ID identification

limited feature extraction / anomaly detection

There are couple of ways to identify the ID.

Variance method (Liu et al. 2022) : Large variance in the magnetic field compared with neighboring intervals (see notebook)
Partial variance increment (PVI) method :
- Vasko et al. (2022)
B-criterion (Burlaga and Ness 1969) : a directional change of the magnetic ﬁeld larger than 30° during 60 s
TS-criterion (Tsurutani and Smith 1979) : \(|ΔB|/|B| \geq 0.5\) within 3 minutes

Traditional methods (B-criterion and TS-criterion) rely on magnetic ﬁeld variations with a certain time lag. B-criterion has, as its main condition. In their methods, the IDs below the thresholds are artiﬁcially abandoned. Therefore, identiﬁcation criteria may affect the statistical results, and there is likely to be a discrepancy between the ﬁndings via B-criterion and TS- criterion.

02-Jun-24 09:00:27: UserWarning: Traceback (most recent call last):
  File "/Users/zijin/projects/ids_finder/.pixi/envs/default/lib/python3.11/site-packages/pdpipe/__init__.py", line 85, in <module>
    from . import skintegrate
  File "/Users/zijin/projects/ids_finder/.pixi/envs/default/lib/python3.11/site-packages/pdpipe/skintegrate.py", line 20, in <module>
    from sklearn.base import BaseEstimator
ModuleNotFoundError: No module named 'sklearn'


02-Jun-24 09:00:27: UserWarning: pdpipe: Scikit-learn or skutil import failed. Scikit-learn-dependent pipeline stages will not be loaded.

02-Jun-24 09:00:27: UserWarning: Traceback (most recent call last):
  File "/Users/zijin/projects/ids_finder/.pixi/envs/default/lib/python3.11/site-packages/pdpipe/__init__.py", line 105, in <module>
    from . import nltk_stages
  File "/Users/zijin/projects/ids_finder/.pixi/envs/default/lib/python3.11/site-packages/pdpipe/nltk_stages.py", line 19, in <module>
    import nltk
ModuleNotFoundError: No module named 'nltk'


02-Jun-24 09:00:27: UserWarning: pdpipe: nltk import failed. nltk-dependent  pipeline stages will not be loaded.

source

pl_format_time

 pl_format_time (df:polars.lazyframe.frame.LazyFrame,
                 tau:datetime.timedelta)

Pipelines

source

detect_events

 detect_events (data:polars.lazyframe.frame.LazyFrame,
                tau:datetime.timedelta, ts:datetime.timedelta, bcols,
                sparse_num=None, method='liu', **kwargs)

References

Burlaga, Leonard F., and Norman F. Ness. 1969. “Tangential Discontinuities in the Solar Wind.” Solar Physics 9 (2): 467–77. https://doi.org/10.1007/BF02391672.

Liu, Y. Y., H. S. Fu, J. B. Cao, Z. Wang, R. J. He, Z. Z. Guo, Y. Xu, and Y. Yu. 2022. “Magnetic Discontinuities in the Solar Wind and Magnetosheath: Magnetospheric Multiscale Mission (MMS) Observations.” Astrophysical Journal 930 (1): 63. https://doi.org/10.3847/1538-4357/ac62d2.

Tsurutani, Bruce T., and Edward J. Smith. 1979. “Interplanetary Discontinuities: Temporal Variations and the Radial Gradient from 1 to 8.5 AU.” Journal of Geophysical Research: Space Physics 84 (A6): 2773–87. https://doi.org/10.1029/JA084iA06p02773.

Vasko, I. Y., K. Alimov, T. Phan, S. D. Bale, F. S. Mozer, and A. V. Artemyev. 2022. “Kinetic-Scale Current Sheets in the Solar Wind at 1 Au: Scale-Dependent Properties and Critical Current Density.” Astrophysical Journal Letters 926 (2): L19. https://doi.org/10.3847/2041-8213/ac4fc4.