Check out https://www.pythonpodcast.com/steven-lott-learn-to-code-episode-283/. This was a fun conversation on Python and learning.
We didn't talk about my books in detail. Instead, we talked about learning and what it takes to get closer to mastery.
It's a thing I worry about. I suspect other writers worry about it, also. Will the reader take the next steps? Or will they simply think they've got it because the read about it?
In the main, the PLoP patterns are less granular than the persnickety GoFCutting through the editorializing, the author is describing two families.
"Design Patterns." (Classic GoF, in part, static type binding work-arounds. And
you need to talk about a "facade" pattern? Really? Although see Fowler's at it
again, coining a ™ term - "fluent API" - for Some Not Egregiously Stupid
Practice, to feed to the credulous who have never reflected on what they are doing.)
"Plug-in Pattern" is a nice example. Enumerates the stuff you kinda know, with
qualities / attributes of its proposal, plus application samples / outcomes of
applying the pattern. The claims to relevance throughout are reminiscent of the
investigation behind Parnas' "Criteria for Decomposing Systems into Modules."
01 Some-Record.
05 Header PIC XXX.
05 Body PIC X(17).
01 ABC-Segment.
05 Field-ABC PIC X(17).
01 DEF-Segment.
05 Field-DEF PIC X(17).
If the files include Occurs Depending On, then the FTP transfer should include the RDW/BDW headers. The SITE RDW (or LOCSITE RDW) are essential. It's much faster to include this overhead. Stingray can process files without the headers, but it's slower.There are two essential Python techniques for building file splitters than involve parsing.
itertools.groupby( enumerate(reader), lambda x: x[0]//batch_size )
import itertools
import stringray.cobol
import collections
import pprint
batch_size= 1000
counts= collections.defaultdict(int)
with open( "some_file.schema", "rb" ) as source:
reader= stringray.cobol.RECFM_VB( source ).bdw_iter()
batches= itertools.groupby(enumerate(reader), lambda x: x[0]//batch_size):
for group, group_iter in batches:
with open( "some_file_{0}.schema".format(group), "wb" ) as target:
for id, row in group_iter:
target.write( row )
counts['rows'] += 1
counts[str(group)] += 1
pprint.pprint( dict(counts) )
import package.moduleimport packagefrom package import thingimport packageimport package.implementation as package
from stingray.workbook.csv import CSV_Workbook
from stingray.workbook.xls import XLS_Workbook
from stingray.workbook.xlsx import XLSX_Workbook
from stingray.workbook.ods import ODS_Workbook
from stingray.workbook.numbers_09 import Numbers09_Workbook
from stingray.workbook.numbers_13 import Numbers13_Workbook
from stingray.workbook.fixed import Fixed_Workbook