Merge branch 'master' into features/fetch_total_amount_of_collateral

apps/dashboard_app/charts/main_chart_figure.py

@@ -1,7 +1,7 @@
 """
 This module includes functions to generate financial charts using token price and liquidity data.
 """
-
+from collections import defaultdict
 import math
 from decimal import Decimal
 import pandas as pd
@@ -408,6 +408,7 @@ def display_user_history_chart(df: pd.DataFrame):
         else:  
             st.error("No data found for this wallet ID.") 
 
+ features/fetch_total_amount_of_collateral
 def get_total_collateral_amount(df: pd.DataFrame) -> pd.DataFrame:
     """
     Calculate the total collateral amount for each token address.
@@ -442,3 +443,67 @@ def get_total_collateral_amount(df: pd.DataFrame) -> pd.DataFrame:
 result = get_total_collateral_amount(mock_data)
 print("Total collateral amount per token address:")
 print(result)
+
+def get_total_debt_amount(debt_df: pd.DataFrame) -> pd.DataFrame:
+    """
+    Computes the total debt amount per token from the provided DataFrame.
+    
+    The function assumes that the DataFrame contains (according to the data.csv):
+    - A column representing token identifiers (e.g., 'Token' or 'Asset').
+    - A 'Debt (USD)' column containing numerical debt values.
+    
+    It groups the data by token and sums the debt amounts accordingly.
+    
+    Args:
+        debt_df (pd.DataFrame): DataFrame containing debt data with at least:
+            - A column for token identifiers.
+            - A 'Debt (USD)' column for debt values.
+    """
+    total_debt = {}
+
+    for _, row in debt_df.iterrows():
+        debt_entry = row.get("Debt")
+        if isinstance(debt_entry, dict):
+            for token, amount in debt_entry.items():
+                total_debt[token] = total_debt.get(token, 0) + amount
+        else:
+            # If the 'Debt' field is not a dict, skip this row or handle accordingly.
+            continue
+
+    result_df = pd.DataFrame(list(total_debt.items()), columns=["token", "total_debt"])
+    return result_df
+
+def get_total_deposit_amount(df: pd.DataFrame) -> dict:
+    """
+    A dataframe with a "Collateral" column containing deposit amount in the format:
+    "TOKEN: amount, TOKEN: amount, ...".
+    This function sums up the deposit amounts per token across all rows.
+    Args: Pandas DataFrame with a "Collateral" column.
+    Returns: A dictionary containing the total deposit amount per token.
+    """
+    totals = defaultdict(float)
+
+    for _, row in df.iterrows():
+        collateral = row.get("Collateral", "")
+
+        if pd.isna(collateral) or not collateral.strip():
+            continue
+
+        tokens = collateral.split(',')
+        for token_entry in tokens:
+            token_entry = token_entry.strip()
+            if not token_entry or ":" not in token_entry:
+                continue
+
+            token, amount_str = token_entry.split(":", 1)
+            token = token.strip()
+            try:
+                amount = float(amount_str.strip())
+            except ValueError:
+
+                continue
+
+            totals[token] += amount
+
+    return dict(totals)
+