The best way to understand how Datashader works is to try out our extensive set of examples. Datashader.org includes static versions of the getting started guide, user manual, and topic examples, but for the full experience with dynamic updating you will need to install them on a live server. To get started, first go to your home directory and download the current list of everything needed for the examples:
- Download the conda ds environment file and save it as
environment.yml.
Then run the following commands in your terminal (command) prompt, from wherever you saved environment.yml:
1. conda env create --file environment.yml
2. source activate ds
3. datashader examples
3. cd datashader-examplesStep 1 will read environment.yml, create a new Conda environment
named ds, and install of the libraries needed into that environment
(including datashader itself). It will use Python 3.6 by default, but
you can edit that file to specify a different Python version if you
prefer (which may require changing some of the dependencies in some
cases).
Step 2 will activate the ds environment, using it for all subsequent
commands. You will need to re-run step 2 after closing your terminal
or rebooting your machine, if you want to use anything in the ds
environment. On Windows, you need to replace source activate ds
with activate ds.
Step 3 will copy the datashader examples from wherever Conda placed
them into a subdirectory datashader-examples, and will then download
the sample data required for the examples. (datashader examples is
a shorthand for datashader copy-examples --path datashader-examples && datashader fetch-data --path datashader-examples.)
The total download size is currently about 4GB to transfer, requiring
about 10GB on disk when unpacked, which can take some time depending on
the speed of your connection. The files involved are specified in the
text file datasets.yml in the datashader-examples directory, and
you are welcome to edit that file or to download the individual files
specified therein manually if you prefer, as long as you put them into
a subdirectory data/ so the examples can find them. Once these
steps have completed, you will be ready to run any of the examples
listed on datashader.org.
Most of the examples are in the form of runnable Jupyter notebooks. Once you have obtained the notebooks and the data they require, you can run them on your own system using Jupyter:
cd datashader-examples
jupyter notebook
If you want the generated notebooks to work without an internet connection or
with an unreliable connection (e.g. if you see Loading BokehJS ... but never
BokehJS sucessfully loaded), then restart the Jupyter notebook server using:
BOKEH_RESOURCES=inline jupyter notebook --NotebookApp.iopub_data_rate_limit=100000000
An example interactive dashboard using bokeh server integrated with a datashading pipeline.
To start, launch it with one of the supported datasets specified:
python dashboard/dashboard.py -c dashboard/nyc_taxi.yml
python dashboard/dashboard.py -c dashboard/census.yml
python dashboard/dashboard.py -c dashboard/opensky.yml
python dashboard/dashboard.py -c dashboard/osm.yml
The '.yml' configuration file sets up the dashboard to use one of the
datasets downloaded above. You can write similar configuration files
for working with other datasets of your own, while adding features to
dashboard.py itself if needed to support them.
For most of these datasets, if you have less than 16GB of RAM on your machine, you will want to add the "-o" option before "-c" to tell it to work out of core instead of loading all data into memory. However, doing so will make interactive use substantially slower than if sufficient memory were available.
To launch multiple dashboards at once, you'll need to add "-p 5001" (etc.) to select a unique port number for the web page to use for communicating with the Bokeh server. Otherwise, be sure to kill the server process before launching another instance.