# Example Usage

Exponential delays
```
Python data_generator/generate_data.py -output-path ./data/syn_data/sample/ -alarms alarms.csv -true-graph true_graph.npy -nodes 2 -edges 1 -T 10000 -delay-dist exp -delay-params-names beta -delay-params-lower 0 -delay-params-upper 0 -skip-prob-range 0.0 0.0 -root-count 100 --add-device -g-add-percentage 0.0 -m-add-percentage 0.0 -m-dest-percentage 0.0 -min-delay 0
```

Normal delays
```
Python data_generator/generate_data.py -output-path ./data/syn_data/sample/ -alarms alarms.csv -true-graph true_graph.npy -nodes 2 -edges 1 -T 10000 -delay-dist normal -delay-params-names loc scale -delay-params-lower 10 2 -delay-params-upper 10 2 -skip-prob-range 0.0 0.0 -root-count 100 --add-device -g-add-percentage 0.0 -m-add-percentage 0.0 -m-dest-percentage 0.0 -min-delay 0
```

Uniform delays
```
Python data_generator/generate_data.py -output-path ./data/syn_data/sample/ -alarms alarms.csv -true-graph true_graph.npy -nodes 2 -edges 1 -T 10000 -delay-dist uniform -delay-params-names low high -delay-params-lower 10 20 -delay-params-upper 10 20 -skip-prob-range 0.0 0.0 -root-count 100 --add-device -g-add-percentage 0.0 -m-add-percentage 0.0 -m-dest-percentage 0.0 -min-delay 0
```

Poisson delays
```
Python data_generator/generate_data.py -output-path ./data/syn_data/sample/ -alarms alarms.csv -true-graph true_graph.npy -nodes 2 -edges 1 -T 10000 -delay-dist poisson -delay-params-names lam -delay-params-lower 1 -delay-params-upper 1 -skip-prob-range 0.0 0.0 -root-count 100 --add-device -g-add-percentage 0.0 -m-add-percentage 0.0 -m-dest-percentage 0.0 -min-delay 0
```

Note that for now, Exponential and Poisson delays add min_delay to sampled delays, while Normal and Uniform skip delays less than min_delay.
