Parker Solar Probe Data Download Example¶

Tamar Ervin

Downloading PSP data with PySPEDAS
Using pyTplot to plot the data

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import pyspedas
from pyspedas import time_string
from pytplot import tplot, get_data
import astrospice
import sunpy 
import sunpy.coordinates as scoords
import sys, os
import datetime
import pandas as pd
import astropy.units as u
import matplotlib.pyplot as plt

for sc in ['psp','solar orbiter'] : kernels = astrospice.registry.get_kernels(sc,'predict')

import pyspedas
from pyspedas import time_string
from pytplot import tplot, get_data
import astrospice
import sunpy 
import sunpy.coordinates as scoords
import sys, os
import datetime
import pandas as pd
import astropy.units as u
import matplotlib.pyplot as plt

for sc in ['psp','solar orbiter'] : kernels = astrospice.registry.get_kernels(sc,'predict') 

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03-Nov-23 12:52:08: sys:1: ResourceWarning: Unclosed socket <zmq.Socket(zmq.PUSH) at 0x17dcca400>

03-Nov-23 12:52:09: /Users/tamarervin/anaconda3/envs/psp38/lib/python3.8/asyncio/base_events.py:654: ResourceWarning: unclosed event loop <_UnixSelectorEventLoop running=False closed=False debug=False>
  _warn(f"unclosed event loop {self!r}", ResourceWarning, source=self)

03-Nov-23 12:52:09: /Users/tamarervin/anaconda3/envs/psp38/lib/python3.8/site-packages/erfa/core.py:154: ErfaWarning: ERFA function "dtf2d" yielded 1 of "dubious year (Note 6)"
  warnings.warn('ERFA function "{}" yielded {}'.format(func_name, wmsg),

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03-Nov-23 12:52:09: sys:1: ResourceWarning: Unclosed socket <zmq.Socket(zmq.PUSH) at 0x168ae64c0>

Download and Plot the Data¶

Example data to download:

FIELDS: Radial, Tangential, Normal (RTN) magnetic field data
SWEAP/SPAN-I Proton: Radial, Tangential, Normal (RTN) proton velocity and proton density data
SWEAP/SPAN-I Alpha Particle: Radial, Tangential, Normal (RTN) alpha particle velocity and density

Data will download to a folder titled 'psp_data' in this same repo!

Don't worry if this takes a while to run! The data is at a very high cadence and takes a bit to download depending on the time range

Plot the data using pyTplot¶

pyTplot is a Python package that works with PySPEDAS to plot space physics data! It already has all the information needed to plot observables in terms of their units! You can also create your own plots of the data using matplotlib.

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### ------- TIME PERIOD OF INTEREST ------- ###
# this is an example of data from the heliospheric current sheet (HCS) crossing during PSP Encounter 15
time_range = ['2023-03-17/12:00', '2023-03-18/12:00']
### ------- TIME PERIOD OF INTEREST ------- ###
# this is an example of data from the heliospheric current sheet (HCS) crossing during PSP Encounter 15
time_range = ['2023-03-17/12:00', '2023-03-18/12:00']

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### ------- FIELDS: MAG RTN DATA ------- ###
fields_vars = pyspedas.psp.fields(trange=time_range, datatype='mag_RTN_4_Sa_per_Cyc')

### print out the variables stored in the magnetic field data
print(fields_vars)

### get the RTN magnetic field
B_RTN = get_data('psp_fld_l2_mag_RTN_4_Sa_per_Cyc')

### plot the data!
tplot(['psp_fld_l2_mag_RTN_4_Sa_per_Cyc'])

### CONVERT TIME FROM JULIAN TIME TO DATETIME OBJECT
date_obj = [datetime.datetime.strptime(time_string(d), '%Y-%m-%d %H:%M:%S.%f') for d in B_RTN.times]

### CREATE DATAFRAME
rd = {'Time': date_obj, 'Br': B_RTN.y[:, 0], 'Bt': B_RTN.y[:, 1], 'Bn': B_RTN.y[:, 2]}
fields = pd.DataFrame(data=rd)

### SAVE DATAFRAME AS CSV
fields.to_csv(os.path.join('results', 'fields.csv'))
### ------- FIELDS: MAG RTN DATA ------- ###
fields_vars = pyspedas.psp.fields(trange=time_range, datatype='mag_RTN_4_Sa_per_Cyc')

### print out the variables stored in the magnetic field data
print(fields_vars)

### get the RTN magnetic field
B_RTN = get_data('psp_fld_l2_mag_RTN_4_Sa_per_Cyc')

### plot the data!
tplot(['psp_fld_l2_mag_RTN_4_Sa_per_Cyc'])

### CONVERT TIME FROM JULIAN TIME TO DATETIME OBJECT
date_obj = [datetime.datetime.strptime(time_string(d), '%Y-%m-%d %H:%M:%S.%f') for d in B_RTN.times]

### CREATE DATAFRAME
rd = {'Time': date_obj, 'Br': B_RTN.y[:, 0], 'Bt': B_RTN.y[:, 1], 'Bn': B_RTN.y[:, 2]}
fields = pd.DataFrame(data=rd)

### SAVE DATAFRAME AS CSV
fields.to_csv(os.path.join('results', 'fields.csv')) 

03-Nov-23 12:49:57: Downloading remote index: https://spdf.gsfc.nasa.gov/pub/data/psp/fields/l2/mag_rtn_4_per_cycle/2023/
03-Nov-23 12:49:58: File is current: psp_data/fields/l2/mag_rtn_4_per_cycle/2023/psp_fld_l2_mag_rtn_4_sa_per_cyc_20230317_v02.cdf
03-Nov-23 12:49:58: File is current: psp_data/fields/l2/mag_rtn_4_per_cycle/2023/psp_fld_l2_mag_rtn_4_sa_per_cyc_20230318_v02.cdf
03-Nov-23 12:50:00: Downloading remote index: https://spdf.gsfc.nasa.gov/pub/data/psp/fields/l2/mag_rtn_4_per_cycle/2023/
03-Nov-23 12:50:01: File is current: psp_data/fields/l2/mag_rtn_4_per_cycle/2023/psp_fld_l2_mag_rtn_4_sa_per_cyc_20230317_v02.cdf
03-Nov-23 12:50:01: File is current: psp_data/fields/l2/mag_rtn_4_per_cycle/2023/psp_fld_l2_mag_rtn_4_sa_per_cyc_20230318_v02.cdf
03-Nov-23 12:50:01: /Users/tamarervin/anaconda3/envs/psp38/lib/python3.8/asyncio/base_events.py:654: ResourceWarning: unclosed event loop <_UnixSelectorEventLoop running=False closed=False debug=False>
  _warn(f"unclosed event loop {self!r}", ResourceWarning, source=self)

['psp_fld_l2_mag_RTN_4_Sa_per_Cyc', 'psp_fld_l2_quality_flags']

No description has been provided for this image

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### ------- SPAN-I: PROTON (HYDROGEN) MOMENTS ------- ###
### download proton data
proton_vars = pyspedas.psp.spi(trange=time_range, datatype='sf00_l3_mom', level='l3')

### print out the variables stored in the proton data
print(proton_vars)

### get the RTN velocity, density, and temperature
Np = get_data('psp_spi_DENS')
vp_RTN = get_data('psp_spi_VEL_RTN_SUN')
Tp = get_data('psp_spi_TEMP')

### CONVERT TIME FROM JULIAN TIME TO DATETIME OBJECT
date_obj = [datetime.datetime.strptime(time_string(d), '%Y-%m-%d %H:%M:%S.%f') for d in Np.times]

### CREATE DATAFRAME
rd = {'Time': date_obj, 'vr': vp_RTN.y[:, 0], 'vt': vp_RTN.y[:, 1], 'vn': vp_RTN.y[:, 2], 'Np': Np.y, 'Tp': Tp.y}
protons = pd.DataFrame(data=rd)

### SAVE DATAFRAME AS CSV
protons.to_csv(os.path.join('results', 'protons.csv'))

### plot the data!
tplot(['psp_spi_DENS', 'psp_spi_VEL_RTN_SUN', 'psp_spi_TEMP'])
### ------- SPAN-I: PROTON (HYDROGEN) MOMENTS ------- ###
### download proton data
proton_vars = pyspedas.psp.spi(trange=time_range, datatype='sf00_l3_mom', level='l3')

### print out the variables stored in the proton data
print(proton_vars)

### get the RTN velocity, density, and temperature
Np = get_data('psp_spi_DENS')
vp_RTN = get_data('psp_spi_VEL_RTN_SUN')
Tp = get_data('psp_spi_TEMP')

### CONVERT TIME FROM JULIAN TIME TO DATETIME OBJECT
date_obj = [datetime.datetime.strptime(time_string(d), '%Y-%m-%d %H:%M:%S.%f') for d in Np.times]

### CREATE DATAFRAME
rd = {'Time': date_obj, 'vr': vp_RTN.y[:, 0], 'vt': vp_RTN.y[:, 1], 'vn': vp_RTN.y[:, 2], 'Np': Np.y, 'Tp': Tp.y}
protons = pd.DataFrame(data=rd)

### SAVE DATAFRAME AS CSV
protons.to_csv(os.path.join('results', 'protons.csv'))

### plot the data!
tplot(['psp_spi_DENS', 'psp_spi_VEL_RTN_SUN', 'psp_spi_TEMP'])

03-Nov-23 12:50:46: Downloading remote index: https://spdf.gsfc.nasa.gov/pub/data/psp/sweap/spi/l3/spi_sf00_l3_mom/2023/

Using LEVEL=L3

03-Nov-23 12:50:47: File is current: psp_data/sweap/spi/l3/spi_sf00_l3_mom/2023/psp_swp_spi_sf00_l3_mom_20230317_v04.cdf
03-Nov-23 12:50:48: File is current: psp_data/sweap/spi/l3/spi_sf00_l3_mom/2023/psp_swp_spi_sf00_l3_mom_20230318_v04.cdf

['psp_spi_QUALITY_FLAG', 'psp_spi_DENS', 'psp_spi_VEL_INST', 'psp_spi_VEL_SC', 'psp_spi_VEL_RTN_SUN', 'psp_spi_T_TENSOR_INST', 'psp_spi_TEMP', 'psp_spi_EFLUX_VS_ENERGY', 'psp_spi_EFLUX_VS_THETA', 'psp_spi_EFLUX_VS_PHI', 'psp_spi_SUN_DIST', 'psp_spi_VENUS_DIST', 'psp_spi_SC_VEL_RTN_SUN', 'psp_spi_QUAT_SC_TO_RTN', 'psp_spi_MAGF_SC', 'psp_spi_MAGF_INST']

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### ------- SPAN-I: ALPHA PARTICLE (HELIUM) MOMENTS ------- ###
### download alpha particle data
alpha_vars = pyspedas.psp.spi(trange=time_range, datatype='sf0a_l3_mom', level='l3')

### print out the variables stored in the alpha particle data
print(alpha_vars)

### READ IN SWEAP VELOCITY (RTN), DENSITY, AND TEMPERATURE DATA
Na = get_data('psp_spi_DENS')
va_RTN = get_data('psp_spi_VEL_RTN_SUN')
Ta = get_data('psp_spi_TEMP')

### CONVERT TIME FROM JULIAN TIME TO DATETIME OBJECT
date_obj = [datetime.datetime.strptime(time_string(d), '%Y-%m-%d %H:%M:%S.%f') for d in Na.times]

### CREATE DATAFRAME
rd = {'Time': date_obj, 'vr': va_RTN.y[:, 0], 'vt': va_RTN.y[:, 1], 'vn': va_RTN.y[:, 2], 'Na': Na.y, 'Ta': Ta.y}
alphas = pd.DataFrame(data=rd)

### SAVE DATAFRAME AS CSV
alphas.to_csv(os.path.join('results', 'alphas.csv'))

### plot the data!
tplot(['psp_spi_DENS', 'psp_spi_VEL_RTN_SUN', 'psp_spi_TEMP'])
### ------- SPAN-I: ALPHA PARTICLE (HELIUM) MOMENTS ------- ###
### download alpha particle data
alpha_vars = pyspedas.psp.spi(trange=time_range, datatype='sf0a_l3_mom', level='l3')

### print out the variables stored in the alpha particle data
print(alpha_vars)

### READ IN SWEAP VELOCITY (RTN), DENSITY, AND TEMPERATURE DATA
Na = get_data('psp_spi_DENS')
va_RTN = get_data('psp_spi_VEL_RTN_SUN')
Ta = get_data('psp_spi_TEMP')

### CONVERT TIME FROM JULIAN TIME TO DATETIME OBJECT
date_obj = [datetime.datetime.strptime(time_string(d), '%Y-%m-%d %H:%M:%S.%f') for d in Na.times]

### CREATE DATAFRAME
rd = {'Time': date_obj, 'vr': va_RTN.y[:, 0], 'vt': va_RTN.y[:, 1], 'vn': va_RTN.y[:, 2], 'Na': Na.y, 'Ta': Ta.y}
alphas = pd.DataFrame(data=rd)

### SAVE DATAFRAME AS CSV
alphas.to_csv(os.path.join('results', 'alphas.csv'))

### plot the data!
tplot(['psp_spi_DENS', 'psp_spi_VEL_RTN_SUN', 'psp_spi_TEMP'])

03-Nov-23 12:50:57: Downloading remote index: https://spdf.gsfc.nasa.gov/pub/data/psp/sweap/spi/l3/spi_sf0a_l3_mom/2023/

Using LEVEL=L3

03-Nov-23 12:50:58: File is current: psp_data/sweap/spi/l3/spi_sf0a_l3_mom/2023/psp_swp_spi_sf0a_l3_mom_20230317_v04.cdf
03-Nov-23 12:50:58: File is current: psp_data/sweap/spi/l3/spi_sf0a_l3_mom/2023/psp_swp_spi_sf0a_l3_mom_20230318_v04.cdf

['psp_spi_QUALITY_FLAG', 'psp_spi_DENS', 'psp_spi_VEL_INST', 'psp_spi_VEL_SC', 'psp_spi_VEL_RTN_SUN', 'psp_spi_T_TENSOR_INST', 'psp_spi_TEMP', 'psp_spi_EFLUX_VS_ENERGY', 'psp_spi_EFLUX_VS_THETA', 'psp_spi_EFLUX_VS_PHI', 'psp_spi_SUN_DIST', 'psp_spi_VENUS_DIST', 'psp_spi_SC_VEL_RTN_SUN', 'psp_spi_QUAT_SC_TO_RTN', 'psp_spi_MAGF_SC', 'psp_spi_MAGF_INST']

Create Parker FIELDS/SWEAP Dataframe¶

Now that we have a dataframe for each instrument, we then use pandas to merge the dataframes as a function of time.

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### merge the PAS and MAG dataframes
merged_df = pd.merge_asof(fields, protons, on='Time', direction='backward')

### merge the HIS and newly merged dataframe
merged_df = pd.merge_asof(alphas, merged_df, on='Time', direction='backward')
merged_df = merged_df.set_index('Time')
### merge the PAS and MAG dataframes
merged_df = pd.merge_asof(fields, protons, on='Time', direction='backward')

### merge the HIS and newly merged dataframe
merged_df = pd.merge_asof(alphas, merged_df, on='Time', direction='backward')
merged_df = merged_df.set_index('Time')

Find the spacecraft trajectory¶

Now we will use astrospice to generate the trajectory of the spacecraft. We transform from inertial coordinates to the solar co-rotating (Carrington) frame!

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### Create SkyCoord for Parker in the inertial (J2000) frame
psp_inertial = astrospice.generate_coords(
    'SOLAR PROBE PLUS', pd.to_datetime(merged_df.index.to_list())

)

### Transform to solar co-rotating frame 
psp_carrington = psp_inertial.transform_to(
    scoords.HeliographicCarrington(observer="self")
)
### Create SkyCoord for Parker in the inertial (J2000) frame
psp_inertial = astrospice.generate_coords(
    'SOLAR PROBE PLUS', pd.to_datetime(merged_df.index.to_list())

)

### Transform to solar co-rotating frame 
psp_carrington = psp_inertial.transform_to(
    scoords.HeliographicCarrington(observer="self")
)

Now we will add the position information to our merged dataframe and save as a CSV file!

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### ADD POSITION INFORMAITON AND SAVE
parker = merged_df.copy()
parker['lon'] = psp_carrington.lon.value
parker['lat'] = psp_carrington.lat.value
parker['rAU'] = psp_carrington.radius.to(u.AU).value
parker['NpR2'] = parker.Np * (parker.rAU ** 2)
parker['BrR2'] = parker.Br * (parker.rAU ** 2)
parker.to_csv(os.path.join('results', 'parker.csv'))
parker
### ADD POSITION INFORMAITON AND SAVE
parker = merged_df.copy()
parker['lon'] = psp_carrington.lon.value
parker['lat'] = psp_carrington.lat.value
parker['rAU'] = psp_carrington.radius.to(u.AU).value
parker['NpR2'] = parker.Np * (parker.rAU ** 2)
parker['BrR2'] = parker.Br * (parker.rAU ** 2)
parker.to_csv(os.path.join('results', 'parker.csv'))
parker

Out[11]:

	vr_x	vt_x	vn_x	Na	Ta	Br	Bt	Bn	vr_y	vt_y	vn_y	Np	Tp	lon	lat	rAU	NpR2	BrR2
Time
2023-03-17 00:00:03.869595	136.413834	-66.148483	49.722652	0.388385	-0.000087	-460.497559	66.652130	25.975363	NaN	NaN	NaN	NaN	NaN	5.436847	-0.599451	0.081274	NaN	-3.041764
2023-03-17 00:00:07.364835	NaN	NaN	NaN	0.000000	NaN	-460.015228	73.225525	21.024397	216.121811	-8.346628	31.184471	72.195038	41.848660	5.438309	-0.599587	0.081272	0.476858	-3.038463
2023-03-17 00:00:10.860136	NaN	NaN	NaN	0.000000	NaN	-461.111633	69.096474	14.254626	201.977798	-8.060006	15.134486	83.939156	41.792698	5.439771	-0.599723	0.081270	0.554408	-3.045589
2023-03-17 00:00:14.355377	NaN	NaN	NaN	0.000000	NaN	-463.271484	45.696793	24.873411	204.622040	-2.734370	-8.305954	83.250435	47.484131	5.441233	-0.599858	0.081269	0.549839	-3.059739
2023-03-17 00:00:17.850618	213.295364	8.470299	84.349762	0.307426	-0.000088	-463.071014	45.171581	26.641287	208.022537	-24.612598	27.923014	88.809814	46.484776	5.442695	-0.599994	0.081267	0.586534	-3.058299
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
2023-03-18 23:59:45.415700	467.251678	-119.165878	4.757739	56.570091	499.240814	323.541870	117.854416	-40.503208	316.523438	-80.164185	51.505405	1178.158325	62.458263	112.948472	-2.228309	0.092909	10.169938	2.792834
2023-03-18 23:59:48.910940	464.533112	-115.335388	-0.120976	52.530193	589.464417	344.583527	48.452587	-47.286186	317.690277	-102.556511	35.999821	1232.727661	65.334381	112.949459	-2.228223	0.092911	10.641370	2.974575
2023-03-18 23:59:52.406241	480.955933	-100.031128	12.107570	50.822128	488.711578	323.028778	136.430649	-9.331066	310.887329	-84.589439	31.938829	1196.579346	63.912716	112.950445	-2.228137	0.092912	10.329698	2.788607
2023-03-18 23:59:55.901482	492.865723	-126.695915	21.139906	62.649345	473.030762	323.584839	119.636749	-75.996742	323.035309	-118.320900	53.034309	1195.040039	65.877647	112.951431	-2.228051	0.092914	10.316784	2.793509
2023-03-18 23:59:59.396723	NaN	NaN	NaN	NaN	NaN	286.590302	182.615662	-59.358253	341.417572	-144.636108	49.197903	1231.612793	69.274925	112.952418	-2.227965	0.092916	10.632902	2.474225

78724 rows × 18 columns

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