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argparse

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What is argparse?

What is argparse?

"The argparse module makes it easy to write user-friendly command-line interfaces. The program defines what arguments it requires, and argparse will figure out how to parse those out of sys.argv. The argparse module also automatically generates help and usage messages and issues errors when users give the program invalid arguments." - from argparse - Parser for command-line options, arguments and sub-commands

The following description is from The argparse module is now part of the Python standard library!

The argparse module provides an easy, declarative interface for creating command line tools, which knows how to:

  1. parse the arguments and flags from sys.argv
  2. convert arg strings into objects for your program
  3. format and print informative help messages

and much more...

The argparse module improves on the standard library optparse module in a number of ways including:

  1. handling positional arguments
  2. supporting sub-commands
  3. allowing alternative option prefixes like + and /
  4. handling zero-or-more and one-or-more style arguments
  5. producing more informative usage messages
  6. providing a much simpler interface for custom types and actions



A simple sample

Let's look at our first sample of using argparse:

# arg.py

import argparse
import sys

def check_arg(args=None):
    parser = argparse.ArgumentParser(description='Script to learn basic argparse')
    parser.add_argument('-H', '--host',
                        help='host ip',
                        required='True',
                        default='localhost')
    parser.add_argument('-p', '--port',
                        help='port of the web server',
                        default='8080')
    parser.add_argument('-u', '--user',
                        help='user name',
                        default='root')

    results = parser.parse_args(args)
    return (results.host,
            results.port,
            results.user)

if __name__ == '__main__':
    h, p, u = check_arg(sys.argv[1:])
    print 'h =',h
    print 'p =',p
    print 'u =',u

If we run it:

$ python arg.py -H 192.17.23.5
h = 192.17.23.5
p = 8080
u = root

Note that the 'host' arg is set as 'required'. So, if we run the code without feeding host ip, we'll get an error like this:

$ python arg.py
usage: arg.py [-h] -H HOST [-p PORT] [-u USER]
arg.py: error: argument -H/--host is required



Help option

Also, we need to look at how the help works:

$ python arg.py -h
usage: arg.py [-h] -H HOST [-p PORT] [-u USER]

Script to learn basic argparse

optional arguments:
  -h, --help            show this help message and exit
  -H HOST, --host HOST  host ip
  -p PORT, --port PORT  port of the web server
  -u USER, --user USER  user name

Notice that we used -H for host-ip mandatory option instead of lower case 'h' because it is reserved for 'help.





Integer input

Another sample code:

# arg.py

import argparse
import sys

def int_args(args=None):
    parser = argparse.ArgumentParser(description='Processing integers.')
    parser.add_argument('integers',
                        metavar='N',
                        type=int,
                        nargs='+',
                        help='integer args')
    return parser.parse_args()

if __name__ == '__main__':
    print int_args(sys.argv[1:])

Just to see how it works, let's request 'help':

$ python arg.py -h
usage: arg.py [-h] N [N ...]

Processing integers.

positional arguments:
  N           integer args

optional arguments:
  -h, --help  show this help message and exit

We need to check the add_argument() method in ArgumentParser.add_argument():

ArgumentParser.add_argument(name or flags...[, action][, nargs][, const][, default][, type][, choices][, required][, help][, metavar][, dest])
  1. name or flags - Either a name or a list of option strings, e.g. foo or -f, --foo.
  2. action - The basic type of action to be taken when this argument is encountered at the command line.
  3. nargs - The number of command-line arguments that should be consumed.
  4. const - A constant value required by some action and nargs selections.
  5. default - The value produced if the argument is absent from the command line.
  6. type - The type to which the command-line argument should be converted.
  7. choices - A container of the allowable values for the argument.
  8. required - Whether or not the command-line option may be omitted (optionals only).
  9. help - A brief description of what the argument does.
  10. metavar - A name for the argument in usage messages.
  11. dest - The name of the attribute to be added to the object returned by parse_args().

Now, it's time to run the code:

$ arg.py 1 2 3 4 5
Namespace(integers=[1, 2, 3, 4, 5])

The '+' in nargs='+', just like '*', makes all command-line args present to be gathered into a list.

If we provide a wrong type arg such as a float type, we'll get an error:

$ arg.py 1.9999 2 3 4 5
usage: arg.py [-h] N [N ...]
arg.py: error: argument N: invalid int value: '1.9999'



Sample - Git commit_activity

For details, check Einsteinish/GitHub-API


Python code:

import json
import requests
import time
import calendar
import argparse
import sys
import operator

DAYS = ['Sunday', 'Monday', 'Tuesday', 'Wednesday', 'Thursday', 'Friday', 'Saturday']
WEEK_IN_SECOND = 604800

def count_commits(repo, weeks, sort):

    # Get commit activity via Github REST API v3 with Python's requests module
    r = requests.get('https://api.github.com/repos/%s/stats/commit_activity' % repo)

    repos = json.loads(r.content)
    # sample : [{'days': [0, 0, 0, 0, 0, 0, 0], 'total': 0, 'week': 1511053200}, ... ]

    # calculate time cut in weeks
    current_epoctime = calendar.timegm(time.gmtime())
    week_cut = current_epoctime-(weeks)*WEEK_IN_SECOND

    # initialize commits for the days of a week
    commits = [0]*7
    # loop through commits week by week. Cut by input 'weeks'
    for r in repos:
       if r['week'] >= week_cut:
           for i,d in enumerate(r['days']):
               commits[i] += d

    # average commits per week
    commits = [c/weeks for c in commits]


    # construct dictionary from the two list : zip(DAYS, commits)
    # then, sort it (default: descending)
    days_commits = dict(zip(DAYS, commits))
    days_commits = sorted(days_commits.items(), key = operator.itemgetter(1), reverse = (sort == 'dsc'))
    # sample :  days_commits =  [('Wednesday', 75.95), ('Thursday', 73.7), ... ]

    print('\n--- Commits (average) ---')
    for item in days_commits:
        print('%s %.1f' %(item[0],item[1]))

    print('\n--- The most commits ---')
    if sort == 'dsc':
        index = 0  # top
    else:
        index = 6  # bottom
    print('%s %.1f' %(days_commits[index][0],days_commits[index][1]))


# setup args including default values and input error handling
def check_arg(args=None):
    parser = argparse.ArgumentParser(description='Github API - stats of commits')
    #parser.add_argument('repo', metavar='repository name', type=str, help='user/repo')
    parser.add_argument('-r', nargs='?', default='kubernetes/kubernetes')
    parser.add_argument('-w', nargs='?', default='52')
    parser.add_argument('-s', nargs='?', default='dsc')
    results = parser.parse_args(args)
    return (results.r, results.w, results.s)

# MAIN
if __name__ == '__main__':

    '''
    Count Github commits via 'stats/commit_activity' - using REST API v3
    Usage :  python ask_ki.py -r kubernetes/kubernetes -w=36 -s=dsc
    (note) All args are optional: '-r', '-w', and '-s'
    '''

    r, w, s = check_arg(sys.argv[1:])
    print('Inputs: repo=%s weeks=%s sort=%s' %(r,w,s))

    # call github api
    count_commits(repo=r, weeks=int(w), sort=s)






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Sponsor Open Source development activities and free contents for everyone.

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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









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