Instruction:
Can you tell what is the following Python 3 function doing
def get_pole(continent,age):
    age=int(age)
    if continent=='congo':continent='nwaf'
    if continent=='kala':continent='neaf'
    if continent=='sam':continent='sac'
    if continent=='ant':continent='eant'
    if continent=='af':
        return [0,0,0] # assume africa fixed
    if continent=='aus':
        cont= [[5,9.7,54.3,-3.3],[10,10.4,52.8,-6.2],[15,11.5,49.8,-9.0],[20,12.4,48.0,-11.8],[25,12.9,48.3,-15.0],[30,12.8,49.9,-18.1],[35,13.5,50.8,-20.9],[40,14.1,52.7,-22.1],[45,14.4,54.7,-22.9],[50,14.7,56.5,-23.6],[55,14.0,57.3,-24.7],[60,12.9,57.9,-25.7],[65,13.6,58.8,-26.3],[70,17.3,60.2,-26.3],[75,19.8,63.3,-26.7],[80,20.5,68.5,-26.6],[85,19.8,74.6,-26.9],[90,17.7,80.9,-28.9],[95,15.9,86.2,-31.1],[100,18.4,89.3,-30.7],[105,17.9,95.6,-32.6],[110,17.3,101.0,-34.8],[115,16.8,105.6,-37.4],[120,16.4,109.4,-40.3],[125,15.7,110.3,-42.3],[130,15.9,111.6,-44.4],[135,15.9,113.1,-46.6],[140,15.6,113.7,-48.3],[145,15.0,113.1,-50.5],[150,15.5,113.5,-52.5],[155,17.6,115.7,-54.3],[160,19.5,117.8,-56.2],[165,19.5,117.8,-56.2],[170,19.5,117.8,-56.2],[175,19.5,117.8,-56.2],[180,19.5,117.8,-56.2],[185,19.5,117.8,-56.2],[190,19.5,117.8,-56.2],[195,19.5,117.8,-56.2],[200,19.5,117.8,-56.2],[205,19.5,117.8,-56.2],[210,19.5,117.8,-56.2],[215,19.5,117.8,-56.2],[220,19.5,117.8,-56.2],[225,19.5,117.8,-56.2],[230,19.5,117.8,-56.2],[235,19.5,117.8,-56.2],[240,19.5,117.8,-56.2],[245,19.5,117.8,-56.2],[250,19.5,117.8,-56.2],[255,19.5,117.8,-56.2],[260,19.5,117.8,-56.2],[265,19.5,117.8,-56.2],[270,19.5,117.8,-56.2],[275,19.5,117.8,-56.2],[280,19.5,117.8,-56.2],[285,19.5,117.8,-56.2],[290,19.5,117.8,-56.2],[295,19.5,117.8,-56.2],[300,19.5,117.8,-56.2],[305,19.5,117.8,-56.2],[310,19.5,117.8,-56.2],[315,19.5,117.8,-56.2],[320,19.5,117.8,-56.2]]
        for rec in cont:
             if age==int(rec[0]): return [rec[1],rec[2],rec[3]]
    if continent=='eur':
        cont= [[5,17.9,-27.1,0.6],[10,18.4,-26.3,1.2],[15,18.9,-24.6,1.8],[20,17.2,-22.7,2.4],[25,20.7,-19.0,3.0],[30,24.9,-19.5,4.3],[35,27.2,-19.3,5.8],[40,28.7,-18.5,7.5],[45,30.3,-18.2,9.0],[50,30.8,-16.7,10.0],[55,32.7,-15.4,11.3],[60,34.8,-15.7,12.6],[65,36.0,-15.8,13.6],[70,35.4,-16.1,14.9],[75,35.5,-15.7,15.5],[80,36.1,-15.2,16.9],[85,37.0,-14.2,18.8],[90,39.6,-13.7,21.9],[95,39.8,-13.7,25.2],[100,40.2,-12.5,28.5],[105,41.6,-11.2,31.7],[110,42.6,-9.8,34.5],[115,43.4,-8.5,37.3],[120,44.5,-6.9,40.3],[125,45.3,-6.3,42.0],[130,45.9,-5.7,43.0],[135,46.6,-5.3,44.0],[140,47.3,-4.9,45.2],[145,47.8,-4.8,46.4],[150,48.6,-4.0,47.9],[155,49.8,-2.2,50.0],[160,50.6,-1.2,52.1],[165,51.4,-0.3,54.2],[170,52.1,0.6,56.3],[175,52.9,1.9,59.6],[180,53.0,2.0,60.0],[185,53.0,2.0,60.4],[190,53.1,2.1,60.8],[195,53.2,2.2,61.1],[200,53.3,2.2,61.5],[205,53.2,2.6,59.7],[210,53.1,2.9,57.8],[215,53.1,3.3,55.9],[220,52.9,3.6,53.6],[225,52.7,4.0,51.4],[230,52.4,4.4,49.1],[235,52.2,4.8,46.8],[240,51.9,5.3,44.5],[245,51.9,5.3,44.5],[250,51.9,5.3,44.5],[255,51.9,5.3,44.5],[260,51.9,5.3,44.5],[265,51.9,5.3,44.5],[270,51.9,5.3,44.5],[275,51.9,5.3,44.5],[280,51.9,5.3,44.5],[285,51.9,5.3,44.5],[290,51.9,5.3,44.5],[295,51.9,5.3,44.5],[300,51.9,5.3,44.5],[305,51.9,5.3,44.5],[310,51.9,5.3,44.5],[315,51.9,5.3,44.5],[320,51.9,5.3,44.5]]
        for rec in cont:
             if age==int(rec[0]): return [rec[1],rec[2],rec[3]]
    if continent=='mad':
        cont= [[5,90.0,0.0,0.0],[10,90.0,0.0,0.0],[15,90.0,0.0,0.0],[20,90.0,0.0,0.0],[25,90.0,0.0,0.0],[30,90.0,0.0,0.0],[35,90.0,0.0,0.0],[40,90.0,0.0,0.0],[45,90.0,0.0,0.0],[50,90.0,0.0,0.0],[55,90.0,0.0,0.0],[60,90.0,0.0,0.0],[65,90.0,0.0,0.0],[70,90.0,0.0,0.0],[75,90.0,0.0,0.0],[80,90.0,0.0,0.0],[85,90.0,0.0,0.0],[90,90.0,0.0,0.0],[95,90.0,0.0,0.0],[100,90.0,0.0,0.0],[105,90.0,0.0,0.0],[110,90.0,0.0,0.0],[115,90.0,0.0,0.0],[120,90.0,0.0,0.0],[125,2.6,-63.3,1.8],[130,2.6,-63.3,3.9],[135,1.5,-57.6,5.7],[140,1.0,-55.9,7.2],[145,0.6,118.3,-8.9],[150,4.5,119.8,-10.9],[155,10.6,130.1,-13.0],[160,14.8,137.5,-15.4],[165,14.8,137.5,-15.4],[170,14.8,137.5,-15.4],[175,14.8,137.5,-15.4],[180,14.8,137.5,-15.4],[185,14.8,137.5,-15.4],[190,14.8,137.5,-15.4],[195,14.8,137.5,-15.4],[200,14.8,137.5,-15.4],[205,14.8,137.5,-15.4],[210,14.8,137.5,-15.4],[215,14.8,137.5,-15.4],[220,14.8,137.5,-15.4],[225,14.8,137.5,-15.4],[230,14.8,137.5,-15.4],[235,14.8,137.5,-15.4],[240,14.8,137.5,-15.4],[245,14.8,137.5,-15.4],[250,14.8,137.5,-15.4],[255,14.8,137.5,-15.4],[260,14.8,137.5,-15.4],[265,14.8,137.5,-15.4],[270,14.8,137.5,-15.4],[275,14.8,137.5,-15.4],[280,14.8,137.5,-15.4],[285,14.8,137.5,-15.4],[290,14.8,137.5,-15.4],[295,14.8,137.5,-15.4],[300,14.8,137.5,-15.4],[305,14.8,137.5,-15.4],[310,14.8,137.5,-15.4],[315,14.8,137.5,-15.4],[320,14.8,137.5,-15.4]]
        for rec in cont:
             if age==int(rec[0]): return [rec[1],rec[2],rec[3]]
    if continent=='nwaf':
        cont= [[5,90.0,0.0,0.0],[10,90.0,0.0,0.0],[15,90.0,0.0,0.0],[20,90.0,0.0,0.0],[25,90.0,0.0,0.0],[30,90.0,0.0,0.0],[35,90.0,0.0,0.0],[40,90.0,0.0,0.0],[45,90.0,0.0,0.0],[50,90.0,0.0,0.0],[55,90.0,0.0,0.0],[60,90.0,0.0,0.0],[65,90.0,0.0,0.0],[70,90.0,0.0,0.0],[75,90.0,0.0,0.0],[80,90.0,0.0,0.0],[85,19.6,6.7,0.0],[90,16.6,6.7,-0.2],[95,16.5,6.7,-0.4],[100,16.5,6.7,-0.5],[105,16.5,6.7,-0.7],[110,16.5,6.7,-0.8],[115,16.5,6.7,-1.0],[120,16.5,6.7,-1.1],[125,16.5,6.7,-1.2],[130,16.5,6.7,-1.2],[135,16.5,6.7,-1.2],[140,16.5,6.7,-1.2],[145,16.5,6.7,-1.2],[150,16.5,6.7,-1.2],[155,16.5,6.7,-1.2],[160,16.5,6.7,-1.2],[165,16.5,6.7,-1.2],[170,16.5,6.7,-1.2],[175,16.5,6.7,-1.2],[180,16.5,6.7,-1.2],[185,16.5,6.7,-1.2],[190,16.5,6.7,-1.2],[195,16.5,6.7,-1.2],[200,16.5,6.7,-1.2],[205,16.5,6.7,-1.2],[210,16.5,6.7,-1.2],[215,16.5,6.7,-1.2],[220,16.5,6.7,-1.2],[225,16.5,6.7,-1.2],[230,16.5,6.7,-1.2],[235,16.5,6.7,-1.2],[240,16.5,6.7,-1.2],[245,16.5,6.7,-1.2],[250,16.5,6.7,-1.2],[255,16.5,6.7,-1.2],[260,16.5,6.7,-1.2],[265,16.5,6.7,-1.2],[270,16.5,6.7,-1.2],[275,16.5,6.7,-1.2],[280,16.5,6.7,-1.2],[285,16.5,6.7,-1.2],[290,16.5,6.7,-1.2],[295,16.5,6.7,-1.2],[300,16.5,6.7,-1.2],[305,16.5,6.7,-1.2],[310,16.5,6.7,-1.2],[315,16.5,6.7,-1.2],[320,16.5,6.7,-1.2]]
        for rec in cont:
             if age==int(rec[0]): return [rec[1],rec[2],rec[3]]
    if continent=='col':
        cont= [[5,62.1,-40.2,1.6],[10,61.8,-40.3,3.3],[15,59.6,-38.1,5.4],[20,58.5,-37.1,7.5],[25,57.7,-36.4,9.6],[30,56.7,-34.5,11.9],[35,56.5,-33.4,14.3],[40,57.1,-32.6,16.6],[45,57.0,-31.4,18.6],[50,58.2,-31.2,20.5],[55,60.7,-31.9,22.0],[60,62.5,-32.8,23.3],[65,63.7,-33.5,24.6],[70,63.5,-33.4,26.1],[75,63.2,-33.9,28.6],[80,62.7,-34.3,31.5],[85,61.2,-34.3,34.4],[90,59.1,-34.5,37.3],[95,57.2,-34.7,40.3],[100,55.7,-34.8,43.3],[105,54.3,-34.9,46.4],[110,53.1,-35.0,49.5],[115,52.2,-35.0,51.7],[120,51.6,-35.0,52.8],[125,50.7,-33.9,54.0],[130,48.5,-33.4,55.4],[135,47.5,-33.3,56.0],[140,47.5,-33.3,56.1],[145,47.5,-33.3,56.1],[150,47.5,-33.3,56.2],[155,47.5,-33.3,56.2],[160,47.5,-33.3,56.2],[165,47.5,-33.3,56.2],[170,47.5,-33.3,56.2],[175,47.5,-33.3,56.2],[180,47.5,-33.3,56.2],[185,47.5,-33.3,56.2],[190,47.5,-33.3,56.2],[195,47.5,-33.3,56.2],[200,47.5,-33.3,56.2],[205,47.5,-33.3,56.2],[210,47.5,-33.3,56.2],[215,47.5,-33.3,56.2],[220,47.5,-33.3,56.2],[225,47.5,-33.3,56.2],[230,47.5,-33.3,56.2],[235,47.5,-33.3,56.2],[240,47.5,-33.3,56.2],[245,47.5,-33.3,56.2],[250,47.5,-33.3,56.2],[255,47.5,-33.3,56.2],[260,47.5,-33.3,56.2],[265,47.5,-33.3,56.2],[270,47.5,-33.3,56.2],[275,47.5,-33.3,56.2],[280,47.5,-33.3,56.2],[285,47.5,-33.3,56.2],[290,47.5,-33.3,56.2],[295,47.5,-33.3,56.2],[300,47.5,-33.3,56.2],[305,47.5,-33.3,56.2],[310,47.5,-33.3,56.2],[315,47.5,-33.3,56.2],[320,47.5,-33.3,56.2]]
        for rec in cont:
             if age==int(rec[0]): return [rec[1],rec[2],rec[3]]
    if continent=='grn':
        cont= [[5,80.9,22.8,1.3],[10,80.9,22.9,2.6],[15,80.9,23.2,4.1],[20,80.6,24.4,5.5],[25,79.5,28.1,6.8],[30,77.3,12.5,8.6],[35,74.8,7.2,10.2],[40,72.6,9.5,11.5],[45,71.4,11.4,12.7],[50,71.0,20.7,14.2],[55,71.8,29.6,16.8],[60,71.9,30.5,17.5],[65,71.3,32.9,17.6],[70,69.8,29.0,17.9],[75,69.0,26.6,18.5],[80,67.6,21.0,19.8],[85,66.3,16.4,21.5],[90,65.9,11.5,24.2],[95,64.2,5.5,26.9],[100,62.7,2.8,30.1],[105,62.4,1.6,33.3],[110,62.1,0.9,36.5],[115,61.8,0.5,39.7],[120,61.8,0.8,43.1],[125,61.9,1.0,44.9],[130,62.2,1.3,46.0],[135,62.4,1.6,47.1],[140,62.7,1.6,48.4],[145,62.9,1.3,49.7],[150,63.2,1.8,51.4],[155,63.7,3.6,53.8],[160,64.1,4.2,56.0],[165,64.4,4.8,58.3],[170,64.7,5.3,60.6],[175,64.8,6.0,64.1],[180,64.9,6.0,64.5],[185,64.9,5.9,64.9],[190,65.0,5.9,65.4],[195,65.0,5.8,65.8],[200,65.1,5.8,66.2],[205,65.1,5.7,66.7],[210,65.2,5.7,67.1],[215,65.2,5.6,67.5],[220,65.2,5.6,67.5],[225,65.2,5.6,67.5],[230,65.2,5.6,67.5],[235,65.2,5.6,67.5],[240,65.2,5.6,67.5],[245,65.2,5.6,67.5],[250,65.2,5.6,67.5],[255,65.2,5.6,67.5],[260,65.2,5.6,67.5],[265,65.2,5.6,67.5],[270,65.2,5.6,67.5],[275,65.2,5.6,67.5],[280,65.2,5.6,67.5],[285,65.2,5.6,67.5],[290,65.2,5.6,67.5],[295,65.2,5.6,67.5],[300,65.2,5.6,67.5],[305,65.2,5.6,67.5],[310,65.2,5.6,67.5],[315,65.2,5.6,67.5],[320,65.2,5.6,67.5]]
        for rec in cont:
             if age==int(rec[0]): return [rec[1],rec[2],rec[3]]
    if continent=='nam':
        cont= [[5,80.9,22.8,1.3],[10,80.9,22.9,2.6],[15,80.9,23.2,4.1],[20,80.6,24.4,5.5],[25,79.5,28.1,6.8],[30,77.3,12.5,8.6],[35,75.4,3.5,10.5],[40,74.5,-1.1,12.6],[45,74.3,-4.3,14.6],[50,75.9,-3.5,16.2],[55,79.8,4.1,17.6],[60,81.6,5.1,19.1],[65,82.6,3.2,20.7],[70,81.6,-6.5,22.4],[75,80.4,-13.1,24.6],[80,78.2,-18.8,27.5],[85,76.2,-21.3,30.5],[90,74.6,-23.0,33.8],[95,72.0,-24.7,36.9],[100,70.0,-24.0,40.2],[105,69.1,-23.3,43.6],[110,68.3,-22.6,47.0],[115,67.6,-21.8,50.4],[120,67.1,-20.4,53.9],[125,67.0,-19.7,55.6],[130,67.0,-19.1,56.7],[135,67.1,-18.7,57.9],[140,67.2,-18.4,59.2],[145,67.1,-18.3,60.5],[150,67.3,-17.6,62.2],[155,67.6,-15.5,64.6],[160,67.6,-14.5,66.8],[165,67.7,-13.6,69.1],[170,67.8,-12.8,71.4],[175,67.7,-11.5,74.8],[180,67.7,-11.5,75.3],[185,67.7,-11.5,75.7],[190,67.7,-11.5,76.1],[195,67.7,-11.5,76.6],[200,67.7,-11.5,77.0],[205,67.7,-11.5,77.4],[210,67.7,-11.5,77.9],[215,67.7,-11.5,78.3],[220,67.7,-11.5,78.3],[225,67.7,-11.5,78.3],[230,67.7,-11.5,78.3],[235,67.7,-11.5,78.3],[240,67.7,-11.5,78.3],[245,67.7,-11.5,78.3],[250,67.7,-11.5,78.3],[255,67.7,-11.5,78.3],[260,67.7,-11.5,78.3],[265,67.7,-11.5,78.3],[270,67.7,-11.5,78.3],[275,67.7,-11.5,78.3],[280,67.7,-11.5,78.3],[285,67.7,-11.5,78.3],[290,67.7,-11.5,78.3],[295,67.7,-11.5,78.3],[300,67.7,-11.5,78.3],[305,67.7,-11.5,78.3],[310,67.7,-11.5,78.3],[315,67.7,-11.5,78.3],[320,67.7,-11.5,78.3]]
        for rec in cont:
             if int(age)==int(rec[0]):
                  pole= [rec[1],rec[2],rec[3]]
                  return pole
    if continent=='par':
        cont= [[5,62.1,-40.2,1.6],[10,61.8,-40.3,3.3],[15,59.6,-38.1,5.4],[20,58.5,-37.1,7.5],[25,57.7,-36.4,9.6],[30,56.7,-34.5,11.9],[35,56.5,-33.4,14.3],[40,57.1,-32.6,16.6],[45,57.0,-31.4,18.6],[50,58.2,-31.2,20.5],[55,60.7,-31.9,22.0],[60,62.5,-32.8,23.3],[65,63.7,-33.5,24.6],[70,63.5,-33.4,26.1],[75,63.2,-33.9,28.6],[80,62.7,-34.3,31.5],[85,61.2,-34.3,34.4],[90,59.1,-34.5,37.3],[95,57.2,-34.7,40.3],[100,55.7,-34.8,43.3],[105,54.3,-34.9,46.4],[110,53.1,-35.0,49.5],[115,52.2,-35.0,51.7],[120,51.6,-35.0,52.8],[125,50.7,-33.9,54.0],[130,48.5,-33.4,55.4],[135,47.5,-33.3,56.0],[140,47.5,-33.3,56.1],[145,47.5,-33.3,56.1],[150,47.5,-33.3,56.2],[155,47.5,-33.3,56.2],[160,47.5,-33.3,56.2],[165,47.5,-33.3,56.2],[170,47.5,-33.3,56.2],[175,47.5,-33.3,56.2],[180,47.5,-33.3,56.2],[185,47.5,-33.3,56.2],[190,47.5,-33.3,56.2],[195,47.5,-33.3,56.2],[200,47.5,-33.3,56.2],[205,47.5,-33.3,56.2],[210,47.5,-33.3,56.2],[215,47.5,-33.3,56.2],[220,47.5,-33.3,56.2],[225,47.5,-33.3,56.2],[230,47.5,-33.3,56.2],[235,47.5,-33.3,56.2],[240,47.5,-33.3,56.2],[245,47.5,-33.3,56.2],[250,47.5,-33.3,56.2],[255,47.5,-33.3,56.2],[260,47.5,-33.3,56.2],[265,47.5,-33.3,56.2],[270,47.5,-33.3,56.2],[275,47.5,-33.3,56.2],[280,47.5,-33.3,56.2],[285,47.5,-33.3,56.2],[290,47.5,-33.3,56.2],[295,47.5,-33.3,56.2],[300,47.5,-33.3,56.2],[305,47.5,-33.3,56.2],[310,47.5,-33.3,56.2],[315,47.5,-33.3,56.2],[320,47.5,-33.3,56.2]]
        for rec in cont:
             if age==int(rec[0]): return [rec[1],rec[2],rec[3]]
    if continent=='eant':
        cont= [[5,8.2,-49.4,0.8],[10,8.2,-49.4,1.5],[15,9.8,-48.4,2.1],[20,10.7,-47.9,2.8],[25,11.4,-48.2,3.8],[30,11.8,-48.3,4.8],[35,12.5,-46.1,6.0],[40,13.6,-41.5,7.4],[45,11.1,-41.1,8.5],[50,9.1,-40.9,9.6],[55,9.4,-43.5,10.3],[60,10.6,-47.4,10.8],[65,8.1,-47.7,11.3],[70,0.4,-43.3,12.2],[75,3.7,138.9,-13.8],[80,2.7,142.7,-16.1],[85,0.6,144.7,-18.8],[90,1.4,-37.0,22.3],[95,2.9,-38.3,25.8],[100,3.1,146.5,-26.8],[105,5.5,148.9,-30.3],[110,7.4,150.7,-33.9],[115,9.0,152.3,-37.6],[120,10.3,153.6,-41.3],[125,9.4,152.4,-43.0],[130,9.1,151.5,-45.3],[135,8.6,150.9,-47.6],[140,8.0,150.1,-49.2],[145,7.3,148.1,-50.7],[150,7.4,147.1,-52.6],[155,9.0,148.0,-55.4],[160,10.5,148.8,-58.2],[165,10.5,148.8,-58.2],[170,10.5,148.8,-58.2],[175,10.5,148.8,-58.2],[180,10.5,148.8,-58.2],[185,10.5,148.8,-58.2],[190,10.5,148.8,-58.2],[195,10.5,148.8,-58.2],[200,10.5,148.8,-58.2],[205,10.5,148.8,-58.2],[210,10.5,148.8,-58.2],[215,10.5,148.8,-58.2],[220,10.5,148.8,-58.2],[225,10.5,148.8,-58.2],[230,10.5,148.8,-58.2],[235,10.5,148.8,-58.2],[240,10.5,148.8,-58.2],[245,10.5,148.8,-58.2],[250,10.5,148.8,-58.2],[255,10.5,148.8,-58.2],[260,10.5,148.8,-58.2],[265,10.5,148.8,-58.2],[270,10.5,148.8,-58.2],[275,10.5,148.8,-58.2],[280,10.4,148.8,-58.2],[285,10.5,148.8,-58.2],[290,10.5,148.8,-58.2],[295,10.5,148.8,-58.2],[300,10.5,148.8,-58.2],[305,10.4,148.8,-58.2],[310,10.5,148.8,-58.2],[315,10.5,148.8,-58.2],[320,10.5,148.8,-58.2]]
        for rec in cont:
             if age==int(rec[0]): return [rec[1],rec[2],rec[3]]
    if continent=='ind':
        cont= [[5,22.7,32.9,-2.3],[10,23.8,33.1,-4.6],[15,27.1,27.4,-6.0],[20,29.6,23.9,-7.5],[25,25.1,33.2,-10.3],[30,22.5,38.5,-13.3],[35,22.6,41.3,-15.9],[40,25.5,42.7,-17.4],[45,24.2,40.1,-19.7],[50,24.0,34.2,-23.5],[55,22.1,29.2,-28.3],[60,19.5,25.2,-34.4],[65,19.0,21.9,-40.2],[70,20.5,18.9,-44.4],[75,21.8,18.2,-47.3],[80,22.3,18.2,-49.1],[85,21.8,22.1,-53.8],[90,20.0,27.5,-58.8],[95,20.7,28.1,-57.8],[100,21.3,28.8,-56.8],[105,21.9,29.6,-55.9],[110,22.6,30.3,-54.9],[115,23.3,31.1,-54.0],[120,24.0,32.0,-53.1],[125,23.4,34.8,-55.2],[130,21.2,36.2,-60.1],[135,21.2,36.2,-61.6],[140,21.9,37.5,-61.5],[145,22.6,39.0,-62.5],[150,24.1,40.4,-62.9],[155,26.9,41.2,-61.6],[160,29.8,42.1,-60.5],[165,29.8,42.1,-60.5],[170,29.8,42.1,-60.5],[175,29.8,42.1,-60.5],[180,29.8,42.1,-60.5],[185,29.8,42.1,-60.5],[190,29.8,42.1,-60.5],[195,29.8,42.1,-60.5],[200,29.8,42.1,-60.5],[205,29.8,42.1,-60.5],[210,29.8,42.1,-60.5],[215,29.8,42.1,-60.5],[220,29.8,42.1,-60.5],[225,29.8,42.1,-60.5],[230,29.8,42.1,-60.5],[235,29.8,42.1,-60.5],[240,29.8,42.1,-60.5],[245,29.8,42.1,-60.5],[250,29.8,42.1,-60.5],[255,29.8,42.1,-60.5],[260,29.8,42.1,-60.5],[265,29.8,42.1,-60.5],[270,29.8,42.1,-60.5],[275,29.8,42.1,-60.5],[280,29.8,42.1,-60.5],[285,29.8,42.1,-60.5],[290,29.8,42.1,-60.5],[295,29.8,42.1,-60.5],[300,29.8,42.1,-60.5],[305,29.8,42.1,-60.5],[310,29.8,42.1,-60.5],[315,29.8,42.1,-60.5],[320,29.8,42.1,-60.5]]
        for rec in cont:
             if age==int(rec[0]): return [rec[1],rec[2],rec[3]]
    if continent=='neaf':
        cont= [[5,90.0,0.0,0.0],[10,90.0,0.0,0.0],[15,90.0,0.0,0.0],[20,90.0,0.0,0.0],[25,90.0,0.0,0.0],[30,90.0,0.0,0.0],[35,90.0,0.0,0.0],[40,90.0,0.0,0.0],[45,90.0,0.0,0.0],[50,90.0,0.0,0.0],[55,90.0,0.0,0.0],[60,90.0,0.0,0.0],[65,90.0,0.0,0.0],[70,90.0,0.0,0.0],[75,90.0,0.0,0.0],[80,90.0,0.0,0.0],[85,27.9,-61.4,0.0],[90,39.8,-61.4,-0.1],[95,40.8,-61.4,-0.2],[100,40.3,-61.4,-0.3],[105,40.6,-61.4,-0.4],[110,40.6,-61.4,-0.5],[115,40.5,-61.4,-0.6],[120,40.5,-61.4,-0.7],[125,40.5,-61.4,-0.7],[130,40.5,-61.4,-0.7],[135,40.5,-61.4,-0.7],[140,40.5,-61.4,-0.7],[145,40.5,-61.4,-0.7],[150,40.5,-61.4,-0.7],[155,40.5,-61.4,-0.7],[160,40.5,-61.4,-0.7],[165,40.5,-61.4,-0.7],[170,40.5,-61.4,-0.7],[175,40.5,-61.4,-0.7],[180,40.5,-61.4,-0.7],[185,40.5,-61.4,-0.7],[190,40.5,-61.4,-0.7],[195,40.5,-61.4,-0.7],[200,40.5,-61.4,-0.7],[205,40.5,-61.4,-0.7],[210,40.5,-61.4,-0.7],[215,40.5,-61.4,-0.7],[220,40.5,-61.4,-0.7],[225,40.5,-61.4,-0.7],[230,40.5,-61.4,-0.7],[235,40.5,-61.4,-0.7],[240,40.5,-61.4,-0.7],[245,40.4,-61.4,-0.7],[250,40.4,-61.4,-0.7],[255,40.4,-61.4,-0.7],[260,40.4,-61.4,-0.7],[265,40.4,-61.4,-0.7],[270,40.4,-61.4,-0.7],[275,40.4,-61.4,-0.7],[280,40.4,-61.4,-0.7],[285,40.4,-61.4,-0.7],[290,40.4,-61.4,-0.7],[295,40.4,-61.4,-0.7],[300,40.4,-61.4,-0.7],[305,40.4,-61.4,-0.7],[310,40.4,-61.4,-0.7],[315,40.4,-61.4,-0.7],[320,40.4,-61.4,-0.7]]
        for rec in cont:
             if age==int(rec[0]): return [rec[1],rec[2],rec[3]]
    if continent=='sac':
        cont= [[5,62.1,-40.2,1.6],[10,61.8,-40.3,3.3],[15,59.6,-38.1,5.4],[20,58.5,-37.1,7.5],[25,57.7,-36.4,9.6],[30,56.7,-34.5,11.9],[35,56.5,-33.4,14.3],[40,57.1,-32.6,16.6],[45,57.0,-31.4,18.6],[50,58.2,-31.2,20.5],[55,60.7,-31.9,22.0],[60,62.5,-32.8,23.3],[65,63.7,-33.5,24.6],[70,63.5,-33.4,26.1],[75,63.2,-33.9,28.6],[80,62.7,-34.3,31.5],[85,61.2,-34.3,34.4],[90,59.1,-34.5,37.3],[95,57.2,-34.7,40.3],[100,55.7,-34.8,43.3],[105,54.3,-34.9,46.4],[110,53.1,-35.0,49.5],[115,52.2,-35.0,51.7],[120,51.6,-35.0,52.8],[125,50.7,-33.9,54.0],[130,50.1,-32.8,54.9],[135,50.0,-32.5,55.1],[140,50.0,-32.5,55.1],[145,50.0,-32.5,55.1],[150,50.0,-32.5,55.1],[155,50.0,-32.5,55.1],[160,50.0,-32.5,55.1],[165,50.0,-32.5,55.1],[170,50.0,-32.5,55.1],[175,50.0,-32.5,55.1],[180,50.0,-32.5,55.1],[185,50.0,-32.5,55.1],[190,50.0,-32.5,55.1],[195,50.0,-32.5,55.1],[200,50.0,-32.5,55.1],[205,50.0,-32.5,55.1],[210,50.0,-32.5,55.1],[215,50.0,-32.5,55.1],[220,50.0,-32.5,55.1],[225,50.0,-32.5,55.1],[230,50.0,-32.5,55.1],[235,50.0,-32.5,55.1],[240,50.0,-32.5,55.1],[245,50.0,-32.5,55.1],[250,50.0,-32.5,55.1],[255,50.0,-32.5,55.1],[260,50.0,-32.5,55.1],[265,50.0,-32.5,55.1],[270,50.0,-32.5,55.1],[275,50.0,-32.5,55.1],[280,50.0,-32.5,55.1],[285,50.0,-32.5,55.1],[290,50.0,-32.5,55.1],[295,50.0,-32.5,55.1],[300,50.0,-32.5,55.1],[305,50.0,-32.5,55.1],[310,50.0,-32.5,55.1],[315,50.0,-32.5,55.1],[320,50.0,-32.5,55.1]]
        for rec in cont:
             if age==int(rec[0]): return [rec[1],rec[2],rec[3]]
    if continent=='ib':
        cont= [[5,0,0,0],[10,0,0,0],[15,77.93,59.14,.12],[20,77.93,59.14,.24],[25,77.93,59.14,.24],[30,-31.21,166.79,1.73],[35,-31.21,166.79,1.73],[40,-27,160,1.73],[45,-23.85,157.12,1.72],[50,-20.6,157.88,2.1],[55,-20.72,162.4,2.61],[60,-16,164,3],[65,-12.95,165.77,3.1],[70,-16.45,167.49,3.1],[75,-16.45,167.49,3.1],[80,-37.17,169,8.04],[85,-38.86,169.85,10.28],[90,-42.64,173.2,16.56],[95,-43.,174.,20],[100,-43.,174.,25],[105,-43.,174.,30],[110,-43.,174.,35],[115,-43.,174.,35],[120,-43.,174.,44.77],[120,-43.86,174.17,44.77],[125,-43.86,174.17,44.77],[130,-46.19,177.47,45.91],[135,-46.19,177.47,45.91],[140,-46.19,177.47,45.91],[145,-46.19,177.47,45.91],[150,-46.19,177.47,45.91],[155,-47.12,179.45,46.29],[160,-47.12,179.45,46.29],[165,-47.12,179.45,46.29],[170,-47.55,180.35,50.62],[175,-46.8,181.1,50.33],[180,-46.8,181.1,50.33],[185,-46.8,181.1,50.33],[190,-46.8,181.1,50.33],[195,-46.8,181.1,50.33],[200,-46.8,181.1,50.33],[205,-46.8,181.1,50.33],[210,-46.8,181.1,50.33],[215,-46.8,181.1,50.33],[220,-46.8,181.1,50.33],[225,-46.8,181.1,50.33],[230,-46.8,181.1,50.33],[235,-46.8,181.1,50.33],[240,-46.8,181.1,50.33],[245,-46.8,181.1,50.33],[250,-46.8,181.1,50.33],[255,-46.8,181.1,50.33],[260,-46.8,181.1,50.33],[265,-46.8,181.1,50.33],[270,-46.8,181.1,50.33],[275,-46.8,181.1,50.33],[280,-46.8,181.1,50.33],[285,-46.8,181.1,50.33],[290,-46.8,181.1,50.33],[295,-46.8,181.1,50.33],[300,-46.8,181.1,50.33],[305,-46.8,181.1,50.33],[310,-46.8,181.1,50.33],[315,-46.8,181.1,50.33],[320,-46.8,181.1,50.33]]
        for rec in cont:
             if age==int(rec[0]): return [rec[1],rec[2],rec[3]]
    if continent=='saf':
        cont= [[0,0,56.0,2.2],[5,0,57.6,2.5],[10,0,53.9,2.5],[15,0,66.5,3.0],[20,0,75.5,4.7],[25,0,84.1,6.8],[30,0,95.8,7.9],[35,0,98.8,8.7],[40,0,107.5,9.2],[45,0,110.9,10.3],[50,0,111.6,13.2],[55,0,115.7,13.9],[60,0,123.5,15.7],[65,0,127.8,17.5],[70,0,137.2,17.5],[75,0,140.3,19.2],[80,0,138.1,19.3],[85,0,142.9,19.6],[90,0,144.7,20.5],[95,0,144.3,20.8],[100,0,150.8,22.3],[105,0,160.2,26.9],[110,0,169.2,32.1],[115,0,170.3,35.6],[120,0,171.3,36.2],[125,0,172.1,37.5],[130,0,170.0,39.4],[135,0,172.6,42.1],[140,0,163.1,40.8],[145,0,155.2,38.1],[150,0,155.0,34.8],[155,0,155.0,33.2],[160,0,157.0,30.7],[165,0,159.5,32.5],[170,0,167.6,28.8],[175,0,167.8,27.7],[180,0,167.4,25.9],[185,0,168.4,21.6],[190,0,158.8,18.2],[195,0,147.9,17.8],[200,0,144.4,19.2],[205,0,137.4,20.7],[210,0,133.6,23.1],[215,0,129.9,26.4],[220,0,127.2,27.2],[225,0,128.0,29.4],[230,0,130.0,31.4],[235,0,133.6,35.3],[240,0,137.4,36.5],[245,0,143.1,39.6],[250,0,145.4,40.4],[255,0,145.6,41.8],[260,0,144.8,41.9],[265,0,141.6,47.1],[270,0,140.3,46.8],[275,0,138.2,51.1],[280,0,138.6,51.6],[285,0,136.5,51.8],[290,0,135.8,52.8],[295,0,136.8,53.5],[300,0,136.9,55.4],[305,0,138.9,56.3],[310,0,139.9,59.5],[315,0,138.9,60.8],[320,0,132.5,61.6]]
        for rec in cont:
             if age==int(rec[0]): return [rec[1],rec[2],rec[3]]
    return 'NONE'

Response: Returns the pole longitude and angle for the specified continent and age.


Instruction:
How would you explain what the following Python 3 function does
def main():
    cmap = 'RdYlBu'
    date = 2016.
    if not ccrs:
        print("-W- You must intstall the cartopy module to run plot_magmap.py")
        sys.exit()
    dir_path = '.'
    lincr = 1  # level increment for contours
    if '-WD' in sys.argv:
        ind = sys.argv.index('-WD')
        dir_path = sys.argv[ind+1]
    if '-h' in sys.argv:
        print(main.__doc__)
        sys.exit()
    if '-fmt' in sys.argv:
        ind = sys.argv.index('-fmt')
        fmt = sys.argv[ind+1]
        if fmt == 'jpg':
            print('jpg not a supported option')
            print(main.__doc__)
            sys.exit()
    else:
        fmt = 'png'
    if '-cm' in sys.argv:
        ind = sys.argv.index('-cm')
        cmap = sys.argv[ind+1]
    if '-el' in sys.argv:
        ind = sys.argv.index('-el')
        el = sys.argv[ind+1]
    else:
        el = 'B'
    if '-alt' in sys.argv:
        ind = sys.argv.index('-alt')
        alt = sys.argv[ind+1]
    else:
        alt = 0
    if '-lon0' in sys.argv:
        ind = sys.argv.index('-lon0')
        lon_0 = float(sys.argv[ind+1])
    else:
        lon_0 = 0
    if '-mod' in sys.argv:
        ind = sys.argv.index('-mod')
        mod = sys.argv[ind+1]
        ghfile = ''
    elif '-f' in sys.argv:
        ind = sys.argv.index('-f')
        ghfile = sys.argv[ind+1]
        mod = 'custom'
        date = ''
    else:
        mod, ghfile = 'cals10k', ''
    if '-age' in sys.argv:
        ind = sys.argv.index('-age')
        date = float(sys.argv[ind+1])
    if '-alt' in sys.argv:
        ind = sys.argv.index('-alt')
        alt = float(sys.argv[ind+1])
    else:
        alt = 0

    # doesn't work correctly with mod other than default
    Ds, Is, Bs, Brs, lons, lats = pmag.do_mag_map(
        date, mod=mod, lon_0=lon_0, alt=alt, file=ghfile)
    ax = plt.axes(projection=ccrs.Mollweide(central_longitude=lon_0))
    ax.coastlines()
    xx, yy = meshgrid(lons, lats)
    if mod == 'custom':
        str_date = 'Custom'
    else:
        str_date = str(date)
    if el == 'B':
        levmax = Bs.max()+lincr
        levmin = round(Bs.min()-lincr)
        levels = np.arange(levmin, levmax, lincr)
        plt.contourf(xx, yy, Bs, levels=levels, cmap=cmap,
                     transform=ccrs.PlateCarree(central_longitude=lon_0))
        plt.title('Field strength ($\mu$T): '+ str_date)
    if el == 'Br':
        levmax = Brs.max()+lincr
        levmin = round(Brs.min()-lincr)
        plt.contourf(xx, yy, Brs,
                     levels=np.arange(levmin, levmax, lincr),
                     cmap=cmap, transform=ccrs.PlateCarree(central_longitude=lon_0))
        plt.title('Radial field strength ($\mu$T): '+ str_date)
    if el == 'I':
        levmax = Is.max()+lincr
        levmin = round(Is.min()-lincr)
        plt.contourf(xx, yy, Is,
                     levels=np.arange(levmin, levmax, lincr),
                     cmap=cmap, transform=ccrs.PlateCarree(central_longitude=lon_0))
        plt.contour(xx, yy, Is, levels=np.arange(-80, 90, 10),
                    colors='black', transform=ccrs.PlateCarree(central_longitude=lon_0))
        plt.title('Field inclination: '+ str_date)
    if el == 'D':
        plt.contourf(xx, yy, Ds,
                     levels=np.arange(-180, 180, 10),
                     cmap=cmap, transform=ccrs.PlateCarree(central_longitude=lon_0))
        plt.contour(xx, yy, Ds, levels=np.arange(-180,
                                                 180, 10), colors='black')
        # cs=m.contourf(x,y,Ds,levels=np.arange(-180,180,10),cmap=cmap)
        # cs=m.contourf(x,y,Ds,levels=np.arange(-180,180,10),cmap=cmap)
        # m.contour(x,y,Ds,levels=np.arange(-180,180,10),colors='black')
        plt.title('Field declination: '+ str_date)
    cbar = plt.colorbar(orientation='horizontal')
    figname = 'geomagnetic_field_' + str_date + '.'+fmt
    plt.savefig(figname)
    print('Figure saved as: ', figname)

Response: This function is the main function for the plot_magmap. py script. It will create a contour map of the desired field model and plot the contour.


Instruction:
Can you generate a brief explanation for the following Python 3 code
def main():
    if '-h' in sys.argv:
        print(main.__doc__)
        sys.exit()
    if '-WD' in sys.argv:
        ind=sys.argv.index('-WD')
        dir_path=sys.argv[ind+1]
    # interactive entry
    if '-i' in sys.argv:
        infile=input("Magic txt file for unpacking? ")
        dir_path = '.'
        input_dir_path = '.'
    # non-interactive
    else:
        infile = pmag.get_named_arg("-f", reqd=True)
        # if -O flag is present, overwrite is False
        overwrite = pmag.get_flag_arg_from_sys("-O", true=False, false=True)
        # if -sep flag is present, sep is True
        sep = pmag.get_flag_arg_from_sys("-sep", true=True, false=False)
        data_model = pmag.get_named_arg("-DM", default_val=3, reqd=False)
        dir_path = pmag.get_named_arg("-WD", default_val=".", reqd=False)
        input_dir_path = pmag.get_named_arg("-ID", default_val=".", reqd=False)

    #if '-ID' not in sys.argv and '-WD' in sys.argv:
    #    input_dir_path = dir_path
    if "-WD" not in sys.argv and "-ID" not in sys.argv:
        input_dir_path = os.path.split(infile)[0]
    if not input_dir_path:
        input_dir_path = "."

    ipmag.download_magic(infile, dir_path, input_dir_path, overwrite, True, data_model, sep)

Response:
NAME
        download_magic. py DESCRIPTION download_magic. py FILE UPLOAD_MAGIC. txt FILE and unpacks a smartbook. txt file into the current tab delimited MagIC format txt files for use with the MagIC - Py programs.


Instruction:
Can you tell what is the following Python 3 function doing
def main():
    args = sys.argv
    if "-h" in args:
        print(main.__doc__)
        sys.exit()
    pltspec = ""
    verbose = pmagplotlib.verbose
    dir_path = pmag.get_named_arg('-WD', '.')
    dir_path = os.path.realpath(dir_path)
    meas_file = pmag.get_named_arg('-f', 'measurements.txt')
    fmt = pmag.get_named_arg('-fmt', 'png')
    save_plots = False
    interactive = True
    if '-sav' in args:
        verbose = False
        save_plots = True
        interactive = False
    if '-spc' in args:
        ind = args.index("-spc")
        pltspec = args[ind+1]
        verbose = False
        save_plots = True
    ipmag.quick_hyst(dir_path, meas_file, save_plots,
                     interactive, fmt, pltspec, verbose)

Response:
NAME
        quick_hyst.py

    DESCRIPTION
        makes plots of hysteresis data

    SYNTAX
        quick_hyst.py [command line options]

    OPTIONS
        -h prints help message and quits
        -f: specify input file, default is measurements.txt
        -spc SPEC: specify specimen name to plot and quit
        -sav save all plots and quit
        -fmt [png,svg,eps,jpg]


Instruction:
Here you have a function in Python 3, explain what it does
def smooth(x,window_len,window='bartlett'):

    if x.ndim != 1:
        raise ValueError("smooth only accepts 1 dimension arrays.")

    if x.size < window_len:
        raise ValueError("Input vector needs to be bigger than window size.")

    if window_len<3:
        return x

    # numpy available windows
    if not window in ['flat', 'hanning', 'hamming', 'bartlett', 'blackman']:
        raise ValueError("Window is on of 'flat', 'hanning', 'hamming', 'bartlett', 'blackman'")

    # padding the beggining and the end of the signal with an average value to evoid edge effect
    start=[average(x[0:10])]*window_len
    end=[average(x[-10:])]*window_len
    s=start+list(x)+end

    #s=numpy.r_[2*x[0]-x[window_len:1:-1],x,2*x[-1]-x[-1:-window_len:-1]]
    if window == 'flat': #moving average
        w=ones(window_len,'d')
    else:
        w=eval('numpy.'+window+'(window_len)')
    y=numpy.convolve(old_div(w,w.sum()),s,mode='same')
    return array(y[window_len:-window_len])

Response: smooth the data using a sliding window with requested size


Instruction:
Can you tell what is the following Python 3 function doing
def main():
    plot,fmt=0,'svg'
    if '-h' in sys.argv:
        print(main.__doc__)
        sys.exit()
    if '-f' in sys.argv:
        ind=sys.argv.index('-f')
        meas_file=sys.argv[ind+1]
    else:
        print("missing -f\n")
        sys.exit()
    if '-w' in sys.argv:
        ind=sys.argv.index('-w')
        window_len=int(sys.argv[ind+1])
    else:
        window_len=3
    if '-t' in sys.argv:
        ind=sys.argv.index('-t')
        t_begin=int(sys.argv[ind+1])
        t_end=int(sys.argv[ind+2])
    else:
        t_begin=''
        t_end=''
    if '-sav' in sys.argv:plot=1
    if '-fmt' in sys.argv:
        ind=sys.argv.index('-fmt')
        fmt=sys.argv[ind+1]

    # read data from file
    Data=numpy.loadtxt(meas_file,dtype=numpy.float)
    T=Data.transpose()[0]
    M=Data.transpose()[1]
    T=list(T)
    M=list(M)
    # cut the data if -t is one of the flags
    if t_begin:
        while T[0]<t_begin:
            M.pop(0);T.pop(0)
        while T[-1]>t_end:
            M.pop(-1);T.pop(-1)

    # prepare the signal:
    # from M(T) array with unequal deltaT
    # to M(T) array with deltaT=(1 degree).
    # if delataT is larger, then points are added using linear fit between
    # consecutive data points.
    # exit if deltaT is not integer
    i=0
    while i<(len(T)-1):
        if (T[i+1]-T[i])%1>0.001:
            print("delta T should be integer, this program will not work!")
            print("temperature range:",T[i],T[i+1])
            sys.exit()
        if (T[i+1]-T[i])==0.:
            M[i]=average([M[i],M[i+1]])
            M.pop(i+1);T.pop(i+1)
        elif (T[i+1]-T[i])<0.:
            M.pop(i+1);T.pop(i+1)
            print("check data in T=%.0f ,M[T] is ignored"%(T[i]))
        elif (T[i+1]-T[i])>1.:
            slope,b=polyfit([T[i],T[i+1]],[M[i],M[i+1]],1)
            for j in range(int(T[i+1])-int(T[i])-1):
                M.insert(i+1,slope*(T[i]+1.)+b)
                T.insert(i+1,(T[i]+1.))
                i=i+1
        i=i+1

    # calculate the smoothed signal
    M=array(M,'f')
    T=array(T,'f')
    M_smooth=[]
    M_smooth=smooth(M,window_len)

    #plot the original data and the smooth data
    PLT={'M_T':1,'der1':2,'der2':3,'Curie':4}
    pmagplotlib.plot_init(PLT['M_T'],5,5)
    string='M-T (sliding window=%i)'%int(window_len)
    pmagplotlib.plot_xy(PLT['M_T'],T,M_smooth,sym='-')
    pmagplotlib.plot_xy(PLT['M_T'],T,M,sym='--',xlab='Temperature C',ylab='Magnetization',title=string)

    #calculate first derivative
    d1,T_d1=[],[]
    for i in range(len(M_smooth)-1):
        Dy=M_smooth[i-1]-M_smooth[i+1]
        Dx=T[i-1]-T[i+1]
        d1.append(old_div(Dy,Dx))
    T_d1=T[1:len(T-1)]
    d1=array(d1,'f')
    d1_smooth=smooth(d1,window_len)

    #plot the first derivative
    pmagplotlib.plot_init(PLT['der1'],5,5)
    string='1st derivative (sliding window=%i)'%int(window_len)
    pmagplotlib.plot_xy(PLT['der1'],T_d1,d1_smooth,sym='-',xlab='Temperature C',title=string)
    pmagplotlib.plot_xy(PLT['der1'],T_d1,d1,sym='b--')

    #calculate second derivative
    d2,T_d2=[],[]
    for i in range(len(d1_smooth)-1):
        Dy=d1_smooth[i-1]-d1_smooth[i+1]
        Dx=T[i-1]-T[i+1]
        #print Dy/Dx
        d2.append(old_div(Dy,Dx))
    T_d2=T[2:len(T-2)]
    d2=array(d2,'f')
    d2_smooth=smooth(d2,window_len)

    #plot the second derivative
    pmagplotlib.plot_init(PLT['der2'],5,5)
    string='2nd derivative (sliding window=%i)'%int(window_len)
    pmagplotlib.plot_xy(PLT['der2'],T_d2,d2,sym='-',xlab='Temperature C',title=string)
    d2=list(d2)
    print('second derivative maximum is at T=%i'%int(T_d2[d2.index(max(d2))]))

    # calculate Curie temperature for different width of sliding windows
    curie,curie_1=[],[]
    wn=list(range(5,50,1))
    for win in wn:
        # calculate the smoothed signal
        M_smooth=[]
        M_smooth=smooth(M,win)
        #calculate first derivative
        d1,T_d1=[],[]
        for i in range(len(M_smooth)-1):
            Dy=M_smooth[i-1]-M_smooth[i+1]
            Dx=T[i-1]-T[i+1]
            d1.append(old_div(Dy,Dx))
        T_d1=T[1:len(T-1)]
        d1=array(d1,'f')
        d1_smooth=smooth(d1,win)
        #calculate second derivative
        d2,T_d2=[],[]
        for i in range(len(d1_smooth)-1):
            Dy=d1_smooth[i-1]-d1_smooth[i+1]
            Dx=T[i-1]-T[i+1]
            d2.append(old_div(Dy,Dx))
        T_d2=T[2:len(T-2)]
        d2=array(d2,'f')
        d2_smooth=smooth(d2,win)
        d2=list(d2)
        d2_smooth=list(d2_smooth)
        curie.append(T_d2[d2.index(max(d2))])
        curie_1.append(T_d2[d2_smooth.index(max(d2_smooth))])

    #plot Curie temp for different sliding window length
    pmagplotlib.plot_init(PLT['Curie'],5,5)
    pmagplotlib.plot_xy(PLT['Curie'],wn,curie,sym='.',xlab='Sliding Window Width (degrees)',ylab='Curie Temp',title='Curie Statistics')
    files = {}
    for key in list(PLT.keys()): files[key]=str(key) + "." +fmt
    if plot==0:
        pmagplotlib.draw_figs(PLT)
        ans=input(" S[a]ve to save plot, [q]uit, Return to continue:  ")
        if ans=="q": sys.exit()
        if ans=="a": pmagplotlib.save_plots(PLT,files)
    else: pmagplotlib.save_plots(PLT,files)
    sys.exit()

Response:
NAME curie. py curie. py
            is a simple curie. py curie. py curie. py curie. py curie. py curie. py curie. py curie. py curie. py curie. py curie. py curie. py curie. py curie. py curie. py curie. py curie. py curie. py curie. py curie. py curie. py


Instruction:
Write a Python 3 function for
naming jr6_jr6_magic.py DESCRIPTION converts JR6 .jr6 format files to magic_measurements format files SYNTAX jr6_jr6_magic.py [command line options] OPTIONS -h: prints the help message and quits. -f FILE: specify input file, or -F FILE: specify output file, default is magic_measurements.txt -Fsa: specify er_samples format file for appending, default is new er_samples.txt (Not working yet) -spc NUM : specify number of characters to designate a specimen, default = 1 -loc LOCNAME : specify location/study name -A: don't average replicate measurements -ncn NCON: specify sample naming convention (6 and 7 not yet implemented) -mcd [SO-MAG,SO-SUN,SO-SIGHT...] supply how these samples were oriented -JR IODP samples measured on the JOIDES RESOLUTION -v NUM : specify the volume in cc of the sample, default 2.5^3cc Sample naming convention: [1] XXXXY: where XXXX is an arbitrary length site designation and Y is the single character sample designation. e.g., TG001a is the first sample from site TG001. [default] [2] XXXX-YY: YY sample from site XXXX (XXX, YY of arbitary length) [3] XXXX.YY: YY sample from site XXXX (XXX, YY of arbitary length) [4-Z] XXXX[YYY]: YYY is sample designation with Z characters from site XXX [5] site name same as sample [6] site is entered under a separate column -- NOT CURRENTLY SUPPORTED [7-Z] [XXXX]YYY: XXXX is site designation with Z characters with sample name XXXXYYYY NB: all others you will have to customize your self or e-mail ltauxe@ucsd.edu for help. INPUT JR6 .jr6 format file

Response:
def main(command_line=True, **kwargs):
    """
    NAME
        jr6_jr6_magic.py

    DESCRIPTION
        converts JR6 .jr6 format files to magic_measurements format files

    SYNTAX
        jr6_jr6_magic.py [command line options]

    OPTIONS
        -h: prints the help message and quits.
        -f FILE: specify  input file, or
        -F FILE: specify output file, default is magic_measurements.txt
        -Fsa: specify er_samples format file for appending, default is new er_samples.txt (Not working yet)
        -spc NUM : specify number of characters to designate a  specimen, default = 1
        -loc LOCNAME : specify location/study name
        -A: don't average replicate measurements
        -ncn NCON: specify sample naming convention (6 and 7 not yet implemented)
        -mcd [SO-MAG,SO-SUN,SO-SIGHT...] supply how these samples were oriented
        -JR  IODP samples measured on the JOIDES RESOLUTION
        -v NUM : specify the volume in cc of the sample, default 2.5^3cc
       Sample naming convention:
            [1] XXXXY: where XXXX is an arbitrary length site designation and Y
                is the single character sample designation.  e.g., TG001a is the
                first sample from site TG001.    [default]
            [2] XXXX-YY: YY sample from site XXXX (XXX, YY of arbitary length)
            [3] XXXX.YY: YY sample from site XXXX (XXX, YY of arbitary length)
            [4-Z] XXXX[YYY]:  YYY is sample designation with Z characters from site XXX
            [5] site name same as sample
            [6] site is entered under a separate column -- NOT CURRENTLY SUPPORTED
            [7-Z] [XXXX]YYY:  XXXX is site designation with Z characters with sample name XXXXYYYY
            NB: all others you will have to customize your self
                 or e-mail ltauxe@ucsd.edu for help.

    INPUT
        JR6 .jr6 format file
    """
# initialize some stuff
    noave=0
    #volume=2.5**3 #default volume is a 2.5cm cube
    volume = 2.5 * 1e-6 #default volume is a 2.5 cm cube, translated to meters cubed
    inst=""
    samp_con,Z='1',""
    missing=1
    demag="N"
    er_location_name="unknown"
    citation='This study'
    args=sys.argv
    meth_code="LP-NO"
    specnum=1
    version_num=pmag.get_version()
    Samps=[] # keeps track of sample orientations

    user=""
    mag_file=""
    dir_path='.'
    MagRecs=[]
    ErSamps=[]
    SampOuts=[]

    samp_file = 'er_samples.txt'
    meas_file = 'magic_measurements.txt'
    tmp_file= "fixed.jr6"
    meth_code,JR="",0
    #
    # get command line arguments
    #

    if command_line:
        if '-WD' in sys.argv:
            ind = sys.argv.index('-WD')
            dir_path=sys.argv[ind+1]
        if '-ID' in sys.argv:
            ind = sys.argv.index('-ID')
            input_dir_path = sys.argv[ind+1]
        else:
            input_dir_path = dir_path
        output_dir_path = dir_path
        if "-h" in args:
            print(main.__doc__)
            return False
        if '-F' in args:
            ind=args.index("-F")
            meas_file = args[ind+1]
        if '-Fsa' in args:
            ind = args.index("-Fsa")
            samp_file = args[ind+1]
            #try:
            #    open(samp_file,'r')
            #    ErSamps,file_type=pmag.magic_read(samp_file)
            #    print 'sample information will be appended to ', samp_file
            #except:
            #    print samp_file,' not found: sample information will be stored in new er_samples.txt file'
            #    samp_file = output_dir_path+'/er_samples.txt'
        if '-f' in args:
            ind = args.index("-f")
            mag_file= args[ind+1]
        if "-spc" in args:
            ind = args.index("-spc")
            specnum = int(args[ind+1])
        if "-ncn" in args:
            ind=args.index("-ncn")
            samp_con=sys.argv[ind+1]
        if "-loc" in args:
            ind=args.index("-loc")
            er_location_name=args[ind+1]
        if "-A" in args: noave=1
        if "-mcd" in args:
            ind=args.index("-mcd")
            meth_code=args[ind+1]
        if "-JR" in args:
            meth_code=meth_code+":FS-C-DRILL-IODP:SP-SS-C:SO-V"
            meth_code=meth_code.strip(":")
            JR=1
            samp_con='5'
        if "-v" in args:
            ind=args.index("-v")
            volume=float(args[ind+1])*1e-6 # enter volume in cc, convert to m^3
    if not command_line:
        dir_path = kwargs.get('dir_path', '.')
        input_dir_path = kwargs.get('input_dir_path', dir_path)
        output_dir_path = dir_path
        meas_file = kwargs.get('meas_file', 'magic_measurements.txt')
        mag_file = kwargs.get('mag_file')
        samp_file = kwargs.get('samp_file', 'er_samples.txt')
        specnum = kwargs.get('specnum', 1)
        samp_con = kwargs.get('samp_con', '1')
        er_location_name = kwargs.get('er_location_name', '')
        noave = kwargs.get('noave', 0) # default (0) means DO average
        meth_code = kwargs.get('meth_code', "LP-NO")
        volume = float(kwargs.get('volume', 0))
        if not volume:
            volume = 2.5 * 1e-6 #default volume is a 2.5 cm cube, translated to meters cubed
        else:
            #convert cm^3 to m^3
            volume *= 1e-6
        JR = kwargs.get('JR', 0)
        if JR:
            if meth_code == "LP-NO":
                meth_code = ""
            meth_code=meth_code+":FS-C-DRILL-IODP:SP-SS-C:SO-V"
            meth_code=meth_code.strip(":")
            samp_con='5'

    # format variables
    mag_file = input_dir_path+"/" + mag_file
    meas_file = output_dir_path+"/" + meas_file
    samp_file = output_dir_path+"/" + samp_file
    tmp_file = output_dir_path+"/" + tmp_file
    if specnum!=0:
        specnum=-specnum
    if "4" in samp_con:
        if "-" not in samp_con:
            print("option [4] must be in form 4-Z where Z is an integer")
            return False, "option [4] must be in form 4-Z where Z is an integer"
        else:
            Z=samp_con.split("-")[1]
            samp_con="4"
    if "7" in samp_con:
        if "-" not in samp_con:
            print("option [7] must be in form 7-Z where Z is an integer")
            return False, "option [7] must be in form 7-Z where Z is an integer"
        else:
            Z=samp_con.split("-")[1]
            samp_con="7"

    ErSampRec,ErSiteRec={},{}

    # parse data

    # fix .jr6 file so that there are spaces between all the columns.
    pre_data=open(mag_file, 'r')
    tmp_data=open(tmp_file, 'w')
    line=pre_data.readline()
    while line !='':
        line=line.replace('-',' -')
        #print "line=", line
        tmp_data.write(line)
        line=pre_data.readline()
    tmp_data.close()
    pre_data.close()

    data=pd.read_csv(tmp_file, delim_whitespace=True,header=None)

    if JR==0: #
        data.columns=['er_specimen_name','step','x','y','z','expon','sample_azimuth','sample_dip',              'sample_bed_dip_direction','sample_bed_dip','bed_dip_dir2','bed_dip2','param1','param2','param3','param4','measurement_csd']
        cart=np.array([data['x'],data['y'],data['z']]).transpose()
    else: # measured on the Joides Resolution JR6
        data.columns=['er_specimen_name','step','negz','y','x','expon','sample_azimuth','sample_dip',              'sample_bed_dip_direction','sample_bed_dip','bed_dip_dir2','bed_dip2','param1','param2','param3','param4','measurement_csd']
        cart=np.array([data['x'],data['y'],-data['negz']]).transpose()
    dir= pmag.cart2dir(cart).transpose()
    data['measurement_dec']=dir[0]
    data['measurement_inc']=dir[1]
    data['measurement_magn_moment']=dir[2]*(10.0**data['expon'])*volume # the data are in A/m - this converts to Am^2
    data['measurement_magn_volume']=dir[2]*(10.0**data['expon']) # A/m  - data in A/m
    data['sample_dip']=-data['sample_dip']
    DGEOs,IGEOs=[],[]
    for ind in range(len(data)):
        dgeo,igeo=pmag.dogeo(data.iloc[ind]['measurement_dec'],data.iloc[ind]['measurement_inc'],data.iloc[ind]['sample_azimuth'],data.iloc[ind]['sample_dip'])
        DGEOs.append(dgeo)
        IGEOs.append(igeo)
    data['specimen_dec']=DGEOs
    data['specimen_inc']=IGEOs
    data['specimen_tilt']='1'
    if specnum!=0:
        data['er_sample_name']=data['er_specimen_name'][:specnum]
    else:
        data['er_sample_name']=data['er_specimen_name']

    if int(samp_con) in [1, 2, 3, 4, 5, 7]:
        data['er_site_name']=pmag.parse_site(data['er_sample_name'],samp_con,Z)
    # else:
    #     if 'er_site_name' in ErSampRec.keys():er_site_name=ErSampRec['er_site_name']
    #     if 'er_location_name' in ErSampRec.keys():er_location_name=ErSampRec['er_location_name']

    # Configure the er_sample table

    for rowNum, row in data.iterrows():
        sampleFlag=0
        for sampRec in SampOuts:
            if sampRec['er_sample_name'] == row['er_sample_name']:
                sampleFlag=1
                break
        if sampleFlag == 0:
            ErSampRec['er_sample_name']=row['er_sample_name']
            ErSampRec['sample_azimuth']=str(row['sample_azimuth'])
            ErSampRec['sample_dip']=str(row['sample_dip'])
            ErSampRec['magic_method_codes']=meth_code
            ErSampRec['er_location_name']=er_location_name
            ErSampRec['er_site_name']=row['er_site_name']
            ErSampRec['er_citation_names']='This study'
            SampOuts.append(ErSampRec.copy())

    # Configure the magic_measurements table

    for rowNum, row in data.iterrows():
        MagRec={}
#        MagRec['measurement_description']='Date: '+date
        MagRec["er_citation_names"]="This study"
        MagRec['er_location_name']=er_location_name
        MagRec['er_site_name']=row['er_site_name']
        MagRec['er_sample_name']=row['er_sample_name']
        MagRec['magic_software_packages']=version_num
        MagRec["treatment_temp"]='%8.3e' % (273) # room temp in kelvin
        MagRec["measurement_temp"]='%8.3e' % (273) # room temp in kelvin
        MagRec["measurement_flag"]='g'
        MagRec["measurement_standard"]='u'
        MagRec["measurement_number"]='1'
        MagRec["er_specimen_name"]=row['er_specimen_name']
        MagRec["treatment_ac_field"]='0'
        if row['step'] == 'NRM':
            meas_type="LT-NO"
        elif row['step'][0:2] == 'AD':
            meas_type="LT-AF-Z"
            treat=float(row['step'][2:])
            MagRec["treatment_ac_field"]='%8.3e' %(treat*1e-3) # convert from mT to tesla
        elif row['step'][0] == 'TD':
            meas_type="LT-T-Z"
            treat=float(row['step'][2:])
            MagRec["treatment_temp"]='%8.3e' % (treat+273.) # temp in kelvin
        else: # need to add IRM, and ARM options
            print("measurement type unknown", row['step'])
            return False, "measurement type unknown"
        MagRec["measurement_magn_moment"]=str(row['measurement_magn_moment'])
        MagRec["measurement_magn_volume"]=str(row['measurement_magn_volume'])
        MagRec["measurement_dec"]=str(row['measurement_dec'])
        MagRec["measurement_inc"]=str(row['measurement_inc'])
        MagRec['magic_method_codes']=meas_type
        MagRecs.append(MagRec.copy())
    pmag.magic_write(samp_file,SampOuts,'er_samples')
    print("sample orientations put in ",samp_file)
    MagOuts=pmag.measurements_methods(MagRecs,noave)
    pmag.magic_write(meas_file,MagOuts,'magic_measurements')
    print("results put in ",meas_file)
    print("exit!")
    return True, meas_file


Instruction:
Can you implement a function in Python 3 that
names extract_methods.py DESCRIPTION reads in a magic table and creates a file with method codes SYNTAX extract_methods.py [command line options] OPTIONS -h: prints the help message and quits. -f FILE: specify magic format input file, default is magic_measurements.txt -F FILE: specify method code output file, default is magic_methods.txt

Response:
def main():
    """
    NAME
        extract_methods.py

    DESCRIPTION
        reads in a magic table and creates a file with method codes

    SYNTAX
        extract_methods.py [command line options]

    OPTIONS
        -h: prints the help message and quits.
        -f FILE: specify magic format input file, default is magic_measurements.txt
        -F FILE: specify method code output file, default is magic_methods.txt
    """
    citation='This study'
    args=sys.argv
    outfile='magic_methods.txt'
    infile='magic_measurements.txt'
#
# get command line arguments
#
    dir_path='.'
    if '-WD' in args:
        ind=args.index("-WD")
        dir_path=args[ind+1]
    if "-h" in args:
        print(main.__doc__)
        sys.exit()
    if '-F' in args:
        ind=args.index("-F")
        outfile=args[ind+1]
    if '-f' in args:
        ind=args.index("-f")
        infile=args[ind+1]
    infile=dir_path+'/'+infile
    outfile=dir_path+'/'+outfile
    data,file_type=pmag.magic_read(infile)
    MethRecs=[]
    methods=[]
    for rec in data:
        meths=rec['magic_method_codes'].split(":")
        for meth in meths:
            if meth not in methods:
                MethRec={}
                methods.append(meth)
                MethRec['magic_method_code']=meth
                MethRecs.append(MethRec)
    pmag.magic_write(outfile,MethRecs,'magic_methods')


Instruction:
Write a Python 3 script to
name get_fisher_parameters is the main function of the gofish module. It is the main function of the gofish module. It is used to calculate the fisher parameters from dec inc data.

Response:
def main():
    """
    NAME
       gofish.py

    DESCRIPTION
       calculates fisher parameters from dec inc data

    INPUT FORMAT
       takes dec/inc as first two columns in space delimited file

    SYNTAX
       gofish.py [options]  [< filename]

    OPTIONS
        -h prints help message and quits
        -i for interactive filename entry
        -f FILE, specify input file
        -F FILE, specifies output file name
        < filename for reading from standard input

    OUTPUT
       mean dec, mean inc, N, R, k, a95, csd

    """
    if '-h' in sys.argv: # check if help is needed
        print(main.__doc__)
        sys.exit() # graceful quit
    if '-i' in sys.argv: # ask for filename
        file=input("Enter file name with dec, inc data: ")
        f=open(file,'r')
        data=f.readlines()
    elif '-f' in sys.argv:
        dat=[]
        ind=sys.argv.index('-f')
        file=sys.argv[ind+1]
        f=open(file,'r')
        data=f.readlines()
    else:
        data = sys.stdin.readlines()  # read from standard input
    ofile = ""
    if '-F' in sys.argv:
        ind = sys.argv.index('-F')
        ofile= sys.argv[ind+1]
        out = open(ofile, 'w + a')
    DIs= [] # set up list for dec inc data
    for line in data:   # read in the data from standard input
        if '\t' in line:
            rec=line.split('\t') # split each line on space to get records
        else:
            rec=line.split() # split each line on space to get records
        DIs.append((float(rec[0]),float(rec[1])))
#
    fpars=pmag.fisher_mean(DIs)
    outstring='%7.1f %7.1f    %i %10.4f %8.1f %7.1f %7.1f'%(fpars['dec'],fpars['inc'],fpars['n'],fpars['r'],fpars['k'],fpars['alpha95'], fpars['csd'])
    if ofile == "":
        print(outstring)
    else:
        out.write(outstring+'\n')


Instruction:
Implement a Python 3 function for
naming trmaq_magic.py DESCTIPTION does non-linear trm acquisisiton correction SYNTAX trmaq_magic.py [-h][-i][command line options] OPTIONS -h prints help message and quits -i allows interactive setting of file names -f MFILE, sets magic_measurements input file -ft TSPEC, sets thellier_specimens input file -F OUT, sets output for non-linear TRM acquisition corrected data -sav save figures and quit -fmt [png, svg, pdf] -DM [2, 3] MagIC data model, default 3 DEFAULTS MFILE: trmaq_measurements.txt TSPEC: thellier_specimens.txt OUT: NLT_specimens.txt

Response:
def main():
    """
    NAME
        trmaq_magic.py

    DESCTIPTION
        does non-linear trm acquisisiton correction

    SYNTAX
        trmaq_magic.py [-h][-i][command line options]

    OPTIONS
        -h prints help message and quits
        -i allows interactive setting of file names
        -f MFILE, sets magic_measurements input file
        -ft TSPEC, sets thellier_specimens input file
        -F OUT, sets output for non-linear TRM acquisition corrected data
        -sav save figures and quit
        -fmt [png, svg, pdf]
        -DM [2, 3] MagIC data model, default 3

    DEFAULTS
        MFILE: trmaq_measurements.txt
        TSPEC: thellier_specimens.txt
        OUT: NLT_specimens.txt
    """
    meas_file = 'trmaq_measurements.txt'
    tspec = "thellier_specimens.txt"
    output = 'NLT_specimens.txt'
    data_model_num = int(float(pmag.get_named_arg("-DM", 3)))
    if '-h' in sys.argv:
        print(main.__doc__)
        sys.exit()
    if '-i' in sys.argv:
        meas_file = input(
            "Input magic_measurements file name? [trmaq_measurements.txt] ")
        if meas_file == "":
            meas_file = "trmaq_measurements.txt"
        tspec = input(
            " thellier_specimens file name? [thellier_specimens.txt] ")
        if tspec == "":
            tspec = "thellier_specimens.txt"
        output = input(
            "File for non-linear TRM adjusted specimen data: [NLTspecimens.txt] ")
        if output == "":
            output = "NLT_specimens.txt"
    if '-f' in sys.argv:
        ind = sys.argv.index('-f')
        meas_file = sys.argv[ind+1]
    if '-ft' in sys.argv:
        ind = sys.argv.index('-ft')
        tspec = sys.argv[ind+1]
    if '-F' in sys.argv:
        ind = sys.argv.index('-F')
        output = sys.argv[ind+1]
    if '-sav' in sys.argv:
        save_plots = True
    else:
        save_plots = False
    fmt = pmag.get_named_arg("-fmt", "svg")
    #
    PLT = {'aq': 1}
    if not save_plots:
        pmagplotlib.plot_init(PLT['aq'], 5, 5)
    #
    # get name of file from command line
    #
    comment = ""
    #
    #
    meas_data, file_type = pmag.magic_read(meas_file)
    if 'measurements' not in file_type:
        print(file_type, "This is not a valid measurements file ")
        sys.exit()

    if data_model_num == 2:
        spec_col = "er_specimen_name"
        lab_field_dc_col = "specimen_lab_field_dc"
        int_col = "specimen_int"
        meth_col = "magic_method_codes"
        treat_dc_col = "treatment_dc_field"
        magn_moment_col = "measurement_magn_moment"
        experiment_col = "magic_experiment_name"
        outfile_type = "pmag_specimens"
    else:
        spec_col = "specimen"
        lab_field_dc_col = "int_treat_dc_field"
        int_col = "int_abs"
        meth_col = "method_codes"
        treat_dc_col = "treat_dc_field"
        magn_moment_col = "magn_moment"
        experiment_col = "experiment"
        outfile_type = "specimens"

    sids = pmag.get_specs(meas_data)
    specimen = 0
    #
    # read in thellier_specimen data
    #
    nrm, file_type = pmag.magic_read(tspec)
    PmagSpecRecs= []
    while specimen < len(sids):
        #
        # find corresoponding paleointensity data for this specimen
        #
        s = sids[specimen]
        blab, best = "", ""
        for nrec in nrm:   # pick out the Banc data for this spec
            if nrec[spec_col] == s:
                try:
                    blab = float(nrec[lab_field_dc_col])
                except ValueError:
                    continue
                best = float(nrec[int_col])
                TrmRec = nrec
                break
        if blab == "":
            print("skipping ", s, " : no best ")
            specimen += 1
        else:
            print(sids[specimen], specimen+1, 'of ',
                  len(sids), 'Best = ', best*1e6)
            MeasRecs = []
    #
    # find the data from the meas_data file for this specimen
    #
            for rec in meas_data:
                if rec[spec_col] == s:
                    meths = rec[meth_col].split(":")
                    methcodes = []
                    for meth in meths:
                        methcodes.append(meth.strip())
                    if "LP-TRM" in methcodes:
                        MeasRecs.append(rec)
            if len(MeasRecs) < 2:
                specimen += 1
                print('skipping specimen -  no trm acquisition data ', s)
    #
    #  collect info for the PmagSpecRec dictionary
    #
            else:
                TRMs, Bs = [], []
                for rec in MeasRecs:
                    Bs.append(float(rec[treat_dc_col]))
                    TRMs.append(float(rec[magn_moment_col]))
                # calculate best fit parameters through TRM acquisition data, and get new banc
                NLpars = nlt.NLtrm(Bs, TRMs, best, blab, 0)
    #
                Mp, Bp = [], []
                for k in range(int(max(Bs)*1e6)):
                    Bp.append(float(k)*1e-6)
                    # predicted NRM for this field
                    npred = nlt.TRM(Bp[-1], NLpars['xopt']
                                    [0], NLpars['xopt'][1])
                    Mp.append(npred)
                pmagplotlib.plot_trm(
                    PLT['aq'], Bs, TRMs, Bp, Mp, NLpars, rec[experiment_col])
                if not save_plots:
                    pmagplotlib.draw_figs(PLT)
                print('Banc= ', float(NLpars['banc'])*1e6)
                trmTC = {}
                for key in list(TrmRec.keys()):
                    # copy of info from thellier_specimens record
                    trmTC[key] = TrmRec[key]
                trmTC[int_col] = '%8.3e' % (NLpars['banc'])
                trmTC[meth_col] = TrmRec[meth_col]+":DA-NL"
                PmagSpecRecs.append(trmTC)
                if not save_plots:
                    ans = input("Return for next specimen, s[a]ve plot  ")
                    if ans == 'a':
                        Name = {'aq': rec[spec_col]+'_TRM.{}'.format(fmt)}
                        pmagplotlib.save_plots(PLT, Name)
                else:
                    Name = {'aq': rec[spec_col]+'_TRM.{}'.format(fmt)}
                    pmagplotlib.save_plots(PLT, Name)

                specimen += 1

    pmag.magic_write(output, PmagSpecRecs, outfile_type)


Instruction:
Write a Python 3 script to
name huji_sample_magic.py DESCRIPTION takes tab delimited Hebrew University sample file and converts to MagIC formatted tables SYNTAX huji_sample_magic.py [command line options] OPTIONS -f FILE: specify input file -Fsa FILE: specify sample output file, default is: samples.txt -Fsi FILE: specify site output file, default is: sites.txt -Iso: import sample orientation info - default is to set sample_az/dip to 0,0 -ncn NCON: specify naming convention: default is #1 below -mcd: specify sampling method codes as a colon delimited string: [default is: FS-FD:SO-POM:SO-SUN] FS-FD field sampling done with a drill FS-H field sampling done with hand samples FS-LOC-GPS field location done with GPS FS-LOC-MAP field location done with map SO-POM a Pomeroy orientation device was used SO-ASC an ASC orientation device was used SO-MAG orientation with magnetic compass -loc: location name, default="unknown" -DM: data model number (MagIC 2 or 3, default 3) INPUT FORMAT Input files must be tab delimited: Samp Az Dip Dip_dir Dip Orientation convention: Lab arrow azimuth = mag_azimuth; Lab arrow dip = 90-field_dip e.g. field_dip is degrees from horizontal of drill direction Magnetic declination convention: Az is already corrected in file Sample naming convention: [1] XXXXY: where XXXX is an arbitrary length site designation and Y is the single character sample designation. e.g., TG001a is the first sample from site TG001. [default] [2] XXXX-YY: YY sample from site XXXX (XXX, YY of arbitary length) [3] XXXX.YY: YY sample from site XXXX (XXX, YY of arbitary length) [4-Z] XXXX[YYY]: YYY is sample designation with Z characters from site XXX [5] site name = sample name [6] site name entered in site_name column in the orient.txt format input file -- NOT CURRENTLY SUPPORTED [7-Z] [XXX]YYY: XXX is site designation with Z characters from samples XXXYYY NB: all others you will have to either customize your self or e-mail ltauxe@ucsd.edu for help. OUTPUT output saved in samples will overwrite any existing files

Response:
def main():
    """
    NAME
        huji_sample_magic.py

    DESCRIPTION
        takes tab delimited Hebrew University sample file and converts to MagIC formatted tables

    SYNTAX
        huji_sample_magic.py [command line options]

    OPTIONS
        -f FILE: specify input file
        -Fsa FILE: specify sample output file, default is: samples.txt
        -Fsi FILE: specify site output file, default is: sites.txt
        -Iso:  import sample orientation info - default is to set sample_az/dip to 0,0
        -ncn NCON:  specify naming convention: default is #1 below
        -mcd: specify sampling method codes as a colon delimited string:  [default is: FS-FD:SO-POM:SO-SUN]
             FS-FD field sampling done with a drill
             FS-H field sampling done with hand samples
             FS-LOC-GPS  field location done with GPS
             FS-LOC-MAP  field location done with map
             SO-POM   a Pomeroy orientation device was used
             SO-ASC   an ASC orientation device was used
             SO-MAG   orientation with magnetic compass
        -loc: location name, default="unknown"
        -DM: data model number (MagIC 2 or 3, default 3)

    INPUT FORMAT
        Input files must be tab delimited:
            Samp  Az Dip Dip_dir Dip
        Orientation convention:
             Lab arrow azimuth = mag_azimuth; Lab arrow dip = 90-field_dip
                e.g. field_dip is degrees from horizontal of drill direction

         Magnetic declination convention:
             Az is already corrected in file

         Sample naming convention:
            [1] XXXXY: where XXXX is an arbitrary length site designation and Y
                is the single character sample designation.  e.g., TG001a is the
                first sample from site TG001.    [default]
            [2] XXXX-YY: YY sample from site XXXX (XXX, YY of arbitary length)
            [3] XXXX.YY: YY sample from site XXXX (XXX, YY of arbitary length)
            [4-Z] XXXX[YYY]:  YYY is sample designation with Z characters from site XXX
            [5] site name = sample name
            [6] site name entered in site_name column in the orient.txt format input file  -- NOT CURRENTLY SUPPORTED
            [7-Z] [XXX]YYY:  XXX is site designation with Z characters from samples  XXXYYY
            NB: all others you will have to either customize your
                self or e-mail ltauxe@ucsd.edu for help.

    OUTPUT
            output saved in samples  will overwrite any existing files
    """
    args = sys.argv
    if "-h" in args:
        print(main.__doc__)
        sys.exit()
    #
    # initialize variables
    Z = 1
    # get arguments from the command line
    orient_file = pmag.get_named_arg("-f", reqd=True)
    data_model_num = int(float(pmag.get_named_arg("-DM", 3)))
    if data_model_num == 2:
        samp_file = pmag.get_named_arg("-Fsa", "er_samples.txt")
        site_file = pmag.get_named_arg("-Fsi", "er_sites.txt")
    else:
        samp_file = pmag.get_named_arg("-Fsa", "samples.txt")
        site_file = pmag.get_named_arg("-Fsi", "sites.txt")
    samp_con = pmag.get_named_arg("-ncn", "1")
    if "4" in samp_con:
        if "-" not in samp_con:
            print("option [4] must be in form 3-Z where Z is an integer")
            sys.exit()
        else:
            Z = samp_con.split("-")[1]
            #samp_con = "4"
        print(samp_con)#, Z)
    meths = pmag.get_named_arg("-mcd", 'FS-FD:SO-POM:SO-SUN')
    location_name = pmag.get_named_arg("-loc", "unknown")
    if "-Iso" in args:
        ignore = 0
    else:
        ignore = 1

    convert.huji_sample(orient_file, meths, location_name, samp_con, ignore)


Instruction:
Write a Python 3 script to
name vector_mean.py DESCRIPTION calculates vector mean of vector data INPUT FORMAT takes dec, inc, int from an input file SYNTAX vector_mean.py [command line options] [< filename] OPTIONS -h prints help message and quits -f FILE, specify input file -F FILE, specify output file < filename for reading from standard input OUTPUT mean dec, mean inc, R, N

Response:
def main():
    """
    NAME
       vector_mean.py

    DESCRIPTION
       calculates vector mean of vector data

    INPUT FORMAT
       takes dec, inc, int from an input file

    SYNTAX
       vector_mean.py [command line options]  [< filename]

    OPTIONS
        -h prints help message and quits
        -f FILE, specify input file
        -F FILE, specify output file
        < filename for reading from standard input

    OUTPUT
       mean dec, mean inc, R, N

    """
    if '-h' in sys.argv: # check if help is needed
        print(main.__doc__)
        sys.exit() # graceful quit
    if '-f' in sys.argv:
        dat=[]
        ind=sys.argv.index('-f')
        file=sys.argv[ind+1]
    else:
        file = sys.stdin  # read from standard input
    ofile=""
    if '-F' in sys.argv:
        ind = sys.argv.index('-F')
        ofile= sys.argv[ind+1]
        out = open(ofile, 'w + a')
    DIIs=numpy.loadtxt(file,dtype=numpy.float) # read in the data
#
    vpars,R=pmag.vector_mean(DIIs)
    outstring='%7.1f %7.1f   %10.3e %i'%(vpars[0],vpars[1],R,len(DIIs))
    if ofile == "":
        print(outstring)
    else:
        out.write(outstring + "\n")


Instruction:
Make a summary of the following Python 3 code
def main():
    kappa, cutoff = 0, 180
    rev, anti, boot = 0, 0, 0
    spin,n,v,mm97 = 0,0,0,0
    if '-h' in sys.argv:
        print(main.__doc__)
        sys.exit()
    if '-f' in sys.argv:
        ind = sys.argv.index("-f")
        in_file = sys.argv[ind + 1]
        vgp_df=pd.read_csv(in_file,delim_whitespace=True,header=None)
    else:
        vgp_df=pd.read_csv(sys.stdin,delim_whitespace=True,header=None)
    if '-c' in sys.argv:
        ind = sys.argv.index('-c')
        cutoff = float(sys.argv[ind + 1])
    if '-k' in sys.argv:
        ind = sys.argv.index('-k')
        kappa = float(sys.argv[ind + 1])
    if '-n' in sys.argv:
        ind = sys.argv.index('-n')
        n = int(sys.argv[ind + 1])
    if '-a' in sys.argv: anti = 1
    if '-r' in sys.argv: rev=1
    if '-b' in sys.argv: boot = 1
    if '-v' in sys.argv: v = 1
    if '-p' in sys.argv: spin = 1
    if '-mm97' in sys.argv: mm97=1
    #
    #
    if len(list(vgp_df.columns))==2:
        vgp_df.columns=['vgp_lon','vgp_lat']
        vgp_df['dir_k'],vgp_df['dir_n_samples'],vgp_df['lat']=0,0,0
    else:
        vgp_df.columns=['vgp_lon','vgp_lat','dir_k','dir_n_samples','lat']
    N,S_B,low,high,cutoff=pmag.scalc_vgp_df(vgp_df,anti=anti,rev=rev,cutoff=cutoff,kappa=kappa,n=n,spin=spin,v=v,boot=boot,mm97=mm97)
    if high!=0:
        print(N, '%7.1f %7.1f  %7.1f %7.1f ' % (S_B, low, high, cutoff))
    else:
        print(N, '%7.1f  %7.1f ' % (S_B, cutoff))

Response: NAME calculate_scalc_scalc_scalc. py DESCRIPTION calculate_scalc_scalc_scalc_scalc_scalc_scalc_scalc_scalc_scalc_scalc_scalc_scalc_scalc_scalc_scalc_scalc_scalc_scalc_scalc_scalc s


Instruction:
Implement a function in Python 3 to
name irmaq_magic. py AttributeNames is the entry point for the IRM acquisition curves.

Response:
def main():
    """
    NAME
        irmaq_magic.py

    DESCRIPTION
       plots IRM acquisition curves from measurements file

    SYNTAX
        irmaq_magic [command line options]

    INPUT
       takes magic formatted magic_measurements.txt files

    OPTIONS
        -h prints help message and quits
        -f FILE: specify input file, default is: magic_measurements.txt/measurements.txt
        -obj OBJ: specify  object  [loc, sit, sam, spc] for plot, default is by location
        -N ; do not normalize by last point - use original units
        -fmt [png,jpg,eps,pdf] set plot file format [default is svg]
        -sav save plot[s] and quit
        -DM MagIC data model number, default is 3
    NOTE
        loc: location (study); sit: site; sam: sample; spc: specimen
    """
    FIG = {}  # plot dictionary
    FIG['exp'] = 1  # exp is figure 1
    dir_path = './'
    plot, fmt = 0, 'svg'
    units = 'T',
    XLP = []
    norm = 1
    LP = "LP-IRM"
    if len(sys.argv) > 1:
        if '-h' in sys.argv:
            print(main.__doc__)
            sys.exit()
        data_model = int(pmag.get_named_arg("-DM", 3))
        if '-N' in sys.argv:
            norm = 0
        if '-sav' in sys.argv:
            plot = 1
        if '-fmt' in sys.argv:
            ind = sys.argv.index("-fmt")
            fmt = sys.argv[ind + 1]
        if data_model == 3:
            in_file = pmag.get_named_arg("-f", 'measurements.txt')
        else:
            in_file = pmag.get_named_arg("-f", 'magic_measurements.txt')
        if '-WD' in sys.argv:
            ind = sys.argv.index('-WD')
            dir_path = sys.argv[ind + 1]
        dir_path = os.path.realpath(dir_path)
        in_file = pmag.resolve_file_name(in_file, dir_path)
        if '-WD' not in sys.argv:
            dir_path = os.path.split(in_file)[0]
        plot_by = pmag.get_named_arg("-obj", "loc")
        if data_model == 3:
            plot_key = 'location'
            if plot_by == 'sit':
                plot_key = 'site'
            if plot_by == 'sam':
                plot_key = 'sample'
            if plot_by == 'spc':
                plot_key = 'specimen'
        else:
            plot_key = 'er_location_name'
            if plot_by == 'sit':
                plot_key = 'er_site_name'
            if plot_by == 'sam':
                plot_key = 'er_sample_name'
            if plot_by == 'spc':
                plot_key = 'er_specimen_name'

    # set defaults and get more information if needed
    if data_model == 3:
        dmag_key = 'treat_dc_field'
    else:
        dmag_key = 'treatment_dc_field'
    #
    if data_model == 3 and plot_key != 'specimen':
        # gonna need to read in more files
        print('-W- You are trying to plot measurements by {}'.format(plot_key))
        print('    By default, this information is not available in your measurement file.')
        print('    Trying to acquire this information from {}'.format(dir_path))
        con = cb.Contribution(dir_path)
        meas_df = con.propagate_location_to_measurements()
        if meas_df is None:
            print('-W- No data found in {}'.format(dir_path))
            return
        if plot_key not in meas_df.columns:
            print('-W- Could not find required data.')
            print('    Try a different plot key.')
            return
        else:
            print('-I- Found {} information, continuing with plotting'.format(plot_key))
        # need to take the data directly from the contribution here, to keep
        # location/site/sample columns in the measurements table
        data = con.tables['measurements'].convert_to_pmag_data_list()
        file_type = "measurements"
    else:
        data, file_type = pmag.magic_read(in_file)
    # read in data
    sids = pmag.get_specs(data)
    pmagplotlib.plot_init(FIG['exp'], 6, 6)
    #
    #
    # find desired intensity data
    #
    # get plotlist
    #
    plotlist = []
    if data_model == 3:
        intlist = ['magn_moment', 'magn_volume', 'magn_mass', 'magnitude']
    else:
        intlist = ['measurement_magnitude', 'measurement_magn_moment',
                    'measurement_magn_volume', 'measurement_magn_mass']
    IntMeths = []
    # get all the records with this lab protocol
    #print('data', len(data))
    #print('data[0]', data[0])
    if data_model == 3:
        data = pmag.get_dictitem(data, 'method_codes', LP, 'has')
    else:
        data = pmag.get_dictitem(data, 'magic_method_codes', LP, 'has')
    Ints = {}
    NoInts, int_key = 1, ""
    for key in intlist:
        # get all non-blank data for intensity type
        Ints[key] = pmag.get_dictitem(data, key, '', 'F')
        if len(Ints[key]) > 0:
            NoInts = 0
            if int_key == "":
                int_key = key
    if NoInts == 1:
        print('No intensity information found')
        sys.exit()
    for rec in Ints[int_key]:
        if rec[plot_key] not in plotlist:
            plotlist.append(rec[plot_key])
    plotlist.sort()
    for plt in plotlist:
        print(plt)
        INTblock = []
        # get data with right intensity info whose plot_key matches plot
        data = pmag.get_dictitem(Ints[int_key], plot_key, plt, 'T')
        # get a list of specimens with appropriate data
        sids = pmag.get_specs(data)
        if len(sids) > 0:
            title = data[0][plot_key]
        for s in sids:
            INTblock = []
            # get data for each specimen
            if data_model == 3:
                sdata = pmag.get_dictitem(data, 'specimen', s, 'T')
            else:
                sdata = pmag.get_dictitem(data, 'er_specimen_name', s, 'T')
            for rec in sdata:
                INTblock.append([float(rec[dmag_key]), 0, 0,
                                 float(rec[int_key]), 1, 'g'])
            pmagplotlib.plot_mag(FIG['exp'], INTblock, title, 0, units, norm)
        files = {}
        for key in list(FIG.keys()):
            files[key] = title + '_' + LP + '.' + fmt
        if plot == 0:
            pmagplotlib.draw_figs(FIG)
            ans = input(" S[a]ve to save plot, [q]uit,  Return to continue:  ")
            if ans == 'q':
                sys.exit()
            if ans == "a":
                pmagplotlib.save_plots(FIG, files)
            if plt != plotlist[-1]: # if it isn't the last plot, init the next one
                pmagplotlib.plot_init(FIG['exp'], 6, 6)
        else:
            pmagplotlib.save_plots(FIG, files)
        pmagplotlib.clearFIG(FIG['exp'])


Instruction:
Create a Python 3 function to
return a slice covering all combinations with coordinate i along a given axis.

Response:
def all_but_axis(i, axis, num_axes):
    """
    Return a slice covering all combinations with coordinate i along
    axis.  (Effectively the hyperplane perpendicular to axis at i.)
    """
    the_slice  = ()
    for j in range(num_axes):
        if j == axis:
            the_slice = the_slice + (i,)
        else:
            the_slice = the_slice + (slice(None),)
    return the_slice


Instruction:
Can you tell what is the following Python 3 function doing
def array_map(f, ar):
    "Apply an ordinary function to all values in an array."
    flat_ar = ravel(ar)
    out = zeros(len(flat_ar), flat_ar.typecode())
    for i in range(len(flat_ar)):
        out[i] = f(flat_ar[i])
    out.shape = ar.shape
    return out

Response: Apply an ordinary function to all values in an array.


Instruction:
Given the following Python 3 function, write the documentation
def on_plot_select(self,event):
        if not self.xdata or not self.ydata: return
        pos=event.GetPosition()
        width, height = self.canvas.get_width_height()
        pos[1] = height - pos[1]
        xpick_data,ypick_data = pos
        xdata_org = self.xdata
        ydata_org = self.ydata
        data_corrected = self.map.transData.transform(vstack([xdata_org,ydata_org]).T)
        xdata,ydata = data_corrected.T
        xdata = list(map(float,xdata))
        ydata = list(map(float,ydata))
        e = 4e0

        index = None
        for i,(x,y) in enumerate(zip(xdata,ydata)):
            if 0 < sqrt((x-xpick_data)**2. + (y-ypick_data)**2.) < e:
                index = i
                break
        if index==None: print("Couldn't find point %.1f,%.1f"%(xpick_data,ypick_data))

        self.change_selected(index)

Response: Select data point if cursor is in range of a data point


Instruction:
Can you tell what is the following Python 3 function doing
def on_change_plot_cursor(self,event):
        if not self.xdata or not self.ydata: return
        pos=event.GetPosition()
        width, height = self.canvas.get_width_height()
        pos[1] = height - pos[1]
        xpick_data,ypick_data = pos
        xdata_org = self.xdata
        ydata_org = self.ydata
        data_corrected = self.map.transData.transform(vstack([xdata_org,ydata_org]).T)
        xdata,ydata = data_corrected.T
        xdata = list(map(float,xdata))
        ydata = list(map(float,ydata))
        e = 4e0

        if self.plot_setting == "Zoom":
            self.canvas.SetCursor(wx.Cursor(wx.CURSOR_CROSS))
        else:
            self.canvas.SetCursor(wx.Cursor(wx.CURSOR_ARROW))
        for i,(x,y) in enumerate(zip(xdata,ydata)):
            if 0 < sqrt((x-xpick_data)**2. + (y-ypick_data)**2.) < e:
                self.canvas.SetCursor(wx.Cursor(wx.CURSOR_HAND))
                break
        event.Skip()

Response: change the shape of the cursor


Instruction:
Write a Python 3 function that can
return a tuple with first entry being a boolean which specifies if the user entered all values and second entry being a dictionary of input names to parsed values.

Response:
def get_values(self):
        """
        Applies parsing functions to each input as specified in init before returning a tuple with first entry being a boolean which specifies if the user entered all values and a second entry which is a dictionary of input names to parsed values.
        """
        return_dict = {}
        for i,ctrl in enumerate(self.list_ctrls):
            if hasattr(self.parse_funcs,'__getitem__') and len(self.parse_funcs)>i and hasattr(self.parse_funcs[i],'__call__'):
                try: return_dict[self.inputs[i]] = self.parse_funcs[i](ctrl.GetValue())
                except: return_dict[self.inputs[i]] = ctrl.GetValue()
            else:
                return_dict[self.inputs[i]] = ctrl.GetValue()
        return ('' not in list(return_dict.values()), return_dict)


Instruction:
Write a Python 3 script for
naming upload_magic.py DESCRIPTION This program will prepare your MagIC text files for uploading to the MagIC database it will check for all the MagIC text files and skip the missing ones SYNTAX upload_magic.py INPUT MagIC txt files OPTIONS -h prints help message and quits -all include all the measurement data, default is only those used in interpretations -DM specify which MagIC data model number to use (2 or 3). Default is 3. OUTPUT upload file: file for uploading to MagIC database

Response:
def main():
    """
    NAME
        upload_magic.py

    DESCRIPTION
        This program will prepare your MagIC text files  for uploading to the MagIC database
        it will check for all the MagIC text files and skip the missing ones

    SYNTAX
        upload_magic.py

    INPUT
        MagIC txt files

    OPTIONS
        -h prints help message and quits
        -all include all the measurement data, default is only those used in interpretations
        -DM specify which MagIC data model number to use (2 or 3).  Default is 3.

    OUTPUT
        upload file:  file for uploading to MagIC database
    """
    if '-h' in sys.argv:
        print(main.__doc__)
        sys.exit()
    else:
        data_model_num = pmag.get_named_arg("-DM", 3)
        dataframe = extractor.command_line_dataframe([['cat', False, 0], ['F', False, ''], ['f', False, '']])
        checked_args = extractor.extract_and_check_args(sys.argv, dataframe)
        dir_path, concat = extractor.get_vars(['WD', 'cat'], checked_args)
        data_model_num = int(float(data_model_num))
        if data_model_num == 2:
            ipmag.upload_magic2(concat, dir_path)
        else:
            ipmag.upload_magic(concat, dir_path)


Instruction:
How would you explain what the following Python 3 function does
def d2_func(x, y, z):
    '''
    #=================================================
    /poly fit for every SF grid data
    #=================================================
    '''
    X, Y = np.meshgrid(x, y, copy=False)
    X = X.flatten()
    Y = Y.flatten()
    A = np.array([np.ones(len(X)), X, X**2, Y, Y**2, X*Y]).T
    Z = np.array(z)
    B = Z.flatten()
    # print(A.shape,B.shape)
    coeff, r, rank, s = np.linalg.lstsq(A, B, rcond=None)
    return -coeff[5]

Response: Compute the D2 function for the given data.


Instruction:
Here you have a function in Python 3, explain what it does
def grid_list(data):
    '''
    #=================================================
    /process the grid data
    /convert to list data for poly fitting
    #=================================================
    '''
    a = []
    b = []
    M = []
    for i in data:
        a.append(i[0])  # np.array([i[1] for i in data], dtype=np.float64)
        b.append(i[1])  # np.array([i[0] for i in data], dtype=np.float64)
        M.append(i[2])  # np.array([i[2] for i in data], dtype=np.float64)
    a = np.array(a, dtype=np.float64).tolist()
    b = np.array(b, dtype=np.float64).tolist()
    M = np.array(M, dtype=np.float64).tolist()
    a = list(set(a))
    b = list(set(b))
    return a, b, M

Response: Convert a list of data into a list of lists


Instruction:
Write a Python 3 script for
fitting the FORC distribution for a given set of values

Response:
def fit(self, SF, x_range, y_range, matrix_z):
        '''
        #=================================================
        /the main fitting process
        /xx,yy,zz = Hb,Ha,p
        /p is the FORC distribution
        /m0,n0 is the index of values on Ha = Hb
        /then loop m0 and n0
        /based on smooth factor(SF)
        /select data grid from the matrix_z for curve fitting
        #=================================================
        '''
        xx, yy, zz = [], [], []
        m0, n0 = [], []
        for m, n in itertools.product(np.arange(0, len(x_range), step=SF), np.arange(0, len(y_range), step=SF)):
            if x_range[m] > y_range[n]:  # Ha nearly equal Hb
                m0.append(m)
                n0.append(n)

        aa, bb, cc = [], [], []
        for m, n in zip(m0, n0):
            s = 0
            try:
                grid_data = []
                a_ = x_range[m+s]
                b_ = y_range[n-s]
                for i, j in itertools.product(np.arange(3*SF+1), np.arange(3*SF+1)):
                    try:
                        grid_data.append(
                            [x_range[m+s+i], y_range[n-s-j], matrix_z.item(n-s-j, m+s+i)])
                    except:
                        try:
                            for i, j in itertools.product(np.arange(3), np.arange(3)):
                                grid_data.append(
                                    [x_range[m+i], y_range[n-j], matrix_z.item(n-j, m+i)])
                        except:
                            pass
                # print(grid_data)
                '''
                #=================================================
                /when SF = n
                /data grid as (2*n+1)x(2*n+1)
                /grid_list: convert grid to list
                /every grid produce on FORC distritution p
                /the poly fitting use d2_func
                #=================================================
                '''
                x, y, z = grid_list(grid_data)
                try:
                    p = d2_func(x, y, z)
                    # print(p)
                    xx.append((a_-b_)/2)
                    yy.append((a_+b_)/2)
                    zz.append(p)
                except Exception as e:
                    # print(e)
                    pass
            except:
                pass

        '''
        #=================================================
        /the data will be save as pandas dataframe
        /all the data with nan values will be delete be dropna()
        #=================================================
        '''
        # print(zz)
        df = pd.DataFrame({'x': xx, 'y': yy, 'z': zz})
        #df = df.replace(0,np.nan)
        df = df.dropna()
        '''
        #=================================================
        /due to the space near Bc = zero
        /the Bi values when Bc <0.003 will be mirrored to -Bc
        #=================================================
        '''
        df_negative = df[(df.x < 0.03)].copy()
        df_negative.x = df_negative.x*-1
        df = df.append(df_negative)
        df = df.drop_duplicates(['x', 'y'])
        df = df.sort_values('x')
        # plt.scatter(df.x,df.y,c=df.z)
        # plt.show()
        '''
        #=================================================
        /reset the Bc and Bi range by X,Y
        /use linear interpolate to obtain FORC distribution
        #=================================================
        '''
        xrange = [0, int((np.max(df.x)+0.05)*10)/10]
        yrange = [int((np.min(df.y)-0.05)*10)/10,
                  int((np.max(df.y)+0.05)*10)/10]
        X = np.linspace(xrange[0], xrange[1], 200)
        Y = np.linspace(yrange[0], yrange[1], 200)
        self.yi, self.xi = np.mgrid[yrange[0]:yrange[1]:200j, xrange[0]:xrange[1]:200j]

        #self.xi,self.yi = np.mgrid[0:0.2:400j,-0.15:0.15:400j]
        z = df.z/np.max(df.z)
        z = np.asarray(z.tolist())
        self.zi = griddata((df.x, df.y), z, (self.xi, self.yi), method='cubic')


Instruction:
How would you explain what the following Python 3 function does
def rawData(self, fileAdres=None):
        # skip skiprows
        skiprows = None
        skip_from = [b'Field', b'Moment']
        with open(fileAdres, 'rb') as fr:
            #f = fr.read()
            for i, line in enumerate(fr, 1):
                # print(line.split())
                if skip_from == line.split():
                    skiprows = i+2
                    break
                # else:
                #    print('file format wrong, cannot find the data row.')
        skiprows = 34 if skiprows == None else skiprows
        df = pd.read_csv(fileAdres, skiprows=skiprows, sep='\s+',
                         delimiter=',', names=['H', 'M'], skipfooter=1,
                         engine='python')

        H = df.H  # measured field
        M = df.M  # measured magnetic moment
        '''
        #=================================================
        /datainterval_H/_M
        /slice the measured data into pieces
        /for every measured FORC
        #=================================================
        '''
        dataInterval_H = []
        dataInterval_M = []
        # print(H)
        cretia = df.H.mean()  # edge of linear programing for selecting data
        H0 = df.H.max()  # the maximum field
        self.x, self.y, self.z = [[], [], []]
        for i in np.arange(1, len(H)):
            dataInterval_H.append(H[i])
            dataInterval_M.append(M[i])
            if abs(H[i]-H0) <= 0.001:  # when the filed reach the max, a new forc
                if len(dataInterval_H) >= 0 and len(dataInterval_H) <= 200:
                    # print(dataInterval_H)
                    Ha = dataInterval_H[0]
                    dataInterval_H.pop(-1)
                    dataInterval_M.pop(-1)
                    Hb = dataInterval_H[1:-1]
                    Hm = dataInterval_M[1:-1]
                    for t in np.arange(len(Hb)):
                        self.x.append(Hb[t])
                        self.y.append(Ha)
                        self.z.append(Hm[t])
                        # print(Ha)
                dataInterval_H = []
                dataInterval_M = []
        self.rawdf = df
        '''
        #=================================================
        transfer the data set to matrix as len(x)*len(y) with z value
        /mesh up the rawdata
        /select the data area by X,Y ranges
        /obtain regular spaced data potins by np.linspace
        /use interplote to caculate the Hm values
        /loop Ha(Y),Hb(X)
        /fill every position with Hm, else with np.nan
        #=================================================
        '''
        self.z = self.z/np.max(self.z)
        # print(int(np.min(self.x)*100)/100,np.max(self.x))
        xrange = [int((np.min(self.x)-0.1)*10)/10,
                  int((np.max(self.x)+0.1)*10)/10]
        yrange = [int((np.min(self.y)-0.1)*10)/10,
                  int((np.max(self.y)+0.1)*10)/10]
        X = np.linspace(xrange[0], xrange[1], 200)
        Y = np.linspace(yrange[0], yrange[1], 200)
        yi, xi = np.mgrid[yrange[0]:yrange[1]:200j, xrange[0]:xrange[1]:200j]

        #X = np.linspace(-0.2,0.3,200)
        #Y = np.linspace(-0.2,0.3,200)
        #xi,yi = np.mgrid[-0.2:0.3:200j,-0.2:0.3:200j]

        zi = griddata((self.x, self.y), self.z, (xi, yi),
                      method='linear')  # !!! must linear
        self.matrix_z = zi
        self.x_range = X
        self.y_range = Y

Response: read the raw data from a file and store it in the self. x y z.


Instruction:
Can you generate a brief explanation for the following Python 3 code
def get_data_model():
    #print("-I- getting data model, please be patient!!!!")
    url = 'http://earthref.org/services/MagIC-data-model.txt'
    offline = True # always get cached data model, as 2.5 is now static
    #try:
    #    data = urllib2.urlopen(url)
    #except urllib2.URLError:
    #    print '-W- Unable to fetch data model online\nTrying to use cached data model instead'
    #    offline = True
    #except httplib.BadStatusLine:
    #    print '-W- Website: {} not responding\nTrying to use cached data model instead'.format(url)
    #    offline = True
    if offline:
        data = get_data_offline()
    data_model, file_type = pmag.magic_read(None, data)
    if file_type in ('bad file', 'empty_file'):
        print('-W- Unable to read online data model.\nTrying to use cached data model instead')
        data = get_data_offline()
        data_model, file_type = pmag.magic_read(None, data)
    ref_dicts = [d for d in data_model if d['column_nmb'] != '>>>>>>>>>>']
    file_types = [d['field_name'] for d in data_model if d['column_nmb'] == 'tab delimited']
    file_types.insert(0, file_type)
    complete_ref = {}

    dictionary = {}
    n = 0
    for d in ref_dicts:
        if d['field_name'] in file_types:
            complete_ref[file_types[n]] = dictionary
            n += 1
            dictionary = {}
        else:
            dictionary[d['field_name_oracle']] = {'data_type': d['data_type'], 'data_status': d['data_status']}
    return complete_ref

Response: Get the data model document from the EarthRef site.


Instruction:
How would you explain what the following Python 3 function does
def read_upload(up_file, data_model=None):
    print("-I- Running validation for your upload file")

    ## Read file
    f = open(up_file)
    lines = f.readlines()
    f.close()
    data = split_lines(lines)
    data_dicts = get_dicts(data)
    ## initialize
    invalid_data = {}
    missing_data = {}
    non_numeric = {}
    bad_vocab = {}
    bad_coords = {}
    invalid_col_names = {}
    missing_file_type = False
    ## make sure you have the data model
    if not data_model:
        data_model = get_data_model()
    reqd_file_types = ['er_locations']
    provided_file_types = set()
    if not data_model:
        return False, None
    ## Iterate through data
    # each dictionary is one tab delimited line in a csv file
    for dictionary in data_dicts:
        for k, v in list(dictionary.items()):
            if k == "file_type": # meta data
                provided_file_types.add(v)
                continue
            file_type = dictionary['file_type']
            # need to deal with pmag_criteria type file, too
            item_type = file_type.split('_')[1][:-1]
            if item_type == 'criteria':
                item_name = dictionary.get('criteria_definition')
            elif item_type == 'result':
                item_name = dictionary.get('pmag_result_name', None)
            elif item_type in ('specimen', 'sample', 'site', 'location'):
                item_name = dictionary.get('er_' + item_type + '_name', None)
            elif item_type == 'age':
                # get the lowest level er_*_name column that is filled in
                for dtype in ('specimen', 'sample', 'site', 'location'):
                    item_name = dictionary.get('er_' + dtype + '_name', None)
                    if item_name:
                        break
            elif item_type == 'measurement':
                exp_name = dictionary.get('magic_experiment_name')
                meas_num = dictionary.get('measurement_number')
                item_name = exp_name + '_' + str(meas_num)
            else:
                item_name = None

            if file_type not in list(data_model.keys()):
                continue
            specific_data_model = data_model[file_type]

            ## Function for building problems list
            def add_to_invalid_data(item_name, item_type, invalid_data,
                                    validation, problem_type):
                """
                correctly create or add to the dictionary of invalid values
                """
                if item_name:
                    if item_type not in invalid_data:
                        invalid_data[item_type] = {}
                    if item_name not in invalid_data[item_type]:
                        invalid_data[item_type][item_name] = {}
                    if problem_type not in invalid_data[item_type][item_name]:
                        invalid_data[item_type][item_name][problem_type] = []
                    invalid_data[item_type][item_name][problem_type].append(validation)

            ## Validate for each problem type

            # check if column header is in the data model
            invalid_col_name = validate_for_recognized_column(k, v, specific_data_model)
            if invalid_col_name:
                if item_type not in list(invalid_col_names.keys()):
                    invalid_col_names[item_type] = set()
                invalid_col_names[item_type].add(invalid_col_name)
                # skip to next item, as additional validations won't work
                # (key is not in the data model)

                ## new style
                add_to_invalid_data(item_name, item_type, invalid_data,
                                    invalid_col_name, 'invalid_col')
                # skip to next item, as additional validations won't work
                # (key is not in the data model)
                continue

            # make a list of missing, required data
            missing_item = validate_for_presence(k, v, specific_data_model)
            #print 'k, v', k, v
            if missing_item:
                if item_type not in list(missing_data.keys()):
                    missing_data[item_type] = set()
                missing_data[item_type].add(missing_item)
                if item_name:
                    # don't double count if a site is missing its parent location
                    if item_type == 'age' and missing_item == 'er_location_name':
                        pass
                    # ignore er_synthetic_name (data model is incorrect here)
                    if missing_item == 'er_synthetic_name':
                        pass
                    else:
                        add_to_invalid_data(item_name, item_type, invalid_data,
                                            missing_item, 'missing_data')

            # vocabulary problems
            vocab_problem = validate_for_controlled_vocab(k, v, specific_data_model)
            if vocab_problem:
                if item_type not in list(bad_vocab.keys()):
                    bad_vocab[item_type] = set()
                bad_vocab[item_type].add(vocab_problem)
                add_to_invalid_data(item_name, item_type, invalid_data,
                                    vocab_problem, 'vocab_problem')

            # illegal coordinates
            coord_problem = validate_for_coordinates(k, v, specific_data_model)
            if coord_problem:
                if item_type not in list(bad_coords.keys()):
                    bad_coords[item_type] = set()
                bad_coords[item_type].add(coord_problem)
                add_to_invalid_data(item_name, item_type, invalid_data,
                                    coord_problem, 'coordinates')

            # make a list of data that should be numeric, but aren't
            number_fail = validate_for_numericality(k, v, specific_data_model)
            if number_fail:
                if item_type not in list(non_numeric.keys()):
                    non_numeric[item_type] = set()
                non_numeric[item_type].add(number_fail)
                add_to_invalid_data(item_name, item_type, invalid_data,
                                    number_fail, 'number_fail')

    ## Print out all issues

    for file_type, invalid_names in list(invalid_col_names.items()):
        print("-W- In your {} file, you are using the following unrecognized columns: {}".format(file_type, ', '.join(invalid_names)))

    for file_type, wrong_cols in list(non_numeric.items()):
        print("-W- In your {} file, you must provide only valid numbers, in the following columns: {}".format(file_type, ', '.join(wrong_cols)))

    for file_type, empty_cols in list(missing_data.items()):
        print("-W- In your {} file, you are missing data in the following required columns: {}".format(file_type, ', '.join(empty_cols)))

    for file_type in reqd_file_types:
        if file_type not in provided_file_types:
            print("-W- You have not provided a(n) {} type file, which is required data".format(file_type))
            missing_file_type = True

    for file_type, vocab_types in list(bad_vocab.items()):
        print("-W- In your {} file, you are using an unrecognized value for these controlled vocabularies: {}".format(file_type, ', '.join(vocab_types)))

    for file_type, coords in list(bad_coords.items()):
        print("-W- In your {} file, you are using an illegal value for these columns: {}.  (Latitude must be between -90 and +90)".format(file_type, ', '.join(coords)))

    if any((invalid_col_names, non_numeric, missing_data, missing_file_type, bad_vocab, bad_coords)):
        return False, invalid_data
    else:
        print("-I- validation was successful")
        return True, None

Response: Read a file and validate it


Instruction:
Create a Python 3 function for
splitting a MagIC upload format file into lists.

Response:
def split_lines(lines):
    """
    split a MagIC upload format file into lists.
    the lists are split by the '>>>' lines between file_types.
    """
    container = []
    new_list = []
    for line in lines:
        if '>>>' in line:
            container.append(new_list)
            new_list = []
        else:
            new_list.append(line)
    container.append(new_list)
    return container