diff --git a/.gitignore b/.gitignore
index 7b45ec2..8c5111a 100644
--- a/.gitignore
+++ b/.gitignore
@@ -5,6 +5,7 @@ data/facebook_auth/facebook_auth_tim.csv
 data/facebook_auth/facebook_auth_yuan.csv
 data/embeddings
 data/query_results
+data/assimilation_scores
 data/spec_data.tsv
 data/all_FB_interests_2016/
 data/top_interests_complete.json
diff --git a/data/queries/List_of_Mexican-American_communities_music_interests.json b/data/queries/List_of_Mexican-American_communities_music_interests.json
new file mode 100644
index 0000000..55261a2
--- /dev/null
+++ b/data/queries/List_of_Mexican-American_communities_music_interests.json
@@ -0,0 +1,4072 @@
+{
+    "interests": [
+        {
+            "name": "Rocksteady", 
+            "and":["6003314921218"]
+        }, 
+        {
+            "name": "Christian hip hop", 
+            "and":["6003140211647"]
+        }, 
+        {
+            "name": "Noise music", 
+            "and":["6003258005082"]
+        }, 
+        {
+            "name": "Flamenco", 
+            "and":["6003074062797"]
+        }, 
+        {
+            "name": "Psychedelic trance", 
+            "and":["6003419806154"]
+        }, 
+        {
+            "name": "Skate punk", 
+            "and":["6002985722723"]
+        }, 
+        {
+            "name": "New jack swing", 
+            "and":["6002969744692"]
+        }, 
+        {
+            "name": "Power metal", 
+            "and":["6003677535718"]
+        }, 
+        {
+            "name": "Neue Deutsche H\u00e4rte", 
+            "and":["6003159533034"]
+        }, 
+        {
+            "name": "Underground music", 
+            "and":["6003437235770"]
+        }, 
+        {
+            "name": "Mexican cumbia", 
+            "and":["6003497930280"]
+        }, 
+        {
+            "name": "Broken beat", 
+            "and":["6003425345554"]
+        }, 
+        {
+            "name": "Mexican rock music", 
+            "and":["6002971551929"]
+        }, 
+        {
+            "name": "Hardcore (electronic dance music genre)", 
+            "and":["6003374770477"]
+        }, 
+        {
+            "name": "Tango music", 
+            "and":["6009909180927"]
+        }, 
+        {
+            "name": "Alternative country", 
+            "and":["6003133345814"]
+        }, 
+        {
+            "name": "Avant-garde metal", 
+            "and":["6014498774112"]
+        }, 
+        {
+            "name": "Hardcore hip hop", 
+            "and":["6003290070051"]
+        }, 
+        {
+            "name": "A cappella", 
+            "and":["6003153751465"]
+        }, 
+        {
+            "name": "Post-punk revival", 
+            "and":["6003472592263"]
+        }, 
+        {
+            "name": "Reggae en Espa\u00f1ol", 
+            "and":["6003364047823"]
+        }, 
+        {
+            "name": "Baroque pop", 
+            "and":["6003605762820"]
+        }, 
+        {
+            "name": "Instrumental", 
+            "and":["6003356636987"]
+        }, 
+        {
+            "name": "Western swing", 
+            "and":["6003257250286"]
+        }, 
+        {
+            "name": "Zouk", 
+            "and":["6003169339149"]
+        }, 
+        {
+            "name": "Brega (music)", 
+            "and":["6003463723544"]
+        }, 
+        {
+            "name": "Vocal music", 
+            "and":["6003289088651"]
+        }, 
+        {
+            "name": "Hard trance", 
+            "and":["6003192799672"]
+        }, 
+        {
+            "name": "Funk ostenta\u00e7\u00e3o", 
+            "and":["6016917656327"]
+        }, 
+        {
+            "name": "Shoegazing", 
+            "and":["6003127203524"]
+        }, 
+        {
+            "name": "Korean hip hop", 
+            "and":["6012875345920"]
+        }, 
+        {
+            "name": "Stoner rock", 
+            "and":["6003257838288"]
+        }, 
+        {
+            "name": "Folk", 
+            "and":["6003300256738"]
+        }, 
+        {
+            "name": "Freestyle rap", 
+            "and":["6003259015102"]
+        }, 
+        {
+            "name": "Samba-enredo", 
+            "and":["6003541789765"]
+        }, 
+        {
+            "name": "Huapango", 
+            "and":["6003420589431"]
+        }, 
+        {
+            "name": "Symphonic metal", 
+            "and":["6003198743801"]
+        }, 
+        {
+            "name": "Surf music", 
+            "and":["6003257449969"]
+        }, 
+        {
+            "name": "Cabaret", 
+            "and":["6003647797113"]
+        }, 
+        {
+            "name": "Argentine cumbia", 
+            "and":["6003985876266"]
+        }, 
+        {
+            "name": "Beatboxing", 
+            "and":["6003382908804"]
+        }, 
+        {
+            "name": "Oldschool jungle", 
+            "and":["6003339214796"]
+        }, 
+        {
+            "name": "Music of India", 
+            "and":["6003651347113"]
+        }, 
+        {
+            "name": "Belgian rock", 
+            "and":["6003539998663"]
+        }, 
+        {
+            "name": "Ra\u00ef", 
+            "and":["6003434733851"]
+        }, 
+        {
+            "name": "Groove metal", 
+            "and":["6003646941516"]
+        }, 
+        {
+            "name": "Christmas carol", 
+            "and":["6003304554260"]
+        }, 
+        {
+            "name": "Vocal trance", 
+            "and":["6003647207516"]
+        }, 
+        {
+            "name": "Sambass", 
+            "and":["6003352340000"]
+        }, 
+        {
+            "name": "Afrobeat", 
+            "and":["6003484812986"]
+        }, 
+        {
+            "name": "Anatolian rock", 
+            "and":["6004114757224"]
+        }, 
+        {
+            "name": "Tech trance", 
+            "and":["6003122415922"]
+        }, 
+        {
+            "name": "Pinoy hip hop", 
+            "and":["6003185572008"]
+        }, 
+        {
+            "name": "Intelligent dance music", 
+            "and":["6003672270062"]
+        }, 
+        {
+            "name": "Jazz rap", 
+            "and":["6003362198821"]
+        }, 
+        {
+            "name": "Balearic trance", 
+            "and":["6009884266155"]
+        }, 
+        {
+            "name": "Acid trance", 
+            "and":["6003331848888"]
+        }, 
+        {
+            "name": "Post-rock", 
+            "and":["6002897827910"]
+        }, 
+        {
+            "name": "Urban contemporary gospel", 
+            "and":["6003369642335"]
+        }, 
+        {
+            "name": "Rumba (dance)", 
+            "and":["6003234292967"]
+        }, 
+        {
+            "name": "Ghazal", 
+            "and":["6003147013549"]
+        }, 
+        {
+            "name": "Jingle", 
+            "and":["6003605467820"]
+        }, 
+        {
+            "name": "Deejay (Jamaican)", 
+            "and":["6004933825545"]
+        }, 
+        {
+            "name": "Crunk", 
+            "and":["6003375372077"]
+        }, 
+        {
+            "name": "Gothic metal", 
+            "and":["6003337414843"]
+        }, 
+        {
+            "name": "Visual kei", 
+            "and":["6003109324984"]
+        }, 
+        {
+            "name": "Freestyle music", 
+            "and":["6003386969196"]
+        }, 
+        {
+            "name": "Chicano rap", 
+            "and":["6004108615609"]
+        }, 
+        {
+            "name": "Old-school hip hop", 
+            "and":["6003341960423"]
+        }, 
+        {
+            "name": "Mainstream Top 40", 
+            "and":["6003377746787"]
+        }, 
+        {
+            "name": "Experimental music", 
+            "and":["6002969799129"]
+        }, 
+        {
+            "name": "Thai rock", 
+            "and":["6003278226799"]
+        }, 
+        {
+            "name": "Texas blues", 
+            "and":["6003451628671"]
+        }, 
+        {
+            "name": "Country rap", 
+            "and":["6003199481065"]
+        }, 
+        {
+            "name": "Latin jazz", 
+            "and":["6004111763189"]
+        }, 
+        {
+            "name": "Fingerstyle guitar", 
+            "and":["6003224974661"]
+        }, 
+        {
+            "name": "Boogie (genre)", 
+            "and":["6003254827401"]
+        }, 
+        {
+            "name": "Comedy rock", 
+            "and":["6003331792577"]
+        }, 
+        {
+            "name": "Symphonic rock", 
+            "and":["6003386614418"]
+        }, 
+        {
+            "name": "Grindcore", 
+            "and":["6003251083686"]
+        }, 
+        {
+            "name": "Melodic death metal", 
+            "and":["6003376287381"]
+        }, 
+        {
+            "name": "Contemporary classical music", 
+            "and":["6003274787171"]
+        }, 
+        {
+            "name": "Pinoy pop", 
+            "and":["6003361706460"]
+        }, 
+        {
+            "name": "Cool jazz", 
+            "and":["6003022285156"]
+        }, 
+        {
+            "name": "Grime (music)", 
+            "and":["6003061520481"]
+        }, 
+        {
+            "name": "Highlife", 
+            "and":["6002839697279"]
+        }, 
+        {
+            "name": "Acid jazz", 
+            "and":["6003348618232"]
+        }, 
+        {
+            "name": "Texas country music", 
+            "and":["6003305455369"]
+        }, 
+        {
+            "name": "Paisley Underground", 
+            "and":["6003314537514"]
+        }, 
+        {
+            "name": "Cuban rumba", 
+            "and":["718607324904571"]
+        }, 
+        {
+            "name": "Deathcore", 
+            "and":["6002968692692"]
+        }, 
+        {
+            "name": "Boogie rock", 
+            "and":["6002948891555"]
+        }, 
+        {
+            "name": "Indie folk", 
+            "and":["6003278095799"]
+        }, 
+        {
+            "name": "Arabesque (Turkish music)", 
+            "and":["6003360934193"]
+        }, 
+        {
+            "name": "Euro disco", 
+            "and":["6003258987667"]
+        }, 
+        {
+            "name": "Hardstyle", 
+            "and":["6003718613316"]
+        }, 
+        {
+            "name": "Roots rock", 
+            "and":["6003009405850"]
+        }, 
+        {
+            "name": "Middle of the road (music)", 
+            "and":["6003386652818"]
+        }, 
+        {
+            "name": "Acid house", 
+            "and":["6003693206316"]
+        }, 
+        {
+            "name": "Screamo", 
+            "and":["6003314274714"]
+        }, 
+        {
+            "name": "Bounce music", 
+            "and":["6003057052932"]
+        }, 
+        {
+            "name": "Noise rock", 
+            "and":["6003461066225"]
+        }, 
+        {
+            "name": "Indian classical music", 
+            "and":["6003240881810"]
+        }, 
+        {
+            "name": "Carnatic music", 
+            "and":["6003156706484"]
+        }, 
+        {
+            "name": "Lullaby", 
+            "and":["6004102481609"]
+        }, 
+        {
+            "name": "Viking metal", 
+            "and":["6003650529946"]
+        }, 
+        {
+            "name": "Pinoy rock", 
+            "and":["6003393889131"]
+        }, 
+        {
+            "name": "Dance-punk", 
+            "and":["6003427999575"]
+        }, 
+        {
+            "name": "Ax\u00e9 (music)", 
+            "and":["6003179968552"]
+        }, 
+        {
+            "name": "Concert band", 
+            "and":["6003342577987"]
+        }, 
+        {
+            "name": "National Socialist black metal", 
+            "and":["6003055117556"]
+        }, 
+        {
+            "name": "Street punk", 
+            "and":["6003707861116"]
+        }, 
+        {
+            "name": "Protopunk", 
+            "and":["6003243830767"]
+        }, 
+        {
+            "name": "Oi!", 
+            "and":["6003209872430"]
+        }, 
+        {
+            "name": "German rock", 
+            "and":["6003107169433"]
+        }, 
+        {
+            "name": "Space rock", 
+            "and":["6002948479355"]
+        }, 
+        {
+            "name": "Marching band", 
+            "and":["6002960318920"]
+        }, 
+        {
+            "name": "Children's music", 
+            "and":["6003200511682"]
+        }, 
+        {
+            "name": "Miami bass", 
+            "and":["6003139637826"]
+        }, 
+        {
+            "name": "Tech house", 
+            "and":["6003075191185"]
+        }, 
+        {
+            "name": "Andalusian classical music", 
+            "and":["6003745678504"]
+        }, 
+        {
+            "name": "Big beat", 
+            "and":["6003052955245"]
+        }, 
+        {
+            "name": "Avant-garde jazz", 
+            "and":["6003351627243"]
+        }, 
+        {
+            "name": "Krautrock", 
+            "and":["6003486587669"]
+        }, 
+        {
+            "name": "Baroque music", 
+            "and":["6003417864712"]
+        }, 
+        {
+            "name": "Music of the Philippines", 
+            "and":["6003280576223"]
+        }, 
+        {
+            "name": "Boogaloo", 
+            "and":["6003243677542"]
+        }, 
+        {
+            "name": "Barbershop music", 
+            "and":["6003092601017"]
+        }, 
+        {
+            "name": "Vocaloid", 
+            "and":["6003354833037"]
+        }, 
+        {
+            "name": "Desi Hip Hop", 
+            "and":["6003767308605"]
+        }, 
+        {
+            "name": "Melodic hardcore", 
+            "and":["6003142928647"]
+        }, 
+        {
+            "name": "Hip hop soul", 
+            "and":["6003349217527"]
+        }, 
+        {
+            "name": "Minimal techno", 
+            "and":["6003139973592"]
+        }, 
+        {
+            "name": "Outlaw country", 
+            "and":["6003157605284"]
+        }, 
+        {
+            "name": "Argentine tango", 
+            "and":["6003226791885"]
+        }, 
+        {
+            "name": "Concept album", 
+            "and":["6003315581763"]
+        }, 
+        {
+            "name": "PBR&B", 
+            "and":["6009743139264"]
+        }, 
+        {
+            "name": "Afro-Cuban jazz", 
+            "and":["6003254226816"]
+        }, 
+        {
+            "name": "Swedish death metal", 
+            "and":["6003247985413"]
+        }, 
+        {
+            "name": "Teutonic thrash metal", 
+            "and":["6003001045450"]
+        }, 
+        {
+            "name": "Chamber music", 
+            "and":["6003337916179"]
+        }, 
+        {
+            "name": "Ska punk", 
+            "and":["6003142140447"]
+        }, 
+        {
+            "name": "UK hardcore", 
+            "and":["6003647110620"]
+        }, 
+        {
+            "name": "Horror punk", 
+            "and":["6003114006700"]
+        }, 
+        {
+            "name": "Ragtime", 
+            "and":["6003202093612"]
+        }, 
+        {
+            "name": "Jangle pop", 
+            "and":["6003351443119"]
+        }, 
+        {
+            "name": "M\u00e1kina", 
+            "and":["6003357385187"]
+        }, 
+        {
+            "name": "Pop-rap", 
+            "and":["6022204664592"]
+        }, 
+        {
+            "name": "Regional Mexican", 
+            "and":["6003250320511"]
+        }, 
+        {
+            "name": "Crossover thrash", 
+            "and":["6004206371989"]
+        }, 
+        {
+            "name": "French rock", 
+            "and":["6003337494443"]
+        }, 
+        {
+            "name": "Jazz-funk", 
+            "and":["6002995734844"]
+        }, 
+        {
+            "name": "Technical death metal", 
+            "and":["6003073183197"]
+        }, 
+        {
+            "name": "Hip house", 
+            "and":["6003012068797"]
+        }, 
+        {
+            "name": "Instrumental rock", 
+            "and":["6003351932200"]
+        }, 
+        {
+            "name": "Mor lam", 
+            "and":["6003279300980"]
+        }, 
+        {
+            "name": "Music of Brazil", 
+            "and":["6002933742373"]
+        }, 
+        {
+            "name": "UK Singles Chart", 
+            "and":["6003186196105"]
+        }, 
+        {
+            "name": "Electric folk", 
+            "and":["6003584207507"]
+        }, 
+        {
+            "name": "Soul jazz", 
+            "and":["6003326982288"]
+        }, 
+        {
+            "name": "Psychedelic folk", 
+            "and":["6003107511301"]
+        }, 
+        {
+            "name": "Kuduro", 
+            "and":["6003453309042"]
+        }, 
+        {
+            "name": "Post-industrial music", 
+            "and":["6002897198710"]
+        }, 
+        {
+            "name": "Classical period (music)", 
+            "and":["6003179189835"]
+        }, 
+        {
+            "name": "Lovers rock", 
+            "and":["6003221572267"]
+        }, 
+        {
+            "name": "Son Huasteco", 
+            "and":["6003343186056"]
+        }, 
+        {
+            "name": "Bhajan", 
+            "and":["6003971423096"]
+        }, 
+        {
+            "name": "Nueva canci\u00f3n", 
+            "and":["6003058651132"]
+        }, 
+        {
+            "name": "Irish rock", 
+            "and":["6003391451144"]
+        }, 
+        {
+            "name": "Post-bop", 
+            "and":["6003428308312"]
+        }, 
+        {
+            "name": "Balearic beat", 
+            "and":["6003179485324"]
+        }, 
+        {
+            "name": "Electronic body music", 
+            "and":["6002926010162"]
+        }, 
+        {
+            "name": "Serbian rock", 
+            "and":["6003269760902"]
+        }, 
+        {
+            "name": "Red Dirt (music)", 
+            "and":["6003186916235"]
+        }, 
+        {
+            "name": "Reggae rock", 
+            "and":["6004615176296"]
+        }, 
+        {
+            "name": "New Orleans rhythm and blues", 
+            "and":["6003183696012"]
+        }, 
+        {
+            "name": "Music of Africa", 
+            "and":["6003359282604"]
+        }, 
+        {
+            "name": "Hot R&B/Hip-Hop Songs", 
+            "and":["6002972712194"]
+        }, 
+        {
+            "name": "Doo-wop", 
+            "and":["6002920914155"]
+        }, 
+        {
+            "name": "African popular music", 
+            "and":["6003023077356"]
+        }, 
+        {
+            "name": "Baladi", 
+            "and":["6003859682234"]
+        }, 
+        {
+            "name": "Bouncy techno", 
+            "and":["6003093989670"]
+        }, 
+        {
+            "name": "Riddim", 
+            "and":["6003157650884"]
+        }, 
+        {
+            "name": "Hard bop", 
+            "and":["6003121907134"]
+        }, 
+        {
+            "name": "Modal jazz", 
+            "and":["6002998040773"]
+        }, 
+        {
+            "name": "Hungarian rock", 
+            "and":["6003283896979"]
+        }, 
+        {
+            "name": "Southern soul", 
+            "and":["6002951281955"]
+        }, 
+        {
+            "name": "Choro", 
+            "and":["6003287919141"]
+        }, 
+        {
+            "name": "Nu jazz", 
+            "and":["6003291882938"]
+        }, 
+        {
+            "name": "Persian traditional music", 
+            "and":["6003382424181"]
+        }, 
+        {
+            "name": "Swamp pop", 
+            "and":["6003246876099"]
+        }, 
+        {
+            "name": "Political hip hop", 
+            "and":["6003231078588"]
+        }, 
+        {
+            "name": "Bassline (music genre)", 
+            "and":["6003142207584"]
+        }, 
+        {
+            "name": "Andean music", 
+            "and":["6003030856207"]
+        }, 
+        {
+            "name": "Novelty song", 
+            "and":["6003319834418"]
+        }, 
+        {
+            "name": "Trot", 
+            "and":["6013312981795"]
+        }, 
+        {
+            "name": "Psychobilly", 
+            "and":["6003108983584"]
+        }, 
+        {
+            "name": "Jumpstyle", 
+            "and":["6003331455977"]
+        }, 
+        {
+            "name": "Turntablism", 
+            "and":["6003369016202"]
+        }, 
+        {
+            "name": "Khyal", 
+            "and":["6003012449797"]
+        }, 
+        {
+            "name": "Schlager music", 
+            "and":["6003540731636"]
+        }, 
+        {
+            "name": "Shibuya-kei", 
+            "and":["6003225780698"]
+        }, 
+        {
+            "name": "Folktronica", 
+            "and":["6003143944128"]
+        }, 
+        {
+            "name": "Ry\u016bk\u014dka", 
+            "and":["6003055238756"]
+        }, 
+        {
+            "name": "Sufi rock", 
+            "and":["6003435317683"]
+        }, 
+        {
+            "name": "Christian alternative rock", 
+            "and":["6003256042942"]
+        }, 
+        {
+            "name": "Italo dance", 
+            "and":["6003087462992"]
+        }, 
+        {
+            "name": "Islamic music", 
+            "and":["6003171044561"]
+        }, 
+        {
+            "name": "Nueva trova", 
+            "and":["6003173276578"]
+        }, 
+        {
+            "name": "Russian pop", 
+            "and":["6003324473654"]
+        }, 
+        {
+            "name": "Free improvisation", 
+            "and":["6003207572686"]
+        }, 
+        {
+            "name": "Bakersfield sound", 
+            "and":["6003374787477"]
+        }, 
+        {
+            "name": "Country blues", 
+            "and":["6003446597480"]
+        }, 
+        {
+            "name": "Music of Ireland", 
+            "and":["6003136092990"]
+        }, 
+        {
+            "name": "Celtic punk", 
+            "and":["6003030316185"]
+        }, 
+        {
+            "name": "Psychedelic pop", 
+            "and":["6003349888800"]
+        }, 
+        {
+            "name": "Duranguense", 
+            "and":["6003288662251"]
+        }, 
+        {
+            "name": "Ottoman classical music", 
+            "and":["6003056772244"]
+        }, 
+        {
+            "name": "Cover band", 
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+            "name": "20th-century Classical Music", 
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+        {
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+            "name": "Coup\u00e9-D\u00e9cal\u00e9"
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+        {
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+            "name": "Latin Power Music"
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+        {
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+            "name": "Big band"
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+            "name": "Chamam\u00e9"
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+            "name": "Zouk (club)"
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+            "name": "Worldbeat"
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+        {
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+            "name": "RAPEROS"
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+        {
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+            "name": "Samba rock"
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+        {
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+            "name": "Alternative metal"
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+        {
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+            "name": "B\u00e9l\u00e9"
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+        {
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+            "name": "Punk rock"
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+        {
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+            "name": "Salsa Bachata"
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+        {
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+            "name": "Rap rock"
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+        {
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+            "name": "Electro house"
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+        {
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+            "name": "Ballroom dance"
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+        {
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+            "name": "M\u00fasica sertaneja"
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+                    "key": "2447983"
+                }, 
+                {
+                    "country": "US", 
+                    "key": "2448003"
+                }, 
+                {
+                    "country": "US", 
+                    "key": "2448103"
+                }, 
+                {
+                    "country": "US", 
+                    "key": "2481500"
+                }, 
+                {
+                    "country": "US", 
+                    "key": "2481868"
+                }, 
+                {
+                    "country": "US", 
+                    "key": "2482020"
+                }, 
+                {
+                    "country": "US", 
+                    "key": "2482028"
+                }, 
+                {
+                    "country": "US", 
+                    "key": "2485496"
+                }, 
+                {
+                    "country": "US", 
+                    "key": "2485662"
+                }, 
+                {
+                    "country": "US", 
+                    "key": "2485721"
+                }, 
+                {
+                    "country": "US", 
+                    "key": "2485855"
+                }, 
+                {
+                    "country": "US", 
+                    "key": "2486502"
+                }, 
+                {
+                    "country": "US", 
+                    "key": "2486609"
+                }, 
+                {
+                    "country": "US", 
+                    "key": "2486614"
+                }, 
+                {
+                    "country": "US", 
+                    "key": "2486647"
+                }, 
+                {
+                    "country": "US", 
+                    "key": "2502897"
+                }, 
+                {
+                    "country": "US", 
+                    "key": "2503395"
+                }, 
+                {
+                    "country": "US", 
+                    "key": "2503567"
+                }, 
+                {
+                    "country": "US", 
+                    "key": "2503802"
+                }, 
+                {
+                    "country": "US", 
+                    "key": "2504080"
+                }, 
+                {
+                    "country": "US", 
+                    "key": "2504680"
+                }, 
+                {
+                    "country": "US", 
+                    "key": "2504748"
+                }, 
+                {
+                    "country": "US", 
+                    "key": "2504792"
+                }, 
+                {
+                    "country": "US", 
+                    "key": "2505343"
+                }, 
+                {
+                    "country": "US", 
+                    "key": "2505346"
+                }, 
+                {
+                    "country": "US", 
+                    "key": "2505456"
+                }, 
+                {
+                    "country": "US", 
+                    "key": "2505557"
+                }, 
+                {
+                    "country": "US", 
+                    "key": "2505581"
+                }, 
+                {
+                    "country": "US", 
+                    "key": "2505612"
+                }, 
+                {
+                    "country": "US", 
+                    "key": "2505707"
+                }, 
+                {
+                    "country": "US", 
+                    "key": "2505727"
+                }, 
+                {
+                    "country": "US", 
+                    "key": "2505987"
+                }, 
+                {
+                    "country": "US", 
+                    "key": "2525347"
+                }, 
+                {
+                    "country": "US", 
+                    "key": "2525495"
+                }, 
+                {
+                    "country": "US", 
+                    "key": "2525599"
+                }, 
+                {
+                    "country": "US", 
+                    "key": "2525613"
+                }, 
+                {
+                    "country": "US", 
+                    "key": "2525846"
+                }, 
+                {
+                    "country": "US", 
+                    "key": "2525895"
+                }, 
+                {
+                    "country": "US", 
+                    "key": "2526382"
+                }, 
+                {
+                    "country": "US", 
+                    "key": "2526451"
+                }, 
+                {
+                    "country": "US", 
+                    "key": "2526710"
+                }, 
+                {
+                    "country": "US", 
+                    "key": "2526788"
+                }, 
+                {
+                    "country": "US", 
+                    "key": "2526905"
+                }, 
+                {
+                    "country": "US", 
+                    "key": "2527052"
+                }, 
+                {
+                    "country": "US", 
+                    "key": "2527085"
+                }, 
+                {
+                    "country": "US", 
+                    "key": "2527150"
+                }, 
+                {
+                    "country": "US", 
+                    "key": "2527171"
+                }, 
+                {
+                    "country": "US", 
+                    "key": "2527622"
+                }, 
+                {
+                    "country": "US", 
+                    "key": "2527664"
+                }, 
+                {
+                    "country": "US", 
+                    "key": "2527717"
+                }, 
+                {
+                    "country": "US", 
+                    "key": "2527729"
+                }, 
+                {
+                    "country": "US", 
+                    "key": "2527880"
+                }, 
+                {
+                    "country": "US", 
+                    "key": "2528056"
+                }, 
+                {
+                    "country": "US", 
+                    "key": "2528522"
+                }, 
+                {
+                    "country": "US", 
+                    "key": "2528447"
+                }, 
+                {
+                    "country": "US", 
+                    "key": "2528589"
+                }, 
+                {
+                    "country": "US", 
+                    "key": "2529119"
+                }, 
+                {
+                    "country": "US", 
+                    "key": "2529222"
+                }, 
+                {
+                    "country": "US", 
+                    "key": "2529338"
+                }, 
+                {
+                    "country": "US", 
+                    "key": "2529381"
+                }, 
+                {
+                    "country": "US", 
+                    "key": "2529151"
+                }, 
+                {
+                    "country": "US", 
+                    "key": "2529571"
+                }, 
+                {
+                    "country": "US", 
+                    "key": "2529759"
+                }, 
+                {
+                    "country": "US", 
+                    "key": "2529761"
+                }, 
+                {
+                    "country": "US", 
+                    "key": "2529764"
+                }, 
+                {
+                    "country": "US", 
+                    "key": "2529766"
+                }, 
+                {
+                    "country": "US", 
+                    "key": "2529898"
+                }, 
+                {
+                    "country": "US", 
+                    "key": "2530066"
+                }, 
+                {
+                    "country": "US", 
+                    "key": "2530084"
+                }, 
+                {
+                    "country": "US", 
+                    "key": "2530706"
+                }, 
+                {
+                    "country": "US", 
+                    "key": "2530970"
+                }, 
+                {
+                    "country": "US", 
+                    "key": "2531386"
+                }, 
+                {
+                    "country": "US", 
+                    "key": "2531475"
+                }, 
+                {
+                    "country": "US", 
+                    "key": "2531623"
+                }, 
+                {
+                    "country": "US", 
+                    "key": "2532136"
+                }, 
+                {
+                    "country": "US", 
+                    "key": "2532254"
+                }, 
+                {
+                    "country": "US", 
+                    "key": "2532292"
+                }, 
+                {
+                    "country": "US", 
+                    "key": "2532348"
+                }, 
+                {
+                    "country": "US", 
+                    "key": "2532346"
+                }, 
+                {
+                    "country": "US", 
+                    "key": "2532354"
+                }, 
+                {
+                    "country": "US", 
+                    "key": "2532456"
+                }, 
+                {
+                    "country": "US", 
+                    "key": "2540995"
+                }, 
+                {
+                    "country": "US", 
+                    "key": "2541005"
+                }, 
+                {
+                    "country": "US", 
+                    "key": "2541287"
+                }, 
+                {
+                    "country": "US", 
+                    "key": "2541696"
+                }, 
+                {
+                    "country": "US", 
+                    "key": "2542058"
+                }, 
+                {
+                    "country": "US", 
+                    "key": "2542224"
+                }, 
+                {
+                    "country": "US", 
+                    "key": "2542244"
+                }, 
+                {
+                    "country": "US", 
+                    "key": "2542404"
+                }, 
+                {
+                    "country": "US", 
+                    "key": "2542395"
+                }, 
+                {
+                    "country": "US", 
+                    "key": "2542527"
+                }, 
+                {
+                    "country": "US", 
+                    "key": "2542532"
+                }, 
+                {
+                    "country": "US", 
+                    "key": "2542718"
+                }, 
+                {
+                    "country": "US", 
+                    "key": "2542879"
+                }, 
+                {
+                    "country": "US", 
+                    "key": "2543020"
+                }, 
+                {
+                    "country": "US", 
+                    "key": "2540892"
+                }
+            ], 
+            "name": "cities", 
+            "location_types": [
+                "home"
+            ]
+        }
+    ], 
+    "name": "List_of_Mexican-American_communities",
+    "behavior": [ 
+        {
+            "or": [
+                6003133212372
+            ],
+            "not": [
+                6015559470583, 
+                6025978286383
+            ], 
+            "name": "Hispanic and Not Expats and Not close friends of expats"
+        }
+    ]
+}
\ No newline at end of file
diff --git a/data/queries/music_genre_interests_grouped.json b/data/queries/music_genre_interests_grouped.json
index 69bef13..fcc19d6 100644
--- a/data/queries/music_genre_interests_grouped.json
+++ b/data/queries/music_genre_interests_grouped.json
@@ -988,6 +988,7 @@
             "Conjunto", 
             "Grupera", 
             "Trival", 
+            "Latin dance",
             "Chicano rock", 
             "Reggae en Espa\u00f1ol", 
             "Cumbia", 
diff --git a/src/analysis/__init__.py b/src/analysis/__init__.py
new file mode 100644
index 0000000..a60141c
--- /dev/null
+++ b/src/analysis/__init__.py
@@ -0,0 +1 @@
+"" 
diff --git a/src/analysis/compare_non_expat_subpopulation_assimilation.ipynb b/src/analysis/compare_non_expat_subpopulation_assimilation.ipynb
index 0423cc5..d1643e1 100644
--- a/src/analysis/compare_non_expat_subpopulation_assimilation.ipynb
+++ b/src/analysis/compare_non_expat_subpopulation_assimilation.ipynb
@@ -10,7 +10,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -30,7 +30,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 61,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -48,7 +48,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 62,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [
     {
@@ -372,14 +372,24 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 87,
+   "execution_count": 94,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "image/png": 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6nrvUxMyZM721tbXWYYiIjCs33HDD4+5e+I+b+lFyl5pobW1l48aNtQ5DRGRcMbMHB66lYXkREZHUUXIXERFJGSV3ERGRlFFyFxERSRkldxERkZRRchcREUkZJXcREZGUUXIXERFJGSV3ERGRlFFyFxERSRkldxERkZRRchcREUkZJXcREZGU0X+FE5ExobGxkc2bN9c6jHHJT9sJ++RTVS3T0NDAE088MUIRSa0puYvImLB582bcvdZhjE+d06red2Y2QsHIWKBheRERkZRRchcREUkZJXcREZGUUXIXERFJGSV3SQ3dICQyOvRaG/uU3EVERFJGyV1ERCRlxkxyN7OtBc+XmNk58fHxZvbeUYrjU2a2f5XLLDIzN7OlibK9YtlJ8fnlZnZ0Yv55ZnZymTYvNrO7zex2MzvfzLJl6r7LzE4ZIMZWMzuymu0ajHjcdh3p9YhI7a1bt4558+ZRV1dHc3MzM2bMwMx2mKZMmcKUKVP6nmezWU488cS+ZTOZDBMnTiSTydDc3ExzczN1dXXMmzePdevW7bCOfFmpGIrNf9Fy9zExAVsLni8Bzql1XBXGvgi4FbgqUfZ54GbgpPi8FbgfmA7sE+tny7R5IGBxWgcsG4YYr6hymbpBrGcDsHCgegsWLPDhFk5nGa90/IbgtJ2qXmQo+xvwrq4ub2tr81wu52vXrvXp06c74IBPnjzZGxoavL6+vq9s8eLFfuWVV3pjY6Obme+0006+atUqb21t9dWrV3tTU5NPnz7dZ8+e7WvXrvVcLudNTU3e1NTkuVzOe3t7PZfLeVtbm3d1dbm77xBDsflpBGz0St6LK6k0GlO55A50JpLkh4A7Y3L8TmL+RUAOuAc4LpZPAX4B3AjcBhzsLyTau4DzgDuAq4BJcd6FwOHx8d7AdcAtwO+AqSViXwRcAVwDzIoJ+RbgjHzcsd5K4NyY9PetYt98BDi9zPzkvroQ+EqM+77EtvwGeDKu+yNAHfAF4Pq4Lz+Q2Jb1QBdwZyw7Km7/zcA34rJ1cV23x337EeBwYCtwd6w7qVTMSu5SSMdvCGqQ3Ds6OjyXy7m7e0dHh7e2tnpdXZ3X1dV5LpfzXC7n2WzWAc9kMt7R0eHu7rlczidMmODZbHaHNlpbW721tdVzuVxf3XxZUnJ+cvli89NoPCb37TEh5Kc/lUjuDwP18fH0xPxbgEnATOAhYFfCz+vuFOvMBO6NibcVeA7YM867BDjKE8kdeElMjnvH8p2ACSVizyf3DwHLgdcDFyTjjvWycbsurmK/ZAkfTt5Qpk5hcv9/hEsurwbuTcaYWOb9wCfi43pgI9AW6/UAbXFeO/Aj4igD8DXgvcAC4OpEe/ljsYESPfe4zo3Axrlz51Z8MleK2EPQNH4nGaRBJvehTJlMxnt7e93dPZPJuJn1zevt7fXe3t5+9d19h/JkG2bW9zxfN99uUuH8/PLF5qcRFSb3sfTb8s+4+575J2a2BFhYpN6twMVmdhlwWaL8h+7+DPCMma0HXgf8GPisme0LPA/sRuhZA9zv7jfHxzcQEn7SK4FH3P16AHev5L8yXAJ8F3gVYSh9n4L58wkfLl5lZhl3f76CNr8GXOPuv6qgbt5lse07zWxWiTpvBeab2eHx+TRgd6AX+J273x/L30xI5NfHr79MAh4jJPyXmdnZhP181UBBufu5hJELFi5c6FVsT8XCuS/jkb5eNfoG+3oxM9rb2+nu7ma//fajvb2dnp4eHnroIQC6u7sByGazbNu2jUwmQ3t7e9+8CRMmYGa84hWv6GujpaWlb36+7ty5c/utOzk/GUOx+S9mY+aGuiq8A/gqIeHcYGb5DyiFZ6kD/wk0AQviB4dHgYlx/j8SdbfT/5/oWJE2y3L3vwLbgLcQLge80JhZhpCojyZcOlg2UHtmdlqM/6PVxMGO21bqHdOAE919zzi1uXs+QfcU1PtWot4r3b3T3TcDryH01E8AvllljCIyjq1atYqlS5eyfv16VqxYwZYtW9i+fTvbt2/nne98J4cddhiZTEgxzz//PPPnz+cnP/kJhx9+ONu3b2fSpEkccsghvO997+NLX/oSPT09bNmyhSOPPJIVK1awfv16enp66OnpYf369Wzbto3169ezdOlSVq1a1S+GYvNfzMZSz31AMUE2u/t6M+sGjiRcVwc42MzOACYThpZPAd4DPObu28xsP6ClitX9HtjVzPZ29+vNbCphdOG5AZY7FdjZ3bcX9EQ+ANzj7hvM7A/Ar83sEnffVGJbjwUOAN5cYQ9/IE8DUxPPfwYsM7Nc3D+vAP5SZLlfAD80sy+7+2Nm1hjb6QF63f17ZvZHwuWAYusRkRRavHgxACeeeCJ33XUXu+66K5lMhieeeKIvKQNMnjwZoO/O9wkTJnDCCSewzz77cPrpp/Pggw/y8Y9/nN7eXnbbbTcAlixZQnt7O2edddYO62hvb+f000/vW3dhDIXzX8zGVXIn3MT1bTObRuhRftndt8Qk+jvC8PBc4NPu/rCZXQz8yMw2Eq7j/77SFbl7r5kdAZxtZpOAZ4D9CTeMlVvuusIyM9sZWAH8S6zzsJmdBZwJHFOiqTXAg4QPAQDfd/dPVRp/EbcCz5nZLYREfBbhUsSNFlawCTikyPbcaWafAK6KH662EXrqzwAXxDIINwsS215jZs8A/xovlYhICi1evHhIibTSZcvVG2oMaWVpuEZpZp2Eu+2/WOtYpDILFy70jRs3DmubZqZr7uOYjt8QdE6DzierWmQo+1vHqnbM7AZ3L3Y/2g7G4zV3ERERKWO8DcsX5e6do7EeMzuA8OM0Sfe7+6FDaPMHhK+gJa1w958VqXsM8OGC4mvd/YTBrj9N1JMQGR16rY19qUjuoyUm3H5Jd4htVvzBwN0vIHx/XkREpCQNy4uIiKSMkruIiEjKKLmLiIikjK65i8iYoZ+gHRw/baeq911DQ8MIRSNjgZK7iIwJugN7aEbnO0MyXmhYXkREJGWU3EVERFJGyV1ERCRllNxFRERSRsldREQkZZTcRUREUkbJXUREJGWU3EVERFJGyV1ERCRllNxFRERSRsldREQkZZTcRUREUkbJXUREJGWU3EVERFJGyV1ERCRllNxFRERSRsldREQkZZTcRUREUkbJXUREJGWU3EVERFJGyV1ERCRllNxFRERSRsldREQkZZTcRUREUkbJXUREJGWU3EVERFJGyV1ERCRllNxFRERSRsldRIassbERMxv1ic5pNVlvsamxsbHWh0Gkz4RaByAi49/mzZtx99Ffcee02qy3CDOrdQgifdRzFxERSRkldxERkZRRchcREUkZJXcREZGUUXKXcUk3L0ma6HyW4abkLiIikjJK7iIiIimj5C4iMkasW7eOGTNm9PuBnPnz5zNv3jwymQwTJ04kk8nQ3NxMc3MzmUyGbDbbV7e5uZl169YVbXvevHnU1dUxb948DjjgACZOnNhvXdlslhNPPLFkTIXzB1pPsVjGs3Gzfe5e8wnYWvB8CXBOfHw88N5RiuNTwP5VLrMIcGBpomyvWHZSfH45cHRi/nnAyWXavBi4G7gdOB/Ilqk7C7gCuAW4E7hyBPbLu4BTyszvO16VTgsWLPChCKeujBU1Ox6n7VSb9RYxlH0AeFdXl0+dOtXje4cfeOCBPn369L7nbW1t3tbW5qtXr/aZM2f69OnTfdq0aT5lyhRvaGjw2bNn+8qVK32XXXbxpqYm7+rq6mu/q6vL29raPJfLeW9vrx9yyCEO+Otf/3qfOnWqm5mbmR900EHe0NDgmUzGly9f3hdTXV2dn3nmmX7llVfuML9Q4XpyuZy3tbXtEMt4Nha2D9jolbxvV1JppKdyyX2sTzG53wpclSj7PHBzIrm3AvcD04F9Yv1yCftAwOK0DlhWpu43gA8nns+vwT5Qcn+RU3If2j4AvKOjw7PZrAO+bNkyd3fP5XJeV1fnmUzGAc/lcu7u3tra2jdls1nP5XKey+W8o6PDc7mct7a2ekdHR1/7+fK8+vp6X7ZsmdfX13s2m/XVq1f76tWrvb6+3nO5nM+aNcvr6+v7Ylq9enXfssn5hQrXk6+fjGU8Gwvbl5rkDnQmkuSHYu/0VuA7ifkXATngHuC4WD4F+AVwI3AbcLC/kGjvir3nO4CrgElx3oXA4fHx3sB1sUf8O2BqidgXxZ7zNbEXbXGZM/Jxx3orgXNj0t+3in3zEeD0MvMvBw4rEdc1wA/iPlsDZOK8rwMb4/Z/MrHMA8AnE/vsVUWOx3sIIwq3ANck5n8f+Gk8BmeWiPX9cb0b586dW+m5XBSxN6Np7Ew1McaS+1CmfAIHfMuWLe7u3tvbu0Od3t5ed3fPZDJ9ve18eW9vr2cymb6/mUymL7Z8eTLWLVu29LXb09PjPT09fW0VxtTT09O3bHJ+ocL15OsnYxnPxsL2UWFyHyvX3CeZ2c35iTA8XswpwF7uPp8wXJ83H3gH8K/AqWa2K/AscKi7vxbYD1htL3zfZHfgq+7eAWwBDkuuxMxeAnyX0CN+DbA/8MwA23ApIfHtQ0iO/yiY/0XgbcAd7n7NAG3l48gCRxOSZilfBf7XzNab2aq47XmvA/4vsAfwcuDdsXyVuy8k7Lc3mtn8xDKPx332deCkIus7FTgg7pd3Jcr3BI6I6zrCzJoLF3T3c919obsvbGpqKrNJlankBNc0OpMEQ9l/7e3tZLNZAFauXAlAd3c3dXV1ZDKZvucAc+fOpaWlhZaWFrLZLN3d3XR3d9Pe3k53dzdz586lvb29L658eV59fT0rV66kvr6ebDbLmjVrWLNmDfX19XR3dzNr1izq6+v7YlqzZk3fssn5hQrXk6+fjGU8G1fbV+s3hXhiV9pz/ykhiR4FTEnM/1Ri2bXAIUAWOIfQy7+ZkJxnE3ru9yTqrwA+ER9fCBxOSFDXVhj7IkLPfTbwy7jOtybjjvUWAA8BNxB70BW0fR7wPxXUawSOJIxgPAo0xbiuSdR5X74twgejG+O+2QT8Ryx/ANgtPv5n4OdFjsca4GrgOGBGYv55iXX9BPi3cjFrWD5danY8xljPfSjL6pr72DcWto+UDsvXEXrhXybccDYhzk8OLa8FDo5tfJd4bTsmrtY43Z6ofxLQ6Tsm9/lAd4WxLwKuiI9/HuOqK4g7A/w21v0OcEIF7Z4GXFbpB4HEclcQRiIWAb9MlL8v7rc24F6gIbHNSxL7aGZ8vBDYUHg8/IXE/ynCh5UZReZfASwqF6eSe7oouQ89ubuH5NHY2NhvyH6PPfbwjo4ONzOvr693M/M5c+b4nDlz3Mx8woQJfXXnzJlTNNl0dXV5R0eHZzIZ7+jo8Le+9a1eX1/fb10TJkzYIXEXxlQ4f6D1pCWx59V6+ypN7uPmX76aWQZodvf1ZtZN6KlOibMPNrMzgMmEpHYKYYj8MXffZmb7AS1VrO73wK5mtre7X29mU4Fn3P25AZY7FdjZ3bcX/OLUBwijBRvM7A/Ar83sEnffVGJbjwUOAN7s7s+XW6GZvQn4jbv/Pcb5cuBPhH3xOjNrAx4kDJmfC+wE9ABPmtks4O3AhgG2K7m+l7v7b4Hfmtk7gX7D7yIyOIsXL2bx4sVjqu1qlxvJbRgLxsv2jZvkTugNf9vMphFuWvuyu2+JSfR3wI+BucCn3f1hM7sY+JGZbSQMy/++0hW5e6+ZHQGcbWaTCEP6+wNbB1juusIyM9uZMPT/L7HOw2Z2FnAmcEyJptYQEvKv4/Z9391L3YewADjHzJ4jjBB8M34gWQT8Gvgc4TLDNcAP3P15M7uJcDPdfcC15bapiC+Y2e6EY/ALwo11e1bZhoiIjCALvfzxy8w6CcP6X6x1LGNJTO4nuftBtY6lmIULF/rGjRsHvbyZMd7P3TSp2fHonAadT47+eosYyj7Q+SyVMrMbPNwQXdZYuVtepCp6I5Q00fksw208DcsX5e6do7EeMzuA8OM0Sfe7+6FDaPMHhBvckla4+8+K1D0G+HBB8bXufkKxtt19A1VcSxcRkfQY98l9tMSE2y/pDrHNij8YuPsFwAXDuX4REUknDcuLiIikjJK7iIhIymhYXkSGRcFvO4wKP22nmqy3mIaGhlqHINJHyV1EhqyWd3ua+rgVAAAVDUlEQVSPzi21IuOLhuVFRERSRsldREQkZZTcRUREUkbJXUREJGWU3EVERFJGyV1ERCRllNxFRERSRsldREQkZZTcRUREUkbJXUREJGWU3EVERFJGyV1ERCRllNxFRERSRsldREQkZZTcRUREUkbJXUREJGWU3EVERFJGyV1ERCRllNxFRERSRsldREQkZZTcRUREUkbJXUREJGWU3EVERFJGyV1ERCRllNxFRERSRsldREQkZZTcRUREUkbJXUREJGUm1DoAERGAxsZGNm/eXOswUsdP2wn75FMD1mtoaOCJJ54YhYhkNCi5i8iYsHnzZty91mGkT+e0ivarmY1CMDJaNCwvIiKSMkruIiIiKaPkLiIikjJK7iIiIimj5C6SAroZSmpB593YpeQuIiKSMkruIiIiKaPkLiIiw+LEE08km81iZgNOzc3NrFu3jnXr1jFv3jzq6uqYMWNG3/KZTIZMJkNzczPNzc3U1dUxb9481q1bV1EsyXYLlys3LzXcvewEbC14vgQ4Jz4+HnjvQG0MxwR8Cti/ymUWAQ4sTZTtFctOis8vB45OzD8POLlMmxcDdwO3A+cD2dHY/lHax1cC0+P0wUT5rsClw7muBQsWuAyf8FIe39KwDWPSaTtVVG0w+z+5zPLlyz2TyXhdXZ0Dns1m3cw8vt864JMnT/bp06f7tGnTfPr06T516lRvamryXC7ny5Yt80wm41OmTPGZM2f6smXL3Mz8pS99qc+ePdvXrl3ruVzO29ravKurq2xcXV1d3tbW5rlcznt7e3dYrty88QDY6JW8nw9YoUxyH+tTTO63Alclyj4P3JxI7q3A/TGh7RPrl0zYwIGAxWkdsKzW2zkC+60VuH0k16HkPrzSkBjTsA1j0igl9/r6ep81a5YD3tDQ4Llcru95Psm3trZ6Lpfz1tZWb21t9Ww2662trX3Lr169um+eu/vs2bM9m816Lpfzjo4Od/cdHpfS0dHhuVxuh7L8cuXmjQejktyBzkSS/BBwZ0yO30nMvwjIAfcAx8XyKcAvgBuB24CD/YWkclfsPd8BXAVMivMuBA6Pj/cGrgNuAX4HTC0R+yLgCuAaYFZMyLcAZ+TjjvVWAufGpL9vJTsuLvcR4PQy86cAF8RtvBU4LJYvjmW3A59P7uv44eMG4OfA64ANwH3AuxL7/zLgR/FDyXLgo8BNwG+AxlhvA7AwPp4JPJBY/vvAT+MxOTOx/gdi3e8Az8T98QUSyR6oi2XXx236QCzfJe7nm+N2vaHI/ng/sBHYOHfu3CGf5PICEr2j8TzJCKgiuQ/lmBWW9/b29iszM+/t7XUz60v4Zta3fE9Pj5uZZzIZd3fPZDJ9beXLko9LyWQy3tvbu0NZfrly88YDKkzulVxzn2RmN+cnwvB4MacAe7n7fMJwfd584B3AvwKnmtmuwLPAoe7+WmA/YLW98J2K3YGvunsHsAU4LLkSM3sJ8F3gw+7+GmB/QiIq51LgPYSe+Y3APwrmfxF4G3CHu18zQFv5OLLA0YQkWcp/A0+6+x5xv+Ti9n8eeBOwJ7C3mR0S608GNrj7AuBp4DPAW4BD2XG/zwOOJCT/04G/u/tewK+B91YQ/p7AEcAewBFm1lww/xTgj+6+p7ufXDBvadymvQkfso4zs7YYz8/cfU/gNYQkvwN3P9fdF7r7wqampgrClGpU8oIfy5PU3lCOWX19PbNmzQLCP6Hp7u7ue55/e29paaG7u5uWlhZaWlrIZrO0tLT0Lb9mzRpaWlqYO3cuADvvvDPZbJbu7m7a29sBdnhcSnt7O93d3TuU5ZcrNy9NKknuz8Q3+T3jG/epJerdClxsZkcBzyXKf+juz7j748B6QkIy4LNmdiuhh7oboWcNcL+75xPDDYReY9IrgUfc/XoAd3/K3Z+jvEsIyX0xYSi90PwY06vMrNKbDL8GXOPuvypTZ3/gq/kn7r6ZkBA3uPumGPfFwL6xSi8vfFi4Dfilu2+Lj1sT7a5396fdfRPwJKEXT5F6pfzC3Z9092cJoy0tFSyT91bgvfGD3m+BGYQPZNcDx5hZJ7CHuz9dRZsiMs4dd9xxbNq0ibq6OjZv3swBBxzAY489BtD3QWDTpk28+93vZvPmzWzZsoWJEyfS09PD+vXred/73sfJJ5/M448/ztatW/ngBz/Io48+Sjab5cgjj2TFihWsX7+epUuXsmrVqrKxrFq1iqVLl7J+/Xq2bdu2w3Ll5qXJcP5XuHcQktS7gP82s45YXviR3IH/BJqABe6+zcweACbG+cle9XZgUsHyVqTNstz9r2a2jdAL/jChBx8aC8n8a4Re+PHAMhIJuRgzOy3G/4EBVl0s1nK/+rDNX/g4/DxxX7j782aWPFbJffR84vnzvHBMn+OFD28T2VHhPq7mPDDgRHf/Wb8ZZvsSzoOLzOwL7r62inZFZBw7++yzAVizZg0A27Zt61enp6en7/GcOXM488wzgXCX/V133cX06dN56qmn2Lp1a187jY2NACxZsoT29nZOP/10Fi9eXDaW/Px8u8WWKzcvDYYluccE2ezu682smzBEOyXOPtjMziAMOS8iDPm+B3gsJvb9qK7n+HtgVzPb292vN7OphNGFgXrvpwI7u/v2gl9V+gBwj7tvMLM/AL82s0tir7jYth4LHAC82d2fH2CdVxGuif9XXLaB0Ns9y8xmApsJowlnD9DOYDwALCDck3B4lcs+DUwtMe9nwDIzy8Xj9wrgL4Rr9X9x9/PMbDLwWkDJXeRF5Oyzz+5L8tUYicS6ePHiku2Wm5cWw/U99zrg22Z2G+HGri+7+5Y473fAjwk3e33a3R8mDEUvNLONhF787ytdkbv3Eq4Xn21mtwBX079nWmy569z9smSZme0MrABOinUeBs4CzizT1BrCJYRfx/sQSl2mgHDNvMHMbo+x7ufujxBu4FtPuLnvRnf/4UDxD8IXCUn4OkLirZi7/w24Nsb9hYLZ3yQM5d9oZrcD3yB8SFwE3GxmNxHukzhriPGLiMgg2UjeyBKvv2519y+O2EpkXFq4cKFv3Lix1mGkhpmN+5vS0rANY1LnNOh8csBqg9n/Omajz8xucPeFA9XTL9SJiIikzHDeUNePu3eOZPt5ZnYA4etlSfe7+6FDaPMHQFtB8YoSN5IdQ7hRL+ladz9hsOsXqYZ6T1ILOu/GrhFN7qMlJtx+SXeIbVb8wcDdLyD8WI2IiEjNaVheREQkZZTcRUREUkbJXUREJGVScc1dRNKh4AemZBj4aTtVtF8bGhpGIRoZLUruIjIm6M7rkTM631uSsUTD8iIiIimj5C4iIpIySu4iIiIpo+QuIiKSMkruIiIiKaPkLiIikjJK7iIiIimj5C4iIpIySu4iIiIpo+QuIiKSMkruIiIiKaPkLiIikjJK7iIiIimj5C4iIpIySu4iIiIpo+QuIiKSMkruIiIiKaPkLiIikjJK7iIiIimj5C4iIpIySu4iIiIpo+QuIiKSMkruIiIiKaPkLiIikjJK7iIiIimj5C4iIpIySu4iIiIpo+QuIiKSMkruIiLjVGNjI2ZWdKJzWsl5xabGxsZab44MIyV3EZFxavPmzbh70QkoOa/YtHnz5hpvjQwnJXcREZGUUXIXERFJGSV3ERGRlFFyFxERSRkld5EaMLNahyAyZDqPxy4ldxERkZRRchcREUkZJfdBMrOtBc+XmNk58fHxZvbeUYrjU2a2f5XLLDIzN7OlibK9YtlJ8fnlZnZ0Yv55ZnZymTaXm9m9sY2Zg9kWERkfTjzxRLLZLEDZH8apq6vre9zc3My6desAWLduHfPmzaOuro7m5maam5v7Hs+YMaPoMqUk25o3b17J+sXqlYqjXDtDUWmsw6KaHznQtMMPRGwteL4EOKfWcVUY+yLgVuCqRNnngZuBk+LzVuB+YDqwT6yfLdPmXnGZB4CZA8WwYMECfzELLz2RoSl7Hp220/C1lbB8+XLPZDLe2NjowA5TJpPZ4S/g06ZN81NOOcVnz57tTU1Nvnz5cm9ra/NcLudr16712bNn+y677OLLli3z6dOneyaT8VNOOcWvuuoq32WXXbypqcm7urqKxtLV1dXXVm9vr+dyOW9ra+tXv1i9pqYmb2pq6hfH2rVrS7YzFJXGOhBgo1fyPl9JJU1Fk1nJ5A50JpLkh4A7Y3L8TmL+RUAOuAc4LpZPAX4B3AjcBhzsLyTau4DzgDuAq4BJcd6FwOHx8d7AdcAtwO+AqSViXwRcAVwDzAIsLnNGPu5YbyVwbkz6+1a4X5TcK6DkLsOhFsm9vr7eZ82a5blczgGfPXt2XzLPZrO+bNmyvgRfV1fnra2t3tHR4blczltbW72+vt5zuZy7e195Lpfz+vp6b21t9dWrV3tHR4e7e98y+eeF8ssn5XK5fvWL1WttbfXW1tZ+cSTXXWq9g1FprANRch/55L49Jr389KcSyf1hoD4+np6YfwswCZgJPATsCkwAdop1ZgL3xsTbCjwH7BnnXQIc5YnkDrwEuA/YO5bvBEwoEXs+uX8IWA68HrggGXesl43bdXEV+6VkcgfeD2wENs6dO9dfzCjo8WjSNNippEEk98FMyV464Fu2bNnhuZl5JpPx3t5eNzMHvLe31929r7y3t7evbk9Pj2cyGXd37+3t9Uwm0/e8UH75pPwyA9XLZDJuZv3iKFz3cKk01oFQYXLXNffBe8bd98xPwKkl6t0KXGxmRxESdN4P3f0Zd38cWA+8jpDIP2tmtwI/B3Yj9KwB7nf3m+PjGwgJP+mVwCPufj2Auz/l7s9R3iXAe4DFQLGLP/NjTK8ysyGfK+5+rrsvdPeFTU1NQ21u3KvkBapJU7mpFudkfX09s2bNIpfLAbDzzjuTyYS3h2w2y8qVKwHIZDLU1dXR0tJCe3s73d3dtLS0UF9fT3d3N0BfeXd3N/X19bS0tLBmzRra29sB6O7uZu7cuX3PC+WXT+ru7u5Xv1i9uXPn0tLS0i+O5LpLrXcwKo112NT65ByvE5UPy9cB+wFfBu4m9M47gU8mll0LHBzb+C7x2jahF9wap9sT9U8COuPjCwk99/lAd4WxLwKuiI9/HuOqK4g7A/w21v0OcEKFbT+AhuUHRLkel0iFyp5HuuZetp6uuWsadHKPCbI1lmWBRwk3qHUShvInAjMIQ9+7Ah8Gzo7194svjkqTe+Gw/FQGGJaPj/cBDknGHR8vA74dH+8KPAg0VbBflNwroOQuw6EWyd09JPgJEyZUNWQ/Z86cvkTW1dXlHR0dnslkfM6cOT5nzpy+x8kPDcllSkm21dHRUfaDQGG9UnGUa2coKo21nEqTu4W6Ui0z2+ruUxLPlwAL3X25mXUCW4GzCEPu0wjD299298/F+bsCLwfmAme6+3nxK2Q/InwQuJlwLfztcRVXuPu8uK6TgCnu3mlmF8Z5l5rZ3sDZhGv5zwD7u/sOX9mLyy8iJPGDCsrzca8l3JD3L+7+1zjvo8Ae7n5Mif3xIeBjwGzgMeBKdz+21P5buHChb9y4sdTs1DMz9NqToSp7HnVOg84nh6etYVxGhsbMbnD3hQPW04EZffkk6u5frHUstaLkrjdFGTol9xefSpO7bqgTERFJmQm1DuDFyN07R2M9ZnYA4cdpku5390OH0OYPgLaC4hXu/rPBtvlipN6OpIHO47FLyT3FYsId1qQ7lA8GIiIyOjQsLyIikjJK7iIiIimj5C4iIpIySu4iIuNYqX+3Wm5esamhoaHGWyLDSTfUiYiMUwPdrT4638uRsUg9dxERkZRRchcREUkZJXcREZGUUXIXERFJGSV3ERGRlFFyFxERSRkldxERkZRRchcREUkZJXcREZGUUXIXERFJGSV3ERGRlFFyFxERSRkldxERkZSxgf6rkMhIMLNNwIPANODJIn9nAo9X2Wx+2WrmF5aVe65YFatiVay1jnV3d582YMvurklTzSbg3BJ/Nw62rWrmF5aVe65YFatiVaxjLdZSk4blpdZ+VOLvUNqqZn5hWbnnirXy+Yp1YIq1/2PFOvD8itanYXkZk8xso7svrHUclVCsI0OxjgzFOjLGWqzquctYdW6tA6iCYh0ZinVkKNaRMaZiVc9dREQkZdRzFxERSRkldxERkZRRchcREUkZJXcZd8xskZn9yszWmNmiWsczEDObbGY3mNlBtY6lHDNrj/v0UjNbVut4yjGzQ8zsPDP7oZm9tdbxlGNmLzOz/zWzS2sdSzHx/PxW3J//Wet4yhnr+zKp1ueokruMKjM738weM7PbC8rfZmZ3m9m9ZnbKAM04sBWYCPx5jMcKsAK4ZGSi7ItpyLG6+13ufjzw78CIfaVnmGK9zN2PA5YAR4zxWO9z96UjFWMxVcb9buDSuD/fNZpxVhtrLfZlQUzVxDoq52hJ1f6ijiZNQ5mAfYHXArcnyuqAPwIvA14C3AK8GtgDuKJg2hnIxOVmAReP8Vj3B/6D8AI/aCzHGpd5F3AdcORYjzUutxp47TiJ9dKRfn0NMu6VwJ6xTtdoxTiYWGuxL4ch1hE9R0tNExAZRe5+jZm1FhS/DrjX3e8DMLPvAAe7+xlAuaHszUD9SMQJwxOrme0HTCa8iT5jZle6+/NjMdbYzuXA5Wb2Y6BruOMcrljNzIDPAT9x9xtHIs7hirUWqombMPo1B7iZGozmVhnrnaMb3Y6qidXM7mIUztFSNCwvY8FuwEOJ53+OZUWZ2bvN7BvARcA5IxxboapidfdV7v5fhER53kgk9jKq3a+LzOwrcd9eOdLBFagqVuBEwqjI4WZ2/EgGVkS1+3WGma0B9jKzlSMdXBml4v4+cJiZfZ2h/ZTqcCoa6xjal0ml9mstz1H13GVMsCJlJX9dyd2/T3hDqoWqYu2r4H7h8IcyoGr36wZgw0gFM4BqY/0K8JWRC6esamP9GzDqb+5FFI3b3XuAY0Y7mAGUinWs7MukUrHW8hxVz13GhD8DzYnnc4CHaxTLQBTryFCsI288xa1Yh0jJXcaC64HdzazNzF5CuAHt8hrHVIpiHRmKdeSNp7gV61DV4o5DTS/eCVgHPAJsI3ziXRrLDwT+QLjrdFWt41SsinW8xTpe41asIzPpH8eIiIikjIblRUREUkbJXUREJGWU3EVERFJGyV1ERCRllNxFRERSRsldREQkZZTcRUREUkbJXUREJGWU3EVERFLm/wNiQ4mC1DmCCAAAAABJRU5ErkJggg==\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0xe494e10>"
+       "<matplotlib.figure.Figure at 0xba03c88>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0xfb54390>"
       ]
      },
      "metadata": {},
@@ -387,6 +397,7 @@
     }
    ],
    "source": [
+    "import numpy as np\n",
     "first_gen_data = pd.read_csv('../../data/query_results/Hispanic_MX_music.tsv',\n",
     "                             sep='\\t', index_col=False)\n",
     "count_var = 'mau_audience'\n",
@@ -407,8 +418,12 @@
     "first_gen_assim = compute_assimilation(source_pct, dest_pct, first_gen_pct,\n",
     "                                       TOP_DEST_PCT=top_dest_pct, \n",
     "                                       score_type='/')\n",
-    "# plot everything together\n",
+    "## plot everything together\n",
+    "# boxplot\n",
     "all_gen_assim = [first_gen_assim] + expat_assim_scores\n",
+    "all_gen_assim_log = [np.log(d) for d in all_gen_assim]\n",
+    "all_gen_assim_medians = [np.median(d) for d in all_gen_assim_log]\n",
+    "all_gen_assim_median_error = [1.253*np.std(d)/len(d)**.5 for d in all_gen_assim_log]\n",
     "all_gen_names = ['Hispanic_MX_1', 'Hispanic_MX_2_communities',\n",
     "                 'Hispanic_MX_2_Spanish', 'Hispanic_MX_2_interest']\n",
     "Y = pd.np.arange(len(all_gen_assim))+1\n",
@@ -416,6 +431,13 @@
     "plt.yticks(Y, all_gen_names)\n",
     "plt.xscale('log')\n",
     "plt.title('Assimilation scores by generation', fontsize=18)\n",
+    "plt.show()\n",
+    "# dot plot (median plus errors)\n",
+    "plt.scatter(all_gen_assim_medians, Y, color='b')\n",
+    "plt.errorbar(all_gen_assim_medians, Y, xerr=all_gen_assim_median_error, color='b', fmt='none')\n",
+    "plt.yticks(Y, all_gen_names)\n",
+    "# plt.xscale('log')\n",
+    "plt.title('Assimilation scores by generation', fontsize=18)\n",
     "plt.show()"
    ]
   },
@@ -426,6 +448,356 @@
     "If nothing else it seems like the first generation is more consistent (lower variance). This might mean that the second generation is branching out more to new music tastes."
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Top-k assimilation scores per generation"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "What are the top-scoring interests for each group?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 107,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "top/bottom 10 scoring interests for Hispanic_MX_1\n",
+      "interest_name\n",
+      "Criolla                      175.396415\n",
+      "Music of Mexico               46.172732\n",
+      "Trova                         40.199336\n",
+      "Timba                         29.513820\n",
+      "Mento                         17.292604\n",
+      "UK funky                       9.278731\n",
+      "Urban contemporary gospel      8.709856\n",
+      "Guajira (music)                7.657640\n",
+      "Latin dance                    6.448261\n",
+      "Zouk (club)                    4.533323\n",
+      "Name: mau_audience_pct, dtype: float64\n",
+      "interest_name\n",
+      "Biguine                         0.001259\n",
+      "Byzantine music                 0.001181\n",
+      "Women's music                   0.000994\n",
+      "Irish rock                      0.000899\n",
+      "New Orleans rhythm and blues    0.000726\n",
+      "Adult hits                      0.000675\n",
+      "Straight-ahead jazz             0.000556\n",
+      "Abwe                            0.000540\n",
+      "New Romanticism                 0.000370\n",
+      "Canadian rock                   0.000203\n",
+      "Name: mau_audience_pct, dtype: float64\n",
+      "top/bottom 10 scoring interests for Hispanic_MX_2_communities\n",
+      "interest_name\n",
+      "Criolla                      35.811338\n",
+      "Music of Mexico              24.456523\n",
+      "Urban contemporary gospel    11.548914\n",
+      "Timba                         9.935463\n",
+      "Mento                         8.783681\n",
+      "Slow jam                      8.631714\n",
+      "Trova                         7.525084\n",
+      "Crunk                         5.706522\n",
+      "Roots reggae                  5.322890\n",
+      "Guajira (music)               5.287897\n",
+      "Name: mau_audience_pct, dtype: float64\n",
+      "interest_name\n",
+      "Absolute music             0.001675\n",
+      "Australian folk music      0.001652\n",
+      "Canadian rock              0.001315\n",
+      "Slack-key guitar           0.001287\n",
+      "Scottish highland dance    0.001223\n",
+      "Adult album alternative    0.001112\n",
+      "New rave                   0.000873\n",
+      "Music of Greece            0.000815\n",
+      "Pep band                   0.000644\n",
+      "New Acoustic Music         0.000582\n",
+      "Name: mau_audience_pct, dtype: float64\n",
+      "top/bottom 10 scoring interests for Hispanic_MX_2_Spanish\n",
+      "interest_name\n",
+      "Music of Mexico          77.047224\n",
+      "Hot R&B/Hip-Hop Songs     3.852361\n",
+      "West Coast hip hop        2.201349\n",
+      "Southern hip hop          2.166953\n",
+      "Bouyon music              2.115022\n",
+      "Spoken word               1.432890\n",
+      "Space age pop             1.391130\n",
+      "Electro house             1.329982\n",
+      "Roots reggae              1.246352\n",
+      "Crunk                     1.192398\n",
+      "Name: mau_audience_pct, dtype: float64\n",
+      "interest_name\n",
+      "Bakersfield sound           0.000250\n",
+      "Outlaw country              0.000244\n",
+      "Old-time music              0.000216\n",
+      "British blues               0.000214\n",
+      "Adult contemporary music    0.000175\n",
+      "1970s in music              0.000138\n",
+      "Alternative country         0.000101\n",
+      "Western swing               0.000092\n",
+      "Psychedelic rock            0.000066\n",
+      "Rock and roll               0.000015\n",
+      "Name: mau_audience_pct, dtype: float64\n",
+      "top/bottom 10 scoring interests for Hispanic_MX_2_interest\n",
+      "interest_name\n",
+      "Music of Mexico              18.049703\n",
+      "Urban contemporary gospel    11.771546\n",
+      "Mento                         8.953007\n",
+      "Chicago hip hop               7.315175\n",
+      "Slow jam                      6.855194\n",
+      "Roots reggae                  5.193329\n",
+      "Crunk                         5.044948\n",
+      "Trova                         4.980269\n",
+      "Bouyon music                  4.154663\n",
+      "Southern hip hop              3.972897\n",
+      "Name: mau_audience_pct, dtype: float64\n",
+      "interest_name\n",
+      "Klezmer                  0.001177\n",
+      "Music of Greece          0.001177\n",
+      "Vocal jazz               0.001039\n",
+      "Pep band                 0.000929\n",
+      "New Acoustic Music       0.000841\n",
+      "Heartland rock           0.000803\n",
+      "Christmas carol          0.000589\n",
+      "Globus (music)           0.000519\n",
+      "Hot R&B/Hip-Hop Songs    0.000208\n",
+      "Old-time music           0.000020\n",
+      "Name: mau_audience_pct, dtype: float64\n",
+      "               Hispanic_MX_1  Hispanic_MX_2_communities  \\\n",
+      "0                    Criolla                    Criolla   \n",
+      "1            Music of Mexico            Music of Mexico   \n",
+      "2                      Trova  Urban contemporary gospel   \n",
+      "3                      Timba                      Timba   \n",
+      "4                      Mento                      Mento   \n",
+      "5                   UK funky                   Slow jam   \n",
+      "6  Urban contemporary gospel                      Trova   \n",
+      "7            Guajira (music)                      Crunk   \n",
+      "8                Latin dance               Roots reggae   \n",
+      "9                Zouk (club)            Guajira (music)   \n",
+      "\n",
+      "   Hispanic_MX_2_Spanish     Hispanic_MX_2_interest  \n",
+      "0        Music of Mexico            Music of Mexico  \n",
+      "1  Hot R&B/Hip-Hop Songs  Urban contemporary gospel  \n",
+      "2     West Coast hip hop                      Mento  \n",
+      "3       Southern hip hop            Chicago hip hop  \n",
+      "4           Bouyon music                   Slow jam  \n",
+      "5            Spoken word               Roots reggae  \n",
+      "6          Space age pop                      Crunk  \n",
+      "7          Electro house                      Trova  \n",
+      "8           Roots reggae               Bouyon music  \n",
+      "9                  Crunk           Southern hip hop  \n"
+     ]
+    }
+   ],
+   "source": [
+    "# print separately\n",
+    "top_k = 10\n",
+    "for g_name, g_scores in izip(all_gen_names, all_gen_assim):\n",
+    "    print('top/bottom %d scoring interests for %s'%(top_k, g_name))\n",
+    "    g_scores.sort_values(inplace=True, ascending=False)\n",
+    "    print(g_scores.head(top_k))\n",
+    "    print(g_scores.tail(top_k))\n",
+    "# print together\n",
+    "top_k_interests = pd.concat([pd.Series(g_scores.sort_values(inplace=False, ascending=False).head(top_k).index.tolist()) for g_scores in all_gen_assim], axis=1)\n",
+    "top_k_interests.columns = all_gen_names\n",
+    "top_k_interests.index = np.arange(top_k)\n",
+    "print(top_k_interests)\n",
+    "# print(top_k_interests.to_latex())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "How does this look if we stick to the high percentiles (85-95)?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 109,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "?np.percentile"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 110,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "top/bottom 10 scoring interests for Hispanic_MX_1\n",
+      "interest_name\n",
+      "Latin dance                 6.448261\n",
+      "Zouk (club)                 4.533323\n",
+      "African popular music       4.504263\n",
+      "Slow jam                    3.999991\n",
+      "Baltimore club              3.777769\n",
+      "Samba-cancao                3.620362\n",
+      "Bouyon music                3.518510\n",
+      "Roots reggae                3.333326\n",
+      "Crunk                       3.238088\n",
+      "Hot R&B/Hip-Hop Songs       3.111104\n",
+      "Kwaito                      2.833327\n",
+      "Breakbeat hardcore          2.747468\n",
+      "Southern hip hop            2.715271\n",
+      "Urban adult contemporary    2.669076\n",
+      "Chicago hip hop             2.563486\n",
+      "West Coast hip hop          2.428566\n",
+      "Name: mau_audience_pct, dtype: float64\n",
+      "top/bottom 10 scoring interests for Hispanic_MX_2_communities\n",
+      "interest_name\n",
+      "Roots reggae                5.322890\n",
+      "Guajira (music)             5.287897\n",
+      "Bouyon music                4.795397\n",
+      "Chicago hip hop             4.367236\n",
+      "Urban adult contemporary    4.253308\n",
+      "Baltimore club              4.157609\n",
+      "UK funky                    4.076087\n",
+      "Latin dance                 3.921477\n",
+      "Southern hip hop            3.821332\n",
+      "West Coast hip hop          3.668478\n",
+      "Music of Texas              2.694363\n",
+      "Electro house               2.649457\n",
+      "Cakewalk                    2.586748\n",
+      "Dark ambient                2.517583\n",
+      "Acid rock                   2.511751\n",
+      "Horrorcore                  2.351589\n",
+      "Name: mau_audience_pct, dtype: float64\n",
+      "top/bottom 10 scoring interests for Hispanic_MX_2_Spanish\n",
+      "interest_name\n",
+      "Roots reggae                  1.246352\n",
+      "Crunk                         1.192398\n",
+      "Contemporary R&B              0.977907\n",
+      "Alternative rock              0.590695\n",
+      "Chumba                        0.563760\n",
+      "Swing (dance)                 0.525322\n",
+      "Contemporary worship music    0.436601\n",
+      "Christian hip hop             0.385236\n",
+      "Country music                 0.321030\n",
+      "Organ (music)                 0.321030\n",
+      "1990s in music                0.311152\n",
+      "Waltz                         0.304979\n",
+      "Nu metal                      0.295348\n",
+      "Christian rock                0.288927\n",
+      "Texas country music           0.231142\n",
+      "Name: mau_audience_pct, dtype: float64\n",
+      "top/bottom 10 scoring interests for Hispanic_MX_2_interest\n",
+      "interest_name\n",
+      "Bouyon music                4.154663\n",
+      "Southern hip hop            3.972897\n",
+      "Latin dance                 3.896788\n",
+      "Urban adult contemporary    3.646620\n",
+      "West Coast hip hop          3.531464\n",
+      "Cakewalk                    3.090031\n",
+      "Baltimore club              2.472025\n",
+      "Horrorcore                  2.241121\n",
+      "Music of Texas              2.214647\n",
+      "Zydeco                      2.017979\n",
+      "Electro house               1.891856\n",
+      "New rave                    1.891856\n",
+      "Dark ambient                1.869598\n",
+      "Christian hip hop           1.618588\n",
+      "Samba-cancao                1.618588\n",
+      "Doo-wop                     1.434657\n",
+      "Name: mau_audience_pct, dtype: float64\n"
+     ]
+    }
+   ],
+   "source": [
+    "low_pct = 85\n",
+    "top_pct = 95\n",
+    "for g_name, g_scores in izip(all_gen_names, all_gen_assim):\n",
+    "    print('top/bottom %d scoring interests for %s'%(top_k, g_name))\n",
+    "    g_scores.sort_values(inplace=True, ascending=False)\n",
+    "    g_scores_i = g_scores[(g_scores > np.percentile(g_scores, low_pct)) & \n",
+    "                          (g_scores < np.percentile(g_scores, top_pct))]\n",
+    "    print(g_scores_i)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "How many of the top assimilation scores are Latin-American, for each generation?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Let's figure out which genres are Latin American."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 147,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "133 Latin genres\n",
+      "['Abwe', 'Afro-Cuban jazz', 'Andean music', 'Argentine cumbia', 'Argentine rock', 'Axe (music)', 'Bachata (music)', 'Bachaton', 'Baiao (music)', 'Bele', 'Biguine', 'Bolero', 'Bomba (Puerto Rico)', 'Boogaloo', 'Bossa nova', 'Brazilian hip hop', 'Brazilian rock', 'Brega (music)', 'Cadence rampa', 'Cadence-lypso', 'Cancion', 'Candombe', 'Capoeira music', 'Chacarera', 'Chamame', 'Champeta', 'Changui', 'Charanga (Cuba)', 'Chicano rap', 'Chicano rock', 'Choro', 'Chutney music', 'Coco (music)', 'Coimbra Fado', 'Conjunto', 'Corrido', 'Criolla', 'Cuarteto', 'Cuban rumba', 'Cueca', 'Cumbia', 'Cumbia villera', 'Danza', 'Danzon', 'Decima', 'Deejay (Jamaican)', 'Duranguense', 'Fado', 'Flamenco', 'Forro', 'Funk carioca', 'Funk ostentacao', 'Grupera', 'Guaguanco', 'Guajira (music)', 'Guaracha', 'Gwo ka', 'Huapango', 'Huayno', 'Jarocho', 'Jongo', 'Joropo', 'Lambada', 'Latin American music', 'Latin ballad', 'Latin dance', 'Latin hip hop', 'Latin jazz', 'Latin pop', 'Latin soul', 'List of Caribbean music genres', 'Mambo (music)', 'Marchinha', 'Mariachi', 'Merengue music', 'Merengue tipico', 'Mexican cumbia', 'Mexican pop music', 'Milonga (music)', 'Moombahton', 'Music of Brazil', 'Music of Cuba', 'Music of Ecuador', 'Music of Mexico', 'Musica popular brasileira', 'Musica sertaneja', 'Narcocorrido', 'Norteno (music)', 'Nueva cancion', 'Nueva trova', 'Orquesta tipica', 'Pachanga', 'Pagode', 'Pagode (music)', 'Peruvian cumbia', 'Plena', 'Punta', 'Punto guajiro', 'Ranchera', 'Rapping', 'Reggae', 'Reggae en Espanol', 'Reggae fusion', 'Reggaeton', 'Regional styles of Mexican music', 'Rock en espanol', 'Rondalla', 'Rumba', 'Salsa dura', 'Salsa music', 'Salsa romantica', 'Samba', 'Samba rock', 'Samba-cancao', 'Sambass', 'Ska', 'Soca music', 'Son (music)', 'Son Huasteco', 'Son Jarocho', 'Son montuno', 'Songo music', 'Tango', 'Tango music', 'Tecno brega', 'Tejano music', 'Timba', 'Trival', 'Trova', 'Vallenato', 'Ye-ye', 'Zamba (artform)', 'Zouk']\n",
+      "12 valid genres\n",
+      "Hispanic_MX_1 has 10/50 Latin interests with median score 7.053\n",
+      "Hispanic_MX_2_communities has 7/50 Latin interests with median score 7.525\n",
+      "Hispanic_MX_2_Spanish has 4/50 Latin interests with median score 0.133\n",
+      "Hispanic_MX_2_interest has 7/50 Latin interests with median score 1.619\n"
+     ]
+    }
+   ],
+   "source": [
+    "import json\n",
+    "import unicodedata\n",
+    "genre_categories = json.load(open('../../data/queries/music_genre_interests_grouped.json'))\n",
+    "latin_genre_categories = ['Latin music genres', 'Latin American styles of music',\n",
+    "                          'Latin American music', 'South American music',\n",
+    "                          'Caribbean music', 'Cuban styles of music']\n",
+    "latin_genres = reduce(lambda x,y: x | y, [set(c['genres']) for c in genre_categories if c['super-genre'] in latin_genre_categories])\n",
+    "# clean up to match assimilation names\n",
+    "latin_genres = [unicodedata.normalize('NFKD', x).encode('ascii', 'ignore') for x in latin_genres]\n",
+    "print('%d Latin genres'%(len(latin_genres)))\n",
+    "print(sorted(latin_genres))\n",
+    "assim_genres_str = [i.encode('utf-8') for i in all_gen_assim[0].index]\n",
+    "print('%d valid genres'%(len(set(assim_genres_str) & set(latin_genres))))\n",
+    "# now compute number of Latin genres in top-k\n",
+    "top_k = 50\n",
+    "for g_name, g_scores in izip(all_gen_names, all_gen_assim):\n",
+    "    g_scores_i = g_scores.sort_values(inplace=False, ascending=False).head(top_k)\n",
+    "    g_scores_i.index = [i.encode('utf-8') for i in g_scores_i.index]\n",
+    "    g_scores_latin = g_scores_i[g_scores_i.index.isin(latin_genres)]\n",
+    "    print('%s has %d/%d Latin interests with median score %.3f'%\n",
+    "          (g_name, g_scores_latin.shape[0], top_k, np.median(g_scores_latin)))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "What is the median assimilation score of the Latin interests?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -436,14 +808,40 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 79,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "U:\\miniconda\\miniconda2\\envs\\assimilation_py27\\lib\\site-packages\\numpy\\core\\fromnumeric.py:225: FutureWarning: reshape is deprecated and will raise in a subsequent release. Please use .values.reshape(...) instead\n",
+      "  return reshape(newshape, order=order)\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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PPsn27dtx93yfs88+m/3797N8+XJ2794NwHnnnccll1wCwDnnnFO2rtHRUQYHB7nxxhs58cQTueKKK7jqqqvYvn17firc1pYtWyqus+nN9mOg+U5AGvg+8BngAeBmYBmwBxiI+1wHjAPfA64oWPZNwP3Ad4B/AVYAm4Fr4/l/CHwDWD1bDevXr/dQ0e4RkRAVj5+PHjX3ZeI+gC9fvtzT6bSn02kHPJVKOeBr1qzxdDrtiUTCAc9ms57JZNzdPZvNemtrq2ezWXd3z2Qyns1mp/VJp9OeSqXyfXLLpdPpfJ9ycusrfD00NOSZTKbktgqfNxtg3Kv5nq+mU+gUh4gDvx+/vgm4tChEOuLHZNz+euAI4DHgTfG8o4hGTZuBa4E/Bu4DVpXZ7oVxMI339PTMZydq0qRpHtOsyoTIXKZEIuFm5kD+Mdeeez41NeWJRMLd3aempvJt7u6JRMKnpqam9cmtJ9cnt5yZ5fuUk1tf4evJyUlPJBIlt1X4vNlQZYgsxOmsn7j7P8XPPwd8oGj+u8zsQqKQWAscF//j/8zdvwXg7r+C/F0ZG4ABYGOuvZi7Xw9cDzAwMODzKd59XouLHLZC76KqdMzl1rt8+XJWr14NwOOPP54/hbVmzRqOPPJIfvzjH3Po0CHGxsbo6+sDYGxsjNbWVsbGxtiwYQN9fX2MjY0B5PusW7eOp556Kt8nt9y6detob2+ftbbc+nLL9fX1MTw8PG37hdsqrG3RqiZpQieikcgTBa//M/BV4pEI0As8SjyiAHYSjTZeD4yVWN9m4DaiU18D1dSg01kijVHx+JnH6azcqatUKuXLli2bNjoxs2nz165d67t27fJsNuu9vb2+detW7+3t9Ww267t27fK1a9f6Mccck+/T2dnpK1as8LVr1/ru3bt99+7dfswxx3hnZ6ePjIzMWtvIyEh+3VNTUz44OOgtLS0+ODhYclu9vb0V19koNNnprLfGr28A/qogRH6X6JpHAlgDPB0HRfHprBVMP53128BDQKZSDQoRkcaoZ4iMjIx4a2trxdNdXV1d3tXV5YlEwjOZTP4Le2RkxDOZjCcSiZJ9RkZGvKura9p6qv2yL1x3JpPxrVu3zrqtZlVtiJjX8XSNmaWBO4F7gROAR4D3xm2Xuvu4me0E3hyHxkvAre6+08zeBFwDHAm8CJwCnEU0AtlqZm8APg/8kbv/sFwNAwMDPj4+Hlq/TmeJBKp4/OxYCTuem9syVfaR+TOzve5e8acYdb0m4u6PE13jKHZSQZ/NZZb9FvCWouad8YS7/78y6xYRkQWiX6yLiEgwhcgsNGQWaT46LpuLQkRERIIpREREJJhCREREgilERKRuZvsLu6Xmr1q1qsEVy1zpr/iKSF1UcwHcd9S/DqkvjURERCSYQkRERIIpREREJJhCREREgilEREQkmEJERESCKURERCSYQkRERIIpREREJJhCREREgilEREQkmEJERESCKURERCSYQkRERIIpREREJJhCREREgilEREQkmEJERESCKURERCSYQkRERIIpREREJJhCREREgilEREQkmEJERESCKURERCSYQkRERIIpREREJJhCREREgilEREQkmEJERESCKURERCSYQkRERIIpREREJJhCREREgilEREQkmEJERESCKURERCSYQkRERIIpREREJJhCREREgilEREQkmEJERESCKURERCSYQkRERIIpREREJJhCREREgilEREQkmEJERESCKURERCSYQkREaqqjowMzmzGxY2XJ9lJTR0dHo9+GVEkhIiI1NTExgbvPmICS7aWmiYmJBr8LqZZCREREgilEREQkmEJERESCKUSkJsys0SXIYUqfvcZSiIiISLCGhYiZPV+m/SIzOy9+vtnMXrmwlYmISLWabiTi7sPuvit+uRlQiIhISZs2bQKo6rcnbW1tbNu2DYDR0VH6+/tJJpN0d3fT3d1NIpEglUrl+3d3dzM6Olp224Xr6O/vL9m3VJ9cWyKRoK2tjUQiUXb5ENXUVVPV3rc91wm4DPhA/Px/Adn4+cnA54DngSuB7wD/DKyJ5+8ALgXOivs8DHwbOBJYD9wD7AXuAtZWqmP9+vUu9Rd9lERm+Sx89Kj5r6PAxo0bHXDAly9fnn8OuJnln6dSKV+9erWfe+653tLS4hs3bvTe3l7PZrO+a9cuP+aYY/zoo4/2FStWeEdHh69Zs8Yvv/xyX7t2rXd2dvrIyMiMbY+MjOTXMTU15dls1nt7e6f1LdWns7PTOzs7fXBw0NPptA8NDXlvb68PDg7OWD5ENXVVCxj3ar7rq+kUMgFvAb4UP78P+BcgBXwU+PP4H/iP4vmfBP7aC0Ikfr4HGIifp4D7gc749dnATZXqUIgsDIWI5CxUiJiZX3zxxfmgSCaT3tbW5oAPDQ3lwySVSnk6nfZMJuNDQ0NuZp7NZt3dPZPJeDab9XQ67alUyrPZrGez2WntmUxmxrZz8wvllputTzqdzq8zN69we6W2NRfV1FWtZgiRFPAYsAL4OnA18Nb4+XHAS4D5bwLh0z57iPQDv4pHJd8GvgvsLrPtC4FxYLynp2fOO0/mrvB/gZo0lTTHEJnPNDk5Oe21mXkikci3T01Nubt7IpHwqakpTyQS+fbc69xjIpGYUV9ufqFc/9n6JBKJfC25ecXbm49q6qoWVYZI3a6JuPsB4HHgAqIRxH3ABuA1wD7gQFwowEGgpcIqDfieux8fT69z941ltn29uw+4+0BnZ2cN3o1Uo5oPnKalPy3U58nMuPjiiwFIpVIkk0na2toAGB4eBqJrJalUinXr1tHX18fw8DBmxtjYGAB9fX2MjY3R09NDKpVibGyMsbGxae19fX0zasvNL5RbbrY+PT09+Vpy8wq3V2pbc1FNXTVX5w/TDuDHwCnAmvj5V+N5zxf0OwvYWbBMbiRyG7Ahfn4E8Cjw1vh1CshUqkGnsxYG5f73KYedsp8FXRPRNZG5TkQX0Q8A7fHrHwD/1asPkT9h+oX144F7iS7Gfw94f6UaFCILQyEiOQsVIu7Tg6TS1Nra6lu3bnX36Ms2k8l4IpHwrq4u7+rqcjPzlpaWfP+urq5Zv3wL15HJZMqGTXGfXJuZeWtrq5tZ2eVDVFNXNaoNkdw1iSVrYGDAx8fHG13GkmdmNT2VIYtX2c/CjpWw47n5rWOefaV6ZrbX3Qcq9Wu634mIiMjioRCRmtD/BKVR9NlrLIWIiIgEU4iIiEgwhYiIiARTiIiISDCFiIjUXKm/oluuvdS0atWqBr8DqValPzUiIjIns90t5TsWrg5ZGBqJiIhIMIWIiIgEU4iIiEgwhYiIiARTiIiISDCFiIiIBFOIiIhIMIWIiIgEU4iIiEgwhYiIiARTiIiISDCFiIiIBFOIiIhIMIWIiIgEU4iIiEgwhYiIiARTiIiISDCFiIiIBFOIiIhIMIWIiIgEU4iIiEgwhYiIiARTiIiISDBz90bXUFdm9hwwATw7h8VWAs/NYV5hW6XnxY+ra1Rbpboq1VPclqpRXdXUVq7OZt1ns9U4l9rms8+qrafW+6xSHeXqmq3GZtlnxW2NOgaa5Xtjnbt3Vlyzuy/pCbgeGJ/rMnOZV9hW6XmJx5rUVqmuSvUUtzVqn1XYV02xz2pV23z22Rzqqek+q1THYt5nxW363qhuOhxOZ91W42VKzbttDs+LH+eq3HKV6qpUT7m2+dZVbl65/VSqjmbbZ6VqWuh9Vm09td5nleooV1epmpptn5Vrq9ZS/d6Y1ZI/nQVgZuPuPtDoOkpp1tqatS5QbSGatS5o3tqatS5ortoOh5EIREPTZtWstTVrXaDaQjRrXdC8tTVrXdBEtR0WIxEREamPw2UkIiIidaAQERGRYAoREREJdtiHiJklzOxKM7vGzM5vdD2FzOwkM7vPzIbN7KRG11PIzNrNbK+Zvb3RtRQys754f91sZhc3up4cMzvTzG4ws6+Z2cZG11PIzF5tZjea2c1NUEu7mX0m3lfnNrqeQs20n4o18vO1qEPEzG4ys1+Y2YNF7aeZ2cNm9qiZXV5hNe8AXgUcAJ5sstoceB5oq1VtNaoLYDvwxVrUVMva3H2fu18EvAuoyS2QNarrFnd/P7AZOLsWddWwtsfcfUutappnje8Ebo731Rn1qimktnrvp3nWVpfPV1Xm8svEZpuA/wS8EXiwoC0J/BB4NXAE8B3gOOB1wO1F028BlwN/Hi97c5PVloiXWwN8vonqOgV4N9EH9u3NtM/iZc4A7gfe00x1xcsNAW9stn1W68//PGr8MHB83GekHvWE1lbv/VSj2mr6+apmamERc/d7zSxd1Px7wKPu/hiAmf098A53/zgw49SLmT0JTMUvDzZTbQUmgNZmqcvMNgDtRAf9i2Z2p7sfaoba4vXcCtxqZncAI81Ql5kZ8Ang/7j7v863plrWVm9zqZFoxN0FfJsFOFMyx9oeqnc9obWZ2T7q8PmqxqI+nVXGq4CfFLx+Mm4r5yvAJjO7Bri3noUxx9rM7J1m9r+BzwLXNktd7j7o7n9J9AV9Qy0CpFa1xdeRPhXvtzubpS5gG9EI7iwzu6iOdcHc99krzGwYeIOZfbjOteWUq/ErwJ+Y2XWE/4mP+SpZW4P2U7Fy+20hP1/TLOqRSBlWoq3sLyrd/QVgoc5zzrW2rxAdVPU2p7ryHdx31r6UGea6z/YAe+pVTIG51vUp4FP1K2eaudb2b8CCfvFQpkZ3nwQuWOBaipWrrRH7qVi52hby8zXNUhyJPAl0F7zuAn7aoFqKNWttzVoXNG9tzVoXNHdtOc1co2qbg6UYIt8CjjWzXjM7gugC8K0NrimnWWtr1rqgeWtr1rqguWvLaeYaVdtcLORV/DrcvTAK/Izf3J67JW7/A+AHRHcxDKq25q+rmWtr1rqavbbFUKNqm/+kP8AoIiLBluLpLBERWSAKERERCaYQERGRYAoREREJphAREZFgChEREQmmEBERkWAKERERCaYQERGRYP8fKYjvNSi4lK8AAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0xcf940b8>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "scores have different median: grand median 7.774E-01 stat=7.008E+00 (p=3.008E-02)\n",
+      "scores have different variance: stat=3.385E+00 (p=3.460E-02)\n"
+     ]
+    },
     {
      "data": {
       "image/png": 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\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x9296358>"
+       "<matplotlib.figure.Figure at 0xcf94278>"
       ]
      },
      "metadata": {},
@@ -461,7 +859,7 @@
      "data": {
       "image/png": 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\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0xf367a90>"
+       "<matplotlib.figure.Figure at 0xe60c6a0>"
       ]
      },
      "metadata": {},
@@ -479,7 +877,7 @@
      "data": {
       "image/png": "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\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0xf34dd30>"
+       "<matplotlib.figure.Figure at 0xfc7d4a8>"
       ]
      },
      "metadata": {},
@@ -495,6 +893,8 @@
     }
    ],
    "source": [
+    "import pandas as pd\n",
+    "from itertools import izip\n",
     "import utils\n",
     "reload(utils)\n",
     "from utils import simplify_audience_output, clean_pop_data, compute_assimilation\n",
@@ -507,6 +907,8 @@
     "                            sep='\\t', index_col=False)\n",
     "source_data = pd.read_csv('../../data/query_results/all_MX_music.tsv',\n",
     "                          sep='\\t', index_col=False)\n",
+    "expat_data = pd.read_csv('../../data/query_results/Hispanic_MX_music.tsv',\n",
+    "                         sep='\\t', index_col=False)\n",
     "ethnic_group_data = [white_data, black_data, hispanic_data]\n",
     "ethnic_group_names = ['white', 'black', 'hispanic']\n",
     "# process\n",
@@ -532,10 +934,18 @@
     "source_data = clean_pop_data(source_data, shared_interests, count_var=count_var)\n",
     "source_pct = source_data.loc[:, count_var_pct]\n",
     "source_pct.index = source_data.loc[:, 'interest_name']\n",
+    "# expat data\n",
+    "expat_data = replace_min_count(expat_data, count_var, MIN_COUNT)\n",
+    "expat_data = clean_pop_data(expat_data, shared_interests, count_var=count_var)\n",
+    "expat_pct = expat_data.loc[:, count_var_pct]\n",
+    "expat_pct.index = expat_data.loc[:, 'interest_name']\n",
     "# compute assimilation\n",
     "# and plot, test, etc.\n",
     "top_dest_pct = 50\n",
     "Y = pd.np.arange(len(ethnic_group_data))+1\n",
+    "# include expats for comparison\n",
+    "expat_data = [expat_pct, mx_interest_pct, spanish_pct, mx_communities_pct]\n",
+    "expat_names = ['Hispanic_MX', 'MX_interest', 'Spanish', 'MX_communities']\n",
     "for e_pct, e_name in izip(expat_data, expat_names):\n",
     "    ethnic_assim_scores = []\n",
     "    for g_pct in ethnic_group_pcts:\n",
@@ -563,6 +973,20 @@
     " Similar distributions, but we also have interesting clumps with the Hispanic destination population (see `MX_interest` and `MX_communities`). This means that the population is either (1) neatly assimilating or (2) not at all assimilating to the Hispanic population. This effect persists even after increasing `TOP_DEST_PCT` (from 50 to 90)."
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "What are the low-scoring vs. high-scoring interests for `hispanic`?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
diff --git a/src/analysis/compute_interest_pct_assimilation_scores.py b/src/analysis/compute_interest_pct_assimilation_scores.py
index 479adbb..75c0832 100644
--- a/src/analysis/compute_interest_pct_assimilation_scores.py
+++ b/src/analysis/compute_interest_pct_assimilation_scores.py
@@ -6,15 +6,84 @@
 
 @author: stewart
 """
-from utils import replace_min_count, clean_pop_data, compute_assimilation
+import os
+from src.analysis.utils import clean_pop_data, compute_assimilation
 from argparse import ArgumentParser
+import pandas as pd
+from itertools import izip
+
+def load_data(f, count_var='mau_audience', shared_interests=None):
+    data = pd.read_csv(f, sep='\t', index_col=False)
+    data = clean_pop_data(data, shared_interests=shared_interests, count_var=count_var, replace_min=True)
+    count_var_pct = '%s_pct'%(count_var)
+    # convert to percent
+    data_pct = data.loc[:, count_var_pct]
+    data_pct.index = data.loc[:, 'interest_name']
+    data_name = data.loc[:, 'name'].iloc[0]
+    return data_name, data_pct
 
 def main():
     parser = ArgumentParser()
-    parser.add_argument('--source_file', default='')
-    parser.add_argument('--dest_files', default=[''])
-    parser.add_argument('--expat_files')
-    pass
+    parser.add_argument('--source_file', default='data/query_results/all_MX_music.tsv')
+    parser.add_argument('--dest_files', default=['data/query_results/US_white_music.tsv',
+                                                 'data/query_results/US_African_American_music.tsv',
+                                                 'data/query_results/US_Hispanic_music.tsv',
+                                                 'data/query_results/all_US_music.tsv'])
+    # full population expat files
+#    parser.add_argument('--expat_files', default=['data/query_results/Hispanic_MX_music.tsv',
+#                                                  'data/query_results/US_hispanic_non_expats_mexican_aligned_music_interests.tsv',
+#                                                  'data/query_results/Hispanic_Spanish_non_expat_music_interests.tsv',
+#                                                  'data/query_results/hispanic_non_expat_MX_community_music_interests.tsv'])
+    # subpopulation expat files
+    parser.add_argument('--expat_files', default=['data/query_results/hispanic_mexican_community_music_interests_education.tsv',
+                                                  'data/query_results/hispanic_mexican_community_music_interests_genders.tsv',
+                                                  'data/query_results/hispanic_mexican_community_music_interests_language.tsv',
+                                                  'data/query_results/hispanic_mexican_community_music_interests_age_ranges.tsv',
+                                                  'data/query_results/hispanic_MX_music_interests_education.tsv',
+                                                  'data/query_results/hispanic_MX_music_interests_genders.tsv',
+                                                  'data/query_results/hispanic_MX_music_interests_language.tsv',
+                                                  'data/query_results/hispanic_MX_music_interests_region.tsv',
+                                                  'data/query_results/hispanic_MX_music_interests_age_ranges.tsv',
+                                                  'data/query_results/hispanic_non_expat_MX_aligned_music_interests_education.tsv',
+                                                  'data/query_results/hispanic_non_expat_MX_aligned_music_interests_genders.tsv',
+                                                  'data/query_results/hispanic_non_expat_MX_aligned_music_interests_language.tsv',
+                                                  'data/query_results/hispanic_non_expat_MX_aligned_music_interests_regions.tsv',
+                                                  'data/query_results/hispanic_non_expat_MX_aligned_music_interests_age_ranges.tsv',
+                                                  'data/query_results/hispanic_non_expat_Spanish_music_interests_education.tsv',
+                                                  'data/query_results/hispanic_non_expat_Spanish_music_interests_genders.tsv',
+                                                  'data/query_results/US_white_music_interests_ages.tsv'])
+    parser.add_argument('--out_dir', default='data/assimilation_scores')
+    parser.add_argument('--top_dest_pct', default=50)
+    args = parser.parse_args()
+    source_file = args.source_file
+    dest_files = args.dest_files
+    expat_files = args.expat_files
+    out_dir = args.out_dir
+    top_dest_pct = args.top_dest_pct
+    if(not os.path.exists(out_dir)):
+        os.mkdir(out_dir)
+
+    ## load data
+    source_data = pd.read_csv(source_file, sep='\t', index_col=False)
+    count_var = 'mau_audience'
+    count_var_pct = '%s_pct'%(count_var)
+    source_data = clean_pop_data(source_data, shared_interests=None, count_var=count_var, replace_min=True)
+    shared_interests = source_data.loc[:, 'interest_name'].unique()
+    source_pct = source_data.loc[:, count_var_pct]
+    source_pct.index = source_data.loc[:, 'interest_name']
+    dest_names, dest_data = zip(*[load_data(f, count_var=count_var, shared_interests=shared_interests) for f in dest_files])
+    expat_names, expat_data = zip(*[load_data(f, count_var=count_var, shared_interests=shared_interests) for f in expat_files])
+    
+    ## compute assimilation for all combos
+    for d_name, d_data in izip(dest_names, dest_data):
+        for e_name, e_data in izip(expat_names, expat_data):
+            if(d_name != e_name):
+                assim_scores = compute_assimilation(source_pct, d_data, e_data,
+                                                    TOP_DEST_PCT=top_dest_pct,
+                                                    score_type='/')
+                # write to file
+                out_file = os.path.join(out_dir, 'assim_scores_%s_vs_%s_topDestPct_%d'%(e_name, d_name, top_dest_pct))
+                assim_scores.to_csv(out_file, sep='\t', index=False)
 
 if __name__ == '__main__':
     main()
\ No newline at end of file
diff --git a/src/data_processing/compare_young_old_lang_use.ipynb b/src/data_processing/compare_young_old_lang_use.ipynb
index 31f27e6..9ec22f5 100644
--- a/src/data_processing/compare_young_old_lang_use.ipynb
+++ b/src/data_processing/compare_young_old_lang_use.ipynb
@@ -2260,8 +2260,8 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 2 (Spyder)",
-   "language": "python2",
+   "display_name": "Python 2",
+   "language": "python",
    "name": "python2"
   },
   "language_info": {
diff --git a/src/data_processing/mine_facebook_audience_presets.py b/src/data_processing/mine_facebook_audience_presets.py
index f599202..3720f3c 100644
--- a/src/data_processing/mine_facebook_audience_presets.py
+++ b/src/data_processing/mine_facebook_audience_presets.py
@@ -148,7 +148,14 @@
                          6015559470583
                        ],
                     "name" : "US African American"
-                }
+                },
+            9 : {
+                    "not":[
+                         6015559470583,
+                         6025978286383,
+                       ],
+                    "name" : "Not Expats OR Close Friends of Expats"
+                },
         }
 INTEREST_DICT = {
          # music
@@ -280,7 +287,7 @@ def mine_audience(locations, genders, age_ranges, behaviors, interests, query_na
 #    out_dir = args.out_dir
 #    mine_audience(locations, behaviors, interests, genders, age_ranges, query_name, out_dir)
     
-@profile
+#@profile
 def mine_all_demos(behavior, interest, query_name, out_dir,
                    demo_names=['age','gender','education','region','language'],
                    ages=[1,2,3,4,5],
@@ -328,8 +335,8 @@ def mine_all_demos(behavior, interest, query_name, out_dir,
     
 if __name__ == '__main__':
     #    mine_audience(locations, genders, age_ranges, behaviors, interests, query_name, out_dir)
-    # all US music interests
-#    mine_audience([0], [0], [0], [], 0, 'all_US_music', 'data/query_results/')
+    # US non-expat music interests
+    mine_audience([0], [0], [0], [9], 0, 'all_US_music', 'data/query_results/')
     # US white music interests
 #    mine_audience([0], [0], [0], [5], 0, 'US_white_music', 'data/query_results/')
     # US black music interests