Justification for the tau (data)

::: {#cell-1 .cell 0=‘h’ 1=‘i’ 2=‘d’ 3=‘e’ execution_count=3}

Code
from utils.config import JunoConfig

:::

Code
JunoConfig(tau=180).get_and_process_data().export()
JunoConfig(tau=20).get_and_process_data().export()
28-Feb-24 19:54:43: UserWarning: Distributing <class 'pandas.core.frame.DataFrame'> object. This may take some time.
(_deploy_ray_func pid=36713) RuntimeWarning: overflow encountered in exp
(_deploy_ray_func pid=36720) RuntimeWarning: overflow encountered in exp [repeated 32x across cluster]
(_deploy_ray_func pid=36718) RuntimeWarning: overflow encountered in exp [repeated 21x across cluster]
(_deploy_ray_func pid=36717) RuntimeWarning: overflow encountered in exp [repeated 17x across cluster]
(_deploy_ray_func pid=36712) RuntimeWarning: overflow encountered in exp [repeated 22x across cluster]
(_deploy_ray_func pid=36712) RuntimeWarning: overflow encountered in exp [repeated 5x across cluster]
(_deploy_ray_func pid=36716) RuntimeWarning: overflow encountered in exp [repeated 8x across cluster]
(_deploy_ray_func pid=36716) RuntimeWarning: overflow encountered in exp [repeated 3x across cluster]
28-Feb-24 19:54:52: UserWarning: Distributing <class 'pandas.core.frame.DataFrame'> object. This may take some time.
28-Feb-24 19:55:04: UserWarning: Distributing <class 'pandas.core.frame.DataFrame'> object. This may take some time.
28-Feb-24 19:55:14: UserWarning: Distributing <class 'pandas.core.frame.DataFrame'> object. This may take some time.
28-Feb-24 19:55:24: UserWarning: Distributing <class 'pandas.core.frame.DataFrame'> object. This may take some time.
28-Feb-24 19:55:31: UserWarning: Distributing <class 'pandas.core.frame.DataFrame'> object. This may take some time.
28-Feb-24 19:55:37: UserWarning: Distributing <class 'pandas.core.frame.DataFrame'> object. This may take some time.
28-Feb-24 19:55:42: UserWarning: Distributing <class 'pandas.core.frame.DataFrame'> object. This may take some time.
2024-02-28 19:55:44.719 | INFO     | discontinuitypy.datasets:write:40 - Dataframe written to ../data/05_reporting/events.JNO.fit.ts_1.00s_tau_180s.arrow
JunoConfig(name='JNO', data=<LazyFrame [4 cols, {"BX SE": Float64 … "time": Datetime(time_unit='us', time_zone=None)}] at 0x2A3CBBBD0>, ts=datetime.timedelta(seconds=1), tau=datetime.timedelta(seconds=180), events=shape: (42_984, 94)
┌─────────────┬────────────┬─────┬──────────┬───┬────────────┬───────────┬────────────┬────────────┐
│ time        ┆ index_diff ┆ len ┆ std      ┆ … ┆ v.ion.chan ┆ B.change  ┆ v.Alfven.c ┆ v.Alfven.c │
│ ---         ┆ ---        ┆ --- ┆ ---      ┆   ┆ ge.l       ┆ ---       ┆ hange      ┆ hange.l    │
│ datetime[ns ┆ f64        ┆ u32 ┆ f64      ┆   ┆ ---        ┆ f64       ┆ ---        ┆ ---        │
│ ]           ┆            ┆     ┆          ┆   ┆ f64        ┆           ┆ f64        ┆ f64        │
╞═════════════╪════════════╪═════╪══════════╪═══╪════════════╪═══════════╪════════════╪════════════╡
│ 2011-08-25  ┆ 1.457943   ┆ 180 ┆ 2.655685 ┆ … ┆ 0.0        ┆ 0.030584  ┆ 0.394842   ┆ 81.897526  │
│ 15:33:00    ┆            ┆     ┆          ┆   ┆            ┆           ┆            ┆            │
│ 2011-08-25  ┆ 0.37205    ┆ 180 ┆ 0.937567 ┆ … ┆ -0.032502  ┆ 0.242502  ┆ 3.52999    ┆ -50.821981 │
│ 17:49:30    ┆            ┆     ┆          ┆   ┆            ┆           ┆            ┆            │
│ 2011-08-25  ┆ 0.802401   ┆ 180 ┆ 1.548479 ┆ … ┆ -0.012796  ┆ 0.024578  ┆ 0.411154   ┆ -69.345459 │
│ 17:51:00    ┆            ┆     ┆          ┆   ┆            ┆           ┆            ┆            │
│ 2011-08-25  ┆ 1.47258    ┆ 180 ┆ 2.647456 ┆ … ┆ 0.013626   ┆ -0.271046 ┆ -3.9196    ┆ 90.960015  │
│ 18:36:00    ┆            ┆     ┆          ┆   ┆            ┆           ┆            ┆            │
│ 2011-08-25  ┆ 1.005952   ┆ 180 ┆ 1.614257 ┆ … ┆ -0.009481  ┆ 0.087428  ┆ 1.359867   ┆ 79.308791  │
│ 20:39:00    ┆            ┆     ┆          ┆   ┆            ┆           ┆            ┆            │
│ …           ┆ …          ┆ …   ┆ …        ┆ … ┆ …          ┆ …         ┆ …          ┆ …          │
│ 2016-06-29  ┆ 1.286955   ┆ 180 ┆ 3.264029 ┆ … ┆ NaN        ┆ 0.939178  ┆ NaN        ┆ NaN        │
│ 17:37:30    ┆            ┆     ┆          ┆   ┆            ┆           ┆            ┆            │
│ 2016-06-29  ┆ 0.858761   ┆ 180 ┆ 2.697777 ┆ … ┆ NaN        ┆ 0.663428  ┆ NaN        ┆ NaN        │
│ 17:49:30    ┆            ┆     ┆          ┆   ┆            ┆           ┆            ┆            │
│ 2016-06-29  ┆ 0.864486   ┆ 180 ┆ 2.724823 ┆ … ┆ -0.011618  ┆ 0.872487  ┆ 111.977492 ┆ 1179.18351 │
│ 17:51:00    ┆            ┆     ┆          ┆   ┆            ┆           ┆            ┆ 2          │
│ 2016-06-29  ┆ 0.891457   ┆ 180 ┆ 3.344919 ┆ … ┆ -0.023165  ┆ 0.083389  ┆ 10.257005  ┆ -1231.4827 │
│ 23:37:30    ┆            ┆     ┆          ┆   ┆            ┆           ┆            ┆ 57         │
│ 2016-06-29  ┆ 0.933747   ┆ 180 ┆ 2.632613 ┆ … ┆ -0.030708  ┆ 2.284448  ┆ 283.917387 ┆ -979.80710 │
│ 23:39:00    ┆            ┆     ┆          ┆   ┆            ┆           ┆            ┆ 6          │
└─────────────┴────────────┴─────┴──────────┴───┴────────────┴───────────┴────────────┴────────────┘, method='fit', mag_meta=Meta(dataset=None, parameters=None), bcols=None, plasma_data=<LazyFrame [14 cols, {"radial_distance": Float64 … "B_background_z": Float64}] at 0x174C37B50>, plasma_meta=PlasmaDataset(dataset=None, parameters=None, density_col='plasma_density', velocity_cols=['v_x', 'v_y', 'v_z'], speed_col=None, temperature_col=None), ion_temp_data=None, ion_temp_meta=TempMeta(dataset=None, parameters=None, para_col=None, perp_cols=None), e_temp_data=None, e_temp_meta=TempMeta(dataset=None, parameters=None, para_col=None, perp_cols=None), timerange=[datetime.datetime(2011, 8, 25, 0, 0, tzinfo=TzInfo(UTC)), datetime.datetime(2016, 6, 30, 0, 0, tzinfo=TzInfo(UTC))], split=8, fmt='arrow')