This file gives you access to a ruby module, MesaReader, containing three classes: MesaData, MesaProfileIndex, and MesaLogDir. The primary use is intended for plotting in Tioga, but as the tools become more sophisticated, analysis can be done on the fly with irb or standalone scripts.
Obivously you need an installation of Ruby. I haven't found any conflicts with Ruby 1.8.7, 1.9.3, or 2.0+, and I'm told it now works with 1.8.6. If you are doing a manual installation, you will also need to have tiogga, a plotting package, installed since MesaReader gathers data into Dvectors, which are a part of Tioga.
As of this writing, MesaReader is now packaged as a gem. If you have RubyGems working (and you probably do), you should be able to install it via
gem install mesa_reader
You may need to have superuser privileges, so just prepend sudo to the
previous command if rubygems barks at you about privileges. Tioga is a
prerequisite since MesaReader loads data into Dvectors, but Tioga installation
should occur automatically.
If you can't use a RubyGems installation, you can manually place the
mesa_reader.rb file in useful location for installation. Copying it to
your work directory may be sufficient if you just plan to use this for plotting
purposes since it will copy over with the rest of the directory whenever you
make a new work directory.
Another possible installation idea that I've been using is to clone the git
repository somewhere sensible on my machine and then create a pointer file in
my Ruby path (see below). For instance, I have a file in /usr/lib/ruby/
called mesa_reader.rb that has one line:
require '/Users/wmwolf/Documents/MESA_Reader/mesa_reader/lib/mesa_reader.rb'
That way, the source file is easily accessible in my git repo while also being readable from Ruby's path. That is, when you tell ruby to require MesaReader, it will find the file in its path, which will then load the file from your local repo.
To clone the git repo on your machine (assuming you
have git installed), cd to the directory where you want it installed and clone
it. You'll want to change the path to where you want to install the file, but
the commands are
cd ~/Documents
git clone https://github.com/wmwolf/MESA_Reader.git MesaReader
which will install the contents of the repo (currently a RubyGems-style directory for MesaReader and this README) into a directory called MesaReader in your Documents directory. Then any time you want to update to the latest release, you can do
cd ~/Documents/MesaReader
git pull
Or, hopefully, you can make your own additions and push updates for me to add!
For a more permanent solution, put mesa_reader.rb in your ruby path. For me
(on a mac), placing it in /usr/lib/ruby/ did the trick. To see where you
should install the file for global use enter the following line into your
terminal
ruby -e 'puts $:'
This should give you a list of directories that would work for installation. To update to a newer version of these tools, just move the new file to the same place.
In your program (or irb), just start with
require 'mesa_reader'
If the file is in the same directory as your current working one, it will be
read from there. Otherwise, ruby will search through its available paths for a
file called mesa_reader.rb and load the first one it finds. Note that in Ruby
2.0+, the working directory is no longer on the search. You must either use
require_relative or explicitly indicate that you want to search the current
directory with require './mesa_reader. This will also work in earlier versions of ruby, so if you are requiring a local copy, you should get in this
habit.
If you want direct access to the classes, you will also need to put in the line
include MesaReader
This takes all of the contents of the module MesaReader and drops them into
your current namespace. If you don't do this, you'll just have to add
MesaReader:: in front of any class name, like MesaReader::MesaData or
MesaReader::MesaLogDir. For subsequent examples, I'll assume you have
included the entire module (i.e., no more typing out MesaReader::).
To create a simple MesaData instance, use the class #initialize method:
s = MesaData.new(FILEPATH)
where FILEPATH is a string that is the path (relative or fully-qualified) to
the file you wish to read in. For instance, if you are in the work directory
and you want to read in the history file, you would use 'LOGS/history.data'
in place of FILEPATH. You can load history or profile files since the basic
MesaData object doesn't know the difference (though when loading history
files, it does know to throw out backups, retries, and restarts, ensuring that
the model numbers are monotonically increasing).
To create a MesaProfileIndex instance, we'll use that class' #initialize
method as well.
m = MesaProfileIndex.new(FILEPATH)
where now FILEPATH is a string containing the path to your profiles index
file, like 'LOGS/profiles.index'.
Finally, to make a MesaLogDir instance, we use that class' #initialize
method again. Unlike the first two examples, though, this class can take many
more initialization parameters. To use just two of them, here's an example:
l = MesaLogDir.new('log_path' => '~/mesa/star/work/LOGS', 'history_file' => 'history.data')
You can also set 'profile_prefix', 'profile_suffix', and 'index_file',
which denote the part of the name of profile before the number, the suffix of a
profile file, and the full name of the index file. The defaults are
'log_path' => 'LOGS'
'profile_prefix' => 'profile'
'profile_suffix' => 'data'
'history_file' => 'history.data'
'index_file' => 'profiles.index
Normally these shouldn't need to be altered, and so long as you don't have custom-named log directories, l = MesaLogDir.new, with no options, should suffice.
There are many publicly accessible instance methods for MesaData objects.
Their usage is detailed below. For convenience, we'll assume that s is an
instance of the MesaData class. That is, assume we have already done
s = MesaData.new('LOGS/history.data')
Returns an array of Dvectors containing the columns from the source file. This isn't very useful and is really only a diagnostic tool.
Returns an array of strings that are the names of each of the data columns.
These are the strings at the top of each data column from the source file.
Together with #bulk_data forms a hash that is accessed using the #data
command to convert data column names into Dvectors.
Accepts a string and returns the corresponding Dvector from #bulk_data. This
is the main usage of the class.
s.data('model_number') => [1.0, 2.0, 3.0, 4.0, ...]
Returns nil and prints a warning if no such key exists in s.bulk_names.
Accepts a string and a model number (float or integer) and returns the value of
s.data(key) at the index corresponding to the given model number. If no such
data category exists, returns nil and prints a warning. An exception will be
thrown if model_number is not a data category or if the given model number is
outside the range of s.data('model_number').
Accepts a string and returns true if the entry is found in s.bulk_names and
false otherwise.
Returns a string containing the name of the file that was read into the instance.
Accepts a string and returns the corresponding value from #header_data. The
key value must be in s.header_names or else it will return nil (and a
warning will be printed). Works in much the same way as #data but with the
header data rather than the bulk data.
Accepts a string and returns true if the string is in s.header_names.
Returns an array of all the data in the header row of the source file.
Returns an array of strings containing all the names of the header data entries.
Accepts an arbitrary (at least one) number of strings as arguments, each of
which must be a member of s.bulk_names. Then yields each member of
s.data(key1), s.data(key2), etc. into a [required] block and performs a
user-specified test on those data. Returns an array of integers containing the
indices of the set members that passed the test. For example
s.where('star_age', 'log_L') { |age, lum| age > 1e6 and lum > 2 }
returns an array containing the indices , i such that
s.data('star_age')[i] > 1e6 and s.data('log_L')[i] > 2. A common usage
would then be to feed these indices back in to get a subset of an array from
s.data('star_age'). For instance, one could get all the values of the
luminosity for times later than a million years via
s.data('luminosity').values_at(*s.where('star_age) { |age| age > 1e6 })
Depending on the type of file read in to the object (specifically what ends up
in s.bulk_names), you can also access the bulk data through some shorthand
without using the #data method. So long as the data name isn't already a
defined method on the MesaData class, you can simply use its name as a method
which just returns data("#{name}"). That is, if you load in a history file,
s.data('model_number')
and
s.model_number
should return the same thing. This is essentially just syntatic sugar, but it
makes for more readable code. The #data way of doing things is always invoked
at some point, so it is the "preferred" method of accessing data, but magic
methods should, for nearly all cases, perform just as well unless you have
unfortunately named data categories (starting with numbers, or they are
tragically named the same as an already existing method, like, nil?).
If the method name used is not in s.bulk_names but is in s.header_names,
then it returns the appropriate header data instead. If the name is in both
the header and data names, the data entry wins (this sometimes happens with
things like version_number and the like). If you use an invalid method (i.e.
one that is not explicitly defined in the class or its superclass, the basic
Ruby object Object, or a method implicitly defined from the data and header
categories via magic methods) a NameError will be thrown, just like it is for
any other case of a bad method call.
There are five publicly accessible methods for the MesaProfileIndex class,
though likely the only useful methods are #have_profile_with_model_number?
and #profile_with_model_number, which allow you to obtain a model number from
a model number. In practice, this class isn't very useful on its own, but is
used extensively in the MesaLogDir class.
Accepts an integer, a model number, and returns true if there is a profile
available with that model number. Otherwise returns false.
Accepts an integer, a profile number, and returns true if there is a profile
with that profile number. Otherwise returns false.
Returns a Dvector containing all the model numbers that have profiles available.
Returns a Dvector containing all the profile numbers available.
Accepts an integer model number and returns the profile number that corresponds
to it. Returns nil if there is no such profile.
We'll suppose we've already made an instance for the purpose of examples via
l = MesaLogDir.new
In addition to the methods defined below, all the methods of MesaProfileIndex
are available and are simply called on the internal MesaProfileIndex created
within the MesaLogDir structure.
Returns an array of strings containing names of all the files in the directory
returned by l.log_path.
Returns a MesaData instance made from l.history_file in l.log_path. This
object is created at initialization and is thus "free". There's no need to
catch this in a variable to spare the MesaData initialization process each time
it is called.
Alias for history.
Returns the name of the history data file in l.log_path.
Returns the name of the profile index file in l.log_path.
Returns a string containing the given path to the logs directory.
Returns a MesaProfileIndex instance built from l.index_file
Accepts two possible integer arguments, 'model_number' or 'profile_number'
which specify a profile to be loaded. If neither are given, the profile with
the largest model number (i.e., the last saved profile) is selected. Returns a
MesaData object built from this profile. If the model number provided has no
profile (i.e. l.profiles.profile_with_model_number(params['model_number']) => nil), then the default model number is selected. If a profile number is used,
it will attempt to use it no matter what, triggering an error if the given
profile number is invalid. For example
p = l.profile_data('model_number' => 300)
would set p to be a MesaData object built from the profile data associated
with model number 300. If no such model number existed, though, it would pull
data from the profile with the largest model number. If we instead used
p = l.profile_data('model_number' => 300, 'profile_number' => 15)
then p would be set to a MesaData object with profile number 15. The
'model_number' entry is entirely ignored. If there was no profile with
profile number 15, an exception will be raised. Essentially there is never a
time when it is helpful to specify both a model number and a profile number,
and again, if neither are specified, the profile with the largest model number
is used.
Each of these objects are made as this is called. That is, they aren't "sitting around" like the history MesaData object. As such, these should be captured in a variable so that they aren't re-constructed each time they are needed.
Returns the string containing the profile prefix as defined in the
MesaProfileIndex class.
Returns the string containing the profile suffix as defined in the
MesaProfileIndex class.
Nearly identical to #where in the MesaData class, but ensures that the
returned model numbers have corresponding profile files. Accepts an arbitrary
number (at least one) of strings that must be in l.history_data.bulk_names
and yields successive values of l.history_data.data(key) for each key to a
user-specified block that should return a boolean. Only those model numbers
that have available profiles are tested, so the returned Dvector of model
numbers (not indices, like in #where) have available profiles and pass the
test provided by the user. As an example
models_to_plot = l.select_models('log_center_T', 'log_center_Rho') { |log_tc, log_rhoc| log_tc > 8 and log_rhoc > 3 }
will return a Dvector of model numbers that have profiles available for reading in and have central temperatures exceeding 1e8 and have central densities exceeding 1e3.
The uses for these classes are pretty generic. As stated earlier, they were
developed primarily to ease plotting MESA data in Tioga, but they are also
quite useful for manipulating the data in their own rite for numerical
purposes. The only reason you might not want to do that is that Ruby isn't the
fastest language available, but then again, if you are dealing with such large
MESA data sets that the computational timescales are getting too long for your
comfort, you are in a pretty remarkable situation. As a practical note, if you
use this in irb and you make an instance of MesaData, I'd recommend
following that up with a semi-colon and nil unless you want to see a ton of
numbers fly up your screen. For instance, do this
l = MesaLogDir.new; nil
The nil keeps irb from outputting all the data held in l. Or consider using
the wonderful irb replacement Pry which can make
exploring your data outside of plots a much more pleasant experience (for
instance a simple semi-colon will suppress outputting the return value). If you
have any problems with or suggestions for further development of these classes,
please contact me or better yet, make some commits and push the changes for
deployment!