Merge branch 'main' of ssh://dep.sokaris.link:2222/Simon/Cycles

.python-version

@@ -0,0 +1 @@
+3.10

cycles/Analysis/boillinger_band.py

@@ -0,0 +1,50 @@
+import pandas as pd
+
+class BollingerBands:
+    """
+    Calculates Bollinger Bands for given financial data.
+    """
+    def __init__(self, period: int = 20, std_dev_multiplier: float = 2.0):
+        """
+        Initializes the BollingerBands calculator.
+
+        Args:
+            period (int): The period for the moving average and standard deviation.
+            std_dev_multiplier (float): The number of standard deviations for the upper and lower bands.
+        """
+        if period <= 0:
+            raise ValueError("Period must be a positive integer.")
+        if std_dev_multiplier <= 0:
+            raise ValueError("Standard deviation multiplier must be positive.")
+            
+        self.period = period
+        self.std_dev_multiplier = std_dev_multiplier
+
+    def calculate(self, data_df: pd.DataFrame, price_column: str = 'close') -> pd.DataFrame:
+        """
+        Calculates Bollinger Bands and adds them to the DataFrame.
+
+        Args:
+            data_df (pd.DataFrame): DataFrame with price data. Must include the price_column.
+            price_column (str): The name of the column containing the price data (e.g., 'close').
+
+        Returns:
+            pd.DataFrame: The original DataFrame with added columns:
+                          'SMA' (Simple Moving Average),
+                          'UpperBand',
+                          'LowerBand'.
+        """
+        if price_column not in data_df.columns:
+            raise ValueError(f"Price column '{price_column}' not found in DataFrame.")
+
+        # Calculate SMA
+        data_df['SMA'] = data_df[price_column].rolling(window=self.period).mean()
+
+        # Calculate Standard Deviation
+        std_dev = data_df[price_column].rolling(window=self.period).std()
+
+        # Calculate Upper and Lower Bands
+        data_df['UpperBand'] = data_df['SMA'] + (self.std_dev_multiplier * std_dev)
+        data_df['LowerBand'] = data_df['SMA'] - (self.std_dev_multiplier * std_dev)
+
+        return data_df

cycles/Analysis/rsi.py

@@ -0,0 +1,109 @@
+import pandas as pd
+import numpy as np
+
+class RSI:
+    """
+    A class to calculate the Relative Strength Index (RSI).
+    """
+    def __init__(self, period: int = 14):
+        """
+        Initializes the RSI calculator.
+
+        Args:
+            period (int): The period for RSI calculation. Default is 14.
+                          Must be a positive integer.
+        """
+        if not isinstance(period, int) or period <= 0:
+            raise ValueError("Period must be a positive integer.")
+        self.period = period
+
+    def calculate(self, data_df: pd.DataFrame, price_column: str = 'close') -> pd.DataFrame:
+        """
+        Calculates the RSI and adds it as a column to the input DataFrame.
+
+        Args:
+            data_df (pd.DataFrame): DataFrame with historical price data.
+                                    Must contain the 'price_column'.
+            price_column (str): The name of the column containing price data.
+                                Default is 'close'.
+
+        Returns:
+            pd.DataFrame: The input DataFrame with an added 'RSI' column.
+                          Returns the original DataFrame with no 'RSI' column
+                          if the period is larger than the number of data points.
+        """
+        if price_column not in data_df.columns:
+            raise ValueError(f"Price column '{price_column}' not found in DataFrame.")
+
+        if len(data_df) < self.period:
+            print(f"Warning: Data length ({len(data_df)}) is less than RSI period ({self.period}). RSI will not be calculated.")
+            return data_df.copy()
+
+        df = data_df.copy()
+        delta = df[price_column].diff(1)
+
+        gain = delta.where(delta > 0, 0)
+        loss = -delta.where(delta < 0, 0) # Ensure loss is positive
+
+        # Calculate initial average gain and loss (SMA)
+        avg_gain = gain.rolling(window=self.period, min_periods=self.period).mean().iloc[self.period -1:self.period]
+        avg_loss = loss.rolling(window=self.period, min_periods=self.period).mean().iloc[self.period -1:self.period]
+
+
+        # Calculate subsequent average gains and losses (EMA-like)
+        # Pre-allocate lists for gains and losses to avoid repeated appending to Series
+        gains = [0.0] * len(df)
+        losses = [0.0] * len(df)
+
+        if not avg_gain.empty:
+            gains[self.period -1] = avg_gain.iloc[0]
+        if not avg_loss.empty:
+            losses[self.period -1] = avg_loss.iloc[0]
+
+
+        for i in range(self.period, len(df)):
+            gains[i] = ((gains[i-1] * (self.period - 1)) + gain.iloc[i]) / self.period
+            losses[i] = ((losses[i-1] * (self.period - 1)) + loss.iloc[i]) / self.period
+        
+        df['avg_gain'] = pd.Series(gains, index=df.index)
+        df['avg_loss'] = pd.Series(losses, index=df.index)
+
+        # Calculate RS
+        # Handle division by zero: if avg_loss is 0, RS is undefined or infinite.
+        # If avg_loss is 0 and avg_gain is also 0, RSI is conventionally 50.
+        # If avg_loss is 0 and avg_gain > 0, RSI is conventionally 100.
+        rs = df['avg_gain'] / df['avg_loss']
+        
+        # Calculate RSI
+        # RSI = 100 - (100 / (1 + RS))
+        # If avg_loss is 0:
+        #   If avg_gain > 0, RS -> inf, RSI -> 100
+        #   If avg_gain == 0, RS -> NaN (0/0), RSI -> 50 (conventionally, or could be 0 or 100 depending on interpretation)
+        # We will use a common convention where RSI is 100 if avg_loss is 0 and avg_gain > 0,
+        # and RSI is 0 if avg_loss is 0 and avg_gain is 0 (or 50, let's use 0 to indicate no strength if both are 0).
+        # However, to avoid NaN from 0/0, it's better to calculate RSI directly with conditions.
+
+        rsi_values = []
+        for i in range(len(df)):
+            avg_g = df['avg_gain'].iloc[i]
+            avg_l = df['avg_loss'].iloc[i]
+
+            if i < self.period -1 : # Not enough data for initial SMA
+                 rsi_values.append(np.nan)
+                 continue
+
+            if avg_l == 0:
+                if avg_g == 0:
+                    rsi_values.append(50) # Or 0, or np.nan depending on how you want to treat this. 50 implies neutrality.
+                else:
+                    rsi_values.append(100) # Max strength
+            else:
+                rs_val = avg_g / avg_l
+                rsi_values.append(100 - (100 / (1 + rs_val)))
+        
+        df['RSI'] = pd.Series(rsi_values, index=df.index)
+        
+        # Remove intermediate columns if desired, or keep them for debugging
+        # df.drop(columns=['avg_gain', 'avg_loss'], inplace=True)
+
+        return df

cycles/utils/data_utils.py

@@ -0,0 +1,60 @@
+import pandas as pd
+
+def aggregate_to_daily(data_df: pd.DataFrame) -> pd.DataFrame:
+    """
+    Aggregates time-series financial data to daily OHLCV format.
+
+    The input DataFrame is expected to have a DatetimeIndex.
+    'open' will be the first 'open' price of the day.
+    'close' will be the last 'close' price of the day.
+    'high' will be the maximum 'high' price of the day.
+    'low' will be the minimum 'low' price of the day.
+    'volume' (if present) will be the sum of volumes for the day.
+
+    Args:
+        data_df (pd.DataFrame): DataFrame with a DatetimeIndex and columns
+                                like 'open', 'high', 'low', 'close', and optionally 'volume'.
+                                Column names are expected to be lowercase.
+
+    Returns:
+        pd.DataFrame: DataFrame aggregated to daily OHLCV data.
+                      The index will be a DatetimeIndex with the time set to noon (12:00:00) for each day.
+                      Returns an empty DataFrame if no relevant OHLCV columns are found.
+    
+    Raises:
+        ValueError: If the input DataFrame does not have a DatetimeIndex.
+    """
+    if not isinstance(data_df.index, pd.DatetimeIndex):
+        raise ValueError("Input DataFrame must have a DatetimeIndex.")
+
+    agg_rules = {}
+
+    # Define aggregation rules based on available columns
+    if 'open' in data_df.columns:
+        agg_rules['open'] = 'first'
+    if 'high' in data_df.columns:
+        agg_rules['high'] = 'max'
+    if 'low' in data_df.columns:
+        agg_rules['low'] = 'min'
+    if 'close' in data_df.columns:
+        agg_rules['close'] = 'last'
+    if 'volume' in data_df.columns:
+        agg_rules['volume'] = 'sum'
+    
+    if not agg_rules:
+        # Log a warning or raise an error if no relevant columns are found
+        # For now, returning an empty DataFrame with a message might be suitable for some cases
+        print("Warning: No standard OHLCV columns (open, high, low, close, volume) found for daily aggregation.")
+        return pd.DataFrame(index=pd.to_datetime([])) # Return empty DF with datetime index
+
+    # Resample to daily frequency and apply aggregation rules
+    daily_data = data_df.resample('D').agg(agg_rules)
+    
+    # Adjust timestamps to noon if data exists
+    if not daily_data.empty and isinstance(daily_data.index, pd.DatetimeIndex):
+        daily_data.index = daily_data.index + pd.Timedelta(hours=12)
+    
+    # Remove rows where all values are NaN (these are days with no trades in the original data)
+    daily_data.dropna(how='all', inplace=True)
+
+    return daily_data

cycles/utils/gsheets.py

@@ -0,0 +1,128 @@
+import threading
+import time
+import queue
+from google.oauth2.service_account import Credentials
+import gspread
+import math
+import numpy as np
+from collections import defaultdict
+
+
+class GSheetBatchPusher(threading.Thread):
+
+    def __init__(self, queue, timestamp, spreadsheet_name, interval=60, logging=None):
+        super().__init__(daemon=True)
+        self.queue = queue
+        self.timestamp = timestamp
+        self.spreadsheet_name = spreadsheet_name
+        self.interval = interval
+        self._stop_event = threading.Event()
+        self.logging = logging
+
+    def run(self):
+        while not self._stop_event.is_set():
+            self.push_all()
+            time.sleep(self.interval)
+        # Final push on stop
+        self.push_all()
+
+    def stop(self):
+        self._stop_event.set()
+
+    def push_all(self):
+        batch_results = []
+        batch_trades = []
+        while True:
+            try:
+                results, trades = self.queue.get_nowait()
+                batch_results.extend(results)
+                batch_trades.extend(trades)
+            except queue.Empty:
+                break
+            
+        if batch_results or batch_trades:
+            self.write_results_per_combination_gsheet(batch_results, batch_trades, self.timestamp, self.spreadsheet_name)
+
+
+    def write_results_per_combination_gsheet(self, results_rows, trade_rows, timestamp, spreadsheet_name="GlimBit Backtest Results"):
+        scopes = [
+            "https://www.googleapis.com/auth/spreadsheets",
+            "https://www.googleapis.com/auth/drive"
+        ]
+        creds = Credentials.from_service_account_file('credentials/service_account.json', scopes=scopes)
+        gc = gspread.authorize(creds)
+        sh = gc.open(spreadsheet_name)
+        
+        try:
+            worksheet = sh.worksheet("Results")
+        except gspread.exceptions.WorksheetNotFound:
+            worksheet = sh.add_worksheet(title="Results", rows="1000", cols="20")
+
+        # Clear the worksheet before writing new results
+        worksheet.clear()
+
+        # Updated fieldnames to match your data rows
+        fieldnames = [
+            "timeframe", "stop_loss_pct", "n_trades", "n_stop_loss", "win_rate",
+            "max_drawdown", "avg_trade", "profit_ratio", "initial_usd", "final_usd"
+        ]
+
+        def to_native(val):
+            if isinstance(val, (np.generic, np.ndarray)):
+                val = val.item()
+            if hasattr(val, 'isoformat'):
+                return val.isoformat()
+            # Handle inf, -inf, nan
+            if isinstance(val, float):
+                if math.isinf(val):
+                    return "∞" if val > 0 else "-∞"
+                if math.isnan(val):
+                    return ""
+            return val
+
+        # Write header if sheet is empty
+        if len(worksheet.get_all_values()) == 0:
+            worksheet.append_row(fieldnames)
+
+        for row in results_rows:
+            values = [to_native(row.get(field, "")) for field in fieldnames]
+            worksheet.append_row(values)
+        
+        trades_fieldnames = [
+            "entry_time", "exit_time", "entry_price", "exit_price", "profit_pct", "type"
+        ]
+        trades_by_combo = defaultdict(list)
+
+        for trade in trade_rows:
+            tf = trade.get("timeframe")
+            sl = trade.get("stop_loss_pct")
+            trades_by_combo[(tf, sl)].append(trade)
+        
+        for (tf, sl), trades in trades_by_combo.items():
+            sl_percent = int(round(sl * 100))
+            sheet_name = f"Trades_{tf}_ST{sl_percent}%"
+
+            try:
+                trades_ws = sh.worksheet(sheet_name)
+            except gspread.exceptions.WorksheetNotFound:
+                trades_ws = sh.add_worksheet(title=sheet_name, rows="1000", cols="20")
+            
+            # Clear the trades worksheet before writing new trades
+            trades_ws.clear()
+
+            if len(trades_ws.get_all_values()) == 0:
+                trades_ws.append_row(trades_fieldnames)
+            
+            for trade in trades:
+                trade_row = [to_native(trade.get(field, "")) for field in trades_fieldnames]
+                try:
+                    trades_ws.append_row(trade_row)
+                except gspread.exceptions.APIError as e:
+                    if '429' in str(e):
+                        if self.logging is not None:
+                            self.logging.warning(f"Google Sheets API quota exceeded (429). Please wait one minute. Will retry on next batch push. Sheet: {sheet_name}")
+                        # Re-queue the failed batch for retry
+                        self.queue.put((results_rows, trade_rows))
+                        return  # Stop pushing for this batch, will retry next interval
+                    else:
+                        raise

cycles/utils/storage.py

@@ -0,0 +1,210 @@
+import os
+import json
+import pandas as pd
+import csv
+from collections import defaultdict
+
+RESULTS_DIR = "results"
+DATA_DIR = "data"
+
+class Storage:
+
+    """Storage class for storing and loading results and data"""
+    def __init__(self, logging=None, results_dir=RESULTS_DIR, data_dir=DATA_DIR):
+
+        self.results_dir = results_dir
+        self.data_dir = data_dir
+        self.logging = logging
+
+        # Create directories if they don't exist
+        os.makedirs(self.results_dir, exist_ok=True)
+        os.makedirs(self.data_dir, exist_ok=True)
+
+    def load_data(self, file_path, start_date, stop_date):
+        """Load data with optimized dtypes and filtering, supporting CSV and JSON input
+        Args:
+            file_path: path to the data file
+            start_date: start date
+            stop_date: stop date
+        Returns:
+            pandas DataFrame
+        """
+        # Determine file type
+        _, ext = os.path.splitext(file_path)
+        ext = ext.lower()
+        try:
+            if ext == ".json":
+                with open(os.path.join(self.data_dir, file_path), 'r') as f:
+                    raw = json.load(f)
+                data = pd.DataFrame(raw["Data"])
+                # Convert columns to lowercase
+                data.columns = data.columns.str.lower()
+                # Convert timestamp to datetime
+                data["timestamp"] = pd.to_datetime(data["timestamp"], unit="s")
+                # Filter by date range
+                data = data[(data["timestamp"] >= start_date) & (data["timestamp"] <= stop_date)]
+                if self.logging is not None:
+                    self.logging.info(f"Data loaded from {file_path} for date range {start_date} to {stop_date}")
+                return data.set_index("timestamp")
+            else:
+                # Define optimized dtypes
+                dtypes = {
+                    'Open': 'float32',
+                    'High': 'float32', 
+                    'Low': 'float32',
+                    'Close': 'float32',
+                    'Volume': 'float32'
+                }
+                # Read data with original capitalized column names
+                data = pd.read_csv(os.path.join(self.data_dir, file_path), dtype=dtypes)
+
+
+                # Convert timestamp to datetime
+                if 'Timestamp' in data.columns:
+                    data['Timestamp'] = pd.to_datetime(data['Timestamp'], unit='s')
+                    # Filter by date range
+                    data = data[(data['Timestamp'] >= start_date) & (data['Timestamp'] <= stop_date)]
+                    # Now convert column names to lowercase
+                    data.columns = data.columns.str.lower()
+                    if self.logging is not None:
+                        self.logging.info(f"Data loaded from {file_path} for date range {start_date} to {stop_date}")
+                    return data.set_index('timestamp')
+                else: # Attempt to use the first column if 'Timestamp' is not present
+                    data.rename(columns={data.columns[0]: 'timestamp'}, inplace=True)
+                    data['timestamp'] = pd.to_datetime(data['timestamp'], unit='s')
+                    data = data[(data['timestamp'] >= start_date) & (data['timestamp'] <= stop_date)]
+                    data.columns = data.columns.str.lower() # Ensure all other columns are lower
+                    if self.logging is not None:
+                        self.logging.info(f"Data loaded from {file_path} (using first column as timestamp) for date range {start_date} to {stop_date}")
+                    return data.set_index('timestamp')
+        except Exception as e:
+            if self.logging is not None:
+                self.logging.error(f"Error loading data from {file_path}: {e}")
+            # Return an empty DataFrame with a DatetimeIndex
+            return pd.DataFrame(index=pd.to_datetime([]))
+
+    def save_data(self, data: pd.DataFrame, file_path: str):
+        """Save processed data to a CSV file.
+        If the DataFrame has a DatetimeIndex, it's converted to float Unix timestamps
+        (seconds since epoch) before saving. The index is saved as a column named 'timestamp'.
+
+        Args:
+            data (pd.DataFrame): data to save.
+            file_path (str): path to the data file relative to the data_dir.
+        """
+        data_to_save = data.copy()
+
+        if isinstance(data_to_save.index, pd.DatetimeIndex):
+            # Convert DatetimeIndex to Unix timestamp (float seconds since epoch)
+            # and make it a column named 'timestamp'.
+            data_to_save['timestamp'] = data_to_save.index.astype('int64') / 1e9
+            # Reset index so 'timestamp' column is saved and old DatetimeIndex is not saved as a column.
+            # We want the 'timestamp' column to be the first one.
+            data_to_save.reset_index(drop=True, inplace=True)
+            # Ensure 'timestamp' is the first column if other columns exist
+            if 'timestamp' in data_to_save.columns and len(data_to_save.columns) > 1:
+                cols = ['timestamp'] + [col for col in data_to_save.columns if col != 'timestamp']
+                data_to_save = data_to_save[cols]
+        elif pd.api.types.is_numeric_dtype(data_to_save.index.dtype):
+            # If index is already numeric (e.g. float Unix timestamps from a previous save/load cycle),
+            # make it a column named 'timestamp'.
+            data_to_save['timestamp'] = data_to_save.index
+            data_to_save.reset_index(drop=True, inplace=True)
+            if 'timestamp' in data_to_save.columns and len(data_to_save.columns) > 1:
+                cols = ['timestamp'] + [col for col in data_to_save.columns if col != 'timestamp']
+                data_to_save = data_to_save[cols]
+        else:
+            # For other index types, or if no index that we want to specifically handle,
+            # save with the current index. pandas to_csv will handle it.
+            # This branch might be removed if we strictly expect either DatetimeIndex or a numeric one from previous save.
+            pass # data_to_save remains as is, to_csv will write its index if index=True
+
+        # Save to CSV, ensuring the 'timestamp' column (if created) is written, and not the DataFrame's active index.
+        full_path = os.path.join(self.data_dir, file_path)
+        data_to_save.to_csv(full_path, index=False) # index=False because timestamp is now a column
+        if self.logging is not None:
+            self.logging.info(f"Data saved to {full_path} with Unix timestamp column.")
+    
+    
+    def format_row(self, row):
+        """Format a row for a combined results CSV file
+        Args:
+            row: row to format
+        Returns:
+            formatted row
+        """
+
+        return {
+            "timeframe": row["timeframe"],
+            "stop_loss_pct": f"{row['stop_loss_pct']*100:.2f}%",
+            "n_trades": row["n_trades"],
+            "n_stop_loss": row["n_stop_loss"],
+            "win_rate": f"{row['win_rate']*100:.2f}%",
+            "max_drawdown": f"{row['max_drawdown']*100:.2f}%",
+            "avg_trade": f"{row['avg_trade']*100:.2f}%",
+            "profit_ratio": f"{row['profit_ratio']*100:.2f}%",
+            "final_usd": f"{row['final_usd']:.2f}",
+        }
+    
+    def write_results_chunk(self, filename, fieldnames, rows, write_header=False, initial_usd=None):
+        """Write a chunk of results to a CSV file
+        Args:
+            filename: filename to write to
+            fieldnames: list of fieldnames
+            rows: list of rows
+            write_header: whether to write the header
+            initial_usd: initial USD
+        """
+        mode = 'w' if write_header else 'a'
+        
+        with open(filename, mode, newline="") as csvfile:
+            writer = csv.DictWriter(csvfile, fieldnames=fieldnames)
+            if write_header:
+                csvfile.write(f"# initial_usd: {initial_usd}\n")
+                writer.writeheader()
+            
+            for row in rows:
+                # Only keep keys that are in fieldnames
+                filtered_row = {k: v for k, v in row.items() if k in fieldnames}
+                writer.writerow(filtered_row)
+    
+    def write_results_combined(self, filename, fieldnames, rows):
+        """Write a combined results to a CSV file
+        Args:
+            filename: filename to write to
+            fieldnames: list of fieldnames
+            rows: list of rows
+        """
+        fname = os.path.join(self.results_dir, filename)
+        with open(fname, "w", newline="") as csvfile:
+            writer = csv.DictWriter(csvfile, fieldnames=fieldnames, delimiter='\t')
+            writer.writeheader()
+            for row in rows:
+                writer.writerow(self.format_row(row))
+        if self.logging is not None:
+            self.logging.info(f"Combined results written to {fname}")
+    
+    def write_trades(self, all_trade_rows, trades_fieldnames):
+        """Write trades to a CSV file
+        Args:
+            all_trade_rows: list of trade rows
+            trades_fieldnames: list of trade fieldnames
+            logging: logging object
+        """
+
+        trades_by_combo = defaultdict(list)
+        for trade in all_trade_rows:
+            tf = trade.get("timeframe")
+            sl = trade.get("stop_loss_pct")
+            trades_by_combo[(tf, sl)].append(trade)
+
+        for (tf, sl), trades in trades_by_combo.items():
+            sl_percent = int(round(sl * 100))
+            trades_filename = os.path.join(self.results_dir, f"trades_{tf}_ST{sl_percent}pct.csv")
+            with open(trades_filename, "w", newline="") as csvfile:
+                writer = csv.DictWriter(csvfile, fieldnames=trades_fieldnames)
+                writer.writeheader()
+                for trade in trades:
+                    writer.writerow({k: trade.get(k, "") for k in trades_fieldnames})
+            if self.logging is not None:
+                self.logging.info(f"Trades written to {trades_filename}")

cycles/utils/system.py

@@ -0,0 +1,19 @@
+import os
+import psutil
+
+class SystemUtils:
+
+    def __init__(self, logging=None):
+        self.logging = logging
+
+    def get_optimal_workers(self):
+        """Determine optimal number of worker processes based on system resources"""
+        cpu_count = os.cpu_count() or 4
+        memory_gb = psutil.virtual_memory().total / (1024**3)
+        # Heuristic: Use 75% of cores, but cap based on available memory
+        # Assume each worker needs ~2GB for large datasets
+        workers_by_memory = max(1, int(memory_gb / 2))
+        workers_by_cpu = max(1, int(cpu_count * 0.75))
+        if self.logging is not None:
+            self.logging.info(f"Using {min(workers_by_cpu, workers_by_memory)} workers for processing")
+        return min(workers_by_cpu, workers_by_memory)

docs/analysis.md

@@ -0,0 +1,78 @@
+# Analysis Module
+
+This document provides an overview of the `Analysis` module and its components, which are typically used for technical analysis of financial market data.
+
+## Modules
+
+The `Analysis` module includes classes for calculating common technical indicators:
+
+-   **Relative Strength Index (RSI)**: Implemented in `cycles/Analysis/rsi.py`.
+-   **Bollinger Bands**: Implemented in `cycles/Analysis/boillinger_band.py`.
+
+## Class: `RSI`
+
+Found in `cycles/Analysis/rsi.py`.
+
+Calculates the Relative Strength Index.
+### Mathematical Model  
+1. **Average Gain (AvgU)** and **Average Loss (AvgD)** over 14 periods:  
+   $$
+   \text{AvgU} = \frac{\sum \text{Upward Price Changes}}{14}, \quad \text{AvgD} = \frac{\sum \text{Downward Price Changes}}{14}
+   $$  
+2. **Relative Strength (RS)**:  
+   $$
+   RS = \frac{\text{AvgU}}{\text{AvgD}}
+   $$  
+3. **RSI**:  
+   $$
+   RSI = 100 - \frac{100}{1 + RS}
+   $$  
+
+### `__init__(self, period: int = 14)`
+
+-   **Description**: Initializes the RSI calculator.
+-   **Parameters**:
+    -   `period` (int, optional): The period for RSI calculation. Defaults to 14. Must be a positive integer.
+
+### `calculate(self, data_df: pd.DataFrame, price_column: str = 'close') -> pd.DataFrame`
+
+-   **Description**: Calculates the RSI and adds it as an 'RSI' column to the input DataFrame. Handles cases where data length is less than the period by returning the original DataFrame with a warning.
+-   **Parameters**:
+    -   `data_df` (pd.DataFrame): DataFrame with historical price data. Must contain the `price_column`.
+    -   `price_column` (str, optional): The name of the column containing price data. Defaults to 'close'.
+-   **Returns**: `pd.DataFrame` - The input DataFrame with an added 'RSI' column (containing `np.nan` for initial periods where RSI cannot be calculated). Returns a copy of the original DataFrame if the period is larger than the number of data points.
+
+## Class: `BollingerBands`
+
+Found in `cycles/Analysis/boillinger_band.py`.
+
+## **Bollinger Bands**  
+### Mathematical Model  
+1. **Middle Band**: 20-day Simple Moving Average (SMA)  
+   $$
+   \text{Middle Band} = \frac{1}{20} \sum_{i=1}^{20} \text{Close}_{t-i}
+   $$  
+2. **Upper Band**: Middle Band + 2 × 20-day Standard Deviation (σ)  
+   $$
+   \text{Upper Band} = \text{Middle Band} + 2 \times \sigma_{20}
+   $$  
+3. **Lower Band**: Middle Band − 2 × 20-day Standard Deviation (σ)  
+   $$
+   \text{Lower Band} = \text{Middle Band} - 2 \times \sigma_{20}
+   $$  
+
+
+### `__init__(self, period: int = 20, std_dev_multiplier: float = 2.0)`
+
+-   **Description**: Initializes the BollingerBands calculator.
+-   **Parameters**:
+    -   `period` (int, optional): The period for the moving average and standard deviation. Defaults to 20. Must be a positive integer.
+    -   `std_dev_multiplier` (float, optional): The number of standard deviations for the upper and lower bands. Defaults to 2.0. Must be positive.
+
+### `calculate(self, data_df: pd.DataFrame, price_column: str = 'close') -> pd.DataFrame`
+
+-   **Description**: Calculates Bollinger Bands and adds 'SMA' (Simple Moving Average), 'UpperBand', and 'LowerBand' columns to the DataFrame.
+-   **Parameters**:
+    -   `data_df` (pd.DataFrame): DataFrame with price data. Must include the `price_column`.
+    -   `price_column` (str, optional): The name of the column containing the price data (e.g., 'close'). Defaults to 'close'.
+-   **Returns**: `pd.DataFrame` - The original DataFrame with added columns: 'SMA', 'UpperBand', 'LowerBand'.

docs/utils_storage.md

@@ -0,0 +1,73 @@
+# Storage Utilities
+
+This document describes the storage utility functions found in `cycles/utils/storage.py`.
+
+## Overview
+
+The `storage.py` module provides a `Storage` class designed for handling the loading and saving of data and results. It supports operations with CSV and JSON files and integrates with pandas DataFrames for data manipulation. The class also manages the creation of necessary `results` and `data` directories.
+
+## Constants
+
+-   `RESULTS_DIR`: Defines the default directory name for storing results (default: "results").
+-   `DATA_DIR`: Defines the default directory name for storing input data (default: "data").
+
+## Class: `Storage`
+
+Handles storage operations for data and results.
+
+### `__init__(self, logging=None, results_dir=RESULTS_DIR, data_dir=DATA_DIR)`
+
+-   **Description**: Initializes the `Storage` class. It creates the results and data directories if they don't already exist.
+-   **Parameters**:
+    -   `logging` (optional): A logging instance for outputting information. Defaults to `None`.
+    -   `results_dir` (str, optional): Path to the directory for storing results. Defaults to `RESULTS_DIR`.
+    -   `data_dir` (str, optional): Path to the directory for storing data. Defaults to `DATA_DIR`.
+
+### `load_data(self, file_path, start_date, stop_date)`
+
+-   **Description**: Loads data from a specified file (CSV or JSON), performs type optimization, filters by date range, and converts column names to lowercase. The timestamp column is set as the DataFrame index.
+-   **Parameters**:
+    -   `file_path` (str): Path to the data file (relative to `data_dir`).
+    -   `start_date` (datetime-like): The start date for filtering data.
+    -   `stop_date` (datetime-like): The end date for filtering data.
+-   **Returns**: `pandas.DataFrame` - The loaded and processed data, with a `timestamp` index. Returns an empty DataFrame on error.
+
+### `save_data(self, data: pd.DataFrame, file_path: str)`
+
+-   **Description**: Saves a pandas DataFrame to a CSV file within the `data_dir`. If the DataFrame has a DatetimeIndex, it's converted to a Unix timestamp (seconds since epoch) and stored in a column named 'timestamp', which becomes the first column in the CSV. The DataFrame's active index is not saved if a 'timestamp' column is created.
+-   **Parameters**:
+    -   `data` (pd.DataFrame): The DataFrame to save.
+    -   `file_path` (str): Path to the data file (relative to `data_dir`).
+
+### `format_row(self, row)`
+
+-   **Description**: Formats a dictionary row for output to a combined results CSV file, applying specific string formatting for percentages and float values.
+-   **Parameters**:
+    -   `row` (dict): The row of data to format.
+-   **Returns**: `dict` - The formatted row.
+
+### `write_results_chunk(self, filename, fieldnames, rows, write_header=False, initial_usd=None)`
+
+-   **Description**: Writes a chunk of results (list of dictionaries) to a CSV file. Can append to an existing file or write a new one with a header. An optional `initial_usd` can be written as a comment in the header.
+-   **Parameters**:
+    -   `filename` (str): The name of the file to write to (path is absolute or relative to current working dir).
+    -   `fieldnames` (list): A list of strings representing the CSV header/column names.
+    -   `rows` (list): A list of dictionaries, where each dictionary is a row.
+    -   `write_header` (bool, optional): If `True`, writes the header. Defaults to `False`.
+    -   `initial_usd` (numeric, optional): If provided and `write_header` is `True`, this value is written as a comment in the CSV header. Defaults to `None`.
+
+### `write_results_combined(self, filename, fieldnames, rows)`
+
+-   **Description**: Writes combined results to a CSV file in the `results_dir`. Uses tab as a delimiter and formats rows using `format_row`.
+-   **Parameters**:
+    -   `filename` (str): The name of the file to write to (relative to `results_dir`).
+    -   `fieldnames` (list): A list of strings representing the CSV header/column names.
+    -   `rows` (list): A list of dictionaries, where each dictionary is a row.
+
+### `write_trades(self, all_trade_rows, trades_fieldnames)`
+
+-   **Description**: Writes trade data to separate CSV files based on timeframe and stop-loss percentage. Files are named `trades_{tf}_ST{sl_percent}pct.csv` and stored in `results_dir`.
+-   **Parameters**:
+    -   `all_trade_rows` (list): A list of dictionaries, where each dictionary represents a trade.
+    -   `trades_fieldnames` (list): A list of strings for the CSV header of trade files.
+

docs/utils_system.md

@@ -0,0 +1,49 @@
+# System Utilities
+
+This document describes the system utility functions found in `cycles/utils/system.py`.
+
+## Overview
+
+The `system.py` module provides utility functions related to system information and resource management. It currently includes a class `SystemUtils` for determining optimal configurations based on system resources.
+
+## Classes and Methods
+
+### `SystemUtils`
+
+A class to provide system-related utility methods.
+
+#### `__init__(self, logging=None)`
+
+-   **Description**: Initializes the `SystemUtils` class.
+-   **Parameters**:
+    -   `logging` (optional): A logging instance to output information. Defaults to `None`.
+
+#### `get_optimal_workers(self)`
+
+-   **Description**: Determines the optimal number of worker processes based on available CPU cores and memory.
+    The heuristic aims to use 75% of CPU cores, with a cap based on available memory (assuming each worker might need ~2GB for large datasets). It returns the minimum of the workers calculated by CPU and memory.
+-   **Parameters**: None.
+-   **Returns**: `int` - The recommended number of worker processes.
+
+## Usage Examples
+
+```python
+from cycles.utils.system import SystemUtils
+
+# Initialize (optionally with a logger)
+# import logging
+# logging.basicConfig(level=logging.INFO)
+# logger = logging.getLogger(__name__)
+# sys_utils = SystemUtils(logging=logger)
+sys_utils = SystemUtils()
+
+
+optimal_workers = sys_utils.get_optimal_workers()
+print(f"Optimal number of workers: {optimal_workers}")
+
+# This value can then be used, for example, when setting up a ThreadPoolExecutor
+# from concurrent.futures import ThreadPoolExecutor
+# with ThreadPoolExecutor(max_workers=optimal_workers) as executor:
+#     # ... submit tasks ...
+#     pass
+``` 

main.py

@@ -1,21 +1,16 @@
 import pandas as pd
 import numpy as np
-from trend_detector_simple import TrendDetectorSimple
-import csv
 import logging
 import concurrent.futures
 import os
-import psutil
 import datetime
-import gspread
-from google.oauth2.service_account import Credentials
-from collections import defaultdict
-import threading
 import queue
-import time
-import math
-import json
-from taxes import Taxes
+
+from cycles.trend_detector_simple import TrendDetectorSimple
+from cycles.taxes import Taxes
+from cycles.utils.storage import Storage
+from cycles.utils.gsheets import GSheetBatchPusher
+from cycles.utils.system import SystemUtils
 
 # Set up logging
 logging.basicConfig(
@@ -27,50 +22,8 @@ logging.basicConfig(
     ]
 )
 
-def get_optimal_workers():
-    """Determine optimal number of worker processes based on system resources"""
-    cpu_count = os.cpu_count() or 4
-    memory_gb = psutil.virtual_memory().total / (1024**3)
-    # Heuristic: Use 75% of cores, but cap based on available memory
-    # Assume each worker needs ~2GB for large datasets
-    workers_by_memory = max(1, int(memory_gb / 2))
-    workers_by_cpu = max(1, int(cpu_count * 0.75))
-    return min(workers_by_cpu, workers_by_memory)
-
-def load_data(file_path, start_date, stop_date):
-    """Load data with optimized dtypes and filtering, supporting CSV and JSON input"""
-    # Determine file type
-    _, ext = os.path.splitext(file_path)
-    ext = ext.lower()
-    if ext == ".json":
-        with open(file_path, 'r') as f:
-            raw = json.load(f)
-        data = pd.DataFrame(raw["Data"])
-        # Convert columns to lowercase
-        data.columns = data.columns.str.lower()
-        # Convert timestamp to datetime
-        data["timestamp"] = pd.to_datetime(data["timestamp"], unit="s")
-        # Filter by date range
-        data = data[(data["timestamp"] >= start_date) & (data["timestamp"] <= stop_date)]
-        return data.set_index("timestamp")
-    else:
-        # Define optimized dtypes
-        dtypes = {
-            'Open': 'float32',
-            'High': 'float32', 
-            'Low': 'float32',
-            'Close': 'float32',
-            'Volume': 'float32'
-        }
-        # Read data with original capitalized column names
-        data = pd.read_csv(file_path, dtype=dtypes)
-        # Convert timestamp to datetime
-        data['Timestamp'] = pd.to_datetime(data['Timestamp'], unit='s')
-        # Filter by date range
-        data = data[(data['Timestamp'] >= start_date) & (data['Timestamp'] <= stop_date)]
-        # Now convert column names to lowercase
-        data.columns = data.columns.str.lower()
-        return data.set_index('timestamp')
+# Global queue for batching Google Sheets updates
+results_queue = queue.Queue()
 
 def process_timeframe_data(min1_df, df, stop_loss_pcts, rule_name, initial_usd, debug=False):
     """Process the entire timeframe with all stop loss values (no monthly split)"""
@@ -165,21 +118,6 @@ def process_timeframe(timeframe_info, debug=False):
     results_rows, all_trade_rows = process_timeframe_data(data_1min, df, [stop_loss_pct], rule, initial_usd, debug=debug)
     return results_rows, all_trade_rows
 
-def write_results_chunk(filename, fieldnames, rows, write_header=False):
-    """Write a chunk of results to a CSV file"""
-    mode = 'w' if write_header else 'a'
-    
-    with open(filename, mode, newline="") as csvfile:
-        writer = csv.DictWriter(csvfile, fieldnames=fieldnames)
-        if write_header:
-            csvfile.write(f"# initial_usd: {initial_usd}\n")
-            writer.writeheader()
-        
-        for row in rows:
-            # Only keep keys that are in fieldnames
-            filtered_row = {k: v for k, v in row.items() if k in fieldnames}
-            writer.writerow(filtered_row)
-
 def aggregate_results(all_rows):
     """Aggregate results per stop_loss_pct and per rule (timeframe)"""
     from collections import defaultdict
@@ -218,6 +156,15 @@ def aggregate_results(all_rows):
         })
     return summary_rows
 
+def get_nearest_price(df, target_date):
+        if len(df) == 0:
+            return None, None
+        target_ts = pd.to_datetime(target_date)
+        nearest_idx = df.index.get_indexer([target_ts], method='nearest')[0]
+        nearest_time = df.index[nearest_idx]
+        price = df.iloc[nearest_idx]['close']
+        return nearest_time, price
+
 if __name__ == "__main__":
     # Configuration
     # start_date = '2022-01-01'
@@ -229,23 +176,16 @@ if __name__ == "__main__":
 
     debug = False
     
-    results_dir = "results"
-    os.makedirs(results_dir, exist_ok=True)
     timestamp = datetime.datetime.now().strftime("%Y%m%d%H%M")
 
-    timeframes = ["1min", "5min", "15min"]
-    stop_loss_pcts = [0.01, 0.02, 0.03, 0.04, 0.05]
+    storage = Storage(logging=logging)
+    system_utils = SystemUtils(logging=logging)
 
-    data_1min = load_data('./data/btcusd_1-min_data.csv', start_date, stop_date)
+    timeframes = ["1D"]
+    stop_loss_pcts = [0.01, 0.02, 0.03]
 
-    def get_nearest_price(df, target_date):
-        if len(df) == 0:
-            return None, None
-        target_ts = pd.to_datetime(target_date)
-        nearest_idx = df.index.get_indexer([target_ts], method='nearest')[0]
-        nearest_time = df.index[nearest_idx]
-        price = df.iloc[nearest_idx]['close']
-        return nearest_time, price
+    # Load data once
+    data_1min = storage.load_data('btcusd_1-min_data.csv', start_date, stop_date)
 
     nearest_start_time, start_price = get_nearest_price(data_1min, start_date)
     nearest_stop_time, stop_price = get_nearest_price(data_1min, stop_date)
@@ -259,8 +199,7 @@ if __name__ == "__main__":
         for stop_loss_pct in stop_loss_pcts
     ]
     
-    workers = get_optimal_workers()
-    logging.info(f"Using {workers} workers for processing")
+    workers = system_utils.get_optimal_workers()
 
     # Process tasks with optimized concurrency
     with concurrent.futures.ProcessPoolExecutor(max_workers=workers) as executor:
@@ -274,57 +213,17 @@ if __name__ == "__main__":
                 all_trade_rows.extend(trades)
 
     # Write all results to a single CSV file
-    combined_filename = os.path.join(results_dir, f"{timestamp}_backtest_combined.csv")
+    combined_filename = os.path.join(f"{timestamp}_backtest_combined.csv")
     combined_fieldnames = [
         "timeframe", "stop_loss_pct", "n_trades", "n_stop_loss", "win_rate",
-        "max_drawdown", "avg_trade", "profit_ratio", "final_usd", "total_fees_usd"
+        "max_drawdown", "avg_trade", "profit_ratio", "final_usd"
     ]
-
-    def format_row(row):
-        return {
-            "timeframe": row["timeframe"],
-            "stop_loss_pct": f"{row['stop_loss_pct']*100:.2f}%",
-            "n_trades": row["n_trades"],
-            "n_stop_loss": row["n_stop_loss"],
-            "win_rate": f"{row['win_rate']*100:.2f}%",
-            "max_drawdown": f"{row['max_drawdown']*100:.2f}%",
-            "avg_trade": f"{row['avg_trade']*100:.2f}%",
-            "profit_ratio": f"{row['profit_ratio']*100:.2f}%",
-            "final_usd": f"{row['final_usd']:.2f}",
-            "total_fees_usd": f"{row.get('total_fees_usd'):.2f}",
-        }
-
-    with open(combined_filename, "w", newline="") as csvfile:
-        writer = csv.DictWriter(csvfile, fieldnames=combined_fieldnames, delimiter='\t')
-        writer.writeheader()
-        for row in all_results_rows:
-            writer.writerow(format_row(row))
-
-    logging.info(f"Combined results written to {combined_filename}")
+    storage.write_results_combined(combined_filename, combined_fieldnames, all_results_rows)
 
     # Now, group all_trade_rows by (timeframe, stop_loss_pct)
-    from collections import defaultdict
-    trades_by_combo = defaultdict(list)
-    for trade in all_trade_rows:
-        tf = trade.get("timeframe")
-        sl = trade.get("stop_loss_pct")
-        trades_by_combo[(tf, sl)].append(trade)
-
     trades_fieldnames = [
         "entry_time", "exit_time", "entry_price", "exit_price", "profit_pct", "type", "fee_usd"
     ]
-
-    for (tf, sl), trades in trades_by_combo.items():
-        sl_percent = int(round(sl * 100))
-        trades_filename = os.path.join(results_dir, f"trades_{tf}_ST{sl_percent}pct.csv")
-        with open(trades_filename, "w", newline="") as csvfile:
-            writer = csv.DictWriter(csvfile, fieldnames=trades_fieldnames)
-            writer.writeheader()
-            for trade in trades:
-                row = {k: trade.get(k, "") for k in trades_fieldnames}
-                fee = trade.get("fee_usd")
-                row["fee_usd"] = f"{float(fee):.2f}"
-                writer.writerow(row)
-        logging.info(f"Trades written to {trades_filename}")
+    storage.write_trades(all_trade_rows, trades_fieldnames)
 
     

pyproject.toml

@@ -0,0 +1,14 @@
+[project]
+name = "cycles"
+version = "0.1.0"
+description = "Add your description here"
+readme = "README.md"
+requires-python = ">=3.10"
+dependencies = [
+    "gspread>=6.2.1",
+    "matplotlib>=3.10.3",
+    "pandas>=2.2.3",
+    "psutil>=7.0.0",
+    "scipy>=1.15.3",
+    "seaborn>=0.13.2",
+]

test_bbrsi.py

@@ -0,0 +1,132 @@
+import logging
+import seaborn as sns
+import matplotlib.pyplot as plt
+import pandas as pd
+
+from cycles.utils.storage import Storage
+from cycles.utils.data_utils import aggregate_to_daily
+from cycles.Analysis.boillinger_band import BollingerBands
+from cycles.Analysis.rsi import RSI
+
+logging.basicConfig(
+    level=logging.INFO,
+    format="%(asctime)s [%(levelname)s] %(message)s",
+    handlers=[
+        logging.FileHandler("backtest.log"),
+        logging.StreamHandler()
+    ]
+)
+
+config_minute = {
+    "start_date": "2022-01-01",
+    "stop_date": "2023-01-01",
+    "data_file": "btcusd_1-min_data.csv"
+}
+
+config_day = {
+    "start_date": "2022-01-01",
+    "stop_date": "2023-01-01",
+    "data_file": "btcusd_1-day_data.csv"
+}
+
+IS_DAY = True
+
+def no_strategy(data_bb, data_with_rsi):
+    buy_condition = pd.Series([False] * len(data_bb), index=data_bb.index)
+    sell_condition = pd.Series([False] * len(data_bb), index=data_bb.index)
+    return buy_condition, sell_condition
+
+def strategy_1(data_bb, data_with_rsi):
+    # Long trade: price move below lower Bollinger band and RSI go below 25
+    buy_condition = (data_bb['close'] < data_bb['LowerBand']) & (data_bb['RSI'] < 25)
+    # Short only: price move above top Bollinger band and RSI goes over 75
+    sell_condition = (data_bb['close'] > data_bb['UpperBand']) & (data_bb['RSI'] > 75)
+    return buy_condition, sell_condition
+
+
+if __name__ == "__main__":
+
+    storage = Storage(logging=logging)
+
+    if IS_DAY:
+        config = config_day
+    else:
+        config = config_minute
+
+    data = storage.load_data(config["data_file"], config["start_date"], config["stop_date"])
+
+    if not IS_DAY:
+        data_daily = aggregate_to_daily(data)
+        storage.save_data(data, "btcusd_1-day_data.csv")
+        df_to_plot = data_daily
+    else:
+        df_to_plot = data
+
+    bb = BollingerBands(period=30, std_dev_multiplier=2.0)
+    data_bb = bb.calculate(df_to_plot.copy())
+
+    rsi_calculator = RSI(period=13)
+    data_with_rsi = rsi_calculator.calculate(df_to_plot.copy(), price_column='close')
+    
+    # Combine BB and RSI data into a single DataFrame for signal generation
+    # Ensure indices are aligned; they should be as both are from df_to_plot.copy()
+    if 'RSI' in data_with_rsi.columns:
+        data_bb['RSI'] = data_with_rsi['RSI']
+    else:
+        # If RSI wasn't calculated (e.g., not enough data), create a dummy column with NaNs
+        # to prevent errors later, though signals won't be generated.
+        data_bb['RSI'] = pd.Series(index=data_bb.index, dtype=float)
+        logging.warning("RSI column not found or not calculated. Signals relying on RSI may not be generated.")
+
+    strategy = 1
+    if strategy == 1:
+        buy_condition, sell_condition = strategy_1(data_bb, data_with_rsi)
+    else:
+        buy_condition, sell_condition = no_strategy(data_bb, data_with_rsi)
+
+    buy_signals = data_bb[buy_condition]
+    sell_signals = data_bb[sell_condition]
+    
+    # plot the data with seaborn library
+    if df_to_plot is not None and not df_to_plot.empty:
+        # Create a figure with two subplots, sharing the x-axis
+        fig, (ax1, ax2) = plt.subplots(2, 1, figsize=(16, 8), sharex=True)
+
+        # Plot 1: Close Price and Bollinger Bands
+        sns.lineplot(x=data_bb.index, y='close', data=data_bb, label='Close Price', ax=ax1)
+        sns.lineplot(x=data_bb.index, y='UpperBand', data=data_bb, label='Upper Band (BB)', ax=ax1)
+        sns.lineplot(x=data_bb.index, y='LowerBand', data=data_bb, label='Lower Band (BB)', ax=ax1)
+        # Plot Buy/Sell signals on Price chart
+        if not buy_signals.empty:
+            ax1.scatter(buy_signals.index, buy_signals['close'], color='green', marker='o', s=20, label='Buy Signal', zorder=5)
+        if not sell_signals.empty:
+            ax1.scatter(sell_signals.index, sell_signals['close'], color='red', marker='o', s=20, label='Sell Signal', zorder=5)
+        ax1.set_title('Price and Bollinger Bands with Signals')
+        ax1.set_ylabel('Price')
+        ax1.legend()
+        ax1.grid(True)
+
+        # Plot 2: RSI
+        if 'RSI' in data_bb.columns: # Check data_bb now as it should contain RSI
+            sns.lineplot(x=data_bb.index, y='RSI', data=data_bb, label='RSI (14)', ax=ax2, color='purple')
+            ax2.axhline(75, color='red', linestyle='--', linewidth=0.8, label='Overbought (75)')
+            ax2.axhline(25, color='green', linestyle='--', linewidth=0.8, label='Oversold (25)')
+            # Plot Buy/Sell signals on RSI chart
+            if not buy_signals.empty:
+                ax2.scatter(buy_signals.index, buy_signals['RSI'], color='green', marker='o', s=20, label='Buy Signal (RSI)', zorder=5)
+            if not sell_signals.empty:
+                ax2.scatter(sell_signals.index, sell_signals['RSI'], color='red', marker='o', s=20, label='Sell Signal (RSI)', zorder=5)
+            ax2.set_title('Relative Strength Index (RSI) with Signals')
+            ax2.set_ylabel('RSI Value')
+            ax2.set_ylim(0, 100) # RSI is typically bounded between 0 and 100
+            ax2.legend()
+            ax2.grid(True)
+        else:
+            logging.info("RSI data not available for plotting.")
+
+        plt.xlabel('Date') # Common X-axis label
+        fig.tight_layout() # Adjust layout to prevent overlapping titles/labels
+        plt.show()
+    else:
+        logging.info("No data to plot.")
+    

trend_detector_simple.py

@@ -1,849 +0,0 @@
-import pandas as pd
-import numpy as np
-import logging
-from scipy.signal import find_peaks
-from matplotlib.patches import Rectangle
-from scipy import stats
-import concurrent.futures
-from functools import partial
-from functools import lru_cache
-import matplotlib.pyplot as plt
-
-# Color configuration
-# Plot colors
-DARK_BG_COLOR = '#181C27'
-LEGEND_BG_COLOR = '#333333'
-TITLE_COLOR = 'white'
-AXIS_LABEL_COLOR = 'white'
-
-# Candlestick colors
-CANDLE_UP_COLOR = '#089981'  # Green
-CANDLE_DOWN_COLOR = '#F23645'  # Red
-
-# Marker colors
-MIN_COLOR = 'red'
-MAX_COLOR = 'green'
-
-# Line style colors
-MIN_LINE_STYLE = 'g--'  # Green dashed
-MAX_LINE_STYLE = 'r--'  # Red dashed
-SMA7_LINE_STYLE = 'y-'  # Yellow solid
-SMA15_LINE_STYLE = 'm-'  # Magenta solid
-
-# SuperTrend colors
-ST_COLOR_UP = 'g-'
-ST_COLOR_DOWN = 'r-'
-
-# Cache the calculation results by function parameters
-@lru_cache(maxsize=32)
-def cached_supertrend_calculation(period, multiplier, data_tuple):
-    # Convert tuple back to numpy arrays
-    high = np.array(data_tuple[0])
-    low = np.array(data_tuple[1])
-    close = np.array(data_tuple[2])
-    
-    # Calculate TR and ATR using vectorized operations
-    tr = np.zeros_like(close)
-    tr[0] = high[0] - low[0]
-    hc_range = np.abs(high[1:] - close[:-1])
-    lc_range = np.abs(low[1:] - close[:-1])
-    hl_range = high[1:] - low[1:]
-    tr[1:] = np.maximum.reduce([hl_range, hc_range, lc_range])
-    
-    # Use numpy's exponential moving average
-    atr = np.zeros_like(tr)
-    atr[0] = tr[0]
-    multiplier_ema = 2.0 / (period + 1)
-    for i in range(1, len(tr)):
-        atr[i] = (tr[i] * multiplier_ema) + (atr[i-1] * (1 - multiplier_ema))
-
-    # Calculate bands
-    upper_band = np.zeros_like(close)
-    lower_band = np.zeros_like(close)
-    for i in range(len(close)):
-        hl_avg = (high[i] + low[i]) / 2
-        upper_band[i] = hl_avg + (multiplier * atr[i])
-        lower_band[i] = hl_avg - (multiplier * atr[i])
-
-    final_upper = np.zeros_like(close)
-    final_lower = np.zeros_like(close)
-    supertrend = np.zeros_like(close)
-    trend = np.zeros_like(close)
-    final_upper[0] = upper_band[0]
-    final_lower[0] = lower_band[0]
-    if close[0] <= upper_band[0]:
-        supertrend[0] = upper_band[0]
-        trend[0] = -1
-    else:
-        supertrend[0] = lower_band[0]
-        trend[0] = 1
-    for i in range(1, len(close)):
-        if (upper_band[i] < final_upper[i-1]) or (close[i-1] > final_upper[i-1]):
-            final_upper[i] = upper_band[i]
-        else:
-            final_upper[i] = final_upper[i-1]
-        if (lower_band[i] > final_lower[i-1]) or (close[i-1] < final_lower[i-1]):
-            final_lower[i] = lower_band[i]
-        else:
-            final_lower[i] = final_lower[i-1]
-        if supertrend[i-1] == final_upper[i-1] and close[i] <= final_upper[i]:
-            supertrend[i] = final_upper[i]
-            trend[i] = -1
-        elif supertrend[i-1] == final_upper[i-1] and close[i] > final_upper[i]:
-            supertrend[i] = final_lower[i]
-            trend[i] = 1
-        elif supertrend[i-1] == final_lower[i-1] and close[i] >= final_lower[i]:
-            supertrend[i] = final_lower[i]
-            trend[i] = 1
-        elif supertrend[i-1] == final_lower[i-1] and close[i] < final_lower[i]:
-            supertrend[i] = final_upper[i]
-            trend[i] = -1
-    return {
-        'supertrend': supertrend,
-        'trend': trend,
-        'upper_band': final_upper,
-        'lower_band': final_lower
-    }
-
-def calculate_supertrend_external(data, period, multiplier):
-    # Convert DataFrame columns to hashable tuples
-    high_tuple = tuple(data['high'])
-    low_tuple = tuple(data['low'])
-    close_tuple = tuple(data['close'])
-    
-    # Call the cached function
-    return cached_supertrend_calculation(period, multiplier, (high_tuple, low_tuple, close_tuple))
-
-def calculate_okx_fee(amount, is_maker=True):
-    fee_rate = 0.0008 if is_maker else 0.0010
-    return amount * fee_rate
-
-class TrendDetectorSimple:
-    def __init__(self, data, verbose=False, display=False):
-        """
-        Initialize the TrendDetectorSimple class.
-        
-        Parameters:
-        - data: pandas DataFrame containing price data
-        - verbose: boolean, whether to display detailed logging information
-        - display: boolean, whether to enable display/plotting features
-        """
-        
-        self.data = data
-        self.verbose = verbose
-        self.display = display
-        
-        # Only define display-related variables if display is True
-        if self.display:
-            # Plot style configuration
-            self.plot_style = 'dark_background' 
-            self.bg_color = DARK_BG_COLOR
-            self.plot_size = (12, 8)
-            
-            # Candlestick configuration
-            self.candle_width = 0.6
-            self.candle_up_color = CANDLE_UP_COLOR
-            self.candle_down_color = CANDLE_DOWN_COLOR
-            self.candle_alpha = 0.8
-            self.wick_width = 1
-            
-            # Marker configuration
-            self.min_marker = '^'
-            self.min_color = MIN_COLOR
-            self.min_size = 100
-            self.max_marker = 'v'
-            self.max_color = MAX_COLOR
-            self.max_size = 100
-            self.marker_zorder = 100
-            
-            # Line configuration
-            self.line_width = 1
-            self.min_line_style = MIN_LINE_STYLE
-            self.max_line_style = MAX_LINE_STYLE
-            self.sma7_line_style = SMA7_LINE_STYLE
-            self.sma15_line_style = SMA15_LINE_STYLE
-            
-            # Text configuration
-            self.title_size = 14
-            self.title_color = TITLE_COLOR
-            self.axis_label_size = 12
-            self.axis_label_color = AXIS_LABEL_COLOR
-            
-            # Legend configuration
-            self.legend_loc = 'best'
-            self.legend_bg_color = LEGEND_BG_COLOR
-        
-        # Configure logging
-        logging.basicConfig(level=logging.INFO if verbose else logging.WARNING,
-                           format='%(asctime)s - %(levelname)s - %(message)s')
-        self.logger = logging.getLogger('TrendDetectorSimple')
-        
-        # Convert data to pandas DataFrame if it's not already
-        if not isinstance(self.data, pd.DataFrame):
-            if isinstance(self.data, list):
-                self.data = pd.DataFrame({'close': self.data})
-            else:
-                raise ValueError("Data must be a pandas DataFrame or a list")
-
-    def calculate_tr(self):
-        """
-        Calculate True Range (TR) for the price data.
-        
-        True Range is the greatest of:
-        1. Current high - current low
-        2. |Current high - previous close|
-        3. |Current low - previous close|
-        
-        Returns:
-        - Numpy array of TR values
-        """
-        df = self.data.copy()
-        high = df['high'].values
-        low = df['low'].values
-        close = df['close'].values
-        
-        tr = np.zeros_like(close)
-        tr[0] = high[0] - low[0]  # First TR is just the first day's range
-        
-        for i in range(1, len(close)):
-            # Current high - current low
-            hl_range = high[i] - low[i]
-            # |Current high - previous close|
-            hc_range = abs(high[i] - close[i-1])
-            # |Current low - previous close|
-            lc_range = abs(low[i] - close[i-1])
-            
-            # TR is the maximum of these three values
-            tr[i] = max(hl_range, hc_range, lc_range)
-            
-        return tr
-    
-    def calculate_atr(self, period=14):
-        """
-        Calculate Average True Range (ATR) for the price data.
-        
-        ATR is the exponential moving average of the True Range over a specified period.
-        
-        Parameters:
-        - period: int, the period for the ATR calculation (default: 14)
-        
-        Returns:
-        - Numpy array of ATR values
-        """
-        
-        tr = self.calculate_tr()
-        atr = np.zeros_like(tr)
-        
-        # First ATR value is just the first TR
-        atr[0] = tr[0]
-        
-        # Calculate exponential moving average (EMA) of TR
-        multiplier = 2.0 / (period + 1)
-        
-        for i in range(1, len(tr)):
-            atr[i] = (tr[i] * multiplier) + (atr[i-1] * (1 - multiplier))
-            
-        return atr
-    
-    def detect_trends(self):
-        """
-        Detect trends by identifying local minima and maxima in the price data
-        using scipy.signal.find_peaks.
-        
-        Parameters:
-        - prominence: float, required prominence of peaks (relative to the price range)
-        - width: int, required width of peaks in data points
-        
-        Returns:
-        - DataFrame with columns for timestamps, prices, and trend indicators
-        - Dictionary containing analysis results including linear regression, SMAs, and SuperTrend indicators
-        """
-        df = self.data
-        # close_prices = df['close'].values
-        
-        # max_peaks, _ = find_peaks(close_prices)
-        # min_peaks, _ = find_peaks(-close_prices)
-        
-        # df['is_min'] = False
-        # df['is_max'] = False
-        
-        # for peak in max_peaks:
-        #     df.at[peak, 'is_max'] = True
-        # for peak in min_peaks:
-        #     df.at[peak, 'is_min'] = True
-        
-        # result = df[['timestamp', 'close', 'is_min', 'is_max']].copy()
-        
-        # Perform linear regression on min_peaks and max_peaks
-        # min_prices = df['close'].iloc[min_peaks].values
-        # max_prices = df['close'].iloc[max_peaks].values
-        
-        # Linear regression for min peaks if we have at least 2 points
-        # min_slope, min_intercept, min_r_value, _, _ = stats.linregress(min_peaks, min_prices)
-        # Linear regression for max peaks if we have at least 2 points
-        # max_slope, max_intercept, max_r_value, _, _ = stats.linregress(max_peaks, max_prices)
-
-        # Calculate Simple Moving Averages (SMA) for 7 and 15 periods        
-        # sma_7 = pd.Series(close_prices).rolling(window=7, min_periods=1).mean().values
-        # sma_15 = pd.Series(close_prices).rolling(window=15, min_periods=1).mean().values
-        
-        analysis_results = {}
-        # analysis_results['linear_regression'] = {
-        #     'min': {
-        #         'slope': min_slope,
-        #         'intercept': min_intercept,
-        #         'r_squared': min_r_value ** 2
-        #     },
-        #     'max': {
-        #         'slope': max_slope,
-        #         'intercept': max_intercept,
-        #         'r_squared': max_r_value ** 2
-        #     }
-        # }
-        # analysis_results['sma'] = {
-        #     '7': sma_7,
-        #     '15': sma_15
-        # }
-        
-        # Calculate SuperTrend indicators
-        supertrend_results_list = self._calculate_supertrend_indicators()
-        analysis_results['supertrend'] = supertrend_results_list
-        
-        return analysis_results
-        
-    def _calculate_supertrend_indicators(self):
-        """
-        Calculate SuperTrend indicators with different parameter sets in parallel.
-        Returns:
-        - list, the SuperTrend results
-        """
-        supertrend_params = [
-            {"period": 12, "multiplier": 3.0, "color_up": ST_COLOR_UP, "color_down": ST_COLOR_DOWN},
-            {"period": 10, "multiplier": 1.0, "color_up": ST_COLOR_UP, "color_down": ST_COLOR_DOWN},
-            {"period": 11, "multiplier": 2.0, "color_up": ST_COLOR_UP, "color_down": ST_COLOR_DOWN}
-        ]
-        data = self.data.copy()
-        
-        # For just 3 calculations, direct calculation might be faster than process pool
-        results = []
-        for p in supertrend_params:
-            result = calculate_supertrend_external(data, p["period"], p["multiplier"])
-            results.append(result)
-        
-        supertrend_results_list = []
-        for params, result in zip(supertrend_params, results):
-            supertrend_results_list.append({
-                "results": result,
-                "params": params
-            })
-        return supertrend_results_list
-
-    def plot_trends(self, trend_data, analysis_results, view="both"):
-        """
-        Plot the price data with detected trends using a candlestick chart.
-        Also plots SuperTrend indicators with three different parameter sets.
-
-        Parameters:
-        - trend_data: DataFrame, the output from detect_trends()
-        - analysis_results: Dictionary containing analysis results from detect_trends()
-        - view: str, one of 'both', 'trend', 'supertrend'; determines which plot(s) to display
-
-        Returns:
-        - None (displays the plot)
-        """
-        if not self.display:
-            return  # Do nothing if display is False
-
-        plt.style.use(self.plot_style)
-
-        if view == "both":
-            fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(self.plot_size[0]*2, self.plot_size[1]))
-        else:
-            fig, ax = plt.subplots(figsize=self.plot_size)
-            ax1 = ax2 = None
-            if view == "trend":
-                ax1 = ax
-            elif view == "supertrend":
-                ax2 = ax
-
-        fig.patch.set_facecolor(self.bg_color)
-        if ax1: ax1.set_facecolor(self.bg_color)
-        if ax2: ax2.set_facecolor(self.bg_color)
-
-        df = self.data.copy()
-
-        if ax1:
-            self._plot_trend_analysis(ax1, df, trend_data, analysis_results)
-
-        if ax2:
-            self._plot_supertrend_analysis(ax2, df, analysis_results['supertrend'])
-
-        plt.tight_layout()
-        plt.show()
-    
-    def _plot_candlesticks(self, ax, df):
-        """
-        Plot candlesticks on the given axis.
-        
-        Parameters:
-        - ax: matplotlib.axes.Axes, the axis to plot on
-        - df: pandas.DataFrame, the data to plot
-        """
-        from matplotlib.patches import Rectangle
-        
-        for i in range(len(df)):
-            # Get OHLC values for this candle
-            open_val = df['open'].iloc[i]
-            close_val = df['close'].iloc[i]
-            high_val = df['high'].iloc[i]
-            low_val = df['low'].iloc[i]
-            
-            # Determine candle color
-            color = self.candle_up_color if close_val >= open_val else self.candle_down_color
-            
-            # Plot candle body
-            body_height = abs(close_val - open_val)
-            bottom = min(open_val, close_val)
-            rect = Rectangle((i - self.candle_width/2, bottom), self.candle_width, body_height, 
-                            color=color, alpha=self.candle_alpha)
-            ax.add_patch(rect)
-            
-            # Plot candle wicks
-            ax.plot([i, i], [low_val, high_val], color=color, linewidth=self.wick_width)
-    
-    def _plot_trend_analysis(self, ax, df, trend_data, analysis_results):
-        """
-        Plot trend analysis on the given axis.
-        
-        Parameters:
-        - ax: matplotlib.axes.Axes, the axis to plot on
-        - df: pandas.DataFrame, the data to plot
-        - trend_data: pandas.DataFrame, the trend data
-        - analysis_results: dict, the analysis results
-        """
-        # Draw candlesticks
-        self._plot_candlesticks(ax, df)
-        
-        # Plot minima and maxima points
-        self._plot_min_max_points(ax, df, trend_data)
-        
-        # Plot trend lines and moving averages
-        if analysis_results:
-            self._plot_trend_lines(ax, df, analysis_results)
-        
-        # Configure the subplot
-        self._configure_subplot(ax, 'Price Chart with Trend Analysis', len(df))
-    
-    def _plot_min_max_points(self, ax, df, trend_data):
-        """
-        Plot minimum and maximum points on the given axis.
-        
-        Parameters:
-        - ax: matplotlib.axes.Axes, the axis to plot on
-        - df: pandas.DataFrame, the data to plot
-        - trend_data: pandas.DataFrame, the trend data
-        """
-        min_indices = trend_data.index[trend_data['is_min'] == True].tolist()
-        if min_indices:
-            min_y = [df['close'].iloc[i] for i in min_indices]
-            ax.scatter(min_indices, min_y, color=self.min_color, s=self.min_size, 
-                      marker=self.min_marker, label='Local Minima', zorder=self.marker_zorder)
-
-        max_indices = trend_data.index[trend_data['is_max'] == True].tolist()
-        if max_indices:
-            max_y = [df['close'].iloc[i] for i in max_indices]
-            ax.scatter(max_indices, max_y, color=self.max_color, s=self.max_size, 
-                      marker=self.max_marker, label='Local Maxima', zorder=self.marker_zorder)
-    
-    def _plot_trend_lines(self, ax, df, analysis_results):
-        """
-        Plot trend lines on the given axis.
-        
-        Parameters:
-        - ax: matplotlib.axes.Axes, the axis to plot on
-        - df: pandas.DataFrame, the data to plot
-        - analysis_results: dict, the analysis results
-        """
-        x_vals = np.arange(len(df))
-        
-        # Minima regression line (support)
-        min_slope = analysis_results['linear_regression']['min']['slope']
-        min_intercept = analysis_results['linear_regression']['min']['intercept']
-        min_line = min_slope * x_vals + min_intercept
-        ax.plot(x_vals, min_line, self.min_line_style, linewidth=self.line_width, 
-               label='Minima Regression')
-
-        # Maxima regression line (resistance)
-        max_slope = analysis_results['linear_regression']['max']['slope']
-        max_intercept = analysis_results['linear_regression']['max']['intercept']
-        max_line = max_slope * x_vals + max_intercept
-        ax.plot(x_vals, max_line, self.max_line_style, linewidth=self.line_width, 
-               label='Maxima Regression')
-
-        # SMA-7 line
-        sma_7 = analysis_results['sma']['7']
-        ax.plot(x_vals, sma_7, self.sma7_line_style, linewidth=self.line_width, 
-               label='SMA-7')
-
-        # SMA-15 line
-        sma_15 = analysis_results['sma']['15']
-        valid_idx_15 = ~np.isnan(sma_15)
-        ax.plot(x_vals[valid_idx_15], sma_15[valid_idx_15], self.sma15_line_style, 
-               linewidth=self.line_width, label='SMA-15')
-    
-    def _configure_subplot(self, ax, title, data_length):
-        """
-        Configure the subplot with title, labels, limits, and legend.
-        
-        Parameters:
-        - ax: matplotlib.axes.Axes, the axis to configure
-        - title: str, the title of the subplot
-        - data_length: int, the length of the data
-        """
-        # Set title and labels
-        ax.set_title(title, fontsize=self.title_size, color=self.title_color)
-        ax.set_xlabel('Date', fontsize=self.axis_label_size, color=self.axis_label_color)
-        ax.set_ylabel('Price', fontsize=self.axis_label_size, color=self.axis_label_color)
-        
-        # Set appropriate x-axis limits
-        ax.set_xlim(-0.5, data_length - 0.5)
-        
-        # Add a legend
-        ax.legend(loc=self.legend_loc, facecolor=self.legend_bg_color)
-    
-    def _plot_supertrend_analysis(self, ax, df, supertrend_results_list=None):
-        """
-        Plot SuperTrend analysis on the given axis.
-        
-        Parameters:
-        - ax: matplotlib.axes.Axes, the axis to plot on
-        - df: pandas.DataFrame, the data to plot
-        - supertrend_results_list: list, the SuperTrend results (optional)
-        """
-        self._plot_candlesticks(ax, df)
-        self._plot_supertrend_lines(ax, df, supertrend_results_list, style='Both')
-        self._configure_subplot(ax, 'Multiple SuperTrend Indicators', len(df))
-        
-    def _plot_supertrend_lines(self, ax, df, supertrend_results_list, style="Horizontal"):
-        """
-        Plot SuperTrend lines on the given axis.
-        
-        Parameters:
-        - ax: matplotlib.axes.Axes, the axis to plot on
-        - df: pandas.DataFrame, the data to plot
-        - supertrend_results_list: list, the SuperTrend results
-        """
-        x_vals = np.arange(len(df))
-
-        if style == 'Horizontal' or style == 'Both':
-            if len(supertrend_results_list) != 3:
-                raise ValueError("Expected exactly 3 SuperTrend results for meta calculation")
-
-            trends = [st["results"]["trend"] for st in supertrend_results_list]
-
-            band_height = 0.02 * (df["high"].max() - df["low"].min())
-            y_base = df["low"].min() - band_height * 1.5
-            
-            prev_color = None
-            for i in range(1, len(x_vals)):
-                t_vals = [t[i] for t in trends]
-                up_count = t_vals.count(1)
-                down_count = t_vals.count(-1)
-
-                if down_count == 3:
-                    color = "red"
-                elif down_count == 2 and up_count == 1:
-                    color = "orange"
-                elif down_count == 1 and up_count == 2:
-                    color = "yellow"
-                elif up_count == 3:
-                    color = "green"
-                else:
-                    continue  # skip if unknown or inconsistent values
-
-                ax.add_patch(Rectangle(
-                    (x_vals[i-1], y_base),
-                    1,
-                    band_height,
-                    color=color,
-                    linewidth=0,
-                    alpha=0.6
-                ))
-                # Draw a vertical line at the change of color
-                if prev_color and prev_color != color:
-                    ax.axvline(x_vals[i-1], color="grey", alpha=0.3, linewidth=1)
-                prev_color = color
-
-            ax.set_ylim(bottom=y_base - band_height * 0.5)
-        if style == 'Curves' or style == 'Both':
-            for st in supertrend_results_list:
-                params = st["params"]
-                results = st["results"]
-                supertrend = results["supertrend"]
-                trend = results["trend"]
-                
-                # Plot SuperTrend line with color based on trend
-                for i in range(1, len(x_vals)):
-                    if trend[i] == 1:  # Uptrend
-                        ax.plot(x_vals[i-1:i+1], supertrend[i-1:i+1], params["color_up"], linewidth=self.line_width)
-                    else:  # Downtrend
-                        ax.plot(x_vals[i-1:i+1], supertrend[i-1:i+1], params["color_down"], linewidth=self.line_width)
-            self._plot_metasupertrend_lines(ax, df, supertrend_results_list)
-            self._add_supertrend_legend(ax, supertrend_results_list)
-
-    def _plot_metasupertrend_lines(self, ax, df, supertrend_results_list):
-        """
-        Plot a Meta SuperTrend line where all individual SuperTrends agree on trend.
-
-        Parameters:
-        - ax: matplotlib.axes.Axes, the axis to plot on
-        - df: pandas.DataFrame, the data to plot
-        - supertrend_results_list: list, each item contains SuperTrend 'results' and 'params'
-        """
-        x_vals = np.arange(len(df))
-
-        if len(supertrend_results_list) != 3:
-            raise ValueError("Expected exactly 3 SuperTrend results for meta calculation")
-
-        trends = [st["results"]["trend"] for st in supertrend_results_list]
-        supertrends = [st["results"]["supertrend"] for st in supertrend_results_list]
-        params = supertrend_results_list[0]["params"]  # Use first config for styling
-
-        trends_arr = np.stack(trends, axis=1)
-        meta_trend = np.where((trends_arr[:,0] == trends_arr[:,1]) & (trends_arr[:,1] == trends_arr[:,2]), trends_arr[:,0], 0)
-
-        for i in range(1, len(x_vals)):
-            t1, t2, t3 = trends[0][i], trends[1][i], trends[2][i]
-            if t1 == t2 == t3:
-                meta_trend = t1
-                # Average the 3 supertrend values
-                st_avg_prev = np.mean([s[i-1] for s in supertrends])
-                st_avg_curr = np.mean([s[i] for s in supertrends])
-                color = params["color_up"] if meta_trend == 1 else params["color_down"]
-                ax.plot(x_vals[i-1:i+1], [st_avg_prev, st_avg_curr], color, linewidth=self.line_width)
-
-    def _add_supertrend_legend(self, ax, supertrend_results_list):
-        """
-        Add SuperTrend legend entries to the given axis.
-        
-        Parameters:
-        - ax: matplotlib.axes.Axes, the axis to add legend entries to
-        - supertrend_results_list: list, the SuperTrend results
-        """
-        for st in supertrend_results_list:
-            params = st["params"]
-            period = params["period"]
-            multiplier = params["multiplier"]
-            color_up = params["color_up"]
-            color_down = params["color_down"]
-            
-            ax.plot([], [], color_up, linewidth=self.line_width, 
-                    label=f'ST (P:{period}, M:{multiplier}) Up')
-            ax.plot([], [], color_down, linewidth=self.line_width, 
-                    label=f'ST (P:{period}, M:{multiplier}) Down')
-    
-    def backtest_meta_supertrend(self, min1_df, initial_usd=10000, stop_loss_pct=0.05, debug=False):
-        """
-        Backtest a simple strategy using the meta supertrend (all three supertrends agree).
-        Buys when meta supertrend is positive, sells when negative, applies a percentage stop loss.
-        
-        Parameters:
-        - min1_df: pandas DataFrame, 1-minute timeframe data for more accurate stop loss checking (optional)
-        - initial_usd: float, starting USD amount
-        - stop_loss_pct: float, stop loss as a fraction (e.g. 0.05 for 5%)
-        - debug: bool, whether to print debug info
-        """
-        df = self.data.copy().reset_index(drop=True)
-        df['timestamp'] = pd.to_datetime(df['timestamp'])
-        
-        # Get meta supertrend (all three agree)
-        supertrend_results_list = self._calculate_supertrend_indicators()
-        trends = [st['results']['trend'] for st in supertrend_results_list]
-        trends_arr = np.stack(trends, axis=1)
-        meta_trend = np.where((trends_arr[:,0] == trends_arr[:,1]) & (trends_arr[:,1] == trends_arr[:,2]), 
-                              trends_arr[:,0], 0)
-        
-        position = 0  # 0 = no position, 1 = long
-        entry_price = 0
-        usd = initial_usd
-        coin = 0
-        trade_log = []
-        max_balance = initial_usd
-        drawdowns = []
-        trades = []
-        entry_time = None
-        current_trade_min1_start_idx = None
-
-        min1_df['timestamp'] = pd.to_datetime(min1_df.index)
-
-        for i in range(1, len(df)):
-            if i % 100 == 0 and debug:
-                self.logger.debug(f"Progress: {i}/{len(df)} rows processed.")
-
-            price_open = df['open'].iloc[i]
-            price_high = df['high'].iloc[i]
-            price_low = df['low'].iloc[i]
-            price_close = df['close'].iloc[i]
-            date = df['timestamp'].iloc[i]
-            prev_mt = meta_trend[i-1]
-            curr_mt = meta_trend[i]
-            
-            # Check stop loss if in position
-            if position == 1:
-                stop_price = entry_price * (1 - stop_loss_pct)
-                
-                if current_trade_min1_start_idx is None:
-                    # First check after entry, find the entry point in 1-min data
-                    current_trade_min1_start_idx = min1_df.index[min1_df.index >= entry_time][0]
-                
-                # Get the end index for current check
-                current_min1_end_idx = min1_df.index[min1_df.index <= date][-1]
-                
-                # Check all 1-minute candles in between for stop loss
-                min1_slice = min1_df.loc[current_trade_min1_start_idx:current_min1_end_idx]
-                if (min1_slice['low'] <= stop_price).any():
-                    # Stop loss triggered, find the exact candle
-                    stop_candle = min1_slice[min1_slice['low'] <= stop_price].iloc[0]
-                    # More realistic fill: if open < stop, fill at open, else at stop
-                    if stop_candle['open'] < stop_price:
-                        sell_price = stop_candle['open']
-                    else:
-                        sell_price = stop_price
-                    if debug:
-                        print(f"STOP LOSS triggered: entry={entry_price}, stop={stop_price}, sell_price={sell_price}, entry_time={entry_time}, stop_time={stop_candle.name}")
-                    btc_to_sell = coin
-                    usd_gross = btc_to_sell * sell_price
-                    exit_fee = calculate_okx_fee(usd_gross, is_maker=False)  # taker fee
-                    usd = usd_gross - exit_fee
-                    trade_log.append({
-                        'type': 'STOP',
-                        'entry': entry_price,
-                        'exit': sell_price,
-                        'entry_time': entry_time,
-                        'exit_time': stop_candle.name,
-                        'fee_usd': exit_fee
-                    })
-                    coin = 0
-                    position = 0
-                    entry_price = 0
-                    current_trade_min1_start_idx = None
-                    continue
-                
-                # Update the start index for next check
-                current_trade_min1_start_idx = current_min1_end_idx
-
-            # Entry: only if not in position and signal changes to 1
-            if position == 0 and prev_mt != 1 and curr_mt == 1:
-                # Buy at open, fee is charged in USD
-                entry_fee = calculate_okx_fee(usd, is_maker=False)
-                usd_after_fee = usd - entry_fee
-                coin = usd_after_fee / price_open
-                entry_price = price_open
-                entry_time = date
-                usd = 0
-                position = 1
-                current_trade_min1_start_idx = None  # Will be set on first stop loss check
-                trade_log.append({
-                    'type': 'BUY',
-                    'entry': entry_price,
-                    'exit': None,
-                    'entry_time': entry_time,
-                    'exit_time': None,
-                    'fee_usd': entry_fee
-                })
-            
-            # Exit: only if in position and signal changes from 1 to -1
-            elif position == 1 and prev_mt == 1 and curr_mt == -1:
-                # Sell at open, fee is charged in USD
-                btc_to_sell = coin
-                usd_gross = btc_to_sell * price_open
-                exit_fee = calculate_okx_fee(usd_gross, is_maker=False)
-                usd = usd_gross - exit_fee
-                trade_log.append({
-                    'type': 'SELL',
-                    'entry': entry_price,
-                    'exit': price_open,
-                    'entry_time': entry_time,
-                    'exit_time': date,
-                    'fee_usd': exit_fee
-                })
-                coin = 0
-                position = 0
-                entry_price = 0
-                current_trade_min1_start_idx = None
-
-            # Track drawdown
-            balance = usd if position == 0 else coin * price_close
-            if balance > max_balance:
-                max_balance = balance
-            drawdown = (max_balance - balance) / max_balance
-            drawdowns.append(drawdown)
-
-        # If still in position at end, sell at last close
-        if position == 1:
-            btc_to_sell = coin
-            usd_gross = btc_to_sell * df['close'].iloc[-1]
-            exit_fee = calculate_okx_fee(usd_gross, is_maker=False)
-            usd = usd_gross - exit_fee
-            trade_log.append({
-                'type': 'EOD',
-                'entry': entry_price,
-                'exit': df['close'].iloc[-1],
-                'entry_time': entry_time,
-                'exit_time': df['timestamp'].iloc[-1],
-                'fee_usd': exit_fee
-            })
-            coin = 0
-            position = 0
-            entry_price = 0
-
-        # Calculate statistics
-        final_balance = usd
-        n_trades = len(trade_log)
-        wins = [1 for t in trade_log if t['exit'] is not None and t['exit'] > t['entry']]
-        win_rate = len(wins) / n_trades if n_trades > 0 else 0
-        max_drawdown = max(drawdowns) if drawdowns else 0
-        avg_trade = np.mean([t['exit']/t['entry']-1 for t in trade_log if t['exit'] is not None]) if trade_log else 0
-
-        trades = []
-        total_fees_usd = 0.0
-        for trade in trade_log:
-            if trade['exit'] is not None:
-                profit_pct = (trade['exit'] - trade['entry']) / trade['entry']
-            else:
-                profit_pct = 0.0
-            trades.append({
-                'entry_time': trade['entry_time'],
-                'exit_time': trade['exit_time'],
-                'entry': trade['entry'],
-                'exit': trade['exit'],
-                'profit_pct': profit_pct,
-                'type': trade.get('type', 'SELL'),
-                'fee_usd': trade.get('fee_usd', 0.0)
-            })
-            # Sum up USD fees
-            fee_usd = trade.get('fee_usd', 0.0)
-            total_fees_usd += fee_usd
-
-        results = {
-            "initial_usd": initial_usd,
-            "final_usd": final_balance,
-            "n_trades": n_trades,
-            "win_rate": win_rate,
-            "max_drawdown": max_drawdown,
-            "avg_trade": avg_trade,
-            "trade_log": trade_log,
-            "trades": trades,
-            "total_fees_usd": total_fees_usd,
-        }
-        if n_trades > 0:
-            results["first_trade"] = {
-                "entry_time": trade_log[0]['entry_time'],
-                "entry": trade_log[0]['entry']
-            }
-            results["last_trade"] = {
-                "exit_time": trade_log[-1]['exit_time'],
-                "exit": trade_log[-1]['exit']
-            }
-        return results
-    

uv.lock

@@ -0,0 +1,823 @@
+version = 1
+revision = 2
+requires-python = ">=3.10"
+resolution-markers = [
+    "python_full_version >= '3.12'",
+    "python_full_version == '3.11.*'",
+    "python_full_version < '3.11'",
+]
+
+[[package]]
+name = "cachetools"
+version = "5.5.2"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/6c/81/3747dad6b14fa2cf53fcf10548cf5aea6913e96fab41a3c198676f8948a5/cachetools-5.5.2.tar.gz", hash = "sha256:1a661caa9175d26759571b2e19580f9d6393969e5dfca11fdb1f947a23e640d4", size = 28380, upload-time = "2025-02-20T21:01:19.524Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/72/76/20fa66124dbe6be5cafeb312ece67de6b61dd91a0247d1ea13db4ebb33c2/cachetools-5.5.2-py3-none-any.whl", hash = "sha256:d26a22bcc62eb95c3beabd9f1ee5e820d3d2704fe2967cbe350e20c8ffcd3f0a", size = 10080, upload-time = "2025-02-20T21:01:16.647Z" },
+]
+
+[[package]]
+name = "certifi"
+version = "2025.4.26"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/e8/9e/c05b3920a3b7d20d3d3310465f50348e5b3694f4f88c6daf736eef3024c4/certifi-2025.4.26.tar.gz", hash = "sha256:0a816057ea3cdefcef70270d2c515e4506bbc954f417fa5ade2021213bb8f0c6", size = 160705, upload-time = "2025-04-26T02:12:29.51Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/4a/7e/3db2bd1b1f9e95f7cddca6d6e75e2f2bd9f51b1246e546d88addca0106bd/certifi-2025.4.26-py3-none-any.whl", hash = "sha256:30350364dfe371162649852c63336a15c70c6510c2ad5015b21c2345311805f3", size = 159618, upload-time = "2025-04-26T02:12:27.662Z" },
+]
+
+[[package]]
+name = "charset-normalizer"
+version = "3.4.2"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/e4/33/89c2ced2b67d1c2a61c19c6751aa8902d46ce3dacb23600a283619f5a12d/charset_normalizer-3.4.2.tar.gz", hash = "sha256:5baececa9ecba31eff645232d59845c07aa030f0c81ee70184a90d35099a0e63", size = 126367, upload-time = "2025-05-02T08:34:42.01Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/95/28/9901804da60055b406e1a1c5ba7aac1276fb77f1dde635aabfc7fd84b8ab/charset_normalizer-3.4.2-cp310-cp310-macosx_10_9_universal2.whl", hash = "sha256:7c48ed483eb946e6c04ccbe02c6b4d1d48e51944b6db70f697e089c193404941", size = 201818, upload-time = "2025-05-02T08:31:46.725Z" },
+    { url = "https://files.pythonhosted.org/packages/d9/9b/892a8c8af9110935e5adcbb06d9c6fe741b6bb02608c6513983048ba1a18/charset_normalizer-3.4.2-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:b2d318c11350e10662026ad0eb71bb51c7812fc8590825304ae0bdd4ac283acd", size = 144649, upload-time = "2025-05-02T08:31:48.889Z" },
+    { url = "https://files.pythonhosted.org/packages/7b/a5/4179abd063ff6414223575e008593861d62abfc22455b5d1a44995b7c101/charset_normalizer-3.4.2-cp310-cp310-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:9cbfacf36cb0ec2897ce0ebc5d08ca44213af24265bd56eca54bee7923c48fd6", size = 155045, upload-time = "2025-05-02T08:31:50.757Z" },
+    { url = "https://files.pythonhosted.org/packages/3b/95/bc08c7dfeddd26b4be8c8287b9bb055716f31077c8b0ea1cd09553794665/charset_normalizer-3.4.2-cp310-cp310-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:18dd2e350387c87dabe711b86f83c9c78af772c748904d372ade190b5c7c9d4d", size = 147356, upload-time = "2025-05-02T08:31:52.634Z" },
+    { url = "https://files.pythonhosted.org/packages/a8/2d/7a5b635aa65284bf3eab7653e8b4151ab420ecbae918d3e359d1947b4d61/charset_normalizer-3.4.2-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:8075c35cd58273fee266c58c0c9b670947c19df5fb98e7b66710e04ad4e9ff86", size = 149471, upload-time = "2025-05-02T08:31:56.207Z" },
+    { url = "https://files.pythonhosted.org/packages/ae/38/51fc6ac74251fd331a8cfdb7ec57beba8c23fd5493f1050f71c87ef77ed0/charset_normalizer-3.4.2-cp310-cp310-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:5bf4545e3b962767e5c06fe1738f951f77d27967cb2caa64c28be7c4563e162c", size = 151317, upload-time = "2025-05-02T08:31:57.613Z" },
+    { url = "https://files.pythonhosted.org/packages/b7/17/edee1e32215ee6e9e46c3e482645b46575a44a2d72c7dfd49e49f60ce6bf/charset_normalizer-3.4.2-cp310-cp310-musllinux_1_2_aarch64.whl", hash = "sha256:7a6ab32f7210554a96cd9e33abe3ddd86732beeafc7a28e9955cdf22ffadbab0", size = 146368, upload-time = "2025-05-02T08:31:59.468Z" },
+    { url = "https://files.pythonhosted.org/packages/26/2c/ea3e66f2b5f21fd00b2825c94cafb8c326ea6240cd80a91eb09e4a285830/charset_normalizer-3.4.2-cp310-cp310-musllinux_1_2_i686.whl", hash = "sha256:b33de11b92e9f75a2b545d6e9b6f37e398d86c3e9e9653c4864eb7e89c5773ef", size = 154491, upload-time = "2025-05-02T08:32:01.219Z" },
+    { url = "https://files.pythonhosted.org/packages/52/47/7be7fa972422ad062e909fd62460d45c3ef4c141805b7078dbab15904ff7/charset_normalizer-3.4.2-cp310-cp310-musllinux_1_2_ppc64le.whl", hash = "sha256:8755483f3c00d6c9a77f490c17e6ab0c8729e39e6390328e42521ef175380ae6", size = 157695, upload-time = "2025-05-02T08:32:03.045Z" },
+    { url = "https://files.pythonhosted.org/packages/2f/42/9f02c194da282b2b340f28e5fb60762de1151387a36842a92b533685c61e/charset_normalizer-3.4.2-cp310-cp310-musllinux_1_2_s390x.whl", hash = "sha256:68a328e5f55ec37c57f19ebb1fdc56a248db2e3e9ad769919a58672958e8f366", size = 154849, upload-time = "2025-05-02T08:32:04.651Z" },
+    { url = "https://files.pythonhosted.org/packages/67/44/89cacd6628f31fb0b63201a618049be4be2a7435a31b55b5eb1c3674547a/charset_normalizer-3.4.2-cp310-cp310-musllinux_1_2_x86_64.whl", hash = "sha256:21b2899062867b0e1fde9b724f8aecb1af14f2778d69aacd1a5a1853a597a5db", size = 150091, upload-time = "2025-05-02T08:32:06.719Z" },
+    { url = "https://files.pythonhosted.org/packages/1f/79/4b8da9f712bc079c0f16b6d67b099b0b8d808c2292c937f267d816ec5ecc/charset_normalizer-3.4.2-cp310-cp310-win32.whl", hash = "sha256:e8082b26888e2f8b36a042a58307d5b917ef2b1cacab921ad3323ef91901c71a", size = 98445, upload-time = "2025-05-02T08:32:08.66Z" },
+    { url = "https://files.pythonhosted.org/packages/7d/d7/96970afb4fb66497a40761cdf7bd4f6fca0fc7bafde3a84f836c1f57a926/charset_normalizer-3.4.2-cp310-cp310-win_amd64.whl", hash = "sha256:f69a27e45c43520f5487f27627059b64aaf160415589230992cec34c5e18a509", size = 105782, upload-time = "2025-05-02T08:32:10.46Z" },
+    { url = "https://files.pythonhosted.org/packages/05/85/4c40d00dcc6284a1c1ad5de5e0996b06f39d8232f1031cd23c2f5c07ee86/charset_normalizer-3.4.2-cp311-cp311-macosx_10_9_universal2.whl", hash = "sha256:be1e352acbe3c78727a16a455126d9ff83ea2dfdcbc83148d2982305a04714c2", size = 198794, upload-time = "2025-05-02T08:32:11.945Z" },
+    { url = "https://files.pythonhosted.org/packages/41/d9/7a6c0b9db952598e97e93cbdfcb91bacd89b9b88c7c983250a77c008703c/charset_normalizer-3.4.2-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:aa88ca0b1932e93f2d961bf3addbb2db902198dca337d88c89e1559e066e7645", size = 142846, upload-time = "2025-05-02T08:32:13.946Z" },
+    { url = "https://files.pythonhosted.org/packages/66/82/a37989cda2ace7e37f36c1a8ed16c58cf48965a79c2142713244bf945c89/charset_normalizer-3.4.2-cp311-cp311-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:d524ba3f1581b35c03cb42beebab4a13e6cdad7b36246bd22541fa585a56cccd", size = 153350, upload-time = "2025-05-02T08:32:15.873Z" },
+    { url = "https://files.pythonhosted.org/packages/df/68/a576b31b694d07b53807269d05ec3f6f1093e9545e8607121995ba7a8313/charset_normalizer-3.4.2-cp311-cp311-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:28a1005facc94196e1fb3e82a3d442a9d9110b8434fc1ded7a24a2983c9888d8", size = 145657, upload-time = "2025-05-02T08:32:17.283Z" },
+    { url = "https://files.pythonhosted.org/packages/92/9b/ad67f03d74554bed3aefd56fe836e1623a50780f7c998d00ca128924a499/charset_normalizer-3.4.2-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:fdb20a30fe1175ecabed17cbf7812f7b804b8a315a25f24678bcdf120a90077f", size = 147260, upload-time = "2025-05-02T08:32:18.807Z" },
+    { url = "https://files.pythonhosted.org/packages/a6/e6/8aebae25e328160b20e31a7e9929b1578bbdc7f42e66f46595a432f8539e/charset_normalizer-3.4.2-cp311-cp311-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:0f5d9ed7f254402c9e7d35d2f5972c9bbea9040e99cd2861bd77dc68263277c7", size = 149164, upload-time = "2025-05-02T08:32:20.333Z" },
+    { url = "https://files.pythonhosted.org/packages/8b/f2/b3c2f07dbcc248805f10e67a0262c93308cfa149a4cd3d1fe01f593e5fd2/charset_normalizer-3.4.2-cp311-cp311-musllinux_1_2_aarch64.whl", hash = "sha256:efd387a49825780ff861998cd959767800d54f8308936b21025326de4b5a42b9", size = 144571, upload-time = "2025-05-02T08:32:21.86Z" },
+    { url = "https://files.pythonhosted.org/packages/60/5b/c3f3a94bc345bc211622ea59b4bed9ae63c00920e2e8f11824aa5708e8b7/charset_normalizer-3.4.2-cp311-cp311-musllinux_1_2_i686.whl", hash = "sha256:f0aa37f3c979cf2546b73e8222bbfa3dc07a641585340179d768068e3455e544", size = 151952, upload-time = "2025-05-02T08:32:23.434Z" },
+    { url = "https://files.pythonhosted.org/packages/e2/4d/ff460c8b474122334c2fa394a3f99a04cf11c646da895f81402ae54f5c42/charset_normalizer-3.4.2-cp311-cp311-musllinux_1_2_ppc64le.whl", hash = "sha256:e70e990b2137b29dc5564715de1e12701815dacc1d056308e2b17e9095372a82", size = 155959, upload-time = "2025-05-02T08:32:24.993Z" },
+    { url = "https://files.pythonhosted.org/packages/a2/2b/b964c6a2fda88611a1fe3d4c400d39c66a42d6c169c924818c848f922415/charset_normalizer-3.4.2-cp311-cp311-musllinux_1_2_s390x.whl", hash = "sha256:0c8c57f84ccfc871a48a47321cfa49ae1df56cd1d965a09abe84066f6853b9c0", size = 153030, upload-time = "2025-05-02T08:32:26.435Z" },
+    { url = "https://files.pythonhosted.org/packages/59/2e/d3b9811db26a5ebf444bc0fa4f4be5aa6d76fc6e1c0fd537b16c14e849b6/charset_normalizer-3.4.2-cp311-cp311-musllinux_1_2_x86_64.whl", hash = "sha256:6b66f92b17849b85cad91259efc341dce9c1af48e2173bf38a85c6329f1033e5", size = 148015, upload-time = "2025-05-02T08:32:28.376Z" },
+    { url = "https://files.pythonhosted.org/packages/90/07/c5fd7c11eafd561bb51220d600a788f1c8d77c5eef37ee49454cc5c35575/charset_normalizer-3.4.2-cp311-cp311-win32.whl", hash = "sha256:daac4765328a919a805fa5e2720f3e94767abd632ae410a9062dff5412bae65a", size = 98106, upload-time = "2025-05-02T08:32:30.281Z" },
+    { url = "https://files.pythonhosted.org/packages/a8/05/5e33dbef7e2f773d672b6d79f10ec633d4a71cd96db6673625838a4fd532/charset_normalizer-3.4.2-cp311-cp311-win_amd64.whl", hash = "sha256:e53efc7c7cee4c1e70661e2e112ca46a575f90ed9ae3fef200f2a25e954f4b28", size = 105402, upload-time = "2025-05-02T08:32:32.191Z" },
+    { url = "https://files.pythonhosted.org/packages/d7/a4/37f4d6035c89cac7930395a35cc0f1b872e652eaafb76a6075943754f095/charset_normalizer-3.4.2-cp312-cp312-macosx_10_13_universal2.whl", hash = "sha256:0c29de6a1a95f24b9a1aa7aefd27d2487263f00dfd55a77719b530788f75cff7", size = 199936, upload-time = "2025-05-02T08:32:33.712Z" },
+    { url = "https://files.pythonhosted.org/packages/ee/8a/1a5e33b73e0d9287274f899d967907cd0bf9c343e651755d9307e0dbf2b3/charset_normalizer-3.4.2-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:cddf7bd982eaa998934a91f69d182aec997c6c468898efe6679af88283b498d3", size = 143790, upload-time = "2025-05-02T08:32:35.768Z" },
+    { url = "https://files.pythonhosted.org/packages/66/52/59521f1d8e6ab1482164fa21409c5ef44da3e9f653c13ba71becdd98dec3/charset_normalizer-3.4.2-cp312-cp312-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:fcbe676a55d7445b22c10967bceaaf0ee69407fbe0ece4d032b6eb8d4565982a", size = 153924, upload-time = "2025-05-02T08:32:37.284Z" },
+    { url = "https://files.pythonhosted.org/packages/86/2d/fb55fdf41964ec782febbf33cb64be480a6b8f16ded2dbe8db27a405c09f/charset_normalizer-3.4.2-cp312-cp312-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:d41c4d287cfc69060fa91cae9683eacffad989f1a10811995fa309df656ec214", size = 146626, upload-time = "2025-05-02T08:32:38.803Z" },
+    { url = "https://files.pythonhosted.org/packages/8c/73/6ede2ec59bce19b3edf4209d70004253ec5f4e319f9a2e3f2f15601ed5f7/charset_normalizer-3.4.2-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:4e594135de17ab3866138f496755f302b72157d115086d100c3f19370839dd3a", size = 148567, upload-time = "2025-05-02T08:32:40.251Z" },
+    { url = "https://files.pythonhosted.org/packages/09/14/957d03c6dc343c04904530b6bef4e5efae5ec7d7990a7cbb868e4595ee30/charset_normalizer-3.4.2-cp312-cp312-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:cf713fe9a71ef6fd5adf7a79670135081cd4431c2943864757f0fa3a65b1fafd", size = 150957, upload-time = "2025-05-02T08:32:41.705Z" },
+    { url = "https://files.pythonhosted.org/packages/0d/c8/8174d0e5c10ccebdcb1b53cc959591c4c722a3ad92461a273e86b9f5a302/charset_normalizer-3.4.2-cp312-cp312-musllinux_1_2_aarch64.whl", hash = "sha256:a370b3e078e418187da8c3674eddb9d983ec09445c99a3a263c2011993522981", size = 145408, upload-time = "2025-05-02T08:32:43.709Z" },
+    { url = "https://files.pythonhosted.org/packages/58/aa/8904b84bc8084ac19dc52feb4f5952c6df03ffb460a887b42615ee1382e8/charset_normalizer-3.4.2-cp312-cp312-musllinux_1_2_i686.whl", hash = "sha256:a955b438e62efdf7e0b7b52a64dc5c3396e2634baa62471768a64bc2adb73d5c", size = 153399, upload-time = "2025-05-02T08:32:46.197Z" },
+    { url = "https://files.pythonhosted.org/packages/c2/26/89ee1f0e264d201cb65cf054aca6038c03b1a0c6b4ae998070392a3ce605/charset_normalizer-3.4.2-cp312-cp312-musllinux_1_2_ppc64le.whl", hash = "sha256:7222ffd5e4de8e57e03ce2cef95a4c43c98fcb72ad86909abdfc2c17d227fc1b", size = 156815, upload-time = "2025-05-02T08:32:48.105Z" },
+    { url = "https://files.pythonhosted.org/packages/fd/07/68e95b4b345bad3dbbd3a8681737b4338ff2c9df29856a6d6d23ac4c73cb/charset_normalizer-3.4.2-cp312-cp312-musllinux_1_2_s390x.whl", hash = "sha256:bee093bf902e1d8fc0ac143c88902c3dfc8941f7ea1d6a8dd2bcb786d33db03d", size = 154537, upload-time = "2025-05-02T08:32:49.719Z" },
+    { url = "https://files.pythonhosted.org/packages/77/1a/5eefc0ce04affb98af07bc05f3bac9094513c0e23b0562d64af46a06aae4/charset_normalizer-3.4.2-cp312-cp312-musllinux_1_2_x86_64.whl", hash = "sha256:dedb8adb91d11846ee08bec4c8236c8549ac721c245678282dcb06b221aab59f", size = 149565, upload-time = "2025-05-02T08:32:51.404Z" },
+    { url = "https://files.pythonhosted.org/packages/37/a0/2410e5e6032a174c95e0806b1a6585eb21e12f445ebe239fac441995226a/charset_normalizer-3.4.2-cp312-cp312-win32.whl", hash = "sha256:db4c7bf0e07fc3b7d89ac2a5880a6a8062056801b83ff56d8464b70f65482b6c", size = 98357, upload-time = "2025-05-02T08:32:53.079Z" },
+    { url = "https://files.pythonhosted.org/packages/6c/4f/c02d5c493967af3eda9c771ad4d2bbc8df6f99ddbeb37ceea6e8716a32bc/charset_normalizer-3.4.2-cp312-cp312-win_amd64.whl", hash = "sha256:5a9979887252a82fefd3d3ed2a8e3b937a7a809f65dcb1e068b090e165bbe99e", size = 105776, upload-time = "2025-05-02T08:32:54.573Z" },
+    { url = "https://files.pythonhosted.org/packages/ea/12/a93df3366ed32db1d907d7593a94f1fe6293903e3e92967bebd6950ed12c/charset_normalizer-3.4.2-cp313-cp313-macosx_10_13_universal2.whl", hash = "sha256:926ca93accd5d36ccdabd803392ddc3e03e6d4cd1cf17deff3b989ab8e9dbcf0", size = 199622, upload-time = "2025-05-02T08:32:56.363Z" },
+    { url = "https://files.pythonhosted.org/packages/04/93/bf204e6f344c39d9937d3c13c8cd5bbfc266472e51fc8c07cb7f64fcd2de/charset_normalizer-3.4.2-cp313-cp313-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:eba9904b0f38a143592d9fc0e19e2df0fa2e41c3c3745554761c5f6447eedabf", size = 143435, upload-time = "2025-05-02T08:32:58.551Z" },
+    { url = "https://files.pythonhosted.org/packages/22/2a/ea8a2095b0bafa6c5b5a55ffdc2f924455233ee7b91c69b7edfcc9e02284/charset_normalizer-3.4.2-cp313-cp313-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:3fddb7e2c84ac87ac3a947cb4e66d143ca5863ef48e4a5ecb83bd48619e4634e", size = 153653, upload-time = "2025-05-02T08:33:00.342Z" },
+    { url = "https://files.pythonhosted.org/packages/b6/57/1b090ff183d13cef485dfbe272e2fe57622a76694061353c59da52c9a659/charset_normalizer-3.4.2-cp313-cp313-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:98f862da73774290f251b9df8d11161b6cf25b599a66baf087c1ffe340e9bfd1", size = 146231, upload-time = "2025-05-02T08:33:02.081Z" },
+    { url = "https://files.pythonhosted.org/packages/e2/28/ffc026b26f441fc67bd21ab7f03b313ab3fe46714a14b516f931abe1a2d8/charset_normalizer-3.4.2-cp313-cp313-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:6c9379d65defcab82d07b2a9dfbfc2e95bc8fe0ebb1b176a3190230a3ef0e07c", size = 148243, upload-time = "2025-05-02T08:33:04.063Z" },
+    { url = "https://files.pythonhosted.org/packages/c0/0f/9abe9bd191629c33e69e47c6ef45ef99773320e9ad8e9cb08b8ab4a8d4cb/charset_normalizer-3.4.2-cp313-cp313-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:e635b87f01ebc977342e2697d05b56632f5f879a4f15955dfe8cef2448b51691", size = 150442, upload-time = "2025-05-02T08:33:06.418Z" },
+    { url = "https://files.pythonhosted.org/packages/67/7c/a123bbcedca91d5916c056407f89a7f5e8fdfce12ba825d7d6b9954a1a3c/charset_normalizer-3.4.2-cp313-cp313-musllinux_1_2_aarch64.whl", hash = "sha256:1c95a1e2902a8b722868587c0e1184ad5c55631de5afc0eb96bc4b0d738092c0", size = 145147, upload-time = "2025-05-02T08:33:08.183Z" },
+    { url = "https://files.pythonhosted.org/packages/ec/fe/1ac556fa4899d967b83e9893788e86b6af4d83e4726511eaaad035e36595/charset_normalizer-3.4.2-cp313-cp313-musllinux_1_2_i686.whl", hash = "sha256:ef8de666d6179b009dce7bcb2ad4c4a779f113f12caf8dc77f0162c29d20490b", size = 153057, upload-time = "2025-05-02T08:33:09.986Z" },
+    { url = "https://files.pythonhosted.org/packages/2b/ff/acfc0b0a70b19e3e54febdd5301a98b72fa07635e56f24f60502e954c461/charset_normalizer-3.4.2-cp313-cp313-musllinux_1_2_ppc64le.whl", hash = "sha256:32fc0341d72e0f73f80acb0a2c94216bd704f4f0bce10aedea38f30502b271ff", size = 156454, upload-time = "2025-05-02T08:33:11.814Z" },
+    { url = "https://files.pythonhosted.org/packages/92/08/95b458ce9c740d0645feb0e96cea1f5ec946ea9c580a94adfe0b617f3573/charset_normalizer-3.4.2-cp313-cp313-musllinux_1_2_s390x.whl", hash = "sha256:289200a18fa698949d2b39c671c2cc7a24d44096784e76614899a7ccf2574b7b", size = 154174, upload-time = "2025-05-02T08:33:13.707Z" },
+    { url = "https://files.pythonhosted.org/packages/78/be/8392efc43487ac051eee6c36d5fbd63032d78f7728cb37aebcc98191f1ff/charset_normalizer-3.4.2-cp313-cp313-musllinux_1_2_x86_64.whl", hash = "sha256:4a476b06fbcf359ad25d34a057b7219281286ae2477cc5ff5e3f70a246971148", size = 149166, upload-time = "2025-05-02T08:33:15.458Z" },
+    { url = "https://files.pythonhosted.org/packages/44/96/392abd49b094d30b91d9fbda6a69519e95802250b777841cf3bda8fe136c/charset_normalizer-3.4.2-cp313-cp313-win32.whl", hash = "sha256:aaeeb6a479c7667fbe1099af9617c83aaca22182d6cf8c53966491a0f1b7ffb7", size = 98064, upload-time = "2025-05-02T08:33:17.06Z" },
+    { url = "https://files.pythonhosted.org/packages/e9/b0/0200da600134e001d91851ddc797809e2fe0ea72de90e09bec5a2fbdaccb/charset_normalizer-3.4.2-cp313-cp313-win_amd64.whl", hash = "sha256:aa6af9e7d59f9c12b33ae4e9450619cf2488e2bbe9b44030905877f0b2324980", size = 105641, upload-time = "2025-05-02T08:33:18.753Z" },
+    { url = "https://files.pythonhosted.org/packages/20/94/c5790835a017658cbfabd07f3bfb549140c3ac458cfc196323996b10095a/charset_normalizer-3.4.2-py3-none-any.whl", hash = "sha256:7f56930ab0abd1c45cd15be65cc741c28b1c9a34876ce8c17a2fa107810c0af0", size = 52626, upload-time = "2025-05-02T08:34:40.053Z" },
+]
+
+[[package]]
+name = "contourpy"
+version = "1.3.2"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "numpy" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/66/54/eb9bfc647b19f2009dd5c7f5ec51c4e6ca831725f1aea7a993034f483147/contourpy-1.3.2.tar.gz", hash = "sha256:b6945942715a034c671b7fc54f9588126b0b8bf23db2696e3ca8328f3ff0ab54", size = 13466130, upload-time = "2025-04-15T17:47:53.79Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/12/a3/da4153ec8fe25d263aa48c1a4cbde7f49b59af86f0b6f7862788c60da737/contourpy-1.3.2-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:ba38e3f9f330af820c4b27ceb4b9c7feee5fe0493ea53a8720f4792667465934", size = 268551, upload-time = "2025-04-15T17:34:46.581Z" },
+    { url = "https://files.pythonhosted.org/packages/2f/6c/330de89ae1087eb622bfca0177d32a7ece50c3ef07b28002de4757d9d875/contourpy-1.3.2-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:dc41ba0714aa2968d1f8674ec97504a8f7e334f48eeacebcaa6256213acb0989", size = 253399, upload-time = "2025-04-15T17:34:51.427Z" },
+    { url = "https://files.pythonhosted.org/packages/c1/bd/20c6726b1b7f81a8bee5271bed5c165f0a8e1f572578a9d27e2ccb763cb2/contourpy-1.3.2-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:9be002b31c558d1ddf1b9b415b162c603405414bacd6932d031c5b5a8b757f0d", size = 312061, upload-time = "2025-04-15T17:34:55.961Z" },
+    { url = "https://files.pythonhosted.org/packages/22/fc/a9665c88f8a2473f823cf1ec601de9e5375050f1958cbb356cdf06ef1ab6/contourpy-1.3.2-cp310-cp310-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:8d2e74acbcba3bfdb6d9d8384cdc4f9260cae86ed9beee8bd5f54fee49a430b9", size = 351956, upload-time = "2025-04-15T17:35:00.992Z" },
+    { url = "https://files.pythonhosted.org/packages/25/eb/9f0a0238f305ad8fb7ef42481020d6e20cf15e46be99a1fcf939546a177e/contourpy-1.3.2-cp310-cp310-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:e259bced5549ac64410162adc973c5e2fb77f04df4a439d00b478e57a0e65512", size = 320872, upload-time = "2025-04-15T17:35:06.177Z" },
+    { url = "https://files.pythonhosted.org/packages/32/5c/1ee32d1c7956923202f00cf8d2a14a62ed7517bdc0ee1e55301227fc273c/contourpy-1.3.2-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:ad687a04bc802cbe8b9c399c07162a3c35e227e2daccf1668eb1f278cb698631", size = 325027, upload-time = "2025-04-15T17:35:11.244Z" },
+    { url = "https://files.pythonhosted.org/packages/83/bf/9baed89785ba743ef329c2b07fd0611d12bfecbedbdd3eeecf929d8d3b52/contourpy-1.3.2-cp310-cp310-musllinux_1_2_aarch64.whl", hash = "sha256:cdd22595308f53ef2f891040ab2b93d79192513ffccbd7fe19be7aa773a5e09f", size = 1306641, upload-time = "2025-04-15T17:35:26.701Z" },
+    { url = "https://files.pythonhosted.org/packages/d4/cc/74e5e83d1e35de2d28bd97033426b450bc4fd96e092a1f7a63dc7369b55d/contourpy-1.3.2-cp310-cp310-musllinux_1_2_x86_64.whl", hash = "sha256:b4f54d6a2defe9f257327b0f243612dd051cc43825587520b1bf74a31e2f6ef2", size = 1374075, upload-time = "2025-04-15T17:35:43.204Z" },
+    { url = "https://files.pythonhosted.org/packages/0c/42/17f3b798fd5e033b46a16f8d9fcb39f1aba051307f5ebf441bad1ecf78f8/contourpy-1.3.2-cp310-cp310-win32.whl", hash = "sha256:f939a054192ddc596e031e50bb13b657ce318cf13d264f095ce9db7dc6ae81c0", size = 177534, upload-time = "2025-04-15T17:35:46.554Z" },
+    { url = "https://files.pythonhosted.org/packages/54/ec/5162b8582f2c994721018d0c9ece9dc6ff769d298a8ac6b6a652c307e7df/contourpy-1.3.2-cp310-cp310-win_amd64.whl", hash = "sha256:c440093bbc8fc21c637c03bafcbef95ccd963bc6e0514ad887932c18ca2a759a", size = 221188, upload-time = "2025-04-15T17:35:50.064Z" },
+    { url = "https://files.pythonhosted.org/packages/b3/b9/ede788a0b56fc5b071639d06c33cb893f68b1178938f3425debebe2dab78/contourpy-1.3.2-cp311-cp311-macosx_10_9_x86_64.whl", hash = "sha256:6a37a2fb93d4df3fc4c0e363ea4d16f83195fc09c891bc8ce072b9d084853445", size = 269636, upload-time = "2025-04-15T17:35:54.473Z" },
+    { url = "https://files.pythonhosted.org/packages/e6/75/3469f011d64b8bbfa04f709bfc23e1dd71be54d05b1b083be9f5b22750d1/contourpy-1.3.2-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:b7cd50c38f500bbcc9b6a46643a40e0913673f869315d8e70de0438817cb7773", size = 254636, upload-time = "2025-04-15T17:35:58.283Z" },
+    { url = "https://files.pythonhosted.org/packages/8d/2f/95adb8dae08ce0ebca4fd8e7ad653159565d9739128b2d5977806656fcd2/contourpy-1.3.2-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:d6658ccc7251a4433eebd89ed2672c2ed96fba367fd25ca9512aa92a4b46c4f1", size = 313053, upload-time = "2025-04-15T17:36:03.235Z" },
+    { url = "https://files.pythonhosted.org/packages/c3/a6/8ccf97a50f31adfa36917707fe39c9a0cbc24b3bbb58185577f119736cc9/contourpy-1.3.2-cp311-cp311-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:70771a461aaeb335df14deb6c97439973d253ae70660ca085eec25241137ef43", size = 352985, upload-time = "2025-04-15T17:36:08.275Z" },
+    { url = "https://files.pythonhosted.org/packages/1d/b6/7925ab9b77386143f39d9c3243fdd101621b4532eb126743201160ffa7e6/contourpy-1.3.2-cp311-cp311-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:65a887a6e8c4cd0897507d814b14c54a8c2e2aa4ac9f7686292f9769fcf9a6ab", size = 323750, upload-time = "2025-04-15T17:36:13.29Z" },
+    { url = "https://files.pythonhosted.org/packages/c2/f3/20c5d1ef4f4748e52d60771b8560cf00b69d5c6368b5c2e9311bcfa2a08b/contourpy-1.3.2-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:3859783aefa2b8355697f16642695a5b9792e7a46ab86da1118a4a23a51a33d7", size = 326246, upload-time = "2025-04-15T17:36:18.329Z" },
+    { url = "https://files.pythonhosted.org/packages/8c/e5/9dae809e7e0b2d9d70c52b3d24cba134dd3dad979eb3e5e71f5df22ed1f5/contourpy-1.3.2-cp311-cp311-musllinux_1_2_aarch64.whl", hash = "sha256:eab0f6db315fa4d70f1d8ab514e527f0366ec021ff853d7ed6a2d33605cf4b83", size = 1308728, upload-time = "2025-04-15T17:36:33.878Z" },
+    { url = "https://files.pythonhosted.org/packages/e2/4a/0058ba34aeea35c0b442ae61a4f4d4ca84d6df8f91309bc2d43bb8dd248f/contourpy-1.3.2-cp311-cp311-musllinux_1_2_x86_64.whl", hash = "sha256:d91a3ccc7fea94ca0acab82ceb77f396d50a1f67412efe4c526f5d20264e6ecd", size = 1375762, upload-time = "2025-04-15T17:36:51.295Z" },
+    { url = "https://files.pythonhosted.org/packages/09/33/7174bdfc8b7767ef2c08ed81244762d93d5c579336fc0b51ca57b33d1b80/contourpy-1.3.2-cp311-cp311-win32.whl", hash = "sha256:1c48188778d4d2f3d48e4643fb15d8608b1d01e4b4d6b0548d9b336c28fc9b6f", size = 178196, upload-time = "2025-04-15T17:36:55.002Z" },
+    { url = "https://files.pythonhosted.org/packages/5e/fe/4029038b4e1c4485cef18e480b0e2cd2d755448bb071eb9977caac80b77b/contourpy-1.3.2-cp311-cp311-win_amd64.whl", hash = "sha256:5ebac872ba09cb8f2131c46b8739a7ff71de28a24c869bcad554477eb089a878", size = 222017, upload-time = "2025-04-15T17:36:58.576Z" },
+    { url = "https://files.pythonhosted.org/packages/34/f7/44785876384eff370c251d58fd65f6ad7f39adce4a093c934d4a67a7c6b6/contourpy-1.3.2-cp312-cp312-macosx_10_13_x86_64.whl", hash = "sha256:4caf2bcd2969402bf77edc4cb6034c7dd7c0803213b3523f111eb7460a51b8d2", size = 271580, upload-time = "2025-04-15T17:37:03.105Z" },
+    { url = "https://files.pythonhosted.org/packages/93/3b/0004767622a9826ea3d95f0e9d98cd8729015768075d61f9fea8eeca42a8/contourpy-1.3.2-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:82199cb78276249796419fe36b7386bd8d2cc3f28b3bc19fe2454fe2e26c4c15", size = 255530, upload-time = "2025-04-15T17:37:07.026Z" },
+    { url = "https://files.pythonhosted.org/packages/e7/bb/7bd49e1f4fa805772d9fd130e0d375554ebc771ed7172f48dfcd4ca61549/contourpy-1.3.2-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:106fab697af11456fcba3e352ad50effe493a90f893fca6c2ca5c033820cea92", size = 307688, upload-time = "2025-04-15T17:37:11.481Z" },
+    { url = "https://files.pythonhosted.org/packages/fc/97/e1d5dbbfa170725ef78357a9a0edc996b09ae4af170927ba8ce977e60a5f/contourpy-1.3.2-cp312-cp312-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:d14f12932a8d620e307f715857107b1d1845cc44fdb5da2bc8e850f5ceba9f87", size = 347331, upload-time = "2025-04-15T17:37:18.212Z" },
+    { url = "https://files.pythonhosted.org/packages/6f/66/e69e6e904f5ecf6901be3dd16e7e54d41b6ec6ae3405a535286d4418ffb4/contourpy-1.3.2-cp312-cp312-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:532fd26e715560721bb0d5fc7610fce279b3699b018600ab999d1be895b09415", size = 318963, upload-time = "2025-04-15T17:37:22.76Z" },
+    { url = "https://files.pythonhosted.org/packages/a8/32/b8a1c8965e4f72482ff2d1ac2cd670ce0b542f203c8e1d34e7c3e6925da7/contourpy-1.3.2-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:f26b383144cf2d2c29f01a1e8170f50dacf0eac02d64139dcd709a8ac4eb3cfe", size = 323681, upload-time = "2025-04-15T17:37:33.001Z" },
+    { url = "https://files.pythonhosted.org/packages/30/c6/12a7e6811d08757c7162a541ca4c5c6a34c0f4e98ef2b338791093518e40/contourpy-1.3.2-cp312-cp312-musllinux_1_2_aarch64.whl", hash = "sha256:c49f73e61f1f774650a55d221803b101d966ca0c5a2d6d5e4320ec3997489441", size = 1308674, upload-time = "2025-04-15T17:37:48.64Z" },
+    { url = "https://files.pythonhosted.org/packages/2a/8a/bebe5a3f68b484d3a2b8ffaf84704b3e343ef1addea528132ef148e22b3b/contourpy-1.3.2-cp312-cp312-musllinux_1_2_x86_64.whl", hash = "sha256:3d80b2c0300583228ac98d0a927a1ba6a2ba6b8a742463c564f1d419ee5b211e", size = 1380480, upload-time = "2025-04-15T17:38:06.7Z" },
+    { url = "https://files.pythonhosted.org/packages/34/db/fcd325f19b5978fb509a7d55e06d99f5f856294c1991097534360b307cf1/contourpy-1.3.2-cp312-cp312-win32.whl", hash = "sha256:90df94c89a91b7362e1142cbee7568f86514412ab8a2c0d0fca72d7e91b62912", size = 178489, upload-time = "2025-04-15T17:38:10.338Z" },
+    { url = "https://files.pythonhosted.org/packages/01/c8/fadd0b92ffa7b5eb5949bf340a63a4a496a6930a6c37a7ba0f12acb076d6/contourpy-1.3.2-cp312-cp312-win_amd64.whl", hash = "sha256:8c942a01d9163e2e5cfb05cb66110121b8d07ad438a17f9e766317bcb62abf73", size = 223042, upload-time = "2025-04-15T17:38:14.239Z" },
+    { url = "https://files.pythonhosted.org/packages/2e/61/5673f7e364b31e4e7ef6f61a4b5121c5f170f941895912f773d95270f3a2/contourpy-1.3.2-cp313-cp313-macosx_10_13_x86_64.whl", hash = "sha256:de39db2604ae755316cb5967728f4bea92685884b1e767b7c24e983ef5f771cb", size = 271630, upload-time = "2025-04-15T17:38:19.142Z" },
+    { url = "https://files.pythonhosted.org/packages/ff/66/a40badddd1223822c95798c55292844b7e871e50f6bfd9f158cb25e0bd39/contourpy-1.3.2-cp313-cp313-macosx_11_0_arm64.whl", hash = "sha256:3f9e896f447c5c8618f1edb2bafa9a4030f22a575ec418ad70611450720b5b08", size = 255670, upload-time = "2025-04-15T17:38:23.688Z" },
+    { url = "https://files.pythonhosted.org/packages/1e/c7/cf9fdee8200805c9bc3b148f49cb9482a4e3ea2719e772602a425c9b09f8/contourpy-1.3.2-cp313-cp313-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:71e2bd4a1c4188f5c2b8d274da78faab884b59df20df63c34f74aa1813c4427c", size = 306694, upload-time = "2025-04-15T17:38:28.238Z" },
+    { url = "https://files.pythonhosted.org/packages/dd/e7/ccb9bec80e1ba121efbffad7f38021021cda5be87532ec16fd96533bb2e0/contourpy-1.3.2-cp313-cp313-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:de425af81b6cea33101ae95ece1f696af39446db9682a0b56daaa48cfc29f38f", size = 345986, upload-time = "2025-04-15T17:38:33.502Z" },
+    { url = "https://files.pythonhosted.org/packages/dc/49/ca13bb2da90391fa4219fdb23b078d6065ada886658ac7818e5441448b78/contourpy-1.3.2-cp313-cp313-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:977e98a0e0480d3fe292246417239d2d45435904afd6d7332d8455981c408b85", size = 318060, upload-time = "2025-04-15T17:38:38.672Z" },
+    { url = "https://files.pythonhosted.org/packages/c8/65/5245ce8c548a8422236c13ffcdcdada6a2a812c361e9e0c70548bb40b661/contourpy-1.3.2-cp313-cp313-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:434f0adf84911c924519d2b08fc10491dd282b20bdd3fa8f60fd816ea0b48841", size = 322747, upload-time = "2025-04-15T17:38:43.712Z" },
+    { url = "https://files.pythonhosted.org/packages/72/30/669b8eb48e0a01c660ead3752a25b44fdb2e5ebc13a55782f639170772f9/contourpy-1.3.2-cp313-cp313-musllinux_1_2_aarch64.whl", hash = "sha256:c66c4906cdbc50e9cba65978823e6e00b45682eb09adbb78c9775b74eb222422", size = 1308895, upload-time = "2025-04-15T17:39:00.224Z" },
+    { url = "https://files.pythonhosted.org/packages/05/5a/b569f4250decee6e8d54498be7bdf29021a4c256e77fe8138c8319ef8eb3/contourpy-1.3.2-cp313-cp313-musllinux_1_2_x86_64.whl", hash = "sha256:8b7fc0cd78ba2f4695fd0a6ad81a19e7e3ab825c31b577f384aa9d7817dc3bef", size = 1379098, upload-time = "2025-04-15T17:43:29.649Z" },
+    { url = "https://files.pythonhosted.org/packages/19/ba/b227c3886d120e60e41b28740ac3617b2f2b971b9f601c835661194579f1/contourpy-1.3.2-cp313-cp313-win32.whl", hash = "sha256:15ce6ab60957ca74cff444fe66d9045c1fd3e92c8936894ebd1f3eef2fff075f", size = 178535, upload-time = "2025-04-15T17:44:44.532Z" },
+    { url = "https://files.pythonhosted.org/packages/12/6e/2fed56cd47ca739b43e892707ae9a13790a486a3173be063681ca67d2262/contourpy-1.3.2-cp313-cp313-win_amd64.whl", hash = "sha256:e1578f7eafce927b168752ed7e22646dad6cd9bca673c60bff55889fa236ebf9", size = 223096, upload-time = "2025-04-15T17:44:48.194Z" },
+    { url = "https://files.pythonhosted.org/packages/54/4c/e76fe2a03014a7c767d79ea35c86a747e9325537a8b7627e0e5b3ba266b4/contourpy-1.3.2-cp313-cp313t-macosx_10_13_x86_64.whl", hash = "sha256:0475b1f6604896bc7c53bb070e355e9321e1bc0d381735421a2d2068ec56531f", size = 285090, upload-time = "2025-04-15T17:43:34.084Z" },
+    { url = "https://files.pythonhosted.org/packages/7b/e2/5aba47debd55d668e00baf9651b721e7733975dc9fc27264a62b0dd26eb8/contourpy-1.3.2-cp313-cp313t-macosx_11_0_arm64.whl", hash = "sha256:c85bb486e9be652314bb5b9e2e3b0d1b2e643d5eec4992c0fbe8ac71775da739", size = 268643, upload-time = "2025-04-15T17:43:38.626Z" },
+    { url = "https://files.pythonhosted.org/packages/a1/37/cd45f1f051fe6230f751cc5cdd2728bb3a203f5619510ef11e732109593c/contourpy-1.3.2-cp313-cp313t-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:745b57db7758f3ffc05a10254edd3182a2a83402a89c00957a8e8a22f5582823", size = 310443, upload-time = "2025-04-15T17:43:44.522Z" },
+    { url = "https://files.pythonhosted.org/packages/8b/a2/36ea6140c306c9ff6dd38e3bcec80b3b018474ef4d17eb68ceecd26675f4/contourpy-1.3.2-cp313-cp313t-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:970e9173dbd7eba9b4e01aab19215a48ee5dd3f43cef736eebde064a171f89a5", size = 349865, upload-time = "2025-04-15T17:43:49.545Z" },
+    { url = "https://files.pythonhosted.org/packages/95/b7/2fc76bc539693180488f7b6cc518da7acbbb9e3b931fd9280504128bf956/contourpy-1.3.2-cp313-cp313t-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:c6c4639a9c22230276b7bffb6a850dfc8258a2521305e1faefe804d006b2e532", size = 321162, upload-time = "2025-04-15T17:43:54.203Z" },
+    { url = "https://files.pythonhosted.org/packages/f4/10/76d4f778458b0aa83f96e59d65ece72a060bacb20cfbee46cf6cd5ceba41/contourpy-1.3.2-cp313-cp313t-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:cc829960f34ba36aad4302e78eabf3ef16a3a100863f0d4eeddf30e8a485a03b", size = 327355, upload-time = "2025-04-15T17:44:01.025Z" },
+    { url = "https://files.pythonhosted.org/packages/43/a3/10cf483ea683f9f8ab096c24bad3cce20e0d1dd9a4baa0e2093c1c962d9d/contourpy-1.3.2-cp313-cp313t-musllinux_1_2_aarch64.whl", hash = "sha256:d32530b534e986374fc19eaa77fcb87e8a99e5431499949b828312bdcd20ac52", size = 1307935, upload-time = "2025-04-15T17:44:17.322Z" },
+    { url = "https://files.pythonhosted.org/packages/78/73/69dd9a024444489e22d86108e7b913f3528f56cfc312b5c5727a44188471/contourpy-1.3.2-cp313-cp313t-musllinux_1_2_x86_64.whl", hash = "sha256:e298e7e70cf4eb179cc1077be1c725b5fd131ebc81181bf0c03525c8abc297fd", size = 1372168, upload-time = "2025-04-15T17:44:33.43Z" },
+    { url = "https://files.pythonhosted.org/packages/0f/1b/96d586ccf1b1a9d2004dd519b25fbf104a11589abfd05484ff12199cca21/contourpy-1.3.2-cp313-cp313t-win32.whl", hash = "sha256:d0e589ae0d55204991450bb5c23f571c64fe43adaa53f93fc902a84c96f52fe1", size = 189550, upload-time = "2025-04-15T17:44:37.092Z" },
+    { url = "https://files.pythonhosted.org/packages/b0/e6/6000d0094e8a5e32ad62591c8609e269febb6e4db83a1c75ff8868b42731/contourpy-1.3.2-cp313-cp313t-win_amd64.whl", hash = "sha256:78e9253c3de756b3f6a5174d024c4835acd59eb3f8e2ca13e775dbffe1558f69", size = 238214, upload-time = "2025-04-15T17:44:40.827Z" },
+    { url = "https://files.pythonhosted.org/packages/33/05/b26e3c6ecc05f349ee0013f0bb850a761016d89cec528a98193a48c34033/contourpy-1.3.2-pp310-pypy310_pp73-macosx_10_15_x86_64.whl", hash = "sha256:fd93cc7f3139b6dd7aab2f26a90dde0aa9fc264dbf70f6740d498a70b860b82c", size = 265681, upload-time = "2025-04-15T17:44:59.314Z" },
+    { url = "https://files.pythonhosted.org/packages/2b/25/ac07d6ad12affa7d1ffed11b77417d0a6308170f44ff20fa1d5aa6333f03/contourpy-1.3.2-pp310-pypy310_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:107ba8a6a7eec58bb475329e6d3b95deba9440667c4d62b9b6063942b61d7f16", size = 315101, upload-time = "2025-04-15T17:45:04.165Z" },
+    { url = "https://files.pythonhosted.org/packages/8f/4d/5bb3192bbe9d3f27e3061a6a8e7733c9120e203cb8515767d30973f71030/contourpy-1.3.2-pp310-pypy310_pp73-win_amd64.whl", hash = "sha256:ded1706ed0c1049224531b81128efbd5084598f18d8a2d9efae833edbd2b40ad", size = 220599, upload-time = "2025-04-15T17:45:08.456Z" },
+    { url = "https://files.pythonhosted.org/packages/ff/c0/91f1215d0d9f9f343e4773ba6c9b89e8c0cc7a64a6263f21139da639d848/contourpy-1.3.2-pp311-pypy311_pp73-macosx_10_15_x86_64.whl", hash = "sha256:5f5964cdad279256c084b69c3f412b7801e15356b16efa9d78aa974041903da0", size = 266807, upload-time = "2025-04-15T17:45:15.535Z" },
+    { url = "https://files.pythonhosted.org/packages/d4/79/6be7e90c955c0487e7712660d6cead01fa17bff98e0ea275737cc2bc8e71/contourpy-1.3.2-pp311-pypy311_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:49b65a95d642d4efa8f64ba12558fcb83407e58a2dfba9d796d77b63ccfcaff5", size = 318729, upload-time = "2025-04-15T17:45:20.166Z" },
+    { url = "https://files.pythonhosted.org/packages/87/68/7f46fb537958e87427d98a4074bcde4b67a70b04900cfc5ce29bc2f556c1/contourpy-1.3.2-pp311-pypy311_pp73-win_amd64.whl", hash = "sha256:8c5acb8dddb0752bf252e01a3035b21443158910ac16a3b0d20e7fed7d534ce5", size = 221791, upload-time = "2025-04-15T17:45:24.794Z" },
+]
+
+[[package]]
+name = "cycler"
+version = "0.12.1"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/a9/95/a3dbbb5028f35eafb79008e7522a75244477d2838f38cbb722248dabc2a8/cycler-0.12.1.tar.gz", hash = "sha256:88bb128f02ba341da8ef447245a9e138fae777f6a23943da4540077d3601eb1c", size = 7615, upload-time = "2023-10-07T05:32:18.335Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/e7/05/c19819d5e3d95294a6f5947fb9b9629efb316b96de511b418c53d245aae6/cycler-0.12.1-py3-none-any.whl", hash = "sha256:85cef7cff222d8644161529808465972e51340599459b8ac3ccbac5a854e0d30", size = 8321, upload-time = "2023-10-07T05:32:16.783Z" },
+]
+
+[[package]]
+name = "cycles"
+version = "0.1.0"
+source = { virtual = "." }
+dependencies = [
+    { name = "gspread" },
+    { name = "matplotlib" },
+    { name = "pandas" },
+    { name = "psutil" },
+    { name = "scipy" },
+    { name = "seaborn" },
+]
+
+[package.metadata]
+requires-dist = [
+    { name = "gspread", specifier = ">=6.2.1" },
+    { name = "matplotlib", specifier = ">=3.10.3" },
+    { name = "pandas", specifier = ">=2.2.3" },
+    { name = "psutil", specifier = ">=7.0.0" },
+    { name = "scipy", specifier = ">=1.15.3" },
+    { name = "seaborn", specifier = ">=0.13.2" },
+]
+
+[[package]]
+name = "fonttools"
+version = "4.58.0"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/9a/cf/4d037663e2a1fe30fddb655d755d76e18624be44ad467c07412c2319ab97/fonttools-4.58.0.tar.gz", hash = "sha256:27423d0606a2c7b336913254bf0b1193ebd471d5f725d665e875c5e88a011a43", size = 3514522, upload-time = "2025-05-10T17:36:35.886Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/72/07/06d01b7239d6632a0984ef29ab496928531862b827cd3aa78309b205850d/fonttools-4.58.0-cp310-cp310-macosx_10_9_universal2.whl", hash = "sha256:0bcaa65cddbc7d32c77bd0af0b41fdd6448bad0e84365ca79cf8923c27b21e46", size = 2731632, upload-time = "2025-05-10T17:34:55.331Z" },
+    { url = "https://files.pythonhosted.org/packages/1d/c7/47d26d48d779b1b084ebc0d9ec07035167992578768237ef553a3eecc8db/fonttools-4.58.0-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:25590272f89e94ab5a292d518c549f3a88e6a34fa1193797b7047dfea111b048", size = 2303941, upload-time = "2025-05-10T17:34:58.624Z" },
+    { url = "https://files.pythonhosted.org/packages/79/2e/ac80c0fea501f1aa93e2b22d72c97a8c0d14239582b7e8c722185a0540a7/fonttools-4.58.0-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:614435e9a87abe18bd7bc7ceeb8029e8f181c571317161e89fa3e6e0a4f20f5d", size = 4712776, upload-time = "2025-05-10T17:35:01.124Z" },
+    { url = "https://files.pythonhosted.org/packages/f2/5c/b41f9c940dc397ecb41765654efc76e06782bfe0783c3e2affc534be181c/fonttools-4.58.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:0154bd86d9a9e880f6e937e4d99c2139a624428dd9852072e12d7a85c79d611e", size = 4743251, upload-time = "2025-05-10T17:35:03.815Z" },
+    { url = "https://files.pythonhosted.org/packages/3d/c4/0d3807d922a788b603a3fff622af53e732464b88baf0049a181a90f9b1c6/fonttools-4.58.0-cp310-cp310-musllinux_1_2_aarch64.whl", hash = "sha256:5b3660df0b02c9cebbf7baf66952c2fd055e43e658aceb92cc95ba19e0a5c8b6", size = 4795635, upload-time = "2025-05-10T17:35:06.134Z" },
+    { url = "https://files.pythonhosted.org/packages/46/74/627bed8e2c7e641c9c572f09970b0980e5513fd29e57b394d4aee2261e30/fonttools-4.58.0-cp310-cp310-musllinux_1_2_x86_64.whl", hash = "sha256:c43b7f1d0b818427bb1cd20903d1168271abdcde10eb6247b1995c4e1ed63907", size = 4904720, upload-time = "2025-05-10T17:35:09.015Z" },
+    { url = "https://files.pythonhosted.org/packages/f9/f2/7e5d082a98eb61fc0c3055e8a0e061a1eb9fc2d93f0661854bf6cb63c519/fonttools-4.58.0-cp310-cp310-win32.whl", hash = "sha256:5450f40c385cdfa21133245f57b9cf8ce45018a04630a98de61eed8da14b8325", size = 2188180, upload-time = "2025-05-10T17:35:11.494Z" },
+    { url = "https://files.pythonhosted.org/packages/00/33/ffd914e3c3a585003d770457188c8eaf7266b7a1cceb6d234ab543a9f958/fonttools-4.58.0-cp310-cp310-win_amd64.whl", hash = "sha256:c0553431696eacafee9aefe94dc3c2bf5d658fbdc7fdba5b341c588f935471c6", size = 2233120, upload-time = "2025-05-10T17:35:13.896Z" },
+    { url = "https://files.pythonhosted.org/packages/76/2e/9b9bd943872a50cb182382f8f4a99af92d76e800603d5f73e4343fdce61a/fonttools-4.58.0-cp311-cp311-macosx_10_9_universal2.whl", hash = "sha256:9345b1bb994476d6034996b31891c0c728c1059c05daa59f9ab57d2a4dce0f84", size = 2751920, upload-time = "2025-05-10T17:35:16.487Z" },
+    { url = "https://files.pythonhosted.org/packages/9b/8c/e8d6375da893125f610826c2e30e6d2597dfb8dad256f8ff5a54f3089fda/fonttools-4.58.0-cp311-cp311-macosx_10_9_x86_64.whl", hash = "sha256:1d93119ace1e2d39ff1340deb71097932f72b21c054bd3da727a3859825e24e5", size = 2313957, upload-time = "2025-05-10T17:35:18.906Z" },
+    { url = "https://files.pythonhosted.org/packages/4f/1b/a29cb00c8c20164b24f88780e298fafd0bbfb25cf8bc7b10c4b69331ad5d/fonttools-4.58.0-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:79c9e4f01bb04f19df272ae35314eb6349fdb2e9497a163cd22a21be999694bd", size = 4913808, upload-time = "2025-05-10T17:35:21.394Z" },
+    { url = "https://files.pythonhosted.org/packages/d1/ab/9b9507b65b15190cbfe1ccd3c08067d79268d8312ef20948b16d9f5aa905/fonttools-4.58.0-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:62ecda1465d38248aaf9bee1c17a21cf0b16aef7d121d7d303dbb320a6fd49c2", size = 4935876, upload-time = "2025-05-10T17:35:23.849Z" },
+    { url = "https://files.pythonhosted.org/packages/15/e4/1395853bc775b0ab06a1c61cf261779afda7baff3f65cf1197bbd21aa149/fonttools-4.58.0-cp311-cp311-musllinux_1_2_aarch64.whl", hash = "sha256:29d0499bff12a26733c05c1bfd07e68465158201624b2fba4a40b23d96c43f94", size = 4974798, upload-time = "2025-05-10T17:35:26.189Z" },
+    { url = "https://files.pythonhosted.org/packages/3c/b9/0358368ef5462f4653a198207b29885bee8d5e23c870f6125450ed88e693/fonttools-4.58.0-cp311-cp311-musllinux_1_2_x86_64.whl", hash = "sha256:1871abdb0af582e2d96cc12d88889e3bfa796928f491ec14d34a2e58ca298c7e", size = 5093560, upload-time = "2025-05-10T17:35:28.577Z" },
+    { url = "https://files.pythonhosted.org/packages/11/00/f64bc3659980c41eccf2c371e62eb15b40858f02a41a0e9c6258ef094388/fonttools-4.58.0-cp311-cp311-win32.whl", hash = "sha256:e292485d70402093eb94f6ab7669221743838b8bd4c1f45c84ca76b63338e7bf", size = 2186330, upload-time = "2025-05-10T17:35:31.733Z" },
+    { url = "https://files.pythonhosted.org/packages/c8/a0/0287be13a1ec7733abf292ffbd76417cea78752d4ce10fecf92d8b1252d6/fonttools-4.58.0-cp311-cp311-win_amd64.whl", hash = "sha256:6df3755fcf9ad70a74ad3134bd5c9738f73c9bb701a304b1c809877b11fe701c", size = 2234687, upload-time = "2025-05-10T17:35:34.015Z" },
+    { url = "https://files.pythonhosted.org/packages/6a/4e/1c6b35ec7c04d739df4cf5aace4b7ec284d6af2533a65de21972e2f237d9/fonttools-4.58.0-cp312-cp312-macosx_10_13_universal2.whl", hash = "sha256:aa8316798f982c751d71f0025b372151ea36405733b62d0d94d5e7b8dd674fa6", size = 2737502, upload-time = "2025-05-10T17:35:36.436Z" },
+    { url = "https://files.pythonhosted.org/packages/fc/72/c6fcafa3c9ed2b69991ae25a1ba7a3fec8bf74928a96e8229c37faa8eda2/fonttools-4.58.0-cp312-cp312-macosx_10_13_x86_64.whl", hash = "sha256:c6db489511e867633b859b11aefe1b7c0d90281c5bdb903413edbb2ba77b97f1", size = 2307214, upload-time = "2025-05-10T17:35:38.939Z" },
+    { url = "https://files.pythonhosted.org/packages/52/11/1015cedc9878da6d8d1758049749eef857b693e5828d477287a959c8650f/fonttools-4.58.0-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:107bdb2dacb1f627db3c4b77fb16d065a10fe88978d02b4fc327b9ecf8a62060", size = 4811136, upload-time = "2025-05-10T17:35:41.491Z" },
+    { url = "https://files.pythonhosted.org/packages/32/b9/6a1bc1af6ec17eead5d32e87075e22d0dab001eace0b5a1542d38c6a9483/fonttools-4.58.0-cp312-cp312-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:ba7212068ab20f1128a0475f169068ba8e5b6e35a39ba1980b9f53f6ac9720ac", size = 4876598, upload-time = "2025-05-10T17:35:43.986Z" },
+    { url = "https://files.pythonhosted.org/packages/d8/46/b14584c7ea65ad1609fb9632251016cda8a2cd66b15606753b9f888d3677/fonttools-4.58.0-cp312-cp312-musllinux_1_2_aarch64.whl", hash = "sha256:f95ea3b6a3b9962da3c82db73f46d6a6845a6c3f3f968f5293b3ac1864e771c2", size = 4872256, upload-time = "2025-05-10T17:35:46.617Z" },
+    { url = "https://files.pythonhosted.org/packages/05/78/b2105a7812ca4ef9bf180cd741c82f4522316c652ce2a56f788e2eb54b62/fonttools-4.58.0-cp312-cp312-musllinux_1_2_x86_64.whl", hash = "sha256:874f1225cc4ccfeac32009887f722d7f8b107ca5e867dcee067597eef9d4c80b", size = 5028710, upload-time = "2025-05-10T17:35:49.227Z" },
+    { url = "https://files.pythonhosted.org/packages/8c/a9/a38c85ffd30d1f2c7a5460c8abfd1aa66e00c198df3ff0b08117f5c6fcd9/fonttools-4.58.0-cp312-cp312-win32.whl", hash = "sha256:5f3cde64ec99c43260e2e6c4fa70dfb0a5e2c1c1d27a4f4fe4618c16f6c9ff71", size = 2173593, upload-time = "2025-05-10T17:35:51.226Z" },
+    { url = "https://files.pythonhosted.org/packages/66/48/29752962a74b7ed95da976b5a968bba1fe611a4a7e50b9fefa345e6e7025/fonttools-4.58.0-cp312-cp312-win_amd64.whl", hash = "sha256:2aee08e2818de45067109a207cbd1b3072939f77751ef05904d506111df5d824", size = 2223230, upload-time = "2025-05-10T17:35:53.653Z" },
+    { url = "https://files.pythonhosted.org/packages/0c/d7/d77cae11c445916d767cace93ba8283b3f360197d95d7470b90a9e984e10/fonttools-4.58.0-cp313-cp313-macosx_10_13_universal2.whl", hash = "sha256:4809790f2371d8a08e59e1ce2b734c954cf09742e75642d7f4c46cfdac488fdd", size = 2728320, upload-time = "2025-05-10T17:35:56.455Z" },
+    { url = "https://files.pythonhosted.org/packages/77/48/7d8b3c519ef4b48081d40310262224a38785e39a8610ccb92a229a6f085d/fonttools-4.58.0-cp313-cp313-macosx_10_13_x86_64.whl", hash = "sha256:b00f240280f204ce4546b05ff3515bf8ff47a9cae914c718490025ea2bb9b324", size = 2302570, upload-time = "2025-05-10T17:35:58.794Z" },
+    { url = "https://files.pythonhosted.org/packages/2c/48/156b83eb8fb7261056e448bfda1b495b90e761b28ec23cee10e3e19f1967/fonttools-4.58.0-cp313-cp313-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:5a62015ad463e1925544e9159dd6eefe33ebfb80938d5ab15d8b1c4b354ff47b", size = 4790066, upload-time = "2025-05-10T17:36:01.174Z" },
+    { url = "https://files.pythonhosted.org/packages/60/49/aaecb1b3cea2b9b9c7cea6240d6bc8090feb5489a6fbf93cb68003be979b/fonttools-4.58.0-cp313-cp313-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:2ceef6f6ab58061a811967e3e32e630747fcb823dcc33a9a2c80e2d0d17cb292", size = 4861076, upload-time = "2025-05-10T17:36:03.663Z" },
+    { url = "https://files.pythonhosted.org/packages/dc/c8/97cbb41bee81ea9daf6109e0f3f70a274a3c69418e5ac6b0193f5dacf506/fonttools-4.58.0-cp313-cp313-musllinux_1_2_aarch64.whl", hash = "sha256:c7be21ac52370b515cdbdd0f400803fd29432a4fa4ddb4244ac8b322e54f36c0", size = 4858394, upload-time = "2025-05-10T17:36:06.087Z" },
+    { url = "https://files.pythonhosted.org/packages/4d/23/c2c231457361f869a7d7374a557208e303b469d48a4a697c0fb249733ea1/fonttools-4.58.0-cp313-cp313-musllinux_1_2_x86_64.whl", hash = "sha256:85836be4c3c4aacf6fcb7a6f263896d0e9ce431da9fa6fe9213d70f221f131c9", size = 5002160, upload-time = "2025-05-10T17:36:08.178Z" },
+    { url = "https://files.pythonhosted.org/packages/a9/e0/c2262f941a43b810c5c192db94b5d1ce8eda91bec2757f7e2416398f4072/fonttools-4.58.0-cp313-cp313-win32.whl", hash = "sha256:2b32b7130277bd742cb8c4379a6a303963597d22adea77a940343f3eadbcaa4c", size = 2171919, upload-time = "2025-05-10T17:36:10.644Z" },
+    { url = "https://files.pythonhosted.org/packages/8f/ee/e4aa7bb4ce510ad57a808d321df1bbed1eeb6e1dfb20aaee1a5d9c076849/fonttools-4.58.0-cp313-cp313-win_amd64.whl", hash = "sha256:75e68ee2ec9aaa173cf5e33f243da1d51d653d5e25090f2722bc644a78db0f1a", size = 2222972, upload-time = "2025-05-10T17:36:12.495Z" },
+    { url = "https://files.pythonhosted.org/packages/9b/1f/4417c26e26a1feab85a27e927f7a73d8aabc84544be8ba108ce4aa90eb1e/fonttools-4.58.0-py3-none-any.whl", hash = "sha256:c96c36880be2268be409df7b08c5b5dacac1827083461a6bc2cb07b8cbcec1d7", size = 1111440, upload-time = "2025-05-10T17:36:33.607Z" },
+]
+
+[[package]]
+name = "google-auth"
+version = "2.40.1"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "cachetools" },
+    { name = "pyasn1-modules" },
+    { name = "rsa" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/94/a5/38c21d0e731bb716cffcf987bd9a3555cb95877ab4b616cfb96939933f20/google_auth-2.40.1.tar.gz", hash = "sha256:58f0e8416a9814c1d86c9b7f6acf6816b51aba167b2c76821965271bac275540", size = 280975, upload-time = "2025-05-07T01:04:55.3Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/a1/b1/1272c6e80847ba5349f5ccb7574596393d1e222543f5003cb810865c3575/google_auth-2.40.1-py2.py3-none-any.whl", hash = "sha256:ed4cae4f5c46b41bae1d19c036e06f6c371926e97b19e816fc854eff811974ee", size = 216101, upload-time = "2025-05-07T01:04:53.612Z" },
+]
+
+[[package]]
+name = "google-auth-oauthlib"
+version = "1.2.2"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "google-auth" },
+    { name = "requests-oauthlib" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/fb/87/e10bf24f7bcffc1421b84d6f9c3377c30ec305d082cd737ddaa6d8f77f7c/google_auth_oauthlib-1.2.2.tar.gz", hash = "sha256:11046fb8d3348b296302dd939ace8af0a724042e8029c1b872d87fabc9f41684", size = 20955, upload-time = "2025-04-22T16:40:29.172Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/ac/84/40ee070be95771acd2f4418981edb834979424565c3eec3cd88b6aa09d24/google_auth_oauthlib-1.2.2-py3-none-any.whl", hash = "sha256:fd619506f4b3908b5df17b65f39ca8d66ea56986e5472eb5978fd8f3786f00a2", size = 19072, upload-time = "2025-04-22T16:40:28.174Z" },
+]
+
+[[package]]
+name = "gspread"
+version = "6.2.1"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "google-auth" },
+    { name = "google-auth-oauthlib" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/91/83/42d1d813822ed016d77aabadc99b09de3b5bd68532fd6bae23fd62347c41/gspread-6.2.1.tar.gz", hash = "sha256:2c7c99f7c32ebea6ec0d36f2d5cbe8a2be5e8f2a48bde87ad1ea203eff32bd03", size = 82590, upload-time = "2025-05-14T15:56:25.254Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/27/76/563fb20dedd0e12794d9a12cfe0198458cc0501fdc7b034eee2166d035d5/gspread-6.2.1-py3-none-any.whl", hash = "sha256:6d4ec9f1c23ae3c704a9219026dac01f2b328ac70b96f1495055d453c4c184db", size = 59977, upload-time = "2025-05-14T15:56:24.014Z" },
+]
+
+[[package]]
+name = "idna"
+version = "3.10"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/f1/70/7703c29685631f5a7590aa73f1f1d3fa9a380e654b86af429e0934a32f7d/idna-3.10.tar.gz", hash = "sha256:12f65c9b470abda6dc35cf8e63cc574b1c52b11df2c86030af0ac09b01b13ea9", size = 190490, upload-time = "2024-09-15T18:07:39.745Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/76/c6/c88e154df9c4e1a2a66ccf0005a88dfb2650c1dffb6f5ce603dfbd452ce3/idna-3.10-py3-none-any.whl", hash = "sha256:946d195a0d259cbba61165e88e65941f16e9b36ea6ddb97f00452bae8b1287d3", size = 70442, upload-time = "2024-09-15T18:07:37.964Z" },
+]
+
+[[package]]
+name = "kiwisolver"
+version = "1.4.8"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/82/59/7c91426a8ac292e1cdd53a63b6d9439abd573c875c3f92c146767dd33faf/kiwisolver-1.4.8.tar.gz", hash = "sha256:23d5f023bdc8c7e54eb65f03ca5d5bb25b601eac4d7f1a042888a1f45237987e", size = 97538, upload-time = "2024-12-24T18:30:51.519Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/47/5f/4d8e9e852d98ecd26cdf8eaf7ed8bc33174033bba5e07001b289f07308fd/kiwisolver-1.4.8-cp310-cp310-macosx_10_9_universal2.whl", hash = "sha256:88c6f252f6816a73b1f8c904f7bbe02fd67c09a69f7cb8a0eecdbf5ce78e63db", size = 124623, upload-time = "2024-12-24T18:28:17.687Z" },
+    { url = "https://files.pythonhosted.org/packages/1d/70/7f5af2a18a76fe92ea14675f8bd88ce53ee79e37900fa5f1a1d8e0b42998/kiwisolver-1.4.8-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:c72941acb7b67138f35b879bbe85be0f6c6a70cab78fe3ef6db9c024d9223e5b", size = 66720, upload-time = "2024-12-24T18:28:19.158Z" },
+    { url = "https://files.pythonhosted.org/packages/c6/13/e15f804a142353aefd089fadc8f1d985561a15358c97aca27b0979cb0785/kiwisolver-1.4.8-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:ce2cf1e5688edcb727fdf7cd1bbd0b6416758996826a8be1d958f91880d0809d", size = 65413, upload-time = "2024-12-24T18:28:20.064Z" },
+    { url = "https://files.pythonhosted.org/packages/ce/6d/67d36c4d2054e83fb875c6b59d0809d5c530de8148846b1370475eeeece9/kiwisolver-1.4.8-cp310-cp310-manylinux_2_12_i686.manylinux2010_i686.whl", hash = "sha256:c8bf637892dc6e6aad2bc6d4d69d08764166e5e3f69d469e55427b6ac001b19d", size = 1650826, upload-time = "2024-12-24T18:28:21.203Z" },
+    { url = "https://files.pythonhosted.org/packages/de/c6/7b9bb8044e150d4d1558423a1568e4f227193662a02231064e3824f37e0a/kiwisolver-1.4.8-cp310-cp310-manylinux_2_12_x86_64.manylinux2010_x86_64.whl", hash = "sha256:034d2c891f76bd3edbdb3ea11140d8510dca675443da7304205a2eaa45d8334c", size = 1628231, upload-time = "2024-12-24T18:28:23.851Z" },
+    { url = "https://files.pythonhosted.org/packages/b6/38/ad10d437563063eaaedbe2c3540a71101fc7fb07a7e71f855e93ea4de605/kiwisolver-1.4.8-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:d47b28d1dfe0793d5e96bce90835e17edf9a499b53969b03c6c47ea5985844c3", size = 1408938, upload-time = "2024-12-24T18:28:26.687Z" },
+    { url = "https://files.pythonhosted.org/packages/52/ce/c0106b3bd7f9e665c5f5bc1e07cc95b5dabd4e08e3dad42dbe2faad467e7/kiwisolver-1.4.8-cp310-cp310-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:eb158fe28ca0c29f2260cca8c43005329ad58452c36f0edf298204de32a9a3ed", size = 1422799, upload-time = "2024-12-24T18:28:30.538Z" },
+    { url = "https://files.pythonhosted.org/packages/d0/87/efb704b1d75dc9758087ba374c0f23d3254505edaedd09cf9d247f7878b9/kiwisolver-1.4.8-cp310-cp310-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:d5536185fce131780ebd809f8e623bf4030ce1b161353166c49a3c74c287897f", size = 1354362, upload-time = "2024-12-24T18:28:32.943Z" },
+    { url = "https://files.pythonhosted.org/packages/eb/b3/fd760dc214ec9a8f208b99e42e8f0130ff4b384eca8b29dd0efc62052176/kiwisolver-1.4.8-cp310-cp310-musllinux_1_2_aarch64.whl", hash = "sha256:369b75d40abedc1da2c1f4de13f3482cb99e3237b38726710f4a793432b1c5ff", size = 2222695, upload-time = "2024-12-24T18:28:35.641Z" },
+    { url = "https://files.pythonhosted.org/packages/a2/09/a27fb36cca3fc01700687cc45dae7a6a5f8eeb5f657b9f710f788748e10d/kiwisolver-1.4.8-cp310-cp310-musllinux_1_2_i686.whl", hash = "sha256:641f2ddf9358c80faa22e22eb4c9f54bd3f0e442e038728f500e3b978d00aa7d", size = 2370802, upload-time = "2024-12-24T18:28:38.357Z" },
+    { url = "https://files.pythonhosted.org/packages/3d/c3/ba0a0346db35fe4dc1f2f2cf8b99362fbb922d7562e5f911f7ce7a7b60fa/kiwisolver-1.4.8-cp310-cp310-musllinux_1_2_ppc64le.whl", hash = "sha256:d561d2d8883e0819445cfe58d7ddd673e4015c3c57261d7bdcd3710d0d14005c", size = 2334646, upload-time = "2024-12-24T18:28:40.941Z" },
+    { url = "https://files.pythonhosted.org/packages/41/52/942cf69e562f5ed253ac67d5c92a693745f0bed3c81f49fc0cbebe4d6b00/kiwisolver-1.4.8-cp310-cp310-musllinux_1_2_s390x.whl", hash = "sha256:1732e065704b47c9afca7ffa272f845300a4eb959276bf6970dc07265e73b605", size = 2467260, upload-time = "2024-12-24T18:28:42.273Z" },
+    { url = "https://files.pythonhosted.org/packages/32/26/2d9668f30d8a494b0411d4d7d4ea1345ba12deb6a75274d58dd6ea01e951/kiwisolver-1.4.8-cp310-cp310-musllinux_1_2_x86_64.whl", hash = "sha256:bcb1ebc3547619c3b58a39e2448af089ea2ef44b37988caf432447374941574e", size = 2288633, upload-time = "2024-12-24T18:28:44.87Z" },
+    { url = "https://files.pythonhosted.org/packages/98/99/0dd05071654aa44fe5d5e350729961e7bb535372935a45ac89a8924316e6/kiwisolver-1.4.8-cp310-cp310-win_amd64.whl", hash = "sha256:89c107041f7b27844179ea9c85d6da275aa55ecf28413e87624d033cf1f6b751", size = 71885, upload-time = "2024-12-24T18:28:47.346Z" },
+    { url = "https://files.pythonhosted.org/packages/6c/fc/822e532262a97442989335394d441cd1d0448c2e46d26d3e04efca84df22/kiwisolver-1.4.8-cp310-cp310-win_arm64.whl", hash = "sha256:b5773efa2be9eb9fcf5415ea3ab70fc785d598729fd6057bea38d539ead28271", size = 65175, upload-time = "2024-12-24T18:28:49.651Z" },
+    { url = "https://files.pythonhosted.org/packages/da/ed/c913ee28936c371418cb167b128066ffb20bbf37771eecc2c97edf8a6e4c/kiwisolver-1.4.8-cp311-cp311-macosx_10_9_universal2.whl", hash = "sha256:a4d3601908c560bdf880f07d94f31d734afd1bb71e96585cace0e38ef44c6d84", size = 124635, upload-time = "2024-12-24T18:28:51.826Z" },
+    { url = "https://files.pythonhosted.org/packages/4c/45/4a7f896f7467aaf5f56ef093d1f329346f3b594e77c6a3c327b2d415f521/kiwisolver-1.4.8-cp311-cp311-macosx_10_9_x86_64.whl", hash = "sha256:856b269c4d28a5c0d5e6c1955ec36ebfd1651ac00e1ce0afa3e28da95293b561", size = 66717, upload-time = "2024-12-24T18:28:54.256Z" },
+    { url = "https://files.pythonhosted.org/packages/5f/b4/c12b3ac0852a3a68f94598d4c8d569f55361beef6159dce4e7b624160da2/kiwisolver-1.4.8-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:c2b9a96e0f326205af81a15718a9073328df1173a2619a68553decb7097fd5d7", size = 65413, upload-time = "2024-12-24T18:28:55.184Z" },
+    { url = "https://files.pythonhosted.org/packages/a9/98/1df4089b1ed23d83d410adfdc5947245c753bddfbe06541c4aae330e9e70/kiwisolver-1.4.8-cp311-cp311-manylinux_2_12_i686.manylinux2010_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:c5020c83e8553f770cb3b5fc13faac40f17e0b205bd237aebd21d53d733adb03", size = 1343994, upload-time = "2024-12-24T18:28:57.493Z" },
+    { url = "https://files.pythonhosted.org/packages/8d/bf/b4b169b050c8421a7c53ea1ea74e4ef9c335ee9013216c558a047f162d20/kiwisolver-1.4.8-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:dace81d28c787956bfbfbbfd72fdcef014f37d9b48830829e488fdb32b49d954", size = 1434804, upload-time = "2024-12-24T18:29:00.077Z" },
+    { url = "https://files.pythonhosted.org/packages/66/5a/e13bd341fbcf73325ea60fdc8af752addf75c5079867af2e04cc41f34434/kiwisolver-1.4.8-cp311-cp311-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:11e1022b524bd48ae56c9b4f9296bce77e15a2e42a502cceba602f804b32bb79", size = 1450690, upload-time = "2024-12-24T18:29:01.401Z" },
+    { url = "https://files.pythonhosted.org/packages/9b/4f/5955dcb376ba4a830384cc6fab7d7547bd6759fe75a09564910e9e3bb8ea/kiwisolver-1.4.8-cp311-cp311-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:3b9b4d2892fefc886f30301cdd80debd8bb01ecdf165a449eb6e78f79f0fabd6", size = 1376839, upload-time = "2024-12-24T18:29:02.685Z" },
+    { url = "https://files.pythonhosted.org/packages/3a/97/5edbed69a9d0caa2e4aa616ae7df8127e10f6586940aa683a496c2c280b9/kiwisolver-1.4.8-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:3a96c0e790ee875d65e340ab383700e2b4891677b7fcd30a699146f9384a2bb0", size = 1435109, upload-time = "2024-12-24T18:29:04.113Z" },
+    { url = "https://files.pythonhosted.org/packages/13/fc/e756382cb64e556af6c1809a1bbb22c141bbc2445049f2da06b420fe52bf/kiwisolver-1.4.8-cp311-cp311-musllinux_1_2_aarch64.whl", hash = "sha256:23454ff084b07ac54ca8be535f4174170c1094a4cff78fbae4f73a4bcc0d4dab", size = 2245269, upload-time = "2024-12-24T18:29:05.488Z" },
+    { url = "https://files.pythonhosted.org/packages/76/15/e59e45829d7f41c776d138245cabae6515cb4eb44b418f6d4109c478b481/kiwisolver-1.4.8-cp311-cp311-musllinux_1_2_i686.whl", hash = "sha256:87b287251ad6488e95b4f0b4a79a6d04d3ea35fde6340eb38fbd1ca9cd35bbbc", size = 2393468, upload-time = "2024-12-24T18:29:06.79Z" },
+    { url = "https://files.pythonhosted.org/packages/e9/39/483558c2a913ab8384d6e4b66a932406f87c95a6080112433da5ed668559/kiwisolver-1.4.8-cp311-cp311-musllinux_1_2_ppc64le.whl", hash = "sha256:b21dbe165081142b1232a240fc6383fd32cdd877ca6cc89eab93e5f5883e1c25", size = 2355394, upload-time = "2024-12-24T18:29:08.24Z" },
+    { url = "https://files.pythonhosted.org/packages/01/aa/efad1fbca6570a161d29224f14b082960c7e08268a133fe5dc0f6906820e/kiwisolver-1.4.8-cp311-cp311-musllinux_1_2_s390x.whl", hash = "sha256:768cade2c2df13db52475bd28d3a3fac8c9eff04b0e9e2fda0f3760f20b3f7fc", size = 2490901, upload-time = "2024-12-24T18:29:09.653Z" },
+    { url = "https://files.pythonhosted.org/packages/c9/4f/15988966ba46bcd5ab9d0c8296914436720dd67fca689ae1a75b4ec1c72f/kiwisolver-1.4.8-cp311-cp311-musllinux_1_2_x86_64.whl", hash = "sha256:d47cfb2650f0e103d4bf68b0b5804c68da97272c84bb12850d877a95c056bd67", size = 2312306, upload-time = "2024-12-24T18:29:12.644Z" },
+    { url = "https://files.pythonhosted.org/packages/2d/27/bdf1c769c83f74d98cbc34483a972f221440703054894a37d174fba8aa68/kiwisolver-1.4.8-cp311-cp311-win_amd64.whl", hash = "sha256:ed33ca2002a779a2e20eeb06aea7721b6e47f2d4b8a8ece979d8ba9e2a167e34", size = 71966, upload-time = "2024-12-24T18:29:14.089Z" },
+    { url = "https://files.pythonhosted.org/packages/4a/c9/9642ea855604aeb2968a8e145fc662edf61db7632ad2e4fb92424be6b6c0/kiwisolver-1.4.8-cp311-cp311-win_arm64.whl", hash = "sha256:16523b40aab60426ffdebe33ac374457cf62863e330a90a0383639ce14bf44b2", size = 65311, upload-time = "2024-12-24T18:29:15.892Z" },
+    { url = "https://files.pythonhosted.org/packages/fc/aa/cea685c4ab647f349c3bc92d2daf7ae34c8e8cf405a6dcd3a497f58a2ac3/kiwisolver-1.4.8-cp312-cp312-macosx_10_13_universal2.whl", hash = "sha256:d6af5e8815fd02997cb6ad9bbed0ee1e60014438ee1a5c2444c96f87b8843502", size = 124152, upload-time = "2024-12-24T18:29:16.85Z" },
+    { url = "https://files.pythonhosted.org/packages/c5/0b/8db6d2e2452d60d5ebc4ce4b204feeb16176a851fd42462f66ade6808084/kiwisolver-1.4.8-cp312-cp312-macosx_10_13_x86_64.whl", hash = "sha256:bade438f86e21d91e0cf5dd7c0ed00cda0f77c8c1616bd83f9fc157fa6760d31", size = 66555, upload-time = "2024-12-24T18:29:19.146Z" },
+    { url = "https://files.pythonhosted.org/packages/60/26/d6a0db6785dd35d3ba5bf2b2df0aedc5af089962c6eb2cbf67a15b81369e/kiwisolver-1.4.8-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:b83dc6769ddbc57613280118fb4ce3cd08899cc3369f7d0e0fab518a7cf37fdb", size = 65067, upload-time = "2024-12-24T18:29:20.096Z" },
+    { url = "https://files.pythonhosted.org/packages/c9/ed/1d97f7e3561e09757a196231edccc1bcf59d55ddccefa2afc9c615abd8e0/kiwisolver-1.4.8-cp312-cp312-manylinux_2_12_i686.manylinux2010_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:111793b232842991be367ed828076b03d96202c19221b5ebab421ce8bcad016f", size = 1378443, upload-time = "2024-12-24T18:29:22.843Z" },
+    { url = "https://files.pythonhosted.org/packages/29/61/39d30b99954e6b46f760e6289c12fede2ab96a254c443639052d1b573fbc/kiwisolver-1.4.8-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:257af1622860e51b1a9d0ce387bf5c2c4f36a90594cb9514f55b074bcc787cfc", size = 1472728, upload-time = "2024-12-24T18:29:24.463Z" },
+    { url = "https://files.pythonhosted.org/packages/0c/3e/804163b932f7603ef256e4a715e5843a9600802bb23a68b4e08c8c0ff61d/kiwisolver-1.4.8-cp312-cp312-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:69b5637c3f316cab1ec1c9a12b8c5f4750a4c4b71af9157645bf32830e39c03a", size = 1478388, upload-time = "2024-12-24T18:29:25.776Z" },
+    { url = "https://files.pythonhosted.org/packages/8a/9e/60eaa75169a154700be74f875a4d9961b11ba048bef315fbe89cb6999056/kiwisolver-1.4.8-cp312-cp312-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:782bb86f245ec18009890e7cb8d13a5ef54dcf2ebe18ed65f795e635a96a1c6a", size = 1413849, upload-time = "2024-12-24T18:29:27.202Z" },
+    { url = "https://files.pythonhosted.org/packages/bc/b3/9458adb9472e61a998c8c4d95cfdfec91c73c53a375b30b1428310f923e4/kiwisolver-1.4.8-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:cc978a80a0db3a66d25767b03688f1147a69e6237175c0f4ffffaaedf744055a", size = 1475533, upload-time = "2024-12-24T18:29:28.638Z" },
+    { url = "https://files.pythonhosted.org/packages/e4/7a/0a42d9571e35798de80aef4bb43a9b672aa7f8e58643d7bd1950398ffb0a/kiwisolver-1.4.8-cp312-cp312-musllinux_1_2_aarch64.whl", hash = "sha256:36dbbfd34838500a31f52c9786990d00150860e46cd5041386f217101350f0d3", size = 2268898, upload-time = "2024-12-24T18:29:30.368Z" },
+    { url = "https://files.pythonhosted.org/packages/d9/07/1255dc8d80271400126ed8db35a1795b1a2c098ac3a72645075d06fe5c5d/kiwisolver-1.4.8-cp312-cp312-musllinux_1_2_i686.whl", hash = "sha256:eaa973f1e05131de5ff3569bbba7f5fd07ea0595d3870ed4a526d486fe57fa1b", size = 2425605, upload-time = "2024-12-24T18:29:33.151Z" },
+    { url = "https://files.pythonhosted.org/packages/84/df/5a3b4cf13780ef6f6942df67b138b03b7e79e9f1f08f57c49957d5867f6e/kiwisolver-1.4.8-cp312-cp312-musllinux_1_2_ppc64le.whl", hash = "sha256:a66f60f8d0c87ab7f59b6fb80e642ebb29fec354a4dfad687ca4092ae69d04f4", size = 2375801, upload-time = "2024-12-24T18:29:34.584Z" },
+    { url = "https://files.pythonhosted.org/packages/8f/10/2348d068e8b0f635c8c86892788dac7a6b5c0cb12356620ab575775aad89/kiwisolver-1.4.8-cp312-cp312-musllinux_1_2_s390x.whl", hash = "sha256:858416b7fb777a53f0c59ca08190ce24e9abbd3cffa18886a5781b8e3e26f65d", size = 2520077, upload-time = "2024-12-24T18:29:36.138Z" },
+    { url = "https://files.pythonhosted.org/packages/32/d8/014b89fee5d4dce157d814303b0fce4d31385a2af4c41fed194b173b81ac/kiwisolver-1.4.8-cp312-cp312-musllinux_1_2_x86_64.whl", hash = "sha256:085940635c62697391baafaaeabdf3dd7a6c3643577dde337f4d66eba021b2b8", size = 2338410, upload-time = "2024-12-24T18:29:39.991Z" },
+    { url = "https://files.pythonhosted.org/packages/bd/72/dfff0cc97f2a0776e1c9eb5bef1ddfd45f46246c6533b0191887a427bca5/kiwisolver-1.4.8-cp312-cp312-win_amd64.whl", hash = "sha256:01c3d31902c7db5fb6182832713d3b4122ad9317c2c5877d0539227d96bb2e50", size = 71853, upload-time = "2024-12-24T18:29:42.006Z" },
+    { url = "https://files.pythonhosted.org/packages/dc/85/220d13d914485c0948a00f0b9eb419efaf6da81b7d72e88ce2391f7aed8d/kiwisolver-1.4.8-cp312-cp312-win_arm64.whl", hash = "sha256:a3c44cb68861de93f0c4a8175fbaa691f0aa22550c331fefef02b618a9dcb476", size = 65424, upload-time = "2024-12-24T18:29:44.38Z" },
+    { url = "https://files.pythonhosted.org/packages/79/b3/e62464a652f4f8cd9006e13d07abad844a47df1e6537f73ddfbf1bc997ec/kiwisolver-1.4.8-cp313-cp313-macosx_10_13_universal2.whl", hash = "sha256:1c8ceb754339793c24aee1c9fb2485b5b1f5bb1c2c214ff13368431e51fc9a09", size = 124156, upload-time = "2024-12-24T18:29:45.368Z" },
+    { url = "https://files.pythonhosted.org/packages/8d/2d/f13d06998b546a2ad4f48607a146e045bbe48030774de29f90bdc573df15/kiwisolver-1.4.8-cp313-cp313-macosx_10_13_x86_64.whl", hash = "sha256:54a62808ac74b5e55a04a408cda6156f986cefbcf0ada13572696b507cc92fa1", size = 66555, upload-time = "2024-12-24T18:29:46.37Z" },
+    { url = "https://files.pythonhosted.org/packages/59/e3/b8bd14b0a54998a9fd1e8da591c60998dc003618cb19a3f94cb233ec1511/kiwisolver-1.4.8-cp313-cp313-macosx_11_0_arm64.whl", hash = "sha256:68269e60ee4929893aad82666821aaacbd455284124817af45c11e50a4b42e3c", size = 65071, upload-time = "2024-12-24T18:29:47.333Z" },
+    { url = "https://files.pythonhosted.org/packages/f0/1c/6c86f6d85ffe4d0ce04228d976f00674f1df5dc893bf2dd4f1928748f187/kiwisolver-1.4.8-cp313-cp313-manylinux_2_12_i686.manylinux2010_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:34d142fba9c464bc3bbfeff15c96eab0e7310343d6aefb62a79d51421fcc5f1b", size = 1378053, upload-time = "2024-12-24T18:29:49.636Z" },
+    { url = "https://files.pythonhosted.org/packages/4e/b9/1c6e9f6dcb103ac5cf87cb695845f5fa71379021500153566d8a8a9fc291/kiwisolver-1.4.8-cp313-cp313-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:3ddc373e0eef45b59197de815b1b28ef89ae3955e7722cc9710fb91cd77b7f47", size = 1472278, upload-time = "2024-12-24T18:29:51.164Z" },
+    { url = "https://files.pythonhosted.org/packages/ee/81/aca1eb176de671f8bda479b11acdc42c132b61a2ac861c883907dde6debb/kiwisolver-1.4.8-cp313-cp313-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:77e6f57a20b9bd4e1e2cedda4d0b986ebd0216236f0106e55c28aea3d3d69b16", size = 1478139, upload-time = "2024-12-24T18:29:52.594Z" },
+    { url = "https://files.pythonhosted.org/packages/49/f4/e081522473671c97b2687d380e9e4c26f748a86363ce5af48b4a28e48d06/kiwisolver-1.4.8-cp313-cp313-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:08e77738ed7538f036cd1170cbed942ef749137b1311fa2bbe2a7fda2f6bf3cc", size = 1413517, upload-time = "2024-12-24T18:29:53.941Z" },
+    { url = "https://files.pythonhosted.org/packages/8f/e9/6a7d025d8da8c4931522922cd706105aa32b3291d1add8c5427cdcd66e63/kiwisolver-1.4.8-cp313-cp313-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:a5ce1e481a74b44dd5e92ff03ea0cb371ae7a0268318e202be06c8f04f4f1246", size = 1474952, upload-time = "2024-12-24T18:29:56.523Z" },
+    { url = "https://files.pythonhosted.org/packages/82/13/13fa685ae167bee5d94b415991c4fc7bb0a1b6ebea6e753a87044b209678/kiwisolver-1.4.8-cp313-cp313-musllinux_1_2_aarch64.whl", hash = "sha256:fc2ace710ba7c1dfd1a3b42530b62b9ceed115f19a1656adefce7b1782a37794", size = 2269132, upload-time = "2024-12-24T18:29:57.989Z" },
+    { url = "https://files.pythonhosted.org/packages/ef/92/bb7c9395489b99a6cb41d502d3686bac692586db2045adc19e45ee64ed23/kiwisolver-1.4.8-cp313-cp313-musllinux_1_2_i686.whl", hash = "sha256:3452046c37c7692bd52b0e752b87954ef86ee2224e624ef7ce6cb21e8c41cc1b", size = 2425997, upload-time = "2024-12-24T18:29:59.393Z" },
+    { url = "https://files.pythonhosted.org/packages/ed/12/87f0e9271e2b63d35d0d8524954145837dd1a6c15b62a2d8c1ebe0f182b4/kiwisolver-1.4.8-cp313-cp313-musllinux_1_2_ppc64le.whl", hash = "sha256:7e9a60b50fe8b2ec6f448fe8d81b07e40141bfced7f896309df271a0b92f80f3", size = 2376060, upload-time = "2024-12-24T18:30:01.338Z" },
+    { url = "https://files.pythonhosted.org/packages/02/6e/c8af39288edbce8bf0fa35dee427b082758a4b71e9c91ef18fa667782138/kiwisolver-1.4.8-cp313-cp313-musllinux_1_2_s390x.whl", hash = "sha256:918139571133f366e8362fa4a297aeba86c7816b7ecf0bc79168080e2bd79957", size = 2520471, upload-time = "2024-12-24T18:30:04.574Z" },
+    { url = "https://files.pythonhosted.org/packages/13/78/df381bc7b26e535c91469f77f16adcd073beb3e2dd25042efd064af82323/kiwisolver-1.4.8-cp313-cp313-musllinux_1_2_x86_64.whl", hash = "sha256:e063ef9f89885a1d68dd8b2e18f5ead48653176d10a0e324e3b0030e3a69adeb", size = 2338793, upload-time = "2024-12-24T18:30:06.25Z" },
+    { url = "https://files.pythonhosted.org/packages/d0/dc/c1abe38c37c071d0fc71c9a474fd0b9ede05d42f5a458d584619cfd2371a/kiwisolver-1.4.8-cp313-cp313-win_amd64.whl", hash = "sha256:a17b7c4f5b2c51bb68ed379defd608a03954a1845dfed7cc0117f1cc8a9b7fd2", size = 71855, upload-time = "2024-12-24T18:30:07.535Z" },
+    { url = "https://files.pythonhosted.org/packages/a0/b6/21529d595b126ac298fdd90b705d87d4c5693de60023e0efcb4f387ed99e/kiwisolver-1.4.8-cp313-cp313-win_arm64.whl", hash = "sha256:3cd3bc628b25f74aedc6d374d5babf0166a92ff1317f46267f12d2ed54bc1d30", size = 65430, upload-time = "2024-12-24T18:30:08.504Z" },
+    { url = "https://files.pythonhosted.org/packages/34/bd/b89380b7298e3af9b39f49334e3e2a4af0e04819789f04b43d560516c0c8/kiwisolver-1.4.8-cp313-cp313t-macosx_10_13_universal2.whl", hash = "sha256:370fd2df41660ed4e26b8c9d6bbcad668fbe2560462cba151a721d49e5b6628c", size = 126294, upload-time = "2024-12-24T18:30:09.508Z" },
+    { url = "https://files.pythonhosted.org/packages/83/41/5857dc72e5e4148eaac5aa76e0703e594e4465f8ab7ec0fc60e3a9bb8fea/kiwisolver-1.4.8-cp313-cp313t-macosx_10_13_x86_64.whl", hash = "sha256:84a2f830d42707de1d191b9490ac186bf7997a9495d4e9072210a1296345f7dc", size = 67736, upload-time = "2024-12-24T18:30:11.039Z" },
+    { url = "https://files.pythonhosted.org/packages/e1/d1/be059b8db56ac270489fb0b3297fd1e53d195ba76e9bbb30e5401fa6b759/kiwisolver-1.4.8-cp313-cp313t-macosx_11_0_arm64.whl", hash = "sha256:7a3ad337add5148cf51ce0b55642dc551c0b9d6248458a757f98796ca7348712", size = 66194, upload-time = "2024-12-24T18:30:14.886Z" },
+    { url = "https://files.pythonhosted.org/packages/e1/83/4b73975f149819eb7dcf9299ed467eba068ecb16439a98990dcb12e63fdd/kiwisolver-1.4.8-cp313-cp313t-manylinux_2_12_i686.manylinux2010_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:7506488470f41169b86d8c9aeff587293f530a23a23a49d6bc64dab66bedc71e", size = 1465942, upload-time = "2024-12-24T18:30:18.927Z" },
+    { url = "https://files.pythonhosted.org/packages/c7/2c/30a5cdde5102958e602c07466bce058b9d7cb48734aa7a4327261ac8e002/kiwisolver-1.4.8-cp313-cp313t-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:2f0121b07b356a22fb0414cec4666bbe36fd6d0d759db3d37228f496ed67c880", size = 1595341, upload-time = "2024-12-24T18:30:22.102Z" },
+    { url = "https://files.pythonhosted.org/packages/ff/9b/1e71db1c000385aa069704f5990574b8244cce854ecd83119c19e83c9586/kiwisolver-1.4.8-cp313-cp313t-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:d6d6bd87df62c27d4185de7c511c6248040afae67028a8a22012b010bc7ad062", size = 1598455, upload-time = "2024-12-24T18:30:24.947Z" },
+    { url = "https://files.pythonhosted.org/packages/85/92/c8fec52ddf06231b31cbb779af77e99b8253cd96bd135250b9498144c78b/kiwisolver-1.4.8-cp313-cp313t-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:291331973c64bb9cce50bbe871fb2e675c4331dab4f31abe89f175ad7679a4d7", size = 1522138, upload-time = "2024-12-24T18:30:26.286Z" },
+    { url = "https://files.pythonhosted.org/packages/0b/51/9eb7e2cd07a15d8bdd976f6190c0164f92ce1904e5c0c79198c4972926b7/kiwisolver-1.4.8-cp313-cp313t-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:893f5525bb92d3d735878ec00f781b2de998333659507d29ea4466208df37bed", size = 1582857, upload-time = "2024-12-24T18:30:28.86Z" },
+    { url = "https://files.pythonhosted.org/packages/0f/95/c5a00387a5405e68ba32cc64af65ce881a39b98d73cc394b24143bebc5b8/kiwisolver-1.4.8-cp313-cp313t-musllinux_1_2_aarch64.whl", hash = "sha256:b47a465040146981dc9db8647981b8cb96366fbc8d452b031e4f8fdffec3f26d", size = 2293129, upload-time = "2024-12-24T18:30:30.34Z" },
+    { url = "https://files.pythonhosted.org/packages/44/83/eeb7af7d706b8347548313fa3a3a15931f404533cc54fe01f39e830dd231/kiwisolver-1.4.8-cp313-cp313t-musllinux_1_2_i686.whl", hash = "sha256:99cea8b9dd34ff80c521aef46a1dddb0dcc0283cf18bde6d756f1e6f31772165", size = 2421538, upload-time = "2024-12-24T18:30:33.334Z" },
+    { url = "https://files.pythonhosted.org/packages/05/f9/27e94c1b3eb29e6933b6986ffc5fa1177d2cd1f0c8efc5f02c91c9ac61de/kiwisolver-1.4.8-cp313-cp313t-musllinux_1_2_ppc64le.whl", hash = "sha256:151dffc4865e5fe6dafce5480fab84f950d14566c480c08a53c663a0020504b6", size = 2390661, upload-time = "2024-12-24T18:30:34.939Z" },
+    { url = "https://files.pythonhosted.org/packages/d9/d4/3c9735faa36ac591a4afcc2980d2691000506050b7a7e80bcfe44048daa7/kiwisolver-1.4.8-cp313-cp313t-musllinux_1_2_s390x.whl", hash = "sha256:577facaa411c10421314598b50413aa1ebcf5126f704f1e5d72d7e4e9f020d90", size = 2546710, upload-time = "2024-12-24T18:30:37.281Z" },
+    { url = "https://files.pythonhosted.org/packages/4c/fa/be89a49c640930180657482a74970cdcf6f7072c8d2471e1babe17a222dc/kiwisolver-1.4.8-cp313-cp313t-musllinux_1_2_x86_64.whl", hash = "sha256:be4816dc51c8a471749d664161b434912eee82f2ea66bd7628bd14583a833e85", size = 2349213, upload-time = "2024-12-24T18:30:40.019Z" },
+    { url = "https://files.pythonhosted.org/packages/1f/f9/ae81c47a43e33b93b0a9819cac6723257f5da2a5a60daf46aa5c7226ea85/kiwisolver-1.4.8-pp310-pypy310_pp73-macosx_10_15_x86_64.whl", hash = "sha256:e7a019419b7b510f0f7c9dceff8c5eae2392037eae483a7f9162625233802b0a", size = 60403, upload-time = "2024-12-24T18:30:41.372Z" },
+    { url = "https://files.pythonhosted.org/packages/58/ca/f92b5cb6f4ce0c1ebfcfe3e2e42b96917e16f7090e45b21102941924f18f/kiwisolver-1.4.8-pp310-pypy310_pp73-macosx_11_0_arm64.whl", hash = "sha256:286b18e86682fd2217a48fc6be6b0f20c1d0ed10958d8dc53453ad58d7be0bf8", size = 58657, upload-time = "2024-12-24T18:30:42.392Z" },
+    { url = "https://files.pythonhosted.org/packages/80/28/ae0240f732f0484d3a4dc885d055653c47144bdf59b670aae0ec3c65a7c8/kiwisolver-1.4.8-pp310-pypy310_pp73-manylinux_2_12_i686.manylinux2010_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:4191ee8dfd0be1c3666ccbac178c5a05d5f8d689bbe3fc92f3c4abec817f8fe0", size = 84948, upload-time = "2024-12-24T18:30:44.703Z" },
+    { url = "https://files.pythonhosted.org/packages/5d/eb/78d50346c51db22c7203c1611f9b513075f35c4e0e4877c5dde378d66043/kiwisolver-1.4.8-pp310-pypy310_pp73-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:7cd2785b9391f2873ad46088ed7599a6a71e762e1ea33e87514b1a441ed1da1c", size = 81186, upload-time = "2024-12-24T18:30:45.654Z" },
+    { url = "https://files.pythonhosted.org/packages/43/f8/7259f18c77adca88d5f64f9a522792e178b2691f3748817a8750c2d216ef/kiwisolver-1.4.8-pp310-pypy310_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:c07b29089b7ba090b6f1a669f1411f27221c3662b3a1b7010e67b59bb5a6f10b", size = 80279, upload-time = "2024-12-24T18:30:47.951Z" },
+    { url = "https://files.pythonhosted.org/packages/3a/1d/50ad811d1c5dae091e4cf046beba925bcae0a610e79ae4c538f996f63ed5/kiwisolver-1.4.8-pp310-pypy310_pp73-win_amd64.whl", hash = "sha256:65ea09a5a3faadd59c2ce96dc7bf0f364986a315949dc6374f04396b0d60e09b", size = 71762, upload-time = "2024-12-24T18:30:48.903Z" },
+]
+
+[[package]]
+name = "matplotlib"
+version = "3.10.3"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "contourpy" },
+    { name = "cycler" },
+    { name = "fonttools" },
+    { name = "kiwisolver" },
+    { name = "numpy" },
+    { name = "packaging" },
+    { name = "pillow" },
+    { name = "pyparsing" },
+    { name = "python-dateutil" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/26/91/d49359a21893183ed2a5b6c76bec40e0b1dcbf8ca148f864d134897cfc75/matplotlib-3.10.3.tar.gz", hash = "sha256:2f82d2c5bb7ae93aaaa4cd42aca65d76ce6376f83304fa3a630b569aca274df0", size = 34799811, upload-time = "2025-05-08T19:10:54.39Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/d0/ea/2bba25d289d389c7451f331ecd593944b3705f06ddf593fa7be75037d308/matplotlib-3.10.3-cp310-cp310-macosx_10_12_x86_64.whl", hash = "sha256:213fadd6348d106ca7db99e113f1bea1e65e383c3ba76e8556ba4a3054b65ae7", size = 8167862, upload-time = "2025-05-08T19:09:39.563Z" },
+    { url = "https://files.pythonhosted.org/packages/41/81/cc70b5138c926604e8c9ed810ed4c79e8116ba72e02230852f5c12c87ba2/matplotlib-3.10.3-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:d3bec61cb8221f0ca6313889308326e7bb303d0d302c5cc9e523b2f2e6c73deb", size = 8042149, upload-time = "2025-05-08T19:09:42.413Z" },
+    { url = "https://files.pythonhosted.org/packages/4a/9a/0ff45b6bfa42bb16de597e6058edf2361c298ad5ef93b327728145161bbf/matplotlib-3.10.3-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:8c21ae75651c0231b3ba014b6d5e08fb969c40cdb5a011e33e99ed0c9ea86ecb", size = 8453719, upload-time = "2025-05-08T19:09:44.901Z" },
+    { url = "https://files.pythonhosted.org/packages/85/c7/1866e972fed6d71ef136efbc980d4d1854ab7ef1ea8152bbd995ca231c81/matplotlib-3.10.3-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:a49e39755580b08e30e3620efc659330eac5d6534ab7eae50fa5e31f53ee4e30", size = 8590801, upload-time = "2025-05-08T19:09:47.404Z" },
+    { url = "https://files.pythonhosted.org/packages/5d/b9/748f6626d534ab7e255bdc39dc22634d337cf3ce200f261b5d65742044a1/matplotlib-3.10.3-cp310-cp310-musllinux_1_2_x86_64.whl", hash = "sha256:cf4636203e1190871d3a73664dea03d26fb019b66692cbfd642faafdad6208e8", size = 9402111, upload-time = "2025-05-08T19:09:49.474Z" },
+    { url = "https://files.pythonhosted.org/packages/1f/78/8bf07bd8fb67ea5665a6af188e70b57fcb2ab67057daa06b85a08e59160a/matplotlib-3.10.3-cp310-cp310-win_amd64.whl", hash = "sha256:fd5641a9bb9d55f4dd2afe897a53b537c834b9012684c8444cc105895c8c16fd", size = 8057213, upload-time = "2025-05-08T19:09:51.489Z" },
+    { url = "https://files.pythonhosted.org/packages/f5/bd/af9f655456f60fe1d575f54fb14704ee299b16e999704817a7645dfce6b0/matplotlib-3.10.3-cp311-cp311-macosx_10_12_x86_64.whl", hash = "sha256:0ef061f74cd488586f552d0c336b2f078d43bc00dc473d2c3e7bfee2272f3fa8", size = 8178873, upload-time = "2025-05-08T19:09:53.857Z" },
+    { url = "https://files.pythonhosted.org/packages/c2/86/e1c86690610661cd716eda5f9d0b35eaf606ae6c9b6736687cfc8f2d0cd8/matplotlib-3.10.3-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:d96985d14dc5f4a736bbea4b9de9afaa735f8a0fc2ca75be2fa9e96b2097369d", size = 8052205, upload-time = "2025-05-08T19:09:55.684Z" },
+    { url = "https://files.pythonhosted.org/packages/54/51/a9f8e49af3883dacddb2da1af5fca1f7468677f1188936452dd9aaaeb9ed/matplotlib-3.10.3-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:7c5f0283da91e9522bdba4d6583ed9d5521566f63729ffb68334f86d0bb98049", size = 8465823, upload-time = "2025-05-08T19:09:57.442Z" },
+    { url = "https://files.pythonhosted.org/packages/e7/e3/c82963a3b86d6e6d5874cbeaa390166458a7f1961bab9feb14d3d1a10f02/matplotlib-3.10.3-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:fdfa07c0ec58035242bc8b2c8aae37037c9a886370eef6850703d7583e19964b", size = 8606464, upload-time = "2025-05-08T19:09:59.471Z" },
+    { url = "https://files.pythonhosted.org/packages/0e/34/24da1027e7fcdd9e82da3194c470143c551852757a4b473a09a012f5b945/matplotlib-3.10.3-cp311-cp311-musllinux_1_2_x86_64.whl", hash = "sha256:c0b9849a17bce080a16ebcb80a7b714b5677d0ec32161a2cc0a8e5a6030ae220", size = 9413103, upload-time = "2025-05-08T19:10:03.208Z" },
+    { url = "https://files.pythonhosted.org/packages/a6/da/948a017c3ea13fd4a97afad5fdebe2f5bbc4d28c0654510ce6fd6b06b7bd/matplotlib-3.10.3-cp311-cp311-win_amd64.whl", hash = "sha256:eef6ed6c03717083bc6d69c2d7ee8624205c29a8e6ea5a31cd3492ecdbaee1e1", size = 8065492, upload-time = "2025-05-08T19:10:05.271Z" },
+    { url = "https://files.pythonhosted.org/packages/eb/43/6b80eb47d1071f234ef0c96ca370c2ca621f91c12045f1401b5c9b28a639/matplotlib-3.10.3-cp312-cp312-macosx_10_13_x86_64.whl", hash = "sha256:0ab1affc11d1f495ab9e6362b8174a25afc19c081ba5b0775ef00533a4236eea", size = 8179689, upload-time = "2025-05-08T19:10:07.602Z" },
+    { url = "https://files.pythonhosted.org/packages/0f/70/d61a591958325c357204870b5e7b164f93f2a8cca1dc6ce940f563909a13/matplotlib-3.10.3-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:2a818d8bdcafa7ed2eed74487fdb071c09c1ae24152d403952adad11fa3c65b4", size = 8050466, upload-time = "2025-05-08T19:10:09.383Z" },
+    { url = "https://files.pythonhosted.org/packages/e7/75/70c9d2306203148cc7902a961240c5927dd8728afedf35e6a77e105a2985/matplotlib-3.10.3-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:748ebc3470c253e770b17d8b0557f0aa85cf8c63fd52f1a61af5b27ec0b7ffee", size = 8456252, upload-time = "2025-05-08T19:10:11.958Z" },
+    { url = "https://files.pythonhosted.org/packages/c4/91/ba0ae1ff4b3f30972ad01cd4a8029e70a0ec3b8ea5be04764b128b66f763/matplotlib-3.10.3-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:ed70453fd99733293ace1aec568255bc51c6361cb0da94fa5ebf0649fdb2150a", size = 8601321, upload-time = "2025-05-08T19:10:14.47Z" },
+    { url = "https://files.pythonhosted.org/packages/d2/88/d636041eb54a84b889e11872d91f7cbf036b3b0e194a70fa064eb8b04f7a/matplotlib-3.10.3-cp312-cp312-musllinux_1_2_x86_64.whl", hash = "sha256:dbed9917b44070e55640bd13419de83b4c918e52d97561544814ba463811cbc7", size = 9406972, upload-time = "2025-05-08T19:10:16.569Z" },
+    { url = "https://files.pythonhosted.org/packages/b1/79/0d1c165eac44405a86478082e225fce87874f7198300bbebc55faaf6d28d/matplotlib-3.10.3-cp312-cp312-win_amd64.whl", hash = "sha256:cf37d8c6ef1a48829443e8ba5227b44236d7fcaf7647caa3178a4ff9f7a5be05", size = 8067954, upload-time = "2025-05-08T19:10:18.663Z" },
+    { url = "https://files.pythonhosted.org/packages/3b/c1/23cfb566a74c696a3b338d8955c549900d18fe2b898b6e94d682ca21e7c2/matplotlib-3.10.3-cp313-cp313-macosx_10_13_x86_64.whl", hash = "sha256:9f2efccc8dcf2b86fc4ee849eea5dcaecedd0773b30f47980dc0cbeabf26ec84", size = 8180318, upload-time = "2025-05-08T19:10:20.426Z" },
+    { url = "https://files.pythonhosted.org/packages/6c/0c/02f1c3b66b30da9ee343c343acbb6251bef5b01d34fad732446eaadcd108/matplotlib-3.10.3-cp313-cp313-macosx_11_0_arm64.whl", hash = "sha256:3ddbba06a6c126e3301c3d272a99dcbe7f6c24c14024e80307ff03791a5f294e", size = 8051132, upload-time = "2025-05-08T19:10:22.569Z" },
+    { url = "https://files.pythonhosted.org/packages/b4/ab/8db1a5ac9b3a7352fb914133001dae889f9fcecb3146541be46bed41339c/matplotlib-3.10.3-cp313-cp313-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:748302b33ae9326995b238f606e9ed840bf5886ebafcb233775d946aa8107a15", size = 8457633, upload-time = "2025-05-08T19:10:24.749Z" },
+    { url = "https://files.pythonhosted.org/packages/f5/64/41c4367bcaecbc03ef0d2a3ecee58a7065d0a36ae1aa817fe573a2da66d4/matplotlib-3.10.3-cp313-cp313-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:a80fcccbef63302c0efd78042ea3c2436104c5b1a4d3ae20f864593696364ac7", size = 8601031, upload-time = "2025-05-08T19:10:27.03Z" },
+    { url = "https://files.pythonhosted.org/packages/12/6f/6cc79e9e5ab89d13ed64da28898e40fe5b105a9ab9c98f83abd24e46d7d7/matplotlib-3.10.3-cp313-cp313-musllinux_1_2_x86_64.whl", hash = "sha256:55e46cbfe1f8586adb34f7587c3e4f7dedc59d5226719faf6cb54fc24f2fd52d", size = 9406988, upload-time = "2025-05-08T19:10:29.056Z" },
+    { url = "https://files.pythonhosted.org/packages/b1/0f/eed564407bd4d935ffabf561ed31099ed609e19287409a27b6d336848653/matplotlib-3.10.3-cp313-cp313-win_amd64.whl", hash = "sha256:151d89cb8d33cb23345cd12490c76fd5d18a56581a16d950b48c6ff19bb2ab93", size = 8068034, upload-time = "2025-05-08T19:10:31.221Z" },
+    { url = "https://files.pythonhosted.org/packages/3e/e5/2f14791ff69b12b09e9975e1d116d9578ac684460860ce542c2588cb7a1c/matplotlib-3.10.3-cp313-cp313t-macosx_10_13_x86_64.whl", hash = "sha256:c26dd9834e74d164d06433dc7be5d75a1e9890b926b3e57e74fa446e1a62c3e2", size = 8218223, upload-time = "2025-05-08T19:10:33.114Z" },
+    { url = "https://files.pythonhosted.org/packages/5c/08/30a94afd828b6e02d0a52cae4a29d6e9ccfcf4c8b56cc28b021d3588873e/matplotlib-3.10.3-cp313-cp313t-macosx_11_0_arm64.whl", hash = "sha256:24853dad5b8c84c8c2390fc31ce4858b6df504156893292ce8092d190ef8151d", size = 8094985, upload-time = "2025-05-08T19:10:35.337Z" },
+    { url = "https://files.pythonhosted.org/packages/89/44/f3bc6b53066c889d7a1a3ea8094c13af6a667c5ca6220ec60ecceec2dabe/matplotlib-3.10.3-cp313-cp313t-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:68f7878214d369d7d4215e2a9075fef743be38fa401d32e6020bab2dfabaa566", size = 8483109, upload-time = "2025-05-08T19:10:37.611Z" },
+    { url = "https://files.pythonhosted.org/packages/ba/c7/473bc559beec08ebee9f86ca77a844b65747e1a6c2691e8c92e40b9f42a8/matplotlib-3.10.3-cp313-cp313t-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:f6929fc618cb6db9cb75086f73b3219bbb25920cb24cee2ea7a12b04971a4158", size = 8618082, upload-time = "2025-05-08T19:10:39.892Z" },
+    { url = "https://files.pythonhosted.org/packages/d8/e9/6ce8edd264c8819e37bbed8172e0ccdc7107fe86999b76ab5752276357a4/matplotlib-3.10.3-cp313-cp313t-musllinux_1_2_x86_64.whl", hash = "sha256:6c7818292a5cc372a2dc4c795e5c356942eb8350b98ef913f7fda51fe175ac5d", size = 9413699, upload-time = "2025-05-08T19:10:42.376Z" },
+    { url = "https://files.pythonhosted.org/packages/1b/92/9a45c91089c3cf690b5badd4be81e392ff086ccca8a1d4e3a08463d8a966/matplotlib-3.10.3-cp313-cp313t-win_amd64.whl", hash = "sha256:4f23ffe95c5667ef8a2b56eea9b53db7f43910fa4a2d5472ae0f72b64deab4d5", size = 8139044, upload-time = "2025-05-08T19:10:44.551Z" },
+    { url = "https://files.pythonhosted.org/packages/3d/d1/f54d43e95384b312ffa4a74a4326c722f3b8187aaaa12e9a84cdf3037131/matplotlib-3.10.3-pp310-pypy310_pp73-macosx_10_15_x86_64.whl", hash = "sha256:86ab63d66bbc83fdb6733471d3bff40897c1e9921cba112accd748eee4bce5e4", size = 8162896, upload-time = "2025-05-08T19:10:46.432Z" },
+    { url = "https://files.pythonhosted.org/packages/24/a4/fbfc00c2346177c95b353dcf9b5a004106abe8730a62cb6f27e79df0a698/matplotlib-3.10.3-pp310-pypy310_pp73-macosx_11_0_arm64.whl", hash = "sha256:a48f9c08bf7444b5d2391a83e75edb464ccda3c380384b36532a0962593a1751", size = 8039702, upload-time = "2025-05-08T19:10:49.634Z" },
+    { url = "https://files.pythonhosted.org/packages/6a/b9/59e120d24a2ec5fc2d30646adb2efb4621aab3c6d83d66fb2a7a182db032/matplotlib-3.10.3-pp310-pypy310_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:cb73d8aa75a237457988f9765e4dfe1c0d2453c5ca4eabc897d4309672c8e014", size = 8594298, upload-time = "2025-05-08T19:10:51.738Z" },
+]
+
+[[package]]
+name = "numpy"
+version = "2.2.6"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/76/21/7d2a95e4bba9dc13d043ee156a356c0a8f0c6309dff6b21b4d71a073b8a8/numpy-2.2.6.tar.gz", hash = "sha256:e29554e2bef54a90aa5cc07da6ce955accb83f21ab5de01a62c8478897b264fd", size = 20276440, upload-time = "2025-05-17T22:38:04.611Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/9a/3e/ed6db5be21ce87955c0cbd3009f2803f59fa08df21b5df06862e2d8e2bdd/numpy-2.2.6-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:b412caa66f72040e6d268491a59f2c43bf03eb6c96dd8f0307829feb7fa2b6fb", size = 21165245, upload-time = "2025-05-17T21:27:58.555Z" },
+    { url = "https://files.pythonhosted.org/packages/22/c2/4b9221495b2a132cc9d2eb862e21d42a009f5a60e45fc44b00118c174bff/numpy-2.2.6-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:8e41fd67c52b86603a91c1a505ebaef50b3314de0213461c7a6e99c9a3beff90", size = 14360048, upload-time = "2025-05-17T21:28:21.406Z" },
+    { url = "https://files.pythonhosted.org/packages/fd/77/dc2fcfc66943c6410e2bf598062f5959372735ffda175b39906d54f02349/numpy-2.2.6-cp310-cp310-macosx_14_0_arm64.whl", hash = "sha256:37e990a01ae6ec7fe7fa1c26c55ecb672dd98b19c3d0e1d1f326fa13cb38d163", size = 5340542, upload-time = "2025-05-17T21:28:30.931Z" },
+    { url = "https://files.pythonhosted.org/packages/7a/4f/1cb5fdc353a5f5cc7feb692db9b8ec2c3d6405453f982435efc52561df58/numpy-2.2.6-cp310-cp310-macosx_14_0_x86_64.whl", hash = "sha256:5a6429d4be8ca66d889b7cf70f536a397dc45ba6faeb5f8c5427935d9592e9cf", size = 6878301, upload-time = "2025-05-17T21:28:41.613Z" },
+    { url = "https://files.pythonhosted.org/packages/eb/17/96a3acd228cec142fcb8723bd3cc39c2a474f7dcf0a5d16731980bcafa95/numpy-2.2.6-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:efd28d4e9cd7d7a8d39074a4d44c63eda73401580c5c76acda2ce969e0a38e83", size = 14297320, upload-time = "2025-05-17T21:29:02.78Z" },
+    { url = "https://files.pythonhosted.org/packages/b4/63/3de6a34ad7ad6646ac7d2f55ebc6ad439dbbf9c4370017c50cf403fb19b5/numpy-2.2.6-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:fc7b73d02efb0e18c000e9ad8b83480dfcd5dfd11065997ed4c6747470ae8915", size = 16801050, upload-time = "2025-05-17T21:29:27.675Z" },
+    { url = "https://files.pythonhosted.org/packages/07/b6/89d837eddef52b3d0cec5c6ba0456c1bf1b9ef6a6672fc2b7873c3ec4e2e/numpy-2.2.6-cp310-cp310-musllinux_1_2_aarch64.whl", hash = "sha256:74d4531beb257d2c3f4b261bfb0fc09e0f9ebb8842d82a7b4209415896adc680", size = 15807034, upload-time = "2025-05-17T21:29:51.102Z" },
+    { url = "https://files.pythonhosted.org/packages/01/c8/dc6ae86e3c61cfec1f178e5c9f7858584049b6093f843bca541f94120920/numpy-2.2.6-cp310-cp310-musllinux_1_2_x86_64.whl", hash = "sha256:8fc377d995680230e83241d8a96def29f204b5782f371c532579b4f20607a289", size = 18614185, upload-time = "2025-05-17T21:30:18.703Z" },
+    { url = "https://files.pythonhosted.org/packages/5b/c5/0064b1b7e7c89137b471ccec1fd2282fceaae0ab3a9550f2568782d80357/numpy-2.2.6-cp310-cp310-win32.whl", hash = "sha256:b093dd74e50a8cba3e873868d9e93a85b78e0daf2e98c6797566ad8044e8363d", size = 6527149, upload-time = "2025-05-17T21:30:29.788Z" },
+    { url = "https://files.pythonhosted.org/packages/a3/dd/4b822569d6b96c39d1215dbae0582fd99954dcbcf0c1a13c61783feaca3f/numpy-2.2.6-cp310-cp310-win_amd64.whl", hash = "sha256:f0fd6321b839904e15c46e0d257fdd101dd7f530fe03fd6359c1ea63738703f3", size = 12904620, upload-time = "2025-05-17T21:30:48.994Z" },
+    { url = "https://files.pythonhosted.org/packages/da/a8/4f83e2aa666a9fbf56d6118faaaf5f1974d456b1823fda0a176eff722839/numpy-2.2.6-cp311-cp311-macosx_10_9_x86_64.whl", hash = "sha256:f9f1adb22318e121c5c69a09142811a201ef17ab257a1e66ca3025065b7f53ae", size = 21176963, upload-time = "2025-05-17T21:31:19.36Z" },
+    { url = "https://files.pythonhosted.org/packages/b3/2b/64e1affc7972decb74c9e29e5649fac940514910960ba25cd9af4488b66c/numpy-2.2.6-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:c820a93b0255bc360f53eca31a0e676fd1101f673dda8da93454a12e23fc5f7a", size = 14406743, upload-time = "2025-05-17T21:31:41.087Z" },
+    { url = "https://files.pythonhosted.org/packages/4a/9f/0121e375000b5e50ffdd8b25bf78d8e1a5aa4cca3f185d41265198c7b834/numpy-2.2.6-cp311-cp311-macosx_14_0_arm64.whl", hash = "sha256:3d70692235e759f260c3d837193090014aebdf026dfd167834bcba43e30c2a42", size = 5352616, upload-time = "2025-05-17T21:31:50.072Z" },
+    { url = "https://files.pythonhosted.org/packages/31/0d/b48c405c91693635fbe2dcd7bc84a33a602add5f63286e024d3b6741411c/numpy-2.2.6-cp311-cp311-macosx_14_0_x86_64.whl", hash = "sha256:481b49095335f8eed42e39e8041327c05b0f6f4780488f61286ed3c01368d491", size = 6889579, upload-time = "2025-05-17T21:32:01.712Z" },
+    { url = "https://files.pythonhosted.org/packages/52/b8/7f0554d49b565d0171eab6e99001846882000883998e7b7d9f0d98b1f934/numpy-2.2.6-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:b64d8d4d17135e00c8e346e0a738deb17e754230d7e0810ac5012750bbd85a5a", size = 14312005, upload-time = "2025-05-17T21:32:23.332Z" },
+    { url = "https://files.pythonhosted.org/packages/b3/dd/2238b898e51bd6d389b7389ffb20d7f4c10066d80351187ec8e303a5a475/numpy-2.2.6-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:ba10f8411898fc418a521833e014a77d3ca01c15b0c6cdcce6a0d2897e6dbbdf", size = 16821570, upload-time = "2025-05-17T21:32:47.991Z" },
+    { url = "https://files.pythonhosted.org/packages/83/6c/44d0325722cf644f191042bf47eedad61c1e6df2432ed65cbe28509d404e/numpy-2.2.6-cp311-cp311-musllinux_1_2_aarch64.whl", hash = "sha256:bd48227a919f1bafbdda0583705e547892342c26fb127219d60a5c36882609d1", size = 15818548, upload-time = "2025-05-17T21:33:11.728Z" },
+    { url = "https://files.pythonhosted.org/packages/ae/9d/81e8216030ce66be25279098789b665d49ff19eef08bfa8cb96d4957f422/numpy-2.2.6-cp311-cp311-musllinux_1_2_x86_64.whl", hash = "sha256:9551a499bf125c1d4f9e250377c1ee2eddd02e01eac6644c080162c0c51778ab", size = 18620521, upload-time = "2025-05-17T21:33:39.139Z" },
+    { url = "https://files.pythonhosted.org/packages/6a/fd/e19617b9530b031db51b0926eed5345ce8ddc669bb3bc0044b23e275ebe8/numpy-2.2.6-cp311-cp311-win32.whl", hash = "sha256:0678000bb9ac1475cd454c6b8c799206af8107e310843532b04d49649c717a47", size = 6525866, upload-time = "2025-05-17T21:33:50.273Z" },
+    { url = "https://files.pythonhosted.org/packages/31/0a/f354fb7176b81747d870f7991dc763e157a934c717b67b58456bc63da3df/numpy-2.2.6-cp311-cp311-win_amd64.whl", hash = "sha256:e8213002e427c69c45a52bbd94163084025f533a55a59d6f9c5b820774ef3303", size = 12907455, upload-time = "2025-05-17T21:34:09.135Z" },
+    { url = "https://files.pythonhosted.org/packages/82/5d/c00588b6cf18e1da539b45d3598d3557084990dcc4331960c15ee776ee41/numpy-2.2.6-cp312-cp312-macosx_10_13_x86_64.whl", hash = "sha256:41c5a21f4a04fa86436124d388f6ed60a9343a6f767fced1a8a71c3fbca038ff", size = 20875348, upload-time = "2025-05-17T21:34:39.648Z" },
+    { url = "https://files.pythonhosted.org/packages/66/ee/560deadcdde6c2f90200450d5938f63a34b37e27ebff162810f716f6a230/numpy-2.2.6-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:de749064336d37e340f640b05f24e9e3dd678c57318c7289d222a8a2f543e90c", size = 14119362, upload-time = "2025-05-17T21:35:01.241Z" },
+    { url = "https://files.pythonhosted.org/packages/3c/65/4baa99f1c53b30adf0acd9a5519078871ddde8d2339dc5a7fde80d9d87da/numpy-2.2.6-cp312-cp312-macosx_14_0_arm64.whl", hash = "sha256:894b3a42502226a1cac872f840030665f33326fc3dac8e57c607905773cdcde3", size = 5084103, upload-time = "2025-05-17T21:35:10.622Z" },
+    { url = "https://files.pythonhosted.org/packages/cc/89/e5a34c071a0570cc40c9a54eb472d113eea6d002e9ae12bb3a8407fb912e/numpy-2.2.6-cp312-cp312-macosx_14_0_x86_64.whl", hash = "sha256:71594f7c51a18e728451bb50cc60a3ce4e6538822731b2933209a1f3614e9282", size = 6625382, upload-time = "2025-05-17T21:35:21.414Z" },
+    { url = "https://files.pythonhosted.org/packages/f8/35/8c80729f1ff76b3921d5c9487c7ac3de9b2a103b1cd05e905b3090513510/numpy-2.2.6-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:f2618db89be1b4e05f7a1a847a9c1c0abd63e63a1607d892dd54668dd92faf87", size = 14018462, upload-time = "2025-05-17T21:35:42.174Z" },
+    { url = "https://files.pythonhosted.org/packages/8c/3d/1e1db36cfd41f895d266b103df00ca5b3cbe965184df824dec5c08c6b803/numpy-2.2.6-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:fd83c01228a688733f1ded5201c678f0c53ecc1006ffbc404db9f7a899ac6249", size = 16527618, upload-time = "2025-05-17T21:36:06.711Z" },
+    { url = "https://files.pythonhosted.org/packages/61/c6/03ed30992602c85aa3cd95b9070a514f8b3c33e31124694438d88809ae36/numpy-2.2.6-cp312-cp312-musllinux_1_2_aarch64.whl", hash = "sha256:37c0ca431f82cd5fa716eca9506aefcabc247fb27ba69c5062a6d3ade8cf8f49", size = 15505511, upload-time = "2025-05-17T21:36:29.965Z" },
+    { url = "https://files.pythonhosted.org/packages/b7/25/5761d832a81df431e260719ec45de696414266613c9ee268394dd5ad8236/numpy-2.2.6-cp312-cp312-musllinux_1_2_x86_64.whl", hash = "sha256:fe27749d33bb772c80dcd84ae7e8df2adc920ae8297400dabec45f0dedb3f6de", size = 18313783, upload-time = "2025-05-17T21:36:56.883Z" },
+    { url = "https://files.pythonhosted.org/packages/57/0a/72d5a3527c5ebffcd47bde9162c39fae1f90138c961e5296491ce778e682/numpy-2.2.6-cp312-cp312-win32.whl", hash = "sha256:4eeaae00d789f66c7a25ac5f34b71a7035bb474e679f410e5e1a94deb24cf2d4", size = 6246506, upload-time = "2025-05-17T21:37:07.368Z" },
+    { url = "https://files.pythonhosted.org/packages/36/fa/8c9210162ca1b88529ab76b41ba02d433fd54fecaf6feb70ef9f124683f1/numpy-2.2.6-cp312-cp312-win_amd64.whl", hash = "sha256:c1f9540be57940698ed329904db803cf7a402f3fc200bfe599334c9bd84a40b2", size = 12614190, upload-time = "2025-05-17T21:37:26.213Z" },
+    { url = "https://files.pythonhosted.org/packages/f9/5c/6657823f4f594f72b5471f1db1ab12e26e890bb2e41897522d134d2a3e81/numpy-2.2.6-cp313-cp313-macosx_10_13_x86_64.whl", hash = "sha256:0811bb762109d9708cca4d0b13c4f67146e3c3b7cf8d34018c722adb2d957c84", size = 20867828, upload-time = "2025-05-17T21:37:56.699Z" },
+    { url = "https://files.pythonhosted.org/packages/dc/9e/14520dc3dadf3c803473bd07e9b2bd1b69bc583cb2497b47000fed2fa92f/numpy-2.2.6-cp313-cp313-macosx_11_0_arm64.whl", hash = "sha256:287cc3162b6f01463ccd86be154f284d0893d2b3ed7292439ea97eafa8170e0b", size = 14143006, upload-time = "2025-05-17T21:38:18.291Z" },
+    { url = "https://files.pythonhosted.org/packages/4f/06/7e96c57d90bebdce9918412087fc22ca9851cceaf5567a45c1f404480e9e/numpy-2.2.6-cp313-cp313-macosx_14_0_arm64.whl", hash = "sha256:f1372f041402e37e5e633e586f62aa53de2eac8d98cbfb822806ce4bbefcb74d", size = 5076765, upload-time = "2025-05-17T21:38:27.319Z" },
+    { url = "https://files.pythonhosted.org/packages/73/ed/63d920c23b4289fdac96ddbdd6132e9427790977d5457cd132f18e76eae0/numpy-2.2.6-cp313-cp313-macosx_14_0_x86_64.whl", hash = "sha256:55a4d33fa519660d69614a9fad433be87e5252f4b03850642f88993f7b2ca566", size = 6617736, upload-time = "2025-05-17T21:38:38.141Z" },
+    { url = "https://files.pythonhosted.org/packages/85/c5/e19c8f99d83fd377ec8c7e0cf627a8049746da54afc24ef0a0cb73d5dfb5/numpy-2.2.6-cp313-cp313-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:f92729c95468a2f4f15e9bb94c432a9229d0d50de67304399627a943201baa2f", size = 14010719, upload-time = "2025-05-17T21:38:58.433Z" },
+    { url = "https://files.pythonhosted.org/packages/19/49/4df9123aafa7b539317bf6d342cb6d227e49f7a35b99c287a6109b13dd93/numpy-2.2.6-cp313-cp313-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:1bc23a79bfabc5d056d106f9befb8d50c31ced2fbc70eedb8155aec74a45798f", size = 16526072, upload-time = "2025-05-17T21:39:22.638Z" },
+    { url = "https://files.pythonhosted.org/packages/b2/6c/04b5f47f4f32f7c2b0e7260442a8cbcf8168b0e1a41ff1495da42f42a14f/numpy-2.2.6-cp313-cp313-musllinux_1_2_aarch64.whl", hash = "sha256:e3143e4451880bed956e706a3220b4e5cf6172ef05fcc397f6f36a550b1dd868", size = 15503213, upload-time = "2025-05-17T21:39:45.865Z" },
+    { url = "https://files.pythonhosted.org/packages/17/0a/5cd92e352c1307640d5b6fec1b2ffb06cd0dabe7d7b8227f97933d378422/numpy-2.2.6-cp313-cp313-musllinux_1_2_x86_64.whl", hash = "sha256:b4f13750ce79751586ae2eb824ba7e1e8dba64784086c98cdbbcc6a42112ce0d", size = 18316632, upload-time = "2025-05-17T21:40:13.331Z" },
+    { url = "https://files.pythonhosted.org/packages/f0/3b/5cba2b1d88760ef86596ad0f3d484b1cbff7c115ae2429678465057c5155/numpy-2.2.6-cp313-cp313-win32.whl", hash = "sha256:5beb72339d9d4fa36522fc63802f469b13cdbe4fdab4a288f0c441b74272ebfd", size = 6244532, upload-time = "2025-05-17T21:43:46.099Z" },
+    { url = "https://files.pythonhosted.org/packages/cb/3b/d58c12eafcb298d4e6d0d40216866ab15f59e55d148a5658bb3132311fcf/numpy-2.2.6-cp313-cp313-win_amd64.whl", hash = "sha256:b0544343a702fa80c95ad5d3d608ea3599dd54d4632df855e4c8d24eb6ecfa1c", size = 12610885, upload-time = "2025-05-17T21:44:05.145Z" },
+    { url = "https://files.pythonhosted.org/packages/6b/9e/4bf918b818e516322db999ac25d00c75788ddfd2d2ade4fa66f1f38097e1/numpy-2.2.6-cp313-cp313t-macosx_10_13_x86_64.whl", hash = "sha256:0bca768cd85ae743b2affdc762d617eddf3bcf8724435498a1e80132d04879e6", size = 20963467, upload-time = "2025-05-17T21:40:44Z" },
+    { url = "https://files.pythonhosted.org/packages/61/66/d2de6b291507517ff2e438e13ff7b1e2cdbdb7cb40b3ed475377aece69f9/numpy-2.2.6-cp313-cp313t-macosx_11_0_arm64.whl", hash = "sha256:fc0c5673685c508a142ca65209b4e79ed6740a4ed6b2267dbba90f34b0b3cfda", size = 14225144, upload-time = "2025-05-17T21:41:05.695Z" },
+    { url = "https://files.pythonhosted.org/packages/e4/25/480387655407ead912e28ba3a820bc69af9adf13bcbe40b299d454ec011f/numpy-2.2.6-cp313-cp313t-macosx_14_0_arm64.whl", hash = "sha256:5bd4fc3ac8926b3819797a7c0e2631eb889b4118a9898c84f585a54d475b7e40", size = 5200217, upload-time = "2025-05-17T21:41:15.903Z" },
+    { url = "https://files.pythonhosted.org/packages/aa/4a/6e313b5108f53dcbf3aca0c0f3e9c92f4c10ce57a0a721851f9785872895/numpy-2.2.6-cp313-cp313t-macosx_14_0_x86_64.whl", hash = "sha256:fee4236c876c4e8369388054d02d0e9bb84821feb1a64dd59e137e6511a551f8", size = 6712014, upload-time = "2025-05-17T21:41:27.321Z" },
+    { url = "https://files.pythonhosted.org/packages/b7/30/172c2d5c4be71fdf476e9de553443cf8e25feddbe185e0bd88b096915bcc/numpy-2.2.6-cp313-cp313t-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:e1dda9c7e08dc141e0247a5b8f49cf05984955246a327d4c48bda16821947b2f", size = 14077935, upload-time = "2025-05-17T21:41:49.738Z" },
+    { url = "https://files.pythonhosted.org/packages/12/fb/9e743f8d4e4d3c710902cf87af3512082ae3d43b945d5d16563f26ec251d/numpy-2.2.6-cp313-cp313t-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:f447e6acb680fd307f40d3da4852208af94afdfab89cf850986c3ca00562f4fa", size = 16600122, upload-time = "2025-05-17T21:42:14.046Z" },
+    { url = "https://files.pythonhosted.org/packages/12/75/ee20da0e58d3a66f204f38916757e01e33a9737d0b22373b3eb5a27358f9/numpy-2.2.6-cp313-cp313t-musllinux_1_2_aarch64.whl", hash = "sha256:389d771b1623ec92636b0786bc4ae56abafad4a4c513d36a55dce14bd9ce8571", size = 15586143, upload-time = "2025-05-17T21:42:37.464Z" },
+    { url = "https://files.pythonhosted.org/packages/76/95/bef5b37f29fc5e739947e9ce5179ad402875633308504a52d188302319c8/numpy-2.2.6-cp313-cp313t-musllinux_1_2_x86_64.whl", hash = "sha256:8e9ace4a37db23421249ed236fdcdd457d671e25146786dfc96835cd951aa7c1", size = 18385260, upload-time = "2025-05-17T21:43:05.189Z" },
+    { url = "https://files.pythonhosted.org/packages/09/04/f2f83279d287407cf36a7a8053a5abe7be3622a4363337338f2585e4afda/numpy-2.2.6-cp313-cp313t-win32.whl", hash = "sha256:038613e9fb8c72b0a41f025a7e4c3f0b7a1b5d768ece4796b674c8f3fe13efff", size = 6377225, upload-time = "2025-05-17T21:43:16.254Z" },
+    { url = "https://files.pythonhosted.org/packages/67/0e/35082d13c09c02c011cf21570543d202ad929d961c02a147493cb0c2bdf5/numpy-2.2.6-cp313-cp313t-win_amd64.whl", hash = "sha256:6031dd6dfecc0cf9f668681a37648373bddd6421fff6c66ec1624eed0180ee06", size = 12771374, upload-time = "2025-05-17T21:43:35.479Z" },
+    { url = "https://files.pythonhosted.org/packages/9e/3b/d94a75f4dbf1ef5d321523ecac21ef23a3cd2ac8b78ae2aac40873590229/numpy-2.2.6-pp310-pypy310_pp73-macosx_10_15_x86_64.whl", hash = "sha256:0b605b275d7bd0c640cad4e5d30fa701a8d59302e127e5f79138ad62762c3e3d", size = 21040391, upload-time = "2025-05-17T21:44:35.948Z" },
+    { url = "https://files.pythonhosted.org/packages/17/f4/09b2fa1b58f0fb4f7c7963a1649c64c4d315752240377ed74d9cd878f7b5/numpy-2.2.6-pp310-pypy310_pp73-macosx_14_0_x86_64.whl", hash = "sha256:7befc596a7dc9da8a337f79802ee8adb30a552a94f792b9c9d18c840055907db", size = 6786754, upload-time = "2025-05-17T21:44:47.446Z" },
+    { url = "https://files.pythonhosted.org/packages/af/30/feba75f143bdc868a1cc3f44ccfa6c4b9ec522b36458e738cd00f67b573f/numpy-2.2.6-pp310-pypy310_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:ce47521a4754c8f4593837384bd3424880629f718d87c5d44f8ed763edd63543", size = 16643476, upload-time = "2025-05-17T21:45:11.871Z" },
+    { url = "https://files.pythonhosted.org/packages/37/48/ac2a9584402fb6c0cd5b5d1a91dcf176b15760130dd386bbafdbfe3640bf/numpy-2.2.6-pp310-pypy310_pp73-win_amd64.whl", hash = "sha256:d042d24c90c41b54fd506da306759e06e568864df8ec17ccc17e9e884634fd00", size = 12812666, upload-time = "2025-05-17T21:45:31.426Z" },
+]
+
+[[package]]
+name = "oauthlib"
+version = "3.2.2"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/6d/fa/fbf4001037904031639e6bfbfc02badfc7e12f137a8afa254df6c4c8a670/oauthlib-3.2.2.tar.gz", hash = "sha256:9859c40929662bec5d64f34d01c99e093149682a3f38915dc0655d5a633dd918", size = 177352, upload-time = "2022-10-17T20:04:27.471Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/7e/80/cab10959dc1faead58dc8384a781dfbf93cb4d33d50988f7a69f1b7c9bbe/oauthlib-3.2.2-py3-none-any.whl", hash = "sha256:8139f29aac13e25d502680e9e19963e83f16838d48a0d71c287fe40e7067fbca", size = 151688, upload-time = "2022-10-17T20:04:24.037Z" },
+]
+
+[[package]]
+name = "packaging"
+version = "25.0"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/a1/d4/1fc4078c65507b51b96ca8f8c3ba19e6a61c8253c72794544580a7b6c24d/packaging-25.0.tar.gz", hash = "sha256:d443872c98d677bf60f6a1f2f8c1cb748e8fe762d2bf9d3148b5599295b0fc4f", size = 165727, upload-time = "2025-04-19T11:48:59.673Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/20/12/38679034af332785aac8774540895e234f4d07f7545804097de4b666afd8/packaging-25.0-py3-none-any.whl", hash = "sha256:29572ef2b1f17581046b3a2227d5c611fb25ec70ca1ba8554b24b0e69331a484", size = 66469, upload-time = "2025-04-19T11:48:57.875Z" },
+]
+
+[[package]]
+name = "pandas"
+version = "2.2.3"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "numpy" },
+    { name = "python-dateutil" },
+    { name = "pytz" },
+    { name = "tzdata" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/9c/d6/9f8431bacc2e19dca897724cd097b1bb224a6ad5433784a44b587c7c13af/pandas-2.2.3.tar.gz", hash = "sha256:4f18ba62b61d7e192368b84517265a99b4d7ee8912f8708660fb4a366cc82667", size = 4399213, upload-time = "2024-09-20T13:10:04.827Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/aa/70/c853aec59839bceed032d52010ff5f1b8d87dc3114b762e4ba2727661a3b/pandas-2.2.3-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:1948ddde24197a0f7add2bdc4ca83bf2b1ef84a1bc8ccffd95eda17fd836ecb5", size = 12580827, upload-time = "2024-09-20T13:08:42.347Z" },
+    { url = "https://files.pythonhosted.org/packages/99/f2/c4527768739ffa4469b2b4fff05aa3768a478aed89a2f271a79a40eee984/pandas-2.2.3-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:381175499d3802cde0eabbaf6324cce0c4f5d52ca6f8c377c29ad442f50f6348", size = 11303897, upload-time = "2024-09-20T13:08:45.807Z" },
+    { url = "https://files.pythonhosted.org/packages/ed/12/86c1747ea27989d7a4064f806ce2bae2c6d575b950be087837bdfcabacc9/pandas-2.2.3-cp310-cp310-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:d9c45366def9a3dd85a6454c0e7908f2b3b8e9c138f5dc38fed7ce720d8453ed", size = 66480908, upload-time = "2024-09-20T18:37:13.513Z" },
+    { url = "https://files.pythonhosted.org/packages/44/50/7db2cd5e6373ae796f0ddad3675268c8d59fb6076e66f0c339d61cea886b/pandas-2.2.3-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:86976a1c5b25ae3f8ccae3a5306e443569ee3c3faf444dfd0f41cda24667ad57", size = 13064210, upload-time = "2024-09-20T13:08:48.325Z" },
+    { url = "https://files.pythonhosted.org/packages/61/61/a89015a6d5536cb0d6c3ba02cebed51a95538cf83472975275e28ebf7d0c/pandas-2.2.3-cp310-cp310-musllinux_1_2_aarch64.whl", hash = "sha256:b8661b0238a69d7aafe156b7fa86c44b881387509653fdf857bebc5e4008ad42", size = 16754292, upload-time = "2024-09-20T19:01:54.443Z" },
+    { url = "https://files.pythonhosted.org/packages/ce/0d/4cc7b69ce37fac07645a94e1d4b0880b15999494372c1523508511b09e40/pandas-2.2.3-cp310-cp310-musllinux_1_2_x86_64.whl", hash = "sha256:37e0aced3e8f539eccf2e099f65cdb9c8aa85109b0be6e93e2baff94264bdc6f", size = 14416379, upload-time = "2024-09-20T13:08:50.882Z" },
+    { url = "https://files.pythonhosted.org/packages/31/9e/6ebb433de864a6cd45716af52a4d7a8c3c9aaf3a98368e61db9e69e69a9c/pandas-2.2.3-cp310-cp310-win_amd64.whl", hash = "sha256:56534ce0746a58afaf7942ba4863e0ef81c9c50d3f0ae93e9497d6a41a057645", size = 11598471, upload-time = "2024-09-20T13:08:53.332Z" },
+    { url = "https://files.pythonhosted.org/packages/a8/44/d9502bf0ed197ba9bf1103c9867d5904ddcaf869e52329787fc54ed70cc8/pandas-2.2.3-cp311-cp311-macosx_10_9_x86_64.whl", hash = "sha256:66108071e1b935240e74525006034333f98bcdb87ea116de573a6a0dccb6c039", size = 12602222, upload-time = "2024-09-20T13:08:56.254Z" },
+    { url = "https://files.pythonhosted.org/packages/52/11/9eac327a38834f162b8250aab32a6781339c69afe7574368fffe46387edf/pandas-2.2.3-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:7c2875855b0ff77b2a64a0365e24455d9990730d6431b9e0ee18ad8acee13dbd", size = 11321274, upload-time = "2024-09-20T13:08:58.645Z" },
+    { url = "https://files.pythonhosted.org/packages/45/fb/c4beeb084718598ba19aa9f5abbc8aed8b42f90930da861fcb1acdb54c3a/pandas-2.2.3-cp311-cp311-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:cd8d0c3be0515c12fed0bdbae072551c8b54b7192c7b1fda0ba56059a0179698", size = 15579836, upload-time = "2024-09-20T19:01:57.571Z" },
+    { url = "https://files.pythonhosted.org/packages/cd/5f/4dba1d39bb9c38d574a9a22548c540177f78ea47b32f99c0ff2ec499fac5/pandas-2.2.3-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:c124333816c3a9b03fbeef3a9f230ba9a737e9e5bb4060aa2107a86cc0a497fc", size = 13058505, upload-time = "2024-09-20T13:09:01.501Z" },
+    { url = "https://files.pythonhosted.org/packages/b9/57/708135b90391995361636634df1f1130d03ba456e95bcf576fada459115a/pandas-2.2.3-cp311-cp311-musllinux_1_2_aarch64.whl", hash = "sha256:63cc132e40a2e084cf01adf0775b15ac515ba905d7dcca47e9a251819c575ef3", size = 16744420, upload-time = "2024-09-20T19:02:00.678Z" },
+    { url = "https://files.pythonhosted.org/packages/86/4a/03ed6b7ee323cf30404265c284cee9c65c56a212e0a08d9ee06984ba2240/pandas-2.2.3-cp311-cp311-musllinux_1_2_x86_64.whl", hash = "sha256:29401dbfa9ad77319367d36940cd8a0b3a11aba16063e39632d98b0e931ddf32", size = 14440457, upload-time = "2024-09-20T13:09:04.105Z" },
+    { url = "https://files.pythonhosted.org/packages/ed/8c/87ddf1fcb55d11f9f847e3c69bb1c6f8e46e2f40ab1a2d2abadb2401b007/pandas-2.2.3-cp311-cp311-win_amd64.whl", hash = "sha256:3fc6873a41186404dad67245896a6e440baacc92f5b716ccd1bc9ed2995ab2c5", size = 11617166, upload-time = "2024-09-20T13:09:06.917Z" },
+    { url = "https://files.pythonhosted.org/packages/17/a3/fb2734118db0af37ea7433f57f722c0a56687e14b14690edff0cdb4b7e58/pandas-2.2.3-cp312-cp312-macosx_10_9_x86_64.whl", hash = "sha256:b1d432e8d08679a40e2a6d8b2f9770a5c21793a6f9f47fdd52c5ce1948a5a8a9", size = 12529893, upload-time = "2024-09-20T13:09:09.655Z" },
+    { url = "https://files.pythonhosted.org/packages/e1/0c/ad295fd74bfac85358fd579e271cded3ac969de81f62dd0142c426b9da91/pandas-2.2.3-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:a5a1595fe639f5988ba6a8e5bc9649af3baf26df3998a0abe56c02609392e0a4", size = 11363475, upload-time = "2024-09-20T13:09:14.718Z" },
+    { url = "https://files.pythonhosted.org/packages/c6/2a/4bba3f03f7d07207481fed47f5b35f556c7441acddc368ec43d6643c5777/pandas-2.2.3-cp312-cp312-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:5de54125a92bb4d1c051c0659e6fcb75256bf799a732a87184e5ea503965bce3", size = 15188645, upload-time = "2024-09-20T19:02:03.88Z" },
+    { url = "https://files.pythonhosted.org/packages/38/f8/d8fddee9ed0d0c0f4a2132c1dfcf0e3e53265055da8df952a53e7eaf178c/pandas-2.2.3-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:fffb8ae78d8af97f849404f21411c95062db1496aeb3e56f146f0355c9989319", size = 12739445, upload-time = "2024-09-20T13:09:17.621Z" },
+    { url = "https://files.pythonhosted.org/packages/20/e8/45a05d9c39d2cea61ab175dbe6a2de1d05b679e8de2011da4ee190d7e748/pandas-2.2.3-cp312-cp312-musllinux_1_2_aarch64.whl", hash = "sha256:6dfcb5ee8d4d50c06a51c2fffa6cff6272098ad6540aed1a76d15fb9318194d8", size = 16359235, upload-time = "2024-09-20T19:02:07.094Z" },
+    { url = "https://files.pythonhosted.org/packages/1d/99/617d07a6a5e429ff90c90da64d428516605a1ec7d7bea494235e1c3882de/pandas-2.2.3-cp312-cp312-musllinux_1_2_x86_64.whl", hash = "sha256:062309c1b9ea12a50e8ce661145c6aab431b1e99530d3cd60640e255778bd43a", size = 14056756, upload-time = "2024-09-20T13:09:20.474Z" },
+    { url = "https://files.pythonhosted.org/packages/29/d4/1244ab8edf173a10fd601f7e13b9566c1b525c4f365d6bee918e68381889/pandas-2.2.3-cp312-cp312-win_amd64.whl", hash = "sha256:59ef3764d0fe818125a5097d2ae867ca3fa64df032331b7e0917cf5d7bf66b13", size = 11504248, upload-time = "2024-09-20T13:09:23.137Z" },
+    { url = "https://files.pythonhosted.org/packages/64/22/3b8f4e0ed70644e85cfdcd57454686b9057c6c38d2f74fe4b8bc2527214a/pandas-2.2.3-cp313-cp313-macosx_10_13_x86_64.whl", hash = "sha256:f00d1345d84d8c86a63e476bb4955e46458b304b9575dcf71102b5c705320015", size = 12477643, upload-time = "2024-09-20T13:09:25.522Z" },
+    { url = "https://files.pythonhosted.org/packages/e4/93/b3f5d1838500e22c8d793625da672f3eec046b1a99257666c94446969282/pandas-2.2.3-cp313-cp313-macosx_11_0_arm64.whl", hash = "sha256:3508d914817e153ad359d7e069d752cdd736a247c322d932eb89e6bc84217f28", size = 11281573, upload-time = "2024-09-20T13:09:28.012Z" },
+    { url = "https://files.pythonhosted.org/packages/f5/94/6c79b07f0e5aab1dcfa35a75f4817f5c4f677931d4234afcd75f0e6a66ca/pandas-2.2.3-cp313-cp313-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:22a9d949bfc9a502d320aa04e5d02feab689d61da4e7764b62c30b991c42c5f0", size = 15196085, upload-time = "2024-09-20T19:02:10.451Z" },
+    { url = "https://files.pythonhosted.org/packages/e8/31/aa8da88ca0eadbabd0a639788a6da13bb2ff6edbbb9f29aa786450a30a91/pandas-2.2.3-cp313-cp313-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:f3a255b2c19987fbbe62a9dfd6cff7ff2aa9ccab3fc75218fd4b7530f01efa24", size = 12711809, upload-time = "2024-09-20T13:09:30.814Z" },
+    { url = "https://files.pythonhosted.org/packages/ee/7c/c6dbdb0cb2a4344cacfb8de1c5808ca885b2e4dcfde8008266608f9372af/pandas-2.2.3-cp313-cp313-musllinux_1_2_aarch64.whl", hash = "sha256:800250ecdadb6d9c78eae4990da62743b857b470883fa27f652db8bdde7f6659", size = 16356316, upload-time = "2024-09-20T19:02:13.825Z" },
+    { url = "https://files.pythonhosted.org/packages/57/b7/8b757e7d92023b832869fa8881a992696a0bfe2e26f72c9ae9f255988d42/pandas-2.2.3-cp313-cp313-musllinux_1_2_x86_64.whl", hash = "sha256:6374c452ff3ec675a8f46fd9ab25c4ad0ba590b71cf0656f8b6daa5202bca3fb", size = 14022055, upload-time = "2024-09-20T13:09:33.462Z" },
+    { url = "https://files.pythonhosted.org/packages/3b/bc/4b18e2b8c002572c5a441a64826252ce5da2aa738855747247a971988043/pandas-2.2.3-cp313-cp313-win_amd64.whl", hash = "sha256:61c5ad4043f791b61dd4752191d9f07f0ae412515d59ba8f005832a532f8736d", size = 11481175, upload-time = "2024-09-20T13:09:35.871Z" },
+    { url = "https://files.pythonhosted.org/packages/76/a3/a5d88146815e972d40d19247b2c162e88213ef51c7c25993942c39dbf41d/pandas-2.2.3-cp313-cp313t-macosx_10_13_x86_64.whl", hash = "sha256:3b71f27954685ee685317063bf13c7709a7ba74fc996b84fc6821c59b0f06468", size = 12615650, upload-time = "2024-09-20T13:09:38.685Z" },
+    { url = "https://files.pythonhosted.org/packages/9c/8c/f0fd18f6140ddafc0c24122c8a964e48294acc579d47def376fef12bcb4a/pandas-2.2.3-cp313-cp313t-macosx_11_0_arm64.whl", hash = "sha256:38cf8125c40dae9d5acc10fa66af8ea6fdf760b2714ee482ca691fc66e6fcb18", size = 11290177, upload-time = "2024-09-20T13:09:41.141Z" },
+    { url = "https://files.pythonhosted.org/packages/ed/f9/e995754eab9c0f14c6777401f7eece0943840b7a9fc932221c19d1abee9f/pandas-2.2.3-cp313-cp313t-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:ba96630bc17c875161df3818780af30e43be9b166ce51c9a18c1feae342906c2", size = 14651526, upload-time = "2024-09-20T19:02:16.905Z" },
+    { url = "https://files.pythonhosted.org/packages/25/b0/98d6ae2e1abac4f35230aa756005e8654649d305df9a28b16b9ae4353bff/pandas-2.2.3-cp313-cp313t-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:1db71525a1538b30142094edb9adc10be3f3e176748cd7acc2240c2f2e5aa3a4", size = 11871013, upload-time = "2024-09-20T13:09:44.39Z" },
+    { url = "https://files.pythonhosted.org/packages/cc/57/0f72a10f9db6a4628744c8e8f0df4e6e21de01212c7c981d31e50ffc8328/pandas-2.2.3-cp313-cp313t-musllinux_1_2_aarch64.whl", hash = "sha256:15c0e1e02e93116177d29ff83e8b1619c93ddc9c49083f237d4312337a61165d", size = 15711620, upload-time = "2024-09-20T19:02:20.639Z" },
+    { url = "https://files.pythonhosted.org/packages/ab/5f/b38085618b950b79d2d9164a711c52b10aefc0ae6833b96f626b7021b2ed/pandas-2.2.3-cp313-cp313t-musllinux_1_2_x86_64.whl", hash = "sha256:ad5b65698ab28ed8d7f18790a0dc58005c7629f227be9ecc1072aa74c0c1d43a", size = 13098436, upload-time = "2024-09-20T13:09:48.112Z" },
+]
+
+[[package]]
+name = "pillow"
+version = "11.2.1"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/af/cb/bb5c01fcd2a69335b86c22142b2bccfc3464087efb7fd382eee5ffc7fdf7/pillow-11.2.1.tar.gz", hash = "sha256:a64dd61998416367b7ef979b73d3a85853ba9bec4c2925f74e588879a58716b6", size = 47026707, upload-time = "2025-04-12T17:50:03.289Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/0d/8b/b158ad57ed44d3cc54db8d68ad7c0a58b8fc0e4c7a3f995f9d62d5b464a1/pillow-11.2.1-cp310-cp310-macosx_10_10_x86_64.whl", hash = "sha256:d57a75d53922fc20c165016a20d9c44f73305e67c351bbc60d1adaf662e74047", size = 3198442, upload-time = "2025-04-12T17:47:10.666Z" },
+    { url = "https://files.pythonhosted.org/packages/b1/f8/bb5d956142f86c2d6cc36704943fa761f2d2e4c48b7436fd0a85c20f1713/pillow-11.2.1-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:127bf6ac4a5b58b3d32fc8289656f77f80567d65660bc46f72c0d77e6600cc95", size = 3030553, upload-time = "2025-04-12T17:47:13.153Z" },
+    { url = "https://files.pythonhosted.org/packages/22/7f/0e413bb3e2aa797b9ca2c5c38cb2e2e45d88654e5b12da91ad446964cfae/pillow-11.2.1-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:b4ba4be812c7a40280629e55ae0b14a0aafa150dd6451297562e1764808bbe61", size = 4405503, upload-time = "2025-04-12T17:47:15.36Z" },
+    { url = "https://files.pythonhosted.org/packages/f3/b4/cc647f4d13f3eb837d3065824aa58b9bcf10821f029dc79955ee43f793bd/pillow-11.2.1-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:c8bd62331e5032bc396a93609982a9ab6b411c05078a52f5fe3cc59234a3abd1", size = 4490648, upload-time = "2025-04-12T17:47:17.37Z" },
+    { url = "https://files.pythonhosted.org/packages/c2/6f/240b772a3b35cdd7384166461567aa6713799b4e78d180c555bd284844ea/pillow-11.2.1-cp310-cp310-manylinux_2_28_aarch64.whl", hash = "sha256:562d11134c97a62fe3af29581f083033179f7ff435f78392565a1ad2d1c2c45c", size = 4508937, upload-time = "2025-04-12T17:47:19.066Z" },
+    { url = "https://files.pythonhosted.org/packages/f3/5e/7ca9c815ade5fdca18853db86d812f2f188212792780208bdb37a0a6aef4/pillow-11.2.1-cp310-cp310-manylinux_2_28_x86_64.whl", hash = "sha256:c97209e85b5be259994eb5b69ff50c5d20cca0f458ef9abd835e262d9d88b39d", size = 4599802, upload-time = "2025-04-12T17:47:21.404Z" },
+    { url = "https://files.pythonhosted.org/packages/02/81/c3d9d38ce0c4878a77245d4cf2c46d45a4ad0f93000227910a46caff52f3/pillow-11.2.1-cp310-cp310-musllinux_1_2_aarch64.whl", hash = "sha256:0c3e6d0f59171dfa2e25d7116217543310908dfa2770aa64b8f87605f8cacc97", size = 4576717, upload-time = "2025-04-12T17:47:23.571Z" },
+    { url = "https://files.pythonhosted.org/packages/42/49/52b719b89ac7da3185b8d29c94d0e6aec8140059e3d8adcaa46da3751180/pillow-11.2.1-cp310-cp310-musllinux_1_2_x86_64.whl", hash = "sha256:cc1c3bc53befb6096b84165956e886b1729634a799e9d6329a0c512ab651e579", size = 4654874, upload-time = "2025-04-12T17:47:25.783Z" },
+    { url = "https://files.pythonhosted.org/packages/5b/0b/ede75063ba6023798267023dc0d0401f13695d228194d2242d5a7ba2f964/pillow-11.2.1-cp310-cp310-win32.whl", hash = "sha256:312c77b7f07ab2139924d2639860e084ec2a13e72af54d4f08ac843a5fc9c79d", size = 2331717, upload-time = "2025-04-12T17:47:28.922Z" },
+    { url = "https://files.pythonhosted.org/packages/ed/3c/9831da3edea527c2ed9a09f31a2c04e77cd705847f13b69ca60269eec370/pillow-11.2.1-cp310-cp310-win_amd64.whl", hash = "sha256:9bc7ae48b8057a611e5fe9f853baa88093b9a76303937449397899385da06fad", size = 2676204, upload-time = "2025-04-12T17:47:31.283Z" },
+    { url = "https://files.pythonhosted.org/packages/01/97/1f66ff8a1503d8cbfc5bae4dc99d54c6ec1e22ad2b946241365320caabc2/pillow-11.2.1-cp310-cp310-win_arm64.whl", hash = "sha256:2728567e249cdd939f6cc3d1f049595c66e4187f3c34078cbc0a7d21c47482d2", size = 2414767, upload-time = "2025-04-12T17:47:34.655Z" },
+    { url = "https://files.pythonhosted.org/packages/68/08/3fbf4b98924c73037a8e8b4c2c774784805e0fb4ebca6c5bb60795c40125/pillow-11.2.1-cp311-cp311-macosx_10_10_x86_64.whl", hash = "sha256:35ca289f712ccfc699508c4658a1d14652e8033e9b69839edf83cbdd0ba39e70", size = 3198450, upload-time = "2025-04-12T17:47:37.135Z" },
+    { url = "https://files.pythonhosted.org/packages/84/92/6505b1af3d2849d5e714fc75ba9e69b7255c05ee42383a35a4d58f576b16/pillow-11.2.1-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:e0409af9f829f87a2dfb7e259f78f317a5351f2045158be321fd135973fff7bf", size = 3030550, upload-time = "2025-04-12T17:47:39.345Z" },
+    { url = "https://files.pythonhosted.org/packages/3c/8c/ac2f99d2a70ff966bc7eb13dacacfaab57c0549b2ffb351b6537c7840b12/pillow-11.2.1-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:d4e5c5edee874dce4f653dbe59db7c73a600119fbea8d31f53423586ee2aafd7", size = 4415018, upload-time = "2025-04-12T17:47:41.128Z" },
+    { url = "https://files.pythonhosted.org/packages/1f/e3/0a58b5d838687f40891fff9cbaf8669f90c96b64dc8f91f87894413856c6/pillow-11.2.1-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:b93a07e76d13bff9444f1a029e0af2964e654bfc2e2c2d46bfd080df5ad5f3d8", size = 4498006, upload-time = "2025-04-12T17:47:42.912Z" },
+    { url = "https://files.pythonhosted.org/packages/21/f5/6ba14718135f08fbfa33308efe027dd02b781d3f1d5c471444a395933aac/pillow-11.2.1-cp311-cp311-manylinux_2_28_aarch64.whl", hash = "sha256:e6def7eed9e7fa90fde255afaf08060dc4b343bbe524a8f69bdd2a2f0018f600", size = 4517773, upload-time = "2025-04-12T17:47:44.611Z" },
+    { url = "https://files.pythonhosted.org/packages/20/f2/805ad600fc59ebe4f1ba6129cd3a75fb0da126975c8579b8f57abeb61e80/pillow-11.2.1-cp311-cp311-manylinux_2_28_x86_64.whl", hash = "sha256:8f4f3724c068be008c08257207210c138d5f3731af6c155a81c2b09a9eb3a788", size = 4607069, upload-time = "2025-04-12T17:47:46.46Z" },
+    { url = "https://files.pythonhosted.org/packages/71/6b/4ef8a288b4bb2e0180cba13ca0a519fa27aa982875882392b65131401099/pillow-11.2.1-cp311-cp311-musllinux_1_2_aarch64.whl", hash = "sha256:a0a6709b47019dff32e678bc12c63008311b82b9327613f534e496dacaefb71e", size = 4583460, upload-time = "2025-04-12T17:47:49.255Z" },
+    { url = "https://files.pythonhosted.org/packages/62/ae/f29c705a09cbc9e2a456590816e5c234382ae5d32584f451c3eb41a62062/pillow-11.2.1-cp311-cp311-musllinux_1_2_x86_64.whl", hash = "sha256:f6b0c664ccb879109ee3ca702a9272d877f4fcd21e5eb63c26422fd6e415365e", size = 4661304, upload-time = "2025-04-12T17:47:51.067Z" },
+    { url = "https://files.pythonhosted.org/packages/6e/1a/c8217b6f2f73794a5e219fbad087701f412337ae6dbb956db37d69a9bc43/pillow-11.2.1-cp311-cp311-win32.whl", hash = "sha256:cc5d875d56e49f112b6def6813c4e3d3036d269c008bf8aef72cd08d20ca6df6", size = 2331809, upload-time = "2025-04-12T17:47:54.425Z" },
+    { url = "https://files.pythonhosted.org/packages/e2/72/25a8f40170dc262e86e90f37cb72cb3de5e307f75bf4b02535a61afcd519/pillow-11.2.1-cp311-cp311-win_amd64.whl", hash = "sha256:0f5c7eda47bf8e3c8a283762cab94e496ba977a420868cb819159980b6709193", size = 2676338, upload-time = "2025-04-12T17:47:56.535Z" },
+    { url = "https://files.pythonhosted.org/packages/06/9e/76825e39efee61efea258b479391ca77d64dbd9e5804e4ad0fa453b4ba55/pillow-11.2.1-cp311-cp311-win_arm64.whl", hash = "sha256:4d375eb838755f2528ac8cbc926c3e31cc49ca4ad0cf79cff48b20e30634a4a7", size = 2414918, upload-time = "2025-04-12T17:47:58.217Z" },
+    { url = "https://files.pythonhosted.org/packages/c7/40/052610b15a1b8961f52537cc8326ca6a881408bc2bdad0d852edeb6ed33b/pillow-11.2.1-cp312-cp312-macosx_10_13_x86_64.whl", hash = "sha256:78afba22027b4accef10dbd5eed84425930ba41b3ea0a86fa8d20baaf19d807f", size = 3190185, upload-time = "2025-04-12T17:48:00.417Z" },
+    { url = "https://files.pythonhosted.org/packages/e5/7e/b86dbd35a5f938632093dc40d1682874c33dcfe832558fc80ca56bfcb774/pillow-11.2.1-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:78092232a4ab376a35d68c4e6d5e00dfd73454bd12b230420025fbe178ee3b0b", size = 3030306, upload-time = "2025-04-12T17:48:02.391Z" },
+    { url = "https://files.pythonhosted.org/packages/a4/5c/467a161f9ed53e5eab51a42923c33051bf8d1a2af4626ac04f5166e58e0c/pillow-11.2.1-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:25a5f306095c6780c52e6bbb6109624b95c5b18e40aab1c3041da3e9e0cd3e2d", size = 4416121, upload-time = "2025-04-12T17:48:04.554Z" },
+    { url = "https://files.pythonhosted.org/packages/62/73/972b7742e38ae0e2ac76ab137ca6005dcf877480da0d9d61d93b613065b4/pillow-11.2.1-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:0c7b29dbd4281923a2bfe562acb734cee96bbb129e96e6972d315ed9f232bef4", size = 4501707, upload-time = "2025-04-12T17:48:06.831Z" },
+    { url = "https://files.pythonhosted.org/packages/e4/3a/427e4cb0b9e177efbc1a84798ed20498c4f233abde003c06d2650a6d60cb/pillow-11.2.1-cp312-cp312-manylinux_2_28_aarch64.whl", hash = "sha256:3e645b020f3209a0181a418bffe7b4a93171eef6c4ef6cc20980b30bebf17b7d", size = 4522921, upload-time = "2025-04-12T17:48:09.229Z" },
+    { url = "https://files.pythonhosted.org/packages/fe/7c/d8b1330458e4d2f3f45d9508796d7caf0c0d3764c00c823d10f6f1a3b76d/pillow-11.2.1-cp312-cp312-manylinux_2_28_x86_64.whl", hash = "sha256:b2dbea1012ccb784a65349f57bbc93730b96e85b42e9bf7b01ef40443db720b4", size = 4612523, upload-time = "2025-04-12T17:48:11.631Z" },
+    { url = "https://files.pythonhosted.org/packages/b3/2f/65738384e0b1acf451de5a573d8153fe84103772d139e1e0bdf1596be2ea/pillow-11.2.1-cp312-cp312-musllinux_1_2_aarch64.whl", hash = "sha256:da3104c57bbd72948d75f6a9389e6727d2ab6333c3617f0a89d72d4940aa0443", size = 4587836, upload-time = "2025-04-12T17:48:13.592Z" },
+    { url = "https://files.pythonhosted.org/packages/6a/c5/e795c9f2ddf3debb2dedd0df889f2fe4b053308bb59a3cc02a0cd144d641/pillow-11.2.1-cp312-cp312-musllinux_1_2_x86_64.whl", hash = "sha256:598174aef4589af795f66f9caab87ba4ff860ce08cd5bb447c6fc553ffee603c", size = 4669390, upload-time = "2025-04-12T17:48:15.938Z" },
+    { url = "https://files.pythonhosted.org/packages/96/ae/ca0099a3995976a9fce2f423166f7bff9b12244afdc7520f6ed38911539a/pillow-11.2.1-cp312-cp312-win32.whl", hash = "sha256:1d535df14716e7f8776b9e7fee118576d65572b4aad3ed639be9e4fa88a1cad3", size = 2332309, upload-time = "2025-04-12T17:48:17.885Z" },
+    { url = "https://files.pythonhosted.org/packages/7c/18/24bff2ad716257fc03da964c5e8f05d9790a779a8895d6566e493ccf0189/pillow-11.2.1-cp312-cp312-win_amd64.whl", hash = "sha256:14e33b28bf17c7a38eede290f77db7c664e4eb01f7869e37fa98a5aa95978941", size = 2676768, upload-time = "2025-04-12T17:48:19.655Z" },
+    { url = "https://files.pythonhosted.org/packages/da/bb/e8d656c9543276517ee40184aaa39dcb41e683bca121022f9323ae11b39d/pillow-11.2.1-cp312-cp312-win_arm64.whl", hash = "sha256:21e1470ac9e5739ff880c211fc3af01e3ae505859392bf65458c224d0bf283eb", size = 2415087, upload-time = "2025-04-12T17:48:21.991Z" },
+    { url = "https://files.pythonhosted.org/packages/36/9c/447528ee3776e7ab8897fe33697a7ff3f0475bb490c5ac1456a03dc57956/pillow-11.2.1-cp313-cp313-macosx_10_13_x86_64.whl", hash = "sha256:fdec757fea0b793056419bca3e9932eb2b0ceec90ef4813ea4c1e072c389eb28", size = 3190098, upload-time = "2025-04-12T17:48:23.915Z" },
+    { url = "https://files.pythonhosted.org/packages/b5/09/29d5cd052f7566a63e5b506fac9c60526e9ecc553825551333e1e18a4858/pillow-11.2.1-cp313-cp313-macosx_11_0_arm64.whl", hash = "sha256:b0e130705d568e2f43a17bcbe74d90958e8a16263868a12c3e0d9c8162690830", size = 3030166, upload-time = "2025-04-12T17:48:25.738Z" },
+    { url = "https://files.pythonhosted.org/packages/71/5d/446ee132ad35e7600652133f9c2840b4799bbd8e4adba881284860da0a36/pillow-11.2.1-cp313-cp313-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:7bdb5e09068332578214cadd9c05e3d64d99e0e87591be22a324bdbc18925be0", size = 4408674, upload-time = "2025-04-12T17:48:27.908Z" },
+    { url = "https://files.pythonhosted.org/packages/69/5f/cbe509c0ddf91cc3a03bbacf40e5c2339c4912d16458fcb797bb47bcb269/pillow-11.2.1-cp313-cp313-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:d189ba1bebfbc0c0e529159631ec72bb9e9bc041f01ec6d3233d6d82eb823bc1", size = 4496005, upload-time = "2025-04-12T17:48:29.888Z" },
+    { url = "https://files.pythonhosted.org/packages/f9/b3/dd4338d8fb8a5f312021f2977fb8198a1184893f9b00b02b75d565c33b51/pillow-11.2.1-cp313-cp313-manylinux_2_28_aarch64.whl", hash = "sha256:191955c55d8a712fab8934a42bfefbf99dd0b5875078240943f913bb66d46d9f", size = 4518707, upload-time = "2025-04-12T17:48:31.874Z" },
+    { url = "https://files.pythonhosted.org/packages/13/eb/2552ecebc0b887f539111c2cd241f538b8ff5891b8903dfe672e997529be/pillow-11.2.1-cp313-cp313-manylinux_2_28_x86_64.whl", hash = "sha256:ad275964d52e2243430472fc5d2c2334b4fc3ff9c16cb0a19254e25efa03a155", size = 4610008, upload-time = "2025-04-12T17:48:34.422Z" },
+    { url = "https://files.pythonhosted.org/packages/72/d1/924ce51bea494cb6e7959522d69d7b1c7e74f6821d84c63c3dc430cbbf3b/pillow-11.2.1-cp313-cp313-musllinux_1_2_aarch64.whl", hash = "sha256:750f96efe0597382660d8b53e90dd1dd44568a8edb51cb7f9d5d918b80d4de14", size = 4585420, upload-time = "2025-04-12T17:48:37.641Z" },
+    { url = "https://files.pythonhosted.org/packages/43/ab/8f81312d255d713b99ca37479a4cb4b0f48195e530cdc1611990eb8fd04b/pillow-11.2.1-cp313-cp313-musllinux_1_2_x86_64.whl", hash = "sha256:fe15238d3798788d00716637b3d4e7bb6bde18b26e5d08335a96e88564a36b6b", size = 4667655, upload-time = "2025-04-12T17:48:39.652Z" },
+    { url = "https://files.pythonhosted.org/packages/94/86/8f2e9d2dc3d308dfd137a07fe1cc478df0a23d42a6c4093b087e738e4827/pillow-11.2.1-cp313-cp313-win32.whl", hash = "sha256:3fe735ced9a607fee4f481423a9c36701a39719252a9bb251679635f99d0f7d2", size = 2332329, upload-time = "2025-04-12T17:48:41.765Z" },
+    { url = "https://files.pythonhosted.org/packages/6d/ec/1179083b8d6067a613e4d595359b5fdea65d0a3b7ad623fee906e1b3c4d2/pillow-11.2.1-cp313-cp313-win_amd64.whl", hash = "sha256:74ee3d7ecb3f3c05459ba95eed5efa28d6092d751ce9bf20e3e253a4e497e691", size = 2676388, upload-time = "2025-04-12T17:48:43.625Z" },
+    { url = "https://files.pythonhosted.org/packages/23/f1/2fc1e1e294de897df39fa8622d829b8828ddad938b0eaea256d65b84dd72/pillow-11.2.1-cp313-cp313-win_arm64.whl", hash = "sha256:5119225c622403afb4b44bad4c1ca6c1f98eed79db8d3bc6e4e160fc6339d66c", size = 2414950, upload-time = "2025-04-12T17:48:45.475Z" },
+    { url = "https://files.pythonhosted.org/packages/c4/3e/c328c48b3f0ead7bab765a84b4977acb29f101d10e4ef57a5e3400447c03/pillow-11.2.1-cp313-cp313t-macosx_10_13_x86_64.whl", hash = "sha256:8ce2e8411c7aaef53e6bb29fe98f28cd4fbd9a1d9be2eeea434331aac0536b22", size = 3192759, upload-time = "2025-04-12T17:48:47.866Z" },
+    { url = "https://files.pythonhosted.org/packages/18/0e/1c68532d833fc8b9f404d3a642991441d9058eccd5606eab31617f29b6d4/pillow-11.2.1-cp313-cp313t-macosx_11_0_arm64.whl", hash = "sha256:9ee66787e095127116d91dea2143db65c7bb1e232f617aa5957c0d9d2a3f23a7", size = 3033284, upload-time = "2025-04-12T17:48:50.189Z" },
+    { url = "https://files.pythonhosted.org/packages/b7/cb/6faf3fb1e7705fd2db74e070f3bf6f88693601b0ed8e81049a8266de4754/pillow-11.2.1-cp313-cp313t-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:9622e3b6c1d8b551b6e6f21873bdcc55762b4b2126633014cea1803368a9aa16", size = 4445826, upload-time = "2025-04-12T17:48:52.346Z" },
+    { url = "https://files.pythonhosted.org/packages/07/94/8be03d50b70ca47fb434a358919d6a8d6580f282bbb7af7e4aa40103461d/pillow-11.2.1-cp313-cp313t-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:63b5dff3a68f371ea06025a1a6966c9a1e1ee452fc8020c2cd0ea41b83e9037b", size = 4527329, upload-time = "2025-04-12T17:48:54.403Z" },
+    { url = "https://files.pythonhosted.org/packages/fd/a4/bfe78777076dc405e3bd2080bc32da5ab3945b5a25dc5d8acaa9de64a162/pillow-11.2.1-cp313-cp313t-manylinux_2_28_aarch64.whl", hash = "sha256:31df6e2d3d8fc99f993fd253e97fae451a8db2e7207acf97859732273e108406", size = 4549049, upload-time = "2025-04-12T17:48:56.383Z" },
+    { url = "https://files.pythonhosted.org/packages/65/4d/eaf9068dc687c24979e977ce5677e253624bd8b616b286f543f0c1b91662/pillow-11.2.1-cp313-cp313t-manylinux_2_28_x86_64.whl", hash = "sha256:062b7a42d672c45a70fa1f8b43d1d38ff76b63421cbbe7f88146b39e8a558d91", size = 4635408, upload-time = "2025-04-12T17:48:58.782Z" },
+    { url = "https://files.pythonhosted.org/packages/1d/26/0fd443365d9c63bc79feb219f97d935cd4b93af28353cba78d8e77b61719/pillow-11.2.1-cp313-cp313t-musllinux_1_2_aarch64.whl", hash = "sha256:4eb92eca2711ef8be42fd3f67533765d9fd043b8c80db204f16c8ea62ee1a751", size = 4614863, upload-time = "2025-04-12T17:49:00.709Z" },
+    { url = "https://files.pythonhosted.org/packages/49/65/dca4d2506be482c2c6641cacdba5c602bc76d8ceb618fd37de855653a419/pillow-11.2.1-cp313-cp313t-musllinux_1_2_x86_64.whl", hash = "sha256:f91ebf30830a48c825590aede79376cb40f110b387c17ee9bd59932c961044f9", size = 4692938, upload-time = "2025-04-12T17:49:02.946Z" },
+    { url = "https://files.pythonhosted.org/packages/b3/92/1ca0c3f09233bd7decf8f7105a1c4e3162fb9142128c74adad0fb361b7eb/pillow-11.2.1-cp313-cp313t-win32.whl", hash = "sha256:e0b55f27f584ed623221cfe995c912c61606be8513bfa0e07d2c674b4516d9dd", size = 2335774, upload-time = "2025-04-12T17:49:04.889Z" },
+    { url = "https://files.pythonhosted.org/packages/a5/ac/77525347cb43b83ae905ffe257bbe2cc6fd23acb9796639a1f56aa59d191/pillow-11.2.1-cp313-cp313t-win_amd64.whl", hash = "sha256:36d6b82164c39ce5482f649b437382c0fb2395eabc1e2b1702a6deb8ad647d6e", size = 2681895, upload-time = "2025-04-12T17:49:06.635Z" },
+    { url = "https://files.pythonhosted.org/packages/67/32/32dc030cfa91ca0fc52baebbba2e009bb001122a1daa8b6a79ad830b38d3/pillow-11.2.1-cp313-cp313t-win_arm64.whl", hash = "sha256:225c832a13326e34f212d2072982bb1adb210e0cc0b153e688743018c94a2681", size = 2417234, upload-time = "2025-04-12T17:49:08.399Z" },
+    { url = "https://files.pythonhosted.org/packages/33/49/c8c21e4255b4f4a2c0c68ac18125d7f5460b109acc6dfdef1a24f9b960ef/pillow-11.2.1-pp310-pypy310_pp73-macosx_10_15_x86_64.whl", hash = "sha256:9b7b0d4fd2635f54ad82785d56bc0d94f147096493a79985d0ab57aedd563156", size = 3181727, upload-time = "2025-04-12T17:49:31.898Z" },
+    { url = "https://files.pythonhosted.org/packages/6d/f1/f7255c0838f8c1ef6d55b625cfb286835c17e8136ce4351c5577d02c443b/pillow-11.2.1-pp310-pypy310_pp73-macosx_11_0_arm64.whl", hash = "sha256:aa442755e31c64037aa7c1cb186e0b369f8416c567381852c63444dd666fb772", size = 2999833, upload-time = "2025-04-12T17:49:34.2Z" },
+    { url = "https://files.pythonhosted.org/packages/e2/57/9968114457bd131063da98d87790d080366218f64fa2943b65ac6739abb3/pillow-11.2.1-pp310-pypy310_pp73-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:f0d3348c95b766f54b76116d53d4cb171b52992a1027e7ca50c81b43b9d9e363", size = 3437472, upload-time = "2025-04-12T17:49:36.294Z" },
+    { url = "https://files.pythonhosted.org/packages/b2/1b/e35d8a158e21372ecc48aac9c453518cfe23907bb82f950d6e1c72811eb0/pillow-11.2.1-pp310-pypy310_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:85d27ea4c889342f7e35f6d56e7e1cb345632ad592e8c51b693d7b7556043ce0", size = 3459976, upload-time = "2025-04-12T17:49:38.988Z" },
+    { url = "https://files.pythonhosted.org/packages/26/da/2c11d03b765efff0ccc473f1c4186dc2770110464f2177efaed9cf6fae01/pillow-11.2.1-pp310-pypy310_pp73-manylinux_2_28_aarch64.whl", hash = "sha256:bf2c33d6791c598142f00c9c4c7d47f6476731c31081331664eb26d6ab583e01", size = 3527133, upload-time = "2025-04-12T17:49:40.985Z" },
+    { url = "https://files.pythonhosted.org/packages/79/1a/4e85bd7cadf78412c2a3069249a09c32ef3323650fd3005c97cca7aa21df/pillow-11.2.1-pp310-pypy310_pp73-manylinux_2_28_x86_64.whl", hash = "sha256:e616e7154c37669fc1dfc14584f11e284e05d1c650e1c0f972f281c4ccc53193", size = 3571555, upload-time = "2025-04-12T17:49:42.964Z" },
+    { url = "https://files.pythonhosted.org/packages/69/03/239939915216de1e95e0ce2334bf17a7870ae185eb390fab6d706aadbfc0/pillow-11.2.1-pp310-pypy310_pp73-win_amd64.whl", hash = "sha256:39ad2e0f424394e3aebc40168845fee52df1394a4673a6ee512d840d14ab3013", size = 2674713, upload-time = "2025-04-12T17:49:44.944Z" },
+    { url = "https://files.pythonhosted.org/packages/a4/ad/2613c04633c7257d9481ab21d6b5364b59fc5d75faafd7cb8693523945a3/pillow-11.2.1-pp311-pypy311_pp73-macosx_10_15_x86_64.whl", hash = "sha256:80f1df8dbe9572b4b7abdfa17eb5d78dd620b1d55d9e25f834efdbee872d3aed", size = 3181734, upload-time = "2025-04-12T17:49:46.789Z" },
+    { url = "https://files.pythonhosted.org/packages/a4/fd/dcdda4471ed667de57bb5405bb42d751e6cfdd4011a12c248b455c778e03/pillow-11.2.1-pp311-pypy311_pp73-macosx_11_0_arm64.whl", hash = "sha256:ea926cfbc3957090becbcbbb65ad177161a2ff2ad578b5a6ec9bb1e1cd78753c", size = 2999841, upload-time = "2025-04-12T17:49:48.812Z" },
+    { url = "https://files.pythonhosted.org/packages/ac/89/8a2536e95e77432833f0db6fd72a8d310c8e4272a04461fb833eb021bf94/pillow-11.2.1-pp311-pypy311_pp73-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:738db0e0941ca0376804d4de6a782c005245264edaa253ffce24e5a15cbdc7bd", size = 3437470, upload-time = "2025-04-12T17:49:50.831Z" },
+    { url = "https://files.pythonhosted.org/packages/9d/8f/abd47b73c60712f88e9eda32baced7bfc3e9bd6a7619bb64b93acff28c3e/pillow-11.2.1-pp311-pypy311_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:9db98ab6565c69082ec9b0d4e40dd9f6181dab0dd236d26f7a50b8b9bfbd5076", size = 3460013, upload-time = "2025-04-12T17:49:53.278Z" },
+    { url = "https://files.pythonhosted.org/packages/f6/20/5c0a0aa83b213b7a07ec01e71a3d6ea2cf4ad1d2c686cc0168173b6089e7/pillow-11.2.1-pp311-pypy311_pp73-manylinux_2_28_aarch64.whl", hash = "sha256:036e53f4170e270ddb8797d4c590e6dd14d28e15c7da375c18978045f7e6c37b", size = 3527165, upload-time = "2025-04-12T17:49:55.164Z" },
+    { url = "https://files.pythonhosted.org/packages/58/0e/2abab98a72202d91146abc839e10c14f7cf36166f12838ea0c4db3ca6ecb/pillow-11.2.1-pp311-pypy311_pp73-manylinux_2_28_x86_64.whl", hash = "sha256:14f73f7c291279bd65fda51ee87affd7c1e097709f7fdd0188957a16c264601f", size = 3571586, upload-time = "2025-04-12T17:49:57.171Z" },
+    { url = "https://files.pythonhosted.org/packages/21/2c/5e05f58658cf49b6667762cca03d6e7d85cededde2caf2ab37b81f80e574/pillow-11.2.1-pp311-pypy311_pp73-win_amd64.whl", hash = "sha256:208653868d5c9ecc2b327f9b9ef34e0e42a4cdd172c2988fd81d62d2bc9bc044", size = 2674751, upload-time = "2025-04-12T17:49:59.628Z" },
+]
+
+[[package]]
+name = "psutil"
+version = "7.0.0"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/2a/80/336820c1ad9286a4ded7e845b2eccfcb27851ab8ac6abece774a6ff4d3de/psutil-7.0.0.tar.gz", hash = "sha256:7be9c3eba38beccb6495ea33afd982a44074b78f28c434a1f51cc07fd315c456", size = 497003, upload-time = "2025-02-13T21:54:07.946Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/ed/e6/2d26234410f8b8abdbf891c9da62bee396583f713fb9f3325a4760875d22/psutil-7.0.0-cp36-abi3-macosx_10_9_x86_64.whl", hash = "sha256:101d71dc322e3cffd7cea0650b09b3d08b8e7c4109dd6809fe452dfd00e58b25", size = 238051, upload-time = "2025-02-13T21:54:12.36Z" },
+    { url = "https://files.pythonhosted.org/packages/04/8b/30f930733afe425e3cbfc0e1468a30a18942350c1a8816acfade80c005c4/psutil-7.0.0-cp36-abi3-macosx_11_0_arm64.whl", hash = "sha256:39db632f6bb862eeccf56660871433e111b6ea58f2caea825571951d4b6aa3da", size = 239535, upload-time = "2025-02-13T21:54:16.07Z" },
+    { url = "https://files.pythonhosted.org/packages/2a/ed/d362e84620dd22876b55389248e522338ed1bf134a5edd3b8231d7207f6d/psutil-7.0.0-cp36-abi3-manylinux_2_12_i686.manylinux2010_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:1fcee592b4c6f146991ca55919ea3d1f8926497a713ed7faaf8225e174581e91", size = 275004, upload-time = "2025-02-13T21:54:18.662Z" },
+    { url = "https://files.pythonhosted.org/packages/bf/b9/b0eb3f3cbcb734d930fdf839431606844a825b23eaf9a6ab371edac8162c/psutil-7.0.0-cp36-abi3-manylinux_2_12_x86_64.manylinux2010_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:4b1388a4f6875d7e2aff5c4ca1cc16c545ed41dd8bb596cefea80111db353a34", size = 277986, upload-time = "2025-02-13T21:54:21.811Z" },
+    { url = "https://files.pythonhosted.org/packages/eb/a2/709e0fe2f093556c17fbafda93ac032257242cabcc7ff3369e2cb76a97aa/psutil-7.0.0-cp36-abi3-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:a5f098451abc2828f7dc6b58d44b532b22f2088f4999a937557b603ce72b1993", size = 279544, upload-time = "2025-02-13T21:54:24.68Z" },
+    { url = "https://files.pythonhosted.org/packages/50/e6/eecf58810b9d12e6427369784efe814a1eec0f492084ce8eb8f4d89d6d61/psutil-7.0.0-cp37-abi3-win32.whl", hash = "sha256:ba3fcef7523064a6c9da440fc4d6bd07da93ac726b5733c29027d7dc95b39d99", size = 241053, upload-time = "2025-02-13T21:54:34.31Z" },
+    { url = "https://files.pythonhosted.org/packages/50/1b/6921afe68c74868b4c9fa424dad3be35b095e16687989ebbb50ce4fceb7c/psutil-7.0.0-cp37-abi3-win_amd64.whl", hash = "sha256:4cf3d4eb1aa9b348dec30105c55cd9b7d4629285735a102beb4441e38db90553", size = 244885, upload-time = "2025-02-13T21:54:37.486Z" },
+]
+
+[[package]]
+name = "pyasn1"
+version = "0.6.1"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/ba/e9/01f1a64245b89f039897cb0130016d79f77d52669aae6ee7b159a6c4c018/pyasn1-0.6.1.tar.gz", hash = "sha256:6f580d2bdd84365380830acf45550f2511469f673cb4a5ae3857a3170128b034", size = 145322, upload-time = "2024-09-10T22:41:42.55Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/c8/f1/d6a797abb14f6283c0ddff96bbdd46937f64122b8c925cab503dd37f8214/pyasn1-0.6.1-py3-none-any.whl", hash = "sha256:0d632f46f2ba09143da3a8afe9e33fb6f92fa2320ab7e886e2d0f7672af84629", size = 83135, upload-time = "2024-09-11T16:00:36.122Z" },
+]
+
+[[package]]
+name = "pyasn1-modules"
+version = "0.4.2"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "pyasn1" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/e9/e6/78ebbb10a8c8e4b61a59249394a4a594c1a7af95593dc933a349c8d00964/pyasn1_modules-0.4.2.tar.gz", hash = "sha256:677091de870a80aae844b1ca6134f54652fa2c8c5a52aa396440ac3106e941e6", size = 307892, upload-time = "2025-03-28T02:41:22.17Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/47/8d/d529b5d697919ba8c11ad626e835d4039be708a35b0d22de83a269a6682c/pyasn1_modules-0.4.2-py3-none-any.whl", hash = "sha256:29253a9207ce32b64c3ac6600edc75368f98473906e8fd1043bd6b5b1de2c14a", size = 181259, upload-time = "2025-03-28T02:41:19.028Z" },
+]
+
+[[package]]
+name = "pyparsing"
+version = "3.2.3"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/bb/22/f1129e69d94ffff626bdb5c835506b3a5b4f3d070f17ea295e12c2c6f60f/pyparsing-3.2.3.tar.gz", hash = "sha256:b9c13f1ab8b3b542f72e28f634bad4de758ab3ce4546e4301970ad6fa77c38be", size = 1088608, upload-time = "2025-03-25T05:01:28.114Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/05/e7/df2285f3d08fee213f2d041540fa4fc9ca6c2d44cf36d3a035bf2a8d2bcc/pyparsing-3.2.3-py3-none-any.whl", hash = "sha256:a749938e02d6fd0b59b356ca504a24982314bb090c383e3cf201c95ef7e2bfcf", size = 111120, upload-time = "2025-03-25T05:01:24.908Z" },
+]
+
+[[package]]
+name = "python-dateutil"
+version = "2.9.0.post0"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "six" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/66/c0/0c8b6ad9f17a802ee498c46e004a0eb49bc148f2fd230864601a86dcf6db/python-dateutil-2.9.0.post0.tar.gz", hash = "sha256:37dd54208da7e1cd875388217d5e00ebd4179249f90fb72437e91a35459a0ad3", size = 342432, upload-time = "2024-03-01T18:36:20.211Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/ec/57/56b9bcc3c9c6a792fcbaf139543cee77261f3651ca9da0c93f5c1221264b/python_dateutil-2.9.0.post0-py2.py3-none-any.whl", hash = "sha256:a8b2bc7bffae282281c8140a97d3aa9c14da0b136dfe83f850eea9a5f7470427", size = 229892, upload-time = "2024-03-01T18:36:18.57Z" },
+]
+
+[[package]]
+name = "pytz"
+version = "2025.2"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/f8/bf/abbd3cdfb8fbc7fb3d4d38d320f2441b1e7cbe29be4f23797b4a2b5d8aac/pytz-2025.2.tar.gz", hash = "sha256:360b9e3dbb49a209c21ad61809c7fb453643e048b38924c765813546746e81c3", size = 320884, upload-time = "2025-03-25T02:25:00.538Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/81/c4/34e93fe5f5429d7570ec1fa436f1986fb1f00c3e0f43a589fe2bbcd22c3f/pytz-2025.2-py2.py3-none-any.whl", hash = "sha256:5ddf76296dd8c44c26eb8f4b6f35488f3ccbf6fbbd7adee0b7262d43f0ec2f00", size = 509225, upload-time = "2025-03-25T02:24:58.468Z" },
+]
+
+[[package]]
+name = "requests"
+version = "2.32.3"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "certifi" },
+    { name = "charset-normalizer" },
+    { name = "idna" },
+    { name = "urllib3" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/63/70/2bf7780ad2d390a8d301ad0b550f1581eadbd9a20f896afe06353c2a2913/requests-2.32.3.tar.gz", hash = "sha256:55365417734eb18255590a9ff9eb97e9e1da868d4ccd6402399eaf68af20a760", size = 131218, upload-time = "2024-05-29T15:37:49.536Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/f9/9b/335f9764261e915ed497fcdeb11df5dfd6f7bf257d4a6a2a686d80da4d54/requests-2.32.3-py3-none-any.whl", hash = "sha256:70761cfe03c773ceb22aa2f671b4757976145175cdfca038c02654d061d6dcc6", size = 64928, upload-time = "2024-05-29T15:37:47.027Z" },
+]
+
+[[package]]
+name = "requests-oauthlib"
+version = "2.0.0"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "oauthlib" },
+    { name = "requests" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/42/f2/05f29bc3913aea15eb670be136045bf5c5bbf4b99ecb839da9b422bb2c85/requests-oauthlib-2.0.0.tar.gz", hash = "sha256:b3dffaebd884d8cd778494369603a9e7b58d29111bf6b41bdc2dcd87203af4e9", size = 55650, upload-time = "2024-03-22T20:32:29.939Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/3b/5d/63d4ae3b9daea098d5d6f5da83984853c1bbacd5dc826764b249fe119d24/requests_oauthlib-2.0.0-py2.py3-none-any.whl", hash = "sha256:7dd8a5c40426b779b0868c404bdef9768deccf22749cde15852df527e6269b36", size = 24179, upload-time = "2024-03-22T20:32:28.055Z" },
+]
+
+[[package]]
+name = "rsa"
+version = "4.9.1"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "pyasn1" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/da/8a/22b7beea3ee0d44b1916c0c1cb0ee3af23b700b6da9f04991899d0c555d4/rsa-4.9.1.tar.gz", hash = "sha256:e7bdbfdb5497da4c07dfd35530e1a902659db6ff241e39d9953cad06ebd0ae75", size = 29034, upload-time = "2025-04-16T09:51:18.218Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/64/8d/0133e4eb4beed9e425d9a98ed6e081a55d195481b7632472be1af08d2f6b/rsa-4.9.1-py3-none-any.whl", hash = "sha256:68635866661c6836b8d39430f97a996acbd61bfa49406748ea243539fe239762", size = 34696, upload-time = "2025-04-16T09:51:17.142Z" },
+]
+
+[[package]]
+name = "scipy"
+version = "1.15.3"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "numpy" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/0f/37/6964b830433e654ec7485e45a00fc9a27cf868d622838f6b6d9c5ec0d532/scipy-1.15.3.tar.gz", hash = "sha256:eae3cf522bc7df64b42cad3925c876e1b0b6c35c1337c93e12c0f366f55b0eaf", size = 59419214, upload-time = "2025-05-08T16:13:05.955Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/78/2f/4966032c5f8cc7e6a60f1b2e0ad686293b9474b65246b0c642e3ef3badd0/scipy-1.15.3-cp310-cp310-macosx_10_13_x86_64.whl", hash = "sha256:a345928c86d535060c9c2b25e71e87c39ab2f22fc96e9636bd74d1dbf9de448c", size = 38702770, upload-time = "2025-05-08T16:04:20.849Z" },
+    { url = "https://files.pythonhosted.org/packages/a0/6e/0c3bf90fae0e910c274db43304ebe25a6b391327f3f10b5dcc638c090795/scipy-1.15.3-cp310-cp310-macosx_12_0_arm64.whl", hash = "sha256:ad3432cb0f9ed87477a8d97f03b763fd1d57709f1bbde3c9369b1dff5503b253", size = 30094511, upload-time = "2025-05-08T16:04:27.103Z" },
+    { url = "https://files.pythonhosted.org/packages/ea/b1/4deb37252311c1acff7f101f6453f0440794f51b6eacb1aad4459a134081/scipy-1.15.3-cp310-cp310-macosx_14_0_arm64.whl", hash = "sha256:aef683a9ae6eb00728a542b796f52a5477b78252edede72b8327a886ab63293f", size = 22368151, upload-time = "2025-05-08T16:04:31.731Z" },
+    { url = "https://files.pythonhosted.org/packages/38/7d/f457626e3cd3c29b3a49ca115a304cebb8cc6f31b04678f03b216899d3c6/scipy-1.15.3-cp310-cp310-macosx_14_0_x86_64.whl", hash = "sha256:1c832e1bd78dea67d5c16f786681b28dd695a8cb1fb90af2e27580d3d0967e92", size = 25121732, upload-time = "2025-05-08T16:04:36.596Z" },
+    { url = "https://files.pythonhosted.org/packages/db/0a/92b1de4a7adc7a15dcf5bddc6e191f6f29ee663b30511ce20467ef9b82e4/scipy-1.15.3-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:263961f658ce2165bbd7b99fa5135195c3a12d9bef045345016b8b50c315cb82", size = 35547617, upload-time = "2025-05-08T16:04:43.546Z" },
+    { url = "https://files.pythonhosted.org/packages/8e/6d/41991e503e51fc1134502694c5fa7a1671501a17ffa12716a4a9151af3df/scipy-1.15.3-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:9e2abc762b0811e09a0d3258abee2d98e0c703eee49464ce0069590846f31d40", size = 37662964, upload-time = "2025-05-08T16:04:49.431Z" },
+    { url = "https://files.pythonhosted.org/packages/25/e1/3df8f83cb15f3500478c889be8fb18700813b95e9e087328230b98d547ff/scipy-1.15.3-cp310-cp310-musllinux_1_2_aarch64.whl", hash = "sha256:ed7284b21a7a0c8f1b6e5977ac05396c0d008b89e05498c8b7e8f4a1423bba0e", size = 37238749, upload-time = "2025-05-08T16:04:55.215Z" },
+    { url = "https://files.pythonhosted.org/packages/93/3e/b3257cf446f2a3533ed7809757039016b74cd6f38271de91682aa844cfc5/scipy-1.15.3-cp310-cp310-musllinux_1_2_x86_64.whl", hash = "sha256:5380741e53df2c566f4d234b100a484b420af85deb39ea35a1cc1be84ff53a5c", size = 40022383, upload-time = "2025-05-08T16:05:01.914Z" },
+    { url = "https://files.pythonhosted.org/packages/d1/84/55bc4881973d3f79b479a5a2e2df61c8c9a04fcb986a213ac9c02cfb659b/scipy-1.15.3-cp310-cp310-win_amd64.whl", hash = "sha256:9d61e97b186a57350f6d6fd72640f9e99d5a4a2b8fbf4b9ee9a841eab327dc13", size = 41259201, upload-time = "2025-05-08T16:05:08.166Z" },
+    { url = "https://files.pythonhosted.org/packages/96/ab/5cc9f80f28f6a7dff646c5756e559823614a42b1939d86dd0ed550470210/scipy-1.15.3-cp311-cp311-macosx_10_13_x86_64.whl", hash = "sha256:993439ce220d25e3696d1b23b233dd010169b62f6456488567e830654ee37a6b", size = 38714255, upload-time = "2025-05-08T16:05:14.596Z" },
+    { url = "https://files.pythonhosted.org/packages/4a/4a/66ba30abe5ad1a3ad15bfb0b59d22174012e8056ff448cb1644deccbfed2/scipy-1.15.3-cp311-cp311-macosx_12_0_arm64.whl", hash = "sha256:34716e281f181a02341ddeaad584205bd2fd3c242063bd3423d61ac259ca7eba", size = 30111035, upload-time = "2025-05-08T16:05:20.152Z" },
+    { url = "https://files.pythonhosted.org/packages/4b/fa/a7e5b95afd80d24313307f03624acc65801846fa75599034f8ceb9e2cbf6/scipy-1.15.3-cp311-cp311-macosx_14_0_arm64.whl", hash = "sha256:3b0334816afb8b91dab859281b1b9786934392aa3d527cd847e41bb6f45bee65", size = 22384499, upload-time = "2025-05-08T16:05:24.494Z" },
+    { url = "https://files.pythonhosted.org/packages/17/99/f3aaddccf3588bb4aea70ba35328c204cadd89517a1612ecfda5b2dd9d7a/scipy-1.15.3-cp311-cp311-macosx_14_0_x86_64.whl", hash = "sha256:6db907c7368e3092e24919b5e31c76998b0ce1684d51a90943cb0ed1b4ffd6c1", size = 25152602, upload-time = "2025-05-08T16:05:29.313Z" },
+    { url = "https://files.pythonhosted.org/packages/56/c5/1032cdb565f146109212153339f9cb8b993701e9fe56b1c97699eee12586/scipy-1.15.3-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:721d6b4ef5dc82ca8968c25b111e307083d7ca9091bc38163fb89243e85e3889", size = 35503415, upload-time = "2025-05-08T16:05:34.699Z" },
+    { url = "https://files.pythonhosted.org/packages/bd/37/89f19c8c05505d0601ed5650156e50eb881ae3918786c8fd7262b4ee66d3/scipy-1.15.3-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:39cb9c62e471b1bb3750066ecc3a3f3052b37751c7c3dfd0fd7e48900ed52982", size = 37652622, upload-time = "2025-05-08T16:05:40.762Z" },
+    { url = "https://files.pythonhosted.org/packages/7e/31/be59513aa9695519b18e1851bb9e487de66f2d31f835201f1b42f5d4d475/scipy-1.15.3-cp311-cp311-musllinux_1_2_aarch64.whl", hash = "sha256:795c46999bae845966368a3c013e0e00947932d68e235702b5c3f6ea799aa8c9", size = 37244796, upload-time = "2025-05-08T16:05:48.119Z" },
+    { url = "https://files.pythonhosted.org/packages/10/c0/4f5f3eeccc235632aab79b27a74a9130c6c35df358129f7ac8b29f562ac7/scipy-1.15.3-cp311-cp311-musllinux_1_2_x86_64.whl", hash = "sha256:18aaacb735ab38b38db42cb01f6b92a2d0d4b6aabefeb07f02849e47f8fb3594", size = 40047684, upload-time = "2025-05-08T16:05:54.22Z" },
+    { url = "https://files.pythonhosted.org/packages/ab/a7/0ddaf514ce8a8714f6ed243a2b391b41dbb65251affe21ee3077ec45ea9a/scipy-1.15.3-cp311-cp311-win_amd64.whl", hash = "sha256:ae48a786a28412d744c62fd7816a4118ef97e5be0bee968ce8f0a2fba7acf3bb", size = 41246504, upload-time = "2025-05-08T16:06:00.437Z" },
+    { url = "https://files.pythonhosted.org/packages/37/4b/683aa044c4162e10ed7a7ea30527f2cbd92e6999c10a8ed8edb253836e9c/scipy-1.15.3-cp312-cp312-macosx_10_13_x86_64.whl", hash = "sha256:6ac6310fdbfb7aa6612408bd2f07295bcbd3fda00d2d702178434751fe48e019", size = 38766735, upload-time = "2025-05-08T16:06:06.471Z" },
+    { url = "https://files.pythonhosted.org/packages/7b/7e/f30be3d03de07f25dc0ec926d1681fed5c732d759ac8f51079708c79e680/scipy-1.15.3-cp312-cp312-macosx_12_0_arm64.whl", hash = "sha256:185cd3d6d05ca4b44a8f1595af87f9c372bb6acf9c808e99aa3e9aa03bd98cf6", size = 30173284, upload-time = "2025-05-08T16:06:11.686Z" },
+    { url = "https://files.pythonhosted.org/packages/07/9c/0ddb0d0abdabe0d181c1793db51f02cd59e4901da6f9f7848e1f96759f0d/scipy-1.15.3-cp312-cp312-macosx_14_0_arm64.whl", hash = "sha256:05dc6abcd105e1a29f95eada46d4a3f251743cfd7d3ae8ddb4088047f24ea477", size = 22446958, upload-time = "2025-05-08T16:06:15.97Z" },
+    { url = "https://files.pythonhosted.org/packages/af/43/0bce905a965f36c58ff80d8bea33f1f9351b05fad4beaad4eae34699b7a1/scipy-1.15.3-cp312-cp312-macosx_14_0_x86_64.whl", hash = "sha256:06efcba926324df1696931a57a176c80848ccd67ce6ad020c810736bfd58eb1c", size = 25242454, upload-time = "2025-05-08T16:06:20.394Z" },
+    { url = "https://files.pythonhosted.org/packages/56/30/a6f08f84ee5b7b28b4c597aca4cbe545535c39fe911845a96414700b64ba/scipy-1.15.3-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:c05045d8b9bfd807ee1b9f38761993297b10b245f012b11b13b91ba8945f7e45", size = 35210199, upload-time = "2025-05-08T16:06:26.159Z" },
+    { url = "https://files.pythonhosted.org/packages/0b/1f/03f52c282437a168ee2c7c14a1a0d0781a9a4a8962d84ac05c06b4c5b555/scipy-1.15.3-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:271e3713e645149ea5ea3e97b57fdab61ce61333f97cfae392c28ba786f9bb49", size = 37309455, upload-time = "2025-05-08T16:06:32.778Z" },
+    { url = "https://files.pythonhosted.org/packages/89/b1/fbb53137f42c4bf630b1ffdfc2151a62d1d1b903b249f030d2b1c0280af8/scipy-1.15.3-cp312-cp312-musllinux_1_2_aarch64.whl", hash = "sha256:6cfd56fc1a8e53f6e89ba3a7a7251f7396412d655bca2aa5611c8ec9a6784a1e", size = 36885140, upload-time = "2025-05-08T16:06:39.249Z" },
+    { url = "https://files.pythonhosted.org/packages/2e/2e/025e39e339f5090df1ff266d021892694dbb7e63568edcfe43f892fa381d/scipy-1.15.3-cp312-cp312-musllinux_1_2_x86_64.whl", hash = "sha256:0ff17c0bb1cb32952c09217d8d1eed9b53d1463e5f1dd6052c7857f83127d539", size = 39710549, upload-time = "2025-05-08T16:06:45.729Z" },
+    { url = "https://files.pythonhosted.org/packages/e6/eb/3bf6ea8ab7f1503dca3a10df2e4b9c3f6b3316df07f6c0ded94b281c7101/scipy-1.15.3-cp312-cp312-win_amd64.whl", hash = "sha256:52092bc0472cfd17df49ff17e70624345efece4e1a12b23783a1ac59a1b728ed", size = 40966184, upload-time = "2025-05-08T16:06:52.623Z" },
+    { url = "https://files.pythonhosted.org/packages/73/18/ec27848c9baae6e0d6573eda6e01a602e5649ee72c27c3a8aad673ebecfd/scipy-1.15.3-cp313-cp313-macosx_10_13_x86_64.whl", hash = "sha256:2c620736bcc334782e24d173c0fdbb7590a0a436d2fdf39310a8902505008759", size = 38728256, upload-time = "2025-05-08T16:06:58.696Z" },
+    { url = "https://files.pythonhosted.org/packages/74/cd/1aef2184948728b4b6e21267d53b3339762c285a46a274ebb7863c9e4742/scipy-1.15.3-cp313-cp313-macosx_12_0_arm64.whl", hash = "sha256:7e11270a000969409d37ed399585ee530b9ef6aa99d50c019de4cb01e8e54e62", size = 30109540, upload-time = "2025-05-08T16:07:04.209Z" },
+    { url = "https://files.pythonhosted.org/packages/5b/d8/59e452c0a255ec352bd0a833537a3bc1bfb679944c4938ab375b0a6b3a3e/scipy-1.15.3-cp313-cp313-macosx_14_0_arm64.whl", hash = "sha256:8c9ed3ba2c8a2ce098163a9bdb26f891746d02136995df25227a20e71c396ebb", size = 22383115, upload-time = "2025-05-08T16:07:08.998Z" },
+    { url = "https://files.pythonhosted.org/packages/08/f5/456f56bbbfccf696263b47095291040655e3cbaf05d063bdc7c7517f32ac/scipy-1.15.3-cp313-cp313-macosx_14_0_x86_64.whl", hash = "sha256:0bdd905264c0c9cfa74a4772cdb2070171790381a5c4d312c973382fc6eaf730", size = 25163884, upload-time = "2025-05-08T16:07:14.091Z" },
+    { url = "https://files.pythonhosted.org/packages/a2/66/a9618b6a435a0f0c0b8a6d0a2efb32d4ec5a85f023c2b79d39512040355b/scipy-1.15.3-cp313-cp313-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:79167bba085c31f38603e11a267d862957cbb3ce018d8b38f79ac043bc92d825", size = 35174018, upload-time = "2025-05-08T16:07:19.427Z" },
+    { url = "https://files.pythonhosted.org/packages/b5/09/c5b6734a50ad4882432b6bb7c02baf757f5b2f256041da5df242e2d7e6b6/scipy-1.15.3-cp313-cp313-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:c9deabd6d547aee2c9a81dee6cc96c6d7e9a9b1953f74850c179f91fdc729cb7", size = 37269716, upload-time = "2025-05-08T16:07:25.712Z" },
+    { url = "https://files.pythonhosted.org/packages/77/0a/eac00ff741f23bcabd352731ed9b8995a0a60ef57f5fd788d611d43d69a1/scipy-1.15.3-cp313-cp313-musllinux_1_2_aarch64.whl", hash = "sha256:dde4fc32993071ac0c7dd2d82569e544f0bdaff66269cb475e0f369adad13f11", size = 36872342, upload-time = "2025-05-08T16:07:31.468Z" },
+    { url = "https://files.pythonhosted.org/packages/fe/54/4379be86dd74b6ad81551689107360d9a3e18f24d20767a2d5b9253a3f0a/scipy-1.15.3-cp313-cp313-musllinux_1_2_x86_64.whl", hash = "sha256:f77f853d584e72e874d87357ad70f44b437331507d1c311457bed8ed2b956126", size = 39670869, upload-time = "2025-05-08T16:07:38.002Z" },
+    { url = "https://files.pythonhosted.org/packages/87/2e/892ad2862ba54f084ffe8cc4a22667eaf9c2bcec6d2bff1d15713c6c0703/scipy-1.15.3-cp313-cp313-win_amd64.whl", hash = "sha256:b90ab29d0c37ec9bf55424c064312930ca5f4bde15ee8619ee44e69319aab163", size = 40988851, upload-time = "2025-05-08T16:08:33.671Z" },
+    { url = "https://files.pythonhosted.org/packages/1b/e9/7a879c137f7e55b30d75d90ce3eb468197646bc7b443ac036ae3fe109055/scipy-1.15.3-cp313-cp313t-macosx_10_13_x86_64.whl", hash = "sha256:3ac07623267feb3ae308487c260ac684b32ea35fd81e12845039952f558047b8", size = 38863011, upload-time = "2025-05-08T16:07:44.039Z" },
+    { url = "https://files.pythonhosted.org/packages/51/d1/226a806bbd69f62ce5ef5f3ffadc35286e9fbc802f606a07eb83bf2359de/scipy-1.15.3-cp313-cp313t-macosx_12_0_arm64.whl", hash = "sha256:6487aa99c2a3d509a5227d9a5e889ff05830a06b2ce08ec30df6d79db5fcd5c5", size = 30266407, upload-time = "2025-05-08T16:07:49.891Z" },
+    { url = "https://files.pythonhosted.org/packages/e5/9b/f32d1d6093ab9eeabbd839b0f7619c62e46cc4b7b6dbf05b6e615bbd4400/scipy-1.15.3-cp313-cp313t-macosx_14_0_arm64.whl", hash = "sha256:50f9e62461c95d933d5c5ef4a1f2ebf9a2b4e83b0db374cb3f1de104d935922e", size = 22540030, upload-time = "2025-05-08T16:07:54.121Z" },
+    { url = "https://files.pythonhosted.org/packages/e7/29/c278f699b095c1a884f29fda126340fcc201461ee8bfea5c8bdb1c7c958b/scipy-1.15.3-cp313-cp313t-macosx_14_0_x86_64.whl", hash = "sha256:14ed70039d182f411ffc74789a16df3835e05dc469b898233a245cdfd7f162cb", size = 25218709, upload-time = "2025-05-08T16:07:58.506Z" },
+    { url = "https://files.pythonhosted.org/packages/24/18/9e5374b617aba742a990581373cd6b68a2945d65cc588482749ef2e64467/scipy-1.15.3-cp313-cp313t-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:0a769105537aa07a69468a0eefcd121be52006db61cdd8cac8a0e68980bbb723", size = 34809045, upload-time = "2025-05-08T16:08:03.929Z" },
+    { url = "https://files.pythonhosted.org/packages/e1/fe/9c4361e7ba2927074360856db6135ef4904d505e9b3afbbcb073c4008328/scipy-1.15.3-cp313-cp313t-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:9db984639887e3dffb3928d118145ffe40eff2fa40cb241a306ec57c219ebbbb", size = 36703062, upload-time = "2025-05-08T16:08:09.558Z" },
+    { url = "https://files.pythonhosted.org/packages/b7/8e/038ccfe29d272b30086b25a4960f757f97122cb2ec42e62b460d02fe98e9/scipy-1.15.3-cp313-cp313t-musllinux_1_2_aarch64.whl", hash = "sha256:40e54d5c7e7ebf1aa596c374c49fa3135f04648a0caabcb66c52884b943f02b4", size = 36393132, upload-time = "2025-05-08T16:08:15.34Z" },
+    { url = "https://files.pythonhosted.org/packages/10/7e/5c12285452970be5bdbe8352c619250b97ebf7917d7a9a9e96b8a8140f17/scipy-1.15.3-cp313-cp313t-musllinux_1_2_x86_64.whl", hash = "sha256:5e721fed53187e71d0ccf382b6bf977644c533e506c4d33c3fb24de89f5c3ed5", size = 38979503, upload-time = "2025-05-08T16:08:21.513Z" },
+    { url = "https://files.pythonhosted.org/packages/81/06/0a5e5349474e1cbc5757975b21bd4fad0e72ebf138c5592f191646154e06/scipy-1.15.3-cp313-cp313t-win_amd64.whl", hash = "sha256:76ad1fb5f8752eabf0fa02e4cc0336b4e8f021e2d5f061ed37d6d264db35e3ca", size = 40308097, upload-time = "2025-05-08T16:08:27.627Z" },
+]
+
+[[package]]
+name = "seaborn"
+version = "0.13.2"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "matplotlib" },
+    { name = "numpy" },
+    { name = "pandas" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/86/59/a451d7420a77ab0b98f7affa3a1d78a313d2f7281a57afb1a34bae8ab412/seaborn-0.13.2.tar.gz", hash = "sha256:93e60a40988f4d65e9f4885df477e2fdaff6b73a9ded434c1ab356dd57eefff7", size = 1457696, upload-time = "2024-01-25T13:21:52.551Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/83/11/00d3c3dfc25ad54e731d91449895a79e4bf2384dc3ac01809010ba88f6d5/seaborn-0.13.2-py3-none-any.whl", hash = "sha256:636f8336facf092165e27924f223d3c62ca560b1f2bb5dff7ab7fad265361987", size = 294914, upload-time = "2024-01-25T13:21:49.598Z" },
+]
+
+[[package]]
+name = "six"
+version = "1.17.0"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/94/e7/b2c673351809dca68a0e064b6af791aa332cf192da575fd474ed7d6f16a2/six-1.17.0.tar.gz", hash = "sha256:ff70335d468e7eb6ec65b95b99d3a2836546063f63acc5171de367e834932a81", size = 34031, upload-time = "2024-12-04T17:35:28.174Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/b7/ce/149a00dd41f10bc29e5921b496af8b574d8413afcd5e30dfa0ed46c2cc5e/six-1.17.0-py2.py3-none-any.whl", hash = "sha256:4721f391ed90541fddacab5acf947aa0d3dc7d27b2e1e8eda2be8970586c3274", size = 11050, upload-time = "2024-12-04T17:35:26.475Z" },
+]
+
+[[package]]
+name = "tzdata"
+version = "2025.2"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/95/32/1a225d6164441be760d75c2c42e2780dc0873fe382da3e98a2e1e48361e5/tzdata-2025.2.tar.gz", hash = "sha256:b60a638fcc0daffadf82fe0f57e53d06bdec2f36c4df66280ae79bce6bd6f2b9", size = 196380, upload-time = "2025-03-23T13:54:43.652Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/5c/23/c7abc0ca0a1526a0774eca151daeb8de62ec457e77262b66b359c3c7679e/tzdata-2025.2-py2.py3-none-any.whl", hash = "sha256:1a403fada01ff9221ca8044d701868fa132215d84beb92242d9acd2147f667a8", size = 347839, upload-time = "2025-03-23T13:54:41.845Z" },
+]
+
+[[package]]
+name = "urllib3"
+version = "2.4.0"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/8a/78/16493d9c386d8e60e442a35feac5e00f0913c0f4b7c217c11e8ec2ff53e0/urllib3-2.4.0.tar.gz", hash = "sha256:414bc6535b787febd7567804cc015fee39daab8ad86268f1310a9250697de466", size = 390672, upload-time = "2025-04-10T15:23:39.232Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/6b/11/cc635220681e93a0183390e26485430ca2c7b5f9d33b15c74c2861cb8091/urllib3-2.4.0-py3-none-any.whl", hash = "sha256:4e16665048960a0900c702d4a66415956a584919c03361cac9f1df5c5dd7e813", size = 128680, upload-time = "2025-04-10T15:23:37.377Z" },
+]