From e18196b95e137b0b0810b4c44fc65d98b1cfa860 Mon Sep 17 00:00:00 2001 From: David A Minton Date: Thu, 8 Jul 2021 13:35:22 -0400 Subject: [PATCH] Updated whm swifter vs swiftest comparison example with streamlined initial conditions generator --- .../swiftest_vs_swifter.ipynb | 6 +- .../whm_swifter_comparison/Untitled.ipynb | 169 -------- .../whm_swifter_comparison/cb.swiftest.in | Bin 64 -> 87 bytes examples/whm_swifter_comparison/init_cond.py | 362 +++-------------- .../whm_swifter_comparison/param.swifter.in | 52 +-- .../whm_swifter_comparison/param.swiftest.in | 64 +-- examples/whm_swifter_comparison/pl.swifter.in | 56 ++- .../whm_swifter_comparison/pl.swiftest.in | Bin 700 -> 1529 bytes .../swiftest_vs_swifter.ipynb | 245 ++++++++++++ examples/whm_swifter_comparison/tp.swifter.in | 24 +- .../whm_swifter_comparison/tp.swiftest.in | Bin 280 -> 559 bytes .../whm_swiftest_vs_swifter.ipynb | 363 ------------------ 12 files changed, 396 insertions(+), 945 deletions(-) delete mode 100644 examples/whm_swifter_comparison/Untitled.ipynb mode change 100644 => 100755 examples/whm_swifter_comparison/init_cond.py create mode 100644 examples/whm_swifter_comparison/swiftest_vs_swifter.ipynb delete mode 100644 examples/whm_swifter_comparison/whm_swiftest_vs_swifter.ipynb diff --git a/examples/rmvs_swifter_comparison/1pl_1tp_encounter/swiftest_vs_swifter.ipynb b/examples/rmvs_swifter_comparison/1pl_1tp_encounter/swiftest_vs_swifter.ipynb index 8232207e5..f2566d9e7 100644 --- a/examples/rmvs_swifter_comparison/1pl_1tp_encounter/swiftest_vs_swifter.ipynb +++ b/examples/rmvs_swifter_comparison/1pl_1tp_encounter/swiftest_vs_swifter.ipynb @@ -81,8 +81,8 @@ { "data": { "text/plain": [ - "[,\n", - " ]" + "[,\n", + " ]" ] }, "execution_count": 6, @@ -91,7 +91,7 @@ }, { "data": { - "image/png": "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\n", + "image/png": "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\n", "text/plain": [ "
" ] diff --git a/examples/whm_swifter_comparison/Untitled.ipynb b/examples/whm_swifter_comparison/Untitled.ipynb deleted file mode 100644 index 7d5a299df..000000000 --- a/examples/whm_swifter_comparison/Untitled.ipynb +++ /dev/null @@ -1,169 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "import numpy as np\n", - "import astropy.constants as const" - ] - }, - { - "cell_type": "code", - "execution_count": 31, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "6.6743e-11\n" - ] - } - ], - "source": [ - "AU2M = np.longdouble(const.au.value)\n", - "GMSunSI = np.longdouble(const.GM_sun.value)\n", - "print(const.G.value)\n", - "Rsun = np.longdouble(const.R_sun.value)\n", - "GC = np.longdouble(const.G.value)\n", - "JD = 86400\n", - "year = np.longdouble(365.25 * JD)\n", - "c = np.longdouble(299792458.0)\n", - "MSun_over_Mpl = np.array([6023600.0,\n", - " 408523.71,\n", - " 328900.56,\n", - " 3098708.,\n", - " 1047.3486,\n", - " 3497.898,\n", - " 22902.98,\n", - " 19412.24,\n", - " 1.35e8], dtype=np.longdouble)\n", - "\n", - "MU2KG = np.longdouble(GMSunSI / GC) #Conversion from mass unit to kg\n", - "DU2M = np.longdouble(AU2M) #Conversion from radius unit to centimeters\n", - "TU2S = np.longdouble(year) #Conversion from time unit to seconds\n", - "GU = np.longdouble(GC / (DU2M**3 / (MU2KG * TU2S**2)))\n", - "\n", - "GMSun = np.longdouble(GMSunSI / (DU2M**3 / TU2S**2))" - ] - }, - { - "cell_type": "code", - "execution_count": 34, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "int" - ] - }, - "execution_count": 34, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "type(JD)" - ] - }, - { - "cell_type": "code", - "execution_count": 39, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([6.0236000000000000000e+06, 4.0852371000000002095e+05,\n", - " 3.2890055999999999767e+05, 3.0987080000000000000e+06,\n", - " 1.0473486000000000331e+03, 3.4978980000000001382e+03,\n", - " 2.2902979999999999563e+04, 1.9412240000000001601e+04,\n", - " 1.3500000000000000000e+08], dtype=float128)" - ] - }, - "execution_count": 39, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "MSun_over_Mpl" - ] - }, - { - "cell_type": "code", - "execution_count": 54, - "metadata": {}, - "outputs": [], - "source": [ - "np.set_printoptions(threshold = np.inf, precision=23)" - ] - }, - { - "cell_type": "code", - "execution_count": 59, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "39.476926408897626292\n" - ] - } - ], - "source": [ - "print(GU)" - ] - }, - { - "cell_type": "code", - "execution_count": 60, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "39.47692640889762571987376\n" - ] - } - ], - "source": [ - "print(\"{:.23f}\".format(GU))" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.8.6" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/examples/whm_swifter_comparison/cb.swiftest.in b/examples/whm_swifter_comparison/cb.swiftest.in index 2386b53c8a2bcee968968e01db63bf30bc75c07a..058975b813b2ede68061d83c64275e30fd103ddb 100644 GIT binary patch literal 87 zcmWN`u@L|<2m`^KUd&(t5)}0Px9|=w*~|437p$0B5w!4#V!s5&61QdL>g)+_&4sf2 RI~R7~DCJW7wlL81u^;=Z5pDng literal 64 zcmd;JU|=xH*zksXud@ROkPX6j{SWxW@f6#``25D)QG73^*uwa(zDpQecDd{U@d2$e B4EX>6 diff --git a/examples/whm_swifter_comparison/init_cond.py b/examples/whm_swifter_comparison/init_cond.py old mode 100644 new mode 100755 index 289f14f75..aac82eed9 --- a/examples/whm_swifter_comparison/init_cond.py +++ b/examples/whm_swifter_comparison/init_cond.py @@ -1,323 +1,59 @@ +#!/usr/bin/env python3 +import swiftest import numpy as np import sys from astroquery.jplhorizons import Horizons import astropy.constants as const from scipy.io import FortranFile -#Values from JPL Horizons -AU2M = np.longdouble(const.au.value) -GMSunSI = np.longdouble(const.GM_sun.value) -Rsun = np.longdouble(const.R_sun.value) -GC = np.longdouble(const.G.value) -JD = 86400 -year = np.longdouble(365.25 * JD) -c = np.longdouble(299792458.0) -MSun_over_Mpl = np.array([6023600.0, - 408523.71, - 328900.56, - 3098708., - 1047.3486, - 3497.898, - 22902.98, - 19412.24, - 1.35e8], dtype=np.longdouble) - -MU2KG = np.longdouble(GMSunSI / GC) #Conversion from mass unit to kg -DU2M = np.longdouble(AU2M) #Conversion from radius unit to centimeters -TU2S = np.longdouble(year) #Conversion from time unit to seconds -GU = np.longdouble(GC / (DU2M**3 / (MU2KG * TU2S**2))) - -GMSun = np.longdouble(GMSunSI / (DU2M**3 / TU2S**2)) - -# Simulation start, stop, and output cadence times -t_0 = 0 # simulation start time -deltaT = 0.25 * JD / TU2S # simulation step size -end_sim = 1 * year / TU2S # simulation end time -t_print = deltaT #year / TU2S #output interval to print results - - -# Solar oblatenes values: From Mecheri et al. (2004), using Corbard (b) 2002 values (Table II) -J2 = 0.0 #np.longdouble(2.198e-7) * (Rsun / DU2M)**2 -J4 = 0.0 #np.longdouble(-4.805e-9) * (Rsun / DU2M)**4 - -tstart = '2021-01-28' -tend = '2021-01-29' -tstep = '1d' -planetid = { - 'mercury' : '1', - 'venus' : '2', - 'earthmoon' : '3', - 'mars' : '4', - 'jupiter' : '5', - 'saturn' : '6', - 'uranus' : '7', - 'neptune' : '8', - 'plutocharon' : '9' -} -npl = 9 - -#Planet Msun/M ratio -MSun_over_Mpl = { - 'mercury' : np.longdouble(6023600.0), - 'venus' : np.longdouble(408523.71), - 'earthmoon' : np.longdouble(328900.56), - 'mars' : np.longdouble(3098708.), - 'jupiter' : np.longdouble(1047.3486), - 'saturn' : np.longdouble(3497.898), - 'uranus' : np.longdouble(22902.98), - 'neptune' : np.longdouble(19412.24), - 'plutocharon' : np.longdouble(1.35e8) -} - -#Planet radii in meters -Rpl = { - 'mercury' : np.longdouble(2439.4e3), - 'venus' : np.longdouble(6051.8e3), - 'earthmoon' : np.longdouble(6371.0084e3), # Earth only for radius - 'mars' : np.longdouble(3389.50e3), - 'jupiter' : np.longdouble(69911e3), - 'saturn' : np.longdouble(58232.0e3), - 'uranus' : np.longdouble(25362.e3), - 'neptune' : np.longdouble(24622.e3), - 'plutocharon' : np.longdouble(1188.3e3) -} - -pdata = {} -plvec = {} -Rhill = {} -THIRDLONG = np.longdouble(1.0) / np.longdouble(3.0) - -for key,val in planetid.items(): - pdata[key] = Horizons(id=val, id_type='majorbody',location='@sun', - epochs={'start': tstart, 'stop': tend, - 'step': tstep}) - plvec[key] = np.array([pdata[key].vectors()['x'][0], - pdata[key].vectors()['y'][0], - pdata[key].vectors()['z'][0], - pdata[key].vectors()['vx'][0], - pdata[key].vectors()['vy'][0], - pdata[key].vectors()['vz'][0] - ]) - - Rhill[key] = np.longdouble(pdata[key].elements()['a'][0]) * (3 * MSun_over_Mpl[key])**(-THIRDLONG) - -asteroidid = { - '100001' : 'Ceres', - '100002' : 'Pallas', - '100003' : 'Juno', - '100004' : 'Vesta' +sim = swiftest.Simulation() + +sim.param['MU2KG'] = swiftest.MSun +sim.param['TU2S'] = swiftest.YR2S +sim.param['DU2M'] = swiftest.AU2M +sim.param['T0'] = 0.0 +sim.param['TSTOP'] = 1.0 +sim.param['DT'] = 0.25 * swiftest.JD2S / swiftest.YR2S +sim.param['CHK_QMIN_COORD'] = "HELIO" +sim.param['CHK_QMIN'] = swiftest.RSun / swiftest.AU2M +sim.param['CHK_QMIN_RANGE'] = f"{swiftest.RSun / swiftest.AU2M} 1000.0" +sim.param['CHK_RMIN'] = swiftest.RSun / swiftest.AU2M +sim.param['CHK_RMAX'] = 1000.0 +sim.param['CHK_EJECT'] = 1000.0 +sim.param['ISTEP_OUT'] = 1 +sim.param['ISTEP_DUMP'] = 1 +sim.param['OUT_FORM'] = "XV" +sim.param['OUT_STAT'] = "UNKNOWN" +sim.param['GR'] = 'NO' + +bodyid = { + "Sun": 0, + "Mercury": 1, + "Venus": 2, + "Earth": 3, + "Mars": 4, + "Jupiter": 5, + "Saturn": 6, + "Uranus": 7, + "Neptune": 8, + "Ceres": 101, + "Pallas": 102, + "Juno": 103, + "Vesta": 104 } -ntp = 4 -tdata = {} -tpvec = {} -for key,val in asteroidid.items(): - tdata[key] = Horizons(id=val, id_type='smallbody', location='@sun', - epochs={'start': tstart, 'stop': tend, - 'step': tstep}) - tpvec[key] = np.array([tdata[key].vectors()['x'][0], - tdata[key].vectors()['y'][0], - tdata[key].vectors()['z'][0], - tdata[key].vectors()['vx'][0], - tdata[key].vectors()['vy'][0], - tdata[key].vectors()['vz'][0] - ]) - - -if __name__ == '__main__': - # Convert from AU-day to AU-year just because I find it easier to keep track of the sim progress - for plid in plvec: - plvec[plid][3:] *= year / JD - - for tpid in tpvec: - tpvec[tpid][3:] *= year / JD - - # Names of all output files - swifter_input = "param.swifter.in" - swifter_pl = "pl.swifter.in" - swifter_tp = "tp.swifter.in" - swifter_bin = "bin.swifter.dat" - swifter_enc = "enc.swifter.dat" - - swiftest_input = "param.swiftest.in" - swiftest_pl = "pl.swiftest.in" - swiftest_tp = "tp.swiftest.in" - swiftest_cb = "cb.swiftest.in" - swiftest_bin = "bin.swiftest.dat" - swiftest_enc = "enc.swiftest.dat" - - iout = int(np.ceil(t_print / deltaT)) - rmin = Rsun / DU2M - rmax = np.longdouble(1000.0) - #Make Swifter files +for name, id in bodyid.items(): + sim.add(name, idval=id) - plfile = open(swifter_pl, 'w') - print(npl+1, f'! Planet input file generated using init_cond.py using JPL Horizons data for the major planets (and Pluto) for epoch {tstart}' ,file=plfile) - print(1,GMSun,file=plfile) - print('0.0 0.0 0.0',file=plfile) - print('0.0 0.0 0.0',file=plfile) - for i, plid in enumerate(plvec): - print(i + 2,"{:.23g}".format(GMSun * MSun_over_Mpl[plid]**-1),Rhill[plid], file=plfile) - print(Rpl[plid] / DU2M, file=plfile) - print(plvec[plid][0],plvec[plid][1],plvec[plid][2], file=plfile) - print(plvec[plid][3],plvec[plid][4],plvec[plid][5], file=plfile) - plfile.close() - - tpfile = open(swifter_tp, 'w') - print(ntp,file=tpfile) - for tpid, tp in tpvec.items(): - print(tpid, file=tpfile) - print(tp[0],tp[1],tp[2], file=tpfile) - print(tp[3],tp[4],tp[5], file=tpfile) - tpfile.close() - - sys.stdout = open(swifter_input, "w") - print('! Swifter input file generated using init_cond.py') - print('T0 ',t_0) - print('TSTOP ',end_sim) - print('DT ',deltaT) - print('PL_IN ',swifter_pl) - print('TP_IN ',swifter_tp) - print('IN_TYPE ASCII') - print('ISTEP_OUT ',iout) - print('ISTEP_DUMP ',iout) - print('BIN_OUT ',swifter_bin) - print('OUT_TYPE REAL8') - print('OUT_FORM XV') - print('OUT_STAT NEW') - print('J2 ',J2) - print('J4 ',J4) - print('CHK_CLOSE yes') - print('CHK_RMIN ',rmin) - print('CHK_RMAX ',rmax) - print('CHK_EJECT ',rmax) - print('CHK_QMIN ',rmin) - print('CHK_QMIN_COORD HELIO') - print('CHK_QMIN_RANGE ',rmin,rmax) - print('ENC_OUT ',swifter_enc) - print('EXTRA_FORCE no') - print('BIG_DISCARD no') - print('RHILL_PRESENT yes') - - sys.stdout = sys.__stdout__ - #Now make Swiftest files - #cbfile = open(swiftest_cb, 'w') - cbfile = FortranFile(swiftest_cb, 'w') - #print(1.0,file=cbfile) - #print(rmin,file=cbfile) - #print(J2,file=cbfile) - #print(J4,file=cbfile) - Msun = np.double(1.0) - cbfile.write_record(np.double(GMSun)) - cbfile.write_record(np.double(rmin)) - cbfile.write_record(np.double(J2)) - cbfile.write_record(np.double(J4)) - cbfile.close() - - #plfile = open(swiftest_pl, 'w') - plfile = FortranFile(swiftest_pl, 'w') - #print(npl,file=plfile) - plfile.write_record(npl) - - name = np.empty(npl, dtype=np.int32) - px = np.empty(npl, dtype=np.double) - py = np.empty(npl, dtype=np.double) - pz = np.empty(npl, dtype=np.double) - vx = np.empty(npl, dtype=np.double) - vy = np.empty(npl, dtype=np.double) - vz = np.empty(npl, dtype=np.double) - mass = np.empty(npl, dtype=np.double) - Gmass = np.empty(npl, dtype=np.double) - radius = np.empty(npl, dtype=np.double) - for i, plid in enumerate(plvec): - name[i] = i + 2 - px[i] = plvec[plid][0] - py[i] = plvec[plid][1] - pz[i] = plvec[plid][2] - vx[i] = plvec[plid][3] - vy[i] = plvec[plid][4] - vz[i] = plvec[plid][5] - Gmass[i] = GMSun * MSun_over_Mpl[plid]**-1 - radius[i] = Rpl[plid] / DU2M - plfile.write_record(name.T) - plfile.write_record(px.T) - plfile.write_record(py.T) - plfile.write_record(pz.T) - plfile.write_record(vx.T) - plfile.write_record(vy.T) - plfile.write_record(vz.T) - plfile.write_record(Gmass.T) - plfile.write_record(radius.T) - #for i, plid in enumerate(plvec): - # print(i + 2,"{:.23g}".format(np.longdouble(MSun_over_Mpl[plid]**-1)), file=plfile) - # print(Rpl[plid] / DU2M, file=plfile) - # print(plvec[plid][0], plvec[plid][1], plvec[plid][2], file=plfile) - # print(plvec[plid][3], plvec[plid][4], plvec[plid][5], file=plfile) - plfile.close() - #tpfile = open(swiftest_tp, 'w') - tpfile = FortranFile(swiftest_tp, 'w') - #print(ntp,file=tpfile) - tpfile.write_record(ntp) - #for tpid, tp in tpvec.items(): - # print(tpid, file=tpfile) - # print(tp[0],tp[1],tp[2], file=tpfile) - # print(tp[3],tp[4],tp[5], file=tpfile) - - name = np.empty(ntp, dtype=np.int32) - px = np.empty(ntp, dtype=np.double) - py = np.empty(ntp, dtype=np.double) - pz = np.empty(ntp, dtype=np.double) - vx = np.empty(ntp, dtype=np.double) - vy = np.empty(ntp, dtype=np.double) - vz = np.empty(ntp, dtype=np.double) - for i, tpid in enumerate(tpvec): - name[i] = int(tpid) - px[i] = tpvec[tpid][0] - py[i] = tpvec[tpid][1] - pz[i] = tpvec[tpid][2] - vx[i] = tpvec[tpid][3] - vy[i] = tpvec[tpid][4] - vz[i] = tpvec[tpid][5] - tpfile.write_record(name.T) - tpfile.write_record(px.T) - tpfile.write_record(py.T) - tpfile.write_record(pz.T) - tpfile.write_record(vx.T) - tpfile.write_record(vy.T) - tpfile.write_record(vz.T) - - tpfile.close() - - sys.stdout = open(swiftest_input, "w") - print('! Swiftest input file generated using init_cond.py') - print('T0 ',t_0) - print('TSTOP ',end_sim) - print('DT ',deltaT) - print('CB_IN ',swiftest_cb) - print('PL_IN ',swiftest_pl) - print('TP_IN ',swiftest_tp) - print('IN_TYPE REAL8') - print('ISTEP_OUT ',iout) - print('ISTEP_DUMP ',iout) - print('BIN_OUT ',swiftest_bin) - print('OUT_TYPE REAL8') - print('OUT_FORM XV') - print('OUT_STAT REPLACE') - print('CHK_CLOSE yes') - print('CHK_RMIN ',rmin) - print('CHK_RMAX ',rmax) - print('CHK_EJECT ',rmax) - print('CHK_QMIN ',rmin) - print('CHK_QMIN_COORD HELIO') - print('CHK_QMIN_RANGE ',rmin,rmax) - print('ENC_OUT ',swiftest_enc) - print('EXTRA_FORCE no') - print('BIG_DISCARD no') - print('ROTATION no') - print('GR no') - print('MU2KG ',MU2KG) - print('DU2M ',DU2M) - print('TU2S ',TU2S) - +sim.param['PL_IN'] = "pl.swiftest.in" +sim.param['TP_IN'] = "tp.swiftest.in" +sim.param['CB_IN'] = "cb.swiftest.in" +sim.param['BIN_OUT'] = "bin.swiftest.dat" +sim.param['ENC_OUT'] = "enc.swiftest.dat" +sim.save("param.swiftest.in") +sim.param['PL_IN'] = "pl.swifter.in" +sim.param['TP_IN'] = "tp.swifter.in" +sim.param['BIN_OUT'] = "bin.swifter.dat" +sim.param['ENC_OUT'] = "enc.swifter.dat" +sim.save("param.swifter.in", codename="Swifter") - sys.stdout = sys.__stdout__ diff --git a/examples/whm_swifter_comparison/param.swifter.in b/examples/whm_swifter_comparison/param.swifter.in index b092c2553..5cf0cb8b9 100644 --- a/examples/whm_swifter_comparison/param.swifter.in +++ b/examples/whm_swifter_comparison/param.swifter.in @@ -1,26 +1,26 @@ -! Swifter input file generated using init_cond.py -T0 0 -TSTOP 1.0 -DT 0.0006844626967830253251 -PL_IN pl.swifter.in -TP_IN tp.swifter.in -IN_TYPE ASCII -ISTEP_OUT 1 -ISTEP_DUMP 1 -BIN_OUT bin.swifter.dat -OUT_TYPE REAL8 -OUT_FORM XV -OUT_STAT NEW -J2 4.7535806948127356533e-12 -J4 -2.2473967953572827815e-18 -CHK_CLOSE yes -CHK_RMIN 0.0046504672609621575315 -CHK_RMAX 1000.0 -CHK_EJECT 1000.0 -CHK_QMIN 0.0046504672609621575315 -CHK_QMIN_COORD HELIO -CHK_QMIN_RANGE 0.0046504672609621575315 1000.0 -ENC_OUT enc.swifter.dat -EXTRA_FORCE no -BIG_DISCARD no -RHILL_PRESENT yes +! VERSION Swifter parameter file converted from Swiftest +T0 0.0 +TSTOP 1.0 +DT 0.0006844626967830253 +ISTEP_OUT 1 +ISTEP_DUMP 1 +OUT_FORM XV +OUT_TYPE REAL8 +OUT_STAT UNKNOWN +IN_TYPE ASCII +PL_IN pl.swifter.in +TP_IN tp.swifter.in +BIN_OUT bin.swifter.dat +ENC_OUT enc.swifter.dat +CHK_QMIN 0.004650467260962157 +CHK_RMIN 0.004650467260962157 +CHK_RMAX 1000.0 +CHK_EJECT 1000.0 +CHK_QMIN_COORD HELIO +CHK_QMIN_RANGE 0.004650467260962157 1000.0 +EXTRA_FORCE NO +BIG_DISCARD NO +CHK_CLOSE YES +J2 4.7535806948127355e-12 +J4 -2.2473967953572827e-18 +RHILL_PRESENT YES diff --git a/examples/whm_swifter_comparison/param.swiftest.in b/examples/whm_swifter_comparison/param.swiftest.in index c32a270f5..73818e198 100644 --- a/examples/whm_swifter_comparison/param.swiftest.in +++ b/examples/whm_swifter_comparison/param.swiftest.in @@ -1,29 +1,35 @@ -! Swiftest input file generated using init_cond.py -T0 0 -TSTOP 1.0 -DT 0.0006844626967830253251 -CB_IN cb.swiftest.in -PL_IN pl.swiftest.in -TP_IN tp.swiftest.in -IN_TYPE REAL8 -ISTEP_OUT 1 -ISTEP_DUMP 1 -BIN_OUT bin.swiftest.dat -OUT_TYPE REAL8 -OUT_FORM XV -OUT_STAT REPLACE -CHK_CLOSE yes -CHK_RMIN 0.0046504672609621575315 -CHK_RMAX 1000.0 -CHK_EJECT 1000.0 -CHK_QMIN 0.0046504672609621575315 -CHK_QMIN_COORD HELIO -CHK_QMIN_RANGE 0.0046504672609621575315 1000.0 -ENC_OUT enc.swiftest.dat -EXTRA_FORCE no -BIG_DISCARD no -ROTATION no -GR no -MU2KG 1.988409870698050917e+30 -DU2M 149597870700.0 -TU2S 31557600.0 +! VERSION Swiftest parameter input +T0 0.0 +TSTOP 1.0 +DT 0.0006844626967830253 +ISTEP_OUT 1 +ISTEP_DUMP 1 +OUT_FORM XV +OUT_TYPE REAL8 +OUT_STAT UNKNOWN +IN_TYPE ASCII +PL_IN pl.swiftest.in +TP_IN tp.swiftest.in +CB_IN cb.swiftest.in +BIN_OUT bin.swiftest.dat +ENC_OUT enc.swiftest.dat +CHK_QMIN 0.004650467260962157 +CHK_RMIN 0.004650467260962157 +CHK_RMAX 1000.0 +CHK_EJECT 1000.0 +CHK_QMIN_COORD HELIO +CHK_QMIN_RANGE 0.004650467260962157 1000.0 +MU2KG 1.988409870698051e+30 +TU2S 31557600.0 +DU2M 149597870700.0 +EXTRA_FORCE NO +BIG_DISCARD NO +CHK_CLOSE YES +FRAGMENTATION NO +ROTATION NO +TIDES NO +ENERGY NO +GR NO +YARKOVSKY NO +YORP NO +MTINY 0.0 diff --git a/examples/whm_swifter_comparison/pl.swifter.in b/examples/whm_swifter_comparison/pl.swifter.in index d0d4e7ff9..7412144e0 100644 --- a/examples/whm_swifter_comparison/pl.swifter.in +++ b/examples/whm_swifter_comparison/pl.swifter.in @@ -1,40 +1,36 @@ -10 ! Planet input file generated using init_cond.py using JPL Horizons data for the major planets (and Pluto) for epoch 2021-01-28 -1 39.47692640889762629 +9 +0 39.476926408897625196 0.0 0.0 0.0 0.0 0.0 0.0 -2 6.553709809565313959502e-06 0.0014751229680863789154 +1 6.5537098095653139645e-06 0.0014751253039664285066 1.6306381826061645943e-05 -0.1030256860922895 0.2897796047098886 0.01422904600374035 --11.74004209950937 3.8343124110162736 1.3902496665973592 -3 9.6633133995815387361564e-05 0.006759127649782299051 +0.36019833403308620934 -0.07157757063116521046 -0.038889932331457412185 +0.012062683987023428416 10.539199589223686515 0.86012493216791845955 +2 9.663313399581537916e-05 0.006759112363391176217 4.0453784346544178454e-05 -0.06110218027254217 -0.7245466901305982 -0.01346904300924688 -7.311995449678243 0.5941125721336201 -0.4137913843379075 -4 0.00012002693582795246295385 0.0100447565675466429165 +-0.71276554591539231787 0.0894943770131735733 0.042358444034962597358 +-0.96047232050779632014 -7.363179644470093107 -0.045627977257453471387 +3 0.000120026935827952453094 0.010044757472678654026 4.25875607065040958e-05 --0.6061796342297583 0.7761214554702035 -3.4750047790977e-05 --5.054824314301841 -3.891667468503358 0.00019720338148272726 -5 1.2739802010675942316241e-05 0.0072464490746299085006 +0.27645830888837641393 -0.97837771886398083865 4.5542715832163949185e-05 +5.9448497026859876977 1.6852493323830119935 -9.895818943662129852e-05 +4 1.2739802010675941456e-05 0.007246754169100752911 2.265740805092889601e-05 -0.2751944175855944 1.51937688993241 0.02508924593104206 --4.835983593209577 1.344855094041679 0.14681413000004515 -6 0.037692251088985682938581 0.3552852357486060849 +-1.4965217056830220077 0.729867855162097956 0.052005223740499352536 +-2.049353987860530548 -4.1577626275819368415 -0.03686191825212072444 +5 0.037692251088985676735 0.3552713962079929143 0.00046732617030490929307 -3.200135438345358 -3.953498213518368 -0.05517737289975112 -2.111393749129838 1.8660266890185446 -0.05498941067210089 -7 0.011285899820091272946487 0.43763064566943408597 +4.027841704615886087 -3.0231618001306270749 -0.077559557972985263 +1.6231826570873460391 2.3366237981055781438 -0.046019759896080974796 +6 0.011285899820091272997 0.43765160695836118215 0.00038925687730393611812 -5.607382165725712 -8.258649105608766 -0.07958445228024298 -1.5748468603228847 1.1414574661825514 -0.08250331331320372 -8 0.0017236589478267728883093 0.4690969274244374022 +6.2788354074558432316 -7.724005035333701308 -0.11559390097316769863 +1.4696075442034620881 1.282966226939726742 -0.08077393754283409384 +7 0.0017236589478267730203 0.4695227539643713788 0.00016953449859497231466 -15.28225422201768 12.53905314208462 -0.1514143582550325 --0.9198472198098231 1.0454390993472462 0.01574538863031621 -9 0.0020336100526728304385693 0.7807192056765467829 +14.869154031353570389 12.9936724365634095335 -0.1443982771709022006 +-0.95437109658589562686 1.0170745961532793757 0.016089151184688745742 +8 0.0020336100526728302319 0.78127049251990261927 0.000164587904124493665 -29.47483071169769 -5.147686530859088 -0.5733441819169969 -0.19191677740340274 1.1385110364087574 -0.027844325148353527 -10 2.9242167710294538257026e-07 0.05383468172776979939 -7.943294877391593783e-06 -14.14000920780611 -31.14141812522779 -0.7565722591093476 -1.073396108697069 0.23003123192799815 -0.33424529561177047 +29.55509611047864027 -4.6450138458072487424 -0.585533781429422695 +0.17223467348300621534 1.1421766618084267115 -0.027457548207218328868 diff --git a/examples/whm_swifter_comparison/pl.swiftest.in b/examples/whm_swifter_comparison/pl.swiftest.in index 7bdc4a619165c705f845ed6e32d16145b1630c7f..e144eae8fefeea04d75aebc32718c9ee298c1297 100644 GIT binary patch literal 1529 zcmXw($F;;Z3`O^AAV@{!L=bjLl!U8YRd&m1ESJs@}riaW1b9Rfe8vz*QnI86yun*THae&j$uKj$0i z{509-Z@11Aw8}X2y@AYOs56H>XGR3Lh4R~oiG}}~M27<3i}(YEbtI#QB8KPt+iwoX zaO72{wjBDSnj6-q9}Gw}UP0(T8SJxSUKvPQ!w1Otr-FX4iA!1%4~W44z0rok)sv~m zTCh<^gJe7L1Eu1?vGjU=hB*8=4#FRhmND-yzGFnJWewiNLxF{kLyw)5>5aZ7ZAkPS za|s;cwp0jZtm;DJV^P7w?N*?4S~0@9G{T5sC|0AOmEQR0=d8Mx4tkOvYw6hAmslLX zqW)#A`~-}%q>H>v<59u%uYG>F#l@q-`O$C&0tg3mVfGM6Tstt>bD2@KAuBsDwD{U~ z`wzO4Ah_*zihHS!+EYk{XEFen*cy#V9j=89C>Gds-UJ4?ja5PJOX=nuEMY9bM}ecs zl@SVbn$cr|u?7AyGLu$^UF?If0N}U{ug3bsbK@pTdwF9-kt literal 700 zcmd;JU|`?`Vi4c}Vih1}0%B$$W&vVWAZ7z%b|41HgVb^XF-V;U5O)h@89a&KWpC;5 zy6y7p2QxVx4&70o{xw(3VP28sg60W&4o4nj zHuhE8I`D)RdaifZaR8YG!Uwhm9^Bu3$$p_)U{{U)^ZoUI=dJv6^@Y9STMv~u{~z|- zZrodacme-`^wP`Ab6XS+l*%z2`zNgFa5ptT*?PLj0ozx3zl>Gv55UZe(0yeor_yV` zz2t7>imBcETiu@a2w5=g|6tF0`rn3`_D&bIGE5!T?BD8tp?hikmi>~Kb2fD+9^OCe z-PNa8BOdKxHrG8*XrSYC>ds&A^=ZfztTZkN(=wwJ}Odf*9ZH( z+7^-F=AZ0SZ6vO)EIbQ0kN<~Bewp1j`xVZ|ZueE++W*h;iz?4eP5bY875e8+9kQRn z^R{4;`kMVeU)H{OFSm97`_9)Y{qhs+e;k^?ZrMC%fAo_0M7NZyaQ_}>V&wc+bK7pw znaeXjgh<*;`$Y5IFO#=lFYUp%aQiE}$0w?U(|i`&Kff+;m0z~q{<-Ge%{{g;_DxqZ zL{F_qu)pigq" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "pldiff['dr'].plot.line(x=\"time (y)\")\n", + "print()" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n" + ] + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "tpdiff['dr'].plot.line(x=\"time (y)\")\n", + "print()" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n" + ] + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "pldiff['dv'].plot.line(x=\"time (y)\")\n", + "print()" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n" + ] + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "tpdiff['dv'].plot.line(x=\"time (y)\")\n", + "print()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "swiftestOOF", + "language": "python", + "name": "swiftestoof" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.10" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/examples/whm_swifter_comparison/tp.swifter.in b/examples/whm_swifter_comparison/tp.swifter.in index 4043c1929..a9fa06f46 100644 --- a/examples/whm_swifter_comparison/tp.swifter.in +++ b/examples/whm_swifter_comparison/tp.swifter.in @@ -1,13 +1,13 @@ 4 -100001 -2.894380502059049 0.2060633316227693 -0.5267473116107563 --0.36781773526648104 3.511709550412678 0.17870205470035164 -100002 -2.402157114026988 -2.063650689101573 1.221462187067896 -1.9929454155842872 1.7670860403833373 -1.3920313116840413 -100003 --1.76242073921186 -2.766072818539065 0.6998178789690445 -2.514640194836968 -1.5368452873925367 0.24681696899054972 -100004 --2.13688673996785 1.023684546777855 0.2293900047351895 --1.3918193851434775 -3.7994616097035703 0.2829665907639622 +101 +2.3071617894844269614 1.6438449758645010679 -0.37312906258436789875 +-2.256588666826445461 2.8302735208962828827 0.50519430783805206563 +102 +3.011471099377928784 -1.1061264985150089935 0.5067865823770466571 +0.69505215270382913404 2.5183263418638507098 -1.8031340524448678953 +103 +-0.51350730399144917104 -3.139963346661017951 0.7339670445581878422 +3.0598116277417892524 -0.1107568728194456082 -0.09922455469700767241 +104 +-2.070517783632789044 -0.7764919020604850175 0.27514297675486260042 +1.7817875607764876778 -3.94088558602991294 -0.09896621676031464546 diff --git a/examples/whm_swifter_comparison/tp.swiftest.in b/examples/whm_swifter_comparison/tp.swiftest.in index 01954c3d71e48c09ad4c42e2426e7f0f5f111c6b..a9fa06f462e4b0ecb3c2ae57b64a7afb245e1e51 100644 GIT binary patch literal 559 zcmW-f$&p1d2m|kq;w3OH#3CU7#76!{+n}Y(d0;qx!x}`-=43jT$PbqpI&Rl6%V6yN z{_I8ve2rd{K`l+!`Sa_=njtyP%u3?(J7L~YZnO098Sl?-Sz%MI6JvU0F%-p5Tm!OU zyV1QqJ^B6N${bSNDU?ZTqkb~7m#J&klB~wD%ay#Nye-BTk(NqQm~cj77*p1^^OVQp zO_XQmyf*cc#8J^gXf7;vAr4S1g^3GYFhYhDzTSeebi`6AvTE>R`>##y0QByBcwIIR zAz)pc_7R3G+W5OxYj+7Gx6PXE35Cf=SAh#HU<{X%jNV4KpkPf1E!7JBxOf-RR|2I@ oPP6ob+oKHfwYN2TZ4rpOX){Vqa~1Xd35(8>4mJfS^#PLSA0*dq00000 literal 280 zcmd;JU|?VYVi4c}VgVpt*v81P2#6O0@e&{gi75bat-+UX8`RkyB+sa*%`<0qFb_B_ z_Q2@Rex?X(;Rp%F10XdZJZ-;Bvq!=ydy&cqUeQeq2i9NHKlw_5?Z9SVSsv!Z5B4B6 zAbj%2WL??Y5B6`6)p;-7{n?(s;Z@m-lxOyF-}SC6i#ZEdxBuO@)Znh``}f6U?Us7{ z-`>J;!`k1nEDrlbg3_P3eA^E*Pi>lm?r(7(2jN9r%zs?}*jJxz6@5MJ$NmFr9w_*v r^1-}T);(V{?A9DS+|HhFoXEsegZ~x!T=efMhC9u5!YW`%^ diff --git a/examples/whm_swifter_comparison/whm_swiftest_vs_swifter.ipynb b/examples/whm_swifter_comparison/whm_swiftest_vs_swifter.ipynb deleted file mode 100644 index 997defb80..000000000 --- a/examples/whm_swifter_comparison/whm_swiftest_vs_swifter.ipynb +++ /dev/null @@ -1,363 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "import numpy as np\n", - "import matplotlib.pyplot as plt\n", - "import pandas as pd\n", - "import swiftestio as swio\n", - "from astroquery.jplhorizons import Horizons" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Reading Swifter file param.swifter.in\n" - ] - } - ], - "source": [ - "inparfile = 'param.swifter.in'\n", - "param = swio.read_swifter_param(inparfile)\n", - "swifterdat = swio.swifter2xr(param)" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Reading Swiftest file param.swiftest.in\n" - ] - } - ], - "source": [ - "param_file_name = 'param.swiftest.in'\n", - "config = swio.read_swiftest_config(param_file_name)\n", - "swiftestdat = swio.swiftest2xr(config)" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "swiftdiff = swiftestdat - swifterdat" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [], - "source": [ - "swiftdiff = swiftdiff.rename({'time' : 'time (y)'})" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [], - "source": [ - "swiftdiff['dr'] = np.sqrt(swiftdiff['px']**2 + swiftdiff['py']**2 + swiftdiff['pz']**2)\n", - "swiftdiff['dv'] = np.sqrt(swiftdiff['vx']**2 + swiftdiff['vy']**2 + swiftdiff['vz']**2)" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[,\n", - " ,\n", - " ,\n", - " ,\n", - " ,\n", - " ,\n", - " ,\n", - " ,\n", - " ,\n", - " ,\n", - " ,\n", - " ,\n", - " ]" - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": "iVBORw0KGgoAAAANSUhEUgAAAZAAAAEGCAYAAABLgMOSAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuMywgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/Il7ecAAAACXBIWXMAAAsTAAALEwEAmpwYAAAfVUlEQVR4nO3de5QU5bnv8e8jA6KCQQRkLuBwlWFAEQhgtlsJbDigRoKiAS+gIYfESKKJJrB1ubNZMZHE5ESMFw5qEkSXk8SooBkxCHrkgARRQEFCGBFlYAQcQe4OMzznj244w6SBnuruKob+fdbqNV1Vb1U976D9m7eq+21zd0REROrrlKgLEBGRhkkBIiIigShAREQkEAWIiIgEogAREZFAcqIuIEytWrXywsLCqMsQEWlQ3n777U/dvXXd9VkVIIWFhSxbtizqMkREGhQz+yjRel3CEhGRQBQgIiISiAJEREQCyap7ICKSvQ4cOEB5eTn79++PupQTVtOmTSkoKKBx48ZJtVeAiEhWKC8vp3nz5hQWFmJmUZdzwnF3KisrKS8vp0OHDknto0tYIpIV9u/fz9lnn63wOAoz4+yzz67XCE0BIiJZQ+FxbPX9/ShAREQkEAWIiEhIvvKVryRcf9NNN/Hss8+GXE3qFCAiIiFZvHhx1CWkld6FJSISkmbNmrF7927cne9973ssWLCADh060FC/GVYjEBGRkD3//POsXbuW9957j8cee6zBjkwUICIiIXvjjTcYM2YMjRo1Ii8vj0GDBkVdUiAKEBGRCJwMbylWgIiIhOySSy6hpKSEmpoaKioqeO2116IuKRDdRBcRCdnIkSNZsGABPXv2pGvXrlx66aVRlxSIAkREJCS7d+8GYpevHnrooYirSZ0uYYmISCAKEBERCUQBIiIigShAREQkEAWIiIgEogAREZFAFCAiIiHYuHEjX/3qVykqKqK4uJhp06ZFXVLK9DkQEZEQ5OTk8Otf/5revXuza9cu+vTpw5AhQ+jevXvUpQUW6QjEzIaZ2VozKzOzyQm2m5k9GN/+rpn1rrO9kZktN7OXwqtaRKT+cnNz6d079hLWvHlzioqK2LRpU8RVpSayEYiZNQIeBoYA5cBbZjbH3d+v1Ww40CX+6A88Gv95yG3AGuDMUIoWkZPClBdX8/7mnWk9Zve8M/nJ14qTarthwwaWL19O//79j9/4BBblCKQfUObu6929CigBRtRpMwJ40mOWAC3MLBfAzAqAy4HHwyxaRCQVu3fv5uqrr+aBBx7gzDMb9t++Ud4DyQc21lou58jRxdHa5AMVwAPAj4HmxzqJmU0AJgC0b98+pYJF5OSQ7Egh3Q4cOMDVV1/N9ddfz1VXXRVJDekU5Qgk0WT4db/XMWEbM7sC2Orubx/vJO4+w937unvf1q1bB6lTRCRl7s748eMpKirihz/8YdTlpEWUAVIOtKu1XABsTrLNvwFXmtkGYpe+BpnZU5krVUQkNYsWLWLWrFksWLCAXr160atXL0pLS6MuKyVRXsJ6C+hiZh2ATcBo4Lo6beYAE82shNjlrc/dvQL4z/gDMxsI3OnuN4RUt4hIvV188cW4173I0rBFFiDuXm1mE4FXgEbA79x9tZl9J759OlAKXAaUAXuBm6OqV0REjhTpBwndvZRYSNReN73WcwduPc4xXgdez0B5IiJyDJrKREREAlGAiIhIIAoQEREJRAEiIiKBKEBEREKwf/9++vXrxwUXXEBxcTE/+clPoi4pZZrOXUQkBKeeeioLFiygWbNmHDhwgIsvvpjhw4czYMCAqEsLTCMQEZEQmBnNmjUDYnNiHThwALNEszU1HBqBiEj2eXkyfPJeeo/ZticMn3rMJjU1NfTp04eysjJuvfVWTecuIiLJadSoEStWrKC8vJylS5eyatWqqEtKiUYgIpJ9jjNSyLQWLVowcOBA5s6dS48ePSKtJRUagYiIhGDbtm3s2LEDgH379vHqq6/SrVu3aItKkUYgIiIhqKioYNy4cdTU1HDw4EGuvfZarrjiiqjLSokCREQkBOeffz7Lly+Puoy00iUsEREJRAEiIiKBKEBERCQQBYiIiASiABERkUAUICIiEogCREQkRDU1NVx44YUN/jMgoAAREQnVtGnTKCoqirqMtFCAiIiEpLy8nL/+9a9861vfirqUtNAn0UUk6/xi6S/4x2f/SOsxu7XsxqR+k47Z5vbbb+eXv/wlu3btSuu5o6IRiIhICF566SXatGlDnz59oi4lbTQCEZGsc7yRQiYsWrSIOXPmUFpayv79+9m5cyc33HADTz31VOi1pItGICIiIbjvvvsoLy9nw4YNlJSUMGjQoAYdHqAAERGRgHQJS0QkZAMHDmTgwIFRl5EyjUBERCQQBYiIiAQSaYCY2TAzW2tmZWY2OcF2M7MH49vfNbPe8fXtzOw1M1tjZqvN7LbwqxcRyW6RBYiZNQIeBoYD3YExZta9TrPhQJf4YwLwaHx9NXCHuxcBA4BbE+wrIiIZFOUIpB9Q5u7r3b0KKAFG1GkzAnjSY5YALcws190r3P0dAHffBawB8sMsXkQk20UZIPnAxlrL5fxrCBy3jZkVAhcCf09/iSIicjRRvo3XEqzz+rQxs2bAX4Db3X1nwpOYTSB2+Yv27dsHq1REJA0KCwtp3rw5jRo1Iicnh2XLlkVdUkqiDJByoF2t5QJgc7JtzKwxsfB42t2fO9pJ3H0GMAOgb9++dQNKRCRUr732Gq1atYq6jLSI8hLWW0AXM+tgZk2A0cCcOm3mAGPj78YaAHzu7hVmZsATwBp3/1/hli0iIhDhCMTdq81sIvAK0Aj4nbuvNrPvxLdPB0qBy4AyYC9wc3z3fwNuBN4zsxXxdXe5e2mIXRCRBuqTn/+cL9akdzr3U4u60fauu47ZxswYOnQoZsa3v/1tJkyYkNYawhbpVCbxF/zSOuum13ruwK0J9vu/JL4/IiJywlq0aBF5eXls3bqVIUOG0K1bNy655JKoywpMc2GJSNY53kghU/Ly8gBo06YNI0eOZOnSpQ06QDSViYhICPbs2XP4mwj37NnD3/72N3r06BFxVanRCEREJARbtmxh5MiRAFRXV3PdddcxbNiwiKtKjQJERCQEHTt2ZOXKlVGXkVa6hCUiIoEoQEREJBAFiIiIBKIAERGRQBQgIiISiAJEREQCUYCIiIRkx44djBo1im7dulFUVMSbb74ZdUkp0edARERCcttttzFs2DCeffZZqqqq2Lt3b9QlpUQBIiISgp07d/LGG2/whz/8AYAmTZrQpEmTaItKkQJERLLOwj/9k0837k7rMVu1a8a/X9v1qNvXr19P69atufnmm1m5ciV9+vRh2rRpnHHGGWmtI0y6ByIiEoLq6mreeecdbrnlFpYvX84ZZ5zB1KlToy4rJRqBiEjWOdZIIVMKCgooKCigf//+AIwaNarBB4hGICIiIWjbti3t2rVj7dq1AMyfP5/u3btHXFVqNAIREQnJb3/7W66//nqqqqro2LEjv//976MuKSUKEBGRkPTq1Ytly5ZFXUba6BKWiIgEogAREZFAFCAiIhKIAkRERAJRgIiISCAKEBERCUQBIiISgrVr19KrV6/DjzPPPJMHHngg6rJSos+BiIiE4LzzzmPFihUA1NTUkJ+fz8iRI6MtKkUagYiIhGz+/Pl06tSJc889N+pSUqIRiIhkndf+MIOtH61P6zHbnNuRr940Iam2JSUljBkzJq3nj4JGICIiIaqqqmLOnDlcc801UZeSMo1ARCTrJDtSyISXX36Z3r17c84550RWQ7ocdwRiZo3M7AeZOLmZDTOztWZWZmaTE2w3M3swvv1dM+ud7L4iIieiZ5555qS4fAVJBIi71wAj0n1iM2sEPAwMB7oDY8ys7uT4w4Eu8ccE4NF67CsickLZu3cv8+bN46qrroq6lLRI9hLWIjN7CPgjsOfQSnd/J4Vz9wPK3H09gJmVEAuq92u1GQE86e4OLDGzFmaWCxQmsW/a3Prbr7EjpyIThxaRkHynzy9Zv9WjLoO31iyk8otNVG7dFOp5m/oZ5J2T3nd9JRsgX4n/nBL/aYADg1I4dz6wsdZyOdA/iTb5Se4bK9RsArHRC+3btw9UaLXtY3vO/kD7isiJ4SBOjUUfIFE56AfTfsxjBoiZ/TD+9CVigWG1Nqf6L2EJ1tU95tHaJLNvbKX7DGAGQN++fQPV/L8nvhpkNxE5gaxZs4YurYuiLuOkcrwRSPP4z/OALwOzib14fw14I8VzlwPtai0XAJuTbNMkiX1FRCSDjhkg7j4FwMz+BvR2913x5f8G/pziud8CuphZB2ATMBq4rk6bOcDE+D2O/sDn7l5hZtuS2FdERDIo2Xsg7YGqWstVxG5kB+bu1WY2EXgFaAT8zt1Xm9l34tunA6XAZUAZsBe4+Vj7plKPiIjUT7IBMgtYambPE7vXMBKYmerJ3b2UWEjUXje91nMHbk12XxERCU9SU5m4+8+I/fW/HdgB3Ozu92WwLhGRk85vfvMbiouL6dGjB2PGjGH//ob97s6k58Jy93fcfVr8sTyTRYmInGw2bdrEgw8+yLJly1i1ahU1NTWUlJREXVZKNJmiiEhIqqur2bdvH9XV1ezdu5e8vLyoS0qJJlMUkayz48UPqNq85/gN66FJ3hm0+Fqno27Pz8/nzjvvpH379px22mkMHTqUoUOHprWGsGkEIiISgu3btzN79mw+/PBDNm/ezJ49e3jqqaeiLislGoGISNY51kghU1599VU6dOhA69atAbjqqqtYvHgxN9xwQ+i1pItGICIiIWjfvj1Llixh7969uDvz58+nqKhhT62iABERCUH//v0ZNWoUvXv3pmfPnhw8eJAJE6L7Yqt00CUsEZGQTJkyhSlTphy/YQOhEYiIiASiABERkUAUICIiEogCREREAlGAiIhIIAoQEREJRAEiIhKSadOm0aNHD4qLi3nggQeiLidlChARkRCsWrWKxx57jKVLl7Jy5Upeeukl1q1bF3VZKVGAiIiEYM2aNQwYMIDTTz+dnJwcLr30Up5//vmoy0qJPokuIlnn5Zdf5pNPPknrMdu2bcvw4cOPur1Hjx7cfffdVFZWctppp1FaWkrfvn3TWkPYFCAiIiEoKipi0qRJDBkyhGbNmnHBBReQk9OwX4IbdvUiIgEca6SQSePHj2f8+PEA3HXXXRQUFERSR7ooQEREQrJ161batGnDxx9/zHPPPcebb74ZdUkpUYCIiITk6quvprKyksaNG/Pwww9z1llnRV1SShQgIiIhWbhwYdQlpJXexisiIoEoQEREJBAFiIiIBKIAERGRQBQgIiISiAJEREQCiSRAzKylmc0zs3XxnwnfDG1mw8xsrZmVmdnkWuvvN7N/mNm7Zva8mbUIrXgRkYC++c1v0qZNG3r06HF43WeffcaQIUPo0qULQ4YMYfv27RFWWD9RjUAmA/PdvQswP758BDNrBDwMDAe6A2PMrHt88zygh7ufD/wT+M9QqhYRScFNN93E3Llzj1g3depUBg8ezLp16xg8eDBTp06NqLr6iypARgAz489nAl9P0KYfUObu6929CiiJ74e7/83dq+PtlgANe0IZEckKl1xyCS1btjxi3ezZsxk3bhwA48aN44UXXoigsmCi+iT6Oe5eAeDuFWbWJkGbfGBjreVyoH+Cdt8E/pj+EkXkZPXPf/6UXbvXpPWYzZsV0bXrPfXeb8uWLeTm5gKQm5vL1q1b01pXJmUsQMzsVaBtgk13J3uIBOu8zjnuBqqBp49RxwRgAkD79u2TPLWIiBxPxgLE3f/jaNvMbIuZ5cZHH7lAosgtB9rVWi4ANtc6xjjgCmCwuztH4e4zgBkAffv2PWo7EckeQUYKmXLOOedQUVFBbm4uFRUVtGmT6ILMiSmqeyBzgHHx5+OA2QnavAV0MbMOZtYEGB3fDzMbBkwCrnT3vSHUKyKSEVdeeSUzZ8ZuCc+cOZMRI0ZEXFHyogqQqcAQM1sHDIkvY2Z5ZlYKEL9JPhF4BVgD/MndV8f3fwhoDswzsxVmNj3sDoiI1NeYMWO46KKLWLt2LQUFBTzxxBNMnjyZefPm0aVLF+bNm8fkyf/yptQTViQ30d29EhicYP1m4LJay6VAaYJ2nTNaoIhIBjzzzDMJ18+fPz/kStJDn0QXEZFAFCAiIhKIAkRERAJRgIiISCAKEBERCUQBIiIigShARERCUt/p3O+77z46d+7MeeedxyuvvHJ4/dtvv03Pnj3p3Lkz3//+9zk0GccXX3zBN77xDTp37kz//v3ZsGHD4X2GDRtGixYtuOKKK9LWHwWIiEhI6jOd+/vvv09JSQmrV69m7ty5fPe736WmpgaAW265hRkzZrBu3TrWrVt3+JhPPPEEZ511FmVlZfzgBz9g0qRJh8/zox/9iFmzZqW1PwoQEZGQ1Gc699mzZzN69GhOPfVUOnToQOfOnVm6dCkVFRXs3LmTiy66CDNj7NixR+xz6FijRo1i/vz5h0cngwcPpnnz5mntT1TTuYuIROaedeWs2r0vrcfs0ew0ftql/l9NdLTp3Ddt2sSAAQMOtysoKGDTpk00btyYgoKCf1l/aJ927WJz0Obk5PClL32JyspKWrVqFbhfx6IRiIjICSjRJONmdtT1x9onUzQCEZGsE2SkkClHm869oKCAjRv//3fqlZeXk5eXR0FBAeXl5f+yvvY+BQUFVFdX8/nnn//LJbN00ghERCRCR5vO/corr6SkpIQvvviCDz/8kHXr1tGvXz9yc3Np3rw5S5Yswd158sknj9jn0LGeffZZBg0alNERCO6eNY8+ffq4iGSn999/P+oSfPTo0d62bVvPycnx/Px8f/zxx/3TTz/1QYMGeefOnX3QoEFeWVl5uP29997rHTt29K5du3ppaenh9W+99ZYXFxd7x44d/dZbb/WDBw+6u/u+fft81KhR3qlTJ//yl7/sH3zwweF9Lr74Ym/VqpU3bdrU8/Pzfe7cuQlrTPR7ApZ5gtdU86N/md9Jp2/fvr5s2bKoyxCRCKxZs4aioqKoyzjhJfo9mdnb7t63bltdwhIRkUAUICIiEogCREREAlGAiIhIIAoQEREJRAEiIiKBKEBEREIS1XTuK1as4KKLLqK4uJjzzz+fP/7xj2npjwJERCQkUU3nfvrpp/Pkk08ePtbtt9/Ojh07Uu6PAkREJCRRTefetWtXunTpAkBeXh5t2rRh27ZtKfdHkymKSNaZ8uJq3t+8M63H7J53Jj/5WnG99wt7OvelS5dSVVVFp06d6t/JOhQgIiInoETTTKU6nXtFRQU33ngjM2fO5JRTUr8ApQARkawTZKSQKWFN575z504uv/xy7r333iNGNqnQPRARkQiFMZ17VVUVI0eOZOzYsVxzzTXpKz7RFL0n60PTuYtkr2yezn3WrFmek5PjF1xwweHH8uXLE9ao6dyPQtO5i2QvTeeeHE3nLiIiGRdJgJhZSzObZ2br4j/POkq7YWa21szKzGxygu13mpmbWatE+4uISOZENQKZDMx39y7A/PjyEcysEfAwMBzoDowxs+61trcDhgAfh1KxiIgcIaoAGQHMjD+fCXw9QZt+QJm7r3f3KqAkvt8hvwF+DGTPTRwRkRNIVAFyjrtXAMR/tknQJh/YWGu5PL4OM7sS2OTuK493IjObYGbLzGxZOj66LyIiMRn7IKGZvQq0TbDp7mQPkWCdm9np8WMMTeYg7j4DmAGxd2EleW4RETmOjI1A3P0/3L1HgsdsYIuZ5QLEf25NcIhyoF2t5QJgM9AJ6ACsNLMN8fXvmFmisBIROWFENZ37Rx99RJ8+fejVqxfFxcVMnz49Lf2J6hLWHGBc/Pk4YHaCNm8BXcysg5k1AUYDc9z9PXdv4+6F7l5ILGh6u/snYRQuIhJUVNO55+bmsnjxYlasWMHf//53pk6dyubNm1PuT1QBMhUYYmbriL2TaiqAmeWZWSmAu1cDE4FXgDXAn9x9dUT1ioikLKrp3Js0acKpp54KxEYpBw8eTEt/IplM0d0rgcEJ1m8GLqu1XAqUHudYhemuT0ROci9Phk/eS+8x2/aE4VPrvVtY07lv3LiRyy+/nLKyMu6///7DEzCmQp9EFxE5ASWaZiqV6dzbtWvHu+++S1lZGTNnzmTLli0p16jp3EUk+wQYKWRKWNO5H5KXl0dxcTELFy5k1KhRKdWuEYiISITCmM69vLycffv2AbB9+3YWLVrEeeedl3LtGoGIiIRkzJgxvP7663z66acUFBQwZcoUJk+ezLXXXssTTzxB+/bt+fOf/wxAcXEx1157Ld27dycnJ4eHH36YRo0aAfDoo49y0003sW/fPoYPH87w4cMBGD9+PDfeeCOdO3emZcuWlJSUALEZdu+4447Dl8DuvPNOevbsmXJ/NJ27iGQFTeeeHE3nLiIiGacAERGRQBQgIiISiAJEREQCUYCIiEggChAREQlEASIiEpKopnM/ZOfOneTn5zNx4sS09EcBIiISkqimcz/knnvu4dJLL01bfxQgIiIhiWo6d4iNWrZs2cLQoUl9mWtSNJWJiGSdXyz9Bf/47B9pPWa3lt2Y1G/S8RvWEcZ07i1btuSOO+5g1qxZzJ8/P3Af69IIRETkBJTO6dwfeeQRLrvsssPhki4agYhI1gkyUsiUMKZzf/PNN1m4cCGPPPIIu3fvpqqqimbNmh2+3xKURiAiIhEKYzr3p59+mo8//pgNGzbwq1/9irFjx6YcHqARiIhIaKKazj1TNJ27iGQFTeeeHE3nLiIiGacAERGRQBQgIpI1sumSfRD1/f0oQEQkKzRt2pTKykqFyFG4O5WVlTRt2jTpfbLqJrqZbQM+Crh7K+DTNJbTEKjP2SEr+ty6deucn/3sZ4WFhYWnufspp5xyysGoawrTwYMHj9lnd2fDhg377r777g3btm2rrrP5XHdvXXefrAqQVJjZskTvQjiZqc/ZQX3ODpnosy5hiYhIIAoQEREJRAGSvBlRFxAB9Tk7qM/ZIe191j0QEREJRCMQEREJRAEiIiKBKEDqMLNhZrbWzMrMbHKC7WZmD8a3v2tmvaOoM52S6PP18b6+a2aLzeyCKOpMp+P1uVa7L5tZjZmNCrO+dEumv2Y20MxWmNlqM/s/YdeYbkn8d/0lM3vRzFbG+3xzFHWmk5n9zsy2mtmqo2xP7+uXu+sRfwCNgA+AjkATYCXQvU6by4CXAQMGAH+Puu4Q+vwV4Kz48+HZ0Oda7RYApcCoqOvO8L9xC+B9oH18uU3UdYfQ57uAX8SftwY+A5pEXXuK/b4E6A2sOsr2tL5+aQRypH5Ambuvd/cqoAQYUafNCOBJj1kCtDCz3LALTaPj9tndF7v79vjiEqCAhi2Zf2eA7wF/AbaGWVwGJNPf64Dn3P1jAHfPhj470Nxi3wfbjFiA1P0EdoPi7m8Q68fRpPX1SwFypHxgY63l8vi6+rZpSOrbn/HE/oJpyI7bZzPLB0YC00OsK1OS+TfuCpxlZq+b2dtmNja06jIjmT4/BBQBm4H3gNvc/WSf3iStr1/6RsIjWYJ1dd/nnEybhiTp/pjZV4kFyMUZrSjzkunzA8Akd6+J/YHaoCXT3xygDzAYOA1408yWuPs/M11chiTT5/8BrAAGAZ2AeWa20N13Zri2KKX19UsBcqRyoF2t5QJif53Ut01DklR/zOx84HFguLtXhlRbpiTT575ASTw8WgGXmVm1u78QSoXplex/15+6+x5gj5m9AVwANNQASabPNwNTPXZzoMzMPgS6AUvDKTESaX390iWsI70FdDGzDmbWBBgNzKnTZg4wNv5uhgHA5+5eEXahaXTcPptZe+A54MYG/Bdpbcfts7t3cPdCdy8EngW+20DDA5L773o28O9mlmNmpwP9gTUh15lOyfT5Y2IjLszsHOA8YH2oVYYvra9fGoHU4u7VZjYReIXYuzh+5+6rzew78e3Tib0j5zKgDNhL7K+YBivJPv8XcDbwSPwv8mpvwDOZJtnnk0Yy/XX3NWY2F3gXOAg87u4J3wraECT5b/xT4A9m9h6xSzuT3L1BT2tvZs8AA4FWZlYO/ARoDJl5/dJUJiIiEoguYYmISCAKEBERCUQBIiIigShAREQkEAWIiIgEogARCcjMWpjZd2st55nZsxk619fN7L+O0+ZXZjYoE+cXSURv4xUJyMwKgZfcvUcI51oMXHmszymY2bnAY+4+NNP1iIBGICKpmAp0in+Hxv1mVnjoexjM7CYzeyH+fRMfmtlEM/uhmS03syVm1jLerpOZzY1PYLjQzLrVPYmZdQW+cPdPzax5/HiN49vONLMNZtbY3T8CzjaztiH+DiSLKUBEgpsMfODuvdz9Rwm29yA2TXo/4GfAXne/EHgTODTb7Qzge+7eB7gTeCTBcf4NeAfA3XcBrwOXx7eNBv7i7gfiy+/E24tknKYyEcmc1+Iv+LvM7HPgxfj694DzzawZsS/r+nOtGX9PTXCcXGBbreXHgR8DLxCbiuJ/1tq2FchLVwdEjkUBIpI5X9R6frDW8kFi/++dAuxw917HOc4+4EuHFtx9Ufxy2aVAozpzVjWNtxfJOF3CEgluF9A86M7x75340MyugcPfV53o++bXAJ3rrHsSeAb4fZ31XYEGOwmiNCwKEJGA4t+LssjMVpnZ/QEPcz0w3sxWAqtJ/NW6bwAX2pHfbPU0cBaxEAEgfmO9M7AsYC0i9aK38Yo0AGY2DXjR3V+NL48CRrj7jbXajAR6u/s9EZUpWUb3QEQahp8T+5InzOy3wHBi3+tQWw7w65DrkiymEYiIiASieyAiIhKIAkRERAJRgIiISCAKEBERCUQBIiIigfw/hLGPYHYEDhoAAAAASUVORK5CYII=\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "swiftdiff['dr'].plot.line(x=\"time (y)\")" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[]" - ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": "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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "swiftdiff['dv'].sel(id=2).plot.line(x=\"time (y)\")" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[,\n", - " ,\n", - " ,\n", - " ,\n", - " ,\n", - " ,\n", - " ,\n", - " ,\n", - " ,\n", - " ,\n", - " ,\n", - " ,\n", - " ]" - ] - }, - "execution_count": 9, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": "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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "swiftdiff['pz'].plot.line(x=\"time (y)\")" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[,\n", - " ,\n", - " ,\n", - " ,\n", - " ,\n", - " ,\n", - " ,\n", - " ,\n", - " ,\n", - " ,\n", - " ,\n", - " ,\n", - " ]" - ] - }, - "execution_count": 10, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": "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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "swiftdiff['vx'].plot.line(x=\"time (y)\")" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[,\n", - " ,\n", - " ,\n", - " ,\n", - " ,\n", - " ,\n", - " ,\n", - " ,\n", - " ,\n", - " ,\n", - " ,\n", - " ,\n", - " ]" - ] - }, - "execution_count": 11, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": "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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "swiftdiff['vy'].plot.line(x=\"time (y)\")" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[,\n", - " ,\n", - " ,\n", - " ,\n", - " ,\n", - " ,\n", - " ,\n", - " ,\n", - " ,\n", - " ,\n", - " ,\n", - " ,\n", - " ]" - ] - }, - "execution_count": 12, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": "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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "swiftdiff['vz'].plot.line(x=\"time (y)\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.8.6" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -}