This repo contains an example workload for lambda/CFFS based on the pySat library. It uses the example code provided here, which calculates the probability of perturbations in the magnetic field by lat,long bins over a time window.

You'll first need to get the dependencies. This is a bit challenging as the system requires pandas>=0.23,<0.25 which is very old. We provide an anaconda environment file that should work:

$ conda env create -f environment.yml

There is also a requirements.txt that should work with python3.7.

You can now run the example local application:

$ python3 seasonalOccurence.py

The first run will download the dataset which may take a bit of extra time. Future runs will use the cached version. As output, you will see two images representing the final data that would be used by a domain expert: agg.png which represents the partitioned and aggregated data, and full.png which represents the non-partitioned run. If you open them, you will notice that they are similar, but not identical. This is due to the simple aggregation strategy that we use.

Most of the application-specific code is deep in the pysat library (pysat.ssnl.occur_prob.by_orbit2D()), our modifications primarily focus on how that function gets called and distributed.

The basic flow of the code is as follows:

1. The publicly available cnofs dataset for some date-time range is downloaded to a common datasets directory (datasets/cnofs). This download is cached between runs.
2. We decide on a partitioning of the dataset by date-time range (e.g. split by day).
3. Each partition is processed by pysat (processRange()), resulting in a results dict containing a matrix of probabilities with one cell for each lat,long bin.
4. The per-partition results are aggregated by multiplying the probabilities.
5. The aggregated results are plotted and stored to (agg.png)

The processRange function returns a dictionary with the following fields (defined by pysat.ssnl.occur_prob.by_orbit2D):

• 'bin_y', 'bin_x': Vectors containing the latitude and longitude (respectively) boundaries for each bin.
• 'count': A matrix containing the number of unique orbit crossings for each bin (e.g. the number of unique measurements).
• 'prob': A matrix containing the probability measure for each cell. Ultimately, this is what the application wants.

The aggregation strategy we use (multiplying probabilities per-partition) is not identical to the non-partitioned algorithm. We don't have great visibility into the core algorithm, but presumably it's doing some additional calculations across the entire range. However, the results are qualitatively similar and clearly outputs the same trends, just with less resolution.