!pip install matplotlib pandas pyarrow seaborn

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import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt

plt.rcParams["font.family"] = "sans"
plt.rcParams["font.size"] = 8
sns.set_palette('muted')

Shot Metadata

This notebook contains a demonstration of plotting several of the summary statistics that accompany the shot metadata.

Firstly, we’re going to load all the shot data into a pandas dataframe:

summary = pd.read_parquet('https://mastapp.site/parquet/level2/shots')
summary

	shot_id	campaign	ohmnic_energy_total	radii_s_area_max_current	equi_max_li3_time	generic_dt_energy_max_current	generic_s_area_max	divertor_config	generic_dt_total_energy	generic_plasma_elongation_max_current	...	nbi_energy_ss_max_current	nbi_total_injected_energy_ss	nbi_injected_energy_ss_ruby_time	nbi_power_ss_max_current	nbi_power_truby_ss	shot_scenario	scenario	rad_o2ratio	radii_c2ratio	shot_flat_top_duration
0	11766	M5	0.114530	22.644779	0.270	-192676.851518	23.362010	Conventional	430281.62	1.601573	...	NaN	NaN	NaN	NaN	NaN	None	NaN	NaN	NaN	NaN
1	11767	M5	0.134566	22.002382	0.150	-82970.042801	23.469263	Conventional	254662.14	1.594044	...	NaN	NaN	NaN	NaN	NaN	None	NaN	NaN	NaN	NaN
2	11768	M5	0.129432	23.027416	0.270	-113603.034418	23.507900	Conventional	401462.44	1.612653	...	NaN	NaN	NaN	NaN	NaN	None	NaN	NaN	NaN	NaN
3	11769	M5	0.156508	22.759919	0.290	-127591.593586	23.938997	Conventional	283884.00	1.673236	...	NaN	NaN	NaN	NaN	NaN	None	NaN	NaN	NaN	NaN
4	11771	M5	0.131940	22.525058	0.290	-164363.251971	23.959898	Conventional	309404.78	1.655904	...	NaN	NaN	NaN	NaN	NaN	None	NaN	NaN	NaN	NaN
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
11568	30467	M9	0.094553	25.402852	0.030	31972.688440	25.416924	Conventional	2307703.20	1.930563	...	0.137379	0.340425	0.000195	2.180491	NaN	S6	3.0	NaN	NaN	NaN
11569	30468	M9	0.095056	25.509430	0.030	-146041.829662	25.541964	Conventional	2943505.20	1.938207	...	0.155259	0.288250	0.000208	1.985447	NaN	S8	2.0	NaN	NaN	NaN
11570	30469	M9	0.074943	25.318623	0.035	133309.409983	25.418210	Conventional	840205.30	1.933172	...	0.293718	0.328446	0.000189	2.288206	NaN	S6	3.0	NaN	NaN	NaN
11571	30470	M9	0.236737	27.009387	0.035	-501857.330985	28.405008	Conventional	4641571.50	1.924374	...	0.274879	0.294526	0.000207	2.107413	NaN	S8	2.0	NaN	NaN	NaN
11572	30471	M9	0.372848	24.728233	0.035	-506018.886237	25.858627	Conventional	25309066.00	1.882437	...	0.136419	0.171676	0.000202	NaN	NaN	S8	2.0	NaN	NaN	NaN

11573 rows × 189 columns

Summary Statistics About Shots

Let’s look at a summary of simple counts of different shot metadata.

fig, axes = plt.subplots(2, 2, figsize=(10, 5))
ax1, ax2, ax3, ax4 = axes.flatten()

sns.histplot(summary, y='heating', hue='campaign', multiple="stack", ax=ax1)
sns.histplot(summary, y='plasma_shape', hue='campaign', multiple="stack", ax=ax2)
sns.histplot(summary, y='current_range', hue='campaign', multiple="stack", ax=ax3)
sns.histplot(summary, y=summary.pellets.astype(str), hue='campaign', multiple="stack", ax=ax4)

for ax in axes.flatten():
    ax.set_xlabel('No. Shots')

plt.tight_layout()

Plasma Beta (\(\beta\)) v.s Confinement Time (\(\tau_E\))

This plot can show how the efficiency of energy confinement varies with plasma pressure.

plt.figure()
sns.scatterplot(summary, y='generic_max_energy_time', x='generic_max_beta_max_current', hue='heating')
plt.xlim(0, 18)
plt.ylim(0, 1)
plt.ylabel('Confinement time $\\tau_E$ (s)')
plt.xlabel('Plasma Beta $\\beta$ (%)')
plt.show()

Plasma Temperature (\(T_e\)) vs. Plasma Density (\(n_e\))

This can reveal the relationship between temperature and density, which is critical for achieving the conditions necessary for fusion.

plt.figure()
sns.scatterplot(summary, y='thomson_temp_max_current', x='thomson_density_max_current', hue='current_range', alpha=0.8)
plt.xlim(0, .8e20)
plt.ylim(0, 1750)
plt.ylabel('Temperature $T_e$ (eV)')
plt.xlabel('Density $n_e$ ($m^{-3}$)')
plt.show()

Plasma Current (\(I_p\)) vs. Confinement Time (\(\tau_E\))

This can indicate how the plasma current affects the confinement time, providing insights into stability and performance.

plt.figure()
sns.scatterplot(summary, y='plasma_time_avg_current', x='generic_max_energy_time', hue='current_range', alpha=0.8)
plt.xlim(0, 1)
plt.xlabel('Confinement Time $\\tau_E$ (s)')
plt.ylabel('Average Plasma Current  $I_p$ (kA)')
plt.show()