Tricks to make saving images quicker?

Comienza con la función pre_outcome_chart y mide el tiempo empleado en pd.read_csv, fplt.plot y plt.savefig para la carga de trabajo de 10k-image. Compara el bucle actual con el enfoque de multiprocessing propuesto y define la finalización como una reducción medida y reproducible del tiempo total de guardado sin cambiar los gráficos generados.

I am using a friends script (so I don't know all the details in this script), but I wonder if there are simple tricks I can do here to make it save quicker.
I want to be able to save around 10k images.

I plan to incorporate multiprocessing to make it even quicker.

Thank you.

def pre_outcome_chart(month, save_dir, sample_df, test = True):
     if test:
          sample_df = sample_df.sample(100)
    
     for index, row in sample_df.iterrows():
            ticker = row['ticker']
            input_date = row['entry_date']
          
            #nput_date_string = input_date.strftime('%Y-%m-%d')
            startDate = pd.to_datetime(input_date).date() - relativedelta(months=month)
            endDate = pd.to_datetime(input_date).date() + relativedelta(days =1)
            startDateString = startDate.strftime('%Y%m%d')
            endDateString = endDate.strftime('%Y%m%d')
         
            filename = os.path.join(DATA_DIR, ticker + '.csv')
            df = pd.read_csv(filename, index_col=0, parse_dates=True)
            dt_range = pd.date_range(start=startDate, end=endDate)
            df = df[df.index.isin(dt_range)]
            # Drop any rows with missing data
            df.dropna(inplace=True)

            # Compute moving averages
            df['SMA50'] = df['Close'].rolling(window=50, min_periods=1).mean()
            df['SMA100'] = df['Close'].rolling(window=100, min_periods=1).mean()
            df['SMA200'] = df['Close'].rolling(window=200, min_periods=1).mean()

            # Define addplots
            ema10 = fplt.make_addplot(df['Close'].ewm(span=10, min_periods=1).mean(), color='#CBC3E3')
            ema20 = fplt.make_addplot(df['Close'].ewm(span=20, min_periods=1).mean(), color='#87CEEB')
            sma50 = fplt.make_addplot(df['SMA50'], color='red')
            sma100 = fplt.make_addplot(df['SMA100'], color='yellow')
            sma200 = fplt.make_addplot(df['SMA200'], color='white')

            # Define market colors and style
            mc = fplt.make_marketcolors(up='black',down='#f76757',
                                       edge={'up': '#13eda4', 'down': '#f76757'},
                                       wick={'up': '#13eda4', 'down': '#f76757'},
                                       volume={'up': '#13eda4', 'down': '#f76757'},
                                        )

            s = fplt.make_mpf_style(marketcolors=mc,facecolor='black',figcolor='black',
                                    gridcolor='gray',
                                    gridstyle='dotted',
                                    rc={'xtick.color':'white',
                                      'ytick.color':'white',
                                      'axes.labelcolor':'white',
                                      'text.color':'white',
                                      'axes.edgecolor': 'gray',
                                      'grid.alpha': 0.7,
                                      'grid.linewidth': 0.5,
                                    })



            # Plot the data
            fig, axlist = fplt.plot(
                df,
                type='candle',
                addplot=[ema10, ema20, sma50, sma100, sma200],
                style=s,
                figsize=(12,6),
                update_width_config={'candle_linewidth':1.0, 'candle_width':0.525, 'volume_width': 0.525},
                tight_layout=True,
                volume=True,
                ylabel='Price',
                xrotation=0,
                returnfig=True
            )

            # Add legend
            ax = axlist[0]
            legend_handles = [ax.lines[i] for i in range(len(ax.lines))]
            legend_labels = ['EMA10', 'EMA20', 'SMA50', 'SMA100', 'SMA200']
            ax.legend(legend_handles, legend_labels)

            # Get the index of the input_date candle
            input_date_index = df.index.get_loc(pd.to_datetime(input_date, format='%Y-%m-%d').floor('D'))

            # Get the coordinates of the input_date candle
            x_coord = input_date_index
            y_coord = df['High'].iloc[input_date_index]

            # Calculate the maximum value of the chart
            chart_max = max(df['High'].max(), df[['SMA50', 'SMA100', 'SMA200']].max().max())

            # Calculate the maximum range of the chart
            chart_range = df['High'].max() - df['Low'].min()

            # Calculate the desired arrow length as a fraction of the maximum range
            arrow_length_fraction = 0.1  # Adjust this value to control the arrow length
            arrow_length = arrow_length_fraction * chart_range

            # Calculate the desired gap size
            gap_size = arrow_length

            # Adjust the ylim to create the gap
            axlist[0].set_ylim(df['Low'].min(), 1.2 * df['High'].max() + gap_size)

            # Annotate the input_date above the input_date candle
            axlist[0].annotate('',
                               xy=(x_coord, y_coord),
                               xytext=(x_coord, y_coord+arrow_length),
                               arrowprops=dict(arrowstyle='->', color='yellow', linewidth=3),
                               color='yellow', ha='center', va='bottom')

            # Add the date tag at the top of the chart
            ax.annotate(ticker + '  '  + pd.to_datetime(input_date).date().strftime('%Y-%m-%d'),
                        xy=(x_coord, y_coord),
                        xytext=(x_coord, chart_max+arrow_length),
                        color='yellow', ha='center', va='bottom')
            # Save chart
           
            plt.savefig(
                f"{save_dir}/"
                + f"{ticker}_"
                + input_date
                + 'M_'
                 + '.png', dpi=300, bbox_inches='tight')
            plt.close(fig)`