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"""
Provide basic method to process data describing tables.
Created by Eugeniy E. Mikhailov 2024/05/27
The basic idea that we will have an *input* table
with data description and we (re)generate *output* table
based on the input table with processed rows.
If output table already have processed rows with entries different from NA
such rows are skipped.
Super handy for bulk processing data files where only a few parameters changed.
"""
import pandas as pd
import warnings
def loadInOutTables(inputFileName=None, outputFileName=None, comment=None):
"""Load the input and the output tables from files.
The output table loaded only if the corresponding file exists.
Otherwise it is a clone of the input table.
Parameters
==========
inputFileName : path or string
Path to the input table filename. If this file does not exists,
return None for both tables.
outputFileName : path or string or None
Path to the outpyt table filename. If such file does not exit,
clone the input table to the output one.
comment : string or None (default)
String which indicates a comment in the input `csv` file.
Usually it is either '#' or '%'. If set to None, internally changed to '#'.
"""
if not inputFileName:
return None, None
if not comment:
comment = '#'
tIn = pd.read_csv(inputFileName, comment=comment)
tIn.columns = tIn.columns.str.removeprefix(' '); # clean up leading white space in columns names
try:
tOut=pd.read_csv(outputFileName, comment=comment)
except Exception:
tOut=tIn.copy(deep=True)
return tIn, tOut
def ilocRowOrAdd(tbl, row):
"""Find a row in a table (`tbl`) similar to a provided `row`.
NA in both sets treated as a match.
If similar 'row' not found in the table, insert it.
"""
tSub = tbl[row.keys()]
res = (tSub == row) | (tSub.isna() & row.isna() )
res = res.all(axis=1) # which rows coincide
if res.any():
# we have a similar row
i = res[res].index[0]
else:
# we need to create new row since tbl does not has it
i=len(tbl)
updateTblRowAt(tbl, i, row)
return i
def updateTblRowAt(tbl, i, row):
"""Update row with position 'i' in the table ('tbl') with values from 'row'."""
for k in row.keys():
tbl.at[i, k] = row[k]
return
def isRedoNeeded(row, cols2check):
"""Check is Redo required in a given row.
Redo is required if *all* required entries in 'cols2check' are NA
or we are missing columns in cols2check list
Parameters
==========
row: pandas row
row to perform check on
cols2check: list of strings
List of strings with column names which considered as generated outputs.
"""
for c in cols2check:
if c not in row.keys():
return True
if row[cols2check].isna().all():
return True
return False
def reflowTable(tIn, tOut, process_row_func=None, postProcessedColums=None, extraInfo=None, redo=False):
"""Reflow/update table tOut in place based on the inputs specified in table tIn.
Effectively maps unprocessed rows to ``process_row_func``.
Parameters
==========
postProcessedColums : list of strings
List of column names which need to be generated
extraInfo : dictionary (optional)
Dictionary of additional parameter supplied to ``process_row_func``
process_row_func : function
Function which will take a row from the input table and generate
row with post processed entries (columns).
Expected to have signature like:
``rowOut = process_row_func(rowIn, extraInfo=userInfo)``
redo : True or False (default)
Flag indicating if reflow is needed unconditionally
(i.e. True forces reflow of all entries).
"""
if not process_row_func:
warnings.warn("process_row_func is not provided, exiting reflowTable")
return
if not postProcessedColums:
warnings.warn("postProcessedColums are not provided, exiting reflowTable")
return
for index, rowIn in tIn.iterrows():
iOut = ilocRowOrAdd(tOut, rowIn)
rowOutBefore = tOut.iloc[iOut]
if not (redo or isRedoNeeded(rowOutBefore, postProcessedColums) ):
continue
# processing data describing row
rowOut = process_row_func(rowOutBefore, extraInfo=extraInfo)
updateTblRowAt(tOut, iOut, rowOut)
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