Usage
Usage¶
To use arcos4py in a project
import arcos4py
Basic usage example of the main module¶
To use the main class, generate a new class instance of ARCOS
from arcos4py import ARCOS
ts = ARCOS(data,["x, y"], 't', 'id', 'm', 'clTrackID')
| t | x | y | m | id | Position | |
|---|---|---|---|---|---|---|
| 0 | 1 | 0.228724716134052 | -0.158939933368972 | 0 | 1 | 0 |
| 1 | 1 | 0.880322831777765 | -0.117711550077457 | 0 | 2 | 0 |
| 2 | 1 | 1.93057074895645 | 0.0786037381335957 | 0 | 3 | 0 |
| 3 | 1 | 2.95877070488632 | 0.189801493820322 | 0 | 4 | 0 |
| 4 | 1 | 3.90293266588805 | -0.0413798066471996 | 0 | 5 | 0 |
| .. | . | ............... | ................. | . | .. | . |
Prepare the input data.¶
interpolate Measurments¶
If the measurement column contains missing values, run interpolate_measurements() first.
ts.interpolate_measurements()
Clip measurement to provided quantile range¶
Clipping can be performed to remove extreme outliers from the dataset, but it is not necessary.
ts.clip_meas(clip_low: = 0.001, clip_high=0.999)
Rescale and Binarize the measurement¶
Rescaling and detrending are optional for the algorithm to work but recommended. There are three options available: ['none', 'lm', 'runmed']. Rumned is the default.
However, ARCOS requires binarized data to detect and track collective event clusters. Binarization is done by setting a threshold (binThr) and defining measurements below this threshold as 0 and above as 1.
ts.bin_measurements(smoothK: int = 1, biasK = 1, peakThr = 1,binThr = 1, polyDeg = 1, biasMet = "runmed",)
Detect collective events¶
events_df = ts.trackCollev(eps = 1, minClsz = 1, nPrev = 1)
print(events_df)
| t | id | x | y | clTrackID | m | Position | |
|---|---|---|---|---|---|---|---|
| 0 | 2 | 41 | 4.15698907764003 | 3.91461390425413 | 1 | 1 | 0 |
| 1 | 3 | 32 | 3.89042167730585 | 2.98886585399189 | 1 | 1 | 0 |
| 2 | 3 | 40 | 3.08624924975602 | 4.193936843095 | 1 | 1 | 0 |
| 3 | 3 | 41 | 3.99750905085216 | 3.9553900675078 | 1 | 1 | 0 |
| 4 | 3 | 42 | 5.06006349489829 | 4.0631364410516 | 1 | 1 | 0 |
| .. | . | .. | ... | .. | . | . | . |
TrackCollev returns a pandas DataFrame object containing a column with the collecive event id.
Perform calculations without main class¶
All functions from the ARCOS class are also accessible individually through the tools module, such as:
from arcos4py.tools import trackCollev
Additional modules¶
In addition to the ARCOS algorithm and its helper classes, plots are generated with the plotting module, collective event statistics using the stats module. Please see the Modules Page for further details.