diff --git a/EGRIP_eigenvalues.csv b/EGRIP_eigenvalues.csv new file mode 100644 index 0000000..9b56ad4 --- /dev/null +++ b/EGRIP_eigenvalues.csv @@ -0,0 +1,745 @@ +bag section Top_depth Bottom_depth Center_depth number_grains e1 e1_weighted e2 e2_weighted e3 e3_weighted +203 1 111.1 111.19 111.15 814 0.206199 0.177432 0.278516 0.282642 0.515285 0.539927 +203 2 111.19 111.28 111.24 403 0.212429 0.209158 0.256304 0.244297 0.531267 0.546545 +203 3 111.28 111.38 111.33 1320 0.210208 0.190255 0.284137 0.273131 0.505654 0.536613 +216 1 118.25 118.34 118.3 1418 0.223402 0.203068 0.260919 0.248231 0.515679 0.548701 +216 2 118.34 118.43 118.39 480 0.196948 0.186845 0.272547 0.261362 0.530505 0.551792 +216 3 118.43 118.53 118.48 532 0.235517 0.215122 0.27496 0.244015 0.489522 0.540863 +216 4 118.53 118.62 118.57 1196 0.220991 0.228057 0.298551 0.258792 0.480457 0.513151 +216 5 118.62 118.71 118.66 1293 0.213433 0.195048 0.290115 0.240998 0.496452 0.563954 +216 6 118.71 118.8 118.75 1554 0.21506 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135.39 135.35 1117 0.273126 0.244747 0.276702 0.25179 0.450173 0.503463 +252 1 138.05 138.14 138.1 757 0.221557 0.21008 0.319936 0.270233 0.458507 0.519687 +252 2 138.14 138.23 138.19 744 0.226082 0.201449 0.314993 0.290789 0.458925 0.507762 +252 3 138.23 138.33 138.28 904 0.236793 0.214366 0.318224 0.292914 0.444984 0.492721 +252 4 138.33 138.42 138.37 879 0.214792 0.185562 0.31013 0.27784 0.475078 0.536597 +252 5 138.42 138.51 138.46 816 0.185401 0.17681 0.261367 0.239009 0.553232 0.584181 +252 6 138.51 138.6 138.55 1192 0.204412 0.193175 0.291195 0.267459 0.504393 0.539365 +253 1 138.6 138.69 138.65 1074 0.229704 0.193219 0.311799 0.296144 0.458496 0.510637 +253 2 138.69 138.78 138.74 1019 0.22108 0.207614 0.319328 0.26149 0.459592 0.530896 +253 3 138.78 138.88 138.83 1058 0.228605 0.219243 0.298486 0.258541 0.472908 0.522215 +253 4 138.88 138.97 138.92 999 0.234234 0.219734 0.327662 0.284835 0.438104 0.495431 +253 5 138.97 139.06 139.01 1130 0.236527 0.205427 0.285478 0.242706 0.477995 0.551868 +266 1 145.75 145.84 145.8 1095 0.195126 0.167929 0.348699 0.302561 0.456174 0.52951 +266 2 145.84 145.93 145.89 1352 0.17583 0.164497 0.334559 0.301904 0.489611 0.5336 +266 3 145.93 146.03 145.98 1407 0.163434 0.146819 0.339905 0.318433 0.496661 0.534748 +266 4 146.03 146.12 146.07 1394 0.197618 0.177455 0.302386 0.313532 0.499996 0.509013 +266 5 146.12 146.21 146.16 1230 0.191453 0.166042 0.38427 0.352754 0.424277 0.481204 +266 6 146.21 146.3 146.25 1273 0.19732 0.192462 0.32782 0.2966 0.474859 0.510939 +293 1 160.6 160.69 160.65 879 0.20506 0.194008 0.364559 0.333332 0.430381 0.47266 +293 2 160.69 160.78 160.74 1111 0.187796 0.179546 0.335885 0.304618 0.476319 0.515836 +293 3 160.78 160.88 160.83 979 0.19703 0.190106 0.375301 0.310823 0.427669 0.499071 +293 4 160.88 160.97 160.92 1216 0.190683 0.169022 0.319934 0.302235 0.489383 0.528742 +293 5 160.97 161.06 161.01 840 0.190337 0.187966 0.331196 0.292028 0.478467 0.520006 +293 6 161.06 161.15 161.1 661 0.223726 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3180 0.0155468 0.0200798 0.293769 0.308009 0.690684 0.671911 +2666 4 1466.03 1466.12 1466.07 2990 0.0160518 0.0187938 0.296512 0.319415 0.687436 0.661792 +2666 5 1466.12 1466.21 1466.16 2525 0.0120983 0.0141884 0.303261 0.334499 0.684641 0.651313 +2666 6 1466.21 1466.3 1466.25 2864 0.0135631 0.0168322 0.298462 0.321791 0.687975 0.661377 +2696 1 1482.25 1482.34 1482.3 2348 0.0153764 0.0217104 0.299519 0.330589 0.685104 0.6477 +2696 2 1482.34 1482.43 1482.39 2475 0.0132927 0.0153725 0.315202 0.333307 0.671506 0.65132 +2696 3 1482.43 1482.53 1482.48 2533 0.0134635 0.0152679 0.306371 0.343789 0.680165 0.640943 +2696 4 1482.53 1482.62 1482.57 2341 0.0130188 0.0161886 0.290449 0.323727 0.696533 0.660085 +2696 5 1482.62 1482.71 1482.66 2713 0.0147248 0.0175395 0.282484 0.310728 0.702791 0.671733 +2696 6 1482.71 1482.8 1482.75 2856 0.014504 0.0173628 0.305357 0.328335 0.680139 0.654302 +2726 2 1498.84 1498.93 1498.89 2574 0.012112 0.0147994 0.334193 0.312818 0.653695 0.672383 +2726 3 1498.93 1499.03 1498.98 2250 0.011902 0.0138153 0.334193 0.312818 0.653695 0.672383 +2726 4 1499.03 1499.12 1499.07 2497 0.0120142 0.0132596 0.31656 0.310496 0.671538 0.675689 +2726 5 1499.12 1499.21 1499.16 2434 0.0120306 0.0155214 0.350579 0.312382 0.637407 0.674358 +2726 6 1499.21 1499.3 1499.25 2503 0.0116513 0.0142339 0.381909 0.320051 0.60606 0.664428 +2756 1 1515.25 1515.34 1515.3 2588 0.011899 0.0150355 0.354849 0.31081 0.6335 0.674956 +2756 2 1515.34 1515.43 1515.39 2385 0.0101783 0.0120252 0.341864 0.329277 0.646237 0.655688 +2756 3 1515.43 1515.53 1515.48 2683 0.0112705 0.0121976 0.366195 0.334357 0.623627 0.653618 +2756 4 1515.53 1515.62 1515.57 2275 0.0101817 0.0122769 0.322246 0.296818 0.666483 0.690984 +2756 5 1515.62 1515.71 1515.66 2562 0.0120266 0.0137083 0.340469 0.318638 0.64935 0.669085 +2756 6 1515.71 1515.8 1515.75 2370 0.0104792 0.011574 0.339667 0.327485 0.648307 0.658807 +2786 1 1531.75 1531.84 1531.8 3036 0.0108215 0.0135154 0.349051 0.32598 0.64047 0.662446 +2786 2 1531.84 1531.93 1531.89 2745 0.00973033 0.0115285 0.288492 0.313375 0.700687 0.673109 +2786 3 1531.93 1532.03 1531.98 2525 0.00953739 0.0107229 0.286433 0.321243 0.703837 0.667229 +2786 4 1532.03 1532.12 1532.07 2744 0.00985514 0.0119383 0.291884 0.327076 0.698579 0.662201 +2786 5 1532.12 1532.21 1532.16 2803 0.00961942 0.0115515 0.271543 0.30438 0.718602 0.683681 +2786 6 1532.21 1532.3 1532.25 3238 0.0114098 0.0136881 0.280219 0.314939 0.710162 0.67351 +2817 1 1548.8 1548.89 1548.85 3584 0.0104709 0.0123137 0.261788 0.29261 0.726803 0.693702 +2817 2 1548.89 1548.98 1548.94 3034 0.0181134 0.0199757 0.278451 0.276765 0.711078 0.710921 +2817 3 1548.98 1549.08 1549.03 3418 0.0108122 0.0111427 0.287065 0.2828 0.694821 0.697225 +2817 4 1549.08 1549.17 1549.12 3139 0.0171597 0.0183357 0.276448 0.273483 0.712739 0.715375 +2817 5 1549.17 1549.26 1549.21 3084 0.0115272 0.0120514 0.285602 0.285771 0.697239 0.695893 +2817 6 1549.26 1549.35 1549.3 3470 0.0118086 0.0129208 0.312955 0.295845 0.675518 0.692104 +2846 1 1564.75 1564.84 1564.8 2165 0.0116237 0.0140231 0.284968 0.278715 0.703223 0.708364 +2846 2 1564.84 1564.93 1564.89 2580 0.0120996 0.0151484 0.326339 0.319103 0.662038 0.666874 +2846 3 1564.93 1565.03 1564.98 2762 0.0126947 0.0145094 0.348995 0.331056 0.638905 0.653796 +2846 4 1565.03 1565.12 1565.07 2851 0.0127562 0.0151839 0.307857 0.306855 0.679449 0.678635 +2846 5 1565.12 1565.21 1565.16 2920 0.0130251 0.016379 0.312874 0.310716 0.674369 0.6741 +2846 6 1565.21 1565.3 1565.25 2597 0.0124532 0.0170562 0.32981 0.323231 0.657165 0.66039 +2876 1 1581.25 1581.34 1581.3 2462 0.010687 0.0126292 0.346902 0.329472 0.640644 0.653472 +2876 2 1581.34 1581.43 1581.39 2615 0.0113897 0.0142245 0.29645 0.334429 0.692864 0.652942 +2876 3 1581.43 1581.53 1581.48 2711 0.0106319 0.0132058 0.301403 0.334796 0.687208 0.650979 +2876 4 1581.53 1581.62 1581.57 2860 0.0115793 0.0154469 0.269595 0.315034 0.719773 0.67176 +2876 5 1581.62 1581.71 1581.66 2823 0.0116198 0.0163386 0.27734 0.307659 0.711081 0.676895 +2876 6 1581.71 1581.8 1581.75 2867 0.0111439 0.0142084 0.30415 0.312075 0.684231 0.671586 +2906 1 1597.75 1597.84 1597.8 2087 0.0111485 0.0151002 0.304527 0.323394 0.684329 0.662398 +2906 2 1597.84 1597.93 1597.89 1874 0.0106347 0.0123849 0.33682 0.362215 0.652031 0.622685 +2906 3 1597.93 1598.03 1597.98 2342 0.0120104 0.0134695 0.322617 0.358147 0.666748 0.629468 +2906 4 1598.03 1598.12 1598.07 2028 0.0117329 0.0125553 0.354347 0.361933 0.633643 0.624597 +2906 5 1598.12 1598.21 1598.16 1906 0.0099312 0.0109799 0.329531 0.36081 0.658736 0.626634 +2906 6 1598.21 1598.3 1598.25 2018 0.0101003 0.0120009 0.318686 0.349011 0.671214 0.638988 +2936 1 1614.25 1614.34 1614.3 3046 0.0135277 0.0157301 0.322063 0.323998 0.664409 0.660272 +2936 2 1614.34 1614.43 1614.39 3252 0.013622 0.0166549 0.377127 0.348521 0.609251 0.634825 +2936 3 1614.43 1614.53 1614.48 2843 0.0124837 0.0143483 0.342134 0.327373 0.645382 0.658279 +2936 4 1614.53 1614.62 1614.57 2827 0.0121274 0.0137228 0.34287 0.326894 0.645003 0.659383 +2936 5 1614.62 1614.71 1614.66 2851 0.0121248 0.0158636 0.368032 0.33338 0.619843 0.650757 +2936 6 1614.71 1614.8 1614.75 3106 0.0128285 0.0150385 0.325846 0.31854 0.661326 0.666421 +2966 1 1630.75 1630.84 1630.8 2629 0.0137752 0.0169475 0.301008 0.309249 0.685216 0.673803 +2966 2 1630.84 1630.93 1630.89 2775 0.0139209 0.0163568 0.302616 0.308215 0.683464 0.675428 +2966 3 1630.93 1631.03 1630.98 2740 0.013193 0.0169106 0.300414 0.321722 0.686393 0.661367 +2966 4 1631.03 1631.12 1631.07 2522 0.0122993 0.0152036 0.321365 0.332088 0.666336 0.652709 +2966 5 1631.12 1631.21 1631.16 2482 0.0117483 0.0132314 0.321365 0.332088 0.666336 0.652709 +2966 6 1631.21 1631.3 1631.25 2544 0.0119369 0.0158058 0.303809 0.320215 0.684443 0.666554 +2997 1 1647.8 1647.89 1647.85 4106 0.0163627 0.0179553 0.334964 0.349761 0.653099 0.634434 +2997 2 1647.89 1647.98 1647.94 4108 0.0171262 0.0193359 0.274598 0.271012 0.709039 0.711032 +2997 3 1647.98 1648.08 1648.03 4550 0.0160464 0.0185243 0.295392 0.281248 0.687482 0.699416 +2997 4 1648.08 1648.17 1648.12 3932 0.0169748 0.0196157 0.288082 0.282521 0.695871 0.698955 +2997 5 1648.17 1648.26 1648.21 3857 0.0159837 0.018044 0.295884 0.286864 0.687141 0.693521 +2997 6 1648.26 1648.35 1648.3 3892 0.0146419 0.0168719 0.298838 0.283424 0.685179 0.698532 +3027 1 1664.3 1664.39 1664.35 2335 0.0114427 0.0131922 0.281767 0.271887 0.703591 0.711241 +3027 2 1664.39 1664.48 1664.44 2628 0.0135136 0.0141993 0.322242 0.343691 0.666315 0.643116 +3027 3 1664.48 1664.58 1664.53 2264 0.0119237 0.0142733 0.304064 0.302424 0.682422 0.683377 +3027 4 1664.58 1664.67 1664.62 2949 0.0155118 0.0183268 0.315589 0.309662 0.672488 0.676065 +3027 5 1664.67 1664.76 1664.71 2490 0.0125041 0.0145283 0.295372 0.303994 0.689116 0.677679 +3027 6 1664763 1664855 1664.8 2973 0.0148196 0.0172075 0.309688 0.318869 0.677808 0.666603 +3056 1 1680.25 1680.34 1680.3 3007 0.0147447 0.0163503 0.31491 0.300627 0.670271 0.682166 +3056 2 1680.34 1680.43 1680.39 3084 0.015127 0.0163587 0.322476 0.303579 0.662779 0.68007 +3056 3 1680.43 1680.53 1680.48 3129 0.0137228 0.0148389 0.296854 0.295971 0.688019 0.68767 +3056 4 1680.53 1680.62 1680.57 3009 0.0132437 0.0151131 0.282133 0.286287 0.704144 0.698874 +3056 5 1680.62 1680.71 1680.66 3397 0.0145354 0.0157679 0.30576 0.306159 0.680996 0.678728 +3056 6 1680.71 1680.8 1680.75 2632 0.0149628 0.0155215 296 0.292551 0.689465 0.691681 +3086 1 1696.75 1696.84 1696.8 3266 0.0129584 0.0161478 0.327945 0.313859 0.657092 0.67062 +3086 2 1696.84 1696.93 1696.89 3585 0.0104572 0.0115895 0.278184 0.280366 0.708858 0.703487 +3086 3 1696.93 1697.03 1696.98 3748 0.0107439 0.0121964 0.281491 0.273092 0.708051 0.715318 +3086 4 1697.03 1697.12 1697.07 3461 0.0109583 0.012236 0.260232 0.27265 0.729024 0.715153 +3086 5 1697.12 1697.21 1697.16 3635 0.0111941 0.0123466 0.262694 0.268849 0.726348 0.718915 +3086 6 1697.21 1697.3 1697.25 3649 0.0104174 0.0122563 0.265484 0.265941 0.723322 0.721712 +3117 1 1713.8 1713.89 1713.85 2497 0.0153159 0.0186542 0.260928 0.271382 0.728654 0.716362 +3117 2 1713.89 1713.98 1713.94 2308 0.0145771 0.0155391 0.345898 0.341255 0.638786 0.640091 +3117 3 1713.98 1714.08 1714.03 2200 0.0129823 0.0145207 0.355548 0.363126 0.629875 0.621335 +3117 4 1714.08 1714.17 1714.12 2530 0.0149721 0.0183298 0.330348 0.336419 0.65667 0.649061 +3117 5 1714.17 1714.26 1714.21 2340 0.0131671 0.0159437 0.319412 0.321437 0.665616 0.660233 +3117 6 1714.26 1714.35 1714.3 2177 0.0142279 0.0167534 0.33029 0.336595 0.656543 0.647461 diff --git a/GreenlandSG.py b/GreenlandSG.py new file mode 100644 index 0000000..c1fc134 --- /dev/null +++ b/GreenlandSG.py @@ -0,0 +1,174 @@ +#%% + +import rasterio +import netCDF4 as nc +import numpy as np +from scipy.interpolate import RegularGridInterpolator +import gc +import pickle +from SavitzkyGolay import sgolay2d + +fp = r'./greenland_vel_mosaic250_vx_v1.tif' +img = rasterio.open(fp) +vx = img.read(1) + +fp = r'./greenland_vel_mosaic250_vy_v1.tif' +img = rasterio.open(fp) +vy = img.read(1) + + +y = np.array(img.xy(np.arange(vy.shape[0]),np.zeros(vy.shape[0]))[1]) +x = np.array(img.xy(np.zeros(vy.shape[1]),np.arange(vy.shape[1]))[0]) + + + + +##Add nans +vx[vx<-1e9]=np.nan +vy[vy<-1e9]=np.nan + +#flip on axis 0 to makes postitive +vx = np.flip(vx,0) +vy = np.flip(vy,0) +y = np.flip(y,0) + +#Trim +# xmin = 100000 +# xmax = 295000 +# ymin = -2260000 +# ymax = -1040000 +xmin = 110000 +xmax = 310000 +ymin = -1940000 +ymax = -1500000 + + +xminind = np.abs(x-xmin).argmin() +xmaxind = np.abs(x-xmax).argmin() +yminind = np.abs(y-ymin).argmin() +ymaxind = np.abs(y-ymax).argmin() + +x = x[xminind:xmaxind] +y = y[yminind:ymaxind] +vx = vx[yminind:ymaxind,xminind:xmaxind] +vy = vy[yminind:ymaxind,xminind:xmaxind] + + +#Savitzky-Golay filtering +window=51 +order =5 +vx_filt = sgolay2d(vx,window,order) +vy_filt = sgolay2d(vy,window,order) + +dudx,dudy = sgolay2d(vx,window,order,'both')/(x[1]-x[0]) +dvdx,dvdy = sgolay2d(vy,window,order,'both')/(y[1]-y[0]) + + +fn = 'BedMachineGreenland-v5.nc' +ds = nc.Dataset(fn) + + +surface=np.squeeze(ds['surface'][:]) +thickness=np.squeeze(ds['thickness'][:]) +bed=np.squeeze(ds['bed'][:]) +y2 = np.squeeze(ds['y'][:]) +x2 = np.squeeze(ds['x'][:]) + +y2=np.flip(y2,0) +surface = np.flip(surface,0) +thickness = np.flip(thickness,0) +bed = np.flip(bed,0) + +window=51 +order =5 +dbeddx,dbeddy = sgolay2d(bed,window,order,'both')/(x[1]-x[0]) +dsurfdx,dsurfdy = sgolay2d(surface,window,order,'both')/(y[1]-y[0]) + + +fn = 'Greenland1km.nc' +ds = nc.Dataset(fn) +acc = np.squeeze(ds['presprcp'][:]) +temp = np.squeeze(ds['presartm'][:]) +y3 = np.squeeze(ds['y'][:]) +x3 = np.squeeze(ds['x'][:]) + + + + + + +class gl: + def __init__(self): + self.vx_interp = RegularGridInterpolator((x,y), vx.T) + self.vy_interp = RegularGridInterpolator((x,y), vy.T) + self.dudx_interp = RegularGridInterpolator((x,y), dudx.T) + self.dudy_interp = RegularGridInterpolator((x,y), dudy.T) + self.dvdx_interp = RegularGridInterpolator((x,y), dvdx.T) + self.dvdy_interp = RegularGridInterpolator((x,y), dvdy.T) + self.surf_interp = RegularGridInterpolator((x2,y2), surface.T) + self.thick_interp = RegularGridInterpolator((x2,y2), thickness.T) + self.bed_interp = RegularGridInterpolator((x2,y2), bed.T) + self.accumulation_interp = RegularGridInterpolator((x3,y3), acc.T) + self.surftemp_interp = RegularGridInterpolator((x3,y3), temp.T) + self.dbeddx_interp = RegularGridInterpolator((x2,y2),dbeddx.T) + self.dbeddy_interp = RegularGridInterpolator((x2,y2),dbeddy.T) + self.dsurfdx_interp = RegularGridInterpolator((x2,y2), dsurfdx.T) + self.dsurfdy_interp = RegularGridInterpolator((x2,y2), dsurfdy.T) + + self.xmin = np.max([x.min(),x2.min(),x3.min()]) + self.xmax = np.min([x.max(),x2.max(),x3.max()]) + self.ymin = np.max([y.min(),y2.min(),y3.min()]) + self.ymax = np.min([y.max(),y2.max(),y3.max()]) + + + def interps(self,xi,yi): + self.xi=xi + self.yi=yi + + if xi.ndim==2: + pts = np.reshape([xi,yi],(2,-1)).T + elif xi.ndim==1: + pts = np.stack((xi,yi),1) + else: + pts=[xi,yi] + + self.vx=self.vx_interp(pts) + self.vy=self.vy_interp(pts) + self.dudx = self.dudx_interp(pts) + self.dudy = self.dudy_interp(pts) + self.dvdx = self.dvdx_interp(pts) + self.dvdy = self.dvdy_interp(pts) + self.surface = self.surf_interp(pts) + self.thickness = self.thick_interp(pts) + self.bed = self.bed_interp(pts) + self.accumulation = self.accumulation_interp(pts) + self.surftemp = self.surftemp_interp(pts) + + self.surftemp = self.surftemp_interp(pts) + self.dbeddx = self.dbeddx_interp(pts) + self.dbeddy = self.dbeddy_interp(pts) + self.dsurfdx = self.dsurfdx_interp(pts) + self.dsurfdy = self.dsurfdy_interp(pts) + if xi.ndim==2: + self.vx=self.vx.reshape(xi.shape) + self.vy=self.vy.reshape(xi.shape) + self.dudx=self.dudx.reshape(xi.shape) + self.dudy=self.dudy.reshape(xi.shape) + self.dvdx=self.dvdx.reshape(xi.shape) + self.dvdy=self.dvdy.reshape(xi.shape) + self.surface=self.surface.reshape(xi.shape) + self.thickness=self.thickness.reshape(xi.shape) + self.bed=self.bed.reshape(xi.shape) + self.accumulation=self.accumulation.reshape(xi.shape) + self.surftemp=self.surftemp.reshape(xi.shape) + self.dbeddx=self.dbeddx.reshape(xi.shape) + self.dbeddy=self.dbeddy.reshape(xi.shape) + self.dsurfdx=self.dsurfdx.reshape(xi.shape) + self.dsurfdy=self.dsurfdy.reshape(xi.shape) + + self.vmag=np.sqrt(self.vx**2+self.vy**2) + + + + +# %% \ No newline at end of file diff --git a/Save2DpathGerber.py b/Save2DpathGerber.py new file mode 100644 index 0000000..f587bf7 --- /dev/null +++ b/Save2DpathGerber.py @@ -0,0 +1,22 @@ +#%% + +import numpy as np +import track +import pickle +import GreenlandSG as Greenland + +dt=50 + +xc = 244691 +yc = -1544921 + +data = Greenland.gl() + +p=track.path2d(100000,dt,xc,yc,data) + +filename = 'path2dSGdt'+str(dt)+'.pkl' + +with open(filename, 'wb') as f: + pickle.dump(p,f) + +# %% diff --git a/SavitzkyGolay.py b/SavitzkyGolay.py new file mode 100644 index 0000000..d8e40fc --- /dev/null +++ b/SavitzkyGolay.py @@ -0,0 +1,82 @@ +# from https://scipy.github.io/old-wiki/pages/Cookbook/SavitzkyGolay +import numpy as np +import scipy.signal + +def sgolay2d ( z, window_size, order, derivative=None): + """ + """ + # number of terms in the polynomial expression + n_terms = ( order + 1 ) * ( order + 2) / 2.0 + + if window_size % 2 == 0: + raise ValueError('window_size must be odd') + + if window_size**2 < n_terms: + raise ValueError('order is too high for the window size') + + half_size = window_size // 2 + + # exponents of the polynomial. + # p(x,y) = a0 + a1*x + a2*y + a3*x^2 + a4*y^2 + a5*x*y + ... + # this line gives a list of two item tuple. Each tuple contains + # the exponents of the k-th term. First element of tuple is for x + # second element for y. + # Ex. exps = [(0,0), (1,0), (0,1), (2,0), (1,1), (0,2), ...] + exps = [ (k-n, n) for k in range(order+1) for n in range(k+1) ] + + # coordinates of points + ind = np.arange(-half_size, half_size+1, dtype=np.float64) + dx = np.repeat( ind, window_size ) + dy = np.tile( ind, [window_size, 1]).reshape(window_size**2, ) + + # build matrix of system of equation + A = np.empty( (window_size**2, len(exps)) ) + for i, exp in enumerate( exps ): + A[:,i] = (dx**exp[0]) * (dy**exp[1]) + + # pad input array with appropriate values at the four borders + new_shape = z.shape[0] + 2*half_size, z.shape[1] + 2*half_size + Z = np.zeros( (new_shape) ) + # top band + band = z[0, :] + Z[:half_size, half_size:-half_size] = band - np.abs( np.flipud( z[1:half_size+1, :] ) - band ) + # bottom band + band = z[-1, :] + Z[-half_size:, half_size:-half_size] = band + np.abs( np.flipud( z[-half_size-1:-1, :] ) -band ) + # left band + band = np.tile( z[:,0].reshape(-1,1), [1,half_size]) + Z[half_size:-half_size, :half_size] = band - np.abs( np.fliplr( z[:, 1:half_size+1] ) - band ) + # right band + band = np.tile( z[:,-1].reshape(-1,1), [1,half_size] ) + Z[half_size:-half_size, -half_size:] = band + np.abs( np.fliplr( z[:, -half_size-1:-1] ) - band ) + # central band + Z[half_size:-half_size, half_size:-half_size] = z + + # top left corner + band = z[0,0] + Z[:half_size,:half_size] = band - np.abs( np.flipud(np.fliplr(z[1:half_size+1,1:half_size+1]) ) - band ) + # bottom right corner + band = z[-1,-1] + Z[-half_size:,-half_size:] = band + np.abs( np.flipud(np.fliplr(z[-half_size-1:-1,-half_size-1:-1]) ) - band ) + + # top right corner + band = Z[half_size,-half_size:] + Z[:half_size,-half_size:] = band - np.abs( np.flipud(Z[half_size+1:2*half_size+1,-half_size:]) - band ) + # bottom left corner + band = Z[-half_size:,half_size].reshape(-1,1) + Z[-half_size:,:half_size] = band - np.abs( np.fliplr(Z[-half_size:, half_size+1:2*half_size+1]) - band ) + + # solve system and convolve + if derivative == None: + m = np.linalg.pinv(A)[0].reshape((window_size, -1)) + return scipy.signal.fftconvolve(Z, m, mode='valid') + elif derivative == 'col': + c = np.linalg.pinv(A)[1].reshape((window_size, -1)) + return scipy.signal.fftconvolve(Z, -c, mode='valid') + elif derivative == 'row': + r = np.linalg.pinv(A)[2].reshape((window_size, -1)) + return scipy.signal.fftconvolve(Z, -r, mode='valid') + elif derivative == 'both': + c = np.linalg.pinv(A)[1].reshape((window_size, -1)) + r = np.linalg.pinv(A)[2].reshape((window_size, -1)) + return scipy.signal.fftconvolve(Z, -r, mode='valid'), scipy.signal.fftconvolve(Z, -c, mode='valid') \ No newline at end of file diff --git a/SupplementaryMaterial/UpstreamCorrection_data_10Jun2021.ods b/SupplementaryMaterial/UpstreamCorrection_data_10Jun2021.ods new file mode 100644 index 0000000..7d5a5d1 Binary files /dev/null and b/SupplementaryMaterial/UpstreamCorrection_data_10Jun2021.ods differ diff --git a/SupplementaryMaterial/UpstreamCorrection_data_10Jun2021.xls b/SupplementaryMaterial/UpstreamCorrection_data_10Jun2021.xls new file mode 100644 index 0000000..ac855b5 Binary files /dev/null and b/SupplementaryMaterial/UpstreamCorrection_data_10Jun2021.xls differ diff --git a/agedepth.py b/agedepth.py new file mode 100644 index 0000000..9f74e31 --- /dev/null +++ b/agedepth.py @@ -0,0 +1,37 @@ +#%% + +import numpy as np +from scipy.interpolate import interp1d,griddata +import pandas as pd + + + +data = pd.read_excel("./SupplementaryMaterial/UpstreamCorrection_data_10Jun2021.xls", + sheet_name=4) + + + + + +depth = data['depth'].to_numpy()[2:].astype(float) +age = data['age'].to_numpy()[2:].astype(float) +dist = data['upstream distance'].to_numpy()[2:].astype(float)*1000 + + +def time2depth(t): + return np.interp(t,age,depth) + + + +def dist2depth(s): + return np.interp(s,dist,depth) + +def depth2time(d): + return np.interp(d,depth,age) + +def depth2dist(d): + return np.interp(d,depth,dist) + + +def depth2time(d): + return np.interp(d,depth,age) diff --git a/densityfromdepth.py b/densityfromdepth.py new file mode 100644 index 0000000..3342db6 --- /dev/null +++ b/densityfromdepth.py @@ -0,0 +1,34 @@ +import numpy as np +from scipy.interpolate import interp1d + +#Digitised from Fig. 9 "Initial results from geophysical surveys and shallow coring of the +# Northeast Greenland Ice Stream (NEGIS)" by Vallelonga et al. (2014)" +rawdata = np.array([[0.3383916990920882, -0.35901626644076146],\ +[0.4369649805447471, 5.930440910760732],\ +[0.5783398184176395, 16.985123041666967],\ +[0.6846952010376135, 30.20859729750667],\ +[0.8040207522697794, 49.28549115454492],\ +[0.8695201037613488, 63.75470137373827],\ +[0.9019455252918288, 76.07611164841097],\ +[0.9136186770428015, 86.5542085573124],\ +[0.9149156939040207, 101.19735398564033]]) + +raw_d = rawdata[:,1] +raw_rho = rawdata[:,0] +raw_rho = raw_rho/raw_rho[-1] + + + + +def densityfromdepth(depth): + # depth in m + # normalised density where 1 = ice sheet density + # from Fig. 9 "Initial results from geophysical surveys and shallow coring of the + # Northeast Greenland Ice Stream (NEGIS)" by Vallelonga et al. (2014) + # Digitised using https://apps.automeris.io/wpd/ + # + f = interp1d(raw_d,raw_rho,kind='cubic',fill_value=1,bounds_error=False) + density = f(depth) + density[density>1]=1 + return density + diff --git a/divide_data.py b/divide_data.py new file mode 100644 index 0000000..22c86f3 --- /dev/null +++ b/divide_data.py @@ -0,0 +1,778 @@ +import numpy as np +import pyproj + +# Define the source (EPSG:4326) and destination (EPSG:3413) coordinate systems +source_crs = pyproj.CRS('EPSG:4326') +dest_crs = pyproj.CRS('EPSG:3413') + +# Create a transformer object to convert between the coordinate systems +transformer = pyproj.Transformer.from_crs(source_crs, dest_crs) + +def locations(): + return ['GRIP','DomeC','Talos','DomeF','NGRIP'] + + +class data: + def __init__(self,location): + self.location = location + if location == 'GRIP': + self.acc = 0.23 + self.H = 3029 + self.largest_ev =grip_largestev[:,0] + self.largest_ev_d = grip_largestev[:,1] + elif location == 'DomeC': + self.acc = 0.0284 #https://www.researchgate.net/figure/Main-characteristics-of-the-Dome-C-and-Dome-Fuji-drilling-sites_tbl1_29619841 + self.H = 3273 + self.largest_ev = domec_largestev[:,1] + self.largest_ev_d = domec_largestev[:,0] + elif location == 'Talos': + self.acc = 0.08 + self.H = 1795 + self.largest_ev = talos_largestev[:,1] + self.largest_ev_d = talos_largestev[:,0] + elif location == 'DomeF': + self.acc = 0.0299 + self.H = 3028 + self.largest_ev = domef_largestev[:,1] + self.largest_ev_d = domef_largestev[:,0] + elif location == 'NGRIP': + self.H = 3085 + self.lat = 75.10 + self.lon = -42.32 + self.acc = 0.195 + + self.xc,self.yc = transformer.transform(self.lat, self.lon) + self.largest_ev = ngrip_largestev[:,1] + self.largest_ev_d = ngrip_largestev[:,0] + + self.middle_ev = ngrip_middleev[:,1] + self.middle_ev_d = ngrip_middleev[:,0] + + + + + + + + def Temperature(self,d): + if self.location == 'GRIP': + self.T = np.interp(d,grip_T[:,1],grip_T[:,0]) + elif self.location == 'DomeC': + self.T = np.interp(d,domec_T[:,1],domec_T[:,0]) + elif self.location == 'Talos': + Ts = -41 + T1500 = -20 #Montagnat 2012 + + self.T = Ts + (T1500-Ts)*d/1500 + elif self.location == 'DomeF': + self.T = np.interp(d,domef_T[:,1],domef_T[:,0]) + elif self.location == 'NGRIP': + self.T = np.interp(d,ngrip_T[:,1],ngrip_T[:,0]) + + return self.T + + def woodcock(self,d): + e1 = np.interp(d,self.largest_ev_d,self.largest_ev) + if self.location == 'NGRIP': + e2 = np.interp(d,self.middle_ev_d,self.middle_ev) + else: + e2 = (1-e1)/2 + + e3 = 1 - e1 - e2 + + return np.log(e1/e2)/np.log(e2/e3) + + + +grip_largestev = np.array([[0.45271398494784987, 141.38615298986065],\ +[0.4259552791423662, 252.0509159547954],\ +[0.5249255862352685, 357.7789608692498],\ +[0.6055525978268761, 471.5412724148781],\ +[0.5971674370205453, 578.116303269366],\ +[0.6042397696198243, 700.3484742153283],\ +[0.6539820438980715, 786.7410400987346],\ +[0.6488154296638677, 913.0145439585704],\ +[0.6678544636159041, 995.7045713041168],\ +[0.7206035379812696, 1074.1838685477826],\ +[0.6630508433560003, 1169.3124894126759],\ +[0.6976986182029379, 1287.3823294533313],\ +[0.7476526389661933, 1401.3866369818263],\ +[0.7607688212375674, 1511.7368051690344],\ +[0.8041647508651357, 1570.5660286039256],\ +[0.8078672894029957, 1653.377053940905],\ +[0.8024889286837837, 1752.038816155652],\ +[0.8059494712387774, 1803.293565326816],\ +[0.8496176463470706, 1897.6235994482495],\ +[0.8472460857149773, 1988.3720930232562],\ +[0.8662548701691553, 2067.117585848075],\ +[0.9284478377658931, 2177.0805604626967],\ +[0.895554533794739, 2287.793722624205],\ +[0.939283207898749, 2390.012825787092],\ +[0.9399789463494908, 2480.737119763812],\ +[0.9745359727028531, 2586.9733562422866],\ +[0.9569791641458754, 2697.5655204123614],\ +[0.9333482564189435, 2816.0951528204637],\ +[0.6760278779372262, 2861.5177988045402],\ +[0.878741862885076, 2895.4214360041633],\ +[0.7717796384580016, 2947.5473707136466],\ +[0.7014132565399416, 2971.771168598602],\ +[0.8580875057474044, 3002.093265251797]]) + + + + +domec_largestev = np.array([[215.1898734177214, 0.4413851083404715],\ +[269.4394213381554, 0.45382690199479014],\ +[332.73056057866177, 0.4771792350073497],\ +[341.772151898734, 0.46001959892699407],\ +[382.45931283905963, 0.45843476483050105],\ +[400.54249547920415, 0.4849342061200823],\ +[432.1880650994574, 0.5394953628797734],\ +[445.7504520795659, 0.5067383648995908],\ +[454.79204339963826, 0.5036138165385334],\ +[463.83363471971063, 0.4630623675926807],\ +[477.396021699819, 0.5082780791641552],\ +[500, 0.4770748953960148],\ +[509.04159132007226, 0.5207339727659515],\ +[545.2079566003616, 0.5207114128499872],\ +[599.4575045207956, 0.5425099316505047],\ +[653.7070524412295, 0.5689868130241216],\ +[707.9566003616636, 0.5362044351384792],\ +[762.2061482820976, 0.606346033861024],\ +[834.5388788426764, 0.6203360017483937],\ +[879.7468354430379, 0.6218672560444714],\ +[933.9963833634719, 0.6374279580808562],\ +[979.2043399638336, 0.6311619414217683],\ +[1037.9746835443038, 0.6935034491996519],\ +[1132.9113924050635, 0.6809685958919803],\ +[1173.598553345389, 0.7074539372340839],\ +[1227.8481012658226, 0.6933850096408392],\ +[1264.014466546112, 0.7073975374441731],\ +[1322.7848101265822, 0.7089203317717642],\ +[1372.51356238698, 0.7494351208541749],\ +[1422.242314647378, 0.7104177461938954],\ +[1440.3254972875225, 0.729119916528311],\ +[1467.4502712477397, 0.7836838932774977],\ +[1481.012658227848, 0.7540458036793813],\ +[1494.575045207957, 0.7711913398122592],\ +[1512.6582278481014, 0.7431098844156806],\ +[1526.2206148282098, 0.7961228669423207],\ +[1557.8661844484627, 0.7695924057682886],\ +[1557.8661844484627, 0.7695924057682886],\ +[1562.3869801084993, 0.7602328606325942],\ +[1594.0325497287522, 0.7680103916612911],\ +[1598.5533453887883, 0.7602103007166299],\ +[1616.6365280289333, 0.7118559408366205],\ +[1621.1573236889694, 0.7929447387808481],\ +[1643.76130198915, 0.7492659214844425],\ +[1643.76130198915, 0.7944900930244035],\ +[1670.8860759493673, 0.7554868183116019],\ +[1688.9692585895118, 0.6884190081391948],\ +[1702.5316455696202, 0.7445508990479011],\ +[1711.5732368896925, 0.7102372668661809],\ +[1738.6980108499097, 0.7351716139857379],\ +[1743.2188065099458, 0.8256171370761645],\ +[1743.2188065099458, 0.8256171370761645],\ +[1770.343580470163, 0.8100056752288598],\ +[1774.8643761301992, 0.8474297558241597],\ +[1788.4267631103075, 0.8489807500467061],\ +[1801.9891500904164, 0.8411750191230538],\ +[1806.5099457504525, 0.8645640119990554],\ +[1824.593128390597, 0.8551960068948744],\ +[1833.6347197106693, 0.8302390998593531],\ +[1851.7179023508138, 0.8582979953399674],\ +[1865.2802893309222, 0.8582895353714808],\ +[1874.321880650995, 0.8052624528973631],\ +[1874.321880650995, 0.8052624528973631],\ +[1896.9258589511755, 0.834877982579515],\ +[1901.4466546112121, 0.8707426089837816],\ +[1919.5298372513566, 0.8130315239575734],\ +[1928.571428571429, 0.8738445974288747],\ +[1942.1338155515373, 0.88475231679762],\ +[1942.1338155515373, 0.88475231679762],\ +[1955.6962025316457, 0.8738276774919014],\ +[1978.3001808318268, 0.8862892110726888],\ +[1996.3833634719713, 0.8940752020698723],\ +[2018.987341772152, 0.8909421937403283],\ +[2018.987341772152, 0.8737881976389639],\ +[2028.0289330922242, 0.9112094582447681],\ +[2041.591320072333, 0.883130822837685],\ +[2059.6745027124775, 0.929903168610697],\ +[2068.71609403255, 0.873757177754513],\ +[2082.2784810126586, 0.8550352674936287],\ +[2091.320072332731, 0.8472323565594719],\ +[2095.840867992767, 0.9049293416382025],\ +[2136.5280289330926, 0.9158201410699746],\ +[2154.611211573237, 0.9236061320671581],\ +[2172.6943942133817, 0.8939652224795464],\ +[2177.2151898734182, 0.8986407650631503],\ +[2208.860759493671, 0.93760737991251],\ +[2217.902350813744, 0.9376017399335188],\ +[2226.943942133816, 0.9297988289993621],\ +[2249.5479204339968, 0.9282252748608513],\ +[2276.6726943942135, 0.9578379845535077],\ +[2276.6726943942135, 0.9578379845535077],\ +[2281.19349005425, 0.9469189852267801],\ +[2303.7974683544307, 0.8751699924917781],\ +[2321.8806509945757, 0.9578097846585523],\ +[2335.4430379746836, 0.9655985956452313],\ +[2358.0470162748647, 0.9390737744501902],\ +[2385.1717902350815, 0.9359379461311508],\ +[2385.1717902350815, 0.9359379461311508],\ +[2394.213381555154, 0.9468484854893916],\ +[2425.858951175407, 0.9561854707091216],\ +[2448.4629294755878, 0.9624091875257765],\ +[2448.4629294755878, 0.9624091875257765],\ +[2471.066907775769, 0.9483599998590007],\ +[2489.1500904159134, 0.9670621701934161],\ +[2498.1916817359856, 0.9748538011695908],\ +[2516.2748643761306, 0.9514507083461115],\ +[2529.837251356239, 0.9654773360969231],\ +[2547.9204339963835, 0.965466056138941],\ +[2570.5244122965646, 0.973249227146629],\ +[2584.086799276673, 0.9857164007064074],\ +[2597.649186256782, 0.96543503625449],\ +[2606.690777576854, 0.9529537627472339],\ +[2611.2115732368898, 0.9233213131281086],\ +[2624.7739602169986, 0.968537024699583],\ +[2642.857142857143, 0.9482528402581701],\ +[2642.857142857143, 0.9482528402581701],\ +[2660.9403254972876, 0.9622766480194862],\ +[2660.9403254972876, 0.9030173887602269],\ +[2669.9819168173603, 0.9232846532646666],\ +[2679.0235081374326, 0.973181547398736],\ +[2697.106690777577, 0.9388622752380248],\ +[2715.1898734177216, 0.920137544987645],\ +[2715.1898734177216, 0.9560049913814072],\ +[2728.7522603978305, 0.9481992604577548],\ +[2746.835443037975, 0.9731392475563029],\ +[2769.439421338156, 0.9356982470240299],\ +[2773.9602169981918, 0.9434926979897],\ +[2787.5226039783006, 0.9731138676508431],\ +[2810.1265822784817, 0.9497079548378683],\ +[2810.1265822784817, 0.99025376380473],\ +[2823.6889692585896, 0.9496994948693817],\ +[2837.2513562386985, 0.870158871158205],\ +[2855.334538878843, 0.7422723475355054],\ +[2868.8969258589514, 0.7750124255787147],\ +[2877.9385171790236, 0.8732523996348114],\ +[2896.0216998191686, 0.881038390631995],\ +[2896.0216998191686, 0.8997518409243925],\ +[2909.584086799277, 0.9605620944061983],\ +[2923.146473779386, 0.9090916461336183]]) + + +ngrip_largestev = np.array([[158.96980461811722, 0.41628959276018096],\ +[180.28419182948488, 0.38009049773755654], +[212.2557726465364, 0.5520361990950227], +[265.5417406749556, 0.4343891402714932], +[293.9609236234458, 0.46153846153846156], +[318.82770870337475, 0.4524886877828054], +[372.11367673179393, 0.4298642533936652], +[425.39964476021316, 0.4796380090497737], +[478.6856127886323, 0.48416289592760176], +[535.5239786856129, 0.420814479638009], +[592.3623445825932, 0.49321266968325794], +[645.6483126110124, 0.4524886877828054], +[698.9342806394316, 0.49321266968325794], +[755.7726465364121, 0.4298642533936652], +[812.6110124333926, 0.6923076923076923], +[869.4493783303731, 0.5972850678733032], +[929.8401420959148, 0.6968325791855203], +[979.5737122557726, 0.6380090497737557], +[1039.9644760213146, 0.6380090497737557], +[1089.6980461811722, 0.6606334841628959], +[1153.6412078152753, 0.6742081447963801], +[1199.822380106572, 0.7058823529411765], +[1249.55595026643, 0.6877828054298643], +[1306.3943161634106, 0.4660633484162896], +[1317.051509769094, 0.7239819004524888], +[1324.1563055062165, 0.6696832579185521], +[1331.2611012433395, 0.7104072398190046], +[1341.918294849023, 0.6380090497737557], +[1349.0230905861458, 0.7330316742081449], +[1359.6802841918297, 0.6289592760180995], +[1359.6802841918297, 0.5429864253393665], +[1380.9946714031971, 0.6515837104072398], +[1391.651865008881, 0.6832579185520362], +[1402.309058614565, 0.7330316742081449], +[1409.4138543516874, 0.6832579185520362], +[1420.0710479573713, 0.6923076923076923], +[1427.1758436944938, 0.7194570135746606], +[1430.7282415630552, 0.6606334841628959], +[1437.8330373001777, 0.6696832579185521], +[1444.9378330373002, 0.7058823529411765], +[1455.595026642984, 0.7058823529411765], +[1466.252220248668, 0.6696832579185521], +[1469.8046181172294, 0.7149321266968326], +[1473.3570159857904, 0.7647058823529411], +[1487.5666074600354, 0.6380090497737557], +[1498.2238010657193, 0.6515837104072398], +[1498.2238010657193, 0.6832579185520362], +[1501.7761989342807, 0.7194570135746606], +[1512.4333925399646, 0.7285067873303168], +[1530.195381882771, 0.746606334841629], +[1530.195381882771, 0.7104072398190046], +[1537.3001776198935, 0.6877828054298643], +[1540.852575488455, 0.7104072398190046], +[1547.9573712255774, 0.746606334841629], +[1555.0621669626998, 0.7058823529411765], +[1562.1669626998223, 0.6923076923076923], +[1565.7193605683838, 0.7104072398190046], +[1579.9289520426291, 0.7647058823529411], +[1587.0337477797511, 0.6787330316742082], +[1601.2433392539965, 0.7647058823529411], +[1604.795737122558, 0.7104072398190046], +[1608.348134991119, 0.746606334841629], +[1611.9005328596804, 0.7239819004524888], +[1619.0053285968029, 0.751131221719457], +[1629.6625222024868, 0.7104072398190046], +[1633.2149200710478, 0.7420814479638009], +[1640.3197158081707, 0.7058823529411765], +[1647.4245115452932, 0.7782805429864253], +[1665.1865008880995, 0.7013574660633484], +[1690.0532859680284, 0.7647058823529411], +[1693.6056838365898, 0.6968325791855203], +[1750.4440497335704, 0.7285067873303168], +[1803.7300177619895, 0.7963800904977376], +[1864.120781527531, 0.7647058823529411], +[1871.2255772646536, 0.7918552036199096], +[1878.3303730017765, 0.7782805429864253], +[1881.8827708703375, 0.6923076923076923], +[1892.5399644760214, 0.7873303167420814], +[1910.3019538188278, 0.7285067873303168], +[1938.721136767318, 0.755656108597285], +[1952.930728241563, 0.7375565610859729], +[1963.587921847247, 0.751131221719457], +[1977.7975133214918, 0.7104072398190046], +[1992.0071047957372, 0.7873303167420814], +[2013.321492007105, 0.7420814479638009], +[2020.4262877442275, 0.7737556561085972], +[2045.2930728241563, 0.6968325791855203], +[2070.159857904085, 0.7647058823529411], +[2091.474245115453, 0.7375565610859729], +[2109.2362344582593, 0.8190045248868778], +[2123.4458259325042, 0.7873303167420814], +[2130.550621669627, 0.7828054298642534], +[2151.865008880995, 0.7692307692307692], +[2166.07460035524, 0.8009049773755657], +[2201.5985790408527, 0.8190045248868778], +[2222.91296625222, 0.8144796380090498], +[2233.570159857904, 0.8099547511312217], +[2304.6181172291294, 0.8144796380090498], +[2343.6944937833036, 0.8235294117647058], +[2354.3516873889876, 0.8235294117647058], +[2400.532859680284, 0.8144796380090498], +[2453.8188277087033, 0.8009049773755657], +[2503.552397868561, 0.8371040723981901], +[2539.076376554174, 0.9321266968325792], +[2546.1811722912967, 0.832579185520362], +[2581.70515097691, 0.9502262443438914], +[2581.70515097691, 0.8552036199095023], +[2588.809946714032, 0.8733031674208145], +[2588.809946714032, 0.9276018099547512], +[2592.3623445825933, 0.9004524886877828], +[2599.467140319716, 0.9457013574660633], +[2613.6767317939607, 0.9321266968325792], +[2620.7815275310836, 0.9411764705882353], +[2634.9911190053285, 0.9773755656108597], +[2642.0959147424514, 0.9004524886877828], +[2645.6483126110124, 0.9411764705882353], +[2659.857904085258, 0.9140271493212669], +[2674.0674955595027, 0.9547511312217195], +[2674.0674955595027, 0.9230769230769231], +[2688.2770870337476, 0.8914027149321267], +[2713.1438721136765, 0.9185520361990951], +[2745.115452930728, 0.8642533936651584], +[2752.220248667851, 0.9366515837104072], +[2801.953818827709, 0.8552036199095023], +[2841.030195381883, 0.9457013574660633], +[2841.030195381883, 0.8823529411764706], +[2887.2113676731797, 0.8642533936651584], +[2901.4209591474246, 0.832579185520362]]) + + +ngrip_middleev = np.array([[151.86500888099465, 0.35746606334841635],\ +[180.28419182948488, 0.36651583710407243],\ +[205.15097690941383, 0.2398190045248868],\ +[265.5417406749556, 0.3438914027149321],\ +[293.9609236234458, 0.31221719457013575],\ +[315.2753108348135, 0.32579185520361986],\ +[372.11367673179393, 0.3529411764705883],\ +[421.8472468916518, 0.29864253393665163],\ +[482.2380106571936, 0.29864253393665163],\ +[531.9715808170515, 0.31221719457013575],\ +[585.2575488454708, 0.31221719457013575],\ +[645.6483126110124, 0.30316742081447967],\ +[698.9342806394316, 0.31221719457013575],\ +[755.7726465364121, 0.37104072398190047],\ +[809.0586145648313, 0.16742081447963797],\ +[865.8969804618117, 0.2624434389140271],\ +[926.2877442273535, 0.16742081447963797],\ +[976.0213143872114, 0.2262443438914028],\ +[1039.9644760213146, 0.20814479638009042],\ +[1039.9644760213146, 0.20814479638009042],\ +[1089.6980461811722, 0.2262443438914028],\ +[1150.0888099467143, 0.21266968325791857],\ +[1196.269982238011, 0.19457013574660642],\ +[1249.55595026643, 0.1809954751131222],\ +[1306.3943161634106, 0.31221719457013575],\ +[1309.9467140319716, 0.17647058823529405],\ +[1334.8134991119005, 0.21266968325791857],\ +[1338.365896980462, 0.2579185520361992],\ +[1352.5754884547068, 0.17194570135746612],\ +[1352.5754884547068, 0.2624434389140271],\ +[1366.7850799289522, 0.32126696832579194],\ +[1380.9946714031971, 0.2262443438914028],\ +[1384.5470692717586, 0.2036199095022625],\ +[1405.8614564831264, 0.17194570135746612],\ +[1409.4138543516874, 0.22171945701357465],\ +[1416.5186500888099, 0.2398190045248868],\ +[1423.6234458259328, 0.19909502262443435],\ +[1437.8330373001777, 0.23529411764705888],\ +[1452.0426287744226, 0.22171945701357465],\ +[1455.595026642984, 0.23076923076923073],\ +[1462.6998223801065, 0.2579185520361992],\ +[1469.8046181172294, 0.20814479638009042],\ +[1476.9094138543514, 0.1809954751131222],\ +[1476.9094138543514, 0.2579185520361992],\ +[1494.6714031971583, 0.2624434389140271],\ +[1505.3285968028422, 0.2398190045248868],\ +[1508.8809946714032, 0.19004524886877827],\ +[1508.8809946714032, 0.22171945701357465],\ +[1519.538188277087, 0.23076923076923073],\ +[1530.195381882771, 0.2036199095022625],\ +[1533.747779751332, 0.23529411764705888],\ +[1540.852575488455, 0.2036199095022625],\ +[1547.9573712255774, 0.2036199095022625],\ +[1558.6145648312613, 0.24434389140271495],\ +[1565.7193605683838, 0.21719457013574672],\ +[1576.3765541740677, 0.17194570135746612],\ +[1583.4813499111901, 0.23076923076923073],\ +[1594.138543516874, 0.23076923076923073],\ +[1601.2433392539965, 0.19004524886877827],\ +[1615.4529307282419, 0.21719457013574672],\ +[1622.5577264653643, 0.18552036199095034],\ +[1633.2149200710478, 0.2036199095022625],\ +[1636.7673179396093, 0.2398190045248868],\ +[1643.8721136767317, 0.18552036199095034],\ +[1654.5293072824156, 0.15384615384615397],\ +[1661.634103019538, 0.24434389140271495],\ +[1686.500888099467, 0.1809954751131222],\ +[1693.6056838365898, 0.25339366515837103],\ +[1753.9964476021314, 0.23076923076923073],\ +[1803.7300177619895, 0.1583710407239819],\ +[1864.120781527531, 0.2036199095022625],\ +[1871.2255772646536, 0.17647058823529405],\ +[1881.8827708703375, 0.24434389140271495],\ +[1881.8827708703375, 0.24434389140271495],\ +[1888.98756660746, 0.19909502262443435],\ +[1906.7495559502663, 0.2036199095022625],\ +[1935.1687388987566, 0.21719457013574672],\ +[1956.4831261101244, 0.23076923076923073],\ +[1963.587921847247, 0.20814479638009042],\ +[1981.3499111900533, 0.25339366515837103],\ +[1988.4547069271757, 0.19457013574660642],\ +[2013.321492007105, 0.22171945701357465],\ +[2016.873889875666, 0.19457013574660642],\ +[2041.7406749555948, 0.2624434389140271],\ +[2063.0550621669627, 0.2036199095022625],\ +[2091.474245115453, 0.20814479638009042],\ +[2105.683836589698, 0.15384615384615397],\ +[2130.550621669627, 0.18552036199095034],\ +[2155.417406749556, 0.20814479638009042],\ +[2162.522202486679, 0.16742081447963797],\ +[2205.1509769094137, 0.14932126696832582],\ +[2219.360568383659, 0.17194570135746612],\ +[2233.570159857904, 0.17194570135746612],\ +[2301.0657193605684, 0.16742081447963797],\ +[2347.246891651865, 0.16742081447963797],\ +[2350.7992895204266, 0.14932126696832582],\ +[2396.9804618117228, 0.14932126696832582],\ +[2450.2664298401423, 0.14932126696832582],\ +[2503.552397868561, 0.13122171945701366],\ +[2542.628774422735, 0.11312217194570129],\ +[2542.628774422735, 0.040723981900452566],\ +[2560.390763765542, 0.05882352941176472],\ +[2578.1527531083484, 0.040723981900452566],\ +[2588.809946714032, 0.09502262443438914],\ +[2592.3623445825933, 0.08597285067873306],\ +[2595.9147424511543, 0.040723981900452566],\ +[2620.7815275310836, 0.045248868778280604],\ +[2620.7815275310836, 0.045248868778280604],\ +[2638.54351687389, 0.03619909502262453],\ +[2638.54351687389, 0.03619909502262453],\ +[2659.857904085258, 0.08597285067873306],\ +[2670.5150976909417, 0.045248868778280604],\ +[2677.619893428064, 0.07692307692307698],\ +[2720.2486678507994, 0.05882352941176472],\ +[2745.115452930728, 0.09502262443438914],\ +[2755.772646536412, 0.040723981900452566],\ +[2805.50621669627, 0.12217194570135737],\ +[2841.030195381883, 0.03619909502262453],\ +[2841.030195381883, 0.09502262443438914],\ +[2887.2113676731797, 0.09502262443438914],\ +[2904.9733570159856, 0.09502262443438914],\ +[2922.735346358792, 0.05429864253393668]]) + + + +talos_largestev = np.array([[17.16417910447757, 0.3978056426332288],\ +[27.61194029850745, 0.45564263322884013],\ +[39.55223880597012, 0.39075235109717865],\ +[47.014925373134275, 0.5177115987460815],\ +[57.462686567164155, 0.4387147335423198],\ +[69.40298507462683, 0.4612852664576803],\ +[75.37313432835816, 0.4838557993730408],\ +[91.79104477611935, 0.4838557993730408],\ +[102.2388059701492, 0.47398119122257054],\ +[108.20895522388054, 0.4782131661442006],\ +[118.65671641791045, 0.5148902821316615],\ +[129.10447761194024, 0.5148902821316615],\ +[141.04477611940297, 0.4782131661442006],\ +[149.99999999999994, 0.5021943573667712],\ +[158.95522388059698, 0.5360501567398119],\ +[169.40298507462683, 0.5233542319749217],\ +[179.85074626865668, 0.5275862068965518],\ +[190.29850746268653, 0.5148902821316615],\ +[202.2388059701492, 0.49514106583072104],\ +[212.686567164179, 0.5557993730407524],\ +[223.1343283582089, 0.5586206896551724],\ +[239.5522388059701, 0.5854231974921631],\ +[250, 0.5572100313479624],\ +[260.4477611940298, 0.5445141065830722],\ +[270.89552238805965, 0.5811912225705329],\ +[278.35820895522386, 0.5586206896551724],\ +[288.80597014925365, 0.5529780564263324],\ +[300.7462686567163, 0.5783699059561128],\ +[309.7014925373134, 0.5882445141065831],\ +[320.1492537313432, 0.5656739811912226],\ +[339.5522388059701, 0.5741379310344829],\ +[348.50746268656707, 0.5967084639498433],\ +[360.44776119402985, 0.5769592476489028],\ +[370.89552238805965, 0.5952978056426332],\ +[379.8507462686567, 0.634796238244514],\ +[388.8059701492536, 0.6291536050156741],\ +[399.2537313432835, 0.5614420062695925],\ +[411.1940298507462, 0.6206896551724138],\ +[420.1492537313432, 0.6277429467084641],\ +[432.0895522388059, 0.6277429467084641],\ +[439.55223880597003, 0.6390282131661442],\ +[449.99999999999994, 0.6489028213166144],\ +[458.955223880597, 0.6418495297805643],\ +[467.9104477611939, 0.6644200626959248],\ +[482.8358208955223, 0.6799373040752351],\ +[496.26865671641787, 0.6362068965517242],\ +[517.1641791044776, 0.6630094043887147],\ +[539.5522388059701, 0.6630094043887147],\ +[557.4626865671642, 0.7250783699059562],\ +[567.9104477611941, 0.7067398119122257],\ +[585.8208955223879, 0.6813479623824452],\ +[609.7014925373132, 0.7053291536050157],\ +[632.0895522388059, 0.7053291536050157],\ +[633.5820895522386, 0.6996865203761755],\ +[660.4477611940297, 0.718025078369906],\ +[670.8955223880596, 0.718025078369906],\ +[676.8656716417909, 0.7462382445141067],\ +[691.7910447761194, 0.764576802507837],\ +[694.7761194029849, 0.7532915360501568],\ +[702.2388059701491, 0.7702194357366772],\ +[708.2089552238804, 0.6954545454545455],\ +[720.1492537313434, 0.765987460815047],\ +[729.1044776119402, 0.718025078369906],\ +[741.044776119403, 0.812539184952978],\ +[751.4925373134326, 0.7617554858934169],\ +[758.9552238805968, 0.7405956112852665],\ +[767.9104477611941, 0.8012539184952978],\ +[779.8507462686566, 0.7575235109717868],\ +[788.8059701492537, 0.8139498432601882],\ +[797.7611940298507, 0.8195924764890282],\ +[808.2089552238806, 0.812539184952978],\ +[820.1492537313432, 0.7857366771159875],\ +[830.5970149253731, 0.8689655172413793],\ +[839.5522388059701, 0.8308777429467085],\ +[849.9999999999998, 0.8661442006269593],\ +[861.9402985074626, 0.8647335423197493],\ +[878.3582089552237, 0.8675548589341693],\ +[890.2985074626865, 0.9169278996865203],\ +[899.2537313432833, 0.8929467084639499],\ +[911.1940298507461, 0.9112852664576803],\ +[921.641791044776, 0.9211598746081505],\ +[929.1044776119402, 0.9239811912225706],\ +[941.0447761194027, 0.8985893416927899],\ +[949.9999999999998, 0.9042319749216301],\ +[960.4477611940297, 0.9253918495297806],\ +[967.9104477611938, 0.9380877742946708],\ +[978.3582089552237, 0.9282131661442006],\ +[990.2985074626863, 0.9394984326018809],\ +[1000.7462686567162, 0.9423197492163009],\ +[1005.2238805970148, 0.9338557993730408],\ +[1024.6268656716416, 0.9437304075235109],\ +[1048.507462686567, 0.9352664576802507],\ +[1055.9701492537313, 0.9169278996865203],\ +[1067.9104477611938, 0.9366771159874608],\ +[1085.820895522388, 0.9394984326018809],\ +[1108.2089552238804, 0.9352664576802507],\ +[1130.597014925373, 0.9366771159874608],\ +[1149.9999999999998, 0.945141065830721],\ +[1157.4626865671642, 0.9155172413793103],\ +[1160.4477611940297, 0.9437304075235109],\ +[1167.9104477611938, 0.8731974921630095],\ +[1176.8656716417909, 0.9719435736677116],\ +[1190.2985074626863, 0.9747648902821316],\ +[1206.7164179104475, 0.9634796238244514],\ +[1229.1044776119402, 0.9804075235109718],\ +[1247.014925373134, 0.9747648902821316],\ +[1267.9104477611938, 0.9818181818181818],\ +[1279.8507462686566, 0.9804075235109718],\ +[1300.7462686567162, 0.9860501567398119],\ +[1323.1343283582087, 0.9818181818181818],\ +[1342.5373134328356, 0.9832288401253919],\ +[1357.462686567164, 0.9818181818181818],\ +[1378.3582089552235, 0.9775862068965517],\ +[1411.1940298507461, 0.9056426332288401],\ +[1421.641791044776, 0.9761755485893417],\ +[1441.044776119403, 0.9663009404388715],\ +[1457.462686567164, 0.9437304075235109],\ +[1481.3432835820893, 0.89576802507837]]) + + + +grip_T = np.array([[-32.29312606747692, 1421.9810171318868],\ +[-32.221837174187485, 1585.5989284663256],\ +[-32.03712413123537, 1191.639782062812],\ +[-31.888495589824835, 1702.5160138727306],\ +[-31.743607515037507, 99.86908366818705],\ +[-31.741077199380626, 176.6641638544403],\ +[-31.649545780836164, 954.6427166789051],\ +[-31.6062003734966, 270.17582943472144],\ +[-31.361694871435216, 690.9178169976699],\ +[-31.324290205203106, 1826.1494371422832],\ +[-31.270548500925493, 457.21016196770506],\ +[-30.66255765406735, 1909.7323641124226],\ +[-29.835859523475555, 2000.0206275732899],\ +[-28.976432309930665, 2083.636558660693],\ +[-27.754234840796393, 2177.3297468859237],\ +[-26.36762186082714, 2261.033688952697],\ +[-23.495878610856707, 2418.441325555363],\ +[-21.97768921672979, 2495.489437307304],\ +[-19.04125789692264, 2616.179993454183],\ +[-16.40158859817762, 2730.1432103654934],\ +[-12.937531457049268, 2864.277443611091],\ +[-10.265463116523787, 2961.5515785594257],\ +[-8.912899384198177, 3011.8608546416167]]) + + +domec_T = np.array([[-53.85382059800665, 8.336338456108535],\ +[-54.385382059800676, 43.05509732761459],\ +[-54.252491694352166, 94.99896419172427],\ +[-53.58803986710964, 175.73446479402753],\ +[-52.92358803986711, 256.46996539633085],\ +[-50.93023255813954, 458.26076281525013],\ +[-50.00000000000001, 544.7315722035095],\ +[-48.671096345514954, 671.5605410755526],\ +[-46.94352159468439, 821.426653265098],\ +[-43.62126245847176, 1086.5360269463604],\ +[-40.43189368770765, 1317.0225499297953],\ +[-36.71096345514951, 1553.2060184297147],\ +[-34.05315614617941, 1714.4852032869649],\ +[-32.059800664451835, 1829.6709198744757],\ +[-29.800664451827252, 1950.5919452479416],\ +[-26.74418604651163, 2117.587257256412],\ +[-23.953488372093034, 2249.9789002017906],\ +[-20.4983388704319, 2422.6903393614816],\ +[-17.574750830564795, 2560.8364727275516],\ +[-14.38538205980067, 2704.7179148795776],\ +[-11.727574750830577, 2825.581395348837],\ +[-9.20265780730898, 2940.6903853974054],\ +[-6.279069767441868, 3073.062846708048],\ +[-3.8870431893687822, 3182.417346335924],\ +[-2.9568106312292457, 3222.6987792807654]]) + + +domef_largestev = np.array([[117.85714285714289, 0.43231450719822806],\ +[200.0000000000001, 0.4187006275378369],\ +[292.8571428571429, 0.4935031376891842],\ +[396.42857142857156, 0.49238095238095225],\ +[496.42857142857156, 0.5113990402362495],\ +[596.4285714285716, 0.505610926541159],\ +[700, 0.43782207456626066],\ +[800, 0.6211812476928756],\ +[900.0000000000002, 0.6448504983388704],\ +[1007.1428571428573, 0.660797342192691],\ +[1100, 0.5169952011812478],\ +[1196.4285714285713, 0.7204946474713916],\ +[1303.5714285714284, 0.672875599852344],\ +[1403.5714285714287, 0.738405315614618],\ +[1503.5714285714287, 0.7620745662606128],\ +[1599.9999999999998, 0.781077888519749],\ +[1703.5714285714282, 0.8032115171650056],\ +[1796.4285714285713, 0.8082465854558878],\ +[1900.0000000000002, 0.8179771133259506],\ +[1992.8571428571424, 0.8834772978959026],\ +[2092.857142857143, 0.9009449981543005],\ +[2189.2857142857147, 0.8191731266149871],\ +[2292.857142857143, 0.9172757475083056],\ +[2389.2857142857147, 0.9052713178294574],\ +[2492.857142857142, 0.9491103728313031]]) + + +domef_T = np.array([[-52.87425149700599, 328.935308433073],\ +[-51.59680638722555, 435.41540548622623],\ +[-50.55888223552894, 529.7334685075646],\ +[-49.20159680638723, 627.0722866082695],\ +[-47.84431137724551, 724.4111047089743],\ +[-46.24750499001996, 824.7767558670723],\ +[-44.89021956087824, 934.2958419336724],\ +[-43.612774451097806, 1028.59567102093],\ +[-42.01596806387226, 1125.916255187554],\ +[-40.339321357285435, 1220.1856943846776],\ +[-38.58283433133733, 1329.6743905611431],\ +[-36.746506986027946, 1423.9316738022128],\ +[-35.22954091816368, 1533.4386039127592],\ +[-33.233532934131745, 1633.7738651807226],\ +[-31.556886227544908, 1731.0883713693202],\ +[-29.161676646706596, 1825.3031087642012],\ +[-27.165668662674648, 1931.7285040151128],\ +[-25.169660678642714, 2025.973631300128],\ +[-23.17365269461078, 2114.128624602196],\ +[-20.69860279441118, 2214.4274180019984],\ +[-18.54291417165669, 2287.344920390644],\ +[-17.42514970059881, 2366.4315704765863],\ +[-16.147704590818364, 2430.280729649106],\ +[-14.790419161676652, 2488.033676860651],\ +[-13.9121756487026, 2530.5977569829893],\ +[-11.596806387225548, 2621.773505364423],\ +[-9.20159680638723, 2722.078376742252],\ +[-6.886227544910184, 2825.434393089582],\ +[-4.491017964071858, 2916.604063492989],\ +[-3.3732534930139764, 2968.2851106556677]]) + +ngrip_T = np.array([[-31.650568308198764, 2.824858757062117],\ +[-31.596490057386895, 200.56497175141243],\ +[-31.541577694737313, 432.2033898305085],\ +[-31.72953089550247, 793.7853107344635],\ +[-32.06831932025446, 1025.4237288135594],\ +[-32.30757039903911, 1302.25988700565],\ +[-32.15604008185417, 1460.45197740113],\ +[-31.660299612972107, 1607.3446327683619],\ +[-31.11618065750256, 1720.338983050848],\ +[-30.474609635659952, 1793.7853107344636],\ +[-29.487716313003247, 1901.129943502825],\ +[-28.353463232350194, 1997.175141242938],\ +[-27.31819142310601, 2070.621468926554],\ +[-26.03671760309622, 2149.717514124294],\ +[-23.966869077805956, 2268.3615819209044],\ +[-20.616519862983232, 2426.553672316384],\ +[-17.660983584679034, 2539.5480225988704],\ +[-15.099426131055651, 2641.2429378531074],\ +[-11.947039459050671, 2754.2372881355936],\ +[-10.420058721473378, 2810.7344632768363],\ +[-8.745440188620496, 2867.23163841808],\ +[-5.642405133680327, 2974.5762711864413],\ +[-3.57380777614663, 3042.372881355933],\ +[-2.7364985097201853, 3070.621468926554]]) + + + + diff --git a/doubleegrip.py b/doubleegrip.py new file mode 100644 index 0000000..eeed087 --- /dev/null +++ b/doubleegrip.py @@ -0,0 +1,512 @@ +#%% +import numpy as np# numpy for arrays +from tqdm import tqdm +import track +import pickle +import copy +from tqdm import tqdm +import agedepth +from matplotlib import pyplot as plt +import mcfab as mc +with open('path2dSGdt10.pkl', 'rb') as f: + path2d = pickle.load(f) + + + + + +depthsupper = np.array([5,25,50,75,100,150,200,250]) +depthslower = np.arange(375,1875,250) +depths = np.concatenate((depthsupper,depthslower)) +#colors = ['#03045e', '#0077b6', '#00b4d8','#90e0ef','#caf0f8','#f72585','#7209b7','#3a0ca3','#4361ee','#4cc9f0'] +#default colors +colors = ['#1f77b4', '#ff7f0e', '#2ca02c', '#d62728', '#9467bd', '#8c564b', '#e377c2', '#7f7f7f', '#bcbd22', '#17becf'] + + +#depths = np.linspace(100,1800,16) +times=-agedepth.depth2time(depths) + + +paths=[] +for t in times: + nt = path2d.nt - np.abs(path2d.t - t).argmin() + paths.append(track.path3d(copy.deepcopy(path2d),nt)) + + +for p in paths: + p.optimizeacc() + +import stoll +stoll_d,e_s,e_z,e_n = stoll.eigenvalues(dmin=depths[0],dmax=depths[-1]) +# From data we know smallest eigenvalue is approximately in streamline direction, +# and the largest eigenvalue is approximately perpendicular to the streamline direction. + + +L=12 +npoints = 10000 + + +depths = np.zeros(len(paths)) +ev_s = np.zeros(len(paths)) +ev_n = np.zeros(len(paths)) +ev_z = np.zeros(len(paths)) + +paths2 = copy.deepcopy(paths) +ev_s2 = np.zeros(len(paths)) +ev_n2 = np.zeros(len(paths)) +ev_z2 = np.zeros(len(paths)) + +for i in tqdm(range(len(paths))): + paths[i].Temperature() + paths2[i].Temperature() + paths[i].fabric_mc(npoints) + paths2[i].fabric_mc(npoints,x='Reduced') + depths[i] = paths[i].d[-1] + + + w,v = np.linalg.eig(paths[i].a2[-1,:2,:2]) + ev_n[i] = np.max(w) + ev_s[i] = np.min(w) + ev_z[i] = paths[i].a2[-1,2,2] + + w2,v2 = np.linalg.eig(paths2[i].a2[-1,:2,:2]) + ev_n2[i] = np.max(w2) + ev_s2[i] = np.min(w2) + ev_z2[i] = paths2[i].a2[-1,2,2] + +fig,ax = plt.subplots() +ax.scatter(stoll_d,e_z,s=0.3,marker='.',color=colors[0]) +ax.scatter(stoll_d,e_n,s=0.3,marker='.',color=colors[1]) +ax.scatter(stoll_d,e_s,s=0.3,marker='.',color=colors[2]) + +ax.plot(depths,ev_z,color=colors[0]) +ax.plot(depths,ev_n,color=colors[1]) +ax.plot(depths,ev_s,color=colors[2]) + +ax.plot(depths,ev_z2,color=colors[0],linestyle='--') +ax.plot(depths,ev_n2,color=colors[1],linestyle='--') +ax.plot(depths,ev_s2,color=colors[2],linestyle='--') + +ax.set_xlabel('Depth (m)') +ax.set_ylabel('Eigenvalue') + + + +#%% +import cartopy.crs as ccrs +import cartopy.mpl.geoaxes as geoaxes +from mpl_toolkits.axes_grid1.inset_locator import inset_axes +from meso_fab_mc import BuildHarmonics +import matplotlib.patheffects as path_effects + +plt.rcParams.update({ + "text.usetex": True, + "font.family": "serif", + "font.serif": ["Palatino"], + "font.size" : 11, + "figure.autolayout" : True, + +}) + +L=8 +mmax = 4 +vmax = 1.5 + + +def loadEGRIP(loc): + + if loc==0: + filename = 'stereo_EGRIP266_2_20.txt' + depth = 145.93 + elif loc==1: + filename = 'stereo_EGRIP1906_6_20.txt' + depth = 1048.3 + else: + filename = 'stereo_EGRIP2635_4_20.txt' + depth = 1499.07 + + # load data as tab delimited with header + data = np.loadtxt(filename, delimiter='\t', skiprows=1) + + # extract columns + lon = data[:,0] + lat = data[:,1] + + # convert to radians + lon = lon*np.pi/180 + lat = lat*np.pi/180 + + # convert to xyz + x = np.cos(lat)*np.cos(lon) + y = np.cos(lat)*np.sin(lon) + z = np.sin(lat) + + # create array of xyz + xyz = np.array([x,y,z]).T + m = np.ones(len(xyz)) + a2 = np.einsum('pi,pj->ij',xyz,xyz)/len(xyz) + w,v = np.linalg.eig(a2[:2,:2]) + epf_n = np.max(w) + epf_s = np.min(w) + epf_z =a2[2,2] + + w = np.array([epf_n,epf_z,epf_s]) + + return xyz,w,depth + +ev_exp = np.zeros((3,3)) +d_exp = np.zeros(3) +for loc in range(3): + _,ev_exp[loc,:],d_exp[loc] = loadEGRIP(loc) + +import seaborn as sns +import cmocean +colors = sns.color_palette("deep", 3) +colors_bright = sns.color_palette("bright", 3) + + + + +# Create vertical eigenvalue plot +fig,ax = plt.subplots(figsize=(4,4)) + + +ax.plot(ev_z,depths,linewidth=2,color =colors[0]) +ax.plot(ev_n,depths,linewidth=2,color =colors[1]) +ax.plot(ev_s,depths,linewidth=2,color =colors[2]) + +ax.plot(ev_z2,depths,linewidth=2,color =colors[0],linestyle='--') +ax.plot(ev_n2,depths,linewidth=2,color =colors[1],linestyle='--') +ax.plot(ev_s2,depths,linewidth=2,color =colors[2],linestyle='--') + + +ax.scatter(e_z,stoll_d,s=0.3,color=colors[0],alpha=0.5) +ax.scatter(e_n,stoll_d,s=0.3,color=colors[1],alpha=0.5) +ax.scatter(e_s,stoll_d,s=0.3,color=colors[2],alpha=0.5) + +#Highlight these points +ax.scatter(ev_exp[:,1],d_exp,s=100,color=colors_bright[0],marker='x') +ax.scatter(ev_exp[:,0],d_exp,s=100,color=colors_bright[1],marker='x') +ax.scatter(ev_exp[:,2],d_exp,s=100,color=colors_bright[2],marker='x') + + +ax.set_title('Eigenvalues at EGRIP') + + +ax.set_xlabel('Eigenvalues of $\mathbf{A}^{(2)}$') +ax.set_ylabel('Depth (m)') +ax.set_ylim(0,2100) +#flip y axis +ax.invert_yaxis() +ax.grid() + + +nearestdepths = np.concatenate(([100],depthslower[1::2])) + + +#Custom legend +from matplotlib.lines import Line2D +legend_elements = [Line2D([0], [0], color='k', lw=2, label='SpecCAF'), + Line2D([0], [0], color='k', lw=2, linestyle='--', label=r"$\tilde{\lambda}' = \tilde{\lambda}/4$"), + Line2D([0], [0], marker='o', color='w', label='EGRIP', + markerfacecolor='k', markersize=4), + Line2D([0], [0], marker='x', color='w', label='Pole figures', + markerfacecolor='k', markersize=8,markeredgecolor='k')] +# put legend outside top right +ax.legend(handles=legend_elements,fontsize=9,ncol=2,loc='lower center') + + + + + +# # Get aspect ratio in fig coords for axes +# def get_aspect(ax): +# pos = ax.get_position() +# return (pos.ymax-pos.ymin)/(pos.xmax-pos.xmin) + + +# ar = get_aspect(ax2) + +# for n in nearestdepths: + +# j = np.abs(depths - n).argmin() +# pcol=inset(fig,ax2,0.93,depths[j],j,r=0.1,vmax=vmax) + + +# cbax2 = ax2.inset_axes([0.9,-0.14,0.3,0.04]) +# cbar=fig.colorbar(pcol, cax=cbax2, ticks=[0,vmax],orientation="horizontal", format='%.1f') +# cbar.set_label('$f^*$',labelpad=-10,fontsize=9) + +fig.savefig('eigenvaluesdouble.pdf', bbox_inches='tight') + +#%% +# Create three subplots sharing y axis, y is depth + +fig,axs = plt.subplots(1, 3, sharey=True,figsize=(6,4)) + +colors = sns.color_palette("cmo.ice", 3) + + + +# Plot smallest eigenvalue in left subplot +axs[0].plot(ev_s,depths,linewidth=2,color =colors[0],label='SpecCAF') +axs[0].plot(ev_s2,depths,linewidth=2,color =colors[1],label=r"$\tilde{\lambda}' = 0.25\tilde{\lambda}$") +axs[0].scatter(e_s,stoll_d,s=0.3,color=colors[2],alpha=0.5,label='EGRIP') + +# Plot middle eigenvalue in middle subplot +axs[1].plot(ev_z,depths,linewidth=2,color =colors[0]) +axs[1].plot(ev_z2,depths,linewidth=2,color =colors[1]) +axs[1].scatter(e_z,stoll_d,s=0.3,color=colors[2],alpha=0.5) + +# Plot largest eigenvalue in right subplot +axs[2].plot(ev_n,depths,linewidth=2,color =colors[0]) +axs[2].plot(ev_n2,depths,linewidth=2,color =colors[1]) +axs[2].scatter(e_n,stoll_d,s=0.3,color=colors[2],alpha=0.5) + +#Highlight these points +axs[0].scatter(ev_exp[:,2],d_exp,s=100,color=colors[2],marker='x',label='Pole figures') +axs[1].scatter(ev_exp[:,1],d_exp,s=100,color=colors[2],marker='x') +axs[2].scatter(ev_exp[:,0],d_exp,s=100,color=colors[2],marker='x') + +# Set titles +axs[0].set_xlabel('$e_s$') +axs[1].set_xlabel('$e_z$') +axs[2].set_xlabel('$e_n$') + +# fig title +fig.suptitle('Eigenvalues at EGRIP') + +# grids +for ax in axs: + ax.grid() + +# legend - move verticall down a bit +fig.legend(loc='lower center',ncol=4,bbox_to_anchor=(0.5, -0.07)) +# Set y label +axs[0].set_ylabel('Depth (m)') +#flip y axis +axs[0].invert_yaxis() + +fig.savefig('eigenvaluessplit.pdf', bbox_inches='tight') + + +#%% +L=8 +mmax = 8 +vmax=2 + +def J(odf): + J=0 + Sff = 0 + for l in range(0,odf.L+1,2): + Sff = 0*Sff + for m in range(0,l+1,1): + Sff=Sff+np.abs(odf.f[odf.sh.idx(l,abs(m))])**2 + J=J+Sff + return J + +loc = 2 +xyz,epf,depth = loadEGRIP(loc=loc) + + +def angle_correction(xyz): + angle_corrector = 124.94 #Westhoff average of two peaks + + a2 = np.einsum('pi,pj->ij',xyz,xyz)/len(xyz) + w,v = np.linalg.eig(a2[:2,:2]) + + # Find eigenvector corresponding to largest eigenvalue + idx = np.argmax(w) + v = v[:,idx] + + # Find angle between eigenvector and y axis + angle_v = 90 - np.arctan2(v[1],v[0])*180/np.pi + + # Correct angle + angle = angle_corrector - angle_v + # Convert xyz to phi,theta + phi = np.arctan2(xyz[:,1],xyz[:,0]) + theta = np.arccos(xyz[:,2]) + + # Update phi + phi = phi - angle*np.pi/180 + + # Convert back to xyz + xyz[:,0] = np.cos(phi)*np.sin(theta) + xyz[:,1] = np.sin(phi)*np.sin(theta) + xyz[:,2] = np.cos(theta) + + return xyz + +xyz = angle_correction(xyz) +m = np.ones(len(xyz)) + +#get path with nearest depth +j = np.abs(depths-depth).argmin() + +epf = np.sort(epf) +espec = np.sort(np.array([ev_n[j],ev_z[j],ev_s[j]])) +espec2 = np.sort(np.array([ev_n2[j],ev_z2[j],ev_s2[j]])) + +n1 = paths[j].n[-1,...] +m1 = paths[j].m[-1,...] + +n2 = paths2[j].n[-1,...] +m2 = paths2[j].m[-1,...] + +fig,ax = plt.subplots(1,3,figsize=(6,3.3),subplot_kw=\ + {'projection':ccrs.AzimuthalEquidistant(90,90)}) +odf1 = BuildHarmonics(n1,m1,L,mmax) +odf2 = BuildHarmonics(n2,m2,L,mmax) +odf_exp = BuildHarmonics(xyz,m,L,mmax) + +odf1.plot(fig,ax[0],hemisphere=True) +odf2.plot(fig,ax[1],hemisphere=True) +odf_exp.plot(fig,ax[2],hemisphere=True) + + +J1 = J(odf1) +J2 = J(odf2) +J_exp = J(odf_exp) + +# ax[0].set_title('(a) SpecCAF'\ +# +'\n'+r'$e_{1,2,3} = '+'{:.2f}, {:.2f}, {:.2f}'.format(espec[0],espec[1],espec[2])+'$') +# ax[1].set_title(r"(b) $\tilde{\lambda}' = \tilde{\lambda}/4$"\ +# +'\n'+r'$e_{1,2,3} = '+'{:.2f}, {:.2f}, {:.2f}'.format(espec2[0],espec2[1],espec2[2])+'$') +# ax[2].set_title('(c) EGRIP ice core data'\ +# +'\n'+r'$e_{1,2,3} = '+'{:.2f}, {:.2f}, {:.2f}'.format(epf[0],epf[1],epf[2])+'$') + + +ax[0].set_title('(a) SpecCAF \n $J={:.2f}$'.format(J1)) +ax[1].set_title(r"(b) $\tilde{\lambda}' = 0.25\tilde{\lambda}$" +"\n$J={:.2f}$".format(J2)) +ax[2].set_title('(c) EGRIP ice core data\n$J={:.2f}$'.format(J_exp)) +#figure title +fig.suptitle('Pole figures at {:.0f} m'.format(depth),y=1.05) +fig.savefig('polefigs' + str(loc) +'.pdf', bbox_inches='tight') + + +#%% +# fig = plt.figure(figsize=(6.5,5.5)) + +# subfigs = fig.subfigures(nrows=2, ncols=1) + +fig,axs = plt.subplots(nrows=2,ncols=3,figsize=(6,6),subplot_kw=\ + {'projection':ccrs.AzimuthalEquidistant(90,90)}) + + + + +rowletter = ['(a)','(b)','(c)'] + +for row in range(2): + + + loc = row+1 + xyz,epf,depth = loadEGRIP(loc=loc) + xyz = angle_correction(xyz) + m = np.ones(len(xyz)) + + #get path with nearest depth + j = np.abs(depths-depth).argmin() + + epf = np.sort(epf) + espec = np.sort(np.array([ev_n[j],ev_z[j],ev_s[j]])) + espec2 = np.sort(np.array([ev_n2[j],ev_z2[j],ev_s2[j]])) + + n1 = paths[j].n[-1,...] + m1 = paths[j].m[-1,...] + + n2 = paths2[j].n[-1,...] + m2 = paths2[j].m[-1,...] + + # ax = subfig.subplots(nrows=1, ncols=3,subplot_kw=\ + # {'projection':ccrs.AzimuthalEquidistant(90,90)}) + ax = axs[row,:] + odf1 = BuildHarmonics(n1,m1,L,mmax) + odf2 = BuildHarmonics(n2,m2,L,mmax) + odf_exp = BuildHarmonics(xyz,m,L,mmax) + + pcol1 = odf1.plot(fig,ax[0],hemisphere=True,colorbar=True,pad=0.05) + pcol2 = odf2.plot(fig,ax[1],hemisphere=True,colorbar=True,pad=0.05) + pcol_exp = odf_exp.plot(fig,ax[2],hemisphere=True, colorbar=True,pad=0.05) + + + + J1 = J(odf1) + J2 = J(odf2) + J_exp = J(odf_exp) + + fmax1 = pcol1.get_clim()[1] + fmax2 = pcol2.get_clim()[1] + fmax_exp = pcol_exp.get_clim()[1] + + def titlestr(J,fmax): + return '\n $J={:.2f}$'.format(J)# + r'\; \rho^*_{max} =' + '{:.2f}$'.format(fmax) + + # add J and fmax to plot + colnumeral = ['i','ii','iii'] + + ax[0].set_title('(' +colnumeral[0] + ') SpecCAF' + titlestr(J1,fmax1)) + ax[1].set_title('(' +colnumeral[1] + r") $\tilde{\lambda}' = \tilde{\lambda}/4$" + titlestr(J2,fmax2)) + ax[2].set_title('(' +colnumeral[2] + ') EGRIP ice core data' + titlestr(J_exp,fmax_exp)) + + #subfig.suptitle(rowletter[row] + f' Pole figures at {depth:.0f} m',y=0.96) + #fig.suptitle(rowletter[row] + f' Pole figures at {depth:.0f} m',y=0.96) + + +fig.text(0.5, 1.0, '(a) Pole figures at 1048 m', ha='center', va='center',fontsize=13) +fig.text(0.5, 0.5, '(b) Pole figures at 1499 m', ha='center', va='center',fontsize=13) + +# Add custom colorbar from 0 to 1, horiztonal +# with customticklabels so max is \rho^*_{max} +# cbax = fig.add_axes([0.1, 0.03, 0.8, 0.015]) +# cbar=fig.colorbar(pcol_exp, cax=cbax, ticks=[0,fmax_exp],orientation="horizontal") +# cbar.ax.set_xticklabels(['0',r'$\rho^*_{max}$']) +# cbar.set_label(r'$\rho^*$',labelpad=-10) + +fig.savefig('polefigs.pdf',bbox_inches='tight') + + #Add vertical text relative to ax[0] centre in fig coords + + # #Get ax[0] centre in fig coords + # ax0centre = ax[0].transAxes.transform([0,0.5]) + # #Get fig coords of ax[0] centre + # ax0centre = fig.transFigure.inverted().transform(ax0centre) + + # fig.text(ax0centre[0]-0.1,ax0centre[1],f'Depth $= {depth:.0f}$ m',rotation=90,va='center',ha='center') + + +#%% + +# Calculate effective strain along paths +# and plot against depth +depths = np.zeros(len(paths)) +strains = np.zeros(len(paths)) +times = np.zeros(len(paths)) +for i in range(len(paths)): + p = paths[i] + D = 0.5*(p.gradu+np.transpose(p.gradu,(0,2,1))) + effSR = np.sqrt(0.5*np.einsum('pij,pji->p',D,D)) + + strain = np.cumsum(effSR)*p.dt + + p.strain = strain + + depths[i] = p.d[-1] + strains[i] = strain[-1] + times[i] = -p.t[0] + +fig,ax = plt.subplots() +ax.plot(depths,strains) + +plt.figure() +plt.plot(depths,times) + +# interpolate to find value at depth=500 +from scipy.interpolate import interp1d +f = interp1d(depths,strains) +f2 = interp1d(depths,times) +print(f(500)) +print(f2(500)) + + diff --git a/egrip_temperature.py b/egrip_temperature.py new file mode 100644 index 0000000..587b611 --- /dev/null +++ b/egrip_temperature.py @@ -0,0 +1,36 @@ +import numpy as np + + +raw = np.array([[-30.907258064516128, 9.333333333333186],\ +[-31.84811827956989, 105.3333333333332],\ +[-32.016129032258064, 185.3333333333332],\ +[-32.016129032258064, 270.6666666666665],\ +[-31.948924731182792, 339.99999999999983],\ +[-31.98252688172043, 451.99999999999983],\ +[-32.016129032258064, 579.9999999999999],\ +[-32.1505376344086, 718.6666666666665],\ +[-32.45295698924731, 974.6666666666664],\ +[-32.45295698924731, 1129.333333333333],\ +[-32.21774193548387, 1235.9999999999998],\ +[-31.948924731182792, 1321.333333333333],\ +[-31.444892473118276, 1417.3333333333333],\ +[-30.571236559139784, 1518.6666666666665],\ +[-29.798387096774192, 1588],\ +[-28.891129032258064, 1673.333333333333],\ +[-28.18548387096774, 1737.3333333333333],\ +[-27.311827956989248, 1811.9999999999998],\ +[-26.034946236559136, 1897.3333333333333],\ +[-25.19489247311828, 1945.3333333333333],\ +[-24.052419354838708, 2025.333333333333],\ +[-22.47311827956989, 2067.9999999999995],\ +[-21.56586021505376, 2099.9999999999995],\ +[-19.751344086021504, 2158.6666666666665],\ +[-15.215053763440856, 2286.6666666666665],\ +[-12.459677419354836, 2388],\ +[-10.477150537634412, 2430.6666666666665],\ +[-7.284946236559136, 2526.6666666666665],\ +[-5.1680107526881685, 2580],\ +[-2.883064516129032, 2649.333333333333]]) + +T = raw[:,0] +depth = raw[:,1] \ No newline at end of file diff --git a/gerber.py b/gerber.py new file mode 100644 index 0000000..557c765 --- /dev/null +++ b/gerber.py @@ -0,0 +1,102 @@ +#%% +import numpy as np +from scipy.interpolate import interp1d,griddata +import pandas as pd +import pyproj + +filename = 'UpstreamCorrection_data_10Jun2021.csv' + +data = np.genfromtxt(filename,delimiter=',') + +s = -np.flipud(data[3:,5])*1e3 +surf = np.flipud(data[3:,10]) +acc_s = np.flipud(data[3:,20]) + + +line1 = pd.read_excel("./SupplementaryMaterial/UpstreamCorrection_data_10Jun2021.xls", + sheet_name=1) +line2 = pd.read_excel("./SupplementaryMaterial/UpstreamCorrection_data_10Jun2021.xls", + sheet_name=2) +line3 = pd.read_excel("./SupplementaryMaterial/UpstreamCorrection_data_10Jun2021.xls", + sheet_name=3) + + + +P = pyproj.Proj('epsg:3413') + +def LonLat_To_XY(Lon,Lat): + return P(Lon,Lat) + + +x1,y1 = LonLat_To_XY(line1['longitude'].to_numpy(),line1['latitude '].to_numpy()) +acc1 = line1['acc rate [m/yr]'].to_numpy() +basalmelt1 = line1['basal melt [m/yr]'].to_numpy() +f1 = line1['basal sliding'].to_numpy() + +x2,y2 = LonLat_To_XY(line2['longitude'].to_numpy(),line2['latitude '].to_numpy()) +acc2 = line2['acc rate [m/yr]'].to_numpy() +basalmelt2 = line2['basal melt [m/yr]'].to_numpy() +f2 = line2['basal sliding'].to_numpy() + +x3,y3 = LonLat_To_XY(line3['longitude'].to_numpy(),line3['latitude '].to_numpy()) +x3 = x3[~np.isnan(x3)] +y3 = y3[~np.isnan(y3)] +acc3 = line3['acc rate [m/yr]'].to_numpy() +basalmelt3 = line3['basal melt [m/yr]'].to_numpy() +f3 = line3['basal sliding'].to_numpy() + + +x = np.concatenate((x1,x2,x3),axis=0) +y = np.concatenate((y1,y2,y3),axis=0) +acc = np.concatenate((acc1,acc2,acc3),axis=0) +basalmelt = np.concatenate((basalmelt1,basalmelt2,basalmelt3),axis=0) +f = np.concatenate((f1,f2,f3),axis=0) + +nans = np.isnan(x) +y=y[~nans] +acc = acc[~nans] +basalmelt = basalmelt[~nans] +f = f[~nans] +x = x[~nans] + + +class gerb(): + def __init__(self): + self.surf_interp = interp1d(s,surf,fill_value='extrapolate') + self.acc_interp = interp1d(s,acc_s,fill_value='extrapolate') + + def interps(self,si): + self.si = si + + self.surf_s = self.surf_interp(si) + self.acc_s = self.acc_interp(si) + + def interps_xy(self,xi,yi): + + if xi.ndim==2: + pts = np.reshape([xi,yi],(2,-1)).T + elif xi.ndim==1: + pts = np.stack((xi,yi),1) + else: + pts=[xi,yi] + + + self.acc = griddata(np.stack((x,y),1),acc,pts) + self.f = griddata(np.stack((x,y),1),f,pts) + self.basalmelt = griddata(np.stack((x,y),1),basalmelt,pts) + + # self.acc2 = griddata(np.stack((x2,y2),1),acc2,pts) + # self.f2 = griddata(np.stack((x2,y2),1),f2,pts) + + # self.acc3 = griddata(np.stack((x3,y3),1),acc3,pts) + # self.f3 = griddata(np.stack((x3,y3),1),f3,pts) + + + + + + + + + +# %% diff --git a/grip_data.py b/grip_data.py new file mode 100644 index 0000000..2f60f1d --- /dev/null +++ b/grip_data.py @@ -0,0 +1,69 @@ +import numpy as np + + +largest_raw = np.array([[0.45271398494784987, 141.38615298986065],\ +[0.4259552791423662, 252.0509159547954],\ +[0.5249255862352685, 357.7789608692498],\ +[0.6055525978268761, 471.5412724148781],\ +[0.5971674370205453, 578.116303269366],\ +[0.6042397696198243, 700.3484742153283],\ +[0.6539820438980715, 786.7410400987346],\ +[0.6488154296638677, 913.0145439585704],\ +[0.6678544636159041, 995.7045713041168],\ +[0.7206035379812696, 1074.1838685477826],\ +[0.6630508433560003, 1169.3124894126759],\ +[0.6976986182029379, 1287.3823294533313],\ +[0.7476526389661933, 1401.3866369818263],\ +[0.7607688212375674, 1511.7368051690344],\ +[0.8041647508651357, 1570.5660286039256],\ +[0.8078672894029957, 1653.377053940905],\ +[0.8024889286837837, 1752.038816155652],\ +[0.8059494712387774, 1803.293565326816],\ +[0.8496176463470706, 1897.6235994482495],\ +[0.8472460857149773, 1988.3720930232562],\ +[0.8662548701691553, 2067.117585848075],\ +[0.9284478377658931, 2177.0805604626967],\ +[0.895554533794739, 2287.793722624205],\ +[0.939283207898749, 2390.012825787092],\ +[0.9399789463494908, 2480.737119763812],\ +[0.9745359727028531, 2586.9733562422866],\ +[0.9569791641458754, 2697.5655204123614],\ +[0.9333482564189435, 2816.0951528204637],\ +[0.6760278779372262, 2861.5177988045402],\ +[0.878741862885076, 2895.4214360041633],\ +[0.7717796384580016, 2947.5473707136466],\ +[0.7014132565399416, 2971.771168598602],\ +[0.8580875057474044, 3002.093265251797]]) + + + +T_raw = np.array([[-32.29312606747692, 1421.9810171318868],\ +[-32.221837174187485, 1585.5989284663256],\ +[-32.03712413123537, 1191.639782062812],\ +[-31.888495589824835, 1702.5160138727306],\ +[-31.743607515037507, 99.86908366818705],\ +[-31.741077199380626, 176.6641638544403],\ +[-31.649545780836164, 954.6427166789051],\ +[-31.6062003734966, 270.17582943472144],\ +[-31.361694871435216, 690.9178169976699],\ +[-31.324290205203106, 1826.1494371422832],\ +[-31.270548500925493, 457.21016196770506],\ +[-30.66255765406735, 1909.7323641124226],\ +[-29.835859523475555, 2000.0206275732899],\ +[-28.976432309930665, 2083.636558660693],\ +[-27.754234840796393, 2177.3297468859237],\ +[-26.36762186082714, 2261.033688952697],\ +[-23.495878610856707, 2418.441325555363],\ +[-21.97768921672979, 2495.489437307304],\ +[-19.04125789692264, 2616.179993454183],\ +[-16.40158859817762, 2730.1432103654934],\ +[-12.937531457049268, 2864.277443611091],\ +[-10.265463116523787, 2961.5515785594257],\ +[-8.912899384198177, 3011.8608546416167]]) + + +largest_ev_d = largest_raw[:,1] +largest_ev = largest_raw[:,0] + +T = T_raw[:,0] +T_d = T_raw[:,1] diff --git a/iceexperiments.csv b/iceexperiments.csv new file mode 100644 index 0000000..ad0e979 --- /dev/null +++ b/iceexperiments.csv @@ -0,0 +1,44 @@ +strainrate,T +1.00E-05,-3 +3.40E-08,-5 +1.37E-04,-5.2 +7.98E-05,-5.4 +1.08E-04,-5.6 +1.78E-04,-5.6 +6.00E-07,-7 +2.50E-06,-7 +2.50E-06,-7 +1.00E-05,-7 +2.50E-06,-17 +1.10E-05,-9.1 +2.31E-06,-9.3 +9.05E-05,-9.3 +1.13E-05,-9.3 +1.18E-06,-9.5 +4.40E-05,-9.6 +2.42E-04,-9.7 +1.35E-05,-9.7 +5.02E-05,-9.8 +2.50E-06,-10 +1.00E-05,-10 +6.60E-09,-10 +7.50E-08,-15 +3.60E-08,-15 +1.40E-08,-15 +3.30E-09,-15 +7.00E-10,-15 +2.10E-07,-19 +1.80E-08,-19 +6.80E-09,-19 +1.30E-09,-19 +4.90E-10,-19 +9.49E-05,-20.1 +1.19E-04,-20.4 +2.30E-08,-21 +7.20E-10,-21 +1.28E-04,-29.9 +2.60E-06,-30 +7.20E-06,-30 +2.80E-05,-30 +1.25E-04,-30.5 +1.46E-04,-30.6 \ No newline at end of file diff --git a/path2dSGdt10.pkl b/path2dSGdt10.pkl new file mode 100644 index 0000000..ba8c35f Binary files /dev/null and b/path2dSGdt10.pkl differ diff --git a/plotdivide.py b/plotdivide.py new file mode 100644 index 0000000..a067912 --- /dev/null +++ b/plotdivide.py @@ -0,0 +1,237 @@ +#%% +import numpy as np +import matplotlib.pyplot as plt +import track +import divide_data +from tqdm import tqdm +import cartopy.crs as ccrs +import cartopy.mpl.geoaxes as geoaxes +from mpl_toolkits.axes_grid1.inset_locator import inset_axes +from mcfab import BuildHarmonics +import matplotlib.patheffects as path_effects + + +plt.rcParams.update({ + "text.usetex": True, + "font.family": "serif", + "font.serif": ["Palatino"], + "font.size" : 11, + "figure.autolayout" : True, + +}) +colors = ['#1f77b4', '#ff7f0e', '#2ca02c', '#d62728', '#9467bd', '#8c564b', '#e377c2', '#7f7f7f', '#bcbd22', '#17becf'] + +npoints=10000 +location = 'GRIP'#,'DomeF','DomeC','Talos'] +#locations = ['DomeF'] + + +param = 'Richards' +legend = 'Richards et al. (2021)' +sr_type = 'SR' +model = 'R1' + + +p = track.divide(dt=100,location=location,dh=0.9) +p.depth() + + + + +data =divide_data.data(location) + +ev1 = data.largest_ev +ev2 = ev3 = (1 - ev1)/2 + + + +a2,a4,n,m = track.fabric(p,npoints) + +a2r,a4r,nr,mr = track.fabric(p,npoints,x='Reduced') + +eigvals = np.linalg.eigvals(a2) + +eigvals = np.sort(eigvals,axis=1) + +eigvalsr = np.linalg.eigvals(a2r) + +eigvalsr = np.sort(eigvalsr,axis=1) +#%% + +fig,ax = plt.subplots(1,1,figsize=(4,4)) + +ax.scatter(ev2,data.largest_ev_d,color=colors[0],s=10) +ax.scatter(ev1,data.largest_ev_d,color=colors[1],s=10) +ax.scatter(ev3,data.largest_ev_d,color=colors[2],s=10) + + + +ax.plot(eigvals[:,1],p.d,color=colors[0],linewidth=2) +ax.plot(eigvals[:,2],p.d,color=colors[1],linewidth=2) +ax.plot(eigvals[:,0],p.d,color=colors[2],linewidth=2) + + + + +vmax = 1.5 +if vmax<1: + ax.set_title('Eigenvalues at GRIP') +else: + ax.set_title(r"Eigenvalues at GRIP, $\tilde{\lambda}' = \tilde{\lambda}/4$") + + +ax.set_xlabel('Eigenvalues of $\mathbf{A}^{(2)}$') +ax.set_ylabel('Depth (m)') +ax.grid() +ax.set_ylim(0,3500) +ax.invert_yaxis() + + + +#Custom legend +from matplotlib.lines import Line2D +legend_elements = [Line2D([0], [0], color='k', lw=2, label='SpecCAF'), + Line2D([0], [0], marker='o', color='w', label='GRIP', + markerfacecolor='k', markersize=4)] +# put legend outside top right +ax.legend(handles=legend_elements,fontsize=9,ncol=2,loc='lower center') + + + +# ar = get_aspect(ax2) + +nearestdepths = np.linspace(0,p.d[-1],4) +L=8 +mmax = 8 + + + +def inset(fig,ax,insx,insy,j,r=0.1,hemisphere=True,vmax=0.7): + centre=ax.transAxes.inverted().transform(ax.transData.transform((insx, insy))) + + ins = inset_axes(ax,width="100%", height="100%", + bbox_to_anchor=(centre[0]-r,centre[1]-r,2*r,2*r), + bbox_transform=ax.transAxes, loc='center', + axes_class=geoaxes.GeoAxes, + axes_kwargs=dict(map_projection=ccrs.AzimuthalEquidistant(0,90) + )) + + #odf = Reconstruct(paths[j].mmc.n,paths[j].mmc.m) + odf = BuildHarmonics(n[j,...],m[j],L,mmax) + pcol = odf.plot(fig,ins,hemisphere=hemisphere,vmax=vmax,colorbar=False) + + geo = ccrs.RotatedPole() + + + text = [] + text.append(ins.text(0,90,'$z$',transform=geo,ha='center',va='center',color='white')) + # #ax.text(90,0,'$y$',transform=geo,ha='center',va='center') + # #ax.text(0,0,'$x$',transform=geo,ha='center',va='center') + # text.append(ins.text(s_dir,0,'$s$',transform=geo,ha='center',va='center',color='white')) + # text.append(ins.text(s_dir+90,0,'$n$',transform=geo,ha='center',va='center',color='white')) + # #text.append(ax.text(s_dir+270,0,'$-n$',transform=geo,ha='center',va='center',color='white')) + + for tex in text: + tex.set_path_effects([path_effects.Stroke(linewidth=1, foreground='black'), + path_effects.Normal()]) + + return pcol + + + +for d in nearestdepths: + + j = np.abs(p.d - d).argmin() + pcol=inset(fig,ax,1.15,p.d[j],j,r=0.1,vmax=vmax) + + +cbax2 = ax.inset_axes([0.9,-0.12,0.3,0.04]) +cbar=fig.colorbar(pcol, cax=cbax2, ticks=[0,vmax],orientation="horizontal", format='%.1f') +cbar.set_label('$f^*$',labelpad=-10,fontsize=9) + +if vmax<1: + fig.savefig('gripcomparison.pdf',bbox_inches='tight') +else: + fig.savefig('gripcomparisonlam.pdf',bbox_inches='tight') + + + +#%% +import seaborn as sns +colors = sns.color_palette("deep", 3) + +fig,ax = plt.subplots(1,1,figsize=(4,4)) + +ax.scatter(ev2,data.largest_ev_d,color=colors[0],s=6) +ax.scatter(ev1,data.largest_ev_d,color=colors[1],s=6) +ax.scatter(ev3,data.largest_ev_d,color=colors[2],s=6) + + + +ax.plot(eigvals[:,1],p.d,color=colors[0],linewidth=2) +ax.plot(eigvals[:,2],p.d,color=colors[1],linewidth=2) +ax.plot(eigvals[:,0],p.d,color=colors[2],linewidth=2) + +ax.plot(eigvalsr[:,1],p.d,color=colors[0],linewidth=2,linestyle='--') +ax.plot(eigvalsr[:,2],p.d,color=colors[1],linewidth=2,linestyle='--') +ax.plot(eigvalsr[:,0],p.d,color=colors[2],linewidth=2,linestyle='--') + + + +ax.set_title('Eigenvalues at GRIP') + + +ax.set_xlabel('Eigenvalues of $\mathbf{A}^{(2)}$') +ax.set_ylabel('Depth (m)') +ax.grid() +ax.set_ylim(0,3700) +ax.invert_yaxis() + + +from matplotlib.lines import Line2D +#Custom legend +legend_elements = [Line2D([0], [0], color='k', lw=2, label='SpecCAF'), + Line2D([0], [0], color='k', lw=2, linestyle='--', label=r"$\tilde{\lambda}' = \tilde{\lambda}/4$"), + Line2D([0], [0], marker='o', color='w', label='GRIP', + markerfacecolor='k', markersize=4)] +# put legend outside top right +ax.legend(handles=legend_elements,fontsize=9,ncol=2,loc='lower center') + +fig.savefig('gripcomparisondouble.pdf',bbox_inches='tight') + + +#%% +# plot 3 vertical subplots for each eigenvalue, sharing y axis +fig,ax = plt.subplots(1,3,figsize=(6,4),sharey=True) + +import cmocean +colors = sns.color_palette("cmo.ice", 3) + +#plot smallest eigenvalue in left plot, etc. +ax[0].plot(eigvals[:,0],p.d,color=colors[0],linewidth=2,label='SpecCAF') +ax[1].plot(eigvals[:,1],p.d,color=colors[0],linewidth=2) +ax[2].plot(eigvals[:,2],p.d,color=colors[0],linewidth=2) + +ax[0].plot(eigvalsr[:,0],p.d,color=colors[1],linewidth=2,label=r"$\tilde{\lambda}' = 0.25\tilde{\lambda}$") +ax[1].plot(eigvalsr[:,1],p.d,color=colors[1],linewidth=2) +ax[2].plot(eigvalsr[:,2],p.d,color=colors[1],linewidth=2) + +ax[0].scatter(ev3,data.largest_ev_d,color=colors[2],s=6,label='GRIP') +ax[1].scatter(ev2,data.largest_ev_d,color=colors[2],s=6) +ax[2].scatter(ev1,data.largest_ev_d,color=colors[2],s=6) + +ax[0].set_xlabel('$e_1$') +ax[1].set_xlabel('$e_2$') +ax[2].set_xlabel('$e_3$') + +ax[0].set_ylabel('Depth (m)') + +fig.suptitle('Eigenvalues at GRIP') + +for a in ax: + a.grid() + a.invert_yaxis() + +fig.legend(loc='lower center',ncol=3,bbox_to_anchor=(0.5,-0.08)) + +fig.savefig('gripcomparisonsplit.pdf',bbox_inches='tight') \ No newline at end of file diff --git a/quickplot.py b/quickplot.py new file mode 100644 index 0000000..489992a --- /dev/null +++ b/quickplot.py @@ -0,0 +1,413 @@ +#%% +import numpy as np# numpy for arrays +from tqdm import tqdm +import track +import pickle +import copy +from tqdm import tqdm +import agedepth +from matplotlib import pyplot as plt +import mcfab as mc +with open('path2dSGdt10.pkl', 'rb') as f: + path2d = pickle.load(f) + + + + + +depthsupper = np.array([5,25,50,75,100,150,200,250]) +depthslower = np.arange(375,1875,250) +depths = np.concatenate((depthsupper,depthslower)) +#colors = ['#03045e', '#0077b6', '#00b4d8','#90e0ef','#caf0f8','#f72585','#7209b7','#3a0ca3','#4361ee','#4cc9f0'] +#default colors +colors = ['#1f77b4', '#ff7f0e', '#2ca02c', '#d62728', '#9467bd', '#8c564b', '#e377c2', '#7f7f7f', '#bcbd22', '#17becf'] + + +#depths = np.linspace(100,1800,16) +times=-agedepth.depth2time(depths) + + +paths=[] +for t in times: + nt = path2d.nt - np.abs(path2d.t - t).argmin() + paths.append(track.path3d(copy.deepcopy(path2d),nt)) + + +for p in paths: + p.optimizeacc() + +import stoll +stoll_d,e_s,e_z,e_n = stoll.eigenvalues(dmin=depths[0],dmax=depths[-1]) +# From data we know smallest eigenvalue is approximately in streamline direction, +# and the largest eigenvalue is approximately perpendicular to the streamline direction. + + +L=12 +npoints = 10000 + + +depths = np.zeros(len(paths)) +ev_s = np.zeros(len(paths)) +ev_n = np.zeros(len(paths)) +ev_z = np.zeros(len(paths)) +for i in tqdm(range(len(paths))): + paths[i].Temperature() + paths[i].fabric_mc(npoints,) + depths[i] = paths[i].d[-1] + + ## Due to lack of vertical shear we know one eigenvector is (0,0,1), + # so eigenvalue is just the vertical component of the fabric tensor. + # TODO: update to find closest to vertical eigenvalue + w,v = np.linalg.eig(paths[i].a2[-1,:2,:2]) + ev_n[i] = np.max(w) + ev_s[i] = np.min(w) + ev_z[i] = paths[i].a2[-1,2,2] + +fig,ax = plt.subplots() +ax.scatter(stoll_d,e_z,s=0.3,marker='.',color=colors[0]) +ax.scatter(stoll_d,e_n,s=0.3,marker='.',color=colors[1]) +ax.scatter(stoll_d,e_s,s=0.3,marker='.',color=colors[2]) + +ax.plot(depths,ev_z,color=colors[0]) +ax.plot(depths,ev_n,color=colors[1]) +ax.plot(depths,ev_s,color=colors[2]) + +ax.set_xlabel('Depth (m)') +ax.set_ylabel('Eigenvalue') + + + +#%% +import cartopy.crs as ccrs +import cartopy.mpl.geoaxes as geoaxes +from mpl_toolkits.axes_grid1.inset_locator import inset_axes +from mcfab import BuildHarmonics +import matplotlib.patheffects as path_effects + +plt.rcParams.update({ + "text.usetex": True, + "font.family": "serif", + "font.serif": ["Palatino"], + "font.size" : 11, + "figure.autolayout" : True, + +}) + +L=8 +mmax = 4 +vmax = 1.5 + +def plot(ax,odf,s_dir,hemisphere=True): + X,Y,F = odf.plot(hemisphere=hemisphere) + pcol = ax.pcolormesh(X,Y,F,transform=ccrs.PlateCarree(),vmax=vmax,vmin=0) + pcol.set_edgecolor('face') + ax.set_aspect('equal') + ax.axis('off') + kwargs_gridlines = {'ylocs':np.arange(-90,90+30,30), \ + 'xlocs':np.arange(s_dir-360,s_dir+360,45),\ + 'linewidth':0.5, 'color':'black', 'alpha':0.25, \ + 'linestyle':'-'} + + gl = ax.gridlines(crs=ccrs.PlateCarree(),**kwargs_gridlines)#,xlocs=[s_dir,s_dir+90,s_dir+180,s_dir+270]) + + + if hemisphere: + gl.ylim = (0,90) + + geo = ccrs.RotatedPole() + + text = [] + text.append(ax.text(0,90,'$z$',transform=geo,ha='center',va='center',color='white')) + #ax.text(90,0,'$y$',transform=geo,ha='center',va='center') + #ax.text(0,0,'$x$',transform=geo,ha='center',va='center') + text.append(ax.text(s_dir,0,'$s$',transform=geo,ha='center',va='center',color='white')) + text.append(ax.text(s_dir+90,0,'$n$',transform=geo,ha='center',va='center',color='white')) + #text.append(ax.text(s_dir+270,0,'$-n$',transform=geo,ha='center',va='center',color='white')) + + for tex in text: + tex.set_path_effects([path_effects.Stroke(linewidth=1, foreground='black'), + path_effects.Normal()]) + # ax.text(s_dir+180,0,'$-s$',transform=geo,ha='center',va='center',color = 'white') + # ax.text(s_dir+270,0,'$-n$',transform=geo,ha='center',va='center') + + + return pcol + +def inset(fig,ax,insx,insy,j,tind=-1,r=0.1,lon=90,lat=90,hemisphere=True,vmax=0.7): + centre=ax.transAxes.inverted().transform(ax.transData.transform((insx, insy))) + long_s = np.arctan2(paths[j].vy_s[-1],paths[j].vx_s[-1])*180/np.pi + + ins = inset_axes(ax,width="100%", height="100%", + bbox_to_anchor=(centre[0]-r,centre[1]-r,2*r,2*r), + bbox_transform=ax.transAxes, loc='center', + axes_class=geoaxes.GeoAxes, + axes_kwargs=dict(map_projection=ccrs.AzimuthalEquidistant(long_s+90,lat) + )) + + #odf = Reconstruct(paths[j].mmc.n,paths[j].mmc.m) + odf = BuildHarmonics(paths[j].n[tind,...],paths[j].m[tind,...],L,mmax) + pcol = odf.plot(fig,ins,hemisphere=hemisphere,vmax=vmax,colorbar=False) + + geo = ccrs.RotatedPole() + + s_dir = long_s + text = [] + text.append(ins.text(0,90,'$z$',transform=geo,ha='center',va='center',color='white')) + #ax.text(90,0,'$y$',transform=geo,ha='center',va='center') + #ax.text(0,0,'$x$',transform=geo,ha='center',va='center') + text.append(ins.text(s_dir,0,'$s$',transform=geo,ha='center',va='center',color='white')) + text.append(ins.text(s_dir+90,0,'$n$',transform=geo,ha='center',va='center',color='white')) + #text.append(ax.text(s_dir+270,0,'$-n$',transform=geo,ha='center',va='center',color='white')) + + for tex in text: + tex.set_path_effects([path_effects.Stroke(linewidth=1, foreground='black'), + path_effects.Normal()]) + + return pcol + + + + + + +# Create vertical eigenvalue plot +fig,ax2 = plt.subplots(figsize=(4,4)) + + +ax2.plot(ev_z,depths,linewidth=2,color =colors[0]) +ax2.plot(ev_n,depths,linewidth=2,color =colors[1]) +ax2.plot(ev_s,depths,linewidth=2,color =colors[2]) + +ax2.scatter(e_z,stoll_d,s=0.3,color=colors[0]) +ax2.scatter(e_n,stoll_d,s=0.3,color=colors[1]) +ax2.scatter(e_s,stoll_d,s=0.3,color=colors[2]) + +if vmax<1: + ax2.set_title('Eigenvalues at EGRIP') +else: + ax2.set_title(r"Eigenvalues at EGRIP, $\tilde{\lambda}' = \tilde{\lambda}/4$") + + + +ax2.set_xlabel('Eigenvalues of $\mathbf{A}^{(2)}$') +ax2.set_ylabel('Depth (m)') +ax2.set_ylim(0,2000) +#flip y axis +ax2.invert_yaxis() +ax2.grid() + + +nearestdepths = np.concatenate(([100],depthslower[1::2])) + + +#Custom legend +from matplotlib.lines import Line2D +legend_elements = [Line2D([0], [0], color='k', lw=2, label='Model'), + Line2D([0], [0], marker='o', color='w', label='EGRIP', + markerfacecolor='k', markersize=4)] +# put legend outside top right +ax2.legend(handles=legend_elements,fontsize=9,ncol=2,loc='lower center') + + + + + +# # Get aspect ratio in fig coords for axes +# def get_aspect(ax): +# pos = ax.get_position() +# return (pos.ymax-pos.ymin)/(pos.xmax-pos.xmin) + + +# ar = get_aspect(ax2) + +for n in nearestdepths: + + j = np.abs(depths - n).argmin() + pcol=inset(fig,ax2,0.93,depths[j],j,r=0.1,vmax=vmax) + + +cbax2 = ax2.inset_axes([0.9,-0.14,0.3,0.04]) +cbar=fig.colorbar(pcol, cax=cbax2, ticks=[0,vmax],orientation="horizontal", format='%.1f') +cbar.set_label('$f^*$',labelpad=-10,fontsize=9) + + + +if npoints>=10000: + if vmax<1: + fig.savefig('eigenvalues.pdf', bbox_inches='tight') + else: + fig.savefig('eigenvalueslam.pdf', bbox_inches='tight') + + +#%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +def inset(fig,ax,insx,insy,j,tind=-1,r=0.1,lon=90,lat=90,hemisphere=False,vmax=0.7): + centre=ax.transAxes.inverted().transform(ax.transData.transform((insx, insy))) + long_s = np.arctan2(paths[j].vy_s[-1],paths[j].vx_s[-1])*180/np.pi + + ins = inset_axes(ax,width="100%", height="100%", + bbox_to_anchor=(centre[0]-r,centre[1]-r,2*r,2*r), + bbox_transform=ax.transAxes, loc='center', + axes_class=geoaxes.GeoAxes, + axes_kwargs=dict(map_projection=ccrs.Orthographic(long_s+90,lat) + )) + + #odf = Reconstruct(paths[j].mmc.n,paths[j].mmc.m) + odf = BuildHarmonics(paths[j].n[tind,...],paths[j].m[tind,...],L,mmax) + pcol = odf.plot(fig,ins,hemisphere,vmax=vmax,colorbar=False) + + geo = ccrs.RotatedPole() + + s_dir = long_s + text = [] + text.append(ins.text(0,90,'$z$',transform=geo,ha='center',va='center',color='white')) + #ax.text(90,0,'$y$',transform=geo,ha='center',va='center') + #ax.text(0,0,'$x$',transform=geo,ha='center',va='center') + text.append(ins.text(s_dir,0,'$s$',transform=geo,ha='center',va='center',color='white')) + text.append(ins.text(s_dir+90,0,'$n$',transform=geo,ha='center',va='center',color='white')) + text.append(ins.text(s_dir+270,0,'$-n$',transform=geo,ha='center',va='center',color='white')) + + for tex in text: + tex.set_path_effects([path_effects.Stroke(linewidth=1, foreground='black'), + path_effects.Normal()]) + + + return pcol + + +''' Plot paths and surface and bed''' + +fig,ax = plt.subplots(figsize=(7,5)) + +max = np.max(paths[-1].surf) +200 +min = np.min(paths[-1].bed) - 100 +ax.plot(-paths[-1].s/1e3,paths[-1].surf,color='k',linewidth=2) +ax.plot(-paths[-1].s/1e3,paths[-1].bed,color='k',linewidth=2) +ax.fill_between(-paths[-1].s/1e3,paths[-1].surf,max,color='#87ceeb') +ax.fill_between(-paths[-1].s/1e3,paths[-1].bed,min,color='#876445') + + +ax.set_xlim(0,-paths[-1].s[0]/1e3) +ax.set_ylim(min,max) +ax.invert_xaxis() +for p in paths: + #ice blue color + ax.plot(-p.s/1e3,p.z,'k--',linewidth=0.7) + + + +ax.set_xlabel('Distance upstream (km)') +ax.set_ylabel('Height above sea-level (m)') + + +for n in nearestdepths: + + j = np.abs(depths - n).argmin() + pcol=inset(fig,ax,-paths[j].s[-1]/1e3, paths[j].z[-1],j,r=0.08,lon=180,lat=20,vmax=vmax) + + + ## Plot small figures along line + splot = paths[j].s[0] + ds = 50e3 + while splot<-40e3: + ind = np.abs(paths[j].s-splot).argmin() + inset(fig,ax,-paths[j].s[ind]/1e3, paths[j].z[ind],j,\ + r=0.07,tind=ind,lon=180,lat=20,hemisphere=False,vmax=vmax) + + splot = splot+ds + +cbax = ax.inset_axes([0.1,0.25,0.3,0.04]) +cbar=fig.colorbar(pcol, cax=cbax, ticks=[0,vmax],orientation="horizontal", format='%.1f') +cbar.set_label('$f^*$',labelpad=-10,fontsize=9) + + +if npoints>=10000: + fig.savefig('paths.pdf', bbox_inches='tight') + +#%%%% +# plot strain-rate T graph with strain-rate log scale +fig,ax = plt.subplots(figsize=(7,5)) + +secperyr = 365*24*60*60 + +for p in paths[-2:]: + D = 0.5*(p.gradu + np.transpose(p.gradu,axes=(0,2,1))) + effectiveSR = np.sqrt(0.5*np.einsum('pij,pji->p',D,D))/secperyr + + ax.scatter(effectiveSR,p.T) + +ax.set_xlabel('Effective strain-rate (s$^{-1}$)') +ax.set_ylabel('Temperature (C)') + +# set x log scale +ax.set_xscale('log') + + + +#%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +import netCDF4 as nc + + +fn = 'BedMachineGreenland-v5.nc' +ds = nc.Dataset(fn) + + + +xmin = paths[-1].xp.min()-50e3 +xmax = paths[-1].xp.max()+50e3 +ymin = paths[-1].yp.min()-50e3 +ymax = paths[-1].yp.max()+50e3 + + +surface=np.squeeze(ds['surface'][:]) +thickness=np.squeeze(ds['thickness'][:]) +bed=np.squeeze(ds['bed'][:]) +y = np.squeeze(ds['y'][:]) +x = np.squeeze(ds['x'][:]) + +y=np.flip(y,0) +surface = np.flip(surface,0) +thickness = np.flip(thickness,0) +bed = np.flip(bed,0) + + +xminind = np.abs(x-xmin).argmin() +xmaxind = np.abs(x-xmax).argmin() +yminind = np.abs(y-ymin).argmin() +ymaxind = np.abs(y-ymax).argmin() + + +x = x[xminind:xmaxind] +y = y[yminind:ymaxind] +surface = surface[yminind:ymaxind,xminind:xmaxind] +thickness = thickness[yminind:ymaxind,xminind:xmaxind] +bed = bed[yminind:ymaxind,xminind:xmaxind] + + +Y,X = np.meshgrid(y,x) + + +''' Plot 3d paths''' + +fig,ax = plt.subplots(subplot_kw={'projection': '3d'}) + +for p in paths[1:]: + #ice blue color + w,v = np.linalg.eig(p.a2) + ind = np.argmax(np.abs(w),1) + + w = np.sort(w) + + ax.scatter(p.xp/1e3,p.yp/1e3,p.z,s=w[:,2]) + + #ax.plot(p.xp/1e3,p.yp/1e3,p.z,) + +ax.set_xlabel('x (km)') +ax.set_ylabel('y (km)') +ax.set_zlabel('z (m)') + + + +ax.plot_surface(X/1e3,Y/1e3,surface.T,color='#87ceeb',alpha=0.3) +ax.plot_surface(X/1e3,Y/1e3,bed.T,color='#876445',alpha=1) + + diff --git a/stereo_EGRIP1906_6_20.txt b/stereo_EGRIP1906_6_20.txt new file mode 100644 index 0000000..2faae88 --- /dev/null +++ b/stereo_EGRIP1906_6_20.txt @@ -0,0 +1,814 @@ +# azimuth latitude +35.6005 5.6073 +31.3949 61.1655 +212.432 1.84517 +55.3455 77.6827 +63.2987 77.2672 +36.3538 62.0256 +215.774 15.4283 +207.739 46.0555 +215.218 10.9163 +213.11 24.0066 +208.354 8.90079 +203.358 27.1495 +11.848 74.2001 +213.619 17.2143 +42.3142 42.6037 +31.2078 47.805 +30.899 59.9827 +202.408 16.9471 +214.914 83.8428 +31.7242 3.57712 +191.31 80.0211 +218.517 68.492 +26.9144 28.6387 +213.225 4.57521 +204.317 33.9374 +29.6963 11.4488 +211.697 7.22467 +207.505 81.3565 +37.1114 78.4216 +36.7452 28.1106 +37.2837 0.207123 +32.4645 23.856 +201.607 5.38718 +177.955 75.9255 +222.151 26.9546 +350.703 76.1291 +155.691 86.7182 +214.643 23.8677 +25.1682 44.0786 +219.052 13.7147 +216.132 32.1482 +28.8419 6.84885 +208.934 63.6478 +203.767 20.0454 +217.095 43.7234 +31.3688 13.5763 +32.9513 60.8935 +142.572 79.165 +202.931 34.5577 +213.538 31.7796 +189.215 51.9829 +207.925 24.6434 +209.956 58.2422 +203.682 25.0664 +24.4937 42.4006 +17.1191 75.2919 +46.0734 14.874 +203.148 41.6184 +199.707 32.7343 +27.9039 44.1221 +153.682 87.0638 +28.4427 9.84892 +19.0395 14.0977 +164.862 88.0777 +215.368 22.5984 +79.7817 78.652 +212.487 7.96741 +72.7066 78.6721 +187.872 55.6133 +29.617 10.3885 +219.456 32.4948 +33.2046 11.5532 +52.0882 72.8076 +27.3724 68.3931 +213.372 2.49638 +208.686 25.1468 +35.736 49.5094 +33.3917 6.16271 +22.7001 70.4365 +49.85 4.12141 +35.7285 17.6727 +204.783 80.7229 +182.549 60.976 +211.134 15.7963 +347.141 69.1187 +192.974 70.4587 +24.2941 81.0128 +31.2271 28.3579 +2.28558 82.1211 +203.012 0.673943 +203.826 77.9507 +35.3191 25.0578 +47.2983 3.77764 +29.9469 1.62028 +34.1258 22.7516 +30.1419 25.6245 +208.225 19.4277 +211.678 23.5333 +15.9982 79.2736 +202.251 21.131 +24.6705 72.9915 +199.868 42.0488 +205.312 29.8488 +37.9764 22.901 +32.8598 74.1931 +210.831 18.7345 +22.5642 57.5354 +43.6717 5.73232 +16.3149 74.8824 +206.094 28.3908 +19.3768 65.8515 +32.9913 22.9762 +42.857 16.8851 +195.154 72.26 +197.15 10.4671 +215.387 62.0771 +82.6177 85.6778 +181.677 78.2057 +36.0289 11.8976 +158.113 76.8075 +217.987 32.5292 +33.3552 5.31503 +37.8688 53.4256 +212.609 12.6624 +215.169 1.67052 +216.299 3.33306 +211.579 32.2007 +19.5254 51.8451 +48.07 24.4255 +357.45 84.9779 +157.732 84.6786 +197.349 63.3601 +213.633 32.5512 +208.888 9.72054 +34.7632 11.7788 +210.971 58.5599 +43.2799 1.05666 +208.894 8.66537 +211.406 52.6614 +353.921 85.2139 +38.1301 22.2275 +24.4494 58.8575 +141.163 84.2761 +214.523 11.5104 +211.524 27.0179 +212.246 30.4078 +35.5985 18.6425 +231.469 73.2608 +216.56 22.5234 +213.362 6.66182 +211.611 13.475 +111.478 78.7297 +172.798 75.3377 +20.783 48.4185 +33.5933 50.2436 +23.8178 25.0523 +35.7217 48.263 +35.7893 44.2665 +41.9471 21.2638 +40.6403 21.7029 +115.489 82.9607 +202.806 48.8147 +27.9411 19.5821 +178.038 78.458 +210.803 54.9234 +191.655 82.9617 +215.537 6.22085 +220.641 0.558601 +211.501 28.2373 +337.436 85.1598 +34.0148 22.7505 +201.373 71.9526 +220.519 72.7729 +209.953 21.3477 +235.177 68.6698 +49.1887 16.1811 +9.52219 80.9264 +213.212 0.655342 +18.9006 60.7908 +208.584 6.89558 +36.6033 55.3715 +125.471 85.7339 +30.5501 69.2227 +183.652 47.2405 +221.082 75.8769 +43.3504 52.2619 +25.9899 69.1283 +31.0739 21.7858 +217.181 40.2203 +37.4224 0.548401 +213.452 47.1782 +28.5344 43.9404 +1.34793 47.4244 +46.0535 34.9262 +31.3671 45.9385 +29.9197 4.59223 +211.252 26.7034 +175.861 82.3127 +201.454 50.1994 +189.133 59.7223 +40.453 71.0363 +33.9006 0.363533 +211.969 22.0076 +206.6 13.5615 +219.064 55.4698 +228.77 82.6354 +207.701 30.1463 +5.36165 47.2337 +210.634 2.32519 +215.53 1.85491 +40.8097 39.6308 +212.299 21.7299 +214.724 30.9508 +22.0225 71.3244 +43.5657 31.4241 +40.053 42.3619 +28.1502 14.1964 +33.3975 21.5834 +27.6256 14.5676 +207.659 3.30018 +31.6954 8.94944 +26.6276 4.3306 +177.45 82.2425 +184.807 69.2697 +217.36 4.20316 +39.7288 68.6861 +200.248 44.2444 +213.104 14.5798 +209.223 2.31135 +45.4685 70.9188 +29.6856 38.9876 +11.3564 56.0456 +97.0479 83.8971 +25.3978 10.1003 +201.959 69.4318 +188.539 70.5502 +17.9804 83.0495 +27.9996 21.3303 +31.9507 1.5518 +33.8606 42.8871 +174.222 84.7401 +20.7063 13.7147 +171.738 70.6333 +210.864 17.7539 +209.058 3.90558 +209.673 15.4922 +207.953 28.27 +205.582 37.7061 +15.5115 4.38685 +208.574 53.3531 +67.7753 75.4833 +34.463 3.26598 +214.326 35.8599 +211.054 58.5857 +182.502 73.0357 +133.516 79.0305 +257.522 12.1826 +35.4118 15.2647 +28.629 20.4508 +36.944 8.54105 +28.4433 18.2714 +33.0716 71.0123 +200.122 56.7272 +207.172 32.1823 +28.936 4.41695 +235.26 72.9646 +194.794 73.2135 +203.457 23.9463 +32.3888 29.7003 +212.689 61.4702 +217.733 36.2153 +197.67 57.3817 +28.797 59.0395 +33.6275 72.4365 +119.991 82.1617 +203.879 29.765 +44.6842 54.0958 +35.5403 13.6204 +211.546 62.8051 +32.5435 64.3538 +157.355 84.6948 +208.848 54.6445 +218.532 70.7515 +208.396 10.9936 +199.311 50.002 +34.1288 3.00713 +209.68 4.69547 +35.752 24.2145 +39.0369 63.6677 +111.251 85.9599 +64.5734 75.9947 +214.696 33.2571 +46.0626 58.3897 +196.281 82.2626 +29.0479 49.0702 +31.1047 3.22713 +129.898 83.9859 +199.832 45.9911 +36.8874 5.65088 +213.881 18.9227 +232.768 72.9549 +41.6125 5.59151 +200.648 48.7374 +214.885 16.3241 +199.937 49.1519 +27.6688 30.056 +207.411 27.2934 +194.216 74.3611 +42.1923 35.2449 +213.041 64.9878 +243.5 85.0126 +150.841 82.279 +207.338 23.1195 +56.8682 79.2777 +211.158 20.3205 +230.771 69.8334 +199.385 13.7131 +213.837 18.5633 +206.044 8.96567 +202.317 84.716 +36.1954 26.7081 +35.5785 41.8941 +208.681 23.9604 +206.176 5.51759 +205.766 7.41035 +59.8128 78.7109 +35.5852 58.3016 +202.185 55.8294 +21.7289 64.8099 +33.499 60.2923 +27.259 58.6768 +29.7062 30.0441 +195.683 65.0163 +30.7981 28.5541 +211.272 72.8341 +212.995 2.68269 +214.997 47.3602 +208.581 32.2396 +197.759 36.6862 +203.587 3.03484 +227.254 76.9766 +211.655 32.9261 +216.032 25.5161 +34.8852 64.1005 +194.142 44.1382 +199.256 29.9849 +212.248 34.7362 +40.1924 12.2714 +52.2607 79.2476 +27.2433 25.1628 +211.125 55.3686 +210.204 1.41254 +31.5735 33.0356 +206.733 51.3358 +210.04 12.3997 +17.854 67.1263 +42.7164 27.8164 +199.292 63.7555 +218.765 85.4508 +188.767 71.807 +217.274 37.6273 +211.652 25.8819 +32.7224 74.3531 +35.4845 8.02191 +203.116 73.8742 +208.977 75.3322 +80.5227 86.686 +12.2595 85.5766 +210.551 10.8635 +212.182 60.4443 +211.075 55.854 +36.8357 45.2447 +201.343 16.7685 +205.322 78.5663 +36.0464 4.49341 +24.0851 52.0342 +45.7968 86.6156 +38.0008 78.7017 +209.947 46.3342 +211.854 6.83218 +200.491 76.0529 +202.948 24.0721 +218.393 35.984 +260.449 66.287 +226.965 0.634338 +33.4647 37.9657 +212.631 66.7888 +205.658 71.1411 +43.7337 17.4503 +38.0096 31.9459 +204.989 67.0032 +204.625 53.8333 +39.7791 6.57429 +41.3428 9.9363 +183.88 82.4258 +209.377 51.7918 +29.8693 61.7307 +205.197 3.5131 +25.986 16.9016 +29.5669 39.4776 +159.121 80.9498 +69.3995 82.4124 +340.098 71.0281 +29.389 19.4428 +30.1272 4.9306 +195.973 21.8782 +41.8123 52.2014 +205.225 61.564 +207.245 43.7187 +27.958 2.58396 +204.865 12.7415 +188.239 71.2403 +181.248 73.8795 +192.25 72.3996 +46.3384 63.0395 +24.1084 55.3977 +27.6713 56.6683 +17.5055 24.4643 +85.9572 76.1935 +33.43 41.2963 +205.306 59.4846 +27.6005 27.6262 +206 63.5258 +29.7948 30.0064 +37.8857 65.9229 +24.2909 21.3791 +123.191 82.7657 +198.338 67.3467 +82.4655 79.472 +209.528 34.8533 +82.9454 78.9401 +46.9614 71.0958 +211.341 37.3834 +182.039 78.8274 +209.164 58.2775 +173.566 73.2349 +206.691 60.0149 +71.6101 79.0741 +206.238 32.766 +29.3763 14.5065 +204.903 39.6998 +56.6125 77.3077 +38.5774 58.7532 +31.8901 5.41151 +21.4075 10.1283 +215.005 1.71594 +205.865 25.8243 +202.525 18.9039 +27.2084 50.5923 +174.256 82.0399 +214.274 21.1279 +188.481 81.0898 +20.8873 26.9986 +210.571 21.2441 +206.619 22.383 +215.62 33.5211 +217.551 6.31348 +199.932 43.5984 +201.853 45.1394 +215.478 55.0087 +222.168 59.2715 +32.1248 4.18611 +225.736 58.7931 +30.7781 20.7037 +34.4709 49.3458 +34.0216 4.36382 +190.902 55.2174 +209.66 35.0621 +219.179 62.5516 +18.5516 22.9745 +216.966 6.77055 +206.53 38.6714 +25.5439 47.9521 +39.5429 61.4273 +301.229 85.1015 +219.189 74.5579 +205.481 15.7176 +216.751 10.7599 +43.6716 69.2602 +220.256 20.0904 +215.936 3.84036 +145.099 82.8829 +188.154 73.4406 +229.789 83.4598 +218.891 10.781 +217.811 2.16558 +26.5143 47.4265 +17.6974 13.4752 +33.4491 55.3942 +198.915 50.0813 +214.153 4.92493 +28.6066 4.07763 +203.021 40.6464 +279.443 76.2736 +58.5377 73.5959 +263.251 76.894 +208.588 32.1756 +31.936 21.862 +204.535 32.4812 +262.624 78.2086 +210.711 31.5458 +192.276 84.5928 +207.261 71.801 +253.22 79.5992 +18.1882 39.1268 +208.935 67.6443 +208.313 17.0914 +34.0003 14.1179 +79.1955 71.6226 +257.064 76.1488 +120.984 85.0994 +176.845 72.5919 +21.1864 30.8176 +35.9181 30.474 +40.1293 37.1387 +196.971 8.0541 +30.7357 37.5106 +27.2534 10.4351 +197.189 74.708 +32.7773 57.3008 +346.891 43.413 +29.9054 15.4578 +194.764 82.5495 +29.4494 28.5945 +129.802 78.35 +27.7031 13.2219 +193.602 66.7826 +203.771 70.1943 +188.84 81.0516 +37.283 31.4724 +35.7196 32.3204 +211.304 73.3068 +19.2265 32.7269 +30.9687 8.90072 +44.2643 43.6465 +216.848 43.2443 +212.838 9.07761 +33.0319 32.1587 +221.123 23.5521 +191.125 56.314 +194.491 64.2563 +195.985 53.6823 +42.6614 53.6188 +32.5909 28.2106 +206.184 72.8365 +106.972 82.1801 +196.603 55.1765 +45.1517 14.003 +35.4564 21.7785 +224.181 74.1859 +124.517 84.1753 +217.634 21.6871 +212.98 71.7851 +37.6396 68.9213 +199.729 55.6396 +31.8862 74.1427 +34.7635 12.2225 +204.377 78.2146 +35.9123 37.813 +217.836 19.9781 +42.531 53.4112 +209.167 25.1512 +35.2907 49.8191 +33.03 24.9935 +41.6474 51.8504 +17.8478 63.0818 +336.271 62.7773 +206.665 53.5746 +227.91 73.7403 +31.1546 2.36259 +34.8622 41.7325 +48.451 44.8922 +209.415 28.4136 +213.584 3.75709 +213.647 19.5778 +35.2714 52.4726 +29.7719 27.2483 +172.914 85.1968 +22.5127 27.7666 +53.854 51.9349 +34.3162 27.8831 +203.116 41.9286 +34.1128 3.77123 +206.317 56.8061 +200.461 25.2809 +213.363 25.8384 +26.9063 74.4735 +208.895 30.7979 +40.6572 44.6575 +197.337 60.1539 +212.866 17.8014 +41.314 48.0465 +43.9052 9.71791 +213.933 36.3894 +147.939 80.1014 +41.6355 66.4465 +205.778 51.9808 +54.3727 53.165 +188.544 71.5035 +48.397 60.9933 +87.6258 70.0848 +34.2375 26.8507 +25.1516 1.39792 +125.839 76.6886 +219.611 4.60997 +19.1715 19.1948 +68.2418 73.7557 +12.1443 83.4852 +26.6613 23.3121 +218.389 77.6269 +209.895 1.59734 +18.6068 17.5545 +25.5307 37.1846 +28.4077 16.6717 +25.5617 16.3045 +206.466 56.1111 +29.3966 45.9652 +209.152 19.136 +20.9644 44.3375 +207.902 48.1191 +29.0884 15.3815 +207.631 44.8084 +28.03 11.1558 +28.105 37.9833 +211.307 47.9438 +214.963 17.0698 +15.9537 59.2514 +28.864 4.94898 +48.6934 66.2407 +224.856 81.9691 +218.198 24.105 +31.8401 50.8125 +187.337 70.4506 +224.159 24.6539 +34.1923 31.5196 +219.888 31.9296 +208.679 39.1011 +202.076 52.8278 +207.623 77.5311 +40.794 65.2219 +211.174 51.812 +211.467 25.6396 +28.1098 41.5139 +19.6751 49.9591 +31.2 3.4393 +37.2613 26.1044 +224.52 53.7471 +217.95 54.6455 +35.5382 44.0371 +38.0528 56.0788 +30.2522 11.7738 +195.295 1.74622 +209.299 11.5396 +27.1809 4.99155 +219.812 30.67 +36.669 21.0827 +210.93 40.9593 +38.8645 30.1086 +37.803 26.329 +355.857 81.8883 +222.034 5.88007 +229.513 4.9297 +207.149 7.87527 +211.215 36.6692 +190.964 75.4053 +25.3313 5.3017 +31.7144 9.06899 +35.3658 83.0111 +16.4322 52.4572 +26.5204 14.3009 +182.376 78.892 +245.205 83.9301 +220.277 6.53569 +330.81 85.1868 +216.978 68.6735 +31.006 32.0699 +203.357 15.3325 +34.6207 41.8417 +48.0376 63.46 +199.558 49.8236 +206.361 76.7045 +210.324 21.6851 +208.988 39.2042 +210.799 33.3074 +30.822 29.8863 +23.3982 48.9735 +200.783 56.2528 +25.4468 34.6334 +223.883 6.06586 +29.8075 42.6724 +33.4651 51.9863 +210.048 33.6736 +223.757 25.7242 +73.0415 77.2864 +62.5588 82.8751 +37.8644 5.92216 +218.712 31.273 +182.212 71.7974 +39.7146 53.2827 +63.9905 82.1046 +35.2399 58.7505 +32.7728 55.0725 +201.219 26.7 +22.5279 8.04607 +191.805 49.8998 +199.372 48.6955 +202.533 82.4236 +26.6075 42.5306 +21.9513 42.4359 +220.634 1.95255 +66.7435 82.2284 +53.527 63.2715 +27.3959 5.23521 +124.2 70.8131 +25.5893 4.94083 +33.8726 8.30989 +186.975 80.7431 +316.738 89.1533 +19.9058 21.4477 +88.101 78.0701 +15.3005 8.66386 +108.813 83.1877 +213.534 8.23553 +37.1326 14.2397 +35.8089 43.3448 +208.884 29.7801 +130.537 70.8387 +35.1176 66.1256 +126.401 86.3452 +68.7104 84.1366 +211.572 27.477 +27.5522 13.632 +34.4897 58.6637 +54.1113 61.1203 +176.045 71.6874 +209.335 20.2545 +240.262 69.0733 +193.68 32.1708 +8.75786 77.8887 +36.9546 77.7441 +38.0246 65.6534 +41.598 62.6285 +9.06635 82.365 +205.551 23.184 +209.852 40.8964 +32.0472 19.0481 +208.876 20.1863 +24.3943 32.2394 +225.662 40.5134 +216.463 76.9278 +199.574 64.2237 +29.9648 6.56853 +25.5737 53.2218 +44.9059 67.1102 +36.3299 29.1688 +174.171 69.3093 +30.254 15.7509 +184.139 75.8165 +199.104 11.6129 +54.8988 58.8435 +29.3044 18.3376 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+3.61099 67.3694 +30.6585 7.90224 +205.72 64.7213 +205.991 64.5151 +86.535 77.0387 +255.154 38.3174 +217.461 81.7785 +189.165 30.4875 +12.3511 54.2276 +58.1055 74.5935 +338.171 57.918 +214.146 6.79787 +135.181 75.7603 +261.32 39.2076 +215.855 7.24261 diff --git a/stoll.py b/stoll.py new file mode 100644 index 0000000..b817771 --- /dev/null +++ b/stoll.py @@ -0,0 +1,85 @@ +import numpy as np + +data= np.genfromtxt('./EGRIP_eigenvalues.csv',delimiter='\t',skip_header=1) +def eigenvalues(dmin=0,dmax=10000): + depth = data[:,4] + + + eig1 = data[:,7] + eig2 = data[:,9] + eig3 = data[:,11] + # eig3>eig2>eig1 + + + #Sort eigenvalues: below d=dswitch, e_n>e_z>e_s, above d=dwitch, e_z>e_n>e_s + e_s = np.zeros_like(eig1) + e_n = np.zeros_like(eig1) + e_z = np.zeros_like(eig1) + + dswitch = 250 + e_n = np.where(depth>dswitch,eig3,eig2) + e_z = np.where(depth>dswitch,eig2,eig3) + e_s = eig1 + + + + # Trim to depth range + e_s = e_s[(depth>dmin) & (depthdmin) & (depthdmin) & (depthdmin) & (depth