Week 8 Lecture: Getting Things Done

Transcription

1 Week 8 Lecture: Input and Output, and Moving to Getting Things Done Introduction to Programming for GIS & Remote Sensing GEO GEO

2 Where We Are To this point we ve covered: Basics of Python Basics of VBA A little exposure to other languages (IDL, etc.) Understanding of data types Algorithms Programming structures Controlling flow Object orientation

3 Where Are We Going? What s left? Geospatial data Getting acquainted with our tools Projects, what are the problems? Real-world examples and guest lectures Working to solve your own problems

4 Quick Return to Objects Why is object oriented programming so common to scripting environments: Objects vs. Actions Data vs. Logic

5 Concepts That Make OO Useful Inheritance Generalize and abstract functionality Specialize as necessary Independence Hiding functionality and data can reduce errors Black-box ideal Reusability/Modifiability

6 Data Types, Everything is an Object Classes aren t the only way to define objects Strings Numbers Operators Functions, etc. all are objects

7 Since All Things Are Objects Many have their own sets of methods, like strings: Pyt tho on

8 Numbers as Objects Pyt tho on

9 This Brings Us to Getting Things Done A huge factor in getting useful work done is input and output, done using OO in Python: User interaction Simple Command-line GUI Files Libraries for interacting with files, like ArcGIS Visual output

10 Simple User Interaction Pyt tho on

11 Application Interaction: Command line Pyt tho on

12 Command Line Arguments We can feed information to Python scripts from the command line as well Pyt tho on

13 GUI Interfaces Tkinter is a simple GUI toolkit included in recent Python versions: Web applications Python is heavily used server-side Many, many libraries Won t get into it any further but see me if you re interested Pyt tho on

14 Real Data For any more than a few arguments, file handling is a necessity Configuration files Contain many arguments to control program function Data files Contain data to be operated on, munging g

15 Reading and Writing Files In Python it was easy: Pyt tho on

16 We Can Use Multiple Tools For example, Excel and VBA can be used for data formatting, filename/configuration file creation, etc. Even can feed results back into Python

17 Alternative, Language-Specific Tools for Processing Loop over all files in a directory:

18 So once we have data we need to do something with it: Reformat Change from tab to comma-delimited Add/remove headers Project/georeference Display Summarize/Calculate statistics

19 Perform Complex Search/Replace Operations Normal search/replace can be effective for most operations (tabs/commas) but when more power is needed: Regular expressions Maybe we read in every line from a file Perform search and replace to change commas to tabs Add header information Regular expressions are incredibly powerful, complex tools

20 Return to Example From Week 2

21 Using the Tools You Have Most software we use for geo-processing can be accessed either programmatically via Python or from command-line calls:

22 Displaying and Summarizing Data Obviously, we can tie into ArcGIS tools for data analysis and summary Eample of COM/ArcObjects Other options: e.g. Various statistical libraries, Matplotlib, command line scripting calls to ERDAS spatial modeler, COM scripting

23 Matplotlib for Data Summary and Visual Exploration

24 Even More Sophisticated

25 In Lab This Week Focus on different ways of doing input/output User interaction File I/O Basic data formatting, file processing Display and summarization Supplemental reading: