We are hiring ! See our job offers.

Tip revision: **f8a7d6c933c4f84d39c07083de531b541c948137** authored by ** Tamas Nepusz ** on **07 August 2022, 21:11:11 UTC**

**chore: bumped version to 0.9.4**

Tip revision: **f8a7d6c**

README.rst

```
=====
plfit
=====
-------------------------------------------------
Fitting power-law distributions to empirical data
-------------------------------------------------
:Author: Tamas Nepusz
This program fits power-law distributions to empirical (discrete or
continuous) data, according to the method of Clauset, Shalizi and
Newman [1]_.
Requirements
------------
You only need a recent C compiler (e.g., ``gcc``), CMake_ and ``make`` to
compile the main executable. If you also want to compile the Python module with
the same functionality, you will also need SWIG_.
.. _SWIG: http://www.swig.org
.. _CMake: http://www.cmake.org
It is possible (although a bit more complicated) to compile the program
and the Python module without CMake_ or ``make``; see the
`Compiling without CMake or make`_ section below.
Compilation
-----------
Compiling with CMake
^^^^^^^^^^^^^^^^^^^^
This is easy. Assuming that you have downloaded the source code already,
just do this from the command line::
$ mkdir build
$ cd build
$ cmake ..
$ make
If you have permission to write in ``/usr/local``, you can also invoke
``make install`` at this point, which will place the executable in
``/usr/local/bin``. Adjusting ``CMAKE_INSTALL_PREFIX`` using ``ccmake``
will let you install ``plfit`` in a different folder.
The above code snippet compiles the main executable (``plfit``) but not
the Python module. If you need the Python module as well, the dance goes
like this::
$ mkdir build
$ cd build
$ cmake .. -DPLFIT_COMPILE_PYTHON_MODULE=ON
$ make
Compiling without CMake or make
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
CMake is the preferred way to build ``plfit``; however, those who do
not want to install CMake may rest assured that it is possible to compile
``plfit`` without CMake as well.
``plfit`` consists of two separate executables: ``plfit`` itself, which
implements the power-law fitting procedure, and ``plgen``, which generates
random data according to some power-law distribution. The two executables
are compiled nearly from the same source files; the only difference is
that ``main.c`` is not needed for ``plgen`` and ``plgen.c`` is not needed
for ``plfit``.
I like to use a separate build folder for compiling stuff, so we start
again by creating one::
$ mkdir build
$ cd build
Now we simply compile ``plfit`` and ``plgen`` using ``gcc``, making sure that
``plgen.c`` is excluded when we compile ``plfit`` and ``main.c`` is excluded
when we compile ``plgen``::
$ gcc -o plfit -I../src -lm `ls ../src/*.c | grep -F -v plgen.c`
$ gcc -o plgen -I../src -lm `ls ../src/*.c | grep -F -v main.c`
To build the Python interface as well, some extra legwork is needed
with SWIG::
$ swig -Wall -O -python -module plfit -interface _plfit_python -outcurrentdir ../src/plfit.i
$ gcc -shared -fPIC -o _plfit_python.so -I../src -I/usr/include/python3.8 \
-lm plfit_wrap.c ../src/*.c
where ``/usr/include/python3.8`` should be replaced with the directory
where the ``Python.h`` file of your Python interpreter is.
That's it. The Python interface itself consists of two files:
``plfit.py`` and ``_plfit_python.so``. Both of them are to be found in the
``build`` folder.
Usage
-----
From the command line
^^^^^^^^^^^^^^^^^^^^^
Prepare a data file consisting of your input data, one number (i.e. sample)
per line. So, for instance, if you want to find out whether the degrees
of some graph follow a power-law distribution or not, just dump the
degrees into the input file, one vertex per line. If you are interested
in whether the magnitude of earthquakes follow a power-law, dump the
magnitudes into the input file, one earthquake per line. There is no
need to calculate the cumulative distribution function or the probability
density function before, nor to normalize the input data. Futhermore,
inline comments using # (hash) are supported. Then simply run::
$ ./plfit input_data.txt
For some inspiration, see the sample data files under the directory ``data``.
The program will assume that the data is discrete when the file contains
integers only; otherwise it will assume that the data is continuous. If your
data file contains integers only but you know that they come from a continuous
distribution, use ``-c`` to force continuous fitting::
$ ./plfit -c input_data.txt
The ``-h`` switch gives you a nice help message::
$ ./plfit -h
You may also ask ``plfit`` to calculate the first four moments of the input
data (besides doing a regular power law fitting) using the ``-M`` switch. You
can then use the values of the moments to decide whether the distribution is
skewed and fat-tailed enough to be considered a "real" power-law.
``plfit`` has two output modes: a human-readable and a brief (but easily
parseable) format. By default, the results are printed in human-readable
format, which looks like this::
input.txt:
Central moments
mean = 2.71550
variance = 43.36610
std.dev. = 6.58529
skewness = 23.30801
kurtosis = 798.21995
ex.kurt. = 795.21995
Discrete MLE
alpha = 2.58306
xmin = 2.00000
L = -9155.61707
D = 0.00428
p = 0.99996
The moments are printed only if ``-M`` is given in the command line. The output
of the brief mode (when ``-b`` is given) looks like this::
input.txt: M 2.7155 43.3661 23.308 798.22
input.txt: D 2.58306 2 -9155.62 0.00428253 0.999965
where the line containing ``M`` (i.e. the first one) lists the first four
central moments (mean, variance, skewness, kurtosis) and the line containing
``D`` (for discrete data) or ``C`` (for continuous data) contains the fitted
exponent, minimum X value, log-likelihood (L), Kolmogorov-Smirnov statistic (D)
and p-value (p). Note that the ``M`` line does not list the standard deviation
(which is simply the square root of the variance) and the excess kurtosis
(which is simply the kurtosis minus 3).
From Python
^^^^^^^^^^^
The Python module is just a thin wrapper around the original C calls,
so take a look at ``plfit.h`` and use your instincts to figure out
how the corresponding Python functions are named ;) This should
get you started::
>>> import plfit
>>> data = [float(line) for line in open("input_file.txt")]
>>> plfit.plfit_discrete(data)
>>> plfit.alpha, plfit.xmin, plfit.L
You can also invoke ``dir(plfit)`` to examine the namespace of the
``plfit`` module, listing all the functions and data types.
There's also a test script in ``test/test_python_module.py``.
Using multiple CPU cores when fitting power-laws
------------------------------------------------
``plfit`` has experimental support for parallel computation on multi-core
machines. This is not tested thoroughly yet, but I am using it regularly on my
machine so it should be okay in general.
The default compilation settings of ``plfit`` compile the executable in
sequential mode, therefore ``plfit`` will use only a single core of your machine.
However, some parts of the calculation may be speeded up by distributing the
work among multiple CPU cores. If you want to enable parallelized computing,
launch ``ccmake .`` in the build directory, set the ``PLFIT_USE_OPENMP``
option to ``ON``, and recompile everything by calling ``make``. If you are
compiling ``plfit`` without CMake, add the necessary compiler switch to enable
OpenMP in the compiler. For ``gcc``, you should add the ``-fopenmp`` switch, so
the compiler invocation should start with ``gcc -fopenmp``.
Theoretically, the results provided by ``plfit`` in parallelized mode should be
exactly the same as the results provided in sequential mode (apart from the
exact p-values of course, which tend to fluctuate between runs). If you have
a dataset for which this does not hold, please file a bug report for ``plfit``
on GitHub_.
.. _GitHub: http://github.com/ntamas/plfit
Frequently asked questions
--------------------------
Where did the p-values go?
^^^^^^^^^^^^^^^^^^^^^^^^^^
Before version 0.7, ``plfit`` used an approximation algorithm to calculate the
p-value; this involved using the standard p-value formula of the
Kolmogorov-Smirnov test with the D statistic obtained directly from comparing
the fitted distribution with the empirical data. The approximation was really
fast, but unfortunately its results did not match the ones based on the more
exact resampling procedure reported in the original paper [1]_, and this has
been the source of some confusion. From version 0.7, ``plfit`` is able to
perform the same p-value estimation procedure from [1]_, but it is quite
time-consuming, especially for continuous data where there are a lot of
possible lower cutoff thresholds to try, since ``plfit`` has to fit power-laws
to about 2500 additional synthetic datasets in order to get a reasonable
estimate on the p-value that is more or less accurate to the second decimal
digit. So, the bottom line is that the approximation is fast but inaccurate,
and the exact calculation is slow. Since I still want to pretend that ``plfit``
is snappy, I decided not to calculate the p-value by default. If you want the
p-value, you have to add ``-p exact`` or ``-p approximate`` to the command line
options of ``plfit``, depending on whether you prefer the exact value or the
approximation that was used in ``plfit`` 0.6 or earlier.
I am getting different p-values every time I run the algorithm
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
I assume that you are calculating the exact p-value (i.e. you use ``-p exact``).
This is normal; the algorithm simply generates 2500 synthetic data sets with the
fitted exponent and cutoff, repeats the whole fitting procedure on the synthetic
data, and counts how many times the synthetic D-score is above the D-score of the
original fit. The fraction of the synthetic datasets with a D-score higher than the
D-score of the original fit then gives us the p-value. Of course there is quite a
bit of randomness involved, so the estimated p-value will be different every time,
but the first two decimal digits should be stable. If you want to make the procedure
fully deterministic, you have to pre-seed the random number generator with an
arbitrary integer using the ``-s`` switch.
I want more accurate p-values!
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
Sure, if you have plenty of time, ``plfit`` will allow you to do that. Just use
the ``-e`` switch with your required precision (e.g., 0.01 will give you
a p-value that is accurate up to the second decimal digit), and of course don't
forget to add ``-p exact``, otherwise ``-e`` will not do anything at all. For a
given precision *eps*, ``plfit`` will use ``1 / (4 * eps^2)`` iterations, so
be prepared for a long wait when *eps* is small. When multiple CPU cores are
available and ``plfit`` was compiled with OpenMP (see `Using multiple CPU cores
when fitting power-laws`_), the calculation will be parallelized, but it will
still take quite a bit of time.
References
----------
.. [1] Clauset A, Shalizi CR and Newman MEJ: Power-law distributions
in empirical data. SIAM Review 51, 661-703 (2009).
```