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FORTRAN Short Course Week 4. Kate Thayer-Calder March 10, 2009. Topics for this week. Searching in Unix Grep, Regular Expressions Multi-Dimensional Arrays User Defined Datatypes Missing data Reading and writing scientific data. Unix Wildcards.

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fortran short course week 4
FORTRAN Short CourseWeek 4
  • Kate Thayer-Calder
  • March 10, 2009
topics for this week
Topics for this week
  • Searching in Unix
    • Grep, Regular Expressions
  • Multi-Dimensional Arrays
  • User Defined Datatypes
  • Missing data
  • Reading and writing scientific data
unix wildcards
Unix Wildcards
  • * - matches all files with none or more of the pattern
    • ls *a returns all files ending in ‘a’
    • ls a* returns all files starting with ‘a’
  • ? - matches exactly one character
    • ?ouse would return house and mouse, but not grouse.
slide4
grep
  • Searches through a file looking for a specific string or pattern, returns the lines where the string occurs
    • grep -i ‘alien’ ufo.txt (case insensitive)
    • grep -w ‘abduct’ ufo.txt (whole word only)
    • grep -riw ‘saucer’ * (recursively thru subdirectories)
  • or lines where it does not occur:
    • grep -v ‘censor’ ufo.txt
  • Can grep multiple files, just add them to the list on the line: grep -i ‘parameterization’ *.txt
  • Unix lexicon: “Can’t grep dead trees.”
regular expressions
Regular Expressions
  • aka RegEx, is a special string for describing a pattern of text
  • RegExs can be used with grep or other unix commands and programs for sifting through text
  • They can get really huge, confusing, and powerful, we’ll just look at a few simple options.
  • For more: http://www.regular-expressions.info or just man regex
grep combinations
grep combinations
  • Just some ideas for using grep with other Unix commands:
  • ls -al | grep ‘Jan’
  • ps -ef | grep ‘501’
  • man ftp | grep -i ‘directory’
  • head -30 ‘mydata.txt’ | grep ‘temperature’
slide8
But...
  • Searching has become much easier than it once was, usually your desktop search engine will filter through files looking for your keywords
  • So, let’s talk about more Fortan!
multi dimensional arrays
Multi Dimensional Arrays
  • type, dimension(dim1,dim2,...) :: name
  • REAL, dimension(lon,lat,height,time) :: temp
  • Higher dimensional arrays are usually stored contiguously in memory or binary files, in COLUMN MAJOR order
  • See example Multiarrays.f90
column major
Column Major
  • Fortran fills up each dimension in order
    • So for i,j,k array, i fills first, then j, then k
  • But do loops work inside out
    • Write out k first, then j, then i
  • To fix this, write your do-loops from the last index in to the first.
  • Do time=1,days
  • Do lon=1,360
  • Do lat=1,180
  • Read (10,fmt) Data(lat,lon,time)
  • enddo
  • enddo
  • enddo
array transformation
Array Transformation

lons

  • Reshape function is pretty cool
  • Matrix = RESHAPE( Source, Shape )
  • A = RESHAPE( B, (/3,2/) )
  • Another way to index your array elements uses ‘mod’ and integer division
  • lat = array(MOD(i,num_lats)+1)
  • lon = array(i/num_lats + 1)

lats

allocatable arrays
Allocatable Arrays
  • Sometimes, you don’t know how large you want your array to be until runtime.
  • Fortran 90 has “allocatable arrays” that can be declared without fixed dimensions, and filled in when the program is running.
  • These can be filled from stdin, or a variable in a file, or a calculation based on previous work, or any other run-time value.
  • See example Multiarrays2.f90
where statements
WHERE statements
  • An easy way to initialize or set sections of arrays
  • WHERE (array expression)
    • array assignment block
  • ELSEWHERE
    • array assignment block 2
  • END WHERE
  • This is called “masking”
forall construct
FORALL Construct
  • This statement indicates to the compiler that the operations can be performed in parallel (no operations depend on the value of the operation on other elements in the array)
  • FORALL (triplet)
    • variable = expression
atmospheric data
Atmospheric Data
  • You’ll see data stored in arrays in many ways:
    • MyData(pressure, temp, mixingratio, height)
    • MyPressure(height), MyTemp(height), MyMixingRatio(height)
    • Pressure(lat,lon,height,time), Temperature(lat,lon,height,time)
the perils of parallel arrays
The Perils of Parallel Arrays
  • It is common in our science to see people using multiple arrays of data that are all the same shape but for different variables (Temperature, Pressure, u wind, v wind, ...)
  • This is considered bad form in computer science, it would be better to have one array with multiple values possible at each point. Why?
  • This gets confusing if you implement a 5-D array, however.
user defined data types
User Defined Data Types
  • Fortran gives us a nice way to describe more complex data structures by creating new data types.
  • Instead of 4 arrays with different variables in each, we can have one array with four values at each point.
  • TYPE name
      • DataType :: Component_name
      • ....
      • END TYPE name
  • We can create variables with this type or arrays of variables of this type
  • TYPE (name) :: VariableName
  • TYPE (name), Dimension(d1,d2,d3,d4) :: ArrayName
        • Example: StdAtmos.f90
inf and nan
INF and NaN
  • INF is defined as the value given to any Real that is outside the limits of the type.
  • Fortran has +INF and -INF
  • NaN (Not a Number) is produced as the result of an improper floating point calculation.
  • NaN is not equal to either INF. In fact, in the IEEE standard, NaN is not even equal to itself.
  • INF or NaN are occasionally used as placeholders for missing data.
  • See Example: WriteExample2.f90
missing data
Missing Data
  • Any observational dataset is going to have holes.
  • If missing data is not given as an “outside the bounds” value (-9999 or 9999.0) it is often replaced with INF or NaN.
  • Most Fortran implementations will read INF or NaN in as a Real value (it is a real Real), we need to check for it before doing calculations, or we’ll get a runtime error.
  • See Example: ReadBadData.f90
netcdf data
NetCDF Data
  • NetCDF is an I/O library that is widely used in the earth sciences.
  • Once the files are installed, you can use their procedures to open and access the files
  • Each files is “self-describing,” all of the data is annotated (dimension, units, range of values, missing data values, etc...)
  • Examples: read_netCDF.f90 with data from NCEP (NCEP.Precip.0100-1204.nc)
zonal average example
Zonal Average Example
  • Modelers and Dynamicists like to look at the atmosphere in latitudinal bands.
  • Don’t have to worry about missing data here...
  • Loading in precip data is pretty simple if you know the parameters.
  • When you do a zonal average, first average in time at each point and then average across all longitudes.
  • Could come up with a less memory intensive way to get the same result...
  • Example: PlayWithPrecip.f90
what did we talk about
What did we talk about?
  • Searching in Unix
    • Grep, Regular Expressions
  • Multi-Dimensional Arrays
  • User Defined Datatypes
  • Missing data
  • Reading and writing scientific data