Explore Python Classes and Their Use in Keras

In this Keras article, we will learn about Python Classes and Their Use in Keras. Classes are one of the fundamental building blocks of the Python language, which may be applied in the development of machine learning applications. As we shall see, the Python syntax for developing classes is simple and can be applied to implement callbacks in Keras. 

in this tutorial, you will discover the Python classes and their functionality. 

After completing this tutorial, you will know:

  • Why Python classes are important
  • How to define and instantiate a class and set its attributes 
  • How to create methods and pass arguments
  • What is class inheritance
  • How to use classes to implement callbacks in Keras

Kick-start your project with my new book Python for Machine Learning, including step-by-step tutorials and the Python source code files for all examples.

Let’s get started.

Tutorial Overview

This tutorial is divided into six parts; they are:

  • Introduction to Classes
  • Defining a Class
  • Instantiation and Attribute References
  • Creating Methods and Passing Arguments
  • Class Inheritance
  • Using Classes in Keras

Introduction to Classes

In object-oriented languages, such as Python, classes are one of the fundamental building blocks. 

They can be likened to blueprints for an object, as they define what properties and methods/behaviors an object should have.

Python Fundamentals, 2018.

Creating a new class creates a new object, where every class instance can be characterized by its attributes to maintain its state and methods to modify its state.

Defining a Class

The class keyword allows for the creation of a new class definition, immediately followed by the class name:

class MyClass:

In this manner, a new class object bound to the specified class name (MyClass, in this case) is created. Each class object can support instantiation and attribute references, as we will see shortly.

Instantiation and Attribute References

Instantiation is the creation of a new instance of a class.

To create a new instance of a class, we can call it using its class name and assign it to a variable. This will create a new, empty class object:

x = MyClass()

Upon creating a new instance of a class, Python calls its object constructor method, __init()__, which often takes arguments that are used to set the instantiated object’s attributes. 

We can define this constructor method in our class just like a function and specify attributes that will need to be passed in when instantiating an object.

Python Fundamentals, 2018.

Let’s say, for instance, that we would like to define a new class named Dog:

class Dog:
 family = "Canine"
 def __init__(self, name, breed):
 self.name = name
 self.breed = breed

Here, the constructor method takes two arguments, name and breed, which can be passed to it upon instantiating the object:

dog1 = Dog("Lassie", "Rough Collie")

In the example that we are considering, name and breed are known as instance variables (or attributes) because they are bound to a specific instance. This means that such attributes belong only to the object in which they have been set but not to any other object instantiated from the same class. 

On the other hand, family is a class variable (or attribute) because it is shared by all instances of the same class.

You may also note that the first argument of the constructor method (or any other method) is often called self. This argument refers to the object that we are in the process of creating. It is good practice to follow the convention of setting the first argument to self to ensure the readability of your code for other programmers. 

Once we have set our object’s attributes, they can be accessed using the dot operator. For example, considering again the dog1 instance of the Dog class, its name attribute may be accessed as follows:


Producing the following output:


Creating Methods and Passing Arguments

In addition to having a constructor method, a class object can also have several other methods for modifying its state. 

The syntax for defining an instance method is familiar. We pass the argument self … It is always the first argument of an instance method.

Python Fundamentals, 2018.

Similar to the constructor method, each instance method can take several arguments, with the first one being the argument self that lets us set and access the object’s attributes:

class Dog:
 family = "Canine"
 def __init__(self, name, breed):
 self.name = name
 self.breed = breed
 def info(self):
 print(self.name, "is a female", self.breed)

Different methods of the same object can also use the self argument to call each other:

class Dog:
 family = "Canine"
 def __init__(self, name, breed):
 self.name = name
 self.breed = breed
 self.tricks = []
 def add_tricks(self, x):
 def info(self, x):
 print(self.name, "is a female", self.breed, "that", self.tricks[0])

An output string can then be generated as follows:

dog1 = Dog("Lassie", "Rough Collie")
dog1.info("barks on command")

We find that, in doing so, the barks on command input is appended to the tricks list when the info() method calls the add_tricks() method. The following output is produced:

Lassie is a female Rough Collie that barks on command

Class Inheritance

Another feature that Python supports is class inheritance

Inheritance is a mechanism that allows a subclass (also known as a derived or child class) to access all attributes and methods of a superclass (also known as a base or parent class). 

The syntax for using a subclass is the following:

class SubClass(BaseClass):

It is also possible that a subclass inherits from multiple base classes, too. In this case, the syntax would be as follows:

class SubClass(BaseClass1, BaseClass2, BaseClass3):

Class attributes and methods are searched for in the base class and also in subsequent base classes in the case of multiple inheritances. 

Python further allows a method in a subclass to override another method in the base class that carries the same name. An overriding method in the subclass may be replacing the base class method or simply extending its capabilities. When an overriding subclass method is available, it is this method that is executed when called, rather than the method with the same name in the base class. 

Using Classes in Keras

A practical use of classes in Keras is to write one’s own callbacks. 

A callback is a powerful tool in Keras that allows us to look at our model’s behavior during the different stages of training, testing, and prediction. 

Indeed, we may pass a list of callbacks to any of the following:

  • keras.Model.fit()
  • keras.Model.evaluate()
  • keras.Model.predict()

The Keras API comes with several built-in callbacks. Nonetheless, we might wish to write our own, and for this purpose, we shall look at how to build a custom callback class. In order to do so, we can inherit several methods from the callback base class, which can provide us with information of when:

  • Training, testing, and prediction starts and ends
  • An epoch starts and ends
  • A training, testing, and prediction batch starts and ends

Let’s first consider a simple example of a custom callback that reports back every time that an epoch starts and ends. We will name this custom callback class, EpochCallback, and override the epoch-level methods, on_epoch_begin() and on_epoch_end(), from the base class, keras.callbacks.Callback:

import tensorflow.keras as keras
class EpochCallback(keras.callbacks.Callback):
    def on_epoch_begin(self, epoch, logs=None):
        print("Starting epoch {}".format(epoch + 1))
    def on_epoch_end(self, epoch, logs=None):
        print("Finished epoch {}".format(epoch + 1))

In order to test the custom callback that we have just defined, we need a model to train. For this purpose, let’s define a simple Keras model:

from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense, Flatten
def simple_model():
    model = Sequential()
    model.add(Flatten(input_shape=(28, 28)))
    model.add(Dense(128, activation="relu"))
    model.add(Dense(10, activation="softmax"))
    return model

We also need a dataset to train on, for which purpose we will be using the MNIST dataset:

from tensorflow.keras.datasets import mnist
from tensorflow.keras.utils import to_categorical
# Loading the MNIST training and testing data splits
(x_train, y_train), (x_test, y_test) = mnist.load_data()
# Pre-processing the training data
x_train = x_train / 255.0
x_train = x_train.reshape(60000, 28, 28, 1)
y_train_cat = to_categorical(y_train, 10)

Now, let’s try out the custom callback by adding it to the list of callbacks that we pass as input to the keras.Model.fit() method:

model = simple_model()

The callback that we have just created produces the following output:

Starting epoch 1
Finished epoch 1
Starting epoch 2
Finished epoch 2
Starting epoch 3
Finished epoch 3
Starting epoch 4
Finished epoch 4
Starting epoch 5
Finished epoch 5

We can create another custom callback that monitors the loss value at the end of each epoch and stores the model weights only if the loss has decreased. To this end, we will be reading the loss value from the log dict, which stores the metrics at the end of each batch and epoch. We will also be accessing the model corresponding to the current round of training, testing, or prediction, by means of self.model

Let’s call this custom callback, CheckpointCallback:

import numpy as np
class CheckpointCallback(keras.callbacks.Callback):
    def __init__(self):
        super(CheckpointCallback, self).__init__()
        self.best_weights = None
    def on_train_begin(self, logs=None):
        self.best_loss = np.Inf
    def on_epoch_end(self, epoch, logs=None):
        current_loss = logs.get("loss")
        print("Current loss is {}".format(current_loss))
        if np.less(current_loss, self.best_loss):
            self.best_loss = current_loss
            self.best_weights = self.model.get_weights()
            print("Storing the model weights at epoch {} \n".format(epoch + 1))

We can try this out again, this time including the CheckpointCallback into the list of callbacks:

model = simple_model()
          callbacks=[EpochCallback(), CheckpointCallback()],

The following output of the two callbacks together is now produced:

Starting epoch 1
Finished epoch 1
Current loss is 0.6327750086784363
Storing the model weights at epoch 1
Starting epoch 2
Finished epoch 2
Current loss is 0.3391888439655304
Storing the model weights at epoch 2
Starting epoch 3
Finished epoch 3
Current loss is 0.29216915369033813
Storing the model weights at epoch 3
Starting epoch 4
Finished epoch 4
Current loss is 0.2625095248222351
Storing the model weights at epoch 4
Starting epoch 5
Finished epoch 5
Current loss is 0.23906977474689484
Storing the model weights at epoch 5

Other classes in Keras

Besides callbacks, we can also make derived classes in Keras for custom metrics (derived from keras.metrics.Metrics), custom layers (derived from keras.layers.Layer), custom regularizer (derived from keras.regularizers.Regularizer), or even custom models (derived from keras.Model, for such as changing the behavior of invoking a model). All you have to do is follow the guideline to change the member functions of a class. You must use exactly the same name and parameters in the member functions.

Below is an example from Keras documentation:

class BinaryTruePositives(tf.keras.metrics.Metric):
  def __init__(self, name='binary_true_positives', **kwargs):
    super(BinaryTruePositives, self).__init__(name=name, **kwargs)
    self.true_positives = self.add_weight(name='tp', initializer='zeros')
  def update_state(self, y_true, y_pred, sample_weight=None):
    y_true = tf.cast(y_true, tf.bool)
    y_pred = tf.cast(y_pred, tf.bool)
    values = tf.logical_and(tf.equal(y_true, True), tf.equal(y_pred, True))
    values = tf.cast(values, self.dtype)
    if sample_weight is not None:
      sample_weight = tf.cast(sample_weight, self.dtype)
      values = tf.multiply(values, sample_weight)
  def result(self):
    return self.true_positives
  def reset_states(self):
m = BinaryTruePositives()
m.update_state([0, 1, 1, 1], [0, 1, 0, 0])
print('Intermediate result:', float(m.result()))
m.update_state([1, 1, 1, 1], [0, 1, 1, 0])
print('Final result:', float(m.result()))

This reveals why we would need a class for the custom metric: A metric is not just a function but a function that computes its value incrementally, once per batch of training data during the training cycle. Eventually, the result is reported at the result() function at the end of an epoch and reset its memory using the reset_state() function so you can start afresh in the next epoch.

For the details on what exactly has to be derived, you should refer to Keras’ documentation.

Original article sourced at: https://machinelearningmastery.com

#python #keras 

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Explore Python Classes and Their Use in Keras
Chloe  Butler

Chloe Butler


Pdf2gerb: Perl Script Converts PDF Files to Gerber format


Perl script converts PDF files to Gerber format

Pdf2Gerb generates Gerber 274X photoplotting and Excellon drill files from PDFs of a PCB. Up to three PDFs are used: the top copper layer, the bottom copper layer (for 2-sided PCBs), and an optional silk screen layer. The PDFs can be created directly from any PDF drawing software, or a PDF print driver can be used to capture the Print output if the drawing software does not directly support output to PDF.

The general workflow is as follows:

  1. Design the PCB using your favorite CAD or drawing software.
  2. Print the top and bottom copper and top silk screen layers to a PDF file.
  3. Run Pdf2Gerb on the PDFs to create Gerber and Excellon files.
  4. Use a Gerber viewer to double-check the output against the original PCB design.
  5. Make adjustments as needed.
  6. Submit the files to a PCB manufacturer.

Please note that Pdf2Gerb does NOT perform DRC (Design Rule Checks), as these will vary according to individual PCB manufacturer conventions and capabilities. Also note that Pdf2Gerb is not perfect, so the output files must always be checked before submitting them. As of version 1.6, Pdf2Gerb supports most PCB elements, such as round and square pads, round holes, traces, SMD pads, ground planes, no-fill areas, and panelization. However, because it interprets the graphical output of a Print function, there are limitations in what it can recognize (or there may be bugs).

See docs/Pdf2Gerb.pdf for install/setup, config, usage, and other info.


#Pdf2Gerb config settings:
#Put this file in same folder/directory as pdf2gerb.pl itself (global settings),
#or copy to another folder/directory with PDFs if you want PCB-specific settings.
#There is only one user of this file, so we don't need a custom package or namespace.
#NOTE: all constants defined in here will be added to main namespace.
#package pdf2gerb_cfg;

use strict; #trap undef vars (easier debug)
use warnings; #other useful info (easier debug)

#configurable settings:
#change values here instead of in main pfg2gerb.pl file

use constant WANT_COLORS => ($^O !~ m/Win/); #ANSI colors no worky on Windows? this must be set < first DebugPrint() call

#just a little warning; set realistic expectations:
#DebugPrint("${\(CYAN)}Pdf2Gerb.pl ${\(VERSION)}, $^O O/S\n${\(YELLOW)}${\(BOLD)}${\(ITALIC)}This is EXPERIMENTAL software.  \nGerber files MAY CONTAIN ERRORS.  Please CHECK them before fabrication!${\(RESET)}", 0); #if WANT_DEBUG

use constant METRIC => FALSE; #set to TRUE for metric units (only affect final numbers in output files, not internal arithmetic)
use constant APERTURE_LIMIT => 0; #34; #max #apertures to use; generate warnings if too many apertures are used (0 to not check)
use constant DRILL_FMT => '2.4'; #'2.3'; #'2.4' is the default for PCB fab; change to '2.3' for CNC

use constant WANT_DEBUG => 0; #10; #level of debug wanted; higher == more, lower == less, 0 == none
use constant GERBER_DEBUG => 0; #level of debug to include in Gerber file; DON'T USE FOR FABRICATION
use constant WANT_STREAMS => FALSE; #TRUE; #save decompressed streams to files (for debug)
use constant WANT_ALLINPUT => FALSE; #TRUE; #save entire input stream (for debug ONLY)

#DebugPrint(sprintf("${\(CYAN)}DEBUG: stdout %d, gerber %d, want streams? %d, all input? %d, O/S: $^O, Perl: $]${\(RESET)}\n", WANT_DEBUG, GERBER_DEBUG, WANT_STREAMS, WANT_ALLINPUT), 1);
#DebugPrint(sprintf("max int = %d, min int = %d\n", MAXINT, MININT), 1); 

#define standard trace and pad sizes to reduce scaling or PDF rendering errors:
#This avoids weird aperture settings and replaces them with more standardized values.
#(I'm not sure how photoplotters handle strange sizes).
#Fewer choices here gives more accurate mapping in the final Gerber files.
#units are in inches
use constant TOOL_SIZES => #add more as desired
#round or square pads (> 0) and drills (< 0):
    .010, -.001,  #tiny pads for SMD; dummy drill size (too small for practical use, but needed so StandardTool will use this entry)
    .031, -.014,  #used for vias
    .041, -.020,  #smallest non-filled plated hole
    .051, -.025,
    .056, -.029,  #useful for IC pins
    .070, -.033,
    .075, -.040,  #heavier leads
#    .090, -.043,  #NOTE: 600 dpi is not high enough resolution to reliably distinguish between .043" and .046", so choose 1 of the 2 here
    .100, -.046,
    .115, -.052,
    .130, -.061,
    .140, -.067,
    .150, -.079,
    .175, -.088,
    .190, -.093,
    .200, -.100,
    .220, -.110,
    .160, -.125,  #useful for mounting holes
#some additional pad sizes without holes (repeat a previous hole size if you just want the pad size):
    .090, -.040,  #want a .090 pad option, but use dummy hole size
    .065, -.040, #.065 x .065 rect pad
    .035, -.040, #.035 x .065 rect pad
    .001,  #too thin for real traces; use only for board outlines
    .006,  #minimum real trace width; mainly used for text
    .008,  #mainly used for mid-sized text, not traces
    .010,  #minimum recommended trace width for low-current signals
    .015,  #moderate low-voltage current
    .020,  #heavier trace for power, ground (even if a lighter one is adequate)
    .030,  #heavy-current traces; be careful with these ones!
#Areas larger than the values below will be filled with parallel lines:
#This cuts down on the number of aperture sizes used.
#Set to 0 to always use an aperture or drill, regardless of size.
use constant { MAX_APERTURE => max((TOOL_SIZES)) + .004, MAX_DRILL => -min((TOOL_SIZES)) + .004 }; #max aperture and drill sizes (plus a little tolerance)
#DebugPrint(sprintf("using %d standard tool sizes: %s, max aper %.3f, max drill %.3f\n", scalar((TOOL_SIZES)), join(", ", (TOOL_SIZES)), MAX_APERTURE, MAX_DRILL), 1);

#NOTE: Compare the PDF to the original CAD file to check the accuracy of the PDF rendering and parsing!
#for example, the CAD software I used generated the following circles for holes:
#CAD hole size:   parsed PDF diameter:      error:
#  .014                .016                +.002
#  .020                .02267              +.00267
#  .025                .026                +.001
#  .029                .03167              +.00267
#  .033                .036                +.003
#  .040                .04267              +.00267
#This was usually ~ .002" - .003" too big compared to the hole as displayed in the CAD software.
#To compensate for PDF rendering errors (either during CAD Print function or PDF parsing logic), adjust the values below as needed.
#units are pixels; for example, a value of 2.4 at 600 dpi = .0004 inch, 2 at 600 dpi = .0033"
use constant
    HOLE_ADJUST => -0.004 * 600, #-2.6, #holes seemed to be slightly oversized (by .002" - .004"), so shrink them a little
    RNDPAD_ADJUST => -0.003 * 600, #-2, #-2.4, #round pads seemed to be slightly oversized, so shrink them a little
    SQRPAD_ADJUST => +0.001 * 600, #+.5, #square pads are sometimes too small by .00067, so bump them up a little
    RECTPAD_ADJUST => 0, #(pixels) rectangular pads seem to be okay? (not tested much)
    TRACE_ADJUST => 0, #(pixels) traces seemed to be okay?
    REDUCE_TOLERANCE => .001, #(inches) allow this much variation when reducing circles and rects

#Also, my CAD's Print function or the PDF print driver I used was a little off for circles, so define some additional adjustment values here:
#Values are added to X/Y coordinates; units are pixels; for example, a value of 1 at 600 dpi would be ~= .002 inch
use constant
    CIRCLE_ADJUST_MINY => -0.001 * 600, #-1, #circles were a little too high, so nudge them a little lower
    CIRCLE_ADJUST_MAXX => +0.001 * 600, #+1, #circles were a little too far to the left, so nudge them a little to the right
    SUBST_CIRCLE_CLIPRECT => FALSE, #generate circle and substitute for clip rects (to compensate for the way some CAD software draws circles)
    WANT_CLIPRECT => TRUE, #FALSE, #AI doesn't need clip rect at all? should be on normally?
    RECT_COMPLETION => FALSE, #TRUE, #fill in 4th side of rect when 3 sides found

#allow .012 clearance around pads for solder mask:
#This value effectively adjusts pad sizes in the TOOL_SIZES list above (only for solder mask layers).
use constant SOLDER_MARGIN => +.012; #units are inches

#line join/cap styles:
use constant
    CAP_NONE => 0, #butt (none); line is exact length
    CAP_ROUND => 1, #round cap/join; line overhangs by a semi-circle at either end
    CAP_SQUARE => 2, #square cap/join; line overhangs by a half square on either end
    CAP_OVERRIDE => FALSE, #cap style overrides drawing logic
#number of elements in each shape type:
use constant
    RECT_SHAPELEN => 6, #x0, y0, x1, y1, count, "rect" (start, end corners)
    LINE_SHAPELEN => 6, #x0, y0, x1, y1, count, "line" (line seg)
    CURVE_SHAPELEN => 10, #xstart, ystart, x0, y0, x1, y1, xend, yend, count, "curve" (bezier 2 points)
    CIRCLE_SHAPELEN => 5, #x, y, 5, count, "circle" (center + radius)
#const my %SHAPELEN =
#Readonly my %SHAPELEN =>
    rect => RECT_SHAPELEN,
    line => LINE_SHAPELEN,
    curve => CURVE_SHAPELEN,
    circle => CIRCLE_SHAPELEN,

#This will repeat the entire body the number of times indicated along the X or Y axes (files grow accordingly).
#Display elements that overhang PCB boundary can be squashed or left as-is (typically text or other silk screen markings).
#Set "overhangs" TRUE to allow overhangs, FALSE to truncate them.
#xpad and ypad allow margins to be added around outer edge of panelized PCB.
use constant PANELIZE => {'x' => 1, 'y' => 1, 'xpad' => 0, 'ypad' => 0, 'overhangs' => TRUE}; #number of times to repeat in X and Y directions

# Set this to 1 if you need TurboCAD support.
#$turboCAD = FALSE; #is this still needed as an option?

#CIRCAD pad generation uses an appropriate aperture, then moves it (stroke) "a little" - we use this to find pads and distinguish them from PCB holes. 
use constant PAD_STROKE => 0.3; #0.0005 * 600; #units are pixels
#convert very short traces to pads or holes:
use constant TRACE_MINLEN => .001; #units are inches
#use constant ALWAYS_XY => TRUE; #FALSE; #force XY even if X or Y doesn't change; NOTE: needs to be TRUE for all pads to show in FlatCAM and ViewPlot
use constant REMOVE_POLARITY => FALSE; #TRUE; #set to remove subtractive (negative) polarity; NOTE: must be FALSE for ground planes

#PDF uses "points", each point = 1/72 inch
#combined with a PDF scale factor of .12, this gives 600 dpi resolution (1/72 * .12 = 600 dpi)
use constant INCHES_PER_POINT => 1/72; #0.0138888889; #multiply point-size by this to get inches

# The precision used when computing a bezier curve. Higher numbers are more precise but slower (and generate larger files).
#$bezierPrecision = 100;
use constant BEZIER_PRECISION => 36; #100; #use const; reduced for faster rendering (mainly used for silk screen and thermal pads)

# Ground planes and silk screen or larger copper rectangles or circles are filled line-by-line using this resolution.
use constant FILL_WIDTH => .01; #fill at most 0.01 inch at a time

# The max number of characters to read into memory
use constant MAX_BYTES => 10 * M; #bumped up to 10 MB, use const

use constant DUP_DRILL1 => TRUE; #FALSE; #kludge: ViewPlot doesn't load drill files that are too small so duplicate first tool

my $runtime = time(); #Time::HiRes::gettimeofday(); #measure my execution time

print STDERR "Loaded config settings from '${\(__FILE__)}'.\n";
1; #last value must be truthful to indicate successful load


#use Package::Constants;
#use Exporter qw(import); #https://perldoc.perl.org/Exporter.html

#my $caller = "pdf2gerb::";

#sub cfg
#    my $proto = shift;
#    my $class = ref($proto) || $proto;
#    my $settings =
#    {
#        $WANT_DEBUG => 990, #10; #level of debug wanted; higher == more, lower == less, 0 == none
#    };
#    bless($settings, $class);
#    return $settings;

#use constant HELLO => "hi there2"; #"main::HELLO" => "hi there";
#use constant GOODBYE => 14; #"main::GOODBYE" => 12;

#print STDERR "read cfg file\n";

#our @EXPORT_OK = Package::Constants->list(__PACKAGE__); #https://www.perlmonks.org/?node_id=1072691; NOTE: "_OK" skips short/common names

#print STDERR scalar(@EXPORT_OK) . " consts exported:\n";
#foreach(@EXPORT_OK) { print STDERR "$_\n"; }
#my $val = main::thing("xyz");
#print STDERR "caller gave me $val\n";
#foreach my $arg (@ARGV) { print STDERR "arg $arg\n"; }

Download Details:

Author: swannman
Source Code: https://github.com/swannman/pdf2gerb

License: GPL-3.0 license


Ray  Patel

Ray Patel


Lambda, Map, Filter functions in python

Welcome to my Blog, In this article, we will learn python lambda function, Map function, and filter function.

Lambda function in python: Lambda is a one line anonymous function and lambda takes any number of arguments but can only have one expression and python lambda syntax is

Syntax: x = lambda arguments : expression

Now i will show you some python lambda function examples:

#python #anonymous function python #filter function in python #lambda #lambda python 3 #map python #python filter #python filter lambda #python lambda #python lambda examples #python map

Shardul Bhatt

Shardul Bhatt


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.


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

Lawrence  Lesch

Lawrence Lesch


Superdom: Better and Simpler ES6 DOM Manipulation


You have dom. It has all the DOM virtually within it. Use that power:

// Fetch all the page links
let links = dom.a.href;

// Links open in a new tab
dom.a.target = '_blank';

Only for modern browsers

Getting started

Simply use the CDN via unpkg.com:

<script src="https://unpkg.com/superdom@1"></script>

Or use npm or bower:

npm|bower install superdom --save


It always returns an array with the matched elements. Get all the elements that match the selector:

// Simple element selector into an array
let allLinks = dom.a;

// Loop straight on the selection
dom.a.forEach(link => { ... });

// Combined selector
let importantLinks = dom['a.important'];

There are also some predetermined elements, such as id, class and attr:

// Select HTML Elements by id:
let main = dom.id.main;

// by class:
let buttons = dom.class.button;

// or by attribute:
let targeted = dom.attr.target;
let targeted = dom.attr['target="_blank"'];


Use it as a function or a tagged template literal to generate DOM fragments:

// Not a typo; tagged template literals
let link = dom`<a href="https://google.com/">Google</a>`;

// It is the same as
let link = dom('<a href="https://google.com/">Google</a>');

Delete elements

Delete a piece of the DOM

// Delete all of the elements with the class .google
delete dom.class.google;   // Is this an ad-block rule?


You can easily manipulate attributes right from the dom node. There are some aliases that share the syntax of the attributes such as html and text (aliases for innerHTML and textContent). There are others that travel through the dom such as parent (alias for parentNode) and children. Finally, class behaves differently as explained below.

Get attributes

The fetching will always return an array with the element for each of the matched nodes (or undefined if not there):

// Retrieve all the urls from the page
let urls = dom.a.href;     // #attr-list
  // ['https://google.com', 'https://facebook.com/', ...]

// Get an array of the h2 contents (alias of innerHTML)
let h2s = dom.h2.html;     // #attr-alias
  // ['Level 2 header', 'Another level 2 header', ...]

// Get whether any of the attributes has the value "_blank"
let hasBlank = dom.class.cta.target._blank;    // #attr-value
  // true/false

You also use these:

  • html (alias of innerHTML): retrieve a list of the htmls
  • text (alias of textContent): retrieve a list of the htmls
  • parent (alias of parentNode): travel up one level
  • children: travel down one level

Set attributes

// Set target="_blank" to all links
dom.a.target = '_blank';     // #attr-set
dom.class.tableofcontents.html = `
  <ul class="tableofcontents">
    ${dom.h2.map(h2 => `
        <a href="#${h2.id}">

Remove an attribute

To delete an attribute use the delete keyword:

// Remove all urls from the page
delete dom.a.href;

// Remove all ids
delete dom.a.id;


It provides an easy way to manipulate the classes.

Get classes

To retrieve whether a particular class is present or not:

// Get an array with true/false for a single class
let isTest = dom.a.class.test;     // #class-one

For a general method to retrieve all classes you can do:

// Get a list of the classes of each matched element
let arrays = dom.a.class;     // #class-arrays
  // [['important'], ['button', 'cta'], ...]

// If you want a plain list with all of the classes:
let flatten = dom.a.class._flat;     // #class-flat
  // ['important', 'button', 'cta', ...]

// And if you just want an string with space-separated classes:
let text = dom.a.class._text;     // #class-text
  // 'important button cta ...'

Add a class

// Add the class 'test' (different ways)
dom.a.class.test = true;    // #class-make-true
dom.a.class = 'test';       // #class-push

Remove a class

// Remove the class 'test'
dom.a.class.test = false;    // #class-make-false


Did we say it returns a simple array?

dom.a.forEach(link => link.innerHTML = 'I am a link');

But what an interesting array it is; indeed we are also proxy'ing it so you can manipulate its sub-elements straight from the selector:

// Replace all of the link's html with 'I am a link'
dom.a.html = 'I am a link';

Of course we might want to manipulate them dynamically depending on the current value. Just pass it a function:

// Append ' ^_^' to all of the links in the page
dom.a.html = html => html + ' ^_^';

// Same as this:
dom.a.forEach(link => link.innerHTML = link.innerHTML + ' ^_^');

Note: this won't work dom.a.html += ' ^_^'; for more than 1 match (for reasons)

Or get into genetics to manipulate the attributes:

dom.a.attr.target = '_blank';

// Only to external sites:
let isOwnPage = el => /^https?\:\/\/mypage\.com/.test(el.getAttribute('href'));
dom.a.attr.target = (prev, i, element) => isOwnPage(element) ? '' : '_blank';


You can also handle and trigger events:

// Handle click events for all <a>
dom.a.on.click = e => ...;

// Trigger click event for all <a>


We are using Jest as a Grunt task for testing. Install Jest and run in the terminal:

grunt watch

Download Details:

Author: franciscop
Source Code: https://github.com/franciscop/superdom 
License: MIT license

#javascript #es6 #dom 

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