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Merge branch 'ReadDB' into Packagers

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darkicewolf50 2024-11-30 14:57:44 -07:00
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ReadDB.py
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@ -1,42 +1,75 @@
import pandas as pd
import json
from filelock import FileLock, Timeout
import time
# Define the path to the Excel file and the lock file
excel_file_path = "./interview_database.xlsx"
lock_file_path = "./interview_database.xlsx.lock"
def ReadDatabase():
"""
Reads the Database
``REQUIRES``: None
``PROMISES``: JSON (Interview Avaiable Slots)
``PROMISES``: JSON (Interview Available Slots)
``Develop in part by``: Ahmad
``Developed in part by``: Ahmad
``Contact``: ahmad.ahmad1@ucalgary.ca
"""
# Load the updated Excel file into a pandas DataFrame
excel_file_path = "./interview_database.xlsx"
df = pd.read_excel(excel_file_path)
# Retry parameters
max_retries = 60 # Maximum number of retries if the file is locked
retry_interval = 0.5 # Wait time (in seconds) between retries
# Initialize the dictionary to store the structured data
interview_data = {}
retries = 0
while retries < max_retries:
try:
# Attempt to acquire a shared read (non-blocking) access
with FileLock(lock_file_path, timeout=0): # Non-blocking, checks if the lock exists
# Load the Excel file into a pandas DataFrame
df = pd.read_excel(excel_file_path)
# Group the DataFrame by Date, Start Time, and Slot for organization
for _, row in df.iterrows():
date = str(row['Date'])
start_time = str(row['Start Time'])
slot = int(row['Slot']) if not pd.isna(row['Slot']) else 0
#Returns amount of interviewees in the slot and if it's empty it will return 0
interviewee_amount = len(str(row['Interviewee Name']).split()) if str(row['Interviewee Name']) != "nan" else 0
# Initialize the dictionary to store the structured data
interview_data = {}
# Check if the slot is available for an interviewee to attend
avaliable_slots = interviewee_amount != slot
if avaliable_slots:
# Initialize nested structure if not present
if date not in interview_data:
interview_data[date] = {}
#Adds the start time and duration if not present
if start_time not in interview_data[date]:
interview_data[date][start_time] = {
'Meeting Duration': row['Meeting Duration'],
}
return interview_data
# Group the DataFrame by Date, Start Time, and Slot for organization
for _, row in df.iterrows():
date = str(row['Date'])
start_time = str(row['Start Time'])
slot = int(row['Slot']) if not pd.isna(row['Slot']) else 0
# Returns the number of interviewees in the slot; returns 0 if empty
interviewee_amount = len(str(row['Interviewee Name']).split()) if str(row['Interviewee Name']) != "nan" else 0
# Check if the slot is available for an interviewee to attend
available_slots = interviewee_amount != slot
if available_slots:
# Initialize nested structure if not present
if date not in interview_data:
interview_data[date] = {}
# Add the start time and duration if not present
if start_time not in interview_data[date]:
interview_data[date][start_time] = {
'Meeting Duration': row['Meeting Duration'],
}
return interview_data # Successfully read the database
except Timeout:
# File is locked; wait and retry
retries += 1
print(f"File is locked, retrying ({retries}/{max_retries})...")
time.sleep(retry_interval)
# If max retries are exceeded, raise an error
raise RuntimeError("Unable to access the database after multiple attempts due to a file lock.")
# Example usage of the ReadDatabase function
if __name__ == "__main__":
try:
data = ReadDatabase()
print(json.dumps(data, indent=4))
except RuntimeError as e:
print(e)