diff --git a/.github/workflows/check-working-examples.yaml b/.github/workflows/check-working-examples.yaml index b303072ed..4d6112841 100644 --- a/.github/workflows/check-working-examples.yaml +++ b/.github/workflows/check-working-examples.yaml @@ -36,10 +36,10 @@ jobs: for i in *.py; do # Skip these examples since they have additional dependencies - if [[ $i == *11* ]]; then + if [[ $i == *15* ]]; then continue fi - if [[ $i == *16* ]]; then + if [[ $i == *19* ]]; then continue fi diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml index e849037e6..eaa1d829f 100644 --- a/.pre-commit-config.yaml +++ b/.pre-commit-config.yaml @@ -7,7 +7,7 @@ repos: stages: [commit] - repo: https://github.com/psf/black - rev: 21.9b0 + rev: 22.6.0 hooks: - id: black name: black @@ -23,7 +23,7 @@ repos: # args: [--no-strict-optional, --ignore-missing-imports] - repo: https://github.com/pre-commit/pre-commit-hooks - rev: v4.0.1 + rev: v4.3.0 hooks: - id: trailing-whitespace - id: end-of-file-fixer @@ -40,3 +40,4 @@ repos: rev: '4.0.1' hooks: - id: flake8 + args: [--max-line-length=120] diff --git a/README.md b/README.md index 507305c58..ccf39cd6f 100644 --- a/README.md +++ b/README.md @@ -3,7 +3,7 @@ FLORIS is a controls-focused wind farm simulation software incorporating steady-state engineering wake models into a performance-focused Python framework. It has been in active development at NREL since 2013 and the latest -release is [FLORIS v3.1.1](https://github.com/NREL/floris/releases/latest) +release is [FLORIS v3.2](https://github.com/NREL/floris/releases/latest) in March 2022. The software is in active development and engagement with the development team @@ -76,11 +76,11 @@ and importing FLORIS: DATA ROOT = PosixPath('/Users/rmudafor/Development/floris') - VERSION = '3.1.1' + VERSION = '3.2' version_file = <_io.TextIOWrapper name='/Users/rmudafor/Development/fl... VERSION - 3.1.1 + 3.2 FILE ~/floris/floris/__init__.py diff --git a/docs/_tutorials/index.md b/docs/_tutorials/index.md index 775869548..ddbefaf62 100644 --- a/docs/_tutorials/index.md +++ b/docs/_tutorials/index.md @@ -69,7 +69,7 @@ initial 3x1 layout to a 2x2 rectangular layout. ```python x_2x2 = [0, 0, 800, 800] y_2x2 = [0, 400, 0, 400] -fi.reinitialize( layout=(x_2x2, y_2x2) ) +fi.reinitialize( layout_x=x_2x2, layout_y=y_2x2 ) x, y = fi.get_turbine_layout() @@ -483,9 +483,9 @@ fi_gch = FlorisInterface("inputs/gch.yaml") fi_cc = FlorisInterface("inputs/cc.yaml") # Assign the layouts, wind speeds and directions -fi_jensen.reinitialize(layout=(X, Y), wind_directions=wind_directions, wind_speeds=wind_speeds) -fi_gch.reinitialize(layout=(X, Y), wind_directions=wind_directions, wind_speeds=wind_speeds) -fi_cc.reinitialize(layout=(X, Y), wind_directions=wind_directions, wind_speeds=wind_speeds) +fi_jensen.reinitialize(layout_x=X, layout_y=Y, wind_directions=wind_directions, wind_speeds=wind_speeds) +fi_gch.reinitialize(layout_x=X, layout_y=Y, wind_directions=wind_directions, wind_speeds=wind_speeds) +fi_cc.reinitialize(layout_x=X, layout_y=Y, wind_directions=wind_directions, wind_speeds=wind_speeds) def time_model_calculation(model_fi: FlorisInterface) -> Tuple[float, float]: """ @@ -535,7 +535,7 @@ X = np.linspace(0, 6*7*D, 7) Y = np.zeros_like(X) wind_speeds = [8.] wind_directions = np.arange(0., 360., 2.) -fi_gch.reinitialize(layout=(X, Y), wind_directions=wind_directions, wind_speeds=wind_speeds) +fi_gch.reinitialize(layout_x=X, layout_y=Y, wind_directions=wind_directions, wind_speeds=wind_speeds) ``` ```python diff --git a/docs/index.md b/docs/index.md index 53f5ab851..b3d8498e6 100644 --- a/docs/index.md +++ b/docs/index.md @@ -12,7 +12,7 @@ permalink: / FLORIS is a controls-focused wind farm simulation software incorporating steady-state engineering wake models into a performance-focused Python framework. It has been in active development at NREL since 2013 and the latest -release is [FLORIS v3.1.1](https://github.com/NREL/floris/releases/latest) +release is [FLORIS v3.2](https://github.com/NREL/floris/releases/latest) in March 2022. The software is in active development and engagement with the development team @@ -85,11 +85,11 @@ and importing FLORIS: DATA ROOT = PosixPath('/Users/rmudafor/Development/floris') - VERSION = '3.1.1' + VERSION = '3.2' version_file = <_io.TextIOWrapper name='/Users/rmudafor/Development/fl... VERSION - 3.1.1 + 3.2 FILE ~/floris/floris/__init__.py diff --git a/examples/00_getting_started.ipynb b/examples/00_getting_started.ipynb index 6351cba90..8b9d4629a 100644 --- a/examples/00_getting_started.ipynb +++ b/examples/00_getting_started.ipynb @@ -112,7 +112,7 @@ "source": [ "x_2x2 = [0, 0, 800, 800]\n", "y_2x2 = [0, 400, 0, 400]\n", - "fi.reinitialize( layout=(x_2x2, y_2x2) )\n", + "fi.reinitialize(layout_x=x_2x2, layout_y=y_2x2)\n", "\n", "x, y = fi.get_turbine_layout()\n", "\n", @@ -210,7 +210,7 @@ }, { "data": { - "image/png": "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", + "image/png": "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", "text/plain": [ "
" ] @@ -386,7 +386,7 @@ "yaw_angles[:, :, 0] = 25\n", "print(yaw_angles)\n", "\n", - "fi.calculate_wake( yaw_angles=yaw_angles )" + "fi.calculate_wake(yaw_angles=yaw_angles)" ] }, { @@ -438,7 +438,8 @@ "\n", "# Pass the new data to FlorisInterface\n", "fi.reinitialize(\n", - " layout=(x, y),\n", + " layout_x=x,\n", + " layout_y=y,\n", " wind_directions=wind_directions,\n", " wind_speeds=wind_speeds\n", ")\n", @@ -459,7 +460,7 @@ "yaw_angles[1, :, 1] = 10 # At 265 degrees, yaw the second turbine -25 degrees\n", "\n", "# 6. Calculate the velocities at each turbine for all atmospheric conditions with the new yaw settings\n", - "fi.calculate_wake( yaw_angles=yaw_angles )\n", + "fi.calculate_wake(yaw_angles=yaw_angles)\n", "\n", "# 7. Get the total farm power\n", "turbine_powers = fi.get_turbine_powers() / 1000.0\n", @@ -505,7 +506,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 9, @@ -514,7 +515,7 @@ }, { "data": { - "image/png": "iVBORw0KGgoAAAANSUhEUgAAA34AAAG5CAYAAADRW+YxAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/YYfK9AAAACXBIWXMAAAsTAAALEwEAmpwYAAEAAElEQVR4nOz9e7A123YXhv3GnN291n58j3PuOffqPnX1uGCQCWDJYIeKIaYCRoaIJDYmpkA4EFEVwCF2EsCVKjspk0Aq2OCyjS1QjESpiigSKQgmtikSbEiMeRshCYSkq6t7rs6559zz/L6916N7zpE/xpizZ8/VvR57r/36vvmr853e3T17dq9+zDF+4zWJmVFQUFBQUFBQUFBQUFDw4sLc9QUUFBQUFBQUFBQUFBQU3CwK8SsoKCgoKCgoKCgoKHjBUYhfQUFBQUFBQUFBQUHBC45C/AoKCgoKCgoKCgoKCl5wFOJXUFBQUFBQUFBQUFDwgqMQv4KCgoKCgoKCgoKCghcchfgVFDwgENFfJqLfrn//JiL6L+7gGj5PRExE1W2fu6CgoKCg4D6BiH4FEb1x19dRULAPCvEreKlBRDMi+h4i+hIRPSOiv0tEvybZ/5uI6Hny71JJz7fqfiKiP0RE7+q/P0REdM1rIiL6KSL60W3tmPn7mflXXedcBQUFBQUF++C+yEvt578ion8z2/5biOgniej0+r+2oODFRCF+BS87KgBfBvDLATwB8L8D8ANE9Hkgkqvz8A/A/wLATwH423r8dwH49QB+IYD/DoBfB+B3XPOa/hkAHwfwjUT0T16zr4KCgoKCgmPgXshLZmYAvx3A/4qIvgUAiOh1AH8YwG9n5sur/sCCghcdhfgVvNRg5gtm/reY+aeZ2TPznwfwRQDfOnHIdwL4PhU8Yf0PM/MbzPwViOD5rde8rO8E8GcB/AX9exRE9FuJ6K8m67+KiP4hEX1IRP8hEf2XSVjobyWiv0pE/xciep+IvphZap+oJfdNIvoKEf3bRGR1n9XjvkZEPwXgn7/m7ysoKCgoeGC4T/KSmX8cwB8A8D1EZAD8ewB+CMDfJaI/T0TvqKz780T0GQAgov8+Ef1w6IOI/iIR/Y1k/a8Q0a/Xvz9FRD+k/XyRiP7VpN0JEf1J7f9HARQDbcGDQSF+BQUJiOgTAH4OgB8Z2ff1EG/c9yWbvwXAf5us/7e67arnPwXwLwD4fv33G4mo2eO41wD8IIDfD+BjAP4hgP9u1uyX6vbXAPyfIQIzhNn8SQAdgG8G8IsB/CqIRRUA/ucAfq1u/za9voKCgoKClxh3LS8B/DsACCL7fhmA/w1Er/1PAHw9gM8BWAD497X9XwPwBSJ6jYhqiNfxU0T0iIhOIPLtryiR/H/q9X0awK8E8HuI6FdrP/8mgG/Sf78aWwy0BQX3DYX4FRQoVBB8P4DvZeZ/MNLktwD4K8z8xWTbOYAPk/UPAZxfI8/vfwxgBeC/APCfAqixn4ft2wH8CDP/GWbuINbPt7I2X2LmP87MDsD3AvgkgE+o8P52AL9HLbpvA/h3AfxGPe43APgjzPxlZn4PwP/pir+toKCgoOAFwH2QlyrL/mcA/kcAfjczP2Pmd5n5h5j5kpmfQbyCv1zbLwD8DQgh/VYIsfv/QkjjPwXgHzHzuxAP3uvM/H9g5jUz/xSAP46hTPwDzPweM38ZIm8LCh4ESlW+ggIAauH7UwDWAH7XRLPfAuD/mG17DuBxsv4YwPMktCU9x/8LwH9PV38HM3//yDm+E8APKHnriOiHdNv/Y8dP+BQk9wKA5EDQZpWxt5L9lyprzwG8CiGYbyby1yT9DfoG8KUd11JQUFBQ8ILiHslLMPOPqNz6ET3uFGK4/OcAvKLNHhGRVaL4XwL4FQDe0L/fhxDDla4D4i38FBF9kJzKAvgr+neRiQUPFoX4Fbz0UGvj9wD4BIBvZ+Z2pM0vgwz2P5jt+hFIovpf1/VfiJGwFwBg5l8ztj05x2cA/LMAfgkR/U908ymAORG9xsxf23L4mwA+k/2mz0w3H+DLEKH3mhLOsb4/m6x/bs9+CwoKCgpeINwXebkF/zqAnwvglzLzW0T0iwD8HUhIKCDk7g8D+BkAfxBC/P44RAb+B9rmywC+yMxfmDhHkInh2otMLHgwKKGeBQXAHwPw8wD8Og0FGcN3AvghDR1J8X0A/jUi+jQRfQoidP7kFa/jNwP4cYjQ+kX67+dALJP/0x3H/qcAfgER/XqS+fV+J4Cv2+ekzPwmJLT0DxPRYyIyRPRNRPTLtckPAPhXiegzRPQKgN932M8qKCgoKHhBcF/k5RQeQfL6PiCiVyH5eCn+fxAZ+0sA/HVm/hGIh++XAvivtM1fB/CMiH6vFnKxRPSPJ1W2fwDA7yeiV9Rg+7uP/BsKCm4MhfgVvNTQBPTfASFZb1E//9BvStrMITH93zvSxX8MSQL/YQB/H0LA/uMrXs53AvgPmfmt9B+A/wg7ksfVG/gvQoq2vAvg5wP4mxAr5j74LQAaAD8KsYD+ICQHEBBr6H8OyYf42wD+zCE/qqCgoKDg4eOeycsp/BEAJwC+Binm8p+lO5n5AiLHfoSZ17r5v4bkwL+tbRykoNkvglQt/RqAPwGZwgIA/veQ8M4vQoymf+rIv6Gg4MZAI6HVBQUFDxyag/EGgN/EzP+fu76egoKCgoKCgoKCu0Xx+BUUvCAgol9NRE+JaAbg34DkNPy1O76sgoKCgoKCgoKCe4BC/AoKXhz80wB+EhKW8usA/PotORgFBQUFBQUFBQUvEUqoZ0FBQUFBQUFBQUFBwQuO4vErKCgoKCgoKCgoKCh4wVGIX0FBQUFBQUFBQUFBwQuOBzGB+xOy/HHUd30ZBQUFBQW3gJ/A6mvM/PpdX8dDQZGRBQUFBS8HrisfHwTx+zhq/JHq6+/6MgoKCgoKbgG/tvvxL931NTwkFBlZUFBQ8HLguvLxKKGeWkL+B4noHxDRjxHRP01ErxLRXySif6TLV7QtEdG/R0Q/QUR/j4j+iWNcQ0FBQUFBwX1EkZEFBQUFBfcBx8rx+6MA/jNm/scA/EIAPwbg9wH4S8z8BQB/SdcB4NcA+IL++y4Af+xI11BQUFBQUHAfUWRkQUFBQcGd49rEj4ieAPhnAHwPADDzmpk/APAdAL5Xm30vgF+vf38HgO9jwV8D8JSIPnnd6ygoKCgoKLhvKDKyoKCgoOC+4Bgev28A8A6A/4SI/g4R/QkiOgPwCWZ+U9u8BeAT+venAXw5Of4N3TYAEX0XEf1NIvqbH8Id4TILCgoKCgpuHUVGFhQUFBTcCxyD+FUA/gkAf4yZfzGAC/QhKwAAllniD5opnpm/m5m/jZm/7QnsES6zoKCgoOA+gGra+u8FQ5GRBQUFBQX3Ascgfm8AeIOZ/xtd/0GIkPtqCE/R5du6/ysAPpsc/xndVlBQUFBwT7CLnF3n30uGIiMLCgoKCu4Frk38mPktAF8mop+rm34lgB8F8OcAfKdu+04Af1b//nMAfotWLvunAHyYhLsUFBQUFNwQCjm7fRQZWVBQUFBwX3Csefx+N4DvJ6IGwE8B+FcgpPIHiOi3AfgSgN+gbf8CgG8H8BMALrVtQUFBQcEVUEjag0CRkQUFBQUFd46jED9m/rsAvm1k168cacsAfucxzltQUFDwEPAikTNTvTi/5bZQZGRBQUFBwX3AsTx+BQUFBS88XiQCBxQSV1BQUFBQ8DKhEL+CgoKXGi8KmbuvJI7qY9QQKygoKCgoeLlBNQHd9fooxK+goODeoZCxm8Fdk7D7dj8KCgoKCgpuAvdVjynEr6Cg4EZxXwe/Q3FfSMtdkbf78vsLCgoKCgpuGy+KLlOIX0FBwd54yAPffSIut03e7vK3P+R3pqCgoKDgxcXLKJ8K8SsoeKB46APWXROx2yRft/Vbb/OduOvnV1BQUFDw8uAh6zz3SV4W4ldQcId4yAMZcPeD2YtA3m7jHbiL5/TQ3+2CgoKCgpvDQ5URd633XBeF+BUUHAkPZRC7b4PWTZO3m/y9N/nMH+p1b8N9e/cKCgoKCo6Pog9dDzepFxXiV/BS4C4GofsyoLyIxUCO+TyPeZ038Z7d6H20d/uOmjs+f0FBQcHLivtKzu6D7nTXFbCncIx7U4hfwUG4rwPFXeK4xOHFLfrxIpO1m7iPN0nK7gvhInM/rqOgoOBmMTXOcsu3fCUvNu6bjnZXJO4+ELf7QGDHUIjfDeC+fXgF07jvpO22B477Hrp4X0jaMUnZTZKw+0KsyN69EC4oKLh/eCj60m0T1Pt4X25fH7l9uXHfyNpNvAeF+N0A7tuL86LjvnjJ9vlAH6qHbReu87uuQ6KuQ5qOQYqOSWju0gt3XwhiwcuBMDYVb8/LA3tiAQDc+ju+knH4bvu7eF+I2O3qEA83/z7HXT+/+8QLCvG7AdwHF/PLirvMK7MnRyQBt1X+/xbIxlUIzVWJyHWI2Oh1EgFEIF2OrZMxfVtjQCC5r1PHggCj6yBdhP3SF8X9AJEB0jaQPski/i374kVn5+y3S39933GZ/31X8B7Pf/jv3fVVFNwSto2phRS+WIgyrRICuItoHQOHkMz7pJgfEzeljz6UHP59cd+e/03eg0L8bgDHJAC3gfv2wt8GbiJksD6rr93nMXFXHqQBaTOmJxrG9ERICRJM+BswtRXiQ4HsUE+AyGi/oR9to/tNZYdEKz1XerwSp8nrzcCeATDgPcAAJ38DDGYGQhtmsFdFg2UdlPzN2j7s91670f3wADs5pwPA4TwAs9c24VQs1xL7Tq4n9qmNdZ2T48P1xrb3CHZWxNKLjn28P9fxCxXSeP9QPbaj26/yrPYmjVt0sbt4Rw4huzeSN/7Aio8FlCrUx0WRsDeA+tHtWbTuCi/aB3HdcEOqKjz+lp8DM5slivyhF0HT6xuXl3hvRttPeK9wm2F9QoqYWUgMkZKmhDCx1/1KhDKSROj/lu0O7DrdpmSMPbznnhwpEWM9F5GSoJQ8HQl3GSI5dubJq9nku8mG+/MtC9EueNER5IfHtGJudlC/bfJ1l6JYiOHtoz5Vvchl934mC863jyA8c3sgERh93gfqMMfQ56auO1zfQyBmL+q8sznuuuL1TaIQvxuAbYwu7/hCCiZxzPwuqmucf8u3AM/fQfu1n0VUpA85xahMyTYqaeGcvPDIStrmnnh0rnPPYzjl2L5sOdgx2Dnso+S1HQfHJGv0sIIlCq6IoDzaLbl+U6QweAm3KYfXzdcqxPD4qOaibm4QPwW7caKfji+5TjXV1wYOIJex7+wdOoRsHvz+XCdH/gGQxTHcNbG6L5Wt7wKF+N0Anvxjn8PsM58DnAMwoqjvAm+GdsVdsa89FPvJ7WObbkDQHfK7bzi/iECjeVK68+AQwKwhlj/9RZw/BvCxk63CKIYC3iBeRq/JbZG4+3hvb+qabqLf23j/C+4/5h+TsPh0rAxKea5w50q078zW/QBAE8Rv35yvEmZ6fJy9fgagH1fysSAfb3I5mu6fGkemiN12mbz9ee0il1OE9ZBz3DTuNDKlVHS+dyjE7wZw/o2fx+LHfxTcdQD2GDimBoWNAgzTnqRAYuKAOEmk9hgAXkRDSJLXNJrrdASvWPOp1zdPu2PAP0QRPrbwuC1h9CIr+zdxD/e2Ym/BMe75dX/b2PFkxvN8Cl4u1KdC/MI74h1HBTrdBgBcDd+jnNTl+33H057EHYVFijfx5jB7NAPN5uCuA3cOHAzjO4jgwDiQjSm7yGPA2Jg61fZQUgnYq+t4Gbb18yJ7qEr0TY/b0MsK8TsyqsfnsMahnlcIt3dyINnjAR/+EvTWlbuwMt1/JX802ekomD05G6wPLZSHCZldx12lr+v03R909XfqJt6NY77j17m+61xHfqzZc1Tefr2ZV+QK13dVAhquax9hfteW8IK7wQbxax28M8NtgfhlhDCE+20jhpFchVzCibC9DW/ilpzDfUjh2Lly7BOa9yKSw7Ov/wyq178OfrUCWQsOclh/qu9awHn4rgO8g287sPPgrgU7J2TRe9nXOckJd7KUY/zBXsSxNofqa94xcnPWVeU9cFg4asELiFsg+IX4HRn1x57i9EmN+ec/LoUsEmwOMNMDznU9RXspVAco8ddV2q+nGB+PMBxTQc9x/ot+EahqVDh5Cdd1Tu6zd0i9i+xCKG9Yd3qS4JXU/QwAoR/0x2y7Ht5fcO28t7vew5FzbZznQEG67Zp2hk2PHLv7W7r6t3aj3+mO37qXEnEEj/N179++59m3r4KHjyeffQUggluuAQC+86LMA3Bt5vnT7ZHoZR4itx73FAKbpHEjfHCP8NK4TytETpGyvvDI+G/eJ8w09HHXc47dBJ78gp8P99Uvw/MS6HrZweH5epZqzJUBjAXmFmQaMJ3IOgwYku/NMNIW0H1ayTmrVMwqe9k5IZGui4QxymkllHFfRi7Bss2vu8Hv2aavHaLrbWuX4jqRIDc5rt6Fsf+25MQxom/2xW3KvkL8jozq0Rma8znoyeck1NPrgMNe1kOFQ2Ygiw1PleibUJ6vojTve76rtpO2N++pummibB4/hX31dfDzDzSm3UJIHTTslgHQcD3kFRLJs6F0v4bvctIOSI4ZueaJ65Pfzv1+n+aPcv9ORmIKIJmqgCNBDefgpI+w3ffn6Zy8y0FoOhcreOoFZde3RShOvd87+uiP358ITgrjsft68Pd4BZK745ix3In8GAqm6Ml31175OvLr2Xb8VIjnxvWWdJCXAo9/8beKJ0fHRLdcwi0W4PUa3eUCfrWEWyzBbRvJW04Aw3o1k/WeMPqe0O3yIlola1u8iPuElgLbC9UAx/Em5ud8KKC6RvX0KexJA79eA64D2rUQsPVa9KR2rcQrPCMxlrJ3AHeAZ/hgIPVDQh/GIE5lHKDy1AAgGDJg6tdhDVBbYCbTCDF0TlYystS/41REWbRQJK6uN/TGv5U8BoLJnZN1JZu+7Xri6XvdkL3b+G1TOIZBrm97OHnr+7/6oH0sYnVTkT/2miGoB13Xnp6+Y9yzQvyODHsyR/3JTwNVDawWOqDonGOsSn4oJsLAIOxwrNx8Wv4eLC9SVKBZC8j0Sr0ItESBZ594jxJFHbzZVvex7xDmCovtfdpHuNbkGHZb78vWD+CI3oCdSuiUAnwQQdjcXn/mc2BLwOMnsajP4HlGQZQ8u2Q7JQRqUGfH8/C49JiN6p7Zb9BjwxvGgXyG/M9weCSY2toQ2HsJ1TNJERwM/8aAmPZ9se4jpoHw5NAsXOaAbPZCE8HyGkgjB6Lpga4bksqu6+fRG7kHWwsC5IRzH0/pnoRvbxJ5pffOZtuvT24DiRzPy9tBQHeQujEyd0g4aMGLhea1V2CNg3v/Z8GtelG8KObUzFDNHoGa18DGgupGDacMd7kEtyu4ywX8eg2/WoHXa7jlEsDQU7jLS9gTw+H+sfDSfUJLAWyElwbsQwy3kUI5Zvjd3ody94eg+dhjmCevgi8syF4AxmqhZSvjrrEyhBszJD7eRQM6ula9cA4IRvWu7YlTkB3sR573MJ9QCJcD/OaYmJPHvq9hn317FeeBHOrctXFctEb+mTqSSibSeWht4rlUkhkGzJFht/dMBkLs9e8gKwPp5Ph3DIsNumMgpsxg1xPQMePgTaQjpe13pTVcj5BeDd4d/m1NnXMfQ+ahv/G6ZBQoxO/oMHUN+oafB//sXZB7BAAgL8ItKOqUEgMgIQE0vi0IBcJQ0SYSJXqwXatXhhcxbcucVLJMjo1eJz/sIxDTjf4pGSQCkZgiVPn2ZJ0D0QzkVff7lGDK3zJZtQ5W6f2JpECPTYkpkBDWoUVOQid7T9SGpRAjH/MEqaDZCcxJjYtv/CcBIlh2Mgedd/HZy2TdDJNaMcGg4CXzQnAokKHkugHI9vD+THjOKP8N+b3I27Gf3Ef5YDRFMtPtQZn3qVWWAS/hXORGnk08sRE+UxHA9cAyS6lnFIgCligRmAm9BaAWVA+4IOCDwHTy7L3L1pP8kClFINm377vRr0+Tzp3ey50h4yPEbw+COd7XmPA/1Lu5PxG9L1ONFNwemk+8hvmnPg76+CvwywXQdfCXF+LxAeDbDkAH3y6AFhJiRwRvAZo34CevgKoaHgZU1+LWJhIi2K7hg/ewbcV7uF7Dr/qQUqB/R/Ow0rHth5LFgG3hpbsqlwbkYaa72m/DXXoJq1ceYf3pbwK3a5huJeNuuwLYg3SJtpV11wJQOUIk+hJJGKcBC3li7rVqYxAjZRi6XZ+FGrdDLiB3rZIfB3RKlLo2hn9GOcE+7g9jewhJHSWGPP4eRYTt7AC4fjx0HnDD48Z0kdh3NKwGnVDJojXiMSILmBqkDoeBt1JJJUWCGshn4pxIz9WvRXIY59BNvZV632J6S/Bqxvs53B8j30awi7Rdp2L6MaPV9u9rH1IXom5u7/ssxO/YsAbrRx8Dzp7CuFYJgL7oLg/1lAGGdHAh9XQA2FDaKVfi8+0B6fEjyjmnbUz+UgbypQ3za0nPH8+3XQHeUDjzvojCVOT9uVXR58BMQxsiwAAk2n/StiehsbppTpBTT5cOcGRsHAz7Sp/9xOBx0O+C16nbGOjAHjQ/BZjxzD7Bat3CQsiegYc18hutESFmKi+n1Osy3ol1MKyH50osvye5Vybe26FgiKRQCWwgjxQJsz5XDuSSEcmness2yFq4o1d430Jfwz4hwja0BeT9T9eTvih6st3gXdz4DqJAHa5DFYTgfYQxMNYAtU7oFJ59zA/JvFaRKHYiqFywNLdg78Btq8aDIVkM7xEl/cjlTn8nk0QubLd28BspO46CB3Cb0cJOhHRm3rwNwg+APWXrel6Tt9PrM7lHknsj9kb/NH69BS8s6iePYD/+afDlh6j8U4mOCQSLPXixAHdr8HIB7lr4xUK+u0AM12ugW8O3LbAAnHoNyavhlSzM2QmoeYKZqUBN0w8xq1a8hgshhN3lArxewS1WvVcEGHgMw98bRO+qXkS724u400s4UbRmG3ZNHn6TqB6dYnXyKta0gOELoDao6hYgAwsHIiPyjQwM6/N0ncizQNZcG//2QXfqWpUXrciJGAqqyyBjgifOAqAKYAs00PMpiSQoiaSMTCYyhgHoNQzIUDQeBs+jhnUGD2TIF/RbyCPQ5x4CG7It9VYO1uWH6/fTv4c0Ig/YDyfu8vsYFYMXMuhRiWeSjInkkaqq916GbUZlsP4NMiArMncjwibxTvZhsSE/0yXEPNxrkcPsnRDoPdM/dldg3U3W9g2z3VbFet80h5vIoTwa8SMiC+BvAvgKM/9aIvoGAH8awMcA/C0Av5mZ10Q0A/B9AL4VwLsA/iVm/uljXcddw56eYDl/Fe1qCcJKPUBCYAw8iEhTlFWJpZiyLBHkGVkjyMcsS46eIYwp+9n22FaV/9jGZQr4No9Qtn/DqxSwj0coPy7/8LO2e3udQEOFH3q/kGya8oLloZSe4zPgMPAn4RdEBqa2ANVxAGQQqGrwYXsm5I28Rk46EByYAaNWPhOeJwkZC+smfc5ATwD19xh28boovj/pe5WSRe5JJJG+Z7K9TzeU7YNjENTwfj0lxP3N5P7ZJB5KCsLQyWBsgkU3CuGhp3srQczfs7xNbhiJFtQhmSQNhRFB3QEueeYBUbAmfZN62lVgkbXgqgHmJyBrhWxZm4SvspzbqSexWwOuhV+3gOvEqJNcf+qRo0yQk5K0KUHW5/gl5wbkeibIYX+ucRIX15Ow3UhiM4GUf5e5kBsTaNtIYXqO+18V+OooMlJQv/IEfP4YqCx4dan5XktgvYQY7zxoPgPmM8BWMuTYug9lU2Io3sIWfnEJtPqtAfDOwa9XwHKh3kMk3hoCNQ0qW4GenKN58liIoanES+K9eAoXSyGElwu4xVK9hkuIt0Pe2TSnUJYT2yfyDWVbRgp1X04MpyYgn5qvEDiA0B0QNnpVr2Hz2hMs6RHW9SlM/RgAYCHEzngxkFu3BrOPBnOrqTG2khw8Aw/jRYci9mLMy2RckBMUvHbp0jPgeo8fqYeR1cMoepPTcRzTepIP8hRKJiuIOzojj2GdDKCGyJ51Jc+dxfvIrhPjhhOZ4VdrlSdtb2hG8v5MeRnHCOPEmL2RGxkw2Z4RvKnsWrlPWZt+fQ8ilp5XczCJjBgqlSRGcjmrRX7EEFor69aATFIltj+hhIm3bSTh7FyU0dx1snSu3+c9glNh7HrD97iLpI2lMGzKxQlDzEa7cZl5HRzT4/e/BPBjAB7r+h8C8O8y858mov8IwG8D8Md0+T4zfzMR/UZt9y8d8TruFGbW4J3u43jeMog8vGdR/j3E+wPAknwthkTZt6TKPjyM6f9Ol4EQWFIiEJxX+rr37ZSnhLAIkB5L0dNEcTvHPgAkynb4mhPCqKGKITSRWPMOg0cmeJbCwBsJQVhPXtac3E4QwM3tfrMvAKPx6TmZ9RnpyAb1AfnIz7NBPtKBV0M6X/kk3nj/Eb72DDidyfOpK8asZhgCKsNSsMxuPj/PgCFWsuiVcIbB2CtxZNmHQPD6dyGSQ32P+v1hEJpoZ3jjHcjft3gsD9uRYX2fepIZDBuWPcgYWEhoMWkuAvkO7J1Yab0DupW+X0MCKCGy272Gu54ntj3PPNTaZ+/bKLkUzz13a6DbPIa913wNC7IVaDYD7BmMrnMYvFmUD9YCB9yuwOuV5KxkhpbcwhuwNRToiiGnW8NNDww13acYznSI6Qs9z1+RkUSwp3MsPv5NaJcLVNyBjEHlOyEA3sOsL8HOgdqlGE1WCzl0vQRQgWY1YCtU7IUYepalRsz41RJYLQHXwi0WQLcGL5YAWDz1QCSEvl0DvIZfhDl3HVDVqOcW9OgM/tVzIYV1I982EXwnCnooPhOK0rilrG94CLN11/Zh5Vf1IgaMVTDNsa+ieJWiEftOPTB7/RHeWTzCe88B5z2cF/nEDMwaB0PAzIoBs6k8DBFqqzoQOeH+6ECaRmHYiT4S1+X5WS/P17CLRnbS9AsAcV30I6+EcjieGlZPYwxrVO9iMEi6xMuoeYgbek6W3rMhc3SdrI+VTMnMAMiUUGSskEc7kvvoOpEdnRBEhOku2lA0pwPAgyiYWDU8J3oTZDI+3xFSOSkrJqJb9iGX4/IgeFN1Yzvy/o9GqGgbIsCKQYesEnSjpNI0QiqtlTbWSk7xINy1J4iRLLatzkPZRVIe5qUcw0FpD+E3HcEjuQtHIX5E9BkA/zyAPwDgXyOJt/tnAfzL2uR7AfxbEKH2Hfo3APwggH+fiIh3lpx8GDB1hUVX44OlKOCWgJkOZFaVfiYPD8CzKPsOXoxIkmksA1kkBGHwG3p7UrKYbk/b9cq8DjKBKGRKvOGUrEG8GTHskEDE0dNPJli7uLe8EUEdR3F79CoNPEkZmdNQxD7PLVvq/aEQogiNf0+VfkYMlQ19ptjwoIYBeYr4pR6qqRDFgbcLYv45OcM77zl8cAl8aA06BzQ1UGnRjEq/NJ0/OBLASouH1ZUQ+LoSe5NVYlVZIY6WWIxg1Bd/ooQsega8rjM4kkW5ZK/UP5DI/vj8vYqkMbxXGbkcLMP7ybIevJYWHeCByq8BsHq8gQoGZCrYZgYYI+cgI8I7CaNEuwS59eC55EvOCB/bLYI3F7pmKsd2G8GHejXzbZtt0K0T8ubVLsJ9+6pSYVODzh4Bj55KuBtIDCyt5CrRainCvFuPn5Myi6FhIPPoaVx0T35zr1x4GcJx0euYkMHsmCgb/XifvbcxEexmaCIdeOTRWz9f1JDPIiMFpqlhTua4NI+wquawvIL3HhUvUasnpa4NaGZQeQcyFgYdAAJ1ayWCSgzXl+K56daAbyUEDyQyqqlA9gTm/ImQwpAj3K4lfFS9hbxYiPK2uBR5o2OFlO5v4bueyPm204h5D6obWBhg1qA6eQpqlBhWjZBH9koMJb+Q1yt0F5dCFJerDVI4Sfz2JIbSZtwNsW/Im0mmoZgikRt92/1eyfrJOS5WwJvvAkwWtQXO5haVBTxbOA90joUQGiGGhjy8rhMBs6pGpX/XKhcr8iAjhlVJlxDdwamuYNhp0JOQoahL+E70Hgasygqjn5fxouwYYxEisigayhPdJxg9kwgYDhFYsciMpvf4xOMYyGIgj0Avr4L3aczzmMolayVGrK7FyBg8Xka3h5oNXnMYW/UedmshU22rXi6NRBkpfiO/Zwup29Pj2DfP5OzYsWH7NuPmxPq4h1F1ww7g9XKw329E/mTrRODgdbQVUAmJNPO5/G1rCW8NilnopgsEMZDCVvKY1+u4PT1Xft158ZaptI3r4Fgevz8C4H8L4JGufwzAB8wcAqzfAPBp/fvTAL4MAMzcEdGH2v5rR7qWOwXNGlwuDb7yNUQNqFZtPyj7dSVKfaODV50sKyuDSmUC2QrKVlDiWQijLoPST8w9ecS4Um+zdZMr91MeJPhpz08MUeSehKX78/YbbTTEFQYECY0gm5JHaUkpiQzbdABOP4th4RK12rCHUW+T71rAd5OEAux7Uphb8bIQxTgo2UqKDLgW7YJxdmpxUgFVZUCG0HYAd7JcEaFzfSh8pSzO6Mde6xcZ9tu45GQbb+5LSGIQglbJehVSGSF92OgBVPIIGWhTg4NT0ijJ5pvEcMzLGMhiRfLZV6pJWArWWF2yEEPrGYATKy0ZWFOBqhns7CQKLyGDHdAuwF2XFMtJLKaDZxLCSvu8OMqIXr/Ow2MiodeBdSzUZ4zoYfMdGQshTte5W4v3cMnJeRkgE6sZ4uQM5vErQN2IUrpeivBaLsSjEX5HgCrO0lVO0obEqp/mYZy8sffocweHx0wSwozMDcp851bKmLeYGWo2+nhh8EdQZKSU9T8/xQon+KirMbMzmIpAjQNIPC3OLeE9w7oF2HtU+ppXsKCqhrUNyFhY34mlXiv/0nohCvR6AVouVMFdAX4tii8AdB3IVrBNDZycAI8ei9GFIUUuWvGg+JWGkS4X8JeX8nfmLRQr/xp+dQGsAN+JkYc7J0ZPsjB1Azu3oEdP0bz+mnzXZOGdB7yDW64kjHSxgG/bvkDNag3wHtVJPcd9/SedK8cTnsCM+A2IYJ0Vk7mm17A6P8N61cF4RuctProEProwWLXQYiPA2YkYQk9mFpUBZg1LMUzNf2yZ0TkhgwQPx6LDeO6jqSpd1sarTPRqLBVdoU7WobJLDKVD72EozsYaqQL0BnLjhTDG9ZD7ByWPRCBYkKlgqI5jMhHBkO7nfqzWGwyZ0mIFWq/ArgO1CzE2BvnUKUnM9BMJuWx78pZ6FcmAADDJ1BWmOQdsJaqVrfprCB6t9Uq9hkpcWi3Ek8ivvclhHrkSf+qm93CqoNku7+Ewl348RDOPJtkVgjqcTsoDnZLnFUQOT7UNsJUagSpQVYPqCpjNYJ881fVaiTnkvqsxittWv30ZA7hVz/INpEFcm/gR0a8F8DYz/y0i+hXXvqK+3+8C8F0A8PoDqkFjmhne/2AFrBiLzmKxZjgvA9xsFrw/8ro2mmwdlP+q6pX54BkyiZIvYRCBJEq4oE1IYyjupDVQYIj1+2EtlKlkUUkiQjipfj2T3h/ijW15W5Pv30IqR71HGJJWYIw05qSyJ52b++RGkJGPrIITQUwiaEhYDeBkLiF0qzjImiz0cBi+io0wQ2Nr8OwcP/2T7+FHfmKFZi4JGrN5jZPTCnVFODu1qGvC6YlFUwFVE8gFYa3GwHUHOE9onZBGeScoPn9jw9+0sQ/oDUFhPYzpYd2MEEYixPcpCEQJSdX3K3gew3tFyk+Cd5HlHWPyADOcA2orgrJWYlIZXQ8EMNseiuHYJFwn5DhaNjDVCWx9otZ/Cb3h9QrctTDBUho9fLvDRnvBs2fYaCpIcy/hRNhobyTI2mc5iTxFJrsV0C7BS10HAVUN08yBpx8DmplULF0twMsLIYPcC2jKBaq+HJQL67wwTPj2rJ0U3LlXMXyvnOcsJMdN5RZueAePYM28bygysgdZAzuf4WvP53jzA8QomJl1aCoJ+ZtX57AGmFWi+NdGqyRzB+8ZhiUXzLIowpUVUtc0LYgMLDtUcICSQwCgbgU4B1ovwN5J2GjwFgIgJYYEAPMT2LO5hpOKQRNVrQUkAF4LqeSYY6jew/UKAHqv4YAgOvh2qQRRC1UYg4oq4KwGzh8DdQ2GGYaVOp2yYC2KeAwnVe+BW61j+OowD2uLNyXzIubbtx2TY4ro5e2bxyd4/uEl/tbfusAHyxqnM8IrrzQ4mREenQsRebayWK4ZHqIv1Uo+m0aWJzPCvBEv4bwRORaWVdRpxGsIksdFuh4IYZCBleF+G/k+HYMIlXXR+G4MwdahNkPf3msKDKseIbn8gTxKeCqQ5OoHXSGSxeF+ww6whKpyqOYuyjoYIzmPoQqq6yQEOpFrMWw0rYaabId3fY65cwC3ov/49bCNqUBNBZi5GN41RUHCTsVA6ddL9R7q+x7CTYEkj3bC2DlGGKfesx3exNh+TEZdI+R0eM79v4fN/R2w7gZexvy8IT2E6loIoa1hTk5QPX6SEMSQd7yO/9yzD3FdHENa/DIA/0Mi+nYAc0j+wh8F8JSIKrVofgbAV7T9VwB8FsAbRFQBeAJJYB+Amb8bwHcDwBdovp+56R7AVAa+6/DFL3dYs8XpnPDkkcWTOYEs43LJaFuD5RpY5QSwoqj3THmCliZT9sNAZgPBQtxviGOuWSCHVslhZUm8iwACMfTMcF6IYecQlcBtxC8NG0y3b3iIQhgFRsILJ9pO7h/JP9tKDgE4hlrpPACvXiaCJYKpZzD1XD5CL1Yu367jQGpMPzgCiPlakUjUM3Dd4GtffY7n7y8xPz8BAHRtg/VavF4fNfIAbS3HNkr8ZnOLWQWcngohPJ8b1JWEBa9boPPAqgUWaxPfhej5s/17E565bNdl1t5k744xlJBEHhwTvYI0PCbc18pQ9B7Ku0WoK6nHYEBYrBnMwfuky/Auh7C+8PbRcD+DIglkFs+vdR2Cd5CMha0amPkZyDv4dgVeax6QlwuN9moyYu0EYvw+KQGNpCNsD88zWx8QHdaeN3LlQvxtEFw5CQrtw80PBNCAQ3VdH6rNahfhmPAAg5cwCBAywPwUdPYI9OongNUS7vkHwPKi7yvz7DHCb8sElZ4j9bhxvJ5xT15/fRR/y7BZ0lc2T1k4Tx8+mvX5YqHISAUZA5o1MMZiVots6hzgQVh2Fq0DFurFbzR8uzbitZjXFtYATdXAEMVxueMO3nt0vIb3jMqvJMzcA5UqtpYrUNWgskKqJMzcinfH+5hPiNVSIkLWkhOIWGxj3XtcvJc83rNTUdaYJI9XLGHg1VLCR1dL9Rou5dtd9cRQQkmhE5JLCB7cIk5Qzp3rvYdVDTDBVBXMvAHOT6NHAbbuo8YY4K6Db9cabiZLt1oPws/8Og81m1Z0t+UUAvt7Ge3JHNy2+LonDs8vHd56y+HDZ3MAwPy0AQF4/KTCvCE8Oa9wdkpoasayBTrnsVgBy6VBXec6k/RfWalhcDqjASGUPHtJuRD7mRgnO89oY2hpkH1hKb/KZjpNFWQjUTSGBuNoiMgJnkWiYPATcsgaeuo5kEb1Kga5qwaKyq9R+VZl3hoxIsZY2GoGas5gTh8LGXNt9Gqz6yT0GdiMWHItyFWDbZHwVX64rtMbxefpuj5VwUhUFlsLqk9Bp4/6FAVm8Q52LbDWFIVWcvjjuxDP0etmORnso0q0zS4vospdsn2/0QCZvZOURb0EpAXNACBUuh5WRd0efrl7v1ojkn2xiJsSOkBsTGMykOoGsDWoaeS7vyau3QMz/34Avx8A1Jr5v2bm30RE/3cA/wKkatl3Avizesif0/X/Wvf/v1+E3AUA6jKx6NYOz5+vcNlW+ADA1z6Q23x6UuFsTnjlCfD0FBLucAk4VZiqjlCrEt/qOxEIX+eCd1DWc+U+7E+9OkFZX7swYG2SQ9nek0ErEQFSUI1kYOw8o229RsME8mWyPofbpwqQEHFP7PJ8sklPYH6OTYLYtxn3GgYLWyQUgWz4Dui8hG9oX5WtpBw4e7jVAr5d6T4dZMJk9aqhe1uDbIXnz5ZYXgoBAQDvvIT06N8AUHVW1+XBdp3HBYCPnqsXTIlhVRvMauB0bjCvCR9/DJAhPLtkPF/qs1bi13X6fKNHELofg/UxQhi2GcpIZVDmiYd9BA7is3bgaHw4nxmczQieCcv1lk87cIG8iQG2RQ0CmhvTiXfW1g3soznccgFuxcIWaRa7nuBNXEZ4R8L+KLMj2U1PrM8+y1nr92cEL2hGgc3F8uI9AQyEaW8CGM/BQvIWz0VgVA3s2RPg6etw770lVRJjPuCQxEUiFgRuJN/99j7vLl6I9hHuyDgBjIJdzynhtpnQHSGag/vyAqHIyAQkJGnVAl96WxT4ygJnc4OzGeNsJuPsugOWnUHngUYHqZUqjqHoR1DEZ5URD6FpYCoCGw9PDt57ONaCMd0SzCxKNYBKc4it92IAtDOgPkE1OwOMjdUigyIbK4/6DrSUfEB0LRheFXBIgSaoIbKqgOoUsDUMAxTCScGx+EyYqoJdC14uAediOCknBBAIuYAuFpdKcwQH+YJWQgxDaX06aVCdzrWwhRWmpEUs+jzBLhJDt26FdLbrfnvXClF13ZYwurAevmc72GdPZvC+w9tvX8C4Gp973eDN95ZYtwynsrFzEinzTiO/eT6vMJ8BT84qPJoDJyeMzhOeXQIX7TAiptb8RC3smkRR9TKRCJjr+zarGU0lBLGyGhHlxQjROsnRN4H4JV5CIKmvENd7Y3ZY2mAwDSQxpPBovQSr3kWDMC9xh845OF/BhXdUZzePKRJuDXjWAjYtjJOwZVPPQPMz0PkrAFiIV9cC64WM2Z3tc+DzKJdAxmxYz6ZYcnnag867GOZeBIYypmpA8xNQyK0lo8RmBV5e9qQwHLcrNSIoq2HaoZToYUgIN8ggZe/mnoSwNwjTSMrEUAZOV7TeJtfyfROEMB3yteIrLy9wDNxkfMjvBfCniejfBvB3AHyPbv8eAH+KiH4CwHsAfuMNXsOtg8DwrsN62WI1Uk75cgF8eOFhDPDzv77CBx95rML7WhF8IIGZgSauBx1rBxH0Bgi53lFZj94bFRTqlehCDmAWEmjQh1M8Oqvw/nOGC+Vs9aNyGA6Ofe6XXu+IBzD3EsY2GSHcCCPdstxGCtP7wqrNh+pfYTAwOpJY36LTkr/EjGp+DjYG3LVROY5epRBWaCuwMfjU6wZf/vIa62VmQUL/UbtA/MK6Cj2nhC/cX+cMVkvg4lLfh/cMmsrg1ceE1x8bvPEOb4R+bngAhzwiGgeiZdP279FGeGhuWKDhek4IiQguGaier4DXH1l4MNpdTpyMAEqSum7zyba0Lfr9vnPouhbV/FwFQJ/75n3PlyYJYPQSZ/tzr10o4Q0MB+WsDYBeUGYevjjkRgFH0UtIQbbuIoC5YAjCqFvDf/BVoKphP/ZJ+Hffip7QiMyLt+GJC8pgUkVuXwIYwBgKbyLTF+OZIID5uV4SvHwyUqfHWa8Zy1U/Jl0uCR+ECJgKmNXA+ZwxrwDHBpcrgPXbqaIiLu9MTwRV6TYetXEwBphZhjWEuvFaJEsrbXMrnhcvnpcKqmx7JYRqIKywhq1OpUoxhxA8D1AfghdC79AKeYtho50aDJUQBo+haSxQnwFVLaGkQUE2FmG+UB/C6ZbqPQyVf4NnIITVhfnNgL566IA0OjFu8hp+7YA14rcWJ7NXMs7GopobkJ2D6VTJo+1zwcLUGpCxKC2Jj07K4ftWC1m0baw+6dsO9ekc7brDsw8X+ImvXODJowqf/ewj/KOfXiahp/o8NTqma2s8fwZ88KHcv6axeHRu8fSc8NqZwVvvMxZLGT+CwXwsRSJdv9Dh1xqKOpU1BENSjK22wEnDqDXtRuyLLOkY+grbTPYZw8m2oP9Mk0Qg1ZNk/6yqUVvCvD5Dhw5t28GQPOvKyntjq94rCCSEUD2ExoVUihp0cgp7+kTzVlfA6hLcrRPilxW5m6p5MDYv4kSag/Tl5TsI3wQAkAXVM9CjJ0DzCanO4Drw8iKSwbyS9mShmC3ho9fJNRycI+7nLWGgEx7K0HDEY36tXEOg1y2OgKMSP2b+ywD+sv79UwB+yUibJYB/8ZjnvU+QCaMNrLWwgSDY4B0jnMyAV58anM0J7z4DPEycRofUOiR/6zIq2sPt/aCjy/w46rcFmMzjNyyW0ivzRj2As0pC+CoLLNeaAL2pC4+CNtXr5Bje2ebY4FyJ3+8o/Td+bD5vzBtvLrFugVkdlOt+QnmKXrZsmSR+p+3SZWWBR6eEJ+eE+Yzw3jMePN90ztRtyN+hMUw5sm4bMTw0sh433J8/E9YJ6TXkijBsP4pwI27iNQw30u1xHfGYoWFhJwGcQteCLz4CzU/FMotNobbf9UyEtdKQAE4fP25hfZlRZCQAzSNOxxrm/jVpnfxbrOX9OZsDZzP5TD+8BHysOSQdOB0rKu2wMh5O/25t8MBA98l5G2uEEFYe1hAIkj/Yah4hqXJdmbYngU7CR62XdAHjvYadz4UQzs5Bxka5xu0qEkP2Tj2FXkLvupAXlYeRakEOz+K9Oz/XMFII8QrQcvK+XYm3sGvFk5hMdM+jRLAfCzibZsK7ThLjXKrg8iD8bKD4Wiuar610rtMK5mQGsqeyvZIy+TCVzkYgpH8+t/jkJ+Z4dum3GnpyJ7dnxsXC42IBfPxVwideIXz5nWAolTaU6ThBdocoDoSAi4qjNTMaRrVNiKiprPw7bWSKptoCFytgFZxi4Qwe8Po7gvEzELywPVQM7bJIqCD/ufNYdsCqFU9kUzWobYXVukWYOjHPoe4TdfR6w3YN13SBGBJg52cw1SvA+lILF0UFUxbZbxrePQyVh/5myzKXcbkiwqzzdF4Clx9KOLOtgGoG8+gpqDmRPNblBfjyIzGexN+oBGtSJvYGTMoNo5mXMK9w3RtlMWwXjZIeUwXMNjx8U6kTmadwiBEr9kH7r46HkRH+UMAMOIeT0wavfMzgjC3mM8KjU4uTGdDUBssWWDnCuwsJR3j0KLVWDa1Qsi2sy3Ks2uNwe2+N6r1dso9Czl8yp5xUvqIYq64/BM6LV6pz8o/AmFUJwRyp6ghs5vyN5QZOFYTZO8QzWw637RfqOajQla6zQ2UMqKphALjVArReyECUJWj3VjELcg6PXn2CRxczzE4ld6GZN6hnEoMSQjiDNTMN6QSAujKoK+BEc/xO5hLq2dSkVkfCoiV89JF49k5PN3P98lDPMP71xoJAOhGXUwaE6PWlfP9QsKa5f32op5dQTy9e0zrm6mg4qxZ1qdTFZZWk2bje9duSeZmA/nnZpJgLWYu6PgFr8jkh8fgOpvrw/TaMWDd93o431/OCLwFTU0METFQDRSgylLSZtm5OnGNgJSXQ+RPQ6WP4t9+Y7GsDY4JpimBO9DE1R+BW7FlEouAFgffgtsPJjPCxJ71iqY5AEGTsCYXKRD7JvzY4GPQV6UKIVni1QwSLJ1jVuFofxjENmwuheHFO3eA9lBzqxsq4WddyDS07tNzBOQ82+Xg1Uq04Xa8b8RSesM5rKvObknMyXY16CWXOQgmdM6G8f9cX6mBA5j4FEi+KA2YWpjkFzs/EK8cAbB9SKlPBdODVpVR+XK2UGGbhpOkyhn8OSeEGacy3ew9grV4Sfc4JeaTPfj3sqzW+8ZtfxZNPGLz5zhprV+H8SROLodUzlY0axlTPNA++MZg1wPlJhbMTwqwGWm/w1gccPXlVPR79kkeyHOI0cR46zQQBC/EEfuwcWDyjWNgMECNuLHQcSZr2oe9knhLj47o09OmYKnVBsLaMk9kcrjPy/k3lxIeQegoOhnawDq8VadsWlgBz/irgWvjFs14Gxrz2fHqHTEay7atix3fRxH3psVEByT2DIQelWwLrBRgfgI0Bzc9gXpPCxv7Z++DF8425lGnCI4jRImm6vme46EY0SpK3t6uA2VTxskGUjhnKtjx/cWNapMzQuxlOenUU4ndk+M7D1BU+/5kKa2/EU0YGHy4B02ooS014dDZMTAbk3Yihm1nY5RjR6ytRaZXPEDtuZKAJgjNVzj0DrEvSKp/MjLVDlKDpIFUpgdyWs7dtfYzcHUrsdpG6QZtMad+sppWuk84DZGGokiU7qRi5eAbvOiERExUjw/0i3wG+w6sfO8PbH1WYnc4AAE1TRWE2m0vxlvm8kuVMiN6skfyUMO2DhywdEz64RBy0JB+PMJ9PE7y8eMsm8dtc3wzZDPsygpcQvjSx3Zi+cFCt76+Bx2IteQ+17QlfrOZJYRkskj3hk6XbIHw2L6lNgKlqWDsHewe/vAC7rs/VTInhlqk7APTkY59JeDeqemY5ERNVPEcJX9h+VcKXom5A83PQ/Ax8+RzurS8NhWGGQyaH3zW57wYmKq9tPabgpQAzw7cdjPV4/Un/Sss+4SypfcWzEL7LtXzzTaKxTEWspMp9/7qFjUEZ1nOaoKCRRKmFXOxICAl1VcMaQmOluiO4g3OsRWUYNpBKNWiZQCp1DBubvsYwS7n/5jwJH7Vg7uTbc2v1FsrStMtoWAaSsUzL+DN78e63i2ERjUrmeTP2HHhs+qkrmKU6aSjG0WlYaStzr2LDSzgkemEsCOGl+RiWhsmxZ5hZBU8VvvIu4b0LC1QnODsXo2jTVKgqSMXrGjg7sagrEtlIomyv1lKg7bIlvH8JGEuwFU0SPTMhI1PjZ25ED+9NkGNNxaisrNdWXpkPF0EfGhpL8xSXAIPDx7tA8BwTLlcO57MKnWsTD9+Q6PXRMVmUTLoeCKdr4S+fwVY1zNlT8PP3pU0oWLfBYAN5Sn5Hvi8Sqmz/Ru535mFjRl/kzAOXz+CffSAe5LPHME8+BvfuW/Jeb6QE6G9PPILbvIHxHMAgj13aBcI3PA7o82A3onHy1IU8Z34kDz7m0cf7pufbKHRG2Xrfx7Dvq6MQvyPDr1eomwoftfoBW6CuCfWsJ3WNJiIHYtbUOkm3DjakXrmoYCuhCxZRQIQcc0/mwIjz+nFYdzLXTfDq59U0ofl2BjJtT+69GxRkOaKX7tDpGwLidu8n26UTxlNcJ622ZWCNgUwaLuXqyXmw03lTvFMvlVev0RTRG5ILQwB1a3zdJ8+wwBznpxVkmqgKs6YP922dyMfOSX7l0gGLpQkFswCo95cAWxFO6qHgGvPYAbu9dqlgC/P9xb8N1LqeTNuAntBVRm5k/t71ChrrQMZgz1h0QvQsAY0dErs6evp6gif7Ny3nA8JHJJZKa2FMLSHR3Qq+W8JfLuMzIYx4Y/V5Dp9blr8wMU3H6LQO+fQNOVHaMvn7Rl/a7koevqoGNSfA/BSoGqka+PwjuPffxiBuLmCipHbcfQ3Ct6vU9tgxxdP3ksI5+FWLxkpRkviKJaH4KRk0kHGtNmIUk23j70pK+NK0BSCReRnCeX3cL4NliAj0HnCaq7+QsA9UxsBaQmMsmsqAyME5j84rEdRxzYXqkLoeqpBa3w4NWR4wIXyUnYx3sEDVwFQnIKMlQIjEA+i1yIt3QLcGrbS8fy2es+gdDEaprgWH0v1r9GGlMJLHNz+FqeqYphKK2nAXKpOuNYx0HQvYRKU0kszME+jcYFww1sDMGnzucwavLKWi6+mJVQJm0TkxkLcdC+FpgYtOZGMgdcaoXKx2Gz9NlHXyc+qqj3Cx1BvZg2FcDOmBvErYp/eyXLSM56v+/YpTbSXvWG50yD18+XvYt9vUuXoSyTiZVei6dRbdNBGJMQFGkpseyGK7hJmdbHwVPWkM1zUklYN6KVNu1I31oJjodbu0XWCButEQwA780XtgG3LVf7YvCHMF5ORsigDm1ysEcEi6pghgxGTOek8ij0UAr4NC/I4MXrd4/SmjbsR9H+YjkspOXufXk7ZE/eTY8g54mEDcwjK2GZIdS14V8mniZbJjrkO4NqZJyMhXbDvwtJGSRkoGq34QCpazyCyEo8VJUGN7StsHFhIOGJ63NxcHdqLz67hOY99FYG56f8Lv6oYeHqAnAlOeoNUSZGqcvfIIP+eRDJJtF6yKvTfX4hCPriwjMVMPbkrShMyGdVIBJAKDkr560qZEDQBDJmsHh3evn8gdzOBgWNBbufGuEMtjSwRcqM5qc48eMqKXWsKRhG+G4gkkwpmMlTAa9lIZbN2BOrF8k3cyJE+QukH45j6evKSvaTLnR4nbYH1iXr8NchfAfkcIJ4FtJUpdNQM1c3mZuhZ+eQl+/x2Zwy89Jw4jeIP2I33sCuEcTYoftJ8mgBsJ7i9I8cqCcfjOwV9e4rX5Mzx+zUmBMRbvhmMaGJUcGzUsUbRlMPoQz4Do8QgekSvkc+cVAFO51pNDDR91jNYBKyWCUkG7QlPVqGopXLFq3Yb8jZUfrYfR8PfQZiMFYSwVgdWZYmqYZgYyRsZKAFJsZqVz/sm8giFsNBDBMJcbZeGkcE48J94BbQgpZZlL7OwUZJ+IwTSEkXoH7lZAGya6X8s0GOi/Zwkb7UmgmdV49PgUr7bAmRP5CJLnTJZQQ2TgmdHCo9QTs944iThvcZR1MappSLQI4fQcVQJdjVFPCG28TPEQU8E0JaaqgGbkndioiRD1mOn3aJP4DXWuPuS4w7xi1JVBZYD1egVwi4p6OVpxFg2Thxj74Xx+ht0gEoaqGvbkMfzyeaLb5MtEn0rXU9z0WO0k7w/NSSxqtM3zN5UHuDsXfZx4Sf+bnrsUlJHaqaJlg2s9EgG8DgrxOzL8usV5oyWkqZOqgjqHC1x44EFBVkGQTAxK4J7Qke6jabIWPRzKmqKwSciikQbhf703bED4AjnryVo4hpQq9H/36Kc4yD4uJjBYCWJGyuKg4iI5i/sRpntg/Y97xVuP3TxXsp6FeMZfzZyFbU6QO9+H8+1NDMDAySN87KnBOx9JOJIx4r2tKy8WR4tIxsS7y7FqKiWCyyQCJCVkYvNN7gmnyknqdQNAakmPRrQxqyLGPbl5HPoBHt04xx+GxC6GbUK8rhUciAzE+WpggqLmOIY3UbcSqzGHCdqdPr+RMFtgwzuXeu82c/jC8xvP7cPUcx+Un5724MnmPb14oU1ViRevqoXoVTVgKmkXSmAvnsN/9G6vnG2bvDYXyiNVxgbXHVdHyN41iF6+fyfRO4I1s+D+gtsO3fML1N0z+Itn8CQuGUsWrOXrmSyIAK+TmDNMXAIAKJgSddxgGXk8IIo9APa6LXzqLCwjkEpGIJlhTEW/zLAh87Kxj1nmmvNOJvw+m1tYa3G5FGU1yGSfzA+TzKAry1gEZBhy108EPpKqwIBxDiYJiyfboJpXIFsDvgN3a/DqQib+jkRQz2El9CgSQecQ5wBKqjiy7/qx0HWQCqQSNmrOH0kJf02Y8usV0K1kcvv1SucedTDWYjYz+PirogYF+Qj0Rs4QvWRIn5s+r97E2z8/Cs/SAx33RVOmPGvps7M0JGtTzzVgyms81j6tdZDatIOh1MR2oW+d5kGrz1oiwHfo3BptK6kmu3JJ++iYTcIHAMZ1MESguoGxp2DXwj//YNQAPpXvPtCPpuRlvh4wafRM5OkI6OwJaH4KfvbeZJuHAjJ0cFTLJuEbrl8HhfgdGX61xqm5gLEt4FvAUl/EIg4KGkoYPn6W+F/ikEtHcYCPlqTcK5eQqZ5gAcHCFb10wYMT16XNMJHCxWPBALEmlLOP2yJJi17B7GMPSD56in1M7N9G4NJzBEwWm0jaTfWxUeBjC6mbGti2KfvLBZ5+osPHHkE9a0K8SUlYnK8HXkkoxzayKYg2vW7yvcCAPs903kPCKPkaLCf2x3uTkLVt+ZPpscOpNagXbPruEkiKGFC/NNwnjrNvxZLnvZZD9+KFhbx3gdxN5eNtI3iD9fRZXYXgDdaT9ocSPACwMtGyVL+TKngyAbNOvuy9FGHQCdr95YVY0TOCF7DVm7eHJ2+wPa5vevWuQ/BG+0T/nu/bR8GLBe46+FWHk9VHmFOn1nkJ72MvA6cjC2aGh9GlfsahEIaSjEg2QggXjMpLmwhOmfzdk1EjZT9mSZsQbUIJEQimsX59iOF6P7YzvAdW6w6d8xvjbbwHyfHh2qM4132x2mPM49LfHomfphCQhQkhpW4t4lqrOhrXinfn9IkUf1kvpPiVkUq/wSMYw/iMi16Ifr7SUKgjEL+kYEfXAk6iDWIREFMJETx9BD5/CqprNWxZ1LWDbYXoScqDyrNgxA7eskDOKOTSpYZKxPtDScSTUWLVPxNkMjIcl7RJCR31bYIO1rebGpOGuk66PTUKhN/UP2OOxFXaMOA8utaDyWlxsyRCxu3Id8/WCZBqs2RBVSV6VrcCt0v4C3lek4RvIv1hLIJmUi/aN+0h2RdlDVWgU81VX17AffVnMl11eK40nHjvQmY5thgbjyWPjtHPMWVjIX5Hhrtc4GMXPy0vpmt1qXOfsEMIUwsK6cAr5lz/kgevl+6bJlpbyFL+Qk9ZYqb2j/Uz9THtc+y+x0xWHtzy4u/6yPf10KTnCUo8M2LuQ1xKiFKcpPrtr+DT3/A22s4lHlzXE/isyExOxszAo0qSZ5gKoxEhI2SL+t8UBHgk54jbKe0jkWub5Lo3KPSN0RsRwrbwfoYmHEJ7vOSHsJcQo5Ecu525dcyHhV+m+8cG/0O9cwFjXjrvxSoe5t2yFmQsWOKQRBm1ieIZfku71smQdXLdrhVruvMb38jOcMzs+tL9txmWuXG9I32MnuOAvgtePLQffIT6zZ8Av/+WfksENHNw3ch3VdUgMuCqkX1VDZABV5ocbyzALOZT9kIE2asXj8FEShp9JE6BLIZlv91k67ofPfHaqKYYQ0v1GP1doZ0F4vQRuzBJDsO5MFzGqpDBMwju56SN1yn7wty0ruuAzsGSgTl7FUQMt7yIc3xSpbmBro1/x/D44CXcmM8tWx+Myx7oFnLMQq4M6wu8/oWP8PoJAJVtMVcz95xNyBniPhOTkqxM2RcmQs/vJ49sg8qFkXGJ0Z9v4tnk2DBgb2s70Wfow/puI9w39+CRF8+sAcfUCFnXd9e1kgPargDXSohvqFuQz9sXZU0WCZXL2XT7Ltnr9pPNzB4gCzRzUDOTaR3aNXjxHP6Dd3p5m/Sxc36/5Dy5gXRnFMwB8nRfOXqdaJebNIIW4ndsOI/q2bvwH30txtTvVGJHQhUPJm07lMJhn5Qsonuq3x6QKq+D3dSfL92WHMea45fvihsIvcTc0g+QkhMabE//jnkdg/iNZJtBJGuBuFG0+BoM2NDAgygEPSrwwevjPZgl0Z7mZ+CLD/GULuCtk4ptCKGzPl6C/Jb0HdC+uN/OSrAG3tbooR1uG0wvgGSQSj27mTd4YD1LryfHVbyv+3rhJi2GGcFK2+zjjRtpx6mAj1ZFeeYMqOIp81CJmVkJXSgCZCzUdNyfwzkpj+6ckLm2UxKnOaSu66vfZfdr22B/He9c3HcHXrrpvsYI3+0Jt4L7ie7Dj8DPPgLVZ4Ahmc6g8yC3kHGVSAxGBDW0hGILqthqOWxbCVE0ukQtE6GzrcXDHhLAiMChcAe8kkUliSyBhE69jYE8pN7Fvlw+xW3SV+aVy/0+2XxmG3OPbkMUr8PvI8+1H5w/u868uqP3Dn51CeMd7OkjkQ7duh/XTK8OxivNI1Hy64zzlXb5HsSiHk4mnj+hS6xWK6mczUmaQSA0uczJo4u2tNk49R5kbDRv7Qo4tNjKKJgBItgg1438DTKgSgyNYUoSSUiUf0MjYh8hMiB5wdmg+9LlaAGzcI5B+0RWT0bKbJHFJLnqqBoJMa5nMMZoKPIS/vmHwOqtaaIXsCXSZpJ87TKkHpDucBXCl/d1KOG7CSNpIX5Hhm9b4GtvSvjFulWF0Q9JB8RrJKWkKX704pdJhthIoEYw6WHbMphFshE3DPbxIB8va8PJ9kgU++2c9uUZMZo9sdJtktd0Penf8/DaBufnYft4Tj/czmkbqTgZf6P3So4CmesHs8FHua/HpW5gX/8U6nd+SoSd5glGzy5GBFEecnoA0d8QWDu8wJPHbSFvm9v3eA+vEiI7WE/asZd7yVCSxuplUxIGyf9BFb4T9EoIGfHEGjOqaoX5qsgJcZc5pzzYt4hhl973AtZ1iO9Q/lvjbdhPAGwjdcf01k212buYyhbP3759bzv+kLYFLxbadz9A9+Yb4I/ek6qS1gLNDGY2lwm/60ZCoZsZYBrJdwWkHSt9CoVIvAfTQtaVMMJYsJZ55JDHlpJGMiDbSN6grQFDaGytMlmMQGHibRhA0gN1HjVmePj4LfYhqck8azkhzL13CSE8iAwmSAlPTpx2ERoG4L2X8M9unVglgxEUCCX5OXoW+2PT9Y253Gx6pmD80imN1h+hVoMZSOsRaIpLjEoJBmhC3I5kP0KUS3In9EKzX5kVwBi9J/ve+11jkp3Yvn//MaLGO0m1ab1GjHmNGOuLgKV1CgiYLkKXGl6nZHAkcXtOS7Qtz13Krooh1VZga/XbNXJMuwLaNfziGfDh1+Sc1yB6gzvoeX/PXn/Q6PaxKtWTsvcAQ+p9IHwBhfgdGW6xxOJHfwTUNKJgDjxFEofM0Wqi21xiRRmQMx5V5vbBbStQDyVUa6+qgTt+y9i9regEZ299CVhebA6IclDWSfgjxHMGz6Uf91oy93/n3tGNY9J++3be+8G5+qbUtwrCNu9r47ARgTvwxA5DYmUZiFsFgOJ8rxHpLfJd/GbAvv9GfCfPMIZPeyVuSuBCaGe0ivZGhENImKyPvAc7yNhUX/t4yfYOpdzS5z5et63n2jJuHHzMlm+tEL+XF8s338HiWYv2Z98VAuM6GKueDQBGSdvoOhkYmcwUpplJeHUzA1mrFW8rUA0pUsUAkYwDBFGqY7GwYKALQ1acD0Dz5RKyyRoBwFZCTiVXV9dDtUv1aNCA1BklimL85WTpKXgYVQeYII0RG2Nyf46Ynz0WEghIoTdr9V8tlZJbmauW0nZJYbPRaKQxZERxYy43AIEAzr72RZj5eSQxxH3KSxzLB+u5ch30ob7vSZK767oPCM88KvLnGjBG2neRtV2G1th3Sta2GFtHrgeQCBg2JMYYU8m7aK1+B5XIJ/aS4qHeXb+4EE+kpntIl1sI1a5olz3SIY45/dDGcQdGyuwV2bNnX1PtroNC/I6M7tkF2ssV/JtvHszYj/lgHwoRewjY57m0P/WTOPumzwPzx8kANObpSEIAx7yYg1j1ZH8q92MV1LCBEtKUD2Y98RmQIE7aDg7Jt/H49kwAD0NSVZnxrhcKAwOIHwj2XYRstE3cPvGej+ZvHDig7kGsdl3HdUjbdc69u68rCJeJ33Kdca2MUy8fuG1x+VNfhH30GGY+U++AAVkLdh2chrARe1k68XoQ63x1StJMIIa1evWqUK3S9qRxNhMvRCCHdaPksAaZBjSbAdAQ76iEdlphWKpZBg8jEeQbsKI2BS9iII1hEuw4GbbmAptKvIqiNBuwrXqSmISVS0hqCBs1gFbZFoOxjN0yzAaiyOBA8ggydRAZAEaqJUMOF8NYB7Qr8PoSvhtWfxzkYGfh+JM5YBueoBGimI0XZvEcWDyPFZp7wjIcA7ZGwewbAbVrXLkr4rcL24hfbDNOXAbPIjW8JoWOQsRMeDeZdHsSJRPPySz5gk6jXryTOY81tDT+i6ffQahGrndneGZ+L+KhmyTvkJSI9LqOOv3QXgbeqxG94vG7x2jf+xAdV2g/vNh8SW7Amn2VPsmMWw9fBFz3t13nGa3+wQ+HTg7u70YU4DvynkyGJ4f9+1zXkYwk2+7rTu/vtmOvSNL2uq4bPHabF+5Or+vAdgUPGxdfegNAT97IkPxtK1SnM1Bdw87noKqGaWrQ/BRmpiRRSZpXYhiW1Gn4J1Z99eGEDA7OF8iiTfaTATVKDJtGtjUzUDUDbC2kkUi9goGcJF4r54UgmuBNTBRuYEAQAzmM0ycEhTusk04IYEx/TiIYStZBei/0t+q1sJcQOp9XbtTcrw3vUkL2RsME0zY54ZvKBXOp91C2mdXleB9THqyAMSK4K90hIB9PQjhriJKJef7p30Ag3HrQMFImLmjY73URzp/2R5r+E94HAsAZodsgH0KQpV6A18gX+TukObBfyngdvLxd16fAJH0ekk++02sX242Qu33Taib2y285wIO3zzmx+fuvWpBla5GXI0bl7ItC/I6M7sNnWDxzuHjzIyn68IBwG4TQ7Fvu7Jq4id+ya+LM6ktvDjfsE1Z6kziGMHoRcMTncF2CfsigfZVzHSoUbuJ6DunTu0L0XkY8e+s5AMA2oXCLiWN2kBFxPYZ8JtuNEWJY17BzIYpmJuvVyQxUSw4fWQJ3neZNtcBKloQFoNvBHM81Fl66EXpaS6j6kCRWgK6bkKNY1wBIikZp7jCck+kfiPv8uEASQ14z0IeehjFcvYxTZHIUB+SNx/37kq998rXzNs8+HO97qk9A7wtFz2jwUBFM7ykN5JjE0wkt3LNx/dk/jjn4PJK7FtoBkeSnkTmsqRP9CTbv1TbksjkSZt9H6aTXi+R6kxSI4X2cOO8+ETSHRKwcSNKm2m2bK7Y//2Hkbed5Bn1fpa+rk7RjpUqU4i73FM9/8qcx/+afD79ub/AsiTVqsI5ksOg3czp4ZfsA7j+CONilB7NamtLj+n2DY0eW6QfGg8EMMXQFUKNnfsxgyYNN6aCYXvfgHOFaCTHUUXIsIQPUHoN0T/jc1nbrZ5c7+7oJ7CKkt3MNhWTug+PM53P1gf86578qSSvhnAU5uqUYRV2r89FZ2iR+keiFOeKy/Rc6H11GDE1lE9JoxGs4a0B1A3si5NA0J6CzGnbWSBEoDTNl76XSoHoR0TmYthOPYnTKbclBROJFTNeNiWGmppkpabRA1QB2puGgtvf8AUoMlYx5rQ1gqCcm3gGDgnBXRCoDd5GJsdywdJqj4KU0dYg/jd5K8k5ywFAlJM1I/lgSojrwZLGPzyVUspR7sZZlKBASvFWppxEjY882j9XUb964X7vHs2uN8weOl1eJXNkdGbI/Sdk33WLsfh+SZjHWfq8q1BN97lu0bOo82/s44LceQvCIECvYXwOF+N0ALn/mZ3H55TfjAz1EYTrYIzYICeiXNLY/mwaBsv1kw0S4WTGPLFac8v5iv8kkuNkxUVinIRWxnWx3HojVuOLx0AiH7HyU/FZr+kpf2e9Nr5XIyHHG9H9TsDjvd9855Ml5DRVRIbx++6tYPVvu1cch57sK7nvfxySqt+VBvinctMfrmKTrOKS1ePgKgG6pZM7272fu6QvyaMMDuAcxTENIgdWAFA7aVr3HEdaiOpXwUvEiNjBNDZzMhbRpwRcGaw6iTOfiWXMSnRDEMLptI4bx7yDDqmG+ICkRpLruSaMxMuVCqJZYz5RUhnDRibEwGmozI3H8FPMxIpGhnod6w4QnLRbVSsmZFvpgJwTWaPEdIWsdJAfcJ4W4krDEkXNt8xBNhh32B2eruz1Zfd+HkZOd59l90IHNd59jpxzYFca/5ZoOJW/bjrvJXPn7Vugs9qHFcqiqQbaSb7uq+221RhSo86V9+63J69oXhfjdABbvre7s3Ka6pVDKW1S4j6ncX4W4BAVEVqifJiD8bS1OPvdZ4PEn4C77PAZKQ07SEJNsHcl0E9AwkuC1JErWE89mmO8v9WoOPZ3cT0+Yho3cwv3a7GM/oneVcx3j3SjeyuuhkLmCQ7F+b5gGQXX/DQYZlm5Lt8djbEYEM8I4tm+TNEpbWw8NgGE7GUr2KRGtK1BTwzTiRaxO5qCm0fUzmEYnRCcCt2sQPLhdS5hp18mk2kk10+G1D3MRgwcwkEdk4a/iOaNB22gEHYy7pL+ZEoPuSJhoOCTKjbgyjq3FoIbKcZgbcW9v0j7enH0V8D28TYcUCpNruYIn7QBD3FXH1SulCByiGxwp5P8YhPU6hcOukw8/2betJHqAjJA4a4Gq0r916hpjYxSAdAYpotN1QNfCr1vwcgluW3DXauRBWkTn+vK2EL8bgFvcfmhTEIquvV0lLBfON4EQYHkTpHYvAtvK89xGMp79/R9D88ojye8Aeo8kpRPFq1IS5mzM26QhM0Q6A0IQ5gZUUe/5NNJnfxz6dWOG66E/e0VPG/tIPkOuOWNIMlPSGacr8ZKvQNjchlDRK07VIOvMHob69v28i+Pvtb8Kkb+B0NhCHvdDIYkFAOC77D3oeEOGUTf8pjgnfip7cvlwmBdR2vp2nBCSIbh11odpASwmyWL0IloJM61OTzQHUYiiOTmVvMCq7j19WsmUvXoR4XXic6c5iksx5mWeQlP1CmS8jhiTmt2vrF2yI/lzz3Fsagy9yTD061R7vqEqz1vbj13HxjUcn3BtXsPhY+5N5qIfxUO5T1973lsGxICvodZhCWv1b9t75UwFWG1jbfzWOOhITvJ42XWxGio7B7+8kG1dJ/MEZyHJO3/LoF3J8buX6D66WlEXqq+ukG7PPjs+Iglb3PKJR3BM8nkVchkVkPWHu9te0UN1p56thDwaG6rKJdbllFwGL6jmapAxoggZ0+/TstJxoK20rbVakMHG0uQU5w8av/a4fZR0esBzXIIScurcsH3yd2wfySnrIE2yjTmpnMbxfLfyLAoKXgC4xabEyoldLlum5OOGJ3DEezi2b3T/hBdR9u0bempG9w8qmGZ9hEI1pmlAVQUza4BK8wGrM9i5FqsxRsefvsQ+OiWHxKJUehcL2kjkxzjhy7cP9mfEbjhp+mYf++CqY96NFKDao2jJIddxnYrH+xx/SF/HOs94B1chkQdeL5GcJqbkBGM29VNOqD5BJinyg+SY0Ca2Dbmm6XWFaCw1PnsXq57CO7B38Gsn+aRO1uFCzqlW8d0gb8f36N5EOkghfvcI3D6cIggeVyepR0duPcbVvYOpx3RvQqnn31BcxvoYudZtiFbwa3z8kWxeo4+gqLgr1Cs6VOBfxTNpLA3I5ZBoZoTT2lgZMBBXCvkzhobtCUI81ctqqlQYqdc1VpXb/3f2IbyBqPaTOcP7Phw4lL6OXs8k9DfJhYkENIRn6bZ4SUn4MAYhw8m6tpNw5M1tiMWVOJ53tBDUoE22P2kzuV7wUmLDC6gIY+CUfNyQRSPew4CreBHj+JlFfkx5EcPRm8RQ58IzZsNb2K0mjhkJPQWRzF1YVTBNjWo+A1WhuugcZl4LeaxDEZXkN7pOlNVOC9m4DsRaMTKZmoLbDnBeQlLT+zNBHnNsG/Nvwuh1E9WMj+F1uolKyNciX1lkUW647WshhMiimLGKvniPGmUHfSE5LpkvkDLZGGTqjt/HcfqJ3JDbRwKFdW67vk1S6Md3oRCQvtduu3vkpqtoH0rgbrIoWiF+N4ApAXZfcRWSdF9JarAKH+UZaB/73p/RO7LjOnb1vU/o7k6CesC9mLqeQ4jnhldzz2ODsjOY5H7XuUwgxsAuv/dVQ11vxNuah/gGiyaQ/E1RgG4IaYTco7At8b4iIbNysp7QEvpw4aRIUl/QKdmux9JAcCfbQvt43OAH6ubk3MNdw+PDDu/x/O//vUNubcEDBI+Ma1Pj2K6x3IyMvMFAteElzMb0GFYazp2dKyWGoc0GWbTjqQDbwku3hZYOftskAVzH/eupUNNsO8jI9BRNLVEVtRSQMbUUkjD1HFRVgDEwGspmKp26Irku1vn+ouIdlG3fzx3HsfLoZgQGOvmbwlx5e1bXvincRKji4PekhkJKjJMJGYoRMpE4JR6u9FhjpOvkmFhrIIz9o5eTFMyJhsIQ2cJZzYAsmiWJamHfR8tsRL2kx/n+ueZ9s7u+7njbXs/brGh9G+kQhfjdAMaE2n1EEGQPjahuRXdY0Os+pG5fLxd1hxOLqas9iIzvGW67jwfz0JDhY+Rd5lb2mzt/qCR49Wu+Lgm8kfklr5q7uSceeuXUgoeBq8rNrapVJg96wpe1mwgrTY/eFTa6q126PR+DthWoGdufbt8VYrpBAMM1jLSbajM4LxnxJmphM1lWfVidMTC1jX+TtQDV8psSAgNro3eIkjzDfTxCkmueFEpLK5VG0pVHHYyDBufMDVn9OsXtiSEMeyjqMeUgkK2eEHHXz83XE2gH5jZplxznUyIl6z7ki12xgNtev+EA3ERo4n2rTn3Mvu4i770Qv5cYd0VQb6MgzL64CumdIhvX8YLmluljkvE+VOr4z/sow/GBntUUGxb7A84XcMh5pzyfe5PJLULxqgRrzEN6TIK5I0KmP+cNE9CCgjEc1Xu4Jax0LKQ0PSZgyos45j0MmPIixuuaIH5kzR4hpvt7FXeRw7h9tc7abfa9My87O3YKG+NiUiwt/E02jSoYErODESptD8LV+/D4QWj8kXBfiddNhhveFOG5iX5vps+7i5orxK/g1nEXhPOYZPMQUrYvqThG6OxU8YObIJEBx3iWR/E8H4E85tdzW+eP15EJ7GtNmTIh/G/Se5cT0FLApuCusGtcOoQY7soxPDSsFMDeuYaT+5O8w22kEBiGmKYI230yJuwbcpq3nyKdw7bbj51q5ya2bztmH9xEdeer4qZJwG14lV6cc9zMs7hPFa0L8St4KXAbZHNMmbjVMFoNa7rJuRz3CXs9uDrtEb2u15nOZCrP5xDcxNQjxzRa3OTUKFO4zTk/Cwr2wSHyYG81cN+wUiCGlo6FlKbH5hgbC/atXDoVXhqP21LBdKzN6P4RErnvsVPtpvrb1nbXOQ7p4yq4SVJ5l56ifXBfCM5NhJzui/tyD6ZwbeJHRJ8F8H0APgFxhH83M/9RInoVwP8NwOcB/DSA38DM75P44/8ogG8HcAngtzLz377udRQU3DUOUSZuMtz1OmTzGITgut7LfYjjTfzG6xgH8ud5DMJ/EyG6t5nXG6//DgXwfUCRkQ8bx/QeBhxcqTRgJLJgKrR0soCNYtKLCMCo4uqDN3PCmxhIbSRYg/YTRW+CpzGb9Wp3hdDN+Rmn2+Kgc+zT12HYPffvfcJdkqSr4L4Sq/tOygOO4fHrAPzrzPy3iegRgL9FRH8RwG8F8JeY+Q8S0e8D8PsA/F4AvwbAF/TfLwXwx3RZUPDS4FBl/rbyIu+aNALHzZUcw30njQHHJI8BN+H5vsnrf0FQZOQLjKlvatuYfdWpK+LxY8TwGhVL8+vdRgqBnhjG9m4ktHMvcphiO1lKryEQusOnk9hNHvfvazfY359w0oeAh0KcctxXIjqFaxM/Zn4TwJv69zMi+jEAnwbwHQB+hTb7XgB/GSLUvgPA97Fkzv41InpKRJ/UfgoKCkZw06GqxyCWd6HobxKbmxUck/k818E1p/zYhn1DX6/1/G8g5/VFQpGRLyeuMmZfNawU2F2xNB+zNsK+k8qmh1YsHW+X5e5NEK1DQj6nCkgdGja67ZjNc+4/Zm0S0cNrVZfc6IeDh0b4Ao6a40dEnwfwiwH8NwA+kQiqtyBhLoAIvC8nh72h24pQKyi4I1yVWN51hdZjVmU9BFclmFchjLfxG2/CYzmGl90LWGRkwTZcNawU2Py29jWKjXkPp0apjR4mzjkoZDPhcYxtp4paZduNpckCUlOVh7dVGma/fdyKBGyPMMg4B+2+JZDDOUZIJR/Jdnmfite86HhonsqjET8iOgfwQwB+DzN/lE7Yy8xMRAdJfCL6LgDfBQCvlxo0BQX3Evclr/EQHEo+jumlugphvC2yCFztt16FNN6Xd+E2UWRkwXVxjKkr9iGEh+QYDo/b/5jJqXim2icEbIocBkQiNjLVDSCkbidZ3EH4Uo+h2+H5mfTiHUAqD8WhJDRFKch1HNzX/PajSAsiqiEC7fuZ+c/o5q+G8BQi+iSAt3X7VwB8Njn8M7ptAGb+bgDfDQBfoPn9vHsFBQV74yaKlNwGbnI6jH1wk1N95Lit33pXc4jeFYqMLLgpHPot7TWa7KgQnYeTxvFly/iR5w1GxFDUjOhlzfYZO0MfU5Rnrz7sfuPtIYTsStNNZDmSV8WViOM1zzmGEsJ6f3CMqp4E4HsA/Bgz/zvJrj8H4DsB/EFd/tlk++8ioj8NSVj/sOQuFBQUHIKrFFS4D9gVjnVTyMnjUXMUJ5D+1pcxty+gyMiC+4R87LxOEZq+z2myFMaaXUalXSOSa3nn+L5PH3JNW/rZc07W4IHcyzumbQ8hYceaF3XM8bctBPbGMBK6W3A3OIbH75cB+M0AfpiI/q5u+zcgwuwHiOi3AfgSgN+g+/4CpEz1T0BKVf8rR7iGgoKCgoI9ERS12yCABUVGFtxfHBI+GrCTxCWkaVfEwt7EsKKd3s19YyP2GfX2IokA0PHehq3JfMa9Dj6cPE4hkMq79MJNhhMX3DiOUdXzrwKYent+5Uh7BvA7r3vegoKCFxf33XO3C3ft4boLQnfXv/m+osjIgoeG6043dFDI+I6w0oCxkMeNcW5PQrpPteOpojSj15b1fygOkXfXPdfo+W/B+5YT1qn8y4eAhx62WjLCCwoKbhUPldTdNbG5bTJ3F7/3ob4bBQUvM67iNcyxb1jp8Lz7eRN3nWPsfFcpXHbVwmE3OvXHPue/gSIkOZm8L5PEH8djej9+y1VRiF9BQcGDV7hfJlJ227/1ob8bBQUFt4/reg0DrlNwylyBHk0WrtmGK5BWOdf0b9snxHRfTBfpucGKzHdcFG0KebjtMfDQqqAW4ldQ8ILgoSvohbwdH7f5Ttz18ysoKHi4uM4chlPYRhr3Ha8O9Srue/5d13LMuXWPOb3PXVTnvsn5X48yr+898Wbui0L8CgruIR4iibsPiv9tkbfb+K238Q68iAS0oKDgxcMxwklTHFKgZhv2mW5nl1w6hNjsuq5jzqt6LW/rHvfvPkzxdN9J5U2gEL+CggNx35TY+zK4vCges5t4vjdxvTf9Ht6X96qgoKBgDMcKJx3DMQhBn8N3jIw8wY1Uw9xzCotDkIeR3ri8utHet+MmiPNNohC/gpcad0Xi7otS/aJUf7zvZO2+X982PLT8hYKCgoIxHNtruAvH8iqm2JdEXkW2H1L85lDsQ9Jv0oMbcBNy86Z/27FRiF/BreI+vfzHxs16oe7GnvXQQhrvK1l7aKW3gTLBbkFBQcFN4hg5iFPYRhCvo09cpfrqvpgiUMeUxTd5/dtwG79tXxTidwN4kcnNbeK+eMWmkA+eL3K1xfvmpbvO9RyDOB2TFN3GnEBkH8ZkuYVsFhS8mCh60WHICcoxZXBOCo9hWL7Z65W+X4R36D78hkL8bgD3nbC8zLhJz9mDDue7wcHoLknadYjEMQjZTRKu+0qSHvrktgUFBcfHXepF9zXXahseWgTRfY22yfHQophuAoX4vcC4q/DAh4CbGKTG+nzoXrkp3LXX7Crk4hgkzFgCqP9HAGBMv57uI71G3U8ggJDsN3IfY1/D/QDpgqa3IZwz/MhsO9D3GRpR34cxoX04dnDHkj6SHTRotLl+HXiP5z/8947XX8G9xLZx8ZiV/gruD25CH9k33+1lMsbfd1IXcGzd6KEVfNsXN/G7CvG7AVSPy229Du56kL7qR96c3+5zv8vwvSuTtoQUUWV7gmQMiIwQj0CU4nYlJ8bAVoFkSVsio+31byLAKInKtlM1PEf8mw68j+zlPMyAZzBY/tZ/nPwtzX38e7QNMzjZJ21ZF2HdQ07DyrNCW9kGpP3L0oOz8yK2Y073xR/Wtw1ttuzf+PtIsLMyfr7o2DbGX7f+YSGO9xPNq/Jd7/t89vLSnWwfu2/rXbgNj+J9J1/3/fqmcNf6ZsBtFlErEvYGYHcMRgW3jxsPNzQG5z/3m2CaJlGUBTyhHNOUp4QGjSZ29PtorH3w5gy2jZ/kpr06A+8cBwLje+LEHvBebltGkiJpSsmS9wlx6kS4s/RDRMO+wdqnntMrqdJtYL+TvNy3kMpDVIxJz6jfowrZxh9j6/fr3hQ8DIx5JYL3ZtdYvUvJLt7E+wk702c+k8WuSa/pADI1+VwPkPvXIW924p07xvv2UFJIHnp4ZsDLUMW6EL8bQBzgCm4dN6Gk7wwrtBaP/vFvgWmfo3v/bT0oIVYg8ISizZsscfOcAw/L8Oi+SeaF2TjdhDdn0M2EJ+eaXp3bDOnMrzQ92hjTT/YT+7VXvLKbx9Rvn3qXDuv72l0UFFwZwTg6UIwr+RanFPBjEMNdymkhhjeHaj5UN9mN+3bD+Gab4Xa/jSjuSSYH/WXvyRR5m8Je78o98CY9qJDKOyRd983Ae5MoxO8G8OQLn8LJN3wjfNtle0bCs9KQMMcDJXvoKcrDrmKjjfNvepj2GKCOJe9uIPTrKDlEJP8beLdGtqU5V4P8qTRHKu/aGix/5mfQzFo0571G3Svo2+7JsE8elYVTv//lGah24b4WFDkGSTsGbvI62B9vcuKClwPN05wE8IYinivWvjNb9weMeYr2zQW7zptcSON2nH/8DEA/XoQxKSd0k0bSZJyZInhT5HDb+LeVUKZ9TBDVfc9zlXOmuAticl/k6kOpSv1QUIjfDeDsm78Rix//h+B2PdyhBEOIGWn+UVJcIRZfGLYnUELm8hC87OQ7SdL0/snQw0NwzHFiYmy8yqAZ8rCMpel8p7AthhX269pw6zlmr31s87w7BMExFPLbULzvC4G577jKu7kvjvmcj/PeBe/04V7T8j693GjOagD998LObxABrobvSE7owv4xz80GacSuXLDd3sTrhJjKOV7ud745a0BVDd91Eq6fjWf7EMGpYwIOIX5T4+k0qZx+h3aNZ/l1mXpr83uHl8kb9jKgEL8jw56foaoYzZyAucQfXF/5Twc+d91L3In7rpRtM0LtVLxzJyy01sdgYJv27m3D7Mk5gMwyufPZb1fmD3oW1/C2XpdU3PQ781DIrblGH7t/4w7l9QrnvpIRRa/zEGtwfm33xZJccDeoT5X4tSLPvDObxM8NPUMh9C8nhsHsEIgZtxxD7MK2PIzvEGJYcg+Pg7PPfhL1Jz8D33YgYwaeP3YdfOvA3oHbVpeyDu/BzoE7p6TRybvhdd31OtEuIriNRO7qIzdvbRtvDyWVBQUBN2k8DijE78hoXnsFZ48rnHz+43Fg2GWlGhskJgeVPZX7fRTlqyiKx1DAj00SjtnfdX7fo1/wLUDdiCBitWg6h7xIiaw7/VsLj2T7+3/TQm3va546bjyudOu5+j4PP3ZSGG57p69JjK8inI/Zx17v5o5v+jauc58+Dmmz7znvu5Gp4Gbw6OseAQB853Xp4NosBLALpJAH28N75dbTIYM5aezJZB4+uiu8lGPlyCly1pPL8d+6T5jpyxAs/eQXfAvc22/Ad0sAAOvzZe8BY8EGoMqAGwOYmSQiGwOECs0gcKzonFSCNibaxtP0CvYeHLyLzsm6c9HbyF033KZLr9t7wjm0Fm/qb7xl33bdL2AfZf+hRAdNn/tux/rbIFRTOOS334Y3uBC/I6N6dIbmpAFOP6WWKw/2XV+tkPuQllyp3SccYVJJvoIivu/+sevY3X7/F/2qg9GVBpI9iPNVfqs5f4zqE58GX3wARi35guFUpKG6aUgvo88l1KkBKAg1oG8f2g1+QnZ9g9VAJhnDEFWW+5VMFwAGEAiotktzTzlvC+6tq4N9fd/MDLhOrsF7ed9d/p7n1z/y3vN+AnPqvT8otOeI39RVyPmuY6byGza8aKlJeuM3De3V1yGs+x47FgK66fnb2lXBC4on3/ptAInHj10Ht1iiu1yA1yv45RJ+vYZbLFURHxLAsF7NZL0njLp0HMPyrupFDEjDS/N9uTdxkhjegjfxvoOqCtWrT2DnFrxeA64Dt2shVuu1yBUvBEueL/frzgEOStiG5D+O3WFMivsZ0PGHjAGYZAkIUbRGjLTWADDigQxtjdFje8KZBgBxeg6VcSF8NZLKhGDCO7hW17uu92JqhepANsNvOFZ6yPg4vT0s/zrk7JjE6iZIIpnjk969r/OAUNni8XuAsCcz1F/3SdD8BLxeJQNHUlQkPNfcOhRLz6eeIT9UwtkN9suBPlPqIQNqNr8YQpu8qIx3icIv/cmOVKn3SPPiIo7g8djbG7GPt/OK13NdRbf53OfB5IDTU1AUQi5eTySBg3uL/naG7bF9dj0JEaIgeDYIYHJMmGdOC9jEdYR1/TOdwBtGzhuniUgJqhzX56dS1nc4rQETeo1+TLNP3tFIPL2PAhEsoT9BqKYW27gt7A95mtssrXt6PDfegQM88dcx0ByLRB5CdgOZnDoHmW3fw/Zqo6OPfEd46F1bgwtuD/WrT1FVjO7NnwGvO8BYGGPRNBXo7ByMJ6CmAZMFWQvXefB6BbdYgNdr+NVKiOHlJXi9HvUUTnkJg8cv3z9FDMlOEz9KQ0uBjfDSgG3E8Bi5h1O4T2SxfvoI1dNXwfMZ/OVzkLWRnBEgHr+ozjgZ1nWM564FnBgIuG0l1LPTqJqu1aWTNuqdC2N7bmSXdQf2rRIvbIzJA2KH5N3JZYzKSoaR32BlXlpjjPyWygJsAFuhVs8kgyK5JF2XATN4MGl0zOYY3hrIpBpVg2HEB13RR1LJXRfbDGUsyzHBOLsFB3mqbjGC7GppDVdPL5iWk4f0sd9vtTvSII4hKwvxOzKormG/8PPgP3oXppvLRp94SQAZ0NL18EJEImB6hRtIlPigvJM0DJUmCf3+0N6HDpOqlD7rs9fW0Sv1ochLWmiGhv0P8t9yhXvspWQgen0C4dH529LiKYM2SjrjfeLkPiXkCazCPCXCCYGGkOYw4AmB9UAY+MauOxcacfME0WrmsGczLL7p20DGglwLgEGui8SG2Mkt9J3sU9IShZx66Sgl9gCI+99MG+9LT/xTUL5dl5Tvj/do8COz9eE5aVu7AWnlxDs4JLX9OVksrkxAZcAhhIdD4SOxwvbvuYyyZEwSBhROEd6hRKCptz0oCXCqNIR9mi/SW4szK3KAH3lPdpDGXV5Nnnr3svON7p84xyjZ3LuvHdd/wHVsbX+ksNaCh4vZJz6G+SdeBb16Dr+8BLpWvHxaBdu3LYAWfp14fKoafGpATx7B4zGobgBTgeoa7ESW+MUC3LZwywW6iyV4vUJ3uQBcF0NKY5hoRha3bd+XLAYcEl46GlqaYkeY6egxiqnQ07tA89pjdJ/+ZvB6CWqX8N7DdCswC6mHd6BuBQAwIbSSaxnjvdelyBUio/qIeq+CXhLbII4zQn6cGBlDNEpCJsWo2Ml4HMhk26r80FDUTAbmMoAD6QJ6j+NAhxCyCfSyhrvhe5eeYzSUNJBJ6okjE0lobBXCX2uQoUgmJQxWZaeGxTI4k5/j9QzCXLg5yQy5lek+7lxGOtWLGe4jp2SVh79rAru8XleJoLlKu0NJ1nb5Za/UZ8AxPYGF+B0ZZC26R6+DT5+CXAtmLwSAWde5JwMxJC4QFz+ttO9S+vP9Nt/H+t4FUgTkShjlyvwIGdqwEO3qI7aLLcDCQPsqooGQRpLpYwgkBdIKI54GSo4LHDgSA5JrTj1RSPab4MFK8wbCz0gG7uh56r1NYlXseoLjNRHdM8yjJ2DPeD57Bev1GhYViAim8jDsJR1BibNFIFBy8SbcCyXyweNm4n0MBDAltkIWicWyR9ABNQ3xjN5hJNt7MhzakA9hNa4/T4oNshYuy2+2D9uCoSMQv7CeE3c3NIhQ+gzUOLLxPeTezo1BlIFIGI0KSpKww7pKrK52GNITJ37X6/VqQXZOCKNz4K7tvZRtOyB/GyFH8ZbsJoqHkMT0WAphO2PENKxnYcJBV8pJIm1c94gyMEn8aLRdag3tf+N42/767u+8igXHQf30MepPfz344iMYZlBVS1XPrgW6Dry8BLdr+OVCvrnLS4C9EsFOFEp3CRemS+ociAjEBqZuYOYN6rMTUN2ATQWqKiGHzsEtxWsYwkrd5RK+XYPbNMywV7J85zaJ3DW9iLZJ9u2qXJp5EwNSsrcr1PQ+oHp0gvXZx7CmCxhzASIDy05kosp7o0uK+pGOsSG6w3VAu1KdqpVxsmtVznnRI8ZkDhlRASzJeE81CDORLRoGKsSK+/BQJUkAeoN0IDGuG+oFzimJlHBVVjIkBuaubxP6wDh5BCAkaqu3socU+tNoLcak7IlLyDvLI2369aQPIjD3nkiYIE9Nb4QlAzQWMHXczkR97mWSo0l6/Ma5VScO9y2Ex/Z5l7rNJcV9dJ2DnL4nJHJbhettETH7XM8uT+AhKMTvyLCnc7x7/nms2g7kWxABlkQwqWM/KvsW6YAHmCR0zcTQyl5pZwAmKuo+WUIHOu4JAveWGSEGQCgUskkuM8V7jMyNKfrA4d6nbW0OJZVjx21tE8hTIHWISnZPOuTZRCJaEcC1Dmg60DF60piEa/z0848DDBgSL6MhDwOvlyD9W435NORHl5bkWVF4DwJRJI5tKLxH8dieNBLR4FgCyfMHgYhlXUNA1c+LQDxVfRg8k9AXgnc2MVLIdcrvI+7zFky3AnsP41vpI5BKl63nhDDdHt63jBz2bTISGb3G4R1PiOHg2Tr5ba4Fuow8xneHhCEZAtUGqGeiEJiQD2J1vTdAcLeO5FCWa1EQWlFmgeE5BgJlh6dxQ/hMEMFt4a2HhrVuI6bbPKLTfUyRxh3XVfDCoXrlCdrXPw1+9BS0uhBvT7uGWQMwc9D5KWArVMyArdSYZ0Txd130EvJqKd7CywsZe1oZX3yr5BBLVcohpFELiOBxAzw5AWoNJ63qfnhZryV8dLkAt2u4xQJ+uZK/V9p/N/wup4rQbCtOs4ssBuSkMWBYyGb8m7lKqOdNkcfZJ57iXXwci5phayHZhkRht2jBDBiIYdxwB2aGhehNFXcAESr4hCw6EBkZ5xNDaSSNGspJXvoSEskx/5xcl2wbRqaQdyIVg3yKZWMBkAVRA4DiuaERUUS9TiBkUg3R0cBMCHpASLuJ9R+C3GjXMg1YlCWdLMMxyXXG92kPUpl7JAOmSGbcPyJbZPxPI2WgKlVOIjMDSbY9glTLMCJbSeUsjAFqAzKznjjqfo6eT5HFEiKbfhNhypC+Smww5HLb6X5dd53u20YAd8u4FNvI5u7UoeNX785RiN+RYWYzXLgzPF97EDp4ZlSQgSQo6hXJhxqU+qjAwyfbepImpNEBIBjLutT1oKhHRR6xr+A96gM0tYBIGhKZKPzBg9R7jIR0UvQe9dugIZvEXnMTU4W8X49IyeUUoTuUEKZ5bxPEc4OwZiRjgzymoX+DPlly6wIJ9v0xxAx6/bNYrht8cAHMaoYxwKxi1JZhCLCWNYpRB0NiMXSpXZHAelu9CkHN60yIIGWkMRK8+P6M78/bpdv7v/t3cNAmhonS4BzRiSoXK0SUPYgIdnYmQjBE4PhWLXQS+kHdCuzdJBEk7+LfPEYK0+c6QQwH3sWMFObew17gjzx374deRh/Cj4PA1GOMAUwl1mVbgWpRXkFWl3oz2hbsWhHu3VqU1XYtym8uhEOuZe6ly71x+l6QtePEcuSY8C5FQmiGYShk+ntI+T6E26PHZl66GDIe5Jf3MbdvU2gNhVwp9vKCgwj2dI7u/DW4+gz2XBR441twtwKcA7ULWS4vAd+BnIQAol2LktfUwPwE5vwxYCuISCRRkl0HXlzCLxcxhBTdGrySPvpw0gWwWsTKksF7SEziNawt6PQc/vFjwFrxHqrYc6sVuG3hVyGcdClkcbFQZX5I/MJ6WoRmfy/ieJGaUPmPnQfC39m3lReu2QV2vOFZ3Be7SGb1+AyXbYP3ngPeV+gcUFdyf2bVDIaAxvq4JCLJmQfgSLxaBk5TJhgGQuiMa8GeYdSobVmu31IFGII16g1WI7tNDJ0hCoeiQdwPyFggjX1euXggKSnkInqBevoQ9AyvegT0OCQyKEnziTn0RobBqgHNGhiGyBIgjssAEvKiKQpKEL0aHRHzH8M5ElkwElIa9+VtkcrdTaJ4CEkcnDNgxKDZX0fw4G32zYP1LQbD4Lm1kiMcyCLIgCoLmjXq+ZX8SwrtYmE9H72N7Dqg63TakE6K83SBKLbw63bU8DlWSCZeYyYv8+8zl4H5bz3GVEiF+B0ZpqmwcjUuWwCopZhPxRq5KA+4o+AF4jigeQ5KteaCBQ9R4vEB0Fu2cmVeP65ecVcSyWNK/HBdBk0Cwaj1LBBGRoiQjF6j8DtJBtCAGLoYWaYwg95Ll5DNgbeS4YOiGLyZg1BFjveJ0uMyArnVWxn7xqZ3KQ6ESWhhThCmSGNYJwLmJ3jrXcYHF/2HW1cGtX5hlQ1LjkshhHJ/KyPrleSHw4Z1I2TdGoYxQyInd5Xh4nvEeusCmdRQUA5yZvguEXiDNKb7ZPvmMYNl2peR+1L5FmDAqsfPegaRhSXxoJnmVEJ94MVY6qTSGXVimaN2GUkhBu8GNEeyJ3xstzxHoM/5A4AQVjWZYztFEJNvbII0Rs+i7wC37gfr/By2AmwNYy0wPwWfWZCpAWtF4HQJKVytgHbVX28uOCLpTN75QPAo+w5y4mWy9qFdchxnx6RETtYnchZyoZyEVG/2MUUIC15EmKaGPZljUT/C0jew3RLsPWrPqNiDqhkqU4OMFVJobO/FWS/EQLJaiEK2EmKIdi2dd60ock0Fa88lxBMUQ0kBgFcr8cavhBi65QJoW1QrmWKgzysURY/WbfSohH2AhpRaC3p8jubpUw0rlcGbnddQ1RV8u9Kw0jXcYqWVLDcrlU4VEtmXGKZtAnKSGNtNeTbqw7/DOMfdjqqF9SuPcbEyeO8joGOD2gKnVMEaoPUe3gPLjuEYqODgWGSP50AEgdp6NErkqrCtUZKo8ohJ9CfHnQ69ShC9ptsomTPsYlRVGD6tRvsQVyBDMKbuDedk+lDUNLKGaGBE5hCtot7E8O6i0zSfxOMYjchIDJiuE+LpkvkJ09QJ0kIyxgJ1DZrNQHSuMoFApupDVBOCwq16D9cr6X8tJHHDULgHQZwMDx3Nb0xfljHCl50va9u3y8+5STI3310POA9uVwPS6DciaEZ+D5lIHKkSAy7ZCmY+B6pKyKSSxpTIcSseWu66vhhR18K38gy4a5P7oDp+RuQ2fscOongVFOJ3ZNCswdvvEf7BG4DX2ztr5MHWOgA3kmqEpmJYiqlHqG3vIaqs0KpQXY884Jk1jU00+UAQvZiawNyHEqZKfdyWK/UZEYgk02y2n/IibS4DmZzyHLEQECIlMmKNIw1FhMYgUkoyQ/4bdHcceNGfQz9ejp5Ip6SHY6hHIBdg3xOIYO1LBu8NkjFBCPpQkAp0eo6PvvIcHz7zeHRegwhorUFLBusOYBBaBzAROgdUKihrtbAarbIYiKLRwSAQRmu18jTSpXgWq0AeA5nUnO5+Ke9NIJ39festaaTvE4PVCOEjz/CchpUm9xzDdyi8Z5WR+1brsrKytFBiiEAIdQkDsgbWMowxkooRyKxbaXiGFgDQe27y57bhNdwk8hvewgljwFain3mWQ4XVnEwSjwtDWfcilLsVQnhsRCVKLzUz4PQcVM/EJ+878HIJrFfwqwtRchU0IpSjKMkE62YuXy4Ee+FMMRk9UxSz39R7AMO5D889xJYqogUvDqiqUJ2f4kP3Cj5YecwrB1MTmrmDRQfvGQYtvGdYXoGZUbMQu7qWib9r7gBjULMowiZ4YtoF2HuY9aInie1axrdWPH5Uz0H2XDyKtkIFEu+Kjie8WgOujcTQLxfghfSVhpIC6AlhuwT8ErxWD1XnxMAzs6DzM/Crj6UIjaliWCm3LXy7hl8upSDNYgluV/ArCfVzSjLzPK3ci+gdjyrBaZuAnETmGCjRO5TL3XlTw/2zp2f4ygcLfPge42JtcLkCyFos10DdWFgDnJ0YVBY4mVWoLDDX2RYqG2SQRL10XuSP872OUlu59kplXW38QCbWGnFTqxHeECdRVZquoEZ3aCE2QxomGgzkIVJrYDAHjHH9395rhJbqOIDqLl5N670uI3mpgfgF/WQNapdg14FaMUaQevB6edXF7SwPQ7Y7iSZJJ7SHtSBTgeqqT1GwYaoK2xvW236KjRhe2rXAei37E3K3kV9/BfKYvyNT5LHfP9FXfvzIeTf6mFwfa++Bbt2fd+LYqBcYCR2HMaCqgmlq0Olct1VC1oNn0SUksZXoH27XMgYEL+7I9ZojsLY7I35E9M8B+KOQCOo/wcx/8K6u5ZgwdY31ssOJZay9wXINPFsbdA5o6kAAVamvhsp9XVH8O3h3Uk+RDGBDJT8uVckPx5kQSsiA5z7PTBwTotznhLD3/mBje+7hyT2Rk0RwJDyxPybrg/PlNHkEeGN/fyzFsFYhlARTibvfxDw9Lc6iBTuoW0WLYE8GK10GgheUZ1XyA6Go50A1wwfvXeJHfmKFZi4xNvOTGmenFrOacHpWoa4IpzODqgGa2sB5oPOEtmNwR1h3wAoGnvt3I5A8Y2mDFAZLUTAIhXWTEcT4zBLCGI6z1L9PlLxHQsJkfz14r8LYzFojqJ9qpGOx3DZKar1eiNeBzqrgjOvqMQqE0PoWcIDlDtYHskggO4OtGpCxMOzgXQe/VuFIvfCVzoNiZOOzi8WAQjnnMEjHnIxA4nQ9enJduOH99ih8M89aYMQ5EYzv9rA967q8i4kF0HViHV7LHGbxzTYWqBvQfA57/hioagkbXV3CL54B6xXgTT+XX+hTv+ZI+PRliUQQubALlVPT7cPYE0Z2v2KzrK/kuPw8eZ9TeYIvK15U+UiVhZnP8GxZ4asfAU0I97MGM2tgDHBSVzCG0FQnYugkSZlw3MF7D+uFEFZeCGHlPUAVmkqKStjZmXhvjIENOV6t5PuZTseN1UK3rwDf9t+r68SQd3IqFn7Ip0TW9nlBy6XIjVB8ZrVUpVkNWYEYdg5SnfQSWPekTbwwDFM14sGZNbBzqVRK1koFUwbgWXIVuxZOCaJfrcBdK+GlbavT3AwJ3dT0A1PEr1d47eQxOaZK949WA3YMezKDa1v8+E8u8eGywumc8MorDc4awsnMwzlGt7a4WAPvk0HbpQZziSQ6mRHmM0JlgJmSwkaJnCULxwArMVy7nhgyA5XpiSEgufRWx/U+2ka9iFqDpCIPsiR59yCxviPoUwwPL8Z4dkmhNc1fzMnhIJw03a/X4wEyNWxdwVYzMf4pSQwh0Nyu5N3tlqKrbCGE+vB0KXKLuRM5AWxGtYDkva/E6Mik61QhRuV0nZCg9UoIZruWYkzR2KrvV5ZnHw2CuREyXFvSZirkNMq1Ca8iwW72ZTPDZWZc3FXQLM2N3Eh3MKFNcv9ki0T8tAyseqKYehXjMSTkkOpGCGFdw5ycoKpnoFq8zfL8O/B6Lc+/XcM9e4br4k6IHxFZAP8BgP8BgDcA/A0i+nPM/KN3cT3HhKkqdK7Dm1/t0MHiZEZ4cl6hPgEcExYrYLEQpT8o94EIiucvkMGhUh/XM+Xd5Mp80GVJvYcGqA2pMm+FPKqH0RADDHhV4sESdtnmxDD1+AUiF37vSPjgYJ37gTbs3+015Ik+J4jiCAnM1w07wLEWxwGs75QIVqDZDGZ+Jh9a139gRJqDZoYeI44Tw+qgXtfgusHbb13g2ftLzE9lGo923WC5kE+snumykWVVi5JzOjdoasLpzKKpgNMThjWAB2GlhcsWa6BbUfJOZMvwDkSjQb49I5HJ/p4Uhm3hXg+PCffRqlAMoai1YZARwthUjKoWhX+xZnhW4hcFrCpEOkgGAsgqLGNoIZu+jeZTChHsJI/QVqhmZzC2Atwafr0WqzsQ/cSR7OngqQ9s+BxjVVk32A9SARo+riAkUy9VrCwbrIthX7DS6dAaraND4pcSwQEJTI7hwTlYcpJWi3hOJguan8I+fg1oZuDlJfzzD4D1EsgJXC64gpBMwjCBnqClCeahj17ohec2FJxTRJA9D8kzsBHiGfssBPCFlo9kDExTw5DB6UzGndYBrbdovaQ8XGguXK3ja2NFfs2qCtYQZtUcxkgOmNecLM8erV/De4/Kr1FjJeOGAwCDyjRAZWFnp4CxsJqPHDwktF7EqpHwSgyBvpJkC/HSGwsyDDqZAyfzfs4EW0n1R+/gl5eirC0WEma3XIo3JngMO9d7DTsH+KVMWbFGzDmMS4hyaMmCThrg7ES8h2TEewgNLWUW5bDr4FZrVRZX4K6TgjVd15fXV2wLl4shnNn2vt34dzpWmMI7hj07AbcdHs9avPveGm+84/HueyInZ6cNKgs8eVxj3gCPzy0enxGsNViuGZ0zuFgBl0sTdaZGb3uMnKlFNs1rgrWSZ29DwUkDGDJanJPhHOCIASWHIRLGxIiV4OGTpY0yUp5JrVUurfEwuhR5SPJuQOSljNpODfAhAinMmayF0HSsD9EvlW/FoOEg4akArJeQZ9OcCCHUyubRu9eKAYI6MYSwHcmd3wgbHRqvoxzrWjD6itWxcAwYZCt5H5sGZM/Em1jV8m54ed/EaLnSb2at5x+StdEIlYnIlClCuBGN4jnqDxsRKBNVpyejThIZNVWxul8f3z9GDOO+oKhp6C+7DljqJmx+W0wWVNegZgaqG9j5DNfFXXn8fgmAn2DmnwIAIvrTAL4DwMMWbBJPh9XlGl97e4mLVh5wM5dR6vGjCmdzwqtPKhAB7z8HLldAXatiXNNICGBPCoFNb0+l222m7BvDiccntNkkh0DvRawrOWZWiZeHiNE5oHMeoXJ2JHBRrw7EbrunL/UibvMGStsJ4hdJ3eb+SeKZFS2xJljkegIIBxjuPUy2OYE5fSIKxWohVi4gksY8TBT1DGRrvPfO+/joawusTk8AAM3JDI1+pPVM3oGqHhLA57XV7bqs5E7NZhazBjg7tTidEV45NfBE+PCC8dFl9g7UuQdw01uYrodxR4TXuNewf2eQrQ8NDuEdEu+g/P3khHA6I7TOYdUCjQ2hn3KQUxISiGDvEVQPIFWwVu6P5WGbIChd1wKdE4/l/Azm5AxutRDFDcmzom6DuG9UFWX98TnBi55BvSfM6Yg+bBMEaUZwNkjlCBHc6g1MjuHsush7YPkcvHgGgMDNDPaV1wFj4d/9qpY+H3oicyLYx/MOiWBa3CV6ATNv4pQnMICS4zjzQMY2uzyBLydeTPkIqIxs8HxB+IdviJxrKuD8BDidSchf5xmrTpRA53svTQjn65dO0qsrr0o+w1aEyjisycF5hmEJGzVeQj7zMPOKWlBtUM3EEGjhJb8weGi6FajVYlQhfHQdQvBUsY1h1x6oDEwtOVfEEEIYls6LsrtagJcLwIXiM60Wn+FIDoPCHcNJAxF0Mschtw5oZTs7J56BUKG0MkB1CtAjUdaTIhbyYavBt21jHhK7DrzWXCTnetLYaiXVvNroZMgdj/49Oz9Fu1ji3a9+gNrV+MQ54Y13lugcMDsVGfnh+xIpE/Sl+bzCyYzw9InoTU1l0HqDjy4ZH2r0Rt2o7pTpTTGaKubYGzFONmKknDei50iOvbxna69T+YHRupTwSR8xCssMCaExvFGt2yYkMvUiSnqGpLkYIhit99D5Fp3zINOiqvTd5EAG5T0LUTCVX4vXEBZUz2DrE5C1ojcxy7jv1sBqCUBSV0zImc/THrKc+ck5p6MXj+XvzgErDTXVYipkLTCbgU5OJTqFKnnfWyGCfrUQj2P4Xnh6/sMrh5GOHLN3RdOAW85F3CjeYs2gHQGSM3q5Rnb2K+OuiN+nAXw5WX8DwC+9o2s5KiTfzMM5B6eDk9dk6sulx+US+PBSQjh/7ucq/OTPejhVAo0DvCp3ff61PGoXrRNhgNF2wzDgwZWEtmHgcn7YRwhPa3VQb/V6L4JyD7nOk4bwdE547zkjKGbRA6Q9hssIZ+grIAfvQP8Sa3pyJGM+65Mzj94kEUw9fpMhpzQ41uuVBY9gIBRxnR3QOrh2DQPAzs/AK0hIT+45ivfRgo3BN3ymwc++eSnWWwC+8/HZhxLgLoQm2qBg03Cp51i3HusWWKwDMfM4mRm8+pjw6ITws+/2Q4DZ+g4AfcDB0DoVcixHj6Sp9WGf4Z54Rnzoly3hsgU+/ojQud7zF4hB780bhoLG500+9huIi1fW0b8jeqx38KsVPDtUJ2cSvuvd4FnF50TD96v3vgUyFIgWD663J2AuuRHJNj2PICMyWThp8MT1xVU8Dh7NwzXkXrT1Ev5rPwvUDcxrn4R/901gtdC2gQBmBDQgEL4RATdVgOUqXrvNIjPjnsCXHC+sfATJvJrMEmXSdoS2A1Yt8JFqI/OGMK+B05ko6R0DyzWwbOVd6VSOheW6C8RQc4gJaAw0XNTCGMKsmcEQgeHgPcMrIWxZDEUDpdpJ6B0AVGxgq7mQweZMPC+avxXzjtulKMxrCfkMhBBdK4q164BO15V4UVOD7AnM+dOenYCEgLkuTlUhBKyFXUuRp4GnENBQz6F3hrUCYQwtDeNZnh/IAFmpRGysBWrxiEoxCyGLoYjFQEl2HXwoeqPX63XaGt/2hS38uovKvWlqOPZ4/myNn/jKEk8fV/jcZx/jH31pCdfKdXXVUDauiLBaAZdLfb61xePHFk9OCedPDN75kLHUyEWvRM/r+Bb0p6DzOMtYAVgq56gsDaKprAFmtRjBmxqY17K9dYDzjHUHdJ2+dzEapjd6m/i3GijDuu9lWmgLpEZv0VDmdYXKGsyrBsRrtJ2LBje2QU6qUSAYStV44XwH+E7WiSRnvjmHnZ/LO7VeSl64a6MRtPf8hTChCSKY6TzQAjd68YgvEiDvd9f275u2FY/VDObsKegVIfncrsDLS2BxIcba6G2bMoLy+DnjtXBCwuygj1zmxPDNoAtmFa5jekRi/Oz1Xgz62JU6EUDGYsobOFkkbSMNY7TrK+HeFnchou8C8F0A8Pr9vcwNhDndJIxwqMwbIjQ18MpjgyfnhA8vZGBS54+68If95YpQvv8QBO/MPrBGhGddAU0lA+AhCMo9bdFq92lzLETPR1inoeIe1qU4TBCQYk2lMCF43mdGmr74MxdYrTzm9ZDUpTATii1tebBEwMkMeHxGOJ0TPrq83v3KjVYpsvFsup32MVbMLUaNeH2fg/GOh2RugnNK+FJomz2ntM1wHfDOicW7ZRD2eGE3hNr2YzgpKLTRR448THTrdQzJYfCUcUYWN8NHJtCuwR++Czp9FHM68mT5vZB7L/Pr3dVnLqQLjoqHKiPzirOAjBlBWV+18u/5Ut6f87mSQAt8cCEpE0Cv1IdQvUAEa+vRsQEcUHUh0kP6lnxl8R5ZQzB2rt+0g/cerZc8Qkq8LSHqoMIa8ND5diXlm2wDQ1YI4al4C6H5XqZdiqdwtRA50kqYNoX52QCJJmnXvRekbbVaoAGaU+AkTAtDstQCU7xaqZdupSF2kvcsVYGHxWci4QuKbagoOtjugW6VzFE47jVhhngzKZTKF9Jo1OtEtlLCSOLq1+repq4i16gs4ZUnNRbL8Ym34zlVkLhQnbRiPLvweHYBPDoFPvcJg596KxjG9dWKxCrvNcieeJbeqKjvXSjamqbV1BY4aYB5JYRwJY4XDd/UN4cYVjv2sfr68LpMjGrRCKRYoVtJncqepWXMK4OmqlHVNVbrrpeFefXHQEBjikIgOEL83boFQBIVc/pYns1qAb+67KdSyvPfN9IizGAd3iV/ZzIuXF/0HoZIGQ+sF4B7LqGM7GXqinoOPH1NCpi1K2B5AVw8k3NchQhGY/GQhOURKxtEENNEcEACgQ0iGHEgERztY2f0y/GY311Ji68A+Gyy/hndFsHM3w3guwHgCzR/GJoDi9WhmdV4+hSoW83xe1Rh3gBnpwbrFlh7wnuXgDEGjx6lYQl9cZeN3L4YiifLvIDHWM7fWH6W9NlPFSBVIRmWKFaHJEhZZecZzjMul2LRmlV9X9uqOwLD0M60XT4RubSd8OjtyvnLlsNt2fVksfSDUM9k3bCTEIyqhrUGvl2DV5cSt59VlOzD9hzIe5y98ghnz2vMQ6jnvElCPOUB9jl+w9DOqrYgAk5PDGY14WQmU0Gcnaj10xHWLfDVD2Tgms/7kM74zPfM/QvPzphkX+ZE6td5Yj9vtA8hWfPa42wmuQ/OM2Y2KF9usNxrPsuN5HgersMDZFDXM5AhuMVCnlVaICiGZfp+G9BrAdm0DQH53JCUMuapPiYqim0cl073sLUCaH/M5BQROeanoCcfg3/nZzf7msJYX7t+wy4UwndV7JSPwAOVkd6DO4dZQ3h81m82UVYlecRWq17b8CoROt+PPV2MHBh6Ylpn+iIeSbEzIMnfWvvBuuRpkYSLGorhpA7i1fLeA7E68bBKcaxOHNIFfAcQUM1mYgA+Y1mGatRO8rFk6hopr09tVrAjm+NUiKGTfwTQzIJMA9CZRLWE0G1bQaqceCGCrgNWS8k1XCykGIfLiGEyAXjcFsaiCdI4JIQdmFvxaq57wpi2MZ/7LOiVGp//plfxytcZvP1uiw8uDE4fV7EYWtMM8+FT2Vhb4PzM4uzU4HRGMJbw1Q+4L4KWyb40xXsMofAd0NshxoZV54F1F+Qs4+kpcD4jvH+RKPdEAxII9AEeUX4GXSfI4LA/RCLFqBgxei5ayW09mc3hO4PO9VEwMWdejRMhGsbHVAnVcSh4klpQ2wJtK8aOx69JbvziOcgN6xXkheti4ZY0XSJPYzCZPhTXedBXUDbEm8jA+gK0DA9B8tXN170i0QDP3gcWzzfmXw7rG+Gb7Pt0hh1FZTby7WPOXR5WSptFZSaMmSGPcGOu2/QFHMkhHByzo4DMtGfwcNwV8fsbAL5ARN8AEWi/EcC/fEfXclRw52DrCl/4hgorJ8VcPAwuW+DyuVokK8LJfCwOPQ0/kGUeXz5WmTEW2SDZLwVdWIkdwZggUMMgo8VcKBR18fAOWPkwRUSv7BMkd2JfYjdV7CUld1cldrlnMCV3adgn0H/8OVHoyy4H0sEw1mr1yEqus1vBt0u4Sy0BvmsKAWsB1+GVV87w1Q8s5pqzIMQvCDErJarnRqp7nljUFWE+kxLWdUXwLFbsdSfKzKIDnn9EYBaBZgzQzPYneBv5edk7Fd6X9N5P5fTl0R2WkiqgRtrJhPWEyjIWa4+uc6gNYjXPKoa76P3M1qMihS4qUf1zy4g7vJBzI0oDry/hu3Y0D5PyvIWYI5eFtcTtQ0E2ED5TUz5Mre8iXonA2kn4phByD08fwZw8EmvvV3+mz8/Iz4dp4bGRw7Ct7b4FHxKiuHPOv0IWgRdZPjKD1x2sZXzqVd022K//dEXGRPG0XKx6UgiMGR+H/QBJeoOOa50Pnpdw7kAejRSZUWJjqZfVta1hrZBCAsDs4BzDhyqjOlFeJISau9xmeeRx7PJewvGqCtbOAGMkBJCMFIHRatMySfdS8wqHsoiSnC0OynjwGnbi6SEpDw06Fa8hPzUatikGal6n4aRrCS8NEQKR8A0JYBg/YhjpRJVF7pIQVGZQXcOhwpe/ynjvkgA0OH0k961uKhCAszMbZWNTEU5OxPhZVQZtJ0VeWge8+ywo1IS6CV7f7QQwl5FW9SJg04hu9B2rjehhs5ol8skCrQOeLTGsGUabusm+yIdbZorvpGPgYunw+KRC69q4PXihYpRSjI7JomTS9UAwuxa+ewZb1bDnr8A/e09PrONwjD7J2HDwgpme1MR9kdAgW4/sVtfDdSVesxjl4oDFMylOZmuYs0egJ6/Bvf9VSVfYSAkYpo+QNyMRMlnoZp7HjkwW0vA4wPQyLaTixFSNTG5h2GePPv0heFenC6oFwpcTwmEKxYOdwJ2ZOyL6XQD+c0hA7v+VmX/kLq7l2PCrFarG4u2L/mWYNYTTemz6Blk2GhlhLaPRqI4474wSu4252AgAy0coXm4pq88qLInEpNV1ug/94JTGmZNul8Eu2z9G1rYQOln3G8em2F7VMyONE2Qu7QuQD3q6jVjkDDyIJLyHjIUlvfldC6/z5/Cqg3edTAGAEaKXzf8Wlx2B3Bof/7ozfNQ2OD9vUFfA+VmF+VwKFlSVhXMM1nn9PIsQuWwJ7bK3bqbeu6oBZgm5m67aqb81F3ITJM6o0Kq06muYN9IAOlcSku2hXLbczHTICUYCr+8dg7Fsgdo5zQ8NRV3kPtVaLTMSvUAIQ8EF9AS7n/w9HCMDoK1mMnGya0WILS8A9jD6zMbmZ8yJ396Eb2yev5zg5W2mCF8+aXyyf2/Cl56bDNCcgGYnwEw8zHzxDO6dN4aEbypJPu7PrKHJ9m1z/A03Z9eZE9kt553c/xLjRZaPcA5uvUZlHNqO4TdC8Hqk4fnWbBqpckRCCN4oPpYjnDfs7gOy1aoe8s87oAuhhqEYmpGKk7U1qGsDgof3DOdbOOdhSImgCWOXGrECQUynq2Ep3SxjjZNKjsbAmgpUzWC0CimFfKkwpYTmF6JdSago+95LGMr5KxFkLYSFMO+b6wCSqS9Q16DqDLBPVPiIZs/tWo5bSyiqXy2l4rX2HcJdcw/hgBgm44CpDExV4+s+eY7zdYWmBs7OKjQ1oarEuAmSaa9kiiPgYk1oLxGLk1WVVMKutxg/N42cpNNcYVjR3CS6le3DOgOJi2lrXgwPlyvgeTAiKyFMDQ/5u5m+i3LM0DjR60f5ccP3ddZYdGMGvAPASFIUAllsl0AzlwtgTshiIEFBFxuSysHlTblVN/OVZBnIEqckLpCgJFyUO/BH74HtR7CvfBz+o/fAi+cH/ebh6YeE+TACOCRdOQHcSKGYzFnvSWS8p5E4j1/HTVa+vrPEAGb+CwD+wl2d/6bg1y0+/jEDqgCGhFTOaj+YRLRKkoIBAErawkTk8j70BIq1nDszQGmYJKXhcZvEzOREaocnbRvxmiRdmVdkbG69uJ9kwlIjDRCYBHEgoKGxhkMQp1sBSM5Y7AuMkEPQXx8PFmBV1p2WsnadhNYkoTQWGCj/Oydujwq4tlvXMLbB048/xi94iijAYOR6PQPeyOBbW6DBpvdtm2eX0Asn8dz66K2TffKeBYVHPLzieRPiuymMhKjJjQrWdVZDAkMGNPmZ+j5mlVQHHl9CH8Jrk4nbA9ELnryw7seXhh3IGFSqlpGxsOFGdGuxOoe5jFie2+QE7il5y59fTvDyktbhXcrnOlJLeXq+nFjtIoBjJG93CCeJq7dqQLUsAcj0DYsL8PvvyPV4vhLBG23Pm8RviuDF/TvOPbZvo3T1tgTUlwgvqnxk5+EXS7x+eoFHlYNjLfTCMn8pA/CeYg4Vs6QdMACXTRUQPR3Ra3f49eTG81xuMffZwqzVf73mD/YpC4TKGtSVRdUIEWw7h1YV9j7Vog9tN6GQycR8b2MpCCLuDKgyMPVpDB+VScEtDGvBlbWU9zfrSzn/1vDRsPQqI8P4CVBdAc0ZyD6VOe9srcLBa7XrVsJIQ54i+2FoaEIK7XyG86en+MznT7HsZJ4+htQO8El6SwPgVKfvqENqY4wq6T2+VQgHTiKd0mVqeA4e5Di8cj/OpBGDochdKidNuC6byL4J8hbOF45N921GRiFblwupjUNTOdTWYF4R2q6Fd2tU1MvRvDLtRhRTZug0Os+gPgyADMz8TJ5bLi/H0hr2Rcxr99m6G28/OFZvSNrUdfAffg109qQnfls8f1O58VOpCbkHboMAYkjcZF+mB2ekdnvRssxzt4MATsPs2L8bDygj/GGA2w7zqsWr54CBzEtkSJSoULqXwocRSFsMefM9octz1HJP2h5kzQzaEigo1GHQ4XAuDNupdZGUnMVQSoJafEgJaN+mJ1qIvQ2ukxPPBvsoQERay8gcY7PjCC2x4GKh4bh9mKPFgw97Y8DKcq9GiQEwUOg3iN1kWF8YYJfg+TmePKrw9oeSBD5DKIzTe2qjl009bOJJ640BY541azgSMKgQo0jaoGQtTPStEk7JmnfD0N1gaOBECKVeXyAliUMBFu/vFiNCfI+TkE0gyX1xUnWsYi/eO8MgrShHRCAmGRS7DtSFkCcNceLg0csI3g4SR95tEr6cvG0835z4JSTuCERPFhmZDFX0qhpU1UDdAEaq6sE58HoJXi/gL58DSynTHTFGuK5A9PLtxyB6O/uMfRXC9zLAr1u4Zxeo3XO49aXMT0cEDyPV/0AyHQERmI04JMiIzyJWEA7KnoADeVQ55LnP+wtFc4OocNHQNSQDU0hVrHzsS9E5yYcHPCwx5o1UaVyuu759WBDF3xBzmVXpM4FcanJRVOpNptyz02mIvI5rHYzvQLaS8v7zxyC8ImPo+lLmpQ1znQYiqHMQUpUQwJH53qRYSL8evYVVA5yfg2wt+UdkpPp1uwLWK/ilTK9DXQdjDepZhcePgFPfe9iC502M2L3s8Sq3Qs0OWZcH5pPbSQhDqPhoQxG6KeIVc9WjJ26kzQihS/fnzz/URdjWNvXkUTxGthtDqAzDWsLMyg/quharlRYYyiJipuoU2CzaJY1YIvagqkE1Ey8fL54L8cvSHzbkWtSXUhmYy7wp/Wi7/Ni5z1YwT17rw1HvE6Zy/SYqYN83FOJ3ZPjlGmfVUixuvgVIPDSwpBWd+qUhJ2GISrQMeyVnvTcrkLBRcgYgSJOU8In30KM3dQlxChKPgveQM0U4bpf2FEgZ0Cf5BoEQyFpunZiyGHEIrsGGtM29ibv3Z1aXMem9MeiMDGAYIQOcEMno8cvC9MaU/fUSr7zq8epjya+Up8QDIcbMGlnBMNR7dqMA8wDT8Lk635O20XBb2hQyhxTLuUo+ZXofU4u50XfXogORUYJHej0EwxLuQN6BvVay817nwWLAS7mGNC/vIIKX7k+fa07Uc4I34dkdJXlXJXjGQubSmsnSWFG8Qs4Ne2At4VW8WoMvnks4jh8hmwirmwI2n99oo+1UH3E9+x3pb5s4ZvdcRiPXl+MI4SsF9x/cdXDLFU7bZ5hTK1O5MMOBtBQ/o4vbkiWQTOkSpGEggEbL4ksIhOVEm6+UPAYjJQuBjOFvG9U/8vVUHA09jP34imwpY33IF4z9RM+k2Qjp672X6iULJDcngDQ0XBr2kRQGQuB9B3QtbLcGjIWtZqD5I7muroVfPge6Ns7txiYU+LCb3sBQqMMG4pfMeeraWAqTo15AoKoBqhrmyVz+thZUWTS1x6wBZiSfu1HZ2CYyLr2vgRxZSCilIR6E8YY2QQYGAyYRDeRSGvWSrtNEm97jl+wnGiFz+nsHfefj28Q6B+KqBm12cB1j3XWwECNnlUfIZHnv0SscPX09ISRjxSNsK1Azh+FOnv3lRxodksvXifSHMSP4FEncZQzdVtgsRTMHzc9A1Qzug7clx29C95qao28fXMvYeM+J3S4U4ndkdJcLvLb4El6LlhEP6jTcUMMOybte2WMNowyW//B3aBs/NO7bAJumyjHCdShhmqzit6XPsTZb+xrZvusD3Oe6tvUP7O+pSdpuVkQUMg4KFmmIyZABevtn8anPfg1t5/viJUl1ynwi+Y1Q2YRwRQ9cIP4Q8pR7ZANi2EhC/pG4DXuPb/gdwxDb5H+bJDszLMhryMm7E4wLrn93XSt/d7KMEx1PkbcRUj5KyJO2k+Rt7Lm6ib7iTzyAxIVjiYTMkZEcHFMDpgIbo9ZvKwqPno91jiPftcDyQnJ1ulamXtgwUlzNW5fu29dblx+Xn2ts395euy19HNp3wYuD7qPnaN78cfAHb4vXyBhwPQPbWkK36gbRA04GXDX9uAsoWfQy/yuzzH0b1z1cDNJjeDP0oHkakkjO1wO5i2FYhFhMY4PiCXy2HiA50+P3YKoYSDjXxiicEcFUboS/rRF1LhACr+tOEhVhnEwVYc9eAdkKfnUBXi3imGpc24+7YcLuPDx0xCM4uj0UpFlfAt6Bmzle+6bneO2UwCwGvhDtQgM5lRHouOyNlGkUC+uG0DbeX07XOVlsyrf82HCSKC2DAR3TZGHf4i6b8lUwqCoenmfm2dsoMgcGkZV3zFiYyih5J5nfcb0CawVZhLoFE1Evk1FOY+kOU4bwK6Q/MHv5zpsZaHYCqmYy7+CzD/o5aEf6GJ34/dB89h3G0dFIlX3l6B457FedDP4YKMTv2HAO9fN34T/62oZVZYOcjX0QuwhUruhOtRtrs4sUbRwf2iexh2nbUWtpQi6SRVxhn2wbBMdn10Eb7SSYYyP+IrvmdK416ttQIG1JYCNFc6GEsIYEuYD8vrhA4ANR7+S+z07gn72PV+0FHDolaVpQJiNdfdjPcJChPGyVPeBFUHDwrqZtAtlK+go5oroR8GkIaELW0n4Sr+7gtm7xwtLo/n59ktDtI1SA3gAyaLMpNIbrVyHyEKUTUDIvVtJI6JTcEZGEXaZwkjvIrtVJlx2wvpQ5tNTiHgUhtgiE1JiTt+03jO7fRu6u66Ub63+XoJrqa5++t7UteLHQffgMuLgA2blucKBuAZhFomA5Nar5vox5JaGJJkw6Xs80+UvWfdVICKKtNEw6CYI3kgPLWszCscgiJhPDRGWpJC8hmf0crzTYN70+lEn5HLL7YKMozZSIRk+I85xHzj2lRuYXxEpCPk1Vwzx+Dfz8fcA7eKRpH9lps0qRNDHv22But5i3JQT91Fxi3XYwcBr9wgMjYpwmKV4Db8iazdoBfuOYbdgnd22KnO2Dq1b3TD24xF69lpIKQQDIGJBUOBPjMUNCcL2TuSC9EyMi+zhOU4yg4ZvJc5+MlJkmerA1uO5z1Y2xYN+BFxdDsrenZ28g0+854btPRs1C/I4M33XgL/6YDJzLRUY21H4VSAaFoE59OSQmJSEzgfykhAqxvwFGhUu2zR8igBiABZgzhY8HCyDJQdsgNTz8Ox6SkJONbSPr8eNPz5FfDw83B09U2BdzvTgOEswsCnvqbfWchK0oprwV4eNv5rCvfR3OvvS3hSi4FtFr6xPP7aDPHd6nsfuQHzt1zLZjx47f55hd5wIOsvTJ5gniwn44SCYEHYw+MSNW3zL6TQXSpt+WsfIKGCP7Bj+NxXLJWpDAa/lxL5Mrwzkhzr7T/d1Q6MXbsR9J69ePR9bG2u1dLGWvvibe+wNJ21j7SYJ3nfCbggeB9bsfYPGjPwz/0Xsw8xOZsLyZgZpG8npmjUw2XWuYYJhSxsjUBSZ8u6Qxg5Dx3RghGFIJS+c0U7IIY+VfVYuxR0kiW1lHVel6BYBiwQcGCZHzXv5mrYDIXkhhohDnXsSYk5htlz6zQjU5WZzwIm5D8BRt5P/n40rwZq4XEvnazGVSb6IY9hmQE8CN9TgBY0b8lGCn+56s3kG3vOi9iBrVotQGCIZdIjG2sXoEQ3+A9Jncq6FdOVkTtyDoCvfxTsEO5FoZt10nOoTKoligLnr+Mrk65VgYq0a9T6QMMC7Tt8lzIki0Sw2qwvdWxTnLuG2BdgV/+Uxy1TUaaC9il54z7E8I13Xy2QfXEI/ng2XylFF2cG17GF33ur5roBC/I8MvlmjffANmdgrvlEw41yuzgQwFiw2HlyIQFd3v0g8xJTt6bGYdOwR3YlW/J9aOXb9928c1qUQvVsD5q5g9/wBYL4cD48SAtTN8daAIpJ7SZHvIfE/bJYvR693oN2+Te2qjqzdbH+kvvfZAwgZe194IEgR79KxpXk/fV2rJU2tesFyyj98TOw9w2xP3SNj0m3NeCbjfe8DdRt52hVD2t+JwErcvcTqEzO0bLnIIOdslgEaPm7jm+2QFLbgdrN58Gx3N0H60gHl2KQp+ZWGUrBmdyDtMrGx0/iOqRYmk+TySRRgLahqQaUAxRNSASDwJRNAxwYHdGrCSN0saEREnb7bhnKoShcmmbZ2Qw7onh2koqlXSGEmTjG0eUKIoirfYLeU7cNqG0/ExQT9XW7Y9I4wpeq/RRGhgiMAAw1QNzGwudOviwz6lZF/DS5xdOtNDUg9g2BeqdD97B/OTc5BtALCEuzNrflnIdev/kaa7cBIJRXG8yAzK3vVsNNWXxozMOa7h4TsYU88vJW2swcP7RIil6zmpi33zYcRurF2Q56YCk8x5DFJjijUgU0V9h52kNUi+us4NGYzhyMb8bdE4yfn3qlJ9BKI3uJYt553afx3Ct3dKxRH090L8jozu+SXajxbwz96cdC0HbHuAN0nOirI1javcd/Ye7U/8OE6/8fNAdSobx0JjpfWO8yYGACAZgBkDwcYAbEqU+j/iwEXJ8XnDeA7qBWRiZOiP8tklJ+9Ofp2pIEq9qSq4+3WOxK0nx+MD5L7kbKr96Lu+J1kb7fMI3i4510j7g/ve3H4lUjZx3M5v4Qpk7jqGl4IXA361xvLL/3/2/jz4li07CwO/tTPznN9w731D1atXc6kQhYRkWiAUQIcByWYWGOFuGiugjQB1y0QjDDbYSKajm+7GtLDbTAEBLSwGYWEhBgcKN5MAIyDCEiCQhKRCqFRSTapXr1694d77G87JzL36j7X2zp07M8/8G+/64t13Tmbu3LlPnvPbX35r2p+Ee/h2YD6PYs2zlwdGeHBTS5h7XYOWsn5czGVWIRiEYtyuOsFIZal1+GcgV8LNj2S7Eq8iVTNQVUTBhyAQGSJYQrQGLSUNwPu4xk4MPQ3XD2HgQfgVEnXgiqoTiE6FIeki6q6USAVyOgW7bhzMOiWzhvmHB1KWdIeEE3wyB4e0AqeeNLGniQgmKhCKgaEND+VnYiBLUlImq13zxOsg+mT679udvwmcvzkUcWE7FzrRg4nedq9NwDZ1AEb6uzUYiSqZFHjpOWm7MeHjWdNYSKvm6BoZLvyexVPHBM1ZD1Ey2lfbQKpsS+VXWeexEa9kvex7FeNlN/TiJWPfaxmiHVIieuPJzkuvM3pspO9t8tw3NvRegRYw4Xdg1K+/hadvLrH8+Gcm29zlhxty0xbH60ZY2PW24Ox7/6m86YWmboe7/NuYwk7Vs3a8D9vc803bbvKdHKKvjce+wf3cVQAeuo9DXctwv/C5H/hRAJ03zxUEdzSXvLP5DFRVKI6OQFUFN5+DqgcogifQkQhD78H1EjgXIQPfikCE7/otVazl3sPwGvbP5xpmGryIuj1TT2JRdotXOwbaFoQ6eg/Zt+pFTPKdgjcxFYSACsH+vugtJFkZnF2FuPI4nIyTnISghpzjGEXh0GWDsIa96cM+t/Jw7rUKJ1Z7kHLRFcXXVA7YWFhh2M7aFk/eGD8n7ytgLB1ijWdocG6OsblzlQCMaQZIgl7yVBx93ztvcmPFdSBCP609ENIbQi4nEUStddsSOJMcT8NiA3pRZxoZo1W1Wf9uxAjg5Tts2t59WSXWus013rEtRNG6vq8yv32XKtSbGGc39eBdR/67Cb8Do37jLXiqsDxfTk4+vj28gs9xU6KIpsqYXcm1rlaEbvtZFm8+Hey7qw+0V3lvb/qe7DWB7piDts1n3nZ8V9H3dgL6cMLY8GygvlARossFkCO4uE9DAxNRmG5T4UQYHh+ByiAMZyiPT0FlBTqax8IizD4RhepFbGq4ZQNulrFvKi7610iEoctEIpUi3Gg+l5C3kJs4mwOFCsVQ3Tdk8Adh2LaSM0z68E16LIjCUDylyLyIYX/IPYz73SAcFEBX1CPFIARwIpRQblz/nF1yxPIwwno53RZYnxaRC8CYUuAApi6XO3BX2I5CyqmY6udgdh95xJMWxHQaEaP7OB/ToP5A8lmmpr9UsIdrhAiYUP09OSZ1CZqubS8Nwnc5r6s+F7BWOE/O16tE0Q6pFNsIOrnm9lE5O6dQ3FDqxHXkv5vwuwKcffynUL3/Z0gia2KBiYaYDR6C4kKQvUmEMbKzt687xMNzepdNirbkYYS6L4ajJ8e7Pw4eHw9n/aZjT0JWAL0fSdtQvbLfrxaPScIQ43ja9Lz+ZyCiZBzoJsctsK3wWz4536r9TeM6RfomuG5v8nUI0EMInn362PUzbnPNXQxZNy3+DTeL5ZmuQRaEl6NEhIV9nShM24oQW4DeOhvZr32pp088dioSqxmKozmoPAbmM1BZATMp/sLei9eDVSCCwfUSrq4BllC2SW9hLgyzENRYpKaaSxjqbC5FL4oSVEooaggjjQVrWD0vTPq30vZEV/APxbC8XTAVUggMBV2+f0Vhrnhe2pYoVhPt9RmrJyeizTkVZ/KeQ3VllxToCukCLGKH26bL426kOFc4Fj1c4Rkg5ntv5mlZt2bblBhZiW3m9DX9r52rx0TIhp6sldfYUCxukhe/kaDbcHy7LDe08poT+8audZUFzqy4yy3Hm//6I3DzT6CXrHwIpCLSBTE5nhzeic3seG/VUho/Hut7aB9lMThvXd+UFvPI92sYgys0RCU5n8KKrEA8RlkffTEtlVHTfohIY9S1vca2U/LZptATQ8n3Jwnmso8DabD8MRIxlq++iievPF7d+RY4pMf2KgTePiJtl/FcpUi9LQL4Jr1iV5tTfHOfy3A7cfnaAgBAVRBtCUcEIZfNgZHzVCSlorG3XVCcn4rK9c91/e14vKpAVYWy50WsUBwfS+ipK+S67CXEtKmBWryG8Atw00gxkrbtCVAAozmJeX5iJxpdl4NYSOEaOMlNlJL+lYaiVsJ3HP4lQi0aVMOzR3I89VrJUocJ0o1gpc7CGgN/R57mxPOmYYkx9DQ11LLmU6YGYwAsYYUcCnDVLdAuRIhr6CGHapZtO/TmTAnQ2GDFg/k2+der2m8giqZw6MiKHjYJsd9HVB4wn7wbz3b3ftW1DlmcbOfwy5HPs9H1idQwJPMSlZXMB9UM/un+z5km/K4A7dKjXV6sb3hDoGsMA72OkNN9HtzDQ8T4wSTOntSKnApR5+R9UeD4/R9AUXr48/P+5JWKx3QbQVCiT9Isk7HX91KhvH8cUPE58j41NBxS0AyF3uZVZbcdxza/mdsmau8qrtsDZ2Lw2YZv9PvX1xad+AtiMMwwqSgEACrkt5oLv3QuD8eaEVEofYrQapehDwkzXbonAPqCMcwHMv87uCPxFpYnx6BqBjerQMencEdzfViTZZDQ1uC6Vm/iEp5ZhOJiIWuxYVwU9j9T2HbZdnI8GDbL4DVTQ20h25SEO8qapCOGVNBwLuX4P9lMjdiB00JRj9SbNpWHF/rfMHxvlYjbJBQxjnnF8dG+NvU+xf0bhFLm52zhJdx6rryS0P8NwvnXOTj2Kvq17vgWHrUV52x8/rZeubA2cFlKReKy1LkibGvl4BDyzZA1gmvNX65r+IsL8JPH8JeXK8e9CUz4XQHaC/nyI7llyInsphGI9iqQy4Pr+uwbi9u6/4e6SnRMSR1yhOVrb+DoHS+KhTZ4HlMraSReqLdTydkR4npDUWiiI2vd7jyWgeDV0+loSOpr3JqjYolzgdmJ0J7g1GOkxt4x4RmWLYnLl3gfPaNxvz4gsOesGIFH23RtN8VhPZBXJ4JuW0GiHNeRf2wwBI4McCWhrdVg1fT/RjgXfhPCMAhCYFoUdvsz8Rj3S7tm0QnG3FtYXIi3cpnlInbtZE4uNbw0LVaD+UMUR3Px4Ol8zU2tFU0bzUVcglrW4hvLXg5gFIDJNQfHYvnT/pwY92dzJaUi8CaMXusewFMe2NbzsoGI2zbEc0y0bSpENz5vtO12vLRVUbUbyhPf9DPttQzXvkXTiMBQA1BRSO5u+loUYK3WK9tlVxU1dN3U4LYFt02shspNA395oeKu0VzkA3yODWDC7wpQP1ntDdl9Bb6rwbMkRAN2+czrxtXWr+06nL0EwcE8nkloLgFxofReSG/qBdU2lIXtUqw8px5R52Sf5m5Q6UBVksvhHJyjZFtCh8TavuKzcV80BnHJcX+wQOdiVNuHV6AToRrSmwpRDgn34X3o90Dfwaa47aLRYNgU7UWfBXviLguWoWpcjAw8gcn8vOrY6PEsvDT9e94ktFT252GkZ6P7XUE9sUhV1eUixkqmM1B5It7Foujm6VQgtg2wbAB0lTu5baRYi/eT4a2rBGBuQBvMa2uE4XWFzW/8ELzDg/RaT9CYQNhT4F1l5ePrqi6+9XW2EKYbjccNC/2Qc0DluhzR8BwSXwsRdUXWpndxFW4abszhn2/By1pCk1vJK/VNE98fKlfPlnO4I+B6tz/Qm4JWeZ4k2GvHNXhKg3U5x0pxt2Zc7Q6eonjuHp6WfUJ33QEnlW1Jf2WYbYZR0RNEJSCWtkRkykSeCM/4mgpLXXxWjxUxlFfaBK9rJ2RDSFRWNnvtZ+dOaKqIjAWJvNeqfypMe9XaZDssdj92rB86nIZjpV7bLFQ4L4iUFE7q+kTcF68Vd2UhXwbDnhiLjglz4xSfemTzh/aReg8DdvciUowK2dSLGNBOhJeKF7HRcxyAJcidj15j4E2sJDzMzeagskIxDzlAM7jjmRyrqqRAitwpaPGT+MDa6lqFbQuEaqPpEhVrBODkfDciDHsexWvCRt6ug3mbpo8fakmcTfrap+/upB3O0RxPziKVYhXVbKmJlIM5iWSKQi2p1tqFJrveb3pYgRVdFdQYTaSFf5pGc0a9VNT1yxiFBN/2X1v5G9n4dl1hte6rjL4x4XcFmArxvG5sK5Ruq2ANgvQ67usu0ncqRKnX78R3MSVAxzApSre8L+lYdhWco2Jzw77Cww379RNseMDYYi6W87YQlfm4trrOJmI3yxXteVIDEVKXQySVx0OVP92ORIoeUSKQrYb9hgJGaehw9MTm+zQviLJxxrbSJBO5WZGkUOzhUPAeT3/oBw/Xn+FWgnNhNjK3rZvv3YShra1HjJiJKJQ23L9udq1UGIY2A7GY5RrG/RuEl055CQficlDZNCx58TSen3vyck+fqzT8bFaBChWGRQFXVaBChCOck+2yRFigXjohjZBIiq2EB+tQLTM8/MaHaHmQFgNRkvsX788tMTArrkJQHSqEURppm7SIDrmMB1KBpYZL5kRwBVGFrn3CRbEfZMdp/XcVDJocfguhGnua6pHkgXYROS1C4TxuMtEWo2zaJDrHTwrTW+vVVGwj4q4j396E3xUgJ7WbAFV0awTo3mgOFxy7TgwH7+c2iA8ZK+731CfYSpxvWC9obUjq5lc8qJc1t6pvg+3HoQ9M+3hDbyj8Nvaxg3jdFBY2arhN2IUzVz4eZZzRCb6s3URYaXr2urDRde2mwkqB1QVqxo7n4aVybJ14XH087WdURJIuIF84OF3DsKgknymEy1EV8poqWWdxXnbHin74HMfr6fV74oC1IGgiGjmJlsj/BWEa+smWduI0OiFFum9sms29lEnefiiUimhI6953xjw1iLnU4ObA4M4r1hNwbq39jNtwD/yIqOrEUbx3HlGgx3MzEdaLRIkpEP173xvDAUTJIUMXD+EVu+nPdBOFzkz43VPctPi8jjy9XbCLGF4nOrbxlOaW6EOK8y406nB9Dizj+yCzuu8yjoCNx7MihGztNUdIZWMhOUFI2wiudR7RfcTlJh7UqxSeBsO+mJrndvIerggrXRVSmmLKizgVVjp6LPMi+pCGsUIY5iGm63IPNxF+k17MiRDQVaGgUx6+eI0glopiGBaYhP5RrFKaRCBkQks+c3oc/bZx5yZIw9m5e9tbUN3rdn9/HorPbdvPD+d+6P9UEZreaA7oCbpt4ivHbf2st7WvbWHCz3AluCnheRWCc1NxtomgOEQ47VQu5lWIyIBDfp+HmNL3kSR7idnsHm8rYvcSk4PORkTtAT15U8Lztqx7aDCMYdVcNfU3v6oC99ScPZkbPxFWGseQpARsmmsYjxdDkTcQXWtyD6eF4XRV1Nh3Xjk0a9+1mxZ+MXx/pO1UH1N9TeFZiGi47YJr2PfVPRNeRd+H7vO6l0xaBRN+hnuF6xSc+UPEtYXWToQxHRKbhrzuVBBoi/t0iNzIHFN5Pbsgf3DbB4cwWtzE8inXuS6owbAPtuWHjR7VNg0rDbgYDylNz82xSXjpVOXS/O9zXU7iqjaD/YN2bnDOuj7Hzl3VbqyvVW3H223PW/fB4HUb1k99FtaMve3LIpnwMxh2xDYPEVcZ+rqP4NxXGFylBxM4jJi+Cu9l+D4POb6rMFpcJcXGcd9ykjMYdsUhvYcBq7yIg7DSgBXhpbkha7KAjWIYXqrbtZ8MMe3a9r2JGLQbFrwJ/fum/5GGnsD1a+qG66zra+q8DsP7f9M53XcFt0E85rhpoXUb78kqmPAzGK4B1+GJ3EVc3mQBoHXl2g+F3PJ+kGVLNswZ2gS7ei83+r6v0Ciwj9c1x23NCTYYprDtnL6RsWiikNlKL+JF6H8/L+Iq7+FUX+u8icC0eBz0vcYjONX/2Di6Pqbn+k3F2pVVe94Tt606asBtCmk8NO6awJuCCT+D4Z5gF3F5kw/cV1FoZxPsIzQ3FY3X4YU9pOdyDFeZM5rjpotRGQxXjV28hwGr/ha3Xu8wnJdtj3kP82tstQxGEEyZN2Zq7Vhu1wtE9hP3acJDOeaBzLEuVD14FzcRWvH6e3igNhWamyyHZNgPN+1JvCqY8DMYnmHsarW+KdyUWAzYVTRu42W8ivDWKVy1eAy4N0vLGAxXgG0qlOZYVZhG+t5cEE7NUusE36gwnGqTPUxvIxBzcdj14Ub7iOdNCUYANNFnHEdYd3aDUsi75junorI9sFfpWQpDPQTui1dvFfYSfkT03wL4DwAsAfw4gN/KzG/qsW8E8LWQyIP/lJn/ru7/lQD+OIACwH/PzN+0zxgMBsP14bo8M4cUmNcpOqYE11WHs+aYzBW6CuxZ6fQ+wzjSsA/2ieJYO++NFKWZKkizbq3aQZGrZH3Edeu3blvQZvycfu9TAmwTb9o6obTNMjebh4l24z+UULtPlU0PWUDMctL39/h9F4BvZOaGiP4wgG8E8PuI6IsAfDWALwbwbgB/n4h+hp7zpwD8MgCfBPDPieg7mflH9hyHwWC4R7hrnsiAm/ZIBlyHZ3IK5t3rwTjScK3Y1Xs49ne7aR72KkPTuhllsucV676uXZJnqhhO8tA/KSZyz2PWbizEckqsTTkJV4rHA6wBC2zmObwz3sArFGv5fb2v4Z0p9hJ+zPz3ks3vAfDr9f1XAfh2Zl4A+Aki+giAn6fHPsLMHwUAIvp2bWukZjAYdsZdy29MsatQuk2CEThQ0Zx7BuNIw23BLoJwXRhp1/dw3gjzwdoqpyuPrqlMvEIcAmsE4oaRCpOhqb1Gq8Vijlw8biLAdhKRazs9jMi8zVh3b++a0DtEKOohc/x+G4C/ou/fAyG5gE/qPgD4RLb/5x9wDAaDwbARrjJs9TpE5VV71rYVltcdznoHYRxpuHXYZB7cac1aDSVdXx146pqrw0vTvtdWGI6VT6fHEnTVtvPeWJ/bhrWu7H+dCFsz7+4i4vyKdXxvo5dwFw/qs4y1wo+I/j6Ad44c+v3M/De1ze8H0AD4tkMNjIi+DsDXAcBLVoPGYDDcIdyWNR73wW3yRN5mGEca7juuovhMwC550Zt6E/NrbDIvb2u+2sbftotpbN84ikOb425DXEcuPrk1o+M2WMsWzPxLVx0not8C4NcA+CXMHP6qPgXgfUmz9+o+rNifX/ebAXwzAHyIjkyyGwyGe4m7ms84hduS53hdMI40PKvYRxAG7CIM10UX5GHnmyyH0fW92Z/SxkV00mtt3LLDvpLm0ELtOiXWlLfyWai8eZXYt6rnrwTwXwL4cmY+Tw59J4C/TER/BJK4/iEA/wwAAfgQEX0QQmZfDeA37jMGg8FgeJZw10NUx3BfC8IYRxqeRRwy53onQ9IW8mRtqOkUVhTDmb7W6s+ySS7iOgwK2uzBF4cYzxQ2Mfbl+ZVXiUNWDr3t2Dc+5E8CmAP4LiICgO9h5t/OzD9MRN8BSUhvAPwOZm4BgIi+HsDfhZSq/nPM/MN7jsFgMBgMB8B9CFG9ZTCONBg2wCG8hwG7hpf2x7OdV3GT664bxyEE8yEjLq5ynddbl6N+R3IBDyFQqYs8ub34EB3xHys/cNPDMBgMBsOW2OXB7Vdf/Oj3MfOXXcFw7iWMIw3PIq7C+HTIsPNDVDo+7Hiuzlh3neH6t93oeNX34iv+zQ/sxY+WEW4wGAyGK8NVhqYaDIZnF7vOLbssYbENNl3/cBOEUNSDLJez4fIcu2AqpPRKRNoWOZs3gU3Ca29SvJrwMxgMBoPBYDA8ExgTjId8ED9EqGmObUXkNkJxqwI1Wy/zc+AcwzW4ihDSq1kz9+YMoib8DAaDwWAwGAzPLNY9iF+nMAT2FxubCMVdvIjXuczPVeYYboPrKD52nZ5KE34Gg8FgMBgMBsME7rLnape1Eg+FbddcPDRuw7qDq7DLsiB7X/MuFHchos8C+NhNj2NDvB3Aazc9iHsEu5+Hg93Lw8Lu5+GQ38sPMPNLNzWYuwbjyGcWdi8PC7ufh4Pdy8MivZ978eOdEH53CUT0L6wa3eFg9/NwsHt5WNj9PBzsXj47sO/6cLB7eVjY/Twc7F4eFoe8n7fdC2owGAwGg8FgMBgMhj1hws9gMBgMBoPBYDAY7jlM+B0e33zTA7hnsPt5ONi9PCzsfh4Odi+fHdh3fTjYvTws7H4eDnYvD4uD3U/L8TMYDAaDwWAwGAyGew7z+BkMBoPBYDAYDAbDPYcJvwOCiH4lEf0oEX2EiL7hpsdzF0BEP0lE/5qIvp+I/oXue5GIvouIfkxfX9D9RER/Qu/vDxLRl97s6G8eRPTniOhVIvqhZN/W94+Ivkbb/xgRfc1NfJabxsS9/ANE9Cn9fX4/EX1lcuwb9V7+KBH9imT/Mz8PENH7iOh/IaIfIaIfJqLfpfvtt/kMw/42todx5O4wfjwsjCMPhxvlSGa2fwf4B6AA8OMAfhqAGYAfAPBFNz2u2/4PwE8CeHu2778B8A36/hsA/GF9/5UA/jYAAvALAHzvTY//pv8B+MUAvhTAD+16/wC8COCj+vqCvn/hpj/bLbmXfwDA7x1p+0X6Nz4H8EH92y9sHoj3510AvlTfPwTwb/We2W/zGf1nfxs73zfjyN3vnfHj1d9P48jd7uWNcaR5/A6HnwfgI8z8UWZeAvh2AF91w2O6q/gqAH9R3/9FAL8u2f+tLPgeAM8T0btuYHy3Bsz8jwG8nu3e9v79CgDfxcyvM/MbAL4LwK+88sHfMkzcyyl8FYBvZ+YFM/8EgI9A5gCbBwAw86eZ+V/q+ycAPgzgPbDf5rMM+9s4HIwjN4Dx42FhHHk43CRHmvA7HN4D4BPJ9id1n2E1GMDfI6LvI6Kv030vM/On9f0rAF7W93aPN8O298/u62p8vYZW/LkQdgG7lxuDiD4PwM8B8L2w3+azDPsud4Nx5GFhc9DhYRy5B66bI034GW4av5CZvxTArwLwO4joF6cHWXzZVnp2R9j92xt/GsDnA/jZAD4N4L+70dHcMRDRAwB/HcDvZubH6TH7bRoMG8E48opg9+4gMI7cAzfBkSb8DodPAXhfsv1e3WdYAWb+lL6+CuB/goQBfCaEp+jrq9rc7vFm2Pb+2X2dADN/hplbZvYA/izk9wnYvVwLIqoghPZtzPw3dLf9Np9d2He5A4wjDw6bgw4I48jdcVMcacLvcPjnAD5ERB8kohmArwbwnTc8plsNIjoloofhPYBfDuCHIPctVCb6GgB/U99/J4DfrNWNfgGAtxKXuKHDtvfv7wL45UT0goZp/HLd98wjy4/5DyG/T0Du5VcT0ZyIPgjgQwD+GWweACAVyAB8C4APM/MfSQ7Zb/PZhf1tbAnjyCuBzUEHhHHkbrhRjtynKo39G1Tp+UpIZZ4fB/D7b3o8t/0fpKrTD+i/Hw73DMDbAPwDAD8G4O8DeFH3E4A/pff3XwP4spv+DDf9D8D/CAmvqCGx3V+7y/0D8NsgydcfAfBbb/pz3aJ7+Zf0Xv2gTrzvStr/fr2XPwrgVyX7n/l5AMAvhISo/CCA79d/X2m/zWf7n/1tbH2/jCP3u3/Gj1d/P40jd7uXN8aRpCcZDAaDwWAwGAwGg+GewkI9DQaDwWAwGAwGg+Gew4SfwWAwGAwGg8FgMNxzmPAzGAwGg8FgMBgMhnsOE34Gg8FgMBgMBoPBcM9hws9gMBgMBoPBYDAY7jlM+BkMBoPBYDAYDAbDPYcJP4PBYDAYDAaDwWC45zDhZzAYDAaDwWAwGAz3HCb8DAaDwWAwGAwGg+Gew4SfwWAwGAwGg8FgMNxzmPAzGAwGg8FgMBgMhnsOE34Gg8FgMBgMBoPBcM9hws9gMBgMBoPBYDAY7jlM+BkMBoPBYDAYDAbDPYcJP4PBYDAYDAaDwWC45zDhZzAYDAaDwWAwGAz3HCb8DAaDwWAwGAwGg+Gew4SfwWAwGAwGg8FgMNxzmPAzGAwGg8FgMBgMhnsOE34Gg8FgMBgMBoPBcM9hws9gMBgMBoPBYDAY7jlM+BkMBoPBYDAYDAbDPYcJP4PBYDAYDAaDwWC45zDhZzAYDAaDwWAwGAz3HCb8DAaDwWAwGAwGg+Gew4SfwWAwGAwGg8FgMNxzmPAzGAwGg8FgMBgMhnsOE34Gg8FgMBgMBoPBcM9hws9gMBgMBoPBYDAY7jlM+BkMBoPBYDAYDAbDPYcJP4PBYDAYDAaDwWC45zDhZzAYDAaDwWAwGAz3HCb8DAaDwWAwGAwGg+Gew4SfwWAwGAwGg8FgMNxzmPAzGAwGg8FgMBgMhnsOE34Gg8FgMBgMBoPBcM9hws9gMBgMBoPBYDAY7jlM+BkMBoPBYDAYDAbDPYcJP4PBYDAYDAaDwWC45zDhZzAYDAaDwWAwGAz3HCb8DAaDwWAwGAwGg+Gew4SfwWAwGAwGg8FgMNxzmPAzGAwGg8FgMBgMhnsOE34Gg8FgMBgMBoPBcM9hws9gMBgMBoPBYDAY7jlM+BkMBoPBYDAYDAbDPYcJP4PBYDAYDAaDwWC45zDhZzAYDAaDwWAwGAz3HCb8DAaDwWAwGAwGg+Gew4SfwXCHQER/gYj+oL7/RUT0ozc0Diain34T1zYYDAaD4baAiD5PObG86bEYDOtgws/wTIOI5kT0LUT0MSJ6QkTfT0S/KmvzG4jow3r8R4jo12XH/zMieoWIHhPRnyOi+QHG9Y+I6I1VfTHzP2HmL9j3WgaDwWAwrMNt4ksi+h+I6M9n+76ciD5HRO/apU+D4VmACT/Ds44SwCcAfDmA5wD8XwF8BxF9HgAQ0XsA/A8A/nMAjwD8FwD+MhG9Q4//CgDfAOCXAPgAgJ8G4P+xz4D02r8IAAP4tfv0ZTAYDAbDgXCb+PJ3AfhVRPTLtO8jAH8WwO9h5k/v2KfBcO9hws/wTIOZz5j5DzDzTzKzZ+b/GcBPAPi52uS9AN5k5r/Ngv8fgDMAn6/HvwbAtzDzDzPzGwD+XwB+y57D+s0AvgfAX9D+R0FEX0FEn0y2v5SI/pVaWv8qEf2VJCz0K4jok0T0e4joVSL6NBH91uTcORH9f4jo40T0GSL6M0R0nBz/L/ScnyKi37bn5zMYDAbDHcNt4ktm/hyA3wngm4noFMD/HcCPM/NfUP57hYjeIqJ/TERfDABE9EEiepOInG7/WSJ6NfRJRH+JiH63vn9OvZufJqJPEdEfJKJCjxXKl68R0UcB/OpdPoPBcBMw4WcwJCCilwH8DAA/rLv+BYAPE9Gv1cn+1wFYAPhBPf7FAH4g6eIHALxMRG/bYxi/GcC36b9foWNaN+4ZgP8JIhZfBPA/AvgPs2bvhFhp3wPgawH8KSJ6QY99E+Rz/2wAP13b/N+0718J4PcC+GUAPgTgl+78yQwGg8FwL3DTfMnMfxXAv4Tw3dfpPwD42xCueoce/zZt/xMAHgP4OdruFwN4SkQ/U7e/HMB36/u/AKCB8OHPAfDLAfyf9Nj/GcCv0f1fBuDX7zJ+g+EmYMLPYFAQUQUhiL/IzP8GAJi5BfCtAP4yhMD+MoD/hJnP9LQHAN5KugnvH+44hl8ICYH5Dmb+PgA/DuA3bnDqL4CE4fwJZq6Z+W8A+GdZmxrA/1OP/y0ATwF8ARERhDD/M2Z+nZmfAPhDAL5az/sNAP48M/+Qfu4/sMtnMxgMBsP9wG3gS8X/BcC/D+G2T+g4/hwzP2HmBYSvvoSIntP23w3gy4nonbr913T7g5Dw1B9QQfuVAH63ejlfBfBH0efEP8bMn2Dm1wH8v/cYv8FwrTDhZzAA0NCPvwRgCeDrk/2/FMB/A+ArAMwgFsH/noh+tjZ5CiGLgPD+ycg1/gwRPdV//9XEUL4GwN9j5td0+y9jRbhngncD+BQzc7LvE1mbzzFzk2yfQ4j4JQAnAL5Pw2DeBPB3dH/oO+3rYxuMx2AwGAz3ELeIL8HMnwHwGtTrqJ7GbyKiHyeixwB+Upu+XV+/W8f3iwH8YwD/SMf55QD+CTN7iPG1AvDphBP/vxAPImCcaLjDsNKzhmce6vH6FgAvA/hKZq6Twz8bwD9m5n+h2/+ciL4XEu74/RCy+RIA36HHvwTAZzT/oAdm/u0AfvuKcRxDLIkFEb2iu+cAnieiL2HmH5g6F8CnAbyHiCgRf++DeAzX4TUAFwC+mJk/NdH3+5Lt92/Qp8FgMBjuGW4LX67AbwTwVXrNn4SkN7wBgPT4dwP4bwF8Ut//UwB/BsAlujDPT0A8lm/PjKUBxomGOwvz+BkMwJ8G8DMB/AfMfJEd++cAflGwWBLRz4FU3Aw5C98K4GuJ6IuI6HlIlbO/sOM4fh2AFsAXQQj0Z+u4/gkk728V/lc99+uJqCSirwLw8za5qFo4/yyAP5pUX3uPVmADhKR/i37GE0gSvcFgMBiePdwWvpzCQ4ho+xwkkuUPpQeZ+ccghs7/I4DvZubHAD4D4H8PFX5aFfTvAfjviOgRETki+nwi+nLt5jsA/KdE9F7Nk/+GA38Gg+HKYMLP8EyDiD4A4D+BiKxXktCS3wQAzPzdkByBv0ZETwD8dQB/iJn/nh7/O5DQlv8FwMchIR+7CqOvgeTSfZyZXwn/APxJAL+JViwOy8xLAP87SNGWNyGk9j9DCHAT/D4AHwHwPRoe8/cBfIH2/bcB/DEA/1Db/MPtP5rBYDAY7jJuGV9O4Vu1308B+BFIhewc3w1JffhEsk2QQjABvxkSrvojEI/hXwMQ1gf8swD+LqQ4zb8E8DcO+xEMhqsD9VOCDAbDfYGG2PwZZv7zaxsbDAaDwWAwGO41zONnMNwTENGXE9E7NdTzawD8byBFWgwGg8FgMBgMzzisuIvBcH/wBZDcg1MAHwXw6zVXwWAwGAwGg8HwjMNCPQ0Gg8FgMBgMBoPhnsNCPQ0Gg8FgMBgMBoPhnsOEn8FgMBgMBoPBYDDcc9yJHL/nqOB3oLrpYRgMBoPhGvARLF5j5pduehx3BcaRBoPB8GxgX368E8LvHajwx8oP3PQwDAaDwXAN+DXNv/3YTY/hLsE40mAwGJ4N7MuPFuppMBgMBoPBYDAYDPccJvwMBoPBYDAYDAaD4Z7DhJ/BYDAYDAaDwWAw3HOY8DMYDAaDwWAwGAyGew4TfgaDwWAwGAwGg8Fwz2HCz2AwGAwGg8FgMBjuOUz4GQwGg8FgMBgMBsM9hwk/g8FgMBgMBoPBYLjnuBMLuBsMBoPhboAq2r+TZv8uDAaDwWAw9HEQjx8RPU9Ef42I/g0RfZiI/rdE9CIRfRcR/Zi+vqBtiYj+BBF9hIh+kIi+9BBjMBgMBsPhQRVt9c8whHGkwWAwGG4DDhXq+ccB/B1m/kIAXwLgwwC+AcA/YOYPAfgHug0AvwrAh/Tf1wH40wcag8FgMBhWYFsRZ0LuYDCONBgMBsNOOCQn7x3qSUTPAfjFAH4LADDzEsCSiL4KwFdos78I4B8B+H0AvgrAtzIzA/getYS+i5k/ve9YDAaD4VnDXRJnrrw7Yz0UjCMNBoPh2cZt4ulD5Ph9EMBnAfx5IvoSAN8H4HcBeDkhqlcAvKzv3wPgE8n5n9R9RmoGg+GZxW0ihlV4FsXbnjCONBgMhjuOu8LR63AI4VcC+FIAv5OZv5eI/ji6kBUAADMzEfE2nRLR10HCXPCS1aAxGAw3jNs66d+0EKPKikOvgXGkwWAwHBi3lZNX4ab5GjiM8PskgE8y8/fq9l+DkNpnQngKEb0LwKt6/FMA3pec/17d1wMzfzOAbwaAD9HRVoRoMBgM63BbSOMmieCmRNttIL9rhHGkwWAwZLgtHLwp7gtv7S38mPkVIvoEEX0BM/8ogF8C4Ef039cA+CZ9/Zt6yncC+Hoi+nYAPx/AW5a7YDAYDoGbIpKbIITrEm33hexuCsaRBoPhvuKuiLfbwmO3IULmUPEhvxPAtxHRDMBHAfxWSMXQ7yCirwXwMQC/Qdv+LQBfCeAjAM61rcFgMAxwE6RyHQRx1ZP/dZLcXSH+G4ZxpMFguNW4rXP5TYu22yDWDnkPDiL8mPn7AXzZyKFfMtKWAfyOQ1zXYDDcDG4rQeS4KsK4CiK4SnK7zu/rpkn6NsI40mAwXAduGzdfNx/chEi7a5xnGeEGw5a4bRPrdeC2TWyHmNwP+ZkO8Zu4intsgs9gMFw3nkWOvC246nn4OoSVGUGvFib8DIYtcVv/mG8brpIg9vkObpNIOyQJXdfvkgr7/RsMhmkYR94u3JUIlasUZdeTwnE3fvcm/AyGLXEb4r1vC65NbOwxoe46xtskEIGrFVzOxJzBYDgQjCNvD25bZEuK2zy2MdxoBe4DcrQJP4NhS9x2a+ZdsTpNYZv7u8tn3ctbeKDJ9yqEFrnb8b1TYQ99BsOzjNvOkfcRty165LaNJ8d1R67cJuOqCT+DYUvMX6puegj3CttMiLuIm32FyCEn7Nsizm4TCRkMhvuFXTiSW1uK8pA4hLA5BE9cBefdlHHxvvCmCT+DYUvMTvuk5o2wDoKrFoC7XGftOG45EZC7Oe8be39j1zYYDDeHlCMPyY/c2pxyCBxCOF2lCLoNBtL7IvLGYMLPYNgSswczAAD71YRmD7774xDC5TpJ5D6TRY51D3TkimsaicFguE2YPZhN8uM+vHjbvYK31Qh8pSLtFnDeTRo47yJM+BkMW+Lo0RzAUPhtM+mvE41XgbsoRG/DhH7T1seb+K1sApexx138fRkMhsPj6NF8a37cZp475FxzE/PrsygQDbcHJvwMhi3x4PM/D+Xb3wFuW9nBANjDNx7MDLCP+8AMZhaiYu79Y7C2Y+mEWUgoHkfXPiA9N15b3ss1ws5kP2ckE/tM+kjbh3El+8L4uj6QjCEZN7Lz8msbNsZ1CKnDPvSISL+tDzUGg+EaQITnfu6XAqQiIvJjm/CbcqX3HY96L5zihZu47fhzijs7XtULhfZAn+eUJ33gyIRzI5dNcG3cvONi81nDXeGhm/gtmPAzGLbEC1/yxWg+8REwC2GxZyG5AgCRcAWR/AvHiAA44SciAAQQABCISEQiKJJl7CNpH3lUOtFjYVTpuaxdZ9a7uC3X7jiNescp6TOS6GhfafvQNhtXes0pbCIOJ9pwT8COCdGO9DtB2xE+2HcPD2Dtonv46D1seAb3RL2eP9Le60MLvI/9dUaB/R4oNiGKbUlvmzFsS1TbWfLvBlkbDIZxHL37HXjw0inaz34aQOBHB5Ss/ChcRUQAOTBrVAe5yIkgdBwTaIX0fcKdOU9SwoFpO7jQV9jf5zzpe+RYwnsr56aNDJyJATg9r2ck5RjOOspXyNsD7NuOt4COi9Jx9Tgs4cWRvocG6b7A5nwMoZ3vcyy37WCsm2IfHthX8O0VirzHuK+T+0z4GQxboHrxeRQPjlH8tM8H17U8+DetPNC3jWyHh/626SbnMPFlk0r3x+7CjonjExMSQyf6kUM8PoGtnGDWTHp7nbtu8h85fzpPJNmfknNO+D1iT9uHhwsHsAdpWwKAEF4aiV9Ee2yf9C1v02PhgYZ6RoC4z4U2buXnk99RG63iYsn23XsffmetEK738tvxImTDb5J92NfKuYPfX9gehtRuSkSbEO2mZGrCz2C425i/+x2onn8O1YMTmXuaWuehFvDtgB8HvKBzBacCRnZE+2GYJybnlWweyblwdJ6Z6Cu0zU2XY9dex3HUM6R2RlKx+wZDqhp6kejRHqeF12RELrHklqlh2fU4kTJO7PElkR4fu2Yytp4RuOO63nFKjcYuCvLB/Zqc77n7bTALf0WD64QX2Hs1+HbG1sibaTv2wpmtcGbsK3BlYqCN3ugNsQ1/XaVxdh1M+BkMW6B68TkUL78bOHsLaNsoHHoigdXCCQZYJ0+eIKJAbm2L1PoWJqs44YGB1idtsgktHkcyybXdpJbs74Wd+nZg8ZOmE5PSGnLMdq68l/k5vYltW+KeFNRTE2Z44OgeJmIf2Vw/9tlWjZ1XtFs1poGYTcQiqaiEWskp7HMknuayjPtFuKrIjO30vR4bGAXaFqxGDG6DSGzjAxu3Htw08ntstU3bgn0LFx/Upr9v9is8vgnuSniOwWAYR/XoIYp3v184koqOH8OcBOVH75U/+16hODcFrxMrR7V+wHnRq5SmOUTe7IxmUSz4jmc549OOCxMOHsMKrkl2rm+DiTlzY0NuEL/ruXol1/aukYncLY2xY0dW88KK+5JyYDC0ZgbV8I/I6Vsnt8URqCT5/fXaETgx+lLCqSACOQIjNeD2DaK974Ah3JgaZ5VHeyIyGl/l1bfqKEj29Yyz0ZOb37fD1Tsw4WcwbIHy4QP4t70MnD4E1ONHzVIsR77V7aYjkrZW0gr5gIk4A3RiTcIjScJSiCETEwfLIHdho0BidUsmxECwifUQRJqLN2Gp02NdiEw3nN44A/KJmvuEG4Vlj1BDCEhfhDLrRNf6bCJswE2jx5reGNZ6TjfxmO4hKrcl0LE+Rq+xsm8GECyeq8mS4/uJh4EJQc+ACsMivnIUkgVoPgMdH3VtilKJ0gFFv3onB2HYNCoiWxGNbQuuaz3edMeDZxJAcQvKeBsMht3g5jMUD07gX3gHcPJQ+FA5kJslwAwK0Qlt8Kw00xyZpksUiLwYiIpmVecWS9tCvU9hf8yTUI9WyolBREQe5L7HKmCSCzkL9U/SB9KH+FSw5hyZi9DQT0/kdikJvXaxbzXAjXDkJvwYxzn6GcMtmObCjQRxdo1NDcz9vrr7Lh6+6f7zPvKr8Yr2w+umB5LfFQUDa4jo6Ywb0UBbOjAKkHMocoMsOVDhwFA+HfOOMqvh1aN943OT490UJvwMhi0wf8eLqE/fjiU9RVFdAkRw3oOIQNzKw7BOCiGso8vhS0I9oNshF61tNFdMCIBibliY1DvvHQWrZ/AS9sIThuF8HSmN7A/emrBvQHbjYqnfVxJmEsIf0zyNogulBFw3aaLqJrngpSINTUnCIeNEmoyb27orDODb+AqdHNHWGloknio5LyPG2F8aWjT1GZU4NiTGfTyS60TjWB/r+pw+R3+HcYd68uqlbK4a19S1VCwKoZVAUYCKUh7S5nNQUQBlKfsKEZoxdKtpsPyJfzt6HYPBcLvhjo9QPvcQlw9fAhePZZ8XXnQ6rzv4Hk8ihNrrbENxPvEqFIPwaaNXLnJg8Aaq54WCITL1xORevZCH1gsflCsy0HFZGMcmnBg/S8L5kQMhlaEjj+XRQckrEiEaBUByTAUpJdtBZMTrpYKUWYxsbTCsKj+G5wSN7uCQphIMrmkbZHP/DkZX2Z7mzn0iekb7zsc80sd0X0OeW8+n+htq20F7TtqOMuiU+E2vTSQGWOfWptRsAhN+BsMWKB8+wGeL9+FJ6VHO5A+wdCrWIIU+HCTZ2nEb95HGnZMKOKfCz2mBmODo6PazRMYEsmSvxwgUvXRKMvp/0mnFJRYxIBGbnEzQkSQDMYZ9WRGSgRWy7W1TEjozELf5BJVevzeuIILlXlAIhQB0IvXZNsnaQWUJorKzADv1kAarmSsQ8hy66ycPEG0jDwh1reKxkf6bugv7QTKJh+Iu+j4de/yIvv/Z14rMEQvqIcTlOmLcxiO5tq9Jy66ISLRZf/2B9E9yBcqj2Wh/BoPhdqOYz1A9/wifdh9Ec9TAEaFwynHwkfs48CUDDo0wWuRIbUd6HgfDqnAgAT0uJOVHAF1KmoqjwKfQo4EjKV6ve418lxpdwcpTqYjUh/yUCyNf9PmSYpvNuZHyOTGOLzceo99X2O/0tW1VdDqgKiSSKI0aCnwZc9SjSu08Wb7t+o71C0KkThCLfSMsAo/6oQiS8SrPaa7d2GfNebS7TRP7eyJ8nB+38WYOzt9SeG4iOjcRnGN9S27HfjDhZzBsgeLhKZ4uZnj1qZBP40X4eQ8UhfzhzooWREDpGKVjfe/F+RU0Ccl+UmIqYvBJJxRF83G0ZBKCGEsIlLkjM+6LyXx/FIZexROxEmcIoUEnKtXKSCpAg7h0kO1glYzezWBFRfeZAlLi5MSbyV5yNqiV3DFSwUW+iZYzisTTF4eDsKAQMshecvTCxJuKyJRMNKQRzkkYoysAFEDhuhDHwMVtIx7ZpgaaWrbrWryKTRM/i4wvIbVke5VAnMqrWGdJHSMyTh+AkrYUyGKCwDgPL3EdCdHgHMq2pwiLJtt0Y8/78uOhLgaD4daDqhLFw1O8cT7D+bJC4wFHHp6BgiQUvXQMR0BViKArnYdLOLFwUpVaDKoERx1/OQrzpVRgjmIRGuauoowocGPHlaEPQI2yOS9G3pT/OZdyH/pcCRKeU5HpKOxL57nAo+EcDAxdFK7JOn7fdl7LNEe/JyLVcxSjg4LXUyJbKC6PEfpOjqW8CIDhAXigWZWKEvoKaSdQ4yoAV4LKqltvtygghdCSkEVm1cu15ocrvwdODQbCIBrRN7ZKF31eyw2vYyJtn8ie3hjS64U2B6g5QEXoa5z/O6wpBLcDTPgZDFugeHCCJ5fAx14BqrJA4YCTI3mdV6FqtPyBLlsRVi0LqXkW8vAJGTp9wC5Iya4Qi2bhhBSd7i8cS5tAkK6rUF2A1WCnE1sgRfJqhOwshtGyqgKyCzntCAiYFo2BlILQc74jy86CCt2XkkgIAdXYdhCoBECEgj1ATkSlK+DAoLaWybVZiueoXgopNhKC2CekZPxBGKbHI9k1yXgQiRNto6GjHZkw0InLQHDkgLIAzecyaxdlVyzFt1EYoqnB9ULeh5DJPL8wEaNdmGWfmGiCwGiFMMxJK5BLJNA8h25CGPaIaoUoTMeTj0HEtGyHqN04LldMnAODwXBH4eZzFCfHeP0x41NvEMoCOJmXKArhx8JBlnUA0DLHlDzPEqkiKVvCX468cqUKM8fRgFqmIpHkXCKg0OjHgjzgCAUJj3rto0AwdAXvHSAishOP0eiqkTtBPHZG2EwoEk/yZvREZsKl48qQWwh9ddKHCxWjEb2Xvbyy4O0MfapHNITOSipIiGSpYyGuUI8gisMgOiJvZgIx49mYUxj3eQ0h1WM1hobWwLtpLlxRAMUcdHQU0wLgii69A5Cw1LpWD2KjBtelbE8YXGWImxtde/vjvRiJnNk0jDXs17z3VLBuaoSN5w5SKEz4GQw3guL0BPWihqsZngssGuD8nLBsADiHugGqStrOK4f5TMhuVoqRbFYyCgdUpewPwo+IJQxfqvODEYSij6QY2oZQzlQ0AuhZRsViKtbGKBgdg0AonBCEI47W1UFNjWBZZVYPZOd5ZA13iVUxoeQ05WHkKVEZPo8cL7wQlfONEBE5FDQDKoeiOlZikIEKmQkRcFuDmoX0OUZokcw0hDCziHahMr5/vOTh/roG6kXsP61AR0UJhhML6PwIKKqu8Em9lOIGi0vwcgGqF9p1Gu5S9K/n+/czEkMoXpBZDKVKnp5T9PvKSScSJmUEFn4ICbHRhIcvElMkuUy8RZFHo2Kwj2EojMFguFtwRzMUx0fwyxrUOPgWeFIT6oQfoVUZy5LgHHBUkXBk1efKshBPWrARORIuaj3QIDiSlCejiOTYFlABiIRn0XGmI9+LvimDE6snJjUyJhhYO5efxuhw98riwUvFIxCMr6FU9JQg7Ob6aaPrCmMsNC0keABBcKS51cVcI3UcCvmAGmnixUipgpB9C6ove9cai7wZGFcj/0zwac677OV++AZofO+cyGUsUTkEJ4XEylINro/6BtdYKGypHFsLt6owlK6mja4pBjyLEeNr7q0LvLYB325qhO2uvVlkzS4w4WcwbAoiFPMZLs6W+Fc/dAEuKlQl8NxzFWYV4dFpidMKqEohON8QzmrCZY34sF7pX1xZyHZZdvsLB8yKvkAsi0JfRaSl80/LQoKsr0QdqQFDYTg4Tr5HhGkbR9QjPyFDQgH1RBYhhwKxXXT1KemxisM2iEQNXYm5ifphChZhVvhaX5soFlsVbY4ZaD1cW8dzqSjg5idCbprkzvVCPG+LCwgJNqC21GEpibksfDQTiyhGvIaAPIFkhNiRiIbdcAPUDXCpoTThOJVANQMdHYMePi9k5hm8vAAuzsGL884yGib88IDB1PtuUIwLRPkiMnLI+oqfORNgXZGXIl4rjj1rG2vuaN/5OldjgnBMDPb7nBKEBoPhrsBVJdy8wqdfOcO//Ih4rR48LDGvCA9OS8wr4OTYaaS9w7JhXDYOixpomeC5z4lAnyurYigOC01hCzwJqLZhmXtCDZPWIwrHYPQEOpE4xo/yit52zpmOGC5E6KBELO6obUJ0jhbszuDVw5kurJ4YXUNePTAIV50SgIFPQzRO4RsNEw1G2FZ4VPkbrkJxNOsqUhKJmPKJYVUNlT1vYRpKCqz3FpYj+3PxOJZDzw1Qe/EmxrzBRHwXpYjBsgIdnej7UrpsaylUVi/AlxdAvUwEXi4AR3jVhSQX9K47EIDxM2dG2chvI2kPmbCbNMIGmPAzGPYDVdvnEVFRwB3PUF9c4IXqMT72CvD4aYv5yRwAcKSvs6MKsxJ48KDEwwcljueE06MCTcu4XBCeXCA+PZc6jkBuxYggBIQkS61VMlNH0qwUK+W8klenwrDxwLIRYdi0iOuRB6IqXOiTJ72HIbovWEwpI78xMuz65xim6jQcxxFJREeIVGEA7NG0Htwu9bMrURUNitQLiFQc9knN+RpoGY4biGwpQUfHcCfPAQD88hy4PJMxToSFcibidg8bRWL17JOx5DDUwGIBLM4QY5yqGejkAdwLL0l3l2fwT9/sLWNBGTmuDBudCEnZ1HKZWiw39Q4O8glXWCq3DRs1GAx3CK5AUc1whHMcN0t8/KcW+GypvHgsnp/ZkYTEHB2VmM8ID08LHM1FGBYO8I3D+SXjSSuCMHBhURDKUnkz7FMuS9sAgHMER1146awCSuXMIlTc1zSMxoutrvFQvsx4Mgo83R4xqG7LmzEahjS/MRhWkwidMM4gGsNiPUEkEjy8F6HYeo7XiMV0Ct1G04lB7sRgf7uWqJU2iEwGuRnc0RwUQzAZ1CzAywsxrk7xZW5QXSUQx0JJgekonEwg9sI524X8CzmQ7NU7WILmx+DTR6BqBniIgfjyDFicy2dJ+k75Ng+3zHP4kRk9p9I15JzhvrTt1uGke8CEn+FasIvQunXQdcu4bfHRj76Fd7zzEch7PL4U4eISC80SwKJmPD6TP9LZrEBRAC88KvHiA0JVenzqNUbrhV18GQSfJvJm0RNl2c2FrQ/kJtvBAVQ48dIdVUJwRxVQHsl5jy+6YLqwRnbhu3XTwqTsM2+hV2XQWUWVYEe2vfbRcEdqAOBCaZiBJZUxKwscHR1jsWzRes3fS38q0VOUbecIx7kFmiVc24iHtizhHr0dfnEBXpyNntolVasXauo40H0hhU6dQQC6fGCB9MKX1L9GOE7LBXhxAT57Qy58/ADF298Nbhv41z8j1wt9R2+Z/kZifqZuO99dL4zdKRHpjYsCMBtvyMdkPT+cpx+ud714Trhu9CKG+5ePNw31DInq4cFIz4kV6SzJz2C4qyBH4NLhyeMFPvbxM3zee4/w2ccN3nzcDtqyZ1ycA2cXMpdWWiX7+Njh9Ijw4nMlZo+AN58Cj8+FG1slr7btC8A2BBlEISivoWZJ4OYi4czCUfQeVqWIxNO5GFEXDXCxBOo2NZTquXqtJnCfYzRR8HH/WORJp9t9QRj3RwGRRMQMonF0HAQ4csL3jlAWjFkl3F83LRrNgy8oFHChaKQrAseFbabYRvoOHrUGMYe/baOoK5yDm52ATiqgWcIvLiSXEABT4Jj+dx3n/JgzH9IJ2vg+FofJ23DGB5lnrYtkoS4UJXAydOmD9hJcX0qEDZSvqiPQ7Aj04HmAAD5/Aj5/LCGwSVEaSt7LfepzbnQSRiGY8Wea574h93b3LTeCjvPwLjDhdwO4FyLojsOVO3j8SoJWMcFi0eCVz17ixecqvHWuIilag4JnhuMDr2eGb4DXH7d48wnwM95f4dEJ8PginEuxHZDOf2E/QEH4ZaIrGsEkOgSXtfw7W8jx508Zb3sAfPZJX+S1oGg9CgIwTtlRaAUZpMSgW6FdkWwX0fUTuogxgdoV9fYzs5Br0+DBcYXLS7VCwkfxECa5QCqcEVWYpONxvbgPiSHLBXhxjuL0OXjNC+RYfSa89gVVvKEh1DI9HguthBDOTJnmfbg+IQzOc74TUPDA5VP48yfA/ATFO94H/+rHO0vfBgKQ4/UmrIIuE4B54ZgYgukGFshctMX98buZFoDD0M41AtBgMNw96DquzIzHj5d49bPA8y+e4o03G/gsxC7lSQDwqt6WS8JyyXhy0eJkDvz091Z4fNaibTnOI0EADotBKccNno37PBbQevl3sUyEnSPMK+BtDxlvngHLpk9sHAWf7k3EYZjyAm1yIg7lM0uDmK+ftXPkO0NpuF7wDgYuZEKLLj0uzrbkMa8cZrM5LhZB/EgD4i6Ev3+3Ak/pjtCntouPAUFThSJmy4UI0OOH4PoSvLzowvWRIXj4goU6OO9c9z1FDsvaxBFMGVojF7mOa5G1Rce1gIq2ZinjPnsTYAKdPIR76f3wT14HnT+WMXm/lnOjxy9wYM73CScOjK+ZsbMTgOH5Y9hH7/PsARN+B4QJut0E1W0CVdN/VFQ6XSfV4Qs+9BwumxIf+dglqqNjAEBRqndMWcAVTsJTCuDBqcPJEeH5hyUIwJtnwFvnNAxRof52t79PTEBqNexeywKYqwXzqGIUhcxZn3vaee0CQgiJnMujfcbPnk3nDhMEsmZf77hec14VUTRfCUKM6a6ighKG33ssmWVzCssLbV8Mqn/tglx4TY5r5FqT544Ugpns20I4DdiNI7k2Y8CdATPgGQ9OK/ysL3qExZLxqVcbFGWBogj8OORJQDx6IfTz9Ihweiq58j/5GQ8QxfBNYMiXMcQy2BpHuFHad9spx4Vw0KoAjmfCo8ta/iHnwjXbm92mzGMV9oOi8beg8XNiVE7sozu3boF59gzGsdxbEmrP3bH0NZ4T7ms4HvPMKR7zvoVfnKM6PkVbL4b8GG9+ZqBMx5Ard848fsi4d8rQOsanY+IQ6AtAHRdfPAafP4Z7+QPwKvxGkXkocwHYXTtwY1+8pePaVQAeAib8DojieP+FFe8bbrsQ3OZBhEoxUbnZEd5cnuKsqfDCux7gSHMXTk8lif30pMCsAh6cFJjPnSSWM3BRA08bgvdANSO8OEtzE+QaZcxRQH+/6wrAVAXD6bbs5zjPhGIvTSthKotGrI3Hs5H8PddV4pyqGNrlIkBffW9/DN9Mj2WhKi7k+FGXxxCELBGB2yXqusVMw0wKamIOwlRuQsj9C2ElsXJozP1rQOUMxfEJ4Fv4J2/Eyp9dLP+KdY+AYbL6YGF5jGxn8fp5SeuR4718ASLQ8QPQg+fhH7+uuRR53kOwlPevka8tlGKqhHXE1BpHE/t6n20dTPQZFDvxwcQ5vrndgvBZFKzsPXxdY3l6gic8xxKMt71HcvqqWQlHwOlpgVlJODkWnjw+clJRs3BY1sqVC+DNSyHB41PgGEBVElwwiI6kOQBD3gxFXyotmpYWg6lKFYBa8IUhoaGN8qYjxvF8mNvnMM2juSE1L5qW8+mARynGyQzOSbdjDmDgUiJUBYGZsVwuNFdQORMtCmQFXzJedRmPuiwHMF3uiXwLKkoU8yNQUcGfPdbq2Vn17qmcv1idlLtjW1cKHeGzQc571pYn+NKz5No/eB588QQrkXPvFCby9gZj3mT/AQVfwMGEHxEVAP4FgE8x868hog8C+HYAbwPwfQD+Y2ZeEtEcwLcC+LkAPgfgP2LmnzzUOG4St1XkPIueyH2+i6n7RaUDuMHsaIZ3vucRiipUKyvEe6ZVyhgOdQucNYQLtZRVJVBWwNGRbuuaf7NSXssybOvitrodQkSIhJi8CjuZV4Swlktd/D0hosLpcg5YLeqmiGlY4UyXfUjIhgCQLhFRkpeCYKAuHxAapsJB3IQqZZKU7lup/lmgQQV0BV3aejIJfYqQHHtQUaCoKlBRAb4FN0v4p2+CmmV/ncGJReBXFnUBMFzDCENyGa32idXkQ1KQBrNjUClE6l/9RFLlc0PBlwqxvDT1xJpFqxa1nV50PWu6bjHbkX15X89CiKdx5NA4uot441pDx6+Abw8pJq+Dd69bXK77TEQevqnx8LljvOvdmjd3UmJWEWYzMXqCxDPl4dA0wHnTr+ZZOODouMvTK9TTV5YdL3ZCTvlTl34o8wJiJFU9PWSqCyKv9UCb86Zy3KxkzJAaO3Mj51DkFStEWv9c7TMuo9RfMsIh8HNqKJXteD0gFngJ1T89M+qlcE+JFrKMU+BKHw2lkUfjEhD9ImrjhlSCcwWoKLTCt1b9XJzDN8vEkBrOGV8sPvJsYlhduV5guj8XfAOe5S05l4DqGDQ/Bs2PwE0D/+QNYHGOiJTvpwqrxabZ/jFOXMPBvbZTfRwIh/T4/S4AHwbwSLf/MIA/yszfTkR/BsDXAvjT+voGM/90IvpqbfcfHXAcN4bi+G4VJngWBOFOuXx5jEXYXzpws0Qxq/DyO0rUXGDZAOdtWI9IwkXmui7RvOqIKhBTUXSEEJ/JVcR5LwThGYB67dp0gXcdVkkeoGmvXBGOq2WwJMi29kEq2gL5pGsZOWjkSEJcwp+ciDgg1BiL6xWxB1ohoJCTQDG0o7PwkfZZoBNikXySMtRFJI+MmMAgV8hyE0UBR5V8gGYpZHR5Dt80kXxcSi4jaxL1tteRz6ry0+2EEMxJxzmgmoOKGTCbw7kCXC/Bi3PwG6/KekRTZBMwJfhGxNpgfaR8/4jgy/evJbvYbv011gq+++0dNI5UjgwCqxjhoLViptxfPA6veXViMuAqPJS3jcPbZQ1e1jg+meNd75RQTXKEyxZoW+XFOaFKxNusQhRtwegZOAmQ18CRSo2RP0MEfzCG1k1flBWJ0TOkNpQOurA7tO2QR4UffRRnAOISDdHoGbmUe2It9qHtwvXlImGuDnyKWIGS9TMSOk6VyFnl2on0CoJHAVnUHhhWvi64GXr0BobTFiCnBmxZP094VsfRLMHNpSyLoMVjEPh8C6HX3/b7Cb3etl/NueVMDMLVHK6cAWDh3MU5/JuflXNGIms25t6AEf4MfW7Lwd3mxHPAHjiI8COi9wL41QD+awD/OUkQ778P4Ddqk78I4A9ASO2r9D0A/DUAf5KIiO+Bybd8YKGe+8BNCK6rRLq+yloUBNQLnJ6UePfLmjoGEXWy5IuUh3a66DqRhICECd0zQJqUXhSMgsQrl5JMGSuIcU+cOVLvWyTE1LPWkZtYMNM1geTa/W0hErH6qTk0tvF9ekmIipJrrVp4tjs1b9M/N97WQAjwIHJwYPH+OSeJ7q6Ag66h19ZSHaxZgNWrBwiBEca9eetCTibDN8dIZ5NwElcArgTKmcQclRXIBYG6AOoF/OIcePI60NR7WxV7xHEAodcbQ3K9TYXe2PFnXPAZRyqC8ItFbkfEEJebfcxV4jH2talH7ArFZMBVicrbFPLqLxdozs7x4vMVinnnlQPUqOnVKxhEm++EXWr09IxYhIscR09Y8Mq5ojNaEjQfjqBr03b7KfAn0HnWwvGMP4FuW4ya3f4gvGQZhY7TRYVyNIySD2bPpA06ViX0fxNxPJyPZcif6/ZPhm2yF352TpdpcCCWCuUEcZE638r83tbgulYDZAtuu6UOgsjbSeBpH7KdGEmnQjenUibGeNc55dwCVFTgogCKSovNkC7uvgAvL6Vy53LRu2+b8O9GHr20r3jecP9OHJyNZ18cyuP3xwD8lwAe6vbbALzJzKGszicBvEffvwfAJwCAmRsiekvbv3agsdwYZqfV6H7fHu4Lu0+4CaGXYzvhVwDNEsfzRmP/vVoqZfHWynm1KHaiLVjh0hh+FlYBWIlARRlDRGPwqKHXrh1MOqs8arI9FGdj2/19QnGhbycHYyvZT921YnJ6jK1JLJFAqPKWjjPNhidQV3HMM9i3oLYGWvV8qQcsVoz0jVw3zxdY5cXbcY2gUbIhJ78DJ4spEpVAUYBdIUTD8hlQL4Gmhj9/qsSzzK49YsXb1po4RjIHXCNoH6HX6zOMbcW5k9e4P/hjMI5EedQXWGPf9xRfcrZ/lXiM52wpImPfu3gic1yBmOzG0v393KoUE9/An5/heN6i8SJ3qkJ50mkEii523hkygwdNOSdJFYCWJgnyKYQ5UjBipjwahBi6/YFHRYMFgSZ8R5kYS3k0xRhf5vs3E2cZN6Z96Ki68VBsl3+7JB84aEqlUxG1UgQHIHJd3RjPUiWTw6LsrRgc2cd0Ak5z+IDJKJiN8vIGhtOJNW+TvsbWpBUxp6KuJK34WSj/OqRFT0SsLsD1Ev58IZ8vrM+Xc802BtZtBV7AmKF1U0/etpE1O2Bv4UdEvwbAq8z8fUT0FXuPqOv36wB8HQC8dEdq0Dx698Pe9tQXdAgheI8fkG4tqCpRFg1enn8Wb3+hAbVLMDOcb7pXD1DrwWC4tulIRWPlKcTrBwEV+mYOSgnd//vE0Omv8K4jCxBpHxr7QmnvGdKJJJ2wGDG0MrJlEgIhFs3s3FRUhX+ZtVPadKQsfaT9jxDJmHUwHfs2FsJVbVwBFhMyoOEtwXqIspAy0nHdIU48jjWwWMK3DdDU4HqpoaCZeAvYJfY/nrtdSIi835NMVlgoN87PW2Xl3LDv+wDjyA4P3yUcOfgNeY68uPb3pu1yHt1ERHI7/vvahqtzATo4ZwOBt6mInOxrRVrJTRaVcSXDX17gfSefBleymDb5RvmwjV4xgEFt04k2UXQI3EhBJBElgqtTOylf9sRRFH56LORzobc7a9vn3NByXVXqiLHiG5Fr4g4dd8JtUZFKH0HeRp6MfJq2DXzRbXO6P4g59qtz6aZE2iY8u4kXLux3DmAHOK0GSoFjVbg5KPdSV5En9NWqx7FpZRkm/Ye2EaGXRuAgE2wpVoRf9ppt4Z2bOr6Kdw9pSN0Xh2CLfxfAryWirwRwBMlf+OMAnieiUi2a7wXwKW3/KQDvA/BJIioBPAdJYO+Bmb8ZwDcDwIfo6E6onOO3P0L59pflwZA9uGnB6QOx/vNN2+VFAcMH5uA10PdxduDw40wmCI4HemO5KWF4nwUpzWZwvsbJxevw9QLU1hI+grDQpxJXECsaHy9QsovfqxBAyJuj8L22OqkD/d8NQrx5IqDY979/7hNCJIj8c6S/lclKUiPnIf08q9vTyr7HLXBbk4sSSSzlRg6Abhek+zXZwinhkMssei3QNOL1C2sUtQ241TDStqtW1qvAGbBpIvaaEMzRc26QZMb630TgTfc1Jfzun9AbgXGk4uj5E1BRwC/FA95Zvbt8mm5dt3FOy9sFjAnHKdGYn5NfY2WbNeKxmI0e7vczHx9PDtpFRB7AA7ird5LRoH78GA8uXwctLzqDJDAy7/sev1HKb4Ebgb6xMfBnNCqmfXacF8RQd+1MQKVLD8Q2LTq65P51MPQE9rBuHtsgSnslv67oI37bq0Rbfu6AX5VPGUjiZAGwvBKB9JWhbUBRwIVj0lW4PtQY2srfYdvI+3oR93FIdWiajmfTPuJn24BnN+DYwTlr+jyYN24LQ2rX9+rPsQ/2Fn7M/I0AvhEA1Jr5e5n5NxHRXwXw6yFVy74GwN/UU75Tt/9XPf4P70PuAgCc/rQPAs7Bn59L/EL8IxKLBzy0Akf6hxWsUMl+SFle+WOLLTrLVbIITWf46nxEU1hV7n1TbCzs0okW2duRY1Hw9jxQI9caTORjx3nQPO6Pbv7+du+E6OHq73NHx0BzieqtV+Avz0ChopVvkHq/aGzCnRA7g0k7Hh+2H3yzW4i2lX0D8ttMFyQaLE4U+qRk0Nnv0vuJ4+F/PHKeWluJNGkyJRcRbuQ6UonfTfDkBVIJ220LqDUwkA03TRfymVkJY1/ApKWw25wWzPuQzbax/VN/xyvHt6HwGp2KtwzL3EREruvjPsE4ssPzX/alOs8DwnP6sNa08Esp0ORrMcS0S7Hu+7oWa38tf8teDTVTfzep97A71heN+f5VAnBb8TjWx1S7bUXk6LU2FJFTGBN5q/ImV4GoAZoLlJ/9GPD0zc4LBchrXE81vMYTtV2aMhCO6f/S7yHjg5xrOg/gyDk5QsLh+CcaOyE5uuY+9bhxg/4nu6Peo1S/XfjsCU/2RFwQcFkfQMenYaki5o5bWfmU5fmGe3zL4n1jLx65JLJmUrTFa64wdu4q3rJ+x9rswqvrDKYbXwubC7lteHVXXGV8yO8D8O1E9AcB/CsA36L7vwXAXyKijwB4HcBXX+EYrhXH73sfmk/+ODATURFJzXM3V3hMh25hwwexBDQ5oQz7HzzKb/FD2soyn0yi3SKUE8I0EwmUnhvj5DOBMXm99FwVzun++ELJBBlOSPpKj0Vx4gC/hH/jddD5E7jXX+mHOcYVYgvpy7luPxHg1Ss4EFfUtcnEf287FWZjoZ6rsOo3lHoNtW2Uvb398r/e7yAX8IFgAmLbfh/59dBKYnlY6kEWVZJ/3DSRkKTPNZP1DiTT9bV+Yt7WK9ftXzNODD/LJtbDqfYbWxFX9LktAe0i7p4Rj98UnimOLB89wOxkhuanPgYf8mqbYOV3IGY4V6IgAmYFqvkMKE7kcFGKZyGEXhdF97ti+dvhptF/rYjFpoVvaqBt4WsNFWsacCtrzclDbV8QjgnBVQIzxTrvYS/nNbYtRs+d6mPqWtLnbsaVKQ/lqnFNgpdAu4Q7ewpm1+cE5RoOnNOLUPHd/enNUaxzZT7vDhSMvIzeAx55u+ZzTRqdc/4ZPXnF5vr7ubGNZ+weMKuIY6DHp3osX2R9Fz7bgTdH+9pAaE3ywxWKtlXX3oY/p/o+JH/uioMKP2b+RwD+kb7/KICfN9LmEsD/4ZDXvQ0oTo5Rve0FVC9+mVhCAHAMEQNkAkv/ILnzVCR/qNx7DZYXjpNOnNxTD5a2jWGlcnH5wWT7wJCJYWwCWvWAtqG3cO2PdIsHvU1/8ON/MH1BxDmZ5OMZ0U9jFtRi5oDXPws6epjcV0YsQOJbSPhgq99x8t0mJMa97w/dbyP01wtNyf7F/f3PsvFD9DaCfxcv8bYT1SaTdux7Q8PIFkJm1AM60n70/k5N4hv2KX3sT0hyzc37Xn+NMcLata/NjVP3Hc8yR85ffjuq5x6ievTF4Hoh4dTLpXj1lsvufaP82dSAr+X30wTeSn4zQaQ1bTSSuaIAk0NBDjgqADqSPF4A5OQYqYhE4QY06BsJTfNLua6vl+JpbFsVja2Mw7fwdauicqlz/WZCsCcA9xCPQM5R4yJym7+x/Fw3Xq9uJVzBWP7oD4CDqN9jzt4W1zGfbFUQ7gax8l5sw0+94/tz4Mpr7Sh+thJpe1xr15SFQ/LpIXA3MsLvAMrnHgKf94VoL56A6gt5QF9qcnMMJWvVC8WIoQ6pSz5alNJjLE4f3d0RVRZSF5OYqR9OQcmxNKRhzO2fpRONlAAApX5JREFUlRruLwidWLtiuF0nZOL+6J3pCxNOBWx+fBAamR7TCWQghNAJ6/Sc7DrsQ9G87DNtUK1pypLVvvZT4tFrsmpVsa+k7209VF0nI7t2I8yVE+ChJp8NBOJ+Qj7DOiJYMZ6NP9MegmXtpH5F38m+uXT7eOdWP2jsd78Mdx/VC49QfuHPgn/yOanK65xErLgiCRN33e9fjWGhcJIIwRZYLiFLuYScIS2sFAVjOxCJwdjJvgagBZra5LiKFAZkbp9r9UDMwXysuUxJOHrIHw4eSHLdb1wNflw3cZxBOAbByM1SjLi+7URl04rwxIjAi6Kx/7qq7aYCcPShec38tsoT6OozPH31CP7N19de5y6BQi75Qfu8GyJyF+wjXPb5rWx93S2i6Nl7DakFutxH6uaHJBdSUlaSVBWdN0J7Iop1B+IizKFNmt6Sof7sZ7b7fCMw4XcgFCdHWDz3Mtqj50HNJUAODlK2OK+o5BAKfEiRFwpVD70HcVL4hSFVFlNBFV69H91PqfCKyk77ioIKnUgChj/8PDk4Gbsgxh52YYmZUM3DLymWtk7OBTIBqjmNaehkGGtom74i2Z+eE8V0CDNJ8glSj2gSZhgX505i3WMIYqs5fE0LFCXci2+XUKNGclBi0rImMlNdd/3FisNeP6HehzA5FWFYuSDMSIb9gCTiBBcIadvJ8gBesVXnrZ28N5igd7IaDq5zGHG70nO1o3Vxm3HsIzYP8hk3NgLsLh4N9xezt7+I+tE7wKcvwjWXMqfUC5nballrC74FaU4R+Ubm8pIANwf5Utbq8qyh9JDXuN3xrISIt1L9z/tONDYqxnSO5hDyqUbEMcEoBsbg6RsaCTuh1Xm4RCQ4OFdINUNXADOnQrHzQMqDnlQ5lHL1lF0/iMc2EY9eKxyqYFSBKTmROt62jX9rQ6/h9PaUiMyxSvjx49dBz30hyM1BVZkI5f0xNZ7bIqAoPHPElAmgH6mD5H36PNYt6N4d4/gWYPmMSV+8oq/BvmCA7/XZP7ZSAG0hjkYxmUOJwbNi2EXp/tTJQSS/A33O7KfuJOkxyXsKYi1Lren5UjZBfDbvovPAXow4HPIju/1pZJ+E4NYIy0T4Vp8RfZJXGSPH/OQ9PwSHmvA7EMqHpzgrn0fNHlTVkLVmEgJjBjmp8ul8CyZ5hSM4Jz9ix2E9mFDSGMm2CJtQxjh4AF36R46uBHHv70zb5NW1wg+XQjWrQTWoUIbfxxLMQVRyqD7Z8+CpIEUiQHXS6gmHkLs34lHrz0cTgjQ/norYvG1oky/O7fUmhQkmPES4sptQuLPSgLQccdsA9SWwaEAQ0uaiAFUzmVycWIpl8dBEuIQ/ahWJoYpVIGlZu66NYcLdIlXhHhQDEdStoaeYEIZxb07qKRkHYTolLmkoRHfGVXrrbspLt8H1143hOsTZITx/XaP9r/WshXo+yygenOD85O24vDhD4eYgAFUl4q7gFgU3IHJwGqESFqKWNT0ZaIJIlDBMamvdXkDWKAt5g1op0DlQ5QAqQTyTbebOYu8KsRcG8QgghuY3jRj96mUUiWiT0E7v9ToqxtAXi7Id5v7w0K0CsrlMPHcJFwOJt7PjNnJF4lUogNKByxJUzDuPQlECIFDhwCT806sxoH13nkgVwyoUuZUHU79sEuGowjIYRDH995rvv/zIvxF+bRr4Zb0RX9wro9CYaOkJkUzs5EbtrBYAZdtAMBxk10qPp+3HjOXdRqq5kiYj11wl4FZgMtWmtzkiQOPzYxC7rI+TyTNn2oZDm6Sv8HzqE3Gd1jbYgNev4rd5k9xnwu9AKB89xHl7ijcvvKztBcDpgt4VycKlRVjgOyzmHRb6jmLNw2n+V1zYm4I4E1FEvpVX3XY+rI0j68BQIqryRUQDkcZiKSoeCWU0hESvm/YRTCJh3bnu8b8TqMnJalQRyxTF+ST13vleDwCiWAx/pFGIIsl1TP6wo6jU8wZLZoDloSDkUCbXzRf+jsdTYRhDWUO+XtaWAJSlkrOXcCVfd58xCNg4dv2wrpB749SdX87Fyqv7KViHw7m+CwVCXGYgLD3QSIhTDLcFglqMRXFy8ZhbRdOJJ5vQw0Qd9d7AEtwXglQk9yei3yZOnqvGAbHOHaICbXfdKxR8e3oTD1H45MrCM2Oj/QXp2us5B3f6EO74BM2rn97oeoa7AypLlI8e4Jwe4aI4RVW1IAJqUq70NRzXYM/xteAFmD0KKkEVwZXHKhIl3MpxCyKCg2zHZXTaFq5dyO8xFIdqFhrRUXeijT3ArVT2Dwa3VtZbBRhUOBCVQFWqaATgnPw5xDnb9UUbQ9Ye87KoNKdGvroTil34at8g6WNeXOCgoXgDvOYi6tyUe/Y867Ntfz+DYogqFQWcK4DCRa9cf501OQYVljww/IVrh7L8TfRWuONTFLPu0TKOq21UYDYqoDtjJ6v3g5PIGx+4bgX2eXg+xHrK9wWuuB0e09uG6/yNXKfhw4TfgVCcHONiWeHxBVD7GQoHzEoRdmXBGomor/CSbgevz1Ty6sjDkXz5ZRCF6tornOwv4n41WlasRh9tR6xiriv/HxcR57BI6Ig1JAouTsQPx+1UUIa+pO/+tvSr7bi7dud1nDhHewNcrH7p0v0xBDQIUl24NatwKZ5RJyKYnBIqKSnrgqAajsncWXEp5AJyJxYnRWJunZ3cnwg/n35mFqHoa6Du+uaknTRWL6PTinZlCZrPuwIFruyWDUnWoIsiMbzWy94Yuu/JJyGnRbcPnVQfVmVVuKwv7xGFZ/isQXiGPl3/eBxGnlsKgPJQ13jdNaFII2GvwYs5HSbk9Ph1is2rEWsH8+gd0Ao6ed+rGYrnX4R7+BwAoH36BO350/tl+TcAAKgsUJwc43x5hNfPGYXzaD3DUYvWA/OiwazwcA6YOQ8qgWoWDKXye3BoxCjq5bVolyI0WomwKViMb0QOReFAJcFVfZEY+CEs7B2qYpNXz1Yj3jxqE69ik4SLglUkeqBpdc7uBCeAKBCpBOBmYK507iP1MnJsE7yPoWhbFxGiHrfgbdQx+CgYG63W2C1rEYXeoAhOJgCjl1E8kKESJnsetslEZEAvEsR1i3ITAP/mqz2fjuRFyWenogBDwlqpKEFlATo60UW8odEyLsulTDvT/pLomSgaByLS948HUeml8N06sXOIh/67MpdNFIQ1bIm7EsFiwu9AKB+c4HOvL/DJzzAeXxZY1PJAuWwAOBEpR0cFCgfMKzGmzSoSotPn91kFhFQ4RxxrscjcIduOwrM6x7oqwiHygyvUm1g4VtEYRKF4GkVTced5pE5EFs6nOavJ/nBeItriq4pX0oFw5+3qKlcCIK8OTN8J0aSPdJxRXE4IxHw/Ydq76aCvrpUHgFJuagEWLwOgDwLhvAbsJdyFmmV8TWPrg0iMoapZGGm0PLMf5Hf2loBI90+Ix154rmfAN/JZm/RcjtZnuBKYzUHFKbiQECAifejwIgZ5uQCapTzI1IvOu5heH+GyebhSXxiOFcsZK5Qj54bvFePHV/UVz+mT9cCrGc5LvIpRiOZackDKE2JzLBcy3Kf8wWRdyGwY9xoxemhch9Db5LOw9yiefxHliy+BmdG++Trqn/yxrlCS4V6CigLlowf45OcIP/AThJOTElUBnBwRqgI4motjzXsAxGg9UJBH64XbPAOl82pQ9XAEzAuNqCkZjoCqUOModbnyhEaMqmjgmeFY0i0KqKdRy9tLmKl4E4MXkTQElSBL8bhgeAx/4yo4JZUj8SLq+mbCGx7U9gUi+S6nMM4XIZjElwDp+gphwkq8jfJhinBTASTGu5jf13TiULe51SI4rS6gnUSKBKEoHjvdl3kiBxVJV3kkHzw3es6wWJoW2lnmnJNdK9sfi2YUIjadVmtF5UDzuQjMGFkjQjJtPzRAcpc/GXKxwnp13nfCsQlhsCoiQw2Aejh3dWNfndu4as7cVnhep+HyqnHT198Ud2WcOUz4HQjFyRz1osFHPnqB188djmaER49mmFfA6UkJR4CDw3IJnC8JyxrwRKhboNCHwKoklKW8L1UMVoWIw7IkFCSi0TkRiM4JWYpI636AngEGo9Xnec+MYHSTPHgVOUEkZaIxOHcK8nE7iLLYJoanahgreX0VkeuckiV5MdzJBdXAyQAoXh/qCeUgIrnvCSW0UTRydm0ZS+LdCWG0SkiFhmAGgu/tb8P+Noo1By8E70q4WdmRCJGE2foW3CwkFEUFIRXB0puJu2BlRCLgctGYC67sgaAnCPO2mTdRxGED+CW47trFsEkm+WEVJej4BOTKWNqcmxrcLIHlpTwYLBd6P/XHEDx8mfAChWt0+2Pu4VRIZ/gcRQhNzb/Hbo2uXb2GPY/hlJcw8w5OibUxj2C4L4OHmYlCO1NCjxyN7Du8B3IfHGIc7D3cw+cwe+d74J8+xuLjH5UH5UG7u0mkhtWgwsHNK7RNgzl51BcFniwZn3NiJJ3NHKqKULhODB7PC5QFcDQDChIDpPfAZS3C8ClxFIrMwCwIP+dRFSIOZ2q0rIJIdCz6iTqR6JnRctsThqSvjhrhCBZxCE7EIgjklOOIQDM1KnILCsZER5ofr+eFYmHqYaSwCLaGh1IQj6lIRGJwa31MAQDQFSMDoCoZehNAxTzLa6TOkxYsysxaYbQF6mUsVuaXKhIbrZg6IhJlXP05PTXebSUWE6wUjIO2QaRNGCwR7NErjnvuF9jRtIvgxXQa6pt6LaO31nX5/PGzMZDmSUbRGARlK8aBKCLDEibJupIAQG71vDuYK4titN02ApInuHCzcw83d1+3YfS6ryXXuzl+N+F3IFBZwddLPCwX+MwZ47OvtJgfzwEAsyNZDOf4uMS8IpwcF5hVwMlJgdM5oSoJdQO0ILSNw+USMVCzE4LyWoR5Xf/GSw1XcFE8SpuqFPIsnISaFg6oim7+EgeWEChDOGPRimW1iIbGTiDG90EHZN7EXERGz5s+uYcQVrGehnMo9iGiESoWnb5y75XAcERJUZNk3cNorWR4ZpB6+gqdlJ0Kg4JFeLVOfvqFD/mYKvx8KyGv+h6+6USh9xKiQiVofgR38kgm/3oBbmv4xYVYePWa5FuA9YsKhBlIIhB3mNwjsWdCa0z4RXEYkzG1Tdh2/XNDO83JoLYGlheAZ/joXXNANQdVc9DJI1BZAb6FvzwDX54Diwv9rpK+9LR4jUDYkbD6FUwjkWXijUaEICEn+CyncECKWT5hagzIw0ZzIZhbZTPyywWg7AtGkcMIwG1wE6S4CmvDWl2B2fs+CPYei5/4MRN8zyKIQFWFy4saP/DD5zh9OMPxnPD8cxWemwGzWYEWDpcLxltLEYNVJb/zmb5WGiFzOicUBXCkkTNHM5aUNCrQeKBhhm+E2wjiNSwLNVzq334ZXslHb6E6jSQaphAxV5GHV+5itDHnnFlzEXVOBTpuiVEmkDy8EH1SeA9QCeccnKuA6EUUIyjFEFTXzX9evErUBqFYy9wbchVDaD848hRlxkMORdraRlMM+qGpoYAMKvlH7lSYuSgBOC0U053DbSMiUUNR2TeQB5hG1mIE94TfJiIx3c6FYgCP9BmPTXkJcwNr1j5/j7CQfJKzn89NfkVfHS+GIm8hbFUicqgogGoG0FFXBE5fJQw2Q9tGUdgJxzbmSYont0lCXDvxGO5FsULEHVY8bi9k1s376wToXeGN22LEDTDhdyBQVQLc4K03z0CNwwdeKvDqmws8Pfdoapk064Xc7mqur5oAPZsVqErgwUmBk2OHh3PgaOZwvgCeXhKWDVAUmQCM23L9INaWIVTUUSIS+6IxiL95STEXUQg0nMOoW6Bu5dou8ShG4TfhNQzP1JTv14dr8U76Xl+5By8eD/c2evYQ+wjbjkg+KzlZk1e9jqVjNG2LupFeCiXjEALoVJAFge3Y6bWLaNGN3joK5ybE2jRw0SsGUFGheHgk7S7PxYqaFhxQMRYtuJmA6vb32/VE21RlrkhuWwpBYqlcCiBUzeP6QpuqFXQ2h3v4PPDCy+CLp+Anb0g/eUiqc1Fs5UtWRCE4Id5y4UXe9yuOAtNCMHoCx0Uae9/90MIxvR88ce6UJzAlmdwrt60A7H22SW/g7fL87YLixZdQvPh21J/+JPyTtwbH7wpxG/YEEVxV4fJyiTffOMflQua3Vz8nRtGjeYmHj0oczwmPHng8OCUwHM4XjLMzh9Z3QvCs7IQg0HHirAJKB8xnlIhCh1kl3kIxcjLaFqhbltV5lJ+CEHTZdpnk3VdFAUeSb++IUDoRi6VG3TC8ZtCL95BUqBBCSGiIOmmTCJTEyBi8idzZ/cgDoBKFE3Kj6qjzJhJpJBEDIIT1boMXTERAEI3Bo9eC2qX2rRwTDDFBWCDkvkuoKodjAOBZPV4ElDPQbKZiIax7qPkhzFIZNck1R13rtnoWJ8JJVwrFLURir8+AXHQmfUyt29u1y72G42KzOxa8s013jYm+xkLthbtCPr++FoV8/1UJOjrS/fpjD7n/6WOC9yIcw1qSrYT++rqO77lJisbx+GdKxePknJ2JxqsQi1MpE9ucM4Wxvg7FT+TcreJxE34HBLceb75xjh//uMd8RvhZX/Qi/vVrl9Grwl5i92Mcvf6ofOtxCeBy4VE+lT+eqnJ4cAy85+0lPvppj0Z/lG2rQqYI230hGMVdgYF3MPzxhmfREJoe9gcdURWEWQk8PGbMCuCNs75oBACn4iEKwiD48pDQ4MxJPIHhwXvQNgq8IML0Wom3MG0XhaHnEfHImJUl5rMCF4smCrwo9HTC6YRgZ7UNYYudKAwCS+9TXjbZh8l0AZBDOT8Bqhn48qy7qbmwo37IZ5xecrGWtvPZsTwsdBchmLWJQjCEcC4vwMsLsQofP4R7+f3wr30yljBPc9xyb2AUgkGk5WsW6v7ce8dwnRcw7JsQgpt4AgfVRPcQgOvE2aQAzHBrvHYrxjlFVGs9fMwonnsB5UvvRPvkLSx+7MPDhzvDM4UQLl9ftlicL+LvoG3kd9HULRZLmc/KSiaKBycFHhwT3vZ8gboFXn1T/nSrmXKkCr5QpCNEv+QpEwBQOCdCcC6OrXmlETAEtAxcNixOK5bol04QcuS+XBwG3srFYuUk57Ao5LXU7bJCLMRGGloqOfLiNQzbQMdP8ZX6qQohraAvInWfeisdexEJJJzrwCDnpBI4M0hTFqhZiDBslnCtFsjJC9ZErlE+ikIwCzlNOYlZi7hUgBPBGitb6/ITCLnnQZg2Igy5qWORsjTFYSpsNM8130ZEBuwjJleFkvaun5wzdu2x/qUvBljuyVYeSL3XYckPVxSgsoSrjvW7KYBSUz4K/WMJt1JFoTzbhO+mjvt5WXdrHI/AreMJ75FH6kzdgxybiMpNw1ZXXWtdrv76MQyNxevPuXqBaMLvgCAiVLMKb3uR8J6X53j9MaOsSpSVhhXqa6kVXJwySlkGMedwNCcczwkPTxxOjghvncvCnVHYTb1GwddtTwm+bn8Yt7xWhYSKHlWMqpA8wrPLrrgLMPTSxe2BiOvvT9cXzMND8zb5NXIMPYE8vI7aXik7HpB7kKauBSApsLLBH72SH9FI2MahcKilDraZYJjBF09Bpw81VDNLaL9F1qzrxK6T9D4CaOrcacvzLtfY4XOVFYoX3obiuRfQPn6rF9a5T5VTwz0BAa4EiqqI4q4og5GPYrpCmP+XDfD6E8Zl4/Hutzs8OiG8ecbxoa+NHCNvgg2r9YhL8HgOYlA8fHXb58uQS39UibHzZC5eQwZwsZT2jf42m5A2kBRQAzrOq5Vgl5kgLLJIloJETDqS0FLntBibrukrRdS8rgMtgrD1vpfC0EXN+KFYzLyJpAXWCi/KtmwbAARHJaiao5gdgVwhYaesa/zVSxFk9QLw7UAQ8iaCMBY/kyqoHDyLg6JoLA8l6j51VSXbrlShKG1kTcWliB8VhqQhp7mnijJhSGPCMBOF8RF/TShqwJhA3NjTGNIawnijUZQH4whPPFMex66a9pQA1OU2mmVa030097H3jFOoICw7j6M7OZH9pVQVpzIIeP3sdQ2va19y8j1xXUveaC+HsZic9zvb9DgPFRsYTv3awjrrheG2HsZt2q+rMj5sf7hnLBN+BwJ7D48C7//AQ/Ac+KnXaqAscPygwOxIPH0hxHM2K1FVwMPTUnL9jguxQFYOzBQJ55U3JWRzPh+GeHbEFfbLdiDPVPiFipwzzf8Lr5Xm/pVhbgVQt4BnxmUDnOkqAFUxFH6ToZ65MMzapcIvtBl6/DLvHTB63MX1Ebvw1YJIw3AITeuxWCzhMGJBnSBJx21XETQS59DKCqCXU0FFCTc/lty45QX85VPN8euLxnxZjLyIS6+KZ95uymq4ajmJ9HhGZKva9Cyp1Rz04BR0dAr/1mtSFXQME2Ei3eFxS2o8PrJ/naU0OXltX5PjmTpnh4l24/Guum7cf4ViesKIsa14dCcPQA8eonjwHLit0b7xOSw+8uHe5151vgm+ZwPMDK5rzOcznD487oygKgDn8xJHc8KsIhwfF5iVhOMjh6oQL9zjM8b5QgygORdSZsAcQxcUEea3EDEDtJAp6XwJPLmUdlVBOJkDL5wynl4SLuvubztwbBCVbRCCetzHAmPZdojaAcfUgmUmCmMEDZx4CQvlM+Xg0kGiKdnDhxxtbuFDaGGvrxApI68lifDK89uL1svah14L2ZBD4QpQOYebPxDx5SWXj+sFuG1AtaY55EVoUkEYxaA+ZuYVrcPaiakQ9LXMyw2S8NIQsaE5c1WpIaaV7isB9uC2FmEYBeJShGFyjVQYrhSFQIw62VQYpvsGojDmr2fXiGkRI32sWUJpEBWTFV7rexHHjw2WZ+z16UX463cde8u4I56jS06hrEBVBXIl3MkpqJqBykrFokTgyFqWUlCO6xp+cSnb6lmMn3GimFs6xims86+tE4br+gf2E3pTbbcVhLvAhN+BwIslXFXh02+UeOOywPGjOR48nGFeER4+6ASeeNwL1A2j9Q7LhlF7h7MzwJUUw1WKknBSjhd16VX7TKp7xgIuJEaaMoRXErRqZlivRfIdWgaWNUB1106uxSi1L0CEWRdCCX3NxVm/j1V5fIOKoHpeWKOQ1BoalpOgUCFU73V4ZSAm24t1VKx4DTPaSHYawhkWCs7EW1f1MwmdiZU4MwEYK39C1yCqRPBBK7UtLsDnbwGsy2L4rlroQMitWAKi1y4t6DIhFrcWfGm7XPABIvTKOTAXSzA3S/inbwFvvLpCfDIGomEqHCdgVejMxHWGFsrV4k0S9bN9Wwq+UUG6ZSjP9Pg26PsQHr413uqN+nIF3Mmp/Dt9CBQF/PkZ2sdvyqLrU9/RJuMz3G94D162ODmZ4Yu/8BFK5bqy1BrPJHl8dQN4SLGzty4ITdt59OZHfcHXRbD0PYWuoGFqQuZNzKNfAt8Ib0ooaLIGudTFCqH+QdDpsfD41oaH61jwSs/V8YcYEA+OfMmRR7VNSAXQ43UMIdfxJjws4asOpSvgKkJJOn9zi9YzWt/qWonSh1e3UuFU8CknFokQDHzofQN4huMGQA3X1sJ15RFoXspDq2dJBWiWoFoUM7nE49fqQ3vR57jIdUUmCF2fA7vthL9CAZOUd8KxIqx3W4HmxyJAVDj45QJoFqDlIlatptxQOeDJvrE299b1hGEUP3pOti5uLvRWCsMVojDtaxthuEoUpuPJhda4MOx72yjN7fMtsGjAWgyOB+cqNLSUqhmomqF8+AhUzTpvIlEUgrxcwF9egusl/OJyC2G4WrStFYYtr/W+beMB3FToXUdBGxN+B0K7WGA2n+Fn/ozn8f6a0HiAqMCyARiybMPjxoEZKBtJyp7PCMczYDajuI5fT9DpPkcpgUGXZmAVO9DF4GX5BkAEnW8Yauvq8usS8ihdJ9qmwjNTb54j1jX9dBzEIBCcrv0nnj1ZciKKNVBnwQxW2TzYQMceKnLKJK7bnsFgcCuVOn3uEUymlbBgPYFRYJgTUbTjyzr0kushYk/WcNLcCFfAaWUzgnrdvFbQujyX+Pe4REMn+OTj+clF4DcVhD1RdwihF/aXFUCSLI6yAhUzsZ62aoW7PAc/fWNolc366k3mewg9OY8H+7pTh9bVsb6mxGdvXBPnrk/mT4j0CoXeqrZj7VcSwRaevdgXEWg2h5sfwR0dwx2fgmYzscxenMOfP0X92qsHDeO8TYnvhsODPUuoV1liCYe6dWjrJDWhoI6fCkJZAHON4nCOENaVhXq9wjJt6X6p+KwFvrr1wCN/Bv4K2ymvEmSq8FrpuvXA00tolWuWqJjIj/LqJranEJ/xCZ3HUY+FR+ew3XXVF4Jh3imIpGophfV6O4NqWRCcc6gKh6NKim4t6xaNPvzHfPeQjxxynUGdSA37gicv5Lv7FvBL4S8iWb7p+CHo9DlZluDyDGhrMXq6vtBjX/T67IRgUpgA6IRgEIYpB+VtMl4gXY4CfKa7E2FVzkBHJ6CHL4CKUitiXoIWFyJU1Du4qSDs5YyPiUEAgzx7xWSOt0+rlutlB4XLtI91Yi4VhnE5pr5g6frsF2ZZJQzzcQzn7vHP1hNR7CX0dHk5mhLDnkUEzmYSjnx0BHr0HFDNRBgywPVCPIWLBfz5uVRV741lvc9vFVadvU8Y6f6evzUTzQYw4XcgtGcXeO5tx3ihBU5arShWsYq3kOwtbQtddwgknioX16vjKOiI9dVJ23z9vOhRUy9Z8I6F8EchBGhbFmLUscpC7aTeN/0zTY5jTGCpZY1YxJj8F5LTIa/hOHPy2vfejC22Pr1Ae/YHkp2X7h+2HfHWaflsIgeEhHd1Mco9I7UqSkI560K4qBeQZR10nLm3Lk7yI2v05aJslbBLt0fX6JtoE6AP7Uwa50uFxOhXktgd13DyWg66qYHLS3D9WD9ju95rl+0fC3OZWrh9rSdtVchMft3Y93ZibqzNLt67TQTdWN+r2m9dTGWqEtyGfVA1A83moPkRUFZw8yMJoWKAlwu0F+doz85Qv/bZ0fDe9YJvvZgzD+CzAW5bNGfneOdLBX7hcTEQUZLX1jcI6rv+/2WKyyPo++24awttG6cmoTLlsTQEVF4dyTQ5KxnHs25figH/5Mczw2neLo16CZiqcD281sjffCzIJS9Ny0DLqIkBtFrMpsLlonezUnWpgwCYOxEo41IvYSxalVW6ZqlwHdIdipPnASL48zdFhAFRpHVCr29MHOQL5sbR1DM4FgmTbpfjfEveyxIYzRJYnAlvMImX6egE9NzbJC+/vgSfPwEuzwZ8S9n2WNho561DbxxTxWZiu7FoiS1yC6XPce/iqrD7Ke7LxzWWZ7h5jiGy/Um7ifhscmLMgIaa+vPsXKLIX25+jOLBI9DRkVTPrJfwFxfw50/hz88kfHRkXOvELrlpDlubY6gP+2NFaNblL3bt1lxjD5jwOxDa8ws8nDd4+UWpVOZZvE7sRWgxQshkF9JYRK+YV8sly6LpcV2fhJByLxel7Ma9xc27ddAyK0841oQJLJuEJgRZz7M20bY7nomhsD/GvSDOKrEv9R7qxkAIAqJqKSQ+h2qasS+C1r3uxu5Iw2fkJ06e5LO3NZi1lLRaneC9kFEI2Yg5fn0SyddHGgiwsVDMfb1zYLVyOrArxCtH8gpdI4h0MdkQJkKtVtuqa3B9AfZSzhm64PyqpPRtvXTd+byVsBs7vtJ7mGEbb91aYbdBH6v2jV7jpsMzXaHWUvmHsgRp+XUqS/GmhzCa5QL+yWM0r31G1+HacHwrxrHN+cA4QRruD7hp0T49x3G1xFHZRh4Ys5szMBQleZupP5mJjX1+XasKf/XajYiysfPH2uX3IW1DCBypr4H/VCwHb2YwXsp7jb4hwHvGsm6SCqEZz8eqz0Mj7DbgtkFbL9RDcwp//lj7z1V+EJe+d5zj+rfaX+KNjYhhoRMel0yooFh13APNAlheAmdvSb5kdQQ6OgU99xL44gn4yevdovWDKs/d54giizLeyipXc/Tg9scTqlanRed4oup1FGF5gbrQPquenS6XlBe1G/zych5bs6zSyr4GSzCFZzO3ts1AEPLIuU0tIbxPH2djLuGOT+COT1C+7SVQNQPXS7SP30L79DFweTExrqEHddtQz5zrQkQDMOS4vO8pHt23sugYTPgdCM3jM5yUlyhPOFq0HPmBVwzcbTs0PS+ZixYtjqJhVIQRRREk6/mESVxFE8n8Q/DqxVKWHQiuZDsaDQmBlfuCDx27xB0jpJaTZCpgGN3nA4AQOhn2p8cYgK5p1O1L2kSSCou4++4aYLHOhZwAAPBtvF8ErBZzq4Tc6P4VYo6hhJHEFTkn4o0KcKnHtdQ1Qdk6WkObzhMX3jcXcQ2emPPAfq0lcEzUbSToxvZv4K3rtvfw2u0oqFaFja7va834txjHuvarxN2gL02eZ1eAygouJNGXlQi7atad27axCh7XNfzTJ+ClFj0IfxObjG/FZ9qmj02FnYV83k9wXaN58hQvlmeYnTYIc3r+qxD2mQh9isvpAKB+oP+wl3xzQknyxH6FX6dAD4TcO8iDd8n9SqJuhP+6V9b0CM8cCpvGNIhufVx5Hct7n0qFyCtbE/rbAVTN4WbH4MvHA+PvQRDdJdkcFi3kejyf48J5Mf/SdxsuPAs4CT98cgl+8jnQ0UO4d3wA/tWPoWcwHRGA+bwVlwrK1rCNAjAa6HR/JgCBTmRNL1mUCQf0eZ6o//mkTT72POQzePYyUc7JeCfPHe+rw5DLB9ebEjlrhGB6/8k34LPHaJJ1Y6mawT14hOrl94DmR/BnT9C+8Tn486cr1tHtrjkVtjr43leItFQEAocTgrvAhN+B0Dx+ihN+jKq5lCRiXUcHCBMtae5YJ8TC/jTEZbB0QPSwJaWZPXfE4NOJOQhG6YtVKFEuWJI23XZCMD7xFjInfev1cjGWYsJi2COBKatiHvK5oWdm5Tg2EW95WThO3svKvPK9ka7US65PNEz9iSn57lgT0WW9GxWiYUHdGE7qs6T13bxy3eaEWFsRwriXd25qnFN9byCOdvXSjfd1GJF2UG8dSJLiizKupQQnobmhhDYVWbls30rCe9uIoGsa8MV5LJ3tF8vJv619vHabWhi3WVvJ8OyhPbvAaf0mjuvLjj70GFO3wTrnhhbpGqxR/rC2A4M52R/bURRJ0mf+kJw9oE0cL8J40mMTYpHX5f2sEJFDU8zmoOw17s88i3nee169eqyidXgN97GXOuEKrf5ZxrA5qi/hz16XMM9BPnvfQDp81snmhrG5bJ1H8oDzC188BT14TkJNw1IUE0i9fzKOibmQJgRgOEw04Lp1wmRYhIt6Y6AQDdRrs4cAXHvu1HiHIm9KJOZCcPAZszWD0/NHRWRbo33jNbRvvCYf//Qhyre/AzR/P+qf+gT82ZOVom3dZ8qvve48oBOCU7w5JS4PARN+B0J7foHjp69hvjgHtbpeiYYPUtsIfYWcL247QRbEFICet0mRl/ePCJ6+4M2Dvqx8SAs/bN8/Z3BK+KPy4+f0+uL+vp7nEMMH4DRBIx1DfENyXaLseuFSBFCRHco/BHXeyXBORO6VE3HM3Mo+38r3E5JB2Iv3RHPsOHgSmSV8MuTeJWJ5pWiLwxj/XleJpG0Wgd2k77HQysnxbjK+bQXWDrl0u1zr4N65GGJbgGayCC4DIuBcCSpct2guFUBYKDf5HcqCyQ28LlLMTSOCbqmCrqnh61oXRt7uHmzyGbfp45DeOsvpe3ZRv/4W5m/9FPzZW5nQUsNZGgo4ss2BE8j1wgejMHSFOvec9E8i6KKfMPH8dQVN+uCMFxkdX/mwL/JKIjQTw6kIQD3G6JyKoZ+kz3jdvHxiPp4tMNXXYJmiTMw536qXSSKHyDmtGeD0njs4VMqrXqJo2hq8PNc8Ko6RNWDucvqisVUfyPcpeLbOkJtjVSrFmCGVCJgdg44fgMoj+CevrxV9twWbCIWBENlBAPa8fyvPnRY9k+PZtl0mADeFP3siYq+aoXrv56F+5VPwZ08G19xYbK/BWF8BNyEATfgdCM3jpyg+9sMomxq8uOyTVi+xrfP4dVjvneh5AJMfue9567SvQEpxkswsa8iOx2Pc/VEPjmVjDZZUz8k1MXwfX6b6QyJ8++dwPsaxc9PPG8cT2vlOwPlxm+o6sTTaJu6f9np1w91SXBygGEh37c2vsWtlxl08RXuJkU3z3sLDo3MycToHQF/DPi2CE99re+cKoJAHHSSlqlNPKvsWaFvxwrXdP395GT263GhobtvGh5dDiqNt8uIOEa55XW0N9xPL198EPvYRWdYgrjGk/AGgZ8AL6QfsQdEdqCGOwTgY24Vt6s4LyEMAQ0GHWM4z5Wl9JejfPWnoXTAkamnPnjClrE/S0tcqVIk05K4TnhzKkSKLHkkx8EDGA+DkHnVUyyL40sidRICm9yqmiwA9AU1OxBCrgEPDYrzWvHdWoZeKpuF6tJ3QGhit1wi/jUTdrrnyWTtmL99bOQPNKq0WKaHyvDiHf/ymFHhJkfV51RWtNzXkbpNKsU1u/FjfG1XevmGsC8PsRQPVS/izJ3AnpwPhd8hrT10/xXUKQBN+B0Lz+Cnaz70O//or8kDIDG5aIIROJp6+vmjqY+MvdYc/sikRcpW48cngAA+8OwusLcaxq/Dqt9n9GgcRBuEhLHs4onwfODvuutrswUIfRFsiyECJiGPWKqUTY1HvLIcwWmYRYJzs9x7cLMCN5lDGXMq2E3c9j+btFm3bXH/X9rtdw8I7DUD92huof+pT8J97RR+CVDSFeaDQv/WiiH/r0fhCGhpN4RynxhnSgldO1kkgB3JlPCcWAkvD+VUcMntJVWeGFEtTYyEzwEsws55OWZtOiMY5L/QNRNFGE+KSUqEXHubGBGiyP0bQBK9m2rcLHtFunwxb5sewn7zvjKlpRFKW5x48djKATOAF5GJuSgD22m4g7Mb6SMXUIHJmoi3r76iodE2PQl9LkCtFPjdhAfEl+PJMirwwj38GJPPYmLDaR+ilnyM9Z490iziuiXOvQ/Dtkit/nZWt6fgE5dvfCQCoP/HR8f4PiHW8eZ0Fzkz4HRCLV16Bf/oY/uIsEoy4xtFZ+BBe1PU7rAU96JeSp9v9xNv2YSPJhVcf3uacbdzyk23ziXbzLnunrXuITcKDsp36lvoTTf51xu985E+Nkt9A0e2LHcXnCf3t5A8Z1B8HoGthRWuy9MN6nsvPC9ZtdAIt/lZT0bbidzP4PSpxhvzSLpTWg9vwUCWvrETOzEAbwmhZclhjeC1Hsdb17RNjyoTVcwW2FS5XJdJ2Gcv1nrOfYLtxo4/hVmHx6udQ10C7AERgeYAXgPddVL73CMsHBeEUc2+y7a54hW6nx2ni3DCflSoui+D5L3viklwZRWgnOEPEQKisrNEDRfDoZQIQ6AvGIDLBqh113gsCsxdtI+27/dnfUvzsrj+f99pk3s6Aqb/rMbE2dmysj028c3nbKY+WV+9tuOcI9x6Aq9Q4KIKfg4EwRHPEa8uSRayh9KiX8j5sp+Of4pApITUy7rteJG2XnPphH1MCcL1QvBbB5xzc6UMUDx7BnT6Ev7xA89lX4M+fbnztfXLl94WFet5SnP3YR1C99/OAkyNED0NSZAVA8sCbTvLI5vV0As5/aG760A4YizLZvfFYyMrILkfTfW0yoMk2ewjbHja4sZuI2jQUJ30XHgCYgSI9JfldgGM6YkcGrE2SBwFOzkveh4Xvx47JS0LKoT/frxYHr8Vp9vyh7SMA9rWC7TJZ7jPemzpXzt+dGA4h0g5hsTSxeI/BjKc/8iNwpw/hSl1ntJwBJWlRI/Xql+LB40xIEWQ+IgDctioQE2OS5l9Tq+uq+c5zT9omLoUwJSbDQtwbi8hhH1GAqQczFZEiFMOyPJ13M0ZEhMiG6LGUFehjjnC8ZuA6DeeMobHJPdF7jsAD6CqAjn8/WDHX62cKVcPDmgFhPJ57RkQwA2VigOx9l6FNsj+MNbxy+A71eww59k0D+BocojN8UhwttO99pvWi7rbl3x967dqVfcc+1wj70b52F3pTY9m2WNpkH2UFd3QMOjqGOz6Fmx+B2cOfP0Xz1hvwP/XxQZ+rI6I2v/6mfd5kpWsTfgfE6//2U8C//dTadts83ByidOu+mFrLZP9+r+6z5aVzt8F13POruqe3Ebcl1O86RcV1hG1c9ee5ju/NhN6zhc/9yMcBDOdncjSYE8M8HNs6JwWVKimcRKVUxKVS83R1TVMqC7hSwj3ldS7HiwKuzEQbUU9YwLPm47Lm8HpZI1dz1mI1Zu+lEIr3A8/kcDvjE+cSAbqHV1PeqJBU4ZjkLad5zCJAkwiP/o3u7kWOgbcueE/QGQgBoE29e9wZN9PQ0t52CI/kgQgaDGHVHLGpkFnRfpM8/1XjWFUkrRvPZmJok/FtLN5G+ryVxdJ26Yt0Afe5LOIe16ydHQGONHz3Av7yEss3PwnO1u7baHwrxrHJuSlWPQ9cRf79Ouwt/IjofQC+FcDLkFngm5n5jxPRiwD+CoDPA/CTAH4DM79BMuP9cQBfCeAcwG9h5n+57zhuA5ZPh4sfA9s9BO4jWHYFXeE1r1Lg7CPQdrnP+1xveB/2KeC97bVv3nhwaNw1wfAsit9VeJYWbDeO7BA4Mp+T0vk456OB2Bk5Jz2ezrW5eMz7IPXCuaqU17KMXrkgMqOIrEoRmaUcQ6FCNObbhV45hqaLtzHkFLfRUxlyjBEKqYCB1oM0LHRKBI95HdeJx9hHHgqaHx/h6rXcMVFB9CDYIq1lrxC8deduIwS3FD+jwmpdhM3W1xjuP8RSRuvOWZseNPrZIevSzuegUtaoDWvVUjWTkGxHEo1UL6QK9nKB9uwp/MUFeLlY4R3c/TdyVwXeFA7h8WsA/B5m/pdE9BDA9xHRdwH4LQD+ATN/ExF9A4BvAPD7APwqAB/Sfz8fwJ/W1zuP5dl42d+pL/KmHsglJ+Fq0ZHy4QTOIe5XGNcmo9pWEK8WuZvdh9vqBTXcHO6jSLotAvSaYBypaC6b0f3pvJfz05TAmzyeish1XsRs/1j7dW1XiUnxSJZdeKcKSFcWQDHTENckBLTUR7JCClgRORFASZ6zeB1bzZnu/lHD6ols9JW7XGiMiMd4AxOhPJFGMcVtK/nqJqNariDne6Uw2DQdYluv2Jpjq8Z1ZZ6sTcIuQ36sC8sZlbHATn+tWtmOPTJ3a9U2tby/OIdfLOL2eH7f+vt/15c2ulUeP2b+NIBP6/snRPRhAO8B8FUAvkKb/UUA/whCal8F4FtZ/lK+h4ieJ6J3aT93Gs2lPNz7ZrsvyJXX/UCu6+tcgxC4SrGxlwduC/G7+WfQ+3rNnsgcU9+rH3/m2u9az1DIqsGwC4wjO9Tn4wawPgeu5qd1QjCd2/cRjdJ+2nuY799GZK7z6PXGSSQej6KAq4rojYSKSvFWalhr1XkkOfFcpn1yyHfk4Jns1qfteSPTPPAQ3kqIx+Katt6PCsZNefCmI1L2z63eX3Qcsq+NPk8qnpL80q5qdmfA6CrraiEjF9audTGEmkIRnm4Q8E0j4dKtrFEb3+tatb6uAV2/dlcvXdfuar1121xvl2tv23ZfHDTHj4g+D8DPAfC9AF5OiOoVSJgLIIT3ieS0T+q+HqkR0dcB+DoAeOmOpCLWT3bzbm1z1vWLRAFVu113lztyFZ9x8BBRT//h5kTt16zfugtxTQnPdsW1NhaFG643exjCXT8B3kdP43UYTQz3D886R7YX4/NFi/XzfuCgnFOG57WDv8+pOWiVaMzP21U0rgxr3Vg0LibbT3nypvp2BXXhrckDP5UayqoVS0O+pFTXLEGliwVqoiDQqqJUFH3xlwvBtJpzKCCmoa7MXWgswpL1sd1YjqCGX4Ycw6woTHd9vc5YMbTQPh1i4h2VVps9iKdV1/PPHbsLaz3GNqT/kQov6D5CXPaZnDZNKrhCQnbjfqJulei4JFLI50y3C/2MI58pEfFxmaO28yoze1maTIvqhKJJvtY2vlundhWuWyAHXEURldu4bNI2OBhbENEDAH8dwO9m5sfpJMDMTERbfQpm/mYA3wwAH6KjOxEX1Dy+ApdKBqruhoflpgRqwK5CdZ9xb3LNrv/tJfGhxMZVCLGrttpeR3hywEb3Z0NxvQlu2uK9Ke6jgL9OGEduxpHbctyqOXvdnDx17qrz8nN29UzKuVcf1pqfOxSIm3s1N73WYF9cp9EhVkolSI5kqFiqazumx0FOxpKvB5v0Sfo+Ch0E8USAS8RVGPfUElpblTjv0PvDy6u19/Zz8la2eUS0slbU7oSuirLBEklyrAv5TQR0OBYKFm2YL3nT1Z1vojL1TVbilj6uP///IMKPiCoIoX0bM/8N3f2ZEJ5CRO8C8Kru/xSA9yWnv1f33XlsG+K5CXKS4RWeqtuEKc/VtQnXie9inbBr6+nvcK2wG7lmfr2p/jcSqjt+phztiol5Z3G5os9DCAb240L5kKIpPIQdMqduk8++L3lcl3Dc9L6YQBzCOFKwCUe6DaIIUgSuGeWWNXPm5HzcTP+G+WCeSYCK/mfdxTM5lU+/qWgcE36T5+wgADcRwGN9rWu/7tgmx7cZx33G1a4/d/jn4kP2edi+Dncfb7XHTyuQfQuADzPzH0kOfSeArwHwTfr6N5P9X09E3w5JWH/rPuQuXBWuQkxeJdaJkJsWrh57TOr6XWwltDZsyysEZ45cJO7zGxkYFg4genLxeJVC6pBLo3C7+re5i9dx6rMfUhyFe3DdnkMTeJvBOHI77DqfbSMYV4pFoCcYtzbeTYx/lWDcRiwCfcG4fxjrMDc9L4I2ZRBcJfymCqlNi8fVwrU/5nFsnle4eSG2q5hXbzrK47YX17qJ8V215+023fNDePz+XQD/MYB/TUTfr/v+KwiZfQcRfS2AjwH4DXrsb0HKVH8EUqr6tx5gDLcC2zy8Hxq7hjYeGrdNqF6Fx3Qb8Visb7ISt+V73QZBPF5FHlwQUtuIjn1JdhfBd0hRZJVe7zyMIxW7cOSmc+A23BN4YRM+WCsSA7LrbyIY9xGL4fxdxCJwqFDU8fvnCtpYNOZ9rJqT/FoP3+rvqPuMm+f4b4ObEHS33UN5W5Yy2ge3SbQdwpB+iKqe/xTIVwSN+CUj7RnA79j3uoY+blJ0rsJNC5dDesMCthGPxXG18/WvGjdVoOSmRdG2Qu6mHwSuU5xZ0ZrDwzhyP2zLbZtwzi68cNAQ1IAdxKL0OS0Yp8Rifl+CMBsPQdV9ynVhDsoLnU15FdNUj2mPpPRdzPr3h/30/Vo/n/bHu0s/efXrXYTVVQjAqc80lQZhuB+4iuWc7kYpMMOdxW0VpKsQCPIQ3suTtx31tu/jmmw3gV3E0L6i5pCW1ZsO9TEY7iuuinOCCNvamNmsfzDvcg437POie5sLy3VXO0RBm73OyebhBy+fAljvVdlmzlzLsy1vzCG75JdvsfZ81uc0x7RX4HW6Dh6yqJL9cBX3z4SfwZBh1weHMRKcPZj1+95y8jahuB8OPWmaYDMYnm3cFg8kIOJq2/SFyVSFLfIa14WoupI2Pqecb/4YehVhg7c1VPI2cM11ibbb8FmfJZjwMxgOhLEHgqPnjkGzuVRm9q2UYPahBPM02e9CcDcRh34XhKlZHLfHXfheDYa7gH0Kd63DVeU1rs1jX1PorK15+Fkmxjp/7hhufqRrxHn4ps3W7uuwe8n+u59ndltxW4WzYRom/AyGK8TJu96O2Qc+H/7srFvslkiWqU3XDZrgszHCCuv5CAkGgkT3HpysFRT2Ix7zbUKoa9t3/UqbsJ5QMuicoMPiu6FJ1k/6WeMivUgriubj639uDPrm5LSRe5L1c5O4bnG+6/XcCma4ioeo25Q8bzDcFK7CmxiwTiSmIm5TL+JKgbhhFexHP+uLQeRE+OlafORcWMq9w8jw07korGsX532v69ql/NZbCJ6V6uS9b3zoqNeGe9zaPx76TRegz/uN5/TOTcc8wWmRz+SAGIuTG3FLOM2wHreN30z4GQxXiNPP/2lo3/oc6OwJgGQCyCaC/GE6nyji8WSxWVJiZGbEhWsJIOhit2l72QAIcKnoZK/7qSs/QYT+orYEUoJnLrRdcjxsh0VydT97n1xf2yXb3dCysRIp4Wl/6WfIxtX12e9PTg9tw/W2s6aPim59kEgfLhAW4k1J3Iv4jZ7d5KGEw8NJWHQ3HPdhW453lm8vv4feey/ttxj7Zp95E4Iaf9jbhdy6pSi2PtVgeOZxSG/iRmss7lAle5U4dPMZ5m97Hs3HPxLns004cpQfI29Rx4nKkwM+jPyhvEEO7NI+oOe6yDFClX2+C1wXzkm5mAY8mXMyyZyeHlvBaeHagbJ34bSxe9nb3xOmCc8lhtRem0Skhu3+8eQ63gvttT47p4t+GvQd3nuvQjodD3fX0zahv3yx+1QkX2c0S7jPty2U1YSfwXCFOP3CLwDXS3Db6MN9KxNd2ybbDG6zV92POEmmbXVfQC4ac0vgGlGpO9e3wQpBMWjfCTeeyHRfKRTSc5Jm+TqDKwksjm2760cxMtJnJHokJOyoE9PhwUM/PxEBLjlGBFCht4eihbt7iAgPGp13uGujoyJS77HrEf/gYSiIQ/bgtu2s0t7L+3DMe3AjYVbM8pq28U0T943fr/UPf+tEoYWWGgxXi32K3uxV8CwrbpOKx+rlF3DygfcAH3gv0NQyHzW18IlP5iKdw7q5KfBjxqP5XBTE5ApOHMxNa7hweI3EYyctBpw3Of9FD+UGbaeuH/av8gBuyoFRwAKdaNX/JcbcvpBOBWknXPM2nTG27AvXtP9g8A1G5NB/FE6JKE/4tsevUXxDeBIYcOX0PciMuOzVc9x2BtdggGU1/PYMuD6m8nT8q7/LxGDL3G1vazA9RMSNCT+D4YrgjubA294BujwDnrwhD/Ds9ZXjJEVEnRAAIJNbJ5xkPwOcWQ3HwkgikvCT3GqW7ud+GyHU0FcmPBkiDIKlLbW8ed+dl7QBWMRaGs4SsA2B5dbg2Hh8f29yHBNEvbabC+WBZ3YwVv2caTseCtbJa2fX4Ik2Y2MZ9BdEo3MdSToHcg5ELh5zZQEuC8nVCO011IpcIeRZFHIOsvsXjBW+FYOEb8FNo0TXyrG2FeJsW3DdyG8sPLApQmip5eIYDLcP60TjriGnxekD0EvvAb/1WaCU+YpCyKcXrozGtjC3RXpMoj5Cm+hFGuPGxEsUBCRUPOZchsTAmu5P0ivYtx0HppEd6IQoeiGn6U1YwSObirQNDaqjc+rOnFhM8xFNfKb+Y8no55s0tq4QRcKNazhw7Lw1HCPa06nYpB53yiCFT6NR1pF49KgAo1BR6jq+TQy1aR8IPDy4vj5L+TaKx2io9R7tk7dWjn8TmPAzGK4I5YMTNI/eATxsQC+8U0ReWyuJqAewDX/cbUcuYRsMart2naBKchGAXohDbz/QEWbUiwQ47qxj3iVWMkj4aDxHrW6hn7TP8MrdvnjVhJSJIksn1sSJiTcnvx6RioWNW/U+qfWXfZNMjG3cj7ZBtKZNkeyUwJsgwRsTkxs+CEz32YlubrJFqsJRDcOZHL8MbLhP81UpiEMViiIiC9B8nhwrgcKBijLxjnZ9c9uCl0ssP/ETo5/XYDDcTmziTRwTh+Xzj9A89xLw4HkRfMw6d7MsGsgMapqOE1N+ZAZFHm3Ug5LN4QOODNzHAHH3UJ4aW5W/IqMFLks9SaF99DLJPg77QihoLlBjSGM2p6cCNeO+Lr0gEa2pV2rQPjW8IikklwhSjTziRvte4xnt8cFOHtH0o67wtm4SnTRyjVXG4uG52xphw2+onWybn+l3EKSD57YoHEUkih518pvfEyb8DIYrgjuaY/HwJdSLSxDOJVSQ5I+24AYggmMNnWAvVqRAOtzG/SEMMNJmRmoUrZkywVMMQ+hC+7rJnUE+xMAnRKrx8+Q9ABVWogg0rH6MqFIROhxX2N0j3V4fEwQR9vcEKKmVjYHSAWWBGBbpHMDBqqZWOIT93PUbvVDqdWrauC2eqlYEeRCW3OpwhkIxF4+bhgutIsVAiNuS4aDAwRbnbtTnGvLVIDCxerf1sK8RUh7cByKgKACIB9JgMNwvjInD6tEjXD54CYuLcxTNAswM5xrRZRDuc1Bu9B5OxVfkR0C3WQNjxoUfRQ5U717TRr4MBlWKETGJgVX3Bw9fPAf5tRI+SLiwM4aOCNDezdH9Cd8FYUkhf1FDHinwXBSTafNOdKoWRSpmQx9y25y2SaKH0nBENaQy+3i/uK2799HYqkbWfL3KZ0BMbi8iN2sb23MwdCTPfwcoFGPCz2C4Irj5DK+2L+OsYdS+hSOgmjMcAYWGRRQuPDyLGCOI0CrQinGSW53XvbaRfQDgSF910gvi0XELEUqSk+bgZVt8M9GSSeFcgu7TY9RZN6N9M06sndhkZmkXyVOJMoTCJEI0JdLB/tSiGa4VP1NGmGp1o2B9C+3aNjke+vKdZzWMvXCgwoGoBKDhFsBQPDrXsXYgubbRfy24qUUwqmhEXfceCuIkHwQf97cj0hj/bYgy+eyjnr4JYrxKT+TmHsg1ZDevRo8ZDIb7heL0FK8078Qbl4yy8CACZjN5LZ0YIwvyqlvayIFODZIOjQariGgjFWGBrwjy6vS10FcioAjcp6KxE5U+GmWpx4XcE1iU8G4kCw3TSzmPE66jmEOmYjPhz5AzRpwJzyhiM04MyMXkWLuRPhhIwlzD8VZvDkBUAb7s5ZmLuA7bkFeG8mYYT+AzzSuPuZm1CMomhPuH3PEWaBqkzwDreGxgJAYGPNqdM7F/TYRPbxwBe0T6rBOzkzmaG3LnNjDhZzBcEcoHp7hYlDi7BJZtidYDRIzWAwWxRmB6eAbKQgRh6TrSI6K4XRCjcB7kCAUpUVEQcB25CUd5dcgxAO6JSnXBqYD0IKdCSicdx+Lpo+ABjATquuNJe9IQUUcliILAFITxQXvQRSw0WiYJpYnVy0SYppW9wpjjZ2hqsG9BzUJfRXBFIdhmgtC33bFMPAavHqtFLRJn1od8Ueo9dCoW53M4dyL7Y+iitmHWAgUNuF4CbfeKukZOcJSJQ8oIaZD/kAlEGiOXLT2O8dpJe0LRv15OXHmyvOsf74+r33aKBMmZt89geFZQPvcAF4sSTy8Bcoy2BZwTjnSBI4nhPVAVjErFYVV4ECRIgAioSERa4ZL9kHMBwCknNvrqoiFV52sADq1Gt4g4k3M63nSRi4QbOy70kcviPoRovc6gSkTK1dQzxvZeiTOja8KTmacxCsQYBtoJxd4yEdFzlAjOnDc3NKxKvw16uXqBayJvJoZWcgBcF9FRVXCzeWdgVeNrl3rCqkPVoBq8jpH360lDK02IspxP4/EthOImIjGMo4eEXyk7J+fXqT4lEibhyANQpAk/g+GKQEdzLC5bvPIa8MpjEU4nRw6lA47mQOGAeVWgcDLZtDL3w7fAkmSbIKRHxHDoiBDohFXwHjongrBUYVjodkFBPCKKyJB7XFAgIunLk4/kF71mgFouIdsMkHokQzgkEfc8jwAiYU1tD9sDwVobhWW8PgByKF0JFDO46hjknPThtGiO90C7BLctqFkqUSwHonBAahOicbDNrGJRPIncorMAtk3XJohAzXmj6gR88gAoKhDpk4pvJOR0uQDqJbhZgpaLLswIyUQ/4TWkTBhK28xrGEglGAmURIaiMiOXRLRzqKiWERKtE3OpMHT98YTy1t1yDib4DIZnDcXpMc7OGvzoxwDWQi4nx4SiAI5mDgUBR7MuAKNlEQYtB15UuxxkTT1WkRi4pnQdN8q2GB8L5/W9ikXqtEdJDHIEp2LSKb+G2S2IyDYxSgIhAiYsO5AKMgZRZjAdcGF3nILXMImuSdtGw6oX4RQNrqqjgnB0QOfBDO81DDSkmAQxi1a9ca0KKxVYIZ+sx5EZbw7EY+SefL8XQ2vbAi06sQoMPXgMIBQWi4ZWTe8I+6OhFeC2BjdqXNVoHKrrLkoHQ1G2jlflnKFxVc6Z2L+HF3HM+Nof7zBHdleY8DMYrgjFvMJy0cAvPah2mJUANQXOa8b5uUPdyMNx0wKVJr6XJcE5YF6SCMOZCLZZBVQloyARjFBhJykFYiGlFiIew7YSR+GmhWJBfSJypBZTF7yKKhCdB4HgNCE+kGIRLJqxopcQoOwN4rATjdHCyhxDVcfIcIoYGyUixy3gAedFjBW+ViIrgCoIw0IEjJeqkqiXkoNWL3rWTg4Cr1xDaL4FyhGhBwBtX1DFQgN+CW6XUURySmxFJeJwNgedPACKEuQKKXLSLEDLhYz18kLGOxUGk14zF2kq9IaCUO3JcdhBgPUFWa8t9YmI87WJMgtqEHPiPdRzpryEE/kPBoPhnsI5FMdHaOoGzUWD+bxEWRB87bC4AM5AWDYAg1C3wGzmMNMo8FnpUDhgNhM+nEndKJSF8GVZchexzwBUEC5beYVGmGzKk0DSjjrxGDiycBKx4yjhTQCu0HSLkI4X51CvulAMrW0iHNOIHE7CMEO46jqD6mA7eBHRRfSIHu7CSAsGyJUgkurOEsXZVVTt8uCXPaMqMG04nUzHSIVh7jUci7rxHrIubtIuF5nOSVRSUQKzOag4EXIrSsRq1E0twrZeijisl0CdGIYRLplyeMa1ioHRNfPG5YZXKhKO3dT4GmyhQRgecC1AE34GwxXBzWdo6ho/+clL/PhPyR/t8WmFoxnw8LTCrAIenhQ4ncsku6gZvnY4XwKP9VE5CMKqBMpC3oeKwFVJkjdYBvJjFK7brgqdUCisyiBE10CXVqJO+E2RXnhOD1bPKBSTEBoAcHDd+0h+Red5hHocNbLDUedl7MSij0Ix5Dx0BUTktXS1jIO1SI4KwZZnui3HnRZpId/C+RZwBYqiBM2OQCfPgYjAzVJCMhfnKgRVzKVhLUgFYNPlTbhM6Lkpb2FfgMX9WkSHmhbgS2CBSG7sPVDNQUUJevAc8NxLgCPw4hJYXgDnT0V4RtGkXxIxmPuCLhJWLghjhdUg+EL7rq9I2ImAAxJiiqEq4RrhXvTbk0Mi7LLy1WF/CGeZynMwGAz3CuQc3NEc50+X+KEPP4GrZA4/OqkwrwgPH5SYV8CDkwKnc0JREpgIyxq4qB0ulkBRdAZTQI2iyXapIZ9VpYbUSqbCqlBDaqgTRl1oqXgUgdazrtIg+wP3BSGYbgcuGxOJQMeTkTdRJNE4FKN1ChIBGVYTCEZY6RMIXkQG4JO8vLCGXORPdJyZCr84Lt/nUaccV3gJ4yQt/NYLZyUHV1QoypnM8eoi5VY9a/VCPG0qCHOPHxd9Q2+PT/PomikD60Q+I3RJISk80z8WeYtIjK2uAM1PQKcPQeVMElBU1KJeAstL+de2k8bWSZ7NhWLgfe8HqRA9rk2QL/beiUsTfgbDQbHNOkSbwh3PsDxf4IXZJfhsidfeaDA/mQMA5sdCcrMjMWHOj0oczQiPHhQ4OSI8PC5QN8DTp8CTc6CsHGaVsFpOdinJyXF9VYVYlbJvHgRipcRXCNnVLXBZyz+KQlA/QyYMgxDMBSOBI+mtI8GhaAxeRUShWBDDOYIrVbMwo209ls1Cr6tE5VQAIhDXUBiGfY1XL2EgOwbc7ATu6AFABL+8AC8vBh6/9HUr7yCQWDCVwMoRostIQyyjHmgu5V84Xlag2THo4QsgKsAXT8Bnb0lYjl6LMmJc6R3EiOUy8RCOWS17ba8hXNRgMNxTEMGVBai+wLtPn+InXgEeP20xOw4C8AgAUM3kMXU2L3F6WmI+Izx6UOJkDswKJ0LwKfD4HCiUuMpSX0MkjXJmyp2B41K+jFE26kUsC31Vg6X3IgiXDVC3YlAtHEdjbMdxoc+c+/r7KeNKl/DoNG+y5jWqYNV6AEVYKi6ErSJN2/DwXgqr+STaJvJo0efRIPiiITV4Bn3d1QFQDnMgUHkEVx6LsZKgeXgSsUKt8lPOp2ty83vbUwIx5dc14rArAFfLv2XiRSQnyw1VM9DRC6BqLt9AvQAWF/DnT3rXiKw1wbeDMNJk36a5+915GUcfACb8DFvhKgTSfYErKdt2ALc4O6vx8IRxcdHiYqnCRFWaU/YhIiwXwOVC/rirOWNWAS89X+D9LxE+8ZqH2tFQcYzyz0YQvDiyxQXH/XUD6KUTogNKRygL4HjOeHQMFI7w+tNu8nGu32cgLtZLd/sJBYJlTV697xMXZzkLhXbmidXKGoQgIfc0EhhV6XB6dISLRRMoLYoNiuRYxPZxfyBlvRvR+cQNfFOrtZNQlCXcw7fBnz+WnIcRxG84RIeEz+ay4wGBwAqdaoMAjB5AdJ4ynw00XCNUfm1qCVE5exNMBJqfwr39veDFGfit14R0whNIDLfUMJdg/dVrcHZNchm5ONcRzqDPEA46fm5+TXiO4TbxsyoY430bDIb7DdIYSPYeH/mJp3jf+x/h4nyBtpYJsF7U2k69K45wdk44Owcen8lcM5sVmFfAC88VeN9LhDeeeDy56K4RPDOsQjB9Tvcua1Mwmhao2773MOVLEYSEWQmczkUYeg88vQQa3/UZp0ztu80Mpa3uj6KOO66kyKnKo5nwC9zWTOxPRWYIMy0KB6fisHKEglpcLkXA6clyD3Q+LiIBh3vUhUOykp2Lc3cNtF7ahJx65+CogDt5TmoTqFE1DgzouDHZF4vIKF/EdIws/y16v1KRFPPZ9VgbCVReAq8GLopG0DCeBlg0wKU+XXgGFyVofgz3tneBihJ8LsZWDsbWCb6Nj2axbx5ybhhPEIIInznjSO0z1js4AEz43SBMRN0e5KLtkKgKQlU6tC3BxZi69QjOICmh3EXrTbcPD+39hnH32DlQy+WlREk8fyrJ9IvOiSSvYWIG984PtaoKLTwjbQJCcEqfXSgKwcg2vW1PDBfIOLQhYNkwCnhUZYGmbnrjiuQd4vnjNiX9hwlUCYGo10dbL4CmRnX8AO3TN0buVnIfsnNpcJ/CRw4k005sE7pvpp9rEAkMmWjSxHg+fwI+fyIewEcvgt98bXTMY4h5D+sEYPpZM5LrGmwgAMO++P1kfUz1bXhmEfhxk8XBDXcU+tUeHzk45+K8vSk8My6WwPyScTwnzCvC4zNG5jRJvCjKMZ5RKE9G3oq6oR/Ol+oJ30r7yxp4cin7nz8hvP0R45U3OqE3JN2wX16iANT9MQrQdafkrBDahKmyjZfIubkvkhyTFMWJbRhVwTg5qrBc6LJRGa+HKt6UlZDscvm796yh/eE+xam8reHbGsQebnaM4vQI7flbA2Mp0N171stFAeiK/vHcwJp+OfHCeiwaUDMBmBtao0jzg3bUNkAQe8HY+tJ7wU/eAJ8/7iJqcv7iIQcOja7hh5fxZ24MXce/O8CE3zOMqxQ79wFUbS7SRlHKYqtUFHjljRpLLnF00g/xDGEsx0cFjmaEhw9LHM8JJ0cOjQfOF4RPfg6oZtSFr4R8hsn8Bui2kkpiuXTUhbDMK2BecQxjWdTA43NGkziOgoZcF/JJSb5gd6yfJ9iFquh26pVLjyfvA4LH72jmcLFs4sQ/6CPmnw0fFqcsZpSEb7j5sSR9T2CwhtINIIonJGEvm1bFnAi5vCmMFpMxGBIc0kBqIvL2QCIQGS0c3vmuh/jIJ2u0VOLoKHCkvFZzIbRqVmI2LyT/77TA8ZxweuLgHFA3hDefMpYtoawoSYPop0f08uOLPm92nr0hbxIkNSKEgpYFcFRB8+cZn30MgDqe7CJW5LUrnta/Bw799mnbHHldrOHxbX7bQRw7MLrc8GHIyooeohEvCJhg/cwMmCD45QLu9KGImvBckAw3evg4E1LRA7iJgXVCEA0E04ShdR0SY6t7+f3g88fJRwxf+LgATDE0umbPOoETMSH03J7PpTDhd6O4S8JrbxF0B7Hv9yMLwZZ487yALwqcPgIePprhaEY4PakwnwEPH5RwJBPfsma07PB0ATxedMQ1n4u4y3P6UsJKK4HG4i7Jq6MuRyFUAW09cFlrZbNEyAWCAzoyyRPbc0EYlptI2+Rkl4ZthnMI3ZqFsSiMQ7f0hJOwT2KH1ntcXi51PCG/IOQkaJhoWNw+vIbiLkCchGPCum8BcqCygqvmcPDwC83zS9r3cgVyi16eRzB2DjCsXhaQlrTOMegjWKL1Czs+hXvwAnh5CX7z1fE+cozkHtwkTPAZprBu/vXN9r+dq4yyMVG5JVjWO2Uq8bHPANXxCapj4PTBDLOKcHpayetJiVkFzCQpHcsGaD3hcsk4e8vJOriluMqO54EbVxtHi0Ly/kJxNEdAVQnvFEWX4xd4Ewz4hDebFnirFoelIw6rDgx4MW5n+e9TuX7d2n/D3L6cT3OjZy9nHl3uXxifFI8hFE4Kqy2WNYhbOIxzM5AYUpOKof2F6zuDKo1wChUlXFFIEZWLJyD4UePp4Nx86YU8723MALupUXYf7qtmoIcvgi/Pd7v2LYEJvxtE+ej+3f67JGansO/DQbgH5Qnj5LkT/Dv/zjHe/UE1JzrJtwMJgXkQWhYv3dGRkNELJAVYXCAfF4qxsMbry74yEWHy/Ozjmn8yb3KohKzLK0gVMwKjctioGllOOgMhqOLNab9BuBEQcwxKx2INTfaFPlnXOwprH3XLPXgRBU2YpD0KALOQlL6qKhkgVckgIs9pvh55DyoKOEdCSFTJdZaX4MsnUqoaCaGNrf+XH5sq5pLvX1mNbOJYLtIKLe4yOwKVFfz5E7SvfUoqkYVrTFYhy4m1f62xxWzHFrLttZ1Yq2hsnaL8+gPBZyGehgzF8WovdszZ3UEABnB9wN9dxn37jOsmsEq4XoVgpgLgusbzb3uIf+/fO41hceQc6hZxGQfWZR2aklCVwBySe/4AnTgrE550JB454UrubXeRLMI7UsVTvF8eYfpU3oSmWnDHdU6LpAGJEEt4NI9u2cT4CXQ+KCKOnEq6zmDYBlGsPop0aSXo4vDJNeVjhCqfDOhSSp4ZvmYQWszQFUdLi7fI9rhBtfB15L6wrzOocsKvFYggy0AszsBPXwdYP9dYcZd8Afmk8md/ewXfrloSAhgpcLYB7zIBsyNZcunoBNw04Kdvgi+eDq+fbK/kwJz7VnCwdDm+fx/cP+Vxh1A97BPbbSWK+yDmNgUVu39Wl59LjGpeoXFA7cQbdzIHTlxXVnqm6w1VMTzTR7KJ8xRLX6RE5VTYhbh9n4ixgpRcij7ZEEEJAvFfEUmGlGQyiyVJ7l5oDySEBUSxJhDRIUP1kXhkkSJdroFbOZ4ltMfbpRl9Y+v4dWWok3X80BFVj4ycQ8Gs6/gRXDGHFCtpZdH0ZglenMPXCz1XQkfzdYZGF3pfV7VzqrLYgISS4znhgIFyBqoqoJrDlVKC2i8vwYsL8Buf0QV2V5DNxOK0kwvLjlUUm6omNrJwfO/6+f6RBXHza0weNzyzKB9pldgJQRL4slghStZ64crDc3AQk1fBm1f5jHDdNQfYezQXl6iOSrSFhLhJ1WnCkesbP4mksmZV9r1bIERxEzQOsxo/WQMZdV/jgSJwpvIaoCkJ1On2MdFG6CJUAp+Gf+Ix486omYR8pueCAFlAPXj+kiWN0mtyf826IEYRwzOVV5PlG9JZtOPokGrRcXb4/B1/9vnUZaKuFx0DEYIE4VZHBVAUKESlAm0D9rqsw+VTICzrEAVfJuJGFoMfRM6sM6ymYm9foeecePTcTNcCngG+BS8v4S/OgLc+N23IRehywuCa8toEf05V9Zzk4T1gwu8GUR4NLZrP0oPPXangRyE+ZNvzwDiaA+98G/Di88EbJ68uEpOPydUEEVolAa6Q9X3KmFvnRZSpYAuEE0gmkov2IQMIvyUGUsJQAqFkrTYhG1nvhyHeMaEmLesfg+u7PnvesSQEJP38yNrIdib4JhaeTdt24SQs6z+xl1eC5lEKCxM7sPegRhdqb5bgppHPGUhuE4EHjJPOpp68nHQGZagJKEpwWUgZ6bICFSXgCvkOmgV4cSnLNSwv5TprlmroedWmPHrx+BYks07oZddc6d1bI/QGfRqeWRRznXfn/f3chnmujzFRxOVw36pz9hKRASomDykiA67SCLvNeA81Dr68wINjwue/JxFrpMZP5anIlboeHRFQoFs8XZYw0PXwVGg5zZ8aGjJ12wkXEyhGqiAxagJdWGb3/5xPlUfFV9g7llawZjBI12ilsFZtbN0XZ+lyAWN8iA32x+2MV8N4gKHQI5a0hwIecE4+u3Mo4OQGo5RnDd9IZWlfA20NXuj6d0FdIzGkThg9U77dSeD19ic8t07okYZLUSkVOotCeDcUsNF1/Hh5CT5/C1guupu5bpkkhMPThtZtPXqbGlx3gQm/G8TstBrs8+3+X6rhMBh48LZE4Tzm1QKlA5qm1bV3OLMIhlw1Hz174OA1SyZTCTyR9+z7nqJEFEnbKcE13B6SBcQyyUqKuXdOTZpCwJQUTBEfoNg0fWgYQz5jH2msZ7woJeGfhF65r4QIRXi1IuDaWj5/28gkHBeN7UiHAFmE1eeiS+9Dvsj6QMz9/9t7l1hZkvQ87PsjMqvOuefe26+ZaQ6HreFQGi1oGJZpguJClhe2qBFhYyxAi4EWpiUBAwMibC8MmwQ3Wko2LFiGCQNjmYBsyB4bsA3NQgZFGga8okhJoCgOpRF7HpzuZvdMT/d9nkdVZsbvxf9HZGRUZlVmPU7VvSc+4JyszIyMjIrKjD+++F89wiaUWSNkrC5R2wKAESFjDGALdewmwNUSElpzHfH1c6BajCJ4bdccQMis0c6N0ej1XdcxH91A9HIC9wwPLx9TmchN/+JA/GyFawZIo8cYc9FA+Cb6HO6TRA7dYxsMmbcew7KnfvocF7MlXr9gWON9vqV9q+4GDkQiJ70mT+Qlt3IoyCkvO2NCFS1sNow0CEkq6zYRqc4C5SbSFYKTcCT+WvlJKjv99/TnO9dQfJ7aItGtJQJ4dA+ibt1hMZhEfvvmOW4XNhuVr86JjOJIhqq7Q5+FzGjtXSxvpxA7YFXWMdS50oqDpjFC5IwFGwuytn0EXAPUNVAv4aoF+FKieAdrnXTRkXkr2Su7q3J2tCZvrAvFDjga8SOiLwD4W5Cx928z818/VluOhdn92eC5fWr+MpncD6YSQVsA94pL2KYG01JWB1XrVFA7KBKpLX0wC2lt61tCxPpHAJSUKZFqhYsnXjFJi+oIA8rqCmAwy6R0YG3NN0WgtGRUTruOUPUroQBakuSFdDK4U3Qt4vMRKCVrI1YRgUQordj+bzDLjAd1Hy3TGABK3oKnvJVjcURN51TAVELuqiXczVUrTJtVTeFGzdnYlT/nBoVLe+kGQRX2V7WH2/rpxeU2Er2s8QOQ5SPQysehiU9K4ryc6yWACVLyOCRvXcMbyaPHWJ9DrngyiQz36CGTk4PK7FEjuSuaxQ3Oi0twcQNysjjqt8bLCa/FYxcRJeiWI+1YK/tIVIahVOpT5+tFuBYrxwP8uJfI01C+My4yIp6l1/bI2+SerRxN5HSnmIv2ud3E92BOLGpi2RuRYPWJi/3tAKxau/RZxexijpnWzU40igyVoypzybRy1liE4AH+cyBWDbhpWvla1WiqK2lLU69P7O57aILlTHvNAImbYEEzVNfQfCCc75knTcVRiB+JbvWXAfwZAO8C+C0i+hoz/94x2nMsvPlv/iRofiZCyDl5gMFgp5NBrz4Pk2M9F6nVO6ta4qks/lSuvaYzWHRWwXwVHO7bmbhzXK49tvJwc1IegKujwSs6x3FbVu7Rc3wkTtFE7Oy1C7z++G35XYJ9uxIo10g/uqb7O3MT/WY6QMdbQAfYtgz5c/6apitsAlZ+t54+G+r3Tf27bjDa9Fuuq3uozZvMOow3UQH8qiADIkw4ESK6UijMm9B5np0TB/W6gSSqbcCugauVxNV1+xuiZ1AeIzg2CI3eazbUOVV49E58tzTLHEPmhibad8nUfQhZPgpe+9FPwL76GtyyAtcVmqVM5NxyGWQhgJ6tC59XtIUJiUyftz7yOJZwpvdIEZcbo7Vcd4917Vm5diTBO0ZUUmqu8MZH/wJvsJjnA077hpESCorIEVyjFjGQsn7hUuc4HYIVz19i2Zgu9A3JqSkT7akWC9vMW3aZ+A+NzZs0a0E2Ilj9hFVqbh0eiQy4c9y0RI5kn+P+Zr2Ht9xxTuSsc/Ie15Ueb+SvrnUOuka+bXBvaLtigrzdI1nbZsG09x474Fgav58C8DYzfwsAiOirAL4I4M4ItuLhfcxffxX1H35HcpKQaV+EyHOYGWrIrtbo3lwOJnoB5XgwmKNoS6QPTPSiQtbA/PXyAsQmeEk9gL60flKcLn3F9xuoI33JaPUe3X0xRYixz0nhqFWTQZKqFvxhwh6Vi4isuf8A9uMP1IzvpluH09TnQ0Knx+yg/TzQ9tA/7W+bfBE93fZxZ2DvnPPH0t+buqc765/xfbrPQmyq0mal9+VsT93yOaQu8IIHkHdFhUy4c9wnwSRUBURTBzNNrsTfjz2hC0LFCbmLvsM6x+x293DCZege64TM0HO9D+1ce49xiwN97+sw4Tu9hZsj4s7LRwC4+GM/BipncDc3EraRjProGJlgehLX1OCmEYLYNOC6gqtkYSaQxaoCNzWc7veRxRgxcRx6/4ZIY1pHep20eXfyKPU4IPEYSa+1iWHRIImcbyaR+4ZbXME8+QH4+SOxkuhYrOi4r3OOFbNJoB370nmHLzc07+hD0Oyt6PQ2fIuRlkBxsdDusfEDUrm7S3M07qf2LwWShpX+Yk+ovXbOW7SE/VbWgh2YRYHBqlEU09Hos5YJmKhRa9u1xeLijuRN7jtiwbSnPf2ycMN3GqhrHzgW8fsMgHei/XcB/MkjteUoOPvMmyjPC5R/5C0RUl7j518q1fi1Qsb/+D6pY7Sihf4JX6vBi4oyRq8wdM+tf/hG++ZMeIinEL1tSeG67xXqpFVCEhOblXIkYqp59D74858GnV2AF4uEsHgi480ZZMtaD3WIcY8AA3qOdVc3g9a45zzHq6BSuEt0w/lYAwbA+x+EFev4XFxf9zP33SOc7ruuW2/4Ll6geGEypGnzmELawjVrBuSx7862JA7oFTjSjulaurVle+7f+x5vMV4MnR8kk1nTF+POy0cAOH/rLdTvvg1GDdQA16L5cXU3zQoYIFuIlbqxQGHgbAGyZ6Lh9z63Rnx/yNrwnAeCt1zKhLWqRaO/rIMJmSeNXCnBrGvR/DepbEa3zg3axviYR5/Gsfceoe7WzHxb8tjWuUoiV8rs+T3l6hrNu98GlbPWLM9bIKXauaCd0n+xrOtbQI1dErp37f04cGC13q2wxfVb3lLmF5vG5sTSaNBKaAeZswdytva+SN6LkXJzU53rfNJ779u513jytq3Vyz4WSU82uAsRfRnAlwHgk6fbzK1Rvv4qZp/9o+DLp6rFExU4gUTDx+hdnWp9dKKtV313BkeOTNC0jHfghTcX7SuHtq6OyVujp+PjTgVQty1xXcF0se87DGDjg72OoI40gxg76V9XdvPLb8GPPgQequkRV6J1ctHv5UMa+37zxCb63dbdS9o8TkKM6pttBfsO5ieTJhPbLEBMJRsTBMXg77LpGR9aqOmta/f2bFyYObBgug1hdtfwMstIe+8c5euvonzt39CF0UYi7jU13GIJuBq8WMp4Wst5t1hCCEOjJLER+dQALvGtZe+K4FywpjHGgklyfPKMQHYGpjM5bwswGYnwTEomgxiUujwhbKoaqGvdryX3l3MtqayqQCK5rjvfexNpbPdXtY5TNI4AerR7dmM8gG2I37o63eVzNM8v4X7w9rhFscGKtpBbtxhV3Oco3G+d+2v/2N91l3nIuvnHxvtvkvtTZHNozzR5O3SfMedHKRgGz788Gr/3ALwV7f+IHgtg5q8A+AoAfJ7OXrrl4Nnrr2L5mT8GrhYgVykJqNX3y4EaMUmjmCAAkb17S74QokNFEZy8j5/XGElhdMz3fB3Unqf4fMdMMzX1dN26O0TV14G2TMcUL+2NNSr22LzSOyh7whqbIXT839pjoinSm3bIcnucYwIWmSiwP+ZXdrfw3+KqAn/0wcr32cqBd8fVsd7J/ZbmBWsHq13Jd6fMyEFvTF0b2jVK+O3SLxj3fbYhaePvv337DnltW/ClG+q3wUb5CLzcMrJ45QHwo38c7tkj4OY5QEZCzxsD65xoNXxgCEDMxSP/LzRivs31EmgcuNaIuVUFdg24Wsr5atmWR/ucst+vfa4xJ369gIw1dQ+ZhAEKC7YsmkWSRHRMFmSMEEdjEMJ0GQsOfsj+vmJ+7io1UVUfYk8aw7aRNru6UvN1jrSF/fIo3bbnN5PItK7V49MXe+QeN7h+ssDyvY8AsJjwApF5PxDMEWN3gHUmmysNidvBnY2c5p7jrP7g3Dnm62sPRYvcnXtxO91aacM47ErsDkE2bwv7JDtba6m3+M067Q4uW97SizrHZMpMgDUajbQtG/aNmLWTad+L5unj7b5PhGMRv98C8Hki+hxEoH0JwF88UltuHWZWwtyb4+r+p7C8uYZFAwLBcqPRHSlEe1yJWpWaNOjAY1Sz5jVJFMiNbBlIIjZxIJXtNa5brwabEfttH6FR7et9yP0wmY7N+IA0V1tvRKV0Ih75v0U1Sd2x35ofUAldO/44ApTuU3wO0DaS7kY+Yx1fMnRf2nRQ91pWDW3MOsnwoY5j8kj37oNmZ/AOyqyCnBoV6P46n5BbI06SmvB0B5KUHCaCQQc4n5ZmhZAiEQTOrfpR+u+YCo0xA/GWxOpQq4jr7tm9x+517MUUemdSuYd2HrqOTOzG4E7LRwCwF+dYPHwTfO8NmEbyaRnNF0b1QhZHayFvsm0kHQoziCvJS2YMUFpgPgPxTHwDHctxRrLVZ9r7J9U+nP0S8K4YweRTNZCNLNh6E1TuJY8NJOcZwljmvLyIynoSScyALWBBgLUwxoAKC54XShRbwkghQJXpyEHWRUvRNKpfs2vERLZpggbV+0Zy3QT/xyHSOH5/PAH05HLxzndRvPkWQnCPprVAYl3Y7VgahYpHjCVDBLGzSN3ut8V7CGY8r4iOUVyXJ6o+oWA4vj2JG0UAOfxr5yfhHGsbuwS1GxwnIbg9JrNda6/OjeUehpJzKzvj5CD8FIzaZ2f079geX4l5ET5Hczz9TP438vNCHzejE1dgVNNbsJors6bqiqzyOJmjBysw5wDHEtTGL175cq4NeLMrjkL8mLkmop8H8KsQI/VfYeavH6Mtx4CZz1C++iquzGtYlK+ggA761MCgATPgE2cTRwk2AZCrRVbp1oc3NmgAo4QRBINGw/0LX5Dn3DMm2ZpODplA6Vb2A/EKmsX2Gj8QUKxhCwNJNChwEx7+FYIZEdRV89K4Lh2cOBKSHisksrufhh7urtg5IM4D5P3X1ibppjBpIDA0iyxABVo/Pn2Bnz6X64wBeUFdlCAzl8A+/piu7rTfgcGNRrVSoigTHBHo5NMDOD/h8O32307Jo/ZFIIT+vLWr/TbkVG19Xfp7m+Q8AHYDK4z+/ioY0omAX5mcssq3E+HbgbjsTPDGaPx2IHhj+3Avmsk9aPGyiecq7rp8BIDi/gWuZ69hwRUI12BmWCsEsDDnICJYdiBDsFwDZCTcPxHI1TDecqbWfJ/1QrZK4qgR8iOkEV2LGjKAYaCwoOIcmJ+12p84vQtDynqZ4hfz6hohB1qsXfS+gdVSohOqWSqQksV0LG+EGDZYIYzsJ4ZAS0Bd205ji0AWTUmgsxmYzgNpJGPbLOix+aqPjFg3kaZRFymdi8ijU1LpzVwb+BD7qRmrRzpu1I8+Qv3oo55ye7Ag2AM2mb/umu93b0gIKFnT3U+DpPlrkv1ufAF0runUGZXp9MBQ4L5d0UNGgXQuF88rkxJ6rEtg07nlACEetK46nPw65DN+NMcAZv77AP7+se5/TFBZonhwgaeLMzx6zjDk4Biw5GBD8lIRYoWR6I/WaL43TXJqCg7HAUloCgDk88OpRq9rxtBq+hhot64llW05dJKQhqSm0bHOvj8PbkcB083FE84zI2g1w8k40WhUl2rnKK7Eo4/4sc87102LEFJgcJO81BqRKoQLrlpz2nW54ZJ7Aujkh2Poqq4hYH6WXKPaPxfV4QV6WjeULBojArwogWIuiUmJNEl4tLroGGh0tUiTk7LPaVO36QgC/ARiHTmM25MORvFvYgZIY9BecvceKTk33XIAwANmwB2CHLW/raufZEo7fYCkPZqTnKh55m3WMWYFPhO9cbjL8hEQU89rd4GnlQPhHoiA2ayBoTYHqkENZoZBJdtGNYK8hEUFEKEoKhARzOwCFMxFCcY5kF+0A0J6HVMvhTBqAmtUSxlP/UJb0Ejp+Aq0C4SN08U9BqwFFdFiXtAuWn1NRAaGd6ZWwuRNUf1Cn5qiunBeTFfbxT4eIItd8iaaRxbDnFTWRGQyDUDDkIVJAkTmQL6LmVmQKeV7Bk2kAUP9IOEXNJNxg9UH02s9rYU9P+uMHZ5somkJpDdtZR/2v/F+k0Ls2f8uCaZMnrfNd9x33Ys2zh3DLPRkCPMInEIu7JchncOdBlkDe/8ePr4q8M5HwPlMzHznJWNWsgg3IwTKMKsGkCWfNBiOGaB4v9X8WTUHNORQKEkkI1pATyq9FYIxEkzGWCWTJbdWjvAaQgRz00CWIMRKKFZXeLQEoSVNHXLpTUwRk8cNpNJP9lMSoLcmdivX6DfUj6rdNIRW6ckt4TQkxJlMZFarL5oX8CzmPxyTw15/S3RNaodIYtpfQ6Qy8iOkxgXzom4/RGWJVIso4c9pNpOJh43DoeuAW9diylQLKeSmku+mJkKd+2OAAEbE0A/jg+YZK8QwMTONiWHQEiZkMFgYJ0RvSNto1pgeDZizpqY18eRhSDvpV0lHR7e9JZyCX95etYcZdwL23jmuqxluGqB2Do2TxdDGMaxp0DigNEIEZ9bBEFDaBmQIs7IJC6I1GiWDShJdDWanJNHBNELejLouGGuFKJay8GqcgyG/UBn5FoKECLLT3GI+x5iO0c5rGyPNl2sgzoH1is9+SxoBKizANkR6hjGwPvcogNifMRCeiDQi1jw2DZz6OXq5tU672CWL7XvZkkUhkWKSBvCSw7Wyda01YXqtyieRQwYEA3YN6mjBVH6EKMeqRmElK9FaaX4eUnoECxrTmr325hiOzFwDoVSNbDCncw2sc519rrx/53r0kYJNROrUNEWdxeA12GdAmYFUli8lTk2+ZeJ3BJC1MPfO8PijK3z4hw1sWWBZIwxmyxrBH6ssjZDCGcEYYF4KSZwVsl9Yvw8hit4CUUmb09VFp9aJjYtIZNDIS9lAGk13n0iELhBSCipplHsTIk0kiV20bPU8+fYQiDgyJRW1OgV7dIeuut2nHXBh668D0DFVDSaovs2JAFghk8m+ZRE+xjXiLwnAUAOYuRBjEnJIxsq1Rv1FwNEEYKkrk7JPUWJvr00d0hquaBeDFjE6PkQak7IUkzNuIInHF6smq0QgsoAtQLMLwBZgY0FF0f4O1UJIYbWQQETVsjvRj+r0wiyIhlSbOBCcJhC+tE60RIo2+J6kArqt0/aQtO61QVvYo3EE0GvWitRfsqftnXus8ZlcRzTj81OEx64avl389DLRy9gV9uIcP3jk8NvfZFzVBqUFLu6VKC1w74xQWuBsLmUNAbUTGVQ33jpGZEZhZCF1Vgg5nBdOXP8Mi/ycybNaGKcLrKr54waOWV0mvGZRxlfrt061iuqjbyDE0HKji42ymOitcYR0MqipQaw5ROulkkUN5lZHsgNox/SwENeVIzKmFgDNQDxvSaExaiVj4LV0/nhgZs4HwPGazEo+e3PUoGFUDSTQjulpYJvE3FS0cF3it6KJdF15Nhh0hhlCmJ2YuzYxqUwXExM5wBANpabykJziVnw/Z3M1d22JZkwkvYls2x6R98FfXzWvQiC9FY+awta1BBVyMeF0bb8NYJeFOE9Ax6QG3AfxPMYY/qLIjVNvZyZ+RwDNShRnc3z86Aa/+U+e4/6DM8xKwoOHJR7cLzErgXvnNpg3LipgeUNYVoCDQd0ARSGTwXTrTbBnMxGO1gBlQbLVRcRCx73CuyuQECwHHVtZiGIFvyrDgSSaxLS0jyTKNj3e7gfSRRw0j4YAgyIyZ23JY0s2faAbhDa0hFHMOV2HNOpAzd17AljZt1DiZ5qWBHoC6LygF+Hj/S1N5GsoPhTnwNzAgJQYNkoGa7h6IVFbbb8ZaarxC2TJtOV6yWB0TfA/maBFFPPWBhwSzOuKrdceGiv+iGfnoIuHIFsCZERTuLyBW1wDyxv5bkMaPq/ZGwhOE4hYRMACcRvpY5j6F1JH05eYhSaa4xVtYaIl7NMQptrn0N6xGsGYCO4ohPvuuQ1ZvA3ktA4ZY2DPz+DqCq+cMYqFQVUDV5cGN0uAycAxMCvlub93RigscD5vt2czWTAtrMiyZcOoHfB8wapck+ewMJ74iWyZlapFVMJYWIdS3SzacUFkSqMkccmyDQTRRPKCWzlh9d03ZGCpBFmCKRxAJHIyBHUzkbWLROz0i4vsCaKaOlKzBFUVEFvjBDNQTxJ9cJlEHjTiGxkIoS1BZdmSHTXVhLGtzPABYSpJqdGJktrUQFVrO1tt2QrB6yGJcbmhiNcckclxZDGuwwGopbyrBq/lzv5Ae4zXxqqfpGovyUofwtoQnCf4Ulq9JpVFLBpbH4gnkETvR+nJo9MUJa4JeZ9TUI91S/pdAqztLTfNpHG7sXoXmbRpkfRQOA7JPZwszMTvCDBWtCqWl3g4u8HTxw2qmvH4yRzzM8mgWs7lp5mfWZzNCPfOLeYl8OCeRWmFDd0sgaoBrhcIE0mrdtPFggIJDKQw+KV3SaK14kc3K4DCSHlrgJmVv7LgsGDonAhQx7LKynWX8HkTm7DfR/zCOdcpu0IWB8haSjINGbEOiUmiWpT4Y1YqANRUltjBqWlL3Tg06nhmqYHTzx3NHwDnHeZdl/gFAtjIql7Ydw0MEWBK2PMZyBQiFOsl3PI6pO8AADaFXqPC2gvHJoqe6gW3d6hONYDkBak3CUoir6blfSCDuExC0ogbecgW11IM/hYGKOcw5/eBV94AkYG7uQLfXAI3l/5hkTrCg9cVpIFweb7qo5Q6boVYooVb0fzpc7+OCK4KxO2J4AoJTMxDUwLYXuef9R6T0A11td9jf2Rum4A6m7APs1H76htoHq8Gesi4W6BZiapyePvb1/j+U4NZCbzysMT5nPDwfgFrgIYNrhaM58/FSqZUIjgrCWXZLohao+TQAOdzXQy13g3ComlksbNxwKIp0DQIbhGF5faz35JTdwwHY0jqUpJoiNqxkxqVNZ4YurBv2JuYelIYkUQGjL4vhsVkxmr+QKtEkaBWKKyLUNRG/G7JYQV2EWn0C5e1ugv0+LJTXevCn7oyeE1fvNhorIy3xQyYzUWbBiCNMMqNBrNRX3MhjTV4udT8hmo9Ei9EIiJcqVbRcSungG7ZTVpFj44/Y6odHC7bd091mAQve+RDqGKDpQrELIpUI+n9I8lY2FmphFGIo+SR1H1ju37twUy1CalA2GtqGxd+C1epeav/XUO71PJpwJSzd/xOyONY0thOOcbLniH5Mcb0dD/y8nALqcdY7MzE7xgwagPpajx5dIXPfvYhvvPuDa4vXQjrXNdCAOuqwCWApzP5qcq5vGyzmcH5DHh4UeD1+wRjHD56yriu5AEtC0JRyBtWKTkLpFB/9Zb4ybbSa01y3BoKJp1lIauq84Ixt8DsjNA4YFkzrhdA7UmlCr+aPMn0+6vngjYxHO/XKnri13hu4s+TC2aEIebUGvJoCDDGwBiCJaCcG8BVWFQNmFqPRqMjlNUtK6GyqDr7fmuQ7JPprtZhAXINqCxhzu7LYH59KQIwIXUrJK9pQs6nQBb9wJ9G1opMOWVfe8UP9jEpCdo236nriWAwRXUOWFwL0YMOiLM5zPkF8MonwNfPwM8eoY2ahYjYaD8NEC42DhjQ4KUpKShd4fVEELEw7n6nIZIWBmD/QCaBZFijCMbXjiVtKcb4BE4lgGTMqqZxnwLrgP6LdHYP5Q+/heb50/a5zbizMNbC1TWePV3g8hnhEsDVlYx7M10cfeW+xfkZ4eF9i1fOCJVjPL1iPFsYzGbyXvpFz6sbLxel/kJl4Wwm1i9nJcFa4KxkzEqgsEZctMFodLHz2omFSbGiLZRt0CIGtwijZqZKDI2QtcI69bsHoNG7a/iw7Z4kasAaHbPSRcZ46w0r/JbYAGRhC4kwbZgh/uuiTQw+izpWuFiL6GXRUi1UYq0h0OObWAetVfe4+gEaseIRM6MzGFOESNakWjOv4QoEURPfO20Da9oORMRvSIu4QtYGCCD7+jCOJK5ci2Ts3kDwNqW98No+H7k1LLD2tatvHCY1TY2IorEFMCuDhlKsdVQTaYtowoAkkE7dRqDVz1zVEmFcg+z0fZdJpBEArJ2gYdyeJI71SxwjI4fq2k2Luf9F2E3IxO9IICI0NeP9969wdm+GB/cIHz5pUHtTQD+B9ZN6090CwHIJXN7UIAJ++BMFHp4D33si52SYVqExED2JrZ98+n2/ypgebz83LJVfedJmGKUF3nhAqBvgRuWD5xqeXPo0gNZEHMOTQu+PGDSBeo2ed4HU6UAdiKCWgw3aLxdIo373kJSiJX6OVVvW+OMNLuYGhSU0Da/4gK3uD7yg/jsHwUBBY+WiItw4cHUFkEFx7wLumkHdgKrtqnGou9Wgsda50oxgWhnfLbo7JfvGrJb12i1Pllb827rlOmRtuQAvJWw63X8NeO1N8McfRKrZlCT1Ey5yRuoD2tVgT3pNIoz9ezJAADsCmXYngDxw7VgN4NrB/QDRRk8B64Qi3btA8clPA67B8rvfkucnIwOiLakWFaqFlzVdOfbYMR4/BX7wRPzNX31o8doDg1nJ+OCxkLRSiV9d6yJocI2QOpYqr4LLhG0JoiHgfEYoS5FxZzM/JIgv4bIRTWNhPRHUBVbvDqFjeFF7ItgSRf/ZkgERUBqXkEPW76tyTc1KHWtwFjUrFe3hEClMSKMTbSJpdO9gmQIAZGGsgTEFyBiYe2KpweoyYaob0RrVatK5FNcASrWGMQFMtYWu1milXqMXBXUhArGQEi4taD6HvbjfBiXzvolBa7hQ01LxS+RqoVrKAcLXQxCH/MSHtIihWB9R3Mr0NC7HvZ877dhwnsDSx3UVh93zjV5bpxDDotUqWtE2mpkG0wlBdooQIE7MZpUsKlnnuoZbqq9orW3xQY967p2SxXUkERirVZwmP1sZvb32cNO140hlv3PmIQhhJn7HAEvY5LPzGf7Ev/YGqJjjnfcXmN+bY3Y2AwDMgoZPtl57V84LlAVw/9xgPiPcPzcoC+B6Sfj4OQdzF2spMvHsmnaaSLgBrUA1yfy81fi113rfwFnBovmTxVcsa0bVxH6DfstJ3RylnvD1p9o56FYFaWIu2mr6EPYp3McTPvRe07dfWEJhCYumCcfjMmF/IMJXm9Ji9fzacz4Vw9QXe0SksV74H/ZWwmmx+jesaqFuG/vSenFHeK0X5FthZB1D32NK8uSh36S3/ICmb5sAMewcaDaHffV12Fdeg7u+QvX+O+Cb67XtzbhbYHaw1uL8YoZ57cmaCJeiVLN86+WWWKQwAzdLh/N5AdcI8WtTHat2S1cdGzUbaTwxjBYmAWCpcux6Qa1rhB6bFYRZIdG4z0uADON6CVzeyGTby7PYhx7RcUu84hu/sh+Iob9WI5cqB7JG5Lg1kg5K3NnFfQEamMZr31qZGBEEtC4MMUH0ZLBolnJfrgEQbFmA5lb87o1t5Wu9ALsaVC3ATQOqZeGGXDM6QE3qe0gxYQzJ3KN6rAXNCsBofsWiAMjqIqsskAoxrMDLpUSr9j6IPcHIUs3fFPLoMaRhXOevGCO9R+dY2J9OItP9TSQScKp9ld+RB8tBCbv0P9kCMAbGFjD3zkDFfVBRgIoCbIo2/RI7JYoV3HKh2sSlBJCrK7hFEjwuub8ZYCzd75W6WayXKevI5JAFzVD7hkCGJsu2IbeRfSATvyOAHYMbh6uK8OGzAigKzO8XmJ2Vwcfv/NxiXhLOzy1mJXBxZlEW4ruwFLNtLCrg2cKguhIhOJt3g7yYhPClAWA8wSrU73hWeP8H2S+tRj4zKvQYcGD1iZA2PL9hv2An10UED1g115RUEtxbJiV8qz59/STODJzr+Pyp64El0TRaAkijjNaNw1IHHEOujdrmA74kPn1xBND4eOzb5/fDiqh2kjEGVJQw9p4MdJePRVuVrJy2zv2Rn0NIe9EVMp0yQKSqTfbTsNlRnRgQeoN19gnL2Rx0dgGa3wPfPIf76H30mXoOmgwOCMXOfVeu2Xw8Fcorx8N+v5DeBhtXbUeYTW4yFxqqe2rZwfL7InxkQOf3YB88hLl4CK4rNE8eYfGtb4TJXCZ8GTHYMWxR4MEr57hsZG4/n0l0z7Mzi6IgnJ8ZXYQ0qB2wqIHrBeO9j4AiWgCNt34OlS52pjmn+6yN/etQSzysYN1SGMKDc+BTrzA+eAw0IU2Nl3OJBY2hVQuVZJ+Cm4EnZbK/XFn8ZFgiibBtCZYMrLXixqCpoUi1dsyN+LU7pxFLE0JIDGtERjQkU0Mv6zwRNGqWaZS8WTDIzGDOJLcscUsceHkDbqpA5FqzUambBwKd+fNItYiF6wYsc5WkXFgAHXnl/QyLEmZ+BtgHYJ8SggnsaqAS6xSuK/Ffr+vWRzLV9CUyr3WpaEleanES9kdqFeP8ucGqJFzi72vb7xjX7QOfJXWFtnSuGadhW1euHf/1uVbSBmCtllEu0ZWLogAVJcgUMPcu5HM5k4jialnjSaH8VeBqCbdYhEiz3WrtGhmyXl6to1Vu7dmoz0do/KZqBfeZOiNFJn5HgETFqnBx/xyffJPw8JU5ZiVw/6LA2bxVmUkUT0JVA9dMeHYD2KXX1hGoAM4LwjlU+w4hd4ZEm+fNVfzWEzzNKxtWIona6J1eGdTofL1qRJuXBm0xYBQEmLIldMA4UrcpeIv4IChJg5I2PU6kKSF8ygjoWALfLviGBDMVn7yd1ZE8+FRE97Rwes86rIBa13XA7zWdAVajfLoGMAaWDEwhJhRkjKx2LRfgxRWcRtFcIXhB+K1G5ByV8iG+JhCtoeO8em06qR8ifLYAyjmomIFmZ3Lq5gru+jnw6PvdQT8hfBtXQeMBcJP/xRiH/LGEb8Ckp/e6jSunSd2hzvWrtf3tG1n3gIat/9oBIbmGkI6ti8oZzMV9mPMLmHsXMke7fIbm+VNU3/vDfjOrjIwI7mYBO7P43GfP8alPEWrHYDaoGpnG1Q1w0xCqRTtBIgPAAuclBaIXrErS/UAAkRxPr0MIFuZTJ/ltaX0wNHmWHz3v1pUiePUxBxeEML/2r1ZiYe8/eXcHToOlgUS7h9ZFg7xPf2R1I8FohBAWJVBogBjnHJgbNJ4Q+vspe/Db4PfuFz/9cR+5tBHTPqOpLCwszNkFyJaStqmuwMtrcF2BrCeCPX6BAOAjWXuiWERyziaLnOl+nH+2qcG8BKrr1m9OZTOMmDLS/D7w4DXxfwOD66UQ1moJ3FwBddUGWluRE1EUbU/O0hRFK3LKdo/3LEqukMI0GrZ/RpPytI6shYlbMlYPyJA0f24ckCVOcZHeJ253IIad8yy/wbIBL278kZ5rIas9RiPNzmYw84coXp+BijK8uFxVMqdaLuBubkSLuFx05OpKLuAVDBPDTbq2fRDDsaam+5SZmfgdAbys0Fxd4+xihj/yWXmIlxWwgMWyErOVeUEoz4C5+hXEefsMqc8uxSuYMmfzPmwMGTCccCgZl5jbaJxNJDwicxOvJRMjxOh4ROCEjLV5/AAlZaDgtB78GwiQ/IIMn9vIJ5Q3YUCLV9Ik9YKMk2KuQz7FALcpGvQLgQG4mqPEDgmphE8Bkajsk3x/QaC5eoXY9Wr0yGgIbgNDBDJGcuKRpHHwobaxFFMYjsxdCAMaPaDV/K1o7dxgDsDeaJ3x/jqt3qCWEOJfUZSgohSiV8zkOzID9QJYLuAunwjR05XgDgYFpj/dfxzcQwymEL34e/Scm2x+k9xrXZnbJHp95W+V6BUFzPwM5uwc5uyepPwwRgTw9SXqJx/Dvf/OKKKXiWBGjOb6BvNZgYqBK0e6qClyb8V1IfYnV/llDXVSAXm51Ua2ThYVTUvwgFTzx50h1rEslDoGriqEJOaki6xttGmtI1n8FLndvzC6Ce3rqsQRLJFEAbgQZEze2yZcIzYsTYdIiJy1hlBaC1sYFAZYLpdo/KQhhu9jTqJBBs2k12p6DX4NUtWocQ3IlrCzc5iLV8Ssb3kN5/0Evf+27wQfvc0TjCjQGbEeC7Jw034i13yUbFdLvt3ljcgbQGR0UQK2hJnfAy5eEdlXV+DlDbC8Bm6ueyxnCN4nfyVFkT5wKSFESsRi0ubnJkP+bwnB8/filDhE5qND5IfTB28jMaTo8yoplP1h0tjFqvljx6zROcAtgHoBdzlAzqxqCmdz2Pv3UczeAJUz6fdaAgTx4gbu5hru+koiyq5gvc6vD8H0dCB+RptTcbXubc1H96kBzMTvCOCmQXN1jc/80AzlPeCsFMdua0Qb5ljs8p0nPyzExW8dI+Swa8mbaKwK1ZZ580cRZi6YNQopk3GdorLwx9AKJYoImf/sCVhYp1FH8XafAfbkTUJYMyt5U62iMM8ot14y4W6PbzofreokE9hA9NLjwYwiHeBkpdJ4Exgyol00Ruo24oQvoaodwJXkv3MNqF6KoGsk51KsRWtTLnTTNAwSrnXau23NMj08wQkJayUsNKwFkyZuD36HGoa7WoKvr9UOfxmE3qB/RF+74HeT7+6P95CjdUSuU1fY7/+uK23ruXazj0REIgfNVMdqAFePj9Hc9ZVbW35PZppUzoT8z+a66nomGl5Dstq6uEFzdYXm+ffhrq9GEdsp7cm4m3CXV3j9Fca/WgJV0y7yxQj54PRfCGLshx/9x51rVo+HRcZ1aw9KHtfNEVPNjIdJZU08x08DhyVlU/KYXtfxSY9cH9a1J0bjJL2RaRrMCgNbWNTLJpDIto7CN8R/KQAt0fMIka5hYLyHJREAhqsb0QoSwZw9AJ3dh7t6GogdOyVJNl0U9dsC7BdGTVdOtvuecKWLn7qNy6UmnMb7Flbyd+OXkY2MfxcPQK99Sp6dxRX48glQV103jCHrlgGZSMnx2Gy07dNu3ZvMR9vDqwuraWCa1Hy0vbZLXGMz0o4Jaeda0702rQvdunq/S9p2j0E1egMsr2UhIb3GSJAgKuew9x+i/MSboNkMcA7u5gbN1XO4q+fiXx69/HGbhrSblIwDaXvXBq0ZGahmm7QXY5GJ3xHg6gb1s0ucFws8nDcoNKJXaVxIUh4cxIOzt5I4P9B3Uh5w9OD6z56QKdnxJAyRBs0TMX8+ImtAl4BtIl+biZcwS2LXDhgEIaTsui826zl/rY8gmtxBvoA3e+iWDbUZCppEpbW6cphO/mVQJOfUCbwR8uMayXsUa7QibR3pPsV9EGvepphdxuf7SF3aZna6KqpBVHzCWCixMyTkzoomkv3vrDl+UFdthDT/uVp27+9vtUY7J5vo+BCx20RwttDSrbSv7/oJxK633QP19Ne1PWmbrK0LBTaTyd66CjWjKWeyLWaixStlC+auKc3lJeqPP5bV7wFBOdjEqX6BGXca9eU1zssb4EzyrPZhiKiFfKB+f+Uc95/n7rW91XeI5EA9aytYX8QvzqboED9qpV5M7kz0mSI56K1w/LolSYHgG8iQsc6xQ72sYIAQECYNBJMuxg4GPFtjPsdNjeb6OezZBVDOwcvrTpvDGrKf+Ya2oJ0Np6Q2jWi9Ekm6r1wSydpPh0OOuygK9PIauLkC47E07+we6NU3QURwj78P1jy3g5GrQ2AdT7SUGCZRtMk4DKYs6klVJM3rRryOOqVzb6lLywY/QK17ZXbVX1f83YYiaG/WTMX36pfNq5qyARkezRtXc+c24Osr8PUVgEetBhwEmp/BnF+g/OQPwZzdA7ND8+wp3PMncJfPI+KezGsHCN1Q3t6+64a0hUNEcFPd2yATvyOAlxXqp8/xavkcZxc1iGsAQq5klYtBdQMGwzjZhgFXTRcNuw45IiU2BCcDKOs1QUjAj5wt0WIEUtSW8aQoIVZJ0vT2yyQvRg854SAtlaTqdenqWGvKidAfXV+x9tX19TGiVTawOityp86goQxtQIdk+HYTALh6s1ZuCpkbInIMEWKUbjUCVmGErBF0lCb5XYlaksMs2jFPVDVaFtTUFHEC3cgPwYN7ju2ipVu5JqlzFy3dYN1bkLoxhK73XiPrH7p2sD1bmGEO1mWsOMrbAlSWaqqrjvSF5nTS2R43Vddp/uo6ONPHzvNjSdkuCdzH53PKuAuonzzDPX4G667F9LAHvDI5DOqxVRM2FkHYneDSyueV66IyHGRhUnVCND2xWm11l1Sumx4Pvg28+pFUtsU60XboiDSZLC4RXuvZ+LlDRKJsEgHUEz6rFiujUkbovSiS9UA0V7EFaHYGQ0BTLSO3hnQuscMEN6hLmuR4RAiTACEBK2l1ugSRHAsJvH4OtiXMa2/CPf4+sLxpx8BNqYtCc/R4RAA3piwaTB0UEdXoOBBF1h7IUYuBe/alJ6KV/kG3XR491w7VNeQLlxKrteUSlf8QYSIwsLiGW1zDPf6BXGsszMV9FK++AfPDnwUvF6g+/hDu2ZPOczlEBNearKL7uw9dsy0R3AaZ+B0Bzc0CzbPnuF89glveqNZOCFm7kuZz7Ogo7U0qPZHxpEhJjTepRFSeUkLCTVRXdFzz+wRRskJ42vN9xK4XsRlmdLhz/dBEcXApd2L5tfdIjoe+iFeSWAQI69bvAwAZyTtE3mwyPk/tIES61poQUVazUPZ+dq4R0qoJcSXBrGs1j7UPRc2DPmmjtHFJn0wNDT1IDLGeyPW1d5AMrCGR+9TObT4+pY6J2rp1vnUsgkhMbyV3EtkCrLmWyBYhhLbX5vo2cKN5kzR/Ei+XcNdXcMul5Lzy5shj27nmu4251mMsscumnxkA0Fxe4ezqY5zVy1USFPzJuhqiMMwSBXLDRP3PO5OQID3Pfvz21/nhmvxSK6+aiCb2Xqmubt35TddSKDdMD1fORAR0k86lrxBRK9+H/dy7RI/S/bi8jmPWWone6B0umyoEOJPF1oFI1n2BziKf98429AH3H0/P72uccTXYNeoiMQEJAevDIKnwz/9Q3tfefLUJ8Ry4/5j8tINkLLyXm8njprqG2rOu3IoWMF1IWHsPB/fsiRA9AHR2juK1T4B+6DOoPngP7unj/v4ZaGcK6klrNejDqfBE8BCLopn4HQPOoXp6ifnH74iNe62TsSj8P4IGT9cGg8M1hRV7rQztCE7hOglrrMeY2yLRqmjHWoKQkJ51WDWXWbmO01WJIMraQybxGwjX9qyHdurmpI197R05sK+QItWWMUemmqyatQZgTUbqA5CwPx6VVzInx7sav7Fkrd0dJsq3Sdrae44gb1v6rPX6z03WqPWRmt21cuvKy0kv7MRnktSPkjSXCPkkuP681RC7kSluVFmI/MtqlsuauNgtFkGLy1W9FZFb9x2n1HEIMpdNPjMAoH5+hfLJB+CrJ5Kc2y++Ma+SIb/YZnSRzZh2UmzEWoI1xH+wmkiv8SkAwp/R6yiqX7dAWw4xOfP7SiIBaW8gjokWkFk1kJ5wRmXVIgd6ZatV1LJeoxhrIwbkdr/xaD9S//p+gqeL1NqfZCT5OhkDQhvBmoLliQY5q5etnIzqbP3jBvzg44BnQ0HRNlnl9JWf6ivvZTgAzM9AswvQ7Az87GPw5dO1/bqCExvnguZxFy3rNvcdSexWrxtRbisCKG3hm2tU778DFAVmb/0YKuZACvvaven4lO+W4hAEMBO/I6F69Bj83W+CIm0QBzKhg1IwfWyXIdlrffzyY+z7FerRa/SYT2fQIro+bKKlTBeZQESml6vXtB84KR++x4ptSryfEhOOC669X2d/j4PVFOKwafDuG0THaLl2ble4//R7TdWKjWnLIa+dZA7pJ3HGyAocU/jstbY+6XwcLpfIANbC+HL+GupJytqIX6hsNRdR0wC1JsVVv1EfGEj8KutRz/A+NWttnfu577blM9HL6EP9+Cmab/9LoKrakJtWTd6THAydlfNokZMdq+FGJB+Jo4m+7sfnTVAbyjtJiKw8IjPBeJ/MwHHbXh+1n9X3WsajpExKQH1wifheoRzAoZ6oD9LIE53jsebSv3vpQq7sE3n/eNGIaCeBmYV4ecsUduI3zo0ERXEa1GyAQPVFrO4cT8ekuPwUN4v4/Dqf+aEgaOw0yudczOSLElTOxSpneQP3/JHkAOz7rr7pzq3W2XOvuA0b3StGLvBuY40zxZVim9y0KcZa9BwNdQ337AnM2XmH+G3CJg3gNtfskwBm4nckLD98hOUf/AHck486g8GmF2+w3ASc3Mt1Qpg0eB1p0jz297vtuoLQ6/FdJH/cm2ToSnFn0kQkCyHGSFXGiIGKMeFa6kyM/LXy2WvXBtvufSK9b6NrIlNbB1frflXDm9q2Wwnuw3UVjm0inaP7TXFKGrRDk7c8BmWsg1tWqB49Qf3ed1Ym5iFvX/z+A2ExxmvXYQyoLMKCjl/AARlQ4ccU3bdGy0m0Y1gdb2yUn0HzR5A3/0c0kWcnMUiYAfixoemch/e9B5RbRYupk0mlb0s75vVrQtstq9sBqeaS0nsai0AAYyuWGHFwNiAknk+Pg904Ihfvr5TrOT/kajKktRu8J8TiyFow+d/ZAj7nrrFo8w8uwNVCtHrVorfuSblp43aE3dU+2IXoyX503VS3i10I38g567j5ydBC9AirogH5PNaix1zch339kwAD1bvf3tjWje3Z8Zqs8XsJcPPe91BhhqYp5MUJK3wy+Q0vk0+Eh3aDeIhd9TTvPz4Vmya1UwjSimkm0F1hHKiTB3eiS6a0c139e0T8Vdfdo8dctXsoMSEKv21yoziSabT6G/bTCD/sy+l+7I/oV3kpeu7Qs1I5BEZYmWWnfqfsOsfbv/Y46z47BldKtOK6IhNaOPWNjIgcN81ByFiMKQPvNsTmNkjabtdsT9ayZi9jKq6+9R3Y1z8Jd3OlxELGFGJE/s4SDK1zXgYOIVlBK6hEyWsHk/1eEunPU9+1FMhiIJNl2ZJI40mkP0+tSbe1SkLF1DssaElD2i07BM1j8O9HSy6da8kee+1mqvHinunDBlIZY9N4OkTW+q4dImPptUMkyV8fZJouAhoj0TrJE1oTzOiZoCQuWRR0DVxVQfL5NRrUSveXy97vMSoY2RZ+9nI9r5yb4qoxun0D195GSqNd3DAOmcoo1GEL2PsPYO4/hLl3gebyEvWH34O7ej7uvmuOr7v/MZCJ35HAVYXn3/gXMLO5THZdMrhFA3ccrcvVKtRCRX0P/rS2rCShHMsZe8nluLqo5wRZExcYUXdaqOeiXiLVnthnUkwAa/o+HcTX/Eip6e7KNZyU6x4TwpWW6dt6QqXXp59HYJ8T+n2saO1jcN1Nm36ca9s6dvv++2hDjtCZsQ88//Z3Qe9/KATKGNhStXiF+NBSUcrWdo/Dn1MrAQBKkFjHxwbELUnkphGy6BrJeu4cPLEiH5XaE0rm3UikfGjLxhnlkzLBPLRDIoVsmkI1keozHD6T9ofudy0kuguFQesItItxqUvHpslEkuesH8nCY6zhjBcifdsGfLNa4uvErNT5xUOnwc8YYDWh9+mX/LZOzOmjujdq65Lj0YXRxwnEru/8OpPPoXYOtOvgqY121OhtlcdvQ/m++cq6Omg215QO92DOL0CzGbiu4S6fof7oQ7h3v5M8I+vfg6nfY0ydh5SjmfgdEZff/eDYTdgaeydMvfcY8FXYS937a/9t9MXLjNvSBh3yPvtczTtEO/cpRA7RvlNaDc04Ddw8uQaeXId9v0CZjrcpsYrPU0K+TCnBlMgKYTJFqduIYBkjKVBsCSoLPWclt6UP+OLrVh+3dhsHEZF9qoVsynHxjSMtswt5DPv+HPXXhbSuxLw+mNnbIglgg/ZzKovXWrUMLXIqcQuWGemiY6up7V4XVTXR334jeZNKk90NmspQ93hCtfEe4fh488zBukaYQY72zxtV1/bkba/RsOO6jAWdnYPKGVCUMLMzSeZezKTscoHm5gru+gr1xz+QXMYj2nDIeAW3Gfk6E78j4tkHXRXyuh9+RSt3CzgUodn3d6E91jeFbE7pn31852MQzEOS71PHi0ZGjm1OmTV9GftEKh/TMTQdD+PzqUxYIU4b6uwr30s8jWnJYykE0RSeVBZtNF9rQWUJMjOYsmy1lsHMUutTf2Ly5utg9bfzpJJD4CjZV9NXdisGMIOEOCGKvX0z0F+h7jSAzBrZdAoyZNRYPnL8nBS0LdQ9nuDsO2jbPsja+romkrdQYIvvTlbz0hZtwJ2iFDNrK8cBAE0Dpzlp3c0NmsuPwIuF5Kid0M59xUAYIxv3kTZpLDLxOyKWl9Wt3u+Q5PGQpKRjAronDPfFalLXqd+tr+6BVLE7kdbb0Yi+WOQnYzscmzRmZKSoVD4OjZHriGAqM6aQxvSem0gjma62YHN5Whm7AzkrCzVjLYJWkoyFKYVc2rIAbAmayXGf61OC2fQQ08YHsGqEMDgnKWBCkCsHapRsAoA3edVJ6NB3WZGJPbKIel1BpsutQ8wtDh0cb3SQuB00RIfQPu1EPqZE2PY5aHXLmtbIJLlrURQgY9uoo00jOWqrSgLv1DXczTXcYiE+mk09rc1puwbLHJ7Yjb3XPpCJ3xFRPx+iA+NB5fhBcfe7Aaa4BRPPVNBX+yMfISTuBM49VfBMIarbkPF9kbJDLQTsUwObcTfAWVuYkaC57h/fvMxL5VlXNmnuN7ue4IU6U2ITjeHbksYhchdfs8lsdah8XHeq0ev4ElqrZNLCzMpg5krWhEk3FXOpT8lmm96m1eyR0YBzProxgJDz1vkAXJHZa1+qKTWHZa4AxyCfSiMhDIeyutkXbiui+tj77DUlT0refGoj8lFxEfxoO6mQkrRIkrNWy2gak7i9svjQCJFrNMBO04AXN5ICSXPUsp7b5vvsQ4N2zEjbhySAmfgdEUOCbRLUBWIKAdwF64Xt7WCX77qJ/E75PoMEp4eoDk04hgjoLgJtLPFs1pDfnUjhBlJ9qj6RxzCnfpmQTT0z9olB+Tgg8/zYvm4MH5Id6TVkN4/h68jipvLrNIxSdrxp6hDB3GTGunp8jW+kpeAP2Amoo1vj/QM1bQapXySF9Bo+XQZJWV9XlJZnrVxIozureauci3IVx36C/riWETIa+RYS2s9pwDT/uS/AjEEn4J40z1/f0/b4axm0ge3iIDtx4YG0HETR+Th6N2lqDv2TKPGaQsn7cWo/S0DU6BjWzRU06KDXFHvC1rRpkCRXbQO4ZTA/5qo1RfY5bXtrn5QyaH/k9hDRuQ+Z/ugQBHAn4kdE/xWAfw/AEsA3AfwlZn6s534RwF+BjMf/MTP/qh7/AoC/BcAC+NvM/Nd3acOLDInQuSfssa4p5Kep9v9QbiR2++w3hf/OU77PFALKWxLkrbRnSjx3ITKekB6CpB3CdHcdxvZDk80dM/aMLCO3R3OtWrtyYLxI5MCYMZzq/rFg3fg8TsPYTxaBaSapY66Jz/eZpG68ZkDLGJ+bShpXyw23K23PUF3huI9SGnK1KtnVPLFt9NaW/HgiJOk4KJT1ZeRjRLKCz6Km6YgD3IT0SNF3QnscbenNSBO8x4ST26Od3IAhnYSQXg7lu0S3Q35j4ssO3LjomCdz3JLnTc3eM0nbpX7gsKmVDp0eaTrx27+7za4av18D8IvMXBPR3wDwiwD+CyL6cQBfAvCvAPhhAL9ORH9cr/llAH8GwLsAfouIvsbMv7djO15IeMF2ahizcnpQXG8usgmDk4UBTPklQr9s0c59aGZ3/V32qR3eri3re/u2TEVfFA3fqWpIM0Yhy8gtERZG63Gj8yi5NTBmT5EXm8a8TeNr3/VjNZGhfM/YNZ5IDWsm92HGuqnO9vj4OqbWNfa6Tef2UX4qdvXdP3ZQsn1qqE6lrpctTdJOxI+Z/0G0+xsA/oJ+/iKArzLzAsC3iehtAD+l595m5m8BABF9VcveOaEGALxuZfKWTDfXYa8ayQNgnQDmPfoFpnBrzSM2YGCVegrSFe3Jz8qa33Vqe9aurm/7DI947vaxKNFMHEiP5ru4xYD/opDalx1ZRm6PIfm4aVzZRm6ZCf7S3kR+qiayvX61fUOayE1kNu6LjWX9mJDIxnUkbZgkNr3l/fmYqg+ZsQ7V0deuoXJpXevKrqu7D2MJ3j6CrHXv9WIHVLvNQGGHILmnl5v4xEw9E/xlAP+bfv4MRMh5vKvHAOCd5Pif3GMbXhqsI4WngLtETFNBug9S6ScNe/kOWsdeyJA+d3v5ffdAcocwRDgP+lz2/FZH04pvwBCpzYF3joosI/eAKbJx7HgwZRz27/xYObCRKAKTyWL4Xj3t3uRWMMU3MjVf3UaruHrNOJNY16vxS/twPXkcqrsZcc14QtdsrQW87QW6U4uQehu4Dd/zFyVHsMdG4kdEvw7gh3pO/RIz/z0t80sAagB/d18NI6IvA/gyAHwyx6A5OZwSMT00Cd2WnB1aI5lOJPZJhA/hhbdXUjmEPZLgMdhZ+3rLOH5GrZcPWUaeLqbKqTHv79RxdipRBKZpFcdoFKWuYbII9BPGsSSxLxL3UATtIa1iGmys39RzHGlM2+VWo/yPMA8d93tNIW/pd+9r1/C1efS+bRzDbPYk0jkw87+z7jwR/YcA/l0A/za3yUveA/BWVOxH9BjWHE/v+xUAXwGAz9PZ6bCMjJPDKZFQoBWWB9dIbvB92YX8rIv4OYTRfjK3EIjoEEGHgBETwyOaR4/5vQ/VL3cZWUa+PDiELNlpwWuEf+PowGGJf2M6XvfdaRd/xjHmqZPuNcKvceWadb59G4KMjSV0Y7Rok8jhjlrA28nvmzEFp6YV3TWq5xcA/OcA/i1mvopOfQ3A/0JEfxPiuP55AL8JCX70eSL6HESYfQnAX9ylDRkZp4ZtJg+H0BTtQjy3IY379KscSyIPQa7Xa2pvfwA/hLnabeBUzWBvE1lGZngcwkQVOKwGcqzmse9dHyS8E6Ox7mTWuoZEmU2JvZuRxG9ElGp2E4jfyPsOwdj9yYG0/0YGAc04cexqH/LfAZgD+DWS0La/wcz/ETN/nYj+d4hDeg3grzJzAwBE9PMAfhUSqvpXmPnrO7YhI+OFx6mQRY99kIhdJv7HIJEehyRQ2xHq0yWb63BqRPRIyDIyYzIORRKBce/lVL/2tQHPNpjfbzSXP4BZqyloY3CvsZq3TQQSGE8i5b7baexCYJ0dtEsrGr0D+MflwGPHB3GaoPIE8Xk64/+m+Oyxm5GR8VLg1P3QgNPVFk0lkcfCqfbfWPzM97/+j5n5J4/djhcFWUZmjMWx0vmMHTun1TmSnI3I0zgWY9o31VxzGzK0jdnlPnPongKBu6umpz/9G7+5k3zMHuEZGXcMp6Zd7MO+tEX7JkCHSBNyCDJ5G9q2F51cZmTcRYwZ//dhXr5tNOwp6ZJGx91c52c5Mfr0qIBeazSNvXX2aNY2kscN2rg+YsZuXE7MMYSqmZiGep+ks23EDu4sJ0Bcj4VM/DIyMjbits0N90U0T83csE/wHzLn5CGxUz7LjIyMk8Wu4z2VtP3YOxDQpt+PcGx7dKwa0aZN+RBXEAXN2SS3hrjSLotoQ/ccw8v2uXi3kajuIOcOQdLSaLNT8KJrGjPxy8jIODlsO/E4dTPWY/tO7hMvKmHNyMg4LPapVfTYxjexbc/msWqX9EhtwJztxvddRtJdlt8Okbf3EPIp1YgePR+ttudF1Rpm4peRkfHSYJeV6lMnjR57zdd4IiQyIyPjbmHTWL3NeDx2bNyH5UVMFHfN9buL3NrH8ts+bTduI1/vsW1NQn7IW0gOfwhk4peRkZGBu0EaU9x2OoyMjIyMMVg3Hu863u6iPfSYQhSH/Li3GX9XfSd3l1uHWEw8hHvIreVITrDS5y8o4fPIxC8jIyNjR9xF0jiEU/OrzMjIeLkwdrzdZWzdxtRzCNuYxe+TLHrskzR6pH18SIuU24g10PfMvGwyLRO/jIyMjCPiGHn6PF420pmRkZHhsc+xdd1YeRDLib0YcXYxFAxnp8jSO5jXbsJQnsZ12Fmm3QFXikz8MjIyMu4osqYyIyMjYzMOEbBmHaaSyV1Ixj41kkO4rcBmhzQxnYpT1RRm4peRkZGRMRmHMBPKyMjIeFFxGyaoQ9iHv+AU7BrVeRst47GiYh/LxPRQyMQvIyMjI+MoOKaZa0ZGRsYxcNvawyHcplYxxS7EcRfT1FONin2bsjATv4yMjIyMjIyMjIwTwSmZLHrcluniIYLl7BOeeJ6qKecmZOKXkZGRkZGRkZGR8RJjKpk8lin+PiOXHgL7JJ47BdbZEpn4ZWRkZGRkZGRkZGQEnIpJ6hRsSxqPFYHzGNrLTPwyMjIyMjIyMjIyMibhRdEibsKpaxn3CWI+fRtVIvoQwB8cux1b4hMAfnDsRrzgyH24H+R+3B25D/eDTf34WWb+5G015kXHCywj8/u0H+R+3B25D/eD3I+746Dy8YUgfi8yiOgfMfNPHrsdLzJyH+4HuR93R+7D/SD3YwaQn4N9Iffj7sh9uB/kftwdh+7D2/cqzMjIyMjIyMjIyMjIyLhVZOKXkZGRkZGRkZGRkZHxkiMTv8PjK8duwEuA3If7Qe7H3ZH7cD/I/ZgB5OdgX8j9uDtyH+4HuR93x0H7MPv4ZWRkZGRkZGRkZGRkvOTIGr+MjIyMjIyMjIyMjIyXHJn4HQhE9AUi+gYRvU1Ev3Ds9pw6iOg7RPTPiOi3iegf6bHXiejXiOj3dfuaHici+m+1b3+HiH7iuK0/DojoV4jo+0T0u9GxyX1GRD+n5X+fiH7uGN/lmBjox79GRO/p8/jbRPSz0blf1H78BhH92ej4nX3niegtIvp/iej3iOjrRPSf6PH8PGb04i6/L1OR5eN2yDJyd2T5uB+clIxk5vy35z8AFsA3AfwYgBmAfwrgx4/drlP+A/AdAJ9Ijv2XAH5BP/8CgL+hn38WwP8NgAD8NIB/eOz2H6nP/jSAnwDwu9v2GYDXAXxLt6/p59eO/d1OoB//GoD/rKfsj+v7PAfwOX3P7V1/5wF8GsBP6OcHAP6l9lV+HvNf3/Nyp9+XLfory8ft+i3LyMP0YZaP0/vxZGRk1vgdBj8F4G1m/hYzLwF8FcAXj9ymFxFfBPB39PPfAfDvR8f/Jxb8BoBXiejTR2jfUcHM/x+Aj5PDU/vszwL4NWb+mJkfAfg1AF84eONPCAP9OIQvAvgqMy+Y+dsA3oa873f6nWfm95n5n+jnZwD+OYDPID+PGf240+/LnpDl4wZkGbk7snzcD05JRmbidxh8BsA70f67eixjGAzgHxDRPyaiL+uxN5n5ff38AYA39XPu32FM7bPcl8P4eTWx+BVvfoHcjxtBRD8K4F8H8A+Rn8eMfuTfeRqyfNwf8pi0H2T5uCWOLSMz8cs4FfwpZv4JAH8OwF8loj8dn2TRcecQtBOQ+2wn/PcA/iiAPwHgfQD/9VFb84KAiO4D+D8A/KfM/DQ+l5/HjIytkeXjAZD7bWtk+bglTkFGZuJ3GLwH4K1o/0f0WMYAmPk93X4fwP8FMQ34njdR0e33tXju32FM7bPclz1g5u8xc8PMDsD/AHkegdyPgyCiEiLQ/i4z/596OD+PGX3Iv/MEZPm4V+QxaUdk+bgdTkVGZuJ3GPwWgM8T0eeIaAbgSwC+duQ2nSyI6IKIHvjPAH4GwO9C+sxHLPo5AH9PP38NwH+gUY9+GsCTSFV+1zG1z34VwM8Q0WtqrvEzeuxOI/GJ+fOQ5xGQfvwSEc2J6HMAPg/gN3HH33kiIgD/I4B/zsx/MzqVn8eMPtzp92UKsnzcO/KYtCOyfJyOk5KRUyLB5L9JEXx+FhK155sAfunY7TnlP0ikp3+qf1/3/QXgDQD/D4DfB/DrAF7X4wTgl7Vv/xmAnzz2dzhSv/2vEDOLCmLn/Ve26TMAfxnihP02gL907O91Iv34P2s//Y4OwJ+Oyv+S9uM3APy56PidfecB/CmIicrvAPht/fvZ/DzmvzXPzJ19Xyb2U5aP2/ddlpGH6cMsH6f348nISNJKMjIyMjIyMjIyMjIyMl5SZFPPjIyMjIyMjIyMjIyMlxyZ+GVkZGRkZGRkZGRkZLzkyMQvIyMjIyMjIyMjIyPjJUcmfhkZGRkZGRkZGRkZGS85MvHLyMjIyMjIyMjIyMh4yZGJX0ZGRkZGRkZGRkZGxkuOTPwyMjIyMjIyMjIyMjJecmTil5GRkZGRkZGRkZGR8ZLj/wfoMywMRBG1lgAAAABJRU5ErkJggg==", + "image/png": "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", "text/plain": [ "
" ] @@ -530,16 +531,16 @@ "\n", "fig, axarr = plt.subplots(2, 2, figsize=(15,8))\n", "\n", - "horizontal_plane = fi.calculate_horizontal_plane( wd=[wind_directions[0]], height=90.0 )\n", + "horizontal_plane = fi.calculate_horizontal_plane(wd=[wind_directions[0]], height=90.0)\n", "visualize_cut_plane(horizontal_plane, ax=axarr[0,0], title=\"270 - Aligned\")\n", "\n", - "horizontal_plane = fi.calculate_horizontal_plane( wd=[wind_directions[0]], yaw_angles=yaw_angles[0:1,0:1] , height=90.0 )\n", + "horizontal_plane = fi.calculate_horizontal_plane(wd=[wind_directions[0]], yaw_angles=yaw_angles[0:1,0:1] , height=90.0)\n", "visualize_cut_plane(horizontal_plane, ax=axarr[0,1], title=\"270 - Yawed\")\n", "\n", - "horizontal_plane = fi.calculate_horizontal_plane( wd=[wind_directions[1]], height=90.0 )\n", + "horizontal_plane = fi.calculate_horizontal_plane(wd=[wind_directions[1]], height=90.0)\n", "visualize_cut_plane(horizontal_plane, ax=axarr[1,0], title=\"280 - Aligned\")\n", "\n", - "horizontal_plane = fi.calculate_horizontal_plane( wd=[wind_directions[1]], yaw_angles=yaw_angles[1:2,0:1] , height=90.0 )\n", + "horizontal_plane = fi.calculate_horizontal_plane(wd=[wind_directions[1]], yaw_angles=yaw_angles[1:2,0:1] , height=90.0)\n", "visualize_cut_plane(horizontal_plane, ax=axarr[1,1], title=\"280 - Yawed\")" ] }, @@ -572,7 +573,7 @@ }, { "data": { - "image/png": "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", + "image/png": "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", "text/plain": [ "
" ] @@ -584,7 +585,7 @@ }, { "data": { - "image/png": "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", + "image/png": "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", "text/plain": [ "
" ] @@ -656,7 +657,7 @@ }, { "data": { - "image/png": "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", + "image/png": "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", "text/plain": [ "
" ] @@ -731,9 +732,9 @@ "Calculating AEP for 1440 wind direction and speed combinations...\n", "Number of turbines = 25\n", "Model AEP (GWh) Compute Time (s)\n", - "Jensen 843.233 3.977 \n", - "GCH 843.909 6.434 \n", - "CC 839.267 10.937\n" + "Jensen 843.233 3.353 \n", + "GCH 843.909 5.335 \n", + "CC 839.267 9.463 \n" ] } ], @@ -765,9 +766,9 @@ "fi_cc = FlorisInterface(\"inputs/cc.yaml\")\n", "\n", "# Assign the layouts, wind speeds and directions\n", - "fi_jensen.reinitialize(layout=(X, Y), wind_directions=wind_directions, wind_speeds=wind_speeds)\n", - "fi_gch.reinitialize(layout=(X, Y), wind_directions=wind_directions, wind_speeds=wind_speeds)\n", - "fi_cc.reinitialize(layout=(X, Y), wind_directions=wind_directions, wind_speeds=wind_speeds)\n", + "fi_jensen.reinitialize(layout_x=X, layout_y=Y, wind_directions=wind_directions, wind_speeds=wind_speeds)\n", + "fi_gch.reinitialize(layout_x=X, layout_y=Y, wind_directions=wind_directions, wind_speeds=wind_speeds)\n", + "fi_cc.reinitialize(layout_x=X, layout_y=Y, wind_directions=wind_directions, wind_speeds=wind_speeds)\n", "\n", "def time_model_calculation(model_fi: FlorisInterface) -> Tuple[float, float]:\n", " \"\"\"\n", @@ -828,7 +829,7 @@ "Y = np.zeros_like(X)\n", "wind_speeds = [8.]\n", "wind_directions = np.arange(0., 360., 2.)\n", - "fi_gch.reinitialize(layout=(X, Y), wind_directions=wind_directions, wind_speeds=wind_speeds)" + "fi_gch.reinitialize(layout_x=X, layout_y=Y, wind_directions=wind_directions, wind_speeds=wind_speeds)" ] }, { @@ -875,7 +876,7 @@ "[Serial Refine] Processing pass=1, turbine_depth=4 (78.6 %)\n", "[Serial Refine] Processing pass=1, turbine_depth=5 (85.7 %)\n", "[Serial Refine] Processing pass=1, turbine_depth=6 (92.9 %)\n", - "Optimization wall time: 2.130 s\n" + "Optimization wall time: 2.718 s\n" ] } ], @@ -917,7 +918,7 @@ }, { "data": { - "image/png": "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", + "image/png": "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", "text/plain": [ "
" ] @@ -942,9 +943,6 @@ } ], "metadata": { - "interpreter": { - "hash": "abb86f6b47589d310a8582323f08589acc6fd65b639f664d8b854acb0023e70a" - }, "jekyll": { "layout": "default", "nav_order": 1, @@ -952,7 +950,7 @@ "title": "Overview" }, "kernelspec": { - "display_name": "Python 3 (ipykernel)", + "display_name": "Python 3.10.4 ('floris')", "language": "python", "name": "python3" }, @@ -966,7 +964,12 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.2" + "version": "3.10.4" + }, + "vscode": { + "interpreter": { + "hash": "853a8652e3619d46ff0e51baac54f380b0862f9ec17aef8c5e0b66472a177ac0" + } } }, "nbformat": 4, diff --git a/examples/01_opening_floris_computing_power.py b/examples/01_opening_floris_computing_power.py index 5cbe863d7..f7c0871b8 100644 --- a/examples/01_opening_floris_computing_power.py +++ b/examples/01_opening_floris_computing_power.py @@ -33,7 +33,7 @@ fi = FlorisInterface("inputs/gch.yaml") # Convert to a simple two turbine layout -fi.reinitialize( layout=( [0, 500.], [0., 0.] ) ) +fi.reinitialize(layout_x=[0, 500.], layout_y=[0., 0.]) # Single wind speed and wind direction print('\n============================= Single Wind Direction and Wind Speed =============================') diff --git a/examples/03_making_adjustments.py b/examples/03_making_adjustments.py index d3eef7310..42c8b6f3b 100644 --- a/examples/03_making_adjustments.py +++ b/examples/03_making_adjustments.py @@ -58,7 +58,7 @@ 5.0 * fi.floris.farm.rotor_diameters[0][0][0] * np.arange(0, N, 1), 5.0 * fi.floris.farm.rotor_diameters[0][0][0] * np.arange(0, N, 1), ) -fi.reinitialize( layout=( X.flatten(), Y.flatten() ) ) +fi.reinitialize(layout_x=X.flatten(), layout_y=Y.flatten()) horizontal_plane = fi.calculate_horizontal_plane(height=90.0) visualize_cut_plane(horizontal_plane, ax=axarr[3], title="3x3 Farm", minSpeed=MIN_WS, maxSpeed=MAX_WS) diff --git a/examples/04_sweep_wind_directions.py b/examples/04_sweep_wind_directions.py index d2d32938d..338769c2b 100644 --- a/examples/04_sweep_wind_directions.py +++ b/examples/04_sweep_wind_directions.py @@ -40,7 +40,7 @@ D = 126. layout_x = np.array([0, D*6]) layout_y = [0, 0] -fi.reinitialize(layout = [layout_x, layout_y]) +fi.reinitialize(layout_x=layout_x, layout_y=layout_y) # Sweep wind speeds but keep wind direction fixed wd_array = np.arange(250,291,1.) diff --git a/examples/05_sweep_wind_speeds.py b/examples/05_sweep_wind_speeds.py index 70e353c7b..b23ef74b6 100644 --- a/examples/05_sweep_wind_speeds.py +++ b/examples/05_sweep_wind_speeds.py @@ -40,7 +40,7 @@ D = 126. layout_x = np.array([0, D*6]) layout_y = [0, 0] -fi.reinitialize(layout = [layout_x, layout_y]) +fi.reinitialize(layout_x=layout_x, layout_y=layout_y) # Sweep wind speeds but keep wind direction fixed ws_array = np.arange(5,25,0.5) diff --git a/examples/06_sweep_wind_conditions.py b/examples/06_sweep_wind_conditions.py index 975701d6a..ab2db3ba7 100644 --- a/examples/06_sweep_wind_conditions.py +++ b/examples/06_sweep_wind_conditions.py @@ -42,7 +42,7 @@ D = 126. layout_x = np.array([0, D*6, D*12, D*18,D*24]) layout_y = [0, 0, 0, 0, 0] -fi.reinitialize(layout = [layout_x, layout_y]) +fi.reinitialize(layout_x=layout_x, layout_y=layout_y) # Define a ws and wd to sweep # Note that all combinations will be computed diff --git a/examples/07_calc_aep_from_rose.py b/examples/07_calc_aep_from_rose.py index 2e23e92cc..34053f8e6 100644 --- a/examples/07_calc_aep_from_rose.py +++ b/examples/07_calc_aep_from_rose.py @@ -56,7 +56,8 @@ # floris object and assign the layout, wind speed and wind direction arrays. D = fi.floris.farm.rotor_diameters[0] # Rotor diameter for the NREL 5 MW fi.reinitialize( - layout=[[0.0, 5* D, 10 * D], [0.0, 0.0, 0.0]], + layout_x=[0.0, 5 * D, 10 * D], + layout_y=[0.0, 0.0, 0.0], wind_directions=wd_array, wind_speeds=ws_array, ) diff --git a/examples/08_calc_aep_from_rose_use_class.py b/examples/08_calc_aep_from_rose_use_class.py new file mode 100644 index 000000000..358fbc19e --- /dev/null +++ b/examples/08_calc_aep_from_rose_use_class.py @@ -0,0 +1,74 @@ +# Copyright 2022 NREL + +# Licensed under the Apache License, Version 2.0 (the "License"); you may not +# use this file except in compliance with the License. You may obtain a copy of +# the License at http://www.apache.org/licenses/LICENSE-2.0 + +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, WITHOUT +# WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the +# License for the specific language governing permissions and limitations under +# the License. + +# See https://floris.readthedocs.io for documentation + + +import numpy as np +import pandas as pd +import matplotlib.pyplot as plt +from scipy.interpolate import NearestNDInterpolator +from floris.tools import FlorisInterface, WindRose, wind_rose + +""" +This example demonstrates how to calculate the Annual Energy Production (AEP) +of a wind farm using wind rose information stored in a .csv file. + +The wind rose information is first loaded, after which we initialize our Floris +Interface. A 3 turbine farm is generated, and then the turbine wakes and powers +are calculated across all the wind directions. Finally, the farm power is +converted to AEP and reported out. +""" + +# Read in the wind rose using the class +wind_rose = WindRose() +wind_rose.read_wind_rose_csv("inputs/wind_rose.csv") + +# Show the wind rose +wind_rose.plot_wind_rose() + +# Load the FLORIS object +fi = FlorisInterface("inputs/gch.yaml") # GCH model +# fi = FlorisInterface("inputs/cc.yaml") # CumulativeCurl model + +# Assume a three-turbine wind farm with 5D spacing. We reinitialize the +# floris object and assign the layout, wind speed and wind direction arrays. +D = 126.0 # Rotor diameter for the NREL 5 MW +fi.reinitialize( + layout=[[0.0, 5* D, 10 * D], [0.0, 0.0, 0.0]] +) + +# Compute the AEP using the default settings +aep = fi.get_farm_AEP_wind_rose_class(wind_rose=wind_rose) +print("Farm AEP (default options): {:.3f} GWh".format(aep / 1.0e9)) + +# Compute the AEP again while specifying a cut-in and cut-out wind speed. +# The wake calculations are skipped for any wind speed below respectively +# above the cut-in and cut-out wind speed. This can speed up computation and +# prevent unexpected behavior for zero/negative and very high wind speeds. +# In this example, the results should not change between this and the default +# call to 'get_farm_AEP()'. +aep = fi.get_farm_AEP_wind_rose_class( + wind_rose=wind_rose, + cut_in_wind_speed=3.0, # Wakes are not evaluated below this wind speed + cut_out_wind_speed=25.0, # Wakes are not evaluated above this wind speed +) +print("Farm AEP (with cut_in/out specified): {:.3f} GWh".format(aep / 1.0e9)) + +# Finally, we can also compute the AEP while ignoring all wake calculations. +# This can be useful to quantity the annual wake losses in the farm. Such +# calculations can be facilitated by enabling the 'no_wake' handle. +aep_no_wake = fi.get_farm_AEP_wind_rose_class(wind_rose=wind_rose, no_wake=True) +print("Farm AEP (no_wake=True): {:.3f} GWh".format(aep_no_wake / 1.0e9)) + + +plt.show() \ No newline at end of file diff --git a/examples/09_compare_farm_power_with_neighbor.py b/examples/09_compare_farm_power_with_neighbor.py new file mode 100644 index 000000000..9dc0f845b --- /dev/null +++ b/examples/09_compare_farm_power_with_neighbor.py @@ -0,0 +1,79 @@ +# Copyright 2022 NREL + +# Licensed under the Apache License, Version 2.0 (the "License"); you may not +# use this file except in compliance with the License. You may obtain a copy of +# the License at http://www.apache.org/licenses/LICENSE-2.0 + +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, WITHOUT +# WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the +# License for the specific language governing permissions and limitations under +# the License. + +# See https://floris.readthedocs.io for documentation + + +import numpy as np +import pandas as pd +from floris.tools import FlorisInterface +import matplotlib.pyplot as plt + +""" +This example demonstrates how to use turbine_wieghts to define a set of turbines belonging to a neighboring farm which +impacts the power production of the farm under consideration via wake losses, but whose own power production is not +considered in farm power / aep production + +The use of neighboring farms in the context of wake steering design is considered in example examples/10_optimize_yaw_with_neighboring_farm.py +""" + + +# Instantiate FLORIS using either the GCH or CC model +fi = FlorisInterface("inputs/gch.yaml") # GCH model matched to the default "legacy_gauss" of V2 + +# Define a 4 turbine farm turbine farm +D = 126. +layout_x = np.array([0, D*6, 0, D*6]) +layout_y = [0, 0, D*3, D*3] +fi.reinitialize(layout_x = layout_x, layout_y = layout_y) + +# Define a simple wind rose with just 1 wind speed +wd_array = np.arange(0,360,4.) +fi.reinitialize(wind_directions=wd_array, wind_speeds=[8.]) + + +# Calculate +fi.calculate_wake() + +# Collect the farm power +farm_power_base = fi.get_farm_power() / 1E3 # In kW + +# Add a neighbor to the east +layout_x = np.array([0, D*6, 0, D*6, D*12, D*15, D*12, D*15]) +layout_y = np.array([0, 0, D*3, D*3, 0, 0, D*3, D*3]) +fi.reinitialize(layout_x = layout_x, layout_y = layout_y) + +# Define the weights to exclude the neighboring farm from calcuations of power +turbine_weights = np.zeros(len(layout_x), dtype=int) +turbine_weights[0:4] = 1.0 + +# Calculate +fi.calculate_wake() + +# Collect the farm power with the neightbor +farm_power_neighbor = fi.get_farm_power(turbine_weights=turbine_weights) / 1E3 # In kW + +# Show the farms +fig, ax = plt.subplots() +ax.scatter(layout_x[turbine_weights==1],layout_y[turbine_weights==1], color='k',label='Base Farm') +ax.scatter(layout_x[turbine_weights==0],layout_y[turbine_weights==0], color='r',label='Neighboring Farm') +ax.legend() + +# Plot the power difference +fig, ax = plt.subplots() +ax.plot(wd_array,farm_power_base,color='k',label='Farm Power (no neighbor)') +ax.plot(wd_array,farm_power_neighbor,color='r',label='Farm Power (neighboring farm due east)') +ax.grid(True) +ax.legend() +ax.set_xlabel('Wind Direction (deg)') +ax.set_ylabel('Power (kW)') +plt.show() diff --git a/examples/08_opt_yaw_single_ws.py b/examples/10_opt_yaw_single_ws.py similarity index 97% rename from examples/08_opt_yaw_single_ws.py rename to examples/10_opt_yaw_single_ws.py index cc29d0e26..8762d1e2e 100644 --- a/examples/08_opt_yaw_single_ws.py +++ b/examples/10_opt_yaw_single_ws.py @@ -32,7 +32,8 @@ # Reinitialize as a 3-turbine farm with range of WDs and 1 WS D = 126.0 # Rotor diameter for the NREL 5 MW fi.reinitialize( - layout=[[0.0, 5 * D, 10 * D], [0.0, 0.0, 0.0]], + layout_x=[0.0, 5 * D, 10 * D], + layout_y=[0.0, 0.0, 0.0], wind_directions=np.arange(0.0, 360.0, 3.0), wind_speeds=[8.0], ) diff --git a/examples/09_opt_yaw_multiple_ws.py b/examples/11_opt_yaw_multiple_ws.py similarity index 98% rename from examples/09_opt_yaw_multiple_ws.py rename to examples/11_opt_yaw_multiple_ws.py index aa464ffb5..34b3bcf8d 100644 --- a/examples/09_opt_yaw_multiple_ws.py +++ b/examples/11_opt_yaw_multiple_ws.py @@ -32,7 +32,8 @@ # Reinitialize as a 3-turbine farm with range of WDs and 1 WS D = 126.0 # Rotor diameter for the NREL 5 MW fi.reinitialize( - layout=[[0.0, 5 * D, 10 * D], [0.0, 0.0, 0.0]], + layout_x=[0.0, 5 * D, 10 * D], + layout_y=[0.0, 0.0, 0.0], wind_directions=np.arange(0.0, 360.0, 3.0), wind_speeds=np.arange(2.0, 18.0, 1.0), ) diff --git a/examples/10_optimize_yaw.py b/examples/12_optimize_yaw.py similarity index 99% rename from examples/10_optimize_yaw.py rename to examples/12_optimize_yaw.py index b1a521896..067f351ff 100644 --- a/examples/10_optimize_yaw.py +++ b/examples/12_optimize_yaw.py @@ -45,7 +45,7 @@ def load_floris(): 5.0 * fi.floris.farm.rotor_diameters_sorted[0][0][0] * np.arange(0, N, 1), 5.0 * fi.floris.farm.rotor_diameters_sorted[0][0][0] * np.arange(0, N, 1), ) - fi.reinitialize(layout=(X.flatten(), Y.flatten())) + fi.reinitialize(layout_x=X.flatten(), layout_y=Y.flatten()) return fi diff --git a/examples/13_optimize_yaw_with_neighboring_farm.py b/examples/13_optimize_yaw_with_neighboring_farm.py new file mode 100644 index 000000000..8f7bcb1aa --- /dev/null +++ b/examples/13_optimize_yaw_with_neighboring_farm.py @@ -0,0 +1,312 @@ +# Copyright 2022 NREL + +# Licensed under the Apache License, Version 2.0 (the "License"); you may not +# use this file except in compliance with the License. You may obtain a copy of +# the License at http://www.apache.org/licenses/LICENSE-2.0 + +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, WITHOUT +# WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the +# License for the specific language governing permissions and limitations under +# the License. + +# See https://floris.readthedocs.io for documentation + + +import matplotlib.pyplot as plt +import numpy as np +import pandas as pd +from floris.tools import FlorisInterface +from floris.tools.optimization.yaw_optimization.yaw_optimizer_sr import ( + YawOptimizationSR, +) +from scipy.interpolate import NearestNDInterpolator + + +""" +This example demonstrates how to perform a yaw optimization and evaluate the performance over a full wind rose. + +The beginning of the file contains the definition of several functions used in the main part of the script. + +Within the main part of the script, we first load the wind rose information. We then initialize our Floris Interface +object. We determine the baseline AEP using the wind rose information, and then perform the yaw optimization over 72 +wind directions with 1 wind speed per direction. The optimal yaw angles are then used to determine yaw angles across +all the wind speeds included in the wind rose. Lastly, the final AEP is calculated and analysis of the results are +shown in several plots. +""" + +def load_floris(): + # Load the default example floris object + fi = FlorisInterface("inputs/gch.yaml") # GCH model matched to the default "legacy_gauss" of V2 + # fi = FlorisInterface("inputs/cc.yaml") # New CumulativeCurl model + + # Specify the full wind farm layout: nominal and neighboring wind farms + X = np.array( + [ + 0., 756., 1512., 2268., 3024., 0., 756., 1512., + 2268., 3024., 0., 756., 1512., 2268., 3024., 0., + 756., 1512., 2268., 3024., 4500., 5264., 6028., 4878., + 0., 756., 1512., 2268., 3024., + ] + ) / 1.5 + Y = np.array( + [ + 0., 0., 0., 0., 0., 504., 504., 504., + 504., 504., 1008., 1008., 1008., 1008., 1008., 1512., + 1512., 1512., 1512., 1512., 4500., 4059., 3618., 5155., + -504., -504., -504., -504., -504., + ] + ) / 1.5 + + # Turbine weights: we want to only optimize for the first 10 turbines + turbine_weights = np.zeros(len(X), dtype=int) + turbine_weights[0:10] = 1.0 + + # Now reinitialize FLORIS layout + fi.reinitialize(layout_x = X, layout_y = Y) + + # And visualize the floris layout + fig, ax = plt.subplots() + ax.plot(X[turbine_weights == 0], Y[turbine_weights == 0], 'ro', label="Neighboring farms") + ax.plot(X[turbine_weights == 1], Y[turbine_weights == 1], 'go', label='Farm subset') + ax.grid(True) + ax.set_xlabel("x coordinate (m)") + ax.set_ylabel("y coordinate (m)") + ax.legend() + + return fi, turbine_weights + + +def load_windrose(): + # Load the wind rose information from an external file + df = pd.read_csv("inputs/wind_rose.csv") + df = df[(df["ws"] < 22)].reset_index(drop=True) # Reduce size + df["freq_val"] = df["freq_val"] / df["freq_val"].sum() # Normalize wind rose frequencies + + # Now put the wind rose information in FLORIS format + ws_windrose = df["ws"].unique() + wd_windrose = df["wd"].unique() + wd_grid, ws_grid = np.meshgrid(wd_windrose, ws_windrose, indexing="ij") + + # Use an interpolant to shape the 'freq_val' vector appropriately. You can + # also use np.reshape(), but NearestNDInterpolator is more fool-proof. + freq_interpolant = NearestNDInterpolator( + df[["ws", "wd"]], df["freq_val"] + ) + freq = freq_interpolant(wd_grid, ws_grid) + freq_windrose = freq / freq.sum() # Normalize to sum to 1.0 + + return ws_windrose, wd_windrose, freq_windrose + + +def optimize_yaw_angles(fi_opt): + # Specify turbines to optimize + turbs_to_opt = np.zeros(len(fi_opt.layout_x), dtype=bool) + turbs_to_opt[0:10] = True + + # Specify turbine weights + turbine_weights = np.zeros(len(fi_opt.layout_x)) + turbine_weights[turbs_to_opt] = 1.0 + + # Specify minimum and maximum allowable yaw angle limits + minimum_yaw_angle = np.zeros( + ( + fi_opt.floris.flow_field.n_wind_directions, + fi_opt.floris.flow_field.n_wind_speeds, + fi_opt.floris.farm.n_turbines + ) + ) + maximum_yaw_angle = np.zeros( + ( + fi_opt.floris.flow_field.n_wind_directions, + fi_opt.floris.flow_field.n_wind_speeds, + fi_opt.floris.farm.n_turbines + ) + ) + maximum_yaw_angle[:, :, turbs_to_opt] = 30.0 + + yaw_opt = YawOptimizationSR( + fi=fi_opt, + minimum_yaw_angle=minimum_yaw_angle, + maximum_yaw_angle=maximum_yaw_angle, + turbine_weights=turbine_weights, + Ny_passes=[5], + exclude_downstream_turbines=True, + ) + + df_opt = yaw_opt.optimize() + yaw_angles_opt = yaw_opt.yaw_angles_opt + print("Optimization finished.") + print(" ") + print(df_opt) + print(" ") + + # Now create an interpolant from the optimal yaw angles + def yaw_opt_interpolant(wd, ws): + # Format the wind directions and wind speeds accordingly + wd = np.array(wd, dtype=float) + ws = np.array(ws, dtype=float) + + # Interpolate optimal yaw angles + x = yaw_opt.fi.floris.flow_field.wind_directions + nturbs = fi_opt.floris.farm.n_turbines + y = np.stack( + [np.interp(wd, x, yaw_angles_opt[:, 0, ti]) for ti in range(nturbs)], + axis=np.ndim(wd) + ) + + # Now, we want to apply a ramp-up region near cut-in and ramp-down + # region near cut-out wind speed for the yaw offsets. + lim = np.ones(np.shape(wd), dtype=float) # Introduce a multiplication factor + + # Dont do wake steering under 4 m/s or above 14 m/s + lim[(ws <= 4.0) | (ws >= 14.0)] = 0.0 + + # Linear ramp up for the maximum yaw offset between 4.0 and 6.0 m/s + ids = (ws > 4.0) & (ws < 6.0) + lim[ids] = (ws[ids] - 4.0) / 2.0 + + # Linear ramp down for the maximum yaw offset between 12.0 and 14.0 m/s + ids = (ws > 12.0) & (ws < 14.0) + lim[ids] = (ws[ids] - 12.0) / 2.0 + + # Copy over multiplication factor to every turbine + lim = np.expand_dims(lim, axis=np.ndim(wd)).repeat(nturbs, axis=np.ndim(wd)) + lim = lim * 30.0 # These are the limits + + # Finally, Return clipped yaw offsets to the limits + return np.clip(a=y, a_min=0.0, a_max=lim) + + # Return the yaw interpolant + return yaw_opt_interpolant + + +if __name__ == "__main__": + # Load FLORIS: full farm including neighboring wind farms + fi, turbine_weights = load_floris() + nturbs = len(fi.layout_x) + + # Load a dataframe containing the wind rose information + ws_windrose, wd_windrose, freq_windrose = load_windrose() + ws_windrose = ws_windrose + 0.001 # Deal with 0.0 m/s discrepancy + + # Create a FLORIS object for AEP calculations + fi_AEP = fi.copy() + fi_AEP.reinitialize(wind_speeds=ws_windrose, wind_directions=wd_windrose) + + # And create a separate FLORIS object for optimization + fi_opt = fi.copy() + fi_opt.reinitialize( + wind_directions=np.arange(0.0, 360.0, 3.0), + wind_speeds=[8.0] + ) + + # First, get baseline AEP, without wake steering + print(" ") + print("===========================================================") + print("Calculating baseline annual energy production (AEP)...") + aep_bl_subset = 1.0e-9 * fi_AEP.get_farm_AEP( + freq=freq_windrose, + turbine_weights=turbine_weights + ) + print("Baseline AEP for subset farm: {:.3f} GWh.".format(aep_bl_subset)) + print("===========================================================") + print(" ") + + # Now optimize the yaw angles using the Serial Refine method. We first + # create a copy of the floris object for optimization purposes and assign + # it the atmospheric conditions for which we want to optimize. Typically, + # the optimal yaw angles are very insensitive to the actual wind speed, + # and hence we only optimize for a single wind speed of 8.0 m/s. We assume + # that the optimal yaw angles at 8.0 m/s are also optimal at other wind + # speeds between 4 and 12 m/s. + print("Now starting yaw optimization for the entire wind rose for farm subset...") + + # In this hypothetical case, we can only control the yaw angles of the + # turbines of the wind farm subset (i.e., the first 10 wind turbines). + # Hence, we constrain the yaw angles of the neighboring wind farms to 0.0. + turbs_to_opt = (turbine_weights > 0.0001) + + # Optimize yaw angles while including neighboring farm + yaw_opt_interpolant = optimize_yaw_angles(fi_opt=fi_opt) + + # Optimize yaw angles while ignoring neighboring farm + fi_opt_subset = fi_opt.copy() + fi_opt_subset.reinitialize(layout_x= fi.layout_x[turbs_to_opt], layout_y = fi.layout_y[turbs_to_opt]) + yaw_opt_interpolant_nonb = optimize_yaw_angles(fi_opt=fi_opt_subset) + + # Use interpolant to get optimal yaw angles for fi_AEP object + X, Y = np.meshgrid( + fi_AEP.floris.flow_field.wind_directions, + fi_AEP.floris.flow_field.wind_speeds, + indexing="ij" + ) + yaw_angles_opt_AEP = yaw_opt_interpolant(X, Y) + yaw_angles_opt_nonb_AEP = np.zeros_like(yaw_angles_opt_AEP) # nonb = no neighbor + yaw_angles_opt_nonb_AEP[:, :, turbs_to_opt] = yaw_opt_interpolant_nonb(X, Y) + + # Now get AEP with optimized yaw angles + print(" ") + print("===========================================================") + print("Calculating annual energy production with wake steering (AEP)...") + aep_opt_subset_nonb = 1.0e-9 * fi_AEP.get_farm_AEP( + freq=freq_windrose, + turbine_weights=turbine_weights, + yaw_angles=yaw_angles_opt_nonb_AEP, + ) + aep_opt_subset = 1.0e-9 * fi_AEP.get_farm_AEP( + freq=freq_windrose, + turbine_weights=turbine_weights, + yaw_angles=yaw_angles_opt_AEP, + ) + uplift_subset_nonb = 100.0 * (aep_opt_subset_nonb - aep_bl_subset) / aep_bl_subset + uplift_subset = 100.0 * (aep_opt_subset - aep_bl_subset) / aep_bl_subset + print("Optimized AEP for subset farm (including neighbor farms' wakes): {:.3f} GWh (+{:.2f}%).".format(aep_opt_subset_nonb, uplift_subset_nonb)) + print("Optimized AEP for subset farm (ignoring neighbor farms' wakes): {:.3f} GWh (+{:.2f}%).".format(aep_opt_subset, uplift_subset)) + print("===========================================================") + print(" ") + + # Plot power and AEP uplift across wind direction at wind_speed of 8 m/s + X, Y = np.meshgrid( + fi_opt.floris.flow_field.wind_directions, + fi_opt.floris.flow_field.wind_speeds, + indexing="ij", + ) + yaw_angles_opt = yaw_opt_interpolant(X, Y) + + yaw_angles_opt_nonb = np.zeros_like(yaw_angles_opt) # nonb = no neighbor + yaw_angles_opt_nonb[:, :, turbs_to_opt] = yaw_opt_interpolant_nonb(X, Y) + + fi_opt = fi_opt.copy() + fi_opt.calculate_wake(yaw_angles=np.zeros_like(yaw_angles_opt)) + farm_power_bl_subset = fi_opt.get_farm_power(turbine_weights).flatten() + + fi_opt = fi_opt.copy() + fi_opt.calculate_wake(yaw_angles=yaw_angles_opt) + farm_power_opt_subset = fi_opt.get_farm_power(turbine_weights).flatten() + + fi_opt = fi_opt.copy() + fi_opt.calculate_wake(yaw_angles=yaw_angles_opt_nonb) + farm_power_opt_subset_nonb = fi_opt.get_farm_power(turbine_weights).flatten() + + fig, ax = plt.subplots() + ax.bar( + x=fi_opt.floris.flow_field.wind_directions - 0.65, + height=100.0 * (farm_power_opt_subset / farm_power_bl_subset - 1.0), + edgecolor="black", + width=1.3, + label="Including wake effects of neighboring farms" + ) + ax.bar( + x=fi_opt.floris.flow_field.wind_directions + 0.65, + height=100.0 * (farm_power_opt_subset_nonb / farm_power_bl_subset - 1.0), + edgecolor="black", + width=1.3, + label="Ignoring neighboring farms" + ) + ax.set_ylabel("Power uplift \n at 8 m/s (%)") + ax.legend() + ax.grid(True) + ax.set_xlabel("Wind direction (deg)") + + plt.show() diff --git a/examples/12_compare_yaw_optimizers.py b/examples/14_compare_yaw_optimizers.py similarity index 98% rename from examples/12_compare_yaw_optimizers.py rename to examples/14_compare_yaw_optimizers.py index 41caf0306..9fb1fb8f2 100644 --- a/examples/12_compare_yaw_optimizers.py +++ b/examples/14_compare_yaw_optimizers.py @@ -37,7 +37,8 @@ # Reinitialize as a 3-turbine farm with range of WDs and 1 WS D = 126.0 # Rotor diameter for the NREL 5 MW fi.reinitialize( - layout=[[0.0, 5 * D, 10 * D], [0.0, 0.0, 0.0]], + layout_x=[0.0, 5 * D, 10 * D], + layout_y=[0.0, 0.0, 0.0], wind_directions=np.arange(0.0, 360.0, 3.0), wind_speeds=[8.0], ) diff --git a/examples/11_optimize_layout.py b/examples/15_optimize_layout.py similarity index 67% rename from examples/11_optimize_layout.py rename to examples/15_optimize_layout.py index b3eeed6dc..689e9e9ef 100644 --- a/examples/11_optimize_layout.py +++ b/examples/15_optimize_layout.py @@ -1,4 +1,4 @@ -# Copyright 2021 NREL +# Copyright 2022 NREL # Licensed under the Apache License, Version 2.0 (the "License"); you may not # use this file except in compliance with the License. You may obtain a copy of @@ -17,14 +17,15 @@ import numpy as np from floris.tools import FlorisInterface -import floris.tools.optimization.pyoptsparse as opt + +from floris.tools.optimization.layout_optimization.layout_optimization_scipy import LayoutOptimizationScipy """ -This example shows a simple layout optimization using the python module pyOptSparse. +This example shows a simple layout optimization using the python module Scipy. A 4 turbine array is optimized such that the layout of the turbine produces the highest annual energy production (AEP) based on the given wind resource. The turbines -are constrained to a square boundary and a randomw wind resource is supplied. The results +are constrained to a square boundary and a random wind resource is supplied. The results of the optimization show that the turbines are pushed to the outer corners of the boundary, which makes sense in order to maximize the energy production by minimizing wake interactions. """ @@ -37,8 +38,10 @@ wind_directions = np.arange(0, 360.0, 5.0) np.random.seed(1) wind_speeds = 8.0 + np.random.randn(1) * 0.5 -freq = np.abs(np.sort(np.random.randn(len(wind_directions)))) +# Shape frequency distribution to match number of wind directions and wind speeds +freq = np.abs(np.sort(np.random.randn(len(wind_directions)))).reshape((len(wind_directions), len(wind_speeds))) freq = freq / freq.sum() + fi.reinitialize(wind_directions=wind_directions, wind_speeds=wind_speeds) # The boundaries for the turbines, specified as vertices @@ -49,15 +52,23 @@ layout_x = [0, 0, 6 * D, 6 * D] layout_y = [0, 4 * D, 0, 4 * D] fi.reinitialize(layout=(layout_x, layout_y)) -fi.calculate_wake() # Setup the optimization problem -model = opt.layout.Layout(fi, boundaries, freq) -tmp = opt.optimization.Optimization(model=model, solver='SLSQP') +layout_opt = LayoutOptimizationScipy(fi, boundaries, freq=freq) # Run the optimization -sol = tmp.optimize() +sol = layout_opt.optimize() + +# Get the resulting improvement in AEP +print('... calcuating improvement in AEP') +fi.calculate_wake() +base_aep = fi.get_farm_AEP(freq=freq) / 1e6 +fi.reinitialize(layout=sol) +fi.calculate_wake() +opt_aep = fi.get_farm_AEP(freq=freq) / 1e6 +percent_gain = 100 * (opt_aep - base_aep) / base_aep # Print and plot the results -print(sol) -model.plot_layout_opt_results(sol) +print('Optimal layout: ', sol) +print('Optimal layout improves AEP by %.1f%% from %.1f MWh to %.1f MWh' % (percent_gain, base_aep, opt_aep)) +layout_opt.plot_layout_opt_results() \ No newline at end of file diff --git a/examples/13_heterogeneous_inflow.py b/examples/16_heterogeneous_inflow.py similarity index 100% rename from examples/13_heterogeneous_inflow.py rename to examples/16_heterogeneous_inflow.py diff --git a/examples/14_multiple_turbine_types.py b/examples/17_multiple_turbine_types.py similarity index 100% rename from examples/14_multiple_turbine_types.py rename to examples/17_multiple_turbine_types.py diff --git a/examples/15_check_turbine.py b/examples/18_check_turbine.py similarity index 98% rename from examples/15_check_turbine.py rename to examples/18_check_turbine.py index 64b984f33..aa135a757 100644 --- a/examples/15_check_turbine.py +++ b/examples/18_check_turbine.py @@ -32,7 +32,7 @@ fi = FlorisInterface("inputs/gch.yaml") # Make one turbine sim -fi.reinitialize(layout=[[0],[0]]) +fi.reinitialize(layout_x=[0], layout_y=[0]) # Apply wind speeds fi.reinitialize(wind_speeds=ws_array) diff --git a/examples/16_streamlit_demo.py b/examples/19_streamlit_demo.py similarity index 93% rename from examples/16_streamlit_demo.py rename to examples/19_streamlit_demo.py index c86b09bd0..2fb5d5f0b 100644 --- a/examples/16_streamlit_demo.py +++ b/examples/19_streamlit_demo.py @@ -113,7 +113,13 @@ fi = FlorisInterface("inputs/%s.yaml" % fm) # Set the layout, wind direction and wind speed - fi.reinitialize( layout=( X, Y ), wind_speeds=[wind_speed], wind_directions=[wind_direction], turbulence_intensity=turbulence_intensity ) + fi.reinitialize( + layout_x=X, + layout_y=Y, + wind_speeds=[wind_speed], + wind_directions=[wind_direction], + turbulence_intensity=turbulence_intensity + ) fi.calculate_wake(yaw_angles=yaw_angles_base) turbine_powers = fi.get_turbine_powers() / 1000. @@ -139,7 +145,13 @@ fi = FlorisInterface("inputs/%s.yaml" % fm) # Set the layout, wind direction and wind speed - fi.reinitialize( layout=( X, Y ), wind_speeds=[wind_speed], wind_directions=[wind_direction], turbulence_intensity=turbulence_intensity ) + fi.reinitialize( + layout_x=X, + layout_y=Y, + wind_speeds=[wind_speed], + wind_directions=[wind_direction], + turbulence_intensity=turbulence_intensity + ) fi.calculate_wake(yaw_angles=yaw_angles_yaw) turbine_powers = fi.get_turbine_powers() / 1000. diff --git a/examples/17_calculate_farm_power_with_uncertainty.py b/examples/20_calculate_farm_power_with_uncertainty.py similarity index 93% rename from examples/17_calculate_farm_power_with_uncertainty.py rename to examples/20_calculate_farm_power_with_uncertainty.py index cc9390f81..d118c8f7d 100644 --- a/examples/17_calculate_farm_power_with_uncertainty.py +++ b/examples/20_calculate_farm_power_with_uncertainty.py @@ -38,8 +38,8 @@ layout_x = np.array([0, D*6, D*12]) layout_y = [0, 0, 0] wd_array = np.arange(0.0, 360.0, 1.0) -fi.reinitialize(layout=[layout_x, layout_y], wind_directions=wd_array) -fi_unc.reinitialize(layout=[layout_x, layout_y], wind_directions=wd_array) +fi.reinitialize(layout_x=layout_x, layout_y=layout_y, wind_directions=wd_array) +fi_unc.reinitialize(layout_x=layout_x, layout_y=layout_y, wind_directions=wd_array) # Define a matrix of yaw angles to be all 0 # Note that yaw angles is now specified as a matrix whose dimesions are diff --git a/examples/21_demo_time_series.py b/examples/21_demo_time_series.py new file mode 100644 index 000000000..31ba6b6ee --- /dev/null +++ b/examples/21_demo_time_series.py @@ -0,0 +1,89 @@ +# Copyright 2021 NREL + +# Licensed under the Apache License, Version 2.0 (the "License"); you may not +# use this file except in compliance with the License. You may obtain a copy of +# the License at http://www.apache.org/licenses/LICENSE-2.0 + +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, WITHOUT +# WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the +# License for the specific language governing permissions and limitations under +# the License. + +# See https://floris.readthedocs.io for documentation + + +import matplotlib.pyplot as plt +import numpy as np + +from floris.tools import FlorisInterface + +""" +This example demonstrates running FLORIS in time series mode. + +Typically when an array of wind directions and wind speeds are passed in FLORIS, +it is assumed these are defining a grid of wd/ws points to consider, as in a wind rose. +All combinations of wind direction and wind speed are therefore computed, and resulting +matrices, for example of turbine power are returned with martrices whose dimensions are +wind direction, wind speed and turbine number. + +In time series mode, specified by setting the time_series flag of the FLORIS interface to True +each wd/ws pair is assumed to constitute a single point in time and each pair is computed. +Results are returned still as a 3 dimensional matrix, however the index of the (wd/ws) pair +is provided in the first dimension, the second dimension is fixed at 1, and the thrid is +turbine number again for consistency. + +Note by not specifying yaw, the assumption is that all turbines are always pointing into the +current wind direction with no offset. +""" + +# Initialize FLORIS to simple 4 turbine farm +fi = FlorisInterface("inputs/gch.yaml") + +# Convert to a simple two turbine layout +fi.reinitialize(layout_x=[0, 500.], layout_y=[0., 0.]) + +# Create a fake time history where wind speed steps in the middle while wind direction +# Walks randomly +time = np.arange(0, 120, 10.) # Each time step represents a 10-minute average +ws = np.ones_like(time) * 8. +ws[int(len(ws) / 2):] = 9. +wd = np.ones_like(time) * 270. + +for idx in range(1, len(time)): + wd[idx] = wd[idx - 1] + np.random.randn() * 2. + + +# Now intiialize FLORIS object to this history using time_series flag +fi.reinitialize(wind_directions=wd, wind_speeds=ws, time_series=True) + +# Collect the powers +fi.calculate_wake() +turbine_powers = fi.get_turbine_powers() / 1000. + +# Show the dimensions +num_turbines = len(fi.layout_x) +print('There are %d time samples, and %d turbines and so the resulting turbine power matrix has the shape:' % (len(time), num_turbines), turbine_powers.shape) + + +fig, axarr = plt.subplots(3, 1, sharex=True, figsize=(7,8)) + +ax = axarr[0] +ax.plot(time, ws, 'o-') +ax.set_ylabel('Wind Speed (m/s)') +ax.grid(True) + +ax = axarr[1] +ax.plot(time, wd, 'o-') +ax.set_ylabel('Wind Direction (Deg)') +ax.grid(True) + +ax = axarr[2] +for t in range(num_turbines): + ax.plot(time,turbine_powers[:, 0, t], 'o-', label='Turbine %d' % t) +ax.legend() +ax.set_ylabel('Turbine Power (kW)') +ax.set_xlabel('Time (minutes)') +ax.grid(True) + +plt.show() \ No newline at end of file diff --git a/examples/22_get_wind_speed_at_turbines.py b/examples/22_get_wind_speed_at_turbines.py new file mode 100644 index 000000000..b9f68f0ee --- /dev/null +++ b/examples/22_get_wind_speed_at_turbines.py @@ -0,0 +1,47 @@ +# Copyright 2021 NREL + +# Licensed under the Apache License, Version 2.0 (the "License"); you may not +# use this file except in compliance with the License. You may obtain a copy of +# the License at http://www.apache.org/licenses/LICENSE-2.0 + +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, WITHOUT +# WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the +# License for the specific language governing permissions and limitations under +# the License. + +# See https://floris.readthedocs.io for documentation + + +import matplotlib.pyplot as plt +import numpy as np + +from floris.tools import FlorisInterface + +# Initialize FLORIS with the given input file via FlorisInterface. +# For basic usage, FlorisInterface provides a simplified and expressive +# entry point to the simulation routines. +fi = FlorisInterface("inputs/gch.yaml") + +# Create a 4-turbine layouts +fi.reinitialize(layout_x=[0, 0., 500., 500.], layout_y=[0., 300., 0., 300.]) + +# Calculate wake +fi.calculate_wake() + +# Collect the wind speed at all the turbine points +u_points = fi.floris.flow_field.u + +print('U points is 1 wd x 1 ws x 4 turbines x 3 x 3 points (turbine_grid_points=3)') +print(u_points.shape) + +# Collect the average wind speeds from each turbine +avg_vel = fi.get_turbine_average_velocities() + +print('Avg vel is 1 wd x 1 ws x 4 turbines') +print(avg_vel.shape) + +# Show that one is equivalent to the other following averaging +print('Avg Vel is determined by taking the cube root of mean of the cubed value across the points') +print('Average velocity: ', avg_vel) +print('Recomputed: ', np.cbrt(np.mean(u_points**3, axis=(3,4)))) diff --git a/examples/inputs/gch_multiple_turbine_types.yaml b/examples/inputs/gch_multiple_turbine_types.yaml index def970c63..ca2d86ea5 100644 --- a/examples/inputs/gch_multiple_turbine_types.yaml +++ b/examples/inputs/gch_multiple_turbine_types.yaml @@ -13,7 +13,7 @@ logging: solver: type: turbine_grid - turbine_grid_points: 5 + turbine_grid_points: 3 farm: layout_x: diff --git a/floris/simulation/__init__.py b/floris/simulation/__init__.py index ae17a7dfb..3233b27cb 100644 --- a/floris/simulation/__init__.py +++ b/floris/simulation/__init__.py @@ -33,7 +33,7 @@ # that should be included in the simulation package. # Since some of these depend on each other, the order # that they are listed here does matter. -from .base import BaseClass, BaseModel +from .base import BaseClass, BaseModel, State from .turbine import Turbine, Ct, power, axial_induction, average_velocity from .farm import Farm from .grid import Grid, TurbineGrid, FlowFieldGrid, FlowFieldPlanarGrid diff --git a/floris/simulation/base.py b/floris/simulation/base.py index dbd36eab1..8967c3e58 100644 --- a/floris/simulation/base.py +++ b/floris/simulation/base.py @@ -18,20 +18,29 @@ """ from abc import ABC, abstractmethod +from enum import Enum from typing import Any, Dict, Final import attrs -from attrs import define from floris.type_dec import FromDictMixin from floris.logging_manager import LoggerBase +class State(Enum): + UNINITIALIZED = 0 + INITIALIZED = 1 + USED = 2 + + class BaseClass(LoggerBase, FromDictMixin): """ BaseClass object class. This class does the logging and MixIn class inheritance. """ + state = State.UNINITIALIZED + + @classmethod def get_model_defaults(cls) -> Dict[str, Any]: """Produces a dictionary of the keyword arguments and their defaults. @@ -63,15 +72,6 @@ class BaseModel(BaseClass, ABC): NUM_EPS: Final[float] = 0.001 # This is a numerical epsilon to prevent divide by zeros - @property - def model_string(self): - return self.model_string - - @model_string.setter - @abstractmethod - def model_string(self, string): - raise NotImplementedError("BaseModel.model_string") - @abstractmethod def prepare_function() -> dict: raise NotImplementedError("BaseModel.prepare_function") diff --git a/floris/simulation/farm.py b/floris/simulation/farm.py index 186ed0d77..1ba24366b 100644 --- a/floris/simulation/farm.py +++ b/floris/simulation/farm.py @@ -17,7 +17,6 @@ from attrs import define, field import numpy as np from pathlib import Path -import os import copy from floris.type_dec import ( @@ -26,7 +25,7 @@ NDArrayFloat ) from floris.utilities import Vec3, load_yaml -from floris.simulation import BaseClass +from floris.simulation import BaseClass, State from floris.simulation import Turbine @@ -83,10 +82,22 @@ def check_turbine_type(self, instance: attrs.Attribute, value: Any) -> None: if type(val) is str: _floris_dir = Path(__file__).parent.parent fname = _floris_dir / "turbine_library" / f"{val}.yaml" - if not os.path.isfile(fname): + if not Path.is_file(fname): raise ValueError("User-selected turbine definition `{}` does not exist in pre-defined turbine library.".format(val)) self.turbine_definitions[i] = load_yaml(fname) + # This is a temporary block of code that catches that ref_density_cp_ct is not defined + # In the yaml file and forces it in + # A warning is issued letting the user know in future versions defining this value explicitly + # will be required + if not 'ref_density_cp_ct' in self.turbine_definitions[i]: + self.logger.warn("The value ref_density_cp_ct is not defined in the file: %s " % fname) + self.logger.warn("This value is not the simulated air density but is the density at which the cp/ct curves are defined") + self.logger.warn("In previous versions this was assumed to be 1.225") + self.logger.warn("Future versions of FLORIS will give an error if this value is not explicitly defined") + self.logger.warn("Currently this value is being set to the prior default value of 1.225") + self.turbine_definitions[i]['ref_density_cp_ct'] = 1.225 + def initialize(self, sorted_indices): # Sort yaw angles from most upstream to most downstream wind turbine self.yaw_angles_sorted = np.take_along_axis( @@ -94,6 +105,7 @@ def initialize(self, sorted_indices): sorted_indices[:, :, :, 0, 0], axis=2, ) + self.state = State.INITIALIZED def construct_hub_heights(self): self.hub_heights = np.array([turb['hub_height'] for turb in self.turbine_definitions]) @@ -107,6 +119,9 @@ def construct_turbine_TSRs(self): def construc_turbine_pPs(self): self.pPs = np.array([turb['pP'] for turb in self.turbine_definitions]) + def construc_turbine_ref_density_cp_cts(self): + self.ref_density_cp_cts = np.array([turb['ref_density_cp_ct'] for turb in self.turbine_definitions]) + def construct_turbine_map(self): self.turbine_map = [Turbine.from_dict(turb) for turb in self.turbine_definitions] @@ -149,6 +164,7 @@ def finalize(self, unsorted_indices): self.TSRs = np.take_along_axis(self.TSRs_sorted, unsorted_indices[:,:,:,0,0], axis=2) self.pPs = np.take_along_axis(self.pPs_sorted, unsorted_indices[:,:,:,0,0], axis=2) self.turbine_type_map = np.take_along_axis(self.turbine_type_map_sorted, unsorted_indices[:,:,:,0,0], axis=2) + self.state.USED @property def n_turbines(self): diff --git a/floris/simulation/floris.py b/floris/simulation/floris.py index cb0c89c71..57f23af63 100644 --- a/floris/simulation/floris.py +++ b/floris/simulation/floris.py @@ -20,16 +20,16 @@ from floris.utilities import load_yaml import floris.logging_manager as logging_manager -from floris.type_dec import FromDictMixin from floris.simulation import ( + BaseClass, Farm, WakeModelManager, FlowField, - Turbine, Grid, TurbineGrid, FlowFieldGrid, FlowFieldPlanarGrid, + State, sequential_solver, cc_solver, turbopark_solver, @@ -41,7 +41,7 @@ @define -class Floris(logging_manager.LoggerBase, FromDictMixin): +class Floris(BaseClass): """ Top-level class that describes a Floris model and initializes the simulation. Use the :py:class:`~.simulation.farm.Farm` attribute to @@ -73,6 +73,7 @@ def __attrs_post_init__(self) -> None: self.farm.construct_rotor_diameters() self.farm.construct_turbine_TSRs() self.farm.construc_turbine_pPs() + self.farm.construc_turbine_ref_density_cp_cts() self.farm.construct_coordinates() self.farm.set_yaw_angles(self.flow_field.n_wind_directions, self.flow_field.n_wind_speeds) @@ -83,6 +84,7 @@ def __attrs_post_init__(self) -> None: wind_directions=self.flow_field.wind_directions, wind_speeds=self.flow_field.wind_speeds, grid_resolution=self.solver["turbine_grid_points"], + time_series=self.flow_field.time_series, ) elif self.solver["type"] == "flow_field_grid": self.grid = FlowFieldGrid( @@ -91,6 +93,7 @@ def __attrs_post_init__(self) -> None: wind_directions=self.flow_field.wind_directions, wind_speeds=self.flow_field.wind_speeds, grid_resolution=self.solver["flow_field_grid_points"], + time_series=self.flow_field.time_series, ) elif self.solver["type"] == "flow_field_planar_grid": self.grid = FlowFieldPlanarGrid( @@ -103,6 +106,7 @@ def __attrs_post_init__(self) -> None: grid_resolution=self.solver["flow_field_grid_points"], x1_bounds=self.solver["flow_field_bounds"][0], x2_bounds=self.solver["flow_field_bounds"][1], + time_series=self.flow_field.time_series, ) else: raise ValueError( @@ -136,6 +140,7 @@ def initialize_domain(self): # Initialize farm quantities self.farm.initialize(self.grid.sorted_indices) + self.state.INITIALIZED def steady_state_atmospheric_condition(self): """Perform the steady-state wind farm wake calculations. Note that @@ -196,6 +201,7 @@ def finalize(self): # the user-supplied order of things. self.flow_field.finalize(self.grid.unsorted_indices) self.farm.finalize(self.grid.unsorted_indices) + self.state = State.USED ## I/O diff --git a/floris/simulation/flow_field.py b/floris/simulation/flow_field.py index abf973fad..869183614 100644 --- a/floris/simulation/flow_field.py +++ b/floris/simulation/flow_field.py @@ -34,7 +34,8 @@ class FlowField(FromDictMixin): wind_shear: float = field(converter=float) air_density: float = field(converter=float) turbulence_intensity: float = field(converter=float) - reference_wind_height: float = field(converter=float) + reference_wind_height: int = field(converter=int) + time_series : bool = field(default=False) n_wind_speeds: int = field(init=False) n_wind_directions: int = field(init=False) @@ -49,13 +50,17 @@ class FlowField(FromDictMixin): v: NDArrayFloat = field(init=False, default=np.array([])) w: NDArrayFloat = field(init=False, default=np.array([])) het_map: list = field(init=False, default=None) + dudz_initial_sorted: NDArrayFloat = field(init=False, default=np.array([])) turbulence_intensity_field: NDArrayFloat = field(init=False, default=np.array([])) @wind_speeds.validator def wind_speeds_validator(self, instance: attrs.Attribute, value: NDArrayFloat) -> None: """Using the validator method to keep the `n_wind_speeds` attribute up to date.""" - self.n_wind_speeds = value.size + if self.time_series: + self.n_wind_speeds = 1 + else: + self.n_wind_speeds = value.size @wind_directions.validator def wind_directions_validator(self, instance: attrs.Attribute, value: NDArrayFloat) -> None: @@ -74,6 +79,7 @@ def initialize_velocity_field(self, grid: Grid) -> None: # for height, using it here to apply the shear law makes that dimension store the vertical # wind profile. wind_profile_plane = (grid.z_sorted / self.reference_wind_height) ** self.wind_shear + dwind_profile_plane = self.wind_shear * (1 / self.reference_wind_height) ** self.wind_shear * (grid.z_sorted) ** (self.wind_shear - 1) # If no hetergeneous inflow defined, then set all speeds ups to 1.0 if self.het_map is None: @@ -93,7 +99,12 @@ def initialize_velocity_field(self, grid: Grid) -> None: # here to do broadcasting from left to right (transposed), and then transpose back. # The result is an array the wind speed and wind direction dimensions on the left side # of the shape and the grid.template array on the right - self.u_initial_sorted = (self.wind_speeds[None, :].T * wind_profile_plane.T).T * speed_ups + if self.time_series: + self.u_initial_sorted = (self.wind_speeds[:].T * wind_profile_plane.T).T * speed_ups + self.dudz_initial_sorted = (self.wind_speeds[:].T * dwind_profile_plane.T).T * speed_ups + else: + self.u_initial_sorted = (self.wind_speeds[None, :].T * wind_profile_plane.T).T * speed_ups + self.dudz_initial_sorted = (self.wind_speeds[None, :].T * dwind_profile_plane.T).T * speed_ups self.v_initial_sorted = np.zeros(np.shape(self.u_initial_sorted), dtype=self.u_initial_sorted.dtype) self.w_initial_sorted = np.zeros(np.shape(self.u_initial_sorted), dtype=self.u_initial_sorted.dtype) diff --git a/floris/simulation/grid.py b/floris/simulation/grid.py index 9dc9ddbec..a3617395f 100644 --- a/floris/simulation/grid.py +++ b/floris/simulation/grid.py @@ -22,7 +22,7 @@ from attrs import define, field import numpy as np -from floris.utilities import Vec3, rotate_coordinates_rel_west, cosd, sind +from floris.utilities import Vec3, rotate_coordinates_rel_west from floris.type_dec import ( floris_float_type, floris_array_converter, @@ -54,13 +54,17 @@ class Grid(ABC): Args: turbine_coordinates (`list[Vec3]`): The collection of turbine coordinate (`Vec3`) objects. reference_turbine_diameter (:py:obj:`float`): The reference turbine's rotor diameter. - grid_resolution (:py:obj:`int` | :py:obj:`Iterable(int,)`): Grid resolution specific to each grid type + grid_resolution (:py:obj:`int` | :py:obj:`Iterable(int,)`): Grid resolution specific to each grid type. + wind_directions (:py:obj:`NDArrayFloat`): Wind directions supplied by the user. + wind_speeds (:py:obj:`NDArrayFloat`): Wind speeds supplied by the user. + time_series (:py:obj:`bool`): True/false flag to indicate whether the supplied wind data is a time series. """ turbine_coordinates: list[Vec3] = field() reference_turbine_diameter: float grid_resolution: int | Iterable = field() wind_directions: NDArrayFloat = field(converter=floris_array_converter) wind_speeds: NDArrayFloat = field(converter=floris_array_converter) + time_series: bool = field() n_turbines: int = field(init=False) n_wind_speeds: int = field(init=False) @@ -88,7 +92,10 @@ def check_coordinates(self, instance: attrs.Attribute, value: list[Vec3]) -> Non @wind_speeds.validator def wind_speeds_validator(self, instance: attrs.Attribute, value: NDArrayFloat) -> None: """Using the validator method to keep the `n_wind_speeds` attribute up to date.""" - self.n_wind_speeds = value.size + if self.time_series: + self.n_wind_speeds = 1 + else: + self.n_wind_speeds = value.size @wind_directions.validator def wind_directions_validator(self, instance: attrs.Attribute, value: NDArrayFloat) -> None: diff --git a/floris/simulation/solver.py b/floris/simulation/solver.py index 6a404159a..255e49fcf 100644 --- a/floris/simulation/solver.py +++ b/floris/simulation/solver.py @@ -16,7 +16,6 @@ import sys from floris.simulation import Farm -from floris.simulation import Turbine from floris.simulation import TurbineGrid, FlowFieldGrid from floris.simulation import Ct, axial_induction from floris.simulation import FlowField @@ -134,6 +133,7 @@ def sequential_solver(farm: Farm, flow_field: FlowField, grid: TurbineGrid, mode v_wake, w_wake = calculate_transverse_velocity( u_i, flow_field.u_initial_sorted, + flow_field.dudz_initial_sorted, grid.x_sorted - x_i, grid.y_sorted - y_i, grid.z_sorted, @@ -224,6 +224,7 @@ def full_flow_sequential_solver(farm: Farm, flow_field: FlowField, flow_field_gr turbine_grid_farm.construct_rotor_diameters() turbine_grid_farm.construct_turbine_TSRs() turbine_grid_farm.construc_turbine_pPs() + turbine_grid_farm.construc_turbine_ref_density_cp_cts() turbine_grid_farm.construct_coordinates() @@ -233,6 +234,7 @@ def full_flow_sequential_solver(farm: Farm, flow_field: FlowField, flow_field_gr wind_directions=turbine_grid_flow_field.wind_directions, wind_speeds=turbine_grid_flow_field.wind_speeds, grid_resolution=3, + time_series=turbine_grid_flow_field.time_series, ) turbine_grid_farm.expand_farm_properties( turbine_grid_flow_field.n_wind_directions, turbine_grid_flow_field.n_wind_speeds, turbine_grid.sorted_coord_indices @@ -321,6 +323,7 @@ def full_flow_sequential_solver(farm: Farm, flow_field: FlowField, flow_field_gr v_wake, w_wake = calculate_transverse_velocity( u_i, flow_field.u_initial_sorted, + flow_field.dudz_initial_sorted, flow_field_grid.x_sorted - x_i, flow_field_grid.y_sorted - y_i, flow_field_grid.z_sorted, @@ -465,6 +468,7 @@ def cc_solver(farm: Farm, flow_field: FlowField, grid: TurbineGrid, model_manage v_wake, w_wake = calculate_transverse_velocity( u_i, flow_field.u_initial_sorted, + flow_field.dudz_initial_sorted, grid.x_sorted - x_i, grid.y_sorted - y_i, grid.z_sorted, @@ -551,6 +555,7 @@ def full_flow_cc_solver(farm: Farm, flow_field: FlowField, flow_field_grid: Flow turbine_grid_farm.construct_rotor_diameters() turbine_grid_farm.construct_turbine_TSRs() turbine_grid_farm.construc_turbine_pPs() + turbine_grid_farm.construc_turbine_ref_density_cp_cts() turbine_grid_farm.construct_coordinates() turbine_grid = TurbineGrid( @@ -559,6 +564,7 @@ def full_flow_cc_solver(farm: Farm, flow_field: FlowField, flow_field_grid: Flow wind_directions=turbine_grid_flow_field.wind_directions, wind_speeds=turbine_grid_flow_field.wind_speeds, grid_resolution=3, + time_series=turbine_grid_flow_field.time_series, ) turbine_grid_farm.expand_farm_properties( turbine_grid_flow_field.n_wind_directions, turbine_grid_flow_field.n_wind_speeds, turbine_grid.sorted_coord_indices @@ -653,6 +659,7 @@ def full_flow_cc_solver(farm: Farm, flow_field: FlowField, flow_field_grid: Flow v_wake, w_wake = calculate_transverse_velocity( u_i, flow_field.u_initial_sorted, + flow_field.dudz_initial_sorted, flow_field_grid.x_sorted - x_i, flow_field_grid.y_sorted - y_i, flow_field_grid.z_sorted, @@ -703,6 +710,7 @@ def turbopark_solver(farm: Farm, flow_field: FlowField, grid: TurbineGrid, model w_wake = np.zeros_like(flow_field.w_initial_sorted) shape = (farm.n_turbines,) + np.shape(flow_field.u_initial_sorted) velocity_deficit = np.zeros(shape) + deflection_field = np.zeros_like(flow_field.u_initial_sorted) turbine_turbulence_intensity = flow_field.turbulence_intensity * np.ones((flow_field.n_wind_directions, flow_field.n_wind_speeds, farm.n_turbines, 1, 1)) ambient_turbulence_intensity = flow_field.turbulence_intensity @@ -769,20 +777,43 @@ def turbopark_solver(farm: Farm, flow_field: FlowField, grid: TurbineGrid, model # Model calculations # NOTE: exponential - deflection_field = model_manager.deflection_model.function( - x_i, - y_i, - effective_yaw_i, - turbulence_intensity_i, - ct_i, - rotor_diameter_i, - **deflection_model_args - ) + if not np.all(farm.yaw_angles_sorted): + model_manager.deflection_model.logger.warning("WARNING: Deflection with the TurbOPark model has not been fully validated. This is an initial implementation, and we advise you use at your own risk and perform a thorough examination of the results.") + for ii in range(i): + x_ii = np.mean(grid.x_sorted[:, :, ii:ii+1], axis=(3, 4)) + x_ii = x_ii[:, :, :, None, None] + y_ii = np.mean(grid.y_sorted[:, :, ii:ii+1], axis=(3, 4)) + y_ii = y_ii[:, :, :, None, None] + + yaw_ii = farm.yaw_angles_sorted[:, :, ii:ii+1, None, None] + turbulence_intensity_ii = turbine_turbulence_intensity[:, :, ii:ii+1] + ct_ii = Ct( + velocities=flow_field.u_sorted, + yaw_angle=farm.yaw_angles_sorted, + fCt=farm.turbine_fCts, + turbine_type_map=farm.turbine_type_map_sorted, + ix_filter=[ii] + ) + ct_ii = ct_ii[:, :, 0:1, None, None] + rotor_diameter_ii = farm.rotor_diameters_sorted[: ,:, ii:ii+1, None, None] + + deflection_field_ii = model_manager.deflection_model.function( + x_ii, + y_ii, + yaw_ii, + turbulence_intensity_ii, + ct_ii, + rotor_diameter_ii, + **deflection_model_args + ) + + deflection_field[:,:,ii:ii+1,:,:] = deflection_field_ii[:,:,i:i+1,:,:] if model_manager.enable_transverse_velocities: v_wake, w_wake = calculate_transverse_velocity( u_i, flow_field.u_initial_sorted, + flow_field.dudz_initial_sorted, grid.x_sorted - x_i, grid.y_sorted - y_i, grid.z_sorted, @@ -816,6 +847,7 @@ def turbopark_solver(farm: Farm, flow_field: FlowField, grid: TurbineGrid, model rotor_diameter_i, farm.rotor_diameters_sorted[:, :, :, None, None], i, + deflection_field, **deficit_model_args ) diff --git a/floris/simulation/turbine.py b/floris/simulation/turbine.py index ef52e281a..e0498ac31 100644 --- a/floris/simulation/turbine.py +++ b/floris/simulation/turbine.py @@ -79,6 +79,7 @@ def _filter_convert( def power( air_density: float, + ref_density_cp_ct: float, velocities: NDArrayFloat, yaw_angle: NDArrayFloat, pP: float, @@ -91,6 +92,7 @@ def power( Args: air_density (NDArrayFloat[wd, ws, turbines]): The air density value(s) at each turbine. + ref_density_cp_cts (NDArrayFloat[wd, ws, turbines]): The reference density for each turbine velocities (NDArrayFloat[wd, ws, turbines, grid1, grid2]): The velocity field at a turbine. pP (NDArrayFloat[wd, ws, turbines]): The pP value(s) of the cosine exponent relating the yaw misalignment angle to power for each turbine. @@ -134,7 +136,7 @@ def power( # Compute the yaw effective velocity pW = pP / 3.0 # Convert from pP to w - yaw_effective_velocity = ((air_density/1.225)**(1/3)) * average_velocity(velocities) * cosd(yaw_angle) ** pW + yaw_effective_velocity = ((air_density/ref_density_cp_ct)**(1/3)) * average_velocity(velocities) * cosd(yaw_angle) ** pW # Loop over each turbine type given to get thrust coefficient for all turbines p = np.zeros(np.shape(yaw_effective_velocity)) @@ -145,7 +147,7 @@ def power( # type to the main thrust coefficient array p += power_interp[turb_type](yaw_effective_velocity) * np.array(turbine_type_map == turb_type) - return p * 1.225 + return p * ref_density_cp_ct def Ct( @@ -317,6 +319,8 @@ class Turbine(BaseClass): tilt angle to power. generator_efficiency (:py:obj: float): The generator efficiency factor used to scale the power production. + ref_density_cp_ct (:py:obj: float): The density at which the provided + cp and ct is defined power_thrust_table (PowerThrustTable): A dictionary containing the following key-value pairs: @@ -343,8 +347,11 @@ class Turbine(BaseClass): pT: float TSR: float generator_efficiency: float + ref_density_cp_ct: float power_thrust_table: PowerThrustTable = field(converter=PowerThrustTable.from_dict) + + # rloc: float = float_attrib() # TODO: goes here or on the Grid? # use_points_on_perimeter: bool = bool_attrib() @@ -355,6 +362,7 @@ class Turbine(BaseClass): fCt_interp: interp1d = field(init=False) power_interp: interp1d = field(init=False) + # For the following parameters, use default values if not user-specified # self.rloc = float(input_dictionary["rloc"]) if "rloc" in input_dictionary else 0.5 # if "use_points_on_perimeter" in input_dictionary: diff --git a/floris/simulation/wake_combination/fls.py b/floris/simulation/wake_combination/fls.py index 9a0860bfc..4f639f1f8 100644 --- a/floris/simulation/wake_combination/fls.py +++ b/floris/simulation/wake_combination/fls.py @@ -23,8 +23,6 @@ class FLS(BaseModel): deficits to the freestream flow field. """ - model_string = "fls" - def prepare_function(self) -> dict: pass diff --git a/floris/simulation/wake_combination/max.py b/floris/simulation/wake_combination/max.py index cc8b92a28..9e342617f 100644 --- a/floris/simulation/wake_combination/max.py +++ b/floris/simulation/wake_combination/max.py @@ -30,8 +30,6 @@ class MAX(BaseModel): :keyprefix: max- """ - model_string = "max" - def prepare_function(self) -> dict: pass diff --git a/floris/simulation/wake_combination/sosfs.py b/floris/simulation/wake_combination/sosfs.py index 1754d61fd..ca3e6cdc3 100644 --- a/floris/simulation/wake_combination/sosfs.py +++ b/floris/simulation/wake_combination/sosfs.py @@ -23,8 +23,6 @@ class SOSFS(BaseModel): wake velocity deficits to the base flow field. """ - model_string = "sosfs" - def prepare_function(self) -> dict: pass diff --git a/floris/simulation/wake_deflection/curl.py b/floris/simulation/wake_deflection/curl.py deleted file mode 100644 index b49a9f580..000000000 --- a/floris/simulation/wake_deflection/curl.py +++ /dev/null @@ -1,72 +0,0 @@ -# Copyright 2021 NREL - -# Licensed under the Apache License, Version 2.0 (the "License"); you may not -# use this file except in compliance with the License. You may obtain a copy of -# the License at http://www.apache.org/licenses/LICENSE-2.0 - -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, WITHOUT -# WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the -# License for the specific language governing permissions and limitations under -# the License. - -import numpy as np - -from .base_velocity_deflection import VelocityDeflection - - -class Curl(VelocityDeflection): - """ - Stand-in class for the curled wake model. Wake deflection with the curl - model is handled inherently in the wake velocity portion of the model. - Passes zeros for deflection values. See - :cite:`cdm-martinez2019aerodynamics` for additional info on the curled wake - model. - - References: - .. bibliography:: /source/zrefs.bib - :style: unsrt - :filter: docname in docnames - :keyprefix: cdm- - """ - - def __init__(self, parameter_dictionary): - """ - See super-class for initialization details. See - :py:class:`floris.simulation.wake_velocity.curl` for details on - `parameter_dictionary`. - - Args: - parameter_dictionary (dict): Model-specific parameters. - """ - super().__init__(parameter_dictionary) - self.model_string = "curl" - - def function( - self, x_locations, y_locations, z_locations, turbine, coord, flow_field - ): - """ - Passes zeros for wake deflection as deflection is inherently handled in - the wake velocity portion of the curled wake model. - - Args: - x_locations (np.array): An array of floats that contains the - streamwise direction grid coordinates of the flow field - domain (m). - y_locations (np.array): An array of floats that contains the grid - coordinates of the flow field domain in the direction normal to - x and parallel to the ground (m). - z_locations (np.array): An array of floats that contains the grid - coordinates of the flow field domain in the vertical - direction (m). - turbine (:py:obj:`floris.simulation.turbine`): Object that - represents the turbine creating the wake. - coord (:py:obj:`floris.utilities.Vec3`): Object containing - the coordinate of the turbine creating the wake (m). - flow_field (:py:class:`floris.simulation.flow_field`): Object - containing the flow field information for the wind farm. - - Returns: - np.array: Zeros the same size as the flow field grid points. - """ - return np.zeros(np.shape(x_locations)) diff --git a/floris/simulation/wake_deflection/gauss.py b/floris/simulation/wake_deflection/gauss.py index 55e883332..6e0257757 100644 --- a/floris/simulation/wake_deflection/gauss.py +++ b/floris/simulation/wake_deflection/gauss.py @@ -20,7 +20,7 @@ from floris.simulation import FlowField from floris.simulation import Grid from floris.simulation import Turbine -from floris.utilities import cosd, sind, tand +from floris.utilities import cosd, sind @define @@ -79,7 +79,6 @@ class GaussVelocityDeflection(BaseModel): dm: float = field(converter=float, default=1.0) eps_gain: float = field(converter=float, default=0.2) use_secondary_steering: bool = field(converter=bool, default=True) - model_string = "gauss" def prepare_function( self, @@ -343,6 +342,7 @@ def wake_added_yaw( def calculate_transverse_velocity( u_i, u_initial, + dudz_initial, delta_x, delta_y, z, @@ -402,9 +402,6 @@ def calculate_transverse_velocity( lmda = D / 8 kappa = 0.41 lm = kappa * z / (1 + kappa * z / lmda) - # TODO: get this from the z input? - z_basis = np.linspace(np.min(z), np.max(z), np.shape(u_initial)[4]) - dudz_initial = np.gradient(u_initial, z_basis, axis=4) nu = lm ** 2 * np.abs(dudz_initial) decay = eps ** 2 / (4 * nu * delta_x / Uinf + eps ** 2) # This is the decay downstream diff --git a/floris/simulation/wake_deflection/jimenez.py b/floris/simulation/wake_deflection/jimenez.py index 656f377c3..d73c80a86 100644 --- a/floris/simulation/wake_deflection/jimenez.py +++ b/floris/simulation/wake_deflection/jimenez.py @@ -40,7 +40,6 @@ class JimenezVelocityDeflection(BaseModel): kd: float = field(default=0.05) ad: float = field(default=0.0) bd: float = field(default=0.0) - model_string = "jimenez" def prepare_function( self, diff --git a/floris/simulation/wake_deflection/none.py b/floris/simulation/wake_deflection/none.py index 1a748efce..6e2b7beb7 100644 --- a/floris/simulation/wake_deflection/none.py +++ b/floris/simulation/wake_deflection/none.py @@ -26,7 +26,6 @@ class NoneVelocityDeflection(BaseModel): The None deflection model is a placeholder code that simple ignores any deflection and returns an array of zeroes. """ - model_string = "none" def prepare_function( self, diff --git a/floris/simulation/wake_turbulence/crespo_hernandez.py b/floris/simulation/wake_turbulence/crespo_hernandez.py index 5cbf0f730..a5f7f7549 100644 --- a/floris/simulation/wake_turbulence/crespo_hernandez.py +++ b/floris/simulation/wake_turbulence/crespo_hernandez.py @@ -20,7 +20,7 @@ from floris.simulation import FlowField from floris.simulation import Grid from floris.simulation import Turbine -from floris.utilities import cosd, sind, tand +from floris.utilities import cosd, sind @define @@ -57,7 +57,6 @@ class CrespoHernandez(BaseModel): constant: float = field(converter=float, default=0.9) ai: float = field(converter=float, default=0.8) downstream: float = field(converter=float, default=-0.32) - model_string = "crespo_hernandez" def prepare_function(self) -> dict: pass diff --git a/floris/simulation/wake_turbulence/none.py b/floris/simulation/wake_turbulence/none.py index 78c5a2d5e..1d0fd98ed 100644 --- a/floris/simulation/wake_turbulence/none.py +++ b/floris/simulation/wake_turbulence/none.py @@ -25,8 +25,6 @@ class NoneWakeTurbulence(BaseModel): any wake turbulence and just returns an array of the ambient TIs. """ - model_string = "none" - def prepare_function(self) -> dict: pass diff --git a/floris/simulation/wake_velocity/cumulative_gauss_curl.py b/floris/simulation/wake_velocity/cumulative_gauss_curl.py index 764e2a58a..bcfb11b13 100644 --- a/floris/simulation/wake_velocity/cumulative_gauss_curl.py +++ b/floris/simulation/wake_velocity/cumulative_gauss_curl.py @@ -13,9 +13,7 @@ from typing import Any, Dict from attrs import define, field -import numexpr as ne import numpy as np -from numpy import newaxis as na from scipy.special import gamma from floris.simulation import BaseModel @@ -23,7 +21,7 @@ from floris.simulation import FlowField from floris.simulation import Grid from floris.simulation import Turbine -from floris.utilities import cosd, sind, tand, pshape +from floris.utilities import cosd, sind, tand @define @@ -38,8 +36,6 @@ class CumulativeGaussCurlVelocityDeficit(BaseModel): c_f: float = field(default=2.41) alpha_mod: float = field(default=1.0) - model_string = "cumulative_gauss_curl" - def prepare_function( self, grid: Grid, diff --git a/floris/simulation/wake_velocity/gauss.py b/floris/simulation/wake_velocity/gauss.py index a1f1dbb71..c8efdb8ef 100644 --- a/floris/simulation/wake_velocity/gauss.py +++ b/floris/simulation/wake_velocity/gauss.py @@ -31,7 +31,6 @@ class GaussVelocityDeficit(BaseModel): beta: float = field(default=0.077) ka: float = field(default=0.38) kb: float = field(default=0.004) - model_string = "gauss" def prepare_function( self, diff --git a/floris/simulation/wake_velocity/jensen.py b/floris/simulation/wake_velocity/jensen.py index fea1e0e7c..62e0d709c 100644 --- a/floris/simulation/wake_velocity/jensen.py +++ b/floris/simulation/wake_velocity/jensen.py @@ -42,7 +42,6 @@ class JensenVelocityDeficit(BaseModel): """ we: float = field(converter=float, default=0.05) - model_string = "jensen" def prepare_function( self, diff --git a/floris/simulation/wake_velocity/none.py b/floris/simulation/wake_velocity/none.py index 3c891c7ec..831f52380 100644 --- a/floris/simulation/wake_velocity/none.py +++ b/floris/simulation/wake_velocity/none.py @@ -27,8 +27,6 @@ class NoneVelocityDeficit(BaseModel): wake wind speed deficits and returns an array of zeroes. """ - model_string = "none" - def prepare_function( self, grid: Grid, diff --git a/floris/simulation/wake_velocity/turbopark.py b/floris/simulation/wake_velocity/turbopark.py index 7af2d964c..d16382cdf 100644 --- a/floris/simulation/wake_velocity/turbopark.py +++ b/floris/simulation/wake_velocity/turbopark.py @@ -18,11 +18,13 @@ from scipy import integrate from scipy.interpolate import RegularGridInterpolator import scipy.io -import os from floris.simulation import BaseModel +from floris.simulation import Farm from floris.simulation import FlowField from floris.simulation import Grid +from floris.simulation import Turbine +from floris.utilities import cosd, sind, tand @define @@ -36,7 +38,6 @@ class TurbOParkVelocityDeficit(BaseModel): A: float = field(default=0.04) sigma_max_rel: float = field(default=4.0) overlap_gauss_interp: RegularGridInterpolator = field(init=False) - model_string = "turbopark" def __attrs_post_init__(self) -> None: lookup_table_matlab_file = Path(__file__).parent / "turbopark_lookup_table.mat" @@ -71,6 +72,7 @@ def function( rotor_diameter_i: np.ndarray, rotor_diameters: np.ndarray, i: int, + deflection_field: np.ndarray, # enforces the use of the below as keyword arguments and adherence to the # unpacking of the results from prepare_function() *, @@ -88,8 +90,8 @@ def function( x_dist = (x_i - x) * downstream_mask / rotor_diameters # Radial distance between turbine i and the centerlines of wakes from all real/image turbines - r_dist = np.sqrt((y_i - y) ** 2 + (z_i - z) ** 2) - r_dist_image = np.sqrt((y_i - y) ** 2 + (z_i - (-z)) ** 2) + r_dist = np.sqrt((y_i - (y + deflection_field)) ** 2 + (z_i - z) ** 2) + r_dist_image = np.sqrt((y_i - (y + deflection_field)) ** 2 + (z_i - (-z)) ** 2) Cts[:,:,i:,:,:] = 0.00001 diff --git a/floris/tools/floris_interface.py b/floris/tools/floris_interface.py index ab3cc5dd7..ae6d7bf97 100644 --- a/floris/tools/floris_interface.py +++ b/floris/tools/floris_interface.py @@ -14,33 +14,20 @@ from __future__ import annotations -import copy -from typing import Any, Tuple from pathlib import Path -from itertools import repeat, product -from multiprocessing import cpu_count -from multiprocessing.pool import Pool import numpy as np import pandas as pd -import numpy.typing as npt -import matplotlib.pyplot as plt -from scipy.stats import norm from scipy.interpolate import LinearNDInterpolator, NearestNDInterpolator -from numpy.lib.arraysetops import unique -from floris.utilities import Vec3 from floris.type_dec import NDArrayFloat -from floris.simulation import Farm, Floris, FlowField, WakeModelManager, farm, floris, flow_field +from floris.simulation import Floris from floris.logging_manager import LoggerBase -from floris.tools.cut_plane import get_plane_from_flow_data -# from floris.tools.flow_data import FlowData -from floris.simulation.turbine import Ct, power, axial_induction, average_velocity -from floris.tools.interface_utilities import get_params, set_params, show_params -from floris.tools.cut_plane import CutPlane, change_resolution, get_plane_from_flow_data -# from .visualization import visualize_cut_plane -# from .layout_functions import visualize_layout, build_turbine_loc +from floris.simulation import State + +from floris.tools.cut_plane import CutPlane +from floris.simulation.turbine import Ct, power, axial_induction, average_velocity class FlorisInterface(LoggerBase): @@ -70,7 +57,7 @@ def __init__(self, configuration: dict | str | Path, het_map=None): self.floris = Floris.from_dict(self.configuration) else: - raise TypeError("The Floris `configuration` must of type 'dict', 'str', or 'Path'.") + raise TypeError("The Floris `configuration` must be of type 'dict', 'str', or 'Path'.") # Store the heterogeneous map for use after reinitailization self.het_map = het_map @@ -84,22 +71,32 @@ def __init__(self, configuration: dict | str | Path, het_map=None): # Make a check on reference height and provide a helpful warning unique_heights = np.unique(self.floris.farm.hub_heights) - if ((len(unique_heights) == 1) and (self.floris.flow_field.reference_wind_height!=unique_heights[0])): - err_msg = 'The only unique hub-height is not the equal to the specified reference wind height. If this was unintended use -1 as the reference hub height to indicate use of hub-height as reference wind height.' + if (len(unique_heights) == 1) and (self.floris.flow_field.reference_wind_height != unique_heights[0]): + err_msg = "The only unique hub-height is not the equal to the specified reference wind height. If this was unintended use -1 as the reference hub height to indicate use of hub-height as reference wind height." self.logger.warning(err_msg, stack_info=True) + # Check the turbine_grid_points is reasonable + if self.floris.solver["type"] == "turbine_grid": + if self.floris.solver["turbine_grid_points"] > 3: + self.logger.error(f"turbine_grid_points value is {self.floris.solver['turbine_grid_points']} which is larger than the recommended value of less than or equal to 3. High amounts of turbine grid points reduce the computational performance but have a small change on accuracy.") + raise ValueError("turbine_grid_points must be less than or equal to 3.") + def assign_hub_height_to_ref_height(self): # Confirm can do this operation unique_heights = np.unique(self.floris.farm.hub_heights) - if (len(unique_heights) > 1): - raise ValueError("To assign hub heights to reference height, can not have more than one specified height. Current length is {}.".format(len(unique_heights))) + if len(unique_heights) > 1: + raise ValueError( + "To assign hub heights to reference height, can not have more than one specified height. Current length is {}.".format( + len(unique_heights) + ) + ) self.floris.flow_field.reference_wind_height = unique_heights[0] def copy(self): """Create an independent copy of the current FlorisInterface object""" - return FlorisInterface(self.floris.as_dict()) + return FlorisInterface(self.floris.as_dict(), het_map=self.het_map) def calculate_wake( self, @@ -128,9 +125,6 @@ def calculate_wake( # TODO decide where to handle this sign issue if (yaw_angles is not None) and not (np.all(yaw_angles==0.)): - if self.floris.wake.model_strings["velocity_model"] == "turbopark": - # TODO: Implement wake steering for the TurbOPark model - raise ValueError("Non-zero yaw angles given and for TurbOPark model; wake steering with this model is not yet implemented.") self.floris.farm.yaw_angles = yaw_angles # Initialize solution space @@ -157,9 +151,6 @@ def calculate_no_wake( # TODO decide where to handle this sign issue if (yaw_angles is not None) and not (np.all(yaw_angles==0.)): - if self.floris.wake.model_strings["velocity_model"] == "turbopark": - # TODO: Implement wake steering for the TurbOPark model - raise ValueError("Non-zero yaw angles given and for TurbOPark model; wake steering with this model is not yet implemented.") self.floris.farm.yaw_angles = yaw_angles # Initialize solution space @@ -180,12 +171,15 @@ def reinitialize( # turbulence_kinetic_energy=None, air_density: float | None = None, # wake: WakeModelManager = None, - layout: Tuple[list[float], list[float]] | Tuple[NDArrayFloat, NDArrayFloat] | None = None, + layout_x: list[float] | NDArrayFloat | None = None, + layout_y: list[float] | NDArrayFloat | None = None, turbine_type: list | None = None, # turbine_id: list[str] | None = None, # wtg_id: list[str] | None = None, # with_resolution: float | None = None, - solver_settings: dict | None = None + solver_settings: dict | None = None, + time_series: bool | None = False, + layout: tuple[list[float], list[float]] | tuple[NDArrayFloat, NDArrayFloat] | None = None, ): # Export the floris object recursively as a dictionary floris_dict = self.floris.as_dict() @@ -212,11 +206,22 @@ def reinitialize( ## Farm if layout is not None: - farm_dict["layout_x"] = layout[0] - farm_dict["layout_y"] = layout[1] + msg = "Use the `layout_x` and `layout_y` parameters in place of `layout` because the `layout` parameter will be deprecated in 3.3." + self.logger.warning(msg) + layout_x = layout[0] + layout_y = layout[1] + if layout_x is not None: + farm_dict["layout_x"] = layout_x + if layout_y is not None: + farm_dict["layout_y"] = layout_y if turbine_type is not None: farm_dict["turbine_type"] = turbine_type + if time_series: + flow_field_dict["time_series"] = True + else: + flow_field_dict["time_series"] = False + ## Wake # if wake is not None: # self.floris.wake = wake @@ -300,7 +305,7 @@ def get_plane_of_points( # Subset to plane # TODO: Seems sloppy as need more than one plane in the z-direction for GCH if planar_coordinate is not None: - df = df[np.isclose(df.x3, planar_coordinate)] # , atol=0.1, rtol=0.0)] + df = df[np.isclose(df.x3, planar_coordinate)] # , atol=0.1, rtol=0.0)] # Drop duplicates # TODO is this still needed now that we setup a grid for just this plane? @@ -342,7 +347,7 @@ def calculate_horizontal_plane( :py:class:`~.tools.cut_plane.CutPlane`: containing values of x, y, u, v, w """ - #TODO update docstring + # TODO update docstring if wd is None: wd = self.floris.flow_field.wind_directions if ws is None: @@ -361,9 +366,7 @@ def calculate_horizontal_plane( "flow_field_grid_points": [x_resolution, y_resolution], "flow_field_bounds": [x_bounds, y_bounds], } - self.reinitialize( - wind_directions=wd, wind_speeds=ws, solver_settings=solver_settings - ) + self.reinitialize(wind_directions=wd, wind_speeds=ws, solver_settings=solver_settings) # TODO this has to be done here as it seems to be lost with reinitialize if yaw_angles is not None: @@ -441,9 +444,7 @@ def calculate_cross_plane( "flow_field_grid_points": [y_resolution, z_resolution], "flow_field_bounds": [y_bounds, z_bounds], } - self.reinitialize( - wind_directions=wd, wind_speeds=ws, solver_settings=solver_settings - ) + self.reinitialize(wind_directions=wd, wind_speeds=ws, solver_settings=solver_settings) # TODO this has to be done here as it seems to be lost with reinitialize if yaw_angles is not None: @@ -502,7 +503,7 @@ def calculate_y_plane( :py:class:`~.tools.cut_plane.CutPlane`: containing values of x, y, u, v, w """ - #TODO update docstring + # TODO update docstring if wd is None: wd = self.floris.flow_field.wind_directions if ws is None: @@ -521,9 +522,7 @@ def calculate_y_plane( "flow_field_grid_points": [x_resolution, z_resolution], "flow_field_bounds": [x_bounds, z_bounds], } - self.reinitialize( - wind_directions=wd, wind_speeds=ws, solver_settings=solver_settings - ) + self.reinitialize(wind_directions=wd, wind_speeds=ws, solver_settings=solver_settings) # TODO this has to be done here as it seems to be lost with reinitialize if yaw_angles is not None: @@ -553,10 +552,14 @@ def calculate_y_plane( def check_wind_condition_for_viz(self, wd=None, ws=None): if len(wd) > 1 or len(wd) < 1: - raise ValueError("Wind direction input must be of length 1 for visualization. Current length is {}.".format(len(wd))) + raise ValueError( + "Wind direction input must be of length 1 for visualization. Current length is {}.".format(len(wd)) + ) if len(ws) > 1 or len(ws) < 1: - raise ValueError("Wind speed input must be of length 1 for visualization. Current length is {}.".format(len(ws))) + raise ValueError( + "Wind speed input must be of length 1 for visualization. Current length is {}.".format(len(ws)) + ) def get_turbine_powers(self) -> NDArrayFloat: """Calculates the power at each turbine in the windfarm. @@ -564,8 +567,14 @@ def get_turbine_powers(self) -> NDArrayFloat: Returns: NDArrayFloat: [description] """ + + # Confirm calculate wake has been run + if self.floris.state is not State.USED: + raise RuntimeError(f"Can't run function `FlorisInterface.get_turbine_powers` without first running `FlorisInterface.calculate_wake`.") + turbine_powers = power( air_density=self.floris.flow_field.air_density, + ref_density_cp_ct=self.floris.farm.ref_density_cp_cts, velocities=self.floris.flow_field.u, yaw_angle=self.floris.farm.yaw_angles, pP=self.floris.farm.pPs, @@ -598,8 +607,12 @@ def get_turbine_average_velocities(self) -> NDArrayFloat: ) return turbine_avg_vels + def get_turbine_TIs(self) -> NDArrayFloat: + return self.floris.flow_field.turbulence_intensity_field + def get_farm_power( self, + turbine_weights=None, use_turbulence_correction=False, ): """ @@ -610,6 +623,19 @@ def get_farm_power( original wind direction and yaw angles. Args: + turbine_weights (NDArrayFloat | list[float] | None, optional): + weighing terms that allow the user to emphasize power at + particular turbines and/or completely ignore the power + from other turbines. This is useful when, for example, you are + modeling multiple wind farms in a single floris object. If you + only want to calculate the power production for one of those + farms and include the wake effects of the neighboring farms, + you can set the turbine_weights for the neighboring farms' + turbines to 0.0. The array of turbine powers from floris + is multiplied with this array in the calculation of the + objective function. If None, this is an array with all values + 1.0 and with shape equal to (n_wind_directions, n_wind_speeds, + n_turbines). Defaults to None. use_turbulence_correction: (bool, optional): When *True* uses a turbulence parameter to adjust power output calculations. Defaults to *False*. @@ -624,7 +650,34 @@ def get_farm_power( # for turbine in self.floris.farm.turbines: # turbine.use_turbulence_correction = use_turbulence_correction + # Confirm calculate wake has been run + if self.floris.state is not State.USED: + raise RuntimeError(f"Can't run function `FlorisInterface.get_turbine_powers` without running `FlorisInterface.calculate_wake`.") + + if turbine_weights is None: + # Default to equal weighing of all turbines when turbine_weights is None + turbine_weights = np.ones( + ( + self.floris.flow_field.n_wind_directions, + self.floris.flow_field.n_wind_speeds, + self.floris.farm.n_turbines + ) + ) + elif len(np.shape(turbine_weights)) == 1: + # Deal with situation when 1D array is provided + turbine_weights = np.tile( + turbine_weights, + ( + self.floris.flow_field.n_wind_directions, + self.floris.flow_field.n_wind_speeds, + 1 + ) + ) + + # Calculate all turbine powers and apply weights turbine_powers = self.get_turbine_powers() + turbine_powers = np.multiply(turbine_weights, turbine_powers) + return np.sum(turbine_powers, axis=2) def get_farm_AEP( @@ -633,6 +686,7 @@ def get_farm_AEP( cut_in_wind_speed=0.001, cut_out_wind_speed=None, yaw_angles=None, + turbine_weights=None, no_wake=False, ) -> float: """ @@ -646,7 +700,7 @@ def get_farm_AEP( up to 1.0 and are used to weigh the wind farm power for every condition in calculating the wind farm's AEP. cut_in_wind_speed (float, optional): Wind speed in m/s below which - any calculations are ignored and the wind farm is known to + any calculations are ignored and the wind farm is known to produce 0.0 W of power. Note that to prevent problems with the wake models at negative / zero wind speeds, this variable must always have a positive value. Defaults to 0.001 [m/s]. @@ -658,13 +712,26 @@ def get_farm_AEP( The relative turbine yaw angles in degrees. If None is specified, will assume that the turbine yaw angles are all zero degrees for all conditions. Defaults to None. + turbine_weights (NDArrayFloat | list[float] | None, optional): + weighing terms that allow the user to emphasize power at + particular turbines and/or completely ignore the power + from other turbines. This is useful when, for example, you are + modeling multiple wind farms in a single floris object. If you + only want to calculate the power production for one of those + farms and include the wake effects of the neighboring farms, + you can set the turbine_weights for the neighboring farms' + turbines to 0.0. The array of turbine powers from floris + is multiplied with this array in the calculation of the + objective function. If None, this is an array with all values + 1.0 and with shape equal to (n_wind_directions, n_wind_speeds, + n_turbines). Defaults to None. no_wake: (bool, optional): When *True* updates the turbine quantities without calculating the wake or adding the wake to the flow field. This can be useful when quantifying the loss in AEP due to wakes. Defaults to *False*. Returns: - float: + float: The Annual Energy Production (AEP) for the wind farm in watt-hours. """ @@ -676,30 +743,22 @@ def get_farm_AEP( & (len(np.shape(freq)) == 2) ): raise UserWarning( - "'freq' should be a two-dimensional array with dimensions" - + " (n_wind_directions, n_wind_speeds)." + "'freq' should be a two-dimensional array with dimensions" + " (n_wind_directions, n_wind_speeds)." ) # Check if frequency vector sums to 1.0. If not, raise a warning if np.abs(np.sum(freq) - 1.0) > 0.001: - self.logger.warning( - "WARNING: The frequency array provided to get_farm_AEP() " - + "does not sum to 1.0. " - ) + self.logger.warning("WARNING: The frequency array provided to get_farm_AEP() " + "does not sum to 1.0. ") # Copy the full wind speed array from the floris object and initialize # the the farm_power variable as an empty array. wind_speeds = np.array(self.floris.flow_field.wind_speeds, copy=True) - farm_power = np.zeros( - (self.floris.flow_field.n_wind_directions, len(wind_speeds)) - ) + farm_power = np.zeros((self.floris.flow_field.n_wind_directions, len(wind_speeds))) # Determine which wind speeds we must evaluate in floris - conditions_to_evaluate = (wind_speeds >= cut_in_wind_speed) + conditions_to_evaluate = wind_speeds >= cut_in_wind_speed if cut_out_wind_speed is not None: - conditions_to_evaluate = conditions_to_evaluate & ( - wind_speeds < cut_out_wind_speed - ) + conditions_to_evaluate = conditions_to_evaluate & (wind_speeds < cut_out_wind_speed) # Evaluate the conditions in floris if np.any(conditions_to_evaluate): @@ -712,7 +771,9 @@ def get_farm_AEP( self.calculate_no_wake(yaw_angles=yaw_angles_subset) else: self.calculate_wake(yaw_angles=yaw_angles_subset) - farm_power[:, conditions_to_evaluate] = self.get_farm_power() + farm_power[:, conditions_to_evaluate] = ( + self.get_farm_power(turbine_weights=turbine_weights) + ) # Finally, calculate AEP in GWh aep = np.sum(np.multiply(freq, farm_power) * 365 * 24) @@ -722,6 +783,74 @@ def get_farm_AEP( return aep + def get_farm_AEP_wind_rose_class( + self, + wind_rose, + cut_in_wind_speed=0.001, + cut_out_wind_speed=None, + yaw_angles=None, + no_wake=False, + ) -> float: + """ + Estimate annual energy production (AEP) for distributions of wind speed, wind + direction, frequency of occurrence, and yaw offset. + + Args: + wind_rose (wind_rose): An object of the wind rose class + cut_in_wind_speed (float, optional): Wind speed in m/s below which + any calculations are ignored and the wind farm is known to + produce 0.0 W of power. Note that to prevent problems with the + wake models at negative / zero wind speeds, this variable must + always have a positive value. Defaults to 0.001 [m/s]. + cut_out_wind_speed (float, optional): Wind speed above which the + wind farm is known to produce 0.0 W of power. If None is + specified, will assume that the wind farm does not cut out + at high wind speeds. Defaults to None. + yaw_angles (NDArrayFloat | list[float] | None, optional): + The relative turbine yaw angles in degrees. If None is + specified, will assume that the turbine yaw angles are all + zero degrees for all conditions. Defaults to None. + no_wake: (bool, optional): When *True* updates the turbine + quantities without calculating the wake or adding the wake to + the flow field. This can be useful when quantifying the loss + in AEP due to wakes. Defaults to *False*. + + Returns: + float: + The Annual Energy Production (AEP) for the wind farm in + watt-hours. + """ + + # Hold the starting values of wind speed and direction + wind_speeds = np.array(self.floris.flow_field.wind_speeds, copy=True) + wind_directions = np.array(self.floris.flow_field.wind_directions, copy=True) + + # Now set FLORIS wind speed and wind direction + # over to those values in the wind rose class + wind_speeds_wind_rose = wind_rose.df.ws.unique() + wind_directions_wind_rose = wind_rose.df.wd.unique() + self.reinitialize(wind_speeds=wind_speeds_wind_rose, wind_directions=wind_directions_wind_rose) + + # Build the frequency matrix from wind rose + freq = wind_rose.df.set_index(['wd','ws']).unstack().values + + # Now compute aep + aep = self.get_farm_AEP( + freq, + cut_in_wind_speed=cut_in_wind_speed, + cut_out_wind_speed=cut_out_wind_speed, + yaw_angles=yaw_angles, + no_wake=no_wake) + + + # Reset the FLORIS object to the original wind speed and directions + self.reinitialize(wind_speeds=wind_speeds, wind_directions=wind_directions) + + + return aep + + + @property def layout_x(self): """ @@ -742,7 +871,6 @@ def layout_y(self): """ return self.floris.farm.layout_y - def get_turbine_layout(self, z=False): """ Get turbine layout @@ -778,759 +906,6 @@ def generate_heterogeneous_wind_map(speed_ups, x, y, z=None): return [in_region, out_region] -# def global_calc_one_AEP_case(FlorisInterface, wd, ws, freq, yaw=None): -# return FlorisInterface._calc_one_AEP_case(wd, ws, freq, yaw) - -DEFAULT_UNCERTAINTY = {"std_wd": 4.95, "std_yaw": 1.75, "pmf_res": 1.0, "pdf_cutoff": 0.995} - - -def _generate_uncertainty_parameters(unc_options: dict, unc_pmfs: dict) -> dict: - """Generates the uncertainty parameters for `FlorisInterface.get_farm_power` and - `FlorisInterface.get_turbine_power` for more details. - - Args: - unc_options (dict): See `FlorisInterface.get_farm_power` or `FlorisInterface.get_turbine_power`. - unc_pmfs (dict): See `FlorisInterface.get_farm_power` or `FlorisInterface.get_turbine_power`. - - Returns: - dict: [description] - """ - if (unc_options is None) & (unc_pmfs is None): - unc_options = DEFAULT_UNCERTAINTY - - if unc_pmfs is not None: - return unc_pmfs - - wd_unc = np.zeros(1) - wd_unc_pmf = np.ones(1) - yaw_unc = np.zeros(1) - yaw_unc_pmf = np.ones(1) - - # create normally distributed wd and yaw uncertaitny pmfs if appropriate - if unc_options["std_wd"] > 0: - wd_bnd = int(np.ceil(norm.ppf(unc_options["pdf_cutoff"], scale=unc_options["std_wd"]) / unc_options["pmf_res"])) - bound = wd_bnd * unc_options["pmf_res"] - wd_unc = np.linspace(-1 * bound, bound, 2 * wd_bnd + 1) - wd_unc_pmf = norm.pdf(wd_unc, scale=unc_options["std_wd"]) - wd_unc_pmf /= np.sum(wd_unc_pmf) # normalize so sum = 1.0 - - if unc_options["std_yaw"] > 0: - yaw_bnd = int( - np.ceil(norm.ppf(unc_options["pdf_cutoff"], scale=unc_options["std_yaw"]) / unc_options["pmf_res"]) - ) - bound = yaw_bnd * unc_options["pmf_res"] - yaw_unc = np.linspace(-1 * bound, bound, 2 * yaw_bnd + 1) - yaw_unc_pmf = norm.pdf(yaw_unc, scale=unc_options["std_yaw"]) - yaw_unc_pmf /= np.sum(yaw_unc_pmf) # normalize so sum = 1.0 - - unc_pmfs = { - "wd_unc": wd_unc, - "wd_unc_pmf": wd_unc_pmf, - "yaw_unc": yaw_unc, - "yaw_unc_pmf": yaw_unc_pmf, - } - return unc_pmfs - - -# def correct_for_all_combinations( -# wd: NDArrayFloat, -# ws: NDArrayFloat, -# freq: NDArrayFloat, -# yaw: NDArrayFloat | None = None, -# ) -> tuple[NDArrayFloat]: -# """Computes the probabilities for the complete windrose from the desired wind -# direction and wind speed combinations and their associated probabilities so that -# any undesired combinations are filled with a 0.0 probability. - -# Args: -# wd (NDArrayFloat): List or array of wind direction values. -# ws (NDArrayFloat): List or array of wind speed values. -# freq (NDArrayFloat): Frequencies corresponding to wind -# speeds and directions in wind rose with dimensions -# (N wind directions x N wind speeds). -# yaw (NDArrayFloat | None): The corresponding yaw angles for each of the wind -# direction and wind speed combinations, or None. Defaults to None. - -# Returns: -# NDArrayFloat, NDArrayFloat, NDArrayFloat: The unique wind directions, wind -# speeds, and the associated probability of their combination combinations in -# an array of shape (N wind directions x N wind speeds). -# """ - -# combos_to_compute = np.array(list(zip(wd, ws, freq))) - -# unique_wd = wd.unique() -# unique_ws = ws.unique() -# all_combos = np.array(list(product(unique_wd, unique_ws)), dtype=float) -# all_combos = np.hstack((all_combos, np.zeros((all_combos.shape[0], 1), dtype=float))) -# expanded_yaw = np.array([None] * all_combos.shape[0]).reshape(unique_wd.size, unique_ws.size) - -# ix_match = [np.where((all_combos[:, :2] == combo[:2]).all(1))[0][0] for combo in combos_to_compute] -# all_combos[ix_match, 2] = combos_to_compute[:, 2] -# if yaw is not None: -# expanded_yaw[ix_match] = yaw -# freq = all_combos.T[2].reshape((unique_wd.size, unique_ws.size)) -# return unique_wd, unique_ws, freq - - - # def get_set_of_points(self, x_points, y_points, z_points): - # """ - # Calculates velocity values through the - # :py:meth:`~.FlowField.calculate_wake` method at points specified by - # inputs. - - # Args: - # x_points (float): X-locations to get velocity values at. - # y_points (float): Y-locations to get velocity values at. - # z_points (float): Z-locations to get velocity values at. - - # Returns: - # :py:class:`pandas.DataFrame`: containing values of x, y, z, u, v, w - # """ - # # Get a copy for the flow field so don't change underlying grid points - # flow_field = copy.deepcopy(self.floris.flow_field) - - # if hasattr(self.floris.wake.velocity_model, "requires_resolution"): - # if self.floris.velocity_model.requires_resolution: - - # # If this is a gridded model, must extract from full flow field - # self.logger.info( - # "Model identified as %s requires use of underlying grid print" - # % self.floris.wake.velocity_model.model_string - # ) - # self.logger.warning("FUNCTION NOT AVAILABLE CURRENTLY") - - # # Set up points matrix - # points = np.row_stack((x_points, y_points, z_points)) - - # # TODO: Calculate wake inputs need to be mapped - # raise_error = True - # if raise_error: - # raise NotImplementedError("Additional point calculation is not yet supported!") - # # Recalculate wake with these points - # flow_field.calculate_wake(points=points) - - # # Get results vectors - # x_flat = flow_field.x.flatten() - # y_flat = flow_field.y.flatten() - # z_flat = flow_field.z.flatten() - # u_flat = flow_field.u.flatten() - # v_flat = flow_field.v.flatten() - # w_flat = flow_field.w.flatten() - - # df = pd.DataFrame( - # { - # "x": x_flat, - # "y": y_flat, - # "z": z_flat, - # "u": u_flat, - # "v": v_flat, - # "w": w_flat, - # } - # ) - - # # Subset to points requests - # df = df[df.x.isin(x_points)] - # df = df[df.y.isin(y_points)] - # df = df[df.z.isin(z_points)] - - # # Drop duplicates - # df = df.drop_duplicates() - - # # Return the dataframe - # return df - - # def get_flow_data(self, resolution=None, grid_spacing=10, velocity_deficit=False): - # """ - # Generate :py:class:`~.tools.flow_data.FlowData` object corresponding to - # active FLORIS instance. - - # Velocity and wake models requiring calculation on a grid implement a - # discretized domain at resolution **grid_spacing**. This is distinct - # from the resolution of the returned flow field domain. - - # Args: - # resolution (float, optional): Resolution of output data. - # Only used for wake models that require spatial - # resolution (e.g. curl). Defaults to None. - # grid_spacing (int, optional): Resolution of grid used for - # simulation. Model results may be sensitive to resolution. - # Defaults to 10. - # velocity_deficit (bool, optional): When *True*, normalizes velocity - # with respect to initial flow field velocity to show relative - # velocity deficit (%). Defaults to *False*. - - # Returns: - # :py:class:`~.tools.flow_data.FlowData`: FlowData object - # """ - - # if resolution is None: - # if not self.floris.wake.velocity_model.requires_resolution: - # self.logger.info("Assuming grid with spacing %d" % grid_spacing) - # ( - # xmin, - # xmax, - # ymin, - # ymax, - # zmin, - # zmax, - # ) = self.floris.flow_field.domain_bounds # TODO: No grid attribute within FlowField - # resolution = Vec3( - # 1 + (xmax - xmin) / grid_spacing, - # 1 + (ymax - ymin) / grid_spacing, - # 1 + (zmax - zmin) / grid_spacing, - # ) - # else: - # self.logger.info("Assuming model resolution") - # resolution = self.floris.wake.velocity_model.model_grid_resolution - - # # Get a copy for the flow field so don't change underlying grid points - # flow_field = copy.deepcopy(self.floris.flow_field) - - # if ( - # flow_field.wake.velocity_model.requires_resolution - # and flow_field.wake.velocity_model.model_grid_resolution != resolution - # ): - # self.logger.warning( - # "WARNING: The current wake velocity model contains a " - # + "required grid resolution; the Resolution given to " - # + "FlorisInterface.get_flow_field is ignored." - # ) - # resolution = flow_field.wake.velocity_model.model_grid_resolution - # flow_field.reinitialize(with_resolution=resolution) # TODO: Not implemented - # self.logger.info(resolution) - # # print(resolution) - # flow_field.steady_state_atmospheric_condition() - - # order = "f" - # x = flow_field.x.flatten(order=order) - # y = flow_field.y.flatten(order=order) - # z = flow_field.z.flatten(order=order) - - # u = flow_field.u.flatten(order=order) - # v = flow_field.v.flatten(order=order) - # w = flow_field.w.flatten(order=order) - - # # find percent velocity deficit - # if velocity_deficit: - # u = abs(u - flow_field.u_initial.flatten(order=order)) / flow_field.u_initial.flatten(order=order) * 100 - # v = abs(v - flow_field.v_initial.flatten(order=order)) / flow_field.v_initial.flatten(order=order) * 100 - # w = abs(w - flow_field.w_initial.flatten(order=order)) / flow_field.w_initial.flatten(order=order) * 100 - - # # Determine spacing, dimensions and origin - # unique_x = np.sort(np.unique(x)) - # unique_y = np.sort(np.unique(y)) - # unique_z = np.sort(np.unique(z)) - # spacing = Vec3( - # unique_x[1] - unique_x[0], - # unique_y[1] - unique_y[0], - # unique_z[1] - unique_z[0], - # ) - # dimensions = Vec3(len(unique_x), len(unique_y), len(unique_z)) - # origin = Vec3(0.0, 0.0, 0.0) - # return FlowData(x, y, z, u, v, w, spacing=spacing, dimensions=dimensions, origin=origin) - - - # def get_turbine_power( - # self, - # include_unc=False, - # unc_pmfs=None, - # unc_options=None, - # no_wake=False, - # use_turbulence_correction=False, - # ): - # """ - # Report power from each wind turbine. - - # Args: - # include_unc (bool): If *True*, uncertainty in wind direction - # and/or yaw position is included when determining turbine - # powers. Defaults to *False*. - # unc_pmfs (dictionary, optional): A dictionary containing optional - # probability mass functions describing the distribution of wind - # direction and yaw position deviations when wind direction and/or - # yaw position uncertainty is included in the power calculations. - # Contains the following key-value pairs: - - # - **wd_unc** (*np.array*): Wind direction deviations from the - # original wind direction. - # - **wd_unc_pmf** (*np.array*): Probability of each wind - # direction deviation in **wd_unc** occuring. - # - **yaw_unc** (*np.array*): Yaw angle deviations from the - # original yaw angles. - # - **yaw_unc_pmf** (*np.array*): Probability of each yaw angle - # deviation in **yaw_unc** occuring. - - # Defaults to None, in which case default PMFs are calculated - # using values provided in **unc_options**. - # unc_options (dictionary, optional): A dictionary containing values - # used to create normally-distributed, zero-mean probability mass - # functions describing the distribution of wind direction and yaw - # position deviations when wind direction and/or yaw position - # uncertainty is included. This argument is only used when - # **unc_pmfs** is None and contains the following key-value pairs: - - # - **std_wd** (*float*): A float containing the standard - # deviation of the wind direction deviations from the - # original wind direction. - # - **std_yaw** (*float*): A float containing the standard - # deviation of the yaw angle deviations from the original yaw - # angles. - # - **pmf_res** (*float*): A float containing the resolution in - # degrees of the wind direction and yaw angle PMFs. - # - **pdf_cutoff** (*float*): A float containing the cumulative - # distribution function value at which the tails of the - # PMFs are truncated. - - # Defaults to None. Initializes to {'std_wd': 4.95, 'std_yaw': 1. - # 75, 'pmf_res': 1.0, 'pdf_cutoff': 0.995}. - # no_wake: (bool, optional): When *True* updates the turbine - # quantities without calculating the wake or adding the - # wake to the flow field. Defaults to *False*. - # use_turbulence_correction: (bool, optional): When *True* uses a - # turbulence parameter to adjust power output calculations. - # Defaults to *False*. - - # Returns: - # np.array: Power produced by each wind turbine. - # """ - # # TODO: Turbulence correction used in the power calculation, but may not be in - # # the model yet - # # TODO: Turbines need a switch for using turbulence correction - # # TODO: Uncomment out the following two lines once the above are resolved - # # for turbine in self.floris.farm.turbines: - # # turbine.use_turbulence_correction = use_turbulence_correction - - # if include_unc: - # unc_pmfs = _generate_uncertainty_parameters(unc_options, unc_pmfs) - - # mean_farm_power = np.zeros(self.floris.farm.n_turbines) - # wd_orig = self.floris.flow_field.wind_directions # TODO: same comment as in get_farm_power - - # yaw_angles = self.get_yaw_angles() - # self.reinitialize(wind_direction=wd_orig[0] + unc_pmfs["wd_unc"]) - # for i, delta_yaw in enumerate(unc_pmfs["yaw_unc"]): - # self.calculate_wake( - # yaw_angles=list(np.array(yaw_angles) + delta_yaw), - # no_wake=no_wake, - # ) - # mean_farm_power += unc_pmfs["wd_unc_pmf"] * unc_pmfs["yaw_unc_pmf"][i] * self._get_turbine_powers() - - # # reinitialize with original values - # self.reinitialize(wind_direction=wd_orig) - # self.calculate_wake(yaw_angles=yaw_angles, no_wake=no_wake) - # return mean_farm_power - - # return self._get_turbine_powers() - - # def get_power_curve(self, wind_speeds): - # """ - # Return the power curve given a set of wind speeds - - # Args: - # wind_speeds (np.array): array of wind speeds to get power curve - # """ - - # # TODO: Why is this done? Should we expand for evenutal multiple turbines types - # # or just allow a filter on the turbine index? - # # Temporarily set the farm to a single turbine - # saved_layout_x = self.layout_x - # saved_layout_y = self.layout_y - - # self.reinitialize(wind_speed=wind_speeds, layout_array=([0], [0])) - # self.calculate_wake() - # turbine_power = self._get_turbine_powers() - - # # Set it back - # self.reinitialize(layout_array=(saved_layout_x, saved_layout_y)) - - # return turbine_power - - # def get_farm_power_for_yaw_angle( - # self, - # yaw_angles, - # include_unc=False, - # unc_pmfs=None, - # unc_options=None, - # no_wake=False, - # ): - # """ - # Assign yaw angles to turbines, calculate wake, and report farm power. - - # Args: - # yaw_angles (np.array): Yaw to apply to each turbine. - # include_unc (bool, optional): When *True*, includes wind direction - # uncertainty in estimate of wind farm power. Defaults to *False*. - # unc_pmfs (dictionary, optional): A dictionary containing optional - # probability mass functions describing the distribution of wind - # direction and yaw position deviations when wind direction and/or - # yaw position uncertainty is included in the power calculations. - # Contains the following key-value pairs: - - # - **wd_unc** (*np.array*): Wind direction deviations from the - # original wind direction. - # - **wd_unc_pmf** (*np.array*): Probability of each wind - # direction deviation in **wd_unc** occuring. - # - **yaw_unc** (*np.array*): Yaw angle deviations from the - # original yaw angles. - # - **yaw_unc_pmf** (*np.array*): Probability of each yaw angle - # deviation in **yaw_unc** occuring. - - # Defaults to None, in which case default PMFs are calculated - # using values provided in **unc_options**. - # unc_options (dictionary, optional): A dictionary containing values - # used to create normally-distributed, zero-mean probability mass - # functions describing the distribution of wind direction and yaw - # position deviations when wind direction and/or yaw position - # uncertainty is included. This argument is only used when - # **unc_pmfs** is None and contains the following key-value pairs: - - # - **std_wd** (*float*): A float containing the standard - # deviation of the wind direction deviations from the - # original wind direction. - # - **std_yaw** (*float*): A float containing the standard - # deviation of the yaw angle deviations from the original yaw - # angles. - # - **pmf_res** (*float*): A float containing the resolution in - # degrees of the wind direction and yaw angle PMFs. - # - **pdf_cutoff** (*float*): A float containing the cumulative - # distribution function value at which the tails of the - # PMFs are truncated. - - # Defaults to None. Initializes to {'std_wd': 4.95, 'std_yaw': 1. - # 75, 'pmf_res': 1.0, 'pdf_cutoff': 0.995}. - # no_wake: (bool, optional): When *True* updates the turbine - # quantities without calculating the wake or adding the - # wake to the flow field. Defaults to *False*. - - # Returns: - # float: Wind plant power. #TODO negative? in kW? - # """ - - # self.calculate_wake(yaw_angles=yaw_angles, no_wake=no_wake) - - # return self.get_farm_power(include_unc=include_unc, unc_pmfs=unc_pmfs, unc_options=unc_options) - - # def copy_and_update_turbine_map( - # self, base_turbine_id: str, update_parameters: dict, new_id: str | None = None - # ) -> dict: - # """Creates a new copy of an existing turbine and updates the parameters based on - # user input. This function is a helper to make the v2 -> v3 transition easier. - - # Args: - # base_turbine_id (str): The base turbine's ID in `floris.farm.turbine_id`. - # update_parameters (dict): A dictionary of the turbine parameters to update - # and their new valies. - # new_id (str, optional): The new `turbine_id`, if `None` a unique - # identifier will be appended to the end. Defaults to None. - - # Returns: - # dict: A turbine mapping that can be passed directly to `change_turbine`. - # """ - # if new_id is None: - # new_id = f"{base_turbine_id}_copy{self.unique_copy_id}" - # self.unique_copy_id += 1 - - # turbine = {new_id: self.floris.turbine[base_turbine_id]._asdict()} - # turbine[new_id].update(update_parameters) - # return turbine - - # def change_turbine( - # self, - # turbine_indices: list[int], - # new_turbine_map: dict[str, dict[str, Any]], - # update_specified_wind_height: bool = False, - # ): - # """ - # Change turbine properties for specified turbines. - - # Args: - # turbine_indices (list[int]): List of turbine indices to change. - # new_turbine_map (dict[str, dict[str, Any]]): New dictionary of turbine - # parameters to create the new turbines for each of `turbine_indices`. - # update_specified_wind_height (bool, optional): When *True*, update specified - # wind height to match new hub_height. Defaults to *False*. - # """ - # new_turbine = True - # new_turbine_id = [*new_turbine_map][0] - # if new_turbine_id in self.floris.farm.turbine_map: - # new_turbine = False - # self.logger.info(f"Turbines {turbine_indices} will be re-mapped to the definition for: {new_turbine_id}") - - # self.floris.farm.turbine_id = [ - # new_turbine_id if i in turbine_indices else t_id for i, t_id in enumerate(self.floris.farm.turbine_id) - # ] - # if new_turbine: - # self.logger.info(f"Turbines {turbine_indices} have been mapped to the new definition for: {new_turbine_id}") - - # # Update the turbine mapping if a new turbine was provided, then regenerate the - # # farm arrays for the turbine farm - # if new_turbine: - # turbine_map = self.floris.farm._asdict()["turbine_map"] - # turbine_map.update(new_turbine_map) - # self.floris.farm.turbine_map = turbine_map - # self.floris.farm.generate_farm_points() - - # new_hub_height = new_turbine_map[new_turbine_id]["hub_height"] - # changed_hub_height = new_hub_height != self.floris.flow_field.reference_wind_height - - # # Alert user if changing hub-height and not specified wind height - # if changed_hub_height and not update_specified_wind_height: - # self.logger.info("Note, updating hub height but not updating " + "the specfied_wind_height") - - # if changed_hub_height and update_specified_wind_height: - # self.logger.info(f"Note, specfied_wind_height changed to hub-height: {new_hub_height}") - # self.reinitialize(specified_wind_height=new_hub_height) - - # # Finish by re-initalizing the flow field - # self.reinitialize() - - # def set_use_points_on_perimeter(self, use_points_on_perimeter=False): - # """ - # Set whether to use the points on the rotor diameter (perimeter) when - # calculating flow field and wake. - - # Args: - # use_points_on_perimeter (bool): When *True*, use points at rotor - # perimeter in wake and flow calculations. Defaults to *False*. - # """ - # for turbine in self.floris.farm.turbines: - # turbine.use_points_on_perimeter = use_points_on_perimeter - # turbine.initialize_turbine() - - # def set_gch(self, enable=True): - # """ - # Enable or disable Gauss-Curl Hybrid (GCH) functions - # :py:meth:`~.GaussianModel.calculate_VW`, - # :py:meth:`~.GaussianModel.yaw_added_recovery_correction`, and - # :py:attr:`~.VelocityDeflection.use_secondary_steering`. - - # Args: - # enable (bool, optional): Flag whether or not to implement flow - # corrections from GCH model. Defaults to *True*. - # """ - # self.set_gch_yaw_added_recovery(enable) - # self.set_gch_secondary_steering(enable) - - # def set_gch_yaw_added_recovery(self, enable=True): - # """ - # Enable or Disable yaw-added recovery (YAR) from the Gauss-Curl Hybrid - # (GCH) model and the control state of - # :py:meth:`~.GaussianModel.calculate_VW_velocities` and - # :py:meth:`~.GaussianModel.yaw_added_recovery_correction`. - - # Args: - # enable (bool, optional): Flag whether or not to implement yaw-added - # recovery from GCH model. Defaults to *True*. - # """ - # model_params = self.get_model_parameters() - # use_secondary_steering = model_params["Wake Deflection Parameters"]["use_secondary_steering"] - - # if enable: - # model_params["Wake Velocity Parameters"]["use_yaw_added_recovery"] = True - - # # If enabling be sure calc vw is on - # model_params["Wake Velocity Parameters"]["calculate_VW_velocities"] = True - - # if not enable: - # model_params["Wake Velocity Parameters"]["use_yaw_added_recovery"] = False - - # # If secondary steering is also off, disable calculate_VW_velocities - # if not use_secondary_steering: - # model_params["Wake Velocity Parameters"]["calculate_VW_velocities"] = False - - # self.set_model_parameters(model_params) - # self.reinitialize() - - # def set_gch_secondary_steering(self, enable=True): - # """ - # Enable or Disable secondary steering (SS) from the Gauss-Curl Hybrid - # (GCH) model and the control state of - # :py:meth:`~.GaussianModel.calculate_VW_velocities` and - # :py:attr:`~.VelocityDeflection.use_secondary_steering`. - - # Args: - # enable (bool, optional): Flag whether or not to implement secondary - # steering from GCH model. Defaults to *True*. - # """ - # model_params = self.get_model_parameters() - # use_yaw_added_recovery = model_params["Wake Velocity Parameters"]["use_yaw_added_recovery"] - - # if enable: - # model_params["Wake Deflection Parameters"]["use_secondary_steering"] = True - - # # If enabling be sure calc vw is on - # model_params["Wake Velocity Parameters"]["calculate_VW_velocities"] = True - - # if not enable: - # model_params["Wake Deflection Parameters"]["use_secondary_steering"] = False - - # # If yar is also off, disable calculate_VW_velocities - # if not use_yaw_added_recovery: - # model_params["Wake Velocity Parameters"]["calculate_VW_velocities"] = False - - # self.set_model_parameters(model_params) - # self.reinitialize() - - # def show_model_parameters( - # self, - # params=None, - # verbose=False, - # wake_velocity_model=True, - # wake_deflection_model=True, - # turbulence_model=False, - # ): - # """ - # Helper function to print the current wake model parameters and values. - # Shortcut to :py:meth:`~.tools.interface_utilities.show_params`. - - # Args: - # params (list, optional): Specific model parameters to be returned, - # supplied as a list of strings. If None, then returns all - # parameters. Defaults to None. - # verbose (bool, optional): If set to *True*, will return the - # docstrings for each parameter. Defaults to *False*. - # wake_velocity_model (bool, optional): If set to *True*, will return - # parameters from the wake_velocity model. If set to *False*, will - # exclude parameters from the wake velocity model. Defaults to - # *True*. - # wake_deflection_model (bool, optional): If set to *True*, will - # return parameters from the wake deflection model. If set to - # *False*, will exclude parameters from the wake deflection - # model. Defaults to *True*. - # turbulence_model (bool, optional): If set to *True*, will return - # parameters from the wake turbulence model. If set to *False*, - # will exclude parameters from the wake turbulence model. - # Defaults to *True*. - # """ - # show_params( - # self.floris.wake, - # params, - # verbose, - # wake_velocity_model, - # wake_deflection_model, - # turbulence_model, - # ) - - # def get_model_parameters( - # self, - # params=None, - # wake_velocity_model=True, - # wake_deflection_model=True, - # turbulence_model=True, - # ): - # """ - # Helper function to return the current wake model parameters and values. - # Shortcut to :py:meth:`~.tools.interface_utilities.get_params`. - - # Args: - # params (list, optional): Specific model parameters to be returned, - # supplied as a list of strings. If None, then returns all - # parameters. Defaults to None. - # wake_velocity_model (bool, optional): If set to *True*, will return - # parameters from the wake_velocity model. If set to *False*, will - # exclude parameters from the wake velocity model. Defaults to - # *True*. - # wake_deflection_model (bool, optional): If set to *True*, will - # return parameters from the wake deflection model. If set to - # *False*, will exclude parameters from the wake deflection - # model. Defaults to *True*. - # turbulence_model ([type], optional): If set to *True*, will return - # parameters from the wake turbulence model. If set to *False*, - # will exclude parameters from the wake turbulence model. - # Defaults to *True*. - - # Returns: - # dict: Dictionary containing model parameters and their values. - # """ - # model_params = get_params( - # self.floris.wake, params, wake_velocity_model, wake_deflection_model, turbulence_model - # ) - - # return model_params - - # def set_model_parameters(self, params, verbose=True): - # """ - # Helper function to set current wake model parameters. - # Shortcut to :py:meth:`~.tools.interface_utilities.set_params`. - - # Args: - # params (dict): Specific model parameters to be set, supplied as a - # dictionary of key:value pairs. - # verbose (bool, optional): If set to *True*, will print information - # about each model parameter that is changed. Defaults to *True*. - # """ - # self.floris.wake = set_params(self.floris.wake, params, verbose) - - - - - - - # def vis_layout( - # self, - # ax=None, - # show_wake_lines=False, - # limit_dist=None, - # turbine_face_north=False, - # one_index_turbine=False, - # black_and_white=False, - # ): - # """ - # Visualize the layout of the wind farm in the floris instance. - # Shortcut to :py:meth:`~.tools.layout_functions.visualize_layout`. - - # Args: - # ax (:py:class:`matplotlib.pyplot.axes`, optional): - # Figure axes. Defaults to None. - # show_wake_lines (bool, optional): Flag to control plotting of - # wake boundaries. Defaults to False. - # limit_dist (float, optional): Downstream limit to plot wakes. - # Defaults to None. - # turbine_face_north (bool, optional): Force orientation of wind - # turbines. Defaults to False. - # one_index_turbine (bool, optional): If *True*, 1st turbine is - # turbine 1. - # """ - # for i, turbine in enumerate(self.floris.farm.turbines): - # D = turbine.rotor_diameter - # break - # layout_x, layout_y = self.get_turbine_layout() - - # turbineLoc = build_turbine_loc(layout_x, layout_y) - - # # Show visualize the turbine layout - # visualize_layout( - # turbineLoc, - # D, - # ax=ax, - # show_wake_lines=show_wake_lines, - # limit_dist=limit_dist, - # turbine_face_north=turbine_face_north, - # one_index_turbine=one_index_turbine, - # black_and_white=black_and_white, - # ) - - # def show_flow_field(self, ax=None): - # """ - # Shortcut method to - # :py:meth:`~.tools.visualization.visualize_cut_plane`. - - # Args: - # ax (:py:class:`matplotlib.pyplot.axes` optional): - # Figure axes. Defaults to None. - # """ - # # Get horizontal plane at default height (hub-height) - # hor_plane = self.get_hor_plane() - - # # Plot and show - # if ax is None: - # fig, ax = plt.subplots() - # visualize_cut_plane(hor_plane, ax=ax) - # plt.show() - - - ## Functionality removed in v3 def set_rotor_diameter(self, rotor_diameter): diff --git a/floris/tools/floris_interface_legacy_reader.py b/floris/tools/floris_interface_legacy_reader.py index 093cbc5b4..ac9472dd1 100644 --- a/floris/tools/floris_interface_legacy_reader.py +++ b/floris/tools/floris_interface_legacy_reader.py @@ -188,6 +188,7 @@ def _convert_v24_dictionary_to_v3(dict_legacy): "rotor_diameter": tp["rotor_diameter"], "TSR": tp["TSR"], "power_thrust_table": tp["power_thrust_table"], + "ref_density_cp_ct": 1.225 # This was implicit in the former input file } return dict_floris, dict_turbine diff --git a/floris/tools/optimization/__init__.py b/floris/tools/optimization/__init__.py index f40bb816e..917eae2e7 100644 --- a/floris/tools/optimization/__init__.py +++ b/floris/tools/optimization/__init__.py @@ -1 +1 @@ -from . import other, scipy, pyoptsparse, yaw_optimization +from . import other, legacy, yaw_optimization, layout_optimization diff --git a/floris/tools/optimization/layout_optimization/__init__.py b/floris/tools/optimization/layout_optimization/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/floris/tools/optimization/layout_optimization/layout_optimization_base.py b/floris/tools/optimization/layout_optimization/layout_optimization_base.py new file mode 100644 index 000000000..db1480b7c --- /dev/null +++ b/floris/tools/optimization/layout_optimization/layout_optimization_base.py @@ -0,0 +1,114 @@ +# Copyright 2022 NREL + +# Licensed under the Apache License, Version 2.0 (the "License"); you may not +# use this file except in compliance with the License. You may obtain a copy of +# the License at http://www.apache.org/licenses/LICENSE-2.0 + +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, WITHOUT +# WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the +# License for the specific language governing permissions and limitations under +# the License. + +# See https://floris.readthedocs.io for documentation + +import numpy as np +import matplotlib.pyplot as plt +from shapely.geometry import Polygon, LineString + +from ....logging_manager import LoggerBase + +class LayoutOptimization(LoggerBase): + def __init__(self, fi, boundaries, min_dist=None, freq=None): + self.fi = fi.copy() + self.boundaries = boundaries + + self._boundary_polygon = Polygon(self.boundaries) + self._boundary_line = LineString(self.boundaries) + + self.xmin = np.min([tup[0] for tup in boundaries]) + self.xmax = np.max([tup[0] for tup in boundaries]) + self.ymin = np.min([tup[1] for tup in boundaries]) + self.ymax = np.max([tup[1] for tup in boundaries]) + + # If no minimum distance is provided, assume a value of 2 rotor diamters + if min_dist is None: + self.min_dist = 2 * self.rotor_diameter + else: + self.min_dist = min_dist + + # If freq is not provided, give equal weight to all wind conditions + if freq is None: + self.freq = np.ones((self.fi.floris.flow_field.n_wind_directions, self.fi.floris.flow_field.n_wind_speeds)) + self.freq = self.freq / self.freq.sum() + else: + self.freq = freq + + self.initial_AEP = fi.get_farm_AEP(self.freq) + + def __str__(self): + return "layout" + + def _norm(self, val, x1, x2): + return (val - x1) / (x2 - x1) + + def _unnorm(self, val, x1, x2): + return np.array(val) * (x2 - x1) + x1 + + # Public methods + + def optimize(self): + sol = self._optimize() + return sol + + def plot_layout_opt_results(self): + x_initial, y_initial, x_opt, y_opt = self._get_initial_and_final_locs() + + plt.figure(figsize=(9, 6)) + fontsize = 16 + plt.plot(x_initial, y_initial, "ob") + plt.plot(x_opt, y_opt, "or") + # plt.title('Layout Optimization Results', fontsize=fontsize) + plt.xlabel("x (m)", fontsize=fontsize) + plt.ylabel("y (m)", fontsize=fontsize) + plt.axis("equal") + plt.grid() + plt.tick_params(which="both", labelsize=fontsize) + plt.legend( + ["Old locations", "New locations"], + loc="lower center", + bbox_to_anchor=(0.5, 1.01), + ncol=2, + fontsize=fontsize, + ) + + verts = self.boundaries + for i in range(len(verts)): + if i == len(verts) - 1: + plt.plot([verts[i][0], verts[0][0]], [verts[i][1], verts[0][1]], "b") + else: + plt.plot( + [verts[i][0], verts[i + 1][0]], [verts[i][1], verts[i + 1][1]], "b" + ) + + plt.show() + + ########################################################################### + # Properties + ########################################################################### + + @property + def nturbs(self): + """ + This property returns the number of turbines in the FLORIS + object. + + Returns: + nturbs (int): The number of turbines in the FLORIS object. + """ + self._nturbs = self.fi.floris.farm.n_turbines + return self._nturbs + + @property + def rotor_diameter(self): + return self.fi.floris.farm.rotor_diameters_sorted[0][0][0] diff --git a/floris/tools/optimization/layout_optimization/layout_optimization_boundary_grid.py b/floris/tools/optimization/layout_optimization/layout_optimization_boundary_grid.py new file mode 100644 index 000000000..a7dfadf79 --- /dev/null +++ b/floris/tools/optimization/layout_optimization/layout_optimization_boundary_grid.py @@ -0,0 +1,628 @@ +# Copyright 2022 NREL + +# Licensed under the Apache License, Version 2.0 (the "License"); you may not +# use this file except in compliance with the License. You may obtain a copy of +# the License at http://www.apache.org/licenses/LICENSE-2.0 + +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, WITHOUT +# WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the +# License for the specific language governing permissions and limitations under +# the License. + +# See https://floris.readthedocs.io for documentation + + +import numpy as np +import matplotlib.pyplot as plt +from shapely.geometry import Point, Polygon, LineString +from scipy.spatial.distance import cdist + +from .layout_optimization_base import LayoutOptimization + +class LayoutOptimizationBoundaryGrid(LayoutOptimization): + def __init__( + self, + fi, + boundaries, + start, + x_spacing, + y_spacing, + shear, + rotation, + center_x, + center_y, + boundary_setback, + n_boundary_turbines=None, + boundary_spacing=None, + ): + self.fi = fi + + self.boundary_x = np.array([val[0] for val in boundaries]) + self.boundary_y = np.array([val[1] for val in boundaries]) + boundary = np.zeros((len(self.boundary_x), 2)) + boundary[:, 0] = self.boundary_x[:] + boundary[:, 1] = self.boundary_y[:] + self._boundary_polygon = Polygon(boundary) + + self.start = start + self.x_spacing = x_spacing + self.y_spacing = y_spacing + self.shear = shear + self.rotation = rotation + self.center_x = center_x + self.center_y = center_y + self.boundary_setback = boundary_setback + self.n_boundary_turbines = n_boundary_turbines + self.boundary_spacing = boundary_spacing + + def _discontinuous_grid( + self, + nrows, + ncols, + farm_width, + farm_height, + shear, + rotation, + center_x, + center_y, + shrink_boundary, + boundary_x, + boundary_y, + eps=1e-3, + ): + """ + Map from grid design variables to turbine x and y locations. Includes integer design variables and the formulation + results in a discontinous design space. + + TODO: shrink_boundary doesn't work well with concave boundaries, or with boundary angles less than 90 deg + + Args: + nrows (Int): number of rows in the grid. + ncols (Int): number of columns in the grid. + farm_width (Float): total grid width (before shear). + farm_height (Float): total grid height. + shear (Float): grid shear (rad). + rotation (Float): rotation about grid center (rad). + center_x (Float): location of grid x center. + center_y (Float): location of grid y center. + shrink_boundary (Float): how much to shrink the boundary that the grid can occupy. + boundary_x (Array(Float)): x boundary points. + boundary_y (Array(Float)): y boundary points. + + Returns: + grid_x (Array(Float)): turbine x locations. + grid_y (Array(Float)): turbine y locations. + """ + # create grid + nrows = int(nrows) + ncols = int(ncols) + xlocs = np.linspace(0.0, farm_width, ncols) + ylocs = np.linspace(0.0, farm_height, nrows) + y_spacing = ylocs[1] - ylocs[0] + nturbs = nrows * ncols + grid_x = np.zeros(nturbs) + grid_y = np.zeros(nturbs) + turb = 0 + for i in range(nrows): + for j in range(ncols): + grid_x[turb] = xlocs[j] + float(i) * y_spacing * np.tan(shear) + grid_y[turb] = ylocs[i] + turb += 1 + + # rotate + grid_x, grid_y = ( + np.cos(rotation) * grid_x - np.sin(rotation) * grid_y, + np.sin(rotation) * grid_x + np.cos(rotation) * grid_y, + ) + + # move center of grid + grid_x = (grid_x - np.mean(grid_x)) + center_x + grid_y = (grid_y - np.mean(grid_y)) + center_y + + # arrange the boundary + + # boundary = np.zeros((len(boundary_x),2)) + # boundary[:,0] = boundary_x[:] + # boundary[:,1] = boundary_y[:] + # poly = Polygon(boundary) + # centroid = poly.centroid + + # boundary[:,0] = (boundary_x[:]-centroid.x)*boundary_mult + centroid.x + # boundary[:,1] = (boundary_y[:]-centroid.y)*boundary_mult + centroid.y + # poly = Polygon(boundary) + + boundary = np.zeros((len(boundary_x), 2)) + boundary[:, 0] = boundary_x[:] + boundary[:, 1] = boundary_y[:] + poly = Polygon(boundary) + + if shrink_boundary != 0.0: + nBounds = len(boundary_x) + for i in range(nBounds): + point = Point(boundary_x[i] + eps, boundary_y[i]) + if poly.contains(point) is True or poly.touches(point) is True: + boundary[i, 0] = boundary_x[i] + shrink_boundary + else: + boundary[i, 0] = boundary_x[i] - shrink_boundary + + point = Point(boundary_x[i], boundary_y[i] + eps) + if poly.contains(point) is True or poly.touches(point) is True: + boundary[i, 1] = boundary_y[i] + shrink_boundary + else: + boundary[i, 1] = boundary_y[i] - shrink_boundary + + poly = Polygon(boundary) + + # get rid of points outside of boundary + index = 0 + for i in range(len(grid_x)): + point = Point(grid_x[index], grid_y[index]) + if poly.contains(point) is False and poly.touches(point) is False: + grid_x = np.delete(grid_x, index) + grid_y = np.delete(grid_y, index) + else: + index += 1 + + return grid_x, grid_y + + def _discrete_grid( + self, + x_spacing, + y_spacing, + shear, + rotation, + center_x, + center_y, + boundary_setback, + boundary_poly + ): + """ + returns grid turbine layout. Assumes the turbines fill the entire plant area + + Args: + x_spacing (Float): grid spacing in the unrotated x direction (m) + y_spacing (Float): grid spacing in the unrotated y direction (m) + shear (Float): grid shear (rad) + rotation (Float): grid rotation (rad) + center_x (Float): the x coordinate of the grid center (m) + center_y (Float): the y coordinate of the grid center (m) + boundary_poly (Polygon): a shapely Polygon of the wind plant boundary + + Returns + return_x (Array(Float)): turbine x locations + return_y (Array(Float)): turbine y locations + """ + + shrunk_poly = boundary_poly.buffer(-boundary_setback) + if shrunk_poly.area <= 0: + return np.array([]), np.array([]) + # create grid + minx, miny, maxx, maxy = shrunk_poly.bounds + width = maxx-minx + height = maxy-miny + + center_point = Point((center_x,center_y)) + poly_to_center = center_point.distance(shrunk_poly.centroid) + + width = np.max([width,poly_to_center]) + height = np.max([height,poly_to_center]) + nrows = int(np.max([width,height])/np.min([x_spacing,y_spacing]))*2 + 1 + ncols = nrows + + xlocs = np.arange(0,ncols)*x_spacing + ylocs = np.arange(0,nrows)*y_spacing + row_number = np.arange(0,nrows) + + d = np.array([i for x in xlocs for i in row_number]) + layout_x = np.array([x for x in xlocs for y in ylocs]) + d*y_spacing*np.tan(shear) + layout_y = np.array([y for x in xlocs for y in ylocs]) + + # rotate + rotate_x = np.cos(rotation)*layout_x - np.sin(rotation)*layout_y + rotate_y = np.sin(rotation)*layout_x + np.cos(rotation)*layout_y + + # move center of grid + rotate_x = (rotate_x - np.mean(rotate_x)) + center_x + rotate_y = (rotate_y - np.mean(rotate_y)) + center_y + + # get rid of points outside of boundary polygon + meets_constraints = np.zeros(len(rotate_x),dtype=bool) + for i in range(len(rotate_x)): + pt = Point(rotate_x[i],rotate_y[i]) + if shrunk_poly.contains(pt) or shrunk_poly.touches(pt): + meets_constraints[i] = True + + # arrange final x,y points + return_x = rotate_x[meets_constraints] + return_y = rotate_y[meets_constraints] + + return return_x, return_y + + def find_lengths(self, x, y, npoints): + length = np.zeros(len(x) - 1) + for i in range(npoints): + length[i] = np.sqrt((x[i + 1] - x[i]) ** 2 + (y[i + 1] - y[i]) ** 2) + return length + + # def _place_boundary_turbines(self, n_boundary_turbs, start, boundary_x, boundary_y): + # """ + # Place turbines equally spaced traversing the perimiter if the wind farm along the boundary + + # Args: + # n_boundary_turbs (Int): number of turbines to be placed on the boundary + # start (Float): where the first turbine should be placed + # boundary_x (Array(Float)): x boundary points + # boundary_y (Array(Float)): y boundary points + + # Returns + # layout_x (Array(Float)): turbine x locations + # layout_y (Array(Float)): turbine y locations + # """ + + # # check if the boundary is closed, correct if not + # if boundary_x[-1] != boundary_x[0] or boundary_y[-1] != boundary_y[0]: + # boundary_x = np.append(boundary_x, boundary_x[0]) + # boundary_y = np.append(boundary_y, boundary_y[0]) + + # # make the boundary + # boundary = np.zeros((len(boundary_x), 2)) + # boundary[:, 0] = boundary_x[:] + # boundary[:, 1] = boundary_y[:] + # poly = Polygon(boundary) + # perimeter = poly.length + + # # get the flattened turbine locations + # spacing = perimeter / float(n_boundary_turbs) + # flattened_locs = np.linspace(start, perimeter + start - spacing, n_boundary_turbs) + + # # set all of the flattened values between 0 and the perimeter + # for i in range(n_boundary_turbs): + # while flattened_locs[i] < 0.0: + # flattened_locs[i] += perimeter + # if flattened_locs[i] > perimeter: + # flattened_locs[i] = flattened_locs[i] % perimeter + + # # place the turbines around the perimeter + # nBounds = len(boundary_x) + # layout_x = np.zeros(n_boundary_turbs) + # layout_y = np.zeros(n_boundary_turbs) + + # lenBound = np.zeros(nBounds - 1) + # for i in range(nBounds - 1): + # lenBound[i] = Point(boundary[i]).distance(Point(boundary[i + 1])) + # for i in range(n_boundary_turbs): + # for j in range(nBounds - 1): + # if flattened_locs[i] < sum(lenBound[0 : j + 1]): + # layout_x[i] = ( + # boundary_x[j] + # + (boundary_x[j + 1] - boundary_x[j]) + # * (flattened_locs[i] - sum(lenBound[0:j])) + # / lenBound[j] + # ) + # layout_y[i] = ( + # boundary_y[j] + # + (boundary_y[j + 1] - boundary_y[j]) + # * (flattened_locs[i] - sum(lenBound[0:j])) + # / lenBound[j] + # ) + # break + + # return layout_x, layout_y + + def _place_boundary_turbines(self, start, boundary_poly, nturbs=None, spacing=None): + xBounds, yBounds = boundary_poly.boundary.coords.xy + + if xBounds[-1] != xBounds[0]: + xBounds = np.append(xBounds, xBounds[0]) + yBounds = np.append(yBounds, yBounds[0]) + + nBounds = len(xBounds) + lenBound = self.find_lengths(xBounds, yBounds, len(xBounds) - 1) + circumference = sum(lenBound) + + if nturbs is not None and spacing is None: + # When the number of boundary turbines is specified + nturbs = int(nturbs) + bound_loc = np.linspace( + start, start + circumference - circumference / float(nturbs), nturbs + ) + elif spacing is not None and nturbs is None: + # When the spacing of boundary turbines is specified + nturbs = int(np.floor(circumference / spacing)) + bound_loc = np.linspace( + start, start + circumference - circumference / float(nturbs), nturbs + ) + else: + raise ValueError("Please specify either nturbs or spacing.") + + x = np.zeros(nturbs) + y = np.zeros(nturbs) + + if spacing is None: + # When the number of boundary turbines is specified + for i in range(nturbs): + if bound_loc[i] > circumference: + bound_loc[i] = bound_loc[i] % circumference + while bound_loc[i] < 0.0: + bound_loc[i] += circumference + for i in range(nturbs): + done = False + for j in range(nBounds): + if done == False: + if bound_loc[i] < sum(lenBound[0:j+1]): + point_x = xBounds[j] + (xBounds[j+1]-xBounds[j])*(bound_loc[i]-sum(lenBound[0:j]))/lenBound[j] + point_y = yBounds[j] + (yBounds[j+1]-yBounds[j])*(bound_loc[i]-sum(lenBound[0:j]))/lenBound[j] + done = True + x[i] = point_x + y[i] = point_y + else: + # When the spacing of boundary turbines is specified + additional_space = 0.0 + end_loop = False + for i in range(nturbs): + done = False + for j in range(nBounds): + while done == False: + dist = start + i*spacing + additional_space + if dist < sum(lenBound[0:j+1]): + point_x = xBounds[j] + (xBounds[j+1]-xBounds[j])*(dist -sum(lenBound[0:j]))/lenBound[j] + point_y = yBounds[j] + (yBounds[j+1]-yBounds[j])*(dist -sum(lenBound[0:j]))/lenBound[j] + + # Check if turbine is too close to previous turbine + if i > 0: + # Check if turbine just placed is to close to first turbine + min_dist = cdist([(point_x, point_y)], [(x[0], y[0])]) + if min_dist < spacing: + # TODO: make this more robust; pass is needed if 2nd turbine is too close to the first + if i == 1: + pass + else: + end_loop = True + ii = i + break + + min_dist = cdist([(point_x, point_y)], [(x[i-1], y[i-1])]) + if min_dist < spacing: + additional_space += 1.0 + else: + done = True + x[i] = point_x + y[i] = point_y + elif i == 0: + # If first turbine, just add initial turbine point + done = True + x[i] = point_x + y[i] = point_y + else: + pass + else: + break + if end_loop == True: + break + if end_loop == True: + x = x[:ii] + y = y[:ii] + break + return x, y + + def _place_boundary_turbines_with_specified_spacing(self, spacing, start, boundary_x, boundary_y): + """ + Place turbines equally spaced traversing the perimiter if the wind farm along the boundary + + Args: + n_boundary_turbs (Int): number of turbines to be placed on the boundary + start (Float): where the first turbine should be placed + boundary_x (Array(Float)): x boundary points + boundary_y (Array(Float)): y boundary points + + Returns + layout_x (Array(Float)): turbine x locations + layout_y (Array(Float)): turbine y locations + """ + + # check if the boundary is closed, correct if not + if boundary_x[-1] != boundary_x[0] or boundary_y[-1] != boundary_y[0]: + boundary_x = np.append(boundary_x, boundary_x[0]) + boundary_y = np.append(boundary_y, boundary_y[0]) + + # make the boundary + boundary = np.zeros((len(boundary_x), 2)) + boundary[:, 0] = boundary_x[:] + boundary[:, 1] = boundary_y[:] + poly = Polygon(boundary) + perimeter = poly.length + + # get the flattened turbine locations + n_boundary_turbs = int(perimeter / float(spacing)) + flattened_locs = np.linspace(start, perimeter + start - spacing, n_boundary_turbs) + + # set all of the flattened values between 0 and the perimeter + for i in range(n_boundary_turbs): + while flattened_locs[i] < 0.0: + flattened_locs[i] += perimeter + if flattened_locs[i] > perimeter: + flattened_locs[i] = flattened_locs[i] % perimeter + + # place the turbines around the perimeter + nBounds = len(boundary_x) + layout_x = np.zeros(n_boundary_turbs) + layout_y = np.zeros(n_boundary_turbs) + + lenBound = np.zeros(nBounds - 1) + for i in range(nBounds - 1): + lenBound[i] = Point(boundary[i]).distance(Point(boundary[i + 1])) + for i in range(n_boundary_turbs): + for j in range(nBounds - 1): + if flattened_locs[i] < sum(lenBound[0 : j + 1]): + layout_x[i] = ( + boundary_x[j] + + (boundary_x[j + 1] - boundary_x[j]) + * (flattened_locs[i] - sum(lenBound[0:j])) + / lenBound[j] + ) + layout_y[i] = ( + boundary_y[j] + + (boundary_y[j + 1] - boundary_y[j]) + * (flattened_locs[i] - sum(lenBound[0:j])) + / lenBound[j] + ) + break + + return layout_x, layout_y + + def boundary_grid( + self, + start, + x_spacing, + y_spacing, + shear, + rotation, + center_x, + center_y, + boundary_setback, + n_boundary_turbines=None, + boundary_spacing=None, + ): + """ + Place turbines equally spaced traversing the perimiter if the wind farm along the boundary + + Args: + n_boundary_turbs,start: boundary variables + nrows,ncols,farm_width,farm_height,shear,rotation,center_x,center_y,shrink_boundary,eps: grid variables + boundary_x,boundary_y: boundary points + + Returns + layout_x (Array(Float)): turbine x locations + layout_y (Array(Float)): turbine y locations + """ + + boundary_turbines_x, boundary_turbines_y = self._place_boundary_turbines( + start, self._boundary_polygon, nturbs=n_boundary_turbines, spacing=boundary_spacing + ) + # boundary_turbines_x, boundary_turbines_y = self._place_boundary_turbines_with_specified_spacing( + # spacing, start, boundary_x, boundary_y + # ) + + # grid_turbines_x, grid_turbines_y = self._discontinuous_grid( + # nrows, + # ncols, + # farm_width, + # farm_height, + # shear, + # rotation, + # center_x, + # center_y, + # shrink_boundary, + # boundary_x, + # boundary_y, + # eps=eps, + # ) + + grid_turbines_x, grid_turbines_y = self._discrete_grid( + x_spacing, + y_spacing, + shear, + rotation, + center_x, + center_y, + boundary_setback, + self._boundary_polygon, + ) + + layout_x = np.append(boundary_turbines_x, grid_turbines_x) + layout_y = np.append(boundary_turbines_y, grid_turbines_y) + + return layout_x, layout_y + + def reinitialize_bg( + self, + n_boundary_turbines=None, + start=None, + x_spacing=None, + y_spacing=None, + shear=None, + rotation=None, + center_x=None, + center_y=None, + boundary_setback=None, + boundary_x=None, + boundary_y=None, + boundary_spacing=None, + ): + + if n_boundary_turbines is not None: + self.n_boundary_turbines = n_boundary_turbines + if start is not None: + self.start = start + if x_spacing is not None: + self.x_spacing = x_spacing + if y_spacing is not None: + self.y_spacing = y_spacing + if shear is not None: + self.shear = shear + if rotation is not None: + self.rotation = rotation + if center_x is not None: + self.center_x = center_x + if center_y is not None: + self.center_y = center_y + if boundary_setback is not None: + self.boundary_setback = boundary_setback + if boundary_x is not None: + self.boundary_x = boundary_x + if boundary_y is not None: + self.boundary_y = boundary_y + if boundary_spacing is not None: + self.boundary_spacing = boundary_spacing + + def reinitialize_xy(self): + layout_x, layout_y = self.boundary_grid( + self.start, + self.x_spacing, + self.y_spacing, + self.shear, + self.rotation, + self.center_x, + self.center_y, + self.boundary_setback, + self.n_boundary_turbines, + self.boundary_spacing, + ) + + self.fi.reinitialize(layout=(layout_x, layout_y)) + + def plot_layout(self): + plt.figure(figsize=(9, 6)) + fontsize = 16 + + plt.plot(self.fi.layout_x, self.fi.layout_y, "ob") + # plt.plot(locsx, locsy, "or") + + plt.xlabel("x (m)", fontsize=fontsize) + plt.ylabel("y (m)", fontsize=fontsize) + plt.axis("equal") + plt.grid() + plt.tick_params(which="both", labelsize=fontsize) + + plt.show() + + def space_constraint(self, x, y, min_dist, rho=500): + # Calculate distances between turbines + locs = np.vstack((x, y)).T + distances = cdist(locs, locs) + arange = np.arange(distances.shape[0]) + distances[arange, arange] = 1e10 + dist = np.min(distances, axis=0) + + g = 1 - np.array(dist) / min_dist + + # Following code copied from OpenMDAO KSComp(). + # Constraint is satisfied when KS_constraint <= 0 + g_max = np.max(np.atleast_2d(g), axis=-1)[:, np.newaxis] + g_diff = g - g_max + exponents = np.exp(rho * g_diff) + summation = np.sum(exponents, axis=-1)[:, np.newaxis] + KS_constraint = g_max + 1.0 / rho * np.log(summation) + + return KS_constraint[0][0], dist diff --git a/floris/tools/optimization/layout_optimization/layout_optimization_pyoptsparse.py b/floris/tools/optimization/layout_optimization/layout_optimization_pyoptsparse.py new file mode 100644 index 000000000..83aaa23ee --- /dev/null +++ b/floris/tools/optimization/layout_optimization/layout_optimization_pyoptsparse.py @@ -0,0 +1,183 @@ +# Copyright 2022 NREL + +# Licensed under the Apache License, Version 2.0 (the "License"); you may not +# use this file except in compliance with the License. You may obtain a copy of +# the License at http://www.apache.org/licenses/LICENSE-2.0 + +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, WITHOUT +# WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the +# License for the specific language governing permissions and limitations under +# the License. + +# See https://floris.readthedocs.io for documentation + + +import numpy as np +import matplotlib.pyplot as plt +from shapely.geometry import Point +from scipy.spatial.distance import cdist + +from .layout_optimization_base import LayoutOptimization + +class LayoutOptimizationPyOptSparse(LayoutOptimization): + def __init__( + self, + fi, + boundaries, + min_dist=None, + freq=None, + solver=None, + optOptions=None, + timeLimit=None, + storeHistory='hist.hist', + hotStart=None + ): + super().__init__(fi, boundaries, min_dist=min_dist, freq=freq) + + self.x0 = self._norm(self.fi.layout_x, self.xmin, self.xmax) + self.y0 = self._norm(self.fi.layout_y, self.ymin, self.ymax) + + self.storeHistory = storeHistory + self.timeLimit = timeLimit + self.hotStart = hotStart + + try: + import pyoptsparse + except ImportError: + err_msg = ( + "It appears you do not have pyOptSparse installed. " + + "Please refer to https://pyoptsparse.readthedocs.io/ for " + + "guidance on how to properly install the module." + ) + self.logger.error(err_msg, stack_info=True) + raise ImportError(err_msg) + + # Insantiate ptOptSparse optimization object with name and objective function + self.optProb = pyoptsparse.Optimization('layout', self._obj_func) + + self.optProb = self.add_var_group(self.optProb) + self.optProb = self.add_con_group(self.optProb) + self.optProb.addObj("obj") + + if solver is not None: + self.solver = solver + print("Setting up optimization with user's choice of solver: ", self.solver) + else: + self.solver = "SLSQP" + print("Setting up optimization with default solver: SLSQP.") + if optOptions is not None: + self.optOptions = optOptions + else: + if self.solver == "SNOPT": + self.optOptions = {"Major optimality tolerance": 1e-7} + else: + self.optOptions = {} + + exec("self.opt = pyoptsparse." + self.solver + "(options=self.optOptions)") + + def _optimize(self): + if hasattr(self, "_sens"): + self.sol = self.opt(self.optProb, sens=self._sens) + else: + if self.timeLimit is not None: + self.sol = self.opt(self.optProb, sens="CDR", storeHistory=self.storeHistory, timeLimit=self.timeLimit, hotStart=self.hotStart) + else: + self.sol = self.opt(self.optProb, sens="CDR", storeHistory=self.storeHistory, hotStart=self.hotStart) + return self.sol + + def _obj_func(self, varDict): + # Parse the variable dictionary + self.parse_opt_vars(varDict) + + # Update turbine map with turbince locations + self.fi.reinitialize(layout_x = self.x, layout_y = self.y) + + # Compute the objective function + funcs = {} + funcs["obj"] = ( + -1 * self.fi.get_farm_AEP(self.freq) / self.initial_AEP + ) + + # Compute constraints, if any are defined for the optimization + funcs = self.compute_cons(funcs, self.x, self.y) + + fail = False + return funcs, fail + + # Optionally, the user can supply the optimization with gradients + # def _sens(self, varDict, funcs): + # funcsSens = {} + # fail = False + # return funcsSens, fail + + def parse_opt_vars(self, varDict): + self.x = self._unnorm(varDict["x"], self.xmin, self.xmax) + self.y = self._unnorm(varDict["y"], self.ymin, self.ymax) + + def parse_sol_vars(self, sol): + self.x = list(self._unnorm(sol.getDVs()["x"], self.xmin, self.xmax))[0] + self.y = list(self._unnorm(sol.getDVs()["y"], self.ymin, self.ymax))[1] + + def add_var_group(self, optProb): + optProb.addVarGroup( + "x", self.nturbs, varType="c", lower=0.0, upper=1.0, value=self.x0 + ) + optProb.addVarGroup( + "y", self.nturbs, varType="c", lower=0.0, upper=1.0, value=self.y0 + ) + + return optProb + + def add_con_group(self, optProb): + optProb.addConGroup("boundary_con", self.nturbs, upper=0.0) + optProb.addConGroup("spacing_con", 1, upper=0.0) + + return optProb + + def compute_cons(self, funcs, x, y): + funcs["boundary_con"] = self.distance_from_boundaries(x, y) + funcs["spacing_con"] = self.space_constraint(x, y) + + return funcs + + def space_constraint(self, x, y, rho=500): + # Calculate distances between turbines + locs = np.vstack((x, y)).T + distances = cdist(locs, locs) + arange = np.arange(distances.shape[0]) + distances[arange, arange] = 1e10 + dist = np.min(distances, axis=0) + + g = 1 - np.array(dist) / self.min_dist + + # Following code copied from OpenMDAO KSComp(). + # Constraint is satisfied when KS_constraint <= 0 + g_max = np.max(np.atleast_2d(g), axis=-1)[:, np.newaxis] + g_diff = g - g_max + exponents = np.exp(rho * g_diff) + summation = np.sum(exponents, axis=-1)[:, np.newaxis] + KS_constraint = g_max + 1.0 / rho * np.log(summation) + + return KS_constraint[0][0] + + def distance_from_boundaries(self, x, y): + boundary_con = np.zeros(self.nturbs) + for i in range(self.nturbs): + loc = Point(x[i], y[i]) + boundary_con[i] = loc.distance(self._boundary_line) + if self._boundary_polygon.contains(loc)==True: + boundary_con[i] *= -1.0 + + return boundary_con + + def _get_initial_and_final_locs(self): + x_initial = self._unnorm(self.x0, self.xmin, self.xmax) + y_initial = self._unnorm(self.y0, self.ymin, self.ymax) + x_opt, y_opt = self.get_optimized_locs() + return x_initial, y_initial, x_opt, y_opt + + def get_optimized_locs(self): + x_opt = self._unnorm(self.sol.getDVs()["x"], self.xmin, self.xmax) + y_opt = self._unnorm(self.sol.getDVs()["y"], self.ymin, self.ymax) + return x_opt, y_opt diff --git a/floris/tools/optimization/layout_optimization/layout_optimization_pyoptsparse_spread.py b/floris/tools/optimization/layout_optimization/layout_optimization_pyoptsparse_spread.py new file mode 100644 index 000000000..772fa0fab --- /dev/null +++ b/floris/tools/optimization/layout_optimization/layout_optimization_pyoptsparse_spread.py @@ -0,0 +1,218 @@ +# Copyright 2022 NREL + +# Licensed under the Apache License, Version 2.0 (the "License"); you may not +# use this file except in compliance with the License. You may obtain a copy of +# the License at http://www.apache.org/licenses/LICENSE-2.0 + +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, WITHOUT +# WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the +# License for the specific language governing permissions and limitations under +# the License. + +# See https://floris.readthedocs.io for documentation + + +import numpy as np +import matplotlib.pyplot as plt +from shapely.geometry import Point +from scipy.spatial.distance import cdist + +from .layout_optimization_base import LayoutOptimization + +class LayoutOptimizationPyOptSparse(LayoutOptimization): + def __init__( + self, + fi, + boundaries, + min_dist=None, + freq=None, + solver=None, + optOptions=None, + timeLimit=None, + storeHistory='hist.hist', + hotStart=None + ): + super().__init__(fi, boundaries, min_dist=min_dist, freq=freq) + self._reinitialize(solver=solver, optOptions=optOptions) + + self.storeHistory = storeHistory + self.timeLimit = timeLimit + self.hotStart = hotStart + + def _reinitialize(self, solver=None, optOptions=None): + try: + import pyoptsparse + except ImportError: + err_msg = ( + "It appears you do not have pyOptSparse installed. " + + "Please refer to https://pyoptsparse.readthedocs.io/ for " + + "guidance on how to properly install the module." + ) + self.logger.error(err_msg, stack_info=True) + raise ImportError(err_msg) + + # Insantiate ptOptSparse optimization object with name and objective function + self.optProb = pyoptsparse.Optimization('layout', self._obj_func) + + self.optProb = self.add_var_group(self.optProb) + self.optProb = self.add_con_group(self.optProb) + self.optProb.addObj("obj") + + if solver is not None: + self.solver = solver + print("Setting up optimization with user's choice of solver: ", self.solver) + else: + self.solver = "SLSQP" + print("Setting up optimization with default solver: SLSQP.") + if optOptions is not None: + self.optOptions = optOptions + else: + if self.solver == "SNOPT": + self.optOptions = {"Major optimality tolerance": 1e-7} + else: + self.optOptions = {} + + exec("self.opt = pyoptsparse." + self.solver + "(options=self.optOptions)") + + def _optimize(self): + if hasattr(self, "_sens"): + self.sol = self.opt(self.optProb, sens=self._sens) + else: + if self.timeLimit is not None: + self.sol = self.opt(self.optProb, sens="CDR", storeHistory=self.storeHistory, timeLimit=self.timeLimit, hotStart=self.hotStart) + else: + self.sol = self.opt(self.optProb, sens="CDR", storeHistory=self.storeHistory, hotStart=self.hotStart) + return self.sol + + def _obj_func(self, varDict): + # Parse the variable dictionary + self.parse_opt_vars(varDict) + + # Update turbine map with turbince locations + # self.fi.reinitialize(layout=[self.x, self.y]) + # self.fi.calculate_wake() + + # Compute the objective function + funcs = {} + funcs["obj"] = ( + -1 * self.mean_distance(self.x, self.y) + # -1 * np.sum(self.fi.get_farm_power() * self.freq * 8760) / self.initial_AEP + ) + + # Compute constraints, if any are defined for the optimization + funcs = self.compute_cons(funcs, self.x, self.y) + + fail = False + return funcs, fail + + # Optionally, the user can supply the optimization with gradients + # def _sens(self, varDict, funcs): + # funcsSens = {} + # fail = False + # return funcsSens, fail + + def parse_opt_vars(self, varDict): + self.x = self._unnorm(varDict["x"], self.xmin, self.xmax) + self.y = self._unnorm(varDict["y"], self.ymin, self.ymax) + + def parse_sol_vars(self, sol): + self.x = list(self._unnorm(sol.getDVs()["x"], self.xmin, self.xmax))[0] + self.y = list(self._unnorm(sol.getDVs()["y"], self.ymin, self.ymax))[1] + + def add_var_group(self, optProb): + optProb.addVarGroup( + "x", self.nturbs, type="c", lower=0.0, upper=1.0, value=self.x0 + ) + optProb.addVarGroup( + "y", self.nturbs, type="c", lower=0.0, upper=1.0, value=self.y0 + ) + + return optProb + + def add_con_group(self, optProb): + optProb.addConGroup("boundary_con", self.nturbs, upper=0.0) + optProb.addConGroup("spacing_con", 1, upper=0.0) + + return optProb + + def compute_cons(self, funcs, x, y): + funcs["boundary_con"] = self.distance_from_boundaries(x, y) + funcs["spacing_con"] = self.space_constraint(x, y) + + return funcs + + def mean_distance(self, x, y): + + locs = np.vstack((x, y)).T + distances = cdist(locs, locs) + return np.mean(distances) + + + def space_constraint(self, x, y, rho=500): + # Calculate distances between turbines + locs = np.vstack((x, y)).T + distances = cdist(locs, locs) + arange = np.arange(distances.shape[0]) + distances[arange, arange] = 1e10 + dist = np.min(distances, axis=0) + + g = 1 - np.array(dist) / self.min_dist + + # Following code copied from OpenMDAO KSComp(). + # Constraint is satisfied when KS_constraint <= 0 + g_max = np.max(np.atleast_2d(g), axis=-1)[:, np.newaxis] + g_diff = g - g_max + exponents = np.exp(rho * g_diff) + summation = np.sum(exponents, axis=-1)[:, np.newaxis] + KS_constraint = g_max + 1.0 / rho * np.log(summation) + + return KS_constraint[0][0] + + def distance_from_boundaries(self, x, y): + boundary_con = np.zeros(self.nturbs) + for i in range(self.nturbs): + loc = Point(x[i], y[i]) + boundary_con[i] = loc.distance(self.boundary_line) + if self.boundary_polygon.contains(loc)==True: + boundary_con[i] *= -1.0 + + return boundary_con + + def plot_layout_opt_results(self): + """ + Method to plot the old and new locations of the layout opitimization. + """ + locsx = self._unnorm(self.sol.getDVs()["x"], self.xmin, self.xmax) + locsy = self._unnorm(self.sol.getDVs()["y"], self.ymin, self.ymax) + x0 = self._unnorm(self.x0, self.xmin, self.xmax) + y0 = self._unnorm(self.y0, self.ymin, self.ymax) + + plt.figure(figsize=(9, 6)) + fontsize = 16 + plt.plot(x0, y0, "ob") + plt.plot(locsx, locsy, "or") + # plt.title('Layout Optimization Results', fontsize=fontsize) + plt.xlabel("x (m)", fontsize=fontsize) + plt.ylabel("y (m)", fontsize=fontsize) + plt.axis("equal") + plt.grid() + plt.tick_params(which="both", labelsize=fontsize) + plt.legend( + ["Old locations", "New locations"], + loc="lower center", + bbox_to_anchor=(0.5, 1.01), + ncol=2, + fontsize=fontsize, + ) + + verts = self.boundaries + for i in range(len(verts)): + if i == len(verts) - 1: + plt.plot([verts[i][0], verts[0][0]], [verts[i][1], verts[0][1]], "b") + else: + plt.plot( + [verts[i][0], verts[i + 1][0]], [verts[i][1], verts[i + 1][1]], "b" + ) + + plt.show() diff --git a/floris/tools/optimization/layout_optimization/layout_optimization_scipy.py b/floris/tools/optimization/layout_optimization/layout_optimization_scipy.py new file mode 100644 index 000000000..bd2501659 --- /dev/null +++ b/floris/tools/optimization/layout_optimization/layout_optimization_scipy.py @@ -0,0 +1,232 @@ +# Copyright 2022 NREL + +# Licensed under the Apache License, Version 2.0 (the "License"); you may not +# use this file except in compliance with the License. You may obtain a copy of +# the License at http://www.apache.org/licenses/LICENSE-2.0 + +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, WITHOUT +# WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the +# License for the specific language governing permissions and limitations under +# the License. + +# See https://floris.readthedocs.io for documentation + +import numpy as np +import matplotlib.pyplot as plt +from scipy.optimize import minimize +from shapely.geometry import Point +from scipy.spatial.distance import cdist + +from .layout_optimization_base import LayoutOptimization + +class LayoutOptimizationScipy(LayoutOptimization): + def __init__( + self, + fi, + boundaries, + freq=None, + bnds=None, + min_dist=None, + solver='SLSQP', + optOptions=None, + ): + """ + _summary_ + + Args: + fi (_type_): _description_ + boundaries (iterable(float, float)): Pairs of x- and y-coordinates + that represent the boundary's vertices (m). + freq (np.array): An array of the frequencies of occurance + correponding to each pair of wind direction and wind speed + values. If None, equal weight is given to each pair of wind conditions + Defaults to None. + bnds (iterable, optional): Bounds for the optimization + variables (pairs of min/max values for each variable (m)). If + none are specified, they are set to 0 and 1. Defaults to None. + min_dist (float, optional): The minimum distance to be maintained + between turbines during the optimization (m). If not specified, + initializes to 2 rotor diameters. Defaults to None. + solver (str, optional): Sets the solver used by Scipy. Defaults to 'SLSQP'. + optOptions (dict, optional): Dicitonary for setting the + optimization options. Defaults to None. + """ + super().__init__(fi, boundaries, min_dist=min_dist, freq=freq) + + self.boundaries_norm = [ + [ + self._norm(val[0], self.xmin, self.xmax), + self._norm(val[1], self.ymin, self.ymax), + ] + for val in self.boundaries + ] + self.x0 = [ + self._norm(x, self.xmin, self.xmax) + for x in self.fi.layout_x + ] + [ + self._norm(y, self.ymin, self.ymax) + for y in self.fi.layout_y + ] + if bnds is not None: + self.bnds = bnds + else: + self._set_opt_bounds() + if solver is not None: + self.solver = solver + if optOptions is not None: + self.optOptions = optOptions + else: + self.optOptions = {"maxiter": 100, "disp": True, "iprint": 2, "ftol": 1e-9, "eps":0.01} + + self._generate_constraints() + + + # Private methods + + def _optimize(self): + self.residual_plant = minimize( + self._obj_func, + self.x0, + method=self.solver, + bounds=self.bnds, + constraints=self.cons, + options=self.optOptions, + ) + + return self.residual_plant.x + + def _obj_func(self, locs): + locs_unnorm = [ + self._unnorm(valx, self.xmin, self.xmax) + for valx in locs[0 : self.nturbs] + ] + [ + self._unnorm(valy, self.ymin, self.ymax) + for valy in locs[self.nturbs : 2 * self.nturbs] + ] + self._change_coordinates(locs_unnorm) + return -1 * self.fi.get_farm_AEP(self.freq) / self.initial_AEP + + def _change_coordinates(self, locs): + # Parse the layout coordinates + layout_x = locs[0 : self.nturbs] + layout_y = locs[self.nturbs : 2 * self.nturbs] + + # Update the turbine map in floris + self.fi.reinitialize(layout_x=layout_x, layout_y=layout_y) + + def _generate_constraints(self): + tmp1 = { + "type": "ineq", + "fun": lambda x, *args: self._space_constraint(x), + } + tmp2 = { + "type": "ineq", + "fun": lambda x: self._distance_from_boundaries(x), + } + + self.cons = [tmp1, tmp2] + + def _set_opt_bounds(self): + self.bnds = [(0.0, 1.0) for _ in range(2 * self.nturbs)] + + def _space_constraint(self, x_in, rho=500): + x = [ + self._unnorm(valx, self.xmin, self.xmax) + for valx in x_in[0 : self.nturbs] + ] + y = [ + self._unnorm(valy, self.ymin, self.ymax) + for valy in x_in[self.nturbs : 2 * self.nturbs] + ] + + # Calculate distances between turbines + locs = np.vstack((x, y)).T + distances = cdist(locs, locs) + arange = np.arange(distances.shape[0]) + distances[arange, arange] = 1e10 + dist = np.min(distances, axis=0) + + g = 1 - np.array(dist) / self.min_dist + + # Following code copied from OpenMDAO KSComp(). + # Constraint is satisfied when KS_constraint <= 0 + g_max = np.max(np.atleast_2d(g), axis=-1)[:, np.newaxis] + g_diff = g - g_max + exponents = np.exp(rho * g_diff) + summation = np.sum(exponents, axis=-1)[:, np.newaxis] + KS_constraint = g_max + 1.0 / rho * np.log(summation) + + return -1*KS_constraint[0][0] + + def _distance_from_boundaries(self, x_in): + x = [ + self._unnorm(valx, self.xmin, self.xmax) + for valx in x_in[0 : self.nturbs] + ] + y = [ + self._unnorm(valy, self.ymin, self.ymax) + for valy in x_in[self.nturbs : 2 * self.nturbs] + ] + boundary_con = np.zeros(self.nturbs) + for i in range(self.nturbs): + loc = Point(x[i], y[i]) + boundary_con[i] = loc.distance(self._boundary_line) + if self._boundary_polygon.contains(loc)==True: + boundary_con[i] *= 1.0 + + return boundary_con + + def _get_initial_and_final_locs(self): + x_initial = [ + self._unnorm(valx, self.xmin, self.xmax) + for valx in self.x0[0 : self.nturbs] + ] + y_initial = [ + self._unnorm(valy, self.ymin, self.ymax) + for valy in self.x0[self.nturbs : 2 * self.nturbs] + ] + x_opt = [ + self._unnorm(valx, self.xmin, self.xmax) + for valx in self.residual_plant.x[0 : self.nturbs] + ] + y_opt = [ + self._unnorm(valy, self.ymin, self.ymax) + for valy in self.residual_plant.x[self.nturbs : 2 * self.nturbs] + ] + return x_initial, y_initial, x_opt, y_opt + + + # Public methods + + def optimize(self): + """ + This method finds the optimized layout of wind turbines for power + production given the provided frequencies of occurance of wind + conditions (wind speed, direction). + + Returns: + opt_locs (iterable): A list of the optimized locations of each + turbine (m). + """ + print("=====================================================") + print("Optimizing turbine layout...") + print("Number of parameters to optimize = ", len(self.x0)) + print("=====================================================") + + opt_locs_norm = self._optimize() + + print("Optimization complete.") + + opt_locs = [ + [ + self._unnorm(valx, self.xmin, self.xmax) + for valx in opt_locs_norm[0 : self.nturbs] + ], + [ + self._unnorm(valy, self.ymin, self.ymax) + for valy in opt_locs_norm[self.nturbs : 2 * self.nturbs] + ], + ] + + return opt_locs diff --git a/floris/tools/optimization/legacy/__init__.py b/floris/tools/optimization/legacy/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/floris/tools/optimization/pyoptsparse/__init__.py b/floris/tools/optimization/legacy/pyoptsparse/__init__.py similarity index 100% rename from floris/tools/optimization/pyoptsparse/__init__.py rename to floris/tools/optimization/legacy/pyoptsparse/__init__.py diff --git a/floris/tools/optimization/pyoptsparse/layout.py b/floris/tools/optimization/legacy/pyoptsparse/layout.py similarity index 99% rename from floris/tools/optimization/pyoptsparse/layout.py rename to floris/tools/optimization/legacy/pyoptsparse/layout.py index aa2d6b0e5..7c6f32a2e 100644 --- a/floris/tools/optimization/pyoptsparse/layout.py +++ b/floris/tools/optimization/legacy/pyoptsparse/layout.py @@ -61,7 +61,7 @@ def obj_func(self, varDict): self.parse_opt_vars(varDict) # Update turbine map with turbince locations - self.fi.reinitialize(layout=[self.x, self.y]) + self.fi.reinitialize(layout_x=self.x, layout_y=self.y) self.fi.calculate_wake() # Compute the objective function diff --git a/floris/tools/optimization/pyoptsparse/optimization.py b/floris/tools/optimization/legacy/pyoptsparse/optimization.py similarity index 100% rename from floris/tools/optimization/pyoptsparse/optimization.py rename to floris/tools/optimization/legacy/pyoptsparse/optimization.py diff --git a/floris/tools/optimization/pyoptsparse/power_density.py b/floris/tools/optimization/legacy/pyoptsparse/power_density.py similarity index 100% rename from floris/tools/optimization/pyoptsparse/power_density.py rename to floris/tools/optimization/legacy/pyoptsparse/power_density.py diff --git a/floris/tools/optimization/pyoptsparse/yaw.py b/floris/tools/optimization/legacy/pyoptsparse/yaw.py similarity index 100% rename from floris/tools/optimization/pyoptsparse/yaw.py rename to floris/tools/optimization/legacy/pyoptsparse/yaw.py diff --git a/floris/tools/optimization/scipy/__init__.py b/floris/tools/optimization/legacy/scipy/__init__.py similarity index 100% rename from floris/tools/optimization/scipy/__init__.py rename to floris/tools/optimization/legacy/scipy/__init__.py diff --git a/floris/tools/optimization/scipy/base_COE.py b/floris/tools/optimization/legacy/scipy/base_COE.py similarity index 100% rename from floris/tools/optimization/scipy/base_COE.py rename to floris/tools/optimization/legacy/scipy/base_COE.py diff --git a/floris/tools/optimization/scipy/cluster_turbines.py b/floris/tools/optimization/legacy/scipy/cluster_turbines.py similarity index 100% rename from floris/tools/optimization/scipy/cluster_turbines.py rename to floris/tools/optimization/legacy/scipy/cluster_turbines.py diff --git a/floris/tools/optimization/scipy/derive_downstream_turbines.py b/floris/tools/optimization/legacy/scipy/derive_downstream_turbines.py similarity index 100% rename from floris/tools/optimization/scipy/derive_downstream_turbines.py rename to floris/tools/optimization/legacy/scipy/derive_downstream_turbines.py diff --git a/floris/tools/optimization/scipy/layout.py b/floris/tools/optimization/legacy/scipy/layout.py similarity index 100% rename from floris/tools/optimization/scipy/layout.py rename to floris/tools/optimization/legacy/scipy/layout.py diff --git a/floris/tools/optimization/scipy/layout_height.py b/floris/tools/optimization/legacy/scipy/layout_height.py similarity index 100% rename from floris/tools/optimization/scipy/layout_height.py rename to floris/tools/optimization/legacy/scipy/layout_height.py diff --git a/floris/tools/optimization/scipy/optimization.py b/floris/tools/optimization/legacy/scipy/optimization.py similarity index 100% rename from floris/tools/optimization/scipy/optimization.py rename to floris/tools/optimization/legacy/scipy/optimization.py diff --git a/floris/tools/optimization/scipy/power_density.py b/floris/tools/optimization/legacy/scipy/power_density.py similarity index 100% rename from floris/tools/optimization/scipy/power_density.py rename to floris/tools/optimization/legacy/scipy/power_density.py diff --git a/floris/tools/optimization/scipy/power_density_1D.py b/floris/tools/optimization/legacy/scipy/power_density_1D.py similarity index 100% rename from floris/tools/optimization/scipy/power_density_1D.py rename to floris/tools/optimization/legacy/scipy/power_density_1D.py diff --git a/floris/tools/optimization/scipy/yaw.py b/floris/tools/optimization/legacy/scipy/yaw.py similarity index 100% rename from floris/tools/optimization/scipy/yaw.py rename to floris/tools/optimization/legacy/scipy/yaw.py diff --git a/floris/tools/optimization/scipy/yaw_clustered.py b/floris/tools/optimization/legacy/scipy/yaw_clustered.py similarity index 100% rename from floris/tools/optimization/scipy/yaw_clustered.py rename to floris/tools/optimization/legacy/scipy/yaw_clustered.py diff --git a/floris/tools/optimization/scipy/yaw_wind_rose.py b/floris/tools/optimization/legacy/scipy/yaw_wind_rose.py similarity index 100% rename from floris/tools/optimization/scipy/yaw_wind_rose.py rename to floris/tools/optimization/legacy/scipy/yaw_wind_rose.py diff --git a/floris/tools/optimization/scipy/yaw_wind_rose_clustered.py b/floris/tools/optimization/legacy/scipy/yaw_wind_rose_clustered.py similarity index 100% rename from floris/tools/optimization/scipy/yaw_wind_rose_clustered.py rename to floris/tools/optimization/legacy/scipy/yaw_wind_rose_clustered.py diff --git a/floris/tools/optimization/scipy/yaw_wind_rose_parallel.py b/floris/tools/optimization/legacy/scipy/yaw_wind_rose_parallel.py similarity index 100% rename from floris/tools/optimization/scipy/yaw_wind_rose_parallel.py rename to floris/tools/optimization/legacy/scipy/yaw_wind_rose_parallel.py diff --git a/floris/tools/optimization/scipy/yaw_wind_rose_parallel_clustered.py b/floris/tools/optimization/legacy/scipy/yaw_wind_rose_parallel_clustered.py similarity index 100% rename from floris/tools/optimization/scipy/yaw_wind_rose_parallel_clustered.py rename to floris/tools/optimization/legacy/scipy/yaw_wind_rose_parallel_clustered.py diff --git a/floris/tools/uncertainty_interface.py b/floris/tools/uncertainty_interface.py index bd4c80972..1d138c7b9 100644 --- a/floris/tools/uncertainty_interface.py +++ b/floris/tools/uncertainty_interface.py @@ -13,16 +13,16 @@ import copy + import numpy as np from scipy.stats import norm from floris.tools import FlorisInterface -from floris.logging_manager import LoggerBase from floris.utilities import wrap_360 +from floris.logging_manager import LoggerBase class UncertaintyInterface(LoggerBase): - def __init__( self, configuration, @@ -77,7 +77,7 @@ def __init__( will essentially come down to a Gaussian smoothing of FLORIS solutions over the wind directions. This calculation can therefore be really fast, since it does not require additional calculations - compared to a non-uncertainty FLORIS evaluation. + compared to a non-uncertainty FLORIS evaluation. When fix_yaw_in_relative_frame=False, the yaw angles are fixed in the absolute (compass) reference frame, meaning that for each probablistic wind direction evaluation, our probablistic (relative) @@ -118,6 +118,9 @@ def __init__( fix_yaw_in_relative_frame=fix_yaw_in_relative_frame, ) + # Add a _no_wake switch to keep track of calculate_wake/calculate_no_wake + self._no_wake = False + # Private methods def _generate_pdfs_from_dict(self): @@ -132,7 +135,9 @@ def _generate_pdfs_from_dict(self): # create normally distributed wd and yaw uncertaitny pmfs if appropriate unc_options = self.unc_options if unc_options["std_wd"] > 0: - wd_bnd = int(np.ceil(norm.ppf(unc_options["pdf_cutoff"], scale=unc_options["std_wd"]) / unc_options["pmf_res"])) + wd_bnd = int( + np.ceil(norm.ppf(unc_options["pdf_cutoff"], scale=unc_options["std_wd"]) / unc_options["pmf_res"]) + ) bound = wd_bnd * unc_options["pmf_res"] wd_unc = np.linspace(-1 * bound, bound, 2 * wd_bnd + 1) wd_unc_pmf = norm.pdf(wd_unc, scale=unc_options["std_wd"]) @@ -176,10 +181,7 @@ def _expand_wind_directions_and_yaw_angles(self): # Expand wind direction and yaw angle array into the direction # of uncertainty over the ambient wind direction. - wd_array_probablistic = np.vstack( - [np.expand_dims(wd_array_nominal, axis=0) + dy - for dy in unc_pmfs["wd_unc"]] - ) + wd_array_probablistic = np.vstack([np.expand_dims(wd_array_nominal, axis=0) + dy for dy in unc_pmfs["wd_unc"]]) if self.fix_yaw_in_relative_frame: # The relative yaw angle is fixed and always has the nominal @@ -190,8 +192,7 @@ def _expand_wind_directions_and_yaw_angles(self): # not require any additional calculations compared to the # non-uncertainty FLORIS evaluation. yaw_angles_probablistic = np.vstack( - [np.expand_dims(yaw_angles_nominal, axis=0) - for _ in unc_pmfs["wd_unc"]] + [np.expand_dims(yaw_angles_nominal, axis=0) for _ in unc_pmfs["wd_unc"]] ) else: # Fix yaw angles in the absolute (compass) reference frame, @@ -202,8 +203,7 @@ def _expand_wind_directions_and_yaw_angles(self): # it with a relative yaw angle that is 3 deg below its nominal # value. yaw_angles_probablistic = np.vstack( - [np.expand_dims(yaw_angles_nominal, axis=0) - dy - for dy in unc_pmfs["wd_unc"]] + [np.expand_dims(yaw_angles_nominal, axis=0) - dy for dy in unc_pmfs["wd_unc"]] ) self.wd_array_probablistic = wd_array_probablistic @@ -223,12 +223,7 @@ def copy(self): fi_unc_copy.fi = self.fi.copy() return fi_unc_copy - def reinitialize_uncertainty( - self, - unc_options=None, - unc_pmfs=None, - fix_yaw_in_relative_frame=None - ): + def reinitialize_uncertainty(self, unc_options=None, unc_pmfs=None, fix_yaw_in_relative_frame=None): """Reinitialize the wind direction and yaw angle probability distributions used in evaluating FLORIS. Must either specify 'unc_options', in which case distributions are calculated assuming @@ -281,7 +276,7 @@ def reinitialize_uncertainty( will essentially come down to a Gaussian smoothing of FLORIS solutions over the wind directions. This calculation can therefore be really fast, since it does not require additional calculations - compared to a non-uncertainty FLORIS evaluation. + compared to a non-uncertainty FLORIS evaluation. When fix_yaw_in_relative_frame=False, the yaw angles are fixed in the absolute (compass) reference frame, meaning that for each probablistic wind direction evaluation, our probablistic (relative) @@ -300,23 +295,21 @@ def reinitialize_uncertainty( often does not perfectly know the true wind direction, and that a turbine often does not perfectly achieve its desired yaw angle offset. Defaults to fix_yaw_in_relative_frame=False. - + """ # Check inputs - if ((unc_options is not None) and (unc_pmfs is not None)): - self.logger.error( - "Must specify either 'unc_options' or 'unc_pmfs', not both." - ) + if (unc_options is not None) and (unc_pmfs is not None): + self.logger.error("Must specify either 'unc_options' or 'unc_pmfs', not both.") # Assign uncertainty probability distributions if unc_options is not None: self.unc_options = unc_options self._generate_pdfs_from_dict() - + if unc_pmfs is not None: self.unc_pmfs = unc_pmfs - + if fix_yaw_in_relative_frame is not None: self.fix_yaw_in_relative_frame = bool(fix_yaw_in_relative_frame) @@ -330,6 +323,8 @@ def reinitialize( turbulence_intensity=None, air_density=None, layout=None, + layout_x=None, + layout_y=None, turbine_type=None, solver_settings=None, ): @@ -337,6 +332,12 @@ def reinitialize( to directly replace a FlorisInterface object with this UncertaintyInterface object, this function is required.""" + if layout is not None: + msg = "Use the `layout_x` and `layout_y` parameters in place of `layout` because the `layout` parameter will be deprecated in 3.3." + self.logger.warning(msg) + layout_x = layout[0] + layout_y = layout[1] + # Just passes arguments to the floris object self.fi.reinitialize( wind_speeds=wind_speeds, @@ -346,7 +347,8 @@ def reinitialize( reference_wind_height=reference_wind_height, turbulence_intensity=turbulence_intensity, air_density=air_density, - layout=layout, + layout_x=layout_x, + layout_y=layout_y, turbine_type=turbine_type, solver_settings=solver_settings, ) @@ -364,16 +366,26 @@ def calculate_wake(self, yaw_angles=None): yaw_angles: NDArrayFloat | list[float] | None = None, """ self._reassign_yaw_angles(yaw_angles) + self._no_wake = False - def get_turbine_powers(self, no_wake=False): - """Calculates the probability-weighted power production of each - turbine in the wind farm. + def calculate_no_wake(self, yaw_angles=None): + """Replaces the 'calculate_no_wake' function in the FlorisInterface + object. Fundamentally, this function only overwrites the nominal + yaw angles in the FlorisInterface object. The actual wake calculations + are performed once 'get_turbine_powers' or 'get_farm_powers' is + called. However, to allow users to directly replace a FlorisInterface + object with this UncertaintyInterface object, this function is + required. Args: - no_wake (bool, optional): disable the wakes in the flow model. - This can be useful to determine the (probablistic) power - production of the farm in the artificial scenario where there - would never be any wake losses. Defaults to False. + yaw_angles: NDArrayFloat | list[float] | None = None, + """ + self._reassign_yaw_angles(yaw_angles) + self._no_wake = True + + def get_turbine_powers(self): + """Calculates the probability-weighted power production of each + turbine in the wind farm. Returns: NDArrayFloat: Power production of all turbines in the wind farm. @@ -399,9 +411,7 @@ def get_turbine_powers(self, no_wake=False): # Format into conventional floris format by reshaping wd_array_probablistic = np.reshape(self.wd_array_probablistic, -1) - yaw_angles_probablistic = np.reshape( - self.yaw_angles_probablistic, (-1, num_ws, num_turbines) - ) + yaw_angles_probablistic = np.reshape(self.yaw_angles_probablistic, (-1, num_ws, num_turbines)) # Wrap wind direction array around 360 deg wd_array_probablistic = wrap_360(wd_array_probablistic) @@ -409,17 +419,14 @@ def get_turbine_powers(self, no_wake=False): # Find minimal set of solutions to evaluate wd_exp = np.tile(wd_array_probablistic, (1, num_ws, 1)).T _, id_unq, id_unq_rev = np.unique( - np.append(yaw_angles_probablistic, wd_exp, axis=2), - axis=0, - return_index=True, - return_inverse=True + np.append(yaw_angles_probablistic, wd_exp, axis=2), axis=0, return_index=True, return_inverse=True ) wd_array_probablistic_min = wd_array_probablistic[id_unq] yaw_angles_probablistic_min = yaw_angles_probablistic[id_unq, :, :] # Evaluate floris for minimal probablistic set self.fi.reinitialize(wind_directions=wd_array_probablistic_min) - if no_wake: + if self._no_wake: self.fi.calculate_no_wake(yaw_angles=yaw_angles_probablistic_min) else: self.fi.calculate_wake(yaw_angles=yaw_angles_probablistic_min) @@ -430,37 +437,188 @@ def get_turbine_powers(self, no_wake=False): # Reshape solutions back to full set power_probablistic = turbine_powers[id_unq_rev, :] - power_probablistic = np.reshape( - power_probablistic, - (num_wd_unc, num_wd, num_ws, num_turbines) - ) + power_probablistic = np.reshape(power_probablistic, (num_wd_unc, num_wd, num_ws, num_turbines)) # Calculate probability weighing terms wd_weighing = ( - np.expand_dims(unc_pmfs["wd_unc_pmf"], axis=(1, 2, 3)) - ).repeat(num_wd, 1).repeat(num_ws, 2).repeat(num_turbines, 3) + (np.expand_dims(unc_pmfs["wd_unc_pmf"], axis=(1, 2, 3))) + .repeat(num_wd, 1) + .repeat(num_ws, 2) + .repeat(num_turbines, 3) + ) # Now apply probability distribution weighing to get turbine powers return np.sum(wd_weighing * power_probablistic, axis=0) - def get_farm_power(self, no_wake=False): + def get_farm_power(self, turbine_weights=None): """Calculates the probability-weighted power production of the collective of all turbines in the farm, for each wind direction and wind speed specified. Args: - no_wake (bool, optional): disable the wakes in the flow model. - This can be useful to determine the (probablistic) power - production of the farm in the artificial scenario where there - would never be any wake losses. Defaults to False. + turbine_weights (NDArrayFloat | list[float] | None, optional): + weighing terms that allow the user to emphasize power at + particular turbines and/or completely ignore the power + from other turbines. This is useful when, for example, you are + modeling multiple wind farms in a single floris object. If you + only want to calculate the power production for one of those + farms and include the wake effects of the neighboring farms, + you can set the turbine_weights for the neighboring farms' + turbines to 0.0. The array of turbine powers from floris + is multiplied with this array in the calculation of the + objective function. If None, this is an array with all values + 1.0 and with shape equal to (n_wind_directions, n_wind_speeds, + n_turbines). Defaults to None. Returns: NDArrayFloat: Expectation of power production of the wind farm. This array has the shape (num_wind_directions, num_wind_speeds). """ - turbine_powers = self.get_turbine_powers(no_wake=no_wake) + + if turbine_weights is None: + # Default to equal weighing of all turbines when turbine_weights is None + turbine_weights = np.ones( + ( + self.floris.flow_field.n_wind_directions, + self.floris.flow_field.n_wind_speeds, + self.floris.farm.n_turbines + ) + ) + elif len(np.shape(turbine_weights)) == 1: + # Deal with situation when 1D array is provided + turbine_weights = np.tile( + turbine_weights, + ( + self.floris.flow_field.n_wind_directions, + self.floris.flow_field.n_wind_speeds, + 1 + ) + ) + + # Calculate all turbine powers and apply weights + turbine_powers = self.get_turbine_powers() + turbine_powers = np.multiply(turbine_weights, turbine_powers) + return np.sum(turbine_powers, axis=2) + def get_farm_AEP( + self, + freq, + cut_in_wind_speed=0.001, + cut_out_wind_speed=None, + yaw_angles=None, + turbine_weights=None, + no_wake=False, + ) -> float: + """ + Estimate annual energy production (AEP) for distributions of wind speed, wind + direction, frequency of occurrence, and yaw offset. + + Args: + freq (NDArrayFloat): NumPy array with shape (n_wind_directions, + n_wind_speeds) with the frequencies of each wind direction and + wind speed combination. These frequencies should typically sum + up to 1.0 and are used to weigh the wind farm power for every + condition in calculating the wind farm's AEP. + cut_in_wind_speed (float, optional): Wind speed in m/s below which + any calculations are ignored and the wind farm is known to + produce 0.0 W of power. Note that to prevent problems with the + wake models at negative / zero wind speeds, this variable must + always have a positive value. Defaults to 0.001 [m/s]. + cut_out_wind_speed (float, optional): Wind speed above which the + wind farm is known to produce 0.0 W of power. If None is + specified, will assume that the wind farm does not cut out + at high wind speeds. Defaults to None. + yaw_angles (NDArrayFloat | list[float] | None, optional): + The relative turbine yaw angles in degrees. If None is + specified, will assume that the turbine yaw angles are all + zero degrees for all conditions. Defaults to None. + turbine_weights (NDArrayFloat | list[float] | None, optional): + weighing terms that allow the user to emphasize power at + particular turbines and/or completely ignore the power + from other turbines. This is useful when, for example, you are + modeling multiple wind farms in a single floris object. If you + only want to calculate the power production for one of those + farms and include the wake effects of the neighboring farms, + you can set the turbine_weights for the neighboring farms' + turbines to 0.0. The array of turbine powers from floris + is multiplied with this array in the calculation of the + objective function. If None, this is an array with all values + 1.0 and with shape equal to (n_wind_directions, n_wind_speeds, + n_turbines). Defaults to None. + no_wake: (bool, optional): When *True* updates the turbine + quantities without calculating the wake or adding the wake to + the flow field. This can be useful when quantifying the loss + in AEP due to wakes. Defaults to *False*. + + Returns: + float: + The Annual Energy Production (AEP) for the wind farm in + watt-hours. + """ + + # Verify dimensions of the variable "freq" + if not ( + (np.shape(freq)[0] == self.floris.flow_field.n_wind_directions) + & (np.shape(freq)[1] == self.floris.flow_field.n_wind_speeds) + & (len(np.shape(freq)) == 2) + ): + raise UserWarning( + "'freq' should be a two-dimensional array with dimensions (n_wind_directions, n_wind_speeds)." + ) + + # Check if frequency vector sums to 1.0. If not, raise a warning + if np.abs(np.sum(freq) - 1.0) > 0.001: + self.logger.warning("WARNING: The frequency array provided to get_farm_AEP() does not sum to 1.0. ") + + # Copy the full wind speed array from the floris object and initialize + # the the farm_power variable as an empty array. + wind_speeds = np.array(self.fi.floris.flow_field.wind_speeds, copy=True) + farm_power = np.zeros((self.fi.floris.flow_field.n_wind_directions, len(wind_speeds))) + + # Determine which wind speeds we must evaluate in floris + conditions_to_evaluate = wind_speeds >= cut_in_wind_speed + if cut_out_wind_speed is not None: + conditions_to_evaluate = conditions_to_evaluate & (wind_speeds < cut_out_wind_speed) + + # Evaluate the conditions in floris + if np.any(conditions_to_evaluate): + wind_speeds_subset = wind_speeds[conditions_to_evaluate] + yaw_angles_subset = None + if yaw_angles is not None: + yaw_angles_subset = yaw_angles[:, conditions_to_evaluate] + self.reinitialize(wind_speeds=wind_speeds_subset) + if no_wake: + self.calculate_no_wake(yaw_angles=yaw_angles_subset) + else: + self.calculate_wake(yaw_angles=yaw_angles_subset) + farm_power[:, conditions_to_evaluate] = ( + self.get_farm_power(turbine_weights=turbine_weights) + ) + + # Finally, calculate AEP in GWh + aep = np.sum(np.multiply(freq, farm_power) * 365 * 24) + + # Reset the FLORIS object to the full wind speed array + self.reinitialize(wind_speeds=wind_speeds) + + return aep + + def assign_hub_height_to_ref_height(self): + return self.fi.assign_hub_height_to_ref_height() + + def get_turbine_layout(self, z=False): + return self.fi.get_turbine_layout(z=z) + + def get_turbine_Cts(self): + return self.fi.get_turbine_Cts() + + def get_turbine_ais(self): + return self.fi.get_turbine_ais() + + def get_turbine_average_velocities(self): + return self.fi.get_turbine_average_velocities() + # Define getter functions that just pass information from FlorisInterface @property def floris(self): diff --git a/floris/tools/visualization.py b/floris/tools/visualization.py index 06c3a9259..95cf85098 100644 --- a/floris/tools/visualization.py +++ b/floris/tools/visualization.py @@ -67,8 +67,8 @@ def plot_turbines_with_fi(ax, fi, color=None): ax, fi.layout_x, fi.layout_y, - fi.get_yaw_angles()[0, 0], - fi.floris.farm.rotor_diameter[0, 0], + fi.floris.farm.yaw_angles[0, 0], + fi.floris.farm.rotor_diameters[0, 0], color=color, wind_direction=fi.floris.flow_field.wind_directions[0], ) diff --git a/floris/tools/wind_rose.py b/floris/tools/wind_rose.py index 8f3afb56f..e1e1ebe37 100644 --- a/floris/tools/wind_rose.py +++ b/floris/tools/wind_rose.py @@ -634,6 +634,22 @@ def make_wind_rose_from_user_data( self.internal_resample_wind_direction(wd=wd) return self.df + + def read_wind_rose_csv( + self, + filename + ): + + #Read in the csv + self.df = pd.read_csv(filename) + + # Renormalize the frequency column + self.df["freq_val"] = self.df["freq_val"] / self.df["freq_val"].sum() + + # Call the resample function in order to set all the internal variables + self.internal_resample_wind_speed(ws=self.df.ws.unique()) + self.internal_resample_wind_direction(wd=self.df.wd.unique()) + def make_wind_rose_from_user_dist( self, @@ -1283,7 +1299,7 @@ def indices_for_coord(self, f, lat_index, lon_index): ij = [int(round(x / 2000)) for x in delta] return tuple(reversed(ij)) - def plot_wind_speed_all(self, ax=None): + def plot_wind_speed_all(self, ax=None, label=None): """ This method plots the wind speed frequency distribution of the WindRose object averaged across all wind directions. If no axis is provided, a @@ -1297,7 +1313,7 @@ def plot_wind_speed_all(self, ax=None): _, ax = plt.subplots() df_plot = self.df.groupby("ws").sum() - ax.plot(self.ws, df_plot.freq_val) + ax.plot(self.ws, df_plot.freq_val, label=label) def plot_wind_speed_by_direction(self, dirs, ax=None): """ diff --git a/floris/turbine_library/iea_10MW.yaml b/floris/turbine_library/iea_10MW.yaml index e664974ee..bc40bb0fb 100644 --- a/floris/turbine_library/iea_10MW.yaml +++ b/floris/turbine_library/iea_10MW.yaml @@ -5,6 +5,7 @@ pP: 1.88 pT: 1.88 rotor_diameter: 198.0 TSR: 8.0 +ref_density_cp_ct: 1.225 power_thrust_table: power: - 0.000000 diff --git a/floris/turbine_library/iea_15MW.yaml b/floris/turbine_library/iea_15MW.yaml index 6f97283e2..c6bc7986a 100644 --- a/floris/turbine_library/iea_15MW.yaml +++ b/floris/turbine_library/iea_15MW.yaml @@ -5,6 +5,7 @@ pP: 1.88 pT: 1.88 rotor_diameter: 240.0 TSR: 8.0 +ref_density_cp_ct: 1.225 power_thrust_table: power: - 0.000000 diff --git a/floris/turbine_library/nrel_5MW.yaml b/floris/turbine_library/nrel_5MW.yaml index 10bc8ea8d..84da83168 100644 --- a/floris/turbine_library/nrel_5MW.yaml +++ b/floris/turbine_library/nrel_5MW.yaml @@ -5,6 +5,7 @@ pP: 1.88 pT: 1.88 rotor_diameter: 126.0 TSR: 8.0 +ref_density_cp_ct: 1.225 power_thrust_table: power: - 0.0 diff --git a/floris/turbine_library/x_20MW.yaml b/floris/turbine_library/x_20MW.yaml new file mode 100644 index 000000000..436a83b52 --- /dev/null +++ b/floris/turbine_library/x_20MW.yaml @@ -0,0 +1,176 @@ +turbine_type: 'x_20MW' +generator_efficiency: 1.0 +hub_height: 165.0 +pP: 1.88 +pT: 1.88 +rotor_diameter: 252.0 +TSR: 8.0 +power_thrust_table: + power: + - 0.000000 + - 0.000000 + - 0.074000 + - 0.325100 + - 0.376200 + - 0.402700 + - 0.415600 + - 0.423000 + - 0.427400 + - 0.429300 + - 0.429800 + - 0.429800 + - 0.429800 + - 0.429800 + - 0.429800 + - 0.429800 + - 0.429800 + - 0.429800 + - 0.429800 + - 0.429800 + - 0.429800 + - 0.429800 + - 0.429800 + - 0.429800 + - 0.429800 + - 0.429800 + - 0.429800 + - 0.429800 + - 0.429800 + - 0.429800 + - 0.429800 + - 0.429603 + - 0.354604 + - 0.316305 + - 0.281478 + - 0.250068 + - 0.221924 + - 0.196845 + - 0.174592 + - 0.154919 + - 0.137570 + - 0.122300 + - 0.108881 + - 0.097094 + - 0.086747 + - 0.077664 + - 0.069686 + - 0.062677 + - 0.056511 + - 0.051083 + - 0.046299 + - 0.043182 + - 0.033935 + - 0.000000 + - 0.000000 + thrust: + - 0.000000 + - 0.000000 + - 0.770100 + - 0.770100 + - 0.776300 + - 0.782400 + - 0.782000 + - 0.780200 + - 0.777200 + - 0.771900 + - 0.776800 + - 0.776800 + - 0.776800 + - 0.776800 + - 0.776800 + - 0.776800 + - 0.776800 + - 0.776800 + - 0.776800 + - 0.776800 + - 0.776800 + - 0.776800 + - 0.776800 + - 0.776800 + - 0.776800 + - 0.776800 + - 0.776800 + - 0.776800 + - 0.776800 + - 0.767500 + - 0.765100 + - 0.758700 + - 0.505600 + - 0.431000 + - 0.370800 + - 0.320900 + - 0.278800 + - 0.243200 + - 0.212800 + - 0.186800 + - 0.164500 + - 0.145400 + - 0.128900 + - 0.114700 + - 0.102400 + - 0.091800 + - 0.082500 + - 0.074500 + - 0.067500 + - 0.061300 + - 0.055900 + - 0.051200 + - 0.047000 + - 0.000000 + - 0.000000 + wind_speed: + - 0.000000 + - 2.900000 + - 3.000000 + - 4.000000 + - 4.514700 + - 5.000800 + - 5.457400 + - 5.883300 + - 6.277700 + - 6.639700 + - 6.968400 + - 7.263200 + - 7.523400 + - 7.748400 + - 7.937700 + - 8.090900 + - 8.207700 + - 8.287700 + - 8.330800 + - 8.337000 + - 8.367800 + - 8.435600 + - 8.540100 + - 8.681200 + - 8.858500 + - 9.071700 + - 9.320200 + - 9.603500 + - 9.921000 + - 10.272000 + - 10.655700 + - 11.507700 + - 12.267700 + - 12.744100 + - 13.249400 + - 13.782400 + - 14.342000 + - 14.926900 + - 15.535900 + - 16.167500 + - 16.820400 + - 17.493200 + - 18.184200 + - 18.892100 + - 19.615200 + - 20.351900 + - 21.100600 + - 21.859600 + - 22.627300 + - 23.401900 + - 24.181700 + - 24.750000 + - 25.010000 + - 25.020000 + - 50.000000 \ No newline at end of file diff --git a/floris/type_dec.py b/floris/type_dec.py index e4dbce8c7..41c5b2451 100644 --- a/floris/type_dec.py +++ b/floris/type_dec.py @@ -108,6 +108,7 @@ def from_dict(cls, data: dict): # Map the inputs must be provided: 1) must be initialized, 2) no default value defined required_inputs = [a.name for a in cls.__attrs_attrs__ if a.init and a.default is attrs.NOTHING] undefined = sorted(set(required_inputs) - set(kwargs)) + if undefined: raise AttributeError(f"The class defintion for {cls.__name__} is missing the following inputs: {undefined}") return cls(**kwargs) diff --git a/floris/version.py b/floris/version.py index 94ff29cc4..a3ec5a4bd 100644 --- a/floris/version.py +++ b/floris/version.py @@ -1 +1 @@ -3.1.1 +3.2 diff --git a/profiling/profiling.py b/profiling/profiling.py index 97ebfc97b..421dd2766 100644 --- a/profiling/profiling.py +++ b/profiling/profiling.py @@ -46,16 +46,19 @@ def run_floris(): sample_inputs.floris["wake"]["enable_yaw_added_recovery"] = True sample_inputs.floris["wake"]["enable_transverse_velocities"] = True - factor = 100 - TURBINE_DIAMETER = sample_inputs.floris["turbine"]["rotor_diameter"] - sample_inputs.floris["farm"]["layout_x"] = [5 * TURBINE_DIAMETER * i for i in range(factor)] - sample_inputs.floris["farm"]["layout_y"] = [0.0 for i in range(factor)] + N_TURBINES = 100 + N_WIND_DIRECTIONS = 72 + N_WIND_SPEEDS = 25 - factor = 10 - sample_inputs.floris["flow_field"]["wind_directions"] = factor * [270.0] - sample_inputs.floris["flow_field"]["wind_speeds"] = factor * [8.0] + TURBINE_DIAMETER = sample_inputs.floris["farm"]["turbine_type"][0]["rotor_diameter"] + sample_inputs.floris["farm"]["layout_x"] = [5 * TURBINE_DIAMETER * i for i in range(N_TURBINES)] + sample_inputs.floris["farm"]["layout_y"] = [0.0 for i in range(N_TURBINES)] - N = 5 + sample_inputs.floris["flow_field"]["wind_directions"] = N_WIND_DIRECTIONS * [270.0] + sample_inputs.floris["flow_field"]["wind_speeds"] = N_WIND_SPEEDS * [8.0] + + N = 1 for i in range(N): floris = Floris.from_dict(copy.deepcopy(sample_inputs.floris)) + floris.initialize_domain() floris.steady_state_atmospheric_condition() diff --git a/tests/base_test.py b/tests/base_test.py index bf9e36dc1..81681632f 100644 --- a/tests/base_test.py +++ b/tests/base_test.py @@ -13,28 +13,35 @@ # See https://floris.readthedocs.io for documentation -import attr import pytest +from attr import define, field + from floris.simulation import BaseClass, BaseModel -@attr.s(auto_attribs=True) -class ClassTest(BaseClass): - x: int = attr.ib(default=1, converter=int) - model_string: str = attr.ib(default="test", converter=str) +@define +class ClassTest(BaseModel): + x: int = field(default=1, converter=int) + a_string: str = field(default="abc", converter=str) + + def prepare_function() -> dict: + return {} + + def function() -> None: + return None def test_get_model_defaults(): defaults = ClassTest.get_model_defaults() assert len(defaults) == 2 assert defaults["x"] == 1 - assert defaults["model_string"] == "test" + assert defaults["a_string"] == "abc" def test_get_model_values(): - cls = ClassTest(x=4, model_string="new") + cls = ClassTest(x=4, a_string="xyz") values = cls._get_model_dict() assert len(values) == 2 assert values["x"] == 4 - assert values["model_string"] == "new" + assert values["a_string"] == "xyz" diff --git a/tests/conftest.py b/tests/conftest.py index 610eb6755..2643db942 100644 --- a/tests/conftest.py +++ b/tests/conftest.py @@ -90,6 +90,7 @@ def print_test_values(average_velocities: list, thrusts: list, powers: list, axi N_TURBINES = len(X_COORDS) ROTOR_DIAMETER = 126.0 TURBINE_GRID_RESOLUTION = 2 +TIME_SERIES = False ## Unit test fixtures @@ -116,7 +117,8 @@ def turbine_grid_fixture(sample_inputs_fixture) -> TurbineGrid: reference_turbine_diameter=rotor_diameters, wind_directions=np.array(WIND_DIRECTIONS), wind_speeds=np.array(WIND_SPEEDS), - grid_resolution=TURBINE_GRID_RESOLUTION + grid_resolution=TURBINE_GRID_RESOLUTION, + time_series=TIME_SERIES ) @pytest.fixture @@ -154,6 +156,7 @@ def __init__(self): "pP": 1.88, "pT": 1.88, "generator_efficiency": 1.0, + "ref_density_cp_ct": 1.225, "power_thrust_table": { "power": [ 0.000000, diff --git a/tests/reg_tests/cumulative_curl_regression_test.py b/tests/reg_tests/cumulative_curl_regression_test.py index f74ab430d..a2c449fd2 100644 --- a/tests/reg_tests/cumulative_curl_regression_test.py +++ b/tests/reg_tests/cumulative_curl_regression_test.py @@ -1,4 +1,4 @@ -# Copyright 2021 NREL +# Copyright 2022 NREL # Licensed under the Apache License, Version 2.0 (the "License"); you may not # use this file except in compliance with the License. You may obtain a copy of @@ -18,7 +18,7 @@ from floris.simulation import Ct, power, axial_induction, average_velocity from tests.conftest import N_TURBINES, N_WIND_DIRECTIONS, N_WIND_SPEEDS, print_test_values, assert_results_arrays -DEBUG = True +DEBUG = False VELOCITY_MODEL = "cc" DEFLECTION_MODEL = "gauss" @@ -86,25 +86,25 @@ [ [7.9803783, 0.7605249, 1683956.5765064, 0.2548147], [5.4219904, 0.8658607, 511133.7736997, 0.3168748], - [4.9902533, 0.8928102, 385309.6126320, 0.3363008], + [4.9901603, 0.8928170, 385287.3116696, 0.3363059], ], # 9 m/s [ [8.9779256, 0.7596713, 2397236.5542849, 0.2543815], [6.1011855, 0.8307591, 748404.6404163, 0.2943055], - [5.6072171, 0.8555225, 571154.1495386, 0.3099490], + [5.6071092, 0.8555280, 571116.7279097, 0.3099527], ], # 10 m/s [ [9.9754729, 0.7499157, 3283591.8023665, 0.2494847], [6.7984638, 0.8003672, 1048915.4794254, 0.2765986], - [6.2452220, 0.8241201, 806765.4479110, 0.2903098], + [6.2451030, 0.8241256, 806717.2493019, 0.2903131], ], # 11 m/s [ [10.9730201, 0.7276532, 4344222.0129382, 0.2386508], [7.5339320, 0.7749706, 1427833.3888763, 0.2628137], - [6.8971848, 0.7963949, 1094864.8116422, 0.2743869], + [6.8970594, 0.7964000, 1094806.4414958, 0.2743897], ], ] ) @@ -115,25 +115,25 @@ [ [7.9803783, 0.7605249, 1683956.5765064, 0.2548147], [5.4029709, 0.8670436, 505568.1176628, 0.3176840], - [4.9791408, 0.8936138, 382644.8719082, 0.3369155], + [4.9790760, 0.8936185, 382629.3354701, 0.3369191], ], # 9 m/s [ [8.9779256, 0.7596713, 2397236.5542849, 0.2543815], [6.0798429, 0.8317428, 739757.0246720, 0.2949042], - [5.5938124, 0.8562085, 566504.2126629, 0.3104007], + [5.5937356, 0.8562124, 566477.5644593, 0.3104033], ], # 10 m/s [ [9.9754729, 0.7499157, 3283591.8023665, 0.2494847], [6.7754458, 0.8012934, 1038201.8164555, 0.2771174], - [6.2302537, 0.8248100, 800700.5867580, 0.2907215], + [6.2301672, 0.8248140, 800665.5335362, 0.2907239], ], # 11 m/s [ [10.9730201, 0.7276532, 4344222.0129382, 0.2386508], [7.5103959, 0.7755790, 1413729.2052485, 0.2631345], - [6.8817912, 0.7970143, 1087699.9040360, 0.2747304], + [6.8816977, 0.7970181, 1087656.4020125, 0.2747324], ], ] ) @@ -174,6 +174,7 @@ def test_regression_tandem(sample_inputs_fixture): ) farm_powers = power( floris.flow_field.air_density, + floris.farm.ref_density_cp_cts, velocities, yaw_angles, floris.farm.pPs, @@ -318,6 +319,7 @@ def test_regression_yaw(sample_inputs_fixture): ) farm_powers = power( floris.flow_field.air_density, + floris.farm.ref_density_cp_cts, velocities, yaw_angles, floris.farm.pPs, @@ -390,6 +392,7 @@ def test_regression_yaw_added_recovery(sample_inputs_fixture): ) farm_powers = power( floris.flow_field.air_density, + floris.farm.ref_density_cp_cts, velocities, yaw_angles, floris.farm.pPs, @@ -461,6 +464,7 @@ def test_regression_secondary_steering(sample_inputs_fixture): ) farm_powers = power( floris.flow_field.air_density, + floris.farm.ref_density_cp_cts, velocities, yaw_angles, floris.farm.pPs, @@ -530,6 +534,7 @@ def test_regression_small_grid_rotation(sample_inputs_fixture): farm_powers = power( floris.flow_field.air_density, + floris.farm.ref_density_cp_cts, velocities, yaw_angles, floris.farm.pPs, diff --git a/tests/reg_tests/gauss_regression_test.py b/tests/reg_tests/gauss_regression_test.py index d9b7731ca..72e8a63e4 100644 --- a/tests/reg_tests/gauss_regression_test.py +++ b/tests/reg_tests/gauss_regression_test.py @@ -1,4 +1,4 @@ -# Copyright 2021 NREL +# Copyright 2022 NREL # Licensed under the Apache License, Version 2.0 (the "License"); you may not # use this file except in compliance with the License. You may obtain a copy of @@ -26,27 +26,27 @@ [ # 8 m/s [ - [7.9803783, 0.7634300, 1695368.6455473, 0.2568077], - [5.8384411, 0.8436903, 651362.9121753, 0.3023199], - [5.9388958, 0.8385498, 686209.4710003, 0.2990957], + [7.9803783, 0.7634300, 1695368.7987130, 0.2568077], + [5.8384411, 0.8436903, 651363.2435524, 0.3023199], + [5.9388958, 0.8385498, 686209.8630205, 0.2990957], ], # 9 m/s [ - [8.9779256, 0.7625731, 2413659.0651694, 0.2563676], - [6.5698070, 0.8095679, 942487.3932503, 0.2818073], - [6.7192788, 0.8035535, 1012058.4081816, 0.2783886], + [8.9779256, 0.7625731, 2413658.0981405, 0.2563676], + [6.5698070, 0.8095679, 942487.9831258, 0.2818073], + [6.7192788, 0.8035535, 1012059.0934624, 0.2783886], ], # 10 m/s [ - [9.9754729, 0.7527803, 3306006.9741814, 0.2513940], - [7.3198945, 0.7817588, 1312121.9341194, 0.2664185], - [7.4982017, 0.7759067, 1406546.0953528, 0.2633075], + [9.9754729, 0.7527803, 3306006.2306084, 0.2513940], + [7.3198945, 0.7817588, 1312122.9051486, 0.2664185], + [7.4982017, 0.7759067, 1406547.1257826, 0.2633075], ], # 11 m/s [ - [10.9730201, 0.7304328, 4373591.7174990, 0.2404007], - [ 8.1044931, 0.7626381, 1778225.5062060, 0.2564010], - [ 8.2645633, 0.7622021, 1887139.2890270, 0.2561774], + [10.9730201, 0.7304328, 4373596.1594956, 0.2404007], + [8.1044931, 0.7626381, 1778226.0596889, 0.2564010], + [8.2645633, 0.7622021, 1887140.5106744, 0.2561774], ] ] ) @@ -146,27 +146,27 @@ [ # 8 m/s [ - [7.9803783, 0.7605249, 1683956.3885389, 0.2548147], - [5.8919486, 0.8409522, 669924.0459695, 0.3005960], - [5.9689897, 0.8370099, 696648.6988779, 0.2981398], + [7.9803783, 0.7605249, 1683956.5765064, 0.2548147], + [5.8919486, 0.8409522, 669924.4096484, 0.3005960], + [5.9686695, 0.8370262, 696538.0378027, 0.2981500], ], # 9 m/s [ - [8.9779256, 0.7596713, 2397237.3791443, 0.2543815], - [6.6298866, 0.8071504, 970451.1986814, 0.2804268], - [6.7526650, 0.8022101, 1027597.8734084, 0.2776321], + [8.9779256, 0.7596713, 2397236.5542849, 0.2543815], + [6.6298866, 0.8071504, 970451.8269047, 0.2804268], + [6.7523126, 0.8022243, 1027434.5597156, 0.2776401], ], # 10 m/s [ - [9.9754729, 0.7499157, 3283592.6005045, 0.2494847], - [7.3851732, 0.7796164, 1346690.8243164, 0.2652748], - [7.5342846, 0.7749614, 1428043.6798542, 0.2628089], + [9.9754729, 0.7499157, 3283591.8023665, 0.2494847], + [7.3851732, 0.7796164, 1346691.8170923, 0.2652748], + [7.5339044, 0.7749713, 1427816.8489148, 0.2628140], ], # 11 m/s [ - [10.9730201, 0.7276532, 4344217.6993801, 0.2386508], - [8.1726065, 0.7624526, 1824570.7248189, 0.2563058], - [8.2995738, 0.7621067, 1910960.9002259, 0.2561285], + [10.9730201, 0.7276532, 4344222.0129382, 0.2386508], + [8.1726065, 0.7624526, 1824571.5626205, 0.2563058], + [8.2991708, 0.7621078, 1910688.0574225, 0.2561290], ], ] ) @@ -175,27 +175,27 @@ [ # 8 m/s [ - [7.9803783, 0.7605249, 1683956.3885389, 0.2548147], - [5.8919476, 0.8409523, 669923.6972896, 0.3005961], - [5.9632412, 0.8373040, 694654.5960227, 0.2983221], + [7.9803783, 0.7605249, 1683956.5765064, 0.2548147], + [5.8919476, 0.8409523, 669924.0609678, 0.3005961], + [5.9630522, 0.8373137, 694589.4363406, 0.2983281], ], # 9 m/s [ - [8.9779256, 0.7596713, 2397237.3791443, 0.2543815], - [6.6298855, 0.8071504, 970450.6737564, 0.2804268], - [6.7462833, 0.8024669, 1024627.5360075, 0.2777765], + [8.9779256, 0.7596713, 2397236.5542849, 0.2543815], + [6.6298855, 0.8071504, 970451.3019789, 0.2804268], + [6.7460763, 0.8024752, 1024531.8988965, 0.2777812], ], # 10 m/s [ - [9.9754729, 0.7499157, 3283592.6005045, 0.2494847], - [7.3851720, 0.7796164, 1346690.1809469, 0.2652748], - [7.5273470, 0.7751408, 1423886.2807889, 0.2629034], + [9.9754729, 0.7499157, 3283591.8023665, 0.2494847], + [7.3851720, 0.7796164, 1346691.1737223, 0.2652748], + [7.5271249, 0.7751465, 1423754.1608641, 0.2629064], ], # 11 m/s [ - [10.9730201, 0.7276532, 4344217.6993801, 0.2386508], - [8.1726052, 0.7624526, 1824569.8797601, 0.2563058], - [8.2921752, 0.7621269, 1905926.7688633, 0.2561388], + [10.9730201, 0.7276532, 4344222.0129382, 0.2386508], + [8.1726052, 0.7624526, 1824570.7175565, 0.2563058], + [8.2919410, 0.7621275, 1905768.7628771, 0.2561391], ], ] ) @@ -204,27 +204,27 @@ [ # 8 m/s [ - [7.9803783, 0.7605249, 1683956.3885389, 0.2548147], - [5.8728728, 0.8419284, 663306.8379666, 0.3012089], - [5.9488301, 0.8380415, 689655.5729586, 0.2987796], + [7.9803783, 0.7605249, 1683956.5765064, 0.2548147], + [5.8728728, 0.8419284, 663307.1901296, 0.3012089], + [5.9486952, 0.8380484, 689609.1551620, 0.2987839], ], # 9 m/s [ - [8.9779256, 0.7596713, 2397237.3791443, 0.2543815], - [6.6084827, 0.8080116, 960488.8358520, 0.2809176], - [6.7305702, 0.8030991, 1017313.9339292, 0.2781324], + [8.9779256, 0.7596713, 2397236.5542849, 0.2543815], + [6.6084827, 0.8080116, 960489.4504135, 0.2809176], + [6.7304206, 0.8031051, 1017245.0103229, 0.2781358], ], # 10 m/s [ - [9.9754729, 0.7499157, 3283592.6005045, 0.2494847], - [7.3621043, 0.7803735, 1334474.4719693, 0.2656784], - [7.5106603, 0.7755721, 1413886.6252099, 0.2631309], + [9.9754729, 0.7499157, 3283591.8023665, 0.2494847], + [7.3621043, 0.7803735, 1334475.4570600, 0.2656784], + [7.5104978, 0.7755763, 1413790.2904370, 0.2631331], ], # 11 m/s [ - [10.9730201, 0.7276532, 4344217.6993801, 0.2386508], - [8.1489900, 0.7625169, 1808501.7467836, 0.2563388], - [8.2759460, 0.7621711, 1894884.2411821, 0.2561615], + [10.9730201, 0.7276532, 4344222.0129382, 0.2386508], + [8.1489900, 0.7625169, 1808502.4860052, 0.2563388], + [8.2757728, 0.7621716, 1894767.6143032, 0.2561617], ], ] ) @@ -265,6 +265,7 @@ def test_regression_tandem(sample_inputs_fixture): ) farm_powers = power( floris.flow_field.air_density, + floris.farm.ref_density_cp_cts, velocities, yaw_angles, floris.farm.pPs, @@ -409,6 +410,7 @@ def test_regression_yaw(sample_inputs_fixture): ) farm_powers = power( floris.flow_field.air_density, + floris.farm.ref_density_cp_cts, velocities, yaw_angles, floris.farm.pPs, @@ -478,6 +480,7 @@ def test_regression_gch(sample_inputs_fixture): ) farm_powers = power( floris.flow_field.air_density, + floris.farm.ref_density_cp_cts, velocities, yaw_angles, floris.farm.pPs, @@ -542,6 +545,7 @@ def test_regression_gch(sample_inputs_fixture): ) farm_powers = power( floris.flow_field.air_density, + floris.farm.ref_density_cp_cts, velocities, yaw_angles, floris.farm.pPs, @@ -614,6 +618,7 @@ def test_regression_yaw_added_recovery(sample_inputs_fixture): ) farm_powers = power( floris.flow_field.air_density, + floris.farm.ref_density_cp_cts, velocities, yaw_angles, floris.farm.pPs, @@ -685,6 +690,7 @@ def test_regression_secondary_steering(sample_inputs_fixture): ) farm_powers = power( floris.flow_field.air_density, + floris.farm.ref_density_cp_cts, velocities, yaw_angles, floris.farm.pPs, @@ -754,6 +760,7 @@ def test_regression_small_grid_rotation(sample_inputs_fixture): farm_powers = power( floris.flow_field.air_density, + floris.farm.ref_density_cp_cts, velocities, yaw_angles, floris.farm.pPs, diff --git a/tests/reg_tests/jensen_jimenez_regression_test.py b/tests/reg_tests/jensen_jimenez_regression_test.py index d9274160a..a0be63048 100644 --- a/tests/reg_tests/jensen_jimenez_regression_test.py +++ b/tests/reg_tests/jensen_jimenez_regression_test.py @@ -1,4 +1,4 @@ -# Copyright 2020 NREL +# Copyright 2022 NREL # Licensed under the Apache License, Version 2.0 (the "License"); you may not # use this file except in compliance with the License. You may obtain a copy of @@ -117,6 +117,7 @@ def test_regression_tandem(sample_inputs_fixture): ) farm_powers = power( floris.flow_field.air_density, + floris.farm.ref_density_cp_cts, velocities, yaw_angles, floris.farm.pPs, @@ -261,6 +262,7 @@ def test_regression_yaw(sample_inputs_fixture): ) farm_powers = power( floris.flow_field.air_density, + floris.farm.ref_density_cp_cts, velocities, yaw_angles, floris.farm.pPs, @@ -330,6 +332,7 @@ def test_regression_small_grid_rotation(sample_inputs_fixture): farm_powers = power( floris.flow_field.air_density, + floris.farm.ref_density_cp_cts, velocities, yaw_angles, floris.farm.pPs, diff --git a/tests/reg_tests/none_regression_test.py b/tests/reg_tests/none_regression_test.py index 444dffe5a..cb7784643 100644 --- a/tests/reg_tests/none_regression_test.py +++ b/tests/reg_tests/none_regression_test.py @@ -1,4 +1,4 @@ -# Copyright 2020 NREL +# Copyright 2022 NREL # Licensed under the Apache License, Version 2.0 (the "License"); you may not # use this file except in compliance with the License. You may obtain a copy of @@ -118,6 +118,7 @@ def test_regression_tandem(sample_inputs_fixture): ) farm_powers = power( floris.flow_field.air_density, + floris.farm.ref_density_cp_cts, velocities, yaw_angles, floris.farm.pPs, @@ -283,6 +284,7 @@ def test_regression_small_grid_rotation(sample_inputs_fixture): farm_powers = power( floris.flow_field.air_density, + floris.farm.ref_density_cp_cts, velocities, yaw_angles, floris.farm.pPs, diff --git a/tests/reg_tests/turbopark_regression_test.py b/tests/reg_tests/turbopark_regression_test.py index 273f230c6..8f9bcb6da 100644 --- a/tests/reg_tests/turbopark_regression_test.py +++ b/tests/reg_tests/turbopark_regression_test.py @@ -1,4 +1,4 @@ -# Copyright 2021 NREL +# Copyright 2022 NREL # Licensed under the Apache License, Version 2.0 (the "License"); you may not # use this file except in compliance with the License. You may obtain a copy of @@ -18,7 +18,7 @@ from floris.simulation import Ct, power, axial_induction, average_velocity from tests.conftest import N_TURBINES, N_WIND_DIRECTIONS, N_WIND_SPEEDS, print_test_values, assert_results_arrays -DEBUG = True +DEBUG = False VELOCITY_MODEL = "turbopark" DEFLECTION_MODEL = "gauss" COMBINATION_MODEL = "fls" @@ -53,6 +53,35 @@ ) +yawed_baseline = np.array( + [ + # 8 m/s + [ + [7.9803783, 0.7605249, 1683956.5765064, 0.2548147], + [5.9926862, 0.8357973, 704869.1763857, 0.2973903], + [5.3145419, 0.8725432, 479691.1339821, 0.3214945], + ], + # 9 m/s + [ + [8.9779256, 0.7596713, 2397236.5542849, 0.2543815], + [6.7429885, 0.8025994, 1023094.6963579, 0.2778511], + [5.9836502, 0.8362597, 701734.6626599, 0.2976758], + ], + # 10 m/s + [ + [9.9754729, 0.7499157, 3283591.8023665, 0.2494847], + [7.5085974, 0.7756254, 1412651.4697014, 0.2631590], + [6.6781823, 0.8052071, 992930.8979929, 0.2793232], + ], + # 11 m/s + [ + [10.9730201, 0.7276532, 4344222.0129382, 0.2386508], + [8.3071319, 0.7620861, 1916104.8725891, 0.2561179], + [7.3875052, 0.7795398, 1347926.7384587, 0.2652341], + ], + ] +) + # Note: compare the yawed vs non-yawed results. The upstream turbine # power should be lower in the yawed case. The following turbine # powers should higher in the yawed case. @@ -89,6 +118,7 @@ def test_regression_tandem(sample_inputs_fixture): ) farm_powers = power( floris.flow_field.air_density, + floris.farm.ref_density_cp_cts, velocities, yaw_angles, floris.farm.pPs, @@ -199,6 +229,73 @@ def test_regression_rotation(sample_inputs_fixture): assert np.allclose(t3_270, t2_360) +def test_regression_yaw(sample_inputs_fixture): + """ + Tandem turbines with the upstream turbine yawed + """ + sample_inputs_fixture.floris["wake"]["model_strings"]["velocity_model"] = VELOCITY_MODEL + sample_inputs_fixture.floris["wake"]["model_strings"]["deflection_model"] = DEFLECTION_MODEL + + floris = Floris.from_dict(sample_inputs_fixture.floris) + + yaw_angles = np.zeros((N_WIND_DIRECTIONS, N_WIND_SPEEDS, N_TURBINES)) + yaw_angles[:,:,0] = 5.0 + floris.farm.yaw_angles = yaw_angles + + floris.initialize_domain() + floris.steady_state_atmospheric_condition() + + n_turbines = floris.farm.n_turbines + n_wind_speeds = floris.flow_field.n_wind_speeds + n_wind_directions = floris.flow_field.n_wind_directions + + velocities = floris.flow_field.u + yaw_angles = floris.farm.yaw_angles + test_results = np.zeros((n_wind_directions, n_wind_speeds, n_turbines, 4)) + + farm_avg_velocities = average_velocity( + velocities, + ) + farm_cts = Ct( + velocities, + yaw_angles, + floris.farm.turbine_fCts, + floris.farm.turbine_type_map, + ) + farm_powers = power( + floris.flow_field.air_density, + floris.farm.ref_density_cp_cts, + velocities, + yaw_angles, + floris.farm.pPs, + floris.farm.turbine_power_interps, + floris.farm.turbine_type_map, + ) + farm_axial_inductions = axial_induction( + velocities, + yaw_angles, + floris.farm.turbine_fCts, + floris.farm.turbine_type_map, + ) + for i in range(n_wind_directions): + for j in range(n_wind_speeds): + for k in range(n_turbines): + test_results[i, j, k, 0] = farm_avg_velocities[i, j, k] + test_results[i, j, k, 1] = farm_cts[i, j, k] + test_results[i, j, k, 2] = farm_powers[i, j, k] + test_results[i, j, k, 3] = farm_axial_inductions[i, j, k] + + if DEBUG: + print_test_values( + farm_avg_velocities, + farm_cts, + farm_powers, + farm_axial_inductions, + ) + + assert_results_arrays(test_results[0], yawed_baseline) + + def test_regression_small_grid_rotation(sample_inputs_fixture): """ Where wake models are masked based on the x-location of a turbine, numerical precision @@ -238,6 +335,7 @@ def test_regression_small_grid_rotation(sample_inputs_fixture): farm_powers = power( floris.flow_field.air_density, + floris.farm.ref_density_cp_cts, velocities, yaw_angles, floris.farm.pPs, diff --git a/tests/turbine_unit_test.py b/tests/turbine_unit_test.py index ec5796792..192ae5bc6 100644 --- a/tests/turbine_unit_test.py +++ b/tests/turbine_unit_test.py @@ -259,6 +259,7 @@ def test_power(): wind_speed = 10.0 p = power( air_density=AIR_DENSITY, + ref_density_cp_ct=AIR_DENSITY, velocities=wind_speed * np.ones((1, 1, 1, 3, 3)), yaw_angle=np.zeros((1, 1, 1)), pP=turbine.pP * np.ones((1, 1, 1)),