From fb9bb25c08c13d6c19554a806100f7914c40ac9a Mon Sep 17 00:00:00 2001 From: Collin Brown Date: Mon, 23 Mar 2026 16:05:05 -0400 Subject: [PATCH] Added non-conservative Krook operator to python wrapper and JETPLUME --- examplelinfpc.ipynb | 7463 ++++++++++++++++++++++++++++++++++++++++++- linfpclib/linfpc.py | 138 +- src/fpc.f90 | 23 +- src/functions.f90 | 10 +- 4 files changed, 7500 insertions(+), 134 deletions(-) diff --git a/examplelinfpc.ipynb b/examplelinfpc.ipynb index d79f28b..4652d9b 100644 --- a/examplelinfpc.ipynb +++ b/examplelinfpc.ipynb @@ -51,7 +51,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 2, @@ -871,7 +871,7 @@ }, { "data": { - "image/png": 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", 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YAeJzddR5PRQ6hKQMenSchUKHkExhRyxYXb9KRi9kJQsbOWylLCvX8ugovTlaeTyEEMeh0HEW3gtCMo2eIFG+t7pIpx7KtbGCQf0qLHleHfkctTdHXa1Fzw4hxOXQo0NIJpAFg9VScTt2lfaV++UFOoPBhuXk8ia3A+LLy9Wv8ub3Wx8bIcQS9Og4C4UOIU6hlfRr1M6pyiWjtbCU75Wl58oQlDosVVcXaa8OZyk35UzJhBBH4RIQzkLRR0gqsLvopta6VHZRig61AFEnPesJGNnrA8Svf6WVy0MIIVkAPTqEpAqrK4cbnW/mMdETSFpVV2rPjlaCcTgcEThARPAoS9CV3iB6cwhJGV4kv9ZVsufnEhQ6hFhBLUi0PCaJ2jMTKEb92u1LaUcrfFVXV5+MHAoBx49re3MocghJGQKSD7fwN7QeCh1CEiFVD3srXh4jtESTntAB4r064XD9Ap9A/eSASq+OHLLy+Sh2CEkRTEZ2Ft4LQrIJq+EwK2tdKScIFMX4VcyB+mOyl0cWRV4vRQ4hJGugR4eQRFDOS5PsXDLqyf3M2mq101rqQWspCHWFlnwsFKoXNEqPjuzVkWdSFkXA42logxDiGPToOAuFDiGJkozI0TrPqi29dnYEk/IcUazPxZGFDVAftpJzdUQxMm+OvKgnvTqEpAQKHWfhvSAkUdzm0TDz8mghC526unpRo0xGVm7BYLw3x23XTwghGtCjQ0hj5+jRhjMkAw33FRTEe3Po0SEkJdCj4ywUOoQkQiJhJieEgZ2qKyvnKL04yiorIH65B3pwCEkbFDrOwntBiFtwekkIM7vyvDmCEC945BJypTfH46nP02HoihCSRVDoEGIXvVwYo8omdTs9rKyRpdevXnu9sSnnyFGKHXkJCOVxICJ4gkGGrghJMYJDW6L8+c9/hiAImDx5cmzf8ePHMXHiRLRq1QrNmjXD5Zdfjv3798edt3v3bowcORKFhYVo27YtbrvtNoRkD3GUVatWYeDAgcjPz0f37t2xcOHCJEZqDQodQuzi9APeTLDonWPHthJ5/HV18ceVsyQD8d4cef/x4/TkEJJivA5tibB+/Xo8/fTT6NevX9z+W2+9Fa+//jqWLl2K1atXY+/evfjlL38ZOx4OhzFy5EgEAgGsWbMGzz//PBYuXIgZM2bE2uzatQsjR47EsGHDsGnTJkyePBnXX3893nnnnQRHaw0KHUKs4LQHw6q4UfZrtGinHZuSFPHMKENTSi+PsupKyxMkr4VFCEkJHoc2u9TU1OCqq67Cs88+ixYtWsT2HzlyBH//+9/x+OOP47zzzsOgQYOwYMECrFmzBp988gkA4N1338W2bdvwv//7vxgwYAAuuugi3H///Zg7dy4C0b8Z8+bNQ7du3fDYY4+hV69emDRpEn71q19h9uzZCYzWOhQ6hGQSJxfHtDK/jtwuEGi4UKcgNKy60loTS87VIYS4nurq6ritrq5Ot+3EiRMxcuRIDB8+PG5/ZWUlgsFg3P6ePXuiS5cuWLt2LQBg7dq16Nu3L9q1axdrU15ejurqamzdujXWRm27vLw8ZiNVUOgQYhW74SIruTtWRI6dyf+03qvFlNKbo17UE6gXOvK5eot/MoRFSEqQF/VMZpN/4zt37ozi4uLYNmvWLM0+Fy9ejI0bN2oer6qqQl5eHk444YS4/e3atUNVVVWsjVLkyMflY0ZtqqurcezYMZO7kjgsLyfEClqLZSrfp+qhb9aXOuFYuV/jsySKEJRrVynFjixwlKEptciRN3nyQEKI4zhZXr5nzx4UFRXF9ufn5zdou2fPHtxyyy2oqKhAQUFBkj27D/6lIsQqmfJgWMm50fMaKd5LkgQpEIAoipH3ylfFezHq/RElKbJPb6utpVeHEJdTVFQUt2kJncrKShw4cAADBw6Ez+eDz+fD6tWr8Ze//AU+nw/t2rVDIBDA4cOH487bv38/SkpKAAAlJSUNqrDkz2ZtioqK0KRJE6cuuQEUOoTYQcuDYvdhb7XCyqhkXOu4SRhMEATA64Xk9RoKmHC0vFwKBOpFkMYGHx3ChKSCdCcjn3/++fjiiy+wadOm2DZ48GBcddVVsfd+vx8rV66MnbNjxw7s3r0bZWVlAICysjJ88cUXOHDgQKxNRUUFioqK0Lt371gbpQ25jWwjVbhC6MyaNQunnXYamjdvjrZt22LUqFHYsWOH4TkLFy6EIAhxWy663IgLkAVFKuaNMcvjsSOmtMJVqmMCAHg8kAwEmxRNNJaCwfp2QNz8OZIg1Ieu6NUhxFHSLXSaN2+O0tLSuK1p06Zo1aoVSktLUVxcjPHjx2PKlCn44IMPUFlZiWuvvRZlZWU488wzAQAXXHABevfujWuuuQabN2/GO++8g+nTp2PixIkxL9KNN96Ir776ClOnTsX27dvxt7/9DS+99BJuvfXW5G+aAa4QOqtXr8bEiRPxySefoKKiAsFgEBdccAFqa2sNzysqKsK+ffti2zfffJOmEZOcwU5JtpnoMEo6VrZJBLXIMhqLTugq8jGSmCz5fBEPDhDZou/FUCgmdEQAYlTsxIW45AotdcKzFlY8V4QQ1zN79mxcfPHFuPzyy3HOOeegpKQEr776auy41+vFG2+8Aa/Xi7KyMlx99dUYM2YM7rvvvlibbt264c0330RFRQX69++Pxx57DM899xzKy8tTOnZX+J7ffvvtuM8LFy5E27ZtUVlZiXPOOUf3PEEQYrE/QlKC1kNaXa5ttt/psVjpQ6ukXPFeEgRIAASlKBNFhAOBOG+PFAxCFMW4WVYlAIJW2Epd3UWBQ0hCuGGtq1WrVsV9LigowNy5czF37lzdc7p27Yp//vOfhnaHDh2Kzz77LMnR2cMVQkfNkSNHAAAtW7Y0bFdTU4OuXbtCFEUMHDgQDz74IPr06aPbvq6uLm4OgerqagBAMBRCUJ72vhESjE7RHVRN1d0oUDyMY/chGGz4kDbKjTHzBCUT8jIKYWlVWeklI8tVV+FwJBk56tUR5BCUzwfJ44Ho8yGYlwcACPr9kTwcSYLH46m/Do8HgiRBDAYhWP29ycLlIhr174UC3ocI6bz+ZJdwkG2QCIIkuevfLlEUcemll+Lw4cP4+OOPddutXbsW//nPf9CvXz8cOXIEjz76KD788ENs3boVnTp10jxn5syZuPfeexvsX7RoEQoLCx27BkIIIbnF0aNH8dvf/hZHjhyJK9d2kurqahQXF2MtgGZJ2qoBUAakdLzZguuEzk033YS33noLH3/8sa5g0SIYDKJXr1648sorcf/992u20fLodO7cGfv27kUrE+9RLhMMhVDx3nsYMXw4/I2tkkbl0alYuRIjzjuv4X1IJt8mmV8xPe+R1quBJ0feLx4+DOnQIUgHD0I8dAj4/ntg/35IBw5ArKqCdOgQAgcPYs3DD+Psxx+Hv3lzCK1aQWjbFkLHjkC7dhDat4enZUt4OnWCoFUSquW9yVKPTqP9vVDA+xDhx4MH0b5Dh7QInU/hjNA5HRQ6gMtCV5MmTcIbb7yBDz/80JbIAQC/349TTz0VO3fu1G2Tn5+vOYeA3+eD3++3Pd5co1HeBw0R4vf7rQkdrf1W21nFKFSlfjXbAIhebyRsJQiReXI8nsjr0aNAIACprg6e6Aylnro6ePLzIQSD8NTVQQgEIPh8kfwBjwcenw+C1vclR4SOTKP8vdCgsd+HdIo8N+To5BKuuBeSJGHSpElYtmwZ3n//fXTr1s22jXA4jC+++ALt27dPwQhJo8JpT43TTlOrychG5yoTjsPh+pLyaIUVUF+NBXmf1xvJ6XFifIQQXTK1qGeu4gqPzsSJE7Fo0SKsWLECzZs3j62LUVxcHJstccyYMejYsWNsHY777rsPZ555Jrp3747Dhw/jkUcewTfffIPrr78+Y9dBSMqxI670KsOirwIiJeTSsWOQJKl+xmRZ6CjLzwUBQtQbmr2+GUJIY8QVQuepp54CECk7U7JgwQKMGzcOALB79+5I5UeUQ4cOYcKECaiqqkKLFi0waNAgrFmzJjYDIyGuIBGvj5bXxE6ll7qdUvTIi3IqFucUozMli4iIG1EOc8niRxTh8XqBYJAih5A0IC/qmawNEsEVQsdKPrS6pn/27NmYPXt2ikZECMzFhCxIUhnqMsrLUe7XSkQ26VMURYiBAASPB2FBqF/vShm6AiITDHq9kEIhiOEwPJIUP/+OEuU9yeK8HEIyCXN0nMUVQocQV2A0V45eOyv20pmzojeXjsarFAjUz4rs89W/l48r1rQSohMMSoqqxQYowmLM0yGEuAUKHUK0cPJhbdfzo4Wdai695SIU76VwGFIoVJ9oLAgQPZ7IMhDRpiIAURDg9XohShIEUYQUDgN6E6fJXhx6cwhJCnp0nIVChzRurIZarIoUtb1UVlzpze1j5NWJIgaD9YnG8ubzRUrNlcnIfn9caFmSJIh1dRDkHB8zKHgIsQ2FjrNQ6BBiByveCqdydpzOAZIFTCgU8crIokYhiCTFyuaivFJ5tF2sCktOZjYThvTuEEJcAIUOIVpeD738GjsPbSO7icxHY2TPii15ThzZm6NOOpbqJxAEAESrsQBF7k70VQwEIv8xSlKkusPIM0axQ4gtuNaVs1DokMaNLACsCg8r3hynvT5W7IliZFOeo/bWiGKsckoWLnL5uLKsXPJ6I69Rb44YrbKSECk5F2RbgQCEggLjyQlZhUWIbbzRLVkbJAKFDiFWH8BWxYtTD3QzD5PVqi6FWJI8nvplH4BY8rEyPBWUQ1eSBCl6LZJcdSW/ApHrNLpWu54d5XU42Y4Ci5BGDYUOabzohYD0RIXVB6aZ3VShl/wcHbcge2x8PogKwSKvfSUJAkRBgBj1DIWj5edidBblONETFTmxO6Llvcmk0GB5O8limIzsLBQ6hFjBzsParNoqUdFktb3JJng8QLSUXD4nln8jiggrPDpiKASPwtsjh7FEAPB4GvadrkRtO+0YNiNZBoWOs/BeZCGLFy9Gx86d0bN3b2zevDnTw3EnGjkqDY4r2yn3mdlK1p4shKzYU4/BqA8zcaV4L/l8kXlyopscwgqFw7F5dCRJQjgYRBjxXh9JECDYWcnZDd4VvXusd/8JySBc1NNZeC+yjD179mDcddehe/fuyMvLw40335zpIWUH8rwvdh5sypl+nbSnhZnw0TvHyn6VbQmIrELu9dYnKiMqbEKhmKcnjMgyEZJWKbpXI9VReX1uFBCJ3GNCSNbD0FWW8cknn6Curg5333UXgsEgXnn1VYTDYXi1HjykHiuhC6MZhRPt047HJZGSc739Bv0qc3XCghDz6ISCwYiHRz4enS1ZjHp1hGgOjyc/PyKUzK6B4SJCEoKLejoLhU6W8d3evQCA9u3bo0+fPrjooosyPKIsQp0kmwzKyfCs5omY2dNqZyZs9JKPZa+FXHKu0ZcgCEB0jStRFBGOhq3E6HFZ9IQlCZ5gMOLR8XjgUefmWMFonGY42Y7ii2QBzNFxFt6LLKOmpgYA0KxZs4z0/+OPP6JtSQm+/vrrjPSfCL/57W/x2OOPaz8I9cIZivcPzJqFM885p6Fhq/acSqy1Ys8o50exyV4d+HyAIMS8ORLqPTqy6JEQWTJClCQIfr/10m+zMFGmEpMJIY0KCp0sw4rQqaysxG+uvBIdOnVCQWEhuv/sZ7j6mmuwdevWpPv/04MP4rJLL8WJJ56YtK10Mf3OO/GnWbNw5PBhayeoHoabP/8cA/r1M22XSj78179wyRVXoMMpp0A44QQsf+MNw/GcOHgwhJISCB06QOjYEULnzhC6dMHvH3ww0hRAOBzGnysqcMaLL6Ln66/j/I0b8bcff0RYNodo5RWioie6krkEJLUu2Ny//Q0ndu+OgmbNcMaQIfj0009jx56aNw/9Tj0VRS1boqhlS5SdfTbeevttC3eIkNyBycjOwnuRZZgJnWeffRZnlJWhuLgYr778MnZ8+SWefuop/PTTT1j0f/+XVN9Hjx7F3+fPx/jrrkvKTropLS3FySefjP/9xz8SOn/z559jQP/+Do/KHrW1tehfWoq5jz5qqf36t9/Gvk2bsO+zz7Bv40ZULFoEAPjViBEAIvH7OW+/jefXrcO9Z56Jt4cOxZSuXTH/4EEsPn4cQLQaK5rDIwLwNGkS8QYBumJm6IgRWPjii7rjWvLSS5hy2224Z/p0bPz0U/Tv1w/lI0fiwIEDAIBOnTrhzw8+iMp167Dhk09w3rBhuOyXv3REpBOSLVDoOAvvRZZRU1MDv9+P/Pz8Bsc+/vhj3HjzzfjrnDl4et48nHnmmejatSvOP/98rFi+HH+YMiWpvv/5z38iPz8fZ555Zmxf25ISPPfcc3Ht1q9fj4LCQuzatSvhvpy2e8nIkVj80kum7Xbu3InzLrgATYqLcerpp2Pdp5/iv199hQH9+uHGSZMgFBTg1DPOiHvQ//z88yE0aYLfXHON7XHFMPGCXDRiBB64+278z8UXWzqnTatWKGnbFiVt2qCkTRu88d57OLlrV5wzaFCsgurTr75Cec+eGNqlCzo0bYoLWrdGWdOm2BYKAQDCkoSnd+/GsHfeQa+//x3nP/YYXpO9Lwnmujz+xBOYMH48rh07Fr179cK8v/0NhYWFmL9wIQDgkosvxi8uugg9evTAz372M/zpvvvQrFkzfLJunbFhJ6rVCCE5CYVOlvHTTz/penOm/PGPOPfcc3HTTTdpHm/ZsmVSfX/08ccYNHBg3L6+ffti25dfxu27fdo0/O6GG9CtW7eE+3La7umnnYZP169HXV2dbu7I9h07cPfdd+PnP/85tnz2GaZPm4ZRo0cDAPr17YuxV18NANi0eTN2/PvfAIBv9+zBv9auBQCMix5vgCDgwUceQbO2beu3du3it5IS7N6zp2GOj9kDXCsfSJXLEwgE8L/LluG6X/86rlrqtJNOwkf//S+++ukniAC+rKnBZ8eO4Qy/HwDw9wMH8NqBA5g5YADeuuYaXD9kCG6cOxcfbd5sXyxIEgJ1dajcuBHDzzsvttsjCBh+3nlYu3Ztg2sOh0JYvGQJamtrUSaLS7P7Y9SGAodkCfToOAurrrKMmpoaTaHz5ZdfYv369XjZxGvx+OzZeHz2bLRu3RpApPJmZUWFpb6/+eYbdOjQIW5faZ8+2LZtW+zzO++8gw0bNuClxYsBAEPPOw+r3n+/wXszzOzaQpLQoX17BAIBVFVVoWuXLprNbrn1Vpxxxhm4d8YM+P1+nHzSSVj80kv4YssWFBYWouzMM/GzHj3w7//8B0tefhkz7rwTS5ctgxS1P+L883WHcOP11+PXv/xl3JjU7zu0b29cOWSlcklZcRV9v/ydd3C4uhrjoqINiISubjn/fBw6cAAjliyBVxAQliTc3Lo1RkgSqoJBLNi/H0/37YtBHTqgoEUL9PjZz1D5/fdY8OabGKZ1rSZenh9++AHhcBjt2rWL29+ubVts37Ej9vmLL75A2Tnn4Pjx42jWrBmWLV2K3r17G9q2hHqeH1ZgEZfCqitnodDJMmpqazWFzsaNGwEAgwYNMjx/y5YtmDN7Ni6//PLYvmAwaKnvY8eOoaCgIG5f39JSLFu+HAAgSRKm3XUXbvvjH2NCKlHs2n3k0Udx2x//qG1MENCksBBAJM9Ii2+++QYfrF6Nxx9/PO6/f7/fH5efM+aqqzB95syY0Fny8ssAgKuvvLLhXEYKAdKyRQu0bNFC85jluXbUGFVXKfj74sW4aOhQdGjbFqFDhwBRhBgO49X167FsyxbMHj4cXQUBW3btwiNffYWiJk3Qft8+HJck3LhlC7BtW8QTJAgIhsPo1717zPaDDz2EBx96KPb52LFj+GTdOky65ZbYvm2bN6OLjrjU4pRTTsGm9etxpLoaL7/yCsaOH4/V772XvNihuCGkUUKhk2XoeXTkB7hZ2fmWrVtx1513JtR369atcejQobh9paWl+Pbbb1FTU4PXX38d+/btw5Rbb03IfjJ2GyyFoZqX5uCPPwIA2rRpE98uenzTpk3w+Xzo2rVr3OHPNm3C2Guuidm75re/xd333ottX36J1998E+vWrwcAjL3qqjh7sffR8x585BE8+Mgjhte8bf16dOncWV/AqO0bERU833z7Ld77+GO8+vTT9YcEAZIo4r7XX8fNQ4bg4m7dEPjxR3Tt2BHfHjqE/z10CL+PJiTP6dcPnbp0gb9lS+S1agV/y5ZorvDq3ThhAn6tEM1XjRuHy0eNwi9HjYrt69C+PYDI98fr9WL//v1xQ91/4ABKFF6evLw8dI+KqUEDB2J9ZSXmPPkknv7b36xdux5akxlS+BAXQo+Os1DoZBk1NTVo1apVg/2lpaUAgI8++gj/8z//0+C47I3ZsWMHLleEMB584AGMiFbimHHqqac2qFyS+/38889x9z33YMb06WjatKnl69HDabtbtm5Fp06dIh4hDbHg8XggiiJC0URcAPjnW29h+44dcaXlXbp0wbBzz8X7q1bhhkmTAACnDRqE3r16xRtUeQ9uHD8+PnSlbBd9lQVBwii9OdGw1YIlS9C2VSuMHDasvp0oIhwM4lggEPkoRRbrlDweCNHFPjt37ow8QcC+YBBlzZsjr0UL5Ldqhby2bZHXsiWk6ESELVu2jMv9atKkCdq2bRsRKqp7kJeXh0EDB2LlBx9g1GWXRYciYuUHH2CSTl6Z3Kauri65e0NIFkGh4ywUOllGTU0Nmmt4bcrKynDBiBG4edIk1NTUoKysDKIoYv369Xjq6afxzLx5KCgoQM+ePbEumjwrYzV0VX7BBZh25504dOgQWkTDMM2aNUPXrl3xh9tug8fjwYQJE5K/SKfsKgTNRx9/jAuGD9dtOujUU+H3+7Fw4UL0790bO3bswE3/7/8BQH3oKmpv7NVX4/1Vq1BVVRX5LHtzDFALAvX4zBKOa2pqsPOrr2K7dn3zDTZ98QVatmiBLp064clnn8WyN9/EypdfjgkeURSx4KWXMPZXv4LP56vfH13PakSfPpi7Zg3aCgK6CgK++P57LD54EOV5eWjSpAl+064dHv/3v+EpKsIZfj/qAgFs+vxznNCuHcZdeaXpNcfNHh29jim33IKx48dj8MCBOH3wYDzx17+itrYW144ZA0gSpk2fjovKy9Glc2f8VFODRYsXY9Xq1XjnjTcSSya2spo6vTrEZVDoOAuFTpZhVHW1YvlyzH7iCTz86KP46quvUFBQgO7du+OSiy9Gz5498cYbb+BnPXqY9rFw4UKMGzeuwf6+ffti4MCBeOmll/C73/2ufn9pKV5/4w28tHhx5IFqkYULF+La8eMhhcOaxxO1CyDuoXj82DEsf+01vK18WKoemh06dsS8uXPxx9tvx4DTTsPpp52GMVddhQUvvICSkpK49pePGoWJkyejpqYGeXl5uPLXv274ELbygFW/N9i34bPPMOySS2KHp9x1FwBg7G9+g4Vz5+KHH3/Ef1WzVb/30UfY/d13uO6KK6KmImXlYnSRzvtHjcKfly/HvR9/jB+PHkXrvDxc1qoVfh0KIQhgfOfOaF1cjL9v3Yr71q1DUZMmKD3xREzp1y8yeaCVpSBUIcQrRo/G999/jxn33YeqqioM6N8fb7/+eixB+cD332PM+PHYt28fiouL0a+0FO+88QZGGIhUQ1Qerrj3yrFR7BCSs1DoZBGiKOLYsWO6QqegoADT7rgD0+64Q/P4lq1b8bOf/cy0nx+i+SxazJg+HbfdfjsmTJgQW/PotRUrLIy+Ibu+/hrnnnuu7vFE7apZsHAhTj/ttMj8PwZegat+8xu0aN0avxg+HP5oifWsBx5o0K5p06b46fvvIx/s5s4osZFvM/TssyHJ+VEa5dQzb7sNM//4x7hS6gvOOQfS7t2Rz6IISbmeFYCmBQWYPmIEpg4ciMCPP6Luhx8Q+PFH1PzwA4IAIAi4oksXXDN4MPLatkV+mzaR0FWbNggLAkJHj8JTUBBXsr7KQgXfpJtvxqSbb9Y89ndFLlHKocAhLkUAtBfOtWOD0ynEoNDJAl6IehVOPfVUABoJtRbZsmULPvr4Y7zy6qsAgBYtWuCDlStt2Rg5ciT+s3MnvvvuO3Tu3Dmhcci89fbbePIvf0n4/BdffBF/X7AAQKS8fmh0fpYnHn88Em5SVE79dfZsfc+J1T8oTlRK6bXX8+ao2ybRr0cQIHi9kHy+mHcnbgMiq5hH85TEUAiS4t7Ix2UEn8/dKyTreW203iu9PUb2zNoQ4gTRNeiSQpIARc5hY4ZCJwt4ddky/Ps//4klcJ42eHBCdhYluASCmsmK0uFk+PSTT5I6/5prrsE1WrMRq0IU148fn9xkcVrhj2RRihkrf9CM2mkJI/V7xT7B64Xk8cQW8VRv8qKeoVAIYjgcSVTW2AQ5dOVWz0giY7IiON16vYQQTSh0soCZ99yD80eMwKw//xlX/fa3uPTSS1PSz9PPPIOPP/4YO/79b2zYsAG3/L//h7KyspT0lTBWc2GseD7shJ3s2DMan9b4rXp4RLFhezvl5nKXABCd80ctcsJRYSMfE0MhQBAinp3oJgkC4PXGu9b1Hv7Z4AVJRLhonZMN10qyA3p0HIVCJwsYMGAAdn/9NQ4ePJh0uMiI391wAyZNnOiozXFjxmi+dwz1A8dIbFgVGWr7Wvbs7LeK3rlWZkvWyNtpsCnxemNhKWVIKhStyBIQyeMJh8MIh8ORJOboJkkSBK3kcCs/i0xCTwzJFpwSOgQAhU7W0LRpU0fmp0k3yuotrUqupEnAo5GwwLHbJ1DvhTGzZdVLoxeSUh7TEj2q4wIA+P313hxJQigYjIgZAN7ofhFAWM7VkTefrz5spXc9bsTpJSDcfK2EkBgUOiS7UZYKJyJ6Emlnpy89D4fVcJmeqDGyozU+jXaeaK4OAIiCgFC0IktuKVdnicpcHVGEx+/X9xS52WNiJc9JRv1zM/ouEOI09Og4CoUOyX6MckPM9mmh9WBL1p5VrOYNGY3PJGwlRdtIkgT4/RGvTdSbI3tx5HYSIiIoHAgAggAhL8/cQ5UtD3+1l099jA8Kkim8XmvzVBkhiuZtGgmcPJHkBlYeSlYfwOlul+gDVS90pexTTwDJQ/N4IHk8CAWDMZGj9uhIkhRJUg6HIyXl2SwAnB57Nt8LQhoJ9OiQ7CddIatsaqeVqxNFKbsEAGI4HBM58gbUix5Z7MDjgSAIMO09m7w6hLgRn48eHQeh0CGNByfnwckERonLWgnJFq5VlCQE6+ogRgWMsgILUIgdjwee/HwAcPckgYTkAhQ6jkKhQ7KbRPJf0onVXBuj3ByzJGazvB49ESRJEINBCH4/RElCGNEqq+hpSg+PkJeHcHSNLElpM9uwOjmj8tVKe3qwiJNQ6DgKhQ7JHcweTHrz6WTCnpFXRrlfK/9Gr63eeephyK/hcCzJWDmBoLKdJEmRyiyvN1J5JYr1Hh2jqis3PvytlPnrHdOrbiOEuB4KHZK9WC3XTlaIJGvPrvfD6kPXTCApN1GM+yyJIsLBYP38OD6ftkdHEIDoAqeQJITr6mIzJzdAfvC7NUSoJ8qShYKHOI3XG/vnI2HCYfM2jQQKHZK96C3M6JRtp+wZeX70PAV6osvMlixoDPoQovtEUazPyYl6bZQ5OiIAKboQqLz0gyRJ+tPKK38O2fDwd2qc6ko3QpLF50te6PD7GINCh2QX6ge9WV6K/F7vl95qqMWq6ElUfBl5k5R29EJYNnOVQoFAJCwlipFNkiLhKTkHBxHBI+Tl1YewpMgSEKFg0P79cCtOiRSlN4sQ4ioodEhu47R3wYo9p3KAlO20BI5exZX6veocMbp2lRy2kte8gscDyeer9+jInpyowJG9PZIkRRKT1ehN3OjWh79ybGZJ3lYFkZuvl2QP9Og4CoUOyT60vBh6no1Eq2ySsWfFbiLtrPRjsEnRNuFgsN6bE/XUSHI7rzc2j46gDGWpys/DwWBMBAnR43FiQP3ejX90ExlTtl0jyU4odByFMyOT7MJMANhdPTsRQWHWJpXt9EJX6twcjT9yUnQl8rAkNZgMUH4PAFJ0ZXIpusK5pBA5ULyGjh933nuVCzSmayUkC6BHh2QnVnJznP6Pxk7IyqqXyGyfUW6OUX6Oxj5BTiyW5+dQ5JXI4StJEBCOniuGQjFPT8zzg3qBFLeCuVYYyK7Xw+rPzm67VP5nq2Wb/0mTZKFHx1EodEh2YjaHjd1Qgtl/4XbspbO82kzkKMSQXG3liebhqD00sVeF0IlbLkJx/QIiQke22eDeKD87nSPlZDu7ffPhQdKB1xsRO8QReCdJdqL3IHM6X8KuvVQ8iPVyd5Riz6I9AYDH44l4Y1R2BUREjhidUVUURYTCYe3wlTz/jnqcZgnJTt8fq+E/J74PTEQmJCuh0CHZiZ7XJNH/5PUeUFaTkpNJMrabjKwVzrLpRfL4/XHJxXI4KhQKxYmZcDAIKZr/I3t6JAAeny9+zSv5Ia98lcflBrRESCrDnIQkg89Hj46DMBmZkERQzTTcgEx4LtTtNQRRzCsjCBCUf0ij4kReyTxyWnTuHHmfvHK5IMTn5zg57lSi/HklOy6t8ymWiFPIQifZzSKzZs3CaaedhubNm6Nt27YYNWoUduzYEdfm+PHjmDhxIlq1aoVmzZrh8ssvx/79++Pa7N69GyNHjkRhYSHatm2L2267DSHVVBSrVq3CwIEDkZ+fj+7du2PhwoUJ3yarUOiQ7MWKVyWT9tQ2rHhp9NppjS0B24Ji8yjmzAGAUDAY91nuMxRNSpYTkz0+X1y+jq1xE0LMSbPQWb16NSZOnIhPPvkEFRUVCAaDuOCCC1BbWxtrc+utt+L111/H0qVLsXr1auzduxe//OUvY8fD4TBGjhyJQCCANWvW4Pnnn8fChQsxY8aMWJtdu3Zh5MiRGDZsGDZt2oTJkyfj+uuvxzvvvOPMfdOBvjGSvahDPnbzQMzsyTb1HuDqMI2Vvo0qpbSOW6mu0qvMAuJXMFZ5NARBiIWwwtHScwBxYkeK2hDl/8o8Hni83oaeEa2yfrNwmtVwm5Pt0pGrQ0iW8fbbb8d9XrhwIdq2bYvKykqcc845OHLkCP7+979j0aJFOO+88wAACxYsQK9evfDJJ5/gzDPPxLvvvott27bhvffeQ7t27TBgwADcf//9uP322zFz5kzk5eVh3rx56NatGx577DEAQK9evfDxxx9j9uzZKC8vT9n10aNDsh+7c+dYtZfsg9pIgOi1MxpPMujY9kRnP5YX+IxLOIYidycQgATAm5dnvR+ros8MpxOTk8XONRKSCHLVVTJbtDy9uro6bqurqzPt/siRIwCAli1bAgAqKysRDAYxfPjwWJuePXuiS5cuWLt2LQBg7dq16Nu3L9q1axdrU15ejurqamzdujXWRmlDbiPbSBWuEDpW4oNaLF26FD179kRBQQH69u2Lf/7zn2kYLcl5MlnCbNaPnRwTM5EGRObU8XgQlhOOoeHRkd+Hw7EFPmOb3hiVfRBC7OFg6Kpz584oLi6ObbNmzTLsWhRFTJ48GWeddRZKS0sBAFVVVcjLy8MJJ5wQ17Zdu3aoqqqKtVGKHPm4fMyoTXV1NY4dO5bYvbKAK4SOlfigmjVr1uDKK6/E+PHj8dlnn2HUqFEYNWoUtmzZksaRE5IhjPKJtMJf8iFlm+gWOHasfpZkRRvlZ0kQ4GvSRD+h12rODiEkrezZswdHjhyJbdOmTTNsP3HiRGzZsgWLFy9O0whTjytydMzig1rMmTMHF154IW677TYAwP3334+Kigo8+eSTmDdvXsrHTFyElZyaREm2jD2RnByjdsrPyYTDooiqigjN1oIAeL2x8nLT8dlZX8zqbNNOtZPb0ttE3IwT5eXR34mioiIUFRVZOmXSpEl444038OGHH6JTp06x/SUlJQgEAjh8+HCcV2f//v0oKSmJtfn000/j7MlVWco26kqt/fv3o6ioCE2aNLF3fTZwhdBRo44ParF27VpMmTIlbl95eTmWL1+ue05dXV1cfLK6uhoAEAyFEAwGkxhxdhOMPuyCWitSuw29/Ihk8yYkqf4+RJNy4+xpCQ2tvqy0s2pP7TnROyZvypJ3Uaz/rNgfDocREkWEJQkhAAFRBAoKIOXlQcrPh5CfDxQURPrIz4eUlwfR54OnoABBeZ0sSUJIFCP3S/69UeY1yaRqduRk0BqnDrq/F2azQLvlWh0iq/4+pJC0Xr+DQsdaUwm///3vsWzZMqxatQrdunWLOz5o0CD4/X6sXLkSl19+OQBgx44d2L17N8rKygAAZWVl+NOf/oQDBw6gbdu2AICKigoUFRWhd+/esTbqFJOKioqYjVThOqGjFR/UQi/WJ8cCtZg1axbuvffeBvs/WLUKhYWFiQ86R6h4771MD8EVVKxenekhpJ4TTohs0T9oHgDNolvraJM2d90Va340un0PRMRNVVVkawRUrFqV6SG4gsb+9+Ho0aOZHkLKmDhxIhYtWoQVK1agefPmsedocXExmjRpguLiYowfPx5TpkxBy5YtUVRUhN///vcoKyvDmWeeCQC44IIL0Lt3b1xzzTV4+OGHUVVVhenTp2PixInIz88HANx444148sknMXXqVFx33XV4//338dJLL+HNN99M6fW5TujI8cGPP/7YcdvTpk2L8wJVV1ejc+fOGDZ0KFoZeI9ynWAohIr33sOI4cPhd/tsnGZenCTCVsFgEBWrV2PEuefCLy+oZ8VzY3VMybTT8uzIpeNqj044rO3lkSQcP3wYxw4fxtGDB3Fk/37UHjyIo4cOofbgQfx04ACOHTyInw4fxslz52L/Aw+goFkzFHXsiKZt2qCwdWs0bd0aRR07orBNGxR16gS/8h8EI6+OW7wcNrxMwVAIFatWYcTQodq/F+prc9u1OkRW/X1IIT8ePJi+ztLs0XnqqacAAEOHDo3bv2DBAowbNw4AMHv2bHg8Hlx++eWoq6tDeXk5/va3v8Xaer1evPHGG7jppptQVlaGpk2bYuzYsbjvvvtibbp164Y333wTt956K+bMmYNOnTrhueeeS2lpOeAyoaMXH9RCL9YnxwK1yM/PjylLJX6fD36/P7FB5xBZcR/MhIddoaMhJPw+X0OhY9RPqsSNclxqoSNJkYoppaARhPhXuY0ijBXyeOAFgFAInnAYnnAYQigEIfpZrKuDePx4pJu6OghNm0bODYchiCIESQKCQfgEAX6vt+GDz80P/wQFmObvhVGIzg3XmgKy4u9DCkmryHNiUU/lHFomGObfRSkoKMDcuXMxd+5c3TZdu3Y1rX4eOnQoPvvsM8tjcwJXVF1JkoRJkyZh2bJleP/99xvEB7UoKyvDypUr4/alI9ZHXI7TDxm3PbT0hJfeAp+q9vJq42I4HF8mLgjw+Hxx1yvJy0SobIiiGFn/KpHx5gJu+04QQgxxhUfHLD4IAGPGjEHHjh1jcwDccsstOPfcc/HYY49h5MiRWLx4MTZs2IBnnnkmY9dBXIDTD1az8JiZR8aqbasoq8Dk6iGlx8ek6kwCEFYs6aDcREmCoFgWQlAv3Cn3KUkIaSXvq+fVScWSGnZxcsVx5f2l2CGpxInQlQ2PTq7jCo/OU089hSNHjmDo0KFo3759bFuyZEmsze7du7Fv377Y5yFDhmDRokV45pln0L9/f7z88stYvny5YQIzySHS7blJRLzo5dfoeV302mmFsvQ+Gw0t6okR5fCWxiZ4vRCj9mKTA2rZkj1CWmM2uq50oze+RISp2Xck2SVICJFJ81pXuY4r7oSV+OAqjcqH0aNHY/To0SkYESEW0PveWv2P3+x7byZw1MdMHuoSImKnQXvV5onmYUiSFBFHSs8PADH6Gjh6FL6CAu0FPrWuxW1eECfG5LZrIrkBPTqO4gqPDiG2ceq/dLUNtW29dmZ9WQ1zadmweh1GQkujTw8Aj8cTycVpcIpCzET/QIqhUCSZWe31iW5ev78+tKUnyvTumd64rXh/rHqIrNxTq2MzOi+RayWEpA1XeHQIsYTTDw+7D0wnV9FWPhCthM20HqB2ZoFWVBkJHk9sAc/6w/UCRlQInbikY9U4PB5P/DUor0XvmqyO1+rPxeje2fW2OG2PkEShR8dRKHSIO1A/2Kw8KJUPWK02alFgdwxG/Vo5PxlRpHU/1NerJdT0vAuKOXcEQYBPK9EYiFRjxboU4peIUJROC6oKrQZix+69TxQ9caIUp3ZsOGHP7JxEbJHGhRPl5eoZ3hsxDF0Rd2JFNNjxaJjZ1zuu168WRksDWPVQGIkoo9CO2cNTtV9ONFZbUq991cCrE7XltfpHOB0Pc6sCM1XhJAoWQlwNPTrEvdh9MOl5FID6ifTs9JXsA9NKmMlqzo9WRY/VHB2lOJI9MgC8qsnf5LJzNeFgsN6GKEKQ83a0+s3UQ9+q90zZPtnwmrq9U/YIcSJ0RY9ODAod4l70RItRez30RJAVgaMXRjJDL6xmNeFW2V49DqNkWCNRFd0vSRIEjyeSrwNEqqlCIc2HtRQOx/J2IAiRqiwdARU3NrP75GTOU7KVX1ZDp3r2zErL6fUhdqDQcRQKHeJejHIlrO5Ptp1RCMtu+Mtq+EqNneRmPRuq4wLivTrhUCgSxlK0k2LNJYTq6gAAHp8PHjPPmCKXx3TMVrxAVtvZxck8InptCHEtFDrE3Tj5ADETDImGysyOJ9uvnifHCjreFfmdJ+rVEQOBBjblR78gCIAoQgyHY3Ps2OrXCKsCw6kZju20s4LT/RIC0KPjMBQ6xL2kykuTi+2UQkY9Y7HGOcpHrhTNX9Kb+E/O2xG8XgiKzw3GpK5c4oOdkMRwoupKVVjQmGHVFcl+suGBatcTo3e+VVsmeUBxYanoauV6dgQgUmmVl6ffzk6YMVUwfEQI0YAeHeJO7Dy0Es19cbK9VfSSoa2KF6UN9Zw66nZan1XHwoFA3JIPWuOVBAG+vDxI0WRlTVlpNVRlp72TOTSJ2HH6O8DwFbGKE6ErrnUVg3eCuB+rFTeJhH6SEUl2+5Xb6nlbtASK3j6jqiv1Pg37AiIhKykchtfjieTqaDyEBQAeQYDH54ssCKosNdequlIKMLtJ1Mm0M5vwT9nGzH4yeUN6YUVC7ECh4yi8EyR7ScV/3FbsOxF+SqRfJVbK5A1sSpKEUCAQKy3XmwBQkiR45JCVJCEcCkGU+1fbd8MD3c5cSUal5okkLLvh+kluQKHjKMzRIe7EyYdGog9hp0uOE/UAaHmB1J/V3iX1/C4NupUik/8h4rWR59RpMBSvt35NKwAIhyMhrFSEC53AiZBnqpesIISkFUo+4g6shoHUD3uzMEUiIRQreS+JYsWrY7UvoxwdrdCW4ng4GGxgzuvzNQhfxTw9CgEl6syg3CBslS6UfZkIvAZYzS1yqiTd7vhI44QeHUfhnSDuxIowcephYdcLYHWCO633euIkEayGvFR9iuEwpHA4EraSpMgWbaf06jTw8sjXEs3t0exH2VYtepzMtXJiXh073yGn7RFihBPl5V6vM2PJASh0iHsw8qQkIgrs2rPSzsqDTCsx1sizZOZBStQbpbIrCxsxFKoPWak2j9cb89Z41N4cxfWJUY+QBGhXYRmNMZk26rZa8wclkkysZc8uRvb0krUJISmHQoe4Ayv/7as9I8nYs9vOar9qm1reHa1wktXxaeXqaLXTMhX15Bi1FQQBvqjA0ZtAUD4/HAjAX1BQPzmgOgykFnfperCnSkw4+b2zao80Thi6chTeCeIurOSwpGIuFKfCJcr3Tub4mIWlLHgiBI8HHmUejiQ19OoIAgTZAyQnK0fnzlG/etQeDK2EaDveFquCyMyO3rgStZdo31bsUOwQLSh0HIVVV8RdmC0dYFcwpDuPR0+QmXhHEsaGmJLFjM/vjwkWI3uSnLCs45GSBY9mv8ncd6u5POq2WmG7ZPp20p5GKJEQkh4o+Yi7sOK5cNKeGcn8152MR0c9PrPxGnl1VPsFjwfe6LpVghC/xpUkSRBEMdJOkiCFw/GeHFnceDzaZfBOhnacwgkvjF0vkR3o1SFq6NFxFN4J4i7MEnZTZc/M42LWPtFkW6eu18iOhkcitgq5JEGQJHgQce8K0URlIOL9EQOByKKfSltARCgZVVklm5ej9fDX85bpCQWtJGAr9vTGo2xnZE/Plpmn0q49kruw6spRKHQISYToit+m2PHKWLVjhJknSA47QSFWotchiWLkuuRTEY1tK3N1BAEeQYBg9Y9oMt4KO9esPsckLOcIdu2ZCReGtAhJCRQ6xH1oPZzTYS8RL40WVhKq1f2ajc+sjU1RICA6GaDCSyMpys7lNoIkQQwE4ux7/P74RGTlGNT70+GNUPebbJ8MJZFMw9CVo/BOEPehDhepq3mStSfb1BMLVrwwetVOWmN3QqBoYSdcpgonSYIAeDzw5uVFvDqiCCHquZErFDyyBweAGAxGQlxCZFJBSR0S0/OqGI3RiofLruAwE6tOiCC79pTCyey7TIFFAAodh+GdIO4lmVCPkT0rD2Ar/VsZnzo/xkqSsd1Eaa33VgSWJMEbnflYDAbjysyBiDdH9upI0VXLY8LI6HqN8ljUbc2w6l3R+7kmkjCtHpueoLNjT8suIXpQ6DgKy8tJdpDOB4TVvrL5oSVXUMkVWMoFPpXNFJsoivXeHDVaooreiXoaw3eKEJdCyUeyA6e9O9mOHW+TjjCR17kKHTtWX0IOVY6OYvMXFsb2a/anfs9cF0ISgx4dR+GdIO4llVUyiYSH9GwZ9Wc1L8juGNS21e/lqjB1KEnVVgyHI3k3kmQodLw+X2x+Hc0R281dseP1cVowud0eISwvdxQKHeI+1Lk0TlVdmdmzmjCqxEpCs5EtAxFi6VyjHB1FqbhacMl5NlIwCE+0zFxT6EiReXa8fj+k6Jw6gjIRWS1Y1N4cKzlMRlgVEFZLtjNhz2qpOz1hhKQECh2S+zidH2HVM+RkOzsJy2rxo9FOAiKzHkcrqWLLQkAldAQBXr8/ThghKorixqLs26mHtFW7iYiXdNpLBIqdxg1DV47CO0HcR6JVSFZJNmyV7nwhPQ+TWVWVfK7W+dFKKlnYeDyeOK8O5P2IzJsj75dCocgq6Hp9qfcn87BOpEJKaxyJ2rPzM7ZTXWY2BgocQqHjKKy6IrmNU94cpWCwipWQhVEIw2xsVkMiGvvFcFhlSoDP54ssAyHvA+Dx+SI5PECc2DHsIxUiUBkas5L/o+XJ0kqYTsae0TjNbJm1sWqPEGIKJR9xF1byUZwgWa9OqvtVY0WIWQiBSZIEURQBxSzIMa+OIlcnYkKIJCEjvtJKCofr17+yMu5EH9Z6Qs6KmHA6jOQUFC7ECvToOArvBMkOUlmBZbWdlsfCyj6jEFMmwmBKkRL1aMj5OHKuDoB6D0/UCyEnJguI5upo2ZbREhtOeESsYHcCPydDSU4LMebqNE5YdeUoDF0Rd6Cu2tE7nqjdZNs5ndBsF7McEHVIRO3dEQRIkgSIYiT0pBIvsc/RUnMgkrcTM6Hconbi+la/V47BatjHyJ76vVWs2Ei3PWWSuBUb6RTChOQgSQudK6+8EtXV1QCA1157DUuXLk16UKQRYidvIZdIh4BSJgtL9fPlxOXeKN77/H7TPgUAoWPHoDlLstEYkm3jJE70R28LSQVy6CrZjQBwQOhs3boVRUVF2LZtG+68806sWrUKv//9750YG2mMuCFEZbWd0TnpDkcpN+U++T2iOTdeL/zR1cdjAif63oOI+PFEvTVCOAyPJEX2a2zKMFeD+5KoJ8aqd8MJ4WRXpFj9mTotfiimGh8UOo6S9J3w+/2QJAkLFizAtGnTcNVVV2HQoEFOjI2QetyYjKyepM/J5OZUXq/HUz8ZYHQCQGUejlLoAIj3+iiEkebkgGYhSKsCxSxUp+5P75rN+rHSLlX2zOwyP6fxwmRkR0n6TowdOxYDBw7E4cOHMXPmTABAbW1tsmZJYyWTgsYuieRRJNPOrkdEJ0cmNkGgUrhEN4hizM3rkSQgEIjLzYkJoui8O3H9KsWHOkcoEbREpNW5eqyKDjd4XyhoCEkpSQud559/Hh988AF8Ph+aNm2KnTt3on///k6MjZB63BLSsurFcdJzoxYURu3Ung4t4YFIKbl6kkBBECCEQjGhI8hhLNXK5h5E5texRDIiJ9HKJD2R6HTJeartUfw0XujRcZSE78Trr7+Obdu2oaamBj/99BM6d+4MAOjevTu2b9/u2ABJIyTVXh0jcaLEqohJtTcnWfs63hWvzxe/mGcoBC/qE/fk/coZlAUAgteb+Bw3drArduziBm8OIVqwvNxREr6TpaWl2LNnD3744QeMGTMGu3fvRseOHdG+fXv45aoNQhJBK+E0GfGjtqf0dijbaHlvtNqpsdpObpuq0mF5HB4PoJz5WMtTIAiRWY+93vqE4mAwJnyA+qRjIRyOL0lXVmVphYis5i2ZecTs2EskRJWoMHM6p4YCiZCUkrDQ6datG26++WaUlpbinHPOAQB89913+Oabb1BaWurYAEkjRktApLovdX9GoSur7ZLdp9VGS5jJ+2x4gGK5OuEwvIIQEzdAfIWVFAxC8HjgzcuLVWzp2Yyr+DLp33LisZVE4ERDXXZRhwYB69dsxbZsjzReGLpyFNt34ptvvkHXrl1jn2WRAwAdO3ZEx44dnRkZIYC9cmIz9ESKVju79lLZLpE8ICuJwYIAQc7VCQRi3hyl0BEQLTmXJEihUCRsZUVMOZ0M7GRYzKwyLFF7iaK+NoocQqHjKLbn0enZsyemTJmCgwcPpmI8hNTjlgRkt7WzgxWb4XBEzKg8OrLokautvNG1sEz7oVeCEOIibAudDz/8EJs3b8ZJJ52EBx98EMeOHUvFuAhx/r/cbH7w6uUPGe2zIHIkUYQQCMSFqWQrcmKyF4iEtYxy74xKvJPFqlcvHSHOVJPN31HiHJww0FFsC53TTjsNK1euxJIlS/DKK6+ge/fueOaZZyIrIhPiJKn06CQ6SZxBGEi3nVV7iY5Da59RbotyfygEr98fETmSFCd0Yh4dAF6/H1AkJGv2befnZdberr1ESNS+Fa9WovZyQayR5JGrrpLZWHUVI+ElIMrLy1FZWYlHH30UjzzyCHr37o1XX33VybGRxoo6hyLZh55Ve1baaYmHVLQzw0rui/J6VP1IkhT55yQchsfjieTpANrJyB5PpBRdFCGGw5Ci52smP6u9L+p2Wu31jiVqTwujn7ddrP4cE7HndP4QIST5ta4uu+wyvPDCC2jZsiVGjx7txJhIY8dKmCYZe9nWTi8slMTDVhAEIBSKzY3jVax/FcvRiebm+Hy++jl0gkHtMTnpiUhG2GZKIGQqn4zkJgxdOYptoTN//nz88Y9/xC9+8QuceOKJKCoqws9//nNUVVXh4osvTnggH374IS655BJ06NABgiBg+fLlhu1XrVoFrSnsq6qqEh4DaSQkGrZKRTsrnganPUaI5uZEZzwGol4brzeWkAxEqq28Xm9srh0BEfGDcFi/vNzs2pLBij2rIUOr9vRIhz3SeMmQ0Jk7dy5OPPFEFBQU4IwzzsCnn36agotLP7bvxLRp09C3b1+Ulpbi8ssvR2lpKUpLS9G0adOkBlJbW4v+/fvjuuuuwy9/+UvL5+3YsQNFRUWxz23btk1qHCTDJFJK7YS9bKigMsNKeboiNydWPh599fp88EhSXI6OV7FCuSDbCgYhaeXkKcvZnZpTxoo9rVypXBAOTpbUk+wiA+XlS5YswZQpUzBv3jycccYZeOKJJ1BeXo4dO3Zk/XPV8p2YMWMGLrvsMuzfvz8lA7noootw0UUX2T6vbdu2OOGEE5wfEMkMepMEMjRgjpk3I3pcFMXIyuRS/KrlMbETPc3j8cSVnMtz6giSBEk587K6H7kvtUixG6LT85pYnYxQz0aiUHiQHObxxx/HhAkTcO211wIA5s2bhzfffBPz58/HHXfckeHRJYdlofPtt9/ioosuQl5eHi655BJcdtllOO+885CXl5fK8ZkyYMAA1NXVobS0FDNnzsRZZ52l27aurg51dXWxz9XV1QCAYCiEoJx70AgJhkJxrxlBr4TYammxFdta9hTH4u6DQTur9kz3KcWA3piV4Sh18q38XvauiKJ+oq4oQpIkBMNhhMNhhD0eiF4vRJ8Pkt8PMS8PyM8HCgsjrwA8hYWQ8vIg+v2RtoIAEUAYgCcQQDAchhjN89EVAIkm6Rp5afSq3YyO6+2TfwYaYzT8vXDai2R17BnAFX8fXEBar99Bj478nJPJz89HfvR3XCYQCKCyshLTpk2L7fN4PBg+fDjWrl2b3DhcgOU7OX/+fIiiiH/96194/fXXccstt2Dfvn0YMWIELrvsMlx88cVo2bJlKscaR/v27TFv3jwMHjwYdXV1eO655zB06FCsW7cOAwcO1Dxn1qxZuPfeexvs/2DVKhQWFqZ6yK6n4r33Mj0EV1Dxr39legipxe8HSkoimwEHjP6LCwaBL790eGDupOKjjzI9BFfQ2P8+HD16NG19ifBATLJWSD5fXnBb5p577sHMmTPj9v3www8Ih8No165d3P527drlxCLdgiQl/q/yl19+iddffx0rVqxAZWUlTj/9dFx66aW48sork1oKQhAELFu2DKNGjbJ13rnnnosuXbrgxRdf1Dyu5dHp3Lkz9u3di1ZpFGluIxgKoeK99zBi+HD4M5WpnyqPjpVy5ehrMBRCxb/+hRFnnQW/PAdFKj06emPUs2vFs6P25sjenmjb8OHDCHz/PQI//ojgwYMIHjqE4KFDCBw6hFD0/dHvv8ePf/0r2s+ahYLiYvhatUJey5bwt2wJf6tW8Ec/53ftCk/z5pEKLrMJDK2GmxLxyji5T0EwFELFRx9hxM9/Hvm90Bqf3WvUw+UenYz/fXABPx48iPYdOuDIkSNxeaFOUl1djeLiYhw6lHwf1dXVaNGiGHv27ImzpeXR2bt3Lzp27Ig1a9agrKwstn/q1KlYvXo11q1bl9RYMk1S39pevXqhV69emDp1Kr7//nu89tpreO211wAAf/zjHx0ZoB1OP/10fPzxx7rHtX7AAOD3+bjiOjJ8H2wIEls5GnrvDQSK3+fTFzp2xmmnf6N2WqJG670cilFuHk/cZ4/HA8HrjSQTiyKk6MrkUjAIKRCAWFcH3/HjAABfbS28BQXwBwLwhULwiyL8khTZBAF+vx9e+T6pxY76fTLhK7shK4eEjozf57MndOyKOifspYHG/ncynSIvFIpsydoAgKKiIlPR1Lp1a3i93gY5uPv370eJiec3G0h6Hh2ZNm3aYPz48VixYkVGRA4AbNq0Ce3bt89I38QhlA9vJYn+0dfzBDlROq60ncpSdKVQ0Huv9VnLphBZyNPr80EQIutbxaZnQKR83Bv1AnklCUK0lFxZoeUBYufEkUweldZY1Wh9N6y208LueM1+dk5efyrskaxBFjrJblbJy8vDoEGDsHLlytg+URSxcuXKOA9PtuIaP2RNTQ127twZ+7xr1y5s2rQJLVu2RJcuXTBt2jR89913eOGFFwAATzzxBLp164Y+ffrg+PHjeO655/D+++/j3XffzdQlECcwS85NlT29h5hWO3mf8r1RO6v2jNpZGZvSpkFbCYCgnARQsXlCIcgTx3uinyFJDdsp/7tNhcdBz3OnJa6SaUcIAQBMmTIFY8eOxeDBg3H66afjiSeeQG1tbawKK5tJidD5/PPP0a9fP1vnbNiwAcOGDYt9njJlCgBg7NixWLhwIfbt24fdu3fHjgcCAfzhD3/Ad999h8LCQvTr1w/vvfdenA2Speg9/Elygi/6KghCbFmHEBTiRZLgEeNTIAVJgqCYQTliQtBeR0cpJORxehxzGlvHae+aFlrCyU32SFbjZOjKKldccQW+//57zJgxA1VVVRgwYADefvvtBgnK2UhKhM7kyZPx/vvv2zpn6NChMMqLXrhwYdznqVOnYurUqYkMj2QbyYoetXCy6gkxamfVntPtlMe1BKG6jQENvDrBYGzFciC6ajmiXh1lO3WehjI/yKnkXKVdM3t28rYoHkgWkAmhAwCTJk3CpEmTkuvYhWTg3y1CTFCHgxJNZtWzJ9tMZTs7eSJOCDmjZGCtbgEIHk9s5XJBFOGNenPkMz2KTQgEIiInmsysmSTtJFo/c73cLSfb2RmfUyT7/SaEGJKUR+fRRx/F5s2b45ISJUnCl41kfg2SYpwOYSm9DkZ2lcm+Zu307Gnl8pi1M9oH6D/AzTxFGgnLcjKxx++PvAaDsXbKHB2lV0eQJHj8fuN7ou4vE16UdISuZJy4PnqaiIpwOHmPjtbk5Y2VpISOXnUV82SI46QzZ8dqX063M8OO8LMg6qRoO6/HAzFaaSUhPnQV8+pIUmQRULsiwmnPR65D0UOQudBVruKaqitCDElngnI2PGj07ofFHJ24o8ePw4OIN1YpdGJJyoiucVVQYH18enk7xBjeKwIKHadJSY7O+PHjU2GWNDZSOS+J0QMlkflxEsXONdotOVfm7qi2mGcmFILP44E3OqeO7MEBFGErSYLH64VHr39lnk4qxahbPW3JolWtRghxjJR4dK6++upUmCWNBXXYxamqKzN76nCLldwXM3tmybrqvuzkBBlVIan712knhEJAdMVyb7StnKMjCx2vIMCblxdZ30qZhCxvyvGox5aoUFReg5Edp9s5PT67tlIR8iNZBz06zmJb6HzzzTfo2rVrKsZCSGrIlCdA7V0x84g41U6JnrdAECCIIjySBEkQAL8fkiDEha6AaOjK662vxgoEzPt0CqtJzU63c3p8icCQX6OGycjOYjt01bNnT0yZMgUHDx5MxXgI0Z8bximcrspJZ5WPjJnI0epLEbqSJAlCMBhb9kGIihnN0FW0MksAIITDzofbspFMfScJIbaxLXQ+/PBDbN68GSeddBIefPBBHDt2LBXjIsQZ3ODNsdvOikdCL7xmcU4WIRyOlZjLa17F5tSJtonbJ0mxxGQEg+bXYHYtpgPUOM/qvUqmXTK43R7JGtK91lWuY1vonHbaaVi5ciWWLFmCV155Bd27d8czzzwTK08lJCnMclmcIhe8OlpYET7R3BxZvMSqq3w+eD2e2B8FjyRFJggU6hf8jHl1tH7f1flKqUhOdsPDn94ckmIodJwl4aqr8vJyVFZW4tFHH8UjjzyC3r1749VXX3VybITUk6kHZqLtzJJizdpZHYPGZIAN3is/CwIQDkcqrOQqK6F+9XJP1IMDIOLNEYQ4QeSRpMiq5mqvjl6fdrASjrNiM1Wi00rCeCK4QbwRksMkXV5+2WWX4YUXXkDLli0xevRoJ8ZEGiNm1SZOhkDS0c5ptO6PhqdG870iLweiCI8o1ntyZIGDaKjK44lVKHgEIVJajvrcnVioS1l1pexTbwx2rlGPTAsCs/GZVesl2l+mr5ukHXp0nMV21dX8+fOxbdu22Pbtt98CALp06YKLL77Y8QGSRoLZH/Nc/WNvtXLKqldHWa2jLP0GImEojyeyorjSwyMIkcqrKN78/Fh7eL2x9rEwVnRDMBhJarbqlbIiZJwQklbtOF15RYhDsLzcWWwLnWnTpqFv374oLS3F5ZdfjtLSUpSWlqJp06apGB9pbKgezrrH7dizitpbou5La34aK+3UwkPrWpxop9deMRbB5wPy8yF5vRERE90Erxfw+QCfL/Jevg1RcSTIgkd5jt8fEUNa90+N1ZCTnsfKzs88laErp0JnWmE4J66dENIA20Jn//79qRgHIfXoVc04ObeIlgixek662tmY+C+unVqIyW3kh6vfHxE8snfH44Gk8PLI4iUmYuT9yg2InKseRzoeylZDXE61c7pfO1DkNEo4j46zcK0r4i6sigSn7FnJKTEqVzbzQGl5f6y202pv1E5L5Oi9z8+PEzqxMJUgRF4RFTrROXbiwlheL+D3N8wbSjYZWSvnyMxjZdVWsu2s4rS9dIlH4ioYunIWCh2SHaSj0sUoEVpLMJjlzaTbS2QkqhTvBSASppLFiiDUe3Q8noi3B4Agh7GiD9u4/ByfS/50pDMfJ5X2lFDYNHoodJwlJYt6EpIUqf4vO1nRpPZkWO03Fej1oa66UiYgy+TlxXl0BKA+Fweo9/TI++UtOlNyg77sikf1ZkSiuVZOtEu230SuNVeT7wnJAC75t4wQBVoPzmRCWmp7VvNy9MIm6jZa/92rxy7v0wvPmCUZ67Uzsq8Oa6nGI/h8kBRiR/bmyMnIsrgRBCEyQaD8kPb5Gj60ncydSra91v1LNARmtU8je3rjMeqbXp1GDT06zkKhQ9yLVUGSKvQeUHoCIpXttMamZ8uoHwByCwmAIAsdSYrl4MSqrny+iPCRQ1keT8QLJEQW/9R8FEtSfJJyMnk26vE7gRN29MZOcUIcgsnIzsLQFXE3VvJhrKIVbjFq53QIJJl2WmOyEipRf5ZzbJS5NtHEZCFaXh7Lv5HLzqPJyJIk1Xtz9MaWjOdNDye8PDJanrZkSEaY6kHBRIij0KND3Es6EpCT7ddNYkivncn1SKIYETJRb07Mo6MIaUEQIrk5QKQSy6xPp0NahDQiGLpyFgod4l6MqojcYC+dJDNWZXhMiM6CLL/KokX25CiSkeVJBAWfD8jLg+D3Q4p6gnRDV8rx6uUvpZpsLsnO1nETR6HQcRaGroh7yZRHxwyrYaRk2mm1seMlMUh2FZSvoghPfn7MkxOrrgJi+TmCzwdPQUFEFEXziHRHoB6jUXhNOU698abywZ/o98EoGdnsHLMk5GwS34RkCfToEPeRaIKuU/aMHkZ61VTKz+p2yjFYbafu32o7rbbq/tXnR/N0BK8XkiL5WM7NiS0NofIMGYqvROe3sZu4rHVP7VQ+JYLVPo2OmX3H6Nlp1NCj4ywUOsR9GIkEJ+yZYZRUazUcoxZX6WqnbC+jfDArxZkowiPn4ETXv4rz6Mihq4KC2GKgUlQoxoWuzDwUyTy0rSQ4u0EUWP1OJHOcNBoodJyFoSvS+HD6gWLHq5LKdnptVO3klcoF5fw5Xi9QUFBfaQXUTw4oLw0hTyBoV2gl2s5qiE5NMvfUKonYs+qdpOAhxFHo0SHuwsI8MI7aMwo/mHl1zNrI/atDXIm00xqz1XCWqr0gSRHPjLKqyuOBJy8PkpyQjIhHx6NaE0u2JSj7NvJmJCp4nAozuQ2reUi5cr0kITiPjrNQ6BD3oSUeMpWYnKkQlLqdkZDQyxvSeq/hzYl5fTweCNFcHQCRyQHlKizlWlhm4k7Zp9XcGI0x6pKpHBbmzpA0wdCVs1DoEPfhpKhJNr9HacNIRGg92NWCRkvkOLHPznXKXh2VwJE3ye+v9+jk5dW3U3l/lCXqgty/8n4Z3Uv1e/V1JEsieVRW7do9riXerAo60mih0HEW5ugQd5FsCCQRrAohp9tZxWoyrp7YUAgvAYCgIXCUuTqewsLIabI3R+nVkdsJAoRQKD6EZTQ2o3ZWwnZaNq3YS9ZWImTqe0II0YQeHeIOzB7mWqEsM8+B3fCUWRurttR2tTxBWv3rtTPzJsk25IU39drJCcfHjwPHjtVPEqjahLy8epvKVcvl/B25rd8fv66VXojKjick0fucjK1kSCa/hh4dogM9Os5CoUPcg1nIBrAX4rBjz8nxWRVZToTV9OxphYPkz3J4SsOjE/PgAA33ab0ajUXvczqFidP92rVhFm4kRAMmIzsLQ1fEnVh5CLgtlydVITCjh6vWZyNPihyykr0x6k0Q4hb1BBCXiBwX8kr2utxAqsWG8meRbm8TIQQAPTrELVhNpLX6gNArvTayZ2RbXU6thZ6nJ5l9RtdkpV+9cJie0JFDU0B9qApoKHbkuXXU41C+qt+bYfXnayWPS6uN0T1Npl9CHIahK2eh0CHuRF0ybadM2WyfmT3lf9+ZCEHp9aGFVj6PkQ3llpfXUOgoxY2cm6OVq2OUm2PnWoyEhp3cHrv7zUr21a9mY9GzZ/V7S4gCCh1nYeiKuJ9kQyJWPSNO27P68E50n1Fys/xqFMbSytVRLAERt6I5EO/NUY9DCyNxlki7ZHF7dR0hJCXQo0PciV7Spp7HJlGvghUPkB17WjbUHh+969FqpzUWPY+UkW09ZK+OnJsjb0BD4aNMQlbnA+lhJLSSwSxsZtZvouOxY4+eG5Ig9Og4C4UOcT9OlJLbqXZxwp5Rrkwi7exch9qOcn0qdfhKHZJSenCUn9WJylrjMvqsR6IhIaPznRAYTnhrEhHghIBCx2kYuiLuxWlvgFU72RCScEJIyMnFfn+9oFEmI6sFkJEHR8/z5MQ1WMUpcZWKfrPhO0VIjkKPDiFqGtt/4ZLUcFJAIN7b4/FEwlx2Kp7o0SAkITiPjrNQ6BD3YtVLkEzyqFlJcjKow1KJtDMKi2mhV4WlTlBWihHZo6P06gDxOTv5+faqy5zy5lj16qXS+2clF8nMBiE2CIUa5vwnYoNEoNAh7saswklrxlmrtrTsKdsmYs+KXTvtzOb+MTrPLDFZDl0pRY4yGVnp4fH7tcWSWW6OkQBJxuOTjPBJZb/qfuRzUpWUTXISCh1nYY4OcSdWHkZ28h6sTkjopD0lTnkkEm2n98CXK6ny8hp6dOT3eXnx+5Mdm7q9leNmHjE7/Zph104qvzeEkKShR4e4E+WEdHa9Hsnas4KZPa1wlF5oys7DL5nEV7UXRll15ffHh7CA+NCVcq6dZB/WVgVMop4Xszwis3tud4ZmI3v04JAEoEfHWSh0iDswC8eoc02SCSul0p7WfrM+1O2UbZPt1yhHyOuN/DWUxY+y+ko+Lnt61EtAaPWpfrWSY5Vsro46fGYlWdqsX6PrM/ts1XNIAUQMYDKyszB0RbIDvQdDot6FVCQeK0nFrMp2+lfn06j71FrnSm8eHfUK5k54dRK5JnnsZmRqOQ69dk4kNhNCEoYeHZI9aIWB1Mm3TggfqyEOozFayT+x85+9lXwltUdIKzFZ9sroLf2Qn18vdOTcHHU79erlWg90dZ92r0Hv56HESogqFf1axWgiSIodYkAoFP8rlqgNEoFCh+QWdsSJ3NapUIKeEHNin1mfWiJD/VAVxfpjaoGj/Oz3R9rLC3qqN7mtOt9HOSb1ezs5L3qYVTFpCVQn+pX7tuKNsdMfQ1hEBwodZ2HoiuQWiXh0Uh3GMmpnJSnWzsPVyJ5ydXK1WFGKmPz8+vZqj446KTmZOYwSaWNHlDiJVXusqiLEddCjQ7ILvVCVXojAyrwzWv/9O2VPL4Sk/qz13k6Cq1Ffyveyx0Yu61BvshgCGnp99Norx6D33qp3yswzZLedVaycZ9VbYxRiS3R8pFFBj46zuMaj8+GHH+KSSy5Bhw4dIAgCli9fbnrOqlWrMHDgQOTn56N79+5YuHBhysdJsgynQhepsGc3VGO02elXy5Oj9urI7bT61xu/VSFjdh2NgcZ0rcQ28qKeyW4kgmuETm1tLfr374+5c+daar9r1y6MHDkSw4YNw6ZNmzB58mRcf/31eOedd1I8UpJRlOESPe+K0iNiRUwo7em1sfogtmpPTaJ5LcpzjHJX1Ju8SKdWSEpZXq7crxRCWpN8JDJuo+sxey9j5z6b9WWEk2EphrgISRuuCV1ddNFFuOiiiyy3nzdvHrp164bHHnsMANCrVy98/PHHmD17NsrLy1M1TOIm9MIhiT5orYQsrIRgtNqqxZnRJILJoGdPLQplsaL25CgrqpTenUQ9L4n+LNKVqGsklNPRFyEacB4dZ3GN0LHL2rVrMXz48Lh95eXlmDx5su45dXV1qKuri32urq4GAARDIQSDwZSMMxsIRn+jgtng61Q/xM0+W7ERpcF9SCTRVu+9lXZG59rByKMUDkeqr0Qxfr/8EPZ4EIx6a4JK0aMemyhG/hLr5Tfp7bOCXo6LVS9Psjkx0XamvxdWRZKV63ExWfX3IYWk8/rleTyTtUEiZK3QqaqqQrt27eL2tWvXDtXV1Th27BiaNGnS4JxZs2bh3nvvbbD/g1WrUFhYmLKxZgsV772X6SG4gorVqzM9hMwSFToVoggcPx7ZV1OTwQFllopVqzI9BFfQ2P8+HD16NG19uVnofP3117j//vvx/vvvo6qqCh06dMDVV1+Nu+66C3l5ebF2n3/+OSZOnIj169ejTZs2+P3vf4+pU6fG2Vq6dCnuvvtufP311+jRowceeugh/OIXv4gdlyQJ99xzD5599lkcPnwYZ511Fp566in06NHD1pizVugkwrRp0zBlypTY5+rqanTu3BnDhg5Fq5YtMziyzBIMhVDx3nsYMXw4/D6XfyX0wjLyq/zXIYG8jWAohIpVqzDi3HMj98HMnt19evas5PWYoec9Uu+XsxSDwcgWCgG1tUAgENsfPH4cFQBG5OXBLy8B4fMBBQWR0vO8vEjlljyhoBqtXKFk/mrbsWfVm2J2HqLfh9WrMWLoUP3fCy17RteahVVXWfX3IYX8ePBgpofgCrZv3w5RFPH000+je/fu2LJlCyZMmIDa2lo8+uijACLP1gsuuADDhw/HvHnz8MUXX+C6667DCSecgBtuuAEAsGbNGlx55ZWYNWsWLr74YixatAijRo3Cxo0bUVpaCgB4+OGH8Ze//AXPP/88unXrhrvvvhvl5eXYtm0bCgoKLI85a7+1JSUl2L9/f9y+/fv3o6ioSNObAwD5+fnIl+cIUeD3+eCXJ0lrxGTFfbAqYJJo5/f74Vc/wFPZr1KUWJ0o0My+2p6MUmjJm98fCWnJISmvFwiH4fd6I/dBuTSE+r18n8zCM3Yf6lr2zERCivb5fb76B7yVcxMVYS4nK/4+pJB0ijw3e3QuvPBCXHjhhbHPJ510Enbs2IGnnnoqJnT+8Y9/IBAIYP78+cjLy0OfPn2wadMmPP744zGhM2fOHFx44YW47bbbAAD3338/Kioq8OSTT2LevHmQJAlPPPEEpk+fjssuuwwA8MILL6Bdu3ZYvnw5fvOb31ges2uqruxSVlaGlStXxu2rqKhAWVlZhkZEMoaTk9Zlsh3g/MPPij11IrIiX0ezFN2qJ8upa8kyQUBIsjhZXl5dXR23KfNUneLIkSNoqYiKrF27Fuecc05cKKu8vBw7duzAoUOHYm208mzXrl0LIFJZXVVVFdemuLgYZ5xxRqyNVVwjdGpqarBp0yZs2rQJQOQiN23ahN27dwOIhJ3GjBkTa3/jjTfiq6++wtSpU7F9+3b87W9/w0svvYRbb701E8MnmcRmkqnrSGVyqla1lPqz0jsjb/J+uTpLXtxTb5xa5exOXwchxDadO3dGcXFxbJs1a5aj9nfu3Im//vWv+N3vfhfbp5dDKx8zaqM8rjxPq41VXBO62rBhA4YNGxb7LOfSjB07FgsXLsS+fftiogcAunXrhjfffBO33nor5syZg06dOuG5555jaXljRCvco34wJlvCrWXPaojJ6RJyvT6s9KsVPpG9NurycvWsyFa9Q07itIi1KioTtcf5cYgDhMPJ/yrJ5eV79uxBUVFRbL9W+gYA3HHHHXjooYcMbX755Zfo2bNn7PN3332HCy+8EKNHj8aECROSG3AKcY3QGTp0KCSDPxJasx4PHToUn332WQpHRbICrYeX3nfJiujQ8kgkas9sskEtIZLIPC5aOUBWhYnssRHF+MkAleJH6emRj+ldl1mpeTIY2bOyPIdeWz17Vsai3mf0s6ZniljAifwa2UZRUVGc0NHjD3/4A8aNG2fY5qSTToq937t3L4YNG4YhQ4bgmWeeiWunl0MrHzNqozwu72vfvn1cmwEDBphejxLXCB1CEiYV/02bCRirXhrlgjV6eSyJJiBb7VerT3mTF+eUhYwsdmQb8tpXWiEwLbtGn+1iJ6HZKH9IRik0rAgip7xJSnv0+BCX0qZNG7Rp08ZS2++++w7Dhg3DoEGDsGDBAnhUf2/Kyspw1113IRgMxhLYKyoqcMopp6BFixaxNitXroyb+06ZZ9utWzeUlJRg5cqVMWFTXV2NdevW4aabbrJ1ba7J0SEkIaw8jJz+T1pZDu6ELTVOjtVIfCiTjZUrlStzdOTcHLUwskIy98iOhyURQeL0cg4UMMRB3LzW1XfffYehQ4eiS5cuePTRR/H999+jqqoqLm/mt7/9LfLy8jB+/Hhs3boVS5YswZw5c+Kmd7nlllvw9ttv47HHHsP27dsxc+ZMbNiwAZMmTQIACIKAyZMn44EHHsBrr72GL774AmPGjEGHDh0watQoW2OmR4dkF1b+E1fPV2P0n3S67enlzFiZA8gIu/3K+9VeHeV/ZsplItQVWHr9O+m9UIeWrIjCZHN1rHqrrB532stFGgVOhq6cpqKiAjt37sTOnTvRqVOnuGNy+klxcTHeffddTJw4EYMGDULr1q0xY8aMWGk5AAwZMgSLFi3C9OnTceedd6JHjx5Yvnx5bA4dAJg6dSpqa2txww034PDhwzj77LPx9ttv25pDB6DQIcQ6TvzXbjVUZSWR2M6Y1OfK4kYdxlLOjaNeC0steIz60AsTWclVMhMNVkNLVjx5VsNZVu0poaghOci4ceNMc3kAoF+/fvjoo48M24wePRqjR4/WPS4IAu677z7cd999docZB4UOyT2UIkGd3JtIPoyejWTtqW2b7TOzZdROGV5RenHkSQLVQgaIX71cLXbU+TpWxIwVjOxZuR9attJpz6otQgxwYkFOLupZD4UOIW6uhrH7ADZqI1+n2pujFD1eb2SmZKBhbo46p0dOXLYS/jMbp5Vr0BObiZKqn7ubv08kK1Cul5soFDr1UOiQ3MSOl8WOUNB7rw7b2OnTyGNkx55ZO1mUKJd/UG6yZ0ee6l4pbrREj3ISQfU1KT8nGu6x8j4ZnLBjFsIjJAEodJyFQofkLmZhISA1E//ZDWVZtefUA1T2xmjl38j7lX0r32t9djpJ2Kl7bDSGRIVTqsUXIcRxKHRI40WdiKr1XtnWrj0rbfW8ROo+nQphKcNXoqhtQzmPjtKDo5XHo9Vvsg99q/ZS5ZFxkz3SKKFHx1kodEhuojfjcLIYeYQS8QwIgrbgULaRcdpLJL9qbUDD/BszMajXjx1vjpP21Gj9jBJNRrbTByE2odBxFgodkvvoiR63PJC0xqEVUnNS+KhDVVriRm6jfFW/N8tRsZvDkog9q6jznsw8RXZEnZu+T4SQOCh0SO6jFgxOTGRnVAqe7PjkfVo49UBVz6EjCxhJahi6Ul6jEx6aRK8h2eu22q/T7QixSTic/J8pPUdxY4RChzROrHoa9LwtWu/N2irb6CXw2kmwNXrI6vWrFjbKXB11MrJWfk6ioaNEBYFezpORd8upMJSd8yh4iIOEQsbL1VmBQqceCh3SONBK9jVqa6WNlWUb7Hg/rI7Pbvm6WTulNwfQ9uhoCQ2j+5RIvpLd8xLtzwpO5kMRQjIKhQ5pPDiVkJxK0u0ZUAoddb6O2vsjvzrZt5PtnO43U/ZIo4ceHWeh0CGkMaOVq6MldOTPylc9+OAnJCkodJyFQoc0Hpzy5mglINv1FunlA5nZSDbxWatP9V9UZehKveyD8jwrVVdG/Wq1NfucLtThNIo3QrIWCh3SeLDqiTB6sGnl0diZi0V9jvJzonktdlAKFOW49Tw6WuXlRn2rc5eSHS+QHntWoeAhaSAcTt4j4/YofTqh0CGNA6dLgZ22pxQXehVEqfLmKEvKgfr36uosO0LRav9G7e3as4rToTeWmROHCYWS/0pR6NRDoUMaB8oHebIT7akfbE6IDzMPklY7p2Z6ll+V+TryPqUnR32e3n6zsemJOuUx9Xsnr9XMnlXhYnQdhCQBhY6zUOiQ3MRKeMWpfhKxZXV8SuGgJSqSmaRQeb56okBlG/VsyXr2jD6btUt1ro4de1byjtySS0QIMYVChzROEhUJeuEjp/+r1wthWZm7x04f6v6UNtWTBcrtnBQd2SgQsnHMJKugR8dZKHQIkdF7mCeycGcioSh1O6thICuzAqvbqL1GyskC1fdBS+hohbms9Gt0nVYEkJ37Z9bG6hgobEiaodBxFgod0nixI1q01rYC7Isgq94Yo/CV3lis2lPa1Gonh668Xn2hoyV2jPo1EzN2vTxmbew8Jay0tXKthBBXQqFDiFXseAacaKfOoUkWWcTIISq9cJjXG3+O8r2TISy9MSZzPFVQ4JC0IkKSkp3xjzMGylDoEGIFvVCPlmfDirfGak6PngdJ/d5JtMSMnlfHynWoRZHRe7v3xSmseJQodkjaCEe3ZG0QgEKHEOvYeQhbtWeG0QzMWsLKivjRug6tsJaWHav7jDAbo5P3jxDS6KHQIcQKajGh18bph686KdqsjR27VkrY5eN2c2jM+lbbyQbRwokBSdqgR8dJKHQIsYteiCqVD0E9b1KikxeqBZSW2LEacso1KGhIxqHQcRIKHUISIRO1m0ZhI3Xoyc58O0bVW2rvS7JJyE6EvlKN28ZDGiEikk8mZjKyDIUOIU6g93BMZN4XZTstAaM8x0rirFYbrYkPrc6NozxmFlqzmthrxV6q7jEhJKeh0CEkETKRmKzu20jMWPU4qavGzNaeSjaJWE/gJGovFe0IyTgMXTkJhQ4hiWIU8kl1n/J7GbtLUhiVrTsVprKKWwWIW8dFGgEikhcqDF3JeMybEEI0yeQc66ms7nJTn4QQkiT06BCSjZiJhGTCalaqrpyCYocQDRi6chIKHUISJR2z+OpVKTnVr95q7Hq2nBImbhc4bh8fyXEodJyEQoeQRLC7yGOiD06tGYy1Eoe15sUxEibK1cqVn/WqrtR9WqnMMkPLW+QmgcHlnwnJCSh0CEkEjyK9LZ0PRKUwsVKKbTSbsvKYvNinuo2WmHNqZmSn7DmNPB4PUxhJpuA8Ok5CoUNINmIUbrI6U7KW4HBqHSo7cCZiQlQwdOUkFDqEWMHs4Z8qr47VfrXm0bE7mZ9em0Tn6NFCq4TdTSEivZwoCjFCshYKHUKcwGyyPRmrswfbmfDPSnur3hp1jo7eg9/KPvW49PJw9HKCzOzptUv0HlPMENdAj46TUOgQ4hROzkWjFjBG59mdBdnIniTV56YYeTKsjEvZp5GYsTIu9TnJtrHTjpC0Q6HjJBQ6hLiZZISR1nszm3b6kyRr+TVW5vzRmqGZkEYLZ0Z2EpYVEJLt6JWEa+FkJZEgOGMv1ZMSEkIaNfToEJJrqMNFZguAap0vvzoZKtJqT5FDiAYsL3cSCh1CcgWthGjlZ7eEhdwyDkJcC3N0nIShK0JyATlfRn6vddzt4sJNZeaEkJyBHh1Cchmn16hyAq35fdw0PkIyDj06TkKhQwhJL3ZzhghpdFDoOAmFDiG5htXJCwHjCf2UychWy9OttiOEkDRBoUNILmJ1nhunJ9dLpgKLEBKFHh0ncVUy8ty5c3HiiSeioKAAZ5xxBj799FPdtgsXLoQgCHFbQUFBGkdLSBbgZiHh5rERklEk1JeYJ7oxuV/GNUJnyZIlmDJlCu655x5s3LgR/fv3R3l5OQ4cOKB7TlFREfbt2xfbvvnmmzSOmBBCCCFuxzVC5/HHH8eECRNw7bXXonfv3pg3bx4KCwsxf/583XMEQUBJSUlsa9euXRpHTIjLcbPHxM1jIyTjhB3aCOCSHJ1AIIDKykpMmzYtts/j8WD48OFYu3at7nk1NTXo2rUrRFHEwIED8eCDD6JPnz667evq6lBXVxf7XF1dDQAIhkIIBoMOXEl2EgyF4l4bK1l9H7QmCkxQTATD4frXVAoSrTG6SABl9ffBQXgfIqT3+pmj4ySuEDo//PADwuFwA49Mu3btsH37ds1zTjnlFMyfPx/9+vXDkSNH8Oijj2LIkCHYunUrOnXqpHnOrFmzcO+99zbY/8GqVSgsLEz+QrKcivfey/QQXAHvQ4SKlSszPQRXwO9DhMZ+H44ePZrG3ih0nMQVQicRysrKUFZWFvs8ZMgQ9OrVC08//TTuv/9+zXOmTZuGKVOmxD5XV1ejc+fOGDZ0KFq1bJnyMbuVYCiEivfew4jhw+H3Ze1XImmy+j4YlZPbnHE4GAqhYuVKjDj//NTcB6Oxucyjk7XfBwfhfYjw48GDmR4CSRBXfGtbt24Nr9eL/fv3x+3fv38/SkpKLNnw+/049dRTsXPnTt02+fn5yM/Pb3iuzwe/329v0DkI70OEnLwPCSyvkJL74CIhY5Wc/D4kQGO/D+kVefToOIkrkpHz8vIwaNAgrFS4ykVRxMqVK+O8NkaEw2F88cUXaN++faqGSUhuIU8IqNwSbWfVFiHEAsmWljux+nnu4AqhAwBTpkzBs88+i+effx5ffvklbrrpJtTW1uLaa68FAIwZMyYuWfm+++7Du+++i6+++gobN27E1VdfjW+++QbXX399pi6BEPeSaNJvMoLFxYnGhJDkqaurw4ABAyAIAjZt2hR37PPPP8fPf/5zFBQUoHPnznj44YcbnL906VL07NkTBQUF6Nu3L/75z3/GHZckCTNmzED79u3RpEkTDB8+HP/5z39sj9M1QueKK67Ao48+ihkzZmDAgAHYtGkT3n777ViC8u7du7Fv375Y+0OHDmHChAno1asXfvGLX6C6uhpr1qxB7969M3UJhLgbK94W5XEr7azao8ghxAbZUV4+depUdOjQocH+6upqXHDBBejatSsqKyvxyCOPYObMmXjmmWdibdasWYMrr7wS48ePx2effYZRo0Zh1KhR2LJlS6zNww8/jL/85S+YN28e1q1bh6ZNm6K8vBzHjx+3NU5X5OjITJo0CZMmTdI8tmrVqrjPs2fPxuzZs9MwKkIIISSdiEheqKQ2dPXWW2/h3XffxSuvvIK33nor7tg//vEPBAIBzJ8/H3l5eejTpw82bdqExx9/HDfccAMAYM6cObjwwgtx2223AQDuv/9+VFRU4Mknn8S8efMgSRKeeOIJTJ8+HZdddhkA4IUXXkC7du2wfPly/OY3v7E8Vtd4dAghhBDiLNXV1XGbci65RNm/fz8mTJiAF198UXNqlrVr1+Kcc85BXl5ebF95eTl27NiBQ4cOxdoMHz487rzy8vLY3Hm7du1CVVVVXJvi4mKcccYZhvPraUGhQwghhLgK50JXnTt3RnFxcWybNWtWUiOTJAnjxo3DjTfeiMGDB2u2qaqq0pwXTz5m1EZ5XHmeVhuruCp0RQghhBAnqqYi5+/ZswdFRUWxvVpTrADAHXfcgYceesjQ4pdffol3330XP/30U1xxkNuh0CGEEEJylKKiojiho8cf/vAHjBs3zrDNSSedhPfffx9r165tIJgGDx6Mq666Cs8//zxKSko058UDEJsbT6+N8ri8TzltzP79+zFgwADT61FCoUMIIYS4ivRPGNimTRu0adPGtN1f/vIXPPDAA7HPe/fuRXl5OZYsWYIzzjgDQGTlgrvuugvBYDA2yWRFRQVOOeUUtGjRItZm5cqVmDx5csxWRUVFbO68bt26oaSkBCtXrowJm+rqaqxbtw433XSTrWuj0CGEEEJchXtnRu7SpUvc52bNmgEATj755Ng6k7/97W9x7733Yvz48bj99tuxZcsWzJkzJ65S+pZbbsG5556Lxx57DCNHjsTixYuxYcOGWAm6IAiYPHkyHnjgAfTo0QPdunXD3XffjQ4dOmDUqFG2xkyhQwghhLgK9wodKxQXF+Pdd9/FxIkTMWjQILRu3RozZsyIlZYDkfUpFy1ahOnTp+POO+9Ejx49sHz5cpSWlsbaTJ06FbW1tbjhhhtw+PBhnH322Xj77bdRUFBgazwUOoQQQghJiBNPPBGSxlp6/fr1w0cffWR47ujRozF69Gjd44Ig4L777sN9992X1BgpdAghhBBX4f4JA7MJCh1CCCHEVThXXk44YSAhhBBCchh6dAghhBBXkd3JyG6DQocQQghxFRQ6TsLQFSGEEEJyFnp0CCGEEFdBj46TUOgQQgghroJCx0kYuiKEEEJIzkKPDiGEEOIqOI+Ok1DoEEIIIa6CMyM7CYUOIYQQ4iqYo+MkzNEhhBBCSM5Cjw4hhBDiKujRcRIKHUIIIcRVUOg4CUNXhBBCCMlZ6NEhhBBCXAU9Ok5CoUMIIYS4Cs6j4yQMXRFCCCEkZ6FHhxBCCHEVnDDQSSh0CCGEEFfBHB0nYeiKEEIIITkLPTqEEEKIq6BHx0kodAghhBBXwaorJ6HQIYQQQlwFPTpOwhwdQgghhOQs9OgQQgghroIeHSeh0CGEEEJcBYWOkzB0RQghhJCchR4dQgghxFVwZmQnodAhhBBCXAXLy52EoStCCCGE5Cz06BBCCCGugsnITkKhQwghhLgKCh0nYeiKEEIIITkLPTqEEEKIq6BHx0kodAghhBBXQaHjJBQ6hBBCiKvgPDpOwhwdQgghhOQs9OgQQgghroITBjoJhQ4hhBDiKsJIPuDCHB0Zhq4IIYQQkrO4SujMnTsXJ554IgoKCnDGGWfg008/NWy/dOlS9OzZEwUFBejbty/++c9/pmmkhBBCSKoIO7QRwEVCZ8mSJZgyZQruuecebNy4Ef3790d5eTkOHDig2X7NmjW48sorMX78eHz22WcYNWoURo0ahS1btqR55IQQQoiTUOg4iWuEzuOPP44JEybg2muvRe/evTFv3jwUFhZi/vz5mu3nzJmDCy+8ELfddht69eqF+++/HwMHDsSTTz6Z5pETQgghxK24Ihk5EAigsrIS06ZNi+3zeDwYPnw41q5dq3nO2rVrMWXKlLh95eXlWL58uW4/dXV1qKuri30+cuQIAODgoUNJjD77CYZCOHr0KH48eBB+nyu+EhmB9yEC70ME3ocIvA8R5OeEJElp6C2A5KumQk4MJCdwxbf2hx9+QDgcRrt27eL2t2vXDtu3b9c8p6qqSrN9VVWVbj+zZs3Cvffe22D/z045JYFRE0IIaWz8+OOPKC4uTontvLw8lJSUoKrqPUfslZSUIC8vzxFb2YwrhE66mDZtWpwX6PDhw+jatSt2796dsi9uNlBdXY3OnTtjz549KCoqyvRwMgbvQwTehwi8DxF4HyIcOXIEXbp0QcuWLVPWR0FBAXbt2oVAIOCIvby8PBQUFDhiK5txhdBp3bo1vF4v9u/fH7d///79KCkp0TynpKTEVnsAyM/PR35+foP9xcXFjfoXWKaoqIj3AbwPMrwPEXgfIvA+RPB4UpvaWlBQQHHiMK5IRs7Ly8OgQYOwcuXK2D5RFLFy5UqUlZVpnlNWVhbXHgAqKip02xNCCCGk8eEKjw4ATJkyBWPHjsXgwYNx+umn44knnkBtbS2uvfZaAMCYMWPQsWNHzJo1CwBwyy234Nxzz8Vjjz2GkSNHYvHixdiwYQOeeeaZTF4GIYQQQlyEa4TOFVdcge+//x4zZsxAVVUVBgwYgLfffjuWcLx79+44l+GQIUOwaNEiTJ8+HXfeeSd69OiB5cuXo7S01HKf+fn5uOeeezTDWY0J3ocIvA8ReB8i8D5E4H2IwPuQvQhSemrlCCGEEELSjitydAghhBBCUgGFDiGEEEJyFgodQgghhOQsFDqEEEIIyVkodKJceuml6NKlCwoKCtC+fXtcc8012Lt3b6aHlVa+/vprjB8/Ht26dUOTJk1w8skn45577nFsls5s4k9/+hOGDBmCwsJCnHDCCZkeTlqZO3cuTjzxRBQUFOCMM87Ap59+mukhpZUPP/wQl1xyCTp06ABBEAzXz8tlZs2ahdNOOw3NmzdH27ZtMWrUKOzYsSPTw0o7Tz31FPr16xebMLGsrAxvvfVWpodFbEChE2XYsGF46aWXsGPHDrzyyiv473//i1/96leZHlZa2b59O0RRxNNPP42tW7di9uzZmDdvHu68885MDy3tBAIBjB49GjfddFOmh5JWlixZgilTpuCee+7Bxo0b0b9/f5SXl+PAgQOZHlraqK2tRf/+/TF37txMDyWjrF69GhMnTsQnn3yCiooKBINBXHDBBaitrc300NJKp06d8Oc//xmVlZXYsGEDzjvvPFx22WXYunVrpodGrCIRTVasWCEJgiAFAoFMDyWjPPzww1K3bt0yPYyMsWDBAqm4uDjTw0gbp59+ujRx4sTY53A4LHXo0EGaNWtWBkeVOQBIy5Yty/QwXMGBAwckANLq1aszPZSM06JFC+m5557L9DCIRejR0eDgwYP4xz/+gSFDhsDv92d6OBnlyJEjKV3EjriHQCCAyspKDB8+PLbP4/Fg+PDhWLt2bQZHRtzAkSNHAKBR/z0Ih8NYvHgxamtrudxQFkGho+D2229H06ZN0apVK+zevRsrVqzI9JAyys6dO/HXv/4Vv/vd7zI9FJIGfvjhB4TD4dhs5DLt2rVDVVVVhkZF3IAoipg8eTLOOussW7PP5wpffPEFmjVrhvz8fNx4441YtmwZevfunelhEYvktNC54447IAiC4bZ9+/ZY+9tuuw2fffYZ3n33XXi9XowZMwZSDkwcbfc+AMB3332HCy+8EKNHj8aECRMyNHJnSeQ+EEKAiRMnYsuWLVi8eHGmh5IRTjnlFGzatAnr1q3DTTfdhLFjx2Lbtm2ZHhaxSE4vAfH999/jxx9/NGxz0kknIS8vr8H+b7/9Fp07d8aaNWuy3kVp9z7s3bsXQ4cOxZlnnomFCxfGrTGWzSTyfVi4cCEmT56Mw4cPp3h0mScQCKCwsBAvv/wyRo0aFds/duxYHD58uFF6OAVBwLJly+LuR2Nj0qRJWLFiBT788EN069Yt08NxBcOHD8fJJ5+Mp59+OtNDIRZwzaKeqaBNmzZo06ZNQueKoggAqKurc3JIGcHOffjuu+8wbNgwDBo0CAsWLMgZkQMk931oDOTl5WHQoEFYuXJl7MEuiiJWrlyJSZMmZXZwJO1IkoTf//73WLZsGVatWkWRo0AUxZx4NjQWclroWGXdunVYv349zj77bLRo0QL//e9/cffdd+Pkk0/Oem+OHb777jsMHToUXbt2xaOPPorvv/8+dqykpCSDI0s/u3fvxsGDB7F7926Ew2Fs2rQJANC9e3c0a9Yss4NLIVOmTMHYsWMxePBgnH766XjiiSdQW1uLa6+9NtNDSxs1NTXYuXNn7POuXbuwadMmtGzZEl26dMngyNLLxIkTsWjRIqxYsQLNmzeP5WkVFxejSZMmGR5d+pg2bRouuugidOnSBT/99BMWLVqEVatW4Z133sn00IhVMlv05Q4+//xzadiwYVLLli2l/Px86cQTT5RuvPFG6dtvv8300NLKggULJACaW2Nj7Nixmvfhgw8+yPTQUs5f//pXqUuXLlJeXp50+umnS5988kmmh5RWPvjgA82f/dixYzM9tLSi97dgwYIFmR5aWrnuuuukrl27Snl5eVKbNm2k888/X3r33XczPSxig5zO0SGEEEJI4yZ3EjAIIYQQQlRQ6BBCCCEkZ6HQIYQQQkjOQqFDCCGEkJyFQocQQgghOQuFDiGEEEJyFgodQgghhOQsFDqEEEIIyVkodAghhBCSs1DoEEIIISRnodAhhDTg2muvxfTp0zM9DEIISRquXk4IiSMcDuONN97Am2++memhEEJI0tCjQ0iO0bZtWzz33HNx+9avX4+CggLs2rXL9Pw1a9bA7/fjtNNOS9oWIYRkGgodQnKMvn37Ytu2bXH7br/9dvzud79Dt27dTM9/7bXXcMkll0AQhKRtEUJIpqHQISTHKC0tjRMn77zzDjZs2IC7774bADB06NDYMeV7mRUrVuDSSy+1ZAsAvvrqK7z22msOXwUhhDgDhQ4hOYbSCyNJEqZNm4bbbrsNrVu3Nj33yy+/xN69e3H++edbtvXWW2818PoQQohbYDIyITlGaWkpvv32W9TU1OD111/Hvn37MGXKFEvnvvbaaxgxYgQKCgos2Vq9ejXuvvtutGrVCkuWLMG4cePw/PPPIxAIoE+fPliyZElKrpEQQqxCoUNIjlFaWgoA+Pzzz3H33XdjxowZaNq0qaVzV6xYgRtuuMGyrXPPPRf9+vXDwoULUVxcjGHDhqGyshJerxeHDx927qIIISRBGLoiJMdo1qwZunbtij/84Q/weDyYMGGCpfMOHDiADRs24OKLL7Zla/fu3TjxxBPh8/lw6NAhTJ06FVu3bsUJJ5zg1CURQkjCUOgQkoP07dsXn3zyCf70pz/B57PmuH399ddx+umnN8jlMbL17bffokOHDgCA5s2bY8uWLRgwYAB+/etfY/ny5Y5cCyGEJANDV4TkIIlUQSmrraza+uabb9C+fXsAwH/+8x/06NED11xzDdauXYu6ujrbYyCEEKeh0CGEAADOPvtsXHnllbbOKS0txVdffYW+ffuiZ8+e+Pzzz1FYWIghQ4Zg9OjRKRopIYRYh0KHEAIAmDp1qu1ziouLUVlZmYLREEKIMzBHhxBCCCE5C4UOIY2McePGab4nhJBcRJAkScr0IAghhBBCUgE9OoQQQgjJWSh0CCGEEJKzUOgQQgghJGeh0CGEEEJIzkKhQwghhJCchUKHEEIIITkLhQ4hhBBCchYKHUIIIYTkLBQ6hBBCCMlZKHQIIYQQkrP8fxTspsu7RibnAAAAAElFTkSuQmCC", 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"text/plain": [ "
" ] @@ -1441,6 +1441,464 @@ "lp.plot_fs1_re_im_cart(foldername,outputflnm,flnm=flnm,specnum='02')" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Collisions\n", + "\n", + "Test the non-conservative krook operator" + ] + }, + { + "cell_type": "code", + "execution_count": 52, + "metadata": {}, + "outputs": [], + "source": [ + "Knlist = [10e99, 10e3, 5e3, 3e3, 2e3, 10e2] \n", + "plumeinput.params['Kn'] = 7.0\n", + "plumeinput.params['collision_type'] = 1" + ] + }, + { + "cell_type": "code", + "execution_count": 53, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "1" + ] + }, + "execution_count": 53, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "plumeinput.params['collision_type']" + ] + }, + { + "cell_type": "code", + "execution_count": 54, + "metadata": {}, + "outputs": [], + "source": [ + "selectedrootidx = [1,1,1,1,2,1] #set by hand!" + ] + }, + { + "cell_type": "code", + "execution_count": 55, + "metadata": {}, + "outputs": [], + "source": [ + "vperpmin = .0\n", + "vperpmax = 3\n", + "vparmin = -3\n", + "vparmax = 3\n", + "delv = .15\n", + "elecdircontribution = 0. #If equal to 1. 2. or 3., will return contribution to signatures by Eperp1, Eperp2, Epar in isolation. Leave alone if unsure what this means. Note, this relates to the contribution by the individual elements of the susc tensor...\n", + "plumeinput.set_fpc(vperpmin,vperpmax,vparmin,vparmax,delv,elecdircontribution=elecdircontribution)" + ] + }, + { + "cell_type": "code", + "execution_count": 56, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "****** 1e+100 ******\n", + "OVERWRITING OPTION; TODO CHECK THAT plume_input IS CORRECT INSTEAD...\n", + "mkdir data/howes2017\n", + "./plume.e inputs/howes2017/roots.in >> outlog\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "mkdir: data/howes2017: File exists\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "mkdir data/howes2017\n", + "./plume.e inputs/howes2017/fpc.in >> outlog\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "mkdir: data/howes2017: File exists\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saving figure to figures folder!\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "****** 10000.0 ******\n", + "OVERWRITING OPTION; TODO CHECK THAT plume_input IS CORRECT INSTEAD...\n", + "mkdir data/howes2017\n", + "./plume.e inputs/howes2017/roots.in >> outlog\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "mkdir: data/howes2017: File exists\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "mkdir data/howes2017\n", + "./plume.e inputs/howes2017/fpc.in >> outlog\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "mkdir: data/howes2017: File exists\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saving figure to figures folder!\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "****** 5000.0 ******\n", + "OVERWRITING OPTION; TODO CHECK THAT plume_input IS CORRECT INSTEAD...\n", + "mkdir data/howes2017\n", + "./plume.e inputs/howes2017/roots.in >> outlog\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "mkdir: data/howes2017: File exists\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "mkdir data/howes2017\n", + "./plume.e inputs/howes2017/fpc.in >> outlog\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "mkdir: data/howes2017: File exists\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saving figure to figures folder!\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "****** 3000.0 ******\n", + "OVERWRITING OPTION; TODO CHECK THAT plume_input IS CORRECT INSTEAD...\n", + "mkdir data/howes2017\n", + "./plume.e inputs/howes2017/roots.in >> outlog\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "mkdir: data/howes2017: File exists\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "mkdir data/howes2017\n", + "./plume.e inputs/howes2017/fpc.in >> outlog\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "mkdir: data/howes2017: File exists\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saving figure to figures folder!\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "****** 2000.0 ******\n", + "OVERWRITING OPTION; TODO CHECK THAT plume_input IS CORRECT INSTEAD...\n", + "mkdir data/howes2017\n", + "./plume.e inputs/howes2017/roots.in >> outlog\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "mkdir: data/howes2017: File exists\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "mkdir data/howes2017\n", + "./plume.e inputs/howes2017/fpc.in >> outlog\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "mkdir: data/howes2017: File exists\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saving figure to figures folder!\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "****** 1000.0 ******\n", + "OVERWRITING OPTION; TODO CHECK THAT plume_input IS CORRECT INSTEAD...\n", + "mkdir data/howes2017\n", + "./plume.e inputs/howes2017/roots.in >> outlog\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "mkdir: data/howes2017: File exists\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "mkdir data/howes2017\n", + "./plume.e inputs/howes2017/fpc.in >> outlog\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "mkdir: data/howes2017: File exists\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saving figure to figures folder!\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "for _indx,_kn in enumerate(Knlist):\n", + " print('******',_kn,'******')\n", + " plumeinput.params['Kn'] = _kn\n", + " roots = lfpc.compute_roots(plumeinput,inputfldr+'roots',filetag)\n", + " lp.plot_roots(roots,flnm=flnm,xlim=[-0.002,.004],ylim=[-.004,0.002])\n", + " \n", + " inputflnm = inputfldr+'fpc'\n", + " outputflnm = 'fpc'\n", + " elecdircontribution = 0\n", + " cdataflnms = lfpc.compute_fpc_from_root(plumeinput,roots[selectedrootidx[_indx]],inputflnm,outputflnm)\n", + " iondatacpar = lfpc.loadlinfpccepar(cdataflnms[0])\n", + " lp.plotlinfpc_gyro(iondatacpar,filetag+'/'+filetag+'_Kn_'+str(_kn)+'.png',plotresonant=True)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#reset when done\n", + "del Knlist\n", + "plumeinput.params['Kn'] = None\n", + "plumeinput.collision_type = 0" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, { "cell_type": "markdown", "metadata": {}, @@ -1452,14 +1910,6845 @@ }, { "cell_type": "code", - "execution_count": 52, + "execution_count": 58, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Warning: outlog is large and is not displayed here. Please check the outlog file...\n" + "-=-=-=-=-=-=-=-=-=-\n", + "Root Search:\n", + "-=-=-=-=-=-=-=-=-=-\n", + "Global Plasma Parameters:\n", + "k_perp rho_ref = 1.30000 \n", + "k_par rho_ref = 0.100000E-02\n", + "Beta_ref,par = 1.00000 \n", + "v_t,ref,par/c = 0.100000E-03\n", + "-=-=-=-=-=-=-=-=-=-\n", + "Parameters for Species : 1\n", + "T_||,ref/T_||s = 1.00000 \n", + "m_ref/m_s = 1.00000 \n", + "T_perp/T_par|s = 1.00000 \n", + "q_ref/q_s = 1.00000 \n", + "n_s/n_ref = 1.00000 \n", + "v_drift s/v_A,ref = 0.00000 \n", + "-=-=-=-=-=-=-=-=-=-\n", + "Parameters for Species : 2\n", + "T_||,ref/T_||s = 1.00000 \n", + "m_ref/m_s = 1836.00 \n", + "T_perp/T_par|s = 1.00000 \n", + "q_ref/q_s = -1.00000 \n", + "n_s/n_ref = 1.00000 \n", + "v_drift s/v_A,ref = 0.00000 \n", + "-=-=-=-=-=-=-=-=-=-\n", + "Searching over:\n", + "omega/Omega_ref \\in [ 1.000E-05, 1.000E-02]\n", + "gamma/Omega_ref \\in [-2.000E-03,-2.000E-06]\n", + "-=-=-=-=-=-=-=-=-=-\n", + " finding minima\n", + " minima found\n", + " 7 possible local minima found:\n", + "ir = 128 ii = 128\n", + " 1.0000E-02 -2.0000E-06\n", + "ir = 16 ii = 125\n", + " 1.2588E-03 -4.8828E-05\n", + "ir = 0 ii = 80\n", + " 1.0000E-05 -7.5125E-04\n", + "ir = 15 ii = 65\n", + " 1.1807E-03 -9.8539E-04\n", + "ir = 22 ii = 26\n", + " 1.7270E-03 -1.5942E-03\n", + "ir = 28 ii = 2\n", + " 2.1953E-03 -1.9688E-03\n", + "ir = 128 ii = 0\n", + " 1.0000E-02 -2.0000E-03\n", + "Refining roots:\n", + "Dispersion Solutions \n", + " 1 1.2362E-03 -4.4355E-05\n", + " 2 3.4468E-14 -7.4376E-04\n", + " 3 1.1473E-03 -9.8322E-04\n", + " 4 1.7166E-03 -1.5983E-03\n", + " 5 2.2089E-03 -1.9704E-03\n", + " 6 2.2089E-03 -1.9704E-03\n", + " 7 1.4252E+01 8.9696E+00\n", + " 8 0.0000E+00 0.0000E+00\n", + " 9 0.0000E+00 0.0000E+00\n", + " 10 0.0000E+00 0.0000E+00\n", + " 11 0.0000E+00 0.0000E+00\n", + " 12 0.0000E+00 0.0000E+00\n", + " 13 0.0000E+00 0.0000E+00\n", + " 14 0.0000E+00 0.0000E+00\n", + " 15 0.0000E+00 0.0000E+00\n", + " 16 0.0000E+00 0.0000E+00\n", + " 17 0.0000E+00 0.0000E+00\n", + " 18 0.0000E+00 0.0000E+00\n", + " 19 0.0000E+00 0.0000E+00\n", + " 20 0.0000E+00 0.0000E+00\n", + "Input for Root 1: 1.2362E-03 -4.4355E-05\n", + "Dispersion Solutions: \n", + " 1 1.2362E-03 -4.4355E-05\n", + "Scan over kperp from 1.3000E+00 to 1.0000E+02\n", + " =>data/howes2017/sweepsweep1_tensor_kperp_1300_100000\n", + "Root 1 In: 1.2362E-03 -4.4355E-05\n", + "Root 1 Out: 3.7512E-02 -3.7403E-02\n", + "Input for Root 1: 1.2362E-03 -4.4355E-05\n", + "Dispersion Solutions: \n", + " 1 1.2362E-03 -4.4355E-05\n", + "Scan over kperp from 1.3000E+00 to 1.0000E-01\n", + " =>data/howes2017/sweepsweep2_tensor_kperp_1300_100\n", + "Root 1 In: 1.2362E-03 -4.4355E-05\n", + "Root 1 Out: 1.0011E-03 -4.6981E-07\n", + "Input for Root 1: 1.2362E-03 -4.4355E-05\n", + "Dispersion Solutions: \n", + " 1 1.2362E-03 -4.4355E-05\n", + "Scan over tauS for species 2 from 1.0000E+00 to 1.0000E+02\n", + " =>data/howes2017/sweepsweep1_tensor_tauS_s2_1000_100000\n", + "Root 1 In: 1.2362E-03 -4.4355E-05\n", + "Root 1 Out: 1.1319E-03 -2.0535E-05\n", + "Input for Root 1: 1.2362E-03 -4.4355E-05\n", + "Dispersion Solutions: \n", + " 1 1.2362E-03 -4.4355E-05\n", + "Scan over tauS for species 2 from 1.0000E+00 to 1.0000E-01\n", + " =>data/howes2017/sweepsweep2_tensor_tauS_s2_1000_100\n", + "Root 1 In: 1.2362E-03 -4.4355E-05\n", + "Root 1 Out: 1.5471E-03 -2.9932E-04\n", + "Input for Root 1: 1.2362E-03 -4.4355E-05\n", + "Dispersion Solutions: \n", + " 1 1.2362E-03 -4.4355E-05\n", + "Scan over kperp from 1.300000E+000 to 1.000000E+001\n", + "Scan over kpar from 1.000000E-003 to 1.000000E+001\n", + "Parameter 1: 1.3000E+00\n", + "Root 1: 1.2362E-03 -4.4355E-05\n", + "Parameter 1: 1.7350E+00\n", + "Root 1: 1.4327E-03 -5.7938E-05\n", + "Parameter 1: 2.1700E+00\n", + "Root 1: 1.6788E-03 -7.3372E-05\n", + "Parameter 1: 2.6050E+00\n", + "Root 1: 1.9538E-03 -9.5239E-05\n", + "Parameter 1: 3.0400E+00\n", + "Root 1: 2.2430E-03 -1.2441E-04\n", + "Parameter 1: 3.4750E+00\n", + "Root 1: 2.5392E-03 -1.6009E-04\n", + "Parameter 1: 3.9100E+00\n", + "Root 1: 2.8393E-03 -2.0140E-04\n", + "Parameter 1: 4.3450E+00\n", + "Root 1: 3.1417E-03 -2.4782E-04\n", + "Parameter 1: 4.7800E+00\n", + "Root 1: 3.4454E-03 -2.9912E-04\n", + "Parameter 1: 5.2150E+00\n", + "Root 1: 3.7499E-03 -3.5526E-04\n", + "Parameter 1: 5.6500E+00\n", + "Root 1: 4.0549E-03 -4.1620E-04\n", + "Parameter 1: 6.0850E+00\n", + "Root 1: 4.3603E-03 -4.8193E-04\n", + "Parameter 1: 6.5200E+00\n", + "Root 1: 4.6659E-03 -5.5243E-04\n", + "Parameter 1: 6.9550E+00\n", + "Root 1: 4.9716E-03 -6.2767E-04\n", + "Parameter 1: 7.3900E+00\n", + "Root 1: 5.2773E-03 -7.0764E-04\n", + "Parameter 1: 7.8250E+00\n", + "Root 1: 5.5831E-03 -7.9231E-04\n", + "Parameter 1: 8.2600E+00\n", + "Root 1: 5.8889E-03 -8.8166E-04\n", + "Parameter 1: 8.6950E+00\n", + "Root 1: 6.1946E-03 -9.7565E-04\n", + "Parameter 1: 9.1300E+00\n", + "Root 1: 6.5003E-03 -1.0742E-03\n", + "Parameter 1: 9.5650E+00\n", + "Root 1: 6.8059E-03 -1.1774E-03\n", + "Parameter 1: 1.0000E+01\n", + "Root 1: 7.1114E-03 -1.2852E-03\n", + "Input for Root 1: 1.2362E-03 -4.4355E-05\n", + "Dispersion Solutions: \n", + " 1 1.2362E-03 -4.4355E-05\n", + "Scan over kperp from 1.300000E+000 to 1.000000E+000\n", + "Scan over kpar from 1.000000E-003 to 1.000000E+001\n", + "Parameter 1: 1.3000E+00\n", + "Root 1: 1.2362E-03 -4.4355E-05\n", + "Parameter 1: 1.2850E+00\n", + "Root 1: 1.2305E-03 -4.3836E-05\n", + "Parameter 1: 1.2700E+00\n", + "Root 1: 1.2249E-03 -4.3311E-05\n", + "Parameter 1: 1.2550E+00\n", + "Root 1: 1.2193E-03 -4.2780E-05\n", + "Parameter 1: 1.2400E+00\n", + "Root 1: 1.2139E-03 -4.2243E-05\n", + "Parameter 1: 1.2250E+00\n", + "Root 1: 1.2085E-03 -4.1700E-05\n", + "Parameter 1: 1.2100E+00\n", + "Root 1: 1.2032E-03 -4.1150E-05\n", + "Parameter 1: 1.1950E+00\n", + "Root 1: 1.1979E-03 -4.0593E-05\n", + "Parameter 1: 1.1800E+00\n", + "Root 1: 1.1928E-03 -4.0030E-05\n", + "Parameter 1: 1.1650E+00\n", + "Root 1: 1.1877E-03 -3.9460E-05\n", + "Parameter 1: 1.1500E+00\n", + "Root 1: 1.1827E-03 -3.8883E-05\n", + "Parameter 1: 1.1350E+00\n", + "Root 1: 1.1777E-03 -3.8300E-05\n", + "Parameter 1: 1.1200E+00\n", + "Root 1: 1.1729E-03 -3.7710E-05\n", + "Parameter 1: 1.1050E+00\n", + "Root 1: 1.1681E-03 -3.7114E-05\n", + "Parameter 1: 1.0900E+00\n", + "Root 1: 1.1634E-03 -3.6511E-05\n", + "Parameter 1: 1.0750E+00\n", + "Root 1: 1.1587E-03 -3.5902E-05\n", + "Parameter 1: 1.0600E+00\n", + "Root 1: 1.1541E-03 -3.5286E-05\n", + "Parameter 1: 1.0450E+00\n", + "Root 1: 1.1497E-03 -3.4664E-05\n", + "Parameter 1: 1.0300E+00\n", + "Root 1: 1.1452E-03 -3.4036E-05\n", + "Parameter 1: 1.0150E+00\n", + "Root 1: 1.1409E-03 -3.3402E-05\n", + "Parameter 1: 1.0000E+00\n", + "Root 1: 1.1366E-03 -3.2762E-05\n", + "Input for Root 1: 1.2362E-03 -4.4355E-05\n", + "Dispersion Solutions: \n", + " 1 1.2362E-03 -4.4355E-05\n", + "Scan over kperp from 1.300000E+000 to 1.000000E+001\n", + "Scan over kpar from 1.000000E-003 to 1.000000E+000\n", + "Parameter 1: 1.3000E+00\n", + "Root 1: 1.2362E-03 -4.4355E-05\n", + "Parameter 1: 1.7350E+00\n", + "Root 1: 1.4327E-03 -5.7938E-05\n", + "Parameter 1: 2.1700E+00\n", + "Root 1: 1.6788E-03 -7.3372E-05\n", + "Parameter 1: 2.6050E+00\n", + "Root 1: 1.9538E-03 -9.5239E-05\n", + "Parameter 1: 3.0400E+00\n", + "Root 1: 2.2430E-03 -1.2441E-04\n", + "Parameter 1: 3.4750E+00\n", + "Root 1: 2.5392E-03 -1.6009E-04\n", + "Parameter 1: 3.9100E+00\n", + "Root 1: 2.8393E-03 -2.0140E-04\n", + "Parameter 1: 4.3450E+00\n", + "Root 1: 3.1417E-03 -2.4782E-04\n", + "Parameter 1: 4.7800E+00\n", + "Root 1: 3.4454E-03 -2.9912E-04\n", + "Parameter 1: 5.2150E+00\n", + "Root 1: 3.7499E-03 -3.5526E-04\n", + "Parameter 1: 5.6500E+00\n", + "Root 1: 4.0549E-03 -4.1620E-04\n", + "Parameter 1: 6.0850E+00\n", + "Root 1: 4.3603E-03 -4.8193E-04\n", + "Parameter 1: 6.5200E+00\n", + "Root 1: 4.6659E-03 -5.5243E-04\n", + "Parameter 1: 6.9550E+00\n", + "Root 1: 4.9716E-03 -6.2767E-04\n", + "Parameter 1: 7.3900E+00\n", + "Root 1: 5.2773E-03 -7.0764E-04\n", + "Parameter 1: 7.8250E+00\n", + "Root 1: 5.5831E-03 -7.9231E-04\n", + "Parameter 1: 8.2600E+00\n", + "Root 1: 5.8889E-03 -8.8166E-04\n", + "Parameter 1: 8.6950E+00\n", + "Root 1: 6.1946E-03 -9.7565E-04\n", + "Parameter 1: 9.1300E+00\n", + "Root 1: 6.5003E-03 -1.0742E-03\n", + "Parameter 1: 9.5650E+00\n", + "Root 1: 6.8059E-03 -1.1774E-03\n", + "Parameter 1: 1.0000E+01\n", + "Root 1: 7.1114E-03 -1.2852E-03\n", + "Input for Root 1: 1.2362E-03 -4.4355E-05\n", + "Dispersion Solutions: \n", + " 1 1.2362E-03 -4.4355E-05\n", + "Scan over kperp from 1.300000E+000 to 1.000000E+000\n", + "Scan over kpar from 1.000000E-003 to 1.000000E+000\n", + "Parameter 1: 1.3000E+00\n", + "Root 1: 1.2362E-03 -4.4355E-05\n", + "Parameter 1: 1.2850E+00\n", + "Root 1: 1.2305E-03 -4.3836E-05\n", + "Parameter 1: 1.2700E+00\n", + "Root 1: 1.2249E-03 -4.3311E-05\n", + "Parameter 1: 1.2550E+00\n", + "Root 1: 1.2193E-03 -4.2780E-05\n", + "Parameter 1: 1.2400E+00\n", + "Root 1: 1.2139E-03 -4.2243E-05\n", + "Parameter 1: 1.2250E+00\n", + "Root 1: 1.2085E-03 -4.1700E-05\n", + "Parameter 1: 1.2100E+00\n", + "Root 1: 1.2032E-03 -4.1150E-05\n", + "Parameter 1: 1.1950E+00\n", + "Root 1: 1.1979E-03 -4.0593E-05\n", + "Parameter 1: 1.1800E+00\n", + "Root 1: 1.1928E-03 -4.0030E-05\n", + "Parameter 1: 1.1650E+00\n", + "Root 1: 1.1877E-03 -3.9460E-05\n", + "Parameter 1: 1.1500E+00\n", + "Root 1: 1.1827E-03 -3.8883E-05\n", + "Parameter 1: 1.1350E+00\n", + "Root 1: 1.1777E-03 -3.8300E-05\n", + "Parameter 1: 1.1200E+00\n", + "Root 1: 1.1729E-03 -3.7710E-05\n", + "Parameter 1: 1.1050E+00\n", + "Root 1: 1.1681E-03 -3.7114E-05\n", + "Parameter 1: 1.0900E+00\n", + "Root 1: 1.1634E-03 -3.6511E-05\n", + "Parameter 1: 1.0750E+00\n", + "Root 1: 1.1587E-03 -3.5902E-05\n", + "Parameter 1: 1.0600E+00\n", + "Root 1: 1.1541E-03 -3.5286E-05\n", + "Parameter 1: 1.0450E+00\n", + "Root 1: 1.1497E-03 -3.4664E-05\n", + "Parameter 1: 1.0300E+00\n", + "Root 1: 1.1452E-03 -3.4036E-05\n", + "Parameter 1: 1.0150E+00\n", + "Root 1: 1.1409E-03 -3.3402E-05\n", + "Parameter 1: 1.0000E+00\n", + "Root 1: 1.1366E-03 -3.2762E-05\n", + "Input for Root 1: 1.2362E-03 -4.4355E-05\n", + "Dispersion Solutions: \n", + " 1 1.2362E-03 -4.4355E-05\n", + "Scan over betap from 1.000000E+000 to 5.000000E+000\n", + "Scan over alphS for species 1 from 1.000000E+000 to 5.000000E+000\n", + "Parameter 1: 1.0000E+00\n", + "Root 1: 1.2362E-03 -4.4355E-05\n", + "Parameter 1: 1.2000E+00\n", + "Root 1: 1.0965E-03 -4.5613E-05\n", + "Parameter 1: 1.4000E+00\n", + "Root 1: 9.9005E-04 -4.7867E-05\n", + "Parameter 1: 1.6000E+00\n", + "Root 1: 9.0557E-04 -5.0722E-05\n", + "Parameter 1: 1.8000E+00\n", + "Root 1: 8.3649E-04 -5.3871E-05\n", + "Parameter 1: 2.0000E+00\n", + "Root 1: 7.7864E-04 -5.7109E-05\n", + "Parameter 1: 2.2000E+00\n", + "Root 1: 7.2929E-04 -6.0311E-05\n", + "Parameter 1: 2.4000E+00\n", + "Root 1: 6.8652E-04 -6.3404E-05\n", + "Parameter 1: 2.6000E+00\n", + "Root 1: 6.4900E-04 -6.6348E-05\n", + "Parameter 1: 2.8000E+00\n", + "Root 1: 6.1571E-04 -6.9123E-05\n", + "Parameter 1: 3.0000E+00\n", + "Root 1: 5.8591E-04 -7.1721E-05\n", + "Parameter 1: 3.2000E+00\n", + "Root 1: 5.5902E-04 -7.4142E-05\n", + "Parameter 1: 3.4000E+00\n", + "Root 1: 5.3459E-04 -7.6389E-05\n", + "Parameter 1: 3.6000E+00\n", + "Root 1: 5.1225E-04 -7.8468E-05\n", + "Parameter 1: 3.8000E+00\n", + "Root 1: 4.9172E-04 -8.0387E-05\n", + "Parameter 1: 4.0000E+00\n", + "Root 1: 4.7275E-04 -8.2152E-05\n", + "Parameter 1: 4.2000E+00\n", + "Root 1: 4.5515E-04 -8.3772E-05\n", + "Parameter 1: 4.4000E+00\n", + "Root 1: 4.3876E-04 -8.5254E-05\n", + "Parameter 1: 4.6000E+00\n", + "Root 1: 4.2344E-04 -8.6605E-05\n", + "Parameter 1: 4.8000E+00\n", + "Root 1: 4.0907E-04 -8.7832E-05\n", + "Parameter 1: 5.0000E+00\n", + "Root 1: 3.9556E-04 -8.8940E-05\n", + "Input for Root 1: 1.2362E-03 -4.4355E-05\n", + "Dispersion Solutions: \n", + " 1 1.2362E-03 -4.4355E-05\n", + "Scan over betap from 1.000000E+000 to 5.000000E-001\n", + "Scan over alphS for species 1 from 1.000000E+000 to 5.000000E+000\n", + "Parameter 1: 1.0000E+00\n", + "Root 1: 1.2362E-03 -4.4355E-05\n", + "Parameter 1: 9.7500E-01\n", + "Root 1: 1.2568E-03 -4.4287E-05\n", + "Parameter 1: 9.5000E-01\n", + "Root 1: 1.2784E-03 -4.4241E-05\n", + "Parameter 1: 9.2500E-01\n", + "Root 1: 1.3008E-03 -4.4216E-05\n", + "Parameter 1: 9.0000E-01\n", + "Root 1: 1.3243E-03 -4.4214E-05\n", + "Parameter 1: 8.7500E-01\n", + "Root 1: 1.3488E-03 -4.4234E-05\n", + "Parameter 1: 8.5000E-01\n", + "Root 1: 1.3744E-03 -4.4277E-05\n", + "Parameter 1: 8.2500E-01\n", + "Root 1: 1.4013E-03 -4.4343E-05\n", + "Parameter 1: 8.0000E-01\n", + "Root 1: 1.4295E-03 -4.4433E-05\n", + "Parameter 1: 7.7500E-01\n", + "Root 1: 1.4591E-03 -4.4547E-05\n", + "Parameter 1: 7.5000E-01\n", + "Root 1: 1.4902E-03 -4.4686E-05\n", + "Parameter 1: 7.2500E-01\n", + "Root 1: 1.5230E-03 -4.4852E-05\n", + "Parameter 1: 7.0000E-01\n", + "Root 1: 1.5576E-03 -4.5048E-05\n", + "Parameter 1: 6.7500E-01\n", + "Root 1: 1.5942E-03 -4.5277E-05\n", + "Parameter 1: 6.5000E-01\n", + "Root 1: 1.6330E-03 -4.5545E-05\n", + "Parameter 1: 6.2500E-01\n", + "Root 1: 1.6741E-03 -4.5859E-05\n", + "Parameter 1: 6.0000E-01\n", + "Root 1: 1.7177E-03 -4.6229E-05\n", + "Parameter 1: 5.7500E-01\n", + "Root 1: 1.7643E-03 -4.6668E-05\n", + "Parameter 1: 5.5000E-01\n", + "Root 1: 1.8140E-03 -4.7194E-05\n", + "Parameter 1: 5.2500E-01\n", + "Root 1: 1.8673E-03 -4.7826E-05\n", + "Parameter 1: 5.0000E-01\n", + "Root 1: 1.9245E-03 -4.8593E-05\n", + "Input for Root 1: 1.2362E-03 -4.4355E-05\n", + "Dispersion Solutions: \n", + " 1 1.2362E-03 -4.4355E-05\n", + "Scan over betap from 1.000000E+000 to 5.000000E+000\n", + "Scan over alphS for species 1 from 1.000000E+000 to 5.000000E-001\n", + "Parameter 1: 1.0000E+00\n", + "Root 1: 1.2362E-03 -4.4355E-05\n", + "Parameter 1: 1.2000E+00\n", + "Root 1: 1.0965E-03 -4.5613E-05\n", + "Parameter 1: 1.4000E+00\n", + "Root 1: 9.9005E-04 -4.7867E-05\n", + "Parameter 1: 1.6000E+00\n", + "Root 1: 9.0557E-04 -5.0722E-05\n", + "Parameter 1: 1.8000E+00\n", + "Root 1: 8.3649E-04 -5.3871E-05\n", + "Parameter 1: 2.0000E+00\n", + "Root 1: 7.7864E-04 -5.7109E-05\n", + "Parameter 1: 2.2000E+00\n", + "Root 1: 7.2929E-04 -6.0311E-05\n", + "Parameter 1: 2.4000E+00\n", + "Root 1: 6.8652E-04 -6.3404E-05\n", + "Parameter 1: 2.6000E+00\n", + "Root 1: 6.4900E-04 -6.6348E-05\n", + "Parameter 1: 2.8000E+00\n", + "Root 1: 6.1571E-04 -6.9123E-05\n", + "Parameter 1: 3.0000E+00\n", + "Root 1: 5.8591E-04 -7.1721E-05\n", + "Parameter 1: 3.2000E+00\n", + "Root 1: 5.5902E-04 -7.4142E-05\n", + "Parameter 1: 3.4000E+00\n", + "Root 1: 5.3459E-04 -7.6389E-05\n", + "Parameter 1: 3.6000E+00\n", + "Root 1: 5.1225E-04 -7.8468E-05\n", + "Parameter 1: 3.8000E+00\n", + "Root 1: 4.9172E-04 -8.0387E-05\n", + "Parameter 1: 4.0000E+00\n", + "Root 1: 4.7275E-04 -8.2152E-05\n", + "Parameter 1: 4.2000E+00\n", + "Root 1: 4.5515E-04 -8.3772E-05\n", + "Parameter 1: 4.4000E+00\n", + "Root 1: 4.3876E-04 -8.5254E-05\n", + "Parameter 1: 4.6000E+00\n", + "Root 1: 4.2344E-04 -8.6605E-05\n", + "Parameter 1: 4.8000E+00\n", + "Root 1: 4.0907E-04 -8.7832E-05\n", + "Parameter 1: 5.0000E+00\n", + "Root 1: 3.9556E-04 -8.8940E-05\n", + "Input for Root 1: 1.2362E-03 -4.4355E-05\n", + "Dispersion Solutions: \n", + " 1 1.2362E-03 -4.4355E-05\n", + "Scan over betap from 1.000000E+000 to 5.000000E-001\n", + "Scan over alphS for species 1 from 1.000000E+000 to 5.000000E-001\n", + "Parameter 1: 1.0000E+00\n", + "Root 1: 1.2362E-03 -4.4355E-05\n", + "Parameter 1: 9.7500E-01\n", + "Root 1: 1.2568E-03 -4.4287E-05\n", + "Parameter 1: 9.5000E-01\n", + "Root 1: 1.2784E-03 -4.4241E-05\n", + "Parameter 1: 9.2500E-01\n", + "Root 1: 1.3008E-03 -4.4216E-05\n", + "Parameter 1: 9.0000E-01\n", + "Root 1: 1.3243E-03 -4.4214E-05\n", + "Parameter 1: 8.7500E-01\n", + "Root 1: 1.3488E-03 -4.4234E-05\n", + "Parameter 1: 8.5000E-01\n", + "Root 1: 1.3744E-03 -4.4277E-05\n", + "Parameter 1: 8.2500E-01\n", + "Root 1: 1.4013E-03 -4.4343E-05\n", + "Parameter 1: 8.0000E-01\n", + "Root 1: 1.4295E-03 -4.4433E-05\n", + "Parameter 1: 7.7500E-01\n", + "Root 1: 1.4591E-03 -4.4547E-05\n", + "Parameter 1: 7.5000E-01\n", + "Root 1: 1.4902E-03 -4.4686E-05\n", + "Parameter 1: 7.2500E-01\n", + "Root 1: 1.5230E-03 -4.4852E-05\n", + "Parameter 1: 7.0000E-01\n", + "Root 1: 1.5576E-03 -4.5048E-05\n", + "Parameter 1: 6.7500E-01\n", + "Root 1: 1.5942E-03 -4.5277E-05\n", + "Parameter 1: 6.5000E-01\n", + "Root 1: 1.6330E-03 -4.5545E-05\n", + "Parameter 1: 6.2500E-01\n", + "Root 1: 1.6741E-03 -4.5859E-05\n", + "Parameter 1: 6.0000E-01\n", + "Root 1: 1.7177E-03 -4.6229E-05\n", + "Parameter 1: 5.7500E-01\n", + "Root 1: 1.7643E-03 -4.6668E-05\n", + "Parameter 1: 5.5000E-01\n", + "Root 1: 1.8140E-03 -4.7194E-05\n", + "Parameter 1: 5.2500E-01\n", + "Root 1: 1.8673E-03 -4.7826E-05\n", + "Parameter 1: 5.0000E-01\n", + "Root 1: 1.9245E-03 -4.8593E-05\n", + " Predicting FPC (gyrotropic coords)...\n", + "Input for Root 1: 1.2362E-03 -4.4355E-05\n", + "Dispersion Solutions: \n", + " 1 1.2362E-03 -4.4355E-05\n", + " WARNING! The gyro routine does not support computing moments at this time due to computational demand!\n", + " Assuming data folder already exists...\n", + " Assuming subfolder howes2017 already exists\n", + " Calculating fpc for species 1\n", + " Writing omega/kpar V_a normalization to file...\n", + " Calculating fpc for species 2\n", + " Writing omega/kpar V_a normalization to file...\n", + " Predicting FPC (gyrotropic coords)...\n", + "Input for Root 1: 1.2362E-03 -4.4355E-05\n", + "Dispersion Solutions: \n", + " 1 1.2362E-03 -4.4355E-05\n", + " WARNING! The gyro routine does not support computing moments at this time due to computational demand!\n", + " Assuming data folder already exists...\n", + " Assuming subfolder howes2017 already exists\n", + " Calculating fpc for species 1\n", + " Writing omega/kpar V_a normalization to file...\n", + " Calculating fpc for species 2\n", + " Writing omega/kpar V_a normalization to file...\n", + " Predicting FPC (gyrotropic coords)...\n", + "Input for Root 1: 1.2362E-03 -4.4355E-05\n", + "Dispersion Solutions: \n", + " 1 1.2362E-03 -4.4355E-05\n", + " WARNING! The gyro routine does not support computing moments at this time due to computational demand!\n", + " Assuming data folder already exists...\n", + " Assuming subfolder howes2017 already exists\n", + " Calculating fpc for species 1\n", + " Writing omega/kpar V_a normalization to file...\n", + " Calculating fpc for species 2\n", + " Writing omega/kpar V_a normalization to file...\n", + " Predicting FPC (cartesian coords)...\n", + "Input for Root 1: 1.2362E-03 -4.4355E-05\n", + " Assuming data folder already exists...\n", + " assuming subfolder howes2017 already exists\n", + " Writing fpc for species 1\n", + " Writing omega/kpar V_a normalization to file...\n", + " Writing fpc for species 2\n", + " Writing omega/kpar V_a normalization to file...\n", + "-=-=-=-=-=-=-=-=-=-\n", + "Root Search:\n", + "-=-=-=-=-=-=-=-=-=-\n", + "Global Plasma Parameters:\n", + "k_perp rho_ref = 1.30000 \n", + "k_par rho_ref = 0.100000E-02\n", + "Beta_ref,par = 1.00000 \n", + "v_t,ref,par/c = 0.100000E-03\n", + "-=-=-=-=-=-=-=-=-=-\n", + "Parameters for Species : 1\n", + "T_||,ref/T_||s = 1.00000 \n", + "m_ref/m_s = 1.00000 \n", + "T_perp/T_par|s = 1.00000 \n", + "q_ref/q_s = 1.00000 \n", + "n_s/n_ref = 1.00000 \n", + "v_drift s/v_A,ref = 0.00000 \n", + "-=-=-=-=-=-=-=-=-=-\n", + "Parameters for Species : 2\n", + "T_||,ref/T_||s = 1.00000 \n", + "m_ref/m_s = 1836.00 \n", + "T_perp/T_par|s = 1.00000 \n", + "q_ref/q_s = -1.00000 \n", + "n_s/n_ref = 1.00000 \n", + "v_drift s/v_A,ref = 0.00000 \n", + "-=-=-=-=-=-=-=-=-=-\n", + "Searching over:\n", + "omega/Omega_ref \\in [ 1.000E-05, 1.000E-02]\n", + "gamma/Omega_ref \\in [-2.000E-03,-2.000E-06]\n", + "-=-=-=-=-=-=-=-=-=-\n", + " finding minima\n", + " minima found\n", + " 7 possible local minima found:\n", + "ir = 128 ii = 128\n", + " 1.0000E-02 -2.0000E-06\n", + "ir = 16 ii = 125\n", + " 1.2588E-03 -4.8828E-05\n", + "ir = 0 ii = 80\n", + " 1.0000E-05 -7.5125E-04\n", + "ir = 15 ii = 65\n", + " 1.1807E-03 -9.8539E-04\n", + "ir = 22 ii = 26\n", + " 1.7270E-03 -1.5942E-03\n", + "ir = 28 ii = 2\n", + " 2.1953E-03 -1.9688E-03\n", + "ir = 128 ii = 0\n", + " 1.0000E-02 -2.0000E-03\n", + "Refining roots:\n", + "Dispersion Solutions \n", + " 1 1.2362E-03 -4.4355E-05\n", + " 2 3.4468E-14 -7.4376E-04\n", + " 3 1.1473E-03 -9.8322E-04\n", + " 4 1.7166E-03 -1.5983E-03\n", + " 5 2.2089E-03 -1.9704E-03\n", + " 6 2.2089E-03 -1.9704E-03\n", + " 7 1.4252E+01 8.9696E+00\n", + " 8 0.0000E+00 0.0000E+00\n", + " 9 0.0000E+00 0.0000E+00\n", + " 10 0.0000E+00 0.0000E+00\n", + " 11 0.0000E+00 0.0000E+00\n", + " 12 0.0000E+00 0.0000E+00\n", + " 13 0.0000E+00 0.0000E+00\n", + " 14 0.0000E+00 0.0000E+00\n", + " 15 0.0000E+00 0.0000E+00\n", + " 16 0.0000E+00 0.0000E+00\n", + " 17 0.0000E+00 0.0000E+00\n", + " 18 0.0000E+00 0.0000E+00\n", + " 19 0.0000E+00 0.0000E+00\n", + " 20 0.0000E+00 0.0000E+00\n", + "Input for Root 1: 1.2362E-03 -4.4355E-05\n", + "Dispersion Solutions: \n", + " 1 1.2362E-03 -4.4355E-05\n", + "Scan over kperp from 1.3000E+00 to 1.0000E+02\n", + " =>data/howes2017/sweepsweep1_tensor_kperp_1300_100000\n", + "Root 1 In: 1.2362E-03 -4.4355E-05\n", + "Root 1 Out: 3.7512E-02 -3.7403E-02\n", + "Input for Root 1: 1.2362E-03 -4.4355E-05\n", + "Dispersion Solutions: \n", + " 1 1.2362E-03 -4.4355E-05\n", + "Scan over kperp from 1.3000E+00 to 1.0000E-01\n", + " =>data/howes2017/sweepsweep2_tensor_kperp_1300_100\n", + "Root 1 In: 1.2362E-03 -4.4355E-05\n", + "Root 1 Out: 1.0011E-03 -4.6981E-07\n", + "Input for Root 1: 1.2362E-03 -4.4355E-05\n", + "Dispersion Solutions: \n", + " 1 1.2362E-03 -4.4355E-05\n", + "Scan over tauS for species 2 from 1.0000E+00 to 1.0000E+02\n", + " =>data/howes2017/sweepsweep1_tensor_tauS_s2_1000_100000\n", + "Root 1 In: 1.2362E-03 -4.4355E-05\n", + "Root 1 Out: 1.1319E-03 -2.0535E-05\n", + "Input for Root 1: 1.2362E-03 -4.4355E-05\n", + "Dispersion Solutions: \n", + " 1 1.2362E-03 -4.4355E-05\n", + "Scan over tauS for species 2 from 1.0000E+00 to 1.0000E-01\n", + " =>data/howes2017/sweepsweep2_tensor_tauS_s2_1000_100\n", + "Root 1 In: 1.2362E-03 -4.4355E-05\n", + "Root 1 Out: 1.5471E-03 -2.9932E-04\n", + "Input for Root 1: 1.2362E-03 -4.4355E-05\n", + "Dispersion Solutions: \n", + " 1 1.2362E-03 -4.4355E-05\n", + "Scan over kperp from 1.300000E+000 to 1.000000E+001\n", + "Scan over kpar from 1.000000E-003 to 1.000000E+001\n", + "Parameter 1: 1.3000E+00\n", + "Root 1: 1.2362E-03 -4.4355E-05\n", + "Parameter 1: 1.7350E+00\n", + "Root 1: 1.4327E-03 -5.7938E-05\n", + "Parameter 1: 2.1700E+00\n", + "Root 1: 1.6788E-03 -7.3372E-05\n", + "Parameter 1: 2.6050E+00\n", + "Root 1: 1.9538E-03 -9.5239E-05\n", + "Parameter 1: 3.0400E+00\n", + "Root 1: 2.2430E-03 -1.2441E-04\n", + "Parameter 1: 3.4750E+00\n", + "Root 1: 2.5392E-03 -1.6009E-04\n", + "Parameter 1: 3.9100E+00\n", + "Root 1: 2.8393E-03 -2.0140E-04\n", + "Parameter 1: 4.3450E+00\n", + "Root 1: 3.1417E-03 -2.4782E-04\n", + "Parameter 1: 4.7800E+00\n", + "Root 1: 3.4454E-03 -2.9912E-04\n", + "Parameter 1: 5.2150E+00\n", + "Root 1: 3.7499E-03 -3.5526E-04\n", + "Parameter 1: 5.6500E+00\n", + "Root 1: 4.0549E-03 -4.1620E-04\n", + "Parameter 1: 6.0850E+00\n", + "Root 1: 4.3603E-03 -4.8193E-04\n", + "Parameter 1: 6.5200E+00\n", + "Root 1: 4.6659E-03 -5.5243E-04\n", + "Parameter 1: 6.9550E+00\n", + "Root 1: 4.9716E-03 -6.2767E-04\n", + "Parameter 1: 7.3900E+00\n", + "Root 1: 5.2773E-03 -7.0764E-04\n", + "Parameter 1: 7.8250E+00\n", + "Root 1: 5.5831E-03 -7.9231E-04\n", + "Parameter 1: 8.2600E+00\n", + "Root 1: 5.8889E-03 -8.8166E-04\n", + "Parameter 1: 8.6950E+00\n", + "Root 1: 6.1946E-03 -9.7565E-04\n", + "Parameter 1: 9.1300E+00\n", + "Root 1: 6.5003E-03 -1.0742E-03\n", + "Parameter 1: 9.5650E+00\n", + "Root 1: 6.8059E-03 -1.1774E-03\n", + "Parameter 1: 1.0000E+01\n", + "Root 1: 7.1114E-03 -1.2852E-03\n", + "Input for Root 1: 1.2362E-03 -4.4355E-05\n", + "Dispersion Solutions: \n", + " 1 1.2362E-03 -4.4355E-05\n", + "Scan over kperp from 1.300000E+000 to 1.000000E+000\n", + "Scan over kpar from 1.000000E-003 to 1.000000E+001\n", + "Parameter 1: 1.3000E+00\n", + "Root 1: 1.2362E-03 -4.4355E-05\n", + "Parameter 1: 1.2850E+00\n", + "Root 1: 1.2305E-03 -4.3836E-05\n", + "Parameter 1: 1.2700E+00\n", + "Root 1: 1.2249E-03 -4.3311E-05\n", + "Parameter 1: 1.2550E+00\n", + "Root 1: 1.2193E-03 -4.2780E-05\n", + "Parameter 1: 1.2400E+00\n", + "Root 1: 1.2139E-03 -4.2243E-05\n", + "Parameter 1: 1.2250E+00\n", + "Root 1: 1.2085E-03 -4.1700E-05\n", + "Parameter 1: 1.2100E+00\n", + "Root 1: 1.2032E-03 -4.1150E-05\n", + "Parameter 1: 1.1950E+00\n", + "Root 1: 1.1979E-03 -4.0593E-05\n", + "Parameter 1: 1.1800E+00\n", + "Root 1: 1.1928E-03 -4.0030E-05\n", + "Parameter 1: 1.1650E+00\n", + "Root 1: 1.1877E-03 -3.9460E-05\n", + "Parameter 1: 1.1500E+00\n", + "Root 1: 1.1827E-03 -3.8883E-05\n", + "Parameter 1: 1.1350E+00\n", + "Root 1: 1.1777E-03 -3.8300E-05\n", + "Parameter 1: 1.1200E+00\n", + "Root 1: 1.1729E-03 -3.7710E-05\n", + "Parameter 1: 1.1050E+00\n", + "Root 1: 1.1681E-03 -3.7114E-05\n", + "Parameter 1: 1.0900E+00\n", + "Root 1: 1.1634E-03 -3.6511E-05\n", + "Parameter 1: 1.0750E+00\n", + "Root 1: 1.1587E-03 -3.5902E-05\n", + "Parameter 1: 1.0600E+00\n", + "Root 1: 1.1541E-03 -3.5286E-05\n", + "Parameter 1: 1.0450E+00\n", + "Root 1: 1.1497E-03 -3.4664E-05\n", + "Parameter 1: 1.0300E+00\n", + "Root 1: 1.1452E-03 -3.4036E-05\n", + "Parameter 1: 1.0150E+00\n", + "Root 1: 1.1409E-03 -3.3402E-05\n", + "Parameter 1: 1.0000E+00\n", + "Root 1: 1.1366E-03 -3.2762E-05\n", + "Input for Root 1: 1.2362E-03 -4.4355E-05\n", + "Dispersion Solutions: \n", + " 1 1.2362E-03 -4.4355E-05\n", + "Scan over kperp from 1.300000E+000 to 1.000000E+001\n", + "Scan over kpar from 1.000000E-003 to 1.000000E+000\n", + "Parameter 1: 1.3000E+00\n", + "Root 1: 1.2362E-03 -4.4355E-05\n", + "Parameter 1: 1.7350E+00\n", + "Root 1: 1.4327E-03 -5.7938E-05\n", + "Parameter 1: 2.1700E+00\n", + "Root 1: 1.6788E-03 -7.3372E-05\n", + "Parameter 1: 2.6050E+00\n", + "Root 1: 1.9538E-03 -9.5239E-05\n", + "Parameter 1: 3.0400E+00\n", + "Root 1: 2.2430E-03 -1.2441E-04\n", + "Parameter 1: 3.4750E+00\n", + "Root 1: 2.5392E-03 -1.6009E-04\n", + "Parameter 1: 3.9100E+00\n", + "Root 1: 2.8393E-03 -2.0140E-04\n", + "Parameter 1: 4.3450E+00\n", + "Root 1: 3.1417E-03 -2.4782E-04\n", + "Parameter 1: 4.7800E+00\n", + "Root 1: 3.4454E-03 -2.9912E-04\n", + "Parameter 1: 5.2150E+00\n", + "Root 1: 3.7499E-03 -3.5526E-04\n", + "Parameter 1: 5.6500E+00\n", + "Root 1: 4.0549E-03 -4.1620E-04\n", + "Parameter 1: 6.0850E+00\n", + "Root 1: 4.3603E-03 -4.8193E-04\n", + "Parameter 1: 6.5200E+00\n", + "Root 1: 4.6659E-03 -5.5243E-04\n", + "Parameter 1: 6.9550E+00\n", + "Root 1: 4.9716E-03 -6.2767E-04\n", + "Parameter 1: 7.3900E+00\n", + "Root 1: 5.2773E-03 -7.0764E-04\n", + "Parameter 1: 7.8250E+00\n", + "Root 1: 5.5831E-03 -7.9231E-04\n", + "Parameter 1: 8.2600E+00\n", + "Root 1: 5.8889E-03 -8.8166E-04\n", + "Parameter 1: 8.6950E+00\n", + "Root 1: 6.1946E-03 -9.7565E-04\n", + "Parameter 1: 9.1300E+00\n", + "Root 1: 6.5003E-03 -1.0742E-03\n", + "Parameter 1: 9.5650E+00\n", + "Root 1: 6.8059E-03 -1.1774E-03\n", + "Parameter 1: 1.0000E+01\n", + "Root 1: 7.1114E-03 -1.2852E-03\n", + "Input for Root 1: 1.2362E-03 -4.4355E-05\n", + "Dispersion Solutions: \n", + " 1 1.2362E-03 -4.4355E-05\n", + "Scan over kperp from 1.300000E+000 to 1.000000E+000\n", + "Scan over kpar from 1.000000E-003 to 1.000000E+000\n", + "Parameter 1: 1.3000E+00\n", + "Root 1: 1.2362E-03 -4.4355E-05\n", + "Parameter 1: 1.2850E+00\n", + "Root 1: 1.2305E-03 -4.3836E-05\n", + "Parameter 1: 1.2700E+00\n", + "Root 1: 1.2249E-03 -4.3311E-05\n", + "Parameter 1: 1.2550E+00\n", + "Root 1: 1.2193E-03 -4.2780E-05\n", + "Parameter 1: 1.2400E+00\n", + "Root 1: 1.2139E-03 -4.2243E-05\n", + "Parameter 1: 1.2250E+00\n", + "Root 1: 1.2085E-03 -4.1700E-05\n", + "Parameter 1: 1.2100E+00\n", + "Root 1: 1.2032E-03 -4.1150E-05\n", + "Parameter 1: 1.1950E+00\n", + "Root 1: 1.1979E-03 -4.0593E-05\n", + "Parameter 1: 1.1800E+00\n", + "Root 1: 1.1928E-03 -4.0030E-05\n", + "Parameter 1: 1.1650E+00\n", + "Root 1: 1.1877E-03 -3.9460E-05\n", + "Parameter 1: 1.1500E+00\n", + "Root 1: 1.1827E-03 -3.8883E-05\n", + "Parameter 1: 1.1350E+00\n", + "Root 1: 1.1777E-03 -3.8300E-05\n", + "Parameter 1: 1.1200E+00\n", + "Root 1: 1.1729E-03 -3.7710E-05\n", + "Parameter 1: 1.1050E+00\n", + "Root 1: 1.1681E-03 -3.7114E-05\n", + "Parameter 1: 1.0900E+00\n", + "Root 1: 1.1634E-03 -3.6511E-05\n", + "Parameter 1: 1.0750E+00\n", + "Root 1: 1.1587E-03 -3.5902E-05\n", + "Parameter 1: 1.0600E+00\n", + "Root 1: 1.1541E-03 -3.5286E-05\n", + "Parameter 1: 1.0450E+00\n", + "Root 1: 1.1497E-03 -3.4664E-05\n", + "Parameter 1: 1.0300E+00\n", + "Root 1: 1.1452E-03 -3.4036E-05\n", + "Parameter 1: 1.0150E+00\n", + "Root 1: 1.1409E-03 -3.3402E-05\n", + "Parameter 1: 1.0000E+00\n", + "Root 1: 1.1366E-03 -3.2762E-05\n", + "Input for Root 1: 1.2362E-03 -4.4355E-05\n", + "Dispersion Solutions: \n", + " 1 1.2362E-03 -4.4355E-05\n", + "Scan over betap from 1.000000E+000 to 5.000000E+000\n", + "Scan over alphS for species 1 from 1.000000E+000 to 5.000000E+000\n", + "Parameter 1: 1.0000E+00\n", + "Root 1: 1.2362E-03 -4.4355E-05\n", + "Parameter 1: 1.2000E+00\n", + "Root 1: 1.0965E-03 -4.5613E-05\n", + "Parameter 1: 1.4000E+00\n", + "Root 1: 9.9005E-04 -4.7867E-05\n", + "Parameter 1: 1.6000E+00\n", + "Root 1: 9.0557E-04 -5.0722E-05\n", + "Parameter 1: 1.8000E+00\n", + "Root 1: 8.3649E-04 -5.3871E-05\n", + "Parameter 1: 2.0000E+00\n", + "Root 1: 7.7864E-04 -5.7109E-05\n", + "Parameter 1: 2.2000E+00\n", + "Root 1: 7.2929E-04 -6.0311E-05\n", + "Parameter 1: 2.4000E+00\n", + "Root 1: 6.8652E-04 -6.3404E-05\n", + "Parameter 1: 2.6000E+00\n", + "Root 1: 6.4900E-04 -6.6348E-05\n", + "Parameter 1: 2.8000E+00\n", + "Root 1: 6.1571E-04 -6.9123E-05\n", + "Parameter 1: 3.0000E+00\n", + "Root 1: 5.8591E-04 -7.1721E-05\n", + "Parameter 1: 3.2000E+00\n", + "Root 1: 5.5902E-04 -7.4142E-05\n", + "Parameter 1: 3.4000E+00\n", + "Root 1: 5.3459E-04 -7.6389E-05\n", + "Parameter 1: 3.6000E+00\n", + "Root 1: 5.1225E-04 -7.8468E-05\n", + "Parameter 1: 3.8000E+00\n", + "Root 1: 4.9172E-04 -8.0387E-05\n", + "Parameter 1: 4.0000E+00\n", + "Root 1: 4.7275E-04 -8.2152E-05\n", + "Parameter 1: 4.2000E+00\n", + "Root 1: 4.5515E-04 -8.3772E-05\n", + "Parameter 1: 4.4000E+00\n", + "Root 1: 4.3876E-04 -8.5254E-05\n", + "Parameter 1: 4.6000E+00\n", + "Root 1: 4.2344E-04 -8.6605E-05\n", + "Parameter 1: 4.8000E+00\n", + "Root 1: 4.0907E-04 -8.7832E-05\n", + "Parameter 1: 5.0000E+00\n", + "Root 1: 3.9556E-04 -8.8940E-05\n", + "Input for Root 1: 1.2362E-03 -4.4355E-05\n", + "Dispersion Solutions: \n", + " 1 1.2362E-03 -4.4355E-05\n", + "Scan over betap from 1.000000E+000 to 5.000000E-001\n", + "Scan over alphS for species 1 from 1.000000E+000 to 5.000000E+000\n", + "Parameter 1: 1.0000E+00\n", + "Root 1: 1.2362E-03 -4.4355E-05\n", + "Parameter 1: 9.7500E-01\n", + "Root 1: 1.2568E-03 -4.4287E-05\n", + "Parameter 1: 9.5000E-01\n", + "Root 1: 1.2784E-03 -4.4241E-05\n", + "Parameter 1: 9.2500E-01\n", + "Root 1: 1.3008E-03 -4.4216E-05\n", + "Parameter 1: 9.0000E-01\n", + "Root 1: 1.3243E-03 -4.4214E-05\n", + "Parameter 1: 8.7500E-01\n", + "Root 1: 1.3488E-03 -4.4234E-05\n", + "Parameter 1: 8.5000E-01\n", + "Root 1: 1.3744E-03 -4.4277E-05\n", + "Parameter 1: 8.2500E-01\n", + "Root 1: 1.4013E-03 -4.4343E-05\n", + "Parameter 1: 8.0000E-01\n", + "Root 1: 1.4295E-03 -4.4433E-05\n", + "Parameter 1: 7.7500E-01\n", + "Root 1: 1.4591E-03 -4.4547E-05\n", + "Parameter 1: 7.5000E-01\n", + "Root 1: 1.4902E-03 -4.4686E-05\n", + "Parameter 1: 7.2500E-01\n", + "Root 1: 1.5230E-03 -4.4852E-05\n", + "Parameter 1: 7.0000E-01\n", + "Root 1: 1.5576E-03 -4.5048E-05\n", + "Parameter 1: 6.7500E-01\n", + "Root 1: 1.5942E-03 -4.5277E-05\n", + "Parameter 1: 6.5000E-01\n", + "Root 1: 1.6330E-03 -4.5545E-05\n", + "Parameter 1: 6.2500E-01\n", + "Root 1: 1.6741E-03 -4.5859E-05\n", + "Parameter 1: 6.0000E-01\n", + "Root 1: 1.7177E-03 -4.6229E-05\n", + "Parameter 1: 5.7500E-01\n", + "Root 1: 1.7643E-03 -4.6668E-05\n", + "Parameter 1: 5.5000E-01\n", + "Root 1: 1.8140E-03 -4.7194E-05\n", + "Parameter 1: 5.2500E-01\n", + "Root 1: 1.8673E-03 -4.7826E-05\n", + "Parameter 1: 5.0000E-01\n", + "Root 1: 1.9245E-03 -4.8593E-05\n", + "Input for Root 1: 1.2362E-03 -4.4355E-05\n", + "Dispersion Solutions: \n", + " 1 1.2362E-03 -4.4355E-05\n", + "Scan over betap from 1.000000E+000 to 5.000000E+000\n", + "Scan over alphS for species 1 from 1.000000E+000 to 5.000000E-001\n", + "Parameter 1: 1.0000E+00\n", + "Root 1: 1.2362E-03 -4.4355E-05\n", + "Parameter 1: 1.2000E+00\n", + "Root 1: 1.0965E-03 -4.5613E-05\n", + "Parameter 1: 1.4000E+00\n", + "Root 1: 9.9005E-04 -4.7867E-05\n", + "Parameter 1: 1.6000E+00\n", + "Root 1: 9.0557E-04 -5.0722E-05\n", + "Parameter 1: 1.8000E+00\n", + "Root 1: 8.3649E-04 -5.3871E-05\n", + "Parameter 1: 2.0000E+00\n", + "Root 1: 7.7864E-04 -5.7109E-05\n", + "Parameter 1: 2.2000E+00\n", + "Root 1: 7.2929E-04 -6.0311E-05\n", + "Parameter 1: 2.4000E+00\n", + "Root 1: 6.8652E-04 -6.3404E-05\n", + "Parameter 1: 2.6000E+00\n", + "Root 1: 6.4900E-04 -6.6348E-05\n", + "Parameter 1: 2.8000E+00\n", + "Root 1: 6.1571E-04 -6.9123E-05\n", + "Parameter 1: 3.0000E+00\n", + "Root 1: 5.8591E-04 -7.1721E-05\n", + "Parameter 1: 3.2000E+00\n", + "Root 1: 5.5902E-04 -7.4142E-05\n", + "Parameter 1: 3.4000E+00\n", + "Root 1: 5.3459E-04 -7.6389E-05\n", + "Parameter 1: 3.6000E+00\n", + "Root 1: 5.1225E-04 -7.8468E-05\n", + "Parameter 1: 3.8000E+00\n", + "Root 1: 4.9172E-04 -8.0387E-05\n", + "Parameter 1: 4.0000E+00\n", + "Root 1: 4.7275E-04 -8.2152E-05\n", + "Parameter 1: 4.2000E+00\n", + "Root 1: 4.5515E-04 -8.3772E-05\n", + "Parameter 1: 4.4000E+00\n", + "Root 1: 4.3876E-04 -8.5254E-05\n", + "Parameter 1: 4.6000E+00\n", + "Root 1: 4.2344E-04 -8.6605E-05\n", + "Parameter 1: 4.8000E+00\n", + "Root 1: 4.0907E-04 -8.7832E-05\n", + "Parameter 1: 5.0000E+00\n", + "Root 1: 3.9556E-04 -8.8940E-05\n", + "Input for Root 1: 1.2362E-03 -4.4355E-05\n", + "Dispersion Solutions: \n", + " 1 1.2362E-03 -4.4355E-05\n", + "Scan over betap from 1.000000E+000 to 5.000000E-001\n", + "Scan over alphS for species 1 from 1.000000E+000 to 5.000000E-001\n", + "Parameter 1: 1.0000E+00\n", + "Root 1: 1.2362E-03 -4.4355E-05\n", + "Parameter 1: 9.7500E-01\n", + "Root 1: 1.2568E-03 -4.4287E-05\n", + "Parameter 1: 9.5000E-01\n", + "Root 1: 1.2784E-03 -4.4241E-05\n", + "Parameter 1: 9.2500E-01\n", + "Root 1: 1.3008E-03 -4.4216E-05\n", + "Parameter 1: 9.0000E-01\n", + "Root 1: 1.3243E-03 -4.4214E-05\n", + "Parameter 1: 8.7500E-01\n", + "Root 1: 1.3488E-03 -4.4234E-05\n", + "Parameter 1: 8.5000E-01\n", + "Root 1: 1.3744E-03 -4.4277E-05\n", + "Parameter 1: 8.2500E-01\n", + "Root 1: 1.4013E-03 -4.4343E-05\n", + "Parameter 1: 8.0000E-01\n", + "Root 1: 1.4295E-03 -4.4433E-05\n", + "Parameter 1: 7.7500E-01\n", + "Root 1: 1.4591E-03 -4.4547E-05\n", + "Parameter 1: 7.5000E-01\n", + "Root 1: 1.4902E-03 -4.4686E-05\n", + "Parameter 1: 7.2500E-01\n", + "Root 1: 1.5230E-03 -4.4852E-05\n", + "Parameter 1: 7.0000E-01\n", + "Root 1: 1.5576E-03 -4.5048E-05\n", + "Parameter 1: 6.7500E-01\n", + "Root 1: 1.5942E-03 -4.5277E-05\n", + "Parameter 1: 6.5000E-01\n", + "Root 1: 1.6330E-03 -4.5545E-05\n", + "Parameter 1: 6.2500E-01\n", + "Root 1: 1.6741E-03 -4.5859E-05\n", + "Parameter 1: 6.0000E-01\n", + "Root 1: 1.7177E-03 -4.6229E-05\n", + "Parameter 1: 5.7500E-01\n", + "Root 1: 1.7643E-03 -4.6668E-05\n", + "Parameter 1: 5.5000E-01\n", + "Root 1: 1.8140E-03 -4.7194E-05\n", + "Parameter 1: 5.2500E-01\n", + "Root 1: 1.8673E-03 -4.7826E-05\n", + "Parameter 1: 5.0000E-01\n", + "Root 1: 1.9245E-03 -4.8593E-05\n", + " Predicting FPC (gyrotropic coords)...\n", + "Input for Root 1: 1.2362E-03 -4.4355E-05\n", + "Dispersion Solutions: \n", + " 1 1.2362E-03 -4.4355E-05\n", + " WARNING! The gyro routine does not support computing moments at this time due to computational demand!\n", + " Assuming data folder already exists...\n", + " Assuming subfolder howes2017 already exists\n", + " Calculating fpc for species 1\n", + " Writing omega/kpar V_a normalization to file...\n", + " Calculating fpc for species 2\n", + " Writing omega/kpar V_a normalization to file...\n", + " Predicting FPC (gyrotropic coords)...\n", + "Input for Root 1: 1.2362E-03 -4.4355E-05\n", + "Dispersion Solutions: \n", + " 1 1.2362E-03 -4.4355E-05\n", + " WARNING! The gyro routine does not support computing moments at this time due to computational demand!\n", + " Assuming data folder already exists...\n", + " Assuming subfolder howes2017 already exists\n", + " Calculating fpc for species 1\n", + " Writing omega/kpar V_a normalization to file...\n", + " Calculating fpc for species 2\n", + " Writing omega/kpar V_a normalization to file...\n", + " Predicting FPC (gyrotropic coords)...\n", + "Input for Root 1: 1.2362E-03 -4.4355E-05\n", + "Dispersion Solutions: \n", + " 1 1.2362E-03 -4.4355E-05\n", + " WARNING! The gyro routine does not support computing moments at this time due to computational demand!\n", + " Assuming data folder already exists...\n", + " Assuming subfolder howes2017 already exists\n", + " Calculating fpc for species 1\n", + " Writing omega/kpar V_a normalization to file...\n", + " Calculating fpc for species 2\n", + " Writing omega/kpar V_a normalization to file...\n", + " Predicting FPC (cartesian coords)...\n", + "Input for Root 1: 1.2362E-03 -4.4355E-05\n", + " Assuming data folder already exists...\n", + " assuming subfolder howes2017 already exists\n", + " Writing fpc for species 1\n", + " Writing omega/kpar V_a normalization to file...\n", + " Writing fpc for species 2\n", + " Writing omega/kpar V_a normalization to file...\n", + "-=-=-=-=-=-=-=-=-=-\n", + "Root Search:\n", + "-=-=-=-=-=-=-=-=-=-\n", + "Global Plasma Parameters:\n", + "k_perp rho_ref = 1.30000 \n", + "k_par rho_ref = 0.100000E-02\n", + "Beta_ref,par = 1.00000 \n", + "v_t,ref,par/c = 0.100000E-03\n", + "-=-=-=-=-=-=-=-=-=-\n", + "Parameters for Species : 1\n", + "T_||,ref/T_||s = 1.00000 \n", + "m_ref/m_s = 1.00000 \n", + "T_perp/T_par|s = 1.00000 \n", + "q_ref/q_s = 1.00000 \n", + "n_s/n_ref = 1.00000 \n", + "v_drift s/v_A,ref = 0.00000 \n", + "-=-=-=-=-=-=-=-=-=-\n", + "Parameters for Species : 2\n", + "T_||,ref/T_||s = 1.00000 \n", + "m_ref/m_s = 1836.00 \n", + "T_perp/T_par|s = 1.00000 \n", + "q_ref/q_s = -1.00000 \n", + "n_s/n_ref = 1.00000 \n", + "v_drift s/v_A,ref = 0.00000 \n", + "-=-=-=-=-=-=-=-=-=-\n", + "Searching over:\n", + "omega/Omega_ref \\in [ 1.000E-05, 1.000E-02]\n", + "gamma/Omega_ref \\in [-2.000E-03,-2.000E-06]\n", + "-=-=-=-=-=-=-=-=-=-\n", + " finding minima\n", + " minima found\n", + " 7 possible local minima found:\n", + "ir = 128 ii = 128\n", + " 1.0000E-02 -2.0000E-06\n", + "ir = 16 ii = 125\n", + " 1.2588E-03 -4.8828E-05\n", + "ir = 0 ii = 80\n", + " 1.0000E-05 -7.5125E-04\n", + "ir = 15 ii = 65\n", + " 1.1807E-03 -9.8539E-04\n", + "ir = 22 ii = 26\n", + " 1.7270E-03 -1.5942E-03\n", + "ir = 28 ii = 2\n", + " 2.1953E-03 -1.9688E-03\n", + "ir = 128 ii = 0\n", + " 1.0000E-02 -2.0000E-03\n", + "Refining roots:\n", + "Dispersion Solutions \n", + " 1 1.2362E-03 -4.4355E-05\n", + " 2 3.4468E-14 -7.4376E-04\n", + " 3 1.1473E-03 -9.8322E-04\n", + " 4 1.7166E-03 -1.5983E-03\n", + " 5 2.2089E-03 -1.9704E-03\n", + " 6 2.2089E-03 -1.9704E-03\n", + " 7 1.4252E+01 8.9696E+00\n", + " 8 0.0000E+00 0.0000E+00\n", + " 9 0.0000E+00 0.0000E+00\n", + " 10 0.0000E+00 0.0000E+00\n", + " 11 0.0000E+00 0.0000E+00\n", + " 12 0.0000E+00 0.0000E+00\n", + " 13 0.0000E+00 0.0000E+00\n", + " 14 0.0000E+00 0.0000E+00\n", + " 15 0.0000E+00 0.0000E+00\n", + " 16 0.0000E+00 0.0000E+00\n", + " 17 0.0000E+00 0.0000E+00\n", + " 18 0.0000E+00 0.0000E+00\n", + " 19 0.0000E+00 0.0000E+00\n", + " 20 0.0000E+00 0.0000E+00\n", + "Input for Root 1: 1.2362E-03 -4.4355E-05\n", + "Dispersion Solutions: \n", + " 1 1.2362E-03 -4.4355E-05\n", + "Scan over kperp from 1.3000E+00 to 1.0000E+02\n", + " =>data/howes2017/sweepsweep1_tensor_kperp_1300_100000\n", + "Root 1 In: 1.2362E-03 -4.4355E-05\n", + "Root 1 Out: 3.7512E-02 -3.7403E-02\n", + "Input for Root 1: 1.2362E-03 -4.4355E-05\n", + "Dispersion Solutions: \n", + " 1 1.2362E-03 -4.4355E-05\n", + "Scan over kperp from 1.3000E+00 to 1.0000E-01\n", + " =>data/howes2017/sweepsweep2_tensor_kperp_1300_100\n", + "Root 1 In: 1.2362E-03 -4.4355E-05\n", + "Root 1 Out: 1.0011E-03 -4.6981E-07\n", + "Input for Root 1: 1.2362E-03 -4.4355E-05\n", + "Dispersion Solutions: \n", + " 1 1.2362E-03 -4.4355E-05\n", + "Scan over tauS for species 2 from 1.0000E+00 to 1.0000E+02\n", + " =>data/howes2017/sweepsweep1_tensor_tauS_s2_1000_100000\n", + "Root 1 In: 1.2362E-03 -4.4355E-05\n", + "Root 1 Out: 1.1319E-03 -2.0535E-05\n", + "Input for Root 1: 1.2362E-03 -4.4355E-05\n", + "Dispersion Solutions: \n", + " 1 1.2362E-03 -4.4355E-05\n", + "Scan over tauS for species 2 from 1.0000E+00 to 1.0000E-01\n", + " =>data/howes2017/sweepsweep2_tensor_tauS_s2_1000_100\n", + "Root 1 In: 1.2362E-03 -4.4355E-05\n", + "Root 1 Out: 1.5471E-03 -2.9932E-04\n", + "Input for Root 1: 1.2362E-03 -4.4355E-05\n", + "Dispersion Solutions: \n", + " 1 1.2362E-03 -4.4355E-05\n", + "Scan over kperp from 1.300000E+000 to 1.000000E+001\n", + "Scan over kpar from 1.000000E-003 to 1.000000E+001\n", + "Parameter 1: 1.3000E+00\n", + "Root 1: 1.2362E-03 -4.4355E-05\n", + "Parameter 1: 1.7350E+00\n", + "Root 1: 1.4327E-03 -5.7938E-05\n", + "Parameter 1: 2.1700E+00\n", + "Root 1: 1.6788E-03 -7.3372E-05\n", + "Parameter 1: 2.6050E+00\n", + "Root 1: 1.9538E-03 -9.5239E-05\n", + "Parameter 1: 3.0400E+00\n", + "Root 1: 2.2430E-03 -1.2441E-04\n", + "Parameter 1: 3.4750E+00\n", + "Root 1: 2.5392E-03 -1.6009E-04\n", + "Parameter 1: 3.9100E+00\n", + "Root 1: 2.8393E-03 -2.0140E-04\n", + "Parameter 1: 4.3450E+00\n", + "Root 1: 3.1417E-03 -2.4782E-04\n", + "Parameter 1: 4.7800E+00\n", + "Root 1: 3.4454E-03 -2.9912E-04\n", + "Parameter 1: 5.2150E+00\n", + "Root 1: 3.7499E-03 -3.5526E-04\n", + "Parameter 1: 5.6500E+00\n", + "Root 1: 4.0549E-03 -4.1620E-04\n", + "Parameter 1: 6.0850E+00\n", + "Root 1: 4.3603E-03 -4.8193E-04\n", + "Parameter 1: 6.5200E+00\n", + "Root 1: 4.6659E-03 -5.5243E-04\n", + "Parameter 1: 6.9550E+00\n", + "Root 1: 4.9716E-03 -6.2767E-04\n", + "Parameter 1: 7.3900E+00\n", + "Root 1: 5.2773E-03 -7.0764E-04\n", + "Parameter 1: 7.8250E+00\n", + "Root 1: 5.5831E-03 -7.9231E-04\n", + "Parameter 1: 8.2600E+00\n", + "Root 1: 5.8889E-03 -8.8166E-04\n", + "Parameter 1: 8.6950E+00\n", + "Root 1: 6.1946E-03 -9.7565E-04\n", + "Parameter 1: 9.1300E+00\n", + "Root 1: 6.5003E-03 -1.0742E-03\n", + "Parameter 1: 9.5650E+00\n", + "Root 1: 6.8059E-03 -1.1774E-03\n", + "Parameter 1: 1.0000E+01\n", + "Root 1: 7.1114E-03 -1.2852E-03\n", + "Input for Root 1: 1.2362E-03 -4.4355E-05\n", + "Dispersion Solutions: \n", + " 1 1.2362E-03 -4.4355E-05\n", + "Scan over kperp from 1.300000E+000 to 1.000000E+000\n", + "Scan over kpar from 1.000000E-003 to 1.000000E+001\n", + "Parameter 1: 1.3000E+00\n", + "Root 1: 1.2362E-03 -4.4355E-05\n", + "Parameter 1: 1.2850E+00\n", + "Root 1: 1.2305E-03 -4.3836E-05\n", + "Parameter 1: 1.2700E+00\n", + "Root 1: 1.2249E-03 -4.3311E-05\n", + "Parameter 1: 1.2550E+00\n", + "Root 1: 1.2193E-03 -4.2780E-05\n", + "Parameter 1: 1.2400E+00\n", + "Root 1: 1.2139E-03 -4.2243E-05\n", + "Parameter 1: 1.2250E+00\n", + "Root 1: 1.2085E-03 -4.1700E-05\n", + "Parameter 1: 1.2100E+00\n", + "Root 1: 1.2032E-03 -4.1150E-05\n", + "Parameter 1: 1.1950E+00\n", + "Root 1: 1.1979E-03 -4.0593E-05\n", + "Parameter 1: 1.1800E+00\n", + "Root 1: 1.1928E-03 -4.0030E-05\n", + "Parameter 1: 1.1650E+00\n", + "Root 1: 1.1877E-03 -3.9460E-05\n", + "Parameter 1: 1.1500E+00\n", + "Root 1: 1.1827E-03 -3.8883E-05\n", + "Parameter 1: 1.1350E+00\n", + "Root 1: 1.1777E-03 -3.8300E-05\n", + "Parameter 1: 1.1200E+00\n", + "Root 1: 1.1729E-03 -3.7710E-05\n", + "Parameter 1: 1.1050E+00\n", + "Root 1: 1.1681E-03 -3.7114E-05\n", + "Parameter 1: 1.0900E+00\n", + "Root 1: 1.1634E-03 -3.6511E-05\n", + "Parameter 1: 1.0750E+00\n", + "Root 1: 1.1587E-03 -3.5902E-05\n", + "Parameter 1: 1.0600E+00\n", + "Root 1: 1.1541E-03 -3.5286E-05\n", + "Parameter 1: 1.0450E+00\n", + "Root 1: 1.1497E-03 -3.4664E-05\n", + "Parameter 1: 1.0300E+00\n", + "Root 1: 1.1452E-03 -3.4036E-05\n", + "Parameter 1: 1.0150E+00\n", + "Root 1: 1.1409E-03 -3.3402E-05\n", + "Parameter 1: 1.0000E+00\n", + "Root 1: 1.1366E-03 -3.2762E-05\n", + "Input for Root 1: 1.2362E-03 -4.4355E-05\n", + "Dispersion Solutions: \n", + " 1 1.2362E-03 -4.4355E-05\n", + "Scan over kperp from 1.300000E+000 to 1.000000E+001\n", + "Scan over kpar from 1.000000E-003 to 1.000000E+000\n", + "Parameter 1: 1.3000E+00\n", + "Root 1: 1.2362E-03 -4.4355E-05\n", + "Parameter 1: 1.7350E+00\n", + "Root 1: 1.4327E-03 -5.7938E-05\n", + "Parameter 1: 2.1700E+00\n", + "Root 1: 1.6788E-03 -7.3372E-05\n", + "Parameter 1: 2.6050E+00\n", + "Root 1: 1.9538E-03 -9.5239E-05\n", + "Parameter 1: 3.0400E+00\n", + "Root 1: 2.2430E-03 -1.2441E-04\n", + "Parameter 1: 3.4750E+00\n", + "Root 1: 2.5392E-03 -1.6009E-04\n", + "Parameter 1: 3.9100E+00\n", + "Root 1: 2.8393E-03 -2.0140E-04\n", + "Parameter 1: 4.3450E+00\n", + "Root 1: 3.1417E-03 -2.4782E-04\n", + "Parameter 1: 4.7800E+00\n", + "Root 1: 3.4454E-03 -2.9912E-04\n", + "Parameter 1: 5.2150E+00\n", + "Root 1: 3.7499E-03 -3.5526E-04\n", + "Parameter 1: 5.6500E+00\n", + "Root 1: 4.0549E-03 -4.1620E-04\n", + "Parameter 1: 6.0850E+00\n", + "Root 1: 4.3603E-03 -4.8193E-04\n", + "Parameter 1: 6.5200E+00\n", + "Root 1: 4.6659E-03 -5.5243E-04\n", + "Parameter 1: 6.9550E+00\n", + "Root 1: 4.9716E-03 -6.2767E-04\n", + "Parameter 1: 7.3900E+00\n", + "Root 1: 5.2773E-03 -7.0764E-04\n", + "Parameter 1: 7.8250E+00\n", + "Root 1: 5.5831E-03 -7.9231E-04\n", + "Parameter 1: 8.2600E+00\n", + "Root 1: 5.8889E-03 -8.8166E-04\n", + "Parameter 1: 8.6950E+00\n", + "Root 1: 6.1946E-03 -9.7565E-04\n", + "Parameter 1: 9.1300E+00\n", + "Root 1: 6.5003E-03 -1.0742E-03\n", + "Parameter 1: 9.5650E+00\n", + "Root 1: 6.8059E-03 -1.1774E-03\n", + "Parameter 1: 1.0000E+01\n", + "Root 1: 7.1114E-03 -1.2852E-03\n", + "Input for Root 1: 1.2362E-03 -4.4355E-05\n", + "Dispersion Solutions: \n", + " 1 1.2362E-03 -4.4355E-05\n", + "Scan over kperp from 1.300000E+000 to 1.000000E+000\n", + "Scan over kpar from 1.000000E-003 to 1.000000E+000\n", + "Parameter 1: 1.3000E+00\n", + "Root 1: 1.2362E-03 -4.4355E-05\n", + "Parameter 1: 1.2850E+00\n", + "Root 1: 1.2305E-03 -4.3836E-05\n", + "Parameter 1: 1.2700E+00\n", + "Root 1: 1.2249E-03 -4.3311E-05\n", + "Parameter 1: 1.2550E+00\n", + "Root 1: 1.2193E-03 -4.2780E-05\n", + "Parameter 1: 1.2400E+00\n", + "Root 1: 1.2139E-03 -4.2243E-05\n", + "Parameter 1: 1.2250E+00\n", + "Root 1: 1.2085E-03 -4.1700E-05\n", + "Parameter 1: 1.2100E+00\n", + "Root 1: 1.2032E-03 -4.1150E-05\n", + "Parameter 1: 1.1950E+00\n", + "Root 1: 1.1979E-03 -4.0593E-05\n", + "Parameter 1: 1.1800E+00\n", + "Root 1: 1.1928E-03 -4.0030E-05\n", + "Parameter 1: 1.1650E+00\n", + "Root 1: 1.1877E-03 -3.9460E-05\n", + "Parameter 1: 1.1500E+00\n", + "Root 1: 1.1827E-03 -3.8883E-05\n", + "Parameter 1: 1.1350E+00\n", + "Root 1: 1.1777E-03 -3.8300E-05\n", + "Parameter 1: 1.1200E+00\n", + "Root 1: 1.1729E-03 -3.7710E-05\n", + "Parameter 1: 1.1050E+00\n", + "Root 1: 1.1681E-03 -3.7114E-05\n", + "Parameter 1: 1.0900E+00\n", + "Root 1: 1.1634E-03 -3.6511E-05\n", + "Parameter 1: 1.0750E+00\n", + "Root 1: 1.1587E-03 -3.5902E-05\n", + "Parameter 1: 1.0600E+00\n", + "Root 1: 1.1541E-03 -3.5286E-05\n", + "Parameter 1: 1.0450E+00\n", + "Root 1: 1.1497E-03 -3.4664E-05\n", + "Parameter 1: 1.0300E+00\n", + "Root 1: 1.1452E-03 -3.4036E-05\n", + "Parameter 1: 1.0150E+00\n", + "Root 1: 1.1409E-03 -3.3402E-05\n", + "Parameter 1: 1.0000E+00\n", + "Root 1: 1.1366E-03 -3.2762E-05\n", + "Input for Root 1: 1.2362E-03 -4.4355E-05\n", + "Dispersion Solutions: \n", + " 1 1.2362E-03 -4.4355E-05\n", + "Scan over betap from 1.000000E+000 to 5.000000E+000\n", + "Scan over alphS for species 1 from 1.000000E+000 to 5.000000E+000\n", + "Parameter 1: 1.0000E+00\n", + "Root 1: 1.2362E-03 -4.4355E-05\n", + "Parameter 1: 1.2000E+00\n", + "Root 1: 1.0965E-03 -4.5613E-05\n", + "Parameter 1: 1.4000E+00\n", + "Root 1: 9.9005E-04 -4.7867E-05\n", + "Parameter 1: 1.6000E+00\n", + "Root 1: 9.0557E-04 -5.0722E-05\n", + "Parameter 1: 1.8000E+00\n", + "Root 1: 8.3649E-04 -5.3871E-05\n", + "Parameter 1: 2.0000E+00\n", + "Root 1: 7.7864E-04 -5.7109E-05\n", + "Parameter 1: 2.2000E+00\n", + "Root 1: 7.2929E-04 -6.0311E-05\n", + "Parameter 1: 2.4000E+00\n", + "Root 1: 6.8652E-04 -6.3404E-05\n", + "Parameter 1: 2.6000E+00\n", + "Root 1: 6.4900E-04 -6.6348E-05\n", + "Parameter 1: 2.8000E+00\n", + "Root 1: 6.1571E-04 -6.9123E-05\n", + "Parameter 1: 3.0000E+00\n", + "Root 1: 5.8591E-04 -7.1721E-05\n", + "Parameter 1: 3.2000E+00\n", + "Root 1: 5.5902E-04 -7.4142E-05\n", + "Parameter 1: 3.4000E+00\n", + "Root 1: 5.3459E-04 -7.6389E-05\n", + "Parameter 1: 3.6000E+00\n", + "Root 1: 5.1225E-04 -7.8468E-05\n", + "Parameter 1: 3.8000E+00\n", + "Root 1: 4.9172E-04 -8.0387E-05\n", + "Parameter 1: 4.0000E+00\n", + "Root 1: 4.7275E-04 -8.2152E-05\n", + "Parameter 1: 4.2000E+00\n", + "Root 1: 4.5515E-04 -8.3772E-05\n", + "Parameter 1: 4.4000E+00\n", + "Root 1: 4.3876E-04 -8.5254E-05\n", + "Parameter 1: 4.6000E+00\n", + "Root 1: 4.2344E-04 -8.6605E-05\n", + "Parameter 1: 4.8000E+00\n", + "Root 1: 4.0907E-04 -8.7832E-05\n", + "Parameter 1: 5.0000E+00\n", + "Root 1: 3.9556E-04 -8.8940E-05\n", + "Input for Root 1: 1.2362E-03 -4.4355E-05\n", + "Dispersion Solutions: \n", + " 1 1.2362E-03 -4.4355E-05\n", + "Scan over betap from 1.000000E+000 to 5.000000E-001\n", + "Scan over alphS for species 1 from 1.000000E+000 to 5.000000E+000\n", + "Parameter 1: 1.0000E+00\n", + "Root 1: 1.2362E-03 -4.4355E-05\n", + "Parameter 1: 9.7500E-01\n", + "Root 1: 1.2568E-03 -4.4287E-05\n", + "Parameter 1: 9.5000E-01\n", + "Root 1: 1.2784E-03 -4.4241E-05\n", + "Parameter 1: 9.2500E-01\n", + "Root 1: 1.3008E-03 -4.4216E-05\n", + "Parameter 1: 9.0000E-01\n", + "Root 1: 1.3243E-03 -4.4214E-05\n", + "Parameter 1: 8.7500E-01\n", + "Root 1: 1.3488E-03 -4.4234E-05\n", + "Parameter 1: 8.5000E-01\n", + "Root 1: 1.3744E-03 -4.4277E-05\n", + "Parameter 1: 8.2500E-01\n", + "Root 1: 1.4013E-03 -4.4343E-05\n", + "Parameter 1: 8.0000E-01\n", + "Root 1: 1.4295E-03 -4.4433E-05\n", + "Parameter 1: 7.7500E-01\n", + "Root 1: 1.4591E-03 -4.4547E-05\n", + "Parameter 1: 7.5000E-01\n", + "Root 1: 1.4902E-03 -4.4686E-05\n", + "Parameter 1: 7.2500E-01\n", + "Root 1: 1.5230E-03 -4.4852E-05\n", + "Parameter 1: 7.0000E-01\n", + "Root 1: 1.5576E-03 -4.5048E-05\n", + "Parameter 1: 6.7500E-01\n", + "Root 1: 1.5942E-03 -4.5277E-05\n", + "Parameter 1: 6.5000E-01\n", + "Root 1: 1.6330E-03 -4.5545E-05\n", + "Parameter 1: 6.2500E-01\n", + "Root 1: 1.6741E-03 -4.5859E-05\n", + "Parameter 1: 6.0000E-01\n", + "Root 1: 1.7177E-03 -4.6229E-05\n", + "Parameter 1: 5.7500E-01\n", + "Root 1: 1.7643E-03 -4.6668E-05\n", + "Parameter 1: 5.5000E-01\n", + "Root 1: 1.8140E-03 -4.7194E-05\n", + "Parameter 1: 5.2500E-01\n", + "Root 1: 1.8673E-03 -4.7826E-05\n", + "Parameter 1: 5.0000E-01\n", + "Root 1: 1.9245E-03 -4.8593E-05\n", + "Input for Root 1: 1.2362E-03 -4.4355E-05\n", + "Dispersion Solutions: \n", + " 1 1.2362E-03 -4.4355E-05\n", + "Scan over betap from 1.000000E+000 to 5.000000E+000\n", + "Scan over alphS for species 1 from 1.000000E+000 to 5.000000E-001\n", + "Parameter 1: 1.0000E+00\n", + "Root 1: 1.2362E-03 -4.4355E-05\n", + "Parameter 1: 1.2000E+00\n", + "Root 1: 1.0965E-03 -4.5613E-05\n", + "Parameter 1: 1.4000E+00\n", + "Root 1: 9.9005E-04 -4.7867E-05\n", + "Parameter 1: 1.6000E+00\n", + "Root 1: 9.0557E-04 -5.0722E-05\n", + "Parameter 1: 1.8000E+00\n", + "Root 1: 8.3649E-04 -5.3871E-05\n", + "Parameter 1: 2.0000E+00\n", + "Root 1: 7.7864E-04 -5.7109E-05\n", + "Parameter 1: 2.2000E+00\n", + "Root 1: 7.2929E-04 -6.0311E-05\n", + "Parameter 1: 2.4000E+00\n", + "Root 1: 6.8652E-04 -6.3404E-05\n", + "Parameter 1: 2.6000E+00\n", + "Root 1: 6.4900E-04 -6.6348E-05\n", + "Parameter 1: 2.8000E+00\n", + "Root 1: 6.1571E-04 -6.9123E-05\n", + "Parameter 1: 3.0000E+00\n", + "Root 1: 5.8591E-04 -7.1721E-05\n", + "Parameter 1: 3.2000E+00\n", + "Root 1: 5.5902E-04 -7.4142E-05\n", + "Parameter 1: 3.4000E+00\n", + "Root 1: 5.3459E-04 -7.6389E-05\n", + "Parameter 1: 3.6000E+00\n", + "Root 1: 5.1225E-04 -7.8468E-05\n", + "Parameter 1: 3.8000E+00\n", + "Root 1: 4.9172E-04 -8.0387E-05\n", + "Parameter 1: 4.0000E+00\n", + "Root 1: 4.7275E-04 -8.2152E-05\n", + "Parameter 1: 4.2000E+00\n", + "Root 1: 4.5515E-04 -8.3772E-05\n", + "Parameter 1: 4.4000E+00\n", + "Root 1: 4.3876E-04 -8.5254E-05\n", + "Parameter 1: 4.6000E+00\n", + "Root 1: 4.2344E-04 -8.6605E-05\n", + "Parameter 1: 4.8000E+00\n", + "Root 1: 4.0907E-04 -8.7832E-05\n", + "Parameter 1: 5.0000E+00\n", + "Root 1: 3.9556E-04 -8.8940E-05\n", + "Input for Root 1: 1.2362E-03 -4.4355E-05\n", + "Dispersion Solutions: \n", + " 1 1.2362E-03 -4.4355E-05\n", + "Scan over betap from 1.000000E+000 to 5.000000E-001\n", + "Scan over alphS for species 1 from 1.000000E+000 to 5.000000E-001\n", + "Parameter 1: 1.0000E+00\n", + "Root 1: 1.2362E-03 -4.4355E-05\n", + "Parameter 1: 9.7500E-01\n", + "Root 1: 1.2568E-03 -4.4287E-05\n", + "Parameter 1: 9.5000E-01\n", + "Root 1: 1.2784E-03 -4.4241E-05\n", + "Parameter 1: 9.2500E-01\n", + "Root 1: 1.3008E-03 -4.4216E-05\n", + "Parameter 1: 9.0000E-01\n", + "Root 1: 1.3243E-03 -4.4214E-05\n", + "Parameter 1: 8.7500E-01\n", + "Root 1: 1.3488E-03 -4.4234E-05\n", + "Parameter 1: 8.5000E-01\n", + "Root 1: 1.3744E-03 -4.4277E-05\n", + "Parameter 1: 8.2500E-01\n", + "Root 1: 1.4013E-03 -4.4343E-05\n", + "Parameter 1: 8.0000E-01\n", + "Root 1: 1.4295E-03 -4.4433E-05\n", + "Parameter 1: 7.7500E-01\n", + "Root 1: 1.4591E-03 -4.4547E-05\n", + "Parameter 1: 7.5000E-01\n", + "Root 1: 1.4902E-03 -4.4686E-05\n", + "Parameter 1: 7.2500E-01\n", + "Root 1: 1.5230E-03 -4.4852E-05\n", + "Parameter 1: 7.0000E-01\n", + "Root 1: 1.5576E-03 -4.5048E-05\n", + "Parameter 1: 6.7500E-01\n", + "Root 1: 1.5942E-03 -4.5277E-05\n", + "Parameter 1: 6.5000E-01\n", + "Root 1: 1.6330E-03 -4.5545E-05\n", + "Parameter 1: 6.2500E-01\n", + "Root 1: 1.6741E-03 -4.5859E-05\n", + "Parameter 1: 6.0000E-01\n", + "Root 1: 1.7177E-03 -4.6229E-05\n", + "Parameter 1: 5.7500E-01\n", + "Root 1: 1.7643E-03 -4.6668E-05\n", + "Parameter 1: 5.5000E-01\n", + "Root 1: 1.8140E-03 -4.7194E-05\n", + "Parameter 1: 5.2500E-01\n", + "Root 1: 1.8673E-03 -4.7826E-05\n", + "Parameter 1: 5.0000E-01\n", + "Root 1: 1.9245E-03 -4.8593E-05\n", + " Predicting FPC (gyrotropic coords)...\n", + "Input for Root 1: 1.2362E-03 -4.4355E-05\n", + "Dispersion Solutions: \n", + " 1 1.2362E-03 -4.4355E-05\n", + " WARNING! The gyro routine does not support computing moments at this time due to computational demand!\n", + " Assuming data folder already exists...\n", + " Assuming subfolder howes2017 already exists\n", + " Calculating fpc for species 1\n", + " Writing omega/kpar V_a normalization to file...\n", + " Calculating fpc for species 2\n", + " Writing omega/kpar V_a normalization to file...\n", + " Predicting FPC (gyrotropic coords)...\n", + "Input for Root 1: 1.2362E-03 -4.4355E-05\n", + "Dispersion Solutions: \n", + " 1 1.2362E-03 -4.4355E-05\n", + " WARNING! The gyro routine does not support computing moments at this time due to computational demand!\n", + " Assuming data folder already exists...\n", + " Assuming subfolder howes2017 already exists\n", + " Calculating fpc for species 1\n", + " Writing omega/kpar V_a normalization to file...\n", + " Calculating fpc for species 2\n", + " Writing omega/kpar V_a normalization to file...\n", + " Predicting FPC (gyrotropic coords)...\n", + "Input for Root 1: 1.2362E-03 -4.4355E-05\n", + "Dispersion Solutions: \n", + " 1 1.2362E-03 -4.4355E-05\n", + " WARNING! The gyro routine does not support computing moments at this time due to computational demand!\n", + " Assuming data folder already exists...\n", + " Assuming subfolder howes2017 already exists\n", + " Calculating fpc for species 1\n", + " Writing omega/kpar V_a normalization to file...\n", + " Calculating fpc for species 2\n", + " Writing omega/kpar V_a normalization to file...\n", + " Predicting FPC (cartesian coords)...\n", + "Input for Root 1: 1.2362E-03 -4.4355E-05\n", + " Assuming data folder already exists...\n", + " assuming subfolder howes2017 already exists\n", + " Writing fpc for species 1\n", + " Writing omega/kpar V_a normalization to file...\n", + " Writing fpc for species 2\n", + " Writing omega/kpar V_a normalization to file...\n", + "-=-=-=-=-=-=-=-=-=-\n", + "Root Search:\n", + "-=-=-=-=-=-=-=-=-=-\n", + "Global Plasma Parameters:\n", + "k_perp rho_ref = 1.30000 \n", + "k_par rho_ref = 0.100000E-02\n", + "Beta_ref,par = 1.00000 \n", + "v_t,ref,par/c = 0.100000E-03\n", + "-=-=-=-=-=-=-=-=-=-\n", + "Parameters for Species : 1\n", + "T_||,ref/T_||s = 1.00000 \n", + "m_ref/m_s = 1.00000 \n", + "T_perp/T_par|s = 1.00000 \n", + "q_ref/q_s = 1.00000 \n", + "n_s/n_ref = 1.00000 \n", + "v_drift s/v_A,ref = 0.00000 \n", + "-=-=-=-=-=-=-=-=-=-\n", + "Parameters for Species : 2\n", + "T_||,ref/T_||s = 1.00000 \n", + "m_ref/m_s = 1836.00 \n", + "T_perp/T_par|s = 1.00000 \n", + "q_ref/q_s = -1.00000 \n", + "n_s/n_ref = 1.00000 \n", + "v_drift s/v_A,ref = 0.00000 \n", + "-=-=-=-=-=-=-=-=-=-\n", + "Searching over:\n", + "omega/Omega_ref \\in [ 1.000E-05, 1.000E-02]\n", + "gamma/Omega_ref \\in [-2.000E-03,-2.000E-06]\n", + "-=-=-=-=-=-=-=-=-=-\n", + " finding minima\n", + " minima found\n", + " 7 possible local minima found:\n", + "ir = 128 ii = 128\n", + " 1.0000E-02 -2.0000E-06\n", + "ir = 16 ii = 125\n", + " 1.2588E-03 -4.8828E-05\n", + "ir = 0 ii = 80\n", + " 1.0000E-05 -7.5125E-04\n", + "ir = 15 ii = 65\n", + " 1.1807E-03 -9.8539E-04\n", + "ir = 22 ii = 26\n", + " 1.7270E-03 -1.5942E-03\n", + "ir = 28 ii = 2\n", + " 2.1953E-03 -1.9688E-03\n", + "ir = 128 ii = 0\n", + " 1.0000E-02 -2.0000E-03\n", + "Refining roots:\n", + "Dispersion Solutions \n", + " 1 1.2362E-03 -4.4355E-05\n", + " Predicting FPC (gyrotropic coords)...\n", + "Input for Root 1: 1.2362E-03 -4.4355E-05\n", + "Dispersion Solutions: \n", + " 1 1.2362E-03 -4.4355E-05\n", + " WARNING! The gyro routine does not support computing moments at this time due to computational demand!\n", + " Assuming data folder already exists...\n", + " Assuming subfolder howes2017 already exists\n", + " Calculating fpc for species 1\n", + " Writing omega/kpar V_a normalization to file...\n", + " Calculating fpc for species 2\n", + " Writing omega/kpar V_a normalization to file...\n", + "-=-=-=-=-=-=-=-=-=-\n", + "Root Search:\n", + "-=-=-=-=-=-=-=-=-=-\n", + "Global Plasma Parameters:\n", + "k_perp rho_ref = 1.30000 \n", + "k_par rho_ref = 0.100000E-02\n", + "Beta_ref,par = 1.00000 \n", + "v_t,ref,par/c = 0.100000E-03\n", + "-=-=-=-=-=-=-=-=-=-\n", + "Parameters for Species : 1\n", + "T_||,ref/T_||s = 1.00000 \n", + "m_ref/m_s = 1.00000 \n", + "T_perp/T_par|s = 1.00000 \n", + "q_ref/q_s = 1.00000 \n", + "n_s/n_ref = 1.00000 \n", + "v_drift s/v_A,ref = 0.00000 \n", + "-=-=-=-=-=-=-=-=-=-\n", + "Parameters for Species : 2\n", + "T_||,ref/T_||s = 1.00000 \n", + "m_ref/m_s = 1836.00 \n", + "T_perp/T_par|s = 1.00000 \n", + "q_ref/q_s = -1.00000 \n", + "n_s/n_ref = 1.00000 \n", + "v_drift s/v_A,ref = 0.00000 \n", + "-=-=-=-=-=-=-=-=-=-\n", + "Searching over:\n", + "omega/Omega_ref \\in [ 1.000E-05, 1.000E-02]\n", + "gamma/Omega_ref \\in [-2.000E-03,-2.000E-06]\n", + "-=-=-=-=-=-=-=-=-=-\n", + " finding minima\n", + " minima found\n", + " 7 possible local minima found:\n", + "ir = 128 ii = 128\n", + " 1.0000E-02 -2.0000E-06\n", + "ir = 16 ii = 125\n", + " 1.2588E-03 -4.8828E-05\n", + "ir = 0 ii = 80\n", + " 1.0000E-05 -7.5125E-04\n", + "ir = 15 ii = 65\n", + " 1.1807E-03 -9.8539E-04\n", + "ir = 22 ii = 26\n", + " 1.7270E-03 -1.5942E-03\n", + "ir = 28 ii = 2\n", + " 2.1953E-03 -1.9688E-03\n", + "ir = 128 ii = 0\n", + " 1.0000E-02 -2.0000E-03\n", + "Refining roots:\n", + "Dispersion Solutions \n", + " 1 1.2362E-03 -4.4355E-05\n", + " Predicting FPC (gyrotropic coords)...\n", + "Input for Root 1: 1.2362E-03 -4.4355E-05\n", + "Dispersion Solutions: \n", + " 1 1.2362E-03 -4.4355E-05\n", + " WARNING! The gyro routine does not support computing moments at this time due to computational demand!\n", + " Assuming data folder already exists...\n", + " Assuming subfolder howes2017 already exists\n", + " Calculating fpc for species 1\n", + " Writing omega/kpar V_a normalization to file...\n", + " Calculating fpc for species 2\n", + " Writing omega/kpar V_a normalization to file...\n", + "-=-=-=-=-=-=-=-=-=-\n", + "Root Search:\n", + "-=-=-=-=-=-=-=-=-=-\n", + "Global Plasma Parameters:\n", + "k_perp rho_ref = 1.30000 \n", + "k_par rho_ref = 0.100000E-02\n", + "Beta_ref,par = 1.00000 \n", + "v_t,ref,par/c = 0.100000E-03\n", + "-=-=-=-=-=-=-=-=-=-\n", + "Parameters for Species : 1\n", + "T_||,ref/T_||s = 1.00000 \n", + "m_ref/m_s = 1.00000 \n", + "T_perp/T_par|s = 1.00000 \n", + "q_ref/q_s = 1.00000 \n", + "n_s/n_ref = 1.00000 \n", + "v_drift s/v_A,ref = 0.00000 \n", + "-=-=-=-=-=-=-=-=-=-\n", + "Parameters for Species : 2\n", + "T_||,ref/T_||s = 1.00000 \n", + "m_ref/m_s = 1836.00 \n", + "T_perp/T_par|s = 1.00000 \n", + "q_ref/q_s = -1.00000 \n", + "n_s/n_ref = 1.00000 \n", + "v_drift s/v_A,ref = 0.00000 \n", + "-=-=-=-=-=-=-=-=-=-\n", + "Searching over:\n", + "omega/Omega_ref \\in [ 1.000E-05, 1.000E-02]\n", + "gamma/Omega_ref \\in [-2.000E-03,-2.000E-06]\n", + "-=-=-=-=-=-=-=-=-=-\n", + " finding minima\n", + " minima found\n", + " 7 possible local minima found:\n", + "ir = 128 ii = 128\n", + " 1.0000E-02 -2.0000E-06\n", + "ir = 16 ii = 125\n", + " 1.2588E-03 -4.8828E-05\n", + "ir = 0 ii = 80\n", + " 1.0000E-05 -7.5125E-04\n", + "ir = 15 ii = 65\n", + " 1.1807E-03 -9.8539E-04\n", + "ir = 22 ii = 26\n", + " 1.7270E-03 -1.5942E-03\n", + "ir = 28 ii = 2\n", + " 2.1953E-03 -1.9688E-03\n", + "ir = 128 ii = 0\n", + " 1.0000E-02 -2.0000E-03\n", + "Refining roots:\n", + "Dispersion Solutions \n", + " 1 1.2362E-03 -4.4355E-05\n", + " Predicting FPC (gyrotropic coords)...\n", + "Input for Root 1: 1.2362E-03 -4.4355E-05\n", + "Dispersion Solutions: \n", + " 1 1.2362E-03 -4.4355E-05\n", + " WARNING! The gyro routine does not support computing moments at this time due to computational demand!\n", + " Assuming data folder already exists...\n", + " Assuming subfolder howes2017 already exists\n", + " Calculating fpc for species 1\n", + " Writing omega/kpar V_a normalization to file...\n", + " Calculating fpc for species 2\n", + " Writing omega/kpar V_a normalization to file...\n", + "-=-=-=-=-=-=-=-=-=-\n", + "Root Search:\n", + "-=-=-=-=-=-=-=-=-=-\n", + "Global Plasma Parameters:\n", + "k_perp rho_ref = 1.30000 \n", + "k_par rho_ref = 0.100000E-02\n", + "Beta_ref,par = 1.00000 \n", + "v_t,ref,par/c = 0.100000E-03\n", + "-=-=-=-=-=-=-=-=-=-\n", + "Parameters for Species : 1\n", + "T_||,ref/T_||s = 1.00000 \n", + "m_ref/m_s = 1.00000 \n", + "T_perp/T_par|s = 1.00000 \n", + "q_ref/q_s = 1.00000 \n", + "n_s/n_ref = 1.00000 \n", + "v_drift s/v_A,ref = 0.00000 \n", + "-=-=-=-=-=-=-=-=-=-\n", + "Parameters for Species : 2\n", + "T_||,ref/T_||s = 1.00000 \n", + "m_ref/m_s = 1836.00 \n", + "T_perp/T_par|s = 1.00000 \n", + "q_ref/q_s = -1.00000 \n", + "n_s/n_ref = 1.00000 \n", + "v_drift s/v_A,ref = 0.00000 \n", + "-=-=-=-=-=-=-=-=-=-\n", + "Searching over:\n", + "omega/Omega_ref \\in [ 1.000E-05, 1.000E-02]\n", + "gamma/Omega_ref \\in [-2.000E-03,-2.000E-06]\n", + "-=-=-=-=-=-=-=-=-=-\n", + " finding minima\n", + " minima found\n", + " 7 possible local minima found:\n", + "ir = 128 ii = 128\n", + " 1.0000E-02 -2.0000E-06\n", + "ir = 16 ii = 125\n", + " 1.2588E-03 -4.8828E-05\n", + "ir = 0 ii = 80\n", + " 1.0000E-05 -7.5125E-04\n", + "ir = 15 ii = 65\n", + " 1.1807E-03 -9.8539E-04\n", + "ir = 22 ii = 26\n", + " 1.7270E-03 -1.5942E-03\n", + "ir = 28 ii = 2\n", + " 2.1953E-03 -1.9688E-03\n", + "ir = 128 ii = 0\n", + " 1.0000E-02 -2.0000E-03\n", + "Refining roots:\n", + "Dispersion Solutions \n", + " 1 1.2362E-03 -4.4355E-05\n", + " Predicting FPC (gyrotropic coords)...\n", + "Input for Root 1: 1.2362E-03 -4.4355E-05\n", + "Dispersion Solutions: \n", + " 1 1.2362E-03 -4.4355E-05\n", + " WARNING! The gyro routine does not support computing moments at this time due to computational demand!\n", + " Assuming data folder already exists...\n", + " Assuming subfolder howes2017 already exists\n", + " Calculating fpc for species 1\n", + " Writing omega/kpar V_a normalization to file...\n", + " Calculating fpc for species 2\n", + " Writing omega/kpar V_a normalization to file...\n", + "-=-=-=-=-=-=-=-=-=-\n", + "Root Search:\n", + "-=-=-=-=-=-=-=-=-=-\n", + "Global Plasma Parameters:\n", + "k_perp rho_ref = 1.30000 \n", + "k_par rho_ref = 0.100000E-02\n", + "Beta_ref,par = 1.00000 \n", + "v_t,ref,par/c = 0.100000E-03\n", + "-=-=-=-=-=-=-=-=-=-\n", + "Parameters for Species : 1\n", + "T_||,ref/T_||s = 1.00000 \n", + "m_ref/m_s = 1.00000 \n", + "T_perp/T_par|s = 1.00000 \n", + "q_ref/q_s = 1.00000 \n", + "n_s/n_ref = 1.00000 \n", + "v_drift s/v_A,ref = 0.00000 \n", + "-=-=-=-=-=-=-=-=-=-\n", + "Parameters for Species : 2\n", + "T_||,ref/T_||s = 1.00000 \n", + "m_ref/m_s = 1836.00 \n", + "T_perp/T_par|s = 1.00000 \n", + "q_ref/q_s = -1.00000 \n", + "n_s/n_ref = 1.00000 \n", + "v_drift s/v_A,ref = 0.00000 \n", + "-=-=-=-=-=-=-=-=-=-\n", + "Searching over:\n", + "omega/Omega_ref \\in [ 1.000E-05, 1.000E-02]\n", + "gamma/Omega_ref \\in [-2.000E-03,-2.000E-06]\n", + "-=-=-=-=-=-=-=-=-=-\n", + " finding minima\n", + " minima found\n", + " 7 possible local minima found:\n", + "ir = 128 ii = 128\n", + " 1.0000E-02 -2.0000E-06\n", + "ir = 16 ii = 125\n", + " 1.2588E-03 -4.8828E-05\n", + "ir = 0 ii = 80\n", + " 1.0000E-05 -7.5125E-04\n", + "ir = 15 ii = 65\n", + " 1.1807E-03 -9.8539E-04\n", + "ir = 22 ii = 26\n", + " 1.7270E-03 -1.5942E-03\n", + "ir = 28 ii = 2\n", + " 2.1953E-03 -1.9688E-03\n", + "ir = 128 ii = 0\n", + " 1.0000E-02 -2.0000E-03\n", + "Refining roots:\n", + "Dispersion Solutions \n", + " 1 1.2362E-03 -4.4355E-05\n", + " Predicting FPC (gyrotropic coords)...\n", + "Input for Root 1: 1.2362E-03 -4.4355E-05\n", + "Dispersion Solutions: \n", + " 1 1.2362E-03 -4.4355E-05\n", + " WARNING! The gyro routine does not support computing moments at this time due to computational demand!\n", + " Assuming data folder already exists...\n", + " Assuming subfolder howes2017 already exists\n", + " Calculating fpc for species 1\n", + " Writing omega/kpar V_a normalization to file...\n", + " Calculating fpc for species 2\n", + " Writing omega/kpar V_a normalization to file...\n", + "-=-=-=-=-=-=-=-=-=-\n", + "Root Search:\n", + "-=-=-=-=-=-=-=-=-=-\n", + "Global Plasma Parameters:\n", + "k_perp rho_ref = 1.30000 \n", + "k_par rho_ref = 0.100000E-02\n", + "Beta_ref,par = 1.00000 \n", + "v_t,ref,par/c = 0.100000E-03\n", + "-=-=-=-=-=-=-=-=-=-\n", + "Parameters for Species : 1\n", + "T_||,ref/T_||s = 1.00000 \n", + "m_ref/m_s = 1.00000 \n", + "T_perp/T_par|s = 1.00000 \n", + "q_ref/q_s = 1.00000 \n", + "n_s/n_ref = 1.00000 \n", + "v_drift s/v_A,ref = 0.00000 \n", + "-=-=-=-=-=-=-=-=-=-\n", + "Parameters for Species : 2\n", + "T_||,ref/T_||s = 1.00000 \n", + "m_ref/m_s = 1836.00 \n", + "T_perp/T_par|s = 1.00000 \n", + "q_ref/q_s = -1.00000 \n", + "n_s/n_ref = 1.00000 \n", + "v_drift s/v_A,ref = 0.00000 \n", + "-=-=-=-=-=-=-=-=-=-\n", + "Searching over:\n", + "omega/Omega_ref \\in [ 1.000E-05, 1.000E-02]\n", + "gamma/Omega_ref \\in [-2.000E-03,-2.000E-06]\n", + "-=-=-=-=-=-=-=-=-=-\n", + " finding minima\n", + " minima found\n", + " 7 possible local minima found:\n", + "ir = 128 ii = 128\n", + " 1.0000E-02 -2.0000E-06\n", + "ir = 16 ii = 125\n", + " 1.2588E-03 -4.8828E-05\n", + "ir = 0 ii = 80\n", + " 1.0000E-05 -7.5125E-04\n", + "ir = 15 ii = 65\n", + " 1.1807E-03 -9.8539E-04\n", + "ir = 22 ii = 26\n", + " 1.7270E-03 -1.5942E-03\n", + "ir = 28 ii = 2\n", + " 2.1953E-03 -1.9688E-03\n", + "ir = 128 ii = 0\n", + " 1.0000E-02 -2.0000E-03\n", + "Refining roots:\n", + "Dispersion Solutions \n", + " 1 1.2362E-03 -4.4355E-05\n", + " 2 3.4468E-14 -7.4376E-04\n", + " 3 1.1473E-03 -9.8322E-04\n", + " 4 1.7166E-03 -1.5983E-03\n", + " 5 2.2089E-03 -1.9704E-03\n", + " 6 2.2089E-03 -1.9704E-03\n", + " 7 1.4252E+01 8.9696E+00\n", + " 8 0.0000E+00 0.0000E+00\n", + " 9 0.0000E+00 0.0000E+00\n", + " 10 0.0000E+00 0.0000E+00\n", + " 11 0.0000E+00 0.0000E+00\n", + " 12 0.0000E+00 0.0000E+00\n", + " 13 0.0000E+00 0.0000E+00\n", + " 14 0.0000E+00 0.0000E+00\n", + " 15 0.0000E+00 0.0000E+00\n", + " 16 0.0000E+00 0.0000E+00\n", + " 17 0.0000E+00 0.0000E+00\n", + " 18 0.0000E+00 0.0000E+00\n", + " 19 0.0000E+00 0.0000E+00\n", + " 20 0.0000E+00 0.0000E+00\n", + "Input for Root 1: 1.2362E-03 -4.4355E-05\n", + "Dispersion Solutions: \n", + " 1 1.2362E-03 -4.4355E-05\n", + "Scan over kperp from 1.3000E+00 to 1.0000E+02\n", + " =>data/howes2017/sweepsweep1_tensor_kperp_1300_100000\n", + "Root 1 In: 1.2362E-03 -4.4355E-05\n", + "Root 1 Out: 3.7512E-02 -3.7403E-02\n", + "Input for Root 1: 1.2362E-03 -4.4355E-05\n", + "Dispersion Solutions: \n", + " 1 1.2362E-03 -4.4355E-05\n", + "Scan over kperp from 1.3000E+00 to 1.0000E-01\n", + " =>data/howes2017/sweepsweep2_tensor_kperp_1300_100\n", + "Root 1 In: 1.2362E-03 -4.4355E-05\n", + "Root 1 Out: 1.0011E-03 -4.6981E-07\n", + "Input for Root 1: 1.2362E-03 -4.4355E-05\n", + "Dispersion Solutions: \n", + " 1 1.2362E-03 -4.4355E-05\n", + "Scan over tauS for species 2 from 1.0000E+00 to 1.0000E+02\n", + " =>data/howes2017/sweepsweep1_tensor_tauS_s2_1000_100000\n", + "Root 1 In: 1.2362E-03 -4.4355E-05\n", + "Root 1 Out: 1.1319E-03 -2.0535E-05\n", + "Input for Root 1: 1.2362E-03 -4.4355E-05\n", + "Dispersion Solutions: \n", + " 1 1.2362E-03 -4.4355E-05\n", + "Scan over tauS for species 2 from 1.0000E+00 to 1.0000E-01\n", + " =>data/howes2017/sweepsweep2_tensor_tauS_s2_1000_100\n", + "Root 1 In: 1.2362E-03 -4.4355E-05\n", + "Root 1 Out: 1.5471E-03 -2.9932E-04\n", + "Input for Root 1: 1.2362E-03 -4.4355E-05\n", + "Dispersion Solutions: \n", + " 1 1.2362E-03 -4.4355E-05\n", + "Scan over kperp from 1.300000E+000 to 1.000000E+001\n", + "Scan over kpar from 1.000000E-003 to 1.000000E+001\n", + "Parameter 1: 1.3000E+00\n", + "Root 1: 1.2362E-03 -4.4355E-05\n", + "Parameter 1: 1.7350E+00\n", + "Root 1: 1.4327E-03 -5.7938E-05\n", + "Parameter 1: 2.1700E+00\n", + "Root 1: 1.6788E-03 -7.3372E-05\n", + "Parameter 1: 2.6050E+00\n", + "Root 1: 1.9538E-03 -9.5239E-05\n", + "Parameter 1: 3.0400E+00\n", + "Root 1: 2.2430E-03 -1.2441E-04\n", + "Parameter 1: 3.4750E+00\n", + "Root 1: 2.5392E-03 -1.6009E-04\n", + "Parameter 1: 3.9100E+00\n", + "Root 1: 2.8393E-03 -2.0140E-04\n", + "Parameter 1: 4.3450E+00\n", + "Root 1: 3.1417E-03 -2.4782E-04\n", + "Parameter 1: 4.7800E+00\n", + "Root 1: 3.4454E-03 -2.9912E-04\n", + "Parameter 1: 5.2150E+00\n", + "Root 1: 3.7499E-03 -3.5526E-04\n", + "Parameter 1: 5.6500E+00\n", + "Root 1: 4.0549E-03 -4.1620E-04\n", + "Parameter 1: 6.0850E+00\n", + "Root 1: 4.3603E-03 -4.8193E-04\n", + "Parameter 1: 6.5200E+00\n", + "Root 1: 4.6659E-03 -5.5243E-04\n", + "Parameter 1: 6.9550E+00\n", + "Root 1: 4.9716E-03 -6.2767E-04\n", + "Parameter 1: 7.3900E+00\n", + "Root 1: 5.2773E-03 -7.0764E-04\n", + "Parameter 1: 7.8250E+00\n", + "Root 1: 5.5831E-03 -7.9231E-04\n", + "Parameter 1: 8.2600E+00\n", + "Root 1: 5.8889E-03 -8.8166E-04\n", + "Parameter 1: 8.6950E+00\n", + "Root 1: 6.1946E-03 -9.7565E-04\n", + "Parameter 1: 9.1300E+00\n", + "Root 1: 6.5003E-03 -1.0742E-03\n", + "Parameter 1: 9.5650E+00\n", + "Root 1: 6.8059E-03 -1.1774E-03\n", + "Parameter 1: 1.0000E+01\n", + "Root 1: 7.1114E-03 -1.2852E-03\n", + "Input for Root 1: 1.2362E-03 -4.4355E-05\n", + "Dispersion Solutions: \n", + " 1 1.2362E-03 -4.4355E-05\n", + "Scan over kperp from 1.300000E+000 to 1.000000E+000\n", + "Scan over kpar from 1.000000E-003 to 1.000000E+001\n", + "Parameter 1: 1.3000E+00\n", + "Root 1: 1.2362E-03 -4.4355E-05\n", + "Parameter 1: 1.2850E+00\n", + "Root 1: 1.2305E-03 -4.3836E-05\n", + "Parameter 1: 1.2700E+00\n", + "Root 1: 1.2249E-03 -4.3311E-05\n", + "Parameter 1: 1.2550E+00\n", + "Root 1: 1.2193E-03 -4.2780E-05\n", + "Parameter 1: 1.2400E+00\n", + "Root 1: 1.2139E-03 -4.2243E-05\n", + "Parameter 1: 1.2250E+00\n", + "Root 1: 1.2085E-03 -4.1700E-05\n", + "Parameter 1: 1.2100E+00\n", + "Root 1: 1.2032E-03 -4.1150E-05\n", + "Parameter 1: 1.1950E+00\n", + "Root 1: 1.1979E-03 -4.0593E-05\n", + "Parameter 1: 1.1800E+00\n", + "Root 1: 1.1928E-03 -4.0030E-05\n", + "Parameter 1: 1.1650E+00\n", + "Root 1: 1.1877E-03 -3.9460E-05\n", + "Parameter 1: 1.1500E+00\n", + "Root 1: 1.1827E-03 -3.8883E-05\n", + "Parameter 1: 1.1350E+00\n", + "Root 1: 1.1777E-03 -3.8300E-05\n", + "Parameter 1: 1.1200E+00\n", + "Root 1: 1.1729E-03 -3.7710E-05\n", + "Parameter 1: 1.1050E+00\n", + "Root 1: 1.1681E-03 -3.7114E-05\n", + "Parameter 1: 1.0900E+00\n", + "Root 1: 1.1634E-03 -3.6511E-05\n", + "Parameter 1: 1.0750E+00\n", + "Root 1: 1.1587E-03 -3.5902E-05\n", + "Parameter 1: 1.0600E+00\n", + "Root 1: 1.1541E-03 -3.5286E-05\n", + "Parameter 1: 1.0450E+00\n", + "Root 1: 1.1497E-03 -3.4664E-05\n", + "Parameter 1: 1.0300E+00\n", + "Root 1: 1.1452E-03 -3.4036E-05\n", + "Parameter 1: 1.0150E+00\n", + "Root 1: 1.1409E-03 -3.3402E-05\n", + "Parameter 1: 1.0000E+00\n", + "Root 1: 1.1366E-03 -3.2762E-05\n", + "Input for Root 1: 1.2362E-03 -4.4355E-05\n", + "Dispersion Solutions: \n", + " 1 1.2362E-03 -4.4355E-05\n", + "Scan over kperp from 1.300000E+000 to 1.000000E+001\n", + "Scan over kpar from 1.000000E-003 to 1.000000E+000\n", + "Parameter 1: 1.3000E+00\n", + "Root 1: 1.2362E-03 -4.4355E-05\n", + "Parameter 1: 1.7350E+00\n", + "Root 1: 1.4327E-03 -5.7938E-05\n", + "Parameter 1: 2.1700E+00\n", + "Root 1: 1.6788E-03 -7.3372E-05\n", + "Parameter 1: 2.6050E+00\n", + "Root 1: 1.9538E-03 -9.5239E-05\n", + "Parameter 1: 3.0400E+00\n", + "Root 1: 2.2430E-03 -1.2441E-04\n", + "Parameter 1: 3.4750E+00\n", + "Root 1: 2.5392E-03 -1.6009E-04\n", + "Parameter 1: 3.9100E+00\n", + "Root 1: 2.8393E-03 -2.0140E-04\n", + "Parameter 1: 4.3450E+00\n", + "Root 1: 3.1417E-03 -2.4782E-04\n", + "Parameter 1: 4.7800E+00\n", + "Root 1: 3.4454E-03 -2.9912E-04\n", + "Parameter 1: 5.2150E+00\n", + "Root 1: 3.7499E-03 -3.5526E-04\n", + "Parameter 1: 5.6500E+00\n", + "Root 1: 4.0549E-03 -4.1620E-04\n", + "Parameter 1: 6.0850E+00\n", + "Root 1: 4.3603E-03 -4.8193E-04\n", + "Parameter 1: 6.5200E+00\n", + "Root 1: 4.6659E-03 -5.5243E-04\n", + "Parameter 1: 6.9550E+00\n", + "Root 1: 4.9716E-03 -6.2767E-04\n", + "Parameter 1: 7.3900E+00\n", + "Root 1: 5.2773E-03 -7.0764E-04\n", + "Parameter 1: 7.8250E+00\n", + "Root 1: 5.5831E-03 -7.9231E-04\n", + "Parameter 1: 8.2600E+00\n", + "Root 1: 5.8889E-03 -8.8166E-04\n", + "Parameter 1: 8.6950E+00\n", + "Root 1: 6.1946E-03 -9.7565E-04\n", + "Parameter 1: 9.1300E+00\n", + "Root 1: 6.5003E-03 -1.0742E-03\n", + "Parameter 1: 9.5650E+00\n", + "Root 1: 6.8059E-03 -1.1774E-03\n", + "Parameter 1: 1.0000E+01\n", + "Root 1: 7.1114E-03 -1.2852E-03\n", + "Input for Root 1: 1.2362E-03 -4.4355E-05\n", + "Dispersion Solutions: \n", + " 1 1.2362E-03 -4.4355E-05\n", + "Scan over kperp from 1.300000E+000 to 1.000000E+000\n", + "Scan over kpar from 1.000000E-003 to 1.000000E+000\n", + "Parameter 1: 1.3000E+00\n", + "Root 1: 1.2362E-03 -4.4355E-05\n", + "Parameter 1: 1.2850E+00\n", + "Root 1: 1.2305E-03 -4.3836E-05\n", + "Parameter 1: 1.2700E+00\n", + "Root 1: 1.2249E-03 -4.3311E-05\n", + "Parameter 1: 1.2550E+00\n", + "Root 1: 1.2193E-03 -4.2780E-05\n", + "Parameter 1: 1.2400E+00\n", + "Root 1: 1.2139E-03 -4.2243E-05\n", + "Parameter 1: 1.2250E+00\n", + "Root 1: 1.2085E-03 -4.1700E-05\n", + "Parameter 1: 1.2100E+00\n", + "Root 1: 1.2032E-03 -4.1150E-05\n", + "Parameter 1: 1.1950E+00\n", + "Root 1: 1.1979E-03 -4.0593E-05\n", + "Parameter 1: 1.1800E+00\n", + "Root 1: 1.1928E-03 -4.0030E-05\n", + "Parameter 1: 1.1650E+00\n", + "Root 1: 1.1877E-03 -3.9460E-05\n", + "Parameter 1: 1.1500E+00\n", + "Root 1: 1.1827E-03 -3.8883E-05\n", + "Parameter 1: 1.1350E+00\n", + "Root 1: 1.1777E-03 -3.8300E-05\n", + "Parameter 1: 1.1200E+00\n", + "Root 1: 1.1729E-03 -3.7710E-05\n", + "Parameter 1: 1.1050E+00\n", + "Root 1: 1.1681E-03 -3.7114E-05\n", + "Parameter 1: 1.0900E+00\n", + "Root 1: 1.1634E-03 -3.6511E-05\n", + "Parameter 1: 1.0750E+00\n", + "Root 1: 1.1587E-03 -3.5902E-05\n", + "Parameter 1: 1.0600E+00\n", + "Root 1: 1.1541E-03 -3.5286E-05\n", + "Parameter 1: 1.0450E+00\n", + "Root 1: 1.1497E-03 -3.4664E-05\n", + "Parameter 1: 1.0300E+00\n", + "Root 1: 1.1452E-03 -3.4036E-05\n", + "Parameter 1: 1.0150E+00\n", + "Root 1: 1.1409E-03 -3.3402E-05\n", + "Parameter 1: 1.0000E+00\n", + "Root 1: 1.1366E-03 -3.2762E-05\n", + "Input for Root 1: 1.2362E-03 -4.4355E-05\n", + "Dispersion Solutions: \n", + " 1 1.2362E-03 -4.4355E-05\n", + "Scan over betap from 1.000000E+000 to 5.000000E+000\n", + "Scan over alphS for species 1 from 1.000000E+000 to 5.000000E+000\n", + "Parameter 1: 1.0000E+00\n", + "Root 1: 1.2362E-03 -4.4355E-05\n", + "Parameter 1: 1.2000E+00\n", + "Root 1: 1.0965E-03 -4.5613E-05\n", + "Parameter 1: 1.4000E+00\n", + "Root 1: 9.9005E-04 -4.7867E-05\n", + "Parameter 1: 1.6000E+00\n", + "Root 1: 9.0557E-04 -5.0722E-05\n", + "Parameter 1: 1.8000E+00\n", + "Root 1: 8.3649E-04 -5.3871E-05\n", + "Parameter 1: 2.0000E+00\n", + "Root 1: 7.7864E-04 -5.7109E-05\n", + "Parameter 1: 2.2000E+00\n", + "Root 1: 7.2929E-04 -6.0311E-05\n", + "Parameter 1: 2.4000E+00\n", + "Root 1: 6.8652E-04 -6.3404E-05\n", + "Parameter 1: 2.6000E+00\n", + "Root 1: 6.4900E-04 -6.6348E-05\n", + "Parameter 1: 2.8000E+00\n", + "Root 1: 6.1571E-04 -6.9123E-05\n", + "Parameter 1: 3.0000E+00\n", + "Root 1: 5.8591E-04 -7.1721E-05\n", + "Parameter 1: 3.2000E+00\n", + "Root 1: 5.5902E-04 -7.4142E-05\n", + "Parameter 1: 3.4000E+00\n", + "Root 1: 5.3459E-04 -7.6389E-05\n", + "Parameter 1: 3.6000E+00\n", + "Root 1: 5.1225E-04 -7.8468E-05\n", + "Parameter 1: 3.8000E+00\n", + "Root 1: 4.9172E-04 -8.0387E-05\n", + "Parameter 1: 4.0000E+00\n", + "Root 1: 4.7275E-04 -8.2152E-05\n", + "Parameter 1: 4.2000E+00\n", + "Root 1: 4.5515E-04 -8.3772E-05\n", + "Parameter 1: 4.4000E+00\n", + "Root 1: 4.3876E-04 -8.5254E-05\n", + "Parameter 1: 4.6000E+00\n", + "Root 1: 4.2344E-04 -8.6605E-05\n", + "Parameter 1: 4.8000E+00\n", + "Root 1: 4.0907E-04 -8.7832E-05\n", + "Parameter 1: 5.0000E+00\n", + "Root 1: 3.9556E-04 -8.8940E-05\n", + "Input for Root 1: 1.2362E-03 -4.4355E-05\n", + "Dispersion Solutions: \n", + " 1 1.2362E-03 -4.4355E-05\n", + "Scan over betap from 1.000000E+000 to 5.000000E-001\n", + "Scan over alphS for species 1 from 1.000000E+000 to 5.000000E+000\n", + "Parameter 1: 1.0000E+00\n", + "Root 1: 1.2362E-03 -4.4355E-05\n", + "Parameter 1: 9.7500E-01\n", + "Root 1: 1.2568E-03 -4.4287E-05\n", + "Parameter 1: 9.5000E-01\n", + "Root 1: 1.2784E-03 -4.4241E-05\n", + "Parameter 1: 9.2500E-01\n", + "Root 1: 1.3008E-03 -4.4216E-05\n", + "Parameter 1: 9.0000E-01\n", + "Root 1: 1.3243E-03 -4.4214E-05\n", + "Parameter 1: 8.7500E-01\n", + "Root 1: 1.3488E-03 -4.4234E-05\n", + "Parameter 1: 8.5000E-01\n", + "Root 1: 1.3744E-03 -4.4277E-05\n", + "Parameter 1: 8.2500E-01\n", + "Root 1: 1.4013E-03 -4.4343E-05\n", + "Parameter 1: 8.0000E-01\n", + "Root 1: 1.4295E-03 -4.4433E-05\n", + "Parameter 1: 7.7500E-01\n", + "Root 1: 1.4591E-03 -4.4547E-05\n", + "Parameter 1: 7.5000E-01\n", + "Root 1: 1.4902E-03 -4.4686E-05\n", + "Parameter 1: 7.2500E-01\n", + "Root 1: 1.5230E-03 -4.4852E-05\n", + "Parameter 1: 7.0000E-01\n", + "Root 1: 1.5576E-03 -4.5048E-05\n", + "Parameter 1: 6.7500E-01\n", + "Root 1: 1.5942E-03 -4.5277E-05\n", + "Parameter 1: 6.5000E-01\n", + "Root 1: 1.6330E-03 -4.5545E-05\n", + "Parameter 1: 6.2500E-01\n", + "Root 1: 1.6741E-03 -4.5859E-05\n", + "Parameter 1: 6.0000E-01\n", + "Root 1: 1.7177E-03 -4.6229E-05\n", + "Parameter 1: 5.7500E-01\n", + "Root 1: 1.7643E-03 -4.6668E-05\n", + "Parameter 1: 5.5000E-01\n", + "Root 1: 1.8140E-03 -4.7194E-05\n", + "Parameter 1: 5.2500E-01\n", + "Root 1: 1.8673E-03 -4.7826E-05\n", + "Parameter 1: 5.0000E-01\n", + "Root 1: 1.9245E-03 -4.8593E-05\n", + "Input for Root 1: 1.2362E-03 -4.4355E-05\n", + "Dispersion Solutions: \n", + " 1 1.2362E-03 -4.4355E-05\n", + "Scan over betap from 1.000000E+000 to 5.000000E+000\n", + "Scan over alphS for species 1 from 1.000000E+000 to 5.000000E-001\n", + "Parameter 1: 1.0000E+00\n", + "Root 1: 1.2362E-03 -4.4355E-05\n", + "Parameter 1: 1.2000E+00\n", + "Root 1: 1.0965E-03 -4.5613E-05\n", + "Parameter 1: 1.4000E+00\n", + "Root 1: 9.9005E-04 -4.7867E-05\n", + "Parameter 1: 1.6000E+00\n", + "Root 1: 9.0557E-04 -5.0722E-05\n", + "Parameter 1: 1.8000E+00\n", + "Root 1: 8.3649E-04 -5.3871E-05\n", + "Parameter 1: 2.0000E+00\n", + "Root 1: 7.7864E-04 -5.7109E-05\n", + "Parameter 1: 2.2000E+00\n", + "Root 1: 7.2929E-04 -6.0311E-05\n", + "Parameter 1: 2.4000E+00\n", + "Root 1: 6.8652E-04 -6.3404E-05\n", + "Parameter 1: 2.6000E+00\n", + "Root 1: 6.4900E-04 -6.6348E-05\n", + "Parameter 1: 2.8000E+00\n", + "Root 1: 6.1571E-04 -6.9123E-05\n", + "Parameter 1: 3.0000E+00\n", + "Root 1: 5.8591E-04 -7.1721E-05\n", + "Parameter 1: 3.2000E+00\n", + "Root 1: 5.5902E-04 -7.4142E-05\n", + "Parameter 1: 3.4000E+00\n", + "Root 1: 5.3459E-04 -7.6389E-05\n", + "Parameter 1: 3.6000E+00\n", + "Root 1: 5.1225E-04 -7.8468E-05\n", + "Parameter 1: 3.8000E+00\n", + "Root 1: 4.9172E-04 -8.0387E-05\n", + "Parameter 1: 4.0000E+00\n", + "Root 1: 4.7275E-04 -8.2152E-05\n", + "Parameter 1: 4.2000E+00\n", + "Root 1: 4.5515E-04 -8.3772E-05\n", + "Parameter 1: 4.4000E+00\n", + "Root 1: 4.3876E-04 -8.5254E-05\n", + "Parameter 1: 4.6000E+00\n", + "Root 1: 4.2344E-04 -8.6605E-05\n", + "Parameter 1: 4.8000E+00\n", + "Root 1: 4.0907E-04 -8.7832E-05\n", + "Parameter 1: 5.0000E+00\n", + "Root 1: 3.9556E-04 -8.8940E-05\n", + "Input for Root 1: 1.2362E-03 -4.4355E-05\n", + "Dispersion Solutions: \n", + " 1 1.2362E-03 -4.4355E-05\n", + "Scan over betap from 1.000000E+000 to 5.000000E-001\n", + "Scan over alphS for species 1 from 1.000000E+000 to 5.000000E-001\n", + "Parameter 1: 1.0000E+00\n", + "Root 1: 1.2362E-03 -4.4355E-05\n", + "Parameter 1: 9.7500E-01\n", + "Root 1: 1.2568E-03 -4.4287E-05\n", + "Parameter 1: 9.5000E-01\n", + "Root 1: 1.2784E-03 -4.4241E-05\n", + "Parameter 1: 9.2500E-01\n", + "Root 1: 1.3008E-03 -4.4216E-05\n", + "Parameter 1: 9.0000E-01\n", + "Root 1: 1.3243E-03 -4.4214E-05\n", + "Parameter 1: 8.7500E-01\n", + "Root 1: 1.3488E-03 -4.4234E-05\n", + "Parameter 1: 8.5000E-01\n", + "Root 1: 1.3744E-03 -4.4277E-05\n", + "Parameter 1: 8.2500E-01\n", + "Root 1: 1.4013E-03 -4.4343E-05\n", + "Parameter 1: 8.0000E-01\n", + "Root 1: 1.4295E-03 -4.4433E-05\n", + "Parameter 1: 7.7500E-01\n", + "Root 1: 1.4591E-03 -4.4547E-05\n", + "Parameter 1: 7.5000E-01\n", + "Root 1: 1.4902E-03 -4.4686E-05\n", + "Parameter 1: 7.2500E-01\n", + "Root 1: 1.5230E-03 -4.4852E-05\n", + "Parameter 1: 7.0000E-01\n", + "Root 1: 1.5576E-03 -4.5048E-05\n", + "Parameter 1: 6.7500E-01\n", + "Root 1: 1.5942E-03 -4.5277E-05\n", + "Parameter 1: 6.5000E-01\n", + "Root 1: 1.6330E-03 -4.5545E-05\n", + "Parameter 1: 6.2500E-01\n", + "Root 1: 1.6741E-03 -4.5859E-05\n", + "Parameter 1: 6.0000E-01\n", + "Root 1: 1.7177E-03 -4.6229E-05\n", + "Parameter 1: 5.7500E-01\n", + "Root 1: 1.7643E-03 -4.6668E-05\n", + "Parameter 1: 5.5000E-01\n", + "Root 1: 1.8140E-03 -4.7194E-05\n", + "Parameter 1: 5.2500E-01\n", + "Root 1: 1.8673E-03 -4.7826E-05\n", + "Parameter 1: 5.0000E-01\n", + "Root 1: 1.9245E-03 -4.8593E-05\n", + " Predicting FPC (gyrotropic coords)...\n", + "Input for Root 1: 1.2362E-03 -4.4355E-05\n", + "Dispersion Solutions: \n", + " 1 1.2362E-03 -4.4355E-05\n", + " WARNING! The gyro routine does not support computing moments at this time due to computational demand!\n", + " Assuming data folder already exists...\n", + " Assuming subfolder howes2017 already exists\n", + " Calculating fpc for species 1\n", + " Writing omega/kpar V_a normalization to file...\n", + " Calculating fpc for species 2\n", + " Writing omega/kpar V_a normalization to file...\n", + " Predicting FPC (gyrotropic coords)...\n", + "Input for Root 1: 1.2362E-03 -4.4355E-05\n", + "Dispersion Solutions: \n", + " 1 1.2362E-03 -4.4355E-05\n", + " WARNING! The gyro routine does not support computing moments at this time due to computational demand!\n", + " Assuming data folder already exists...\n", + " Assuming subfolder howes2017 already exists\n", + " Calculating fpc for species 1\n", + " Writing omega/kpar V_a normalization to file...\n", + " Calculating fpc for species 2\n", + " Writing omega/kpar V_a normalization to file...\n", + " Predicting FPC (gyrotropic coords)...\n", + "Input for Root 1: 1.2362E-03 -4.4355E-05\n", + "Dispersion Solutions: \n", + " 1 1.2362E-03 -4.4355E-05\n", + " WARNING! The gyro routine does not support computing moments at this time due to computational demand!\n", + " Assuming data folder already exists...\n", + " Assuming subfolder howes2017 already exists\n", + " Calculating fpc for species 1\n", + " Writing omega/kpar V_a normalization to file...\n", + " Calculating fpc for species 2\n", + " Writing omega/kpar V_a normalization to file...\n", + " Predicting FPC (cartesian coords)...\n", + "Input for Root 1: 1.2362E-03 -4.4355E-05\n", + " Assuming data folder already exists...\n", + " assuming subfolder howes2017 already exists\n", + " Writing fpc for species 1\n", + " Writing omega/kpar V_a normalization to file...\n", + " Writing fpc for species 2\n", + " Writing omega/kpar V_a normalization to file...\n", + "-=-=-=-=-=-=-=-=-=-\n", + "Root Search:\n", + "-=-=-=-=-=-=-=-=-=-\n", + "Global Plasma Parameters:\n", + "k_perp rho_ref = 1.30000 \n", + "k_par rho_ref = 0.100000E-02\n", + "Beta_ref,par = 1.00000 \n", + "v_t,ref,par/c = 0.100000E-03\n", + "-=-=-=-=-=-=-=-=-=-\n", + "Parameters for Species : 1\n", + "T_||,ref/T_||s = 1.00000 \n", + "m_ref/m_s = 1.00000 \n", + "T_perp/T_par|s = 1.00000 \n", + "q_ref/q_s = 1.00000 \n", + "n_s/n_ref = 1.00000 \n", + "v_drift s/v_A,ref = 0.00000 \n", + "-=-=-=-=-=-=-=-=-=-\n", + "Parameters for Species : 2\n", + "T_||,ref/T_||s = 1.00000 \n", + "m_ref/m_s = 1836.00 \n", + "T_perp/T_par|s = 1.00000 \n", + "q_ref/q_s = -1.00000 \n", + "n_s/n_ref = 1.00000 \n", + "v_drift s/v_A,ref = 0.00000 \n", + "-=-=-=-=-=-=-=-=-=-\n", + "Searching over:\n", + "omega/Omega_ref \\in [ 1.000E-05, 1.000E-02]\n", + "gamma/Omega_ref \\in [-2.000E-03,-2.000E-06]\n", + "-=-=-=-=-=-=-=-=-=-\n", + " finding minima\n", + " minima found\n", + " 7 possible local minima found:\n", + "ir = 128 ii = 128\n", + " 1.0000E-02 -2.0000E-06\n", + "ir = 16 ii = 125\n", + " 1.2588E-03 -4.8828E-05\n", + "ir = 0 ii = 80\n", + " 1.0000E-05 -7.5125E-04\n", + "ir = 15 ii = 65\n", + " 1.1807E-03 -9.8539E-04\n", + "ir = 22 ii = 26\n", + " 1.7270E-03 -1.5942E-03\n", + "ir = 28 ii = 2\n", + " 2.1953E-03 -1.9688E-03\n", + "ir = 128 ii = 0\n", + " 1.0000E-02 -2.0000E-03\n", + "Refining roots:\n", + "Dispersion Solutions \n", + " 1 1.2362E-03 -4.4355E-05\n", + " Predicting FPC (gyrotropic coords)...\n", + "Input for Root 1: 1.2362E-03 -4.4355E-05\n", + "Dispersion Solutions: \n", + " 1 1.2362E-03 -4.4355E-05\n", + " WARNING! The gyro routine does not support computing moments at this time due to computational demand!\n", + " Assuming data folder already exists...\n", + " Assuming subfolder howes2017 already exists\n", + " Calculating fpc for species 1\n", + " Writing omega/kpar V_a normalization to file...\n", + " Calculating fpc for species 2\n", + " Writing omega/kpar V_a normalization to file...\n", + "-=-=-=-=-=-=-=-=-=-\n", + "Root Search:\n", + "-=-=-=-=-=-=-=-=-=-\n", + "Global Plasma Parameters:\n", + "k_perp rho_ref = 1.30000 \n", + "k_par rho_ref = 0.100000E-02\n", + "Beta_ref,par = 1.00000 \n", + "v_t,ref,par/c = 0.100000E-03\n", + "-=-=-=-=-=-=-=-=-=-\n", + "Parameters for Species : 1\n", + "T_||,ref/T_||s = 1.00000 \n", + "m_ref/m_s = 1.00000 \n", + "T_perp/T_par|s = 1.00000 \n", + "q_ref/q_s = 1.00000 \n", + "n_s/n_ref = 1.00000 \n", + "v_drift s/v_A,ref = 0.00000 \n", + "-=-=-=-=-=-=-=-=-=-\n", + "Parameters for Species : 2\n", + "T_||,ref/T_||s = 1.00000 \n", + "m_ref/m_s = 1836.00 \n", + "T_perp/T_par|s = 1.00000 \n", + "q_ref/q_s = -1.00000 \n", + "n_s/n_ref = 1.00000 \n", + "v_drift s/v_A,ref = 0.00000 \n", + "-=-=-=-=-=-=-=-=-=-\n", + "Searching over:\n", + "omega/Omega_ref \\in [ 1.000E-05, 1.000E-02]\n", + "gamma/Omega_ref \\in [-2.000E-03,-2.000E-06]\n", + "-=-=-=-=-=-=-=-=-=-\n", + " finding minima\n", + " minima found\n", + " 7 possible local minima found:\n", + "ir = 128 ii = 128\n", + " 1.0000E-02 -2.0000E-06\n", + "ir = 16 ii = 125\n", + " 1.2588E-03 -4.8828E-05\n", + "ir = 0 ii = 80\n", + " 1.0000E-05 -7.5125E-04\n", + "ir = 15 ii = 65\n", + " 1.1807E-03 -9.8539E-04\n", + "ir = 22 ii = 26\n", + " 1.7270E-03 -1.5942E-03\n", + "ir = 28 ii = 2\n", + " 2.1953E-03 -1.9688E-03\n", + "ir = 128 ii = 0\n", + " 1.0000E-02 -2.0000E-03\n", + "Refining roots:\n", + "Dispersion Solutions \n", + " 1 1.2362E-03 -4.4355E-05\n", + " Predicting FPC (gyrotropic coords)...\n", + "Input for Root 1: 1.2362E-03 -4.4355E-05\n", + "Dispersion Solutions: \n", + " 1 1.2362E-03 -4.4355E-05\n", + " WARNING! The gyro routine does not support computing moments at this time due to computational demand!\n", + " Assuming data folder already exists...\n", + " Assuming subfolder howes2017 already exists\n", + " Calculating fpc for species 1\n", + " Writing omega/kpar V_a normalization to file...\n", + " Calculating fpc for species 2\n", + " Writing omega/kpar V_a normalization to file...\n", + "-=-=-=-=-=-=-=-=-=-\n", + "Root Search:\n", + "-=-=-=-=-=-=-=-=-=-\n", + "Global Plasma Parameters:\n", + "k_perp rho_ref = 1.30000 \n", + "k_par rho_ref = 0.100000E-02\n", + "Beta_ref,par = 1.00000 \n", + "v_t,ref,par/c = 0.100000E-03\n", + "-=-=-=-=-=-=-=-=-=-\n", + "Parameters for Species : 1\n", + "T_||,ref/T_||s = 1.00000 \n", + "m_ref/m_s = 1.00000 \n", + "T_perp/T_par|s = 1.00000 \n", + "q_ref/q_s = 1.00000 \n", + "n_s/n_ref = 1.00000 \n", + "v_drift s/v_A,ref = 0.00000 \n", + "-=-=-=-=-=-=-=-=-=-\n", + "Parameters for Species : 2\n", + "T_||,ref/T_||s = 1.00000 \n", + "m_ref/m_s = 1836.00 \n", + "T_perp/T_par|s = 1.00000 \n", + "q_ref/q_s = -1.00000 \n", + "n_s/n_ref = 1.00000 \n", + "v_drift s/v_A,ref = 0.00000 \n", + "-=-=-=-=-=-=-=-=-=-\n", + "Searching over:\n", + "omega/Omega_ref \\in [ 1.000E-05, 1.000E-02]\n", + "gamma/Omega_ref \\in [-2.000E-03,-2.000E-06]\n", + "-=-=-=-=-=-=-=-=-=-\n", + " finding minima\n", + " minima found\n", + " 7 possible local minima found:\n", + "ir = 128 ii = 128\n", + " 1.0000E-02 -2.0000E-06\n", + "ir = 16 ii = 125\n", + " 1.2588E-03 -4.8828E-05\n", + "ir = 0 ii = 80\n", + " 1.0000E-05 -7.5125E-04\n", + "ir = 15 ii = 65\n", + " 1.1807E-03 -9.8539E-04\n", + "ir = 22 ii = 26\n", + " 1.7270E-03 -1.5942E-03\n", + "ir = 28 ii = 2\n", + " 2.1953E-03 -1.9688E-03\n", + "ir = 128 ii = 0\n", + " 1.0000E-02 -2.0000E-03\n", + "Refining roots:\n", + "Dispersion Solutions \n", + " 1 1.2362E-03 -4.4355E-05\n", + " Predicting FPC (gyrotropic coords)...\n", + "Input for Root 1: 1.2362E-03 -4.4355E-05\n", + "Dispersion Solutions: \n", + " 1 1.2362E-03 -4.4355E-05\n", + " WARNING! The gyro routine does not support computing moments at this time due to computational demand!\n", + " Assuming data folder already exists...\n", + " Assuming subfolder howes2017 already exists\n", + " Calculating fpc for species 1\n", + " Writing omega/kpar V_a normalization to file...\n", + " Calculating fpc for species 2\n", + " Writing omega/kpar V_a normalization to file...\n", + "-=-=-=-=-=-=-=-=-=-\n", + "Root Search:\n", + "-=-=-=-=-=-=-=-=-=-\n", + "Global Plasma Parameters:\n", + "k_perp rho_ref = 1.30000 \n", + "k_par rho_ref = 0.100000E-02\n", + "Beta_ref,par = 1.00000 \n", + "v_t,ref,par/c = 0.100000E-03\n", + "-=-=-=-=-=-=-=-=-=-\n", + "Parameters for Species : 1\n", + "T_||,ref/T_||s = 1.00000 \n", + "m_ref/m_s = 1.00000 \n", + "T_perp/T_par|s = 1.00000 \n", + "q_ref/q_s = 1.00000 \n", + "n_s/n_ref = 1.00000 \n", + "v_drift s/v_A,ref = 0.00000 \n", + "-=-=-=-=-=-=-=-=-=-\n", + "Parameters for Species : 2\n", + "T_||,ref/T_||s = 1.00000 \n", + "m_ref/m_s = 1836.00 \n", + "T_perp/T_par|s = 1.00000 \n", + "q_ref/q_s = -1.00000 \n", + "n_s/n_ref = 1.00000 \n", + "v_drift s/v_A,ref = 0.00000 \n", + "-=-=-=-=-=-=-=-=-=-\n", + "Searching over:\n", + "omega/Omega_ref \\in [ 1.000E-05, 1.000E-02]\n", + "gamma/Omega_ref \\in [-2.000E-03,-2.000E-06]\n", + "-=-=-=-=-=-=-=-=-=-\n", + " finding minima\n", + " minima found\n", + " 7 possible local minima found:\n", + "ir = 128 ii = 128\n", + " 1.0000E-02 -2.0000E-06\n", + "ir = 16 ii = 125\n", + " 1.2588E-03 -4.8828E-05\n", + "ir = 0 ii = 80\n", + " 1.0000E-05 -7.5125E-04\n", + "ir = 15 ii = 65\n", + " 1.1807E-03 -9.8539E-04\n", + "ir = 22 ii = 26\n", + " 1.7270E-03 -1.5942E-03\n", + "ir = 28 ii = 2\n", + " 2.1953E-03 -1.9688E-03\n", + "ir = 128 ii = 0\n", + " 1.0000E-02 -2.0000E-03\n", + "Refining roots:\n", + "Dispersion Solutions \n", + " 1 1.2362E-03 -4.4355E-05\n", + " Predicting FPC (gyrotropic coords)...\n", + "Input for Root 1: 1.2362E-03 -4.4355E-05\n", + "Dispersion Solutions: \n", + " 1 1.2362E-03 -4.4355E-05\n", + " WARNING! The gyro routine does not support computing moments at this time due to computational demand!\n", + " Assuming data folder already exists...\n", + " Assuming subfolder howes2017 already exists\n", + " Calculating fpc for species 1\n", + " Writing omega/kpar V_a normalization to file...\n", + " Calculating fpc for species 2\n", + " Writing omega/kpar V_a normalization to file...\n", + "-=-=-=-=-=-=-=-=-=-\n", + "Root Search:\n", + "-=-=-=-=-=-=-=-=-=-\n", + "Global Plasma Parameters:\n", + "k_perp rho_ref = 1.30000 \n", + "k_par rho_ref = 0.100000E-02\n", + "Beta_ref,par = 1.00000 \n", + "v_t,ref,par/c = 0.100000E-03\n", + "-=-=-=-=-=-=-=-=-=-\n", + "Parameters for Species : 1\n", + "T_||,ref/T_||s = 1.00000 \n", + "m_ref/m_s = 1.00000 \n", + "T_perp/T_par|s = 1.00000 \n", + "q_ref/q_s = 1.00000 \n", + "n_s/n_ref = 1.00000 \n", + "v_drift s/v_A,ref = 0.00000 \n", + "-=-=-=-=-=-=-=-=-=-\n", + "Parameters for Species : 2\n", + "T_||,ref/T_||s = 1.00000 \n", + "m_ref/m_s = 1836.00 \n", + "T_perp/T_par|s = 1.00000 \n", + "q_ref/q_s = -1.00000 \n", + "n_s/n_ref = 1.00000 \n", + "v_drift s/v_A,ref = 0.00000 \n", + "-=-=-=-=-=-=-=-=-=-\n", + "Searching over:\n", + "omega/Omega_ref \\in [ 1.000E-05, 1.000E-02]\n", + "gamma/Omega_ref \\in [-2.000E-03,-2.000E-06]\n", + "-=-=-=-=-=-=-=-=-=-\n", + " finding minima\n", + " minima found\n", + " 7 possible local minima found:\n", + "ir = 128 ii = 128\n", + " 1.0000E-02 -2.0000E-06\n", + "ir = 16 ii = 125\n", + " 1.2588E-03 -4.8828E-05\n", + "ir = 0 ii = 80\n", + " 1.0000E-05 -7.5125E-04\n", + "ir = 15 ii = 65\n", + " 1.1807E-03 -9.8539E-04\n", + "ir = 22 ii = 26\n", + " 1.7270E-03 -1.5942E-03\n", + "ir = 28 ii = 2\n", + " 2.1953E-03 -1.9688E-03\n", + "ir = 128 ii = 0\n", + " 1.0000E-02 -2.0000E-03\n", + "Refining roots:\n", + "Dispersion Solutions \n", + " 1 1.2362E-03 -4.4355E-05\n", + " Predicting FPC (gyrotropic coords)...\n", + "Input for Root 1: 1.2362E-03 -4.4355E-05\n", + "Dispersion Solutions: \n", + " 1 1.2362E-03 -4.4355E-05\n", + " WARNING! The gyro routine does not support computing moments at this time due to computational demand!\n", + " Assuming data folder already exists...\n", + " Assuming subfolder howes2017 already exists\n", + " Calculating fpc for species 1\n", + " Writing omega/kpar V_a normalization to file...\n", + " Calculating fpc for species 2\n", + " Writing omega/kpar V_a normalization to file...\n", + "-=-=-=-=-=-=-=-=-=-\n", + "Root Search:\n", + "-=-=-=-=-=-=-=-=-=-\n", + "Global Plasma Parameters:\n", + "k_perp rho_ref = 1.30000 \n", + "k_par rho_ref = 0.100000E-02\n", + "Beta_ref,par = 1.00000 \n", + "v_t,ref,par/c = 0.100000E-03\n", + "-=-=-=-=-=-=-=-=-=-\n", + "Parameters for Species : 1\n", + "T_||,ref/T_||s = 1.00000 \n", + "m_ref/m_s = 1.00000 \n", + "T_perp/T_par|s = 1.00000 \n", + "q_ref/q_s = 1.00000 \n", + "n_s/n_ref = 1.00000 \n", + "v_drift s/v_A,ref = 0.00000 \n", + "-=-=-=-=-=-=-=-=-=-\n", + "Parameters for Species : 2\n", + "T_||,ref/T_||s = 1.00000 \n", + "m_ref/m_s = 1836.00 \n", + "T_perp/T_par|s = 1.00000 \n", + "q_ref/q_s = -1.00000 \n", + "n_s/n_ref = 1.00000 \n", + "v_drift s/v_A,ref = 0.00000 \n", + "-=-=-=-=-=-=-=-=-=-\n", + "Searching over:\n", + "omega/Omega_ref \\in [ 1.000E-05, 1.000E-02]\n", + "gamma/Omega_ref \\in [-2.000E-03,-2.000E-06]\n", + "-=-=-=-=-=-=-=-=-=-\n", + " finding minima\n", + " minima found\n", + " 7 possible local minima found:\n", + "ir = 128 ii = 128\n", + " 1.0000E-02 -2.0000E-06\n", + "ir = 16 ii = 125\n", + " 1.2588E-03 -4.8828E-05\n", + "ir = 0 ii = 80\n", + " 1.0000E-05 -7.5125E-04\n", + "ir = 15 ii = 65\n", + " 1.1807E-03 -9.8539E-04\n", + "ir = 22 ii = 26\n", + " 1.7270E-03 -1.5942E-03\n", + "ir = 28 ii = 2\n", + " 2.1953E-03 -1.9688E-03\n", + "ir = 128 ii = 0\n", + " 1.0000E-02 -2.0000E-03\n", + "Refining roots:\n", + "Dispersion Solutions \n", + " 1 1.2362E-03 -4.4355E-05\n", + " 2 3.4468E-14 -7.4376E-04\n", + " 3 1.1473E-03 -9.8322E-04\n", + " 4 1.7166E-03 -1.5983E-03\n", + " 5 2.2089E-03 -1.9704E-03\n", + " 6 2.2089E-03 -1.9704E-03\n", + " 7 1.4252E+01 8.9696E+00\n", + " 8 0.0000E+00 0.0000E+00\n", + " 9 0.0000E+00 0.0000E+00\n", + " 10 0.0000E+00 0.0000E+00\n", + " 11 0.0000E+00 0.0000E+00\n", + " 12 0.0000E+00 0.0000E+00\n", + " 13 0.0000E+00 0.0000E+00\n", + " 14 0.0000E+00 0.0000E+00\n", + " 15 0.0000E+00 0.0000E+00\n", + " 16 0.0000E+00 0.0000E+00\n", + " 17 0.0000E+00 0.0000E+00\n", + " 18 0.0000E+00 0.0000E+00\n", + " 19 0.0000E+00 0.0000E+00\n", + " 20 0.0000E+00 0.0000E+00\n", + "Input for Root 1: 1.2362E-03 -4.4355E-05\n", + "Dispersion Solutions: \n", + " 1 1.2362E-03 -4.4355E-05\n", + "Scan over kperp from 1.3000E+00 to 1.0000E+02\n", + " =>data/howes2017/sweepsweep1_tensor_kperp_1300_100000\n", + "Root 1 In: 1.2362E-03 -4.4355E-05\n", + "Root 1 Out: 3.7512E-02 -3.7403E-02\n", + "Input for Root 1: 1.2362E-03 -4.4355E-05\n", + "Dispersion Solutions: \n", + " 1 1.2362E-03 -4.4355E-05\n", + "Scan over kperp from 1.3000E+00 to 1.0000E-01\n", + " =>data/howes2017/sweepsweep2_tensor_kperp_1300_100\n", + "Root 1 In: 1.2362E-03 -4.4355E-05\n", + "Root 1 Out: 1.0011E-03 -4.6981E-07\n", + "Input for Root 1: 1.2362E-03 -4.4355E-05\n", + "Dispersion Solutions: \n", + " 1 1.2362E-03 -4.4355E-05\n", + "Scan over tauS for species 2 from 1.0000E+00 to 1.0000E+02\n", + " =>data/howes2017/sweepsweep1_tensor_tauS_s2_1000_100000\n", + "Root 1 In: 1.2362E-03 -4.4355E-05\n", + "Root 1 Out: 1.1319E-03 -2.0535E-05\n", + "Input for Root 1: 1.2362E-03 -4.4355E-05\n", + "Dispersion Solutions: \n", + " 1 1.2362E-03 -4.4355E-05\n", + "Scan over tauS for species 2 from 1.0000E+00 to 1.0000E-01\n", + " =>data/howes2017/sweepsweep2_tensor_tauS_s2_1000_100\n", + "Root 1 In: 1.2362E-03 -4.4355E-05\n", + "Root 1 Out: 1.5471E-03 -2.9932E-04\n", + "Input for Root 1: 1.2362E-03 -4.4355E-05\n", + "Dispersion Solutions: \n", + " 1 1.2362E-03 -4.4355E-05\n", + "Scan over kperp from 1.300000E+000 to 1.000000E+001\n", + "Scan over kpar from 1.000000E-003 to 1.000000E+001\n", + "Parameter 1: 1.3000E+00\n", + "Root 1: 1.2362E-03 -4.4355E-05\n", + "Parameter 1: 1.7350E+00\n", + "Root 1: 1.4327E-03 -5.7938E-05\n", + "Parameter 1: 2.1700E+00\n", + "Root 1: 1.6788E-03 -7.3372E-05\n", + "Parameter 1: 2.6050E+00\n", + "Root 1: 1.9538E-03 -9.5239E-05\n", + "Parameter 1: 3.0400E+00\n", + "Root 1: 2.2430E-03 -1.2441E-04\n", + "Parameter 1: 3.4750E+00\n", + "Root 1: 2.5392E-03 -1.6009E-04\n", + "Parameter 1: 3.9100E+00\n", + "Root 1: 2.8393E-03 -2.0140E-04\n", + "Parameter 1: 4.3450E+00\n", + "Root 1: 3.1417E-03 -2.4782E-04\n", + "Parameter 1: 4.7800E+00\n", + "Root 1: 3.4454E-03 -2.9912E-04\n", + "Parameter 1: 5.2150E+00\n", + "Root 1: 3.7499E-03 -3.5526E-04\n", + "Parameter 1: 5.6500E+00\n", + "Root 1: 4.0549E-03 -4.1620E-04\n", + "Parameter 1: 6.0850E+00\n", + "Root 1: 4.3603E-03 -4.8193E-04\n", + "Parameter 1: 6.5200E+00\n", + "Root 1: 4.6659E-03 -5.5243E-04\n", + "Parameter 1: 6.9550E+00\n", + "Root 1: 4.9716E-03 -6.2767E-04\n", + "Parameter 1: 7.3900E+00\n", + "Root 1: 5.2773E-03 -7.0764E-04\n", + "Parameter 1: 7.8250E+00\n", + "Root 1: 5.5831E-03 -7.9231E-04\n", + "Parameter 1: 8.2600E+00\n", + "Root 1: 5.8889E-03 -8.8166E-04\n", + "Parameter 1: 8.6950E+00\n", + "Root 1: 6.1946E-03 -9.7565E-04\n", + "Parameter 1: 9.1300E+00\n", + "Root 1: 6.5003E-03 -1.0742E-03\n", + "Parameter 1: 9.5650E+00\n", + "Root 1: 6.8059E-03 -1.1774E-03\n", + "Parameter 1: 1.0000E+01\n", + "Root 1: 7.1114E-03 -1.2852E-03\n", + "Input for Root 1: 1.2362E-03 -4.4355E-05\n", + "Dispersion Solutions: \n", + " 1 1.2362E-03 -4.4355E-05\n", + "Scan over kperp from 1.300000E+000 to 1.000000E+000\n", + "Scan over kpar from 1.000000E-003 to 1.000000E+001\n", + "Parameter 1: 1.3000E+00\n", + "Root 1: 1.2362E-03 -4.4355E-05\n", + "Parameter 1: 1.2850E+00\n", + "Root 1: 1.2305E-03 -4.3836E-05\n", + "Parameter 1: 1.2700E+00\n", + "Root 1: 1.2249E-03 -4.3311E-05\n", + "Parameter 1: 1.2550E+00\n", + "Root 1: 1.2193E-03 -4.2780E-05\n", + "Parameter 1: 1.2400E+00\n", + "Root 1: 1.2139E-03 -4.2243E-05\n", + "Parameter 1: 1.2250E+00\n", + "Root 1: 1.2085E-03 -4.1700E-05\n", + "Parameter 1: 1.2100E+00\n", + "Root 1: 1.2032E-03 -4.1150E-05\n", + "Parameter 1: 1.1950E+00\n", + "Root 1: 1.1979E-03 -4.0593E-05\n", + "Parameter 1: 1.1800E+00\n", + "Root 1: 1.1928E-03 -4.0030E-05\n", + "Parameter 1: 1.1650E+00\n", + "Root 1: 1.1877E-03 -3.9460E-05\n", + "Parameter 1: 1.1500E+00\n", + "Root 1: 1.1827E-03 -3.8883E-05\n", + "Parameter 1: 1.1350E+00\n", + "Root 1: 1.1777E-03 -3.8300E-05\n", + "Parameter 1: 1.1200E+00\n", + "Root 1: 1.1729E-03 -3.7710E-05\n", + "Parameter 1: 1.1050E+00\n", + "Root 1: 1.1681E-03 -3.7114E-05\n", + "Parameter 1: 1.0900E+00\n", + "Root 1: 1.1634E-03 -3.6511E-05\n", + "Parameter 1: 1.0750E+00\n", + "Root 1: 1.1587E-03 -3.5902E-05\n", + "Parameter 1: 1.0600E+00\n", + "Root 1: 1.1541E-03 -3.5286E-05\n", + "Parameter 1: 1.0450E+00\n", + "Root 1: 1.1497E-03 -3.4664E-05\n", + "Parameter 1: 1.0300E+00\n", + "Root 1: 1.1452E-03 -3.4036E-05\n", + "Parameter 1: 1.0150E+00\n", + "Root 1: 1.1409E-03 -3.3402E-05\n", + "Parameter 1: 1.0000E+00\n", + "Root 1: 1.1366E-03 -3.2762E-05\n", + "Input for Root 1: 1.2362E-03 -4.4355E-05\n", + "Dispersion Solutions: \n", + " 1 1.2362E-03 -4.4355E-05\n", + "Scan over kperp from 1.300000E+000 to 1.000000E+001\n", + "Scan over kpar from 1.000000E-003 to 1.000000E+000\n", + "Parameter 1: 1.3000E+00\n", + "Root 1: 1.2362E-03 -4.4355E-05\n", + "Parameter 1: 1.7350E+00\n", + "Root 1: 1.4327E-03 -5.7938E-05\n", + "Parameter 1: 2.1700E+00\n", + "Root 1: 1.6788E-03 -7.3372E-05\n", + "Parameter 1: 2.6050E+00\n", + "Root 1: 1.9538E-03 -9.5239E-05\n", + "Parameter 1: 3.0400E+00\n", + "Root 1: 2.2430E-03 -1.2441E-04\n", + "Parameter 1: 3.4750E+00\n", + "Root 1: 2.5392E-03 -1.6009E-04\n", + "Parameter 1: 3.9100E+00\n", + "Root 1: 2.8393E-03 -2.0140E-04\n", + "Parameter 1: 4.3450E+00\n", + "Root 1: 3.1417E-03 -2.4782E-04\n", + "Parameter 1: 4.7800E+00\n", + "Root 1: 3.4454E-03 -2.9912E-04\n", + "Parameter 1: 5.2150E+00\n", + "Root 1: 3.7499E-03 -3.5526E-04\n", + "Parameter 1: 5.6500E+00\n", + "Root 1: 4.0549E-03 -4.1620E-04\n", + "Parameter 1: 6.0850E+00\n", + "Root 1: 4.3603E-03 -4.8193E-04\n", + "Parameter 1: 6.5200E+00\n", + "Root 1: 4.6659E-03 -5.5243E-04\n", + "Parameter 1: 6.9550E+00\n", + "Root 1: 4.9716E-03 -6.2767E-04\n", + "Parameter 1: 7.3900E+00\n", + "Root 1: 5.2773E-03 -7.0764E-04\n", + "Parameter 1: 7.8250E+00\n", + "Root 1: 5.5831E-03 -7.9231E-04\n", + "Parameter 1: 8.2600E+00\n", + "Root 1: 5.8889E-03 -8.8166E-04\n", + "Parameter 1: 8.6950E+00\n", + "Root 1: 6.1946E-03 -9.7565E-04\n", + "Parameter 1: 9.1300E+00\n", + "Root 1: 6.5003E-03 -1.0742E-03\n", + "Parameter 1: 9.5650E+00\n", + "Root 1: 6.8059E-03 -1.1774E-03\n", + "Parameter 1: 1.0000E+01\n", + "Root 1: 7.1114E-03 -1.2852E-03\n", + "Input for Root 1: 1.2362E-03 -4.4355E-05\n", + "Dispersion Solutions: \n", + " 1 1.2362E-03 -4.4355E-05\n", + "Scan over kperp from 1.300000E+000 to 1.000000E+000\n", + "Scan over kpar from 1.000000E-003 to 1.000000E+000\n", + "Parameter 1: 1.3000E+00\n", + "Root 1: 1.2362E-03 -4.4355E-05\n", + "Parameter 1: 1.2850E+00\n", + "Root 1: 1.2305E-03 -4.3836E-05\n", + "Parameter 1: 1.2700E+00\n", + "Root 1: 1.2249E-03 -4.3311E-05\n", + "Parameter 1: 1.2550E+00\n", + "Root 1: 1.2193E-03 -4.2780E-05\n", + "Parameter 1: 1.2400E+00\n", + "Root 1: 1.2139E-03 -4.2243E-05\n", + "Parameter 1: 1.2250E+00\n", + "Root 1: 1.2085E-03 -4.1700E-05\n", + "Parameter 1: 1.2100E+00\n", + "Root 1: 1.2032E-03 -4.1150E-05\n", + "Parameter 1: 1.1950E+00\n", + "Root 1: 1.1979E-03 -4.0593E-05\n", + "Parameter 1: 1.1800E+00\n", + "Root 1: 1.1928E-03 -4.0030E-05\n", + "Parameter 1: 1.1650E+00\n", + "Root 1: 1.1877E-03 -3.9460E-05\n", + "Parameter 1: 1.1500E+00\n", + "Root 1: 1.1827E-03 -3.8883E-05\n", + "Parameter 1: 1.1350E+00\n", + "Root 1: 1.1777E-03 -3.8300E-05\n", + "Parameter 1: 1.1200E+00\n", + "Root 1: 1.1729E-03 -3.7710E-05\n", + "Parameter 1: 1.1050E+00\n", + "Root 1: 1.1681E-03 -3.7114E-05\n", + "Parameter 1: 1.0900E+00\n", + "Root 1: 1.1634E-03 -3.6511E-05\n", + "Parameter 1: 1.0750E+00\n", + "Root 1: 1.1587E-03 -3.5902E-05\n", + "Parameter 1: 1.0600E+00\n", + "Root 1: 1.1541E-03 -3.5286E-05\n", + "Parameter 1: 1.0450E+00\n", + "Root 1: 1.1497E-03 -3.4664E-05\n", + "Parameter 1: 1.0300E+00\n", + "Root 1: 1.1452E-03 -3.4036E-05\n", + "Parameter 1: 1.0150E+00\n", + "Root 1: 1.1409E-03 -3.3402E-05\n", + "Parameter 1: 1.0000E+00\n", + "Root 1: 1.1366E-03 -3.2762E-05\n", + "Input for Root 1: 1.2362E-03 -4.4355E-05\n", + "Dispersion Solutions: \n", + " 1 1.2362E-03 -4.4355E-05\n", + "Scan over betap from 1.000000E+000 to 5.000000E+000\n", + "Scan over alphS for species 1 from 1.000000E+000 to 5.000000E+000\n", + "Parameter 1: 1.0000E+00\n", + "Root 1: 1.2362E-03 -4.4355E-05\n", + "Parameter 1: 1.2000E+00\n", + "Root 1: 1.0965E-03 -4.5613E-05\n", + "Parameter 1: 1.4000E+00\n", + "Root 1: 9.9005E-04 -4.7867E-05\n", + "Parameter 1: 1.6000E+00\n", + "Root 1: 9.0557E-04 -5.0722E-05\n", + "Parameter 1: 1.8000E+00\n", + "Root 1: 8.3649E-04 -5.3871E-05\n", + "Parameter 1: 2.0000E+00\n", + "Root 1: 7.7864E-04 -5.7109E-05\n", + "Parameter 1: 2.2000E+00\n", + "Root 1: 7.2929E-04 -6.0311E-05\n", + "Parameter 1: 2.4000E+00\n", + "Root 1: 6.8652E-04 -6.3404E-05\n", + "Parameter 1: 2.6000E+00\n", + "Root 1: 6.4900E-04 -6.6348E-05\n", + "Parameter 1: 2.8000E+00\n", + "Root 1: 6.1571E-04 -6.9123E-05\n", + "Parameter 1: 3.0000E+00\n", + "Root 1: 5.8591E-04 -7.1721E-05\n", + "Parameter 1: 3.2000E+00\n", + "Root 1: 5.5902E-04 -7.4142E-05\n", + "Parameter 1: 3.4000E+00\n", + "Root 1: 5.3459E-04 -7.6389E-05\n", + "Parameter 1: 3.6000E+00\n", + "Root 1: 5.1225E-04 -7.8468E-05\n", + "Parameter 1: 3.8000E+00\n", + "Root 1: 4.9172E-04 -8.0387E-05\n", + "Parameter 1: 4.0000E+00\n", + "Root 1: 4.7275E-04 -8.2152E-05\n", + "Parameter 1: 4.2000E+00\n", + "Root 1: 4.5515E-04 -8.3772E-05\n", + "Parameter 1: 4.4000E+00\n", + "Root 1: 4.3876E-04 -8.5254E-05\n", + "Parameter 1: 4.6000E+00\n", + "Root 1: 4.2344E-04 -8.6605E-05\n", + "Parameter 1: 4.8000E+00\n", + "Root 1: 4.0907E-04 -8.7832E-05\n", + "Parameter 1: 5.0000E+00\n", + "Root 1: 3.9556E-04 -8.8940E-05\n", + "Input for Root 1: 1.2362E-03 -4.4355E-05\n", + "Dispersion Solutions: \n", + " 1 1.2362E-03 -4.4355E-05\n", + "Scan over betap from 1.000000E+000 to 5.000000E-001\n", + "Scan over alphS for species 1 from 1.000000E+000 to 5.000000E+000\n", + "Parameter 1: 1.0000E+00\n", + "Root 1: 1.2362E-03 -4.4355E-05\n", + "Parameter 1: 9.7500E-01\n", + "Root 1: 1.2568E-03 -4.4287E-05\n", + "Parameter 1: 9.5000E-01\n", + "Root 1: 1.2784E-03 -4.4241E-05\n", + "Parameter 1: 9.2500E-01\n", + "Root 1: 1.3008E-03 -4.4216E-05\n", + "Parameter 1: 9.0000E-01\n", + "Root 1: 1.3243E-03 -4.4214E-05\n", + "Parameter 1: 8.7500E-01\n", + "Root 1: 1.3488E-03 -4.4234E-05\n", + "Parameter 1: 8.5000E-01\n", + "Root 1: 1.3744E-03 -4.4277E-05\n", + "Parameter 1: 8.2500E-01\n", + "Root 1: 1.4013E-03 -4.4343E-05\n", + "Parameter 1: 8.0000E-01\n", + "Root 1: 1.4295E-03 -4.4433E-05\n", + "Parameter 1: 7.7500E-01\n", + "Root 1: 1.4591E-03 -4.4547E-05\n", + "Parameter 1: 7.5000E-01\n", + "Root 1: 1.4902E-03 -4.4686E-05\n", + "Parameter 1: 7.2500E-01\n", + "Root 1: 1.5230E-03 -4.4852E-05\n", + "Parameter 1: 7.0000E-01\n", + "Root 1: 1.5576E-03 -4.5048E-05\n", + "Parameter 1: 6.7500E-01\n", + "Root 1: 1.5942E-03 -4.5277E-05\n", + "Parameter 1: 6.5000E-01\n", + "Root 1: 1.6330E-03 -4.5545E-05\n", + "Parameter 1: 6.2500E-01\n", + "Root 1: 1.6741E-03 -4.5859E-05\n", + "Parameter 1: 6.0000E-01\n", + "Root 1: 1.7177E-03 -4.6229E-05\n", + "Parameter 1: 5.7500E-01\n", + "Root 1: 1.7643E-03 -4.6668E-05\n", + "Parameter 1: 5.5000E-01\n", + "Root 1: 1.8140E-03 -4.7194E-05\n", + "Parameter 1: 5.2500E-01\n", + "Root 1: 1.8673E-03 -4.7826E-05\n", + "Parameter 1: 5.0000E-01\n", + "Root 1: 1.9245E-03 -4.8593E-05\n", + "Input for Root 1: 1.2362E-03 -4.4355E-05\n", + "Dispersion Solutions: \n", + " 1 1.2362E-03 -4.4355E-05\n", + "Scan over betap from 1.000000E+000 to 5.000000E+000\n", + "Scan over alphS for species 1 from 1.000000E+000 to 5.000000E-001\n", + "Parameter 1: 1.0000E+00\n", + "Root 1: 1.2362E-03 -4.4355E-05\n", + "Parameter 1: 1.2000E+00\n", + "Root 1: 1.0965E-03 -4.5613E-05\n", + "Parameter 1: 1.4000E+00\n", + "Root 1: 9.9005E-04 -4.7867E-05\n", + "Parameter 1: 1.6000E+00\n", + "Root 1: 9.0557E-04 -5.0722E-05\n", + "Parameter 1: 1.8000E+00\n", + "Root 1: 8.3649E-04 -5.3871E-05\n", + "Parameter 1: 2.0000E+00\n", + "Root 1: 7.7864E-04 -5.7109E-05\n", + "Parameter 1: 2.2000E+00\n", + "Root 1: 7.2929E-04 -6.0311E-05\n", + "Parameter 1: 2.4000E+00\n", + "Root 1: 6.8652E-04 -6.3404E-05\n", + "Parameter 1: 2.6000E+00\n", + "Root 1: 6.4900E-04 -6.6348E-05\n", + "Parameter 1: 2.8000E+00\n", + "Root 1: 6.1571E-04 -6.9123E-05\n", + "Parameter 1: 3.0000E+00\n", + "Root 1: 5.8591E-04 -7.1721E-05\n", + "Parameter 1: 3.2000E+00\n", + "Root 1: 5.5902E-04 -7.4142E-05\n", + "Parameter 1: 3.4000E+00\n", + "Root 1: 5.3459E-04 -7.6389E-05\n", + "Parameter 1: 3.6000E+00\n", + "Root 1: 5.1225E-04 -7.8468E-05\n", + "Parameter 1: 3.8000E+00\n", + "Root 1: 4.9172E-04 -8.0387E-05\n", + "Parameter 1: 4.0000E+00\n", + "Root 1: 4.7275E-04 -8.2152E-05\n", + "Parameter 1: 4.2000E+00\n", + "Root 1: 4.5515E-04 -8.3772E-05\n", + "Parameter 1: 4.4000E+00\n", + "Root 1: 4.3876E-04 -8.5254E-05\n", + "Parameter 1: 4.6000E+00\n", + "Root 1: 4.2344E-04 -8.6605E-05\n", + "Parameter 1: 4.8000E+00\n", + "Root 1: 4.0907E-04 -8.7832E-05\n", + "Parameter 1: 5.0000E+00\n", + "Root 1: 3.9556E-04 -8.8940E-05\n", + "Input for Root 1: 1.2362E-03 -4.4355E-05\n", + "Dispersion Solutions: \n", + " 1 1.2362E-03 -4.4355E-05\n", + "Scan over betap from 1.000000E+000 to 5.000000E-001\n", + "Scan over alphS for species 1 from 1.000000E+000 to 5.000000E-001\n", + "Parameter 1: 1.0000E+00\n", + "Root 1: 1.2362E-03 -4.4355E-05\n", + "Parameter 1: 9.7500E-01\n", + "Root 1: 1.2568E-03 -4.4287E-05\n", + "Parameter 1: 9.5000E-01\n", + "Root 1: 1.2784E-03 -4.4241E-05\n", + "Parameter 1: 9.2500E-01\n", + "Root 1: 1.3008E-03 -4.4216E-05\n", + "Parameter 1: 9.0000E-01\n", + "Root 1: 1.3243E-03 -4.4214E-05\n", + "Parameter 1: 8.7500E-01\n", + "Root 1: 1.3488E-03 -4.4234E-05\n", + "Parameter 1: 8.5000E-01\n", + "Root 1: 1.3744E-03 -4.4277E-05\n", + "Parameter 1: 8.2500E-01\n", + "Root 1: 1.4013E-03 -4.4343E-05\n", + "Parameter 1: 8.0000E-01\n", + "Root 1: 1.4295E-03 -4.4433E-05\n", + "Parameter 1: 7.7500E-01\n", + "Root 1: 1.4591E-03 -4.4547E-05\n", + "Parameter 1: 7.5000E-01\n", + "Root 1: 1.4902E-03 -4.4686E-05\n", + "Parameter 1: 7.2500E-01\n", + "Root 1: 1.5230E-03 -4.4852E-05\n", + "Parameter 1: 7.0000E-01\n", + "Root 1: 1.5576E-03 -4.5048E-05\n", + "Parameter 1: 6.7500E-01\n", + "Root 1: 1.5942E-03 -4.5277E-05\n", + "Parameter 1: 6.5000E-01\n", + "Root 1: 1.6330E-03 -4.5545E-05\n", + "Parameter 1: 6.2500E-01\n", + "Root 1: 1.6741E-03 -4.5859E-05\n", + "Parameter 1: 6.0000E-01\n", + "Root 1: 1.7177E-03 -4.6229E-05\n", + "Parameter 1: 5.7500E-01\n", + "Root 1: 1.7643E-03 -4.6668E-05\n", + "Parameter 1: 5.5000E-01\n", + "Root 1: 1.8140E-03 -4.7194E-05\n", + "Parameter 1: 5.2500E-01\n", + "Root 1: 1.8673E-03 -4.7826E-05\n", + "Parameter 1: 5.0000E-01\n", + "Root 1: 1.9245E-03 -4.8593E-05\n", + " Predicting FPC (gyrotropic coords)...\n", + "Input for Root 1: 1.2362E-03 -4.4355E-05\n", + "Dispersion Solutions: \n", + " 1 1.2362E-03 -4.4355E-05\n", + " WARNING! The gyro routine does not support computing moments at this time due to computational demand!\n", + " Assuming data folder already exists...\n", + " Assuming subfolder howes2017 already exists\n", + " Calculating fpc for species 1\n", + " Writing omega/kpar V_a normalization to file...\n", + " Calculating fpc for species 2\n", + " Writing omega/kpar V_a normalization to file...\n", + " Predicting FPC (gyrotropic coords)...\n", + "Input for Root 1: 1.2362E-03 -4.4355E-05\n", + "Dispersion Solutions: \n", + " 1 1.2362E-03 -4.4355E-05\n", + " WARNING! The gyro routine does not support computing moments at this time due to computational demand!\n", + " Assuming data folder already exists...\n", + " Assuming subfolder howes2017 already exists\n", + " Calculating fpc for species 1\n", + " Writing omega/kpar V_a normalization to file...\n", + " Calculating fpc for species 2\n", + " Writing omega/kpar V_a normalization to file...\n", + " Predicting FPC (gyrotropic coords)...\n", + "Input for Root 1: 1.2362E-03 -4.4355E-05\n", + "Dispersion Solutions: \n", + " 1 1.2362E-03 -4.4355E-05\n", + " WARNING! The gyro routine does not support computing moments at this time due to computational demand!\n", + " Assuming data folder already exists...\n", + " Assuming subfolder howes2017 already exists\n", + " Calculating fpc for species 1\n", + " Writing omega/kpar V_a normalization to file...\n", + " Calculating fpc for species 2\n", + " Writing omega/kpar V_a normalization to file...\n", + " Predicting FPC (cartesian coords)...\n", + "Input for Root 1: 1.2362E-03 -4.4355E-05\n", + " Assuming data folder already exists...\n", + " assuming subfolder howes2017 already exists\n", + " Writing fpc for species 1\n", + " Writing omega/kpar V_a normalization to file...\n", + " Writing fpc for species 2\n", + " Writing omega/kpar V_a normalization to file...\n", + "-=-=-=-=-=-=-=-=-=-\n", + "Root Search:\n", + "-=-=-=-=-=-=-=-=-=-\n", + "Global Plasma Parameters:\n", + "k_perp rho_ref = 1.30000 \n", + "k_par rho_ref = 0.100000E-02\n", + "Beta_ref,par = 1.00000 \n", + "v_t,ref,par/c = 0.100000E-03\n", + "-=-=-=-=-=-=-=-=-=-\n", + "Parameters for Species : 1\n", + "T_||,ref/T_||s = 1.00000 \n", + "m_ref/m_s = 1.00000 \n", + "T_perp/T_par|s = 1.00000 \n", + "q_ref/q_s = 1.00000 \n", + "n_s/n_ref = 1.00000 \n", + "v_drift s/v_A,ref = 0.00000 \n", + "-=-=-=-=-=-=-=-=-=-\n", + "Parameters for Species : 2\n", + "T_||,ref/T_||s = 1.00000 \n", + "m_ref/m_s = 1836.00 \n", + "T_perp/T_par|s = 1.00000 \n", + "q_ref/q_s = -1.00000 \n", + "n_s/n_ref = 1.00000 \n", + "v_drift s/v_A,ref = 0.00000 \n", + "-=-=-=-=-=-=-=-=-=-\n", + "Searching over:\n", + "omega/Omega_ref \\in [ 1.000E-05, 1.000E-02]\n", + "gamma/Omega_ref \\in [-2.000E-03,-2.000E-06]\n", + "-=-=-=-=-=-=-=-=-=-\n", + " finding minima\n", + " minima found\n", + " 7 possible local minima found:\n", + "ir = 128 ii = 128\n", + " 1.0000E-02 -2.0000E-06\n", + "ir = 16 ii = 125\n", + " 1.2588E-03 -4.8828E-05\n", + "ir = 0 ii = 80\n", + " 1.0000E-05 -7.5125E-04\n", + "ir = 15 ii = 65\n", + " 1.1807E-03 -9.8539E-04\n", + "ir = 22 ii = 26\n", + " 1.7270E-03 -1.5942E-03\n", + "ir = 28 ii = 2\n", + " 2.1953E-03 -1.9688E-03\n", + "ir = 128 ii = 0\n", + " 1.0000E-02 -2.0000E-03\n", + "Refining roots:\n", + "Dispersion Solutions \n", + " 1 1.2362E-03 -4.4355E-05\n", + " Predicting FPC (gyrotropic coords)...\n", + "Input for Root 1: 1.2362E-03 -4.4355E-05\n", + "Dispersion Solutions: \n", + " 1 1.2362E-03 -4.4355E-05\n", + " WARNING! The gyro routine does not support computing moments at this time due to computational demand!\n", + " Assuming data folder already exists...\n", + " Assuming subfolder howes2017 already exists\n", + " Calculating fpc for species 1\n", + " Writing omega/kpar V_a normalization to file...\n", + " Calculating fpc for species 2\n", + " Writing omega/kpar V_a normalization to file...\n", + "-=-=-=-=-=-=-=-=-=-\n", + "Root Search:\n", + "-=-=-=-=-=-=-=-=-=-\n", + "Global Plasma Parameters:\n", + "k_perp rho_ref = 1.30000 \n", + "k_par rho_ref = 0.100000E-02\n", + "Beta_ref,par = 1.00000 \n", + "v_t,ref,par/c = 0.100000E-03\n", + "-=-=-=-=-=-=-=-=-=-\n", + "Parameters for Species : 1\n", + "T_||,ref/T_||s = 1.00000 \n", + "m_ref/m_s = 1.00000 \n", + "T_perp/T_par|s = 1.00000 \n", + "q_ref/q_s = 1.00000 \n", + "n_s/n_ref = 1.00000 \n", + "v_drift s/v_A,ref = 0.00000 \n", + "-=-=-=-=-=-=-=-=-=-\n", + "Parameters for Species : 2\n", + "T_||,ref/T_||s = 1.00000 \n", + "m_ref/m_s = 1836.00 \n", + "T_perp/T_par|s = 1.00000 \n", + "q_ref/q_s = -1.00000 \n", + "n_s/n_ref = 1.00000 \n", + "v_drift s/v_A,ref = 0.00000 \n", + "-=-=-=-=-=-=-=-=-=-\n", + "Searching over:\n", + "omega/Omega_ref \\in [ 1.000E-05, 1.000E-02]\n", + "gamma/Omega_ref \\in [-2.000E-03,-2.000E-06]\n", + "-=-=-=-=-=-=-=-=-=-\n", + " finding minima\n", + " minima found\n", + " 7 possible local minima found:\n", + "ir = 128 ii = 128\n", + " 1.0000E-02 -2.0000E-06\n", + "ir = 16 ii = 125\n", + " 1.2588E-03 -4.8828E-05\n", + "ir = 0 ii = 80\n", + " 1.0000E-05 -7.5125E-04\n", + "ir = 15 ii = 65\n", + " 1.1807E-03 -9.8539E-04\n", + "ir = 22 ii = 26\n", + " 1.7270E-03 -1.5942E-03\n", + "ir = 28 ii = 2\n", + " 2.1953E-03 -1.9688E-03\n", + "ir = 128 ii = 0\n", + " 1.0000E-02 -2.0000E-03\n", + "Refining roots:\n", + "Dispersion Solutions \n", + " 1 1.2362E-03 -4.4355E-05\n", + " Predicting FPC (gyrotropic coords)...\n", + "Input for Root 1: 1.2362E-03 -4.4355E-05\n", + "Dispersion Solutions: \n", + " 1 1.2362E-03 -4.4355E-05\n", + " WARNING! The gyro routine does not support computing moments at this time due to computational demand!\n", + " Assuming data folder already exists...\n", + " Assuming subfolder howes2017 already exists\n", + " Calculating fpc for species 1\n", + " Writing omega/kpar V_a normalization to file...\n", + " Calculating fpc for species 2\n", + " Writing omega/kpar V_a normalization to file...\n", + "-=-=-=-=-=-=-=-=-=-\n", + "Root Search:\n", + "-=-=-=-=-=-=-=-=-=-\n", + "Global Plasma Parameters:\n", + "k_perp rho_ref = 1.30000 \n", + "k_par rho_ref = 0.100000E-02\n", + "Beta_ref,par = 1.00000 \n", + "v_t,ref,par/c = 0.100000E-03\n", + "-=-=-=-=-=-=-=-=-=-\n", + "Parameters for Species : 1\n", + "T_||,ref/T_||s = 1.00000 \n", + "m_ref/m_s = 1.00000 \n", + "T_perp/T_par|s = 1.00000 \n", + "q_ref/q_s = 1.00000 \n", + "n_s/n_ref = 1.00000 \n", + "v_drift s/v_A,ref = 0.00000 \n", + "-=-=-=-=-=-=-=-=-=-\n", + "Parameters for Species : 2\n", + "T_||,ref/T_||s = 1.00000 \n", + "m_ref/m_s = 1836.00 \n", + "T_perp/T_par|s = 1.00000 \n", + "q_ref/q_s = -1.00000 \n", + "n_s/n_ref = 1.00000 \n", + "v_drift s/v_A,ref = 0.00000 \n", + "-=-=-=-=-=-=-=-=-=-\n", + "Searching over:\n", + "omega/Omega_ref \\in [ 1.000E-05, 1.000E-02]\n", + "gamma/Omega_ref \\in [-2.000E-03,-2.000E-06]\n", + "-=-=-=-=-=-=-=-=-=-\n", + " finding minima\n", + " minima found\n", + " 7 possible local minima found:\n", + "ir = 128 ii = 128\n", + " 1.0000E-02 -2.0000E-06\n", + "ir = 16 ii = 125\n", + " 1.2588E-03 -4.8828E-05\n", + "ir = 0 ii = 80\n", + " 1.0000E-05 -7.5125E-04\n", + "ir = 15 ii = 65\n", + " 1.1807E-03 -9.8539E-04\n", + "ir = 22 ii = 26\n", + " 1.7270E-03 -1.5942E-03\n", + "ir = 28 ii = 2\n", + " 2.1953E-03 -1.9688E-03\n", + "ir = 128 ii = 0\n", + " 1.0000E-02 -2.0000E-03\n", + "Refining roots:\n", + "Dispersion Solutions \n", + " 1 1.2362E-03 -4.4355E-05\n", + " Predicting FPC (gyrotropic coords)...\n", + "Input for Root 1: 1.2362E-03 -4.4355E-05\n", + "Dispersion Solutions: \n", + " 1 1.2362E-03 -4.4355E-05\n", + " WARNING! The gyro routine does not support computing moments at this time due to computational demand!\n", + " Assuming data folder already exists...\n", + " Assuming subfolder howes2017 already exists\n", + " Calculating fpc for species 1\n", + " Writing omega/kpar V_a normalization to file...\n", + " Calculating fpc for species 2\n", + " Writing omega/kpar V_a normalization to file...\n", + "-=-=-=-=-=-=-=-=-=-\n", + "Root Search:\n", + "-=-=-=-=-=-=-=-=-=-\n", + "Global Plasma Parameters:\n", + "k_perp rho_ref = 1.30000 \n", + "k_par rho_ref = 0.100000E-02\n", + "Beta_ref,par = 1.00000 \n", + "v_t,ref,par/c = 0.100000E-03\n", + "-=-=-=-=-=-=-=-=-=-\n", + "Parameters for Species : 1\n", + "T_||,ref/T_||s = 1.00000 \n", + "m_ref/m_s = 1.00000 \n", + "T_perp/T_par|s = 1.00000 \n", + "q_ref/q_s = 1.00000 \n", + "n_s/n_ref = 1.00000 \n", + "v_drift s/v_A,ref = 0.00000 \n", + "-=-=-=-=-=-=-=-=-=-\n", + "Parameters for Species : 2\n", + "T_||,ref/T_||s = 1.00000 \n", + "m_ref/m_s = 1836.00 \n", + "T_perp/T_par|s = 1.00000 \n", + "q_ref/q_s = -1.00000 \n", + "n_s/n_ref = 1.00000 \n", + "v_drift s/v_A,ref = 0.00000 \n", + "-=-=-=-=-=-=-=-=-=-\n", + "Searching over:\n", + "omega/Omega_ref \\in [ 1.000E-05, 1.000E-02]\n", + "gamma/Omega_ref \\in [-2.000E-03,-2.000E-06]\n", + "-=-=-=-=-=-=-=-=-=-\n", + " finding minima\n", + " minima found\n", + " 7 possible local minima found:\n", + "ir = 128 ii = 128\n", + " 1.0000E-02 -2.0000E-06\n", + "ir = 16 ii = 125\n", + " 1.2588E-03 -4.8828E-05\n", + "ir = 0 ii = 80\n", + " 1.0000E-05 -7.5125E-04\n", + "ir = 15 ii = 65\n", + " 1.1807E-03 -9.8539E-04\n", + "ir = 22 ii = 26\n", + " 1.7270E-03 -1.5942E-03\n", + "ir = 28 ii = 2\n", + " 2.1953E-03 -1.9688E-03\n", + "ir = 128 ii = 0\n", + " 1.0000E-02 -2.0000E-03\n", + "Refining roots:\n", + "Dispersion Solutions \n", + " 1 1.2362E-03 -4.4355E-05\n", + " Predicting FPC (gyrotropic coords)...\n", + "Input for Root 1: 1.2362E-03 -4.4355E-05\n", + "Dispersion Solutions: \n", + " 1 1.2362E-03 -4.4355E-05\n", + " WARNING! The gyro routine does not support computing moments at this time due to computational demand!\n", + " Assuming data folder already exists...\n", + " Assuming subfolder howes2017 already exists\n", + " Calculating fpc for species 1\n", + " Writing omega/kpar V_a normalization to file...\n", + " Calculating fpc for species 2\n", + " Writing omega/kpar V_a normalization to file...\n", + "-=-=-=-=-=-=-=-=-=-\n", + "Root Search:\n", + "-=-=-=-=-=-=-=-=-=-\n", + "Global Plasma Parameters:\n", + "k_perp rho_ref = 1.30000 \n", + "k_par rho_ref = 0.100000E-02\n", + "Beta_ref,par = 1.00000 \n", + "v_t,ref,par/c = 0.100000E-03\n", + "-=-=-=-=-=-=-=-=-=-\n", + "Parameters for Species : 1\n", + "T_||,ref/T_||s = 1.00000 \n", + "m_ref/m_s = 1.00000 \n", + "T_perp/T_par|s = 1.00000 \n", + "q_ref/q_s = 1.00000 \n", + "n_s/n_ref = 1.00000 \n", + "v_drift s/v_A,ref = 0.00000 \n", + "-=-=-=-=-=-=-=-=-=-\n", + "Parameters for Species : 2\n", + "T_||,ref/T_||s = 1.00000 \n", + "m_ref/m_s = 1836.00 \n", + "T_perp/T_par|s = 1.00000 \n", + "q_ref/q_s = -1.00000 \n", + "n_s/n_ref = 1.00000 \n", + "v_drift s/v_A,ref = 0.00000 \n", + "-=-=-=-=-=-=-=-=-=-\n", + "Searching over:\n", + "omega/Omega_ref \\in [ 1.000E-05, 1.000E-02]\n", + "gamma/Omega_ref \\in [-2.000E-03,-2.000E-06]\n", + "-=-=-=-=-=-=-=-=-=-\n", + " finding minima\n", + " minima found\n", + " 7 possible local minima found:\n", + "ir = 128 ii = 128\n", + " 1.0000E-02 -2.0000E-06\n", + "ir = 16 ii = 125\n", + " 1.2588E-03 -4.8828E-05\n", + "ir = 0 ii = 80\n", + " 1.0000E-05 -7.5125E-04\n", + "ir = 15 ii = 65\n", + " 1.1807E-03 -9.8539E-04\n", + "ir = 22 ii = 26\n", + " 1.7270E-03 -1.5942E-03\n", + "ir = 28 ii = 2\n", + " 2.1953E-03 -1.9688E-03\n", + "ir = 128 ii = 0\n", + " 1.0000E-02 -2.0000E-03\n", + "Refining roots:\n", + "Dispersion Solutions \n", + " 1 1.2362E-03 -4.4355E-05\n", + " Predicting FPC (gyrotropic coords)...\n", + "Input for Root 1: 1.2362E-03 -4.4355E-05\n", + "Dispersion Solutions: \n", + " 1 1.2362E-03 -4.4355E-05\n", + " WARNING! The gyro routine does not support computing moments at this time due to computational demand!\n", + " Assuming data folder already exists...\n", + " Assuming subfolder howes2017 already exists\n", + " Calculating fpc for species 1\n", + " Writing omega/kpar V_a normalization to file...\n", + " Calculating fpc for species 2\n", + " Writing omega/kpar V_a normalization to file...\n", + "-=-=-=-=-=-=-=-=-=-\n", + "Root Search:\n", + "-=-=-=-=-=-=-=-=-=-\n", + "Global Plasma Parameters:\n", + "k_perp rho_ref = 1.30000 \n", + "k_par rho_ref = 0.100000E-02\n", + "Beta_ref,par = 1.00000 \n", + "v_t,ref,par/c = 0.100000E-03\n", + "-=-=-=-=-=-=-=-=-=-\n", + "Parameters for Species : 1\n", + "T_||,ref/T_||s = 1.00000 \n", + "m_ref/m_s = 1.00000 \n", + "T_perp/T_par|s = 1.00000 \n", + "q_ref/q_s = 1.00000 \n", + "n_s/n_ref = 1.00000 \n", + "v_drift s/v_A,ref = 0.00000 \n", + "-=-=-=-=-=-=-=-=-=-\n", + "Parameters for Species : 2\n", + "T_||,ref/T_||s = 1.00000 \n", + "m_ref/m_s = 1836.00 \n", + "T_perp/T_par|s = 1.00000 \n", + "q_ref/q_s = -1.00000 \n", + "n_s/n_ref = 1.00000 \n", + "v_drift s/v_A,ref = 0.00000 \n", + "-=-=-=-=-=-=-=-=-=-\n", + "Searching over:\n", + "omega/Omega_ref \\in [ 1.000E-05, 1.000E-02]\n", + "gamma/Omega_ref \\in [-2.000E-03,-2.000E-06]\n", + "-=-=-=-=-=-=-=-=-=-\n", + " finding minima\n", + " minima found\n", + " 7 possible local minima found:\n", + "ir = 128 ii = 128\n", + " 1.0000E-02 -2.0000E-06\n", + "ir = 16 ii = 125\n", + " 1.2588E-03 -4.8828E-05\n", + "ir = 0 ii = 80\n", + " 1.0000E-05 -7.5125E-04\n", + "ir = 15 ii = 65\n", + " 1.1807E-03 -9.8539E-04\n", + "ir = 22 ii = 26\n", + " 1.7270E-03 -1.5942E-03\n", + "ir = 28 ii = 2\n", + " 2.1953E-03 -1.9688E-03\n", + "ir = 128 ii = 0\n", + " 1.0000E-02 -2.0000E-03\n", + "Refining roots:\n", + "Dispersion Solutions \n", + " 1 1.2362E-03 -4.4355E-05\n", + " Predicting FPC (gyrotropic coords)...\n", + "Input for Root 1: 1.2362E-03 -4.4355E-05\n", + "Dispersion Solutions: \n", + " 1 1.2362E-03 -4.4355E-05\n", + " WARNING! The gyro routine does not support computing moments at this time due to computational demand!\n", + " Assuming data folder already exists...\n", + " Assuming subfolder howes2017 already exists\n", + " Calculating fpc for species 1\n", + " Writing omega/kpar V_a normalization to file...\n", + " Calculating fpc for species 2\n", + " Writing omega/kpar V_a normalization to file...\n", + "-=-=-=-=-=-=-=-=-=-\n", + "Root Search:\n", + "-=-=-=-=-=-=-=-=-=-\n", + "Global Plasma Parameters:\n", + "k_perp rho_ref = 1.30000 \n", + "k_par rho_ref = 0.100000E-02\n", + "Beta_ref,par = 1.00000 \n", + "v_t,ref,par/c = 0.100000E-03\n", + "-=-=-=-=-=-=-=-=-=-\n", + "Parameters for Species : 1\n", + "T_||,ref/T_||s = 1.00000 \n", + "m_ref/m_s = 1.00000 \n", + "T_perp/T_par|s = 1.00000 \n", + "q_ref/q_s = 1.00000 \n", + "n_s/n_ref = 1.00000 \n", + "v_drift s/v_A,ref = 0.00000 \n", + "-=-=-=-=-=-=-=-=-=-\n", + "Parameters for Species : 2\n", + "T_||,ref/T_||s = 1.00000 \n", + "m_ref/m_s = 1836.00 \n", + "T_perp/T_par|s = 1.00000 \n", + "q_ref/q_s = -1.00000 \n", + "n_s/n_ref = 1.00000 \n", + "v_drift s/v_A,ref = 0.00000 \n", + "-=-=-=-=-=-=-=-=-=-\n", + "Searching over:\n", + "omega/Omega_ref \\in [ 1.000E-05, 1.000E-02]\n", + "gamma/Omega_ref \\in [-2.000E-03,-2.000E-06]\n", + "-=-=-=-=-=-=-=-=-=-\n", + " finding minima\n", + " minima found\n", + " 7 possible local minima found:\n", + "ir = 128 ii = 128\n", + " 1.0000E-02 -2.0000E-06\n", + "ir = 16 ii = 125\n", + " 1.2588E-03 -4.8828E-05\n", + "ir = 0 ii = 80\n", + " 1.0000E-05 -7.5125E-04\n", + "ir = 15 ii = 65\n", + " 1.1807E-03 -9.8539E-04\n", + "ir = 22 ii = 26\n", + " 1.7270E-03 -1.5942E-03\n", + "ir = 28 ii = 2\n", + " 2.1953E-03 -1.9688E-03\n", + "ir = 128 ii = 0\n", + " 1.0000E-02 -2.0000E-03\n", + "Refining roots:\n", + "Dispersion Solutions \n", + " 1 1.2362E-03 -4.4355E-05\n", + " Predicting FPC (gyrotropic coords)...\n", + "Input for Root 1: 1.2362E-03 -4.4355E-05\n", + "Dispersion Solutions: \n", + " 1 1.2362E-03 -4.4355E-05\n", + " WARNING! The gyro routine does not support computing moments at this time due to computational demand!\n", + " Assuming data folder already exists...\n", + " Assuming subfolder howes2017 already exists\n", + " Calculating fpc for species 1\n", + " Writing omega/kpar V_a normalization to file...\n", + " Calculating fpc for species 2\n", + " Writing omega/kpar V_a normalization to file...\n", + "-=-=-=-=-=-=-=-=-=-\n", + "Root Search:\n", + "-=-=-=-=-=-=-=-=-=-\n", + "Global Plasma Parameters:\n", + "k_perp rho_ref = 1.30000 \n", + "k_par rho_ref = 0.100000E-02\n", + "Beta_ref,par = 1.00000 \n", + "v_t,ref,par/c = 0.100000E-03\n", + "-=-=-=-=-=-=-=-=-=-\n", + "Parameters for Species : 1\n", + "T_||,ref/T_||s = 1.00000 \n", + "m_ref/m_s = 1.00000 \n", + "T_perp/T_par|s = 1.00000 \n", + "q_ref/q_s = 1.00000 \n", + "n_s/n_ref = 1.00000 \n", + "v_drift s/v_A,ref = 0.00000 \n", + "-=-=-=-=-=-=-=-=-=-\n", + "Parameters for Species : 2\n", + "T_||,ref/T_||s = 1.00000 \n", + "m_ref/m_s = 1836.00 \n", + "T_perp/T_par|s = 1.00000 \n", + "q_ref/q_s = -1.00000 \n", + "n_s/n_ref = 1.00000 \n", + "v_drift s/v_A,ref = 0.00000 \n", + "-=-=-=-=-=-=-=-=-=-\n", + "Searching over:\n", + "omega/Omega_ref \\in [ 1.000E-05, 1.000E-02]\n", + "gamma/Omega_ref \\in [-2.000E-03,-2.000E-06]\n", + "-=-=-=-=-=-=-=-=-=-\n", + " finding minima\n", + " minima found\n", + " 7 possible local minima found:\n", + "ir = 128 ii = 128\n", + " 1.0000E-02 -2.0000E-06\n", + "ir = 16 ii = 125\n", + " 1.2588E-03 -4.8828E-05\n", + "ir = 0 ii = 80\n", + " 1.0000E-05 -7.5125E-04\n", + "ir = 15 ii = 65\n", + " 1.1807E-03 -9.8539E-04\n", + "ir = 22 ii = 26\n", + " 1.7270E-03 -1.5942E-03\n", + "ir = 28 ii = 2\n", + " 2.1953E-03 -1.9688E-03\n", + "ir = 128 ii = 0\n", + " 1.0000E-02 -2.0000E-03\n", + "Refining roots:\n", + "Dispersion Solutions \n", + " 1 1.2362E-03 -4.4355E-05\n", + " Predicting FPC (gyrotropic coords)...\n", + "Input for Root 1: 1.2362E-03 -4.4355E-05\n", + "Dispersion Solutions: \n", + " 1 1.2362E-03 -4.4355E-05\n", + " WARNING! The gyro routine does not support computing moments at this time due to computational demand!\n", + " Assuming data folder already exists...\n", + " Assuming subfolder howes2017 already exists\n", + " Calculating fpc for species 1\n", + " Writing omega/kpar V_a normalization to file...\n", + " Calculating fpc for species 2\n", + " Writing omega/kpar V_a normalization to file...\n", + "-=-=-=-=-=-=-=-=-=-\n", + "Root Search:\n", + "-=-=-=-=-=-=-=-=-=-\n", + "Global Plasma Parameters:\n", + "k_perp rho_ref = 1.30000 \n", + "k_par rho_ref = 0.100000E-02\n", + "Beta_ref,par = 1.00000 \n", + "v_t,ref,par/c = 0.100000E-03\n", + "-=-=-=-=-=-=-=-=-=-\n", + "Parameters for Species : 1\n", + "T_||,ref/T_||s = 1.00000 \n", + "m_ref/m_s = 1.00000 \n", + "T_perp/T_par|s = 1.00000 \n", + "q_ref/q_s = 1.00000 \n", + "n_s/n_ref = 1.00000 \n", + "v_drift s/v_A,ref = 0.00000 \n", + "-=-=-=-=-=-=-=-=-=-\n", + "Parameters for Species : 2\n", + "T_||,ref/T_||s = 1.00000 \n", + "m_ref/m_s = 1836.00 \n", + "T_perp/T_par|s = 1.00000 \n", + "q_ref/q_s = -1.00000 \n", + "n_s/n_ref = 1.00000 \n", + "v_drift s/v_A,ref = 0.00000 \n", + "-=-=-=-=-=-=-=-=-=-\n", + "Searching over:\n", + "omega/Omega_ref \\in [ 1.000E-05, 1.000E-02]\n", + "gamma/Omega_ref \\in [-2.000E-03,-2.000E-06]\n", + "-=-=-=-=-=-=-=-=-=-\n", + " finding minima\n", + " minima found\n", + " 7 possible local minima found:\n", + "ir = 128 ii = 128\n", + " 1.0000E-02 -2.0000E-06\n", + "ir = 16 ii = 125\n", + " 1.2588E-03 -4.8828E-05\n", + "ir = 0 ii = 80\n", + " 1.0000E-05 -7.5125E-04\n", + "ir = 15 ii = 65\n", + " 1.1807E-03 -9.8539E-04\n", + "ir = 22 ii = 26\n", + " 1.7270E-03 -1.5942E-03\n", + "ir = 28 ii = 2\n", + " 2.1953E-03 -1.9688E-03\n", + "ir = 128 ii = 0\n", + " 1.0000E-02 -2.0000E-03\n", + "Refining roots:\n", + "Dispersion Solutions \n", + " 1 1.2362E-03 -4.4355E-05\n", + " Predicting FPC (gyrotropic coords)...\n", + "Input for Root 1: 1.2362E-03 -4.4355E-05\n", + "Dispersion Solutions: \n", + " 1 1.2362E-03 -4.4355E-05\n", + " WARNING! The gyro routine does not support computing moments at this time due to computational demand!\n", + " Assuming data folder already exists...\n", + " Assuming subfolder howes2017 already exists\n", + " Calculating fpc for species 1\n", + " Writing omega/kpar V_a normalization to file...\n", + " Calculating fpc for species 2\n", + " Writing omega/kpar V_a normalization to file...\n", + "-=-=-=-=-=-=-=-=-=-\n", + "Root Search:\n", + "-=-=-=-=-=-=-=-=-=-\n", + "Global Plasma Parameters:\n", + "k_perp rho_ref = 1.30000 \n", + "k_par rho_ref = 0.100000E-02\n", + "Beta_ref,par = 1.00000 \n", + "v_t,ref,par/c = 0.100000E-03\n", + "-=-=-=-=-=-=-=-=-=-\n", + "Parameters for Species : 1\n", + "T_||,ref/T_||s = 1.00000 \n", + "m_ref/m_s = 1.00000 \n", + "T_perp/T_par|s = 1.00000 \n", + "q_ref/q_s = 1.00000 \n", + "n_s/n_ref = 1.00000 \n", + "v_drift s/v_A,ref = 0.00000 \n", + "-=-=-=-=-=-=-=-=-=-\n", + "Parameters for Species : 2\n", + "T_||,ref/T_||s = 1.00000 \n", + "m_ref/m_s = 1836.00 \n", + "T_perp/T_par|s = 1.00000 \n", + "q_ref/q_s = -1.00000 \n", + "n_s/n_ref = 1.00000 \n", + "v_drift s/v_A,ref = 0.00000 \n", + "-=-=-=-=-=-=-=-=-=-\n", + "Searching over:\n", + "omega/Omega_ref \\in [ 1.000E-05, 1.000E-02]\n", + "gamma/Omega_ref \\in [-2.000E-03,-2.000E-06]\n", + "-=-=-=-=-=-=-=-=-=-\n", + " finding minima\n", + " minima found\n", + " 7 possible local minima found:\n", + "ir = 128 ii = 128\n", + " 1.0000E-02 -2.0000E-06\n", + "ir = 16 ii = 125\n", + " 1.2588E-03 -4.8828E-05\n", + "ir = 0 ii = 80\n", + " 1.0000E-05 -7.5125E-04\n", + "ir = 15 ii = 65\n", + " 1.1807E-03 -9.8539E-04\n", + "ir = 22 ii = 26\n", + " 1.7270E-03 -1.5942E-03\n", + "ir = 28 ii = 2\n", + " 2.1953E-03 -1.9688E-03\n", + "ir = 128 ii = 0\n", + " 1.0000E-02 -2.0000E-03\n", + "Refining roots:\n", + "Dispersion Solutions \n", + " 1 1.2362E-03 -4.4355E-05\n", + " Predicting FPC (gyrotropic coords)...\n", + "Input for Root 1: 1.2362E-03 -4.4355E-05\n", + "Dispersion Solutions: \n", + " 1 1.2362E-03 -4.4355E-05\n", + " WARNING! The gyro routine does not support computing moments at this time due to computational demand!\n", + " Assuming data folder already exists...\n", + " Assuming subfolder howes2017 already exists\n", + " Calculating fpc for species 1\n", + " Writing omega/kpar V_a normalization to file...\n", + " Calculating fpc for species 2\n", + " Writing omega/kpar V_a normalization to file...\n", + "-=-=-=-=-=-=-=-=-=-\n", + "Root Search:\n", + "-=-=-=-=-=-=-=-=-=-\n", + "Global Plasma Parameters:\n", + "k_perp rho_ref = 1.30000 \n", + "k_par rho_ref = 0.100000E-02\n", + "Beta_ref,par = 1.00000 \n", + "v_t,ref,par/c = 0.100000E-03\n", + "-=-=-=-=-=-=-=-=-=-\n", + "Parameters for Species : 1\n", + "T_||,ref/T_||s = 1.00000 \n", + "m_ref/m_s = 1.00000 \n", + "T_perp/T_par|s = 1.00000 \n", + "q_ref/q_s = 1.00000 \n", + "n_s/n_ref = 1.00000 \n", + "v_drift s/v_A,ref = 0.00000 \n", + "-=-=-=-=-=-=-=-=-=-\n", + "Parameters for Species : 2\n", + "T_||,ref/T_||s = 1.00000 \n", + "m_ref/m_s = 1836.00 \n", + "T_perp/T_par|s = 1.00000 \n", + "q_ref/q_s = -1.00000 \n", + "n_s/n_ref = 1.00000 \n", + "v_drift s/v_A,ref = 0.00000 \n", + "-=-=-=-=-=-=-=-=-=-\n", + "Searching over:\n", + "omega/Omega_ref \\in [ 1.000E-05, 1.000E-02]\n", + "gamma/Omega_ref \\in [-2.000E-03,-2.000E-06]\n", + "-=-=-=-=-=-=-=-=-=-\n", + " finding minima\n", + " minima found\n", + " 1 possible local minima found:\n", + "ir = 122 ii = 45\n", + " 9.5317E-03 -1.2976E-03\n", + "Refining roots:\n", + "Dispersion Solutions \n", + " 1 9.5218E-03 -1.2924E-03\n", + "-=-=-=-=-=-=-=-=-=-\n", + "Root Search:\n", + "-=-=-=-=-=-=-=-=-=-\n", + "Global Plasma Parameters:\n", + "k_perp rho_ref = 1.30000 \n", + "k_par rho_ref = 0.100000E-02\n", + "Beta_ref,par = 1.00000 \n", + "v_t,ref,par/c = 0.100000E-03\n", + "-=-=-=-=-=-=-=-=-=-\n", + "Parameters for Species : 1\n", + "T_||,ref/T_||s = 1.00000 \n", + "m_ref/m_s = 1.00000 \n", + "T_perp/T_par|s = 1.00000 \n", + "q_ref/q_s = 1.00000 \n", + "n_s/n_ref = 1.00000 \n", + "v_drift s/v_A,ref = 0.00000 \n", + "-=-=-=-=-=-=-=-=-=-\n", + "Parameters for Species : 2\n", + "T_||,ref/T_||s = 1.00000 \n", + "m_ref/m_s = 1836.00 \n", + "T_perp/T_par|s = 1.00000 \n", + "q_ref/q_s = -1.00000 \n", + "n_s/n_ref = 1.00000 \n", + "v_drift s/v_A,ref = 0.00000 \n", + "-=-=-=-=-=-=-=-=-=-\n", + "Searching over:\n", + "omega/Omega_ref \\in [ 1.000E-05, 1.000E-02]\n", + "gamma/Omega_ref \\in [-2.000E-03,-2.000E-06]\n", + "-=-=-=-=-=-=-=-=-=-\n", + " finding minima\n", + " minima found\n", + " 1 possible local minima found:\n", + "ir = 128 ii = 0\n", + " 1.0000E-02 -2.0000E-03\n", + "Refining roots:\n", + "Dispersion Solutions \n", + " 1 9.6597E-01 -1.3360E-01\n", + "-=-=-=-=-=-=-=-=-=-\n", + "Root Search:\n", + "-=-=-=-=-=-=-=-=-=-\n", + "Global Plasma Parameters:\n", + "k_perp rho_ref = 1.30000 \n", + "k_par rho_ref = 0.100000E-02\n", + "Beta_ref,par = 1.00000 \n", + "v_t,ref,par/c = 0.100000E-03\n", + "-=-=-=-=-=-=-=-=-=-\n", + "Parameters for Species : 1\n", + "T_||,ref/T_||s = 1.00000 \n", + "m_ref/m_s = 1.00000 \n", + "T_perp/T_par|s = 1.00000 \n", + "q_ref/q_s = 1.00000 \n", + "n_s/n_ref = 1.00000 \n", + "v_drift s/v_A,ref = 0.00000 \n", + "-=-=-=-=-=-=-=-=-=-\n", + "Parameters for Species : 2\n", + "T_||,ref/T_||s = 1.00000 \n", + "m_ref/m_s = 1836.00 \n", + "T_perp/T_par|s = 1.00000 \n", + "q_ref/q_s = -1.00000 \n", + "n_s/n_ref = 1.00000 \n", + "v_drift s/v_A,ref = 0.00000 \n", + "-=-=-=-=-=-=-=-=-=-\n", + "Searching over:\n", + "omega/Omega_ref \\in [ 1.000E-05, 1.000E-02]\n", + "gamma/Omega_ref \\in [-2.000E-03,-2.000E-06]\n", + "-=-=-=-=-=-=-=-=-=-\n", + " finding minima\n", + " minima found\n", + " 7 possible local minima found:\n", + "ir = 128 ii = 128\n", + " 1.0000E-02 -2.0000E-06\n", + "ir = 16 ii = 125\n", + " 1.2588E-03 -4.8828E-05\n", + "ir = 0 ii = 80\n", + " 1.0000E-05 -7.5125E-04\n", + "ir = 15 ii = 65\n", + " 1.1807E-03 -9.8539E-04\n", + "ir = 22 ii = 26\n", + " 1.7270E-03 -1.5942E-03\n", + "ir = 28 ii = 2\n", + " 2.1953E-03 -1.9688E-03\n", + "ir = 128 ii = 0\n", + " 1.0000E-02 -2.0000E-03\n", + "Refining roots:\n", + "Dispersion Solutions \n", + " 1 1.2362E-03 -4.4355E-05\n", + " Predicting FPC (gyrotropic coords)...\n", + "Input for Root 1: 1.2362E-03 -4.4355E-05\n", + "Dispersion Solutions: \n", + " 1 1.2362E-03 -4.4355E-05\n", + " WARNING! The gyro routine does not support computing moments at this time due to computational demand!\n", + " Assuming data folder already exists...\n", + " Assuming subfolder howes2017 already exists\n", + " Calculating fpc for species 1\n", + " Writing omega/kpar V_a normalization to file...\n", + " Calculating fpc for species 2\n", + " Writing omega/kpar V_a normalization to file...\n", + "-=-=-=-=-=-=-=-=-=-\n", + "Root Search:\n", + "-=-=-=-=-=-=-=-=-=-\n", + "Global Plasma Parameters:\n", + "k_perp rho_ref = 1.30000 \n", + "k_par rho_ref = 0.100000E-02\n", + "Beta_ref,par = 1.00000 \n", + "v_t,ref,par/c = 0.100000E-03\n", + "-=-=-=-=-=-=-=-=-=-\n", + "Parameters for Species : 1\n", + "T_||,ref/T_||s = 1.00000 \n", + "m_ref/m_s = 1.00000 \n", + "T_perp/T_par|s = 1.00000 \n", + "q_ref/q_s = 1.00000 \n", + "n_s/n_ref = 1.00000 \n", + "v_drift s/v_A,ref = 0.00000 \n", + "-=-=-=-=-=-=-=-=-=-\n", + "Parameters for Species : 2\n", + "T_||,ref/T_||s = 1.00000 \n", + "m_ref/m_s = 1836.00 \n", + "T_perp/T_par|s = 1.00000 \n", + "q_ref/q_s = -1.00000 \n", + "n_s/n_ref = 1.00000 \n", + "v_drift s/v_A,ref = 0.00000 \n", + "-=-=-=-=-=-=-=-=-=-\n", + "Searching over:\n", + "omega/Omega_ref \\in [ 1.000E-05, 1.000E-02]\n", + "gamma/Omega_ref \\in [-2.000E-03,-2.000E-06]\n", + "-=-=-=-=-=-=-=-=-=-\n", + " finding minima\n", + " minima found\n", + " 1 possible local minima found:\n", + "ir = 128 ii = 0\n", + " 1.0000E-02 -2.0000E-03\n", + "Refining roots:\n", + "Dispersion Solutions \n", + " 1 9.6597E-01 -1.3360E-01\n", + "-=-=-=-=-=-=-=-=-=-\n", + "Root Search:\n", + "-=-=-=-=-=-=-=-=-=-\n", + "Global Plasma Parameters:\n", + "k_perp rho_ref = 1.30000 \n", + "k_par rho_ref = 0.100000E-02\n", + "Beta_ref,par = 1.00000 \n", + "v_t,ref,par/c = 0.100000E-03\n", + "-=-=-=-=-=-=-=-=-=-\n", + "Parameters for Species : 1\n", + "T_||,ref/T_||s = 1.00000 \n", + "m_ref/m_s = 1.00000 \n", + "T_perp/T_par|s = 1.00000 \n", + "q_ref/q_s = 1.00000 \n", + "n_s/n_ref = 1.00000 \n", + "v_drift s/v_A,ref = 0.00000 \n", + "-=-=-=-=-=-=-=-=-=-\n", + "Parameters for Species : 2\n", + "T_||,ref/T_||s = 1.00000 \n", + "m_ref/m_s = 1836.00 \n", + "T_perp/T_par|s = 1.00000 \n", + "q_ref/q_s = -1.00000 \n", + "n_s/n_ref = 1.00000 \n", + "v_drift s/v_A,ref = 0.00000 \n", + "-=-=-=-=-=-=-=-=-=-\n", + "Searching over:\n", + "omega/Omega_ref \\in [ 1.000E-05, 1.000E-02]\n", + "gamma/Omega_ref \\in [-2.000E-03,-2.000E-06]\n", + "-=-=-=-=-=-=-=-=-=-\n", + " finding minima\n", + " minima found\n", + " 7 possible local minima found:\n", + "ir = 128 ii = 128\n", + " 1.0000E-02 -2.0000E-06\n", + "ir = 16 ii = 125\n", + " 1.2588E-03 -4.8828E-05\n", + "ir = 0 ii = 80\n", + " 1.0000E-05 -7.5125E-04\n", + "ir = 15 ii = 65\n", + " 1.1807E-03 -9.8539E-04\n", + "ir = 22 ii = 26\n", + " 1.7270E-03 -1.5942E-03\n", + "ir = 28 ii = 2\n", + " 2.1953E-03 -1.9688E-03\n", + "ir = 128 ii = 0\n", + " 1.0000E-02 -2.0000E-03\n", + "Refining roots:\n", + "Dispersion Solutions \n", + " 1 1.2365E-03 -4.4616E-05\n", + " Predicting FPC (gyrotropic coords)...\n", + "Input for Root 1: 1.2365E-03 -4.4616E-05\n", + "Dispersion Solutions: \n", + " 1 1.2365E-03 -4.4616E-05\n", + " WARNING! The gyro routine does not support computing moments at this time due to computational demand!\n", + " Assuming data folder already exists...\n", + " Assuming subfolder howes2017 already exists\n", + " Calculating fpc for species 1\n", + " Writing omega/kpar V_a normalization to file...\n", + " Calculating fpc for species 2\n", + " Writing omega/kpar V_a normalization to file...\n", + "-=-=-=-=-=-=-=-=-=-\n", + "Root Search:\n", + "-=-=-=-=-=-=-=-=-=-\n", + "Global Plasma Parameters:\n", + "k_perp rho_ref = 1.30000 \n", + "k_par rho_ref = 0.100000E-02\n", + "Beta_ref,par = 1.00000 \n", + "v_t,ref,par/c = 0.100000E-03\n", + "-=-=-=-=-=-=-=-=-=-\n", + "Parameters for Species : 1\n", + "T_||,ref/T_||s = 1.00000 \n", + "m_ref/m_s = 1.00000 \n", + "T_perp/T_par|s = 1.00000 \n", + "q_ref/q_s = 1.00000 \n", + "n_s/n_ref = 1.00000 \n", + "v_drift s/v_A,ref = 0.00000 \n", + "-=-=-=-=-=-=-=-=-=-\n", + "Parameters for Species : 2\n", + "T_||,ref/T_||s = 1.00000 \n", + "m_ref/m_s = 1836.00 \n", + "T_perp/T_par|s = 1.00000 \n", + "q_ref/q_s = -1.00000 \n", + "n_s/n_ref = 1.00000 \n", + "v_drift s/v_A,ref = 0.00000 \n", + "-=-=-=-=-=-=-=-=-=-\n", + "Searching over:\n", + "omega/Omega_ref \\in [ 1.000E-05, 1.000E-02]\n", + "gamma/Omega_ref \\in [-2.000E-03,-2.000E-06]\n", + "-=-=-=-=-=-=-=-=-=-\n", + " finding minima\n", + " minima found\n", + " 7 possible local minima found:\n", + "ir = 128 ii = 128\n", + " 1.0000E-02 -2.0000E-06\n", + "ir = 16 ii = 125\n", + " 1.2588E-03 -4.8828E-05\n", + "ir = 0 ii = 80\n", + " 1.0000E-05 -7.5125E-04\n", + "ir = 15 ii = 65\n", + " 1.1807E-03 -9.8539E-04\n", + "ir = 22 ii = 25\n", + " 1.7270E-03 -1.6098E-03\n", + "ir = 28 ii = 2\n", + " 2.1953E-03 -1.9688E-03\n", + "ir = 128 ii = 0\n", + " 1.0000E-02 -2.0000E-03\n", + "Refining roots:\n", + "Dispersion Solutions \n", + " 1 1.2392E-03 -4.6950E-05\n", + " Predicting FPC (gyrotropic coords)...\n", + "Input for Root 1: 1.2392E-03 -4.6950E-05\n", + "Dispersion Solutions: \n", + " 1 1.2392E-03 -4.6950E-05\n", + " WARNING! The gyro routine does not support computing moments at this time due to computational demand!\n", + " Assuming data folder already exists...\n", + " Assuming subfolder howes2017 already exists\n", + " Calculating fpc for species 1\n", + " Writing omega/kpar V_a normalization to file...\n", + " Calculating fpc for species 2\n", + " Writing omega/kpar V_a normalization to file...\n", + "-=-=-=-=-=-=-=-=-=-\n", + "Root Search:\n", + "-=-=-=-=-=-=-=-=-=-\n", + "Global Plasma Parameters:\n", + "k_perp rho_ref = 1.30000 \n", + "k_par rho_ref = 0.100000E-02\n", + "Beta_ref,par = 1.00000 \n", + "v_t,ref,par/c = 0.100000E-03\n", + "-=-=-=-=-=-=-=-=-=-\n", + "Parameters for Species : 1\n", + "T_||,ref/T_||s = 1.00000 \n", + "m_ref/m_s = 1.00000 \n", + "T_perp/T_par|s = 1.00000 \n", + "q_ref/q_s = 1.00000 \n", + "n_s/n_ref = 1.00000 \n", + "v_drift s/v_A,ref = 0.00000 \n", + "-=-=-=-=-=-=-=-=-=-\n", + "Parameters for Species : 2\n", + "T_||,ref/T_||s = 1.00000 \n", + "m_ref/m_s = 1836.00 \n", + "T_perp/T_par|s = 1.00000 \n", + "q_ref/q_s = -1.00000 \n", + "n_s/n_ref = 1.00000 \n", + "v_drift s/v_A,ref = 0.00000 \n", + "-=-=-=-=-=-=-=-=-=-\n", + "Searching over:\n", + "omega/Omega_ref \\in [ 1.000E-05, 1.000E-02]\n", + "gamma/Omega_ref \\in [-2.000E-03,-2.000E-06]\n", + "-=-=-=-=-=-=-=-=-=-\n", + " finding minima\n", + " minima found\n", + " 7 possible local minima found:\n", + "ir = 128 ii = 128\n", + " 1.0000E-02 -2.0000E-06\n", + "ir = 16 ii = 124\n", + " 1.2588E-03 -6.4437E-05\n", + "ir = 0 ii = 77\n", + " 1.0000E-05 -7.9808E-04\n", + "ir = 15 ii = 64\n", + " 1.1807E-03 -1.0010E-03\n", + "ir = 22 ii = 21\n", + " 1.7270E-03 -1.6722E-03\n", + "ir = 29 ii = 0\n", + " 2.2734E-03 -2.0000E-03\n", + "ir = 128 ii = 0\n", + " 1.0000E-02 -2.0000E-03\n", + "Refining roots:\n", + "Dispersion Solutions \n", + " 1 1.2676E-03 -6.9505E-05\n", + " Predicting FPC (gyrotropic coords)...\n", + "Input for Root 1: 1.2676E-03 -6.9505E-05\n", + "Dispersion Solutions: \n", + " 1 1.2676E-03 -6.9505E-05\n", + " WARNING! The gyro routine does not support computing moments at this time due to computational demand!\n", + " Assuming data folder already exists...\n", + " Assuming subfolder howes2017 already exists\n", + " Calculating fpc for species 1\n", + " Writing omega/kpar V_a normalization to file...\n", + " Calculating fpc for species 2\n", + " Writing omega/kpar V_a normalization to file...\n", + "-=-=-=-=-=-=-=-=-=-\n", + "Root Search:\n", + "-=-=-=-=-=-=-=-=-=-\n", + "Global Plasma Parameters:\n", + "k_perp rho_ref = 1.30000 \n", + "k_par rho_ref = 0.100000E-02\n", + "Beta_ref,par = 1.00000 \n", + "v_t,ref,par/c = 0.100000E-03\n", + "-=-=-=-=-=-=-=-=-=-\n", + "Parameters for Species : 1\n", + "T_||,ref/T_||s = 1.00000 \n", + "m_ref/m_s = 1.00000 \n", + "T_perp/T_par|s = 1.00000 \n", + "q_ref/q_s = 1.00000 \n", + "n_s/n_ref = 1.00000 \n", + "v_drift s/v_A,ref = 0.00000 \n", + "-=-=-=-=-=-=-=-=-=-\n", + "Parameters for Species : 2\n", + "T_||,ref/T_||s = 1.00000 \n", + "m_ref/m_s = 1836.00 \n", + "T_perp/T_par|s = 1.00000 \n", + "q_ref/q_s = -1.00000 \n", + "n_s/n_ref = 1.00000 \n", + "v_drift s/v_A,ref = 0.00000 \n", + "-=-=-=-=-=-=-=-=-=-\n", + "Searching over:\n", + "omega/Omega_ref \\in [ 1.000E-05, 1.000E-02]\n", + "gamma/Omega_ref \\in [-2.000E-03,-2.000E-06]\n", + "-=-=-=-=-=-=-=-=-=-\n", + " finding minima\n", + " minima found\n", + " 6 possible local minima found:\n", + "ir = 128 ii = 128\n", + " 1.0000E-02 -2.0000E-06\n", + "ir = 22 ii = 113\n", + " 1.7270E-03 -2.3614E-04\n", + "ir = 0 ii = 109\n", + " 1.0000E-05 -2.9858E-04\n", + "ir = 18 ii = 47\n", + " 1.4148E-03 -1.2664E-03\n", + "ir = 0 ii = 45\n", + " 1.0000E-05 -1.2976E-03\n", + "ir = 128 ii = 0\n", + " 1.0000E-02 -2.0000E-03\n", + "Refining roots:\n", + "Dispersion Solutions \n", + " 1 1.7177E-03 -2.3538E-04\n", + " Predicting FPC (gyrotropic coords)...\n", + "Input for Root 1: 1.7177E-03 -2.3538E-04\n", + "Dispersion Solutions: \n", + " 1 1.7177E-03 -2.3538E-04\n", + " WARNING! The gyro routine does not support computing moments at this time due to computational demand!\n", + " Assuming data folder already exists...\n", + " Assuming subfolder howes2017 already exists\n", + " Calculating fpc for species 1\n", + " Writing omega/kpar V_a normalization to file...\n", + " Calculating fpc for species 2\n", + " Writing omega/kpar V_a normalization to file...\n", + "-=-=-=-=-=-=-=-=-=-\n", + "Root Search:\n", + "-=-=-=-=-=-=-=-=-=-\n", + "Global Plasma Parameters:\n", + "k_perp rho_ref = 1.30000 \n", + "k_par rho_ref = 0.100000E-02\n", + "Beta_ref,par = 1.00000 \n", + "v_t,ref,par/c = 0.100000E-03\n", + "-=-=-=-=-=-=-=-=-=-\n", + "Parameters for Species : 1\n", + "T_||,ref/T_||s = 1.00000 \n", + "m_ref/m_s = 1.00000 \n", + "T_perp/T_par|s = 1.00000 \n", + "q_ref/q_s = 1.00000 \n", + "n_s/n_ref = 1.00000 \n", + "v_drift s/v_A,ref = 0.00000 \n", + "-=-=-=-=-=-=-=-=-=-\n", + "Parameters for Species : 2\n", + "T_||,ref/T_||s = 1.00000 \n", + "m_ref/m_s = 1836.00 \n", + "T_perp/T_par|s = 1.00000 \n", + "q_ref/q_s = -1.00000 \n", + "n_s/n_ref = 1.00000 \n", + "v_drift s/v_A,ref = 0.00000 \n", + "-=-=-=-=-=-=-=-=-=-\n", + "Searching over:\n", + "omega/Omega_ref \\in [ 1.000E-05, 1.000E-02]\n", + "gamma/Omega_ref \\in [-2.000E-03,-2.000E-06]\n", + "-=-=-=-=-=-=-=-=-=-\n", + " finding minima\n", + " minima found\n", + " 1 possible local minima found:\n", + "ir = 122 ii = 45\n", + " 9.5317E-03 -1.2976E-03\n", + "Refining roots:\n", + "Dispersion Solutions \n", + " 1 9.5218E-03 -1.2924E-03\n", + "-=-=-=-=-=-=-=-=-=-\n", + "Root Search:\n", + "-=-=-=-=-=-=-=-=-=-\n", + "Global Plasma Parameters:\n", + "k_perp rho_ref = 1.30000 \n", + "k_par rho_ref = 0.100000E-02\n", + "Beta_ref,par = 1.00000 \n", + "v_t,ref,par/c = 0.100000E-03\n", + "-=-=-=-=-=-=-=-=-=-\n", + "Parameters for Species : 1\n", + "T_||,ref/T_||s = 1.00000 \n", + "m_ref/m_s = 1.00000 \n", + "T_perp/T_par|s = 1.00000 \n", + "q_ref/q_s = 1.00000 \n", + "n_s/n_ref = 1.00000 \n", + "v_drift s/v_A,ref = 0.00000 \n", + "-=-=-=-=-=-=-=-=-=-\n", + "Parameters for Species : 2\n", + "T_||,ref/T_||s = 1.00000 \n", + "m_ref/m_s = 1836.00 \n", + "T_perp/T_par|s = 1.00000 \n", + "q_ref/q_s = -1.00000 \n", + "n_s/n_ref = 1.00000 \n", + "v_drift s/v_A,ref = 0.00000 \n", + "-=-=-=-=-=-=-=-=-=-\n", + "Searching over:\n", + "omega/Omega_ref \\in [ 1.000E-05, 1.000E-02]\n", + "gamma/Omega_ref \\in [-2.000E-03,-2.000E-06]\n", + "-=-=-=-=-=-=-=-=-=-\n", + " finding minima\n", + " minima found\n", + " 7 possible local minima found:\n", + "ir = 128 ii = 128\n", + " 1.0000E-02 -2.0000E-06\n", + "ir = 16 ii = 125\n", + " 1.2588E-03 -4.8828E-05\n", + "ir = 0 ii = 80\n", + " 1.0000E-05 -7.5125E-04\n", + "ir = 15 ii = 65\n", + " 1.1807E-03 -9.8539E-04\n", + "ir = 22 ii = 26\n", + " 1.7270E-03 -1.5942E-03\n", + "ir = 28 ii = 2\n", + " 2.1953E-03 -1.9688E-03\n", + "ir = 128 ii = 0\n", + " 1.0000E-02 -2.0000E-03\n", + "Refining roots:\n", + "Dispersion Solutions \n", + " 1 1.2362E-03 -4.4355E-05\n", + " Predicting FPC (gyrotropic coords)...\n", + "Input for Root 1: 1.2362E-03 -4.4355E-05\n", + "Dispersion Solutions: \n", + " 1 1.2362E-03 -4.4355E-05\n", + " WARNING! The gyro routine does not support computing moments at this time due to computational demand!\n", + " Assuming data folder already exists...\n", + " Assuming subfolder howes2017 already exists\n", + " Calculating fpc for species 1\n", + " Writing omega/kpar V_a normalization to file...\n", + " Calculating fpc for species 2\n", + " Writing omega/kpar V_a normalization to file...\n", + "-=-=-=-=-=-=-=-=-=-\n", + "Root Search:\n", + "-=-=-=-=-=-=-=-=-=-\n", + "Global Plasma Parameters:\n", + "k_perp rho_ref = 1.30000 \n", + "k_par rho_ref = 0.100000E-02\n", + "Beta_ref,par = 1.00000 \n", + "v_t,ref,par/c = 0.100000E-03\n", + "-=-=-=-=-=-=-=-=-=-\n", + "Parameters for Species : 1\n", + "T_||,ref/T_||s = 1.00000 \n", + "m_ref/m_s = 1.00000 \n", + "T_perp/T_par|s = 1.00000 \n", + "q_ref/q_s = 1.00000 \n", + "n_s/n_ref = 1.00000 \n", + "v_drift s/v_A,ref = 0.00000 \n", + "-=-=-=-=-=-=-=-=-=-\n", + "Parameters for Species : 2\n", + "T_||,ref/T_||s = 1.00000 \n", + "m_ref/m_s = 1836.00 \n", + "T_perp/T_par|s = 1.00000 \n", + "q_ref/q_s = -1.00000 \n", + "n_s/n_ref = 1.00000 \n", + "v_drift s/v_A,ref = 0.00000 \n", + "-=-=-=-=-=-=-=-=-=-\n", + "Searching over:\n", + "omega/Omega_ref \\in [ 1.000E-05, 1.000E-02]\n", + "gamma/Omega_ref \\in [-2.000E-03,-2.000E-06]\n", + "-=-=-=-=-=-=-=-=-=-\n", + " finding minima\n", + " minima found\n", + " 6 possible local minima found:\n", + "ir = 128 ii = 128\n", + " 1.0000E-02 -2.0000E-06\n", + "ir = 22 ii = 113\n", + " 1.7270E-03 -2.3614E-04\n", + "ir = 0 ii = 109\n", + " 1.0000E-05 -2.9858E-04\n", + "ir = 18 ii = 47\n", + " 1.4148E-03 -1.2664E-03\n", + "ir = 0 ii = 45\n", + " 1.0000E-05 -1.2976E-03\n", + "ir = 128 ii = 0\n", + " 1.0000E-02 -2.0000E-03\n", + "Refining roots:\n", + "Dispersion Solutions \n", + " 1 1.7177E-03 -2.3538E-04\n", + " Predicting FPC (gyrotropic coords)...\n", + "Input for Root 1: 1.7177E-03 -2.3538E-04\n", + "Dispersion Solutions: \n", + " 1 1.7177E-03 -2.3538E-04\n", + " WARNING! The gyro routine does not support computing moments at this time due to computational demand!\n", + " Assuming data folder already exists...\n", + " Assuming subfolder howes2017 already exists\n", + " Calculating fpc for species 1\n", + " Writing omega/kpar V_a normalization to file...\n", + " Calculating fpc for species 2\n", + " Writing omega/kpar V_a normalization to file...\n", + "-=-=-=-=-=-=-=-=-=-\n", + "Root Search:\n", + "-=-=-=-=-=-=-=-=-=-\n", + "Global Plasma Parameters:\n", + "k_perp rho_ref = 1.30000 \n", + "k_par rho_ref = 0.100000E-02\n", + "Beta_ref,par = 1.00000 \n", + "v_t,ref,par/c = 0.100000E-03\n", + "-=-=-=-=-=-=-=-=-=-\n", + "Parameters for Species : 1\n", + "T_||,ref/T_||s = 1.00000 \n", + "m_ref/m_s = 1.00000 \n", + "T_perp/T_par|s = 1.00000 \n", + "q_ref/q_s = 1.00000 \n", + "n_s/n_ref = 1.00000 \n", + "v_drift s/v_A,ref = 0.00000 \n", + "-=-=-=-=-=-=-=-=-=-\n", + "Parameters for Species : 2\n", + "T_||,ref/T_||s = 1.00000 \n", + "m_ref/m_s = 1836.00 \n", + "T_perp/T_par|s = 1.00000 \n", + "q_ref/q_s = -1.00000 \n", + "n_s/n_ref = 1.00000 \n", + "v_drift s/v_A,ref = 0.00000 \n", + "-=-=-=-=-=-=-=-=-=-\n", + "Searching over:\n", + "omega/Omega_ref \\in [ 1.000E-05, 1.000E-02]\n", + "gamma/Omega_ref \\in [-2.000E-03,-2.000E-06]\n", + "-=-=-=-=-=-=-=-=-=-\n", + " finding minima\n", + " minima found\n", + " 1 possible local minima found:\n", + "ir = 122 ii = 45\n", + " 9.5317E-03 -1.2976E-03\n", + "Refining roots:\n", + "Dispersion Solutions \n", + " 1 9.5218E-03 -1.2924E-03\n", + "-=-=-=-=-=-=-=-=-=-\n", + "Root Search:\n", + "-=-=-=-=-=-=-=-=-=-\n", + "Global Plasma Parameters:\n", + "k_perp rho_ref = 1.30000 \n", + "k_par rho_ref = 0.100000E-02\n", + "Beta_ref,par = 1.00000 \n", + "v_t,ref,par/c = 0.100000E-03\n", + "-=-=-=-=-=-=-=-=-=-\n", + "Parameters for Species : 1\n", + "T_||,ref/T_||s = 1.00000 \n", + "m_ref/m_s = 1.00000 \n", + "T_perp/T_par|s = 1.00000 \n", + "q_ref/q_s = 1.00000 \n", + "n_s/n_ref = 1.00000 \n", + "v_drift s/v_A,ref = 0.00000 \n", + "-=-=-=-=-=-=-=-=-=-\n", + "Parameters for Species : 2\n", + "T_||,ref/T_||s = 1.00000 \n", + "m_ref/m_s = 1836.00 \n", + "T_perp/T_par|s = 1.00000 \n", + "q_ref/q_s = -1.00000 \n", + "n_s/n_ref = 1.00000 \n", + "v_drift s/v_A,ref = 0.00000 \n", + "-=-=-=-=-=-=-=-=-=-\n", + "Searching over:\n", + "omega/Omega_ref \\in [ 1.000E-05, 1.000E-02]\n", + "gamma/Omega_ref \\in [-2.000E-03,-2.000E-06]\n", + "-=-=-=-=-=-=-=-=-=-\n", + " finding minima\n", + " minima found\n", + " 7 possible local minima found:\n", + "ir = 128 ii = 128\n", + " 1.0000E-02 -2.0000E-06\n", + "ir = 16 ii = 125\n", + " 1.2588E-03 -4.8828E-05\n", + "ir = 0 ii = 80\n", + " 1.0000E-05 -7.5125E-04\n", + "ir = 15 ii = 65\n", + " 1.1807E-03 -9.8539E-04\n", + "ir = 22 ii = 26\n", + " 1.7270E-03 -1.5942E-03\n", + "ir = 28 ii = 2\n", + " 2.1953E-03 -1.9688E-03\n", + "ir = 128 ii = 0\n", + " 1.0000E-02 -2.0000E-03\n", + "Refining roots:\n", + "Dispersion Solutions \n", + " 1 1.2362E-03 -4.4355E-05\n", + " Predicting FPC (gyrotropic coords)...\n", + "Input for Root 1: 1.2362E-03 -4.4355E-05\n", + "Dispersion Solutions: \n", + " 1 1.2362E-03 -4.4355E-05\n", + " WARNING! The gyro routine does not support computing moments at this time due to computational demand!\n", + " Assuming data folder already exists...\n", + " Assuming subfolder howes2017 already exists\n", + " Calculating fpc for species 1\n", + " Writing omega/kpar V_a normalization to file...\n", + " Calculating fpc for species 2\n", + " Writing omega/kpar V_a normalization to file...\n", + "-=-=-=-=-=-=-=-=-=-\n", + "Root Search:\n", + "-=-=-=-=-=-=-=-=-=-\n", + "Global Plasma Parameters:\n", + "k_perp rho_ref = 1.30000 \n", + "k_par rho_ref = 0.100000E-02\n", + "Beta_ref,par = 1.00000 \n", + "v_t,ref,par/c = 0.100000E-03\n", + "-=-=-=-=-=-=-=-=-=-\n", + "Parameters for Species : 1\n", + "T_||,ref/T_||s = 1.00000 \n", + "m_ref/m_s = 1.00000 \n", + "T_perp/T_par|s = 1.00000 \n", + "q_ref/q_s = 1.00000 \n", + "n_s/n_ref = 1.00000 \n", + "v_drift s/v_A,ref = 0.00000 \n", + "-=-=-=-=-=-=-=-=-=-\n", + "Parameters for Species : 2\n", + "T_||,ref/T_||s = 1.00000 \n", + "m_ref/m_s = 1836.00 \n", + "T_perp/T_par|s = 1.00000 \n", + "q_ref/q_s = -1.00000 \n", + "n_s/n_ref = 1.00000 \n", + "v_drift s/v_A,ref = 0.00000 \n", + "-=-=-=-=-=-=-=-=-=-\n", + "Searching over:\n", + "omega/Omega_ref \\in [ 1.000E-05, 1.000E-02]\n", + "gamma/Omega_ref \\in [-2.000E-03,-2.000E-06]\n", + "-=-=-=-=-=-=-=-=-=-\n", + " finding minima\n", + " minima found\n", + " 7 possible local minima found:\n", + "ir = 128 ii = 128\n", + " 1.0000E-02 -2.0000E-06\n", + "ir = 16 ii = 124\n", + " 1.2588E-03 -6.4437E-05\n", + "ir = 0 ii = 77\n", + " 1.0000E-05 -7.9808E-04\n", + "ir = 15 ii = 64\n", + " 1.1807E-03 -1.0010E-03\n", + "ir = 22 ii = 21\n", + " 1.7270E-03 -1.6722E-03\n", + "ir = 29 ii = 0\n", + " 2.2734E-03 -2.0000E-03\n", + "ir = 128 ii = 0\n", + " 1.0000E-02 -2.0000E-03\n", + "Refining roots:\n", + "Dispersion Solutions \n", + " 1 1.2676E-03 -6.9505E-05\n", + " Predicting FPC (gyrotropic coords)...\n", + "Input for Root 1: 1.2676E-03 -6.9505E-05\n", + "Dispersion Solutions: \n", + " 1 1.2676E-03 -6.9505E-05\n", + " WARNING! The gyro routine does not support computing moments at this time due to computational demand!\n", + " Assuming data folder already exists...\n", + " Assuming subfolder howes2017 already exists\n", + " Calculating fpc for species 1\n", + " Writing omega/kpar V_a normalization to file...\n", + " Calculating fpc for species 2\n", + " Writing omega/kpar V_a normalization to file...\n", + "-=-=-=-=-=-=-=-=-=-\n", + "Root Search:\n", + "-=-=-=-=-=-=-=-=-=-\n", + "Global Plasma Parameters:\n", + "k_perp rho_ref = 1.30000 \n", + "k_par rho_ref = 0.100000E-02\n", + "Beta_ref,par = 1.00000 \n", + "v_t,ref,par/c = 0.100000E-03\n", + "-=-=-=-=-=-=-=-=-=-\n", + "Parameters for Species : 1\n", + "T_||,ref/T_||s = 1.00000 \n", + "m_ref/m_s = 1.00000 \n", + "T_perp/T_par|s = 1.00000 \n", + "q_ref/q_s = 1.00000 \n", + "n_s/n_ref = 1.00000 \n", + "v_drift s/v_A,ref = 0.00000 \n", + "-=-=-=-=-=-=-=-=-=-\n", + "Parameters for Species : 2\n", + "T_||,ref/T_||s = 1.00000 \n", + "m_ref/m_s = 1836.00 \n", + "T_perp/T_par|s = 1.00000 \n", + "q_ref/q_s = -1.00000 \n", + "n_s/n_ref = 1.00000 \n", + "v_drift s/v_A,ref = 0.00000 \n", + "-=-=-=-=-=-=-=-=-=-\n", + "Searching over:\n", + "omega/Omega_ref \\in [ 1.000E-05, 1.000E-02]\n", + "gamma/Omega_ref \\in [-2.000E-03,-2.000E-06]\n", + "-=-=-=-=-=-=-=-=-=-\n", + " finding minima\n", + " minima found\n", + " 7 possible local minima found:\n", + "ir = 128 ii = 128\n", + " 1.0000E-02 -2.0000E-06\n", + "ir = 17 ii = 122\n", + " 1.3368E-03 -9.5656E-05\n", + "ir = 0 ii = 74\n", + " 1.0000E-05 -8.4491E-04\n", + "ir = 15 ii = 62\n", + " 1.1807E-03 -1.0322E-03\n", + "ir = 22 ii = 16\n", + " 1.7270E-03 -1.7503E-03\n", + "ir = 29 ii = 0\n", + " 2.2734E-03 -2.0000E-03\n", + "ir = 128 ii = 0\n", + " 1.0000E-02 -2.0000E-03\n", + "Refining roots:\n", + "Dispersion Solutions \n", + " 1 1.3032E-03 -9.2973E-05\n", + " Predicting FPC (gyrotropic coords)...\n", + "Input for Root 1: 1.3032E-03 -9.2973E-05\n", + "Dispersion Solutions: \n", + " 1 1.3032E-03 -9.2973E-05\n", + " WARNING! The gyro routine does not support computing moments at this time due to computational demand!\n", + " Assuming data folder already exists...\n", + " Assuming subfolder howes2017 already exists\n", + " Calculating fpc for species 1\n", + " Writing omega/kpar V_a normalization to file...\n", + " Calculating fpc for species 2\n", + " Writing omega/kpar V_a normalization to file...\n", + "-=-=-=-=-=-=-=-=-=-\n", + "Root Search:\n", + "-=-=-=-=-=-=-=-=-=-\n", + "Global Plasma Parameters:\n", + "k_perp rho_ref = 1.30000 \n", + "k_par rho_ref = 0.100000E-02\n", + "Beta_ref,par = 1.00000 \n", + "v_t,ref,par/c = 0.100000E-03\n", + "-=-=-=-=-=-=-=-=-=-\n", + "Parameters for Species : 1\n", + "T_||,ref/T_||s = 1.00000 \n", + "m_ref/m_s = 1.00000 \n", + "T_perp/T_par|s = 1.00000 \n", + "q_ref/q_s = 1.00000 \n", + "n_s/n_ref = 1.00000 \n", + "v_drift s/v_A,ref = 0.00000 \n", + "-=-=-=-=-=-=-=-=-=-\n", + "Parameters for Species : 2\n", + "T_||,ref/T_||s = 1.00000 \n", + "m_ref/m_s = 1836.00 \n", + "T_perp/T_par|s = 1.00000 \n", + "q_ref/q_s = -1.00000 \n", + "n_s/n_ref = 1.00000 \n", + "v_drift s/v_A,ref = 0.00000 \n", + "-=-=-=-=-=-=-=-=-=-\n", + "Searching over:\n", + "omega/Omega_ref \\in [ 1.000E-05, 1.000E-02]\n", + "gamma/Omega_ref \\in [-2.000E-03,-2.000E-06]\n", + "-=-=-=-=-=-=-=-=-=-\n", + " finding minima\n", + " minima found\n", + " 6 possible local minima found:\n", + "ir = 128 ii = 128\n", + " 1.0000E-02 -2.0000E-06\n", + "ir = 22 ii = 113\n", + " 1.7270E-03 -2.3614E-04\n", + "ir = 0 ii = 109\n", + " 1.0000E-05 -2.9858E-04\n", + "ir = 18 ii = 47\n", + " 1.4148E-03 -1.2664E-03\n", + "ir = 0 ii = 45\n", + " 1.0000E-05 -1.2976E-03\n", + "ir = 128 ii = 0\n", + " 1.0000E-02 -2.0000E-03\n", + "Refining roots:\n", + "Dispersion Solutions \n", + " 1 1.7177E-03 -2.3538E-04\n", + " Predicting FPC (gyrotropic coords)...\n", + "Input for Root 1: 1.7177E-03 -2.3538E-04\n", + "Dispersion Solutions: \n", + " 1 1.7177E-03 -2.3538E-04\n", + " WARNING! The gyro routine does not support computing moments at this time due to computational demand!\n", + " Assuming data folder already exists...\n", + " Assuming subfolder howes2017 already exists\n", + " Calculating fpc for species 1\n", + " Writing omega/kpar V_a normalization to file...\n", + " Calculating fpc for species 2\n", + " Writing omega/kpar V_a normalization to file...\n", + "-=-=-=-=-=-=-=-=-=-\n", + "Root Search:\n", + "-=-=-=-=-=-=-=-=-=-\n", + "Global Plasma Parameters:\n", + "k_perp rho_ref = 1.30000 \n", + "k_par rho_ref = 0.100000E-02\n", + "Beta_ref,par = 1.00000 \n", + "v_t,ref,par/c = 0.100000E-03\n", + "-=-=-=-=-=-=-=-=-=-\n", + "Parameters for Species : 1\n", + "T_||,ref/T_||s = 1.00000 \n", + "m_ref/m_s = 1.00000 \n", + "T_perp/T_par|s = 1.00000 \n", + "q_ref/q_s = 1.00000 \n", + "n_s/n_ref = 1.00000 \n", + "v_drift s/v_A,ref = 0.00000 \n", + "-=-=-=-=-=-=-=-=-=-\n", + "Parameters for Species : 2\n", + "T_||,ref/T_||s = 1.00000 \n", + "m_ref/m_s = 1836.00 \n", + "T_perp/T_par|s = 1.00000 \n", + "q_ref/q_s = -1.00000 \n", + "n_s/n_ref = 1.00000 \n", + "v_drift s/v_A,ref = 0.00000 \n", + "-=-=-=-=-=-=-=-=-=-\n", + "Searching over:\n", + "omega/Omega_ref \\in [ 1.000E-05, 1.000E-02]\n", + "gamma/Omega_ref \\in [-2.000E-03,-2.000E-06]\n", + "-=-=-=-=-=-=-=-=-=-\n", + " finding minima\n", + " minima found\n", + " 1 possible local minima found:\n", + "ir = 122 ii = 45\n", + " 9.5317E-03 -1.2976E-03\n", + "Refining roots:\n", + "Dispersion Solutions \n", + " 1 9.5218E-03 -1.2924E-03\n", + "-=-=-=-=-=-=-=-=-=-\n", + "Root Search:\n", + "-=-=-=-=-=-=-=-=-=-\n", + "Global Plasma Parameters:\n", + "k_perp rho_ref = 1.30000 \n", + "k_par rho_ref = 0.100000E-02\n", + "Beta_ref,par = 1.00000 \n", + "v_t,ref,par/c = 0.100000E-03\n", + "-=-=-=-=-=-=-=-=-=-\n", + "Parameters for Species : 1\n", + "T_||,ref/T_||s = 1.00000 \n", + "m_ref/m_s = 1.00000 \n", + "T_perp/T_par|s = 1.00000 \n", + "q_ref/q_s = 1.00000 \n", + "n_s/n_ref = 1.00000 \n", + "v_drift s/v_A,ref = 0.00000 \n", + "-=-=-=-=-=-=-=-=-=-\n", + "Parameters for Species : 2\n", + "T_||,ref/T_||s = 1.00000 \n", + "m_ref/m_s = 1836.00 \n", + "T_perp/T_par|s = 1.00000 \n", + "q_ref/q_s = -1.00000 \n", + "n_s/n_ref = 1.00000 \n", + "v_drift s/v_A,ref = 0.00000 \n", + "-=-=-=-=-=-=-=-=-=-\n", + "Searching over:\n", + "omega/Omega_ref \\in [ 1.000E-05, 1.000E-02]\n", + "gamma/Omega_ref \\in [-2.000E-03,-2.000E-06]\n", + "-=-=-=-=-=-=-=-=-=-\n", + " finding minima\n", + " minima found\n", + " 7 possible local minima found:\n", + "ir = 128 ii = 128\n", + " 1.0000E-02 -2.0000E-06\n", + "ir = 16 ii = 125\n", + " 1.2588E-03 -4.8828E-05\n", + "ir = 0 ii = 80\n", + " 1.0000E-05 -7.5125E-04\n", + "ir = 15 ii = 65\n", + " 1.1807E-03 -9.8539E-04\n", + "ir = 22 ii = 26\n", + " 1.7270E-03 -1.5942E-03\n", + "ir = 28 ii = 2\n", + " 2.1953E-03 -1.9688E-03\n", + "ir = 128 ii = 0\n", + " 1.0000E-02 -2.0000E-03\n", + "Refining roots:\n", + "Dispersion Solutions \n", + " 1 1.2362E-03 -4.4355E-05\n", + " Predicting FPC (gyrotropic coords)...\n", + "Input for Root 1: 1.2362E-03 -4.4355E-05\n", + "Dispersion Solutions: \n", + " 1 1.2362E-03 -4.4355E-05\n", + " WARNING! The gyro routine does not support computing moments at this time due to computational demand!\n", + " Assuming data folder already exists...\n", + " Assuming subfolder howes2017 already exists\n", + " Calculating fpc for species 1\n", + " Writing omega/kpar V_a normalization to file...\n", + " Calculating fpc for species 2\n", + " Writing omega/kpar V_a normalization to file...\n", + "-=-=-=-=-=-=-=-=-=-\n", + "Root Search:\n", + "-=-=-=-=-=-=-=-=-=-\n", + "Global Plasma Parameters:\n", + "k_perp rho_ref = 1.30000 \n", + "k_par rho_ref = 0.100000E-02\n", + "Beta_ref,par = 1.00000 \n", + "v_t,ref,par/c = 0.100000E-03\n", + "-=-=-=-=-=-=-=-=-=-\n", + "Parameters for Species : 1\n", + "T_||,ref/T_||s = 1.00000 \n", + "m_ref/m_s = 1.00000 \n", + "T_perp/T_par|s = 1.00000 \n", + "q_ref/q_s = 1.00000 \n", + "n_s/n_ref = 1.00000 \n", + "v_drift s/v_A,ref = 0.00000 \n", + "-=-=-=-=-=-=-=-=-=-\n", + "Parameters for Species : 2\n", + "T_||,ref/T_||s = 1.00000 \n", + "m_ref/m_s = 1836.00 \n", + "T_perp/T_par|s = 1.00000 \n", + "q_ref/q_s = -1.00000 \n", + "n_s/n_ref = 1.00000 \n", + "v_drift s/v_A,ref = 0.00000 \n", + "-=-=-=-=-=-=-=-=-=-\n", + "Searching over:\n", + "omega/Omega_ref \\in [ 1.000E-05, 1.000E-02]\n", + "gamma/Omega_ref \\in [-2.000E-03,-2.000E-06]\n", + "-=-=-=-=-=-=-=-=-=-\n", + " finding minima\n", + " minima found\n", + " 7 possible local minima found:\n", + "ir = 128 ii = 128\n", + " 1.0000E-02 -2.0000E-06\n", + "ir = 16 ii = 124\n", + " 1.2588E-03 -6.4437E-05\n", + "ir = 0 ii = 77\n", + " 1.0000E-05 -7.9808E-04\n", + "ir = 15 ii = 64\n", + " 1.1807E-03 -1.0010E-03\n", + "ir = 22 ii = 21\n", + " 1.7270E-03 -1.6722E-03\n", + "ir = 29 ii = 0\n", + " 2.2734E-03 -2.0000E-03\n", + "ir = 128 ii = 0\n", + " 1.0000E-02 -2.0000E-03\n", + "Refining roots:\n", + "Dispersion Solutions \n", + " 1 1.2676E-03 -6.9505E-05\n", + " Predicting FPC (gyrotropic coords)...\n", + "Input for Root 1: 1.2676E-03 -6.9505E-05\n", + "Dispersion Solutions: \n", + " 1 1.2676E-03 -6.9505E-05\n", + " WARNING! The gyro routine does not support computing moments at this time due to computational demand!\n", + " Assuming data folder already exists...\n", + " Assuming subfolder howes2017 already exists\n", + " Calculating fpc for species 1\n", + " Writing omega/kpar V_a normalization to file...\n", + " Calculating fpc for species 2\n", + " Writing omega/kpar V_a normalization to file...\n", + "-=-=-=-=-=-=-=-=-=-\n", + "Root Search:\n", + "-=-=-=-=-=-=-=-=-=-\n", + "Global Plasma Parameters:\n", + "k_perp rho_ref = 1.30000 \n", + "k_par rho_ref = 0.100000E-02\n", + "Beta_ref,par = 1.00000 \n", + "v_t,ref,par/c = 0.100000E-03\n", + "-=-=-=-=-=-=-=-=-=-\n", + "Parameters for Species : 1\n", + "T_||,ref/T_||s = 1.00000 \n", + "m_ref/m_s = 1.00000 \n", + "T_perp/T_par|s = 1.00000 \n", + "q_ref/q_s = 1.00000 \n", + "n_s/n_ref = 1.00000 \n", + "v_drift s/v_A,ref = 0.00000 \n", + "-=-=-=-=-=-=-=-=-=-\n", + "Parameters for Species : 2\n", + "T_||,ref/T_||s = 1.00000 \n", + "m_ref/m_s = 1836.00 \n", + "T_perp/T_par|s = 1.00000 \n", + "q_ref/q_s = -1.00000 \n", + "n_s/n_ref = 1.00000 \n", + "v_drift s/v_A,ref = 0.00000 \n", + "-=-=-=-=-=-=-=-=-=-\n", + "Searching over:\n", + "omega/Omega_ref \\in [ 1.000E-05, 1.000E-02]\n", + "gamma/Omega_ref \\in [-2.000E-03,-2.000E-06]\n", + "-=-=-=-=-=-=-=-=-=-\n", + " finding minima\n", + " minima found\n", + " 7 possible local minima found:\n", + "ir = 128 ii = 128\n", + " 1.0000E-02 -2.0000E-06\n", + "ir = 17 ii = 122\n", + " 1.3368E-03 -9.5656E-05\n", + "ir = 0 ii = 74\n", + " 1.0000E-05 -8.4491E-04\n", + "ir = 15 ii = 62\n", + " 1.1807E-03 -1.0322E-03\n", + "ir = 22 ii = 16\n", + " 1.7270E-03 -1.7503E-03\n", + "ir = 29 ii = 0\n", + " 2.2734E-03 -2.0000E-03\n", + "ir = 128 ii = 0\n", + " 1.0000E-02 -2.0000E-03\n", + "Refining roots:\n", + "Dispersion Solutions \n", + " 1 1.3032E-03 -9.2973E-05\n", + " Predicting FPC (gyrotropic coords)...\n", + "Input for Root 1: 1.3032E-03 -9.2973E-05\n", + "Dispersion Solutions: \n", + " 1 1.3032E-03 -9.2973E-05\n", + " WARNING! The gyro routine does not support computing moments at this time due to computational demand!\n", + " Assuming data folder already exists...\n", + " Assuming subfolder howes2017 already exists\n", + " Calculating fpc for species 1\n", + " Writing omega/kpar V_a normalization to file...\n", + " Calculating fpc for species 2\n", + " Writing omega/kpar V_a normalization to file...\n", + "-=-=-=-=-=-=-=-=-=-\n", + "Root Search:\n", + "-=-=-=-=-=-=-=-=-=-\n", + "Global Plasma Parameters:\n", + "k_perp rho_ref = 1.30000 \n", + "k_par rho_ref = 0.100000E-02\n", + "Beta_ref,par = 1.00000 \n", + "v_t,ref,par/c = 0.100000E-03\n", + "-=-=-=-=-=-=-=-=-=-\n", + "Parameters for Species : 1\n", + "T_||,ref/T_||s = 1.00000 \n", + "m_ref/m_s = 1.00000 \n", + "T_perp/T_par|s = 1.00000 \n", + "q_ref/q_s = 1.00000 \n", + "n_s/n_ref = 1.00000 \n", + "v_drift s/v_A,ref = 0.00000 \n", + "-=-=-=-=-=-=-=-=-=-\n", + "Parameters for Species : 2\n", + "T_||,ref/T_||s = 1.00000 \n", + "m_ref/m_s = 1836.00 \n", + "T_perp/T_par|s = 1.00000 \n", + "q_ref/q_s = -1.00000 \n", + "n_s/n_ref = 1.00000 \n", + "v_drift s/v_A,ref = 0.00000 \n", + "-=-=-=-=-=-=-=-=-=-\n", + "Searching over:\n", + "omega/Omega_ref \\in [ 1.000E-05, 1.000E-02]\n", + "gamma/Omega_ref \\in [-2.000E-03,-2.000E-06]\n", + "-=-=-=-=-=-=-=-=-=-\n", + " finding minima\n", + " minima found\n", + " 6 possible local minima found:\n", + "ir = 128 ii = 128\n", + " 1.0000E-02 -2.0000E-06\n", + "ir = 22 ii = 113\n", + " 1.7270E-03 -2.3614E-04\n", + "ir = 0 ii = 109\n", + " 1.0000E-05 -2.9858E-04\n", + "ir = 18 ii = 47\n", + " 1.4148E-03 -1.2664E-03\n", + "ir = 0 ii = 45\n", + " 1.0000E-05 -1.2976E-03\n", + "ir = 128 ii = 0\n", + " 1.0000E-02 -2.0000E-03\n", + "Refining roots:\n", + "Dispersion Solutions \n", + " 1 1.7177E-03 -2.3538E-04\n", + " Predicting FPC (gyrotropic coords)...\n", + "Input for Root 1: 1.7177E-03 -2.3538E-04\n", + "Dispersion Solutions: \n", + " 1 1.7177E-03 -2.3538E-04\n", + " WARNING! The gyro routine does not support computing moments at this time due to computational demand!\n", + " Assuming data folder already exists...\n", + " Assuming subfolder howes2017 already exists\n", + " Calculating fpc for species 1\n", + " Writing omega/kpar V_a normalization to file...\n", + " Calculating fpc for species 2\n", + " Writing omega/kpar V_a normalization to file...\n", + "-=-=-=-=-=-=-=-=-=-\n", + "Root Search:\n", + "-=-=-=-=-=-=-=-=-=-\n", + "Global Plasma Parameters:\n", + "k_perp rho_ref = 1.30000 \n", + "k_par rho_ref = 0.100000E-02\n", + "Beta_ref,par = 1.00000 \n", + "v_t,ref,par/c = 0.100000E-03\n", + "-=-=-=-=-=-=-=-=-=-\n", + "Parameters for Species : 1\n", + "T_||,ref/T_||s = 1.00000 \n", + "m_ref/m_s = 1.00000 \n", + "T_perp/T_par|s = 1.00000 \n", + "q_ref/q_s = 1.00000 \n", + "n_s/n_ref = 1.00000 \n", + "v_drift s/v_A,ref = 0.00000 \n", + "-=-=-=-=-=-=-=-=-=-\n", + "Parameters for Species : 2\n", + "T_||,ref/T_||s = 1.00000 \n", + "m_ref/m_s = 1836.00 \n", + "T_perp/T_par|s = 1.00000 \n", + "q_ref/q_s = -1.00000 \n", + "n_s/n_ref = 1.00000 \n", + "v_drift s/v_A,ref = 0.00000 \n", + "-=-=-=-=-=-=-=-=-=-\n", + "Searching over:\n", + "omega/Omega_ref \\in [ 1.000E-05, 1.000E-02]\n", + "gamma/Omega_ref \\in [-2.000E-03,-2.000E-06]\n", + "-=-=-=-=-=-=-=-=-=-\n", + " finding minima\n", + " minima found\n", + " 1 possible local minima found:\n", + "ir = 122 ii = 45\n", + " 9.5317E-03 -1.2976E-03\n", + "Refining roots:\n", + "Dispersion Solutions \n", + " 1 9.5218E-03 -1.2924E-03\n", + "-=-=-=-=-=-=-=-=-=-\n", + "Root Search:\n", + "-=-=-=-=-=-=-=-=-=-\n", + "Global Plasma Parameters:\n", + "k_perp rho_ref = 1.30000 \n", + "k_par rho_ref = 0.100000E-02\n", + "Beta_ref,par = 1.00000 \n", + "v_t,ref,par/c = 0.100000E-03\n", + "-=-=-=-=-=-=-=-=-=-\n", + "Parameters for Species : 1\n", + "T_||,ref/T_||s = 1.00000 \n", + "m_ref/m_s = 1.00000 \n", + "T_perp/T_par|s = 1.00000 \n", + "q_ref/q_s = 1.00000 \n", + "n_s/n_ref = 1.00000 \n", + "v_drift s/v_A,ref = 0.00000 \n", + "-=-=-=-=-=-=-=-=-=-\n", + "Parameters for Species : 2\n", + "T_||,ref/T_||s = 1.00000 \n", + "m_ref/m_s = 1836.00 \n", + "T_perp/T_par|s = 1.00000 \n", + "q_ref/q_s = -1.00000 \n", + "n_s/n_ref = 1.00000 \n", + "v_drift s/v_A,ref = 0.00000 \n", + "-=-=-=-=-=-=-=-=-=-\n", + "Searching over:\n", + "omega/Omega_ref \\in [ 1.000E-05, 1.000E-02]\n", + "gamma/Omega_ref \\in [-2.000E-03,-2.000E-06]\n", + "-=-=-=-=-=-=-=-=-=-\n", + " finding minima\n", + " minima found\n", + " 7 possible local minima found:\n", + "ir = 128 ii = 128\n", + " 1.0000E-02 -2.0000E-06\n", + "ir = 16 ii = 125\n", + " 1.2588E-03 -4.8828E-05\n", + "ir = 0 ii = 80\n", + " 1.0000E-05 -7.5125E-04\n", + "ir = 15 ii = 65\n", + " 1.1807E-03 -9.8539E-04\n", + "ir = 22 ii = 26\n", + " 1.7270E-03 -1.5942E-03\n", + "ir = 28 ii = 2\n", + " 2.1953E-03 -1.9688E-03\n", + "ir = 128 ii = 0\n", + " 1.0000E-02 -2.0000E-03\n", + "Refining roots:\n", + "Dispersion Solutions \n", + " 1 1.2362E-03 -4.4355E-05\n", + " Predicting FPC (gyrotropic coords)...\n", + "Input for Root 1: 1.2362E-03 -4.4355E-05\n", + "Dispersion Solutions: \n", + " 1 1.2362E-03 -4.4355E-05\n", + " WARNING! The gyro routine does not support computing moments at this time due to computational demand!\n", + " Assuming data folder already exists...\n", + " Assuming subfolder howes2017 already exists\n", + " Calculating fpc for species 1\n", + " Writing omega/kpar V_a normalization to file...\n", + " Calculating fpc for species 2\n", + " Writing omega/kpar V_a normalization to file...\n", + "-=-=-=-=-=-=-=-=-=-\n", + "Root Search:\n", + "-=-=-=-=-=-=-=-=-=-\n", + "Global Plasma Parameters:\n", + "k_perp rho_ref = 1.30000 \n", + "k_par rho_ref = 0.100000E-02\n", + "Beta_ref,par = 1.00000 \n", + "v_t,ref,par/c = 0.100000E-03\n", + "-=-=-=-=-=-=-=-=-=-\n", + "Parameters for Species : 1\n", + "T_||,ref/T_||s = 1.00000 \n", + "m_ref/m_s = 1.00000 \n", + "T_perp/T_par|s = 1.00000 \n", + "q_ref/q_s = 1.00000 \n", + "n_s/n_ref = 1.00000 \n", + "v_drift s/v_A,ref = 0.00000 \n", + "-=-=-=-=-=-=-=-=-=-\n", + "Parameters for Species : 2\n", + "T_||,ref/T_||s = 1.00000 \n", + "m_ref/m_s = 1836.00 \n", + "T_perp/T_par|s = 1.00000 \n", + "q_ref/q_s = -1.00000 \n", + "n_s/n_ref = 1.00000 \n", + "v_drift s/v_A,ref = 0.00000 \n", + "-=-=-=-=-=-=-=-=-=-\n", + "Searching over:\n", + "omega/Omega_ref \\in [ 1.000E-05, 1.000E-02]\n", + "gamma/Omega_ref \\in [-2.000E-03,-2.000E-06]\n", + "-=-=-=-=-=-=-=-=-=-\n", + " finding minima\n", + " minima found\n", + " 7 possible local minima found:\n", + "ir = 128 ii = 128\n", + " 1.0000E-02 -2.0000E-06\n", + "ir = 16 ii = 124\n", + " 1.2588E-03 -6.4437E-05\n", + "ir = 0 ii = 77\n", + " 1.0000E-05 -7.9808E-04\n", + "ir = 15 ii = 64\n", + " 1.1807E-03 -1.0010E-03\n", + "ir = 22 ii = 21\n", + " 1.7270E-03 -1.6722E-03\n", + "ir = 29 ii = 0\n", + " 2.2734E-03 -2.0000E-03\n", + "ir = 128 ii = 0\n", + " 1.0000E-02 -2.0000E-03\n", + "Refining roots:\n", + "Dispersion Solutions \n", + " 1 1.2676E-03 -6.9505E-05\n", + " Predicting FPC (gyrotropic coords)...\n", + "Input for Root 1: 1.2676E-03 -6.9505E-05\n", + "Dispersion Solutions: \n", + " 1 1.2676E-03 -6.9505E-05\n", + " WARNING! The gyro routine does not support computing moments at this time due to computational demand!\n", + " Assuming data folder already exists...\n", + " Assuming subfolder howes2017 already exists\n", + " Calculating fpc for species 1\n", + " Writing omega/kpar V_a normalization to file...\n", + " Calculating fpc for species 2\n", + " Writing omega/kpar V_a normalization to file...\n", + "-=-=-=-=-=-=-=-=-=-\n", + "Root Search:\n", + "-=-=-=-=-=-=-=-=-=-\n", + "Global Plasma Parameters:\n", + "k_perp rho_ref = 1.30000 \n", + "k_par rho_ref = 0.100000E-02\n", + "Beta_ref,par = 1.00000 \n", + "v_t,ref,par/c = 0.100000E-03\n", + "-=-=-=-=-=-=-=-=-=-\n", + "Parameters for Species : 1\n", + "T_||,ref/T_||s = 1.00000 \n", + "m_ref/m_s = 1.00000 \n", + "T_perp/T_par|s = 1.00000 \n", + "q_ref/q_s = 1.00000 \n", + "n_s/n_ref = 1.00000 \n", + "v_drift s/v_A,ref = 0.00000 \n", + "-=-=-=-=-=-=-=-=-=-\n", + "Parameters for Species : 2\n", + "T_||,ref/T_||s = 1.00000 \n", + "m_ref/m_s = 1836.00 \n", + "T_perp/T_par|s = 1.00000 \n", + "q_ref/q_s = -1.00000 \n", + "n_s/n_ref = 1.00000 \n", + "v_drift s/v_A,ref = 0.00000 \n", + "-=-=-=-=-=-=-=-=-=-\n", + "Searching over:\n", + "omega/Omega_ref \\in [ 1.000E-05, 1.000E-02]\n", + "gamma/Omega_ref \\in [-2.000E-03,-2.000E-06]\n", + "-=-=-=-=-=-=-=-=-=-\n", + " finding minima\n", + " minima found\n", + " 7 possible local minima found:\n", + "ir = 128 ii = 128\n", + " 1.0000E-02 -2.0000E-06\n", + "ir = 17 ii = 122\n", + " 1.3368E-03 -9.5656E-05\n", + "ir = 0 ii = 74\n", + " 1.0000E-05 -8.4491E-04\n", + "ir = 15 ii = 62\n", + " 1.1807E-03 -1.0322E-03\n", + "ir = 22 ii = 16\n", + " 1.7270E-03 -1.7503E-03\n", + "ir = 29 ii = 0\n", + " 2.2734E-03 -2.0000E-03\n", + "ir = 128 ii = 0\n", + " 1.0000E-02 -2.0000E-03\n", + "Refining roots:\n", + "Dispersion Solutions \n", + " 1 1.3032E-03 -9.2973E-05\n", + " Predicting FPC (gyrotropic coords)...\n", + "Input for Root 1: 1.3032E-03 -9.2973E-05\n", + "Dispersion Solutions: \n", + " 1 1.3032E-03 -9.2973E-05\n", + " WARNING! The gyro routine does not support computing moments at this time due to computational demand!\n", + " Assuming data folder already exists...\n", + " Assuming subfolder howes2017 already exists\n", + " Calculating fpc for species 1\n", + " Writing omega/kpar V_a normalization to file...\n", + " Calculating fpc for species 2\n", + " Writing omega/kpar V_a normalization to file...\n", + "-=-=-=-=-=-=-=-=-=-\n", + "Root Search:\n", + "-=-=-=-=-=-=-=-=-=-\n", + "Global Plasma Parameters:\n", + "k_perp rho_ref = 1.30000 \n", + "k_par rho_ref = 0.100000E-02\n", + "Beta_ref,par = 1.00000 \n", + "v_t,ref,par/c = 0.100000E-03\n", + "-=-=-=-=-=-=-=-=-=-\n", + "Parameters for Species : 1\n", + "T_||,ref/T_||s = 1.00000 \n", + "m_ref/m_s = 1.00000 \n", + "T_perp/T_par|s = 1.00000 \n", + "q_ref/q_s = 1.00000 \n", + "n_s/n_ref = 1.00000 \n", + "v_drift s/v_A,ref = 0.00000 \n", + "-=-=-=-=-=-=-=-=-=-\n", + "Parameters for Species : 2\n", + "T_||,ref/T_||s = 1.00000 \n", + "m_ref/m_s = 1836.00 \n", + "T_perp/T_par|s = 1.00000 \n", + "q_ref/q_s = -1.00000 \n", + "n_s/n_ref = 1.00000 \n", + "v_drift s/v_A,ref = 0.00000 \n", + "-=-=-=-=-=-=-=-=-=-\n", + "Searching over:\n", + "omega/Omega_ref \\in [ 1.000E-05, 1.000E-02]\n", + "gamma/Omega_ref \\in [-2.000E-03,-2.000E-06]\n", + "-=-=-=-=-=-=-=-=-=-\n", + " finding minima\n", + " minima found\n", + " 7 possible local minima found:\n", + "ir = 128 ii = 128\n", + " 1.0000E-02 -2.0000E-06\n", + "ir = 17 ii = 120\n", + " 1.3368E-03 -1.2687E-04\n", + "ir = 0 ii = 69\n", + " 1.0000E-05 -9.2295E-04\n", + "ir = 16 ii = 60\n", + " 1.2588E-03 -1.0634E-03\n", + "ir = 22 ii = 10\n", + " 1.7270E-03 -1.8439E-03\n", + "ir = 30 ii = 0\n", + " 2.3514E-03 -2.0000E-03\n", + "ir = 128 ii = 0\n", + " 1.0000E-02 -2.0000E-03\n", + "Refining roots:\n", + "Dispersion Solutions \n", + " 1 1.3568E-03 -1.2191E-04\n", + " Predicting FPC (gyrotropic coords)...\n", + "Input for Root 1: 1.3568E-03 -1.2191E-04\n", + "Dispersion Solutions: \n", + " 1 1.3568E-03 -1.2191E-04\n", + " WARNING! The gyro routine does not support computing moments at this time due to computational demand!\n", + " Assuming data folder already exists...\n", + " Assuming subfolder howes2017 already exists\n", + " Calculating fpc for species 1\n", + " Writing omega/kpar V_a normalization to file...\n", + " Calculating fpc for species 2\n", + " Writing omega/kpar V_a normalization to file...\n", + "-=-=-=-=-=-=-=-=-=-\n", + "Root Search:\n", + "-=-=-=-=-=-=-=-=-=-\n", + "Global Plasma Parameters:\n", + "k_perp rho_ref = 1.30000 \n", + "k_par rho_ref = 0.100000E-02\n", + "Beta_ref,par = 1.00000 \n", + "v_t,ref,par/c = 0.100000E-03\n", + "-=-=-=-=-=-=-=-=-=-\n", + "Parameters for Species : 1\n", + "T_||,ref/T_||s = 1.00000 \n", + "m_ref/m_s = 1.00000 \n", + "T_perp/T_par|s = 1.00000 \n", + "q_ref/q_s = 1.00000 \n", + "n_s/n_ref = 1.00000 \n", + "v_drift s/v_A,ref = 0.00000 \n", + "-=-=-=-=-=-=-=-=-=-\n", + "Parameters for Species : 2\n", + "T_||,ref/T_||s = 1.00000 \n", + "m_ref/m_s = 1836.00 \n", + "T_perp/T_par|s = 1.00000 \n", + "q_ref/q_s = -1.00000 \n", + "n_s/n_ref = 1.00000 \n", + "v_drift s/v_A,ref = 0.00000 \n", + "-=-=-=-=-=-=-=-=-=-\n", + "Searching over:\n", + "omega/Omega_ref \\in [ 1.000E-05, 1.000E-02]\n", + "gamma/Omega_ref \\in [-2.000E-03,-2.000E-06]\n", + "-=-=-=-=-=-=-=-=-=-\n", + " finding minima\n", + " minima found\n", + " 8 possible local minima found:\n", + "ir = 128 ii = 128\n", + " 1.0000E-02 -2.0000E-06\n", + "ir = 0 ii = 120\n", + " 1.0000E-05 -1.2687E-04\n", + "ir = 18 ii = 118\n", + " 1.4148E-03 -1.5809E-04\n", + "ir = 0 ii = 63\n", + " 1.0000E-05 -1.0166E-03\n", + "ir = 16 ii = 57\n", + " 1.2588E-03 -1.1103E-03\n", + "ir = 22 ii = 2\n", + " 1.7270E-03 -1.9688E-03\n", + "ir = 32 ii = 0\n", + " 2.5075E-03 -2.0000E-03\n", + "ir = 128 ii = 0\n", + " 1.0000E-02 -2.0000E-03\n", + "Refining roots:\n", + "Dispersion Solutions \n", + " 1 5.9952E-15 -1.2446E-04\n", + " Predicting FPC (gyrotropic coords)...\n", + "Input for Root 1: 5.9952E-15 -1.2446E-04\n", + "Dispersion Solutions: \n", + " 1 1.0521E-18 -1.2446E-04\n", + " WARNING! The gyro routine does not support computing moments at this time due to computational demand!\n", + " Assuming data folder already exists...\n", + " Assuming subfolder howes2017 already exists\n", + " Calculating fpc for species 1\n", + " Writing omega/kpar V_a normalization to file...\n", + " Calculating fpc for species 2\n", + " Writing omega/kpar V_a normalization to file...\n", + "-=-=-=-=-=-=-=-=-=-\n", + "Root Search:\n", + "-=-=-=-=-=-=-=-=-=-\n", + "Global Plasma Parameters:\n", + "k_perp rho_ref = 1.30000 \n", + "k_par rho_ref = 0.100000E-02\n", + "Beta_ref,par = 1.00000 \n", + "v_t,ref,par/c = 0.100000E-03\n", + "-=-=-=-=-=-=-=-=-=-\n", + "Parameters for Species : 1\n", + "T_||,ref/T_||s = 1.00000 \n", + "m_ref/m_s = 1.00000 \n", + "T_perp/T_par|s = 1.00000 \n", + "q_ref/q_s = 1.00000 \n", + "n_s/n_ref = 1.00000 \n", + "v_drift s/v_A,ref = 0.00000 \n", + "-=-=-=-=-=-=-=-=-=-\n", + "Parameters for Species : 2\n", + "T_||,ref/T_||s = 1.00000 \n", + "m_ref/m_s = 1836.00 \n", + "T_perp/T_par|s = 1.00000 \n", + "q_ref/q_s = -1.00000 \n", + "n_s/n_ref = 1.00000 \n", + "v_drift s/v_A,ref = 0.00000 \n", + "-=-=-=-=-=-=-=-=-=-\n", + "Searching over:\n", + "omega/Omega_ref \\in [ 1.000E-05, 1.000E-02]\n", + "gamma/Omega_ref \\in [-2.000E-03,-2.000E-06]\n", + "-=-=-=-=-=-=-=-=-=-\n", + " finding minima\n", + " minima found\n", + " 6 possible local minima found:\n", + "ir = 128 ii = 128\n", + " 1.0000E-02 -2.0000E-06\n", + "ir = 22 ii = 113\n", + " 1.7270E-03 -2.3614E-04\n", + "ir = 0 ii = 109\n", + " 1.0000E-05 -2.9858E-04\n", + "ir = 18 ii = 47\n", + " 1.4148E-03 -1.2664E-03\n", + "ir = 0 ii = 45\n", + " 1.0000E-05 -1.2976E-03\n", + "ir = 128 ii = 0\n", + " 1.0000E-02 -2.0000E-03\n", + "Refining roots:\n", + "Dispersion Solutions \n", + " 1 1.7177E-03 -2.3538E-04\n", + " Predicting FPC (gyrotropic coords)...\n", + "Input for Root 1: 1.7177E-03 -2.3538E-04\n", + "Dispersion Solutions: \n", + " 1 1.7177E-03 -2.3538E-04\n", + " WARNING! The gyro routine does not support computing moments at this time due to computational demand!\n", + " Assuming data folder already exists...\n", + " Assuming subfolder howes2017 already exists\n", + " Calculating fpc for species 1\n", + " Writing omega/kpar V_a normalization to file...\n", + " Calculating fpc for species 2\n", + " Writing omega/kpar V_a normalization to file...\n", + "-=-=-=-=-=-=-=-=-=-\n", + "Root Search:\n", + "-=-=-=-=-=-=-=-=-=-\n", + "Global Plasma Parameters:\n", + "k_perp rho_ref = 1.30000 \n", + "k_par rho_ref = 0.100000E-02\n", + "Beta_ref,par = 1.00000 \n", + "v_t,ref,par/c = 0.100000E-03\n", + "-=-=-=-=-=-=-=-=-=-\n", + "Parameters for Species : 1\n", + "T_||,ref/T_||s = 1.00000 \n", + "m_ref/m_s = 1.00000 \n", + "T_perp/T_par|s = 1.00000 \n", + "q_ref/q_s = 1.00000 \n", + "n_s/n_ref = 1.00000 \n", + "v_drift s/v_A,ref = 0.00000 \n", + "-=-=-=-=-=-=-=-=-=-\n", + "Parameters for Species : 2\n", + "T_||,ref/T_||s = 1.00000 \n", + "m_ref/m_s = 1836.00 \n", + "T_perp/T_par|s = 1.00000 \n", + "q_ref/q_s = -1.00000 \n", + "n_s/n_ref = 1.00000 \n", + "v_drift s/v_A,ref = 0.00000 \n", + "-=-=-=-=-=-=-=-=-=-\n", + "Searching over:\n", + "omega/Omega_ref \\in [ 1.000E-05, 1.000E-02]\n", + "gamma/Omega_ref \\in [-2.000E-03,-2.000E-06]\n", + "-=-=-=-=-=-=-=-=-=-\n", + " finding minima\n", + " minima found\n", + " 7 possible local minima found:\n", + "ir = 128 ii = 128\n", + " 1.0000E-02 -2.0000E-06\n", + "ir = 16 ii = 125\n", + " 1.2588E-03 -4.8828E-05\n", + "ir = 0 ii = 80\n", + " 1.0000E-05 -7.5125E-04\n", + "ir = 15 ii = 65\n", + " 1.1807E-03 -9.8539E-04\n", + "ir = 22 ii = 26\n", + " 1.7270E-03 -1.5942E-03\n", + "ir = 28 ii = 2\n", + " 2.1953E-03 -1.9688E-03\n", + "ir = 128 ii = 0\n", + " 1.0000E-02 -2.0000E-03\n", + "Refining roots:\n", + "Dispersion Solutions \n", + " 1 1.2362E-03 -4.4355E-05\n", + " Predicting FPC (gyrotropic coords)...\n", + "Input for Root 1: 1.2362E-03 -4.4355E-05\n", + "Dispersion Solutions: \n", + " 1 1.2362E-03 -4.4355E-05\n", + " WARNING! The gyro routine does not support computing moments at this time due to computational demand!\n", + " Assuming data folder already exists...\n", + " Assuming subfolder howes2017 already exists\n", + " Calculating fpc for species 1\n", + " Writing omega/kpar V_a normalization to file...\n", + " Calculating fpc for species 2\n", + " Writing omega/kpar V_a normalization to file...\n", + "-=-=-=-=-=-=-=-=-=-\n", + "Root Search:\n", + "-=-=-=-=-=-=-=-=-=-\n", + "Global Plasma Parameters:\n", + "k_perp rho_ref = 1.30000 \n", + "k_par rho_ref = 0.100000E-02\n", + "Beta_ref,par = 1.00000 \n", + "v_t,ref,par/c = 0.100000E-03\n", + "-=-=-=-=-=-=-=-=-=-\n", + "Parameters for Species : 1\n", + "T_||,ref/T_||s = 1.00000 \n", + "m_ref/m_s = 1.00000 \n", + "T_perp/T_par|s = 1.00000 \n", + "q_ref/q_s = 1.00000 \n", + "n_s/n_ref = 1.00000 \n", + "v_drift s/v_A,ref = 0.00000 \n", + "-=-=-=-=-=-=-=-=-=-\n", + "Parameters for Species : 2\n", + "T_||,ref/T_||s = 1.00000 \n", + "m_ref/m_s = 1836.00 \n", + "T_perp/T_par|s = 1.00000 \n", + "q_ref/q_s = -1.00000 \n", + "n_s/n_ref = 1.00000 \n", + "v_drift s/v_A,ref = 0.00000 \n", + "-=-=-=-=-=-=-=-=-=-\n", + "Searching over:\n", + "omega/Omega_ref \\in [ 1.000E-05, 1.000E-02]\n", + "gamma/Omega_ref \\in [-2.000E-03,-2.000E-06]\n", + "-=-=-=-=-=-=-=-=-=-\n", + " finding minima\n", + " minima found\n", + " 7 possible local minima found:\n", + "ir = 128 ii = 128\n", + " 1.0000E-02 -2.0000E-06\n", + "ir = 16 ii = 124\n", + " 1.2588E-03 -6.4437E-05\n", + "ir = 0 ii = 77\n", + " 1.0000E-05 -7.9808E-04\n", + "ir = 15 ii = 64\n", + " 1.1807E-03 -1.0010E-03\n", + "ir = 22 ii = 21\n", + " 1.7270E-03 -1.6722E-03\n", + "ir = 29 ii = 0\n", + " 2.2734E-03 -2.0000E-03\n", + "ir = 128 ii = 0\n", + " 1.0000E-02 -2.0000E-03\n", + "Refining roots:\n", + "Dispersion Solutions \n", + " 1 1.2676E-03 -6.9505E-05\n", + " Predicting FPC (gyrotropic coords)...\n", + "Input for Root 1: 1.2676E-03 -6.9505E-05\n", + "Dispersion Solutions: \n", + " 1 1.2676E-03 -6.9505E-05\n", + " WARNING! The gyro routine does not support computing moments at this time due to computational demand!\n", + " Assuming data folder already exists...\n", + " Assuming subfolder howes2017 already exists\n", + " Calculating fpc for species 1\n", + " Writing omega/kpar V_a normalization to file...\n", + " Calculating fpc for species 2\n", + " Writing omega/kpar V_a normalization to file...\n", + "-=-=-=-=-=-=-=-=-=-\n", + "Root Search:\n", + "-=-=-=-=-=-=-=-=-=-\n", + "Global Plasma Parameters:\n", + "k_perp rho_ref = 1.30000 \n", + "k_par rho_ref = 0.100000E-02\n", + "Beta_ref,par = 1.00000 \n", + "v_t,ref,par/c = 0.100000E-03\n", + "-=-=-=-=-=-=-=-=-=-\n", + "Parameters for Species : 1\n", + "T_||,ref/T_||s = 1.00000 \n", + "m_ref/m_s = 1.00000 \n", + "T_perp/T_par|s = 1.00000 \n", + "q_ref/q_s = 1.00000 \n", + "n_s/n_ref = 1.00000 \n", + "v_drift s/v_A,ref = 0.00000 \n", + "-=-=-=-=-=-=-=-=-=-\n", + "Parameters for Species : 2\n", + "T_||,ref/T_||s = 1.00000 \n", + "m_ref/m_s = 1836.00 \n", + "T_perp/T_par|s = 1.00000 \n", + "q_ref/q_s = -1.00000 \n", + "n_s/n_ref = 1.00000 \n", + "v_drift s/v_A,ref = 0.00000 \n", + "-=-=-=-=-=-=-=-=-=-\n", + "Searching over:\n", + "omega/Omega_ref \\in [ 1.000E-05, 1.000E-02]\n", + "gamma/Omega_ref \\in [-2.000E-03,-2.000E-06]\n", + "-=-=-=-=-=-=-=-=-=-\n", + " finding minima\n", + " minima found\n", + " 7 possible local minima found:\n", + "ir = 128 ii = 128\n", + " 1.0000E-02 -2.0000E-06\n", + "ir = 17 ii = 122\n", + " 1.3368E-03 -9.5656E-05\n", + "ir = 0 ii = 74\n", + " 1.0000E-05 -8.4491E-04\n", + "ir = 15 ii = 62\n", + " 1.1807E-03 -1.0322E-03\n", + "ir = 22 ii = 16\n", + " 1.7270E-03 -1.7503E-03\n", + "ir = 29 ii = 0\n", + " 2.2734E-03 -2.0000E-03\n", + "ir = 128 ii = 0\n", + " 1.0000E-02 -2.0000E-03\n", + "Refining roots:\n", + "Dispersion Solutions \n", + " 1 1.3032E-03 -9.2973E-05\n", + " Predicting FPC (gyrotropic coords)...\n", + "Input for Root 1: 1.3032E-03 -9.2973E-05\n", + "Dispersion Solutions: \n", + " 1 1.3032E-03 -9.2973E-05\n", + " WARNING! The gyro routine does not support computing moments at this time due to computational demand!\n", + " Assuming data folder already exists...\n", + " Assuming subfolder howes2017 already exists\n", + " Calculating fpc for species 1\n", + " Writing omega/kpar V_a normalization to file...\n", + " Calculating fpc for species 2\n", + " Writing omega/kpar V_a normalization to file...\n", + "-=-=-=-=-=-=-=-=-=-\n", + "Root Search:\n", + "-=-=-=-=-=-=-=-=-=-\n", + "Global Plasma Parameters:\n", + "k_perp rho_ref = 1.30000 \n", + "k_par rho_ref = 0.100000E-02\n", + "Beta_ref,par = 1.00000 \n", + "v_t,ref,par/c = 0.100000E-03\n", + "-=-=-=-=-=-=-=-=-=-\n", + "Parameters for Species : 1\n", + "T_||,ref/T_||s = 1.00000 \n", + "m_ref/m_s = 1.00000 \n", + "T_perp/T_par|s = 1.00000 \n", + "q_ref/q_s = 1.00000 \n", + "n_s/n_ref = 1.00000 \n", + "v_drift s/v_A,ref = 0.00000 \n", + "-=-=-=-=-=-=-=-=-=-\n", + "Parameters for Species : 2\n", + "T_||,ref/T_||s = 1.00000 \n", + "m_ref/m_s = 1836.00 \n", + "T_perp/T_par|s = 1.00000 \n", + "q_ref/q_s = -1.00000 \n", + "n_s/n_ref = 1.00000 \n", + "v_drift s/v_A,ref = 0.00000 \n", + "-=-=-=-=-=-=-=-=-=-\n", + "Searching over:\n", + "omega/Omega_ref \\in [ 1.000E-05, 1.000E-02]\n", + "gamma/Omega_ref \\in [-2.000E-03,-2.000E-06]\n", + "-=-=-=-=-=-=-=-=-=-\n", + " finding minima\n", + " minima found\n", + " 7 possible local minima found:\n", + "ir = 128 ii = 128\n", + " 1.0000E-02 -2.0000E-06\n", + "ir = 16 ii = 125\n", + " 1.2588E-03 -4.8828E-05\n", + "ir = 0 ii = 80\n", + " 1.0000E-05 -7.5125E-04\n", + "ir = 15 ii = 65\n", + " 1.1807E-03 -9.8539E-04\n", + "ir = 22 ii = 26\n", + " 1.7270E-03 -1.5942E-03\n", + "ir = 28 ii = 2\n", + " 2.1953E-03 -1.9688E-03\n", + "ir = 128 ii = 0\n", + " 1.0000E-02 -2.0000E-03\n", + "Refining roots:\n", + "Dispersion Solutions \n", + " 1 1.2362E-03 -4.4355E-05\n", + " Predicting FPC (gyrotropic coords)...\n", + "Input for Root 1: 1.2362E-03 -4.4355E-05\n", + "Dispersion Solutions: \n", + " 1 1.2362E-03 -4.4355E-05\n", + " WARNING! The gyro routine does not support computing moments at this time due to computational demand!\n", + " Assuming data folder already exists...\n", + " Assuming subfolder howes2017 already exists\n", + " Calculating fpc for species 1\n", + " Writing omega/kpar V_a normalization to file...\n", + " Calculating fpc for species 2\n", + " Writing omega/kpar V_a normalization to file...\n", + "-=-=-=-=-=-=-=-=-=-\n", + "Root Search:\n", + "-=-=-=-=-=-=-=-=-=-\n", + "Global Plasma Parameters:\n", + "k_perp rho_ref = 1.30000 \n", + "k_par rho_ref = 0.100000E-02\n", + "Beta_ref,par = 1.00000 \n", + "v_t,ref,par/c = 0.100000E-03\n", + "-=-=-=-=-=-=-=-=-=-\n", + "Parameters for Species : 1\n", + "T_||,ref/T_||s = 1.00000 \n", + "m_ref/m_s = 1.00000 \n", + "T_perp/T_par|s = 1.00000 \n", + "q_ref/q_s = 1.00000 \n", + "n_s/n_ref = 1.00000 \n", + "v_drift s/v_A,ref = 0.00000 \n", + "-=-=-=-=-=-=-=-=-=-\n", + "Parameters for Species : 2\n", + "T_||,ref/T_||s = 1.00000 \n", + "m_ref/m_s = 1836.00 \n", + "T_perp/T_par|s = 1.00000 \n", + "q_ref/q_s = -1.00000 \n", + "n_s/n_ref = 1.00000 \n", + "v_drift s/v_A,ref = 0.00000 \n", + "-=-=-=-=-=-=-=-=-=-\n", + "Searching over:\n", + "omega/Omega_ref \\in [ 1.000E-05, 1.000E-02]\n", + "gamma/Omega_ref \\in [-2.000E-03,-2.000E-06]\n", + "-=-=-=-=-=-=-=-=-=-\n", + " finding minima\n", + " minima found\n", + " 7 possible local minima found:\n", + "ir = 128 ii = 128\n", + " 1.0000E-02 -2.0000E-06\n", + "ir = 16 ii = 124\n", + " 1.2588E-03 -6.4437E-05\n", + "ir = 0 ii = 77\n", + " 1.0000E-05 -7.9808E-04\n", + "ir = 15 ii = 64\n", + " 1.1807E-03 -1.0010E-03\n", + "ir = 22 ii = 21\n", + " 1.7270E-03 -1.6722E-03\n", + "ir = 29 ii = 0\n", + " 2.2734E-03 -2.0000E-03\n", + "ir = 128 ii = 0\n", + " 1.0000E-02 -2.0000E-03\n", + "Refining roots:\n", + "Dispersion Solutions \n", + " 1 1.2676E-03 -6.9505E-05\n", + " Predicting FPC (gyrotropic coords)...\n", + "Input for Root 1: 1.2676E-03 -6.9505E-05\n", + "Dispersion Solutions: \n", + " 1 1.2676E-03 -6.9505E-05\n", + " WARNING! The gyro routine does not support computing moments at this time due to computational demand!\n", + " Assuming data folder already exists...\n", + " Assuming subfolder howes2017 already exists\n", + " Calculating fpc for species 1\n", + " Writing omega/kpar V_a normalization to file...\n", + " Calculating fpc for species 2\n", + " Writing omega/kpar V_a normalization to file...\n", + "-=-=-=-=-=-=-=-=-=-\n", + "Root Search:\n", + "-=-=-=-=-=-=-=-=-=-\n", + "Global Plasma Parameters:\n", + "k_perp rho_ref = 1.30000 \n", + "k_par rho_ref = 0.100000E-02\n", + "Beta_ref,par = 1.00000 \n", + "v_t,ref,par/c = 0.100000E-03\n", + "-=-=-=-=-=-=-=-=-=-\n", + "Parameters for Species : 1\n", + "T_||,ref/T_||s = 1.00000 \n", + "m_ref/m_s = 1.00000 \n", + "T_perp/T_par|s = 1.00000 \n", + "q_ref/q_s = 1.00000 \n", + "n_s/n_ref = 1.00000 \n", + "v_drift s/v_A,ref = 0.00000 \n", + "-=-=-=-=-=-=-=-=-=-\n", + "Parameters for Species : 2\n", + "T_||,ref/T_||s = 1.00000 \n", + "m_ref/m_s = 1836.00 \n", + "T_perp/T_par|s = 1.00000 \n", + "q_ref/q_s = -1.00000 \n", + "n_s/n_ref = 1.00000 \n", + "v_drift s/v_A,ref = 0.00000 \n", + "-=-=-=-=-=-=-=-=-=-\n", + "Searching over:\n", + "omega/Omega_ref \\in [ 1.000E-05, 1.000E-02]\n", + "gamma/Omega_ref \\in [-2.000E-03,-2.000E-06]\n", + "-=-=-=-=-=-=-=-=-=-\n", + " finding minima\n", + " minima found\n", + " 7 possible local minima found:\n", + "ir = 128 ii = 128\n", + " 1.0000E-02 -2.0000E-06\n", + "ir = 17 ii = 122\n", + " 1.3368E-03 -9.5656E-05\n", + "ir = 0 ii = 74\n", + " 1.0000E-05 -8.4491E-04\n", + "ir = 15 ii = 62\n", + " 1.1807E-03 -1.0322E-03\n", + "ir = 22 ii = 16\n", + " 1.7270E-03 -1.7503E-03\n", + "ir = 29 ii = 0\n", + " 2.2734E-03 -2.0000E-03\n", + "ir = 128 ii = 0\n", + " 1.0000E-02 -2.0000E-03\n", + "Refining roots:\n", + "Dispersion Solutions \n", + " 1 1.3032E-03 -9.2973E-05\n", + " Predicting FPC (gyrotropic coords)...\n", + "Input for Root 1: 1.3032E-03 -9.2973E-05\n", + "Dispersion Solutions: \n", + " 1 1.3032E-03 -9.2973E-05\n", + " WARNING! The gyro routine does not support computing moments at this time due to computational demand!\n", + " Assuming data folder already exists...\n", + " Assuming subfolder howes2017 already exists\n", + " Calculating fpc for species 1\n", + " Writing omega/kpar V_a normalization to file...\n", + " Calculating fpc for species 2\n", + " Writing omega/kpar V_a normalization to file...\n", + "-=-=-=-=-=-=-=-=-=-\n", + "Root Search:\n", + "-=-=-=-=-=-=-=-=-=-\n", + "Global Plasma Parameters:\n", + "k_perp rho_ref = 1.30000 \n", + "k_par rho_ref = 0.100000E-02\n", + "Beta_ref,par = 1.00000 \n", + "v_t,ref,par/c = 0.100000E-03\n", + "-=-=-=-=-=-=-=-=-=-\n", + "Parameters for Species : 1\n", + "T_||,ref/T_||s = 1.00000 \n", + "m_ref/m_s = 1.00000 \n", + "T_perp/T_par|s = 1.00000 \n", + "q_ref/q_s = 1.00000 \n", + "n_s/n_ref = 1.00000 \n", + "v_drift s/v_A,ref = 0.00000 \n", + "-=-=-=-=-=-=-=-=-=-\n", + "Parameters for Species : 2\n", + "T_||,ref/T_||s = 1.00000 \n", + "m_ref/m_s = 1836.00 \n", + "T_perp/T_par|s = 1.00000 \n", + "q_ref/q_s = -1.00000 \n", + "n_s/n_ref = 1.00000 \n", + "v_drift s/v_A,ref = 0.00000 \n", + "-=-=-=-=-=-=-=-=-=-\n", + "Searching over:\n", + "omega/Omega_ref \\in [ 1.000E-05, 1.000E-02]\n", + "gamma/Omega_ref \\in [-2.000E-03,-2.000E-06]\n", + "-=-=-=-=-=-=-=-=-=-\n", + " finding minima\n", + " minima found\n", + " 7 possible local minima found:\n", + "ir = 128 ii = 128\n", + " 1.0000E-02 -2.0000E-06\n", + "ir = 17 ii = 120\n", + " 1.3368E-03 -1.2687E-04\n", + "ir = 0 ii = 69\n", + " 1.0000E-05 -9.2295E-04\n", + "ir = 16 ii = 60\n", + " 1.2588E-03 -1.0634E-03\n", + "ir = 22 ii = 10\n", + " 1.7270E-03 -1.8439E-03\n", + "ir = 30 ii = 0\n", + " 2.3514E-03 -2.0000E-03\n", + "ir = 128 ii = 0\n", + " 1.0000E-02 -2.0000E-03\n", + "Refining roots:\n", + "Dispersion Solutions \n", + " 1 1.3568E-03 -1.2191E-04\n", + " Predicting FPC (gyrotropic coords)...\n", + "Input for Root 1: 1.3568E-03 -1.2191E-04\n", + "Dispersion Solutions: \n", + " 1 1.3568E-03 -1.2191E-04\n", + " WARNING! The gyro routine does not support computing moments at this time due to computational demand!\n", + " Assuming data folder already exists...\n", + " Assuming subfolder howes2017 already exists\n", + " Calculating fpc for species 1\n", + " Writing omega/kpar V_a normalization to file...\n", + " Calculating fpc for species 2\n", + " Writing omega/kpar V_a normalization to file...\n", + "-=-=-=-=-=-=-=-=-=-\n", + "Root Search:\n", + "-=-=-=-=-=-=-=-=-=-\n", + "Global Plasma Parameters:\n", + "k_perp rho_ref = 1.30000 \n", + "k_par rho_ref = 0.100000E-02\n", + "Beta_ref,par = 1.00000 \n", + "v_t,ref,par/c = 0.100000E-03\n", + "-=-=-=-=-=-=-=-=-=-\n", + "Parameters for Species : 1\n", + "T_||,ref/T_||s = 1.00000 \n", + "m_ref/m_s = 1.00000 \n", + "T_perp/T_par|s = 1.00000 \n", + "q_ref/q_s = 1.00000 \n", + "n_s/n_ref = 1.00000 \n", + "v_drift s/v_A,ref = 0.00000 \n", + "-=-=-=-=-=-=-=-=-=-\n", + "Parameters for Species : 2\n", + "T_||,ref/T_||s = 1.00000 \n", + "m_ref/m_s = 1836.00 \n", + "T_perp/T_par|s = 1.00000 \n", + "q_ref/q_s = -1.00000 \n", + "n_s/n_ref = 1.00000 \n", + "v_drift s/v_A,ref = 0.00000 \n", + "-=-=-=-=-=-=-=-=-=-\n", + "Searching over:\n", + "omega/Omega_ref \\in [ 1.000E-05, 1.000E-02]\n", + "gamma/Omega_ref \\in [-2.000E-03,-2.000E-06]\n", + "-=-=-=-=-=-=-=-=-=-\n", + " finding minima\n", + " minima found\n", + " 8 possible local minima found:\n", + "ir = 128 ii = 128\n", + " 1.0000E-02 -2.0000E-06\n", + "ir = 0 ii = 120\n", + " 1.0000E-05 -1.2687E-04\n", + "ir = 18 ii = 118\n", + " 1.4148E-03 -1.5809E-04\n", + "ir = 0 ii = 63\n", + " 1.0000E-05 -1.0166E-03\n", + "ir = 16 ii = 57\n", + " 1.2588E-03 -1.1103E-03\n", + "ir = 22 ii = 2\n", + " 1.7270E-03 -1.9688E-03\n", + "ir = 32 ii = 0\n", + " 2.5075E-03 -2.0000E-03\n", + "ir = 128 ii = 0\n", + " 1.0000E-02 -2.0000E-03\n", + "Refining roots:\n", + "Dispersion Solutions \n", + " 1 5.9952E-15 -1.2446E-04\n", + " Predicting FPC (gyrotropic coords)...\n", + "Input for Root 1: 1.4335E-03 -1.5475E-04\n", + "Dispersion Solutions: \n", + " 1 1.4335E-03 -1.5475E-04\n", + " WARNING! The gyro routine does not support computing moments at this time due to computational demand!\n", + " Assuming data folder already exists...\n", + " Assuming subfolder howes2017 already exists\n", + " Calculating fpc for species 1\n", + " Writing omega/kpar V_a normalization to file...\n", + " Calculating fpc for species 2\n", + " Writing omega/kpar V_a normalization to file...\n", + "-=-=-=-=-=-=-=-=-=-\n", + "Root Search:\n", + "-=-=-=-=-=-=-=-=-=-\n", + "Global Plasma Parameters:\n", + "k_perp rho_ref = 1.30000 \n", + "k_par rho_ref = 0.100000E-02\n", + "Beta_ref,par = 1.00000 \n", + "v_t,ref,par/c = 0.100000E-03\n", + "-=-=-=-=-=-=-=-=-=-\n", + "Parameters for Species : 1\n", + "T_||,ref/T_||s = 1.00000 \n", + "m_ref/m_s = 1.00000 \n", + "T_perp/T_par|s = 1.00000 \n", + "q_ref/q_s = 1.00000 \n", + "n_s/n_ref = 1.00000 \n", + "v_drift s/v_A,ref = 0.00000 \n", + "-=-=-=-=-=-=-=-=-=-\n", + "Parameters for Species : 2\n", + "T_||,ref/T_||s = 1.00000 \n", + "m_ref/m_s = 1836.00 \n", + "T_perp/T_par|s = 1.00000 \n", + "q_ref/q_s = -1.00000 \n", + "n_s/n_ref = 1.00000 \n", + "v_drift s/v_A,ref = 0.00000 \n", + "-=-=-=-=-=-=-=-=-=-\n", + "Searching over:\n", + "omega/Omega_ref \\in [ 1.000E-05, 1.000E-02]\n", + "gamma/Omega_ref \\in [-2.000E-03,-2.000E-06]\n", + "-=-=-=-=-=-=-=-=-=-\n", + " finding minima\n", + " minima found\n", + " 6 possible local minima found:\n", + "ir = 128 ii = 128\n", + " 1.0000E-02 -2.0000E-06\n", + "ir = 22 ii = 113\n", + " 1.7270E-03 -2.3614E-04\n", + "ir = 0 ii = 109\n", + " 1.0000E-05 -2.9858E-04\n", + "ir = 18 ii = 47\n", + " 1.4148E-03 -1.2664E-03\n", + "ir = 0 ii = 45\n", + " 1.0000E-05 -1.2976E-03\n", + "ir = 128 ii = 0\n", + " 1.0000E-02 -2.0000E-03\n", + "Refining roots:\n", + "Dispersion Solutions \n", + " 1 1.7177E-03 -2.3538E-04\n", + " Predicting FPC (gyrotropic coords)...\n", + "Input for Root 1: 1.7177E-03 -2.3538E-04\n", + "Dispersion Solutions: \n", + " 1 1.7177E-03 -2.3538E-04\n", + " WARNING! The gyro routine does not support computing moments at this time due to computational demand!\n", + " Assuming data folder already exists...\n", + " Assuming subfolder howes2017 already exists\n", + " Calculating fpc for species 1\n", + " Writing omega/kpar V_a normalization to file...\n", + " Calculating fpc for species 2\n", + " Writing omega/kpar V_a normalization to file...\n", + "-=-=-=-=-=-=-=-=-=-\n", + "Root Search:\n", + "-=-=-=-=-=-=-=-=-=-\n", + "Global Plasma Parameters:\n", + "k_perp rho_ref = 1.30000 \n", + "k_par rho_ref = 0.100000E-02\n", + "Beta_ref,par = 1.00000 \n", + "v_t,ref,par/c = 0.100000E-03\n", + "-=-=-=-=-=-=-=-=-=-\n", + "Parameters for Species : 1\n", + "T_||,ref/T_||s = 1.00000 \n", + "m_ref/m_s = 1.00000 \n", + "T_perp/T_par|s = 1.00000 \n", + "q_ref/q_s = 1.00000 \n", + "n_s/n_ref = 1.00000 \n", + "v_drift s/v_A,ref = 0.00000 \n", + "-=-=-=-=-=-=-=-=-=-\n", + "Parameters for Species : 2\n", + "T_||,ref/T_||s = 1.00000 \n", + "m_ref/m_s = 1836.00 \n", + "T_perp/T_par|s = 1.00000 \n", + "q_ref/q_s = -1.00000 \n", + "n_s/n_ref = 1.00000 \n", + "v_drift s/v_A,ref = 0.00000 \n", + "-=-=-=-=-=-=-=-=-=-\n", + "Searching over:\n", + "omega/Omega_ref \\in [ 1.000E-05, 1.000E-02]\n", + "gamma/Omega_ref \\in [-2.000E-03,-2.000E-06]\n", + "-=-=-=-=-=-=-=-=-=-\n", + " finding minima\n", + " minima found\n", + " 7 possible local minima found:\n", + "ir = 128 ii = 128\n", + " 1.0000E-02 -2.0000E-06\n", + "ir = 16 ii = 125\n", + " 1.2588E-03 -4.8828E-05\n", + "ir = 0 ii = 80\n", + " 1.0000E-05 -7.5125E-04\n", + "ir = 15 ii = 65\n", + " 1.1807E-03 -9.8539E-04\n", + "ir = 22 ii = 26\n", + " 1.7270E-03 -1.5942E-03\n", + "ir = 28 ii = 2\n", + " 2.1953E-03 -1.9688E-03\n", + "ir = 128 ii = 0\n", + " 1.0000E-02 -2.0000E-03\n", + "Refining roots:\n", + "Dispersion Solutions \n", + " 1 1.2362E-03 -4.4355E-05\n", + " Predicting FPC (gyrotropic coords)...\n", + "Input for Root 1: 1.2362E-03 -4.4355E-05\n", + "Dispersion Solutions: \n", + " 1 1.2362E-03 -4.4355E-05\n", + " WARNING! The gyro routine does not support computing moments at this time due to computational demand!\n", + " Assuming data folder already exists...\n", + " Assuming subfolder howes2017 already exists\n", + " Calculating fpc for species 1\n", + " Writing omega/kpar V_a normalization to file...\n", + " Calculating fpc for species 2\n", + " Writing omega/kpar V_a normalization to file...\n", + "-=-=-=-=-=-=-=-=-=-\n", + "Root Search:\n", + "-=-=-=-=-=-=-=-=-=-\n", + "Global Plasma Parameters:\n", + "k_perp rho_ref = 1.30000 \n", + "k_par rho_ref = 0.100000E-02\n", + "Beta_ref,par = 1.00000 \n", + "v_t,ref,par/c = 0.100000E-03\n", + "-=-=-=-=-=-=-=-=-=-\n", + "Parameters for Species : 1\n", + "T_||,ref/T_||s = 1.00000 \n", + "m_ref/m_s = 1.00000 \n", + "T_perp/T_par|s = 1.00000 \n", + "q_ref/q_s = 1.00000 \n", + "n_s/n_ref = 1.00000 \n", + "v_drift s/v_A,ref = 0.00000 \n", + "-=-=-=-=-=-=-=-=-=-\n", + "Parameters for Species : 2\n", + "T_||,ref/T_||s = 1.00000 \n", + "m_ref/m_s = 1836.00 \n", + "T_perp/T_par|s = 1.00000 \n", + "q_ref/q_s = -1.00000 \n", + "n_s/n_ref = 1.00000 \n", + "v_drift s/v_A,ref = 0.00000 \n", + "-=-=-=-=-=-=-=-=-=-\n", + "Searching over:\n", + "omega/Omega_ref \\in [ 1.000E-05, 1.000E-02]\n", + "gamma/Omega_ref \\in [-2.000E-03,-2.000E-06]\n", + "-=-=-=-=-=-=-=-=-=-\n", + " finding minima\n", + " minima found\n", + " 7 possible local minima found:\n", + "ir = 128 ii = 128\n", + " 1.0000E-02 -2.0000E-06\n", + "ir = 16 ii = 124\n", + " 1.2588E-03 -6.4437E-05\n", + "ir = 0 ii = 77\n", + " 1.0000E-05 -7.9808E-04\n", + "ir = 15 ii = 64\n", + " 1.1807E-03 -1.0010E-03\n", + "ir = 22 ii = 21\n", + " 1.7270E-03 -1.6722E-03\n", + "ir = 29 ii = 0\n", + " 2.2734E-03 -2.0000E-03\n", + "ir = 128 ii = 0\n", + " 1.0000E-02 -2.0000E-03\n", + "Refining roots:\n", + "Dispersion Solutions \n", + " 1 1.2676E-03 -6.9505E-05\n", + " Predicting FPC (gyrotropic coords)...\n", + "Input for Root 1: 1.2676E-03 -6.9505E-05\n", + "Dispersion Solutions: \n", + " 1 1.2676E-03 -6.9505E-05\n", + " WARNING! The gyro routine does not support computing moments at this time due to computational demand!\n", + " Assuming data folder already exists...\n", + " Assuming subfolder howes2017 already exists\n", + " Calculating fpc for species 1\n", + " Writing omega/kpar V_a normalization to file...\n", + " Calculating fpc for species 2\n", + " Writing omega/kpar V_a normalization to file...\n", + "-=-=-=-=-=-=-=-=-=-\n", + "Root Search:\n", + "-=-=-=-=-=-=-=-=-=-\n", + "Global Plasma Parameters:\n", + "k_perp rho_ref = 1.30000 \n", + "k_par rho_ref = 0.100000E-02\n", + "Beta_ref,par = 1.00000 \n", + "v_t,ref,par/c = 0.100000E-03\n", + "-=-=-=-=-=-=-=-=-=-\n", + "Parameters for Species : 1\n", + "T_||,ref/T_||s = 1.00000 \n", + "m_ref/m_s = 1.00000 \n", + "T_perp/T_par|s = 1.00000 \n", + "q_ref/q_s = 1.00000 \n", + "n_s/n_ref = 1.00000 \n", + "v_drift s/v_A,ref = 0.00000 \n", + "-=-=-=-=-=-=-=-=-=-\n", + "Parameters for Species : 2\n", + "T_||,ref/T_||s = 1.00000 \n", + "m_ref/m_s = 1836.00 \n", + "T_perp/T_par|s = 1.00000 \n", + "q_ref/q_s = -1.00000 \n", + "n_s/n_ref = 1.00000 \n", + "v_drift s/v_A,ref = 0.00000 \n", + "-=-=-=-=-=-=-=-=-=-\n", + "Searching over:\n", + "omega/Omega_ref \\in [ 1.000E-05, 1.000E-02]\n", + "gamma/Omega_ref \\in [-2.000E-03,-2.000E-06]\n", + "-=-=-=-=-=-=-=-=-=-\n", + " finding minima\n", + " minima found\n", + " 7 possible local minima found:\n", + "ir = 128 ii = 128\n", + " 1.0000E-02 -2.0000E-06\n", + "ir = 17 ii = 122\n", + " 1.3368E-03 -9.5656E-05\n", + "ir = 0 ii = 74\n", + " 1.0000E-05 -8.4491E-04\n", + "ir = 15 ii = 62\n", + " 1.1807E-03 -1.0322E-03\n", + "ir = 22 ii = 16\n", + " 1.7270E-03 -1.7503E-03\n", + "ir = 29 ii = 0\n", + " 2.2734E-03 -2.0000E-03\n", + "ir = 128 ii = 0\n", + " 1.0000E-02 -2.0000E-03\n", + "Refining roots:\n", + "Dispersion Solutions \n", + " 1 1.3032E-03 -9.2973E-05\n", + " Predicting FPC (gyrotropic coords)...\n", + "Input for Root 1: 1.3032E-03 -9.2973E-05\n", + "Dispersion Solutions: \n", + " 1 1.3032E-03 -9.2973E-05\n", + " WARNING! The gyro routine does not support computing moments at this time due to computational demand!\n", + " Assuming data folder already exists...\n", + " Assuming subfolder howes2017 already exists\n", + " Calculating fpc for species 1\n", + " Writing omega/kpar V_a normalization to file...\n", + " Calculating fpc for species 2\n", + " Writing omega/kpar V_a normalization to file...\n", + "-=-=-=-=-=-=-=-=-=-\n", + "Root Search:\n", + "-=-=-=-=-=-=-=-=-=-\n", + "Global Plasma Parameters:\n", + "k_perp rho_ref = 1.30000 \n", + "k_par rho_ref = 0.100000E-02\n", + "Beta_ref,par = 1.00000 \n", + "v_t,ref,par/c = 0.100000E-03\n", + "-=-=-=-=-=-=-=-=-=-\n", + "Parameters for Species : 1\n", + "T_||,ref/T_||s = 1.00000 \n", + "m_ref/m_s = 1.00000 \n", + "T_perp/T_par|s = 1.00000 \n", + "q_ref/q_s = 1.00000 \n", + "n_s/n_ref = 1.00000 \n", + "v_drift s/v_A,ref = 0.00000 \n", + "-=-=-=-=-=-=-=-=-=-\n", + "Parameters for Species : 2\n", + "T_||,ref/T_||s = 1.00000 \n", + "m_ref/m_s = 1836.00 \n", + "T_perp/T_par|s = 1.00000 \n", + "q_ref/q_s = -1.00000 \n", + "n_s/n_ref = 1.00000 \n", + "v_drift s/v_A,ref = 0.00000 \n", + "-=-=-=-=-=-=-=-=-=-\n", + "Searching over:\n", + "omega/Omega_ref \\in [ 1.000E-05, 1.000E-02]\n", + "gamma/Omega_ref \\in [-2.000E-03,-2.000E-06]\n", + "-=-=-=-=-=-=-=-=-=-\n", + " finding minima\n", + " minima found\n", + " 7 possible local minima found:\n", + "ir = 128 ii = 128\n", + " 1.0000E-02 -2.0000E-06\n", + "ir = 17 ii = 120\n", + " 1.3368E-03 -1.2687E-04\n", + "ir = 0 ii = 69\n", + " 1.0000E-05 -9.2295E-04\n", + "ir = 16 ii = 60\n", + " 1.2588E-03 -1.0634E-03\n", + "ir = 22 ii = 10\n", + " 1.7270E-03 -1.8439E-03\n", + "ir = 30 ii = 0\n", + " 2.3514E-03 -2.0000E-03\n", + "ir = 128 ii = 0\n", + " 1.0000E-02 -2.0000E-03\n", + "Refining roots:\n", + "Dispersion Solutions \n", + " 1 1.3568E-03 -1.2191E-04\n", + " Predicting FPC (gyrotropic coords)...\n", + "Input for Root 1: 1.3568E-03 -1.2191E-04\n", + "Dispersion Solutions: \n", + " 1 1.3568E-03 -1.2191E-04\n", + " WARNING! The gyro routine does not support computing moments at this time due to computational demand!\n", + " Assuming data folder already exists...\n", + " Assuming subfolder howes2017 already exists\n", + " Calculating fpc for species 1\n", + " Writing omega/kpar V_a normalization to file...\n", + " Calculating fpc for species 2\n", + " Writing omega/kpar V_a normalization to file...\n", + "-=-=-=-=-=-=-=-=-=-\n", + "Root Search:\n", + "-=-=-=-=-=-=-=-=-=-\n", + "Global Plasma Parameters:\n", + "k_perp rho_ref = 1.30000 \n", + "k_par rho_ref = 0.100000E-02\n", + "Beta_ref,par = 1.00000 \n", + "v_t,ref,par/c = 0.100000E-03\n", + "-=-=-=-=-=-=-=-=-=-\n", + "Parameters for Species : 1\n", + "T_||,ref/T_||s = 1.00000 \n", + "m_ref/m_s = 1.00000 \n", + "T_perp/T_par|s = 1.00000 \n", + "q_ref/q_s = 1.00000 \n", + "n_s/n_ref = 1.00000 \n", + "v_drift s/v_A,ref = 0.00000 \n", + "-=-=-=-=-=-=-=-=-=-\n", + "Parameters for Species : 2\n", + "T_||,ref/T_||s = 1.00000 \n", + "m_ref/m_s = 1836.00 \n", + "T_perp/T_par|s = 1.00000 \n", + "q_ref/q_s = -1.00000 \n", + "n_s/n_ref = 1.00000 \n", + "v_drift s/v_A,ref = 0.00000 \n", + "-=-=-=-=-=-=-=-=-=-\n", + "Searching over:\n", + "omega/Omega_ref \\in [ 1.000E-05, 1.000E-02]\n", + "gamma/Omega_ref \\in [-2.000E-03,-2.000E-06]\n", + "-=-=-=-=-=-=-=-=-=-\n", + " finding minima\n", + " minima found\n", + " 8 possible local minima found:\n", + "ir = 128 ii = 128\n", + " 1.0000E-02 -2.0000E-06\n", + "ir = 0 ii = 120\n", + " 1.0000E-05 -1.2687E-04\n", + "ir = 18 ii = 118\n", + " 1.4148E-03 -1.5809E-04\n", + "ir = 0 ii = 63\n", + " 1.0000E-05 -1.0166E-03\n", + "ir = 16 ii = 57\n", + " 1.2588E-03 -1.1103E-03\n", + "ir = 22 ii = 2\n", + " 1.7270E-03 -1.9688E-03\n", + "ir = 32 ii = 0\n", + " 2.5075E-03 -2.0000E-03\n", + "ir = 128 ii = 0\n", + " 1.0000E-02 -2.0000E-03\n", + "Refining roots:\n", + "Dispersion Solutions \n", + " 1 5.9952E-15 -1.2446E-04\n", + " Predicting FPC (gyrotropic coords)...\n", + "Input for Root 1: 1.4335E-03 -1.5475E-04\n", + "Dispersion Solutions: \n", + " 1 1.4335E-03 -1.5475E-04\n", + " WARNING! The gyro routine does not support computing moments at this time due to computational demand!\n", + " Assuming data folder already exists...\n", + " Assuming subfolder howes2017 already exists\n", + " Calculating fpc for species 1\n", + " Writing omega/kpar V_a normalization to file...\n", + " Calculating fpc for species 2\n", + " Writing omega/kpar V_a normalization to file...\n", + "-=-=-=-=-=-=-=-=-=-\n", + "Root Search:\n", + "-=-=-=-=-=-=-=-=-=-\n", + "Global Plasma Parameters:\n", + "k_perp rho_ref = 1.30000 \n", + "k_par rho_ref = 0.100000E-02\n", + "Beta_ref,par = 1.00000 \n", + "v_t,ref,par/c = 0.100000E-03\n", + "-=-=-=-=-=-=-=-=-=-\n", + "Parameters for Species : 1\n", + "T_||,ref/T_||s = 1.00000 \n", + "m_ref/m_s = 1.00000 \n", + "T_perp/T_par|s = 1.00000 \n", + "q_ref/q_s = 1.00000 \n", + "n_s/n_ref = 1.00000 \n", + "v_drift s/v_A,ref = 0.00000 \n", + "-=-=-=-=-=-=-=-=-=-\n", + "Parameters for Species : 2\n", + "T_||,ref/T_||s = 1.00000 \n", + "m_ref/m_s = 1836.00 \n", + "T_perp/T_par|s = 1.00000 \n", + "q_ref/q_s = -1.00000 \n", + "n_s/n_ref = 1.00000 \n", + "v_drift s/v_A,ref = 0.00000 \n", + "-=-=-=-=-=-=-=-=-=-\n", + "Searching over:\n", + "omega/Omega_ref \\in [ 1.000E-05, 1.000E-02]\n", + "gamma/Omega_ref \\in [-2.000E-03,-2.000E-06]\n", + "-=-=-=-=-=-=-=-=-=-\n", + " finding minima\n", + " minima found\n", + " 7 possible local minima found:\n", + "ir = 128 ii = 128\n", + " 1.0000E-02 -2.0000E-06\n", + "ir = 16 ii = 125\n", + " 1.2588E-03 -4.8828E-05\n", + "ir = 0 ii = 80\n", + " 1.0000E-05 -7.5125E-04\n", + "ir = 15 ii = 65\n", + " 1.1807E-03 -9.8539E-04\n", + "ir = 22 ii = 26\n", + " 1.7270E-03 -1.5942E-03\n", + "ir = 28 ii = 2\n", + " 2.1953E-03 -1.9688E-03\n", + "ir = 128 ii = 0\n", + " 1.0000E-02 -2.0000E-03\n", + "Refining roots:\n", + "Dispersion Solutions \n", + " 1 1.2362E-03 -4.4355E-05\n", + " Predicting FPC (gyrotropic coords)...\n", + "Input for Root 1: 1.2362E-03 -4.4355E-05\n", + "Dispersion Solutions: \n", + " 1 1.2362E-03 -4.4355E-05\n", + " WARNING! The gyro routine does not support computing moments at this time due to computational demand!\n", + " Assuming data folder already exists...\n", + " Assuming subfolder howes2017 already exists\n", + " Calculating fpc for species 1\n", + " Writing omega/kpar V_a normalization to file...\n", + " Calculating fpc for species 2\n", + " Writing omega/kpar V_a normalization to file...\n", + "-=-=-=-=-=-=-=-=-=-\n", + "Root Search:\n", + "-=-=-=-=-=-=-=-=-=-\n", + "Global Plasma Parameters:\n", + "k_perp rho_ref = 1.30000 \n", + "k_par rho_ref = 0.100000E-02\n", + "Beta_ref,par = 1.00000 \n", + "v_t,ref,par/c = 0.100000E-03\n", + "-=-=-=-=-=-=-=-=-=-\n", + "Parameters for Species : 1\n", + "T_||,ref/T_||s = 1.00000 \n", + "m_ref/m_s = 1.00000 \n", + "T_perp/T_par|s = 1.00000 \n", + "q_ref/q_s = 1.00000 \n", + "n_s/n_ref = 1.00000 \n", + "v_drift s/v_A,ref = 0.00000 \n", + "-=-=-=-=-=-=-=-=-=-\n", + "Parameters for Species : 2\n", + "T_||,ref/T_||s = 1.00000 \n", + "m_ref/m_s = 1836.00 \n", + "T_perp/T_par|s = 1.00000 \n", + "q_ref/q_s = -1.00000 \n", + "n_s/n_ref = 1.00000 \n", + "v_drift s/v_A,ref = 0.00000 \n", + "-=-=-=-=-=-=-=-=-=-\n", + "Searching over:\n", + "omega/Omega_ref \\in [ 1.000E-05, 1.000E-02]\n", + "gamma/Omega_ref \\in [-2.000E-03,-2.000E-06]\n", + "-=-=-=-=-=-=-=-=-=-\n", + " finding minima\n", + " minima found\n", + " 7 possible local minima found:\n", + "ir = 128 ii = 128\n", + " 1.0000E-02 -2.0000E-06\n", + "ir = 16 ii = 124\n", + " 1.2588E-03 -6.4437E-05\n", + "ir = 0 ii = 77\n", + " 1.0000E-05 -7.9808E-04\n", + "ir = 15 ii = 64\n", + " 1.1807E-03 -1.0010E-03\n", + "ir = 22 ii = 21\n", + " 1.7270E-03 -1.6722E-03\n", + "ir = 29 ii = 0\n", + " 2.2734E-03 -2.0000E-03\n", + "ir = 128 ii = 0\n", + " 1.0000E-02 -2.0000E-03\n", + "Refining roots:\n", + "Dispersion Solutions \n", + " 1 1.2676E-03 -6.9505E-05\n", + " Predicting FPC (gyrotropic coords)...\n", + "Input for Root 1: 1.2676E-03 -6.9505E-05\n", + "Dispersion Solutions: \n", + " 1 1.2676E-03 -6.9505E-05\n", + " WARNING! The gyro routine does not support computing moments at this time due to computational demand!\n", + " Assuming data folder already exists...\n", + " Assuming subfolder howes2017 already exists\n", + " Calculating fpc for species 1\n", + " Writing omega/kpar V_a normalization to file...\n", + " Calculating fpc for species 2\n", + " Writing omega/kpar V_a normalization to file...\n", + "-=-=-=-=-=-=-=-=-=-\n", + "Root Search:\n", + "-=-=-=-=-=-=-=-=-=-\n", + "Global Plasma Parameters:\n", + "k_perp rho_ref = 1.30000 \n", + "k_par rho_ref = 0.100000E-02\n", + "Beta_ref,par = 1.00000 \n", + "v_t,ref,par/c = 0.100000E-03\n", + "-=-=-=-=-=-=-=-=-=-\n", + "Parameters for Species : 1\n", + "T_||,ref/T_||s = 1.00000 \n", + "m_ref/m_s = 1.00000 \n", + "T_perp/T_par|s = 1.00000 \n", + "q_ref/q_s = 1.00000 \n", + "n_s/n_ref = 1.00000 \n", + "v_drift s/v_A,ref = 0.00000 \n", + "-=-=-=-=-=-=-=-=-=-\n", + "Parameters for Species : 2\n", + "T_||,ref/T_||s = 1.00000 \n", + "m_ref/m_s = 1836.00 \n", + "T_perp/T_par|s = 1.00000 \n", + "q_ref/q_s = -1.00000 \n", + "n_s/n_ref = 1.00000 \n", + "v_drift s/v_A,ref = 0.00000 \n", + "-=-=-=-=-=-=-=-=-=-\n", + "Searching over:\n", + "omega/Omega_ref \\in [ 1.000E-05, 1.000E-02]\n", + "gamma/Omega_ref \\in [-2.000E-03,-2.000E-06]\n", + "-=-=-=-=-=-=-=-=-=-\n", + " finding minima\n", + " minima found\n", + " 7 possible local minima found:\n", + "ir = 128 ii = 128\n", + " 1.0000E-02 -2.0000E-06\n", + "ir = 17 ii = 122\n", + " 1.3368E-03 -9.5656E-05\n", + "ir = 0 ii = 74\n", + " 1.0000E-05 -8.4491E-04\n", + "ir = 15 ii = 62\n", + " 1.1807E-03 -1.0322E-03\n", + "ir = 22 ii = 16\n", + " 1.7270E-03 -1.7503E-03\n", + "ir = 29 ii = 0\n", + " 2.2734E-03 -2.0000E-03\n", + "ir = 128 ii = 0\n", + " 1.0000E-02 -2.0000E-03\n", + "Refining roots:\n", + "Dispersion Solutions \n", + " 1 1.3032E-03 -9.2973E-05\n", + " Predicting FPC (gyrotropic coords)...\n", + "Input for Root 1: 1.3032E-03 -9.2973E-05\n", + "Dispersion Solutions: \n", + " 1 1.3032E-03 -9.2973E-05\n", + " WARNING! The gyro routine does not support computing moments at this time due to computational demand!\n", + " Assuming data folder already exists...\n", + " Assuming subfolder howes2017 already exists\n", + " Calculating fpc for species 1\n", + " Writing omega/kpar V_a normalization to file...\n", + " Calculating fpc for species 2\n", + " Writing omega/kpar V_a normalization to file...\n", + "-=-=-=-=-=-=-=-=-=-\n", + "Root Search:\n", + "-=-=-=-=-=-=-=-=-=-\n", + "Global Plasma Parameters:\n", + "k_perp rho_ref = 1.30000 \n", + "k_par rho_ref = 0.100000E-02\n", + "Beta_ref,par = 1.00000 \n", + "v_t,ref,par/c = 0.100000E-03\n", + "-=-=-=-=-=-=-=-=-=-\n", + "Parameters for Species : 1\n", + "T_||,ref/T_||s = 1.00000 \n", + "m_ref/m_s = 1.00000 \n", + "T_perp/T_par|s = 1.00000 \n", + "q_ref/q_s = 1.00000 \n", + "n_s/n_ref = 1.00000 \n", + "v_drift s/v_A,ref = 0.00000 \n", + "-=-=-=-=-=-=-=-=-=-\n", + "Parameters for Species : 2\n", + "T_||,ref/T_||s = 1.00000 \n", + "m_ref/m_s = 1836.00 \n", + "T_perp/T_par|s = 1.00000 \n", + "q_ref/q_s = -1.00000 \n", + "n_s/n_ref = 1.00000 \n", + "v_drift s/v_A,ref = 0.00000 \n", + "-=-=-=-=-=-=-=-=-=-\n", + "Searching over:\n", + "omega/Omega_ref \\in [ 1.000E-05, 1.000E-02]\n", + "gamma/Omega_ref \\in [-2.000E-03,-2.000E-06]\n", + "-=-=-=-=-=-=-=-=-=-\n", + " finding minima\n", + " minima found\n", + " 7 possible local minima found:\n", + "ir = 128 ii = 128\n", + " 1.0000E-02 -2.0000E-06\n", + "ir = 17 ii = 120\n", + " 1.3368E-03 -1.2687E-04\n", + "ir = 0 ii = 69\n", + " 1.0000E-05 -9.2295E-04\n", + "ir = 16 ii = 60\n", + " 1.2588E-03 -1.0634E-03\n", + "ir = 22 ii = 10\n", + " 1.7270E-03 -1.8439E-03\n", + "ir = 30 ii = 0\n", + " 2.3514E-03 -2.0000E-03\n", + "ir = 128 ii = 0\n", + " 1.0000E-02 -2.0000E-03\n", + "Refining roots:\n", + "Dispersion Solutions \n", + " 1 1.3568E-03 -1.2191E-04\n", + " Predicting FPC (gyrotropic coords)...\n", + "Input for Root 1: 1.3568E-03 -1.2191E-04\n", + "Dispersion Solutions: \n", + " 1 1.3568E-03 -1.2191E-04\n", + " WARNING! The gyro routine does not support computing moments at this time due to computational demand!\n", + " Assuming data folder already exists...\n", + " Assuming subfolder howes2017 already exists\n", + " Calculating fpc for species 1\n", + " Writing omega/kpar V_a normalization to file...\n", + " Calculating fpc for species 2\n", + " Writing omega/kpar V_a normalization to file...\n", + "-=-=-=-=-=-=-=-=-=-\n", + "Root Search:\n", + "-=-=-=-=-=-=-=-=-=-\n", + "Global Plasma Parameters:\n", + "k_perp rho_ref = 1.30000 \n", + "k_par rho_ref = 0.100000E-02\n", + "Beta_ref,par = 1.00000 \n", + "v_t,ref,par/c = 0.100000E-03\n", + "-=-=-=-=-=-=-=-=-=-\n", + "Parameters for Species : 1\n", + "T_||,ref/T_||s = 1.00000 \n", + "m_ref/m_s = 1.00000 \n", + "T_perp/T_par|s = 1.00000 \n", + "q_ref/q_s = 1.00000 \n", + "n_s/n_ref = 1.00000 \n", + "v_drift s/v_A,ref = 0.00000 \n", + "-=-=-=-=-=-=-=-=-=-\n", + "Parameters for Species : 2\n", + "T_||,ref/T_||s = 1.00000 \n", + "m_ref/m_s = 1836.00 \n", + "T_perp/T_par|s = 1.00000 \n", + "q_ref/q_s = -1.00000 \n", + "n_s/n_ref = 1.00000 \n", + "v_drift s/v_A,ref = 0.00000 \n", + "-=-=-=-=-=-=-=-=-=-\n", + "Searching over:\n", + "omega/Omega_ref \\in [ 1.000E-05, 1.000E-02]\n", + "gamma/Omega_ref \\in [-2.000E-03,-2.000E-06]\n", + "-=-=-=-=-=-=-=-=-=-\n", + " finding minima\n", + " minima found\n", + " 8 possible local minima found:\n", + "ir = 128 ii = 128\n", + " 1.0000E-02 -2.0000E-06\n", + "ir = 0 ii = 120\n", + " 1.0000E-05 -1.2687E-04\n", + "ir = 18 ii = 118\n", + " 1.4148E-03 -1.5809E-04\n", + "ir = 0 ii = 63\n", + " 1.0000E-05 -1.0166E-03\n", + "ir = 16 ii = 57\n", + " 1.2588E-03 -1.1103E-03\n", + "ir = 22 ii = 2\n", + " 1.7270E-03 -1.9688E-03\n", + "ir = 32 ii = 0\n", + " 2.5075E-03 -2.0000E-03\n", + "ir = 128 ii = 0\n", + " 1.0000E-02 -2.0000E-03\n", + "Refining roots:\n", + "Dispersion Solutions \n", + " 1 5.9952E-15 -1.2446E-04\n", + " Predicting FPC (gyrotropic coords)...\n", + "Input for Root 1: 1.4335E-03 -1.5475E-04\n", + "Dispersion Solutions: \n", + " 1 1.4335E-03 -1.5475E-04\n", + " WARNING! The gyro routine does not support computing moments at this time due to computational demand!\n", + " Assuming data folder already exists...\n", + " Assuming subfolder howes2017 already exists\n", + " Calculating fpc for species 1\n", + " Writing omega/kpar V_a normalization to file...\n", + " Calculating fpc for species 2\n", + " Writing omega/kpar V_a normalization to file...\n", + "-=-=-=-=-=-=-=-=-=-\n", + "Root Search:\n", + "-=-=-=-=-=-=-=-=-=-\n", + "Global Plasma Parameters:\n", + "k_perp rho_ref = 1.30000 \n", + "k_par rho_ref = 0.100000E-02\n", + "Beta_ref,par = 1.00000 \n", + "v_t,ref,par/c = 0.100000E-03\n", + "-=-=-=-=-=-=-=-=-=-\n", + "Parameters for Species : 1\n", + "T_||,ref/T_||s = 1.00000 \n", + "m_ref/m_s = 1.00000 \n", + "T_perp/T_par|s = 1.00000 \n", + "q_ref/q_s = 1.00000 \n", + "n_s/n_ref = 1.00000 \n", + "v_drift s/v_A,ref = 0.00000 \n", + "-=-=-=-=-=-=-=-=-=-\n", + "Parameters for Species : 2\n", + "T_||,ref/T_||s = 1.00000 \n", + "m_ref/m_s = 1836.00 \n", + "T_perp/T_par|s = 1.00000 \n", + "q_ref/q_s = -1.00000 \n", + "n_s/n_ref = 1.00000 \n", + "v_drift s/v_A,ref = 0.00000 \n", + "-=-=-=-=-=-=-=-=-=-\n", + "Searching over:\n", + "omega/Omega_ref \\in [ 1.000E-05, 1.000E-02]\n", + "gamma/Omega_ref \\in [-2.000E-03,-2.000E-06]\n", + "-=-=-=-=-=-=-=-=-=-\n", + " finding minima\n", + " minima found\n", + " 6 possible local minima found:\n", + "ir = 128 ii = 128\n", + " 1.0000E-02 -2.0000E-06\n", + "ir = 22 ii = 113\n", + " 1.7270E-03 -2.3614E-04\n", + "ir = 0 ii = 109\n", + " 1.0000E-05 -2.9858E-04\n", + "ir = 18 ii = 47\n", + " 1.4148E-03 -1.2664E-03\n", + "ir = 0 ii = 45\n", + " 1.0000E-05 -1.2976E-03\n", + "ir = 128 ii = 0\n", + " 1.0000E-02 -2.0000E-03\n", + "Refining roots:\n", + "Dispersion Solutions \n", + " 1 1.7177E-03 -2.3538E-04\n", + " Predicting FPC (gyrotropic coords)...\n", + "Input for Root 1: 1.7177E-03 -2.3538E-04\n", + "Dispersion Solutions: \n", + " 1 1.7177E-03 -2.3538E-04\n", + " WARNING! The gyro routine does not support computing moments at this time due to computational demand!\n", + " Assuming data folder already exists...\n", + " Assuming subfolder howes2017 already exists\n", + " Calculating fpc for species 1\n", + " Writing omega/kpar V_a normalization to file...\n", + " Calculating fpc for species 2\n", + " Writing omega/kpar V_a normalization to file...\n", + "-=-=-=-=-=-=-=-=-=-\n", + "Root Search:\n", + "-=-=-=-=-=-=-=-=-=-\n", + "Global Plasma Parameters:\n", + "k_perp rho_ref = 1.30000 \n", + "k_par rho_ref = 0.100000E-02\n", + "Beta_ref,par = 1.00000 \n", + "v_t,ref,par/c = 0.100000E-03\n", + "-=-=-=-=-=-=-=-=-=-\n", + "Parameters for Species : 1\n", + "T_||,ref/T_||s = 1.00000 \n", + "m_ref/m_s = 1.00000 \n", + "T_perp/T_par|s = 1.00000 \n", + "q_ref/q_s = 1.00000 \n", + "n_s/n_ref = 1.00000 \n", + "v_drift s/v_A,ref = 0.00000 \n", + "-=-=-=-=-=-=-=-=-=-\n", + "Parameters for Species : 2\n", + "T_||,ref/T_||s = 1.00000 \n", + "m_ref/m_s = 1836.00 \n", + "T_perp/T_par|s = 1.00000 \n", + "q_ref/q_s = -1.00000 \n", + "n_s/n_ref = 1.00000 \n", + "v_drift s/v_A,ref = 0.00000 \n", + "-=-=-=-=-=-=-=-=-=-\n", + "Searching over:\n", + "omega/Omega_ref \\in [ 1.000E-05, 1.000E-02]\n", + "gamma/Omega_ref \\in [-2.000E-03,-2.000E-06]\n", + "-=-=-=-=-=-=-=-=-=-\n", + " finding minima\n", + " minima found\n", + " 7 possible local minima found:\n", + "ir = 128 ii = 128\n", + " 1.0000E-02 -2.0000E-06\n", + "ir = 16 ii = 125\n", + " 1.2588E-03 -4.8828E-05\n", + "ir = 0 ii = 80\n", + " 1.0000E-05 -7.5125E-04\n", + "ir = 15 ii = 65\n", + " 1.1807E-03 -9.8539E-04\n", + "ir = 22 ii = 26\n", + " 1.7270E-03 -1.5942E-03\n", + "ir = 28 ii = 2\n", + " 2.1953E-03 -1.9688E-03\n", + "ir = 128 ii = 0\n", + " 1.0000E-02 -2.0000E-03\n", + "Refining roots:\n", + "Dispersion Solutions \n", + " 1 1.2362E-03 -4.4355E-05\n", + " Predicting FPC (gyrotropic coords)...\n", + "Input for Root 1: 1.2362E-03 -4.4355E-05\n", + "Dispersion Solutions: \n", + " 1 1.2362E-03 -4.4355E-05\n", + " WARNING! The gyro routine does not support computing moments at this time due to computational demand!\n", + " Assuming data folder already exists...\n", + " Assuming subfolder howes2017 already exists\n", + " Calculating fpc for species 1\n", + " Writing omega/kpar V_a normalization to file...\n", + " Calculating fpc for species 2\n", + " Writing omega/kpar V_a normalization to file...\n", + "-=-=-=-=-=-=-=-=-=-\n", + "Root Search:\n", + "-=-=-=-=-=-=-=-=-=-\n", + "Global Plasma Parameters:\n", + "k_perp rho_ref = 1.30000 \n", + "k_par rho_ref = 0.100000E-02\n", + "Beta_ref,par = 1.00000 \n", + "v_t,ref,par/c = 0.100000E-03\n", + "-=-=-=-=-=-=-=-=-=-\n", + "Parameters for Species : 1\n", + "T_||,ref/T_||s = 1.00000 \n", + "m_ref/m_s = 1.00000 \n", + "T_perp/T_par|s = 1.00000 \n", + "q_ref/q_s = 1.00000 \n", + "n_s/n_ref = 1.00000 \n", + "v_drift s/v_A,ref = 0.00000 \n", + "-=-=-=-=-=-=-=-=-=-\n", + "Parameters for Species : 2\n", + "T_||,ref/T_||s = 1.00000 \n", + "m_ref/m_s = 1836.00 \n", + "T_perp/T_par|s = 1.00000 \n", + "q_ref/q_s = -1.00000 \n", + "n_s/n_ref = 1.00000 \n", + "v_drift s/v_A,ref = 0.00000 \n", + "-=-=-=-=-=-=-=-=-=-\n", + "Searching over:\n", + "omega/Omega_ref \\in [ 1.000E-05, 1.000E-02]\n", + "gamma/Omega_ref \\in [-2.000E-03,-2.000E-06]\n", + "-=-=-=-=-=-=-=-=-=-\n", + " finding minima\n", + " minima found\n", + " 7 possible local minima found:\n", + "ir = 128 ii = 128\n", + " 1.0000E-02 -2.0000E-06\n", + "ir = 16 ii = 124\n", + " 1.2588E-03 -6.4437E-05\n", + "ir = 0 ii = 77\n", + " 1.0000E-05 -7.9808E-04\n", + "ir = 15 ii = 64\n", + " 1.1807E-03 -1.0010E-03\n", + "ir = 22 ii = 21\n", + " 1.7270E-03 -1.6722E-03\n", + "ir = 29 ii = 0\n", + " 2.2734E-03 -2.0000E-03\n", + "ir = 128 ii = 0\n", + " 1.0000E-02 -2.0000E-03\n", + "Refining roots:\n", + "Dispersion Solutions \n", + " 1 1.2676E-03 -6.9505E-05\n", + " Predicting FPC (gyrotropic coords)...\n", + "Input for Root 1: 1.2676E-03 -6.9505E-05\n", + "Dispersion Solutions: \n", + " 1 1.2676E-03 -6.9505E-05\n", + " WARNING! The gyro routine does not support computing moments at this time due to computational demand!\n", + " Assuming data folder already exists...\n", + " Assuming subfolder howes2017 already exists\n", + " Calculating fpc for species 1\n", + " Writing omega/kpar V_a normalization to file...\n", + " Calculating fpc for species 2\n", + " Writing omega/kpar V_a normalization to file...\n", + "-=-=-=-=-=-=-=-=-=-\n", + "Root Search:\n", + "-=-=-=-=-=-=-=-=-=-\n", + "Global Plasma Parameters:\n", + "k_perp rho_ref = 1.30000 \n", + "k_par rho_ref = 0.100000E-02\n", + "Beta_ref,par = 1.00000 \n", + "v_t,ref,par/c = 0.100000E-03\n", + "-=-=-=-=-=-=-=-=-=-\n", + "Parameters for Species : 1\n", + "T_||,ref/T_||s = 1.00000 \n", + "m_ref/m_s = 1.00000 \n", + "T_perp/T_par|s = 1.00000 \n", + "q_ref/q_s = 1.00000 \n", + "n_s/n_ref = 1.00000 \n", + "v_drift s/v_A,ref = 0.00000 \n", + "-=-=-=-=-=-=-=-=-=-\n", + "Parameters for Species : 2\n", + "T_||,ref/T_||s = 1.00000 \n", + "m_ref/m_s = 1836.00 \n", + "T_perp/T_par|s = 1.00000 \n", + "q_ref/q_s = -1.00000 \n", + "n_s/n_ref = 1.00000 \n", + "v_drift s/v_A,ref = 0.00000 \n", + "-=-=-=-=-=-=-=-=-=-\n", + "Searching over:\n", + "omega/Omega_ref \\in [ 1.000E-05, 1.000E-02]\n", + "gamma/Omega_ref \\in [-2.000E-03,-2.000E-06]\n", + "-=-=-=-=-=-=-=-=-=-\n", + " finding minima\n", + " minima found\n", + " 7 possible local minima found:\n", + "ir = 128 ii = 128\n", + " 1.0000E-02 -2.0000E-06\n", + "ir = 17 ii = 122\n", + " 1.3368E-03 -9.5656E-05\n", + "ir = 0 ii = 74\n", + " 1.0000E-05 -8.4491E-04\n", + "ir = 15 ii = 62\n", + " 1.1807E-03 -1.0322E-03\n", + "ir = 22 ii = 16\n", + " 1.7270E-03 -1.7503E-03\n", + "ir = 29 ii = 0\n", + " 2.2734E-03 -2.0000E-03\n", + "ir = 128 ii = 0\n", + " 1.0000E-02 -2.0000E-03\n", + "Refining roots:\n", + "Dispersion Solutions \n", + " 1 1.3032E-03 -9.2973E-05\n", + " Predicting FPC (gyrotropic coords)...\n", + "Input for Root 1: 1.3032E-03 -9.2973E-05\n", + "Dispersion Solutions: \n", + " 1 1.3032E-03 -9.2973E-05\n", + " WARNING! The gyro routine does not support computing moments at this time due to computational demand!\n", + " Assuming data folder already exists...\n", + " Assuming subfolder howes2017 already exists\n", + " Calculating fpc for species 1\n", + " Writing omega/kpar V_a normalization to file...\n", + " Calculating fpc for species 2\n", + " Writing omega/kpar V_a normalization to file...\n", + "-=-=-=-=-=-=-=-=-=-\n", + "Root Search:\n", + "-=-=-=-=-=-=-=-=-=-\n", + "Global Plasma Parameters:\n", + "k_perp rho_ref = 1.30000 \n", + "k_par rho_ref = 0.100000E-02\n", + "Beta_ref,par = 1.00000 \n", + "v_t,ref,par/c = 0.100000E-03\n", + "-=-=-=-=-=-=-=-=-=-\n", + "Parameters for Species : 1\n", + "T_||,ref/T_||s = 1.00000 \n", + "m_ref/m_s = 1.00000 \n", + "T_perp/T_par|s = 1.00000 \n", + "q_ref/q_s = 1.00000 \n", + "n_s/n_ref = 1.00000 \n", + "v_drift s/v_A,ref = 0.00000 \n", + "-=-=-=-=-=-=-=-=-=-\n", + "Parameters for Species : 2\n", + "T_||,ref/T_||s = 1.00000 \n", + "m_ref/m_s = 1836.00 \n", + "T_perp/T_par|s = 1.00000 \n", + "q_ref/q_s = -1.00000 \n", + "n_s/n_ref = 1.00000 \n", + "v_drift s/v_A,ref = 0.00000 \n", + "-=-=-=-=-=-=-=-=-=-\n", + "Searching over:\n", + "omega/Omega_ref \\in [ 1.000E-05, 1.000E-02]\n", + "gamma/Omega_ref \\in [-2.000E-03,-2.000E-06]\n", + "-=-=-=-=-=-=-=-=-=-\n", + " finding minima\n", + " minima found\n", + " 7 possible local minima found:\n", + "ir = 128 ii = 128\n", + " 1.0000E-02 -2.0000E-06\n", + "ir = 17 ii = 120\n", + " 1.3368E-03 -1.2687E-04\n", + "ir = 0 ii = 69\n", + " 1.0000E-05 -9.2295E-04\n", + "ir = 16 ii = 60\n", + " 1.2588E-03 -1.0634E-03\n", + "ir = 22 ii = 10\n", + " 1.7270E-03 -1.8439E-03\n", + "ir = 30 ii = 0\n", + " 2.3514E-03 -2.0000E-03\n", + "ir = 128 ii = 0\n", + " 1.0000E-02 -2.0000E-03\n", + "Refining roots:\n", + "Dispersion Solutions \n", + " 1 1.3568E-03 -1.2191E-04\n", + " Predicting FPC (gyrotropic coords)...\n", + "Input for Root 1: 1.3568E-03 -1.2191E-04\n", + "Dispersion Solutions: \n", + " 1 1.3568E-03 -1.2191E-04\n", + " WARNING! The gyro routine does not support computing moments at this time due to computational demand!\n", + " Assuming data folder already exists...\n", + " Assuming subfolder howes2017 already exists\n", + " Calculating fpc for species 1\n", + " Writing omega/kpar V_a normalization to file...\n", + " Calculating fpc for species 2\n", + " Writing omega/kpar V_a normalization to file...\n", + "-=-=-=-=-=-=-=-=-=-\n", + "Root Search:\n", + "-=-=-=-=-=-=-=-=-=-\n", + "Global Plasma Parameters:\n", + "k_perp rho_ref = 1.30000 \n", + "k_par rho_ref = 0.100000E-02\n", + "Beta_ref,par = 1.00000 \n", + "v_t,ref,par/c = 0.100000E-03\n", + "-=-=-=-=-=-=-=-=-=-\n", + "Parameters for Species : 1\n", + "T_||,ref/T_||s = 1.00000 \n", + "m_ref/m_s = 1.00000 \n", + "T_perp/T_par|s = 1.00000 \n", + "q_ref/q_s = 1.00000 \n", + "n_s/n_ref = 1.00000 \n", + "v_drift s/v_A,ref = 0.00000 \n", + "-=-=-=-=-=-=-=-=-=-\n", + "Parameters for Species : 2\n", + "T_||,ref/T_||s = 1.00000 \n", + "m_ref/m_s = 1836.00 \n", + "T_perp/T_par|s = 1.00000 \n", + "q_ref/q_s = -1.00000 \n", + "n_s/n_ref = 1.00000 \n", + "v_drift s/v_A,ref = 0.00000 \n", + "-=-=-=-=-=-=-=-=-=-\n", + "Searching over:\n", + "omega/Omega_ref \\in [ 1.000E-05, 1.000E-02]\n", + "gamma/Omega_ref \\in [-2.000E-03,-2.000E-06]\n", + "-=-=-=-=-=-=-=-=-=-\n", + " finding minima\n", + " minima found\n", + " 8 possible local minima found:\n", + "ir = 128 ii = 128\n", + " 1.0000E-02 -2.0000E-06\n", + "ir = 0 ii = 120\n", + " 1.0000E-05 -1.2687E-04\n", + "ir = 18 ii = 118\n", + " 1.4148E-03 -1.5809E-04\n", + "ir = 0 ii = 63\n", + " 1.0000E-05 -1.0166E-03\n", + "ir = 16 ii = 57\n", + " 1.2588E-03 -1.1103E-03\n", + "ir = 22 ii = 2\n", + " 1.7270E-03 -1.9688E-03\n", + "ir = 32 ii = 0\n", + " 2.5075E-03 -2.0000E-03\n", + "ir = 128 ii = 0\n", + " 1.0000E-02 -2.0000E-03\n", + "Refining roots:\n", + "Dispersion Solutions \n", + " 1 5.9952E-15 -1.2446E-04\n", + " Predicting FPC (gyrotropic coords)...\n", + "Input for Root 1: 1.4335E-03 -1.5475E-04\n", + "Dispersion Solutions: \n", + " 1 1.4335E-03 -1.5475E-04\n", + " WARNING! The gyro routine does not support computing moments at this time due to computational demand!\n", + " Assuming data folder already exists...\n", + " Assuming subfolder howes2017 already exists\n", + " Calculating fpc for species 1\n", + " Writing omega/kpar V_a normalization to file...\n", + " Calculating fpc for species 2\n", + " Writing omega/kpar V_a normalization to file...\n", + "-=-=-=-=-=-=-=-=-=-\n", + "Root Search:\n", + "-=-=-=-=-=-=-=-=-=-\n", + "Global Plasma Parameters:\n", + "k_perp rho_ref = 1.30000 \n", + "k_par rho_ref = 0.100000E-02\n", + "Beta_ref,par = 1.00000 \n", + "v_t,ref,par/c = 0.100000E-03\n", + "-=-=-=-=-=-=-=-=-=-\n", + "Parameters for Species : 1\n", + "T_||,ref/T_||s = 1.00000 \n", + "m_ref/m_s = 1.00000 \n", + "T_perp/T_par|s = 1.00000 \n", + "q_ref/q_s = 1.00000 \n", + "n_s/n_ref = 1.00000 \n", + "v_drift s/v_A,ref = 0.00000 \n", + "-=-=-=-=-=-=-=-=-=-\n", + "Parameters for Species : 2\n", + "T_||,ref/T_||s = 1.00000 \n", + "m_ref/m_s = 1836.00 \n", + "T_perp/T_par|s = 1.00000 \n", + "q_ref/q_s = -1.00000 \n", + "n_s/n_ref = 1.00000 \n", + "v_drift s/v_A,ref = 0.00000 \n", + "-=-=-=-=-=-=-=-=-=-\n", + "Searching over:\n", + "omega/Omega_ref \\in [ 1.000E-05, 1.000E-02]\n", + "gamma/Omega_ref \\in [-2.000E-03,-2.000E-06]\n", + "-=-=-=-=-=-=-=-=-=-\n", + " finding minima\n", + " minima found\n", + " 6 possible local minima found:\n", + "ir = 128 ii = 128\n", + " 1.0000E-02 -2.0000E-06\n", + "ir = 22 ii = 113\n", + " 1.7270E-03 -2.3614E-04\n", + "ir = 0 ii = 109\n", + " 1.0000E-05 -2.9858E-04\n", + "ir = 18 ii = 47\n", + " 1.4148E-03 -1.2664E-03\n", + "ir = 0 ii = 45\n", + " 1.0000E-05 -1.2976E-03\n", + "ir = 128 ii = 0\n", + " 1.0000E-02 -2.0000E-03\n", + "Refining roots:\n", + "Dispersion Solutions \n", + " 1 1.7177E-03 -2.3538E-04\n", + " Predicting FPC (gyrotropic coords)...\n", + "Input for Root 1: 1.7177E-03 -2.3538E-04\n", + "Dispersion Solutions: \n", + " 1 1.7177E-03 -2.3538E-04\n", + " WARNING! The" ] } ], @@ -1493,112 +8782,112 @@ }, { "cell_type": "code", - "execution_count": 53, + "execution_count": 64, "metadata": {}, "outputs": [], "source": [ - "import numpy as np\n", - "import matplotlib.pyplot as plt\n", + "# TODO: re implement this option into JET-PLUME! (that is we need to re implement the option that called the test function that returned the single residual value!)" + ] + }, + { + "cell_type": "code", + "execution_count": 65, + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "# import numpy as np\n", + "# import matplotlib.pyplot as plt\n", "\n", - "# Example: define val1 and val2 ranges (edit as needed)\n", - "val1_min, val1_max, n_val1 = np.real(roots[rootidx])-.2, np.real(roots[rootidx])+.2, 100\n", - "val2_min, val2_max, n_val2 = np.imag(roots[rootidx])-.2, np.imag(roots[rootidx])+.2, 20\n", + "# # Example: define val1 and val2 ranges (edit as needed)\n", + "# val1_min, val1_max, n_val1 = np.real(roots[rootidx])-.2, np.real(roots[rootidx])+.2, 100\n", + "# val2_min, val2_max, n_val2 = np.imag(roots[rootidx])-.2, np.imag(roots[rootidx])+.2, 20\n", "\n", - "val1s = np.linspace(val1_min, val1_max, n_val1)\n", - "val2s = np.linspace(val2_min, val2_max, n_val2)\n", + "# val1s = np.linspace(val1_min, val1_max, n_val1)\n", + "# val2s = np.linspace(val2_min, val2_max, n_val2)\n", "\n", - "# Allocate arrays for results\n", - "D_r = np.zeros((n_val2, n_val1))\n", - "D_i = np.zeros((n_val2, n_val1))\n", - "plotval = np.zeros((n_val2, n_val1))\n", - "pky = 'ef' #we can grap the associated Eigen value to see how stiff the results are nearby\n", + "# # Allocate arrays for results\n", + "# D_r = np.zeros((n_val2, n_val1))\n", + "# D_i = np.zeros((n_val2, n_val1))\n", + "# plotval = np.zeros((n_val2, n_val1))\n", + "# pky = 'ef' #we can grap the associated Eigen value to see how stiff the results are nearby\n", "\n", - "# Loop over grid\n", - "for i, v1 in enumerate(val1s):\n", - " for j, v2 in enumerate(val2s):\n", - " om = v1\n", - " gam = v2\n", - " test_disp_data = lfpc.test_disp(om, gam, plumeinput, inputfldr+'disp', filetag, verbose=False)\n", - " d_r = test_disp_data['D_real']\n", - " d_i = test_disp_data['D_imag']\n", - " pv = 0\n", - " D_r[j, i] = d_r\n", - " D_i[j, i] = d_i" + "# # Loop over grid\n", + "# for i, v1 in enumerate(val1s):\n", + "# for j, v2 in enumerate(val2s):\n", + "# om = v1\n", + "# gam = v2\n", + "# test_disp_data = lfpc.test_disp(om, gam, plumeinput, inputfldr+'disp', filetag, verbose=False)\n", + "# d_r = test_disp_data['D_real']\n", + "# d_i = test_disp_data['D_imag']\n", + "# pv = 0\n", + "# D_r[j, i] = d_r\n", + "# D_i[j, i] = d_i" ] }, { "cell_type": "code", - "execution_count": 54, + "execution_count": 66, "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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- "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ - "import numpy as np\n", - "import matplotlib.pyplot as plt\n", - "import matplotlib.colors as colors\n", + "# import numpy as np\n", + "# import matplotlib.pyplot as plt\n", + "# import matplotlib.colors as colors\n", "\n", - "oms = [rt.real for rt in roots]\n", - "gams = [rt.imag for rt in roots]\n", - "lbls = [_i for _i in range(0,len(oms))]\n", + "# oms = [rt.real for rt in roots]\n", + "# gams = [rt.imag for rt in roots]\n", + "# lbls = [_i for _i in range(0,len(oms))]\n", "\n", - "# Plot results\n", - "fig, axes = plt.subplots(1, 2, figsize=(12, 5))\n", + "# # Plot results\n", + "# fig, axes = plt.subplots(1, 2, figsize=(12, 5))\n", "\n", - "# pcolormesh expects 2D meshgrid for coordinates\n", - "V1, V2 = np.meshgrid(val1s, val2s)\n", + "# # pcolormesh expects 2D meshgrid for coordinates\n", + "# V1, V2 = np.meshgrid(val1s, val2s)\n", "\n", - "# symmetric limits\n", - "vmax_r = np.nanmax(np.abs(D_r))\n", - "vmax_i = np.nanmax(np.abs(D_i))\n", + "# # symmetric limits\n", + "# vmax_r = np.nanmax(np.abs(D_r))\n", + "# vmax_i = np.nanmax(np.abs(D_i))\n", "\n", - "if(vmax_r > 1e10):\n", - " vmax_r = 1e10\n", - "if(vmax_i > 1e10):\n", - " vmax_i = 1e10 \n", - "# threshold around zero where it switches from linear to log\n", - "linthresh = 1e-10 # <-- adjust this as needed\n", - "pcm0 = axes[0].pcolormesh(\n", - " V1, V2, D_r, shading='auto', cmap='seismic',\n", - " norm=colors.SymLogNorm(linthresh=linthresh, vmin=-vmax_r, vmax=vmax_r)\n", - ")\n", - "fig.colorbar(pcm0, ax=axes[0])\n", - "axes[0].set_title(\"D_r\")\n", - "axes[0].set_xlabel(r\"$\\omega/\\Omega_r$\")\n", - "axes[0].set_ylabel(r\"$\\gamma/\\Omega_r$\")\n", + "# if(vmax_r > 1e10):\n", + "# vmax_r = 1e10\n", + "# if(vmax_i > 1e10):\n", + "# vmax_i = 1e10 \n", + "# # threshold around zero where it switches from linear to log\n", + "# linthresh = 1e-10 # <-- adjust this as needed\n", + "# pcm0 = axes[0].pcolormesh(\n", + "# V1, V2, D_r, shading='auto', cmap='seismic',\n", + "# norm=colors.SymLogNorm(linthresh=linthresh, vmin=-vmax_r, vmax=vmax_r)\n", + "# )\n", + "# fig.colorbar(pcm0, ax=axes[0])\n", + "# axes[0].set_title(\"D_r\")\n", + "# axes[0].set_xlabel(r\"$\\omega/\\Omega_r$\")\n", + "# axes[0].set_ylabel(r\"$\\gamma/\\Omega_r$\")\n", "\n", - "linthresh = 1e8 # <-- adjust this as needed\n", - "pcm1 = axes[1].pcolormesh(\n", - " V1, V2, D_i, shading='auto', cmap='seismic',\n", - " norm=colors.SymLogNorm(linthresh=linthresh, vmin=-vmax_i, vmax=vmax_i)\n", - ")\n", - "fig.colorbar(pcm1, ax=axes[1])\n", - "axes[1].set_title(\"D_i\")\n", - "axes[1].set_xlabel(r\"$\\omega/\\Omega_r$\")\n", - "axes[1].set_ylabel(r\"$\\gamma/\\Omega_r$\")\n", - "axes[1].scatter(oms,gams)\n", + "# linthresh = 1e8 # <-- adjust this as needed\n", + "# pcm1 = axes[1].pcolormesh(\n", + "# V1, V2, D_i, shading='auto', cmap='seismic',\n", + "# norm=colors.SymLogNorm(linthresh=linthresh, vmin=-vmax_i, vmax=vmax_i)\n", + "# )\n", + "# fig.colorbar(pcm1, ax=axes[1])\n", + "# axes[1].set_title(\"D_i\")\n", + "# axes[1].set_xlabel(r\"$\\omega/\\Omega_r$\")\n", + "# axes[1].set_ylabel(r\"$\\gamma/\\Omega_r$\")\n", + "# axes[1].scatter(oms,gams)\n", "\n", - "for ax in axes:\n", - " ax.set_xlim(val1_min,val1_max)\n", - " ax.set_ylim(val2_min,val2_max)\n", - " ax.scatter(oms,gams,marker='x',color='yellow')\n", + "# for ax in axes:\n", + "# ax.set_xlim(val1_min,val1_max)\n", + "# ax.set_ylim(val2_min,val2_max)\n", + "# ax.scatter(oms,gams,marker='x',color='yellow')\n", "\n", - "plt.tight_layout()\n", - "plt.show()" + "# plt.tight_layout()\n", + "# plt.show()" ] }, { "cell_type": "code", - "execution_count": 55, + "execution_count": 67, "metadata": {}, "outputs": [], "source": [ diff --git a/linfpclib/linfpc.py b/linfpclib/linfpc.py index 8c7ed2b..bd496fb 100644 --- a/linfpclib/linfpc.py +++ b/linfpclib/linfpc.py @@ -26,6 +26,21 @@ def find_nearest(array, value): #random but very useful function idx = (np.abs(array - value)).argmin() return idx +import math + + +class ParamsDict(dict): + def __init__(self, owner, *args, **kwargs): + super().__init__(*args, **kwargs) + self.owner = owner + + def __setitem__(self, key, value): + old_value = self.get(key, None) + super().__setitem__(key, value) + + if key == 'Kn' and old_value != value: #works like a setter + self.owner.recompute_all_nuns() + class plume_input: #class that have dictss with all inputs for each namelist #params @@ -37,11 +52,28 @@ class plume_input: def __init__(self,dataname): self.dataname = dataname + # make these instance-level, not shared across all objects + self.params = ParamsDict(self) + self.fpc = {} + self.species = [{}] + self.maps = {} + self.scan_inputs = [{}] + self.guesses = [{}] + def read_input(): #typically load from sample pass + def recompute_all_nuns(self): + if len(self.species) == 0: + return + if len(self.species[0]) == 0: + return + + for i in range(len(self.species)): + self.species[i]['nu_ns'] = self.get_nuns(self.species[i]) + def set_params(self,betap,kperp,kpar,vtp,nspec,nscan,option,\ - nroot_max,use_map,writeOut): + nroot_max,use_map,writeOut,collision_type=0,Kn=None): use_map_out = '.true.' if(not(use_map)): use_map_out = '.false.' @@ -50,7 +82,8 @@ def set_params(self,betap,kperp,kpar,vtp,nspec,nscan,option,\ if(not(use_map)): usewriteOut_out = '.false.' - self.params = {'betap':betap, + self.params = ParamsDict(self, { + 'betap':betap, 'kperp':kperp, 'kpar':kpar, 'vtp':vtp, @@ -59,8 +92,10 @@ def set_params(self,betap,kperp,kpar,vtp,nspec,nscan,option,\ 'option':int(option), 'nroot_max':int(nroot_max), 'use_map':use_map_out, - 'writeOut':usewriteOut_out - } + 'writeOut':usewriteOut_out, + 'collision_type':int(collision_type), + 'Kn':Kn + }) def set_fpc(self,vperpmin=0,vperpmax=0,vparmin=0,vparmax=0,delv=0,vxmin=None,vxmax=None,vymin=None,vymax=None,vzmin=None,vzmax=None,elecdircontribution=0.): self.fpc = {'vperpmin':vperpmin, @@ -73,10 +108,10 @@ def set_fpc(self,vperpmin=0,vperpmax=0,vparmin=0,vparmax=0,delv=0,vxmin=None,vxm if(vxmin!=None and vxmax!=None and vymin!=None and vymax!=None and vzmin!=None and vzmax!=None): self.fpc['vxmin'] = vxmin self.fpc['vxmax'] = vxmax - self.fpc['vymin'] = vymin - self.fpc['vymax'] = vymax - self.fpc['vzmin'] = vzmin - self.fpc['vzmax'] = vzmax + self.fpc['vymin'] = vymin + self.fpc['vymax'] = vymax + self.fpc['vzmin'] = vzmin + self.fpc['vzmax'] = vzmax def set_maps(self,loggridw,omi,omf,gami,gamf,positive_roots): loggridw_out = '.true.' @@ -95,18 +130,43 @@ def set_maps(self,loggridw,omi,omf,gami,gamf,positive_roots): 'positive_roots':positive_roots_out } + def get_nuns(self, spec): + muS = spec['muS'] + tauS = spec['tauS'] + + # Fortran spec(1)%alph_s -> Python self.species[0]['alphS'] + # If no species exist yet, assume this spec IS the first/reference species + if len(self.species) == 0: + alpha1 = spec['alphS'] + else: + alpha1 = self.species[0]['alphS'] + + try: + Kn = self.params['Kn'] + except: + print("Error with Kn = self.params['Kn'], returning 0 for nu_ns...") + return 0. + + if(Kn is None): + return None + + nu_ns = 1.0 / (math.sqrt(2.0) * Kn) * math.sqrt(muS / (tauS * alpha1)) + + return nu_ns + def make_species(self,tauS, muS, alphS, Qs, Ds, vvS,spec_n=-1): tempspecies = {'tauS':tauS, 'muS':muS, 'alphS':alphS, 'Qs':Qs, 'Ds':Ds, - 'vvS':vvS + 'vvS':vvS, } if(len(self.species[0]) == 0): if(spec_n == -1 or spec_n == 1): print("No species found, creating first species...") self.species = [tempspecies] + idx = 0 else: print("Warning: spec_n ==",spec_n,"but there are no species here...") else: @@ -114,12 +174,17 @@ def make_species(self,tauS, muS, alphS, Qs, Ds, vvS,spec_n=-1): if(spec_n == -1 or num_spec+1 == spec_n): print("Appending species to list. Total species is now ",num_spec+1) self.species.append(tempspecies) + idx = -1 elif(num_spec+1 < spec_n): print("Warning: there are only ",num_spec,"species but user requested we create spec number",spec_n) - print("Please input a valid spec_n...") + print("Please input a valid spec_n... Returning") + return else: print("Replacing species number",spec_n) self.species[spec_n-1] = tempspecies + idx = spec_n - 1 + + self.species[idx]['nu_ns'] = self.get_nuns(self.species[idx]) def make_scan(self,scan_type,scan_style,swi,swf,swlog,ns,nres,heating,eigen): swlog_out = '.true.' @@ -186,8 +251,16 @@ def write_input(self,flnm,outputname,desc='',verbose=False): line = str(key)+'='+"'"+str(outputname)+"'"+'\n' else: line = str(key)+'='+str(self.params[key])+'\n' + if(key == 'Kn'): + if(not(self.params['Kn']) is None): + line = str(key)+'='+str(self.params[key])+'\n' + else: + continue f.write(line) + if(self.params['Kn'] != None and self.params['collision_type'] == 0): + print("Warning, Kn has a finite value but collision_type is 0 (i.e. collisions are off). Was this intentional?") + line = 'dataName'+"='"+str(self.dataname)+"'\n" f.write(line) f.write('/\n\n') @@ -203,6 +276,8 @@ def write_input(self,flnm,outputname,desc='',verbose=False): for spec in self.species: f.write('&species_'+str(specidx)+'\n') for key in spec.keys(): + if(key == 'nu_ns'): + continue line = str(key)+'='+str(spec[key])+'\n' f.write(line) f.write('/\n\n') @@ -238,7 +313,7 @@ def write_input(self,flnm,outputname,desc='',verbose=False): f.close() def load_from_file(self,flnm): - paramkeys = ['betap','kperp','kpar','vtp','nspec','nscan','option','nroot_max','use_map','writeOut','outputName'] #note dataName is missing as that is set by self.dataname and defined at creation + paramkeys = ['betap','kperp','kpar','vtp','nspec','nscan','option','nroot_max','use_map','writeOut','outputName','collision_type','Kn'] #note dataName is missing as that is set by self.dataname and defined at creation fpckeys = ['vperpmin','vperpmax','vparmin','vparmax','delv','elecdircontribution'] specieskeys = ['tauS','muS','alphS','Qs','Ds','vvS'] mapskeys = ['loggridw','omi','omf','gami','gamf','positive_roots'] @@ -276,7 +351,7 @@ def load_from_file(self,flnm): tempparamdict[parse[0]] = parse[1].split('!')[0].replace('\n','') _i += 1 - self.params = tempparamdict + self.params = ParamsDict(self, tempparamdict) if(parse[0] == '&fpc'): @@ -371,15 +446,6 @@ def load_from_file(self,flnm): _i += 1 - #main dict - namelists = {} - params = {} - fpc = {} - species = [{}] - maps = {} - scan_inputs = [{}] - guesses = [{}] - dataname = 'default' def _replace_input_aux(inputflnm,verbose=False): @@ -442,19 +508,23 @@ class containing input parameters if(verbose): print("Reading roots from ",rootflnm) roots = [] - tempfile = open(rootflnm, "r") - for line in tempfile: - parse = line.split() - try: - temp_om = float(parse[4]) - except: - temp_om = float(1*10**99.) - try: - temp_gam = float(parse[5]) - except: - temp_gam = float(1*10**99.) - roots.append(temp_om+temp_gam*1j) - tempfile.close() + try: + tempfile = open(rootflnm, "r") + for line in tempfile: + parse = line.split() + try: + temp_om = float(parse[4]) + except: + temp_om = float(1*10**99.) + try: + temp_gam = float(parse[5]) + except: + temp_gam = float(1*10**99.) + roots.append(temp_om+temp_gam*1j) + except Exception as e: + print(f"Error! Be sure to run the makefile with 'make' to compile PLUME before using this!") + raise + tempfile.close() return np.asarray(roots) diff --git a/src/fpc.f90 b/src/fpc.f90 index 0b9b8fd..23f7136 100644 --- a/src/fpc.f90 +++ b/src/fpc.f90 @@ -374,13 +374,13 @@ subroutine compute_fpc_cart(wrootindex) if (computemoment) then call calc_fs1(omega, vperp, vvz(ivz), phi, ef, bf, hatV_s(is), spec(is)%q_s, spec(is)%alph_s, & spec(is)%tau_s, spec(is)%mu_s, spec(1)%alph_s, elecdircontribution, & - (1., 0.), fs0(ivx, ivy, ivz, is), fs1_SP(ivx, ivy, ivz, is), EpsilonSokhotski_Plemelj) + (1., 0.), fs0(ivx, ivy, ivz, is), fs1_SP(ivx, ivy, ivz, is), EpsilonSokhotski_Plemelj,spec(is)%nu_ns) !We fs1_sp to correctly compute jiEi = int CorEi d3v with residual! fs1(ivx, ivy, ivz, is) = fs1_SP(ivx, ivy, ivz, is) else call calc_fs1(omega, vperp, vvz(ivz), phi, ef, bf, hatV_s(is), spec(is)%q_s, spec(is)%alph_s, & spec(is)%tau_s, spec(is)%mu_s, spec(1)%alph_s, elecdircontribution, & - (1., 0.), fs0(ivx, ivy, ivz, is), fs1(ivx, ivy, ivz, is), 0.) + (1., 0.), fs0(ivx, ivy, ivz, is), fs1(ivx, ivy, ivz, is), 0.,spec(is)%nu_ns) end if end do end do @@ -1308,7 +1308,7 @@ subroutine compute_fpc_gyro(wrootindex) call calc_fs1(omega, vvperp(ivperp), vvpar(ivpar), vvphi(ivphi), ef, bf, hatV_s(is),& spec(is)%q_s, spec(is)%alph_s, & spec(is)%tau_s, spec(is)%mu_s, spec(1)%alph_s, & - elecdircontribution, exbar, fs0(ivperp, ivpar, ivphi, is), fs1(ivperp, ivpar, ivphi, is), 0.) + elecdircontribution, exbar, fs0(ivperp, ivpar, ivphi, is), fs1(ivperp, ivpar, ivphi, is), 0.,spec(is)%nu_ns) !compute fs1 at adjacent locations in vperp1/vperp2 direction to take derivatives with later !Note: delv may not be the best choice here when it is large. @@ -1321,7 +1321,7 @@ subroutine compute_fpc_gyro(wrootindex) call calc_fs1(omega, vperp_adjacent, vvpar(ivpar), phi_adjacent, ef, bf, & hatV_s(is), spec(is)%q_s, spec(is)%alph_s, & spec(is)%tau_s, spec(is)%mu_s, spec(1)%alph_s, elecdircontribution, & - exbar, fs0_temp, fs1_plus_delvperp1(ivperp, ivpar, ivphi, is), 0.) + exbar, fs0_temp, fs1_plus_delvperp1(ivperp, ivpar, ivphi, is), 0.,spec(is)%nu_ns) vperp1_adjacent = vvperp(ivperp)*COS(vvphi(ivphi)) - delv vperp2_adjacent = vvperp(ivperp)*SIN(vvphi(ivphi)) phi_adjacent = ATAN2(vperp2_adjacent, vperp1_adjacent) @@ -1330,7 +1330,7 @@ subroutine compute_fpc_gyro(wrootindex) call calc_fs1(omega, vperp_adjacent, vvpar(ivpar), phi_adjacent, ef, bf, & hatV_s(is), spec(is)%q_s, spec(is)%alph_s, & spec(is)%tau_s, spec(is)%mu_s, spec(1)%alph_s, elecdircontribution, & - exbar, fs0_temp, fs1_minus_delvperp1(ivperp, ivpar, ivphi, is), 0.) + exbar, fs0_temp, fs1_minus_delvperp1(ivperp, ivpar, ivphi, is), 0.,spec(is)%nu_ns) vperp1_adjacent = vvperp(ivperp)*COS(vvphi(ivphi)) vperp2_adjacent = vvperp(ivperp)*SIN(vvphi(ivphi)) + delv phi_adjacent = ATAN2(vperp2_adjacent, vperp1_adjacent) @@ -1339,7 +1339,7 @@ subroutine compute_fpc_gyro(wrootindex) call calc_fs1(omega, vperp_adjacent, vvpar(ivpar), phi_adjacent, ef, bf, & hatV_s(is), spec(is)%q_s, spec(is)%alph_s, & spec(is)%tau_s, spec(is)%mu_s, spec(1)%alph_s, elecdircontribution, & - exbar, fs0_temp, fs1_plus_delvperp2(ivperp, ivpar, ivphi, is), 0.) + exbar, fs0_temp, fs1_plus_delvperp2(ivperp, ivpar, ivphi, is), 0.,spec(is)%nu_ns) vperp1_adjacent = vvperp(ivperp)*COS(vvphi(ivphi)) vperp2_adjacent = vvperp(ivperp)*SIN(vvphi(ivphi)) - delv phi_adjacent = ATAN2(vperp2_adjacent, vperp1_adjacent) @@ -1348,7 +1348,7 @@ subroutine compute_fpc_gyro(wrootindex) call calc_fs1(omega, vperp_adjacent, vvpar(ivpar), phi_adjacent, ef, bf, & hatV_s(is), spec(is)%q_s, spec(is)%alph_s, & spec(is)%tau_s, spec(is)%mu_s, spec(1)%alph_s, elecdircontribution, & - exbar, fs0_temp, fs1_minus_delvperp2(ivperp, ivpar, ivphi, is), 0.) + exbar, fs0_temp, fs1_minus_delvperp2(ivperp, ivpar, ivphi, is), 0.,spec(is)%nu_ns) end do end do end do @@ -1632,7 +1632,7 @@ end function fs0hat ! Collin Brown and Greg Howes, 2025 !------------------------------------------------------------------------------ subroutine calc_fs1(omega, vperp, vpar, phi, ef, bf, hatV_s, q_s, aleph_s, tau_s, mu_s, & - aleph_r, elecdircontribution, exbar, fs0, fs1, epsSokhotski_Plemelj) + aleph_r, elecdircontribution, exbar, fs0, fs1, epsSokhotski_Plemelj, nu_ns) !! Determine species perturbed VDF fs1 at given (vperp,vpar,phi) use vars, only: betap, kperp, kpar, vtp, pi, delv @@ -1689,6 +1689,9 @@ subroutine calc_fs1(omega, vperp, vpar, phi, ef, bf, hatV_s, q_s, aleph_s, tau_s real, intent(in) :: epsSokhotski_Plemelj !! Small value for Sokhotski_Plemelj when computing moments of fs1; zero when just computing fs1 + real, intent(in) :: nu_ns + !! Neutral-species collision frequency (using Krook collision operator) + real :: epsSokhotski_Plemelj_temp !! fixes sign of epsilon @@ -1786,7 +1789,7 @@ subroutine calc_fs1(omega, vperp, vpar, phi, ef, bf, hatV_s, q_s, aleph_s, tau_s do n = -nbesmax, nbesmax !Calculate all parts of solution that dosn't depend on m !epsSokhotski_Plemelj is typically 0 unless using Sokhotski-Plemelj theorem to take moment over this singularity - denom = (omega_temp - kpar_temp*vpar_temp*sqrt(mu_s/(tau_s*aleph_r)) - n*mu_s/q_s) + (0., 1.)*epsSokhotski_Plemelj_temp + denom = (omega_temp - kpar_temp*vpar_temp*sqrt(mu_s/(tau_s*aleph_r)) - n*mu_s/q_s + (0., 1.) * nu_ns) + (0., 1.)*epsSokhotski_Plemelj_temp Wbar_s = 2.*(n*mu_s/(q_s*(omega_temp)) - 1.)*(vpar_temp - hatV_s) - 2.*(n*mu_s/(q_s*(omega_temp)*aleph_s))*vpar_temp emult = (0., 0.) @@ -1969,7 +1972,7 @@ complex function wparth_from_ratio(is,ef) phi = ATAN2(vvy(ivy), vvx(ivx)) call calc_fs1(omega, vperp, vvz(ivz), phi, ef, ef, hatV_s, spec(is)%q_s, spec(is)%alph_s, & spec(is)%tau_s, spec(is)%mu_s, spec(1)%alph_s, 1., & - (1., 0.), fs0(ivx, ivy, ivz, is), fs1_SP(ivx, ivy, ivz, is), EpsilonSokhotski_Plemelj) + (1., 0.), fs0(ivx, ivy, ivz, is), fs1_SP(ivx, ivy, ivz, is), EpsilonSokhotski_Plemelj, spec(is)%nu_ns) end do end do end do diff --git a/src/functions.f90 b/src/functions.f90 index fe9c9ae..85a63b6 100644 --- a/src/functions.f90 +++ b/src/functions.f90 @@ -74,6 +74,7 @@ subroutine read_in_params !default values elecdircontribution = 0. + Kn = 1.0e30 !give default value for backwards compat (note large -> collisionless) call get_unused_unit(input_unit_no) call get_runname(runname) @@ -189,9 +190,12 @@ subroutine spec_read(is) spec(is)%vv_s = vvS !calculate neutral-charged collision frequency - spec(is)%nu_ns = (sqrt(2.d0)*Kn)**(-1.d0)* & - sqrt(spec(is)%mu_s/(spec(is)%tau_s*spec(1)%alph_s)) - + if (Kn > 1.0e25) then + spec(is)%nu_ns = 0.0 !Treat large default Kn as effectively collisionless (streamlines fpc.f90) + else + spec(is)%nu_ns = 1.0/(sqrt(2.0)*Kn) * & + sqrt(spec(is)%mu_s/(spec(is)%tau_s*spec(1)%alph_s)) + endif end subroutine spec_read !-=-=-=-=