#| echo: false
#| output: false
from juno.config import JunoConfig
import polars as pl
from datetime import timedelta
from discontinuitypy.utils.plot import plot_candidates
from beforerr.project import plotsdir
from beforerr.matplotlib import hide_fig_legend, hide_y_axes_label::: {#cell-4 .cell 0=‘e’ 1=‘x’ 2=‘p’ 3=‘o’ 4=‘r’ 5=‘t’ execution_count=9}
from importlib.util import find_spec
import matplotlib.pyplot as plt
if find_spec("scienceplots"):
import scienceplots as scienceplots
plt.style.use(["science", "nature", "notebook"]):::
j_config = JunoConfig().find_events()
events = j_config.events.with_columns(pl.col("radial_distance").round())| time | tstart | tstop | t.d_end | t.d_start | t.d_time | index_diff | len | std | std_prev | std_next | index_std | index_fluctuation | B.after | B.before | b_mag | b_n | bn_over_b | d_star | db_mag | db_over_b | db_over_b_max | fit.stat.chisqr | fit.stat.rsquared | fit.vars.amplitude | fit.vars.c | fit.vars.sigma | rotation_angle | dB_x | dB_y | dB_z | dB_lmn_x | dB_lmn_y | dB_lmn_z | k_x | k_y | k_z | … | B_background_y | B_background_z | n.before | v_x_before | v_y_before | v_z_before | n.after | v_x_after | v_y_after | v_z_after | v_l | v_n | v_k | L_k | j0_k | ion_inertial_length | Alfven_speed | j_Alfven | L_k_norm | j0_k_norm | v.ion.before.l | v.ion.after.l | B.vec.before.l | B.vec.before.m | B.vec.before.n | B.vec.after.l | B.vec.after.m | B.vec.after.n | v.Alfven.before | v.Alfven.after | v.Alfven.before.l | v.Alfven.after.l | n.change | v.ion.change.l | B.change | v.Alfven.change | v.Alfven.change.l |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| datetime[ns] | datetime[ns] | datetime[ns] | datetime[ns] | datetime[ns] | datetime[ns] | f64 | u32 | f64 | f64 | f64 | f64 | f64 | f64 | f64 | f64 | f64 | f64 | f64 | f64 | f64 | f64 | f64 | f64 | f64 | f64 | f64 | f64 | f64 | f64 | f64 | f64 | f64 | f64 | f64 | f64 | f64 | … | f64 | f64 | f64 | f64 | f64 | f64 | f64 | f64 | f64 | f64 | f64 | f64 | f64 | f64 | f64 | f64 | f64 | f64 | f64 | f64 | f64 | f64 | f64 | f64 | f64 | f64 | f64 | f64 | f64 | f64 | f64 | f64 | f64 | f64 | f64 | f64 | f64 |
| 2011-08-25 15:25:30 | 2011-08-25 15:25:00 | 2011-08-25 15:26:00 | 2011-08-25 15:25:44.503 | 2011-08-25 15:25:13.503 | 2011-08-25 15:25:31.189079012 | 1.28895 | 60 | 2.59028 | 0.892715 | 0.907528 | 2.854216 | 1.245153 | 3.41785 | 3.179654 | 3.247682 | -0.698155 | -0.21497 | 1.037873 | 0.238196 | 0.073343 | 0.128613 | 0.418534 | 0.997957 | 5.609134 | -2.6382 | 1.351113 | 128.296648 | -5.15 | 2.05 | 2.13 | -5.913075 | 0.192051 | -0.510453 | 0.491886 | 0.705298 | 0.510493 | … | 2.057567 | 1.459172 | null | null | null | null | null | null | null | null | -396.585303 | 169.76004 | 200.265667 | 541.162948 | 4.124088 | 134.823335 | 41.927135 | 19.165405 | 4.013867 | 0.215184 | NaN | NaN | -2.826809 | -1.260165 | -0.728927 | 3.086266 | -1.452216 | -0.218474 | NaN | NaN | NaN | NaN | null | NaN | 0.238196 | NaN | NaN |
predicate = [
pl.col("index_diff") > 1,
pl.col("db_over_b_max") < 0.3,
]
rs = [1, 3, 5]
figure_opts = {
"figwidth": 3.3,
"figheight": 3,
}
fmt = "svg"
for r in rs:
temp_predicate = predicate + [pl.col("radial_distance").round() == r]
fig, ax = plot_candidates(
j_config,
predicate=temp_predicate,
num=1,
random=False,
add_ids_properties=False,
add_timebars=False,
)[0]
path = plotsdir() / "examples" / f"juno_id_example_r={r}.{fmt}"
hide_fig_legend(fig)
hide_y_axes_label(ax)
fig.set(**figure_opts)
fig.savefig(path)2024-07-12 12:09:11.502 | INFO | discontinuitypy.utils.plot:plot_candidates:265 - Candidates indices: [0]
12-Jul-24 12:09:12: UserWarning: Converting non-nanosecond precision datetime values to nanosecond precision. This behavior can eventually be relaxed in xarray, as it is an artifact from pandas which is now beginning to support non-nanosecond precision values. This warning is caused by passing non-nanosecond np.datetime64 or np.timedelta64 values to the DataArray or Variable constructor; it can be silenced by converting the values to nanosecond precision ahead of time.
2024-07-12 12:09:12.312 | INFO | discontinuitypy.utils.plot:plot_candidates:265 - Candidates indices: [66480]
12-Jul-24 12:09:12: UserWarning: Converting non-nanosecond precision datetime values to nanosecond precision. This behavior can eventually be relaxed in xarray, as it is an artifact from pandas which is now beginning to support non-nanosecond precision values. This warning is caused by passing non-nanosecond np.datetime64 or np.timedelta64 values to the DataArray or Variable constructor; it can be silenced by converting the values to nanosecond precision ahead of time.
2024-07-12 12:09:12.867 | INFO | discontinuitypy.utils.plot:plot_candidates:265 - Candidates indices: [78081]
12-Jul-24 12:09:13: UserWarning: Converting non-nanosecond precision datetime values to nanosecond precision. This behavior can eventually be relaxed in xarray, as it is an artifact from pandas which is now beginning to support non-nanosecond precision values. This warning is caused by passing non-nanosecond np.datetime64 or np.timedelta64 values to the DataArray or Variable constructor; it can be silenced by converting the values to nanosecond precision ahead of time.
Large derivative current density but small when using fitting
j_config = JunoConfig(ts=0.125)
j_fit_config = JunoConfig(ts=0.125).load()
j_der_config = JunoConfig(ts=0.125, method='derivative').load()
print(
len(j_fit_config.events),
len(j_der_config.events)
)
j_config.events = j_fit_config.events.join(j_der_config.events, on='time').with_row_index()
print(len(j_config.events))
j_config.events = j_config.events.filter(
pl.col("radial_distance") > 5,
pl.col("j0_k_right") > 3
)
print(len(j_config.events))
# j_fit_config.events.join(j_der_config.events, on='time')72656 94095
156532
1168indices = [153101, 156356, 156046]
offset = timedelta(seconds=0)
figs = j_config.plot_candidates(
indices=indices,
offset=offset,
add_plasma_params=True,
plot_current_density=True,
plot_fit_data=True,
)04-Mar-24 15:57:37: deriv_data was applied to: fgm_rot_tot_0-der
04-Mar-24 15:57:37: deriv_data was applied to: fgm_rot_tot_0-der
04-Mar-24 15:57:38: deriv_data was applied to: fgm_rot_tot_0-der
offset = timedelta(seconds=0)
figs = j_config.plot_candidates(
num=3,
add_plasma_params=True,
offset=offset,
plot_fit_data=True,
plot_current_density=True,
start_col = "tstart",
end_col = "tstop"
)2024-03-04 15:57:07.447 | INFO | discontinuitypy.datasets:plot_candidates:245 - Candidates indices: [156049 156046 154208]
04-Mar-24 15:57:07: deriv_data was applied to: fgm_rot_tot_0-der
04-Mar-24 15:57:08: deriv_data was applied to: fgm_rot_tot_0-der
04-Mar-24 15:57:08: deriv_data was applied to: fgm_rot_tot_0-der
j_config = JunoConfig(ts=0.125).load()
j_config.events = j_config.events.with_row_index().filter(
pl.col("radial_distance") > 5,
# pl.col("j0_k") > 2
)indices = [72643, 72280, 69537]
offset = timedelta(seconds=2)
figs = j_config.plot_candidates(
indices=indices,
offset=offset,
add_plasma_params=True,
plot_current_density=True,
plot_fit_data=True,
)04-Mar-24 12:01:19: PydanticDeprecatedSince20: Support for class-based `config` is deprecated, use ConfigDict instead. Deprecated in Pydantic V2.0 to be removed in V3.0. See Pydantic V2 Migration Guide at https://errors.pydantic.dev/2.6/migration/
04-Mar-24 12:01:24: deriv_data was applied to: fgm_rot_tot_0-der
04-Mar-24 12:01:25: deriv_data was applied to: fgm_rot_tot_0-der
04-Mar-24 12:01:25: deriv_data was applied to: fgm_rot_tot_0-der
offset = timedelta(seconds=2)
figs = j_config.plot_candidates(
num=3,
add_plasma_params=True,
offset=offset,
plot_fit_data=True,
plot_current_density=True,
)2024-03-04 14:41:02.337 | INFO | discontinuitypy.datasets:plot_candidates:243 - Candidates indices: [71263 71875 71222]
04-Mar-24 14:41:02: deriv_data was applied to: fgm_rot_tot_0-der
04-Mar-24 14:41:03: deriv_data was applied to: fgm_rot_tot_0-der
04-Mar-24 14:41:03: deriv_data was applied to: fgm_rot_tot_0-der
j_config = JunoConfig(ts=0.125, method='derivative').load()
j_config.events = j_config.events.with_row_index().filter(
pl.col("radial_distance") > 5,
pl.col("j0_k") > 4
)02-Mar-24 16:24:50: PydanticDeprecatedSince20: Support for class-based `config` is deprecated, use ConfigDict instead. Deprecated in Pydantic V2.0 to be removed in V3.0. See Pydantic V2 Migration Guide at https://errors.pydantic.dev/2.6/migration/
offset = timedelta(seconds=2)indices=[91384, 93825, 93618, 92389]
figs = j_config.plot_candidates(indices=indices, add_plasma_params=True, offset=offset, plot_current_density=True)02-Mar-24 16:52:58: deriv_data was applied to: fgm_rot_tot_0-der
02-Mar-24 16:52:58: deriv_data was applied to: fgm_rot_tot_0-der
02-Mar-24 16:52:58: deriv_data was applied to: fgm_rot_tot_0-der
02-Mar-24 16:52:59: deriv_data was applied to: fgm_rot_tot_0-der
figs = j_config.plot_candidates(num=3, add_plasma_params=True, offset=offset, plot_current_density=True)2024-03-02 16:53:51.076 | INFO | discontinuitypy.datasets:plot_candidates:243 - Candidates indices: [90260 92835 92303]
02-Mar-24 16:53:51: deriv_data was applied to: fgm_rot_tot_0-der
02-Mar-24 16:53:51: deriv_data was applied to: fgm_rot_tot_0-der
02-Mar-24 16:53:52: deriv_data was applied to: fgm_rot_tot_0-der
offset = timedelta(seconds=30)
figs = j_config.plot_candidates(num=15, add_plasma_params=True, offset=offset)2024-03-02 16:53:24.688 | INFO | discontinuitypy.datasets:plot_candidates:243 - Candidates indices: [92436 90663 92303 90640 92616 93261 93834 94014 94032 93294 92471 92343
92972 93732 92478]--------------------------------------------------------------------------- KeyboardInterrupt Traceback (most recent call last) Cell In[35], line 4 1 #| layout-ncol: 3 2 #| column: screen 3 offset = timedelta(seconds=30) ----> 4 figs = j_config.plot_candidates(num=15, add_plasma_params=True, offset=offset) File ~/micromamba/envs/psp_conjunction/lib/python3.11/site-packages/discontinuitypy/datasets.py:245, in IDsDataset.plot_candidates(self, indices, num, random, predicate, **kwargs) 242 indices = indices.head(num).to_numpy() 243 logger.info(f"Candidates indices: {indices}") --> 245 return [self.plot_candidate(index=i, **kwargs) for i in indices] File ~/micromamba/envs/psp_conjunction/lib/python3.11/site-packages/discontinuitypy/datasets.py:245, in <listcomp>(.0) 242 indices = indices.head(num).to_numpy() 243 logger.info(f"Candidates indices: {indices}") --> 245 return [self.plot_candidate(index=i, **kwargs) for i in indices] File ~/micromamba/envs/psp_conjunction/lib/python3.11/site-packages/discontinuitypy/datasets.py:224, in IDsDataset.plot_candidate(self, event, index, **kwargs) 222 if event is None: 223 event = self.get_event(index) --> 224 data = self.get_event_data(event, **kwargs) 226 return _plot_candidate(event, data, **kwargs) File ~/micromamba/envs/psp_conjunction/lib/python3.11/site-packages/discontinuitypy/datasets.py:100, in IdsEvents.get_event_data(self, event, offset, **kwargs) 97 end = event["t.d_end"] + offset 99 _data = self.data.filter(pl.col("time").is_between(start, end)) --> 100 return df2ts(_data, self.bcols) File ~/micromamba/envs/psp_conjunction/lib/python3.11/site-packages/discontinuitypy/utils/basic.py:292, in df2ts(df, cols, time_col, attrs, name) 289 cols = df.columns.tolist() 291 if isinstance(df, pl.LazyFrame): --> 292 df = df.collect() 294 # Prepare data 295 data = df[cols].to_numpy() File ~/micromamba/envs/psp_conjunction/lib/python3.11/site-packages/polars/lazyframe/frame.py:1940, in LazyFrame.collect(self, type_coercion, predicate_pushdown, projection_pushdown, simplify_expression, slice_pushdown, comm_subplan_elim, comm_subexpr_elim, no_optimization, streaming, background, _eager) 1937 if background: 1938 return InProcessQuery(ldf.collect_concurrently()) -> 1940 return wrap_df(ldf.collect()) KeyboardInterrupt:
