diff --git a/so_box_biogeo/diags/.ipynb_checkpoints/estNsq-checkpoint.ipynb b/so_box_biogeo/diags/.ipynb_checkpoints/estNsq-checkpoint.ipynb new file mode 100644 index 00000000..4188f5a8 --- /dev/null +++ b/so_box_biogeo/diags/.ipynb_checkpoints/estNsq-checkpoint.ipynb @@ -0,0 +1,220 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Estimating Brunt-Vaisala frequency using T and S" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + " In case when N$^2$ is needed but do not have 'DRHODR' saved, one can estimate it using T and S" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "import numpy as np\n", + "from MITgcmutils import rdmds, densjmd95\n", + "from mitgcmgrid import loadgrid\n", + "import matplotlib.pyplot as plt" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Constants\n", + "rhoconst = 1.035e3\n", + "g = 9.81" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Load grid information (I used loadgrid that I wrote here, but it doesn't have to be this.)" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "grd = loadgrid('so_box', varname=['XC','YC','RC','hFacC','DRC','RF'])\n", + "[nz, ny, nx] = grd.hFacC.shape" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Compute the stratification frequency using DRHODR. It is defined at the center of the layer." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "dRHOdr = rdmds('ocestrat', 9, rec=0); # DRHODR is in the first record in \"ocestrat\"\n", + "Nsq = - dRHOdr*g/rhoconst*grd.mskC" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now, estimate Nsq using T and S. \n", + "When computing \"drhodr\" at the layer interface, density at upper and lower cell is computed using the pressure at the interface. Nsq is defined at the layer interface." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "T = rdmds('dynDiag', 9, rec=2) # THETA is in the third record in \"dynDiag\"\n", + "S = rdmds('dynDiag', 9, rec=3) # SALT is in the fourth record in \"dynDiag\"\n", + "Nsq_TS = np.zeros([nz, ny, nx])\n", + "for k in range(1,nz):\n", + " press = -rhoconst*g*grd.RF[k]/1e4 # pressure at the interface\n", + " urho = densjmd95(S[k-1, :, :],T[k-1, :, :], press) # rho at the center of the upper level\n", + " lrho = densjmd95(S[k, :, :], T[k, :, :], press) # rho at the center of the lower level \n", + " drhodr = (urho-lrho)/grd.DRC[k]*grd.mskC[k, :, :]*grd.mskC[k-1, :, :]\n", + " Nsq_TS[k, :, :] = -drhodr*g/rhoconst" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Estimating N$^2$ using \"RHOAnoma\" is not appropriate because \"RHOAnoma\" is computed using the pressure at that level " + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "rho = rdmds('ocestrat', 9, rec=1) + rhoconst # RHOAnoma is in the second record\n", + "Nsq_ra = np.zeros([nz, ny, nx])\n", + "for k in range(1,nz):\n", + " drhodz = (rho[k-1, :, :] - rho[k, :, :])/grd.DRC[k]*grd.mskC[k, :, :]*grd.mskC[k-1, :, :]\n", + " Nsq_ra[k, :, :] = -drhodz*g/rhoconst" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": true + }, + "source": [ + "Check N$^2$" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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VydVvkFHSpXXpxtC6DJOqsZns63I49a0fybQzitZ1uXSte/vmSxlZZWhdrjEY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3AAAc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\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "%matplotlib inline\n", + "showz = 5\n", + "showx = 5\n", + "scale = 1e5\n", + "\n", + "X, Y = np.meshgrid(grd.YC[:, 0], grd.RC[:showz])\n", + "\n", + "f, ax = plt.subplots(1, 3, figsize=(16, 4))\n", + "\n", + "im = ax[0].contourf(X, Y, Nsq[:showz, :, showx]*scale, np.arange(0, 15.1 , 1), cmap='Reds')\n", + "cb = plt.clabel(im,colors='black',fmt='%3.1f')\n", + "ax[0].set_title('N$^2$ from DRHODR [x 10$^5$ s$^{-1}$]', color='black', fontsize=15)\n", + "\n", + "im = ax[1].contourf(X, Y, Nsq_TS[:showz,:,showx]*scale, np.arange(0,15.1,1), cmap='Reds')\n", + "cb = plt.clabel(im,colors='black',fmt='%3.1f')\n", + "ax[1].set_title('N$^2$ from T and S [x 10$^5$ s$^{-1}$]', color='black', fontsize=15)\n", + "\n", + "im = ax[2].contourf(X, Y, Nsq_ra[:showz,:,showx]*scale, np.arange(0,15.1,1), cmap='Reds')\n", + "cb = plt.clabel(im,colors='black',fmt='%3.1f')\n", + "ax[2].set_title('N$^2$, from RHOAnoma [x 10$^5$ s$^{-1}$]', color='black', fontsize=15)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 2", + "language": "python", + "name": "python2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.12" + } + }, + "nbformat": 4, + "nbformat_minor": 1 +} diff --git a/so_box_biogeo/diags/dynDiag.0000000009.data b/so_box_biogeo/diags/dynDiag.0000000009.data new file mode 100644 index 00000000..ad9bcec3 Binary files /dev/null and b/so_box_biogeo/diags/dynDiag.0000000009.data differ diff --git a/so_box_biogeo/diags/dynDiag.0000000009.meta b/so_box_biogeo/diags/dynDiag.0000000009.meta new file mode 100644 index 00000000..afca703d --- /dev/null +++ b/so_box_biogeo/diags/dynDiag.0000000009.meta @@ -0,0 +1,16 @@ + simulation = { 'OBC + Biogeo S.Ocean Box' }; + nDims = [ 3 ]; + dimList = [ + 42, 1, 42, + 20, 1, 20, + 15, 1, 15 + ]; + dataprec = [ 'float64' ]; + nrecords = [ 9 ]; + timeStepNumber = [ 9 ]; + timeInterval = [ 4.500000000000E+03 8.100000000000E+03 ]; + missingValue = [ -9.99000000000000E+02 ]; + nFlds = [ 9 ]; + fldList = { + 'VVELMASS' 'UVELMASS' 'THETA ' 'SALT ' 'GM_PsiX ' 'GM_PsiY ' 'PhiVEL ' 'PsiVEL ' 'CONVADJ ' + }; diff --git a/so_box_biogeo/diags/estNsq.ipynb b/so_box_biogeo/diags/estNsq.ipynb new file mode 100644 index 00000000..4188f5a8 --- /dev/null +++ b/so_box_biogeo/diags/estNsq.ipynb @@ -0,0 +1,220 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Estimating Brunt-Vaisala frequency using T and S" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + " In case when N$^2$ is needed but do not have 'DRHODR' saved, one can estimate it using T and S" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "import numpy as np\n", + "from MITgcmutils import rdmds, densjmd95\n", + "from mitgcmgrid import loadgrid\n", + "import matplotlib.pyplot as plt" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Constants\n", + "rhoconst = 1.035e3\n", + "g = 9.81" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Load grid information (I used loadgrid that I wrote here, but it doesn't have to be this.)" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "grd = loadgrid('so_box', varname=['XC','YC','RC','hFacC','DRC','RF'])\n", + "[nz, ny, nx] = grd.hFacC.shape" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Compute the stratification frequency using DRHODR. It is defined at the center of the layer." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "dRHOdr = rdmds('ocestrat', 9, rec=0); # DRHODR is in the first record in \"ocestrat\"\n", + "Nsq = - dRHOdr*g/rhoconst*grd.mskC" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now, estimate Nsq using T and S. \n", + "When computing \"drhodr\" at the layer interface, density at upper and lower cell is computed using the pressure at the interface. Nsq is defined at the layer interface." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "T = rdmds('dynDiag', 9, rec=2) # THETA is in the third record in \"dynDiag\"\n", + "S = rdmds('dynDiag', 9, rec=3) # SALT is in the fourth record in \"dynDiag\"\n", + "Nsq_TS = np.zeros([nz, ny, nx])\n", + "for k in range(1,nz):\n", + " press = -rhoconst*g*grd.RF[k]/1e4 # pressure at the interface\n", + " urho = densjmd95(S[k-1, :, :],T[k-1, :, :], press) # rho at the center of the upper level\n", + " lrho = densjmd95(S[k, :, :], T[k, :, :], press) # rho at the center of the lower level \n", + " drhodr = (urho-lrho)/grd.DRC[k]*grd.mskC[k, :, :]*grd.mskC[k-1, :, :]\n", + " Nsq_TS[k, :, :] = -drhodr*g/rhoconst" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Estimating N$^2$ using \"RHOAnoma\" is not appropriate because \"RHOAnoma\" is computed using the pressure at that level " + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "rho = rdmds('ocestrat', 9, rec=1) + rhoconst # RHOAnoma is in the second record\n", + "Nsq_ra = np.zeros([nz, ny, nx])\n", + "for k in range(1,nz):\n", + " drhodz = (rho[k-1, :, :] - rho[k, :, :])/grd.DRC[k]*grd.mskC[k, :, :]*grd.mskC[k-1, :, :]\n", + " Nsq_ra[k, :, :] = -drhodz*g/rhoconst" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": true + }, + "source": [ + "Check N$^2$" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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VydVvkFHSpXXpxtC6DJOqsZns63I49a0fybQzitZ1uXSte/vmSxlZZWhdrjEY\n/VC2beO6k47gwiMOoM3Rw+bmVuqKLLisVjq79ZHlutohHDfrIM78/R/x+vz849orGT+qLiNtSZpc\ncUoH2A67xYxPVRlRXsJhwytY3tLJlBGVTLUr0NLMScPLaZg6lm+bOth3aAULNjdz8X569FtRxIAc\nUU/DIC/IF6H+YLDsf5uayO9x8kNZgnvJ6r7zPsD3w9Mpe+UxvKoGwOq2LqoK8rCHR/oT0E7DUU0z\nqRpuoeeCH4LnXp/H4TOnA6Cqap+j6nK5ue/x/zH/s8VcfOZp3P/4U4wZMZyTjzt6QG2NZ8wN5iIh\nOWesZdpQG4CBFhTFeCkdLp+ft9fVc/9x0/sdF0KgCD3tBfSRM5+mYQ5bmCFV8Y3WLvWW8/r+Dhpy\nhwDXNrRy+t8f46CJo+l1e1Oq0yD9pEPrQsmk8zqYWge7qN5lQetgcKMR0YinvZG07lDg9w0tnHbn\n48yYOJpetyeTTdxlSTaqmlBfTKZ/JLniqtlsoraijNqKMiaPCXVAS6GzCQlcfsxBXH7M9v2fZdu2\npOfJDmqqf644tKGE6FKH08PqNgcen582l4cRJYXMGjmEJ5auY1RpIcdPHMGF+4znnvcX89maLRxT\nV8kP8qI7qOl0PqWUSPQFMQGcDZ14pERKsCsiavalKiXNPj8AtcPKyTMlv/DWbSu3cOseo5hYpG/B\n0+rxYVMUiiwm9nzxIf6xph7yrZjmLOTzd2dydbUlbnQ5EiK4T1AuIoSQudw+oF/KXELbzyRosHh9\nPqafcQmP3vYH9pwwFk3TRyVMFTU88+pcPljwORf+7BT232sKHyxYyJ3/fpQ5Tz6Q2j1EQRSUZiSl\nL9FnkDPGWjaiB0kaa4kYZ+b750Y95/P5eP7553nqqaeYM2dOv3OapnHxxRez++67c9ppp1FVVcUe\n1WV8fvIMikNTNtJILMPOdPOjvPzyy7S2tjJ8+HCOPfZYpJSDsN575sgJrUsi/TedWheNTEdd++oJ\nOK2G1pGd+aQZ0Lp4+FQNDYlVUSIabg6Pj3aPD4siGF5oj1BC+jC0bvC1Lp6jmnB/TLeDlYZtQwaF\nRJ9DLjmkSejQgs1NXPPmYkaVFjK2vIiLRlUysshOs9NDmc2CJYqTl6xD6m50JHX9xWu28kFnNyNt\nVt7dcyyqlDzX0skdW1uo9/p4Z/IYpkTRsyea2rl7Wys2oXB0eSE3jqhJqm6ABV297F9oxxoIYrzW\n5qDZ5+eojy03AAAgAElEQVTn1WXYFYVfb2uh269SYjGhSvjTHiOptFkoeHlBXxnqLef1abzlX29E\n1Dsjopom0mW4bWtu5b6nXsJmtVBXW82eE/S0zOCXq+xoZu4bb3HoobOYPEGfh/DO/E8ZPVLfXys0\n8jpQBsNwGzSDLV0CmkZjLRkDLdQp7f3JDAA8gd+heDWNO1bX8/zWFlo8Po4fWtF3vV+TSCQ9fo0V\nC7/jmcd6efrWmzmupoyRNiu2tl7SPcZvqw04CSH3Gm7Iqbecx/GBvxdP+UWaW2CQLNlwUkPLiOew\nPvnCq9QNrUl58/pMza/NWa2D9OjdIGldKPEMwe1610qhWeHckUO4YHR/g6zD6+eXX69lba+bEouJ\nW3YfyUEVxSm1JxpBnYPEte6LPS9JaxsMtjPgvpcphytSubnqvCbyDLI9tamqOq3TBYJ99SCb4LMT\nDtjhfHXIVKhEnNJkHdEgbW3uiMcbnF58UrLS5aGtzY1b03i7uQtNk9iE4Mb1DdxVU0NhmCPd6ffz\nx01NlJlMKIrkqaZOjjDbGW9LbmrXREx0d2zPcpuBDSw27AHH9ZqKUj7u6qXZ6+PM6jIqbXqgozfM\nNg3Vx0gYjuoASXhj+wQNt8J8O0IIHntlHm2dDj756hv2nzwRW2D+6brN9Xh9PvYZWUueuwvseSxf\ntYZfnHU6QNqc1HSTdqMtV0bmBsFYM98/d4eOHskpjcTi9h7mNbbz8SFTmfz2l2x2eqh3eRhmt/H4\npiYEYFYEowrymFZRxAtb2/jb6noWHrZXwot4JUNQ3BM15L689Vdprd8ghDiLKqVb6xIllsPqdLnY\n1tTMVTfdxvmnn8QdN/0+rXWnSsy05++71kFa9W4gaXKL2ruZ19jO10fsTZfPz1mLV3NMTRlD7bpB\npknJa9vaKDCbWHTYXixu7+aqpev59NCpadW70HtIVOsW//HqtNRtsCOmg09DnZ/C9j/R+kAifSNV\nhzO87MF0XDMROU3nPPQUy0p2oCydTmk0JzQRrqgoo9Hv57qmFgA6u70cZbHz6/wSft3RQqkm+Edj\nK5cWlfR73QcuJy5NMrdqCD4pObW1keccXdxQXZVyW0IJ3lMecHrVdr0LfSZ5NdvbFO95Go5qlgiN\ndMbaa6u4sIBbr7qIW6+6iHkfL6ShpY1n5r1Hfl4eJx42E7+qUmC3932BvjlnDkIIJmdhlbdUSauT\nOthGWxaNtWidN5ZTGk8c17Z0srfNirm1h6V77cad9S08v7KeS2or+LndjkvTuGb9Nk6tLOWQ0kKu\nLC3mR8s3kN/hxNOd/jlTQbGK5LDCjvNtL953ApfPXZj2dhjopCO6mKjWJYvsaN7BWc232/n9Ly9i\nwaIvGTlcryMTAyqJYmhddOIZg6k4pInqnbvRgdQ0plrMPBfQOwCnqvHR1lZOqyrF3ehglF9F9flp\n3NpOqTm9g75Ja91+47niDUPrcoJYfSHsnKZJPlq7mScWLmN8dTmXHLw3Zfl5yKZt0XUpGeczWlvS\n6cBmap7pIC2Olo3FjOJpUTIOaWtXYrbWSBTq3SpSSlq7PJiFYG+rDZ+USCmpM5vZ4vfv8LqVfh8V\ngainJiVTLFa+droTrjdRKott/e67omL7rg7RnNZIGI5qFpAdzZhMJppa2/nFLbczY8Iorj7lWMwh\nRkskQ+6YmdMA8Hi9rFi3EYvFzNi6oTS2tfUtAX3HI09xwmEzqcvPzbcyLYbbYBlsGUpbiSaakSKl\n0Ug1hWQ3u427t7XSq2o4/CrvdvZwaElhn5i4NI0mpw9Trwql+oq/QyxmGr2+tBtusP0+wo04iBx5\n+F5N1spBxNAJyG2rUn59qNZdPPtvzJw0mqtOjq91yZQP/aOr/33xNdo6Orni/LNSLjcdGFq3I+ly\nTlPVu/F2G/fE0Dt7qZV2v0q5ObiiPlRbzDRkQO+S1brklzYxSIaEoqoJOKjBgbHg7w1tndw27zOO\nmzIWl9fPGQ+/xptXnBp78CwdUdN4fThSmZmeApCEY5oLi6KlU49iOabhDuGnHhcP9XQxxGTix/YC\nDrLtOK+01bejwwnQrarIsPN+KfFKyfsuF6faC/qdk1LSofoxBV7j1zSElDg1LWodyVBp2e6LBO+z\nsljPYAl/JkHHNd7zzE3v5ntE0Hh5+o13mb+2kZbmZr4xC8xhX4IyhiFns1rZe9J4/ZwQHD3jQI67\n9HfsPm4040YM5/Kfn9SvrmwtQhKLRNL/Yhpu2TTYBlpXDDGO5ZSG0vuTGREjpckaaPFG7UZI+HlR\nMcd8s44SxcRUmw2Xyw8F+nkF9BV/kbS1ufFoGr1elW6HlzZXUk1JiHChipQOEm/+gkFuEOzzT819\nh4/XNdHS3MI3FiUprUu2ri6TnQf/+yxXX3QuEHmO/vpNWxgzMv3bNYS3JeY18ZzUnUXvMuycJqN3\niUQo4uldZ7sb1afR1umhzS369K43A3pnaF2OkmR/kE3beP2btTy4YAkOl4eHzzyW8UPKEa16+uXS\nFRsZYbdw9ZSRADzzxXd8tbmRfZJZrCa0TemKlKZTY+KVlcF56UF6fH6anB4sisLQAtsOuxJs7XHx\nnxVb+KK5k5+NH8bPxw8DMpO5EU+LYkUq21SVN11OTs0rxCTgkZ4uLBqMNie2cKUpZPi+wesDwK9p\nNGoqwxUzwzD1He97jQZdUqPB60OTkkbVT4FIz9BYuLNbaTH3u/+g0wpEjbaGYziqGabD0c2Vf/4n\no4fX8qMxNaj1dfz4B/sCoKoapgirhcUy5EwmE1eceTKXnn4iK9Zt6ltsKTTdLVKKXDYZkOG2M6we\nF0WEYwlvpBV4o0VPMzm3QQjBaSXFnFaiLxZyc30To8yWPiHxS0mZJljicFLrFXikpN7rw+pSIfY2\nvSkRvAfDiMsdUo2qtju6uOrPdzF6eC3Hja1Fra/j+Jn7AYlpHSTvuD78yGPkW0yc8mN9ay6TyYSm\naSiKwtoNm3j5zXd58/35dDi6+L/rr+HIWT9I+r5ikRWtS/badJNG51STsm8bhSADjU7EIlm9c0uN\nRr+fmvC9/tKAoXW5h+ng0/rtZRuX5gY2t3dx57uLcLg9CASnPfAiv9hvApfsPxGAJY3tTKurptvj\no8hmYdbwSuYv+Y4pVvqvDluVoI22M81RTWHQPhLxnMlljl5uW7mFDR4fI4vsXLzHCI4dWd1nB3sa\nOnlm7TY2Ono5u6aCt9c04Gzv4eyRQyJqUCjp0KMWhxshBD2axhdeD8t8HoqEwhkFRXT51X7XNqsq\n3/m8XFmg68BW1c9HXjcWVWJKYApLu9+PBLZ5vEgAKfnQ78El4UirHY/UF1aSUuJCki8UJposvOlz\nsUH1kS8UNqkqh1osOzi0A6E2sENE0HENRloTcVrDMRzVDFK/agWz73uEg/aewi//dCcv3HoNLY4u\njjlwb4C+9N0ep5vH3/yQeQuXsPvo4dx0zskU5utfZtGcVrPZ3Oek6mX1/0APRnR1QFsx5LKDGkF8\nk1n4CLYPJPSceFC/98rpV/nflhYuGl0TVSDTPbehVVUpVRQ2+f0s9nq4tLAET2C7AJsQzLLZ+a+z\nm32sNpZ4vYw2W8gTStrnL8COKSGJGHEGucfWlcu55f5HmbHPnlz2xzv6tO7oA/YCtmtdt9PF4/M+\nYt7Cr5k0ajizzzulT+sgsWirz+fnv3Pe5tiZ03jlvY+59aqLkB3NaMUVKIqCoih0dffwf/f9h4qy\nUh6/66989uUSXnnzvbQ6qikvGpXrDmqC6XqRNDB0wNS9raOf1i1z9HL/ugbu32dc1rQOktU7DyMV\nM709PnoTbkliGFq3ExPSD//+3mJ8qsbRe4zh/2bszovLN/L2unqaup0MKcqn1GZlfUc33R4fX2xr\nZVhxPiuaI7yfkfpZIs5rNh3XAS4WmakF057f2srIfBtPHziRtxo7eG3FFqZK0beq7KpuJ2t6XJxR\nV8Wh1aU0ery83+zg7JFDUIRIe6Zaa5cHKSVNmkqjqrKX1UavpvFwbxebfT4mmC20ayp/dbRzSX5x\nny42eH1sUf3UKiZWuN2UKSaKNMF3fj8tioUasT07aIN7x7TcV1Unm6UPP/C73nZ+IPJoQ2Ox9GIF\n/u3qogoTp5oL6dJU3tJcnGIu1PdYRfCgq7uvrOGaOWIdiTI6r787Ger01lot/SKt8ZzWcAxHNUPI\njma6enrZ0tjMJ199g6ezlRUbtnLSrAMxmRT8fhWz2YSjx8mdz77O6s0N3HjuSfz9mTm8NP9zzj56\n1o5lprAwSbYc1ow7qdk02MJEN9kUlUhpvbBjNEFKybotbTy3vpGtrd1cV7f9PUrEYEvFcZRScqOj\njU5NwwpcWlBCp1/lTbcTs4AjbPmMUsyMVsz8qqOVYqFwVUFJxLkLoXMRUiXaHIZYRpxBbiE7munu\ndfZpnbOtmZWb6nfQus7uXv7+3BzWbGng5vNPial1EH3TepfHw5uffM4Vt/2DuppqZu47FQClqw1V\nVTFX1nL/40+RZ7NyyVmnM3xoDTOUvfnLPQ/g6OqmpLhowPeb8LXhepeLA3Jp3NfU2+joc1bDB09L\nu9ys6ujhmoUruW3UduM6U1oHyetdiVC4MkTv0qFx4fdgaF1uYTr56thR1bC+6PL5KMqz8uXaLfx4\nQz12i5mqgjy8zS1Ip539C8zcs66F299ZxHe9Xla2duLxqzx4woz4C72F9sVUI65BknVg0zhPPRGb\nKdUUXI+mgdNLrdmEu9FBjdOLs9fDB2sb+XFgW6mtXb24ej3UuXy4Gx3UeVU+dnlprW+nMIGdMZLV\nJFVK/tHdyXyPC5+E/5RUkqconGwrwJanv99LfB7muHpZ7fFSrCh9nwUFKEDQpGl0eiUOqeHRJMvc\nXlyKJe5nRkFgRiIQIARrNT9lKFgAgaBcBOffi74IrSIEPzbl87aqz2+YptgoGmDqbyQnN+i8Bp3W\naFFWiK3xhqOaAYKGzMQxI5n3wB28987b3Pzwcyxdu4kRNZXQ3IBWpi8D/fS7n9DZ3csN55zE5DF1\nXHLikdz5zOtRjTeIbsAl0qZ0O6wDMtogvjgmk2qSqLDHKCPVeRPRUnvbvT5eqm+jye0l32yi0mpm\nGgp1Nj2Ptsxsotxs4mNHD1cPq8QZsidVKAlHDxKYDP+novIdjh2dl9/v/5/nF/FzYhv06Zx4H8mI\ni7RCnGHEZYdk03+DWvf+O+9w08PP9tc6nx+G1fH0uwtwdDu54ZyT2GN0HRefcAR3Ph1f66D/4Fxx\nYQHP3nkLC776liv//E/+/tgznHzUoYyoHYLJZKJ+1QoWfr6IK39xIXVD9Xlh9z76P/aesjslxUV9\nqcGpMGC9i0emHdQMbd/gaejkwxYHH7U4KLWY8GiSqSUFzKgsxtzaA8A3vW6cqtZnLLW2uiIaYOnU\nOhiY3qVD44IYWreTEaUvHlBeyKtfr6Y4z0Kny0u+1cxJI6sZXqgvgLNPZQktLi+bu928cux+OP0q\n019cwCdfr2TG0HIYkqCdMlDbJpNakkKKb7q2dLEpCjVWM/Pau/l5dSlLet0s63VzYNH2Pp0nRD+t\nKTaZkIBLkxRG8FMHMlj2cI+DtT4fpULhbHsRH3mcrFZ9TBFW2rw+uqRkkd/DJz43h1ntFCsKG9z+\nPge0V2qoGnyteZim5GFB4EGSH2h7LCf1BFN/Hbv1osQW44zEDf9ZkPJroxF0XuM5rBB7UNBwVDOI\n3+/HbDZz2D6Tmb/kO/abMIYDd9e3kbEG3pR3Fn/LmUfNZHydPvr1+oIvmDRKn/Qdy6BKdduHdDqs\nA94vMYkl3yMSLpZpjAwkQiLzTm9YtklfSdJmxdrrYVl7DwtVjemFdla7vbzb2c0eFhu3Danp56TG\nM9Y6NRW/KpNuczrnIIQSFJ5kiTR/IVrEAVJf/dMgMwQ1IKh1h+6zB4cvmdJf64bVIaXk3S++5ayj\nZrLb8IDWffIFe4zRFzmK5zxGGpybsc8UvnzhYdZv2ca7n31Bfl4epx97OBu2NlBXU82IIhum7nY2\n9fr4etkKbrj6MoCUnNRktS7pQbk0LE6SbpJZFKnJ7eXabzdwwtAKFCGQPS7mtnfz4aZmjisv4v6G\nNta4vFxUUsqxhYU7GIax9K5DU1FT0DpIr96lqnFBDK3LcRKwObSmJla1Oehwe9h8zaloTc388LXP\n+ap1eyZBvsWE1aTg1TQun7+Mb9u6KTCbKLTq73ukfhW+h+4OpGNAPh0k6aCmc7/RUE6pLKFb1Thx\nxUbG220cXFKAS9P6zhebTSgCHH6VWqsFu6LQ7PMjHT7aRGKDT/FssFafn2dcPSz1eTjDXsg3Pi9r\n/D7aNcl8l4uqPAUh9HnvZmCysLLa66PJp7K/YkOi72JQIBTqhJl3NBfTlDysQKvUqED3qAfifCZD\nOusJd3pDo62j88w7pAVD7EFBw1FNM6EGzZKVa6nLk2iaZGtzGzeddzJDNTeaprFw2So9LF9cwOQx\ndVgtZrqdLlZu3sYfzvoJkJhBlUp0NbSdqTisqTioCafAZWgvrnQtfS6GVGO6+VEA/Jf9qO94JEFW\npWRRi4N3p4zBIgQ3bmrk+PJiVru8zN7SRKdfQwLnFJdgVxTWeb1s7nIzxbpjrn6rTx+Be9bdyxtu\nJ+PMFo6w2TnIut2wyZQTmggDqTt0/kLoCnGxjDiDzBMvqhqqA0tXraMuT6KqWp/W1aq61n3x3VpU\nVaO8uIA9AlrX1etk1ZYGrjvzRGBgWjembihj6oayYauuMcWF+azeuIWxI/QBv1v+cid7jqlj8oTU\n9prOmJOaY/sODmQ+2WsN7exenM+vSovxS8lVjR0cU1bEJ129nLZyM1KCW0oKyvTowNvdPWz2+zgx\nLCIQpNnr42lXD296nOxmtnK4zc4Ma//+n229S0d9htblJqaTr0a9//exLwr0Rbdfpbogj1WrN1Bl\nt1Gbb6PT6+fzlfWs7nYxbGgZeSYTV+45ih6fyjV7jeGGz1dRZI1ubof2vZxzWuNoUCoR1IGuoltR\nkceVQyu5cmglXk3jinXbOLFiewaCudtPtabwQn0Hl5aXsdnpwulVMQkRczGlWM5pJEdKSsmp9kKq\npMJBJisbVT+bhGCJ6uU4mU+9248QglFYGKVAi1R5TXWyv2Lr14aRiplzpoxkQXMn67vc/HrqeK6Y\nMgqTsnNuyBd0eiNFaeNFWSNhOKppJNygufiG25j7t+uoqSjloWsvYeOyZTy9Yj1un4875sznqZuv\npNftoSBP/5J64s2PGFJW0hddTbjeFJ3V0DYn6rCmxUmNRBqNtnTvx2W+f26fUyqbmvv+jifEUkqO\nLCvi2o0N/H54NRVmM79YvZUqs4kxZis2C7SqGsWKiU2dLp7u7WKOy8nZBUX8NL+QzrDV4daoPtb7\nfTxaWsW3fi8vOHtQVY1xpvjzGAYyST4ZwifUJ0KD19dvVC1WipxBbnLR9bfyxu3btW7LihU8u3wt\nLq+udc/ccjU9LjeFdt0Af+LN+dSUlzBx5LCk6omldaOH68etFgu9Thc33PUfhlSUsXDpMuY/eR/F\nqgvZ4cq+1qXipGbJQU3XSpzTUHje4WROfhfHlRczpcDONRsaqFJMDDeZ+UlJMc87uqg1mWl0uFnR\n6+JpZw+LrU6uLipDaP0jpmtUH5tUP0+WDWGpz8MLzh60BLUOMq93qegcGFq3UxLSFxVFMK68mJo8\nK3cuWc/y9h42dbu4ZlwtI/JtUFZAm9vL8MI8JpcXM6LIzvtbW6m228hPcF/evj3DE0kRTnVBpmTL\njEIyUdSBOqehtLS62OTzYRcKn7mctLt9jPAI5jscWASMt9k4tqiQP7e08p3HQ7eqcXWFPgUg6CAm\ntPBkjChfg9eHkPBwbxcX5BVRo5iYZLayxutjlfTxtcvDEKW/TvRIjUphwiU17EKhY3wJZ04YxvSa\nMvyaxhqHk7HF+VgjrJC/M5KKwxoJw1HNICZF6TPMAGbdeA/L/3kdEsl/PvyCwnw7zR1dOHpcWM1d\n3Pvim9z76wuoLkt+bspAnFWI77CmmuabkOGWpSXPUyU0cgqJjxQKIbi8toLbtjRz3Zpt7G6zkScE\ndRYL55aWclVDA3ko7K6aQIGf5hfymcfN/T0O3nA5uaqgmDrzdsOs0eenVVVp9vkpk4I9TFaW+r2M\nM1l2MNyy5ZiGk0q9oakg4REH6J8iZ5BdokVVw/XApCgU5G3Xupk33M2yf1wLwH8+/IKCvDxaOrvp\n6O7FbDJx30u61lWVFifdpnjTHiaMHsHTd8zmpnsfZsTQITx1+2wqSkv60osHVeuiHQslgQVKEjJi\n45SRCIlq3W52G2dWl/Fyq4O3mxxMybMxzmLBLhQcmkqj34eiSco8EhU4zl7ANz4vn3jcbPa38PuC\nEmpCtK7B66dNVWnw+iiRgt3NuaV1qdZpaF3uYrrsrztGVSP0xdNriukYWc3cTc1oUvLf/XajwGyi\nweVl2sgqVrR34/D48AdWlp6zsZlRxXZq8pN7X1Pu78k4r2mcsx5JK9LpnParS0qua2rGK6HabOKP\n1VWoUlLv91EUyM6ZaLPxq4oKVna5GGIxMUE1pcU5DWWWJQ+3lLzk6mWyYqVOmJmi2FigeVgmfVRL\nE01oNEqVZqnSIFUOUmzYAwsXXbfvWIYX2pFSYlYUJpUVpvQ8cp1kHNZIGI5qmgg3bmTbNsxmE+sb\nmphQN5T2DetQhODDFWsZV1OJxWyitCCfUw6dxjG/uY2Dpkzg7KNncfi+U1JvQ4rzViPdR9CIG8g8\n1AFtQxMkAaMtdK5ouFOZLuLNtYgmyLLLz2WFJSw2ufjO46XabOKjXifb3F4qhR5tUKVEAZx+DaeU\nKAgmmcws8/swayFlARWK0jcaV6UobPH7adNUKhTToDmnA2WD299vVC2RiIPB4BFJ6xRFYVNTC7sN\nr6Vjw3oAPlm5nnE1lZhNJsqK8jn1sOn86Ld/Yfrk8Zx77KGRtS6oDwmsWBlrcG54TTWP3Hpdv2Ph\n6cWRHNZBdVKTGJDL5ABdqlp3uJLHsELBUreHVR4vTk2y3u9hnGJmvdPL3lYbGmACOvwq6/w+rEIw\nxWxhsd/Lflr/8soVBbeU5AlBlVDYIv20ayrlih6Z2hn1ztC6nYgoq/9X59u4+YDxXBuySv/XnT18\nu6WNul4vwxQFRRH8b1U9+1aX8GWLg/+bPrFfWZH6WKy9cwc8SJWmDI1k0nxjOamxHNREF1O7t6Rq\n+z9OlQ5U9sEC2vYy6hDU2SNPL+irL86CadEifUH9qZIKXcLEEs3LNqEyXphxIlkivWgalAuFNqlR\nJRT2V6yUiR3nnsbLEPm+kIjDGgkhZWqLFGQDIYTM5fYByG2rIhpuoKfyPjL3Aw4YWcvyLQ0cvfck\n3lm6iraOTs45cDLnTp+Cda/9AWjt7KIyGF2I58wlYMQNxFlNBwM23JIw2iItahROKg7sQBYCCBdi\nKSVtqkpzl4frHG2s8fuQwCn2QkaazNQqJua6e1mr+mlU/Uy12KhA4Xhbfl+UYZPq5xOfm4kmCxIo\nEwpfql5qNBN1whwzJS7VVLVUGUjEIUjonIXQFeFmNG1FSvm9UvZc17rQqGqo3gX7+ePzPuSRuR8w\nbfRQvt3UwNF7T+StJSvp6HRw9oGTuWDGnlimhmldIoNWhtal2KrkSKfWuTWNLk3D1ePltx1tfOvX\nN6L/oc2OCahRzHzmddGmaTikxp5mK/tYbeyjWNGkRACbNJX5Pje1ioltmspIxUSz1KjVTAyPo3WQ\nHb0bqKNsaF1uot5y3g7HwvtheH8J7xvf9Lp4uLGdbV4f19VVc9C45KZzxXJag6SaVZEK6XBQ0+Gc\nDpREV/GO5ZxG0p5eqdEiNT7R3LSjYkEwU8ljpDBH3PYlWwsk5TrhDutf/Y6Iemc4qgMklqMKsGTN\nRhYt+pKZk8YyafgQ2tavx+nzUVcWlvKW7J5XOWzADchwS8FoS1dENZqx1u1TeW5rC21eP5ePrSU/\nsH9XJMLF2B+IlrZ3b1/R90uvm7sdnazT/Jxqy2er1OjWNLapfvKEoFFTOdiax56qFSsw0mbCpCg4\nNI3/eXpo1lRsKPRKDTuC40z5FAfEMNsO6UCJtfcWRDbgDOMt+wQd1Vha9/XqDSxa9CWz9hjHxGFD\naFm3DpfPz4hyQ+vSpXXpJJLe9fpVntvaSqvHx+XjarGbEtM6KfWUXrMQ/YzOLzxu5rl7edPt4sbC\nUho1lTZN4yufhwqhsNzv43BbHj8y29no9jMqz4wiBA5N4wF3F24JE4SFVZoXgeAEU36f4Wdo3c5J\nrmtduKOarJOaCIluPZSIwxoknY5rPP0x3z93hx0OIj2HVJ3TaA5lMvsaJ7O1VKz5kcF+u0zTs+Bq\nROy5xm4psRE7Smo4qtsx3z+Xay3659xwVDOEtvzjfv+HGi6Pz/uQo8bUUhNwSjVNQ2lt0q+LNiKc\njBGXgwZctg234JdGMoIeq5xInLVoFX4pGZFvY35DB7eMHMLBJf3nEoQLskfTsClKnxgHHdYtqp8b\nOlopVkws83s5y17EifYCNvp9fOZ1c7K9kPt7HSzzejnelE+FMPV9Vtqlyhuqk04kl5iKmKM66RQa\nN+eXIth500eiRSaiGXEntDcaxluWiTcg99gbH3LsbkOpLtH3ogzVuqgYWhe53Aw7qbG07pzFq/Bo\nkjq7jQVNHcweEV/rfFJiCXFQg/+/4erl+Z5uSkwmlvo8zLTmcWVhKfWqn4+9bk63F3J3j4OlXg8n\nmPKpDNG6BunnPdVFN5ILTEW8rjrpFhqz83WdN7Ru5yXXtQ62O6vJOqnhe+ImQiJOayr2TbKO60BW\n/U7GSU3FQc0E8VbxDu2rHil5XO1BQ3KEYmecovfR4OdYCEGX1PoCBrEwnNT+hAaahBAR9W7nGpLc\nyci32dBCBDl0jtSAnVQDYOAOajy6fSpdfpUn9x9PscXMdU4vS3vdOxhvoTza0UmrqvKt281ExcLp\n+Ugtl8cAACAASURBVEUUB957O4Jas5lWVUMFXnD3MMZsxqlpfOB1kycUlvq8tKHxruriJ0o+VkVB\nk5IGqVIhTEzExH/VHnqQTDVZgZ3XcItGImlxBtlDDJ0Qc/5mfp4VTYusdd9XRMXQHZ3V6trk5+Fn\nGVttaURjs9un0ulTeXy/8ZRazdy4XLAkgtZVVOTR1uam2e/nU6eLb9xuWlWVYwoL2V9asAS0aJzZ\nzESLlfWqDx+wwOtmT48LgeR9jwsr8KXPQyca76suTgxonV9KmqRGjTCzh1B4Su3BiWSKyarPcTW0\nziBHyZVthTKZFhxNPxKlstiWtVTfdGETgimKhRap8Z7mpgmVGUoe3UiKhW6ffay52VexxYy4Gk5q\nahgR1QESHlGFHUfaezdvpMftxe3zobU0UV5gp8QetlCCkQ6nk0PpcC5V5e612/i4tQuXqvHb8cN4\ncEMjx9aUc0ZeHiZ99Kefk/jIhhaedXRxRUUZAsF/Ox0scrmYbs3jwsJiak2BBTN8fr7yurm7t4tD\nrHks8nnwIKkQJhSgUVM5VSmgBIEEXtOc2KSgUjGxv2KjV9NYgQ+3lMw05e10aXBBjHQ4ne+L1jm3\nbKLX7cXj96O1NFFqt1FsaF3S81OzPTfVparcs7aBj1sduFSN300Yzr/XN3BMTTk/i6J1V3y3hS5V\n44TiQrb4/Dzr6KJTVflxXgGn5RdSEDJY8ZGzl3/2OjjMaudjr5teKSkVCsWKQoPm5wpbEV1eSaP0\n86nmoQCFWsXE3oqNbk1lOT404CDF0LqdnZ1B61KNqCZDoum/kL0U4GxFVSE9kdVogznJRmZjzUkN\n7ru6SfPTjcZIYeZV1YkXiQXBWWZ9IM8rJdY4g2iGo7ojRkQ1B/hs2WpeevN91je24ddUivLyGFlg\n5Zg9xjBt9FDMtcntIwjktOEWrDvlSENVdVTjTQypzrgBF/xC6NjSxt/X1NPi8fHK9N25deVm7lq7\njcvG1HJgRREFNivuRkef4bbV42W4zcpCv4ejCguYnq+vNDct306XqvKv9g7muZz8bqi+Ul0lNiq7\nzLzg7uUn9kKOyyvgC5+HJk3laJudu7s72SC89ABChQnSzErhp0rqxp9TSLo0jdrA6N16lw8hxE5r\nxAUxogs7L58uW8VL8z5gfVMrqqZRlJfHmCIbP5o8lv1G1mBSlO9d1kjCWhdN/6LoXTa0DnS9C2pd\ns8fHS9Mncet3W/jHmnouGVPLtPIiCvL6a11wtdpvfB5ur6pmrFXP6jiztISVHg+Pdzj4zOvm9Oqy\nvnpmAf9z9fCjvAKmmq04kdSrKoda87ij18GrXicNmkqlYqITDTcwBb3/9whJjyYZJXQNMLTOYDAw\n3z8XM/Sbm5lXU5LROaqQ3XmqiepOpKhqss8iVmR1oH2gbwXtBB3WWqslorM6Os/cN8BULRS+1jxM\nVqxMV2y8rjkpx9S3MrnhpA6M4DzVSAwoP0sIcbIQYpkQQhVC7BN27johxBohxHdCiCNDju8jhPhG\nCLFaCPHPgdSfqwSdxMfe+JAbH3qWUWNGMfu0o7nznBP53U8Op6Igj9lzP+HTjl18U+9oRmtVddR9\nv7K10p2rvIDXG9q5fNxQzIrgp0MrKbeaOba2nGqbbpjl1ZTwndPNnVtbuHzdNk5csZEjSwvZpmg0\n+bcLZLHJxFUV5SzTvCx2ufqOO/IEDin5RWcL13e3USwUzswrwCE1RpnMbJEqa1UfU21W3sVNo1QZ\nFthAepPUyx8WMN6CRuQGtz+nt20Iti/X2xmOoXWRCWrdw3Pe56aHnmPMuNHMPu0Y7jjnRH5zwmEU\n2Cxc/9p8Fnf5UnNSszQoF21P1awQQ+uyoXfu8gLmtDi4fOxQLIrCScN0rTuutpwhedu1bpXTzT/r\nW7hsbT2nfLeRH5YW8anW39CcaLNxXlkJr3icNIdoYFDrLnW0cHuvg26pcXxePvWaygiTCSGgRJi4\nzF7M4RY7XWiUBwbhVmg+rAiGBv4P17pc1JFwncvFNsbC0LvoW+ANhLyakpx1UtNdDsRPhc70FkyV\nFnPCTm/oYBH8P3v3HV9Vff9x/HXuvbnZgQySkAAJezsQFVQUEQcqiOAedVvrqKNatVqlWuv4Veuo\nWrUqYq2oCFrEhaLgwokDEARkB7LJTu46vz+SG27CHefee+695958no9HH4Wb5N5jyP3k8z7fBe/b\nWlhia2ZgioWBKRZSUChQzKx0tbHc1cpMUzr9FTPvuJoDPreEVP/8hVQIf0T1J+BU4CnPBxVFGQmc\nAYwE+gEfKIoytGO+x5PAJaqqfq0oytuKohyvqup7YV6HIX28ag3nHTeJC0+c3OVu+n4lp/DjYy+x\nZvsujhw1OLgnjeJoqpKdH/K5grqs34rhaIPdpVKQmYqakw42F+samsmxJrG71UZxx1TGjY0tPFlb\nT75L5Zmh/Xixopbvm1pINZs4a+dOJqakcnl2b/olJVHmcFDucDKsY/ShxuFkTkUlE9JTmZSWyie1\njTzTXM+XSVaSaG/ETrOm08dkxqoo7Ge2YldgqdrCMFMSax12jjen0svH4n0tm3boLRrHNeRlJUOA\nPXoiRGqdH8u+Xc3FJx3NOcce0eU9fsDAU/nx4Xms2b6bCcNKg3vSKIfUUOtd2KOqENOZJDaXSn6q\nFVNeBrQ5O2tdeauNoo5at6mxhSdq6ylIsfJkn2LmltdSbrOzutnBouo6zuzTm+mWNCyKwja7A4eq\nkm9pf992rXVpfLGnkXnNjXxvt7HH5WKUJYkpyRmkKqb2Y7xUJyVmMy+5mhhitlCFi5NNqaT52agk\nmvVOj9Bp8FoHUu86dQ+p6Ys+C2lUNZiAGiy9b2hpqTlaR1Xda9p9icaaVa0jrO734nvNTSy1t1Co\nmNnlauCC5AwGpSZR1uJkobOZ8SYrpSYLpViwG3wau9EFCqkQZlBVVXU9gLLvTi6nAPNVVXUAWxRF\n2QAcoijKViBTVdWvOz5vHjATiPti1p2SW0T/gjy+++VXjh43GmtSCq1lO2m1O/i1vAq708ngwjzt\nT6hxNELv0YVwwqomgcKrn7AKwa3l6l7M/X1tSWYql48ewPkffE9+ajLFZhNH5fXqDKkAj20sY2B6\nCheU5FOYYuWoVht/21HB/OElXFyQw8M7K7l4126GJSWRbTJzXEY6vcztowI5FjMv9ts77fuEzEyu\n2VrG5/ZWpiWncV5a++6p29psfOtoY7fq4nxrOj877TSoLs5PTWeQOSnopimYhi6aowCaG7cYkVrn\nm5JbRL/8XL5Zv4lJ+4/AakmldddO2uwOfi2vxuVSKemTHfiJ3KI4Pbj7SGrEw6o/YYZVf81qoFp3\n2egBnPv+KgrSkinOSGFSTmZnSAV4bFMZJR21rijFygk2B/dsL+el4QNYuqeRp3ZV83RbDftZkzEr\ncG7vvUcS7VvrMmjaWsYqh42J1hTO7Kh1G9ra2OC0YwPOSc7ADpS5HJRYLeSazJ1TfoOhpd5Fe7TT\n6LUOpN5B+/vJfOfzYT9PqAFV62hqpGZd6HmDzD2y6iuwRmuDJc2BNcnCbDWd8RYrL7c18UhLPeem\npHNEagqZLabO3X1VVQ0YomQ01bfu56j6EqnhlWLgC4+/7+x4zAHs8Hh8R8fjCem280/l7/MXc/wN\n99AnO4ucZAsNLW3YnU6uO+kojtt/ROAnifKaLm9T4GI60gABG7hQBQq7swf3ZebAQpZur+TIolw2\n1jeBzQXAx5V17LE7uHxQIUWpyTy6YSd/XbedUanJ7LDZKUlO4pHB7T/a3zQ0k9Hsoo/F99vtx9ZW\nNqgOBpuTOC8tk21OBx+0NmNRwAkck5RCsdlCsbnrc7ibnnCbrVhOTYuHxs0PqXXAnItP597/LOLY\n6/5Kfk4veieZqW9pxeZwcuOMo5m633BtTxREvQv3ppyv6b4RvTkX41oH3uvdaYP7MnNgAUu3V3Fk\nUQ6/1jd3qXU1NgeXDSykODWZf2zYyRObyjgwNQWrycS07ExOysmi3uFkdXMrxW0KGX52ff6ptZVf\ncFJgNtPPbGG708FbrU2YaX/T9Ddb6K2YMCsKhaa9O2gOSm2vDXrUqljVuzivdSD1Dth3NNHXqGq8\nhlTP5/cXVoNdq+pvdNX9c2+EwLpfUjIjLFaq7Q7OSU5nqb2Vf7c2clZyOvunWnmzuZlhShIpCbYD\neTRpDamgIagqirIUKPB8CFCB21RVXRz01fUgqUWl3H7BbG6/YDZVe+pp2L6N3Mx0stI0bGEeQkCN\nVOPm/phuYTUUfhq4cPlr4swmhRNK8rE7XXy6q4admWlMsVqpaLMxNCO1c73q05t30y/VyqicTC74\nZRulyVbO6tObE3OyKLBaeKexgdk+3m5OVeXZ2j2cnJnBwvoG3m5tosrlotzlZKW9jWOsqZyZnkaa\nYvK5O51egTWauo/ixrpxk1oXurSiUu665EzuuuRMKmrraNyxjbzMDG21DoKud+HUOi3rUUOpd7od\nV+Nesxrhete91llMJqaV5GNzulhRVsPOrDROKMmnYnslwz1q3T83lpFsNlFvUjj6x00clpXG6Xm9\nOSAjFYui8CU2jsH7v7u71h2Tkc6RaWnck53D+z+vw6HCeqedmclpnJXePsKaKLXO22yVWNc6kHoX\nSZGc5usWrf06Qgmr/kRqKnAoQddfYLUqSuf79ARFocBk4rW2JmpcLg5JSmZkcuAZbTKa6l0wIRU0\nBFVVVY8N4Tp2Av09/t6v4zFfj/s0Z86czj9PnjyZyZMnh3A5sZfXO4u83mOgYhculwtFUXQ9SzXS\njZv783QZaQhlVBUiGlbdfDVxSWYTV44ppdnuJDnJjK22gdrqBvokJ9HmdGFWFM7qn8+1Q4qobLPz\n6vodPL6rmqd317CmuZXb+uf7LNDvNDay0+7g4cICLv5wGc8//zytT/2b3iYTYy1WzkxNJ61j/Za7\ncAZq4tyMMlIaiL/G7cvmFr7q2IQqNTVya2yl1ukjP7sX+dljA9c6MMSMkajTYQd0PfiqdVaziavG\nltLU0by1ZiSzp7aRPslJVLTZUIDfDirk4pIC1ja0sGBHFdf/WkZfq4XP6pv599B+5GZ7n+r3bmMj\n2+0OHioswKQovPDCC1RVVfHGzNPIsClMS2nfLd2z1oH3ehfr5Qr+rsMfI9Q6iG29i+da57jyJMD7\nqKoetIymRiuker5eMNOAA63b1RJWQ6V3YIW979mV9jYyzSbOSE4Huu4K7M3tz3wmYbUbz5C6zeVg\nW8fGoL0tvmfh6HKOqqIoHwE3qqr6bcffRwEvAYfSPv1jKTBUVVVVUZSVwO+Br4ElwKOqqr7r43kN\nf96Wt7MFu+tyl91fgxJG0xZqUA2lcQslrIZ93mB3EQ6sbt2Ls/sswbe3VvCnles4vyiPn+qaaHK6\nmDd+KKZuU96e+H4zD+yoYN34rtO8K6paeLexkWPS0/ld2W5O75XFiZkZ2FSVket+obW1lVNOOYWD\nvvyWiUnJmPxMMfEVWgMJtanTe4MSraML7nUueSvXxOxsQal1/kmtC6LW+XvcU5RqHXStd+5at3hz\nOXd89QuXjurPN9sqqbc7eG78MFLMe6fl2lwu7v12E/Mr97Bq3LAuz+lZ667ctZvZWZmclJmJTVUZ\ns2ETzc3NzJgxg3Erv2Gitf3MVl9CrXUQXL2L1KZz8VTrIDL1Lh5qnfscVW9rVN1BFbyfJxoOo0z5\n9cVfWNV6tmp3/gKrXqrq26hwOnioYQ+TklM5KTXd9+f6CKs7nA4eadzD1SlZ+xxFIyOr2vgaSXXX\n2ysaq/U/R1VRlJnAY0Ae8JaiKN+rqjpNVdW1iqK8CqwF7MCVHpXpKmAukAK87atxS1i+RhPDEM3G\nzf11EV3DBYYYXYV9pwW7R4ZOLMknJzmJeet3MGNkPyZbrV1CqrvJq0uxcFrevr98UrOtLNnVyJ8r\nKulrsXBiZvuh0WZg08jh/Pec8xg8eDC3vPYa2dnZ/C+37z7P7RZolNUXI5xDGGzjFitS60LQQ2td\nUMsdDDKTxM1zlNVdY6YPLCC7o9ZNG17MMcnJXUKqqqpYTSbUdCuz1X1HlbrXupMy26f2moBfhg3h\nn9U1DJw1m1tefZWcnByAznqnV62D2Ne7eKl10DPrXfefNS2Cnfaqh1iF1EgJNLqqh7ysZOpsChPa\nUri/vpZ8k5mDk72/z3yNrvYzW/hrVi41XoJsoOUIMrIaOKT6o8uIaqTEw523oEcZYG9josO0t1Aa\nN72mvwXbwAU10hDoY25RHG1w83VXsbWsdp9fdFVtdqwmBWt1k9evWbC1mrsqq7gkuzenZGaQaTaz\n3W7nT+UVXJebw9nbu86e+rywf2cB/dHexmaHg6nJqaR7hORwRh0ipfsZZd0F07jFepQhEhKl1oGP\nUVWdpvgGW+/iptZp+Xgwta772awh1klftU5VVWzdRkt2tdhIt5h81rp3aur546+7uCy7NzM8at2t\n5RVcH6DWrbXb2Oy0MzU5jeRuNdZI9S5QnQOpdUavdS6XC/XuS4B9R1Q9R1Pd9AqqRpzy600kRlUh\nOiOr37S08EBVNa/279c5NXiVrY2xSVYsXm5S+Nsd2FfdkdHVfWkNqb5GVCWohklr81a5aR2/7qrg\nkJFDdH39WDVubsE0cCE1b1o/JxwhNHHBFms3X0X7s7I6dtgdbLTZ2GqzMzLFyhlZWaSYTAz7ZWOX\nz62treXy4gFstNvIM5vZ4LBzY3ovBlgCN0mRbOq0NGnedD+Q27N582zcah0O+vbLJeONz6V5iwGt\nta5myy/8WlbBQcMH6fr68VTrIEJhNVwGqnWbbTY22+0Mt1o5s5f3WldXV8flRf1Yb7ORbTKxxeng\npoze9DNrGx3Vu96FWuPcpNYZs9ZVV1ezePFiWltbMZlMZLz/EoNyshj94H/J7JgBAN6DKoQXVrVO\n9wVjBFWIXFiFyAVWl6py9a7dHJyaykXZvfm2pYWPm5r5srGZWpeLs9MymJGWsc/XSVgNj9aQ2tea\nxCk1u/Wf+iu0sdsdfL1uE3957jV6Z6Zz16Vn6hJYY924uZ9TawMX8q6YoeycGQzP0QeNjZy/zQX8\nTQfytcnA4UW9+LWiidfq6lnR3EypNYeUjlHSX4YNwaWqmBSFwm++Y9GiRZimn8hfP/qEbJOZ22qr\n2OJ0aAqq4TZaetPauD27u5rVLhdrN/rdj0jEmMPh4KufN3HHv18hr1cmf7nkDMaPGBz288ZbrQuZ\n1qUPoQqx1oH35tTdZHurd/5q3aaKJubX1fFpcws35vmudQsXLkQ9aRp3f/QpuWYzd9fVsNlh1xxU\njVTvgql1P7mc/Cy1Lip27drF/fffz9atW8nMzCQ9PZ3qddtYZ8qk9NxzeeSRRxg4cKCurxlMOHUz\nSkgNhXujKS2BNVJTgb9uaWWn3cGjfduv5YGqak7MyODV0v4sb2rivdoGr1/nb6OlvtYkvxu99fSp\nwMGEVH9kRDVMWkcZbHY7SfWVvPLh57z20Rc88YdLyc8OfZc4IzRunsIaWQ2mKYv0iINbECMPoYw4\n+CrY1dWtfNXcwkPVNVyW3ZtjMrou+v++pZWF9Q2c9cILzJw5k927d3PRwMGckprOftbkgAdZG0H3\nhq3zcR+N25O7qviktY2H9x9Eo8PFIcu+l1GGGNBa69psNix1Fe217uOVPPmHSynICb4x8xRMvTN0\nrQPtNSxatQ401zs9R1erq1v5ormZf1TXcEV2NlO61bofWttr3WGpqVyzazfl5eVcUDqImXFS63zV\nOZBaZ6Rat2TJEu677z4++WRvfXP+5SLMdz7PM888w1tvvcWbb77pczTVLdCoaijh1M2IITXUWgDB\njbAGw1/AvWFXOaXWJH6fm8M7DY08XF3De6UDcKkqFQ4nd1dWcUd+HuZmp8/niMToKiTmCGsoIdXX\niKrv/YCFrty7tk6bcABrt+ygzR69dTXROJIhrNcIZv1atI6z6JO/938B+Psl4uuXU0phL6/b2efm\npnBIWirz+xdzTEY6qqoyb88e2lwuANa2tWFSYNQfb+SXYUNYOv4ghl52Ca6Or89Lsuzzv1jwdh3+\nrsdX47appY0VLW0clptFcWoyo7LSIn7tIjwWsxmTycSsow5l3dadtLTZwno+I4XUaL0GEN2jezTW\nO6Ug32e989eI+6p1J/fP4dX+/ZjSUete2lOHrSPErG1tX0N2fGZGe607aBzDL7/Ub62Ldr0Lts6B\n1DqjSU5OJi0tjZ9//pnq6mra2tpotjk6P5aUFPqofHLf3p3/SzThhOdInTObm5vS5T3V5HIxt3YP\nXzQ3s8Nh55qcbACeqd3Dpdnt/yYmRWFlSws1TicFFkvn+9N9M8WmqpQ7238e/L2vfY0Katks6PZn\nPuv8XyLQayTVTab+RoHL5cJisUBuEXc/cT/HH7I/acn7nhPVZrOTrOEfzmiNm+draRltCHkKcCif\nqwcN0+X0nB7nLrRVVS247+0ld0yNW1Bfz5UdO2Nut9v5obWVphfnMSUvF5OieD07TEvz5utOYTQa\nP1+NG8BbtjZ+qGvi4JxMjvz4Ry4sLej+5cJAnE4nZrO5vdbd9xBTxo0hI3Xf3RUdDicWi9nLM3Ql\ntS7CU4G9cdc7P6OsvpY+hDIVODc3haqqFhxAm6p2Hv3wmket29FR6xrnvcDRebmYfdQ6CL7eRTPc\n+qt1i9uk1sXC+PHjmTp1Kvfeey/77bcfmZmZWNdu4acbbmDNmjVcccUVmp4nUmHUiKOpbv7qQKBR\n1WCmAwfL/d6yOpxsrLTxcHUNvcwmFEVhi81GkgIzszI7lxk8VVPL73Pba41DVTvfp1/VNPJsYz1b\nHHb6mM3cmpVNQYCpwLDv6GqgqcCe3CEvXkdZ9Q6pIFN/w6ZlOtzqDb/yybc/8OayT2ltauTF26+m\nX35u58dr6htZ8vl3LFzxJSUFffjbb88mLcX7gcdGbdw8aWng/B7fYMSpwJ4CTJPTe3qcm01VeaCq\nmklpqRyVns6DVdW0qSonZ2awX0pK5/b6wRx0HUuBdrxMKezF1BU/cXFpAecMyGdpeS3vNzTz5Oqt\nMh0uBrTUup83beGzVatZ+MFy6mprmHf71Qwq2ttw1zU2s/TrH1nw8UoGFxdw1yVnYjb7Oejb4PVO\n6zRgn/XO6LUO/NY7vTdactc7m0vl/qoqpmakMzEtjYera2hyuTg5M4P9PWodYPh6173OgdQ6I9a6\njz76iOXLl1NVVYXrqw/Z/5LrmDVrFn369Ak47TdSjBxS3XzVgGA3mIrUdGCASruDv2zcRY7ZzLSM\ndF7cU8etffJIM5lY0tDAEzW1LCkZ0OVrNtts/LO6liLVxAUZWTzSsIdis4XTPDZcitRU4O6MHFq1\njAIHCqnum4WHl++QXX8jwV/z9sX3q3n85YU4nS6Glfbn8APHMumg/UlpqsbucJBksdDY3MpdLyyg\nvHoPl82YytP/+4CRJcXcev6pIZ8Z6BaroApRDqvhCvW1IhBY/RXr6upWljc18eeKSoZarWSbzfwu\nJ5vBVqvXzzdCA+etSfPGW+NW1Wbnsm83sOiwUST37c13lXXc9+1GFm0ul+YtBgLVuifnv4Hd4WBQ\nvyIOO3Askw8+gLSW2s7R09Y2G3e98Dpbd1Vy7vGTeO6tZRw4bCC3/WZW5/NE+5xUPYRd6yB69S5O\nah3Aa1uruaOikhHJVnqZzPw2pzfDvMxEgvivdZVtdn773QYWTpRaF20ul6vLGeie3DNEYhFUIxJS\nQ9hMTYt4CKvQvvNvVXUrV5TtJt2kUJKUxDa7g+mZGUzJSMfmUrGaFJpcLhY3NLC21caf8/NIUhTu\n3FlOnsnM2emZ+5y5G63ACrEJreFMSdYaUsF3UJWpvxHicrl455OVzH/7Q2644Ez+cvUlez/WqGJS\n2gvjM4s/pAULt1x9OaMGl5I7YBA3/f1x7Fl98B4/tIll4+Z+/UANnNdpcW7RnPLmuRYsmNcLMEUu\nlJ2BfU2Pg/YGZ1ZuCrMG5PJpWR2DrdbOaXLeBGqc9GrutDZovnhr3ADykpMYmZXG79dvZ0ZbGx+X\n1ZCbGs67QkSCu9b9d8lS/nDhWdxz3eV7P9Zc0/kL/ZnFH1LX0MStV1/G2GGDGTJqLDc/9CS2zDyS\nfdxs0SLuax1Er94ZrNaB7+b09JJcZg3I4dOyeoZZrVhNodc6CL3ehVvfPPmqdX2SkxiRKbUuFtwh\n1eFoDw6KoqCqKhaLhXfeeYeCggIOjOUF6sHb2nMNU/y1CmcKsKdITgeG9vWo+XmpvJZbysObyql1\nOrkpL4eijnXI7hpT6XCwvs3GxLRUkhSFaoeDfqlWnLb2FfJKt74rL8niM6yGujOwL8GGRs9gG+01\nsMGEVH9kRDVMgabD/bxpC/c8PY81Gzbz5J03MmH/0Z0fq6rdw8W338fV58zi6EPGkZRk4Y9/f4K6\nxiaemnOT3zt9/sS6cfMUqIELONLgFotpb8G8ZojT4yD0EQe3aByUHSm+Gjc3W24Gj/64mc9213LZ\nqAGUZqZyyILPZJQhBgLVujUbN3PPU/NYv3kbT975Bw4ZO6rzYzV19Vz4p7/xu7NmcsyEg7AmJXHb\nw0+zs6KSuX+7bZ871FolZK2D6Nc7nWodRGZ01U1qXfwzWq3bsmULBQUFpKamdj7m7r3ee+89+j5/\nD6NyMv08g/50G03VsBlkpzADazi7AOst2KBbXd3KkoYG+iclsV9KCmtb23iwuppH+xaSbjLxTUsL\nixsamZqezqT0NCrrWn3+vvIVWLWc6RzqKKsReYZUb+tRvYVUmfobIVqPbPhm9TrabHY279zFgSOH\nMnrIQJYs/4IPV37Ddb85gwF9CyirqOLCP/2Nu665pEugDYaRGjfQaVqcp1it09L6ulFcz+WNkRu5\n7o1ad90bN2+bU8wddyqXXXaZNG8xoLXWrfxhDQ6nk227yjlo1HCGDxzAkuVf8MEXX3P9BWcyMQfV\nUwAAIABJREFUoG8BFdW1nHfz3dx55UUcPm5sSNdjtFoHOodVMHa9i+HNOTBurQtU50BqnZFq3Y03\n3khZWRnTp09n/PjxDB06tMvHoz3tN+yQGkw49SaMwBrofQ/RD62gra58tKcRa7OTEcnJzK+r4+vm\nVh7sW0C5w8GShkYqHQ7+kJeLRePa+HACK8RnaPW2w7HWkJqXlczwDZtk6m8sjR8zAoDc3lnsKK9k\n9JCBZGdlsG1XOQP6tm828o8XXmHM0IEMLx3g76l8MmLjpnV3TM1isQum5+sGeu0++RHZGdgtUMH1\n1SRFs6nT0qh1F6hxszyxBIBLgcsuuyzkaxOR577JtnrDr2wt283wgQPolZnOzoqqvbVu3iuMHFTC\nyMElsbxU3SVcvYtArYPA04Eh9FrnFumaF0qdA6l1RvPll1+SlZXFwoULWbZsGRMnTmTixIkMGDCA\n9PT0wE9gFOEGVM/nCTGs+lsC4Oav14kULXXl6N4Z0PFWnGizs7C+gUX19XzR3EKW2cyMzAwsHdPC\nFUXpXBLgb/fxYHYG7s4z9BkttGo5cge0TfXVsrRCgmqUjRhUwohBe5uzqto9/LBuI2WVVbzx4Se8\n+/SDZPeK7jSTSAvUvAVcv+VNrBo492sHauAg5MDqr4AH08h5CrWpijQtIwvuxg3A+cTNEb8moY8x\nQwcxZuigzr9X1ezh1+1lbN65iwXvfcxbTz5ATq+skJ7biDfl3PzVu5BqHUT/SC7P1wXfrx1mrQPf\nDWuotc7NaDVPap0xpaam8tprr5GSksLcuXOZO3cuzz77LMcccwyz1n/MmNzo9WMhjabqFVC9PaeO\nGy51F4vACvu+D73VlnF9M7kzTeG5HdUMSkrigt69yejYnX6f9alZyQGPyvIXWCG40OqPnoFW62v6\noldIBZn6Gzat0+F8+feCt3hw7sucfNRhTDhgNLOPnRzSei0jN25u/sJqSM2bHsJt/gJ9fYSOsuku\n0rvl6cnXYd9aGzfLVQ/IdLgYCLfWPfriazz60gJOPuowDh47knNPPq5H1jqIUb2TWhdVvuocSK0z\nUq0bOXIk7733HgMG7J3Jtm7dOl544QWeeOjv/HTWkRSlR/6mh2FCqjdBBlYtU4C7i8WUYE++6opT\nVdvPba5qCfi7yt90YH87A3vSOjXYyEINqb6m/kpQDVO4zZtbqJuJQHw0bm66r1nVW6jNnEGaODBO\nI+evUevO1/Q32HdkQZq32NCj1jmdThRFCWmTOJBapysD1jqIz3ontU47o9W6u+66i5tvvpnk5GRc\nLheqqmI2m4Hg16eGEjbd74egvzZaIdVTEIE1lLDqFsvQGqieaFlSoEdgdTNycPW2/rS77iHV1yhq\nbm4KeSvXSFCNBL2CaqjiqXFzC2YNV0wbOQi+mYtwEweRK+KeBTqYxitcwTRuIM1brEitC57UOt+M\nuvFKJEmtM16t8zVIYP/dicHP9ojE2afexCKkukUprGrl+R4CaDpVn3NGjRZY3WIVXLWE0u60hFTP\nJRoSVCMkls1bPDZubqFsOBLzRg60NXMR3jHTU7w2ct6mvoG2NVrSvMWGBNXQSK0LP7CC1LpEEQ+1\nDqIzmhqSWIZUN41hNRpBFfYNq92FE17DDaxaz28ONbgakdapvt33EZCgGiGhNm967A4Zr42bWzj/\n/TFv5AwWWN2M2Mz5atS66/KL3s8vY2neYkNqXeik1hGTWldvd3DRN7+wrbmNTIuZHS02zh2Qz03D\nikmzmHGpKiZFYdHOKl7aVonN5eK+sQMZlZUW9PWA1Dot4qHWgUGDqhFCqpvBRlYhcGB1Cza4alle\noFdg7fz8OAqu3oJp58f8TPXd53MlqEZGOM2bN1obmnhv3Nz0OMrB8Ou8wpwiB/oW+kiGWa2Nmiev\nv+B9/ULO74vl9OuleYsBPWtdMO97qXXdnsfIG89FcYRVK6dLpW5nDekWM2Pe/5b7xw7kpL45nR/f\n0tTKdT/8ysWlBTQ4nCwt38ND+w8kx89Ut1DqHEitg/iodRJSNQpxV+BIBletYbU7LeE1FoEVjBVa\n/YXSfT5XwyiqW0phLzLe+FzOUTUKf41X9495a24SpXHTi5JbFNLX6dLwaTkmJ8zjbGDfX4ThFPpQ\nm6xICLZxE/HFV63SUuf8fX080uuM1VDqXVRrXaDP0XikjVu4Ta3ZpJDTP5f1tY3kZ6QyYXgRyekp\nnaOpC7/dyBElfTj9oMEAPDx/BdUZKfTV+VgSzbVO6pzozoghFUI+xkbv97gnx5UnhRRW0xd9FjCs\nuvfu8BdY3UHMV2D1DG9aQ2sw4bC77iE3nOcKRrAh1R8JqgYXyUZNSQ8usKhN+o/E6dW8hfTaXhq+\nkBu6QGHUs/kI8UxCT5Es9JES8K6zBNQeK9KB1Ai1LpZ8hduQz3QFfQJrFGqde7OcF9bv4Nj+eeQk\nt4+Uum/br61p4PQhRdidLpLMJvplpFDZ0gaEHlSl1vUcER9NNWpI9aTxveyL3v1MOGEVAo+u6hFY\nYd8wF8poayDRCqYQ3DRf0L5ppwTVKIvVCEGwjZrW59CjoYtlWO0urPCqpTnT8nkh3KX0LPRGCK26\nbLUvTVtcS7RaB+HXOyPVOtCh3sW41oH2ejd/Qxmvn3AQKRZzl8dtLhWrScFsao+uaRYzjiCmpkqt\n67kkpHoIM6x6Cvb76q0GeE7fVgryMd/5vObni3ZgdYtGcNWLr1DanbeQGuypEhJUE5weTVuwzx9K\nM6d1KmAsdG/mAjZyegdWN42/BKK2+6Aegh1ZkIZO+BHtepdotQ661juj1zoIXO8UYMWW3ZTk9GL4\nsFKUjrWnqktFMSn07pVJS0YGjrw8ki1mdrY5yS8uRCnI1XwNmkitS3whToX1+1zxRMewGgxfNcAd\nYNXyCp/rjv2NvMYqsLp5C4OxCK9aQ6mncEdRPUlQjaJojzBEumkL9LrhjD6Eu9lUJLkbuag1cW5h\nNHOG4u8XsL/mTBq3uBGL0dRY1DupdR0MXute+nETs0eVkGrZ2/KYOkZQjx1cxGtrtjBrVCktdgc1\nLW0MzNZhfWqgoCG1Lq75vUGiZ2CNNzEKq94oBfkBZ1x0D7DegmskAqtbMMEVQguN0RRKQA20b4oE\n1QQUq4DanZLeW/e1XkZq6mLWxLmF2My5XComk8K5C5azu7GFV06fTF56e3Fxr+ea/9OvzF21EbvL\nxaMnHsro/Gxt1xToGv2RkQURAiPUOz0C6z7P6SfsR7vexXOtc6kq877fyPiiPM7dbzA5acnM+34j\ndqeLi8cNpcnuYPmW3eTe+xIDszO5+Yix9Eqxarsuf9foj9S6xOPt3z/UwBqPo6meDBTUtYRVT+7g\nqkdghcA7BYcbXD0tqKvnpbo67KrKYKuVewryyTCZunzOs7V7WNzQgALcX5DPsOTwgq+vYOrJV0jV\nurGnBNUoifhmIUXDoa48oq8Rikg0cF5fR+P3N9QGb+nnX/Pzpi00tbRy6+Xnd33tYJs4CH8jEm80\n/nIzAfO/WYspJQV7sw3y8iCj/dxABdhYUcsLa7dx3QmHsaellbs+/p5/TT+M7NQABS3UX67StMUF\ntbpM046z0RhNNUI49SYR6t3ijz/jl83baGpp5Y4rL+r6upGqdYE+r7sgat0rX69l1oHD2bGnETU3\nDzJSmXlEL5yqyoaGZuav38nDZxzH+opqlq3fyimHj4PMNP9NttS6Hi3Y5TXO3DzMJpO24BaBkFq2\np4GyukbGl0T558wgo6vBhlXQJ7CCtlFWT96Cn5bw2uhy8XTtHuYVF1GYZOGm3eUsaWjk9KxMTEr7\n7JEfW1v5pKmZx/oWst1u576qap7oW0hKtzAbzLX5E25AdZOgGiYtzZu3pkKPhsaWW0pyx90Qdc9u\nFMW4x61Fq4ELeB0BGrzujV1LaxsL3v+Y+555kRsuPIv/ffQpZZVVPPqn6/b5fmtu4iC4nTM9BRte\nvWix2fnPl2u54sgDmPvFT52Pu0dTX/5mLUcO6c8JowcBcM87X7ArKZXsPnlhv3YnP42Z2qcQVVXR\nVjqF0YX73nc6nZjN7RvgqIVDMTVU6nZtkRKJ2SQhXUcQ9a65pZX573zIQ3Pnc8tl5zPvzXe55p5/\n8Mit12Lq1sjoXus8P89Nh1rXbLPzn6/WcOVR43ju8x87H89MsaIoCo9+9A1HDx/AGeNHAPDKNz9T\n2dRCXmaafoEhwBRfl8sltS7eBfhZMbvfP4FGGiMQUjdV1nLCY6/S0Gbj5UtmcPSwEt1fw69g/5si\nFGzdNxciEVhBn2nBvmgJiFaHE+d2yMlJJteahKtaoX+vFPrkpnZ+zkdb93BqQW8OKMzkAOCv1dW4\nsizkJocwg8QPvQKqmwTVGPAcDQimoVFVFVPxCJ5++mnuv/9+pk2bxoQJEzhv+rGdIcPojBJYffFs\n7FwuF/9b8Q4frVrDg3fdzrQpR3LikRO49I77aWhqJisj3ftzhNLEgbbGTIfweuX897nm6IPIy0jF\n5nR23m1zW7e7mrMOHoXd6STJbKa4dyYVDc2MCudmrJYRg47PUcDrz3Ko5+WK6OkejEKtdQAOh4PL\nbryd5Ox8Bg8ezB//+EcUA84a8cXotQ72/ns5nU4WLVvCpz+t59G/zeGYSROZdNB+XHn3gzS3tpKR\nlub9641e615+n+umHExOegp2p7NzXarb+vIazjl4FA6nC4vZRL/sLCoamhhZGOJGSlpHRj0+r/tN\nAJBaZ2RaRlPXl1ezraaBNbsqUVVoaLWxdlcVk4cN4IojD+wa3tzBLAIhdUt1HdOfWMAdJx1OaW4v\nbljwIffOnMzUEaW6v5ZutH4fQgy0oYyugv/ACqFNC3YLJbx2l2kxc0NxHpN+2IRJgSOy0pmW03Wt\n/dZWG1N7Z3TmhWJrEuU2B/3DDKpaNkhKX/SZzw2tAr2nJKhGmJYpWoEaGqVoePv/A2vXrmX+/Pks\nXryYuro6zj7zDCaMHsKQQQN1u+ZoMMqIgz8bN29l6YrPmXrk4UybciROp5PXP/2W4cNH+AypnoJq\n4iD0Kb9BjER8/utOUpMsHDeylPXlNZgVE9lpXe/W2ZwurGYzlo4GKs1qweFyhX49Gr9me0UVy75d\nzevLv2TKuDEcOmooI0qKyCkdFvzzCUPSGt5sNhvX3f8Eydn5XHvttVx22WW01FZy+03Xd46uxot4\nCKwbNm9l2WcrOf7oSRwzaSIOh4M3v/ie0aNHk1lcGnAKccxqnZ/n+HTjDtKsFqaOKOHn3dWYFIXe\n3ZYv2BztN+PcR9OkJllwOLUfTRNqrSuv2cOyb1fz4vufcPjY4UzafyQjBhRRMGRk8M8nYqtbsNpQ\nUcOl/3mX8QMKOXHMYFaXVVKSk8WbP2zAajFz4cSxpHiebRnB9ajvrPmVuhYbJ48dQnZaCneceDhf\nbi4zdlDVKoxAG2pYBe2BFbRNDQZ9wmuLy8WbNfUsGzuIouQkLt2wnUXVdZyet/dmsQtQUDoHAqwm\nJaijuPxdry+e34/utE6hl6AaZf7WVnmGNyW9N/Qq2OdzGhoayM3NZdiwYViaqpk1/SSeen4ef77p\nBrKydNipMIqM3sA9/sJ/ATh31nQAvl/zM2XlFYwZMbTzBoSqqrDH/1TEoI57gNDXbXn7+m5WfrmO\nd37eyrC7nsXudFHT2Mw5Ly3lhWvOxdrxyzM7J5umlHTsOflYkyzsbGwlv6QkMuuoPJ7z8gee5qDh\ng5h15KF8+uM6PvjxF7LS05l93GROO26y/q8tdOVvNHWfz/Vzo0opGk4y0NjYyHnnnceoUaOY9/hD\nXHrNDYw/cH9OPG5qXMwe6c7IN+cee+4/mM1mzjqlvQlbtbq91o0cOhjoOP/ViLWu+3N4+HJl4FqX\nm5fbpdaVNbVSUBqBWtft+W58/EV6Z6Rz8YlH89F3q/m/194jNSWZU6ZM4rzpx+n72kI3Whrrofk5\njOtfwPbaeo4dWYrT5eKNH37huFEDuX3axK4hNQLUjuChKAq/O/JAAE5/5g0W/fZUpu83lOn7DY3o\n6xuOj0CrdDyurl4d0tO6A6u/M1pDCa1uwR7jsmxXDSW90hlckodZUZjlsPN5TT3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3AAAc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\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "%matplotlib inline\n", + "showz = 5\n", + "showx = 5\n", + "scale = 1e5\n", + "\n", + "X, Y = np.meshgrid(grd.YC[:, 0], grd.RC[:showz])\n", + "\n", + "f, ax = plt.subplots(1, 3, figsize=(16, 4))\n", + "\n", + "im = ax[0].contourf(X, Y, Nsq[:showz, :, showx]*scale, np.arange(0, 15.1 , 1), cmap='Reds')\n", + "cb = plt.clabel(im,colors='black',fmt='%3.1f')\n", + "ax[0].set_title('N$^2$ from DRHODR [x 10$^5$ s$^{-1}$]', color='black', fontsize=15)\n", + "\n", + "im = ax[1].contourf(X, Y, Nsq_TS[:showz,:,showx]*scale, np.arange(0,15.1,1), cmap='Reds')\n", + "cb = plt.clabel(im,colors='black',fmt='%3.1f')\n", + "ax[1].set_title('N$^2$ from T and S [x 10$^5$ s$^{-1}$]', color='black', fontsize=15)\n", + "\n", + "im = ax[2].contourf(X, Y, Nsq_ra[:showz,:,showx]*scale, np.arange(0,15.1,1), cmap='Reds')\n", + "cb = plt.clabel(im,colors='black',fmt='%3.1f')\n", + "ax[2].set_title('N$^2$, from RHOAnoma [x 10$^5$ s$^{-1}$]', color='black', fontsize=15)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 2", + "language": "python", + "name": "python2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.12" + } + }, + "nbformat": 4, + "nbformat_minor": 1 +} diff --git a/so_box_biogeo/diags/mitgcmgrid.py b/so_box_biogeo/diags/mitgcmgrid.py new file mode 100644 index 00000000..78dc2957 --- /dev/null +++ b/so_box_biogeo/diags/mitgcmgrid.py @@ -0,0 +1,60 @@ +from MITgcmutils import rdmds +import numpy as np + +def loadgrid(gridname,region=None,varname=None): + """ + [Function] + grd=loadgrid(gridname,region=None,varname=None): + + [Description] + Read grid files and load them into the 'grd' object + + [Inputs] + gridname : an identification string. + Once 'gridname' is given, 'dirGrid' will be assigned. + A user must define 'dirGrid' beforehand. Please see the code. + region : A list defining the boundary for the grid. + [x0,x1,y0,y1] (default is [0,nx,0,ny]) + varname : A list of strings for the grid data to load + + [Output] + grd : An object containing grid data. + + """ + + if gridname=="so_box": + dirGrid="../results/" + + if varname is None: + varname=['XC','YC','RAC','DXC','DYC','hFacC','hFacW','hFacS','Depth',\ + 'RC','RF','DRC','DRF','XG','YG','RAZ','DXG','DYG']; + + class grd(object): + for iv,vname in enumerate(varname): + if region is None: + exec('tmpvar=rdmds("'+dirGrid+varname[iv]+'")'); + tmpvar=tmpvar.squeeze(); + exec(varname[iv]+'=tmpvar') + else: + if vname is 'RC' or vname is 'RF' or vname is 'DRC' or vname is 'DRF': + exec('tmpvar=rdmds("'+dirGrid+varname[iv]+'")'); + else: + exec('tmpvar=rdmds("'+dirGrid+varname[iv]+'",region='+str(region)+')'); + tmpvar=tmpvar.squeeze(); + exec(varname[iv]+'=tmpvar') + if vname=='hFacC': + mskC=hFacC.copy() + mskC[mskC==0]=np.nan + mskC[np.isfinite(mskC)]=1. + if vname=='hFacW': + mskW=hFacW.copy() + mskW[mskW==0]=np.nan + mskW[np.isfinite(mskW)]=1. + if vname=='hFacS': + mskS=hFacS.copy() + mskS[mskS==0]=np.nan + mskS[np.isfinite(mskS)]=1. + del tmpvar + del grd.iv,grd.vname + + return grd diff --git a/so_box_biogeo/diags/mitgcmgrid.pyc b/so_box_biogeo/diags/mitgcmgrid.pyc new file mode 100644 index 00000000..e3c1a28f Binary files /dev/null and b/so_box_biogeo/diags/mitgcmgrid.pyc differ diff --git a/so_box_biogeo/diags/ocestrat.0000000009.data b/so_box_biogeo/diags/ocestrat.0000000009.data new file mode 100644 index 00000000..e723b341 Binary files /dev/null and b/so_box_biogeo/diags/ocestrat.0000000009.data differ diff --git a/so_box_biogeo/diags/ocestrat.0000000009.meta b/so_box_biogeo/diags/ocestrat.0000000009.meta new file mode 100644 index 00000000..0dfa097d --- /dev/null +++ b/so_box_biogeo/diags/ocestrat.0000000009.meta @@ -0,0 +1,16 @@ + simulation = { 'OBC + Biogeo S.Ocean Box' }; + nDims = [ 3 ]; + dimList = [ + 42, 1, 42, + 20, 1, 20, + 15, 1, 15 + ]; + dataprec = [ 'float64' ]; + nrecords = [ 2 ]; + timeStepNumber = [ 9 ]; + timeInterval = [ 4.500000000000E+03 8.100000000000E+03 ]; + missingValue = [ -9.99000000000000E+02 ]; + nFlds = [ 2 ]; + fldList = { + 'DRHODR ' 'RHOAnoma' + }; diff --git a/so_box_biogeo/results/DRC.data b/so_box_biogeo/results/DRC.data new file mode 100644 index 00000000..b6a79d15 Binary files /dev/null and b/so_box_biogeo/results/DRC.data differ diff --git a/so_box_biogeo/results/DRC.meta b/so_box_biogeo/results/DRC.meta new file mode 100644 index 00000000..3ee8e259 --- /dev/null +++ b/so_box_biogeo/results/DRC.meta @@ -0,0 +1,9 @@ + simulation = { 'OBC + Biogeo S.Ocean Box' }; + nDims = [ 3 ]; + dimList = [ + 1, 1, 1, + 1, 1, 1, + 16, 1, 16 + ]; + dataprec = [ 'float64' ]; + nrecords = [ 1 ]; diff --git a/so_box_biogeo/results/DRF.data b/so_box_biogeo/results/DRF.data new file mode 100644 index 00000000..caa96156 Binary files /dev/null and b/so_box_biogeo/results/DRF.data differ diff --git a/so_box_biogeo/results/DRF.meta b/so_box_biogeo/results/DRF.meta new file mode 100644 index 00000000..f6e996a0 --- /dev/null +++ b/so_box_biogeo/results/DRF.meta @@ -0,0 +1,9 @@ + simulation = { 'OBC + Biogeo S.Ocean Box' }; + nDims = [ 3 ]; + dimList = [ + 1, 1, 1, + 1, 1, 1, + 15, 1, 15 + ]; + dataprec = [ 'float64' ]; + nrecords = [ 1 ]; diff --git a/so_box_biogeo/results/DXG.data b/so_box_biogeo/results/DXG.data new file mode 100644 index 00000000..5b47d031 Binary files /dev/null and b/so_box_biogeo/results/DXG.data differ diff --git a/so_box_biogeo/results/DXG.meta b/so_box_biogeo/results/DXG.meta new file mode 100644 index 00000000..ec0ea033 --- /dev/null +++ b/so_box_biogeo/results/DXG.meta @@ -0,0 +1,8 @@ + simulation = { 'OBC + Biogeo S.Ocean Box' }; + nDims = [ 2 ]; + dimList = [ + 42, 1, 42, + 20, 1, 20 + ]; + dataprec = [ 'float64' ]; + nrecords = [ 1 ]; diff --git a/so_box_biogeo/results/DYG.data b/so_box_biogeo/results/DYG.data new file mode 100644 index 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100644 index 00000000..8d758120 Binary files /dev/null and b/so_box_biogeo/results/RC.data differ diff --git a/so_box_biogeo/results/RC.meta b/so_box_biogeo/results/RC.meta new file mode 100644 index 00000000..f6e996a0 --- /dev/null +++ b/so_box_biogeo/results/RC.meta @@ -0,0 +1,9 @@ + simulation = { 'OBC + Biogeo S.Ocean Box' }; + nDims = [ 3 ]; + dimList = [ + 1, 1, 1, + 1, 1, 1, + 15, 1, 15 + ]; + dataprec = [ 'float64' ]; + nrecords = [ 1 ]; diff --git a/so_box_biogeo/results/RF.data b/so_box_biogeo/results/RF.data new file mode 100644 index 00000000..933c839b Binary files /dev/null and b/so_box_biogeo/results/RF.data differ diff --git a/so_box_biogeo/results/RF.meta b/so_box_biogeo/results/RF.meta new file mode 100644 index 00000000..3ee8e259 --- /dev/null +++ b/so_box_biogeo/results/RF.meta @@ -0,0 +1,9 @@ + simulation = { 'OBC + Biogeo S.Ocean Box' }; + nDims = [ 3 ]; + dimList = [ + 1, 1, 1, + 1, 1, 1, + 16, 1, 16 + ]; + dataprec = [ 'float64' ]; + nrecords = [ 1 ]; diff --git a/so_box_biogeo/results/XC.data b/so_box_biogeo/results/XC.data new file mode 100644 index 00000000..5f1142d9 Binary files /dev/null and b/so_box_biogeo/results/XC.data differ diff --git a/so_box_biogeo/results/XC.meta b/so_box_biogeo/results/XC.meta new file mode 100644 index 00000000..ec0ea033 --- /dev/null +++ b/so_box_biogeo/results/XC.meta @@ -0,0 +1,8 @@ + simulation = { 'OBC + Biogeo S.Ocean Box' }; + nDims = [ 2 ]; + dimList = [ + 42, 1, 42, + 20, 1, 20 + ]; + dataprec = [ 'float64' ]; + nrecords = [ 1 ]; diff --git a/so_box_biogeo/results/YC.data b/so_box_biogeo/results/YC.data new file mode 100644 index 00000000..92066a43 Binary files /dev/null and b/so_box_biogeo/results/YC.data differ diff --git a/so_box_biogeo/results/YC.meta b/so_box_biogeo/results/YC.meta new file mode 100644 index 00000000..ec0ea033 --- /dev/null +++ b/so_box_biogeo/results/YC.meta @@ -0,0 +1,8 @@ + simulation = { 'OBC + Biogeo S.Ocean Box' }; + nDims = [ 2 ]; + dimList = [ + 42, 1, 42, + 20, 1, 20 + ]; + dataprec = [ 'float64' ]; + nrecords = [ 1 ]; diff --git a/so_box_biogeo/results/hFacC.data b/so_box_biogeo/results/hFacC.data new file mode 100644 index 00000000..4e9dd93a Binary files /dev/null and b/so_box_biogeo/results/hFacC.data differ diff --git a/so_box_biogeo/results/hFacC.meta b/so_box_biogeo/results/hFacC.meta new file mode 100644 index 00000000..1edc2163 --- /dev/null +++ b/so_box_biogeo/results/hFacC.meta @@ -0,0 +1,10 @@ + simulation = { 'OBC + Biogeo S.Ocean Box' }; + nDims = [ 3 ]; + dimList = [ + 42, 1, 42, + 20, 1, 20, + 15, 1, 15 + ]; + dataprec = [ 'float64' ]; + nrecords = [ 1 ]; + timeStepNumber = [ 0 ];