diff --git a/examples/basic_functionality.ipynb b/examples/basic_functionality.ipynb index f89e4cc..9bcbe9b 100644 --- a/examples/basic_functionality.ipynb +++ b/examples/basic_functionality.ipynb @@ -37,7 +37,7 @@ "outputs": [ { "data": { - "image/png": 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YIgghrlIoh3FgJsfCIw0NDRg8eDBuu+02zJw50+395cuX44EHHsCSJUswduxYvP3225g6dSoKCgqQk5MDAMjNzYXZbHb77E8//YTMzExF4zGbzS7bqq2tVXhEBCEf0QZ5rY4FVV8kQg1yLAiP8HMsbIzrzCtFLAjCHRtPChXBSaHIOPDE1KlTMXXqVMn3X3vtNcyZMwdz584FACxevBg//vgjli5dikWLFgEAduzYodp4Fi1ahGeffVa17RGEJyyiUiiKWBChCUmhCI/wq0LZ7YyLM0E5FgThDhvJIymUOrS0tGDHjh2YMmWKy/IpU6Zg8+bNmuxzwYIFqKmp4f4VFxdrsh+CAJz3B6OLFIpyLIjQhCIWhEf4EQurnXFN3qaZFIJwwyXHgo1YkHHgMxUVFbDZbEhPT3dZnp6ejvLyctnbueSSS7Bz5040NDSgc+fOWLFiBUaMGCG6rslkgslkclv+wg8FMMXEuS33dHWjIgy4sE8aRpyXDL1e52FNor3CPlcjRatC0aQEEVqQY0F4pNmlj4UdjS3O1zQLSxDuWEXKzVKDPP/R6VyNcoZh3JZ54scff/R7DJ9tLYbeFKP4c++sP47s5GhcNbQzrh6ahfNSYv0eCxE+iEuhqEEeEZqQY0F4hN95u7bZ4vIehWgJwh07NchTlZSUFBgMBrfoxJkzZ9yiGGqTn5+P/Px82GyOCZV5E7ohKtY9YgEAkHByTlU3YfX+chSfa8IbvxzBG78cQW6XDpg5rDMuG5SBxOgIrYZPhAhiUihngzx6zhKhBTkWhEf4EYvqRlfHoolXMYogCAdWUSmU07Fosdq55YR3IiMjkZubizVr1uCqq67ilq9ZswZXXnmlpvvOy8tDXl4eamtrkZiYiHsv7ImEhATF2/n7lQPwU0E5vt1Zig1HzmLHySrsOFmFZ/57ABf3TcfVw7IwoVeqy4w10X7wJIWi5G0i1CDHgvCImec8/GfPKZf3GlusgR4OQbR5xMrNsobDsk2FeOa/Bfjo9pG4oFdq0MbY1qivr8fRo0e514WFhdi9ezeSk5ORk5OD+fPn4+abb8bw4cMxevRovPPOOygqKsK8efOCOGr5REcacOWQLFw5JAuna5vxn92l+GZHKQ6frsMP+8rww74ypMRFYvrgLFw9LAv9MxMUybyI0IZrkGd0rwpFkmMi1CDHghBl45EKbDhyFvVmp/MglHPw8y0IgnBgFW2Q51j2zH8LAAAPfLELu56aIr6Bdsj27dsxadIk7vX8+fMBALNnz8ayZcswa9YsVFZWYuHChSgrK8OAAQOwatUqdOnSJVhD9pn0hCjcOaE77hjfDQdO1eLbnaX4bk8pKupb8MGmQnywqRC90+MxM9fhiKQnRAV7yITGsDlYRr17VSiKWBChBjkWhCg3vf+H13XIsSAId+z8PhYSORZkK7gyceJEMIznk3LPPffgnnvuCdCIHAhzLNREp9NhQFYiBmQlYsG0Pthw5Cy+2VmKNQWncfh0HV5YdQgv/u8QxvVMxcxhWZjSrxOiIw2qj4MIPqIN8ihiQYQo5FgQPkNSKIJwR05VKG9GNNE2EOZYaEWEQY/JfdIxuU86ahot+GFfGb7dWYLtJ6uw/s+zWP/nWcSZjJg2sBOuHtYZI6l0bVjBSqH4uVeUY0GEKuRYED5DEQuCcEe8j4XAsQj4qIhQITEmAjeOysGNo3JwoqIB3+4qxbc7S1BS1YQvt5fgy+0l6NwhGlcPzcJVwzqjK5WuDXlEpVBUbpYIUcixIHymiRwLgnCDbWhl1OsQa3JIV+qbrS5d7ClgQcjhvJRYzL+4Fx64sCe2n6zCtztL8MPeMpRUNeGNX4/ijV+PYlhOEmbmdsblAzORGEOla0MRUSkU1yCPbhZEaEGOBeEzDeRYEIQLDMNw+RMGvQ6psSYkx0biXEMLCk7VuqxHtH20zLFQgl6vw8iuyRjZNRnPTO+PnwpO49udJVj/51nsLKrGzqJqPPtdAS7ql4arh3bGBb2pdG0o4U0KpbQZJEEEE3IsCMVM6JWK9X+eRRPlWBCEC3w9tFGvh06nw5DsJPx66Ax2FVVz79EkZGgQqBwLJURFGDB9cCamD87EmdpmfLfnFL7eUYJD5XVYta8cq/aVo2NsJKYPycTMYZ2pdG0bx25nuKiEWFUowBG1iDDQNSRCA5rSIBQxsbejQglAORYEIYQvW2Dtgu6pDg18WU0T9x5DWRaECqQlRGHu+G5Y/cAErLp/POaO64qUOBMqG1rw4aYTuPzNjbh08QZ8s6Mk2EMlJLDYnflX/D4WBp4jQQncRChBEQtCEbEmI2IiHV8bkkIRhCvCiAUAmIyOPAu2lwVAORaE+vTLTEC/zH54dGofbDhSgW92luCn1tK1f/tqD4wGHa4ckhXsYRICrLz7gljnbcCRgxEVQaWGidCAHAtCEdERBsS01lInKRRBuGLjeQyGVsNArOQsORaEVhgNekzqk4ZJfdJQ02TBaz8dxke/n8TDX+9Fl46xGJKdFOwhEjz4FeNcpVAUsSBCE5JCEYqIitBzjkWDmSIWBMHHZuNHLFodC2NroytekzySQoUG+fn56NevH0aMGBHsofhEYnQEnr6iPy7qm4YWqx13/ns7ymuagz0sggc74aDTOScjANe/+dFOgmjrkGNBuCGsWDMwy5m0GB1h4EKyZit1BCV8w2ZncPRMfdhVR3LNsXAYBpEUsQhZ8vLyUFBQgG3btgV7KD6j1+uw+Pqh6JUehzN1Ztz58XY0W2hSqK3ASqEiDHqXJHudTkdN8oiQhBwLwg1h3eyoCOfXJDrCAFNrghm/Lj9ByMVmZ3Dd27/jotd+w88HzwR7OKpiE6nuItYkz06eBRFA4kxGvHfLCHSIicDekho8/PXesHPqQxWuh4VIJ3Wjge1lQZN4ROhAjgXhhtUmdCycSWNRkQbOUGqhiIXqlFY3oa7ZEuxhaMru4mrsOFkFADhYVutl7dCCzbHgyxi4HAsrL3k7sMMiCOR0jMHSm3Jh1Ovw3z2nsGTdsWAPiQDPsTC6m2NsAQjhM5kg2jLkWBBuWASzIybeDS/KaOCq3Jitdpr1UpHS6iaMffFXTHx5XbCHoik1TS3c32/+egT3fLoDVlt4OKlsjoVRxLGwkBSKCDLnd+uIhVcOAAC8/ONh/HSgPMgjIiw8KZQQZ8SCbhhE6BAyjsWiRYswYsQIxMfHIy0tDTNmzMDhw4eDPayg8tufZ1FY0aD6doWzIyZexCKaF7EAKKlMTdYddsiCKhtavKwZ2vCT/i02Bqv2lWPt4bNBHJF6sJIFvYgUiiJ8RFvgxlE5mD26CwDggeW7wy5qGGp4lELpSQpFhB4h41j89ttvyMvLw5YtW7BmzRpYrVZMmTIFDQ3qG9ahwOZjFZj9wVZM++cG1bctvIlFGZ2ORYeYCJcIBuVZqEdNU3hLoFiaRPqfhIvRLZpj0TrraAmTqEx7ItSrQknx5OX9MLZHRzS22DD3o+2orDcHe0jtFk9SKFZSSVIoIpQIGcdi9erVuPXWW9G/f38MHjwYH374IYqKirBjx45gDy0ofLOjFADQpEF1D/ccC+fXJCspRuBYkLGkFrVN7aMvSK1IDkmkyEM1FHHmWDiPR0wKRYQG4VAVSgyjQY/8G4fhvI4xKK1uwt2f7Awb5z7UYHOvRKVQbI4FSaGIECJkn+Y1NTUAgOTkZMl1zGYzamtrXf6FCwW88LXaeQ5Cx4JXAQ+ZSVHQ6XTOEpr0MFINfsQiXHNXdhdX47kfDrotF3mmhiRWDzkWLTTrSLQhkmIi8d7sEYg3GbH1xDk89Z/9YXvfacuwCgFPORY2kkIRIURIPs4ZhsH8+fMxbtw4DBgwQHK9RYsWITExkfuXnZ0dwFFqB8MwOHa2nnutdtRAmLxd1eg0eJNjIwGAV3KWbnhq0Gyx4fOtRdzrljCd3X76uwOiy82W8DheVgplEM2xINkg0bbokRaHN24cCr0O+GJbMZZtPhHsIbU7OCmUQTrHgnIZiVAiJB2Le++9F3v37sXnn3/ucb0FCxagpqaG+1dcXBygEWrL6VqzS6SgrlldCY0nPSfbwMcUQb0s1OQXQT+HcH2QmCRCE+HgoNY0WjjJgli52XC9pkRoM6l3Gh6b1hcA8PfvC7D+z/AopBAqyJFCUYM8IpQwKv2A2WzG1q1bceLECTQ2NiI1NRVDhw5F165dtRifG/fddx++++47rF+/Hp07d/a4rslkgslkCsi4Asn+0hqX1/VmK1Lj1TtOoRb81jHn4XRNM64cksktIymUujS0uDqHFqsdCL+vLmJMBtHloe6gvv3bMSz63yFcPSwLgDB5m3IsiLbNnHFdcai8Dl/vKMG9n+3Eyryx6JYaF+xhtQucUihPDfLIsSBCB9mOxebNm/Hmm29i5cqVaGlpQVJSEqKjo3Hu3DmYzWZ069YNd955J+bNm4f4+HjVB8owDO677z6sWLEC69atC5gj0xZZLag9rnY1IeHsSEqcCV/fPcZlGVuCNhxmmtsi4SqFiokUdyyaQ1wKteh/hwAA3+50FFUQl0KF9jES4YtOp8PzVw1AYUUDdpyswtyPtmPFPWORGBMR7KGFPU4plFjEgq0KRfcOInSQJYW68sorcc011yArKws//vgj6urqUFlZiZKSEjQ2NuLIkSN44okn8Msvv6BXr15Ys2aN6gPNy8vDJ598gs8++wzx8fEoLy9HeXk5mpqaVN9XW2dNwWmX1zPyN8Gu4oyGsNysUaS+tomMJVUROnPhel5jIsXnMrSKWDSYrXj0m734LcDyDlcplOPvcHUWw5lwLTcrhslowFs35SIzMQrHKxpw7+c7yaANABZPUigDVYUiQg9ZjsWUKVNw4sQJvPLKK5gwYQJiYmJc3u/WrRtmz56N1atX4+eff9ZkoEuXLkVNTQ0mTpyIjIwM7t/y5cs12V9bpk6kXGdFg3p1yIVacLFSoJFGyrFQE+GDI1xlMwadu5MKaJe8/dZvx/DFtmLM/mCrJtuXQjzHIjyvaTgTruVmpUiNN+Hd2cMRHWHAhiMVeGHVoWAPKeyxeJBCUR8LIhSR5Vjk5eUhMjJS1gb79++Piy++2K9BicEwjOi/W2+9VfV9tWVsdgZikxfnVOzWLLyJeYpYhEs1n2BjExid4Tq73cxzRC8flCG6XE1KqoIT0TSKSKFINkiEAv0zE/HadYMBAB9sKsTybUVePkH4g6X1vmD0JIWicrNECBGSVaHaM1Kznufq1XMshOVmxTqCcrrxMDWAA41bxMIanjNUzbyGjvdf2BN3XdANgHYOKj9AYrMzAZOYGUSSt4UtAqhnANFWmTowAw9e1AsA8MTK/dh24lyQRxS+sAqBSE9SKIpYECGEao7F7NmzMXnyZLU2R0jAN0Dvv7An93elhhGLCL3718RkbE3epoiFKrjlWISpw8YmaT80pRd6pccjyqhtEQC+9OryNzdizIu/BkS+5yKFkugqTrppoi1z/4U9cNnADFhsDOZ9vAPF5xqDPaSwxJMUio1YULlZIpRQzbHIyspCly5d1NocIQE/me7+yT0w8jxH5/HKevVyLIQJe2I3PBPlWKhKe8mxYCMW56XEAnD2Q+FHMtREz3MsDpbVoqLejCOn6z18Qh3EkreFhOs1JsIDnU6HV64djAFZCahsaMEd/96OBrO6PZMIZ3TakxRKqCIgiLaMao7FCy+8gA8//FCtzRESsDPZOp3jRtSrk6PWuKo5FgIj1yCSY0G6cXVpL1WhmluPi41UmDSOWIgE20Srr6iNkbdjMYkDQNE+ou0THWnAOzcPR0qcCYfK6/Dg8t2qViAknBMM4lIoilgQoQflWIQYrEyJNY6SYx1d1CpUdSxcDR6dSCUfEzkWqtJeIhbm1shEVGsflCiNO7jrRb67AfArXJxxnU4nGrXQKmGdINQkMyka79ySi0iDHj8VnMbLPx2m/CAV8SyFohwLIvRQ3Hn79ttv9/j+Bx984PNgCO9wzXRaDZeuKY7SvwdO1aq4D+83sQhKKlMVm8CZC1fHoplzLBzfHzZioVWDPDHHQs7321+EUb74qAi3qGKoNwUk2g/Dcjpg0dUD8bev9mDpumM4UdGAF2cOQmI0NdDzFzlSKKoKRYQSih2Lqqoql9cWiwX79+9HdXU1JW8HANYoYm9CI7t2BADsL61Bg9mKWJPiS+qGHGeBavOrizBiEa6RINaYZiMWWufqiKj4AvKdFToWybGRIo4FRSyI0GFmbmfUNVvw/KqD+N/+cuwrrcGbNwzF0JwOwR5aSOOx87aBdSxoAo8IHRRboStWrHBbZrfbcc8996Bbt26qDIqQRngTykqKRlq8CWfqzDh6ph6Ds5P83oec2RE2bBvOjsWiVQfxU8Fp/OfesUiI0nZmzmYTSqHC80HCyn/YiIXWuTp6Ec/iyZX7ceeE7riM10dDbYS9X5Jj3fsAkWPR9snPz0d+fj5sNrpWAHDr2K4YmtMB936+E8XnmnDtW7/j4Ut6447x3UR/a4R32OdtpGiDPFIGEKGHKmpjvV6PBx98EK+//roamyM84MyxcN6EoiMds79qhUuVSKHC1QAGgLfXH0dhRQOWby3WfF/tJceCNaZZCVSExsmJYlKoPSU1yPtspyb7YxFGLDqKOhbheY3DifbWeVsOg7OT8MP943HZoAxY7QwW/e8Qbv9om6qVCdsTLbKkUOH7nCXCD9XSGI8dOwarlUrRaU2LSNiUrdWv1qyGsNysGMZ2JIVioP1NvT1UhbLbGS4ywTrDWicnesox1TIBVUwKJSTUk7eLi4uxYcMG/Pjjj9i5cyfMZjIs2wsJURH41w1D8cJVA2Ey6rHu8FlM/ecG/H6sMthDCzlkSaHawXOWCB8US6Hmz5/v8pphGJSVleGHH37A7NmzVRsYIQ57gzHyIhZsCNqmkqHEnx15fdZg0XUiAyyFKqxowD9//hP3TOqBXunxAdkni9ist9q0h4hFndnKGfqstEzr5ERP260zWzWTuMmJWFQ3tuC57wvQKTEKc8eHhoz05MmTeOutt/D555+juLjYxTmLjIzE+PHjceedd2LmzJnQi9X6JcIGnU6HG0flYFiXJNz72S4cPVOPG9/bgvsm98RfL+wpWqaccMeTFIoa5BGhiOI7/65du1z+7d27FwDw6quvYvHixWqPjxDASo8iRSIWatlm7OzxjaNycNXQzqLrBFoK9fBXe7By9ylMeX09xr74K0qrmwKyXyAwjoWwKlQ4dt6uabQAAGIiDVxuhVHj6mKeHLSKOu1m2OXkWDy4fA/e21iI5344GBIRqr/+9a8YOHAgjhw5goULF+LAgQOoqalBS0sLysvLsWrVKowbNw5PPvkkBg0aRPKhdkKfTgn47t6xuG54ZzAM8MYvR3Dju1tQXtMc7KGFBB6lUO1AckyEH4ojFmvXrtViHCEJwzD4dmcpeneKx4CsxIDsk615zY9YGFSPWLTuw8OMU6ClUCVVTkeitLoJL/xwEPl/GRaQfQdi5k0YsQgFQ1Mp1U2OqkhJvBKVBo01xJ4eyBX1LeiWqsluuaRLlngvkZHS6iZ0be1G3laJjIzEsWPHkJrqftLS0tIwefJkTJ48GU8//TRWrVqFkydPYsSIEUEYKRFoYiKNeOmawRjTPQWPr9iHPwrPYdobG/DqtYMxqU9asIfXpvEoheIiFuH3PCDCF4pV+8Gmo5X421d7cPmbGwO2T2GDPMBpnKnVEZUraetByhBoKZTQ6DpVE8iIhfb7aA85FtWtEYsEnmMRobGG2GPEQsNkU6GNwJbXleJkZYNmY1GLl19+WdSpEGPatGm45pprNB4R0daYMTQL398/Hv0zE3CuoQW3LduG538oCMv7mVpYZTTIs5AUigghVHMsHnvsMa/N88KNQ+XqNaWTi7NBnvPS6VXWYVpt0jc6lkCHaHOSY1xen6kNXKJoIMoosjP2Ma1JzeHYx6K6yeFYJMU4HQsueVujB6cniZWWjoXQKWfL60pRdK5Rs7EQRCDpmhKLb+8Zg1vHnAcAeHdDIa59azP+s7sUxecaqWu3ALZBnqfkbWE5coJoy/jfTa2V0tJSFBdrX5azLcGXCrVY7fjxQDkGd05CTscYD5/yD86xMPKkUK1/qp28bfTgWAS6QZ5wLGcDWNowIDkWNtaxMKKxxaZZw7hgUtPISqGc+QZaN4Dy9P3Uso+EUD7nLWJRWhW4CJyWPPbYYygvL8cHH3wQ7KEQQcRkNOCZ6f0xuntHPPL1XuwpqcFfv9gNAEiJi8SQ7A4YmpOEodlJGJSdhDgVGruGKmKVHlmo3CwRiqj2a/7oo4/U2lTIwJ/J/vSPk3j2vwUAgBMvXqbZPsVkSupLodgcC+lZVk7CEiDtp3DmWevQOv9cahmwsNrsMBr03IMj1mRART1gDsMeBzWiEQttJXWetqvlw1qYn+QtYhEuEar2OMFESHNJ/04YkJWI9zcUYkdRFQpO1aCivgU/HzyNnw+eBgDodEDv9HgMzUnCkOwkTOiViozE6CCPPHB4kkIZNK6aRxBa0H6nCVSgweyc8fz10JmA7FNMpsTOqKsVsWAlVZ6kUFzEwhqYmZRAz9hYXRwLbTyL07XNuOi13zBjSBaXnBcT6fhJhouhyWK22vDKT38CABKj3aVQWpVT9PS90VJeIJTPsQ0BJccSJjOS7XGCifBMVlI0nrqiHwBHlPDAqVrsKqrCruJq7C6qRml1Ew6V1+FQeR0+31qM6AgD3r45FxN6aVRZoY3hUQpFEQsiBPHJsWhoaMBvv/2GoqIitLS0uLx3//33qzKwUKCu2cL9HagZZrEKEgaVcyy4qIjIjY6F3X+gyqIGqipGU4sNM/I3YWBnZ5UvrRyLDzYWoq7Zio+3nMT4nikAgFguxyK8pFDHzzqTk8f0SOH+djaA0l4KNb5nCjYcqeBeq+WIi+EesRB3LB68qBde//lPMhyIdkFUhAG5XTogt0sHbtmZ2mbsKq7GrqJq/PbnWRwsq8Wcj7bh9VlDcPmgzCCONjB4bpDHluMOr4kmIrxR7Fjs2rUL06ZNQ2NjIxoaGpCcnIyKigrExMQgLS0t7B0LhmFQb7YiPioCdc3OTuNm3g+fYRjoNDJGxYx+tR0LOeVmAy6FCpDhtWpfGQ6frsPh03XcMq3KzfK/I+y1i2nVGodbFRU2nyElzoQLeDORWjfIY38vb900DGsPnXV5T8sogXuOhbvR0C011pmcGWJSh4ULF3p8/6mnngrQSIhQJy0hCpf074RL+nfC/It7Yf6Xu/H93jLc9/ku1DZZceOonGAPUVMsHqtCUYM8IvRQ7Fg8+OCDuOKKK7B06VIkJSVhy5YtiIiIwE033YS//vWvWoyxTfHoN/uwfHsx/pM3VhCxcM4wN1vsiI70LH3wFYsnKZRqVaHYPI7gS6HO1plxpq5ZsxltIWKz2FrlbvNPL1cVKiLwVaHqzVY8890BXDE408XoVxP2eBKjXW85rINsZxx5LWpX4LLy8oX4BQ+AQOdYOO8H0wdnYnT3jriwbxpW7CzVfCxasGLFCpfXFosFhYWFMBqN6N69e9g4Fvn5+cjPz4fNFl4RxLZKpFGPf14/FAnREfjsjyI8tmIfqhpbcM/E7ppN1gUbz1WhqEEeEXoodix2796Nt99+GwaDAQaDAWazGd26dcNLL72E2bNn4+qrr9ZinG2G5dsdiYn5a4+6GPL8GebaZotmjgVrgPDLzbJGjF0laQeXvC1DCqV1VagxL/4Ci41B7/R4TfcTDPRiEYsglJt985cj+HpHCb7eUaJZ4QH2eIS5BvxqX1Y7g0iVHQv2gRxh1LsVI9ByFtA9x8K571iTATeMdMzCqh1tDBS7du1yW1ZbW4tbb70VV111VRBGpA15eXnIy8tDbW0tEhMD0wS1vWPQ6/D8jAFIjonEv9Yexcs/HkZ1Ywsem9Y3LJ0LeQ3yQuv+QLRvFPexiIiI4H7c6enpKCoqAgAkJiZyf7cH9DqdixSq3uz8m61+owWsA8OffXX2sVBnH1ZZydut1Xw0lHAcO1vPGYZ8aRKLWlWwvKGVFF8nFrEwBT7HoqRa+1KnrBRKKAniz+xrIYdy9n3RuUWetHxYe86x0LmtFw6GQ0JCAhYuXIgnn3wy2EMhQhydToeHLumNJy7rC8DRC+Phr/eGZa6BmAqBxRjgRrQEoQaKIxZDhw7F9u3b0atXL0yaNAlPPfUUKioq8PHHH2PgwIFajLFN8O3OEm42GXAYhXxngu9k1GroWDjzH3g5FipXheL6WHgsN6u9FGrzUWeirV7nkMvwsdjtMOlVjgyJHI5akSAhrjkWjusay1aFCmC52UDMAUpGLHjfMS3C/c6eLHq3KJC2ORauvx3+bCTfwTEYtK2KFWiqq6tRU1MT7GEQYcLc8d2QFBOJ//tmL77eUYKaJgvevGGo174woQQXVaWIBREmKHYsXnjhBdTVOWaP//73v2P27Nm4++670aNHD3z44YeqD7AtcLq2GfO/3OOyTKdznWFt4uVYaBmxYHMNIo3uydtqzeBzuvQgN8ir5TlrYodmtTEIRF8lre7pLjkWvAZ5QBiWm5URsbDZGZfiCGrARfgMOreGeFoWHvCUnyS23v/2l+PDTYW4bWxXzcakJm+88YbLa4ZhUFZWho8//hiXXnppkEZFhCPX5HZGYnQE8j7biTUFp3Hbh9vwzi25qt0jgo1nKVRrVShyLIgQQrFZNnz4cO7v1NRUrFq1StUBtUXONbS4LdPpdJI/9tpm7RwLs1UkeVvtqlAeZlBYIgIQovVmXAcqoVsryZWBN3XNGr0xYVputlkiYqHX67holNVmx50f78CagtNY+9BEdE2J9Xu/zuZTYhELvzcviackdP47/OpRz/63IGQci9dff93ltV6vR2pqKmbPno0FCxYEaVREuHJxv3R8dNtI3PHv7fj9eCVufPcPLLttBDrGmYI9NL9gGMaj9NgQ4OqLBKEG1CBPBmIGu16nkzTkG1u0MwobWxyz+OzMNgCw9yPVkrdllZvVvlqFN+Nay/wOPlpJofjGJ/udYXMswq3cLBuxMImUXTXq9Wix2WG1M1hT4OjG+/nWIjw2ra/f++UqnBl0LpXbAG1LvHr67fClUHIjG22NwsLCYA+BaGeM7t4Rn99xPmZ/uBX7Smtw7du/4+M5o5CVFLpduvnPT7FiKVw5bqoKRYQQspK3L730UmzevNnrenV1dfjHP/6B/Px8vwfWlhB3LKR/7E0aOhZNrdr7aJ7GVKuIRbCkUDWNFuwprvZqXAcsYhGA3bCORWy4SqFajydKpAO1WJM8fhUlf2Dzjox6HZoFeStaygs89T7R8WIWWvVIIYhwZGDnRHw1bzSykqJx/GwDrlm6GUfP1Ad7WD7Df35GkhSKCBNkRSyuvfZaXHfddYiPj8f06dMxfPhwZGZmIioqClVVVSgoKMDGjRuxatUqXH755Xj55Ze1HndAEftR6yBtyAu13GrS1Bqx4JezZSU1at18LDb3BHEhnDHYqotXqwzga2v+xBu/HAEAdOkYI2ucWqNVxIIvsXJGogJfbjYQeI5YuIf7VXMsWp0Vg17v9rvUsqqYp0hEUoxTGx5ujsWSJUtQUVERNn0siLZH99Q4fDVvNG5+/w8cO9uAa9/ajI9uH4lBnZOCPTTF8CdTPFWFouRtIpSQ9fSeM2cOjh8/jieffBKHDh3CXXfdhfHjx2PEiBG45JJL8O677yInJwc7duzAF198gezsbK3HHVAkpVASBmeTRo7FzqIq/HzwDAC4VKhSPXnbLj9iAagjh9pw5Czu/Wwn51QAwMnKRo+fCdQsDqORY8F3jNhDiW3NRrfZmbAqreisCiXdBIrvTAlzMXzFWeFMh0sHdBJ9TwvEHIZ/zByIcT1ScOeEbtyyUJVCSfHNN99g2bJlwR4GEeZkJkXjq3ljMLhzIqoaLbjhnS0uVQRDhRbePV7snsHeH6jcLBFKyM6xiIyMxI033ogbb7wRAFBTU4OmpiZ07NgRERHhUZ1BClEDTyc9i6BVjsXVS5xyNL4UimuypZIBbOOSyaT9Tn7YtsVmd6lS5Qs3v79V8WcCZXhrZX9aRDbMj0SZrXaPTQrVgh9tYp0otRtRcVIokTKR7MOzutFZ9EAssuEL7HdZr9fh1jHn4byOsThwqhav//ynxuVm3c/frBE5mDUiR7Ce9tc3kPzyyy/BHgLRTkiOjcSnd5yPO/+9HZuPVeLWD7fhzRuH4pL+nbx/uI3AOgyRBr3oPZdVDVDEggglfH6qJSYmolOnTmHvVADiBqAOOknDVkspFEu0hhELpxRK2rg0GfVIjo0EgKDNFGmZOM5Hq5u62PcnlpeUHww51O3LtuHyNzeq7rSxvwnRiEXr96ymyVl9TS23xsorRGA06HFRv3Skxpta3wuOFMqX9QiCcCfOZMQHt47AJf3T0WKz4+5PduDzraHTqNdZgVH8PuBskEeOBRE6UFUoGVhEDDyb3S5pcGqZvM3Cl0Lp1W6QJ6PcrF6vw8xhWXh3QyF+KjiNKUGYJQpUCT6tcizEHhbpCSbodI5u38GQQq09fBYAcOxsA3p3ildtu1IN8gCnFKqKF7FoUeFByjAMF23iRxDYrzXriFc3tuCHfWW4fGAmEmPUmSiRG/EJ9RyLgoICFBUVoaXFtST39OnTgzQior0RFWFA/o3D8PiK/Vi+vRgLvt2HfaU1ePqKfqpJKrWihesZJf6sdTbIIykUETqQYyGDFhEDjy2PKUZ5bTPKa5rRKTFKszFFiUmhVLr3cLO8HnIsACCno6PPQAOvA3kg0WIWR8yJ0MivcHOM4kxGJEZHIIJXfjVYqG3wsqWDhQ3yAOfDs6rRaZz6W26XYRjM/nCb2z4Ap/yIPb/3frYLG49W4KcDp/HR7SP92i9LrcwmmaEasTh+/Diuuuoq7Nu3Dzqdzk1CZ7OFVx8Wom1jNOjx4syByOoQjdd//hOf/VGEA6dqsfQvw5DZhsvRemqOBzgdDqoKRYQS4SXw1QgxI6fFykhGLLYcP4fRL/6CfSU1qo3h4y0nXV679LFgpVBq9bFgy816MXpYWUsgpF9iaDGjLxb10SpiISyX27lDNHQ6HXc9g1m7XO1jZku9ikcs3HMs/E1WPFnZiPV/nuVe8x0l5yyg4xg3tkr5fuOtrwSx5P6KevemmmKEasTir3/9K7p27YrTp08jJiYGBw4cwPr16zF8+HCsW7cu2MMj2iE6nQ73X9gTH9w6AonREdhTXI0r3tzYppO62Xt8pJQUqg08CwhCKeRYyEDUsbDZPUqPGAb4x+pDqo3hyZX7XV6LSqFU62Phvdws4IyaBKs0qhazOGJ5KlpNFgkjYdnJjvK6xjbQbVXtBn1sxEKqQR7gkCSxnKho8Gt/wt8m34BXu++L2G0gt0sHWZ/1FhVsq/z+++9YuHAhUlNTodfrodfrMW7cOCxatAj3339/sIdHtGMm9U7Df+8dh34ZCahsaMFN7/+Bt387pll1P3/wJoUyqHyvIohA4JNjUV1djffeew8LFizAuXPnAAA7d+5EaWmpqoNrK5hFZk/NFptXiYyWhqGrFMrxv1o3HzZZ3ZvRo1bEwtcbvhbdqcWclUBFLDJbpXPOvg7Be5h463oul2aLDT8eKOeiEWLOKvs9O9fgjFh8u6sUX+8oUWUMgHjE4vfjlfjnz0ekPiIboRNzTW5njOyaLOuzepWrbwUKm82GuLg4AEBKSgpOnToFAOjSpQsOHz4czKERBHI6xuDbe8Zg5rDOsDPAov8dwp0f7/B7wkJtnFIoL8nblGNBhBCKHYu9e/eiV69e+Mc//oFXXnkF1dXVAIAVK1ZgwYIFao+vTSBmwPJ7VTw3Y4Do57SUOcSINMhTywCWU24WUC9i4evnb1u2TfX63mLOmVaN1ISOZ1KMo8oWp6sNYvjbbFHnvD7/w0Hc9fEOHDhVC8DpBPNhDf2KerPLcn8ifsJfHt+h4f8uX//5T5/3wSL8zozvmSL7s0JHqy3OqooxYMAA7N27FwAwatQovPTSS9i0aRMWLlyIbt26efl04CkuLsbEiRPRr18/DBo0CF999VWwh0RoTFSEAa9cOwh/nzEAEQYd1hScxoWv/YaHv9qDIi89kgKFt0Ip7P2BYbRt6EkQaqLYsZg/fz5uvfVWHDlyBFFRzuTkqVOnYv369aoOrq0g5ljwe1V0aDUIhWhpGPJvRHqRGW5fDRSGceaOBCrHwp/k7z3F1X7tW4ioY6FVHwvB9yMh2lGRSKwTtZaIXWWxKJ0vfPKHa26Q2Aw9+/A8W+fqWPhjZAs/yf8qGySiBL4mUgsderFeHVIIJx9CRfLwxBNPwN76/Xzuuedw8uRJjB8/HqtWrcIbb7wR5NG5YzQasXjxYhQUFODnn3/Ggw8+iIaGtjV7TaiPTqfDzed3wYp7xmJi71TY7Ay+2lGCya+uw6Pf7EXxueA6GN6Tt533B4paEKGCYsdi27ZtuOuuu9yWZ2Vloby8XJVBtTVEIxY8xyLGJG5I2OwMymqa8ODy3aobwHxYg4id0XhtzZ8Y8fzPKK1uUrwtvrHrrTmbWhEL1kmLitDLlpCwHD1T79e+hQQ2edv1vCWyjoWhDUihVIpYRAq+Q6KORevxnq0XOha+71dooPPLvxokZAfeInRy9xWtwLEQyg1DpV79JZdcgquvvhoA0K1bNxQUFKCiogJnzpzB5MmTgzw6dzIyMjBkyBAAQFpaGpKTkzkZLxH+DMhKxLLbRuLbe8ZgfM8UWO0MvthWjMmvrsO/fj0StEhhizcpFG/iIVQmHQhC8ZM0KioKtbW1bssPHz6M1NRUVQbV1mgRKZ3In6XnNzXjY7UzeHD5bqzYVYor8zf5vH/hDWXr4xe6vBYmo77xyxFU1LfgzV+U68f5s+RSNzsWZ8TCPyOUlZXFSJxHT7ASG7UQCzdr9dAROg4JUY7jZ2fwgymFmvfJDrykQvEBYUM8sXoA7DpCB96fo/d07qQiE74mUgu/Mv5ELEJ5VjI5Odnnju3r16/HFVdcgczMTOh0OqxcudJtnSVLlqBr166IiopCbm4uNmzY4NO+tm/fDrvdjuzsbJ8+T4Quw3I64OM5o/DN3aMxrkcKLDYGr/z0Jx76aq8mOXvekCuFAkJn0oEgFDsWV155JRYuXAiLxZFoqdPpUFRUhEcffRQzZ85UfYBtAW85Fvx8Bz42O4M/T/s/oy7MI0iLd+2PYZBokOfLQ55/8/KWI+KMWPgnhWIjFtERBsUdl881yCvrKRfx5G1Vd8EhvK5Bk0JJnPQl6475ve1IQXlZsYhFjEn9djqeZvekvte+RiyOnXX9jSuKWAgdiyBVWJPDvHnzUFxcLGvd5cuX49NPP5W97YaGBgwePBj/+te/JLf3wAMP4PHHH8euXbswfvx4TJ06FUVFzi7Lubm5GDBggNs/NrEcACorK3HLLbfgnXfekT02IvzI7ZKMT+aOwgtXDYRBr8M3O0twywd/oKZRXv8ZtWCfAZFGKceCIhZE6KH4if7KK69g2rRpSEtLQ1NTEy644AKUl5dj9OjReP7557UYY9DxlGOh14k3/QIcRqoavRa8SWJYQ+nbnaV4+or+vOU+7Is33ggv5WbZmWZ/ZTONLY4ci5hIA64YnIk/CuVLFNS+2YqXmw1M5+2EKFYKFfyIhVq4RSzEHAsJQ9yfSJGnmX+pHAtvEToprl6ymft7fM8U9M9MkP1ZoZPTlhthpaamYsCAARgzZgymT5+O4cOHIzMzE1FRUaiqqkJBQQE2btyIL774AllZWYqM96lTp2Lq1KmS77/22muYM2cO5s6dCwBYvHgxfvzxRyxduhSLFi0CAOzYscPjPsxmM6666iosWLAAY8aM8bqu2eyU5olF6YnQ58ZROchMisK9n+3CluPncPXSTVh220iu9LfWcOVmJSY79Hod9DrH5FYwy48ThBIUm54JCQnYuHEjvvnmG7z44ou49957sWrVKvz222+IjY3VYoxBR6zzNotRrxdt+gUANrtdldlub7OYfGONL1/xpZQla6jrdU6JlRSchMVm96tiRaOZlUIZcMPIHEzply77s2obYmLb89SvxK99CXMsYlwjFuEwQyXHsYiViFj4c/iezp2U5Mlb3xY5fDxnlNffjad9epNjmK02nPIhd0oN/v73v+PIkSOYMGEC3nrrLZx//vnIyclBWloaevfujVtuuQXHjx/He++9h99//x0DBw5UZb8tLS3YsWMHpkyZ4rJ8ypQp2Lx5s8SnXGEYBrfeeismT56Mm2++2ev6ixYtQmJiIvePZFPhy8Teafhq3mhkJEbh2NkGzMjfpGpzW094k0IBbUMaSxBK8PlJOnnyZDz00EN45JFHcNFFF6k5pjaHp+Rkg17nZjyxWO2MKrMM3nTX/FnPI7xkZl8cC2cPC+9fDb6W3J8E7obWiEV8VAQMeh3G93Lm6iy7bYTHz9pUnsURcyK0yutzj1i05liwtcs16CweaIQhfjGbW0pK6E/EwtND2CDhQPgasfAHtxwLL9f88jc2YsyLv+JgWXBm0NPS0rBgwQLs2bMHlZWV2LlzJzZt2oTDhw+jqqoKX3/9tZsD4C8VFRWw2WxIT3edcEhPT5ddMGTTpk1Yvnw5Vq5ciSFDhmDIkCHYt2+f5PoLFixATU0N90+uBIwITfpmJGBl3lj0z3Q01bv9o20or2nWfL9cVSgJGwKgJnlE6CFLCqWkfKCWXVfXr1+Pl19+GTt27EBZWRlWrFiBGTNmaLY/Fk+ziEa9ziVicf2IbHyxzfEQstkZqGH3ekva4s+QupTV9KF8JjuLHiHjs3yHymy1IVrCQPRGXbPDsYhrnbke3DmRe29i7zQs/csw3P3pTtHPsqdmy/FKnKxswKwROT6NgdueyLkOVB8LtghAoCMWWprTwmMQm82XdCxU3C8fyXKzPuZY+INSKRQ7cbBqXxn6ZsiXXGlBUlISkpKSArY/Yc4YwzCy88jGjRvHlceVg8lkgslkUjQ+IrRJT4jC8rtG45qlm3GovA53frwdX941WlExBqVYZDxvjQYdYAmPiSaifSDLsXj99dddXp89exaNjY3cQ6W6uhoxMTFIS0vT1LFgE/xuu+22gCaKS0mdAIehZOLlWEzolYpZI7Jx1ZLNsNrUiVhsOlLB/f3i1e7yAr6hxDdUfHEsWCdGjpFlNOhh1OtgtTN+VYaqb+1jEdc6Yz+ocxI+njMS2R0cOtcuHaUldmzE4vp3tgAA+mcmYkBWouT63hAvN+vz5jzCnuurh2bh4n7pnNHNhr4tbXSGasfJKmQmRSEjMdrruvx+L4BEjoVUNTA/Dt9jjoVUVSgNG1pKIRyL3Mo0vlZfCkVSUlJgMBjcohNnzpxxi2KoTX5+PvLz82ETqQxIhB9xJiPevWU4rszfhL0lNXjk67345/VDNPu9WWRJoShiQYQWsqboCgsLuX/PP/88hgwZgoMHD+LcuXM4d+4cDh48iGHDhuHvf/+7poOdOnUqnnvuOa5+ujfMZjNqa2td/vnCq9cNRp9O8aLvGfU6l1r9VjvD3SRsdsZvo/RUdRMe+cbR4TY+yojrR7rPyPPvSXzDzRcpFOsIyZWFcAncflSGqmt2VOKI42ntx/dMxXkpDoeiX2YCHprSS/SzJVVN+GSLswmbsHuzUsQb5Kl/Q7fZGU4rf9+FPTF1YAb3HtfHog3OUO0sqsLMpZsx9Z/ySn0KmyeK2e6xEn1g/Cm9KhZ5YpHKsZCqzKIlQmdGbs5QEHygoBEZGYnc3FysWbPGZfmaNWu8JmH7S15eHgoKCrBt2zZN90O0HbKTY7DkL8Ng1Ovw3Z5TqlTHk0KOFIqd5KNys0SooPhJ+uSTT+LNN99E7969uWW9e/fG66+/jieeeELVwfmLmgl4Uka6Qa9zkXfY7Qw3C6lGYvEvB09zf0vpxvmacVfHQvn+2H3IjXawYWK/IhbNbI6FdADt3sk9Mam3M/firZtyAQAnKxvxxMr93PL41spKviLmWGjRx6LoXCPMVjtMRj1yBBVIjCp+f9Tmmx0lAIBqmWUZhRELsZm/aImIRbPF7vMsnadzJ/Vb9iVi4W/te6U5Fiy+TBq0Zerr67F7927s3r0bgGMya/fu3Vw52fnz5+O9997DBx98gIMHD+LBBx9EUVER5s2bF8RRE+HK+d064tkrHRUWX/npMNYUnPbyCd+QJYWiiAURYih2LMrKyrgeFnxsNhtOn9bmx+craibgSRWMEZtx5Dphq2CQ7uJ17JaSVfFH4L8Uii1/J++rwUYslm0uVLwvljqza46FFPdd2BMAMGt4tuSxCTs9K0Xs5q1FVajD5XUAgJ7pcW7HYghwFRBPzyvh+TiioC8LwzAu/V4A8e9krIfcHF8jUB6rQkk2yFP+3WniOU5SZac94dbHQqZjEUy3gmEYnDx5Ek1N6lWn2r59O4YOHYqhQ4cCcDgSQ4cOxVNPPQUAmDVrFhYvXoyFCxdiyJAhWL9+PVatWoUuXbqoNgaC4POXUV1wy+guYBjgvs93YuWuUtX3IUsKxRbzoHKzRIig+El44YUX4o477sD27du5mdzt27fjrrvuanPVoUwmExISElz++YpUwqdBIKuw2e3OiIUKUha+4SIVCuVXZOKPxp9ys3KlUGxTt8+3FmP+l7sV7w9wRiziPEQsAEfX1H3PTMGLMwdKGof+OnPiUii/NilKaasM6jyR/BH23Ktd8UoKT46T0NAVNoPzhFgUS7wqlPR1L6xokL0/Pp5ym9R0ShstVu7vPx5Tfv9zj1jIlEIFUQvFMAx69uyJkpIS1bY5ceJEMAzj9m/ZsmXcOvfccw9OnDgBs9mMHTt2YMKECartX4r8/Hz069cPI0Z4rk5HhCdPXt4Pk/ukodlixwPLd+PJlfv9bgjLR5YUSu+UVhNEKKD4SfrBBx8gKysLI0eORFRUFEwmE0aNGoWMjAy89957WoyxTSD1IGcdjqwkRyLr+J6pqt4I5BgafB07v+eG1snbAPDuLcO5v7/dWeqTbKheZsQCcEiddDqd5LFp4VhoIYVinU6xwgDssQVKU+up6pVQUsRW8JKDMFoBSCVvO89BVlI01j00EcNykgAAJ3x1LDyWmxX/7viiLmpo7cGSGB2BxGjlMjyhNMzTZAT/vWAqofR6PXr27InKysrgDSJAUI5F+ybCoMe7twzH/ZN7AAA+3nIS1729BSVVjapsX4kUivpYEKGC4s7bqampWLVqFf78808cOnQIDMOgb9++6NVLPLk2XJCMWLT+6H/52wWoN1uREmfiZqPV0MjLqSrFN+BqeYafT+Vm7Z47gQrp3MG1MpAjeV3ZflnHwlOOhRBpx0LRrt0QrQqlQeCA/W6InWd+8n8g8OSMCZOg+Y6rt3KfYvkHYqvzk7dNRj3OS4nFwKxE7CyqRmGlb46FL1IoX5xStmu8JzmXEjxJoVwmDYKcY/HSSy/h4YcfxtKlSzFgwICgjoUgtMSg12H+lN4YmtMBDyzfjT3F1Zj+r034/r5xyEzyXhnPE3KkUM6cTZJCEaGBYseCpVevXgF3Jurr63H06FHuNZvgl5ycjJwc//oXeEMqYsFGJ6IiDFwis5rJVnJmKcw8x6KuyZn/4ovtIacTKB+hYdlitcv+LAsnhTLJn/FVI2LRYLbi250lmNK/E9ITogCIO4NaVYUC3KV0gPP7EyhNrSfFHn8MwsiNNydSzEgWu278685WZurUWsr2bJ1vORaezp3Ub9mX3yubnB4jI9omB09RKr6jFuzc7ZtuugmNjY0YPHgwIiMjER3tamCdO3cuSCMjCG2Y1CcN3983Dnf8ezsOldfhyZX78d7s4R4nV8xWm8dy9fKqQrXdYh4EIYbip+Htt9/u8f0PPvjA58F4Y/v27Zg0aRL3ev78+QCA2bNnu2hxtcBbxEJsmRo3AjnJnFIRC298sLEQPx4oR7/MBKTEmZA3qYczeVtB1OGivmn4+eAZAA7jJ1ZhXym287ZUozQxJGedFZzzv39fgC+2FePdDYVY/8gkfLmtGD/sLXNbT4vkbU8RC/bceyqZqiaeHCe+Y9sgqPBksXl2IsW+/2JSqIRo522IHQrrsPjanNCTkyB1vL74cQGNWPAci2DbGIsXLw7uAAIE9bEg+GQnx+DNG4Zi2hsb8MuhM/hhXxkuH5Tpsk5dswX/21+OFTtLsaWwEreOOQ9PX9FfdHvOYimepFCBLeZBEP6i2LGoqqpyeW2xWLB//35UV1dj8uTJqg1MDDbBLxhINtXyMOOsBnIci9wuHfDuBkdVptpmZ8TCm1G28PsCAMAfhY7ZxTvGd/No8Eqx+PqhGPD0jwBc5RpyYWdplfQRkJx1VvD9YJ2honMOvSzbL0SIFl859tqIfa8C3SDPkxHO//7VNLlWg7NYGSBSerti+QJily2a19mW/f7465x7eggnSJQk9iUyVd+aY+Fr13kA2PP0FNzwzhYUlNV6HDe/SEOwe5zMnj07qPsPFHl5ecjLy0NtbS0SE31vvEmEDz3T43H3xB5445cjeOa7AozvkYrEmAjY7QyWrDuKf6096lK44sNNJzAgMxEzczu7bcsq49nnVECQFIoIDRQnb69YscLl3/fff4/jx4/j+uuvx/nnn6/FGNsEUoas2AysL7kNUsgxrC7p3wl5k7oD8G9Ws9lqU1xuFnAkXbOlNn2p689+RklVHinHR4ltKPcyaSGF8uTAGQL8IBEeH5uoCLh+/2oEvSvMXmZxxWQ9YrIB/jJW1uevnNDT56IiDNj86GRcPijDZbkvkSm2uaM//VMSoyOQneyQEnlyzPnvtYVmWceOHcMTTzyBG264AWfOOJz01atX48CBA0EeGUFoS96k7uieGouKejNeWHUQdc0W3PXJDrzy059ottjRLTUWD1/SG3eM7woAeHzlPhwqd2/Qy/6mZZWbbQO/eYKQgyqtZvV6PR588EG8/vrramyuTSKlDBKVsigwyr0h52ai0+lw1dAst+VKjbLmFhv3GSVSKMDpFPgSsWiRoTMVIuW8KTlmvlPoKbqjReCAdRrEHNYIrvN2cCIWo7p1RIeYiNYxOK8nPxoGeP9uiiUbeks6dkYsWsP/Pp58b/kpmUnRyBIUHvBFdlUno7mjHJzddZ3jPlnZgAeX7+YMEr7THuxEzt9++w0DBw7EH3/8gW+//Rb19Y4yxHv37sXTTz8d1LERhNaYjAa8OHMQAGD59mJcungD1hScRqRBjxevHohf5l+AvEk9sGBqX0zolYpmix13f7KTm4hgUSKFonKzRKigmgV87NgxWK3y9f2hhpQh6ynHQg3kSh6iItylGEpn2pstdsXJ2yyRrQlqSiMWDMP4FLFQI3mbvwlPDlHgIxb+GdVKET6wjHpnOd+5/3b2qxFKobxdazHHw1tvFbPF9UHrc8RChlMWIZgA8GVXrKEgJa+SC/vd5zuTcz7ajhW7SnH1ks0ABI5FkGcvH330UTz33HNYs2YNIiOderhJkybh999/D+LICCIwjDgvGTeOchSNKa1uQqeEKCy/63xcPzKHi8Lq9TosnjUEmYlRKKxowKL/HXLZhiwpFBexICkUERoonmZjE6ZZGIZBWVkZfvjhh7DW3fLlGjqdU3KjdY6FXONSrAeEUqOsyWLjZnqVHkOkjzc//vGpIYVS4gTwr6knx0KLvB6uKpRIdMsZsXCtyPT4yv3okRqH28d19Xv/djvDRUuE58xo0KGyoQUAcLKyEXVmKxKiItxzLLxcazGn2Fs1I7NaORYyPid0Tn1xYlSLWLSOhf89PHrGEQVgK0/x3/MlMqgm+/btw2effea2PDU1Naz6W1DyNuGJR6f2wdEz9UiIisCiqwciNd69cklybCReumYwbnr/D3y3+xSeurwfNxEoSwqlYpVJgggEip+Gu3btcnmt1+uRmpqKV1991WvFqFCGL+GIjTRyvRfEDEO9XufifAC+l4eUa6jHijkWCg3iJovNj4iFbzkW/PWVJG+LnXdAWWUf/iYsHsatxQ1dTo4F3zjeWngOn/1RBAB+Oxa7iqpwy/tb8fClvXHL6PPcZuqF57am0YKEqAhU1LuWfvV2rUWrQnlxWNltGv3sPi7nmglL5foSmVLLsWBlgB6Tty1tJ2KRlJSEsrIydO3q+l3ctWsXsrLcZZmhCiVvE55IiIrAl3eN9rremO4dkZkYhVM1zVh3+AwuHeDI71JUFYocCyJEUPw0XLt2rRbjaPPwZzdjTQanY+Eh94IvBfG1oZVcxyLCoIfJqHepHKPUTmq22HwqNwv47ljwj09JYz3h+YyK0KPZYlcohZIXsdDifu6pKlSEiCymUaSLta8898NB1JmteOo/B3Dd8GyYra7bNup1Lt+d6kYLspOB0qoml/W8zZqL9rGQ+TvgnCsfDWg5PUCEDpRvjoX/yduAs/Ou5wZ5zusU7ByLG2+8Ef/3f/+Hr776CjqdDna7HZs2bcJDDz2EW265JahjI4i2hl6vwxWDM/H2+uP4754yzrHgJvI8TKoZRCLYBNGWUZxjMXnyZFRXV7str62t1bzcbDDRuzgWTn/MKDGzLzQYvc3USsE3rB6+pLfHdYWzpr5IoZwz6cq+GhE+Jm+zjoheJ30uxRA2lmObEH25vUT2DZi/BU8OkRZSKKURC75B7q/WNoH3PZn8yjrsL3WtViJ0KqsaW/Cf3aX4tDViwo3DW8RCNMdCfN3R3ToCAC7pn+4Yg5/h/62F3hu0CR1Zf6RQYlJEJbC/H08OEf87GuwKMc8//zxycnKQlZWF+vp69OvXDxMmTMCYMWPwxBNPBHVsBNEWuWKwo9/FzwdPcxOT7L3ckwzY6KcslCACjeKn4bp169DS0uK2vLm5GRs2bFBlUG0Rvg3Cr7s/LKeD6PoOw9zOe+1fxGLFPWMwVGJfLHEmIyrqndfGr6pQSnMsfJVCydCYiiEcH7v/nw+exidbTuLWsd7lQi4RC5FxXz0sC9/uLFU9YrH28BkcLq8DINXHgn2QiH9/mi02xeeLT1p8FPf3qZpmkf27bruqsQX/J9Ljw3tVKHnlZgFgyV+G4X/7y3FZawlYfxLYdxVVYVdRtdf1hOfeF/9R9apQVga1zRbc/N4fbuu0pT4WERER+PTTT7Fw4ULs2rULdrsdQ4cORc+ePYM6LoJoq/TPTEDXlFgUVjTg54LTGNg5EScqGwB4vn+QFIoINWQ/DffudRoWBQUFKC8v517bbDasXr06rLS1QtiIg8modzHyZgzNFF1faLT46liwN5PkWA+dyFoR5lkorgpl9UMK5WfEQkniNuBeXYj/+c3HKmU5FvxN/HLojMt7hYum4Zudpa2OhXo39L0l1bjtw23ca7lSKH54pdliB883UIxYgiEf4Xe1psmCjMRoFFY0uCxv8ZLQKibXkfoZdIiN5Cqs8MfgSxRhy3Hv0Qr+Plh86mNhVkcKxRY/sNrt+GjTCewpqXFbpy1FLI4cOYKePXuie/fu6N69e1DHQhChgE7nkEO98csRfLOzBO+sP45mix2ju3XEgEzp/B1K3iZCDdmOxZAhQ6DT6aDT6UQlT9HR0XjzzTdVHVxbgpWiREUY0MJ7qHeIETf4mwWaeF9vCaxxKUcmJJRjKJZCtfhTbtbXHAvv5fbEEBqFJt7n5R42f/b8RUEZQJ1OxxnBdgZobLHilR//xKUDOmFk12RFY+VzsEwgOxKxtNlzYZYoLyr8binFm9PoJoVqsKBnWhznWMRGGtDQYkOL1UvEQsT4lVuK2Z+qUHEyowfC35QvDiTbNDAxWr0+Fg0t4tfXtUFecCMWvXv3RkZGBi644AJccMEFmDhxInr39izVDEWoKhShJlcMysAbvxzBhiMVABwThouvH+JRKk3lZolQQ7Y1V1hYiGPHjoFhGGzduhWFhYXcv9LSUtTW1oZ3VShexIKf7GqSMIjNAgPbl+ZbgFNzrSSxmdunD1WhfC83697gSw6sI6LUkRHmWPAdE7k5EVKHuOKeMa3vt5ZjtTN4f0MhPthUiOve9q9Gv1AKJFbdysQ5Fs7vGf+8+utYeHM4hVKo6qYWREc65X/9sxLdxgQ4zvvSdcewtjX6I/ZdkJJCuY/B96pQwgTxT+eOEl9P8AVQuqtmiw21rVKo1Dg/Qkjg5Vh4iERU8zqfB1sWUVZWhldeeQUJCQl4/fXX0bdvX2RkZOD666/HW2+9FdSxqUleXh4KCgqwbds27ysThBd6psejT6d47vWr1w5GeoLnewdFLIhQQ/Y0W5cuXQAA9iBXIwkWnBQqQu9S9lGuoeSLzMJmZzjdt7CZlxhn6lzLgfpiKCmJkPDxN8dCacRCaDxGukQs5DoW7tcuKymay2XRcRELRjQXwReE4xZz4NhEdLNVfIa6SWPHQmhw1zdbuf0vvLI/fj7ocByE13rT0Ur8Y7Uj8nPixcvEy83K9Ff9iVg0mJ2NOi/qm4axPVJE1/M3efts6+8t0qhHgp8RiwgZs5Jneb/vYM9epqen44YbbsANN9wAADh69Ciee+45fPrpp/jqq68wb968oI6PINoqfzm/C55cuR93XdANk/qkeV3fKGPSgSDaErKeht999x2mTp2KiIgIfPfddx7XnT59uioDa2uwBpHJaHCZOZSLL/4Y33iQk/NQLjB+WWfmcHkdvt97CndO6OZRC84vNxvpY7lZYaTGG86IhbL9CY1ffsTDFykUNx7eOeciFgyDDjHO88YwjGyHUoicamEmkXNpcZFC+WdUejOghdeixWZ3yYWRaoZYWt3o8loswdhb520WZx8L5Q/Tep5jcbbevdAEi7/lZs/UOX5vafEmn78PLKJ5NQLO8vqIBLuPRX19PTZu3Ih169bht99+w+7du9G3b1/cd999uOCCC4I6NoJoy9w0KgcX9klDZlK0rPXlRm9P1zbjUHkdLuiV6vcYCcIfZDkWM2bMQHl5OdLS0jBjxgzJ9XQ6XdhqUdmZZqEUSi6+RCxcezx4n9EfnJ3okrjKyq8uWbwegGPG88WZgyQ/39RicxqQCiMIcqQcYnCOjNHgZU1X3KpCGXyJWLgv48/COx0L11ya2iYrEmN8S9YV2p+iEYuIVsfCIi6F8jdi4S0KIHR+Wqx2Lq8owqDnXWuhFIr/NyP6XZCfY+Hd0JaCH7GYPbqL5HoRQimUUsei1mHop3lJhpcD60h5Kn7QliIWHTp0QHJyMm6++WY88cQTGDduHDWQIwgZ6HQ62U4FwM+xkL4/2e0Mbnl/Kw6frsOy20ZgYm/vkRCC0ApZ1qPdbkdaWhr3t9S/cHUqANeqUErlPoBvM698o0pOzsPL1wzGrWPOw61jznPsU2AobT9Z5fHzTRbfHQt/O28rjZAIDVTXHAt52xCbPecbbKyv4ohQONc5Xee7LEq4TzFDm5VCtUhIobTOsRA6sS1WO1panelIo14yOsXfqtlqF60KJXdi3x9dcUOLw7G4bFAGrhoqXalOeO6V7ouVHqb5U6KrFSlnjU8F37EIst76sssug81mw8cff4x///vf+Oyzz3Dw4MGgjokgwhF2ksXT/enHA+U4fNpRwvy/e8oCMi6CkML3YvjtDGfEwqBY7sOiNIGb3yxLzkxvdnIMnpneH106xgBwvxHxjRaxBGerjeGOTWn5V2e5WWVGr8XHHAuh9ERujoXVZsfq/WU4W2cWjVjwz5GOF7HgzySzM9W+IDS2xXMs3A33FhWrQnnr2uwWsbDZudmyCIMeUa2Oj3Ac/PPeYLaKzrDJlUL5k2NRb3aMKzeng0eJUmK0a9RJ6a7ONThkVslx3ktBeyNSjhSqji+FCm7EYuXKlaioqMCaNWswbtw4/PLLL5g4cSI6deqE66+/PqhjI4hwwluDPIZh8OavR7nXvxw6HfSIJtG+kSWFeuONN2Rv8P777/d5MG0ZAy9521dsDAM95M/MO0u/6hRpuNmxCu1rvtEido+y2O1Ox0KhNCnQDfKE8PMCPDkWvx46g3mf7MTVw7JEzynfGGaNYJudgYVXWrWi3nfHwiIo0SoasWClUPyqUFY1Ixae3zfqdXjq8n5Y+H0BAMc1ZR9UJqOeK+fKGvAsTbwyqY0tNonO29pXhWKlUN66YQursSh1/FkHTakTLgZfCsWIFKe22RnU8SRewc6xYBk0aBBsNhssFgvMZjNWr16Nb7/9NtjDIoiwgb03SE0mrD18BgVltYiNNCDSqEdVowVbC89xRStqmy2Yv3wPLhvUCVcN7RywcRPtF1mOxeuvvy5rYzqdLmwdC74UyldsdgYRCux1X3tK6HTiMhL+LIZYWNVmZzhDX+lxis2yy8FX6ZUQvpPgyT5k5SvnGlq8Vihi32cYxiUSU9esPHmfRaihF0vK56pC8ZK0XXIsJPocyMWbsa7T6XD7uK5IT4hC3mc7XZK3Iwx6zmCvN7uehwazwLFQ0CBPiK8Ri+Jzjfi1tdytsGGkEDfHQmGOBTs2X5tf8nEmb9tFHZzGFqvL67KaJlhsdr8dcl95/fXXsW7dOmzYsAF1dXUYMmQILrjgAtx1112YMGFCUMakBdTHggg27P2l2WrHpqMV6JeRgA68hrlfbisBANw4Kgd1zVZ8sa0Yq/eXc47FusNn8fPB0zhypo4cCyIgyHIsCgsLtR5Hm4ffIE8O/713HK7410aXZUoNF197SrBjFeZY8I00sbFY7YyLll4JMZGOr1KjQqPX4qPzJIRfxtVTHwvWQLbZxeaFXeEnb/MjGWzvAl8QhqjFZvD5Ttqxs/VYtOqQS25Cs49SPBahsX5xv3SsO3zGTboUa3LmejgjSzrEsxELwXngG78NLeJSKPl9LLzrisVYuauU+zvG5Pm3yu/NASgvsOBraWYxIngJmmJRP74zmRIXiYr6Fmw8WoFJQUrS/PTTTzFx4kTccccdmDBhAhISEoIyDq3Jy8tDXl4eamtrKTmdCArsvfC/e07hv3tOYWTXZHx512gAjuj1b3+eBQBcOSQLZ+vN+GJbMX4qKMfCK/tDp9OhqNLR2PRkZSNqmixuElCCUBu/iq+zBpy/pRZDAaURi4Gd3R9CSo0ki48yIXZ14cwnX04j6ljwy4oqdCxYI1Q4s+oNXx0ZIXzfy9NpZiMqDONd+sLvY8E39mqbfI9YCB0LYTM6wPkda7HZMfej7VzHaxb/Ixauxz1zWGeM7d4Rz/y3wGU5X97G/144Ixau15r/utFs8ysPwOBjuVn++R2YpcwQVKq6smkQsdhXWoMCQXd2wFkJLDbSgGkDM/Dv309iW+G5oDkW27dvD8p+CaK9IXw2bi08hz3F1RicnYSNRyrQZLEhKyka/TMTYLbaYdTrcLrWjFM1zchKikbROWcZ8AOnajCmu3hfH4JQC5+suffffx8DBgxAVFQUoqKiMGDAALz33ntqj61N0alVNpGZFI07J3QDADxwUU9F21BquDhnRJUZLnqJiEWLAimUrxGLBrNvEQt/der8fhCeIkP8iIW30risHKe60YJlm09wy2v9kEIJ9ymeY+GcSRc6FQDQ7EO5Yz7CiEV8lBGpIpWNnAn5dhcnl82xqHOLWPClUFa/ukP7WhWKjSbNu6A7UuKUlYFVLoVynBO5JXQ9wXcwxY6ZPbfRkUbcMb4bfp4/AY9c2sfv/fpDdXU1Xn31VcydOxd33HEHXnvtNdTU1AR1TAQRblzUNx2XDczAE5f1xWWDMgAAH7U+j34qKAfgiDrrdDpERRjQPTUOAHCodYLiZKXTsdhfSr9PQnsURyyefPJJvP7667jvvvswerQjHPf777/jwQcfxIkTJ/Dcc8+pPsi2wLXDO6NrSiyG5iQh0qDHdcM7cz9guSiVWrDGnNisticMEkaZixRKxMmx2BhO129SaOjHRvoWsahpnf2XKzGTQi8zx4LNlbAzjKgBd8PIHO7vhNZmgqXVTS7r1Db5LoUSylzEcyw8n/tmPyMWwkhNnMmIEeclY3iXDhjAm+UXi1iY5EYsJHIs5MLPsVDSkJB1+jrI7DPy/X3j8O/fT+DL7SU+S6GUNncUI9LoeRtOx0KP7OQYv/fnL9u3b8cll1yC6OhojBw5EgzD4PXXX8cLL7yAn376CcOGDQv2EAkiLOiUGIX8vzh+T3uKq/HD3jL8d+8p/N/UPvj5oCOfbEq/dG79vhnxOHy6DgfLanFh33QUn+M7Fu7RUIJQG8WOxdKlS/Huu+/ihhtu4JZNnz4dgwYNwn333Re2jkWEQY/R3Ttyr3ukxSvehtLZV3b2X2kiNWtkC+0km5ccC78iFibfIhb7TzlmUPpmKD+ffPiTxnJyLOwM42b4Rhr0eHZ6f+71wKxEdEuNxfGzrlED/yIWrvsUm+026nXQ66QdJH87bwsjCXFRRkQa9fj67jEuy/mOBT8XhsuxMHvOsfCncpFR7+ooyrXdWacvQaaOeEBWIv7v0j74cnsJGEZZV3X2PAo7ePuCt9wkthJYTIRf6lXVePDBBzF9+nS8++67MBodY7JarZg7dy4eeOABrF+/PsgjJIjwY3B2EobmJGFXUTWuXrIZ5xpakBgdgRFdk7l1+mYkYOXuUzhYXgez1YayWmffJYpYEIFA8RPRZrNh+PDhbstzc3Nhtfo+k9seOKOwsdqO1oZ2fTOVJUbqZchIxGZnLf7kWPgQsWAYBntLHDc6pXp4IQaFUig7435+Rnfv6HLcer0O/TPdx+VrjoXdznAVi7hxixixOp2Oqwwlhr+dt4XHHS9RPYmTQvGStx05Fg6j3T152zmuphab4i7sfPjXU0nkg3X62GiTHORGu4SomWPRu5Nnx9oZsfAvsqcW27dvx//93/9xTgUAGI1GPPLII5R/QRAaMmdcVwDOSPrlgzJc8jD7ZDjshYNltSipagLDOO/lxysa/KpqSBByUOxY3HTTTVi6dKnb8nfeeQd/+ctfVBlUuHLZGxu9r8Tjj8JKAMD53Tp6WdMVqapQfMQSl212xmfHgsuxUCDTqTNbuSZj3gwrb7iUm/Vgh7IGss3OuM3ci0laokTOg69Vob7eWYJD5XUuy6T0+Z76pfjfIM89YiEG12FbWG6WzbEQRCz45XGb/JRCecs5kIJ1+hKi5c/s8/NzlOyLkyqqIIVKi4/CFYMzJd9nHfZoPyWDapGQkICioiK35cXFxYiP9++3TBCENJcNzMCHt47Akr8Mw/I7z8fTV/R3eZ+N/p+oaMCfrc+bbqmxyEx05NEVnCI5FKEtPsXV33//ffz00084//zzAQBbtmxBcXExbrnlFsyfP59b77XXXlNnlO2UkirHjEQ/hTIhqapQfMTesvIcC08z5mKwVaGUVCziV6nyxWBi5ULJsZEuM/+eIhbOqlDuORZiuSxiuR++RixW7y93WyZllHqSv/kfsXA1+KXOvVjTw0iD3mW52Wpz9t3gJZVb7YxfUijXiIX87bAJ5coiFs6/lSRwqxmxAICuHaVzJzgpVBuJWMyaNQtz5szBK6+8gjFjxkCn02Hjxo14+OGHXWSyBEGoi06nw6Q+0tXgUuNM6BgbicqGFi4HIyc5BhabHadqmnH0bD1GKZysJAglKHYs9u/fzyXmHTt2DACQmpqK1NRU7N+/n1uvPZSg9cYHtw7HKz/+KVo+Ug5VjY7Z/KSYSC9rusJKO8prpaVXYtEMq90Os485FqxEo6HFKlunzkpljHplncVZPrxtJJauO4qpAzJw/Gw9t9yTbchVhWLcIxYGmYnUSpsAsogZ8FJGqSfHzuxnjoXQoZI69yaD+xgijXqXnhotVjs3VmHVMWEzQCXwz4tNgYPijFjIdyzkyuiEqJljAXjuh9HWpFCvvPIKdDodbrnlFk4CGxERgbvvvhsvvvhikEenHtQgjwg1dDod+mYkYOPRCvx0wDGZ1aVjDOwMsPbwWbecQYJQG8WOxdq1a7UYR1gyuU86JvdJx3mP/qD4s1abnZt9TVLY0IY1lEqqmvD1jhLRdcSiGVZecy6l5V9jW6VQDONILpZjAPkrJbmgVyou6JUKAHjmuwPcclk5FnZ3AztCxMgXi1iINTCTg9i2pIxSTzPTaudYSCHmXEYYXJ1A/rZcOoXb7X5JtvR6HXQ6x/dJbsTCbmc4eVacl67bLvuSOB5vWFWUQnnbDnvN24oUKjIyEv/85z+xaNEiHDt2DAzDoEePHoiJCX7FKjWhBnlEKNI3Ix4bj1Zw98OcjrFcZJY/CUcQWqDOVBuhOjU8uY3STpl8Q+mhr/a4vMcaTmIGFN9gVRyx4Bk8DTITuH1tACgGvxKUR8fCxqsKJWxWJzKOKJFchxab3WPlKT71ZivmLNuGb3eWiG5LKmLRLTVWcptKDHax6yzXUBd+B/Q6xzniD5mfoO0SseCVLvYVpb0sLDyJl6ccFSG+Jm9bVZZCRXiIfLASw2BLoRobG5GXl4esrCykpaVh7ty5yMjIwKBBg8LOqSCIUGVS7zSXSOx5HWPQLcVRHv+Yh4gFwzCorDdrPj4ivFFs0TU3N+Pll1/GtGnTMHz4cAwbNszlH6EO1a2ORXyU0aNEQgy9B0PHwjOshfBLxSoucavXcUZPg1meY8EaZqo4Fvy/ZUihxPpYiCVvS0mS5Mp83lp3DL8cOoP5X+4RnW2Wula906UrgcmNWPztyz0Y9cIvqG6V1LGwx335oAz8+MAEyc8bWsvesrDXSafTceeKn6DNj+RY7QzXyM9XVaSzl4W8c83P6fBkpEvtBwDyPt3pVkZXCi7HQoXvr2M70ieK3yAvmDz99NNYtmwZLrvsMlx//fVYs2YN7r777qCOiSAIV8b0SMG6hybi3kk9cMf4rhjTPQXd0xyTVSVVjS75cCxnaptx+7JtyH3uZ/z79xMBHjERTih+St1+++1Ys2YNrrnmGowcOZJyKTSCNQY7KMyvAMRLmLKwxryYY9HEizT40gk7KsKAxhabbKmQs9KQ/98hu4eIRV2zBQ9/tReXD85w6bztlmMhKoUSPw/HzjSgn4wywBW82R8xA1RqtttTlSw5fSwYhsE3Ox0yuHWHz2LG0CzuPdYAv3Z4ttdqXJFGPbc/vrNp0OtgsbkmaPNzT2x2housRBkNPsm3HMn0dvkRC56zp+Q7xb8EG49WYNXeMlw3Itvr56w2dSMWnhyUtiKF+vbbb/H+++/j+uuvB+CoEjh27FjYbDYYRHJyCIIIDtnJMXjokt7c69Q4E+JNRtSZrThZ2Yhe6a73/r//cBBrD58FAGw7UYVbRp8XyOESYYRix+KHH37AqlWrMHbsWC3GE/bY7YzHiAJLdaMjYpEks4MwH0+Ttaz857c/K9zeY0vFGvU6WWMUwhpzcmfznVISdRV5Qjt02aYTWH2gHKsPlGNQZ4dOWky7LzYOk4QhN+2NDdj3zBTEe6k+xDeKxRwuqXKzKXHSDqUcKdSZOqdDkxpvcnmPdbzkGMSRBqdjwc9biNDr0Qw7dw6tNlcHwGq3c5+LjvTNseB335YD+73T6aTPqxjCyZEomXIjNpKiZF+eEMvxYWkrUqji4mKMHz+eez1y5EgYjUacOnUK2dnenTGCIIKDTqdDt9RY7CmpwfGz9ejSMQZbjp/D2O4dYTTocbjcWWSmvKYpiCMlQh3FFl1WVhbVKfcDuUZ3FedYqBuxsNgY2O0M/v59geQ6SvMrWFjDXG5jNIuPFajE4AcphBELvlHLrwolRwollnDNUl7jveEhv/pWi0hlGb3EtfJ0TuQ4FvzKH8JdOKsZeTeI+Y5VLM+xYCtosaVrhd/r7SeqUHSuEYB4LxA5KM2xsPK6g/sTSZUbgbBxUj71IxbC68/2sZDr9GiFzWZDZKTrPcloNFJzVIIIAbqlOvMsHvhiN2Z/sBWfbXX0o+E/z05VK2vmSxB8FEcsXn31Vfzf//0f3nrrLXTp0kWLMYU1Zqvdo7HKwiZQJfsUsfAkhbK7NTYToqQHAB/WGLLIdJ64qlAqzPjyTU9hxSu+nIx1LCyi0QORiIUHo1iO7cofS6NIjw+p7XtyLKx2Bhab3WNuSmGF07G48d0/8PP8CeiR5pgQsClwLM7v1hH/3XMKgKtjIXQihdGYI2eclUfkfN/F4CIWCh1VTzP/cpAvvVK33CzfQYkzGXHO6syNaWqN/sQEWQrFMAxuvfVWmEzOKFhzczPmzZuH2FhnwYFvv/02GMMjCMID3VuLghwqr8P/WvsqvbvhOK7NzXZp/Hq6thk2O+P2jGAYBr8eOoND5XWYM66rz/d2IrxR7FgMHz4czc3N6NatG2JiYhAR4WqEnjt3TrXBhQtbH78QI5//BQDbRMy74c5KWdITohTvz1NjPKuN8drgTSidkQtrGIkZ7WJwfSxUqQrl/LtFYIjy5WRszoNY5MhbxKJvRgIO8nqSyJHo8Ify7c5SAI4KHS/OHASGcTXW+XjLcWm22Dw6FnXNrtf4sW/348t5o1vHLV/Cc93wzpxjEefiWLhGEzz19vD14WNUmLzNORZ+RsDk9rJQu0EeX4oXazLgHK94C5v/FGwp1OzZs92W3XTTTUEYCUEQShnYOQkA8P3eU9yyzMRonG7teRVp1MNqc0hcK+rNSE+IwuKf/8Th8jq8dt0QvLbmMN7dUAgAOK9jLC4blBHwYyDaPoodixtuuAGlpaV44YUXkJ6eTsnbMkiLj4LJqIfZaped2Mw6Fr4Y+Z7KvbbYvPcX8N2x0HP7kAOb7xGpipTEVd/Phy/nYWdlxAxh0c7bPCP17ZtyMeFlZx8XOeVUxZy828d1xfleOp96k4c1WWwe8zuEu2UbHzIMwzWck2MQp8Q5vwtsd3XAWcGINeY9fa+lEuC9wUbe5JaAbbH6XmXssztG4cZ3/wAgP0LCfs/US952bidWUP2JjXYFWwr14YcfBnX/BEH4zoSeKRiSnYTdxdXcslM1TZxjkZUUjWaLDWU1zSiraUZSTAQW/3yk9b3D+HjLSe5zRecasa+kBvtP1eDa3M7Q63zLzSTCD8WOxebNm/H7779j8ODBWownbGEdC7ldm9kfui8Ri3qztOPgiFh4lkKlxvnnWCiVrqgdsRAa82I9J8TkWmLlPvlOibA3gpxrKSarkVNxy5tj4c1BFZt1r2m0YHr+Rpxq1dLKiVjwZWR8YzcQEQs2/0R2joXdt+aOADCmewom9U7F2sNnRbvSi++PjbiplLzNdywEkSw2TyjYUiiCIEIXnU6HJy/vi5lLf+eWnapuxqnWZO20eBMsNrvDsahucpk0eW9jocu2ymqacN/nO3GishHbCs9hd3E1UuJNWH7n+TTh3M5R7Fj06dMHTU1UMUApkUYDAKvsiMXZ1ohFmg/Rg8GdpTvEWmx2l+Z7YvgasWANOvk5Fuolv/JtQaFdKGZki10HsZln/rIoQU8LOUnUYkaqnGR1qf4ZbDdqbwnybsY4w2DJb0dxsrKRWySnGpdUVTKjwTXHQqwuOou/ORZypUn+dnJncyWUJourlWPBvx5CyZOzKlRw+1gQBBHa5HZJxlOX98OZOjPe33gcFhuD3UXVAIBOiVGOCZOiapyqaUZts7utkJUUjdLqJmw7UYUTrc+Tb3c5ZL7HKxpQUtWE7GRqltmeUfxEfPHFF/G3v/0N69atQ2VlJWpra13+EeKwSbpyZrkZhsGZ1ohFmg8Riy4dY/HTgxPw9yv7u71ntTOiNws+HWKVV6ICgAij0nKzKnbe5kmhhMa82HDEbEexyAl/4sWXiIVYtETKaXBdR/ycsIn1SiMWDICNR1xLDMs57XyngH9dhRELT+PxtRwre+495Qzx8UcKBTjPh1zHQvUcC0HyNh9ngzx1SzMTBNH+uH1cVzw6tQ+yOzgcgG0nqgAAnRKikNFqc5RVN+HAKYdNN3VAJ1w3vDMm9k7F/It7AQCXbxgf5Xqv2llUFZBjINouiqe/Lr30UgDAhRde6LKcYRjodDrYREpqEk6j1CxjlrumycL1lOjkg2MBAL3S411KjrJYbXavydtDc5J82qficrN+GoJ8PJWblTvjLR6x4JUANQgdC+/XUsxGlROxkJLzREcYUNNk8RoVEhrje0tq3NZROtPO3yeXY9HqHMqV+CmBLZssV5rEJW/7+H0yKo1Y2P2LkAjhj1sYmeAa5FHEIqDk5+cjPz+fnmtEWNI5OQbHKxpQ0Ook8Ccyy2qaUd46wXlJ/05ck1V+ARMAuOn8LuidHo/HVuxDY4sNO05W4cohWSDaL4qfUmvXrvW+EuFGpILEZrb+f1q8CdF+JGuKGcoWG+NSVk5In07xGJbTwaf9RSiVQqmY/OpSbtZNBSTTsRAxEHumxWFi71SkxJncEtPkJG+L5ljIcCykkuAiZUa+5BjjSs87P3eGM8Jblx05XSf5OV+vLhvpkHn5eDkWvu1Rr7RvhupVofg5Fq6/ezYiRDkWgSUvLw95eXmora1FYqK0xJQgQpHsDtEurzslRHHPwaNn6lFc5bBF+mUmcOtkJrp+Zkz3jhjfMxURBj3yPttJEQtCuWNxwQUXSL63e/duf8YS1rBJwHKMUVYHn+OnTlFMgmK1e45Y9Ez3vflhpNG1UpA32LK0/pYHBTwnb8utKiQ2063X67DstpGi6zfLilj4lrwtRYRB3jmWcwmUSpTSEpy5N8JSsE/+54CibclBpzB5m5VC+VoMwKgwp8NZXUutPhbO7UhNKPgz0UAQBMFHaGOkJ5i4ZYdbJ4viTUZ0S3H2qEmIdjUb2QqHA7IczseR0/WiPTCI9oPfT8SamhosWbIEw4YNQ25urhpjCktMPkQsNHEsbAwaPDTIi/axNCjAKzcrUxbDzvj629AMcM2x8FUKlRStrDGglhELKSJb8zO8nWM5URq5M+3vzx6Oi/ul46EpvZ2f5RwcBkW8hHA1Ye1spcnbvhYDYKtQyelPAjgjbmo9QPkRM2GhAMCRc+KpYSNBEIQShEnWnRKjkJYQ5eJIjOnR0WWyhl/xKSspmnvud+4Qw1W/LKnS5plAhAY+P6V+/fVX3HTTTcjIyMCbb76JadOmYfv27WqOLazgciw8zHK3WO2obbagvLUcaGZStOS6chAzeA6W16KqUTpiEe2H1IIrNyt3htlPTbwLLjkWrm/JTf5NVpi07mu5WX8M0UjZEQvvxyx3HBf2Tce7twxHxzh+xMJxzaoaW1x6ewi5fWxXWR3KxWANfeWORYCTt1XKseBHPsQqacVEGKiMI0EQqjGuZwpyu3RAn07xmDuuK7JabY5R3ZJ566S6fe6GkTkw6HV4fdYQbplBr0O31DgADhkV0X5R9AQuKSnBc889h27duuGGG25Ahw4dYLFY8M033+C5557D0KFDtRonx5IlS9C1a1dERUUhNzcXGzZs0HyfasDKXzzNcl+6eD0GPfMTp2uMi/IvUVPMcHxp9WH8fPC05Gf8SQ7lcizkRixU7LwthO9MyJVCJcUocyzWHj6DOcu2eZydkVvqVi5stONcQwvWHj4j6WDIybHwR4LGGtPCaMVzMwa4vP7rhT193gfnWMg8Xez3yVepmeJys3a1pVDO36tY5JBkUARBqElCVAS+uXsMVj8wAU9c3o+buBjV1dnAdULPFLfPPTu9P35fMBkjuya7LO+R5nAsjpBj0a6R/UScNm0a+vXrh4KCArz55ps4deoU3nzzTS3H5sby5cvxwAMP4PHHH8euXbswfvx4TJ06FUVFRQEdhy+wM5CeZrmPVziqOK07fBaAey17pfiSVOpPxELubDqLRcXO20JTkG/Qy53xVhqx2Fp4Dr8cOoPnvj8ouY6Ykerrdb18UAbnvD389V7c9uE2/OvXo6LryonSxPnhRLLfLaFT2L11xorFH5ub/fp6c5KaLTb869cj2F1S3TomX/tYtO5PxrnbfKyCy+tRr9ys54gFORYEQQSCcT1TkBQTgWE5SejSMdbt/UijHmnx7hUre7Y6Fv/8+QjO1DbDbmfwyZaT2F/qXpWQCF9kP/Z/+uknzJ07F88++ywuu+wyGAyBf8i99tprmDNnDubOnYu+ffti8eLFyM7OxtKlSwM+FqWwhkKTjHKzLP4Y+YB0ZSGP+/SjTr6RyyORK11RL2IhzCmwe5BGSZEoI8fivI7ueS91ZmlpmfBUzB7dBf15FTbkkJEYhZ/nX4BXrh3slp/xzc4S0c/IOWZfvh8s7Cy9UNonNOqNej2uH5EDwHPjRjGcVaE8H8w764/jlZ/+xGd/OCYYAlFu9sZ3/3COU61ys/xmjKJSKCo1SxCE9qTEmbD+kUn47I7zFX2OrSjZZLHhjV+P4OWfDuOJlftxwztbtBgm0UaR/QTesGED6urqMHz4cIwaNQr/+te/cPbsWS3H5kJLSwt27NiBKVOmuCyfMmUKNm/eLPoZs9ncZhr4RbVKG9gOunLwt8uu3JnUPx5z9iRRI8dCacRCnQZ5rvCjFHLLzcrJOfhk7ii3ZekiMzfcOARG6tNX9Fesk9frdOiRFoeoCIObzEfq3Mnt/eArrDEtjMAJz6FBr8OkPmn45W8X4Mt5oxXtw1kVyvN6wtkwX6VQeoV9M1giVJJC8c+dWFQriiIWBEEEiISoCNEJDk+M65mCG0Y6JpI+2VKEpeuOAQDqzFbc8sFWXPbGBln9n4jQRvYTcfTo0Xj33XdRVlaGu+66C1988QWysrJgt9uxZs0a1NVJ17FXg4qKCthsNqSnp7ssT09PR3l5uehnFi1ahMTERO5fdna2pmP0BGuwyylRyuKvFEov04BN5zXFOS/FPewpF6VSKKufVXz4CG1BvmMhVzMvh84dYvDgRb1clqXyyrAKEZ4LX6IEfLtVmBch5QzJTVj3FXZ2XZgvIjSyWee2e2qcrI7jfAwyk7eF5yAQUihP+/cVfuSud6d4XNzP9V5HPSwIgmjrXDE4Q3T5+j/P4sCpWuwvdZ3grWmyqPqMJoKP4qm2mJgY3H777di4cSP27duHv/3tb3jxxReRlpaG6dOnazFGF4SzvWzHbzEWLFiAmpoa7l9xcbHm45OC1Uc3K4hY+KupVmJgvT97OB65tDfG9XBP1JKL0ogFK5nSJmIh/reQhCgjYiINuH6EfKezXiB98hTl8SdRm4XvIJoM4oa7EK1v1AZOCiUdsdDp/JNbsT6KN8dC6ED7XhVKWgq14cjZ1s6y7qWa1cqx4DvYOujw7i3Dwd805VgQBNHWEcvJ4PPJlpNobpWEby08h9y/r8E/Vh8KxNCIAOGXRde7d2+89NJLKCkpweeff67WmERJSUmBwWBwi06cOXPGLYrBYjKZkJCQ4PIvWLAhxWYZvQ9YApm8fWHfdNwzsYdf5SzZ2XSLzBwLNmKhRrlOodzJ5lIVSno8XTrGYtdTF+PFmYNk7+u2sV1dro0nI15O3xJv8A1nodEsZURrPQHEGsFmXs7QAxf1dLmW/hrccsvNCp0XLcrN3vz+Vnz2hzO0z+bj9EiL88t54sOvLhXT2nmbf+3JsSAIoq3TiaeASIkz4cI+aS7vr9hVin/+cgQAcPcnO2C1M3hn/fGAjpHQFlXEwQaDATNmzMB3332nxuZEiYyMRG5uLtasWeOyfM2aNRgzZoxm+1ULX5K3AyWFUgufcyxU0KjHCvJRGJk5Fga9TrFEJzMpGgULL8XUAZ0AeO7bYZXpZHmCnzAuTN6WcsrkVsLyFaNIjsVfL+zpErHwVyKkl5ljIdyNrw0I5ZSbLTrXCIZhuMjFv28X78ruC5FGPV67bjBeuGogUlp7hvCdFpJCEQTR1jG4FKHQo6uIvHrpumM419CCyoYW0W3IzYsk2iYhVWZk/vz5uPnmmzF8+HCMHj0a77zzDoqKijBv3rxgD80rbPJ2s5KqUH4nbwe2S6/SHIvGVlmYGjOxf7ukFwrKarGvNZFXrhTKn1l1tmupJ0PUymvCoNTQ/vKu0fho8wk8eXk/bplbxELiGmsthRJWheqaEgudTucyHn+/f+z58pYvYhA40L5eU4OM5G2GcThTbFQu3s9eM0KuHtZZdEwARSwIgggthnfpgBlDs/DLoTMobC2nzzLs785J4ugIA05WNqCq0YL7P98FO8Pg5WsGIzMpyqu0imh7hJRjMWvWLFRWVmLhwoUoKyvDgAEDsGrVKnTp0iXYQ/NKtC8RC7/Lzfr1ccVw5Watng3BM3XNeGrlAaxpbdQXZ/L/a5gWH4Xv7h2LrgtWAXA1rD0Z2f7MqrOf9RSV4EczlBq8I7smuzUgUitiwXdWfIE9FjZiwR4av/Sq/xELx/+BkkKx59Lm4XoyAGqbHTk2Op17pExt+OeQHAuCIEKBr+eNxudbi/HYtD7oGGfC2ocmwmqz4/lVB/HhphNu6zdZbLjg5XUuy254dwuykqKx6dHJgRk0oRoh5VgAwD333IN77rkn2MNQjDPHQtyxEAv9+Z28LcOz6JehXt6JXCnUs98VYPUBZ66Mv5IvFp1OB73OEaGQK4XyJ7+DNa5tHlpD850ONZLUhc0EpXqAeDLGf3t4InKS3ftxKIErN9uaM8R+1/jOk1o5Ft7Kvwp3E2vy7fskZ38Mw6Cu2SGDijMZVcuvkB6T82/qY0EQRCgw/LxkDD/PdVLMaNDj6Sv6Y94F3fHmr0fwyRbvjY1Lq5tQ02hBYoz3HlNE2yHAc9rtl2gvydvCSXW9DjD5qBVn8TZj/OGtI/C5wgY4nmATer1VQhKGRNWIWLCwxuG2E1VYuu4Y7HbGixTK93PMRSw0kkKJ4Rax8KEqVJeOsX4l6QNOCRYrhWINbP4x+mt0c1IoL6ou4XmN9fH7JKfcLMMA9a2ORbyK31sp+I6jP80rCYIg2gLpCVF4bsZA/HD/OLx9c67X5/+xinru77pmC7YWnnOTx360+QTyPtspW4ZNaAs9qQKEiW2QJxGxEM4wx0Qa/Tb+PBmyVw/NwqQ+aarOBLA3CLGSnHyEZpuvhqAYrGOR99lO/GP1IXy1o9jj7L0/s+rOiIX09m1+SKHEEEY9pB0Lv3flEYNACsUOi59j4e/RclWhvHgWwiIFvjqqcpK3GTBoMDu+32p+b6XolR7H/e1vzhVBEERboX9mIi7p3wn1Zs/2wtEzTsfi1g+34bq3f8e3u0pd1nn6uwP4YW8Zfi44rclYCWXQkypARHuRQgmN3wQVkkL5jsWorsmw2Oy4qF866putuP/Cnn5vXwhraNV5uVEIpUm+SlfE0OsB8E7xjpNVSI6VbmDnX46FwxCVilgwDONSeleLiIWUvErrqhoRBqFj4RiHQYXSwSx6LmKhzLHwNaGaHbq3iAV7vaVkaGoyulsKthw/B4CqQvlDXV0dJk+eDIvFApvNhvvvvx933HFHsIdFEIQXXlh1EB9tPoGL+qZjx8kqAMA/Vh9CSVUj5o7v5pITp0Z5d8J/yLEIEFyDPIsNp2ubsb+0BpP7pHFRCaHtlBovbQzLhT+bPahzIh6/zL+EXW+wM8UNIo6F3c5g1f4yDM3p4GYoahGxYKlqtCApJlJyfXVyLMQNUeFiVXIsZCZve8tL8BfWkWAdZXYY/O+cvyPQyzD0HWMRSKF8nNk38HKEdhVVoW9GApcbxYcdj1qN8Txx5ZBMvP7znwCATolRXtYmpIiJicFvv/2GmJgYNDY2YsCAAbj66qvRsWPHYA+NINo16QkmnK41S75f3WhBdaMFB045O3afrTNj8c9HUNdsxZVDMrnljQoaEBPaQY5FgIgyOnMsLl28HlWNFrw+azCuGuooLyk0ntRwLFz07gHoacE6FqwGnc+3u0rx0Fd7oNMBPVLjXN7T0rGobmyB3S6dqOyP/e0tx0Ko91SjEWBXQek9qRwRrcvNRrdK+1gnUix525uEyRsGCadbiPCrHedzxMKxoZ8KTuOngtOY0i8d79wy3GUdhnGeWzUiUN44LyUWmx+djCNn6jG6GxnBvmIwGBAT47gPNDc3w2azUa18gmgDLLttJFbvL0fHuEg89Z8DLu99dsco3PfZLsl+F+v/PIvuPHvinMR6RGChHIsA0SEmEjodUG+2oqrRUa5y1T5nZSThLL7ajoXfgncZsA5CQ4vNzaj8/VglAIdhJnycqynxENp6VY0Wj8m/cruEi8E6ClaJ8KvQuFfDEB2a08HldUSQGuSx15o9RNa/4cuDvCXxe0MnsyqU8G3fcyxcX/8kotdlwHCOZCAcC8DRkPGCXqmaV6AKJuvXr8cVV1yBzMxM6HQ6rFy50m2dJUuWoGvXroiKikJubi42bNigaB/V1dUYPHgwOnfujEceeQQpKSkqjZ4gCF/pm5GABy/uhTHd3X+PI89Lxg/3j8fg7CTRz+p0wA/7TnGvybFoG5BjESASYyK4Ts0sNU0W7m+h8ct23vUHfnOtQEQs+Nr2RkEuSaRReiZbTa260PiqbmzxaGRbPZSK9Ya3iIWwv4UaHcajIw3o3CHabQxC/DgsWQjzGMTGYfbTsWC/Ft6cJKED52/yNh9hMqA9wBGL9kJDQwMGDx6Mf/3rX6LvL1++HA888AAef/xx7Nq1C+PHj8fUqVNRVOQsWZmbm4sBAwa4/Tt1ymF4JCUlYc+ePSgsLMRnn32G06elEz3NZjNqa2td/hEEoR090uLw5V2j0T/TWQLfaNCjU2IUzhf0c2JpMNtcJFInKxtE1yMCCzkWAWRw5ySX17U8x0IYllcjYsE3sgNhApmMes7YEsqhTEZnVKLBS9UofxA6ULXNVo+SB0/N7bzhLcdC6LSoZYh2jHXmjEj5i1rnWAjla2JGub+JdHKrQgkdO3/LzfKZ++/tLq8ZxnluhR2/Cd+ZOnUqnnvuOVx99dWi77/22muYM2cO5s6di759+2Lx4sXIzs7G0qVLuXV27NiB/fv3u/3LzMx02VZ6ejoGDRqE9evXS45n0aJFSExM5P5lZ2erc6AEQUgysmsyunR0ly5LPWNLq5tQ3ei0o349dAbVjeJRi8PldRj/0q/4cnuxOoMlJCHHIoB0ECQR8yMWwh9OpwR1EzUDYQPpdDpnnoUggZufdFwnkoOhFm6RGcazke2P8Wv0UhVKuFxKtqQUfjK6lM3tb36DN9wcCw2+X86qUJ7XEzYo9LX/i5hzJOTng6dRXtMEQJ2cGcI7LS0t2LFjB6ZMmeKyfMqUKdi8ebOsbZw+fZqLOtTW1mL9+vXo3bu35PoLFixATU0N96+4mIwRgggEYtF2oQQYcFbaBIDOHaKREmeCnQEeX7EfG46cdVv/me8OoPhcEx75eq+q4yXcoeTtACLsGVHrQQolpSn0lUBIoQCHDKWmyeLmWPCNan4vjz6d4lXdvzAowMBzgzyp/Ag5sIalVOdtoWOhVsSiA+97JBWNkXKmLuyTpsoYhM3htJAFya0KJTzPvvZ/kRuBeGHVIcf6JIUKCBUVFbDZbEhPT3dZnp6ejvLycolPuVJSUoI5c+aAYRgwDIN7770XgwYNklzfZDLBZPI/akwQhDLESpZPG9gJL1w1EL3S45D32U4My+mAsppm7C6uBgCM75mCvSU1qKg344d9ZfhhXxmOvTANBr0OVpsdRoOemucFEHIsAogwYtHAK40mNBDT1Y5YqLo1adieFI+v2IcBmYn4y/k5+GJbMSJ5OhP+ob5y7WBV9y90oBjGc08Hf6oncTkWEnIqm2B5RxXyZgBBxELiXil2WBmJUW5VjnzFXQql/jfMWRVKWY6Fz/tTeAwkhQosQoeRYRjZTmRubi52796teJ/5+fnIz8+HzUZlLAkiEDw8pTf+OF6J28Z25ZbpdDrcOCoHALD50Qth0Osw/8vdnGNx6YAMlNc0g19T6scD5TBbbXj0m3145NI+XMl/QnvIsQggHTx0uebbRlo0r/O3i7dcWCnUgVO1OHCqFss96Bk3/t8kdO4gXQrWF8SMQ0+JzH5VhfKQY2Gx2bG7pJp7ndulA56d3t/nffHh599IJTaLSaGykqJVcwCEJV21cCw8VYX683Qdjpyux2WDMlwiFsJIihKkE+HFzzFFLAJDSkoKDAaDW3TizJkzblEMtcnLy0NeXh5qa2uRmJio6b4IgnCU2N72+EWSNgt73+2S7Cy93j01FmnxrpOx93y6k/v7798XuETrl20qxFXDOiMxWtomI3yHciwCiKdGbazxFGnQY/7FvVTfd6AmV5XMyifHSp8PXxE7Tu2qQknnWPztyz24//NdABzJ1t/cPQaZSdFu6/nCTaO6cH9LSZ7EnB01y5UKm9DJyU9QioFz3Nzfm/L6euR9thPr/zzrEhnyp4mcVHEyqRwayrEIDJGRkcjNzcWaNWtclq9ZswZjxowJ0qgIgtAKOROhURHOG3ZmYjQ6xnm2J/i5nc/8twBPrtwvut7Ooiqs/9M9R4OQDzkWAcSTd2zXuISlLkBiKCXVrKSau/mDUAplYxiPydtaVYX6bo+ztrba1zQxJgKPTesDQLp5nJgzpWanaINe55I8x7exI1UqH8wOl2EYFFY04Io3N+KbHSUu62w7cc7F8PfPsVDWbDBQeUvtgfr6euzevZuTKxUWFmL37t1cOdn58+fjvffewwcffICDBw/iwQcfRFFREebNmxfEURMEESwuH5wJvQ4Y1TUZer0OMV6kTqXVTS6v/7v3lOh6Vy/ZjFs+2IqSqkbVxtreIClUAIk06hEbaXDJrWBh7UCt1BWBUm2kKXIsNNDl64U6bM9yJ4sKfSz2ldag+FwjspPFZV0RKvbpYOFKsUpJoUSWq+3gxEUZuUR8fjTEZNT7XWqWv02bncHVSzahqtGCv321BzNzO3Pr1DVbXZLnF0zt6/P+pM6OVFRLi+9ve2X79u2YNGkS93r+/PkAgNmzZ2PZsmWYNWsWKisrsXDhQpSVlWHAgAFYtWoVunTpIrVJVaAcC4Jom2QlRWPLYxciIcoxYRsd6dmcFToW/Oey3c5Ar9e55PMdOVOvulS7vUARiwATIVEKk51V12oWNFCTq0KdoxR6nbrSHBax4zRbpI0CNSIWADD+pbVoMIuX0dWmYpLnUqxis+z+NqwTwg9F88+FKUKd24qBd4xVvFrlfGqbLVzE4vmrBqAfr7mSUqQ6hUtGLMixUI2JEydyFZv4/5YtW8atc8899+DEiRMwm83YsWMHJkyYoPm48vLyUFBQgG3btmm+L4IglJEWH4Wo1sg5P4IuB1OrY/HH8UoMfvYnfLzlpMskpFQ/DMI75FgEGKlZTnaGWSsHIFDJ23KlUFrIoABxx6zZg0EtrNSlBKHDcORMveh6Wmjx2V2LRSZarHZRh6PZg4PlC/xryD8X3VLjVNk+ey35EQmhnLC+2coZ/vFR/iXiSTlekjkW5FgQBEG0CbxJoYTodMD6P89i1jtbUGe24smV+/HexuPc+9WNFvx+rBKV9Wa1hxr2kBQqwLhLdRwlE9kQnFY5Fl1TYr2vpAJxMqvyaHWcYiVAPUUs3rhhiM/7EjoMwm7j3HpaRCz04qVYqxpaMP6ltW59RAD1HQv+YfGv52vXDcbT/zmAOeO7inxKwfZbt9nIkw4KHam6ZisX7fP3PEudH+mqUDQvQxAE0RZQWk62ttmKWz7Y6rLspdWHub+XrDuGs3VmdIiJwK6npgg/TniAnowBRmj4srOtdi7HQl0j9Is7z8ejU/tg6oBOqm5XipFdk3ENTwMvhVYVdcROn1TE4prczuiR5nuDPqFhedP7f6BCZHZDCwOUk0IJDu2/e0+JOhUA0GxRVwrlErHgnfjOHWLw/q0jMKZ7il/bZ/2Ehhbn8QiN/DqzhfsN+eusSl0mqYiFBqkzBEEQhA8ojVh442yd41nOynDrmi2iz3fCHXo0BhhhV0nWaGGNI7UlS+d364h5F3QPmBTKoNfhlWsHY2TXZI/raSUjEXPMpCIWEX46N2LH8M+fj6i+HzH0Ej0ePDWLUz1iwTt+LfINWGelwewcd5PF5hKlqW+2cr8hf79TUwdkYGTXZPRKd5VySZ1TreR8RNshPz8f/fr1w4gRI4I9FIIgPMDPsfjHzIGqbrvebMXw537G8Od+Rm2zeL4f4YSejAFGaIxYuYgFO+sa8CFpgkkiSZ1FKxmJ2Ky1VFJulMJkLzn7YmfX+cevTfK243+hFMpTE2r1cyx0on+rBeus8JPi7Yxr5KWhxcblYPh7nqMiDPjyrtH4/r7xLsulIxaUYxHuUPI2QYQGfClUj7Q4TOqdqtq2Bzz9I5eDd6isTrXthithYsaGDkJjxNpalpPRSAoVLExGz0a7FrP4gHhZXWFS7t0Tu6NbaizundTDr32JGdNscjH/JhehpRRKYPMKHQ0+npLYfRqDxhEL9hi3n6xyWV5nds4YMQzDVfZSK4Ig/G7aJMrNkmNBEATRNojhlZtNi4+StEH+eOxCxPohm/KnqW57gRyLACM0RoURi7BxLLyUHNWsEaBYVSjBTP0DF/XEr3+bqKhLuBhix8DV1OY3j9OwrK4wmVko21n9gHP2XSpy4yt8+1uTiIXEJmub+I4FVMuxYBF+h6RacpBjQRAE0TbgP4NS402SeZzpCVH4at4Yn/fjqS8W4YAciwAjdBzY2VZnjkXAh6QJ3qRQWjSNA8SNPaFjoVZnaLEZ8h/2laGmyeLiWGhxTaUiFsLXWpZElUreVgspw72myVXjyv52tCoIQA3yCIIg2jadO0TjxlE5uHdSD0RFGDzen/tlJiA9wbeJxTrKsfAKORYBRmgsWVqnQ1mDMFxmQb3nWARPCqVWIrswER8Ajp6pxwNf7EIsr+yuFsle7Plzz7Fwfa1lBIzvV2nT7FCeY2FVOWIhRLJBXrjMAhCSUPI2QYQGOp0OL1w1EA9d0huAax7n9MGZAIArWv8HPOcjeuLez3bhme8O+D7QdgA5FgFGaIuwRgsTZlKoFqvnX61Ws71ixqhU8q2/SB3D2sNnXV5X1KnfwVNKCmUR6Hb0Oh06xDjkWWqX4+NHLLS4nlIBiOpGiYiFRt8papDXfqHkbYIITfj350VXD0T+jcPw4tXOalGe8hG9sWzzCb8+H+6QYxFkWJkFa7uEiV+BxhbxXgosWslWPElyLuiVik/mjFJtX0Ijng9/lluL2tfOrtSuNzdhrwqDXofP7jgfF/RKxRd3nq/uGLRO3pbYZllNM/e3nWG435BWEQurhKZWi2MmCIIg/Kd/VgL3d6zJiMsGZbgoCfydbxQqIQgn5FgEGKEpIuxjES4Ri4YWz6VNtSo362mz1w7vjHE9/WvaxifFQ/I3P5KgRcREKsdCmE+i1+vQNyMBH90+EoM6J6k6Bu2Tt8W3WVLVyP1ttTG8iIV636k1D07g/pZyICliQRAE0Ta5cWQO5l/cC9/cLZ6oLYz2S/HYtD6iy+uarWgwW7HofwdxsKwWgPoFUkIVo/dVCFWRSN5mw2paJMEGgyYvEYuIADbIk/OeL0RFGLDzyYth0Ovwwg8HsXx7MfeeVvIrFqk+FkLHQsvvE9851MIhltpm8bkm7m+rnYHepn6ORVpCFPe31MMiXPKhCIIgwg2jQY/7L+wp+b5d5BkdbzICOofTAAAX90vHHeO74YVVh9zWHfH8z0iKiUB1owWr9pXhzvHd8PfvD+LD20ZgbA/1JjBDEYpYBBlh8naY+BUu3ZLF0C55O3COBQAkx0YiMTrCTRbDv2kNzEpUfb86iYhFkzBioeH3iV9cS4vrKSzexdYeL+ZHLOx2zvD3VjBA2b6dx9MiEbEgx4IgCCI04c/JLbp6IMb3TMHvj12IR6c6IxQDMhOh0+nQOz1edBtsvl/xuSY8+Z8DaLHZcd/nuzQddyhAjkWAEZoitjDtY/GX83MAACO7Jou+r1WOhSdbL1BGNgDYWq/njCGZeH/2cA32xzoWrp5FU4u7FEortE7eFibid0uNAwCUVDkjFhYbwxn+kSo6FvzDkYpYkBQq/KGqUAQRnrA5GBEGHW4YmYOP54xCnMnoUgo/OtLx9+d3no83bxgqa7sWL3KonUVVmLl0M/YUV/s28BCAHIsAI/Qb2GYrrCGqUepBwLlhRA5W3DMGy24TfyCrqYfnw3fM0uJNku+pjVByxDqMs8ec5yKrUQvWpnXLsRDc1LSUQmmdvC0MVXdLjQXgnrDOvlarPwng+l2RlkKFyY+VkISqQhFEePL6rCGYNTwb3907zmV5FK8HFduPKjk2Ehf1TZe13TqzFRNfXouaRvEy8zOXbsaOk1W46f0/fBx524eejAFGKmIRbjkWer0OQ3M6ICbSKCoZ0Wq2l2/g9kiLE7ynyS4BuBuZWiQU8+GSt4VVoYQRC02dKf7f6u9HKEHqmhLrcX11IxbO45FK3taoxyNBEAShMRmJ0fjHNYPQNyPBZTm/uW2Uy9/yb/gnKhuxr7SGe22zM/i54DRKqho5CRabxxGOUPJ2gBHKOyxsuVm7+PvhgFGvc5tl1koKxTe0e6bFYfOxSu61ludWaGSykSitnBmpPhbNVqEUSpv9A67OlBad1IWRgo5xJiREGVErcUNW07HgO8PSjgV5FgRBEOGElGOh1H7YXVzFVaG8/M2NXOWo9gA5FkHGYrWjqcXGy7EI8oA0IMKgd6v5rNVMPj95uUtH1xnuQMmCAKCutdu21knqQimURdBzITZSu58435eIUNGoZxFGLKIjDMhIjEZtc53bunqdulEw/qaECfEsFLEgCIIIL9i8CsC/prIFZbX4eMtJvL/hOE5UNoqu02C2uvTWCBfo0RhghKbPnR/vQN+nVqO02pGQGi7J23zEohNaGdx8I7BTomtuQyBzLFhHSitnhj0WYblZq7DztoaeqkvEQoP9WATd22MiDchIEs9XiTTqVY1I6XQ6LipU1aqVZTuYs1DEgiAIIrzgRymSYiJd3vv8DvlNZkuqmvDkyv2STgUA9H/6Rzzz3QHlg2zj0JMxwEjZPs/+twBAeHbzFYtOaCWF4ldFEjaw0/LUSs2Wa3U92VMqlEIJJWda4hKx0EIKZXONFERHGNwS8lnUTNxmYZ3CqoYWAECHWNeHDFWFIgiCCC/4UijhZNLo7h1x9bAsWdvZW1LjfSUAyzafkD22UIEciwCjc4tZCN8PPyJEnAitjDJ+xCIlztUQ1DLHQsqB0CxJXUoKZQ9c509+NEYLKZRQ1hUVYXC56fOJNPoespaCvabnWh2LZMHsFRH+ULlZgmhf8CfJhBELQJvJu4p6M+7/fBe2HK/0vnIIQI5FoPFiZ8ptMx9KiFaF0kig3siLWHSMDVzEQkrypJX8yulYCKVQztdXDZU3s+IrrsnbGlSFEuTlREcaXMLUfNRsjsfCfl/YJkjJgoiF2eq5CSQR+lC5WYJoXyREO6MUCVHu+Q9WgWMhVEb4whMr9uO7Padw/TtbsLekGqeqm7x/qA1DjkWA8WZ+tdjCz7EQ85W0msnnl1uNNbkaoZrmG0gY1tolbzv+F5abZWf5bxiZg7/PGKDJvlm0lkLdNvY8l9cxkQaYAuhYsM7iuUZHxKKjIAJmtgQuOkQQBEFoT2J0BL6eNxrf3TtWdALUxrPRXr12ML69ewz3OiMxCjeMzFG0v7R4k0tp2un/2oQxL/7qw8jbDuRYtDGEybfhSiCSt40GvUvt6UAmb3PLNTpOnYQUytoqhZoz7jzEaVxtQutys106xuLjOSO519ERBsla4mqWmmVhHVEux0IQFm+WqBZFEARBhC7Dz0vGoM5Jou/xIxYzczsjOzmae31Br1Qsunqgon2dqTNzxXvCBXIsAow321aqZn4oIybv0sIQBdzDlHzjWlMplMTGNY9YSEihAlGxyDVioc1x8q9fVIQBURK5FJo4FoKIhbsUKvx+qwRBEIQ0NkEeIz93U8zWeeryfrjpfGVRDAB4+j/7caa2mbMJ2QqQVpsde0uqA1qoRSnhV0C3jeMteVuYsBoOiP3YtDK4hcSajKiodxiGWkYspLatVblZ9vwJTy0bsQhExSK+86JFVSYA4B9edKQBJqmIhRZVoQTnWBixEHZsJQiCIMIbR3+ss6Lvic0L3z6uKwDgky1Fivbz0e8n8dHvJ5Eab8LY7h2xu7ga/71vHF5afRgfbzmJuy7ohgVT+yodfkAgxyLAeLMzhQmr4YBYjoVUdR+14TeI09KxkCqfq1m52dZjKa1uQvG5RmQnxwBwRiy0igjx4TtNWiXj83NIogMesXB9nchL6rukfzrGt3ZVJQiCINoHD17cC80WG2aIFEfJlOiz5A9n68xYufsUAODGd//g8jHe/u14m3UsSAoVYLzZttYAlgsNFGIROzUqKYjBlpjtlurous2X0mjVOwPwELHQLMfC+fdVSzYBcIRKWSmYlsfKEggpVDSv86lBr5OsCqWlFIolhlcMYMaQLE3LFxMEQRBtj8ToCLw4cxDO79aRW/b+7OG4amgW5l3QHQDwj5mOPIulfxnGraNGgRF+kndbJmQci+effx5jxoxBTEwMkpKSgj0czQhHKZSwOzTg3mNCLT6743zMHNYZy251JP3ypTOpEs3V1EAyx0LjcrMAOKkXP78kIiA5FtpLofplJODOCd3w9BX9AEAyeVuTqlCCa8qPfgUiIkQQBEG0fS7sm47XZw1BbOtE5qwROTj090sxdWAGt851w7NV3+8Lqw6K2lfBJmSkUC0tLbj22msxevRovP/++8Eejs/wcyxS4kyoqDe7vG8JRymUyLIUjYz8XunxePW6wdzrBrOV+1vLBmeBT9523a7Nzrj0sAhExIKPVoa2TqfDY9Oc4V7piIUGDfIE55hfvjjQ55cIDvn5+cjPz4fNRhXACIKQj/BZ9fClvRFjMuCCXqm48d0/VNnHO+uPY2h2Ej7fVozJvVNx69iu2H7iHM7UmRFnMuLomXrcNva8gEfXQ8axePbZZwEAy5Ytk/0Zs9kMs9lpuNfW1qo9LMXwr6/YtW5pJ1WhUjWSQgmpa3Y6Fpr2sQiwFEq42eJzjS59FgKRHM+/roEytKUiE1pETIRBn2hexEKrCA3RtsjLy0NeXh5qa2uRmJgY7OEQBBGiJERFYMHUvmhqUXeS4qUfD6OwogHr/zyLW0afh2ve+t3l/c4dojGlfydV9+mNsH46Llq0CImJidy/7Gz1Q1H+IGaKCculhgPCJm6AdjkWQup5EQstkTLktbLvhU5SRb3ZJWIRCKkOPwQbKGlQIHMs+M5ipFHvUmkrQoP9EQRBEOGN2rLdwooG7u8thZVu7x872+C2TGvC+um4YMEC1NTUcP+Ki4uDPSSvIam2XJvYV8SOiJ+UqyVaGJxiiEVD9Drv19vn/Qm2+87643jpx8MAHJGwQEQs+MG1wDkW4vuRWu4P/HNsMupdXgeinC9BEAQRXuj1OiyeNUQT20RMYiWmGNGaoDoWzzzzDHQ6ncd/27dv93n7JpMJCQkJLv/aElqWP21L8L/XD1zUE/+8fkjA9v3adUPQuUO0S3UGLRAzNI0aJlALd/dTwWl8vtVRJztQ9xH+DStQfUlMErkUWuTP8J3FqAiDyzXW8toSBEEQ4cuMoVn487mpuHtid833JaYY0Zqg5ljce++9uP766z2uc9555wVmMEGgnfgVLgboAxf1Cui+c7t0wMb/m6z5fsScRC1tz7bglAajGgVfChUTaUBjq141WYMqYwZhxILnWJBfQRAEQfhDBO+ZsuKeMbhqyWbV92HjPad3nDyHjMRoZCZFq74fPkF1LFJSUpCS0r6aTOkk/g5n2mA1NNURm7HXqtQs0Dac0mCo9vjlg1PiTCg61wgA6BirvmPBP8dREQaXaxyoCA1BEAQRnvBLtmslD2+x2jHr7d/BANhaeA4AcOLFyzTZF0vIVIUqKirCuXPnUFRUBJvNht27dwMAevTogbi4uOAOTgF8LXp7abAVDI1foBFLMdCyClVbiFjYgnBdY3gRi6E5SU7HQoNiAHznwWTUuziKbeH8EwRBEKELv5piTIS4Od4hJgJVjRaf97H28FkcLAtsRdSQcSyeeuopfPTRR9zroUOHAgDWrl2LiRMnBmlUynny8r7YX1qDOyZ0wwcbC4M9nIDQDvwKUUNTy1nttjBjHhTtpkGPNQ9OgI1hsPloJf6z+xQAIFmDiAX/mgojFm3g9BMEQRAhzLCcDtzf2cnRmDEkE+caLbh8UAYq6s3o0ykek/uko6bJgsHP/uTTPppa3CtjNrXYNC2gEzKOxbJlyxT1sGirdOkYi98XTIZOp8Oyze3EsRCtCxVeiEWfwl8KFZzr2jM9HgBQcq6JW6aFFEovjFjwXidGa9dskSAIggh/RnfviHdvGY5uqbHQ6XRYfP1Q0fUSoyN83ofF5v6c/vXQGVw2KENkbXUIGccinGCNUJ1IlsWVQzIDPRzNCcMKum6IJTJrKRVqC1KcYF/XnulOCWRClO83XilO1zRzfxsNDsfi7Ztz0WyxIVWjzvEEQRBE++HifumK1jfqdYr6nZVWN7kta9C4vxc5FkHkltFd8NwPB7nXX9x5PoZkJwVvQBpx+aAM/Gf3KQwOw2PzhNmiXRf1tuFYBNez6NIxFm/cMBTxUUZN8lmqm1q4v881mAEAlwS4gylBEARBfHP3GHyxtQiDOifiyf8c8Gtb/GebFpBjEURuH9sVG49WYN3hswCA87t1DPKItOH5qwZiTPeOuLhf+zLKzFZbsIegKfGm4N8+pg/WLsLXzHMMy2vMmu2HIAiCIDyR26UDcrt0wA97y/zeltYR9+BbBu0YvV6HodkdOMciXIkzGTFrRE6wh6EpYnP3Wnb9ttq0i4bI5Y4J3bCzqBqXa6jVbCtU1JNjQRAEQQQXNZQCYjJ8NSHHIsgEW05CqITIZeySHKvZ7lLjTRiWk4SdRdWa7cMb8VER+GTuqKDtP5B07qBtQyGibZKfn4/8/HzYbOEdfSQIIjRQw2LUuqAO9Y8NMgl+ZPsTbZsuHWM027ZOp3NoLu88X7N9EE7euXl4sIdABIG8vDwUFBRg27ZtwR4KQRAELuiViqgIPUZ2TcbVQ7N82obW89kUsQgyfxmVgy3HK3FR37RgD4VQiaE5SdhVVI3bxnbVdD86nU5TuRXhIDXehH6ZCcEeBkEQBNHOSYyOwO6npiDSoIedYfDtrlLF2yDHIsyJijDg3VtoNjTU4YcW/337SJRUNaFvhvbGaKRYy29CVfypIU4QBEEQahIV4Whup/PRQdBagE9WCUGoAN/4jI+KCIhTATgat3kaC+E7F/ZxRBHnXdA9yCMhCIIgCFf4jXnfu2U4xvdMkfU5sb5bakIRC4JQgWE5HXDXhG44L0W7hG0x+FKoKf3ScUn/ThjdPTzLFgeaJTcNw/GzDejTKT7YQyEIgiAIN/52cS8UnWvEhX3TcFG/dJz36A9eP6N1xIIcC4JQAZ1OhwXT+gZ8v3zHIjrSgJm5nQM+hnDFZDQELPJEEARBEEq578Keyj+ksWdBUiiCCGH4ORaGNtCNmyAIgiCI4PPz/AtEl1O5WYIgJOFHLKgjCkEQBEG0Xx6a0gsAMHdcV/RIixNdh6pCEQQhCd+xsNnJtSAIgiCI9krepB6YNjADXVvzPT+eMxKPrdiHlDgTdrU21KWqUARBSMKXQlEXd4IgCIJov+h0OnRLjeMqRo3vmYoNj0zGt3ePQb/WnEGtTQVyLAgihOGXmyO/giAIgiAIITqdDp07RAOgHAuCIGRCEQuCIAiCIMRg5yEpYkEQhCzIsSAIgiAIQgwdHJ4F5VgQBCELyt0mCIIgCEIMTjmt8SQkORYEESYwFLEgCIIgCEIETgql8X7IsSCIMIEiFgRBEARBiMFJoSjHgiAIOVCOBUF4prGxEV26dMFDDz0U7KEQBEEEFGfyNkmhCIKQAUUsCMIzzz//PEaNGhXsYRAEQQQctjy91rYCORYEESZQjgVBSHPkyBEcOnQI06ZNC/ZQCIIgAg6Xu63xfsixIIgwoWNsZLCHQBA+sX79elxxxRXIzMyETqfDypUr3dZZsmQJunbtiqioKOTm5mLDhg2K9vHQQw9h0aJFKo2YIAgitAiUFMqo6dYJgtCcd27OxWdbi/DYZX2DPRSC8ImGhgYMHjwYt912G2bOnOn2/vLly/HAAw9gyZIlGDt2LN5++21MnToVBQUFyMnJAQDk5ubCbDa7ffann37Ctm3b0KtXL/Tq1QubN2/2Oh6z2eyyrdraWj+OjiAIIvhkJEajd3o8OsRoOwmpY9qRfqK2thaJiYmoqalBQkJCsIdDEAQRUELhHqjT6bBixQrMmDGDWzZq1CgMGzYMS5cu5Zb17dsXM2bMkBWFWLBgAT755BMYDAbU19fDYrHgb3/7G5566inR9Z955hk8++yzbsvb8nkjCILQCiXPDpJCEQRBEG2WlpYW7NixA1OmTHFZPmXKFFnRBwBYtGgRiouLceLECbzyyiu44447JJ0KwOGI1NTUcP+Ki4v9OgaCIIj2AkmhCIIgiDZLRUUFbDYb0tPTXZanp6ejvLxck32aTCaYTCZNtk0QBBHOkGNBEARBtHnYUoksDMO4LZPDrbfeqtKICIIgCCEkhSIIgiDaLCkpKTAYDG7RiTNnzrhFMdQmPz8f/fr1w4gRIzTdD0EQRLhAjgVBEATRZomMjERubi7WrFnjsnzNmjUYM2aMpvvOy8tDQUEBtm3bpul+CIIgwgWSQhEEQRBBpb6+HkePHuVeFxYWYvfu3UhOTkZOTg7mz5+Pm2++GcOHD8fo0aPxzjvvoKioCPPmzQviqAmCIAgh5FgQBEEQQWX79u2YNGkS93r+/PkAgNmzZ2PZsmWYNWsWKisrsXDhQpSVlWHAgAFYtWoVunTpEqwhEwRBECJQHwuCIIh2At0DlZGfn4/8/HzYbDb8+eefdN4IgmiXUB8LgiAIgvATyrEgCIJQBjkWBEEQBEEQBEH4DTkWBEEQBEEQBEH4TbtK3mbTSWpra4M8EoIgiMDD3vvaUWqdX7A5FlarFQA9OwiCaJ8oeXa0q+TtkpISZGdnB3sYBEEQQaW4uBidO3cO9jBCBnp2EARByHt2tCvHwm6349SpU4iPj4dOp5P9udraWmRnZ6O4uDjsK4K0l2NtL8cJtJ9jbS/HCfh+rAzDoK6uDpmZmdDrSQkrF7Fnx4gRI2QldctZz9M6Uu/JXd5Wfhdyz5dW21LyGW/r+nK9pN6j6+X/Z/y5Xp7ep+vlRMmzo11JofR6vV+zdAkJCWFvsLC0l2NtL8cJtJ9jbS/HCfh2rImJiRqNJnwRe3YYDAZZ517Oep7WkXpP6fJg/y7kni+ttqXkM97W9eV6Sb1H18v/z/hzvTy9T9fLFbnPDpqyIgiCIAiF5OXlqbaep3Wk3lO6PNioOS5ftqXkM97W9eV6Sb1H18v/z/hzvTy9T9fLN9qVFMpX2lNTqfZyrO3lOIH2c6zt5TiB9nWshH/QdyW0oOsVWtD1cociFjIwmUx4+umnYTKZgj0UzWkvx9pejhNoP8faXo4TaF/HSvgHfVdCC7peoQVdL3coYkEQBEEQBEEQhN9QxIIgCIIgCIIgCL8hx4IgCIIgCIIgCL8hx4IgCIIgCIIgCL8hx4IgCIIgCIIgCL8hx4IgCIIgCIIgCL8hx6KVJUuWoGvXroiKikJubi42bNjgcf3ffvsNubm5iIqKQrdu3fDWW28FaKT+o+RYv/32W1x88cVITU1FQkICRo8ejR9//DGAo/UdpdeUZdOmTTAajRgyZIi2A1QRpcdqNpvx+OOPo0uXLjCZTOjevTs++OCDAI3Wd5Qe56efforBgwcjJiYGGRkZuO2221BZWRmg0frG+vXrccUVVyAzMxM6nQ4rV670+plQvh8RweP7779H79690bNnT7z33nvBHg4hg6uuugodOnTANddcE+yhEF4oLi7GxIkT0a9fPwwaNAhfffVVsIcUGBiC+eKLL5iIiAjm3XffZQoKCpi//vWvTGxsLHPy5EnR9Y8fP87ExMQwf/3rX5mCggLm3XffZSIiIpivv/46wCNXjtJj/etf/8r84x//YLZu3cr8+eefzIIFC5iIiAhm586dAR65MpQeJ0t1dTXTrVs3ZsqUKczgwYMDM1g/8eVYp0+fzowaNYpZs2YNU1hYyPzxxx/Mpk2bAjhq5Sg9zg0bNjB6vZ755z//yRw/fpzZsGED079/f2bGjBkBHrkyVq1axTz++OPMN998wwBgVqxY4XH9UL4fEcHDYrEwPXv2ZEpKSpja2lqmR48eTGVlZbCHRXjh119/Zb777jtm5syZwR4K4YVTp04xu3btYhiGYU6fPs1kZWUx9fX1wR1UACDHgmGYkSNHMvPmzXNZ1qdPH+bRRx8VXf+RRx5h+vTp47LsrrvuYs4//3zNxqgWSo9VjH79+jHPPvus2kNTFV+Pc9asWcwTTzzBPP300yHjWCg91v/9739MYmJiyBkRSo/z5ZdfZrp16+ay7I033mA6d+6s2RjVRo5jEcr3IyJ4bNq0ycXJvv/++5nPPvssiCMi5LJ27VpyLEKQgQMHMkVFRcEehua0eylUS0sLduzYgSlTprgsnzJlCjZv3iz6md9//91t/UsuuQTbt2+HxWLRbKz+4suxCrHb7airq0NycrIWQ1QFX4/zww8/xLFjx/D0009rPUTV8OVYv/vuOwwfPhwvvfQSsrKy0KtXLzz00ENoamoKxJB9wpfjHDNmDEpKSrBq1SowDIPTp0/j66+/xmWXXRaIIQeMUL0fEf4hRzLnSTp46tQpZGVlca87d+6M0tLSQAy93eLvNSMCi5rXa/v27bDb7cjOztZ41MGn3TsWFRUVsNlsSE9Pd1menp6O8vJy0c+Ul5eLrm+1WlFRUaHZWP3Fl2MV8uqrr6KhoQHXXXedFkNUBV+O88iRI3j00Ufx6aefwmg0BmKYquDLsR4/fhwbN27E/v37sWLFCixevBhff/018vLyAjFkn/DlOMeMGYNPP/0Us2bNQmRkJDp16oSkpCS8+eabgRhywAjV+xHhHw0NDRg8eDD+9a9/ib6/fPlyPPDAA3j88cexa9cujB8/HlOnTkVRUREAgGEYt8/odDpNx9ze8feaEYFFretVWVmJW265Be+8804ghh102r1jwSK8oTIM4/EmK7a+2PK2iNJjZfn888/xzDPPYPny5UhLS9NqeKoh9zhtNhtuvPFGPPvss+jVq1eghqcqSq6p3W6HTqfDp59+ipEjR2LatGl47bXXsGzZsjYdtQCUHWdBQQHuv/9+PPXUU9ixYwdWr16NwsJCzJs3LxBDDSihfD8ifGPq1Kl47rnncPXVV4u+/9prr2HOnDmYO3cu+vbti8WLFyM7OxtLly4FAGRlZblEKEpKSpCRkRGQsbdX/L1mRGBR43qZzWZcddVVWLBgAcaMGROooQeVdu9YpKSkwGAwuM16njlzxm0WkKVTp06i6xuNRnTs2FGzsfqLL8fKsnz5csyZMwdffvklLrroIi2H6TdKj7Ourg7bt2/HvffeC6PRCKPRiIULF2LPnj0wGo349ddfAzV0xfhyTTMyMpCVlYXExERuWd++fcEwDEpKSjQdr6/4cpyLFi3C2LFj8fDDD2PQoEG45JJLsGTJEnzwwQcoKysLxLADQqjejwjtkCMdHDlyJPbv34/S0lLU1dVh1apVuOSSS4IxXALqSJWJwCHnejEMg1v/v717j2nyeuMA/m0LOKUiUpS74CxDRCK3YIhTQZn3gUHFoEFqUUN0m5fJnEs0aIiLF9Qg6nQDZBdRd9E/nDqJ46Is6lCIIG4Iw3u9bGqGgCDy/v7wxzsqRVsq1sL3kzSxp+d93+f0DY1Pz3lOVSqMGTMGsbGxpgjTJLp9YmFlZYXAwEDk5ORotefk5LSbXYaEhLTpf/z4cQQFBcHS0rLTYjVWR8YKPJupUKlU2Lt3r1msTzd0nDY2NigtLUVJSYn4SEhIgJeXF0pKSjB8+PDXFbrBOnJPR4wYgVu3buHRo0diW0VFBaRSKVxdXTs13o7qyDjr6uoglWp/xMlkMgC6l4GYK3P9PKLOo8/SQQsLC6SkpCAsLAz+/v5ITExkImpC+i73HD9+PGbMmIEjR47A1dUVv//+++sOlaDf/SosLMT+/ftx6NAh+Pn5wc/PD6WlpaYI9/UyTc34m6VlG8v09HShvLxcWLJkiWBtbS1cuXJFEARB+PTTT4XY2Fixf8v2jkuXLhXKy8uF9PR0s9ne0dCx7t27V7CwsBC2b98uaDQa8fHw4UNTDUEvho7zeea0K5ShY62pqRFcXV2F6dOnCxcvXhTy8/MFT09PYd68eaYagl4MHWdmZqZgYWEh7NixQ6iqqhJOnTolBAUFCcHBwaYagl5qamqE4uJiobi4WAAgbN68WSguLha31e1Kn0f0auC53cNu3rwpABB+++03rX7JycmCl5fXa46OdOE9My+8X/oznyrVTjRz5kz8888/WLt2LTQaDYYOHYojR47A3d0dAKDRaLSKcQYOHIgjR45g6dKl2L59O5ydnZGamopp06aZagh6M3Ssu3btQlNTExYtWqRV3BsXF4c9e/a87vD1Zug4zZmhY5XL5cjJycGHH36IoKAgKBQKREdHIzk52VRD0Iuh41SpVKipqUFaWho+/vhj2NraYsyYMVi/fr2phqCXoqIihIWFic+XLVsG4L+/ua70eUSdw5hlr2QavGfmhferfRJB6EJrAoiIiLoZiUSCgwcPYurUqWLb8OHDERgYiB07dohtQ4YMQWRkJD7//HMTREmt8Z6ZF94v/XHGgoiIyMw8evQIlZWV4vPq6mqUlJTAzs4OAwYMwLJlyxAbG4ugoCCEhIRg9+7duHbtWpfcFc1c8J6ZF96vDjL1WiwiIiIyTG5urgCgzSMuLk7ss337dsHd3V2wsrISAgIChPz8fNMFTLxnZob3q2O4FIqIiIiIiIzW7bebJSIiIiIi4zGxICIiIiIiozGxICIiIiIiozGxICIiIiIiozGxICIiIiIiozGxIHqJpKQk+Pn5mez6q1atwoIFC/Tqu3z5cnz00UedHBERERFRW9xulro1iUTywtfj4uKQlpaGhoYGKBSK1xTVf+7cuQNPT09cuHABHh4eL+1/9+5dDBo0CBcuXMDAgQM7P0AiIiKi/2NiQd3a7du3xX/v378fq1evxp9//im29ezZE3369DFFaACAdevWIT8/H7/88ovex0ybNg1KpRLr16/vxMiIiIiItHEpFHVrjo6O4qNPnz6QSCRt2p5fCqVSqTB16lSsW7cODg4OsLW1xZo1a9DU1ITExETY2dnB1dUVGRkZWte6efMmZs6cib59+0KhUCAyMhJXrlx5YXz79u1DRESEVtsPP/wAX19f9OzZEwqFAuHh4aitrRVfj4iIQHZ2ttHvDREREZEhmFgQdcCvv/6KW7duoaCgAJs3b0ZSUhKmTJmCvn374syZM0hISEBCQgKuX78OAKirq0NYWBjkcjkKCgpw6tQpyOVyTJgwAY2NjTqv8eDBA5SVlSEoKEhs02g0iImJgVqtxqVLl5CXl4eoqCi0nngMDg7G9evXcfXq1c59E4iIqEtpbGyEUqlEYWHhKz3v4cOH4e/vj+bm5ld6XnrzMLEg6gA7OzukpqbCy8sLarUaXl5eqKurw2effQZPT0+sXLkSVlZW4ofzvn37IJVK8dVXX8HX1xfe3t7IzMzEtWvXkJeXp/MaV69ehSAIcHZ2Fts0Gg2ampoQFRUFDw8P+Pr6YuHChZDL5WIfFxcXAHjpbAgRUVemUqkgkUjaPCorK00d2htr9+7dcHd3x4gRI8Q2iUSCQ4cOtenbMnuvjylTpkAikWDv3r2vKFJ6UzGxIOoAHx8fSKX//fk4ODjA19dXfC6TyaBQKHD37l0AwLlz51BZWYnevXtDLpdDLpfDzs4Ojx8/RlVVlc5r1NfXAwDeeustsW3YsGEYO3YsfH19MWPGDHz55Zd48OCB1nE9e/YE8GyWhIioO5swYQI0Go3WQ9fGFu3NHHc327Ztw7x58zrl3HPnzsW2bds65dz05mBiQdQBlpaWWs8lEonOtpZp3+bmZgQGBqKkpETrUVFRgVmzZum8hr29PQBoJQ4ymQw5OTk4evQohgwZgm3btsHLywvV1dVin/v37wMA+vXrZ/xAiYjMWI8ePbTq5hwdHSGTyRAaGooPPvgAy5Ytg729Pd577z0AQHl5OSZNmgS5XA4HBwfExsbi77//Fs9XW1uLOXPmQC6Xw8nJCSkpKQgNDcWSJUvEPrq+4be1tcWePXvE5y+ruWuZDdi0aROcnJygUCiwaNEiPHnyROzT0NCATz75BG5ubujRowc8PT2Rnp4OQRCgVCqxadMmrRjKysoglUrb/TLr/PnzqKysxOTJkw18l5/NkOuaHQoNDRX7RERE4OzZs/jrr78MPj+ZDyYWRK9BQEAALl++jP79+0OpVGo92tt1atCgQbCxsUF5eblWu0QiwYgRI7BmzRoUFxfDysoKBw8eFF8vKyuDpaUlfHx8OnVMRETmLCsrCxYWFigsLMSuXbug0WgwevRo+Pn5oaioCMeOHcOdO3cQHR0tHpOYmIjc3FwcPHgQx48fR15eHs6dO2fQdfWtucvNzUVVVRVyc3ORlZWFPXv2aCUnc+bMwb59+5CamopLly7hiy++gFwuh0QigVqtRmZmptZ1MzIyMHLkSAwaNEhnXAUFBXjnnXdgY2Nj0HgAwM3NTWtWqLi4GAqFAqNGjRL7uLu7o3///jh58qTB5yfzYWHqAIi6g9mzZ2Pjxo2IjIzE2rVr4erqimvXruGnn35CYmIiXF1d2xwjlUoRHh6OU6dOietYz5w5gxMnTmDcuHHo378/zpw5g3v37sHb21s87uTJkxg5cqS4JIqIqLs6fPiwVg3axIkT8f333wMAlEolNmzYIL62evVqBAQEYN26dWJbRkYG3NzcUFFRAWdnZ6Snp+Prr78WZziysrJ0fn6/SOuau5bfUsrMzIStrS3y8vIwbtw4AEDfvn2RlpYGmUyGwYMHY/LkyThx4gTmz5+PiooKHDhwADk5OQgPDwcAvP322+I15s6di9WrV+Ps2bMIDg7GkydP8O2332Ljxo3txnXlyhWtmr7WYmJiIJPJtNoaGhrE2Q2ZTAZHR0cAwOPHjzF16lSEhIQgKSlJ6xgXFxfW/3VxTCyIXoNevXqhoKAAK1asQFRUFGpqauDi4oKxY8e+8NuhBQsWID4+Hhs2bIBUKoWNjQ0KCgqwdetW/Pvvv3B3d0dKSgomTpwoHpOdnY01a9a8jmEREb3RwsLCsHPnTvG5tbW1+O/WO+4Bz2rhcnNztRKRFlVVVaivr0djYyNCQkLEdjs7O3h5eRkUU+uau9aer7nz8fHR+s+8k5MTSktLAQAlJSWQyWQYPXq0zms4OTlh8uTJyMjIQHBwMA4fPozHjx9jxowZ7cZVX1+vVdPX2pYtW8QEpsWKFSvw9OnTNn3j4+NRU1ODnJwcrVpE4FkNIOv/ujYmFkT/p1KpoFKp2rQnJSVpfevSeiq6ha6dnZ7/VsbR0RFZWVkGxTRu3Di4uLhg//79iImJgbe3N44dO9Zu/59//hkymQzTp0836DpERF2RtbU1lEplu6+11tzcjPfff1/nj4s6OTnh8uXLel1TIpHg+d8ebl0b0VJz991337U5tnVt3Ivq9vSZkZ43bx5iY2OxZcsWZGZmYubMmejVq1e7/e3t7cXE5XmOjo5t3sfevXvj4cOHWm3Jyck4duwYzp492yZxAp7VALL+r2tjjQXRG0wikWD37t1oamrSq39tbS0yMzNhYcHvDIiIDBEQEICLFy/Cw8OjTS1cS4JiaWmJ06dPi8c8ePAAFRUVWufp168fNBqN+Pzy5cta39J3pObueb6+vmhubkZ+fn67fSZNmgRra2vs3LkTR48ehVqtfuE5/f398ccff7RJivT1448/Yu3atThw4IDOOo6WGRl/f/8OnZ/MAxMLojfcsGHDEBsbq1ff6OhoDB8+vJMjIiLqehYtWoT79+8jJiZG3L3o+PHjUKvVePr0KeRyOeLj45GYmIgTJ06grKwMKpWqzXKfMWPGIC0tDefPn0dRURESEhK0Zh9mz54Ne3t7REZG4uTJk6iurkZ+fj4WL16MGzdu6BWrh4cH4uLioFarcejQIVRXVyMvLw8HDhwQ+8hkMqhUKqxcuRJKpVJrCZcuYWFhqK2txcWLFw14154pKyvDnDlzsGLFCvj4+OD27du4ffu2uEshAJw+fRo9evR4aRxk3phYEBERUbfn7OyMwsJCPH36FOPHj8fQoUOxePFi9OnTR0weNm7ciFGjRiEiIgLh4eF49913ERgYqHWelJQUuLm5YdSoUZg1axaWL1+utQSppeZuwIABiIqKgre3N9RqNerr6w3akWnnzp2YPn06Fi5ciMGDB2P+/Pmora3V6hMfH4/GxsaXzlYAgEKhQFRUlM4lWi9TVFSEuro6JCcnw8nJSXxERUWJfbKzszF79uwXLsci8ycROjrnRURERNTNhYaGws/PD1u3bjV1KG0UFhYiNDQUN27cgIODw0v7l5aWIjw8XGdxuTHu3buHwYMHo6ioSOcPFFLXwRkLIiIioi6koaEBlZWVWLVqFaKjo/VKKoBntRsbNmx45VvCVldXY8eOHUwqugFWeBIRERF1IdnZ2YiPj4efnx+++eYbg46Ni4t75fEEBwcjODj4lZ+X3jxcCkVEREREREbjUigiIiIiIjIaEwsiIiIiIjIaEwsiIiIiIjIaEwsiIiIiIjIaEwsiIiIiIjIaEwsiIiIiIjIaEwsiIiIiIjIaEwsiIiIiIjLa/wDTQbYKXANtAgAAAABJRU5ErkJggg==", + "image/png": 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", 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" ] @@ -81,67 +81,44 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 3, "metadata": {}, "outputs": [ { - "ename": "TypeError", - "evalue": "cannot unpack non-iterable IrasaSpectrum object", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[0;32mIn[4], line 3\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mpyrasa\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mirasa\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m irasa\n\u001b[0;32m----> 3\u001b[0m freq_irasa, psd_ap, psd_p \u001b[38;5;241m=\u001b[39m irasa(sig, \n\u001b[1;32m 4\u001b[0m fs\u001b[38;5;241m=\u001b[39mfs, \n\u001b[1;32m 5\u001b[0m band\u001b[38;5;241m=\u001b[39m(\u001b[38;5;241m1\u001b[39m, \u001b[38;5;241m100\u001b[39m), \n\u001b[1;32m 6\u001b[0m psd_kwargs\u001b[38;5;241m=\u001b[39m{\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mnperseg\u001b[39m\u001b[38;5;124m'\u001b[39m: duration\u001b[38;5;241m*\u001b[39mfs, \n\u001b[1;32m 7\u001b[0m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mnoverlap\u001b[39m\u001b[38;5;124m'\u001b[39m: duration\u001b[38;5;241m*\u001b[39mfs\u001b[38;5;241m*\u001b[39moverlap\n\u001b[1;32m 8\u001b[0m },\n\u001b[1;32m 9\u001b[0m hset_info\u001b[38;5;241m=\u001b[39m(\u001b[38;5;241m1\u001b[39m, \u001b[38;5;241m2\u001b[39m, \u001b[38;5;241m0.05\u001b[39m))\n\u001b[1;32m 11\u001b[0m f, axes \u001b[38;5;241m=\u001b[39m plt\u001b[38;5;241m.\u001b[39msubplots(ncols\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m2\u001b[39m, figsize\u001b[38;5;241m=\u001b[39m(\u001b[38;5;241m8\u001b[39m, \u001b[38;5;241m4\u001b[39m))\n\u001b[1;32m 12\u001b[0m axes[\u001b[38;5;241m0\u001b[39m]\u001b[38;5;241m.\u001b[39mset_title(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mPeriodic\u001b[39m\u001b[38;5;124m'\u001b[39m)\n", - "\u001b[0;31mTypeError\u001b[0m: cannot unpack non-iterable IrasaSpectrum object" - ] + "data": { + "image/png": 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KtWvX6p23YsUKdOnSBQEBAVKETUREREREZUiaWIwcORKZmZmYM2cOUlNTERAQgB07duhmeUpNTTVY0yI7OxubN2/GF198IUXIRERERERUDknXsZBCTk4OHB0dkZ2dzW5RRGR2+BlYPfy5EZG5qsrnH1dHIiIiIiKiGmNiQURERERENcbEgoiIiIiIaoyJBRERUQWioqLg7++PkJAQqUMhIpI9JhZEREQVqOkCeaIo4nRajpGjIiKSJyYWMvTLsWvo+tGfOJx8S+pQiIiomoo1Wrzx0zEM+Wo/Dl7MlDocIqJax8RChnafuYG0nAL8fYEVERGRqVIKAu4UlqBIo8ULa+NwPj1X6pCIiGoVEwsZ0prX0iJERPWSQiFg4ciO6NS4AXIKSjBhVSzScwukDouIqNYwsZCh0rxCq2WCQURkyqwslPhmXDCaOtvgyq18hK+OQ15RidRhERHVCiYWMlTaYsG8gojI9DnbWWL1xM5oaGOB41ez8fL6BJRotFKHRURkdEwsZKg0oWCXKCKi+qGpiy2+HR8CS5UCf55Ox/vbEyHyM56I6hkmFjJUmlCw0iEiqj+CmjTEopEdIQjAuoOX8c3ei1KHRERkVEws5EjU+4OIiOqJsHaemD3QDwDw0Y7T+OXYNYkjIiIyHiYWMvTvGAumFkRE9U14D19MCG0KAIjcdBSxl25KGxARkZEwsZAhDt4mIpKHqKgo+Pv7IyQkxGjXFAQB7wz2x6P+7ijSaPH82jhcuHHbaNcnIpIKEwsZ4uBtIiJ5iIiIQGJiImJjY416XaVCwJejAtHBpwGy8ooxYdUhZNwuNOo9iIjqGhMLGRJ1g7clDoSIiGqNtVqJFeOD0djJBik38xG+Jg75RRqpwyIiqjYmFjKk5QJ5RERmwcXOEqsmhqCBjQWOpmThlY0J0PCzn4hMFBMLGdK1WEgcBxER1b7mrnb4Zlww1CoFohOv44NfuMYFEZkmJhYyxDEWRETmJaSpExY80wEAsPrAJazYlyRxREREVcfEQoa0HGNBRGR2Brf3wqywNgCAD3ecwm/HUyWOiIioaphYyJDIFgsiIrP0Qq9mGNu1CUQRmLHpCOIvc40LIjIdTCxkiAvkERGZJ0EQ8O4T/nikjRsKS7SYvCYOSRl3pA6LiKhSmFjIEBfIIyIyXyqlAl+NDkQ7b0fcyivGxFWHkMk1LojIBDCxkKHShgo2WBARmScbtQorJgSjUUNrXMrMw+S1cSgo5hoXRCRvTCxk6N/EgpkFEZG5crO3wuqJIXCwUiEhOQszNh7hGhdEJGtMLGSIYyyIiOQhKioK/v7+CAkJkeT+Ldzs765xoVRg58k0fLTjlCRxEBFVBhMLGeIYCyIieYiIiEBiYiJiY2Mli6FLM2d8+nR7AMCKfUlYtZ9rXBCRPDGxkCEukEdERPcb2tEbbz7eGgAw55dE/H4yTeKIiIgMMbGQId3YCuYVRER0z0u9m+PZzo0hisArGxKQkHxL6pCIiPRInlgsXrwYvr6+sLKyQlBQEPbu3fvA8oWFhZg9ezaaNGkCS0tLNG/eHCtXrqyjaOtGaT7BFgsiIiolCAI+GNoWfVu76ta4uJzJNS6ISD4kTSw2bdqEGTNmYPbs2UhISEDPnj0RFhaG5OTkCs955pln8Oeff2LFihU4c+YMNmzYgDZt2tRh1LWPYyyIiKg8KqUCX4/uhLZeDsi8U4QJq2Jx606R1GEREQGQOLFYsGABwsPDMXnyZPj5+WHRokXw8fHBkiVLyi2/c+dO7NmzBzt27ED//v3RtGlTdO7cGaGhoXUcee3Sau/9yRYLIiIqw9ZShZUTQuDdwBpJGXfwPNe4ICKZkCyxKCoqQnx8PAYMGKC3f8CAAThw4EC55/z8888IDg7G/Pnz4e3tjVatWuH1119Hfn5+XYRcZ0oTCuYVRERUHncHK6yaGAJ7KxXiLt/Caz8chZbN3EQkMZVUN87IyIBGo4G7u7vefnd3d6SllT/bxcWLF7Fv3z5YWVlh69atyMjIwNSpU3Hz5s0Kx1kUFhaisLBQt52Tk2O8h6glImeFIiKih2jlbo9lY4MwfuUh/Ho8FV4NrDB7kL/UYRGRGZN88LYgCHrboiga7Cul1WohCAK+//57dO7cGQMHDsSCBQuwevXqClst5s2bB0dHR93Lx8fH6M9gbGyxICKiyght7oL5T91d4+KbvUn4du9FiSMiInMmWWLh4uICpVJp0DqRnp5u0IpRytPTE97e3nB0dNTt8/PzgyiKuHLlSrnnzJo1C9nZ2bpXSkqK8R6ilnBWKCIiqqxhgY10a1zM/fUUtiaUXx8SEdU2yRILtVqNoKAgREdH6+2Pjo6ucDB29+7dce3aNdy+fVu37+zZs1AoFGjUqFG551haWsLBwUHvJXecFYqIiKripd7NMam7LwDgjR+PYdeZdIkjIiJzJGlXqMjISHz77bdYuXIlTp06hVdffRXJycmYMmUKgLutDePGjdOVHz16NJydnTFx4kQkJiYiJiYGb7zxBiZNmgRra2upHsPodOvjscWCiIgqQRAEvD3ID0M7eqFEK2Lqd4e5gB4R1TnJBm8DwMiRI5GZmYk5c+YgNTUVAQEB2LFjB5o0aQIASE1N1VvTws7ODtHR0Xj55ZcRHBwMZ2dnPPPMM5g7d65Uj1Ar/m2xYGJBRESVo1AI+PSpDriVV4yYszcwcXUsfprSDS3c7KUOjYjMhCCa2dfiOTk5cHR0RHZ2tmy7RfWc/xdSbuajdytXrJnUWepwiKgeMYXPQDkypZ/bncISjP72HxxNyYKXoxV+eikUXg3qT6s+EdWtqnz+ST4rFBkqXSDPrDI+IiIyCltLFVZNCEEzV1tcyy7A+JWHkJXH1bmJqPYxsZAxM2tMIiIiI3GyVWPtpM7wcLDCufTbmLQ6FvlFXJ2biGoXEwsZ4hgLIiJ5iIqKgr+/P0JCQqQOpcoaNbTBmkmd4WClwuHkLESsP4xijVbqsIioHmNiIUO6xIKf/0REkoqIiEBiYiJiY2OlDqVaWnvYY+WEEFiqFPjrdDpmbj7O1nAiqjVMLGSodP0KtlgQEVFNBTd1QtToTlAqBGw+fAUf/3Za6pCIqJ5iYiFDpd8mMa8gIiJj6O/vjnnD2wEAlsVcxDcxFyWOiIjqIyYWMlTaYiFyXigiIjKSZ4J98NbjbQAAH+44hc3xVySOiIjqGyYWMiTqBm9LHAgREdUrU3o3Q3gPXwDAm5uPYdfpdIkjIqL6hImFDHGMBRER1QZBEDB7oB+GBXpDoxUx9fvDOJx8S+qwiKieYGIhQ1q2WBARUS1RKATMf6o9erdyRX6xBpNWx+J8eq7UYRFRPcDEQoZKGyo4JSAREdUGC6UCS8Z0QkefBsjKK8bYFYdwLStf6rCIyMQxsZAhLpBHRES1zUatwqoJIWjuaovU7AKMW3kIt+4USR0WEZkwJhYypOV0s0REVAca2qqxNrwLPByscD79NiatiUVeUYnUYRGRiWJiIUOibvC2tHEQEVH9593AGmvDO8PR2gIJyVmI+P4wijVaqcMiIhPExEKGOMaCiIjqUit3e6ycEAwrCwV2nbmBtzYfg5bfbhFRFTGxkCGOsSAioroW1MQJi5/rBKVCwJbDV/HxztNSh0REJoaJhQxxulkiIpJCvzbu+GREewDA8piLWB5zQeKIiMiUMLGQIS6QR0REUnkqqBFmhbUBAHy04zQ2x1+ROCIiMhVMLGRGb1wF8woiIpLAi72b4/mevgCANzcfw1+nr0scERGZAiYWMnN/XsEWCyIiksqsMD8MC/SGRiti6veHEX/5ptQhEZHMMbGQmfuTCY6xICIiqSgUAuY/1R59WruioFiLSavjcO56rtRhEZGMMbGQGS1bLIiISCYslAosfq4TAhs3QHZ+McatPIRrWflSh0VEMsXEQmbuTyaYVxARkdRs1CqsHB+CFm52SM0uwNgV/+DWnSKpwyIiGWJiITN6Y7eZWRARkQw0tFVj7aTO8HS0woUbdzBxdSzyikqkDouIZIaJhcxwjAUREcmRVwNrrJ3UGQ1sLHAkJQtTvz+MYo1W6rCISEaYWMjM/bkEx1gQEUkrKioK/v7+CAkJkToUWWjpbo8V40NgZaHA7jM38OZPx6Dlt2BEdA8TC5lhiwURkXxEREQgMTERsbGxUociG0FNGmLxc52gVAjYmnAV8347JXVIRCQTTCxkRryvVZljLIiISI76tXHH/BHtAQDf7E3Csj0XJI6IiOSAiYXM6LdYMLEgIiJ5GhHUCP8Z2AYAMO+30/gp/orEERGR1JhYyIzedLMSxkFERPQwL/Rqjhd6NQMAvLX5GA6cz5A4IiKSkuSJxeLFi+Hr6wsrKysEBQVh7969FZbdvXs3BEEweJ0+fboOI65degvkcZAFERHJ3MzH2+DJjl7QaEW8vCEBqdlcQI/IXEmaWGzatAkzZszA7NmzkZCQgJ49eyIsLAzJyckPPO/MmTNITU3VvVq2bFlHEdc+EVwgj4iITIdCIeDjEe3h7+mAzDtFmPr9YRSVcBpaInMkaWKxYMEChIeHY/LkyfDz88OiRYvg4+ODJUuWPPA8Nzc3eHh46F5KpbKOIq599ycTHGNBRESmwMpCiaVjguBgpUJCchY+/DVR6pCISAKSJRZFRUWIj4/HgAED9PYPGDAABw4ceOC5gYGB8PT0xCOPPIJdu3Y9sGxhYSFycnL0XnLG6WaJiMgUNXa2wcKRHQEAa/6+jG0JV6UNiIjqnGSJRUZGBjQaDdzd3fX2u7u7Iy0trdxzPD09sXz5cmzevBlbtmxB69at8cgjjyAmJqbC+8ybNw+Ojo66l4+Pj1Gfw9i0bLEgIiIT9YifO17u1wIAMGvLcZxJy5U4IiKqSyqpAxAEQW9bFEWDfaVat26N1q1b67a7deuGlJQUfPbZZ+jVq1e558yaNQuRkZG67ZycHFknF/cP2GZaQUREpmZG/1Y4kpKFvecyMOW7ePx3Wnc4WFlIHRYR1QHJWixcXFygVCoNWifS09MNWjEepGvXrjh37lyFxy0tLeHg4KD3krP7Gym4QB4REZkapULAF6MC4d3AGkkZd/DGj0dZnxGZCckSC7VajaCgIERHR+vtj46ORmhoaKWvk5CQAE9PT2OHJ5n7Z4XiGAsiIjJFTrZqLH6uE9RKBX4/eR3LYi5KHRIR1QFJu0JFRkZi7NixCA4ORrdu3bB8+XIkJydjypQpAO52Y7p69SrWrl0LAFi0aBGaNm2Ktm3boqioCN999x02b96MzZs3S/kYRsUxFkREVB908GmAd4f4Y/bWE5i/8zTaezsitIWL1GERUS2SNLEYOXIkMjMzMWfOHKSmpiIgIAA7duxAkyZNAACpqal6a1oUFRXh9ddfx9WrV2FtbY22bdvi119/xcCBA6V6BKPTW3lbfPCYEyIiIjkb3bkxDl/OwubDV/DyhgT88koPeDpaSx0WEdUSQTSzjo85OTlwdHREdna2LMdbnE/PRf8F/85ydfGjgVAomFgQkXHI/TNQrvhzq778Ig2GLzmAU6k5CGzcAJte6Aa1StJltIioCqry+cf/2TJTdlyFWWV9RERU71irlVg6phPsuXgeUb3HxEJmyo6r4DgLIiIydU2cbbGIi+cR1XtMLGSmbB7BxIKIiOqDsovnnU7LkTgiIjI2JhYyUzaRYF5BRET1xYz+rdCzpQvyizV4eunf2HgomWtcENUjTCxkhi0WRERUX5UuntfRpwFyC0owc8txjP7mH1zKuCN1aERkBEwsZMZwjIVEgRAREdUCJ1s1Nr8UircH+cHKQoG/L2bisUUxWLrnAko0WqnDI6IaYGIhMwazQrHFgoiI6hmlQsDkns3wx4ze6NHCBYUlWnz822k8uXg/Tl7Lljo8IqomJhYywxYLIiIyF42dbbAuvDPmP9UeDlYqnLiagyFf78cnO0+joFgjdXhEVEVMLGSmbAMFWyyIiKg+EwQBzwT74H+v9cbAdh7QaEUs2X0BYV/sxT8XM6UOj4iqgImFzJRNJNhiQURE5sDN3gqLnwvC0jFBcLO3RFLGHYxcfhAf7TiFYo69IDIJTCxkpmwiwVmhiIjInDwe4IHoyN4YFeIDAFgecxGjvzmI6zkFEkdGRA/DxEJmuPI2ERGZO0drC3w8oj2WPNcJdpYqxF66hUFf7sWB8xlSh0ZED8DEQmYMEgnmFUREZKbC2nli+8s90MbDHhm3izBmxT+I2nUeWvYTJpIlJhZyY9AVSpowiIiI5MDXxRZbp3bH00GNoBWBT38/g/A1scjKK5I6NCIqg4mFzHCMBRERkT5rtRKfPt0B80e0h6VKgV1nbmDQl/twNCVL6tCI6D4qqQMgfRxjQUREVL5nQnzQ1tsBU78/jMuZeXh66d8Y160JmrrYwsPBCh6Od19ONmooFILU4RKZHSYWMlM2kWBeQURE9K+2Xo7Y/nIPvPHjUfx+8jq+3ZdkUMZCKcDN3grN3ezwf4P90cLNToJIicwPu0LJjOECedLEQURUn6SkpKBPnz7w9/dH+/bt8eOPP0odEtWAg5UFlo4JwsKRHfBcl8bo7+eGAG8HuNhZQhCAYo2Iq1n5iDl7AxNXH8LNOxyPQVQX2GIhM+wKRURkfCqVCosWLULHjh2Rnp6OTp06YeDAgbC1tZU6NKomQRAwLLARhgU20ttfrNEiPbcQ17Ly8doPR5F8Mw9TvovHd+FdoFbx+1Si2sT/YTJTNo9gYkFEVHOenp7o2LEjAMDNzQ1OTk64efOmtEFRrbBQKuDdwBohTZ2wYnww7CxVOJR0E//33xMQWacS1SomFjJj2GIhUSBERHUoJiYGTzzxBLy8vCAIArZt22ZQZvHixfD19YWVlRWCgoKwd+/eat0rLi4OWq0WPj4+NYya5K6luz2+ejYQCgHYGJuClfsvSR0SUb1Wra5QKSkpuHTpEvLy8uDq6oq2bdvC0tLS2LGZpbKJBL9dISI5M1Z9cOfOHXTo0AETJ07EiBEjDI5v2rQJM2bMwOLFi9G9e3csW7YMYWFhSExMROPGjQEAQUFBKCwsNDj3jz/+gJeXFwAgMzMT48aNw7ffflvlGMk09W3jhv8M9MPcX0/hw18T0dzVFn1au0kdFlG9VOnE4vLly1i6dCk2bNiAlJQUvV941Wo1evbsiRdeeAEjRoyAQsGGkOoqm0iwxYKI5KY26oOwsDCEhYVVeHzBggUIDw/H5MmTAQCLFi3C77//jiVLlmDevHkAgPj4+Afeo7CwEMOGDcOsWbMQGhpaqbiofgjv4Yuz13PxQ9wVvLw+AVsjQtHCzV7qsIjqnUp94k+fPh3t2rXDuXPnMGfOHJw8eRLZ2dkoKipCWloaduzYgR49euCdd95B+/btERsbW9tx11sGLRZll+ImIpKQFPVBUVER4uPjMWDAAL39AwYMwIEDByp1DVEUMWHCBPTr1w9jx459aPnCwkLk5OTovch0CYKAD54MQEjThsgtLEH4mjjc4kxRREZXqRYLtVqNCxcuwNXV1eCYm5sb+vXrh379+uHdd9/Fjh07cPnyZYSEhBg9WHNgMMZCK1EgRETlkKI+yMjIgEajgbu7u95+d3d3pKWlVeoa+/fvx6ZNm9C+fXvd+I1169ahXbt25ZafN28e3n///RrFTfJiqVJi6ZggDI3aj8uZeRi+5ACau9rB1lIJG7UKtmolbCxV8HWxwcB2nrBUKaUOmcjkVCqx+PTTTyt9wYEDB1Y7GIJB+wRnhSIiOZGyPhAE/ZWURVE02FeRHj16QFuFb2pmzZqFyMhI3XZOTg4He9cDznaWWDE+BMMX70dSxh0kZdwpt9xHO05jYvemeK5LEzhaW9RxlESmi+tYyEzZMRbMK4jI3Lm4uECpVBq0TqSnpxu0YhiLpaUlJyWpp1p72OPP1/rgn6RM5BVpcKew5O6fRSW4XVCCv06nIzW7APN3nkHUX+fxbOfGmNTDF14NrKUOnUj2jJZY/Oc//0FaWhpWrlxprEuaJS6QR0Smztj1gVqtRlBQEKKjozFs2DDd/ujoaAwdOtQo9yDz4uFohaEdvcs9VlSixfaj17A85iLOXM/Ft/uSsPrAJbz6aCtM7dO80q1kRObIaInF1atXkZKSYqzLma2yLfVMLIjI1FSnPrh9+zbOnz+v205KSsKRI0fg5OSExo0bIzIyEmPHjkVwcDC6deuG5cuXIzk5GVOmTDF2+GTm1CoFRgQ1wvBO3th99gaW7bmAgxdv4tPfzyA1Ox/vDwmAUsHkgqg8RpsXds2aNfjrr7+qfF51Fzzav38/VCqVbiXV+qJsIsG0gohMTXXqg7i4OAQGBiIwMBAAEBkZicDAQPzf//0fAGDkyJFYtGgR5syZg44dOyImJgY7duxAkyZNjB4/EXB3TE/f1m7Y+EI3vD+kLQQB+O5gMiK+P4yCYo3U4RHJkqQLTpQueDR79mwkJCSgZ8+eCAsLQ3Jy8gPPy87Oxrhx4/DII4/UUaR1p2wDBRfIIyJz0KdPH4iiaPBavXq1rszUqVNx6dIlFBYWIj4+Hr169ar1uKKiouDv78+ZDs3c+NCmiBrdCWqlAjtPpmHsin+QnVcsdVhEsiOIVfzNdc6cOQ88XvrtUmV06dIFnTp1wpIlS3T7/Pz88OSTT+oWPCrPqFGj0LJlSyiVSmzbtg1Hjhyp9D1zcnLg6OiI7OxsODg4VPq8urIpNhlvbT6u2/5xSjeENHWSMCIiqk+M+RlozPpA7uRed1Dd+PtCJl5YF4fcghK0dLPDmkmdOaib6r2qfP5VeYzF1q1b9baLi4uRlJQElUqF5s2bV7oiKV3waObMmXr7H7bg0apVq3DhwgV89913mDt3blXDl72yC+RpufQ2EcmUseoDIlPRrbkzfpzSDeNXHsK59NsIXxOHrVNDYWXBNS+IgGokFgkJCQb7cnJyMGHCBL3ZOh6mOgsenTt3DjNnzsTevXuhUlUu9MLCQhQWFurFKmeGs0JJFAgR0UMYqz4gMiVtPByw+aVQDPl6P06l5uCTnafx7hNtpQ6LSBaMMsbCwcEBc+bMwTvvvFPlcyu74JFGo8Ho0aPx/vvvo1WrVpW+/rx58+Do6Kh7yX2Bo7KJBMdYEJEpqUl9QGQqGjW0wWdPtwcArNp/CX+eui5xRETyYLTB21lZWcjOzq50+aoueJSbm4u4uDhMmzYNKpUKKpUKc+bMwdGjR6FSqSqcgWTWrFnIzs7WveQ+Ja7BAnkSxUFEVF1VrQ+ITFG/Nu6Y2L0pAOCNn47hek6BtAERyUCVu0J9+eWXetuiKCI1NRXr1q3D448/XunrVHXBIwcHBxw/flxv3+LFi/HXX3/hp59+gq+vb7n3MbXVU8uOqeA6FkQkV8aqD+QsKioKUVFR0Gg4vSgZmhnWBv9cvInE1By8uukI1oV34RoXZNaqnFgsXLhQb1uhUMDV1RXjx4/HrFmzqnSthy14NGvWLFy9ehVr166FQqFAQECA3vlubm6wsrIy2G/KyqYRHGNBRHJlzPpAriIiIhAREaGbFYXofpYqJb4aHYjBX+7DgQuZWBZzAVP7tJA6LCLJVDmxSEpKMtrNR44ciczMTMyZMwepqakICAjQW/AoNTX1oWta1DcGs0KxxYKIZMqY9QGRqWruaof3h7bFmz8dw+d/nEWnxg3RtZmz1GERSaLK61iYOrnPRf7t3ouY++sp3fbKCcHo18ZwzAkRUXXI/TNQrvhzowcRRREzNh3Bf49cg72VCj9O6YY2HnyfUP1Qlc8/ow3eXrx48UMXS6KHM5huVitRIERE1cT6gMyNIAj4ZER7hDRtiNyCEkxYGYtrWflSh0VU54yWWGzevBmrV6821uXMlsF0s9KEQURUbawPyBxZWSjx7bgQtHSzQ1pOAcatPISsvCKpwyKqU0ZLLP78809cvHjRWJczW4YL5DG1ICLTwvqAzJWjjQXWTOoMDwcrnE+/jclr4lBQzBnFyHwYLbEg4yibR5jZEBgiIiKT5tXAGmsmdYaDlQpxl2/hhXXxuFNYInVYRHWiyrNClUpMTERycjKKivSb+YYMGVLjoMxZ2USC080SkdzV5/qA61hQdbT2sMc344IxftUhxJy9gdHfHMTKCSFwtjOddbWIqqPKicXFixcxbNgwHD9+HIIg6H4RFoS7C8Lww7dmON0sEZkKc6gPuI4FVVeXZs5Y/3xXhK+OxdEr2Xhq6d9YO6kzfJxspA6NqNZUuSvU9OnT4evri+vXr8PGxgYnT55ETEwMgoODsXv37loI0bwYjrGQKBAioodgfUD0YJ0aN8RPL4XCu4E1kjLuYPiSAzh5LVvqsIhqTZUTi7///htz5syBq6srFAoFFAoFevTogXnz5uGVV16pjRjNisGsUGyxICKZYn1A9HDNXe2wZWoo2njY40ZuIZ779h+cu54rdVhEtaLKiYVGo4GdnR0AwMXFBdeuXQMANGnSBGfOnDFudGaobCLBvIKI5Ir1AVHluDtYYdOL3dDBpwGy8ooxdsUhXLmVJ3VYREZX5cQiICAAx44dAwB06dIF8+fPx/79+zFnzhw0a9bM6AGam7KJBMdYEJFcsT4gqjxHawusnvDvOhdjVxzCjdxCqcMiMqoqJxZvv/02tPeWg547dy4uX76Mnj17YseOHfjyyy+NHqC54RgLIjIVrA+IqqahrRrrwrvoxlyMX3kIOQXFUodFZDRVnhXqscce0/29WbNmSExMxM2bN9GwYUPdTCBUfZwViohMBesDoqrzcLTCd5O74OmlB5CYmoNxKw5hbXhnOFhZSB0aUY0ZZYE8JycnViJGYjBYm3kFEZkQ1gdED+frYou1k7qggY0FjqRkYcy3/yA7jy0XZPoqlVhMmTIFKSkplbrgpk2b8P3339coKHNm2BWKmQURyYe51QdRUVHw9/dHSEiI1KFQPePv5YD1k7vCyVaNY1eyMfrbg7h1p+jhJxLJWKW6Qrm6uiIgIAChoaEYMmQIgoOD4eXlBSsrK9y6dQuJiYnYt28fNm7cCG9vbyxfvry24663DLtCSRMHEVF5zK0+4AJ5VJv8vRyw4fmueO7bgzh5LQfPfnMQ30/uwhW6yWQJYiUXSkhPT8eKFSuwceNGnDhxQu+Yvb09+vfvjxdeeAEDBgyolUCNpbRyyM7OhoODg9ThGJizPREr9yfptuc+GYAxXZtIGBER1SfG+AysL/VBVci97iDTdj49F89+8w9u5Bailbsdvp/cFa72TC5IHqry+VfpxOJ+WVlZuHz5MvLz8+Hi4oLmzZubTJ9auVcO7/18EqsPXNJtfzC0LcZ2aypZPERUvxj7M9CU64OqkHvdQabvwo3bGP3NQVzPKURzV1tseL4r3ByspA6LqEqff1WeFQoAGjRogAYNGlTnVHqIsnkeu0IRkZyxPiAyjuaudtj0QjeM/uYgLty4g5HLD2L9813g6WgtdWhElWaUWaHIeMomEtVoUCIiIiIT1NTFFpte7KZb52L44gNIvJYjdVhElcbEQma4QB4REZH58nGywaYXu6K5qy1Sswvw9NID2HU6XeqwiCqFiYXMcIE8IiIi89aooQ22vNQdoc2dcadIg/A1sfju4GWpwyJ6qColFqIo6gbpUW3RTySYVxCRHLE+IKpdjjYWWD2xM54JbgStCLy97QRW3zdrJJEcVTmxaNmyJa5cuVJb8Zg9rbbMNjMLIpIh1gdEtU+tUuCTEe3xUp/mAID3tidi7d+XpA2K6AGqlFgoFAq0bNkSmZmZtRWP2eMYCyIyBeZSH3DlbZKaIAh487HWmNL7bnLxf/89iXXsFkUyVeUxFvPnz8cbb7xhsCgSGYfBrFBgZkFE8mQO9UFERAQSExMRGxsrdShkxgRBwFuPt8aLvZsBAP7vvyew80SaxFERGaryOhZjxoxBXl4eOnToALVaDWtr/fmVb968abTgzFHZ6WXZE4qI5Ir1AVHdEQQBMx9vg7xCDdYdvIwZmxKwybEbOvg0kDo0Ip0qJxaLFi2qhTColEFXKPaFIiKZYn1AVLcEQcC7T/gj5VYedp+5gfA1cdgWEYpGDW2kDo0IQDUSi/Hjx9dGHHRP2TSCeQURyRXrA6K6p1Iq8PXoTnhqyQGcTsvF44v2IrS5M3q1csXQjl6wt7KQOkQyY9Vax+LChQt4++238eyzzyI9/e6iLTt37sTJkyeNGpw54joWRGRKWB8Q1T07SxVWTgiBr4stbheW4I/E63h72wmMXHYQBcUaqcMjM1blxGLPnj1o164d/vnnH2zZsgW3b98GABw7dgzvvvuu0QM0N2UTibJjLoiI5IL1AZF0vBpY43+RvfHfiO54fUArONmqkZiag3e21d/JFEj+qpxYzJw5E3PnzkV0dDTUarVuf9++ffH3339XOYDFixfD19cXVlZWCAoKwt69eyssu2/fPnTv3h3Ozs6wtrZGmzZtsHDhwirfU85KEwmFcG9bwliIiB7E2PUBEVWNUiGgg08DTOvXEl8/GwiFAPwYfwWbYpOlDo3MVJUTi+PHj2PYsGEG+11dXas8n/mmTZswY8YMzJ49GwkJCejZsyfCwsKQnFz+fwhbW1tMmzYNMTExOHXqFN5++228/fbbWL58eVUfQ7ZKF8hTKe7+07ArFBHJlTHrAyKqmdAWLnj9sdYAgHf+exLf/3MZRSXah5xFZFxVTiwaNGiA1NRUg/0JCQnw9vau0rUWLFiA8PBwTJ48GX5+fli0aBF8fHywZMmScssHBgbi2WefRdu2bdG0aVOMGTMGjz322ANbOUxNaSKhvNdkwcHbRCRXxqwPiKjmpvRqjkf93VFUosXsrSfQ59Nd2Bx/ReqwyIxUObEYPXo03nrrLaSlpUEQBGi1Wuzfvx+vv/46xo0bV+nrFBUVIT4+HgMGDNDbP2DAABw4cKBS10hISMCBAwfQu3fvKj2DnJXmESpdYsHMgojkyVj1AREZh0Ih4OvRgfi/wf5ws7fEtewCvPbjUaz9+5LUoZGZqHJi8eGHH6Jx48bw9vbG7du34e/vj169eiE0NBRvv/12pa+TkZEBjUYDd3d3vf3u7u5IS3vwapKNGjWCpaUlgoODERERgcmTJ1dYtrCwEDk5OXovOdONsbiXWDCvICK5MlZ9QETGY6lSYlIPX8S82Rcv9ipdqfsktiaw5YJqX5XXsbCwsMD333+POXPmICEhAVqtFoGBgWjZsmW1AhAEQW9bFEWDfWXt3bsXt2/fxsGDBzFz5ky0aNECzz77bLll582bh/fff79asUmhtOuTrisU+0IRkUwZuz4gIuOxslBiZlgbFJZosfrAJbz+4zG42FmiZ0tXqUOjeqzKicW5c+fQsmVLNG/eHM2bN6/2jV1cXKBUKg1aJ9LT0w1aMcry9fUFALRr1w7Xr1/He++9V2FiMWvWLERGRuq2c3Jy4OPjU+24a1vZMRZMK4hIroxVH8hZVFQUoqKioNFwbQAyPYIg4P8G+yMnvxhbEq7irZ+O4Y/I3rCzrPKvf0SVUuWuUK1bt4a3tzdGjx6NZcuW4cyZM9W6sVqtRlBQEKKjo/X2R0dHIzQ0tNLXEUURhYWFFR63tLSEg4OD3kvOShsoOMaCiOTOWPWBnEVERCAxMRGxsbFSh0JULQqFgLnDAtDYyQbXsgswf+dpqUOieqzKiUVqaio+++wzODg4YOHChfDz84OnpydGjRqFpUuXVulakZGR+Pbbb7Fy5UqcOnUKr776KpKTkzFlyhQAd1sb7h8AGBUVhe3bt+PcuXM4d+4cVq1ahc8++wxjxoyp6mPIlli2xYJ5BRHJlDHrAyKqPTZqFeYNbwcAWPv3ZcReuilxRFRfCWINl3Y+f/485s6di++//x5arbbKzcWLFy/G/PnzkZqaioCAACxcuBC9evUCAEyYMAGXLl3C7t27AQBfffUVli1bhqSkJKhUKjRv3hzPP/88XnzxRSgUlcuRcnJy4OjoiOzsbFm2Xoz59h/sO5+Bps42uJSZh3HdmmDO0ACpwyKieqI2PwNrWh/ImdzrDqLKeOunY9gUl4KGNhZ46/E2eCbYRzdZDFFFqvL5V+XE4vbt29i3bx92796NPXv24MiRI/Dz80OfPn3Qu3dvDB06tEbB1za5Vw6jvzmIAxcy0czVFhdv3MGYro0x98l2UodFRPWEMT8DTb0+qAq51x1ElZGdX4xnlx9EYurdGTL9PR3wZKAXwgI84eNkI3F0JFdV+fyr8uidhg0bwsnJCWPHjsXbb7+NHj16wNHRsdrBkj7d4G2BC+QRkbyxPiAyLY7WFvjvtO5Yc+ASFv3vHBJTc5CYmoNPfz+DtZO6oFtzZ6lDJBNX5TEWgwYNgkajwbp167B27VqsX78ep06dqo3YzFLZ6WY5xoKI5Ir1AZHpsVAqMLlnM+x+ow/mDG2LAG8HFGtEfLv3otShUT1Q5cRi27ZtyMjIQHR0NHr06IE///wTffr0gYeHB0aNGlUbMZqV0p5pKqWgt01EJDesD4hMl4udJcZ1a4pFIwMBALvP3kB6boHEUZGpq/ZExu3bt4dGo0FxcTEKCwuxc+dObNmyxZixmaV/WywU97aZWBCRvLE+IDJdLdzsENi4ARKSs/DfhGt4/t5q3UTVUeUWi4ULF2Lo0KFwcnJC586dsWHDBrRu3Rpbt25FRkZGbcRoVnQtFgqOsSAieWN9QFQ/PBXUCADwY3wKe0pQjVS5xeL7779Hnz598Pzzz6NXr16cHcPIdC0WAhfIIyJ5Y31AVD8Mbu+F97cn4uz124g5l4FeLV0gCJyGlqquyolFXFxcbcRB95R+U6BbloN5BRHJFOsDovrB0doCj7X1wPaj1zB+5SE0dbbB2G5N8VyXxrCyUEodHpmQao2xyMrKwooVK3Dq1CkIggA/Pz+Eh4dzmkEjKG2xUHGMBRGZANYHRPXDO4P8kFdYgr3nMnApMw8f/JKIb2Iu4uMR7dCntZvU4ZGJqPIYi7i4ODRv3hwLFy7EzZs3kZGRgYULF6J58+Y4fPhwbcRoVnTrWHCMBRHJHOsDovrDzcEKKyaE4PD/PYoPhwXA09EKaTkFeH5tHKITr0sdHpmIKicWr776KoYMGYJLly5hy5Yt2Lp1K5KSkjB48GDMmDGjFkI0L/+2WHCMBRHJG+sDovrHzlKF57o0wa7X+2BQO08Ua0S89F08Ys7ekDo0MgHVarF46623oFL924tKpVLhzTffZH9bIxDLtFgwryAiuWJ9QFR/WVko8cWojhjc3hMlWhHv/nwSxRqt1GGRzFU5sXBwcEBycrLB/pSUFNjb2xslKHMmlll5my0WRCRX5lAfREVFwd/fHyEhIVKHQlTnVEoFPh7RHk62aiRl3MFP8VekDolkrsqJxciRIxEeHo5NmzYhJSUFV65cwcaNGzF58mQ8++yztRGjWdHqZoViiwURyZs51AcRERFITExEbGys1KEQScLOUoVpfVsAABZGn8UrGxIwbuUhpGVzlW4yVOVZoT777DMIgoBx48ahpKQEAGBhYYGXXnoJH3/8sdEDNDdagwXymFkQkTyxPiAyD891bYwV+5JwNSsfPx+9BgCYsSkB30/uquthQQRUo8VCrVbjiy++wK1bt3DkyBEkJCTg5s2bWLhwISwtLWsjRrNi2BVKwmCIiB6A9QGRebBUKTH/qfbo2swJk3v4wkatxMGLN7F413mpQyOZqXRikZeXh4iICHh7e8PNzQ2TJ0+Gp6cn2rdvDxsbm9qM0ayUbbEQ2WJBRDLD+oDI/HRv4YKNL3TD24P98cHQAADAoj/PIe7STYkjIzmpdGLx7rvvYvXq1Rg0aBBGjRqF6OhovPTSS7UZm1kqTSM4eJuI5Ir1AZF5GxHUCMMCvaHRipi+8Qiy84qlDolkotJjLLZs2YIVK1Zg1KhRAIAxY8age/fu0Gg0UCq53LuxcIE8IpI71gdE9MGTATicfAuXM/Mwa+sxRI3uBEHgeAtzV+kWi5SUFPTs2VO33blzZ6hUKly7dq1WAjNX2ntTRKsUd/9pmFcQkdywPiAiO0sVvno2ECqFgB3H07Dl8FWpQyIZqHRiodFooFar9fapVCrdTCBkHKVjKhQCx1gQkTyxPiAiAGjfqAFefbQVAODdn09iz9kb0LKrhVmrdFcoURQxYcIEvZk+CgoKMGXKFNja2ur2bdmyxbgRmpnS/48qJcdYEJE8sT4golJTejfHrtPpiLt8C+NXHkJbLwesf74rHK0tpA6NJFDpxGL8+PEG+8aMGWPUYKicMRZaKaMhIjLE+oCISikVApaNDcKi/53D1oSrOHktB/N2nMLHI9pLHRpJoNKJxapVq2ozDrpHNyuUwBYLIpIn1gdEdD9nO0t88GQAnujghWeW/Y2NsSno1twZT7T3goIL6JmVKi+QR7VLLNNiwbyCiIiITEFnXyeM69YEADB94xE8unAPrucUSBwV1SUmFjKjG2NRmlhwXigiIiIyEbPC/DCxe1PYW6lw4cYdLNl9QeqQqA4xsZCZ0q5PCq5jQURERCbGWq3Eu0+0xeLnOgEANsWmICuvSOKoqK4wsZCZ0mnaVFx5m4iIiExUjxYu8PN0QH6xBp/+fgY3cgulDonqABMLmSnNI7jyNhEREZkqQRDwYq9mAIDv/0lG70934UhKlrRBUa1jYiEzulmhFFwgj4iIiEzX0I5eeH9IW7R0s0NekQb/998TuJqVjws3bksdGtUSJhYyU9r1iV2hiIiIyJQJgoDxoU2x/vmusLNU4diVbHT/+C88vigGp1JzpA6PaoHkicXixYvh6+sLKysrBAUFYe/evRWW3bJlCx599FG4urrCwcEB3bp1w++//16H0da+fxfIu/tPw7yCiIiITJmrvSWm9Wuh2y7WiFi5L0nCiKi2SJpYbNq0CTNmzMDs2bORkJCAnj17IiwsDMnJyeWWj4mJwaOPPoodO3YgPj4effv2xRNPPIGEhIQ6jrz2lJ1ulmMsiIiIyNQ937MZPn2qPT4Y2hYA8N8j15BxmwO66xtJE4sFCxYgPDwckydPhp+fHxYtWgQfHx8sWbKk3PKLFi3Cm2++iZCQELRs2RIfffQRWrZsie3bt9dx5LVHLDPdLMdYEBERkalTKgQ8HeyDsd2aooNPAxRptHjrp2OciraekSyxKCoqQnx8PAYMGKC3f8CAAThw4EClrqHVapGbmwsnJ6cKyxQWFiInJ0fvJWeGLRZMLIiIiKj+eO3RVrBQCvjzdDqC5v4Pzyz9G9l5xVKHRUYgWWKRkZEBjUYDd3d3vf3u7u5IS0ur1DU+//xz3LlzB88880yFZebNmwdHR0fdy8fHp0Zx17ayLRbsCkVEJJ2oqCj4+/sjJCRE6lCI6o1erVzx05RQtHCzg0Yr4tClm9h25KrUYZERSD54WxAEvW1RFA32lWfDhg147733sGnTJri5uVVYbtasWcjOzta9UlJSahxzbWKLBRGRfERERCAxMRGxsbFSh0JUr3TwaYDoV3vhzcdbAwB+PZ4qcURkDJIlFi4uLlAqlQatE+np6QatGGVt2rQJ4eHh+OGHH9C/f/8HlrW0tISDg4PeS67uH09Ruo4FmFcQERFRPSQIAoZ08AIAxF66ifScAokjopqSLLFQq9UICgpCdHS03v7o6GiEhoZWeN6GDRswYcIErF+/HoMGDartMOvU/d2e2GJBRERE9V2jhjbo4NMAogiMWn4QC/44g5izN/BDbApKNFqpw6MqUkl588jISIwdOxbBwcHo1q0bli9fjuTkZEyZMgXA3W5MV69exdq1awHcTSrGjRuHL774Al27dtW1dlhbW8PR0VGy5zCW+5MIjrEgIiIiczAqxAdHU7JwMeMOvvzrvG7/9ZwCvPxISwkjo6qSNLEYOXIkMjMzMWfOHKSmpiIgIAA7duxAkyZNAACpqal6a1osW7YMJSUliIiIQEREhG7/+PHjsXr16roO3+juTyzYYkFERETm4NnOjRHS1AlHU7Lw1V/ncCkzDwDw1V/nEdrCGUFNKp79k+RF0sQCAKZOnYqpU6eWe6xssrB79+7aD0hC9+cQSkEw2EdERERUH7Vws0MLNzuMCGoEURQxcXUsdp+5gRFL/kZYgAdmhfmhsbON1GHSQ0g+KxT9Sy+x4AJ5REREZIYEQcDCZzpieKA3BAH47UQanv3mIAqKNVKHRg/BxEJG9LpCKTnGgoiIiMxTQ1s1FozsiJ3Te8HT0QpXs/Kx5sAlqcOih2BiISNavelmFQb7iIiIiMxJaw97vDbg7loXUbvO43ZhicQR0YMwsZARbTljLNhiQUREROZsWKA3mrnaIqegBOv/ucxpaGWMiYWMlLdAHsdYEBERkTlTKgS82KsZAOCjHacRNPd/SEi+JXFUVB4mFjJS3uBtdoUiIiIic/dkoDcaNbQGAGTnF2PCqlik3MyTOCoqi4mFjOiPsbj7J9MKIiIiMneWKiW2T+uBLVND4e/pgOz8Yiz63zmpw6IymFjIiOZeYiEIgOLeGAsNB1kQERERoaGtGp0aN8S84e0AAJsPX8Gnv5/mNLQywsRCRoo1d5MIC6UCFveaLJhYEBEREf2rg08DDOngBQCI2nUBr/1wlGNSZYKJhYyUznKgVip061gUc+YDIiIiIj2fPd0Bn4xoBwulgF+Pp+LbvUnIKSiWOiyzx8RCRkqTCJVS0LVYFGtEZuFERERE91GrFBgZ0hj/N9gfAPDhjlNo/94f+PVYKkRRRF4R17uQAhMLGSmvKxQAlLA7FBEREZGBMV2boL+fu247Yv1hDFt8AO3f+wMnrmZLGJl5YmIhI6UtFhYKARb3ukLdv5+IiIiI/iUIAqKeC8TqiSFoYGMBADiSkoUSrYg9Z29IHJ35YWIhI7rEQqXfYlFcwhYLIiIiovJYqpTo09oNX44KRHCThrr9J65mI7+IM0bVJSYWMnJ/VyiV4r4WCy1bLIiIiIgepFcrV/z0UijWTOoMAPjtRBravrsTEd8fRlEJf5eqC0wsZEQ3eFshQBD+7Q7FrlBERERElePv6aD7u1YEfj2eio2xyRJGZD6YWMhIyb0WC7Xq7j9LaXeo0v1ERERE9GCu9pYG+xZEn8Wp1BwJojEvTCxkpOi+Fgvg38SiiC0WRERERJX2Sr8W8HWxxa7X+6CdtyOy8ooRvjqWYy5qGRMLGdEN3laWtliwKxQRERFRVUUOaI1dr/eBr4stvgvvAu8G1riWXYBlMReQcjMPvxy7xnXCagETCxkpuW/w9v1/sisUERERUfU42ljgrbA2AICv/zqPnvN3Ydr6BKz9+7LEkdU/TCxkpEjXYnG3pUJ17092hSIiIiKqvifae+KJDl56iw6/+/NJvP7jUbZcGBETCxmpqMWimFOkEREREVWbIAiY+2SAwf6f4q/g7wuZEkRUPzGxkJGyYyzUpV2htMykiYiIiGrC0doCG57vip4tXfDLyz3Qu5UrAOCtLcdw5VaexNHVD0wsZKSYXaGIiIiIak235s5YF94FAd6O+PSp9vBuYI2Um/n4+LfTUodWLzCxkJHSlbdV7ApFREREVKvcHKywZEwnAMDvJ9Ow80QaTqXm4MTVbI67qCaV1AHQvwynm2VXKCIiIqLa0r5RA3Rq3ACHk7Mw5bt43f7lY4MwoK2HhJGZJrZYyEjJvcRCrSxdII/rWBARERHVppcfaWmwb++5DAkiMX1ssZCRooq6QnEdCyKiGsnNzUW/fv1QXFwMjUaDV155Bc8//7zUYRGRDPRp5YoxXRsjNasAXg2sse7gZaw7eBlWFgrk5JfgvSFtYa1WSh2mSWBiISMVdYViiwURUc3Y2Nhgz549sLGxQV5eHgICAjB8+HA4OztLHRoRSezuVLTtAADXsvKx7uDdhfO+2ZsEAOjg0wCjuzSWLD5Twq5QMlJSZlYodoUiIjIOpVIJGxsbAEBBQQE0Gg0HZxKRAa8G1mjfyBFWFv/+ihxz9oaEEZkWyROLxYsXw9fXF1ZWVggKCsLevXsrLJuamorRo0ejdevWUCgUmDFjRt0FWgeKKlogj12hiKiei4mJwRNPPAEvLy8IgoBt27YZlKlKfVGerKwsdOjQAY0aNcKbb74JFxcXI0VPRPXJT1NCcfidR7H5pVAAwM6TaVj39yUUcZbOh5I0sdi0aRNmzJiB2bNnIyEhAT179kRYWBiSk5PLLV9YWAhXV1fMnj0bHTp0qONoa19pi4VK12LBrlBEZB7u3LmDDh064Ouvvy73eGXqi6CgIAQEBBi8rl27BgBo0KABjh49iqSkJKxfvx7Xr1+vk2cjItOiVilgo1aho08D9Gl9dxG9d/57Ej0++Qu3C0skjk7eJE0sFixYgPDwcEyePBl+fn5YtGgRfHx8sGTJknLLN23aFF988QXGjRsHR0fHOo629hXrZoUqbbG41xWKGTIR1XNhYWGYO3cuhg8fXu7xytQX8fHxOHHihMHLy8tL71ru7u5o3749YmJiKoynsLAQOTk5ei8iMi9KhYBVE0LwqL87ACA9txA/xqVIHJW8SZZYFBUVIT4+HgMGDNDbP2DAABw4cMBo9zGlyqG4oq5QXMeCiMyYMeqL69ev6z7/c3JyEBMTg9atW1dYft68eXB0dNS9fHx8qv8ARGSyBEHA4uc6YUJoUwDAmgOXkHgtB/N+O4UNh8rvYWPOJEssMjIyoNFo4O7urrff3d0daWlpRruPKVUOxWW6QqkU7ApFRGSM+uLKlSvo1asXOnTogB49emDatGlo3759heVnzZqF7Oxs3Sslhd9SEpkrC6UCU/s2BwBcyszDwC/3Ytmei5i15TiejNqP7PxiiSOUD8mnmxUEQW9bFEWDfTUxa9YsREZG6rZzcnJkm1wYTDerYlcoIqJSNakvgoKCcOTIkUrfy9LSEpaWllUJj4jqMTd7q3L3H0nJQof3/8BLfZrjzcdaG/V3WFMkWYuFi4sLlEqlwbdN6enpBt9K1YSlpSUcHBz0XnJVoi3tCnX3TVk61qKEXaGIyIzVVX1BRPQgn4xop7c9uL2n7u9Ldl/A6bTcug5JdiRLLNRqNYKCghAdHa23Pzo6GqGhoRJFJa3SacxKWyxKu0IVsSsUEZkx1hdEJAcjQxrj23HBAIBH2rjh82c6YFTIv71gwr7Yi0mrY6UKTxYk7QoVGRmJsWPHIjg4GN26dcPy5cuRnJyMKVOmALjbjenq1atYu3at7pzSpuzbt2/jxo0bOHLkCNRqNfz9/aV4BKPSjbFQsCsUEZmX27dv4/z587rtpKQkHDlyBE5OTmjcuPFD6wsiorrQ398dW6eGopmrHSxVSnw8oj0KijXYduTutNZ/nU7HjdxCuNqbZ1dKSROLkSNHIjMzE3PmzEFqaioCAgKwY8cONGnSBMDdBfHKrmkRGBio+3t8fDzWr1+PJk2a4NKlS3UZeq0o7fKkVrErFBGZl7i4OPTt21e3XTo2bvz48Vi9evVD64vaEhUVhaioKGg0mlq9DxGZjsDGDfW2m7rY6m2HfPg/BDdpCA9HK8wZGgAnW3VdhicpyQdvT506FVOnTi332OrVqw32iWL9/SXbsCvU3QSDXaGIqL7r06fPQz/fH1Rf1JaIiAhEREQgJyenXq6fREQ119zVzmBf3OVbAICGNmq8P6QtNKKo+/2uPqv/T2hCSlsm/u0Kda/FgokFERERkSw96u+OKb2bw7mclomjV7Iw+tuD6PPpbuQX1f+WTyYWMqJbefteVyjdAnma+ttKQ0RERGTKrCyUmBnWBnFv98cAf3c0c7WF773uUceuZOPgxZu4mpWPo1eypA20DkjeFYr+VaIp02Jxb9pZLpBHREREJG+CIGD5vVmjAODFdXH4/eR13fbSPRfQqKE1GjW0kSK8OsEWCxkpKrtAnpIrbxMRERGZogH+Hnrbu8/cwNgVhySKpm4wsZCRf1fevttSUdpywa5QRERERKalXSPDCR+SMu5g8po4pOcWSBBR7WNiISOlXaFKWypKx1qwxYKISBpRUVHw9/dHSEiI1KEQkYlp5mKL1u72Bvv/d+o6hi8+gIJiTb0b0M3EQkZ0XaFUZbtCscWCiEgKERERSExMRGysea+mS0RVp1Iq8Nv0njj3YRi+Hh2od+zKrXwEvPs7On0QjZSbeRJFaHxMLGSkdFpZC0XZrlBssSAiIiIyNQqFAAulAoPbe+GVfi30jpVoReQXa/C/U3cHeJeuZ2bKOCuUTGi0IkoX2GZXKCIiIqL6ZVq/lrBQKvB7YhpOXM3R7X9/eyL+Op2O/ecz8PkzHTAssJGEUdYMWyxk4v7kQaXUX8eihF2hiIiIiEyaWqXAy4+0RFdfZ4Nje89lQCsC83acliAy42FiIRP3JxalCUVpV6gitlgQERER1Qt37huw/fYgP71jno5WdR2OUTGxkIn7B2iX7QpVwsSCiIiIqF54OvhuV6cuvk54MtBb79jRK9l4cV2cFGEZBRMLmShNHhQCoFTod4XirFBERNLgdLNEZGydGjfE/yJ7Y/XEznC2VRsc//3kdQxfvB8HL2ZKEF3NMLGQibKrbgN3pym7/xgREdUtTjdLRLWhhZsdrNVKCIKA957wRxNnG73jh5OzMGr5Qfwv8bpEEVYPEwuZKC6zON7dv7MrFBEREVF9NqG7L/4X2RvuDpYGxyavjcMPsSkSRFU9TCxkQreGxb1kAgAs7g3e1op3p6MlIiIiovrHQqlAdGRvHH9vAB5v66F37M3NxxCx/jBEUf6/CzKxkInS7k6q+1ssVP/+nWtZEBEREdVfDlYWsLeywKJRHXFw1iMIC/g3wfj1WCrOpd+WMLrK4QJ5MlG6VoW6nK5QwN3EwspCWedxEREREVHdsbJQwsNRiXnD26Gxsw2W7bkIANhwKBlXbuUjtLkzJnb3lTjK8jGxkIniB3SFuntc/s1fRERERGQcDWzUmBXmh7TsAvz3yDWs2n8JABCdeB3Pdm4syy+c2RVKJsrrCqVQCLqpZ9kVioiIiMj8POrvbrBv4Bd7seCPMxJE82BMLGSipJxZoe5uM7EgIiIiMleD23vhg6Ft9fZdzLiDL/86j8ISTQVnSYOJhUyU1xUK+Lc7FLtCERHVPS6QR0RyMLZbU/w8rTuCmzTU2x9/+Ra++vMc0nMKJIpMHxMLmShvHQvg35mhuJYFEVHd4wJ5RCQX7Rs1QGgLF719o7/5B59Hn8Vji2IkikofEwuZKG2xUCnKtFjca8Hg6ttERERE5q25q225+2/lFePPU9cl7xrFxEImShMLtUr/n0TFrlBEREREBKBfGze0crcr91j4mjjM2nIcF27clmwxPSYWMlHR4G01u0IREREREQB7Kwv8Nr0XTs15HK3d7Q2Obzl8FY98vgcbY1MkiI6JhWwUsSsUERERET2EUiHAWq3EL6/0wN43+6K/n5tBmVlbjksQGRML2ShtkbBgVygiIiIieggLpQI+TjYVLpQnRXcoJhYyoZsVqmyLBbtCEREREVEFnuvSpNz9vrN24JUNCXWaYEieWCxevBi+vr6wsrJCUFAQ9u7d+8Dye/bsQVBQEKysrNCsWTMsXbq0jiKtXUW6dSzKTDfLlbeJiIiIqALdmjtj/8x+BmuhAcDPR6/Bd9YONJ35Kx5fFIM7hSW1GoukicWmTZswY8YMzJ49GwkJCejZsyfCwsKQnJxcbvmkpCQMHDgQPXv2REJCAv7zn//glVdewebNm+sk3trM+EoHb6sMVt6+u13ErlBEREREVA7vBtZYOSEE7Rs5wsqi/F/vT6flYkvC1VqNQ9LEYsGCBQgPD8fkyZPh5+eHRYsWwcfHB0uWLCm3/NKlS9G4cWMsWrQIfn5+mDx5MiZNmoTPPvus1mMVRRFvbT6GpXsu1EqCoZtutuzK2+wKRUREREQP0bOlK36e1gO7X++L0V0ao2szJ4MyZ9NyazUGVa1e/QGKiooQHx+PmTNn6u0fMGAADhw4UO45f//9NwYMGKC377HHHsOKFStQXFwMCwuLWov3wIVM/BB3BQBw8loOPn2qPbYfvYZlMRfRxsMeY7s2QZdmzkjKuIOkjNuwVCnRqXFDWKvvDqjRaEWcSs3BP0k3cfJqNrwbWqOjTwM0dbFF3KWb+HrXeQAVd4U6e/02svOLcfNOEf46nQ5nWzWCmzZEiUbElVv5uF1YgkYNrZFTUIwrt/Jx9VY+rtzKh6WFAj1auKCxkw1KtCJu5BZCK4pwtlWjUUMbpOcWQCsCNmolbt9rHlMrFVAqBFy5lY/rOQVQqxSwVCmgFUVk3i6CpYUSDlYq2KpVyM4vBgB4OlrBw9EKapUCOfklyC0oRk5BCfKKSmCrVsHeSgW1SoEbuYW4nluInPxiWFkoYaMufalgo1ZCK96NUSEIsLdSwd3BCgBQWKJBYbEWhSXau38v0d7b1qBYI8LKQglbSyWUCgG37hShWCNCIQAKhQBBEKAQAKUgQCEIEARAIQhQKHBvW7h3DLqypccF4e7PX6sVodGK0IoitCJ0fxdF4F4RCIIAAdBdv/TvxmXcCxo7vtLLCfdd+N99+n9qtLj789Te/ZlqRRH3p+z3h6Y79769Iu6eJ977dxAhQqsFRNydXc1SpYBapTB4xvu/F9D7u14Z/S8P9I/du7sIaMt8xyAIFf8LGevrCFEESrR3/y8Mbu8JG7VkH+NmISoqClFRUdBopF10ioiosjwcrfDRsHb4du9FHLx4U+/YwHaetXpvyWqkjIwMaDQauLu76+13d3dHWlpaueekpaWVW76kpAQZGRnw9DT8YRUWFqKwsFC3nZOTU614Q5s7Y+6TAXjv55PYfvQabt4pRNylWygs0eJ8+m38kXgd4T18sWT3Bd05lioF3B2sIOLuL8sFxQ9vdShNREo52aoBAEv3XMDSPRfKO+Wh1v9TftcyIjJt3Zo5w8aJiUVtioiIQEREBHJycuDo6Ch1OERElVb6O+T9ujV3rtV7Sl4jCWW+ThRF0WDfw8qXt7/UvHnz8P7779cwyrvXH9O1CZq52GLCqljsP58JAOji6wQLpQL7zmfokopW7nbILShBanYBkm/m6a5hb6lCcNOG6ODTAFdu5eNIShYu3riNdt6OaO5mB1EEng7y0bvvfwb6wdnOEpsPX7n3Tf7dN0Xm7SKcS78NtVIBzwZWsLNU4eqtfDjaWMC7gTUaNbSGdwNrZN4pwqGkm7pWADcHSygVAq5nFyA1pwCudpawUCpw517LgkIBFJeIKNZo4e5gBe+G1ijR3P12VCEIaGirRlGJBjn5JbhTVAIHq7utRGk5BUjLLkCxRgsHawvYW6ngYGUBawsl8opKkFNQgqISLVzsLeFubwlHawsUlGiRX1SCvCIN8oo0yC+6+42gi70aAgRk5RchPacQynvfPluqlLC0UOj+XtqSolQIKCjWIr+4BMUaEU42aqjvtbCI974J12j//Xa7tNWh9Pj9rQ/a+46LogiNKEKAoGv9UAoCFIp7LSCKuy0g4n3XElH6p/HH5Bi7B56xO/SVPq/ht/v37buvjEIQ7v0MoWs1Kv1vrN+SIJazD3fPw72WJdw9t7TVSKMVUViiQVGJVndvvU+IMp8XZT89yn6clD1e2vJVej/dM+qeV9RrXanwQtWkVipgoRR0C2gSERGVFRbgidUHLuHYlew6u6dkiYWLiwuUSqVB60R6erpBq0QpDw+PcsurVCo4O5efgc2aNQuRkZG67ZycHPj4+JRbtjJCW7jg4xHtEPnDUdiqlfj8mQ6wt7TA4K/3IuVmPkZ0aoTPnm4PALiUmYebd4oAiHC2tYSPkw2UiqolUg1t1ZgZ1gYzw9qgoFgDUTRs1agurVaEQmGk33SIiIiISDas1Ur8PK0HHlsYgzPXczEqpPq//1aWZImFWq1GUFAQoqOjMWzYMN3+6OhoDB06tNxzunXrhu3bt+vt++OPPxAcHFzh+ApLS0tYWloaL3AAwzs1gq+LLZxtLdGooQ0AYMtL3RF/+Rb6+7npEgVfF1v4utg+8FoPSirKqmgBlOpiUkFERERUv309OhAXbtzGo/4etX4vSdvRIyMj8e2332LlypU4deoUXn31VSQnJ2PKlCkA7rY2jBs3Tld+ypQpuHz5MiIjI3Hq1CmsXLkSK1aswOuvv17nsQc2bojGzja6bVd7Szwe4GEwXSwRERERkVRautvj8QBPg14ztUHSMRYjR45EZmYm5syZg9TUVAQEBGDHjh1o0uTuCoKpqal6a1r4+vpix44dePXVVxEVFQUvLy98+eWXGDFihFSPQEREREREAASxLtf5loHSmT2ys7Ph4OAgdThERHWKn4HVw58bEZmrqnz+sd8OERERERHVGBMLIiIiIiKqMSYWRERERERUY0wsiIiIiIioxphYEBERVSAqKgr+/v4ICQmROhQiItljYkFERFSBiIgIJCYmIjY2VupQiIhkj4kFERERERHVGBMLIiIiIiKqMUlX3pZC6XqAOTk5EkdCRFT3Sj/7zGxt1Bpj3UFE5qoq9YbZJRa5ubkAAB8fH4kjISKSTm5uLhwdHaUOw2Sw7iAic1eZekMQzexrK61Wi2vXrsHe3h6CIDy0fE5ODnx8fJCSkvLQZcxNHZ+1/jGX5wT4rJUliiJyc3Ph5eUFhYK9YStLq9WiVatWiI+Pr7DuCAkJMRjkbYrvy/KeQ873qe51qnpeZcrXtExFx/g+qv171MX7qLJlH1auOsfrqt4wuxYLhUKBRo0aVfk8BwcHk/nPXFN81vrHXJ4T4LNWBlsqqk6hUECtVj/wZ6dUKiv89zCl9+WDnkOO96nudap6XmXK17TMw87n+6j27lEX76PKln1YuZocr+16g19XERERVUJERESNjpuKunoOY92nutep6nmVKV/TMvXlPQTUzbMY8x518T6qbNmaftZI+T4yu65QVZWTkwNHR0dkZ2ebzLcE1cVnrX/M5TkBPivJE/+tyBj4PqKaqqv3EFssHsLS0hLvvvsuLC0tpQ6l1vFZ6x9zeU6Az0ryxH8rMga+j6im6uo9xBYLIiIiIiKqMbZYEBERERFRjTGxICIiIiKiGmNiQURERERENcbEgoiIiIiIaoyJxQMsXrwYvr6+sLKyQlBQEPbu3St1SDU2b948hISEwN7eHm5ubnjyySdx5swZvTKiKOK9996Dl5cXrK2t0adPH5w8eVKiiI1j3rx5EAQBM2bM0O2rb8959epVjBkzBs7OzrCxsUHHjh0RHx+vO14fnrekpARvv/02fH19YW1tjWbNmmHOnDnQarW6Mqb6nDExMXjiiSfg5eUFQRCwbds2veOVea7CwkK8/PLLcHFxga2tLYYMGYIrV67U4VNQVf3yyy9o3bo1WrZsiW+//VbqcMgEDRs2DA0bNsRTTz0ldShkolJSUtCnTx/4+/ujffv2+PHHH6t/MZHKtXHjRtHCwkL85ptvxMTERHH69Omira2tePnyZalDq5HHHntMXLVqlXjixAnxyJEj4qBBg8TGjRuLt2/f1pX5+OOPRXt7e3Hz5s3i8ePHxZEjR4qenp5iTk6OhJFX36FDh8SmTZuK7du3F6dPn67bX5+e8+bNm2KTJk3ECRMmiP/884+YlJQk/u9//xPPnz+vK1Mfnnfu3Lmis7Oz+Msvv4hJSUnijz/+KNrZ2YmLFi3SlTHV59yxY4c4e/ZscfPmzSIAcevWrXrHK/NcU6ZMEb29vcXo6Gjx8OHDYt++fcUOHTqIJSUldfw0VBnFxcViy5YtxStXrog5OTliixYtxMzMTKnDIhPz119/iT///LM4YsQIqUMhE3Xt2jUxISFBFEVRvH79uujt7a33e2FVMLGoQOfOncUpU6bo7WvTpo04c+ZMiSKqHenp6SIAcc+ePaIoiqJWqxU9PDzEjz/+WFemoKBAdHR0FJcuXSpVmNWWm5srtmzZUoyOjhZ79+6tSyzq23O+9dZbYo8ePSo8Xl+ed9CgQeKkSZP09g0fPlwcM2aMKIr15znLJhaVea6srCzRwsJC3Lhxo67M1atXRYVCIe7cubPOYqfK279/v/jkk0/qtl955RVx/fr1EkZEpmrXrl1MLMho2rVrJyYnJ1frXHaFKkdRURHi4+MxYMAAvf0DBgzAgQMHJIqqdmRnZwMAnJycAABJSUlIS0vTe3ZLS0v07t3bJJ89IiICgwYNQv/+/fX217fn/PnnnxEcHIynn34abm5uCAwMxDfffKM7Xl+et0ePHvjzzz9x9uxZAMDRo0exb98+DBw4EED9ec6yKvNc8fHxKC4u1ivj5eWFgIAAk352OXtY9zXgwV1qr127Bm9vb912o0aNcPXq1boInWSipu8hIsC476O4uDhotVr4+PhUKxYmFuXIyMiARqOBu7u73n53d3ekpaVJFJXxiaKIyMhI9OjRAwEBAQCge7768OwbN27E4cOHMW/ePINj9ek5AeDixYtYsmQJWrZsid9//x1TpkzBK6+8grVr1wKoP8/71ltv4dlnn0WbNm1gYWGBwMBAzJgxA88++yyA+vOcZVXmudLS0qBWq9GwYcMKy5Bx3blzBx06dMDXX39d7vFNmzZhxowZmD17NhISEtCzZ0+EhYUhOTkZwN3P4LIEQajVmEleavoeIgKM9z7KzMzEuHHjsHz58mrHoqr2mWag7Ae8KIr16kN/2rRpOHbsGPbt22dwzNSfPSUlBdOnT8cff/wBKyurCsuZ+nOW0mq1CA4OxkcffQQACAwMxMmTJ7FkyRKMGzdOV87Un3fTpk347rvvsH79erRt2xZHjhzBjBkz4OXlhfHjx+vKmfpzVqQ6z1Vfnl2OwsLCEBYWVuHxBQsWIDw8HJMnTwYALFq0CL///juWLFmCefPmwdvbW6+F4sqVK+jSpUutx03yUdP3EBFgnPdRYWEhhg0bhlmzZiE0NLTasbDFohwuLi5QKpUG3/Klp6cbfGNoql5++WX8/PPP2LVrFxo1aqTb7+HhAQAm/+zx8fFIT09HUFAQVCoVVCoV9uzZgy+//BIqlUr3LKb+nKU8PT3h7++vt8/Pz0/3bUR9+Xd94403MHPmTIwaNQrt2rXD2LFj8eqrr+o+GOvLc5ZVmefy8PBAUVERbt26VWEZqjuV6VLbuXNnnDhxAlevXkVubi527NiBxx57TIpwSYbMqVs21Z7KvI9EUcSECRPQr18/jB07tkb3Y2JRDrVajaCgIERHR+vtj46OrlEWJweiKGLatGnYsmUL/vrrL/j6+uod9/X1hYeHh96zFxUVYc+ePSb17I888giOHz+OI0eO6F7BwcF47rnncOTIETRr1qxePGep7t27G0wbfPbsWTRp0gRA/fl3zcvLg0Kh/7GlVCp1083Wl+csqzLPFRQUBAsLC70yqampOHHihEk/u6mqTJdalUqFzz//HH379kVgYCDeeOMNODs7SxEuyVBlu2U/9thjePrpp7Fjxw40atQIsbGxdR0qyVhl3kf79+/Hpk2bsG3bNnTs2BEdO3bE8ePHq3U/doWqQGRkJMaOHYvg4GB069YNy5cvR3JyMqZMmSJ1aDUSERGB9evX47///S/s7e11bypHR0dYW1vr1nr46KOP0LJlS7Rs2RIfffQRbGxsMHr0aImjrzx7e3vduJFStra2cHZ21u2vD89Z6tVXX0VoaCg++ugjPPPMMzh06BCWL1+u6ydZX/5dn3jiCXz44Ydo3Lgx2rZti4SEBCxYsACTJk0CYNrPefv2bZw/f163nZSUhCNHjsDJyQmNGzd+6HM5OjoiPDwcr732GpydneHk5ITXX38d7dq1M5i8gOrOw7qvDRkyBEOGDKnrsMiEPOw99Pvvv9d1SGSCHvQ+6tGjh956UDVS4zmp6rGoqCixSZMmolqtFjt16qSbktWUASj3tWrVKl0ZrVYrvvvuu6KHh4doaWkp9urVSzx+/Lh0QRvJ/dPNimL9e87t27eLAQEBoqWlpdimTRtx+fLlesfrw/Pm5OSI06dPFxs3bixaWVmJzZo1E2fPni0WFhbqypjqc+7atavc/5vjx48XRbFyz5Wfny9OmzZNdHJyEq2trcXBgwdXe8pAqhqUmSK4sLBQVCqV4pYtW/TKvfLKK2KvXr3qODoyBXwPkTFI/T4S7gVBRERE1SQIArZu3Yonn3xSt69Lly4ICgrC4sWLdfv8/f0xdOhQDrwlA3wPkTFI/T5iVygiIqJqeFj3tfrapZaMh+8hMgZZvY+M3gZCRERkBh7WfU0U62eXWjIevofIGOT0PmJXKCIiIiIiqjFON0tERERERDXGxIKIiIiIiGqMiQUREREREdUYEwsiIiIiIqoxJhZE9VRRURFatGiB/fv3G/W6v/zyCwIDA423SicRERHVC0wsyCRMmDABgiAYvO6ft5n0LV++HE2aNEH37t11+wRBwLZt2wzKTpgwQW8xnQcZPHgwBEHA+vXrjRQpERER1QdMLMhkPP7440hNTdV7+fr6GpQrKiqSIDr5+eqrrzB58uRaufbEiRPx1Vdf1cq1iYiIyDQxsSCTYWlpCQ8PD72XUqlEnz59MG3aNERGRsLFxQWPPvooACAxMREDBw6EnZ0d3N3dMXbsWGRkZOiud+fOHYwbNw52dnbw9PTE559/jj59+mDGjBm6MuV9w9+gQQOsXr1at3316lWMHDkSDRs2hLOzM4YOHYpLly7pjpe2Bnz22Wfw9PSEs7MzIiIiUFxcrCtTWFiIN998Ez4+PrC0tETLli2xYsUKiKKIFi1a4LPPPtOL4cSJE1AoFLhw4UK5P6vDhw/j/PnzGDRoUBV/ysClS5fKbR3q06ePrsyQIUNw6NAhXLx4scrXJyIiovqJiQXVC2vWrIFKpcL+/fuxbNkypKamonfv3ujYsSPi4uKwc+dOXL9+Hc8884zunDfeeAO7du3C1q1b8ccff2D37t2Ij4+v0n3z8vLQt29f2NnZISYmBvv27YOdnR0ef/xxvZaTXbt24cKFC9i1axfWrFmD1atX6yUn48aNw8aNG/Hll1/i1KlTWLp0Kezs7CAIAiZNmoRVq1bp3XflypXo2bMnmjdvXm5cMTExaNWqFRwcHKr0PADg4+Oj1yqUkJAAZ2dn9OrVS1emSZMmcHNzw969e6t8fSIiIqqnamU9byIjGz9+vKhUKkVbW1vd66mnnhJFURR79+4tduzYUa/8O++8Iw4YMEBvX0pKighAPHPmjJibmyuq1Wpx48aNuuOZmZmitbW1OH36dN0+AOLWrVv1ruPo6CiuWrVKFEVRXLFihdi6dWtRq9XqjhcWForW1tbi77//rou9SZMmYklJia7M008/LY4cOVIURVE8c+aMCECMjo4u99mvXbsmKpVK8Z9//hFFURSLiopEV1dXcfXq1RX+vKZPny7269fPYD8A0crKSu/naGtrK6pUKnHo0KEG5fPz88UuXbqIgwcPFjUajd6xwMBA8b333qswBiIiMj2FhYVi8+bNxX379hn1utu3bxc7duxoUJdQ/cIWCzIZffv2xZEjR3SvL7/8UncsODhYr2x8fDx27doFOzs73atNmzYAgAsXLuDChQsoKipCt27ddOc4OTmhdevWVYopPj4e58+fh729ve4+Tk5OKCgo0Oum1LZtWyiVSt22p6cn0tPTAQBHjhyBUqlE7969y72Hp6cnBg0ahJUrVwK4OytTQUEBnn766Qrjys/Ph5WVVbnHFi5cqPdzPHLkCIYMGVJu2fDwcOTm5mL9+vVQKPQ/LqytrZGXl1dhDEREUuPEH1XHiT+oJlRSB0BUWba2tmjRokWFx+6n1WrxxBNP4JNPPjEo6+npiXPnzlXqnoIgQBRFvX33j43QarUICgrC999/b3Cuq6ur7u8WFhYG1y2drtXa2vqhcUyePBljx47FwoULsWrVKowcORI2NjYVlndxccHx48fLPebh4WHwc7S3t0dWVpbevrlz52Lnzp04dOgQ7O3tDa5z8+ZNvWckIpKjxx9/3KA7aXmfXUVFRVCr1XUVlmx99dVXeO+992rl2qUTf4wZM6ZWrk/SY4sF1UudOnXCyZMn0bRpU7Ro0ULvVZqgWFhY4ODBg7pzbt26hbNnz+pdx9XVFampqbrtc+fO6X1L36lTJ5w7dw5ubm4G93F0dKxUrO3atYNWq8WePXsqLDNw4EDY2tpiyZIl+O233zBp0qQHXjMwMBCnT582SIoqa/PmzZgzZw5++OGHcsdxlLbIBAYGVuv6RER1hRN//IsTf1BtY2JB9VJERARu3ryJZ599Vvch9scff2DSpEnQaDSws7NDeHg43njjDfz55584ceIEJkyYYNDdp1+/fvj6669x+PBhxMXFYcqUKXqtD8899xxcXFwwdOhQ7N27F0lJSdizZw+mT5+OK1euVCrWpk2bYvz48Zg0aRK2bduGpKQk7N69Gz/88IOujFKpxIQJEzBr1iy0aNFCrwtXefr27Ys7d+7g5MmTVfip3XXixAmMGzcOb731Ftq2bYu0tDSkpaXh5s2bujIHDx6EpaXlQ+MgIpIzTvyhjxN/UI1JPMaDqFLGjx9f7uBiUbw7ePv+Adelzp49Kw4bNkxs0KCBaG1tLbZp00acMWOGbqB1bm6uOGbMGNHGxkZ0d3cX58+fb3Ctq1evigMGDBBtbW3Fli1bijt27NAbvC2KopiamiqOGzdOdHFxES0tLcVmzZqJzz//vJidnV1h7NOnTxd79+6t287PzxdfffVV0dPTU1Sr1WKLFi3ElStX6p1z4cIFEYA4f/78Sv3MRo0aJc6cOVNvH8oZjF42xlWrVokADF73x/vCCy+IL774YqXiICKSCif+4MQfVLc4xoJMwv3f0JS1e/fucve3bNkSW7ZsqfA8Ozs7rFu3DuvWrdPt+/XXX/XKeHl54ffff9fbV3YsgoeHB9asWVPhfcqLfdGiRXrbVlZWWLBgARYsWFDhdVJTU6FSqTBu3LgKy9zvP//5D/r374///Oc/ujESYgVdo+6PccKECZgwYUKF171x4wZ++uknxMXFVSoOIiIp9e3bF0uWLNFt3z8m70ETf5R14cIF5OfnG33ij/tVZuKP0vFzVZn4o3PnzkaZ+KN///56+9566y1oNBqDsqUTf0RHR3PiDzPDxIJI5goLC5GSkoJ33nkHzzzzDNzd3St1Xrt27TB//nxcunQJ7dq1M1o8SUlJWLx4cbmrnhMRyQ0n/uDEH1R3mFgQydyGDRsQHh6Ojh076rWuVMb48eONHk/nzp3RuXNno1+XiEhqnTp1wubNm9G0aVOoVIa/It0/8Ufjxo0B/Dvxx/0tB5WZ+GPTpk1wc3Or1ngGQH/ij7ItCaXKTvwRExPzwGsGBgZiyZIlEEURgiBUOabSiT9+++03Tvxhpjh4m+g+u3fvNuimJLUJEyZAo9EgPj4e3t7eUodDRFRvceIPTvxBNcPEgoiIiAh3x9Xt378fGo0Gjz32GAICAjB9+nQ4OjrqkodPP/0UvXr1wpAhQ9C/f3/06NEDQUFBetf5/PPP4ePjg169emH06NF4/fXX9bog2djYICYmBo0bN8bw4cPh5+eHSZMmIT8/v0otGEuWLMFTTz2FqVOnok2bNnj++edx584dvTLh4eEoKip66DTlAODs7Izhw4eX20XrYeLi4pCXl4e5c+fC09NT9xo+fLiuzIYNG/Dcc889sDsWmTZBrGg0JxERERE9VJ8+fdCxY0fZtXgDwP79+9GnTx9cuXKlUmP0jh8/jv79+5c7uLwmbty4gTZt2iAuLo5j9OoxtlgQERER1TOFhYU4f/58jSb+MCZO/GEeOHibiIiIqJ7hxB8kBXaFIiIiIiKiGmNXKCIiIiIiqjEmFkREREREVGNMLIiIiIiIqMaYWBARERERUY0xsSAiIiIiohpjYkFERERERDXGxIKIiIiIiGqMiQUREREREdUYEwsiIiIiIqqx/wf6KZsa/xnrKwAAAABJRU5ErkJggg==", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" } ], "source": [ "from pyrasa.irasa import irasa\n", "\n", "irasa_out = irasa(sig, \n", - " fs=fs, \n", - " band=(1, 100), \n", - " psd_kwargs={'nperseg': duration*fs, \n", - " 'noverlap': duration*fs*overlap\n", - " },\n", - " hset_info=(1, 2, 0.05))\n", + " fs=fs, \n", + " band=(1, 100), \n", + " psd_kwargs={'nperseg': duration*fs, \n", + " 'noverlap': duration*fs*overlap\n", + " },\n", + " hset_info=(1, 2, 0.05))\n", "\n", "f, axes = plt.subplots(ncols=2, figsize=(8, 4))\n", "axes[0].set_title('Periodic')\n", - "axes[0].plot(freq_irasa, psd_p[0,:])\n", + "axes[0].plot(irasa_out.freqs, irasa_out.periodic[0,:])\n", "axes[0].set_ylabel('Power (a.u.)')\n", "axes[0].set_xlabel('Frequency (Hz)')\n", "axes[1].set_title('Aperiodic')\n", - "axes[1].loglog(freq_irasa, psd_ap[0,:])\n", + "axes[1].loglog(irasa_out.freqs, irasa_out.aperiodic[0,:])\n", "axes[1].set_ylabel('Power (a.u.)')\n", "axes[1].set_xlabel('Frequency (Hz)')\n", "\n", "f.tight_layout()\n" ] }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "True" - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "int(freq_irasa[0]) == 1\n", - "\n", - "bool(np.logical_and(freq_irasa[0] == 1, freq_irasa[-1] == 100))" - ] - }, { "cell_type": "markdown", "metadata": {}, @@ -151,7 +128,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 4, "metadata": {}, "outputs": [ { @@ -185,112 +162,92 @@ " \n", " 0\n", " 0\n", - " 10.0\n", - " 1.437975\n", - " 0.414345\n", + " 9.5\n", + " 1.436964\n", + " 0.417941\n", " \n", " \n", "\n", "" ], "text/plain": [ - " ch_name cf bw pw\n", - "0 0 10.0 1.437975 0.414345" + " ch_name cf bw pw\n", + "0 0 9.5 1.436964 0.417941" ] }, - "execution_count": 5, + "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# %% get periodic stuff\n", - "from pyrasa.utils.peak_utils import get_peak_params\n", - "get_peak_params(psd_p, freqs=freq_irasa)" + "irasa_out.get_peaks()" ] }, { "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/aperiodic_utils.py:189: UserWarning: The first frequency appears to be 0 this will result in slope fitting problems. Frequencies will be evaluated starting from the next highest in Hz\n", - " warnings.warn('The first frequency appears to be 0 this will result in slope fitting problems. Frequencies will be evaluated starting from the next highest in Hz')\n" - ] - }, - { - "ename": "ValueError", - "evalue": "No objects to concatenate", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[0;32mIn[13], line 4\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[38;5;66;03m# %% get aperiodic stuff\u001b[39;00m\n\u001b[1;32m 2\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mpyrasa\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mutils\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01maperiodic_utils\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m compute_slope\n\u001b[0;32m----> 4\u001b[0m ap_params, gof_params \u001b[38;5;241m=\u001b[39m \u001b[43mcompute_slope\u001b[49m\u001b[43m(\u001b[49m\u001b[43maperiodic_spectrum\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mpsd_ap\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 5\u001b[0m \u001b[43m \u001b[49m\u001b[43mfreqs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mfreq_irasa\u001b[49m\u001b[38;5;241;43m-\u001b[39;49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\n\u001b[1;32m 6\u001b[0m \u001b[43m \u001b[49m\u001b[43mfit_func\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mfixed\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m 7\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;66;43;03m#fit_bounds=[0, 40]\u001b[39;49;00m\n\u001b[1;32m 8\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 9\u001b[0m ap_params\n", - "File \u001b[0;32m~/git/pyrasa/pyrasa/utils/aperiodic_utils.py:225\u001b[0m, in \u001b[0;36mcompute_slope\u001b[0;34m(aperiodic_spectrum, freqs, fit_func, ch_names, scale, fit_bounds)\u001b[0m\n\u001b[1;32m 222\u001b[0m gof_list\u001b[38;5;241m.\u001b[39mappend(gof)\n\u001b[1;32m 224\u001b[0m \u001b[38;5;66;03m# combine & return\u001b[39;00m\n\u001b[0;32m--> 225\u001b[0m df_aps \u001b[38;5;241m=\u001b[39m \u001b[43mpd\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mconcat\u001b[49m\u001b[43m(\u001b[49m\u001b[43map_list\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 226\u001b[0m df_gof \u001b[38;5;241m=\u001b[39m pd\u001b[38;5;241m.\u001b[39mconcat(gof_list)\n\u001b[1;32m 228\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m df_aps, df_gof\n", - "File \u001b[0;32m~/git/pyrasa/.pixi/envs/default/lib/python3.12/site-packages/pandas/core/reshape/concat.py:382\u001b[0m, in \u001b[0;36mconcat\u001b[0;34m(objs, axis, join, ignore_index, keys, levels, names, verify_integrity, sort, copy)\u001b[0m\n\u001b[1;32m 379\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m copy \u001b[38;5;129;01mand\u001b[39;00m using_copy_on_write():\n\u001b[1;32m 380\u001b[0m copy \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mFalse\u001b[39;00m\n\u001b[0;32m--> 382\u001b[0m op \u001b[38;5;241m=\u001b[39m \u001b[43m_Concatenator\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 383\u001b[0m \u001b[43m \u001b[49m\u001b[43mobjs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 384\u001b[0m \u001b[43m \u001b[49m\u001b[43maxis\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43maxis\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 385\u001b[0m \u001b[43m \u001b[49m\u001b[43mignore_index\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mignore_index\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 386\u001b[0m \u001b[43m \u001b[49m\u001b[43mjoin\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mjoin\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 387\u001b[0m \u001b[43m \u001b[49m\u001b[43mkeys\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mkeys\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 388\u001b[0m \u001b[43m \u001b[49m\u001b[43mlevels\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mlevels\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 389\u001b[0m \u001b[43m \u001b[49m\u001b[43mnames\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mnames\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 390\u001b[0m \u001b[43m \u001b[49m\u001b[43mverify_integrity\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mverify_integrity\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 391\u001b[0m \u001b[43m \u001b[49m\u001b[43mcopy\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcopy\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 392\u001b[0m \u001b[43m \u001b[49m\u001b[43msort\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43msort\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 393\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 395\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m op\u001b[38;5;241m.\u001b[39mget_result()\n", - "File \u001b[0;32m~/git/pyrasa/.pixi/envs/default/lib/python3.12/site-packages/pandas/core/reshape/concat.py:445\u001b[0m, in \u001b[0;36m_Concatenator.__init__\u001b[0;34m(self, objs, axis, join, keys, levels, names, ignore_index, verify_integrity, copy, sort)\u001b[0m\n\u001b[1;32m 442\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mverify_integrity \u001b[38;5;241m=\u001b[39m verify_integrity\n\u001b[1;32m 443\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mcopy \u001b[38;5;241m=\u001b[39m copy\n\u001b[0;32m--> 445\u001b[0m objs, keys \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_clean_keys_and_objs\u001b[49m\u001b[43m(\u001b[49m\u001b[43mobjs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mkeys\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 447\u001b[0m \u001b[38;5;66;03m# figure out what our result ndim is going to be\u001b[39;00m\n\u001b[1;32m 448\u001b[0m ndims \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_get_ndims(objs)\n", - "File \u001b[0;32m~/git/pyrasa/.pixi/envs/default/lib/python3.12/site-packages/pandas/core/reshape/concat.py:507\u001b[0m, in \u001b[0;36m_Concatenator._clean_keys_and_objs\u001b[0;34m(self, objs, keys)\u001b[0m\n\u001b[1;32m 504\u001b[0m objs_list \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mlist\u001b[39m(objs)\n\u001b[1;32m 506\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(objs_list) \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m0\u001b[39m:\n\u001b[0;32m--> 507\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mNo objects to concatenate\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m 509\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m keys \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m 510\u001b[0m objs_list \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mlist\u001b[39m(com\u001b[38;5;241m.\u001b[39mnot_none(\u001b[38;5;241m*\u001b[39mobjs_list))\n", - "\u001b[0;31mValueError\u001b[0m: No objects to concatenate" - ] - } - ], - "source": [ - "# %% get aperiodic stuff\n", - "from pyrasa.utils.aperiodic_utils import compute_slope\n", - "\n", - "ap_params, gof_params = compute_slope(aperiodic_spectrum=psd_ap,\n", - " freqs=freq_irasa-1, \n", - " fit_func='fixed',\n", - " #fit_bounds=[0, 40]\n", - " )\n", - "ap_params" - ] - }, - { - "cell_type": "code", - "execution_count": 12, + "execution_count": 7, "metadata": {}, "outputs": [ { "data": { + "text/html": [ + "
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OffsetExponentfit_typech_name
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73.5, 74. , 74.5, 75. , 75.5, 76. , 76.5,\n", - " 77. , 77.5, 78. , 78.5, 79. , 79.5, 80. , 80.5, 81. , 81.5, 82. ,\n", - " 82.5, 83. , 83.5, 84. , 84.5, 85. , 85.5, 86. , 86.5, 87. , 87.5,\n", - " 88. , 88.5, 89. , 89.5, 90. , 90.5, 91. , 91.5, 92. , 92.5, 93. ,\n", - " 93.5, 94. , 94.5, 95. , 95.5, 96. , 96.5, 97. , 97.5, 98. , 98.5,\n", - " 99. ])" + " Offset Exponent fit_type ch_name\n", + "0 -1.293737 1.013686 fixed 0" ] }, - "execution_count": 12, + "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "freq_irasa-1" + "# %% get aperiodic stuff\n", + "slope_fit = irasa_out.get_slopes()\n", + "slope_fit.aperiodic_params" ] }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 8, "metadata": {}, "outputs": [ { @@ -325,10 +282,10 @@ " \n", " \n", " 0\n", - " 0.000208\n", - " 0.998746\n", - " -1682.182526\n", - " -1685.475831\n", + " 0.000278\n", + " 0.998247\n", + " -1618.772746\n", + " -1625.359356\n", " fixed\n", " 0\n", " \n", @@ -338,16 +295,16 @@ ], "text/plain": [ " mse r_squared BIC AIC fit_type ch_name\n", - "0 0.000208 0.998746 -1682.182526 -1685.475831 fixed 0" + "0 0.000278 0.998247 -1618.772746 -1625.359356 fixed 0" ] }, - "execution_count": 9, + "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "gof_params" + "slope_fit.gof" ] }, { @@ -361,7 +318,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 9, "metadata": {}, "outputs": [ { @@ -370,13 +327,13 @@ "Text(0.5, 0, 'Frequency (Hz)')" ] }, - "execution_count": 7, + "execution_count": 9, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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", + "image/png": 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", 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" ] @@ -407,22 +364,22 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 8, + "execution_count": 10, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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", 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", 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" ] @@ -474,22 +431,22 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 11, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 9, + "execution_count": 11, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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", 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", 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" ] @@ -526,22 +483,22 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 12, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 10, + "execution_count": 12, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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", 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" ] @@ -574,22 +531,22 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 13, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 11, + "execution_count": 13, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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", 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++SXS09ORmprqsf6WLVuwfft2nDx5Ei1atABQeYm0xsj94K4fG0JEjY7fSj02mw1ZWVmIj3e/ulR8fDz27Nnj9TmffPIJ4uLi8Nprr6Fdu3bo1q0bXnrpJVy5cqU+mlyvWOMnIl/xW4+/qKgIdrsdYWFhbsvDwsJQUFDg9TknT57Erl27YDKZ8PHHH6OoqAhTp07FhQsXqq3zW61WWK1W+X5JSYlyO+FDbjV+/zWDiBohvx/crXr6sRCi2lOSHQ4HJEnCBx98gL59++KRRx7B0qVLsWbNmmp7/ampqTCbzfKtthee9hf2+InIV/wW/KGhodBqtR69+8LCQo9vAU7h4eFo164dzGazvCw6OhpCCJw+fdrrc+bOnYvi4mL5lpeXp9xO+JBbj9/hv3YQUePjt+A3GAyIjY2FxWJxW26xWNC/f3+vz7nvvvvw888/49KlS/Kyo0ePQqPRoH379l6fYzQaERIS4nZrCBzs8RORj/i11JOSkoJ//vOfeOedd3D48GG8+OKLyM3NRVJSEoDK3npiYqK8/pgxY9CyZUs899xzOHToEHbs2IGZM2di4sSJCAgI8Ndu+IRgjZ+IfMSvwzkTEhJw/vx5LFq0CPn5+YiJicHmzZsRGRkJAMjPz0dubq68fnBwMCwWC6ZPn464uDi0bNkSTz/9NBYvXuyvXfAZ13n42eMnIiVJoonNAFZSUgKz2Yzi4mJVl31WbD+BJV/8CADYN+8BtG7GmSSJyLva5prfR/WQdw7WeojIRxj8KuU+ZYP/2kFEjQ+DX6U4jp+IfIXBr1IOTtJGRD7C4FcpTtJGRL7C4FcpzsdPRL7C4Fcr1viJyEcY/CrFGj8R+QqDX6Xcavx+bAcRNT4MfpVyDfsmdnI1EfkYg1+l3Gfn9GNDiKjRYfCrlOCoHiLyEQa/SjkcHNVDRL7B4Fcp16hn8BORkhj8KsUzd4nIVxj8KsUaPxH5CoNfpXjNXSLyFQa/SgmeuUtEPsLgVymO4yciX2Hwq5R72DP5iUg5DH6VEuzxE5GPMPhVyq3Gz+QnIgUx+FWKs3MSka8w+FWK8/ETka8w+FVK8MxdIvIRBr9Kca4eIvIVBr9Kca4eIvIVBr9KscZPRL7C4Fcp9viJyFcY/GrFHj8R+QiDX6XY4yciX/F78C9fvhxRUVEwmUyIjY3Fzp07q11327ZtkCTJ4/bjjz/WY4vrB6dlJiJf8WvwZ2RkIDk5GfPmzUN2djYGDhyI4cOHIzc394bPO3LkCPLz8+Vb165d66nF9cd9Wmb/tYOIGh+/Bv/SpUsxadIkTJ48GdHR0UhLS0NERATS09Nv+LzWrVujTZs28k2r1dZTi+sPZ+ckIl/xW/DbbDZkZWUhPj7ebXl8fDz27Nlzw+f27t0b4eHheOCBB7B169Ybrmu1WlFSUuJ2awg4OycR+Yrfgr+oqAh2ux1hYWFuy8PCwlBQUOD1OeHh4Vi5ciU2bNiAjRs3onv37njggQewY8eOal8nNTUVZrNZvkVERCi6H77CGj8R+YrO3w2QJMntvhDCY5lT9+7d0b17d/l+v379kJeXh9dffx2DBg3y+py5c+ciJSVFvl9SUtIgwt99yga/NYOIGiG/9fhDQ0Oh1Wo9eveFhYUe3wJu5N5778WxY8eqfdxoNCIkJMTt1hC4hr1gj5+IFOS34DcYDIiNjYXFYnFbbrFY0L9//xpvJzs7G+Hh4Uo3z+84OycR+YpfSz0pKSkYN24c4uLi0K9fP6xcuRK5ublISkoCUFmmOXPmDNauXQsASEtLQ8eOHXH77bfDZrPh/fffx4YNG7BhwwZ/7oZPsMZPRL7i1+BPSEjA+fPnsWjRIuTn5yMmJgabN29GZGQkACA/P99tTL/NZsNLL72EM2fOICAgALfffjs+//xzPPLII/7aBZ/hOH4i8hVJNLECcklJCcxmM4qLi1Vd739m5X/w7ckLAIC/PHknnopT/wFpIvKP2uaa36dsIO/cD+76rx1E1Pgw+FVKsMZPRD7C4Fcp16xn7BORkhj8KsVRPUTkKwx+lXJwVA8R+QiDX6XcT+Bi8hORchj8KuUa9cx9IlISg1+lWOMnIl9h8KuUw+HyM3OfiBTE4FcpB2v8ROQjDP4GgLlPREpi8KsUa/xE5CsMfpXiOH4i8hUGv0pxrh4i8hUGv0ox64nIVxj8KuVW42eth4gUxOBXKdb4ichXGPwqJVwmbRCcmJmIFMTgVymeuUtEvsLgVynOzklEvsLgVyn3Gj+Dn4iUw+BXKbcaP3OfiBTE4FcpjuohIl9h8KsUa/xE5CsMfpVijZ+IfIXBr1LuPX4/NoSIGh0Gv0qxxk9EvsLgVynOx09EvsLgVylmPRH5CoNfpTgfPxH5it+Df/ny5YiKioLJZEJsbCx27txZo+ft3r0bOp0OvXr18m0D/YSjeojIV/wa/BkZGUhOTsa8efOQnZ2NgQMHYvjw4cjNzb3h84qLi5GYmIgHHnignlpa/9xr/H5sCBE1On4N/qVLl2LSpEmYPHkyoqOjkZaWhoiICKSnp9/web/97W8xZswY9OvXr55aWv9cs54dfiJSkt+C32azISsrC/Hx8W7L4+PjsWfPnmqft3r1apw4cQILFizwdRP9imfuEpGv6Pz1wkVFRbDb7QgLC3NbHhYWhoKCAq/POXbsGObMmYOdO3dCp6tZ061WK6xWq3y/pKSk7o2uR6zxE5Gv+P3griRJbveFEB7LAMBut2PMmDF4+eWX0a1btxpvPzU1FWazWb5FRETccpvrA2v8ROQrfgv+0NBQaLVaj959YWGhx7cAACgtLUVmZiamTZsGnU4HnU6HRYsW4b///S90Oh2++eYbr68zd+5cFBcXy7e8vDyf7I/SXDv57PATkZL8VuoxGAyIjY2FxWLB448/Li+3WCwYOXKkx/ohISE4ePCg27Lly5fjm2++wb/+9S9ERUV5fR2j0Qij0ahs432sak2fNX4iUpLfgh8AUlJSMG7cOMTFxaFfv35YuXIlcnNzkZSUBKCyt37mzBmsXbsWGo0GMTExbs9v3bo1TCaTx/KGrmpphzV+IlKSX4M/ISEB58+fx6JFi5Cfn4+YmBhs3rwZkZGRAID8/PybjulvjKoGPWOfiJQkiSZWRygpKYHZbEZxcTFCQkL83RyvbBUOdJv/hXz/13e1xV9H9/Zji4hIzWqba34f1UOeqvb4WeohIiUx+FWoas43sS9lRORjDH4V8qjxM/eJSEG1Dn4hBH766SdcuXLFF+0heB7MZamHiJRUp+Dv2rUrTp8+7Yv2ELzV+P3UECJqlGod/BqNBl27dsX58+d90R4CIBxV7rPHT0QKqlON/7XXXsPMmTPx/fffK90eAmv8RORbdTqB69lnn0VZWRnuuusuGAwGBAQEuD1+4cIFRRrXVLHGT0S+VKfgT0tLU7gZ5Io1fiLypToF//jx45VuB7nglA1E5Et1Hsd/4sQJzJ8/H6NHj0ZhYSEAYMuWLfjhhx8Ua1xTxRO4iMiX6hT827dvxx133IG9e/di48aNuHTpEgDgf//7X6O/JGJ9qJrzrPETkZLqFPxz5szB4sWLYbFYYDAY5OVDhw7Ff/7zH8Ua11R51Pgd1axIRFQHdQr+gwcPul08xalVq1Yc368Azxo/e/xEpJw6BX/z5s2Rn5/vsTw7Oxvt2rW75UY1dZ6lHv+0g4gapzoF/5gxYzB79mwUFBRAkiQ4HA7s3r0bL730EhITE5VuY5PDg7tE5Et1Cv5XXnkFHTp0QLt27XDp0iX07NkTgwYNQv/+/TF//nyl29jkcBw/EflSncbx6/V6fPDBB1i0aBGys7PhcDjQu3dvdO3aVen2NUmeUzYw+YlIOXUK/mPHjqFr167o3LkzOnfurHSbmjzPKRv80gwiaqTqFPzdu3dHeHg4Bg8ejMGDB2PIkCHo3r270m1rsqr28NnjJyIl1anGn5+fj9dffx0hISFYtmwZoqOjER4ejmeeeQYrVqxQuo1NTtUePmOfiJRUp+APCwvD6NGjsWLFCvz44484evQoHnroIWzYsAEvvPCC0m1scnixdSLypTqVei5duoRdu3Zh27Zt2L59Ow4cOIDo6GhMnz4dgwcPVrqNTY7HOH6euUtECqpT8N92221o0aIFxo0bh/nz52PAgAEwm81Kt63JYo+fiHypTsE/YsQI7Nq1C++99x7y8vKQm5uLIUOGIDo6Wun2NUnMeSLypTrV+Ddt2oSioiJYLBYMGDAAX3/9NYYMGYI2bdrgmWeeUbqNTQ57/ETkS3Xq8TvdeeedsNvtKC8vh9VqxZYtW7Bx40al2tZkca4eIvKlOvX4ly1bhpEjR6JFixbo27cv1q1bh+7du+Pjjz9GUVGR0m1sctjjJyJfqlOP/4MPPsCQIUMwZcoUDBo0CCEhIUq3q0nz6OEz94lIQXUK/szMTKXbQS6qnqnLHj8RKanO19y9ePEi3njjDUyePBlTpkzB0qVLUVxcXOvtLF++HFFRUTCZTIiNjcXOnTurXXfXrl2477770LJlSwQEBKBHjx5YtmxZXXdBtThXDxH5Up2CPzMzE507d8ayZctw4cIFFBUVYdmyZejcuTP2799f4+1kZGQgOTkZ8+bNQ3Z2NgYOHIjhw4cjNzfX6/pBQUGYNm0aduzYgcOHD2P+/PmYP38+Vq5cWZfdUC1HlaTnFbiISEmSqMMMYAMHDkSXLl3w9ttvQ6errBZVVFRg8uTJOHnyJHbs2FGj7dxzzz3o06cP0tPT5WXR0dF47LHHkJqaWqNtjBo1CkFBQXjvvfdqtH5JSQnMZjOKi4tVe2ziPyfOY/Tb38r32zUPwO459/uxRUSkZrXNtTr3+GfPni2HPgDodDrMmjWrxvV/m82GrKwsxMfHuy2Pj4/Hnj17arSN7Oxs7Nmzp9FNE1G1h8/ZOYlISXU6uBsSEoLc3Fz06NHDbXleXh6aNWtWo20UFRXBbrcjLCzMbXlYWBgKCgpu+Nz27dvj3LlzqKiowMKFCzF58uRq17VarbBarfL9kpKSGrXPnziOn4h8qU49/oSEBEyaNAkZGRnIy8vD6dOnsX79ekyePBmjR4+u1bYkSXK7L4TwWFbVzp07kZmZiRUrViAtLQ3r1q2rdt3U1FSYzWb5FhERUav2+YPHFbhY4yciBdWpx//6669Do9EgMTERFRUVACovx/j8889jyZIlNdpGaGgotFqtR+++sLDQ41tAVVFRUQCAO+64A2fPnsXChQur/cCZO3cuUlJS5PslJSWqD39nD18jVf7MHj8RKalWwV9WVoaZM2di06ZNKC8vx2OPPYZp06bBbDajS5cuCAwMrPG2DAYDYmNjYbFY8Pjjj8vLLRYLRo4cWePtCCHcSjlVGY1GGI3GGm9PDZw1fZ1GA5vdwRo/ESmqVsG/YMECrFmzBmPHjkVAQAA+/PBDOBwOfPTRR3V68ZSUFIwbNw5xcXHo168fVq5cidzcXCQlJQGo7K2fOXMGa9euBQD8/e9/R4cOHeRjC7t27cLrr7+O6dOn1+n11cqZ8xoNADt7/ESkrFoF/8aNG7Fq1Sp5Bs6xY8fivvvug91uh1arrfWLJyQk4Pz581i0aBHy8/MRExODzZs3IzIyEkDlJR5dx/Q7HA7MnTsXOTk50Ol06Ny5M5YsWYLf/va3tX5tNXPW+LXXjnWwx09ESqrVOH6DwYCcnBy0a9dOXhYQEICjR4+qvm7u1BDG8VsOncWUtZkIMelQcrUC5gA9/rsg/uZPJKImyafj+O12OwwGg9synU4nH+AlZcg1fm3lr4dz9RCRkmpV6hFCYMKECW4HS69evYqkpCQEBQXJyzgn/625Pqrn2rBW5j4RKahWwT9+/HiPZc8++6xijaFKzh7/tQ4/e/xEpKhaBf/q1at91Q5y4ezx6zQat/tEREqo87TM5DvOM3U17PETkQ8w+FXI2cOXh3P6sS1E1Pgw+FXoeo2f4/iJSHkMfhVyuEzZUHnfn60hosaGwa9C16dsqOzxs8ZPREpi8KuQXOO/9tth7hORkhj8KiTP1aO5/uthnZ+IlMLgV6Hr0zJfvyAN6/xEpBQGvwqJKsM5K5cx+YlIGQx+FZLn6tF4LiMiulUMfhWqOpzTdRkR0a1i8KuQM+I1GtdSj3/aQkSND4Nfhbwd3BWcuIGIFMLgVyHHtYK+RuKoHiJSHoNfha5Py+wa/Ex+IlIGg1+FnBGvdS31OPzTFiJqfBj8KlR1dk6ANX4iUg6DX4UcXoKfNX4iUgqDX4U8LrYO1viJSDkMfhWSp2WWAGf2M/eJSCkMfhVy9u41kiT3+jlXDxEphcGvQs6Ql6TKXj/AGj8RKYfBr0LOkJckCRJ4FS4iUhaDX4W81vj91xwiamQY/CrkrcbvYK2HiBTC4FchbzV+VnqISCkMfhVyHccvSazxE5Gy/B78y5cvR1RUFEwmE2JjY7Fz585q1924cSMefPBBtGrVCiEhIejXrx++/PLLemxt/XBOzyCxxk9EPuDX4M/IyEBycjLmzZuH7OxsDBw4EMOHD0dubq7X9Xfs2IEHH3wQmzdvRlZWFoYOHYpf//rXyM7OrueW+5Zrj1/DHj8RKcyvwb906VJMmjQJkydPRnR0NNLS0hAREYH09HSv66elpWHWrFm4++670bVrV/z5z39G165d8emnn9Zzy33LGfISXM/cZfATkTL8Fvw2mw1ZWVmIj493Wx4fH489e/bUaBsOhwOlpaVo0aJFtetYrVaUlJS43dROHs6pcT1z148NIqJGxW/BX1RUBLvdjrCwMLflYWFhKCgoqNE23njjDVy+fBlPP/10teukpqbCbDbLt4iIiFtqd33gmbtE5Et+P7grucxACVSGXtVl3qxbtw4LFy5ERkYGWrduXe16c+fORXFxsXzLy8u75Tb7Gkf1EJEv6fz1wqGhodBqtR69+8LCQo9vAVVlZGRg0qRJ+OijjzBs2LAbrms0GmE0Gm+5vfXJrcZfZRkR0a3yW4/fYDAgNjYWFovFbbnFYkH//v2rfd66deswYcIEfPjhhxgxYoSvm+kXwsuoHuY+ESnFbz1+AEhJScG4ceMQFxeHfv36YeXKlcjNzUVSUhKAyjLNmTNnsHbtWgCVoZ+YmIg333wT9957r/xtISAgAGaz2W/7oTQhT9nAM3eJSHl+Df6EhAScP38eixYtQn5+PmJiYrB582ZERkYCAPLz893G9P/jH/9ARUUFXnjhBbzwwgvy8vHjx2PNmjX13XyfcZudkzV+IlKYX4MfAKZOnYqpU6d6faxqmG/bts33DVIBh/A8c5fBT0RK8fuoHvLkjHi3Gr//mkNEjQyDX4W81/gZ/USkDAa/Cjkclf+61/j92CAialQY/CrkrcbPDj8RKYXBr0Leavw8uEtESmHwq5DDS42fwU9ESmHwq5DrmbsSeOYuESmLwa9Crr171viJSGkMfhWqsFemvF6rgfZarafCOdSHiOgWMfhVqNxeGfI6rQSdtvJX5PwwICK6VQx+FXIGv16rgZ49fiJSGINfhSoczlKPBP21Hn85e/xEpBAGvwrJpR6NBjqt5LaMiOhWMfhVqNzl4K6BNX4iUhiDX4Uq5Bq/JPf4bezxE5FCGPwq5Ozx67Qal1E9DH4iUgaDX4WcI3j0Wul6qYfTcxKRQhj8KuRa49dpWOohImUx+FXo+qgensBFRMpj8KtQhduoHg7nJCJlMfhVyPXMXR1P4CIihTH4Vch1rh49R/UQkcIY/CokT9mg0UDPUg8RKYzBr0JyqUcnQae5VurhcE4iUgiDX2WEENdP4NJooNddm52TPX4iUgiDX2XsLj17vVaCXsODu0SkLAa/yrgGvF7LGj8RKY/BrzLlLhdccb0CF4OfiJTC4FcZ1zN0XUf18MxdIlIKg19lnAdxtRoJGo3LFbg4qoeIFOL34F++fDmioqJgMpkQGxuLnTt3Vrtufn4+xowZg+7du0Oj0SA5Obn+GlpPbC7z9AC4XuqpYKmHiJTh1+DPyMhAcnIy5s2bh+zsbAwcOBDDhw9Hbm6u1/WtVitatWqFefPm4a677qrn1tYP13l6AMhz9fBi60SkFL8G/9KlSzFp0iRMnjwZ0dHRSEtLQ0REBNLT072u37FjR7z55ptITEyE2Wyu59bWD2fAO6+85TyBy8YaPxEpxG/Bb7PZkJWVhfj4eLfl8fHx2LNnj59a5X+2Cvcev07LE7iISFk6f71wUVER7HY7wsLC3JaHhYWhoKBAsdexWq2wWq3y/ZKSEsW27Qvy1beu1fh5sXUiUprfD+5KkuR2XwjhsexWpKamwmw2y7eIiAjFtu0Lrtfbdf2X4/iJSCl+C/7Q0FBotVqP3n1hYaHHt4BbMXfuXBQXF8u3vLw8xbbtC9fn4pfc/i3nwV0iUojfgt9gMCA2NhYWi8VtucViQf/+/RV7HaPRiJCQELebmlUd1aNnqYeIFOa3Gj8ApKSkYNy4cYiLi0O/fv2wcuVK5ObmIikpCUBlb/3MmTNYu3at/JwDBw4AAC5duoRz587hwIEDMBgM6Nmzpz92QXHlVUf1cK4eIlKYX4M/ISEB58+fx6JFi5Cfn4+YmBhs3rwZkZGRACpP2Ko6pr93797yz1lZWfjwww8RGRmJU6dO1WfTfaa6Hj9n5yQipfg1+AFg6tSpmDp1qtfH1qxZ47FMiMYdgHKN/9r4/evTMrPHT0TK8PuoHnLner1d139Z4ycipTD4VabaUo/D0ei/7RBR/WDwq0x1wzmFcL86FxFRXTH4VcY5/bJzjh5njx8AKhj8RKQABr/KVFRT4weuT9lMRHQrGPwq4yz1OOfocY7qAXiAl4iUweBXmetz9VT29DUaCVoNZ+gkIuUw+FWmosokbcD1q3Gx1ENESmDwq0zVUo/rzyz1EJESGPwqI8/Vo7l+UFfHyy8SkYIY/CrjtdRz7Wfn1bmIiG4Fg19lKuRSz/Uev1zqYY+fiBTA4FcZm9ceP6dmJiLlMPhVpuoJXACnZiYiZTH4VcY5LYPriVs6DWfoJCLlMPhVxlZlkrbKnzknPxEph8GvMtdLPdd/NXrW+IlIQQx+lbk+H7/rOH7W+IlIOQx+lble6vFy5i6HcxKRAhj8KuP9BC5nqYc9fiK6dQx+lXH26vWuUzbwgutEpCAGv8rYqlxzFwAMOk7LTETKYfCrjLcTuK73+FnqIaJbx+BXmQovPX6O4yciJTH4VcY5LbPeyzh+XmydiJTA4FeZcm+lnms/2yrY4yeiW8fgVxm51KPxLPVwHD8RKYHBrzJVL7YOuAQ/D+4SkQIY/CpT7uXMXV5snYiUxOBXmYobzM7JHj8RKYHBrzLlDs8pGzg7JxEpye/Bv3z5ckRFRcFkMiE2NhY7d+684frbt29HbGwsTCYTOnXqhBUrVtRTS+tH+Q3n42ePn4hunV+DPyMjA8nJyZg3bx6ys7MxcOBADB8+HLm5uV7Xz8nJwSOPPIKBAwciOzsbf/jDH/C73/0OGzZsqNd2554vw7+yTuNquV3R7dodAuJatrtdgYujeohIQTp/vvjSpUsxadIkTJ48GQCQlpaGL7/8Eunp6UhNTfVYf8WKFejQoQPS0tIAANHR0cjMzMTrr7+OJ554ol7avPPYOUx9fz9KrRX4V1Ye3k6MQ7ld4B87TqCZUYdhPcPQo00IThVdhiQBHVoEQpIkXLhsg0MIhAYbYa2w4/P/5eN44SU8ckc4YtqZ4XAInDh3SX4d91E9lT87P2iEEPjh5xJIEtCjTQiEEDhbakWF3YGI2wJRVm5H/sUr+Ln4Ks4WX0WY2YQ+HZqjmUmPoktWXLpaAXOAHs0D9ZCk668jhIDN7oAQgEmvxdVyO346XwbHtU8jW4UDpVcr0KlVENo2D4AQAoWlVkgAWgYbodW4b+uXsnJculohL5MkoMxmx9mSq9BpJLQMNiKyZSC0GgkOIWCrcOCXy+XQaIDbAg0IMurctmetcOBquR1Xyu24Yqv8Vwgg0KBFoEGHMlsFiq+UQyNJ0Gok6LQSDFoNjHotNNL1D9aQAD1CTDpIkgSHQ8AuBOyOyluFQ0CSgGCDDuLae+58Pfu1xyRIcL5tkgRIkgTp2s9CAA5R+TpCAAICDlHZ/soqXuVjeq0GBp2m8l+tBpIGkAA4BHDJWgGHQ0CnvbYfGg00114X0rXXlN/T668tQYJA5T44HAKXbXZcsVVAkqTK90SqbLdWU3lfbj9QuV24L3PdduUq1b8+XNaT14WX/Xe+P3B9nyofs1U4YBcCRp0GJr0WWklChcMh/16c/1Yluf7spQ3eHvPYhuvzXO5UfUp126+6banaO9U/z3W1IKMOJr22+gbfIr8Fv81mQ1ZWFubMmeO2PD4+Hnv27PH6nP/85z+Ij493W/bQQw9h1apVKC8vh16v93iO1WqF1WqV75eUlNSpvQ6HQPr2E3jjqyNw/u19e/ICfrM2Cy2CDPj8YD4A4M2vj2H2wz2Q+sWPsDsEWgQZ0LlVELJzLyLQoMVHSf2R9H4WcoouAwCWbzuBlkGGylC1Xg9J11E9gYbKX9OXP5zFQ8t2oPhKOQpKrgK4HjZOOo1U7Rm+Bp3G7SSw0GADQkx6WCscMOo1OFdildvQzKjDlXJ7tdsKNGjhEAJXyyu3p5Eqw1+nkVBuF7CW2932py5aBhkAoDJ4r4W8UpwfNtVts+r7SlSf3nymF0b2auez7fst+IuKimC32xEWFua2PCwsDAUFBV6fU1BQ4HX9iooKFBUVITw83OM5qampePnll2+5vTMyDuDT//4MAHiiT3uMuacDxq3ai/+cPA+gMih6hofgh59LsPjzw/LzLly24cJlGwCg5GoFxrz9Lc5ftiE02IDeHW7DNz8W4vy1x016DcJCTHgwOszt0/7hmDbYc6IIm7LP4MjZUgBAkEELSZJw6Vq46rUSJEmSgz3EpEPb5gFoHWLCyXOXcPqXK7BVOCBJQKBei8s2O4ou2VB0yeZ1f52hHWLSwXitLTqNhACDFj+dL0OZrfLbh0bCtd4bcK7U6rGdAL3WLUQNOg3ahJjgEAIFJVdRetX9w8Gk10AIwFrhkN+XqvRaCSa9FgHX2nXFZkdZuR0Bei3MAXq5B1nhcMBaUXmDcPbOgavllb3IG6ka+sZrvXNxrbfq7M1X/gvg2n1nD1hzrXetce2RS5L8flXYK79deTsbW3+tp+/s4db1A8ig1SDQqK38FuLyzUbuaQNu++Mvzm8QJr0WWo0Ea7nDbeiyVlP5bUWrqby59oxdmy2q7ETVXXJ9WFR51P2xqk+sevf6gqrvW13bU9/8WuoB3L9WAZVvVtVlN1vf23KnuXPnIiUlRb5fUlKCiIiIWrdzxB3hsBwqwKJHY/BUXHtIkoQ/PBKN+Zu+BwAkxEUgJb4b4pftwMWycnRtHYx/JfXHiaJLOHa2FLYKB/74//0gh9mSUXdiWM8wFF8px88Xr0CSgC6tgt1G8zgFG3VY+nQvvDisG46fuwSTToveHZpDr9Xg/CUrdFoNmgfoIQAUlFxF8wC9W5kEqCwfXLhkQ6tmRgQYtLhis+Po2VJYKxzQayVcLXcgNNiA1iEmSBJwtvgqAgxatGse4PHeltkq5JAPNwdAI1V+wBVeW6bVSNBrNWjXPAABhuq/rgohUGqtgLgWynqNRl6/+Eo5Tv9SBr1WA5NOC5NBgwC9Fia91u3bUF04S0LOQNFpNNBoKmdBdX4TKLlaDr2msuRg1Gmg0VT/N3krhLhWmnH59mHUadzec4fzcVz/wAGuB4frMsA9KG/0f+lGbXL9YADg8WF3fV2Xn72EqfMD0LVEVvXD0JvKspyo8z40dFU/NJTmt+APDQ2FVqv16N0XFhZ69Oqd2rRp43V9nU6Hli1ben2O0WiE0Wi85fY+HNMGOyPvR6tm17c19p4OyPrpFxz6uQS/j++OVs2M+NvoPnhndw7+8Eg0zIF69OlwG/p0uA1CCGzYfwYH8i6id4fmeCC6NQDAHKCHOcCzROVNRItARLQIdFvWOsTkdr9d8wCvzw026hDs8mEQYNDirojm1b5WiKn6NgUadIhs6f6n0zrE5NGWm5EkqdrXqXxfzLXaXk0FGnRy+aw6vqyvupIkyW0ElzcajQSNR7XZdyTp+jEMzyp3/ag8XtT0At/J1x92fgt+g8GA2NhYWCwWPP744/Jyi8WCkSNHen1Ov3798Omnn7ot++qrrxAXF+e1vq8019AHKn85yxJ6uS0b0DUUA7qGejxXkiT8+fE78ObXR5HyYPcm2YshIpUQfrR+/Xqh1+vFqlWrxKFDh0RycrIICgoSp06dEkIIMWfOHDFu3Dh5/ZMnT4rAwEDx4osvikOHDolVq1YJvV4v/vWvf9X4NYuLiwUAUVxcrPj+EBH5Q21zza81/oSEBJw/fx6LFi1Cfn4+YmJisHnzZkRGRgIA8vPz3cb0R0VFYfPmzXjxxRfx97//HW3btsVbb71Vb0M5iYgaA0mIpjVoraSkBGazGcXFxQgJCfF3c4iIblltc83vUzYQEVH9YvATETUxDH4ioiaGwU9E1MQw+ImImhgGPxFRE8PgJyJqYhj8RERNDIOfiKiJ8fu0zPXNeaJyXS/IQkSkNs48q+lEDE0u+EtLKy9kUpc5+YmI1Ky0tBRm882nM29yc/U4HA78/PPPaNasWa2mRnZewCUvL6/RzvHDfWz4Gvv+AdxHb4QQKC0tRdu2baHR3LyC3+R6/BqNBu3bt6/z80NCQhrtH5sT97Hha+z7B3Afq6pJT9+JB3eJiJoYBj8RURPD4K8ho9GIBQsWKHL9XrXiPjZ8jX3/AO6jEprcwV0ioqaOPX4ioiaGwU9E1MQw+ImImhgGfw0tX74cUVFRMJlMiI2Nxc6dO/3dpDpJTU3F3XffjWbNmqF169Z47LHHcOTIEbd1hBBYuHAh2rZti4CAAAwZMgQ//PCDn1p861JTUyFJEpKTk+VljWEfz5w5g2effRYtW7ZEYGAgevXqhaysLPnxhryPFRUVmD9/PqKiohAQEIBOnTph0aJFcDgc8joNbf927NiBX//612jbti0kScKmTZvcHq/J/litVkyfPh2hoaEICgrCo48+itOnT9e+MYJuav369UKv14u3335bHDp0SMyYMUMEBQWJn376yd9Nq7WHHnpIrF69Wnz//ffiwIEDYsSIEaJDhw7i0qVL8jpLliwRzZo1Exs2bBAHDx4UCQkJIjw8XJSUlPix5XWzb98+0bFjR3HnnXeKGTNmyMsb+j5euHBBREZGigkTJoi9e/eKnJwc8e9//1scP35cXqch7+PixYtFy5YtxWeffSZycnLERx99JIKDg0VaWpq8TkPbv82bN4t58+aJDRs2CADi448/dnu8JvuTlJQk2rVrJywWi9i/f78YOnSouOuuu0RFRUWt2sLgr4G+ffuKpKQkt2U9evQQc+bM8VOLlFNYWCgAiO3btwshhHA4HKJNmzZiyZIl8jpXr14VZrNZrFixwl/NrJPS0lLRtWtXYbFYxODBg+Xgbwz7OHv2bDFgwIBqH2/o+zhixAgxceJEt2WjRo0Szz77rBCi4e9f1eCvyf5cvHhR6PV6sX79enmdM2fOCI1GI7Zs2VKr12ep5yZsNhuysrIQHx/vtjw+Ph579uzxU6uUU1xcDABo0aIFACAnJwcFBQVu+2s0GjF48OAGt78vvPACRowYgWHDhrktbwz7+MknnyAuLg5PPfUUWrdujd69e+Ptt9+WH2/o+zhgwAB8/fXXOHr0KADgv//9L3bt2oVHHnkEQMPfv6pqsj9ZWVkoLy93W6dt27aIiYmp9T43ubl6aquoqAh2ux1hYWFuy8PCwlBQUOCnVilDCIGUlBQMGDAAMTExACDvk7f9/emnn+q9jXW1fv167N+/H999953HY41hH0+ePIn09HSkpKTgD3/4A/bt24ff/e53MBqNSExMbPD7OHv2bBQXF6NHjx7QarWw2+145ZVXMHr0aACN43foqib7U1BQAIPBgNtuu81jndpmEYO/hqrO5CmEqNXsnmo0bdo0/O9//8OuXbs8HmvI+5uXl4cZM2bgq6++gslkqna9hryPDocDcXFx+POf/wwA6N27N3744Qekp6cjMTFRXq+h7mNGRgbef/99fPjhh7j99ttx4MABJCcno23bthg/fry8XkPdv+rUZX/qss8s9dxEaGgotFqtxydqYWGhx6dzQzJ9+nR88skn2Lp1q9tspW3atAGABr2/WVlZKCwsRGxsLHQ6HXQ6HbZv34633noLOp1O3o+GvI/h4eHo2bOn27Lo6Gjk5uYCaPi/x5kzZ2LOnDl45plncMcdd2DcuHF48cUXkZqaCqDh719VNdmfNm3awGaz4Zdffql2nZpi8N+EwWBAbGwsLBaL23KLxYL+/fv7qVV1J4TAtGnTsHHjRnzzzTeIiopyezwqKgpt2rRx21+bzYbt27c3mP194IEHcPDgQRw4cEC+xcXFYezYsThw4AA6derU4Pfxvvvu8xiGe/ToUURGRgJo+L/HsrIyj3nltVqtPJyzoe9fVTXZn9jYWOj1erd18vPz8f3339d+n+t0SLqJcQ7nXLVqlTh06JBITk4WQUFB4tSpU/5uWq09//zzwmw2i23bton8/Hz5VlZWJq+zZMkSYTabxcaNG8XBgwfF6NGjVT1MriZcR/UI0fD3cd++fUKn04lXXnlFHDt2THzwwQciMDBQvP/++/I6DXkfx48fL9q1aycP59y4caMIDQ0Vs2bNktdpaPtXWloqsrOzRXZ2tgAgli5dKrKzs+Vh4TXZn6SkJNG+fXvx73//W+zfv1/cf//9HM7pS3//+99FZGSkMBgMok+fPvLwx4YGgNfb6tWr5XUcDodYsGCBaNOmjTAajWLQoEHi4MGD/mu0AqoGf2PYx08//VTExMQIo9EoevToIVauXOn2eEPex5KSEjFjxgzRoUMHYTKZRKdOncS8efOE1WqV12lo+7d161av//fGjx8vhKjZ/ly5ckVMmzZNtGjRQgQEBIhf/epXIjc3t9Zt4eycRERNDGv8RERNDIOfiKiJYfATETUxDH4ioiaGwU9E1MQw+ImImhgGPxFRE8PgJyJqYhj8RI2AzWZDly5dsHv3bkW3+9lnn6F3795ulzykho/BT6ozYcIESJLkcTt+/Li/m6ZaK1euRGRkJO677z55mbfrugKV7+9jjz1Wo+3+6le/giRJ+PDDDxVqKakBg59U6eGHH0Z+fr7brepMokBlT5eAv/71r5g8ebJPtv3cc8/hr3/9q0+2Tf7B4CdVMhqNaNOmjdtNq9ViyJAhmDZtGlJSUhAaGooHH3wQAHDo0CE88sgjCA4ORlhYGMaNG4eioiJ5e5cvX0ZiYiKCg4MRHh6ON954A0OGDEFycrK8jrcecvPmzbFmzRr5/pkzZ5CQkIDbbrsNLVu2xMiRI3Hq1Cn5cWdv+vXXX0d4eDhatmyJF154AeXl5fI6VqsVs2bNQkREBIxGI7p27YpVq1ZBCIEuXbrg9ddfd2vD999/D41GgxMnTnh9r/bv34/jx49jxIgRtXyXgVOnTnn9djVkyBB5nUcffRT79u3DyZMna719UicGPzU47777LnQ6HXbv3o1//OMfyM/Px+DBg9GrVy9kZmZiy5YtOHv2LJ5++mn5OTNnzsTWrVvx8ccf46uvvsK2bduQlZVVq9ctKyvD0KFDERwcjB07dmDXrl0IDg7Gww8/7PbNY+vWrThx4gS2bt2Kd999F2vWrHH78EhMTMT69evx1ltv4fDhw1ixYgWCg4MhSRImTpyI1atXu73uO++8g4EDB6Jz585e27Vjxw5069YNISEhtdofAIiIiHD7VpWdnY2WLVti0KBB8jqRkZFo3bo1du7cWevtk0rd8lyjRAobP3680Gq1IigoSL49+eSTQojK6ZV79erltv4f//hHER8f77YsLy9PABBHjhwRpaWlwmAwiPXr18uPnz9/XgQEBLhN1QxAfPzxx27bMZvN8pTVq1atEt27dxcOh0N+3Gq1ioCAAPHll1/KbY+MjHSbH/2pp54SCQkJQgghjhw5IgAIi8Xidd9//vlnodVqxd69e4UQQthsNtGqVSuxZs2aat+vGTNmiPvvv99jOQBhMpnc3segoCCh0+nEyJEjPda/cuWKuOeee8SvfvUrYbfb3R7r3bu3WLhwYbVtoIaF19wlVRo6dCjS09Pl+0FBQfLPcXFxbutmZWVh69atCA4O9tjOiRMncOXKFdhsNvTr109e3qJFC3Tv3r1WbcrKysLx48fRrFkzt+VXr151K8Pcfvvt0Gq18v3w8HAcPHgQAHDgwAFotVoMHjzY62uEh4djxIgReOedd9C3b1989tlnuHr1Kp566qlq23XlypVqry28bNkyDBs2zG3Z7NmzYbfbPdadNGkSSktLYbFYPK5+FRAQgLKysmrbQA0Lg59UKSgoCF26dKn2MVcOhwO//vWv8eqrr3qsGx4ejmPHjtXoNSVJgqhyeQrX2rzD4UBsbCw++OADj+e2atVK/lmv13ts1zkcMiAg4KbtmDx5MsaNG4dly5Zh9erVSEhIQGBgYLXrh4aGyh8sVbVp08bjfWzWrBkuXrzotmzx4sXYsmUL9u3b5/HBBgAXLlxw20dq2Bj81OD16dMHGzZsQMeOHaHTef5Jd+nSBXq9Ht9++y06dOgAAPjll19w9OhRt553q1atkJ+fL98/duyYWy+3T58+yMjIQOvWretUTweAO+64Aw6HA9u3b/foiTs98sgjCAoKQnp6Or744gvs2LHjhtvs3bs30tPTIYSAJEm1btOGDRuwaNEifPHFF16PIzi/0fTu3bvW2yZ14sFdavBeeOEFXLhwAaNHj5ZHn3z11VeYOHEi7HY7goODMWnSJMycORNff/01vv/+e0yYMMGjnHH//ffjb3/7G/bv34/MzEwkJSW59d7Hjh2L0NBQjBw5Ejt37kROTg62b9+OGTNm4PTp0zVqa8eOHTF+/HhMnDgRmzZtQk5ODrZt24b/9//+n7yOVqvFhAkTMHfuXHTp0sWtROXN0KFDcfnyZfzwww+1eNcqff/990hMTMTs2bNx++23o6CgAAUFBbhw4YK8zrfffguj0XjTdlDDweCnBq9t27bYvXs37HY7HnroIcTExGDGjBkwm81yuP/lL3/BoEGD8Oijj2LYsGEYMGAAYmNj3bbzxhtvICIiAoMGDcKYMWPw0ksvuZVYAgMDsWPHDnTo0AGjRo1CdHQ0Jk6ciCtXrtTqG0B6ejqefPJJTJ06FT169MCUKVNw+fJlt3UmTZoEm82GiRMn3nR7LVu2xKhRo7yWoG4mMzMTZWVlWLx4McLDw+XbqFGj5HXWrVuHsWPH3rDcRA0Lr7lLTdaQIUPQq1cvpKWl+bspHnbv3o0hQ4bg9OnTCAsLu+n6Bw8exLBhw7wefL4V586dQ48ePZCZmen1BDpqmNjjJ1IRq9WK48eP449//COefvrpGoU+UHns4LXXXnM7mUwJOTk5WL58OUO/keHBXSIVWbduHSZNmoRevXrhvffeq9Vzx48fr3h7+vbti759+yq+XfIvlnqIiJoYlnqIiJoYBj8RURPD4CciamIY/ERETQyDn4ioiWHwExE1MQx+IqImhsFPRNTEMPiJiJqY/x+EBnx7RApuQgAAAABJRU5ErkJggg==", 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" ] @@ -619,12 +576,12 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 14, "metadata": {}, "outputs": [ { "data": { - "image/png": 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", + "image/png": 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", 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" ] @@ -658,27 +615,27 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 16, "metadata": {}, "outputs": [], "source": [ - "freq_irasa, psd_ap, psd_p = irasa(sig, \n", - " fs=fs, \n", - " band=(1, 100), \n", - " irasa_kwargs={'nperseg': duration*fs, \n", - " 'noverlap': duration*fs*overlap\n", - " },\n", - " hset_info=(1, 2, 0.025))" + "irasa_out = irasa(sig, \n", + " fs=fs, \n", + " band=(1, 100), \n", + " psd_kwargs={'nperseg': duration*fs, \n", + " 'noverlap': duration*fs*overlap\n", + " },\n", + " hset_info=(1, 2, 0.025))" ] }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 19, "metadata": {}, "outputs": [ { "data": { - "image/png": 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", + "image/png": 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" ] @@ -689,23 +646,22 @@ ], "source": [ "f, axes = plt.subplots(ncols=2, figsize=(8, 4))\n", - "axes[0].set_title('Periodic')\n", - "axes[0].plot(freq_irasa, psd_p[0,:])\n", + "axes[0].plot(irasa_out.freqs, irasa_out.periodic[0,:], label='periodic')\n", "axes[0].set_ylabel('Power (a.u.)')\n", "axes[0].set_xlabel('Frequency (Hz)')\n", - "axes[1].set_title('Aperiodic')\n", "\n", - "axes[1].loglog(freq[1:], psd[1:])\n", - "axes[1].loglog(freq_irasa, psd_ap[0,:])\n", + "axes[1].loglog(freq[1:], psd[1:], label='psd')\n", + "axes[1].loglog(irasa_out.freqs, irasa_out.aperiodic[0,:], label='aperiodic')\n", "axes[1].set_ylabel('Power (a.u.)')\n", "axes[1].set_xlabel('Frequency (Hz)')\n", + "plt.legend()\n", "\n", "f.tight_layout()" ] }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 20, "metadata": {}, "outputs": [ { @@ -739,45 +695,43 @@ " \n", " 0\n", " 0\n", - " 9.5\n", - " 1.436504\n", - " 0.411332\n", + " 10.0\n", + " 1.436404\n", + " 0.423565\n", " \n", " \n", "\n", "" ], "text/plain": [ - " ch_name cf bw pw\n", - "0 0 9.5 1.436504 0.411332" + " ch_name cf bw pw\n", + "0 0 10.0 1.436404 0.423565" ] }, - "execution_count": 15, + "execution_count": 20, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# %% get periodic stuff\n", - "from pyrasa.utils.peak_utils import get_peak_params\n", - "get_peak_params(psd_p, freqs=freq_irasa)\n" + "irasa_out.get_peaks()" ] }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 21, "metadata": {}, "outputs": [], "source": [ "# %% get aperiodic stuff\n", - "from pyrasa.utils.aperiodic_utils import compute_slope\n", - "ap_params_f, gof_params_f = compute_slope(aperiodic_spectrum=psd_ap, freqs=freq_irasa, fit_func='fixed')\n", - "ap_params_k, gof_params_k = compute_slope(aperiodic_spectrum=psd_ap, freqs=freq_irasa, fit_func='knee')\n" + "slopes_f = irasa_out.get_slopes(fit_func='fixed')\n", + "slopes_k = irasa_out.get_slopes(fit_func='knee')" ] }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 22, "metadata": {}, "outputs": [ { @@ -812,19 +766,19 @@ " \n", " \n", " 0\n", - " 0.019271\n", - " 0.932297\n", - " -780.588556\n", - " -783.881861\n", + " 0.019573\n", + " 0.931418\n", + " -772.202215\n", + " -778.788824\n", " fixed\n", " 0\n", " \n", " \n", " 0\n", - " 0.000020\n", - " 0.999928\n", - " -2133.013680\n", - " -2142.893595\n", + " 0.000028\n", + " 0.999901\n", + " -2062.763593\n", + " -2075.936813\n", " knee\n", " 0\n", " \n", @@ -834,84 +788,17 @@ ], "text/plain": [ " mse r_squared BIC AIC fit_type ch_name\n", - "0 0.019271 0.932297 -780.588556 -783.881861 fixed 0\n", - "0 0.000020 0.999928 -2133.013680 -2142.893595 knee 0" - ] - }, - "execution_count": 17, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "pd.concat([gof_params_f, gof_params_k])" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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\n", - "
" - ], - "text/plain": [ - " mse r_squared BIC AIC fit_type ch_name\n", - "0 0.007478 0.978371 -2900.221645 -2908.998768 custom 0\n", - "0 0.000337 0.998999 -4744.265350 -4753.042473 custom 0" + "0 0.006699 0.980793 -2965.641163 -2974.418286 custom 0\n", + "0 0.000291 0.999139 -4832.361294 -4841.138417 custom 0" ] }, "execution_count": 6, diff --git a/examples/irasa_mne.ipynb b/examples/irasa_mne.ipynb index 380c96f..5f51fd0 100644 --- a/examples/irasa_mne.ipynb +++ b/examples/irasa_mne.ipynb @@ -233,20 +233,36 @@ "text": [ "Effective window size : 3.410 (s)\n", "Plotting power spectral density (dB=True).\n", - "Plotting power spectral density (dB=True).\n", - "Plotting power spectral density (dB=False).\n" + "Plotting power spectral density (dB=True).\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ + "/var/folders/6x/k2wvgw51691cj5qd77pzcrfw0000gn/T/ipykernel_29797/1044382114.py:4: FutureWarning: The value of `amplitude='auto'` will be removed in MNE 1.8.0, and the new default will be `amplitude=False`.\n", + " raw.compute_psd(method='welch', n_per_seg=nperseg, n_overlap=nperseg//2, fmin=0.25, fmax=50).plot(axes=axes[0])\n", "/Users/fabian.schmidt/git/pyrasa/.pixi/envs/default/lib/python3.12/site-packages/mne/viz/utils.py:167: UserWarning: FigureCanvasAgg is non-interactive, and thus cannot be shown\n", " (fig or plt).show(**kwargs)\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/irasa_mne/mne_objs.py:64: UserWarning: Zero value in PSD for channels MEG 0111, MEG 0121, MEG 0131, MEG 0141, MEG 0211, MEG 0221, MEG 0231, MEG 0241, MEG 0311, MEG 0321, MEG 0331, MEG 0341, MEG 0411, MEG 0421, MEG 0431, MEG 0441, MEG 0511, MEG 0521, MEG 0531, MEG 0541, MEG 0611, MEG 0621, MEG 0631, MEG 0641, MEG 0711, MEG 0721, MEG 0731, MEG 0741, MEG 0811, MEG 0821, MEG 0911, MEG 0921, MEG 0931, MEG 0941, MEG 1011, MEG 1021, MEG 1031, MEG 1041, MEG 1111, MEG 1121, MEG 1131, MEG 1141, MEG 1211, MEG 1221, MEG 1231, MEG 1241, MEG 1311, MEG 1321, MEG 1331, MEG 1341, MEG 1411, MEG 1421, MEG 1431, MEG 1441, MEG 1511, MEG 1521, MEG 1531, MEG 1541, MEG 1611, MEG 1621, MEG 1631, MEG 1641, MEG 1711, MEG 1721, MEG 1731, MEG 1741, MEG 1811, MEG 1821, MEG 1831, MEG 1841, MEG 1911, MEG 1921, MEG 1931, MEG 1941, MEG 2011, MEG 2021, MEG 2031, MEG 2041, MEG 2111, MEG 2121, MEG 2131, MEG 2141, MEG 2211, MEG 2221, MEG 2231, MEG 2241, MEG 2311, MEG 2321, MEG 2331, MEG 2341, MEG 2411, MEG 2421, MEG 2431, MEG 2441, MEG 2511, MEG 2521, MEG 2531, MEG 2541, MEG 2611, MEG 2621, MEG 2631, MEG 2641.\n", + "/var/folders/6x/k2wvgw51691cj5qd77pzcrfw0000gn/T/ipykernel_29797/1044382114.py:5: FutureWarning: The value of `amplitude='auto'` will be removed in MNE 1.8.0, and the new default will be `amplitude=False`.\n", + " irasa_results.aperiodic.plot(axes=axes[1])\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Plotting power spectral density (dB=False).\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/fabian.schmidt/git/pyrasa/pyrasa/irasa_mne/mne_objs.py:61: UserWarning: Zero value in PSD for channels MEG 0111, MEG 0121, MEG 0131, MEG 0141, MEG 0211, MEG 0221, MEG 0231, MEG 0241, MEG 0311, MEG 0321, MEG 0331, MEG 0341, MEG 0411, MEG 0421, MEG 0431, MEG 0441, MEG 0511, MEG 0521, MEG 0531, MEG 0541, MEG 0611, MEG 0621, MEG 0631, MEG 0641, MEG 0711, MEG 0721, MEG 0731, MEG 0741, MEG 0811, MEG 0821, MEG 0911, MEG 0921, MEG 0931, MEG 0941, MEG 1011, MEG 1021, MEG 1031, MEG 1041, MEG 1111, MEG 1121, MEG 1131, MEG 1141, MEG 1211, MEG 1221, MEG 1231, MEG 1241, MEG 1311, MEG 1321, MEG 1331, MEG 1341, MEG 1411, MEG 1421, MEG 1431, MEG 1441, MEG 1511, MEG 1521, MEG 1531, MEG 1541, MEG 1611, MEG 1621, MEG 1631, MEG 1641, MEG 1711, MEG 1721, MEG 1731, MEG 1741, MEG 1811, MEG 1821, MEG 1831, MEG 1841, MEG 1911, MEG 1921, MEG 1931, MEG 1941, MEG 2011, MEG 2021, MEG 2031, MEG 2041, MEG 2111, MEG 2121, MEG 2131, MEG 2141, MEG 2211, MEG 2221, MEG 2231, MEG 2241, MEG 2311, MEG 2321, MEG 2331, MEG 2341, MEG 2411, MEG 2421, MEG 2431, MEG 2441, MEG 2511, MEG 2521, MEG 2531, MEG 2541, MEG 2611, MEG 2621, MEG 2631, MEG 2641.\n", "These channels might be dead.\n", " super().plot(\n", - "/var/folders/6x/k2wvgw51691cj5qd77pzcrfw0000gn/T/ipykernel_37697/1044382114.py:12: UserWarning: This figure includes Axes that are not compatible with tight_layout, so results might be incorrect.\n", + "/var/folders/6x/k2wvgw51691cj5qd77pzcrfw0000gn/T/ipykernel_29797/1044382114.py:12: UserWarning: This figure includes Axes that are not compatible with tight_layout, so results might be incorrect.\n", " f.tight_layout()\n" ] }, @@ -294,7 +310,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 5, "metadata": {}, "outputs": [ { @@ -303,7 +319,7 @@ "Text(0.5, 0, 'R2')" ] }, - "execution_count": 13, + "execution_count": 5, "metadata": {}, "output_type": "execute_result" }, @@ -332,7 +348,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 6, "metadata": {}, "outputs": [ { @@ -345,7 +361,7 @@ " )" ] }, - "execution_count": 14, + "execution_count": 6, "metadata": {}, "output_type": "execute_result" }, @@ -381,7 +397,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 7, "metadata": {}, "outputs": [], "source": [ @@ -423,7 +439,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 8, "metadata": {}, "outputs": [], "source": [ @@ -441,5232 +457,5232 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 9, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: 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np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: 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* (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: 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(Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: 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* (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: 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* (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: 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* (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: 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(Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: 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* (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in power\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n", - "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:137: RuntimeWarning: overflow encountered in multiply\n", + "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n", " y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n" ] } @@ -5677,7 +5693,7 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 10, "metadata": {}, "outputs": [ { @@ -5686,13 +5702,13 @@ "" ] }, - "execution_count": 20, + "execution_count": 10, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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", + "image/png": 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", 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" ] @@ -5703,7 +5719,6 @@ ], "source": [ "ap_data = knee_epoched.aperiodic_params.groupby(['event_id', 'ch_name'])['Knee Frequency (Hz)'].mean().reset_index()\n", - "ap_data\n", "sns.pointplot(ap_data, x='event_id', y='Knee Frequency (Hz)')" ] }, @@ -5716,16 +5731,16 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 11, "metadata": {}, "outputs": [], "source": [ - "df_periodic = periodic.get_peaks(smoothing_window=2)" + "df_periodic = irasa_epoched.periodic.get_peaks(smoothing_window=2)" ] }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 12, "metadata": {}, "outputs": [], "source": [ @@ -5735,7 +5750,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 13, "metadata": {}, "outputs": [ { @@ -5744,13 +5759,13 @@ "" ] }, - "execution_count": 17, + "execution_count": 13, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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", 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", 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