geodezyx.time_series package

Submodules

geodezyx.time_series.ts_class module

Created on Fri Aug 2 13:55:33 2019

@author: psakicki

class geodezyx.time_series.ts_class.Attitude(R=0, P=0, Y=0, T=0, sR=0, sP=0, sY=0, devID='NULL', angtype='deg')

Bases: object

Qcalc()

RPYget()

RPYset(R=0, P=0, Y=0, sR=0, sP=0, sY=0)

Tset(T=0)

class geodezyx.time_series.ts_class.Point(A=0.0, B=0.0, C=0.0, T=0.0, initype='XYZ', sA=0.0, sB=0.0, sC=0.0, name='noname', anex=None)

Bases: object

ENUcalc_pt(refENU)

ENUset(E=nan, N=nan, U=nan, sE=nan, sN=nan, sU=nan)

FLHset(F=0, L=0, H=0, sF=0, sL=0, sH=0)

NEDset(N=nan, E=nan, D=nan, sN=nan, sE=nan, sD=nan)

Tset(T=0)

UTMcalc_pt(ellips='wgs84')

UTMset(Eutm=nan, Nutm=nan, Uutm=nan, sEutm=nan, sNutm=nan, sUutm=nan)

XYZset(X=0, Y=0, Z=0, sX=0, sY=0, sZ=0)

add_offset(dA, dB, dC, coortype='ENU')

helmert_trans(params='itrf2008_2_etrf2000', invert=False)

keysanex()

velocity_trans(vx, vy, vz, epoc_init='auto', epoc_end='auto'): auto == epoc of the measures

class geodezyx.time_series.ts_class.TimeSerieObs(typeobs='NULL', filepath='')

Bases: object

LES DIFFERENCES AVEC TSPOINT * Les objets ne contiennent qu’un type de données sous une seul forme (a la difference d’un point qui peut exister sous plusieurs formes) * Dans un fichier en input, il peut y avoir plusieurs “devices”

=> les fonctions de lectures produisent donc obligatoirement des listes de TS (le cas échéant une liste à 1 élt) => la methode readfile() nécessite donc l’indice de la device

add_obs(inObs)

aleaobs()

del_data()

enddate()

interp_get(T)

interp_set(interptype='slinear')

interval_nominal()

meta_set(path='', devID='NULL', name='')

plot(diapt=10, alpha=0.8, fig=1, new_style=True)

readfile(filein, indtab=0)

startdate()

timewin(windows, mode='keep'): IL EST TRES DANGEREUX DE L’APPLIQUER UN FENETRAGE A SOI MEME

to_list()

class geodezyx.time_series.ts_class.TimeSeriePoint(stat='STAT')

Bases: object

ENUcalc(refENU)

Method to determine the ENU components based on a reference point

Parameters:: refENU (Point Object or TimeSeriePoint Object) – Reference point.
Return type:: None.

ENUcalc_from_first_posi()

Method to determine the ENU components based directly on the mean/median position

Return type:: None.

ENUcalc_from_mean_posi(mean_type='median')

Method to determine the ENU components based directly on the mean/median position

Return type:: None.

UTMcalc()

Method to determine the UTM E and N projected coordinates

Return type:: None.

add_offset(dA, dB, dC, coortype='ENU'): Method to add an offset to all points in the TimeSerie

add_point(point_inp)

Method to add a Point in the TimeSerie Object

Parameters:: point_inp (Point Object)
Return type:: None.

aleapt()

Method to get a random Point in the TimeSeries

Return type:: Point Object

decimate(dec)

Method to decimate a TimeSerie

Parameters:: dec (int) – keep 1/dec point in the TimeSerie.
Return type:: None.

del_data()

Method to purge the data in the TimeSeriePoint

Return type:: None.

discont_manu_click(fig=1)

manual discontinuities are both recorded in the “main” discont list and in a new discont_manu list, thus the manual discontinuites can be identified

IMPORTANTcursor objects (multi , cid): must be stored as global variables like this : multi , cid = tsout.discont_manu_click()
NOTEThis method was created before point_n_click_plot():: this other one is more complete both has to be merged ASAP !!!!!

enddate()

Method to get the last epoch of the data in the TimeSerie

Return type:: DateTime

find_point(tin, tol=0.001, stop_when_found=True)

Method to find a specific point according to its timestamp

Parameters:

tin (float or datetime) – timestamp of the researched point.
tol (float, optional) – tolerence of the research. The default is 0.001.
stop_when_found (bool, optional) – Stop the research when a point is found. The default is True.

Returns:

Point Object – Point Found.
int or list of int – index of the point.

from_list(T, A, B, C, coortype='XYZ', sA=[], sB=[], sC=[])

Method to load data from lists to the TimeSerie

Parameters:

T (float) – Time.
A (list of float) – X, F (latitude), E..
B (list of float) – Y, L (longitude), N.
C (list of float) – Z, H (hight), U.
coortype (str, optional) – The coordinates type. The default is ‘XYZ’.
sA (list of float, optional) – sigma of A component. The default is [].
sB (list of float, optional) – sigma of B component. The default is [].
sC (list of float, optional) – sigma of C component. The default is [].

Return type:

None.

from_uniq_point(Point, startdate, enddate, pas=1)

initype()

interp_get(T, coortype='ENU')

Method to get the coordinate interpolators

Parameters:

T (float or list of float) – Time (IN POSIX Time) where the interpolation is wished.
coortype (str, optional) – The coordinates type. The default is ‘ENU’.

Returns:

DESCRIPTION.

Return type:

tsout

interp_set(interptype='slinear')

Method to set the coordinate interpolators

Parameters:: interptype (TYPE, optional) – Interpolation type. The default is ‘slinear’.
Return type:: None.

interval_nominal()

Method to get the nominal internal between two epochs.

Returns:: interval nominal.
Return type:: float

len_period(output_seconds=False)

Method to get the period length

Return type:: timedelta or

mean_posi(coortype='XYZ', outtype='point', mean_type='median')

Method to determine the mean position of the TimeSerie

Parameters:

coortype (TYPE, optional) – The coordinates type. The default is ‘XYZ’.
outtype (TYPE, optional) – ‘point’ or ‘tuple’. The default is ‘point’.
mean_type (TYPE, optional) – ‘mean’ or ‘median’. The default is ‘median’.

Return type:

Point or coordinates tuple

meta_set(path='', stat='STAT', name='')

Set meta data about the TimeSerie

Parameters:

path (str, optional) – file path. The default is ‘’.
stat (str, optional) – station 4-char. code. The default is ‘STAT’.
name (str, optional) – free name of for the TS, like the experience, the periode , the software … The default is ‘’.

Return type:

None.

property nbpts

Method to have the length of the TimeSerie

Returns:: Length of the TimeSerie.
Return type:: int

plot(coortype='ENU', diapt=2, alpha=0.8, fig=1, errbar=True, symbol='.', errbar_width=1, ylim=None)

Plot data in a TimeSerie Object

Parameters:

coortype (str, optional) – The coordinates type. The default is ‘ENU’.
diapt (float, optional) – Point diameter. The default is 2.
alpha (float, optional) – Alpha (transparency) of points. The default is 0.8.
fig (int or Figure object, optional) – Figure ID where the data will be plotted can accept a int (id of a Figure) OR the figure Object itself. The default is 1.
errbar (bool, optional) – Plot the error bars. The default is True.
symbol (str, optional) – symbol. The default is ‘.’.
errbar_width (float, optional) – coefficient for the error bar size. The default is 1.
ylim (tuple, optional) – Y-axis limits. The default is None.

Return type:

The matplotlib Figure object.

plot_discont(fig=1)

Plot discontinuties of a TimeSerie Object contained in discont list

Parameters:: fig (int or Figure object, optional) – Figure ID where the data will be plotted can accept a int (id of a Figure) OR the figure Object itself. The default is 1.
Return type:: None.

readfile(filein)

Method to read the data form a file Should be used with care

Parameters:: filein (str) – path of the file.
Return type:: None.

rm_duplicat_pts(coortype='XYZ')

Remove duplicate points from the time series.

Parameters:: coortype (str, optional) – The coordinate type to consider for duplication check. The default is “XYZ”.
Return type:: None

set_discont(indiscont)

Method to set the discontinuties list

Parameters:: indiscont (list of time) – Discontinuities in the TimeSerie.
Return type:: None.

sort()

Internal method to sort the point in the TimeSerie Object

Return type:: None.

startdate()

Method to get the first epoch of the data in the TimeSerie

Return type:: DateTime

time_win(windows, mode='keep'): IL EST TRES DANGEREUX DE L’APPLIQUER UN FENETRAGE A SOI MEME

to_dataframe(coortype='XYZ', anex_key_list=None)

Export the TimeSerie Object as DataFrame

Parameters:

coortype (str or iterable of str.) – The coordinates type exported to the DataFrame. ‘XYZ’, ‘FLH’, ‘ENU’, ‘NED’ can be also an iterable like (‘XYZ’,’FLH’) The default is ‘XYZ’.
anex_key_list (list of str, optional) – list of point’s ‘anex’ keys to be added as columns in the DataFrame. The default is None.

Returns:

df – output DataFrame.

Return type:

DataFrame

to_list(coortype='XYZ', specific_output=None, time_as_datetime=False)

Export the TimeSerie Object as Lists (Numpy Arrays)

Parameters:

coortype (str, optional)
list. (The coordinates type exported to the)
'XYZ'. (The default is)
specific_output (int, optional) – ask for a specific list, ranges between 0 and 6. The default is None.
time_as_datetime (bool, optional) – if True the Time list is exported in datetime if False the Time list is exported in Posix time

Returns:

A,B,C,T,sA,sB,sC – A = X, F (latitude), E. B = Y, L (longitude), N. C = Z, H (hight), U. T = Time sA = sigma of A component sB = sigma of B component sC = sigma of C component

Return type:

lists

class geodezyx.time_series.ts_class.point_n_click_plot

Bases: object

This method allow to do “point and click” on a plot, to localize offsets for instance

Usage

Data have to be ploted already in a figure

PnC = point_n_click_plot()
multi , cid = PnC(fig=1,Xdata_are_time=True)
PnC.selectedX

i.e.

Create an object point_n_click_plot (here it is PnC in the exemple below)

Call the object like a function with as 1st argument the id of the plot figure or the plot figure itself

Make your selection using the SPACE key

Get your results in a list called PnC.selectedX

Important

cursor objects (i.e. multi & cid) must be stored as global variables when you call the method like this :

multi , cid = PnC(fig=1)

geodezyx.time_series.ts_export module

Created on Fri May 12 15:56:33 2023

@author: psakicki

geodezyx.time_series.ts_export.export_ts(ts, outdir, coordtype='ENU', outprefix='', write_header=False)

Export a time series to a custom .ts.dat format.

Parameters:

ts (TimeSeries) – Input time series object containing geodetic data.
outdir (str) – Directory where the output file will be saved.
coordtype (str, optional) – Coordinate type to use for the export (e.g., ‘ENU’), by default ‘ENU’.
outprefix (str, optional) – Prefix to add to the output file name, by default an empty string.
write_header (bool, optional) – If True, writes a header to the output file, by default False.

Notes

The function writes a .ts.dat file containing the time series data.
The file includes geodetic coordinates (East, North, Up or other types) and their uncertainties.
The header, if enabled, describes the columns in the file.

Returns:: The function writes the output file to the specified directory and does not return any value. write_header not well implemented !!!
Return type:: None

geodezyx.time_series.ts_export.export_ts_as_hector_enu(tsin, outdir, outprefix, coordtype='ENU')

export to a HECTOR .enu (and not .neu !) compatible format This format is simpler : just gives MJD E N U

This format is necessary to force a sampling period.

outfile will be writed in /outdir/outprefixSTAT.enu

geodezyx.time_series.ts_export.export_ts_as_midas_tenu(tsin, outdir, outprefix, coordtype='ENU', export_step=True)

Export a time series to a MIDAS .tneu compatible format.

Parameters:

tsin (TimeSeries) – Input time series object containing geodetic data.
outdir (str) – Directory where the output .tneu file will be saved.
outprefix (str) – Prefix to add to the output file name.
coordtype (str, optional) – Coordinate type to use for the export, by default ‘ENU’.
export_step (bool, optional) – If True, exports a step file containing discontinuities, by default True.

Notes

The function writes a .tneu file containing the time series data in MIDAS format.
If export_step is True and the time series contains discontinuities, a separate step file is created.
The .tneu file includes geodetic displacements (East, North, Up) relative to the first point.

Returns:: The function writes the output file(s) to the specified directory and does not return any value.
Return type:: None

geodezyx.time_series.ts_export.export_ts_as_neu(tsin, outdir, outprefix, coordtype='ENU')

Export a time series to a HECTOR .neu compatible format.

Parameters:

tsin (TimeSeries) – Input time series object containing geodetic data.
outdir (str) – Directory where the output .neu file will be saved.
outprefix (str) – Prefix to add to the output file name.
coordtype (str, optional) – Coordinate type to use for the export, by default ‘ENU’.

Notes

The function writes a .neu file containing the time series data in HECTOR format.
The header includes metadata such as the reference epoch, reference position, and field descriptions.
The data section includes geodetic displacements (North, East, Up) relative to the first point.
If the time series lacks XYZ attributes, default values are used for the reference position.

Returns:: The function writes the output file to the specified directory and does not return any value.
Return type:: None

geodezyx.time_series.ts_export.export_ts_as_pbo_pos(tsin, outdir, outprefix='', force=None, force_1st_pt_as_ref=True, verbose=False)

Export a time series to the GAMIT/GLOBK PBO position (.pos) format.

Parameters:

tsin (TimeSeries) – Input time series object containing geodetic data.
outdir (str) – Directory where the output .pos file will be saved.
outprefix (str, optional) – Prefix to add to the output file name, by default an empty string.
force (str, optional) – Determines the data source to use for the export. Can be ‘data’ or ‘data_xyz’. If None, the function decides based on the available attributes, by default None.
force_1st_pt_as_ref (bool, optional) – If True, forces the first point of the time series to be used as the reference position, by default True.
verbose (bool, optional) – If True, enables verbose logging for debugging purposes, by default False.

Notes

The function writes a .pos file containing the time series data in the PBO format.
The header includes metadata such as the reference epoch, reference position, and field descriptions.
The data section includes geodetic coordinates (X, Y, Z) and their uncertainties, along with other metadata.

Returns:: outfile_path – Path to the exported PBO file
Return type:: str

geodezyx.time_series.ts_export.export_ts_as_spotgins(tsin, outdir, ac, data_src='unknown', version=3)

Export time series to SPOTGINS format.

Parameters:

tsin (TimeSeries) – Input time series object
outdir (str) – Output directory path
ac (str) – Analysis Center acronym (3-4 characters)
data_src (str, optional) – Data source identifier, by default “unknown”
version (int, optional) – SPOTGINS format version (2 or 3), by default 2

Returns:

outfile_path – Path to the exported SPOTGINS file

Return type:

str

geodezyx.time_series.ts_export.export_ts_figure_pdf(fig, export_path, filename, close=False): fig can accept a int (id of a Figure) OR the figure Object itself

geodezyx.time_series.ts_export.export_ts_plot(tsin, export_path, coortype='ENU', export_type=('pdf', 'png'), plot_B=False, close_fig_after_export=True): Very beta … to be implemented : merge w/ the export_figure_pdf fct

geodezyx.time_series.ts_fcts module

Created on Fri Aug 2 17:38:41 2019

@author: psakicki

geodezyx.time_series.ts_fcts.add_offset_point(ptin, dA, dB, dC, coortype='ENU')

ONLY IMPLEMENTED FOR ENU FOR THE MOMENT 150415 : remark still necessary ???

coortype == ‘UXYZ’ :: specific case where we correct an Up offset directly in the XYZ coords very usefull for an antenna offset in for a moving GPS (but works only for the up)

geodezyx.time_series.ts_fcts.add_offset_smart_for_GINS_kine(tsin, tslist_offset_3ple, list_windows, coortype='XYZ'): tslist_offset_3ple : list of len N containing (dX,dY,dZ) offsets list_windows : list of len N-1 containing dates of changes

geodezyx.time_series.ts_fcts.add_offset_ts(tsin, dA, dB, dC, coortype='ENU')

return a copy of the tsin (tsin won’t be affected)

coortype == ‘UXYZ’ :: specific case where we correct an Up offset directly in the XYZ coords very usefull for an antenna offset in for a moving GPS (but works only for the up)

geodezyx.time_series.ts_fcts.baselines_calc(ts_list, plani_only=False, substract_offset=<function median>, symetric_calc=False, symetric_storage=True)

Parameters:

ts_list (list) – list of TimeSeries.
plani_only (bool, optional) – If True, compute the baseline varation on the East and North component only. The default is False.
substract_offset (function or None, optional) – A function to substract the offset. Can be np.mean (substract the mean value), np.median (substract the median value), or lambda x: x[0] (substract the 1st value) The default is np.median.
symetric_calc (bool, optional) – If True, compute the baseline variation 2 times, for stat1 > stat2 and stat2 > stat1 Might be useful to compare planimetic computation The default is False.
symetric_storage (bool, optional) – If True, store the baseline variation 2 times, dictstore[stat1][stat2] and dictstore[stat2][stat1] symetric_calc overrides this option The default is True.

Returns:

dictstore – A dictionnary of dictionnaries of Pandas Series, containing the baseline variations e.g. dictstore[stat1][stat2] = bl_series

Return type:

dict

geodezyx.time_series.ts_fcts.bool_cleaner(tsin, boollist, verbose=False): A partir d’une liste de bool de meme longeur que le nbre de points on ne conserve que les points True

geodezyx.time_series.ts_fcts.compar(tstup, coortype='ENU', seuil=3.0, win=[], mode='keep', Dtype='2D3D', namest=0, namend=10, alpha=0.8, diapt=5, verbose=True, print_report=True, plot=True, interp=True)

Compare time series data.

This function compares multiple time series data by calculating differences between a reference time series and other time series. It supports various coordinate types and can perform outlier cleaning using the Median Absolute Deviation (MAD) method.

Parameters:

tstup (tuple of TimeSeriePoint) – Tuple of time series to compare. The first one is the reference.
coortype (str, optional) – Coordinate type (‘ENU’, ‘XYZ’, etc.). Default is ‘ENU’.
seuil (float, optional) – Threshold for MAD cleaning. Default is 3.
win (list, optional) – Time window for comparison. Default is an empty list.
mode (str, optional) – Mode for time window (‘keep’ or ‘del’). Default is ‘keep’.
Dtype (str, optional) – Type of distance calculation (‘2D’, ‘3D’, ‘2D3D’). Default is ‘2D3D’.
namest (int, optional) – Start index for name slicing. Default is 0.
namend (int, optional) – End index for name slicing. Default is 10.
alpha (float, optional) – Alpha value for plotting. Default is 0.8.
diapt (float, optional) – Marker size for plotting. Default is 5.
verbose (bool, optional) – If True, print detailed logs. Default is True.
print_report (bool, optional) – If True, print comparison report. Default is True.
plot (bool, optional) – If True, generate comparison plots. Default is True.
interp (bool, optional) – If True, perform interpolation. Default is True.

Returns:

output – If plot is True, returns a tuple (dicolist, fig). Otherwise, returns dicolist.

Return type:

list or tuple

geodezyx.time_series.ts_fcts.compar_elts_in_ts(ts1, ts2): ts2 must contains less elts than ts1 (ts2 = cleaned one)

geodezyx.time_series.ts_fcts.compar_plot(dico_list_in, namest=0, namend=10, alpha=0.8, diapt=1.5, new_style=True, colormap='gnuplot')

Generate comparison plots for time series data.

This function creates plots to compare multiple time series data. It supports different coordinate types and allows customization of plot appearance.

Parameters:

dico_list_in (list of dict) – List of dictionaries containing time series data to plot.
namest (int, optional) – Start index for name slicing. Default is 0.
namend (int, optional) – End index for name slicing. Default is 10.
alpha (float, optional) – Alpha value for plot markers. Default is 0.8.
diapt (float, optional) – Marker size for plot markers. Default is 1.5.
new_style (bool, optional) – If True, use a new style for the plots. Default is True.
colormap (str, optional) – Colormap to use for the plots. Default is ‘gnuplot’.

Returns:

fig – The generated figure containing the comparison plots.

Return type:

matplotlib.figure.Figure

geodezyx.time_series.ts_fcts.decimate_cleaner(tsin, minval, in_place=False): in_place DOES’NT WORK !!!

geodezyx.time_series.ts_fcts.decimate_cleaner_2(tsin, N, in_place=False): keep a value every N vals

geodezyx.time_series.ts_fcts.detrend_ts(tsin, coortype='ENU', t_origin=None)

geodezyx.time_series.ts_fcts.dist_btwn_2pts(ptA, ptB, coortype='XYZ')

geodezyx.time_series.ts_fcts.dist_diff_btwn_2pts(ptA, ptB)

geodezyx.time_series.ts_fcts.find_pts_from_ts_with_time(tin, tstupin, tol=0.001)

geodezyx.time_series.ts_fcts.helmert_trans(tsin, params='itrf2008_2_etrf2000', invert=False)

geodezyx.time_series.ts_fcts.interpolator_light(T, X, Y, Z)

geodezyx.time_series.ts_fcts.interpolator_with_extrapolated(T, X, Y, Z)

geodezyx.time_series.ts_fcts.linear_regress_find_coeff(tsin, coortype='ENU')

geodezyx.time_series.ts_fcts.linear_regress_ts(tsin, coortype='ENU', titledetails=''): doit être cablé ASAP linear_regress_find_coeff

geodezyx.time_series.ts_fcts.linear_regress_ts_discont(tsin, coortype='ENU')

geodezyx.time_series.ts_fcts.mad_cleaner(tsin, seuil=3.5, method='dist', coortype='ABC', detrend_first=False, output_detrended=False, verbose=False)

methodmethode d’élimination :

dist : on élimine les point qu sont trop loin en distance de la posi de ref indep : on traite les point independaments

dist est a privilégier

output_detrended ne marche que si detrend_first est activé

geodezyx.time_series.ts_fcts.mean_list_of_pts(ptslisin): useful for merge fct ONLY IMPLEMENTED FOR ENU coords for the moment

geodezyx.time_series.ts_fcts.mean_posi_multi(tstup)

geodezyx.time_series.ts_fcts.merge(tsin, N): merge N points in one

geodezyx.time_series.ts_fcts.merge_ts(ts_list_in)

Merge several TimeSeriePoint into one

Parameters:: ts_list_in (list of TimeSeriePoint) – list of TimeSeriePoint.
Returns:: ts_out – merged TimeSeriePoint.
Return type:: TimeSeriePoint

geodezyx.time_series.ts_fcts.print4compar(dA, dB, dC, dD, coortype)

geodezyx.time_series.ts_fcts.print4compar_tabular(dicolist, split=0, print_2D3D_if_any=True)

geodezyx.time_series.ts_fcts.refENU_for_tslist(tslist_in, tsref_marker=0): tsref_marker : indice of the reference time serie OR the ‘all’ keyword in this case all the time series mean position will be averaged

geodezyx.time_series.ts_fcts.retrend_ts(tsin, a_coef, b_coef, coortype='ENU', t_origin=None): a_coefs,b_coefs = 3-tuple/list for the 3 component a_coef = m/s

geodezyx.time_series.ts_fcts.rotate_points_class(tsattin, ptslin, Rtype='R1', xyzreftuple=([1, 0, 0], [0, 1, 0], [0, 0, 1]), angtype='deg')

geodezyx.time_series.ts_fcts.rotate_pt_cls_solo(tsattin, pointin, Rtype='R1', xyzreftuple=([1, 0, 0], [0, 1, 0], [0, 0, 1]), angtype='deg')

ENTREEtsattinune TS d’attitude (N angles): pointin : UN Point en entrée

SORTIE : une TSpoint de N points

geodezyx.time_series.ts_fcts.round_time(tsin, round_to, mode='round')

geodezyx.time_series.ts_fcts.sigma_cleaner(tsin, seuil=3, coortype='ABC', cleantype='any', verbose=False)

geodezyx.time_series.ts_fcts.std_dev_cleaner(tsin, stddev_threshold, coortype='ABC', cleantype='any', verbose=False): A rebooted (1807) version of sigma_cleaner just remove values in a timeserie with a high sigma/std deviation

geodezyx.time_series.ts_fcts.time_gap(tsin, marge=2, mode='del'): ENTREE une TimeSerie SORTIE une window (liste de listes)

geodezyx.time_series.ts_fcts.time_win(tsin, windows, mode='keep', outbool=False)

geodezyx.time_series.ts_fcts.time_win_T(Tin, win, mode='del')

geodezyx.time_series.ts_fcts.time_win_multi(inplis)

geodezyx.time_series.ts_fcts.ts_from_list(A, B, C, T, initype, sA=[], sB=[], sC=[], stat='STAT', name='NoName')

geodezyx.time_series.ts_fcts.velocity_trans(tsin, vx, vy, vz, epoc_init='auto', epoc_end='auto')