# 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
0.319, 0.043, ..., 0.644, 0.708
Values:array([[0.3190775 , 0.04266251, 0.58435288, 0.19796241, 0.73769976],
[0.00802536, 0.22622086, 0.85263886, 0.52117018, 0.23658822],
[0.22859141, 0.28637725, 0.75970833, 0.95115741, 0.31560759],
[0.7067321 , 0.88800128, 0.20141595, 0.64350078, 0.70829665]])
[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)
• y: 4
• x: 5
• 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])
• (y, x)
float64
𝟙
0.319, 0.043, ..., 0.644, 0.708
Values:array([[0.3190775 , 0.04266251, 0.58435288, 0.19796241, 0.73769976],
[0.00802536, 0.22622086, 0.85263886, 0.52117018, 0.23658822],
[0.22859141, 0.28637725, 0.75970833, 0.95115741, 0.31560759],
[0.7067321 , 0.88800128, 0.20141595, 0.64350078, 0.70829665]])

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

[8]:

scipp.Dataset (840 Bytes out of 1.13 KB)
• y: 4
• x: 5
• aux
(x)
int64
m
0, 1, 2, 3, 4
Values:array([0, 1, 2, 3, 4])
• 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])
• a
(y, x)
float64
𝟙
0.319, 0.043, ..., 0.644, 0.708
σ = 0.780, 0.896, ..., 0.768, 0.006
Values:array([[0.3190775 , 0.04266251, 0.58435288, 0.19796241, 0.73769976],
[0.00802536, 0.22622086, 0.85263886, 0.52117018, 0.23658822],
[0.22859141, 0.28637725, 0.75970833, 0.95115741, 0.31560759],
[0.7067321 , 0.88800128, 0.20141595, 0.64350078, 0.70829665]])Variances (σ²):array([[6.09149305e-01, 8.02919767e-01, 2.54898352e-01, 3.04294168e-01,
7.26285145e-03],
[8.35132324e-01, 1.45966768e-01, 2.33159223e-01, 9.15620961e-01,
8.33350991e-01],
[8.16832506e-01, 2.91349767e-01, 1.75377229e-02, 8.56537731e-02,
6.38848348e-01],
[3.00856392e-04, 2.02239956e-01, 6.54426902e-01, 5.89832101e-01,
3.59998041e-05]])
• b
(y, x)
float64
𝟙
0.319, 0.043, ..., 0.644, 0.708
σ = 0.780, 0.896, ..., 0.768, 0.006
Values:array([[0.3190775 , 0.04266251, 0.58435288, 0.19796241, 0.73769976],
[0.00802536, 0.22622086, 0.85263886, 0.52117018, 0.23658822],
[0.22859141, 0.28637725, 0.75970833, 0.95115741, 0.31560759],
[0.7067321 , 0.88800128, 0.20141595, 0.64350078, 0.70829665]])Variances (σ²):array([[6.09149305e-01, 8.02919767e-01, 2.54898352e-01, 3.04294168e-01,
7.26285145e-03],
[8.35132324e-01, 1.45966768e-01, 2.33159223e-01, 9.15620961e-01,
8.33350991e-01],
[8.16832506e-01, 2.91349767e-01, 1.75377229e-02, 8.56537731e-02,
6.38848348e-01],
[3.00856392e-04, 2.02239956e-01, 6.54426902e-01, 5.89832101e-01,
3.59998041e-05]])
• c
(y)
float64
𝟙
0.584, 0.853, 0.760, 0.201
σ = 0.505, 0.483, 0.132, 0.809
• aux
()
int64
m
2
Values:array(2)
• x
()
int64
m
2
Values:array(2)
Values:array([0.58435288, 0.85263886, 0.75970833, 0.20141595])Variances (σ²):array([0.25489835, 0.23315922, 0.01753772, 0.6544269 ])
• scalar
()
float64
m/s
1.23
Values:array(1.23)

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)  [0.319078, 0.0426625, ..., 0.643501, 0.708297]  [0.609149, 0.80292, ..., 0.589832, 3.59998e-05]
b                         float64  [dimensionless]  (y, x)  [0.319078, 0.0426625, ..., 0.643501, 0.708297]  [0.609149, 0.80292, ..., 0.589832, 3.59998e-05]
c                         float64  [dimensionless]  (y)  [0.584353, 0.852639, 0.759708, 0.201416]  [0.254898, 0.233159, 0.0175377, 0.654427]
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, -0.33999, ..., 0.791506, 0.871205]  [1.84316, 2.13632, ..., 0.892357, 5.44641e-05]
b                         float64            [m/s]  (y, x)  [0, -0.33999, ..., 0.791506, 0.871205]  [1.84316, 2.13632, ..., 0.892357, 5.44641e-05]
c                         float64            [m/s]  (y)  [0.326289, 1.03887, 0.934441, 0.247742]  [1.30722, 1.61622, 0.0265328, 0.990082]
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

Variables:(3)
var
scipp.Variable (320 Bytes)
• (y: 4, x: 5)
float64
m/s
0.0, -0.340, ..., 0.792, 0.871
σ = 1.358, 1.462, ..., 0.945, 0.007
Values:array([[ 0.        , -0.33999044,  0.32628871, -0.14897156,  0.51490538],
[ 0.        ,  0.26838046,  1.0388746 ,  0.63116813,  0.28113231],
[ 0.28116744,  0.35224402,  0.93444125,  1.16992361,  0.38819733],
[ 0.86928048,  1.09224157,  0.24774161,  0.79150596,  0.87120488]])Variances (σ²):array([[1.84316397e+00, 2.13631930e+00, 1.30721770e+00, 1.38194863e+00,
9.32569951e-01],
[2.52694339e+00, 1.48430482e+00, 1.61621828e+00, 2.64871464e+00,
2.52424841e+00],
[1.23578590e+00, 4.40783063e-01, 2.65328210e-02, 1.29585593e-01,
9.66513665e-01],
[4.55165635e-04, 3.05968830e-01, 9.90082460e-01, 8.92356986e-01,
5.44641036e-05]])
x
scipp.Variable (40 Bytes)
• (x: 5)
int64
m
0, 1, 2, 3, 4
Values:array([0, 1, 2, 3, 4])
y
scipp.Variable (32 Bytes)
• (y: 4)
int64
m
0, 1, 2, 3
Values:array([0, 1, 2, 3])
DataArrays:(1)
array
scipp.DataArray (392 Bytes)
• y: 4
• x: 5
• 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])
• (y, x)
float64
m/s
0.0, -0.340, ..., 0.792, 0.871
σ = 1.358, 1.462, ..., 0.945, 0.007
Values:array([[ 0.        , -0.33999044,  0.32628871, -0.14897156,  0.51490538],
[ 0.        ,  0.26838046,  1.0388746 ,  0.63116813,  0.28113231],
[ 0.28116744,  0.35224402,  0.93444125,  1.16992361,  0.38819733],
[ 0.86928048,  1.09224157,  0.24774161,  0.79150596,  0.87120488]])Variances (σ²):array([[1.84316397e+00, 2.13631930e+00, 1.30721770e+00, 1.38194863e+00,
9.32569951e-01],
[2.52694339e+00, 1.48430482e+00, 1.61621828e+00, 2.64871464e+00,
2.52424841e+00],
[1.23578590e+00, 4.40783063e-01, 2.65328210e-02, 1.29585593e-01,
9.66513665e-01],
[4.55165635e-04, 3.05968830e-01, 9.90082460e-01, 8.92356986e-01,
5.44641036e-05]])
Datasets:(1)
dataset
scipp.Dataset (840 Bytes out of 1.13 KB)
• y: 4
• x: 5
• aux
(x)
int64
m
0, 1, 2, 3, 4
Values:array([0, 1, 2, 3, 4])
• 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])
• a
(y, x)
float64
m/s
0.0, -0.340, ..., 0.792, 0.871
σ = 1.358, 1.462, ..., 0.945, 0.007
Values:array([[ 0.        , -0.33999044,  0.32628871, -0.14897156,  0.51490538],
[ 0.        ,  0.26838046,  1.0388746 ,  0.63116813,  0.28113231],
[ 0.28116744,  0.35224402,  0.93444125,  1.16992361,  0.38819733],
[ 0.86928048,  1.09224157,  0.24774161,  0.79150596,  0.87120488]])Variances (σ²):array([[1.84316397e+00, 2.13631930e+00, 1.30721770e+00, 1.38194863e+00,
9.32569951e-01],
[2.52694339e+00, 1.48430482e+00, 1.61621828e+00, 2.64871464e+00,
2.52424841e+00],
[1.23578590e+00, 4.40783063e-01, 2.65328210e-02, 1.29585593e-01,
9.66513665e-01],
[4.55165635e-04, 3.05968830e-01, 9.90082460e-01, 8.92356986e-01,
5.44641036e-05]])
• b
(y, x)
float64
m/s
0.0, -0.340, ..., 0.792, 0.871
σ = 1.358, 1.462, ..., 0.945, 0.007
Values:array([[ 0.        , -0.33999044,  0.32628871, -0.14897156,  0.51490538],
[ 0.        ,  0.26838046,  1.0388746 ,  0.63116813,  0.28113231],
[ 0.28116744,  0.35224402,  0.93444125,  1.16992361,  0.38819733],
[ 0.86928048,  1.09224157,  0.24774161,  0.79150596,  0.87120488]])Variances (σ²):array([[1.84316397e+00, 2.13631930e+00, 1.30721770e+00, 1.38194863e+00,
9.32569951e-01],
[2.52694339e+00, 1.48430482e+00, 1.61621828e+00, 2.64871464e+00,
2.52424841e+00],
[1.23578590e+00, 4.40783063e-01, 2.65328210e-02, 1.29585593e-01,
9.66513665e-01],
[4.55165635e-04, 3.05968830e-01, 9.90082460e-01, 8.92356986e-01,
5.44641036e-05]])
• c
(y)
float64
m/s
0.326, 1.039, 0.934, 0.248
σ = 1.143, 1.271, 0.163, 0.995
• aux
()
int64
m
2
Values:array(2)
• x
()
int64
m
2
Values:array(2)
Values:array([0.32628871, 1.0388746 , 0.93444125, 0.24774161])Variances (σ²):array([1.3072177 , 1.61621828, 0.02653282, 0.99008246])
• scalar
()
float64
m/s
1.23
Values:array(1.23)
[13]:

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