Merge pull request #69 from zzadxz/67-refactor-and-test-functions-of-…

…dashboarduse_casespy

67 refactor and test functions of dashboarduse casespy

backend/dashboard/calculations.py

@@ -0,0 +1,236 @@
+from datetime import datetime, timedelta
+from calendar import monthrange
+from rapidfuzz import process
+from dateutil.relativedelta import relativedelta
+
+def _get_closest_match(query: str, choices: dict, score_cutoff: int = 75) -> str:
+    """
+    Returns the best match for query in the keys of choices dict if the score 
+    is above the score_cutoff.
+    """
+    match, score, _ = process.extractOne(query, choices.keys())
+    if score >= score_cutoff:
+        return match
+    return None
+
+def _get_company_env_score(transaction: dict, ESG_scores: dict[str, dict]):
+    """
+    Returns environmental score for a transaction's company IF the company is in the ESG_scores dict.
+    Otherwise, return 0.
+    """
+    company_name = _get_closest_match(transaction['merchant_name'], ESG_scores)
+    if company_name is not None:
+        return ESG_scores[company_name]['environment_score']
+    else:
+        return 0
+
+def _is_green(transaction: dict, ESG_scores: dict[str,dict]):
+    company_env_score = _get_company_env_score(transaction, ESG_scores)
+    return company_env_score > 500
+
+def _company_tier(company_env_score: int) -> int:
+    """
+    Returns the tier of the company based on its environmental score, 
+    worst tier is 4 and best tier is 1.
+    """
+    if company_env_score > 560:
+        return 1
+    elif company_env_score > 520:
+        return 2
+    elif company_env_score > 500:
+        return 3
+    else:
+        return 4
+
+
+def _get_start_end_dates(frequency: str, current_date: datetime) -> tuple[datetime]:
+    """
+    Helper function, get start and end dates of the week or month of the current_date.
+    """
+    if frequency == "weekly":
+        # Get the start of the week (Monday)
+        start_date = current_date - timedelta(days=current_date.weekday())
+        # Get the end of the week (Sunday)
+        end_date = start_date + timedelta(days=6)
+    elif frequency == "monthly":
+        # Get the first day of the month
+        start_date = current_date.replace(day=1)
+        # Get the last day of the month
+        last_day = monthrange(current_date.year, current_date.month)[1]
+        end_date = current_date.replace(day=last_day)
+    else:
+        raise ValueError("Frequency must be 'weekly' or 'monthly'")
+
+    return start_date, end_date
+
+
+def _increment_current_date(frequency: str, start_date: datetime) -> datetime:
+    # Increment current date
+    if frequency == "weekly":
+        current_date = start_date - timedelta(days=1)
+    elif frequency == "monthly":
+        current_date = start_date - timedelta(days=start_date.day)
+
+    return current_date
+
+
+def _count_green_transactions_in_period(transactions: list[dict], start_date: datetime, end_date: datetime, ESG_scores: dict[str,dict]) -> int:
+    """
+    Helper function that counts green transactions within a specified date range.
+    """
+    return sum(
+        1 for transaction in transactions
+        if start_date <= datetime.strptime(transaction['time_completed'], "%Y-%m-%dT%H:%M:%S.%fZ") <= end_date
+        and _is_green(transaction, ESG_scores)
+    )
+
+
+def _get_unique_companies(transactions: list[dict], ESG_scores: dict[str, dict]):
+    """
+    Return set of top companies shopped at for all time
+    """
+    unique_companies = set()
+
+    for transaction in transactions:
+        company_name = _get_closest_match(transaction['merchant_name'], ESG_scores)
+        if company_name:
+            unique_companies.add(company_name)
+    return unique_companies
+
+
+def calculate_score(transactions: list[dict], start: datetime, end: datetime, ESG_scores: dict[str, dict]) -> float:
+    """
+    Calculate the environmental impact score of a user
+    """
+    lst_of_transactions = []
+    for transaction in transactions:
+        transaction_date = datetime.strptime(transaction['time_completed'], "%Y-%m-%dT%H:%M:%S.%fZ")
+        if start <= transaction_date <= end:
+            lst_of_transactions.append(transaction)
+
+    # Env_Contribution
+    env_contribution = 0
+    total_spending = 0
+    for transaction in lst_of_transactions:
+        company_env_score = _get_company_env_score(transaction, ESG_scores)
+        transaction_amount = transaction['amount']
+        env_contribution += company_env_score * transaction_amount
+        total_spending += transaction_amount
+
+    return int(env_contribution / total_spending) if total_spending != 0 else None
+
+
+def calculate_company_esg_scores(transactions: dict[int, dict], ESG_scores: dict[str,dict]) -> list[dict[str, float]]:
+    """
+    Calculate the ESG score of the companies that the user has made transactions with
+    """
+    company_ESG_scores = []
+    for transaction in transactions:
+        company_env_score = _get_company_env_score(transaction, ESG_scores)
+        company_ESG_scores.append({transaction['merchant_name']: company_env_score})
+
+    return company_ESG_scores
+
+
+def calculate_total_green_transactions(transactions: list[dict], ESG_scores: dict[str,dict]) -> int:
+    """
+    25th percentile: 245.0
+    50th percentile: 500.0
+    75th percentile: 520.0
+    90th percentile: 560.0
+    """
+    green_transactions = 0
+    for transaction in transactions:
+        if _is_green(transaction, ESG_scores):
+            green_transactions += 1
+
+    return green_transactions
+
+
+def find_most_purchased_companies(transactions: dict[int, dict], ESG_scores: dict[str,dict]) -> list[dict[str, float]]:
+    """
+    Gets most purchased companies of all time, returns a list of dictionary, where dictionaries 
+    contain the company name, the ESG score of the company and the amount spent on that company
+    """
+    sorted_transactions = sorted(transactions, key=lambda dic: dic['amount'], reverse=True)
+    top_5_companies = []
+    for transaction in sorted_transactions[:5]:
+        company_env_score = _get_company_env_score(transaction, ESG_scores)
+        top_5_companies.append({
+            'Company Name': transaction['merchant_name'],
+            'ESG Score': company_env_score,
+            'Amount Spent': transaction['amount']
+        })
+
+    return top_5_companies
+
+
+def calculate_historical_scores(frequency: str, transactions: dict[int, dict], esg_scores: dict[str, dict]) -> list[int]:
+    """
+    Return list of environmental scores for the past 12 weeks or months.
+    The scores go from most to least recent! So scores[0] is this month, scores[-1] is 10 months ago
+    """
+    scores = []
+    current_date =  datetime.now() - relativedelta(months=1)
+
+    if frequency == "weekly":
+        delta_days = 7
+    elif frequency == "monthly":
+        delta_days = 30
+    else:
+        raise ValueError("Frequency must be 'weekly' or 'monthly'")
+
+    for _ in range(12):
+        # Get start and end dates based on frequency
+        start_date, end_date = _get_start_end_dates(frequency, current_date)
+
+        # Calculate the score for the current period
+        score = calculate_score(transactions, start_date, end_date, esg_scores)
+
+        scores.append(score)
+
+        current_date = _increment_current_date(frequency, start_date)
+
+    return scores
+
+
+def calculate_historical_green_transactions(frequency: str, transactions: dict[int, dict], ESG_scores: dict[str,dict]) -> list[int]:
+    """
+    Return a list of green transaction counts for the past 12 weeks or months.
+    List goes from most recent as first element to least recent as last element.
+    """
+    green_transaction_counts = []
+    current_date =  datetime.now() - relativedelta(months=1)
+
+    for _ in range(12):
+        # Get start and end dates for this period
+        start_date, end_date = _get_start_end_dates(frequency, current_date)
+
+        # Get the number of green transactions for the current period
+        green_count = _count_green_transactions_in_period(transactions, start_date, end_date, ESG_scores)
+
+        green_transaction_counts.append(green_count)
+
+        current_date = _increment_current_date(frequency, start_date)
+
+    return green_transaction_counts
+
+
+def find_companies_in_each_tier(transactions: list[dict], ESG_scores: dict[str, dict]) -> list[int]:
+    """
+    Returns list of length 4, where the first element is the number of companies in the highest tier
+    that the user shopped at for all time.
+    """
+    # Set of unique companies transacted with this month
+    unique_companies_this_month = _get_unique_companies(transactions, ESG_scores)
+
+    # Indices correspond to tiers 1-4
+    company_tier_counts = [0, 0, 0, 0]  
+
+    # Classify each unique company into a tier and count
+    for company_name in unique_companies_this_month:
+        company_env_score = ESG_scores[company_name]['environment_score']
+        tier_index = _company_tier(company_env_score) - 1 
+        company_tier_counts[tier_index] += 1
+
+    return company_tier_counts

backend/dashboard/urls.py

@@ -5,12 +5,11 @@
     get_monthly_green_transactions,
     get_monthly_carbon_score,
     get_total_green_transactions,
-    get_top_companies,
+    get_top_5_companies,
     get_total_co2_score,
     get_company_tiers,
     get_co2_score_change,
     get_green_transaction_change,
-    get_map_data
 )
 
 
@@ -20,10 +19,9 @@
     path('get_monthly_green_transactions/', get_monthly_green_transactions, name='get_monthly_green_transactions'),
     path('get_monthly_carbon_score/', get_monthly_carbon_score, name='get_monthly_carbon_score'),
     path('get_total_green_transactions/', get_total_green_transactions, name='get_total_green_transactions'),
-    path('get_top_5_companies/', get_top_companies, name='get_top_companies'),
+    path('get_top_5_companies/', get_top_5_companies, name='get_top_5_companies'),
     path('get_total_co2_score/', get_total_co2_score, name='get_total_co2_score'),
     path('get_company_tiers/', get_company_tiers, name='get_company_tiers'),
     path('get_co2_score_change/', get_co2_score_change, name='get_co2_score_change'),
     path('get_green_transaction_change/', get_green_transaction_change, name='get_green_transaction_change'),
-    path('get_map_data/', get_map_data, name='get_map_data')
 ]