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)
dims=['y', 'x'], shape=[4, 5], unit=dimensionless, variances=Falsevalues yx

Type the name of a variable at the end of a cell to generate an HTML respresentation:

[3]:

var

[3]:
Show/Hide data repr Show/Hide attributes
scipp.Variable (160 Bytes)
    • (y: 4, x: 5)
      float64
      0.66, 0.07, ..., 0.26, 0.6
      Values:
      array([[0.65780539, 0.06505393, 0.34145527, 0.74368694, 0.05012935],
       [0.40935821, 0.47425872, 0.66428449, 0.4985452 , 0.43700121],
       [0.49794469, 0.28874962, 0.43060269, 0.6405841 , 0.35433544],
       [0.31362005, 0.3457533 , 0.13291178, 0.26009168, 0.59534877]])
[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
(dims=['y', 'x'], shape=[4, 5], unit=dimensionless, variances=False)values yx yy(dims=['y'], shape=[4], unit=m, variances=False)values y xx(dims=['x'], shape=[5], unit=m, variances=False)values x
[5]:
Show/Hide data repr Show/Hide attributes
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
      0.66, 0.07, ..., 0.26, 0.6
      Values:
      array([[0.65780539, 0.06505393, 0.34145527, 0.74368694, 0.05012935],
       [0.40935821, 0.47425872, 0.66428449, 0.4985452 , 0.43700121],
       [0.49794469, 0.28874962, 0.43060269, 0.6405841 , 0.35433544],
       [0.31362005, 0.3457533 , 0.13291178, 0.26009168, 0.59534877]])

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)
(dims=['y', 'x'], shape=[4, 5], unit=dimensionless, variances=True)variances yxvalues yx yy(dims=['y'], shape=[4], unit=m, variances=False)values y xx(dims=['x'], shape=[5], unit=m, variances=False)values x

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)
bb(dims=['y', 'x'], shape=[4, 5], unit=dimensionless, variances=True)variances yxvalues yx aa(dims=['y', 'x'], shape=[4, 5], unit=dimensionless, variances=True)variances yxvalues yx yy(dims=['y'], shape=[4], unit=m, variances=False)values y scala..scalar(dims=[], shape=[], unit=m/s, variances=False)values xx(dims=['x'], shape=[5], unit=m, variances=False)values x auxaux(dims=['x'], shape=[5], unit=m, variances=False)values x

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
bb(dims=['y', 'x'], shape=[4, 5], unit=dimensionless, variances=True)variances yxvalues yx aa(dims=['y', 'x'], shape=[4, 5], unit=dimensionless, variances=True)variances yxvalues yx yy(dims=['y'], shape=[4], unit=m, variances=False)values y cc(dims=['y'], shape=[4], unit=dimensionless, variances=True)variances yvalues y scala..scalar(dims=[], shape=[], unit=m/s, variances=False)values xx(dims=['x'], shape=[5], unit=m, variances=False)values x auxaux(dims=['x'], shape=[5], unit=m, variances=False)values x
[8]:
Show/Hide data repr Show/Hide attributes
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
      0.66, 0.07, ..., 0.26, 0.6
      σ = 0.62, 0.38, ..., 0.13, 0.47
      Values:
      array([[0.65780539, 0.06505393, 0.34145527, 0.74368694, 0.05012935],
       [0.40935821, 0.47425872, 0.66428449, 0.4985452 , 0.43700121],
       [0.49794469, 0.28874962, 0.43060269, 0.6405841 , 0.35433544],
       [0.31362005, 0.3457533 , 0.13291178, 0.26009168, 0.59534877]])

      Variances (σ²):
      array([[0.39057516, 0.1439567 , 0.08855967, 0.8733477 , 0.17032368],
       [0.02730934, 0.20264379, 0.21115033, 0.9964424 , 0.24788929],
       [0.99048862, 0.19774823, 0.01145165, 0.86301724, 0.10338583],
       [0.00122493, 0.2213885 , 0.04630171, 0.01656389, 0.21871896]])
    • b
      (y, x)
      float64
      0.66, 0.07, ..., 0.26, 0.6
      σ = 0.62, 0.38, ..., 0.13, 0.47
      Values:
      array([[0.65780539, 0.06505393, 0.34145527, 0.74368694, 0.05012935],
       [0.40935821, 0.47425872, 0.66428449, 0.4985452 , 0.43700121],
       [0.49794469, 0.28874962, 0.43060269, 0.6405841 , 0.35433544],
       [0.31362005, 0.3457533 , 0.13291178, 0.26009168, 0.59534877]])

      Variances (σ²):
      array([[0.39057516, 0.1439567 , 0.08855967, 0.8733477 , 0.17032368],
       [0.02730934, 0.20264379, 0.21115033, 0.9964424 , 0.24788929],
       [0.99048862, 0.19774823, 0.01145165, 0.86301724, 0.10338583],
       [0.00122493, 0.2213885 , 0.04630171, 0.01656389, 0.21871896]])
    • c
      (y)
      float64
      0.34, 0.66, 0.43, 0.13
      σ = 0.3, 0.46, 0.11, 0.22
        • aux
          ()
          int64
          m
          2
          Values:
          array(2)
        • x
          ()
          int64
          m
          2
          Values:
          array(2)
      Values:
      array([0.34145527, 0.66428449, 0.43060269, 0.13291178])

      Variances (σ²):
      array([0.08855967, 0.21115033, 0.01145165, 0.04630171])
    • 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, 2, 3, 4]
  x                           int64              [m]  (x)  [0, 1, 2, 3, 4]
  y                           int64              [m]  (y)  [0, 1, 2, 3]
Data:
  a                         float64  [dimensionless]  (y, x)  [0.657805, 0.065054, 0.341455, 0.743687, ..., 0.345753, 0.132912, 0.260092, 0.595349]  [0.390575, 0.143957, 0.088560, 0.873348, ..., 0.221389, 0.046302, 0.016564, 0.218719]
  b                         float64  [dimensionless]  (y, x)  [0.657805, 0.065054, 0.341455, 0.743687, ..., 0.345753, 0.132912, 0.260092, 0.595349]  [0.390575, 0.143957, 0.088560, 0.873348, ..., 0.221389, 0.046302, 0.016564, 0.218719]
  c                         float64  [dimensionless]  (y)  [0.341455, 0.664284, 0.430603, 0.132912]  [0.088560, 0.211150, 0.011452, 0.046302]
    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, 2, 3, 4]
  x                           int64              [m]  (x)  [0, 1, 2, 3, 4]
  y                           int64              [m]  (y)  [0, 1, 2, 3]
Data:
  a                         float64            [m/s]  (y, x)  [0.000000, -0.729084, -0.389111, 0.105634, ..., 0.425277, 0.163481, 0.319913, 0.732279]  [1.181802, 0.808693, 0.724883, 1.912189, ..., 0.334939, 0.070050, 0.025060, 0.330900]
  b                         float64            [m/s]  (y, x)  [0.000000, -0.729084, -0.389111, 0.105634, ..., 0.425277, 0.163481, 0.319913, 0.732279]  [1.181802, 0.808693, 0.724883, 1.912189, ..., 0.334939, 0.070050, 0.025060, 0.330900]
  c                         float64            [m/s]  (y)  [-0.389111, 0.313559, 0.529641, 0.163481]  [0.724883, 0.360766, 0.017325, 0.070050]
    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
Show/Hide data repr Show/Hide attributes
scipp.Variable (320 Bytes)
    • (y: 4, x: 5)
      float64
      m/s
      0.0, -0.73, ..., 0.32, 0.73
      σ = 1.09, 0.9, ..., 0.16, 0.58
      Values:
      array([[ 0.        , -0.72908429, -0.38911064,  0.10563431, -0.74744152],
       [ 0.        ,  0.07982762,  0.31355932,  0.1097    ,  0.03400089],
       [ 0.61247197,  0.35516204,  0.52964131,  0.78791845,  0.43583259],
       [ 0.38575266,  0.42527655,  0.16348149,  0.31991276,  0.73227899]])

      Variances (σ²):
      array([[1.18180233e+00, 8.08693260e-01, 7.24883090e-01, 1.91218890e+00,
        8.48583866e-01],
       [8.26325957e-02, 3.47896091e-01, 3.60765632e-01, 1.54883401e+00,
        4.16347998e-01],
       [1.49851023e+00, 2.99173302e-01, 1.73251975e-02, 1.30565878e+00,
        1.56412420e-01],
       [1.85318951e-03, 3.34938667e-01, 7.00498545e-02, 2.50595151e-02,
        3.30899910e-01]])
x
Show/Hide data repr Show/Hide attributes
scipp.Variable (40 Bytes)
    • (x: 5)
      int64
      m
      0, 1, 2, 3, 4
      Values:
      array([0, 1, 2, 3, 4])
y
Show/Hide data repr Show/Hide attributes
scipp.Variable (32 Bytes)
    • (y: 4)
      int64
      m
      0, 1, 2, 3
      Values:
      array([0, 1, 2, 3])
DataArrays:(1)
array
Show/Hide data repr Show/Hide attributes
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, -0.73, ..., 0.32, 0.73
      σ = 1.09, 0.9, ..., 0.16, 0.58
      Values:
      array([[ 0.        , -0.72908429, -0.38911064,  0.10563431, -0.74744152],
       [ 0.        ,  0.07982762,  0.31355932,  0.1097    ,  0.03400089],
       [ 0.61247197,  0.35516204,  0.52964131,  0.78791845,  0.43583259],
       [ 0.38575266,  0.42527655,  0.16348149,  0.31991276,  0.73227899]])

      Variances (σ²):
      array([[1.18180233e+00, 8.08693260e-01, 7.24883090e-01, 1.91218890e+00,
        8.48583866e-01],
       [8.26325957e-02, 3.47896091e-01, 3.60765632e-01, 1.54883401e+00,
        4.16347998e-01],
       [1.49851023e+00, 2.99173302e-01, 1.73251975e-02, 1.30565878e+00,
        1.56412420e-01],
       [1.85318951e-03, 3.34938667e-01, 7.00498545e-02, 2.50595151e-02,
        3.30899910e-01]])
Datasets:(1)
dataset
Show/Hide data repr Show/Hide attributes
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, -0.73, ..., 0.32, 0.73
      σ = 1.09, 0.9, ..., 0.16, 0.58
      Values:
      array([[ 0.        , -0.72908429, -0.38911064,  0.10563431, -0.74744152],
       [ 0.        ,  0.07982762,  0.31355932,  0.1097    ,  0.03400089],
       [ 0.61247197,  0.35516204,  0.52964131,  0.78791845,  0.43583259],
       [ 0.38575266,  0.42527655,  0.16348149,  0.31991276,  0.73227899]])

      Variances (σ²):
      array([[1.18180233e+00, 8.08693260e-01, 7.24883090e-01, 1.91218890e+00,
        8.48583866e-01],
       [8.26325957e-02, 3.47896091e-01, 3.60765632e-01, 1.54883401e+00,
        4.16347998e-01],
       [1.49851023e+00, 2.99173302e-01, 1.73251975e-02, 1.30565878e+00,
        1.56412420e-01],
       [1.85318951e-03, 3.34938667e-01, 7.00498545e-02, 2.50595151e-02,
        3.30899910e-01]])
    • b
      (y, x)
      float64
      m/s
      0.0, -0.73, ..., 0.32, 0.73
      σ = 1.09, 0.9, ..., 0.16, 0.58
      Values:
      array([[ 0.        , -0.72908429, -0.38911064,  0.10563431, -0.74744152],
       [ 0.        ,  0.07982762,  0.31355932,  0.1097    ,  0.03400089],
       [ 0.61247197,  0.35516204,  0.52964131,  0.78791845,  0.43583259],
       [ 0.38575266,  0.42527655,  0.16348149,  0.31991276,  0.73227899]])

      Variances (σ²):
      array([[1.18180233e+00, 8.08693260e-01, 7.24883090e-01, 1.91218890e+00,
        8.48583866e-01],
       [8.26325957e-02, 3.47896091e-01, 3.60765632e-01, 1.54883401e+00,
        4.16347998e-01],
       [1.49851023e+00, 2.99173302e-01, 1.73251975e-02, 1.30565878e+00,
        1.56412420e-01],
       [1.85318951e-03, 3.34938667e-01, 7.00498545e-02, 2.50595151e-02,
        3.30899910e-01]])
    • c
      (y)
      float64
      m/s
      -0.39, 0.31, 0.53, 0.16
      σ = 0.85, 0.6, 0.13, 0.26
        • aux
          ()
          int64
          m
          2
          Values:
          array(2)
        • x
          ()
          int64
          m
          2
          Values:
          array(2)
      Values:
      array([-0.38911064,  0.31355932,  0.52964131,  0.16348149])

      Variances (σ²):
      array([0.72488309, 0.36076563, 0.0173252 , 0.07004985])
    • scalar
      ()
      float64
      m/s
      1.23
      Values:
      array(1.23)
[13]:

<module 'scipp' from '/usr/share/miniconda/envs/test/conda-bld/scipp_1638972693602/_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'>

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