dempstert/nmi-finder

Installation

You can install the NMI Checker package directly from GitHub using the following command:

pip install git+https://github.com/Threadlet/nmi-finder.git

Usage

The primary method for checking NMIs is process_input, which can take either a single string or a list of strings as input. Based on the input type, it will process and return the results accordingly.

Example:

NOTE: We have used NMIs directly in this example but process_input method is capable to process OCR output of bills in the form of string datatype, it can search for NMI and run it through all the processes.

1. Using a list of strings:

from nmi_checker import RangeChecker
r = RangeChecker()
results = r.process_input('123')
print(results)

range_checker = RangeChecker()
test_strings = ["2501000000", "QB05414270", "QB09999999", "12345", "QB0A999999"]
results = range_checker.process_input(test_strings)
print(results)

Output:

[
 {'original': '2501000000',
  'output': (('2501000000', 'PWCLNSP'), True),
  'reason': 'NMI Found, Checksum Passed, Found in Range'},
 {'original': 'QB05414270',
  'output': 
{
 'original': '2501000000',
 'output': (('2501000000', 'PWCLNSP'), True),
 'reason': 'NMI Found, Checksum Passed, Found in Range'
}
,
  'reason': 'NMI Found, Checksum Passed, Found in Range'},
 {'original': 'QB09999999',
  'output': (('QB09999999', 'ENERGEXP'), True),
  'reason': 'NMI Found, Checksum Passed, Found in Range'},
 {'original': None,
  'output': (None, False),
  'reason': 'Not in given Ranges of AEMO'},
 {'original': 'QB0A999999',
  'output': (None, False),
  'reason': 'Not in given Ranges of AEMO'}
]

2. Using a single string:

result = range_checker.process_input("2501000000")
print(result)

Output:

{
 'original': '2501000000',
 'output': (('2501000000', 'PWCLNSP'), True),
 'reason': 'NMI Found, Checksum Passed, Found in Range'
}

New Methods in RangeChecker class:

There are two new methods added to the RangeChecker class: to_df and to_csv.

NOTE : The to_df and to_csv methods can only be called when the input to process_input is a list of strings.

to_df:

This method converts the result of process_input into a DataFrame.

Usage:

r = RangeChecker()
result = r.process_input(["2501000000", "QB05414270", "QB09999999", "12345", "QB0A999999"])
out_df = r.to_df()

out_df now contains the output dataframe.

to_csv:

This method saves the result of process_input to a CSV file. It requires a filename as an argument.

Usage:

r = RangeChecker()
result = r.process_input(["2501000000", "QB05414270", "QB09999999", "12345", "QB0A999999"])
r.to_csv("csv_fun_check.csv")

This will generate a CSV file named csv_fun_check.csv.