SwarmFACE package

Subpackages

Submodules

SwarmFACE.esaL2 module

SwarmFACE.esaL2.dual(dtime_beg, dtime_end)[source]

Retrieve the Level-2 dual-satellite FAC density from the ESA database

Parameters

  • dtime_beg (str) – start time in ISO format ‘YYYY-MM-DDThh:mm:ss’

  • dtime_end (str) – end time in ISO format

Returns

FAC_L2 – the Level-2 dual-satellite FAC and IRC densities

Return type

DataFrame

SwarmFACE.esaL2.single(dtime_beg, dtime_end, sat)[source]

Retrieve the Level-2 single-satellite FAC density from the ESA database

Parameters

  • dtime_beg (str) – start time in ISO format ‘YYYY-MM-DDThh:mm:ss’

  • dtime_end (str) – end time in ISO format

  • sat ([str]) – satellite, e.g. [‘A’]

Returns

FAC_L2 – the Level-2 single-satellite FAC and IRC densities

Return type

DataFrame

SwarmFACE.fac module

SwarmFACE.fac.bi_dualJfac(dt, R, B, dB, dt_along=5, er_db=0.5, angTHR=30.0, errTHR=0.1, use_filter=True, saveconf=False)[source]

Estimate the FAC density with dual-satellite Boundary Integral method

Parameters

  • dt (pandas.Index) – time stamps

  • R (numpy.array) – satellites vector position in GEO frame

  • B (numpy.array) – magnetic field vector in GEO frame

  • dB (numpy.array) – magnetic perturbation vector in GEO frame

  • dt_along (integer) – along track separation in sec.

  • er_db (float) – error in magnetic field measurements

  • angTHR (float) – minimum accepted angle between the magnetic field vector and the quad plane

  • errTHR (float) – accepted error for normal current density

  • use_filter (boolean) – ‘True’ for data filtering

  • saveconf (boolean) – ‘True’ to add the quad’s parameters in the results

Returns

j_df – results, including FAC and normal current densities

Return type

DataFrame

SwarmFACE.fac.calcBI(R4, dB4)[source]

Compute curlB using the discrete BI integral along a four-point configuration.

Parameters

  • R4 (numpy.array) – position vectors of the apexes in format [index, apex, comp]

  • dB4 (numpy.array) – magnetic perturbation vectors at the apexes in format [index, apex, comp]

Returns

curl of magnetic field perturbation

Return type

numpy.array

SwarmFACE.fac.ffunct(tau3d, tauast=0.13, taunul=0.07, intpol='Linear')[source]

Compute coefficients for a smooth transition between 1D and 2D gradient estimators in the SVD method.

Parameters

  • tau3d (numpy.array) – S eigenvalues ratio

  • tauast (float) – value between [0, 1]. tauast >= taunul. The interval [taunul, tauast] is used to match the 1D and 2D gradient estimators

  • taunul (float) – value between [0, 1]. Below this threshold value the quad is considered degenerated

  • intpol (string) – interpolation method used for matching the 1D and 2D gradient estimators. Should be ‘Linear’, ‘Cubic’, or None

Returns

f – coefficient to smooth transition between 1D and 2D gradient estimators

Return type

numpy.array

SwarmFACE.fac.ls_dualJfac(dt, R, B, dB, dt_along=5, er_db=0.5, angTHR=30.0, errTHR=0.1, use_filter=True, saveconf=False)[source]

Estimate the FAC density with dual-satellite Least-Squares method

Parameters

  • dt (pandas.Index) – time stamps

  • R (numpy.array) – satellites vector position in GEO frame

  • B (numpy.array) – magnetic field vector in GEO frame

  • dB (numpy.array) – magnetic perturbation vector in GEO frame

  • dt_along (integer) – along track separation in sec.

  • er_db (float) – error in magnetic field measurements

  • angTHR (float) – minimum accepted angle between the magnetic field vector and the quad plane

  • errTHR (float) – accepted error for normal current density

  • use_filter (boolean) – ‘True’ for data filtering

  • saveconf (boolean) – ‘True’ to add the quad’s parameters in the results

Returns

j_df – results, including FAC and normal current densities

Return type

DataFrame

SwarmFACE.fac.qmatrix_intpol(R, tauast=0.13, taunul=0.07, intpol='Linear')[source]

Compute the SVD reciprocal matrix of a four-point planar configuration. A smooth transition between 1D and 2D gradient estimators is allowed.

Parameters

  • R (numpy.array) – position vectors of the apexes in format [index, apex, comp]

  • tauast (float) – value between [0, 1]. tauast >= taunul. The interval [taunul, tauast] is used to match the 1D and 2D gradient estimators

  • taunul (float) – value between [0, 1]. Below this threshold value the quad is considered degenerated

  • intpol (string) – interpolation method used for matching the 1D and 2D gradient estimators. Should be ‘Linear’, ‘Cubic’, or None

Returns

  • Q (numpy.array) – reciprocal matrix

  • AD (numpy_array) – quad degeneracy

  • Vt (numpy.array) – orthogonal matrix from SVD decomposition of R = USVt

  • S (numpy.array) – diagonal matrix from SVD decomposition of R = USVt

SwarmFACE.fac.recivec2s(R4s)[source]

Compute the reciprocal vectors of a four-point planar configuration

Parameters

R4s (numpy.array) – position vectors of the apexes in format [index, apex, comp]

Returns

  • Q4s (numpy.array) – canonical base (reciprocal) vectors

  • Rc (numpy_array) – position of the mesocenter

  • nuvec (numpy.array) – normal to the spacecraft plane

  • condnum (numpy.array) – condition number

  • nonplanar (numpy.array) – nonplanarity

  • tracer (numpy.array) – trace of the position tensor

  • traceq (numpy.array) – trace of the reciprocal tensor

SwarmFACE.fac.recivec3s(R)[source]

Compute the reciprocal vectors for a three-satellites configuration

Parameters

R (numpy.array) – satellites vector position in format [index, sc, comp]

Returns

  • Rc (numpy.array) – position of the mesocenter

  • Rmeso (numpy.array) – satellites position in the mesocentric frame

  • nuvec (numpy.array) – normal to the spacecraft plane

  • Q3S (numpy.array) – generalized reciprocal vectors

  • Qtens (numpy.array) – reciprocal tensor

  • Rtens (numpy.array) – position tensor

SwarmFACE.fac.singleJfac(t, R, B, dB, alpha=None, N2d=None, N3d=None, tincl=None, res='LR', er_db=0.5, angTHR=30.0, use_filter=True)[source]

Estimate the FAC density with single-satellite method

Parameters

  • t (pandas.Index) – time stamps

  • R (numpy.array) – satellite vector position in GEO frame

  • B (numpy.array) – magnetic field vector in GEO frame

  • dB (numpy.array) – magnetic perturbation vector in GEO frame

  • alpha (float) – angle in the tangential plane between the (projection of) current sheet normal and the satellite velocity

  • N3d ([float, float, float]) – current sheet normal vector in GEO frame

  • N2d ([float, float, float]) – projection of the current sheet normal on the tangential plane

  • tincl ([datetime, datetime]) – time interval when the information on current sheet inclination is valid

  • res (str) – data resolution, ‘LR’ or ‘HR’

  • er_db (float) – error in magnetic field measurements

  • angTHR (float) – minimum accepted angle between the magnetic field vector and the tangential plane

  • use_filter (boolean) – ‘True’ for data filtering

Returns

j_df – results, including FAC and IRC densities

Return type

DataFrame

SwarmFACE.fac.svd_dualJfac(dt, R, B, dB, dt_along=5, er_db=0.5, tauast=0.13, taunul=0.07, intpol='Linear', angTHR=30.0, use_filter=True, saveconf=False)[source]

Estimate the FAC density with dual-satellite Singular Values Decomposition method

Parameters

  • dt (pandas.Index) – time stamps

  • R (numpy.array) – satellites vector position in GEO frame

  • B (numpy.array) – magnetic field vector in GEO frame

  • dB (numpy.array) – magnetic perturbation vector in GEO frame

  • dt_along (integer) – along track separation in sec.

  • er_db (float) – error in magnetic field measurements

  • tauast (float) – value between [0, 1]. tauast >= taunul. The interval [taunul, tauast] is used to match the 1D and 2D gradient estimators

  • taunul (float) – value between [0, 1]. Below this threshold value the quad is considered degenerated

  • intpol (string) – interpolation method used for matching the 1D and 2D gradient estimators. Should be ‘Linear’, ‘Cubic’, or None

  • angTHR (float) – minimum accepted angle between the magnetic field vector and the quad plane

  • use_filter (boolean) – ‘True’ for data filtering

  • saveconf (boolean) – ‘True’ to add the quad’s parameters in the results

Returns

j_df – results, including FAC and normal current densities

Return type

DataFrame

SwarmFACE.fac.threeJfac(dt, R, B, dB, er_db=0.5, angTHR=30.0, use_filter=True)[source]

Estimate the FAC density with three-satellite method

Parameters

  • dt (pandas.Index) – time stamps

  • R (numpy.array) – satellites vector position in GEO frame

  • B (numpy.array) – magnetic field vector in GEO frame

  • dB (numpy.array) – magnetic perturbation vector in GEO frame

  • er_db (float) – error in magnetic field measurements

  • angTHR (float) – minimum accepted angle between the magnetic field vector and the spacecraft plane

  • use_filter (boolean) – ‘True’ for data filtering

Returns

j_df – results, including FAC and normal current densities

Return type

DataFrame

SwarmFACE.fac_qi module

SwarmFACE.fac_qi.fac_qi(dtime_beg, dtime_end, swB=False, jTHR=0.05, saveplot=False)[source]

High-level routine to automatically estimate the quality indices of FAC structures

Parameters

  • dtime_beg (str) – start time in ISO format ‘YYYY-MM-DDThh:mm:ss’

  • dtime_end (str) – end time in ISO format

  • swB (boolean) – ‘True’ to the MVA based quality indices for Swarm B

  • jTHR (float) – threshold to neglect small FAC densities

  • saveplot (boolean) – ‘True’ for plotting the results

Returns

  • input_df (list) – list of DataFrames (one per satellite per quarter-orbit section) with input data

  • RBdBAng_df (list) – list of DataFrrames (one per satellite per quarter-orbit section) with intermediate variables

  • fac_df (list) – list of DataFrrames (one per satellite per quarter-orbit section) with estimated single=satellite FAC data

  • qimva_df (DataFrame) – MVA results, FAC planarity, and FAC inclination

  • qicc_df (DataFrame) – results from the correlation analysis between the magnetic field perturbations at the lower Swarm satellites

  • param (dict) – parameters used in the analysis

SwarmFACE.fac_qi.qorbsCC(sats, qimva_df, qorbs_dBmva)[source]

Perform correlation analysis between the magnetic field perturbations at the lower Swarm satellite

Parameters

  • sats (list) – satellites entering the analysis, e.g. [‘A’, ‘C’] or [‘A’, ‘B’, ‘C’]

  • qimva_df (DataFrame) – MVA results, FAC planarity, and FAC inclination

  • qorbs_dBmva (list) – list of DataFrrames (one per satellite per quarter-orbit section) with magnetic field perturbation in MVA frame

Returns

  • qicc_df (DataFrame) – results from the correlation analysis between the magnetic field perturbations at the lower Swarm satellites

  • Bcc_df (list) – list of DataFrrames (one per quarter-orbit section) with magnetic field perturbation of the reference satellite along the maximum magnetic variance direction

SwarmFACE.fac_qi.qorbsMVA(sats, orbs, qorbs_dB, qorbs_fac)[source]

Automatically estimate the MVA related quality indices of FAC structures

Parameters

  • sats (list) – satellites entering the analysis, e.g. [‘A’, ‘C’] or [‘A’, ‘B’, ‘C’]

  • orbs (array) – array of integers of size [nr. sat, 2], indicate for each satellite the start and end orbit numbers

  • qorbs_dB (list) – list of DataFrrames (one per satellite per quarter-orbit section) with magnetic field perturbation in GEO frame

  • fac_df (list) – list of DataFrrames (one per satellite per quarter-orbit section) with estimated single=satellite FAC data

Returns

  • qimva_df (DataFrame) – MVA results, FAC planarity, and FAC inclination

  • qorbs_dBmva (list) – list of DataFrrames (one per satellite per quarter-orbit section) with magnetic field perturbation in MVA frame

SwarmFACE.find_3sat_conj module

SwarmFACE.find_3sat_conj.find_3sat_conj(dtime_beg, dtime_end, delT=120, delN=300, delE=1200, jTHR=0.05, saveplot=True)[source]

High-level routine to find Swarm conjunctions above the auroral oval

Parameters

  • dtime_beg (str) – start time in ISO format ‘YYYY-MM-DDThh:mm:ss’

  • dtime_end (str) – end time in ISO format

  • delT (float) – time window (in sec.) to find conjunctions

  • delN (float) – spatial range along North-South (in km) to find conjunctions

  • delE (float) – spatial range along East-West (in km) to find conjunctions

  • jTHR (float) – threshold to neglect small FAC densities

  • saveplot (boolean) – ‘True’ for plotting the results

Returns

  • conj_df (DataFrame) – details on the identified conjunctions

  • param (dict) – parameters used in the analysis

SwarmFACE.j1sat module

SwarmFACE.j1sat.j1sat(dtime_beg, dtime_end, sat, res='LR', use_filter=True, alpha=None, N3d=None, N2d=None, tincl=None, er_db=0.5, angTHR=30.0, savedata=True, saveplot=True)[source]

High-level routine to estimate the FAC density with single-satellite method

Parameters

  • dtime_beg (str) – start time in ISO format ‘YYYY-MM-DDThh:mm:ss’

  • dtime_end (str) – end time in ISO format

  • sat ([str]) – satellite, e.g. [‘A’]

  • res (str) – data resolution, ‘LR’ or ‘HR’

  • use_filter (boolean) – ‘True’ for data filtering

  • alpha (float) – angle in the tangential plane between the (projection of) current sheet normal and the satellite velocity

  • N3d ([float, float, float]) – current sheet normal vector in GEO frame

  • N2d ([float, float, float]) – projection of the current sheet normal on the tangential plane

  • tincl ([ISO time, ISO time]) – time interval when the information on current sheet inclination is valid

  • er_db (float) – error in magnetic field measurements

  • angTHR (float) – minimum accepted angle between the magnetic field vector and the tangential plane

  • savedata (boolean) – ‘True’ for saving the results in an ASCII file

  • saveplot (boolean) – ‘True’ for plotting the results

Returns

  • j_df (DataFrame) – results

  • input_df (DataFrame) – input data

  • param (dict) – parameters used in the analysis

SwarmFACE.j2satBI module

SwarmFACE.j2satBI.j2satBI(dtime_beg, dtime_end, sats, tshift=None, dt_along=5, use_filter=True, er_db=0.5, angTHR=30.0, errTHR=0.1, saveconf=False, savedata=True, saveplot=True)[source]

High-level routine to estimate the FAC density with dual-satellite Boundary Integral method

Parameters

  • dtime_beg (str) – start time in ISO format ‘YYYY-MM-DDThh:mm:ss’

  • dtime_end (str) – end time in ISO format

  • sats ([str, str]) – satellite pair, e.g. [‘A’, ‘C’]

  • tshift ([float, float]) – array of time shifts (in seconds) to be introduced in satellite data in order to achieve the desired quad configuration

  • dt_along (int) – quad’s length in the along track direction (in seconds of satellite travel distance)

  • use_filter (boolean) – ‘True’ for data filtering

  • er_db (float) – error in magnetic field measurements

  • angTHR (float) – minimum accepted angle between the magnetic field vector and the quad’s plane

  • errTHR (float) – accepted error for the current density along the normal direction

  • saveconf (boolean) – ‘True’ for adding the quad’s parameters in the results

  • savedata (boolean) – ‘True’ for saving the results in an ASCII file

  • saveplot (boolean) – ‘True’ for plotting the results

Returns

  • j_df (DataFrame) – results

  • input_df (DataFrame) – input data

  • param (dict) – parameters used in the analysis

SwarmFACE.j2satLS module

SwarmFACE.j2satLS.j2satLS(dtime_beg, dtime_end, sats, tshift=None, dt_along=5, use_filter=True, er_db=0.5, angTHR=30.0, errTHR=0.1, saveconf=False, savedata=True, saveplot=True)[source]

High-level routine to estimate the FAC density with dual-satellite Least Squares method

Parameters

  • dtime_beg (str) – start time in ISO format ‘YYYY-MM-DDThh:mm:ss’

  • dtime_end (str) – end time in ISO format

  • sats ([str, str]) – satellite pair, e.g. [‘A’, ‘C’]

  • tshift ([float, float]) – array of time shifts (in seconds) to be introduced in satellite data in order to achieve the desired quad configuration

  • dt_along (int) – quad’s length in the along track direction (in seconds of satellite travel distance)

  • use_filter (boolean) – ‘True’ for data filtering

  • er_db (float) – error in magnetic field measurements

  • angTHR (float) – minimum accepted angle between the magnetic field vector and the quad’s plane

  • errTHR (float) – accepted error for the current density along the normal direction

  • saveconf (boolean) – ‘True’ for adding the quad’s parameters in the results

  • savedata (boolean) – ‘True’ for saving the results in an ASCII file

  • saveplot (boolean) – ‘True’ for plotting the results

Returns

  • j_df (DataFrame) – results

  • input_df (DataFrame) – input data

  • param (dict) – parameters used in the analysis

SwarmFACE.j2satSVD module

SwarmFACE.j2satSVD.j2satSVD(dtime_beg, dtime_end, sats, tshift=None, dt_along=5, use_filter=True, er_db=0.5, tauast=0.13, taunul=0.07, intpol='Linear', angTHR=30.0, saveconf=False, savedata=True, saveplot=True)[source]

High-level routine to estimate the FAC density with dual-satellite Singular Value Decomposition method

Parameters

  • dtime_beg (str) – start time in ISO format ‘YYYY-MM-DDThh:mm:ss’

  • dtime_end (str) – end time in ISO format

  • sats ([str, str]) – satellite pair, e.g. [‘A’, ‘C’]

  • tshift ([float, float]) – array of time shifts (in seconds) to be introduced in satellite data in order to achieve the desired quad configuration

  • dt_along (int) – quad’s length in the along track direction (in seconds of satellite travel distance)

  • use_filter (boolean) – ‘True’ for data filtering

  • er_db (float) – error in magnetic field measurements

  • angTHR (float) – minimum accepted angle between the magnetic field vector and the quad’s plane

  • tauast (float) – value between [0, 1]. tauast >= taunul. The interval [taunul, tauast] is used to match the 1D and 2D gradient estimators

  • taunul (float) – value between [0, 1]. Below this threshold value the quad is considered degenerated

  • intpol (str) – interpolation method used for matching the 1D and 2D gradient estimators. Should be ‘Linear’, ‘Cubic’, or None

  • saveconf (boolean) – ‘True’ for adding the quad’s parameters in the results

  • savedata (boolean) – ‘True’ for saving the results in an ASCII file

  • saveplot (boolean) – ‘True’ for plotting the results

Returns

  • j_df (DataFrame) – results

  • input_df (DataFrame) – input data

  • param (dict) – parameters used in the analysis

SwarmFACE.j3sat module

SwarmFACE.j3sat.j3sat(dtime_beg, dtime_end, tshift=[0, 0, 0], use_filter=True, er_db=0.5, angTHR=30.0, savedata=True, saveplot=True)[source]

High-level routine to estimate the FAC density with three-satellite method

Parameters

  • dtime_beg (str) – start time in ISO format ‘YYYY-MM-DDThh:mm:ss’

  • dtime_end (str) – end time in ISO format

  • tshift ([float, float, float]) – array of time shifts (in seconds) to be introduced in satellite data in order to achieve a more favorable configuration

  • use_filter (boolean) – ‘True’ for data filtering

  • er_db (float) – error in magnetic field measurements

  • angTHR (float) – minimum accepted angle between the magnetic field vector and the spacecraft plane

  • savedata (boolean) – ‘True’ for saving the results in an ASCII file

  • saveplot (boolean) – ‘True’ for plotting the results

Returns

  • j_df (DataFrame) – results

  • input_df (DataFrame) – input data

  • param (dict) – parameters used in the analysis

SwarmFACE.mva1sat module

SwarmFACE.mva1sat.get_data_mva1sat(dtime_beg, dtime_end, sat, use_filter=True)[source]

Prepare and plot data on the screen to interactively select the MVA interval

Parameters

  • dtime_beg (str) – start time in ISO format ‘YYYY-MM-DDThh:mm:ss’

  • dtime_end (str) – end time in ISO format

  • sat ([str]) – satellite, e.g. [‘A’]

  • use_filter (boolean) – ‘True’ for data filtering

Returns

  • j_df (DataFrame) – FAC density from single-satellite method

  • input_df (DataFrame) – input data (includes magnetic field perturbation)

  • param (dict) – parameters used in the analysis

  • span_sel (dict) – new MVA start and stop time

  • span (reference) – reference to SpanSelector to prevent it from being garbage collected

SwarmFACE.mva1sat.perform_mva1sat(j_df, input_df, param, span_sel, savedata=True, saveplot=True)[source]

Perform the MVA analysis on the data provided by get_data_mva1sat

Parameters

  • j_df (DataFrame) – FAC density from single-satellite method

  • input_df (DataFrame) – input data (includes magnetic field perturbation)

  • param (dict) – parameters used in the analysis

  • span_sel (dict) – new MVA start and stop time

  • savedata (boolean) – ‘True’ for saving the results in an ASCII file

  • saveplot (boolean) – ‘True’ for plotting the results

Returns

  • jcorr_df (DataFrame) – corrected (for current sheet inclination) FAC density

  • dBmva_df (DataFrame) – magnetic field perturbation in MVA frame

  • mva_df (DataFrame) – MVA results, FAC planarity and inclination

  • param (dict) – parameters used in the analysis

SwarmFACE.utils module

SwarmFACE.utils.GapsAsNaN(df_ini, ind_gaps)[source]

Given a DataFrame object, replace with NaN the magnetic field data and model for certain location

Parameters

  • df_ini (DataFrame) – containes columns with magetic field data and model

  • ing_gaps (numpy.array) – integer-location based indexing

Returns

  • df_out (DataFrame) – new object with NaN

  • index – index values at replaced location

SwarmFACE.utils.R_in_GEOC(Rsph)[source]

Returns Swarm position in cartesian GEO frame and the rotation matrix from NEC to GEO

Parameters

Rsph (numpy.array) – satellite position in spherical GEO frame

Returns

  • R (numpy.array) – satellite position in cartesian GEO frame

  • numpy.array – array of rotation matrices from NEC to GEO

SwarmFACE.utils.SortVertices(R4s, dB4s)[source]

Sort the quad’s vertices in correct order; compute quad’s parameters and the trace of the BI and LS reciprocal tensors.

Parameters

  • R4s (numpy.array) – position vectors of the apexes in format [index, apex, comp]

  • dB4s (numpy.array) – magnetic perturbation vectors at the apexes in format [index, apex, comp]

Returns

  • R4sa, dB4sa (numpy.arrays) – position and magnetic perturbation vectors sorted in correct order

  • trBI (numpy.array) – trace of the reciprocal tensor in LS method

  • Rmeso (numpy.array) – position of the mesocenter

  • nuvec (numpy.array) – normal to the quad

  • satsort (numpy.array) – satellite indeces in the correct order

  • trLS (numpy.array) – trace of the reciprocal tensor in LS method

  • EL, EM, el, em (numpy.arrays) – parameters of the four-point configuration

SwarmFACE.utils.find_ao_margins(df, fac_qnt='FAC_flt_sup', rez_qd=100)[source]

Estimate the times of auroral oval (AO) encounter (central point and edges) for a quarte-orbit section. Works with quasi-dipole latitude QDLat (not time) dependence.

Parameters

  • df (DataFrame) – Swarm data for a quarter-orbit section, including FAC data

  • fac_qnt (string) – name of the column with FAC data to work with

  • rez_qd – resolution to interpolate the QDLat

Returns

  • t64[0], t64[1], t64[2] (datetime-like) – AO start, stop, and central time

  • float, float – QDLat and QDLon of AO center

  • qd_trend, qd_sign (float) – trend and sign of QDLat in the analysed quarte-orbit section

  • ti_arr, qd_arr (numpy.array) – interpolated times and corresponding QDLat values

SwarmFACE.utils.find_tshift2sat(dti, Rsph, sats)[source]

Compute orbital phase lag [in sec.] between two satellites

Parameters

  • dti (Datetimeindex) – One second separated timestamps

  • Rshp (numpy.array) – satellites position in spherical GEO frame

  • sats ([str, str]) – satellite pair, e.g. [‘A’, ‘C’]

Returns

tshift – optimal time shift in sec. to aligned the two sensors side by side

Return type

[float, float]

SwarmFACE.utils.get_db_data(sats, tbeg, tend, Bmodel, frame='GEOC')[source]

Return Swarm magnetic perturbation in a DataFrame structure

Parameters

  • sats (str or list of str) – satellites

  • tbeg (datetime-like) – start and stop times

  • tend (datetime-like) – start and stop times

  • Bmodel (string) – magnetic field model used to compute the magnetic field perturbations (currently CHAOS)

  • frame (string) – reference frame for the output, i.e. ‘GEOC’ for GEO frame or None for NEC frame

Returns

dB – Swarm magnetic field perturbation data

Return type

DataFrame

SwarmFACE.utils.mva(v, cdir=None)[source]

Return results of Minimum Variance Analysis on an array of 3D vector v. If cdir (3D vector) is provided, the analysis is performed in the plane perpendiculat to it

Parameters

  • v (numpy.array) – 3D vectors

  • cdir (numpy.array) – direction to constrain MVA

Return type

array with eigenvalues and eigenvectors from MVA

SwarmFACE.utils.normvec(v)[source]

Return array of unit vectors

Parameters

v (numpy.array) – vectors

Return type

numpy.array of unit vectors

SwarmFACE.utils.res_param(res)[source]

Return sampling step and sampling frequency acording to Swarm magnetic data resolution

Parameters

res (string) – ‘HR’ or ‘LR’

Return type

sampling step (string) and sampling frequency (int)

SwarmFACE.utils.rotvecax(v, ax, ang)[source]

Rotates vector v by angle ang around a normal vector ax. Uses Rodrigues’ formula when v is normal to ax

Parameters

  • v (numpy.array) – vector(s)

  • ax (numpy.array) – normal vector(s)

  • ang (numpy.array) – angle(s)

Return type

vector or array of vectors

SwarmFACE.utils.sign_ang(V, N, R)[source]

Return the signed angle between vectors V and N, perpendicular to R; positive sign corresponds to right hand rule along R

Parameters

  • V (numpy.array) – vectors

  • N (numpy.array) – vectors

  • R (numpy.array) – vectors

Return type

angle(s)

SwarmFACE.utils.sign_dang(ang2, ang1)[source]

Return the difference between two angles in [-180, 180] deg. interval

Parameters

  • ang1 (numpy.array) – angles in degrees

  • ang2 (numpy.array) – angles in degrees

Return type

numpy.array of angles

SwarmFACE.utils.split_into_sections(df, begend_arr)[source]

Splits the original DataFrame object df in more DataFrame objects according to time moments from begend_arr array.

Parameters

  • df (DataFrame) – time indexed DataFrame object

  • begend_arr (list) – time moments

  • Returns – list of DataFrame objects

Module contents