The Python HTMLgen Module
The table facilities have also been extended to create fancy bar charts. Figure 2 shows a bar chart I generated from the output of the Linux ps command. The chart was created by the HTMLgen bar-chart module. The code for psv.py is the 20 lines of Python code shown in Listing 2. The original output from ps v looks something like the following:
PID TTY STAT TIME PAGEIN TSIZ DSIZ RSS LIM %MEM COMMA 555 p1 S 0:01 232 237 1166 664 xx 2.1 -tcsh 1249 p2 S 0:00 424 514 2613 1676 xx 5.4 xv ps ...
I use the Python operating system module's popen function to return a file input pipe for the output stream from the command:
inpipe = os.popen("ps vax", "r");I then read in the first line from the input pipe and split it into a list of column names.
colnames = string.split(inpipe.readline())Now, I create the chart object, and the chart object's datalist object:
chart = barchart.StackedBarChart() ... chart.datalist = barchart.DataList()Datalists can have multiple data segments per bar, which results in a stacked bar chart such as the one in Figure 2. I need to tell the datalist object how many data segments are present by setting the list of segment_names. I decided the bars on my chart will have two segments, one for TSIZ (program text memory size) and one for DSIZ (program data memory size). To accomplish this, I need to copy the two column names from colnames into segment_names. Because lists in Python are numbered from zero, the two colnames I'm interested in are columns 5 (TSIZ) and 6 (DSIZ). I can extract them from the colnames list with a single slicing statement:
chart.datalist.segment_names = colnames[5:7] data = chart.datalistThe [5:7] notation is a slicing notation. In Python, you can slice single items and ranges of items out of strings, lists and other sequence data types. The notation [low:high] means slice out a new list from low to high minus 1. On the second line, I assign the variable called “data” to the variable “chart.datalist” to shorten the length of the following lines to fit the column width required in Linux Journal.
After initializing the chart, I use a for loop to read the remaining lines from the ps output pipe. I extract the columns I need by using string.split(line) to break the line into a list of columns. I extract the text of each command by taking all the words from column 10 onward and joining them into a new barname string:
barname = string.join(cols[10:], " " )
I use the string module's atoi function to convert the ASCII strings in the numeric fields to integers. The last statement in the loop assembles the data into a tuple:
( barname, tsize, dsize )A tuple is a Python structure much like a list, except that a tuple is immutable—you cannot insert or delete elements from a tuple. Although the two are similar, their differences lead to quite different implementation efficiencies. Python has both a tuple and a list, because this allows the programmer to choose the one most appropriate to the situation. Many features of Python and its modules are designed to be high-level interfaces to services that are then implemented efficiently in compiled languages such as C. This allows Python to be used for computer graphics programming using OpenGL and for numerical programming using fast numerical libraries.
Back to the example. The last statement in the loop inserts the tuple into the chart's datalist.
data.load_tuple(( barname, tsize, dsize ))
When the last line is processed, the loop terminates and I sort the data in decreasing order of TSIZ:
data.sort(key=colnames, direction="decreasing")After that, I create the final document and save it to a file.
doc = HTMLgen.SimpleDocument(title='Memory') doc.append(chart) doc.write("psv.html")Loading psv.html into a browser results in the chart seen in Figure 2. By altering the bar chart's parameters, such as the GIFs used for the chart's “atoms”, I can build the chart in different styles.
Practical Task Scheduling Deployment
July 20, 2016 12:00 pm CDT
One of the best things about the UNIX environment (aside from being stable and efficient) is the vast array of software tools available to help you do your job. Traditionally, a UNIX tool does only one thing, but does that one thing very well. For example, grep is very easy to use and can search vast amounts of data quickly. The find tool can find a particular file or files based on all kinds of criteria. It's pretty easy to string these tools together to build even more powerful tools, such as a tool that finds all of the .log files in the /home directory and searches each one for a particular entry. This erector-set mentality allows UNIX system administrators to seem to always have the right tool for the job.
Cron traditionally has been considered another such a tool for job scheduling, but is it enough? This webinar considers that very question. The first part builds on a previous Geek Guide, Beyond Cron, and briefly describes how to know when it might be time to consider upgrading your job scheduling infrastructure. The second part presents an actual planning and implementation framework.
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