diff --git a/data/module1_flow/light_module_desc_single_module-2.0.0.yaml b/data/module1_flow/light_module_desc_single_module-2.0.0.yaml new file mode 100644 index 00000000..cf6a3653 --- /dev/null +++ b/data/module1_flow/light_module_desc_single_module-2.0.0.yaml @@ -0,0 +1,203 @@ +format_version: "0.2.0" +geometry_version: "0.2.0" + +geom: + # ArcLight + 0: { min: [-15.14 , -15.51, 0], max: [+12.03, +15.51, 0] } + # LCM + 1: { min: [-15.14 , -5.17, 0], max: [+12.03, +5.17, 0] } + +tpc_center_offset: + 0: [+15.30, 0, 0] + 1: [-15.30, 0, 0] + +det_center: + 0: [0, -46.53, -31.49] + 1: [0, -25.85, -31.49] + 2: [0, -15.51, -31.49] + 3: [0, -5.16, -31.49] + 4: [0, 15.51, -31.49] + 5: [0, 36.19, -31.49] + 6: [0, 46.53, -31.49] + 7: [0, 56.87, -31.49] + 8: [0, -46.53, +31.49] + 9: [0, -25.84, +31.49] + 10: [0, -15.51, +31.49] + 11: [0, -5.17, +31.49] + 12: [0, 15.51, +31.49] + 13: [0, 36.19, +31.49] + 14: [0, 46.53, +31.49] + 15: [0, 56.87, +31.49] + +sipm_center: + 0: [15.14, -60.07, -31.49] + 1: [15.14, -55.37, -31.49] + 2: [15.14, -48.87, -31.49] + 3: [15.14, -44.17, -31.49] + 4: [15.14, -37.67, -31.49] + 5: [15.14, -32.97, -31.49] + 6: [15.14, -29.07, -31.49] + 7: [15.14, -24.37, -31.49] + 8: [15.14, -17.87, -31.49] + 9: [15.14, -13.17, -31.49] + 10: [15.14, -6.67, -31.49] + 11: [15.14, -1.97, -31.49] + 12: [15.14, 1.97, -31.49] + 13: [15.14, 6.67, -31.49] + 14: [15.14, 13.17, -31.49] + 15: [15.14, 17.87, -31.49] + 16: [15.14, 24.37, -31.49] + 17: [15.14, 29.07, -31.49] + 18: [15.14, 32.97, -31.49] + 19: [15.14, 37.67, -31.49] + 20: [15.14, 44.17, -31.49] + 21: [15.14, 48.87, -31.49] + 22: [15.14, 55.37, -31.49] + 23: [15.14, 60.07, -31.49] + 24: [15.14, -60.07, 31.49] + 25: [15.14, -55.37, 31.49] + 26: [15.14, -48.87, 31.49] + 27: [15.14, -44.17, 31.49] + 28: [15.14, -37.67, 31.49] + 29: [15.14, -32.97, 31.49] + 30: [15.14, -29.07, 31.49] + 31: [15.14, -24.37, 31.49] + 32: [15.14, -17.87, 31.49] + 33: [15.14, -13.17, 31.49] + 34: [15.14, -6.67, 31.49] + 35: [15.14, -1.97, 31.49] + 36: [15.14, 1.97, 31.49] + 37: [15.14, 6.67, 31.49] + 38: [15.14, 13.17, 31.49] + 39: [15.14, 17.87, 31.49] + 40: [15.14, 24.37, 31.49] + 41: [15.14, 29.07, 31.49] + 42: [15.14, 32.97, 31.49] + 43: [15.14, 37.67, 31.49] + 44: [15.14, 44.17, 31.49] + 45: [15.14, 48.87, 31.49] + 46: [15.14, 55.37, 31.49] + 47: [15.14, 60.07, 31.49] + +ch_to_vert_bin: # 0:ACL 1:LCM + 0: [-1,-1,-1,-1,0,1,2,3,4,5,12,13,14,15,16,17,-1,-1,-1,-1,0,1,2,3,4,5,12,13,14,15,16,17, -1,-1,-1,-1,0,1,2,3,4,5,12,13,14,15,16,17,-1,-1,-1,-1,0,1,2,3,4,5,12,13,14,15,16,17] + 1: [-1,-1,-1,-1,6,7,8,9,10,11,18,19,20,21,22,23,-1,-1,-1,-1,6,7,8,9,10,11,18,19,20,21,22,23,-1,-1,-1,-1,6,7,8,9,10,11,18,19,20,21,22,23,-1,-1,-1,-1,6,7,8,9,10,11,18,19,20,21,22,23] + +adc_to_det_type: + 0: 0 + 1: 1 + +det_side: #TPC side 0: -z direction 1: +z direction + 0: 0 + 1: 0 + 2: 0 + 3: 0 + 4: 0 + 5: 0 + 6: 0 + 7: 0 + 8: 1 + 9: 1 + 10: 1 + 11: 1 + 12: 1 + 13: 1 + 14: 1 + 15: 1 + +det_geom: + 0: 0 + 1: 1 + 2: 1 + 3: 1 + 4: 0 + 5: 1 + 6: 1 + 7: 1 + 8: 0 + 9: 1 + 10: 1 + 11: 1 + 12: 0 + 13: 1 + 14: 1 + 15: 1 + +det_adc: + 0: #TPC + # -z, increasing y (det-> adc) + 0: 0 + 1: 1 + 2: 1 + 3: 1 + 4: 0 + 5: 1 + 6: 1 + 7: 1 + # +z, increasing y + 8: 0 + 9: 1 + 10: 1 + 11: 1 + 12: 0 + 13: 1 + 14: 1 + 15: 1 + + 1: + # -z, increasing y + 0: 0 + 1: 1 + 2: 1 + 3: 1 + 4: 0 + 5: 1 + 6: 1 + 7: 1 + # +z, increasing y + 8: 0 + 9: 1 + 10: 1 + 11: 1 + 12: 0 + 13: 1 + 14: 1 + 15: 1 + + +det_chan: + 0: + 0: [4,5,6,7,8,9] + 1: [4,5] + 2: [6,7] + 3: [8,9] + 4: [10,11,12,13,14,15] + 5: [10,11] + 6: [12,13] + 7: [14,15] + 8: [20,21,22,23,24,25] + 9: [20,21] + 10: [22,23] + 11: [24,25] + 12: [26,27,28,29,30,31] + 13: [26,27] + 14: [28,29] + 15: [30,31] + + 1: + 0: [52,53,54,55,56,57] + 1: [52,53] + 2: [54,55] + 3: [56,57] + 4: [58,59,60,61,62,63] + 5: [58,59] + 6: [60,61] + 7: [62,63] + 8: [36,37,38,39,40,41] + 9: [36,37] + 10: [38,39] + 11: [40,41] + 12: [42,43,44,45,46,47] + 13: [42,43] + 14: [44,45] + 15: [46,47] \ No newline at end of file diff --git a/data/module1_flow/module0.yaml b/data/module1_flow/module0.yaml new file mode 100644 index 00000000..abddd9cf --- /dev/null +++ b/data/module1_flow/module0.yaml @@ -0,0 +1,50 @@ +# Argon properties +temperature: 87.17 # K +e_field: 0.50 # kV/cm +lifetime: 2.6e+3 # us +long_diff: 4.0e-6 # cm * cm / us +tran_diff: 8.8e-6 # cm * cm / us +singlet_fraction: 0.375 +tau_s: 0.001 # us +tau_t: 0.752 # us +#tau_t: 0.620 # us + +# Charge simulation parameters +drift_length: 30.27225 # cm +time_interval: [0, 200.] # us +response_sampling: 0.1 # us +#response_sampling: 0.05 # us +response_bin_size: 0.04434 # cm +time_padding: 190 # us +time_window: 189.1 # us + +# Charge geometry parameters +tpc_offsets: # cm + - [0, -21.8236, 0] +tile_map: + - [[7,5,3,1],[8,6,4,2]] + - [[16,14,12,10],[15,13,11,9]] +module_to_io_groups: + 1: [1,2] + +# Light simulation parameters +light_gain: [-5.2589, -5.1955, -5.1616, -5.0982, -5.6851, -5.6870, -58.5344, -58.1440, -58.7968, -59.8208, -55.1488, -57.2672, -5.5680, -5.2243, -5.4509, -5.4291, -5.2672, -5.4278, -57.8816, -54.9824, -54.2272, -54.5856, -56.7616, -58.1696, -5.1424, -6.1382, -6.2451, -5.9392, -4.9338, -5.0266, -51.6864, -50.1568, -49.3440, -51.5904, -48.4992, -46.8160, -6.0134, -6.3974, -6.0077, -6.1254, -6.1280, -6.2048, -0.0000, -0.0000, -0.0000, -0.0000, -0.0000, -0.0000, -5.8694, -5.9027, -5.9392, -6.0058, -6.0083, -6.0454, -59.9296, -61.7792, -63.2704, -60.4672, -61.1776, -60.7680, -6.7821, -6.8288, -6.7693, -6.9325, -6.7930, -6.7757, -55.9552, -57.2032, -56.1856, -53.5232, -59.9296, -0.0000, -6.2221, -6.0813, -6.0646, -6.2138, -6.2310, -6.3558, -42.9824, -41.6768, -51.2832, -49.4976, -42.1312, -44.9792, -6.1901, -0.0000, -5.9878, -5.8035, -6.1069, -6.4064, -0.0000, -55.6160, -56.4864, -55.6416, -54.8032, -55.4816] # PE/us / ADC +sipm_response_model: 1 # arbitrary impulse +impulse_model: 'larndsim/bin/sipm_impulse.npy' +impulse_tick_size: 0.01 # us + +light_det_noise_sample_spacing: 0.01 # us +light_trig_threshold: [ + -1500, -9e+9, -1500, -9e+9, -1500, -9e+9, -1500, -9e+9, -1500, -9e+9, -1500, -9e+9, -1500, -9e+9, -1500, -9e+9] # ArcLight=~no trigger, LCM=-1500 ADC, every 6 channels summed +light_trig_window: [0.9, 1.66] # us +light_nbit: 10 +op_channel_efficiency: [0.38, 0.38, 0.38, 0.38, 0.38, 0.38, 0.38, 0.38, 0.38, 0.38, 0.38, 0.38, 0.38, 0.38, 0.38, 0.38, 0.38, 0.38, 0.38, 0.38, 0.38, 0.38, 0.38, 0.38, 0.38, 0.38, 0.38, 0.38, 0.38, 0.38, 0.38, 0.38, 0.38, 0.38, 0.38, 0.38, 0.38, 0.38, 0.38, 0.38, 0.38, 0.38, 0.38, 0.38, 0.38, 0.38, 0.38, 0.38, 0.38, 0.38, 0.38, 0.38, 0.38, 0.38, 0.38, 0.38, 0.38, 0.38, 0.38, 0.38, 0.38, 0.38, 0.38, 0.38, 0.38, 0.38, 0.38, 0.38, 0.38, 0.38, 0.38, 0.38, 0.38, 0.38, 0.38, 0.38, 0.38, 0.38, 0.38, 0.38, 0.38, 0.38, 0.38, 0.38, 0.38, 0.38, 0.38, 0.38, 0.38, 0.38, 0.38, 0.38, 0.38, 0.38, 0.38, 0.38] # ad hoc PDE scale factor to better improve data/sim agreement + +# Light geometry parameters +n_op_channel: 96 +tpc_to_op_channel: + - [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47] + - [48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95] +module_to_tpcs: + 1: [0, 1] +lut_vox_div: [14, 26, 8] diff --git a/data/module1_flow/module1_layout-2.3.16.yaml b/data/module1_flow/module1_layout-2.3.16.yaml new file mode 100644 index 00000000..84687300 --- /dev/null +++ b/data/module1_flow/module1_layout-2.3.16.yaml @@ -0,0 +1,16403 @@ +chip_channel_to_position: + 11000: + - 3 + - 68 + 11001: + - 2 + - 69 + 11002: + - 1 + - 69 + 11003: + - 0 + - 69 + 11004: + - 2 + - 68 + 11005: + - 1 + - 68 + 11010: + - 0 + - 68 + 11011: + - 2 + - 67 + 11012: + - 1 + - 67 + 11013: + - 0 + - 67 + 11014: + - 3 + - 66 + 11015: + - 2 + - 66 + 11016: + - 1 + - 66 + 11017: + - 0 + - 66 + 11018: + - 2 + - 65 + 11019: + - 0 + - 64 + 11020: + - 0 + - 65 + 11021: + - 1 + - 65 + 11026: + - 1 + - 64 + 11027: + - 2 + - 64 + 11028: + - 0 + - 63 + 11029: + - 1 + - 63 + 11030: + - 2 + - 63 + 11031: + - 3 + - 63 + 11032: + - 3 + - 64 + 11033: + - 4 + - 63 + 11034: + - 5 + - 63 + 11035: + - 6 + - 63 + 11036: + - 4 + - 64 + 11037: + - 5 + - 64 + 11041: + - 6 + - 64 + 11042: + - 3 + - 65 + 11043: + - 4 + - 65 + 11044: + - 5 + - 65 + 11045: + - 6 + - 65 + 11046: + - 4 + - 66 + 11047: + - 5 + - 66 + 11048: + - 6 + - 66 + 11049: + - 3 + - 67 + 11050: + - 4 + - 67 + 11051: + - 5 + - 67 + 11052: + - 6 + - 68 + 11053: + - 6 + - 67 + 11058: + - 5 + - 68 + 11059: + - 4 + - 68 + 11060: + - 6 + - 69 + 11061: + - 5 + - 69 + 11062: + - 4 + - 69 + 11063: + - 3 + - 69 + 12000: + - 10 + - 68 + 12001: + - 9 + - 69 + 12002: + - 8 + - 69 + 12003: + - 7 + - 69 + 12004: + - 9 + - 68 + 12005: + - 8 + - 68 + 12010: + - 7 + - 68 + 12011: + - 9 + - 67 + 12012: + - 8 + - 67 + 12013: + - 7 + - 67 + 12014: + 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b/event_display/module0_flow/module0flow_evd_example.ipynb @@ -18,7 +18,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 1, "id": "81557d67-5a69-4fc5-91d9-ae333bffe2b4", "metadata": { "tags": [] @@ -27,10 +27,10 @@ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 2, + "execution_count": 1, "metadata": {}, "output_type": "execute_result" } @@ -55,7 +55,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 2, "id": "00a3d01f-92be-4d86-8ecd-a68f7a423f69", "metadata": { "tags": [] @@ -84,7 +84,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 3, "id": "ab8119df-4892-425c-bd09-a68fa977ab72", "metadata": { "tags": [] @@ -92,9 +92,9 @@ "outputs": [ { "data": { - "image/png": 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\n", + "image/png": 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", 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\n", + "image/png": 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kRYsWLeKLL77gkUceafIYfvvtN8xmc53GPDiCWbNmMXPmTGbOnMnLL79cp/ckJCSwd+9em2V1SQTXrFnDyJEj6devH0uXLq2yRHm7du04cuRIpeUVy659qp+SkkL//v2RZZnNmzcTHh5e7fEXLVrEhAkTGDt2LJ988kmlpOzuu++udF6NGde1x6roOlUxtujIkSM2N/IVDzmuPpZKpWLevHm88cYbJCcn4+/vT0hICEOHDiUmJqbGz6M+auoCKMtynVumKsr5b9q0iZ9//rnWEuFX69KlCwqFgtOnTzfYsY4ePcr58+cJDg6utG7s2LEA5OXl4e3tXeUxgoKCgP91NbxWWlqadZu66tKlC76+vvz888/MmTNHjDMSBAclEiNBsAOlUsmjjz7K/Pnz2bZtG/v27av0xHT48OEsWbIEs9lc7dPX+qoY0O0srUiyLDNx4kQWLVrEp59+WqmiV1O4cOECM2bMwMvLi0mTJjX58evrzTffZObMmbz66qu8/vrrdX6fh4cHXbp0qdex1q5dy8iRI+nduzcrVqyosmAAwL333svkyZP5448/rNeyyWTi22+/pXv37jYJ2IULF+jfvz9ms5nNmzcTFRVV7fEXL17MhAkTeOSRR/j888+rvNn08/OrsjW1seKq7jO844470Ol0LF682Ob3uWJy4armCauYSBZg//79bNiwgffee6/qD+M6dO/enfDwcNauXYvZbLa2GqWnp3Po0CHGjBlT6z4qWm82btzIsmXLGDp0aL1i2LJlCxaLhWbNmjXYsebPn09+fr7NsoMHDzJt2jRmzpxJv379quzmV6F58+ZERUXx448/Mm3aNJvr6vLly2zatIn77ruvbif4J7VazYsvvsiLL77Im2++yT/+8Y9K22RlZXHmzJlKhSgEQWg6IjEShAZ29OjRKvvHx8XF2UxmOm7cOObOncuYMWNwcXHhgQcesNn+wQcf5LvvvuOuu+7i2WefpVu3bqjVai5evMimTZsYMWIE9957b71iq3gqvXDhQjw8PNDpdMTExFR74whXxo6UlJRQVFQEXJkktmJG+Lvuuss6LuDy5cvWSTErnrivWrWKgIAAAgIC6Nevn3Wf+/bts3bTKiwsRJZl6z67du1qvel85pln+OKLLxg3bhzt2rVj9+7d1n1otVo6depk/ffMmTOZNWsWmzZtuu6WnYqfnclkIisri23btrFo0SKUSiXLly+vcTLaClu2bLGWUzebzaSkpFjPrV+/ftZ9lJaW8vvvvwNYz2vLli1kZ2fj5uZmLcIBVz7z48ePA1daGEpLS637bNOmjbWl8b333uMf//gHd9xxB8OGDbP5vKDqwiDXa/v27YwcOZLg4GBefvllDh48aLO+TZs2eHp6Aleu9Q8//JD777+ff/7znwQGBvLRRx9x6tQpm4lzs7KyGDBgABkZGXzxxRdkZWWRlZVlXR8eHm5tLfnxxx8ZP348HTt2ZNKkSezZs8fm+J06dao2UavQGHFVx9fXl1dffZXXXnsNX19fhgwZwt69e5k5cyYTJkywaS3evHkze/fupX379siyzJ49e5g7dy533HFHnbpF1vXaUigUvP/++4wePZoRI0bw17/+lZKSEt588000Gg0vvfSSzX5VKhX9+vVjw4YN1mX33Xcfq1at4pVXXsHPz8/mmvP09LSe16+//spnn33GPffcQ1RUFEajkX379jF//nyaNWvGhAkTGuxYHTt2rPazadu2bZ2+H/71r38xevRoBg4cyMSJEwkODubMmTP885//RKPRVFtyuybPP/88J06c4PXXX2fPnj2MGTOGiIgICgoK2Lp1KwsXLmTWrFkiMRIEe7LD3EmCcFOqmCS0utdnn31W6T09e/aUAfnhhx+ucp9Go1H+17/+JXfo0EHW6XSyu7u73KpVK3nSpEnymTNnrNtFRUXJw4YNq/T+fv36VZo8df78+XJMTIysVCplQF60aFGN5xUVFVXtOV09gWvFpIhVva6NYezYsdVue3U8NR372klOp0+fLkuSJJ84caLG86lpgteKl0ajkQMDA+V+/frJs2fPlrOysmrc59X69etXbcxXT7JZMRFkXc7t9ddfr3bbqyfcrenYDf11X1NM156rLMtyZmam/Nhjj8m+vr6yTqeTb7vtNnndunU229R0DV17rjVdQ9demzVp6Lhq8+9//1tu0aKFrNFo5MjISPn111+XDQaDzTY7duyQu3fvLnt6esparVaOj4+X//Wvf1Xarjr1ubZkWZZXrFghd+3aVdbpdLKXl5d8zz332Ew4W6Gq3+WaPpertz1x4oR83333yVFRUbJOp5N1Op3cqlUr+fnnn5dzcnIa9FhVqW3S1qqsX79eHjJkiOzt7S2rVCo5JCREfuSRR2y+e69V0wSvFX7++Wd52LBhckBAgKxSqWQfHx95wIAB8ieffCLr9fo6xycIQsOTZNkJSy0JgiBco1u3btbuL4IgCIIgCPUlEiNBEJxeYWEhAQEBHDx4sFKpdEEQBEEQhLoQiZEgCIIgCIIgCLc8MY+RIAiCIAiCIAi3PJEYCYIgCIIgCIJwyxOJkSAIgiAIgiAItzyRGAmCIAiCIAiCcMsTiZEgCIIgCIIgCLc8kRgJgiAIgiAIgnDLE4mRIAiCIAiCIAi3PJEYCYIgCIIgCIJwyxOJkSAIgiAIgiAItzyRGAmCIAiCIAiCcMsTiZEgCIIgCIIgCLc8kRgJgiAIgiAIgnDLE4mRIAiCIAiCIAi3PJEYCYIgCIIgCIJwy1PZOwDBVlpaGi+++CKrVq2irKyMFi1a8MUXX5CQkACALMvMmjWLhQsXkpeXR/fu3fnwww9p27atdR96vZ4ZM2bwww8/UFZWxsCBA/noo48IDw+vcxwWi4X09HQ8PDyQJKnBz1O4MbIsU1RURGhoKAqFYz7fENeQY6vPNVReXo7BYKh1nxqNBp1O11AhCoIgCEKTEomRA8nLy6NXr14MGDCAVatWERgYyLlz5/D29rZu88477zBv3jwWL15MixYteOuttxg8eDCnTp3Cw8MDgKlTp/LLL7+wZMkS/Pz8mD59OsOHDycxMRGlUlmnWNLT04mIiGiM0xQaUGpqar0S3qYkriHnUNs1VF5eTnSMO5cyzbXuKzg4mOTkZJEcCYIgCE5JkmVZtncQwhV///vf2bFjB9u2batyvSzLhIaGMnXqVF588UXgSutQUFAQc+fOZdKkSRQUFBAQEMA333zDAw88APzvBvX3339n6NChdYqloKAAb29vUlNT8fT0bJgTtBODwcB7770HwPTp09FoNHaO6MYVFhYSERFBfn4+Xl5e9g6nSjfTNXQzqus1VFhYiJeXF8dPR+PhUX3LUlGRhTYtzlNQUCB+3oIgCIJTEi1GDmTlypUMHTqU+++/ny1bthAWFsbkyZOZOHEiAMnJyWRmZjJkyBDre7RaLf369WPnzp1MmjSJxMREjEajzTahoaHEx8ezc+fOahMjvV6PXq+3/ruoqAgAT09Pp7/JMRgM1ifYnp6eN0ViVMGRuqjdzNfQzayu15CHuxJPjxpanOXaW5QEQRAEwZE55uCEW1RSUhIff/wxzZs3Z82aNTz11FM888wzfP311wBkZmYCEBQUZPO+oKAg67rMzEw0Gg0+Pj7VblOVOXPm4OXlZX2JLlBCfYlr6OYmWaRaX4IgCILgzERi5EAsFgudO3dm9uzZdOrUiUmTJjFx4kQ+/vhjm+2ufcIry3KtT31r2+all16ioKDA+kpNTb3+ExFuSeIaurlJltpfgiAIguDMRGLkQEJCQmjTpo3NstatW3PhwgXgysBmoFLLT1ZWlrUVKTg4GIPBQF5eXrXbVEWr1Vq7PImuT8L1ENfQzU0y1/4SBEEQBGcmEiMH0qtXL06dOmWz7PTp00RFRQEQExNDcHAw69ats643GAxs2bKFnj17ApCQkIBarbbZJiMjg6NHj1q3EQRBqC9JrqXFSJTxEQRBEJycKL7gQKZNm0bPnj2ZPXs2o0ePZs+ePSxcuJCFCxcCV7rQTZ06ldmzZ9O8eXOaN2/O7NmzcXV1ZcyYMQB4eXkxfvx4pk+fjp+fH76+vsyYMYN27doxaNAge56eIAjOzCJfedW0XhAEQRCcmEiMHEjXrl1Zvnw5L730Em+88QYxMTHMnz+fhx9+2LrNCy+8QFlZGZMnT7ZO8Lp27VrrHEYA77//PiqVitGjR1sneF28eHGd5zASBEG4liTX3CokWowEQRAEZye60jmY4cOHc+TIEcrLyzlx4oS1VHcFSZKYOXMmGRkZlJeXs2XLFuLj42220el0LFiwgJycHEpLS/nll19EhTBBEG6MpQ6vejCZTLz66qvExMTg4uJCbGwsb7zxBhbL/3YkyzIzZ84kNDQUFxcX+vfvz7FjxxrohARBEATBlkiMBEEQhFpJJrnWV33MnTuXTz75hA8++IATJ07wzjvv8O6777JgwQLrNu+88w7z5s3jgw8+YO/evQQHBzN48GDrHFmCIAiC0JBEYiQIgiDUqqIrXU2v+ti1axcjRoxg2LBhREdHc9999zFkyBD27dsHXGktmj9/Pq+88gqjRo0iPj6er776itLSUr7//vtGOENBEAThVicSI0EQBKF2dexKV1hYaPPS6/VV7q53795s2LCB06dPA3Do0CG2b9/OXXfdBUBycjKZmZkMGTLE+h6tVku/fv3YuXNn45yjIAiCcEsTxRcEQRCEWtU2iWvFumvHM77++uvMnDmz0vYvvvgiBQUFtGrVCqVSidls5u233+ahhx4C/jdf27XzrwUFBZGSknL9JyIIgiAI1RCJkSAIglAryQKSufr+chWJUWpqqs3kvlqttsrt//Of//Dtt9/y/fff07ZtWw4ePMjUqVMJDQ1l7Nix/9uvJNm8T5blSssEQRAEoSGIxEgQBEGoXW2V5/5c5+npaZMYVef555/n73//Ow8++CAA7dq1IyUlhTlz5jB27FiCg4OBKy1HISEh1vdlZWVVakUSBEEQhIYgxhgJgiAItWro4gulpaUoFLZ/gpRKpbVcd0xMDMHBwaxbt8663mAwsGXLFnr27HnD5yMIgiAI1xItRoIgCELt6thiVFd33303b7/9NpGRkbRt25YDBw4wb948xo0bB1zpQjd16lRmz55N8+bNad68ObNnz8bV1ZUxY8Zc92kIgiAIQnVEYiQIgiDUroETowULFvDaa68xefJksrKyCA0NZdKkSfzjH/+wbvPCCy9QVlbG5MmTycvLo3v37qxduxYPD4/rOgVBEARBqIlIjARBEIRaSWaQzNUXPZDM9dufh4cH8+fPZ/78+dXvU5KYOXNmlVXtBEEQBKGhicRIEARBqF0DtxgJgiAIgqMRiZEgCIJQO/nPV03rBUEQBMGJicRIEARBqJVkkZAsNXSlq2GdIAiCIDgDkRgJgiAItRMtRoIgCMJNTiRGgiAIQu3MEphqaBWqoTCDIAiCIDgDkRgJgiAItRMtRoIgCMJNTiRGgiAIQu0s0pVXTesFQRAEwYmJxEgQBEGonSxdedW0XhAEQRCcmEiMBEEQhFpJJgmphjFGNa0TBEEQBGcgEiNBEAShdmKMkSAIgnCTE4mRIAiCUDsxxkgQBEG4yYnESBAEQaidGGMkCIIg3OREYiQIgiDUzvLnq6b1giAIguDEFPYOQKjenDlzkCSJqVOnWpfJsszMmTMJDQ3FxcWF/v37c+zYMZv36fV6/va3v+Hv74+bmxv33HMPFy9ebOLoBUG4mcgWBbK5hpdF/DkRBEEQnJv4S+ag9u7dy8KFC2nfvr3N8nfeeYd58+bxwQcfsHfvXoKDgxk8eDBFRUXWbaZOncry5ctZsmQJ27dvp7i4mOHDh2M2m5v6NARBuFlUdKWr6SUIgiAITkwkRg6ouLiYhx9+mM8++wwfHx/rclmWmT9/Pq+88gqjRo0iPj6er776itLSUr7//nsACgoK+OKLL3jvvfcYNGgQnTp14ttvv+XIkSOsX7/eXqckCIKzs9ThJQiCIAhOTCRGDmjKlCkMGzaMQYMG2SxPTk4mMzOTIUOGWJdptVr69evHzp07AUhMTMRoNNpsExoaSnx8vHWbquj1egoLC21eglAf4hq6yYkWI0EQBOEmJxIjB7NkyRL279/PnDlzKq3LzMwEICgoyGZ5UFCQdV1mZiYajcampenabaoyZ84cvLy8rK+IiIgbPRXhFiOuoZucWVH7SxAEQRCcmPhL5kBSU1N59tln+fbbb9HpdNVuJ0m2T2ZlWa607Fq1bfPSSy9RUFBgfaWmptYveOGWJ66hm1zFPEY1vQRBEATBiYly3Q4kMTGRrKwsEhISrMvMZjNbt27lgw8+4NSpU8CVVqGQkBDrNllZWdZWpODgYAwGA3l5eTatRllZWfTs2bPaY2u1WrRabUOfknALEdfQTU7+81XTekEQBEFwYqLFyIEMHDiQI0eOcPDgQeurS5cuPPzwwxw8eJDY2FiCg4NZt26d9T0Gg4EtW7ZYk56EhATUarXNNhkZGRw9erTGxEgQBKEmskWq9SUIgiAIzky0GDkQDw8P4uPjbZa5ubnh5+dnXT516lRmz55N8+bNad68ObNnz8bV1ZUxY8YA4OXlxfjx45k+fTp+fn74+voyY8YM2rVrV6mYgyAIQp3VVmBBFF8QBEEQnJxIjJzMCy+8QFlZGZMnTyYvL4/u3buzdu1aPDw8rNu8//77qFQqRo8eTVlZGQMHDmTx4sUolUo7Rt70LBYT5xN3YijT2zsUQaiW2awnedcOlDoV0Qm9kCQH/T01SzUXWDCLet2CIAiCc5NkWRY9w4VKCgsL8fLyoqCgAE9PT3uHU2+XMo7z4SsfoTAVE+Wl5oJ/OHClQIBGo7FzdDfOGX4+zhCjI9j5w8fs27GTjMta4tp4MO6191AoGr+Xc11/PhXb5S7sgqdL9c/SCstM+D65T/y8BaEK6enpfPfdd4SFhdGmTRuio6Px9va2d1iCIFxDtBgJN6WVH32Gd46CwN4XkdUmKAgDRFcfwfEc2XKS8M5JaI+HsebHEDr0+56u/R+xd1iVyPKVV03rBUGwJcsyu3fvZv369VgsFi5evMiZM2eAK13fo6KirC9fX99aK8wKgtC4RGIk3HSy0w6hOwk+7S+gctdzMbE5RIg/NoJj8lQZUJlUeFrUdGqeg6uHl71DqpoYYyQI9VJcXMyKFSs4d+4cXbt2Ze/evQwbNozY2FhSUlKsryNHjiDLMh4eHjaJkr+/v0iUBKGJicRIaHAVvTPt8YV+4cwhlrz5DV6xF/CMvEzZgea06hnHjlRTk8ciCHWhcIMyyUye0kCzNnraJtxt75CqJJsVyDWMMappnSDcak6fPs3PP/+MJEk88sgjREREsHfvXmRZxsXFhVatWtGqVSsAysvLuXDhgjVROnbsGLIs4+bmZpMoBQYGikRJEBqZSIyEBpVVUM7RM7koZAm/ABfaRXs32bEtFgsbP16Ml7IQv+YZJB2KpEu3OLqPeZ4dc+Y0WRyCUB8ZOQoiYkBl0JCvdmmSYxaVG9mwP6N+b6ptEldRrlsQMBqNrFu3jr1799K8eXNGjBiBm5sbRqMR+N+Dw6vpdDpatGhBixYtgCvTcKSmpnL+/HlSUlJYs2YNFosFFxcXIiMjiYqKIjo6mqCgoCYZjygItxKRGAkNKjmjiHY+VwZen8wrhuimO/bhtV9hOi8TePs5Ss+FIEsB9HhkGhZRLEtwYF4qGaVFgdakRt1EFelOphbS3su9fm8SXekEoUZZWVksXbqUnJwc7rzzTrp27Wpt4an4b13qXWk0GuLi4oiLiwOuJFsXL17k/PnzXLhwgQ0bNmA2m9FqtdZEKSoqipCQkFuu+qwgNDSRGAkNysNdQ3a+Hi+NGksTXl1p5/aw97Nz+PU9grnIFf2FWB6a8wgqlTsGg6HpAhGEenLR6bBYFJgNSvy8PGp/QwMI8tGSklVQr/fIsoRcQ/JT0zpBuJnJsszevXtZu3Ytfn5+PPnkkwQGBtpsU5/E6FpqtZqYmBhiYmIAMJlMpKWlWbvebdmyBaPRiFqttkmUwsLCRKIkCPUkEiOhQbUJ9+KcuohLpSb6NAtqsuMeWpaIS2wyhiJXSk7EcvsLQwmJ7NBkxxeE6yWbvDGX6ii+EIx/nGuTHDPS353iouL6vcny56um9YJwiykpKeHnn3/mzJkzdOvWjUGDBqFWqyttdyOJ0bVUKpU1+QEwm81kZGRYu95t376djRs3olKpCA8Pt3a9Cw8PR6USt32CUBPxGyI0uLigpnnqDZCTmczBzVsw+m/E3TOfC98Po8tfXYnpOKjJYhCEGxHaJYxcuRC3TilEdBnZZMcN96tfEiaKLwiCrbNnz7JixQpkWeahhx6yjhGqSkMmRtdSKpWEh4cTHh5O7969sVgsZGZmWhOlP/74gy1btqBUKgkLC7NJlG6Gef0EoSGJxEhwWnp9PstnfoVRn0lQvzTKSzoydHYHWvTsa+/QBKHO0jMuovFVUpbrwqXMVHuHUz0xxkgQgCtd2TZs2MDu3buJi4tj5MiRuLvXbcxeYyRG11IoFISGhhIaGkrPnj2RZZlLly5Zu94lJiaybds263YVrU+RkZFotdpGj08QHJlIjASntW/VAjTSOQJ7JpN/JBL829Ky1wB7hyUI9WLIyMQ9VI+XRk/u6Yv2DqdaYoyRIMDly5dZunQp2dnZDB06lO7du9ephHZjthjV5djBwcEEBwfTvXt3ZFnm8uXL1kTp4MGD7NixA0mSCAkJsUmUXFyaplKmIDgKkRgJTmntT++zddl5WvZJojjHHTk3iL4Tu9s7LEGol4yLpzh8UaJbZ5ki1xLCdGH2Dql6ci3lukViJNzEZFkmMTGRNWvW4O3tzYQJEwgODq7XPiRJsktiVFUcgYGBBAYG0rVrV2RZJjc319r17tixY+zatQuAoKAga9e7qKgoXF2bZhykINiLSIwEp5NycjvbFqYRN/AC5nINF45G8vDzdxPdqou9QxOEekncvg4p2wMMakr1agxKnb1Dqt5N0JVu27ZtfPrpp5w7d46ffvqJsLAwvvnmG2JiYujdu7e9wxMcVGlpKStXruTUqVMkJCQwdOjQKgss1MZREqNrSZKEn58ffn5+JCQkIMsy+fn51vLgp0+fZs+ePQAEBARYW5Sio6Pr3IVQEJyFSIwEp2KxmFi96DOie2Xj6lnOmW3NaTfQnegOotiC4HwKzyejQ4Naa8TVvQyXYD97h1Qt2Swhm2voSlfDOkewdOlSHn30UR5++GEOHDiAXq8HoKioiNmzZ/P777/bOULBESUlJbFixQpMJhMPPPAArVq1uu59OWpidC1JkvDx8cHHx4dOnToBUFBQQEpKCufPnycpKYl9+/YB4OfnZ5MoeXp62jN0QbhhIjESnMq37/4dvbkY36jLnPotAY+gYu545DV7hyXUQ2pOKZcKygj1cSHU59bulqGUFCh8C7AAhUUulJqN9g6pWs4+xuitt97ik08+4bHHHmPJkiXW5T179uSNN96wY2SCIzKbzWzcuJGdO3cSExPDyJEjb/im31kSo6p4eXnRvn172rdvD1x5oFAxRiklJYX9+/cD4O3tbe12FxUVhbe3d53GYAmCoxCJkeA0kk+uJzcpi4jbLpLyR3Mk9zK6jhiEVutr79CEOsot1pOVW0ZLf3eOZhbi665Fp751JyB08fJBWWYGsxKtAoqysuwdUvWcvCvdqVOn6Nu3csVKT09P8vPzmz4gwWHl5OSwdOlSLl26xKBBg+jZs2eD3Nw7c2J0LQ8PD+Lj44mPjweuzOd0daJ08OBB4Mrv19WJkq+vr0iUBIcmEiPBKRzZu4Glny6mZc/T5Kb6UZrhRe9JIfQcPNbeoQn1UFhuxM/lyrwZHloVpQbzLZ0YyZSh98nHrDRRarHghePOBSRbJOQaii/UtM4RhISEcPbsWaKjo22Wb9++ndjYWPsEJTgUWZY5ePAgq1atwsPDg/HjxxMaGtpg+7+ZEqNrubm50aZNG9q0aQNAWVkZFy5csBZ0OHLkCLIs4+7ubtP1zt/fXyRKgkMRiZHgFHb88COt2mRQlubH2UMRtOyop98dr9o7LKGeovzc2HUmm8wSPQol+Lrd2pML5uaU4pLpj8qgQWVWgsrL3iFV60piVMMErw6eGE2aNIlnn32WL7/8EkmSSE9PZ9euXcyYMYN//OMf9g5PsLOysjJ+/fVXjh8/TqdOnbjjjjsafPLTmzkxupaLiwstW7akZcuWAJSXl5Oammot6LB69WosFguurq7WRCkqKoqgoCCRKAl2JRIjwaFZLEY2ffsZ7iHnQGMk67cE/KKyeejFOeLL0wlJkkTPFgH2DsNh+LioueRVAgoLvp56ug7uau+QqmeppVz3dSRGaWlpvPjii6xatYqysjJatGjBF198QUJCAnDlCf6sWbNYuHAheXl5dO/enQ8//JC2bdvW+1gvvPACBQUFDBgwgPLycvr27YtWq2XGjBk8/fTT9d6fcPNISUlh2bJlGAwG7r//fmurR0O7lf9m6XQ6mjdvTvPmzQEwGAykpqZau96tW7cOs9mMTqezSZSCg4NRKBy3JV24+YjESHBYFouJJe+9Q17uPoKaFXFyTUcUvmWMmzkZF5cge4cnCDes1FxMiV6DGTCrLbRLcNwJimX5yqum9fWRl5dHr169GDBgAKtWrSIwMJBz587h7e1t3eadd95h3rx5LF68mBYtWvDWW28xePBgTp06hYeHR73P4e233+aVV17h+PHjWCwW2rRpI8oN38LMZjNbtmxh27ZtREVFce+99+Ll1XittrdSi1FtNBoNcXFxxMXFAWA0Grl48aI1Udq4cSMmkwmtVktERIS1611ISAhK5a3b/VpofCIxEhzW8T0ryU47SkDbTA5viEdV5E7Hh+IIjhTzFQk3h9J8DWYLSAqZjAwPks4cILZ5J3uHVaWGrko3d+5cIiIiWLRokXXZ1eN/ZFlm/vz5vPLKK4waNQqAr776iqCgIL7//nsmTZpUvxP4k6urK126iO+QW11ubi7Lli0jPT2d22+/nV69ejV6y4RIjKqnVquJiYkhJiYGAJPJRHp6unWM0tatW9mwYQNqtdomUQoNDUWlEreyQsMRV5PgkPbu+JlfPl5L216pFJyIwJjvRrP+Zu58+Al7hyYIDSYo0oXTf5RiNCoI8C3j9LE0h02M6tqVrrCw0GaxVqtFq9VW2nzlypUMHTqU+++/ny1bthAWFsbkyZOZOHEiAMnJyWRmZjJkyBCbffXr14+dO3ded2Ik3NpkWebw4cP8/vvvuLm5MW7cOMLDw5vk2CIxqjuVSkVkZCSRkZHAlda9jIwMa4vSzp072bRpEyqVivDwcGvXu/Dw8OuafFcQKojESHA4RmM+y79ZSUzX06Rc9MRyMhS/EBcenPZ3lEqdvcMThAaTnp7H2Rx32kkWckqU3NMh2t4hVUuWFTUXX5CvrIuIiLBZ/vrrrzNz5sxK2yclJfHxxx/z3HPP8fLLL7Nnzx6eeeYZtFotjz32GJmZmQAEBdl2mw0KCiIlJeUGz0a4FZWXl/Pbb79x9OhROnTowJ133lll0t5YRGJ0/ZRKJeHh4YSHh9OrVy8sFguZmZmkpKRw4EIBp7JMbDiwEW1JBmFhYdZEKSIiosGLaAg3N5EYCQ7FaCzhv/+eTfs2Z7Gg5OjRcNq3ymfE9KfQaBy3YpcgXA8XVxPdWufgprHg52kgMibe3iFVq65d6VJTU20mwqzuxtNisdClSxdmz54NQKdOnTh27Bgff/wxjz32mHW7awesy7J8Sw9iF65Pamoqy5Yto6ysjFGjRtGuXbsmj0EkRg1HoVAQGhqKWedFULiZAHctpzLjyb+cTG5mKomJiWzbtg2FQkFISIi1611kZGSTJsOC8xGJkQOZM2cOy5Yt4+TJk7i4uNCzZ0/mzp1rLXcJdavSpNfrmTFjBj/88ANlZWUMHDiQjz76qMm6C9yI5d/+wLmUZMLioHhHK7x1MoMnPY5/WCt7hyYIDc5crmD/4SACu5/BYHDwr+M6dqXz9PS0SYyqExISUqn6V+vWrVm6dCkAwcHBAGRmZhISEmLdJisrq1IrkiBUx2KxsHXrVrZu3Up4eDhjx461KfDR1ERi1LBc1EpyDXoC0GJRqOh5W1dcNT2QZZns7GzrGKXDhw+zc+dOJEkiODjYJlFycXGx92kIDsTB/xLfWrZs2cKUKVPo2rUrJpOJV155hSFDhnD8+HHc3NyAulVpmjp1Kr/88gtLlizBz8+P6dOnM3z4cBITEx26mkvS8a0UX1xBm86XObi+NW5GJZ3uDiSiRT97hyYIjUJWmWgfn4a3Tyn5yfaOpmYNXZWuV69enDp1ymbZ6dOniYqKAiAmJobg4GDWrVtHp05Xxl0ZDAa2bNnC3Llz63SMlStX1i8oYPDgweJG6SaRn5/PsmXLuHjxIn379qVv3752Lf0sWowanr+HjoIyEydziwn0c8FVc+W2VpIkAgICCAgIoGvXrsiyTG5urnWM0vHjx9m9ezdwpXvu1SXCK+63hFuTSIwcyOrVq23+vWjRIgIDA0lMTKRv3751qtJUUFDAF198wTfffMOgQYMA+Pbbb4mIiGD9+vUMHTq0yc+rLvZs/YkNK5YQ0/4S2YlxGC8GEjNEzz1PvCq6zQg3LYVZRYnZhBEZlYveobuJyZZaxhjVsK4q06ZNo2fPnsyePZvRo0ezZ88eFi5cyMKFC4ErNzZTp05l9uzZ1vlPZs+ejaurK2PGjKnTMUaOHFmvmCRJ4syZM8TGxtbrfYLjOXr0KL/++is6nY7HH3/cOojfnkRi1DjiAt2JC6y57L4kSfj5+eHn50fnzp2RZZn8/HxronTmzBn27NkDQEBAAJGRkURHRxMVFXVdUwMIzkskRg6soKAAAF9fX6BuVZoSExMxGo0224SGhhIfH8/OnTurTYz0ej16vd7672srSzWm3OwkVi1cS9xtWWSe9yMn1YvQOCMDJ/4VhcJxW7gEW/a8hpxVid6Aqdgdc5mWtDQPDu7bTKeuDjqXkUVCbsAJXrt27cry5ct56aWXeOONN4iJiWH+/Pk8/PDD1m1eeOEFysrKmDx5srXr8Nq1a+t1o5KZmUlgYGCdthU3QM5Pr9ezatUqDh06RHx8PMOGDUOnc4yiPSIxchySJOHj44OPjw8dO3YErtxzVSRK58+fJzExEbhyD1bR9S4qKqpR57oS7E8kRg5KlmWee+45evfuTXz8lQHZdanSlJmZiUajwcfHp9I2Fe+vypw5c5g1a1ZDnkKdyLLML5//k7hup8Co4vLWtrhFFvKXlx8kIFCMK3Im9rqGnJmnTkeAmwGN1oSXQk3a+Yt06mrvqKrW0PMYAQwfPpzhw4dXu16SJGbOnFllVbu6GDt2bL26xT3yyCN1Gh8lOKa0tDSWLl1KSUkJI0eOpH379g7VAisSI8fm5eVF+/btad++PQBFRUVcuHDBOk7pwIEDAHh7e1u73UVHR+Pt7d3k19m3335L27Ztrd2MhYYjEiMH9fTTT3P48GG2b99ead31VGmqbZuXXnqJ5557zvrvwsLCSmV3G8PqH94G9TmwKDiwvCsxbTO4+9mJhDfr3ujHFhqWva4hZ+btG8x5MkC6MsmrxuzA82/I0pVXTesdzNWTx9bFxx9/3EiRCI3JYrGwY8cONm/eTEhICI888oi1p4UjEYmRc/Hw8KBt27bW4lYlJSU2idKhQ4eAKwVnrk6UfH19Gz1RWrRoEY899phIjBqBSIwc0N/+9jdWrlxpraJToS5VmoKDgzEYDOTl5dm0GmVlZdGzZ89qj1ndJIyNafV375J0dB9+cSWc3RKPu4eFe5+fRFhsryaNQ2gY9riGnJ2sUlOMGYtZQiUB5Nk7pGo1RouRINyogoICfvzxR9LS0ujRowe33347KpVj3tqIxMi5ubm50bp1a1q3bg1AWVkZFy5csHa/O3r0KLIs4+7ublPMISAgoMETpfLycofpInqzccxvj1uULMv87W9/Y/ny5WzevJmYmBib9XWp0pSQkIBarWbdunWMHj0agIyMDI4ePco777zTtCdUDYvFyK4N73HywG5C22SQvLEtRpOCUX/vLpIi4ZZSjhGNix6l2ogSC4VpDpwYmSVkcw2JUQ3r7CEvLw9ZlvH19eXy5cts3bqVli1bWrsmC87v+PHjrFy5EkmS6NGjB6GhoZw/fx53d3dcXV3R6XSo1WqH6U4nEqObi4uLCy1btrROqaLX620SpdWrV2OxWHB1dSUqKspa0CEoKOiGr8ny8nLxILKRiMTIgUyZMoXvv/+en3/+GQ8PD+uYIC8vL1xcXOpUpcnLy4vx48czffp0/Pz88PX1ZcaMGbRr185apc6ezOZS/u/1l0g6WcRtw9LIOhVCSYEbXUf70KHPffYOTxCalAmZIrMFvUnJJQP4F16yd0jVcqYWo88//5w5c+ZgsVh44YUX+O6772jfvj2vv/46zzzzDE8++aS9QxRugMFgYPXq1Rw4cIDWrVvTokULtFotSqWS8vJyioqKAFCr1bi4uODu7o6Liws6nQ6NRmO3uEVidHPTarXWezO4cp1evHjR2vVu/fr1mM1mdDodkZGR1q53wcHB9S4jL1qMGo9IjBxIRf/2/v372yxftGgRjz/+OFC3Kk3vv/8+KpWK0aNHWyd4Xbx4sd3nMJJlmf9+8B4p23R0GXGE/AxvslL8aD3AlbsffcOusQmCPbh5huDncQLJpEaR583l/GJ7h1QtZ0qMFixYwLFjxygtLSUyMpLk5GQCAgIoLCykb9++IjFyYunp6SxbtozCwkLuvvtuOnTowOnTpwHQaDTWxEeWZYxGI2VlZdYKmWq1GldXV5tESa1uunF9IjG6tWg0GmJjY63l/41GI2lpadYWpU2bNmEymdBoNNZEKSoqitDQ0Frv1/R6vUiMGolIjBxIXb4w61KlSafTsWDBAhYsWNCA0d24Axt+IHNDPl2GHcEE5ByMpWVHBQ9O+6e9QxMEu+gxYCBJ2zajU8j4KhUoTfaOqAZOVHxBqVSi0+nQ6XQ0a9aMgIAA4MogaUfpViXUjyzL7Ny5k40bNxIUFMSTTz6Jv79/tX83JUmySZQsFgtGo5GSkhLy8/NRKBTWFiUPDw9rotSY45NEYnRrU6vVREdHEx0dDYDZbLZJlLZu3YrRaEStVhMREWGTKCkUCptkSbQYNR6RGAlNIj31BPt/W09wh1xcPctI2hdLTDMvHnzxDXGjItyyAoPj8NcpUapNeHgX4+nmuFXpLBYJi7n67h6Wes5j1JhUKpX1xmHLli3W5RVdrATnUlRUxPLly0lOTqZnz57cfvvt9e4BoVAobArEWCwWDAYDxcXFFBQUWBOpihalisS6IRMlkRgJV1MqlURGRhIZGUmfPn0wm81kZmZau97t3LmTTZs2IUkSAQEB/PWvf7W+V4wxajwiMRIa3aE9v/PZzK0EhBiJb5dJzroEWvX15PYn/o5SKZ54CLc2vWs5RXolJ7N1dIqy2Duc6sl/vmpa7yA2btxovWm4ejLGsrIyvvjiC3uFJVyHkydPsnLlSpRKJY8++qi1W1IFSZKQJAmLpX6/OwqFwpr8wP8SpcLCQnJzc1EoFGg0Gtzc3GwSpRvpki4SI6EmSqWSsLAwwsLC6NWrF/n5+Xz33XdkZ2dXGhsnWowaj0iMhEaVm32WZQt+wd3dTFz3s6SfDsTfM4DBE59HpXKzd3iCYHd5xRJuShmF0kJWYZm9w6mWM40xcnd3r3J5YGAggYGBTRyNcD2MRiNr1qwhMTGRli1bcs899+Dq6lrltg2RcFSXKOXn51dKlNzc3K4rURKJkVBXqamp/Oc//0GlUhESElJpXi4xxqjxiMToT1dPTFlXr776qkNOIucojMZidn04h45uBpSDTpF+3g9FoR+DXxstkiJB+JOvexk+OhM9QkvIlsz2DqdazpQYXevixYt8/PHH7Ny5k8zMTCRJIigoiJ49e/LUU0+JiYgdTGZmJkuXLiU/P59hw4aRkJBQY5frxuiOfW2iZDabrXMEZmdno1Qq0Wg0uLu72yRKNVUXE4mRUBcHDx7k119/JTQ0lAceeIAffvjBmoAbDAYMBgMmk0l0pWsk9asPeBObP38+f/zxBwcOHKjTa8GCBeTn59s7bIe2e8knpKwPRW6RAaVayjN96PvACPzD29g7NEFwCElnDkCxDkOJjtyUQHzUjvuVLFukWl+OaPv27bRu3Zrly5fToUMHHnvsMR555BE6dOjAihUraNu2LTt27LB3mAJXCizs3r2bzz//HKVSyZNPPkmXLl1qTXzqW+r4eiiVSlxcXPDy8sLX19faKpmbm8v58+c5c+YMp0+f5uLFi+Tl5VFWVlape59IjISaWCwW1qxZw88//0z79u0ZO3Ysbm5umEwma2K0adMm/Pz8AJg3bx7r16+npKTEZj9paWk88sgj+Pn54erqSseOHUlMTLSul2WZmTNnEhoaiouLC/379+fYsWM2+9Dr9fztb3/D398fNzc37rnnHi5evNjIn4BjEC1GV1m+fHmdu1lcXR5bsCXLMpkXt5G65zj+gy6DJJN+IJLwjgG06zfS3uEJgsPITL+MJIFCp0ehMmMxO25LqmxRIFuqvwGtaZ09TZs2jQkTJvD+++9Xu37q1Kns3bu3iSMTrlZcXMzPP//M2bNnue222xg4cGC9Ch80dcJRkSi5uLgAYDKZMBgMZGdnc/nyZZRKJVqt1mayWUGoTnl5OT/99BNJSUnccccddOvWzfpAwGw2W38XhgwZwvbt2+nWrRtpaWmMGzeOxx9/nDfeuDLlSV5eHr169WLAgAGsWrWKwMBAzp07h7e3t/VY77zzDvPmzWPx4sW0aNGCt956i8GDB3Pq1Cnrve3UqVP55ZdfWLJkCX5+fkyfPp3hw4eTmJho96lfGptIjP60aNEim0G6tfn0008JCgpqxIic17LP5rP0g3y63JlLTEwGuWs7I8fIjJj4pr1DEwSH4u1j5nSWO+pSDQU++fi6Vz2GwhE4a1e6o0eP8u2331a7ftKkSXzyySdNGJFwrTNnzrBixQokSeLhhx+mWbNm9Xq/I1Q2ValUqFQq6zioikQpKysLWZatlRIryoXrdDq0Wq1DxC7YV3Z2NkuWLKGkpIRHHnmkUoERk8lkTYwkSSIsLAyAb7/9FoVCgcn0v3ke5s6dS0REBIsWLbIuqygPDlceIMyfP59XXnmFUaNGAfDVV18RFBTE999/z6RJkygoKOCLL77gm2++YdCgQdZjRUREsH79eoYOHdoon4OjcMxHfHYwduzYevXXHDNmDG5ujvt0116OH9rMjg3HaX/7Ufyjs0k9FEVQdx1PvbEQtVq0sgnC1Y7sPIOkMKPWGSiQjJSXOe5XsizX/nJEISEh7Ny5s9r1u3btIiQkpAkjEiqYTCZWrVrF999/T1hYGH/961/rnRSBYyRG16pIknx8fPDx8bG2LJWVlZGcnMyZM2c4c+YMGRkZFBQUoNfrRTe7W9DZs2f5/PPPkSSJiRMnVkqKwDYxgiutSyqVCqVSiSRJNpMUr1y5ki5dunD//fcTGBhIp06d+Oyzz6zrk5OTyczMZMiQIdZlWq2Wfv36Wb8nExMTMRqNNtuEhoYSHx9f43fpzUK0GNWiuLi4Uj9hT09PO0Xj2IqKLrB8wWLcLFqiO6SSn+aLXORJv8dnOOQfLkGwN58A8HHXo0SiJNeTywGiKl1DmzFjBk899RSJiYkMHjyYoKAgJEkiMzOTdevW8fnnnzN//nx7h3nLycrKYunSpeTk5FTqOlRfjj52p+LmValUolar8fb2trYoXbp0CVmWUavVaLVam8lmNRqN+Nt5k5JlmV27drF+/XqaNWvGX/7yl2ofzl89xghqLtWdlJTExx9/zHPPPcfLL7/Mnj17eOaZZ9BqtTz22GNkZmYCVOrxFBQUREpKCnCl+IlGo8HHx6fSNhXvv5mJxKgKycnJPP3002zevJny8nLrclmWkSQJs9lxK0fZi8ViYdE7L1NSoqLN7Ucpz3EnLyWMye/+HQ+PSHuHJwgOyZBbjlYJKCyY1QYsOO44BNmsQK5hgtea1tnT5MmT8fPz4/333+fTTz+1fn8rlUoSEhL4+uuvGT16tJ2jvHXIsszevXtZt24dPj4+TJw48Ya7pTt6YlShIs6KRKniSb8syxiNRvR6PcXFxciyjEajQafT2SRKarVaJEo3AZPJxK+//sqhQ4fo1asXt99+e40FRK4eYwQ1l+q2WCx06dKF2bNnA9CpUyeOHTvGxx9/zGOPPWbd7trrqOK6rEldtrkZiMSoCg8//DAAX375pfXpolCzrWu+5Zcv4xn08C5kIPdMGA89/xRe3s3tHZogOCylBYry3CnIcefMsVCad9bbO6RqOWuLEcADDzzAAw88gNFoJDs7GwB/f3+bLihC4yspKWHlypWcPn2arl27Mnjw4Ab5GTjL3+jq4pQkCY1GY53EsyJRKi8vp7Cw0JpIubi42BRzENev8ykqKuI///kPly5dYtSoUbRr167W91TVla66xCgkJIQ2bWwr/7Zu3ZqlS5cCEBwcDFxpFbq6C3FWVpb1AUVwcLC1NP3VrUZZWVn07NmzjmfqvERiVIXDhw9bJ5UTaibLFtYu+4Jt/z3C6CfPofQu5uTWVjw0dRjRLbraOzxBcGhuvl7ofIrx9C4lMjIPv6Da/0jaizMnRhXUarUYT2Qn586dY8WKFVgsFh566CFatGjRYPt2lsQI6lY9r6pEyWAwUFpaWilR8vDwsM6hJBIlx5aWlsZ//vMfAB5//HFrEYWaWCwWZFmu1JWuum53vXr14tSpUzbLTp8+TVRUFAAxMTEEBwezbt06OnXqBFyZG2nLli3MnTsXgISEBNRqNevWrbO2pmdkZHD06FHeeeedep618xGJURW6du1KamqqSIzqYOWPH/PZ7HQ63paFZ+Rl8va3ZOiD3YnvPsreoQmCw0vPSCY12xV/vYqAsAKGjrrD3iHVoObECJzn5vRqqampvP7663z55Zf2DuWmZDKZ2LBhA7t37yYuLo4RI0Y0+HQXztaV7nrep9VqrTfDFosFo9ForXCnUChQq9W4urraJEr1KXcuNK4jR46wcuVKgoKCeOCBB+r8O1BRca6uLUbTpk2jZ8+ezJ49m9GjR7Nnzx4WLlzIwoULgSvX0tSpU5k9ezbNmzenefPmzJ49G1dXV8aMGQOAl5cX48ePZ/r06fj5+eHr68uMGTNo166dtUrdzUz81lTh888/56mnniItLY34+PhKT2Hat29vp8gcS35OKod+OUm39kVEtU8lNyWAhP4D6DZinL1DEwSnkHO5AH93I65qCxaLjK+fA4/Hk6Urr5rWO6Hc3Fy++uorkRg1guzsbJYuXUpWVhZDhgzhtttua5TWnZutxag2CoWiUqJkMBgoKioiPz/f2uLk6uqKu7s7Op0OFxeXm37+GUdksVjYuHEjO3bsoEOHDgwfPrxeCWtViVFNY4y6du3K8uXLeemll3jjjTeIiYlh/vz51iEiAC+88AJlZWVMnjyZvLw8unfvztq1a22Stffffx+VSsXo0aMpKytj4MCBLF68+Ja4hkRiVIXLly9z7tw5nnjiCeuyqwdNiuILUJCfzrvT/4+CSxK3DU/GlOuBW2kEnUfcb+/QBMFpeLvIZJSpMZolPBSO/QfHYpawKKq/AbWYHfPmdOXKlTWuT0pKaqJIbh2yLLN//35Wr16Nt7c3EyZMaNQujM7UYtQYFAqFtZUI/pcoFRYWkpuba02krk6UdDrdLXGTa096vZ5ly5Zx5syZ634wUHG/eW2LUU3TywwfPpzhw4dXu16SJGbOnMnMmTOr3Uan07FgwQIWLFhQr3hvBiIxqsK4cePo1KkTP/zwgyi+UAVZlln1n68ozSuiVb9zGEwSpWejeeifT6FSibmKBKGu8kstGCQzSpWZ7CLH/jp21jFGI0eOrPXGWXzHN5zS0lJ++eUXTp48SUJCAkOHDm30sS/O8vNrqgTu2kTJbDZjMBjIz88nJycHpVKJRqPBzc3NJlGqqTKaUD+5ubn88MMPFBUVMWbMmOuanwv+12JU13Ldwo1z7L/EdpKSksLKlSuv+0K+2W1f/jWHvi2lRZ9MvP3KSEmMZtCDffEL7mjv0ATBqXi4qQlQGVEYNQT5l9f+Bjty1sQoJCSEDz/8kJEjR1a5/uDBgyQkJDRtUDep5ORkli9fjslkYvTo0bRu3bpJjqtQKJymxcgecSqVSlxcXKyTzFYkSnl5eeTk5KBQKNBoNHh4eFgr3olE6folJSXx448/4ubmxoQJE/D397/ufdW3K51w40RiVIXbb7+dQ4cOicSoCpnJezj1/SHadEvDNTaT5K2taZEQS5c7xjT4sUxmC7vPZaNVKHBzUdMmzKvBjyEIjUGWZXadzUEtgUqtpFOUT5XbWcqM+KpkdCoLmhKXJo6yfmS55uTHUe9LExIS2L9/f7WJkbN0w3JkZrOZTZs2sWPHDqKjo7n33nvFROjVcIRrrbpEKTs7G4vFglKpRKfT2ZQG12q1IlGqhSzL7NmzhzVr1hAbG8tf/vIX62d8varrSicSo8YjEqMq3H333UybNo0jR47Qrl27St0A7rnnHjtFZl+ybGHv4u/ReRfi1fYC2adDcfNTc9eTTzfK8Y5cLKBDiBcqhYJDaQWNcgxBaAwn0gtp6e+Gq0bFicwiyo1mdOrK/fm1kgGL2oJCkvFUW+wQaT04afGF559/npKSkmrXN2vWjE2bNjVhRDeXnJwcli1bRmZmJgMHDqRnz55NfgPtLDfsjpqEX5somUwmDAYDly9ftpaK1mq1uLu74+bmZk2UnKULY1Mwm8389ttvHDhwgNtuu43Bgwc3yHVZXVe6msYYCTdGJEZVeOqppwB44403Kq27VYsvmEylrPri31xMKSasx2lKT4dyOd2bkdNHoXOp+mn4jfLQqcgrMRLgocVocYybxvxSAycuFqBUSCTE+qGsYTC6cOvydlOTXWwgUqOizGRGVc11opS05EW44epxEb3GsecgsVgUWCzV/6GvaZ099enTp8b1bm5u9OvXr4miuXnIsszBgwdZtWoVHh4ejBs3rk7zstzKnCWRUKlUqFQqXF1dATAajRgMBrKysqzrKxKlq1uUnOX8GlpJSQn//e9/SUtLY8SIEXTs2LHB9l3fct3CjROJURUsDnIT7ki2/fQjp34qJnJYEoV5bujT/Qjp3Izm7Yc22jGbBXlwMqOQ41lFtA13jG50R87n087fA6PZwv7kXLrG+dk7JMEBhXq7kmQo5sTlIuKC3VEpq04aLnu3oFNLP0yqZFJbDWjiKOtHtlx51bTe0Rw+fJj4+Pg6P7k9duwYLVu2FPO/1KKsrIzffvuNY8eO0bFjR+68807rZKT24KgtMddyljivpVarUavVuLm5IcuytUUpKysLWZZRqVTodDo8PDxwcXFBp9Oh0WhuiUQpMzOTJUuWYDKZGDt2LBEREQ26fzHGqOmJb3+hVmnnD5BxYjfhvTKgyI2CwzF0GBNNzxFPN/oXX6sQx+qnrvhzEkuVQsLihH/ghKYTG+he6zZqlQcqCUwKGReFaxNEdf2csfhCp06dyMzMJCAgoE7b9+jRg4MHDxIbG9vIkTmvlJQUli1bhl6v57777qNt27b2DglwntYYZ0yMriZJkjVRAqyJkl6vp7i4GLiSSFWMUarooqdWq53mZ1RXx48fZ8WKFfj7+/Pggw82yrg6Mcao6YnEqBp79uxh8+bNZGVlVWpBmjdvnp2iqp+PPvqId999l4yMDNq2bcv8+fNr7VZyrY0/f8WmrzKIic8lou1l8n7rQ6u7/Ok18lkk6dabAyE6xI3jmcVYgIS4xulCKNw6YgKK2XEkh8iYcgrStwF32zukajljYiTLMq+99pq1S1BtDAZDI0fkvMxmM1u2bGH79u1EREQwatQovLwcoyXfWVpinCXO+qgqUTIajZSXl1NUVARcSZRcXFysiVJFi5KzkmWZzZs3s3XrVuLj47nnnnsarSR9dWOMHOV372YkEqMqzJ49m1dffZWWLVtWmsfIWZ54/Oc//2Hq1Kl89NFH9OrVi08//ZQ777yT48ePExkZWef9rP8sieDm2QR1SCZ9dzs6P9iMng+MQ5IcczxBYwvzcSXMx7Gf7AvOI+X0WUrOF6H3KKIoLdfe4dTIGccY9e3bl1OnTtV5+x49etxwFambUV5eHsuWLSMtLY3+/fvTu3dvhyp4IEmSU/xtdoYYb5QkSWg0GmviU5EolZWVUVhYCFxJlComm61IlBp7rquGYjAYWLFiBSdOnOD222+nd+/ejfpzrW6MUVBQUKMd81YnEqMq/Pvf/+bLL7/k8ccft3co123evHmMHz+eCRMmADB//nzWrFnDxx9/zJw5c+q8nxidmdjbkrh4NggPTxcS7rn3lk2KBKGhyXoDASYtOrMaD5Nj3xg4Y4vR5s2b7R2C0zt8+DC//fYbrq6ujBs3jvDwcHuH5LRuxhaj2lybKFksFoxGIyUlJeTn56NQKKwtSh4eHuh0OlxcXBxynF9+fj5LliwhLy+PBx98kJYtWzb6MSu60l3dYiTGGDUux7vyHIBCoaBXr172DuO6GQwGEhMT+fvf/26zfMiQIezcubNe+wrsc5ySw7FIZW4MfW0sOldRbEAQGkpkSBQpB4qQyzT4So41nu5azpgYCdevvLyc33//nSNHjtC+fXvuuusuhy0R7EwtMbdaYnQthUKBVqu1XksWiwWDwUBxcTEFBQXWRKqiRalisll7J0opKSn897//RavVMn78eAIDA5vkuCaTCaVSaXONi3LdjUskRlWYNm0aH374IfPnz7d3KNclOzsbs9lcqak1KCiIzMzMKt+j1+vR6/XWf1c0eWu0Ssy5rbhjRldCorpWep/JbMFkkauco0W4tVR3Dd0Myo1XSm5XV13uenW8axhFae+j8dTTdkTDVjNqcLIEFuebx0iov9TUVJYtW0ZpaSn33nsv7du3t3dIN41bPTG6lkKhsCY/8L9EqbCwkLy8PJtEqaJFSafT2bSgNLbExER+//13IiMjuf/+++s8ZrEhmEymSkmhKL7QuERiVIUZM2YwbNgw4uLiaNOmTaW+r8uWLbNTZPVz7VM0WZarfbI2Z84cZs2aVWm5j/cA+nz6BC7ulfuzpueXknK5FBeVAoVSQftI7waJW6jZ8Yv57DqTYe8wKqnuGnJ2Ry7mYzZaKDdZiPBv2DFmRen5XFK0xNelEPfMpnkCeb1Ei9GtYe3atezatQtvb28efPBBoqOj7R1SrZylxchZ4rSn2hIlhUKBRqPBzc3NOtlsYyVKZrOZ1atXs2/fPrp27crQoUObNCGD/7UYXU10pWtcIjGqwt/+9jc2bdrEgAED8PPzc7ovM39/f5RKZaXWoaysrGoH7L300ks899xz1n8XFhYSERGBb0jLKpMigMz8cjqHewNwJKOgYYIXarRiz0WUuWayM0rsHUol1V1Dzk5vMNMu5EoFoGOXChs0MVqXqyWvbS+iVFGsUgbhyDMZWSwSlhpajGpaJziPjIwMJEmioKCAr7/+Gm9vb2JiYoiNjSUmJgY3Nzd7h1iJs/yNvhXHGN2oaxMls9mMwWAgLy+P7OxslEolGo0Gd3d3m0TpRouDlJaW8uOPP3LhwgWGDx9OQkJCQ5xOvZnNZtFi1MREYlSFr7/+mqVLlzJs2DB7h3JdNBoNCQkJrFu3jnvvvde6fN26dYwYMaLK91zd5/dqzfpUPyO8p4ua1LxSfN00mCziy76x7T2fy7nTBfgGuvK7h+OVFa7uGnJ2JotMsd5EXpkBT13DFkjYkq3lcoQXCXIcuqzsBt13g6ulxUh0pbs5BAUFUVJSwvjx4zl//jxJSUkkJydz4MABAIKDg62JUmRkpFOXXW5qIjG6cUql0jo3EvwvUcrNzSU7O9s6hsnd3R1XV1dcXFzQarX1SpSysrL44YcfMBgMPPbYY0RFRTXW6dSquq50N+PfWkchEqMq+Pr6EhcXZ+8wbshzzz3Ho48+SpcuXejRowcLFy7kwoULPPXUU/Xaj0JR/SXSLMiDtLxSLpXq6R4nijI0pqS8UnYevoy7n5avFcVois32DumW0T3Oj5OZhXjq1A1eqr3caKYcmYtmI6Uqx75huhm60m3bto1PP/2Uc+fO8dNPPxEWFsY333xDTEwMvXv3tnd4DkGlUmEymdBqtbRs2dJaeauoqMiaJB09epRdu3ahVCoJDw8nNjaW2NhYQkND7VLG21lajECMMWpo1yZKJpMJg8FAdnY2siyjVCptEqWKFqXqrpmTJ0+yfPlyfHx8GDt2LN7e3k14NpVV1ZVOtBg1LpEYVWHmzJm8/vrrLFq0qEkH2TWkBx54gJycHN544w0yMjKIj4/n999/b/AnH2E+roQ16B6Fa/10IJ15nxylT3t/TrZxwdegIG1d1UU0hIYnSRKtQxp+Mr1cvZHCbD3qZm5oNAr05Y6d7Dp7YrR06VIeffRRHn74YQ4cOGAtFFJUVMTs2bP5/fff7RyhY6hIjK7l4eFBhw4d6NChA7Isk52dbU2UduzYwaZNm9BqtURHR1sTJWfsit6YxGfR+FQqFSqVynrvVpEoZWVlIcsyKpUKnU5nkyhVtL5s27aNTZs20bp1a0aOHOkQraFVdaUTY4wal0iMqvB///d/nDt3jqCgIKKjoysVX9i/f7+dIqufyZMnM3nyZLsd/9jWk/z2XSKFFplRE7rRuXsLu8XirM4VlTLr00N4Z1hYGV+GS7KBsO2FPPxQS/743t7RNT2DwcD7f/+JnNRy7hrbjv7DK1dKdBa/nsri8rFC9NE6VhzJIS2r1N4h1chiVmCpYQ4zi9mx5zd76623+OSTT3jsscdYsmSJdXnPnj1544037BiZY6kuMbqaJEkEBAQQEBBA9+7dsVgspKWlkZycTFJSEmvWrMFiseDh4WFNkmJiYvDw8GiUmJ2li5qzxHkzuTpRkmXZmihdunTJul6pVJKYmEhSUhL9+vWjX79+DpPEiqp0TU8kRlUYOXKkvUNwegaDgf9+vIUMbRmWQgUfv7SeD1ZFiX6x9SDLMg+/vweVWcHlfh6YlRLyoWKe+Ht7BgW78oy9A2wg6fml5BQZaBPmhVJR0+B+C28/u5jko8V4hRn59t87nToxyi83orZAkVkmxWhG43j1NGzIcs2tQo5+v3fq1Cn69u1babmnpyf5+flNH5CDUqlU1kkl60qhUBAREUFERAR9+/bFYDBw4cIFkpKSSEpK4tChQwAEBARYE6WoqIb9e+AoN7I1EYmRfUmShFqtRq1W4+bmhizL5Ofns2HDBoqKiuxaZKE61RVfEPdSjUckRlV4/fXX7R2C01MoFJRHB9K/pZrLObns/o+WM0cvEJ/Q3N6hOY1X/3OcvNMFmDv4IHspCVuVR3hPf0a1CL5p5gg6n12MXm8m2seVXWez6d0ioNptc3PzSD9Zioe/GUOeGkO2c399eWjVuFmU5JtlFAUmylwt9g6pRs7elS4kJISzZ89WKj+9fft2YmNj7ROUA1IqlbW2GNVGo9HQrFkzmjVrBkBJSYm1NenkyZP88ccfSJJEeHi4tZBDeHj4dZdCdqaEw1nivBVcvnyZTZs2oVQq6dOnDy1aOF6vFjHGqOk5952F4LBUKhXN2oTg72EhNy+d8K5ehMdUXfa7IW1cuZdVn+5GnaMiuL07rXtGkbglFaIb/dAN6kR6ITtXXsDY3B2jtwLPA4W4d/Lk62ld7B1ag8otNtA2yBMAbR0mT1UadbgGlHJxnQfN+jVshbjrVV6u5/t/ruPU9hRU5RDa249Jb93HV2//RurhEjoPC+OecZWrO2aVlOPnJVHip0XRQodPqf37s9eksROjOXPm8PLLL/Pss89aJ9eWZZlZs2axcOFC8vLy6N69Ox9++CFt27at9/4nTZrEs88+y5dffokkSaSnp7Nr1y5mzJjBP/7xjxuK/WaiUqmwWCxYLJYGK6Tg5uZGfHw88fHxyLJMXl6etTVpz549bN26FbVaTXR0tDVRCgwMrHMrkCRJ1uTIkVuOHDm2W82ZM2fYvXs3/v7+9OvXz2ZyckdSVVc6McaocYnE6E++vr6cPn0af3//Om0fGRnJtm3b7FrG0dG17xZF8oksVF7t6TFAibevZ6Mer6ysjBVfbsdDYUEVrOdydgnnVhdR7K8hwsH/YF5t45Esvnj7IGUxWsxxrrhsz8HHLPGviR1QKRTkFus5nppn7zAbRKSfK3sv5OKmVqFS1fzz8fX1YcBDrdi/4TwJ96n421v3N1GUNduwZB/ndp3GzctCic6F4yWlPPviFxSfMqAzm1j7aSmDH+pmrZpUwV1SMvHRtnwmFxPcXksnrWMketVpzMRo7969LFy4kPbt29ssf+edd5g3bx6LFy+mRYsWvPXWWwwePJhTp07Ve7zKCy+8QEFBAQMGDKC8vJy+ffui1WqZMWMGTz/99HXHfrOpuAkzm82NUmFOkiR8fX3x9fWlS5cuWCwWMjMzrYUcNmzYwNq1a3Fzc7OOTYqNjcXLq+ELoDQ1Z2rZullZLBb27dvHiRMnaN68Od27d0ehUKDX6x3yHsFsNtskQWazGaPRKLrSNSKRGP0pPz+fVatW1fnLNycnp979sG813VuG0CkuECwWNJrGv+lTKpWYMaOOKcdskpBcXfDxk2mukckuurKNo//MCo0mlv07kSxfFSVRLrTdVkxUqyAWTOuMSqEgp1hPUkYR0Z7OWS3xWv4eOnq10GI0y2hUNd+EKRQKHpjSn79MqvwEzZ4yMwpw9zJQEijjEeqCr8qAh0pJia+ai+f1SFQdb1ZRGS29PfFXapAMBm5rH2KH6OvOYlFgsdRQfKGGdTUpLi7m4Ycf5rPPPuOtt96yLpdlmfnz5/PKK68watQoAL766iuCgoL4/vvvmTRpUr2P9fbbb/PKK69w/PhxLBYLbdq0wd3d/brivllVXKsmk6lS4aHGoFAoCA0NJTQ0lN69e2M0GklNTbUmSkeOHAHAz8/PmiRFR0fbPGhwphYjkRjZj16vZ+vWraSnp9OtWzdatWqFJElYLBbrNeRoru1KV9GyJVqMGo/j3F04gLFjx9o7hJuORqUErq/feL2PpdHQqks0qZvPoy5R49WnIx4hvtzu786SxK8B7DLHRl0dySjkja+34a0tIzUhjKCDpQTIKv4+rg2qP+NOzS2lVZAHxUVFdo624UiShKaW1qKrOVJSBNDtjlYs23OCvKIwBjdLQC1BCzcdl/PK+DHlBPdPj6zyBtPXVUl+noEClR43o4ULl4vo4Ou4N+mN1WI0ZcoUhg0bxqBBg2wSo+TkZDIzMxkyZIh1mVarpV+/fuzcufO6EiMAV1dXunS5ubqkNqSrEyN7UKvV1gINcKUnQMX4pKSkJPbt24ckSYSEhFi38/X1dcibWsFxFBQUsGHDBvR6PUOGDCEkxPZBlKNeP9d2pROJUeNzrDsMO7JYHHvgs7MwFuopOl+ETxtfpFpaABrD06/8ha3d9nBi3xmMXmVkmQzsyv1foQJH/fIzWCxM/uce2njmcahHOLHZBfiFq3jrqe5EeP7vyWjLYA92nclGbSy3Y7TOp/hCEbLRgkdcw3fHadcpFu/5Y9jw+y4O5ebg7eJKgdGIRWlm/Au3Ed+s6rF1Sp3EySMXkOJ8ycov5Ld1Jdzd0nFbjWRZQrbUnhhdWxhEq9VW2+1jyZIl7N+/n71791Zal5l5Za6uoCDbzy8oKIiUlJR6xQ5XJr2uiiRJ6HQ6mjVrxogRI/D19a33vm8m9k6MruXi4kKbNm1o06YNcKV3R0Vr0v79+9m+fTsqlQofHx/Cw8MJCwtz2ERJtBjZx8WLF9m6dSuurq4MGzYMT0/bbv0VPxNHvGaubTEqL7/yt190pWs8IjESGoyxUM+PT+4mJ7WY2Pae3Pl/PVGom6a1qIIkSfQb0p1+Q7oDoDeZyS8s5ZMFTRpGvb3y0zGMhSYOdAwluKCIHslp9Js6wiYpAnDRqBjQJojLufn2CdQJXdyawc+vHUSBzJAZbYm7u+HHBUZEhfD4X0dhsVjILzWiUSlx0ShrLD+eX2pBPnQMkz4Uo1YL2uor8jmCurYYRURE2Cx//fXXmTlzZqXtU1NTefbZZ1m7dm2NTz+vvVm53u5SBw4cYP/+/ZjNZlq2bIksy5w5cwalUkmrVq346KOPmD59Otu3b7fehN+KKm7CHCUxupa3tzedO3emc+fOyLJMVlYWp06d4vjx4xw+fJgDBw6g1WoJDg4mNDSUkJCQRps/qb4c8cb7ZibLMseOHSMxMZHw8HD69OnjEJO21se15brLy8tRKpUO13PiZiI+WaHBXFiXRlFqPgkt3Vi9MwP/z47TfXI7u8akVSnxcXfsJyvPfHmQExuTKeoRgDJHJmhXEdzXlZ4xVbceSJKErokTTme29/OT+HmacdUq2fzBEWLvjGi01kyFQoFvHa+3GF93luaEUpCvIzPAjVCjYz9JNlskzDXc2Jn/bE1KTU21eSJb3ZPNxMREsrKybOYNMZvNbN26lQ8++IBTp04BV1qOru72kpWVVakVqS4qWoMWLVpkja+wsJDx48fTu3dvJk6cyJgxY5g2bRpr1qyp9/5vFo7WYlQTSZIICgrC09MTLy8vXFxcyM3NJSMjg4yMDHbv3o0sy7i7uxMSEkJoaCjBwcF264YkWoyajtlsZufOnSQlJREfH0/nzp1rTUwdMXG9tiudKNXd+ERiJDQYhYcSo2Qmp8RIgI+SE+tTSXiiFSoXx662ZU9PLz3C1l0ZlLX3wVUyE346n8hxnXjxrlb2Du2mkVNczsYDOYwbEEywbObED6dp86j9P9/LpQbMrlCs1SAbzZw5mAWT7R1V9eraYuTp6Vmpq0pVBg4caB1YX+GJJ56gVatWvPjii8TGxhIcHMy6devo1KkTcGXi6C1btjB37tx6x//uu++ybt06m9g8PT2ZOXMmQ4YM4dlnn+Uf//iHzZimW9HVVemcRcXAeYVCQXBwMMHBwXTq1AmDwUBmZqY1UTpz5gxwpQptSEgIISEhBAUFNenTd5EYNb7S0lI2bdpEbm4uffr0qXWesopWaGdIjESp7sYnEqOrXLx4kfDwcHuH4fCMZgsHkvOQJOgU7YPqz/lnom+PwOedfSiMJtoGu/LV+kton9/NA/N6otCIFo5r/fdwOon/TaHgNg/wUOGy8hIZYTpevauVQ35BNxaDycLB85Wvp4YSPyyCX7enUV5qQq1SsvHT4zS7OwqNt0vtb25EWSUGLqQZKQk2U+6pwsPNwb+Oa0mMqGfxBQ8PD+Lj422Wubm54efnZ10+depUZs+eTfPmzWnevDmzZ8/G1dWVMWPG1Dv8goICsrKyKnWTu3z5snVclLe3NwaDod77vpk4U4vR1apqjdFoNERGRhIZGQlcmWg2MzOT9PR0kpKSOHbsGAqFgsDAQGui5Ofn16hFekRi1LgqJm0FuPPOO+s8BYuj/s01m82VxhiJxKhxOfhf4qYVHx/PggULePTRR+0dikPbczaHlh5uyMjsPZtLj5ZXvngklQIPPxdcS/SoPbS4aJQk7stmRE45riFudo7asfx74xkWf38GUwcPJG8N6t/SkcuMPDIi3mG/oBvL3rM5tPS8cj3tO5fLbS3q9oesrro/3oax6y6wbW8eg7r6olUpOPyvw8Q/0w5doP3Knrfyd2epu4QxX4/cwhVVDYUNHEFjT/BalRdeeIGysjImT55sneB17dq11zVmZMSIEYwbN4733nuPrl27IkkSe/bsYcaMGYwcORKAPXv20KJFiwY+C+fijIlRXb8z3dzciIuLIy4uDlmWKSgosLYmHT16lAMHDqBWq23GJ3l6ejbYd/Kt9t3e1M6dO8fOnTvx9fVlwIABuLrW7fvd0YsvXNuVThReaFwiMbrK7NmzmTJlCitWrGDhwoX4+fnZOySHJMn8OahcgmsefgW2DeLsihTiQxV0ae1J9KBwXOx48+mIVp/L4qfPTmGM0FIWpiV0fxHK9r68/VgH+kT52Du8Jifxv+vJYmn4p6mSUkG7sW0ozj/M+YxyurX24syhUjT/SaH931o3+PHqqrDcRISnO5dCNBRpleDgT5KbIjHavHmzzb8lSWLmzJlVFm+or08//ZRp06bx4IMPWm/6VSoVY8eO5f333wegVatWfP755zd8LGfmrIlRfW9qJUnC29sbb29vWrdujcViITs725oo7d27F4vFgqurq7U1KSQkpM4329UdU7QYNTyLxcL+/fs5duwYcXFx9OjRw6aVpS4cMSmSZblS8QXRla7xicToKpMnT+bOO+9k/PjxtG3bloULF3LPPffYOyyHExfszrG0K/PotAi3HUsQ2TOYU1sLyC6S6DEkijYvdHDILxx7ySkz8OXiYxRHajC0csd7ex4aNxVrXu6LVnVrdje8+npqGVn72JTr0ezOaKIHhHFx+XnO/5aG2SyjdrXv5y0pLHhnGyjwkCiMkWkd79hloi1mBRZqmODV7LhzhBmNRu6++24+/fRT3n//fZKSkpBlmbi4OJsJXjt27Gi/IB2Eo1elq8mNJB0VXeoCAwPp0KEDRqORS5cuWROlc+fOAVe6W1YkScHBwfWaBFckRg3PYDCwdetW0tLS6NKlC23atKn3PYejthhVjPMTXemalkiMrhETE8PGjRv54IMP+Mtf/kLr1q0rDczcv3+/naJzDEHeLgRVMz4j5PZQBhot6AuMRA0Ld7gvGnvKLTfy2tfHuVhoRt/RDc8LpQRrlSx8s/ctmxRBzddTQ1Lp1ESNjkNy0xCptxB1T0Ttb2pEegNYvGQ8FJCtkXjovmZ2jac29uhK11DUajVHjx5FkiTc3d1p3769vUNyWM5afKGhqdVqwsPDreOOy8rKrEnShQsXOHHiBJIkERAQYE2UAgICah2fJBKjhlNQUMDGjRspKytj0KBBhIWF3dD+HO1+5eqW7QqiK13jE4lRFVJSUli6dCm+vr6MGDFC1IuvB0khEX6nfW84HZHJIvP6h/s5dzif7J6euJ4tw+9wGaNfbE+oh32LANxKJKWCqHsafh6j66FQKsjPU2BWmMAo4alxrD/K13LmxAjgscce44svvuCf//ynvUNxaAqFAkmSnLLFqDG5uLgQGxtLbGwssixTVFRkTZROnDjBoUOHUKlUBAcHWxMlb29vm5ttR7vxdmZpaWls2bIFFxcXhg0bhpfX9U/efb1zozW2iocTolx30xJ3/Nf47LPPmD59OoMGDeLo0aMEBDj2pIuC4ys3W3jy3T0UJuWQ1t0HnxQDETvLaD8plsd6R9o7PMFONAoLXuUS5QUSKhkMON4f5qtZZLDUkPw0wvCwBmUwGPj8889Zt24dXbp0wc3NtiDMvHnz7BSZY5EkCaVS6VSJUcUYo6ZqjZEkyVqWvmXLllgsFpv5kxITE7FYLOh0OmsRh5CQENGVrgHIssyJEyfYu3cvoaGh9OvXr8EmbXW05KiqFiMxxqjxicToKnfccQd79uzhgw8+4LHHHrN3OMJNYvY3h7lwJJvCPt54lBgIP1/M7S+14qkhcfYOTbCjnFIzbkFm1BYZhVom3+TYXZecvcXo6NGjdO7cGYDTp0/brHO0GyJ7U6lUTpUYgX3H7ygUCvz9/fH396ddu3aYTCaysrKsiVJSUhIAOp0Os9lMSkoKISEhDXZDf6swm83s3r2bs2fP0qZNGxISEhqktLqjzmNU8Tsoxhg1LZEYXcVsNnP48GExl1EtsgrLKSw1Ehfk7nBfJI5me1IOh7Ykk9MnANkILf4oImpgGE8NiROfnQNIzy/FYLQQHeBe+8YNzNNVQbGsQmO2YEFCtliaPIb6sFgkLDW0alkcvNx4xdwmQu2cLTFytO9SlUpFaGgooaGhwJWb2czMTI4fP055eTmbN29GkiT8/PysrUmBgYH1rqR2KykrK2PTpk3k5OTQq1cvmjVr2DGZjnYNQeUWox9//JE9e/bU6b1z5szh5Zdf5tlnn2X+/PnAlQRw1qxZLFy40Dr9wYcffkjbtm2t79Pr9cyYMYMffviBsrIyBg4cyEcffXRL3Rc7bhkhO1i3bt0t9cO/HslZxWRcKkVrgp2ns+0dTqMz5eXx++Pv8evot7i8/1S93pt4Pp8PZ+0jp7MXklqm69FLdPxLKG9P7OiQX8K3muNpBeTn6JFLzexLymmw/Vr0ev6Y8w0/DX+Dk1/+Vu12QTo13u6luHqWoVJYkCXH/jquaDGq6SXcHJwxMXLk71SdTkd0dDQxMTEoFAr+8pe/0KNHD9zd3Tlz5gxr167lhx9+YN26dRw9epScnBzR5e4qOTk5/PrrrxQVFXHHHXc0eFJUwdGuoWvHGF24cIH169ezYsUKOnbsyLRp0ygqKqr0vr1797Jw4cJKRWbeeecd5s2bxwcffMDevXsJDg5m8ODBNvuYOnUqy5cvZ8mSJWzfvp3i4mKGDx/uVMVYbpRoMRLqJadIT3OfK33zlcXldo6m8X1734ec2F9ArtGDU9vep/ezfen2woNIdag89MV/T3C2pQuypKDfukzcegbz9wcSmihyoTYlZUZaeF9pKcrKMTbYfje//xtr/7UfF4xs2pLIS0EehA/rW2m7jEITLpeUBBaX8odJItfBu9JZZKmWMUaOdVNRnePHj3PhwgUMBoPNcjE1w/+oVCqnvBFy9GSiorufu7s7zZs3p3nz5siyTF5enrXb3aFDh0hMTESr1drMn3Q9kxrfDJKTk9m+fTs+Pj4MGDCg0tjAhuCo1821XemmT59Obm4uaWlpDBs2jO3bt1eaV6u4uJiHH36Yzz77jLfeesu6XJZl5s+fzyuvvMKoUaMA+OqrrwgKCuL7779n0qRJFBQU8MUXX/DNN98waNAgAL799lsiIiJYv349Q4cObYrTtjuRGAk1mjn3D16Y3otg3yu/fKG+LhxJL8RVrUSlqTk5SMoqJqugHIsMEf6uRPg630SvGSmlxGov0s09n0yDLxe++RFDbhF93plU4/v+vvwIpywlFIa40WxTAfmuXkz+iygRfCNkWWb7mWx0Colys0zvFv5VPuEr0Rv55N2DnEsp4LbBETw2umWV+/P10HEiqwiVQsLdreG+CnMyS5AsMh09TrOm3J/9L/9flYlRhJ8LW921FJarMMsSZ1ILIMxxi704+xijpKQk7r33Xo4cOWIzHqXiGnLGRKCxOGOLkTOoahyUJEn4+vri6+tL27ZtMZvNXL582Zoo7d6925pMhYSEEBoaSnBw8E0/zkSWZQ4ePMjhw4eJiYmhZ8+ejV4h2NGuo+rKdfv4+HD//fdz//33V3rPlClTGDZsGIMGDbJJjJKTk8nMzGTIkCHWZVqtln79+rFz504mTZpEYmIiRqPRZpvQ0FDi4+PZuXOnSIwEAeDIoSyeuu9X+oyKZerkBEJ9XPHz0KI3WfDU1TyxXU6xno7h3gAczSh0ysQooU8AG5cZ8FUV0t41mQxzIBlbdyBbJlbbavSv1adZeygHZaSG7rsvo9XClNf70DqscSYvvVVkFJQR4eWCj6Qg9WIhC2fvIqpzAG26hBIZ8L+niL+tSWHrjjRSQiUufnyIYG8tQ4ZEV9pfXJA7Yb4umCwy7tqG+yrsM6YjJ77ejkoy08vzCOeKXDDm5KD287PZTiFbCLcUo3IHV6ORjZsy4TbHncvIYqllgleLY3cFfPbZZ4mJiWH9+vXExsayZ88ecnJymD59Ov/617/sHZ5DcbaqdHDlptbi4OP06kKpVBIcHExwcDCdOnXCYDCQmZlpTZTOnDkDgK+vr7U1KSgo6KaaVsRoNLJt2zZSU1Pp3Lkz8fHxjZq0yLJsLVPvSKor111d6+GSJUvYv38/e/furbQuMzMTgKCgIJvlQUFBpKSkWLfRaDT4+PhU2qbi/beCm+c3ycmdP3+eN998k40bN5KZmUloaCiPPPIIr7zyik3lmgsXLjBlyhQ2btyIi4sLY8aM4V//+pfNNkeOHOHpp59mz549+Pr6MmnSJF577bXr+qUvbe5CUYGCdSuTSUzMJi7Wk9YJAYy5qzkARkMxh39eyeWj6QS0DqPdvcPQaK8kADJQZjRjNFtQKh3rC6euBn44iZzk2Zw+0gyDlEKk+yUOlYZVmxStPXmZb3anUdDMldj1uWjOl8HoGDqG+1S5vVB3vm5adp/LRG2GQ1svIJVaCM4wc3p3Jl4DI/ByvfI7UJKUjKlED2VqLoWoefO9PWzaepJI71ICi9JIGN6O6K79AdCpG36wc0DHloRoyyg0eeOhLeCylw5JXfkhgodWzVmLCxGXSjGUSFj0jn0jKtfSlc7RW4x27drFxo0brZNwKhQKevfuzZw5c3jmmWc4cOCAvUN0GM7Wlc7RbmirUxFnfebN0Wg0REZGEhl5ZWqHkpISMjMzSU9PJykpiWPHjqFQKAgMDLQmSn5+fg1Src0eioqK2LhxI8XFxdx+++1ERDTNvIiOeA1VVZVOr9dXOY1Mamoqzz77LGvXrq2xNfHa86zLteio8zw1FpEYOYiTJ09isVj49NNPadasGUePHmXixImUlJRYn2aazWaGDRtGQEAA27dvJycnh7FjxyLLMgsWLACgsLCQwYMHM2DAAPbu3cvp06d5/PHHcXNzY/r06fWO614srFequBinJTrElQHBMhcOn2fmT4uIGjmQ/Jw8vCUZvcKdkwuTkFUr6XLfIwB0jfHlaFoBKoVEx0jvBvusmpLS1ZUH18/kt/tf5uIJHevLOvHQa3dWue3Z/FJmLT1OWZwLrqeLKSwxcaSlNz8+3K6Jo7456dRKvAr0/PrVfvSFEr6tgzEYwAsFhWWmK4lRaR4+O1fzaMxFdqbdxvpmccS28qBZ82BUJj1ppUlcfHIPvf91iYSBD1R7rKzCcvQmMxG+9e/PrtRoaD6uP3u/3Y6Xq5L427uhrOIJX59IP6aHqrisdMNgkXH0ycxl+cqrpvWOzGw24+5+ZUyZv78/6enptGzZkqioKE6dql9hlZuds3Wlgyslsx09mWuIm0s3Nzfi4uKIi4tDlmUKCgrIyMggPT2do0ePcuDAATQajc1Es56enk5xY5uZmcmmTZvQaDQMGzYMb2/vJjmuo974V9eVrqrEJzExkaysLBIS/jeO2Ww2s3XrVj744APrd1xmZiYhISHWbbKysqytSMHBwRgMBvLy8mxajbKysujZs2fDnpwDE4mRg7jjjju44447rP+OjY3l1KlTfPzxx9bEaO3atRw/fpzU1FRrGdD33nuPxx9/nLfffhtPT0++++47ysvLWbx4MVqtlvj4eE6fPs28efN47rnn6v3LH2f5kZLUYezUeZDkr2dhppFRLT+kTRx4uZwgLEJCp1RgDjBzzhJGykkjXf58ryRJtPuzK50zk1Qqhi9/B0PmJRRaDSqfyq0/x7OLeeb9Pyhs60ZQtpnm2woxhGp5dHonIr1d7BD1zal9n2jOLz1HRlE5qSklGLy1NOvmSbjvn59xeT5SYSrtAk9RFubKEX0LypQKVGoVzYIWk+F6mpPrurPu60PVJkanMgoxlJhxUSvYl59Dl1i/KrerSb+3nqD3tHuQZRmVf9VjoXRKBdH+WnyKjJzVKNA7ePEF2SIh11CuW3bwct3x8fEcPnyY2NhYunfvzjvvvINGo2HhwoXExsbaOzyH4oyJkTO4nhaj2vbn7e2Nt7c3rVu3xmKxkJ2dbU2U9uzZgyzLuLq6WscnhYSE4OLieH+TTp06xR9//EFQUBD9+/dH28RPipwpMarqsxk4cCBHjhyxWfbEE0/QqlUrXnzxRWJjYwkODmbdunV06tQJuDLp9ZYtW5g7dy4ACQkJqNVq1q1bx+jRowHIyMjg6NGjvPPOO41yjo5IJEYOrKCgAF9fX+u/d+3aRXx8vDUpAhg6dCh6vZ7ExEQGDBjArl276Nevn80vztChQ3nppZc4f/48MTEx9YrhWxIY0/w3zp25A+MxBfrmZfx8rBeBft708IrGRXInXOvL6YtFJJ3Yxf1PR9/weTsqTXBQlcsL9EYmf7qPvLYudHB3Y5JFxbpeRiJ6hjC0ZeANHTMtt5SL2aWYkOkW54da6ZzdIxqKQinh0zOKjC3pxIa4ce8zHdHorvoa8wwlPCidrOxouvodZXX5PQR4+7E0KYtHVD35Zn9L8JPpoC+r9hiFJQZrtbqca6rVXSooIzmzBDMyXeJ80aqq7oonSRIqf/9az8fTVcGDA6NY6WXmVHFJHT4B+3H24guvvvoqJSVXPuO33nqL4cOH06dPH/z8/PjPf/5j5+gci0qloqys+t8RR+RMXccaqwpaRZe6wMBAOnTogNFo5NKlS9bxSefOnQPA29vbmigFBQWhrqKrb1Mxm83s2bOH06dP06pVK7p06dLk8zk5aouR2WxGkiSba7u6FiMPDw/i4+Ntlrm5ueHn52ddPnXqVGbPnm2tiDh79mxcXV0ZM2YMAF5eXowfP57p06fj5+eHr68vM2bMoF27dtYqdbcCkRg5qHPnzrFgwQLee+8967LMzMxKA+d8fHzQaDTWgXGZmZlER0fbbFPxnszMzGoTI71ej16vt/67sLAQAG+Vmt903egbt5H/nO5PUK6MRpLYVxbOgUItQ5p7sT8pl7Kcozz+cltadbStWlKiN5J0qQSdRkFMgDuqm+zGfumRDN769BDmbq74GMw8Ua5CdlcTflsQE4dVXQ2tPs5nFdM+0AuTxcKhlHy6xPrW/iY7qe4aakjHd2SyaVEyHn4Shel6si+UENrC638bqLRcUkVzIqQ/MS47aJd5goLcYMwqE/P3KogoyyIpNpL8fXF8sewEzVr70au5PyrV/65LP08dJy8XoVYq0Ols/0CfyyimXaAnsixz6Hwe3ZrVnvzUxEUt4e6qRKOyoFEr2Houl75xjvkzNlskzDW0GJkdvMXo6opKsbGxHD9+nNzcXHx8fBzypsienLX4gqOWXa7Q1NeZWq0mPDzcOj9jWVmZNUm6cOECJ06cQJIkAgICrImSv79/kyWZFZPdZmVlcdttt9Gy5Y3/zbxejvgdYDKZKhXV0Ov1112R8IUXXqCsrIzJkydbJ3hdu3atTTGH999/H5VKxejRo60TvC5evPiWmnxYJEaNbObMmcyaNavGbfbu3UuXLl2s/05PT+eOO+7g/vvvZ8KECTbbVvXLe+3TjqoG11X33gpz5sypMs4WGk8KXItYb0nggditHDp1J24RruQfM3IhppAfc3OJLC+hM4V8v+MigzWZRAd6EuHvSmGZgc1bU9G6qYkIcmNrTha3tw2u8bNwJquOZ/H2v/ZR1t0Xz3IjPfacY29sGV4tQrm9S8MMGK34O2+xgMLxvrdtVHcN3ai0vFLSc8oI9XNBpVFgKjFh8VBRkmHk8O9ptokRkOTdlS7du3L6+BkSYi+zZstGNDkt0fbUUyyFIanVlMe6oFYrCFOp+G5zEmMH/a8aXGygO8HeOoxmGS8X2yeplj9/HmZZRmqAH0ihwcT2UylkR3qQazbyfx8cou/7A254v43B2VuMqnJ1i7zwP6IrXeO4uiudPbi4uBAbG0tsbCyyLFNUVER6ejoZGRmcOHGCQ4cOoVKpbMYneXt7N0rSkJuby8aNGzGZTAwZMoTgYPveGzhLYlRdi1FVNm/ebPNvSZKYOXMmM2fOrPY9Op2OBQsWWMet34pEYtTInn76aR588MEat7m6hSc9PZ0BAwbQo0cPFi5caLNdcHAwf/zxh82yvLw8jEajzeC5a8sqZmVlAZXLNF7tpZde4rnnnrP+u7CwkIiICFq3aklq0RlyC/JY49aFobFnuHgwDnOgBVWGnnBFGQU+LqzzDKWdPgZjUhm7U0px6xPGhaxiXCyQEOVDuSxTLMsYzRan7w4myzJTvjvI/vUXKE3wxuiioOWifMwuOpJbKPj4L21RNNCXbIswT45fKkKSuK6xLk2pumvoRhTrTaSkFtPK34OTqUW06+BL52GhHP0tAzd3LX5RtiXgy4sNZJ2IIi3GiK+nLxnZ58m+qEYVW0YP+SKrFHHo89W4B2oY0CwQnUZJgKsGi8Vi85TUVVP1V2ObCE+OZxSBBAkxN35TPahrHH8s2EdRgZGyCC8ySssrxeIoboYJXjds2MCGDRvIysqqVNr5yy+/tFNUjsfZqtKBc7UYOUKckiTh6emJp6cnrVq1wmKxkJubax2flJiYiMViwcXFxWai2YaYYPXChQts3boVT09P7rjjDmtRFHtx5K5017bUVDfGSGg4IjFqZP7+/vjXYawBQFpaGgMGDCAhIYFFixZVujnq0aMHb7/9NhkZGdaqImvXrkWr1VorkfTo0YOXX34Zg8FgLeG9du1aQkNDK3Wxu5pWq63yl60AM3u3GgnwK0NSashw6UBo51PEHctB45lOaokbhxQtSc0J5aTGwLdnMrk90Jtpf9/IuIkdKVHBlrOXCQv0oFTh/EnRhZJynvpqPxcP5qFt5orsqcD/cB5J4SoKI8P4YFL3BkuKAAI9dQR6OsdEftVdQzeisMyA75/zZfnq1BSVGxn1Skc6DA2jMLuM2C5+5KQV4xvqBvpCjr/zLu3MelL29SFT4U5MsIL4jhkUl1nYWxSA5yEzl7royC8389XeFPq3CiC7zFDnRMTXXUv35g13jhMHxPDlqpOUuGop04K/q5F525KZ0S+uwY7RUJy9Kt2sWbN444036NKlCyEhIQ55I+QonLUrnbNwhMToWgqFwnq/0q5dO0wmE1lZWdaud0lJSQB4enraTDR79VQhtZFlmcOHD3Pw4EEiIyPp3bu3Xcc3XR2XI14/N9piJFwfkRg5iPT0dPr3709kZCT/+te/uHz5snVdRRPzkCFDaNOmDY8++ijvvvsuubm5zJgxg4kTJ+LpeWXuoDFjxjBr1iwef/xxXn75Zc6cOcPs2bP5xz/+cV2/+CPu7cSZLefIyVLhHZrH5XId6FrSu88ywotPYdp1G0EhR1hZGgQaE+lhpfxfei7qtBLefWYb0bcF8fcXu6BRKQnwcO6nHD/8foCVn2xF6eeGJS6OwjAd7Q5n4Kcwct9bfegTFYyuiRK/rALnGhh9vUK8XDifWUJubhGySqKllzeyDBsPZ3LuVD7lC4+hyzbRorcKlTEV6VAZd7T6nUSDmrNnY/D2KEH2VVO8X02gqZDCfDf0eXqyQ3QsXZfML8tPM3emfcuQ+ssKvNWlpGskIneeYbePBA6YGJktCsw1TPBqdvAJXj/55BMWL17Mo48+au9QHJ4zdqVTKBQOmXA4K5VKRWhoqLXYU3l5uXWi2fT0dE6dOoUkSfj5+VkTpYCAgGrHohiNRnbs2EFKSgodOnSgQ4cODpWMOGIrfUOPMRLqRiRGDmLt2rWcPXuWs2fPWgdKVqj4slcqlfz2229MnjyZXr162UzwWsHLy4t169YxZcoUunTpgo+PD88995xNF6f6+vsHD/H0iA8J8FZg8Smg3DuAQ5dvJ9w7hbadz3FkT0va9y1kxyktHidMFEVKFAdpUTbTUaQ3M/HpDdxxeySPPtAa33omR6mXi0lJKkDSKritfTBKOw20OZ14glV//Y5gSxEHeiVgjFYTuK8YRZKa9k93YHBsaO07qYcj245wbHc6LToG0HlwZ5t15UYzF7JLG/R4jenXHefReXoSFOBGz+ZVl66ujiRJ9GxlO5nd7lOX8fRzoV8UZKhUnDyVi3cLLV2DmqMOWEHhGXdKyv3w9VYjJbuiijeg6OFGM5cwTvhDJx8X9skmcgLA90w57y8+Rv/3w6uJoO7O7j9N4oZkIlp40XPEbXV+X89+EazfloRWnYtHShKqny5j+ftQFPV4EtsUZBlkS83rHZnBYLil5uK4Ec6YGDkDR+pKV186nY7o6Ghrz5OioiJra9Lp06c5cuQISqWSoKAga7c7X19fJEmiuLiYjRs3UlhYSP/+/YmKirLvyTgJk8lUZVc6kRg1LpEYOYjHH3+cxx9/vNbtIiMj+fXXX2vcpl27dmzdurVB4tqy8yIdE6Lo3C+Wc7vOoAmWiItRUuQbxMbToxkS9C1Ro0Af0IZnRnqybkMuhfuyaR7jgSrElbSyclRKFek/JjKvsJi7726F3myhc7QP7rram9D3H8giN68Mtbuas9kl3NktvMm7lhUVFfPBxO/paEkh2LuYQy17En0sE4Xkyn2vdmJ0txu/qa6wZ80+vnt3G6UXsvFTyOxWmch79iIDJ95j3abUYMbLxXl+dUssMr2j/Sg1mdmfkktC9A2OlTLLeLipievohVl5ibS0AhQp+bjnLsLkH86G4kfgfC4aZQCH1aH0UhxC756PT7OeaFub6eXnx4AiA6/sPIXbYF8GhHqxYd8FBnaJvO6QLBYzH037D/q8XPLyvVj9f7/wxoa36/TeaYPj2LZmF4oCBX1dT7E0I4Giy9l4hTVssn2jZLmWeYwcfIzRhAkT+P7773nttdfsHYrDc8bEyJFaH6rjzInRtTw8PPDw8KBFixbIskxeXp61kMPBgwdJTExEq9Xi4+NDTk4OGo2Gu+66yyELnsiy7JAtRmazucqudGKMUeNynrsrwS5aanSkXijkiWl38NrhVPKOGEj3TmFw//78VlzC9sy70ZrV9GoWweEz+fz1IX/+6B7G/gu5/O2OlmxIvszXm85wMsSNkQEutAvxAlnmRGYRCdG1f0EWlhrRuGvp1yKAUpOZYxfyCYxvuuo1OYdO8vpD39Gx+BiBnvl8PuwBlNkmsi9KPP9GB0Z3CGvQ46364Sga01Fcwky4a0y4m1Qc/OksAyf+bxtfNw0H9c4zMNpNp8Zdp0ZpkDiVUUxC9I3tLzLEnZTLpRwqKeZkThH7NifhqggmqF0CmdKd9G2t4vuyYiLVagI634vHmbN4yoUEmbKweEfi5qclSJaIdtOSZzZjdFVxdH/GDSVGZoMRY+FlXLxKcPUu4PTZSPKzMvEOrP1aVSoUlJvV+FiKcVEYiVIUclGpxqvWdzYtiyxhqSExcsTiC1e3lFssFhYuXMj69etp3759pbEN8+bNa+rwHJYzJkbO0JXuZkqMriZJEr6+vvj6+hIfH4/ZbOby5ctkZGRw/PhxJEli2LBhDjmxrCOrKjESXekan0iMhBqVmcwoXZS4ubnSsls4W39OouBQGWvy1iN5tOZCWHMs5zXkbduHPjiGj7/PYfLTXQmIdefI5UICNRp6mVRk5VgoLDODLJNZpMfLtW4DLsOiPUhKKQJkZCCv1NCo53u1tC0H+GLCIkKLivD3KuL7YaPId/VHSpeY+UYP7u4Q0uDHDI1yY+veCCg3Eaa2oNSYCbvNs9J2XRqgIlpTScspZXdSNkZkOkT73PD+wnxdebB/NAAlt4XyZZoeRdopjmd3pHdrPxQaV9xCLuJeJuGv1nHRtT2ty/ZwKDMCT8/g/2fvvMPjqM6+fc/ubC/SqvdmyVVu2MbYBmxTbFMChJoQihMC5AVD+AwpQAKEBMibUJJAQkjeBAiQQEIoCRCwwdg0G9y7JRf1XlYrafvuzPeHI2HZsi3JKrPyubnmwpqdOeeZnbMz53ee5zyH19aU4W8Jo2sLYk82kmk1smZ3A5GoMuB1tvRGIxabiarKeJAk7OYI9oS+JV2RZRkLIfSRKDqdDl/Ygq/dC2nJxz95OFHhmN05Dfb1Nm/e3OPvadOmAbBjx44e+2PB2zCcdGWl0+qkdIG20ev1pKWlkZaWRmdnJx0dHZoWRVr1GInkCyODEEaCY9KRamDehIMdtIu+cQZr3i3DcMCIYu9E8Wwjae7p1Pj209DoIDV6gK9PyOKF7z7PV356IZjNVBxopW5DO0qHxOt/3EMgwcoVZ+ZRmNK39JxnTcugvq2MT8paCapRzi4+esrxwWT3i+/wp7tWkkc9BY5G/m/JldTak1H267jyuuIhEUUA1999IYUTXdRtK8dT2Uj6hCwuvOvY6d61zutbavhkfRtKVZAHfz6H3KTB62zZnGZu+91CYOHBjEflbkKeDiJVZcjZOTQ1drKvM58pgdfw0UrD9u3UviWj80bR10dwX+zk97UVWDYE+MOru7nlqkkDskOn0/G9v93Byz/+M+3NQa740VeRZZn/bCvjhX9vxNtgwJiSwJ9+MBen4cjJyZlNNfgtTsLoMRCF6PANAPSVqALRY9y3qAaF0YcffjjSJsQkXfMaehux1irCY6RNJEmKCXGtRRsPn2OkKAqhUEiE0g0xsfHEE4wYU/ITuh8Y6ekpPPDkFfx52ev491hgkpftf/8IOerENcmHsm8HHSVNqOGLee/WVRjT01ADOgqTbHimmWkxRXnruX2kZjopSLGjhrw0rPgXdqkF2+zzkZIKerXh6/Pz+HxNDWt/VcK/jDtIHBfHzMsmM2basUfUVVVlR42HQKB/nUwlFOJvP36PXKmefGsjfz3nIlqyUknYDPO+MYG7zinqV3n9wWi2sPCqc+CqIati2Ilu6uBAloTZF+Xmb67kzHOz+eXdcwb9RSRJElPzE1h38x+hKZ28vX+gY9wcbIYsauriyHWV8FmnleyUQipr/KTPTqS9NUSbTSUjw8wnn9RwxaJ8kl3W41d2OCEfuk9f4rqr0olb/C347+jjs3/bwnZzIqpVwVLazhWPfsa7Pzz9iGu3NASIZFjQySp6ooOa8n2wiNU5RqtWrWLZsmWsW7euO3tnFx6Ph7lz5/L73/+eM844Y4Qs1B5dYqi3EWvBwDkZhVEseB21amMkEunhHQoGgwDCYzTEaM93KNA0YyfkkjEtiU5AqZFJzoxgroojstqF35VJvZpPcnwEfVw6kZCOkD9Ci19FOicNeXEGU8bH85f/20kwGuWz5Q8Qee9p2la+S/tLD6EGPL3WKUkSDZ82Y1CbUW0SH/9jJ7+7bQ37Njf1enwXmyvayLCbKUjoX0e3ZPs+LOE28s0NvHLmBZQV5DPpM1iwaAz3XjC+X2UJIL0mQnxJkM5EHd4UmVXvVPLTR9YPWeegrbGNhI920l7fxvYqHadkQEs0melKKfGSHatTYdoVY/jO8qlcdG4eqlVH3ngHNy0cwzufVg2ozprHbqPz08/ofO9lyh77bvf+jn1g3+DBn2fGYNah3+hmR3Nnj3MlSUKNqshKhJDOgF6GeA2++BT1+JsW+dWvftVjSYNDiYuL4+abbxbziw6jSwzF0iKvseQxOpnQqug4HC3aeLjHVgij4UEII0G/uf7HFxE1S9haJdSAASnDT3ymFal5Eg2JhWzNrqZCqWDzqlZ2+b0YihVOi7fTqQNfoY2AqlL20bsEDuzBYWnBpGtkX00D7e4KNtduwR1wH1GnJKuYwk0Egnoi5iRqy300lnf2Yt2XRBUFq1Gm4pDU1m3e4HGvr9wWT0teFs+edzmlU8eSvjeK44IxLL9uav+/LAHn/3gqERO4NnSgmnX4xppZ+UEViy7+O299WH7C5SuKwuaaTXxU+THvlazk765KqnNbeCfJjPmLzznt2/OxdVpRonriO0GV/aSnGhk3MYHJOfHYdTr26SKETBJuXwRVVSmta+e9TWX87bMtVLmPbI+HEnI3ECyvwG6sxWGspXZzBZ17twCQNz8LohC3pwP3nHhqXTJ33v8ZVYesQxWJRPDn5rBl7CTeTjuTyHgX8WkpJ/y9DDZdC7wea9MiW7duZcmSJUf9fNGiRWzcuHEYLdI+h3qMYgUtdmyPhtYF3GASC8JIqzYeHkoXCAQAIYyGGiGMBP0mLt7J9568lM40O4YmCZ3XTk2Dj+Z9HnZ6D1Ce/B61Y6MYLmnAaOikesde9q7dBM0BtgeCZJZH2P6v9/CHDIT9Clu8Zp7ZOIalt/+Rx0//mOUX/4ldFaU96pz2lVxa21JpbVVJznQgmWWMjmOHeOQkWtlc3UZZQ0f3vn31xxZTAOeOTWPvhNPxTU8hpSZIamYy585KZUeZ+6R6oQ0WX1syhudeWIS+wErC9hB2t0pzrow3BP/7s/X84o9bTqj8P7zwAGsfe5h7l/6de+76Ozsymll90QF2FnTy2jT45fVPMfZ0E9XebMZbNtC6ycDGj2vZ2dTBxgNN6Mv91IXDrN9fx0cflhOORNm4vQxbVZDkRpVHfvEqjZ2NR61/zz/+RfX+dIy6ZlpaE6lv+DKf3NPLZvGLZ88gSTJgqggQnBqHrk3hB89t6z5Gr9fjm5CNMWykbWYOttuvJNE88BjyereP9bubqHcP7lpXUUU67qZFGhoajshAdyiyLPdYUFsQu8JI689nLXa+hxqt3xPQ7jyow0NZA4EAOp1OhLcOMUIYCQbEmKIcHv37rdz777v42ZorOPe749mVt5NyUw2tdRbG7juAJ0lPNL2F3MBO9ry2jbw9BwilyiSrUUr8eTSH7Hg8cexrSaZsciWtmdWUX7SKJrWeL9bv6lFfzsQEnDmJdNYE0csSsg4yxhwZGnMoqXEW5o5NwnXIorI20/EfKLJOx8M/OZ3LrNO4aNEM5s7NItuj4mgIsWFP88C+sJOccelx/OvFC4jPs+DYHyRlk4+ObCP+NAP/+mMp1/+/99hX0z6gsjv3lLLK2kHnqftQiquQqhMY94evYqqYzjlJRhzONlbt0uHvaCDbupaQuh3LjhUYzXpyMJKdYMQajrBxwwHSKup5+LcbObCvkQ3ez3mu7Rnc+9v4439eOGr9xs4Q4YiR5qZk1n4xm7x5WdiLprGlws36fc1kqUbu+/YUcr7w4vBGaT3VjmF1Iz9742AblySJe26Yz9LUMXzz4oX85LLiAX0PAJ3BCHu2NJPVAXu2NuMLDV7HNlY9RpmZmWzfvv2on2/bto309KFJphKrxKIwAu0Lj5NxjhFo/75olWg0eoTHSHiLhh4hjAQnjCRJLL5iIpH8ZjB1ovea+WyXk+ne7XQkmyA1wqTx1XwzN590h4GmaVbK28ez+McX0RKeRJU3B6I6pLAe+54cJFVH8bT8I+o5/96ppBpNRCqDnD4/k4z83ld6qXH7WFvaxNrSJqrdPmaN/XJB0QlZfVsdZlKGg3kL0zh/chrpJiNqFKx6PZHA8Mfc/311Ga++Ucqf3tyDLxhbHRWAdbub2F3jweUw8dzzi1CnGDElKRRtC+I3SbROMrH3QBtXXvsWm0tb+l1+meLEYg9ibHRifPNUEvbkEcpr5tTTp1CUNR6HLUq5L4XqgIPydjtFuZsJOpNJCbZz3vkFzDgjC2tbJzVGG+1eA7s+2ktGtpetW4wEQmbiiuJQjpGLOvcrC3GPnc3H45ehLr+CunNuZm99Oy7ZwIQkJy1tQeaOS2T8V7MYs6EdQ0ilZrKdLa9t48n3twIwKcPJt78xlovmZJ5QJ6LNGyJBd7BT65L0tA1ientFlY67aZHzzz+f++67rzsM5VD8fj/3338/F1544QhYpl26OmOxJIxiyWOkdTsHE62GqR2KVm083GMk1jAaHoQ/TjAo6CQdj9/zQ376zAs45HJyvmNh/WsLOG/ix6xPyMfYcYB9m15i0ilzWTPBjq25Hf/Mr3DqgguxbVuH+sJaKmrakdKiLL78NGYWHjmfJ3dyIt9dtRh3XScZY4++Hk5Nq59pmfEAbK9vJyXbMeDrkoC0dDtb9jSTLJuZWJR43HMGk2A4itEdYbrLSTga5e0NNVwxL3dYbThRJiTbqWwLQiYkOcyseO4S3l5byT/+uY/vT0jjtZpGDlgUIlUhll+3irO/PYY7r5uCxdi3x9NlV93I3177gJnTIixaPg2n2c6kjIm89uweChPz2fDZX1FNKmU1aSQZg1hMfky6AzTVuckf72TM+ETee1XCk6WnQrJiKo+S6DyFxYsaGGObRnSaijH16GsSWYomknSjjVBdmHFOOx3hCHtrO5iU2iXCVVRUHr9xGpcG/Yx7t5G1l9gJ6YOs/MMf6Wyewd1f++YgfNOQ6bJwwOGhtSOAzikzJW7w1g5ROfZSRVrt6v3oRz/itddeY+zYsSxbtoxx48YhSRK7d+/mt7/9LdFolHvvvXekzdQUsegx0mo4VG8IYaQttGpfb6F0QhgNPUIYCQaNorR8/nL/fd1/39v0Ov9eN4evFH7E+wnTCXTuxrj2BTLTFpJcE+FHD67kr09cwqQZC3hkxgIA3vt0LVt2tfLLP7/H5RcUk5+a2aMOW5wJW9xx5l9IEIxEkZA4Rnbh4zI+3cGmcjdRFGbNTmdMH9deGkyMsg6PctBLVekPkpvbN4+XllBVlUO7V0ZZx1fPyKOl0U+KT8+tYzP5eXUn9alm2mwqb764F6Ujwn3/79Q+lX/WKbM465RZR+xvC65nV9SBN2ES1v3raK3LZL/ey4IJ22mucfKvZ97GMa6AJdcuxGq3Yw9EMJ1mZKzXwzP/9wL33Hsjxdku9jd7SXEd52VkNOKTIkRVlcZwmCm5KTR1hmhsDpAYb8YkHxyB/8kVk/jehjZyP9lKxZk7aQsbaXxkP7k5q7h67ll9/UqPiiRJnHnK0ISFKSoox/lci6SmpvLZZ5/xP//zP9x9993dnVJJkli8eDG/+93vSE0dnvXRYoVYzEoH2hccWu2ADyWxIIxAm/emt+QLYg2joUeE0gmGjId+/lXunLuVL4JzOE/ey1rHFCrVHCZU7iIzdz8L973G3o8/RPnvy3dfw34++KiaVuMBZIOHF/9ZctSy/aEoDYdk9jqUWfkJlLf5KWvzMSs/YcD2S5LEjPwETi1KIhiK8vGWBlZtqaczEB5wmQOxYf4ZmaxTfBgL7JxaOLweq8HgQEeA2b142rJznXgkhXXVbQRqAuRtDxKRoW2MkZ0VbWyuaCFwAnNkbr7hDLauWcuelWFQ9eSfpcM/6UL0qkS22UtiaBOhzz5k/fNvcu+PZjFpnBXFZiF0ioeJc7J4+7OP2d3UQZzDSIrz2MJoXnEqzlwrWwxB8qYmkZVoY3qui1mFSRSlfemxnJzqJHV6EsHmJizbUolm14FOZvX6dQO+zuFCUSWix9i0GkoHkJubyzvvvENzczOff/4569ato7m5mXfeeYe8vLyRNk9zCI/R0HCyhtLFAlpsO72l6xYeo6FHeIwEQ8qSH/+Eul+tIs96JQsOPMvHtlkYG1uZ3LGdgE7iH3e/jDNlNdf8bhmbDmwmEIUW/TomGhZh0ht7LbPO7WPn1hbag2EUm47LT8/r8bkkSUzLPXqo3UBobgowMcGOqqrsrmpn1jCG1OWlOMg7a+DhgCPNKQUJmHsJi1s0I4OSzHYSGuPInRjPi8/uxtoQxpRvYrca5Jc3rUKv03HRD6Zz+fz8fr+4XHFO7v3lN/j9Jf+LnFeMJbybyRmnUlE7lsmJu3mnaSamcUn4UscjGWFaoo3Gjioq9B8zOfMCdE0JTMmOQz5kxO5oGPQ6zprSN0/NYzdN5dueCoxb3scTNJOY18I151zdr2sbCWI1lO5QXC4Xs2Yd6V0U9CQWhVEscDIKI9Cm6IgFRCjdyCCEkWBI0VmcZM6ejGNnCxPtXyGz9VVWppxKdcIYcspKkRztNDZG+dENz6PMdlHvqIWoSlo4n5b03h8AVfVeImqUeeMTqPd4+cnN/0dKm59LfnYh6UVHJm0YDCKSiqqquANh7Hbxs+kviqKyrqQZIioFWQ7SXBYkSWJ8ehzj0+NgajrXXzQOgI93NvDnZR+xN1vG0RHl/55aw6cf1/LEj+b1+wVrjYtj2Ts/5umffUC2MQvVkYo3YMdmDyHnTyTJ0EyOq5mVn5VR01wDKR/i91q5yP8xf/FejnxYfZ3NLWz5y7tsXF2Cz2Mi5ZRULr/3MuKS+i7EkyxG3rjvckrKJ1P68V4cuQ7OmHhav65rJIjVUDpB/4lFYRRLne+TSRiJULqBoSgKqqoeIYxEKN3QI0LpBEPOnNnZtE80U7NvEyvLirnIs5oGUzzmAgMJSgTZGWTsmGZm5xaQUtjIGONUVu3wkZ7R+8sjM8VKS0cH4UCAcEsDhjaJpvVe/vWDd4bsGk6bkER5OIji0PU5s53gSzYdaCVbNTDGaGbPgbZjHjttTAI6k8SpmztpSdDjdllZ+WEFNy97d0AdNaPFwtIfLsA7Nh39RD1zZmfSETQy3lJGwYwFVO34gmR9O/akatp89ehaz6TU7SPHXE3Vyrd6lLXht2+w853VBHdYCZXLlP2nirf/+Gm/bQIYlzeOr1x7IQvOnK+5l3JvqH3YBKODWM1Kp3ViwcbBJlaEkdbo+u2JdN3DjxBGgiEnzmbknLOncNdz3+ebc1Re3H0659es4Rx1A+eM2Uq2FAIU9pb8hRZvPUW6Uwn6JQy63senMxNtrP/re7z+9GN8/OIzSG2fYkivxNvqHbJrsJpkZhUlMj5TiKKBoCoquq6X43F60A6zgR/8+VzKJ6lMLa/E644SLrTRUOdh+f0fDKj+OIedy74+m7POnUL+Zd/E3eYgrX096xocuA0uWre9xW55HUk7JnFX0VxmOLyMC+3m06df7FFO+Z46VJMPqXAX5sI9GOztBP1HpoEejSjq8TfB6ECSJPR6fUwJo1jgZAyli5Vr1Zp46/rtiTlGw48QRoLhwxLPhff/mJ98v5jPa8fR5k9Eh8oZeZuIZw+tgWSMDTMp0KWz5OxkDNaCoxaVoosQ2tKGtNNEsDKDcHMKs7898IUxBUPL9DEJlCtB9gX8FB5l/alDGZsRx6NPXMJOQwb2SoWiOj/VyUY2NnRw+dfe5s9/3T1wY+Kz6bSmk5lo4SwqqGEM9f4k/Buz0W8y8o8v/sOGhgzi931CpEVP/ecfd59qmlyINWpG15ROsCoPX9DIV24+Y+C2xBDRPmyC0YMsyzGVlU5rHdveOBmEkaqq+HwVRKPB7n06nfa7mlprP12/PTHHaPjRfmsVxCxrdzWx8rNqVm+p7/EimHvx+Tzy9DVUH8hHjZgwAWcpO1niX0mK24nd4mNqUT7R4NFnNHz/L3fimD4JXbpE4WwzX33ibOZdcz5KIIISEKOcWkPW65g3KYV5U1PJSrT26ZyCdCf/+sdFuApthJqCOCq8dOr0lIaDPPfLrSy57g027+3/grDIRiITT0c2J5Ia3kSx1Y4hXEe8U2XS/8RzZV0pyhfVmMPtAFS89JfuUy+7/RJSzz8bR0GAhAlRbvrzN0jK7Huq5037WljxaTUrN9QSiR5rxo72EKF0JxfCYzR0jGZhFA67aWtbT33DW7S0fEIk4ufYsxMFvXG0UDoxx2joEbPIBUNCU3uA1mof4+JseDoj7KnxMCErvvtz+/iZnP3EcrY/eB+pCVUYJJWZ+naKGp5jx8443KEgxWdMPmr5smzgpifu6LGvZWMTG3+zBzUYIu20ZKYsK0aShfaPZZIcJt78y0WsK2ni//64Hem1RtrHmmiaaiaxMcAN1/2H5fefxjVLCvtV7imnTmD926UUWzfRuiuTM1qD/OL/ngagc94X/OW6PzJ1zG5OzTtA1f5kQtXbCPpzWPvQNlRfCmddciuF147tV52qqlK2r52p8Q4CwSjrSps5fUJKv8oYSRSOk3xhuAwRDAuyLMeUMNLaiH9vxIKNJ4rfX0W4o4OGd1WUSDu6HD8Bo49QqAWjUZvLTWhxHlRvoXTCYzQ8CGEkGHQiUYUPdjZgCisYdRItvhAuo+2I45yTZzPtgZ/wnzseZuqY/TjNQazUsvVf/2GnbTx/f6sGm9NB8bQwl18wiaTkY2ecq/+8GWMkgF4Pa18oo3JdC4ufngM2IY5iGZOsZ/6kNFK/a+aH9Z+ilnbgkFWaM03IDj2/enADdqueS87svX34fD7effItlFCUs/5nMQlJCcTn5mPYUU8kp42M9CA12ZO48Z7/4G9UuercTMZPCaKoEvEJjWzYM42VN/0vyfPvItjmJdFhYPVTe7CNiSN9bt+9Raqq4g9HMOl1uINhPN7YkhIqcKyB7tE7Bn5yEmvCKJYYrR4jVVXwB6pp2eamaYcLPaDX+5BcLmRZu/NztbgGlphjNHKIHqNg0Nlc6Wbh2CSiZljf3EazPkJ+sr3XY22TTiN57iQ+rJlFu2pGQWVu8WYq42yY8hJISayksfVjfvXDZ7npx2/y8x/9g5f/tY6gcmSnMmVaAtVNIYxGiYJMM5tWNrP5mdKhvlzBMDE+P543Xr6A876Vjy0SJrvCS9gi480zcf9D67j9hx/T4e+5+G4kEuHny/5A87bttO3bycsP/5loNIqSnk7IkUY4I5NFM1qY/o3LkdNamTqtlQ8/KWGdNIl3dk0lGrIyNqMaSyBKMOLH1xFFp0JjRYidf+lf29LpdNgTzGxpa6c8HOC0cUkD/i521rTxeWkzH+9pJBgZnnkgYo7RyUWsCSNJkjQvOEb7HKNQqAVvWyObVtrZ7A2wKdBJ0zYn+mAAnW7oxuGDEYU9TUGavcO3+PpQI+YYjRxCGAkGHVWFOIuRs2emo08xcsWCvGOOxuRc+x12KIX8Q7qAjw0zUOJCXJX3Bh35MiuLs/hg4nReX7CAKotMe2M70psb2Vhac0Q5yXNTmXpxNjv3dGIySRQXW2nb2z6UlyoYAX6wbDaX3zKdVtWIY0sAJRzFn2Vh/fpGrrn2PzR4fN3Hbl6/D2uwg7pEEyVt8azf18ml13+Xr1/2MK9Yk6kzj8fk2Y6ZEF6HmVWZuTisdawubMAUCVJfn0BKWgWNXjuqfzdNjWEiYZXEBANqsP+dm6/MyWLClESWnJ5FomPgseLtnWGK05xMS49jR7VnwOX0B5Uvw+l62/r7bTzyyCPMmjULh8NBSkoKl1xyCSUlJT3rVFUeeOABMjIysFgsLFiwgJ07d574xQiOSywKI62N+h/OaBdGgUAVnqo22jwyWzdls3N7JlU+CSkwtMFJpa0KpxZkoOhtNHv712a77oXW2o6YYzRyCGEkGHQmZ8WxpdbD3mYvcwqTMOiP3cxy8rMwJ8XTUWthdyCNt+WzkA0qP9tVwjl73yCu4+9MVr+g3Wrio7PGszkjhVB5Xa9lTfjmeBSrgX37/XT4FCZcnDUUlygYYb556QR++sAcdCkycXsDuMoidBSbacsxcuXSd7jlu2/wyRd1JKY40LVY6Kg0oeR2IidH8LRBQ3IlJVXtvFripqGhnc0rXid9s49ZrR1sLE6i01LGvuAkPtuUj6z6cCQ00rZ5NSRBeUUAi11P8bX9m9cEoNdJFKY6cJgNJ3T94f/mxm71homznFhZfeVYouh48496Y82aNdx6662sW7eOlStXEolEWLRoEV7vl2n3f/GLX/D444/z1FNPsX79etLS0jj33HPp6OgYnIsSHJVYy0oHo1dwxAKqGsXvr0buDJOV30RiZisme5CUSS2Y0ofuPayqKk6LGUmSKEx20OzrX5vV4vwiEKF0I4kQRhokGAwybdo0JEliy5YtPT6rrKzkK1/5CjabjaSkJG6//XZCoVCPY7Zv3878+fOxWCxkZmby4IMPDusLw2KUOa0wiVPHJJIaZ+n1mEhU4a0vqvnLqgO8/lElU3OnsKcimY9qDVTvlFmrj6PCqEPWt3FKVQNnOV7kex8/yaPrP2FRqoVVv9vIE0v+wlt/29ajXNlq5PI/zGXaZUYW3e0g+8Ls4bhkwQiw6IxsPnrnMsbPTSM+3siN47NwJppRJzqRaltZvX43L33WiG5uPr5aI7tbGzAgYT29Bjm7Dd2BNCrdEKqOMLX831j9HVxc1cZczypMfgnd6ROZfKqRffY5TEnfRdSncMZXahl3RQ5LnphJ2sL0Ebv2qbnx7G7uJCApFKY6hqXOwc5K9+6777J06VImTZrE1KlTefbZZ6msrGTjxo0H61NVfvWrX3Hvvfdy6aWXUlxczPPPP4/P5+Ovf/3r4F2YoFdiLStdl8dIy+JoNHuMgsEmotEA4TIDmU1pXFYY5Ia7SkmckoOk670fMBhIkoQv4GNHbRuf7m8kL35gA0VaE0cilG7kEMkXNMj3v/99MjIy2Lp1a4/90WiUCy64gOTkZD755BNaWlq4/vrrUVWVJ598EoD29nbOPfdcFi5cyPr16yktLWXp0qXYbDbuvPPOkbicXtlW2UZVq4+LJmcQDEdZsbaRKZZEVrKTNemd2CydNOieYkGjnVxaqVdDVIyvxLlHJXnH54x1x/Faxql88euNBJtquOz287rLPvDqC3z05h5a/2Zl7sdrOPvhu0bwSgVDiSzreO6ps7jzN1+QG2/hGruJ50ur2VKQil5n4/xkF/W2YppC7bSW+PD7GgnKHlyVRehOaeeiFGhzp5FqL8GyoZIDrg9wWauQWnJZEtrOm5lTmXvmRBLX7cHgCRHZ8wmz/nDrSF82cVYjMwsShrXOvmala2/vGb5qMpn6FP7h8RwMCUxIOHhdZWVl1NfXs2jRoh5lzZ8/n88++4ybb765P+YL+kmshdLBl/OMtNbJ7WI0CyN/oJKQp4PWzWkEylJIPL2UrEmZNO8yD8n92NcSAvTopSgTko0oaphsu/645/WGFsMwRSjdyCE8RhrjP//5DytWrODRRx894rMVK1awa9cuXnzxRaZPn84555zDY489xh//+MfuzshLL71EIBDgueeeo7i4mEsvvZR77rmHxx9/XFMPY5fNiCxL6CRwWgyk6BQmZ8QTkCP405tRX5+L/rU55AdrMLYayK+LYo7L5N+npvLCGCu1ISudCTqK9Zt587k22rwHvWYf/mE3m1/bTaqriYtP+YgP3tcTbqke4asVDCU6nY7Hvzsbfb6FjSVNpO1oJs4bYXurl7X1bUxPdGIuGk/AbadjYjn2v52OS07mnVcfYUzS5dS0Z+ELp3Dj2aswqll0tE7hnKYAwaYWKJ7G0wdknvReTtSk8lTlXP78xy9wdwSPb9goI4p63A0gOzubuLi47u2RRx45btmqqrJ8+XJOP/10iosPLtRcX18PQGpqz8x/qamp3Z8Jho5YC6XTWse2N0arMFKUKMFAHfW7GmlRQ0Ti3agOFaMzHdAP+r1p9oZJsscxNTMJi8lOeyCCboB1aPVeiHTdI4fwGGmIhoYGbrzxRt544w2s1iMXwVy7di3FxcVkZGR071u8eDHBYJCNGzeycOFC1q5dy/z583uMKixevJi7776b8vJy8vOPnfJ6uMhPsYMOtlS7aWwNUFDlZ8OBIPFSDo1lGXhMnZQluqmpTaEorZEWTyLF9jIaollsSI6y5owQhoo4vkieQNHuHTw1Zx9xlhBGs51ZM+pJstbw/PunMWtGCINr5EKeBMODJEl85bRspo5x8a1bGmCth6yL4tnW3MG2pnb8W9zYEs343p9GML+dnzx8CwB71waJhCaRZRqLS/d3WstklCQTVTkGZp12FdOzM2neWMee1lwaU60oHQbefnA3VfUq9/949ghf9fByvHC5rs+qqqpwOp3d+/sywrls2TK2bdvGJ598csRnh3eqtOwRGE3Isozf7x9pM/qMFkf9TxaCwXq8nmY++0cuHRXxZE+qIiHbh8WciaqWD3p9Okki+t/MtIqqDlgUHYrW2k7XoMShHiMxx2h4EMJII6iqytKlS/nOd77DzJkzKS8vP+KY+vr6I0ZPXS4XRqOxewS1vr6evLy8Hsd0nVNfX39UYRQMBgkGvxwFPzwcZii4YcEYfv+TjzFv70RnNdJRpWNRdjGn/DkH/55SmlbsoqXcgw4dEzJr2FxWRKGzHL9tHC2JTTQbvqDGM5MpCSUYQiq65OnMyH+VeH0NpTW5nHNZPqf94CbCDM/k9JOdkWhDh5OTbOftv32V/3trD++8tAOjUU+ZQUYxS8gNM4gbX8mPb7+EsSkHF2c1mPWMSbFhCTcg04Eq24juaaHDZOXN91cy5yYHbGrCtVnHxDOr+SK3kFbFivXzauDkEkZ9DaVzOp09hNHxuO222/jXv/7FRx99RFbWl5O009LSgIPPrfT0Lwc3Ghsbj3gOCgafWJtj1IVWPQCgvc73YOEPVOFv8eArzyTssRBSJeLH2bFYsoHyQb/uBKtMeWsnW3w+jPoodrtxwGVpdaAlEomg1/f0tgmP0fAgQumGmAceeKB7JOto24YNG3jyySdpb2/n7rvvPmZ5vf2AD/9h9zbCerRzu3jkkUd6hL9kZw990gKdTsc135qK2+2ntMpHYrKBr37/VBZMmMt5X13KdU//gsJFs6lrS2LnzhkUxtfS1iCTE6xjvj8Fl6UWc8JqQnIIydbKZPvbOMKl/Pq18/n7x/OYfv0SpF4WlhUMDSPRhnrDJOu59ZJJ/O8j56KrM5C5OYSpPoIvxYrHXcTvf7Sb7XtbADjvnlNo9azn6X/rsdCMMVSHSgizzkK7JHHFxTP404uXcNvNQRLqVO5RXmZiZz3Z0bIRubaRREVFlY6x9TP9gqqqLFu2jNdee41Vq1YdMWiTn59PWloaK1eu7N4XCoVYs2YNc+fOHZRrEhydWJtjpMXO7eGMxlA6RQkRDNShNMOECfUUTa1g/GQPaXm5GAwJQyY88hIMFCXqyY0fuCg6FK21n0gk0iOMDsQco+FCCKMhZtmyZezevfuYW3FxMatWrWLdunWYTCZkWaaw8GAq4JkzZ3L99dcDB0dQD4+td7vdhMPh7hHU3o5pbGwEjozVP5S7774bj8fTvVVVVQ3ad3AsnLnxfPP/zmH68mKu+9fZ5F9c0OPzad/9MY4sB/8xTeWVtrM5JbucxjIj8eF2LvXIJBgqqZizh3BKA2vr9azcMpW6UAILL4hgSssZlmsQHGQ42pCqqnj84T51LIoLE/jlC2eRdEYCxvoQzk0+/KkyjVOt/OXNEu5/+HMs01KY/+NvkuKqZd3e8djMJQQNCkHVSGu2GySZKrePM69fwuToAeIjfm7O/zfnmj+ivnI3neHY6TieKJE+bP3h1ltv5cUXX+Svf/0rDoeD+vp66uvru8O3JEnijjvu4OGHH+b1119nx44dLF26FKvVytVXXz14FybolVgTRqD9RV5HozAKBOsPpuquVUlI8JKZECYhV8ZiztJ8Mgz4cmBZazZ2eYwORXiMhgcRSjfEJCUlkZR0/BXuf/Ob3/Czn/2s++/a2loWL17MK6+8wuzZB0N25syZw0MPPURdXV13aMmKFSswmUzMmDGj+5h77rmHUCiE0WjsPiYjI+OIELtD6WvmqKEgYUw8CWPie/9Qp2Pur3/LL659m/qJC1hfNZXT1Y1U7zZw6cQvOM0X5OfJBfhSIjgvOI26SBGnWS1sVYJMaggxJfvYHqOWziCVzT7yU2zEWwdn5Kkv7K5tR1FUJmY6NfdAHigDbUOqqrKrth29JDE+4+ghWFFF5a0NNShRBZ1exwUzMpCPs0ZWQaaD5586hw8213DvvZ8yUzGiT7WzNxwhuS3MHXeu4cbl09gzdyydXh1tqSbCoRIaok2EK8fwz3WVFCTYWdPsI8WRgqmzmayUA/wwfDsH7t+H0VFO7tQULp2ZwbxxSdhPcH0ibXM8r1D/OntPP/00AAsWLOix/9lnn2Xp0qXAwQydfr+fW265BbfbzezZs1mxYgUOx/CkKD+ZEckXho5RJYz8VYTaPbjXp9O5K424GeU4ihyYLQcHJrUujECbbScajfbwGLndbjHHaJgQHiONkJOTQ3Fxcfc2duzBORBjxozpjrtftGgREydO5Nprr2Xz5s188MEH3HXXXdx4443dMf1XX301JpOJpUuXsmPHDl5//XUefvhhli9frskff18wW51EklzoI1BhyOGjz+cy9qtnkHHxg6TmZvA9xYclIZ5NLXXUxjcQl5jAjQum0doeYH9jz4Ug99Z18EVpM/saOugMhCmpaWdsoo0dFR6CkeHpBGwud+NQJBJ0etYfaB2WOrXM+gOtuCQ9dkViU9nRv4+K5k5AZW5REnodlDV39rmOs6dn8tN75+DIt3Pz7HyK4+1s0QdwTY0nXOsjkj2ZA4WLcJh1JKTryE0wkDItj/PGpzIh1UGq3UzRNedR35BNgz6O+CIPbWfFEY1KjPXooMTPys9rR1WH53AGe4FXVVV73bpEERzssDzwwAPU1dURCARYs2ZNd9Y6wdASi3OMYsVjNFqIRkMEgg14yj20N1mRspqw5HVgS83GIB/sk2j5fkDfphqMBIeG0jU0NJCUlERZWRl//vOfef/993skRnnkkUeYNWsWDoeDlJQULrnkEkpKSnqUp6oqDzzwABkZGVgsFhYsWMDOnTt7HBMMBrnttttISkrCZrNx0UUXUV198mX1FcIohtDr9bz99tuYzWbmzZvHlVdeySWXXNIjtXdcXBwrV66kurqamTNncsstt7B8+XKWL18+rLYGI1E+KWnki33NlNQdOQm/tqmTb920kssuepM//3PPMcuy6HUEjDIJET++iXakWQ4sxdPAmU7ktLsI6COcOnYX5nEOPrVL7Dd9TpLdiMtooLUjiBL6UvAEvGHGxttobQlQ2eolJ95COKJgkuDdDbXUtPoG+6s4gnAoisNswGqUUaP97U6OPtSogs0o4zAbiISP/n14QxHykuzYjDL5yQdTtAJ8vr2Rbyz9D9/42tv88597j3p+dn4c3/vGFBp8QeSIyli/nv0dPp7fWk16kgFbkZP6mefiKnAzY2IdtrZdfLi3mabOIPubO8k//yJ09jC7Owv4euA/pDR1cuAUJ+kmA5HWEN6OMKFRfD/VPvwnGD3EWiid1jq2vTHaQukCgWpQFTorXESDdvxNyZizdVgtOcec9yw4PocKo9TUVGpra7FYLHR0dPDNb36TZcuWdR+7Zs0abr31VtatW8fKlSuJRCIsWrQIr9fbfcwvfvELHn/8cZ566inWr19PWloa5557Lh0dXw4e33HHHbz++uu8/PLLfPLJJ3R2dnLhhRfGlOd4MBChdBolLy+v14dnTk4Ob7311jHPnTx5Mh999NFQmdYndlR7mJ4Rj06S2FrrgUMyZoejCnfd9QlbKluRotD0i83MOjUNVVWZnB3f60PUpESJq45Ql6ujXtKxYmMrKWdnUfn5dmo+T2WiEuXr5r/Q4ryVfwcs+D//A/nuM3D/aR9rnDaY+t9y/huza9TpSHNa2F7lodUTwO+NYEbH57ubOHdGBnsq28hLdZAcN/hu69QEC1vrPUhIZCQfmZb9ZCMpzsK2+naQIDv16KGPeiR21bfRGQxT0+ZnXJaD8toOvn/XGtrKvASm2Ak/u5uZp6eTm2o/4vxgJIrLaGDh5DQkk476yiZOzUllTU0bWyo8mMbHk55gpzwgkW/zkZ2ukq2PUlnfSaHLhqLChP93HZvvfJfsc2pYUvse+xxX8mtTJxdWgy/RxjXywBYYjAX6mpVOMDqIRWGk9Q746BNGB+eRxo8L0ZQVwBTvxzXRisXyZXbJWAilA+2Jt2g02mOOUWpqKpIk8fDDDzNp0qQeHqN33323x7nPPvssKSkpbNy4kTPPPBNVVfnVr37Fvffey6WXXgrA888/T2pqKn/961+5+eab8Xg8/OlPf+KFF17gnHPOAeDFF18kOzub999/n8WLFw/DVWsD4TESDAk2k4wnEAYgcthLYH9NB231fmRZh84mY822YA5AoiKztqS51/IuuDCfBtkArSHsi5L5xtQsbGYLc75yLmdMyKRqSxr22rHc5nuOTFlhQ1sDn3/xB9QWBVtrqLscr6Swp9WLZNGRYDcxb2wSxjCcnp7A7AwXzZ1BXl9dTmZzhFWfVfLR7kZq2wbXi5SbZOOMSSmcPimZMb104E82xqTaOX1SMqdPTCYv6ejCKCfZjskgIwFGWYfqU3nz+xvxmST8s53I7jBeVcFh632ez5TsePZ3+Nnd2snEMS6+/vVpzHLEcVtaGt9zOChAosUTol1ykRwXj2vSbHZU1TEp1YlOLyFJkHL6IopSmjBVmjjbtpkHCpKw5thYqfgoyBrd9zIqQVRSj7GNtIWCwSTWhFEXsSA6YsHG4xGN+gmGDr6vXWMTOeXeRopv9+NMy0OWv5wDqHVhpOXkC71lpTObzUiS1Otal114PB4AEhISACgrK6O+vp5FixZ1H2MymZg/fz6fffYZABs3biQcDvc4JiMjg+Li4u5jThaEMBIMCePTnbhDEbbXu8lP9BGNfilOclJtZGXayfLL6BONTC1KJNFqxGLQIx1l2PnuC8ah6HXIbREiKSbkiEq604w7pGfOIz9n3oXz2J7+PSYHp3Ov9xm+Z/yMr1umM39KHMGw0j0vfHqei9PGJzE11wWArNchW2XcwTAbmzycMzUNu15HuS/IzDFJjE+0c6DO27tRJ4AWH8QjSV++D7tJ5pQCF0azzPT8BFpqfDQGQsguI8m1ERI9Oi7/1ngS7L0ngJAkibnjkpkzPpmx6U6SXBasGRYUHcQ5jVzbCteWR6mpWYQ34mR+oY/8BCuvlZQzIe9gkgxFJzH57ASqysaQJEWZ0fQxP1QcTHFLpMSNbu/fYM8xEmgbWZZRVRVFiY07GwvP01iwsa/4/TWoikL5pjI+/3s1FTv86GRLD28RaF8EalW4HZ58QVVVgsHgcRMcqarK8uXLOf3007vnY3ZlKj48M3FqamqPNTCNRiMul+uox5wsCGEkGDLS9E1sePx/efOuB3nu//0PLXWlAFhNMj95fB7TZ6RwccTGtFmpbG3ysKOpgzinEV8wwsbSBhrbvnQV6ySJwnwHxuYw1UqYlQ1tvLGlloJkG0gSk6+/hRafm7fkbxNXOZXMYCtJkz+nalIzVhk4xnNv4dQ0aqQwtgQj9e0hJIueA/4gqkGP1SSjk7T/cD9ZyEm0MTM/gdwkGzNnp5A9xcXFxjjOOz+fp145h29eNr7PZU3KjiMx08onn1bxqVXPC9Od2KamEqcWsbU+k531mwla4rD4Fd5avZ+W+gP8cfl9bNjUAIqBstpkDJVvsGFPNebMMGfmu45ot6MJMcfo5KKrUxZLXqNYSb6gZRv7ij9QSWNJA588OpHdr0xk90oL/o7W/y7q2hMtCo/D0ZqNh6fr7lo8/XhZ6ZYtW8a2bdv429/+dsRnva1xebzr1qpwHErEHCPBkLHr83UYDDUkzKuk5PXZ/PMXj3LTE38AID3eykO/PhNFUdHper7MVn5QwjQ1TOneRrbmJNHaGSI9zsKE8S72bGxGaY/y3ppqsgrt/M85B9d7kiSJRXOmULOpGjXpOpL0qQTUNymaup+d7ilAIgBVezcwZlLPxSGNso7zp2V8mZ1mbBLRqMIXB1ppbQyTFGc+6R4MsUCczcR37pvV3YYGQnaSjf2z4gm/00BJYjLxNV6KSt7FL4UI2CvZ19TGJRPTeXX1ATZ8Wo1qKaV9ppvUtDb2hGaR4fuI3H1vMFv1sf7THKapYQ7sb4K5Y0iJswzyFY8sYo7RyUVXpywSiXQv/aBlYuEZPVqEUSTSSTjUSuseFbXZjqRKOOyd2Jxj0Ot7es5joWOtxQiOw0Pp+iKMbrvtNv71r3/x0UcfdWczhoPrW8JBr1DXUi9wcI3LQ9fADIVCuN3uHl6jxsbGk25BbeExEgwZuRPGEe000l7rInFaGdtW5tPcUNrjmK4ObdeDKaqoWINBJEkiCBBWOTMzkVyLmVy7CYuqw9UQgWwToVIfgUOygGWlOInE2fHr9HyqvwC76Sri69xkTVpNamopoPLK/a/y4p2PsP2VNUfYe+jDUa/XMacoiVOLkhibLtZM0TIDFUVdyJKORpMef2uYvR+3km2xoK5XMEUVNtXs5O4VpZTV+3jrtRBl+/Lw1CTgnmyg6PI67EUFnDG5lmpbdne7zZdVqg5LEz8aOPb8ooObYPQQax6jrud3LIiOWLDxWAQCNaiqihUveaeVMmbeHsbODOOMO9Jjr3VhpNV7cbgwCgQCQO/CSFVVli1bxmuvvcaqVavIz8/v8Xl+fj5paWmsXLmye18oFGLNmjXdomfGjBkYDIYex9TV1bFjxw4hjASCwSJn7CzGzMikYt1Y0KmYHFFCgWPP15H1OkiNZ09QoU6WMco6DP+d+F6YZMU5xo7OE6E+y0BLuYdXvvgyx35GgoWw3chuk506v8Qf1keo3ppAZoOP/JSdZGXtYHxGOjUWmQ9eeJ5Iq3uovwJBDKCXJZrjJcJtIToJ47KnM3vJ17EYrTjrd7O3spq1Ze20NUaYdPZpeDuS2PLJVRj1E3BnFGELNWAJmQmrHewJKmzVG5iUlzjSlzXoKKjH3QSjh65O2cmWqnco0bJA6A8e9272bzpAx+4k4nwOkmUTiVPiMZszjzhW68KoCy3Z6A2GadO5aOPLhD6BQABJko5IyABw66238uKLL/LXv/4Vh8NBfX099fX13ZnrJEnijjvu4OGHH+b1119nx44dLF26FKvVytVXXw0cXOrlhhtu4M477+SDDz5g8+bNXHPNNUyePLk7S93JggilEwwpC2/6ETrjn9m/JZWv/jCP9Jxpxz3n9Jm5RBWVyYEwm8rcvF/WTJLdyPicOIocVj6uqidabKF9jJW1O1u4fk5O97mmEJyZf9A1/FnW1zD+NULpqhaazvWQkllKRt5b5KzLpGTceL545W/M/Z9bhurSBTFAVFXZscuN3yVDKEpcrpE5dTsw+mvYs6SA2WOqmblzH8+3XUm2XWHs1Flcc9OlfPzuW6zbkojNnoir2YSpJZ5509LQZ4xBf4IeLO1yvHlEQhiNJmLVYxQLaNVL0RfCYQ+fv1+Je7eZ4Cf5mG1Rxs/ehd01Db2+d2+GllFVFZ1OWz6CzRVuLpwzlaCiY2eNh0mZcT0y0h3O008/DcCCBQt67H/22We7F8z+/ve/j9/v55ZbbsHtdjN79mxWrFiBw/FlRMwTTzyBLMtceeWV+P1+zj77bJ577rkec51OBoQwEgwpsmzj7G/fxtn9PE+vk4i3GjlrUs8sKhdfkMNnu5qwNkSwBiVK9vdcPNYnqXhDERr8IRISLJzx+G1Uvvcpz+/YSGfESfK0evyuVjJ2tFJStp+5CGF0MtMaCmPe6MU4046hLojPqqN+/kVkfvo2Pl8WVnkz+pCLS+c1M+7Us5h9Wh6SJDH//IuZD4TqKmiMm0781akYswpH+nKGFDHH6OQi1oRRF1ruiI+GOUaett3U7Vaxu2U6VZAlBUO2jNWad9RzYkGwaslGo+6gEImzGKkLHfz9BYPBo84v6kt7kiSJBx54gAceeOCox5jNZp588kmefPLJ/hs9itCWTBYIjsPi4lRsER1yZxT3qQ6C7lCPeUbfOCuPvXIIW7aZc6ako7O7yPjKwbz8Xm8iG/edQzAtDvesPUAn7rp9I3QlAi0QUaIEjeDLNYFBR0Snsq29k7fPupKayhAGtRVdk4tTplSz4MzCI16exvRcsi5dgn3y9BG6guFDhNKdXMSaMNJSx/ZoxLowUlWVyl1foGuygD6KraAO29gGEsbrMJkyjnqOlu+NFj1GLruRT/ZUsWZPFePTncCXaxgJhh5ttQaBoA84863om0OETBLSikZ+u3J/92c6nY4LT8lkRkHvczxMVQGqX8yCxgDJ4+rY8Lcnaa7YPlymCzRGW0QhqCrQHkaq8jFhXhq3L7uIS04JEbdpL2pQpimlkz2NmXyxqylmOzSDgUi+cHJxaFa6WCFWki/EKuFwG03rVPybx+DZUkhIH8UxTiEuZQJ6fe+ZC7UujEB7oroozUHr3vWkyx3YzQcXLA8EAsddw0gwOAhhJIg58nMc6FpDyFYd5jOcNNX2fQHWs844i3DR6TT/fSHRNhtK/Ho++90v2bfxHfFCPQmRkNAnSBhtepzBCJmJVnQ6HXlFZ+BwWfDVJ6Afa2WyOp5kd5QNJc0jbfKIITxGJxex6DHSWge3N2JZvPn8FXTWm5EklUBIj2lMLdlntmOzjTnqOUIYDYze0nULj9HwIOYYCWKOmy8tZNfndXh8USomOjDWefp8rsWRQMHp4zEnf0zbW9OJTq7AMKmELW/+hbadTZxyzdfQ6cSozMnCum31tBZYCRslLAY945JtwEHPY9o8mYYKG87sEI2GdFrbWjE7Rl+2ub6icuz0CrHZ1RMcjVjMShcLoiMWbOwNVVVpa96O1+Al4azNhKQoJfvHUtTWgsmUfszztCg8utCqfdFotEfSg9EYStfc3Ex9fT1msxlZlrs3vV7f499dm06nG5awRyGMBDHH1CQHungTxuoAwSQjzX+p4tWF1Vw+N+u45+5XghSMzyRx/HWs2fkcnupkIoYwieOqaPrCTfOZ20jJmzUMVyHQAs/8Yx+m5hCKrMPjV3Dov3wkZoy/gAP1H5Fm2UGCM8CmyihfHZcwgtaOLMfzCgmP0egi1jxGoM2R/96IRWEUDrdQsbkNd4Odiu2ZGG1BZly1j6Ipp6DTGXo9J1auU4vtprd1jEZbKN1rr73GU089RVZWFqqqHiGKDt1MJhNer5eLL76Yr371q0NqlxBGgn6jqiprtzcQ7QjjSLYwbWzSsNtQfEoSravrCBQZMGTbee/Tmj4Jo3mzM/+7ivs0zvjxMrau/ISOA3tg21QsyfHEpRQMvfECzRCpC6MWWTF1KkiTrMj1Pj5srWb29DTGTF7Eph0VKKEJKJ064i3t2Ey9dwBOBiLH8RlFhDAaVcSqMNJ6Z1yLnfC+4PdX4WsMYHRECHRYUVWZ5DQD8a6Jxz1Xy9esRY+RqqpEo9EjhNFo8xitWbOGtrY2rrrqKlpbWwmHw4RCIUKhUPe/fT4foVAIi8XCO++8g8vlEsJIoD32N3SS0BomziCzv7IDb248NtPwNqWr5uey7v1aJCR8OWb2lnqIKCpyP9aQSUov5OzrColE/LRUbicurQCT9eQNlTopscuE4w2YKwPMHRPPFL0RNaKypaSZuVPSuOLrN7J97x46vV7OP+PkWv37cNTjrGN07DWOBLGGTqdDkqSYE0ZaJxbE2+GoqoKnZQ/u7QlYzK2Mn7cb2a4Ql2TEZEo9xnkHr1Pr90Vr9nWFr472OUaZmZnce++93HzzzX06/pvf/OawPI+EMBL0G5tRpk5ViQPCEsj64X+oZDnN6I06XB1RmOYg+EYD2xs7mJ7m7HdZsmwhteDUIbBSoGXWbGskzaKnI92IeVULodyDC90FFRWj8cvY7slF40fKRE2hHieUTgij0Ycsy0IYDTKxYOPhBION1JVUEDEbaG+II9Rpw5XUQc6kieh0R1/8MxaEkRY9Rl2/udE+x+i2227rMZdRVdXu+3FoMhVVVdHr9dxwww1YrdYht0sII0G/SU+w4BnjpKI1QH5hEiZ5+FdFznWaOW2ig8xcO++bFCZlBdi3v3lAwkhw8qFEIqz+1dvceM4p/MalYp2XyMQZKZTZwGAyMHPc8IeHah1FUpGOkZJbzDEafcSiMIoFb0ws2Hgoba07WPNqElW7E0jMbqK1xcSUSfU4neP6dL7WhMfhaM2+rt/caJ9jlJ2d3f3vQ0Xg0Tj99NOH0pxuhDASDIjxOfGQM7I2FHnc5PrtNOxcS8Tqwvevz2CemCMkOD47fvp/BBptsPVTjPNmkuo0cvMC0XaOhQIcq/ugHOMzQWwiy3JMZaWLFWJJGClKlJb63dTvzCPs1xEKGpGdCq5CG0Zj8jHPjYXr1KLHqLdQutHoMTqcuro6amtr8fl86PV6zGYzFovl4BIaeXnDJgyFMBIMmBq3jzq3n3ibkcJUx7DXr7y8GneRgfme3XzaOpG8C/OH3QZB7KFGo5S8so6UDiOSp52kpHySktO6P1cUlc0VbqKKyuTsOCxG8ZgEiKJwLPkTFdJo1KHX62PKY6TT6TTfGddaJ/x4BIMN4AsyYVwD7REFR1YrWVNtFBbPRJKOnTo5FkLpQHv29RZKNxrnGB3Krl27uPvuu1mzZg2RSIRoNNqdsjwYDPLee+9x7rnnoijKkKfsFm98wYAIRxW27G8l0WpkR4ObvXUdnDs5DVk/9DnmS17/lL3PfUBq2z5St9WzuXMcBslL8dULh7zuvrCvvoO6eh/IEnMmJA3LdyLoOxG3m/oGJ1PjN7F1TxHTSv7OTTV/7P78071NGEIqZQ2dbNjTwpSCeOaNT9bcy3O4UVCRRLruk4pYDKXTOrES7teFP1CFZ59EiiOAszGRrLFe8hekY7MVHfdcIYwGxtFC6UajMOoSPz/84Q9pbm7mb3/7GxMnTiQSiXQLJJ/Px4QJEwDEOkYC7VLe1IlTlvEHFObkJBJQFLZWtjEjf2jXeal59W3+fvMbzEqoIdXaSll7ASgy51+TSWLx0VffHk6qa7xMjLMTUVS2lLmZWXj0THfVbh/+UJTCFLvmHs6jkVBjC59+/cfk26tRDHq8uS4s7T5sh8yTq2vx41R0zEx2YdBJ7G73UVrfwbj0k3v+mhBGJx+xJoxA++FbsSSMFCVMS+VOSt5LIVATjz2zDVuugl5vxWA4fgbXWBBGsRRKZ7FYRsqkIae8vJwf//jHnHfeeSNtCmIoWzAg4qxGWjtDNHcG6QiE0eslTIahbU6t67ez+vYXyI9rwaAE2C6PZZsrEUsqzH58+ZDW3R+6snMFolHMxqNPKNxT107QH8Gh17OhrHW4zDtp8VfUseIrz7Bnix9LXDvBRedx/sPfImnpmT1ejIl2E3UdAcJRhbCiElEVrMOcjl6LdC3weqxNMLqINWE0HKPJg0GsCKNgsI7qTc34gjoUSxBS3FgynJgt2X0SE7Fwnaqqaq7d9OYxGq2hdF3f/Te+8Q1KS0tpaGgYYYuEx0gwQFKcZsbnx1PR6GVvu480vYVpufFDWmfN+9swS5AgN7ObImoyIpTtScU504jUh4wmw8XEQhcHajowmPScku066nGd/giT/js3q8rjHy7zTlratpbT3mLAYeugw2yhWarH1uImeeaEHqOG8yel8JEKn9a7sZplpo1xkZ0w9ClCtc7B5AvH8hgJRhuxlnxBayP/vRELNnbhce9g6wcZ1JclkzK+kqQJLTjiJ2MxH38x9UPR+jVrzb6TJV03fPnd/+AHP2DJkiXs3LmTM888k/j4+O7kCwALFiwQyRdOVt5++20efPBBtm3bhs1m48wzz+S1117r/ryyspJbb72VVatWYbFYuPrqq3n00UcxGo3dx2zfvp1ly5bxxRdfkJCQwM0338yPf/zjQf/xj8t0Mi5z+MKLxnx9Pnue/ZSdehfupAjBgBGvz0XUbkJVVfyhKGVNXnKSrDjMhmGz63BS48ykxh3/AZYSZ2JbrQeDTic8EsNA0pkTSRv3ERV7knm9aRITOlqgqgaZFHbVtJOdaMFpMSLrdZw1NY2zpqYdv9CTiKikoop03ScVseYxioUwtViwESAaDREMNiLpzSimIK0eCxt3pzFxcQSDIb5PZcRCKB1oz76TJV33obzxxhs0NDSwdetWVq1aRSgUIhKJoKoqXq+X8vJycnKGJxWy6I1piH/+85/ceOONPPzww5x11lmoqsr27du7P49Go1xwwQUkJyfzySef0NLSwvXXX4+qqjz55JMAtLe3c+6557Jw4ULWr19PaWkpS5cuxWazceedd47UpQ0K1rwsLtv5a9a/8DKfv7eCSGMms8+Gy75zKlFF5bNdTRQnOdlY0sLsCclYjhHGpgVyEm2kx1uIKipmg7ZtHQ0Y4uNY+O/vEW1pwXfnczTWVtHsXcsp5/6ARFVmc6mb6WNdOC3G4xd2EnJQ+AhhdDIRa1nptNbB7Y1YEUbBYA3uvR5S7UHsc6o50GqnsTGVaNTY5+85VoSR1jiZ0nV3RWvccccdnH322dx3331kZWV1Z6WLRqOEw2Hi4+OHzSYhjDRCJBLhu9/9Lr/85S+54YYbuvePG/flAmorVqxg165dVFVVkZGRAcBjjz3G0qVLeeihh3A6nbz00ksEAgGee+45TCYTxcXFlJaW8vjjj7N8+fKYfkB5Pe2s+MPHNOzfyKTFZ3D2t27q/qyxI0C61YRR1pHntFLb5mNMyvCnEO8vBr0OoYmGD0mnQ05O5pbn7+LlB/6X1kYPjnAUo6yjKMFGVaufSZlCGPVGFLV7/lxvCGE0+pBlGZ/PN9Jm9JlYER2xYKPPt5/S9/S0laSgqKnknF5C7vQQWTnX9rmM/gqjfY1BImEdelmhKHV4vCNaTL5wMqbrjkQi3H777eTm5gJ9W/B1qNDWjLOTmE2bNlFTU4NOp2P69Omkp6dz3nnnsXPnzu5j1q5dS3FxcbcoAli8eDHBYJCNGzd2HzN//vweLtfFixdTW1tLeXn5sF3PUPDJKxvZvnoP7oYQnubUHp8l2UzUB0NUuL3s83jJSbSNkJU9UVWVfQ0dNLYHRtqUk5oat4+KFm/336Wf76R0vZn2+nY+WbWPCreXXS0dFKbaR9BKbSOSL5x8iDlGg08s2BiNBthXspMPdtjYGPRjzGhk7tkqM+ZNxGg8+rzZw+mPAPT4I5iiFibGx+PASktneCCm9xtJkjR3T06mdN1d3/03v/lN3njjDRobG7vTdCuKMiKDCMJjpBEOHDgAwAMPPMDjjz9OXl4ejz32GPPnz6e0tJSEhATq6+tJTe0pCFwuF0ajkfr6egDq6+vJy8vrcUzXOfX19eTn974IajAYJBgMdv/d3t4+WJc2aBjMeqJRJ0qgGIujZwdWp5M4a3IqHcEI00yyZh50nx9oZVyqnabOIL5QhLyk0dvx1mob2lXrwW6UMck6tlS6mZbjwmgxIpFAOJSHq6aZMQVxTDXK6HTaaDdaJCxF0UlH7yQrxE4HWtA3YjGUTuvemFiwMRCooWx7BF+rHUUFXXYLcdkZWCzZAyqvL+9jnU5C+e/3ElVVTCfxszgajSJJUo9seaN9jpHH4+Gpp55i3bp1zJw5E7vdjtlsxmQyYTAY+Na3vjVs/TohjIaYBx54gJ/85CfHPGb9+vUoysGcTvfeey+XXXYZAM8++yxZWVn84x//4OabbwZ6f8Ac7go+/Ji+uLMfeeSR49o50pz59TnIskQ0AvOunHnE55Ik4RzBpAu9YdRLOC0GnBYD26o95CWNtEVDh1bbUDCsMC71YGabOs9Bz13+1LFcfKeP+v1tTDu3SHPtRouIULqTj1hMvqCVQbGjEQvCyOerJFHqIHdMI3pVz5g8kE02zP3MRtefUDqHSU+LKcCutiBGE2Rahy+kuS/2NbYHqGr1kRpnJss1tFlKI5FID28RjF6PURdr165l3rx5VFZWsnv3boLBYHcChnA43GOKyVAjhNEQs2zZMr72ta8d85i8vDw6OjoAmDhxYvd+k8lEQUEBlZWVAKSlpfH555/3ONftdhMOh7u9Qmlpad3eoy4aGxsBjvA2Hcrdd9/N8uVfrgXU3t5OdvbARoeGCtlg4MyrzxhpM/qFpJMobeigIxAhK2H0Ls4G2m1DTovMtmoPhsPW2pp69jSmnj2ChsUYynGE0bE+E8QmsSaMIDbm72jZxmjUh7uqlJo3ppHfaSBhWhnmdCNGYyKy3L8Q9f7OMcpL1KZHJBRR2FffySnZ8ZQ0dGAx6Em0D52tvQmj0T7HaP369SNtQjdCGA0xSUlJJCUd300wY8YMTCYTJSUlnH766QCEw2HKy8u7J6PNmTOHhx56iLq6OtLT04GDCRlMJhMzZszoPuaee+4hFAp1p/BesWIFGRkZR4TYHYrJZOrhpu16oGklHOpECIVCBAIHPQXt7e09UpsPNWPi9XQGIySb9Rh1kUH7PrvK0dILVqttKNkMDj0oqoLVKI+4PVqhv20oIgWOuY6RKgWP+pkgNok1YRQrHiMt4/dXcWB7I9UmE4k2iKoS8QXxmM39H+SKlax0x7PPH44SbznYXU6PM9PqDQ25MDo8+cBo9xjBwetubW1FURRkWe6xDee1C2GkEZxOJ9/5zne4//77yc7OJjc3l1/+8pcAXHHFFQAsWrSIiRMncu211/LLX/6S1tZW7rrrLm688UaczoPrCV199dX85Cc/YenSpdxzzz3s3buXhx9+mPvuu69fD6cuD5YWRvwHk5///OcjbcKg0tHRQVxc3Eib0SujtQ2NNo7XhoxG43890cf/7aSlpQ3rwINgaIk1YRQLaD2Uzust4bMPUqmriqPR7mfaeVU4EiZisWQOuMxYF0ZxFgMl4Sjbaz0EowrzioY2Jj4ajfYaSjea5xhVVFTw0ksvsWfPHgKBAHq9Hlk+OF/c5XLx61//ethsEcJIQ/zyl79ElmWuvfZa/H4/s2fPZtWqVbhcB7PA6PV63n77bW655RbmzZvXY4HXLuLi4li5ciW33norM2fOxOVysXz58h4hTn0hIyODqqoqHA7HUR8aXaFSVVVV3cJMq8SKrX21U1VVOjo6emQo1Bp9aUMw+u7NSDPYbchsNlNWVkYoFDpu3UajcdSPap5M6PV6kZVukNGyMIpEOgiHvcQneKjTJ5KW0YEly4TJmIxe3/9Q8K6501qgwh+k3B8ky2wkw2TA0s900KcWJA6RZUdysoXSdXZ2cuutt7J27VrOPvtsrFYr4XCYUCiEz+fr/r0MV2p1IYw0hMFg4NFHH+0hdA4nJyeHt95665jlTJ48mY8++uiEbNHpdGRl9W2ipdPp1HRH8VBixda+2KlVT1EX/WlDMLrujRYYzDZkNptH7UtZcHRizWMUC8IItBUCfSh+fxW1W+oxl2cws7gBpyNKXG4i5gFmo+tCC/elyh+iLhimLhhGQiLJqCfLZCRZo+sYnUzJF5qamli9ejU7d+7snjrSG8N1n8Q6RgKBQCAQCI6gax0jrXbkYxGtdcK7UFUVn7+Sis1horoogZIczBk+zE4nZtPAwui0MscooqjUh8KoqkrJ7gDr32xlxadu9vqCI25bb0Sj0R5zjFRVHfWhdOPGjdOMh1EII4FAIBAIBEfQNWodK+F0WuzkHo5WQ+nCkXYq9+xn46eZ7Ku1YZ9RQt4pYDZnoNcPbN6gVoRRXTBMRFXxuiUuScnguln5GHeFCPrRhH2Hc7jHqCuMebR6jLKzs/nud7/L448/Tnl5OZWVlTQ2NuJ2u2lvb++xPuJwIELpBAPGZDJx//33x8QoRqzYGit2Diaxcs3CTsHJRlfnrLfQHsHA0Kow8vsqKNvmJiTZSZxUyX63zClO44AXddUSNcGDwsJXoxDnhIisw5RpYVK8BRSf5oTR4ckXuoTBaBVGsiyTk5PDnXfeycsvv8zEiRORJAmDwYDf7+eUU07hN7/5DYqi9Fj0dsjsGfIaBKMWk8nEAw88MNJm9IlYsTVW7BxMYuWahZ2Ck41DhVEsoLUObqxwMIxuH/s2pNDRaqehJp6cKbVUlUtMnZV+QuXCyN6XqKpSGwgRjqh0lvn51NWM3h2l0hBinNNIW5tvxGw7Goen6+5abmS0CaOuZAo7d+7kxhtvZOzYsSxZsgRFUQiFQkSjUdrb2ykoKBhWu4QwEggEAoFAcARdnbNYEUaxgBY9RuFwKyF/K6mJnbTGWcme2ILkMFAwLgmdzjDgcrUgjJpCYUKqSsQvc+1FRZjaIrz07wNMOjeu+15oTVBHIpEeIqhLGBkMA78XWqTruy8tLSUYDPLpp58e8/jh8BaBmGMkEAgEAoGgF4TH6OTA76+iuSSEWpdIkkkiyWDkshvCjBk394TK1YIwqg6E8IZV3AcimFujqAYdaq6JC4rs3R1zrbWbw0PpujLSac3OE6XrelJTUznttNOora0dYYsOIjxGAoFAIBAIjkAIo8FHax4jVVXw+atoKgkSMsm4S1MIBOJprahj/OS0Eyx7ZIWRqqrsaA3wyYs1WKr1vDUhiCrB3vgABbbUEbXtWBweSjda1zDq+u4zMzORJInly5dz//33I8syZrMZk8mEXq/HbrcP65xZIYwEAoFAIBAcQaxlpQPtrhHUhdaEUSjUjK+tirrSeJypTSR2GHBmGsgYm4JO179FUA9npK+zJRxhwx4/7g6IWCS2f9CIv8jC+VemoZOk7vTQWhNHhyc7Ga1rGHUlU9ixYwcffvghAO+99x6pqQdFqyzLNDc3c8MNN/DQQw8dkcZ8qBChdILjUl5ezg033EB+fj4Wi4UxY8Zw//33d6eQ7KLLJX3o9vvf/77HMdu3b2f+/PlYLBYyMzN58MEHh/zh+bvf/Y78/HzMZjMzZszg448/HtL6DueRRx5h1qxZOBwOUlJSuOSSSygpKelxzNKlS4/47k477bQexwSDQW677TaSkpKw2WxcdNFFVFdXD+elDBjRhk4M0YYEI4HwGA0NIy0YDsUfqKJ6l0Jri569H0/EKMvM/bqbjLxTB62OkbovG1v8dPyzCakygP/zBhrtCoYMA6e4rCNu27E41GP0j3/8gx/84AeEQqETelaP9DvscFRV7f4dFBcX88QTT/DMM8/w+9//nrvvvpu77rqL22+/nf/3//4fZ511FjB8c4yEx0hwXPbs2YOiKDzzzDMUFhayY8cObrzxRrxeL48++miPY5999lmWLFnS/XdcXFz3v9vb2zn33HNZuHAh69evp7S0lKVLl2Kz2bjzzjuHxPZXXnmFO+64g9/97nfMmzePZ555hvPOO49du3aRk5MzJHUezpo1a7j11luZNWsWkUiEe++9l0WLFrFr1y5sNlv3cUuWLOHZZ5/t/tto7Ll2xB133MG///1vXn75ZRITE7nzzju58MIL2bhx47CMopwIog2dGKINCUaCWBNG8KVHRosdXtBWR1xVo/h9FZTvCxCXHsAQkpAkGU+tDdPs5EEof+RC6VRVZY87SDiiIlV6UUMKXm+A1CIDaUbDiNt3LA6dYzRlyhSys7NZu3YteXl5jBs3jldeeYXi4uI+l6eFd9jhSJKEXq9HURRyc3PJzc3t0znDgRBGguOyZMmSHh3VgoICSkpKePrpp4/o1MbHx5OW1ntc8ksvvUQgEOC5557DZDJRXFxMaWkpjz/+OMuXLx+SRv/4449zww038O1vfxuAX/3qV7z33ns8/fTTPPLII4NeX2+8++67Pf5+9tlnSUlJYePGjZx55pnd+00m01G/O4/Hw5/+9CdeeOEFzjnnHABefPFFsrOzef/991m8ePHQXcAgINrQiSHakGAkiLWsdFrr4PaGlkLpgsFG3E3VtLdI6MpSCDfGkzKlgZzJ6UjSiQ+UDER4hKMKnSGFeLP+hO6nJxKlvT1Ce44JKWpHV+8nOt5O0Vgrel3PcrXWbg4NpRs3bhxXXHEFW7duZfXq1XzwwQf9FjNaeIcdzsaNG4lGo5x6at88k4qiDFuiDBFKJxgQHo+HhISEI/YvW7aMpKQkZs2axe9///vuGF6AtWvXMn/+/B6T6BYvXkxtbS3l5eWDbmMoFGLjxo0sWrSox/5Fixbx2WefDXp9fcXj8QAc8f2tXr2alJQUxo4dy4033khjY2P3Zxs3biQcDve4loyMDIqLi0f0Wk4E0YYGjmhDguEg1jxGXR0nrQiP3tCSfX5/Ffs3q8jtBuTGRBLjg6QUBkjNnjUo5fdXGHUEo5Q0qqhRG1trQ8c/4RhUB8Ic+NCDGlLQtQSRfSrmAjPz0iw9jtOaKILe5xiZTCbi4+O57LLLcDqdfS5Lq++w3/72t/z85z8HwO/3EwqFiEQiRKNRFEVBURRUVSUcDgPwgx/8gJtuumlYbBMeI0G/2b9/P08++SSPPfZYj/0//elPOfvss7FYLHzwwQfceeedNDc386Mf/QiA+vp68vLyepzTNcmuvr6e/Pz8QbWzubmZaDTaXcehddbX1w9qXX1FVVWWL1/O6aef3sMVft5553HFFVeQm5tLWVkZP/7xjznrrLPYuHEjJpOJ+vp6jEYjLperR3kjeS0ngmhDA0e0IcFwEYvJF2IBLQgjRYnQ2bGfigMhXMle9IU1KJ1xJBfpMZlOPIwO+n+dNW1hZmYefNa6/SEUVUE3QOHyRZ0X844Ast+L0hnGcH4qt3ylkBSThf0tnYxJNGo6lO7wBV4HmnxBi++wLt59911uvvlmotEoZrMZi8WCyWTCZDJhNpsxm83o9Xpyc3NZu3YtkydPHha7hDA6iXnggQf4yU9+csxj1q9fz8yZM7v/rq2tZcmSJVxxxRXdbtkuujqvANOmTQPgwQcf7LH/8AfQcDyYeqtzpB6Ey5YtY9u2bXzyySc99l911VXd/y4uLmbmzJnk5uby9ttvc+mllx61vJGOpRdtaPgZbW1IoF1iMZRO621ZK/YFg/Xs2dqC3xtl56YCslI7mZrfRMa08YNuY1/LS3PKbKtrI89lpz0UQCcNLEVzZyTKgYoQaljBVuLH75AYXxTHvHwX8QaZLVVBQLvPvsM9RoORrltL7zA4GOlRU1NDWVkZfr8fv99PIBAgFAoRDAYJhULdXiRVVens7OTss88eFtuEMDqJWbZsGV/72teOecyho/O1tbUsXLiQOXPm8Ic//OG45Z922mm0t7fT0NBAamoqaWlpR4xQdIX6HD6aMRgkJSWh1+t7rXMo6jset912G//617/46KOPyMrKOuax6enp5ObmsnfvXgDS0tIIhUK43e4eI/6NjY3MnXtii/CdCKINDS+jsQ0JtEvXBOlYEUZdaMEjczS00hH3+6uw2lTMUUi2qjQ2OZBPryAhffqg1dHfQat4i4xRH6XR18qUdOPxTzgK1f4g0v4AjiwzLY445EgUfZpEXZufoMmIokYAQ/fxWrknQHcI2WCl69baOwwOtourrrqqx2CelhBzjE5ikpKSGD9+/DG3rh9jTU0NCxYs4JRTTuHZZ5/tU9rEzZs3YzabiY+PB2DOnDl89NFHPVI0r1ixgoyMjCPCowYDo9HIjBkzWLlyZY/9K1euHNaOoKqqLFu2jNdee41Vq1b1KdyrpaWFqqoq0tPTAZgxYwYGg6HHtdTV1bFjx44R7dSKNjQ8jOY2JNA2sizHjDDSUgf3aGhhjpGihAgG64mz64jUphBudDEmu4PELCcGg+v4BfSRgXjzrUY9WfGmIxIk9IdPa73Y1nvJ2+FjktPGnLsLGFtkQa900uFzU5TYUxRpqd10/dYOD6Ub6AKnWnmHHYr03zWkwuEwkUike4tGo93bofOMhvv3IjxGguNSW1vLggULyMnJ4dFHH6Wpqan7s64MWP/+97+pr69nzpw5WCwWPvzwQ+69915uuumm7h/01VdfzU9+8hOWLl3KPffcw969e3n44Ye57777huzBtHz5cq699lpmzpzZ7aWorKzkO9/5zpDU1xu33norf/3rX3nzzTdxOBzdIzdxcXFYLBY6Ozt54IEHuOyyy0hPT6e8vJx77rmHpKQkvvrVr3Yfe8MNN3DnnXeSmJhIQkICd911F5MnT+7OMKZlRBs6MUQbEowUsSSMQBvC43iMtH2BQB2qGqVyh4fKkgJcGW0kJfrImTFrUJ+jIzGHxx+NsuMTL1lbgrgzDQSdetKdepKMetLtPcXFSN+H3uj6rQ3mAq9aeIcdjk6nG7Z1ifqLEEaC47JixQr27dvHvn37jgjf6XqwGAwGfve737F8+XIURaGgoIAHH3yQW2+9tfvYuLg4Vq5cya233srMmTNxuVwsX76c5cuXD5ntV111FS0tLTz44IPU1dVRXFzMO++806ec+YPF008/DcCCBQt67H/22WdZunQper2e7du385e//IW2tjbS09NZuHAhr7zyCg6Ho/v4J554AlmWufLKK/H7/Zx99tk899xzMbH+jGhDJ4ZoQ4KRIpaEkZZG/o+GFoRbIFCFqio07HFiNip4Wx24cqMkpE4bkvqG876U+4PYt3sx6iPQoNKY40SSIMt09NA8LbWbrkQngznHSAvvsFhCUkf6FyoQCAQCgUCTPPXUU4wbN45zzz13pE05LsFgkNLSUsxmMwaD4fgnjACrV68mHA6P2PcZjQZpaHybQFM9258xsLfcSWpRA2fcmkBe0TWDWld5eTlr1qzh61//+hGLTQ8Vz+2sx/tCBZHt0DzJQvJNaaTY9FyYHI9d7jkAFAwGiUQijB07VjPtxe1285vf/IbrrruuO2T6+9//Pn6/nyeffHKErRscuhI/PPfcc0yfPp2pU6eOtEk90KYfSyAQCAQCwYgTi8kXtMxIe4wCgWpQFYItfqxNcRRHHBSlqCSkzRj0uoY7lC6kKLQ1tjEx0MxpmfWkFkdIsemJl+UjRNGhaMljdLQ5RiealU5LdLWL119/nZtvvvmI+U8jjRBGAoFAIBAIeiXWQulGWnj0hZG0zx+oRlHClGwMU211o5u8F0NOBKsle9DrGm5htNMToGZdgBeNyfyfI4GquINiIsvcu7eqy3OhRWE02Om6tUTX3KLf/va3FBYWcvPNN/OrX/2Ktra2kTXsv4g5RgKBQCAQCHpFlmWxwOsgMpLCLRr1EQo1U7Grke3rk/BUJYKhgfxCO7JsH/T6hlsYvf1xKx3bw7TvbEVnV/HF25g2C7LMRw+T05Iogt7nGI02j1EXWVlZvPjiizz99NP8+te/Zv/+/XzrW9/C5XIhyzImkwlZlo9YlHyoEcJIIBAIBAJBr8Sax0jrjKSNfn8NqhJl/5ZO9u3JJ7uwAXOah+Sxi0bMpsEiqqhU7Qug90goNgMRWcGZbMZp0BN3lDC6kciadzwGO123lolGo+j1ev7nf/6H9PR0Lr30Uv7xj3+QkJCAoijIskxraysbNmwgIyNj2OwSwkggEAgEAkGvxJow0noo3Uja5w9UEY100lpuo2BMM4GwnuwJARxx44akvuEUHhXBEEkNAfQShKck0TndwqmLXWSajJoSPsdjKNJ1axW9Xo/X6+W1117jqaee4swzz+Tqq6/GbDYTDAaJRqN0dHR0r2M4XAhhJBAIBAKBoFdkWSYYDI60GaOKkRBGkUgn4VArdSUerAETLa0OcibWkJSfjV5vHZI6h1MYravqRIoolBfKqIYok+c4cZh0xwyj60JLwmko0nVrlY0bN/Lkk0/y/vvvc9VVV/GLX/xCE0tHCGEkEAgEAoGgV2IpK10seIxgZISRP1CNp9WNcW8z0zomMH5cEy3JfjImDt3izsMljBRVZfMqD83oCO/2IEWjNEyyM+m8ZBINR+/mqqqKTqfTlDA6mTxG3/rWt3A6nbz66qucdtppgDbCG0VWOkFMk5eX1/0yHImMJqtXr+6u/5JLLhn2+gUnjmhDAsHRiaVQOtDW6H9vjJR9AX8VG9Z4kGudOKxhkqIymYVBbPaiIauzK+vbULOx0Yf/k044EEQO6/Hn2zHEy2SaDcesf7js6w8n0xyja665ho8//pjTTjsNRVGALwc3RhIhjAQjzqEdw962hQsXHvP8rtWc4+LihsniL5k7dy51dXVceeWVw1634Eui0Shz587lsssu67Hf4/GQnZ3Nj370o2OeL9qQQNA7sZSVrqtDpWWP0Uh4tMJhD+GwBydhGpviCDg76chtxJCbjV4/dB3u4brOldvbaUzVoUogJZnQxxmYMtN51DTdhzLSnfDDORnWMerie9/7Xve/u1J4awHtWCI4aenqGB6+PfPMM0iSxC233HLM8x0OB2lpaSPygDMajaSlpWGxWIa9bsGX6PV6nn/+ed59911eeuml7v233XYbCQkJ3Hfffcc8X7QhgaB3Yi2UTuuMhDDy+6sASEjU01yXzLY1k+nUmRkzbd6Q1z3U90RVVWo+dWNoj6CTJBwHgjjzrNhlHcnG488v0lKHHL7M1Hbo9zZa5xhpFW21CMFJSVfH8NDN7Xbzve99j3vuuYcrrriiX+U999xzxMfH89ZbbzFu3DisViuXX345Xq+X559/nry8PFwuF7fddluPkdC8vDx+9rOfcd1112G328nNzeXNN9+kqamJiy++GLvdzuTJk9mwYcNgfwWCQaCoqIhHHnmE2267jdraWt58801efvllnn/+eYzG448cHopoQwLBQWItlC4WGE5hpKoqgUA1FW1xpE69npxb55F4RTmpp1iw2wto8YVp7AwPWd1DLYzK/WH8sg69O4Jpewed02yMmWIjw2xEf5y6tRpKd+j8Ihi9oXRaRQgjgeZoa2vjkksuYf78+fz0pz8dUBk+n4/f/OY3vPzyy7z77rusXr2aSy+9lHfeeYd33nmHF154gT/84Q+8+uqrPc574oknmDdvHps3b+aCCy7g2muv5brrruOaa65h06ZNFBYWct1112k6VONk5rbbbmPq1Klcd9113HTTTdx3331MmzZtQGWJNiQQxJYwioXkC8PdEQ+H3ezbvgtPXQoFLiezxqeg2ieRPC2HSo+EbHRis8WzrzU06HUPh/BYs8WDpTlKUInQMs2CmqonZ4yJTFP/BsO0wtGEkfAYDR8iK51AUyiKwtVXX41er+fFF18c8EM1HA7z9NNPM2bMGAAuv/xyXnjhBRoaGrDb7UycOJGFCxfy4YcfctVVV3Wfd/7553PzzTcDcN999/H0008za9asbq/VD37wA+bMmUNDQwNpaWkneLWCwUaSJJ5++mkmTJjA5MmT+eEPfzjgskQbEghiSxiB9sPphlu4tTRt5t2/JJA9sYOmZBPtYQ+ydReOuAV0BHXkJtgAqG/rHPS6h1oYRRSFDZ+10WRUUfSg7wxhyo3HopdINx0/jE6LHqOuULpDEaF0w4sQRgJNcc8997B27Vq++OILnE7ngMuxWq3dHVqA1NRU8vLysNvtPfY1Njb2OG/KlCk9PgeYPHnyEfsaGxtFp1aj/PnPf8ZqtVJWVkZ1dTV5eXkDKke0IYEgNoWR1j1Gw2WfqqoEgrWYLAqh9xp54YO1xMudnPo/jZjNmRhDCnsa2pF1EjoiwPHFRH/rH0r+8nkzHe/UIXuihHLMKKkmcqfaSTMakHV9EzxaE0bCYzTyiFA6gWZ45ZVXePTRR3n55ZcpKjqxFKIGQ88HvCRJve7rShHZ23ldD8ze9h1+nkAbrF27lieeeII333yTOXPmcMMNNwz45SzakEDwZVY6LYuNQ9FaR7c3huu7DIWacdd7yRtfjnPOTnIzt5NWUEbG7GJ0OpmceAN2nR8TPvJdgyuKuhjK+1GyrQ2rN4LBH0Vv1WPPMJGXbSLT0rcwuq51jLTE4cLo4BwxMcdoOBEeI4Em2LJlC9/61rf4+c9/zuLFi0faHEEM4vf7uf7667n55ps555xzGDt2LMXFxTzzzDN85zvfGWnzBIKYRK/Xo6oqiqJoYlX64xELHqPhIhCoZv/n7ezZkEbN/iQmFdcxIT+KwzG1+xi7aeju6VCGqvmjCtGmKJZJLnwJEqZUI+MWxGOQJDL6kI2uC60J6cOFUTgcRlVV4TEaRoQwEow4zc3NXHLJJSxYsIBrrrmG+vr6Hp/r9XqSk5NHyDpBrPDDH/4QRVH43//9XwBycnJ47LHHWL58OUuWLBlwSJ1AcDLT1UmLRCIxI4y0zHAJN1VVaKndir/MSs2+ZPydZurbrJw+OYDZnDrk9R+0YeiE0ert7SS+68UaiGKcbcV8Whx5WSZSTDImfd+8QFoU0IfPMQoGgwBCGA0jQhgJRpy3336biooKKioqSE9PP+Lz3NxcysvLh98wQcywZs0afvvb37J69WpsNlv3/htvvJFXX32VG264gffff1/znSaBQGt0CaNYW+RVqwyXfcFgI22VzYR9VsZPr6Sz00T+WC9FsxYiScMjcI8mPEJRhQMtYeItOtIcAwvh2+kOoqoSwaCODpPMuNyDoWZZ/cxGp7VQumg02sNjFAgEACGMhhMhjAQjzvXXX8/1118/aOUtXbqUpUuX9tj3wAMP8MADD/TY99xzz/X4uzfxdfiDPS8vT5OjTCc78+fPP+oE8ffee6/f5Yk2JBAc5FCPUSyg9VA6GB5PRSBQheeAgYC9g9aKRNwN8Zx1xQFs1rwhr/tQehOCexqizM5Npdrjo67dS7qzf+KoNRChZo0HNdeEGq/DMN2CxXCwnkxz/8rSmpA+PJSuSxj1dy0+wcDRllQWCAbAD37wA+x2Ox6PZ9jr/vjjj7Hb7bz00kvDXrdg8BBtSCDoHSGMBp+htk9RonS4d1O3NYHyqngOlMeTN6OalIJEjMakIa37UI4WSmc1GdBJEtlxVjyB/ieh+WBDG8krPaSUejF5IqQVHfQWJRpkrP0I99Riuu7DQ1a7Ei9ozc7RjPAYCWKaNWvWEA4fXLXb4XAMe/0zZ85ky5YtAD3SOAtiB9GGBIKj09VJiyVhpGWGw75gsB4VBckVoHFDOhZrEFsiZGSfgiQN33j40YSHooTYVteGNxSmMLH/3dDqhnYku4rZF6HxFDNZOQe9KVnm/ntVtNZeIpFIj7A5sYbR8COEkSCmyc3NHdH6LRYLhYWFI2qD4MQQbUggODqx6DHSMsPh0fL5q2guaSQzS2XOknbc3nhmnCljteb0uYxoNEo4HCYUCnX/3+Fw9Gvw5mjCaGyyEVVVkKT+d0HL20Ls/7gTo0vCOcVC6jQj8n+ryOynMNKix6i3OUYiVffwIoSRQCAQCASCXhluYaSqKp/vb0HWSSjAqQWJ/T5fy4kihloYKUqY3evX8uYjhVitIeZOa+eC/6fDaB6DwZDw32MUIpEIoVCoh/AJh8Pd/45EIkQiEap2daKXdWRPOOhN768wOhoDFST//KSJYG0QXVClOSwzJtkCQJws45T7n1RCa8Kot1A64TEaXoQwEggEAoFA0CvDnZWuosVLbryVBJuR8lYvrd4QCba+ewLa2trYu3cvmZmZJCcna3LS+lAKo0Cglj2bvSiuVsJhAz6LB50+E4s5C7fbTV1dHc2V7ZR97CPkDaKXIxiiOsxWPdbJTsiKJ6zqUH1Bal5rRdpvJJAYIhqGxMT+iVQYXOERUFRK1jSjK/XSmWTAVugkLelg+8zqZ9IF0KbH6PDkCyKUbvgRwkggEAgEAkGvDLfHKM5ioKrJR4LNiMcfITelf5PpGxsb8fsCfLqqFFdKOUXjDgqkQ9P4jyRD3RGvq1nH+g9zaWsxM2ZCHaljQJKMmM3ZeL0qoVCIA590UrUjTHuDjJwURVUgIdtIcdIEZNmIUwKHDqyndbJnfxWWFg/hgNI9F7OvDLbwqPOHsTb7MJvCeKwyKVOtdBWf2c803XDwXmhNGB2+jpHwGA0/QhgJBAKBQCDoleEWRi6bic5glJ0N7eQkWzEb+i6M2traKN9RS9kb7fjdOjZb29HdoOD3+xk/frwmOsFDGUoXjYZQo424zDb8gTgSZR2pp1iQDQ4MhjgMBi+KotDU5sO930FEpxJtl3G36Ci4KIcF+WlgkPisuoXZ8XaSciRqi61E/U3kTE4kFAr1y57BFka7yjpJTDHhthtIKtKRW3BQ7Nr0Olz9aCdaprd03WKO0fAihJFAIBAIBIJeGYmsdNkJVrITrP0+r2TPXvb+qxPrLhsEdDgyO6na6WXq9AmaEEUwtMLIH6im4gs/2WY748+oIC1BxexMxmLORpKk7rDCoF7GWuQj0GhAdkVwyjp8bX7cnX70NgONgRBVRj/tRh/mWW0UjUtGlmUikUi/xM5gC6Mtn7ahb5KY1Ryi/PQUjP9NsJdpMg6onlgIpRMeo+FHCCOBQCAQCAS9EitZ6Xw+H42NTfjCClY9GC0KxpQIcfEOXC7XSJvXg6ESRt7OnZSsN+GLQmdJBgXfLEWSMrBYsoGD91Kv13PqYidVpX50ET+eMpmwCvqOOj6p1JOcYWFGjoon2ok9xcB5kydiMBgwGo3IstwvITFQ4REOBTEYe3pJNlX5aHnLjbMsyNbT7NjivhQLWZaBzSPTYiidmGM08ghhJBAIBAKBoFd0Oh06nU7zwqi6uhq7w4IhRUdDxEvUJ+PKMjDz7IIeczZGmqHqiEejfnZt3s8XG9MJtFmZf9ZeksfJGAzxyPLBjHI6nQ6Xy0VCQgJTZxi7xU6X8Bns72kgAtDrcVO+ZRPxaekk5eRhshz0HG4/4MVZHUSVocOqI+e/axeZdDqSDKOnKyvmGI08o6c1CQQCgUAgGHT0er2mU2ADmM1mTCYTM7+SQtkWN3HpMlnjXKSlpY20aT0YqlA6f6CGlsowEa8FgymC2RXEnprT7S3qYrjXbeuvEKzYsYu9W1uw7Gsmo6AGV2o6Sbl5BAOdRM8xE/DriJ9oIsF1UDxkmg3oTkBsaslj1JVqXswxGlmEMBIIBAKBQHBUuuaXaJmcnBwyMzNxuUowO3ej0+lITEzEYOh/GuehZMiEka8MQ0eIU0+pRpEUcvKN6A2mI4TRcNLfUDp/u4d17xxg3w4bJoOfMfkVTFwQorW1BdtWMxP8fjrNBhqnf5k2PGsA2ei0Stfgg5hjNLIIYSQQCAQCgeCoxIIwgoOereTkZAKBALIsa3INIxj8OUaRiJfqXSVUb8jA12wnpagRV66M0ZiEXt//JBYDQVUUJJ2u575+CqPGijJa64JkOsLo/VZCdQZqt3rRnzOBtXhJOiAht6n4pyqQAwZJItV4YsJXSx6jrt/YoaF0Yo7R8KM7/iECgUAgEAhOVmJFGMGXHV2n06nJDuVQdMQDgWqat/to31iINWLA4vSRmB+P2ZI16HX1hhIO0/H+BwRKSlAPayd9vV5/Zwfu+jqiikp5q5MOFNSAGUUJ80WtnrqKKE16mbpME949AQDSTUb0uhP7PrUojI4WSqeqKp+WNvF5aTOflDQO6ULBJzNCGAkEAoFAIDgqsSaMtNTZPZyhCKXz+croKLUT5whCuxVXegiDxY7FnDmo9RyN4N59BMtraH17NeX/+xztq9agRKP9us7mynI6PT5avfGEIkbcIT2mJC/FF06isymAFYmoUYe5LkR77kHhkGXWVpjkiXJ4KN2f//xntm/f3t2emzqCJJuNFKc5yXVaqWjxdp9bXl7ODTfcQH5+PhaLhTFjxnD//fcfsfZUZWUlX/nKV7DZbCQlJXH77bcfccz27duZP38+FouFzMxMHnzwwZNKhIlQOoFAIBAIBEclloSRFihzhwhFYUyCAfkwj8ZgC6NwuJ3Omn3YA0ZcOY1Yspqx5howGVPQ6y2DVs/RUPx+9v/9Y1Z9BNFWCTsB5Nc3MXbqJiJXTuyTSA0HA7Q3NeKIt1IwtpXKMpWc9E6Kl2SSM3Ei1DXieLENc1OYzjkOEsea0UsSaaYTF0ZaEtGHe4waGhpYt24d7733Htu3b+eCiy5h3vnfIDPeQn1HgLHZzu5z9+zZg6IoPPPMMxQWFrJjxw5uvPFGvF4vjz76KHBQeF1wwQUkJyfzySef0NLSwvXXX4+qqjz55JMAtLe3c+6557Jw4ULWr19PaWkpS5cuxWazceeddw7zNzIyCGEkEAgEAoHgqMRCVrouRrqju781RFF6Ejajgc8P1DMxeWi9GoFANd6qIHJLMonNcegnVhGfacIyTGF0wZISKmskgh6JvZ15OHUBTnXsJiF1N95NfjrzC4g0NyMnJR21DIPJTMGMU2mqKGfOWTqmewNY7PEk5+QS0Olp3x/E4okStejRO3WMzTWSajRg1I2uoKfD5xjdfffdlJSUkJSURGFhIVVVVRRlOihp6SQn1UbcIes3LVmyhCVLlnT/XVBQQElJCU8//XS3MFqxYgW7du2iqqqKjIwMAB577DGWLl3KQw89hNPp5KWXXiIQCPDcc89hMpkoLi6mtLSUxx9/nOXLl4/472s4GF2tSiAQCAQCwaAiPEZ9R0FHnNmIrJMw95IRr6tjORheI1VV8fkr8Zfp8QcMqHn1hJLAlZqKyTT0YXRRv59gWRmFBRLx+k7MuhBGScGsUyitSyHNXAqRCIG9e49blsXuIGfSZMaeNo+MojHoDQYSs3Op8gehKUL4FAeBYjORcVYcZh2ZgxBGN9AFaIeKo2WlS01N5Tvf+Q4PPfQQyQ4zp+QlkBp3fG+gx+MhISGh+++1a9dSXFzcLYoAFi9eTDAYZOPGjd3HzJ8/v0eK8MWLF1NbW0t5efmJXmJMIDxGAoFAIBAIjoosy8Jj1EeSLPB5WSN6nR6jFAJ6ZsYbTPvCYQ9+bzXuRgW5oA5j1EzuIjM2ew56/dBn5NNbLNjPOAM5YSdntmzFsKoSf8iE16ZjjzufjOoOpIQolkmT+lym2WYna0Ix0XAYvcHAh9sbyFjvxV4WonGeFVuRHgmJjEEKoxvp9nIoR0u+MJAkIvv37+fJJ5/kscce695XX19Pampqj+NcLhdGo5H6+vruY/Ly8noc03VOfX09+fn5/bYl1hAeI4FAIBAIBEdFeIz6jssiMzZBx5h4ley4I8XJYHqMAoEqdv6ng/UfjmP33hTIasbgTBnWtYvkpCRsZ55J3v+7nMVLTRQk76M2pGdcsI6Kllx0Nht6p/P4BR2G3mDAG4mwdX07xhkODLdmIhXoyRxjJdmox3JISuvRQm/punfs2MHtt9/eLeJ62zZs2NCjnNraWpYsWcIVV1zBt7/97R6f9SYED/ecHX5MV1vVkogcSoTHSCAQCAQCwVGRZZlgMDjSZvSJWOm8nagwUlUVf6Aaz34L0TY77T4TpIeRDWZMpvRBsrJvSJKEKTODtGXXk/CVCk7dsAl/nZtdLhOKw9Hv8gIhhb2NIba0dmLzwlfPyUNW4SPVjNWkI3MQFnXVYme/N49RamoqN954IxdffPFRzzvUw1NbW8vChQuZM2cOf/jDH3ocl5aWxueff95jn9vtJhwOd3uF0tLSur1HXTQ2NnbbcjIghJFAIBAIBIKjEkseoxPt6EZ9YVBU9HZtLg7bRTjcQiTcSUZKmMjMKuzWIDnTHJhMGeh0I5PGWtLpMOXnY8zOxrJ/P1Jp4EEbHAAA5cFJREFUKboBeHZK68PofDLnSImYJ+hQVZWoWd+9gGymeXCEkZZEEfQ+x0hRFPLz8xk/fvxxz6+pqWHhwoXMmDGDZ599Ft1hySnmzJnDQw89RF1dHenpB8XzihUrMJlMzJgxo/uYe+65h1Ao1L1A8ooVK8jIyDgixG60IkLpBAKBQCAQHBW9Xh8zwuhEiLT6CexpJbjPgxIamjlVgxVK5/dXo0RC2PNCFE5zU3RWCHNKKhbz8GSjOxaSLGMeNw7JZhuQ+NAjIUXAqZeZbrLw2u5a3thfSSQxikuWscuDF0anJXF0InOMamtrWbBgAdnZ2Tz66KM0NTVRX1/fw/uzaNEiJk6cyLXXXsvmzZv54IMPuOuuu7jxxhtx/jfc8eqrr8ZkMrF06VJ27NjB66+/zsMPP3zSZKQD4TESCAQCgUBwDIbCY7S1wo3PF0Ev6zi1KHHQyh1I501VVMI1nbRva6NpbxBXlhHJ1IZ5bAKSfnA7g4MhjFRVod1Tymd/duPen0qSC6af60evt2IypQ2WqSfMQK8xLUFiVUMn9R4f1e1B6ko7MU03kGt1kmUZPE+eVpMvHDrHKBgM9kkYrVixgn379rFv3z6ysnqK4677oNfrefvtt7nllluYN28eFouFq6++ujudN0BcXBwrV67k1ltvZebMmbhcLpYvX87y5csH4xJjAiGMjoGiKNTW1uJwODT14xEIBAKBdlFVlY6ODjIyMo4IZ4lFBjsrXXsgTNgbZXyCnVZ/iPKmTvKS7YNWfn9QowqBfW1EPUE2vNrGrm1+4pw6lnw7iSSLjCkvblDrGwxhFAo1UbmtispSF4FGMyZLGyGvQmJ2BjqdtpISDKTvlGg3sHFPMyUfunFs7ECXZsFCHL5zIGsQ5hfB4CS/GGyi0SiSJPV4ZgQCgR6ps4/G0qVLWbp06XGPy8nJ4a233jrmMZMnT+ajjz46blmjFSGMjkFtbS3Z2cOX3UUgEAgEo4eqqqojRm9jkcEOpTPodISVgx3TzlCUNOPgegH61enVSUh6CTWk0FQTorUtjE4nU7G+k7ixJnQ2A4Zk66DZNxj4fOVs/0Jh27ZU0nNacOU24cwuwKyBMLpDUVV1QAMDUUWlMxghatHhyzPjiCq0OCFBr8cpD+5Ag5YGvSORSI8wOhh4um7BwBHC6Bg4/ptNpaqqqjv+sr80dTZR+GQhAPtu20eyPXnQ7BMIBANH/DYFQ0V7ezvZ2dnd75BYZ7BD6SxGPempVva6vcQ5jH1arLKvdIVH9XVyvSRJmPLiiDaH0NkhM8uIbITPt7STMt5CaIuPvHNTsOYNzr08UY+RqkbpcO+manc8RmMEv99Acn7kv2F0KYNi42Ax0AQH9aEwujwzxjVu9G0h/BNsJOToybcYB03IdNmmdWHU11A6weAhhNEx6PrBOJ3OAQujgC4A/23TDqcDp31g5QgEgsFF/DYFQ42WOl0nwlDMMcpLtpGXbBvUMgeKJOswT01g1pJONv7HTZsnjKc1RPPnHjpDYeoO+Fn4wHh0phPvMp2oMAoGG2ne20GG0Yop101mUSOOvAQsliwkSVthmwMVRlX+IN7tfpK2+gkWmZGzYeICF5nmwc22p7Xfp/AYaQNt/YoEAoFAIBBoilhL193vcDpAbzFQcEUmU4uMJFtgerYJORwmUO/F0BkheMCDqpz4vJQTFUZ+fxX6YARLaxJxlbnkJOhIzEsa1kVdhxJFVdnSFqSzLEDdxSkEE6yYCxzk2A0kGgZvLF+L6xhFo9EeiRdUVe3zHCPB4CE8RoIBUdPmx+0N4bIZyYwfvDAIgUAgEGgLWZZRFGXE1n5Zs7OO+gYfRfkuTslPOO7xA7VRdpkJZdmxVgdRgwo+FVLSLGTMcBL1hglXd2DMGTnPsqJECAZrsWdYKZrUiTKpndQZVmTZjsEweJn9BouBtJemUJgNH7Tgaw9i+LSZoMtIxy4jSecZBr3taUkUwZEeo0gkgqIowmM0zAhhJOg3NW1+znlsDf5wFItBz/t3zicz3iLEkkAgEIxCujprkUgEg2F4Fw9947MKlEovE+wWtm1sYFyGA5vp6DacqEdmwtcyCUUVZFUhP8dIy84QzdtCGP8bRifZDBgSB/5+OxH7gsE6FCWCJSOR7KVuoqEo5tQkzOZszXXyYWDCqMIfomW/F2OSjKXIjL45SFBSyR3kMDoteowOF0bBYBBACKNhRggjQb9xe0P4w1FuO6uQJ1ftw+0NAfQqlgQCwdChqiqRSGRQUykL+obBYOgR9jKa6brOkRBGNdVe5lrMOPR6HAq0ByLHFEYniinFxMxl+QT2tOLb7ad0k6/7s8IiI+GaTuR484DXNzqRjrjfX03Y20nH7jDmNBVrVhIAFou2stF10V9hpKoqVcEwik0mdWUL0RBE5jpIm2gibZDSdB+OloTR4aF0gUAAEMJouBHCSDBgDhU+vYklIYwEgqEjFApRV1eHz+c7/sGCQUeSJLKysrDbR2b9neHkUI/RcON0yJR6gki+AAeUEBcfJ4PdYHR0dQY9poJ4wvUhHAkyHa0RHKkyeqsBU2H8CS36OlCPUTQaIhCoo3FFFPenhejtXgruqMeWlIXB4BqwPUNJf6/RHY7S2eHFoEToSDGQVh8kHC8zd14i+iEQMFoSRXCkx6hLGBkHMZ294PgIYSQYVIQYEgiGHkVRKCsrQ6/Xk5GRgdE4eGlsBcdHVVWampqorq6mqKho1HuOujprI+GZvGh+Lq99UoXTKHNBXsZxjx9o8oXD0dsMWE9JYDIQcUexT3dgKjgxUXQo/bUvGKxFVaNEQwdzZikhI2o0itmizTC6LvqzjlF1IMSOT+vRrW7CEumk5bRMXIkyp0Y8qEoc0iAulqzFULpoNHqEMDIajaNikehYQggjgUAgiDFCoRCKopCdnY3Vqq3FJ08WkpOTKS8vJxwOnzTCaCQ8RnFWE99cVNjn45s7Q1R0GtD5oxQm6jCdwIKghkQL1vFOJLMeOckyKJ3ogZYRDrcjSRIp5yiYksqwZIYwJSRi0diirofSX/FX7QtQXqsypq2CyrCDnH17iM9OwbHlAEpaKnrL4A28jlQikWPR2xwjEUY3/AgZKhCcJHSNpB5tW7p06RHHORwOZs6cyWuvvdajrPb2du69917Gjx+P2WwmLS2Nc845h9dee+2oL8O6ujquvvpqxo0bh06n44477hjiKx79iJHEkUNrnaqhZCSFUX+paPYztyidOWPSKGs7cXuN2Q4MydZBu98DDaWLi5tCaur5pOSfTtaSHJzjkzAa4zEY4gbFrqGgP+LDE47SXNWAoTpMvClEmiXI/noXee4mjGlpgyqKDkVLv+NI5P+zd+bxUdT3/3/O3kd2N9lcm5uEhEAIAnIfgnJqPWq1Yj1AWm21trZ81fqt+q1Fq/j1tqX9tvbXqoioeOF9QZVLQCCAHIFAIBe5j002e1/z+yPumkCAJOTYwDwfjzyyO/OZmffMXp/XvC//STlGUqnu/kfyGElInCdUV1eHH69evZqHHnqIoqKi8DJtux+el156iUsvvZTm5maeeuoprrvuOjZv3syUKVNobm5m+vTptLS08OijjzJhwgQUCgUbNmzgvvvuY9asWURHR590fI/HQ3x8PA8++CDPPfdcn56rhIRE79G++EKkIwoAAiLA2bcd6nXOpiqdXK4jKiqXqKhc/P5WgkFPb5vXq3RHGFV6vBQXtpBZVs5hZyYOn4KJumK8xkzUWVl9Yl/oBmCk0FkoneQx6n8kYSQhcZ5gsVjCj00mE4IgdFjWnujoaCwWCxaLhX/84x+88cYbfPDBB0yZMoUHHniA0tJSDh8+THLy9zH/w4YN44YbbjjlF/mQIUP485//DMCLL77Yi2cmESJUMr+/6G5p/osvvpgxY8bw/PPPd7p+yJAhLFmyJCK8iYIgsGbNGq6++uqBNmXAGUweo+wEPVsOViDIBIbFR27SuiiKOL0BZIKARtl9z69CYQAMvW9YL9Id8VfdaKW+LEicTI7o1GBQBgkkxWLJjUGRkDCgtvUXJ3qMpFC6gUESRucQlc0uQCqAING7KJVKFAoFPp+PYDDIG2+8wU033dRBFIU4Hyp0RSrt+4v1F71dmn/Hjh3o9fpe2ZdE7zGYhFFslJpMYwCVSoVKHnmhpiEPRVmLiFlU4Q8EUco9pERHrog7G7rikXEGApQfLsdV4iBoc3BhfB1qv5ayETmMGxrfp16dSPIYdVaVThJG/Y8kjM4RQpMiIGIbrkrCbfDh8Xh46qmnsNlszJ49m4aGBqxWK8OHDx9o0yROIFQy//nrx5Cd0PcCtbjOzpLVe3q1NH98fHyv7EeidxnIqnTnGqGJuFymJieuzePzbVVDv9ogiiK+oNjnwrGroXTHHS6KDntJra6k0W6gXJnMlKF1tOZoiBmS0We2RVqOZmfCSMox6n8i613xHZWVldx8883Exsai0+kYM2YMBQUF4fWiKLJ06VKSk5PRarVcfPHFHDhwoMM+PB4Pd911F3Fxcej1eq666iqOHz/e36fSb4QmRS5fAKvDGxZKVyzfzJxnNoRFyUARsicSbJE4MzfccANRUVHodDqeffZZnn76aS677LKILHEq0ZHshCjyU0x9/tdT8eX3+/n1r39NdHQ0sbGx/M///E/4fTVkyJAOYXaCIPCvf/2LH/3oR+h0OnJycvjggw/C69evX48gCPznP/9h/Pjx6HQ6pk6d2iF3DuDDDz9k3LhxaDQasrKyePjhhzt4P44cOcKMGTPQaDTk5eWxdu3aHp3bucpg8hj1VrnuviL03enxuWlweKlscaFSBPvt+P6gyK4yL5V1cnaVufv0OnVVGNWVluMocWFqdBB0yRkhlGPQuZmcpETWh8IgEnOMTiy+IHmM+p+IE0ZWq5Vp06ahVCr59NNPKSws5JlnnumQzP3kk0/y7LPP8te//pUdO3ZgsViYO3cura2t4TFLlixhzZo1vPHGG2zevBm73c4VV1xx3tzxKq6zhxuuhsTSQHKicJOIbJ577jn27NlDdXU1TU1N3HPPPUDbHf2YmBgOHjw4wBZKDFZWrFiBQqHgm2++4S9/+QvPPfcc//rXv045/uGHH2bBggXs3buXH/zgB9x00000NTV1GPPggw/yzDPPsHPnThQKBT/72c/C6z7//HNuvvlmfvOb31BYWMgLL7zAyy+/zGOPPQa09YS65pprkMvlbNu2jX/84x/893//d9+c/CBlMAkjIKKFUYghJpEWTzNB7Awx918YXY3NywhzNDnmKNKjjDS5+u417YowcgeCeEtKyG0pw46eFGUz8oAM2wXJXDmp70qRS+W6JU5FxAmjJ554grS0NF566SUmTpzIkCFDmD17NkOHDgXa3szPP/88Dz74INdccw35+fmsWLECp9PJa6+9BkBLSwv//ve/eeaZZ5gzZw5jx47l1VdfZd++faxbt24gT69fqGx2ccfKArRKOfkpkVvKUyJysVgsZGdnk3BC0qtMJuP6669n1apVVFVVnbSdw+EYNJMniYEhLS2N5557jtzcXG666Sbuuuuu01YpXLx4MTfccAPZ2dksW7YMh8PB9u3bO4x57LHHmDlzJnl5efz+979ny5Yt4a7xjz32GL///e+55ZZbyMrKYu7cufzpT3/ihRdeAGDdunUcPHiQlStXMmbMGGbMmMGyZcv67gIMQkIhR4Phsx1pk93TkWxUEadXdmmswxugsD5ISbOMkiZfj48Zq1NS0mInKIpUORwY1X3bg+tMr0d1XT3qI2Woa53Uew00BbUMTWilKS6ZqLjYAbWtv5FyjCKDiBNGH3zwAePHj+e6664jISGBsWPH8v/+3/8Lry8pKaGmpoZ58+aFl6nVambOnMmWLVsAKCgowOfzdRiTnJxMfn5+eExneDwebDZbh7/BSMg784+F46R8HoleZ9myZaSlpTFp0iReeeUVCgsLOXLkCC+++CJjxozBbrefcts9e/awZ88e7HY79fX17Nmzh8LCwn60XmKgmTx5cocJyZQpUzhy5MgpvfkXXHBB+LFer8dgMFBXV3fKMUlJSQDhMQUFBTzyyCNERUWF/37+859TXV2N0+nk4MGDpKenk5r6/d3pKVOmnP2JnkMIgoBCoRg0wijSJrzt6Wm57uO2AJOz4hmTbgZ5zz1MWpWM9HgotDWQnShD2Yt5RoGgiNv3fVhgMHjmEMGKNZv4zxcqaivUtHrVoBBQDDcwIc3Qp69jJHqMOivXLeUY9T8RV3zh2LFj/P3vf+fuu+/mgQceYPv27fzmN79BrVazaNEiampqAEhMTOywXWJiImVlZQDU1NSgUqmIiYk5aUxo+854/PHHefjhh3v5jAaOWP25WeVGYmCJiYlh27Zt/O///i+PPvooZWVlxMTEMGrUKJ566ilMplN7KceOHRt+XFBQwGuvvUZGRgalpaX9YLnEYESp7HhHXRCEkyZc7ceEJjuhMcFgkIcffphrrrnmpH1rNJpOJ6iRNmGKBAaLMAoRqaF0PRVGOgXU2z3ER6lxenycOH2rs/tocgQx6+UkRJ1+amfQyBmu6V1PUbPLT3WrHKNGTavVwfCEtvnH6T5LXq+XhkP1aP1+AqKSidpizDoPR8dO4a7Jab1qX2dE2ue8swavkseo/4k4YRQMBhk/fnw4lGHs2LEcOHCAv//97yxatCg87sQ3dFfU/5nG3H///dx9993h5zabjbS0vv9wSkj0N4sXL2bx4sWdruvKD7bJZOLxxx/n8ccf79ZxI3WyItF/bNu27aTnOTk5HSYEvcmFF15IUVER2dnZna7Py8ujvLycqqqqcAn6rVu39oktgxm5XD4ocnTPdrIriiKiGEQm65v3Y0+FUVq0igqrlZK6INnmjjcLnL4ANqeS0YlGCutsRKkC6FR9GyJ3IrX2IOPT26pK7q1qC/U705yr5mgJNemJKHdUcsyTQkCQcVVyIbHJWlSqvr+xG0nCKBgMIoqilGMUAUScMEpKSiIvL6/DshEjRvDOO+8A3zeprKmpCYdMQFvYRMiLZLFY8Hq9WK3WDl6juro6pk6despjq9VqyW0pISEh0YdUVFRw9913c/vtt7Nr1y6WL1/OM88802fHe+ihh7jiiitIS0vjuuuuQyaTsXfvXvbt28ejjz7KnDlzyM3NZdGiRTzzzDPYbDYefPDBPrNnsDJQHiO33QcCqLUKBFlbUYWvD9Uj+ET8MoEZI0/uc9PT4gvBoJ+Wll2IYgBN1ETkMgG1InIyDtKiO89HcniCxGp1AMTp1Ni99n4XRtEagaMNdlJMWpxeL6A6rTASRRFr8VEq69KI1TsINmnRqDwUj8piYV5Wn9sbaaF0oc+WFEo38EScMJo2bdpJpVYPHz5MRkZbLfvMzEwsFgtr164Nh+V4vV42bNjAE088AcC4ceNQKpWsXbuWBQsWAFBdXc3+/ft58skn+/Fsek6k9SCSkJAYHBTXnTrHKxKOs2jRIlwuFxMnTkQul3PXXXfxi1/8opet+5758+fz0Ucf8cgjj/Dkk0+iVCoZPnw4t912G9BWWGDNmjXceuut4YI/f/nLX7j00kv7zKbByEAJo+NFVsp3VhFweIm/IBGfVoFOp2RIohGry0tpg4PM+O9Lx/fUI+P322hs3IS9soI6UrGkeREFHWpF7zZf7al9pyNOr2B/jY0Gpwp30Eu+pf/D6BMNShqdTo7U2RmZ2Hb804mPYDBIdVIyXgPUJGaS01xOIFZB9JhEzPHmPrc3UoWRFEo38EScMPqv//ovpk6dyrJly1iwYAHbt2/nn//8J//85z+Bti+VJUuWsGzZMnJycsjJyWHZsmXodDpuvPFGoC3M59Zbb+Wee+4hNjYWs9nMvffey6hRo5gzZ85Anl6XaN/B/lSd5SXhJCEh0Z4YvQqtUs6S1Xv67ZhapZyYbuQyrl+/Pvz473//+0nrT8w162zy2NzcHH588cUXnzRmzJgxJy2bP38+8+fPP6Vdw4YNY9OmTWc89vnMQAmj2gN1WD85REOdQPnWRnJuzMZmVDIk0UCt08PI5Gig40S3ux6jsuMHOfbuLoRjfhA1BCfnkjlCRK3XsK+25xXgOqN9DpzX5cft8OFx+nDZ/Wh0ChKGGHu0z1FJIc/CwHkYYnVKYnVdG1vnC9CckkbSlT6O6Zs5OspErqWBsaMs/SZYIkkYhcJUTwylM5v7XiRKdCTihNGECRNYs2YN999/P4888giZmZk8//zz3HTTTeEx9913Hy6XizvvvBOr1cqkSZP44osvMBgM4THPPfccCoWCBQsW4HK5mD17Ni+//HKfxbH3JqGqcnfNymb5l8UndZbvTDhJSEic36REa1l3z8x+7RMm3Zg5fxgIYSQGRZz1Dqpr5ODw0lpspeTLSvJ+ksMxl5OhaQZM2jZh/vmBGpocPpKiNdDiJkoloD/Nvl3eICX1XsqPHSRLmUjc8Hns+/YIJvsxrN/YqRrvQYipRhBFIKbTfVTZvMgFSDR0/eZAaDJec6wFK0HEoIjbEcBW7ycpW98jYRSpnM4rE62Uc6FRR4LKR/JPVIiIyHxxJCV0fq37gkgSRqcKpZM8Rv1PxAkjgCuuuIIrrrjilOsFQWDp0qUsXbr0lGM0Gg3Lly9n+fLlfWBh/3CqCUdnwmkgKa6zo+3neGYJCYmTSYnWSkJFok9QKBT9XnzB4/KjH2JGri3HOyqa3Jx4ilvcJMRqyRja8U76cauL2iYXBTtriYpqRacKEKW3Y9ariY1SEadXY9IpkcvaJsOHKjyM1IqodCPw2rykG4McGaZH0VCHrEJk9dPVjLqqmYs6iTIRRZH1B+2Y1FqSEzWUNDnJ7GaTVrlSjqPSz+E1zXibHJizbJTutpB1YTxKVeTkNZ0NpxNGWrmcYXotw/Ra3IEg1R4vNV41cZr+8XiJohjuzxUJSDlGkUNECiOJrjHQE6CBCN2RkJCQkOh/5HJ5v3uMXK1eFCo5itEx5GSZiVMoMCUYaA4EyGg3zub2YW2so+TVJozfeChOCCLGiXjUrRhGaYhNVBMTr+DbXXaai91YRuoZnRuHLC6KoM9PnceLX3ChzN9N8UYF9hYjSfEeLhg1ErU64SS7iirdjEZHnELNgVoPiuiu3xgMCQWVRkbFfjf1u5wEvEG0Og3G5FqczcMwJXT9t72yxYcrIEMpC5JxiuIMA0VX83g0chmZOg2ZXQzDOxeRcowiB0kYnYNUNrv65Tih0J0dJU2SOJKQkJA4hxmIUDqXvS2/J+/iIRw83ky0WcvxgJdRSd8XWwgGRaobW3HXHkWxX47CKmBuEXD4YFiNm611bpRaOUflSrTHfEy+PJGLMuKxyWVEixoO+WqRN6zClWNgZKaBMaMFBL+PmNhJ6A2dt+vw+0XUcnB6A7Q4vaTEKoCuiaOQUFCo5aAIIMplIAe3V4lSI+uWV87pDYBcy7hUE2VNDhodrcTqI0ccRXKeXqQVXzhVjpEkjPofSRidgyz/sjicFN3XYXYp0VqsCVFnHighISEhMWhRKBS43e5+Paar1UttSSuiCMPSTZR6fKQZNZii2yaLXref/RsqOdJ0mJS6MsqHJdMc1OCVyTC4AziNArGBACNq3HgNerTNQfJio8AvkqJQUFzloKzIhqY8DWezEoUlQHJWEoaoEdha1cQG3cREKVGdkJs8NFlDUYkLfCKJSYpTltHujNBkXKmRo1QLqBMU4Avi9UJlhQa3veviMyCCWtFmm0Yhp9kdeUIkksQHgMcfpMnpRx1hwkjKMYocJGF0jvHCwnGkRGvDSdEDnX8kISEhITH4GYgco/3rq1m/soyAN0ju5BhGzbZgMqiRfZcn1FDRiqOhGX9hGVFbAoyrbMVhqaHeLKfVGYMtqCbZJ6JyBvEZBAxKGVsONpCqiKZiTw1VzW4St9pR+tIpzZYR7QlQOdyNTlOMfLSIp9VMaroOS3RbnlKsXk2sXo1eLWdM7ulKO5wZuVxALZeh1Qk4GkVkLjANUeN1d10YGdRy6pvs7C53Ewz6yY7t/zLdpyPSvDKt7gDHjnpJVakpdIokJ0VejtGJoXRSjlH/Iwmjc4yUaC35KaaBNkNCQkJC4hyiv3OMRFGk8biTgE/EVu2hZFsz/uYAl96XF15ffqCJdSuKcNc5uCAooPOKxNWLTFQcwWe2cUyhpiVuKF6tSL7o4kh2GoocgVLvMex1XlKOCcTUqXCqBWJLRAJeEa9STkBvI74B1FOOsP1fo4ibqEGsC+CSCyRka8jJ1RIXpUGnkuMVlQjIUarkqBUy0owCUepTh9W1FwqW8VE0FLvwe4MENTJSx0Thcfm7JSiyzCFvVeSE0IWINGFU0eRhVFQUgiAQ7VQTiCAHW2ehdJLHaGCQhJGEhISEhITEaenvHCOfO0D6KBO1JXZk/iBalRxHvQ+lWo4oimzYVc2BRjt1qUYCdV5ksfuJ1RjweXXUVg9lbOanNJYkkNLwLfIoNcGxUThTNTSqUzEnJGD0H6fFDhXIEWVyRlybjk4p58vtpVQXthCToCfGHSCqMkjzh04S8g3EJsDhd2qpStQjpisIukR+dfMFWJJ0lNlc5CcbKaxuILcLwkgURWJSNYz4SQwua4BgQCQ6RYMmSkEwEESuGPyVXiNNGKWZ1ew76iBFqaYZN0p55HmMpByjgSdy3hUSp6TxhHC4E59HOpXNLorr7ANthoSEhIRED+lvYeSyezHGapi1OItxlycRl6kj+xIzxlgthZUtyFxBLsiJ446fjSFrQQ5brbOIiatCGeVENtfP8ehMhOZEmltisTXKcG6U07QlSPOOZhr3OJElpTHqThV5f0hEfU0ccy5OYcJ0C1n5Zjyihiatn6KCVEwNIsZ8Az/7yQgW/DCPCy5NRnvYR3CzC61MidIewO8KIAQF3L7AGQsOtBdGUTEaEocYyZ2ewNgfpJA9LoHUXPM5IYog8oovGDRyhueq8Vv8jEiLHMEGJ4fSffLJJzidzm6H0nk8HsaMGYMgCOzZs6fDuvLycq688kr0ej1xcXH85je/wevtOJ/ct28fM2fORKvVkpKSwiOPPBJxr2NfI3mMIphQOew7Vhaw7p6ZpERrqWx2ccfKgm53nO9NuiLMQmPaN6OVkJA4v7n44osZM2YMzz//fKfrBUFgzZo1XH311f1ql8SZ6W9hpFDKiUsz4Gr1kjfHQsAXBEBrUCK0BrAHRUaYdLi8fkaOiUU4XsOx8gTk80dz8ZzR1LX6+XLbAeIbCjHrW9npTaRum4pYu4BO5qB6pJPgHB2WfAUx5iAtPh9qlZxKpw+NQU2pXcDYoqZ1uIyMoBw0AjIZKBRqYmtEbOkyvJ9YeTvuGPkWJZUKgZph0cwYajjDmbUhiiJagwq5TkFxrZt4mYx4w7khiNoTSR4jALVChsWooqkpsmxrL4yam5u55557KC0t5fe//z033XQTP/zhD0lPTz/jfu677z6Sk5P59ttvOywPBAJcfvnlxMfHs3nzZhobG7nlllsQRTHc79NmszF37lwuueQSduzYweHDh1m8eDF6vZ577rmn9086QpGEUQSTEq3lHwvHccuL27E6vOFiCi5fgBU/m0jsAAijMwmzE8VcyN7nrx+DViXn9pUF/W6zhITE4KC6upqYmBgASktLyczMZPfu3YwZM2ZgDZPod2Gkj1aTNSYeaBMRPncAl92LNkrJCKMKm8PL5gorsRoV2hgNF105lG83+dCbDYCISaNCnxkg0OJGEGUU1CQQr/SjdgdRKkTM9UFcm118vr6VYKIOq9NDnEHL3bek47o+FX8wiDfgp9zqZv2HdazeVIJKreTg5lq8eXKSSoPYc9Vkl5RhOBYgODmN4fEJZwzPOnEyvvuwm1y1nnKrmxqzg1FJZ1fUIZKItFC69giCEFG2BQIB5HJ5W/5TdDQHDx4kJiaGGTNm8P777+N0Ovnv//7v0+7j008/5YsvvuCdd97h008/7bDuiy++oLCwkIqKCpKTkwF45plnWLx4MY899hhGo5FVq1bhdrt5+eWXUavV5Ofnc/jwYZ599lnuvvvuiLpefYkkjPqRg9U2fPGGbjVmPZX4GQhRBHQQZp2dx4liLkS2VNJbQkLiDFgsloE2QeIUyOXyfq9KF0IQBFRaBSrt91OWybnxTM6NbzcqlWFjU/jgi92srWiB46VYqrZgSXHw+dF0vHUixnwrZckGcpq9mLRe6pwqcuoDlKmCJLlVOP1edhxpYmpuLDG6tt+3DL2W6FgH1W9UUUOAHKOSpkwZpul6hsTLqDcZqZcJRMeoMXfhd7l9KJ0oigg+GaZoJSM1cj5sOE5GbACjytir128gCIVfnS+T6bPF7/d3yC8C8Pl8/OxnP+PJJ5884/a1tbX8/Oc/57333kOnO7lT7tatW8nPzw+LIoD58+fj8XgoKCjgkksuYevWrcycObND+N78+fO5//77wzeqzgekHKN+5KZ/f8OcZzb0WwPWnlDZ7OqSfe2FWchLFPIiDZRok5CQiHyCwSD33XcfZrMZi8XC0qVLw+sEQeC9994DCP8Ijx07FkEQuPjiiwFYv349EydORK/XEx0dzbRp0ygrK+vnszj/GIgGr90lzhLDohtnkG5qwWk/whGDjgMuJQpR4JKEKsa2WhkRVYpoDFCfnUjOwlHkLBzO6JnJXDg2hUkjUvnyrUYeuH8Pf3vvMNYWN5VFVgQN2NRyahpFWq0ikzJjuHhMDNljkxiZE8+ITBNmvQqt6syhcO2FkSAI1ASb+LbRxrb6Zhqslfyn7D98W/8tnoCnry9XnzIYhFEk2daZMOpquW5RFFm8eDF33HEH48eP73RMTU0NiYmJHZbFxMSgUqmoqak55ZjQ89CY8wHJY9SP3DY9k5c3W8NhcZFGKB8ICOc0dYWUaC3r7pkZfnym3knFdfZwnyUJCYnzixUrVnD33XfzzTffsHXrVhYvXsy0adOYO3duh3Hbt29n4sSJrFu3jpEjR6JSqfD7/Vx99dX8/Oc/5/XXX8fr9bJ9+/aImuCcq4SEUSSHR0GbndmjJuBFRWy+i50fV6ENVqJ1QklJFm5fK5dnfs3hOT/nR7OyKGtwsvVoA5PHWfD7g2zcWUPVX1rYUtJA0d5Wrh4fQ2y6itYYJZ5qH153EGQi3qQYZg1Losrmwu23k2zsXrlsURTxBXzUutexa30CCfYkHFuacNzRgHChgF6hJzsmu1euyUC+ZpH8Xokk2wKBQAdh5Pf7CQQCZGVlnXa7HTt2sGXLFmw2G/fff/9px3Z2vie+N04cMxgEbm8jCaN+JMkU2WUXQ2FyocfdES5dGRvyLC1ZvQetUt4t8SUhIXFucMEFF/DHP/4RgJycHP7617/yn//85yRhFB/fFiYVGxsbDrFramqipaWFK664gqFDhwIwYsSIfrT+/CU0aQsGgx2aUEYigkyGOcGC03WcuHleqlNlNK6Lw5Xs4ahVgeuYmctNAnqVwNBEPVvKG1Br5Pg9Io0tHtQ+kbRMPUa1mk1bW1AmKIi6QIXB5ycuVkX2FDOiUYtMEEgxatldZSO5m9FvoihysOkgpYV1iBtVlBiPo4/S4jkURD9JT6bpDGFLoginmax6nA5sdXW01NWQkJWNMS7+lGP7gvOtktnZ4vf7O3yuPJ42j+G2bdswmU7dm3LIkCE8+uijbNu27STv0vjx47nppptYsWIFFouFb775psN6q9WKz+cLe4UsFstJnqG6ujqAkzxJ5zKSMJLoN0KepR0lTSxZvSdiPWcSEucNfj8sWwabN8P06fDAA6Do25+FCy64oMPzpKSk8I/vmTCbzSxevJj58+czd+5c5syZw4IFC0hKSuoLUyXaERJGJ07gIhGZrC1LoEVsoaS6AvkuAW9sA85gNFGiHIeQxOFNe4gxqKgoa0TjV7J2RwUur4i1rBXTBSp+PDsFs0bF4eIWPt1azb4mPzK9nIunakkbFU2p1cve6macPh9DY7v+mSktLQVg09dbaYny4t9iIrE4jaBMpGZmAfp4gZGxI5HLTnONRRGOfglaMySOBEVb+LrX5aKlvoaKnQep39mIT+XDMj4edVTUgAmjSPY0RJJtJ4bSud1uoO37Uqs9/TzpL3/5C48++mj4eVVVFfPnz2f16tVMmjQJgClTpvDYY49RXV0d/r784osvUKvVjBs3LjzmgQcewOv1olKpwmOSk5MZMmRIr51rpCMJowiistmF1eGN6ByksyUlWotVKsQgIREZLFsGS5e2TbTWrWtb9tBDfXpIpbJjyJEgCASDwS5v/9JLL/Gb3/yGzz77jNWrV/M///M/rF27lsmTJ/e2qRLtaC+MuttbpS/x+AN8s6cW/CKZQ6NJi/++qptWrqXlKy0JO9JQOpWIoyswREfhtyv56u8C5d98yrBgC8F0E86AheoDIvmtNdTljcBiiSJRoeCo3UPAGURX6cedCJu+bqS1toULLopnxthUVMquT6P2799PQUEBZksa8pShjNXH8pn1W3w73fgSm1CqRUZOSCNJfwah33Icee0B1NZavJrPOG4cT83mahqP23EpvRRuT6DZHoVc7SP6cCuzbzlOcm5evwrawSCMIolTCaOufNZOLOMdFdU2xxo6dCipqakAzJs3j7y8PBYuXMhTTz1FU1MT9957Lz//+c8xGtvcnTfeeCMPP/wwixcv5oEHHuDIkSMsW7aMhx566Lx6HSVhFCFI/X4kJCT6nc2b20QRtP3fvHlg7WlH6I5lZ5XQxo4dy9ixY7n//vuZMmUKr732miSM+pjQpDrSCjDsOtTAUL8CmSBQdMRKWrweQRAQRRG9So/L4MMuiHgSrbhlTqK9ChRKFbFJfqxWA5/Y9Ez1CjRM/IZ4jZaxggyPspkaIcBxd5AjTh9ORxB5AASTHNsBB4eroqj/ppZtI0oYnm9k1kXJGM2xKFSdFx4SRZGdO3dSWFhIfn4+ZSoVl6QPxSxocI0X+OCiFWi1XuKHCbgOtPJp3aekp6eTlpZ2chhVMIjvwAZ2verC6UhkzOgSHPK1lOzIZMuRZESZD1OUDY/DjFbhpa7ORGujHXtTA6b4/guHGgzCKJJsC5XrDuF2u1EqlWHv59kil8v5+OOPufPOO5k2bRparZYbb7yRp59+OjzGZDKxdu1afvWrXzF+/HhiYmK4++67ufvuu3vFhsGCJIwihFB+z12zsln+ZfFAmyMhIXE+MH16m6colK8wffpAWxQmISEBrVbLZ599RmpqKhqNhqamJv75z39y1VVXkZycTFFREYcPH2bRokUDbe45T3uPUSShVMrxBwOo5ALid/PcUI8anVxHxhVqDsiP4Jf5SRwVwHXMifxbFX4hQG2JihgzyJs1GL/OxqqQ4wvaGXb4GPZ4M/Z4Lcf3VCJGa9DJ1QRb/Tib/BDw47So8e2OYu9WP2u3H2ZIko95kwykpyQQFRuLJsqAIAgEAgG2bNnCsWPHmDBhAlGpUXy0ZisZshxGJirw1HpBbcA77yjJ5nTG6cZRW1nL7t27KSgowGg0km4xk26JIy59OEJzKbVfl7Jt5xgCoojd7SY+u4ZSmxybU40nqEFUuTDE1mOKcpKYCTHmGFrqavtVGIWIJPFxIpFk24keI4/Hg0bTs7z0IUOGdJrjlZ6ezkcffXTabUeNGsXGjRt7dNxzBUkYRRhSzo2EhES/8cADbf/b5xhFCAqFgr/85S888sgjPPTQQ1x00UWsXr2aQ4cOsWLFChobG0lKSuLXv/41t99++0Cbe84TmrQNVC+jU3FhTiw7AvX4PQFG53yfRyOKIlqFlvS4BHJ+qkar0BKljEI7U0vJseMc3mjFaBKx2NQo7SLKIgOixs+2inh0o0pJ/HotWkUN05pjqFCb2KjJw2kFIU2D3KfArRRRemX43CKyag27iuUcOWjjp1fWolfJ0BuNaE0xFBYXU9dsY8aMGegT9Gyt3ooxtpVP3tnJ/rQYbNVWUsrNtNQ3cvmUy0nUJ5I/PB+fz0d1dTXl5eUcOXaM/YdL0Sh2ktJYhfJbHzLRh90ZRRAfhceTcMsgOq4et1eJ16lFpfeQrI7GuScLv1aN4dbofq1OJxVf6B4nVqXraqluid5HEkYRRqhyW+jxmUpfS0hISPQYhaLPc4ras379+pOWhfoWwcmTqdtuu43bbrutw7I1a9b0hWkSZyBSPUYymcCkvIQOy0Ieo1htLFcOvfIkMTAidgSXjgvyyYptVKxtBnkAoVGNJ9mP2yujxJnAUXcmcp0FMyXEeeu4ythKrSGeMstIhoxL44IhscS7ZazeWIp/TyPm4gCtOjXvbYAsrx2L4iBEe3GoPCiGXMD+sia+3bUOj6IZguBy7mDfwSBpZUMhzce8yZeQqP/eo6NUKklNTSMtzkDAv5u9/2nA7fFy2Gog1haDQggQM7yZo54EhmdVoRyZRNM+D/X1QbzOADFyBS5bJg53kLoKyKyU41E1o840Icj7voWlFErXPfx+f4f8S7fb3WOPkcTZIQmjCKO7PYEkJCQkJCT6mkgVRqdDJpxaAMhkMn5wy2S+Sd3PjlXl+H0+XAo/hqQguugggSgNlvQU6nbWUxM9jPyReuqP2Jk8LpW8cRby42JoaPWQWmHg6IZ6VBYF5gBYv5XjTTOwXpWIv8LH2EuTmZSeQpxSTtMxDR++cAiZLECK2U/2xAvI/J0WTZyaCzNSTrLR0eymuWAbJW95KatI51hNHKNH7MamFBDmx3HFjWOw+618eWA3LpWAOy+daTIDpq8E6o6D0yniI4igBpkBxIAIsv71GEWS+Ihk/H5/ByF0NqF0EmdH3982OEsef/xxBEFgyZIl4WWiKLJ06VKSk5PRarVcfPHFHDhwoMN2Ho+Hu+66i7i4OPR6PVdddRXHjx/vZ+t7Rkq0Vgqpk5CQkJCIGCK1+EJndHUyLpPJmDL3An7w+1FoZ/jw6WQkDZORNDWBC+cnMv/WyYy5aTQXX5zP+NShzJqRi1sbIDZax666ZipbHVTWlqMyKlBEyWmp8eO3yWj1KpG3Cvi9GtKT4rHodejUWpKNMeiCcgSPkladjHlXD2fmBRlMyxxOTev3N0H9QZF6rw+fy4vQWEV5+Ti8rdkoNfEUleVz2UXFjJtlID1VTlJuJi51Pk2HE2jermTtJ7Aj20aF3I47S834pSNI/HU2DQplm7eon0PpIlUYRVqj4s5C6SRhNDBEtMdox44d/POf/zyp78WTTz7Js88+y8svv8ywYcN49NFHmTt3LkVFRRgMBgCWLFnChx9+yBtvvEFsbCz33HMPV1xxBQUFBRHfgyESKK6zE6NXSQJNQkJCQmJQeoy6SvbwDDJ/n0pZWRlNTVaCNjUjxmagjdIycuY0Du5oAK8KR7MLg6qJZkcLarXAtq17sL5aiio7FrdfCyolKo1AXVDEbZehO+ZjX2kLLlFEDMCe3ZVcnlHMcZ2LgwYTFZoaLozJY1fZMaLkLRS2JnGgrIlWjRy5oGR0QIEtbh6e6ErUgp9U0UVm7jEMeVmojDb2OpV4bAJJmKn52EpLmhxRKWP7kRj0Y+1cPVbLyJFaCutcVLUoaSpxcuGwqH4RBJGcYxQSRZEkjE7sDyblGA0cESuM7HY7N910E//v//2/Do2rRFHk+eef58EHH+Saa64BYMWKFSQmJvLaa69x++2309LSwr///W9WrlzJnDlzAHj11VdJS0tj3bp1zJ8/f0DOaTCxZPUetEo56+6ZKYkjCQkJifOcwSSMejLhlcvlZGZmYjAYMBgM4bv1eq0ay4gYCg+VQ0wTQ9IzuCBJTmNzK9VHtpOZ2UKZu5a9xkQcgpGgQaBZpkXVBK1ZCg5+VcWRygr8BhkWHThLR5KbVUjSVbUk+w7w+tZj2IPN1KviKfvEjrmqlayhfhRzL2S0RYs9GE3Z6CDVK6pJHiNj2MXRKCdfRp69nm31TUQrUphkieVQZjUNrV7cBgH1LjvWkTp25clId3gwuCCOINWVNt4u3cOF2cmkpqb2y8Q7ksRHeyLNrs76GEkeo4EhYoXRr371Ky6//HLmzJnTQRiVlJRQU1PDvHnzwsvUajUzZ85ky5Yt3H777RQUFODz+TqMSU5OJj8/ny1btpxSGHk8HjweT/i5zWbrgzOLPE5sKPvCwnG4vAGWrN6D1eGVhJGEhITEeU6kVqXrjJ5OegVBID4+/qTlqfE6kqKH4nUno9IbWLvvGAHrcZrVIg6liEtsYbbyEHZ5PK2NBtTIsMWbaPVr+VZIRa0Cr9NHXYKaccmlxKmdmL+0U/BFARWaNMo8emJzaxHfj6FpiAaXWU16qw1nlIjVGYW7wUV0vBxbnQurJ5FEYyqY0tCJblyuANutLRTHCfgDAn4CaP0iah/ENCtoVqppVjqJF7REx5uJboT/fL0JDV+TmJhIWloa6enp4aagvUUkh9JFom0nhtJJOUYDR0QKozfeeINdu3axY8eOk9bV1NQAkJjYsR5/YmIiZWVl4TEqlYqYmJiTxoS274zHH3+chx9++GzN73cqm124vD37sQo1lgV4/idjAKlkuITEYCGSw1XOdc63az+Ycoz6ArlSifa7qmFJGj9VQTm7d6VjP6rGHG0nb0gcQ8wHKDyiRgyoyXHW0NgSx1Gjg9ZmGYJejs0tcMhoIM90lHUHRiKq3VyYtJ98XRoFLRb8WVomHrXhDqhxTyzjs0/raS42IztWTlAXizlFy+GgiM7qJN2sZ1SKmmq7jz11XkxTo9ACjqYAQZ2aKJmfyy9IQqHQ4s7ScLy8lTEWAyNSh7MzwYDGXUVFRQU7d+5kx44dxMTEhEWS2Ww+a9EQieKjPZFmV2ehdJIwGhgiThhVVFTw29/+li+++OK0b4oT39RdSaQ705j777+/Q4dfm81GWlpaFy0fOG5fWQCAViknRt955+1TEWosG3osISER+YTKujqdTrRa6UbGQOD1tn1fni85qzKZDJlMNiiEUV9PegOBAHK5gNcPbo+c+tYoSt0e1tmnkCJWMiS6EbVXRUVtKnPU5bw7OgvR6kfudJAcX09ts5EyawL56cfw+WTUVzq4sKmYpgQ/cm0cSqedo1tdtBYex1dfjNNmINNfjq4mjsNfD6XRdIS8EWmMTY8m2aDip9MSucLrY2NTK96gSIG8mWCBh42lVmZolGw81EQgKsgFPgMlNg8pcVrio4YzfPhwvF4vlZWVlJeXc/DgQfbu3Yteryc1NZX09HQSExN79B6PZGEUibZ1Fkon5RgNDBEnjAoKCqirq2PcuHHhZYFAgI0bN/LXv/6VoqIioM0rlJSUFB5TV1cX9iJZLBa8Xi9Wq7WD16iuro6pU6ee8thqtTpi34ihYgin4vnrxzAh03xW3p4TQ+q6uk5CQqJ/kcvlREdHU1dXB4BOp4uoH/lznWAwSH19PTqdrsNk5lxHoVAMCmHU1wQCARoaa4ke6iXgEVAqghyuU3C4TMlxTQ6FTi83Ze5kYu63BILw40AzJVoLMTIfF5rKcTpUTM04RqzCS5XDQHVTPBnx9UQ3F5KS52anKwlfg5ua5hS8ai+zVVU4A2YCtiamHJWxtVBJaVQDDXYPE4aYSTBqiFcpucRsZH2TjayLDGw97uOD14+yPrEal1ZGbjS89aMAVyYZiW83l1CpVGRmZpKZmUkgEKC2tpZ9JbUcdwTY99U29IKblJQU0tPTSUlJQaXq2s3XweBRjaTvTCnHKHKIuG/02bNns2/fvg7LfvrTnzJ8+HD++7//m6ysLCwWC2vXrmXs2LFA2527DRs28MQTTwAwbtw4lEola9euZcGCBQBUV1ezf/9+nnzyyf49obMk1PA1VAwhFO52ItkJUWcdArf8y+Kw16m996izcDsJCYmBxWKxAITFkUT/IpPJSE9Pj6jJVV8zWISRIAh9NjH3eDwUFRXhcDiYcnka5Rlejh60U1OhwJIsopEH8Ns1fNE4iQuj9uPzyjH6gngPJJKcdQhZiwmHECQnzUqzDZQ2A6P0HnRKH0X1scicHpJ1VmLqolmfcJTyKCVvDZVhqfczvtGDWmmjtkXD8Q/dGH0e9g5t4bIfWRiVbMKsUjAr1shXQiv5s4xUl/kIVAbQ1TqpHK+HbTa2zVMwSyNH34kXSC6Xo41JYHxsJllxURSU1tFUuo+aynI2btyIIAgkJSWRlpZGWloaer3+jNcrUj8fkWZXmxfy+9dEyjEaOCJOGBkMBvLz8zss0+v1xMbGhpcvWbKEZcuWkZOTQ05ODsuWLUOn03HjjTcCYDKZuPXWW7nnnnuIjY3FbDZz7733MmrUqHCVukihstmF1eE9pUcm1PB1R0lTuBhCbx23PS8sHEdKtDZcorv9caRwOwmJyCM0SUlISMDn8w20OecdKpUKmSziWwH2KoNJGPXFxNdut7Nu3Tq8Xi/z5s0jJiaGcZM8bDm8nb0bG6gv9KDwKxBVXtxKkS1xCcyYdATbmjQyk8rQ62x8dDSDjIRKhFozlqxjGPVORKeVMpuZXSW5pI0uYOOB0Vw6fA83iTK+rTDRlF7PUbS8m28jSvTgrdxN9NFM3GYz5tIAW4dbqbO5mZQVS7RWxexYI/JcsP8kGtsTNfiCIjZHENkhF60pVdSn2bluyDgMipPFkcsXJCG6zSsUHaUnMX8UE8eNwW63U15eTkVFBdu3b+ebb77BbDaH85JiYmI6XPNIDFcLEWm2iaLYaR+jSI1gOteJOGHUFe677z5cLhd33nknVquVSZMm8cUXX4R7GAE899xzKBQKFixYgMvlYvbs2bz88ssRFQ8e8sSERMepSInWYk3ovYoxpyq4kJ9i6rVjSEhI9A9yuTyivtckzl3kcvmgqEoHvR/K1dzczNq1axEEgcsvvxyTqe33sri+GI/RgWyoA8ebFpRNeuwX7UWXFGT4dBPxqVOZrK6iasMR3t05jPJmMz5E/Fov1XV6MnUOjjbHEKVt5fLxm/mmeCTRRiuuoIIolZeaiizGRbUSZznKN74odPWxeP0qGoYfI64yhqp4CHzrpOh9K1tHNfOTq1MYlmhgVqwRYYzA65c5URa6if+qBedoPV6HnG9muTHqrcyNi8Gk7PjdkWRQcri2iQqlkkDAR3ZsWz5jVFQUeXl55OXl4fF4qKyspKKiggMHDvDtt9+i1+tJT08nLS2NxMTEiBMf7YnE5q6AFEoXIQwKYbR+/foOzwVBYOnSpSxduvSU22g0GpYvX87y5cv71rizIOSJuWtWNsu/LO7344Yen46zqXgnISEhIXHuMNg8Rr01Aa6rq2PdunXo9Xrmzp2LTqcLrxtuHk69qx5BaEAdDS4ciDEik6+3MCHpQobFDEPItGG0fMiImjJMWg+iT01JsxlPwEuMxoncZSTDUolH5WfE0G+xOQw0eZQ4/TLcQVDJfLgPp7M4rpXjCYdpaU5is0+JRldFTGUqB5pb0Vf6qa708c1oHbU2D5OzzMyJM/CFqYwWQSCokqE97MI6zIxnQwwbvqnFenOQa7PMmFXfTwUFQSA3XvndMyWdoVarycrKIisri0AgQE1NDeXl5ZSWlnLw4EGUSiUJCQlAZFcxjBRxFLpGJ5brjo6OHiCLzm8GhTA61wiFsYX+R3J57FDFOwkJCQmJ85vBIoyg9ya95eXlbNiwgfj4eGbNmnVS8QGFXMHkpMk4/U4Ct1bjc4pkjbEwM/0iUgwpbYO0JoQJNzDtmg8p23yIyio7SZZS9jUZ+eRoNtdllfJ12RBMcoHxkw9w8KiIw6Gl2m4kNrmYKpeGguNpjIw+QGarHqcvBn9+DPJJE8Aq58j7FSiP+fCr2kI7A0GRRn+AaKWchVcl8ZcjNbjS1ASiZNj8PizrWlDkaKnb6WJ9tI3L4k1oe+h1lsvlpKSkkJKSwuTJk2lsbKS8vJySkhIA1q5dS1JSUtib1F5UDiR9FW7ZE0KfKalcd2QgCaMBYLCJjeevH4NWJR90dktISEhI9B4KhWJQhNKFJrxn6zE6fPgwW7duJT09nRkzZpwyZFWr1DI1eSpBcTNKmZKJlomYtebw+oDLh3u/FevBkSjIZfpVXrbv+4SyY/F43Goq3Sp2V6dzxYh9iMfjqcqLwVfZQqK7HpMrSFl9CnnpR3ErHQSCAk2qJKZeNZNhqWZ2VDWibbBS7fdgHRqNXi1ndEY065vtBIER+iiSxjbSVGLAGa/AsNuBL06BNuBGZlEwMkrbY1F0IoIgEBcXR1xcHBkZGXz00UeMGDGCxsZGtm3bxrZt24iLiwsXb4iOjh4QcRJpFfNOFUon5RgNDJIwGiDah8+FKs+FHkca2b2Y3yQhISEhMTg5XzxGoiiyd+9e9uzZw7Bhw5g0adIZC23EaGKYZJmEXqVHr2yr1ib6gnjL7PibHNR87WTLe814vUEaazWMvmIcNvdexJYGnDY9+UnHUQsCdpccS24W+TdfgiLoY+f7b6Pe2IAYkFHhUpCiDkKym1iljGBAIOhXs/NrBXPFr/FZN6B5J5nD18/GrjHhVGl5d58Vrz2AL70IfWUOYhT40+1YJzZy3dhh5Eb1TcRKSHwMHTqUCRMm4Ha7OX78OBUVFezdu5fdu3cTFRUVLt6QkJDQ78VMIs1jJOUYRQaSMBog2ofPhSrPnbi8N2lfha64zt4nx5CQkJCQOHcZLMKovceouwSDQbZv305RURGjR49m9OjRp5xAO30B1HIZclnb+gR9QnhdwO5j7eNF7NzaxLiMKGKUKuxOP4Eg+EURddQIrr5tLK0x9Wxbthqxwo3dq0SmkOFMHs344UMIeOw0zZ+O07IWGqxsPujB69KjrzvK7m3r8cTmsnVDE0077HhydSQ7bUR7mrD/32rMylQOVo+g0WjGYI9BHFmDzraTjOETycoaQrlFh1loBuJPeS3s7gDVNh/pZhVqRfdEy4nFFzQaDdnZ2WRnZxMIBKiqqqKiooKSkhIOHjyIWq0ON5VNSkoKN7HuC0KexEgTRlK57shAEkb9SLTu1J6hvs4zksLgJCQkJCTOBrlcPqiEUXcJBAJs2rSJsrIyJk+eTG5u7inHHqz3EW+KorzZTYo+gEH9/aQ24PDStKmOTRvqcToCHBCcZCSICBZQiKCMk6FPU3JUp8YpDOWbSdczouZzgr4gG+OGoWiSc7zVh16uwWgcyqgfRGEvK0Gbup91uxrwe/1MNKdzYYaGVksT9iVD8A6fxMEWL8r9+5Ht3AstCi4xbafQPYSislT0ZRnoZtu5alw2gkpOjCKWUlshOeacTs/P4QlQVgu50THsrWhmzBAVsm5c19OJUrlcHg6nE0WRhoYGysvLKS8v5+jRo8hkMpKTk8NjtNrenx9FiigCyWMUaUjCqB+xGDVhz9BA9AN6/vox4bC4ymZXn4ul4jo7WpVURlhCQkLiXEChUOB2uwfajD7B6/Xy5ZdfUl9fzyWXXEJ6evppx+s0GnLiDeTEG9h2tIrc9sLI7kMdLWPiUD0b9tlRy+XU2bzUBr0YtTJSJ+jZpNWToTIRL5NzyZRs/t96FwHAb1CTJxMoa2ogSaUkzxIFRKEdloE+bSiauPUU1RiYOT6RgEdBykWjyB6SQoxJR2GpDU+imo83tTBM1oijJZoM83HKxGic8Saqmi0cqgyQnWmgxNPESOMIAkEx7PFqT7XNx7DoGFRyGfFqLU6vjyh193/PzxQeJwgC8fHxxMfHM27cOFpaWqioqKCiooKtW7eydetW4uPjwyF3oTLpZ0OklRKXcowiC0kY9TMhz9BACKPshKh+6VUUyplasnrPacc1Ss1iJSQkJAYNgymULlSuuys4nU7WrVtHi91B3qSZlHu16Owe4qJOPTG1Od24fQFKGh2YNR0n/8oEHcFWLzOvSsDVKlJY5sESr2JIvIYJU80oLkvG7FCRExOFTq2gyulGNMoRK12MTYphjCUemwvGxn4fWSJTKIgyZDJm8hAs1krKXAHilSJFtgDKI41UyeqoVaqIctrRC03Emhopqc5EdHtY+JiFvFFDeW9XFR99XInW3sKYSw3UB2RwCm2Qblaxt7yZeLWWBq+TVFX3Juk9LXBgMpkwmUzk5+fjcrnCeUl79uxh165dGI3GsEiKi4vrcV5SpIgikDxGkYYkjAYxkVq0IZQztaOkqVNxFLL7jpUFrLtnZkSXK5eQkJCQaGMwNXjtqjCqa7Ty/tpNVLSocLamcvQbByNyRaJUitMKoxHxCvZX1BCrkxMd1XEqJQgC6iFGgk4/Yy6NRVbQQny8ivE3pGEYGU1li5eyFj91Tg9um4vDVjsuTwBjIMDCWVmozGq8UQGcPg+6ExqwCoJAkjkVo8fPjtIa5ifFkaDW8m2dFWvRPnT7viRLrsYXlHNB1n422C5mTPpQavwa0lMS+YXsz+yoScV8LJ4xY8YhF6I7PT+VXMaFQ1S4fD7SlOpuC4ne8MpotVpycnLIycnB7/dTVVVFeXk5xcXFHDhwAI1GQ2pqKmlpaSQnJ3cQFn1tW28i5RhFFpIwGsT0R9GGnpISrcV6imp2KdFa/rFwHLe8uB2rwxtxtktISEhInMxg8hidibJGB3tLatix/zBet5HKQxqijnpQt3rZd1mQqMuUjE47dTlphUxgaOyphZOgkKPOMpHmC5AxPQb1UBMydduUK8Wkwuv3srO2kRi9ksMOB0GNDJtFzb5mB+OStNTa7AxJPHmK5vIFKWlsa9IeG2VG448iEBTxCAGGj6pGZ2rE7JPjsWpRyvxMkRURpZlLVkI0ioCMY84YYg6AoqQAi64KzEMgcSQYk+GEcxUEAV0Pw+F7W3woFArS09NJT08nGAxSX18fDrkrLi5GLpeTnJxMeno6qampXRIVkSKMpFC6yEISRoOM9tXl4MyCqLe9Sicev6fERpCHS0JCQkLizAwmYXSmSW9xRTXb9xYRLI0h4bCapkCQnKMB/ATx7PViu8SP1eHDHNXz3yp5lApNjhlZlBLhhDyezFgVmbEqvIEgq7e5kesUKLzwxsojbLisiRumRiOXGU7a55G6AHK/mmRBSZXNSZGiBbVMTmKWnjTTbFqTLZQc/w+tu2ORKawEXA4q95WibxlCfW05zjolZWXDGBt3nLL3Skib7kXpqAedGbLngLx3qsH1pVdGJpORmJhIYmIi48ePp6WlJVy84euvvwYgISEhHHJnNBpP2kekiCKQQukiDUkYDRJCAmf5l8VolfIui5ze8ir19PgSEhISEucGg0UYhThVKN3Ro0cp/nY7UVEpqFv1uLx+UsoC2OJkRNkFAjlK5ELbjcCzEUYAcuPpt5cLAugVmL61441V4knT4nF7USs799So5XLwyEgxaTGp5JT6W8hLDU2gNcSap2FYnENZ7ge4GxXIN4Lzk8/Yn2dCVlZJS0MGw4YcRCn3caQkBZvVxIU/dxOUq3tNFEH/NlE1mUyMGjWKUaNG4XK5KC8vp6Kigt27d1NQUIDJZOqQlySV65Y4HZIwGiSEBI7V4SVGr+qWyDnd2FN5gE70NJ3N8SUkJCQkBj+DQRj5A0G2FDfh9+hwt3rIT+q4fv/+/RQUFDBy6FCaojIpcTjw2B2o6uV4VSLHr9MzebaRaL0KXQ+qsHUXmQAxCQpsGQaMziBum0hrVRD/KYSFVu3ncIMHNQJO0UdcfEcbnd4AJXVGdHkLMQluSvQ7sO2vQN5aQfqoOuTeZtwOAblfg8qZRDDoAlEEy8g+Ob/+Fh9arZbc3Fxyc3Px+XzhfkmHDx9m//79aDQakpKSSEtLIzs7u8t5SX1JIBBAJpN1KCQheYwGjoF/R0h0mZRoba8JkjN5gDrzNPXm8SUkJCQkBheDQRgdrLYxOimagFdDUb0dj9+LWiFDFEV27txJYWEh+fn5XHjhheyusOIZFSQhT01Tgx+jTsWEESZSTFoM2r5rMNoeQRCINiop9QYIWH34tSqM9QE8YrDT8ekxKtKiRepa3cRr5OhVHe08UOYlT2+i1eXncJ2PGcPnUZJmR2E+jCp4DPPkGmLcGmRH7TSX1BKTHSAYPRyiEnv1vCKhwIFSqSQjI4OMjAyCwSB1dXWUl5dTVlZGSUkJ27ZtIzs7m9zcXHJycvqkX1JX8Pv9HbxFIOUYDSSSMDpP6YoHSBJBEhISEhIhBkNVukSjhvJGJyk6Gc0ON0n6Npu3bNnCsWPHmDBhAnl5eQCkRutocfpJjtaSMkZLlHpgpkQZRjnbM9TIBAGDUo5fhHpP58II2sRGYicheqIo4nYK6I1KhKAMl69tHwaFGr82j/ioPFymcjTqdATLURJzDoLPBQl5vX5OkSCM2iOTybBYLFgsFvLy8nC5XLhcLoqKilizZg2CIJCRkUFubi7Dhw8nOjq632zz+/0dPFeBQAC/3y95jAYISRidx/S1ByhSy4lLSEhISHQfhUJBMBgkGAz2uH9MX5Ng1ODyeNl6uAxLlIyAX8X69euprq5mxowZZGZmdhibYOz7yac/KHKgyotGoUQm95MT3/H3sN4RRGYLEP+NHW+Mgoppuh7l6AREQAaFVjueYICANsBBjwNRLTLSpAMgSp/dNjhhBMQOg5aKtop0vUykCaMTMZvNDB06lBkzZmCz2Th8+DBFRUWsW7eOzz//nMTExHBIXlJSUp+ex4nCyOPxAEjCaICQhJFEnxHJ5cQlJCQkJLpHaPLm9/tRqSL3ZleqWY8jRo7X6+Xzz7+ipaWFOXPmkJzc+wKgKxQ3eBiTGI9CJrC/viWc/B8iWifHqxApH69FDATAG6TJ1X1hpJAJxESD4AsSFP3MyTWhkp9GwMrlbeW6z3OMRiPjx49n/PjxeDweiouLKSoqYvv27WzcuBGj0ciwYcMYPnw4Q4YMOSns7WwJBAInVaQDSRgNFJIwGiDOF2+KJIgkJCQkzg1Ck7dID6cTBAGHw8GGDRsIBAJceumlxMbGDpg9RrWcBocHi0GDx+9HEDrmBRkUAiq1Ame6DE2ZG4UniKGH04L8NM13wmtgJ9WR7DE6UZi2R61WM3LkSEaOHEkgEKC8vJxDhw5RVFTEzp07UavVHfKSekO8nJhjFBJGUo7RwCAJowFC8qZISEhISAwm2nuMIpmamhq+/PJLBEFg1qxZxMTEDKg9ySYlFVY7e+tbSTef7G3wCyBr9hFfG8ARo8CdqkKp6rmgiAQxEsnCCLpml1wuJzMzk8zMTC699FJqa2vDIundd99FJpMxZMiQcMidyWTqkS3tQ+ncbjcNDQ0oFIpe90xJdI3IDBI+T5CqvElISEhIDBYGgzAqKSlhxYoVGI1Grr32WqKiorDZbFitVhwOx4B5u9JiVIywqInWnXw/Wi8Df5wCt91LwB9E7vej6L82QH1CJAsjURS7nSMnCAIWi4WLL76Y22+/nSVLljB//nwAPv/8c55//nleeOEF1q9fT01NTbdyxNqH0q1du5bx48cjiiJ///vfqaysPOP2H3/8MZMmTUKr1RIXF8c111zTYX15eTlXXnkler2euLg4fvOb3+D1ejuM2bdvHzNnzkSr1ZKSksIjjzzSr72oIgnJYyQhISEhISFxRkJ3sCNVGB04cIA1a9aQkZHBggULUKvVBAIBnE4ndrsdm81Ga2srwWAQtVqNWq2OiD42giDg1oro41So/GCo9VPjPHVVuhCiKLK/xotWrsbp8zAqWRUxQiSShRGcvV0mk4mJEycyceJE3G43R44coaioiG3btrFhwwZMJlO4wl16evppvT/tQ+muvPJK3nrrLW688UbeeOMN7rrrLr7++msmTZrU6bbvvPMOP//5z1m2bBmzZs1CFEX27dsXXh8IBLj88suJj49n8+bNNDY2cssttyCKIsuXLwfAZrMxd+5cLrnkEnbs2MHhw4dZvHgxer2ee+6556yu02Bk4L8RTuDxxx/n3Xff5dChQ2i1WqZOncoTTzxBbm5ueIwoijz88MP885//xGq1MmnSJP72t78xcuT3Dco8Hg/33nsvr7/+Oi6Xi9mzZ/N///d/pKamDsRpSZyC4jq71DBWQkJCYhAQyR6j7du38+mnn5Kfn8/VV18dnmjK5XIMBgMGg4HExEScTicOh4OWlhYcDgfBYBClUolarUap7J/eRSciQ8B33IkQrya+yYdDFPD4ziyMWtwBAkEVCo2KoE+k3OohwxxZCfuRKIx62xOi0WgYNWoUo0aNIhAIUFpayqFDhzh06BDbt29Ho9GQk5NDbm4u2dnZJ+UOnVh8ISEhgZiYGDZs2EBDQ8MpS4f7/X5++9vf8tRTT3HrrbeGl7efL3/xxRcUFhZSUVERLj7yzDPPsHjxYh577DGMRiOrVq3C7Xbz8ssvo1aryc/P5/Dhwzz77LPcfffdEfka9iURF0q3YcMGfvWrX7Ft2zbWrl2L3+9n3rx5OByO8Jgnn3ySZ599lr/+9a/s2LEDi8XC3LlzaW1tDY9ZsmQJa9as4Y033mDz5s3Y7XauuOKKiE8aPV8IFZ9YsnoPc57ZQGWza6BNkpCQkJA4DZEojERR5Msvv+TTTz9l8uTJXHPNNae8Oy+TyYiKiiIxMZHs7Gyys7NJSUlBo9HgcrloamqitbUVr9fbr2FEKgEM8Roa7X6OIVAZq8aoO/P0TKeSIchljEmLZmxGLM2eyAl9ivQwrL4qNy+Xyxk6dCiXX345S5Ys4ec//zkTJ06krq6Ot99+m6eeeopVq1axc+fO8Jy1s3LdoaIOcXFxp/Rq7tq1i8rKSmQyGWPHjiUpKYnLLruMAwcOhMds3bqV/Pz8DhUZ58+fj8fjoaCgIDxm5syZHQTb/PnzqaqqorS0tNeuzWAh4jxGn332WYfnL730EgkJCRQUFDBjxgxEUeT555/nwQcfDMdRrlixgsTERF577TVuv/12Wlpa+Pe//83KlSuZM2cOAK+++ippaWmsW7cuHBcqMXCEik/sKGliyeo9WB1eyWskISEhEcFEWlW6YDDIRx99xO7du5kzZw5Tp07t8t1tmUyGXq9Hr9cTHx+P2+0Oe5JCoXcKhQKNRoNSqezTu+aakmMoUGB0yVC6RVrtUFrpgZTTb9dWituH3euj1eNBK4cDx1rQ6pRkWXR9Zm9XONdD6bp6jOTkZJKTk7nkkkuwWq0UFRVRVFTEJ598wscff0xycjItLS0kJSWFt3O73V2qdnfs2DEAli5dyrPPPsuQIUN45plnmDlzJocPH8ZsNlNTU0NiYmKH7WJiYlCpVNTU1ABtxUqGDBnSYUxom5qamg69v84HIk4YnUhLSwvQ1owL2hIra2pqmDdvXniMWq1m5syZbNmyhdtvv52CggJ8Pl+HMcnJyeTn57Nly5ZTCiOPxxNurAVtcZcSfUdKtBZrQtRAmyEhISEh0QUiyWPk8/l45513OHz4MFdffTWjR4/u8b4EQUCr1aLVaomNjcXj8eB0OsPhdna7HblcHs5L6s1Jta++Hs/G3eSnzyMzR8OuwgZqPX5iDV2rSJZuggNVdahl4Kx1MBaRZpuHEiBzAMVRJAuj05Xr7ktiYmKYPHkykydPxuVyceTIEfbs2RN+j4V4+eWXOXDgwGlt3LFjB8FgW7jlgw8+yLXXXgu0ORNSU1N56623uP3224HOX4MTr8GJYyL59etrIloYiaLI3XffzfTp08nPzwcIK9wTFXBiYiJlZWXhMSqV6qQSnYmJieHtO+Pxxx/n4Ycf7s1TkJCQkJCQOCeIFGHkcrl4/fXXqamp4YYbbiAnJ6fX9i0IAhqNBo1Gg9lsDoskm82G3W6nubkZQRDCIulsQrICLhf2rzawb2c01zyYiiwI2kQNjXsPklVdhDg8/owT0xitgpjvgi0OlgcRFDKi5VDpHtjXKJJD6QZKGLVHq9USHx9PdXU1KSkpLFy4MLxu1qxZFBUV8frrr59y+yFDhoRD8fLy8sLL1Wo1WVlZlJeXA2CxWPjmm286bGu1WvH5fOF5tMViOWluXFdXB5w81z4fiGhh9Otf/5q9e/eyefPmk9Z1pm7P9EY/05j777+fu+++O/zcZrORlpbWTaslJCQkJCTOPSKhKl1LSwurVq3CbrezaNGiPi+oFBJAMTEx+Hy+8N19m81GS0tLj0WSGAjg+uYbmtYfpa58PIVHreRZDJQdrOdCcyOuxEyCIsi7MX/XJOo5UOvEIxcYnqzvwdn2LoIgDLgAORUDbVdtbS0rV67EbDZz8803d8jvUavVmM1mhg8fftp9jBs3DrVaTVFREdOnTwfaPKmlpaVkZGQAMGXKFB577DGqq6vD4XpffPEFarWacePGhcc88MADeL1eVCpVeExycvJJIXbnAz0SRh988EG3t5k7dy5abddzSO666y4++OADNm7c2OGLz2KxAG1eofYxmXV1dR3Ur9frxWq1dvAa1dXVMXXq1FMeM/TlJiEhISEhIdGRgfYY1dXVsWrVKgRB4Gc/+xlxcXH9enylUkl0dDTR0dH4/X6cTietra3YbDZsNhuiKKJSqdBoNGdszuk+cADr9u0c2qMhw1bBf95Xs0kpRzC0MP2HsXjMsQREETldn8BnJukhaeAFEUS2xwj6rvhCV6ivr+eVV17BZDJx8803n5RP1NUcI6PRyB133MEf//hH0tLSyMjI4KmnngLguuuuA2DevHnk5eWxcOFCnnrqKZqamrj33nv5+c9/jtFoBODGG2/k4YcfZvHixTzwwAMcOXKEZcuW8dBDDw24gBwIeiSMrr766m6NFwSBI0eOkJWVdcaxoihy1113sWbNGtavX39S0ldmZiYWi4W1a9cyduxYALxeLxs2bOCJJ54A2lS0Uqlk7dq1LFiwAIDq6mr279/Pk08+2S3bJfqH3izbXdnswurwSmXAJSQkJHqR0GRyIIRRRUUFr732GkajkZtvvhmDwdDvNrRHoVBgNBoxGo1YLJYOZcDP1CvJW1GBs2gf7qMt2FwWWoQAw7aUYUppwPJfI2jJykIpCASIbHFxOiIhXC0SaWhoYMWKFURFRbFw4cJOHQYlJSU0NTV1aX9PPfUUCoWChQsX4nK5mDRpEl9++WXYKSCXy/n444+58847mTZtGlqtlhtvvJGnn346vA+TycTatWv51a9+xfjx44mJieHuu+/uEEF1PtHjULqamhoSEhK6NLY7X2C/+tWveO2113j//fcxGAzhuEeTyYRWq0UQBJYsWcKyZcvIyckhJyeHZcuWodPpuPHGG8Njb731Vu655x5iY2Mxm83ce++9jBo1KlylTiIyaF+2W6uUs+6emWclZiqbXcx5ZgMuX6BX9ichISEh0YYgCCgUin6vSldUVMTbb79NSkoKP/nJT7p0N70/ad8rKSEh4bS9kmQuF45du3A7a2j0yVBNbSVDZ8NWrSdKbGHatBz0RvOgFxWRLIwGyrampiZeeeUVdDodixYtQqc7uThGUVERL7zwApMnT+7SPpVKJU8//XQHoXMi6enpfPTRR6fdz6hRo9i4cWOXjnmu0yNhdMstt3QrLO7mm28Ou+zOxN///ncALr744g7LX3rpJRYvXgzAfffdh8vl4s477ww3eP3iiy86CLDnnnsOhULBggULwg1eX3755TO6tyX6l94u2211eHH5Atw1K5vlXxazv7JF8h5JSEhI9BIKhaJfPUa7du3io48+Yvjw4VxzzTWn7OkSKYR6JUVFRREfH4/L5cLhcGCz2XDYbNi2bcNdUsaRj60ci01FrI+ioTCDnCmHiB7TzLHtX5CYn4Yp5kLU6sSIFRdnIpKF0UDY1dzczIoVK1CpVCxatAi9/uSQx6NHjzJr1ix++ctf8thjj/W7jRJt9Ogb5qWXXurW+JDY6QpdiUsVBIGlS5eydOnSU47RaDQsX76c5cuXd/nYEgNDX5TtDomg21e2NTCTvEcSEhISZ09/CSNRFNm0aRNfffUV48aN4wc/+MGA5oX0hPa9kmKNRvb9/gWC+w6zv1Bkpy2XtOw6qguzEGQibp+C0ncmcfxLN6k/3MewKx2o1QkYjSNRqfo3l6o3iOQco/4WbS0tLaxYsQK5XM6iRYuIijp5vlNWVsasWbO4/vrreeyxxyJWVJ4PDK5vGQmJHnDXrGxcvgBWh3egTZGQkJAY1PSHMAoGg3z66ad89dVXXHzxxVx++eWDThSdSPGTb3LgvVoK92gRvSIxcidFu5PImrmL1Gn7iUuvR2NwES0P4vo8k5aj1RxuUPHt8Sbc3uBAm98jInVy35/V8mw2GytWrADaoq06i546fvw4s2bN4oorruCZZ56J2Ot2vtAr3zSbNm3i5ptvZsqUKVRWVgKwcuXKTstsS0j0NyEvUXGdnf2VLVQ2u7q8bWWzq1vjJSQkJM5l+loY+f1+3nnnHXbu3MkVV1zBzJkzB/1E0VdVhfPQYWpteio9ZvbZ8hH9alLtTjSbFRjERjTRPhImFxNoicLviKKi6QImpI1lcvoYjjQMfEPd7hLJoXT9hd1u55VXXiEQCLBo0SJMJtNJY2pqapg9ezazZs1i+fLl5/01iwTOWhi98847zJ8/H61Wy+7du/F4PAC0traybNmyszZQQuJsaV/g4Yrlm5nzzIYuiZ1QIYeuju8ukuiSkJAYbMjl8j4TRm63m1WrVnH48GEWLFgQ7rMy2Al6PCRPTCQ+thWl3I8oU6GWyRlpqMajT0EMmkkea2TUYgOJl1SSMK4ZfYoCpUKDGASfx4PL5SIYHDyeo0gXRn1tm8Ph4JVXXsHr9XLLLbd0aB0Tor6+ntmzZzNp0iT+8Y9/DHqv6LnCWWcxPvroo/zjH/9g0aJFvPHGG+HlU6dO5ZFHHjnb3UtInDWhAg9Wh5fiOjtLVu+huM5+xnyjUCGH0OPezE8KiS5Ayn2SkJAYNPRVVbrW1lZWrVpFS0sLN998c7hB5bmAOjOT2FtuZp76Hco3FHO0+lvQ+nArs0nTB8i8fATG+DiGTsggfugm/D477oCNErcMRC85KQYIuLDZbOEy4F3plTTQRLIw6kucTievvPIKLpeLW265BbPZfNKYpqYm5s6dS35+Pi+++GLEv5bnE2ctjIqKipgxY8ZJy41GI83NzWe7ewmJXiElWktKtDbsPbpjZcEpBUl/eHH6UnRJSEhI9BV9EUrX2NjIq6++SiAQ4Kc//WmXW4EMJhQxMSTcdgva7PU41n0GlnRUqSnh9Vq9AZNxNIaoEdhsewkGvYwyp4fXBwKBDmXA7XY7gUAg3FA20qr1RXLxBeg70eZyuVi5ciV2u53Fixef1IT43Xff5a233mLHjh2MGDGCV199NeJeu/Ods/bbJSUlUVxcfNLyzZs3d6mhq4REXxASQFqlnBi9Krw8JVrLPxaOO2Uxhr4On5OQkJAYzPS2x6iyspIXX3wRhULBrbfeek6KohCCUonu4ouRDc1CmZLSYZ36u0plMpmK6OjxmM1TO6wP9UqyWCzk5OQwdOhQUlJSUKlUOBwOmpqasNvt+Hy+fjuf03E+htK53W5effVVWlpaWLRoEfHx8SeNSUtLY+PGjVRXV7Nu3Tp+/OMfU1dX1+u2SPScs5apt99+O7/97W958cUXEQSBqqoqtm7dyr333stDDz3UGzZKSHSbUPhc6HF7YtsJpRM50ZMjISEhIfE9vekxKi4u5s033yQxMZEbbrih04aX5xpelxNFTDR+nw+XzUlUjAFBJkOj73rLihN7Jbndbux2OzabDafTSWtrKwqFAo1Gg1KpHBCBEunCqLfxeDysWrWKpqYmFi1aRGJi4kljHA4H9957L3l5eXzwwQeUlpby0UcfdRpqJzFwnLUwuu+++2hpaeGSSy7B7XYzY8YM1Go19957L7/+9a97w0YJiR5xpvC04jq71PhVQkJCohsoFApcrrP3pu/du5f333+foUOHct1116FUKnvBusjHbbfj93jZ/LaV2hITWWNqGX+5BXU3hFF7ZDIZOp0OnU4XFklOp5OWlhYcDgd2ux2FQoFarUalUvWbWIl0YdSbtnm9Xl577TXq6+tZuHAhSUlJJ41xuVz88Ic/RBAE3n//fbRaLSNGjGDEiBG9ZodE79ArgY2PPfYYDz74IIWFhQSDQfLy8jptYCUhEQm0r1InNX6VkJCQ6Dq9UZVuy5YtrF27ljFjxnDFFVecV4nnHqed5no71UfNgJyqYi1Bv79bHqNTIQgCWq0WrVaL2WzG4/HgdDqx2WzY7XYcDgdyuRy1Wo1are5T4XK+CCOfz8frr79OTU0NN998MyknhEhCmzfp2muvxeVy8fnnn58XntHBTK9lfOl0OsaPH99bu5Noj98Py5bB5s0wfTo88ABIyXon4/cT/9wTrHznU+L9l8H/PtLpdQqF2e0oaWLJ6j1dKn4geZckJCQkzi6UThRF1q5dy9atW5k+fTqzZs2K6MlzX+Cx24lONJA2ooHqo1Gk57mQKUw99hidCkEQ0Gg0aDSaTkVSqDiWRqNBrVZLpaJ7gN/v54033qCyspKbbrqJtLS0k8b4fD6uv/56GhoaWLt2reQ0GARIs+tIpb0Y8vth/XoQRVi3rm19f+ZvDRZhtmwZCc/+L4miiPjst2DUnPI6pURrsSa0fUE1diGXKORdeuXWieGCDl0VSaEiDt0VVT3dTkJCQqKv6KkwCgQCvP/+++zbt49LL72USZMm9YF1kU8wGESl1jD12kT8Xg9KtQWlWoOij0MJQ16imJgYfD5fOBfJZrPR0tKCIAjhCne9IZLOdY+R3+/nzTffpLy8nBtvvLHT8vJ+v5+bbrqJsrIyvvzyy04bvEpEHhE4u5UA2oTI0qVtYqg9otgmUAbKloEQZl1l82aE766X0IXr1JXS3QAvLByHyxtgyeo9XPePrQAdQvBOJWAqm10U19m5Y2UBQLgYxKmobHZhdXjDVfSkPkcSEhKRhlwu73ZVOq/Xy5tvvklJSQnXXnst+fn5fWRd5JN14QQyx47H53bhdthx2+2I/dy4ValUYjKZMJlM+P1+nE4ndrudlpaWXuuVFKnCqDfKiAcCAd5++22OHTvGDTfcQGZmZqdjFi9eTGFhIevXr++0watEZCIJo35AHoAHNsGoLxbD/Hld87hs3nyyKAIQhDavzZnoTS9Pe1tEETZuhEceiTgPkm38JKLWrkOGiCgICGe4TqHS3be8uD1cga64zt7puPbcNSub5V8Wh7fpTMCEyn6HKtwB7K9sYckbewB4/idjOuyz/XitUs7zPxnTaXU8SSBJSEgMJN31GDkcDl577TUaGhq46aabpDYetHkrVFodKq0OY9zAlidXKBQYjUaMRiOJiYm91ispUoVRiJ7aFgwGeeeddyguLub6669n6NChnY75xS9+wc6dO9mwYcNJvYwkIpuBn82eBzywCZauBxlfw9YtbQvP5HGZPr3NOxMSJJdc0iY+pkyBYBDmzesoSvx++NOf4NVX28anp8OGDW3br13bFoqnULRtc9998OSTXRc27W0RhLbjd9eD1A/heOW3L+GLTSUsDh7HPH9W2zHOQKh0d2Wzi+v+sbWDkDkVIXESCsE7UcBYHV6K6+y4fAGev34MWpWc21cWdCgFvr+ypcM+Q+tCoqv9+pBtIHmPJCQkBpbuCCOr1cqrr76Kx+Nh8eLFnVbrkogcQr2SDAYDCQkJHUSSw+EIiyS1Wn3GKoKR2uA1JNh6IoyCwSBr1qyhqKiIBQsWkJOT0+n+f/3rX7NhwwY2bNjQadluicim2zNTq9WKKIqYzWbq6+vZuHEjubm557Vr/ExML2/XSfdMIV4hAbFxI1x0EezbB25323YffNAmaEKiJCR4AHbtgpZ2k+1jxzru96uv2v6vXQsvvQRlZR2FzQMPnCxc4HtbLr4Y5PI2mzZt6uhBCuVBtRdmCxfC//zP9+Jn2TL44x+/t+HFF0Emg+ZmiIkh/ofXIVd0bGjXbRQK/jLtBub9cjLmFX+DSy9tE3FyOUyd2mbrqlUd7fuO/ZUtJwmZ9g1eQ2F3AEMTosIheL+bnxses72kiac+LwqLH61SzoRM80n9kOTBAMKf/sTK4wfYkToSfjk5fJ3yU0xolXKWf/l90+QTeytJwkhCQmKg6KowqqmpYdWqVSiVSm699VYplGiQ0VmvpJBI6mqvpEj1GPVUFL3//vscOHCAH//4x+Tm5p40RhRF7r77bj755BM2btzYaYU6icinW8LoX//6F48//jjBYJD77ruPVatWccEFF/DHP/6R3/zmN/ziF7/oKzsHL34/ygCIQPijOHnyqUPRTpVbtH49jBgBQ4Z0XBcSPN2htPT7x6LYtu8XX2wTS9AmXF5+uW1daKwgtNn10ENtgiIkqACKi2HOnDYPVYiHH27b/g9/aDun55/vaEPoWABWKwnPPcGvpt8Evz19Hs6ZkAcDDLnxR7BlU8cV7e0N2bdyJenX/YR7t5QxevUB7knPZ8KSqRj+8iyr3vyYb77OI2rGDeFCC+0bxr512wS+WrSEnFf385vUkfxtygIe+agQrVLOip9NJFavCm93ojD63LmZoZtXIQDTS/fguLGcgv+3OrzvdffMxOrwUtns4vbv8pMkJCQkIoGuCKOSkhJWr16N2WzmpptuQq/X95N1fU+r28fewgYEmcC4/HjUinO/1Hj7XklxcXEdeiWF8pPkcjkajSbcK+lcCqUTRZEPP/yQffv2cc0115CXl9fpmPvvv5+33nqLjRs3kp6e3pvmSvQj3RJGy5cv58CBAzidTtLT0ykpKSE+Ph6bzcaMGTMkYdQJuqf/zMyydqII2jwrIdauhf/8B/buBft3+S2nckGXlkJFRe8buXt3m+emPSUlHZ+LYpvdL70E5eUnjz1xPLR5j+TyzoXeCQjAhOMHumv5Sfxq65voTxRFp+LYMYxPLONX3x1/eukehCmbobSUacDUkt0s2fkOsjeS4aabSJHJYMsWmD6d/GCQkRtfDW93V/lm3OlDEKdNw/jatvC49uF8VY2t/Obr1xmy/5Pw+0EA9Fs2se+Oe9HOXBgWU+29Qu09VxISEhIDiUKhIBAInHLie+DAAdasWUNGRgYLFixArVYPgJV9x669teR4ZYiIFOyvZ+oYy0Cb1K+cqVeS0+lEJpPh9/sjUhh1N8RPFEU+/vhj9uzZw49+9KNTRkc9/PDDvPzyy2zcuFHKoxvkdEsYhe4IaDQasrOziY+PB8BoNEbkByASUG76mjMWvty4ses77GY1oC5xoig6FX5/R2/TmTh2rE1MdeGLSATkwWDbMc6CCccP0N13YnuR0v78BEBwu78/jxBr14JG02E7ZXkZyvIy2Lyh47j164l58/22MLxlj7Nk82vI6Hg9BODWrW9zxZhUUqLmhJeHwvfah9VJSEhIDCShKmWBQOCkZPzt27fz6aefkp+fz9VXX31ONm4Vgt95G0QRMRiZeTT9xYm9krxeLw6Hg9bWVoLBIIFAAKvVGpG9kroyZxVFkc8++4yCggKuuuoqLrjggk7HPf744/ztb39j/fr1DBs2rLdNlehnuiWMFAoFbrcbjUbDhnZhU62trb1u2LmC4tt9HcPozje6KHQEYHL5XuqXPwNPL+vRceKfewJDc03/XG+3u2vjvvqKlGEZfHPb7ajLvz5JFIXQ+r1kLH8S9n4DM2bAqlWkAN/86MfItmxBVbifnaZ0jv3wXanRrISExIAREkN+vz/8WBRFvvrqKzZt2sSkSZOYP3/+OXuzdPiIWIoONSIKAmPz4gfanIhCpVKhUqmIiYnBaDTicDjCUUV90SupJ3S1+IIoinzxxRds376dK664grFjx3Y67rnnnuPpp5/myy+/ZOTIkX1hskQ/0y1h9OWXX4bd4u0bVblcLv7973/3rmW9xP/93//x1FNPUV1dzciRI3n++ee56KKL+u34Mlvr+SuKuokM0G37umcbL1tGwjOPI9DmfeK7O3oRgdWK8an/PWnxiQJOgLY8rXY3HYzPPBl+PKWlmYrLpjH/V//m89/NksSRhIREv9NeGEFbUvpHH33E7t27mTNnDlOnTj1nRRFAgklLwqTUgTYj4hEEAYVCQWpqaodeSTabrdd6JZ2NbadDFEX+85//sG3bNi677DLGjRvX6bi//e1vPPzww6xbt47Ro0f3hakSA0C3JHtUVFSnb6iEhAQuvPDCXjOqt1i9ejVLlizhwQcfZPfu3Vx00UVcdtlllJ+YI9OHiGpVvx1rsCMC2n3f9iycbvPmjiFxkSKKTkNPQv7SWmrZ8cSPUCz941mHHQ46/P62oiXz5rX9P9vz9/v5w3r4/BXQ/e8zA3c9e/u8euPYp7NpIO2VGHBCwigQCODz+XjzzTfZs2cPV199NdOmTTunRZFE12mfgxbqlZScnExOTg5Dhw4lOTkZhUKB3W6nqakJu93erf5YZ2MXnF4crV+/nq+//pp58+YxceLETsf861//4oEHHuCzzz5j/PjxfWKrxMBwVo1kjh8/zt///ne2bNlCTU0NgiCQmJjI1KlTueOOO0hLS+stO3vEs88+y6233sptt90GwPPPP8/nn3/O3//+dx5//PF+scF51y/RP/W85DXqAgIgt7W0VbE7U1+kE5k+HXHt2nP+OguANuBD8+enwBzV/es0mFm2DHHpUgRRRFy3ru21Povz1z395+/6i4G47ClQ6QfmevbyefXKsU9jk+2hhzH872MI3eljJnHOEBJGdrudd955h5qaGm644YZOe7pInL+IothpuNyJvZJcLhd2u71DrySlUhkuA94XnE4Ubdy4kY0bNzJ79mymTJnS6ZhXXnmF//qv/+KTTz5h8uTJfWKjxMDR4yDPzZs3M2LECNasWcPo0aNZtGgRN998M6NHj+a9995j5MiRfP11D8OiegGv10tBQQHz5s3rsHzevHls2bKl3+xwPvA7+qBcwjmLALi/2nDGcSfRhWau5xI9vk6DGPdXG9om44Agimd9/uLGLeEvwN7YX0/p7fPqjWOfanlls4t9b3wcXnfGvmwS5xwhYfTOO+/Q0NDAokWLzmtRVNrgYEtRPV8fqqfJ7qG+1Y3LK/3qd6Vct0wmQ6/Xk5iYSE5ODjk5OaSnp6PVanG5XDQ1NWGz2fB6vb3WMPZ0HqOvv/6ar776iosvvpjp06d3uv3q1au58847ee+99/o1LUOi/+ixx+i//uu/uO2223juuedOuX7JkiXs2LGjx8adDQ0NDQQCgZO6DicmJlJTU9PpNh6PB4/HE37e8l3DVJvN1mM7Wp0u/jkZHtj2vQo9r4sxdEL76yECh7PycB7qflnykYnJRNVW9dq1FYFgWhqCzYbQ0tLBRuHEcenpiEOGIJSWIisvP8mGE8+R756LgH/6dGQVFcjKyrps+9lcp8GKLiuP/PVfIgOCnP35yzJzGb9pc6/tr6f09nn1xrFPtfxYvYPqxBymlu5p8yYJAp4JE/CexXfkuUjoN6O3JnORhP27thIOh4Nrr72W1NTzO9+m6HgL+QlGRES+2FvDBRYjxZ5WMix6kmN0A23egNHdPkaCIIR7JcXGxnapV9LZcOL227ZtY926dcyYMYOZMzvvp7hmzRp+9rOf8c477zB79uyzOr5E5CKIPfzm1mq17Nmzp9PuvwCHDh1i7NixuFwD04OlqqqKlJQUtmzZ0sEd+thjj7Fy5UoOHTp00jZLly7l4Ycf7nVb5MBDwK8ALaCkTZGGCwV8R2cTZ04Yc+KkWjjNfmg3pjsvcvt9n7iPE/93tt2Jj0+HCLQAVtomYK8Cj0KPvGxaYD+Q0e74AdoEqfyE8wm2O0Yoqlnx3WP3d/as/M4WgP8Bbv7ucQUw87v9isBXwLzv9idvNzbmO5sAaoFEQA2Uf3eek4DNQKgGX+g9Ev2drX7A3u78NN/ZaAeWA3+iZ9dpsCIHHgCm8/11O5vz7+39DUY7TnXs09kUKddtMFBRUXHOCQer1cqKFStwuVx4vV7i4+PJy8sjLy+PhISEgTav33ltQynzcxPw+AOs2V/NwokZABRZ7UwYGjvA1g0c77zzDg6Hg0WLFp3VfkRRPKlXks/nQyaToVarUavV3RJJXq8Xn8/HsGHDwqF6oTLz06ZNY/bs2Z3u7+OPP2bBggW8/vrrXHXVVWd1ThKRTY+FUVZWFn/4wx/46U9/2un6l156iT/96U8cO3bsrAzsKV6vF51Ox1tvvcWPfvSj8PLf/va37Nmzp0O58RAneoyCwSBNTU3Exsae1d0Jm81GWloaFRUVGI3GHu/nfEG6Xt1HumbdR7pm3UO6Xl1HFEVaW1tJTk6OqN4tvYnf7+fo0aMUFhZSVFSEx+MhLi6OvLw8Ro4cSXx8/HlRiGHtriqUAfAFRBoDXiamxdLo9GKJ15EWe/56jN5++21cLhcLFy7s1f2275XU2tqK1+sF6HKvJI/Hg9/vJzc3F4VCQUFBAR999BGTJ09m3rx5nb5nv/jiC6655hpefvllfvzjH/fq+UhEHj0Opbv33nu54447KCgoYO7cuSQmJiIIAjU1Naxdu5Z//etfPP/8871oavdQqVSMGzeOtWvXdhBGa9eu5Yc//GGn24TuPrQnOjq612wyGo3ShKIbSNer+0jXrPtI16x7SNera7RvaXEuolAoyM3NJTc3t4NI+uabb9i4cWNYJIU8SeeqSJo4PI5DFTaiVTJmZyRR2exiWJwRk+78rkjb3VC6rtK+V5LP58PpdIZFUij9ITSX66wMePs+Rnv27OGjjz5iwoQJpxRF69ev55prruGf//ynJIrOE3osjO68805iY2N57rnneOGFFwgE2oIp5HI548aN45VXXmHBggW9ZmhPuPvuu1m4cCHjx49nypQp/POf/6S8vJw77rhjQO2SkJCQkOgZ/kAQhfzc9MIMVk4USceOHesgkmJjY8MiKXQT9VzBpFMxKTcu/DzNrB9AayKHvhJG7VEqlZhMJkwm00m9klpbW0/bK2nfvn28//77XHjhhVx22WWnLMZw1VVXsXz5cm688cY+PReJyOGsynVff/31XH/99fh8PhoaGgCIi4vrsxKL3eX666+nsbGRRx55hOrqavLz8/nkk0/IyMgYaNMkJCQkJHrA858fZvSQGIZbDKREa5HJzp1J9rmAQqFg2LBhDBs2rINI2rFjB5s2bcJsNofD7c41kSTxPf0hjNoT6pVkNBpJTEzE6XTicDhoaWnBbrcTCARQqdq8eJWVlWzbto0xY8ZwxRVXdGrn9u3b+cEPfsCTTz55ypQRiXOTsxJGIZRKJUlJSb2xq17nzjvv5M477xxQG9RqNX/84x9PCtOT6BzpenUf6Zp1H+madY9IuV57nj3K0XFRmHONZOaYGJZoYLjFQLyhe0nYEl1j165drF+/nh//+Mekp6d3a9v2IikQCIRF0s6dO9m8eXNYJOXl5WGxWKTX7xyiv4VRe07XK+no0aNs376dUaNGceWVV3Zq4+7du7n00kt55JFHpAij85AeF184ExUVFfzxj3/kxRdf7IvdS0hISEich/xUt5qaC1VEK0GeowKLjuHjE0iL05NraRNJ0ed5fkdv8vXXX/Pwww/z1VdfMX78eK677roeiaT2BAIBSkpKOHDgAIcOHcLtdhMTExP2JEkiafDz+uuvIwgCP/nJTwbalDCHDh3izTffJDc3l+uuu67TQg379+/n4osv5r777uO+++7rdRsef/xx3n33XQ4dOoRWq2Xq1Kk88cQTHSo8L168mBUrVnTYbtKkSWzbti383OPxcO+99/L666/jcrmYPXs2//d//3fOVcEcCPpMGH377bdceOGF4dwjCQkJCQmJs+VHWW8ibw7QECfi0gTRVHnx5erJGmHGcpGZoEdk7AVx5Ce3eZP06l4JjDjvaWhoYM2aNbz55pusX7++10VSYWEhhw4dwuVyhUVSXl4eSUlJkkgahLz22mvI5XKuv/76gTYFgOLiYt544w2GDRvGtdde22lhhkOHDjFz5kx+/etf84c//KFP7Lj00kv5yU9+woQJE/D7/Tz44IPs27ePwsJC9Pq2/LTFixdTW1vLSy+9FN5OpVJhNpvDz3/5y1/y4Ycf8vLLLxMbG8s999xDU1MTBQUFnZ6bRNfpsTD64IMPTrv+2LFj3HPPPZIwkpCQkJDoNX6S+CZOWYBmjY+A1YeglREwKhlbpeL4cAVaa4CWkQIytYDCoOPqhcOYNCyO7IQo1AppwtAb9KVIKi0tDXuSXC4X0dHRYZGUnJwsiaRBwqpVq1AqlQNehAva5qOvvfYaQ4cOZcGCBZ0Kh+LiYmbMmMFPf/pTHn300X57n9XX15OQkMCGDRuYMWMG0CaMmpubee+99zrdpqWlhfj4eFauXBkWnlVVVaSlpfHJJ58wf/78frH9XKXHwkgmkyEIwmk7ewuCMKiFUTAYpKqqCoPBIH0ZS0hISEQAD608wOFdTTi2WHEaZMitAbRyBcOPy2mIE2iKDWDAw8zU43y1LwVrShTJI6NIm2xmWIqBiUNjSDdrUfRh0YbzoY9RiPr6etasWcNbb70VFkkLFizgxz/+MWlpaYiiSGNjI3FxcWfeWTtCIinkSXI6nURHRzNixAhGjhwpiaQIZ+XKlWg0Gq677roBtaO0tJRVq1YxZMgQrr/+ehSKkz3IpaWlzJgxgwULFvDUU0/16/uquLiYnJwc9u3bR35+PtAmjN577z1UKhXR0dHMnDmTxx57LNxA+csvv2T27Nk0NTURExMT3tfo0aO5+uqrefjhh/vN/nORHgujlJQU/va3v3H11Vd3un7Pnj2MGzduUAuj48ePk5aWNtBmSEhISEiEEGQoYlJQp+WToJ9MlCsajTGa+Cpwa9XoK7xEy70kDnfQ0KKjzKVD1uDDMUSBx9VMXVINLhrwNJXhKduFv6UO6JOIcioqKs6rmP8TRdK4ceNQKpW4XC527NjR4wlnMBgMi6SDBw/idDoxmUxhT1JKSookkiKMV155BZ1ON6C9f8rLy3n11VdJS0vjJz/5SacVk48fP86MGTO4/PLL+ctf/tKv7yNRFPnhD3+I1Wpl06ZN4eWrV68mKiqKjIwMSkpK+MMf/oDf76egoAC1Ws1rr73GT3/6UzweT4f9zZs3j8zMTF544YV+O4dzkR4HX48bN45du3adUhidyZs0GDAYDABn1em93l5P9vJsAIrvKiY+Kr7X7JOQkOg50mdz8OMLBCltdHGo1s6+0hYO7WhA9m4jQb9AucqAx6VGo5aBT4a8WQ7KWC5IT8fj87K9uonoaT9l0rxkrpyZSk68jvgoVa9MjGw2G2lpaeHfkPOF+Ph4fvGLX/CLX/yCyspKfvSjH7Fr1y48Hg/Tpk0Lh9t194ajTCYjKyuLrKwsfvCDH1BWVsaBAwfYu3cvW7duxWQyhT1JkkiKDERRHFBv6fHjx1m1ahUpKSmdiqK//vWv7N27l08//ZT58+fz5z//ud/fN7/+9a/Zu3cvmzdv7rC8fV5Wfn4+48ePJyMjg48//phrrrnmlPsbyEqA5xI9Fka/+93vcDgcp1yfnZ3NV1991dPdRwShN9jZdHp3y9ygaXtsMBowRkkd4yUkIgHps3luEBsTzbhscE0IcHB6C28NPcbx463oD9qJifNzzCAid4GxKUjCjy1cPTMZtcdPfc0BrGUuiv+vgr/ubCEhRc/UH6QxKtVEbqKBGP3ZV7Y7nycpH374YbiwgiAI4Zyk++67jwkTJpyVSMrMzCQzMzMskgoLC9m3bx/btm3DaDSGPUmpqann9WswkAzkJL2qqopXX30Vi8XCDTfc0KmnKD09nf/5n//B7Xbz0Ucfceedd/Lcc8+h1Wr7xca77rqLDz74gI0bN57Rq5yUlERGRgZHjhwBwGKx4PV6sVqtHULp6urqmDp1ap/afT7QY2F00UUXnXa9Xq9n5syZPd29hISEhIREl9Gq5FyYYebCu8zY3D4OVLZQWG3DfLSFgvcraC5xkxqrQAv4lHKiTGpkNUGiSv24ZXaqd7Sy6ttGlCY5Yy5N5cK8eHItBnKlynY94he/+AU333wzUVFR4ee/+MUvwuF2vS2SLrvsMsrLyzlw4EAHkRTyJEkiqX8ZKGFUU1PDypUriY+P58Ybbww3dW1PY2Mjf/jDH7jssst4+eWX2bJlC1999RUajabP7RNFkbvuuos1a9awfv16MjMzz7hNY2MjFRUV4X6hoRDVtWvXhotbVFdXs3//fp588sk+tf98oEc5Rnv37iU/P7/LbtIDBw6Qm5vbadJbJGOz2TCZTLS0tPTYY1RnryPxmUQAau+pJSEqoTdNHLRUNruwOrzE6FWkRPfPHRoJifZIn83zgwa7hz3lVj5YW0ad3UWaX4GnycfxjfU4BQGlNYixPkhjgkCd2o2y3o9hpplLfzESuUxAECAtRsfwJAND46PQKM9c2a43fjvOB+rq6sI5SRs2bDgrkdSeYDBIeXl5OCfJbrdjMBjCIiktLU0SSX3Miy++SGxsLD/84Q/77Zh1dXW8/PLLxMTEsHDhwk6FTnNzM3PmzCE9PZ3Vq1d36k3qS+68805ee+013n///Q69i0wmE1qtFrvdztKlS7n22mtJSkqitLSUBx54gPLycg4ePBgOz/3lL3/JRx99xMsvv4zZbObee++lsbFRKtfdC/RIGMnlcmpqaoiP71pMvtFoZM+ePWRlZXXbwIFEEkZ9Q2WziznPbMDlC6BVyll3z0xJHEn0O5H42ZRuGPQdoihS1ezi3+8XcfBQI9FpUXgKbDR/Y8PvDVIXJ+IvsyOXCxjHRzPvjjwUMhkOl4/Cr2pQuwQsU2MYmxfLhekxDInVo5B3fnNQEkbdpzORFKpudzYFLILBIBUVFRw4cOAkkZSXl0d6erokkvqAf//738TFxfWbMKqvr2fFihUYDAYWLVrUaUhca2sr8+bNIzY2lnfffbdTb1Jfc6r32ksvvcTixYtxuVxcffXV7N69m+bmZpKSkrjkkkv405/+1OFmgdvt5ne/+x2vvfZahwavUsGws6dHLhxRFPnDH/6ATqfr0niv19uTw0ico1gdXly+AHfNymb5l8VYHV5pEihx3iPdMOhbBEEgJUbHQ4vHEgiKVDQ5OTTTxtf7aynda8USp6a50U3Q4SN9VCwKmQyfP0DVqqOM3NHK0VgFhfvr+VIZQB2jZsoPM5k9PpnhFiOpMVpkfVj++3wgISGB22+/ndtvv72DSPrd737HxIkTw56k7ookmUxGRkYGGRkZXHbZZR1E0vbt24mKiurgSTrXy6v3F/0ZStfY2Mgrr7yCXq9n4cKFnYoih8PBD37wA4xGI2+//faAiCLgjEXJtFotn3/++Rn3o9FoWL58OcuXL+8t0yS+o0fCaMaMGRQVFXV5/JQpU/otoU1i8CBN+iQkvke6YdB/yGUCQ+L0DInTM3tEIscudnCoxkZpg5Ngu4lLa4uX9DI3Cq9IapMHdZqXOJmHrQVKNlR4KBtRT/SwKOKTdcyZnspwi4EEg3oAz+zcoK9EkiAIpKenk56ezqWXXkpFRQWFhYUUFhayY8eOsEgKeZIkkdRz+ksYNTU1sWLFCjQaDYsWLer0hr3L5eKqq65CoVCwZs2afsklkhi89EgYrV+/vpfNkJCQkJAA6YZBf6OUy9qKLFgMuLwBiuvsHKyxUWl1YYxRUzsxGr3QRJPJxxhfPfs9arw+GSqXSPx7jdRPaKTBqmT3uioSc4xMn5tCukmBTHN+leruKzoTSW+++WaviqT58+dz/PjxsCdpx44d6PX6sCdJEkndpz/KdTc3N/PKK6+gUqlYtGgRer3+pDEej4drrrkGj8fDZ5991uVIJ4nzl8FVDUFCQkJCQqKP0KrkjEo1MSrVhM3t43BNKwcMGspm2zEfruDbrx3srhVQeALEueXIjH5G1TuoRU2wyk7jcRcHs6OojVFgGHcVb+ysYnx2gGGJBqKkynZnzYki6d133+01T1JaWhppaWlhkRTyJO3cuTMskvLy8sjIyJBEUhfoa49RS0sLK1asQCaTsWjRok57hnm9Xq677jqamppYu3ZtuEKihMTpkD7dEhLnCYIgnPZv8eLFJ40zGAyMHz+ed999t8O+bDYbDz74IMOHD0ej0WCxWJgzZw7vvvvuKWOo3333XebOnUt8fDxGo5EpU6Z0KZZaQmIgMGqUjB9i5papQ7hzdg6X/mgCsktGo86Lw/KjVNwXqjFaXBiEIO5YkVHOVnQGP7UlrWx64Ri5wflseqaYZ/68i9uWbuCxt/ez73gzbl9goE/tnCAhIYE77riD//znP1RWVrJw4UI+/PBDMjMzmTZtGn/+8585fvx4t/cbEknz589nyZIl3HrrrYwaNYojR47wyiuv8Mwzz/DRRx9x7NgxgsFgH5zZuUFfCqPW1lZeeeUVAG655ZZOC5z4/X5uvPFGjh8/zmeffSYVQZHoMtItLAmJ84Tq6urw49WrV/PQQw91yBVsnwf40ksvcemll9Lc3MxTTz3Fddddx+bNm5kyZQrNzc1Mnz6dlpYWHn30USZMmIBCoWDDhg3cd999zJo1i+jo6JOOv3HjRubOncuyZcuIjo7mpZde4sorr+Sbb75h7NixfXruEhJnQ1yUmrhsNVN+E0v1LW6KalopqrGxbV0J+4ubyHfaqUoyY5iSTtnaJjwHHMSV+6nNaSX+uJMWlZfVb5WwbmYiFp2GKXNS+cGU1NNWtpPoOiGRdMcdd3TwJN17771MnDgxXN0uJSWlW/sVBIHU1FRSU1OZN28eVVVVHDhwgMLCQgoKCtDpdAwfPpyRI0cyZMgQyZPUjr4SRna7nRUrVuD3+1m8eDEmk+mkMYFAgFtuuYWioiK++uqrDk1QJSTOhCSMJCTOEywWS/ixyWRCEIQOy9oTHR2NxWLBYrHwj3/8gzfeeIMPPviAKVOm8MADD1BaWsrhw4dJTk4ObzNs2DBuuOGGUya2Pv/88x2eL1u2jPfff58PP/xQEkYSgwJBEEiO1pIcrWXmsHguzU/iUE0rB6tt+INBTIJA+T47im1BAioBZXMAhUXGBSU+akzQsrkBl1ZNaWEzhZvqGTYvgfyUaKmyXS9yOpE0adKkcLhdT0RSSkoKKSkpzJ07l6qqqnC43a5du8IiKS8vj8zMzPNeJPWFMHI4HLzyyit4PB5++tOfdip4gsEgt912G7t27WL9+vXExcX1qg0S5z6SMJKQkDgtSqUShUKBz+cjGAzyxhtvcNNNN3UQRSG6E8MdDAZpbW3FbDb3prkSEv2CrENluwSO1bdVtvNfncbXyiAlW4rJGJrK0INujqQqCTT5MSNAmY+AXeSwtYa6oiaO5EWjHKIjOSOKiZmx5CWbSDSqpd46vUB7kVRbWxsu3NCbImnOnDlUV1eHPUm7du1Cq9V28CSdjw03g8Fgr76HnU4nK1euxOl0snjx4k5/N0RR5M4772TTpk1s3LiRxMTEXju+xPlDrwijTZs28cILL3D06FHefvttUlJSWLlyJZmZmUyfPr03DiEhITEAeDwennrqKWw2G7Nnz6ahoQGr1crw4cPPet/PPPMMDoeDBQsW9IKlEhIDR/vKdu6RFmYMNTJn49P8+KancdT7MFW1kLqlEd9eDy6zHF1jkFaPG0ejHV2Zh2qTgtoqkR1T9WRNMDNsTNx3niQDMfqB6bdyrpGYmNhnIik5OZnk5OSwSAp5knbv3h0WSSFP0vkiknrTY+RyuVi5ciWtra3ccsstnXqBRFFkyZIlfP7552zYsKHTG3cSEl3hrIXRO++8w8KFC7npppvYvXs3Ho8HaEuOW7ZsGZ988slZGykhIdG/3HDDDcjlclwuFyaTiaeffprLLruM2tpa4NTdu7vK66+/ztKlS3n//fdJSEjoDZMlJCICjVJOXpIBx/7/cOu0dGpccLC6lfRJKWzfVIPvs1rcInhFH+pGPw2xIsb9HgIaOarPbdiOOFm/sZ69SXoCw7QIviBCnQ+/V+T/t3fn8TFd/QPHP5PJisgqCyKkdlEqtkQQW/BU1fKoamtpKUoQ4deNtqpKq7ZWa6s+tFqlLfpUH1WxbymaUrHUTiyJEJHILpn7+2PMlUkmkVW27/v1mpfMnXPvPXPnzjjfe8753r79n+BfHeXO9kWVPUjKPtzuueeeY+DAgUUKkrp160Z0dLTak2QIkho1akTTpk3x8vKq0EFScQVGqampfPvtt8THxzN8+HCT/18oisIbb7zBxo0b2bt3L3Xq1CnyfkXlVeTAaNasWSxbtoxhw4axbt06dbmfnx8zZ84s6uaFEKVg4cKFdO/enerVqxv9R1SjRg0cHBw4ffp0obe9fv16Ro4cyY8//kj37t2Lo7pClEm21ubUcqmOj6cjsYlp+DeowQE/N/Zsu4Lu3D0yUm2wjlRIr2qGpRlobDSk3b6Pcuc+18+nYnZIoZr9XcysMrn1ZzW+2HyTjX1daPNkDUa+1FQSNxQDV1dXXnvtNV577TWjIGnKlClFDpLc3d1xd3enW7du3Lx5Uw2Sjh07hrW1tdqTVBGDpOIIjNLS0li7di137txh2LBhuQ6Ne++991izZg179+6lXr16RdqnEEUOjM6cOUOnTp1yLK9evTp3794t6uaFEKXAzc2N+vXr51huZmbG4MGDWbNmDe+9916O4QpJSUlYWVlhbm76p+X777/nlVde4fvvv+fpp58ukboLURY5VbPCr74Vvk848VLHuvwTfY+9f0VzIyKeVLNM7h5NIPl2OmZ3MohPA6uk+zg7aqiVoqV5g0iO9bAj6qoD9+7Zsemrf/h5zxXemtIav8Y1JEAqJnkFSe3bt2fQoEE5gqT8BACGRDdubm507dqVmzdvqsPtDEGSoSfpiSeeqBBBUlEDo/T0dL7//ntiYmIYOnQo7u7uJst9+OGHLFu2jN27d9OgQYNC708IgyIHRu7u7pw/f566desaLd+/fz9eXl5F3bwQooyZPXs2u3fvpl27dnz44Ye0bt0aCwsL9u3bx5w5czhy5IjJdN3ff/89w4YN49NPP6V9+/ZER0cD+jThplKuClERaTQa3O1scLezoXODGkT2SOaf6Huc8L/L7s0XiY5QqHJLR6aLOS5XU8FOh4N1IlbJTvj416Stf0OiUjKZv/wY775xgJbdatGrRx2auttJZrtiZCpI+uGHH4yCpOvXr1O1alXefffdfG83a5DUpUsXYmJi1J6kv//+GysrK6OepNwuMpV1RQmM7t+/z7p164iKiuKll17Ktbdu/vz5LFiwgF27dtG0adOiVFcIVZG/cWPGjGHSpEn85z//QaPRcOPGDcLCwpg6dWqBfiyEEOWDg4MDf/zxBx999BGzZs3iypUrODg40Lx5cz755JNcg5zly5eTkZHB+PHjGT9+vLp8+PDhrF69+jHVXoiyI3tmu75P1SLiejynb8STfCOF2ON3ybh2m132blgessbKpzon41Iwt7KglmsVrkXc4fKCi6w7cpuanVyp29SBhq62NHarLpntilH2IGnDhg3MnTuXK1eu8NRTT2Fvb1/o4Xaurq64urqqQZKhJ8kQJGXtSSpPQVJhA6OMjAzWr1/PtWvXePHFF/HwMD2n7vPPP+eDDz5gx44dPPnkk0WtrhCqIn/LXn/9deLj4+nSpQupqal06tQJKysrpk6dSlBQUHHUUQhRzEaMGMGIESNMvqYoyiPXt7OzY86cOcyZMyff+9y9e3e+ywpR2VhozWjoaktDV1tSm7tz7mYi/7RK4PgZZ878aYuu6l1uxafxjEM17iSn07xlDTJPxZKZoMPmSCI3ItNIfkHhnyO3WYcON/eq9GjrThN3Oxwls12xcXV15c6dO9y/f5+9e/cSERGRY7jdv//97wJnRcsaJAUEBHDr1i21J+n48eNqkNSkSRPq169f5oOkwgRGGRkZ/PDDD1y5coUXXngBT09Pk+VWrFjBtGnT+P333/Hx8SmO6gqhKpZv1ocffsi0adM4deoUOp2Opk2bFuh+JkIIIYTQs7bQ0ry2Hc1r29HL243dDZ1ZfyuCWm72uFla4lHVmrPRd7mrtcHZVYdDrI54bQZnF18mtbqCQwwkVbHgYqebeDxlT7MWrjRys6WhazVsrS1K++2VewMGDGDIkCE88cQTdOzYkXHjxhEdHZ1jTpIhcUNhgiQXFxdcXFzo0qVLjiDJ0tJS7Ukqq0GSoigFusltZmYmP/30ExcvXmTIkCG5JlH4+uuvmTJlClu2bKF9+/bFVV0hVMX2bapSpQqtW7curs0JIYQQlZ6ttQXPtKlNz9U1+d+hqyTodFy6m0SKsyV1B9Tm/IGbxF9Ix+xWJpiDLjade9UssY5VsA6Lw+JIHAea3Sa8qR3OzR1o4GpLYzdb6rtUw9qi/E/yLw2m5rO4ubkxbty4HEFSSEgIvr6+auKGwtxfp0aNGgQEBKg9SYbhdhEREVhaWtKwYUM1SLKwKBuBb0F6jHQ6HRs3buTcuXM8//zzPPHEEybLrVu3jvHjx/PLL7/QsWPH4qyuEKoiB0YhISEml2s0Gqytralfvz7PPvus3N1eCCGEKCRLczOe9a3DhZhEGldzomumwj/R9zjcyI6II7dJvJBI6j8JmEVrSKtjwb14Hc737qNLzsTCVov50Xscc7zNWWstO/ztsK9VhfYtXWlaszp1natiIZntik1uQdLkyZPx8/MrcpDUuXNnOnfuzO3bt9WepBMnTpSpICm/gZFOp2PTpk38888/DBo0KNfMchs3bmTkyJFs2LCBrl27Fnd1hVAVOTA6evQof/31F5mZmTRq1AhFUTh37hxarZbGjRuzZMkSpkyZwv79+yVriBBCCFFIZmYaGrjZqs8Nme2udkjm5I0EtuyMJD09k9qN7di7+CS3zM04V02habJCkrmGWn+lolHg9tVUXKpYsKXdLY51dMHZRd+D1NjNFg+HKpLZrhiZCpJ++OGHYulJcnZ2NgqSDD1JP/zwAxYWFmqQ1KBBg8ceJOUnMFIUhV9++YWTJ0/y73//m8aNG5ss9+uvvzJ06FDWrVtHr169SqK6QqiKHBgZeoNWrVpF9erVAUhISGDkyJH4+/vz6quv8sILLzB58mR+//33IldYCCGEEHpmZho8nari6VSVHk1duXQ7iX+i73GlS02iI+KoUsuaVBsL7oQnYn8jmSrJYJaucNcyDdejidhFR3PU0YUDyRos3KxpEehKyzqONHGvjlt1a8lsV4yyB0kbNmwotuF2zs7OdOrUiU6dOhEbG6v2JP3444+lEiQ9KjBSFIXNmzdz/Phx+vfvn+uF899//53BgwfzzTff8Mwzz5RUdYVQFTkw+uSTTwgNDVWDItDf3HXGjBkEBgYyadIk3n33XQIDA4u6KyGEEELkImtmu8CmrpyPSeSf6Htci0smsfV9jv/vOlUiUki3VMjMTKOafSaukUmE375HlT91JFgqbD0Uy+mG1anW1o4GDR1o4WEvme1KgJubm3rrAlNBkiFxQ243Ns2Lk5OTUZBk6EkyBEkNGjRQgyRLy5L5XPMKjBRF4X//+x9Hjx6lX79+NG/e3GS5Xbt2MXDgQFauXMnAgQNLpJ5CZFfkwCg+Pp6YmJgc0f6tW7dISEgAwN7envT09KLuSgghhBCPcOh8LBpFIRMY2KoWiWkZnL15jzo1qnL++j2qVjXn3qVYMk5EE25mgea6lkwlE5IySbuWzJ2b9zl3KZ6Dt8+ifcKG+k848lQnNzq1cKWhq61ktitmWYOkqKgodU5ScHAwfn5+RQ6SOnbsSMeOHblz544aJP3000+Ym5urQVLDhg2LNUjS6XQmAyNFUdi6dSvh4eH07duXFi1amFx///799O3bl88//5whQ4YUW72EeJRiGUr3yiuvMH/+fNq0aYNGo+Hw4cNMnTqVfv36AXD48GEaNmxY1F2JCio2SYJmIYQoDjEJqThYmlPL3oZbiWlci0vBw7EKPp6O+Hg6cicpnX+iEzhT3Zq7DVywuZFEtdPxnLCLJfNqMlZX07lbzxxtRDKaKjrSjiRw6VIGMUfjuNw2Bnd/F+q7SWa7kuLu7p5rkNShQwd1uF1hgiRHR0f8/f3x9/c3CpI2bNhQ7EGSqR4jRVEIDQ3l8OHDPP300zz11FMm1z106BBPP/008+fPz/V+e4/TkiVL+OSTT4iKiqJZs2YsWrRIsuJVYEUOjJYvX87kyZN5/vnnycjI0G/U3Jzhw4ezcOFCABo3bszKlSuLuitRwThUtcTGQsvYNeFsn9KZWvY2pV0lIYQo16pZm3M5NZFa2BCbnE59e1uj1x2rWuL3hDO+Xk7cTEjjdHQC5+pVx/5Je/7edp07lxKxvamgUzTc14DWSovlpTRiMzK4FhrNtcPX2azEE8kFtLYZdPSux5uvDaWhu72a2c5UT8HSpUsZO3as+jwiIoKgoCAOHz6Mo6MjY8aM4Z133pE5TVmYCpJ++OGHYg+S4uLicgRJ9evXV4MkKyurAm8/e2CkKAo7d+4kLCyMXr165Xp7l7/++otevXoxa9YsRo8eXeD9Frf169cTHBzMkiVL6NChA8uXL6d3796cOnWKOnXqlHb1RAkocn7OatWq8eWXXxIbG6tmqIuNjWXFihVUrVoVgJYtW9KyZct8bW/OnDm0adMGW1tbXFxc6NevH2fOnDEqoygKM2bMoGbNmtjY2BAQEMDJkyeNyqSlpTFhwgScnZ2pWrUqffv25dq1a0V9u6IY1bK3YdlQH1LuZxInvUZCCFFkVSzNqe1ShX9iE3F1tMGuiukr/xqNBjc7a7o0cmGUvxdjejZg4v/50GNMY2r1ccUy0BGbmlWwvQuZFmAen8G9O2lo48xpcciejpfb0j7On8idWiYuWseKvRfZeiKaK7FJgIZVq1YRFRWlPoYPH67uOyEhgR49elCzZk2OHDnC4sWLmTdvHgsWLHg8B6kcMgRJe/bs4erVqzz33HP8+OOP1KlTh86dO/P5558TFRVVqG07ODjQoUMHXn31VSZOnEhAQAAJCQls3LiRTz75hPXr1xMREUFaWlq+t5k9MNqzZw/79+8nMDCQdu3amVwnIiKCHj16MG3aNCZMmFCo91LcFixYwMiRIxk1ahRNmjRh0aJFeHh4sHTp0tKumighReoxun//PoGBgSxfvpyGDRvy5JNPFrlCe/bsYfz48bRp04aMjAymTZtGYGAgp06dUgOtuXPnsmDBAlavXk3Dhg2ZNWsWPXr04MyZM9ja6q+OBQcHs3nzZtatW4eTkxNTpkyhT58+hIeHo9VK139Z4SQTeoUQoljVtK9CTfsq+S6fNbNd18YuXO6WxPHr8Rz55xanw25x+1gcltfSoK4N1lcV0GiofltHspsWv/MO3KqSwFn7K1yt58CfF62o3rY/NxQHFBs7k5ntvvvuO1JTU1m9ejVWVlZ4e3tz9uxZFixYQEhIiPQaPUL2niRD4gbDfZIMc5Lc3NwKvG1DkNShQwfu3r2r9iRt3LgRrVar9iQ1atQoz56krIHRvn372LNnD926dcPX19dk+dOnT9OtWzcmT57M1KlTC1zvkpCenk54eDhvvvmm0fLAwEAOHjxYSrUSJa1IgZGFhQUnTpwo1h+xrVu3Gj1ftWoVLi4uhIeH06lTJxRFYdGiRUybNo0BAwYA8PXXX+Pq6sratWsZM2YM8fHxfPXVV6xZs4bu3bsD8O233+Lh4cH27dvp2bNnsdVXCCGEqCgstGY0cLWlgastTzd353znRI5ciOWfa/FYazScD40i6XgyljoFq3RIrKnQJD6T23uiSPjPHWLszfF2ac9PPx/m8+8342yezri+fowePRozM/0glbCwMDp37mzUsO7ZsydvvfUWly9fpl69eqX19ssdd3d3goKCCAoKUoOkH374gUmTJtGhQ4ciBUn29vb4+fnh5+dHfHy8GiRt2rQJrVbLE088oQZJ1tbW6nqKogD6XskDBw6wc+dOAgIC8Pf3N7mfc+fO0a1bN8aMGcP06dMLdyBKwO3bt8nMzMTV1dVouaurK9HR0SWyP2dn52LfriiYIg+lGzZsGF999VVx1MWk+Ph4QD8eFuDSpUtER0cbpf+2srKic+fOagQfHh6u9mYZ1KxZE29v7zyj/LS0NBISEoweQgghRGVkbaHFu5YdL3fyYvoAb/r6efKv0U2p9VpdHEZ7ovzblky7m2Blyd10cxyi72N/Pp3Gd2sRpPVgQJ/n6NWpHVOmTGH27NnqdqOjo002Ng2vicIxBEmG4XaDBg3ihx9+oHbt2gQEBPDFF18U+vja2dnh6+vLyJEjCQ4Oplu3biQnJ/Pzzz8zb948vv/+e/7++29SU1PVwOjSpUts376djh070qlTJ6PtKYrC7t27OXfuHF27duXFF19k5syZRT4GJcFUEoni7tX85ptv6Nu3L7t37y7W7YqCK3LyhfT0dFauXEloaCitW7dWh7sZFGXMsKIohISE4O/vj7e3N/DwR9PUj+qVK1fUMpaWljg4OOQok9ePwpw5c3j//fcLXV8hhBCivJkxY8Yj/+87cuQIrVu3xsfTgTvN3Tlw4hKT3ltG7RbemLs1hD9uc9Ndh1YHVS3NsDW3wNHWnrfGjsXVIpWZM2ca9QaYamyaWi4KJ2tP0o0bN9i4cSPr169n0qRJ+Pv7q4kbCtOTZAiSfH19iY+P5/Tp05w6dYqff/4ZMzMzvLy8ADhx4gR+fn506dIlx+caGRnJc889R2xsLE2aNKFHjx5kZmZibl7kZmmxcXZ2RqvV5mg3xsTE5GiDFsU333zDiBEjaNGiBR9//DGZmZl069at2LYvCqbIZ+CJEydo1aoVAGfPnjV6rag/cEFBQRw/fpz9+/fneK0wEfyjyrz11luEhISozxMSEvDw8ChgrYUQQojyIygoiOeffz7PMnXr1lX/To2/zdRhfenQrh0fzxzD2ZhETvnFc2p3FMrVJCyrZXC8jgutWtbExlJL+/btSUhI4ObNm7i6uuLm5maysQk5L3qKoqtZs6bJICl7drvCBknt27dXP+NTp04REREBQLNmzejevbvJdpeFhQXVq1enXbt2eHp6MmLECF555RVmzZpV5PdbXCwtLfHx8SE0NJT+/fury0NDQ3n22WeLZR+KouDo6MiBAwcwMzPjgw8+YM6cOWRkZMi0j1JS5MBo165dxVGPHCZMmMAvv/zC3r17qV27trrc8MWNjo42SlGZNYJ3c3MjPT2duLg4o16jmJgY/Pz8ct2nlZVVodJSCiGEEOWVs7Nzvuc2XL9+nS5duuDj48PqVavQarXUdKhCpwY1uPZkLU5HJ3A+JpH0DB1N3e0AOHr0KNbW1tjb2wPg6+vL22+/TXp6unq/nG3btlGzZk2jAEwUv+xB0oYNG/IMkrK3o/JSvXp1NUi6efMmLi4uJoOimJgYunXrRseOHfnqq68wMzPjs88+Izk5uVjfa3EICQlh6NChtG7dGl9fX1asWEFkZKRR6vmi0Gg09O7dW00KNmXKFBYtWsTHH3/M/fv36dOnT7HsR+RfkecYGZw6dYqtW7fyyy+/qI/NmzcXeDuKohAUFMTGjRvZuXNnjkmY9erVw83NjdDQUHVZeno6e/bsUYMeHx8fLCwsjMpERUWp3bpCCCGEKJgbN24QEBCAh4cH8+bN49atW0RHRxMdHY2ZmYY6TlVIv3gE7bldNLJJREm4ycqVK5k2bRqjR49WLzy+8MILWFlZMWLECE6cOMGmTZuYPXu2ZKR7zGrWrMmECRPYu3cvV65cYeDAgaxfvx4PDw8CAgJ49tln6dixIzqdrsDbdnV1NflZxsbG0r17d5566ilWrlypJuQwMzOjWrVqRX5PxW3w4MEsWrSImTNn0rJlS/bu3cuWLVvw9PQstn1otVp1KGmXLl2YOnUqdnZ2zJs3j59//rnY9iPyp8g9RhcvXqR///5ERESg0WhyjBPOzMws0PbGjx/P2rVr+e9//4utra3a3W5nZ4eNjQ0ajYbg4GBmz55NgwYNaNCgAbNnz6ZKlSq88MILatmRI0cyZcoUnJyccHR0ZOrUqTRv3lzNUieEEEKI/Nu2bRvnz5/n/PnzRiM54OEcIQsLC5YvXcL58+fR6XR4eXkxc+ZMxo8fr5a1s7MjNDSU8ePH07p1axwcHAgJCTEayi4eL0OQNGHCBG7cuMG4ceP43//+h6IodOvWjUGDBjFgwIBCDbczuHv3Lj169KBhw4Z888035ebWKePGjWPcuHHFvt2s0zs0Gg0ZGRmYm5vTsWNHtFotCxYsYOHChdy/f59BgwYV+/6FaUUOjCZNmkS9evXYvn07Xl5eHD58mNjYWKZMmcK8efMKvD3DTbMCAgKMlq9atYoRI0YA8Prrr5OSksK4ceOIi4ujXbt2bNu2Tb2HEcDChQsxNzfnueeeIyUlhW7durF69epy80UUQgghypIRI0ao/w/nplevXvTq1euR22revDl79+4tppqJ4vTPP/+wd+9eDh8+jKurKxs2bGDdunVGiRsKGiQlJCTQs2dPateuzdq1a8tUkoXSYAiCUlJSSE9Pp0qVKlhYWJCZmYlWq8XPzw9zc3PmzZvH4sWLycjIYMiQIaVd7UqhyEPpwsLCmDlzJjVq1MDMzAwzMzP8/f2ZM2cOEydOLPD2FEUx+cj6Y6zRaJgxYwZRUVGkpqayZ88eNWudgbW1NYsXLyY2Npbk5GQ2b94siRSEEEIIIfLQpUsXjh49ylNPPZVjuN2AAQP4/vvv8fDwoEuXLixZsuSRKcATExP517/+hYODAz/++KM6r6yyUhQFc3NzoqOj6dSpEz169KBdu3YcP37c6OJ927ZtefPNN3F3d2fJkiV8++23pVjryqPIgVFmZqY6LtTZ2ZkbN24A4OnpyZkzZ4q6eSGEEEII8ZhoNBqTc2gMQdK+ffu4fPky/fv3zxEk3bx502id5ORk+vbti6WlJZs2bZIEV+iPb1paGgMHDqRRo0aMHDkSDw8POnTokGNufqtWrXj77bdp1qwZs2bNYsOGDaVU68qjyIGRt7c3x48fB6Bdu3bMnTuXAwcOMHPmTDWXvRBCCCGEqBhq1arFxIkTcwRJtWvXpkuXLixdupQrV67Qv39/7t+/z+bNm7GxsSntapeqrHPurays8Pf3Z/HixYwZM4b//ve/vPTSSwwePJhNmzYZrdeiRQssLCy4ffu2jHx6DIo8yHP69OkkJSUBMGvWLPr06UPHjh1xcnJi/fr1Ra6gEEIIIYQomwxB0sSJE7l+/TobNmxg7dq1jB8/Hk9PT/7++2+qVq1a2tUsVYa5Q6mpqWrK70OHDnHnzh01HfrSpUuxsLBg2LBhLF26lJdeegmA27dvExkZybJly2jbtm1pvo1KociBUdYbUHl5eXHq1Cn1g5a0m0IIIYQQlUPWICksLAw7OzuqV69e2tUqVYqioNVqyczMpFWrVqSmplKtWjVOnDjBb7/9xssvv6wGjp999hkAw4YNo1OnTtSpUwcnJye++eYb7OzsSvNtVBrFdh+jrBwdHSUoEkKIAohNSi/tKgghRLHx9fWladOmpV2NUqXT6dT28OrVq2ndujXh4eEcPnyYiRMnMnXqVNavX6+OvAJ9cPTXX39Rp04dQD8nSYKix6dY8iXu2LGDHTt2EBMTk+NGYP/5z3+KYxdCCFFhXb+bwtg14dhYaHGoWrkzNgkhREVhuIHt6NGjuXTpEr6+vurQuUWLFmFubs7YsWPJzMxkyJAhajKzli1bAsb3OhKPR5EDo/fff5+ZM2fSunVr3N3d5QMUQogCiktKJ+V+Jl+/0hYnCYyEEKJCyczMZMeOHVhbWxMbG4uTkxMA8+bNw9ramjFjxhAfH09wcLDRPZ6kTf34FTkwWrZsGatXr2bo0KHFUR8hhKi0JCgSouxYsmQJn3zyCVFRUTRr1oxFixbRsWPH0q6WKOMMiRay+uqrr3Bzc2PVqlWsWrWKYcOG4eLiAugTl2VkZGBlZVXpb3xbFhT5E0hPT8fPz6846iKEEEIIUerWr19PcHAwS5YsoUOHDixfvpzevXtz6tQpde6HENllDYp+/fVXqlevTu3atfHy8uLDDz/k/v37LF68mIyMDF5++WVcXV0B+Oijj0qz2iKLIidfGDVqFGvXri2OugghhBBClLoFCxYwcuRIRo0aRZMmTVi0aBEeHh4sXbq0tKsmyjBDUNSnTx/Gjx9PYGAgkydP5ptvvgFg7ty5DB06lOXLl7NixQquXbtWmtUVJhSqxygkJET9W6fTsWLFCrZv386TTz6JhYWFUdkFCxYUrYZCCCGEEI9Jeno64eHhvPnmm0bLAwMDOXjwYCnVSpRlWZMk/Prrr9y7d48//viDiIgIVq1axYoVK0hOTmbs2LHMmjULc3NzPvzwQzp27Ejt2rVLufYiq0IFRkePHjV6bsieceLEiSJXSAghhBCitNy+fZvMzEx1mJOBq6sr0dHRpVQrUVZln1Ok0WjUhGTu7u7Url2buXPn8s0336DT6Rg3bhwzZswgICCAgICA0qu4MKlQgdGuXbuKux5CCCGEEGVG9oxgkjpZmGIIil5//XUOHz5MRkYGjRs3Vl9v2rQp06ZNY+7cuaxZs4Y7d+4wffp0OnfuDOhHXhnSeovSV+RPYs6cOSbvVfSf//yHjz/+uKibF0IIUUyu303hxPV4rt9NKe2qCFFmOTs7o9Vqc/QOxcTE5OhFEpVX1vt2vv3223z33Xe0adMGMzMz1q1bx8KFC9XXGzRowFtvvYWHhwfW1tbAw8BbgqKypchZ6ZYvX24y+UKzZs14/vnneeONN4q6CyGEEEV0/W4K3efvIeV+JjYWWrZP6Uwte5vSrpYQZY6lpSU+Pj6EhobSv39/dXloaCjPPvtsKdZMlBVZe3nCwsJwdnbmxx9/xM/Pj8uXL/PFF1+wYsUKMjIy+L//+z8AvLy8WLZsGY6OjoD0QJZVRQ5To6OjcXd3z7G8Ro0aREVFFXXzQgghioHhJrITutYn5X4mcUnppV0lIcqskJAQVq5cyX/+8x9Onz7N5MmTiYyMZOzYsaVdNVGKPvzwQ6OgaPfu3XTo0IFZs2apQ+rq1q3L+PHj6devH6tWrWLOnDnq+hIUlX1FDow8PDw4cOBAjuUHDhygZs2aRd28EEKIYiS9REI82uDBg1m0aBEzZ86kZcuW7N27ly1btuDp6VnaVROl5LnnnmPevHlGAU39+vWZOXMm6enpbN26VV1et25dxo0bx8CBA/nkk0/YuHGj0bYkKCq7ijyUbtSoUQQHB3P//n26du0KwI4dO3j99deZMmVKkSsohBBCCPG4jRs3jnHjxpV2NUQZsHXrVn766SfeeecdkpKSqFatGgC1a9dm9OjRaDQaZs+ejbm5OdOmTQP0HQevvvoq3t7eDBgwoDSrLwqgyIHR66+/zp07dxg3bhzp6fqhGdbW1rzxxhu89dZbRa6gEEIIIYQQpaVXr1688847rFmzBgsLC1555RV1VJSLiwtjxozBwsKCOXPmcP/+fWbMmAFAnTp1qFOnDpAzrbcom4ocGGk0Gj7++GPeeecdTp8+jY2NDQ0aNMDKyqo46ieEEKIEXb+bQlxSOg5VLWWYnRBCZGMIaN5//33Mzc1Zvnw5Op2OkSNHUqtWLUCfyXDkyJFYWFgwe/ZsYmJiWLJkidF2JCgqH4otR2C1atVo06YN3t7eEhRVQJLmV4iKx5Cprs/i/XSfv0e+30KIUnX58mVGjhxJvXr1sLGx4YknnuC9995TRyQZaDSaHI9ly5YZlYmIiKBz587Y2NhQq1YtZs6ciaIoBa6TVqtVU3O/8847BAUFsWLFCpYtW8bVq1fVck5OTowYMYIpU6Zw7dq1Qrx7URYUucdIVHyS5leIiilrprrFO88Tl5Qu320hRKn5559/0Ol0LF++nPr163PixAleffVVkpKSmDdvnlHZVatW0atXL/W5nZ2d+ndCQgI9evSgS5cuHDlyhLNnzzJixAiqVq36yPnvWTPGGTLQmZmZqX+/8cYbWFpaMn/+fDIzMxk9ejR169YFwMHBgaCgIN58802j9UX5IYGReCRpPAlRscn3WQhRFvTq1cso2PHy8uLMmTMsXbo0R2Bkb2+Pm5ubye189913pKamsnr1aqysrPD29ubs2bMsWLCAkJCQXLPCGQKZa9euUb16dapXr66+ljU4mjx5MpaWlsyZM4eMjAzGjh2Ll5cXgJqYQVEUCYrKIfnERL5J40kIIURpMwxrEpVDfHy8ev+frIKCgnB2dqZNmzYsW7bM6LwICwujc+fORlM7evbsyY0bN7h8+bLJ/RiCngsXLuDv78+XX34JYDT8zhAcAYwfP553332X7777jk8//ZSbN28CcPv2bUBScpdX0mMkhBBCiHJDrsJXHhcuXGDx4sXMnz/faPkHH3xAt27dsLGxYceOHUyZMoXbt28zffp0AKKjo9XhbQaurq7qa/Xq1TN6zZBg4dy5c7Rp04aEhAROnz4N5AxwsvYcjR49GisrK9555x0sLCz4/fffadSoET/88IOcp+WUBEZCCFEBSHY5UdHdvn2bgQMHMnnyZPr165fj9axzQ0TZMmPGDN5///08yxw5coTWrVurz2/cuEGvXr0YNGgQo0aNMiprCIAAWrZsCcDMmTONlmc/Fww9P9mXG4KiyMhI2rRpw8svv4yzszNhYWGA6XlCZmZm6vk2fPhwLCwsGDp0KO3atePTTz+VoKgck09OCCHKOckuJyo6RVFwdnbG09OTVatWAZCRkWFURoIivTlz5tCmTRtsbW1xcXGhX79+nDlzxqjMiBEjcmR1a9++vVGZtLQ0JkyYgLOzM1WrVqVv376FzrYWFBTE6dOn83x4e3ur5W/cuEGXLl3w9fVlxYoVj9x++/btSUhIUIezubm5ER0dbVQmJiYGeNhzBA+DoqioKFq1asXzzz/PwoULcXJy4syZM9y/fz/XTHYajUZ97YUXXuDAgQP89NNPagpvUT5JYCSEEOVc1gQpKfcziUtKf/RKQpQjhqCnY8eOXL16levXr2Nubk5CQgJTp06lffv2HDt2rHQrWUbs2bOH8ePH88cffxAaGkpGRgaBgYEkJSUZlevVqxdRUVHqY8uWLUavBwcHs2nTJtatW8f+/ftJTEykT58+ZGZmFrhOzs7ONG7cOM+HtbU1ANevXycgIIBWrVqxatWqfPW+HD16FGtra+zt7QHw9fVl7969Rmm+t23bRs2aNY2G2Gm1Wm7dukXTpk3p37+/mvK7evXqJCQkYGZmluf9h7IGR+3bt1dv+irKrzIZGO3du5dnnnmGmjVrotFo+Pnnn41eVxSFGTNmULNmTWxsbAgICODkyZNGZYrzSocQQpQHMoROVHR9+vTh9OnT3L17l5iYGAIDA9myZQvBwcE0bNgQeDhkytCAP3nyJPv37y+1Oj9uW7duZcSIETRr1owWLVqwatUqIiMjCQ8PNypnZWWFm5ub+sia4CA+Pp6vvvqK+fPn0717d5566im+/fZbIiIi2L59e4nV/caNGwQEBODh4cG8efO4desW0dHRRr0/mzdv5ssvv+TEiRNcuHCBlStXMm3aNHW+D+h7cKysrBgxYgQnTpxg06ZNzJ4922RGui+++ILAwEA12QJAkyZNUBRF7YHKytDzZCA9lRVLmQyMkpKSaNGiBZ9//rnJ1+fOncuCBQv4/PPPOXLkCG5ubvTo0YN79+6pZYrzSocQQgghSp+joyP9+/dn1KhR9OzZE0VR2LFjB88//zxVqlQBHjZUDf9Onz6dTp06YWZmxvfff19qdS8t8fHxADkyu+3evRsXFxcaNmzIq6++atTgDw8P5/79+wQGBqrLatasibe3NwcPHiyxum7bto3z58+zc+dOateujbu7u/owsLCwYMmSJfj6+vLkk0/y6aefMnPmTKMEDXZ2doSGhnLt2jVat27NuHHjCAkJISQkJMc+p06dyvr1642WOTs7k5iYyIULF4yWv/POO3Tv3p3U1NRC3SxWlH1lMvlC79696d27t8nXFEVh0aJFTJs2jQEDBgDw9ddf4+rqytq1axkzZox6pWPNmjV0794dgG+//RYPDw+2b99Oz549H9t7EZWDYU5HLXsbmQQvyoXYpHScqlqWdjWEyBfDBPhbt25x+/ZtDh06xKxZsxg9ejTOzs7qXJGszMzMSEtL4+TJk3z00Ue0bt2aJk2alNI7KB2KohASEoK/v7/RHJ7evXszaNAgPD09uXTpEu+88w5du3YlPDwcKysroqOjsbS0xMHBwWh7rq6uOebuFKcRI0YwYsSIPMtkv9dRbpo3b87evXsfWc5w3yEDRVGwsbHB0dHR6IL77Nmz+eSTT9i3b5867E9UPGWyxygvly5dIjo62ugqhpWVFZ07d1avYhT2SkdaWhoJCQlGDyEexTDxvfv8PRy5fEcmwYtSd/1uCieux5s8/xyqWmJjoWXsmnA5P0W5YWZmxvHjx+natavas2HIHgbkCIoMo0N+/fVXMjIyaNGiBV27djXqeagMI0iCgoI4fvx4jp6ywYMH8/TTT+Pt7c0zzzzDb7/9xtmzZ/nf//6X5/YqQ+Y/jUaDs7MzHh4enD17FtCPVHrvvffYsWMHbdq0qRTnTmVV7gIjw5WKrFlFDM8NrxX2SsecOXOws7NTHx4eHsVce1EQeTXuyhLDxPeU+5lciEmUSfCi1I1ZE55rcF7L3oZlQ31yPT+v300p8985Ufl8/vnn9O/fHx8fH9atW0ejRo3YuXNnruUNjfeffvoJb29vGjdurL5mGAJlCKZ0Ol2FbOhOmDCBX375hV27dlG7du08y7q7u+Pp6cm5c+cAfVa39PR04uLijMrFxMTkaH9VVFZWVty8eZOvv/6at956i23bttGhQweTvZOi4ih3gZGBqfz0j7qK8agyb731FvHx8erj6tWrxVJXUXDlPf2wDKETWeUV5F+/m8L5mMRi32dewXluQ+iy9n6Wt++cqJhu3bpFz549mTt3LiEhIaxcuZLGjRvTsGFDtm7dCpju+TEMozt06BB+fn5GKZTDwsJ49tlnOX36NElJSTkyjymKUq7njyiKQlBQEBs3bmTnzp05bmZqSmxsLFevXlV71Hx8fLCwsCA0NFQtExUVxYkTJ/Dz8yuxupcFhs++RYsWLFq0iNGjR/PLL7/QpUsXdDqdBEUVXJmcY5QXNzc3QN8rlLVLPOtVjKxXOrL2GsXExOT5hbayslIzmojilXXeTX7KHrl0R+15WbzzPHFJ6RJsiHLJEGyk3M/ExkLL9imd1XM5+2sOVS2LrZexMN8XQ++n4W/5zonSlpiYiI2NDf/973956qmn1OVdu3bl22+/5c6dOzmSChiu6P/6669oNBp8fHwwNzdXXzt+/DibN2+mcePG7N69m8TERGbPns2zzz4LPLzwWl6HjY0fP561a9fy3//+F1tbW3WkjJ2dHTY2NiQmJjJjxgwGDhyIu7s7ly9f5u2338bZ2Zn+/furZUeOHMmUKVNwcnLC0dGRqVOn0rx5c3XudkVl+MybNm3K/fv32bJlC4GBgSZv9CoqnnL3CderVw83Nzejqxjp6ens2bNHDXoq85WOx+F8TGKBriYXpPfHUDZ4/TFsLLR417IrjiqXeeVl2KAouLzuMWR4bdHglkYBk4GcF6Kyq1evHj///LMaFBmu5gcGBnL8+HHu3LmTY52sw+iaNWtGo0aN1Nfi4uL47bff8PDwoE6dOmzYsIEOHTowa9YsEhIS2LVrF0uWLOHmzZs5giKdTldSb7NYLV26lPj4eAICAoyyuhkyr2m1WiIiInj22Wdp2LAhw4cPp2HDhoSFhWFra6tuZ+HChfTr14/nnnuODh06UKVKFTZv3lxpekwGDhzI8ePHJSiqZMpkj1FiYiLnz59Xn1+6dIljx47h6OhInTp1CA4OZvbs2TRo0IAGDRowe/ZsqlSpwgsvvABU7isdJckwadsQtJhqyJmStWFo6P15VNlFg1vSpp5jvq6eF0cWuKxZ5R63vHoURMWR12da36WayaBo0LIwo/MiP9sSoiLJzMzEzMwsRwpud3d3RowYwY8//shbb71l1LuTdRjd6NGjjW66efHiRcLCwliwYAEvvfQSoM9ytnbtWoYMGYK7uzvnz59n1qxZLF26VO1FMmwX9MGZoaFcFnuUHjUM0MbGht9///2R27G2tmbx4sUsXry4uKpWrjg7O6vJPSQoqjzKZGD0559/0qVLF/W5Ie/88OHDWb16Na+//jopKSmMGzeOuLg42rVrx7Zt23Jc6TA3N+e5554jJSWFbt26sXr16kpzpaNYZWQw8cD3+B2cxyF/f3YNHMWkDScKPNSmIGUNDcVHBUamggqgQIGSYRtAgYOS4pgfYipwlIZv2fW40rFnPy9OXI8neN0xoODnqRDlVW7/Z9vb27N8+fIcy7MPo2vVqpU6jE6n0/Hnn3+SkZHBc889p66TmJhIZmYmr7zyCk8//TTW1tZ07dqVDRs20LdvXzQaDfv37+fGjRv069cPS0tLaUsIUUGVycAoICAgzyseGo2GGTNmMGPGjFzLVPYrHcWpxuL5BO9fixkK7N9D+/RMMO9Q2tUCcm88FqT3pbDzKrIGZYWVNbCShm7Z96j5QvDoz9FQLj/z7bJuL/t5avg3v9spDLknlyirDMPaDFfys/co/fTTTzRp0oT69eur68TGxrJ9+3b8/PywtNR/bxITEwkPD6dly5YMHDhQLevu7o5OpyMuLg5HR0dWrFjBjh07OHnyJMePH6ddu3YEBQUZ3QMne50A7t27Z3TRVghRtknfoHikKofD9EERgKJQ5XBY6VbIhOyNx8eRLjvrsL/lQ30KvH72+VQl2cAVxSO38yt7Nre85gbllUo7vx5H1sbynhlSVGxmZmYmhzeZmZmRmprKoUOH6Nu3L15eXuprZ86c4cCBAwwdOlRddu7cOY4dO0anTp3UZVevXuXu3bvY2tri6OiITqfjyJEjpKenU7VqVXr06MGiRYv4+OOPATh16hQxMTE56pSZmcm6deswMzNjzJgxXLhwoSQOhRCiGJXJHiNRtiS39aXqvj364EijIbmtb2lX6ZGyXt0u6flD9V2qPbpQlroYrsAXZj6VKBuyn1+GLIpAjl7LRc+3zLF+fubb5aUg8/byklewI0M8RXn1559/cvnyZZYsWULLli1p27YtoO+90el09OvXTy174sQJYmNjjeYSHTlyhDt37qjlfv31VxISEvj444955ZVXAEhJSeH9998nMTGR27dvs3nzZjp06MDXX39tNC/l1Vdf5fr16yxevJgWLVowbty4x3MQhBCFIj1G4pFuTZjCIv8XuNcxAGbM4NaEKaVdJVVsPucgldYV76wZ/HK7Am9q4r0oH7L2+hnkp9cyv3PfHqWw540hkcrineeNeitN7VPOTVHetGvXjj179uDt7c3BgwfV5b179+bmzZvqbTkSExMJDQ1Fp9MZZaz9448/sLS0pHNn/ZzVH3/8kRYtWqjPAczNzUlPT6dOnTp8+umn7NixgwsXLvDrr7+qZbKm/W7RogXdunUr0fcthCg66TEqQ0ozM1qezM35rMMQAif469NnX48v7RoB+uM1dk14nsPQ8pqXUZLH2VQGv+K6yi/Kjqy9fjaWWsasCVdfK+6gpTjVsrdRz0nD9g37FKK8s7CwoGPHjnTs2FFdZsgklzVpQrVq1Xj11VeNstVGRkZy4sQJGjZsSMOGDcnMzCQsLIzhw4fj4eGhlvvmm28YOHAgo0aNwtbWlqpVq+Ls7MzevXsZMWIEGRkZmJubc/PmTY4ePYqHh4dR2nAhRNkkgVEZUZTMaJWVoVH69SttccrnjWOzpj9eNtSnQMPgDNvIz4R3Q8PzyKU7BK8/ZhQEyWdb8WQ9jwrdM5mRAbNnU2v/fg61bkfka8E42FUpsQC6lr2N0bloCJSu300xCvCEqAg0Go3JTHLZA6gbN24QHx9Pz549Adi8eTMajYY2bdqoCRvi4uI4evQo06ZNM0q+cO7cOUaMGAE8TJl9+PBhbt26xdNPP11Sb00IUYwkMCojKuId5x/X0LX8BEXw8Bi/26cpn/x+huH/OZzrHJCsDMFQbFI6Y9eE5zl3JOt7rmVvQ1wBAy949PBAUTYVZHiaSbNnw4wZoChU374db2sLePddk4FRSXy3sgdKQlQG2W/c2b59e/bu3UtqaioAmzZtwsHBwSi73dq1a3F3d6dFixbqcLmwsDDi4uLUIMvCwkJdbmFhQUBAwGN6R0KIopDASJSIrD1gjwo8inu/j9K2nmOuvTmGbWTtFcqaktvGQsu7fZoy89dTRusVx/AnwzbGrgkv1P2YROnKa3havs6J/ftBeZj9kf37jV7Oem6X5DA7ISqT7JntDMPtqlatCsBXX33F+fPnqVevnlpm/vz5dO/eHXd3d3XZ2rVradWqFU888YS67ObNm5w8eZJ69erJMDohygkJjEpRfodllUem5vaUpIJerc+tNyf7cLtFz7dU55DUd6mmZpPLvu3sjWJTgUz2nqDsdatlb8OyoT4M/8/hQt2PSZS+3IanZT1vcuXvD9u364MijUb/nJzn9jcj26rnePZtGsoa/hZCFEz2QMnc3JzGjRurzzMyMmjevDnPPvssVapUASA9PZ2tW7cyefJkzM3NURQFjUbD4cOHiYmJ4V//+tdjfQ9CiMKTwKiUZL9R5OPsVamIimsyeW4JEuq7VNMnnnjAVBCW11Ck63dTCF6nT8TwhEu1XHsSDMMCJVVy+fCoHsqs58QjA6O339b/u3+/Pih68DyvoDv7Ng1l87U/IUSBmZub89///tdoWVhYGNeuXSMwMBDAaHidDKMTonyRwKiUSIaygsnPvJvinEyeVxCSn96h7LImimhT1zHf60swVDaVSOY4c3N4912TLxVk/k++AzEhRIHpdDo0Go0a/IA+gcPOnTuNepauX78uw+iEKIckMCpl0vDNW9Z5N4YscgVpIBb0+OZ3UnthJ6o75aN3SZR9hQmOy6wH2fDUnqrh44HHlzxFiPIk+1A7wzJDr5BhGN2lS5eIiYmRbHRClDMSGInSk5FBjcXzWbPhN2pk9IaPZuYoUsvehh/H+jJoWZiaRa4k59vIpHaRXxUmuM2SDY/t26mTeh8biw7yXRCiEI4dO8bSpUu5efOm0U1ihRDlgwRG4rHTZGQw8cD3NPg+CMvIy7gCyoK/obo1jJyUo7x3LbscWeRKokG6fKgPtext8jdR/jE7H5NY/nsmxKNl7715+239ELuSlC0bXvU/D7H9h7fznzRCCKGqUaMGKSkpXLx4kSeffFKG0QlRzkhgJB67pqu/YND+tZihqMs0hvTEJgIjKPw9gfLDMGSolr2NmmAh343BEm7IGoYSBq8/JtnpKoNsvTdArvOOcvXgnPTcvouJSi14rX3e5U1kwytQ0gghhKp27dqsWbOGlJQUEhMTS7s6QogCksBIPHbOfx82CooAFI0GzYP0xCUl+5yJYplAXxwN2TwY5rKUdG+ZyIeMDGos/Nh46Gdx9+Y84l5G+fLgnLRVFILRcGtxHZg3O/fyuWTDE0IUno2NDTY28lstRHkjgZF4LLS6TJqt/JQ1oTsxc7JBhwYzFBQg0t4N65Ev4/r223Azqdj3nVsAVCwT6IujIfsIJdlbJvIv4d33cVnwEa6K8nDoZ15BcC5JDbLT6jKpsfBjOP6nfh2NJse9jPKS9btVI6M3/H1EPSfNUKhyOCxnvWbONA6EijGYF0IIIcorCYxEidNkZLBm/Tt4Rx5HAyiXIazOkzT3dCS5rS9dzdozf4gPbRLvl8j+8wqAijyBPpebcoqK5frdFC6v+x8dHgQcGkWBr7/Oewhltt7EGgmpYN4hx7bHh/2Ay4G1DwNsBwf9Y+jQnL03JoKt8WE/4L3/O/13a/4xqFtXDa50aEhu64ttlk3UWDwfFnxUYr2cQgghRHklgZEoGQ8SLLS5dhLPrVZ4PAiKADRApkbfYLMLP0Sw7ipTAEsry4c3ujV1VbsISiyDWFGHIeWzV0GUrrikdA7Vaorf5WP6oAjg4kX9I7fgIltvYpXDYeCXMzBqc+3kw20CxMXB3btgZpavYKvNtZNG3y0uX4YuXbiXofClUovACVNwzbKJKofDSryXUwghhCiPJDAqDVnmKtjf6YrWvhyn88wl+UCNxfMJfpBgQQE0WVZRgNbXT2M9/280QBDwfPJF2nd5g7ikdLS6TOq+0B8O7tOvYGh45pKYoTjqW6R1i3K1ffZseO89/d+hoTRY+RVrzBywv9NNf148CBDzPZFelJgvfJ/jxXZ1cN20Xh8QGSgKfPQR6HQwffrD8ylbb2Jy63ZM2v8dA07uwvFbS6LqdkDTdxZHajfD//Ixo+9IrgFLtmDL/sfv+dPDN+f6ZmYkt21Lmw2/6XuIsqTCT27ri+3+PaZ7OU2d40IIIUQlIYFRaZg9++FchZV/M77DTfh3S/XlcpWaefZslBkz0CgKyvbt+sbZu+9S5XCYmmBBA2pwZPjXJuNhpisN4PznQYKs1tHs7h52/riWqnejH+5DUWD2bBovWMgerLFOHw6zZ1Bj/hz2rPoat++t4eXhxo3SPOqbNRBh927Yti3X9YwSNuS1bh4Bl1aXicfn81izb1/OSfvZGr+WVyPpSCTKyuOM73CTGro/YMFHDybSw/1OB2Dky48njbMwkmmm5db4KfrAKLuUFHj/fdi7V39OAGRm6oe13b0LDg5U/eMAk8L2YwZwF4KvRxIz8TJnbyeTaWePefxd422ePw9duz6cc6TV6reZhWXkZfDwJcbHF5fwMP33T6MBnS7nfKgHFxZuTZiCa3Vr/bnn66sP6AID9eetTqfvqVUU9Rz3zFAkKBdCCFEpSMuqNOzfrw6d0SgKba6d5EaWoWdHDjRjeqch/P5/XctMcJR1gniNJ1ujNdM3klJ37cE6y3tJ3bUH63f1V6Wr7tuN4R7hGkBnbY1ZaqrJ7WuAASd2UufATeMr3wZpaZinpeEJKAs/hmOHcdm1S1/2LvpG6Zo1MHz4w6DBVLCS/Sr8rl3QvTtcvap/PnQoTJ+uJmxYsv0MUw79SP1DCyDycs5169eHevXg2DF9AxiMgiaHqpYEH/oR373f6XvP5h+DTesf1tPfX18+y3EwHMuBJ3bgdCIty0R6sIq8rA9EQeaFlIIai+cb9xZlt2sXuLjo/46Le7g8Lo6qFy8andsawCU8TH8fL1PbunRJ/8jOwUHdtgZ97+vu7zZw/Y13eVVzHdvuXWDfPqPfGPbvh+HjCd63hsbLXoL0NGjXTn9uffDBw/lG9eo97JF68H5sIX/Z7YQQQohyTgKj0uDvr+9dURQUjYY/azXhhYkvqVd8/a/8DUDcOP8yExhlnSDusn074zu8AJM6E+/TDsvduzBDP9E73qcd1uivSqd+tQrPLD0/ZjVromRrHBoogEviHdNBUTYagD/+yFn24kX9/AvQBx2BgfqGKugbfZmZ+mApuz17Hv794Kp/rW3b2D6pA3bPPk3Vg/tyr9eVK/pHdrt2QYMG1Dp9mlfNbhj1nnHxor7nafdu2LIFFi0ybkSjPx517kab3G/WAFQ8XlUOHXh0oWyfpUHWHtOsy9R/ra0hlwsHRrIERgqg1emAB0P9dH9gu38/ZGbqU+A/+I2517od2tkfMvHgevViBbt3wx9/GM83yqXuJrPbVTSlcXNdIYQQZYr86peG118n6fft3P/rKOnNmmOmKA+HwfCwt+CeqUZ8YRTDf/hZJ4gberlAHwB9dyiSIelX+N7S8+FEb3NzNnh3Y/KBtfr1NBp46SU0X39tMpDQANaZ93M0HHOjpKSo6xm/oMCcOfo5Hykpxss//DDHUCSTHvQi1dJoHs5zKozLl8HGBmtr69z3U62a/mGoJpCqtcQ6M934vZmbo2Rk6HvesgSg4vHS6Ez27eR//VyWK4AmP0ERgIcH1KkDu3ejAdpHRmC7+guCTkXhcuB7tViSrz9HoxJxf7oH/TTt+d/6kQ+DIoPs+8wlMDKV3a6iSXj3fWw/+lD/eyXZ+oQQolKSwKg0zJ1L1TD9cDrlrz/oV/1SjgZTnbvRxCyeXzxDV3KZB5RvGRlodbqH84Q0Go7UbkYggLk5n3UYQs0BzflsYwSBD4aw1Zg/h4ER28msboe5owN4esLBg/pMW7nQADqtFk3W4MXaWj/kJzIS5epVNA+CRQ2Qblsd7b0EtNk3lFsDMz9BkUHWXqSiyqvBm5kJ8fHqU32AmJ6zAZ2RgSaPTGPi8VDyOH/zLUuQawiz8nMxQLVnj77X6AEzFOpu+YmxN417GKsc/ZMjrQdS+7lRvDJtJh6JtwtVXQU45NEM2wp8zmVPxy7Z+oQQonIqhv/lRYFlm2Nkl5aYY46BBopt6Erqrj1G+0vdlY9GvyFddvfuNOjYCt8s6bYVMy0DI7ZTY/4c00PTZs/GZeHHeMbf1E8ov3RJP2xn+3bTcyYM2wWSXdyNj0XbthAQAPXro+nYEUWjr4UOsHgQFBXtGn4hZe9x02jAykr/KKKsDebs+7zy/c981mGIDPEpJcnt/PSftUF+7mxvb2/8vGZNo6cFCooMsvTsKEC165FGCU0AzFJTCd6/ls4hIwje/x1mBbkwkK1+ra+d1s+vKq5e7MfB8BsWGKj/N4+6G9KxG35fFLknmRBCVErSuioN/v4ooaEPG8A6XY7G0SOHrhRgeFxu84DyknVYiSXGjTezzAw842/qkyCYmeW8aeX+/QVu7Bl6o6pGXeOqnSse8Q+SMOzdq388oOnShbQLl7CMvGw8P6MwDAkaimNdRYG0NH3PmKk5R6DPUHb5cr42b/I9SUOt1BlldMuexS0348fre0v//htatACNBk1kJFCEcxfAzAzFxG+HUREU7M+dLvIVMAslE5cFH+mz25WX4WVZ7vmUn57yL3yfo0cTV+6G7qThwN64SqpyIYSodCQwKg1vv03S79vVSf126clGc2sUIMG6qr7RlZFhOuApwPC4XOcB5SL7sJLcGl5qr1bWm1ZmZEBGRr7nCmXdluFf27Sk3Nc1N0fx8kKTJUNcQfelKsrV79yGx+UWFGm1+nkh8fG5zuPIk7U1vP46xKYVfF1RfLLftyojQ39xYMECoyGRRjQa2LnzYfnAwMKfs1k9IigC/Xfjbv3GuPxlIllJAWnK2/CyLPd8yk/CkkwzLSdHTeJNx678OsEfV+mVFUKISqfCD6VbsmQJ9erVw9raGh8fH/btK8Jk+uJibo6i1RoFA9kzVdmnJuKyaK7+qqcJBRoe92Ae0J7F3+VrGFaOYSW5lFPQp+XOqsbi+SgP0mgr2df18oLOpm9mayinaDScrlEv9+Fx/v5Yd+n8sDzF0MB8HDIz9T1fhQmKQB+IzZ1bvHUSRWcIlFq2zL3M2rUP/549Gwxp5kuYAvzh4c2tFm2KvB0oh8PL/P2zDL3V95QLIYQQeanQgdH69esJDg5m2rRpHD16lI4dO9K7d28iHwxjKU3JbX151PXevK7Qxvu0U9cvif/0v/B9jpiQN6Fbt1xrmV7Hk1sTphgt0x48kGvAxxNP6OcZZZ9zAeisrNlXtyUnRk3miEcz0zvs0kU/ZPDtt9G8/z706IHGy6tgbyy/NKUUbnXpAj166Bvb2d9bebpaX9lEROSvnInPsEhz5LTaPBOaKGhwOnm0SIFYkl9H9tVtqf89KE/Dy95+m5iQN9lXtyWL/F/I8VslhBBCZFehA6MFCxYwcuRIRo0aRZMmTVi0aBEeHh4sXbq0tKvGrQlTWOT/Avc6Bugbwe+8o28IOzjk6wqtYf2otv4l8p9+ppmWW5Pf0AcydevmeF0B7g56IUfvU5R3azVgM2rwGd6LuTk4OubY3u2xExg6eBYnR02i1Y1/jBtyjo76+wtt26Zf33CVfts2/Y1SS0JAQO6vOTiYPCbqawVlCIYM73HbNv3fw4c/DNDK29X6yqZFi9xfGzr04d/+/sZBt5cXGju7wu/X01N/TywTNECTW5e43aItuoJs095e/1vk5QXvvcfltZsYOniW/vegPA0vMzfn1uQ3GDp4liQsEUIIkS8V9n+K9PR0wsPDefPNN42WBwYGcvDgwVKqVRYPhrcFTvDHu9aDhtGDzEkxb77L2Q2/5T0B2FSa7JISEQHe3hAZCVotae41WVKnAz1MBGOnRown9PRNXtVcx7ZLJ30j8ODBhwkiAF56Sf9eDTp31je6lv4BwJHazfC/8vfD+x9NmpT7pGnDNvfu1c/J0mrB70HmsP379cuuXNHfVNUULy9949JwI1jQBypbtuiHrn39dc51g4P1+5092/h1jQYmTNDXYe9e/XySyEi4e1ff2FQU4+QLXl764Ce3xBmG95Y1wcbNJNPvQ5SuLVugVy84fFifmbBFC7CwgI4djXtZTH2mqan679fVq/p12rUDS8uH5/HBg+Drq//7m2+MMzsOHWq8zXPn1HNMAU67eHHV8J3kKrYnjuvPx9xoNDB5svH37Xouc6eEEEKICqbCBka3b98mMzMTV1fjNAOurq5ER0ebXCctLY20tIeT2+MfTKZOSEgodD3uJd6DB/P07yXcw1qnzweXeC8BXVoyifcSSEgwHuhyadRrvJj5FB892xyv81Emt3vxVhK6tGSSE++hS0vm+MUoEu+ZrmdBymYtb1S348fV10/diGfh8j9wjbwFYLTt8zfjWN76WfzGtKdpzQcBX0iI/t/kZP2/kyZhmZGBeVgYGb6+pE+ZQmKMfp8Xrt9iZas+DGrpivvJo/rXg4Igr88gOFj/yM6w34wMLOfP1++vnX7IofmhQ+q+gYevG5alp+u3GRSE5SefYLFuHQD3n39eX5/k5IevZ1130iR9kGOqPlnrYdiPufnD4/Ko95acrJ43j/oMxaPdSbmtfjf/PHcdR5vcE1uY/E5k98svppdn/3yzfaaA0fcrB8N5DDBxovE5NHHiw3MxOBhSU7H597/RnjjBvQaNGdZ2PC9n/U46WqmvZzZrRqafH+aHDumz22m1ZPj55fi+ledzzvC5AQX6jczzc84Hw/8ZSl7ZCoUQQpQ5GqWC/nLfuHGDWrVqcfDgQXx9HyYI+PDDD1mzZg3//PNPjnVmzJjB+++//zirKYQQooK6evUqtWvXLu1qCCGEyKcK22Pk7OyMVqvN0TsUExOToxfJ4K233iIky9VZnU7HnTt3cHJyQlOEyfgJCQl4eHhw9epVqlevXujtVBZyvApOjlnByTErGDle+acoCvfu3aNmtpv5CiGEKNsqbGBkaWmJj48PoaGh9O/fX10eGhrKs88+a3IdKysrrKysjJbZm8igVljVq1eXBkUByPEqODlmBSfHrGDkeOWPXVGSagghhCgVFTYwAggJCWHo0KG0bt0aX19fVqxYQWRkJGPHji3tqgkhhBBCCCHKkAodGA0ePJjY2FhmzpxJVFQU3t7ebNmyBU9Pz9KumhBCCCGEEKIMqdCBEcC4ceMYN25cqdbBysqK9957L8cwPWGaHK+Ck2NWcHLMCkaOlxBCiIquwmalE0IIIYQQQoj8MivtCgghhBBCCCFEaZPASAghhBBCCFHpSWAkhBBCCCGEqPQkMBJCCCGEEEJUehIYlbAlS5ZQr149rK2t8fHxYd++faVdpcdizpw5tGnTBltbW1xcXOjXrx9nzpwxKqMoCjNmzKBmzZrY2NgQEBDAyZMnjcqkpaUxYcIEnJ2dqVq1Kn379uXatWtGZeLi4hg6dCh2dnbY2dkxdOhQ7t69W9JvsUTNmTMHjUZDcHCwukyOV07Xr1/npZdewsnJiSpVqtCyZUvCw8PV1+WYGcvIyGD69OnUq1cPGxsbvLy8mDlzJjqdTi0jx0wIIUSlpYgSs27dOsXCwkL58ssvlVOnTimTJk1Sqlatqly5cqW0q1bievbsqaxatUo5ceKEcuzYMeXpp59W6tSpoyQmJqplPvroI8XW1lbZsGGDEhERoQwePFhxd3dXEhIS1DJjx45VatWqpYSGhip//fWX0qVLF6VFixZKRkaGWqZXr16Kt7e3cvDgQeXgwYOKt7e30qdPn8f6fovT4cOHlbp16ypPPvmkMmnSJHW5HC9jd+7cUTw9PZURI0Yohw4dUi5duqRs375dOX/+vFpGjpmxWbNmKU5OTsqvv/6qXLp0Sfnxxx+VatWqKYsWLVLLyDETQghRWUlgVILatm2rjB071mhZ48aNlTfffLOUalR6YmJiFEDZs2ePoiiKotPpFDc3N+Wjjz5Sy6Smpip2dnbKsmXLFEVRlLt37yoWFhbKunXr1DLXr19XzMzMlK1btyqKoiinTp1SAOWPP/5Qy4SFhSmA8s8//zyOt1as7t27pzRo0EAJDQ1VOnfurAZGcrxyeuONNxR/f/9cX5djltPTTz+tvPLKK0bLBgwYoLz00kuKosgxE0IIUbnJULoSkp6eTnh4OIGBgUbLAwMDOXjwYCnVqvTEx8cD4OjoCMClS5eIjo42Oj5WVlZ07txZPT7h4eHcv3/fqEzNmjXx9vZWy4SFhWFnZ0e7du3UMu3bt8fOzq5cHufx48fz9NNP0717d6Plcrxy+uWXX2jdujWDBg3CxcWFp556ii+//FJ9XY5ZTv7+/uzYsYOzZ88C8Pfff7N//37+9a9/AXLMhBBCVG7mpV2Biur27dtkZmbi6upqtNzV1ZXo6OhSqlXpUBSFkJAQ/P398fb2BlCPganjc+XKFbWMpaUlDg4OOcoY1o+OjsbFxSXHPl1cXMrdcV63bh1//fUXR44cyfGaHK+cLl68yNKlSwkJCeHtt9/m8OHDTJw4ESsrK4YNGybHzIQ33niD+Ph4GjdujFarJTMzkw8//JAhQ4YAcp4JIYSo3CQwKmEajcbouaIoOZZVdEFBQRw/fpz9+/fneK0wxyd7GVPly9txvnr1KpMmTWLbtm1YW1vnWk6O10M6nY7WrVsze/ZsAJ566ilOnjzJ0qVLGTZsmFpOjtlD69ev59tvv2Xt2rU0a9aMY8eOERwcTM2aNRk+fLhaTo6ZEEKIykiG0pUQZ2dntFptjqujMTExOa7GVmQTJkzgl19+YdeuXdSuXVtd7ubmBpDn8XFzcyM9PZ24uLg8y9y8eTPHfm/dulWujnN4eDgxMTH4+Phgbm6Oubk5e/bs4bPPPsPc3Fx9L3K8HnJ3d6dp06ZGy5o0aUJkZCQg55gp//d//8ebb77J888/T/PmzRk6dCiTJ09mzpw5gBwzIYQQlZsERiXE0tISHx8fQkNDjZaHhobi5+dXSrV6fBRFISgoiI0bN7Jz507q1atn9Hq9evVwc3MzOj7p6ens2bNHPT4+Pj5YWFgYlYmKiuLEiRNqGV9fX+Lj4zl8+LBa5tChQ8THx5er49ytWzciIiI4duyY+mjdujUvvvgix44dw8vLS45XNh06dMiRAv7s2bN4enoCco6ZkpycjJmZ8c++VqtV03XLMRNCCFGplULCh0rDkK77q6++Uk6dOqUEBwcrVatWVS5fvlzaVStxr732mmJnZ6fs3r1biYqKUh/JyclqmY8++kixs7NTNm7cqERERChDhgwxmRa4du3ayvbt25W//vpL6dq1q8m0wE8++aQSFhamhIWFKc2bN68QaYGzZqVTFDle2R0+fFgxNzdXPvzwQ+XcuXPKd999p1SpUkX59ttv1TJyzIwNHz5cqVWrlpque+PGjYqzs7Py+uuvq2XkmAkhhKisJDAqYV988YXi6empWFpaKq1atVLTVVd0gMnHqlWr1DI6nU557733FDc3N8XKykrp1KmTEhERYbSdlJQUJSgoSHF0dFRsbGyUPn36KJGRkUZlYmNjlRdffFGxtbVVbG1tlRdffFGJi4t7DO+yZGUPjOR45bR582bF29tbsbKyUho3bqysWLHC6HU5ZsYSEhKUSZMmKXXq1FGsra0VLy8vZdq0aUpaWppaRo6ZEEKIykqjKIpSmj1WQgghhBBCCFHaZI6REEIIIYQQotKTwEgIIYQQQghR6UlgJIQQQgghhKj0JDASQgghhBBCVHoSGAkhhBBCCCEqPQmMhBBCCCGEEJWeBEZCCCGEEEKISk8CIyGEEEIIIUSlJ4GREEIIIYQQotKTwEiIYhQQEEBwcHBpV6PMCwgIQKPRoNFoOHbsWGlXp8BGjBih1v/nn38u7eoIIYQQohhIYCREIZkKgjZu3MgHH3xQOhV6oLwEZ6+++ipRUVF4e3ury5YsWUK9evWwtrbGx8eHffv2PXI7e/fu5ZlnnqFmzZqPLVD59NNPiYqKKvH9CCGEEOLxkcBIiGLk6OiIra1taVejXKhSpQpubm6Ym5sDsH79eoKDg5k2bRpHjx6lY8eO9O7dm8jIyDy3k5SURIsWLfj8888fR7UBsLOzw83N7bHtTwghhBAlTwIjIQphxIgR7Nmzh08//VQdUnX58uUcvTUBAQFMmDCB4OBgHBwccHV1ZcWKFSQlJfHyyy9ja2vLE088wW+//aauoygKc+fOxcvLCxsbG1q0aMFPP/1ktP+ffvqJ5s2bY2Njg5OTE927dycpKSnXegFs3boVf39/7O3tcXJyok+fPly4cKFIdTWsFxQURFBQkLrt6dOnoyhKgY7pggULGDlyJKNGjaJJkyYsWrQIDw8Pli5dmud6vXv3ZtasWQwYMCDf+6pbty6LFi0yWtayZUtmzJihPs/tGAshhBCiYpLASIhC+PTTT/H19VWHg0VFReHh4WGy7Ndff42zszOHDx9mwoQJvPbaawwaNAg/Pz/++usvevbsydChQ0lOTgZg+vTprFq1iqVLl3Ly5EkmT57MSy+9xJ49ewCIiopiyJAhvPLKK5w+fZrdu3czYMAAFEXJs15JSUmEhIRw5MgRduzYgZmZGf3790en0xW6rlnXMzc359ChQ3z22WcsXLiQlStX5vt4pqenEx4eTmBgoNHywMBADh48mO/tFJe8jrEQQgghKibz0q6AEOWRnZ0dlpaW6nCwvLRo0YLp06cD8NZbb/HRRx/h7OzMq6++CsC7777L0qVLOX78OM2bN2fBggXs3LkTX19fALy8vNi/fz/Lly+nc+fOREVFkZGRwYABA/D09ASgefPm6v5yq9fAgQONnn/11Ve4uLhw6tQpdZ5PQeravn17dVseHh4sXLgQjUZDo0aNiIiIYOHChep6j3L79m0yMzNxdXU1Wu7q6kp0dHS+tlGcHnWMhRBCCFHxSI+RECXsySefVP/WarU4OTkZNbINwUBMTAynTp0iNTWVHj16UK1aNfXxzTffqMPeWrRoQbdu3WjevDmDBg3iyy+/JC4u7pH1uHDhAi+88AJeXl5Ur16devXqARjN4SlIXbNq3749Go1Gfe7r68u5c+fIzMx89AHKIus2QD+sMPuyx6Gwx1gIIYQQ5ZcERkKUMAsLC6PnGo3GaJmh4a/T6dRhbf/73/84duyY+jh16pQ6z0ir1RIaGspvv/1G06ZNWbx4MY0aNeLSpUt51uOZZ54hNjaWL7/8kkOHDnHo0CFAP4ytMHUtTs7Ozmi12hy9QzExMTl6kUpK1iCusMdYCCGEEOWXBEZCFJKlpWWBe0QepWnTplhZWREZGUn9+vWNHlnnMGk0Gjp06MD777/P0aNHsbS0ZNOmTbnWKzY2ltOnTzN9+nS6detGkyZNirUH5I8//sjxvEGDBmi12nytb2lpiY+PD6GhoUbLQ0ND8fPzK7Z6ZpU1CLt//z5Xr141ej2vYyyEEEKIikfmGAlRSHXr1uXQoUNcvnyZatWq4ejoWORt2traMnXqVCZPnoxOp8Pf35+EhAQOHjxItWrVGD58OIcOHWLHjh0EBgbi4uLCoUOHuHXrFk2aNMm1Xg4ODjg5ObFixQrc3d2JjIzkzTffLHJ9Da5evUpISAhjxozhr7/+YvHixcyfP79A2wgJCWHo0KG0bt0aX19fVqxYQWRkJGPHjlXLfP7552zatIkdO3aoyxITEzl//rz6/NKlSxw7dgxHR0fq1KmT6/5WrVpF9+7d8fT05NNPPyU+Pp4LFy5w8+ZNLl++nOcxFkIIIUTFI4GREIU0depUhg8fTtOmTUlJSSm2YVYffPABLi4uzJkzh4sXL2Jvb0+rVq14++23AahevTp79+5l0aJFJCQk4Onpyfz58+ndu3eu9apbty7r1q1j4sSJeHt706hRIz777DMCAgKKpc7Dhg0jJSWFtm3botVqmTBhAqNHjy7QNgYPHkxsbCwzZ85Ub/y6ZcsWNfkB6JM0ZE0xDvDnn3/SpUsX9XlISAgAw4cPZ/Xq1bnu75lnnmHixIlcvHiRAQMG8MEHHzBnzhx69epFq1at8jzGQgghhKh4NIrknxVCFEFAQAAtW7bMcV+g4l6nONWtW5fg4GCje04VhkajYdOmTfTr169Y6iWEEEKI0iNzjIQQpWLJkiVUq1aNiIiI0q5KgY0dO5Zq1aqVdjWEEEIIUYxkKJ0Q4rH77rvvSElJAchzHlBZNXPmTKZOnQqAu7t7KddGCCGEEMVBhtIJIYQQQgghKj0ZSieEEEIIIYSo9CQwEkIIIYQQQlR6EhgJIYQQQgghKj0JjIQQQgghhBCVngRGQgghhBBCiEpPAiMhhBBCCCFEpSeBkRBCCCGEEKLSk8BICCGEEEIIUelJYCSEEEIIIYSo9CQwEkIIIYQQQlR6EhgJIYQQQgghKr3/B06StuVzaxmOAAAAAElFTkSuQmCC", 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" ] }, - "metadata": { - "needs_background": "light" - }, + "metadata": {}, "output_type": "display_data" } ], @@ -168,7 +166,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.7" + "version": "3.11.5" } }, "nbformat": 4, diff --git a/event_display/proto_nd_flow/protondflow_evd.py b/event_display/proto_nd_flow/protondflow_evd.py index b4346a1d..e43cb74b 100644 --- a/event_display/proto_nd_flow/protondflow_evd.py +++ b/event_display/proto_nd_flow/protondflow_evd.py @@ -7,6 +7,7 @@ import yaml import matplotlib import matplotlib.pyplot as plt +from mpl_toolkits.mplot3d import Axes3D import mpl_toolkits.mplot3d.art3d as art3d from matplotlib.colors import ListedColormap from mpl_toolkits.axes_grid1.inset_locator import InsetPosition @@ -24,6 +25,10 @@ class ProtoNDFlowEventDisplay: - nhits (int): hit threshold for events to be made available in interactive display - hits_dset (str): dataset of hits within the file that you want to display options are 'raw_hits', 'calib_prompt_hits', and 'calib_final_hits' + - tracklets (bool): bool denoting whether or not file contains 'combined/tracklets' dataset; + default is False. Right now, tracklets plotting is only set up to plot + with hits dataset from which tracklets were made (either 'calib_prompt_hits' + OR 'calib_final_hits') In order to run the display, set up a Jupyter Notebook, import everything in this file, and execute the run() method, e.g.: @@ -36,12 +41,13 @@ class ProtoNDFlowEventDisplay: g = '/path/to/geometry/file/name_of_geometry_file' hd = 'hits_dataset_you_want_to_display' - evd = ProtoNDFlowEventDisplay(filedir=d, filename=f, geometry_file=g,nhits=1, hits_dset=hd) + evd = ProtoNDFlowEventDisplay(filedir=d, filename=f, geometry_file=g,nhits=1, hits_dset=hd, tracklets=False) test_evd.run() ''' - def __init__(self, filedir, filename, geometry_file=None, nhits=1, hits_dset='calib_final_hits'): + def __init__(self, filedir, filename, geometry_file=None, nhits=1, hits_dset='calib_final_hits', tracklets=False): f = h5py.File(filedir+filename, 'r') self.filename = filename + self.tracklets = tracklets # Set name of hits dataset to be used self.hits_dset = hits_dset @@ -49,14 +55,14 @@ def __init__(self, filedir, filename, geometry_file=None, nhits=1, hits_dset='ca # Load datasets events = f['charge/events/data'] self.events = events[events['nhit'] > nhits] - try: + if self.tracklets: self.tracks = f['combined/tracklets/data'] self.tracks_ref = f['charge/events/ref/combined/tracklets/ref'] self.tracks_region = f['charge/events/ref/combined/tracklets/ref_region'] - self.hits_trk_ref = f['combined/tracklets/ref/charge/hits/ref'] - self.hits_trk_region = f['combined/tracklets/ref/charge/hits/ref_region'] - self.hits_drift = f['combined/hit_drift/data'] - except KeyError: + self.hits_trk_ref = f['combined/tracklets/ref/charge/'+self.hits_dset+'/ref'] + self.hits_trk_region = f['combined/tracklets/ref/charge/'+self.hits_dset+'/ref_region'] + #self.hits_drift = f['combined/hit_drift/data'] + else: print("No tracklets found") self.hits = f['charge/'+self.hits_dset+'/data'] self.hits_ref = f['charge/events/ref/charge/'+self.hits_dset+'/ref'] @@ -260,6 +266,7 @@ def get_event_start_time(self, event): ext_trig_ref = self.ext_trigs_ref[self.ext_trigs_region[ev_id,'start']:self.ext_trigs_region[ev_id,'stop']] ext_trig_ref = np.sort(ext_trig_ref[ext_trig_ref[:,0] == ev_id, 1]) + print("EST from earliest light system trigger in event.") return np.min(self.ext_trigs[ext_trig_ref]['ts']) # Second Choice: # Try to determine the start time from a 'bump' in charge. @@ -296,9 +303,11 @@ def get_event_start_time(self, event): start_time = time_bins[t0_bin_index] # Check if qsum exceed threshold if start_time < max_ts: + print("EST from `bump in charge'.") return start_time # Fallback is to use the first hit return event['ts_start'] + print("EST from first hit start time.") # Set up axes def set_axes(self): @@ -390,9 +399,9 @@ def set_axes(self): self.ax_zyx.set_box_aspect((2, 2, 4)) self.ax_zyx.xaxis.set_major_locator(plt.MaxNLocator(3)) self.ax_zyx.yaxis.set_major_locator(plt.MaxNLocator(3)) - self.ax_zyx.w_xaxis.set_pane_color((1.0, 1.0, 1.0, 1.0)) - self.ax_zyx.w_yaxis.set_pane_color((1.0, 1.0, 1.0, 1.0)) - self.ax_zyx.w_zaxis.set_pane_color((1.0, 1.0, 1.0, 1.0)) + self.ax_zyx.xaxis.set_pane_color((1.0, 1.0, 1.0, 1.0)) + self.ax_zyx.yaxis.set_pane_color((1.0, 1.0, 1.0, 1.0)) + self.ax_zyx.zaxis.set_pane_color((1.0, 1.0, 1.0, 1.0)) self.ax_zyx.zaxis.labelpad = 20 def clear_axes(self): @@ -434,8 +443,8 @@ def display_event(self, ev_id): vmin=min(self.hits[hit_ref][self.charge]), vmax=max(self.hits[hit_ref][self.charge])) mcharge = plt.cm.ScalarMappable(norm=norm, cmap=cmap) - hits_anode1 = hits[hits[self.x_vals]*self.convert_to_mm <= 0] - hits_anode2 = hits[hits[self.x_vals]*self.convert_to_mm > 0] + hits_anode1 = hits[hits['io_group']== 1] + hits_anode2 = hits[hits['io_group']== 2] if self.hits_dset == 'raw_hits': q_anode1 = self.charge_from_ADC(hits_anode1[self.charge], self.vref_mv, self.vcm_mv, self.ped_mv) @@ -472,100 +481,42 @@ def display_event(self, ev_id): self.ax_time_1.axvline(x=trig, c='g') self.ax_time_2.axvline(x=trig, c='g') - unassoc_hit_mask = np.ones(event['nhit']).astype(bool) + unassoc_hit_mask = np.ones(len(hits['id'])).astype(bool) - if 'ntracks' in event.dtype.name and event['ntracks']: - track_ref = event['track_ref'] + ev_id = event['id'] + + if self.tracklets and (self.hits_dset == 'calib_final_hits' or self.hits_dset == 'calib_prompt_hits'): + track_ref = self.tracks_ref[self.tracks_region[ev_id,'start']:self.tracks_region[ev_id,'stop']] + track_ref = np.sort(track_ref[track_ref[:,0] == ev_id, 1]) tracks = self.tracks[track_ref] track_start = tracks['start'] track_end = tracks['end'] - for i, track in enumerate(tracks): - - hit_trk_ref = track['hit_ref'] - hits_trk = self.hits[hit_trk_ref] - - # Difference between the z coordinate using the event ts_start (used in the track fitter) - # and the start time found by get_event_start_time - z_correction = (self._get_z_coordinate(hits_trk['iogroup'][0], hits_trk['iochannel'][0], event_start_time) - - self._get_z_coordinate(hits_trk['iogroup'][0], hits_trk['iochannel'][0], event['ts_start'])) - - self.ax_zy.plot((track_start[i][0], track_end[i][0]), - (track_start[i][1], track_end[i][1]), - c='C{}'.format(i+1), alpha=0.75, lw=1) - - self.ax_xy.plot((track_start[i][2], track_end[i][2]), - (track_start[i][1], track_end[i][1]), - c='C{}'.format(i+1), alpha=0.75, lw=1) - - hits_anode1 = hits_trk[hits_trk[self.x_vals]*self.convert_to_mm <= 0] - hits_anode2 = hits_trk[hits_trk[self.x_vals]*self.convert_to_mm >0] - - if self.hits_dset == 'raw_hits': - self.ax_zy.scatter(hits_trk['px'], hits_trk['py'], lw=0.2, ec='C{}'.format( - i+1), c=cmap(norm(self.charge_from_ADC(hits_trk[self.charge]), self.vref_mv, self.vcm_mv, self.ped_mv)), s=5, alpha=0.75) - - hit_xvals = [self._get_z_coordinate(io_group, io_channel, time) for io_group, io_channel, time in zip( - hits_trk['iogroup'], hits_trk['iochannel'], hits_trk['ts']-track['t0'])] - - self.ax_xy.scatter(hit_xvals, hits_trk['py'], lw=0.2, ec='C{}'.format( - i+1), c=cmap(norm(self.charge_from_ADC(hits_trk[self.charge], self.vref_mv, self.vcm_mv, self.ped_mv))), s=5, alpha=0.75) - self.ax_zyx.scatter(hits_trk['px'], hit_xvals, hits_trk['py'], lw=0.2, ec='C{}'.format( - i+1), c=cmap(norm(self.charge_from_ADC(hits_trk[self.charge], self.vref_mv, self.vcm_mv, self.ped_mv))), s=5, alpha=0.75) - else: - self.ax_zy.scatter(hits_trk['px'], hits_trk['py'], lw=0.2, ec='C{}'.format( - i+1), c=cmap(norm(hits_trk[self.charge])), s=5, alpha=0.75) - - hit_xvals = [self._get_z_coordinate(io_group, io_channel, time) for io_group, io_channel, time in zip( - hits_trk['iogroup'], hits_trk['iochannel'], hits_trk['ts']-track['t0'])] - - self.ax_xy.scatter(hit_xvals, hits_trk['py'], lw=0.2, ec='C{}'.format( - i+1), c=cmap(norm(hits_trk[self.charge])), s=5, alpha=0.75) - self.ax_zyx.scatter(hits_trk['px'], hit_xvals, hits_trk['py'], lw=0.2, ec='C{}'.format( - i+1), c=cmap(norm(hits_trk[self.charge])), s=5, alpha=0.75) - - self.ax_zyx.plot((track_start[i][0], track_end[i][0]), - (track_start[i][2], track_end[i][2]), - (track_start[i][1], track_end[i][1]), - c='C{}'.format(i+1), alpha=0.5, lw=4) - - unassoc_hit_mask[np.in1d(hits['hid'], hits_trk['hid'])] = 0 - - - ev_id = event['id'] - - ''' For now, all tracklet plotting is just commented out''' - ''' - track_ref = self.tracks_ref[self.tracks_region[ev_id,'start']:self.tracks_region[ev_id,'stop']] - track_ref = np.sort(track_ref[track_ref[:,0] == ev_id, 1]) - tracks = self.tracks[track_ref] - track_start = tracks['start'] - track_end = tracks['end'] - for itrk, (ts, te) in enumerate(zip(track_start, track_end)): - hit_ref = self.hits_trk_ref[self.hits_trk_region[tracks[itrk]['id'],'start']:self.hits_trk_region[tracks[itrk]['id'],'stop']] - hit_ref = np.sort(hit_ref[hit_ref[:,0] == tracks[itrk]['id'], 1]) - hits_trk = self.hits[hit_ref] - hits_drift_trk = self.hits_drift[hit_ref] - self.ax_zyx.scatter(hits_trk['px'], hits_drift_trk[self.z_vals]*self.convert_to_mm, hits_trk['py'], lw=0.2, ec='C{}'.format( + for itrk, (ts, te) in enumerate(zip(track_start, track_end)): + hit_ref = self.hits_trk_ref[self.hits_trk_region[tracks[itrk]['id'],'start']:self.hits_trk_region[tracks[itrk]['id'],'stop']] + hit_ref = np.sort(hit_ref[hit_ref[:,0] == tracks[itrk]['id'], 1]) + hits_trk = self.hits[hit_ref] + self.ax_zyx.scatter(hits_trk[self.z_vals]*self.convert_to_mm, hits_trk[self.x_vals]*self.convert_to_mm, hits_trk[self.y_vals]*self.convert_to_mm+self.y_offset, lw=0.2, ec='C{}'.format( itrk+1), c=cmap(norm(hits_trk[self.charge])), s=5, alpha=0.75) - self.ax_xy.scatter(hits_drift_trk[self.z_vals]*self.convert_to_mm, hits_trk['py'], lw=0.2, ec='C{}'.format( + self.ax_xy.scatter(hits_trk[self.x_vals]*self.convert_to_mm, hits_trk[self.y_vals]*self.convert_to_mm+self.y_offset, lw=0.2, ec='C{}'.format( itrk+1), c=cmap(norm(hits_trk[self.charge])), s=5, alpha=0.75) - self.ax_zy.scatter(hits_trk['px'], hits_trk['py'], lw=0.2, ec='C{}'.format( + self.ax_zy.scatter(hits_trk[self.z_vals]*self.convert_to_mm, hits_trk[self.y_vals]*self.convert_to_mm+self.y_offset, lw=0.2, ec='C{}'.format( itrk+1), c=cmap(norm(hits_trk[self.charge])), s=5, alpha=0.75) - self.ax_zy.plot((ts[0], te[0]), - (ts[1], te[1]), + self.ax_zy.plot((ts[2]*self.convert_to_mm, te[2]*self.convert_to_mm), + (ts[1]*self.convert_to_mm+self.y_offset, te[1]*self.convert_to_mm+self.y_offset), c='C{}'.format(itrk+1), alpha=0.75, lw=1) - self.ax_xy.plot((ts[2], te[2]), - (ts[1], te[1]), + self.ax_xy.plot((ts[0]*self.convert_to_mm, te[0]*self.convert_to_mm), + (ts[1]*self.convert_to_mm+self.y_offset, te[1]*self.convert_to_mm+self.y_offset), c='C{}'.format(itrk+1), alpha=0.75, lw=1) - self.ax_zyx.plot((ts[0], te[0]), - (ts[2], te[2]), - (ts[1], te[1]), + self.ax_zyx.plot((ts[2]*self.convert_to_mm, te[2]*self.convert_to_mm), + (ts[0]*self.convert_to_mm, te[0]*self.convert_to_mm), + (ts[1]*self.convert_to_mm+self.y_offset, te[1]*self.convert_to_mm+self.y_offset), c='C{}'.format(itrk+1), alpha=0.5, lw=4) - unassoc_hit_mask[np.in1d(hits['id'], hits_trk['id'])] = 0 + unassoc_hit_mask[np.in1d(hits['id'], hits_trk['id'])] = 0 + if np.any(unassoc_hit_mask): - ''' - - unassoc_hits = hits#[unassoc_hit_mask] + unassoc_hits = hits[unassoc_hit_mask] + else: + unassoc_hits = hits BG = np.asarray([1., 1., 1., ]) my_cmap = cmap(np.arange(cmap.N)) alphas = np.linspace(0, 1, cmap.N) @@ -588,10 +539,16 @@ def display_event(self, ev_id): self.ax_xy.scatter(hit_xvals, unassoc_hits[self.y_vals]*self.convert_to_mm+self.y_offset, lw=0, ec='C0', c=cmap( norm(self.charge_from_ADC(unassoc_hits[self.charge], self.vref_mv, self.vcm_mv, self.ped_mv))), s=5, alpha=1) else: + if self.tracklets: + a = 0.75 + else: + a = 1.0 hit_xvals = unassoc_hits[self.x_vals]*self.convert_to_mm self.ax_zyx.scatter(unassoc_hits[self.z_vals]*self.convert_to_mm, hit_xvals, unassoc_hits[self.y_vals]*self.convert_to_mm+self.y_offset, lw=0, ec='C0', c=cmap( - norm(unassoc_hits[self.charge])), s=5, alpha=1) + norm(unassoc_hits[self.charge])), s=5, alpha=a) self.ax_zy.scatter(unassoc_hits[self.z_vals]*self.convert_to_mm, unassoc_hits[self.y_vals]*self.convert_to_mm+self.y_offset, lw=0, ec='C0', c=cmap( - norm(unassoc_hits[self.charge])), s=5, alpha=1) + norm(unassoc_hits[self.charge])), s=5, alpha=a) self.ax_xy.scatter(hit_xvals, unassoc_hits[self.y_vals]*self.convert_to_mm+self.y_offset, lw=0, ec='C0', c=cmap( - norm(unassoc_hits[self.charge])), s=5, alpha=1) \ No newline at end of file + norm(unassoc_hits[self.charge])), s=5, alpha=a) + #plt.gcf() + #plt.show() \ No newline at end of file diff --git a/event_display/proto_nd_flow/protondflow_evd_example.ipynb b/event_display/proto_nd_flow/protondflow_evd_example.ipynb index 35d9ef75..88067ec2 100644 --- a/event_display/proto_nd_flow/protondflow_evd_example.ipynb +++ b/event_display/proto_nd_flow/protondflow_evd_example.ipynb @@ -18,7 +18,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 23, "id": "ab903276-e787-4142-bbb1-4becf42f76c1", "metadata": { "tags": [] @@ -27,15 +27,18 @@ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 1, + "execution_count": 23, "metadata": {}, "output_type": "execute_result" } ], "source": [ + "import ipympl\n", + "import matplotlib\n", + "%matplotlib widget\n", "from protondflow_evd import *\n", "plt.ion()" ] @@ -51,12 +54,13 @@ "- `filename` (str): name of file; must be flow file run through `proto_nd_flow`\n", "- `geometry_file` (str): full path and name of geometry file describing module to be displayed\n", "- `nhits` (int): hit threshold for events to be made available in interactive display (default=1)\n", - "- `hits_dset` (str): dataset of hits within the file that you want to display. Options are 'raw_hits', 'calib_prompt_hits', and 'calib_final_hits' (default)" + "- `hits_dset` (str): dataset of hits within the file that you want to display. Options are `raw_hits`, `calib_prompt_hits`, and `calib_final_hits` (default)\n", + "- `tracklets` (bool): boolean denoting whether or not the file contains the `combined/tracklets` dataset. Default is False. Only will work if `hits_dset` matches `hits_dset` used to build tracklets." ] }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 32, "id": "d3cc7962-6f70-446b-a4d1-f5f1da4ad23a", "metadata": { "tags": [] @@ -65,8 +69,9 @@ "source": [ "# This set of inputs allows you to look at a Module1 charge-only file\n", "# This file originates from the same raw data file as the input file in the Module0FlowEventDisplay example\n", - "directory = '/global/cfs/cdirs/dune/users/sfogarty/muon_samples/'\n", - "file = 'packet_2022_02_09_17_23_09_CET.module1_flow.h5'\n", + "directory = '/global/cfs/cdirs/dune/users/ehinkle/nd_prototypes_ana/flow_tests/Module1_Data/TRACKLET_OUTPUT/NOMINAL_E_FIELD/'\n", + "#directory = '/global/cfs/cdirs/dune/users/ehinkle/nd_prototypes_ana/flow_tests/Module1_Data/OUTPUT/'\n", + "file = 'packet_2022_02_11_07_40_23_CET.EDH_FLOW.proto_nd_flow.calib_final_hits.TRACKLETS_HDBSCAN_1_15_9_3421.h5'\n", "geometry = '/global/cfs/cdirs/dune/www/data/Module1/TPC12/module1_layout-2.3.16.yaml'" ] }, @@ -83,6 +88,14 @@ " - `n`+`Enter` : proceed to the `n`th available event (may not correspond with event ID number if `nhits` > 1)" ] }, + { + "cell_type": "code", + "execution_count": null, + "id": "af6a3dc8-97df-4000-81ef-d5fc38a55c77", + "metadata": {}, + "outputs": [], + "source": [] + }, { "cell_type": "code", "execution_count": null, @@ -91,11 +104,18 @@ "tags": [] }, "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "EST from earliest light system trigger in event.\n" + ] + }, { "data": { - "image/png": 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\n", 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", "text/plain": [ - "
" + "
" ] }, "metadata": {}, @@ -103,7 +123,7 @@ } ], "source": [ - "evd = ProtoNDFlowEventDisplay(filedir=directory, filename=file, geometry_file=geometry, nhits=1, hits_dset='calib_final_hits')\n", + "evd = ProtoNDFlowEventDisplay(filedir=directory, filename=file, geometry_file=geometry, nhits=1, hits_dset='calib_final_hits', tracklets=True)\n", "evd.run()" ] }, @@ -132,7 +152,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.7" + "version": "3.11.7" } }, "nbformat": 4, diff --git a/scripts/proto_nd_scripts/analysis/hip_selection/data_mc_all_metrics.py b/scripts/proto_nd_scripts/analysis/hip_selection/data_mc_all_metrics.py new file mode 100644 index 00000000..7b937023 --- /dev/null +++ b/scripts/proto_nd_scripts/analysis/hip_selection/data_mc_all_metrics.py @@ -0,0 +1,260 @@ +################################################################################ +## ## +## CONTAINS: Script to plot contents in output file from proton selection ## +## being run over Bern Module Data. ## +## ## +################################################################################ + +import h5py, glob, argparse +import numpy as np +import matplotlib.pyplot as plt +import sys +import file_parsing +import json +from plot_all_metrics import plot_event_hit_summ_metrics, plot_channel_metrics, plot_track_metrics + +def main(file_dir, is_sim, sel_event_dict): + + is_sim = bool(is_sim == 'True') + # initialize plotting datasets + event_hit_summ_dict = dict() + channel_metric_dict = dict() + track_summ_dict = dict() + print("Is MC?:", is_sim) + if is_sim: + sample_type = 'MC' + else: + sample_type = 'data' + + count = 0 + + for file in glob.glob(file_dir+'/*.h5'): # Loop over files files + + if count > 10: break + count+=1 + f = h5py.File(file,'r') + + if 'calib_final_hits' in file: + hits_dset = 'calib_final_hits' + elif 'calib_prompt_hits' in file: + hits_dset = 'calib_prompt_hits' + else: + print("No hits dataset detected.") + + # Prepare datasets for plotting + events = f['charge/events/data'] + tracks = f['combined/tracklets/data'] + tracks_ref = f['charge/events/ref/combined/tracklets/ref'] + tracks_region = f['charge/events/ref/combined/tracklets/ref_region'] + hits_trk_ref = f['combined/tracklets/ref/charge/'+hits_dset+'/ref'] + hits_trk_region = f['combined/tracklets/ref/charge/'+hits_dset+'/ref_region'] + #hits_drift = f['combined/hit_drift/data'] + hits = f['charge/'+hits_dset+'/data'] + hits_ref = f['charge/events/ref/charge/'+hits_dset+'/ref'] + hits_region = f['charge/events/ref/charge/'+hits_dset+'/ref_region'] + #if not is_sim: + # charge_hits = hits#f['combined/q_calib_el/data'] + # charge_hits_ref = hits_ref#f['charge/events/ref/combined/q_calib_el/ref'] + # charge_hits_region = hits_region#f['charge/events/ref/combined/q_calib_el/ref_region'] + #else: + # charge_hits = hits + # charge_hits_ref = hits_ref + # charge_hits_region = hits_region + ext_trigs = f['charge/ext_trigs/data'] + ext_trigs_ref = f['charge/events/ref/charge/ext_trigs/ref'] + ext_trigs_region = f['charge/events/ref/charge/ext_trigs/ref_region'] + print("Available datasets:",f.keys(),'\n') + #sel_reco = f['high_purity_sel']['hips']['sel_reco']['data'] + #if is_sim: + # sel_truth = f['high_purity_sel']['hips']['sel_truth']['data'] + # mc_truth_events = f['mc_truth/events/data'] + + print("File:", file) + #sel_mask = (sel_reco['sel'] == True) + #sel_event_ids = sel_reco[sel_mask]['event_id'] + #print("Selected Event Ids:", sel_event_ids) + #if is_sim==True: + #sel_truth_mask = (sel_truth['sel'] == True) + #sel_truth_protons = sel_truth[sel_mask]['hips'] + #sel_truth_sel = sel_truth[sel_truth_mask]['event_id'] + #sel_pdg_mask = (sel_truth[sel_truth_mask]['pdg_id'] != 0) + #sel_truth_pdg = sel_truth[sel_truth_mask]['pdg_id'][sel_pdg_mask] + #print("Selected Proton?:", sel_truth_protons) + #print("Selected True?:", sel_truth_sel) + #print("Selected PDG IDs:", sel_truth_pdg) + #for event in sel_event_ids: + #event_sel_mask = f['high_purity_sel']['hips']['sel_truth']['data']['event_id'] == event + #zero_mask = f['high_purity_sel']['hips']['sel_truth']['data'][event_sel_mask]['pdg_id'] != 0. + #print('Selected event true PID:', f['high_purity_sel']['hips']['sel_truth']['data'][event_sel_mask]['pdg_id'][zero_mask], "| Event ID:", event) + + ### partition file by selected events + #sel_event_mask = np.isin(events['id'], sel_event_ids) + #print("Events:", events[sel_event_mask]) + + # TO DO: Make this variable based on input file + sel_event_id_file = open(file_dir+'/'+sel_event_dict) + sel_event_id_data = json.load(sel_event_id_file) + sel_event_pdgs = sel_event_id_data.keys() + for pdg in sel_event_pdgs: + #if pdg == '13': continue + sel_event_ids = sel_event_id_data[pdg] + for event_id in sel_event_ids: + + # Prepare datasets for plotting + events = f['charge/events/data'] + tracks = f['combined/tracklets/data'] + tracks_ref = f['charge/events/ref/combined/tracklets/ref'] + tracks_region = f['charge/events/ref/combined/tracklets/ref_region'] + hits_trk_ref = f['combined/tracklets/ref/charge/'+hits_dset+'/ref'] + hits_trk_region = f['combined/tracklets/ref/charge/'+hits_dset+'/ref_region'] + #hits_drift = f['combined/hit_drift/data'] + hits = f['charge/'+hits_dset+'/data'] + hits_ref = f['charge/events/ref/charge/'+hits_dset+'/ref'] + hits_region = f['charge/events/ref/charge/'+hits_dset+'/ref_region'] + + # Get track information related to given event_id + track_ref = tracks_ref[tracks_region[int(event_id),'start']:tracks_region[int(event_id),'stop']] + track_ref = np.sort(track_ref[track_ref[:,0] == event_id, 1]) + tracks = tracks[track_ref] + track_start = tracks['start'] + track_end = tracks['end'] + track_charge_data = tracks['q'][0] + track_length_data = tracks['length'][0] + track_num_hits_data = tracks['nhit'][0] + track_theta_data = tracks['theta'][0] + track_phi_data = tracks['phi'][0] + track_ts_start_data = tracks['ts_start'][0] + track_ts_end_data = tracks['ts_end'][0] + track_dx_data = tracks['dx'][0] + track_dq_data = tracks['dq'][0] + track_start_pt_data = tracks['start'][0] + track_end_pt_data = tracks['end'][0] + + zero_dq_mask = track_dq_data != 0. + + track_dx_dist = np.array([np.sqrt(i[0]**2 + i[1]**2 + i[2]**2) for i in list(track_dx_data)]) + track_dx_dist = track_dx_dist[zero_dq_mask][::-1] + track_dq_data = track_dq_data[zero_dq_mask][::-1] + track_dqdx = track_dq_data / track_dx_dist + track_rr = np.zeros(len(track_dqdx)) + track_rr = np.cumsum(track_dx_dist[::-1])[::-1]-0.5*track_dx_dist + #print("Residual range:", track_rr) + #print("Track dqdx:", track_dqdx) + #print("PDG:", pdg) + #print("Track dx:", track_dx_data) + #print("Track dx dist:", track_dx_dist) + #print("Track dq:", track_dq_data) + #print("Track start pt:", track_start_pt_data) + + charge_hits_dset = hits_dset + charge_hits = hits + charge_hits_ref = hits_ref + charge_hits_region = hits_region + + for itrk, (ts, te) in enumerate(zip(track_start, track_end)): + hit_ref = hits_trk_ref[hits_trk_region[tracks[itrk]['id'],'start']:hits_trk_region[tracks[itrk]['id'],'stop']] + hit_ref = np.sort(hit_ref[hit_ref[:,0] == tracks[itrk]['id'], 1]) + hits_trk = charge_hits[hit_ref] + # Get hit information related to given event_id + #charge_hit_ref = charge_hits_ref[charge_hits_region[int(event_id),'start']:charge_hits_region[int(event_id),'stop']] + #charge_hit_ref = np.sort(charge_hit_ref[charge_hit_ref[:,0] == event_id, 1]) + + # Event-level hit metrics + charge_hits_data = hits_trk['Q'] + ts_hits_data = hits_trk['ts_pps'] + num_charge_hits = len(charge_hits_data) + + # Channel-level hit metrics + iogroup_hits = hits_trk['io_group'] + iochannel_hits = hits_trk['io_channel'] + chipid_hits = hits_trk['chip_id'] + channelid_hits = hits_trk['channel_id'] + channel_id = np.array([int(str(iogroup_hits[i])+str(iochannel_hits[i])+str(chipid_hits[i])+str(channelid_hits[i])) for i in range(num_charge_hits)]) + unique_channels, unique_channel_hit_counts = np.unique(channel_id, return_counts=True) + num_channels = len(unique_channels) + #print("String of channels:", channel_id) + #print("Number of unique channels:", num_channels) + #print("Hits per channel:", unique_channel_hit_counts) + #print("Length of hits per channel:", len(unique_channel_hit_counts)) + + for i in range(num_channels): + channel = unique_channels[i] + hits_per_channel = unique_channel_hit_counts[i] + channel_mask = np.argwhere(channel_id == channel).flatten() + channel_hit_amps = charge_hits_data[channel_mask] + channel_hit_ts = ts_hits_data[channel_mask] / 10. # convert to us + max_hit_amp = max(channel_hit_amps) + min_hit_amp = min(channel_hit_amps) + first_hit_idx = np.argmin(channel_hit_ts) + last_hit_idx = np.argmax(channel_hit_ts) + first_hit_amp = channel_hit_amps[first_hit_idx] + last_hit_amp = channel_hit_amps[last_hit_idx] + first_last_hit_delta_t = abs(channel_hit_ts[last_hit_idx] - channel_hit_ts[first_hit_idx]) + #print("Channel hit amplitudes:", channel_hit_amps) + #print("Channel hit timestamps:", channel_hit_ts) + #print("Maximum hit amplitude:", max_hit_amp) + #print("Minimum hit amplitude:", min_hit_amp) + #print("First hit amplitude:", first_hit_amp) + #print("Last hit amplitude:", last_hit_amp) + #print("First/Last hit delta t:", first_last_hit_delta_t) + + channel_metric_dict[(file, pdg, charge_hits_dset, event_id, channel)]=dict( + hit_mult = int(hits_per_channel), + max_hit_amp = float(max_hit_amp), + min_hit_amp = float(min_hit_amp), + first_hit_amp = float(first_hit_amp), + last_hit_amp = float(last_hit_amp), + first_last_hit_delta_t = float(first_last_hit_delta_t), + event_pdg = int(pdg), + hits_dset = str(charge_hits_dset) + ) + + event_hit_summ_dict[(file, pdg, charge_hits_dset, event_id)]=dict( + event_pdg = int(pdg), + total_charge=float(sum(charge_hits_data)), + num_hits=int(num_charge_hits), + num_channels=int(num_channels), + hits_dset = str(charge_hits_dset) + ) + + track_summ_dict[(file, pdg, charge_hits_dset, event_id)]=dict( + event_pdg = int(pdg), + total_charge = float(track_charge_data), + length = float(track_length_data), + hits_in_track = int(track_num_hits_data), + avg_q_per_unit_length = float(track_charge_data/track_length_data), + theta = float(track_theta_data), + phi = float(track_phi_data), + ts_start = float(track_ts_start_data), + ts_end = float(track_ts_end_data), + dx = [float(i) for i in list(track_dx_dist)], + dq = [float(i) for i in list(track_dq_data)], + start_pt = [float(i) for i in list(track_start_pt_data)], + end_pt = [float(i) for i in list(track_end_pt_data)], + dqdx = [float(i) for i in list(track_dqdx)], + rr = [float(i) for i in list(track_rr)], + hits_dset = str(hits_dset) + ) + + ## Save all Python dictionaries to JSON files + file_parsing.save_dict_to_json(event_hit_summ_dict, sample_type+"_event_hit_summ_dict", True) + file_parsing.save_dict_to_json(channel_metric_dict, sample_type+"_channel_metric_dict", True) + file_parsing.save_dict_to_json(track_summ_dict, sample_type+"_track_summ_dict", True) + + # PLOT: Signal Event Info + plot_event_hit_summ_metrics(event_hit_summ_dict, is_sim) + plot_channel_metrics(channel_metric_dict, is_sim) + plot_track_metrics(track_summ_dict, is_sim) + +if __name__=='__main__': + parser = argparse.ArgumentParser() + parser.add_argument('-d', '--file_dir', default=None, required=True, type=str, \ + help='''string corresponding to the path of the directory containing processed files for plotting''') + parser.add_argument('-mc', '--is_sim', default=False, required=True, type=str, \ + help='''str corresponding to bool whether files are simulation (MC) or data''') + #parser.add_argument('-hd', '--hits_dset', default='calib_final_hits', required=True, type=str,\ + # help='''str corresponding to hits dataset name associated with tracklets''') + parser.add_argument('-sed', '--sel_event_dict', default=None, required=True, type=str,\ + help='''str corresponding name of json file containing selected event ids''') + args = parser.parse_args() + main(**vars(args)) \ No newline at end of file diff --git a/scripts/proto_nd_scripts/analysis/hip_selection/data_mc_hit_level_metrics.py b/scripts/proto_nd_scripts/analysis/hip_selection/data_mc_hit_level_metrics.py new file mode 100644 index 00000000..4a4267b2 --- /dev/null +++ b/scripts/proto_nd_scripts/analysis/hip_selection/data_mc_hit_level_metrics.py @@ -0,0 +1,189 @@ +################################################################################ +## ## +## CONTAINS: Script to plot contents in output file from proton selection ## +## being run over Bern Module Data. ## +## ## +################################################################################ + +import h5py, glob, argparse +import numpy as np +import matplotlib.pyplot as plt +import sys +import file_parsing +import json +from plot_hit_level_metrics import plot_event_hit_summ_metrics, plot_channel_metrics + +def main(file_dir, is_sim, hits_dset, sel_event_dict): + + is_sim = bool(is_sim == 'True') + # initialize plotting datasets + event_hit_summ_dict = dict() + channel_metric_dict = dict() + print("Is MC?:", is_sim) + if is_sim: + sample_type = 'MC' + else: + sample_type = 'data' + + count = 0 + + for file in glob.glob(file_dir+'/*.h5'): # Loop over files files + + if count > 10: break + count+=1 + f = h5py.File(file,'r') + + # Prepare datasets for plotting + events = f['charge/events/data'] + tracks = f['combined/tracklets/data'] + tracks_ref = f['charge/events/ref/combined/tracklets/ref'] + tracks_region = f['charge/events/ref/combined/tracklets/ref_region'] + hits_trk_ref = f['combined/tracklets/ref/charge/'+hits_dset+'/ref'] + hits_trk_region = f['combined/tracklets/ref/charge/'+hits_dset+'/ref_region'] + #hits_drift = f['combined/hit_drift/data'] + hits_dsets = ['calib_final_hits', 'calib_prompt_hits'] + hits = [f['charge/calib_final_hits/data'], f['charge/calib_prompt_hits/data']] + hits_ref = [f['charge/events/ref/charge/calib_final_hits/ref'], \ + f['charge/events/ref/charge/calib_prompt_hits/ref']] + hits_region = [f['charge/events/ref/charge/calib_final_hits/ref_region'], \ + f['charge/events/ref/charge/calib_prompt_hits/ref_region']] + #if not is_sim: + # charge_hits = hits#f['combined/q_calib_el/data'] + # charge_hits_ref = hits_ref#f['charge/events/ref/combined/q_calib_el/ref'] + # charge_hits_region = hits_region#f['charge/events/ref/combined/q_calib_el/ref_region'] + #else: + # charge_hits = hits + # charge_hits_ref = hits_ref + # charge_hits_region = hits_region + ext_trigs = f['charge/ext_trigs/data'] + ext_trigs_ref = f['charge/events/ref/charge/ext_trigs/ref'] + ext_trigs_region = f['charge/events/ref/charge/ext_trigs/ref_region'] + print("Available datasets:",f.keys(),'\n') + sel_reco = f['high_purity_sel']['hips']['sel_reco']['data'] + if is_sim: + sel_truth = f['high_purity_sel']['hips']['sel_truth']['data'] + mc_truth_events = f['mc_truth/events/data'] + + print("File:", file) + #sel_mask = (sel_reco['sel'] == True) + #sel_event_ids = sel_reco[sel_mask]['event_id'] + #print("Selected Event Ids:", sel_event_ids) + #if is_sim==True: + #sel_truth_mask = (sel_truth['sel'] == True) + #sel_truth_protons = sel_truth[sel_mask]['hips'] + #sel_truth_sel = sel_truth[sel_truth_mask]['event_id'] + #sel_pdg_mask = (sel_truth[sel_truth_mask]['pdg_id'] != 0) + #sel_truth_pdg = sel_truth[sel_truth_mask]['pdg_id'][sel_pdg_mask] + #print("Selected Proton?:", sel_truth_protons) + #print("Selected True?:", sel_truth_sel) + #print("Selected PDG IDs:", sel_truth_pdg) + #for event in sel_event_ids: + #event_sel_mask = f['high_purity_sel']['hips']['sel_truth']['data']['event_id'] == event + #zero_mask = f['high_purity_sel']['hips']['sel_truth']['data'][event_sel_mask]['pdg_id'] != 0. + #print('Selected event true PID:', f['high_purity_sel']['hips']['sel_truth']['data'][event_sel_mask]['pdg_id'][zero_mask], "| Event ID:", event) + + ### partition file by selected events + #sel_event_mask = np.isin(events['id'], sel_event_ids) + #print("Events:", events[sel_event_mask]) + + # TO DO: Make this variable based on input file + sel_event_id_file = open(file_dir+'/'+sel_event_dict) + sel_event_id_data = json.load(sel_event_id_file) + sel_event_pdgs = sel_event_id_data.keys() + for pdg in sel_event_pdgs: + sel_event_ids = sel_event_id_data[pdg] + for event_id in sel_event_ids: + for x in range(len(hits_dsets)): + charge_hits_dset = hits_dsets[x] + charge_hits = hits[x] + charge_hits_ref = hits_ref[x] + charge_hits_region = hits_region[x] + + # Get hit information related to given event_id + charge_hit_ref = charge_hits_ref[charge_hits_region[int(event_id),'start']:charge_hits_region[int(event_id),'stop']] + charge_hit_ref = np.sort(charge_hit_ref[charge_hit_ref[:,0] == event_id, 1]) + + # Event-level hit metrics + charge_hits_data = charge_hits[charge_hit_ref]['Q'] + ts_hits_data = charge_hits[charge_hit_ref]['ts_pps'] + num_charge_hits = len(charge_hits_data) + + # Channel-level hit metrics + iogroup_hits = charge_hits[charge_hit_ref]['io_group'] + iochannel_hits = charge_hits[charge_hit_ref]['io_channel'] + chipid_hits = charge_hits[charge_hit_ref]['chip_id'] + channelid_hits = charge_hits[charge_hit_ref]['channel_id'] + + channel_id = np.array([int(str(iogroup_hits[i])+str(iochannel_hits[i])+str(chipid_hits[i])+str(channelid_hits[i])) for i in range(num_charge_hits)]) + unique_channels, unique_channel_hit_counts = np.unique(channel_id, return_counts=True) + num_channels = len(unique_channels) + + #print("String of channels:", channel_id) + #print("Number of unique channels:", num_channels) + #print("Hits per channel:", unique_channel_hit_counts) + #print("Length of hits per channel:", len(unique_channel_hit_counts)) + for i in range(num_channels): + + channel = unique_channels[i] + hits_per_channel = unique_channel_hit_counts[i] + channel_mask = np.argwhere(channel_id == channel).flatten() + channel_hit_amps = charge_hits_data[channel_mask] + channel_hit_ts = ts_hits_data[channel_mask] / 10. # convert to us + + max_hit_amp = max(channel_hit_amps) + min_hit_amp = min(channel_hit_amps) + + first_hit_idx = np.argmin(channel_hit_ts) + last_hit_idx = np.argmax(channel_hit_ts) + first_hit_amp = channel_hit_amps[first_hit_idx] + last_hit_amp = channel_hit_amps[last_hit_idx] + first_last_hit_delta_t = abs(channel_hit_ts[last_hit_idx] - channel_hit_ts[first_hit_idx]) + + #print("Channel hit amplitudes:", channel_hit_amps) + #print("Channel hit timestamps:", channel_hit_ts) + #print("Maximum hit amplitude:", max_hit_amp) + #print("Minimum hit amplitude:", min_hit_amp) + #print("First hit amplitude:", first_hit_amp) + #print("Last hit amplitude:", last_hit_amp) + #print("First/Last hit delta t:", first_last_hit_delta_t) + + channel_metric_dict[(file, pdg, charge_hits_dset, event_id, channel)]=dict( + hit_mult = int(hits_per_channel), + max_hit_amp = float(max_hit_amp), + min_hit_amp = float(min_hit_amp), + first_hit_amp = float(first_hit_amp), + last_hit_amp = float(last_hit_amp), + first_last_hit_delta_t = float(first_last_hit_delta_t), + event_pdg = int(pdg), + hits_dset = str(charge_hits_dset) + ) + + event_hit_summ_dict[(file, pdg, charge_hits_dset, event_id)]=dict( + event_pdg = int(pdg), + total_charge=float(sum(charge_hits_data)), + num_hits=int(num_charge_hits), + num_channels=int(num_channels), + hits_dset = str(charge_hits_dset) + ) + + ## Save all Python dictionaries to JSON files + file_parsing.save_dict_to_json(event_hit_summ_dict, sample_type+"_event_hit_summ_dict", True) + file_parsing.save_dict_to_json(channel_metric_dict, sample_type+"_channel_metric_dict", True) + + + # PLOT: Signal Event Info + plot_event_hit_summ_metrics(event_hit_summ_dict, is_sim) + plot_channel_metrics(channel_metric_dict, is_sim) + +if __name__=='__main__': + parser = argparse.ArgumentParser() + parser.add_argument('-d', '--file_dir', default=None, required=True, type=str, \ + help='''string corresponding to the path of the directory containing processed files for plotting''') + parser.add_argument('-mc', '--is_sim', default=False, required=True, type=str, \ + help='''str corresponding to bool whether files are simulation (MC) or data''') + parser.add_argument('-hd', '--hits_dset', default='calib_final_hits', required=True, type=str,\ + help='''str corresponding to hits dataset name associated with tracklets''') + parser.add_argument('-sed', '--sel_event_dict', default=None, required=True, type=str,\ + help='''str corresponding name of json file containing selected event ids''') + args = parser.parse_args() + main(**vars(args)) \ No newline at end of file diff --git a/scripts/proto_nd_scripts/analysis/hip_selection/file_parsing.py b/scripts/proto_nd_scripts/analysis/hip_selection/file_parsing.py new file mode 100644 index 00000000..06130aac --- /dev/null +++ b/scripts/proto_nd_scripts/analysis/hip_selection/file_parsing.py @@ -0,0 +1,57 @@ +################################################################################ +## ## +## CONTAINS: Methods to parse files or objects and/or assist in new file/ ## +## object creation e.g. hdf5 file parsing and json dictionary ## +## creation. ## +## ## +################################################################################ +import json + +####--------------------------- HDF5 FILE PARSING --------------------------#### + +def print_keys_attributes(data_h5): + print('FILE KEYS:', list(data_h5.keys()),'\n') + print('CHARGE KEYS:',list(data_h5['charge'].keys()),'\n') + print('CHARGE EVENT DATA KEYS:',data_h5['charge']['events']['data'].dtype.names,'\n') + print('CHARGE EVENT Q SIZE, DTYPE:',data_h5['charge']['events']['data']['q'].size,\ + ',',data_h5['charge']['events']['data']['q'].dtype,'\n') + print('CHARGE EVENT NHIT SIZE, DTYPE:',data_h5['charge']['events']['data']['nhit'].size,\ + ',',data_h5['charge']['events']['data']['nhit'].dtype,'\n') + print('CHARGE EVENT CHARGE REF KEYS:',list(data_h5['charge']['events']['ref']['charge'].keys()),'\n') + print('CHARGE EVENT CHARGE REF HITS REF DTYPE:',\ + data_h5['charge']['events']['ref']['charge']['hits']['ref'].dtype,'\n') + print('CHARGE HITS DATA KEYS:',data_h5['charge']['hits']['data'].dtype.names,'\n') + print('COMBINED KEYS:',list(data_h5['combined'].keys()),'\n') + print('GEOMETRY INFO KEYS:',list(data_h5['geometry_info'].keys()),'\n') + print('LAR INFO KEYS:',list(data_h5['lar_info'].keys()),'\n') + print('RUN INFO KEYS:',list(data_h5['run_info'].keys()),'\n') + + +def get_charge_datasets(data_h5): + + events_data = data_h5['charge']['events']['data'] + hits_data = data_h5['charge']['hits']['data'] + + return events_data, hits_data + +####----------------------- OUTPUT DICTIONARY TO JSON ----------------------#### + +def tuple_key_to_string(d): + out={} + for key in d.keys(): + string_key="" + max_length=len(key) + for i in range(max_length): + if i> source get_proto_nd_input.sh +# to download all the necessary inputs into the correct directories +# +INPUT_FILE=$1 + +OUTPUT_DIR=`pwd` +OUTPUT_NAME=(${INPUT_FILE//"/"/ }) +OUTPUT_NAME=${OUTPUT_NAME[-1]} +OUTPUT_FILE="${OUTPUT_DIR}/${OUTPUT_NAME}" +OUTPUT_FILE=${OUTPUT_FILE//.h5/.proto_nd_flow.HIP_SEL.h5} +echo ${OUTPUT_FILE} + +# for running on a login node +H5FLOW_CMD='h5flow' +# for running on a single compute node with 32 cores +#H5FLOW_CMD='srun -n32 h5flow' + +# run all stages +WORKFLOW1='yamls/proto_nd_flow/workflows/analysis/hip_sel_workflow.yaml' + +HERE=`pwd` +#cd ndlar_flow +# assumes this is being run from ndlar_flow/scripts/proto_nd_flow/analysis/hip_selection/: +cd ../../../../ + +# avoid being asked if we want to overwrite the file if it exists. +# this is us answering "yes". +if [ -e $OUTPUT_FILE ]; then + rm -i $OUTPUT_FILE +fi + +$H5FLOW_CMD -c $WORKFLOW1 -i $INPUT_FILE -o $OUTPUT_FILE + +echo "Done!" +echo "Output can be found at $OUTPUT_FILE" + +cd ${HERE} + diff --git a/scripts/proto_nd_scripts/run_proto_nd_tracklet_reco.sh b/scripts/proto_nd_scripts/run_proto_nd_tracklet_reco.sh new file mode 100644 index 00000000..3ca39097 --- /dev/null +++ b/scripts/proto_nd_scripts/run_proto_nd_tracklet_reco.sh @@ -0,0 +1,41 @@ +#!/bin/bash +# Runs proto_nd_flow on an example file. +# Before using this script, use +# >> source get_proto_nd_input.sh +# to download all the necessary inputs into the correct directories +# +INPUT_FILE=$1 + +OUTPUT_DIR=`pwd` +OUTPUT_NAME=(${INPUT_FILE//"/"/ }) +OUTPUT_NAME=${OUTPUT_NAME[-1]} +OUTPUT_FILE="${OUTPUT_DIR}/${OUTPUT_NAME}" +OUTPUT_FILE=${OUTPUT_FILE//.h5/.proto_nd_flow.calib_prompt_hits.TRACKLETS_HDBSCAN_1_15_9_3421.h5} +echo ${OUTPUT_FILE} + +# for running on a login node +H5FLOW_CMD='h5flow' +# for running on a single compute node with 32 cores +#H5FLOW_CMD='srun -n32 h5flow' + +# run all stages +WORKFLOW1='yamls/proto_nd_flow/workflows/util/tracklet_workflow.yaml' + +HERE=`pwd` +#cd ndlar_flow +# assumes this is being run from ndlar_flow/scripts/proto_nd_flow: +cd ../../ + +# avoid being asked if we want to overwrite the file if it exists. +# this is us answering "yes". +if [ -e $OUTPUT_FILE ]; then + rm -i $OUTPUT_FILE +fi + +$H5FLOW_CMD -c $WORKFLOW1 -i $INPUT_FILE -o $OUTPUT_FILE + +echo "Done!" +echo "Output can be found at $OUTPUT_FILE" + +cd ${HERE} + diff --git a/src/proto_nd_flow/analysis/hip_selection.py b/src/proto_nd_flow/analysis/hip_selection.py new file mode 100644 index 00000000..55e3df92 --- /dev/null +++ b/src/proto_nd_flow/analysis/hip_selection.py @@ -0,0 +1,1225 @@ +import numpy as np +import numpy.ma as ma +import logging +from scipy.interpolate import interp1d, pchip_interpolate +import scipy.integrate as integrate +import scipy.stats as stats +import scipy.ndimage as ndimage +import scipy.optimize as optimize +from copy import deepcopy + +# Need to have both h5flow and ndlar-flow installed +from h5flow.core import H5FlowStage, resources +from h5flow.data import dereference_chain + +from module0_flow.util.func import mode, condense_array +import proto_nd_flow.util.units as units + +class HIPSelection(H5FlowStage): + ''' + Perform a selection for highly ionizing particles. A HIP event is defined + using the following criteria: + - + - + - + Creates a boolean array of 1:1 with events indicating HIP events, and + creates a boolean array 1:1 with "tracklets if they individually meet the + HIP criteria. + + NOT CURRENTLY IMPLEMENTED: + A dQ/dx profile is generated per event and used to discriminate + stopping protons and muons, as well as through-going muons. + + If the file is a MC file, also generates boolean arrays with the true + value. + + ''' + class_version = '2.0.0' # change for getting around assertion error in geometry files + + default_params = dict( + fid_cut=5.0, # cm + cathode_fid_cut=0.0, # cm + anode_fid_cut=5.0, # cm + profile_dx=2.2, # cm + profile_max_range=200.0, # cm + larpix_gain=250, # e/mV + larpix_noise=5000, # e/cm + proton_classifier_cut=-1.0, + muon_classifier_cut=-1.0, + dqdx_peak_cut=5e4, # e/cm + profile_search_dx=2.2, # cm + remaining_e_cut=85e9, # keV + + curvature_rr_correction=22.6647 / 22, + density_dx_correction_params=[0.78497819, -3.41826874, 198.93022888], + + hits_dset_name='charge/calib_prompt_hits', # '/data' directory may not be necessary ... unclear + hit_drift_dset_name='charge/calib_prompt_hits', # TO DO: Calibrate for electron lifetime + tracklet_dset_name='combined/tracklets', #/merged', # no merged part? + t0_dset_name='combined/t0', # + ext_trigs_dset_name='charge/ext_trigs', + truth_trajectories_dset_name='mc_truth/trajectories', + charge_dset_name = 'charge/calib_prompt_hits', + path='high_purity_sel/hips') # path within hdf5 file vs. file path + + sel_dset_name = 'sel_reco' + sel_truth_dset_name = 'sel_truth' + #event_tracks_dset_name = 'event_tracks_reco' + #event_hits_dset_name = 'event_hits_reco' + + sel_dtype = np.dtype([('sel', 'u1'), + ('hip', 'f8'), + ('pdg_id', 'f8',(1000,)), + ('nhits_over_thresh', 'f8'), + ('event_id', 'f8'), + ('ntracks', 'f8')]) + #('max_dqdx', 'f4'), + #('muon_loglikelihood_mean', 'f8'), + #('proton_loglikelihood_mean', 'f8'), + #('mip_loglikelihood_mean', 'f8'), + #('pid_muon_proton', 'f8'), + #('pid_mip_proton', 'f8')]) + + + def __init__(self, **params): + super(HIPSelection, self).__init__(**params) + + for key,val in self.default_params.items(): + setattr(self, key, params.get(key, val)) + + self.curvature_rr_correction = params.get('curvature_rr_correction', dict()) + self.density_dx_correction_params = params.get('density_dx_correction_params', dict()) + self.larpix_gain = params.get('larpix_gain', dict()) + + + def init(self, source_name): + super(HIPSelection, self).init(source_name) + + self.is_mc = resources['RunData'].is_mc + correction_key = ('medm') + correction_key = ('mc' if self.is_mc + else 'medm') + correction_key = ('high' if (not self.is_mc + and resources['RunData'].charge_thresholds == 'high') + else correction_key) + self.curvature_rr_correction = self.curvature_rr_correction.get(correction_key, self.default_params['curvature_rr_correction']) + self.density_dx_correction_params = self.density_dx_correction_params.get(correction_key, self.default_params['density_dx_correction_params']) + self.larpix_gain = self.larpix_gain.get(correction_key, self.default_params['larpix_gain']) + + attrs = dict() + for key in self.default_params: + attrs[key] = getattr(self, key) + #print(attrs) + self.data_manager.set_attrs(self.path, + classname=self.classname, + class_version=self.class_version, + **attrs) + self.data_manager.create_dset(f'{self.path}/{self.sel_dset_name}', + self.sel_dtype) + #self.data_manager.create_dset(f'{self.path}/{self.event_tracks_dset_name}', + # self.event_tracks_dtype) + #self.data_manager.create_dset(f'{self.path}/{self.hit_profile_dset_name}', + # self.hit_profile_dtype) + #self.data_manager.create_ref(f'{self.path}/{self.hit_profile_dset_name}', self.hits_dset_name) + if self.is_mc: + self.data_manager.create_dset(f'{self.path}/{self.sel_truth_dset_name}', + self.sel_dtype) + + #self.create_dqdx_profile_templates() + #self.data_manager.set_attrs(self.path, + # proton_dqdx=self.proton_range_table['dqdx'], + # muon_dqdx=self.muon_range_table['dqdx'], + # proton_dqdx_width=self.proton_range_table['dqdx_width'], + # muon_dqdx_width=self.muon_range_table['dqdx_width'], + # proton_dedx=self.proton_range_table['dedx_mpv'], + # muon_dedx=self.muon_range_table['dedx_mpv'], + # proton_range=self.proton_range_table['range'], + # muon_range=self.muon_range_table['range'], + # proton_recom=self.proton_range_table['recomb'], + # muon_recom=self.muon_range_table['recomb']) + + def finish(self, source_name): + super(HIPSelection, self).finish(source_name) + sel_dset_name = f'{self.path}/{self.sel_dset_name}' + + if self.rank == 0: + total = len(self.data_manager.get_dset(sel_dset_name)) + #min_tracks = np.min(self.data_manager.get_dset(sel_dset_name)['ntracks']) + #print("Minimum tracks in an event:", min_tracks) + #max_tracks = np.max(self.data_manager.get_dset(sel_dset_name)['ntracks']) + #print("Maximum tracks in an event:", max_tracks) +# + #min_hits = np.min(self.data_manager.get_dset(sel_dset_name)['nhits']) + #print("Minimum hits in an event:", min_hits) + #max_hits = np.max(self.data_manager.get_dset(sel_dset_name)['nhits']) + #print("Maximum hits in an event:", max_hits) +# + #min_charge = np.min(self.data_manager.get_dset(sel_dset_name)['event_charge']) + #print("Minimum charge in an event:", min_charge) + #max_charge = np.max(self.data_manager.get_dset(sel_dset_name)['event_charge']) + #print("Maximum charge in an event:", max_charge) +# + #min_ext_trigs = np.min(self.data_manager.get_dset(sel_dset_name)['next_trigs']) + #print("Minimum ext_trigs in an event:", min_ext_trigs) + #max_ext_trigs = np.max(self.data_manager.get_dset(sel_dset_name)['next_trigs']) + #print("Maximum ext_trigs in an event:", max_ext_trigs) + + #nstopping = np.sum(self.data_manager.get_dset(sel_dset_name)['stop']) + nselected = np.sum(self.data_manager.get_dset(sel_dset_name)['sel']) + #print(f'Stopping: {nstopping} / {total} ({nstopping/total:0.03f})') + print(f'Selected: {nselected} / {total} ({nselected/total:0.03f})') + sel_events_mask = self.data_manager.get_dset(sel_dset_name)['sel'] == 1 + sel_events = self.data_manager.get_dset(sel_dset_name)[sel_events_mask]['event_id'] + print("Sample events:", sel_events) + + if self.is_mc: + sel_truth_dset_name = f'{self.path}/{self.sel_truth_dset_name}' + true_proton = np.sum(self.data_manager.get_dset(sel_truth_dset_name)['hip']) + true_contained_proton = np.sum(self.data_manager.get_dset(sel_truth_dset_name)['sel']) + print(f'True protons: {true_proton} / {total} ({true_proton/total:0.03f})') + print(f'True contained proton events: {true_contained_proton} / {total} ({true_contained_proton/total:0.03f})') + correct = np.sum(self.data_manager.get_dset(sel_truth_dset_name)['sel'] & + self.data_manager.get_dset(sel_dset_name)['sel']) + print(f'Purity: {correct} / {nselected} ({correct/nselected:0.03f})') + print(f'Efficiency: {correct} / {true_contained_proton} ({correct/true_contained_proton:0.03f})') +############ START OF CODE PORTED FROM STOPPING MUON CODE FOR PID + + + + + def create_dqdx_profile_templates(self): + # create range tables used for dQ/dx profile discrimination + self.muon_range_table = dict() + self.proton_range_table = dict() + + # only consider reasonable range values + muon_mask = resources['ParticleData'].muon_range_table['range'] > 0.1 + for key, val in deepcopy(resources['ParticleData'].muon_range_table).items(): + self.muon_range_table[key] = val[muon_mask] + proton_mask = resources['ParticleData'].proton_range_table['range'] > 0.1 + for key, val in deepcopy(resources['ParticleData'].proton_range_table).items(): + self.proton_range_table[key] = val[proton_mask] + + # convert mean dE/dx entries to MPV dE/dx + self.muon_range_table['dedx_mpv'] = resources['ParticleData'].landau_peak( + self.muon_range_table['t'], resources['ParticleData'].mu_mass, + self.profile_dx) / self.profile_dx + self.proton_range_table['dedx_mpv'] = resources['ParticleData'].landau_peak( + self.proton_range_table['t'], resources['ParticleData'].p_mass, + self.profile_dx) / self.profile_dx + + # calculate recombination correction + muon_r = resources['LArData'].ionization_recombination( + self.muon_range_table['dedx_mpv']) + proton_r = resources['LArData'].ionization_recombination( + self.proton_range_table['dedx_mpv']) + w = resources['LArData'].ionization_w + self.muon_range_table['recomb'] = muon_r + self.proton_range_table['recomb'] = proton_r + + self.muon_range_table['dqdx'] = (muon_r * self.muon_range_table['dedx_mpv'] / w) + self.proton_range_table['dqdx'] = (proton_r * self.proton_range_table['dedx_mpv'] / w) + self.muon_range_table['dqdx_width'] = ( + muon_r / w * resources['ParticleData'].landau_width(self.muon_range_table['t'], + resources['ParticleData'].mu_mass, + self.profile_dx) / self.profile_dx) + self.proton_range_table['dqdx_width'] = ( + proton_r / w * resources['ParticleData'].landau_width(self.proton_range_table['t'], + resources['ParticleData'].p_mass, + self.profile_dx) / self.profile_dx) + noise = (self.larpix_noise * np.sqrt(self.profile_dx / resources['Geometry'].pixel_pitch*units.cm) # converting cm -> mm + / resources['Geometry'].pixel_pitch*units.cm) # converting cm -> mm + post_dedx = resources['ParticleData'].landau_peak(50 * units.MeV, + resources['ParticleData'].e_mass, + self.profile_dx) / self.profile_dx + post_dedx_width = resources['ParticleData'].landau_width(50 * units.MeV, + resources['ParticleData'].e_mass, + self.profile_dx) / self.profile_dx + self.muon_range_table['post_dqdx'] = post_dedx * resources['LArData'].ionization_recombination(post_dedx) / w + self.proton_range_table['post_dqdx'] = 1 + self.muon_range_table['post_dqdx_width'] = post_dedx_width * resources['LArData'].ionization_recombination(post_dedx) / w + self.proton_range_table['post_dqdx_width'] = 1 + + self.muon_range_table['mcs_angle'] = resources['ParticleData'].mcs_angle(self.muon_range_table['t'], + resources['ParticleData'].mu_mass, + self.profile_dx) + self.proton_range_table['mcs_angle'] = resources['ParticleData'].mcs_angle(self.proton_range_table['t'], + resources['ParticleData'].p_mass, + self.profile_dx) + self.muon_range_table['post_mcs_angle'] = resources['ParticleData'].mcs_angle(50 * units.MeV, + resources['ParticleData'].e_mass, + self.profile_dx) + self.proton_range_table['post_mcs_angle'] = 1e-9 + + self.muon_range_table['dqdx_gaus_width'] = self.larpix_noise + self.proton_range_table['dqdx_gaus_width'] = self.larpix_noise + + #self.apply_position_resolution(self.muon_range_table, noise=noise) + #self.apply_position_resolution(self.proton_range_table, noise=noise) + + def apply_position_resolution(self, range_table, noise=0): + ''' Update the range table ``dqdx`` and ``dqdx_width`` by smearing the range values by a gaussian ``profile_dx`` ''' + # interpolate dQ/dx MPV and width to apply a gaussian smear + interpolation_pts, dx = np.linspace(-500, 2000, 10 * int(2500 / self.profile_dx), + retstep=True) + + # interpolate central value + rr = np.r_[-5000, 0, range_table['range']] + dqdx = np.r_[0, 0, range_table['dqdx']] + dqdx_width = np.r_[0, 0, range_table['dqdx_width']] + interp_rr = interp1d(rr, dqdx) + dqdx = interp_rr(interpolation_pts) + # apply a position resolution smearing + dqdx_smear = ndimage.uniform_filter(dqdx, int(self.profile_dx / dx), mode='nearest') +# dqdx_smear = ndimage.uniform_filter(dqdx, 1, mode='nearest') + + # interpolate width + interp_rr_width = interp1d(rr, dqdx_width) + dqdx_width = interp_rr_width(interpolation_pts) + # combine position resolution, intrinsic width, and noise contributions + dqdx_width = np.sqrt( + # ndimage.uniform_filtein_fid(self, xyz, cathode_fid=0.0, field_cage_fid=0.0)inr(np.abs(ndimage.convolve(dqdx * dx, [-1, 1], mode='nearest')), int(self.profile_dx / dx), mode='nearest')**2 + ndimage.uniform_filter(np.abs(ndimage.convolve(dqdx * dx, [0], mode='nearest')), 1, mode='nearest')**2 + + dqdx_width**2 + + noise**2) + + # re-align to max + high_val_align = interpolation_pts[np.argmax(dqdx_smear + dqdx_width)] + high_val_interp = interp1d(interpolation_pts - high_val_align, + dqdx_smear + dqdx_width) + low_val_align = interpolation_pts[np.argmax(dqdx_smear - dqdx_width)] + low_val_interp = interp1d(interpolation_pts - low_val_align, + dqdx_smear - dqdx_width) + high_val_interp, low_val_interp = (np.maximum(high_val_interp, low_val_interp), np.minimum(high_val_interp, low_val_interp)) + + # set values + _min, _max = (max(np.min(interpolation_pts - dx * low_val_align), np.min(interpolation_pts - dx * high_val_align)), + min(np.max(interpolation_pts - dx * low_val_align), np.max(interpolation_pts - dx * high_val_align))) + range_table['dqdx'] = 0.5 * (high_val_interp(np.clip(rr[2:], _min, _max)) + low_val_interp(np.clip(rr[2:], _min, _max))) + range_table['dqdx_width'] = 0.5 * (high_val_interp(np.clip(rr[2:], _min, _max)) - low_val_interp(np.clip(rr[2:], _min, _max))) + + @staticmethod + def density_dx_correction(rr, *params): + rr = np.clip(rr, 0, None) + rv = params[0] * np.exp(-rr / params[2]) + params[1] + return rv + + @staticmethod + def dx_estimate(profile_pos, hit_xyz, hit_idx, pixel_pitch, nsamples=10, tol=0.1): + ''' + Calculate the track dx to be associated with each profile point. + + First finds the furthest point along the line that falls on a hit pixel. + Then samples the track length between those points, checking to see if the sample point falls onto a + disabled channel. The track length is calculated as the length between the furthest points, minus the + approximate length on disabled channels + + :param profile_pos: xyz position of each profile point ``shape: (..., nprof, 3)`` + + :param hit_xyz: xyz position of each hit ``shape: (..., nhit, 3)`` + + :param hit_idx: index into ``profile_pos`` of each hit ``shape: (..., nhit)`` + + :param nsamples: number of sample points to estimate disabled fraction of track + + :returns: dx to be associated with each profile point ``shape: (..., nprof)`` + + ''' + dx = np.zeros(profile_pos.shape[:-1]) + for iprof in range(profile_pos.shape[-2]): + if ~np.any(hit_idx >= iprof): + break + hit_mask = hit_idx == iprof + if ~np.any(hit_mask): + continue + + xyz = ma.array(hit_xyz, mask=np.broadcast_to(~hit_mask[...,np.newaxis], hit_xyz.shape)) # (nev, nhit, 3) + valid = np.any(~xyz.mask[...,0], axis=-1) # (nev,) + + # get profile centroid + pos = profile_pos[...,iprof,:] # (nev, 3) + + # get profile trajectory segment directions + dirs = [profile_pos[...,iprof+1,:] - pos if iprof < profile_pos.shape[-2]-1 else profile_pos[...,-2,:] - pos, + profile_pos[...,iprof-1,:] - pos if iprof > 0 else profile_pos[...,1,:] - pos] + dirs = np.concatenate([dr[...,np.newaxis,np.newaxis,np.newaxis,:] for dr in dirs], axis=1) # (nev, ndirection, 1, 1, 3) + + for idr in range(dirs.shape[1]): + invalid_dir = np.all(dirs[:,idr] == 0., axis=-1) + dirs[:,idr][invalid_dir] = -dirs[:,(idr+1)%2][invalid_dir] + dirs = dirs / np.clip(np.linalg.norm(dirs, axis=-1, keepdims=True), 1e-15, None) + + # get active volume + min_xyz,max_xyz = np.min(xyz, axis=-2) - pixel_pitch/2, np.max(xyz, axis=-2) + pixel_pitch/2 + min_xyz = min_xyz.reshape(-1,1,1,1,3) + max_xyz = max_xyz.reshape(-1,1,1,1,3) + c = np.concatenate([min_xyz, max_xyz], axis=2) # (nev, 1, ncorner, 1, 3) + n = np.array([(1,0,0), (0,1,0), (0,0,1)]).reshape(1,1,1,3,3) # (1, 1, 1, naxes, 3) + + # find intersections with active volume planes + pos = pos.reshape(-1, 1, 1, 1, 3) + intersection = HIPSelection.intersection(pos, dirs, c, n) + alpha = np.sum(dirs * (intersection - pos), axis=-1) + + # only use intersections that are within active volume (and in the correct direction relative to the trajectory segment) + within_active_region = ((intersection[...,0] - max_xyz[...,0] <= tol) + & (intersection[...,0] - min_xyz[...,0] >= -tol) + & (intersection[...,1] - max_xyz[...,1] <= tol) + & (intersection[...,1] - min_xyz[...,1] >= -tol) + & (intersection[...,2] - max_xyz[...,2] <= tol) + & (intersection[...,2] - min_xyz[...,2] >= -tol) + & (alpha > 0) & valid.reshape(-1,1,1,1)) # (nev, ndirection, ncorner, naxes) + + intersection = np.take_along_axis(intersection, np.argmax(within_active_region[...,np.newaxis], axis=-2)[...,np.newaxis], axis=-2) # (nev, ndirection, ncorner, 1, 3) + within_active_region = np.take_along_axis(within_active_region[...,np.newaxis], np.argmax(within_active_region[...,np.newaxis], axis=-2)[...,np.newaxis], axis=-2) + intersection = np.take_along_axis(intersection, np.argmax(within_active_region, axis=-3)[...,np.newaxis], axis=-3) # (nev, ndirection, 1, 1, 3) + within_active_region = np.take_along_axis(within_active_region, np.argmax(within_active_region, axis=-3)[...,np.newaxis], axis=-3) + + # calculate track length in active volume + prof_dx = np.linalg.norm(intersection - pos, axis=-1) # (nev, ndirection, 1, 1) + + # correct for disabled channels + disabled_fraction = np.zeros_like(prof_dx) + if 'DisabledChannels' in resources: + sample_pts = np.linspace(pos, intersection, nsamples, axis=0) + sample_pt_disabled = ~resources['DisabledChannels'].is_active(sample_pts).reshape(sample_pts.shape[:-1]) + disabled_fraction = np.sum(sample_pt_disabled, axis=0) / nsamples + + prof_dx *= (1 - disabled_fraction) + + # collect result + dx[...,iprof] = (prof_dx * within_active_region[...,0]).sum(axis=(1,2,3)) # (nev,) + + return dx + + + @staticmethod + def profile_likelihood(profile_rr, profile_dqdx, profile_pos, range_table, type='', mcs_weight=0.0625): + ''' + Calculates the log-likelihood score of a given dqdx v. residual range profile + using a Moyal-distribution approximation. + + Likelihood data is passed via the ``range_table`` parameter which is + a ``dict`` with the following arrays: + + - ``range``: residual range values used in interpolation ``shape: (n_interp_pts,)`` + - ``dqdx``: dQ/dx values used in interpolation ``shape: (n_interp_pts,)`` + - ``dqdx_width``: dQ/dx sigma values ``shape: (n_interp_pts,)`` + + :param profile_rr: residual range ``shape: (..., n)`` + + :param profile_dqdx: dqdx ``shape: (..., n)`` + + :param profile_pos: bin position ``shape: (..., n, 3)`` + + :param range_table: ``dict``, see above. + + :param type: likelihood pdf name, one of ``'abs_exp'``, ``'moyal'``, ``'moyal_gaus'``, ``'gaus'`` + + :returns: likelihood ``shape: (..., n)`` + + ''' + profile_rr, profile_dqdx = np.broadcast_arrays(profile_rr, profile_dqdx) + profile_pos = np.broadcast_to(profile_pos, profile_rr.shape + (3,)) + + interp = interp1d(np.r_[0, range_table['range']], np.r_[range_table['post_dqdx'], range_table['dqdx']]) + interp_width = interp1d(np.r_[0, range_table['range']], np.r_[range_table['post_dqdx_width'], range_table['dqdx_width']]) + interp_angle_width = interp1d(np.r_[0, range_table['range']], np.r_[range_table['post_mcs_angle'], range_table['mcs_angle']]) + min_range = np.min(range_table['range']) + max_range = np.max(range_table['range']) + + # calculate dQ/dx log-likelihood + interp_dqdx = interp(np.clip(profile_rr, min_range, max_range)) + interp_dqdx_width = interp_width(np.clip(profile_rr, min_range, max_range)) + + if type == 'abs_exp': + dqdx_term = stats.expon.logpdf(np.abs(profile_dqdx - interp_dqdx), scale=interp_dqdx_width) + np.log(2) + #dqdx_term = -np.abs(profile_dqdx - interp_dqdx) / interp_dqdx_width - np.log(interp_dqdx_width / 2) + elif type == 'moyal': + dqdx_term = stats.moyal.logpdf(profile_dqdx, loc=interp_dqdx, scale=interp_dqdx_width) + elif type == 'moyal_gaus': + # interp_gaus_width = range_table['dqdx_gaus_width'] + interp_gaus_width = interp1d(range_table['range'], range_table['dqdx_gaus_width'])(np.clip(profile_rr, min_range, max_range)) + smear_values = np.linspace(-5 * interp_gaus_width, +5 * interp_gaus_width, 25).reshape(profile_rr.shape + (25,)) + smeared_profile_dqdx = profile_dqdx[..., np.newaxis] + smear_values + dqdx_term = np.log(np.sum(ma.maximum(stats.moyal.pdf( + smeared_profile_dqdx, loc=interp_dqdx[..., np.newaxis], scale=interp_dqdx_width[..., np.newaxis]), 1e-300) + * ma.maximum(stats.norm.pdf(smear_values, scale=interp_gaus_width[..., np.newaxis]), 1e-300), axis=-1)) + elif type == 'gaus': + dqdx_term = stats.norm.logpdf(profile_dqdx, loc=interp_dqdx, scale=interp_dqdx_width) + else: + dqdx_term = -np.abs(profile_dqdx - interp_dqdx) / np.abs(interp_dqdx) + + # calculate MCS log-likelihood + # pack profile pts + valid_mask = (profile_dqdx > 0) + any_valid = np.any(valid_mask) + npts = np.sum(valid_mask, axis=-1, keepdims=True) + if any_valid: + max_npts = npts.max() + else: + max_npts = 0 + + packed_pos = np.zeros(valid_mask.shape[:-1] + (max_npts, 3)) + packed_dqdx = np.zeros(valid_mask.shape[:-1] + (max_npts,)) + packed_rr = np.zeros(valid_mask.shape[:-1] + (max_npts,)) + place_mask = np.indices(packed_pos.shape)[-2] < npts[..., np.newaxis] + np.place(packed_pos, place_mask, profile_pos[valid_mask]) + place_mask = np.indices(packed_dqdx.shape)[-1] < npts + np.place(packed_dqdx, place_mask, profile_dqdx[valid_mask]) + np.place(packed_rr, place_mask, profile_rr[valid_mask]) + + #interp_angle_width = interp_angle_width(np.clip(packed_rr, min_range, max_range)) + interp_angle_width = interp_angle_width(np.clip(profile_rr, min_range, max_range)) + + d = packed_pos[..., 1:, :] - packed_pos[..., :-1, :] + d = d * place_mask[...,1:,np.newaxis] * place_mask[...,:-1,np.newaxis] + angle = np.zeros_like(packed_dqdx) + norm = np.linalg.norm(d[..., 1:, :], axis=-1) * np.linalg.norm(d[..., :-1, :], axis=-1) + if any_valid and angle.shape[-1] > 1: + angle[..., 2:] = np.sum(d[..., 1:, :] * d[..., :-1, :], axis=-1) / np.maximum(norm, 1e-15) + angle = np.arccos(angle) + angle[..., 0] = 0 + angle[..., 1] = 0 + #angle[..., 0] = angle[..., 1] + #angle[..., -1] = angle[..., -2] + + # and now unpack profile pts + rv_angle_term = np.zeros(valid_mask.shape) + np.place(rv_angle_term, valid_mask, angle[place_mask]) + + #angle_term = stats.norm.logpdf(angle, loc=0, scale=interp_angle_width) + np.log(2) + rv_angle_term = stats.expon.logpdf(rv_angle_term, scale=interp_angle_width) + np.log(2) +# angle_term = -np.abs(angle) / np.pi + #if any_valid: + # np.put_along_axis(angle_term, np.argmin(np.abs(packed_rr), axis=-1)[..., np.newaxis], -np.log(2), axis=-1) + if any_valid: + # don't count the last profile point towards score + np.put_along_axis(rv_angle_term, np.argmin(np.abs(profile_rr), axis=-1)[..., np.newaxis], -np.log(2), axis=-1) + + return dqdx_term, rv_angle_term * mcs_weight + + @staticmethod + def intersection(xyz, dxyz, pxyz, pnorm): + ''' + calculate the intersection of lines with planes + + :param xyz: (..., 3) array representing line origins + :param dxyz: (..., 3) array representing line directions (unit norm) + :param pxyz: (..., 3) array representing a point on the plane + :param pnorm: (..., 3) array representing plane normal (unit norm) + + :returns: (..., 3) array representing the intersection point + ''' + with np.errstate(divide='ignore', invalid='ignore'): + d = np.sum((pxyz - xyz) * pnorm, axis=-1) / np.sum(dxyz * pnorm, axis=-1) + return xyz + dxyz * d[..., np.newaxis] + + + @staticmethod + def profiled_dqdx_kalman(tracks, seed_pt, hit_xyz, hit_q, dx, max_range, search_dx, pixel_pitch, mask=None): + orig_len = len(tracks) + if mask is not None: + tracks = tracks[mask] + seed_pt = seed_pt[mask] + hit_xyz = hit_xyz[mask] + hit_q = hit_q[mask] + + n = len(tracks) + sample_points = int(max_range / dx) + + dq = np.zeros((n, sample_points)) + dn = np.zeros((n, sample_points), dtype=int) + ds = np.zeros((n, sample_points)) + pos = np.zeros((n, sample_points, 3)) + hit_prof_idx = np.full(hit_q.shape, -1, dtype=int) + hit_prof_s = np.full(hit_q.shape, 0, dtype=float) + + hit_mask = ~hit_q.mask + + # find initial point and direction + traj = np.zeros((n, sample_points, 3)) + start_pt = seed_pt[...,0:1,:] + end_pt = start_pt.copy() + traj[...,0:1,:] = seed_pt.copy() + local_mask = np.linalg.norm(hit_xyz - seed_pt, axis=-1, keepdims=True) < search_dx + local_mask = np.broadcast_to(hit_mask[...,np.newaxis] & local_mask, hit_xyz.shape) + curr_direction = ma.array(hit_xyz - seed_pt, mask=~local_mask).mean(axis=-2) + curr_direction /= np.clip(np.linalg.norm(curr_direction, axis=-1, keepdims=True),1e-15,None) + hit_mask = hit_mask & ~local_mask[...,0] + + disabled_channels = resources.get('DisabledChannels', None) + + i = 0 + while (i < sample_points-1) and np.any(hit_mask): + i += 1 + + # collect hits in local region + dr = (hit_xyz - traj[...,i-1,np.newaxis,:]) + dl = np.sum(dr * curr_direction[...,np.newaxis,:], axis=-1, keepdims=True) + forward = dl > 0 + dt = np.linalg.norm(dr - dl * curr_direction[...,np.newaxis,:], axis=-1, keepdims=True) + local_mask = (dl < dx) & (dt < dx/2) & hit_mask[...,np.newaxis] & forward + + # if none found, expand search + if np.any(~((local_mask[...,0]).any(axis=-1)) & hit_mask.any(axis=-1)): + r = np.linalg.norm(dr, axis=-1, keepdims=True) + + # if disabled channels list present and next step is a disabled region, search in a longer line first + if disabled_channels is not None: + proposed_step = traj[...,i-1,:] + curr_direction * dx + step_is_disabled = ~disabled_channels.is_active(proposed_step) + local_mask = local_mask | ( + (dl < 2*dx) & (dt < 3*dx/4) & hit_mask[...,np.newaxis] & forward + & step_is_disabled[...,np.newaxis,np.newaxis] + & ~(local_mask).any(axis=-2, keepdims=True)) + + # then search in a sphere in ever expanding circles + search_factor = 1 + while np.any(~(local_mask[...,0]).any(axis=-1) & hit_mask.any(axis=-1)): + local_mask = (local_mask | ( + (r < search_factor * search_dx) & hit_mask[...,np.newaxis] + & ~(local_mask).any(axis=-2, keepdims=True))) + search_factor += 1 + if search_factor > 5: + break + + # if no more hits found, continue + if not np.any(local_mask): + break + + # calculate new sample point (charge weighted average position) + traj[...,i,:] = ma.average(ma.array(hit_xyz, mask=~np.broadcast_to(local_mask, hit_xyz.shape)), + weights=np.broadcast_to(hit_q[...,np.newaxis], hit_xyz.shape), axis=-2) + end_pt = traj[...,i:i+1,:] + + # calculate new direction + curr_direction = traj[...,i,:] - traj[...,i-1,:] + curr_direction /= np.clip(np.linalg.norm(curr_direction, axis=-1, keepdims=True), 1e-15, None) + + # mask off used hits + hit_mask = hit_mask & ~local_mask[...,0] + + # project hits onto trajectory segments + dr = (hit_xyz[...,np.newaxis,:] - traj[...,np.newaxis,:-1,:]) # (ev, hit, traj-1, 3) + traj_dr = traj[...,np.newaxis,1:,:] - traj[...,np.newaxis,:-1,:] # (ev, 1, traj-1, 3) + traj_l = np.clip(np.linalg.norm(traj_dr, axis=-1, keepdims=True), 1e-15, None) # (ev, 1, traj-1, 1) + traj_dr /= traj_l + alpha = np.sum(dr * traj_dr, axis=-1) / traj_l[...,0] # (ev, hit, traj-1) + + # find closest segment + d = np.linalg.norm(dr - traj_dr * np.clip(alpha[...,np.newaxis], 0, 1) * traj_l, axis=-1) # (ev, hit, traj-1) + d = ma.array(d, mask=(hit_q.mask[...,np.newaxis] | (d > dx/2))) + d.mask[...,i-1:] = True # remove invalid segments + iseg_min = np.argmin(d, axis=-1) # (ev, hit) + iseg_min[np.take_along_axis(d.mask, iseg_min[...,np.newaxis], axis=-1).reshape(iseg_min.shape)] = -1 + + # calculate segment range + s = np.concatenate([np.zeros(traj_l.shape[:-2] + (1,1)), np.cumsum(traj_l, axis=-2)], axis=-2) # (ev, 1, traj-1, 1) + hit_s = np.take_along_axis(s, iseg_min[...,np.newaxis,np.newaxis], axis=-2) + hit_s = hit_s + np.take_along_axis(traj_l * alpha[...,np.newaxis], iseg_min[...,np.newaxis,np.newaxis], axis=-2) # (ev, hit, 1, 1) + hit_s = hit_s[...,0,0] + + # fill bins + bins = np.linspace(0, max_range, sample_points) + hit_prof_idx = np.clip(np.digitize(hit_s, bins=bins) - 1, 0, sample_points-1) + hit_prof_idx[hit_q.mask] = -1 + + sample_point_s = np.zeros_like(ds) + prev_pos = traj[...,0,:] + for i in range(sample_points): + #if not np.any(hit_prof_idx >= i): + # break + + # grab hits from current trajectory point + hit_mask = (hit_prof_idx == i) & (~hit_q.mask) + any_hit_mask = hit_mask.any(axis=-1) + #if not np.any(any_hit_mask): + # continue + + # re-estimate position and only use "local" hits + traj_hit_s = ma.array(hit_s, mask=~hit_mask) + local_pos = (ma.average(ma.array(hit_xyz, mask=~np.broadcast_to(hit_mask[...,np.newaxis], hit_xyz.shape)), + weights=np.broadcast_to(hit_q[...,np.newaxis], hit_xyz.shape), axis=-2) + * any_hit_mask[...,np.newaxis]) + local_pos[~any_hit_mask,:] = prev_pos[~any_hit_mask,:] + prev_pos = local_pos + + hit_mask = hit_mask & (np.linalg.norm(hit_xyz - local_pos[...,np.newaxis,:], axis=-1) < dx) + any_hit_mask = hit_mask.any(axis=-1) + + #if not np.any(any_hit_mask): + # continue + + # fill output arrays + pos[...,i,:] = local_pos + dq[...,i] = (np.sum(ma.array(hit_q, mask=~hit_mask), axis=-1)) * any_hit_mask + dn[...,i] = (np.sum(hit_mask, axis=-1)) * any_hit_mask + local_dir = pos[...,i,:] - pos[...,i-1,:] if i > 0 else traj[...,1,:] - traj[...,0,:] + if i > 0: + sample_point_s[...,i:] += np.linalg.norm(local_dir, axis=-1)[...,np.newaxis] + local_dir /= np.clip(np.linalg.norm(local_dir, axis=-1, keepdims=True), 1e-15, None) + local_s = ma.array(np.sum((hit_xyz - pos[...,i:i+1,:]) * local_dir[...,np.newaxis,:], axis=-1), mask=~hit_mask) + hit_prof_s[hit_mask] = (local_s + sample_point_s[...,i:i+1])[hit_mask] + ds[...,i] = (np.max(local_s, axis=-1) - np.min(local_s, axis=-1)) * any_hit_mask + + r_dq = np.zeros((orig_len,) + dq.shape[1:]) + r_dn = np.zeros((orig_len,) + dn.shape[1:], dtype=int) + r_start_pt = np.zeros((orig_len,) + start_pt.shape[1:]) + r_end_pt = np.zeros((orig_len,) + end_pt.shape[1:]) + r_pos = np.zeros((orig_len,) + pos.shape[1:]) + r_ds = np.zeros((orig_len,) + ds.shape[1:]) + r_hit_prof_idx = np.zeros((orig_len,) + hit_prof_idx.shape[1:], dtype=int) - 1 + r_hit_prof_s = np.zeros((orig_len,) + hit_prof_s.shape[1:], dtype=float) + + np.place(r_dq, np.broadcast_to(mask[..., np.newaxis], r_dq.shape), dq) + np.place(r_dn, np.broadcast_to(mask[..., np.newaxis], r_dn.shape), dn) + np.place(r_ds, np.broadcast_to(mask[..., np.newaxis], r_ds.shape), ds) + np.place(r_start_pt, np.broadcast_to(mask[..., np.newaxis, np.newaxis], r_start_pt.shape), start_pt) + np.place(r_end_pt, np.broadcast_to(mask[..., np.newaxis, np.newaxis], r_end_pt.shape), end_pt) + np.place(r_pos, np.broadcast_to(mask[..., np.newaxis, np.newaxis], r_pos.shape), pos) + np.place(r_hit_prof_idx, np.broadcast_to(mask[..., np.newaxis], r_hit_prof_idx.shape), hit_prof_idx) + np.place(r_hit_prof_s, np.broadcast_to(mask[..., np.newaxis], r_hit_prof_s.shape), hit_prof_s) + + return r_dq, r_dn, r_ds, r_start_pt, r_end_pt, r_pos, r_hit_prof_idx, r_hit_prof_s + + @staticmethod + def mean_neg_loglikelihood(r0, range_table, profile_n, profile_dqdx, profile_rr, profile_pos): + profile_rr = profile_rr - r0 + pt_likelihood_dqdx, pt_likelihood_mcs = HIPSelection.profile_likelihood( + profile_rr, profile_dqdx, profile_pos, range_table) + profile_n, profile_dqdx, profile_rr = np.broadcast_arrays(profile_n, profile_dqdx, profile_rr) + pt_likelihood_mcs = ma.masked_where((profile_n <= 0) | (profile_rr <= 0), pt_likelihood_mcs) + #pt_likelihood_dqdx = ma.masked_where((profile_rr <= 0), pt_likelihood_dqdx) + pt_likelihood_dqdx = ma.masked_where((profile_n <= 0) | (profile_rr <= 0), pt_likelihood_dqdx) + + mean_likelihood = -pt_likelihood_dqdx.mean(axis=-1) - pt_likelihood_mcs.mean(axis=-1) + return mean_likelihood + + + + + + def run(self, source_name, source_slice, cache): + super(HIPSelection, self).run(source_name, source_slice, cache) + + # load arrays of event-level, hit-level, and track-level info + events = cache[source_name] + t0 = cache[self.t0_dset_name].reshape(cache[source_name].shape) + hits = ma.array(cache[self.hits_dset_name], shrink=False) + q = ma.array(cache[self.charge_dset_name], shrink=False) + q = q.reshape(hits.shape) + tracks = ma.array(cache[self.tracklet_dset_name], shrink=False) + #hit_drift = ma.array(cache[self.hit_drift_dset_name].reshape(hits.shape), shrink=False) + #track_hits = ma.array(cache[self.track_hits_dset_name], shrink=False) + #track_hit_drift = ma.array(cache[self.track_hit_drift_dset_name], shrink=False) + #print("Track shape:", tracks.shape) + #print("Track hits shape:", track_hits.shape) + #print("Track hit drift shape:", track_hit_drift.shape) + + if events.shape[0]: + + ## EVENT-LEVEL CALCULATIONS + + # calculate hit positions and charge + hit_q = q['Q'] # convert mV -> ke + # filter out bad channel ids + hit_mask = (hits['y'] != 0.0) & (hits['z'] != 0.0) & ~hit_q.mask & ~hits['t_drift'].mask + hit_q.mask = hit_q.mask | ~hit_mask + hit_xyz = ma.array(np.concatenate([ + hits['x'][..., np.newaxis], hits['y'][..., np.newaxis], + hits['z'][..., np.newaxis]], axis=-1), shrink=False, mask=np.zeros(hits['y'].shape + (3,), dtype=bool) | hit_q.mask[...,np.newaxis] | ~hit_mask[...,np.newaxis]) + hit_in_fid = resources['Geometry'].in_fid(hit_xyz.reshape(-1, 3), cathode_fid=self.cathode_fid_cut, \ + field_cage_fid=self.fid_cut, anode_fid=self.anode_fid_cut).reshape(hit_xyz.shape[:-1]) + hit_q.mask = hit_q.mask | ~hit_in_fid + + # find value for the most charge in one hit in each event + #print("HIt q", hit_q[~hit_q.mask]/self.larpix_gain) + #print("HIt q", hit_q.shape) + max_hit_charge = ma.array([int(ma.filled(hit_q[i,:].astype(float) > 37.5, False).sum()) for i in range(len(hits))]) # 75 ke -> 300 mV with conversion in calib hits + #print("test max hit charge:", ma.filled(hit_q[0,:].astype(float)/self.larpix_gain > 300., False)== True) + #print("NEW MAX HIT CHARGE SHAPE:", max_hit_charge.shape) + #print("Max Hit Charge:", max_hit_charge) + ## TRACK-LEVEL CALCULATIONS + + # find all tracks that end in the fiducial volume + track_start = tracks.ravel()['trajectory'][..., 0, :] + track_stop = tracks.ravel()['trajectory'][..., -1, :] + #print("Track start:", track_start) + #print("Track stop:", track_stop) + #track_dqdx = tracks.ravel()['dq']/np.sqrt(np.sum(tracks.ravel()['dx']**2, axis=-1)) + #print("Tracks dq/dx:", track_dqdx[:5]) + #print("Tracks dn:", tracks.ravel()['dn'][:5]) + #track_dqdx_start = track_dqdx[track_dqdx != 0][..., 0] + #track_dqdx_stop = track_dqdx[track_dqdx != 0][..., -1] + #track_dn_start = tracks.ravel()['dn'][..., 0] + #track_dn_stop = tracks.ravel()['dn'][..., -1] + #print("Tracks start shape:", track_start.shape) + #print("Tracks dq shape:", track_dqdx_start.shape) + + + start_in_fid = resources['Geometry'].in_fid( + track_start, field_cage_fid=self.fid_cut, anode_fid=self.anode_fid_cut) + start_in_fid = start_in_fid.reshape(tracks.shape) + stop_in_fid = resources['Geometry'].in_fid( + track_stop, field_cage_fid=self.fid_cut, anode_fid=self.anode_fid_cut) + stop_in_fid = stop_in_fid.reshape(tracks.shape) + contained_in_fid = start_in_fid & stop_in_fid + #print("Track start:", track_start[:5,:]) + #print("Track stop:", track_stop[:5,:]) + #print("Track dq start:", track_dqdx_start[:5]) + #print("Track dq stop:", track_dqdx_stop[:5]) + #print("Track dnhits start:", track_dn_start[:5]) + #print("Track dnhits stop:", track_dn_stop[:5]) + #print("Shape of start_in_fid:", start_in_fid.shape) + #print("Start in fid:", start_in_fid) + #print("Stop in fid:", stop_in_fid) + #print("Contained in fid:", contained_in_fid) + event_ntracks_in_fid = np.zeros(len(tracks), dtype=int) + #print("Start in FID masked tracks:", np.array([int(start_in_fid[i].sum()) for i in range(len(tracks))])) + + # prep arrays to write to file + event_ids = events['id'] + event_next_trigs = events['n_ext_trigs'] + #print("Shape of one event's tracks:", tracks['id'][0].shape) + #print("One event's tracks's mask:", tracks['id'][0].mask) + #print("One event's tracks's ids:", tracks['id'][0]) + #print("Number of valid events for one event:", int((~tracks['id'][0].mask).sum())) + event_ntracks = np.array([int((~tracks['id'][i].mask).sum()) for i in range(len(tracks))]) + event_nhits = events['nhit'] + #event_charge = events['q'] + for i in range(len(tracks)): + if event_ntracks[i] > 0: + event_ntracks_in_fid[i] = int(contained_in_fid[i].sum()) + else: + event_ntracks_in_fid[i] = 0 + + nhits_cut = (event_nhits > 50) & (event_nhits < 5000) + #print("Number of hits:", nhits_cut) + hit_charge_threshold_cut = (max_hit_charge > 1) # cut on number of hits over threshold, which is currently 300 mV + external_trigger_cut = (event_next_trigs > 0) + ntracks_in_fid_cut = (event_ntracks_in_fid == event_ntracks) & (event_ntracks == 1)# & (event_ntracks <= 3) + event_level_cuts = nhits_cut & hit_charge_threshold_cut & external_trigger_cut & ntracks_in_fid_cut + print("Event level cuts:", event_level_cuts) + + + print("Passing Tracks:", tracks['trajectory'][event_level_cuts]) + print("Event id:", events['id'][event_level_cuts]) + + max_tracks = contained_in_fid.shape[1] + #print("Max tracks shape:",max_tracks) + #print("Event level cuts shape:", event_level_cuts.shape) + # make the array of all initial cuts the same length as the tracks array + #event_level_cuts_ext = np.array([np.full(max_tracks, event_level_cuts[i]) for i in range(len(event_level_cuts))]) + #print("Event level cuts extended shape:", event_level_cuts_ext.shape) + + #all_initial_cuts_ext = contained_in_fid & event_level_cuts_ext + #contained_in_fid_red = np.logical_and.reduce(contained_in_fid, -1, dtype=bool) + #print("Contained in FID Reduced:", contained_in_fid_red) + + #print("All initial cuts shape:", all_initial_cuts.shape) + + # Look into unique channels: + #hits_with_channels = ma.array([hits['iogroup'], hits['iochannel'], hits['chipid'], hits['channelid']]) + #print("IO Group:", hits['iogroup']) + #print("IO Channel:", hits['iochannel']) + #print("Chip ID:", hits['chipid']) + #print("Channel ID:", hits['channelid']) + #print("Shape of hits with channels:", hits_with_channels.shape) + + + '''if self.is_mc: + # lookup the track's true trajectory + track_traj = cache[self.truth_trajectories_dset_name] + #print("True Trajectory PID situation:", track_traj['pdgId']) + + if track_traj.shape[0]: + #print("track ids pre-reshaping:", track_traj['trackID']) + track_traj = track_traj.reshape(tracks.shape[0:1] + (-1,)) + track_traj = condense_array(track_traj, track_traj['trackID'].mask) + track_pdg = condense_array(track_traj, track_traj['pdgId'].mask) + + #print("track ids post-reshaping:", track_traj['trackID']) + #print("pdg ids post-reshaping:", track_pdg['pdgId']) + proton_mask_true = track_pdg['pdgId'] == 2212 + proton_mask = np.tile(proton_mask_true[..., np.newaxis], (1,1,3)) + + + #print("Proton mask:", proton_mask) + true_xyz_start = ma.masked_where(~proton_mask, track_traj['xyz_start']) + true_xyz_end = ma.masked_where(~proton_mask, track_traj['xyz_end']) + + #n_protons = len(track_pdg[proton_mask_true]) + #true_xyz_start = true_xyz_start[~true_xyz_start.mask].reshape((n_protons,3)) + #true_xyz_end = true_xyz_end[~true_xyz_end.mask].reshape((n_protons,3)) + #print("True xyz start:", true_xyz_start) + #print("Proton trajectories:", proton_trajectories) + # Look at all possible proton trajectories + #i_primary_traj = proton_trajectories + #print("Track trajectory shape:", track_traj.shape) + #print("Proton trajectories axis shape:", i_primary_traj.shape) + #track_true_traj = d + #print("Track true trajectories only protons:", track_true_traj) + #track_true_traj = track_true_traj.reshape(-1) + #true_xyz_start = proton_trajectories['xyz_start'] #track_true_traj['xyz_start'] + #true_xyz_end = proton_trajectories['xyz_end']#track_true_traj['xyz_end'] + + # find if trajectory ends in the fiducial volume + #is_muon = ma.abs(track_true_traj['pdgId']) == 1 + #print("PDG ID shape:", track_traj['pdgId'].shape) + #is_proton = ma.array([int(((track_traj['pdgId'][i] == 2212).astype(float)).sum(axis=-1))>=1 for i in range(len(track_traj))]) + + is_proton = np.empty(tracks.shape[0], dtype=bool) + for i in range(len(tracks)): + all_pdg = track_traj['pdgId'][i].ravel() == 2212 + is_proton[i] = np.sum(all_pdg.astype(int)) + #print("What is is_proton?:", is_proton) + + #if len(is_proton): + #print("True start:", true_xyz_start[is_proton]) + #print("True start:", true_xyz_end[is_proton]) + else: + track_true_traj = np.empty(tracks.shape[0], dtype=track_traj.dtype) + is_muon = np.zeros(track_true_traj.shape, dtype=bool) + is_proton = np.zeros(track_true_traj.shape, dtype=bool) + true_xyz_start = track_true_traj['xyz_start'] + true_xyz_end = track_true_traj['xyz_end'] + # define seed point based on + start_in_fid = resources['Geometry'].in_fid( + track_start, field_cage_fid=self.fid_cut, anode_fid=self.anode_fid_cut) + seed_pt = track_start.reshape(tracks.shape + (3,)) + seed_track_mask = all_initial_cuts + max_seed_pts = int(max(np.sum((ma.filled(seed_track_mask.astype(float), 0.0)), axis=-1).max(), 1)) + seed_pt_idx = ma.argsort(ma.array(seed_track_mask, mask=~seed_track_mask | seed_track_mask.mask), axis=-1, fill_value=0)[..., ::-1, np.newaxis] + seed_pt = np.take_along_axis(seed_pt, seed_pt_idx, axis=1)[...,:max_seed_pts,:] + seed_pt = ma.array(seed_pt, mask=np.indices(seed_pt.shape)[1] >= np.sum(seed_track_mask, axis=-1, keepdims=True)[...,np.newaxis]) + #print("Seed point:", seed_pt[~seed_pt.mask]) + #print("Seed point shape:", seed_pt.shape) + + event_passes_initial_cuts = ((t0['type'] != 0) + #& (veto_q < self.veto_charge_cut) + #& (active_proj_length > self.projected_length_cut) + #& (ma.sum(is_throughgoing, axis=-1) == 0) + & (ma.sum(seed_track_mask, axis=-1) >= 1)) + + # now check the likelihood of a stopping muon + + # broadcast into appropriate shape for kalman fit + tracks_km = np.broadcast_to(tracks[:,np.newaxis], (tracks.shape[0], max_seed_pts, tracks.shape[1]), subok=True).reshape(-1, tracks.shape[1]) + tracks_km.mask = np.broadcast_to(tracks.mask[:,np.newaxis], (tracks.shape[0], max_seed_pts, tracks.shape[1]), subok=True).reshape(-1, tracks.shape[1]) + hit_xyz_km = np.broadcast_to(hit_xyz[:,np.newaxis], (hit_xyz.shape[0], max_seed_pts) + hit_xyz.shape[1:], subok=True).reshape(-1, *hit_xyz.shape[1:]) + hit_xyz_km.mask = np.broadcast_to(hit_xyz.mask[:,np.newaxis], (hit_xyz.shape[0], max_seed_pts) + hit_xyz.shape[1:], subok=True).reshape(-1, *hit_xyz.shape[1:]) + hit_q_km = np.broadcast_to(hit_q[:,np.newaxis], (hit_q.shape[0], max_seed_pts) + hit_q.shape[1:], subok=True).reshape(-1, *hit_q.shape[1:]) + hit_q_km.mask = np.broadcast_to(hit_q.mask[:,np.newaxis], (hit_q.shape[0], max_seed_pts) + hit_q.shape[1:], subok=True).reshape(-1, *hit_q.shape[1:]) + kalman_mask = (event_passes_initial_cuts[...,np.newaxis] & ~seed_pt.mask[...,0]).ravel() + + # first generate the dQ/dx profile + dq, dn, ds, start_pt, end_pt, pos, hit_prof_idx, hit_prof_s = self.profiled_dqdx_kalman( + tracks_km, seed_pt.reshape(-1, 1, 3), hit_xyz_km, hit_q_km, + mask=kalman_mask, + dx=self.profile_dx, search_dx=self.profile_search_dx, + max_range=self.profile_max_range, pixel_pitch=resources['Geometry'].pixel_pitch) + #ds += resources['Geometry'].pixel_pitch # correct for pixel edges + ds = self.dx_estimate(pos, hit_xyz_km, hit_prof_idx, resources['Geometry'].pixel_pitch) + profile_n = dn + profile_dqdx = dq / ma.maximum(ds, resources['Geometry'].pixel_pitch) * (dn > 0) + profile_dqdx[dn <= 0] = 0 + + # make an initial guess for the stopping point (maximum 2 dQ/dx bins) + profile_rr = np.linalg.norm(pos[...,1:,:] - pos[...,:-1,:], axis=-1) + profile_rr = np.concatenate((np.zeros(profile_rr.shape[:-1]+(1,)), profile_rr), axis=-1) + profile_rr = np.cumsum(profile_rr, axis=-1) + + i_max = np.argsort(profile_dqdx, axis=-1)[...,-2:] + profile_offset0 = np.take_along_axis(profile_rr, i_max[...,0:1], axis=-1) + profile_offset1 = np.take_along_axis(profile_rr, i_max[...,1:2], axis=-1) + + # refine guess by using the hit with the largest charge + hit_near_stop0 = (hit_prof_idx == i_max[...,0:1]) + hit_near_stop1 = (hit_prof_idx == i_max[...,1:2]) + profile_offset0[hit_near_stop0.any(axis=-1)] = np.take_along_axis( + hit_prof_s, np.argmax(ma.array(hit_q_km, mask=~hit_near_stop0), axis=-1)[...,np.newaxis], axis=-1)[hit_near_stop0.any(axis=-1)] + profile_offset1[hit_near_stop1.any(axis=-1)] = np.take_along_axis( + hit_prof_s, np.argmax(ma.array(hit_q_km, mask=~hit_near_stop1), axis=-1)[...,np.newaxis], axis=-1)[hit_near_stop1.any(axis=-1)] + + profile_rr0 = profile_offset0 - profile_rr + profile_rr1 = profile_offset1 - profile_rr + + # perform a fit for the stopping point assuming a muon or a proton + proton_score = np.full(profile_dqdx.shape[:-1], 1e+303) + muon_r0 = np.zeros(profile_dqdx.shape[:-1]) + proton_r0 = np.zeros(profile_dqdx.shape[:-1]) + max_range = 0 #self.profile_dx # within +/- 1 profile bins + sample_factor = 1 #20 # resolution is profile bin/10 + + for i in range(proton_r0.shape[0]): + if np.any((profile_n[i] > 0)): + valid_mask = profile_n[i] > 0 + + muon_offset = [] + proton_offset = [] + muon_likelihood = [] + proton_likelihood = [] + + for j,rr in enumerate([profile_rr0[i], profile_rr1[i]]): + rr_range = (np.maximum(-max_range, rr[valid_mask].min()), + np.minimum(+max_range, rr[valid_mask].max())) + rr_offset = np.expand_dims( + np.linspace(rr_range[0], rr_range[1], + np.clip(sample_factor * int(np.diff(rr_range) / self.profile_dx),1,None)), + axis=-1) + close_dqdx = np.take_along_axis(profile_dqdx[i:i + 1], np.argmin(np.abs(rr[np.newaxis,...] - rr_offset), axis=-1)[..., np.newaxis], axis=-1) + mask = np.ones_like((close_dqdx > self.dqdx_peak_cut)) # ignore dQ/dx mask + #if not np.any(mask): + # continue + + muon_likelihood.append(self.mean_neg_loglikelihood( + rr_offset + muon_r0[i], self.muon_range_table, profile_n[i:i + 1], profile_dqdx[i:i + 1], rr[np.newaxis,...], pos[i:i + 1])) + #muon_r0[i] = rr_offset[ma.argmin(ma.array(muon_likelihood, mask=~mask), axis=0)] + muon_r0[i] + muon_offset.append(rr_offset[ma.argmin(ma.array(muon_likelihood[j], mask=~mask), axis=0)]) + + proton_likelihood.append(self.mean_neg_loglikelihood( + rr_offset + proton_r0[i], self.proton_range_table, profile_n[i:i + 1], profile_dqdx[i:i + 1], rr[np.newaxis,...], pos[i:i + 1])) + #proton_r0[i] = rr_offset[ma.argmin(ma.array(proton_likelihood, mask=~mask), axis=0)] + proton_r0[i] + proton_offset.append(rr_offset[ma.argmin(ma.array(proton_likelihood[j], mask=~mask), axis=0)]) + + muon_j_min = np.argmin([np.min(ll) if ll is not np.nan else 1e+303 for ll in muon_likelihood]) + proton_j_min = np.argmin([np.min(ll) if ll is not np.nan else 1e+303 for ll in proton_likelihood]) + proton_score[i] = ma.filled(proton_likelihood[proton_j_min].astype(float), 1e+303) + muon_r0[i] = muon_offset[muon_j_min] + proton_r0[i] = proton_offset[proton_j_min] + profile_rr[i] = [profile_rr0[i], profile_rr1[i]][muon_j_min] + + # use only the dQ/dx profile from the most proton-like seed point + ibest_seed = ma.argmin(ma.array(proton_score, mask=np.all(profile_n == 0, axis=-1)).reshape(-1, max_seed_pts), axis=-1)[...,np.newaxis] + profile_dqdx = np.take_along_axis(profile_dqdx.reshape(ibest_seed.shape[0], max_seed_pts, -1), ibest_seed[...,np.newaxis], axis=1)[:,0] + profile_n = np.take_along_axis(profile_n.reshape(ibest_seed.shape[0], max_seed_pts, -1), ibest_seed[...,np.newaxis], axis=1)[:,0] + pos = np.take_along_axis(pos.reshape(ibest_seed.shape[0], max_seed_pts, -1, 3), ibest_seed[...,np.newaxis,np.newaxis], axis=1)[:,0] + profile_rr = np.take_along_axis(profile_rr.reshape(ibest_seed.shape[0], max_seed_pts, -1), ibest_seed[...,np.newaxis], axis=1)[:,0] + muon_r0 = np.take_along_axis(muon_r0.reshape(ibest_seed.shape[0], max_seed_pts), ibest_seed, axis=1)[:,0] + proton_r0 = np.take_along_axis(proton_r0.reshape(ibest_seed.shape[0], max_seed_pts), ibest_seed, axis=1)[:,0] + dq = np.take_along_axis(dq.reshape(ibest_seed.shape[0], max_seed_pts, -1), ibest_seed[...,np.newaxis], axis=1)[:,0] + dn = np.take_along_axis(dn.reshape(ibest_seed.shape[0], max_seed_pts, -1), ibest_seed[...,np.newaxis], axis=1)[:,0] + ds = np.take_along_axis(ds.reshape(ibest_seed.shape[0], max_seed_pts, -1), ibest_seed[...,np.newaxis], axis=1)[:,0] + start_pt = np.take_along_axis(start_pt.reshape(ibest_seed.shape[0], max_seed_pts, -1, 3), ibest_seed[...,np.newaxis,np.newaxis], axis=1)[:,0] + end_pt = np.take_along_axis(end_pt.reshape(ibest_seed.shape[0], max_seed_pts, -1, 3), ibest_seed[...,np.newaxis,np.newaxis], axis=1)[:,0] + hit_prof_idx = np.take_along_axis(hit_prof_idx.reshape(ibest_seed.shape[0], max_seed_pts, -1), ibest_seed[...,np.newaxis], axis=1)[:,0] + hit_prof_s = np.take_along_axis(hit_prof_s.reshape(ibest_seed.shape[0], max_seed_pts, -1), ibest_seed[...,np.newaxis], axis=1)[:,0] + + # calculate likelihood scores for refined dQ/dx profile + muon_likelihood_dqdx, muon_likelihood_mcs = self.profile_likelihood( + (profile_rr - muon_r0[..., np.newaxis]), profile_dqdx, pos, + self.muon_range_table) + proton_likelihood_dqdx, proton_likelihood_mcs = self.profile_likelihood( + (profile_rr - proton_r0[..., np.newaxis]), profile_dqdx, pos, + self.proton_range_table) + mip_likelihood_dqdx, mip_likelihood_mcs = self.profile_likelihood( + np.clip(profile_rr, 1500, 1500), profile_dqdx, pos, + self.muon_range_table) + + muon_likelihood_mcs = ma.masked_where( + (dn == 0) | (profile_rr - muon_r0[..., np.newaxis] <= 0), + muon_likelihood_mcs) + proton_likelihood_mcs = ma.masked_where( + (dn == 0) | (profile_rr - proton_r0[..., np.newaxis] <= 0), + proton_likelihood_mcs) + mip_likelihood_mcs = ma.masked_where( + (dn == 0) | (profile_rr - muon_r0[..., np.newaxis] <= 0), + mip_likelihood_mcs) + muon_likelihood_dqdx = ma.masked_where( + (dn == 0) | (profile_rr - muon_r0[..., np.newaxis] <= 0), + muon_likelihood_dqdx) + proton_likelihood_dqdx = ma.masked_where( + (dn == 0) | (profile_rr - proton_r0[..., np.newaxis] <= 0), + proton_likelihood_dqdx) + mip_likelihood_dqdx = ma.masked_where( + (dn == 0) | (profile_rr - muon_r0[..., np.newaxis] <= 0), + mip_likelihood_dqdx) + + # get end point (for stopping muon assumption) + profile_rr = ma.array(profile_rr - muon_r0[..., np.newaxis], mask=(profile_n <= 0)) + i_stop = np.argmin(np.abs(profile_rr), axis=-1)[..., np.newaxis, np.newaxis] + end_pt = np.take_along_axis(pos, i_stop, axis=-2) + + # correct for rounding error + stop_rr = np.take_along_axis(profile_rr, i_stop[...,0], axis=-1)[...,np.newaxis] + n = end_pt - np.take_along_axis(pos, np.clip(i_stop-1,0,None), axis=-2) + n /= np.clip(np.linalg.norm(n, axis=-1, keepdims=True), 1e-15, None) + end_pt_corr = stop_rr * n + + # check if endpoint in fiducial volume + end_pt_in_fid = resources['Geometry'].in_fid( + end_pt.reshape(-1, 3), cathode_fid=self.cathode_fid_cut, field_cage_fid=self.fid_cut, anode_fid=self.anode_fid_cut) + end_pt_in_fid = end_pt_in_fid.reshape(tracks.shape[0]) + + # estimate residual range for each hit + hit_prof_rr = profile_rr.max(axis=-1, keepdims=True) - hit_prof_s + + # calculate "additional" energy (all energy not associated to the parent muon) assuming nominal michel dE/dx + q_sum = hit_q.sum(axis=-1) - ma.array(dq, mask=(np.around(profile_rr/self.profile_dx) * self.profile_dx < 0) | (profile_n <= 0)).sum(axis=-1) + michel_dedx = resources['ParticleData'].landau_peak(50 * units.MeV, resources['ParticleData'].e_mass, resources['Geometry'].pixel_pitch) + e = q_sum * resources['LArData'].ionization_w / resources['LArData'].ionization_recombination(michel_dedx) + + # calculate active distance to exit detector + #active_proj_length = self.extrapolated_intersection(pos[...,0,:], end_pt.reshape(-1,3)) + + # apply a hit density correction + #profile_dqdx = profile_dqdx * ds / ma.maximum(ds - self.density_dx_correction(profile_rr, *self.density_dx_correction_params), resources['Geometry'].pixel_pitch) * (dn > 0) + # apply a curvature correction + profile_rr = profile_rr * self.curvature_rr_correction + + # find max dqdx + max_dqdx = profile_dqdx.max(axis=-1) + + # select stopping muons + #event_is_contained_muon = (event_is_contained & end_pt_in_fid # stops in fiducial volume + # & (e < self.remaining_e_cut) # has additional energy consistent with a Michel or less + # & (max_dqdx > self.dqdx_peak_cut) # has a prominent dQ/dx peak + # #& (ma.sum(is_stopping & ~is_near_edge, axis=-1) == 1) # only one track stopping in fiducial volume + # & (np.mean(muon_likelihood_dqdx, axis=-1) + # + np.mean(muon_likelihood_mcs, axis=-1) * 0 + # - np.mean(proton_likelihood_dqdx, axis=-1) + # - np.mean(proton_likelihood_mcs, axis=-1) * 0 < self.proton_classifier_cut) # dQ/dx profile more consistent with stopping muon than proton + # & (np.mean(muon_likelihood_dqdx, axis=-1) + # + np.mean(muon_likelihood_mcs, axis=-1) * 0 + # - np.mean(mip_likelihood_dqdx, axis=-1) + # - np.mean(mip_likelihood_mcs, axis=-1) * 0 > self.muon_classifier_cut)) # dQ/dx profile more consistent with stopping muon than MIP + + #PID score muon/proton = (2/pi)arctan((loglikelihood muon - loglikelihood proton) / 100) close to 1 = muon, close to -1 = proton + #print("MUON likelihood dqdx shape:", muon_likelihood_dqdx.shape) + #print("MUON likelihood dqdx:", muon_likelihood_dqdx) + #print("Mean MUON likelihood dqdx -1:", np.mean(muon_likelihood_dqdx, axis=-1)) + #print("Mean Muon likelihood dqdx general:", np.mean(muon_likelihood_dqdx)) + pid_muon_proton = (np.mean(muon_likelihood_dqdx, axis=-1) - (np.mean(proton_likelihood_dqdx, axis=-1))) + + #PID score mip/proton = (2/pi)arctan((loglikelihood mip - loglikelihood proton) / 100) close to 1 = mip, close to -1 = proton + pid_mip_proton = (np.mean(mip_likelihood_dqdx, axis=-1) - (np.mean(proton_likelihood_dqdx, axis=-1))) + + ''' + event_sel = (event_level_cuts) + #& (pid_muon_proton > -1.) + #& (pid_mip_proton > -1.)) # dQ/dx profile more consistent with stopping muon than MIP + #& (-np.mean(muon_likelihood_dqdx, axis=-1) + # + np.mean(proton_likelihood_dqdx, axis=-1)> self.proton_classifier_cut)) + + #track_nhits = tracks.ravel()['nhit'][~tracks['nhit'].mask] + #track_length = tracks.ravel()['length'][~tracks['length'].mask] + #track_theta = tracks.ravel()['theta'][~tracks['theta'].mask] + #track_phi = tracks.ravel()['phi'][~tracks['phi'].mask] + #track_q = tracks.ravel()['q'][~tracks['q'].mask] + + #passing_events = len(event_ids[event_sel]) + + #print("Max Track length:", max_track_length) + + '''if self.is_mc and len(is_proton): + # define true proton events contained in fid as any event with + # at least one proton contained in fid + event_is_true_proton = is_proton + #print("Shape of is_proton:", is_proton.shape) + true_contained = np.full_like(is_proton, False) + for i in range(len(true_contained)): + true_xyz_start_in_fid = resources['Geometry'].in_fid( + true_xyz_start[i], cathode_fid=self.cathode_fid_cut, field_cage_fid=self.fid_cut, anode_fid=self.anode_fid_cut) + true_xyz_end_in_fid = resources['Geometry'].in_fid( + true_xyz_end[i], cathode_fid=self.cathode_fid_cut, field_cage_fid=self.fid_cut, anode_fid=self.anode_fid_cut) + contained = true_xyz_start_in_fid & true_xyz_end_in_fid + true_contained[i] = bool(np.sum(contained)) + #print("Contained:", contained) + #print("Sum contained", bool(np.sum(contained))) + #true_contained.reshape(len(tracks)) + #print("True contained shape:", true_contained.shape)''' + + + sel = np.zeros(len(tracks), dtype=self.sel_dtype) + #print("Event selection:", event_sel) + + if len(sel): + #print("Selection identified:", str(len(sel))+"/32") + sel['sel'] = event_sel + #print("Selected:", sel['sel']) + sel['event_id'] = event_ids + sel['hip'] = event_sel #((pid_muon_proton > -1.)& (pid_mip_proton > -1.)) + sel['nhits_over_thresh'] = max_hit_charge + sel['pdg_id'] = np.zeros(1000) + #sel['muon_loglikelihood_mean'] = np.mean(muon_likelihood_mcs, axis=-1) * 0 + np.mean(muon_likelihood_dqdx, axis=-1) + #sel['proton_loglikelihood_mean'] = np.mean(proton_likelihood_mcs, axis=-1) * 0 + np.mean(proton_likelihood_dqdx, axis=-1) + #sel['mip_loglikelihood_mean'] = np.mean(mip_likelihood_mcs, axis=-1) * 0 + np.mean(mip_likelihood_dqdx, axis=-1) + #sel['max_dqdx'] = max_dqdx + sel['ntracks'] = event_ntracks_in_fid + #sel['pid_muon_proton'] = pid_muon_proton + #sel['pid_mip_proton'] = pid_mip_proton + #sel['next_trigs'] = event_next_trigs[event_sel] + #sel['ntracks'] = event_ntracks[event_sel] + #sel['nhits'] = event_nhits[event_sel] + #sel['event_charge'] = event_charge[event_sel] + + '''if self.is_mc: + event_true_sel = np.zeros(len(tracks), dtype=self.sel_dtype) + if len(event_true_sel): + event_true_sel['sel'] = event_is_true_proton & true_contained + event_true_sel['hip'] = event_is_true_proton + event_true_sel['event_id'] = event_ids + event_true_sel['pdg_id'] = np.concatenate((track_traj['pdgId'],np.zeros((len(tracks), 1000-len(track_traj['pdgId'][0])))), axis=-1) + #event_true_sel['muon_loglikelihood_mean'] = ma.sum(is_muon, axis=-1) >= 1 + #event_true_sel['proton_loglikelihood_mean'] = ma.sum(is_proton & is_true_stopping, axis=-1) >= 1 + #event_true_sel['mip_loglikelihood_mean'] = ma.sum(is_muon & ~is_true_stopping, axis=-1) >= 1''' +# + #event_tracks = np.zeros(len(track_length), dtype=self.event_tracks_dtype) + + #if len(event_tracks): + # event_tracks['nhits'] = track_nhits + # event_tracks['length'] = track_length + # event_tracks['theta'] = track_theta + # event_tracks['phi'] = track_phi + # event_tracks['track_q'] = track_q + # + #hit_profile = np.zeros(hits.shape, dtype=self.hit_profile_dtype) + #if len(hit_profile): + # hit_profile['idx'] -= 1 + # hit_profile['idx'][~hits['id'].mask] = hit_prof_idx[~hits['id'].mask] + # hit_profile['rr'][~hits['id'].mask] = hit_prof_rr[~hits['id'].mask] + + + # reserve data space + sel_slice = self.data_manager.reserve_data( + f'{self.path}/{self.sel_dset_name}', source_slice) + #event_tracks_slice = self.data_manager.reserve_data( + # f'{self.path}/{self.event_tracks_dset_name}', source_slice) + #event_hits_slice = self.data_manager.reserve_data( + # f'{self.path}/{self.hit_profile_dset_name}', int((~hits['id'].mask).sum())) + if self.is_mc: + sel_truth_slice = self.data_manager.reserve_data( + f'{self.path}/{self.sel_truth_dset_name}', + source_slice) + + # write + self.data_manager.write_data(f'{self.path}/{self.sel_dset_name}', + sel_slice, sel) + if self.is_mc: + self.data_manager.write_data( + f'{self.path}/{self.sel_truth_dset_name}', + sel_truth_slice, event_true_sel) + #self.data_manager.write_data(f'{self.path}/{self.event_tracks_dset_name}', + # event_tracks_slice, event_tracks) + #self.data_manager.write_data(f'{self.path}/{self.hit_profile_dset_name}', + # event_hits_slice, hit_profile[~hits['id'].mask]) + #self.data_manager.write_ref(f'{self.path}/{self.hit_profile_dset_name}', + # self.hits_dset_name, np.c_[event_hits_slice, hits['id'].compressed()]) + + ## calculate hit positions and charge + #hit_q = self.larpix_gain * q['q'] # convert mV -> ke +# + ## filter out bad channel ids + #hit_mask = (hits['px'] != 0.0) & (hits['py'] != 0.0) & ~hit_q.mask & ~hit_drift['t_drift'].mask + #hit_q.mask = hit_q.mask | ~hit_mask + #hit_xyz = ma.array(np.concatenate([ + # hits['px'][..., np.newaxis], hits['py'][..., np.newaxis], + # hit_drift['z'][..., np.newaxis]], axis=-1), shrink=False,\ + # mask=np.zeros(hits['px'].shape + (3,), dtype=bool) | hit_q.mask[...,np.newaxis] | ~hit_mask[...,np.newaxis]) + + #Event charge threshold selection + #HIP, MIP selection + #Track fitting + #PIDA + #Void analysis \ No newline at end of file diff --git a/src/proto_nd_flow/reco/charge/calib_prompt_hits.py b/src/proto_nd_flow/reco/charge/calib_prompt_hits.py index 6afa7afd..b18a75ce 100644 --- a/src/proto_nd_flow/reco/charge/calib_prompt_hits.py +++ b/src/proto_nd_flow/reco/charge/calib_prompt_hits.py @@ -60,6 +60,8 @@ class CalibHitBuilder(H5FlowStage): ts_pps f8, PPS packet timestamp [ns] io_group u8, io group ID (PACMAN number) io_channel u8, io channel ID (related to PACMAN number & PACMAN UART Number) + chip_id u8, chip ID (ASIC number on PACMAN UART) + channel_id u8, channel ID (channel number on ASIC) Q f8, hit charge [ke-] E f8, hit energy [MeV] @@ -86,6 +88,8 @@ class CalibHitBuilder(H5FlowStage): ('ts_pps', 'u8'), ('io_group', 'u8'), ('io_channel', 'u8'), + ('chip_id', 'u8'), + ('channel_id', 'u8'), ('Q', 'f8'), ('E', 'f8') ]) @@ -146,20 +150,22 @@ def run(self, source_name, source_slice, cache): mask = ~rfn.structured_to_unstructured(packets_data.mask).any(axis=-1) rh_mask = ~rfn.structured_to_unstructured(raw_hits.mask).any(axis=-1) + has_mc_truth = packet_seg_bt is not None # TODO: change to using RunData "is_mc" field? + # get event boundaries if np.count_nonzero(mask): raw_hits_arr = raw_hits.data[rh_mask] mask = (packets_data['packet_type'] == 0) & mask n = np.count_nonzero(mask) packets_arr = packets_data.data[mask] - packet_frac_bt_arr = packet_frac_bt.data[mask] - packet_seg_bt_arr = packet_seg_bt.data[mask] index_arr = packets_index.data[mask] + if has_mc_truth: + packet_frac_bt_arr = packet_frac_bt.data[mask] + packet_seg_bt_arr = packet_seg_bt.data[mask] else: n = 0 index_arr = np.zeros((0,), dtype=packets_index.dtype) - has_mc_truth = packet_seg_bt is not None # reserve new data calib_hits_slice = self.data_manager.reserve_data(self.calib_hits_dset_name, n) @@ -218,6 +224,8 @@ def run(self, source_name, source_slice, cache): calib_hits_arr['t_drift'] = drift_t calib_hits_arr['io_group'] = packets_arr['io_group'] calib_hits_arr['io_channel'] = packets_arr['io_channel'] + calib_hits_arr['chip_id'] = packets_arr['chip_id'] + calib_hits_arr['channel_id'] = packets_arr['channel_id'] calib_hits_arr['Q'] = self.charge_from_dataword(packets_arr['dataword'],vref,vcm,ped) calib_hits_arr['E'] = self.charge_from_dataword(packets_arr['dataword'],vref,vcm,ped) * 23.6e-3 # hardcoding W_ion and not accounting for finite electron lifetime diff --git a/src/proto_nd_flow/resources/disabled_channels.py b/src/proto_nd_flow/resources/disabled_channels.py new file mode 100644 index 00000000..c5dcc44d --- /dev/null +++ b/src/proto_nd_flow/resources/disabled_channels.py @@ -0,0 +1,290 @@ +import logging +import json +import numpy as np +import random + +from h5flow.core import H5FlowResource, resources + +import proto_nd_flow.util.units as units +from proto_nd_flow.util.lut import LUT, write_lut, read_lut +from proto_nd_flow.util.compat import assert_compat_version + + +class DisabledChannels(H5FlowResource): + ''' + Provides helper functions for identifying the positions of disabled + channels. + + Requires ``RunData`` and ``Geometry`` resources within workflow. + + Parameters: + - ``path``: ``str``, path to stored geometry data within file + - ``disabled_channels_timestamp_dict``: ``str``, path to file mapping disabled channel file timestamps to data file timestamps + - ``disabled_channels_file_dir``: ``str``, path to directory with time dependent disabled channel files + - ``disabled_channels_common_filename``: ``str``, common beginning part of disabled channel file filenames + - ``disabled_channels_file_format``: ``str``, file format for disabled channel files + - ``missing_asic_list``: ``str``, path to file specifying disabled coordinates not in geometry file + + Provides: + - ``disabled_pixel_coords``: 2D coordinates of all disabled channels + - ``disabled_channel_lut``: lookup table to find if a pixel 2D coordinate is disabled + - ``is_active()``: helper function for determining if a 3D point in in an active region + + Example usage:: + + from h5flow.core import resources + + resources['DisabledChannels'].disabled_channel_lut[(io_group,z,y)] + + Example config:: + + resources: + - classname: DisabledChannels + params: + path: 'disabled_channels' + disabled_channels_timestamp_dict: 'data/module0_flow/module1_config_to_data_map.json' + disabled_channels_file_dir: '/global/cfs/cdirs/dune/www/data/Module1/TPC12/disabled/' + disabled_channels_common_filename: 'disabled_channels_' + disabled_channels_file_format: '.json' + missing_asic_list: 'data/module1_flow/module1-network-absent-ASICs.json' + + ''' + class_version = '0.0.0' + + default_path = 'disabled_channels' + + def __init__(self, **params): + super(DisabledChannels, self).__init__(**params) + + self.path = params.get('path', self.default_path) + self.disabled_channels_timestamp_dict = params.get('disabled_channels_timestamp_dict', None) + self.disabled_channels_file_dir = params.get('disabled_channels_file_dir', None) + self.disabled_channels_common_filename = params.get('disabled_channels_common_filename', None) + self.disabled_channels_file_format = params.get('disabled_channels_file_format', None) + self.disabled_channels_file_ts = self.lookup_disabled_channel_file_ts + self.disabled_channels_list = self.disabled_channels_file_dir+self.disabled_channels_common_filename+ \ + self.disabled_channels_file_ts+self.disabled_channels_file_format + self.missing_asic_list = params.get('missing_asic_list', None) + self.is_mc = False + + def init(self, source_name): + super(DisabledChannels, self).init(source_name) + + # create group (if not present) + self.data_manager.set_attrs(self.path) + self.is_mc = resources['RunData'].is_mc + # load data (if present) + self.data = dict(self.data_manager.get_attrs(self.path)) + + if not self.data: + # no data stored in file, generate it + self._disabled_channel_lut, self._disabled_pixel_coords = self.load_disabled_channels_lut( + self.disabled_channels_list, self.missing_asic_list) + self.data['classname'] = self.classname + self.data['class_version'] = self.class_version + self.data['disabled_channels_list'] = (self.disabled_channels_list + if self.disabled_channels_list is not None + else '') + self.data['missing_asic_list'] = (self.missing_asic_list + if self.missing_asic_list is not None + else '') + self.data_manager.set_attrs(self.path, **self.data) + zy_dtype = np.dtype([('z', self._disabled_pixel_coords.dtype), ('y', self._disabled_pixel_coords.dtype)]) + self.data_manager.create_dset(self.path + '/zy', dtype=zy_dtype) + sl = self.data_manager.reserve_data(self.path + '/zy', slice(0, len(self._disabled_pixel_coords))) + self.data_manager.write_data(self.path + '/zy', sl, self._disabled_pixel_coords.view(zy_dtype).ravel()) + + write_lut(self.data_manager, self.path, self.disabled_channel_lut, + 'lut') + else: + assert_compat_version(self.class_version, self.data['class_version']) + + self._disabled_channel_lut = read_lut(self.data_manager, self.path, + 'lut') + self._disabled_pixel_coords = np.c_[self.data_manager[self.path+'/zy/data']['z'], self.data_manager[self.path+'/zy/data']['y']] + + if self.rank == 0: + logging.info(f'N disabled channels: {len(self.disabled_pixel_coords)}') + logging.info(f'Disabled channel LUT size: ' + f'{self.disabled_channel_lut.nbytes/1024/1024:0.02f}MB') + + self._pixel_pitch = resources['Geometry'].pixel_pitch + self._pixel_z_hi_edge = np.sort(np.unique(resources['Geometry'].pixel_coordinates_2D.compress((0,)))) + self._pixel_pitch/2 + self._pixel_y_hi_edge = np.sort(np.unique(resources['Geometry'].pixel_coordinates_2D.compress((1,)))) + self._pixel_pitch/2 + io_group,io_channel,_,_ = resources['Geometry'].pixel_coordinates_2D.keys() + tile_id = resources['Geometry'].tile_id[(io_group,io_channel)] + self._anode_drift_coordinate, idx = np.unique(resources['Geometry'].anode_drift_coordinate[(tile_id,)], return_index=True) + self._tpc_lookup = io_group[idx] + + @property + def disabled_pixel_coords(self): + return self._disabled_pixel_coords + + @property + def disabled_channel_lut(self): + return self._disabled_channel_lut + + def is_active(self, xyz): + ''' + Lookup a specific position to determine if it would fall onto an active pixel + + :param xyz: 3D position ``shape: (..., 3)`` + + :returns: boolean array with ``True == active``, ``shape: (...,)`` + + ''' + pixel_z = self._pixel_z_hi_edge[np.clip(np.digitize(xyz[...,2], bins=self._pixel_z_hi_edge), 0, len(self._pixel_z_hi_edge)-1)] - self._pixel_pitch/2 + pixel_y = self._pixel_y_hi_edge[np.clip(np.digitize(xyz[...,1], bins=self._pixel_y_hi_edge), 0, len(self._pixel_y_hi_edge)-1)] - self._pixel_pitch/2 + tpc = self._tpc_lookup[np.argmin(np.abs(xyz[...,2:3] - self._anode_drift_coordinate.reshape([1,]*(xyz.ndim-1)+[-1])), axis=-1)] + disabled = self.disabled_channel_lut[(tpc.astype(int), pixel_z.astype(int), pixel_y.astype(int))] + return ~disabled + + @staticmethod + def load_disabled_channels_lut(disabled_channels_list=None, + missing_asic_list=None): + ''' + Loads a disabled channels lookup-table from the json formatted filenames:: + + disabled_channels_*.json + missing_asic_list (for Module 1, module1-network-absent-ASICs.json) + + ``disabled_channels_*.json`` files contain ``chip-key: [channel_id]`` pairs of + disabled channels that are defined within the geometry, but should be + considered as disabled. The ``Geometry`` resource is used to find the 2D + locations of these pixels. + + ``missing_asic_list`` contains ``io_group: [[z,y], ...]`` pixel positions + that should be considered as disabled regions. + + Creates a boolean lookup table with keys of + ``(io_group, int(pixel_z), int(pixel_y))`` to determine if a given + pixel position falls onto a disabled channel. + + :returns: ``tuple`` of boolean ``proto_nd_flow.util.lut.LUT`` and ``list`` of pixel 2D coordinates for each disabled channel + + ''' + io_group = list() + zy = np.empty((0, 2)) + + if disabled_channels_list is not None: + # first load disabled channels list + with open(disabled_channels_list, 'r') as fi: + data = json.load(fi) + + # get disabled channels from file + io_channel = list() + chip_id = list() + channel_id = list() + for key in data: + if key == 'All': + continue + io_group_, io_channel_, chip_id_ = key.split('-') + for ch in data[key]: + io_group.append(int(io_group_)) + io_channel.append(int(io_channel_)) + chip_id.append(int(chip_id_)) + channel_id.append(int(ch)) + + if resources['Geometry'].network_agnostic == True: + # add additional entries for each io channel + n_io_channels_per_tile = resources['Geometry'].n_io_channels_per_tile + start_io_channel = ((io_channel_-1)//n_io_channels_per_tile)*n_io_channels_per_tile + 1 + for io_channel in range(start_io_channel, start_io_channel+n_io_channels_per_tile): + io_group.append(int(io_group_)) + io_channel.append(int(io_channel)) + chip_id.append(int(chip_id_)) + channel_id.append(int(ch)) + + pixel_coordinates_2D = resources['Geometry'].pixel_coordinates_2D + chip_key = (np.array(io_group), np.array(io_channel), + np.array(chip_id), np.array(channel_id)) + zy = pixel_coordinates_2D[chip_key] + + if missing_asic_list is not None: + # then load missing asic pixels + with open(missing_asic_list, 'r') as fi: + data = json.load(fi) + + # add to lists + for io_group_ in data: + for asic in data[io_group_]: + io_group.append(int(io_group_)) + zy = np.append(zy, np.array([asic]), axis=0) + + disable_channels_lut = LUT(bool, + (min(io_group), max(io_group)), + (min(zy[:, 0].astype(int)) - 1, + max(zy[:, 0].astype(int)) + 1), + (min(zy[:, 1].astype(int)) - 1, + max(zy[:, 1].astype(int)) + 1), + default=False) + # apply a fudge factor to account for any rounding errors + for dz in (+1, 0, -1): + for dy in (+1, 0, -1): + disable_channels_lut[(io_group, zy[:, 0].astype(int) + dz, + zy[:, 1].astype(int) + dy)] = True + + return disable_channels_lut, zy + + @staticmethod + def convert_ts_str_to_float(filename): + + ''' + Convert timestamp in charge data file name to float so that timestamps can be compared + + :param filename: charge filename ``str`` + + :returns: float with digits of form MMDDhhmmss (M=month, D=day, h=hour(24h), m=min, s=sec) + ''' + + filename = filename.strip('CET') + # Removes year from consideration in charge file timestamp bc year not in disabled channel file timestamp + file_ts_arr = np.array([float(x)/100 for x in filename.split('_') if x and float(x)/100 < 1.]) + file_ts_float = 0. + len_file_ts_arr = len(file_ts_arr) + for i in range(len_file_ts_arr): + file_ts_float += file_ts_arr[i]*pow(10, 2*(len_file_ts_arr - i)) + + return file_ts_float + + + @staticmethod + def lookup_disabled_channel_file_ts(self): + + ''' + Find timestamp for relevant disabled channels file from charge filename + + :param [None] + + :returns: disabled channel dictionary file timestamp of form MM_DD_hh_mm_ss ``str'' + (M=month, D=day, h=hour(24h), m=min, s=sec) + ''' + + dc_file_ts = '' + dc_config_file = open(self.disabled_channels_timestamp_dict) + dc_config = json.load(dc_config_file) + file_ts = self.convert_ts_str_to_float(self.charge_filename) + + # Choose random disabled channels file for MC files + if self.is_mc: + + dc_file_ts = random.choice(list(dc_config.keys())) + + # Choose disabled channels file based on timestamp for data files + else: + + for ts in dc_config.keys(): + + dc_ts = self.convert_ts_str_to_float(ts) + + if file_ts > dc_ts: + dc_file_ts = ts + continue + else: + break + + if dc_file_ts == '': + raise ValueError("Disabled channel file timestamp not found.") + + return dc_file_ts + diff --git a/src/proto_nd_flow/resources/particle_data.py b/src/proto_nd_flow/resources/particle_data.py new file mode 100644 index 00000000..2ff71699 --- /dev/null +++ b/src/proto_nd_flow/resources/particle_data.py @@ -0,0 +1,245 @@ +import numpy as np + +from h5flow.core import H5FlowResource, resources + +from proto_nd_flow.util.compat import assert_compat_version +import proto_nd_flow.util.units as units + + +class ParticleData(H5FlowResource): + ''' + Provides helper functions for calculating and accessing particle + properties. Range tables will be saved and/or loaded to/from metadata + within the output file. + + Requires ``LArData`` resource within workflow. + + Parameters: + - ``path``: ``str``, path to stored particle data within file + - ``muon_range_table_path``: ``str``, path to PDG text file of muon range in LAr + - ``proton_range_table_path``: ``str``, path to NIST text file of proton range in LAr + + Provides: + - ``muon_range_table``: Range, kinetic energy, and for muons in LAr + - ``proton_range_table``: Range, kinetic energy, and for protons in LAr + - ``landau_width``: 1-sigma width of Landau dE/dx distribution in LAr + - ``landau_peak``: MPV of Landau dE/dx distribution in LAr + - ``{}_mass``: for proton (``p``), neutron (``n``), muon (``mu``), electron (``e``), pion (``pi``), pi0 (``pi0``) + + Example usage:: + + from h5flow.core import resources + + resources['ParticleData'].muon_range_table['range'] + + Example config:: + + resources: + - classname: ParticleData + params: + path: 'particle_info' + + ''' + class_version = '0.0.0' + + default_path = 'particle_info' + default_muon_range_table_path = 'PDG_muon_range_table_Ar.txt' + default_proton_range_table_path = 'NIST_proton_range_table_Ar.txt' + + _K = 0.307075 * units.MeV * (units.cm)**2 + + #: electron mass + e_mass = 510.9989461 * units.keV + + #: muon mass + mu_mass = 105.6583745 * units.MeV + + #: proton mass + p_mass = 938.2720813 * units.MeV + + #: neutron mass + n_mass = 939.5654133 * units.MeV + + #: charged pion mass + pi_mass = 139.57039 * units.MeV + + #: neutral pion mass + pi0_mass = 134.9768 * units.MeV + + def __init__(self, **params): + super(ParticleData, self).__init__(**params) + + self.path = params.get('path', self.default_path) + self.muon_range_table_path = params.get('muon_range_table_path', + self.default_muon_range_table_path) + self.proton_range_table_path = params.get('proton_range_table_path', + self.default_proton_range_table_path) + + def init(self, source_name): + super(ParticleData, self).init(source_name) + + if not self.data_manager.attr_exists(self.path, 'classname'): + # no data stored in file, generate it + muon_table = self.load_pdg_range_table(self.muon_range_table_path) + proton_table = self.load_nist_range_table(self.proton_range_table_path) + + self.data = dict() + + # appropriate units from tables + self.data['muon_range'] = (muon_table['range'] * units.g / (units.cm)**2 + / resources['LArData'].density) + self.data['muon_t'] = muon_table['t'] * units.MeV + self.data['muon_dedx'] = (muon_table['dedx'] / units.g + * units.MeV * units.cm**2 + * resources['LArData'].density) + self.data['proton_range'] = (proton_table['range'] * units.g / (units.cm)**2 + / resources['LArData'].density) + self.data['proton_t'] = proton_table['t'] * units.MeV + self.data['proton_dedx'] = (proton_table['dedx'] / units.g + * units.MeV * units.cm**2 + * resources['LArData'].density) + + self.data['classname'] = self.classname + self.data['class_version'] = self.class_version + self.data_manager.set_attrs(self.path, **self.data) + else: + # data exists, check version compatibility + self.data = dict(self.data_manager.get_attrs(self.path)) + assert_compat_version(self.class_version, self.data['class_version']) + + @property + def muon_range_table(self): + ''' + Range v. kinetic energy v. dE/dx for a muon in LAr. ``dict`` with + keys: ``range``, ``t``, and ``dedx`` + + ''' + return dict(range=self.data['muon_range'], t=self.data['muon_t'], + dedx=self.data['muon_dedx']) + + @property + def proton_range_table(self): + ''' + Range v. kinetic energy v. dE/dx for a proton in LAr. ``dict`` with + keys: ``range``, ``t``, and ``dedx`` + + ''' + return dict(range=self.data['proton_range'], t=self.data['proton_t'], + dedx=self.data['proton_dedx']) + + def landau_width(self, t, mass, dx): + ''' Moyal scale factor for Landau dE/dx width in LAr ''' + e = t + mass + p = np.sqrt(e**2 - mass**2) + beta = p / e + + ksi = self._ksi(dx, beta) + + rv = (4 * ksi / 3.59) + return rv + + def landau_peak(self, t, mass, dx): + ''' Moyal peak location for Landau dE/dx distribution in LAr ''' + e = t + mass + p = np.sqrt(e**2 - mass**2) + beta = p / e + gamma = e / mass + + ksi = self._ksi(dx, beta) + I = 188.0 * units.eV # noqa: E741 + + t0 = np.log(2 * self.e_mass * beta**2 * gamma**2 / I) + t1 = np.log(ksi / I) + t2 = 0.200 - beta**2 - self._delta(beta * gamma) + + rv = ksi * (t0 + t1 + t2) + return rv + + def mcs_angle(self, t, mass, dx): + ''' Multiple coulomb scattering characteristic angle ''' + e = t + mass + p = np.sqrt(e**2 - mass**2) + beta = p / e + + x = dx / resources['LArData'].radiation_length # radiation lengths + f = (1 + 0.088 * np.log10(x / beta**2)) + theta0 = (13.6 * units.MeV) / (beta * p) * np.sqrt(x) * f + return theta0 + + def _ksi(self, x, beta): + Z = resources['LArData'].Z + A = resources['LArData'].A + ksi = (self._K / 2) * (Z / A) * (resources['LArData'].density * x) / (beta**2) + return ksi + + @staticmethod + def _delta(betagamma): + #: values from PDG LAr data + a = 0.1956 + x0 = 0.2 + x1 = 3.0 + cbar = 5.2146 + k = 3.00 + x = np.log10(betagamma) + + return (x < x0) * ( + (x < x1) * (2 * np.log(10) * x - cbar + a * (x1 - x)**k) + + (x > x1) * (2 * np.log(10) * x - cbar)) + + @staticmethod + def load_nist_range_table(path): + ''' + Loads particle range, kinetic energy, and dE/dx from a + NIST text file [https://physics.nist.gov/PhysRefData/Star/Text/PSTAR-t.html]. + + :param path: path to range table file + + :returns: ``dict`` with keys ``range``, ``t``, ``dedx`` + + ''' + with open(path, 'r') as fi: + _data = fi.readlines()[15:] + _r = np.empty(len(_data)) + _ke = np.empty(len(_data)) + _dedx = np.empty(len(_data)) + for i, line in enumerate(_data): + row_data = line.strip().split() + if row_data: + _ke[i] = float(row_data[0]) + _r[i] = float(row_data[4]) + _dedx[i] = float(row_data[3]) + + _table = dict(range=_r, + t=_ke, + dedx=_dedx) + + return _table + + @staticmethod + def load_pdg_range_table(path): + ''' + Loads particle range, kinetic energy, and dE/dx from a + PDG text file [https://pdg.lbl.gov/2021/AtomicNuclearProperties/]. + + :param path: path to range table file + + :returns: ``dict`` with keys ``range``, ``t``, ``dedx`` + + ''' + with open(path, 'r') as fi: + _data = fi.readlines()[10:] + _r = np.empty(len(_data)) + _ke = np.empty(len(_data)) + _dedx = np.empty(len(_data)) + for i, line in enumerate(_data): + row_data = line.strip().split() + if row_data: + _ke[i] = float(row_data[0]) + _r[i] = float(row_data[8]) + _dedx[i] = float(row_data[7]) + + _table = dict(range=_r, + t=_ke, + dedx=_dedx) + + return _table diff --git a/src/proto_nd_flow/util/hough.py b/src/proto_nd_flow/util/hough.py new file mode 100644 index 00000000..fe1cca26 --- /dev/null +++ b/src/proto_nd_flow/util/hough.py @@ -0,0 +1,117 @@ +import numpy as np +import numpy.ma as ma +import logging +from collections import defaultdict + +from h5flow.core import H5FlowStage, resources + +from proto_nd_flow.reco.charge.calib_prompt_hits import CalibHitBuilder + + +class hough(H5FlowStage): + ''' + This module was adapted from CalibHitMerger + The goal is to take the charge/calib_prompt_hits and perform a Hough transform + This could also be performed on calib_merged_hits that are in the "final" stage + The outputs are saved as a set of hits along the line that are a subset of the inital input hits + Currently no hough transform implemented!!! Just an empty module + ksutton 8/30/23 + ''' + class_version = '0.0.0' + defaults = dict( + events_dset_name = 'charge/events', + hits_name = 'charge/calib_prompt_hits', + hit_charge_name = 'charge/calib_prompt_hits', + output_name = 'charge/hits/calib_hough_hits', + #mc_hit_frac_dset_name = 'mc_truth/calib_hough_hit_backtrack' + ) + + output_dtype = CalibHitBuilder.calib_hits_dtype + + def __init__(self, **params): + super(hough, self).__init__(**params) + for key in self.defaults: + setattr(self, key, params.get(key, self.defaults[key])) + # self.output_mode = self.output_mode.lower() + # assert self.output_mode in self.valid_output_modes, f'invalid output mode: {self.output_mode}' + + def init(self, source_name): + super(hough, self).init(source_name) + + # self.hit_frac_dtype = np.dtype([ + # ('fraction', f'({self.max_contrib_segments},)f8'), + # ('segment_id', f'({self.max_contrib_segments},)u8') + # ]) + + self.data_manager.create_dset(self.output_name, dtype=self.output_dtype) + # self.data_manager.create_dset(self.mc_hit_frac_dset_name, dtype=self.hit_frac_dtype) + self.data_manager.create_ref(self.hits_name, self.output_name) + self.data_manager.create_ref(source_name, self.output_name) + # self.data_manager.create_ref(self.output_name,self.mc_hit_frac_dset_name) + self.data_manager.create_ref(self.events_dset_name, self.output_name) + + #@staticmethod + def output_hits(self,hits, weights, seg_fracs): + ''' + currently does nothing, need to add in Hough transform here +''' + + new_seg_bt = np.array(seg_fracs[0]) + new_frac_bt = np.array(seg_fracs[1]) + iteration_count = 0 + mask = hits.mask['id'].copy() + new_hits = hits.data.copy() + weights = weights.data.copy() + old_ids = hits.data['id'].copy()[...,np.newaxis] + old_id_mask = hits.mask['id'].copy()[...,np.newaxis] + + new_hit_idx = np.broadcast_to(np.cumsum(~mask.ravel(), axis=0).reshape(mask.shape + (1,)), old_ids.shape)-1 + # back_track = np.full(shape=new_hits.shape,fill_value=0.,dtype=self.hit_frac_dtype) + + return ( + ma.array(new_hits, mask=mask), + np.c_[np.extract(~(old_id_mask | mask[...,np.newaxis]), old_ids), np.extract(~(old_id_mask | mask[...,np.newaxis]), new_hit_idx)], + # ma.array(back_track, mask=mask) + ) + + def run(self, source_name, source_slice, cache): + super(hough, self).run(source_name, source_slice, cache) + + #get the event id, backtracking, and hits from the input file + event_id = np.r_[source_slice] + packet_frac_bt = cache['packet_frac_backtrack'] + packet_seg_bt = cache['packet_seg_backtrack'] + hits = cache[self.hits_name] + + #get the new hits, references, and backtracking for the + # output, ref, back_track = self.output_hits(hits, weights=hits['Q'], seg_fracs=[packet_seg_bt,packet_frac_bt]) + output, ref = self.output_hits(hits, weights=hits['Q'], seg_fracs=[packet_seg_bt,packet_frac_bt]) + + + output_mask = output.mask['id'] #not sure what this does yet + + # first write the new hits to the file + new_nhit = int((~output_mask).sum()) + output_slice = self.data_manager.reserve_data(self.output_name, new_nhit) + output_idx = np.r_[output_slice].astype(output.dtype['id']) + if new_nhit > 0: + ref[:,1] += output_idx[0] # offset references based on reserved region in output file + np.place(output['id'], ~output_mask, output_idx) + + self.data_manager.write_data(self.output_name, output_idx, output[~output_mask]) + #output_bt_slice = self.data_manager.reserve_data(self.mc_hit_frac_dset_name, new_nhit) + #self.data_manager.write_data(self.mc_hit_frac_dset_name, output_idx, back_track[~output_mask]) + + # HACK: Remove duplicate refs. Would be nice to actually understand and + # fix the origin of these duplicates. + ref = np.unique(ref, axis=0) + # sort based on the ID of the prompt hit, to make analysis more convenient + ref = ref[np.argsort(ref[:, 0])] + + # finally, write the references + self.data_manager.write_ref(self.hits_name, self.output_name, ref) + #self.data_manager.write_ref(self.output_name,self.mc_hit_frac_dset_name,np.c_[output_idx,output_idx]) + ev_ref = np.c_[(np.indices(output_mask.shape)[0] + source_slice.start)[~output_mask], output_idx] + self.data_manager.write_ref(source_name, self.output_name, ev_ref) + self.data_manager.write_ref(self.events_dset_name, self.output_name, ev_ref) + diff --git a/src/proto_nd_flow/util/tracklet_merging.py b/src/proto_nd_flow/util/tracklet_merging.py new file mode 100644 index 00000000..841efd38 --- /dev/null +++ b/src/proto_nd_flow/util/tracklet_merging.py @@ -0,0 +1,757 @@ +import numpy as np +import numpy.ma as ma +from scipy import ndimage + +from h5flow.core import H5FlowStage, resources + +from module0_flow.reco.combined.tracklet_reco import TrackletReconstruction +from module0_flow.util.func import condense_array + + +class TrackletMerger(H5FlowStage): + ''' + Merges existing tracks with neighbors based on a multi-dimensional + likelihood ratio metric. The observables used in the likelihood + estimation are: + + - ``sin^2(theta)``: angle between the two track segments + - transverse distance: maximum transverse displacement of track from the axis of the first track [mm] + - missing length: length of line segment between closer two endpoints that crosses active pixels [mm] + - overlap: quadrature sum of 1D overlap of tracks in x, y, and z [mm] + - delta-dQ/dx: difference in raw dQ/dx [mV] + + Requires an input histogram .npz file consisting of 4 arrays: + + - ``'{sig}'``: an array of shape: ``(N0, N1, ... N4)`` representing the number of signal events in each bin of the 5 observables + - ``'{sig}_bins'``: an array of 5 arrays each with shape: ``Ni+1`` representing the bin edges + - ``'{bkg}'``: an array of shape: ``(N0, N1, ... N4)`` representing the number of background events in each bin of the 5 observables + + The selection is performed by normalizing the input histograms to a PDF, + calculating the ``signal/background`` likelihood ratio, and rescaling + to a normalized metric between 0 and 1. The p-value (or inefficiency) + of this metric is calculated based on the signal histogram. The + track merging selection cut is applied on this p-value, e.g. a + ``pvalue_cut = 0.05`` will result in a 95% selection efficiency for + merging neighboring tracks (at least for the sample used to generate + the input histograms). + + Parameters: + - ``pdf_filename``: ``str``, path to .npz file containing multi-dimensional pdf (more details above) + - ``pdf_sig_name``: ``str``, name of array in .npz file containing the "signal" histogram + - ``pdf_bkg_name``: ``str``, name of array in .npz file containing the "background" histogram + - ``pvalue_cut``: ``float``, p-value/inefficiency used as cut for likelihood ratio + - ``max_neighbors``: ``int``, number of neighbor tracks to attempt merge procedure + - ``track_charge_dset_name``: ``str``, path to input charge dataset (1:1 with track hits, requires ``'q'`` field) + - ``hit_drift_dset_name``: ``str``, path to charge hit drift data + - ``hits_dset_name``: ``str``, path to input charge hits dataset + - ``track_hits_dset_name``: ``str``, path to input track-referred charge hits dataset + - ``tracks_dset_name``: ``str``, path to input track dataset + - ``merged_dset_name``: ``str``, path to output track dataset + + All of ``hits_dset_name``, ``hit_drift_dset_name``, ``track_hits_dset_name``, + and ``tracks_dset_name`` are required in the cache. + + Requires both Geometry and DisabledChannels resources in workflow. + + ``merged`` datatype is the same as the + ``TrackletReconstruction.tracklet_dtype``. + + Example config:: + + track_merge: + classname: TrackletMerger + requires: + - 'combined/tracklets' + - name: 'combined/track_hits + path: ['combined/tracklets', charge/hits'] + - name: 'combined/track_hit_drift + path: ['combined/tracklets', charge/hits', 'combined/hit_drift'] + params: + merged_dset_name: 'combined/tracklets/merged' + hit_drift_dset_name: 'combined/hit_drift' + hits_dset_name: 'charge/hits' + track_charge_dset_name: 'charge/hits' + tracks_dset_name: 'combined/tracklets' + pdf_filename: 'joint_pdf.npz' + pvalue_cut: 0.10 + max_neighbors: 5 + + ''' + class_version = '3.1.0' + + default_pdf_filename = 'joint_pdf-2_0_1.npz' + default_pdf_sig_name = 'rereco' + default_pdf_bkg_name = 'origin' + default_pvalue_cut = 0.10 + default_max_neighbors = 5 + + default_hit_drift_dset_name = 'combined/track_hit_drift' + default_hits_dset_name = 'charge/hits' + default_track_charge_dset_name = 'charge/hits' + default_tracks_dset_name = 'combined/tracklets' + default_track_hits_dset_name = 'combined/track_hits' + default_merged_dset_name = 'combined/tracklets/merged' + + merged_dtype = TrackletReconstruction.tracklet_dtype + + missing_track_segments = 150 + cathode_region = 15 + + def __init__(self, **params): + super(TrackletMerger, self).__init__(**params) + + self.pdf_filename = params.get('pdf_filename', self.default_pdf_filename) + self.pdf_sig_name = params.get('pdf_sig_name', self.default_pdf_sig_name) + self.pdf_bkg_name = params.get('pdf_bkg_name', self.default_pdf_bkg_name) + self.pvalue_cut = params.get('pvalue_cut', self.default_pvalue_cut) + self.max_neighbors = params.get('max_neighbors', self.default_max_neighbors) + + self.hit_drift_dset_name = params.get('hit_drift_dset_name', self.default_hit_drift_dset_name) + self.hits_dset_name = params.get('hits_dset_name', self.default_hits_dset_name) + self.track_charge_dset_name = params.get('track_charge_dset_name', self.default_track_charge_dset_name) + self.track_hits_dset_name = params.get('track_hits_dset_name', self.default_track_hits_dset_name) + self.tracks_dset_name = params.get('tracks_dset_name', self.default_tracks_dset_name) + self.merged_dset_name = params.get('merged_dset_name', self.default_merged_dset_name) + + def init(self, source_name): + super(TrackletMerger, self).init(source_name) + + self.r, self.r_bins, self.statistic_bins, self.p_bins = ( + self.load_r_values(self.pdf_filename, self.pdf_sig_name, + self.pdf_bkg_name)) + + self.data_manager.set_attrs(self.merged_dset_name, + classname=self.classname, + class_version=self.class_version, + hits_dset=self.hits_dset_name, + charge_dset=self.track_charge_dset_name, + hit_drift_dset=self.hit_drift_dset_name, + tracks_dset=self.tracks_dset_name, + max_neighbors=self.max_neighbors, + pvalue_cut=self.pvalue_cut, + pdf_filename=self.pdf_filename, + pdf_sig_name=self.pdf_sig_name, + pdf_bkg_name=self.pdf_bkg_name + ) + + self.trajectory_pts = self.data_manager.get_attrs(self.tracks_dset_name)['trajectory_pts'] + self.trajectory_dx = self.data_manager.get_attrs(self.tracks_dset_name)['trajectory_dx'] + self.trajectory_residual_mode = self.data_manager.get_attrs(self.tracks_dset_name).get('trajectory_residual_mode', 1) + + self.merged_dtype = TrackletMerger.merged_dtype(self.trajectory_pts) + self.data_manager.create_dset(self.merged_dset_name, self.merged_dtype) + self.data_manager.create_ref(self.merged_dset_name, self.hits_dset_name) + self.data_manager.create_ref(self.merged_dset_name, self.tracks_dset_name) + self.data_manager.create_ref(source_name, self.merged_dset_name) + + self.pixel_x = np.unique(resources['Geometry'].pixel_xy.compress((0,))) + self.pixel_y = np.unique(resources['Geometry'].pixel_xy.compress((1,))) + + def run(self, source_name, source_slice, cache): + super(TrackletMerger, self).run(source_name, source_slice, cache) + + track_hit_drift = cache[self.hit_drift_dset_name] + track_hits = cache[self.track_hits_dset_name] + track_hit_q = cache[self.track_charge_dset_name] + track_hit_q = track_hit_q.reshape(track_hits.shape) + tracks = cache[self.tracks_dset_name] + track_hit_drift = track_hit_drift.reshape(track_hits.shape) + + # ajacency matrix to represent if tracks should be merged or not (True == to merge) + track_merged = np.expand_dims(np.diagflat(np.ones(tracks.shape[-1], dtype=bool)), axis=0) + track_checked = (track_merged.copy() + | np.expand_dims(tracks['id'].mask, axis=1) + | np.expand_dims(tracks['id'].mask, axis=2)) + track_merged = np.broadcast_to(track_merged, tracks.shape + tracks.shape[-1:]).copy() + track_checked = np.broadcast_to(track_checked, tracks.shape + tracks.shape[-1:]).copy() + + if len(np.r_[source_slice]): + + # iterative approach + for _ in range(self.max_neighbors): + # find neighboring tracks that have not been checked + neighbor = self.find_k_neighbor(tracks, mask=~track_checked)['neighbor'] + + # calculate the p-value for neighbor pair + params = [ + self.calc_2track_deflection_angle(tracks, neighbor), + self.calc_2track_transverse_sin2theta(tracks, neighbor), + self.calc_2track_missing_length(tracks, neighbor, + self.missing_track_segments, + self.pixel_x, self.pixel_y, + resources['DisabledChannels'].disabled_channel_lut, + self.cathode_region), + self.calc_2track_overlap(tracks, neighbor), + self.calc_2track_sin2theta(tracks, neighbor) + ] + pvalue = np.expand_dims(self.score_neighbor(self.r, self.r_bins, self.statistic_bins, self.p_bins, *params), -1) + neighbor = np.expand_dims(neighbor, -1) + + # merge tracks that have large p-values + should_merge = (((pvalue >= self.pvalue_cut) + | np.take_along_axis(track_merged, neighbor, -1)) + & ~neighbor.mask + & ~tracks['id'][..., np.newaxis]) + np.put_along_axis(track_merged, neighbor, should_merge, axis=-1) + np.put_along_axis(track_checked, neighbor, True, axis=-1) + + if np.all(track_checked): + break + + # collect valid associations into track groups + axes = np.arange(track_merged.ndim).astype(int) + new_axes = axes.copy() + new_axes[-1] = axes[-2] + new_axes[-2] = axes[-1] + track_merged = track_merged | np.transpose(track_merged, axes=new_axes) + track_merged = self.create_groups(track_merged) + + # now, collect the hits from the original tracks into the track groups + # get unique track groups, shape: (n_ev, n_grp, n_track) + track_merged = np.unique(track_merged, axis=1) + track_merged_mask = np.ones(track_merged.shape, dtype=bool) + for ev in range(track_merged.shape[0]): + _, index = np.unique(track_merged[ev], axis=0, return_index=True) + track_merged_mask[ev, index] = False + track_grp = ma.array(track_merged, mask=track_merged_mask | ~track_merged, shrink=False) + track_grp_nhit = np.sum(np.expand_dims(tracks['nhit'], axis=1) * track_grp, axis=-1).filled(0) + + track_grp_hits_shape = track_grp.shape[:-1] + (np.max(track_grp_nhit),) + # (n_ev, n_grp, n_hit') + track_grp_hits = np.zeros(track_grp_hits_shape, dtype=track_hits.dtype) + track_grp_hit_drift = np.zeros(track_grp_hits_shape, dtype=track_hit_drift.dtype) + track_grp_hit_q = np.zeros(track_grp_hits_shape, dtype=track_hit_q.dtype) + track_grp_id = np.zeros(track_grp_hits_shape, dtype=int) + track_grp_hits_mask = np.ones(track_grp_hits_shape, dtype=bool) + for grp_idx in range(track_grp_hits_shape[-2]): + mask = np.indices(track_grp_hits[:, grp_idx].shape)[-1] < track_grp_nhit[:, grp_idx, np.newaxis] + + hit_mask = ~track_hits[track_grp[:, grp_idx].filled(False)]['id'].mask + np.place(track_grp_hits[:, grp_idx], mask, track_hits[track_grp[:, grp_idx].filled(0)][hit_mask]) + np.place(track_grp_hit_drift[:, grp_idx], mask, track_hit_drift[track_grp[:, grp_idx].filled(0)][hit_mask]) + np.place(track_grp_hit_q[:, grp_idx], mask, track_hit_q[track_grp[:, grp_idx].filled(0)][hit_mask]) + np.place(track_grp_id[:, grp_idx], mask, grp_idx) + np.place(track_grp_hits_mask[:, grp_idx], mask, False) + + track_grp_hits = ma.array(track_grp_hits, mask=track_grp_hits_mask, shrink=False) + track_grp_hit_q = ma.array(track_grp_hit_q, mask=track_grp_hits_mask, shrink=False) + track_grp_hit_drift = ma.array(track_grp_hit_drift, mask=track_grp_hits_mask, shrink=False) + track_grp_id = ma.array(track_grp_id, mask=track_grp_hits_mask, shrink=False) + + new_shape = track_grp.shape[0:1] + (-1,) + track_grp_hits = track_grp_hits.reshape(new_shape) + track_grp_hit_drift = track_grp_hit_drift.reshape(new_shape) + track_grp_hit_q = track_grp_hit_q.reshape(new_shape) + track_grp_id = track_grp_id.reshape(new_shape) + + # recalculate track parameters + calc_shape = (track_grp_id.shape[0], -1) + merged_tracks = TrackletReconstruction.calc_tracks( + track_grp_hits.reshape(calc_shape), track_grp_hit_q['q'].reshape(calc_shape), track_grp_hit_drift['z'].reshape(calc_shape), + track_grp_id.reshape(calc_shape), self.trajectory_pts, + self.trajectory_dx, self.trajectory_residual_mode) + else: + merged_tracks = ma.masked_all((0, 1), dtype=self.merged_dtype) + track_grp = ma.masked_all((0, 1, 1), dtype=bool) + track_grp_id = ma.masked_all((0, 1), dtype=int) + track_grp_hits = ma.masked_all((0, 1), dtype=track_hits.dtype) + track_grp_hit_drift = ma.masked_all((0, 1), dtype=track_hit_drift.dtype) + track_grp_hit_q = ma.masked_all((0, 1), dtype=track_hit_q.dtype) + + # save to merged track dataset + n_tracks = np.count_nonzero(~merged_tracks['id'].mask) + merged_tracks_mask = ~merged_tracks['id'].mask + + merged_tracks_slice = self.data_manager.reserve_data(self.merged_dset_name, n_tracks) + np.place(merged_tracks['id'], merged_tracks_mask, np.r_[merged_tracks_slice].astype('u4')) + self.data_manager.write_data(self.merged_dset_name, merged_tracks_slice, merged_tracks[merged_tracks_mask]) + + # merged -> tracklet ref + i_ev, i_grp, i_track = np.where(track_grp & np.expand_dims(~tracks['id'].mask, 1) & ~track_grp.mask) + ref = np.c_[merged_tracks['id'][i_ev, i_grp].compressed(), tracks['id'][i_ev, i_track].compressed()] + self.data_manager.write_ref(self.merged_dset_name, self.tracks_dset_name, ref) + + # merged -> hit ref + hit_mask = (np.expand_dims(track_grp_id, 1) + == np.expand_dims(np.indices(merged_tracks.shape)[-1], -1)) + i_ev, i_grp, i_hit = np.where(hit_mask) + ref = np.c_[merged_tracks['id'][i_ev, i_grp].compressed(), + track_grp_hits['id'][i_ev, i_hit].compressed()] + self.data_manager.write_ref(self.merged_dset_name, self.hits_dset_name, ref) + + # event -> merged ref + ev_id = np.broadcast_to(np.expand_dims(np.r_[source_slice], axis=-1), merged_tracks.shape) + ref = np.c_[ev_id[merged_tracks_mask], merged_tracks['id'][merged_tracks_mask]] + self.data_manager.write_ref(source_name, self.merged_dset_name, ref) + + @staticmethod + def create_groups(mask): + ''' + Combine masks of ``n x n`` ajacency matrix such that the mask of + row i is equal to the ``OR`` of the rows that can be reached from + ``i`` and the rows that can reach ``i``. E.g.:: + + arr = [[1,0,1], + [0,1,0], + [0,0,1]] + new_arr = create_groups(arr) + new_arr # [[1,0,1], + [0,1,0], + [1,0,1]] + + and:: + + arr = [[0,1,0], + [0,0,1], + [1,1,0]] + new_arr = create_groups(arr) + new_arr # [[1,1,1], + [1,1,1], + [0,1,1]] + + :param mask: ajacency matrix (``shape: (..., n, n)``) + + :returns: updated ajacency matrix (``shape: (..., n, n)``) + ''' + new_mask = np.zeros_like(mask) + + # get index of masks (starting with True values) + i_mask = np.indices(mask.shape)[-1] + j_mask = np.indices(mask.shape)[-2] + step = 0 + while (step < i_mask.shape[-1]): + # step through indices + # get other index (shape: (..., n, 1)) + ii_mask = np.expand_dims(i_mask[..., step], axis=-1) + jj_mask = np.expand_dims(j_mask[..., step], axis=-1) + # get other mask (shape: (..., n, n)) + other_mask = np.take_along_axis(mask, ii_mask, -2) + # get other matched to current (shape: (..., n, 1)) + other_matched = np.take_along_axis(mask, ii_mask, -1) + # get self matched to current (shape: (..., n, 1)) + self_matched = np.take_along_axis(other_mask, jj_mask, -1) + + # combine with current track(s) + new_mask[:] = (new_mask | (other_mask & other_matched) | (other_mask & self_matched)) + step += 1 + + if np.all(new_mask == mask): + return new_mask + return TrackletMerger.create_groups(new_mask) + + @staticmethod + def find_k_neighbor(tracks, mask=None, k=1): + ''' + Find ``k``-th neighbor based on endpoint distance and require no overlap: + + - ``tracks`` is an (N,M) array of tracks + - ``mask`` is boolean of same shape as ``tracks`` + - ``mask`` true indicates a valid track to search for neighbors + + ''' + ntracks = tracks.shape[-1] + if mask is None: + mask = np.ones(tracks.shape + tracks.shape[-1:], dtype=bool) + mask = (mask + & ~np.diagflat(np.ones(ntracks, dtype=bool)).reshape(1, ntracks, ntracks) + & np.expand_dims(~tracks['id'].mask, axis=1) + & np.expand_dims(~tracks['id'].mask, axis=2)) + + start1 = np.expand_dims(tracks['start'], axis=1) + start2 = np.expand_dims(tracks['start'], axis=2) + end1 = np.expand_dims(tracks['end'], axis=1) + end2 = np.expand_dims(tracks['end'], axis=2) + + endpoint_distance = ma.concatenate(( + ma.sum((start1 - end2)**2, axis=-1, keepdims=True), + ma.sum((end1 - end2)**2, axis=-1, keepdims=True), + ma.sum((start1 - start2)**2, axis=-1, keepdims=True), + ma.sum((end1 - start2)**2, axis=-1, keepdims=True), + ), axis=-1) + endpoint_distance = ma.sqrt(endpoint_distance) + endpoint_distance = ma.array(endpoint_distance.min(axis=-1), mask=~mask, shrink=False) + + neighbor = ma.argsort(endpoint_distance, axis=-1)[..., k - 1].reshape(tracks.shape) + neighbor = ma.array(neighbor, mask=tracks['id'].mask | np.all(~mask, axis=-1), shrink=False) + neighbor.fill_value = -1 + neighbor = ma.array(neighbor.filled(), mask=neighbor.mask, shrink=False) + neighbor.fill_value = -1 + return dict(neighbor=neighbor) + + @staticmethod + def poca(start_xyz0, end_xyz0, start_xyz1, end_xyz1): + ''' + Finds the scale factor to point of closest approach of two lines + each defined by 2 3D points. The scale factor is a number between 0 + and 1 representing the position along the line. To extract the + 3D point of closest approach on each line:: + + s0, s1 = poca(start0, end0, start1, end1) # shape: (N, 1) + poca0 = (1 - s0) * start0 + s0 * end0 # shape: (N, 3) + poca1 = (1 - s1) * start1 + s1 * end1 + + :param {start, end}_xyz(i): start/end point of line i, ``shape: (..., N, 3)`` + + :returns: ``tuple`` of line segment 0 and 1, ``shape: (..., N, 1)`` + ''' + orig_mask0 = start_xyz0.mask | end_xyz0.mask + orig_mask1 = start_xyz1.mask | end_xyz1.mask + orig_mask0, orig_mask1 = np.broadcast_arrays(orig_mask0, orig_mask1) + start_xyz0, end_xyz0, start_xyz1, end_xyz1 = np.broadcast_arrays( + start_xyz0, end_xyz0, start_xyz1, end_xyz1) + + d = start_xyz0 - start_xyz1 + v0, v1 = (end_xyz0 - start_xyz0, end_xyz1 - start_xyz1) + l0, l1 = (np.linalg.norm(v0, axis=-1, keepdims=True), + np.linalg.norm(v1, axis=-1, keepdims=True)) + with np.errstate(divide='ignore', invalid='ignore'): + v0 /= l0 + v1 /= l1 + v0[(l0 == 0)[..., 0]] = 0 + v1[(l1 == 0)[..., 0]] = 0 + v_dp = np.sum(v0 * v1, axis=-1, keepdims=True) + + with np.errstate(divide='ignore', invalid='ignore'): + s0 = (-np.sum(d * v0, axis=-1, keepdims=True) + + np.sum(d * v1, axis=-1, keepdims=True) * v_dp) / (1 - v_dp**2) + s1 = (np.sum(d * v1, axis=-1, keepdims=True) + - np.sum(d * v0, axis=-1, keepdims=True) * v_dp) / (1 - v_dp**2) + + s0 /= l0 + s1 /= l1 + + # handle 0 length line segment + s0[l0 == 0] = 0.5 + s1[l1 == 0] = 0.5 + + # handle parallel segments + parallel_mask = (1 - v_dp**2 == 0)[..., 0] + if np.any(parallel_mask): + # grab mean position + p = (start_xyz0 + end_xyz0 + start_xyz1 + end_xyz1) / 4 + # calculate perpendicular points on other segments + d0 = (start_xyz0 - p) - v0 * np.sum((start_xyz0 - p) * v0, + axis=-1, keepdims=True) + s0[parallel_mask] = np.sum((p + d0) * v0 / l0, axis=-1, + keepdims=True)[parallel_mask] + d1 = (start_xyz1 - p) - v1 * np.sum((start_xyz1 - p) * v1, + axis=-1, keepdims=True) + s1[parallel_mask] = np.sum((p + d1) * v1 / l1, axis=-1, + keepdims=True)[parallel_mask] + + mask0 = np.any(orig_mask0, axis=-1, keepdims=True) + mask1 = np.any(orig_mask1, axis=-1, keepdims=True) + s0 = ma.array(s0, mask=np.broadcast_to(mask0, s0.shape), shrink=False) + s1 = ma.array(s1, mask=np.broadcast_to(mask1, s1.shape), shrink=False) + return s0, s1 + + @staticmethod + def closest_trajectories(tracks0, tracks1): + ''' + :param tracks0: track dtype of shape: ``(..., M,)`` + + :param tracks1: track dtype of shape: ``(..., M,)`` + + :returns: start and end points of closest trajectory segments and points of closest approach, shape: ``(..., M, 3)`` + + ''' + start0 = tracks0['trajectory'][..., :-1, :] # (N, M, n0-1, 3) + end0 = tracks0['trajectory'][..., 1:, :] # (N, M, n0-1, 3) + start1 = tracks1['trajectory'][..., :-1, :] # (N, M, n1-1, 3) + end1 = tracks1['trajectory'][..., 1:, :] # (N, M, n1-1, 3) + + # reshape -> (N, M, n0-1, 1, 3) and (N, M, 1, n1-1, 3) + start0 = np.expand_dims(start0, -2) + end0 = np.expand_dims(end0, -2) + start1 = np.expand_dims(start1, -3) + end1 = np.expand_dims(end1, -3) + + # find point of closest approach + s0, s1 = TrackletMerger.poca(start0, end0, start1, end1) + s0 = ma.clip(s0, 0, 1) + s1 = ma.clip(s1, 0, 1) + + poca0 = (1 - s0) * start0 + s0 * end0 + poca1 = (1 - s1) * start1 + s1 * end1 + poca_d = np.linalg.norm(poca0 - poca1, axis=-1) + poca_d = ma.array(poca_d, mask=(s0.mask | s1.mask), shrink=False) + + # remove segments with 0 length + mask = ((np.linalg.norm(end0 - start0, axis=-1) == 0) + | (np.linalg.norm(end1 - start1, axis=-1) == 0)) + poca_d[mask] = poca_d.max() + + # minimize point of closest approach + min_poca_d0 = np.expand_dims(ma.argmin(poca_d, axis=-1), -1) # (n, M, n0-1, 1) + poca0 = np.take_along_axis(poca0, np.expand_dims(min_poca_d0, -1), -2) # (n, M, n0-1, 1, 3) + poca1 = np.take_along_axis(poca1, np.expand_dims(min_poca_d0, -1), -2) # (n, M, n0-1, 1, 3) + poca_d = np.take_along_axis(poca_d, min_poca_d0, -1) # (n, M, n0-1, 1) + + min_poca_d1 = np.expand_dims(ma.argmin(poca_d, axis=-2), -2) # (n, M, 1, 1) + poca0 = np.take_along_axis(poca0, np.expand_dims(min_poca_d1, -1), -3) # (n, M, 1, 1, 3) + poca1 = np.take_along_axis(poca1, np.expand_dims(min_poca_d1, -1), -3) # (n, M, 1, 1, 3) + poca_d = np.take_along_axis(poca_d, min_poca_d1, -2) # (n, M, 1, 1) + min_poca_d0 = np.take_along_axis(min_poca_d0, min_poca_d1, -2) # (n, M, 1, 1) + + start0 = np.take_along_axis(start0, np.expand_dims(min_poca_d1, -1), -3) # (n, M, 1, 1, 3) + end0 = np.take_along_axis(end0, np.expand_dims(min_poca_d1, -1), -3) # (n, M, 1, 1, 3) + start1 = np.take_along_axis(start1, np.expand_dims(min_poca_d0, -1), -2) # (n, M, 1, 1, 3) + end1 = np.take_along_axis(end1, np.expand_dims(min_poca_d0, -1), -2) # (n, M, 1, 1, 3) + + start0 = start0.reshape(tracks0.shape + (3,)) + end0 = end0.reshape(tracks0.shape + (3,)) + start1 = start1.reshape(tracks1.shape + (3,)) + end1 = end1.reshape(tracks1.shape + (3,)) + poca0 = poca0.reshape(tracks0.shape + (3,)) + poca1 = poca1.reshape(tracks1.shape + (3,)) + + mask = start0.mask | end0.mask | start1.mask | end1.mask | poca0.mask | poca1.mask + start0.mask[mask] = True + end0.mask[mask] = True + start1.mask[mask] = True + end1.mask[mask] = True + poca0.mask[mask] = True + poca1.mask[mask] = True + + return (start0, end0, start1, end1, poca0, poca1) + + @staticmethod + def calc_2track_deflection_angle(tracks, neighbor): + ntracks = tracks.shape[1] + neighbor_tracks = np.take_along_axis(tracks, neighbor, axis=1) + + start, end, neighbor_start, neighbor_end, poca, neighbor_poca = ( + TrackletMerger.closest_trajectories(tracks, neighbor_tracks)) + + orig_mask = poca.mask.copy() | neighbor_poca.mask.copy() + poca = (poca + neighbor_poca) / 2 + + # calculate deflection angle to farthest point on neighboring segment + neighbor_far = np.where( + np.linalg.norm(poca - neighbor_start, axis=-1, keepdims=True) + > np.linalg.norm(poca - neighbor_end, axis=-1, keepdims=True), + neighbor_start, neighbor_end) + ang1 = np.sum((neighbor_far - poca) * (poca - start), axis=-1) + ang1 /= np.linalg.norm((neighbor_far - poca), axis=-1) + 1e-15 + ang1 /= np.linalg.norm((poca - start), axis=-1) + 1e-15 + ang1 = np.arccos(np.clip(ang1, -1, 1)) + + mask = (tracks['id'].mask | neighbor.mask.reshape(ang1.shape) + | (neighbor == -1).reshape(ang1.shape)) + return ma.array(ang1 / np.pi, mask=mask, shrink=False) + + @staticmethod + def calc_2track_transverse_sin2theta(tracks, neighbor): + ntracks = tracks.shape[1] + neighbor_tracks = np.take_along_axis(tracks, neighbor, axis=-1) + + start1, end1, start2, end2, _, _ = TrackletMerger.closest_trajectories( + tracks, neighbor_tracks) + + d = ma.concatenate(( + np.expand_dims(start1 - end2, axis=-1), + np.expand_dims(end1 - end2, axis=-1), + np.expand_dims(start1 - start2, axis=-1), + np.expand_dims(end1 - start2, axis=-1) + ), axis=-1) + i_max = np.expand_dims(ma.argmax(np.sqrt(ma.sum(d * d, axis=-2, keepdims=True)), axis=-1), axis=-1) + d = np.take_along_axis(d, i_max, axis=-1)[..., 0] + d_norm = ma.sqrt(ma.sum(d**2, axis=-1, keepdims=True)) + d_norm[d_norm == 0] = 1 + d /= d_norm + + # transverse d + track_d = end1 - start1 + track_d_mask = np.all(track_d == 0, axis=-1) + track_d[track_d_mask] = (tracks['end'] - tracks['start'])[track_d_mask] + track_d /= ma.sqrt(ma.sum(track_d**2, axis=-1, keepdims=True)) + l_d = np.abs(ma.sum(d * track_d, axis=-1)) + l = np.sqrt(ma.sum(d * d, axis=-1)) + t_d = np.clip(l**2 - l_d**2, 0, 1) + + mask = (tracks['id'].mask | + neighbor.mask.reshape(t_d.shape) + | (neighbor == -1).reshape(t_d.shape)) + return ma.array(t_d, mask=mask, shrink=False) + + @staticmethod + def make_missing_segment(start1, end1, start2, end2): + track_d = np.concatenate(( + np.sum((start1 - end2)**2, axis=-1, keepdims=True), + np.sum((end1 - end2)**2, axis=-1, keepdims=True), + np.sum((start1 - start2)**2, axis=-1, keepdims=True), + np.sum((end1 - start2)**2, axis=-1, keepdims=True), + ), axis=-1) + i_min = np.expand_dims(np.argmin(track_d, axis=-1), axis=-1) + missing_track_start = np.select( + (i_min == 0, + i_min == 1, + i_min == 2, + i_min == 3), + (start1, end1, start1, end1)) + missing_track_end = np.select( + (i_min == 0, + i_min == 1, + i_min == 2, + i_min == 3), + (end2, end2, start2, start2)) + return missing_track_start, missing_track_end + + @staticmethod + def calc_2track_missing_length(tracks, neighbor, missing_track_segments, + pixel_x, pixel_y, disabled_channel_lut, + cathode_region, pixel_pitch=None): + # create missing track segment + _n_steps = missing_track_segments + neighbor_tracks = np.take_along_axis(tracks, neighbor, axis=-1) + start1, end1, start2, end2, poca1, poca2 = TrackletMerger.closest_trajectories( + tracks, neighbor_tracks) + + # _missing_start, _missing_end = TrackletMerger.make_missing_segment( + # start1, end1, start2, end2) + _missing_start, _missing_end = poca1, poca2 + + # interpolate + _missing_x, _dx = np.linspace(_missing_start[..., 0], _missing_end[..., 0], + _n_steps, axis=-1, retstep=True) + _missing_y, _dy = np.linspace(_missing_start[..., 1], _missing_end[..., 1], + _n_steps, axis=-1, retstep=True) + _missing_z, _dz = np.linspace(_missing_start[..., 2], _missing_end[..., 2], + _n_steps, axis=-1, retstep=True) + _ds = np.sqrt(_dx**2 + _dy**2 + _dz**2) + _missing_length = _ds * _n_steps + + pixel_pitch = pixel_pitch if pixel_pitch is not None else resources['Geometry'].pixel_pitch + _ix = np.clip(np.digitize(_missing_x, pixel_x + pixel_pitch / 2) - 1, + 0, len(pixel_x) - 1) + _iy = np.clip(np.digitize(_missing_y, pixel_y + pixel_pitch / 2) - 1, + 0, len(pixel_x) - 1) + + _missing_pixel_x = pixel_x[_ix] + _missing_pixel_y = pixel_y[_iy] + _missing_iogroup = (np.sign(_missing_z) / 2 + 1.5).astype(int) + + _hidden_length = _ds * ( + (disabled_channel_lut[_missing_iogroup, + _missing_pixel_x.astype(int), + _missing_pixel_y.astype(int)].reshape(_missing_iogroup.shape) + | (np.abs(_missing_z) < cathode_region)).sum(axis=-1)) + missing_length = _missing_length - _hidden_length + + mask = (tracks['id'].mask + | neighbor.mask.reshape(missing_length.shape) + | (neighbor == -1).reshape(missing_length.shape)) + return ma.array(missing_length, mask=mask, shrink=False) + + @staticmethod + def calc_2track_overlap(tracks, neighbor): + _ntracks = tracks.shape[1] + neighbor = neighbor.reshape(tracks.shape + (1,)) + _track1_min = np.minimum(tracks['start'], tracks['end']) + _track1_max = np.maximum(tracks['start'], tracks['end']) + _track2_min = np.take_along_axis(np.minimum(tracks['start'], tracks['end']), + neighbor, axis=1) + _track2_max = np.take_along_axis(np.maximum(tracks['start'], tracks['end']), + neighbor, axis=1) + + overlap = (np.minimum(_track2_max, _track1_max) + - np.maximum(_track2_min, _track1_min)) + overlap = np.clip(overlap, 0, None) + overlap = np.sqrt(np.sum(overlap**2, axis=-1)) + mask = (tracks['id'].mask + | neighbor.mask.reshape(overlap.shape) + | (neighbor == -1).reshape(overlap.shape)) + return ma.array(overlap, mask=mask, shrink=False) + + @staticmethod + def calc_2track_sin2theta(tracks, neighbor): + ntracks = tracks.shape[1] + neighbor_tracks = np.take_along_axis(tracks, neighbor, axis=-1) + start1, end1, start2, end2, _, _ = TrackletMerger.closest_trajectories( + tracks, neighbor_tracks) + dxyz = end1 - start1 + mask = np.all(dxyz == 0, axis=-1) + dxyz[mask] = (tracks['end'] - tracks['start'])[mask] + dxyz /= np.sqrt(np.sum((dxyz)**2, axis=-1, keepdims=True)) + + dxyz_neighbor = end2 - start2 + mask = np.all(dxyz_neighbor == 0, axis=-1) + dxyz_neighbor[mask] = (neighbor_tracks['end'] - neighbor_tracks['start'])[mask] + dxyz_neighbor /= np.sqrt(np.sum((dxyz_neighbor)**2, axis=-1, keepdims=True)) + sin2theta = 1 - np.sum(dxyz * dxyz_neighbor, axis=-1)**2 + mask = (tracks['id'].mask | neighbor.mask.reshape(sin2theta.shape) + | (neighbor == -1).reshape(sin2theta.shape)) + return ma.array(sin2theta, mask=mask, shrink=False) + + @staticmethod + def load_r_values(filename, sig_key, bkg_key): + ''' + Load the N-D pdf histogram from an .npz file. Loads and normalizes + the histograms stored under ``{sig_key}`` and ``{bkg_key}`` with + bins stored under ``{key}_bins`` to create a PDF. The likelihood + ratio (``R``) is then calculated and converted to a normalized + value between 0-1 (``r``) with the following transformation:: + + r = 1 - e^(-R) + + Bins with 0 entries are assigned an ``R``-value of 0. + + :param filename: path to .npz file with arrays + + :param sig_key: name of "signal" histogram in .npz file + + :param bkg_key: name of "background" histogram in .npz file + + :returns: ``tuple`` of r histogram (``shape: (N0, N1, ...)``), r bins in each dimension (``shape: (D, Ni)``), an array possible r values (``shape: (1001,)``, and corresponding p-values (``shape: (1001,)``) + + ''' + pdf = dict(np.load(filename, allow_pickle=True)) + + ndimage.gaussian_filter(pdf[sig_key], 1.5, output=pdf[sig_key], mode='nearest') + ndimage.gaussian_filter(pdf[bkg_key], 1.5, output=pdf[bkg_key], mode='nearest') + + sig_norm = np.sum(pdf[sig_key]) + bkg_norm = np.sum(pdf[bkg_key]) + with np.errstate(divide='ignore', invalid='ignore'): + r = 1 - np.exp(-(pdf[sig_key] / sig_norm) / (pdf[bkg_key] / bkg_norm)) + r_inf_mask = (pdf[bkg_key] == 0) & (pdf[sig_key] > 0) + r[r_inf_mask] = 1 + r_zero_mask = (pdf[sig_key] == 0) & (pdf[bkg_key] > 0) + r[r_zero_mask] = 0 + r_undef_mask = (pdf[sig_key] == 0) & (pdf[bkg_key] == 0) + r[r_undef_mask] = 0.5 + r_bins = pdf[sig_key + '_bins'] + + idx = np.where(pdf[sig_key]) + weights = pdf[sig_key][idx].flatten() + + statistic_bins = np.r_[0, np.geomspace(np.min(r[r > 0]), 1, 1000)] + statistic, statistic_bins = np.histogram(r[idx].flatten(), + bins=statistic_bins, weights=weights) + p_bins = 1 - np.cumsum(statistic[::-1])[::-1] / np.sum(statistic) + + return r, r_bins, statistic_bins, p_bins + + @staticmethod + def score_neighbor(r, r_bins, statistic_bins, p_bins, *params): + ''' + Calculates a p-value based on a binned, multi-dimensional PDF + + :param r: likelihood ratio, ``shape: (N,)*D`` + + :param r_bins: bin edge for each parameter, ``shape: (D, N+1)`` + + :param statistic_bins: bins for statistic, range 0-1, ``shape: (n,)`` + + :param p_bins: bins for p value range 0-1, ``shape: (n,)`` + + :param *params: array of parameters to use to calculate p-value, requires ``D`` parameters in the same sequence as listed in the bins, each with the same shape + + :returns: array of same shape as the ``params`` arrays with a p-value between 0-1 + + ''' + i_bin = [np.clip(np.digitize(np.clip(p, b[0], b[-1]), b) - 1, + 0, len(b) - 2) for b, p in zip(r_bins, params)] + statistic = r[tuple(i_bin)] + pvalue = p_bins[np.clip(np.digitize(statistic, statistic_bins), 0, len(statistic_bins) - 2)] + return pvalue diff --git a/src/proto_nd_flow/util/tracklet_reco.py b/src/proto_nd_flow/util/tracklet_reco.py new file mode 100644 index 00000000..c9c2c326 --- /dev/null +++ b/src/proto_nd_flow/util/tracklet_reco.py @@ -0,0 +1,545 @@ +import numpy as np +import numpy.ma as ma + +import sklearn.cluster as cluster +import sklearn.decomposition as dcomp +from skimage.measure import LineModelND, ransac + +from h5flow.core import H5FlowStage, resources + + +class TrackletReconstruction(H5FlowStage): + ''' + Reconstructs "tracklets" or short, collinear track segments from hit + data using HDBSCAN and RANSAC. The track direction is estimated using + a PCA fit. + + Parameters: + - ``tracklet_dset_name``: ``str``, path to output dataset + - ``hits_dset_name``: ``str``, path to input charge hits dataset + - ``charge_dset_name``: ``str``, path to input charge dataset (1:1 with hits dataset, requires ``"Q"`` field) + ** NOTE: change in charge field name from module0_flow datasets ("q") to proto_nd_flow calib datasets ("Q") + - ``hit_drift_dset_name``: ``str``, path to charge hits drift data + ** NOTE: same as hits datasets when using proto_nd_flow calib datasets + - DEPRECATED ``dbscan_eps``: ``float``, dbscan epsilon parameter [cm] + - ``hdbscan_min_samples``: ``int``, hdbscan min neighbor points to consider as "core" point + - ``hdbscan_min_cluster_size``: ``int``, hdbscan min number of points to form a cluster + - ``hdbscan_cluster_sel_eps``: ``int``, hdbscan threshold value [cm] clusters below this size may be merged using DBSCAN* algorithm + - ``ransac_min_samples``: ``int``, min points to run ransac algorithm + - ``ransac_residual_threshold``: ``float``, max distance from trial axis [cm] + - ``ransac_max_trials``: ``int``, number of ransac trials per cluster + - ``max_iterations``: ``int``, max number of fitting iterations before giving up + - ``max_nhit``: ``int``, skip track fitting on events with greater number of hits, ``None`` to apply no cut + + Both ``hits_dset_name``, ``charge_dset_name``, and ``hits_drift_dset_name`` are required in the cache. + + Requires Geometry, RunData, and Units resource in workflow. + + ``tracklets`` datatype:: + + id u4, unique identifier + theta f8, track inclination w.r.t anode + phi f8, track orientation w.r.t anode + yp f8, intersection of track with ``y=0,z=0`` plane [cm] + zp f8, intersection of track with ``y=0,z=0`` plane [cm] + nhit i8, number of hits in track + q f8, charge sum [ke-] + ts_start f8, PPS timestamp of track start [crs ticks] + ts_end f8, PPS timestamp of track end [crs ticks] + residual f8(3,) average track fit error in (x,y,z) [cm] + length f8 track length [cm] + start f8(3,) track start point (x,y,z) [cm] + end f8(3,) track end point (x,y,z) [cm] + trajectory f8(trajectory_pts, 3,) track approximation points (x,y,z) [cm] + trajectory_residual f8(trajectory_pts-1,) track approximation average error [cm] + dx f8(trajectory_pts-1, 3) track approximation displacement (dx,dy,dz) [cm] + dq f8(trajectory_pts-1,) charge along track displacement [ke-] + dn i8(trajectory_pts-1,) nhit along track displacement + + ''' + class_version = '1.1.0' + + default_tracklet_dset_name = 'combined/tracklets' + default_hits_dset_name = 'charge/calib_final_hits' + default_charge_dset_name = 'charge/calib_final_hits' + default_hit_drift_dset_name = 'combined/calib_final_hits' + + #default_dbscan_eps = 2.5 + default_hdbscan_min_samples = 2 + default_hdbscan_min_cluster_size = 5 + default_hdbscan_cluster_sel_eps = 5 + default_ransac_min_samples = 2 + default_ransac_residual_threshold = 0.8 + default_ransac_max_trials = 100 + default_max_iterations = 100 + default_trajectory_pts = 5 + default_trajectory_dx = 1.0 + default_max_nhit = 3000 + default_trajectory_residual_mode = 1 + + @staticmethod + def tracklet_dtype(npts=default_trajectory_pts): + return np.dtype([ + ('id', 'u4'), + ('theta', 'f8'), ('phi', 'f8'), + ('yp', 'f8'), ('zp', 'f8'), + ('nhit', 'i8'), ('q', 'f8'), + ('ts_start', 'f8'), ('ts_end', 'f8'), + ('residual', 'f8', (3,)), ('length', 'f8'), + ('start', 'f8', (3,)), ('end', 'f8', (3,)), + ('trajectory', 'f8', (npts, 3)), + ('trajectory_residual', 'f8', (npts - 1,)), + ('dx', 'f8', (npts - 1, 3)), + ('dq', 'f8', (npts - 1,)), + ('dn', 'i8', (npts - 1,)) + ]) + + def __init__(self, **params): + super(TrackletReconstruction, self).__init__(**params) + + self.tracklet_dset_name = params.get('tracklet_dset_name', self.default_tracklet_dset_name) + self.hits_dset_name = params.get('hits_dset_name', self.default_hits_dset_name) + self.charge_dset_name = params.get('charge_dset_name', self.default_charge_dset_name) + self.hit_drift_dset_name = params.get('hit_drift_dset_name', self.default_hit_drift_dset_name) + + self._hdbscan_min_cluster_size = params.get('hdbscan_min_cluster_size', self.default_hdbscan_min_cluster_size) + self._hdbscan_min_samples = params.get('hdbscan_min_samples', self.default_hdbscan_min_samples) + self._hdbscan_cluster_sel_eps = params.get('hdbscan_cluster_sel_eps', self.default_hdbscan_cluster_sel_eps) + self._ransac_min_samples = params.get('ransac_min_samples', self.default_ransac_min_samples) + self._ransac_residual_threshold = params.get('ransac_residual_threshold', self.default_ransac_residual_threshold) + self._ransac_max_trials = params.get('ransac_max_trials', self.default_ransac_max_trials) + self.max_iterations = params.get('max_iterations', self.default_max_iterations) + self.max_nhit = params.get('max_nhit', self.default_max_nhit) + + self.trajectory_residual_mode = params.get('trajectory_residual_mode', self.default_trajectory_residual_mode) + self.trajectory_pts = params.get('trajectory_pts', self.default_trajectory_pts) + self.trajectory_dx = params.get('trajectory_dx', self.default_trajectory_dx) + self.tracklet_dtype = self.tracklet_dtype(self.trajectory_pts) + + self.hdbscan = cluster.HDBSCAN(min_cluster_size=self._hdbscan_min_cluster_size, \ + min_samples=self._hdbscan_min_samples, \ + cluster_selection_epsilon = self._hdbscan_cluster_sel_eps, \ + allow_single_cluster=True) + + def init(self, source_name): + super(TrackletReconstruction, self).init(source_name) + + self.data_manager.set_attrs(self.tracklet_dset_name, + classname=self.classname, + class_version=self.class_version, + hits_dset=self.hits_dset_name, + charge_dset=self.charge_dset_name, + hit_drift_dset=self.hit_drift_dset_name, + hdbscan_min_cluster_size=self._hdbscan_min_cluster_size, + hdbscan_min_samples=self._hdbscan_min_samples, + ransac_min_samples=self._ransac_min_samples, + ransac_residual_threshold=self._ransac_residual_threshold, + ransac_max_trials=self._ransac_max_trials, + max_iterations=self.max_iterations, + max_nhit=self.max_nhit, + trajectory_pts=self.trajectory_pts, + trajectory_dx=self.trajectory_dx, + trajectory_residual_mode=self.trajectory_residual_mode + ) + + self.data_manager.create_dset(self.tracklet_dset_name, self.tracklet_dtype) + self.data_manager.create_ref(self.tracklet_dset_name, self.hits_dset_name) + self.data_manager.create_ref(source_name, self.tracklet_dset_name) + + def run(self, source_name, source_slice, cache): + super(TrackletReconstruction, self).run(source_name, source_slice, cache) + + events = cache[source_name] # shape: (N,) + hits = cache[self.hits_dset_name] # shape: (N,M) + q = cache[self.charge_dset_name]['Q'] + q = q.reshape(hits.shape) + hit_drift = cache[self.hit_drift_dset_name] # shape: (N,M,1) + hit_drift = hit_drift.reshape(hits.shape) + + + if self.max_nhit is not None: + hits = ma.array(hits, mask=(events['nhit'][..., np.newaxis] > self.max_nhit) | hits['id'].mask, + shrink=False) + hit_drift = ma.array(hit_drift, mask=(events['nhit'][..., np.newaxis] > self.max_nhit) | hits['id'].mask, + shrink=False) + + track_ids = self.find_tracks(hits) + tracks = self.calc_tracks(hits, q, track_ids, self.trajectory_pts, + self.trajectory_dx, self.trajectory_residual_mode) + n_tracks = np.count_nonzero(~tracks['id'].mask) + tracks_mask = ~tracks['id'].mask + + tracks_slice = self.data_manager.reserve_data(self.tracklet_dset_name, n_tracks) + np.place(tracks['id'], tracks_mask, np.r_[tracks_slice].astype('u4')) + self.data_manager.write_data(self.tracklet_dset_name, tracks_slice, tracks[tracks_mask]) + + # track -> hit ref + track_ref_id = np.take_along_axis(tracks['id'], track_ids, axis=-1) + mask = (~track_ref_id.mask) & (track_ids != -1) & (~hits['id'].mask) + ref = np.c_[track_ref_id[mask], hits['id'][mask]] + self.data_manager.write_ref(self.tracklet_dset_name, self.hits_dset_name, ref) + + # event -> track ref + ev_id = np.broadcast_to(np.expand_dims(np.r_[source_slice], axis=-1), tracks.shape) + ref = np.c_[ev_id[tracks_mask], tracks['id'][tracks_mask]] + self.data_manager.write_ref(source_name, self.tracklet_dset_name, ref) + + @staticmethod + def hit_xyz(hits): + xyz = np.concatenate(( + np.expand_dims(hits['x'], axis=-1), + np.expand_dims(hits['y'], axis=-1), + np.expand_dims(hits['z'], axis=-1), + ), axis=-1) + return xyz + + def find_tracks(self, hits): + ''' + Extract tracks from a given hits array + + :param hits: masked array ``shape: (N, n)`` + + [[former input]] :param hit_drift_coord: masked array ``shape: (N, n)`` + + :returns: mask array ``shape: (N, n)`` of track ids for each hit, a value of -1 means no track is associated with the hit + ''' + xyz = self.hit_xyz(hits) + + # Adding masks where hit coordinate is recorded as nan to enable hdbscan + hits['x'].mask = hits['x'].mask | ma.masked_invalid(hits['x']).mask + hits['y'].mask = hits['y'].mask | ma.masked_invalid(hits['y']).mask + hits['z'].mask = hits['z'].mask | ma.masked_invalid(hits['z']).mask + + iter_mask = np.ones(hits.shape, dtype=bool) + iter_mask = iter_mask & (~hits['id'].mask) & (~hits['x'].mask) & (~hits['y'].mask) & (~hits['z'].mask) + track_id = np.full(hits.shape, -1, dtype='i8') + for i in range(hits.shape[0]): + + if not np.any(iter_mask[i]): + continue + + current_track_id = -1 + + for _ in range(self.max_iterations): + + if sum(iter_mask[i]) < self._hdbscan_min_samples: + continue + # hdbscan to find clusters + + #print("Running first HDBSCAN...") + #track_ids = self._do_hdbscan(xyz[i], iter_mask[i]) +# + ##print("First HDBSCAN successful.") +# + #for id_ in np.unique(track_ids): + # if id_ == -1: + # continue + # mask = track_ids == id_ + # if np.sum(mask) <= self._ransac_min_samples: + # continue +# + # # ransac for collinear hits + # inliers = self._do_ransac(xyz[i], mask) + # #mask[mask] = inliers + # iter_mask[i, mask] = inliers +# + #if np.sum(iter_mask[i]) < self._hdbscan_min_samples: + # continue + + #print("Running second HDBSCAN...") + # and a final hdbscan for re-clustering + final_track_ids = self._do_hdbscan(xyz[i], iter_mask[i]) + + #print("Second HDBSCAN successful.")''' + + for id_ in np.unique(final_track_ids): + if id_ < 0: + continue + mask = final_track_ids == id_ + current_track_id += 1 + track_id[i, mask] = current_track_id + iter_mask[i, mask] = False + + if np.all(final_track_ids < 0) or not np.any(iter_mask[i]): + break + + return ma.array(track_id, mask=hits['id'].mask, shrink=False) + + @classmethod + def calc_tracks(cls, hits, hit_q, track_ids, trajectory_pts, trajectory_dx, trajectory_residual_mode): + ''' + Calculate track parameters from hits + + :param hits: masked array, ``shape: (N,M)`` + + :param hit_q: masked array, ``shape: (N,M)`` + + [[former input]] :param hit_drift_coord: masked array, ``shape: (N,M)`` + + :param track_ids: masked array, ``shape: (N,M)`` + + :param trajectory_pts: int + + :param trajectory_dx: float + + :returns: masked array, ``shape: (N,m)`` + ''' + xyz = cls.hit_xyz(hits) + + n_tracks = np.clip(track_ids.max() + 1, 1, np.inf).astype(int) if np.count_nonzero(~track_ids.mask) \ + else 1 + tracks = np.empty((len(hits), n_tracks), dtype=cls.tracklet_dtype(trajectory_pts)) + tracks_mask = np.ones(tracks.shape, dtype=bool) + for i in range(tracks.shape[0]): + for j in range(tracks.shape[1]): + mask = ((track_ids[i] == j) & (~track_ids.mask[i]) + & (~hits['id'].mask[i])) + if np.count_nonzero(mask) < 2: + continue + + # PCA on central hits + centroid, axis = cls.do_pca(xyz[i], mask) + r_min, r_max = cls.projected_limits( + centroid, axis, xyz[i][mask]) + residual = cls.track_residual(centroid, axis, xyz[i][mask]) + yzp = cls.yzp(axis, centroid) + + # run trajectory approximation algo + traj = cls.trajectory_approx(centroid, axis, xyz[i][mask], trajectory_residual_mode, + npts=trajectory_pts, dx=trajectory_dx, + weights=hit_q[i][mask]) # (npts, 3) + d = cls.trajectory_residual(xyz[i][mask], traj, trajectory_residual_mode) # (npts-1, N) + + min_edge_mask = np.indices(d.shape)[0] != np.expand_dims(np.argmin(d, axis=0), 0) # (npts-1, N) + edge_q = ma.sum(ma.array( + np.broadcast_to(hit_q[i][mask][np.newaxis, :], + min_edge_mask.shape), + mask=min_edge_mask, shrink=False), axis=-1) # (npts-1,) + edge_res = ma.mean(ma.array(d, mask=min_edge_mask, + shrink=False), axis=-1) # (npts-1,) + + tracks[i, j]['theta'] = cls.theta(axis) + tracks[i, j]['phi'] = cls.phi(axis) + tracks[i, j]['yp'] = yzp[0] + tracks[i, j]['zp'] = yzp[1] + tracks[i, j]['nhit'] = np.count_nonzero(mask) + tracks[i, j]['q'] = np.sum(hit_q[i][mask]) + tracks[i, j]['ts_start'] = np.min(hits[i][mask]['ts_pps']) + tracks[i, j]['ts_end'] = np.max(hits[i][mask]['ts_pps']) + tracks[i, j]['residual'] = residual + tracks[i, j]['length'] = np.linalg.norm(r_max - r_min) + tracks[i, j]['start'] = r_min + tracks[i, j]['end'] = r_max + + tracks[i, j]['trajectory'] = traj + tracks[i, j]['trajectory_residual'] = edge_res + tracks[i, j]['dx'] = np.diff(traj, axis=0) + tracks[i, j]['dq'] = edge_q + tracks[i, j]['dn'] = np.sum(~min_edge_mask, axis=-1) + + tracks_mask[i, j] = False + + return ma.array(tracks, mask=tracks_mask, shrink=False) + + def _do_hdbscan(self, xyz, mask): + ''' + :param xyz: ``shape: (N,3)`` array of precomputed 3D distances + + :param mask: ``shape: (N,)`` boolean array of valid positions (``True == valid``) + + :returns: ``shape: (N,)`` array of grouped track ids + ''' + + #print("XYZ:", xyz) + #print("Mask:", mask) + #print("XYZ Mask Shape:", xyz[mask].shape) + clustering = self.hdbscan.fit(xyz[mask]) + track_ids = np.full(len(mask), -1) + track_ids[mask] = clustering.labels_ + return track_ids + + def _do_ransac(self, xyz, mask): + ''' + :param xyz: ``shape: (N,3)`` array of 3D positions + + :param mask: ``shape: (N,)`` boolean array of valid positions (``True == valid``) + + :returns: ``shape: (N,)`` boolean array of colinear positions + ''' + model_robust, inliers = ransac(xyz[mask], LineModelND, + min_samples=self._ransac_min_samples, + residual_threshold=self._ransac_residual_threshold, + max_trials=self._ransac_max_trials) + return inliers + + @staticmethod + def trajectory_approx(centroid, axis, xyz, mode, npts, dx, weights=None): + ''' + :param centroid: ``shape: (3,)`` pre-calculated centroid of 3D positions + + :param axis: ``shape: (3,)`` pre-calculated PCA of 3D positions + + :param xyz: ``shape: (N, 3)`` array of 3D positions + + :returns: ``shape: (npts, 3)`` array of piecewise-linear approximation + ''' + # project hits onto PCA axis + s = np.sum((xyz - centroid[np.newaxis, :]) * axis[np.newaxis, :], + axis=-1, keepdims=True) # (N, 1) + + traj = np.empty((npts, 3)) # (M, 3) + traj_s = np.empty((npts, 1)) # (M, 1) + + start_pt = np.argmin(s, axis=0) + end_pt = np.argmax(s, axis=0) + + traj[0] = TrackletReconstruction.local_mean(xyz, xyz[start_pt], dx, weights=weights) + traj[1:] = TrackletReconstruction.local_mean(xyz, xyz[end_pt], dx, weights=weights) + traj_s[0] = s[start_pt] + traj_s[1:] = s[end_pt] + + for i in range(1, npts - 1): + # calculate residuals + d = TrackletReconstruction.trajectory_residual(xyz, traj, mode) # (M, N) + + # use smallest residual per point + i_res_min = np.expand_dims(np.argmin(d, axis=0), axis=0) # (1, N) + res = np.take_along_axis(d, i_res_min, axis=0) # (1, N) + node_d = np.take_along_axis(d, i_res_min, axis=0) # (1, N) + + # find farthest point + mask = node_d < dx # (1, N) + # important for short tracks + # the mask is to prevent breaking track segments into pieces smaller than trajectory_dx + if mask.all() == True: + break + max_pt = ma.argmax(ma.array(res, mask=mask, shrink=False), axis=1) # (1,) + + # update trajectory + new_pt = TrackletReconstruction.local_mean(xyz, xyz[max_pt].ravel(), dx, weights=weights) # (3,) + new_s = np.sum((new_pt - centroid) * axis, axis=-1) # (1,) + traj[i] = new_pt + traj_s[i] = new_s + + order = np.argsort(traj_s, axis=0) # (M, 1) + traj[:] = np.take_along_axis(traj, order, axis=0) + traj_s[:] = np.take_along_axis(traj_s, order, axis=0) + + return traj + + @staticmethod + def local_mean(xyz, pt, dx, weights=None): + ''' + :param xyz: ``shape: (N, 3)`` + + :param pt: ``shape: (3,)`` + + :param dx: ``float`` radius to include in mean + + :param weights: ``shape: (N,)`` relative weights for each pt, ``None`` applies same weights + + :returns: ``shape: (M, 3)`` + ''' + # calculate local mean + r = xyz - np.expand_dims(pt, axis=0) # (N,3) - (1,3) + d = np.linalg.norm(r, axis=-1, keepdims=True) # (N,1) + + mask = np.broadcast_to(d > dx, r.shape) # (N,3) + traj = ma.average(ma.array(np.expand_dims(xyz, axis=1), mask=mask, shrink=False), + axis=0, weights=weights) # (3,) + return traj + + @staticmethod + def do_pca(xyz, mask): + ''' + :param xyz: ``shape: (N,3)`` array of 3D positions + + :param mask: ``shape: (N,)`` boolean array of valid positions (``True == valid``) + + :returns: ``tuple`` of ``shape: (3,)``, ``shape: (3,)`` of centroid and central axis + ''' + centroid = np.mean(xyz[mask], axis=0) + pca = dcomp.PCA(n_components=1).fit(xyz[mask] - centroid) + axis = pca.components_[0] / np.linalg.norm(pca.components_[0]) + + # break degenerate pca axis direction by fixing y component to be negative + if axis[1] > 0: + axis = -axis + return centroid, axis + + @staticmethod + def projected_limits(centroid, axis, xyz): + s = np.dot((xyz - centroid), axis) + xyz_min, xyz_max = np.amin(xyz, axis=0), np.amax(xyz, axis=0) + r_max = np.clip(centroid + axis * np.max(s), xyz_min, xyz_max) + r_min = np.clip(centroid + axis * np.min(s), xyz_min, xyz_max) + return r_min, r_max + + @staticmethod + def track_residual(centroid, axis, xyz): + s = np.dot((xyz - centroid), axis) + res = np.abs(xyz - (centroid + np.outer(s, axis))) + return np.mean(res, axis=0) + + @staticmethod + # mode = 1, shortest distance to the segment ends + # mode = 2, shortest distance to the tractory + def trajectory_residual(xyz, traj, mode): + ''' + :param xyz: ``shape: (N, 3)``, 3D positions + + :param traj: ``shape: (npts, 3)```, trajectory 3D positions + + :returns: distance to nearest trajectory edge ``shape: (npts-1, N)`` + ''' + d0 = np.expand_dims(xyz, axis=0) - np.expand_dims(traj[:-1], axis=1) # (1, N, 3) - (npts-1, 1, 3) + d1 = np.expand_dims(xyz, axis=0) - np.expand_dims(traj[1:], axis=1) + if mode == 1: + dt = np.minimum(np.linalg.norm(d0, axis=-1), np.linalg.norm(d1, axis=-1)) + elif mode == 2: + d = np.expand_dims(np.diff(traj, axis=0), axis=1) # (npts-1, 1, 3) + with np.errstate(divide='ignore', invalid='ignore'): + n = d / np.linalg.norm(d, axis=-1, keepdims=True) + n[np.isnan(n) | np.isinf(n)] = 0 + + dl = np.linalg.norm(d0 * n, axis=-1) # (npts-1, N, 1) + dt = d0 - np.expand_dims(dl, -1) * n # (npts-1, N, 3) - (npts-1, N, 1) * (1, 1, 3) + dt = np.linalg.norm(dt, axis=-1) # (npts-1, N) + + non_overlap_mask = (dl < 0) | (dl > np.linalg.norm(d, axis=-1)) + dt[non_overlap_mask] = np.minimum(np.linalg.norm(d0, axis=-1), + np.linalg.norm(d1, axis=-1))[non_overlap_mask] + + return dt + + @staticmethod + def theta(axis): + ''' + :param axis: array, ``shape: (3,)`` + + :returns: angle of axis w.r.t x-axis + ''' + return np.arctan2(np.linalg.norm(axis[1:]), axis[0]) + + @staticmethod + def phi(axis): + ''' + :param axis: array, ``shape: (3,)`` + + :returns: orientation of axis about x-axis + ''' + return np.arctan2(axis[2], axis[1]) + + @staticmethod + def yzp(axis, centroid): + ''' + :param axis: array, ``shape: (3,)`` + + :param centroid: array, ``shape: (3,)`` + + :returns: y,z coordinate where line intersects ``y=0,z=0`` plane + ''' + if axis[0] == 0: + return centroid[1:] + s = -centroid[0] / axis[0] + return (centroid + axis * s)[1:] diff --git a/yamls/module1_flow/reco/charge/CalibHitBuilder.yaml b/yamls/module1_flow/reco/charge/CalibHitBuilder.yaml index 0800a28c..c45b9cad 100644 --- a/yamls/module1_flow/reco/charge/CalibHitBuilder.yaml +++ b/yamls/module1_flow/reco/charge/CalibHitBuilder.yaml @@ -10,6 +10,10 @@ requires: - name: 'charge/packets_index' path: ['charge/packets'] index_only: True + - name: 'packet_frac_backtrack' + path: ['charge/packets','mc_truth/packet_fraction'] + - name: 'packet_seg_backtrack' + path: ['charge/packets','mc_truth/segments'] params: # inputs events_dset_name: 'charge/events' @@ -17,6 +21,7 @@ params: packets_index_name: 'charge/packets_index' raw_hits_dset_name: 'charge/raw_hits' t0_dset_name: 'combined/t0' + max_contrib_segments: 10 # output calib_hits_dset_name: 'charge/calib_prompt_hits' diff --git a/yamls/module1_flow/resources/DisabledChannels.yaml b/yamls/module1_flow/resources/DisabledChannels.yaml new file mode 100644 index 00000000..8f1e7628 --- /dev/null +++ b/yamls/module1_flow/resources/DisabledChannels.yaml @@ -0,0 +1,13 @@ +classname: DisabledChannels # resources/disabled_channels.py +path: proto_nd_flow.resources.disabled_channels +params: + path: 'disabled_channels' + disabled_channels_timestamp_dict: 'data/module0_flow/module1_config_to_data_map.json' + # download link: https://portal.nersc.gov/project/dune/data/Module1/TPC12/disabled/module1_config_to_data_map.json + # format: key=ASIC config file timestamp + # value=[[row in run log spreadsheet, data file timestamp]] + disabled_channels_file_dir: '/global/cfs/cdirs/dune/www/data/Module1/TPC12/disabled/' + disabled_channels_common_filename: 'disabled_channels_' + disabled_channels_file_format: '.json' + missing_asic_list: 'data/module1_flow/module1-network-absent-ASICs.json' + # download link: https://portal.nersc.gov/project/dune/data/Module1/TPC12/module1-network-absent-ASICs.json diff --git a/yamls/module1_flow/resources/Geometry.yaml b/yamls/module1_flow/resources/Geometry.yaml index ef2f6f12..dddd2d8d 100644 --- a/yamls/module1_flow/resources/Geometry.yaml +++ b/yamls/module1_flow/resources/Geometry.yaml @@ -5,5 +5,5 @@ path: proto_nd_flow.resources.geometry params: path: 'geometry_info' det_geometry_file: 'data/module1_flow/module0.yaml' - crs_geometry_file: '/global/cfs/cdirs/dune/www/data/Module1/TPC12/module1_layout-2.3.16.yaml' - lrs_geometry_file: 'data/proto_nd_flow/light_module_desc-1.0.0.yaml' + crs_geometry_files: ['/global/cfs/cdirs/dune/www/data/Module1/TPC12/module1_layout-2.3.16.yaml'] + lrs_geometry_file: 'data/module1_flow/light_module_desc_single_module-2.0.0.yaml' diff --git a/yamls/module1_flow/resources/ParticleData.yaml b/yamls/module1_flow/resources/ParticleData.yaml new file mode 100644 index 00000000..97935893 --- /dev/null +++ b/yamls/module1_flow/resources/ParticleData.yaml @@ -0,0 +1,8 @@ +classname: ParticleData +path: proto_nd_flow.resources.particle_data +params: + path: 'particle_info' + muon_range_table_path: 'data/module1_flow/PDG_muon_range_table_Ar.txt' + # download link: https://portal.nersc.gov/project/dune/data/Module0/merged/reco_data/PDG_muon_range_table_Ar.txt + proton_range_table_path: 'data/module1_flow/NIST_proton_range_table_Ar.txt' + # download link: https://portal.nersc.gov/project/dune/data/Module0/merged/reco_data/NIST_proton_range_table_Ar.txt \ No newline at end of file diff --git a/yamls/module1_flow/resources/RunData.yaml b/yamls/module1_flow/resources/RunData.yaml index 708379bf..adcf9f23 100644 --- a/yamls/module1_flow/resources/RunData.yaml +++ b/yamls/module1_flow/resources/RunData.yaml @@ -4,7 +4,7 @@ classname: RunData path: proto_nd_flow.resources.run_data params: path: 'run_info' - runlist_file: '/global/cfs/cdirs/dune/www/data/Module1/runlist.txt' + runlist_file: '/global/cfs/cdirs/dune/www/data/Module1/runlist_BR.txt' # download link: https://portal.nersc.gov/project/dune/data/Module0/runlist.txt # download link: https://portal.nersc.gov/project/dune/data/Module0-Run2/runlist.txt defaults: diff --git a/yamls/module1_flow/workflows/combined/combined_reconstruction.yaml b/yamls/module1_flow/workflows/combined/combined_reconstruction.yaml index 7f4b2dd3..be07d434 100644 --- a/yamls/module1_flow/workflows/combined/combined_reconstruction.yaml +++ b/yamls/module1_flow/workflows/combined/combined_reconstruction.yaml @@ -19,7 +19,7 @@ resources: - !include yamls/module1_flow/resources/RunData.yaml - !include yamls/module1_flow/resources/Geometry.yaml - !include yamls/module1_flow/resources/LArData.yaml -# - !include yamls/proto_nd_flow/resources/DisabledChannels.yaml + #- !include yamls/module1_flow/resources/DisabledChannels.yaml t0_reco: !include yamls/module1_flow/reco/combined/T0Reconstruction.yaml diff --git a/yamls/proto_nd_flow/analysis/hip_selection.yaml b/yamls/proto_nd_flow/analysis/hip_selection.yaml new file mode 100644 index 00000000..868d0fcf --- /dev/null +++ b/yamls/proto_nd_flow/analysis/hip_selection.yaml @@ -0,0 +1,36 @@ +classname: HIPSelection # hip_selection.py +path: proto_nd_flow.analysis.hip_selection +requires: + - 'combined/tracklets' + - 'combined/t0' + - 'charge/calib_final_hits' + - name: 'mc_truth/trajectories' + path: ['charge/raw_events', 'mc_truth/events', 'mc_truth/trajectories'] + #- name: 'combined/track_hits' + # path: ['combined/tracklets', 'charge/hits'] + #- name: 'combined/track_hit_drift' + # path: ['combined/tracklets', 'charge/hits', 'combined/hit_drift'] + +params: + # inputs + hits_dset_name: 'charge/calib_final_hits' + ext_trigs_dset_name: 'charge/ext_trigs' + t0_dset_name: 'combined/t0' + tracklet_dset_name: 'combined/tracklets' + hit_drift_dset_name: 'charge/calib_final_hits' + truth_trajectories_dset_name: 'mc_truth/trajectories' + charge_dset_name: 'charge/calib_final_hits' + + # configuration parameters + fid_cut: 5.0 # cm + cathode_fid_cut: 0.0 # cm + anode_fid_cut: 5.0 # cm + profile_dx: 1.0 # cm + larpix_gain: + mc: 250 # e/mV + medm: 221 # e/mV + high: 221 # e/mV + curvature_rr_correction: + mc: 1.0 + medm: 1.0 + high: 1.0 \ No newline at end of file diff --git a/yamls/proto_nd_flow/util/TrackletMerger.yaml b/yamls/proto_nd_flow/util/TrackletMerger.yaml new file mode 100644 index 00000000..7753af71 --- /dev/null +++ b/yamls/proto_nd_flow/util/TrackletMerger.yaml @@ -0,0 +1,26 @@ +classname: TrackletMerger +path: proto_nd_flow.util.tracklet_merging +requires: + - 'combined/tracklets' + - name: 'combined/track_hits' + path: ['combined/tracklets', 'charge/hits'] + - name: 'combined/track_hit_charge' + path: ['combined/tracklets', 'charge/hits', 'combined/q_calib_el'] + - name: 'combined/track_hit_drift' + path: ['combined/tracklets', 'charge/hits', 'combined/hit_drift'] +params: + # inputs + hits_dset_name: 'charge/hits' + track_charge_dset_name: 'combined/track_hit_charge' + track_hits_dset_name: 'combined/track_hits' + track_hit_drift_dset_name: 'combined/track_hit_drift' + tracks_dset_name: 'combined/tracklets' + + # output + merged_dset_name: 'combined/tracklets/merged' + + # configuration parameters + pdf_filename: 'data/module0_flow/joint_pdf-3_0_0.npz' + # download link: https://portal.nersc.gov/project/dune/data/Module0/merged/reco_data/joint_pdf-3_0_0.npz + pvalue_cut: 0.05 + max_neighbors: 5 diff --git a/yamls/proto_nd_flow/util/TrackletReconstruction.yaml b/yamls/proto_nd_flow/util/TrackletReconstruction.yaml new file mode 100644 index 00000000..b8a1e5e7 --- /dev/null +++ b/yamls/proto_nd_flow/util/TrackletReconstruction.yaml @@ -0,0 +1,25 @@ +classname: TrackletReconstruction +path: proto_nd_flow.util.tracklet_reco +requires: + #- 'charge/calib_final_hits' + - 'charge/calib_prompt_hits' +params: + # inputs + hits_dset_name: 'charge/calib_prompt_hits' #'charge/calib_final_hits' + charge_dset_name: 'charge/calib_prompt_hits' #'charge/calib_final_hits' + hit_drift_dset_name: 'charge/calib_prompt_hits' #'charge/calib_final_hits' + + # output + tracklet_dset_name: 'combined/tracklets' + + # configuration parameters + #dbscan_eps: 2.5 + max_iterations: 1 + hdbscan_min_cluster_size: 15 + hdbscan_min_samples: 9 + hdbscan_cluster_sel_eps: 3.421 + ransac_min_samples: 2 + ransac_residual_threshold: 2.444 + ransac_max_trials: 5 + trajectory_pts: 16 + trajectory_residual_mode: 1 # 1: shortest distance to the segment ends # 2: shortest distance to the tractory diff --git a/yamls/proto_nd_flow/util/hough.yaml b/yamls/proto_nd_flow/util/hough.yaml new file mode 100644 index 00000000..7ac9b0d6 --- /dev/null +++ b/yamls/proto_nd_flow/util/hough.yaml @@ -0,0 +1,23 @@ +classname: hough # reco/charge/calib_hit_merger.py +path: proto_nd_flow.util.hough +requires: + - 'charge/events' + - 'charge/calib_prompt_hits' + - name: 'packet_frac_backtrack' + path: ['charge/calib_prompt_hits','charge/packets','mc_truth/packet_fraction'] + - name: 'packet_seg_backtrack' + path: ['charge/calib_prompt_hits','charge/packets','mc_truth/segments'] + + +params: + # inputs + events_dset_name: 'charge/events' + hits_name: 'charge/calib_prompt_hits' + mc_hit_frac_dset_name: 'mc_truth/calib_final_hit_backtrack' + merged_name: 'charge/calib_hough_hits' + max_contrib_segments: 200 + merge_cut: 65 # merge hits with delta t < merge_cut [CRS ticks] + max_merge_steps: 50 # max number of iterations when merging + # adjacent packets in time on the same channel + + diff --git a/yamls/proto_nd_flow/workflows/analysis/hip_sel_workflow.yaml b/yamls/proto_nd_flow/workflows/analysis/hip_sel_workflow.yaml new file mode 100644 index 00000000..246bd484 --- /dev/null +++ b/yamls/proto_nd_flow/workflows/analysis/hip_sel_workflow.yaml @@ -0,0 +1,22 @@ +flow: + source: events + stages: [hip_sel] + drop: [] + + +resources: + - !include yamls/module1_flow/resources/RunData.yaml + - !include yamls/module1_flow/resources/LArData.yaml + - !include yamls/module1_flow/resources/Geometry.yaml + #- !include yamls/proto_nd_flow/resources/ParticleData.yaml + #- !include yamls/module0_flow/resources/DisabledChannels.yaml + +events: + classname: H5FlowDatasetLoopGenerator + path: h5flow.modules + dset_name: 'charge/events' + params: + chunk_size: 32 + +hip_sel: + !include yamls/proto_nd_flow/analysis/hip_selection.yaml \ No newline at end of file diff --git a/yamls/proto_nd_flow/workflows/util/reco_workflow.yaml b/yamls/proto_nd_flow/workflows/util/reco_workflow.yaml new file mode 100644 index 00000000..6984ca93 --- /dev/null +++ b/yamls/proto_nd_flow/workflows/util/reco_workflow.yaml @@ -0,0 +1,26 @@ +# Generates the mid-level event built data for charge data (i.e. hits and +# external triggers) + +flow: + source: raw_events + #source: calib_prompt_hits + #stages: [temp_hit_builder, calib_hit_merger] + stages: [hough] + drop: [] + + +resources: + - !include yamls/proto_nd_flow/resources/RunData.yaml + - !include yamls/proto_nd_flow/resources/LArData.yaml + - !include yamls/proto_nd_flow/resources/Geometry.yaml + +raw_events: + classname: H5FlowDatasetLoopGenerator + path: h5flow.modules + dset_name: 'charge/raw_events' + params: + chunk_size: 32 + +hough: + !include yamls/proto_nd_flow/util/hough.yaml + diff --git a/yamls/proto_nd_flow/workflows/util/tracklet_workflow.yaml b/yamls/proto_nd_flow/workflows/util/tracklet_workflow.yaml new file mode 100644 index 00000000..decccb35 --- /dev/null +++ b/yamls/proto_nd_flow/workflows/util/tracklet_workflow.yaml @@ -0,0 +1,23 @@ +# Generates higher-level event built data for charge data (i.e. tracklets) + +flow: + source: events + stages: [tracklet_reco] + drop: [] + + +resources: + - !include yamls/module1_flow/resources/RunData.yaml + - !include yamls/module1_flow/resources/LArData.yaml + - !include yamls/module1_flow/resources/Geometry.yaml + +events: + classname: H5FlowDatasetLoopGenerator + path: h5flow.modules + dset_name: 'charge/events' + params: + chunk_size: 32 + +tracklet_reco: + !include yamls/proto_nd_flow/util/TrackletReconstruction.yaml +