我有一個 Dash 應用程式正在運行，它呼叫創建和保存圖表的函式。
該應用程式遇到由圖表繪制引起的超時。Matplotlib 列印警告：
Starting a Matplotlib GUI outside of the main thread will likely fail.

據我所知，Dash 應用程式由運行不同執行緒的 Flask 托管。這似乎是 matploblib 的一個問題，因為它不是執行緒保存。我使用threaded=False引數運行應用程式，但問題仍然存在。在除錯應用程式時，Flask 似乎仍在運行多個執行緒。

matplotlib 從 Dash/Flask 保存 matplotlib 圖表

↓ 點擊按鈕，然后..

從 Dash/Flask 保存 matplotlib 圖表

Generating & Downloading High-Throughput Quantities of Subplots in Dash (without any use of `mpl.pyplot`)

In this extended example of the previous code, I added a numeric input component (dcc.Input w/ type='number', max=1200, step=1; you can of course also just type any number 1 <= n <= 1200), so when you click the download button, the file you get is a pdf with potentially hundreds, or thousands, of plots having been generated.

import os
import random
from time import time_ns

import dash
import matplotlib
import matplotlib as mpl

matplotlib.use("agg")

import numpy as np

from dash import dcc
from dash import html
from dash.dependencies import Input
from dash.dependencies import Output
from dash.dependencies import State
from matplotlib.backends.backend_pdf import PdfPages
from matplotlib.figure import Figure

import seaborn as sns

sns.set(
    font_scale=0.2
)  # this erases labels for any blank plots on the last page

ctheme = [
    "k", "gray", "magenta", "fuchsia", "#be03fd", "#1e488f",
    (0.443_137_254_901_960_76, 0.443_137_254_901_960_76,
    0.886_274_509_803_921_53, ), "#75bbfd", "teal", "lime", "g", 
    (0.666_667_4, 0.666_666_3, 0.290_780_141_843_971_38), "y", 
    "#f1da7a", "tan", "orange", "maroon", "r"
] # colors to blend to any scalar-spread palette form


def new_page(m, n):

    fig = Figure()
    axarr = fig.subplots(m, n, sharex="all", sharey="all") 
    arr_ij = [(x, y) for x, y in np.ndindex(m, n)]
    subplots = [axarr[index] for index in arr_ij]

    return (fig, subplots)


def generate_figures(n_plots, m=6, n=5):

    fig_path = f"rand-poiss-hist_N={n_plots}_{time_ns()}.pdf"

    colors = sns.blend_palette(ctheme, n_plots)

    x = 0

    with PdfPages(fig_path) as pdf:

        for _ in range((n_plots // (m * n))   1):

            fig, subplots = new_page(m, n)
            fig.subplots_adjust(wspace=0.5, hspace=0.5)

            for i in range(m * n):  # Random dist plots
                ax = subplots[i]
                x  = 1
                if x <= n_plots:


                    scaled_y = np.random.randint(20, 30)
                    random_data = np.random.poisson(scaled_y, 100)
                    ax.hist(
                        random_data,
                        bins=12,
                        fc=(0, 0, 0, 0),
                        lw=0.75,
                        ec=colors.pop(),
                    )

                    # Axes label properties
                    ax.set_title(f"fig.{x}", size=6)
                    if ax.is_last_row() or ((n_plots - x) <= n):
                        ax.set_xlabel("X Label", size=4)
                    if ax.is_first_col():
                        ax.set_ylabel("Y Label", size=4)
                    # ax.set_xmargin(2)
                    # ax.set_ymargin(2)

            # NOTE:
            # Save figure ~
            # * BUT DO NOT USE PYLAB *
            #   Write figure to output file (png|pdf).
            pdf.savefig(fig)

    return fig_path


app = dash.Dash(__name__)

app.layout = html.Div(
    [
        html.Button(
            "Generate plots",
            id="generate-plot",
            style={
                "width": "30%",
                "fontSize": "1.1rem",
            },
        ),
        html.Br(),
        html.Code("Enter number of plots to generate:"),
        html.Br(),
        dcc.Input(id="range", type="number", min=1, max=1200, step=1),
        dcc.Download(id="download-image"),
    ],
    style={"margin": "10% 40% 10% 40%"}
)


@app.callback(
    Output("download-image", "data"),
    Input("generate-plot", "n_clicks"),
    State("range", "value"),
    prevent_initial_call=True,
)
def generate_downloadable_figure(n_clicks, n_plots):
    if n_clicks > 0:
        fig_path = generate_figures(n_plots)
        return dcc.send_file(fig_path)


if __name__ == "__main__":
    app.run_server(debug=True, dev_tools_hot_reload=True, host="0.0.0.0")