Randomness of Python's random

I'm using Python to generate images using dashed lines for stippling. The period of the dashing is constant, what changes is dash/space ratio. This produces something like this:

However in that image the dashing has a uniform origin and this creates unsightly vertical gutters. So I tried to randomize the origin to remove the gutters. This sort of works but there is an obvious pattern:

Wondering where this comes from I made a very simple test case with stacked dashed straight lines:

• dash ratio: 50%
• dash period 20px
• origin shift from -10px to +10px using random.uniform(-10.,+10.)(*) (after an initial random.seed()

And with added randomness:

So there is still pattern. What I don't understand is that to get a visible gutter you need to have 6 or 7 consecutive values falling in the same range (says, half the total range), which should be a 1/64 probability but seems to happen a lot more often in the 200 lines generated.

Am I misunderstanding something? Is it just our human brain which is seeing patterns where there is none? Could there be a better way to generate something more "visually random" (python 2.7, and preferably without installing anything)?

(*) partial pixels are valid in that context

Annex: the code I use (this is a Gimp script):

#!/usr/bin/env python
# -*- coding: iso-8859-15 -*-
Python script for Gimp (requires Gimp 2.10)
Run on a 400x400 image to see something without having to wait too much
Menu entry is in “Test” submenu of image menubar
import random,traceback

from gimpfu import *
def constant(minShift,maxShift):

return 0
def triangle(minShift,maxShift):

return random.triangular(minShift,maxShift)
def uniform(minShift,maxShift):

return random.uniform(minShift,maxShift)
def gauss(minShift,maxShift):

return random.gauss((minShift+maxShift)/2,(maxShift-minShift)/2)
variants=[(‘Constant’,constant),(‘Triangle’,triangle),(‘Uniform’,uniform),(‘Gauss’,gauss)]
def generate(image,name,generator):

random.seed()

layer=gimp.Layer(image, name, image.width, image.height, RGB_IMAGE,100, LAYER_MODE_NORMAL)

image.add_layer(layer,0)

layer.fill(FILL_WHITE)

path=pdb.gimp_vectors_new(image,name)
# Generate path, horizontal lines are 2px apart, 
# Start on left has a random offset, end is on the right edge right edge
for i in range(1,image.height, 2):
    shift=generator(-10.,10.)
    points=[shift,i]*3+[image.width,i]*3
    pdb.gimp_vectors_stroke_new_from_points(path,0, len(points),points,False)
pdb.gimp_image_add_vectors(image, path, 0)

# Stroke the path
pdb.gimp_context_set_foreground(gimpcolor.RGB(0, 0, 0, 255))
pdb.gimp_context_set_stroke_method(STROKE_LINE)
pdb.gimp_context_set_line_cap_style(0)
pdb.gimp_context_set_line_join_style(0)
pdb.gimp_context_set_line_miter_limit(0.)
pdb.gimp_context_set_line_width(2)
pdb.gimp_context_set_line_dash_pattern(2,[5,5])
pdb.gimp_drawable_edit_stroke_item(layer,path)

def randomTest(image):

image.undo_group_start()

gimp.context_push()
try:
    for name,generator in variants:
        generate(image,name,generator)
except Exception as e:
    print e.args[0]
    pdb.gimp_message(e.args[0])
    traceback.print_exc()

gimp.context_pop()
image.undo_group_end()
return;

Registration
desc=“Python random test”
register(

“randomize-test”,desc,‘’,‘’,‘’,‘’,desc,“*”,

[(PF_IMAGE, “image”, “Input image”, None),],[],

randomTest,menu=“<Image>/Test”,

)
main()

#python

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Why use Python for Software Development

No programming language is pretty much as diverse as Python. It enables building cutting edge applications effortlessly. Developers are as yet investigating the full capability of end-to-end Python development services in various areas. 

By areas, we mean FinTech, HealthTech, InsureTech, Cybersecurity, and that's just the beginning. These are New Economy areas, and Python has the ability to serve every one of them. The vast majority of them require massive computational abilities. Python's code is dynamic and powerful - equipped for taking care of the heavy traffic and substantial algorithmic capacities. 

Programming advancement is multidimensional today. Endeavor programming requires an intelligent application with AI and ML capacities. Shopper based applications require information examination to convey a superior client experience. Netflix, Trello, and Amazon are genuine instances of such applications. Python assists with building them effortlessly. 

5 Reasons to Utilize Python for Programming Web Apps 

Python can do such numerous things that developers can't discover enough reasons to admire it. Python application development isn't restricted to web and enterprise applications. It is exceptionally adaptable and superb for a wide range of uses.

Robust frameworks 

Python is known for its tools and frameworks. There's a structure for everything. Django is helpful for building web applications, venture applications, logical applications, and mathematical processing. Flask is another web improvement framework with no conditions. 

Web2Py, CherryPy, and Falcon offer incredible capabilities to customize Python development services. A large portion of them are open-source frameworks that allow quick turn of events. 

Simple to read and compose 

Python has an improved sentence structure - one that is like the English language. New engineers for Python can undoubtedly understand where they stand in the development process. The simplicity of composing allows quick application building. 

The motivation behind building Python, as said by its maker Guido Van Rossum, was to empower even beginner engineers to comprehend the programming language. The simple coding likewise permits developers to roll out speedy improvements without getting confused by pointless subtleties. 

Utilized by the best 

Alright - Python isn't simply one more programming language. It should have something, which is the reason the business giants use it. Furthermore, that too for different purposes. Developers at Google use Python to assemble framework organization systems, parallel information pusher, code audit, testing and QA, and substantially more. Netflix utilizes Python web development services for its recommendation algorithm and media player. 

Massive community support 

Python has a steadily developing community that offers enormous help. From amateurs to specialists, there's everybody. There are a lot of instructional exercises, documentation, and guides accessible for Python web development solutions. 

Today, numerous universities start with Python, adding to the quantity of individuals in the community. Frequently, Python designers team up on various tasks and help each other with algorithmic, utilitarian, and application critical thinking. 

Progressive applications 

Python is the greatest supporter of data science, Machine Learning, and Artificial Intelligence at any enterprise software development company. Its utilization cases in cutting edge applications are the most compelling motivation for its prosperity. Python is the second most well known tool after R for data analytics.

The simplicity of getting sorted out, overseeing, and visualizing information through unique libraries makes it ideal for data based applications. TensorFlow for neural networks and OpenCV for computer vision are two of Python's most well known use cases for Machine learning applications.

Summary

Thinking about the advances in programming and innovation, Python is a YES for an assorted scope of utilizations. Game development, web application development services, GUI advancement, ML and AI improvement, Enterprise and customer applications - every one of them uses Python to its full potential. 

The disadvantages of Python web improvement arrangements are regularly disregarded by developers and organizations because of the advantages it gives. They focus on quality over speed and performance over blunders. That is the reason it's a good idea to utilize Python for building the applications of the future.

#python development services #python development company #python app development #python development #python in web development #python software development

Python Tricks Every Developer Should Know

Python is awesome, it’s one of the easiest languages with simple and intuitive syntax but wait, have you ever thought that there might ways to write your python code simpler?

In this tutorial, you’re going to learn a variety of Python tricks that you can use to write your Python code in a more readable and efficient way like a pro.

Let’s get started

Swapping value in Python

Instead of creating a temporary variable to hold the value of the one while swapping, you can do this instead

>>> FirstName = "kalebu"
>>> LastName = "Jordan"
>>> FirstName, LastName = LastName, FirstName 
>>> print(FirstName, LastName)
('Jordan', 'kalebu')

#python #python-programming #python3 #python-tutorials #learn-python #python-tips #python-skills #python-development

How to Remove all Duplicate Files on your Drive via Python

Today you’re going to learn how to use Python programming in a way that can ultimately save a lot of space on your drive by removing all the duplicates.

Intro

In many situations you may find yourself having duplicates files on your disk and but when it comes to tracking and checking them manually it can tedious.

Heres a solution

Instead of tracking throughout your disk to see if there is a duplicate, you can automate the process using coding, by writing a program to recursively track through the disk and remove all the found duplicates and that’s what this article is about.

But How do we do it?

If we were to read the whole file and then compare it to the rest of the files recursively through the given directory it will take a very long time, then how do we do it?

The answer is hashing, with hashing can generate a given string of letters and numbers which act as the identity of a given file and if we find any other file with the same identity we gonna delete it.

There’s a variety of hashing algorithms out there such as

• md5
• sha1
• sha224, sha256, sha384 and sha512

#python-programming #python-tutorials #learn-python #python-project #python3 #python #python-skills #python-tips

Generate Random Numbers in Python

There are two types of random number generators: pseudo-random number generator and true random number generator.

Pseudorandom numbers depend on computer algorithms. The computer uses algorithms to generate random numbers. These random numbers are not truly random because they are predictable like the generated numbers using NumPy random seed.

Whereas, truly random numbers are generated by measuring truly physical random parameters so we can ensure that the generated numbers are truly random.

The pseudo-random numbers are not safe to use in cryptography because they can be guessed by attackers.

In Python, the built-in random module generates pseudo-random numbers. In this tutorial, we will discuss both types. So let’s get started.

Table of Contents

• Random number between 0 and 100
• Random number with a step size
• Random floating/double number in a range
• Random number from Iterable
• Using random.choice()
• Using randint()
• Using randrange()
• Excluding certain numbers from a range (Conditional choice)
• Random number of length N
• Random numbers in a two-dimensional array
• Random number probability
• Mean and standard deviation
• Negative random number
• Generate with duplicates
• Using randrange()
• Using choice()
• Generate without duplicates (Unique random numbers)
• Random number on circle
• Shuffle Numbers
• Generate true random number

#python #random #generate random numbers #random numbers #generate random numbers in python

How To Compare Tesla and Ford Company By Using Magic Methods in Python

Magic Methods are the special methods which gives us the ability to access built in syntactical features such as ‘<’, ‘>’, ‘==’, ‘+’ etc…

You must have worked with such methods without knowing them to be as magic methods. Magic methods can be identified with their names which start with __ and ends with __ like init, call, str etc. These methods are also called Dunder Methods, because of their name starting and ending with Double Underscore (Dunder).

Now there are a number of such special methods, which you might have come across too, in Python. We will just be taking an example of a few of them to understand how they work and how we can use them.

1. init

class AnyClass:
    def __init__():
        print("Init called on its own")
obj = AnyClass()

The first example is _init, _and as the name suggests, it is used for initializing objects. Init method is called on its own, ie. whenever an object is created for the class, the init method is called on its own.

The output of the above code will be given below. Note how we did not call the init method and it got invoked as we created an object for class AnyClass.

Init called on its own

2. add

Let’s move to some other example, add gives us the ability to access the built in syntax feature of the character +. Let’s see how,

class AnyClass:
    def __init__(self, var):
        self.some_var = var
    def __add__(self, other_obj):
        print("Calling the add method")
        return self.some_var + other_obj.some_var
obj1 = AnyClass(5)
obj2 = AnyClass(6)
obj1 + obj2

#python3 #python #python-programming #python-web-development #python-tutorials #python-top-story #python-tips #learn-python