This notebook compares the CHAOS-6-Core models evaluated by two different implementations:
This version of the notebook corrects the error caused by the different decimal year/MJD2000 conversions:
The eoxmagmod
library converts the decimal year to MJD2000 so that a whole decimal year is always aligned with the midnight 1st of January.
The chaosmagpy
calculates the MJD2000 as $t_\text{MJD200} = 365.25 \times (t_\text{decimal year} - 2000)$
This version redefines the eoxmagmod
model to use the same conversion as the chaosmagpy
package.
In addition this version also evaluates values between the interpolation nodes.
from numpy import asarray
def decimal_year_to_mjd2000(decimal_year):
return (asarray(decimal_year) - 2000) * 365.25
def mjd2000_to_decimal_year(mjd2000):
return 2000.0 + asarray(mjd2000) / 365.25
from os.path import basename
from eoxmagmod.data import CHAOS6_CORE_LATEST
print(basename(CHAOS6_CORE_LATEST))
print(open(CHAOS6_CORE_LATEST).read())
CHAOS-6-x7_core.shc # CHAOS-6-x7 core field model # Based on model iteration_1 of CF_CHAOS_09d_18.mat # extracted on 09-Oct-2018 12:11:25 1 20 221 6 5 1997.1020 1997.2019 1997.3018 1997.4018 1997.5017 1997.6016 1997.7016 1997.8015 1997.9014 1998.0014 1998.1013 1998.2012 1998.3012 1998.4011 1998.5010 1998.6010 1998.7009 1998.8008 1998.9008 1999.0007 1999.1006 1999.2005 1999.3005 1999.4004 1999.5003 1999.6003 1999.7008 1999.8012 1999.9017 2000.0022 2000.1027 2000.2026 2000.3025 2000.4025 2000.5024 2000.6023 2000.7023 2000.8022 2000.9021 2001.0021 2001.1020 2001.2019 2001.3018 2001.4018 2001.5017 2001.6016 2001.7016 2001.8015 2001.9014 2002.0014 2002.1013 2002.2012 2002.3012 2002.4011 2002.5010 2002.6010 2002.7009 2002.8008 2002.9008 2003.0007 2003.1006 2003.2005 2003.3005 2003.4004 2003.5003 2003.6003 2003.7008 2003.8012 2003.9017 2004.0022 2004.1027 2004.2026 2004.3025 2004.4025 2004.5024 2004.6023 2004.7023 2004.8022 2004.9021 2005.0021 2005.1020 2005.2019 2005.3018 2005.4018 2005.5017 2005.6016 2005.7016 2005.8015 2005.9014 2006.0014 2006.1013 2006.2012 2006.3012 2006.4011 2006.5010 2006.6010 2006.7009 2006.8008 2006.9008 2007.0007 2007.1006 2007.2005 2007.3005 2007.4004 2007.5003 2007.6003 2007.7008 2007.8012 2007.9017 2008.0022 2008.1027 2008.2026 2008.3025 2008.4025 2008.5024 2008.6023 2008.7023 2008.8022 2008.9021 2009.0021 2009.1020 2009.2019 2009.3018 2009.4018 2009.5017 2009.6016 2009.7016 2009.8015 2009.9014 2010.0014 2010.1013 2010.2012 2010.3012 2010.4011 2010.5010 2010.6010 2010.7009 2010.8008 2010.9008 2011.0007 2011.1006 2011.2005 2011.3005 2011.4004 2011.5003 2011.6003 2011.7008 2011.8012 2011.9017 2012.0022 2012.1027 2012.2026 2012.3025 2012.4025 2012.5024 2012.6023 2012.7023 2012.8022 2012.9021 2013.0021 2013.1020 2013.2019 2013.3018 2013.4018 2013.5017 2013.6016 2013.7016 2013.8015 2013.9014 2014.0014 2014.1013 2014.2012 2014.3012 2014.4011 2014.5010 2014.6010 2014.7009 2014.8008 2014.9008 2015.0007 2015.1006 2015.2005 2015.3005 2015.4004 2015.5003 2015.6003 2015.7008 2015.8012 2015.9017 2016.0022 2016.1027 2016.2026 2016.3025 2016.4025 2016.5024 2016.6023 2016.7023 2016.8022 2016.9021 2017.0021 2017.1020 2017.2019 2017.3018 2017.4018 2017.5017 2017.6016 2017.7016 2017.8015 2017.9014 2018.0014 2018.1013 2018.2012 2018.3012 2018.4011 2018.5010 2018.6010 2018.7009 2018.8008 2018.9008 2019.0007 2019.1006 1 0 -29655.45630652 -29654.11082075 -29652.76117716 -29651.40720279 -29650.04872084 -29648.68556348 -29647.31758455 -29645.94466884 -29644.56673295 -29643.18372264 -29641.79561019 -29640.40239183 -29639.00408903 -29637.60075938 -29636.19251145 -29634.77951963 -29633.36203891 -29631.94041525 -29630.51508487 -29629.08656921 -29627.65546967 -29626.22246255 -29624.78829792 -29623.35380856 -29621.91992286 -29620.48767786 -29619.05040875 -29617.61725522 -29616.18953355 -29614.76848434 -29613.35516578 -29611.95798002 -29610.56960825 -29609.19011640 -29607.81927700 -29606.45661606 -29605.10146055 -29603.75299945 -29602.41037944 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799.23292176 799.48200450 799.73170590 799.98184249 800.23224385 800.48275974 800.73326717 800.98367728 801.23393715 801.48401878 801.73390301 801.98356325 802.23294930 802.48197341 802.73050202 802.97834974 803.22527346 803.47096650 803.71505612 803.95710921 804.19664130 804.43312563 804.66726771 804.89718023 805.12231136 805.34219465 805.55650405 805.76398662 805.96591904 806.16259555 806.35446851 806.54210507 806.72614387 806.90725482 807.08610663 807.26333741 807.43952535 807.61515973 807.79062005 807.96617557 808.14199298 808.31814445 808.49461565 808.67131752 808.84810699 809.02481134 809.20125265 809.37727191 809.55274258 809.72755776 809.90160673 810.07475128 810.24680220 810.41750468 810.58654594 810.75357167 810.91820278 811.08005207 811.23873803 811.39389147 811.54515925 811.69220796 811.83549538 811.97391411 812.10719691 812.23509414 812.35736868 812.47317287 812.58296788 812.68654214 812.78369948 812.87426567 812.95809491 813.03507825 813.10515645 813.16833473 813.22469749 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284.30221435 284.23124059 284.15751913 284.08155750 284.00404112 283.92578006 283.84765581 283.77056612 283.69536494 283.62280039 283.55345275 283.48767344 283.42555250 283.36695812 283.31160464 283.25912159 283.20885425 283.16073737 283.11444278 283.06965373 283.02604333 282.98348529 282.94135172 282.89925548 282.85686074 282.81393467 282.77039812 282.72635085 282.68203177 282.63775371 282.59383722 282.55054502 282.50803473 282.46635796 282.42547757 282.38528573 282.34562191 282.30629042 282.26707693 282.22776441 282.18814921 282.14805691 282.10735401 282.06594940 282.02379156 281.98086573 281.93719096 281.89281496 281.84780363 281.80222839 281.75615342 4 2 273.76454050 272.96779756 272.17010486 271.37142483 270.57171780 269.77094027 268.96904331 268.16597278 267.36167433 266.55610034 265.74921683 264.94101033 264.13149166 263.32069200 262.50865595 261.69543443 260.88107761 260.06562875 259.24912020 258.43157035 257.61298049 256.79333189 255.97258541 255.15068767 254.32757985 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3.06562892 3.07877199 3.09191338 3.10505307 3.11819072 3.13132561 3.14445660 3.15758212 3.17070014 3.18380819 3.19690332 3.20998209 3.22304055 3.23607417 3.24907783 3.26204574 3.27497148 3.28784797 3.30066757 3.31342211 3.32610297 3.33870109 3.35120698 3.36361075 3.37590203 3.38807000 3.40010341 3.41199053 3.42371931 3.43527731 3.44671361 3.45795123 3.46897678 3.47977678 3.49033765 3.50059036 3.51057984 3.52029287 3.52971639 3.53883746 3.54764333 3.55612148 3.56425965 3.57204589 3.57946866 3.58651687 3.59317989 3.59944763 3.60531055 3.61075960 3.61578633 3.62038283 3.62454172 3.62825617 3.63151984 3.63432688 3.63667190 3.63855001 3.63995686 3.64088863 3.64134210 3.64131466 3.64080441 3.63981018 3.63833163 3.63636931 3.63392474 3.63100041 3.62759987 3.62372773 3.61936469 3.61453765 3.60925472 3.60352510 3.59735902 3.59080476 3.58384184 3.57648313 3.56874232 3.56063394 3.55217329 3.54337643 3.53426012 3.52484176 3.51513935 3.50517144 3.49495707 3.48451565 3.47386690 3.46303070 3.45202702 3.44087580 3.42959690 3.41821000 3.40673452 3.39518959 3.38359397 3.37196602 3.36032370 3.34868456 3.33706573 3.32548391 3.31395534 3.30249578 3.29112051 3.27984424 3.26868115 3.25764484 3.24674827 3.23600380 3.22536558 3.21490407 3.20462970 3.19455214 3.18468028 3.17507427 3.16568692 3.15652464 3.14759303 3.13889694 3.13044045 3.12222688 3.11425883 3.10653813 3.09906590 3.09184252 3.08486769 3.07814041 3.07165906 3.06542142 3.05942469 3.05366558 3.04814032 3.04284473 3.03777430 3.03292424 3.02828950 3.02386485 3.01964484 3.01562385 3.01179607 3.00815553 3.00469598 3.00141084 2.99829306 2.99533506 2.99252864 2.98986500 2.98733482 2.98492825 2.98262275 2.98042100 2.97831181 2.97628370 2.97432498 2.97243400 2.97058814 2.96877566 2.96698494 2.96520465 2.96342385 2.96163210 2.95981962 2.95797731 2.95609694 2.95417120 2.95219381 2.95015955 2.94806433 2.94590522 2.94368045 2.94138945 2.93903268 2.93661146 2.93412778 2.93158415 2.92898338 2.92632856 2.92362293 2.92086985 2.91807272 2.91523493 2.91235987 2.90945091 2.90651143 9 -2 13.69038920 13.68117849 13.67193949 13.66267109 13.65337216 13.64404158 13.63467822 13.62528095 13.61584861 13.60638002 13.59687396 13.58732921 13.57774447 13.56811844 13.55844977 13.54873707 13.53897896 13.52917402 13.51932081 13.50941792 13.49946391 13.48945741 13.47939703 13.46928145 13.45910938 13.44887960 13.43853439 13.42812851 13.41766090 13.40713049 13.39653621 13.38593523 13.37526887 13.36453590 13.35373499 13.34286466 13.33192326 13.32090892 13.30981955 13.29865279 13.28740602 13.27607632 13.26466045 13.25315484 13.24155561 13.22985855 13.21805911 13.20615244 13.19413329 13.18199608 13.16973482 13.15734312 13.14481415 13.13214068 13.11931509 13.10632940 13.09317528 13.07984411 13.06632702 13.05261493 13.03869869 13.02456910 13.01021698 12.99563329 12.98080915 12.96573599 12.95032082 12.93463762 12.91867891 12.90243774 12.88590772 12.86917552 12.85214673 12.83481682 12.81718184 12.79923840 12.78098367 12.76241536 12.74353175 12.72433179 12.70481506 12.68498190 12.66483341 12.64437145 12.62359864 12.60251835 12.58113469 12.55945252 12.53747736 12.51521541 12.49267349 12.46985899 12.44677987 12.42344458 12.39986203 12.37604158 12.35199297 12.32772634 12.30325222 12.27858160 12.25372599 12.22869743 12.20350860 12.17817272 12.15270360 12.12711555 12.10128239 12.07535955 12.04936300 12.02330903 11.99721419 11.97123757 11.94525361 11.91927886 11.89332983 11.86742289 11.84157422 11.81579970 11.79011494 11.76453521 11.73907543 11.71375017 11.68857359 11.66355941 11.63872082 11.61407044 11.58962028 11.56538164 11.54136518 11.51758093 11.49403832 11.47074627 11.44771315 11.42494684 11.40245480 11.38024400 11.35832105 11.33669213 11.31536308 11.29433938 11.27362617 11.25322828 11.23315025 11.21339631 11.19397040 11.17487616 11.15601509 11.13749587 11.11932138 11.10149431 11.08401720 11.06698484 11.05030339 11.03397515 11.01800245 11.00238762 10.98713308 10.97224132 10.95771494 10.94355666 10.92976937 10.91635612 10.90332017 10.89066497 10.87839425 10.86651200 10.85502249 10.84393031 10.83324033 10.82295765 10.81308760 10.80363567 10.79460748 10.78600874 10.77784522 10.77012268 10.76284685 10.75602339 10.74965779 10.74375539 10.73832127 10.73336020 10.72887663 10.72487453 10.72135738 10.71832802 10.71577603 10.71372075 10.71216274 10.71110173 10.71053656 10.71046428 10.71087758 10.71177264 10.71314463 10.71498766 10.71729473 10.72005768 10.72326728 10.72691335 10.73098483 10.73546993 10.74035622 10.74563065 10.75127969 10.75728931 10.76364508 10.77033221 10.77733564 10.78464019 10.79223062 10.80009173 10.80820847 10.81656587 10.82514906 10.83394312 10.84293313 10.85210405 10.86144080 10.87092828 10.88055141 9 3 -9.29524354 -9.26520184 -9.23520409 -9.20525204 -9.17534743 -9.14549202 -9.11568754 -9.08593571 -9.05623820 -9.02659664 -8.99701255 -8.96748738 -8.93802241 -8.90861880 -8.87927754 -8.84999940 -8.82078496 -8.79163454 -8.76254822 -8.73352584 -8.70456695 -8.67567082 -8.64683645 -8.61806250 -8.58934731 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12.61165361 12.61573924 12.61974723 12.62368045 12.62754208 12.63133555 12.63506453 12.63873294 12.64234488 12.64590465 12.64941673 12.65288572 12.65631633 12.65973190 12.66311845 12.66648092 12.66982419 12.67315306 12.67645408 12.67974978 12.68304425 12.68634129 12.68964434 12.69295641 12.69628010 12.69961753 12.70297031 12.70633960 12.70972602 12.71312967 12.71655012 12.71998638 12.72343690 12.72689957 12.73037168 12.73384995 12.73733054 12.74080906 12.74428062 12.74773977 12.75118054 12.75459644 12.75798039 12.76132474 12.76462127 12.76786118 12.77103513 12.77413323 12.77714512 12.78005994 12.78286639 12.78555276 12.78810698 12.79052941 12.79279277 12.79488388 12.79678929 12.79849533 12.79998061 12.80124114 12.80226295 12.80303185 12.80353353 12.80375349 12.80367711 12.80328964 12.80257625 12.80152213 12.80011245 12.79833247 12.79616750 12.79360297 12.79062436 12.78721724 12.78336732 12.77906040 12.77428247 12.76901969 12.76325848 12.75698549 12.75018768 12.74285230 12.73496696 12.72651964 12.71749872 12.70789301 12.69769181 12.68688495 12.67546281 12.66341639 12.65073733 12.63741791 12.62345111 12.60874871 12.59337979 12.57733944 12.56062353 12.54322867 12.52525262 12.50660060 12.48727155 12.46726519 12.44658202 12.42522334 12.40319127 12.38048873 12.35711953 12.33308832 12.30840065 12.28306301 12.25708281 12.23046848 12.20322947 12.17537632 12.14692066 12.11787523 12.08825392 12.05807171 12.02734471 11.99609015 11.96432622 11.93207203 11.89934746 11.86617303 11.83256980 11.79855921 11.76416297 11.72940290 11.69430079 11.65887828 11.62315672 11.58715703 11.55089963 11.51420365 11.47728635 11.44016614 11.40286052 11.36538601 11.32796350 11.29040336 11.25271863 11.21492121 11.17702183 11.13903007 11.10095430 11.06280184 11.02457901 10.98629127 10.94794336 10.90953937 10.87108286 10.83257692 10.79402427 10.75542730 10.71678822 10.67810903 10.63939165 10.60063795 10.56184977 10.52302905 10.48417773 10.44529778 10.40639118 10.36745987 10.32850576 10.28953074 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9.22227345 9.24459647 9.26675048 9.28874491 9.31058916 9.33229259 9.35386460 9.37531453 9.39665179 9.41788575 9.43902580 9.46008132 10 0 -2.89823223 -2.88792989 -2.87764849 -2.86738888 -2.85715189 -2.84693835 -2.83674909 -2.82658495 -2.81644677 -2.80633538 -2.79625162 -2.78619632 -2.77617031 -2.76617444 -2.75620954 -2.74627645 -2.73637600 -2.72650904 -2.71667639 -2.70687891 -2.69711742 -2.68739277 -2.67770580 -2.66805734 -2.65844824 -2.64887933 -2.63929937 -2.62976175 -2.62026733 -2.61081696 -2.60141151 -2.59210271 -2.58284003 -2.57362431 -2.56445640 -2.55533714 -2.54626740 -2.53724800 -2.52827981 -2.51936367 -2.51050044 -2.50169095 -2.49293607 -2.48423664 -2.47559351 -2.46700754 -2.45847957 -2.45001045 -2.44160105 -2.43325220 -2.42496475 -2.41673957 -2.40857749 -2.40047937 -2.39244606 -2.38447840 -2.37657723 -2.36874340 -2.36097775 -2.35328109 -2.34565427 -2.33809811 -2.33061341 -2.32320098 -2.31586164 -2.30859615 -2.30136613 -2.29421235 -2.28713560 -2.28013665 -2.27321623 -2.26641215 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1.89548467 1.90424532 1.91307910 1.92198541 1.93096363 1.94001324 1.94913374 1.95832473 1.96758587 1.97691689 1.98631765 1.99578804 2.00532808 2.01493787 2.02461756 2.03436741 2.04418772 2.05413324 2.06415077 2.07424073 2.08440356 2.09463971 2.10489321 2.11522002 2.12562049 2.13609488 2.14664340 2.15726616 2.16796317 2.17873436 2.18957954 2.20049840 2.21149053 2.22255542 2.23369241 2.24490075 2.25617954 2.26752776 2.27894425 2.29042773 2.30197677 2.31358981 2.32526515 2.33700096 2.34879528 2.36064600 2.37255090 2.38450762 2.39651365 2.40856640 2.42066316 2.43280117 2.44497758 2.45718950 2.46943401 2.48170816 2.49400896 2.50640101 2.51881389 2.53124454 2.54368989 2.55614688 2.56854453 2.58094776 2.59335367 2.60575944 2.61816230 2.63055958 2.64294872 2.65532722 2.66769275 2.68004304 2.69237597 2.70468953 2.71698184 2.72925110 2.74149557 2.75371360 2.76590357 2.77806390 2.79019304 2.80228949 2.81435172 2.82637828 2.83836767 2.85031841 2.86222900 2.87409793 2.88592365 2.89770454 2.90943899 2.92112530 2.93276174 2.94434650 2.95587774 2.96735353 2.97877191 2.99019291 3.00155167 3.01284599 3.02407363 3.03523228 3.04625931 3.05721336 3.06809194 3.07889254 3.08961255 3.10024931 3.11080005 3.12126193 3.13163200 3.14190722 3.15208443 3.16216035 3.17213158 3.18199455 3.19174558 3.20138080 3.21089618 3.22028751 3.22955037 3.23868016 3.24767208 3.25652109 3.26522196 3.27376926 3.28215737 3.29038049 3.29843267 3.30630786 3.31399998 3.32150299 3.32881094 3.33591809 3.34281891 3.34950814 3.35598088 3.36226626 3.36832407 3.37415065 3.37974286 3.38509810 3.39018710 3.39503829 3.39965085 3.40402452 3.40815969 3.41205730 3.41571889 3.41914661 3.42234320 3.42531196 3.42805681 3.43058223 3.43289328 3.43499548 3.43689489 3.43859797 3.44011159 3.44144302 3.44259986 3.44359001 3.44442165 3.44510322 3.44564331 3.44605068 3.44633416 3.44650262 3.44656488 3.44652979 3.44640617 3.44620286 10 2 1.52828473 1.53042983 1.53253441 1.53459685 1.53661553 1.53858884 1.54051517 1.54239292 1.54422051 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4.89643546 4.89430011 4.89215041 4.88998587 4.88780596 4.88561006 4.88339748 4.88116744 4.87891908 4.87665143 4.87436345 4.87205398 4.86972177 4.86736543 4.86498350 4.86257436 4.86013627 4.85766736 4.85516562 4.85262893 4.85005502 4.84744153 4.84478598 4.84208579 4.83933831 4.83654080 4.83369051 4.83078461 4.82782027 4.82479464 4.82170487 4.81854812 4.81532158 4.81202245 4.80864800 4.80519555 4.80164287 4.79800606 4.79428261 4.79047014 4.78656635 4.78259119 4.77852169 4.77435613 4.77009301 4.76573108 4.76126934 4.75670712 4.75204403 4.74728001 4.74241524 4.73745026 4.73238584 4.72722306 4.72196327 4.71660808 4.71115934 4.70561917 4.69998990 4.69427412 4.68847463 4.68259446 4.67663684 4.67060524 4.66450330 4.65833489 4.65210406 4.64581504 4.63947226 4.63308028 4.62664379 4.62016761 4.61365665 4.60711592 4.60055049 4.59396550 4.58732997 4.58068528 4.57403681 4.56738998 4.56075026 4.55415922 4.54758608 4.54103630 4.53451533 4.52802865 4.52158175 4.51518010 4.50882917 4.50253439 4.49630120 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/usr/local/conda/lib/python3.7/site-packages/spacepy/pycdf/__init__.py:1209: DeprecationWarning: Using or importing the ABCs from 'collections' instead of from 'collections.abc' is deprecated, and in 3.8 it will stop working class CDF(collections.MutableMapping):
# generate random points
from math import pi
from numpy import sin, cos, dstack, linspace
from numpy.random import random, uniform
EARTH_RADIUS = 6371.2 # km
N_coords = 500
X = random((N_coords*2, 2))
X = X[X[..., 1] < sin(X[..., 0]*pi)][:N_coords, ...]
X[..., 0] = pi * (X[..., 0] - 0.5)
X[..., 1] = pi*(2.0*X[..., 1] / cos(X[..., 0]) - 1.0)
X *= 180./pi
lats = X[..., 0]
lons = X[..., 1]
rads = uniform(EARTH_RADIUS, 1.1*EARTH_RADIUS, N_coords)
coords = dstack((lats, lons, rads))[0]
#generate times
from eoxmagmod import load_model_shc
model_vires = load_model_shc(CHAOS6_CORE_LATEST)
N_times = 1000
start, end = model_vires.validity
times = linspace(start, end, num=N_times)
#times = decimal_year_to_mjd2000(model_vires.coefficients._times)
#N_times = len(times)
from numpy import empty
from eoxmagmod.magnetic_model.model import SphericalHarmomicGeomagneticModel
from eoxmagmod.magnetic_model.coefficients import (
SparseSHCoefficientsConstant,
SparseSHCoefficientsTimeDependent,
)
from eoxmagmod.magnetic_model.util import parse_file
from eoxmagmod.magnetic_model.parser_shc import parse_shc_file
def load_coeff_shc_mjd2000(path):
""" Load coefficients from an SHC file (interpolated in the MJD2000 time domain) """
data = parse_file(parse_shc_file, path)
options = {
key: decimal_year_to_mjd2000(data[key])
for key in ("validity_start", "validity_end") if key in data
}
times = data["t"]
if len(times) == 1:
return SparseSHCoefficientsConstant(
data["nm"], data["gh"][:, 0], **options
)
else:
return SparseSHCoefficientsTimeDependent(
data["nm"], data["gh"], decimal_year_to_mjd2000(times), **options
)
model_vires = SphericalHarmomicGeomagneticModel(
load_coeff_shc_mjd2000(CHAOS6_CORE_LATEST)
)
b_nec_vires = empty((N_times, N_coords, 3))
for idx, time in enumerate(times):
b_nec_vires[idx, ...] = model_vires.eval(time, coords, scale=[1, 1, -1])
from chaosmagpy import load_CHAOS_shcfile
model_ref = load_CHAOS_shcfile(CHAOS6_CORE_LATEST)
b_nec_ref = empty((N_times, N_coords, 3))
for idx, time in enumerate(times):
a, b, c = model_ref(time, rads, 90.0 - lats, lons, source_list=['tdep'])
b_nec_ref[idx, :, 0] = -b
b_nec_ref[idx, :, 1] = c
b_nec_ref[idx, :, 2] = -a
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from numpy import zeros, concatenate, isnan
from matplotlib import pyplot as plt
from eoxmagmod import vnorm
node_times = mjd2000_to_decimal_year(model_vires.coefficients._times)
node_val = zeros(node_times.shape)
delta_b_nec = b_nec_vires - b_nec_ref
delta_f = vnorm(b_nec_vires) - vnorm(b_nec_ref)
fig = plt.figure(figsize=(16, 16), dpi=100)
plt.title("eoxmagmod vs chaosmagpy (CHAOS-6-Core, x7)")
times_dy = mjd2000_to_decimal_year(times)
def plot(ax, x0, y0, label):
x, y = [], []
for idx in range(0, N_coords):
x.append(x0)
y.append(y0[:, idx])
x, y = concatenate(x), concatenate(y)
plt.plot(x, y, '.', markersize=2, color='#1f77b4', label='random samples')
plt.plot(node_times, node_val, '+r', markersize=2, label='interpolation nodes')
plt.ylabel(label)
plt.legend(loc='upper right')
plt.text(
0.01, 0.95, 'max: %g nT' % y[~isnan(y)].max(),
horizontalalignment='left', verticalalignment='top', transform=ax.transAxes
)
plt.text(
0.01, 0.05, 'min: %g nT' % y[~isnan(y)].min(),
horizontalalignment='left', verticalalignment='bottom', transform=ax.transAxes
)
plt.grid()
ax = plt.subplot(4, 1, 1)
plot(ax, times_dy, delta_b_nec[..., 0], "delta B_N / nT")
ax = plt.subplot(4, 1, 2)
plot(ax, times_dy, delta_b_nec[..., 1], "delta B_E / nT")
ax = plt.subplot(4, 1, 3)
plot(ax, times_dy, delta_b_nec[..., 2], "delta B_C / nT")
ax = plt.subplot(4, 1, 4)
plot(ax, times_dy, delta_f, "delta F / nT")
plt.show()
%matplotlib inline
from cartopy.feature import LAND, OCEAN, COASTLINE
from cartopy.crs import Mollweide, PlateCarree
from matplotlib import pyplot as plt
from matplotlib.cm import winter as colormap
from matplotlib.colors import Normalize
from matplotlib.colorbar import ColorbarBase
from mpl_toolkits.mplot3d import Axes3D
from eoxmagmod import convert, GEOCENTRIC_SPHERICAL, GEOCENTRIC_CARTESIAN
%matplotlib inline
norm = Normalize(vmin=1,vmax=1.1)
#help(ccrs)
fig = plt.figure(figsize=(16, 16), dpi=100, facecolor='w', edgecolor='k')
ax = plt.subplot(2, 1, 1, projection=Mollweide())
gl = ax.gridlines(crs=PlateCarree(), draw_labels=False, linewidth=1, color='silver', alpha=0.5, linestyle='--')
ax.add_feature(LAND, facecolor=(1.0, 1.0, 0.9))
ax.add_feature(OCEAN, facecolor=(0.9, 1.0, 1.0))
ax.add_feature(COASTLINE, edgecolor='silver')
obj = ax.scatter(
lons, lats, c=rads/EARTH_RADIUS, s=1.5,
cmap=colormap, norm=norm, transform=PlateCarree(),
)
cb = plt.colorbar(obj)
cb.ax.set_ylabel("r/R (R = %g km)" % EARTH_RADIUS)
ax = plt.subplot(2, 1, 2, projection='3d')
cart_coords = convert(coords, GEOCENTRIC_SPHERICAL, GEOCENTRIC_CARTESIAN)/EARTH_RADIUS
obj = ax.scatter(
cart_coords[:, 0], cart_coords[:, 1], cart_coords[:, 2],
c=rads/EARTH_RADIUS, s=1.5,
cmap=colormap, norm=norm,
)
ax.set_aspect('equal','box')
ax.set_xlabel("x/R")
ax.set_ylabel("y/R")
ax.set_zlabel("z/R")
plt.show()