Quick start
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)float640.613, 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]:
- 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)float640.613, 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]:
- 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)float640.613, 0.283, ..., 0.339, 0.914σ = 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)float640.613, 0.283, ..., 0.339, 0.914σ = 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)float640.486, 0.184, 0.780, 0.528σ = 0.531, 0.458, 0.280, 0.685
- aux()int64m2
Values:
array(2) - x()int64m2
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()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) [0.613103, 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
- (y: 4, x: 5)float64m/s0.0, -0.406, ..., 0.417, 1.124σ = 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
- (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, -0.406, ..., 0.417, 1.124σ = 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
- 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, -0.406, ..., 0.417, 1.124σ = 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)float64m/s0.0, -0.406, ..., 0.417, 1.124σ = 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)float64m/s-0.157, 0.217, 0.960, 0.649σ = 0.706, 0.581, 0.345, 0.843
- aux()int64m2
Values:
array(2) - x()int64m2
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()float64m/s1.23
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'>