Implementations of two compression schemes for numeric data from mass spectrometers.
The library provides implementations of 3 different algorithms, 1 designed to compress first order smooth data like retention time or M/Z arrays, and 2 for compressing non smooth data with lower requirements on precision like ion count arrays.
Implementations and unit test are provided in C++, Java, and C#: for Python bindings exist.
For C++, move to src/main/cpp and compile and run tests (on LINUX) with
g++ MSNumpress.cpp MSNumpressTest.cpp -o test && ./test
Ensure that maven (2.2+) is installed. Then, in this directory, run
mvn test
Ensure that Cython and the Python headers are installed on your system. Then
move to src/main/python and compile and run tests (on LINUX) with
python setup.py build_ext --inplace
nosetests test_pymsnumpress.py
Open the MSNumpress.sln solution in Visual Studio. First run 'Restore NuGet Packages'. Now you can 'Run Unit Tests'.
Developers can use the library by directly installing it from
Intended for ion count data, this compression simply rounds values to the nearest integer, and stores these integers in a truncated form which is effective for values relatively close to zero.
Also targeting ion count data, this compression takes the natural logarithm of values, multiplies by a scaling factor and rounds to the nearest integer. For typical ion count dynamic range these values fits into two byte integers, so only the two least significant bytes of the integer are stored.
The scaling factor can be chosen manually, but the library also contains a function for retrieving the optimal Slof scaling factor for a given data array. Since the scaling factor is variable, it is stored as a regular double precision float first in the encoding, and automatically parsed during decoding.
This compression uses a fixed point representation, achieve by multiplication by a scaling factor and rounding to the nearest integer. To exploit the assumed linearity of the data, linear prediction is then used in the following way.
The first two values are stored without compression as 4 byte integers. For each following value a linear prediction is made from the two previous values:
Xpred = (X(n) - X(n-1)) + X(n)
Xres = Xpred - X(n+1)
The residual Xres is then stored, using the same truncated integer
representation as in Numpress Pic.
The scaling factor can be chosen manually, but the library also contains a function for retrieving the optimal Lin scaling factor for a given data array. Since the scaling factor is variable, it is stored as a regular double precision float first in the encoding, and automatically parsed during decoding.
This encoding works on a 4 byte integer, by truncating initial zeros or ones. If the initial (most significant) half byte is 0x0 or 0xf, the number of such halfbytes starting from the most significant is stored in a halfbyte. This initial count is then followed by the rest of the ints halfbytes, in little-endian order. A count halfbyte c of
0 <= c <= 8 is interpreted as an initial c 0x0 halfbytes
9 <= c <= 15 is interpreted as an initial (c-8) 0xf halfbytes
Examples:
int c rest
0 => 0x8
-1 => 0xf 0xf
23 => 0x6 0x7 0x1
This code is open source. It is dual licenced under the Apache 2.0 license as well as the 3-clause BSD licence. See the LICENCE-BSD and the LICENCE-APACHE file for the licences.