These days I've been trying to create a webpage on which one can watch a dynamic plotting demo. I'm using Django to construct the webpage, and recently have learned that matplotlib.animation can do such kind of job. I wonder whether it's possible to use this API, matplitlib.animation, while building the webpage using Django. (I tried to do it using 'HttpResponse' but only to fail.) If there is any other way to do this, please let me know. Thanks in advance.
You need to write the animation to disk and then serve it up via some static interface. Here is an idea of how to accomplish it:
Make your webserver point /animations to /var/www/animations
Save your animations in that folder (You can save mp4 and use HTML5 but if you want to save a GIF this SO has information on what is needed. You could use a model to hold initializing parameters and a hash of those (which would make a decent filename).
Render it in your templates with a regular img tag (or video tag if you use mp4).
Related
I am sorry if the question is confusing or doesn't make sense, this is my first project and I'll try to explain it to the best of my capabilities.
At first, I thought I would just be hosting a single map that I created using folium and inherited the template of the map into the website which is created with flask, therefore I didn't do any hardcoding in the main app.py file, however, it seemed really basic. So now using machine learning algorithms, I want to display different things over the map which makes some predictions for every hour of the day.
THE PROBLEM: It will take longer than expected to generate a new map every hour of the day (plus storing a new map so many times will be expensive in terms of storage).
MY SOLUTION: Have a skeleton map because the area that I will be making predictions on is localized, so have that area as the basic map and change the upper layers that will have the predictions displayed (basically heat map) every hour.
Is this a correct approach? Please recommend a right path to implement this. I am open to trying new things as long as it gets the job done. Would really appreciate any help regarding this. Thanks in advance
Folium does not support WebSockets if anything add new javascript to fetch data dynamically, but the best way would be to fetch data instead of rendering html everytime, it would help with the speed issue as well. Also, look into leaflets that would help too.
If you want to generate your map faster, you can use directly the Javascript library Leaflet to create the map (it is the library that is used to render the map with Folium). You need to send the data of your predictions from your backend in Flask to your front-end in Javascript with Leaflet in your HTML template (Jinja2).
I have an interactive graph generated by Plotly in Python that I saved to an html file using plotly.offline.plot(fig, filename='/tmp/interactiveGraph.html')
I am now trying to embed this interactive graph into some kind of webpage, using either Dash or Django. I'm leaning toward Django at the moment, given that I have an html version of the graph. Which would be better?
My code for the webpage is in a separate file from the code where I am creating the graph.
A lot of the tutorials I've found online just say to add a few lines to the template, but I don't know how to get those lines that they've described.
tl;dr: I'm looking for guidance as how to integrate an html file-for a Plotly interactive graph-with a web python script using Django or Dash
Side Question:
what is the difference between
plotly.offline.plot(fig, include_plotlyjs=False, output_type='div')
and what I have above?
Reference:
https://github.com/ricleal/DjangoPlotLy
https://www.pythonsetup.com/how-implement-data-visualization-django-and-plotly/
I would highly reccomend Django, its a great framework. As for this, the best option is to generate the data via JavaScript and Plotly has a great library for this. If you must use python, then Django can be used. Assuming you are familiar with Django, inside of your view you can collect your data and build your graph ( I would reccomend a celery task for something long running like this unless they are small graphs), and you can then collect the return from creating the graph in div format. This will return a string that holds all the needed html and css for the graphs. Put this inside of you get_context_data() method in your view and place it into the dictionary. You can the use that object inside of a template. I have done this before, if you are having a hard time feel free to DM me. Hope this helps some!
In regards to your side question, I believe having False for including JS will make the graph a bit smaller assuming you have an include for the plotly JS library. They might have done this in a newer release to make the graphs faster as they were significantly slower in the browser from python that the JS rendered one.
I am working on generating some plots using python, but I am generating these plots using matplotlib which is saved as images. If I create an html page as a report with these plots, they are static images. I cannot zoom in or roll over on the plot to see more detailed or specific information on a time series plot.
My question is how can I make these plots dynamic? Can someone suggest the best way to get started and move forward from there?
You should use some additional libraries to achive your goal.
For example, there some good Python web frameworks wich you can use:
CherryPy - allows you to simply write web-app with Python and you can import your plot there.
Plotly Python API - it would simply generate interactive plot, but store it at Plotly platform, but they provide embeded-code option, so you can use it on your site.
I would suggest Plotly, because it is much simpler, but it depends on your needs.
You will definitely want to do it using javascript. It's by far your best option when it comes to quickly make interactive graphs that you can present to a lot of users. Any of these js libraries will do a great job.
You will then want python to provide the data. Depending on the js library you are using, you might be able to parse data from .json, .csv, etc...
If you don't need the data that makes up your plots to change (with user input, for example), then generating and saving flat files with python and having javascript parsing them from some directory might be just enough.
Otherwise, you want to take a look at a python web framework and use one as backend to serve the plots data by request (in that case .json is probably the right format).
Frameworks like Flask, CherryPy, Pyramid or even web2py might be the easier ones to start with.
I have a project with many scripts using Matplotlib. I'd like to build a web interface for this project.
How do you place a Bokeh chart within a Chameleon template? I'm using Pyramid and the Deform bootstrap if that matters.
Does anyone have a good example out there?
There are several different issues to address here.
If you are truly trying to port a lot of Matplotlib plots into interactive JS, then it's possible that the mpld3 project is a good fit for you. However, you should be aware that by using D3, there will be performance implications, depending on how many points are in your plot. Bokeh also does have basic Matplotlib support now, and will only be getting more. Jake is currently refactoring the mpld3 project into an explicit exporter and then D3 renderer, and we will also be potentially building off of this work for the Bokeh Matplotlib support.
To do this with Bokeh, you can grab the raw HTML for a plot by looking at how e.g. HTMLFileSession.dumps() is implemented: https://github.com/ContinuumIO/bokeh/blob/master/bokeh/session.py#L295. The default template is bokeh/templates/base.html; however, this is a full HTML file, and not a fragment. The dumps() method is pretty straightforward, as is the default template, so you should be able to get what you need from looking at those. Hopefully for the next release, we will have finished out a HTMLFragmentSession which will make it easier to embed.
You want to use plot.create_html_snippet. This function returns the code that you want to appear in the HTML, the function also writes out an embed file.
This is what an embed snippet looks like
<script src="http://localhost:5006/static/dc0c7cfd-e657-4c79-8150-6a66be4dccb8.embed.js" bokeh_plottype="embeddata" bokeh_modelid="dc0c7cfd-e657-4c79-8150-6a66be4dccb8" bokeh_modeltype="Plot" async="true"></script>
the following arguments control how the embed file is written out, and where the js code searches for the embed files.
embed_base_url controls the url path (it can be absolute or relative) that the javascript will search for the embed file in.
embed_save_loc controls the directory that python will write out the embed file in. embed_save_loc isn't necessary when server=True
static_path controls the url path (it can absolute or relative) that the javascript will use to construct URLS for bokeh.js and bokeh.css. It defaults to http://localhost:5006/static/, but could just as easily point to a CDN
When running the bokeh server, navigate to http://localhost:5006/bokeh/generate_embed/static . I think this requires you to be running on master because of a bug.
I hope this helps.
Are there any packages that allow for the interactive display of GIS shapefiles? I'm looking to create a simple GUI that displays simple shapefiles (coastlines, etc) but can't seem to find where to start. I was originally tasked with doing this in R but I would like to try and use python and Qt.
Mapnik supports GIS shape files and has a Python interface.
I think you can use a combination of the shapefile library for reading files, and shapely for all the processing of the data.
I have used shapely for working with geo data feeds that have been loaded into a database, to work with the point and polygon data.
Per your comments, if you just want to simply display a shapefile in your PyQt app, then really all you need is to convert it to an SVG and display it directly. I have not used this conversion lib but I am sure there are more like it. Then you can just use a QSvgWidget to load and display it