All-in-One Scientific Computing Package
Project description
scisuit
A computing and visualization library designed with engineers in mind..
Available Libraries
- Plotting,
- Engineering,
- Statistics,
- Roots,
- Integration,
- Fitting,
- Optimization
Plot Library
Interactive charts (Bar, Box-Whisker, Bubble, Direction Field, Histogram, Moody, Psychrometry,
QQnorm, QQplot, Quiver, Scatter). Using the plot.gdi
library existing charts can be extended
or new visualizations can be created.
A simple scatter chart example:
import numpy as np
import scisuit.plot as plt
x = np.arange(1, 6)
y = x**2 - 2*x + 5
plt.scatter(x=x, y=y)
plt.show()
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
Many statistical tests & distributions.
import scisuit.stats as st
#Normal distribution
st.dnorm(0.1, mean=1, sd=2)
st.pnorm(0.1, mean=1, sd=2)
#Binomial distribution
st.dbinom(x=[7, 8, 9], size=9, prob=0.94))
#Weibull distribution
st.dweibull(x=3, shape=2, scale=4)
#log-normal distribution
st.dlnorm(0.1, meanlog=1, sdlog=2)
st.plnorm(0.1, meanlog=1, sdlog=2)
Numerics Library
Procedures for root finding, fitting, integration...
from scisuit.roots import bisect, brentq, Info
def func(x):
return x**2-5
root, info = bisect(f=func, a=0, b=5)
print("**** Bisection method ****")
print(root," ", info)
root, info = brentq(f=func, a=0, b=5)
print("\n **** Brent's method ****")
print(root," ", info)
**** Bisection method ****
2.2360706329345703 Info(err=9.5367431640625e-06, iter=19, conv=True, msg='')
**** Brent's method ****
2.2360684081902256 Info(err=None, iter=8, conv=True, msg='')
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