本文介绍了绘制热图的浏览器渲染速度较慢的处理方法，对大家解决问题具有一定的参考价值，需要的朋友们下面随着小编来一起学习吧！
问题描述
我呈现了一个从0到6的212行x64列的整数df(Final_Df)，作为(无注释的)带注释的热图。我使用fig.write_html()中的一个文件在我的浏览器(Microsoft EDGE)中执行此操作。最终的热图在我的浏览器中呈现得非常慢，以至于我收到"页面未响应"警告，任何放大/缩小图形的操作也都非常慢。考虑到df不是那么大，这很令人惊讶。
有谁能建议为什么会出现这种情况，以及如何加快速度？
谢谢， TIM
def discrete_colorscale(bvals, colors):
#https://chart-studio.plotly.com/~empet/15229/heatmap-with-a-discrete-colorscale/#/
"""
bvals - list of values bounding intervals/ranges of interest
colors - list of rgb or hex colorcodes for values in [bvals[k], bvals[k+1]],0<=k < len(bvals)-1
returns the plotly discrete colorscale
"""
if len(bvals) != len(colors)+1:
raise ValueError('len(boundary values) should be equal to len(colors)+1')
bvals = sorted(bvals)
nvals = [(v-bvals[0])/(bvals[-1]-bvals[0]) for v in bvals] #normalized values
dcolorscale = [] #discrete colorscale
for k in range(len(colors)):
dcolorscale.extend([[nvals[k], colors[k]], [nvals[k+1], colors[k]]])
return dcolorscale
#final_df is a 212 row x 64 col df of ints ranging from 0 to 6
#cell_df is an empty 212x64 df of empty strings to remove cell labelling behaviour
cell_df = final_df.applymap(lambda x: annot_map.get(x, x))
cell_labels = cell_df.values.tolist()
bvals = [0,1,2,3,4,5,6,7]
colors_map = ['rgb(244,244,255)', #whiteish
'rgb(255, 128, 0)', #orange
'rgb(255,0,0)', #red
'rgb(0, 0, 255)', #blue
'rgb(128, 128, 128)', #grey
'rgb(0, 255, 0)', #green
'rgb(192, 192, 192)'] #light grey
dcolorsc = discrete_colorscale(bvals, colors_map)
bvals = np.array(bvals)
tickvals = [np.mean(bvals[k:k+2]) for k in range(len(bvals)-1)]
ticktext = ['param 1',
'param 2',
'param 3',
'param 4',
'param 5',
'param 6',
'param 7']
fig_df = ff.create_annotated_heatmap(final_df.values.tolist(),
x= list(final_df.columns),
y=list(final_df.index),
annotation_text = cell_labels,
colorscale=dcolorsc,
colorbar = dict(thickness=25,
tickvals=tickvals,
ticktext=ticktext),
showscale = True,
zmin=0, zmax=7,
ygap = 1,
xgap = 1,
)
fig_df.update_layout(
xaxis={'title' : 'ID 1'},
yaxis = {'title' : 'ID 2'},
yaxis_nticks = len(final_df.index),
xaxis_nticks = len(final_df.columns)
)
fig_df.write_html(results_file_df)
推荐答案
我怀疑绘图呈现批注的成本非常高。可能的情况是，即使您向annotation_text参数传递了一个212x64空字符串数组，仍然需要逐一检查它们，以确定没有要添加的批注。
我用从0到6的随机整数创建了一个212x64数组，在我的浏览器中呈现它的速度也非常慢，并且我收到了与您相同的页面没有响应&q；警告。
当我使用go.heatmap时，我能够获得似乎与ff.create_annotated_heatmap相同的绘图，这将执行时间从5-6秒缩短到0.66秒，并且它在浏览器中的响应速度也快得多。
这似乎比创建带注释的热图而不使用注释更简单(您需要ff.create_注解_heatmap而不是go.heatmap有什么特殊原因吗？)
import numpy as np
import pandas as pd
import plotly.figure_factory as ff
import plotly.graph_objects as go
import time
start_time = time.time()
def discrete_colorscale(bvals, colors):
#https://chart-studio.plotly.com/~empet/15229/heatmap-with-a-discrete-colorscale/#/
"""
bvals - list of values bounding intervals/ranges of interest
colors - list of rgb or hex colorcodes for values in [bvals[k], bvals[k+1]],0<=k < len(bvals)-1
returns the plotly discrete colorscale
"""
if len(bvals) != len(colors)+1:
raise ValueError('len(boundary values) should be equal to len(colors)+1')
bvals = sorted(bvals)
nvals = [(v-bvals[0])/(bvals[-1]-bvals[0]) for v in bvals] #normalized values
dcolorscale = [] #discrete colorscale
for k in range(len(colors)):
dcolorscale.extend([[nvals[k], colors[k]], [nvals[k+1], colors[k]]])
return dcolorscale
#final_df is a 212 row x 64 col df of ints ranging from 0 to 6
#cell_df is an empty 212x64 df of empty strings to remove cell labelling behaviour
## recreate your dfs
np.random.seed(42)
final_df = pd.DataFrame(np.random.randint(0,6,size=(212, 64)), columns=list(range(64)))
# cell_df = final_df.applymap(lambda x: annot_map.get(x, x))
cell_df = pd.DataFrame(np.array(['']*212*64).reshape(212,64), columns=list(range(64)))
cell_labels = cell_df.values.tolist()
bvals = [0,1,2,3,4,5,6,7]
colors_map = ['rgb(244,244,255)', #whiteish
'rgb(255, 128, 0)', #orange
'rgb(255,0,0)', #red
'rgb(0, 0, 255)', #blue
'rgb(128, 128, 128)', #grey
'rgb(0, 255, 0)', #green
'rgb(192, 192, 192)'] #light grey
dcolorsc = discrete_colorscale(bvals, colors_map)
bvals = np.array(bvals)
tickvals = [np.mean(bvals[k:k+2]) for k in range(len(bvals)-1)]
ticktext = ['param 1',
'param 2',
'param 3',
'param 4',
'param 5',
'param 6',
'param 7']
# fig_df = ff.create_annotated_heatmap(final_df.values.tolist(),
# x= list(final_df.columns),
# y=list(final_df.index),
# annotation_text = cell_labels,
# colorscale=dcolorsc,
# colorbar = dict(thickness=25,
# tickvals=tickvals,
# ticktext=ticktext),
# showscale = True,
# zmin=0, zmax=7,
# ygap = 1,
# xgap = 1,
# )
fig_df = go.Figure([go.Heatmap(
z=final_df,
colorscale=dcolorsc,
colorbar=dict(
thickness=25,
tickvals=tickvals,
ticktext=ticktext),
showscale=True,
zmin=0, zmax=7,
ygap=1,
xgap=1,
)
])
fig_df.update_layout(
xaxis={'title' : 'ID 1'},
yaxis = {'title' : 'ID 2'},
yaxis_nticks = len(final_df.index),
xaxis_nticks = len(final_df.columns)
)
fig_df.show()
print(f"Program executed in {time.time() - start_time} seconds")
## original code with figure_factory annotated heatmap: Program executed in 5.351915121078491 seconds
## modified code with graph_objects heatmap: Program executed in 0.6627509593963623 seconds
# fig_df.write_html(results_file_df)
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