# Scripted Node (Generator)¶

aka Script Node or SN. (iteration 1)

• Introduction
• Features
• Structure
• Templates
• Conveniences
• Examples
• Techniques to improve Python performance
• Limitations
• Future

## Introduction¶

When you want to express an idea in written form and the concept is suitable for a one line Python expression then often you can use a Formula node. If you need access to imports, classes, temporary variables, and functions then you can write a script to load into ScriptNode.

ScriptNode (SN) allows you to write multi-line python programs that define the functionality of a Node, while avoiding some of the boilerplate associated with a regular Node. SN can be used as an environment for experimenting with algorithms. Scripts written for SN are easily converted to full PyNodes.

It’s a prototype so bug reports are welcome.

Here’s a short tutorial to SN1, see An introduction and tutorial for the Scripted Nodes

## Features¶

allows:

• imports and aliasing, ie anything you can import from console works in SN
• nested functions and lambdas
• named inputs and outputs
• named operators (buttons to action something upon button press)

## Structure¶

At present all scripts for SN must (strict list - general):

• have 1 sv_main function as the main workhorse
• sv_main must take 1 or more arguments (even if you don’t use it)
• all function arguments for sv_main must have defaults.
• each script shall define in_sockets and out_sockets
• ui_operators is an optional third output parameter

sv_main()

sv_main() can take int, float and list or nested list. Here are some legal examples:

def sv_main(vecs_in_multi=[[]], vecs_in_flat=[], some_var=1, some_ratio=1.2):
pass

[[]]        # for nested input (lists of lists of any data type currently supported)
[]          # for flat (one list)
int, float  # for single value input


in_sockets

in_sockets = [
[type, 'socket name on ui', input_variable],
[type, 'socket name on ui 2', input_variable2],
# ...
]


out_sockets

out_sockets = [
[type, 'socket name on ui', output_variable],
[type, 'socket name on ui 2', output_variable2],
# ...
]


in_sockets and out_sockets

• Each socket name on ui string shall be unique.

• type are currently limited to

type id type data
‘s’ floats, ints, edges, faces, strings
‘v’ vertices, vectors, 3-tuples
‘m’ matrices, 4 x 4 nested lists

ui_operators

ui_operators = [
['button_name', func1]
]

• Here func1 is the function you want to call when pressing the button.

• Each “button_name” is the text you want to appear on the button. For simplicity it must be a unique and valid python variable name

• with no special characters (|().\/...etc)
• contains no spaces, use single underscores if you need word separation. The UI code replaces underscores with spaces.

return

Simple, only two flavours are allowed at the moment.

return in_sockets, out_sockets
return in_sockets, out_sockets, ui_operators


## Templates¶

Sverchok includes a list of easily accessible examples and templates. They can be accessed from the SN node if nothing is loaded, or from the Template Menu in TextEditor as sv NodeScripts.

## Conveniences¶

We vale our time, i’m sure you do too, so features have been added to help speed up the script creation process.

Text Editor

• has automatic in_sockets list creation when the key cursor is over sv_main. (please note: it doesn’t attempt to detect if you want nested verts or edge/polygon so it assumes you want ‘v’)
• kb shortcut: Ctrl+I -> Generate in_sockets
• can also convert a template description (like kv lang if you know Kivy) into valid ScriptNode ready python. Example available here
• kb shortcut: Ctrl+I -> Convert svlang
• can refresh the Script Node which currently loads that script by hitting Ctrl+Enter

## Examples¶

The best way to get familiarity with SN is to go through the templates folder. They are intended to be lightweight and educational, but some of them will show advanced use cases. The images and animations on this thread on github. may also provide some insight into what’s possible.

A typical nodescript may look like this:

from math import sin, cos, radians, pi
from mathutils import Vector, Euler

in_sockets = [
['s', 'Num Petals',  n_petals],
['s', 'Verts per Petal',  vp_petal],
['s', 'Amp',  amp],
]

# variables
z_float = 0.0
n_verts = n_petals * vp_petal
section_angle = 360.0 / n_verts
position = (2 * (pi / (n_verts / n_petals)))

# consumables
Verts = []

# makes vertex coordinates
for i in range(n_verts):
# difference is a function of the position on the circumference
difference = amp * cos(i * position)
ampline = Vector((arm, 0.0, 0.0))

myEuler = Euler((0.0, 0.0, rad_angle), 'XYZ')

# changes the vector in place, successive calls are accumulative
# we reset at the start of the loop.
ampline.rotate(myEuler)
x_float = ampline.x
y_float = ampline.y
Verts.append((x_float, y_float, z_float))

# makes edge keys, ensure cyclic
Edges = [[i, i + 1] for i in range(n_verts - 1)]
Edges.append([i, 0])

out_sockets = [
['v', 'Verts', [Verts]],
['s', 'Edges', [Edges]],
]

return in_sockets, out_sockets


but we are not forced to have all code inside sv_main, we can also do:

def lorenz(N, verts):
h = 0.01
a = 10.0
b = 28.0
c = 8.0 / 3.0

x0 = 0.1
y0 = 0
z0 = 0
for i in range(N):
x1 = x0 + h * a * (y0 - x0)
y1 = y0 + h * (x0 * (b - z0) - y0)
z1 = z0 + h * (x0 * y0 - c * z0)
x0, y0, z0 = x1, y1, z1

def sv_main(N=1000):

verts = []
in_sockets = [['s', 'N', N]]
out_sockets = [['v','verts', [verts]]]

lorenz(N, verts)
return in_sockets, out_sockets


We can even define classes inside the .py file, or import from elsewhere.

Here’s a ui_operator example, it acts like a throughput (because in and out are still needed by design). You’ll notice that inside func1 the node’s input socket is accessed using SvGetSockeyAnyType(...). It is probably more logical if we could access the input data directly from the variable items_in, currently this is not possible – therefor the solution is to use what sverchok nodes use in their internal code too. The upshot, is that this exposes you to how you might access the socket content of other nodes. Experiment :)

def sv_main(items_in=[[]]):

in_sockets = [
['v', 'items_in', items_in]]

def func1():
# directly from incoming Object_in socket.
sn = bpy.context.node

# safe? or return early
return

verts = SvGetSocketAnyType(sn, sn.inputs['items_in'])
print(verts)

out_sockets = [['v', 'Verts', items_in]]
ui_operators = [['print_names', func1]]

return in_sockets, out_sockets, ui_operators


## Breakout Scripts¶

For lack of a better term, SN scripts written in this style let you pass variables to a script located in /sverchok-master/.. or /sverchok-master/your_module_name/some_library. To keep your sverchok-master folder organized I recommend using a module folder. In the example below, I made a folder inside sverchok-master called sv_modules and inside that I have a file called sv_curve_utils, which contains a function loft. This way of coding requires a bit of setup work, but then you can focus purely on the algorithm inside loft.

from mathutils import Vector, Euler, Matrix
import sv_modules
from sv_modules.sv_curve_utils import loft

def sv_main(verts_p=[], edges_p=[], verts_t=[], edges_t=[]):

in_sockets = [
['v', 'verts_p', verts_p],
['s', 'edges_p', edges_p],
['v', 'verts_t', verts_t],
['s', 'edges_t', edges_t]]

verts_out = []

def out_sockets():
return [['v', 'verts_out', verts_out]]

if not all([verts_p, edges_p, verts_t, edges_t]):
return in_sockets, out_sockets()

# while developing, it can be useful to uncomment this
if 'loft' in globals():
import imp
from sv_modules.sv_curve_utils import loft

verts_out = loft(verts_p[0], verts_t[0])  #  this is your break-out code

# here the call to out_sockets() will pick up verts_out
return in_sockets, out_sockets()


## Techniques to improve Python performance¶

There are many ways to speed up python code. Some slowness will be down to innefficient algorithm design, other slowness is caused purely by how much processing is minimally required to solve a problem. A decent read regarding general methods to improve python code performance can be found on python.org. If you don’t know where the cycles are being consumed, then you don’t know if your efforts to optimize will have any significant impact.

## Limitations¶

Most limitations are voided by increasing your Python and bpy skills.

## Future¶

SN iteration 1 is itself a prototype and is a testing ground for iteration 2. The intention was always to provide multiple programming language interfaces, initially coffeescript because it’s a lightweight language with crazy expressive capacity. iteration 2 might work a little different, perhaps working from within a class but trying to do extra introspection to reduce boilerplate.

The only reason in_sockets needs to be declared at the moment is if you want to have socket names that are different than the function arguments. It would be possible to allow sv_main() to take zero arguments too. So possible configurations should be:

sv_main()
sv_main() + in_sockets
sv_main() + out_sockets
sv_main(a=[],..)
sv_main(a=[],..) + in_sockets
sv_main(a=[],..) + out_sockets
sv_main(a=[],..) + in_socket + out_sockets


etc, with ui_operators optional to all combinations

That’s it for now.