Code
import numpy as npID classification
In this method, TDs and RDs satisfy $ < 0.2$ and $ | | > 0.4$ B BN bg ∣∣ ∣∣ , < D 0.2 B B bg ∣∣ ∣ ∣ , respectively. Moreover, IDs with < 0.4 B BN bg ∣∣ ∣∣ , < D 0.2 B B bg ∣∣ ∣ ∣ could be either TDs or RDs, and so are termed EDs. Similarly, NDs are defined as > 0.4 B BN bg ∣∣ ∣∣ , > D 0.2 B B bg ∣∣ ∣ ∣ because they can be neither TDs nor RDs. It is worth noting that EDs and NDs here are not physical concepts like RDs and TDs. RDs or TDs correspond to specific types of structures in the MHD framework, while EDs and NDs are introduced just to better quantify the statistical results.
Criteria Used to Classify Discontinuities on the Basis of Magnetic Data Type
| Type | \(\|B_n/B\|\) | \(\| \Delta B / B \|\) |
|---|---|---|
| Rotational (RD) | large | small |
| Tangential (TD) | small | large |
| Either (ED) | small | small |
| Neither (ND) | large | large |
Code
BnOverB_RD_lower_threshold = 0.4
dBOverB_RD_upper_threshold = 0.2
BnOverB_TD_upper_threshold = 0.2
dBOverB_TD_lower_threshold = dBOverB_RD_upper_threshold
BnOverB_ED_upper_threshold = BnOverB_RD_lower_threshold
dBOverB_ED_upper_threshold = dBOverB_TD_lower_threshold
BnOverB_ND_lower_threshold = BnOverB_TD_upper_threshold
dBOverB_ND_lower_threshold = dBOverB_RD_upper_thresholdCode
def classify_id(BnOverB, dBOverB):
BnOverB = np.abs(np.asarray(BnOverB))
dBOverB = np.asarray(dBOverB)
s1 = (BnOverB > BnOverB_RD_lower_threshold)
s2 = (dBOverB > dBOverB_RD_upper_threshold)
s3 = (BnOverB > BnOverB_TD_upper_threshold)
s4 = s2 # note: s4 = (dBOverB > dBOverB_TD_lower_threshold)
RD = s1 & ~s2
TD = ~s3 & s4
ED = ~s1 & ~s4
ND = s3 & s2
# Create an empty result array with the same shape
result = np.empty_like(BnOverB, dtype=object)
result[RD] = "RD"
result[TD] = "TD"
result[ED] = "ED"
result[ND] = "ND"
return resultClassification of candidates
Code
sns.jointplot(
candidates_jno_tau_60s,
x='dBOverB', y='BnOverB',
# kind='kde',
kind="hex",
)╭─────────────────────────────── Traceback (most recent call last) ────────────────────────────────╮ │ in <module>:2 │ │ │ │ 1 sns.jointplot( │ │ ❱ 2 │ candidates_jno_tau_60s, │ │ 3 │ x='dBOverB', y='BnOverB', │ │ 4 │ # kind='kde', │ │ 5 │ kind="hex", │ ╰──────────────────────────────────────────────────────────────────────────────────────────────────╯ NameError: name 'candidates_jno_tau_60s' is not defined
Code
sns.jointplot(
all_candidates,
x='dBOverB', y='BnOverB',
# kind='kde',
kind="hex",
)╭─────────────────────────────── Traceback (most recent call last) ────────────────────────────────╮ │ in <module>:2 │ │ │ │ 1 sns.jointplot( │ │ ❱ 2 │ all_candidates, │ │ 3 │ x='dBOverB', y='BnOverB', │ │ 4 │ # kind='kde', │ │ 5 │ kind="hex", │ ╰──────────────────────────────────────────────────────────────────────────────────────────────────╯ NameError: name 'all_candidates' is not defined
Distribution of the DD types
Plot distribution of types for each missions
Code
alt.Chart(all_candidates).encode(
x=alt.X("count()").stack("normalize").title("Share of ID types"),
y=alt.Y('sat').title(None),
color='type',
).mark_bar()
# alt.Chart(distributions).encode(
# alt.X('ratio:Q', title='DD type distribution').axis(format='.0%'),
# y=alt.Y('sat', title=None),
# color='type',
# ).mark_bar()╭─────────────────────────────── Traceback (most recent call last) ────────────────────────────────╮ │ in <module>:1 │ │ │ │ ❱ 1 alt.Chart(all_candidates).encode( │ │ 2 │ x=alt.X("count()").stack("normalize").title("Share of ID types"), │ │ 3 │ y=alt.Y('sat').title(None), │ │ 4 │ color='type', │ ╰──────────────────────────────────────────────────────────────────────────────────────────────────╯ NameError: name 'alt' is not defined
Code
distributions = all_candidates.group_by("sat", "type").agg(pl.count()).with_columns(
(pl.col("count") / pl.sum("count").over("sat")).alias("ratio")
)
count_table = distributions.to_pandas().pivot(index='sat', columns='type', values='count')[['RD', 'TD', 'ED', 'ND']]
ratio_table = distributions.to_pandas().pivot(index='sat', columns='type', values='ratio')[['RD', 'TD', 'ED', 'ND']]
# display(distributions.to_pandas().pivot(index='sat', columns='type', values='ratio')[['RD', 'TD', 'ED', 'ND']].style.format("{:.0%}"))
display(count_table, ratio_table.style.format("{:.0%}"))╭─────────────────────────────── Traceback (most recent call last) ────────────────────────────────╮ │ in <module>:1 │ │ │ │ ❱ 1 distributions = all_candidates.group_by("sat", "type").agg(pl.count()).with_columns( │ │ 2 │ (pl.col("count") / pl.sum("count").over("sat")).alias("ratio") │ │ 3 ) │ │ 4 count_table = distributions.to_pandas().pivot(index='sat', columns='type', values='count │ ╰──────────────────────────────────────────────────────────────────────────────────────────────────╯ NameError: name 'all_candidates' is not defined
Plot distribution of types for each missions over time
Code
alt.Chart(all_candidates).mark_bar(binSpacing=0).encode(
x="yearmonth(time)",
y=alt.Y("count()").stack("normalize").title("Share of ID types"),
row=alt.Row("sat").title(None),
color="type",
).configure_axis(grid=False).properties(height=100)╭─────────────────────────────── Traceback (most recent call last) ────────────────────────────────╮ │ in <module>:1 │ │ │ │ ❱ 1 alt.Chart(all_candidates).mark_bar(binSpacing=0).encode( │ │ 2 │ x="yearmonth(time)", │ │ 3 │ y=alt.Y("count()").stack("normalize").title("Share of ID types"), │ │ 4 │ row=alt.Row("sat").title(None), │ ╰──────────────────────────────────────────────────────────────────────────────────────────────────╯ NameError: name 'alt' is not defined
Code
%%R
plot_type_distribution <- function(data, bin_width = 30) {
data$date_only <- as.Date(data$time)
p <- ggplot(data, aes(date_only, fill = type)) +
geom_histogram(binwidth = bin_width, position = "fill") +
theme_pubr(base_size = 16)
return(p)
}
p1 <- plot_type_distribution(jno_candidates)
p2 <- plot_type_distribution(thb_candidates)
p3 <- plot_type_distribution(sta_candidates)
p <- ggarrange(
p1 + rremove("xlab"),
p2 + rremove("xlab"), p3,
nrow = 3, align = 'hv',
labels=list("JUNO", "ARTEMIS-B", "STEREO-A"), hjust=0, vjust=0,
legend = 'right', common.legend = TRUE
)
# save_plot(filename = "type_distribution")
pUsageError: Cell magic `%%R` not found.