Note
Click here to download the full example code
Merging magnetic field and plasma data
How to merge magnetic field and plasma data on to a set of common time stamps.
First, import the required packages.
import matplotlib
import matplotlib.pyplot as plt
import numpy as np
from astropy.time import Time
from heliopy.data.solo import download
import plasmapy.formulary
from sunpy.timeseries import GenericTimeSeries
Out:
Creating download directory /home/docs/heliopy/data
Set some nice default plotting settings
from astropy.visualization import quantity_support
quantity_support()
matplotlib.rcParams['date.converter'] = 'concise'
Get 12 hours day of MAG data in normal mode.
start_time = "2020-07-07 12:00:00"
end_time = "2020-07-08 00:00:00"
mag_data = download(start_time, end_time, 'MAG-RTN-NORMAL', 'L2')
print(mag_data.columns)
Out:
Downloading http://soar.esac.esa.int/soar-sl-tap/data?retrieval_type=PRODUCT&product_type=SCIENCE&data_item_id=solo_L2_mag-rtn-normal_20200708 to /home/docs/heliopy/data/solar_orbiter/MAG-RTN-NORMAL/L2/solo_L2_mag-rtn-normal_20200708.cdf
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Downloading http://soar.esac.esa.int/soar-sl-tap/data?retrieval_type=PRODUCT&product_type=SCIENCE&data_item_id=solo_L2_mag-rtn-normal_20200707 to /home/docs/heliopy/data/solar_orbiter/MAG-RTN-NORMAL/L2/solo_L2_mag-rtn-normal_20200707.cdf
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96.2% 7569408 / 7868749
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98.0% 7708672 / 7868749
98.1% 7716864 / 7868749
98.2% 7725056 / 7868749
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98.5% 7749632 / 7868749
98.6% 7757824 / 7868749
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99.0% 7790592 / 7868749
99.1% 7798784 / 7868749
99.2% 7806976 / 7868749
99.3% 7815168 / 7868749
99.4% 7823360 / 7868749
99.5% 7831552 / 7868749
99.6% 7839744 / 7868749
99.7% 7847936 / 7868749
99.8% 7856128 / 7868749
99.9% 7864320 / 7868749
100.0% 7872512 / 7868749
['B_RTN_0', 'B_RTN_1', 'B_RTN_2', 'VECTOR_TIME_RESOLUTION', 'VECTOR_RANGE', 'QUALITY_BITMASK', 'QUALITY_FLAG']
Get 12 hours of SWA/PAS ground moments.
swa_data = download(start_time, end_time, 'SWA-PAS-GRND-MOM', 'L2')
print(swa_data.columns)
Out:
Downloading http://soar.esac.esa.int/soar-sl-tap/data?retrieval_type=PRODUCT&product_type=SCIENCE&data_item_id=solo_L2_swa-pas-grnd-mom_20200708 to /home/docs/heliopy/data/solar_orbiter/SWA-PAS-GRND-MOM/L2/solo_L2_swa-pas-grnd-mom_20200708.cdf
0.0% 0 / 4567368
0.2% 8192 / 4567368
0.4% 16384 / 4567368
0.5% 24576 / 4567368
0.7% 32768 / 4567368
0.9% 40960 / 4567368
1.1% 49152 / 4567368
1.3% 57344 / 4567368
1.4% 65536 / 4567368
1.6% 73728 / 4567368
1.8% 81920 / 4567368
2.0% 90112 / 4567368
2.2% 98304 / 4567368
2.3% 106496 / 4567368
2.5% 114688 / 4567368
2.7% 122880 / 4567368
2.9% 131072 / 4567368
3.0% 139264 / 4567368
3.2% 147456 / 4567368
3.4% 155648 / 4567368
3.6% 163840 / 4567368
3.8% 172032 / 4567368
3.9% 180224 / 4567368
4.1% 188416 / 4567368
4.3% 196608 / 4567368
4.5% 204800 / 4567368
4.7% 212992 / 4567368
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5.0% 229376 / 4567368
5.2% 237568 / 4567368
5.4% 245760 / 4567368
5.6% 253952 / 4567368
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6.1% 278528 / 4567368
6.3% 286720 / 4567368
6.5% 294912 / 4567368
6.6% 303104 / 4567368
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Downloading http://soar.esac.esa.int/soar-sl-tap/data?retrieval_type=PRODUCT&product_type=SCIENCE&data_item_id=solo_L2_swa-pas-grnd-mom_20200707 to /home/docs/heliopy/data/solar_orbiter/SWA-PAS-GRND-MOM/L2/solo_L2_swa-pas-grnd-mom_20200707.cdf
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74.3% 1081344 / 1455116
74.9% 1089536 / 1455116
75.4% 1097728 / 1455116
76.0% 1105920 / 1455116
76.6% 1114112 / 1455116
77.1% 1122304 / 1455116
77.7% 1130496 / 1455116
78.3% 1138688 / 1455116
78.8% 1146880 / 1455116
79.4% 1155072 / 1455116
79.9% 1163264 / 1455116
80.5% 1171456 / 1455116
81.1% 1179648 / 1455116
81.6% 1187840 / 1455116
82.2% 1196032 / 1455116
82.8% 1204224 / 1455116
83.3% 1212416 / 1455116
83.9% 1220608 / 1455116
84.4% 1228800 / 1455116
85.0% 1236992 / 1455116
85.6% 1245184 / 1455116
86.1% 1253376 / 1455116
86.7% 1261568 / 1455116
87.3% 1269760 / 1455116
87.8% 1277952 / 1455116
88.4% 1286144 / 1455116
89.0% 1294336 / 1455116
89.5% 1302528 / 1455116
90.1% 1310720 / 1455116
90.6% 1318912 / 1455116
91.2% 1327104 / 1455116
91.8% 1335296 / 1455116
92.3% 1343488 / 1455116
92.9% 1351680 / 1455116
93.5% 1359872 / 1455116
94.0% 1368064 / 1455116
94.6% 1376256 / 1455116
95.1% 1384448 / 1455116
95.7% 1392640 / 1455116
96.3% 1400832 / 1455116
96.8% 1409024 / 1455116
97.4% 1417216 / 1455116
98.0% 1425408 / 1455116
98.5% 1433600 / 1455116
99.1% 1441792 / 1455116
99.6% 1449984 / 1455116
100.0% 1458176 / 1455116
['Half_interval', 'SCET', 'Info', 'validity', 'N', 'V_SRF_0', 'V_SRF_1', 'V_SRF_2', 'V_RTN_0', 'V_RTN_1', 'V_RTN_2', 'P_SRF_0', 'P_SRF_1', 'P_SRF_2', 'P_SRF_3', 'P_SRF_4', 'P_SRF_5', 'P_RTN_0', 'P_RTN_1', 'P_RTN_2', 'P_RTN_3', 'P_RTN_4', 'P_RTN_5', 'TxTyTz_SRF_0', 'TxTyTz_SRF_1', 'TxTyTz_SRF_2', 'TxTyTz_RTN_0', 'TxTyTz_RTN_1', 'TxTyTz_RTN_2', 'T', 'V_SOLO_RTN_0', 'V_SOLO_RTN_1', 'V_SOLO_RTN_2']
Re-index the magnetic field data on to the plasma data timestamps. To do this the magnetic field ~sunpy.timeseries.GenericTimeSeries is first converted to a ~pandas.DataFrame, so the powerful time-series maniuplation tools present in pandas can be used. Then the data is re-indexed using a nearest neighbour method, and re-inserted into a ~sunpy.timeseries.GenericTimeSeries to preserve the unit information
mag_df = mag_data.to_dataframe()
mag_reindexed = mag_data.to_dataframe().reindex(swa_data.index,
method='nearest')
mag_reindexed = GenericTimeSeries(mag_reindexed, units=mag_data.units)
Plot the re-indexed data. Because the SWA data is a lower cadence than the MAG data, the re-indexed data has fewer data points.
fig, ax = plt.subplots()
ax.plot(mag_data.index, mag_data.quantity('B_RTN_0'),
label='Original')
ax.plot(mag_reindexed.index, mag_reindexed.quantity('B_RTN_0'),
label='Re-indexed')
# Zoom in to see individual data points
ax.set_xlim(Time("2020-07-07 23:55:00").plot_date,
Time(end_time).plot_date)
ax.legend()
Out:
<matplotlib.legend.Legend object at 0x7f25b42aba90>
Now the plasma and magnetic field data are on similar timestamps, they can be combined into derived parameters, such as the plasma beta.
First the magnetic field strength is calculated, and then the plasmapy.formularly.beta function is used to calculate the beta from the temperature, density, and magnetic field strength.
mod_B = np.sqrt(mag_reindexed.quantity('B_RTN_0')**2 +
mag_reindexed.quantity('B_RTN_1')**2 +
mag_reindexed.quantity('B_RTN_2')**2)
plasma_beta = plasmapy.formulary.beta(swa_data.quantity('T'),
swa_data.quantity('N'),
mod_B)
Finally, the plasma beta along with the
fig, axs = plt.subplots(nrows=4, sharex=True)
axs[0].plot(swa_data.index, swa_data.quantity('T'), label='Temperature')
axs[1].plot(swa_data.index, swa_data.quantity('N'), label='Density')
axs[2].plot(swa_data.index, mod_B, label='Magnetic field strength')
axs[3].plot(swa_data.index, plasma_beta, label=r'Plasma $\beta$')
axs[3].set_yscale('log')
axs[3].axhline(1, color='black', linewidth=1)
[ax.legend() for ax in axs]
plt.show()
Total running time of the script: ( 0 minutes 29.803 seconds)