# 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)
• 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
0.651, 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

[8]:

scipp.Dataset (5.28 KB out of 5.59 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
0.651, 0.860, ..., 0.736, 0.327
σ = 0.266, 0.701, ..., 0.238, 0.613
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]])Variances (σ²):array([[7.08913566e-02, 4.91558630e-01, 1.36393885e-01, 9.73335179e-01,
2.19480374e-04],
[1.01892712e-01, 1.84912574e-01, 4.23904054e-02, 2.31883466e-01,
8.80681581e-02],
[3.15671080e-02, 3.72781980e-03, 8.01926496e-01, 1.20596261e-01,
2.89413432e-01],
[1.47826795e-01, 4.26541180e-01, 6.58204624e-01, 5.64865778e-02,
3.75219716e-01]])
• b
(y, x)
float64
𝟙
0.651, 0.860, ..., 0.736, 0.327
σ = 0.266, 0.701, ..., 0.238, 0.613
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]])Variances (σ²):array([[7.08913566e-02, 4.91558630e-01, 1.36393885e-01, 9.73335179e-01,
2.19480374e-04],
[1.01892712e-01, 1.84912574e-01, 4.23904054e-02, 2.31883466e-01,
8.80681581e-02],
[3.15671080e-02, 3.72781980e-03, 8.01926496e-01, 1.20596261e-01,
2.89413432e-01],
[1.47826795e-01, 4.26541180e-01, 6.58204624e-01, 5.64865778e-02,
3.75219716e-01]])
• c
(y)
float64
𝟙
0.436, 0.042, 0.005, 0.825
σ = 0.369, 0.206, 0.896, 0.811
• aux
()
int64
m
2
Values:array(2)
• x
()
int64
m
2
Values:array(2)
Values:array([0.43594134, 0.04183491, 0.00459487, 0.82512391])Variances (σ²):array([0.13639388, 0.04239041, 0.8019265 , 0.65820462])
• 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])

[9]:

ab
aux [m]x [m] [𝟙] [𝟙]
000.289±0.1780.289±0.178
110.669±0.0610.669±0.061
220.005±0.8960.005±0.896
330.967±0.3470.967±0.347
440.718±0.5380.718±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

Variables:(3)
var
scipp.Variable (576 Bytes)
• (y: 4, x: 5)
float64
m/s
0.0, 0.258, ..., 0.906, 0.402
σ = 0.463, 0.922, ..., 0.292, 0.753
Values:array([[ 0.        ,  0.25763407, -0.2645005 , -0.65640592,  0.18880634],
[ 0.        , -0.89034258, -0.89165126,  0.20522174, -0.28195817],
[ 0.35488778,  0.82283503,  0.00565169,  1.18929034,  0.88313047],
[ 0.25125015,  1.0791654 ,  1.01490241,  0.90550815,  0.40241182]])Variances (σ²):array([[0.21450307, 0.85093058, 0.31360184, 1.57981033, 0.10758359],
[0.30830697, 0.43390772, 0.21828593, 0.50496998, 0.2873918 ],
[0.04775788, 0.00563982, 1.2132346 , 0.18245008, 0.43785358],
[0.22364716, 0.64531415, 0.99579778, 0.08545854, 0.56766991]])
x
scipp.Variable (296 Bytes)
• (x: 5)
int64
m
0, 1, 2, 3, 4
Values:array([0, 1, 2, 3, 4])
y
scipp.Variable (288 Bytes)
• (y: 4)
int64
m
0, 1, 2, 3
Values:array([0, 1, 2, 3])
DataArrays:(1)
array
scipp.DataArray (1.63 KB)
• 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, 0.258, ..., 0.906, 0.402
σ = 0.463, 0.922, ..., 0.292, 0.753
Values:array([[ 0.        ,  0.25763407, -0.2645005 , -0.65640592,  0.18880634],
[ 0.        , -0.89034258, -0.89165126,  0.20522174, -0.28195817],
[ 0.35488778,  0.82283503,  0.00565169,  1.18929034,  0.88313047],
[ 0.25125015,  1.0791654 ,  1.01490241,  0.90550815,  0.40241182]])Variances (σ²):array([[0.21450307, 0.85093058, 0.31360184, 1.57981033, 0.10758359],
[0.30830697, 0.43390772, 0.21828593, 0.50496998, 0.2873918 ],
[0.04775788, 0.00563982, 1.2132346 , 0.18245008, 0.43785358],
[0.22364716, 0.64531415, 0.99579778, 0.08545854, 0.56766991]])
Datasets:(1)
dataset
scipp.Dataset (5.28 KB out of 5.59 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, 0.258, ..., 0.906, 0.402
σ = 0.463, 0.922, ..., 0.292, 0.753
Values:array([[ 0.        ,  0.25763407, -0.2645005 , -0.65640592,  0.18880634],
[ 0.        , -0.89034258, -0.89165126,  0.20522174, -0.28195817],
[ 0.35488778,  0.82283503,  0.00565169,  1.18929034,  0.88313047],
[ 0.25125015,  1.0791654 ,  1.01490241,  0.90550815,  0.40241182]])Variances (σ²):array([[0.21450307, 0.85093058, 0.31360184, 1.57981033, 0.10758359],
[0.30830697, 0.43390772, 0.21828593, 0.50496998, 0.2873918 ],
[0.04775788, 0.00563982, 1.2132346 , 0.18245008, 0.43785358],
[0.22364716, 0.64531415, 0.99579778, 0.08545854, 0.56766991]])
• b
(y, x)
float64
m/s
0.0, 0.258, ..., 0.906, 0.402
σ = 0.463, 0.922, ..., 0.292, 0.753
Values:array([[ 0.        ,  0.25763407, -0.2645005 , -0.65640592,  0.18880634],
[ 0.        , -0.89034258, -0.89165126,  0.20522174, -0.28195817],
[ 0.35488778,  0.82283503,  0.00565169,  1.18929034,  0.88313047],
[ 0.25125015,  1.0791654 ,  1.01490241,  0.90550815,  0.40241182]])Variances (σ²):array([[0.21450307, 0.85093058, 0.31360184, 1.57981033, 0.10758359],
[0.30830697, 0.43390772, 0.21828593, 0.50496998, 0.2873918 ],
[0.04775788, 0.00563982, 1.2132346 , 0.18245008, 0.43785358],
[0.22364716, 0.64531415, 0.99579778, 0.08545854, 0.56766991]])
• c
(y)
float64
m/s
-0.265, -0.892, 0.006, 1.015
σ = 0.560, 0.467, 1.101, 0.998
• aux
()
int64
m
2
Values:array(2)
• x
()
int64
m
2
Values:array(2)
Values:array([-0.2645005 , -0.89165126,  0.00565169,  1.01490241])Variances (σ²):array([0.31360184, 0.21828593, 1.2132346 , 0.99579778])
• scalar
()
float64
m/s
1.23
Values:array(1.23)
[13]:

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