From cd74dab6503283bdf33ff2f5bad0ba7d4e6ae84f Mon Sep 17 00:00:00 2001
From: Fasil Kebede <46403603+FasilGibdaw@users.noreply.github.com>
Date: Fri, 24 Apr 2026 18:42:59 +0200
Subject: [PATCH 1/3] Update data and data_tools folders
---
lompe/data/sdarn_2010_to_2021.csv | 190 +++++
lompe/data/supermag_stations.csv | 596 +++++++++++++
lompe/data_tools/README | 6 +-
lompe/data_tools/datadownloader.py | 1262 ++++++++++++++++++++++++++--
lompe/data_tools/dataloader.py | 75 +-
lompe/data_tools/supermag_api.py | 551 ++++++++++++
6 files changed, 2590 insertions(+), 90 deletions(-)
create mode 100644 lompe/data/sdarn_2010_to_2021.csv
create mode 100644 lompe/data/supermag_stations.csv
create mode 100644 lompe/data_tools/supermag_api.py
diff --git a/lompe/data/sdarn_2010_to_2021.csv b/lompe/data/sdarn_2010_to_2021.csv
new file mode 100644
index 0000000..36fa4ed
--- /dev/null
+++ b/lompe/data/sdarn_2010_to_2021.csv
@@ -0,0 +1,190 @@
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\ No newline at end of file
diff --git a/lompe/data/supermag_stations.csv b/lompe/data/supermag_stations.csv
new file mode 100644
index 0000000..7db7465
--- /dev/null
+++ b/lompe/data/supermag_stations.csv
@@ -0,0 +1,596 @@
+IAGA,GEOLON,GEOLAT,AACGMLON,AACGMLAT,STATION-NAME,OPERATOR-NUM,OPERATORS
+SON,66.44,25.12,139.81,19.36,Sonmiani,1,INTERMAGNET
+AMU,210.13,61.24,-93.38,61.32,Anchorage,1,THEMIS
+YKC,245.52,62.48,-56.85,69.42,Yellowknife,3,CANMOS,INTERMAGNET,THEMIS
+CBB,254.97,69.12,-47.81,77.05,Cambridge Bay,2,CANMOS,INTERMAGNET
+RES,265.11,74.69,-35.54,82.93,Resolute Bay,2,CANMOS,INTERMAGNET
+BLC,263.99,64.32,-30.06,73.60,Baker Lake,2,CANMOS,INTERMAGNET
+MEA,246.65,54.62,-52.10,61.85,Meanook,2,CANMOS,INTERMAGNET
+SIT,224.67,57.06,-77.83,59.87,Sitka,2,INTERMAGNET,USGS
+PBQ,282.26,55.28,0.20,65.01,Poste de la Baleine,2,CANMOS,INTERMAGNET
+MEK,30.97,62.77,108.66,59.57,Mekrijaervi,2,IMAGE,EMMA
+STJ,307.32,47.60,31.64,52.60,St Johns,2,CANMOS,INTERMAGNET
+ALE,297.50,82.50,86.95,87.14,Alert,2,CANMOS,INTERMAGNET
+LRV,338.30,64.18,66.72,65.01,Leirvogur,1,INTERMAGNET
+BOU,254.76,40.14,-38.69,48.52,Boulder,2,INTERMAGNET,USGS
+FRN,240.28,37.09,-54.89,42.63,Fresno,2,INTERMAGNET,USGS
+VIC,236.58,48.52,-62.05,53.62,Victoria,2,CANMOS,INTERMAGNET
+NEW,242.88,48.27,-54.82,54.65,Newport,2,INTERMAGNET,USGS
+LOV,17.83,59.35,96.13,56.27,Lovo,1,INTERMAGNET
+LER,358.82,60.13,80.96,58.20,Lerwick,3,BGS,SAMNET,INTERMAGNET
+VAL,349.75,51.93,70.41,49.13,Valentia,1,INTERMAGNET
+HAD,355.52,50.98,74.74,47.37,Hartland,3,BGS,SAMNET,INTERMAGNET
+ESK,356.80,55.32,77.25,52.74,Eskdalemuir,3,BGS,SAMNET,INTERMAGNET
+MAB,5.68,50.30,82.86,45.98,Manhay,1,INTERMAGNET
+DOU,4.60,50.10,81.91,45.76,Dourbes,1,INTERMAGNET
+BFE,11.67,55.62,89.56,52.27,Brorfelde,3,DTUSpace,INTERMAGNET,TROMSOE
+CLF,2.27,48.02,79.42,43.11,Chambon la foret,2,INTERMAGNET,BCMT
+OTT,284.45,45.40,2.52,54.98,Ottawa,2,CANMOS,INTERMAGNET
+WNG,9.07,53.75,86.75,50.15,Wingst,2,GFZ,INTERMAGNET
+VRE,292.38,-17.28,3.97,-4.53,Villa Remedios,1,GFZ
+SHE,354.27,-15.95,63.70,-25.71,St. Helena,1,GFZ
+SMA,334.87,36.99,55.49,30.34,Santa Maria/Azoren,1,GFZ
+PNL,303.83,-16.68,14.21,-7.91,Pantanal,1,GFZ
+THY,17.54,46.90,92.25,41.72,Tihany,2,INTERMAGNET,EMMA
+BEL,20.80,51.83,96.27,47.71,Belsk,2,INTERMAGNET,EMMA
+FUR,11.28,48.17,87.05,43.20,Furstenfeldbruk,1,INTERMAGNET
+NGK,12.68,52.07,89.28,48.03,Niemegk,2,GFZ,INTERMAGNET
+BDV,14.02,49.07,89.64,44.35,Budkov,1,INTERMAGNET
+FRD,282.63,38.20,-0.64,48.05,Fredericksburg,2,INTERMAGNET,USGS
+CRP,275.09,10.44,-12.58,21.63,Chiripa,1,INTERMAGNET
+HYB,78.60,17.40,151.87,10.36,Hyderabad,2,GFZ,INTERMAGNET
+IPM,250.58,-27.20,-32.30,-19.28,Isla de Pascua,2,INTERMAGNET,BCMT
+KMH,18.11,-26.54,86.27,-36.25,Keetmanshoop,2,GFZ,INTERMAGNET
+NMP,39.25,-15.09,110.85,-24.90,Nampula,1,INTERMAGNET
+DED,211.21,70.36,-99.27,70.87,Deadhorse,2,INTERMAGNET,USGS
+KEP,323.51,-54.28,25.94,-45.16,King Edward Point,2,INTERMAGNET,BGS
+CDP,256.30,31.00,-35.65,39.45,Chengdu,1,INTERMAGNET
+DMC,235.83,-75.25,-14.70,-65.48,Dome Concordia,2,INTERMAGNET,BCMT
+LON,16.66,45.41,91.18,39.82,Lonjsko Polje,1,INTERMAGNET
+SPG,29.72,60.54,106.73,57.26,Saint Petersburg,1,INTERMAGNET
+TAB,291.18,76.54,27.24,83.89,Thule Air Base,1,DTUSpace
+ROE,8.55,55.17,86.80,51.86,Roemoe,2,DTUSpace,TROMSOE
+THL,290.77,77.47,29.24,84.72,Qaanag,2,DTUSpace,INTERMAGNET
+SVS,294.90,76.02,32.87,83.00,Savissivik,1,DTUSpace
+KUV,302.82,74.57,41.92,80.69,Kullorsuaq,1,DTUSpace
+UPN,303.85,72.78,40.20,78.93,Upernavik,1,DTUSpace
+UMQ,307.87,70.68,42.58,76.38,Uummannaq,1,DTUSpace
+GDH,306.47,69.25,39.39,75.25,Godhavn,2,DTUSpace,INTERMAGNET
+ATU,306.43,67.93,38.19,73.99,Attu,1,DTUSpace
+STF,309.28,67.02,40.87,72.64,Kangerlussuaq,1,DTUSpace
+SKT,307.10,65.42,37.22,71.43,Maniitsoq,1,DTUSpace
+GHB,308.27,64.17,37.85,69.98,Nuuk,1,DTUSpace
+FHB,310.32,62.00,39.05,67.41,Paamiut,1,DTUSpace
+NAQ,314.56,61.16,43.19,65.75,Narssarssuaq,2,DTUSpace,INTERMAGNET
+NRD,343.33,81.60,101.64,81.27,Nord,1,DTUSpace
+DMH,341.37,76.77,84.38,77.34,Danmarkshavn,1,DTUSpace
+DNB,339.78,74.30,78.39,75.19,Daneborg,1,DTUSpace
+SCO,338.03,70.48,71.82,71.63,Ittoqqortoormiit,1,DTUSpace
+AMK,322.37,65.60,53.57,68.99,Tasiilaq,1,DTUSpace
+MCN,322.38,73.93,62.90,77.20,Magic1 north,1,DTUSpace
+MCE,326.10,72.40,63.93,75.20,Magic1 east,1,DTUSpace
+MCW,317.41,72.00,54.95,76.13,Magic1 west,1,DTUSpace
+MCG,321.65,72.60,60.05,76.06,Magic2 GISP,1,DTUSpace
+MCR,313.71,66.48,45.42,71.35,Magic2 Raven,1,DTUSpace
+TDC,347.69,-37.10,49.99,-39.80,Tristan de Cunha,4,DTUSpace,INTERMAGNET,TROMSOE,GFZ
+SUM,321.70,72.30,59.66,75.76,Summit,2,DTUSpace,THEMIS
+HOV,353.20,61.50,77.18,60.26,Faroe Island,2,DTUSpace,THEMIS
+GIM,265.36,56.38,-26.08,66.16,Gillam,3,CARISMA,THEMIS,CANOPUS
+TAL,266.45,69.54,-27.95,78.51,Taloyoak,1,CARISMA
+CNL,248.75,65.75,-54.49,73.08,Contwoyto Lake,1,CARISMA
+DAW,220.89,64.05,-85.34,66.24,Dawson City,1,CARISMA
+EKP,265.95,61.11,-25.96,70.73,Eskimo Point,2,CARISMA,CANOPUS
+FCC,265.91,58.76,-25.59,68.50,Fort Churchill,4,CARISMA,CANOPUS,INTERMAGNET,CANMOS
+FMC,248.79,56.66,-50.02,64.28,Fort McMurray,2,CARISMA,BGS
+FSP,238.77,61.76,-64.89,67.47,Fort Simpson,2,CARISMA,THEMIS
+SMI,248.07,60.03,-52.27,67.48,Fort Smith,2,CARISMA,THEMIS
+ISL,265.34,53.86,-25.79,63.70,Island Lake,2,CARISMA,CANOPUS
+PIN,263.96,50.20,-27.43,59.96,Pinawa,3,CARISMA,THEMIS,CANOPUS
+RAL,256.32,58.22,-40.08,67.00,Rabbit Lake,1,CARISMA
+RAN,267.89,62.82,-23.12,72.45,Rankine Inlet,3,CARISMA,THEMIS,CANOPUS
+NOR,25.79,71.09,109.28,68.19,Nordkapp,2,TROMSOE,IMAGE
+NAL,11.95,78.92,109.96,76.57,New Aalesund,2,TROMSOE,IMAGE
+LYR,15.83,78.20,111.03,75.64,Longyearbyen,2,TROMSOE,IMAGE
+JAN,351.30,70.90,82.78,70.47,Jan Mayen,1,TROMSOE
+HOP,25.01,76.51,114.59,73.53,Hopen Island,2,TROMSOE,IMAGE
+BJN,19.20,74.50,107.71,71.89,Bear Island,2,TROMSOE,IMAGE
+SOR,22.22,70.54,106.04,67.80,Soeroeya,2,TROMSOE,IMAGE
+TRO,18.94,69.66,102.77,67.07,Tromso,2,TROMSOE,IMAGE
+AND,16.03,69.30,100.22,66.86,Andenes,2,TROMSOE,IMAGE
+JCK,16.98,66.40,98.94,63.82,Jaeckvik,2,TROMSOE,IMAGE
+DON,12.50,66.11,95.19,63.75,Doenna,2,TROMSOE,IMAGE
+RVK,10.99,64.94,93.27,62.61,Roervik,2,TROMSOE,IMAGE
+DOB,9.11,62.07,90.19,59.64,Dombas,2,TROMSOE,IMAGE
+SOL,4.84,61.08,86.25,58.82,Solund,2,TROMSOE,IMAGE
+KAR,5.24,59.21,85.69,56.70,Karmoey ,2,TROMSOE,IMAGE
+HRN,15.60,77.00,108.72,74.52,Hornsund,2,IMAGE,INTERMAGNET
+ALT,22.96,69.86,106.08,67.08,Alta,1,IMAGE
+KEV,27.01,69.76,109.22,66.82,Kevo,2,IMAGE,EMMA
+MAS,23.70,69.46,106.36,66.65,Masi,2,IMAGE,EMMA
+KIL,20.77,69.06,103.75,66.37,Kilpisjarvi,3,IMAGE,EMMA,SAMNET
+KAU,23.05,69.02,105.53,66.23,Kautokeino,1,IMAGE
+IVA,27.29,68.56,108.61,65.60,Ivalo,2,IMAGE,EMMA
+ABK,18.82,68.35,101.70,65.74,Abisko,2,IMAGE,INTERMAGNET
+LEK,13.54,68.13,97.39,65.79,Leknes,1,IMAGE
+MUO,23.53,68.02,105.23,65.19,Muonio,2,IMAGE,EMMA
+LOZ,35.02,67.97,114.60,64.77,Lovozero,2,IMAGE,AARI
+KIR,20.42,67.84,102.62,65.14,Kiruna,1,IMAGE
+SOD,26.63,67.37,107.33,64.41,Sodankyla,2,IMAGE,INTERMAGNET
+PEL,24.08,66.90,104.97,64.03,Pello,2,IMAGE,EMMA
+LYC,18.75,64.61,99.33,61.87,Lycksele,2,IMAGE,INTERMAGNET
+OUJ,27.23,64.52,106.27,61.47,Oulujarvi,2,IMAGE,SAMNET
+HAN,26.60,62.25,104.72,59.12,Hankasalmi,3,IMAGE,EMMA,SAMNET
+NUR,24.65,60.50,102.35,57.32,Nurmijarvi,4,IMAGE,EMMA,SAMNET,INTERMAGNET
+UPS,17.35,59.90,95.95,56.88,Uppsala,3,IMAGE,SAMNET,INTERMAGNET
+TAR,26.46,58.26,103.11,54.88,Tartu,2,IMAGE,EMMA
+BRW,203.38,71.32,-106.49,70.64,Barrow,3,GIMA,USGS,INTERMAGNET
+KAV,216.35,70.14,-94.68,71.50,Kaktovik,2,GIMA,INTERMAGNET
+KOT,197.40,66.88,-107.15,65.18,Kotzebue,2,GIMA,210MM
+ARC,214.44,68.12,-94.35,69.17,Arctic Village,1,GIMA
+INK,226.23,68.41,-83.67,71.57,Inuvik,3,GIMA,AUTUMN,THEMIS
+BET,208.45,66.90,-98.41,66.91,Bettles,1,GIMA
+FYU,214.78,66.56,-92.75,67.65,Fort Yukon,2,GIMA,THEMIS
+EAG,218.84,64.78,-87.77,66.59,Eagle,1,GIMA
+CMO,212.14,64.87,-93.82,65.45,College,3,GIMA,USGS,INTERMAGNET
+CGO,212.14,64.87,-93.82,65.46,College Intl Geophys Observatory,2,THEMIS,GIMA
+GAK,214.87,62.39,-89.85,63.40,Gakona,2,GIMA,THEMIS
+TLK,209.90,63.30,-94.74,63.43,Talkeetna,1,GIMA
+CPY,235.30,70.20,-75.95,74.91,Cape Perry,1,GIMA
+PKR,212.57,65.12,-93.62,65.79,Poker Flat,1,GIMA
+DIK,80.70,73.53,156.75,69.34,Dixon,3,RAPIDMAG,GIMA,AARI
+CCS,104.28,77.72,176.59,72.77,Chelyuskin,3,RAPIDMAG,GIMA,AARI
+TIK,128.92,71.59,-161.36,66.71,Tixie,5,RAPIDMAG,GIMA,210MM,AARI,INTERMAGNET
+CWE,190.17,66.17,-112.14,63.47,Wellen,1,AARI
+ARK,40.50,64.60,117.67,61.29,Arkhangelsk,1,AARI
+AAE,38.77,9.03,111.51,-0.06,Addis Ababa,3,INTERMAGNET,BCMT,MAGDAS
+ABG,72.87,18.62,146.22,11.99,Alibag,2,GFZ,INTERMAGNET
+AMS,77.57,-37.80,140.23,-48.87,Martin de Vivias,2,INTERMAGNET,BCMT
+API,188.22,-13.80,-96.46,-15.50,Apia,1,INTERMAGNET
+ASP,133.88,-23.77,-151.93,-33.67,Alice Springs,2,INTERMAGNET,AUS
+BMT,116.20,40.30,-170.01,34.69,Beijing Ming Tombs,1,INTERMAGNET
+BNG,18.57,4.33,91.18,-9.54,Bangui,2,INTERMAGNET,BCMT
+BSL,270.36,30.35,-17.89,40.69,Bay St Louis,2,INTERMAGNET,USGS
+CNB,150.70,-34.10,-131.11,-43.54,Canberra,1,INTERMAGNET
+CAN,149.36,-35.31,-132.35,-45.02,Canberra,3,210MM,AUS,MAGDAS
+CZT,51.87,-46.43,107.41,-53.23,Port Alfred,2,INTERMAGNET,BCMT
+DLR,259.08,29.49,-32.17,38.40,Del Rio,1,INTERMAGNET
+DRV,140.01,-66.67,-122.99,-80.60,Dumont Durville,2,INTERMAGNET,BCMT
+EYR,172.40,-43.40,-102.74,-49.89,Eyrewell,1,INTERMAGNET
+GNA,116.00,-31.80,-171.85,-43.57,Gnangara,2,INTERMAGNET,AUS
+GUA,144.87,13.59,-143.10,5.64,Guam,3,INTERMAGNET,USGS,210MM
+GUI,343.57,28.32,60.99,16.06,Guimar,1,INTERMAGNET
+HBK,27.71,-25.88,96.36,-35.51,Hartebeesthoek,1,INTERMAGNET
+HER,19.23,-34.43,84.09,-42.08,Hermanus,3,INTERMAGNET,EMMA,MAGDAS
+HLP,18.82,54.61,95.32,50.93,Hel,2,INTERMAGNET,EMMA
+HON,202.00,21.32,-89.10,20.88,Honolulu,2,INTERMAGNET,USGS
+HRB,18.19,47.86,93.03,42.92,Hurbanovo,2,INTERMAGNET,EMMA
+IQA,291.48,63.75,15.58,72.21,Iqaluit,2,CANMOS,INTERMAGNET
+IRT,104.45,52.17,178.45,47.79,Irkutsk,2,INTERMAGNET,210MM
+ISK,29.06,41.07,102.04,35.32,Kandilli,1,INTERMAGNET
+KAK,140.18,36.23,-147.05,29.12,Kakioka,1,INTERMAGNET
+KOU,307.27,5.21,24.46,8.62,Kourou,2,INTERMAGNET,BCMT
+LNP,121.17,25.00,-165.89,18.13,Lunping,2,210MM,INTERMAGNET
+MBO,343.03,14.38,58.44,1.20,Mbour,2,INTERMAGNET,BCMT
+MMB,144.19,43.91,-143.33,37.08,Memambetsu,1,INTERMAGNET
+NCK,16.72,47.63,91.68,42.60,Nagycenk,3,SEGMA,INTERMAGNET,EMMA
+MZH,117.40,49.60,-168.51,44.53,Manzaoli,1,Unknown
+PAF,70.26,-49.35,123.64,-58.63,Port aux Francias,2,INTERMAGNET,BCMT
+PHU,105.95,21.03,178.77,14.11,Phuthuy,2,INTERMAGNET,BCMT
+PPT,210.42,-17.57,-73.70,-16.22,Pamatai,2,INTERMAGNET,BCMT
+SBA,166.78,-77.85,-31.24,-79.94,Scott Base,1,INTERMAGNET
+SJG,293.85,18.11,11.85,26.75,San Juan,2,INTERMAGNET,USGS
+SPT,355.65,39.55,72.25,31.31,San Pablo Toledo,1,INTERMAGNET
+SUA,26.25,45.32,99.92,40.18,Surlari,2,GFZ,INTERMAGNET
+TAM,5.53,22.79,78.97,8.92,Tamanrasset,2,INTERMAGNET,BCMT
+TAN,47.55,-18.92,117.81,-28.38,Antananarivo,2,INTERMAGNET,BCMT
+TUC,249.27,32.17,-43.96,39.32,Tucson,2,INTERMAGNET,USGS
+VSS,316.35,-22.40,23.72,-18.53,Vassouras,2,INTERMAGNET,GFZ
+KIV,30.30,50.72,104.63,46.56,Kiev,1,INTERMAGNET
+LNN,30.70,59.95,107.38,56.62,Leningrad,1,Unknown
+NKK,62.12,45.77,135.28,41.74,Novokazalinsk,1,Unknown
+MNK,27.88,54.50,103.27,50.76,Minsk,1,IZMIRAN
+ODE,30.88,46.78,104.50,42.10,Odessa,2,GFZ,IZMIRAN
+TUL,264.22,35.92,-26.09,45.65,Tulsa,1,Unknown
+ASH,58.10,37.95,131.16,33.36,Ashkabad,1,Unknown
+TOL,355.95,39.88,72.55,31.79,Toledo,1,Unknown
+NVS,82.90,55.03,156.55,51.26,Novosibirsk,1,INTERMAGNET
+KGD,73.08,49.82,146.42,45.92,Karaganda,1,Unknown
+AAA,76.92,43.25,150.22,38.83,Alma Ata,1,INTERMAGNET
+TKT,69.62,41.33,142.86,36.91,Tashkent,1,Unknown
+LVV,23.75,49.90,98.46,45.49,Lvov,1,INTERMAGNET
+RSV,12.45,55.85,90.29,52.51,Rude Skov,1,INTERMAGNET
+MOS,37.31,55.48,112.08,51.85,Moscow,1,IZMIRAN
+YSS,142.72,46.95,-144.67,40.38,Yuzhno Sakhalinsk,1,AARI
+HIS,58.05,80.62,143.72,76.10,Heiss Island,1,AARI
+ZGN,123.26,66.75,-164.56,62.01,Zhigansk,2,210MM,MAGDAS
+YAK,129.72,60.02,-157.40,54.88,Yakutsk,4,GFZ,210MM,INTERMAGNET,MAGDAS
+PPI,131.73,42.98,-154.70,36.67,Popov Island,1,210MM
+BJI,116.18,40.06,-170.05,34.43,Beijing,1,210MM
+BOX,38.97,58.03,114.20,54.54,Barok,3,210MM,INTERMAGNET,BCMT
+MUT,121.02,14.37,-166.45,6.90,Muntinlupa,2,210MM,MAGDAS
+PTN,109.25,-0.05,-178.10,-8.49,Pontianak,2,210MM,MAGDAS
+WTK,112.63,-7.56,-174.68,-16.50,Watukosek,1,210MM
+LRM,115.00,-21.00,-172.35,-31.41,Learmonth,2,INTERMAGNET,AUS
+KAT,117.62,-33.68,-170.10,-45.63,Katanning,1,210MM
+KTN,137.71,75.94,-157.19,70.98,Kotelnyy,1,210MM
+CHD,147.89,70.62,-145.89,65.67,Chokurdakh,2,210MM,MAGDAS
+ZYK,150.78,65.75,-141.24,60.54,Zyryanka,1,210MM
+MGD,150.86,59.97,-139.32,54.30,Magadan,4,GFZ,210MM,INTERMAGNET,MAGDAS
+MSR,142.27,44.37,-145.03,37.64,Moshiri,1,210MM
+ONW,141.47,38.43,-145.81,31.38,Onagawa,2,210MM,MAGDAS
+KAG,130.72,31.48,-156.17,24.56,Kagoshima,1,210MM
+YMK,130.62,31.19,-156.28,24.26,Yamakawa,1,210MM
+CBI,142.30,27.15,-145.40,19.58,Chichijima,1,210MM
+YAP,138.50,9.30,-149.26,1.24,Yap,2,210MM,MAGDAS
+KOR,134.50,7.33,-153.20,-0.71,Koror,1,210MM
+BIK,136.05,-1.08,-151.37,-9.35,Biak,2,210MM,MAGDAS
+WEW,143.62,-3.55,-143.40,-11.75,Wewak,1,210MM
+DRW,130.90,-12.40,-156.02,-21.31,Darwin,3,210MM,AUS,MAGDAS
+WEP,141.88,-12.68,-144.40,-21.33,Weipa,1,210MM
+BSV,139.21,-25.54,-145.83,-35.31,Birdsville,1,210MM
+DAL,151.20,-27.18,-132.12,-36.01,Dalby,1,210MM
+ADL,138.65,-34.67,-145.09,-45.42,Adelaide,1,210MM
+CPS,180.63,68.88,-120.92,65.37,Cape Schmidt,3,AARI,210MM,IZMIRAN
+EWA,202.00,21.32,-89.10,20.88,Ewa Beach,2,210MM,MAGDAS
+ASA,170.70,-14.28,-113.81,-19.02,American Samoa,1,210MM
+MCQ,158.95,-54.50,-110.94,-64.39,Macquarie Island,4,210MM,INTERMAGNET,AUS,MAGDAS
+CKA,73.60,68.50,149.04,64.77,Cape Kamenniy,1,Unknown
+AMD,61.21,69.60,138.07,65.95,Amderma,3,RAPIDMAG,GIMA,AARI
+LOP,33.08,68.25,113.15,65.10,Lop,1,Unknown
+MBC,240.64,76.32,-81.35,81.13,Mould Bay,2,CANMOS,INTERMAGNET
+VIZ,76.54,79.30,155.69,74.55,Vieze Island,1,AARI
+PBK,170.90,70.08,-128.73,65.92,Pebek,2,AARI,GIMA
+PGC,294.20,66.10,20.55,74.09,Pangnirtung,1,MACCS
+CY0,291.40,70.50,18.88,78.52,Clyde River,1,MACCS
+CDC,283.40,64.20,3.04,73.47,Cape Dorset,1,MACCS
+CHC,276.00,64.35,-9.45,73.97,Coral Harbour,1,MACCS
+GHC,264.15,68.63,-31.68,77.54,Gjoa Haven,1,MACCS
+IGC,278.20,69.30,-5.39,78.43,Igloolik,1,MACCS
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+GTF,288.05,43.62,7.32,52.69,Grafton,1,MEASURE
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+HLL,339.44,63.77,67.40,64.41,Hella,1,SAMNET
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+YOR,358.95,53.95,78.49,50.90,York,1,SAMNET
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+SER,288.87,-30.00,0.65,-16.22,La Serena,1,SAMBA
+CER,289.40,-33.45,1.15,-19.71,Los Cerrillos,1,SAMBA
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+PAC,289.91,-40.34,1.97,-26.64,Punta Arenas,1,SAMBA
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+FVE,243.98,58.37,-56.85,65.11,Fort Vermilion,1,AUTUMN
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+ROT,245.87,51.07,-52.01,58.10,Rothney,1,AUTUMN
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+VLO,282.24,48.19,-0.38,58.00,Val-dOr,1,AUTUMN
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+GZH,113.34,23.09,-173.79,16.29,Guangzhou,1,INTERMAGNET
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+HLM,210.13,61.24,-93.38,61.32,HLMS,1,GIMA
+HOM,209.53,59.70,-93.12,59.60,Homer,1,GIMA
+TRP,150.24,62.67,-140.57,57.23,Trapper,1,GIMA
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+GCK,20.77,44.63,94.75,39.04,Grocka,1,INTERMAGNET
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+SHU,199.54,55.35,-99.73,53.26,Shumagin,2,INTERMAGNET,USGS
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+B12,335.88,-79.08,29.67,-65.15,m79-336,1,BAS
+B14,337.74,-80.89,29.35,-66.70,m81-338,1,BAS
+B15,2.97,-81.49,37.07,-68.93,m82-003,1,BAS
+B17,347.76,-82.90,30.79,-68.84,m83-348,1,BAS
+B18,336.14,-84.35,26.33,-69.45,m84-336,1,BAS
+B19,2.06,-85.36,30.41,-71.39,m85-002,1,BAS
+B20,95.98,-85.36,30.38,-77.78,m85-096,1,BAS
+B21,28.41,-87.00,29.34,-73.53,m87-028,1,BAS
+B22,68.17,-86.51,31.00,-75.62,m87-069,1,BAS
+B23,316.13,-88.03,20.41,-72.34,m88-316,1,BAS
+B03,291.88,-67.57,8.27,-53.14,m67-292,1,BAS
+M01,263.55,46.87,-27.71,56.59,Glyndon,1,McMAC
+M02,266.75,45.56,-23.05,55.55,Cambridge,1,McMAC
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+M04,263.84,41.36,-26.89,51.07,Bennington,1,McMAC
+M05,263.70,38.50,-26.91,48.18,Americus,1,McMAC
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+M07,263.25,32.98,-27.21,42.57,Richardson,1,McMAC
+M08,261.39,29.44,-29.35,38.74,San Antonio,1,McMAC
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+M10,260.40,24.80,-30.22,33.87,Linares,1,McMAC
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+B01,296.52,-64.82,10.05,-50.62,m65-297,1,BAS
+B02,294.00,-66.03,8.97,-51.69,m66-294,1,BAS
+B04,41.08,-68.58,74.03,-66.63,m68-041,1,BAS
+B05,41.32,-69.29,73.07,-67.09,m69-041,1,BAS
+B06,39.40,-69.67,71.31,-66.95,m70-039,1,BAS
+B07,44.28,-70.70,72.51,-68.45,m70-044,1,BAS
+B08,159.03,-72.77,-65.20,-80.61,m73-159,1,BAS
+B09,42.99,-74.01,65.58,-69.97,m74-043,1,BAS
+B10,39.71,-77.32,57.15,-71.02,m77-040,1,BAS
+B24,77.77,-71.67,90.77,-76.46,m72-078,1,BAS
+B13,77.00,-80.00,56.17,-77.91,m80-077,1,BAS
+B16,347.06,-82.78,30.73,-68.71,m83-347,1,BAS
+B27,3.00,-81.00,37.78,-68.61,m81-003,1,BAS
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+T17,287.87,44.95,7.22,54.06,Derby,1,THEMIS
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+T19,245.33,47.61,-51.74,54.45,Hot Springs,1,THEMIS
+T20,281.62,40.18,-1.88,50.10,Loysburg,1,THEMIS
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+T22,227.05,56.81,-75.31,60.12,Petersburg,1,THEMIS
+T23,274.86,43.66,-11.31,53.91,Remus,1,THEMIS
+T24,271.30,44.82,-16.46,55.04,Shawano,1,THEMIS
+T25,241.07,45.14,-56.04,51.06,Ukiah,1,THEMIS
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+T28,299.54,53.30,23.66,60.48,Goose Bay,1,THEMIS
+T29,291.60,58.10,14.25,66.70,Kuujjuaq,1,THEMIS
+T30,285.58,49.81,4.54,59.25,Chibougamau,1,THEMIS
+T31,280.80,56.50,-1.92,66.31,Sanikiluaq,1,CANMOS
+T32,277.68,49.39,-7.00,59.51,Kapuskasing,1,THEMIS
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+T34,249.33,64.73,-52.99,72.21,Ekati,1,THEMIS
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+T38,224.78,61.01,-79.70,63.93,White Horse,2,THEMIS,AUTUMN
+T39,209.58,62.24,-94.40,62.27,Trapper Creek,1,THEMIS
+T40,204.40,62.95,-99.13,62.11,McGrath,1,THEMIS
+T41,199.56,66.97,-105.56,65.59,Kiana,1,THEMIS
+T42,254.74,55.20,-41.51,63.80,La Ronge,2,THEMIS,AUTUMN
+T43,245.70,50.87,-52.17,57.86,Priddis,1,THEMIS
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+E01,23.86,41.35,97.13,35.21,Nevrokopi,1,ENIGMA
+E02,21.97,39.45,95.14,32.68,Klokotos,1,ENIGMA
+E03,24.10,38.63,97.05,31.88,Kimi,1,ENIGMA
+E04,23.93,38.08,96.84,31.18,Dionyssos,1,ENIGMA
+E05,22.95,36.72,95.78,29.37,Velies,1,ENIGMA
+BRD,260.03,49.87,-33.04,59.20,Brandon,1,CANMOS
+EUA,274.10,80.00,-20.31,87.88,Eureka,1,CANMOS
+ARS,58.57,56.43,132.43,52.90,Arti,1,INTERMAGNET
+CKI,96.84,-12.19,168.90,-22.26,Cocos-Keeling Islands,2,INTERMAGNET,AUS
+CNH,124.86,44.08,-161.26,38.25,Changchun,1,INTERMAGNET
+CYG,126.85,36.37,-159.65,29.94,Cheongyang,1,INTERMAGNET
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+JAI,75.80,26.92,149.20,21.02,Jaipur,1,INTERMAGNET
+KHB,134.69,47.61,-151.91,41.44,Khabarovsk,1,INTERMAGNET
+ORC,315.26,-60.74,20.47,-48.69,Orcadas,1,INTERMAGNET
+PEG,23.90,38.10,96.81,31.21,Pedeli,1,INTERMAGNET
+PET,158.25,52.97,-132.07,46.73,Paratunka,1,GFZ
+PIL,294.47,-31.40,4.76,-18.39,Pilar,1,INTERMAGNET
+WMQ,87.71,43.81,161.14,39.23,Urumqi,1,INTERMAGNET
+PG1,77.20,-85.50,33.68,-76.55,Antartica,2,THEMIS,MIST
+PG2,57.96,-84.42,39.07,-75.24,Antartica,2,THEMIS,MIST
+PG3,37.63,-84.81,36.76,-73.53,Antartica,2,THEMIS,MIST
+PG4,12.25,-83.34,36.39,-70.83,Antartica,2,THEMIS,MIST
+GNG,115.71,-31.36,-172.14,-43.08,Gingin,2,INTERMAGNET,AUS
+BRZ,24.75,56.21,100.97,52.66,Birzai,1,EMMA
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+SZC,19.61,52.91,95.51,48.97,Szczechowo,1,EMMA
+ZAG,20.58,50.28,95.70,45.89,Zagorzyce,1,EMMA
+VYH,18.84,48.49,93.75,43.71,Vyhne,1,EMMA
+WIC,15.52,47.55,90.60,42.48,Conrad Observatorium,1,EMMA
+CST,11.65,46.05,86.90,40.48,Castello Tesino,2,SEGMA,EMMA
+P01,16.66,45.41,91.18,39.82,Lonjsko Polje,1,EMMA
+RNC,12.08,43.97,86.87,37.77,Ranchio,2,SEGMA,EMMA
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+A01,7.39,8.99,80.26,-6.22,Abuja,2,AMBER,MAGDAS
+A02,2.93,36.85,77.88,27.49,Algeria,1,AMBER
+A03,11.52,3.87,84.18,-11.10,Cameroon,1,AMBER
+A04,39.46,14.28,112.06,5.78,Ethiopia,1,AMBER
+A05,17.58,-19.20,88.00,-30.49,Namibia,1,AMBER
+A06,355.87,5.33,69.02,-8.23,Abidjan,2,AMBER,MAGDAS
+A07,346.34,9.58,60.64,-3.02,Conakry,1,AMBER
+A08,125.40,7.00,-162.23,-0.85,Davao,2,AMBER,MAGDAS
+A09,120.00,14.58,-167.46,7.13,Manila,1,AMBER
+A10,319.50,-9.40,31.08,-10.00,Petrolina,1,AMBER
+A11,311.55,-1.45,26.47,0.44,Belem,1,AMBER
+A12,100.61,14.08,173.36,6.47,Bangkok,1,AMBER
+A13,98.35,7.89,171.06,-0.32,Phuket,1,AMBER
+GRK,29.38,60.27,106.34,56.98,Gorkovskaya,1,AARI
+BBG,14.21,78.06,109.67,75.62,Barentsburg,1,AARI
+PRG,76.23,-69.23,97.38,-74.88,Progress,1,AARI
+NVL,11.83,-70.78,53.55,-63.26,Novolazorevskaya,1,AARI
+WHS,224.90,60.70,-79.41,63.64,Whitehorse,2,CANMOS,THEMIS
+T44,281.92,58.47,-0.04,68.14,Inukjuak,2,THEMIS,AUTUMN
+T45,282.24,55.28,0.17,65.01,Kuujjuarapik,2,THEMIS,AUTUMN
+T46,282.71,60.05,1.36,69.60,Puvirnituq,2,THEMIS,AUTUMN
+T47,284.35,62.20,4.23,71.51,Salluit,2,THEMIS,AUTUMN
+T48,293.19,54.80,15.78,63.16,Schefferville,1,THEMIS
+T49,293.73,50.22,15.73,58.42,Spet-lles,2,THEMIS,AUTUMN
+T50,287.55,48.65,7.18,57.83,Saint-Fellicien,2,THEMIS,AUTUMN
+T51,282.22,48.05,-0.43,57.86,Val-dOr,2,THEMIS,AUTUMN
+T52,282.38,53.79,0.26,63.54,Radisson,2,THEMIS,AUTUMN
+T53,281.85,60.82,0.08,70.39,Akulivik,2,THEMIS,AUTUMN
+VNA,351.70,-70.70,41.78,-60.63,Neumayer Station III,1,INTERMAGNET
+CUL,149.58,-30.28,-133.30,-39.54,Culgoora,2,AUS,MAGDAS
+HOB,147.35,-42.88,-132.46,-53.62,Hobart,2,AUS,MAGDAS
+TWN,146.82,-19.27,-138.28,-27.99,Townsville,2,AUS,MAGDAS
+AMA,129.33,28.17,-157.69,21.14,Amami-Oshima,1,MAGDAS
+ANC,282.85,-11.77,-4.48,1.44,Ancon,1,MAGDAS
+ASB,142.17,43.46,-145.11,36.67,Ashibetu,1,MAGDAS
+ASW,32.51,23.50,104.71,15.04,Aswan,1,MAGDAS
+BCL,105.71,9.30,178.37,1.41,Bac_Lieu,1,MAGDAS
+BKL,102.31,-3.86,174.93,-12.90,Bengkulu,1,MAGDAS
+CDO,124.63,8.40,-162.98,0.61,CagayanDeOro,1,MAGDAS
+CEB,123.91,10.36,-163.68,2.66,Cebu,1,MAGDAS
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+CMD,150.67,-34.06,-131.15,-43.50,Camden,1,MAGDAS
+DES,39.12,-6.46,111.84,-16.19,Dal Es Salaam,1,MAGDAS
+DRB,30.56,-29.48,97.67,-38.50,Durban,1,MAGDAS
+EUS,321.57,-3.88,35.01,-6.82,Eusebio,1,MAGDAS
+FYM,35.50,29.18,107.79,22.10,Fayum,1,MAGDAS
+GSI,97.34,1.18,169.98,-7.63,Gunung Sitoli,1,MAGDAS
+HLN,121.55,23.90,-165.57,16.95,Hualien,1,MAGDAS
+HVD,91.67,48.01,165.26,43.69,Khovd,1,MAGDAS
+ICA,284.25,-14.08,-3.19,-0.68,Ica,1,MAGDAS
+ILR,4.68,8.50,77.65,-6.67,Ilorin,1,MAGDAS
+JRS,281.70,0.00,-5.36,12.10,Alem Ecua,1,MAGDAS
+JYP,140.70,2.51,-146.81,-5.66,Jayapura,1,MAGDAS
+KPG,123.40,-10.20,-163.82,-19.04,Kupang,1,MAGDAS
+KRT,32.32,15.33,104.67,5.65,Khartoum,1,MAGDAS
+KUJ,131.23,33.06,-155.59,26.20,Kuju,1,MAGDAS
+LAG,3.27,6.48,76.21,-8.49,Lagos,1,MAGDAS
+LGZ,123.74,13.10,-163.80,5.52,Legazpi,1,MAGDAS
+LKW,99.78,6.30,172.47,-2.00,Langkawi,1,MAGDAS
+LSK,28.19,-15.23,99.88,-26.04,Lusaka,1,MAGDAS
+LWA,104.06,-5.02,176.69,-14.09,Liwa,1,MAGDAS
+MLB,145.18,-38.36,-136.49,-48.87,Melbourne,1,MAGDAS
+MND,124.84,1.44,-162.73,-6.62,Manado Indon,1,MAGDAS
+NAB,36.48,-1.16,109.37,-11.15,Nairobi,1,MAGDAS
+PRP,119.40,-3.60,-168.01,-11.98,ParePare,1,MAGDAS
+PTK,158.25,52.94,-132.06,46.70,St.Paratunka,1,MAGDAS
+ROC,150.31,-23.19,-133.84,-31.83,Rockhampton,1,MAGDAS
+SBH,116.07,6.02,-171.41,-1.86,Jayapura,1,MAGDAS
+SCN,100.30,-0.55,172.94,-9.39,Sicincin,1,MAGDAS
+TGG,121.76,17.66,-165.63,10.34,Tuguegarao,1,MAGDAS
+TIR,76.95,8.48,149.95,0.57,Tirunelveli,1,MAGDAS
+WAD,256.10,51.90,-38.87,60.70,Wadena,1,MAGDAS
+G01,306.28,-29.72,13.72,-20.25,Santa Maria,1,MAGDAS
+KLD,20.20,54.50,96.48,50.79,Kaliningrad,1,IZMIRAN
+T54,283.15,39.10,0.16,48.87,Laurel,1,THEMIS
+T55,208.74,60.55,-94.22,60.35,Kenia College,1,THEMIS
+T56,267.03,45.59,-22.65,55.60,Hrisis,1,THEMIS
+T57,255.39,38.25,-37.64,46.70,Pblolo,1,THEMIS
+T58,237.83,37.42,-57.59,42.49,Stfdford,1,THEMIS
+BRN,132.41,47.80,-154.02,41.77,Baranov,1,THEMIS
+PG0,88.68,-83.67,38.23,-78.32,PG0,2,THEMIS,MIST
+PG5,5.71,-81.96,37.17,-69.46,PG5,2,THEMIS,MIST
+HRP,275.30,49.20,-10.54,59.40,Hornepayne,1,THEMIS
+LCL,252.90,51.00,-43.01,59.29,Lucky Lake,1,THEMIS
+T59,207.60,57.79,-93.90,57.24,Kodkak,1,THEMIS
+T60,210.40,66.63,-96.57,66.96,Toolik Lake,1,THEMIS
+T61,284.56,37.88,1.99,47.50,Wlpsops Island,1,THEMIS
+T62,26.41,65.90,106.30,62.92,Ranua,1,THEMIS
+PKS,252.80,52.50,-43.49,60.79,ParkSite,1,THEMIS
+FTN,237.20,58.90,-65.26,64.32,Fort Nelson,1,THEMIS
+W01,147.21,-41.55,-133.08,-52.16,Launceston,1,BOM
+W02,167.97,-29.03,-113.25,-35.45,Norfolk Island,1,BOM
+W03,141.30,-2.70,-145.87,-10.97,Vanimo,1,BOM
+W04,111.53,-66.28,160.60,-81.07,Casey2,1,BOM
+W05,77.96,-68.58,100.77,-74.97,Davis2,1,BOM
+NR2,24.65,60.50,102.35,57.32,Nurmijarvi2,1,IMAGE
+CPL,78.90,17.30,152.16,10.24,Choutuppal,1,INTERMAGNET
+TTB,311.49,-1.21,26.49,0.67,Tatuocca,1,INTERMAGNET
+R01,267.78,38.90,-21.37,48.96,Missouri,1,MAGSTAR
+R02,282.22,38.68,-1.17,48.56,Virginia,1,MAGSTAR
+R03,266.27,44.90,-23.71,54.85,Hennepin,1,MAGSTAR
+R04,254.76,40.01,-38.68,48.39,Boulder,1,MAGSTAR
+R05,275.83,41.58,-9.99,51.82,Bluesky,1,MAGSTAR
+R06,257.68,31.84,-34.08,40.55,Odessa,1,MAGSTAR
+R07,266.34,47.13,-23.72,57.10,Sugarhills,1,MAGSTAR
+R08,286.44,44.33,5.19,53.65,Atlas,1,MAGSTAR
+R09,287.28,41.65,6.07,50.84,New Britain,1,MAGSTAR
+R10,288.52,42.61,7.85,51.61,Haystack,1,MAGSTAR
+SKD,66.78,66.57,142.18,63.00,Salekhard,1,IZMIRAN
+BEY,70.00,73.30,147.36,69.29,Beliy Island,1,IZMIRAN
+KHS,66.83,71.16,143.71,67.35,Kharasavey,1,IZMIRAN
+GLK,44.47,40.94,117.30,36.21,Gyulagarak,1,IZMIRAN
+KLI,39.52,60.86,115.49,57.48,Klimovskaya,1,IZMIRAN
+KZN,48.74,55.93,122.89,52.37,Kazan,1,IZMIRAN
+MHV,37.73,54.94,112.36,51.27,Mikhnevo,1,IZMIRAN
+NAD,72.51,65.53,147.36,61.94,Nadym,1,IZMIRAN
+WSE,33.10,66.55,112.22,63.39,White Sea,1,IZMIRAN
+POD,90.03,61.58,164.07,57.82,Bor,1,IZMIRAN
+TOP,13.18,56.09,90.99,52.76,Tormestorp,1,IMAGE
+R11,290.40,44.30,10.56,52.98,Augusta,1,MAGSTAR
+R12,242.30,47.50,-55.28,53.74,Grant,1,MAGSTAR
+R13,256.30,40.20,-36.75,48.84,Pawnee,1,MAGSTAR
+Y01,290.71,-31.80,2.03,-18.20,Casleo,1,LISN
+Y02,294.77,-26.84,5.13,-14.03,Tucuman,1,LISN
+Y03,303.93,-15.56,14.59,-7.00,Cuiaba,1,LISN
+Y04,315.79,-2.59,30.09,-2.93,Sao Luiz,1,LISN
+Y05,290.06,-4.19,3.38,7.60,Leticia,1,LISN
+Y06,290.81,-12.59,3.06,0.02,Puerto Maldonado,1,LISN
+Y07,283.12,-11.95,-4.22,1.28,Alta Floresta,1,LISN
+Y08,282.85,-11.78,-4.48,1.43,Ancon,1,LISN
+Y09,288.51,-16.47,0.60,-3.15,Arequipa,1,LISN
+Y10,283.12,-11.95,-4.22,1.28,Delta Jicamarca Piura,1,LISN
+Y11,283.12,-11.95,-4.22,1.28,Delta Jicamarca Tarapoto,1,LISN
+Y12,284.68,-12.04,-2.74,1.17,Huancayo,1,LISN
+Y13,284.68,-12.04,-2.74,1.17,Huancayo GRL,1,LISN
+Y14,284.27,-14.09,-3.17,-0.69,Ica,1,LISN
+Y15,283.12,-11.95,-4.22,1.28,Jicamarca,1,LISN
+Y16,285.08,-14.83,-2.43,-1.38,Nazca,1,LISN
+Y17,279.36,-5.17,-7.96,7.30,Piura,1,LISN
+Y18,283.64,-6.50,-3.57,6.20,Tarapoto,1,LISN
\ No newline at end of file
diff --git a/lompe/data_tools/README b/lompe/data_tools/README
index 74886b9..1addf3e 100644
--- a/lompe/data_tools/README
+++ b/lompe/data_tools/README
@@ -1,9 +1,11 @@
Directory for scripts for data handling.
Currently handles the following data sets:
- - DMSP SSUSI (EDR AURORA, netcdf format)
+ - DMSP SSUSI (EDR AURORA, netcdf format) (as of February 2025, SSUSI data is not supported on "jhuapl" use "cdaweb" option)
- DMSP SSIES (Madrigal)
- - SuperDARN (gridmap)
+ - SuperDARN (gridmap) from Chartier, Alex T
- SuperMAG (netcdf format)
- Iridium (from AMPERE, netcdf format)
+ - CHAMP (cdf format)
+ - SWARM (using viresclient)
diff --git a/lompe/data_tools/datadownloader.py b/lompe/data_tools/datadownloader.py
index 2595951..1144d5c 100644
--- a/lompe/data_tools/datadownloader.py
+++ b/lompe/data_tools/datadownloader.py
@@ -1,93 +1,1237 @@
+import warnings
+import xarray as xr
import numpy as np
+import pandas as pd
import requests
from bs4 import BeautifulSoup
-from urllib.parse import urljoin
import datetime as dt
import os
+import glob
+import shutil
+from lompe.utils.time import date2doy
+import random
+import time
+warnings.filterwarnings("ignore")
-def download_sussi(event, destination='downloads', source='jhuapl'):
- """function to download SSUSI data for a given event date
+# extensive list of functions (including helper functions) to download data from different sources for a given event date
+# the functions are designed to be used in the lompe package for data loading and processing
+# this has to be integrated with the dataloader.py in the lompe package at some level to get lompe data format
+
+# the idea is to merge the two files (datadownloader.py and dataloader.py) into one file in the lompe package, for the time being we need to keep
+# both, some functions imported from dataloader.py are in use in this script
+
+# Note that: lazy importing is implemented here
+
+
+def download_ssusi_cdaweb(event, hemi='north', basepath='./ssusi_tempfiles', tempfile_path='./', source='cdaweb'):
+ """
+
+ Called and used for modelling auroral conductance in cmodel.py.
+
+ Extract the relevant SSUSI info from netcdf-files downloaded from APL server.
+ https://cdaweb.gsfc.nasa.gov/pub/data/dmsp/ (ssusi/data/edr-aurora)
+ Will load all SSUSI data from specified hemisphere into xarray object.
+ Any DMSP Block 5D satellite (F16-19) available in folder will be used.
+
+ Parameters
+ ----------
+ event : str
+ string on format 'yyyy-mm-dd' to specify time of event for model.
+ hemi : str, optional
+ specify hemisphere
+ Default: 'north'
+ basepath : str, optional
+ location of netcdf-files downloaded from APL server (.NC).
+ Default: './ssusi_tempfiles'
+ tempfile_path : str, optional
+ Location for storing processed SSUSI data.
+ Default: './'
+ source : str, optional
+ Specify source of SUSSI data.
+ Default: 'cdaweb' see download_ssusi for 'jhuapl'
+
+ Returns
+ -------
+ savefile : str
+ Path to saved file containing SSUSI data + conductances extracted from the images.
+
+ """
+ '''
+ Using the CDAWeb server here eliminates the need for checking if the data is from the same day or not.
+ The data is already sorted by date and time. From the APL sever, there might be a need to check if the data is from the same day.
+ (see download_ssusi function)
+
+ Extract the relevant info from downloaded netcdf-files (similar to from APL server, but from CDAWeb):
+ Note that these data are gridded on a 363,363 geomagnetic grid. We assume this is AACGM,
+ based on email correspondence with L. Paxton. This is not clear from the documentation.
+ No geographic information of these gridded data exist in the EDR aurora files downloaded
+ Quote from documentation: "This array is a uniform grid in a polar azimuthal equidistant
+ projection of the geomagnetic latitude and geomagnetic local time, with appropriate
+ geomagnetic pole as the origin; that is: 90-ABS(glat) is the radius and geomagnetic
+ local time is the angle of a two-dimensional polar coordinate system."
+ Hence, the mapped latitude array (363x363) is the same for both hemispheres, and
+ the values are therefore always positive.
+ '''
+ if not basepath.endswith('/'):
+ basepath += '/'
+ if not tempfile_path.endswith('/'):
+ tempfile_path += '/'
+
+ # check if the processed file exists
+ savefile = tempfile_path + \
+ event.replace('-', '') + '_ssusi_' + hemi + '.nc'
+ if os.path.isfile(savefile):
+ print('SSUSI file already exists.')
+ return savefile
+ try:
+ import netCDF4
+ except ModuleNotFoundError:
+ raise ModuleNotFoundError(
+ 'read_ssusi: Could not load netCDF4 module. Will not be able to read SSUSI-files from APL.')
+ # downlaoding the files from the server see the ssusi_direct.py for the implementation
+ # from lompe.data_tools.ssusi_direct2 import download_ssusi_files
+ download_ssusi_files(event, basepath=basepath, source=source)
+ ###
+ imgs = [] # List to hold all images. Converted to xarray later.
+ doy_str = f"{date2doy(event):03d}"
+ for sat in ['F16', 'F17', 'F18', 'F19']:
+ files = glob.glob(basepath + '*' + sat.lower() + '*' +
+ event[0:4] + doy_str + '*.nc')
+ files.sort()
+ if len(files) == 0:
+ continue
+
+ ii = 0 # counter
+
+ for file in files:
+ f = netCDF4.Dataset(file)
+
+ mlat = f.variables['LATITUDE_GEOMAGNETIC_GRID_MAP'][:]
+ mlon = f.variables['LONGITUDE_GEOMAGNETIC_' +
+ hemi.upper() + '_GRID_MAP'][:]
+ mlt = f.variables['MLT_GRID_MAP'][:]
+ uthr = f.variables['UT_' + hemi.upper()[0]][:]
+ doy = int(f.variables['DOY'][:])
+ year = int(f.variables['YEAR'][:])
+
+ wavelengths = f.variables['DISK_RADIANCEDATA_INTENSITY_' + hemi.upper()][:]
+ char_energy = f.variables['ELECTRON_MEAN_' +
+ hemi.upper() + '_ENERGY_MAP'][:]
+ energyflux = f.variables['ENERGY_FLUX_' + hemi.upper() + '_MAP'][:]
+
+ f.close()
+
+ mask = uthr == 0
+
+ uthr[mask] = np.nan
+ wavelengths[:, mask] = np.nan
+ char_energy[mask] = np.nan
+ energyflux[mask] = np.nan
+ mlon[mask] = np.nan
+
+ # Applying Robinson formulas to calculate conductances: https://agupubs.onlinelibrary.wiley.com/doi/epdf/10.1029/JA092iA03p02565
+ SP = (40. * char_energy * np.sqrt(energyflux)) / \
+ (16. + char_energy**2)
+ SH = 0.45 * char_energy**0.85 * SP
+
+ if sum(np.isfinite(uthr[:, 181]).flatten()) > 0: # there is data in north
+ # Calculate center time:
+ # there is a sudden transition across midnight in the pass
+ if np.nanstd(uthr) > 0.5:
+ next_day = (uthr > 0) & (uthr < 2)
+ if sum(next_day.flatten()) > 0:
+ uthr[next_day] = uthr[next_day] + 24.
+ center_hr = np.nanmean(uthr, axis=0)[181]
+ hr = int(center_hr)
+ if hr >= 24:
+ hr = hr - 24
+ center_hr = center_hr - 24
+
+ m = int((center_hr - hr) * 60)
+ s = round(((center_hr - hr) * 60 - m) * 60)
+ if s == 60:
+ t0 = dt.datetime(year, 1, 1, hr, m, 59) + \
+ dt.timedelta(seconds=1)
+ else:
+ t0 = dt.datetime(year, 1, 1, hr, m, s)
+
+ dtime = t0 + dt.timedelta(doy - 1)
+ # put into xarray object
+ img = xr.Dataset({'uthr': (['row', 'col'], uthr),
+ 'mlon': (['row', 'col'], mlon),
+ 'mlat': (['row', 'col'], mlat),
+ 'mlt': (['row', 'col'], mlt),
+ '1216': (['row', 'col'], wavelengths[0, :, :]),
+ '1304': (['row', 'col'], wavelengths[1, :, :]),
+ '1356': (['row', 'col'], wavelengths[2, :, :]),
+ 'lbhs': (['row', 'col'], wavelengths[3, :, :]),
+ 'lbhl': (['row', 'col'], wavelengths[4, :, :]),
+ 'E0': (['row', 'col'], char_energy),
+ 'je': (['row', 'col'], energyflux),
+ 'SP': (['row', 'col'], SP),
+ 'SH': (['row', 'col'], SH)})
+ img = img.expand_dims(date=[dtime])
+ img = img.assign({'satellite': sat})
+ img = img.assign(
+ {'orbit': file.split('-')[-1].split('_')[0][3:]})
+ imgs.append(img)
+ ii += 1
+
+ if len(imgs) == 0:
+ print('No SSUSI images found.')
+
+ else: # save as netcdf in specified path
+ imgs = xr.concat(imgs, dim='date')
+ imgs = imgs.assign({'hemisphere': hemi})
+ imgs = imgs.sortby(imgs['date'])
+ print('DMSP SSUSI file saved: ' + savefile)
+
+ imgs.to_netcdf(savefile)
+ shutil.rmtree(basepath)
+ return savefile
+
+
+def download_ssusi(event, hemi='north', basepath='./ssusi_tempfiles', tempfile_path='./', source='jhuapl'):
+ """
+
+ Called and used for modelling auroral conductance in cmodel.py.
+
+ Extract the relevant SSUSI info from netcdf-files downloaded from APL server.
+ https://ssusi.jhuapl.edu/data_products (EDR AURORA)
+ Will load all SSUSI data from specified hemisphere into xarray object.
+ Any DMSP Block 5D satellite (F16-19) available in folder will be used.
+
+ Parameters
+ ----------
+ event : str
+ string on format 'yyyy-mm-dd' to specify time of event for model.
+ hemi : str, optional
+ specify hemisphere
+ Default: 'north'
+ basepath : str, optional
+ location of netcdf-files downloaded from APL server (.NC).
+ Default: './ssusi_tempfiles'
+ tempfile_path : str, optional
+ Location for storing processed SSUSI data.
+ Default: './'
+ source : str, optional
+ Specify source of SUSSI data.
+ Default: 'jhuapl', other option is 'cdaweb'
+
+ Returns
+ -------
+ savefile : str
+ Path to saved file containing SSUSI data + conductances extracted from the images.
+
+ """
+ '''
+ Extract the relevant info from netcdf-files downloaded from APL server:
+ Note that these data are gridded on a 363,363 geomagnetic grid. We assume this is AACGM,
+ based on email correspondence with L. Paxton. This is not clear from the documentation.
+ No geographic information of these gridded data exist in the EDR aurora files downloaded
+ Quote from documentation: "This array is a uniform grid in a polar azimuthal equidistant
+ projection of the geomagnetic latitude and geomagnetic local time, with appropriate
+ geomagnetic pole as the origin; that is: 90-ABS(glat) is the radius and geomagnetic
+ local time is the angle of a two-dimensional polar coordinate system."
+ Hence, the mapped latitude array (363x363) is the same for both hemispheres, and
+ the values are therefore always positive.
+ '''
+ if not basepath.endswith('/'):
+ basepath += '/'
+ if not tempfile_path.endswith('/'):
+ tempfile_path += '/'
+
+ # check if the processed file exists
+ savefile = tempfile_path + \
+ event.replace('-', '') + '_ssusi_' + hemi + '.nc'
+ if os.path.isfile(savefile):
+ print('SSUSI file already exists.')
+ return savefile
+ try:
+ import netCDF4
+ except ModuleNotFoundError:
+ raise ModuleNotFoundError(
+ 'read_ssusi: Could not load netCDF4 module. Will not be able to read SSUSI-files from APL.')
+ # downlaoding the files from the server see the ssusi_direct.py for the implementation
+ # from lompe.data_tools.ssusi_direct2 import download_ssusi_files
+ download_ssusi_files(event, basepath=basepath, source=source)
+ ###
+ imgs = [] # List to hold all images. Converted to xarray later.
+ doy_str = f"{date2doy(event):03d}"
+
+ for sat in ['F16', 'F17', 'F18', 'F19']:
+ files = glob.glob(basepath + '*' + sat + '*' +
+ event[0:4] + event[5:7] + event[8:10] + '*.NC')
+ files.sort()
+ if len(files) == 0:
+ continue
+
+ ii = 0 # counter
+ ii_max = len(files)
+ ii_max_orbit = files[-1].split('_SN.')[-1].split('-')[0]
+
+ extra_orbit = format(int(ii_max_orbit) + 1, '05')
+ extra_file = glob.glob(basepath + '*' + sat +
+ '*_SN.' + extra_orbit + '-' + '*.NC')
+
+ if len(extra_file) == 1:
+ files.append(extra_file[0])
+
+ for file in files:
+ f = netCDF4.Dataset(file)
+
+ mlat = f.variables['LATITUDE_GEOMAGNETIC_GRID_MAP'][:]
+ mlon = f.variables['LONGITUDE_GEOMAGNETIC_' +
+ hemi.upper() + '_GRID_MAP'][:]
+ mlt = f.variables['MLT_GRID_MAP'][:]
+ uthr = f.variables['UT_' + hemi.upper()[0]][:]
+ doy = int(f.variables['DOY'][:])
+ year = int(f.variables['YEAR'][:])
+
+ wavelengths = f.variables['DISK_RADIANCEDATA_INTENSITY_' + hemi.upper()][:]
+ char_energy = f.variables['ELECTRON_MEAN_' +
+ hemi.upper() + '_ENERGY_MAP'][:]
+ energyflux = f.variables['ENERGY_FLUX_' + hemi.upper() + '_MAP'][:]
+
+ f.close()
+
+ mask = uthr == 0
+
+ uthr[mask] = np.nan
+ wavelengths[:, mask] = np.nan
+ char_energy[mask] = np.nan
+ energyflux[mask] = np.nan
+ mlon[mask] = np.nan
+
+ # Applying Robinson formulas to calculate conductances: https://agupubs.onlinelibrary.wiley.com/doi/epdf/10.1029/JA092iA03p02565
+ SP = (40. * char_energy * np.sqrt(energyflux)) / \
+ (16. + char_energy**2)
+ SH = 0.45 * char_energy**0.85 * SP
+
+ if sum(np.isfinite(uthr[:, 181]).flatten()) > 0: # there is data in north
+ # Calculate center time:
+ # there is a sudden transition across midnight in the pass
+ if np.nanstd(uthr) > 0.5:
+ next_day = (uthr > 0) & (uthr < 2)
+ if sum(next_day.flatten()) > 0:
+ uthr[next_day] = uthr[next_day] + 24.
+ center_hr = np.nanmean(uthr, axis=0)[181]
+ if (ii == 0) & (center_hr >= 22):
+ ii = + 1
+ # the image is (likely) from the previous day
+ continue
+
+ if (ii == ii_max) & (center_hr < 22):
+ ii = + 1
+ continue # the image is not from the same day and is skipped
+
+ if (ii == ii_max):
+ doy = doy - 1
+
+ hr = int(center_hr)
+ if hr >= 24:
+ hr = hr - 24
+ center_hr = center_hr - 24
+
+ m = int((center_hr - hr) * 60)
+ s = round(((center_hr - hr) * 60 - m) * 60)
+ if s == 60:
+ t0 = dt.datetime(year, 1, 1, hr, m, 59) + \
+ dt.timedelta(seconds=1)
+ else:
+ t0 = dt.datetime(year, 1, 1, hr, m, s)
+
+ dtime = t0 + dt.timedelta(doy - 1)
+ # put into xarray object
+ img = xr.Dataset({'uthr': (['row', 'col'], uthr),
+ 'mlon': (['row', 'col'], mlon),
+ 'mlat': (['row', 'col'], mlat),
+ 'mlt': (['row', 'col'], mlt),
+ '1216': (['row', 'col'], wavelengths[0, :, :]),
+ '1304': (['row', 'col'], wavelengths[1, :, :]),
+ '1356': (['row', 'col'], wavelengths[2, :, :]),
+ 'lbhs': (['row', 'col'], wavelengths[3, :, :]),
+ 'lbhl': (['row', 'col'], wavelengths[4, :, :]),
+ 'E0': (['row', 'col'], char_energy),
+ 'je': (['row', 'col'], energyflux),
+ 'SP': (['row', 'col'], SP),
+ 'SH': (['row', 'col'], SH)})
+ img = img.expand_dims(date=[dtime])
+ img = img.assign({'satellite': sat})
+ img = img.assign(
+ {'orbit': file.split('_SN.')[-1].split('-')[0]})
+ imgs.append(img)
+ ii += 1
+
+ if len(imgs) == 0:
+ print('No SSUSI images found.')
+ return None
+
+ else: # save as netcdf in specified path
+ imgs = xr.concat(imgs, dim='date')
+ imgs = imgs.assign({'hemisphere': hemi})
+ imgs = imgs.sortby(imgs['date'])
+ print('DMSP SSUSI file saved: ' + savefile)
+
+ imgs.to_netcdf(savefile)
+ shutil.rmtree(basepath)
+ return savefile
+
+
+def fetch_ssusi_urls(sat, year, doy, source):
+ doy_str = f"{doy:03d}" # Zero-pad the day of year to three digits
+ if source == 'jhuapl':
+ url = f"https://ssusi.jhuapl.edu/data_retriver?spc=f{sat}&type=edr-aur&year={year}&Doy={doy_str}"
+ elif source == 'cdaweb':
+ url = f'https://cdaweb.gsfc.nasa.gov/pub/data/dmsp/dmspf{sat}/ssusi/data/edr-aurora/{year}/{doy_str}/'
+ else:
+ print(f"Unsupported source: {source}")
+ return []
+
+ try:
+ response = requests.get(url)
+ response.raise_for_status() # raise an HTTPError on bad response
+ except requests.exceptions.RequestException as e:
+ print(f"Failed to fetch URL probably no data : \n{e}")
+ return []
+
+ from urllib.parse import urljoin
+ soup = BeautifulSoup(response.content, 'html.parser')
+ urls = [urljoin(url, link.get('href')) for link in soup.find_all(
+ 'a', href=True) if link.get('href').lower().endswith('.nc')]
+
+ if not urls:
+ print(
+ f"No .nc files found for satellite F{sat} on day {doy_str} of year {year} from source {source}")
+
+ return urls
+
+
+def download_ssusi_file(file_url, destination):
+ try:
+ response = requests.get(file_url, stream=True)
+ response.raise_for_status() # raise an HTTPError on bad response
+ filename = os.path.join(destination, os.path.basename(file_url))
+
+ with open(filename, 'wb') as file:
+ for chunk in response.iter_content(chunk_size=65536):
+ if chunk: # Filter out keep-alive new chunks
+ file.write(chunk)
+ except requests.exceptions.RequestException as e:
+ print(f"Failed to download {file_url}: {e}")
+
+
+def download_ssusi_files(event, source='jhuapl', basepath='./ssusi_tempfiles/'):
+ """Downloading data from the SSUSI instrument onboard the DMSP satellites for a given event date.
+
+ Args:
+ event strin: format 'YYYY-MM-DD'
+ source (str, optional): data source. Defaults to 'jhuapl'. Another option is 'cdaweb'.
+ """
+ year = int(event[0:4])
+ doy = date2doy(event)
+
+ os.makedirs(basepath, exist_ok=True)
+
+ from joblib import Parallel, delayed
+ import itertools
+ # Use joblib.Parallel to fetch URLs concurrently
+ fetch_args = [(sat, year, doy, source) for sat in [16, 17, 18, 19]]
+ results = Parallel(n_jobs=8, backend='threading')(
+ delayed(fetch_ssusi_urls)(*args) for args in fetch_args)
+ # results = Parallel(n_jobs=-1)(delayed(fetch_ssusi_urls)(*args) for args in fetch_args)
+ all_urls = list(itertools.chain.from_iterable(results))
+
+ # Use joblib.Parallel to download files concurrently
+ download_args = [(url, basepath) for url in all_urls]
+ Parallel(n_jobs=8, backend='threading')(
+ delayed(download_ssusi_file)(*args) for args in download_args)
+
+
+def download_smag(event, tempfile_path='./', hemi='all'):
+ """Download SuperMAG data for a given event date (this can entirely substitute the read_smag function in the lompe package (data_tools))
Example usage:
- event = '2014-08-20'
- detination = 'downloads'
- source = 'cdaweb'
- download_sussi(event, destination, source)
+ event = '2012-04-05'
+ tempfile_path = 'downloads'
+ hemi = 'all'
+ download_smag(event, tempfile_path, hemi)
Args:
event (str): format YYYY-MM-DD
- destination (str, optional): where to save the data. Defaults to 'downloads'.
- source (str, optional): Defaults to 'jhuapl'. cdaweb is the other option.
+ tempfile_path (str, optional): path to save processed file or check if exists already. Defaults to './'.
+ hemi (str, optional): filtering magnetometer stations based on hemsiphere or all the stations. Defaults to 'all'.
+
+ Raises:
+ ValueError: throwing error if the download fails
- Note: I (Fasil) prefer the cdaweb because it is more faster to downlaod.
- but the read_sussi function in lompe package is tailored to the jhuapl data.
+ Returns:
+ saved file path if successful: str
+
+ Note: not sure this is the one which is daily basedlined: need to be checked
"""
- year = int(event[0:4])
- doy = date2doy(event)
- os.makedirs(destination, exist_ok=True)
- # iterate over the satellites with SSUSI data
- for sat in [16, 17, 18, 19]:
- if source == 'jhuapl':
- url = f"https://ssusi.jhuapl.edu/data_retriver?spc=f{sat}&type=edr-aur&year={year}&Doy={doy}"
- elif source == 'cdaweb':
- url = f'https://cdaweb.gsfc.nasa.gov/pub/data/dmsp/dmspf{sat}/ssusi/data/edr-aurora/{year}/{doy}/'
+ start = event + 'T00:00:00'
+ duration = 86400 # Duration in seconds (one day)
+ # lompe username is already registered in the API
+ from lompe.data_tools.supermag_api import SuperMAGGetData, sm_GetUrl, sm_coreurl
+ urlstr = sm_coreurl('inventory.php', 'lompe', start, duration)
+ success, stations = sm_GetUrl(urlstr, 'raw')
+ stations = stations[1:-1]
+ savefile = tempfile_path + event.replace('-', '') + '_supermag.h5'
+ if os.path.isfile(savefile):
+ return savefile
+
+ elif success and stations:
+ basepath = os.path.dirname(__file__)
+ file = basepath + '/../data/supermag_stations.csv'
+ data_temp = pd.read_csv(file, sep=',', nrows=0)
+ df = pd.read_csv(file, sep=',', skiprows=1, header=None,
+ names=data_temp.columns, usecols=range(len(data_temp.columns)))
+ if hemi == 'north':
+ sta2 = df[df['GEOLAT'] > 40].IAGA.values
+ intersection = set(stations).intersection(sta2)
+ stations = list(intersection)
+ elif hemi == 'south':
+ sta2 = df[df['GEOLAT'] < -40].IAGA.values
+ intersection = set(stations).intersection(sta2)
+ stations = list(intersection)
+ elif hemi == 'all':
+ stations = stations
+ print(
+ f"Number of stations available for the selected date is: {len(stations)}")
+
+ # checking the stations and sucess in the geturl inquiry
+ if not success or not stations:
+ raise ValueError(
+ "Failed to fetch stations. Please check the input parameters or API availability.")
+ results = []
+
+ # Function to download data for a given station
+ def download_data(station):
+ success, df = SuperMAGGetData(
+ 'lompe', start, duration, 'geo', station, BASELINE='yearly')
+ if success:
+ return df
+ else:
+ return pd.DataFrame() # Return an empty DataFrame if the download fails
+
+ # Download data serially for each station
+ for station in stations:
+ # print(f"Downloading data for station {station}...")
+ df = download_data(station)
+ if not df.empty:
+ results.append(df)
+ # time.sleep(1) # Optional: Sleep between requests to avoid overwhelming the server
+
+ # Combine results into a single DataFrame
+ if not results:
+ raise ValueError(
+ "No valid data downloaded. Please check API or parameters.")
+
+ df_combined = pd.concat(results, ignore_index=True)
+ # date conversion and cleaning the DataFrame
+ df_combined['tval'] = pd.to_datetime(
+ df_combined['tval'], unit='s', origin='unix')
+ df_combined[['N', 'E', 'Z']] = df_combined[['N', 'E', 'Z']].map(
+ lambda x: x['geo'] if isinstance(x, dict) else np.nan)
+ df_combined[['N', 'E', 'Z']] = df_combined[[
+ 'N', 'E', 'Z']].replace(999999.000000, np.nan)
+
+ # Final DataFrame to save as hdf
+ df_combined.set_index('tval', inplace=True)
+ df_combined.rename(columns={
+ 'glat': 'lat', 'glon': 'lon', 'N': 'Bn', 'E': 'Be', 'Z': 'Bu'}, inplace=True)
+ df_combined['Bu'] = -df_combined['Bu']
+ df_final = df_combined[['Be', 'Bn', 'Bu',
+ 'lat', 'lon']].dropna().sort_index()
+
+ # df_final.to_hdf('20120405_supermag_data.h5', key='df_final', mode='w')
+ finishedfile = tempfile_path + \
+ event.replace('-', '') + '_supermag.h5'
+
+ df_final.to_hdf(finishedfile, key='df_final', mode='w')
+
+ # print("Data processing complete.")
+ return finishedfile
+ else:
+ raise ValueError(
+ 'Something went wrong, check inputs, API availability etc.')
+
+
+def ampere_parsestart(start):
+ # DO NOT EDIT THIS FUNCTION
+
+ # internal helper function adapted from supermag_api.py
+
+ # takes either list of [yyyy, mo, dd, hh, mm, opt_ss]
+ # or string of a normal datetime 'YYYY-MM-DD hh-mm' (optional ss)
+ # or the SuperMAG-ready 'YYYY-MM-DDThh-mm-ss'
+
+ if isinstance(start, list):
+ timestring = "%4.4d-%2.2d-%2.2dT%2.2d:%2.2d" % tuple(start[0:5])
+ elif isinstance(start, dt.date):
+ # good to go, TBD
+ timestring = start.strftime("%Y-%m-%dT%H:%M")
+ else:
+ # is a string, reparse, TBD
+ timestring = start
+
+ return (timestring)
+
+
+def ampere_coreurl(page, logon, start, extent):
+ # DO NOT EDIT THIS FUNCTION
+
+ # internal helper function adapted from supermag_api.py
+ baseurl = "https://ampere.jhuapl.edu/"
+
+ mytime = ampere_parsestart(start)
+ urlstr = baseurl + 'services/' + page + '?'
+ urlstr += '&logon=' + logon
+ urlstr += '&start=' + mytime
+
+ urlstr += '&extent=' + ("%12.12d" % extent)
+
+ return (urlstr)
+
+
+def download_iridium(event, basepath='./', tempfile_path='./', file_name=''):
+ """Download netcdf (dB raw) data to be used by lompe from the AMPERE database (jhuapl) for a given event
+ returns an input for the lompe read_iridium script in dataloader.py in data_tools
+ Example usage:
+ event = '2012-04-05'
+ basepath = 'downloads'
+ tempfile_path = 'downloads'
+ file_name = '20120405_iridium.h5'
+ download_iridium(event, basepath, tempfile_path, file_name)
+
+ Args:
+ event (str): fromat YYYY-MM-DD
+ basepath (str, optional): path to . Defaults to './'.
+ tempfile_path (str, optional): path to. Defaults to './'.
+ file_name (str, optional):name of the file to write the netcdf file. Defaults to ''.
+
+ Returns:
+ saved file: to be used by the lompe read_iridium function in data_tools
+
+ Note:
+ functions "ampere_parsestart" and "ampere_coreurl" are internal helper functions adapted from supermag_api.py
+ credit to the original author of the functions.
+ """
+
+ start = event + 'T00:00:00'
+ duration = 86400 # Duration in seconds (one day)
+ # check if the processed file exists
+ savefile = tempfile_path + event.replace('-', '') + '_iridium.nc'
+
+ # checks if file already exists
+ # checking if the file is not empty
+ if os.path.isfile(savefile) and os.path.getsize(savefile) > 0:
+ return savefile
+ else:
+ import certifi
+ # URL to download data from (lompe username is already registered in the API)
+ urlstr = ampere_coreurl('data-rawdB.php', 'lompe', start, duration)
+ # headers = {"User-Agent": "Mozilla/5.0"}
+ # verify=certifi.where())
+ response = requests.get(
+ urlstr, verify=certifi.where(), stream=True)
+
+ # Check if the request was successful
+ if response.status_code == 200:
+ # Save the downloaded data to a file
+ with open(savefile, 'wb') as file:
+ file.write(response.content)
else:
- print(f"Unsupported source: {source}")
- continue
- # content of the webpage
- response = requests.get(url)
- soup = BeautifulSoup(response.content, 'html.parser')
+ print(f"Failed to retrieve data: {response.status_code}")
+ return savefile
+
+
+def download_supermag(event, tempfile_path='./'):
+ """This downlaods data from superMAG for a given event and returns the hdf file suitable to lompe
+ This is a faster way i can think of to download data from superMAG, the download_smag.py is slow
+ since it uses serial processing to download data for each station. This uses multiprocessing to download (see the supermag_direct.py for the multiprocessing implemetation)
+
+ Args:
+ event (str): format 'YYYY-MM-DD'
+
+ Returns:
+ hdf file: this returns the hdf file with the data for the event, no need of using read_smag in the lompe dataloader.py script
+
+ Note: not sure the data we get here is the one which is daily basedlined: need to be checked
+ """
+ # event = '2012-04-05'
+ start = event + 'T00:00:00'
+ savefile = tempfile_path + event.replace('-', '') + '_supermag.h5'
+
+ if os.path.isfile(savefile): # checks if file already exists
+ return savefile
+ else:
+ # run the function to download the data in the tempfiles folder (later to be deleted if successful)
+ smag_data_for_event(start, event)
+ temp_smag_path = f"./smag_files{event.replace('-', '')}/"
+ files = glob.glob(f'{temp_smag_path}*.txt')
+ df_combined = pd.DataFrame()
+ for file in files:
+ data = pd.read_json(file)
+ df_combined = pd.concat([df_combined, data], axis=0)
+ # date conversion and cleaning the DataFrame
+ df_combined['tval'] = pd.to_datetime(
+ df_combined['tval'], unit='s', origin='unix')
+ df_combined[['N', 'E', 'Z']] = df_combined[['N', 'E', 'Z']].map(
+ lambda x: x['geo'] if isinstance(x, dict) else np.nan)
+ df_combined[['N', 'E', 'Z']] = df_combined[[
+ 'N', 'E', 'Z']].replace(999999.000000, np.nan)
+
+ # Final DataFrame to save as hdf
+ df_combined.set_index('tval', inplace=True)
+ df_combined.rename(columns={
+ 'glat': 'lat', 'glon': 'lon', 'N': 'Bn', 'E': 'Be', 'Z': 'Bu'}, inplace=True)
+ df_combined['Bu'] = -df_combined['Bu']
+ df_final = df_combined[['Be', 'Bn', 'Bu',
+ 'lat', 'lon']].dropna().sort_index()
+
+ # savefile = event.replace('-', '') + '_supermag.h5'
+
+ df_final.to_hdf(savefile, key='df_final', mode='w')
+ # remove the tempfiles folder after the hdf file is created
+ shutil.rmtree(temp_smag_path)
+ return savefile
+
+
+def smag_download_for_station(args, retries=5, backoff_factor=0.5):
+ # DO NOT EDIT THIS FUNCTION
+ urlstr, station, temp_smag_path = args
+ url = urlstr + '&station=' + station.upper()
+ import certifi
+ from requests.exceptions import RequestException
+ import time
+
+ for i in range(retries):
+ try:
+ response = requests.get(url, verify=certifi.where())
+ time.sleep(random.uniform(0.2, 0.5))
+ if response.status_code == 200:
+ if response.content: # Check if the response content is not zero bytes
+ with open(f'{temp_smag_path}{station}_data.txt', 'wb') as file:
+ file.write(response.content)
+ return None
+ else:
+ print(f"Received zero bytes for station {station}")
+ raise RequestException("Received zero bytes.")
+ else:
+ print(
+ f"Failed to retrieve data for station {station}: {response.status_code}")
+ raise RequestException(
+ f"Bad status code: {response.status_code}")
+ except RequestException as e:
+ print(
+ f"Attempt {i + 1} for station {station} failed with error: {e}")
+ time.sleep(backoff_factor * (2 ** i))
+
+ print(
+ f"Failed to download data for station {station} after {retries} attempts.")
+ return None
+
+
+def smag_data_for_event(start, event):
+ # DONOT EDIT THIS FUNCTION
+ temp_smag_path = f"./smag_files{event.replace('-', '')}/"
+ os.makedirs(f'{temp_smag_path}', exist_ok=True)
+ duration = 86400 # Duration in seconds (one day)
+ # lazy importing :)
+ from lompe.data_tools.supermag_api import sm_GetUrl, sm_coreurl, sm_keycheck_data
+ from joblib import Parallel, delayed
+
+ # lompe is already registered in API
+ urlstr = sm_coreurl('data-api.php', 'lompe', start, 86400)
+ # this is hard coded to 'geo', see supermag_api.py to change it according to your needs
+ indices = sm_keycheck_data('geo')
+ urlstr += indices
+ indices = sm_keycheck_data('baseline=daily')
+ urlstr += indices
+
+ urlstr_inv = sm_coreurl('inventory.php', 'lompe', start, duration)
+ success, stations = sm_GetUrl(urlstr_inv, 'raw')
+ stations = stations[1:-1]
+
+ # Create a list of arguments to pass to data_download_for_station
+ args_list = [(urlstr, station, temp_smag_path) for station in stations]
+
+ Parallel(n_jobs=8, backend='threading')(
+ delayed(smag_download_for_station)(args) for args in args_list)
+
+
+def download_champ(event, basepath='./', tempfile_path='./'):
+ """
+ Download CHAMP data from the FTP server and process it in lompe data format.
+ Note that CHAMP data is only available for the year between 2000 and 2010.
+
+ Args:
+ event (str): format 'YYYY-MM-DD'
+ basepath (str, optional): path. Defaults to './'.
+ tempfile_path (str, optional): path. Defaults to './'.
+
+ Returns:
+ savedfile: file name of the processed file if successful
+ """
+ event_date = event.replace('-', '')
+ year = event[:4]
+ savefile = tempfile_path + f'CH_ME_MAG_LR_3_{event_date}_0102.cdf'
+ processed_file = tempfile_path + f'{event_date}_champ.h5'
+
+ # Check if the processed file already exists
+ if os.path.isfile(processed_file):
+ return processed_file
+
+ # Check if the raw file already exists
+ if not os.path.isfile(savefile):
+ from requests_ftp import ftp
+ session = requests.Session()
+ session.mount('ftp://', ftp.FTPAdapter())
+ ftp_url = f"ftp://isdcftp.gfz-potsdam.de/champ/ME/Level3/MAG/V0102/{year}/CH_ME_MAG_LR_3_{event_date}_0102.cdf"
+ try:
+ # Downloading the file and checking if it was successful
+ response = session.get(ftp_url)
+ if response.status_code == 200:
+ with open(savefile, "wb") as file:
+ file.write(response.content)
+ print(f"Downloading {savefile} is successful!")
+ else:
+ print(
+ f"Failed to download the file. Status code: {response.status_code}")
+ return None
+ except Exception as e:
+ print(f"No champ data in this period: {e}")
+ return None
+
+ # Process the downloaded CDF file ot get the magnetic disturbance
+ try:
+ import cdflib
+ import ppigrf
+ cdf_file = cdflib.CDF(savefile)
+ mag = cdf_file.varget('B_NEC') # space magnetometer data
+
+ # geocentric coords of CHAMP orbit
+ theta = 90 - cdf_file.varget('Latitude')
+ phi = cdf_file.varget('Longitude')
+ r = cdf_file.varget('Radius') / 1000
+
+ time = cdflib.cdfepoch.to_datetime(cdf_file.varget('Timestamp'))
+
+ # using IGRF to calculate magnetic disturbance (dB) registered by CHAMP
+ Br, Btheta, Bphi = ppigrf.igrf_gc(r, theta, phi, time[0])
+ B0 = np.vstack((-Btheta.flatten(), Bphi.flatten(), -Br.flatten()))
+ dB = mag.T - B0
+
+ champ_df = pd.DataFrame({
+ 'Be': dB[1],
+ 'Bn': dB[0],
+ 'Bu': -dB[2],
+ 'lon': phi,
+ 'lat': 90 - theta,
+ 'r': r
+ }, index=time)
+ champ_df.to_hdf(processed_file, key='df', mode='w')
+ os.remove(savefile) # remove the raw file after processing
+ return processed_file
+ except Exception as e:
+ print(f"Failed to process the file: {e}")
+ return None
+
+
+def download_sdarn_file(url, save_path):
+ # function to download a file from a URL and save it to a specific path
+ response = requests.get(url)
+ if response.status_code == 200:
+ with open(save_path, 'wb') as f:
+ f.write(response.content)
+ # print(f"Downloaded successfully: {save_path}")
+ else:
+ return None
+
+
+def download_sdarn_files(event, basepath='./'):
+ filepath = os.path.dirname(__file__)
+ # file containing the URLs of the SuperDARN files (zenodo records)
+ file_loc = pd.read_csv(filepath + '/../data/sdarn_2010_to_2021.csv')
+ # Month mapping
+ month_map = {
+ 'Jan': '01',
+ 'Feb': '02',
+ 'Mar': '03',
+ 'Apr': '04',
+ 'May': '05',
+ 'Jun': '06',
+ 'Jul': '07',
+ 'Aug': '08',
+ 'Sep': '09',
+ 'Oct': '10',
+ 'Nov': '11',
+ 'Dec': '12'
+ }
+
+ # Add numerical month to DataFrame (string format)
+ file_loc['Month_Num'] = file_loc['MM'].map(month_map)
+ # event_date = "2019-02-25"
+
+ # Filter the DataFrame based on the year and month of the event date
+ year = event[:4]
+ month = event[5:7]
+
+ # Filter the DataFrame
+ filtered_df = file_loc[(file_loc['year'].astype(
+ str) == year) & (file_loc['Month_Num'] == month)]
+ # Apply function and add to DataFrame
+ event_date_str = event.replace('-', '')
+
+ # URL of the Zenodo record
+ url = filtered_df['url'].tolist()[0]
+
+ # Send a GET request to the URL
+ response = requests.get(url)
+ time.sleep(random.uniform(0.2, 0.5))
+ soup = BeautifulSoup(response.content, "html.parser")
+ # Check if the request was successful (status code 200)
+ if response.status_code == 200:
+
+ # Find all tags with class "download-file"
for link in soup.find_all('a', href=True):
href = link.get('href')
- if href.lower().endswith('.nc'): # looking for .NC (jhuapl) and .nc (cdaweb) files
- file_url = urljoin(url, href)
- try:
- # Download the file
- # probably too much print uncommented the print statement if needed
- # print(f"Downloading {file_url}...")
- response = requests.get(file_url, stream=True)
- response.raise_for_status() # raise an HTTPError on bad response
- filename = os.path.join(
- destination, os.path.basename(file_url))
-
- with open(filename, 'wb') as file:
- # 64KB chuck size
- for chunk in response.iter_content(chunk_size=65536):
- if chunk: # Filter out keep-alive new chunks
- file.write(chunk)
- except requests.exceptions.RequestException as e:
- print(f"Failed to download {file_url}: {e}")
-
- print("Download complete!")
+ if 'grid.nc' in href and event_date_str in href:
+ url_to_download = 'https://zenodo.org' + href
+ # print(url_to_download)
+ save_path = basepath + \
+ url_to_download.split('/')[-1].split('?')[0]
+ download_sdarn_file(url_to_download, save_path)
+ # else:
+ # print('No file found')
+ else:
+ print('Failed to download the file')
return None
-def download_smag():
- pass
+def download_sdarn(event, basepath='./', tempfile_path='./'):
+ # tempfile_path = '/Users/fasilkebede/Documents/LOMPE/Data/SuperDARN/'
+ # event = '2019-02-25'
+ savefile = tempfile_path + event.replace('-', '') + '_superdarn_grdmap.h5'
+ if os.path.isfile(savefile):
+ return savefile
+ else:
+ from lompe.data_tools.dataloader import radar_losvec_from_mag
+ temp_sdarn_path = basepath + f"sdarn_files_{event.replace('-', '')}/"
+ os.makedirs(temp_sdarn_path, exist_ok=True)
+ download_sdarn_files(event, temp_sdarn_path)
+ # looking for the .nc files for the event
+ try:
+ files = glob.glob(
+ f"{temp_sdarn_path}*{event.replace('-', '')}*.nc")
+ files.sort()
+ ddd = pd.DataFrame()
+ for file in files:
+ sm = xr.load_dataset(file)
+ st_abbrev = file.split('/')[-1].split('.')[1]
+ # mjd conversion
+ mjd_epoch = pd.Timestamp('1858-11-17')
+ duration = (sm['mjd_end'] + mjd_epoch) - \
+ (sm['mjd_start'] + mjd_epoch)
-def download_iridium():
- pass
+ time = (sm['mjd_start'] + mjd_epoch) + duration
+ # dff['date'] = unix_epoch + dff.mjd_start
-def download_supermag():
- pass
+ temp = pd.DataFrame()
+ # in degrees AACGM
+ temp.loc[:, 'mlat'] = sm['vector.mlat'].values
+ # in degrees AACGM
+ temp.loc[:, 'mlon'] = sm['vector.mlon'].values
+ # glat, glon from lompe "radar_losvec_from_mag" is a bit different from the vector.glat and vector.glon from the data??
+ # in degrees
+ temp.loc[:, 'vector.glat'] = sm['vector.glat'].values
+ # in degrees
+ temp.loc[:, 'vector.glon'] = sm['vector.glon'].values
+ # in degrees, the angle between los and magnetic north
+ temp.loc[:, 'azimuth'] = sm['vector.kvect'].values
+ # in m/s
+ temp.loc[:, 'vlos'] = sm['vector.vel.median'].values
+ # in m/s
+ temp.loc[:, 'vlos_sd'] = sm['vector.vel.sd'].values
+ # in km
+ temp.loc[:, 'range'] = sm['vector.pwr.median'].values
+ # spectral width in m/s
+ temp.loc[:, 'wdt'] = sm['vector.wdt.median'].values
+ temp.loc[:, 'time'] = pd.to_datetime(time.values).round('s')
+ temp.loc[:, 'radar'] = st_abbrev
+ # temp.set_index = pd.to_datetime(temp['time'], unit='s')
+ # dff['datetime'] = mjd_epoch + dff['mjd_start']
+ ddd = pd.concat([ddd, temp], ignore_index=True)
+ ddd.set_index('time', inplace=True)
+ ddd['glat'], ddd['glon'], ddd['le'], ddd['ln'], ddd['le_m'], ddd['ln_m'] = radar_losvec_from_mag(ddd['mlat'].values,
+ ddd['mlon'].values, ddd['azimuth'].values, ddd.index[0])
+ dd = ddd[ddd['glat'] > 0] # restrict to northern hemisphere
+ df_final = dd.sort_values(by='time')
+ df_final.to_hdf(savefile, key='df', mode='w')
+ # remove the temp files after processing
+ shutil.rmtree(temp_sdarn_path)
-def download_dmsp():
- pass
+ return savefile
+ except Exception as e:
+ print(f"Failed to process the file: {e}")
+ return None
-def download_swarm():
- pass
+def download_swarm(event, tempfile_path='./'):
+ """Download Swarm data for a given event date.
+
+ Args:
+ event (str): Event date in 'YYYY-MM-DD' format.
+ tempfile_path (str, optional): Path to save the file or check if it already exists. Defaults to './'.
+
+ Returns:
+ str: File name of the downloaded file if successful.
+
+ Note:
+ 1. Install viresclient using "pip install viresclient" or make sure that it is installed.
+ 2. The request needs an access token from the Swarm website. Visit https://viresclient.readthedocs.io/en/latest/config_details.html.
+ """
+
+ savefile = tempfile_path + event.replace('-', '') + '_swarm.h5'
+ if os.path.isfile(savefile):
+ return savefile
+
+ try:
+ from viresclient import SwarmRequest
+ except ModuleNotFoundError:
+ print('Please install viresclient using "pip install viresclient"')
+ return
+ # checking if token is present if not directing to the website to configure it
+ try:
+ with open(os.path.expanduser('~/.viresclient.ini'), 'r') as file:
+ lines = file.readlines()
+ for line in lines:
+ if line.startswith("token ="):
+ token_value = line.split('=', 1)[1].strip()
+ if token_value:
+ print("Swarm token is present:", token_value)
+ except:
+ print("Token is missing or empty. \nPlease visit https://viresclient.readthedocs.io/en/latest/config_details.html to configure it")
+ return
+ try:
+ request = SwarmRequest()
+
+ event_start = dt.datetime.strptime(event, '%Y-%m-%d')
+ event_end = event_start + \
+ dt.timedelta(hours=23, minutes=59, seconds=59)
+ df = pd.DataFrame()
+
+ for swarm_satellite in ['A', 'B', 'C']:
+ request.set_collection(f"SW_OPER_MAG{swarm_satellite}_LR_1B")
+ request.set_products(
+ measurements=["F", "B_NEC"],
+ models=["IGRF", "MCO_SHA_2D"],
+ sampling_step="PT10S", # 10 seconds if needed PT1M for 1 minute
+ # quasi-dipole latitude and longitude
+ auxiliaries=["MLT", "OrbitNumber", 'QDLat', 'QDLon']
+ )
+ data = request.get_between(
+ start_time=event_start,
+ end_time=event_end,
+ show_progress=False
+ )
+ df = pd.concat([df, data.as_dataframe(expand=True)])
+
+ # Removing the background field from IGRF
+ df['B_n'] = df['B_NEC_N'] - df['B_NEC_IGRF_N']
+ df['B_e'] = df['B_NEC_E'] - df['B_NEC_IGRF_E']
+ # Upward is negative in the NEC systema
+ df['B_u'] = -(df['B_NEC_C'] - df['B_NEC_IGRF_C'])
+
+ df.sort_values(by='Timestamp', inplace=True)
+ df.reset_index(inplace=True)
+ df.to_hdf(savefile, key='df', mode='w')
+ return savefile
+
+ except Exception as e:
+ print(f"An error occurred while processing the Swarm data: {e}")
+
+
+def download_dmsp_ssies(event, sat, tempfile_path='./', **madrigal_kwargs):
+ """ Download DMSP SSIES ion drift meter data for a full day
+ using FTP-like acess at http://cedar.openmadrigal.org/ftp/ and get relevant parameters for Lompe.
+ Saves hdf file in tempfile_path and returns path
+
+ Args:
+ event (str):
+ string on format 'yyyy-mm-dd' to specify time of event for model.
+ sat (int):
+ Satellite ID for DMSP Block 5D satellite (16-19)
+ tempfile_path (str, optional):
+ Path to dir where processed hdf files are placed. Default: './'
+ **madrigalkwargs (dict):
+ needed to download data from MadrigalWeb (Madrigal needs user specifications through **madrigal_kwargs.)
+ Example usage:
+ event = '2014-07-13'
+ sat = 17
+ tempfile_path = '/Users/fasilkebede/Documents/'
+ madrigal_kwargs = {'user_fullname': 'First','user_email': 'name@host.com', 'user_affiliation': 'University'}
+
+ Returns:
+ savefile(str):
+ Path to hdf-file containing SSIES data for Lompe.
+ """
-def date2doy(date_str):
- date = dt.datetime.strptime(date_str, "%Y-%m-%d")
- return date.timetuple().tm_yday
+ savefile = tempfile_path + \
+ event.replace('-', '') + '_ssies_f' + str(sat) + '.h5'
+ if os.path.exists(savefile):
+ print('File already exists')
+ return savefile
+
+ date_str = event.replace('-', '')
+ year = event[:4]
+ url_base = "https://cedar.openmadrigal.org"
+ url = url_base + \
+ f"/ftp/fullname/{madrigal_kwargs['user_fullname']}/email/{madrigal_kwargs['user_email']}/affiliation/{madrigal_kwargs['user_affiliation']}/kinst/8100/year/{year}/"
+
+ response2 = requests.get(url)
+ soup2 = BeautifulSoup(response2.content, 'html.parser')
+ urls = [link.get('href') for link in soup2.find_all(
+ 'a', href=True) if '/format/hdf5/' in link.get('href')]
+
+ url_ion_drift = url_base + [a['href'] for a in soup2.find_all(
+ 'a', href=True) if 'ion drift' in a.text and f'F{sat}' in a.text][0]
+ url_plasma_temp = url_base + [a['href'] for a in soup2.find_all(
+ 'a', href=True) if 'plasma temp' in a.text and f'F{sat}' in a.text][0]
+ # url_flux_energy = url_base + [a['href'] for a in soup2.find_all(
+ # 'a', href=True) if 'flux/energy' in a.text and f'F{sat}' in a.text][0]
+
+ # downloading the ion drift file
+ ion_drift_hdf5_file_url = url_ion_drift + 'format/hdf5/'
+ response2 = requests.get(ion_drift_hdf5_file_url)
+ soup2 = BeautifulSoup(response2.content, 'html.parser')
+ url_target_file = url_base + [link.get('href') for link in soup2.find_all(
+ 'a', href=True) if '/format/hdf5/fullFilename/' in link.get('href') and date_str in link.get('href')][0]
+
+ response = requests.get(url_target_file, stream=True)
+ # tempfile_path = '/Users/fasilkebede/Documents/'
+ filename = tempfile_path + url_target_file[-27:-1]
+ with open(filename, 'wb') as file:
+ for chunk in response.iter_content(chunk_size=65536):
+ if chunk: # Filter out keep-alive new chunks
+ file.write(chunk)
+
+ # downloading the plasma temperature file
+ plasma_hdf5_file_url = url_plasma_temp + 'format/hdf5/'
+ response2 = requests.get(plasma_hdf5_file_url)
+ soup2 = BeautifulSoup(response2.content, 'html.parser')
+ url_target_file = url_base + [link.get('href') for link in soup2.find_all(
+ 'a', href=True) if '/format/hdf5/fullFilename/' in link.get('href') and date_str in link.get('href')][0]
+
+ response = requests.get(url_target_file, stream=True)
+ # tempfile_path = '/Users/fasilkebede/Documents/'
+ filename2 = tempfile_path + url_target_file[-27:-1]
+ with open(filename2, 'wb') as file:
+ for chunk in response.iter_content(chunk_size=65536):
+ if chunk: # Filter out keep-alive new chunks
+ file.write(chunk)
+
+ dmsp = pd.read_hdf(filename, mode='r', key='Data/Table Layout')
+ dmsp2 = pd.read_hdf(filename2, mode='r', key='Data/Table Layout')
+
+ dmsp.index = np.arange(len(dmsp))
+ dmsp2.index = np.arange(len(dmsp2))
+
+ # set datetime as index
+ times = []
+ for i in range(len(dmsp)):
+ times.append(dt.datetime.fromtimestamp(
+ int(dmsp['ut1_unix'][i]), tz=dt.timezone.utc))
+ dmsp.index = times
+
+ # reindexing due to lower cadence measurements
+ times2 = []
+ for i in range(len(dmsp2)):
+ times2.append(dt.datetime.fromtimestamp(
+ int(dmsp2['ut1_unix'][i]), tz=dt.timezone.utc))
+ dmsp2.index = times2
+ dmsp2 = dmsp2.reindex(index=dmsp.index, method='nearest', tolerance='2sec')
+
+ dmsp.loc[:, 'po+'] = dmsp2['po+']
+ dmsp.loc[:, 'te'] = dmsp2['te']
+ dmsp.loc[:, 'ti'] = dmsp2['ti']
+
+ # Smooth the orbit
+ import matplotlib
+ from scipy import interpolate
+ from lompe.data_tools.dataloader import cross_track_los
+
+ # latitude (geodetic)
+ tck = interpolate.splrep(matplotlib.dates.date2num(dmsp.index[::60].append(dmsp.index[-1:])),
+ pd.concat([dmsp.gdlat[::60], (dmsp.gdlat[-1:])]).values, k=2)
+ gdlat = interpolate.splev(matplotlib.dates.date2num(dmsp.index), tck)
+
+ # longitude (geodetic)
+ negs = dmsp.glon < 0
+ dmsp.loc[negs, 'glon'] = dmsp.glon[negs] + 360
+ tck = interpolate.splrep(matplotlib.dates.date2num(dmsp.index[::60].append(dmsp.index[-1:])),
+ pd.concat([dmsp.glon[::60], (dmsp.glon[-1:])]).values, k=2)
+ glon = interpolate.splev(matplotlib.dates.date2num(dmsp.index), tck)
+
+ # i got negative and above 360 values even negative are excluded before the interpolation, due to the interpolation, handling it here
+ glon[glon < 0] += 360
+ glon = np.mod(glon, 360)
+
+ # altitude (geodetic)
+ tck = interpolate.splrep(matplotlib.dates.date2num(dmsp.index[::60].append(dmsp.index[-1:])),
+ pd.concat([dmsp.gdalt[::60], (dmsp.gdalt[-1:])]).values, k=2)
+ gdalt = interpolate.splev(matplotlib.dates.date2num(dmsp.index), tck)
+
+ # get eastward and northward component of cross track direction
+ le, ln, bearing = cross_track_los(dmsp['hor_ion_v'], gdlat, glon)
+
+ # put together the relevant data
+ ddd = pd.DataFrame() # dataframe to return
+ ddd.index = dmsp.index
+ ddd.loc[:, 'gdlat'] = gdlat
+ ddd.loc[:, 'glon'] = glon
+ ddd.loc[:, 'gdalt'] = gdalt
+ ddd.loc[:, 'hor_ion_v'] = np.abs(dmsp.hor_ion_v)
+ ddd.loc[:, 'vert_ion_v'] = dmsp.vert_ion_v
+ ddd.loc[:, 'bearing'] = bearing
+ ddd.loc[:, 'le'] = le
+ ddd.loc[:, 'ln'] = ln
+
+ # quality flag from Zhu et al 2020 page 8 https://doi.org/10.1029/2019JA027270
+ # flag1 is good, flag2 is also usually acceptable
+
+ flag1 = (dmsp['ne'] > 1e9) & (dmsp['po+'] > 0.85)
+ flag2 = ((dmsp['po+'] > 0.85) & (dmsp['ne'] > 1e8) & (dmsp['ne'] < 1e9)) | ((dmsp['po+'] > 0.75)
+ & (dmsp['po+'] < 0.85) & (dmsp['ne'] > 1e8))
+ flag3 = (dmsp['ne'] < 1e8) | (dmsp['po+'] < 0.75)
+ flag4 = dmsp['po+'].isna()
+
+ ddd.loc[:, 'quality'] = np.zeros(ddd.shape[0]) + np.nan
+ ddd.loc[flag1, 'quality'] = 1
+ ddd.loc[flag2, 'quality'] = 2
+ ddd.loc[flag3, 'quality'] = 3
+ ddd.loc[flag4, 'quality'] = 4
+
+ ddd.to_hdf(savefile, key='df', mode='w')
+ os.remove(filename)
+ os.remove(filename2)
+ return savefile
+
+
+def download_eiscat():
+ pass
+# def date2doy(date_str):
+# date = dt.datetime.strptime(date_str, "%Y-%m-%d")
+# return date.timetuple().tm_yday
if __name__ == '__main__':
- print("This is a module to download SSUSI data for a given event date.")
+ print("This is a module to download data from different sources for a given event date.")
diff --git a/lompe/data_tools/dataloader.py b/lompe/data_tools/dataloader.py
index c87ba46..8232e83 100644
--- a/lompe/data_tools/dataloader.py
+++ b/lompe/data_tools/dataloader.py
@@ -22,13 +22,13 @@
import datetime as dt
import xarray as xr
import numpy as np
-
+from lompe.utils.time import date2doy
# degrees <-> radians conversion
d2r = np.pi / 180.
r2d = 180. / np.pi
-def read_ssusi(event, hemi='north', basepath='./', tempfile_path='./'):
+def read_ssusi(event, hemi='north', basepath='./', tempfile_path='./', source='jhuapl'):
"""
Called and used for modelling auroral conductance in cmodel.py.
@@ -51,6 +51,9 @@ def read_ssusi(event, hemi='north', basepath='./', tempfile_path='./'):
tempfile_path : str, optional
Location for storing processed SSUSI data.
Default: './'
+ source : str, optional
+ Specify source of SUSSI data.
+ Default: 'jhuapl', other option is 'cdaweb'
Returns
-------
@@ -88,10 +91,15 @@ def read_ssusi(event, hemi='north', basepath='./', tempfile_path='./'):
'read_ssusi: Could not load netCDF4 module. Will not be able to read SSUSI-files from APL.')
imgs = [] # List to hold all images. Converted to xarray later.
+ doy_str = f"{date2doy(event):03d}"
for sat in ['F16', 'F17', 'F18', 'F19']:
- files = glob.glob(basepath + '*' + sat + '*' +
- event[0:4]+event[5:7]+event[8:10] + '*.NC')
+ if source == 'jhuapl':
+ files = glob.glob(basepath + '*' + sat + '*' +
+ event[0:4] + event[5:7] + event[8:10] + '*.NC')
+ elif source == 'cdaweb':
+ files = glob.glob(basepath + '*' + sat + '*' +
+ event[0:4] + doy_str + '*.nc')
files.sort()
if len(files) == 0:
continue
@@ -163,8 +171,8 @@ def read_ssusi(event, hemi='north', basepath='./', tempfile_path='./'):
hr = hr - 24
center_hr = center_hr - 24
- m = int((center_hr - hr)*60)
- s = round(((center_hr - hr)*60 - m)*60)
+ m = int((center_hr - hr) * 60)
+ s = round(((center_hr - hr) * 60 - m) * 60)
if s == 60:
t0 = dt.datetime(year, 1, 1, hr, m, 59) + \
dt.timedelta(seconds=1)
@@ -199,7 +207,7 @@ def read_ssusi(event, hemi='north', basepath='./', tempfile_path='./'):
else: # save as netcdf in specified path
imgs = xr.concat(imgs, dim='date')
- imgs = imgs.assign({'hemisphere': hemi})
+ imgs = imgs.assign({'hemisphere': hemi})
imgs = imgs.sortby(imgs['date'])
print('DMSP SSUSI file saved: ' + savefile)
@@ -305,26 +313,32 @@ def read_ssies(event, sat, basepath='./', tempfile_path='./', forcenew=False, **
dmsp = pd.DataFrame()
dmsp2 = pd.DataFrame() # for the density fraction
-
- for i in expList: # get neccessary files
+ date_str = event.replace('-', '')
+ no_data_found = True
+ for i in expList:
fileList = testData.getExperimentFiles(i.id)
filenames = []
for fname in fileList:
filenames.append(fname.name)
-
- ssies = str([s for s in filenames if '_' + str(sat) + 's1' in s][0])
- temp_dens = str(
- [s for s in filenames if '_' + str(sat) + 's4' in s][0])
+ filenames = [filename for filename in filenames if date_str in filename]
+ if len(filenames) == 0:
+ continue
+ no_data_found = False
+ # ssies = str([s for s in filenames if '_' + str(sat) + 's1' in s][0])
+ ssies = [s for s in filenames if '_' + str(sat) + 's1.' in s]
+ # temp_dens = str(
+ # [s for s in filenames if '_' + str(sat) + 's4.' in s][0])
+ temp_dens = [s for s in filenames if '_' + str(sat) + 's4.' in s]
datafile = basepath + 'ssies_temp_' + event + '.hdf5'
result = testData.downloadFile(
- ssies, datafile, **madrigal_kwargs, format="hdf5")
+ ssies[0], datafile, **madrigal_kwargs, format="hdf5")
f = pd.read_hdf(datafile, mode='r', key='Data/Table Layout')
tempdensfile = basepath + 'ssies_tempdens_data_' + event + '.hdf5'
result = testData.downloadFile(
- temp_dens, tempdensfile, **madrigal_kwargs, format="hdf5")
+ temp_dens[0], tempdensfile, **madrigal_kwargs, format="hdf5")
f2 = pd.read_hdf(tempdensfile, mode='r', key='Data/Table Layout')
use = (f.ut1_unix >= usTime) & (f.ut1_unix < ueTime)
@@ -335,8 +349,11 @@ def read_ssies(event, sat, basepath='./', tempfile_path='./', forcenew=False, **
dmsp = pd.concat([dmsp, temp])
dmsp2 = pd.concat([dmsp2, temp2])
- dmsp.index = np.arange(len(dmsp))
- dmsp2.index = np.arange(len(dmsp2))
+ dmsp.index = np.arange(len(dmsp))
+ dmsp2.index = np.arange(len(dmsp2))
+ if no_data_found:
+ print('No data found for ' + event + ' and satellite ' + str(sat))
+ return None
# set datetime as index
times = []
@@ -498,8 +515,8 @@ def read_sdarn(event, basepath='./', tempfile_path='./', hemi='north'):
stime = dt.datetime(tt['start.year'], tt['start.month'], tt['start.day'],
tt['start.hour'], tt['start.minute'], int(tt['start.second']))
- etime = dt.datetime(tt['end.year'], tt['end.month'], tt['end.day'],
- tt['end.hour'], tt['end.minute'], int(tt['end.second']))
+ etime = dt.datetime(tt['end.year'], tt['end.month'], tt['end.day'],
+ tt['end.hour'], tt['end.minute'], int(tt['end.second']))
duration = etime - stime
temp = pd.DataFrame()
@@ -529,7 +546,7 @@ def read_sdarn(event, basepath='./', tempfile_path='./', hemi='north'):
temp.loc[:, 'range'] = tt['vector.pwr.median'] # in km
# spectral width in m/s
temp.loc[:, 'wdt'] = tt['vector.wdt.median']
- temp.loc[:, 'time'] = stime + duration/2
+ temp.loc[:, 'time'] = stime + duration / 2
ddd = pd.concat([ddd, temp], ignore_index=True)
@@ -685,7 +702,7 @@ def read_iridium(event, basepath='./', tempfile_path='./', file_name=''):
fn = files[0]
except:
raise FileNotFoundError(
- 'Cannot find Iridium netcdf in specified folder.')
+ 'Cannot find Iridium netcdf in specified folder.')
iridset = xr.load_dataset(fn, engine='netcdf4')
@@ -757,17 +774,17 @@ def getbearing(lat0, lon0, lat1, lon1):
"""
- lat0 = lat0*d2r
- lon0 = lon0*d2r
- lat1 = lat1*d2r
- lon1 = lon1*d2r
+ lat0 = lat0 * d2r
+ lon0 = lon0 * d2r
+ lat1 = lat1 * d2r
+ lon1 = lon1 * d2r
coslt1 = np.cos(lat1)
sinlt1 = np.sin(lat1)
coslt0 = np.cos(lat0)
sinlt0 = np.sin(lat0)
- cosl0l1 = np.cos(lon1-lon0)
- sinl0l1 = np.sin(lon1-lon0)
+ cosl0l1 = np.cos(lon1 - lon0)
+ sinl0l1 = np.sin(lon1 - lon0)
cosc = sinlt0 * sinlt1 + coslt0 * coslt1 * cosl0l1
# Avoid roundoff problems by clamping cosine range to [-1,1].
@@ -782,8 +799,8 @@ def getbearing(lat0, lon0, lat1, lon1):
sinaz = np.zeros(len(lat0))
cosaz = np.ones(len(lat0))
cosaz[small] = (coslt0[small] * sinlt1[small] - sinlt0[small]
- * coslt1[small]*cosl0l1[small]) / sinc[small]
- sinaz[small] = sinl0l1[small]*coslt1[small]/sinc[small]
+ * coslt1[small] * cosl0l1[small]) / sinc[small]
+ sinaz[small] = sinl0l1[small] * coslt1[small] / sinc[small]
return np.arctan2(sinaz, cosaz)
diff --git a/lompe/data_tools/supermag_api.py b/lompe/data_tools/supermag_api.py
new file mode 100644
index 0000000..4bd4508
--- /dev/null
+++ b/lompe/data_tools/supermag_api.py
@@ -0,0 +1,551 @@
+import datetime
+import re
+import json
+import pandas as pd # dataframes and also to_datetime
+import urllib.request
+
+# the 'certifi' library is required at APL and other sites that
+# require SSL certs for web fetches. If you need this, install certifi
+# (pip install certifi)
+import importlib
+certspec = importlib.util.find_spec("certifi")
+found = certspec is not None
+if found:
+ import certifi
+
+
+"""
+;supermag-api.py
+; ================
+; Author S. Antunes, based on supermag-api.pro by R.J.Barnes
+
+
+; (c) 2021 The Johns Hopkins University Applied Physics Laboratory
+;LLC. All Rights Reserved.
+
+;This material may be only be used, modified, or reproduced by or for
+;the U.S. Government pursuant to the license rights granted under the
+;clauses at DFARS 252.227-7013/7014 or FAR 52.227-14. For any other
+;permission,
+;please contact the Office of Technology Transfer at JHU/APL.
+
+; NO WARRANTY, NO LIABILITY. THIS MATERIAL IS PROVIDED "AS IS."
+; JHU/APL MAKES NO REPRESENTATION OR WARRANTY WITH RESPECT TO THE
+; PERFORMANCE OF THE MATERIALS, INCLUDING THEIR SAFETY, EFFECTIVENESS,
+; OR COMMERCIAL VIABILITY, AND DISCLAIMS ALL WARRANTIES IN THE
+; MATERIAL, WHETHER EXPRESS OR IMPLIED, INCLUDING (BUT NOT LIMITED TO)
+; ANY AND ALL IMPLIED WARRANTIES OF PERFORMANCE, MERCHANTABILITY,
+; FITNESS FOR A PARTICULAR PURPOSE, AND NON-INFRINGEMENT OF
+; INTELLECTUAL PROPERTY OR OTHER THIRD PARTY RIGHTS. ANY USER OF THE
+; MATERIAL ASSUMES THE ENTIRERISK AND LIABILITY FOR USING THE
+; MATERIAL. IN NO EVENT SHALL JHU/APL BE LIABLE TO ANY USER OF THE
+; MATERIAL FOR ANY ACTUAL, INDIRECT, CONSEQUENTIAL, SPECIAL OR OTHER
+; DAMAGES ARISING FROM THE USE OF, OR INABILITY TO USE, THE MATERIAL,
+; INCLUDING, BUT NOT LIMITED TO, ANY DAMAGES FOR LOST PROFITS.
+"""
+# Sample URLs, type into browser if you want to compare the data vs python
+# https://supermag.jhuapl.edu/services/data-api.php?fmt=json&logon=YOURNAME&start=2019-10-15T10:40&extent=3600&all&station=NCK
+# https://supermag.jhuapl.edu/services/indices.php?fmt=json&logon=YOURNAME&start=2019-10-15T10:40&extent=3600&all
+# https://supermag.jhuapl.edu/services/inventory.php?fmt=json&logon=YOURNAME&start=2019-10-15T10:40&extent=3600
+
+
+def sm_coreurl(page, logon, start, extent):
+ # internal helper function
+ baseurl = "https://supermag.jhuapl.edu/"
+
+ mytime = sm_parsestart(start)
+ urlstr = baseurl + 'services/'+page+'?python&nohead'
+ urlstr += '&start='+mytime
+ urlstr += '&logon='+logon
+ urlstr += '&extent=' + ("%12.12d" % extent)
+
+ # print("debug:",urlstr)
+
+ return (urlstr)
+
+# handy helper function when using complicated CVS encoding of lists
+
+
+def sm_csvitem_to_list(myarr):
+ # converts entity of form ['HOP', 'NVS', 'IRT'] to an actual list of HOP, NVS, IRT
+ mylist = []
+ for myline in myarr:
+ myline = re.sub("'", "", myline[1:-1])
+ mylist.append(myline.split(", "))
+ return (mylist)
+
+
+# handy helper function when using complicated CVS encoding of dicts
+def sm_csvitem_to_dict(myarr, **kwargs):
+ # converts entity of form {'X': -12.213, 'Y': -5.5, 'Z': 1.2} to an actual dict of var.X, var.Y, etc
+ # items are presumed strings by default
+ # optional argument "convert=num" will convert them to doubles
+ mylist = []
+ for myline in myarr:
+ myline2 = re.sub(" ", "", myline[1:-1]) # scrub out extra spaces
+ myline2 = re.sub("'", "", myline2)
+ elements = dict(x.split(":") for x in myline2.split(","))
+ # little sanity check to make sure float subitems remain floats
+ try:
+ elements = {item: float(value)
+ for (item, value) in elements.items()}
+ except:
+ pass
+
+ # type(elements)
+ mylist.append(elements)
+ return (mylist)
+
+
+def sm_parsestart(start):
+ # internal helper function
+ # takes either list of [yyyy, mo, dd, hh, mm, opt_ss]
+ # or string of a normal datetime 'YYYY-MM-DD hh-mm' (optional ss)
+ # or the SuperMAG-ready 'YYYY-MM-DDThh-mm-ss'
+
+ if isinstance(start, list):
+ timestring = "%4.4d-%2.2d-%2.2dT%2.2d:%2.2d" % tuple(start[0:5])
+ elif isinstance(start, datetime.date):
+ # good to go, TBD
+ timestring = start.strftime("%Y-%m-%dT%H:%M")
+ else:
+ # is a string, reparse, TBD
+ timestring = start
+
+ return (timestring)
+
+
+def sm_DateToYMDHMS(tval, yr, mo, dy, hr, mt, sc):
+ # not used but here as an example of date conversion
+ julday = (tval/86400.0)+2440587.5
+ # format YYYY-MM-DD HH:MM:SS.ssssss
+ datestr = pd.to_datetime(julday, unit='D', origin='julian')
+ return (datestr)
+
+
+def sm_keycheck_data(flagstring):
+ # internal helper function
+ toggles = ['mlt', 'mag', 'geo', 'decl', 'sza', 'delta=start',
+ 'baseline=daily', 'baseline=yearly', 'baseline=none']
+
+ myflags = ''
+ flags = [x.strip() for x in flagstring.split(',')]
+
+ for i in range(0, len(flags)):
+ chk = flags[i]
+ chk = chk.lower()
+ # check for the '*all', also individual keys, and assemble url flags
+ if chk == 'all':
+ myflags += '&mlt&mag&geo&decl&sza'
+ for ikey in range(0, len(toggles)):
+ if chk == toggles[ikey]:
+ myflags += '&'+toggles[ikey]
+
+ return (myflags)
+
+
+def sm_keycheck_indices(flagstring):
+ # internal helper function
+ # For category='indices', always returns:
+ # tval
+ # additional flags to return data include:
+ # indicesall (or its alias: all)
+ # (or any of)
+ # baseall, sunall, darkall, regionalall, plusall
+ # (or specify individual items to include, from the sets below)
+ #
+ basekeys = ["sme", "sml", "smu", "mlat",
+ "mlt", "glat", "glon", "stid", "num"]
+ # sunkeys: alias allowed of SUN___ -> ___s
+ sunkeys = ["smes", "smls", "smus", "mlats",
+ "mlts", "glats", "glons", "stids", "nums"]
+ # darkkeys: alias allowed of DARK___ -> ___d
+ darkkeys = ["smed", "smld", "smud", "mlatd",
+ "mltd", "glatd", "glond", "stidd", "num"]
+ # regkeys: alias allowed of REGIONAL___ -> ___r
+ regkeys = ["smer", "smlr", "smur", "mlatr",
+ "mltr", "glatr", "glonr", "stidr", "numr"]
+ pluskeys = ["smr", "ltsmr", "ltnum", "nsmr"]
+ indiceskeys = basekeys + sunkeys + darkkeys + regkeys + pluskeys
+ # 'all' means all the above
+
+ imfkeys = ["bgse", "bgsm", "vgse", "vgsm"] # or imfall for all these
+ swikeys = ["pdyn", "epsilon", "newell", "clockgse",
+ "clockgsm", "density"] # % or swiall for all these
+ myflags = ''
+ indices = '&indices='
+ swi = '&swi='
+ imf = '&imf='
+
+ flags = [x.strip() for x in flagstring.split(',')]
+
+ for i in range(0, len(flags)):
+ chk = flags[i]
+ chk = chk.lower()
+
+ # check for the '*all', also individual keys, and assemble url flags
+ if chk == 'all':
+ indices += 'all,'
+ if chk == 'indicesall':
+ indices += 'all,'
+ if chk == 'imfall':
+ imf += 'all,'
+ if chk == 'swiall':
+ swi += 'all,'
+ # available keywords, we allow both the url version and the
+ # aliases of "SUN___ -> ___s", "DARK___ -> ___d", "REGIONAL___ -> ___r"
+
+ for ikey in range(0, len(indiceskeys)):
+ mykey = indiceskeys[ikey]
+ sunkey = "sun"+mykey # allow alias
+ darkkey = "dark"+mykey # allow alias
+ regkey1 = "regional"+mykey # allow alias
+ regkey2 = "reg"+mykey # allow alias
+ if chk == mykey:
+ indices += mykey+',' # base key is correct
+ elif sunkey == mykey:
+ indices += mykey+'s,' # alias, so base key + 's'
+ elif darkkey == mykey:
+ indices += mykey+'d,' # alias, so base key + 'd'
+ elif regkey1 == mykey or regkey2 == mykey:
+ indices += mykey+'r,' # alias, so base key + 'r'
+
+ for ikey in range(0, len(swikeys)):
+ if chk == swikeys[ikey]:
+ swi += swikeys[ikey] + ','
+
+ for ikey in range(0, len(imfkeys)):
+ if chk == imfkeys[ikey]:
+ imf += imfkeys[ikey] + ','
+
+ # more aliases to the user
+ if chk == 'baseall':
+ indices += ','.join(basekeys)
+ if chk == 'sunall':
+ indices += ','.join(sunkeys)
+ if chk == 'darkall':
+ indices += ','.join(darkkeys)
+ if chk == 'regionalall' or chk == 'regall':
+ indices += ','.join(regkeys)
+ if chk == 'plusall':
+ indices += ','.join(pluskeys)
+
+ # clean it up a bit by removing extraneous tags/characters
+ if indices == "&indices=":
+ indices = ""
+ if swi == "&swi=":
+ swi = ""
+ if imf == "&imf=":
+ imf = ""
+ # add them together
+ myflags = indices + swi + imf
+ # a little more cleaning for tidiness, removes extraneous commas
+ myflags = re.sub(',&', '&', myflags)
+ myflags = re.sub(',$', '', myflags)
+
+ return (myflags)
+
+
+def sm_GetUrl(fetchurl, fetch='raw'):
+ # internal helper function
+ # returned data choices are 'raw' or 'json', default is 'raw'
+ # converts an http bytestream into a python list (raw) or list of dict (json)
+ # 'stations' should be 'raw' and returns a list
+ # 'data' should be 'json', returns a list (which converts to a dataframe)
+ # 'indices' should be 'json', returns a list (which converts to a dataframe)
+
+ success = 0 # gets changed to 1 good data is fetched
+ longstring = b"ERROR: Unknown error" # prepare for the worst
+
+ # print("debug: url trying ",fetch,"is",fetchurl)
+ # If the url object throws an error it will be caught here
+ try:
+ # with urllib.request.urlopen(fetchurl,cafile=certifi.where()) as response:
+ # note that 'cafile' is deprecated past python 3.5 but we keep it here
+ # to have stronger backward compatability with earlier versions
+ try:
+ cafile = certifi.where()
+ except:
+ cafile = ''
+ with urllib.request.urlopen(fetchurl, cafile=cafile) as response:
+ longstring = response.read()
+
+ if fetch == 'json':
+ if len(longstring) > 3:
+ # mydata = json.loads(longstring[3:]) # skipping initial OK
+ mydata = json.loads(longstring)
+ else:
+ mydata = [] # just the word 'OK', no data, so return no data
+ success = 1
+ else:
+ # default is raw byte strings, which we split into a list
+ mydata = (longstring.decode('UTF-8')).split('\n')
+ success = 1 # it worked
+ if re.search(r'ERROR', mydata[0]):
+ success = 0 # legit return, but of an err
+
+ except urllib.error.URLError as e:
+ # print, !ERROR_STATE.msg
+ mydata = ['ERROR:HTTP error', e.reason]
+ except:
+ longstring = longstring.decode('UTF-8')
+ mydata = [longstring] # catch-all if nothing below works
+
+ # print("debug: function return type is:",type(mydata),".")
+ # returns a list for 'raw' or a list of dictionaries for 'json'
+ return (success, mydata)
+
+
+# Gets a list of stations.
+# Return value is either '1' plus list of stations, or
+# '0' plus a string with the error message
+# Sample usage:
+# 'extent' is how long a window, in seconds, to grab. (86400 sec = 1 day)
+# (status, stations) =supermaggetinventory('myname',2019,11,2,20,24,00,86400)
+# In this case, 'status'=1 and 'stations' is a list of 184 stations
+
+def SuperMAGGetInventory(logon, start, extent):
+ # One of the core 3 functions
+
+ iarr = ""
+ errstr = ""
+
+ # construct URL
+ urlstr = sm_coreurl('inventory.php', logon, start, extent)
+
+ # get the string array of stations
+ (success, stations) = sm_GetUrl(urlstr, 'raw')
+
+ # if the inventory is valid extract the stations to an array
+ # if an error occurs set the the ERROR keyword to be the error string
+
+ if success:
+ # first data item is how many stations were found
+ numstations = int(stations[0])
+ # print("debug: found",numstations,"stations")
+ if numstations > 0:
+ stations = stations[1:-1] # remove OK,#stations,''
+ else:
+ stations = [] # empty list because no stations found
+ # print('debug, got back:',stations)
+ # success: return true (1) if the call was successful otherwise false (0)
+ # stations: return list with true/1 plus data, or false/0 plus errstr
+ return (success, stations)
+
+
+def SuperMAGGetIndices(logon, start, extent, flagstring='', **kwargs):
+ # One of the core 3 functions
+
+ urlstr = sm_coreurl('indices.php', logon, start, extent)
+ indices = sm_keycheck_indices(flagstring)
+ urlstr += indices
+
+ # get the string array of JSON data
+ (status, data_list) = sm_GetUrl(urlstr, 'json')
+
+ # default is to return a dataframe, but can also return an array
+ if (kwargs.get('FORMAT', 'none')).lower() == 'list':
+ return (status, data_list)
+ else:
+ # default, converts the json 'list of dictionaries' into a dataframe
+ data_df = pd.DataFrame(data_list)
+ return (status, data_df)
+
+
+def SuperMAGGetData(logon, start, extent, flagstring, station, **kwargs):
+ # One of the core 3 functions
+
+ # optional options for 'data':
+ # ALL=&mlt&mag&geo&decl&sza
+ # MLT=&mlt,MAG=&mag,GEO=&geo,DECL=&decl,SZA=&sza,
+ # DELTA='start',BASELINE='none/yearly'
+ # e.g. can pass MLT=1,MAG=1 and they will be evaluated. Full set checked: ALL, MLT, MAG, GEO, DECL, SZA, also values for DELTA, BASELINE
+ # also arg FORMAT='list', otherwise defaults to FORMAT='dataframe' NOT YET DONE!!!!
+
+ # default FORMAT='dataframe', alt is FORMAT='list'
+
+ urlstr = sm_coreurl('data-api.php', logon, start, extent)
+ indices = sm_keycheck_data(flagstring)
+ urlstr += indices
+ urlstr += '&station='+station.upper()
+
+ (status, data_list) = sm_GetUrl(urlstr, 'json')
+
+ # default is to return a dataframe, but can also return an array
+ if (kwargs.get('FORMAT', 'none')).lower() == 'list':
+ return (status, data_list)
+ else:
+ # default, converts the json 'list of dictionaries' into a dataframe
+ data_df = pd.DataFrame(data_list)
+ return (status, data_df)
+
+
+def sm_grabme(dataf, key, subkey):
+ # syntactical sugar to grab nested subitems from a dataframe
+ data = dataf[key]
+ subdata = [temp[subkey] for temp in data]
+ return (subdata)
+
+# Unlike IDL, which returns as Array or Struct,
+# we return as List (of dictionaries) or DataFrame
+
+
+def sm_microtest(choice, userid):
+ # 3 simple unit tests to verify the core fetches work
+ import matplotlib.pyplot as plt
+
+ start = [2019, 11, 15, 10, 40, 00] # alt: start='2019-11-15T10:40'
+
+ if choice == 1 or choice == 4:
+ (status, stations) = SuperMAGGetInventory(userid, start, 3600)
+ print(status)
+ print(stations)
+
+ if choice == 2 or choice == 4:
+ (status, data) = SuperMAGGetData(userid, start,
+ 3600, 'all,delta=start,baseline=yearly', 'HBK')
+ print(status)
+ print(data)
+ print(data.keys())
+
+ tval = data.tval
+ mlt = data.mlt
+ # Python way
+ N_nez = [temp['nez'] for temp in data.N]
+ N_geo = [temp['geo'] for temp in data.N]
+ # or, supermag helper shorthand way
+ N_nez = sm_grabme(data, 'N', 'nez')
+ N_geo = sm_grabme(data, 'N', 'geo')
+ #
+ plt.plot(tval, N_nez)
+ plt.plot(tval, N_geo)
+ plt.ylabel('N_geo vs N_nez')
+ plt.xlabel('date')
+ plt.show()
+
+ if choice == 3 or choice == 4:
+ (status, idxdata) = SuperMAGGetIndices(
+ userid, start, 3600, 'swiall,density,darkall,regall,smes')
+ # print(status)
+ # print(idxdata)
+ idxdata.keys()
+ tval = idxdata.tval
+ hours = list(range(24))
+ y = idxdata.SMLr
+ for i in range(len(tval)-1):
+ plt.plot(hours, y[i])
+ plt.ylabel('SMLr')
+ plt.xlabel('hour')
+ plt.title('SMLr variation by hour, for successive days')
+ plt.show()
+
+
+def supermag_testing(userid):
+
+ start = [2019, 11, 15, 10, 40, 00] # alt: start='2019-11-15T10:40'
+
+ (status, stations) = SuperMAGGetInventory(userid, start, 3600)
+
+ # DATA fetches
+ # BARE CALL, dataframe returned
+ (status, mydata1a) = SuperMAGGetData(userid, start, 3600, '', 'HBK')
+ mydata1a # is 1440 rows x 6 columns dataframe
+ # Index(['tval', 'ext', 'iaga', 'N', 'E', 'Z'], dtype='object')
+ mydata1a.keys()
+
+ # CALL with ALLINDICES, dataframe returned
+ (status, mydata1a) = SuperMAGGetData(userid, start, 3600, 'all', 'HBK')
+ mydata1a # is 1440 rows x 12 columns dataframe
+ # Index(['tval', 'ext', 'iaga', 'glon', 'glat', 'mlt', 'mcolat', 'decl', 'sza', 'N', 'E', 'Z'], dtype='object')
+ mydata1a.keys()
+
+ # BARE CALL, list returned
+ (status, mydata1b) = SuperMAGGetData(
+ userid, start, 3600, '', 'HBK', FORMAT='list')
+ len(mydata1b) # is 1440 rows of dicts (key-value pairs)
+ mydata1b[0:1] # {'tval': 1572726240.0, 'ext': 60.0, 'iaga': 'DOB', 'N': {'nez': -3.942651, 'geo': -5.964826}, 'E': {'nez': 4.492887, 'geo': 0.389075}, 'Z': {'nez': 7.608168, 'geo': 7.608168}}
+
+ # CALL with ALLINDICES, list returned
+ (status, mydata1b) = SuperMAGGetData(
+ userid, start, 3600, 'all', 'HBK', FORMAT='list')
+ mydata1b # is 1440 rows of dicts (key-value pairs)
+ mydata1b[0:1] # {'tval': 1572726240.0, 'ext': 60.0, 'iaga': 'DOB', 'glon': 9.11, 'glat': 62.07, 'mlt': 21.694675, 'mcolat': 30.361519, 'decl': 3.067929, 'sza': 124.698227, 'N': {'nez': -3.942651, 'geo': -5.964826}, 'E': {'nez': 4.492887, 'geo': 0.389075}, 'Z': {'nez': 7.608168, 'geo': 7.608168}}
+
+ ####################
+ # INDICES fetches
+ (status, idxdata) = SuperMAGGetIndices(userid, start, 3600)
+ idxdata # empty!
+
+ (status, idxdata) = SuperMAGGetIndices(
+ userid, start, 3600, 'all,swiall,imfall')
+ idxdata # 1440 rows x 77 columns dataframe
+ idxdata.keys() # Index(['tval', 'SME', 'SML', 'SMLmlat', 'SMLmlt', 'SMLglat', 'SMLglon', 'SMLstid', 'SMU', 'SMUmlat', 'SMUmlt', 'SMUglat', 'SMUglon', 'SMUstid', 'SMEnum', 'SMEs', 'SMLs', 'SMLsmlat', 'SMLsmlt', 'SMLsglat', 'SMLsglon', 'SMLsstid', 'SMUs', 'SMUsmlat', 'SMUsmlt', 'SMUsglat', 'SMUsglon', 'SMUsstid', 'SMEsnum', 'SMEd', 'SMLd', 'SMLdmlat', 'SMLdmlt', 'SMLdglat', 'SMLdglon', 'SMLdstid', 'SMUd', 'SMUdmlat', 'SMUdmlt', 'SMUdglat', 'SMUdglon', 'SMUdstid', 'SMEdnum', 'SMEr', 'SMLr', 'SMLrmlat', 'SMLrmlt', 'SMLrglat', 'SMLrglon', 'SMLrstid', 'SMUr', 'SMUrmlat', 'SMUrmlt', 'SMUrglat', 'SMUrglon', 'SMUrstid', 'SMErnum', 'smr', 'smr00', 'smr06', 'smr12', 'smr18', 'smrnum', 'smrnum00', 'smrnum06', 'smrnum12', 'smrnum18', 'bgse', 'bgsm', 'vgse', 'vgsm', 'clockgse', 'clockgsm', 'density', 'dynpres', 'epsilon', 'newell'], dtype='object')
+ #
+ # just INDICESALL = 67 columns, above 'tval' through 'smrnum18'
+ # just IMFALL = 5 columns, Index(['tval', 'bgse', 'bgsm', 'vgse', 'vgsm'], dtype='object')
+ # just SWIALL = 7 columns, Index(['tval', 'clockgse', 'clockgsm', 'density', 'dynpres', 'epsilon', 'newell'], dtype='object')
+ #
+ # Dataframes are awesome! To manipulate, just pull out what you need
+ import pandas as pd # call once at the top of your code if you are using dataframes
+ tval = idxdata.tval
+ density = idxdata.density
+ vgse = idxdata.vgse
+ # or all as 1 line of code
+ tval, density, vgse = idxdata.tval, idxdata.density, idxdata.vgse
+ # note that vgse is itself a dictionary of values for X/Y/Z, so you can get subitems from it like this
+ vgse_x = [d.get('X') for d in idxdata.vgse]
+
+ # to save the data, there are many formats. Here is how to save as csv
+ idxdata.to_csv('mydata.csv')
+
+ # to read it back in later
+ import pandas as pd
+ import re
+ # you can read it into any variable name, we just used 'mydata2b' as an example
+ mydata2b = pd.read_csv('mydata.csv', index_col=0)
+ # now you can do all the above items again, with one exception: each line of the CVS file got split into a dict (key-value pairs) but items like 'vsge' are part of the pandas structure
+ # the 'd.get()' approach will _not_ work once read from csv
+ # item is a pandas series (not python list)
+ stationlist = mydata2b.SMLrstid
+ # prints a list of stations as a string, but cannot easily access a single item because it is a pandas series
+ print(stationlist[0])
+ # so you can convert a pandas series to a list
+ stationlist2 = sm_csvitem_to_list(
+ mydata2b.SMLrstid) # goal is a list of stations
+ slist = stationlist2[0] # grabs a list of stations for row 0
+ s1 = stationlist2[0][0] # grabs the first station for row 0
+
+ # goal is a dict of coords or other values
+ vgse = sm_csvitem_to_dict(mydata2b.vgse)
+ x = vgse[0]['X'] # grab just the 'X' value for the 1st row of data
+ # grab all the 'X' values as a new list
+ vgse_x = [mydat['X'] for mydat in vgse]
+ vgse_xyz = [(mydat['X'], mydat['Y'], mydat['Z'])
+ for mydat in vgse] # grab all 3
+
+ # We also offer a list format, for users who prefer to work in python lists
+ (status, mydata2c) = SuperMAGGetIndices(
+ userid, start, 3600, 'all,swiall,imfall', FORMAT='list')
+ len(mydata2c) # is 1440 rows of dicts (key-value pairs)
+ mydata2c[0:1] # {'tval': 1572726240.0, 'SME': 58.887299, 'SML': -27.709055, 'SMLmlat': 73.529922, 'SMLmlt': 23.321493, 'SMLglat': 76.510002, 'SMLglon': 25.01, 'SMLstid': 'HOP', 'SMU': 31.178246, 'SMUmlat': 74.702339, 'SMUmlt': 2.090216, 'SMUglat': 79.480003, 'SMUglon': 76.980003, 'SMUstid': 'VIZ', 'SMEnum': 118, 'SMEs': 34.451469, 'SMLs': -16.599854, 'SMLsmlat': 62.368008, 'SMLsmlt': 9.399416, 'SMLsglat': 62.299999, 'SMLsglon': 209.800003, 'SMLsstid': 'T39', 'SMUs': 17.851616, 'SMUsmlat': 73.989975, 'SMUsmlt': 18.228394, 'SMUsglat': 67.93, 'SMUsglon': 306.429993, 'SMUsstid': 'ATU', 'SMEsnum': 54, 'SMEd': 58.887299, 'SMLd': -27.709055, 'SMLdmlat': 73.529922, 'SMLdmlt': 23.321493, 'SMLdglat': 76.510002, 'SMLdglon': 25.01, 'SMLdstid': 'HOP', 'SMUd': 31.178246, 'SMUdmlat': 74.702339, 'SMUdmlt': 2.090216, 'SMUdglat': 79.480003, 'SMUdglon': 76.980003, 'SMUdstid': 'VIZ', 'SMEdnum': 64, 'SMEr': [29.685059, 29.857538, 31.387127, 41.707573, 10.320444, 10.885443, 9.604616, 13.479583, 15.471248, 15.471248, 15.714731, 5.434914, 12.13654, 11.156847, 9.62884, 14.752592, 14.752592, 24.204388, 21.41181, 21.41181, 27.121195, 46.345322, 51.403328, 51.403328], 'SMLr': [-27.709055, 1.320708, -0.208882, -10.529325, -10.529325, -10.529325, -9.248499, -13.123466, -16.599854, -16.599854, -16.599854, -5.449972, -5.449972, -4.470279, -2.942272, -6.352773, -6.352773, -6.352773, -3.560194, -3.560194, -7.514064, -22.651047, -27.709055, -27.709055], 'SMLrmlat': [73.529922, 51.264774, 47.791527, 66.696564, 66.696564, 66.696564, 41.771515, 70.602707, 62.368008, 62.368008, 62.368008, 67.471809, 67.471809, 60.639145, 68.500282, 72.20977, 72.20977, 72.20977, 75.762718, 75.762718, 77.33667, 71.889503, 73.529922, 73.529922], 'SMLrmlt': [23.321493, 2.119074, 3.578985, 4.929673, 4.929673, 4.929673, 5.414416, 8.57761, 9.399416, 9.399416, 9.399416, 11.35623, 11.35623, 12.266475, 13.977451, 16.720993, 16.720993, 16.720993, 19.65963, 19.65963, 21.307804, 22.863134, 23.321493, 23.321493], 'SMLrglat': [76.510002, 55.029999, 52.169998, 71.580002, 71.580002, 71.580002, 47.799999, 71.300003, 62.299999, 62.299999, 62.299999, 61.756001, 61.756001, 53.351002, 58.763, 63.75, 63.75, 63.75, 72.300003, 72.300003, 76.769997, 74.5, 76.510002, 76.510002], 'SMLrglon': [25.01, 82.900002, 104.449997, 129.0, 129.0, 129.0, 132.414001, 203.25, 209.800003, 209.800003, 209.800003, 238.770004, 238.770004, 247.026001, 265.920013, 291.480011, 291.480011, 291.480011, 321.700012, 321.700012, 341.369995, 19.200001, 25.01, 25.01], 'SMLrstid': ['HOP', 'NVS', 'IRT', 'TIK', 'TIK', 'TIK', 'BRN', 'BRW', 'T39', 'T39', 'T39', 'FSP', 'FSP', 'C06', 'FCC', 'IQA', 'IQA', 'IQA', 'SUM', 'SUM', 'DMH', 'BJN', 'HOP', 'HOP'], 'SMUr': [1.976003, 31.178246, 31.178246, 31.178246, -0.208882, 0.356117, 0.356117, 0.356117, -1.128606, -1.128606, -0.885122, -0.015059, 6.686568, 6.686568, 6.686568, 8.399819, 8.399819, 17.851616, 17.851616, 17.851616, 19.60713, 23.694275, 23.694275, 23.694275], 'SMUrmlat': [52.904049, 74.702339, 74.702339, 74.702339, 47.791527, 54.29908, 54.29908, 54.29908, 66.244217, 66.244217, 57.76614, 54.597057, 55.715378, 55.715378, 55.715378, 57.829525, 57.829525, 73.989975, 73.989975, 73.989975, 70.473801, 68.194489, 68.194489, 68.194489], 'SMUrmlt': [0.510692, 2.090216, 2.090216, 2.090216, 3.578985, 6.394085, 6.394085, 6.394085, 9.99274, 9.99274, 11.729218, 12.269058, 13.969843, 13.969843, 13.969843, 16.160952, 16.160952, 18.228394, 18.228394, 18.228394, 21.200783, 22.967857, 22.967857, 22.967857], 'SMUrglat': [56.432999, 79.480003, 79.480003, 79.480003, 52.169998, 59.970001, 59.970001, 59.970001, 64.047997, 64.047997, 51.882999, 47.664001, 45.870998, 45.870998, 45.870998, 48.650002, 48.650002, 67.93, 67.93, 67.93, 70.900002, 71.089996, 71.089996, 71.089996], 'SMUrglon': [58.567001, 76.980003, 76.980003, 76.980003, 104.449997, 150.860001, 150.860001, 150.860001, 220.889999, 220.889999, 239.973999, 245.791, 264.916992, 264.916992, 264.916992, 287.549988, 287.549988, 306.429993, 306.429993, 306.429993, 351.299988, 25.790001, 25.790001, 25.790001], 'SMUrstid': ['ARS', 'VIZ', 'VIZ', 'VIZ', 'IRT', 'MGD', 'MGD', 'MGD', 'DAW', 'DAW', 'C13', 'C10', 'C08', 'C08', 'C08', 'T50', 'T50', 'ATU', 'ATU', 'ATU', 'JAN', 'NOR', 'NOR', 'NOR'], 'SMErnum': [5, 3, 3, 4, 5, 6, 6, 4, 8, 9, 12, 13, 20, 17, 17, 11, 12, 14, 12, 14, 22, 51, 51, 35], 'smr': 0.252399, 'smr00': -0.531382, 'smr06': 0.885406, 'smr12': 1.051192, 'smr18': -0.395618, 'smrnum': 72, 'smrnum00': 26, 'smrnum06': 23, 'smrnum12': 6, 'smrnum18': 17, 'bgse': {'X': 1.07, 'Y': -3.75, 'Z': -0.74}, 'bgsm': {'X': 1.07, 'Y': -3.82, 'Z': -0.06}, 'vgse': {'X': -351.100006, 'Y': -5.5, 'Z': -4.0}, 'vgsm': {'X': 351.100006, 'Y': 6.128625, 'Z': -2.947879}, 'clockgse': 258.340698, 'clockgsm': 268.664337, 'density': 5.03, 'dynpres': 1.25, 'epsilon': 29.468521, 'newell': 2504.155029}
+ # sample accessing
+ print(mydata2c[0]['tval'], mydata2c[0]['density']) # single element
+ result = [(myeach['tval'], myeach['density'])
+ for myeach in mydata2c] # pull out pairs e.g. 'tval, density')
+ # two-line method for extracting any variable set from this
+ # same, pull out pairs, only assign e.g. x=tval, y=density
+ pairsets = [(myeach['tval'], myeach['density'], myeach['vgse'])
+ for myeach in mydata2c]
+ tval, density, vgse = [[z[i] for z in pairsets] for i in (0, 1, 2)]
+ # since 'vgse' is itself an dict of 3 values X/Y/Z, you can pull out nested items like this
+ pairsets = [(myeach['tval'], myeach['density'], myeach['vgse']['X'])
+ for myeach in mydata2c] # same, pull out pairs, only assign e.g. x=tval, y=density
+ tval, density, vgse_x = [[z[i] for z in pairsets] for i in (0, 1, 2)]
+ # the above methods are extensible to any number of variables, just update the (0,1,2) to reflect now many you have
+
+
+# Uncomment to run quick sample tests
+# userid=YOUR_SUPERMAG_USER_ID
+# sm_microtest(1,userid) # sample stations fetch
+# sm_microtest(2,userid) # sample data fetch, with plotting
+# sm_microtest(3,userid) # sample indices fetch, with plotting
From 83d121a07e95cd0b4b8b32093267e19e551d6357 Mon Sep 17 00:00:00 2001
From: Fasil Kebede <46403603+FasilGibdaw@users.noreply.github.com>
Date: Tue, 28 Apr 2026 12:13:50 +0200
Subject: [PATCH 2/3] datadownloader script removed and parts of it in separate
scripts
---
lompe/data_tools/ampere.py | 134 +++
lompe/data_tools/champ.py | 82 ++
lompe/data_tools/datadownloader.py | 1237 ----------------------------
lompe/data_tools/dataloader.py | 11 +-
lompe/data_tools/dmsp_ssies.py | 172 ++++
lompe/data_tools/dmsp_ssusi.py | 262 ++++++
lompe/data_tools/get_lompe_data.py | 170 ++++
lompe/data_tools/superdarn.py | 152 ++++
lompe/data_tools/supermag.py | 156 ++++
lompe/data_tools/swarm.py | 80 ++
lompe/utils/time.py | 46 +-
11 files changed, 1242 insertions(+), 1260 deletions(-)
create mode 100644 lompe/data_tools/ampere.py
create mode 100644 lompe/data_tools/champ.py
delete mode 100644 lompe/data_tools/datadownloader.py
create mode 100644 lompe/data_tools/dmsp_ssies.py
create mode 100644 lompe/data_tools/dmsp_ssusi.py
create mode 100644 lompe/data_tools/get_lompe_data.py
create mode 100644 lompe/data_tools/superdarn.py
create mode 100644 lompe/data_tools/supermag.py
create mode 100644 lompe/data_tools/swarm.py
diff --git a/lompe/data_tools/ampere.py b/lompe/data_tools/ampere.py
new file mode 100644
index 0000000..8cc61cf
--- /dev/null
+++ b/lompe/data_tools/ampere.py
@@ -0,0 +1,134 @@
+import os
+import datetime as dt
+import requests
+from lompe.data_tools.dataloader import read_iridium
+
+
+def ampere_parsestart(start):
+ # DO NOT EDIT THIS FUNCTION
+
+ # internal helper function adapted from supermag_api.py
+
+ if isinstance(start, list):
+ timestring = "%4.4d-%2.2d-%2.2dT%2.2d:%2.2d" % tuple(start[0:5])
+ elif isinstance(start, dt.date):
+ # good to go, TBD
+ timestring = start.strftime("%Y-%m-%dT%H:%M")
+ else:
+ # is a string, reparse, TBD
+ timestring = start
+
+ return (timestring)
+
+
+def ampere_coreurl(page, logon, start, extent):
+ # DO NOT EDIT THIS FUNCTION
+
+ # internal helper function adapted from supermag_api.py
+ baseurl = "https://ampere.jhuapl.edu/"
+
+ mytime = ampere_parsestart(start)
+ urlstr = baseurl + 'services/' + page + '?'
+ urlstr += '&logon=' + logon
+ urlstr += '&start=' + mytime
+
+ urlstr += '&extent=' + ("%12.12d" % extent)
+
+ return (urlstr)
+
+
+def download_iridium_raw(event, basepath='./'):
+ """Download netcdf (dB raw) data to be used by lompe from the AMPERE database (jhuapl) for a given event
+ returns an input for the lompe read_iridium script in dataloader.py in data_tools
+ Example usage:
+ event = '2012-04-05'
+ basepath = 'downloads'
+ download_iridium(event, basepath)
+
+ Args:
+ event (str): fromat YYYY-MM-DD
+ basepath (str, optional): path to . Defaults to './'.
+ tempfile_path (str, optional): path to. Defaults to './'.
+ file_name (str, optional):name of the file to write the netcdf file. Defaults to ''.
+
+ Returns:
+ saved file: to be used by the lompe read_iridium function in data_tools
+
+ Note:
+ functions "ampere_parsestart" and "ampere_coreurl" are internal helper functions adapted from supermag_api.py
+ credit to the original author of the functions.
+ """
+ if not basepath.endswith('/'):
+ basepath += '/'
+
+ start = event + 'T00:00:00'
+ duration = 86400 # Duration in seconds (one day)
+ # check if the processed file exists
+ savefile = basepath + event.replace('-', '') + '_iridium.nc'
+
+ # checks if file already exists
+ # checking if the file is not empty
+ if os.path.isfile(savefile) and os.path.getsize(savefile) > 0:
+ print(f"File {savefile} already exists at {basepath}.")
+ return savefile
+ # return read_iridium(event, basepath='./', tempfile_path='./', file_name='')
+ else:
+ import certifi
+ # URL to download data from (lompe username is already registered in the API)
+ urlstr = ampere_coreurl('data-rawdB.php', 'lompe', start, duration)
+ # headers = {"User-Agent": "Mozilla/5.0"}
+ # verify=certifi.where())
+ response = requests.get(
+ urlstr, verify=certifi.where(), stream=True)
+
+ # Check if the request was successful
+ if response.status_code == 200:
+ # Save the downloaded data to a file
+ with open(savefile, 'wb') as file:
+ file.write(response.content)
+ else:
+ print(f"Failed to retrieve data: {response.status_code}")
+ return savefile
+
+
+def download_iridium(event, basepath='./', tempfile_path='./'):
+ """Download and process iridium data for a given event, returns an input for the lompe read_iridium script in dataloader.py in data_tools
+ Example usage:
+ event = '2012-04-05'
+ basepath = 'downloads'
+ tempfile_path = 'downloads'
+ file_name = '20120405_iridium.h5'
+ download_iridium(event, basepath, tempfile_path, file_name)
+
+ Args:
+ event (str): fromat YYYY-MM-DD
+ basepath (str, optional): path to . Defaults to './'.
+ tempfile_path (str, optional): path to. Defaults to './'.
+ file_name (str, optional):name of the file to write the netcdf file. Defaults to ''.
+
+ Returns:
+ saved file: to be used by the lompe read_iridium function in data_tools
+
+ Note:
+ functions "ampere_parsestart" and "ampere_coreurl" are internal helper functions adapted from supermag_api.py
+ credit to the original author of the functions.
+ """
+ if not basepath.endswith('/'):
+ basepath += '/'
+ if not tempfile_path.endswith('/'):
+ tempfile_path += '/'
+ savefile = tempfile_path + event.replace('-', '') + '_iridium.h5'
+ raw_file_name = basepath + event.replace('-', '') + '_iridium.nc'
+ if os.path.isfile(savefile) and os.path.getsize(savefile) > 0:
+ print(f"File {savefile} already exists at {tempfile_path}.")
+ return savefile
+ elif os.path.isfile(raw_file_name) and os.path.getsize(raw_file_name) > 0:
+ print(
+ f"File {raw_file_name} exists and is converting to lompe data as {savefile}.")
+ return read_iridium(event, basepath=basepath, tempfile_path=tempfile_path)
+ else:
+ print(
+ f"File {savefile} does not exist at {tempfile_path}. Downloading raw data and converting to lompe data as {savefile}.")
+ _ = download_iridium_raw(
+ event, basepath=basepath)
+ return read_iridium(event, basepath=basepath, tempfile_path=tempfile_path)
diff --git a/lompe/data_tools/champ.py b/lompe/data_tools/champ.py
new file mode 100644
index 0000000..84ff693
--- /dev/null
+++ b/lompe/data_tools/champ.py
@@ -0,0 +1,82 @@
+import os
+import numpy as np
+import pandas as pd
+import requests
+
+
+def download_champ(event, basepath='./', tempfile_path='./'):
+ """
+ Download CHAMP data from the FTP server and process it in lompe data format.
+ Note that CHAMP data is only available for the year between 2000 and 2010.
+
+ Args:
+ event (str): format 'YYYY-MM-DD'
+ basepath (str, optional): path. Defaults to './'.
+ tempfile_path (str, optional): path. Defaults to './'.
+
+ Returns:
+ savedfile: file name of the processed file if successful
+ """
+ event_date = event.replace('-', '')
+ year = event[:4]
+ savefile = tempfile_path + f'CH_ME_MAG_LR_3_{event_date}_0102.cdf'
+ processed_file = tempfile_path + f'{event_date}_champ.h5'
+
+ # Check if the processed file already exists
+ if os.path.isfile(processed_file):
+ return processed_file
+
+ # Check if the raw file already exists
+ if not os.path.isfile(savefile):
+ from requests_ftp import ftp
+ session = requests.Session()
+ session.mount('ftp://', ftp.FTPAdapter())
+ ftp_url = f"ftp://isdcftp.gfz-potsdam.de/champ/ME/Level3/MAG/V0102/{year}/CH_ME_MAG_LR_3_{event_date}_0102.cdf"
+ try:
+ # Downloading the file and checking if it was successful
+ response = session.get(ftp_url)
+ if response.status_code == 200:
+ with open(savefile, "wb") as file:
+ file.write(response.content)
+ print(f"Downloading {savefile} is successful!")
+ else:
+ print(
+ f"Failed to download the file. Status code: {response.status_code}")
+ return None
+ except Exception as e:
+ print(f"No champ data in this period: {e}")
+ return None
+
+ # Process the downloaded CDF file ot get the magnetic disturbance
+ try:
+ import cdflib
+ import ppigrf
+ cdf_file = cdflib.CDF(savefile)
+ mag = cdf_file.varget('B_NEC') # space magnetometer data
+
+ # geocentric coords of CHAMP orbit
+ theta = 90 - cdf_file.varget('Latitude')
+ phi = cdf_file.varget('Longitude')
+ r = cdf_file.varget('Radius') / 1000
+
+ time = cdflib.cdfepoch.to_datetime(cdf_file.varget('Timestamp'))
+
+ # using IGRF to calculate magnetic disturbance (dB) registered by CHAMP
+ Br, Btheta, Bphi = ppigrf.igrf_gc(r, theta, phi, time[0])
+ B0 = np.vstack((-Btheta.flatten(), Bphi.flatten(), -Br.flatten()))
+ dB = mag.T - B0
+
+ champ_df = pd.DataFrame({
+ 'Be': dB[1],
+ 'Bn': dB[0],
+ 'Bu': -dB[2],
+ 'lon': phi,
+ 'lat': 90 - theta,
+ 'r': r
+ }, index=time)
+ champ_df.to_hdf(processed_file, key='df', mode='w')
+ os.remove(savefile) # remove the raw file after processing
+ return processed_file
+ except Exception as e:
+ print(f"Failed to process the file: {e}")
+ return None
diff --git a/lompe/data_tools/datadownloader.py b/lompe/data_tools/datadownloader.py
deleted file mode 100644
index 1144d5c..0000000
--- a/lompe/data_tools/datadownloader.py
+++ /dev/null
@@ -1,1237 +0,0 @@
-import warnings
-import xarray as xr
-import numpy as np
-import pandas as pd
-import requests
-from bs4 import BeautifulSoup
-import datetime as dt
-import os
-import glob
-import shutil
-from lompe.utils.time import date2doy
-import random
-import time
-
-warnings.filterwarnings("ignore")
-
-# extensive list of functions (including helper functions) to download data from different sources for a given event date
-# the functions are designed to be used in the lompe package for data loading and processing
-# this has to be integrated with the dataloader.py in the lompe package at some level to get lompe data format
-
-# the idea is to merge the two files (datadownloader.py and dataloader.py) into one file in the lompe package, for the time being we need to keep
-# both, some functions imported from dataloader.py are in use in this script
-
-# Note that: lazy importing is implemented here
-
-
-def download_ssusi_cdaweb(event, hemi='north', basepath='./ssusi_tempfiles', tempfile_path='./', source='cdaweb'):
- """
-
- Called and used for modelling auroral conductance in cmodel.py.
-
- Extract the relevant SSUSI info from netcdf-files downloaded from APL server.
- https://cdaweb.gsfc.nasa.gov/pub/data/dmsp/ (ssusi/data/edr-aurora)
- Will load all SSUSI data from specified hemisphere into xarray object.
- Any DMSP Block 5D satellite (F16-19) available in folder will be used.
-
- Parameters
- ----------
- event : str
- string on format 'yyyy-mm-dd' to specify time of event for model.
- hemi : str, optional
- specify hemisphere
- Default: 'north'
- basepath : str, optional
- location of netcdf-files downloaded from APL server (.NC).
- Default: './ssusi_tempfiles'
- tempfile_path : str, optional
- Location for storing processed SSUSI data.
- Default: './'
- source : str, optional
- Specify source of SUSSI data.
- Default: 'cdaweb' see download_ssusi for 'jhuapl'
-
- Returns
- -------
- savefile : str
- Path to saved file containing SSUSI data + conductances extracted from the images.
-
- """
- '''
- Using the CDAWeb server here eliminates the need for checking if the data is from the same day or not.
- The data is already sorted by date and time. From the APL sever, there might be a need to check if the data is from the same day.
- (see download_ssusi function)
-
- Extract the relevant info from downloaded netcdf-files (similar to from APL server, but from CDAWeb):
- Note that these data are gridded on a 363,363 geomagnetic grid. We assume this is AACGM,
- based on email correspondence with L. Paxton. This is not clear from the documentation.
- No geographic information of these gridded data exist in the EDR aurora files downloaded
- Quote from documentation: "This array is a uniform grid in a polar azimuthal equidistant
- projection of the geomagnetic latitude and geomagnetic local time, with appropriate
- geomagnetic pole as the origin; that is: 90-ABS(glat) is the radius and geomagnetic
- local time is the angle of a two-dimensional polar coordinate system."
- Hence, the mapped latitude array (363x363) is the same for both hemispheres, and
- the values are therefore always positive.
- '''
- if not basepath.endswith('/'):
- basepath += '/'
- if not tempfile_path.endswith('/'):
- tempfile_path += '/'
-
- # check if the processed file exists
- savefile = tempfile_path + \
- event.replace('-', '') + '_ssusi_' + hemi + '.nc'
- if os.path.isfile(savefile):
- print('SSUSI file already exists.')
- return savefile
- try:
- import netCDF4
- except ModuleNotFoundError:
- raise ModuleNotFoundError(
- 'read_ssusi: Could not load netCDF4 module. Will not be able to read SSUSI-files from APL.')
- # downlaoding the files from the server see the ssusi_direct.py for the implementation
- # from lompe.data_tools.ssusi_direct2 import download_ssusi_files
- download_ssusi_files(event, basepath=basepath, source=source)
- ###
- imgs = [] # List to hold all images. Converted to xarray later.
- doy_str = f"{date2doy(event):03d}"
- for sat in ['F16', 'F17', 'F18', 'F19']:
- files = glob.glob(basepath + '*' + sat.lower() + '*' +
- event[0:4] + doy_str + '*.nc')
- files.sort()
- if len(files) == 0:
- continue
-
- ii = 0 # counter
-
- for file in files:
- f = netCDF4.Dataset(file)
-
- mlat = f.variables['LATITUDE_GEOMAGNETIC_GRID_MAP'][:]
- mlon = f.variables['LONGITUDE_GEOMAGNETIC_' +
- hemi.upper() + '_GRID_MAP'][:]
- mlt = f.variables['MLT_GRID_MAP'][:]
- uthr = f.variables['UT_' + hemi.upper()[0]][:]
- doy = int(f.variables['DOY'][:])
- year = int(f.variables['YEAR'][:])
-
- wavelengths = f.variables['DISK_RADIANCEDATA_INTENSITY_' + hemi.upper()][:]
- char_energy = f.variables['ELECTRON_MEAN_' +
- hemi.upper() + '_ENERGY_MAP'][:]
- energyflux = f.variables['ENERGY_FLUX_' + hemi.upper() + '_MAP'][:]
-
- f.close()
-
- mask = uthr == 0
-
- uthr[mask] = np.nan
- wavelengths[:, mask] = np.nan
- char_energy[mask] = np.nan
- energyflux[mask] = np.nan
- mlon[mask] = np.nan
-
- # Applying Robinson formulas to calculate conductances: https://agupubs.onlinelibrary.wiley.com/doi/epdf/10.1029/JA092iA03p02565
- SP = (40. * char_energy * np.sqrt(energyflux)) / \
- (16. + char_energy**2)
- SH = 0.45 * char_energy**0.85 * SP
-
- if sum(np.isfinite(uthr[:, 181]).flatten()) > 0: # there is data in north
- # Calculate center time:
- # there is a sudden transition across midnight in the pass
- if np.nanstd(uthr) > 0.5:
- next_day = (uthr > 0) & (uthr < 2)
- if sum(next_day.flatten()) > 0:
- uthr[next_day] = uthr[next_day] + 24.
- center_hr = np.nanmean(uthr, axis=0)[181]
- hr = int(center_hr)
- if hr >= 24:
- hr = hr - 24
- center_hr = center_hr - 24
-
- m = int((center_hr - hr) * 60)
- s = round(((center_hr - hr) * 60 - m) * 60)
- if s == 60:
- t0 = dt.datetime(year, 1, 1, hr, m, 59) + \
- dt.timedelta(seconds=1)
- else:
- t0 = dt.datetime(year, 1, 1, hr, m, s)
-
- dtime = t0 + dt.timedelta(doy - 1)
- # put into xarray object
- img = xr.Dataset({'uthr': (['row', 'col'], uthr),
- 'mlon': (['row', 'col'], mlon),
- 'mlat': (['row', 'col'], mlat),
- 'mlt': (['row', 'col'], mlt),
- '1216': (['row', 'col'], wavelengths[0, :, :]),
- '1304': (['row', 'col'], wavelengths[1, :, :]),
- '1356': (['row', 'col'], wavelengths[2, :, :]),
- 'lbhs': (['row', 'col'], wavelengths[3, :, :]),
- 'lbhl': (['row', 'col'], wavelengths[4, :, :]),
- 'E0': (['row', 'col'], char_energy),
- 'je': (['row', 'col'], energyflux),
- 'SP': (['row', 'col'], SP),
- 'SH': (['row', 'col'], SH)})
- img = img.expand_dims(date=[dtime])
- img = img.assign({'satellite': sat})
- img = img.assign(
- {'orbit': file.split('-')[-1].split('_')[0][3:]})
- imgs.append(img)
- ii += 1
-
- if len(imgs) == 0:
- print('No SSUSI images found.')
-
- else: # save as netcdf in specified path
- imgs = xr.concat(imgs, dim='date')
- imgs = imgs.assign({'hemisphere': hemi})
- imgs = imgs.sortby(imgs['date'])
- print('DMSP SSUSI file saved: ' + savefile)
-
- imgs.to_netcdf(savefile)
- shutil.rmtree(basepath)
- return savefile
-
-
-def download_ssusi(event, hemi='north', basepath='./ssusi_tempfiles', tempfile_path='./', source='jhuapl'):
- """
-
- Called and used for modelling auroral conductance in cmodel.py.
-
- Extract the relevant SSUSI info from netcdf-files downloaded from APL server.
- https://ssusi.jhuapl.edu/data_products (EDR AURORA)
- Will load all SSUSI data from specified hemisphere into xarray object.
- Any DMSP Block 5D satellite (F16-19) available in folder will be used.
-
- Parameters
- ----------
- event : str
- string on format 'yyyy-mm-dd' to specify time of event for model.
- hemi : str, optional
- specify hemisphere
- Default: 'north'
- basepath : str, optional
- location of netcdf-files downloaded from APL server (.NC).
- Default: './ssusi_tempfiles'
- tempfile_path : str, optional
- Location for storing processed SSUSI data.
- Default: './'
- source : str, optional
- Specify source of SUSSI data.
- Default: 'jhuapl', other option is 'cdaweb'
-
- Returns
- -------
- savefile : str
- Path to saved file containing SSUSI data + conductances extracted from the images.
-
- """
- '''
- Extract the relevant info from netcdf-files downloaded from APL server:
- Note that these data are gridded on a 363,363 geomagnetic grid. We assume this is AACGM,
- based on email correspondence with L. Paxton. This is not clear from the documentation.
- No geographic information of these gridded data exist in the EDR aurora files downloaded
- Quote from documentation: "This array is a uniform grid in a polar azimuthal equidistant
- projection of the geomagnetic latitude and geomagnetic local time, with appropriate
- geomagnetic pole as the origin; that is: 90-ABS(glat) is the radius and geomagnetic
- local time is the angle of a two-dimensional polar coordinate system."
- Hence, the mapped latitude array (363x363) is the same for both hemispheres, and
- the values are therefore always positive.
- '''
- if not basepath.endswith('/'):
- basepath += '/'
- if not tempfile_path.endswith('/'):
- tempfile_path += '/'
-
- # check if the processed file exists
- savefile = tempfile_path + \
- event.replace('-', '') + '_ssusi_' + hemi + '.nc'
- if os.path.isfile(savefile):
- print('SSUSI file already exists.')
- return savefile
- try:
- import netCDF4
- except ModuleNotFoundError:
- raise ModuleNotFoundError(
- 'read_ssusi: Could not load netCDF4 module. Will not be able to read SSUSI-files from APL.')
- # downlaoding the files from the server see the ssusi_direct.py for the implementation
- # from lompe.data_tools.ssusi_direct2 import download_ssusi_files
- download_ssusi_files(event, basepath=basepath, source=source)
- ###
- imgs = [] # List to hold all images. Converted to xarray later.
- doy_str = f"{date2doy(event):03d}"
-
- for sat in ['F16', 'F17', 'F18', 'F19']:
- files = glob.glob(basepath + '*' + sat + '*' +
- event[0:4] + event[5:7] + event[8:10] + '*.NC')
- files.sort()
- if len(files) == 0:
- continue
-
- ii = 0 # counter
- ii_max = len(files)
- ii_max_orbit = files[-1].split('_SN.')[-1].split('-')[0]
-
- extra_orbit = format(int(ii_max_orbit) + 1, '05')
- extra_file = glob.glob(basepath + '*' + sat +
- '*_SN.' + extra_orbit + '-' + '*.NC')
-
- if len(extra_file) == 1:
- files.append(extra_file[0])
-
- for file in files:
- f = netCDF4.Dataset(file)
-
- mlat = f.variables['LATITUDE_GEOMAGNETIC_GRID_MAP'][:]
- mlon = f.variables['LONGITUDE_GEOMAGNETIC_' +
- hemi.upper() + '_GRID_MAP'][:]
- mlt = f.variables['MLT_GRID_MAP'][:]
- uthr = f.variables['UT_' + hemi.upper()[0]][:]
- doy = int(f.variables['DOY'][:])
- year = int(f.variables['YEAR'][:])
-
- wavelengths = f.variables['DISK_RADIANCEDATA_INTENSITY_' + hemi.upper()][:]
- char_energy = f.variables['ELECTRON_MEAN_' +
- hemi.upper() + '_ENERGY_MAP'][:]
- energyflux = f.variables['ENERGY_FLUX_' + hemi.upper() + '_MAP'][:]
-
- f.close()
-
- mask = uthr == 0
-
- uthr[mask] = np.nan
- wavelengths[:, mask] = np.nan
- char_energy[mask] = np.nan
- energyflux[mask] = np.nan
- mlon[mask] = np.nan
-
- # Applying Robinson formulas to calculate conductances: https://agupubs.onlinelibrary.wiley.com/doi/epdf/10.1029/JA092iA03p02565
- SP = (40. * char_energy * np.sqrt(energyflux)) / \
- (16. + char_energy**2)
- SH = 0.45 * char_energy**0.85 * SP
-
- if sum(np.isfinite(uthr[:, 181]).flatten()) > 0: # there is data in north
- # Calculate center time:
- # there is a sudden transition across midnight in the pass
- if np.nanstd(uthr) > 0.5:
- next_day = (uthr > 0) & (uthr < 2)
- if sum(next_day.flatten()) > 0:
- uthr[next_day] = uthr[next_day] + 24.
- center_hr = np.nanmean(uthr, axis=0)[181]
- if (ii == 0) & (center_hr >= 22):
- ii = + 1
- # the image is (likely) from the previous day
- continue
-
- if (ii == ii_max) & (center_hr < 22):
- ii = + 1
- continue # the image is not from the same day and is skipped
-
- if (ii == ii_max):
- doy = doy - 1
-
- hr = int(center_hr)
- if hr >= 24:
- hr = hr - 24
- center_hr = center_hr - 24
-
- m = int((center_hr - hr) * 60)
- s = round(((center_hr - hr) * 60 - m) * 60)
- if s == 60:
- t0 = dt.datetime(year, 1, 1, hr, m, 59) + \
- dt.timedelta(seconds=1)
- else:
- t0 = dt.datetime(year, 1, 1, hr, m, s)
-
- dtime = t0 + dt.timedelta(doy - 1)
- # put into xarray object
- img = xr.Dataset({'uthr': (['row', 'col'], uthr),
- 'mlon': (['row', 'col'], mlon),
- 'mlat': (['row', 'col'], mlat),
- 'mlt': (['row', 'col'], mlt),
- '1216': (['row', 'col'], wavelengths[0, :, :]),
- '1304': (['row', 'col'], wavelengths[1, :, :]),
- '1356': (['row', 'col'], wavelengths[2, :, :]),
- 'lbhs': (['row', 'col'], wavelengths[3, :, :]),
- 'lbhl': (['row', 'col'], wavelengths[4, :, :]),
- 'E0': (['row', 'col'], char_energy),
- 'je': (['row', 'col'], energyflux),
- 'SP': (['row', 'col'], SP),
- 'SH': (['row', 'col'], SH)})
- img = img.expand_dims(date=[dtime])
- img = img.assign({'satellite': sat})
- img = img.assign(
- {'orbit': file.split('_SN.')[-1].split('-')[0]})
- imgs.append(img)
- ii += 1
-
- if len(imgs) == 0:
- print('No SSUSI images found.')
- return None
-
- else: # save as netcdf in specified path
- imgs = xr.concat(imgs, dim='date')
- imgs = imgs.assign({'hemisphere': hemi})
- imgs = imgs.sortby(imgs['date'])
- print('DMSP SSUSI file saved: ' + savefile)
-
- imgs.to_netcdf(savefile)
- shutil.rmtree(basepath)
- return savefile
-
-
-def fetch_ssusi_urls(sat, year, doy, source):
- doy_str = f"{doy:03d}" # Zero-pad the day of year to three digits
- if source == 'jhuapl':
- url = f"https://ssusi.jhuapl.edu/data_retriver?spc=f{sat}&type=edr-aur&year={year}&Doy={doy_str}"
- elif source == 'cdaweb':
- url = f'https://cdaweb.gsfc.nasa.gov/pub/data/dmsp/dmspf{sat}/ssusi/data/edr-aurora/{year}/{doy_str}/'
- else:
- print(f"Unsupported source: {source}")
- return []
-
- try:
- response = requests.get(url)
- response.raise_for_status() # raise an HTTPError on bad response
- except requests.exceptions.RequestException as e:
- print(f"Failed to fetch URL probably no data : \n{e}")
- return []
-
- from urllib.parse import urljoin
- soup = BeautifulSoup(response.content, 'html.parser')
- urls = [urljoin(url, link.get('href')) for link in soup.find_all(
- 'a', href=True) if link.get('href').lower().endswith('.nc')]
-
- if not urls:
- print(
- f"No .nc files found for satellite F{sat} on day {doy_str} of year {year} from source {source}")
-
- return urls
-
-
-def download_ssusi_file(file_url, destination):
- try:
- response = requests.get(file_url, stream=True)
- response.raise_for_status() # raise an HTTPError on bad response
- filename = os.path.join(destination, os.path.basename(file_url))
-
- with open(filename, 'wb') as file:
- for chunk in response.iter_content(chunk_size=65536):
- if chunk: # Filter out keep-alive new chunks
- file.write(chunk)
- except requests.exceptions.RequestException as e:
- print(f"Failed to download {file_url}: {e}")
-
-
-def download_ssusi_files(event, source='jhuapl', basepath='./ssusi_tempfiles/'):
- """Downloading data from the SSUSI instrument onboard the DMSP satellites for a given event date.
-
- Args:
- event strin: format 'YYYY-MM-DD'
- source (str, optional): data source. Defaults to 'jhuapl'. Another option is 'cdaweb'.
- """
- year = int(event[0:4])
- doy = date2doy(event)
-
- os.makedirs(basepath, exist_ok=True)
-
- from joblib import Parallel, delayed
- import itertools
- # Use joblib.Parallel to fetch URLs concurrently
- fetch_args = [(sat, year, doy, source) for sat in [16, 17, 18, 19]]
- results = Parallel(n_jobs=8, backend='threading')(
- delayed(fetch_ssusi_urls)(*args) for args in fetch_args)
- # results = Parallel(n_jobs=-1)(delayed(fetch_ssusi_urls)(*args) for args in fetch_args)
- all_urls = list(itertools.chain.from_iterable(results))
-
- # Use joblib.Parallel to download files concurrently
- download_args = [(url, basepath) for url in all_urls]
- Parallel(n_jobs=8, backend='threading')(
- delayed(download_ssusi_file)(*args) for args in download_args)
-
-
-def download_smag(event, tempfile_path='./', hemi='all'):
- """Download SuperMAG data for a given event date (this can entirely substitute the read_smag function in the lompe package (data_tools))
- Example usage:
- event = '2012-04-05'
- tempfile_path = 'downloads'
- hemi = 'all'
- download_smag(event, tempfile_path, hemi)
-
- Args:
- event (str): format YYYY-MM-DD
- tempfile_path (str, optional): path to save processed file or check if exists already. Defaults to './'.
- hemi (str, optional): filtering magnetometer stations based on hemsiphere or all the stations. Defaults to 'all'.
-
- Raises:
- ValueError: throwing error if the download fails
-
- Returns:
- saved file path if successful: str
-
- Note: not sure this is the one which is daily basedlined: need to be checked
- """
-
- start = event + 'T00:00:00'
- duration = 86400 # Duration in seconds (one day)
- # lompe username is already registered in the API
- from lompe.data_tools.supermag_api import SuperMAGGetData, sm_GetUrl, sm_coreurl
- urlstr = sm_coreurl('inventory.php', 'lompe', start, duration)
- success, stations = sm_GetUrl(urlstr, 'raw')
- stations = stations[1:-1]
- savefile = tempfile_path + event.replace('-', '') + '_supermag.h5'
- if os.path.isfile(savefile):
- return savefile
-
- elif success and stations:
- basepath = os.path.dirname(__file__)
- file = basepath + '/../data/supermag_stations.csv'
- data_temp = pd.read_csv(file, sep=',', nrows=0)
- df = pd.read_csv(file, sep=',', skiprows=1, header=None,
- names=data_temp.columns, usecols=range(len(data_temp.columns)))
- if hemi == 'north':
- sta2 = df[df['GEOLAT'] > 40].IAGA.values
- intersection = set(stations).intersection(sta2)
- stations = list(intersection)
- elif hemi == 'south':
- sta2 = df[df['GEOLAT'] < -40].IAGA.values
- intersection = set(stations).intersection(sta2)
- stations = list(intersection)
- elif hemi == 'all':
- stations = stations
- print(
- f"Number of stations available for the selected date is: {len(stations)}")
-
- # checking the stations and sucess in the geturl inquiry
- if not success or not stations:
- raise ValueError(
- "Failed to fetch stations. Please check the input parameters or API availability.")
- results = []
-
- # Function to download data for a given station
- def download_data(station):
- success, df = SuperMAGGetData(
- 'lompe', start, duration, 'geo', station, BASELINE='yearly')
- if success:
- return df
- else:
- return pd.DataFrame() # Return an empty DataFrame if the download fails
-
- # Download data serially for each station
- for station in stations:
- # print(f"Downloading data for station {station}...")
- df = download_data(station)
- if not df.empty:
- results.append(df)
- # time.sleep(1) # Optional: Sleep between requests to avoid overwhelming the server
-
- # Combine results into a single DataFrame
- if not results:
- raise ValueError(
- "No valid data downloaded. Please check API or parameters.")
-
- df_combined = pd.concat(results, ignore_index=True)
- # date conversion and cleaning the DataFrame
- df_combined['tval'] = pd.to_datetime(
- df_combined['tval'], unit='s', origin='unix')
- df_combined[['N', 'E', 'Z']] = df_combined[['N', 'E', 'Z']].map(
- lambda x: x['geo'] if isinstance(x, dict) else np.nan)
- df_combined[['N', 'E', 'Z']] = df_combined[[
- 'N', 'E', 'Z']].replace(999999.000000, np.nan)
-
- # Final DataFrame to save as hdf
- df_combined.set_index('tval', inplace=True)
- df_combined.rename(columns={
- 'glat': 'lat', 'glon': 'lon', 'N': 'Bn', 'E': 'Be', 'Z': 'Bu'}, inplace=True)
- df_combined['Bu'] = -df_combined['Bu']
- df_final = df_combined[['Be', 'Bn', 'Bu',
- 'lat', 'lon']].dropna().sort_index()
-
- # df_final.to_hdf('20120405_supermag_data.h5', key='df_final', mode='w')
- finishedfile = tempfile_path + \
- event.replace('-', '') + '_supermag.h5'
-
- df_final.to_hdf(finishedfile, key='df_final', mode='w')
-
- # print("Data processing complete.")
- return finishedfile
- else:
- raise ValueError(
- 'Something went wrong, check inputs, API availability etc.')
-
-
-def ampere_parsestart(start):
- # DO NOT EDIT THIS FUNCTION
-
- # internal helper function adapted from supermag_api.py
-
- # takes either list of [yyyy, mo, dd, hh, mm, opt_ss]
- # or string of a normal datetime 'YYYY-MM-DD hh-mm' (optional ss)
- # or the SuperMAG-ready 'YYYY-MM-DDThh-mm-ss'
-
- if isinstance(start, list):
- timestring = "%4.4d-%2.2d-%2.2dT%2.2d:%2.2d" % tuple(start[0:5])
- elif isinstance(start, dt.date):
- # good to go, TBD
- timestring = start.strftime("%Y-%m-%dT%H:%M")
- else:
- # is a string, reparse, TBD
- timestring = start
-
- return (timestring)
-
-
-def ampere_coreurl(page, logon, start, extent):
- # DO NOT EDIT THIS FUNCTION
-
- # internal helper function adapted from supermag_api.py
- baseurl = "https://ampere.jhuapl.edu/"
-
- mytime = ampere_parsestart(start)
- urlstr = baseurl + 'services/' + page + '?'
- urlstr += '&logon=' + logon
- urlstr += '&start=' + mytime
-
- urlstr += '&extent=' + ("%12.12d" % extent)
-
- return (urlstr)
-
-
-def download_iridium(event, basepath='./', tempfile_path='./', file_name=''):
- """Download netcdf (dB raw) data to be used by lompe from the AMPERE database (jhuapl) for a given event
- returns an input for the lompe read_iridium script in dataloader.py in data_tools
- Example usage:
- event = '2012-04-05'
- basepath = 'downloads'
- tempfile_path = 'downloads'
- file_name = '20120405_iridium.h5'
- download_iridium(event, basepath, tempfile_path, file_name)
-
- Args:
- event (str): fromat YYYY-MM-DD
- basepath (str, optional): path to . Defaults to './'.
- tempfile_path (str, optional): path to. Defaults to './'.
- file_name (str, optional):name of the file to write the netcdf file. Defaults to ''.
-
- Returns:
- saved file: to be used by the lompe read_iridium function in data_tools
-
- Note:
- functions "ampere_parsestart" and "ampere_coreurl" are internal helper functions adapted from supermag_api.py
- credit to the original author of the functions.
- """
-
- start = event + 'T00:00:00'
- duration = 86400 # Duration in seconds (one day)
- # check if the processed file exists
- savefile = tempfile_path + event.replace('-', '') + '_iridium.nc'
-
- # checks if file already exists
- # checking if the file is not empty
- if os.path.isfile(savefile) and os.path.getsize(savefile) > 0:
- return savefile
- else:
- import certifi
- # URL to download data from (lompe username is already registered in the API)
- urlstr = ampere_coreurl('data-rawdB.php', 'lompe', start, duration)
- # headers = {"User-Agent": "Mozilla/5.0"}
- # verify=certifi.where())
- response = requests.get(
- urlstr, verify=certifi.where(), stream=True)
-
- # Check if the request was successful
- if response.status_code == 200:
- # Save the downloaded data to a file
- with open(savefile, 'wb') as file:
- file.write(response.content)
- else:
- print(f"Failed to retrieve data: {response.status_code}")
- return savefile
-
-
-def download_supermag(event, tempfile_path='./'):
- """This downlaods data from superMAG for a given event and returns the hdf file suitable to lompe
- This is a faster way i can think of to download data from superMAG, the download_smag.py is slow
- since it uses serial processing to download data for each station. This uses multiprocessing to download (see the supermag_direct.py for the multiprocessing implemetation)
-
- Args:
- event (str): format 'YYYY-MM-DD'
-
- Returns:
- hdf file: this returns the hdf file with the data for the event, no need of using read_smag in the lompe dataloader.py script
-
- Note: not sure the data we get here is the one which is daily basedlined: need to be checked
- """
- # event = '2012-04-05'
- start = event + 'T00:00:00'
- savefile = tempfile_path + event.replace('-', '') + '_supermag.h5'
-
- if os.path.isfile(savefile): # checks if file already exists
- return savefile
- else:
- # run the function to download the data in the tempfiles folder (later to be deleted if successful)
- smag_data_for_event(start, event)
- temp_smag_path = f"./smag_files{event.replace('-', '')}/"
- files = glob.glob(f'{temp_smag_path}*.txt')
- df_combined = pd.DataFrame()
- for file in files:
- data = pd.read_json(file)
- df_combined = pd.concat([df_combined, data], axis=0)
- # date conversion and cleaning the DataFrame
- df_combined['tval'] = pd.to_datetime(
- df_combined['tval'], unit='s', origin='unix')
- df_combined[['N', 'E', 'Z']] = df_combined[['N', 'E', 'Z']].map(
- lambda x: x['geo'] if isinstance(x, dict) else np.nan)
- df_combined[['N', 'E', 'Z']] = df_combined[[
- 'N', 'E', 'Z']].replace(999999.000000, np.nan)
-
- # Final DataFrame to save as hdf
- df_combined.set_index('tval', inplace=True)
- df_combined.rename(columns={
- 'glat': 'lat', 'glon': 'lon', 'N': 'Bn', 'E': 'Be', 'Z': 'Bu'}, inplace=True)
- df_combined['Bu'] = -df_combined['Bu']
- df_final = df_combined[['Be', 'Bn', 'Bu',
- 'lat', 'lon']].dropna().sort_index()
-
- # savefile = event.replace('-', '') + '_supermag.h5'
-
- df_final.to_hdf(savefile, key='df_final', mode='w')
- # remove the tempfiles folder after the hdf file is created
- shutil.rmtree(temp_smag_path)
- return savefile
-
-
-def smag_download_for_station(args, retries=5, backoff_factor=0.5):
- # DO NOT EDIT THIS FUNCTION
- urlstr, station, temp_smag_path = args
- url = urlstr + '&station=' + station.upper()
- import certifi
- from requests.exceptions import RequestException
- import time
-
- for i in range(retries):
- try:
- response = requests.get(url, verify=certifi.where())
- time.sleep(random.uniform(0.2, 0.5))
- if response.status_code == 200:
- if response.content: # Check if the response content is not zero bytes
- with open(f'{temp_smag_path}{station}_data.txt', 'wb') as file:
- file.write(response.content)
- return None
- else:
- print(f"Received zero bytes for station {station}")
- raise RequestException("Received zero bytes.")
- else:
- print(
- f"Failed to retrieve data for station {station}: {response.status_code}")
- raise RequestException(
- f"Bad status code: {response.status_code}")
- except RequestException as e:
- print(
- f"Attempt {i + 1} for station {station} failed with error: {e}")
- time.sleep(backoff_factor * (2 ** i))
-
- print(
- f"Failed to download data for station {station} after {retries} attempts.")
- return None
-
-
-def smag_data_for_event(start, event):
- # DONOT EDIT THIS FUNCTION
- temp_smag_path = f"./smag_files{event.replace('-', '')}/"
- os.makedirs(f'{temp_smag_path}', exist_ok=True)
- duration = 86400 # Duration in seconds (one day)
- # lazy importing :)
- from lompe.data_tools.supermag_api import sm_GetUrl, sm_coreurl, sm_keycheck_data
- from joblib import Parallel, delayed
-
- # lompe is already registered in API
- urlstr = sm_coreurl('data-api.php', 'lompe', start, 86400)
- # this is hard coded to 'geo', see supermag_api.py to change it according to your needs
- indices = sm_keycheck_data('geo')
- urlstr += indices
- indices = sm_keycheck_data('baseline=daily')
- urlstr += indices
-
- urlstr_inv = sm_coreurl('inventory.php', 'lompe', start, duration)
- success, stations = sm_GetUrl(urlstr_inv, 'raw')
- stations = stations[1:-1]
-
- # Create a list of arguments to pass to data_download_for_station
- args_list = [(urlstr, station, temp_smag_path) for station in stations]
-
- Parallel(n_jobs=8, backend='threading')(
- delayed(smag_download_for_station)(args) for args in args_list)
-
-
-def download_champ(event, basepath='./', tempfile_path='./'):
- """
- Download CHAMP data from the FTP server and process it in lompe data format.
- Note that CHAMP data is only available for the year between 2000 and 2010.
-
- Args:
- event (str): format 'YYYY-MM-DD'
- basepath (str, optional): path. Defaults to './'.
- tempfile_path (str, optional): path. Defaults to './'.
-
- Returns:
- savedfile: file name of the processed file if successful
- """
- event_date = event.replace('-', '')
- year = event[:4]
- savefile = tempfile_path + f'CH_ME_MAG_LR_3_{event_date}_0102.cdf'
- processed_file = tempfile_path + f'{event_date}_champ.h5'
-
- # Check if the processed file already exists
- if os.path.isfile(processed_file):
- return processed_file
-
- # Check if the raw file already exists
- if not os.path.isfile(savefile):
- from requests_ftp import ftp
- session = requests.Session()
- session.mount('ftp://', ftp.FTPAdapter())
- ftp_url = f"ftp://isdcftp.gfz-potsdam.de/champ/ME/Level3/MAG/V0102/{year}/CH_ME_MAG_LR_3_{event_date}_0102.cdf"
- try:
- # Downloading the file and checking if it was successful
- response = session.get(ftp_url)
- if response.status_code == 200:
- with open(savefile, "wb") as file:
- file.write(response.content)
- print(f"Downloading {savefile} is successful!")
- else:
- print(
- f"Failed to download the file. Status code: {response.status_code}")
- return None
- except Exception as e:
- print(f"No champ data in this period: {e}")
- return None
-
- # Process the downloaded CDF file ot get the magnetic disturbance
- try:
- import cdflib
- import ppigrf
- cdf_file = cdflib.CDF(savefile)
- mag = cdf_file.varget('B_NEC') # space magnetometer data
-
- # geocentric coords of CHAMP orbit
- theta = 90 - cdf_file.varget('Latitude')
- phi = cdf_file.varget('Longitude')
- r = cdf_file.varget('Radius') / 1000
-
- time = cdflib.cdfepoch.to_datetime(cdf_file.varget('Timestamp'))
-
- # using IGRF to calculate magnetic disturbance (dB) registered by CHAMP
- Br, Btheta, Bphi = ppigrf.igrf_gc(r, theta, phi, time[0])
- B0 = np.vstack((-Btheta.flatten(), Bphi.flatten(), -Br.flatten()))
- dB = mag.T - B0
-
- champ_df = pd.DataFrame({
- 'Be': dB[1],
- 'Bn': dB[0],
- 'Bu': -dB[2],
- 'lon': phi,
- 'lat': 90 - theta,
- 'r': r
- }, index=time)
- champ_df.to_hdf(processed_file, key='df', mode='w')
- os.remove(savefile) # remove the raw file after processing
- return processed_file
- except Exception as e:
- print(f"Failed to process the file: {e}")
- return None
-
-
-def download_sdarn_file(url, save_path):
- # function to download a file from a URL and save it to a specific path
- response = requests.get(url)
- if response.status_code == 200:
- with open(save_path, 'wb') as f:
- f.write(response.content)
- # print(f"Downloaded successfully: {save_path}")
- else:
- return None
-
-
-def download_sdarn_files(event, basepath='./'):
- filepath = os.path.dirname(__file__)
- # file containing the URLs of the SuperDARN files (zenodo records)
- file_loc = pd.read_csv(filepath + '/../data/sdarn_2010_to_2021.csv')
- # Month mapping
- month_map = {
- 'Jan': '01',
- 'Feb': '02',
- 'Mar': '03',
- 'Apr': '04',
- 'May': '05',
- 'Jun': '06',
- 'Jul': '07',
- 'Aug': '08',
- 'Sep': '09',
- 'Oct': '10',
- 'Nov': '11',
- 'Dec': '12'
- }
-
- # Add numerical month to DataFrame (string format)
- file_loc['Month_Num'] = file_loc['MM'].map(month_map)
- # event_date = "2019-02-25"
-
- # Filter the DataFrame based on the year and month of the event date
- year = event[:4]
- month = event[5:7]
-
- # Filter the DataFrame
- filtered_df = file_loc[(file_loc['year'].astype(
- str) == year) & (file_loc['Month_Num'] == month)]
-
- # Apply function and add to DataFrame
- event_date_str = event.replace('-', '')
-
- # URL of the Zenodo record
- url = filtered_df['url'].tolist()[0]
-
- # Send a GET request to the URL
- response = requests.get(url)
- time.sleep(random.uniform(0.2, 0.5))
- soup = BeautifulSoup(response.content, "html.parser")
- # Check if the request was successful (status code 200)
- if response.status_code == 200:
-
- # Find all tags with class "download-file"
- for link in soup.find_all('a', href=True):
- href = link.get('href')
- if 'grid.nc' in href and event_date_str in href:
- url_to_download = 'https://zenodo.org' + href
- # print(url_to_download)
- save_path = basepath + \
- url_to_download.split('/')[-1].split('?')[0]
- download_sdarn_file(url_to_download, save_path)
- # else:
- # print('No file found')
- else:
- print('Failed to download the file')
- return None
-
-
-def download_sdarn(event, basepath='./', tempfile_path='./'):
- # tempfile_path = '/Users/fasilkebede/Documents/LOMPE/Data/SuperDARN/'
- # event = '2019-02-25'
-
- savefile = tempfile_path + event.replace('-', '') + '_superdarn_grdmap.h5'
- if os.path.isfile(savefile):
- return savefile
- else:
- from lompe.data_tools.dataloader import radar_losvec_from_mag
- temp_sdarn_path = basepath + f"sdarn_files_{event.replace('-', '')}/"
- os.makedirs(temp_sdarn_path, exist_ok=True)
- download_sdarn_files(event, temp_sdarn_path)
- # looking for the .nc files for the event
- try:
- files = glob.glob(
- f"{temp_sdarn_path}*{event.replace('-', '')}*.nc")
- files.sort()
- ddd = pd.DataFrame()
- for file in files:
- sm = xr.load_dataset(file)
- st_abbrev = file.split('/')[-1].split('.')[1]
- # mjd conversion
- mjd_epoch = pd.Timestamp('1858-11-17')
- duration = (sm['mjd_end'] + mjd_epoch) - \
- (sm['mjd_start'] + mjd_epoch)
-
- time = (sm['mjd_start'] + mjd_epoch) + duration
-
- # dff['date'] = unix_epoch + dff.mjd_start
-
- temp = pd.DataFrame()
-
- # in degrees AACGM
- temp.loc[:, 'mlat'] = sm['vector.mlat'].values
- # in degrees AACGM
- temp.loc[:, 'mlon'] = sm['vector.mlon'].values
- # glat, glon from lompe "radar_losvec_from_mag" is a bit different from the vector.glat and vector.glon from the data??
- # in degrees
- temp.loc[:, 'vector.glat'] = sm['vector.glat'].values
- # in degrees
- temp.loc[:, 'vector.glon'] = sm['vector.glon'].values
- # in degrees, the angle between los and magnetic north
- temp.loc[:, 'azimuth'] = sm['vector.kvect'].values
- # in m/s
- temp.loc[:, 'vlos'] = sm['vector.vel.median'].values
- # in m/s
- temp.loc[:, 'vlos_sd'] = sm['vector.vel.sd'].values
- # in km
- temp.loc[:, 'range'] = sm['vector.pwr.median'].values
- # spectral width in m/s
- temp.loc[:, 'wdt'] = sm['vector.wdt.median'].values
- temp.loc[:, 'time'] = pd.to_datetime(time.values).round('s')
- temp.loc[:, 'radar'] = st_abbrev
- # temp.set_index = pd.to_datetime(temp['time'], unit='s')
- # dff['datetime'] = mjd_epoch + dff['mjd_start']
- ddd = pd.concat([ddd, temp], ignore_index=True)
- ddd.set_index('time', inplace=True)
- ddd['glat'], ddd['glon'], ddd['le'], ddd['ln'], ddd['le_m'], ddd['ln_m'] = radar_losvec_from_mag(ddd['mlat'].values,
- ddd['mlon'].values, ddd['azimuth'].values, ddd.index[0])
- dd = ddd[ddd['glat'] > 0] # restrict to northern hemisphere
- df_final = dd.sort_values(by='time')
- df_final.to_hdf(savefile, key='df', mode='w')
- # remove the temp files after processing
- shutil.rmtree(temp_sdarn_path)
-
- return savefile
- except Exception as e:
- print(f"Failed to process the file: {e}")
- return None
-
-
-def download_swarm(event, tempfile_path='./'):
- """Download Swarm data for a given event date.
-
- Args:
- event (str): Event date in 'YYYY-MM-DD' format.
- tempfile_path (str, optional): Path to save the file or check if it already exists. Defaults to './'.
-
- Returns:
- str: File name of the downloaded file if successful.
-
- Note:
- 1. Install viresclient using "pip install viresclient" or make sure that it is installed.
- 2. The request needs an access token from the Swarm website. Visit https://viresclient.readthedocs.io/en/latest/config_details.html.
- """
-
- savefile = tempfile_path + event.replace('-', '') + '_swarm.h5'
-
- if os.path.isfile(savefile):
- return savefile
-
- try:
- from viresclient import SwarmRequest
- except ModuleNotFoundError:
- print('Please install viresclient using "pip install viresclient"')
- return
- # checking if token is present if not directing to the website to configure it
- try:
- with open(os.path.expanduser('~/.viresclient.ini'), 'r') as file:
- lines = file.readlines()
- for line in lines:
- if line.startswith("token ="):
- token_value = line.split('=', 1)[1].strip()
- if token_value:
- print("Swarm token is present:", token_value)
- except:
- print("Token is missing or empty. \nPlease visit https://viresclient.readthedocs.io/en/latest/config_details.html to configure it")
- return
- try:
- request = SwarmRequest()
-
- event_start = dt.datetime.strptime(event, '%Y-%m-%d')
- event_end = event_start + \
- dt.timedelta(hours=23, minutes=59, seconds=59)
- df = pd.DataFrame()
-
- for swarm_satellite in ['A', 'B', 'C']:
- request.set_collection(f"SW_OPER_MAG{swarm_satellite}_LR_1B")
- request.set_products(
- measurements=["F", "B_NEC"],
- models=["IGRF", "MCO_SHA_2D"],
- sampling_step="PT10S", # 10 seconds if needed PT1M for 1 minute
- # quasi-dipole latitude and longitude
- auxiliaries=["MLT", "OrbitNumber", 'QDLat', 'QDLon']
- )
- data = request.get_between(
- start_time=event_start,
- end_time=event_end,
- show_progress=False
- )
- df = pd.concat([df, data.as_dataframe(expand=True)])
-
- # Removing the background field from IGRF
- df['B_n'] = df['B_NEC_N'] - df['B_NEC_IGRF_N']
- df['B_e'] = df['B_NEC_E'] - df['B_NEC_IGRF_E']
- # Upward is negative in the NEC systema
- df['B_u'] = -(df['B_NEC_C'] - df['B_NEC_IGRF_C'])
-
- df.sort_values(by='Timestamp', inplace=True)
- df.reset_index(inplace=True)
- df.to_hdf(savefile, key='df', mode='w')
- return savefile
-
- except Exception as e:
- print(f"An error occurred while processing the Swarm data: {e}")
-
-
-def download_dmsp_ssies(event, sat, tempfile_path='./', **madrigal_kwargs):
- """ Download DMSP SSIES ion drift meter data for a full day
- using FTP-like acess at http://cedar.openmadrigal.org/ftp/ and get relevant parameters for Lompe.
- Saves hdf file in tempfile_path and returns path
-
- Args:
- event (str):
- string on format 'yyyy-mm-dd' to specify time of event for model.
- sat (int):
- Satellite ID for DMSP Block 5D satellite (16-19)
- tempfile_path (str, optional):
- Path to dir where processed hdf files are placed. Default: './'
- **madrigalkwargs (dict):
- needed to download data from MadrigalWeb (Madrigal needs user specifications through **madrigal_kwargs.)
- Example usage:
- event = '2014-07-13'
- sat = 17
- tempfile_path = '/Users/fasilkebede/Documents/'
- madrigal_kwargs = {'user_fullname': 'First','user_email': 'name@host.com', 'user_affiliation': 'University'}
-
- Returns:
- savefile(str):
- Path to hdf-file containing SSIES data for Lompe.
- """
-
- savefile = tempfile_path + \
- event.replace('-', '') + '_ssies_f' + str(sat) + '.h5'
- if os.path.exists(savefile):
- print('File already exists')
- return savefile
-
- date_str = event.replace('-', '')
- year = event[:4]
- url_base = "https://cedar.openmadrigal.org"
- url = url_base + \
- f"/ftp/fullname/{madrigal_kwargs['user_fullname']}/email/{madrigal_kwargs['user_email']}/affiliation/{madrigal_kwargs['user_affiliation']}/kinst/8100/year/{year}/"
-
- response2 = requests.get(url)
- soup2 = BeautifulSoup(response2.content, 'html.parser')
- urls = [link.get('href') for link in soup2.find_all(
- 'a', href=True) if '/format/hdf5/' in link.get('href')]
-
- url_ion_drift = url_base + [a['href'] for a in soup2.find_all(
- 'a', href=True) if 'ion drift' in a.text and f'F{sat}' in a.text][0]
- url_plasma_temp = url_base + [a['href'] for a in soup2.find_all(
- 'a', href=True) if 'plasma temp' in a.text and f'F{sat}' in a.text][0]
- # url_flux_energy = url_base + [a['href'] for a in soup2.find_all(
- # 'a', href=True) if 'flux/energy' in a.text and f'F{sat}' in a.text][0]
-
- # downloading the ion drift file
- ion_drift_hdf5_file_url = url_ion_drift + 'format/hdf5/'
- response2 = requests.get(ion_drift_hdf5_file_url)
- soup2 = BeautifulSoup(response2.content, 'html.parser')
- url_target_file = url_base + [link.get('href') for link in soup2.find_all(
- 'a', href=True) if '/format/hdf5/fullFilename/' in link.get('href') and date_str in link.get('href')][0]
-
- response = requests.get(url_target_file, stream=True)
- # tempfile_path = '/Users/fasilkebede/Documents/'
- filename = tempfile_path + url_target_file[-27:-1]
- with open(filename, 'wb') as file:
- for chunk in response.iter_content(chunk_size=65536):
- if chunk: # Filter out keep-alive new chunks
- file.write(chunk)
-
- # downloading the plasma temperature file
- plasma_hdf5_file_url = url_plasma_temp + 'format/hdf5/'
- response2 = requests.get(plasma_hdf5_file_url)
- soup2 = BeautifulSoup(response2.content, 'html.parser')
- url_target_file = url_base + [link.get('href') for link in soup2.find_all(
- 'a', href=True) if '/format/hdf5/fullFilename/' in link.get('href') and date_str in link.get('href')][0]
-
- response = requests.get(url_target_file, stream=True)
- # tempfile_path = '/Users/fasilkebede/Documents/'
- filename2 = tempfile_path + url_target_file[-27:-1]
- with open(filename2, 'wb') as file:
- for chunk in response.iter_content(chunk_size=65536):
- if chunk: # Filter out keep-alive new chunks
- file.write(chunk)
-
- dmsp = pd.read_hdf(filename, mode='r', key='Data/Table Layout')
- dmsp2 = pd.read_hdf(filename2, mode='r', key='Data/Table Layout')
-
- dmsp.index = np.arange(len(dmsp))
- dmsp2.index = np.arange(len(dmsp2))
-
- # set datetime as index
- times = []
- for i in range(len(dmsp)):
- times.append(dt.datetime.fromtimestamp(
- int(dmsp['ut1_unix'][i]), tz=dt.timezone.utc))
- dmsp.index = times
-
- # reindexing due to lower cadence measurements
- times2 = []
- for i in range(len(dmsp2)):
- times2.append(dt.datetime.fromtimestamp(
- int(dmsp2['ut1_unix'][i]), tz=dt.timezone.utc))
- dmsp2.index = times2
- dmsp2 = dmsp2.reindex(index=dmsp.index, method='nearest', tolerance='2sec')
-
- dmsp.loc[:, 'po+'] = dmsp2['po+']
- dmsp.loc[:, 'te'] = dmsp2['te']
- dmsp.loc[:, 'ti'] = dmsp2['ti']
-
- # Smooth the orbit
- import matplotlib
- from scipy import interpolate
- from lompe.data_tools.dataloader import cross_track_los
-
- # latitude (geodetic)
- tck = interpolate.splrep(matplotlib.dates.date2num(dmsp.index[::60].append(dmsp.index[-1:])),
- pd.concat([dmsp.gdlat[::60], (dmsp.gdlat[-1:])]).values, k=2)
- gdlat = interpolate.splev(matplotlib.dates.date2num(dmsp.index), tck)
-
- # longitude (geodetic)
- negs = dmsp.glon < 0
- dmsp.loc[negs, 'glon'] = dmsp.glon[negs] + 360
- tck = interpolate.splrep(matplotlib.dates.date2num(dmsp.index[::60].append(dmsp.index[-1:])),
- pd.concat([dmsp.glon[::60], (dmsp.glon[-1:])]).values, k=2)
- glon = interpolate.splev(matplotlib.dates.date2num(dmsp.index), tck)
-
- # i got negative and above 360 values even negative are excluded before the interpolation, due to the interpolation, handling it here
- glon[glon < 0] += 360
- glon = np.mod(glon, 360)
-
- # altitude (geodetic)
- tck = interpolate.splrep(matplotlib.dates.date2num(dmsp.index[::60].append(dmsp.index[-1:])),
- pd.concat([dmsp.gdalt[::60], (dmsp.gdalt[-1:])]).values, k=2)
- gdalt = interpolate.splev(matplotlib.dates.date2num(dmsp.index), tck)
-
- # get eastward and northward component of cross track direction
- le, ln, bearing = cross_track_los(dmsp['hor_ion_v'], gdlat, glon)
-
- # put together the relevant data
- ddd = pd.DataFrame() # dataframe to return
- ddd.index = dmsp.index
- ddd.loc[:, 'gdlat'] = gdlat
- ddd.loc[:, 'glon'] = glon
- ddd.loc[:, 'gdalt'] = gdalt
- ddd.loc[:, 'hor_ion_v'] = np.abs(dmsp.hor_ion_v)
- ddd.loc[:, 'vert_ion_v'] = dmsp.vert_ion_v
- ddd.loc[:, 'bearing'] = bearing
- ddd.loc[:, 'le'] = le
- ddd.loc[:, 'ln'] = ln
-
- # quality flag from Zhu et al 2020 page 8 https://doi.org/10.1029/2019JA027270
- # flag1 is good, flag2 is also usually acceptable
-
- flag1 = (dmsp['ne'] > 1e9) & (dmsp['po+'] > 0.85)
- flag2 = ((dmsp['po+'] > 0.85) & (dmsp['ne'] > 1e8) & (dmsp['ne'] < 1e9)) | ((dmsp['po+'] > 0.75)
- & (dmsp['po+'] < 0.85) & (dmsp['ne'] > 1e8))
- flag3 = (dmsp['ne'] < 1e8) | (dmsp['po+'] < 0.75)
- flag4 = dmsp['po+'].isna()
-
- ddd.loc[:, 'quality'] = np.zeros(ddd.shape[0]) + np.nan
- ddd.loc[flag1, 'quality'] = 1
- ddd.loc[flag2, 'quality'] = 2
- ddd.loc[flag3, 'quality'] = 3
- ddd.loc[flag4, 'quality'] = 4
-
- ddd.to_hdf(savefile, key='df', mode='w')
- os.remove(filename)
- os.remove(filename2)
- return savefile
-
-
-def download_eiscat():
- pass
-# def date2doy(date_str):
-# date = dt.datetime.strptime(date_str, "%Y-%m-%d")
-# return date.timetuple().tm_yday
-
-
-if __name__ == '__main__':
- print("This is a module to download data from different sources for a given event date.")
diff --git a/lompe/data_tools/dataloader.py b/lompe/data_tools/dataloader.py
index 8232e83..4022e8d 100644
--- a/lompe/data_tools/dataloader.py
+++ b/lompe/data_tools/dataloader.py
@@ -321,12 +321,13 @@ def read_ssies(event, sat, basepath='./', tempfile_path='./', forcenew=False, **
for fname in fileList:
filenames.append(fname.name)
- filenames = [filename for filename in filenames if date_str in filename]
+ filenames = [
+ filename for filename in filenames if date_str in filename]
if len(filenames) == 0:
continue
no_data_found = False
- # ssies = str([s for s in filenames if '_' + str(sat) + 's1' in s][0])
- ssies = [s for s in filenames if '_' + str(sat) + 's1.' in s]
+ # ssies = str([s for s in filenames if '_' + str(sat) + 's1' in s][0])
+ ssies = [s for s in filenames if '_' + str(sat) + 's1.' in s]
# temp_dens = str(
# [s for s in filenames if '_' + str(sat) + 's4.' in s][0])
temp_dens = [s for s in filenames if '_' + str(sat) + 's4.' in s]
@@ -697,12 +698,12 @@ def read_iridium(event, basepath='./', tempfile_path='./', file_name=''):
else:
fn = basepath + event.replace('-', '') + 'Amp_invert.ncdf'
if not os.path.isfile(fn):
- files = glob.glob(basepath + '*' + event.replace('-', '') + '*.ncdf')
+ files = glob.glob(basepath + '*' + event.replace('-', '') + '*.nc')
try:
fn = files[0]
except:
raise FileNotFoundError(
- 'Cannot find Iridium netcdf in specified folder.')
+ 'Cannot find Iridium netcdf in specified folder.')
iridset = xr.load_dataset(fn, engine='netcdf4')
diff --git a/lompe/data_tools/dmsp_ssies.py b/lompe/data_tools/dmsp_ssies.py
new file mode 100644
index 0000000..bd2246b
--- /dev/null
+++ b/lompe/data_tools/dmsp_ssies.py
@@ -0,0 +1,172 @@
+import os
+import datetime as dt
+import numpy as np
+import pandas as pd
+import requests
+from bs4 import BeautifulSoup
+
+
+def download_dmsp_ssies(event, sat, tempfile_path='./', **madrigal_kwargs):
+ """ Download DMSP SSIES ion drift meter data for a full day
+ using FTP-like acess at http://cedar.openmadrigal.org/ftp/ and get relevant parameters for Lompe.
+ Saves hdf file in tempfile_path and returns path
+
+ Args:
+ event (str):
+ string on format 'yyyy-mm-dd' to specify time of event for model.
+ sat (int):
+ Satellite ID for DMSP Block 5D satellite (16-19)
+ tempfile_path (str, optional):
+ Path to dir where processed hdf files are placed. Default: './'
+ **madrigalkwargs (dict):
+ needed to download data from MadrigalWeb (Madrigal needs user specifications through **madrigal_kwargs.)
+ Example usage:
+ event = '2014-07-13'
+ sat = 17
+ tempfile_path = '/Users/fasilkebede/Documents/'
+ madrigal_kwargs = {'user_fullname': 'First','user_email': 'name@host.com', 'user_affiliation': 'University'}
+
+ Returns:
+ savefile(str):
+ Path to hdf-file containing SSIES data for Lompe.
+ """
+
+ savefile = tempfile_path + \
+ event.replace('-', '') + '_ssies_f' + str(sat) + '.h5'
+ if os.path.exists(savefile):
+ print('File already exists')
+ return savefile
+
+ date_str = event.replace('-', '')
+ year = event[:4]
+ url_base = "https://cedar.openmadrigal.org"
+ url = url_base + \
+ f"/ftp/fullname/{madrigal_kwargs['user_fullname']}/email/{madrigal_kwargs['user_email']}/affiliation/{madrigal_kwargs['user_affiliation']}/kinst/8100/year/{year}/"
+
+ response2 = requests.get(url)
+ soup2 = BeautifulSoup(response2.content, 'html.parser')
+ urls = [link.get('href') for link in soup2.find_all(
+ 'a', href=True) if '/format/hdf5/' in link.get('href')]
+
+ url_ion_drift = url_base + [a['href'] for a in soup2.find_all(
+ 'a', href=True) if 'ion drift' in a.text and f'F{sat}' in a.text][0]
+ url_plasma_temp = url_base + [a['href'] for a in soup2.find_all(
+ 'a', href=True) if 'plasma temp' in a.text and f'F{sat}' in a.text][0]
+ # url_flux_energy = url_base + [a['href'] for a in soup2.find_all(
+ # 'a', href=True) if 'flux/energy' in a.text and f'F{sat}' in a.text][0]
+
+ # downloading the ion drift file
+ ion_drift_hdf5_file_url = url_ion_drift + 'format/hdf5/'
+ response2 = requests.get(ion_drift_hdf5_file_url)
+ soup2 = BeautifulSoup(response2.content, 'html.parser')
+ url_target_file = url_base + [link.get('href') for link in soup2.find_all(
+ 'a', href=True) if '/format/hdf5/fullFilename/' in link.get('href') and date_str in link.get('href')][0]
+
+ response = requests.get(url_target_file, stream=True)
+ # tempfile_path = '/Users/fasilkebede/Documents/'
+ filename = tempfile_path + url_target_file[-27:-1]
+ with open(filename, 'wb') as file:
+ for chunk in response.iter_content(chunk_size=65536):
+ if chunk: # Filter out keep-alive new chunks
+ file.write(chunk)
+
+ # downloading the plasma temperature file
+ plasma_hdf5_file_url = url_plasma_temp + 'format/hdf5/'
+ response2 = requests.get(plasma_hdf5_file_url)
+ soup2 = BeautifulSoup(response2.content, 'html.parser')
+ url_target_file = url_base + [link.get('href') for link in soup2.find_all(
+ 'a', href=True) if '/format/hdf5/fullFilename/' in link.get('href') and date_str in link.get('href')][0]
+
+ response = requests.get(url_target_file, stream=True)
+ # tempfile_path = '/Users/fasilkebede/Documents/'
+ filename2 = tempfile_path + url_target_file[-27:-1]
+ with open(filename2, 'wb') as file:
+ for chunk in response.iter_content(chunk_size=65536):
+ if chunk: # Filter out keep-alive new chunks
+ file.write(chunk)
+
+ dmsp = pd.read_hdf(filename, mode='r', key='Data/Table Layout')
+ dmsp2 = pd.read_hdf(filename2, mode='r', key='Data/Table Layout')
+
+ dmsp.index = np.arange(len(dmsp))
+ dmsp2.index = np.arange(len(dmsp2))
+
+ # set datetime as index
+ times = []
+ for i in range(len(dmsp)):
+ times.append(dt.datetime.fromtimestamp(
+ int(dmsp['ut1_unix'][i]), tz=dt.timezone.utc))
+ dmsp.index = times
+
+ # reindexing due to lower cadence measurements
+ times2 = []
+ for i in range(len(dmsp2)):
+ times2.append(dt.datetime.fromtimestamp(
+ int(dmsp2['ut1_unix'][i]), tz=dt.timezone.utc))
+ dmsp2.index = times2
+ dmsp2 = dmsp2.reindex(index=dmsp.index, method='nearest', tolerance='2sec')
+
+ dmsp.loc[:, 'po+'] = dmsp2['po+']
+ dmsp.loc[:, 'te'] = dmsp2['te']
+ dmsp.loc[:, 'ti'] = dmsp2['ti']
+
+ # Smooth the orbit
+ import matplotlib
+ from scipy import interpolate
+ from lompe.data_tools.dataloader import cross_track_los
+
+ # latitude (geodetic)
+ tck = interpolate.splrep(matplotlib.dates.date2num(dmsp.index[::60].append(dmsp.index[-1:])),
+ pd.concat([dmsp.gdlat[::60], (dmsp.gdlat[-1:])]).values, k=2)
+ gdlat = interpolate.splev(matplotlib.dates.date2num(dmsp.index), tck)
+
+ # longitude (geodetic)
+ negs = dmsp.glon < 0
+ dmsp.loc[negs, 'glon'] = dmsp.glon[negs] + 360
+ tck = interpolate.splrep(matplotlib.dates.date2num(dmsp.index[::60].append(dmsp.index[-1:])),
+ pd.concat([dmsp.glon[::60], (dmsp.glon[-1:])]).values, k=2)
+ glon = interpolate.splev(matplotlib.dates.date2num(dmsp.index), tck)
+
+ # i got negative and above 360 values even negative are excluded before the interpolation, due to the interpolation, handling it here
+ glon[glon < 0] += 360
+ glon = np.mod(glon, 360)
+
+ # altitude (geodetic)
+ tck = interpolate.splrep(matplotlib.dates.date2num(dmsp.index[::60].append(dmsp.index[-1:])),
+ pd.concat([dmsp.gdalt[::60], (dmsp.gdalt[-1:])]).values, k=2)
+ gdalt = interpolate.splev(matplotlib.dates.date2num(dmsp.index), tck)
+
+ # get eastward and northward component of cross track direction
+ le, ln, bearing = cross_track_los(dmsp['hor_ion_v'], gdlat, glon)
+
+ # put together the relevant data
+ ddd = pd.DataFrame() # dataframe to return
+ ddd.index = dmsp.index
+ ddd.loc[:, 'gdlat'] = gdlat
+ ddd.loc[:, 'glon'] = glon
+ ddd.loc[:, 'gdalt'] = gdalt
+ ddd.loc[:, 'hor_ion_v'] = np.abs(dmsp.hor_ion_v)
+ ddd.loc[:, 'vert_ion_v'] = dmsp.vert_ion_v
+ ddd.loc[:, 'bearing'] = bearing
+ ddd.loc[:, 'le'] = le
+ ddd.loc[:, 'ln'] = ln
+
+ # quality flag from Zhu et al 2020 page 8 https://doi.org/10.1029/2019JA027270
+ # flag1 is good, flag2 is also usually acceptable
+
+ flag1 = (dmsp['ne'] > 1e9) & (dmsp['po+'] > 0.85)
+ flag2 = ((dmsp['po+'] > 0.85) & (dmsp['ne'] > 1e8) & (dmsp['ne'] < 1e9)) | ((dmsp['po+'] > 0.75)
+ & (dmsp['po+'] < 0.85) & (dmsp['ne'] > 1e8))
+ flag3 = (dmsp['ne'] < 1e8) | (dmsp['po+'] < 0.75)
+ flag4 = dmsp['po+'].isna()
+
+ ddd.loc[:, 'quality'] = np.zeros(ddd.shape[0]) + np.nan
+ ddd.loc[flag1, 'quality'] = 1
+ ddd.loc[flag2, 'quality'] = 2
+ ddd.loc[flag3, 'quality'] = 3
+ ddd.loc[flag4, 'quality'] = 4
+
+ ddd.to_hdf(savefile, key='df', mode='w')
+ os.remove(filename)
+ os.remove(filename2)
+ return savefile
diff --git a/lompe/data_tools/dmsp_ssusi.py b/lompe/data_tools/dmsp_ssusi.py
new file mode 100644
index 0000000..1fa75b9
--- /dev/null
+++ b/lompe/data_tools/dmsp_ssusi.py
@@ -0,0 +1,262 @@
+import warnings
+import xarray as xr
+import numpy as np
+import pandas as pd
+import requests
+from bs4 import BeautifulSoup
+import datetime as dt
+import os
+import glob
+import shutil
+from lompe.utils.time import date2doy
+import random
+import time
+
+warnings.filterwarnings("ignore")
+
+# extensive list of functions (including helper functions) to download data from different sources for a given event date
+# the functions are designed to be used in the lompe package for data loading and processing
+# this has to be integrated with the dataloader.py in the lompe package at some level to get lompe data format
+
+# the idea is to merge the two files (datadownloader.py and dataloader.py) into one file in the lompe package, for the time being we need to keep
+# both, some functions imported from dataloader.py are in use in this script
+
+# Note that: lazy importing is implemented here
+
+
+def download_ssusi(event, hemi='north', basepath='./ssusi_tempfiles', tempfile_path='./', source='cdaweb'):
+ """
+
+ Called and used for modelling auroral conductance in cmodel.py.
+
+ Extract the relevant SSUSI info from netcdf-files downloaded from APL server.
+ https://cdaweb.gsfc.nasa.gov/pub/data/dmsp/ (ssusi/data/edr-aurora)
+ Will load all SSUSI data from specified hemisphere into xarray object.
+ Any DMSP Block 5D satellite (F16-19) available in folder will be used.
+
+ Parameters
+ ----------
+ event : str
+ string on format 'yyyy-mm-dd' to specify time of event for model.
+ hemi : str, optional
+ specify hemisphere
+ Default: 'north'
+ basepath : str, optional
+ location of netcdf-files downloaded from APL server (.NC).
+ Default: './ssusi_tempfiles'
+ tempfile_path : str, optional
+ Location for storing processed SSUSI data.
+ Default: './'
+ source : str, optional
+ Specify source of SUSSI data.
+ Default: 'cdaweb'
+
+ Returns
+ -------
+ savefile : str
+ Path to saved file containing SSUSI data + conductances extracted from the images.
+
+ """
+ '''
+ Using the CDAWeb server here eliminates the need for checking if the data is from the same day or not.
+ The data is already sorted by date and time. From the APL sever, there might be a need to check if the data is from the same day.
+ (see download_ssusi function)
+
+ Extract the relevant info from downloaded netcdf-files (similar to from APL server, but from CDAWeb):
+ Note that these data are gridded on a 363,363 geomagnetic grid. We assume this is AACGM,
+ based on email correspondence with L. Paxton. This is not clear from the documentation.
+ No geographic information of these gridded data exist in the EDR aurora files downloaded
+ Quote from documentation: "This array is a uniform grid in a polar azimuthal equidistant
+ projection of the geomagnetic latitude and geomagnetic local time, with appropriate
+ geomagnetic pole as the origin; that is: 90-ABS(glat) is the radius and geomagnetic
+ local time is the angle of a two-dimensional polar coordinate system."
+ Hence, the mapped latitude array (363x363) is the same for both hemispheres, and
+ the values are therefore always positive.
+ '''
+ if not basepath.endswith('/'):
+ basepath += '/'
+ if not tempfile_path.endswith('/'):
+ tempfile_path += '/'
+
+ # check if the processed file exists
+ savefile = tempfile_path + \
+ event.replace('-', '') + '_ssusi_' + hemi + '.nc'
+ if os.path.isfile(savefile):
+ print('SSUSI file already exists.')
+ return savefile
+ try:
+ import netCDF4
+ except ModuleNotFoundError:
+ raise ModuleNotFoundError(
+ 'read_ssusi: Could not load netCDF4 module. Will not be able to read SSUSI-files from APL.')
+ # downlaoding the files from the server see the ssusi_direct.py for the implementation
+ # from lompe.data_tools.ssusi_direct2 import download_ssusi_files
+ download_ssusi_files(event, basepath=basepath, source=source)
+ ###
+ imgs = [] # List to hold all images. Converted to xarray later.
+ doy_str = f"{date2doy(event):03d}"
+ for sat in ['F16', 'F17', 'F18', 'F19']:
+ files = glob.glob(basepath + '*' + sat.lower() + '*' +
+ event[0:4] + doy_str + '*.nc')
+ files.sort()
+ if len(files) == 0:
+ continue
+
+ ii = 0 # counter
+
+ for file in files:
+ f = netCDF4.Dataset(file)
+
+ mlat = f.variables['LATITUDE_GEOMAGNETIC_GRID_MAP'][:]
+ mlon = f.variables['LONGITUDE_GEOMAGNETIC_' +
+ hemi.upper() + '_GRID_MAP'][:]
+ mlt = f.variables['MLT_GRID_MAP'][:]
+ uthr = f.variables['UT_' + hemi.upper()[0]][:]
+ doy = int(f.variables['DOY'][:])
+ year = int(f.variables['YEAR'][:])
+
+ wavelengths = f.variables['DISK_RADIANCEDATA_INTENSITY_' + hemi.upper()][:]
+ char_energy = f.variables['ELECTRON_MEAN_' +
+ hemi.upper() + '_ENERGY_MAP'][:]
+ energyflux = f.variables['ENERGY_FLUX_' + hemi.upper() + '_MAP'][:]
+
+ f.close()
+
+ mask = uthr == 0
+
+ uthr[mask] = np.nan
+ wavelengths[:, mask] = np.nan
+ char_energy[mask] = np.nan
+ energyflux[mask] = np.nan
+ mlon[mask] = np.nan
+
+ # Applying Robinson formulas to calculate conductances: https://agupubs.onlinelibrary.wiley.com/doi/epdf/10.1029/JA092iA03p02565
+ SP = (40. * char_energy * np.sqrt(energyflux)) / \
+ (16. + char_energy**2)
+ SH = 0.45 * char_energy**0.85 * SP
+
+ if sum(np.isfinite(uthr[:, 181]).flatten()) > 0: # there is data in north
+ # Calculate center time:
+ # there is a sudden transition across midnight in the pass
+ if np.nanstd(uthr) > 0.5:
+ next_day = (uthr > 0) & (uthr < 2)
+ if sum(next_day.flatten()) > 0:
+ uthr[next_day] = uthr[next_day] + 24.
+ center_hr = np.nanmean(uthr, axis=0)[181]
+ hr = int(center_hr)
+ if hr >= 24:
+ hr = hr - 24
+ center_hr = center_hr - 24
+
+ m = int((center_hr - hr) * 60)
+ s = round(((center_hr - hr) * 60 - m) * 60)
+ if s == 60:
+ t0 = dt.datetime(year, 1, 1, hr, m, 59) + \
+ dt.timedelta(seconds=1)
+ else:
+ t0 = dt.datetime(year, 1, 1, hr, m, s)
+
+ dtime = t0 + dt.timedelta(doy - 1)
+ # put into xarray object
+ img = xr.Dataset({'uthr': (['row', 'col'], uthr),
+ 'mlon': (['row', 'col'], mlon),
+ 'mlat': (['row', 'col'], mlat),
+ 'mlt': (['row', 'col'], mlt),
+ '1216': (['row', 'col'], wavelengths[0, :, :]),
+ '1304': (['row', 'col'], wavelengths[1, :, :]),
+ '1356': (['row', 'col'], wavelengths[2, :, :]),
+ 'lbhs': (['row', 'col'], wavelengths[3, :, :]),
+ 'lbhl': (['row', 'col'], wavelengths[4, :, :]),
+ 'E0': (['row', 'col'], char_energy),
+ 'je': (['row', 'col'], energyflux),
+ 'SP': (['row', 'col'], SP),
+ 'SH': (['row', 'col'], SH)})
+ img = img.expand_dims(date=[dtime])
+ img = img.assign({'satellite': sat})
+ img = img.assign(
+ {'orbit': file.split('-')[-1].split('_')[0][3:]})
+ imgs.append(img)
+ ii += 1
+
+ if len(imgs) == 0:
+ print('No SSUSI images found.')
+
+ else: # save as netcdf in specified path
+ imgs = xr.concat(imgs, dim='date')
+ imgs = imgs.assign({'hemisphere': hemi})
+ imgs = imgs.sortby(imgs['date'])
+ print('DMSP SSUSI file saved: ' + savefile)
+
+ imgs.to_netcdf(savefile)
+ shutil.rmtree(basepath)
+ return savefile
+
+
+def fetch_ssusi_urls(sat, year, doy, source):
+ doy_str = f"{doy:03d}" # Zero-pad the day of year to three digits
+ # if source == 'jhuapl':
+ # url = f"https://ssusi.jhuapl.edu/data_retriver?spc=f{sat}&type=edr-aur&year={year}&Doy={doy_str}"
+ if source == 'cdaweb':
+ url = f'https://cdaweb.gsfc.nasa.gov/pub/data/dmsp/dmspf{sat}/ssusi/data/edr-aurora/{year}/{doy_str}/'
+ else:
+ print(f"Unsupported source: {source}")
+ return []
+
+ try:
+ response = requests.get(url)
+ response.raise_for_status() # raise an HTTPError on bad response
+ except requests.exceptions.RequestException as e:
+ print(f"Failed to fetch URL probably no data : \n{e}")
+ return []
+
+ from urllib.parse import urljoin
+ soup = BeautifulSoup(response.content, 'html.parser')
+ urls = [urljoin(url, link.get('href')) for link in soup.find_all(
+ 'a', href=True) if link.get('href').lower().endswith('.nc')]
+
+ if not urls:
+ print(
+ f"No .nc files found for satellite F{sat} on day {doy_str} of year {year} from source {source}")
+
+ return urls
+
+
+def download_ssusi_file(file_url, destination):
+ try:
+ response = requests.get(file_url, stream=True)
+ response.raise_for_status() # raise an HTTPError on bad response
+ filename = os.path.join(destination, os.path.basename(file_url))
+
+ with open(filename, 'wb') as file:
+ for chunk in response.iter_content(chunk_size=65536):
+ if chunk: # Filter out keep-alive new chunks
+ file.write(chunk)
+ except requests.exceptions.RequestException as e:
+ print(f"Failed to download {file_url}: {e}")
+
+
+def download_ssusi_files(event, source='cedaweb', basepath='./ssusi_tempfiles/'):
+ """Downloading data from the SSUSI instrument onboard the DMSP satellites for a given event date.
+
+ Args:
+ event strin: format 'YYYY-MM-DD'
+ source (str, optional): data source. Defaults to 'cedaweb'.
+ """
+ year = int(event[0:4])
+ doy = date2doy(event)
+
+ os.makedirs(basepath, exist_ok=True)
+
+ from joblib import Parallel, delayed
+ import itertools
+ # Use joblib.Parallel to fetch URLs concurrently
+ fetch_args = [(sat, year, doy, source) for sat in [16, 17, 18, 19]]
+ results = Parallel(n_jobs=-1, backend='threading')(
+ delayed(fetch_ssusi_urls)(*args) for args in fetch_args)
+ # results = Parallel(n_jobs=-1)(delayed(fetch_ssusi_urls)(*args) for args in fetch_args)
+ all_urls = list(itertools.chain.from_iterable(results))
+
+ # Use joblib.Parallel to download files concurrently
+ download_args = [(url, basepath) for url in all_urls]
+ Parallel(n_jobs=-1, backend='threading')(
+ delayed(download_ssusi_file)(*args) for args in download_args)
diff --git a/lompe/data_tools/get_lompe_data.py b/lompe/data_tools/get_lompe_data.py
new file mode 100644
index 0000000..3e8d3dc
--- /dev/null
+++ b/lompe/data_tools/get_lompe_data.py
@@ -0,0 +1,170 @@
+import os
+import numpy as np
+import pandas as pd
+import lompe
+from lompe.data_tools import datadownloader, dataloader
+
+from .dmsp_ssusi import download_ssusi
+from .supermag import download_supermag
+from .ampere import download_iridium
+from .champ import download_champ
+from .superdarn import download_sdarn
+from .swarm import download_swarm
+from .dmsp_ssies import download_dmsp_ssies
+
+
+def prepare_event_data(event, data_path="./lompe_data/", sources=None, basepath="./", **kwargs):
+ """
+ Download and load all event datasets once.
+ Returns dict with {supermag, superdarn, champ} as DataFrames.
+ """
+ if sources is None:
+ sources = ["supermag", "iridium", "superdarn"]
+ os.makedirs(
+ data_path, exist_ok=True) # if not already existing create the directory
+ event = pd.to_datetime(event).strftime("%Y-%m-%d")
+ # event = event_time.strftime("%Y-%m-%d")
+
+ def safe_read_hdf(filename):
+ if filename is None or not os.path.exists(filename):
+ return pd.DataFrame()
+ try:
+ return pd.read_hdf(filename)
+ except Exception as e:
+ print(f"[WARN] Could not read {filename}: {e}")
+ return pd.DataFrame()
+ files = {}
+ if "ssusi" in sources:
+ files["ssusi"] = download_ssusi(event, tempfile_path=data_path)
+
+ if "supermag" in sources:
+ files["supermag"] = download_supermag(
+ event, tempfile_path=data_path)
+
+ if "iridium" in sources:
+ files["iridium"] = download_iridium(
+ event, basepath=basepath, tempfile_path=data_path)
+
+ if "champ" in sources:
+ files["champ"] = download_champ(event, tempfile_path=data_path)
+
+ if "superdarn" in sources:
+ files["superdarn"] = download_sdarn(event, tempfile_path=data_path)
+
+ if "swarm" in sources:
+ files["swarm"] = download_swarm(event, tempfile_path=data_path)
+ # smag_file = datadownloader.download_supermag(
+ # event, tempfile_path=data_path)
+ # sdarn_file = datadownloader.download_sdarn(event, tempfile_path=data_path)
+ # champ_file = datadownloader.download_champ(event, tempfile_path=data_path)
+ # iridium_file = datadownloader.download_iridium(
+ # event, tempfile_path=data_path)
+ # file_iridium = dataloader.read_iridium(
+ # event, file_name=iridium_file, tempfile_path=data_path)
+ result = {}
+
+ for item, file in files.items():
+ result[item] = safe_read_hdf(file)
+
+ return result
+
+
+def get_data_subsets(event_data, event, delta_minutes=2, sources=None, **kwargs):
+ '''
+ Extract data subsets for the given time interval [t0, t1]. and prepare lompe.Data objects.
+ Returns: iridium_data, supermag_data, superdarn_data, champ_data'''
+ if sources is None:
+ sources = ["supermag", "iridium", "superdarn"]
+ T0 = pd.to_datetime(event)
+ DT = pd.Timedelta(minutes=delta_minutes)
+ t0, t1 = T0 - DT / 2, T0 + DT / 2
+
+ def ensure_datetimeindex(df):
+ if not isinstance(df.index, pd.DatetimeIndex):
+ try:
+ df = df.copy()
+ df.index = pd.to_datetime(df.index)
+ except Exception as e:
+ raise TypeError(f"Failed to convert index to datetime: {e}")
+ return df
+
+ # --- iridium ---
+ iridium = event_data["iridium"]
+ irid = iridium[(iridium.time >= t0) & (iridium.time <= t1)]
+
+ if not irid.empty:
+ irid_B = np.vstack((irid.B_e.values, irid.B_n.values, irid.B_r.values))
+ irid_coords = np.vstack(
+ (irid.lon.values, irid.lat.values, irid.r.values))
+ else:
+ irid_B = np.empty((3, 0))
+ irid_coords = np.empty((2, 0))
+ iridium_data = lompe.Data(
+ irid_B * 1e-9 if irid_B.size else irid_B,
+ irid_coords,
+ datatype="space_mag_fac", iweight=1.0, error=30e-9
+ )
+
+ # --- SuperMAG ---
+ smag_df = ensure_datetimeindex(event_data["supermag"])
+ smag_df.index = smag_df.index.tz_localize(None)
+ smag = smag_df.loc[t0:t1, :]
+
+ if not smag.empty:
+ smag_B = np.vstack((smag.Be.values, smag.Bn.values, smag.Bu.values))
+ smag_coords = np.vstack((smag.lon.values, smag.lat.values))
+ else:
+ smag_B = np.empty((3, 0))
+ smag_coords = np.empty((2, 0))
+ supermag_data = lompe.Data(
+ smag_B * 1e-9 if smag_B.size else smag_B,
+ smag_coords,
+ datatype="ground_mag", iweight=0.4, error=10e-9
+ )
+
+ # --- CHAMP ---
+ ch_df = ensure_datetimeindex(event_data["champ"])
+ ch = ch_df.loc[t0:t1, :]
+ if not ch.empty:
+ champ_B = np.vstack((ch.Be.values, ch.Bn.values, ch.Bu.values))
+ champ_coords = np.vstack(
+ (ch.lon.values, ch.lat.values, ch.r.values*1000))
+ else:
+ champ_B = np.empty((3, 0))
+ champ_coords = np.empty((3, 0))
+ cham_data = lompe.Data(
+ champ_B * 1e-9 if champ_B.size else champ_B,
+ champ_coords,
+ datatype="space_mag_full", iweight=0.4, error=10e-9
+ )
+
+ # --- SuperDARN ---
+ sd = event_data["superdarn"].loc[t0:t1, :]
+ if not sd.empty:
+ vlos = sd["vlos"].values
+ sd_coords = np.vstack((sd["glon"].values, sd["glat"].values))
+ los = np.vstack((sd["le"].values, sd["ln"].values))
+ else:
+ vlos = np.empty((0,))
+ sd_coords = np.empty((2, 0))
+ los = np.empty((2, 0))
+ superdarn_data = lompe.Data(
+ vlos, sd_coords, LOS=los,
+ datatype="convection", iweight=1.0, error=50
+ )
+
+ return iridium_data, supermag_data, superdarn_data, cham_data
+
+
+# Example usage:
+if __name__ == "__main__":
+ event_time = "2015-03-17 04:00"
+ event_data = prepare_event_data(event_time)
+ iridium_data, supermag_data, superdarn_data, cham_data = get_data_subsets(
+ event_data, event_time, delta_minutes=3)
+
+ # print(iridium_data)
+ # print(supermag_data)
+ # print(superdarn_data)
+ # print(cham_data)
+ print("Data subsets prepared successfully.")
diff --git a/lompe/data_tools/superdarn.py b/lompe/data_tools/superdarn.py
new file mode 100644
index 0000000..558fc98
--- /dev/null
+++ b/lompe/data_tools/superdarn.py
@@ -0,0 +1,152 @@
+import os
+import glob
+import time
+import random
+import shutil
+import requests
+import pandas as pd
+import xarray as xr
+from bs4 import BeautifulSoup
+
+
+def download_sdarn_file(url, save_path):
+ # function to download a file from a URL and save it to a specific path
+ response = requests.get(url)
+ if response.status_code == 200:
+ with open(save_path, 'wb') as f:
+ f.write(response.content)
+ # print(f"Downloaded successfully: {save_path}")
+ else:
+ return None
+
+
+def download_sdarn_files(event, basepath='./'):
+ filepath = os.path.dirname(__file__)
+ # file containing the URLs of the SuperDARN files (zenodo records)
+ file_loc = pd.read_csv(filepath + '/../data/sdarn_2010_to_2021.csv')
+ # Month mapping
+ month_map = {
+ 'Jan': '01',
+ 'Feb': '02',
+ 'Mar': '03',
+ 'Apr': '04',
+ 'May': '05',
+ 'Jun': '06',
+ 'Jul': '07',
+ 'Aug': '08',
+ 'Sep': '09',
+ 'Oct': '10',
+ 'Nov': '11',
+ 'Dec': '12'
+ }
+
+ # Add numerical month to DataFrame (string format)
+ file_loc['Month_Num'] = file_loc['MM'].map(month_map)
+ # event_date = "2019-02-25"
+
+ # Filter the DataFrame based on the year and month of the event date
+ year = event[:4]
+ month = event[5:7]
+
+ # Filter the DataFrame
+ filtered_df = file_loc[(file_loc['year'].astype(
+ str) == year) & (file_loc['Month_Num'] == month)]
+
+ # Apply function and add to DataFrame
+ event_date_str = event.replace('-', '')
+
+ # URL of the Zenodo record
+ url = filtered_df['url'].tolist()[0]
+
+ # Send a GET request to the URL
+ response = requests.get(url)
+ time.sleep(random.uniform(0.2, 0.5))
+ soup = BeautifulSoup(response.content, "html.parser")
+ # Check if the request was successful (status code 200)
+ if response.status_code == 200:
+
+ # Find all tags with class "download-file"
+ for link in soup.find_all('a', href=True):
+ href = link.get('href')
+ if 'grid.nc' in href and event_date_str in href:
+ url_to_download = 'https://zenodo.org' + href
+ # print(url_to_download)
+ save_path = basepath + \
+ url_to_download.split('/')[-1].split('?')[0]
+ download_sdarn_file(url_to_download, save_path)
+ # else:
+ # print('No file found')
+ else:
+ print('Failed to download the file')
+ return None
+
+
+def download_sdarn(event, basepath='./', tempfile_path='./'):
+ # tempfile_path = '/Users/fasilkebede/Documents/LOMPE/Data/SuperDARN/'
+ # event = '2019-02-25'
+
+ savefile = tempfile_path + event.replace('-', '') + '_superdarn_grdmap.h5'
+ if os.path.isfile(savefile):
+ return savefile
+ else:
+ from lompe.data_tools.dataloader import radar_losvec_from_mag
+ temp_sdarn_path = basepath + f"sdarn_files_{event.replace('-', '')}/"
+ os.makedirs(temp_sdarn_path, exist_ok=True)
+ download_sdarn_files(event, temp_sdarn_path)
+ # looking for the .nc files for the event
+ try:
+ files = glob.glob(
+ f"{temp_sdarn_path}*{event.replace('-', '')}*.nc")
+ files.sort()
+ ddd = pd.DataFrame()
+ for file in files:
+ sm = xr.load_dataset(file)
+ st_abbrev = file.split('/')[-1].split('.')[1]
+ # mjd conversion
+ mjd_epoch = pd.Timestamp('1858-11-17')
+ duration = (sm['mjd_end'] + mjd_epoch) - \
+ (sm['mjd_start'] + mjd_epoch)
+
+ time = (sm['mjd_start'] + mjd_epoch) + duration
+
+ # dff['date'] = unix_epoch + dff.mjd_start
+
+ temp = pd.DataFrame()
+
+ # in degrees AACGM
+ temp.loc[:, 'mlat'] = sm['vector.mlat'].values
+ # in degrees AACGM
+ temp.loc[:, 'mlon'] = sm['vector.mlon'].values
+ # glat, glon from lompe "radar_losvec_from_mag" is a bit different from the vector.glat and vector.glon from the data??
+ # in degrees
+ temp.loc[:, 'vector.glat'] = sm['vector.glat'].values
+ # in degrees
+ temp.loc[:, 'vector.glon'] = sm['vector.glon'].values
+ # in degrees, the angle between los and magnetic north
+ temp.loc[:, 'azimuth'] = sm['vector.kvect'].values
+ # in m/s
+ temp.loc[:, 'vlos'] = sm['vector.vel.median'].values
+ # in m/s
+ temp.loc[:, 'vlos_sd'] = sm['vector.vel.sd'].values
+ # in km
+ temp.loc[:, 'range'] = sm['vector.pwr.median'].values
+ # spectral width in m/s
+ temp.loc[:, 'wdt'] = sm['vector.wdt.median'].values
+ temp.loc[:, 'time'] = pd.to_datetime(time.values).round('s')
+ temp.loc[:, 'radar'] = st_abbrev
+ # temp.set_index = pd.to_datetime(temp['time'], unit='s')
+ # dff['datetime'] = mjd_epoch + dff['mjd_start']
+ ddd = pd.concat([ddd, temp], ignore_index=True)
+ ddd.set_index('time', inplace=True)
+ ddd['glat'], ddd['glon'], ddd['le'], ddd['ln'], ddd['le_m'], ddd['ln_m'] = radar_losvec_from_mag(ddd['mlat'].values,
+ ddd['mlon'].values, ddd['azimuth'].values, ddd.index[0])
+ dd = ddd[ddd['glat'] > 0] # restrict to northern hemisphere
+ df_final = dd.sort_values(by='time')
+ df_final.to_hdf(savefile, key='df', mode='w')
+ # remove the temp files after processing
+ shutil.rmtree(temp_sdarn_path)
+
+ return savefile
+ except Exception as e:
+ print(f"Failed to process the file: {e}")
+ return None
diff --git a/lompe/data_tools/supermag.py b/lompe/data_tools/supermag.py
new file mode 100644
index 0000000..aad9bc1
--- /dev/null
+++ b/lompe/data_tools/supermag.py
@@ -0,0 +1,156 @@
+from joblib import Parallel, delayed
+import numpy as np
+import os
+import time
+from urllib.request import urlopen
+import json
+import pandas as pd
+from urllib.error import URLError, HTTPError
+# start_date = '2019-10-15T00:00:00'
+# extent = 86400 # one hour (86400 for one day)
+
+
+def get_smag_stations(start_date, extent, userid="lompe"):
+ """ inventory of stations that have data for the given time range
+
+ Args:
+ start_date (str): eg. '2019-10-15T00:00:00'
+ extent (int): eg. 86400 for one day
+ userid (str, optional): _description_. Defaults to "lompe". plese use your own userid if you have one (please register if not), to avoid hitting SuperMAG limits.
+
+ Returns:
+ list: list of IAGA station codes (e.g. ['AAA', 'ABK', ...])
+ """
+ urlstr = f"https://supermag.jhuapl.edu/services/inventory.php?fmt=json&logon={userid}&start={start_date}&extent={extent}"
+ with urlopen(urlstr) as response:
+ data_response = response.read()
+ data = data_response.decode('utf-8').split('\n')
+
+ # removing ('OK', empty strings)
+ json_parts = [x for x in data if x.strip() not in ("OK", "")]
+ # the first part is the number of stations, so we skip it
+ return json_parts[1:]
+
+
+def get_smag_station_data(start_date, extent, station, userid="lompe", retries=5, sleep=2):
+ """_summary_
+
+ Args:
+ start_date (str): eg. '2019-10-15T00:00:00'
+ extent (int): eg. 86400 for one day
+ station (str): eg. 'ABK'
+ userid (str, optional): _description_. Defaults to "lompe".
+ retries (int, optional): _description_. Defaults to 5.
+ sleep (int, optional): _description_. Defaults to 2.
+
+ Raises:
+ ValueError: _description_
+
+ Returns:
+ pandas dataframe: dataframe with columns ['tval', 'glat', 'glon', 'N.nez', 'E.nez', 'Z.nez', 'station'] and datetime index
+ """
+ data_url = (
+ "https://supermag.jhuapl.edu/services/data-api.php"
+ f"?fmt=json&logon={userid}&start={start_date}&extent={extent}"
+ f"&geo&station={station}"
+ )
+
+ last_error = None
+
+ for attempt in range(retries):
+ try:
+ with urlopen(data_url, timeout=60) as response:
+ text = response.read().decode("utf-8").strip()
+
+ lines = text.splitlines()
+ json_parts = [x for x in lines if x.strip() not in ("OK", "")]
+ json_str = "".join(json_parts).strip()
+
+ if not json_str:
+ raise ValueError(f"Empty response for station {station}")
+
+ parsed = json.loads(json_str)
+
+ df = (
+ pd.json_normalize(parsed)
+ .assign(datetime=lambda x: pd.to_datetime(x["tval"], unit="s", utc=True))
+ .set_index("datetime")
+ )
+
+ df["station"] = station
+ return df
+
+ except (json.JSONDecodeError, ValueError, HTTPError, URLError, TimeoutError) as e:
+ last_error = e
+ time.sleep(sleep * (attempt + 1))
+
+ print(f"Skipping station {station}: {last_error}")
+ return pd.DataFrame()
+
+
+def download_supermag(event, userid="lompe", n_jobs=-1, save=True, tempfile_path="./"):
+ """_summary_
+
+ Args:
+ event (_type_): _description_
+ userid (str, optional): Defaults to "lompe".
+ n_jobs (int, optional): Defaults to -1.
+ save (bool, optional): Defaults to False.
+ tempfile_path (str, optional): Path to check if the file already exists (to avoid downloading it again) Defaults to "./".
+
+ Raises:
+ RuntimeError: _description_
+
+ Returns:
+ if save=true: event_supermag.h5 file
+ if save=false: pandas dataframe with columns ['Be', 'Bn', 'Bu', 'lat', 'lon', 'station'] and datetime index
+ """
+ start = event + "T00:00:00"
+ extent = 86400
+ savefile = os.path.join(
+ tempfile_path, event.replace("-", "") + "_supermag.h5")
+
+ if save and os.path.isfile(savefile):
+ print(f"File {savefile} already exists at {tempfile_path}.")
+ return savefile
+
+ stations = get_smag_stations(start, extent, userid=userid)
+
+ dfs = Parallel(n_jobs=n_jobs, verbose=10)(
+ delayed(get_smag_station_data)(start, extent, station, userid)
+ for station in stations
+ )
+
+ # remove failed / empty downloads
+ dfs = [df for df in dfs if df is not None and not df.empty]
+
+ if not dfs:
+ raise RuntimeError("No station data downloaded successfully.")
+
+ # combine all stations into one dataframe and replace SuperMAG bad values with NaN
+ df_combined = pd.concat(dfs, axis=0).sort_index()
+ df_combined = df_combined.replace(999999.0, np.nan)
+
+ # final Lompe-style dataframe (taking only the nez components and renaming columns)
+ df_final = (
+ df_combined
+ .rename(columns={
+ "glat": "lat",
+ "glon": "lon",
+ "N.nez": "Bn",
+ "E.nez": "Be",
+ "Z.nez": "Bu",
+ })
+ [["Be", "Bn", "Bu", "lat", "lon", "station"]]
+ .dropna()
+ .sort_index()
+ )
+
+ # SuperMAG Z is positive downward; Lompe wants upward
+ df_final["Bu"] = -df_final["Bu"]
+
+ if save:
+ df_final.to_hdf(savefile, key="df_final", mode="w")
+ return savefile
+
+ return df_final
diff --git a/lompe/data_tools/swarm.py b/lompe/data_tools/swarm.py
new file mode 100644
index 0000000..3022e3f
--- /dev/null
+++ b/lompe/data_tools/swarm.py
@@ -0,0 +1,80 @@
+import os
+import datetime as dt
+import pandas as pd
+
+
+def download_swarm(event, tempfile_path='./'):
+ """Download Swarm data for a given event date.
+
+ Args:
+ event (str): Event date in 'YYYY-MM-DD' format.
+ tempfile_path (str, optional): Path to save the file or check if it already exists. Defaults to './'.
+
+ Returns:
+ str: File name of the downloaded file if successful.
+
+ Note:
+ 1. Install viresclient using "pip install viresclient" or make sure that it is installed.
+ 2. The request needs an access token from the Swarm website. Visit https://viresclient.readthedocs.io/en/latest/config_details.html.
+ """
+
+ savefile = tempfile_path + event.replace('-', '') + '_swarm.h5'
+
+ if os.path.isfile(savefile):
+ print(f"File {savefile} already exists at {tempfile_path}.")
+ return savefile
+
+ try:
+ from viresclient import SwarmRequest
+ except ModuleNotFoundError:
+ print('Please install viresclient using "pip install viresclient"')
+ return
+ # checking if token is present if not directing to the website to configure it
+ try:
+ with open(os.path.expanduser('~/.viresclient.ini'), 'r') as file:
+ lines = file.readlines()
+ for line in lines:
+ if line.startswith("token ="):
+ token_value = line.split('=', 1)[1].strip()
+ if token_value:
+ print("Swarm token is present:", token_value)
+ except:
+ print("Token is missing or empty. \nPlease visit https://viresclient.readthedocs.io/en/latest/config_details.html to configure it")
+ return
+ try:
+ request = SwarmRequest()
+
+ event_start = dt.datetime.strptime(event, '%Y-%m-%d')
+ event_end = event_start + \
+ dt.timedelta(hours=23, minutes=59, seconds=59)
+ df = pd.DataFrame()
+
+ for swarm_satellite in ['A', 'B', 'C']:
+ request.set_collection(f"SW_OPER_MAG{swarm_satellite}_LR_1B")
+ request.set_products(
+ measurements=["F", "B_NEC"],
+ models=["IGRF", "MCO_SHA_2D"],
+ sampling_step="PT10S", # 10 seconds if needed PT1M for 1 minute
+ # quasi-dipole latitude and longitude
+ auxiliaries=["MLT", "OrbitNumber", 'QDLat', 'QDLon']
+ )
+ data = request.get_between(
+ start_time=event_start,
+ end_time=event_end,
+ show_progress=False
+ )
+ df = pd.concat([df, data.as_dataframe(expand=True)])
+
+ # Removing the background field from IGRF
+ df['B_n'] = df['B_NEC_N'] - df['B_NEC_IGRF_N']
+ df['B_e'] = df['B_NEC_E'] - df['B_NEC_IGRF_E']
+ # Upward is negative in the NEC systema
+ df['B_u'] = -(df['B_NEC_C'] - df['B_NEC_IGRF_C'])
+
+ df.sort_values(by='Timestamp', inplace=True)
+ df.reset_index(inplace=True)
+ df.to_hdf(savefile, key='df', mode='w')
+ return savefile
+
+ except Exception as e:
+ print(f"An error occurred while processing the Swarm data: {e}")
diff --git a/lompe/utils/time.py b/lompe/utils/time.py
index 122c177..b5cdf2d 100644
--- a/lompe/utils/time.py
+++ b/lompe/utils/time.py
@@ -1,9 +1,10 @@
""" time tools """
import numpy as np
import pandas as pd
+import datetime as dt
-def date_to_doy(month, day, leapyear = False):
+def date_to_doy(month, day, leapyear=False):
""" return day of year (DOY) at given month, day
month and day -- can be arrays, but must have equal shape
@@ -17,11 +18,11 @@ def date_to_doy(month, day, leapyear = False):
KML 2016-04-20
"""
- month = np.array(month, ndmin = 1)
- day = np.array(day, ndmin = 1)
+ month = np.array(month, ndmin=1)
+ day = np.array(day, ndmin=1)
if type(leapyear) == bool:
- leapyear = np.full_like(day, leapyear, dtype = bool)
+ leapyear = np.full_like(day, leapyear, dtype=bool)
# check that shapes match
if month.shape != day.shape:
@@ -38,14 +39,17 @@ def date_to_doy(month, day, leapyear = False):
# flatten arrays:
shape = month.shape
month = month.flatten()
- day = day.flatten()
+ day = day.flatten()
# check if day exceeds days in months
- days_in_month = np.array([0, 31, 28, 31, 30, 31, 30, 31, 31, 30, 31, 30, 31])
- days_in_month_ly = np.array([0, 31, 29, 31, 30, 31, 30, 31, 31, 30, 31, 30, 31])
- if ( (np.any(day[~leapyear] > days_in_month [month[~leapyear]])) |
- (np.any(day[ leapyear] > days_in_month_ly[month[ leapyear]])) ):
- raise ValueError('date2doy: day must not exceed number of days in month')
+ days_in_month = np.array(
+ [0, 31, 28, 31, 30, 31, 30, 31, 31, 30, 31, 30, 31])
+ days_in_month_ly = np.array(
+ [0, 31, 29, 31, 30, 31, 30, 31, 31, 30, 31, 30, 31])
+ if ((np.any(day[~leapyear] > days_in_month[month[~leapyear]])) |
+ (np.any(day[leapyear] > days_in_month_ly[month[leapyear]]))):
+ raise ValueError(
+ 'date2doy: day must not exceed number of days in month')
cumdaysmonth = np.cumsum(days_in_month[:-1])
@@ -64,11 +68,11 @@ def is_leapyear(year):
# if array:
if type(year) is np.ndarray:
- out = np.full_like(year, False, dtype = bool)
+ out = np.full_like(year, False, dtype=bool)
- out[ year % 4 == 0] = True
- out[ year % 100 == 0] = False
- out[ year % 400 == 0] = True
+ out[year % 4 == 0] = True
+ out[year % 100 == 0] = False
+ out[year % 400 == 0] = True
return out
@@ -102,11 +106,17 @@ def yearfrac_to_datetime(fracyear):
Array of datetimes
"""
- year = np.uint16(fracyear) # truncate fracyear to get year
- # use pandas TimedeltaIndex to represent time since beginning of year:
- delta_year = pd.to_timedelta((fracyear - year)*(365 + is_leapyear(year)), unit = 'D')
+ year = np.uint16(fracyear) # truncate fracyear to get year
+ # use pandas TimedeltaIndex to represent time since beginning of year:
+ delta_year = pd.to_timedelta(
+ (fracyear - year)*(365 + is_leapyear(year)), unit='D')
# and DatetimeIndex to represent beginning of years:
start_year = pd.DatetimeIndex(list(map(str, year)))
-
+
# adding them produces the datetime:
return (start_year + delta_year).to_pydatetime()
+
+
+def date2doy(date_str):
+ date = dt.datetime.strptime(date_str, "%Y-%m-%d")
+ return date.timetuple().tm_yday
From 40b0b69638f93cd41b564b49348b5a0a4403b99e Mon Sep 17 00:00:00 2001
From: Fasil Kebede <46403603+FasilGibdaw@users.noreply.github.com>
Date: Tue, 28 Apr 2026 19:08:10 +0200
Subject: [PATCH 3/3] example of using the data tools added
---
lompe/data_tools/data_tools_example.ipynb | 134 ++++++++++++++++++++++
lompe/data_tools/get_lompe_data.py | 4 +-
2 files changed, 136 insertions(+), 2 deletions(-)
create mode 100644 lompe/data_tools/data_tools_example.ipynb
diff --git a/lompe/data_tools/data_tools_example.ipynb b/lompe/data_tools/data_tools_example.ipynb
new file mode 100644
index 0000000..8257d55
--- /dev/null
+++ b/lompe/data_tools/data_tools_example.ipynb
@@ -0,0 +1,134 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "id": "275e7b80",
+ "metadata": {},
+ "source": [
+ "#### Testing to downlaod data from sources and extract subsets for the given time interval [t0, t1]. and prepare lompe.Data objects. This is a test script to check the data downloading and subset extraction functions. The same example used in the lompe paper for comparison with the results in the paper.\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "id": "ed7cfb3e",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from lompe.data_tools import dataloader, datadownloader\n",
+ "from lompe.data_tools.get_lompe_data import get_data_subsets, prepare_event_data\n",
+ "import matplotlib.pyplot as plt\n",
+ "import numpy as np\n",
+ "import pandas as pd\n",
+ "import datetime as dt\n",
+ "import lompe\n",
+ "import apexpy\n",
+ "# cubed sphere grid parameters:\n",
+ "position = (-90, 68) # lon, lat for grid center\n",
+ "orientation = 0 # angle of grid x axis - anti-clockwise from east direction\n",
+ "L, W = 7000e3, 3800e3 # extents [m] of grid\n",
+ "dL, dW = 100e3, 100e3 # spatial resolution [m] of grid \n",
+ "# create grid object\n",
+ "grid = lompe.cs.CSgrid(lompe.cs.CSprojection(position, orientation), L, W, dL, dW, R = 6481.2e3)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "id": "4c2b7d00",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "File ./lompe_data/20120405_supermag.h5 already exists at ./lompe_data/.\n",
+ "File ./lompe_data/20120405_iridium.h5 already exists at ./lompe_data/.\n",
+ "No champ data in this period: [Errno 8] nodename nor servname provided, or not known\n"
+ ]
+ }
+ ],
+ "source": [
+ "event = '2012-04-05'\n",
+ "date_str = event + 'T' + '05:12:00'\n",
+ "T0 = dt.datetime.strptime(date_str, '%Y-%m-%dT%H:%M:%S')\n",
+ "delta_minutes = 4\n",
+ "DT = dt.timedelta(seconds = 60 * delta_minutes) # length of time interval\n",
+ "# apex object for plotting in magnetic\n",
+ "apex = apexpy.Apex(T0, refh = 110)\n",
+ "# making conductance tuples\n",
+ "Kp = 4 # this is the input to the Hardy model\n",
+ "SH = lambda lon = grid.lon, lat = grid.lat: lompe.conductance.hardy_EUV(lon, lat, Kp, T0, 'hall')\n",
+ "SP = lambda lon = grid.lon, lat = grid.lat: lompe.conductance.hardy_EUV(lon, lat, Kp, T0, 'pedersen')\n",
+ "\n",
+ "# Create Emodel object. Pass grid and Hall/Pedersen conductance functions\n",
+ "model = lompe.Emodel(grid, Hall_Pedersen_conductance = (SH, SP))\n",
+ "\n",
+ "# add datasets to model\n",
+ "event_data = prepare_event_data(event, sources=[\"superdarn\", \"supermag\", \"iridium\", \"champ\"])\n",
+ "all_data= get_data_subsets(event_data, T0, delta_minutes=delta_minutes)\n",
+ "model.add_data(*all_data)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "id": "c93b48f8",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "ground_mag: Measurement uncertainty effectively changed from 1e-08 to 1.5811388300841896e-08\n",
+ "space_mag_fac: Measurement uncertainty effectively changed from 3e-08 to 4.743416490252568e-08\n"
+ ]
+ },
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "model.run_inversion(l1 = 1, l2 = 10)\n",
+ "# finally, plot (plot is saved as specified path):\n",
+ "fig = lompe.lompeplot(model, include_data = True, time = T0, apex = apex)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "c747a8cf",
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "base",
+ "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": "3.12.2"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/lompe/data_tools/get_lompe_data.py b/lompe/data_tools/get_lompe_data.py
index 3e8d3dc..272a887 100644
--- a/lompe/data_tools/get_lompe_data.py
+++ b/lompe/data_tools/get_lompe_data.py
@@ -19,7 +19,7 @@ def prepare_event_data(event, data_path="./lompe_data/", sources=None, basepath=
Returns dict with {supermag, superdarn, champ} as DataFrames.
"""
if sources is None:
- sources = ["supermag", "iridium", "superdarn"]
+ sources = ["supermag", "iridium", "superdarn", "champ"]
os.makedirs(
data_path, exist_ok=True) # if not already existing create the directory
event = pd.to_datetime(event).strftime("%Y-%m-%d")
@@ -74,7 +74,7 @@ def get_data_subsets(event_data, event, delta_minutes=2, sources=None, **kwargs)
Extract data subsets for the given time interval [t0, t1]. and prepare lompe.Data objects.
Returns: iridium_data, supermag_data, superdarn_data, champ_data'''
if sources is None:
- sources = ["supermag", "iridium", "superdarn"]
+ sources = ["supermag", "iridium", "superdarn", "champ"]
T0 = pd.to_datetime(event)
DT = pd.Timedelta(minutes=delta_minutes)
t0, t1 = T0 - DT / 2, T0 + DT / 2