scisuit 2.5.1

A computing library designed with engineers in mind..

scisuit

High performance, scientific computing library designed with engineers in mind..

 

Engineering Library

Designed mostly for process engineers.

Examples

1. Psychrometry:

Computation of properties of humid-air.

from scisuit.eng import psychrometry

r = psychrometry(P=101, Tdb=30, Twb=20)

#all of the properties
print(r)

P=101.0 kPa,
Tdb=30.0 C
Twb=20.0 C
Tdp=14.17 C
H=57.06 kJ/kg da
RH=39.82 %
W=0.0106 kg/kg da
V=0.876 m3/kg da

 

2. Food:

A rich class for not only computation of food properties but also to perform food arithmetic.

import scisuit.eng as eng

milk = eng.Food(water=88.13, protein=3.15, cho=4.80, lipid=3.25, ash=0.67)
water = eng.Food(water=100)

#removal of 87% water from milk
powder = milk - 0.87*water 
print(powder)

Type = Food
Weight (unit weight) = 0.13
Temperature (C) = 20.0
water (%) = 8.69
cho (%) = 36.92
protein (%) = 24.23
lipid (%) = 25.0
ash (%) = 5.15
aw = 0.194

   

Statistics Library

Statistical tests & distributions.

from scisuit.stats import linregress

#input values
temperature = [80, 93, 100, 82, 90, 99, 81, 96, 94, 93, 97, 95, 100, 85, 86, 87]
feedrate = [8, 9, 10, 12, 11, 8, 8, 10, 12, 11, 13, 11, 8, 12, 9, 12]
viscosity = [2256, 2340, 2426, 2293, 2330, 2368, 2250, 2409, 2364, 2379, 2440, 2364, 2404, 2317, 2309, 2328]

#note the order of input to factor
result = linregress(yobs=viscosity, factor=[temperature, feedrate])
print(result)

#Output
Multiple Linear Regression  
F=82.5, p-value=4.0997e-08, R2=0.93

Predictor        Coeff        StdError         T             p-value
X0               1566.078         61.59       25.43       9.504e-14
X1               7.621            0.62        12.32       3.002e-09
X2               8.585            2.44        3.52        3.092e-03

   

Numerics Library

Libraries for solving ODE, root finding, fitting, integration...

from scisuit.roots import bisect, brentq, itp

args = {"f":lambda x: x**2-5, "a":0, "b": 4} 

for func in [bisect, brentq, itp]:
    print(func(**args))

Bisection using brute-force method 
Root=2.23607, Error=3.35e-06 (18 iterations).

Brent's method (inverse quadratic interpolation)
Root=2.23607, (7 iterations).

ITP method
Root=2.23607, Error=3.43e-07 (7 iterations).

   

UI Library

For designing notebook-embedded apps, running in web workers, with Python. Requires js and pyodide-py libraries.
This is specifically designed for notebooks on https://www.pebytes.com

Available widgets:

• button
• Inputs (checkbox, radio, textbox)
• label
• layout managers (hpanel, vpanel, table)
• output
• select
• textarea

Knowledge of HTML and CSS can be immensely helpful to modify widgets.

import scisuit.ui as ui
# Define the UI
app = ui.Body()

txt1 = ui.Textbox(value="")
txt2 = ui.Textbox(value="")
btnremove = ui.Button("rmove", style= "color: red; background: yellow")
btnadd = ui.Button("add")


panel = ui.VPanel([txt1, btnremove, txt2, btnadd], halign="start", style="padding: 1em")
app.add(panel)

# Mount the app to the target id
app.mount(target_id=_SYS_CELLID_)


def onBtnRemoveClick(event):
	panel.remove(txt1)

def onBtnAddClick(event):
	panel.add(txt1)
	panel.update()


btnremove.onClick(onBtnRemoveClick)
btnadd.onClick(onBtnAddClick)