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Introduction to IDL

Introduction to IDL . Lecture 2. ITT: solutions for data visualization and image analysis. http://www.ittvis.com/ IDL, programming language ENVI, image processing based on IDL. IDL. Data visualization and image analysis IDL has both numeric and nonnumeric data types

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Introduction to IDL

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  1. Introduction to IDL Lecture 2

  2. ITT: solutions for data visualization and image analysis • http://www.ittvis.com/ • IDL, programming language • ENVI, image processing based on IDL

  3. IDL • Data visualization and image analysis • IDL has both numeric and nonnumeric data types • IDL is an array-oriented language • Build-in functions • Command Line and Development Environment

  4. Array-oriented language • One dimension array (or vector): • array=[1.0, 2.0, 4.0, 8.0] or array (4) • Two dimension array (or matrices, one band image): • Array=[[1,2,3], [5,7,8]] or (Array (3,2)) • Three dimension array (multiple bands image) • Array=findgen(500,300,6) or an image of 500 columns, 300 rows, and 6 bands • Any operation on an array is performed on all elements of the array, without the need for the user to write an explicit loop. The resulting code is easier to read and understand, and executes more efficiently. • For example, you have a landsat image of (500, 300, 6) (i.e. column, row, bands) and you wish to divide each pixel (or element) by 2 or do other calculations: • Image=image/2 • Print, total(image) / n_elements(image) • Print, mean(image) ;the same as above Differ from Matlab: row, column (c, r, b): BSQ; (b, c, r): BIP; (c, b, r): BIL

  5. Compact syntax without using loops. This makes simpler coding and the processing much faster than loops npts=1000000L data=randomu(0L, npts) sum=total(data) sum=0.0 for i=0L, (npts-1L) do sum=sum+data[i] Randomu: Return a array of uniformly distributed random numbers (0,1) of the specified dimensions Needs 0.11 second Needs 2.64s

  6. Functions for array creation • Zeroed array ( *arr() ) • bytarr(), intarr(), uintarr(), ulonarr(), ulong64arr(), fltarr(), dblarr(), complexarr(), strarr() • Index array • bindgen(): byte index array • indgen(): integer index array • ul64indgen(): unsigned integer array • ulindgen(): unsigned long integer array • lindgen(): long integer index array • l64indgen(): 64-bit integer array • findgen(): Floating-point index array • dcindgen(): double precision floating-point index array • cindgen(): complex index array • dcindgen(): complex, double-precision index array • sindgen(): spring array

  7. Array indexing • array dimension myarr(), • array indexing myarr[index] • Two dimension can be • 1-d linear indexing • Arr[14]=56 • 2-d indexing • Arr[2,3]=56 Arr = indgen(4,4)*4 Print, arr 0 4 8 12 16 20 24 28 32 36 40 44 48 52 56 60 Print, arr[3,2] 44 Print, arr[11] 44 Print, arr(*,2), arr(2,*) ?

  8. Array properties-build in functions • n_elements(), size(), min(), max(), mean(), variance(), stddev(), moment(), total()

  9. Other operations for an array • Locating values within an array • Array reordering: • reform(), reverse(), rotate(), transpose(), shift(), sort(), uniq() • Array resizing: • rebin(), congrid(), interpolate(), Arr = indgen(9)*10 Index = where(arr gt 35) Print, arr[index] ?

  10. Other build-in functions and procedures • FFT(image, -1), forward fast Fourier transfer • FFT(image, 1), inverse FFT • ROT(image, angle, scale, /sampling methods) • interpolate, bilinear, cubic, • map_image • read_binary • correlate • …. • plot • tv, tvscl • contour • ….

  11. Command Line and Development Environment

  12. iTools • Allow you to more quickly and easily open, analyze and visualize your data than even before. • http://www.ittvis.com/idl/itools_tutorials.asp • iPlot (plot) • iSurface (surface) • iContour (contour) • iImage (tv, tvscl) • iVolume (volume) • iMap • iVector

  13. IDL Virtual Machine • Simple, no-cost method of distribution • Run IDL .sav file programs without license requirements • Commercial and non-commercial applications

  14. IDL/ENVI programming • Combine the ENVI/IDL together, extending the ENVI interface in IDL in order to implement new methods and algorithms of arbitrary sophistication is both easy and fun (M. Canty, 2007) • See an example and will give more examples as the class moving forward DEMO!

  15. IDL Project • IDL Project allows you to easily develop applications in IDL for distribution among other developers, colleagues, or users who have IDL • Working with an IDL Project allows you to easily prepare a SAVE file of your IDL application for distribution. You can organize, manage, compile, run, and create distributions of all of your application files from within the IDL Project interface. IDL Project offers the flexibility and functionality you need in a development environment. • Access to all files in your application • IDL source code files (.pro) • GUI files (.prc) created with IDL GUIBuilder • Data files • Image files • Other files (help files, .sav files, etc.) • Build application and create an IDL .sav file for distribution and share.

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