How can I speed up the page load of plots created in Bokeh?

After I run my code, the page opens up and for 13 seconds a blank page shows up, and after that, the expected plots.

Is there a way to speed this up? (13 seconds, user wise is sadly too long)

The code produces 44 plots, in two columns(2n loop), differ by parameter (1st loop) and then differ by tool name (3 rd loop)

Also if bokeh, isnt the right tool for this, would happily open to hear a way to plot interactive plots, preferably with python

This is the code:

dateparse = lambda x: pd.datetime.strptime(x, '%Y-%m-%dT%H:%M:%S')



df = pd.read_csv("LUSU.csv",parse_dates=['PM_START_DATE'], date_parser=dateparse)



df.head()

print("time elapsed: {:.2f}s".format(time.time() - start_time))

x = df['PM_START_DATE']

y = df['TASK_VALUE']

tool=df['ENTITY']

tool_list=df['ENTITY'].unique()

param_list=df['PARAMETER'].unique()

#

print("time elapsed: {:.2f}s".format(time.time() - start_time))

colors = itertools.cycle(Spectral11)

output_file('LUSU.html', mode="cdn")

for i in range(0,44,2):

    row = 

    for _ in range(2):

        p = figure(title=param_list[i], x_axis_label='date', y_axis_label='chart value', x_axis_type="datetime", toolbar_location="below")

        for j in range(len(tool_list)):

            df1=((df['PARAMETER']==param_list[i] )& (df['ENTITY']==tool_list[j] ))

            source = ColumnDataSource(data=dict(x=x.loc[df1], y=y.loc[df1], tool=tool.loc[df1]))

            p.line(x='x', y='y',legend='tool', source=source)

            p.scatter(x='x', y='y',legend='tool',size=10,color=next(colors), source=source)

        p.add_tools(HoverTool(tooltips=[("Entity", "@tool"), ("Chart Value", "@y{%0.2f}"), ("Date", "@x{%F}")], formatters={"x": "datetime", "y": 'printf'}))

        #p.xaxis.formatter = DatetimeTickFormatter(days=["%m/%d/%Y"])

        p.legend.location = "top_right"

        p.legend.click_policy = "mute"

        row.append(p)

        i = i + 1

    grid.append(row)



fig=layout(grid)

reset_output()



show(fig)

edited Nov 20 '18 at 13:24

ChesuCR

5,55232153

asked Nov 19 '18 at 18:28

Sam Shaw

111

Is the script still iterating in the for loop or it is stuck in the "show" function when the page is blank? Could you check it? If the iteration is the problem you may use some multiprocessing or multithreading python module
– ChesuCR
Nov 20 '18 at 13:14

Anyway I reckon bokeh is not prepared to work with so many plots. Besides, if many axis share some range, I am afraid that panning and zooming is going to be almost imposible. Let me know if you get this working faster because I am really interested
– ChesuCR
Nov 20 '18 at 13:17

Ah! If you finally use multiprocessing module take into account this. You should use this environment variable BOKEH_SIMPLE_IDS=no
– ChesuCR
Nov 20 '18 at 13:19

add a comment |

After I run my code, the page opens up and for 13 seconds a blank page shows up, and after that, the expected plots.

Is there a way to speed this up? (13 seconds, user wise is sadly too long)

The code produces 44 plots, in two columns(2n loop), differ by parameter (1st loop) and then differ by tool name (3 rd loop)

Also if bokeh, isnt the right tool for this, would happily open to hear a way to plot interactive plots, preferably with python

This is the code:

dateparse = lambda x: pd.datetime.strptime(x, '%Y-%m-%dT%H:%M:%S')



df = pd.read_csv("LUSU.csv",parse_dates=['PM_START_DATE'], date_parser=dateparse)



df.head()

print("time elapsed: {:.2f}s".format(time.time() - start_time))

x = df['PM_START_DATE']

y = df['TASK_VALUE']

tool=df['ENTITY']

tool_list=df['ENTITY'].unique()

param_list=df['PARAMETER'].unique()

#

print("time elapsed: {:.2f}s".format(time.time() - start_time))

colors = itertools.cycle(Spectral11)

output_file('LUSU.html', mode="cdn")

for i in range(0,44,2):

    row = 

    for _ in range(2):

        p = figure(title=param_list[i], x_axis_label='date', y_axis_label='chart value', x_axis_type="datetime", toolbar_location="below")

        for j in range(len(tool_list)):

            df1=((df['PARAMETER']==param_list[i] )& (df['ENTITY']==tool_list[j] ))

            source = ColumnDataSource(data=dict(x=x.loc[df1], y=y.loc[df1], tool=tool.loc[df1]))

            p.line(x='x', y='y',legend='tool', source=source)

            p.scatter(x='x', y='y',legend='tool',size=10,color=next(colors), source=source)

        p.add_tools(HoverTool(tooltips=[("Entity", "@tool"), ("Chart Value", "@y{%0.2f}"), ("Date", "@x{%F}")], formatters={"x": "datetime", "y": 'printf'}))

        #p.xaxis.formatter = DatetimeTickFormatter(days=["%m/%d/%Y"])

        p.legend.location = "top_right"

        p.legend.click_policy = "mute"

        row.append(p)

        i = i + 1

    grid.append(row)



fig=layout(grid)

reset_output()



show(fig)

edited Nov 20 '18 at 13:24

ChesuCR

5,55232153

asked Nov 19 '18 at 18:28

Sam Shaw

111

Is the script still iterating in the for loop or it is stuck in the "show" function when the page is blank? Could you check it? If the iteration is the problem you may use some multiprocessing or multithreading python module
– ChesuCR
Nov 20 '18 at 13:14

Anyway I reckon bokeh is not prepared to work with so many plots. Besides, if many axis share some range, I am afraid that panning and zooming is going to be almost imposible. Let me know if you get this working faster because I am really interested
– ChesuCR
Nov 20 '18 at 13:17

Ah! If you finally use multiprocessing module take into account this. You should use this environment variable BOKEH_SIMPLE_IDS=no
– ChesuCR
Nov 20 '18 at 13:19

add a comment |

After I run my code, the page opens up and for 13 seconds a blank page shows up, and after that, the expected plots.

Is there a way to speed this up? (13 seconds, user wise is sadly too long)

The code produces 44 plots, in two columns(2n loop), differ by parameter (1st loop) and then differ by tool name (3 rd loop)

Also if bokeh, isnt the right tool for this, would happily open to hear a way to plot interactive plots, preferably with python

This is the code:

dateparse = lambda x: pd.datetime.strptime(x, '%Y-%m-%dT%H:%M:%S')



df = pd.read_csv("LUSU.csv",parse_dates=['PM_START_DATE'], date_parser=dateparse)



df.head()

print("time elapsed: {:.2f}s".format(time.time() - start_time))

x = df['PM_START_DATE']

y = df['TASK_VALUE']

tool=df['ENTITY']

tool_list=df['ENTITY'].unique()

param_list=df['PARAMETER'].unique()

#

print("time elapsed: {:.2f}s".format(time.time() - start_time))

colors = itertools.cycle(Spectral11)

output_file('LUSU.html', mode="cdn")

for i in range(0,44,2):

    row = 

    for _ in range(2):

        p = figure(title=param_list[i], x_axis_label='date', y_axis_label='chart value', x_axis_type="datetime", toolbar_location="below")

        for j in range(len(tool_list)):

            df1=((df['PARAMETER']==param_list[i] )& (df['ENTITY']==tool_list[j] ))

            source = ColumnDataSource(data=dict(x=x.loc[df1], y=y.loc[df1], tool=tool.loc[df1]))

            p.line(x='x', y='y',legend='tool', source=source)

            p.scatter(x='x', y='y',legend='tool',size=10,color=next(colors), source=source)

        p.add_tools(HoverTool(tooltips=[("Entity", "@tool"), ("Chart Value", "@y{%0.2f}"), ("Date", "@x{%F}")], formatters={"x": "datetime", "y": 'printf'}))

        #p.xaxis.formatter = DatetimeTickFormatter(days=["%m/%d/%Y"])

        p.legend.location = "top_right"

        p.legend.click_policy = "mute"

        row.append(p)

        i = i + 1

    grid.append(row)



fig=layout(grid)

reset_output()



show(fig)

edited Nov 20 '18 at 13:24

ChesuCR

5,55232153

asked Nov 19 '18 at 18:28

Sam Shaw

111

After I run my code, the page opens up and for 13 seconds a blank page shows up, and after that, the expected plots.

Is there a way to speed this up? (13 seconds, user wise is sadly too long)

The code produces 44 plots, in two columns(2n loop), differ by parameter (1st loop) and then differ by tool name (3 rd loop)

Also if bokeh, isnt the right tool for this, would happily open to hear a way to plot interactive plots, preferably with python

This is the code:

dateparse = lambda x: pd.datetime.strptime(x, '%Y-%m-%dT%H:%M:%S')



df = pd.read_csv("LUSU.csv",parse_dates=['PM_START_DATE'], date_parser=dateparse)



df.head()

print("time elapsed: {:.2f}s".format(time.time() - start_time))

x = df['PM_START_DATE']

y = df['TASK_VALUE']

tool=df['ENTITY']

tool_list=df['ENTITY'].unique()

param_list=df['PARAMETER'].unique()

#

print("time elapsed: {:.2f}s".format(time.time() - start_time))

colors = itertools.cycle(Spectral11)

output_file('LUSU.html', mode="cdn")

for i in range(0,44,2):

    row = 

    for _ in range(2):

        p = figure(title=param_list[i], x_axis_label='date', y_axis_label='chart value', x_axis_type="datetime", toolbar_location="below")

        for j in range(len(tool_list)):

            df1=((df['PARAMETER']==param_list[i] )& (df['ENTITY']==tool_list[j] ))

            source = ColumnDataSource(data=dict(x=x.loc[df1], y=y.loc[df1], tool=tool.loc[df1]))

            p.line(x='x', y='y',legend='tool', source=source)

            p.scatter(x='x', y='y',legend='tool',size=10,color=next(colors), source=source)

        p.add_tools(HoverTool(tooltips=[("Entity", "@tool"), ("Chart Value", "@y{%0.2f}"), ("Date", "@x{%F}")], formatters={"x": "datetime", "y": 'printf'}))

        #p.xaxis.formatter = DatetimeTickFormatter(days=["%m/%d/%Y"])

        p.legend.location = "top_right"

        p.legend.click_policy = "mute"

        row.append(p)

        i = i + 1

    grid.append(row)



fig=layout(grid)

reset_output()



show(fig)

python python-3.x plot bokeh interactive

edited Nov 20 '18 at 13:24

ChesuCR

5,55232153

asked Nov 19 '18 at 18:28

Sam Shaw

111

edited Nov 20 '18 at 13:24

ChesuCR

5,55232153

asked Nov 19 '18 at 18:28

Sam Shaw

111

edited Nov 20 '18 at 13:24

ChesuCR

5,55232153

edited Nov 20 '18 at 13:24

ChesuCR

5,55232153

edited Nov 20 '18 at 13:24

ChesuCR

5,55232153

asked Nov 19 '18 at 18:28

Sam Shaw

111

asked Nov 19 '18 at 18:28

Sam Shaw

111

asked Nov 19 '18 at 18:28

Sam Shaw

111

Is the script still iterating in the for loop or it is stuck in the "show" function when the page is blank? Could you check it? If the iteration is the problem you may use some multiprocessing or multithreading python module
– ChesuCR
Nov 20 '18 at 13:14

Anyway I reckon bokeh is not prepared to work with so many plots. Besides, if many axis share some range, I am afraid that panning and zooming is going to be almost imposible. Let me know if you get this working faster because I am really interested
– ChesuCR
Nov 20 '18 at 13:17

Ah! If you finally use multiprocessing module take into account this. You should use this environment variable BOKEH_SIMPLE_IDS=no
– ChesuCR
Nov 20 '18 at 13:19

add a comment |

Is the script still iterating in the for loop or it is stuck in the "show" function when the page is blank? Could you check it? If the iteration is the problem you may use some multiprocessing or multithreading python module
– ChesuCR
Nov 20 '18 at 13:14

Anyway I reckon bokeh is not prepared to work with so many plots. Besides, if many axis share some range, I am afraid that panning and zooming is going to be almost imposible. Let me know if you get this working faster because I am really interested
– ChesuCR
Nov 20 '18 at 13:17

Ah! If you finally use multiprocessing module take into account this. You should use this environment variable BOKEH_SIMPLE_IDS=no
– ChesuCR
Nov 20 '18 at 13:19

Is the script still iterating in the for loop or it is stuck in the "show" function when the page is blank? Could you check it? If the iteration is the problem you may use some multiprocessing or multithreading python module
– ChesuCR
Nov 20 '18 at 13:14

Anyway I reckon bokeh is not prepared to work with so many plots. Besides, if many axis share some range, I am afraid that panning and zooming is going to be almost imposible. Let me know if you get this working faster because I am really interested
– ChesuCR
Nov 20 '18 at 13:17

Ah! If you finally use multiprocessing module take into account this. You should use this environment variable BOKEH_SIMPLE_IDS=no
– ChesuCR
Nov 20 '18 at 13:19

add a comment |

1 Answer
1

active

oldest

votes

You could try to use webGL to speed it up.
This allows glyphs to render via the GPU.

figure(title=param_list[i], x_axis_label='date', y_axis_label='chart value', x_axis_type="datetime", toolbar_location="below", output_backend="webgl")

More information: https://bokeh.pydata.org/en/latest/docs/user_guide/webgl.html.
Further reading about Bokeh performance issues with a lot of plots: https://github.com/bokeh/bokeh/issues/6294.

answered Nov 20 '18 at 8:32

Jasper

1846

hi, i tried this, the output is that not all graphs are being plotted, and no change in time is seen
– Sam Shaw
Nov 20 '18 at 11:39

add a comment |

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1 Answer
1

active

oldest

votes

1 Answer
1

active

oldest

votes

You could try to use webGL to speed it up.
This allows glyphs to render via the GPU.

figure(title=param_list[i], x_axis_label='date', y_axis_label='chart value', x_axis_type="datetime", toolbar_location="below", output_backend="webgl")

More information: https://bokeh.pydata.org/en/latest/docs/user_guide/webgl.html.
Further reading about Bokeh performance issues with a lot of plots: https://github.com/bokeh/bokeh/issues/6294.

answered Nov 20 '18 at 8:32

Jasper

1846

hi, i tried this, the output is that not all graphs are being plotted, and no change in time is seen
– Sam Shaw
Nov 20 '18 at 11:39

add a comment |

You could try to use webGL to speed it up.
This allows glyphs to render via the GPU.

figure(title=param_list[i], x_axis_label='date', y_axis_label='chart value', x_axis_type="datetime", toolbar_location="below", output_backend="webgl")

More information: https://bokeh.pydata.org/en/latest/docs/user_guide/webgl.html.
Further reading about Bokeh performance issues with a lot of plots: https://github.com/bokeh/bokeh/issues/6294.

answered Nov 20 '18 at 8:32

Jasper

1846

hi, i tried this, the output is that not all graphs are being plotted, and no change in time is seen
– Sam Shaw
Nov 20 '18 at 11:39

add a comment |

You could try to use webGL to speed it up.
This allows glyphs to render via the GPU.

figure(title=param_list[i], x_axis_label='date', y_axis_label='chart value', x_axis_type="datetime", toolbar_location="below", output_backend="webgl")

More information: https://bokeh.pydata.org/en/latest/docs/user_guide/webgl.html.
Further reading about Bokeh performance issues with a lot of plots: https://github.com/bokeh/bokeh/issues/6294.

answered Nov 20 '18 at 8:32

Jasper

1846

You could try to use webGL to speed it up.
This allows glyphs to render via the GPU.

figure(title=param_list[i], x_axis_label='date', y_axis_label='chart value', x_axis_type="datetime", toolbar_location="below", output_backend="webgl")

More information: https://bokeh.pydata.org/en/latest/docs/user_guide/webgl.html.
Further reading about Bokeh performance issues with a lot of plots: https://github.com/bokeh/bokeh/issues/6294.

answered Nov 20 '18 at 8:32

Jasper

1846

answered Nov 20 '18 at 8:32

Jasper

1846

answered Nov 20 '18 at 8:32

Jasper

1846

answered Nov 20 '18 at 8:32

Jasper

1846

hi, i tried this, the output is that not all graphs are being plotted, and no change in time is seen
– Sam Shaw
Nov 20 '18 at 11:39

add a comment |

hi, i tried this, the output is that not all graphs are being plotted, and no change in time is seen
– Sam Shaw
Nov 20 '18 at 11:39

hi, i tried this, the output is that not all graphs are being plotted, and no change in time is seen
– Sam Shaw
Nov 20 '18 at 11:39

add a comment |

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