Map making in Matlab

Map making in Matlab


This weeks post will cover the basics of generating maps in Matlab.  Julie’s recent post showed how to do some of this in Python, but, Matlab is also widely used by the community.  You can get a lot done with Matlab, but in this post we’ll just cover a few of the basics.

We’ll start off by plotting a map of the continental United States, with the states.  We used three  this with three commands: usamap, shaperead, and geoshow.  usamap creates an empty map axes having the Lambert Projection covering the area of the US, or any state or collection of states.  shaperead reads shapefiles (duh) and returns a Matlab geographic data structure, composed of both geographic data and attributes.  This Matlab data structure then interfaces really well with various Matlab functions (duh).  Finally, geoshow plots geographic data, in our case on the map axes we defined.  Here’s some code putting it all together.

hold on
figure1 = figure;
ax = usamap('conus');

set(ax, 'Visible', 'off')
latlim = getm(ax, 'MapLatLimit');
lonlim = getm(ax, 'MapLonLimit');
states = shaperead('usastatehi',...
 'UseGeoCoords', true, 'BoundingBox', [lonlim', latlim']);
geoshow(ax, states, 'FaceColor', [0.5 0.5 0.5])
hold off

Note that ‘usastatehi’ is a shapefile containing the US states (duh) that’s distributed with Matlab. The above code generates this figure:


Now, suppose we wanted to plot some data, say a precipitation forecast, on our CONUS map.  Let’s assume our forecast is being made at many points (lat,long).  To interpolate between the points for plotting we’ll use Matlab’s griddata function.  Once we’ve done this, we use the Matlab’s contourm command.  This works exactly like the normal contour function, but the ‘m’ indicates it plots map data.

xi = min(x):0.5:max(x);
yi = min(y):0.5:max(y);
[XI, YI] = meshgrid(xi,yi);
ZI = griddata(x,y,V,XI,YI);

hold on
figure2 = figure;
ax = usamap('conus');

set(ax, 'Visible', 'off')
latlim = getm(ax, 'MapLatLimit');
lonlim = getm(ax, 'MapLonLimit');
states = shaperead('usastatehi',...
 'UseGeoCoords', true, 'BoundingBox', [lonlim', latlim']);
geoshow(ax, states, 'FaceColor', [0.5 0.5 0.5])

hold off

Here x, y, and V are vectors of long, lat, and foretasted precipitation respectively.  This code generates the following figure:


Wow!  Louisiana is really getting hammered!  Let’s take a closer look.  We can do this by changing the entry to usamap to indicate we want to consider only Louisiana.  Note, usamap accepts US postal code abbreviations.

ax = usamap('LA');

Making that change results in this figure:


Neat!  We can also look at two states and add annotations.  Suppose, for no reason in particular, you’re interested in the location of Tufts University relative to Cornell.  We can make a map to look at this with the textm and scatterm functions.  As before, the ‘m’ indicates the functions  plot on a map axes.

hold on
figure4 = figure;
ax = usamap({'MA','NY'});

set(ax, 'Visible', 'off')
latlim = getm(ax, 'MapLatLimit');
lonlim = getm(ax, 'MapLonLimit');
states = shaperead('usastatehi',...
 'UseGeoCoords', true, 'BoundingBox', [lonlim', latlim']);
geoshow(ax, states, 'FaceColor', [0.5 0.5 0.5])

hold off

This code generates the following figure.


Cool! Now back to forecasts.  NOAA distributes short term Quantitative Precipitation Forecasts (QPFs) for different durations every six hours.  You can download these forecasts in the form of shapefiles from a NOAA server.  Here’s an example of a 24-hour rainfall forecast made at 8:22 AM UTC on April 29.


Wow, that’s a lot of rain!  Can we plot our own version of this map using Matlab!  You bet!  Again we’ll use usamap, shaperead, and geoshow.  The for loop, (0,1) scaling, and log transform are simply to make the color map more visually appealing for the post.  There’s probably a cleaner way to do this, but this got the job done!

figure5 = figure;
ax = usamap('conus');

set(ax, 'Visible', 'off')
latlim = getm(ax, 'MapLatLimit');
lonlim = getm(ax, 'MapLonLimit');
states = shaperead('usastatehi',...
 'UseGeoCoords', true, 'BoundingBox', [lonlim', latlim']);
geoshow(ax, states, 'FaceColor', [0.5 0.5 0.5])
p = colormap(jet);

N = max(size(S));
d = zeros(N,1);
for i = 1:N
 d(i) = log(S(i).QPF);

col = p(y,:);
for i = 1:N
 geoshow(S(i),'FaceColor',col(i,:),'FaceAlpha',0.5)%,'SymbolSpec', faceColors)

This code generates the following figure:


If you are not plotting in the US, Matlab also has a worldmap command.  This works exactly the same as usamap, but now for the world (duh).  Matlab is distibuted with a shapefile ‘landareas.shp’ which contains all of the land areas in the world (duh).  Generating a global map is then trivial:

figure6 = figure;

land = shaperead('landareas.shp', 'UseGeoCoords', true);
geoshow(land, 'FaceColor', [0.15 0.5 0.15])

Which generates this figure.



Matlab also comes with a number of other included that might be of interest.  For instance, shapefiles detailing the locations of major world cities, lakes, and rivers.  We can plot those with the following code:

figure7 = figure;

land = shaperead('landareas.shp', 'UseGeoCoords', true);
geoshow(land, 'FaceColor', [0.15 0.5 0.15])
lakes = shaperead('worldlakes', 'UseGeoCoords', true);
geoshow(lakes, 'FaceColor', 'blue')
rivers = shaperead('worldrivers', 'UseGeoCoords', true);
geoshow(rivers, 'Color', 'blue')
cities = shaperead('worldcities', 'UseGeoCoords', true);
geoshow(cities, 'Marker', '.', 'Color', 'red')

Which generates the figure:


But suppose we’re interested in one country or a group of countries.  worldmap works in the same usamap does.  Also, you can plot continents, for instance Europe.



Those are the basics, but there are many other capabilities, including 3-D projections. I can cover this in a later post if there is interest.


That’s it for now!

9 basic skills for editing and creating vector graphics in Illustrator

This post intends to provide guidance for editing and creating vector graphics using Adobe Illustrator.     The goal is to learn some of the commonly used features to help you get started with your vectorized journey.  Let it be a conceptual diagram, or a logos or cropping people out of your photo, these 9 features (and a fair amount googling) will help you do the job.   Before we begin, it may be worthwhile to distinguish some of the main differences between a raster and a vector graphic.  A raster image is comprised of a collection of squares or pixels, and vector graphics are  based on mathematical formulas that define geometric forms (i.e.  polygons, lines, curves, circles and rectangles), which makes them independent of resolution.

The three main advantages of using vector graphics over raster images are illustrated below:

1. Scalability: Vector graphics scale infinitely without losing any image quality. Raster images guess the colors of missing pixels when sizing up, whereas vector graphics simply use the original mathematical equation to create a consistent shape every time.


2. Edibility: Vector files are not flattened, that is, the original shapes of an image exist separately on different layers; this provides flexibility on modifying different elements without impacting the entire image.


3. Reduced file size: A vector file only requires four data points to recreate a square ,whereas a raster image needs to store many small squares.



9 key things to know when getting started with Adobe Illustrator:

1. Starting a project

You can start a new project simply by clicking File> New, and the following window will appear.  You can provide a number of specifications for your document before starting, but you can also customize your document at any stage by clicking File> Document setup (shortcut Alt+Ctrl+P).


2. Creating basic shapes

Lines & Arrows

Simply use the line segment tool ( A ) and remember to press the shift button to create perfectly straight lines.  Arrows can be added by using the stroke window (Window> Stroke) and (B) will appear, there’s a variety of arrow styles that you can select from and scale (C).  Finally,  in the line segment tool you can provide the exact length of your line.



Some shapes are already specified in Illustrator (e.g. rectangles , stars and circles (A), but many others such as triangles, need to be specified through the polygon tool.  To draw a triangle I need to specify the number of sides =3 as shown in  (B).


To generate curvatures, you can use the pen tool (A).  Specify two points with the pen, hold the click in the second point and a handle will appear, this handle allows you to shape the curve.  If you want to add more curvatures, draw another point (B) and drag the handle in the opposite direction of the curve.  You can then select the color (C) and the width (D) of your wave.


3. Matching colors

If you need to match the color of an image (A) there are a couple of alternatives:

i) Using the “Eyedrop” tool ( B).  Select the component of the image that you want to match, then select the Eyedrop tool and click on the desired color (C).


ii) Using the color picker panel.  Select the image component with the desired color, then double click on the color picker (highlighted in red) and the following panel should appear.  You can see the exact color code and you can copy and paste it on the image that you wish to edit.


4. Extracting exact position and dimensions

In the following example, I want the windows of my house to be perfectly aligned.  First, in (A), I click on one of the windows of my house and the control panel automatically provides its x and y coordinates, as well its width and height.  Since I want to align both of the windows horizontally, I investigate the  Y coordinates  of the first window and copy it onto the y coordinate of he second window as shown in (B).  The same procedure would apply if you want to copy the dimensions from one figure to another.


5. Free-style drawing and editing 

The pencil tool (A) is one of my favorite tools in Illustrator, since it corrects my shaky strokes, and allows me to  paint free style.  Once I added color and filled the blob that I drew, it started resembling more like a tree top (B).  You can edit your figure by right clicking it.  A menu will appear enabling you to rotate, reflect, shear and  scale, among other options.  I only wanted to tilt my tree so I specify a mild rotation  (C).



6. Cropping:

Cropping in Illustrator requires clipping masks and I will show a couple of examples using  Bone Bone, a fluffy celebrity cat.  Once a .png image is imported into illustrator, it can be cropped using  one of the following three methods:

Method 1.  Using the direct selection tool


Method 2. Using shapesSlide2.PNG

Method 3. Using the pen tool for a more detailed cropSlide3.PNG

To reverse  to the original image Select Object> Clipping mask> Release or Alt+Ctrl+7

7. Customize the art-board size

If you want your image to be saved without extra white space (B), you can adapt the size of the canvas with  the Art-board tool (or Shft+8) ( A).  Slide1.PNG

8. Using layers:

Layers can help you organize artwork, specially when working with multiple components in an image.  If the Layers panel is not already in your tools, you can access it through Window>  Layers or through the F7 shortcut.  A panel like the  one below should appear.   You can name the layers by double clicking on them, so you can give them a descriptive name.  Note that you can toggle the visibility of each layer on or off. You can also lock a layer if you want to protect it from further change, like the house layer in the example below.  Note that each layer is color-coded, my current working layer is coded in red, so when I select an element in that layer it will be highlighted in red.  The layers can also have sub-layers to store individual shapes, like in the house layer, which is comprised of a collection of rectangles and a triangle.



9.  Saving vector and exporting raster

Adobe Illustrator, naturally allows you to save images in many vector formats, But you can also export raster images such as .png, .jpeg, .bmp, etc.. To export raster images do File> Export and something like the blurry panel below should show up.  You can specify the  resolution and the color of the background. I usually like a transparent background  since it allows you more flexibility when using your image in programs such as Power Point.


There are many more features available in Illustrator, but this are the ones that I find myself using quite often.  Also, you probably won’t have to generate images from scratch, there are many available resources online. You can download svg images for free which you an later customize in Illustrator.  You can also complement this post by reading Jon Herman’s Scientific figures in Illustrator.

Using HDF5/zlib Compression in NetCDF4

Not too long ago, I posted an entry on writing NetCDF files in C and loading them in R.  In that post, I mentioned that the latest and greatest version of NetCDF includes HDF5/zlib compression, but I didn’t say much more beyond that.  In this post, I’ll explain briefly how to use this compression feature in your NetCDF4 files.

Disclaimer: I’m not an expert in any sense on the details of compression algorithms.  For more details on how HDF5/zlib compression is integrated into NetCDF, check out the NetCDF Documentation.  Also, I’ll be assuming that the NetCDF4 library was compiled on your machine to enable HDF5/zlib compression.  Details on building and installing NetCDF from source code can be found in the documentation too.

I will be using code similar to what was in my previous post.  The code generates three variables (x, y, z) each with 3 dimensions.  I’ve increased the size of the dimensions by an order of magnitude to better accentuate the compression capabilities.

  // Loop control variables
  int i, j, k;
  // Define the dimension sizes for
  // the example data.
  int dim1_size = 100;
  int dim2_size = 50;
  int dim3_size = 200;
  // Define the number of dimensions
  int ndims = 3;
  // Allocate the 3D vectors of example data
  float x[dim1_size][dim2_size][dim3_size]; 
  float y[dim1_size][dim2_size][dim3_size];
  float z[dim1_size][dim2_size][dim3_size];
  // Generate some example data
  for(i = 0; i < dim1_size; i++) {
        for(j = 0; j < dim2_size; j++) {
                for(k = 0; k < dim3_size; k++) {
                        x[i][j][k] = (i+j+k) * 0.2;
                        y[i][j][k] = (i+j+k) * 1.7;
                        z[i][j][k] = (i+j+k) * 2.4;

Next is to setup the various IDs, create the NetCDF file, and apply the dimensions to the NetCDF file.  This has not changed since the last post.

  // Allocate space for netCDF dimension ids
  int dim1id, dim2id, dim3id;
  // Allocate space for the netcdf file id
  int ncid;
  // Allocate space for the data variable ids
  int xid, yid, zid;
  // Setup the netcdf file
  int retval;
  if((retval = nc_create(ncfile, NC_NETCDF4, &ncid))) { ncError(retval); }
  // Define the dimensions in the netcdf file
  if((retval = nc_def_dim(ncid, "dim1_size", dim1_size, &dim1id))) { ncError(retval); }
  if((retval = nc_def_dim(ncid, "dim2_size", dim2_size, &dim2id))) { ncError(retval); }
  if((retval = nc_def_dim(ncid, "dim3_size", dim3_size, &dim3id))) { ncError(retval); }
  // Gather the dimids into an array for defining variables in the netcdf file
  int dimids[ndims];
  dimids[0] = dim1id;
  dimids[1] = dim2id;
  dimids[2] = dim3id;

Here’s where the magic happens.  The next step is to define the variables in the NetCDF file.  The variables must be defined in the file before you tag it for compression.

  // Define the netcdf variables
  if((retval = nc_def_var(ncid, "x", NC_FLOAT, ndims, dimids, &xid))) { ncError(retval); }
  if((retval = nc_def_var(ncid, "y", NC_FLOAT, ndims, dimids, &yid))) { ncError(retval); }
  if((retval = nc_def_var(ncid, "z", NC_FLOAT, ndims, dimids, &zid))) { ncError(retval); }

Now that we’ve defined the variables in the NetCDF file, let’s tag them for compression.

  // OPTIONAL: Compress the variables
  int shuffle = 1;
  int deflate = 1;
  int deflate_level = 4;
  if((retval = nc_def_var_deflate(ncid, xid, shuffle, deflate, deflate_level))) { ncError(retval); }
  if((retval = nc_def_var_deflate(ncid, yid, shuffle, deflate, deflate_level))) { ncError(retval); }
  if((retval = nc_def_var_deflate(ncid, zid, shuffle, deflate, deflate_level))) { ncError(retval); }

The function nc_def_var_deflate() performs this.  It takes the following parameters:

  • int ncid – The NetCDF file ID returned from the nc_create() function
  • int varid – The variable ID associated with the variable you would like to compress.  This is returned from the nc_def_var() function
  • int shuffle – Enables the shuffle filter before compression.  Any non-zero integer enables the filter.  Zero disables the filter.  The shuffle filter rearranges the byte order in the data stream to enable more efficient compression. See this performance evaluation from the HDF group on integrating a shuffle filter into the HDF5 algorithm.
  • int deflate – Enable compression at the compression level indicated in the deflate_level parameter.  Any non-zero integer enables compression.
  • int deflate_level – The level to which the data should be compressed.  Levels are integers in the range [0-9].  Zero results in no compression whereas nine results in maximum compression.

The rest of the code doesn’t change from the previous post.

  // OPTIONAL: Give these variables units
  if((retval = nc_put_att_text(ncid, xid, "units", 2, "cm"))) { ncError(retval); }
  if((retval = nc_put_att_text(ncid, yid, "units", 4, "degC"))) { ncError(retval); }
  if((retval = nc_put_att_text(ncid, zid, "units", 1, "s"))) { ncError(retval); }
  // End "Metadata" mode
  if((retval = nc_enddef(ncid))) { ncError(retval); }
  // Write the data to the file
  if((retval = nc_put_var(ncid, xid, &x[0][0][0]))) { ncError(retval); }
  if((retval = nc_put_var(ncid, yid, &y[0][0][0]))) { ncError(retval); }
  if((retval = nc_put_var(ncid, zid, &z[0][0][0]))) { ncError(retval); }
  // Close the netcdf file
  if((retval = nc_close(ncid))) { ncError(retval); }

So the question now is whether or not it’s worth compressing your data.  I performed a simple experiment with the code presented here and the resulting NetCDF files:

  1. Generate the example NetCDF file from the code above using each of the available compression levels.
  2. Time how long the code takes to generate the file.
  3. Note the final file size of the NetCDF.
  4. Time how long it takes to load and extract data from the compressed NetCDF file.

Below is a figure illustrating the results of the experiment (points 1-3).


Before I say anything about these results, note that individual results may vary.  I used a highly stylized data set to produce the NetCDF file which likely benefits greatly from the shuffle filtering and compression.  These results show a compression of 97% – 99% of the original file size.  While the run time did increase, it barely made a difference until hitting the highest compression levels (8,9).  As for point 4, there was only a small difference in load/read times (0.2 seconds) between the uncompressed and any of the compressed files (using ncdump and the ncdf4 package in R).  There’s no noticeable difference among the load/read times for any of the compressed NetCDF files.  Again, this could be a result of the highly stylized data set used as an example in this post.

For something more practical, I can only offer anecdotal evidence about the compression performance.  I recently included compression in my current project due to the large possible number of multiobjective solutions and states-of-the-world (SOW).  The uncompressed file my code produced was on the order of 17.5 GB (for 300 time steps, 1000 SOW, and about 3000 solutions).  I enabled compression of all variables (11 variables – 5 with three dimensions and 6 with two dimensions – compression level 4).  The next run produced just over 7000 solutions, but the compressed file size was 9.3 GB.  The down side is that it took nearly 45 minutes to produce the compressed file, as opposed to 10 minutes with the previous run.  There are many things that can factor into these differences that I did not control for, but the results are promising…if you’ve got the computer time.

I hope you found this post useful in some fashion.  I’ve been told that compression performance can be increased if you also “chunk” your data properly.  I’m not too familiar with chunking data for writing in NetCDF files…perhaps someone more clever than I can write about this?

Acknowledgement:  I would like to acknowledge Jared Oyler for his insight and helpful advice on some of the more intricate aspects of the NetCDF library.

Useful Linux commands to handle text files and speed up work

Most of us, nice and normal human beings, tend to prefer programs with GUIs over typing commands on a command prompt because the former looks more “real” and is more visual than the latter. However, one thing we don’t know (or, sometimes, don’t want to know) is that learning a few terminal commands can dramatically increase productivity. These commands can save us a lot of time by sparing us from opening and closing programs, navigating through menus and windows, moving the mouse around, as well as moving the hand back and forth from the mouse to the keyboard.

This post will mention and briefly describe some useful “intermediate level” Linux commands (some basic commands are presented in this post by Jon Herman), which can be called from a Linux OS, Cygwin (mostly), or Mac. Among the numerous tedious tasks these commands can greatly simplify is the particularly interesting chore of handling text files, be they scripts or data files. Commands for other tasks are covered as well. Keep in mind that the symbol * is a wild card (character that can mean any string of characters when searching for files), which is really useful when the goal is to repeatedly apply one command to multiple files. For all commands listed here skip the “$” character.


  • Remove columns 30 to 36 (starting from 0) from a comma separated file and export the output to another file.
    $ cut -d',' -f1-30,36 input.file >> output.file

    (More information on the post by Joe Kasprzyk)

  • Print only columns 2 and 4 (starting from 1) of a comma separated file.
    $ awk -F "," '{print $2,$4}' input.file >> output.file
  • Count number of columns in a file separated either by spaces or commas:
    $ head -1 input.file | sed 's/[^, ]//g' | wc -c
    $ awk -F "[, ]" 'END{print NF}' input.file
  • Print lines of a comma separated file in which the value in the 2nd column is lower than 100 and the value in the 5th column is higher than 0.3:
    $ awk -F "," '$2<100 && $5>0.3' input.file >> output.file
  • Print lines between 10th and 20th lines (not inclusive) of a file:
    $ awk 'NR>10 && NR<20' input.file >> output.file
  • Add a string to the end of multiple files:
    $ echo "your string" | tee -a *.set
  • Add a string to the end of one file:
    $ echo "your string" >> file


  • Find all text files in a folder that contain a certain string:
    $ grep -rn './folder' -e your_string
  • Find files recursively (* is a wildcard):
    $ find -type f -name name_of*.file


  • See the contents of a zip/tar file without extracting it. Press q to quit.
    $ less file.tar
  • Count number of lines in a file:
    $ wc -l your.file
  • List all files with a certain extension in a directory:
    $ ls *.ext
  • Print files and folders in tree fashion:
    $ tree
  • Print the size of all subfolders and files in (sub)folders to a certain max depth in the folder hierarchy:
    $ du -h -a --max-depth=2


  • Convert svg files to png (you need to have Inkscape installed):
    $ inkscape input.svg -d 300 -e output.png
  • Convert svg files to pdf-latex (you need to have Inkscape installed):
    $ inkscape input.svg --export-pdf output.pdf --export-latex
  • Rotate a picture:
    $ convert Fig6_prim.png -rotate 90 Fig6_prim_rotated.png


  • See the history of commands you have typed:
    $ history
  • See a calendar (month and year optional):
    $ cal [month] [year]
  • Combine pdf files into one (you need to have pdftk installed):
    $ pdftk file1.pdf file2.pdf file3.pdf cat output newfile.pdf
    or, to merge all pdf files in a directory:
    $ pdftk *.pdf cat output newfile.pdf

    In order to see how to combine only certain pagers of pdf files, as well as how to splits all pages into separate pdf files, see this page.

  • See the manual of a command:
    $ man command

Another useful idea is that of piping outputs of a command to another command. For example, if you want print the number of files in a directory, you can pipe the output of the ls command (list all files in a directory) to the wc -l command (count the number of lines in a string). For this, use the “|” character:

$ ls | wc -l

However, you may want instead to check the number of lines in all the 20 files in a directory at once, which can also be achieved by combining the ls and wc commands with the command xargs. The command then would look like:

$ ls | xargs wc -l

The command xargs breaks down the output from ls into one string per line and then calls wc -l for each line of the output of ls.

Hope all this saves you some time!

Python for automating cluster tasks: Part 2, More advanced commands

This is part 2 in a series about using Python for automating cluster tasks.  Part 1 is here. (For more on Python, check out: another tutorial part one and two, tips on setting up Python and Eclipse, and some specific examples including a cluster submission guide and a script that re-evaluates solutions using a different simulation model)

Edit: Added another example in the “copy” section below!

Welcome back!  Let’s continue our discussion of basic Python commands.  Let’s start by modifying our last code sample to facilitate random seed analysis.  Now, instead of writing one file we will write 50 new files.  This isn’t exactly how we’ll do the final product, but it will be helpful to introduce loops and some other string processing.

Loops and String Processing

import re
import os
import sys
import time
from subprocess import Popen
from subprocess import PIPE

def main():
    #the input filename and filestream are handled outside of the loop.
    #but the output filename and filestream have to occur inside the loop now.
    inFilename = "borg.c"
    inStream = open(inFilename, 'rb')

    for mySeed in range(1,51):
        outFilename = "borgNew.seed" + str(mySeed) + ".c"
        outStream = open(outFilename, 'w')

        print "Working on seed %s" % str(mySeed)

        for line in inStream:
            if "int mySeed" in line:
                newString = " int mySeed = " + str(mySeed) + ";\n"
        outStream.close() #reset the input file so you can read it again

if __name__ == "__main__":

Above, the range function allows us to iterate through a range of numbers. Note that the last member of the range is never included, so range(1,51) goes from 1 to 50. Also, now we have to be concerned with making sure our files are closed properly, and making sure that the input stream gets ‘reset’ every time. There may be a more efficient way to do this code, but sometimes it’s better to be more explicit to be sure that the code is doing exactly what you want it to. Also, if you had to rewrite multiple lines, it would be helpful to structure your loops the way I have them here.

By the way, after you run the sample program, you may want to do something like “rm BorgNew*” to remove all the files you just created.

Calling System Commands

Ok great, so now you can use Python to modify text files. What if you have to do something else in your workflow, such as copy files? Move them? Rename them? Call programs? Basically, you want your script to be able to do anything that you would do on the command line, or call system commands. For some background, check out this post on Stack Overflow, talking about the four or five different ways to call external commands in Python.

The code sample is below. Note that there’s two different ways to use the call command. Using “shell=True” allows you to have access to certain features of the shell such as the wildcard operator. But be careful with this! Accessing the shell directly can lead to problems as discussed here.

import re
import os
import sys
import time
from subprocess import Popen
from subprocess import PIPE
from subprocess import call

def main():

    print "Listing files..."
    call(["ls", "-l"])

    print "Showing the current working directory..."

    print "Now making ten copies of borg.c"
    for i in range(1,11):
        print "Working on file %s" % str(i)
        newFilename = "borgCopy." + str(i) + ".c"
        call(["cp", "borg.c", newFilename])
    print "All done copying!"

    print "Here's proof we did it.  Listing the directory..."
    call(["ls", "-l"])

    print "What a mess.  Let's clean up:"
    call("rm borgCopy*", shell=True)
    #the above is needed if you want to use a wildcard, see:

    print "All done removing!"

if __name__ == "__main__":

You may also remember that there are multiple ways to call the system. You can use subprocess to, in a sense, open a shell and call your favorite Linux commands… or you can use Python’s os library to do some of the tasks directly. Here’s an example of how to create some directories and then copy the files into the directory. Thanks to Amy for helping to write some of this code:

import os
import shutil
import subprocess

print os.getcwd()
global src
src = "myFile.txt" #or whatever your file is called

for i in range(51, 53); #remember this will only do it for 51 and 52
   newFoldername = 'seed'+str(i)
   if not os.path.exists(newFoldername):
   print "Listing files..."["ls", "-l"])
   shutil.copy(src, newFoldername)
   #now, we should change to the new directory to see if the
   #copy worked correctly
   os.chdir(newFoldername)["ls", "-l"])
   #make sure to change back


These two pieces of the puzzle should open up a lot of possibilities to you, as you’re setting up your jobs. Let us know if you want more by posting in the comments below!

Re-evaluating solutions using Python subprocesses

Our series of Python examples continues!  If you’ve missed our previous posts, we have some tutorials in parts one and two, tips on setting up Python and Eclipse, cluster submission guides, and so forth.

Matt has been helping me get up to speed using Python.  He always told me that some processes are a lot easier in Python than in C++, and I suppose I didn’t believe him til recently.  Here is my first shot at a Python program that’s all my own, and I wanted to share it with you here.  The code is below, then some comments follow.  The group has recently begun several GitHub code repositories!  You can link to this code sample at my repository.

import re
import os
import sys
import time
from subprocess import Popen
from subprocess import PIPE

def main():

    #Define the command that you would like to run through the pipe.  This will typically be your
    #executable set up to work with MOEAframework, and associated arguments.  Specifically here
    #we are working with the LRGV problem.
    cmd = ['./lrgvForMOEAFramework',  '-m', 'std-io', '-c', 'combined', '-b', 'AllDecAll']

    #Verify the command
    print "The command to run is: %s" % cmd

    #Use popen to open a child process.  The standard in, out, and error are sent through a pipe.
    child = Popen(cmd, cwd='//home//joka0958//re-evaluator_2013-04-05//', bufsize=LINE_BUFFERED, stdin=PIPE, stdout=PIPE, stderr=PIPE)

    #The current version of the model spits out some lines to the console when it initializes them.
    #When using this python child process, we need to intentionally send and receive all output (i.e. it doesn't
    #automatically do it for us.  Here there are 3 initialization lines to catch:
    print "Reading initializer lines."
    for i in range(0,3):
       line = child.stdout.readline()
       if line:
         print line
         raise Exception("Evaluator died!")

    #Now we want to step through an existing Borg output file, which already contains decision variables and objectives.
    #We are going to step through each line, read in the decision variables from the file, and then evaluate those decision
    #variables in our external program.
    myFilename = "AllDecAllExperimentData.txt"
    fp = open(myFilename, 'rb')
    for line in fp:
        if "#" in line:
            #This "if" statement is helpful if you want to preserve the generation separators and header lines that appeared
            #in the original file.
            print line
            #Read in all the variables on the line (this is both decisions and objectives)
            allVariables = [float(xx) for xx in re.split("[ ,\t]", line.strip())]

            #Only keep what you want
            variables = allVariables[0:8]

            #We want to send the decision variables, separated by a space, and terminated by a newline character
            decvarsAsString = '%f %f %f %f %f %f %f %f\n' % (variables[0], variables[1], variables[2], variables[3], variables[4], variables[5], variables[6], variables[7])

            #We send that string to the child process and catch the result.
            print "Sending to process"
            child.stdin.flush() #you flush, so that the program knows the line was sent

            #Now obtain the program's result
            print "Result:"
            outputLine = child.stdout.readline()
            print outputLine

            #Since this is in a loop, it will operate for every line in the input file.

if __name__ == "__main__":

Basically, your child process gets created in the beginning of this script. It is hanging out, waiting for input. The cool thing about the way input and output is handled here, is that you have complete control over what gets sent to the child program and what gets read in from it. Also, you can send multiple solutions to the program, and read their output sequentially.

Python also gives you lots of great control over input and output formatting in a relatively simple fashion. Notice how we are moving back and forth from strings and floats effortlessly. I also like how the control sequences (for loops, if else statements) are very straightforward and easy to read.

Feel free to adapt this code for your own purposes, and provide comments below!

Using linux “split”

Today I’d like to quickly talk about the linux command “split”.  I like writing about new simple Linux commands as evidenced here and here.

I often write customized C++ scripts to manipulate large data files.  There’s obviously a time and place for this, since you get ultimate control on every aspect of how your data looks going in and coming out.  We’ve written about this before, and I think string processing is an important skill no matter what language.  There’s a post about matlab (and another one here), some sample bash scripting, and a post about python among other things.  You should also see Matt’s series on python data analysis, since I’m doing some shameless plugging!

Anyway… little did I know that something very complicated in C++ can be easily done in linux/unix with “split”!

To split a large file into smaller files with, say, 100 lines, you use: “split -l 100 myLargerFile.txt”  There are also commands to change the filenames of the output files, and so forth.

Read the man page for split, and check out forum posts here and here to get on your way!

grep allows you to find an expression in one or more files in a folder on Linux.  I find it useful for programming.  Say, for example, I want to look for the string “nrec” in a set of source code and header files.  Maybe “nrec” is a variable and I forgot where I declared it (if this sounds a little too specific to be merely an example, you’re right. This is what I’m having to do right this second!).  The grep command is:

grep -in “nrec” *.*

What this means is, search for the “nrec” expression in every file in the folder.  There are two useful flags set here as well.  “i” means that the search is case insensitive (that is, NREC and NrEc and nrec are each treated as equal).  “n” means that the program will show me the line number of each occurrence of my desired phrase.  There are other options that I’m not using, including “inverting” a search to find all occurrences of NOT that phrase, suppressing the file name or only showing the file name, etc.

If you were curious, here’s a sample of the output:

iras.h:144: int num_flow_datapoints; //originally: NRec
SimSysClass.cpp:806: flowrecs=sysstat(nrec)

(If you’re curious, the first instance is in a header file, on line 144.  I’m translating this code from one language to another, and originally the variable was called “nrec”. So in the header file I made a note that now my variable is called something else.  In the second instance, I had copied the original code into my file as a placeholder, so now I know that I need to use my new name in its place.  Also, the “i” flag in grep is helpful since fortran is not case-sensitive, and here you can see there were two different case styles for this variable even in our simple example.)

For more info, please consult some casual reference such as this excellent post about linux command line utilities,  a similar blog post about grep, and of course the Linux man page for the command. Also look at 15 grep tips.  As usual, remember that “man [insert command here]” gives you all the low-down on each command you’d like to learn.

Thanks for reading and please comment with additional tips or questions!