I'm having trouble parsing a file. I have code that parses a file by the word Total: if its value is greater than 20.0 and returns the data. I need to change the search keyword to Tokens eth: with a value greater than 20.0 and output all data between separators ======== and additionally write all sorted values into sort.txt file. I would be grateful for professional help)
Code:
outlist = []
flag = False
def dump(list_, flag_):
if list_ and flag_:
print('\n'.join(list_))
return [], False
with open('total.txt') as file:
for line in map(str.strip, file):
if line.startswith('='):
outlist, flag = dump(outlist, flag)
else:
tokens = line.split()
if len(tokens) == 3 and tokens[1] == 'Total:':
try:
flag = float(tokens[2][:-1]) > 20.0
except ValueError:
pass
outlist.append(line)
dump(outlist, flag)
total.txt
============
| hafuia
| 0xb34a47885262f9d8673dc77de7b583961134f09fb03620b29d282c32ee6932be
| 0xD0b2612a6eE3111114b43b25322C6F08A251D38D
| Total: 47.62874464666479$
|
|
| Tokens eth:
| 20.608732$ MANA
|
| Protocols cro:
| 17.840052$ VVS Finance
| 8.953779$ V3S Finance
============
| asdf
| 0x72e164aa187feaff7cb28a74b7ff800a0dfe916594c70f141069669e9df5a23b
| 0xC7dFe558ed09F0f3b72eBb0A04e9d4e99af0bd0D
| Total: 22.908481672796988$
|
|
| Tokens eth:
| 22.376087$ SOS
============
| asdf
| 0xbce666bca3c862a2ee44651374f95aca677de16b4922c6d5e7d922cc0ac42a3d
| 0x5870923a244f52fF2D119fbf5525421E32EC006e
| Total: 9.077030269778557$
|
|
| Tokens eth:
| 8.942218$ SOS
============
CodePudding user response：
This is how you can parse the file.
def parse_output(filename):
outlist = []
with open(filename) as file:
new_block = False
to_write = False
lines_arr = []
for line in map(str.strip, file):
if line.startswith('======='):
new_block = not new_block
if new_block:
if to_write:
outlist.append(lines_arr)
lines_arr = []
new_block = False
to_write = False
else:
lines_arr.append(line)
if 'Total:' in line:
num = float(line.split()[-1][:-1])
if num > 20:
to_write = True
return outlist
def write_output(outlist, filename):
for block in outlist:
for line in block:
with open(filename, 'a') as out_file:
out_file.write(line '\n')
with open(filename, 'a') as out_file:
out_file.write('=======' '\n')
if __name__ == '__main__':
write_output(parse_output('total.txt'), 'output.txt')
I missed the sorted wallet thing. For sorting, while appending array to outlist, you can use another array for order, or prepend the number to array, sort the outputs, and skip first element while writing.
CodePudding user response：
This is written in such a way that it's easy to get fe. the addresses as well. sorting done with a simple lambda function.
from pprint import pprint
wallet_splitter = "============"
wallet_content_start = "Tokens eth:"
wallet_line_start = "|"
with open("totals.txt") as infile:
wallets = infile.read().split(wallet_splitter)
print(wallets)
wallets_above_20 = []
for wallet in wallets:
total = 0
separate = []
contents = False
for line in wallet.splitlines():
if wallet_content_start in line:
contents = True
elif contents:
if "$" in line:
separate.append(line.replace(wallet_line_start, "").split("$")[0])
total = float(separate[-1])
else:
contents = False
for amount in separate:
if float(amount) > 20:
wallets_above_20.append({
"total": total,
"data": wallet
})
pprint(sorted(wallets_above_20, key = lambda i: i['total'],reverse=True))
CodePudding user response：
This is another simple extensible approach you can use to achieve what you need. The comments will explain the code.
# Create a simple representational object with data for every record.
class RateObject:
# You can change the delimiter to whatever you want.
def __init__(self, text_lines: list, delimiter="Tokens eth:"):
self.text_lines = text_lines
index = [i for i, x in enumerate(text_lines) if delimiter in x][0]
# Get the value from delimiter line
self.value = self._get_value(index)
# Override this method, to change the way you extract the value. From same line or different line etc.
def _get_value(self, delimiter_index: int):
# Case of Tokens eth:
value = self.text_lines[delimiter_index 1]
value = value.strip()
# A bad parsing for numbers, can be improved may be!
number = "".join([x for x in value if x.isdigit() or x == "."])
if number:
return float(number)
else:
# Assume 0 for unknown values
return 0.0
def __str__(self):
# Return the lines as it is
return "".join(self.text_lines)
def __repr__(self):
return "".join(self.text_lines)
# read the source file
with open("src.txt", "r") as src:
line_texts = src.readlines()
# Split the lines into sections, using the delimiter ========
splitters = [index for index, text in enumerate(line_texts) if text == "============\n"]
# Create a list of RateObjects
raw_objects = [RateObject(lt) for lt in [line_texts[splitters[i]:splitters[i 1]] for i in range(len(splitters) - 1)]]
# Filter the objects, to get only the ones with value > 20
selected_objects = list(filter(lambda x: x.value > 20.0, raw_objects))
# Sort the objects by value
sorted_objects = sorted(selected_objects, key=lambda x: x.value, reverse=True)
# print(selected_objects)
# print(sorted_objects)
# Write the sorted objects to a file
with open("sorted.txt", "w") as dst:
dst.write("\n".join([str(x) for x in sorted_objects]))
CodePudding user response：
Here's a simple generator-based approach.
def items(file):
"""
Generator to yield items from filename
whose "Tokens eth:" is above 20.0
"""
with open(file) as lines:
item = []
tokens = 0
capture = False
for line in lines:
if line == "============\n":
if tokens > 20.0:
yield tokens, item
item = []
tokens = 0
continue
if capture:
tokens = float(line.strip().split()[-2].rstrip("$"))
capture = False
if line.startswith("| Tokens eth:"):
# Set flag to capture next line when we get to it
capture = True
item.append(line)
def main():
import sys
print("============")
for tokens, item in sorted(list(items(sys.argv[1]))):
print("".join(item), end="")
print("============")
if __name__ == "__main__":
main()
For simplicity, I made the generator also perform filtering, though it would be easy to remove items with a lower total on the caller's side if you wanted to make this reusable.
Demo: https://ideone.com/UKuC6C
In fact, I would recommend that you parse this haphazard file format just once, and convert it to a standard format like CSV or JSON for further processing if this is more than a one-off.