From 3b9052247f9b14c13f3993b485ec23d0289a6fbf Mon Sep 17 00:00:00 2001
From: Stefano Ariestasia <stefano.ariestasia@gmail.com>
Date: Thu, 8 Dec 2022 20:06:02 +0900
Subject: [PATCH] flake8 fix

---
 freqtrade/data/converter.py | 16 +++++++++-------
 1 file changed, 9 insertions(+), 7 deletions(-)

diff --git a/freqtrade/data/converter.py b/freqtrade/data/converter.py
index 46bf648b8..531a69647 100644
--- a/freqtrade/data/converter.py
+++ b/freqtrade/data/converter.py
@@ -121,16 +121,17 @@ def ohlcv_fill_up_missing_data(dataframe: DataFrame, timeframe: str, pair: str)
             logger.debug(message)
     return df
 
+
 def reduce_mem_usage(pair: str, df: DataFrame) -> DataFrame:
     """ iterate through all the columns of a dataframe and modify the data type
-        to reduce memory usage.        
+        to reduce memory usage.
     """
     # start_mem = df.memory_usage().sum() / 1024**2
     # logger.info(f"Memory usage of dataframe for {pair} is {start_mem:.2f} MB")
-   
+
     for col in df.columns[1:]:
         col_type = df[col].dtype
-        
+
         if col_type != object:
             c_min = df[col].min()
             c_max = df[col].max()
@@ -142,7 +143,7 @@ def reduce_mem_usage(pair: str, df: DataFrame) -> DataFrame:
                 elif c_min > np.iinfo(np.int32).min and c_max < np.iinfo(np.int32).max:
                     df[col] = df[col].astype(np.int32)
                 elif c_min > np.iinfo(np.int64).min and c_max < np.iinfo(np.int64).max:
-                    df[col] = df[col].astype(np.int64)  
+                    df[col] = df[col].astype(np.int64)
             elif str(col_type)[:5] == "float":
                 if c_min > np.finfo(np.float16).min and c_max < np.finfo(np.float16).max:
                     df[col] = df[col].astype(np.float16)
@@ -158,9 +159,10 @@ def reduce_mem_usage(pair: str, df: DataFrame) -> DataFrame:
     # end_mem = df.memory_usage().sum() / 1024**2
     # logger.info("Memory usage after optimization is: {:.2f} MB".format(end_mem))
     # logger.info("Decreased by {:.1f}%".format(100 * (start_mem - end_mem) / start_mem))
-    
+
     return df
 
+
 def trim_dataframe(df: DataFrame, timerange, df_date_col: str = 'date',
                    startup_candles: int = 0) -> DataFrame:
     """
@@ -196,10 +198,10 @@ def trim_dataframes(preprocessed: Dict[str, DataFrame], timerange,
         trimed_df = trim_dataframe(df, timerange, startup_candles=startup_candles)
         if not trimed_df.empty:
             # start_mem = trimed_df.memory_usage().sum() / 1024**2
-            # logger.info(f"Memory usage of dataframe for {pair} before reduced is {start_mem:.2f} MB")
+            # logger.info(f"Memory usage of df for {pair} before reduced is {start_mem:.2f} MB")
             trimed_df = reduce_mem_usage(pair, trimed_df)
             # end_mem = trimed_df.memory_usage().sum() / 1024**2
-            # logger.info(f"Memory usage of dataframe for {pair} after reduced is {end_mem:.2f} MB")
+            # logger.info(f"Memory usage of df for {pair} after reduced is {end_mem:.2f} MB")
             processed[pair] = trimed_df
         else:
             logger.warning(f'{pair} has no data left after adjusting for startup candles, '