BogoToBogo
  • Home
  • About
  • Big Data
  • Machine Learning
  • AngularJS
  • Python
  • C++
  • go
  • DevOps
  • Kubernetes
  • Algorithms
  • More...
    • Qt 5
    • Linux
    • FFmpeg
    • Matlab
    • Django 1.8
    • Ruby On Rails
    • HTML5 & CSS

Network Programming - Server & Client B : File Transfer

python_logo




Bookmark and Share





bogotobogo.com site search:

Note

In this chapter, we're going to extend Python Network Programming I - Basic Server / Client A, and try to file transfer from a server to numerous clients. The main purpose is to check the performance of the server from which clients download files.






Local file transfer

Here is the code to send a file from a local server to a local client.

# server.py

import socket                   # Import socket module

port = 60000                    # Reserve a port for your service.
s = socket.socket()             # Create a socket object
host = socket.gethostname()     # Get local machine name
s.bind((host, port))            # Bind to the port
s.listen(5)                     # Now wait for client connection.

print 'Server listening....'

while True:
    conn, addr = s.accept()     # Establish connection with client.
    print 'Got connection from', addr
    data = conn.recv(1024)
    print('Server received', repr(data))

    filename='mytext.txt'
    f = open(filename,'rb')
    l = f.read(1024)
    while (l):
       conn.send(l)
       print('Sent ',repr(l))
       l = f.read(1024)
    f.close()

    print('Done sending')
    conn.send('Thank you for connecting')
    conn.close()


# client.py

import socket                   # Import socket module

s = socket.socket()             # Create a socket object
host = socket.gethostname()     # Get local machine name
port = 60000                    # Reserve a port for your service.

s.connect((host, port))
s.send("Hello server!")

with open('received_file', 'wb') as f:
    print 'file opened'
    while True:
        print('receiving data...')
        data = s.recv(1024)
        print('data=%s', (data))
        if not data:
            break
        # write data to a file
        f.write(data)

f.close()
print('Successfully get the file')
s.close()
print('connection closed')

Output on a local server:

Server listening....
Got connection from ('192.168.56.10', 62854)
('Server received', "'Hello server!'")
('Sent ', "'1 1234567890\\n
...
('Sent ', "'4567890\\n105
...
('Sent ', "'300 1234567890\\n'")
Done sending

Output on a local client:

file opened
receiving data...
data=1 1234567890
2 1234567890
...
103 1234567890
104 123
receiving data...
data=4567890
105 1234567890
106 1234567890
...
299 1234567890

receiving data...
data=300 1234567890
Thank you for connecting
receiving data...
data=
Successfully get the file
connection closed




multithread tcp file transfer on localhost

Our server code above can only interact with one client. If we try to connect with a second client, however, it simply won't reply to the new client. To let the server interact with multiple clients, we need to use multi-threading. Here is the new server script to accept multiple client connections:

# server2.py
import socket
from threading import Thread
from SocketServer import ThreadingMixIn

TCP_IP = 'localhost'
TCP_PORT = 9001
BUFFER_SIZE = 1024

class ClientThread(Thread):

    def __init__(self,ip,port,sock):
        Thread.__init__(self)
        self.ip = ip
        self.port = port
        self.sock = sock
        print " New thread started for "+ip+":"+str(port)

    def run(self):
        filename='mytext.txt'
        f = open(filename,'rb')
        while True:
            l = f.read(BUFFER_SIZE)
            while (l):
                self.sock.send(l)
                #print('Sent ',repr(l))
                l = f.read(BUFFER_SIZE)
            if not l:
                f.close()
                self.sock.close()
                break

tcpsock = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
tcpsock.setsockopt(socket.SOL_SOCKET, socket.SO_REUSEADDR, 1)
tcpsock.bind((TCP_IP, TCP_PORT))
threads = []

while True:
    tcpsock.listen(5)
    print "Waiting for incoming connections..."
    (conn, (ip,port)) = tcpsock.accept()
    print 'Got connection from ', (ip,port)
    newthread = ClientThread(ip,port,conn)
    newthread.start()
    threads.append(newthread)

for t in threads:
    t.join()



# client2.py
#!/usr/bin/env python

import socket

TCP_IP = 'localhost'
TCP_PORT = 9001
BUFFER_SIZE = 1024

s = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
s.connect((TCP_IP, TCP_PORT))
with open('received_file', 'wb') as f:
    print 'file opened'
    while True:
        #print('receiving data...')
        data = s.recv(BUFFER_SIZE)
        print('data=%s', (data))
        if not data:
            f.close()
            print 'file close()'
            break
        # write data to a file
        f.write(data)

print('Successfully get the file')
s.close()
print('connection closed')

Below is the output from the server console when we run two clients simultaneously:

$ python server2.py
Waiting for incoming connections...
Got connection from  ('127.0.0.1', 55184)
 New thread started for 127.0.0.1:55184
Waiting for incoming connections...
Got connection from  ('127.0.0.1', 55185)
 New thread started for 127.0.0.1:55185
Waiting for incoming connections...




tcp file download from EC2 to local

In the following codes, we made two changes:

  1. ip switched to amazon ec2 ip
  2. To calculate the time to take download a file, we import time module.


# server3.py on EC2 instance
import socket
from threading import Thread
from SocketServer import ThreadingMixIn

# TCP_IP = 'localhost'
TCP_IP = socket.gethostbyaddr("your-ec2-public_ip")[0]
TCP_PORT = 60001
BUFFER_SIZE = 1024

print 'TCP_IP=',TCP_IP
print 'TCP_PORT=',TCP_PORT

class ClientThread(Thread):

    def __init__(self,ip,port,sock):
        Thread.__init__(self)
        self.ip = ip
        self.port = port
        self.sock = sock
        print " New thread started for "+ip+":"+str(port)

    def run(self):
        filename='mytext.txt'
        f = open(filename,'rb')
        while True:
            l = f.read(BUFFER_SIZE)
            while (l):
                self.sock.send(l)
                #print('Sent ',repr(l))
                l = f.read(BUFFER_SIZE)
            if not l:
                f.close()
                self.sock.close()
                break

tcpsock = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
tcpsock.setsockopt(socket.SOL_SOCKET, socket.SO_REUSEADDR, 1)
tcpsock.bind((TCP_IP, TCP_PORT))
threads = []

while True:
    tcpsock.listen(5)
    print "Waiting for incoming connections..."
    (conn, (ip,port)) = tcpsock.accept()
    print 'Got connection from ', (ip,port)
    newthread = ClientThread(ip,port,conn)
    newthread.start()
    threads.append(newthread)

for t in threads:
    t.join()


# client3.py on local machine
#!/usr/bin/env python

#!/usr/bin/env python

import socket
import time

#TCP_IP = 'localhost'
TCP_IP = 'ip-ec2-instance'
TCP_PORT = 60001

BUFFER_SIZE = 1024

s = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
s.connect((TCP_IP, TCP_PORT))

clock_start = time.clock()
time_start = time.time()

with open('received_file', 'wb') as f:
    print 'file opened'
    while True:
        #print('receiving data...')
        data = s.recv(1024)
        #print('data=%s', (data))
        if not data:
            f.close()
            print 'file close()'
            break
        # write data to a file
        f.write(data)

print('Successfully get the file')
s.close()
print('connection closed')

clock_end = time.clock()
time_end = time.time()

duration_clock = clock_end - clock_start
print 'clock:  start = ',clock_start, ' end = ',clock_end
print 'clock:  duration_clock = ', duration_clock

duration_time = time_end - time_start
print 'time:  start = ',time_start, ' end = ',time_end
print 'time:  duration_time = ', duration_time

Server console shows the following output after a connection from my local home machine:

$ python server3.py
TCP_IP= ec2-...
TCP_PORT= 60001
Waiting for incoming connections...
Got connection from  ('108.239.135.40', 56742)
 New thread started for 108.239.135.40:56742

The ip is isp's:

ATT-IP.png

On my local mac:

$ python client3.py
file opened
file close()
Successfully get the file
connection closed
clock:  start =  0.018806  end =  0.038608
clock:  duration_clock =  0.019802
time:  start =  1434991840.37  end =  1434991840.42
time:  duration_time =  0.0457620620728

File downloaded from EC2, received_file is simple, and it looks like this:

From EC2
1
2
3
4
5
6
7
8
9




Download time vs number of clients

Here is the output showing the wall-clock time depending on the number of concurrent connections:

connections.png

Our server is located in California, and the following picture compares the download speed between US and Japan:

time2.png





Python Network Programming



Network Programming - Server & Client A : Basics

Network Programming - Server & Client B : File Transfer

Network Programming II - Chat Server & Client

Network Programming III - SocketServer

Network Programming IV - SocketServer Asynchronous request







Python tutorial



Python Home

Introduction

Running Python Programs (os, sys, import)

Modules and IDLE (Import, Reload, exec)

Object Types - Numbers, Strings, and None

Strings - Escape Sequence, Raw String, and Slicing

Strings - Methods

Formatting Strings - expressions and method calls

Files and os.path

Traversing directories recursively

Subprocess Module

Regular Expressions with Python

Regular Expressions Cheat Sheet

Object Types - Lists

Object Types - Dictionaries and Tuples

Functions def, *args, **kargs

Functions lambda

Built-in Functions

map, filter, and reduce

Decorators

List Comprehension

Sets (union/intersection) and itertools - Jaccard coefficient and shingling to check plagiarism

Hashing (Hash tables and hashlib)

Dictionary Comprehension with zip

The yield keyword

Generator Functions and Expressions

generator.send() method

Iterators

Classes and Instances (__init__, __call__, etc.)

if__name__ == '__main__'

argparse

Exceptions

@static method vs class method

Private attributes and private methods

bits, bytes, bitstring, and constBitStream

json.dump(s) and json.load(s)

Python Object Serialization - pickle and json

Python Object Serialization - yaml and json

Priority queue and heap queue data structure

Graph data structure

Dijkstra's shortest path algorithm

Prim's spanning tree algorithm

Closure

Functional programming in Python

Remote running a local file using ssh

SQLite 3 - A. Connecting to DB, create/drop table, and insert data into a table

SQLite 3 - B. Selecting, updating and deleting data

MongoDB with PyMongo I - Installing MongoDB ...

Python HTTP Web Services - urllib, httplib2

Web scraping with Selenium for checking domain availability

REST API : Http Requests for Humans with Flask

Blog app with Tornado

Multithreading ...

Python Network Programming I - Basic Server / Client : A Basics

Python Network Programming I - Basic Server / Client : B File Transfer

Python Network Programming II - Chat Server / Client

Python Network Programming III - Echo Server using socketserver network framework

Python Network Programming IV - Asynchronous Request Handling : ThreadingMixIn and ForkingMixIn

Python Coding Questions I

Python Coding Questions II

Python Coding Questions III

Python Coding Questions IV

Python Coding Questions V

Python Coding Questions VI

Python Coding Questions VII

Python Coding Questions VIII

Python Coding Questions IX

Python Coding Questions X

Image processing with Python image library Pillow

Python and C++ with SIP

PyDev with Eclipse

Matplotlib

Redis with Python

NumPy array basics A

NumPy Matrix and Linear Algebra

Pandas with NumPy and Matplotlib

Celluar Automata

Batch gradient descent algorithm

Longest Common Substring Algorithm

Python Unit Test - TDD using unittest.TestCase class

Simple tool - Google page ranking by keywords

Google App Hello World

Google App webapp2 and WSGI

Uploading Google App Hello World

Python 2 vs Python 3

virtualenv and virtualenvwrapper

Uploading a big file to AWS S3 using boto module

Scheduled stopping and starting an AWS instance

Cloudera CDH5 - Scheduled stopping and starting services

Removing Cloud Files - Rackspace API with curl and subprocess

Checking if a process is running/hanging and stop/run a scheduled task on Windows

Apache Spark 1.3 with PySpark (Spark Python API) Shell

Apache Spark 1.2 Streaming

bottle 0.12.7 - Fast and simple WSGI-micro framework for small web-applications ...

Flask app with Apache WSGI on Ubuntu14/CentOS7 ...

Fabric - streamlining the use of SSH for application deployment

Ansible Quick Preview - Setting up web servers with Nginx, configure enviroments, and deploy an App

Neural Networks with backpropagation for XOR using one hidden layer

NLP - NLTK (Natural Language Toolkit) ...

RabbitMQ(Message broker server) and Celery(Task queue) ...

OpenCV3 and Matplotlib ...

Simple tool - Concatenating slides using FFmpeg ...

iPython - Signal Processing with NumPy

iPython and Jupyter - Install Jupyter, iPython Notebook, drawing with Matplotlib, and publishing it to Github

iPython and Jupyter Notebook with Embedded D3.js

Downloading YouTube videos using youtube-dl embedded with Python

Machine Learning : scikit-learn ...

Django 1.6/1.8 Web Framework ...








Ph.D. / Golden Gate Ave, San Francisco / Seoul National Univ / Carnegie Mellon / UC Berkeley / DevOps / Deep Learning / Visualization

YouTubeMy YouTube channel

Sponsor Open Source development activities and free contents for everyone.

Thank you.

- K Hong







Python tutorial



Python Home

Introduction

Running Python Programs (os, sys, import)

Modules and IDLE (Import, Reload, exec)

Object Types - Numbers, Strings, and None

Strings - Escape Sequence, Raw String, and Slicing

Strings - Methods

Formatting Strings - expressions and method calls

Files and os.path

Traversing directories recursively

Subprocess Module

Regular Expressions with Python

Regular Expressions Cheat Sheet

Object Types - Lists

Object Types - Dictionaries and Tuples

Functions def, *args, **kargs

Functions lambda

Built-in Functions

map, filter, and reduce

Decorators

List Comprehension

Sets (union/intersection) and itertools - Jaccard coefficient and shingling to check plagiarism

Hashing (Hash tables and hashlib)

Dictionary Comprehension with zip

The yield keyword

Generator Functions and Expressions

generator.send() method

Iterators

Classes and Instances (__init__, __call__, etc.)

if__name__ == '__main__'

argparse

Exceptions

@static method vs class method

Private attributes and private methods

bits, bytes, bitstring, and constBitStream

json.dump(s) and json.load(s)

Python Object Serialization - pickle and json

Python Object Serialization - yaml and json

Priority queue and heap queue data structure

Graph data structure

Dijkstra's shortest path algorithm

Prim's spanning tree algorithm

Closure

Functional programming in Python

Remote running a local file using ssh

SQLite 3 - A. Connecting to DB, create/drop table, and insert data into a table

SQLite 3 - B. Selecting, updating and deleting data

MongoDB with PyMongo I - Installing MongoDB ...

Python HTTP Web Services - urllib, httplib2

Web scraping with Selenium for checking domain availability

REST API : Http Requests for Humans with Flask

Blog app with Tornado

Multithreading ...

Python Network Programming I - Basic Server / Client : A Basics

Python Network Programming I - Basic Server / Client : B File Transfer

Python Network Programming II - Chat Server / Client

Python Network Programming III - Echo Server using socketserver network framework

Python Network Programming IV - Asynchronous Request Handling : ThreadingMixIn and ForkingMixIn

Python Coding Questions I

Python Coding Questions II

Python Coding Questions III

Python Coding Questions IV

Python Coding Questions V

Python Coding Questions VI

Python Coding Questions VII

Python Coding Questions VIII

Python Coding Questions IX

Python Coding Questions X

Image processing with Python image library Pillow

Python and C++ with SIP

PyDev with Eclipse

Matplotlib

Redis with Python

NumPy array basics A

NumPy Matrix and Linear Algebra

Pandas with NumPy and Matplotlib

Celluar Automata

Batch gradient descent algorithm

Longest Common Substring Algorithm

Python Unit Test - TDD using unittest.TestCase class

Simple tool - Google page ranking by keywords

Google App Hello World

Google App webapp2 and WSGI

Uploading Google App Hello World

Python 2 vs Python 3

virtualenv and virtualenvwrapper

Uploading a big file to AWS S3 using boto module

Scheduled stopping and starting an AWS instance

Cloudera CDH5 - Scheduled stopping and starting services

Removing Cloud Files - Rackspace API with curl and subprocess

Checking if a process is running/hanging and stop/run a scheduled task on Windows

Apache Spark 1.3 with PySpark (Spark Python API) Shell

Apache Spark 1.2 Streaming

bottle 0.12.7 - Fast and simple WSGI-micro framework for small web-applications ...

Flask app with Apache WSGI on Ubuntu14/CentOS7 ...

Selenium WebDriver

Fabric - streamlining the use of SSH for application deployment

Ansible Quick Preview - Setting up web servers with Nginx, configure enviroments, and deploy an App

Neural Networks with backpropagation for XOR using one hidden layer

NLP - NLTK (Natural Language Toolkit) ...

RabbitMQ(Message broker server) and Celery(Task queue) ...

OpenCV3 and Matplotlib ...

Simple tool - Concatenating slides using FFmpeg ...

iPython - Signal Processing with NumPy

iPython and Jupyter - Install Jupyter, iPython Notebook, drawing with Matplotlib, and publishing it to Github

iPython and Jupyter Notebook with Embedded D3.js

Downloading YouTube videos using youtube-dl embedded with Python

Machine Learning : scikit-learn ...

Django 1.6/1.8 Web Framework ...


Sponsor Open Source development activities and free contents for everyone.

Thank you.

- K Hong






OpenCV 3 image and video processing with Python



OpenCV 3 with Python

Image - OpenCV BGR : Matplotlib RGB

Basic image operations - pixel access

iPython - Signal Processing with NumPy

Signal Processing with NumPy I - FFT and DFT for sine, square waves, unitpulse, and random signal

Signal Processing with NumPy II - Image Fourier Transform : FFT & DFT

Inverse Fourier Transform of an Image with low pass filter: cv2.idft()

Image Histogram

Video Capture and Switching colorspaces - RGB / HSV

Adaptive Thresholding - Otsu's clustering-based image thresholding

Edge Detection - Sobel and Laplacian Kernels

Canny Edge Detection

Hough Transform - Circles

Watershed Algorithm : Marker-based Segmentation I

Watershed Algorithm : Marker-based Segmentation II

Image noise reduction : Non-local Means denoising algorithm

Image object detection : Face detection using Haar Cascade Classifiers

Image segmentation - Foreground extraction Grabcut algorithm based on graph cuts

Image Reconstruction - Inpainting (Interpolation) - Fast Marching Methods

Video : Mean shift object tracking

Machine Learning : Clustering - K-Means clustering I

Machine Learning : Clustering - K-Means clustering II

Machine Learning : Classification - k-nearest neighbors (k-NN) algorithm




Machine Learning with scikit-learn



scikit-learn installation

scikit-learn : Features and feature extraction - iris dataset

scikit-learn : Machine Learning Quick Preview

scikit-learn : Data Preprocessing I - Missing / Categorical data

scikit-learn : Data Preprocessing II - Partitioning a dataset / Feature scaling / Feature Selection / Regularization

scikit-learn : Data Preprocessing III - Dimensionality reduction vis Sequential feature selection / Assessing feature importance via random forests

Data Compression via Dimensionality Reduction I - Principal component analysis (PCA)

scikit-learn : Data Compression via Dimensionality Reduction II - Linear Discriminant Analysis (LDA)

scikit-learn : Data Compression via Dimensionality Reduction III - Nonlinear mappings via kernel principal component (KPCA) analysis

scikit-learn : Logistic Regression, Overfitting & regularization

scikit-learn : Supervised Learning & Unsupervised Learning - e.g. Unsupervised PCA dimensionality reduction with iris dataset

scikit-learn : Unsupervised_Learning - KMeans clustering with iris dataset

scikit-learn : Linearly Separable Data - Linear Model & (Gaussian) radial basis function kernel (RBF kernel)

scikit-learn : Decision Tree Learning I - Entropy, Gini, and Information Gain

scikit-learn : Decision Tree Learning II - Constructing the Decision Tree

scikit-learn : Random Decision Forests Classification

scikit-learn : Support Vector Machines (SVM)

scikit-learn : Support Vector Machines (SVM) II

Flask with Embedded Machine Learning I : Serializing with pickle and DB setup

Flask with Embedded Machine Learning II : Basic Flask App

Flask with Embedded Machine Learning III : Embedding Classifier

Flask with Embedded Machine Learning IV : Deploy

Flask with Embedded Machine Learning V : Updating the classifier

scikit-learn : Sample of a spam comment filter using SVM - classifying a good one or a bad one




Machine learning algorithms and concepts

Batch gradient descent algorithm

Single Layer Neural Network - Perceptron model on the Iris dataset using Heaviside step activation function

Batch gradient descent versus stochastic gradient descent

Single Layer Neural Network - Adaptive Linear Neuron using linear (identity) activation function with batch gradient descent method

Single Layer Neural Network : Adaptive Linear Neuron using linear (identity) activation function with stochastic gradient descent (SGD)

Logistic Regression

VC (Vapnik-Chervonenkis) Dimension and Shatter

Bias-variance tradeoff

Maximum Likelihood Estimation (MLE)

Neural Networks with backpropagation for XOR using one hidden layer

minHash

tf-idf weight

Natural Language Processing (NLP): Sentiment Analysis I (IMDb & bag-of-words)

Natural Language Processing (NLP): Sentiment Analysis II (tokenization, stemming, and stop words)

Natural Language Processing (NLP): Sentiment Analysis III (training & cross validation)

Natural Language Processing (NLP): Sentiment Analysis IV (out-of-core)

Locality-Sensitive Hashing (LSH) using Cosine Distance (Cosine Similarity)




Artificial Neural Networks (ANN)

[Note] Sources are available at Github - Jupyter notebook files

1. Introduction

2. Forward Propagation

3. Gradient Descent

4. Backpropagation of Errors

5. Checking gradient

6. Training via BFGS

7. Overfitting & Regularization

8. Deep Learning I : Image Recognition (Image uploading)

9. Deep Learning II : Image Recognition (Image classification)

10 - Deep Learning III : Deep Learning III : Theano, TensorFlow, and Keras









Contact

BogoToBogo
contactus@bogotobogo.com

Follow Bogotobogo

About Us

contactus@bogotobogo.com

YouTubeMy YouTube channel
Pacific Ave, San Francisco, CA 94115

Pacific Ave, San Francisco, CA 94115

Copyright © 2024, bogotobogo
Design: Web Master