I've been looking into using the Bokeh library to create animated data visualisations for some small projects I am working on. I feel that the gif format would be the best file format to export these visualisations in. It is widely used and they should be easy to share.
Is it possible (and advisable) to export bokeh animated plots in the gif format?
If so, will I need to make use of any additional tools to do this?
If not, is there a different file format that would be better suited to this?
I found this thread about potential options for creating gifs in Python, but I'm not sure if any of them are relevant to the Bokeh library. Programmatically generate video or animated GIF in Python?
Any help would be much appreciated. Many thanks.
Bokeh plot has a SaveTool which allows you to save the plot canvas manually in PNG format but this would be a lot of work for you to do. Alternatively you could automate this process by implementing Bokeh server app with update() function that updates the data_source property of your plot e.g. each second and saves a screenshot using export_png() function. Then you could use those images to build an animation e.g. using the Python lib you mentioned above.
This is an example script to run with bokeh serve --show app.py:
The content of app.py:
from bokeh.models import ColumnDataSource
from bokeh.plotting import figure, curdoc
from datetime import datetime
from bokeh.io import export_png
import random
source = ColumnDataSource(dict(time = [datetime.now()], value = [random.randint(5, 10)]))
plot = figure(plot_width = 1200, x_axis_type = 'datetime', tools = 'pan,box_select,crosshair,reset,save,wheel_zoom')
plot.line(x = 'time', y = 'value', line_color = 'black', source = source)
counter = 0
def update():
global counter
new_source_data = dict(time = [datetime.now()], value = [random.randint(5, 10)])
source.stream(new_source_data)
counter = counter + 1
export_png(plot, filename = "plot_" + str(counter) + ".png")
curdoc().add_root(plot)
curdoc().add_periodic_callback(update, 1000)
To make this script work you need to have phantomjs installed on your system. So first you need to install nodejs and npm, then install phantomjs like this:
sudo npm install -g phantomjs-prebuilt
If you are using Mac then another option is to use QuickTime Player screen recording to make a movie and then convert it into an animated gif using FFMPEG like explained in this post
Related
I am trying to build a grid plot that updates based on value selected from 'Select' widget using Bokeh.
The graph works but there is no interaction between the widget and the graph. I am not sure how to do this. The goal is to use the 'Select' to update dfPlot then follow the remaining steps.
Here is what i have so far:
output_file('layout.html')
select = Select(title="Option:", options= list(dfExpense['Ident'].unique()), value= "VALUE")
def update_plot(attr, old, new):
dfPlot = dfExpense[dfExpense['Ident'] == select.value]
select.on_change('value', update_plot)
d = []
for x in dfPlot['Account'].unique():
d.append(f's_{x}')
plt = []
for i, x in enumerate(dfPlot['Account'].unique()):
dftemp = dfPlot[dfPlot['Account']==gl]
source1 = ColumnDataSource(dftemp)
d[i] = figure(plot_width = 250, plot_height = 250)
d[i].circle('X', 'Amount', source = source1)
plt.append(d[i])
grid= gridplot([i for i in plt], ncols = 6)
l = row(grid, select)
show(l)
curdoc().add_root(l)
Thanks!
Someone else will probably give you a better answer. I'll just say, I think you might be doing things completely wrong for what you are trying to do (I did the same thing when starting to work with Bokeh).
My understanding after a bit of experience with Bokeh, as it relates to your problem, is as follows:
Using curdoc to make an interactive widget based Bokeh plot means you are using Python to interact with the plot, meaning that you must use a Bokeh server, not just use a .html file. (as a corollary, you won't be using show or output file) https://docs.bokeh.org/en/latest/docs/user_guide/server.html
You can still make a standalone .html file and make it have interactive widgets like sliders, but you will have to write some Javascript. You'll most likely want to do this by utilizing CustomJS within Bokeh, which makes it relatively easy.
https://docs.bokeh.org/en/latest/docs/user_guide/interaction/callbacks.html
I had a similar problem, wanting interactivity without using a Python Bokeh server. CustomJS ended up serving my needs quite well, and even though I'm a novice at Javascript, they make it pretty easy (well, especially if your problem is similar to the examples, it can get tricky otherwise but still not very hard).
In MNE with Python, I would like to keep the interactive plotting window open once the calling def is executed completely.
However, this is not achievable via the following code:
def get_plot():
sample_data_folder = mne.datasets.sample.data_path()
sample_data_raw_file = os.path.join(sample_data_folder, 'MEG', 'sample',
'sample_audvis_raw.fif')
raw = mne.io.read_raw_fif(sample_data_raw_file)
raw.plot()
get_plot()
Such that, once the get_plot() is completed, the plot window is automatically closed.
Also, I am on Windows 10 with PyCharm 2020.1.3.
May I know how to handle this issue?
To get the interactive plot in PyCharm. The Show plots in tool window first need to be disabled.
Disable Settings | Tools | Python Scientific | Show plots in tool
window
Then, matplotlib.use('TkAgg') should allowed to create an interactive plot window.
MNE plot() is based on matplotlib. See the source file plot_raw. Based from the OP, matplotlib equip with block parameter you can pass to plt.show(). This allow the plot to be open even after the function is successfully invoke.
Apparently, mne group have include this parameter as well.
So, the above objective can be achieved simply by setting plot(block=True)
Then, the full code is
import mne
import matplotlib
matplotlib.use('TkAgg')
def get_plot():
sample_data_folder = mne.datasets.sample.data_path()
sample_data_raw_file = os.path.join(sample_data_folder, 'MEG', 'sample',
'sample_audvis_raw.fif')
raw = mne.io.read_raw_fif(sample_data_raw_file)
raw.plot(block=True)
get_plot()
I am looking for a way to plot realtime data line plot or scatter plots from python.
With the plots I want to monitor long-running loops when experimenting with algorithms with scientific computing. I.e. to help me answer the question: Are my results still improving with each iteration or can I cancel the loop?
I am looking for a quick and dirty method. I saw that with Bokeh and Dash one can program dashboards with realtime updates, but an awful lot of boilerplate code seems to be required just to get an updating plot.
Here is a simple "live streaming" example for Bokeh v1.3.0. You can run it with bokeh serve --show app.py
app.py:
from bokeh.plotting import figure, curdoc
from datetime import datetime
import random
plot = figure(plot_width = 1200, x_axis_type = 'datetime', tools = 'pan,box_select,crosshair,reset,save,wheel_zoom')
line = plot.line(x = 'time', y = 'value', line_color = 'black', source = dict(time = [datetime.now()], value = [random.randint(5, 10)]))
def update():
line.data_source.stream(dict(time = [datetime.now()], value = [random.randint(5, 10)]))
curdoc().add_root(plot)
curdoc().add_periodic_callback(update, 1000)
From the last days, I have been trying to use Bokeh to plot real-time data and display on a .html in order to be embeed in a webpage. I have sucessuly adapted one of the bokeh examples to my needs. I am using a buffer of 50 elements on the plot and I am noting the following behaviour:
1) In case I run the script and go to the browser the x_range fully adapts to incomming data and everything works correctly
2) If I click on "Refresh" on the browser the x_range stops to adapt to incoming data and freezes to the last value.
I tried to force the x_axis to initial and end values but the visualization behaves poorly.
I think I am not correctly understanding what does the "Refresh" hit impacts my code and how I can workaround this issue.
""" To view this example, first start a Bokeh server:
bokeh serve --allow-websocket-origin=localhost:8000
And then load the example into the Bokeh server by
running the script:
python animated.py
in this directory. Finally, start a simple web server
by running:
python -m SimpleHTTPServer (python 2)
or
python -m http.server (python 3)
in this directory. Navigate to
http://localhost:8000/animated.html
"""
from __future__ import print_function
import io
from numpy import pi, cos, sin, linspace, roll
from bokeh.client import push_session
from bokeh.embed import server_session
from bokeh.plotting import figure, curdoc
from bokeh.models import ColumnDataSource
fa = open('Accelerometer.txt', 'r')
source = ColumnDataSource(data=dict(x=[], y=[]))
fg = figure(width=250, plot_height=250, title="RT-Test")
fg.line(x='x', y='y', color="olive", source=source)
fg.x_range.follow = "end"
# Visualization scale and aesthetics
fg.xgrid.grid_line_color = None
fg.ygrid.grid_line_color = None
fg.background_fill_color = "snow"
# add the plot to curdoc
curdoc().add_root(fg)
# open a session which will keep our local doc in sync with server
session = push_session(curdoc())
html = """
<html>
<head></head>
<body>
%s
</body>
</html>
""" % server_session(fg, session_id=session.id, relative_urls=False)
with io.open("animated.html", mode='w+', encoding='utf-8') as f:
f.write(html)
print(__doc__)
def update():
line = fa.readline().split(',')
x = float(line[0])
y = float(line[1])
print(x, y)
# construct the new values for all columns, and pass to stream
new_data = dict(x=[x], y=[y])
source.stream(new_data, rollover=50)
curdoc().add_periodic_callback(update, 100)
session.loop_until_closed() # run forever
This kind of usage of the Bokeh server, with the actual code running in a separate process and calling session.loop_until_closed, is discouraged in the strongest terms. In the next release, all of the examples of this sort will be deleted, and mentions of this approach removed from the docs. This usage is inherently inferior in many ways, as outlined here, and I would say that demonstrating it so prominently for so long was a mistake on our part. It is occasionally useful for testing, but nothing else.
So what is the good/intended way to use the Bokeh server? The answer is to have a Bokeh app run in the Bokeh server itself, unlike the code above. This can be done in a variety of ways, but one common way os to wirte a simple script, then execute that script with
bokeh serve -show myapp.py
I don't have access to your "Accelerate.py" dataset, but a rough pass at updating your code would look like:
# myapp.py
from numpy import pi, cos, sin, linspace, roll
from bokeh.plotting import figure, curdoc
from bokeh.models import ColumnDataSource
fa = open('Accelerometer.txt', 'r')
source = ColumnDataSource(data=dict(x=[], y=[]))
fg = figure(width=250, plot_height=250, title="RT-Test")
fg.line(x='x', y='y', color="olive", source=source)
fg.x_range.follow = "end"
fg.xgrid.grid_line_color = None
fg.ygrid.grid_line_color = None
fg.background_fill_color = "snow"
curdoc().add_root(fg)
def update():
line = fa.readline().split(',')
x = float(line[0])
y = float(line[1])
# construct the new values for all columns, and pass to stream
new_data = dict(x=[x], y=[y])
source.stream(new_data, rollover=50)
curdoc().add_periodic_callback(update, 100)
Now if you run this script with the bokeh serve command, then any refresh will get you a brand new session of this app. It's also worth nothing that code written in this way is considerably simpler and shorter.
These kinds of apps can be embedded in Jupyter notebooks, Flask and other web-apps, or made into "regular" python scripts run with python instead of bokeh serve. For more information see Running a Bokeh Server in the User's Guide.
I'm new to Bokeh and Python, and this is my first Stack Overflow question as well.
I'm using Bokeh to plot trajectory profiles of particles diffusing in the brain, but have it be animated. I have been able to successfully create a program that plots the points, but once all the points are plotted, it stops. I want to be able to loop the animation so that once all the points are plotted, it clears itself and starts over.
I am still very unfamiliar with coding terms, and I wasn't able to find something that could do this. I thought I was on the right track with importing using the reset function inside an if statement, but it doesn't seem to work. I have looked at the following as well for reference:
How to animate a circle using bokeh
Here is my code so far plotting a random trajectory:
import numpy as np
from bokeh.plotting import figure, show, gridplot, vplot, hplot, curdoc
from bokeh.io import output_notebook
from bokeh.client import push_session
from bokeh.core.state import State as new
# This is where the actual coding begins.
b = np.random.rand(300, 3)
xlist = b[:, 1]
ylist = b[:, 2]
# create a plot and style its properties. Change chart title here.
p = figure(title='PEG_PLGA15k_F68_R2_P81', title_text_font_size='13pt',
x_range=(min(xlist), max(xlist)), y_range=(min(ylist), max(ylist)),)
# add a text renderer to out plot (no data yet)
r = p.line(x=[], y=[], line_width=3, color='navy')
session = push_session(curdoc())
i = 0
ds = r.data_source
# create a callback that will add a number in a random location
def callback():
global i
ds.data['x'].append(xlist[i])
ds.data['y'].append(ylist[i])
ds.trigger('data', ds.data, ds.data)
if i < xlist.shape[0] - 1:
i = i + 1
else:
new.reset()
# Adds a new data point every 67 ms. Change at user's discretion.
curdoc().add_periodic_callback(callback, 67)
session.show()
session.loop_until_closed()
If all you want is to restart the animation once you reach some condition (like "all points have been plotted") you can just reset the DataSource. So, for instance, on your example you should have:
else:
i = 0
ds.data['x'] = []
ds.data['y'] = []
instead of:
else:
new.reset()
and that should do the trick. Just use your datasource... State is a more general component that should be used on different level and not to manage plot glyphs and datasources.
A couple of quick notes here:
On your question you've mentioned a link to the 0.10 version documentation but from your code I can tell you are not using a newer version (0.11.x). Always be sure to use the right docs for the version of Bokeh you are using since there might be a few changes between one version and another before the project reach 1.0.
You don't need to call ds.trigger('data', ds.data, ds.data) since bokeh property system will automatically detect your changes to the datasource fields inside your callback
You are designing/running your script as a bokeh script that uses a client session to the server (so you'll have a running instance of bokeh server somewhere and your script communicates with it). I'd suggest you to consider running your code as a Bokeh App instead, so your session and your code run inside the bokeh server instance. You can see more details about the difference at the bokeh server section on the official docs.