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Running Python Programs - (os, sys, import)

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Running Python Programs

Interactive Running

Python interpreter executes the code entered on each line immediately, when the Enter key is pressed.


For example, if we type print statement at the >>> prompt, the output is echoed back right away.

>>>
>>> print 'interactive running'
interactive running
>>>

The interactive prompt runs code and echoes results as we go, however, it doesn't save our code in a file. Still, the interactive prompt is a good place to do experiment and testing program files on the fly.

The interactive interpreter is an ideal place to test code we've written in files. We can import our module files interactively and run tests on the tools they define by typing calls at the interactive prompt.

Interactive Run with os.getcwd()

For example, the following tests a function in a precoded module that ships with Python in its standard library. It prints the name of the directory we're currently working in. But we can do the same if we have our own:

>>>
>>> import os
>>> os.getcwd()
'C:\\TEST'
>>>

The os module contains variety of functions to get information on. In some cases, to manipulate local directories, files, processes, and environment variables. Python does its best to offer a unified API across all supported operating systems so our programs can run on any computer with as little platform-specific code as possible.

The current working directory is an invisible property that Python holds in memory at all times. There is always a current working directory, whether we're in the Python Shell, running our own Python script from the command line, or whatever.

>>> os.chdir(SUBDIR)
>>> print(os.getcwd())
C:\TEST\SUBDIR
>>>

We can a Linux-style pathname (forward slashes, no drive letter) even though we're on Windows. This is one of the places where Python tries to paper over the differences between operating systems.

>>> os.chdir('/TEST')
>>> print(os.getcwd())
C:\TEST

We uses the os.getcwd() function to get the current working directory. When er run the graphical Python Shell, the current working directory starts as the directory where the Python Shell executable is. On Windows, this depends on where we installed Python; the default directory is c:\Python32. If we run the Python Shell from the command line, the current working directory starts as the directory we were in when we ran python3.

Visit os.getcwd(), os.path for more info.

We can import and test functions and classes in our Python files.





Tips for Using Interactive Prompt

There are a few tips for the beginners when using interactive prompt:

Python commands only.

We can only type Python code, not system commands. Though there are ways to run system commands (e.g., with os.system), but they are not as direct as typing the command itself.

>>>
>>> os.system
<built-in function system>
>>>

print statements are required in files

We must use print to see our output when we write a file. Thanks to automatic echo feature, we do not have to use the print to see our result.


Don't indent at the interactive prompt.

Any blank space to the left of our code is considered as nested statement. A leading space generates an error message.

Compound (multiline) statement

Be sure to terminate multiline compound statements like for loop at the interactive prompt with a blank line. We must press the Enter key twice to terminate the whole multiline statement and then make it run. For example:

>>>
>>> for x in 'python':
... print(x)
  File "<stdin>", line 2
    print(x)
        ^
IndentationError: expected an indented block
>>>

We needed an indent before print, so let's do it again.

>>>
>>> for x in 'python':
...     print(x)
...
p
y
t
h
o
n
>>>

We needed an indent before the print statement and two enter keys. Note that we do not need the blank line after compound statement in a script file. It is required only at the interactive prompt. In a file, blank lines are simply ignored.




System Command Lines and Files

The programs we type into the interactive prompt go away as soon as the Python interpreter executes them. Because the code we type interactively is never stored in a file, we can't run it again.

To save programs, we need to write our code in files which are called modules. Once we have a code, we can run it by system command lines, by file icon clicks, and by options in the IDLE user interface.




A Script File

Here is a script file named script1.py:

# Python script
import sys            #loading a library module
import math
print(sys.platform)   
print(math.sqrt(1001))
x = "blah ";
print x*3;

Once saved, we can ask Python to run it at the system shell prompt:

C:\workspace>
C:\workspace>python script1.py
win32
31.6385840391
blah blah blah
C:\workspace>

Here is a brief description of the script:

  1. Imports a Python module - to fetch the name of the platform and to get sqrt() from math functions.
  2. Runs three print function calls.
  3. Uses a variable x, created when it's assigned, to hold a string object.
  4. The sys.platform is just a string that identifies the computer. It is in a standard Python module, sys, which we must import to load.
  5. The name of the module could be just stript1 without .py suffix. But files of code we want to import into should end with a .py. Because we may want to import the script file later, it's recommended to use .py suffix.

We used shell command lines to start Python programs. So, we can use all the usual shell syntax. For example, we can route the output of a Python to a file to save it:

C:\workspace>python script1.py > saved_srcipt1

The file saved_script1 will have the same output as its content:

win32
31.6385840391
blah blah blah 



Running Script File from Icons

On Windows, running the script by clicking on a file icon has a problem. We cannot see the result because it disappears as soon as it pops up. Actually, the script exits after printing the result.

But there is a say to work around this. If we want to our script's output to stick around for a while, we can simply put a call to the built-in input function at the bottom of the script as shown below:

# Python script
import sys            #loading a library module
import math
print(sys.platform)   
print(math.sqrt(1001))
x = "blah ";
print x*3;
input()

The input reads the next line of standard input. It waits if there is no input available. So, the net effect will be to pause the script and keeping the output window remained open until we press the Enter key.

script1_output

Putting the input in our script should be used only when all the following conditions have been met:

  1. It is usually only required for Windows.
  2. The script prints text and exits.
  3. Running the script from icon.

If we use the input trick, we may not get to see Python error messages. If our script generates an error, the error message text is written to the pop-up console window. Then it immediately disappears. Adding an input call to our file will not help this time because our script will likely abort long before it reaches this call. So, we won't be able to tell what went wrong.

Because of these kinds of limitations, it is probably best to view icon clicks as a way to launch programs after they have been debugged or have been instrumented to write their output to a file.



The import Search Path

Python looks in several places when we try to import a module. Actually, it looks in all the directories defined in sys.path.

>>> import sys
>>> sys.path
['', 
 '/usr/lib/python3.2',
 ...
 '/usr/lib/python3.2/dist-packages', 
 '/usr/local/lib/python3.2/dist-packages']

Importing the sys module makes all of its functions and attributes available. The sys.path is a list of directory names that constitute the current search path. Python will look through these directories, in this order, for a .py file whose name matches what we're trying to import.

Actually, it is more complicated than that, because not all modules are stored as .py files. Some are built-in modules; they are actually baked right into Python itself. Built-in modules behave just like regular modules, but their Python source code is not available, because they are not written in Python! (Like Python itself, these built-in modules are written in C.)








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









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