Bug Report: Multiprocessing bug

[kenter1]

Describe the bug
When using multiprocessing the images generated have varying plot sizes and adding a lock to the plot function or plotting in a loop works fine.

To Reproduce
Steps to reproduce the behavior:
Plot multiple charts using
python multiprocessing.Pool
map, apply, etc

Expected behavior
The generated charts should be the same as the charts generated in a non multiproc

[DanielGoldfarb]

To save me time looking into this, can you please post exact code you used to reproduce this, and some screen shots of the varying plot sizes? Please also provide the data to input. Thank you.

[kenter1]

To reproduce

using a loop

import pandas as pd
import mplfinance as mpf
from multiprocessing import Pool

def generate_candle_plot(date, n_candles=30):
    mc = mpf.make_marketcolors(
        up='green',
        down='red',
        edge={'up':'green','down':'red'},
        wick={'up':'white','down':'orange'},
        inherit=True,
        alpha=1.0
    )

    s  = mpf.make_mpf_style(base_mpf_style='classic', facecolor='white', marketcolors=mc)

    _ = mpf.plot(df[date < df.index][:n_candles],
        axisoff=True,
        type = 'candle',
        style = s,
        volume = False,
        show_nontrading=False,
        savefig = dict(fname="{}_loop.png".format(str(date)), bboxes='tight'),
        figscale=.5
    )
    
    return 
    
r = requests.get('https://www.alphavantage.co/query?function=TIME_SERIES_DAILY_ADJUSTED&symbol=IBM&outputsize=full&apikey=demo')
df = pd.DataFrame.from_records(r.json()["Time Series (Daily)"]).T[["1. open","2. high","3. low","4. close","6. volume"]]
df.columns = ['Open', 'High', 'Low', 'Close', 'Volume']
df = df.astype(float)
df.index = pd.to_datetime(df.index)

items = df.index[:1000]

for item in items:
    generate_candle_plot(item)

using multiprocessing

import requests 
import pandas as pd
import mplfinance as mpf
from multiprocessing import Pool

def generate_candle_plot(date, n_candles=30):
    mc = mpf.make_marketcolors(
        up='green',
        down='red',
        edge={'up':'green','down':'red'},
        wick={'up':'white','down':'orange'},
        inherit=True,
        alpha=1.0
    )

    s  = mpf.make_mpf_style(base_mpf_style='classic', facecolor='white', marketcolors=mc)

    _ = mpf.plot(df[date < df.index][:n_candles],
        axisoff=True,
        type = 'candle',
        style = s,
        volume = False,
        show_nontrading=False,
        savefig = dict(fname="{}_multi.png".format(str(date)), bboxes='tight'),
        figscale=.5
    )
    
    return 
    
r = requests.get('https://www.alphavantage.co/query?function=TIME_SERIES_DAILY_ADJUSTED&symbol=IBM&outputsize=full&apikey=demo')
df = pd.DataFrame.from_records(r.json()["Time Series (Daily)"]).T[["1. open","2. high","3. low","4. close","6. volume"]]
df.columns = ['Open', 'High', 'Low', 'Close', 'Volume']
df = df.astype(float)
df.index = pd.to_datetime(df.index)

items = df.index[:1000]

with Pool(12) as pool:
    pool.map(generate_candle_plot, items)

[DanielGoldfarb]

@kenter1
Thanks. Will take a look in the next day or so.