README.md

@@ -23,9 +23,6 @@ containing the user's entire cryptocurrency transaction history, the software wi
 
 *The tracking isn't pooled by `ticker`.  Rather, it's tracked at the account/wallet level.
 
-There is a helper Python script at the root of the repo that will assist you in sanitizing your CSV file
-so it can be successfully imported into `cryptools`.
-
 ---
 
 ### Features
@@ -55,20 +52,18 @@ when appreciated cryptocurrency was used to make a tax-deductible charitable con
 * Precision is limited to eight decimal places.  Additional digits will be stripped during
 import and may cause unintended rounding issues.
 
-* Microsoft Excel.  Don't let Excel cause you to bang your head against a wall.
+* Microsoft Excel.  Don't let this cause you to bang your head against a wall.
 Picture this scenario.  You keep your transactions for your input file in a Google Sheet,
 and you're meticulous about making sure it's perfect.
-You then download it as a CSV file and import it into `cryptools`.
+You then download it as a CSV file and import it into `cryptools`.  
 It works perfectly, and you have all your reports.
 Then you realize you'd like to quickly change a memo and re-run the reports, so you open the CSV file in Excel and edit it.
 Then you import it into `cryptools` again and the program panics!
 What happened is most likely that Excel changed the rounding of your precise decimals underneath you!
-Depending on the rounding, `cryptools` may think your input file has been incorrectly prepared
-because you've supposedly spent more coins than you actually owned at that time.
+As a result, it appears your input file has been clearly incorrectly prepared 
+because it appears that you're spending more coins than you actually owned at that time.
 `Cryptools` does not let you spend coins you don't own, and it will exit upon finding such a condition.
-The program is right, and your data is right, but Excel modified your data, so the program crashed for "no reason."
-The solution is to have Excel already open, then in the ribbon's Data tab, you'll import your CSV file "From Text."
-You'll choose Delimited, and Comma, and then highlight every column and choose Text as the data type.
+The program is right, and your data is right, but Excel modified your data, and it can be infuriating when the program crashes for "no reason."
 
 ## Installation
 

clean_input_csv.py

@@ -1,160 +0,0 @@
-#!/usr/bin/env python3
-
-## Purpose:	Allows user to keep additional data in their CSV Input File to increase its usefulness and
-##			enhance readability, yet be able to properly format it prior to importing into `cryptools`.
-##			e.g.:
-##				-Keep an additional first column for flagging/noting important transactions
-##				-Keep additional columns for tracking a running balance
-##				-Rows beneath transactions for life-to-date totals and other calculations and notes
-##				-Ability to use number formatting with parenthesis for negative numbers and commas
-##					-This script will change (1,000.00) to 1000.00
-##
-##			If a column doesn't have a header, this script will exclude it from the sanitized output.
-##			Similarly, this script will exclude transaction rows missing data in either of the first
-##			two fields of the row.
-
-## Usage:
-
-# 1.  Export/Save crypto activity as csv
-# 2.  Move the csv file to your desired directory
-# 3.  Rename file to <unedited>.csv (see variable below)
-# 4.  Build/run this file in an editor or on command line (from same directory), creating the input file
-# 5.  Import the input file into cryptools
-
-import csv
-import re
-import os
-
-unedited = "DigiTrnx.csv" 	# To be replaced with a launch arg, presumably	
-
-stage1 = "stage1.csv"
-
-##		First, writes all header rows.  Then attempts to write all transaction rows.
-##      In the transaction rows, if it finds blank/empty transaction date or proceeds fields,
-##		it discards the row.
-
-##		This allows notes/sums/calculations/etc under the transaction rows to be discarded
-
-with open(unedited) as fin, open(stage1, 'a') as fout:
-	rdr = csv.reader(fin) 	
-	wtr = csv.writer(fout)
-	header = next(rdr)
-	header2 = next(rdr)
-	header3 = next(rdr)
-	header4 = next(rdr)		
-	wtr.writerow(header)
-	wtr.writerow(header2)
-	wtr.writerow(header3)
-	wtr.writerow(header4)
-
-	for row in rdr:
-		if row[0] == "" or row[1] == "":
-			pass
-		else:
-			wtr.writerow(row)
-
-stage2 = "stage2.csv"
-
-##		Iterates over the fields in the first header row to search for empty/blank cells.
-##      Keeps a list of every column index that does contain data, and disregards all the
-##		indices for columns with a blank.
-
-##		Using the indicies of valid columns, writes a new CSV file using only valid columns.
-
-##		This is useful when the input file is also used to manually keep a running tally or
-##		columns with additional notes, but which must be discarded to prepare a proper
-##		CSV input file.
-
-with open(stage1) as fin, open(stage2, 'a') as fout:
-	rdr = csv.reader(fin) 	
-	wtr = csv.writer(fout)
-	header = next(rdr)
-	header2 = next(rdr)
-	header3 = next(rdr)
-	header4 = next(rdr)		
-
-	colListKept = []
-
-	for col in header:
-		if col == "":
-			pass
-		else:
-			colListKept.append(header.index(col))
-
-	output = [v for (i,v) in enumerate(header) if i in colListKept]
-	wtr.writerow(output)
-
-	output = [v for (i,v) in enumerate(header2) if i in colListKept]
-	wtr.writerow(output)
-
-	output = [v for (i,v) in enumerate(header3) if i in colListKept]
-	wtr.writerow(output)
-
-	output = [v for (i,v) in enumerate(header4) if i in colListKept]
-	wtr.writerow(output)
-
-	for row in rdr:
-		output = [v for (i,v) in enumerate(row) if i in colListKept]
-		wtr.writerow(output)
-
-
-stage3 = "InputFile-pycleaned.csv"
-
-##		Performs final formatting changes to ensure values can be successfully parsed.
-##	 	Numbers must have commas removed.  Negative numbers must have parentheses replaced
-##		with a minus sign.  Could also be used to substitute the date separation character.
-
-##	 	i.e., (1.01) -> -1.01  (1,000.00) -> -1000.00
-
-with open(stage2) as fin, open(stage3, 'w') as fout:
-	rdr = csv.reader(fin, quoting=csv.QUOTE_ALL) 	
-	wtr = csv.writer(fout)
-	
-	header = next(rdr)
-	header2 = next(rdr)
-	header3 = next(rdr)
-	header4 = next(rdr)
-	wtr.writerow(header)
-	wtr.writerow(header2)
-	wtr.writerow(header3)
-	wtr.writerow(header4)
-
-	for row in rdr:
-		listRow = []
-		for field in row:
-			fieldStr = str(field) # cast as string, just so there's no funny business
-			try:
-				# Handles negative numbers
-				if fieldStr[0] == "(":
-					fieldStr = fieldStr.replace('(','-').replace(')', '').replace(',', '')
-					listRow.append(fieldStr)
-					continue
-
-			# Uncomment the below and modify as necessary if you want to change date formatting
-				# elif re.search(r'\d\d-\d\d-\d\d',fieldStr):#	Find dates and change formatting
-				# 	fieldStr = fieldStr.replace('-', '/')
-				# 	listRow.append(fieldStr)
-				# 	continue
-
-				# Handle commas in remaining fields
-				else: 
-					try:
-						# if you remove commas from a string and are able to convert to float...
-						fieldStr_test = fieldStr.replace(',', '')
-						fieldStr_float = float(fieldStr_test)
-						# then it is definitely a positive number, so remove the comma.
-						fieldStr = fieldStr.replace(',', '')
-						listRow.append(fieldStr)
-						continue
-					except: # If the 'try' block fails, it's a memo, not a number, so leave any commas
-						listRow.append(fieldStr)
-						continue
-			except: # If the `try` block fails, it's a blank/empty string
-				listRow.append(fieldStr)
-				continue
-		wtr.writerow(listRow)
-
-os.remove(stage1)
-os.remove(stage2)
-
-print("Input file ready")