# Parsing Engines¶

The KeywordParser and PatternReader contained in serpentTools/engines.py are part of the drewtils v0.1.9 package and are provided under the following license

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These are designed to facilitate the parsing of files with a regular structure. For example, the depletion files all contain “chunks” of data that are separated by empty lines. Each chunk leads off with either the name of the material and associated variable, or the metadata, e.g. ZAI, DAYS. These parsers help break up these files into more digestible pieces.

Note

For developers, it is not required that these classes be used. These are bundled with this project to eliminate the need to install extra packages. Some of the readers, like the BranchingReader are not well suited for this type of parsing.

 KeywordParser Class for parsing a file for chunks separated by various keywords. PatternReader Class that can read over a file looking for patterns.

The CSCStreamProcessor is provided to help with reading sparse matrices provided by Serpent. These are current found in the depletion and fission matrix files.

class serpentTools.parsers.base.CSCStreamProcessor(stream, regex, dtype=<class 'float'>)

Read through a block of text and produce sparse matrices

Note

Rows and columns matched by regex will be reduced by one prior to storage. Since we primarily interact with one-indexed MATLAB arrays, we need to convert the indices to something numpy can properly understand

Parameters
• stream (IO stream from an opened file) – Object with a readline function that returns the next line of text to be read

• regex (str or compiled regular expression) –

Regular expression that matches the following:

1. Row of matrix

2. Column of matrix

3. Values to be added into resulting matrix.

All values in match.groups()[2:] will be converted to datatype and appended into data. The rows and columns are used to populate indices and indptr vectors

• datatype (object) – Data type of the numeric values of this matrix.

data

Column matrix of all values matched by regex after first two positions. Each columns can be used to build a sparse matrix with indices and indptr

Type

numpy.ndarray

indices

CSC-format indices pointer array

Type

numpy.ndarray

indptr

CSC-format index pointer array. Row indices for column i are stored in indices[indptr[i]:indptr[i + 1]]. Values for column i are stored in data[indptr[i]:intptr[i + 1]].

Type

numpy.ndarray

line

Last line read after calling process(). Will be the first non-empty line that does not match the passed regular expression

Type

str