!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	uuid	equi_max_li3	ohmnic_max_heating	generic_max_energy_time	generic_min_q95_time	endpoint_url	generic_max_geo_major_radius_time	timestamp	shot_postshot_comment	...	nbi_power_max_ss	nbi_energy_ss_max_power	nbi_max_ss_power_time	nbi_power_ss_max_current	nbi_power_truby_ss	scenario	rad_o2ratio	radii_c2ratio	shot_scenario	shot_flat_top_duration
0	11766	7da2590b-6b7c-5a4e-8125-8b0c38a7c8bf	1.520526	1.038769	0.037496	0.270	https://s3.echo.stfc.ac.uk	0.080	2005-01-13 12:02:00	GOOD PLASMA, RAN FINE. SL JOINT ALARMS RATHER ...	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	None	NaN
1	11767	594cabe7-8b0a-54a2-9e1e-8b250a858acd	1.217211	1.105280	0.019210	0.245	https://s3.echo.stfc.ac.uk	0.080	2005-01-13 12:17:00	OK BUT LOST VERTICAL CONTROL - FA2 JUST DIED A...	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	None	NaN
2	11768	23a8941d-e073-5073-8f5e-e53230f55b43	1.262946	0.849237	0.036312	0.150	https://s3.echo.stfc.ac.uk	0.080	2005-01-13 13:30:00	OK. GOT FA4 BUT NOT FA3	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	None	NaN
3	11769	e12564d4-f0af-5c8d-b15c-ab59f11144b3	1.519628	2.149353	0.057099	0.290	https://s3.echo.stfc.ac.uk	0.290	2005-01-13 13:44:00	SLIDING JOINT ALARMS A BIT LOWER. PLASMA OK.	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	None	NaN
4	11771	1562346b-2097-5d27-8649-07eeb48dc5f7	1.788729	0.824824	0.052359	0.285	https://s3.echo.stfc.ac.uk	0.260	2005-01-13 14:33:00	GOOD PLASMA F/B CONTROL. SLIDING JOINT ALARMS ...	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	None	NaN
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
11568	30467	db7bc878-869c-59c6-bcc6-cb28cba69cf9	6.694992	NaN	-1.000000	0.030	https://s3.echo.stfc.ac.uk	0.165	2013-09-27 14:03:00	'Two times lower DD neutron rate than referenc...	...	2.080261	70.840835	0.12160	2.180491	NaN	3.0	NaN	NaN	S6	NaN
11569	30468	696b1a86-1986-59a4-8cd8-f968dba4ece4	8.221705	NaN	-1.000000	0.030	https://s3.echo.stfc.ac.uk	0.155	2013-09-27 14:21:00	'Good beam.Good repeat.'	...	2.083544	70.921546	0.13065	1.985447	NaN	2.0	NaN	NaN	S8	NaN
11570	30469	dcc5091c-ba43-599e-9c63-f8cb41494a60	2.542882	NaN	-1.000000	0.195	https://s3.echo.stfc.ac.uk	0.170	2013-09-27 14:39:00	'Good shot. Modes present.'	...	2.163367	75.050461	0.19560	2.288206	NaN	3.0	NaN	NaN	S6	NaN
11571	30470	63ccd361-c339-5403-abf3-dec0ad9d4811	11.771064	10.391692	0.008542	0.035	https://s3.echo.stfc.ac.uk	0.170	2013-09-27 15:03:00	'No HF gas.'	...	2.204641	75.045603	0.18445	2.107413	NaN	2.0	NaN	NaN	S8	NaN
11572	30471	0916ffb1-fece-5d3d-81a9-b0504f49929f	59.918600	73.787904	0.021805	0.035	https://s3.echo.stfc.ac.uk	0.125	2013-09-27 15:20:00	'Good shot.'	...	2.062899	71.177179	0.12170	NaN	NaN	2.0	NaN	NaN	S8	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()