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]:
- (y: 4, x: 5)float640.89, 0.68, ..., 0.98, 0.8
Values:
array([[0.89480994, 0.68399103, 0.82038829, 0.65867721, 0.44276753], [0.61658809, 0.41238414, 0.09385746, 0.16852474, 0.57240989], [0.53857184, 0.14089499, 0.60736968, 0.73385062, 0.45890154], [0.46095848, 0.0027758 , 0.69946178, 0.97922237, 0.80368585]])
[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]:
- y: 4
- x: 5
- x(x)int64m0, 1, 2, 3, 4
Values:
array([0, 1, 2, 3, 4]) - y(y)int64m0, 1, 2, 3
Values:
array([0, 1, 2, 3])
- (y, x)float640.89, 0.68, ..., 0.98, 0.8
Values:
array([[0.89480994, 0.68399103, 0.82038829, 0.65867721, 0.44276753], [0.61658809, 0.41238414, 0.09385746, 0.16852474, 0.57240989], [0.53857184, 0.14089499, 0.60736968, 0.73385062, 0.45890154], [0.46095848, 0.0027758 , 0.69946178, 0.97922237, 0.80368585]])
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]:
- y: 4
- x: 5
- aux(x)int64m0, 1, 2, 3, 4
Values:
array([0, 1, 2, 3, 4]) - x(x)int64m0, 1, 2, 3, 4
Values:
array([0, 1, 2, 3, 4]) - y(y)int64m0, 1, 2, 3
Values:
array([0, 1, 2, 3])
- a(y, x)float640.89, 0.68, ..., 0.98, 0.8σ = 0.42, 0.83, ..., 0.02, 0.02
Values:
array([[0.89480994, 0.68399103, 0.82038829, 0.65867721, 0.44276753], [0.61658809, 0.41238414, 0.09385746, 0.16852474, 0.57240989], [0.53857184, 0.14089499, 0.60736968, 0.73385062, 0.45890154], [0.46095848, 0.0027758 , 0.69946178, 0.97922237, 0.80368585]])
Variances (σ²):
array([[1.76229802e-01, 6.96034078e-01, 3.35406980e-01, 7.92418095e-02, 4.00507698e-01], [4.63537620e-01, 2.71467827e-01, 4.33764663e-03, 2.84478602e-01, 2.36026746e-02], [5.63728104e-01, 8.08278144e-02, 5.78965756e-01, 1.46252161e-02, 8.02640577e-01], [2.28552277e-02, 4.99763761e-01, 4.55787648e-01, 3.57000021e-04, 5.36796929e-04]]) - b(y, x)float640.89, 0.68, ..., 0.98, 0.8σ = 0.42, 0.83, ..., 0.02, 0.02
Values:
array([[0.89480994, 0.68399103, 0.82038829, 0.65867721, 0.44276753], [0.61658809, 0.41238414, 0.09385746, 0.16852474, 0.57240989], [0.53857184, 0.14089499, 0.60736968, 0.73385062, 0.45890154], [0.46095848, 0.0027758 , 0.69946178, 0.97922237, 0.80368585]])
Variances (σ²):
array([[1.76229802e-01, 6.96034078e-01, 3.35406980e-01, 7.92418095e-02, 4.00507698e-01], [4.63537620e-01, 2.71467827e-01, 4.33764663e-03, 2.84478602e-01, 2.36026746e-02], [5.63728104e-01, 8.08278144e-02, 5.78965756e-01, 1.46252161e-02, 8.02640577e-01], [2.28552277e-02, 4.99763761e-01, 4.55787648e-01, 3.57000021e-04, 5.36796929e-04]]) - c(y)float640.82, 0.09, 0.61, 0.7σ = 0.58, 0.07, 0.76, 0.68
- aux()int64m2
Values:
array(2) - x()int64m2
Values:
array(2)
Values:
array([0.82038829, 0.09385746, 0.60736968, 0.69946178])
Variances (σ²):
array([0.33540698, 0.00433765, 0.57896576, 0.45578765]) - scalar()float64m/s1.23
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) [0.894810, 0.683991, ..., 0.979222, 0.803686] [0.176230, 0.696034, ..., 0.000357, 0.000537]
b float64 [dimensionless] (y, x) [0.894810, 0.683991, ..., 0.979222, 0.803686] [0.176230, 0.696034, ..., 0.000357, 0.000537]
c float64 [dimensionless] (y) [0.820388, 0.093857, 0.607370, 0.699462] [0.335407, 0.004338, 0.578966, 0.455788]
Attributes:
aux int64 [m] () [2]
x int64 [m] () [2]
scalar float64 [m/s] () [1.230000]
[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) [0.000000, -0.259307, ..., 1.204444, 0.988534] [0.533236, 1.319648, ..., 0.000540, 0.000812]
b float64 [m/s] (y, x) [0.000000, -0.259307, ..., 1.204444, 0.988534] [0.533236, 1.319648, ..., 0.000540, 0.000812]
c float64 [m/s] (y) [-0.091539, -0.642959, 0.747065, 0.860338] [0.774055, 0.707848, 0.875917, 0.689561]
Attributes:
aux int64 [m] () [2]
x int64 [m] () [2]
scalar float64 [m/s] () [1.230000]
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
- (y: 4, x: 5)float64m/s0.0, -0.26, ..., 1.2, 0.99σ = 0.73, 1.15, ..., 0.02, 0.03
Values:
array([[ 0. , -0.25930726, -0.09153862, -0.29044326, -0.55601216], [ 0. , -0.25117087, -0.64295867, -0.55111792, -0.05433918], [ 0.66244337, 0.17330084, 0.74706471, 0.90263627, 0.56444889], [ 0.56697894, 0.00341423, 0.86033799, 1.20444352, 0.9885336 ]])
Variances (σ²):
array([[5.33236134e-01, 1.31964802e+00, 7.74055288e-01, 3.86503000e-01, 8.72546163e-01], [1.40257213e+00, 1.11198974e+00, 7.07848491e-01, 1.13167374e+00, 7.36994551e-01], [8.52864249e-01, 1.22284400e-01, 8.75917292e-01, 2.21264895e-02, 1.21431493e+00], [3.45776740e-02, 7.56092594e-01, 6.89561133e-01, 5.40105331e-04, 8.12120073e-04]])
x
- (x: 5)int64m0, 1, 2, 3, 4
Values:
array([0, 1, 2, 3, 4])
y
- (y: 4)int64m0, 1, 2, 3
Values:
array([0, 1, 2, 3])
DataArrays:(1)
array
- y: 4
- x: 5
- x(x)int64m0, 1, 2, 3, 4
Values:
array([0, 1, 2, 3, 4]) - y(y)int64m0, 1, 2, 3
Values:
array([0, 1, 2, 3])
- (y, x)float64m/s0.0, -0.26, ..., 1.2, 0.99σ = 0.73, 1.15, ..., 0.02, 0.03
Values:
array([[ 0. , -0.25930726, -0.09153862, -0.29044326, -0.55601216], [ 0. , -0.25117087, -0.64295867, -0.55111792, -0.05433918], [ 0.66244337, 0.17330084, 0.74706471, 0.90263627, 0.56444889], [ 0.56697894, 0.00341423, 0.86033799, 1.20444352, 0.9885336 ]])
Variances (σ²):
array([[5.33236134e-01, 1.31964802e+00, 7.74055288e-01, 3.86503000e-01, 8.72546163e-01], [1.40257213e+00, 1.11198974e+00, 7.07848491e-01, 1.13167374e+00, 7.36994551e-01], [8.52864249e-01, 1.22284400e-01, 8.75917292e-01, 2.21264895e-02, 1.21431493e+00], [3.45776740e-02, 7.56092594e-01, 6.89561133e-01, 5.40105331e-04, 8.12120073e-04]])
Datasets:(1)
dataset
- y: 4
- x: 5
- aux(x)int64m0, 1, 2, 3, 4
Values:
array([0, 1, 2, 3, 4]) - x(x)int64m0, 1, 2, 3, 4
Values:
array([0, 1, 2, 3, 4]) - y(y)int64m0, 1, 2, 3
Values:
array([0, 1, 2, 3])
- a(y, x)float64m/s0.0, -0.26, ..., 1.2, 0.99σ = 0.73, 1.15, ..., 0.02, 0.03
Values:
array([[ 0. , -0.25930726, -0.09153862, -0.29044326, -0.55601216], [ 0. , -0.25117087, -0.64295867, -0.55111792, -0.05433918], [ 0.66244337, 0.17330084, 0.74706471, 0.90263627, 0.56444889], [ 0.56697894, 0.00341423, 0.86033799, 1.20444352, 0.9885336 ]])
Variances (σ²):
array([[5.33236134e-01, 1.31964802e+00, 7.74055288e-01, 3.86503000e-01, 8.72546163e-01], [1.40257213e+00, 1.11198974e+00, 7.07848491e-01, 1.13167374e+00, 7.36994551e-01], [8.52864249e-01, 1.22284400e-01, 8.75917292e-01, 2.21264895e-02, 1.21431493e+00], [3.45776740e-02, 7.56092594e-01, 6.89561133e-01, 5.40105331e-04, 8.12120073e-04]]) - b(y, x)float64m/s0.0, -0.26, ..., 1.2, 0.99σ = 0.73, 1.15, ..., 0.02, 0.03
Values:
array([[ 0. , -0.25930726, -0.09153862, -0.29044326, -0.55601216], [ 0. , -0.25117087, -0.64295867, -0.55111792, -0.05433918], [ 0.66244337, 0.17330084, 0.74706471, 0.90263627, 0.56444889], [ 0.56697894, 0.00341423, 0.86033799, 1.20444352, 0.9885336 ]])
Variances (σ²):
array([[5.33236134e-01, 1.31964802e+00, 7.74055288e-01, 3.86503000e-01, 8.72546163e-01], [1.40257213e+00, 1.11198974e+00, 7.07848491e-01, 1.13167374e+00, 7.36994551e-01], [8.52864249e-01, 1.22284400e-01, 8.75917292e-01, 2.21264895e-02, 1.21431493e+00], [3.45776740e-02, 7.56092594e-01, 6.89561133e-01, 5.40105331e-04, 8.12120073e-04]]) - c(y)float64m/s-0.09, -0.64, 0.75, 0.86σ = 0.88, 0.84, 0.94, 0.83
- aux()int64m2
Values:
array(2) - x()int64m2
Values:
array(2)
Values:
array([-0.09153862, -0.64295867, 0.74706471, 0.86033799])
Variances (σ²):
array([0.77405529, 0.70784849, 0.87591729, 0.68956113]) - scalar()float64m/s1.23
Values:
array(1.23)
[13]:
<module 'scipp' from '/usr/share/miniconda/conda-bld/scipp_1636551670183/_test_env_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_pl/lib/python3.7/site-packages/scipp/__init__.py'>