# 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
0.613, 0.283, ..., 0.339, 0.914
Values:array([[0.6131034 , 0.2832231 , 0.48558676, 0.94835275, 0.94932854],
[0.00698108, 0.30465546, 0.1835042 , 0.73350568, 0.37126001],
[0.30883601, 0.50792822, 0.78048415, 0.4991276 , 0.29868537],
[0.44494411, 0.47533736, 0.52786949, 0.33903244, 0.91386923]])
[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
0.613, 0.283, ..., 0.339, 0.914
Values:array([[0.6131034 , 0.2832231 , 0.48558676, 0.94835275, 0.94932854],
[0.00698108, 0.30465546, 0.1835042 , 0.73350568, 0.37126001],
[0.30883601, 0.50792822, 0.78048415, 0.4991276 , 0.29868537],
[0.44494411, 0.47533736, 0.52786949, 0.33903244, 0.91386923]])

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
0.613, 0.283, ..., 0.339, 0.914
σ = 0.218, 0.742, ..., 0.555, 0.009
Values:array([[0.6131034 , 0.2832231 , 0.48558676, 0.94835275, 0.94932854],
[0.00698108, 0.30465546, 0.1835042 , 0.73350568, 0.37126001],
[0.30883601, 0.50792822, 0.78048415, 0.4991276 , 0.29868537],
[0.44494411, 0.47533736, 0.52786949, 0.33903244, 0.91386923]])Variances (σ²):array([[4.77386198e-02, 5.50171244e-01, 2.81505521e-01, 4.77248288e-01,
7.31849683e-01],
[1.35881140e-02, 3.01542696e-01, 2.09810757e-01, 5.11754257e-01,
2.43575149e-02],
[4.91394589e-01, 8.41539714e-02, 7.86341743e-02, 8.89500185e-01,
6.78808360e-04],
[1.62856494e-01, 5.10978527e-01, 4.69227129e-01, 3.07762829e-01,
7.91210274e-05]])
• b
(y, x)
float64
0.613, 0.283, ..., 0.339, 0.914
σ = 0.218, 0.742, ..., 0.555, 0.009
Values:array([[0.6131034 , 0.2832231 , 0.48558676, 0.94835275, 0.94932854],
[0.00698108, 0.30465546, 0.1835042 , 0.73350568, 0.37126001],
[0.30883601, 0.50792822, 0.78048415, 0.4991276 , 0.29868537],
[0.44494411, 0.47533736, 0.52786949, 0.33903244, 0.91386923]])Variances (σ²):array([[4.77386198e-02, 5.50171244e-01, 2.81505521e-01, 4.77248288e-01,
7.31849683e-01],
[1.35881140e-02, 3.01542696e-01, 2.09810757e-01, 5.11754257e-01,
2.43575149e-02],
[4.91394589e-01, 8.41539714e-02, 7.86341743e-02, 8.89500185e-01,
6.78808360e-04],
[1.62856494e-01, 5.10978527e-01, 4.69227129e-01, 3.07762829e-01,
7.91210274e-05]])
• c
(y)
float64
0.486, 0.184, 0.780, 0.528
σ = 0.531, 0.458, 0.280, 0.685
• aux
()
int64
m
2
Values:array(2)
• x
()
int64
m
2
Values:array(2)
Values:array([0.48558676, 0.1835042 , 0.78048415, 0.52786949])Variances (σ²):array([0.28150552, 0.20981076, 0.07863417, 0.46922713])
• 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)  [0.613103, 0.283223, ..., 0.339032, 0.913869]  [0.0477386, 0.550171, ..., 0.307763, 7.9121e-05]
b                         float64  [dimensionless]  (y, x)  [0.613103, 0.283223, ..., 0.339032, 0.913869]  [0.0477386, 0.550171, ..., 0.307763, 7.9121e-05]
c                         float64  [dimensionless]  (y)  [0.485587, 0.183504, 0.780484, 0.527869]  [0.281506, 0.209811, 0.0786342, 0.469227]
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, -0.405753, ..., 0.41701, 1.12406]  [0.144448, 0.904578, ..., 0.465614, 0.000119702]
b                         float64            [m/s]  (y, x)  [0, -0.405753, ..., 0.41701, 1.12406]  [0.144448, 0.904578, ..., 0.465614, 0.000119702]
c                         float64            [m/s]  (y)  [-0.156845, 0.217123, 0.959996, 0.649279]  [0.498113, 0.33798, 0.118966, 0.709894]
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, -0.406, ..., 0.417, 1.124
σ = 0.380, 0.951, ..., 0.682, 0.011
Values:array([[ 0.        , -0.40575277, -0.15684547,  0.4123567 ,  0.41355693],
[ 0.        ,  0.3661395 ,  0.21712344,  0.89362526,  0.44806309],
[ 0.37986829,  0.62475171,  0.95999551,  0.61392695,  0.36738301],
[ 0.54728125,  0.58466496,  0.64927947,  0.4170099 ,  1.12405916]])Variances (σ²):array([[1.44447516e-01, 9.04577832e-01, 4.98113461e-01, 7.94252693e-01,
1.17943914e+00],
[4.11149154e-02, 4.76761403e-01, 3.37980152e-01, 7.94790473e-01,
5.74079420e-02],
[7.43430874e-01, 1.27316543e-01, 1.18965642e-01, 1.34572483e+00,
1.02696917e-03],
[2.46385590e-01, 7.73059414e-01, 7.09893723e-01, 4.65614383e-01,
1.19702202e-04]])
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, -0.406, ..., 0.417, 1.124
σ = 0.380, 0.951, ..., 0.682, 0.011
Values:array([[ 0.        , -0.40575277, -0.15684547,  0.4123567 ,  0.41355693],
[ 0.        ,  0.3661395 ,  0.21712344,  0.89362526,  0.44806309],
[ 0.37986829,  0.62475171,  0.95999551,  0.61392695,  0.36738301],
[ 0.54728125,  0.58466496,  0.64927947,  0.4170099 ,  1.12405916]])Variances (σ²):array([[1.44447516e-01, 9.04577832e-01, 4.98113461e-01, 7.94252693e-01,
1.17943914e+00],
[4.11149154e-02, 4.76761403e-01, 3.37980152e-01, 7.94790473e-01,
5.74079420e-02],
[7.43430874e-01, 1.27316543e-01, 1.18965642e-01, 1.34572483e+00,
1.02696917e-03],
[2.46385590e-01, 7.73059414e-01, 7.09893723e-01, 4.65614383e-01,
1.19702202e-04]])
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, -0.406, ..., 0.417, 1.124
σ = 0.380, 0.951, ..., 0.682, 0.011
Values:array([[ 0.        , -0.40575277, -0.15684547,  0.4123567 ,  0.41355693],
[ 0.        ,  0.3661395 ,  0.21712344,  0.89362526,  0.44806309],
[ 0.37986829,  0.62475171,  0.95999551,  0.61392695,  0.36738301],
[ 0.54728125,  0.58466496,  0.64927947,  0.4170099 ,  1.12405916]])Variances (σ²):array([[1.44447516e-01, 9.04577832e-01, 4.98113461e-01, 7.94252693e-01,
1.17943914e+00],
[4.11149154e-02, 4.76761403e-01, 3.37980152e-01, 7.94790473e-01,
5.74079420e-02],
[7.43430874e-01, 1.27316543e-01, 1.18965642e-01, 1.34572483e+00,
1.02696917e-03],
[2.46385590e-01, 7.73059414e-01, 7.09893723e-01, 4.65614383e-01,
1.19702202e-04]])
• b
(y, x)
float64
m/s
0.0, -0.406, ..., 0.417, 1.124
σ = 0.380, 0.951, ..., 0.682, 0.011
Values:array([[ 0.        , -0.40575277, -0.15684547,  0.4123567 ,  0.41355693],
[ 0.        ,  0.3661395 ,  0.21712344,  0.89362526,  0.44806309],
[ 0.37986829,  0.62475171,  0.95999551,  0.61392695,  0.36738301],
[ 0.54728125,  0.58466496,  0.64927947,  0.4170099 ,  1.12405916]])Variances (σ²):array([[1.44447516e-01, 9.04577832e-01, 4.98113461e-01, 7.94252693e-01,
1.17943914e+00],
[4.11149154e-02, 4.76761403e-01, 3.37980152e-01, 7.94790473e-01,
5.74079420e-02],
[7.43430874e-01, 1.27316543e-01, 1.18965642e-01, 1.34572483e+00,
1.02696917e-03],
[2.46385590e-01, 7.73059414e-01, 7.09893723e-01, 4.65614383e-01,
1.19702202e-04]])
• c
(y)
float64
m/s
-0.157, 0.217, 0.960, 0.649
σ = 0.706, 0.581, 0.345, 0.843
• aux
()
int64
m
2
Values:array(2)
• x
()
int64
m
2
Values:array(2)
Values:array([-0.15684547,  0.21712344,  0.95999551,  0.64927947])Variances (σ²):array([0.49811346, 0.33798015, 0.11896564, 0.70989372])
• scalar
()
float64
m/s
1.23
Values:array(1.23)
[13]:

<module 'scipp' from '/usr/share/miniconda/envs/test/conda-bld/scipp_1642083266382/_h_env_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placeho/lib/python3.7/site-packages/scipp/__init__.py'>