Welcome To Our Shell

Mister Spy & Souheyl Bypass Shell

#=====================================================================
#
#   xyparser v 1.0
#
#   The csv parser into the database
#
#   DOCUMENTATION
#   =============
#
#   The xyparser reads maXYmos csv files  and writes
#   their cycle data to a database.
#
#   Copyright: Ift Informatik GmbH, Fuerth, Germany
#      HRG 11020, AG Fuerth, info@ift-informatik.de
#
#
#   1. Introduction and installation
#   --------------------------------
#
#   To extend the toolchain of the maXYmos family, IfT provides a
#   tool to read csv-files coming from maXYmos BL, TL, NC series.
#   csv files are read and the values are stored in a database.
#
#   This tool comes with three files:
#
#       -   the executable:      xyparrser_<name>.exe
#       -   license file:        <name>.license
#       -   configuration file:  <name>.rule  (THIS file)
#
#   All these three file have to be put into the directory
#   where the csv files are located.
#
#
#   2. The parsing process
#   ----------------------
#
#   To start the parsing of the csv-files, start the exe-file
#   (as a service).
#
#   All the csv-files in the directory will be parsed and
#   the values are written into the database.
#
#   The database parameters are fixed in the license file,
#   and are customized by IfT. For getting a version with
#   a ceratin database connection, call IfT
#
#   Supported database engines:
#
#      -  sqlite
#      -  mysql, mariadb
#      -  MSSql
#      -  PostgreSql
#
#   After a csv-file <xyz>.csv is processed, a file <xyz>.processed
#   is written, so that it is clear which files already are read in
#   and can be removed from the directory.
#
#
#   3.  Configuration
#   -----------------
#
#   The configuration of the parsing can be controled in the .rule file
#   (THIS file).
#
#   A maXYmos csv-file has sections, separated by empty lines.
#   Each section starts with a header, followed by lines of data.
#   All empty lines followed by a single non-empty line are skipped.
#
#   To address a certain value:
#
#      V.<section>_<name>
#
#   where <section> is the identifier of the section, and <name> the
#   name of the variable.
#
#   The section name is the first cell of the section, without
#   spaces and special characters, in lower cases,
#   or even only a substring of it.
#
#   Example 1:
#
#        Process values - curve related;   -->   processvaluescurve
#                                          or    cessvaluesc
#                                          or    svaluescur
#
#   Example 2:
#
#        Process values - EO related;      -->   valueseo
#                                          or    eor
#
#   The first match will be found.
#
#   The <name> is the name of a value in the csv file, without
#   spaces and special characters, in lower cases.
#
#   Example 3:
#
#      XMIN-X  -->  xminx
#
#   Example 4:
#
#      Peak-peak X  -->  peakpeakx
#
#   For example, to address the process value BLOCK-X in the
#   section Process values - curve related, put:
#
#   Example 5:
#
#      V.processvaluescurve_blockx
#
#   If there is no ambiguity, then you even can skip the
#   <name> part: V.processvaluescurve_blockx   is also   V.blockx
#
#   If you skip the <name> part, you get the whole section:
#
#   Example 6:
#
#       V.processvaluescur   delivers the section.
#
#   You also can address the values of variables which many lines.
#   This affects variable names in the headline of sections,
#   for example in the EO related sections and the measuring
#   curve data section. The naming convention is the same,
#   but you get an array of values, due to the count of data
#   lines in the section.
#
#   Example 7:
#
#       V.objectsset_xreference
#
#   returns an array of the X reference settings for each EO.
#
#   Example 8:
#
#       V.time_time
#
#   returns the time column of the measuring data table
#   (be aware that the header of the data section is a
#   single non-empty line, so the following empty lines
#   are skipped).
#
#   Example 9:
#
#       V.time_xabsolute:
#
#   returns the X (ABSOLUTE) column of the measuring data table
#
#
#
#   4.  Mapping to database tables
#   ------------------------------
#
#   The values addressed by certain V.<section>_name, or
#   V.<section> or V.<name> entries can be mapped to certain
#   columns of one or more database tables.
#
#   The tables have to be notated by entries like that:
#
#        E.TABLE  = "<table1>"
#        F.TABLE  = "<table2>"
#        G.TABLE  = "<table3>"
#
#    at the beginning of the .rule file, one or more (up to ten)
#    entries like that, <tablex> is the certain name of the table.
#
#    Values now can be assigned to single columns of the databases
#    by assignment statements like:
#
#        E.<colname>  =  ....
#
#    writes to the column <colname> of the table <table1>.
#
#    All kind of python expressions are allowed here.
#
#    If a value to be entered into the database is an array,
#    then there will be inserted for each array element one
#    dedicated table row. If an array has less values than
#    lines have to be filled, this cell will remain empty.
#
#    To flatten an array (or any datastructure), use the
#    json-function.
#
#    Example 10:
#
#      json(V.eor_result)
#
#    gives the results for all EOs as a json string.
#      
#    A hash function also is offered:
#
#    Example 11:
#
#      hash(V.sequence)
#
#    All data entries come with a column CYCLEKEY with
#    an unique key to identity the cycle given by the csv file,
#    and a column CYCLENUMBER to count the lines in
#    the database table. It is possible to set CYCLEKEY in
#    the rule-file, then the CYCLEKEY will be overriden,
#    but stays unique. With this method a unique hash-table
#    of longer text fields can be provided.
#
#    Example 12:
#
#      E.CYCLEKEY = hash(V.sequence)
#
#    For SQLite and MySQL/MariaSQL tables and columns, which
#    are requested, will be generated if they do not exist.
#
#
#
#     END OF DOCUMENTATION
#
#=====================================================================
#
#   Example:

#   Configuration:

V._delete_csv = 1
V._store_days = 185
V._speed      = 0   #  speed up by doubling they cyclekeys
V._keylen     = 3   #  byte-length of the keys    


#   Table Names:

E.TABLE  = "processv"
F.TABLE  = "eov"
G.TABLE  = "measv"
H.TABLE  = "sequences"

#  Data assignemnts:

#  Process values:

E.DATE        =   V.date
E.TIME        =   V.time
E.CYCLENR     =   V.cyclenumber
E.RESULT      =   V.totalresult
E.PARTNR      =   V.partserialnumber
E.PROGNAME    =   V.measuringprogramname
E.PROGNR      =   V.measuringprogramnumber

E.IPNR        =   V.ipaddress
E.NETWORK     =   V.networkname
E.DEVICE      =   V.devicename
E.COMPANY     =   V.companyname
E.COMMENT     =   V.comment
E.MEASFUNC    =   V.measuringfunc
E.MEASPOINTS  =   V.numberofmeasuringpoints
E.REFGRAPHON  =   V.refgraphon


#  EO values:

F.EONR        =   V.evaluationobjectsset_evaluationobjectsset
F.EOREACTION  =   V.evaluationobjectsset_reaction
F.EOXREF      =   V.evaluationobjectsset_xref
F.EOYREF      =   V.evaluationobjectsset_yref
F.EOXMIN      =   V.evaluationobjectsset_xmin
F.EOXMAX      =   V.evaluationobjectsset_xmax
F.EOYMIN      =   V.evaluationobjectsset_ymin
F.EOYMAX      =   V.evaluationobjectsset_ymax
F.EOENTRY     =   V.evaluationobjectsset_entry
F.EOEXIT      =   V.evaluationobjectsset_exit
F.EOCURVEPART =   V.evaluationobjectsset_curvepart


# Measuring curve data:

G.MEASTIME    =   V.time_time[1:]
G.MEASX       =   V.time_xabs[1:]
G.MEASY       =   V.time_y[1:]


#  Sequences:

H.CYCLEKEY    =   hash(V.sequence)
H.SEQUENCE    =   V.sequence