Create a Timeseries Model¶

The Slycat Timeseries Model provides time series analysis based on clustering and comparative visualization of waveforms. However, unlike Creating a CCA Model, you can’t upload data for a Timeseries model using your web browser. Instead, you’ll use one Python script to synthesize some time series data in a format suitable for use with Slycat, and a second script to compute the model and push it to the Slycat Web Server. This will demonstrate how Slycat’s REST API can be used to control Slycat programmatically, so you can transform and upload your data using any language that supports HTTP networking.

Generate Timeseries Data¶

For this example we’ll ssh into the Slycat Docker container, where the scripts to be run are already installed. Normally, you would run these scripts on the system where your data was located:
```
$ ssh slycat@<docker host ip> -p2222
```

In this case, substitute your docker host IP address. If you’re running docker on a Linux host, this will be localhost. On systems using Boot2Docker, it will be the IP address returned by:

$ boot2docker ip

When prompted, enter password slycat.

Switch to the Slycat source code directory containing the sample client scripts:
```
$ cd src/slycat/web-client
```
The script for synthesizing data is designed to run in parallel, so start some parallel worker processes in the background:
```
$ ipcluster start --daemonize
```
Synthesize some time series data, organized for use with Slycat:
```
$ python slycat-create-sample-timeseries-hdf5.py
```
The script creates a sample-timeseries directory and populates it with a set of random input variables and ten output time series, each containing two variables (additional command line parameters are available to synthesize data of arbitrary size).

Compute a Timeseries Model¶

Now that you have some sample data, run:

$ python slycat-create-timeseries-model-from-hdf5.py --no-verify sample-timeseries

and enter the password slycat when prompted (this script also runs in parallel, using the workers you started previously):

slycat password:
INFO - Storing clustering parameters.
INFO - Storing input table attribute 0
INFO - Storing input table attribute 1
INFO - Storing input table attribute 2
...
INFO - Your new model is located at https://localhost:8092/models/...

View a Timeseries Model¶

Point your web browser to the Slycat home page at https://<docker host ip>, if it isn’t already.
In the Slycat navbar at the top of the page, you should see a gray status dropdown containing two numbers separated by a slash. Those numbers are the number of models being computed, and the number of recently completed models, respectively.
Click on the status dropdown to see a menu containing an entry for all in-progress and recently completed models.
Wait for the sample-timeseries model to be a completed (a green check appears to its left), if it hasn’t already.
Click the sample-timeseries entry in the status dropdown, and the browser opens the new model page.
At the top of the page there is a list of output variables.
At page left is a hierarchical clustering of the output variable timeseries, displayed as a dendrogram.
At page right the raw output timeseries are plotted.
At the bottom of the page is a table containing raw input data.

Interact with a Timeseries Model¶

Click on an output variable name at the top of the page to select that output, updating the rest of the interface.
Click variable names in the raw input table to color timeseries using that variable.
Click individual raw input table rows or shift-click ranges of rows to highlight the corresponding timeseries.
Click nodes in the dendrogram to display only those waveforms.
Double-click nodes in the dendrogram to expand / collapse their children.

Next Steps¶

Next, let’s move on to Create a Parameter Image Model.