E C E

PYQ

Python For Engg


S7

KTU 2019 Scheme


Seventh Semester B.Tech Degree Examination December 2022 (2019 scheme)

Python For Engg Previous Year Question papers 📃


S7


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SYLLABUS

https://pythonforengnrs.blogspot.com/2022/09/syllabus.html

MODEL QP

https://pythonforengnrs.blogspot.com/2022/09/model-question-paper.html

https://pythonforengnrs.blogspot.com/2022/09/python-for-engineers-cst-445-ktu-open.html

Pandas Continued.pdf

Special Function Numerical and ODE.pdf

CST445 VVImp.pdf

NumPy should be imported with SciPy.
9.1 Special Functions
SciPy provides a plethora of special functions, including Bessel functions
(and routines for finding their zeros, derivatives, and integrals),
error functions, the gamma function, Legendre, Laguerre, and Hermite
polynomials (and other polynomial functions), Mathieu functions,
many statistical functions, and a number of other functions.
Most are contained in the scipi.special library, and each has its own
special arguments and syntax, depending on the vagaries of the particular
function.We demonstrate a number of them in the code below
that produces a plot of the different functions called. For more information,
you should consult the SciPy web site on the scipy.special
library.

Solving systems of linear equations
Solving systems of equations is nearly as simple as constructing a coefficient matrix and a column vector. Suppose you have the following system of linear equations to solve:
2x1+4x2+6x3=4
x1−3x2−9x3=−11

8x1+5x2−7x3=1
Ax=b
A=np.array([[2,4,6],[1,-3,-9],[8,5,-7]])
b=np.array([4,-11,1])
x1,x2,x3=scipy.linalg.solve(A,b)
print(x1,x2,x3)
output
-8.52173913043478 9.695652173913041 -2.9565217391304337

Scipy
January 20, 2022


SciPy is a Python library of mathematical routines. Many of the SciPy routines are Python “wrappers,” that is, Python routines that provide a Python interface, for numerical libraries and routines originally written in Fortran, C, or C++. Thus, SciPy lets you take advantage of the decades of work that has gone into creating and optimizing numerical routines for science and engineering. Because the Fortran, C, or C++ code that Python accesses is compiled, these routines typically run very fast. Therefore, there is no real downside—no speed penalty—for using Python in these cases.

SciPy provides a plethora of special functions, including Bessel functions (and routines for finding their zeros, derivatives, and integrals), error functions, the gamma function, Legendre, Laguerre, and Hermite polynomials (and other polynomial functions), Mathieu functions, many statistical functions, and a number of other functions. Most are contained in the scipi.special library, and each has its own special arguments and syntax, depending on the vagaries of the particular function

*Vector plots
VectorPlot is also known as field plot and direction plot. VectorPlot displays a vector field by drawing arrows. By default the direction of the vector is indicated by the direction of the arrow, and the magnitude is indicated by its color. VectorPlot omits any arrows for which the vi etc.*

>>> numpy.array([1,2,3,4])
array([1, 2, 3, 4])
### - simple array creation



>>> numpy.arange(1,100,10)
array([ 1, 11, 21, 31, 41, 51, 61, 71, 81, 91])
### array starting from 1 to 100 with step of 10


>>> numpy.linspace(0,2,10)
array([0. , 0.22222222, 0.44444444, 0.66666667, 0.88888889,
1.11111111, 1.33333333, 1.55555556, 1.77777778, 2. ])
### array of linearly distributed 10 elements between 0 and 2


>>> numpy.logspace(0,2,10)
array([ 1. , 1.66810054, 2.7825594 , 4.64158883,
7.74263683, 12.91549665, 21.5443469 , 35.93813664,
59.94842503, 100. ])
### array of logarithmicly distributed 10 elements between 0 and 2


>>> numpy.zeros(10)
array([0., 0., 0., 0., 0., 0., 0., 0., 0., 0.])
### Array of zeroes

>>> numpy.ones(10)
array([1., 1., 1., 1., 1., 1., 1., 1., 1., 1.])
### Array of ones

>>> numpy.empty(10)
array([1., 1., 1., 1., 1., 1., 1., 1., 1., 1.])
### empty gives un-initialized array, It can be of any value.


>>> numpy.eye()
this returns "Identity matrix"
https://www.geeksforgeeks.org/numpy-eye-python/
Identity matrix is equivalent to 1 in matrix arithmatics

Module 4 Programming in Python Notes - Kerala Notes.pdf


I think it's 3rd Modules as per our syllabus . So keep an eye in your KTU syllabus

CST 283 M2 - KQB KtuQbank.pdf

CST362 Module 1.pdf

https://pythonforengnrs.blogspot.com/2022/09/python-for-engineers-cst-445-ktu-open.html


=>

PYQ

MODULE WISE NOTES 📝


Some Reference or Notes 📝

Study by keeping an eye on your KTU Syllabus