using Speasy: get_data
using SPEDAS
# da = get_data("amda/imf", "2016-6-2", "2016-6-5")
da = get_data("cda/OMNI_HRO_1MIN/Pressure", "2016-6-2", "2016-6-5")
SpeasyVariable{Float32, 2}: Pressure
Time Range: 2016-06-02T00:00:00 to 2016-06-04T23:59:00
Units: nPa
Size: (4320, 1)
Memory Usage: 85.365 KiB
Metadata:
FIELDNAM: Flow pressure
VALIDMIN: Any[0.0]
VALIDMAX: Any[100.0]
SCALEMIN: Any[0.0]
SCALEMAX: Any[100.0]
UNITS: nPa
FORMAT: F5.2
FILLVAL: Any[99.98999786376953]
VAR_TYPE: data
CATDESC: Flow pressure (nPa)
VAR_NOTES: Derived parameters are obtained from the following equations. Flow pressure = (2*10**-6)*Np*Vp**2 nPa (Np in cm**-3, Vp in km/s, subscript p for proton)
DISPLAY_TYPE: time_series
DEPEND_0: Epoch
LABLAXIS: Flow pressure
BIN_LOCATION: 0.0
tplot(da)
using HAPIClient: get_data
da = get_data("CDAWeb/AC_H0_MFI/Magnitude,BGSEc", "2001-1-2", "2001-1-2T12")
2-element Vector{HAPIClient.HAPIVariable{Float64, N, A, Vector{Dates.DateTime}} where {N, A<:AbstractArray{Float64, N}}}:
Magnitude [Time Range: 2001-01-02T00:00:15 to 2001-01-02T11:59:58, Units: nT, Size: (2700,)]
BGSEc [Time Range: 2001-01-02T00:00:15 to 2001-01-02T11:59:58, Units: nT, Size: (2700, 3)]
using SPEDAS
tplot(da)
using SPEDAS: tplot
using PySPEDAS.Projects
using DimensionalData
using CairoMakie
da = themis.fgm(["2020-04-20/06:00", "2020-04-20/08:00"], time_clip=true, probe="d");
keys(da)
# Same as more verbose `pyspedas.projects.themis.fgm(...)`
(:thd_fgs_btotal, :thd_fgs_gse, :thd_fgs_gsm, :thd_fgs_dsl, :thd_fgl_btotal, :thd_fgl_gse, :thd_fgl_gsm, :thd_fgl_dsl, :thd_fgl_ssl, :thd_fgh_btotal, :thd_fgh_gse, :thd_fgh_gsm, :thd_fgh_dsl, :thd_fgh_ssl, :thd_fge_btotal, :thd_fge_gse, :thd_fge_gsm, :thd_fge_dsl, :thd_fge_ssl)
f = Figure()
tplot(f[1,1], [da.thd_fgs_gsm, da.thd_fgs_btotal])
tplot(f[2,1], [DimArray(da.thd_fgl_gsm), DimArray(da.thd_fgl_btotal)])
f
