Line object#

In this tutorial, we show the basics of the line object. We start from the following field:

[1]:

import discretisedfield as df

mesh = df.Mesh(
    p1=(-10e-9, -10e-9, -10e-9), p2=(15e-9, 10e-9, 5e-9), cell=(1e-9, 1e-9, 1e-9)
)


def value_fun(point):
    x, y, z = point
    c = 1e9
    return (x * c, 2 * y * c, 3 * z * c)


field = df.Field(mesh, nvdim=3, value=value_fun, norm=1e6)

We create a line object by passing two points between which the line spans as well as the number of points we want to sample on that line:

[2]:

line = field.line(p1=(-10e-9, 0, 0), p2=(15e-9, 0, 0), n=25)

We can ask for the data on the line:

[3]:

line.data

[3]:
r x y z vx vy vz
0 0.000000e+00 -1.000000e-08 0.0 0.0 -982466.610991 103417.537999 155126.306999
1 1.041667e-09 -8.958333e-09 0.0 0.0 -978240.074002 115087.067530 172630.601295
2 2.083333e-09 -7.916667e-09 0.0 0.0 -972305.585328 129640.744710 194461.117066
3 3.125000e-09 -6.875000e-09 0.0 0.0 -963624.111659 148249.863332 222374.794998
4 4.166667e-09 -5.833333e-09 0.0 0.0 -950255.268139 172773.685116 259160.527674
5 5.208333e-09 -4.791667e-09 0.0 0.0 -928279.121633 206284.249252 309426.373878
6 6.250000e-09 -3.750000e-09 0.0 0.0 -889000.889001 254000.254000 381000.381001
7 7.291667e-09 -2.708333e-09 0.0 0.0 -811107.105654 324442.842262 486664.263392
8 8.333333e-09 -1.666667e-09 0.0 0.0 -639602.149067 426401.432711 639602.149067
9 9.375000e-09 -6.250000e-10 0.0 0.0 -267261.241912 534522.483825 801783.725737
10 1.041667e-08 4.166667e-10 0.0 0.0 267261.241912 534522.483825 801783.725737
11 1.145833e-08 1.458333e-09 0.0 0.0 639602.149067 426401.432711 639602.149067
12 1.250000e-08 2.500000e-09 0.0 0.0 811107.105654 324442.842262 486664.263392
13 1.354167e-08 3.541667e-09 0.0 0.0 889000.889001 254000.254000 381000.381001
14 1.458333e-08 4.583333e-09 0.0 0.0 928279.121633 206284.249252 309426.373878
15 1.562500e-08 5.625000e-09 0.0 0.0 950255.268139 172773.685116 259160.527674
16 1.666667e-08 6.666667e-09 0.0 0.0 963624.111659 148249.863332 222374.794998
17 1.770833e-08 7.708333e-09 0.0 0.0 972305.585328 129640.744710 194461.117066
18 1.875000e-08 8.750000e-09 0.0 0.0 978240.074002 115087.067530 172630.601295
19 1.979167e-08 9.791667e-09 0.0 0.0 982466.610991 103417.537999 155126.306999
20 2.083333e-08 1.083333e-08 0.0 0.0 985578.834374 93864.650893 140796.976339
21 2.187500e-08 1.187500e-08 0.0 0.0 987934.593051 85907.355918 128861.033876
22 2.291667e-08 1.291667e-08 0.0 0.0 989759.478081 79180.758246 118771.137370
23 2.395833e-08 1.395833e-08 0.0 0.0 991201.182589 73422.309821 110133.464732
24 2.500000e-08 1.500000e-08 0.0 0.0 992359.570702 68438.591083 102657.886624

Dimension of the value:

[4]:

line.dim

[4]:

3

Length of the line:

[5]:

line.length

[5]:

2.5e-08

The number of points:

[6]:

line.n

[6]:

25

The columns storing coordinates of points are:

[7]:

line.point_columns

[7]:

['x', 'y', 'z']

Similarly, the columns storing field values are:

[8]:

line.value_columns

[8]:

['vx', 'vy', 'vz']

By default, value columns start with v, but we can rename them:

[9]:

line.value_columns = ["xval", "yval", "zval"]

Line data is now:

[10]:

line.data

[10]:
r x y z xval yval zval
0 0.000000e+00 -1.000000e-08 0.0 0.0 -982466.610991 103417.537999 155126.306999
1 1.041667e-09 -8.958333e-09 0.0 0.0 -978240.074002 115087.067530 172630.601295
2 2.083333e-09 -7.916667e-09 0.0 0.0 -972305.585328 129640.744710 194461.117066
3 3.125000e-09 -6.875000e-09 0.0 0.0 -963624.111659 148249.863332 222374.794998
4 4.166667e-09 -5.833333e-09 0.0 0.0 -950255.268139 172773.685116 259160.527674
5 5.208333e-09 -4.791667e-09 0.0 0.0 -928279.121633 206284.249252 309426.373878
6 6.250000e-09 -3.750000e-09 0.0 0.0 -889000.889001 254000.254000 381000.381001
7 7.291667e-09 -2.708333e-09 0.0 0.0 -811107.105654 324442.842262 486664.263392
8 8.333333e-09 -1.666667e-09 0.0 0.0 -639602.149067 426401.432711 639602.149067
9 9.375000e-09 -6.250000e-10 0.0 0.0 -267261.241912 534522.483825 801783.725737
10 1.041667e-08 4.166667e-10 0.0 0.0 267261.241912 534522.483825 801783.725737
11 1.145833e-08 1.458333e-09 0.0 0.0 639602.149067 426401.432711 639602.149067
12 1.250000e-08 2.500000e-09 0.0 0.0 811107.105654 324442.842262 486664.263392
13 1.354167e-08 3.541667e-09 0.0 0.0 889000.889001 254000.254000 381000.381001
14 1.458333e-08 4.583333e-09 0.0 0.0 928279.121633 206284.249252 309426.373878
15 1.562500e-08 5.625000e-09 0.0 0.0 950255.268139 172773.685116 259160.527674
16 1.666667e-08 6.666667e-09 0.0 0.0 963624.111659 148249.863332 222374.794998
17 1.770833e-08 7.708333e-09 0.0 0.0 972305.585328 129640.744710 194461.117066
18 1.875000e-08 8.750000e-09 0.0 0.0 978240.074002 115087.067530 172630.601295
19 1.979167e-08 9.791667e-09 0.0 0.0 982466.610991 103417.537999 155126.306999
20 2.083333e-08 1.083333e-08 0.0 0.0 985578.834374 93864.650893 140796.976339
21 2.187500e-08 1.187500e-08 0.0 0.0 987934.593051 85907.355918 128861.033876
22 2.291667e-08 1.291667e-08 0.0 0.0 989759.478081 79180.758246 118771.137370
23 2.395833e-08 1.395833e-08 0.0 0.0 991201.182589 73422.309821 110133.464732
24 2.500000e-08 1.500000e-08 0.0 0.0 992359.570702 68438.591083 102657.886624

Line visualisation#

Default plot is:

[11]:

line.mpl()
../../../_images/documentation_notebooks_discretisedfield_line_21_0.png

We can change the size of the plot:

[12]:

line.mpl(figsize=(10, 5))
../../../_images/documentation_notebooks_discretisedfield_line_23_0.png

Also, we can limit the values on the x-axis:

[13]:

line.mpl(xlim=[5e-9, 20e-9])
../../../_images/documentation_notebooks_discretisedfield_line_25_0.png

and we can choose what we want to plot:

[14]:

line.mpl(yaxis=["xval", "yval"])
../../../_images/documentation_notebooks_discretisedfield_line_27_0.png

Similar to all other plots, we can build interactive plots:

[15]:

@df.interact(xlim=line.slider(continuous_update=False), yaxis=line.selector())
def myplot(xlim, yaxis):
    return line.mpl(figsize=(10, 6), yaxis=yaxis, marker="o", xlim=xlim)
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