diff --git a/MANIFEST.in b/MANIFEST.in deleted file mode 100644 index b69d943..0000000 --- a/MANIFEST.in +++ /dev/null @@ -1,2 +0,0 @@ -include optimask/optimask_cython.pyx -include optimask/optimask_cython.c \ No newline at end of file diff --git a/docs/environment.yml b/docs/environment.yml index 7daa18d..aa71498 100644 --- a/docs/environment.yml +++ b/docs/environment.yml @@ -11,4 +11,4 @@ dependencies: - pandoc - numpy - pandas - - cython + - numba diff --git a/docs/source/future.rst b/docs/source/future.rst index d291183..9991901 100644 --- a/docs/source/future.rst +++ b/docs/source/future.rst @@ -3,6 +3,12 @@ What's New? ########### + +Version 1.3 (July 31, 2024) +~~~~~~~~~~~~~~~~~~~~~~~~~~ +- drop cython for numba + various optimizations (speed and memory) +- special cases of NaNs in one row or on columns detected for faster processing + Version 1.2 (June 19, 2024) ~~~~~~~~~~~~~~~~~~~~~~~~~~ - ``np.isnan(x).nonzero()`` replaced by ``np.unravel_index(np.flatnonzero(np.isnan(x)), x.shape)``, 2x faster diff --git a/notebooks/Optimask.ipynb b/notebooks/Optimask.ipynb index 2d0513b..1ad4f53 100644 --- a/notebooks/Optimask.ipynb +++ b/notebooks/Optimask.ipynb @@ -88,7 +88,7 @@ "outputs": [ { "data": { - "image/png": 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"text/plain": [ "
" ] @@ -113,13 +113,13 @@ "output_type": "stream", "text": [ "CPU times: total: 0 ns\n", - "Wall time: 6.75 ms\n" + "Wall time: 2 ms\n" ] }, { "data": { "text/plain": [ - "(35, 33, False)" + "(27, 41, False)" ] }, "execution_count": 4, @@ -148,7 +148,7 @@ "outputs": [ { "data": { - "image/png": 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+ "image/png": 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"text/plain": [ "
" ] @@ -178,7 +178,7 @@ { "data": { "text/plain": [ - "((35, 33), False)" + "((27, 41), False)" ] }, "execution_count": 6, @@ -209,18 +209,18 @@ "name": "stderr", "output_type": "stream", "text": [ - "\tTrial 1 : submatrix of size 36x30 (1080 elements) found.\n", - "\tTrial 2 : submatrix of size 27x39 (1053 elements) found.\n", - "\tTrial 3 : submatrix of size 31x32 (992 elements) found.\n", - "\tTrial 4 : submatrix of size 29x36 (1044 elements) found.\n", - "\tTrial 5 : submatrix of size 35x33 (1155 elements) found.\n", - "Result: the largest submatrix found is of size 35x33 (1155 elements) found.\n" + "\tTrial 1 : submatrix of size 29x38 (1102 elements) found.\n", + "\tTrial 2 : submatrix of size 25x43 (1075 elements) found.\n", + "\tTrial 3 : submatrix of size 27x41 (1107 elements) found.\n", + "\tTrial 4 : submatrix of size 23x46 (1058 elements) found.\n", + "\tTrial 5 : submatrix of size 25x43 (1075 elements) found.\n", + "Result: the largest submatrix found is of size 27x41 (1107 elements) found.\n" ] }, { "data": { "text/plain": [ - "((35, 33), False)" + "((27, 41), False)" ] }, "execution_count": 7, @@ -252,49 +252,50 @@ "output_type": "stream", "text": [ "CPU times: total: 0 ns\n", - "Wall time: 4.92 ms\n" + "Wall time: 3.5 ms\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\tTrial 6 : submatrix of size 30x36 (1080 elements) found.\n", - "\tTrial 7 : submatrix of size 40x26 (1040 elements) found.\n", - "\tTrial 8 : submatrix of size 34x31 (1054 elements) found.\n", - "\tTrial 9 : submatrix of size 33x33 (1089 elements) found.\n", - "\tTrial 10 : submatrix of size 29x37 (1073 elements) found.\n", - "\tTrial 11 : submatrix of size 35x30 (1050 elements) found.\n", - "\tTrial 12 : submatrix of size 42x24 (1008 elements) found.\n", - "\tTrial 13 : submatrix of size 28x35 (980 elements) found.\n", - "\tTrial 14 : submatrix of size 31x34 (1054 elements) found.\n", - "\tTrial 15 : submatrix of size 28x39 (1092 elements) found.\n", - "\tTrial 16 : submatrix of size 27x39 (1053 elements) found.\n", - "\tTrial 17 : submatrix of size 32x32 (1024 elements) found.\n", - "\tTrial 18 : submatrix of size 37x29 (1073 elements) found.\n", - "\tTrial 19 : submatrix of size 34x30 (1020 elements) found.\n", - "\tTrial 20 : submatrix of size 33x33 (1089 elements) found.\n", - "\tTrial 21 : submatrix of size 31x32 (992 elements) found.\n", - "\tTrial 22 : submatrix of size 32x33 (1056 elements) found.\n", - "\tTrial 23 : submatrix of size 31x35 (1085 elements) found.\n", - "\tTrial 24 : submatrix of size 31x34 (1054 elements) found.\n", - "\tTrial 25 : submatrix of size 31x35 (1085 elements) found.\n", - "\tTrial 26 : submatrix of size 29x37 (1073 elements) found.\n", - "\tTrial 27 : submatrix of size 33x33 (1089 elements) found.\n", - "\tTrial 28 : submatrix of size 29x35 (1015 elements) found.\n", - "\tTrial 29 : submatrix of size 29x34 (986 elements) found.\n", - "\tTrial 30 : submatrix of size 33x32 (1056 elements) found.\n", - "\tTrial 31 : submatrix of size 30x36 (1080 elements) found.\n", - "\tTrial 32 : submatrix of size 32x33 (1056 elements) found.\n", - "\tTrial 33 : submatrix of size 31x31 (961 elements) found.\n", - "\tTrial 34 : submatrix of size 28x35 (980 elements) found.\n", - "\tTrial 35 : submatrix of size 36x29 (1044 elements) found.\n" + "\tTrial 6 : submatrix of size 25x44 (1100 elements) found.\n", + "\tTrial 7 : submatrix of size 24x45 (1080 elements) found.\n", + "\tTrial 8 : submatrix of size 31x37 (1147 elements) found.\n", + "\tTrial 9 : submatrix of size 25x43 (1075 elements) found.\n", + "\tTrial 10 : submatrix of size 28x40 (1120 elements) found.\n", + "\tTrial 11 : submatrix of size 30x37 (1110 elements) found.\n", + "\tTrial 12 : submatrix of size 28x39 (1092 elements) found.\n", + "\tTrial 13 : submatrix of size 29x38 (1102 elements) found.\n", + "\tTrial 14 : submatrix of size 27x37 (999 elements) found.\n", + "\tTrial 15 : submatrix of size 34x31 (1054 elements) found.\n", + "\tTrial 16 : submatrix of size 31x37 (1147 elements) found.\n", + "\tTrial 17 : submatrix of size 28x39 (1092 elements) found.\n", + "\tTrial 18 : submatrix of size 31x37 (1147 elements) found.\n", + "\tTrial 19 : submatrix of size 28x39 (1092 elements) found.\n", + "\tTrial 20 : submatrix of size 36x30 (1080 elements) found.\n", + "\tTrial 21 : submatrix of size 28x39 (1092 elements) found.\n", + "\tTrial 22 : submatrix of size 32x35 (1120 elements) found.\n", + "\tTrial 23 : submatrix of size 28x38 (1064 elements) found.\n", + "\tTrial 24 : submatrix of size 33x33 (1089 elements) found.\n", + "\tTrial 25 : submatrix of size 32x36 (1152 elements) found.\n", + "\tTrial 26 : submatrix of size 29x38 (1102 elements) found.\n", + "\tTrial 27 : submatrix of size 24x44 (1056 elements) found.\n", + "\tTrial 28 : submatrix of size 26x43 (1118 elements) found.\n", + "\tTrial 29 : submatrix of size 32x33 (1056 elements) found.\n", + "\tTrial 30 : submatrix of size 28x39 (1092 elements) found.\n", + "\tTrial 31 : submatrix of size 33x33 (1089 elements) found.\n", + "\tTrial 32 : submatrix of size 27x42 (1134 elements) found.\n", + "\tTrial 33 : submatrix of size 24x44 (1056 elements) found.\n", + "\tTrial 34 : submatrix of size 30x39 (1170 elements) found.\n", + "\tTrial 35 : submatrix of size 27x40 (1080 elements) found.\n", + "Result: the largest submatrix found is of size 30x39 (1170 elements) found.\n" ] }, { "data": { "text/plain": [ - "((35, 33), False)" + "((30, 39), False)" ] }, "execution_count": 8, @@ -367,7 +368,8 @@ " \n", " \n", " \n", - " 36\n", + " 40\n", + " 0.0\n", " 0.0\n", " 0.0\n", " 0.0\n", @@ -377,7 +379,6 @@ " 0.0\n", " 0.0\n", " 0.0\n", - " NaN\n", " ...\n", " 0.0\n", " 0.0\n", @@ -391,11 +392,11 @@ " 0.0\n", " \n", " \n", - " 46\n", + " 26\n", + " 0.0\n", " 0.0\n", " 0.0\n", " 0.0\n", - " NaN\n", " 0.0\n", " 0.0\n", " 0.0\n", @@ -415,7 +416,7 @@ " 0.0\n", " \n", " \n", - " 48\n", + " 22\n", " 0.0\n", " 0.0\n", " 0.0\n", @@ -445,24 +446,24 @@ ], "text/plain": [ " feature 0 feature 1 feature 2 feature 3 feature 4 feature 5 \\\n", - "36 0.0 0.0 0.0 0.0 0.0 0.0 \n", - "46 0.0 0.0 0.0 NaN 0.0 0.0 \n", - "48 0.0 0.0 0.0 0.0 0.0 0.0 \n", + "40 0.0 0.0 0.0 0.0 0.0 0.0 \n", + "26 0.0 0.0 0.0 0.0 0.0 0.0 \n", + "22 0.0 0.0 0.0 0.0 0.0 0.0 \n", "\n", " feature 6 feature 7 feature 8 feature 9 ... feature 40 feature 41 \\\n", - "36 0.0 0.0 0.0 NaN ... 0.0 0.0 \n", - "46 0.0 0.0 0.0 0.0 ... 0.0 0.0 \n", - "48 0.0 0.0 0.0 0.0 ... 0.0 0.0 \n", + "40 0.0 0.0 0.0 0.0 ... 0.0 0.0 \n", + "26 0.0 0.0 0.0 0.0 ... 0.0 0.0 \n", + "22 0.0 0.0 0.0 0.0 ... 0.0 0.0 \n", "\n", " feature 42 feature 43 feature 44 feature 45 feature 46 feature 47 \\\n", - "36 0.0 0.0 0.0 0.0 0.0 0.0 \n", - "46 0.0 0.0 0.0 0.0 0.0 0.0 \n", - "48 0.0 0.0 0.0 0.0 0.0 0.0 \n", + "40 0.0 0.0 0.0 0.0 0.0 0.0 \n", + "26 0.0 0.0 0.0 0.0 0.0 0.0 \n", + "22 0.0 0.0 0.0 0.0 0.0 0.0 \n", "\n", " feature 48 feature 49 \n", - "36 0.0 0.0 \n", - "46 0.0 0.0 \n", - "48 0.0 0.0 \n", + "40 0.0 0.0 \n", + "26 0.0 0.0 \n", + "22 0.0 0.0 \n", "\n", "[3 rows x 50 columns]" ] @@ -492,55 +493,55 @@ "Data columns (total 50 columns):\n", " # Column Non-Null Count Dtype \n", "--- ------ -------------- ----- \n", - " 0 feature 0 50 non-null float64\n", + " 0 feature 0 48 non-null float64\n", " 1 feature 1 49 non-null float64\n", - " 2 feature 2 49 non-null float64\n", - " 3 feature 3 48 non-null float64\n", - " 4 feature 4 47 non-null float64\n", - " 5 feature 5 50 non-null float64\n", - " 6 feature 6 50 non-null float64\n", - " 7 feature 7 48 non-null float64\n", + " 2 feature 2 48 non-null float64\n", + " 3 feature 3 49 non-null float64\n", + " 4 feature 4 49 non-null float64\n", + " 5 feature 5 49 non-null float64\n", + " 6 feature 6 49 non-null float64\n", + " 7 feature 7 47 non-null float64\n", " 8 feature 8 49 non-null float64\n", - " 9 feature 9 47 non-null float64\n", - " 10 feature 10 47 non-null float64\n", - " 11 feature 11 49 non-null float64\n", - " 12 feature 12 49 non-null float64\n", - " 13 feature 13 50 non-null float64\n", - " 14 feature 14 47 non-null float64\n", + " 9 feature 9 50 non-null float64\n", + " 10 feature 10 48 non-null float64\n", + " 11 feature 11 47 non-null float64\n", + " 12 feature 12 50 non-null float64\n", + " 13 feature 13 48 non-null float64\n", + " 14 feature 14 50 non-null float64\n", " 15 feature 15 48 non-null float64\n", - " 16 feature 16 48 non-null float64\n", - " 17 feature 17 49 non-null float64\n", + " 16 feature 16 50 non-null float64\n", + " 17 feature 17 50 non-null float64\n", " 18 feature 18 49 non-null float64\n", " 19 feature 19 49 non-null float64\n", - " 20 feature 20 50 non-null float64\n", - " 21 feature 21 48 non-null float64\n", + " 20 feature 20 48 non-null float64\n", + " 21 feature 21 50 non-null float64\n", " 22 feature 22 48 non-null float64\n", - " 23 feature 23 49 non-null float64\n", + " 23 feature 23 48 non-null float64\n", " 24 feature 24 47 non-null float64\n", - " 25 feature 25 49 non-null float64\n", + " 25 feature 25 48 non-null float64\n", " 26 feature 26 48 non-null float64\n", " 27 feature 27 50 non-null float64\n", - " 28 feature 28 50 non-null float64\n", + " 28 feature 28 49 non-null float64\n", " 29 feature 29 49 non-null float64\n", - " 30 feature 30 49 non-null float64\n", - " 31 feature 31 46 non-null float64\n", + " 30 feature 30 48 non-null float64\n", + " 31 feature 31 48 non-null float64\n", " 32 feature 32 49 non-null float64\n", - " 33 feature 33 50 non-null float64\n", - " 34 feature 34 48 non-null float64\n", - " 35 feature 35 47 non-null float64\n", - " 36 feature 36 50 non-null float64\n", + " 33 feature 33 49 non-null float64\n", + " 34 feature 34 49 non-null float64\n", + " 35 feature 35 49 non-null float64\n", + " 36 feature 36 49 non-null float64\n", " 37 feature 37 50 non-null float64\n", - " 38 feature 38 48 non-null float64\n", + " 38 feature 38 50 non-null float64\n", " 39 feature 39 50 non-null float64\n", " 40 feature 40 49 non-null float64\n", - " 41 feature 41 50 non-null float64\n", - " 42 feature 42 47 non-null float64\n", - " 43 feature 43 50 non-null float64\n", + " 41 feature 41 48 non-null float64\n", + " 42 feature 42 50 non-null float64\n", + " 43 feature 43 48 non-null float64\n", " 44 feature 44 49 non-null float64\n", " 45 feature 45 49 non-null float64\n", - " 46 feature 46 50 non-null float64\n", + " 46 feature 46 47 non-null float64\n", " 47 feature 47 48 non-null float64\n", - " 48 feature 48 50 non-null float64\n", + " 48 feature 48 49 non-null float64\n", " 49 feature 49 49 non-null float64\n", "dtypes: float64(50)\n", "memory usage: 19.7 KB\n" @@ -562,45 +563,52 @@ "output_type": "stream", "text": [ "\n", - "Index: 34 entries, 1 to 49\n", - "Data columns (total 33 columns):\n", + "Index: 29 entries, 1 to 49\n", + "Data columns (total 40 columns):\n", " # Column Non-Null Count Dtype \n", "--- ------ -------------- ----- \n", - " 0 feature 0 34 non-null float64\n", - " 1 feature 1 34 non-null float64\n", - " 2 feature 2 34 non-null float64\n", - " 3 feature 5 34 non-null float64\n", - " 4 feature 6 34 non-null float64\n", - " 5 feature 9 34 non-null float64\n", - " 6 feature 11 34 non-null float64\n", - " 7 feature 13 34 non-null float64\n", - " 8 feature 15 34 non-null float64\n", - " 9 feature 19 34 non-null float64\n", - " 10 feature 20 34 non-null float64\n", - " 11 feature 21 34 non-null float64\n", - " 12 feature 22 34 non-null float64\n", - " 13 feature 23 34 non-null float64\n", - " 14 feature 25 34 non-null float64\n", - " 15 feature 27 34 non-null float64\n", - " 16 feature 28 34 non-null float64\n", - " 17 feature 29 34 non-null float64\n", - " 18 feature 30 34 non-null float64\n", - " 19 feature 32 34 non-null float64\n", - " 20 feature 33 34 non-null float64\n", - " 21 feature 36 34 non-null float64\n", - " 22 feature 37 34 non-null float64\n", - " 23 feature 38 34 non-null float64\n", - " 24 feature 39 34 non-null float64\n", - " 25 feature 40 34 non-null float64\n", - " 26 feature 41 34 non-null float64\n", - " 27 feature 43 34 non-null float64\n", - " 28 feature 45 34 non-null float64\n", - " 29 feature 46 34 non-null float64\n", - " 30 feature 47 34 non-null float64\n", - " 31 feature 48 34 non-null float64\n", - " 32 feature 49 34 non-null float64\n", - "dtypes: float64(33)\n", - "memory usage: 9.0 KB\n" + " 0 feature 1 29 non-null float64\n", + " 1 feature 2 29 non-null float64\n", + " 2 feature 3 29 non-null float64\n", + " 3 feature 4 29 non-null float64\n", + " 4 feature 6 29 non-null float64\n", + " 5 feature 8 29 non-null float64\n", + " 6 feature 9 29 non-null float64\n", + " 7 feature 12 29 non-null float64\n", + " 8 feature 13 29 non-null float64\n", + " 9 feature 14 29 non-null float64\n", + " 10 feature 15 29 non-null float64\n", + " 11 feature 16 29 non-null float64\n", + " 12 feature 17 29 non-null float64\n", + " 13 feature 18 29 non-null float64\n", + " 14 feature 19 29 non-null float64\n", + " 15 feature 20 29 non-null float64\n", + " 16 feature 21 29 non-null float64\n", + " 17 feature 22 29 non-null float64\n", + " 18 feature 24 29 non-null float64\n", + " 19 feature 25 29 non-null float64\n", + " 20 feature 26 29 non-null float64\n", + " 21 feature 27 29 non-null float64\n", + " 22 feature 28 29 non-null float64\n", + " 23 feature 30 29 non-null float64\n", + " 24 feature 31 29 non-null float64\n", + " 25 feature 32 29 non-null float64\n", + " 26 feature 33 29 non-null float64\n", + " 27 feature 35 29 non-null float64\n", + " 28 feature 37 29 non-null float64\n", + " 29 feature 38 29 non-null float64\n", + " 30 feature 39 29 non-null float64\n", + " 31 feature 41 29 non-null float64\n", + " 32 feature 42 29 non-null float64\n", + " 33 feature 43 29 non-null float64\n", + " 34 feature 44 29 non-null float64\n", + " 35 feature 45 29 non-null float64\n", + " 36 feature 46 29 non-null float64\n", + " 37 feature 47 29 non-null float64\n", + " 38 feature 48 29 non-null float64\n", + " 39 feature 49 29 non-null float64\n", + "dtypes: float64(40)\n", + "memory usage: 9.3 KB\n" ] } ], @@ -618,16 +626,17 @@ { "data": { "text/plain": [ - "(Index([ 0, 1, 2, 5, 6, 7, 8, 9, 10, 11, 12, 19, 20, 22, 23, 24, 25, 27,\n", - " 29, 30, 32, 33, 34, 35, 37, 39, 41, 42, 45, 46, 47, 48],\n", + "(Index([ 1, 2, 3, 4, 5, 6, 7, 8, 10, 12, 14, 21, 22, 28, 30, 32, 33, 37,\n", + " 38, 39, 40, 41, 43, 45, 46, 47, 48, 49],\n", " dtype='int64'),\n", - " Index(['feature 0', 'feature 1', 'feature 2', 'feature 5', 'feature 6',\n", - " 'feature 8', 'feature 9', 'feature 11', 'feature 12', 'feature 13',\n", - " 'feature 14', 'feature 15', 'feature 17', 'feature 18', 'feature 20',\n", - " 'feature 23', 'feature 25', 'feature 27', 'feature 28', 'feature 29',\n", - " 'feature 30', 'feature 32', 'feature 33', 'feature 34', 'feature 36',\n", - " 'feature 37', 'feature 38', 'feature 39', 'feature 41', 'feature 42',\n", - " 'feature 43', 'feature 44', 'feature 45', 'feature 46', 'feature 48',\n", + " Index(['feature 0', 'feature 1', 'feature 2', 'feature 3', 'feature 4',\n", + " 'feature 8', 'feature 9', 'feature 12', 'feature 13', 'feature 14',\n", + " 'feature 15', 'feature 16', 'feature 17', 'feature 18', 'feature 19',\n", + " 'feature 20', 'feature 21', 'feature 22', 'feature 24', 'feature 25',\n", + " 'feature 26', 'feature 27', 'feature 28', 'feature 30', 'feature 31',\n", + " 'feature 32', 'feature 33', 'feature 34', 'feature 35', 'feature 37',\n", + " 'feature 38', 'feature 39', 'feature 41', 'feature 42', 'feature 43',\n", + " 'feature 44', 'feature 45', 'feature 46', 'feature 47', 'feature 48',\n", " 'feature 49'],\n", " dtype='object'))" ] @@ -660,7 +669,7 @@ "outputs": [ { "data": { - "image/png": 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", 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", 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" ] @@ -699,7 +708,7 @@ "outputs": [ { "data": { - "image/png": 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", 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", 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" ] @@ -738,7 +747,7 @@ "outputs": [ { "data": { - "image/png": "iVBORw0KGgoAAAANSUhEUgAAAI8AAAGFCAYAAADEj6BBAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjguNCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8fJSN1AAAACXBIWXMAAA9hAAAPYQGoP6dpAAAFTUlEQVR4nO3dsW7bZhhAUTroI3U04Cwd/bQeu8SAx7wTOzcoK+ZKpPiT54yBBFD2xQd8+Sn6ZZ7neYLg27MvgHGJh0w8ZOIhEw+ZeMjEQyYesj/WvvD1/XPL6xjOX3/++M9///vn912vYwtfH2+rXmfykImHTDxk4iETD5l4yFav6vzbGVbye5k8ZOIhEw+ZeMjEQ2bbuoCtDnFNHjLxkImHTDxk4iGzbV3AVudwJg+ZeMjEQyYeMvGQiYdMPGTiIRMPmXjIxEN2yrOtMz/+5EhMHjLxkImHTDxk4iE75bZlq9qHyUMmHjLxkImHTDxk4iETD5l4yMRDJh4y8ZCJh+zug1G3fF6XyUMmHjLxkImHTDxkd29btqrrMnnIxEMmHjLxkImHTDxk4iETD5l4yMRDJh4y8ZCJh0w8ZOIhEw+ZeMjEQyYeMvGQiYdMPGTiITvlnw94JE8BWWbykImHTDxk4iETD5l4yDZb1c+y4o52vb9j6Xc0TW+r3m/ykImHTDxk4iETD9nLPM/zmhe+vn9ufS2XdMSt9OvDtsXGxEMmHjLxkImHzG2oTzby2ZnJQyYeMvGQiYdMPGTiIRMPmXjIxEMmHjLxkA1xtnXEu+0webiDeMjEQyYeMvGQDbFt2aqOyeQhEw+ZeMjEQyYeMvGQiYdMPGTiIRMPmXjIxEMmHjLxkImHTDxk4iETD9kQt6Fyn62+NGnykImHTDxk4iETD9nQ25ZHr6yz1c/D5CETD5l4yMRDJh4y8ZCJh0w8ZOIhEw+ZeMgOdbb1u2dVZzjDGvl8zuQhEw+ZeMjEQyYeMvGQHWpVH2E9fbRHfua9136Th0w8ZOIhEw+ZeMgOtW1xn723VZOHTDxk4iETD5l4yGxbN4x8m+jWTB4y8ZCJh0w8ZOIhs23dYKtaZvKQiYdMPGTiIRMPmW0rcuZl8nAH8ZCJh0w8ZOIhs21FV9qqlpg8ZOIhEw+ZeMjEQyYeMqv6DQ5Al5k8ZOIhEw+ZeMjEQ7Z627rq1nHmz7f0O52mt1XvN3nIxEMmHjLxkImH7GWe53nNC1/fP7e+Fg7i68O2xcbEQyYeMvGQiYds9zsJl89Tzn2OdEYmD5l4yMRDJh4y8ZDtfiehjeo8TB4y8ZCJh0w8ZOIhW71t2ZL4lclDJh4y8ZCJh0w8ZJ5J+GQjP33E5CETD5l4yMRDJh4y8ZBZ1Z9shJV8iclDJh4y8ZCJh0w8ZOIhEw+ZeMjEQyYeMvGQOdu6YeTbRLdm8pCJh0w8ZOIhEw+ZbesGW9Uyk4dMPGTiIRMPmXjIbFsXsNX5nMlDJh4y8ZCJh0w8ZENvW+7yW2ern4fJQyYeMvGQiYdMPGTiIRt6VT/zSr703xDTdJzPbfKQiYdMPGTiIRMP2dDb1pkdZaP6PyYPmXjIxEMmHjLxkImHTDxk4iETD5l4yMRD5mzrBl8sXGbykImHTDxk4iETD5lt64YzbFUeaMnhiIdMPGTiIRMPmW3rAjzQksMRD5l4yMRDJh4y8ZCJh0w8ZOIhEw+ZeMjEQ+Zg9MlG/lKhyUMmHjLxkImHTDxktq3oUVvSCFvVEpOHTDxk4iETD5l4yGxb0chb0qOYPGTiIRMPmXjIxENm2zqopbOzaTrOpmfykImHTDxk4iETD9mltq2RviO1xzUtb3Rvq95v8pCJh0w8ZOIhEw+ZeMhe5nme17zw9f1z62vhIL4+rOpsTDxk4iETD5l4yMRDJh4y8ZCJh0w8ZOIh2/021D2+zDbS7aYjM3nIxEMmHjLxkImHbPWdhPArk4dMPGTiIRMPmXjIxEMmHjLxkImH7B9bTbevJMvdxwAAAABJRU5ErkJggg==", + "image/png": "iVBORw0KGgoAAAANSUhEUgAAAI8AAAGFCAYAAADEj6BBAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjkuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/TGe4hAAAACXBIWXMAAA9hAAAPYQGoP6dpAAAFMklEQVR4nO3dMU4bURhG0SFiSZSRoKHMalPSECkle5rUkTAaru3xe+NzyihKjHX1K588hId1XdcFgh+3fgHMSzxk4iETD5l4yMRDJh4y8ZA9bv2NP3/9uebrGNbr0/unv/728bLr69jT39/Pm36fy0MmHjLxkImHTDxk4iHbPNXvcbIuy/G/vnO4PGTiIRMPmXjIxEO2eW1ZHec54lp1ecjEQyYeMvGQiYds89qayRGXzYhcHjLxkImHTDxk4iHbfW2dWkLLcrk1NOKqGvE1ncvlIRMPmXjIxEMmHrLd19YRV8e9cnnIxEMmHjLxkImH7JBPEo7okk83jvKkpMtDJh4y8ZCJh0w8ZOIhG2qqjzJBr+GSX8OpP2vv98/lIRMPmXjIxEMmHrKh1tYRVtWllG+O9MEo0xAPmXjIxEMmHrKrra0jf061hxneJ5eHTDxk4iETD5l4yK62tmZYC5zH5SETD5l4yMRDJh6yoZ4kPLKvngz8zAxr1eUhEw+ZeMjEQyYeMmtrJzOsp+9yecjEQyYeMvGQiYdMPGTiIRMPmXjIxEMmHjLxkImHTDxk4iETD5l4yMRDdlePod7rf7J5ra/b5SETD5l4yMRDJh6yu1pbI66qPRagH43NcMRDJh4y8ZCJh+yu1taIRlyAW7k8ZOIhEw+ZeMjEQyYeMvGQiYdMPGTiIRMPmc+2JjTK95+5PGTiIRMPmXjIxEMmHjJT/cbK7B7l0VWXh0w8ZOIhEw+ZeMisrRsbZTkVLg+ZeMjEQyYeMvGQiYdMPGTiIRMPmXjIxEMmHjLxkImHTDxk4iETD5l4yMRDJh4y8ZCJh0w8ZOIhEw+ZeMjEQyYeMvGQiYdMPGTiIRMPmXjIxEMmHjLxkImHTDxk4iETD5l4yDb/+IBRfpY343B5yMRDJh4y8ZCJh2zz2tpjVVl0c3F5yMRDJh4y8ZCJh2yoH41tVc3F5SETD5l4yMRDJh6yodbWrZz6TG1ZLMCvuDxk4iETD5l4yMRDJh6yQ0717z7Oao43Lg+ZeMjEQyYeMvGQHXJtWU/7cHnIxEMmHjLxkImHTDxk4iETD5l4yMRDJh4y8ZCJh0w8ZOIhEw+ZeMjEQyYeMvGQiYdMPGTiIRMPmXjIxEMmHjLxkImHTDxk4iETD5l4yMRDJh4y8ZCJh0w8ZOIhEw+ZeMjEQyYeMvGQiYdMPGTiIRMPmXjIxEMmHjLxkImHTDxk4iETD5l4yMRDJh4y8ZA93voFbPH69P7pr799vOz6Oka39/vk8pCJh0w8ZOIhEw/ZFGvLqvrfKOvT5SETD5l4yMRDJh4y8ZANNdVHmaCjG+X9cHnIxEMmHjLxkImHbKi1NcqKuIYjLkmXh0w8ZOIhEw+ZeMiGWltHNvOqOsXlIRMPmXjIxEMmHrKz19YRP7NhG5eHTDxk4iETD5l4yM5eW/e6qqxMl4cziIdMPGTiIRMPmScJo3taVae4PGTiIRMPmXjIxEMmHjLxkImHTDxk4iETD5l4yMRDJh4y8ZCJh0w8ZOIh2/0x1FPfLLcsHu2cjctDJh4y8ZCJh0w8ZLuvLYvqOFweMvGQiYdMPGTiIdu8tvwHjtcx8/vq8pCJh0w8ZOIhEw/Z5rU1w7/+q1s+3Tjz++rykImHTDxk4iETD5l4yPzsiWXuuXxLLg+ZeMjEQyYeMvGQiYdMPGTiIRMPmXjIxEPms60D+e43EJ5+/PZ509/n8pCJh0w8ZOIhEw/Zw7qu661fBHNyecjEQyYeMvGQiYdMPGTiIRMPmXjI/gHO16ngeaKl2QAAAABJRU5ErkJggg==", "text/plain": [ "
" ] @@ -803,13 +812,13 @@ "name": "stdout", "output_type": "stream", "text": [ - "CPU times: total: 109 ms\n", - "Wall time: 565 ms\n" + "CPU times: total: 188 ms\n", + "Wall time: 519 ms\n" ] }, { "data": { - "image/png": "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", + "image/png": "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", "text/plain": [ "
" ] @@ -821,7 +830,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "The largest submatrix without NaN is of size 47x15 (705 elements among 1155)\n" + "The largest submatrix without NaN is of size 43x16 (688 elements among 1170)\n" ] } ], @@ -925,7 +934,7 @@ "outputs": [ { "data": { - "image/png": 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+ "image/png": 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"text/plain": [ "
" ] @@ -966,7 +975,7 @@ "outputs": [ { "data": { - "image/png": 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", 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", 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" ] @@ -999,7 +1008,7 @@ "outputs": [ { "data": { - "image/png": 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", + "image/png": 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", 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", + "image/png": 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", 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", + "image/png": 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", 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", 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nn7Ft4vHKzRfH7X1O8c/NKaecwlVUVMj2VXx/nn/++ZzRaORaW1sF+y1evJizWCzsmh3s81CO7u5uDgB3/vnnK+7DcRx33nnncQC4vr4+juPiz3W5cShdU0Q0GuXC4TC3YsUKTqfTcTabjX0nd0zxXG/cuJEDwL344ouybaekpHA9PT1sG92PX331VcLxqaioqCSD6iaqwlixYgU2btwo+Kxfv37Ix/vwww8xadIkTJs2DZFIhH1OOukkgbsRuTlef/31wzEMAGCWELHV4JxzzoHVasUXX3wx5GN7vV4YDAYYDAbk5ubiuuuuw3nnnYc//OEPgv0ikQj++Mc/YsKECUhJSYFer0dKSgrq6+tRW1srOe7pp58u+HvKlCkAgJaWFgDxhAyBQEASrzl37lxUVFQItq1evRoTJkzAnDlzBNsvu+wycBwnsRQtWbJEsEI+fvx4AGCr1eLtra2tMjMj5auvvsLFF1+Miy66SDA/g7021qxZAwCSMV944YWDap+44447YDAYmGvazp078cEHHwhcwz788EMsWrQIxcXFgj6RFfKrr74CAMyZMwcOhwMXXHAB3n//fYl7577o7e3Ftddei7KyMuj1ehgMBnb+5K6Ls846a9DHJrezc889F2+++SY6OjoG9btk75dp06ahvLyc/W0ymTBmzBh2re4Lyuoo/jz66KOC/RoaGrBr1y52/vnnZcmSJejq6hqU66AcU6ZMEViGlZgzZw62bduGpUuX4tNPP93vrMEff/wxTCbToFx3xcyePRsNDQ345JNPcPfdd+Ooo47CF198gUsuuQSnn346OI4DED+fVqsVZ599tuD3dH735/k3VFavXo3jjjsOZWVlkj75fD6JS+S+nof7A82TnFV1X2zZsgWnn346cnJyoNPpYDAYcMkllyAaje63+zqf6667DgDwj3/8g23729/+hsmTJ/+o4pxVVFQOf1Q3URXG+PHj95lAJhl6enrQ0NAgcXsjSIju6+uDTqdDYWHhsLU9MDAAvV6PvLw8wXaNRoPCwkIMDAwM+dhmsxlff/01gLhr4eOPP47XX38dU6ZMwZ133sn2u/nmm/H000/jjjvuwMKFC5GVlQWtVourrroKfr9fctycnBzB3+SWR/tSn+XmSbxtYGBANgaGXIvE46essERKSkrC7YFAQHJsMdXV1TjzzDNx9NFH44UXXhB8N9hrg86jeG6SvVZ++9vf4qKLLkIwGMT333+P3/3udzjjjDOwbds2duyenh588MEH++zTxRdfjEgkgn/84x8466yzEIvFMHv2bDz88MM44YQTEvYjFovhxBNPRGdnJ+69915MnjwZVqsVsVgMRx55pOx1kUxm3gULFuC9997DX//6V1xyySUIBoOYOHEi7rnnHlxwwQWKv0v2fhGfDyB+vcr1X468vDzZZ404To1iOm+99VbceuutssdKVhknBjuvd911F6xWK1555RU8++yz0Ol0WLBgAR599NEhPS/7+vpQXFyctHsiYTAYcNJJJ+Gkk04CED93Z599Nj788EN8/PHHWLJkCQYGBlBYWChRdvLz86HX6/fr+TdUBgYGZOdc6Zm0r+ehHLm5ubBYLMwNXInm5maYzWbZ6zgRra2tOProozF27Fg8+eSTqKyshMlkwoYNG3D99dcP+vofDAUFBTjvvPPw3HPP4c4770R1dTW++eYbPPfcc8PWhoqKigqgKoMqB5Dc3FyYzWbFOIrc3FwAccEwGo2iu7t72EpS5OTkIBKJoK+vTyDgchyH7u7u/coip9VqBULgCSecgJkzZ+KBBx7Ar371K7by/corr+CSSy7BH//4R8Hv+/v7JennBwMJLt3d3ZLvuru7BcpfTk4Ourq6JPtR0gGa+wNFe3s7Tj75ZJSXl+Ptt9+WKFiDvTboPA4MDAgEN7k5SERpaSk7Z/PmzUNhYSEuuugi3Hffffjb3/7G2pwyZYrEwkvwY3Quv/xyXH755fB6vfj6669x33334dRTT8Xu3bslVlo+O3fuxLZt2/Cvf/0Ll156Kdve0NCg+JtkrRdnnHEGzjjjDKb4PvLII7jwwgtRWVkpiD/icyDvl/2BroO77roLv/zlL2X3oVhZk8kEIJ6chR/fqKQsDnZe9Xo9br75Ztx8881wOBz4/PPPcffdd+Okk05CW1tb0tld8/LysHbt2iHFq8mRk5ODZcuW4csvv8TOnTuxZMkS5OTkYP369eA4TjDO3t5eRCKRhPc/fx75DFXp5vfzQD+TdDodjj32WHz88cdob2+XjRtsb2/Hpk2bcPLJJ7NtJpNJNrGOeMzvvfcevF4v3nnnHcF9fqDqBP72t7/Fyy+/jPfffx+ffPIJMjMzJV4SKioqKvuL6iaqcsA49dRT0djYiJycHMyaNUvyIeWF3PCWL1+e8HjJWB6OO+44AHGFjM/bb78Nr9fLvh8OjEYjnn76aQQCATz88MNsu0ajkSS5Wbly5aBd98QceeSRMJlMLHkP8d1330lcp4477jjU1NRg8+bNgu0rVqyARqPBokWLhtSHweB0OrF48WJoNBp89NFHsjXbBnttUD/FY37ttdf2q4+/+tWvcMwxx+Af//gHm7tTTz2VlU6R65Ncwgar1YrFixfjnnvuQSgUQnV1NQBlKwYJ5uLrItnV/sFYSYxGIxYuXMhcL7ds2aK478G8X5Jh7NixGD16NLZt2yZ7TmbNmoW0tDQAYNfM9u3bBcf44IMPhq0/mZmZOPvss3H99dfDZrMxS+ZgzgexePFiBAKBpLMWh8NhRYseuRfTNXrcccfB4/HgvffeE+y3YsUK9r0SSvMolzQq2Wfy6tWrJVkwV6xYAYvFMmylKO68805wHIelS5ciGo0KvotGo7juuusQjUbx29/+lm2vrKxEb2+vILtwKBTCp59+Kvi93P3LcZzAlTMZ9nXdzJw5E3PnzsWjjz6KV199FZdddplioiMVFRWVoaJaBlUYO3fulM3wN3LkSIn72GBYtmwZ3n77bSxYsAA33XQTpkyZglgshtbWVnz22We45ZZbcMQRR+Doo4/GxRdfjIcffhg9PT049dRTYTQasWXLFlgsFtx4440AgMmTJ+ONN97Av//9b4wYMQImkwmTJ0+WbfuEE07ASSedhDvuuAMulwvz5s1j2RGnT5+Oiy++OOnxJGLhwoVYsmQJXnzxRdx5552oqqrCqaeein/9618YN24cpkyZgk2bNuFPf/rTkAsxZ2Vl4dZbb8XDDz+Mq666Cueccw7a2tpw//33S9wmb7rpJqxYsQKnnHIKHnzwQVRUVGDlypV45plncN111w0qVmqoXHjhhaipqcHf//53tLW1oa2tjX1XWlqK0tLSQV8bJ554IhYsWIDbb78dXq8Xs2bNwrfffouXX355v/v56KOP4ogjjsBDDz2E559/Hg8++CBWrVqFuXPn4je/+Q3Gjh2LQCCA5uZmfPTRR3j22WdRWlqKq6++GmazGfPmzUNRURG6u7vxyCOPICMjg1nQJk2aBAD4+9//jrS0NJhMJlRVVWHcuHEYOXIkE1izs7PxwQcfYNWqVUn1na77Rx99FIsXL4ZOp8OUKVPw8MMPo729HccddxxKS0vhcDjw5JNPwmAwYOHChYrHO9j3SzI899xzWLx4MU466SRcdtllKCkpgc1mQ21tLTZv3oz//Oc/AOJxr9nZ2SwjsV6vx7/+9S/B9TcUTjvtNFaDNS8vDy0tLXjiiSdQUVGB0aNHA9h7Pp588klceumlMBgMGDt2LFNU+VxwwQV48cUXce2116Kurg6LFi1CLBbD+vXrMX78eJx//vmy/XA6naisrMQ555yD448/HmVlZfB4PPjyyy/x5JNPYvz48cx6eskll+Dpp5/GpZdeiubmZkyePBlr167FH//4RyxZsgTHH3+84nhnz56NsWPH4tZbb0UkEkFWVhbeffddrF27VrLv5MmT8c4772D58uWYOXOmxGuCz3333cficn//+98jOzsbr776KlauXInHHnsMGRkZiU/EIJk3bx6eeOIJ/Pa3v8X8+fNxww03oLy8nBWdX7duHe6//36BS/d5552H3//+9zj//PNx2223IRAI4K9//atEmTzhhBOQkpKCCy64ALfffjsCgQCWL18Ou90+pL6OHDkSZrMZr776KsaPH4/U1FQUFxcLFp5++9vf4rzzzoNGo8HSpUuHNikqKioqiThkqWtUDhsSZROFKOtdMtlEOY7jPB4P97vf/Y4bO3Ysl5KSwmVkZHCTJ0/mbrrpJq67u5vtF41Guf/7v//jJk2axPY76qijuA8++IDt09zczJ144olcWloaB0Axix3h9/u5O+64g6uoqOAMBgNXVFTEXXfddZzdbhfsN5RsonLs2LGD02q13OWXX85xXDz755VXXsnl5+dzFouFmz9/PvfNN99IsgtS9rz//Oc/guNRJkx+prlYLMY98sgjXFlZGZeSksJNmTKF++CDD2Qzera0tHAXXnghl5OTwxkMBm7s2LHcn/70J0HWUGrjT3/6k+C3Sn2ia4WfBVTcdkVFheK1xM8uONhrw+FwcFdccQWXmZnJWSwW7oQTTuB27dqVVDZR8fiIc845h9Pr9VxDQwPHcRzX19fH/eY3v+Gqqqo4g8HAZWdnczNnzuTuuecezuPxcBzHcS+99BK3aNEirqCggEtJSeGKi4u5c889l9u+fbvg2E888QRXVVXF6XQ6wXmsqanhTjjhBC4tLY3LysrizjnnHK61tVUyHspkSBkP+QSDQe6qq67i8vLyOI1Gw7I/fvjhh9zixYu5kpISLiUlhcvPz+eWLFnCffPNNwnnieMGf78o3ev7yipLAOCuv/562e+UsrBu27aNO/fcc7n8/HzOYDBwhYWF3LHHHss9++yzgv02bNjAzZ07l7NarVxJSQl33333cc8//7xsNlG5MdB3/IyPjz/+ODd37lwuNzeXS0lJ4crLy7krr7ySa25uFvzurrvu4oqLizmtVisYg9y8+P1+7ve//z03evRoLiUlhcvJyeGOPfZY7rvvvlOct2AwyP35z3/mFi9ezJWXl3NGo5EzmUzc+PHjudtvv50bGBgQ7D8wMMBde+21XFFREafX67mKigrurrvu4gKBQMLxchzH7d69mzvxxBO59PR0Li8vj7vxxhu5lStXSs6NzWbjzj77bC4zM5Ndh4Tc/bljxw7utNNO4zIyMriUlBRu6tSpkkyayTwPE/Hdd99xZ511FldQUMDOiclk4lauXCm7/0cffcRNmzaNM5vN3IgRI7i//e1vstlEP/jgA27q1KmcyWTiSkpKuNtuu41lPebPzWCyiXIcx73++uvcuHHjOIPBIDtnwWCQMxqN3MknnzyocauoqKgki4bj/pdWS0VFRUVFRUXlJ8iKFStw6aWX4vbbb5dkrT2c+eCDD3D66adj5cqVWLJkyaHujoqKyk8QNWZQRUVFRUVF5SfNJZdcgv/3//4fHnvsMTz44IOHujv7pKamBh9//DFuueUWTJs2jcXWqxxgLrsMOPPMQ9uH7m7ghBMAqxVIJtlcczOg0QAHKKHRAeFgzfeBbueYY4Bly/b/OIfo+lNjBlVUVFRUVFR+8txxxx244447DnU3BsXSpUvx7bffYsaMGXjppZeGVBNRZQg8+SRwqB3m/u//gK6uuFKnFEt72WWAwwGIkkQdtjQ3A1VVwJYtwLRpe7cfDvN9KDjM5kNVBlVUVFRUVFRUDiO+/PLLQ92FnyfDlMhov2hsBGbOBP6XoOonzeEw34cTh2g+VDdRFRUVFRUVFRWVnwdvvQVMngyYzUBODnD88YDXG/+O76ZHbpfizzHH7D3Wd98BCxbEj1VWBvzmN3uPpcTy5cDIkUBKCjB2LMDPjl1ZCbz9NrBiRbytyy6T/v7++4GXXgLef39vn/iLB3v2AIsWARYLMHUqsG6d8PfD3Wcg3ofly4HFi+PHraoC/pfpGUD8bwCYPl04h2K3yGOOAW68Me5ymZUFFBQAf/97vH+XXw6kpcX78fHHe38TjQJXXhlvw2yO9+/JJxOPR0xLC3DaafE2rVZg4kTgo4/2fv/VV8CcOYDRCBQVAXfeCchk3xfMh9hqm5kJUEmhwc5HMBg/P/n5gMkEzJ8PbNy49/svv4z//osvgFmz4ud87lygri6p4avKoIqKioqKioqKyk+fri7ggguAK64AamvjwvQvfynvmldWFt+fPlu2xJXHBQvi3+/YAZx0Uvz327cD//43sHYtcMMNyu2/+y7w298Ct9wC7NwJXHNNXMlZsyb+/caNwMknA+eeG29TTqm59db49yefvLdvc+fu/f6ee+L7bN0KjBkTHy8pLgeiz8S99wJnnQVs2wZcdFG83f/VQMWGDfF/P/883t933lFu76WXgNzc+G9uvBG47jrgnHPiY9y8Od7/iy8GfL74/rEYUFoKvPkmUFMD/P73wN13x/8eLNdfH1e8vv46PkePPgqkpsa/6+gAliwBZs+Oj235cuCFFwBeXemkGex83H57fHHgpZfiYx81Kj5+m0243z33AI8/DvzwA6DXx6/vZDjU6UxVVFRUVFRUVFRUDjibNnEcwHGi0jCMSy/luDPOkG73+znuiCM47tRTOY7KM118Mcf9+tfC/b75huO02vj+csydy3FXXy3cds45HLdkyd6/zzgj3o9EyPWzqSk+tuef37utujq+rbb2wPYZ4LhrrxXuc8QRHHfddcK+bdmSeBwLF3Lc/Pl7/45EOM5qjfeb6OqKH2vdOvn+chzHLV3KcWedpdyOmMmTOe7+++W/u/tujhs7luNisb3bnn6a41JT914LCxdy3G9/u/d7gOPefVd4nIwMjqPSOIOZD4+H4wwGjnv11b3fh0IcV1zMcY89Fv97zZr4cT7/fO8+K1fGtymdTxnUmEEAsVgMnZ2dSEtLU4O0VVRUVFRUVFQOQziOg9vtRnFxMbTaITi3TZ0KHHdc3E30pJOAE08Ezj477h6YiCuvBNxuYNUqgNrdtAloaABefZXfwbilqqkJGD9eepzaWuDXvxZumzcvebfGREyZsvf/RUXxf3t7gXHjDmyfjzpK+vdQMpvy+6/Txa2xkyfv3VZQEP+3t3fvtmefBZ5/Pu7u6fcDoZAwMcu++M1v4hbIzz6Luw2fddbeftTWxsfC1w/mzQM8HqC9HSgvT3qIg6KxEQiH420RBkPcXZUsroTSOR9k31RlEEBnZyfKysoOdTdUVFRUVFRUVFT2QVtbG0pLS5P/oU4XV+i++y4u+D/1VNzFbv36vXFcYh5+GPjkk7hrX1ra3u2xWNxl8je/kf4mkRAuNjpwnHTb/mAwSNuKxfb+ezD7PJRx8ftPx0g0pjffBG66Ke4medRR8XP0pz/Fz+lgueqq+OLAypXx6+KRR+LHu/FG+bGSW7HS+DQaqetxODz4/iRqQ64/ieZnEKjKIIC0/93cbW1tSE9PP8S9UVFRUVFRUVFREeNyuVBWVsbktiGh0cStLfPmxePLKiricXE33yzd9+23gQcfjCcsGTlS+N2MGUB1dTyOa7CMHx+P0bvkkr3bvvtO3iKXiJSUeOKUZDmQff7+e+E+338fT5BC/QWG1ud98c038XjCpUv3bmtsTP44ZWXAtdfGP3fdBfzjH3FlcMKE+HXAV8K++y6udJaUyB8rLy8eC0jU1++NcQQGNx+jRsX3W7sWuPDC+LZwOB4XOBw1DXmoyiDAXEPTdTqk63TSHXS6eBYfIlHWJa02ns1oKPv6fMr1RTSaeJagoezr9ydeIbBah7ZvIJD4Qk5mX4tl700WDCbO0pTMvmbzXpeOUCjxykwy+5pM8esi2X3D4fj+ShiN8eDfZPeNROJzoURKyt6Vo2T2jUbj504Jg0H4UBvsvrFY/Fobjn31+vhcAPF7gv/A3Z99k7nv1WeE/L7qMyL5fdVnRPz/6jNiaPv+1J8R/7tPhhzSs359PPPiiSfGMzSuXw/09ckrYzt3xpWbO+6IZ5fs7o5vT0kBsrPj2488Mp585Oqr432trY1bHp96Sr79226LJ3+ZMSPurvrBB/HkIZ9/ntw4KiuBTz+NZ43MyRl8SYID2ef//Cee0XL+/Lgb6oYN8UQrQHyuzea4hbW0NH4vDFcZhVGj4tlXP/00bt19+eV4Ih4lS68cy5bFM6GOGQPY7cDq1XuviaVLgSeeiCuGN9wQn/P77osvHii5Kh97LPC3v8XnOhaLzzvfejeY+bBa466rt90Wv97Ky4HHHovft1demcwM7ZtBRxf+hHE6nRwAzhl/LEo//CBZjuM4i0V+PyAeRMonN1d531mzhPtWVCjvO2GCcN8JE5T3ragQ7jtrlvK+ubnCfRcuVN7XYhHuu2SJ8r7iS+vssxPv6/Hs3ffSSxPv29u7d9+lSxPv29S0d99bb028786de/e9777E+27YsHffxx5LvO+aNXv3/dvfEu/74Yd7933xxcT7vvnm3n3ffDPxvhS0zHHxNhLt+7e/7d2XgpOVPhTEzHHxOUm073337d13587E+9566959KdBa6bN06d59e3sT78sPyvd4Eu979tmcgET7qs+I+Ed9Ruz9qM+I+Ed9RsQ/6jMi/tnPZ4QT4ABwTqeTGxI1NRx30kkcl5fHcUYjx40Zw3FPPbX3e34CD6X7i39+NmzguBNOiCcTsVo5bsoUjvvDHxL34ZlnOG7EiHhykDFjOG7FCuH3g0kg09u7t116hsglJbHbpc+YA9FnIJ5U5YQT4vNaUcFxr78u3Ocf/+C4srJ4shqaQ7kEMvxELBwXP9b//Z+0PUrQEghw3GWXxRO0ZGbGk9bceSfHTZ26d/99JZC54QaOGzky3ve8vHjCmv7+vd9/+SXHzZ7NcSkpHFdYyHF33MFx4bByvzs6OO7EE+PzO3o0x330kTCBzGDnw+/nuBtvjN9jRiPHzZsnfLfQ89du37tty5b4Nv67bR+olkEVFRUVFRUVFZWfPuPHx60xSlAdOCBe802uzh+f2bPjMWbJcN118Y8S4vp0cuTlybfLccK/MzOl2w5EnwGguDjxca+6Kv7hw59vQFgvkWhulm7jj8loBF58Mf7h88gjyu2IUbKKEgsX7i0HIYe438XFcUslH4dD+Pdg5sNkAv761/hHjmOOkZ7fadOk2/aBhuOS/MVPEJfLhYyMDDg7O+VjBlX3Dvl9VRew5PdVXcDi/1ddwIa2r/qMiP9ffUYkv6/6jIj/X31GDG3fw+QZ4bLbkVFcDKfTqeZ4OJzQaOJxl/yC6So/GlRlEDxlUH24qKioqKioqKgclqjy2mGKqgz+qFHdRFVUVFRUVFRUVFRUhoZqV/pRoyqDKioqKioqKiqHERzHIRqNIhKJIPY/l0uO4wSfRNv4xxlMW0r/JmpH7vgcxyEWiyEajSIWi7HPUJ3Qxo4dC71eFVVVVA4k6h3G48svv4SV75+uoqKioqLyM4KE+UgkgnA4jHA4LPg/CfZiQT8SiSAUCrH9wuEwQqEQIpGIYH/xb+U+0WiUKUJ8hUisYPCPOZgPKVXi3/L/pjkQz4l4u5wCJqe08dsQ78Pfl/pFY6f9fu50d3ejoKDgUHdDReUnjaoMJiAWi+G7775DCgWp8wglCtpXUVFRUfnJQsqPWGlJ9H8lRYavAO1LIRL/K/5E/5eIQ8nCk4xFSUVFRUXl54GqDPI45phjBAHJe/bsQXt7Oy6//HLJvitWrMAll1wi2Ob1evHPf/4TN954o2C7w+HAqlWrcM4550iO8/bbb+Oss84SbOM4Du+//z4qKysxbdo0wXcdHR347LPPJH3yer14+umncfvtt0vaWLVqFU444QTBtnA4jNdff10yhlgshn//+98oKirCMcccI/iuvr4e69evx0UXXSTY3tzcjDVr1kj61NPTg/feew/XXHONpO2PP/4Yp59+umT7Rx99hPnz5yMnJ0fwndPpxIYNGyTj8Hq9eP3113GVKD1vT08PvvnmG5x99tmS7Rs2bMBpp50mafu1117DySefLFmF3L59O2pqanD++ecLtn///fdobW3FueeeK9je2tqKVatW4UpRUVCfz4f3338fF1xwgWB7LBbD66+/jsWLFyM7O1vwXWNjI3bt2oVTTjlF0rZOp8Ps2bMF2wOBANasWYPFixdLxvff//5Xcq1Fo1F8+umnmDFjBgoLCwfVxqpVqwBAci4cDgd2796NOXPmCLZ3dHSgoaEBCxcuFGyPRCJ4/vnnceaZZ0rarq6uRjgcllz/7733HrKzs7FgwQLJ/tu3b5fMrc/nQ0NDA6ZMmSKZj48//hjHHXecxBtg586d8Pl8knHU1tbC6XTiyCOPFGyvr6/HwMCAZHtLSws2btwouQaDwSDefvtt/PKXv4SJn10QwObNm2E2mzFeVAD5vffew9FHHy25L7799lsEg0Ece+yxgu0dHR2w2+2YNGmSYHskEsFXX32FBQsWwMAvgPu//ubn58PMz0yI+HWg1+sxa9YsybjD4TAmTJgg2N7T04PW1lbJdRMKhbBp0yYceeSRkoLRW7duRXl5ueT693q9aGlpkbSxZcsWaDQayfXBP9+xWAwulwt2ux3d3d3473//i+nTp8Pj8cBut8PhcMBut6OxsRE+nw8GgwF+v599vF4vAoEAs5KRdYzvtqfy40er1UKr1UKn00Gj0UDLKyLNv041Gg37m/9/rVbL/qb/cxwHnU4HnU7Hjq/VasFxHPR6PdufjhGLxaDX69lHp9Mx10idToeUlBRB23T90T3MP1YkEhHsT99Ho1HoKGOtDPzjU5+o3/w2otEoOI5ji+T8dmKxmGDc9G80GoVWqx2Su6f4GamiojL8qMpgAqqrq1FSUiLZ7vf7BS8MIC5cioUrAGy1Vrx/Inw+H7xeL0aPHi3Ybrfb0dPTg4yMjEG1Tcit9oZCIYlABgD9/f1wu90SpaGrqwtNTU2orKwUbKeMXuJjkbuQ3MM/FArJvpR27tyJnJwcZGVlCbb7/X50dnYiLy9PsJ3GnUzb3d3dkvmjtq1Wq0QRbG5uRldXF0aNGiXY3tbWhvz8fHR0dAi2BwIBhEIhWWuy1+uV3d7f3w+9Xi8RhLu6uuByuSTXILXtcrkE28miIHethUIh2e1tbW0wmUwSZWzHjh0wGAwoLi4WbK+vr0dRURH6+voE2+k6l7sOOzs7Ze+j+vp62bab/1dTqKysTNKn0tJSiSDu8XgQDAZlhQav1wsLPz36/6iurkZhYaFEEWxubkYoFEJVVZVge39/P7KysuB2uwXbfT4fQqEQ0tLSJG3QeRLz/fffo7y8XNLfuro66PV6VFRUyI5bPA6bzYZAICCZPyA+5xMnTpRsr6+vR3FxseQ8dXV1ISMjA0ZKqS8ag/hao3HL3Us9PT0oKiqSbN+yZQumTZuGWCyGYDCIQCCAYDCImpoaaDQahMNh1NTUwOv1wufzwel0wuv1oq2tDenp6UxJczqd6OrqYsK7z+djH4/HA6/XC6/XC5fL9ZO3dGm1WpjNZuh0OlgsFhiNRoGiQfekRqORKDv8v0mJicVi0Gg0SElJgdFoZP8C8XupuLgYJpNJoODYbDbk5+fDYDDAZDLBaDTCaDSiubkZ48ePh9VqFShFLpcLaWlpMJlMTHHQ6XQI/K/khNlsRmpqKlOMYrEYBgYGMGLECMH+Wq0W3d3dyM3NRWpqqkAha2trQ0VFhWTcGo0GoVAIRqMRBoOBzYXP52OKDH8xJBaLIRAIyD5D+vr6JO8kIP4MEb8ngfj722q1St4B1HZqaqpgeyQSgdPphNlslrSv1HZvb6/sM8fv9yMlJSWhMshnKOP2er2yYTZ+vx+xWAxmszkpOUhFReXgcUiVQbfbjXvvvRfvvvsuent7MX36dDz55JOYPXs2wuEwfve73+Gjjz7Cnj17kJGRgeOPPx7/7//9P4GAGgwGceutt+L111+H3+/Hcccdh2eeeQalpaX73b++vj6J9QOIP7zFDzUlBYBelIOFjkMPT/52jUYDj8cjeWmEQiG4XC5Z5U4Ov98vWf0HIDg2fyz+/9Vv8ng8EouF3W6XvECBuDAq90JUarutrQ2FhYVobGwUzG00GoVGo4Hb7ZYoFKFQCLt375YoUdT2pk2bBNtra2sxevRobN68WbbtpqYmyXwA8fM9c+ZMwXcdHR3IycmRtN3U1ITx48dj3bp1gu02m02i5FIbJPjwoTkPhUISBbWjo0OinNJvlF7Gci9i6pP4urHZbADiiwhyyvGIESMk2+k6b2trE2zfunUrxo8fj97eXsH27u5u5OXlSYQTmnMSbvk0NjbiiCOOkChkO3fuxJFHHilpm8YnVmTa2tpQUFAgUeSpbb1eL7nH+vr6YLFYJPdyXV0dpk+fjtraWsH2DRs2YMqUKdi+fbtk//LycnR1dQm2O51OAIDRaJQd9ymnnCI5T+vWrcMpp5wiaZvmXLw/zfnAwIBgO11rRqMRHMfB6XTC7XbD5XJh3bp1MJlMCAQC2LFjB1PUGhoakJGRwc4rLWD19vaC4zi43W5EIhEEg0GEQiH4/X5BDNnhjMFggNFohNlsZoqNwWCAXq+HVqtlz0ZSvoxGI7xeL1NKxEoUKU/p6ensOKTUjho1Cnq9nm03GAzo7OxERUUFDAaDoA23243i4mLY7XZUVVXBZDJBr9djzZo1mDp1KgAInkc2mw06nU6isEejUYRCIdnncG1treQZDwArV67EKaecInl+19bWoqKiQnLN7ty5E0ajEaWlpYL9/X4/HA6H7GJBc3MzSkpKoNFoBM/D2tpaidUdiD/np02bhmg0KmjDZrOhsrJSdqECiD/XxIseHo9H9lmo9ExVep4rbaewEjkZwePxyMoI9Ez1i+on7utdIoZchwerCALJj1tJ/qG2+RZGFRWVw49DqgxeddVV2LlzJ15++WUUFxfjlVdewfHHH4+amhqkpqZi8+bNuPfeezF16lTY7XYsW7YMp59+On744Qd2jGXLluGDDz7AG2+8gZycHNxyyy049dRTsWnTpqQefnLICeJA/EHJf1mR9cFmswlW3F0uF1JTUxWtMmI4jkM4HGYPYf5vnE4n8vLyYLfbUV5eLmm7urpa1jIi7mskEpG4wgDxlUC/3y+74tfX14fy8nJEIhFkZmay7a2trSgrK8P69esFv+vr60NWVhY8Hs+g2g6Hw7Db7ZgyZQoaGxsF3wUCAVitVrjdbkEbNO7W1laBBZXadjqdgrb7+/uRkpIiEVr4bYv71dnZiTFjxmDjxo0C9+GNGzdixowZ+PbbbwUWpI6ODmYt5AsbgUBA4PZDDGbOt2/fzgQ9ftutra0CASIYDCIlJQVOp1PQttKcR6NReDwewbVENDY2Yvbs2di0aZPgd6tXr8bRRx+NH374QeBCSde5x+MRtN3T0wOj0YjU1FSBMhiJROByuTBmzBhJ2zTnJEwSH3zwAU488UQ0NTUJLIZ1dXUoKytDW1ub4Nzy55wv4PHPt1gZpLY3b94saHv37t0YPXo0tm3bhhkzZrDtTU1NKC4uhsfjEQhCnZ2dMBqNkhpYwWAQfX19mD9/Prq7uwXftbS0YMqUKairqxNsp3FHIhGYTCZ4PB44HA6sXbsWHMfhrbfeQmdnJ9avX49gMIj+/n44HA5kZmbC4/EgFArB7XbD6XSiu7sb0WgUfX19TCEIhUIIBAKCv3+skPKUkpKCrKwsZGZmIj09HaFQCOPHj0c4HMbo0aORlZWFrKws2Gw2zJw5E729vZg9ezYsFgtMJhPsdjtycnLQ0tKCyspKdv0Eg0Hm6hcIBNj57ezsRF9fn+A+BcAsWvTc5gvRa9euxfz58yVj2L17N0466SSEw2GBguNyudi1TPcUEFegTCYTsrOz2SIOkPj+pmeqXNtibxQgvrAxatQoiYeL0jM1EAigt7cXxx57rESRoeeaGHqXiJVUeqaKoXdLSkqKoI1E41air68Pubm5CAaDgrbpmSpG6XmeqG16fyu1LYaeqeI5H8q7ROl8K5HsuMVyi1zb4utARUXl8OKQKYN+vx9vv/023n//fRb/c//99+O9997D8uXL8fDDD7PYJOKpp57CnDlz0NraivLycjidTrzwwgt4+eWXcfzxxwMAXnnlFZSVleHzzz/HSSedtN/9lHso9vX1MbcwckfUaDRMWADiD+dQKIT09HR0dnbKuluEw2HB3z6fT3Y/p9PJ2nO5XMzixm9brPQRNptNsFoYDAZhtVrZih0xMDAgiUcC4m4ntNoci8WYskvuKzqdjsUoAPEXYiAQQF5eHhoaGgQKKrUtHrfSanQgEGBCeSwWY23wxw3sXfHkt/3DDz8IlIaOjg5MnTpVYpVQapvv4sdvu6OjA+np6cx6S9sjkQh8Ph9KSkrw6aefCoQqUmSDwaCgjcHMObA3ZoLfNt8lk1KQG41GtLe3CxYwaM59Pp+gjY6ODlnXzdraWkUFMSsrCyaTSTBu/nXe2NgoaJvixsRzvmfPHowYMULSBn/OOY5j91J1dTWys7NhsVgE1yApOmPHjsVHH30kiCOkORe7lA7mfPPbHhgYgNVqZfcWXXPhcBgejwdVVVVobGwUeCvs2bMH8+fPZ25vxFdffYXS0lJ8++23+Oqrr1BdXQ2Px4OWlhaWLKSjowM6nQ4ulwu9vb1wuVyIRqNwuVzweDyS+/ZwxWQywWw2s9glspgZDAaYzWZ2HaWmpjI38/T0dFitVvbbjIwMuFwujBgxAhaLBTqdDq2trZgzZw7cbjezSFksFng8HhQVFSESiaC1tZVdX5988gkWLlwIs9mMHTt2YPLkyQDi13NGRgZyc3OxY8cOdu59Ph+MRqMgTgoQ3mM2m01wf9L5FqN0f2/btk32+udfa/xnpPhdwr/Wdu3ahZNPPllynSvd3/xnqlLbfMiCOWfOHIlVsLOzUxKLCwDfffcd5s6dK9kufq4R/HcJH/4zVYzNZkN5ebnEDVhp3EqIzzfBP99ixAuT+2qb//4eTNv88y2ec6W2la41pfOtxFDGrSS3JNu2iorKoeOQKYMUYyS2ZpnNZqxdu1b2N06nExqNhlmnNm3ahHA4jBNPPJHtU1xcjEmTJuG7775TVAaDwaBAMBe7ke0Lr9fL+h0KhdiKmNPpZC80WhEG4quYci459fX1bJ9QKCQb/yaep3A4zNxf+G23tLQIrHYEP57Q7/ez43R1dbEHOPVPzvU1EokwZZKvBDidTqY0OBwO5rbY0dHBtrtcLtYGv+2amhrmxtvR0YGsrCxJDBOl4RYL4eJxe71eNm5+28FgkG3ftWsXU6BbWlqYi5JS24FAAOFwmP2eL3B0dnayxBj8Fdc9e/YwS5ff72dWALvdzs7Xli1bWD+8Xu+g5pwPv+1IJMLmxufzsX643W7WBn/O161bxxKA2O12pKamyrpoBoNBptDxx93S0iJJUgIIr/NIJMJe/jt27MDIkSMBAO3t7ey89Pb2Ij09XSJ0iuecz549e1jCH/64t23bxuYjGo2ya40/5xTTCgz+fIvHJ7ZgxmIxrF+/HqWlpdiyZQu2b98Oq9UKu92Obdu2IRaL4YUXXkB9fT0ikQj6+vrQ3d0tUcgPFWRZIkGUlDa9Xg+z2Yy0tDREo1GUl5czgXTkyJGwWq2w2WwYN24cLBYL/H4/ysvLYbVa0dPTg4qKChQWFsLpdKKwsBA6nQ4tLS2IxWLMgk7Crc/nQ0tLC1PM+ZYzfuxRXV0dxo4dCyBuTbvuuusAQKAUkSUPEF5rO3bsQFFRkeTZS/GOchYZr9fLrmf+M4d/j/HZtGkT6x8fpWeqy+VCMBiUxOIC8tcabZcTwr/++mum3DqdTnb9Kt3f4mfqYNr+6quvcPLJJ0u219XVSWJbgfhiS35+vuS9nui5xn+X8OE/U/l0dXWx+QgEAqwtpXEngn+++Sidb/6zRbxdrm0lOSdR20rnW6ltpWst0flWItlxK8ktQ2lbRUXl0HHI7tS0tDQcddRReOihhzB+/HgUFBTg9ddfx/r162VdVQKBAO68805ceOGFTLjr7u5mLkF8CgoKJG5YfB555BE88MADCfsXDAYVY/BsNhuqqqokq3a9vb2wWq3weDyCB2okEpF1E3W73ezlyHezaG9vZ0K1OOaQrBbitm02G7OO8qGEABR7Ry8rn88nUCaojaamJnZccdwfuax0dnYKkla4XC5UVFTAZrMJXhg0bnHbDoeDveQpVhSIv3DopSmOWaCVb/G4/X4/ioqKJG3TPJGCTt8FAgF2vfDbrqmpYcIUxf2J26ZYLCIQCCAjIwPd3d2CFe9oNIqUlBQW50kKtMfjYQqS1+tlc75x40amgInnnKxq4rZJyRfHapCVVjzngUCAXWtOp5O10dzczMZNsXfica9du1bgHknWKfF1TslrbDYbNBoNmxO+AjcwMMDGsWrVKrYoIJ5zauOzzz7DUUcdJRn3nj17BNcgtS2ec0oWAcQt+pR9sr6+nv2+pqYGaWlp+PLLL9HR0YHvv/8er732Gtra2pj1kZI3kaXkYGWTtFqtSE9PZxY0upZLS0uRkZGBcDiMUaNGIRaLwefzYcSIEWzsaWlpsFqt6O/vx7Rp05Ceno6BgQHk5+cjIyNDkuiC7q2tW7di1KhR7HrZunUrpk2bhra2Nuj1eraYQgoZuSbTgpDD4WDXXXd3N4444ggAQvczirUkaOFBfH/TPNfV1claXcTnm3+ttbW1YcmSJQDi55t+L26brjVxPBS1rRQP1dfXB61Wq6hM0P3tcrnYfbJlyxZJVl0grvjIKUTie4wgCzKNiZ84in9/81GKA1Nqe+vWrbIKn9PphFarlVUO2tvbWZw9/3wrxZCL3yWE+JnKHwMtYADx64be00rjViLZ+DfxtcZHqW2lnAGJ4v7kzlGitvnXGh+l863EUMat5B6abNsqKiqHlkO6bPPyyy/jiiuuQElJCXQ6HWbMmIELL7xQkuAjHA7j/PPPRywWwzPPPLPP4/JfEHLcdddduPnmm9nfLpdLkrkwkbDn8XiQm5srCYr2+XwshoEvIFCWMjFerxfl5eUSNwubzYb09HQWN0B0d3fDaDQyJUucaEXu5UxCVjAYFLRBQlN/f79ghdzlcsFoNLLYO6K9vZ25YxkMBsGLgepUieMlKGuouG1Slnbs2CFIfc+P3eAf3+FwwGQyyY6bjuf1eiXnEIi7KtLqOR9x2x6PBxkZGZIYlcbGRqSnp6Onpwcmk0k245s4/o2uP5fLJZhbUtTEc97T04NZs2ZJ5ry/vx8WiwU9PT0swx4fqnUmdtEhl2X+9nA4jLS0NLS1tQnmye/3Iy0tjcXeEQ6HAxaLBW1tbbBarRKrGWWb44+DrrU9e/ZIyhAAcYGcv9AzMDCARYsWSea8v78faWlpaGxshNVqlVhwQqEQbDabbBsOhwNarRa1tbXo6+vDjh07oNFosGvXLuh0OjzxxBMYGBhAW1sb/H4/enp6WPKWA0VaWhpKSkpQWFiIwsJCaLVajBw5EmazGdnZ2SgpKUFaWhoikQgMBgN0Oh28Xi+OP/54dq1TzM3atWsFSa3I7fWHH34QzAc9U+rr63HkkUcyBYmyEYuvtUgkAp1Op3ithcNhOBwO2XtJ6R774YcfBEoXPQcp1pIIBAJISUlRvL+DwSAGBgZkXQ/F9xixatUqLFq0iP1N2ZnFbXd3dyMzMxOBQIBlr+SjdI8B8etZrk/i+5vu+507d8oqDA6HA0ajUWI9krvHiJqaGtnkZuL7m1CKA1Nq2+/3KyZQUzrfX375JY4++mhB/3U6neRaI+TeJUB8vtxut+yicH9/v+z4lMYthq7zoZxvpWtNqW3x+5tQajvR+VZqW3ytEUrnW4mhjFvJPVTcNs25iorK4YuGOwxyb1Ma8KKiIpx33nnweDxYuXIlgLgQcu6552LPnj1YvXq1wC9+9erVOO644ySrbFOnTsWZZ565T+sf4XK5kJGRgY8++oitZoXDYWzevBkFBQUCt7JYLIb29naJWwbHcXC5XLI1fsSxYioqKslD1i+32w2HwwGn0wmn0yn4v8/nOyDlBLRaLUwmE/tQ3Bv//5QC3mKxsP/LxUKpqPxU4SuVtCgWjUZl3QXFpWj49y2VjuAjrnvH36bRaBKWVxLvz3GcpG3qLx1P/Dsl5ZGeDXLIbQ+FQrJKFLnRihNeUfZTubblsi7Td8nE61FGVvG4w+EwUlNTJeOgcSu1IfbekQuTGSokr/FDAFRUVPaPw8Kh22q1spibTz/9FI899hiAvYpgfX091qxZIwmQnjlzJgwGA1atWsUKf3d1dWHnzp3sGMkwb9489nDx+/2or6/HxIkTBWUFfD4f3nnnHZSWlgqKsvv9frz//vtwuVz49a9/zbbHYjF8++236Ovrwy9/+UtBe6tXr0YkEhHEPALAmjVrEIvFYDKZMG/ePLZ969ataGlpQUlJicAKUFtbi48++ggTJ04UxHdEo1Fs374d/f39khXempoabNq0CRdffLFg+9atW7Ft2zaccsopgtXATz75BIWFhfB4PIJkCdXV1di9ezeqqqoEBaAjkQheffVVZGZm4owzzpCMOyUlRZJ0YevWraxQLb9GWnV1NXuB84tPf/755+A4DmazWXAsKl4/ffp0iftTdXU1du3aJamj+NlnnyEzMxOVlZUCK9Wnn36K8vJyZGRkCCwK69evh06nQywWE2TW9Pv9eOuttzB58mRJQexPPvkEWq1Wcr7ffvttFBcXIzc3V7AavmHDBuTm5oLjOOZeCsQtb6mpqaipqRFcg+FwGGvWrIHZbBas0APxouVTpkyRJK6ora2Fy+XCxIkTBavY1dXVKCoqQnd3t2DO169fj4qKCqSnp0sszTt37gTHcQJrEAA0NDTAYrFI4qRee+01tqiSkZHBCoHv2rULWq0Wvb29sNls6OzsRGdnJ7q7u4e1LEFqaipyc3NRWlqKkpIS5nI3bdo0OBwOLFq0CJmZmcjKysLu3bsxYsQIRCIRwXMoEomgrq4OJpNJcI6AeEzXpEmTJO5ua9euxYgRI5j7J0FWPvGKO8V+1tTUCO6LcDiM3bt3o7CwUPJs9Pl8CAQCkrZra2tZCQQ5gU2ciGLr1q2YMGGCxGoQjUZZeQxxGZ/29nZotVrJ+fb7/SyulG85oIQs4rZra2sxatQohMNh2bYpqRCfhoYGZGdnS8a9detWjBgxAiaTSdA2ucC2t7cLxtHR0YGMjAxotVpBG7FYTBCjKEac4IXGp9frJW0TSufb7/cPetwDAwNs8SEZlKw7YjiOg9/vh0ajGXQbVE4k2XErefXIJUkZbP/5bcj1f19tDyfJzjmApMaooqKikiyHVBn89NNPwXEcxo4di4aGBtx2220YO3YsLr/8ckQiEZx99tnYvHkzPvzwQ0SjURYHmJ2djZSUFGRkZODKK6/ELbfcwmq+3XrrrZg8ebJs/Fyy8LNkEkoF04F4DKDYT97pdCq+ZJQKZQPxeDbxGJRSOwNxy4V4ZbS3t5fVohLT2dmp6NMfDoclwlQwGJQtjEslHsTja29vF9Ta4qPkjgLE3dj4Cg6wd9zi2Bxyz3Q4HILte/bsQXZ2tux58vl8iivIHo9H0ga9jMXj9vv9KCsrk9Rs27hxo2KNy2AwKOsuBcQFRoqt4qOUXEHunO7Zswc5OTmyiUoSWctCoZBkfIFAAG63WyJ8hcNh2dXY9vZ25Ofno6enR7Edn8+HLVu2YOPGjdiwYQNWr16dcP9k0Gg0yM/PR2lpKfLy8tiHVq9nz57NnhE5OTlobW1FUVGR5Fxt3boVoVBIoBzyx81P30/jLi4ulmzfF36/XzaJiFIGPrnnx549e1jscjIEg0HZODelrIfkwsmHiluL60fuC3JXHmzbBoNBkn2Y2k52YUDp2u3t7ZVc55SkSjy3TqdTsX7dUNpO5nwPddxKJJPtkRK1JOvlMhzjPlgcjLaHMufirMQqKioqw80hVQadTifuuusutLe3Izs7G2eddRb+8Ic/wGAwoLm5Gf/9738BQGJhWbNmDVMY/u///g96vR7nnnsuKzr/r3/9a1hcsyKRiCQwmxKyyO0bjUYlClYgEJB9AVAsllL9wUgkIvHTpyLOYqEpGAyC4ziJYhmNRhGJRCQroTabDT6fT7aGIiHuVyQSQSgUkswHx3GyBYyV4n9o3EpZxsTuO/xxiBUpjuPg8/kkiozb7ZYVLCnhkJICrtFoJOeWkkuIj0c1xMRCpJw1BognraC0+UrIXQtyLkUcx6G/v18iZNG5Fguw4jIbYuSu52g0yhRecduU9jwcDqOnpwddXV3YvHkzvF4vdu/eDZPJBJfLBafTif7+fng8HtjtdrS2tg6pNAIpetnZ2cjNzcXIkSMxYsQIVFRUoLy8HI2NjTj99NPhdrsFlk+PxwOn04mOjg6B9RaIexAoCX8+n09WUSP3JD5k6RIfa+fOnQlrnSm1Ta5fYhobGwU1Lalti8UiERYpEYSSEKn0zKFYXjH8jI3ifcXHstlsyMzMTDpD81DaFl9L3d3dyM3NVYz3VppzuWckx3Gyi3XkLpgsyZ7vZMZNSUeSdY9Waltp36EUDU923MkcSynhyeHMUOb8UCrIKioqPw8OqTJ47rnnMvdOMZWVlYN6uZlMJjz11FN46qmnhq1f0WgUHMchFovJugOJC8ECcQUEgKIiIxY2nE4nUlNTJYI7v1C7+CWg0Wjg8/kkq/p2u13WMgjsrcnHx+v1yloYKUuc3MtHo9HA4/FIarRR0hw5AYncD/lQPSsxiSyuAFjRbTE+n0+ifNHKuViBo1IX4uuqr68PRqNRVoikuZAr+eHz+STzQXFt4ix8/f39yMzMlAhyHR0drH6eHPxyDUQoFILD4VCs1See8/7+fsycOVMioDudTllhyu/3o7OzE42NjaiurkZXVxf7VFdXs8QrfX19+xWfZzAYUFVVhQULFiA7O5sVCvf5fCgsLMTo0aNRVFTE4na3bt0KQLo4REXVxVa+YDAIvV4v6aPNZpO1ZvNRUpDlrvOenh7JPWmz2TBx4kSJxdBmsw0pfkYpzkgubobuFfG4qZ5bsigpAr29vZIFK5/Ph4yMDIm1Xu6ZuT9tyy2weTwe5Ofnw263C7bbbLYhZTZMVgGKRqOS+zgUCkGv1ydtzUtm3FRL9EAX9k7mHA71fCuhdJ3/1N0n1Vp9KioqB4PDImbwcIPSycspTBqNRlDnTvyd+AUYDocRiUQk+5MlUSyc9ff3Q6fTybqnUtycWMlRUuA4jpPE21Cf5FJCd3d3o6+vT5LqOxgMsvg48YtJp9PJth0KhRCLxSSWFI/Hg8LCQoRCIcF2JQWVMpHR+Pno9XpZoYOEYPGxotEofD6fxMLY2tqKjIwMiaLm9/sVFVSdTidbR4n6I1bMQ6EQ/H4/pk6dKtje2NiIkpISFn9FhMNhxXFrtVqWeZJPMBiUtWZrtVrY7XYUFBQgHA6jra0Nzc3N+P7779Hd3Y2Wlhbce++96O7uRldX17Bn16SC4uXl5Zg9ezbmzJnDsmW2trbi1FNPFYx7z549SEtLk1jndDqdrGVRq9WyGFtxux0dHRIlsaOjAyNHjpS40yZakFDaTpZSuXtSzrJEtTBp8YgIh8NJC89UtkXuGSVnxezu7saoUaMkrrmJFHqlcdM1KH5OabVa9Pf3S67BUCgEk8kEr9cr2E6eFnJ9UJoPeg7K3Rdy4+7o6MDEiRMlCmqi863UdqIFBLlxe71eWeV4KCiNm7IHJ/I62F/ovTfYxQR+SZfhQOk6T4bDIFdeUtB9MZxK9cHmxzbnKio/V1RlUIFwOKyoYCkVzyWBh4/f75fNQgbEBSGxQO/z+WSVrrq6OlRVVWH37t2KL0Fx2+FwWLHgLWUJ40NlEsTC886dO1FYWIiBgQHFOkRixdLj8UCr1Uq2U8Y28RgcDgdisZhEcW1vb0dOTg7a29tlx8yv10coZUajMcoJND09PZgyZYpg28aNGyVJQfjEYjHJfPj9ftl6aGRpFs+5z+dDJBKRuADu2bMHJSUlaGhokG1b7vxRPKRGo8Hu3btRX1+P3bt3Y/Xq1RgYGEB7ezs6Ojr2u0aewWBAYWEhioqKUFRUhOLiYuj1eqSlpWHu3LnIzs5GRkYGi7Gj8gn88/Gf//wHHR0dEssqxcH19/cPuj/82nJiElnUxNa8uro6VFZWSuJA99W2ktDLr/3GZ2BggNXqI2jcQ3GjVWpbbk7kUtFTaYfhsiwlcvkUzxXFCibrVqqE0rhdLpfEslRXV4dRo0ZJLInA0IRYpXHLtT3UNpRIVmk4GEL6wRh3Msi53h5Mfo6K0aGecxUVlcGhKoMKBAIB2fgxWhHnrz4Hg0GW6EAuXkwOl8uFtLQ02RpLgHyCEDkrWHNzM7KyshSVHDmBlBRduTpHchbDUCjEtvOP1dzczLJAyj3w5V5+/ILsfILBoGyK7mg0CrfbLbHm/fDDDygqKkJbW5usYq6E0+mUjQcLh8MSiwLHcYhGo5K2a2pqkJ+fj66urkHHfyRKr67RaGQVc7mYxM7OTmRmZqKpqQk9PT3YvXs3+3z//fdobW1FT0/PkJQKq9WKoqIiZGZmIjs7G+Xl5Rg5ciRT+iiWbuLEiZJrpLa2Fm63WxKbR5ZN8b1EyrFS0WfxtUzXeTJKosfjkbVOA8pKAykIfOrr6xPG1yoRCARkxxcIBGSFZLlFoGAwOKwxQ0oKi9vtlvSpvr5e9l4ZKn6/X7HenLjt5ubmIc25EomURLFyLBePeyDaDgaDsudbLmnOcCPX9nCT7LgPRvyfUiK3g9H2wZjzwxE15lFF5fBHVQYVIDc9PhSwLnbvpOK6HMcJXio+nw9Go1E2WUs0GoVOp5ONMZSznAHyMV4kPHAcJxC0nE4nrFYrgsGgRNEg1yyluCixJYVSYYtfZGRJEI9jYGAARqNRNqmNUgA9WQzFRCIReL1eiYDudruRm5sr6VNPTw+sVquiICLXvtKcRyIR2TF4vV6YTCaJklFXV8cKWCtZROWQ62sgEEAsFkN+fj7Wr1+Puro67N69G1u2bEFTUxP27NmTdGa/rKwsjBw5ElVVVaioqGDFxU855RSUl5czV8e6ujrEYjEUFBQIFjd++OEHlJSUyKa1NxqNEtfHoSJnYQ8Gg5IEMUC8BIZSmn+yGCZjMZFLAOT3+5GamorMzEzBdkrnn0jxlqtXJrcdkLfuxGIxtLa2Sq7/np6eA66wBAIBWCwWybmgDMlDyXIoN265tklwlmt7OOPE5JRjWowQt+3z+YbVyqF0vuVcnod73MlYEinufLiymCqNW2lhbX89GfZForaHk2TnXMktXkVFRWW4UZVBBZQE7VAoBI7jJMI+JZ3hCzX81NB8AdPn87E4J/FLyOl0IisrSzYBg8vlklXsnE4nNBqNQDnxer3IyspCMBgUCI2hUEhRKYlGowiHwxKrDG0XCyNUVkKv1wuOSYXArVar4Dc0bjkoCYJ4RTwWi0mUbGqjrKxMst1ms8FkMklccBMJrg6HQ9a6GI1GZRUTUvLFimhnZycyMjKg1+sF58Llcim6CAUCAZjNZnR1dWHjxo3YtWsX6urqsHHjRrS0tMi6sCXCYrFgzJgx7FNYWAifz4fFixdj5MiRgnNbW1uLgYEBScKZWCzGYjX50MKD+PpxOp2w2WwYO3asZGyJsuXKQcKwnLAoLlINxOPBCgoKJJk7KSZXjkTb5QQ2Grd4O913Yjdlh8OhWL7E7XZLXERpfErIuZPbbDYUFhZKFiToflE6TjLQPInPt9frhUajkSxwia39g2FffZVrOysrS3JPJrrWkr0OQqEQMjIyJG0rZQpOdKyhugYOdtyRSOSAW17I62Q4a3wmg9PplCzEKGX1/qlAc626WKqoqBwMVGVQBL285ZILULY4ORdLEtr5D2+yTIiTfTQ1NTGhgi/AkCVKLjFJJBKRdeUC9haSFv9GLvlIb28v9Ho9dDqdoK/hcFgxro3KRIgF0lgsxmpfyQnv4m1NTU3IysqSxNqR4CcX+xWJRGTd56LRKAYGBmQTVaSmpkoE0/b2duYyyG+D6ifKrQyTYipWgmOxGDwej8SFTikesqamBhqNBhUVFdBoNGhqasKmTZuwfv16fPrpp2hpaUkqZkqv16OsrAyTJk3C2LFjMXr0aKaczp07V6AYffLJJygsLMSYMWMEgmk4HE6YwVQuKUqilW25+Mn29nZkZGRIFhfcbndCNzil5BPhcFiynfoklxnXbrdLlERazFFCLgYw0bjlFgu2b9+OyZMny1qcElmIk0WuX1TKRiw8U/zwcCFnYaeag2Jll55pw4lcXdOMjAzJuPd1vuVQivVMRG9vr0Q5VlrY2B/kFmJMJtMBVxpCoZDiAocYeo8MF0rX+VAy4/7Y+CkrvCoqKocPqjIogu/XL34QU2ZQuZc7rZbzXTVJsJSrnUeZIvnbqRC8UvIFObdS6nNOTo5ijTy52MBIJCIQnJqbm1kNPrkV8eLiYsWsoXq9flAxF+QO5nQ6BRlLXS4XotEo0tLSJK6G5JolV2bD7/dLXASDwSByc3MlyiAJFOLMrjU1NbBYLLLxSX6/H2lpaYolPsRt861pHMehu7sb1dXV+PDDD9HQ0ICuri7s2bNn0JkFc3JyMGHCBIwfPx5jx47F2LFj2VxMnDhR0Ofa2lq0tbXJKrVGo1GSSKi5uRkmk0k2hsvn8yE9PX1YhFiO4yTK1VdffQWz2Yzp06fL7g8M3o1SzoLJR3y/1NfXIzc3V6I0AHuV/2QEMCV3ZLnSDvX19bBYLJIFDGD4k0vIxRabzWbZ63w4BXetVitrUZZzt6a2h0tZ0ul0kmMlOt/J1iNMhJxy7HK5EIvFZK2Jw3m+k61FN5zJceQIBoPw+/0HfNw/JkXp55g8RkVF5ceDqgyK4K9qyiW9kHOz9Hq9zArHFwg4joPT6ZRkE6Wi3eJjkQVRrHA2NzcjLy8Pzc3NEmHH4XBAp9PJCsR+v19ixaQyERqNRvAbShMvHlt9fT2MRiPS0tIkCha5Zsm5Dvb19Ulc2JxOJ8aMGYPOzk5JzCWlLuf3tbe3F1qtVjZWhtzhlAq1iwUXsiqJ6xWGw2EYDAZJiYwdO3YgLS1Ntng9WY3F57Smpgb19fWoqanBjh07Bp2VMicnB9OmTcPs2bMxfvx4ZGdnIz09Hbm5uaiqqhLM1a5du9DX16e4KJAorkh8rclZUHt7exGJRCRJNQDIumkSStZVpRIYVqtVIrx3dHQgLS1N1ipIrpr7CyVCEl+vzc3NcDqdEpdZIHG8Z7Jtp6SkSK7Njo4OpKenK2b9HQ5liZJeycVBe71e2fM9XAIsPVPl6gKGw2FZBXW42pZLfAXEz7fBYDigyjEtFCjVC1RKunWglZwDneGRnsvJjPtAk4xl80Dwc8yqeajnXEVFZfCoyqAMcoXaCXHtOSoqL5dwJhaLweVySSxbJOiIBWV6YYgTxTgcDlZmgP9CcTqdTLkTt81xHEsZL25b7iVNFhaxIGK325Geni4b2xgOh2WVpWAwCJfLhdGjR0vmg9xm+YKhx+OBwWDAwMCAoKC4x+NhMYl8Ojo6EiqJcqUuyFVNKYGM2ILV39+PgoICyVz19fUxhXLVqlXYuHEjvvnmG6xfv35QCTVKS0sxZcoUzJw5ExqNBuPGjcOcOXNQVVXF2qqtrWWLAnw8Hg+CwaDsnAcCAUmW231BirC4DVLUxOPW6XSKJVXkrN9K0HXv9XoF5R1cLhcqKyslJQ6cTif6+/slmUqBxMkfkhGsHQ4H0tPTJQqLzWYDx3Gy86rkJZCobTkLC41bzrLf3NwsKb8BDF/MFGW5FY/DZrPBYDDICrBKmXETIfesIHdguVhMsRs7MVxKosPhQH5+vuTaoWvvYAjucueb4hXFHGg324OJUpZuOYbzOpezbCZqe7hJ1nr7Y7J8ypGMNVlFReXQoiqDCeC/gOnhLHZL41uhNBoNE4hjsRjLwilnYaSU8XwBmiyGgUBAInSTC5vYrTQ/Px9NTU0C4YwSvogTu1DbgUBAtli6Xq+XJGshC5xYaOrs7EROTo7EshSJRLB7927k5+crWqnEx/N6vbBarfD7/YLxUU1HsWC2e/dujBgxQhJ3Fg6H4Xa7UVFRIWkzGo2ir69P1i2SECvN9DfHcWhqasK3336L9957D9u3b0djY2NCAS0nJwczZ87EhAkTAACTJk3C7NmzBbUM33//febeyxdIlJKl9Pb2yiYXokLtY8aMkQg2lCl1sCgJIU1NTRgxYoRsxtXm5mYcccQRsteaHPyxDUYQ6+zsRG5urmxNTo/HI6nRCMTnxO/3y7oHJkNrayvKy8tl3VP7+/sxadIk2d8plZVIBlpZF4+bxqZUMmM4hMjW1laMHTtWVnC32Wyy4x6urIydnZ2KyrFcjCYwfMKz3W5HVlaWbBbYRIm3hqNtUo6THfeB5GC4NyYqZu9wOPb7Hh5q24cKekfv7/NDRUVFZbCoyqAC4pe/XMFkMXwlh1KBk9WEr3z5/X6YTCakpKQouvuJC2IDcaVJHKdGrqb8vvb09CA7O5tZc/i4XC54PB6JexQpueIYKI/Hg+zsbNjtdtksiGLa29uZYidXBkMuEY2SMEWWK7mkHnKuoHv27EFBQYGsFWJgYABZWVkSwUIuG19PTw/Wrl2L7u5u1NXVYceOHejr60s47sLCQkyePBnHH388jjjiCBx99NFMgXjllVcwd+5c2RhDQBrX5vf7UVhYCI/HIxk3Kc7icaenp0u20/5yMWqRSAQWi0XiEpesaw8l7pATVD0ej+w1Q+6S+8u6detQWVkpe6ympiaUlJQMKrHRUGhvb0dOTo7sXNH1KifUJ2tRk2PPnj0oKyuTHYfL5RLE4xJyCbGGQm9vL7Kzs2WPNTAwIFjsIIZLQe3t7UVeXp7ssTweD0aOHCnb9nCcb6fTqWh17+npkXU3Jdf7/SXRuCle/EBB771kytgkspgPhYNluTtcIA+hH7tlUEVF5ceDqgwqICfAKNXmA/YqFXxrEr3E+EJgU1MTTCYT0tPT4fF4BMcjITkQCAiUGUo2Iy5pQcdXysIpFkj7+vpgNpvhcrkkWUk9Hg/GjRunKDyLS0vQSrXYMkjWAXF2ScokKicc+f1+FBQUSBQuvpLI75ff70dJSQkrKs5vW0557OjoYIKcuP2uri54PB68//77aGxsxMaNG9HW1ibpIx+tVouxY8di4cKFWLBgAebPn4+GhgZYLBaUlZUJ+rtz505kZGTAaDRK2rbb7Zg7d65EIeNbafnj5v+fv2KvVH7j22+/ZUXkxcKc2+1GVVUVuru7JW3LZeeUy3BLbcspBh0dHcjNzZUV5JxOJ2bMmIGuri7Z44l/k6hkgNyqPmVmlbMoUKkVsUuvUvmIROOWqwe3ZcsWjBw5UvY54fV6UVlZKTuWZJPWKC0iyY0BiFtR5eIClQT3RK63ct/19fUhMzNT9rtgMKjobio3jkSut0rXWl5enuzvxKV1iGRiXKntREqJ3LGUYjGTVY6Vxk2lhpK9dpJVMpTOnxJut1t2QWK4FgV+bMXbhzJuVRFUUVE5mKjKIA+qsedwOBIqfmI4joPP5xMIjSTU0jHoXyrkTEXqCSpNQXF74pev3++XxPbwFTU5q5DYEhEOh5lLIn97f38/rFYrs+rxIQucuEwFpa+n0hJycyKOh7RYLBLFNRAIsGQ2cvE7ci5bgUAA6enp6O7uFhQh58co8H8TDAZhNpvh8Xiwbds2vPvuu9iwYQO+++67fbp7AkB2djZmz56NuXPnwmw247jjjkNKSgrGjRvHxtLQ0KAohMsJbI2NjTCbzTAajRKFmrKYimsMUukRSsAiRty+1+uVtSD19/cjNTUVHo9H4o7scrlQUFAgEaATJY+Rg+ZcfB1v27aNuU7LJfnRaDQSQY9iX+WQu0fJyi2+nrq7u5GWliabAbSzsxOLFi2StUAnEjzlkkmNGzdOsl93dzcMBoNszKWc5RuIXzvJWJaUFBy6v+W+Gy7BXa7sB7B3oUruWG63W1Y5TrbtYDAoq/jT+Za7Lz0ej6yiJhdHOxTomlVSUJUyqyZjvaX6f8mQrGJHDPZ80L2XzLiT5WAViR8uhmvcKioqKgcKVRnkQTXt/H6/JFaNYlLkCiuHQiGJRYNeAOJkGHz4yprT6YTBYGDWMBL2PR6PQHAV1yUkwWWwwfGU2Y//Mh0YGEB6ejpaW1tRUlLCtrtcLkk8G398aWlpkiBxEmCVXOLElgiaH8roSVCmUqVyFpQZlC8EejweVFRUwO/3g+M4tLS0sFp+W7ZsQXV19T4LY6empmLq1KkoLCzEMcccg7lz52L69OmsHx988AGys7MlSq3f75cVLpXo6OhgfecLnz6fL6F1R6PRIDMzU3Bter3epJQGOt9Op1PgWkeKuVKCITmBxufzKSpLctaU5uZmFBUVwev1Co5H45Y73319fZg3b55ku9/vT5g9Ve44JSUlkjghSmCklMVUzj0wUZIppbatVqvkWLSIJDfu9vZ2TJ06VbI9Go0mJQwPDAwoWqiUsj4qne9QKJTUnFM9S/H1mUhpCIfDispSMkoinW8x9ExVsgwOh+Du9/sTKmpKLp9y5RiGu8D6gbQ67UuZTqbtfZWNGSzDXetxKCRrvf0xKbtyHA5zrqKiMnhUZVAEWQ3E1ghK7uL1etnLjm9Ronp4hEajQTQahcPhEFhxSIniOE7W2kIPUNre1NSEnJwctj/1iV8TTck6Io4v8/v9TOCVcwfV6/WCMdTX18NqtSI3NxcdHR1sO79tsctaZ2cnRo4ciZ6eHsGxHA4HswLIJWcQC9bd3d3Q6/XMnZY/BrG1FYgLeOvWrcOqVavw1Vdfobq6WuICKUav12PUqFE44ogjMHfuXMyfPx9jx47Ft99+C6vVilGjRgkEMYp5lFNyqOahOB7TbrfvU0nkj6OtrQ3Z2dkSpYziC+Vcxnp6ejBmzBjJHMpluCXovPF/093dDbPZLLFeJYoX6uzsxNFHHy3Zvq8sfeLrtq2tDSUlJZJEIImK03u9XpSWlkq2y8WrJmq7qakJ2dnZkrYTLRw4nU6MGjVK8fhy1mY5IY/GLSaRtdrn8ylmN01GiFQq15Fo3Pz7WPwbJSVRbtxut1vWup3ofCslUUlWOW5qakJxcbHscZJFXA5nqCQ630rjHm4l8VCQaNwul0tWOU4WJcvc4Vr7T2lR4MeEag1VUflxoSqDMlAMlnibVqsVZOIMBoPMdVOc0IQyYdpsNoEbZSAQQG5uLhOQ6GVOFjWKRSIhjQQUsgLS/s3NzcjOzmaxhOL+RiIRuN1uQXkHl8uF8vJytLa2Cvb1er3IzMyUjJtKLkSjUYGgR3UPtVqtrCBGfeL/hjI7ii2lDocDZrNZdr4pIys/q9rmzZtRXl6OWCyGrVu3YuXKlfj888+xY8eOfdYHKy8vx6xZs3DMMcdgzpw5cLvdSE9PR3FxMXJycthKPo1JHIdXU1ODwsJC6PV6RaFVLKgPDAxgwYIFEpdPu92esBB2X1+f4Lrp7OyUjXUj5OJG6VoT4/f7YbVaJceitsXHqampwahRo5ISPD0ej6ySQ9egWPGia14c40XXmpKCJUdHRwcqKyslyigJ7uLjkBWbFnyI+vp6lJeXD6pNfhvkVs2H3KTF7Cv+LxncbreskhMIBBSFS7lzWl9fr6joKmG322WVRKVFAaUYvObmZsW4SiU8Ho+kjA2grKgppbzv7+9PWgjv7++XnfN91fyU2z9Z4TkYDCbljXAwSFY5Hsq4h6LEKVliVYXlwPFjX6hQUfk5oSqDCvCtKvyXD99iyHEc3G43UlNTBUWVPR4PzGYzAoGArLBHL0xx1s7S0lLZ7KCAVKiKRCLo6+vDpEmTBG5YNpsNFosFwWBQ0d1EfCyn04nx48ejqalJ0k9KBiMuUM+3cNI4uru7ZWOi+FDsI9HX1ycreFLb/Ix8AwMDWLlyJTZu3IiNGzfC6XQqtpOZmYk5c+bgyCOPhMlkwmmnnQaLxQKr1cosdytXrlRMqkHKIH+eSAG0Wq2C8g5bt26VjbviI57z3t5enHjiiRLlmFwYxa5yNOdii1BbW5tiXJRS2x6PB2PHjhVYewHIlhwB9o5b7EZJMVnJYLPZcPHFF2NgYECwnRZbxHNOcZJGo1GgvP7www8Js9t6PB6JUuH3+5Geng6fzyfY7na7kZWVJXu+5ZQGus6VrFFyQqaScqzkTqVUKqGtrS3p7JFut1s2O7GS4K40bpvNts/7W64NOXdJJUuiUgIXm82W9LVG51uMkiujUlws1TtNBp/PJ/tMSKQcy53voYxbiWRr6tG7KJnfeL1eRatrMuM+GByMtocy52qhdhUVlYONqgzKoNVqWfkHYG/2MkoUIxaK/X6/wMWIrAIUO0dCY39/PxMqlIQeJZcnsdBIL5nq6mpBvBNlIpUTRsmKmSgBiTipzZgxYwAIFWKKl+rr60NKSgoTKCmmTTw//f39TCB0uVyyCpj4pdzX1we73Y4tW7agsbER27dvV7T+abVajBkzBtOnT8exxx6LoqIiLF68GFqtFg6HAzU1NcjOzobT6ZRNAS/n8knKAZ9QKITU1FRJvJvdbkdeXp5E6O3s7BS49cqV2hBbkRwOB0aMGCHJrEqWP7nSHxzHSQTGxsZGdo6V3MzE47bb7Rg7dqxkP1KSxOeI7gWdTie4/h0Oh2LyDEJ8LEpAIlbUvF4vi03l3xc2mw3FxcWSeyUQCLBtSu2L2+7t7cXRRx8tsd4qxb1SfKP4fJNiLteGEkqJV5xOp6wSJVcWBhhaOn8lwV1JQVWybsqVQNkXSspSorbl6q6RZ0EyeDweWeVYSXCn+344UFKOlRRRpXEHAoGklYZka+rRPT0c5TGGq57fcMURHiyGMufD4XasoqKikgzqU4cHCf+hUAiRSIRZYUgYDgaDiEajAsFAo9HA5XIhFotJ4vOys7OZBQ2Ix3YVFhbC7XYLrH/8WncUG8YnFArB6/UKXiqUSW7r1q3Izc0VKAOUFZUPCU1Go1FgSeTH/5FyQpBFUKvVCpQQygLY1dXFiqYDcUVPq9VK3B97enqQl5fH3FnpOKTYaTQa1NfX4+OPP8bf/vY3rF+/HjU1NQndPtPS0rBw4UJccMEFOPnkk9He3g6z2YzRo0ejtraWCXX9/f0CYYq2U/wfxXaKC86Tmy9fyHW5XKiqqoLb7RbEqoXDYYTDYWRlZaGlpYVtb21tRWFhIbuuqA2KeyRXWHGGWCUFJD8/n2XpJOhaysrKElgZ6+vrMWnSJEnblLGT2ufDcZxs8g4llzeXy4WUlBSkpKQI2qY5Fx+H+kr9ECsQcgqNzWbDxIkTJf0iy7FYUCVFXU6Rl7P0J2rb6/UqCuJypUKampowe/Zsyf6UiTcZK0R3dzemTZsm2a6UuEau7ibtn6zwrJTdVKnQvVJhcLqPhiMrqZKi1t/fL5vNeCh1FQOBwD4t/INhKLF8Sgqn0rhpkeRwYjhj8JSs1skmMPqxkWzWZhUVFZXhQFUGRZhMJkSjUUSjUYkwS/FzYuEdiL+k+MojAHYcEtKi0Sg8Hg+ysrIEboatra0oKChgMXJyZSXkLG58YV4stIpfzMFgkB2Dr3A0NDQgNzeXZecUK5H0cuLHpPGVTf6Lq7e3FxMmTJAkHKFYRKPRyCxknZ2d+PDDD/HJJ59g+/btaGxsRCK0Wi0mTpyIiooK3HjjjcjOzkZ2djYrLVFfX89iMfl4vV7JeQHiJQ6KiopYrJh4bqlch9gaazQa4Xa7BYImuaOJE1BQAgTxsb///ntm3fF4PAIBNJEQSccX144cN26cbPH4aDTKrGpEQ0MD8vLyFC05gUBA4n6pFNfW29uL+fPnS641JYXls88+Y66SXq9XYp3ZVzF6/ri9Xq+shczhcECr1UoE65qaGkEZEjFybSuNW3zOCLJeiq8DijUdrpIFcq6PSuU3GhoaZK1/Q8HtdssWdqfkSXJ9AqTuxUNBSUlUumZqampQVVWVVBtK3hpKeDweWYu7w+EYNkUtkXJ8uFnIaJFkOPB4PD/6JCpDYTit0CoqKiqDRVUGeZDFRKPRJKzhxBd0aQVTo9EwQYLcSu12O6upBsRf4LFYjNUyJGglmRLKkAJSV1eHvLw8BINBVmqBD3/lXS5ZBl+wCYVCSEtLkwgQlLCDxs0vlp6TkwO9Xi+rhPDnTEx9fb1AAKX4xh9++AFvv/02tm/fjvr6etm5BfZm+Zw6dSomT56M+fPnY/bs2bBYLHjttdcwa9YsSaZQn8/HMo/yBTRKouNwOASCFVluKRkMjWPDhg2KCSxisZhsbT6ypNhsNoGg7na7MWHCBNlMinyLKs15U1MTc/EVK1gcx8Fut2PEiBGy2R4pCRARDoeRmpoqsdr5/X4UFhZKrpmOjg5F4WtfK/5iq1QgEJAVaDwej0CZ4I87WYsMKeZiBgYGZIu+82tx8r+rq6tTjMFL1tJBCYfEcaCBQAA5OTmS62Ao41ZCKUlMIBCQVZqHkqBGCaUELko1Bl0ul6yb+lBQutbo+hdTV1eXMNY0GVwul6yifTAE+n0tnBwKhlMZHC7E3i4qBx51zlVUfnwc0kIwbrcby5YtQ0VFBcxmM+bOnYuNGzey7zmOw/3334/i4mKYzWYcc8wxqK6uFhwjGAzixhtvRG5uLqxWK04//XS0t7fvV7+CwSDC4bBkdZdfIJ2sHxzHwePxCIpokwVRHGtFyVBIiSNhOBwOw+PxID8/nwXtA3sVFiAueJCw39PTA4vFwqx8Yhc0vmsY3y1PycpBx+UrkE6nkxV8p/9T26TwyAW7U/xkRkYG1q5di6VLl+LMM8/EvHnzcOGFF+Ltt9+WKIIGgwHTp0/H3XffjdWrV2Pjxo1YtWoV/vKXv+BXv/oVjj76aFgsFtTV1SEtLQ1Go1FWCaWEM3yBhFb7yTpJc+7xeKDVapGfny8Q+p1Op6yA19LSgtTUVBZ3KUar1cq6Msq5NDmdThQVFbFrgXC73cjIyIBGoxEorgMDA9DpdDCbzYoJgQBpbI/cHPGzs/KtiU6nE5mZmRIFqKWlJWHGQnI3HYyVwuPxYMKECZLtXq9XVpHp6elJaGFJFBMoZxknAZ3/XXd3t6xy0NHRIesiOhjEbfv9fuh0OomLpdK4k61hmAilNnw+n+xiV0dHR9IJapRQUljEJW+Inp6eIc+5GP755uNwOGTdSp1O57C5H/p8Ptm2leLdhvN8K3EwYu38fv9hN26lZ9NPZc4PR34KdRJVVH5uHFJl8KqrrsKqVavw8ssvY8eOHTjxxBNx/PHHsyyHjz32GP7yl7/gb3/7GzZu3IjCwkKccMIJcLvd7BjLli3Du+++izfeeANr166Fx+PBqaeeOqSaUQTFk5FgTw91v9+PcDgMi8UiKPXAT5xCq+H0L9+CQcpIJBIRKAqk0Ol0OkFMmE6nY4kC+ElABgYGUFBQwJQfACwRCVkKyf2RjkWuod3d3QKhhx+7GAqFmOBIcXQmk0mgmPT29iI/P19S64/62dPTg7fffhunnXYajj76aCxfvlxSysJgMGDevHm49tpr8a9//QsOhwPPPfccHn74YSxatAharRZ6vZ4pcST0d3R0oLy8nJW0IKgshxz8+D/+PvSyEr+0+OPmQ/XgKMsoMTAwoGhB5jgOZrNZtu4dv6wIwbde8RXXgYEBZGVlscyu/HFT0fDBCjZKChQpB+J+dnd3y5ZXSJTAQsnVVanAuVxRciCuiMq13d/fnzAlvJwQpiSc2Gw22aQ5XV1dsjUMaUFIrr+kGIvHqJTQRly6hFBSiuRcyPeF0riVBPe+vj6UlZVJtifKcphsfJxS2/39/bKK+VDaTpTpVU455i+28ZEr2bIvlPqkpBwHAgHZ8zqcc56sJTEWiw2b9VHpOMm65QLDV65gKG0ny1DmXC3UrqKicig4ZE8ev9+Pt99+G4899hgWLFiAUaNG4f7770dVVRWWL18OjuPwxBNP4J577sEvf/lLTJo0CS+99BJ8Ph9ee+01APHV3BdeeAGPP/44jj/+eEyfPh2vvPIKduzYgc8//1yx7WAwCJfLJfjwkUu6QQ/1SCQiEBo4jmMr/waDAW63m5VcoO/J0gPEX/x0PHoZeb1epqCJy0pQTTh+IgZSbGKxGIvp02q1LHEHKYkkZFPsol6vR0NDA0pLSxUTefCTbFB8I39OKKMgx3EIBAIwm82IxWJ44YUX8Oc//xmLFi3C008/jT179giOeeSRR+K2227D8uXLYbfbsXbtWlx++eVYvHgxOx71ibK2igVGvjsivyBxQ0MDq13Hj+kkRbm3txc6nU5gZeLXVJRzYxML4xQXSIolnbtNmzZh2rRpkpg9ipsk907+NRMMBtnxxbX25FwT6RoA4goBXStbt26VjYOjjJxyx+Jv4wsedB2LrSNKitrOnTtRVVUlW99SKQmCuI7fvnA4HLLlGL7//nvMnDlTtjYlED9/4nb4+4qTBckRCoVkrVcNDQ0oKSmRCG0UHyfnKkeJgsQotd3X1yc77oaGBtnzncidVek7pe3iRROipaVl2Aq1K6FU/6+np0cxg6oSwzVuqkWaDMm2raQ0KI17X/VUk2lbCXIjTsbCk+y4lVBa3Dtci8QrkWx/h1LCREVFRWU4OGTKIAmN4hVRs9mMtWvXoqmpCd3d3TjxxBPZd0ajEQsXLsR3330HIC6Ih8NhwT7FxcWYNGkS20eORx55BBkZGexDK+H85A/8DKL0YiJhnfpMsS8UzJ9oVW/37t1MkBNb1ej/FFMot/opzsrX1dWF/Px8iQDFV/LIEtPa2orc3FwmoPPnnJREcZyT1+tVjHuhuLnm5mb86U9/wrhx43DDDTfgo48+EghoCxYswB//+Eds2rQJX3zxBR544AEceeSRTMgmRUMuAcm+Vm2DwSBTsPjZLvmxUNXV1SyJhE6nEwg2tAorFmYTrRjTSj0//Te/H3zWrl0rsDjJWSDFSkMgEFCMpaK2+a6oDodD1nK2Zs0azJ07V/Y4dK2KBelE45aLAbHZbLKxlY2NjSgtLVU8FmWmHSxybfv9ftnSAKSoUTty7K9ASWVHxPzwww+YNWsWAOniwnC5TSm5Ptrtdtksn8MJ372Yz549e2SvQWD43OSUFLXGxsaESYEOJE6nc9jiHpVQGvfAwMCwufIejihZjpUsqD8VlM63ioqKyoFGwx3C5ba5c+ciJSUFr732GgoKCvD666/jkksuwejRo/Hiiy9i3rx56OjoEKxI//rXv0ZLSws+/fRTvPbaa7j88ssl2StPPPFEVFVV4bnnnpNtNxgMCn7jcrlQVlaGRx99lNVy83q9SE9Pl1gNf+74/X5UV1dj69atsrGZFosF06ZNw4wZMxLGmqmoqKgMFqUFMqVMm3q9XvY7SpglhrwO5Fx55RY2lGq1BoNBmEwm2baVysZEIhFZJUcpS6vP50taGVSyuiban58UbV+QoiY3J0rjU9ru8/lkXXkpMVsyJGp7OOoeJoJCSgYL5Q34sXOg59blciEjIwNOpzNpq72Kioo8hzSb6Msvv4wrrriCxWLNmDEDF154ITZv3sz2EQsBg6lbta99jEaj7EuFCmgDccsYP2ujXFKKnwscx6GtrQ0bN25ETU2NxJqm0WgwatQoTJ8+HWPGjFGL5qqoqCREKTOxWPmgZ42cUpJIWZErC0Qu/nIlRPR6vazSoBTnmpKSIru/xWJRtF6JQwD42+UsqGlpabIKnNVqTVopSrbOJWXUHqwCaTabFRUAs9ks27bRaJSd2+zsbMl5DQaDLBQjGUwmU1JtDydKic6UoIRtP3Z+ytZbFZWfKodUah85ciS++uoreL1euFwuFBUV4bzzzkNVVRVLuS7O9tfb28viKAoLCxEKhWC32wUuTL29vYpucom4/PLL4XK5oNFo8Mknn8BkMsFgMOC0006D2WzG5s2bsW3bNpxwwgl444038Ktf/QqFhYX473//i/7+fvziF7/Af/7zH5x11lmwWCzYuXMnAKCqqgrvvPMOzjnnHFYUu76+HkVFRfj0009x7rnnIhAIIBaLobq6GkajEZs3b8b8+fORm5uL0tJSfPDBBxg3bhy6urrg9XoxZcoUVFVV4e9//zsqKiowffp0fPjhhzjzzDNhMpnQ3NwMm82GyZMno66uDpWVlcjIyMA333yD3NxcFBUVobq6GtOmTUN2dja++OILmM1mFBYWYuPGjZg5cyb8fj++/vpr/PWvf8Xu3bsl81VVVYUlS5bgggsuYL81Go345ptvMHv2bFitVmzatAljxoxBfn4++vv7WVKQ9vZ25Ofno6ioCBzHoaamBhMmTMDmzZsxceJE5o7X3t7OYiJramqwYMECmM1mrFmzBnPmzIHP50NNTQ1mzpyJtLQ01NbWory8HHa7HQ0NDTjiiCMwMDCASCQCh8OB7OxseDweVFVVwW63s7jLaDQKu92O8ePHo7u7GxUVFejp6UEoFILD4WA11goLC1FXVweO41iShby8PJSWlmLr1q0YPXo0PB4P2tvbUV5ejvT0dPT29jL3Yr1ez1bRTSYTW+X0er0Ih8MoKipiv8/OzkZKSgocDgei0SgqKyvR09OD4uJiVFdXw2q1IhaLobCwkMWqUWkNs9nM3E77+/uRkpKC3bt3Y8qUKTCbzSzOkq/Yd3V1CdzuPB4Pc0ul2FCfzwefz4fc3FyW7Mjv98NsNsPj8bBER7QiTglXqPRGdnY2Ox/p6emIxWKwWq0syY7H40EwGERubi60Wi07Nv3r8/nYWMkK0drayly+Q6EQOjs7UVlZiUAggGg0Kog99Pl8LLGTuG2K+wPiwjM/+RIl2NFqtbDb7cxy4fV6AYDFAPNjOmOxGPNA4GcGliMSibB9k3E/pKy4g7FA0PkR94Pa5pfBGQ7onA0FmnOO45Camrpf/VIaN9Hf3w+r1TrovlLG559ycXB+TPihxuv1Iicn5yc93yoqKiqHkqSXxnp6enDxxRejuLgYer2exWKJY7KSwWq1oqioCHa7HZ9++inOOOMMphCuWrWK7RcKhfDVV18xRW/mzJkwGAyCfbq6urBz584hKYPA3lgjv98Pt9uNKVOmgOM4BINB+P1+GI1G7Nq1i8W6UayfTqdjlkRxQoBQKAStVgun04m0tDS0tLSwIuVms5kpFh0dHUy5iEajGDt2LDiOQ0tLC3N38Xq9KCkpAcdxCIfDiMViSE1NFWQSjUaj6OrqQm5uLjIyMpiyo9Pp0NfXh+zsbEliAofDAY7jUFxcjO7ubjz00EOYO3cubrjhBoEimJmZiWuuuQZffPEFHn30Udx9990oLS2FVquFx+OBwWCA1+tFfn4+UlJSBLGL1dXVKCwsZEoOZfokhYGyYpIFtqmpCVqtFoWFhejv70dubi5bsaY6YhTHSfGeJMCQwkXbd+/ejfz8fFgsFuZaEovF0NnZCQBITU1FZmYmEzq7urpYPKDT6WSZJW02G/r6+jBhwgRBDbVoNMqyt/LLjuh0OrS1tSErKwupqakwmUwsZo7iNCl7KX/cVqsV2dnZrI4a33pAiXLoODqdjikr4XAYGRkZcLvdsFgsaG9vZ9caPw6HEhopCXuRSERQpiMcDrNYHoqPlSvfQKvClICI767HT0LEHzfR39+PaDSK/Pz8hDF/dJ37/X5Wq49i5mjc1LY4xk8854Tb7WZKK9+yLR43KaLRaBQej4fNn5ygyhemE3kVUCkbcduDgR8vm4iBgQEAkChE1LbVaj1srBLiOd+ffimNmwgEAknHaR2MTJSHmsMhPo9/j/3U51tFRUXlUJK0ZfCyyy5Da2sr7r33XhQVFe3Xi/rTTz8Fx3EYO3YsGhoacNttt2Hs2LG4/PLLodFosGzZMvzxj3/E6NGjMXr0aPzxj3+ExWLBhRdeCADIyMjAlVdeiVtuuQU5OTnIzs7GrbfeismTJ+P4448fUp8ogQsJnWVlZfD5fMjIyGClJJqbm1nSmWg0ikgkwlwpMzMzJQlGSAEIBALo6+tDQUEBKwsQi8XQ39/PYhQpEySlr+/r60NRURHS0tLQ1dWF9PR05OXlwe/3w+FwIBaLIRQKYevWrRg3bhzsdjtMJhNycnKYkBMIBKDT6ZhiwLc8BINB1NTUwGw2w26345JLLsFbb70lEV6nTJmCE044AcuWLcPAwAArVN/b2wuTyYSKigo0NDQgNzeXKTYGg4G59zQ0NKC4uJj9jqxqoVCICcL81NpUS7G4uBher5fV2SMrTkpKCmw2G/r7+1FWVsZW641GI/r7+5GZmYnu7m4MDAygu7sbxcXFSEtLY0pQT08PBgYGkJGRgYKCAvT19THBvbe3F5WVlcjJyUFHRwcyMzNhNBrR1dXFrIB79uxBNBpFVVUVbDYbizkihb+zsxMajYZZQK1WK4LBIFNkyYJG5SJI0evr64NOp0Nubq6kmDudk9raWmRmZiI7OxttbW2IxWKwWCysbTrn0WiUJQ6ieY3FYvB4PNDpdAmF4FAoxJQ4ymZqtVphs9lkE4mQ0BYIBJCWlgafzydQMEhpp5qcdL5p3P39/bBYLLJCO43b7/czJc5ut0Oj0bAEQYTf74der2eJnfjjpiyFKSkp8Pl8ApfwlJQUWCwWZtnz+/3w+/3QarUSSx0tcNAiDAmqtOBC7dHYEyVjoraNRqPAqjgY+MqoEpFIhJUmEZ9vftuHC/w53x/LYqJx8yGrkziBlhKJyj381DiUiwOhUEiygKWioqKicmBIOoFMWloavvnmG0ybNm2/G3/zzTdx1113MZe4s846C3/4wx/YKj/HcXjggQfw3HPPwW6344gjjsDTTz+NSZMmsWMEAgHcdttteO211+D3+3HcccfhmWeeka2VpQS56r322muIxWLw+/3o6emBVqtlwrRer4fNZoPP52MKDq2ckqsY1RdMSUlhVgTKSkpWHIPBwJRH2v9Q09/fj6+//ho7duwQKIF6vR6TJ0/G7NmzZdPK/xwhJZ8WC6juINV15AtQdN1EIhFmHaW6ZWSxpO38TLUkcJLSTPFOZJEKh8NMmePXvaQMpWTVo0UIKvXgdrsFFi66fqPRKGuDb6GkMfLrGJKCSu1R32kBhBQ8sWWPvjcYDMyVlcYeCARYnU25rKuANGZXr9fLCor8kiOkXFFWS7r3gL2xYGTNp7/pnMZiMaas8wmFQoJ7n6+wy/0/FoshEokoZkekpCVk6U2kaITDYcEcKBVv5xOLxWSTLFAmX/F54sdJ7w90LSUTl0VJTuha2x/rVDQaTeiWC8THGgwGWSmgwSie+1LY6d77sRONRg94tlQl6HmqZtZUkUNNIKOiMvwkrQxOmDABr776KqZPn36g+nTQ4T9cPB4PWlpasHPnToTDYcydOxdTpkwBAKxfvx579uxBT08P8vPzcdxxxwGI1z0jt0WbzYZjjz0WxcXF+Pzzz5klbMOGDVi8eDFGjBiBhoYGVuT43XffxQUXXIDi4mJs3boVvb29OPLII7F8+XKcd955TJhduXIlrr76avz3v/9FVlYWysrKsH79eqSnp6O8vBwbN25EYWEh5syZg+zsbGzatAl5eXnweDzo7u7GUUcdBbPZjJUrV2Lu3Lnw+XxYtWoV1qxZg1deeUWQ7j81NRVXXHEFbr75ZlgsFjQ2NmLq1KlYvXo1xo8fj6ysLOa6WlpayqxfXq8X48ePx1tvvYXTTz8d3377LTiOY21v376duaf29/djxIgRMJvN2Lp1K8rKypCTk4Ovv/4aU6ZMgU6ng8vlAsdxKC0txdq1azF16lTo9Xo0NzcjFAph4sSJWL9+PUaPHo20tDSYzWZs2LABlZWV6OzsRE5ODnQ6HYuxy8jIQFZWFnbt2oWioiIUFxdjz549MBgMKCsrw65du5CWlobU1FTWzsSJE7F161aMHTuWudK6XC5UVlaiqakJlZWVsNvtKC4uxrp16zBu3Dh0dHRg1KhRTMBsbGxEUVERDAYDOI6D1+tFRkYGuru7kZmZCYvFgs7OThQUFECn06GxsRGZmZkCyyRZlKurqzFhwgQ4HA7W/xEjRqC1tRVpaWlIS0uD2+0Gx3HIzs6G1+uF3+9HJBKBx+PBqFGjAID9DgCLSYzFYsjLy2MCL1lWKDbQbrcjLS0N0WiUlfAwm80YGBiA0Whk1mClDIEkRO/ZsweFhYWwWCyw2Wws1g+Quq3yLTZGo1ESpyimsbERI0eORCwWY8fmF3f3+/3gOI65kqamprI2aLsSwWCQKfTkkis+Nrl982s2Uqyk+Fi0KCCeHyX4Y/b5fMxFfLigovaHQ+Frcsk/kH0h1/PBIj7fYqg+6qFSolRUfg6oyqCKyvCT9Jv2iSeewJ133onm5uYD0J1DT19fHysuHgqFkJmZyeoNkhUkEokgLS2NWRLIekMCHQmD5PrpcDiYkAjsFUJ6enqYuxywt1i4x+MBAOaKVlNTw4RJcfIC6g9ZMgAw65HJZILD4WACMbFjxw5cccUVuOqqq7BixQrWfkZGBm699VY88sgjuPnmm1FcXIze3l4mMPFdGwEwCxWt5HIch+7ubuaWaLPZWHwjAOZC2NvbK9hO1lRSjqgdEqxcLhdzEyXEViI6Xz6fD/n5+ZKU4m63G9nZ2UxBoDE7HA5kZWXB5XIxyxspKeRySZYlsvqJj03WZMpWKLawkQJDsaRkbSPrEVnKaF5p/2AwyBQsEorF6cr5487MzGRuydQ2JTshBZXfZ/7/KVU+Wfv49RqpbX4NTlJsSTEkN9jBWHKodiC5bSql3Oe7DdOcyZ1/OUgBE8fv8tvmx3fyXUrlIGs+jRuAxI2XECuqcn3lu8juq205fD7fsCqCdL4PB0UQkJ7v4WYo7p5KxeEJVRFUUVFRUfkxkvTb9rzzzsOXX36JkSNHIi0tDdnZ2YLPjx1+rFM0GhUUcyZXtUgkguzsbKSlpQmSnwQCASaod3V1wWKxMEWBtvMFanGB+XA4DI1Gg8bGRpjNZkQiEVgsFnR3d2PMmDGIRCLMilFQUMAEGooPozYoYU0oFGLWB47jsHr1atx7771YuHAhPvvsM9aXrKwsXHLJJfjwww9x8skno6ysjLkWkkJHMVQmkwmpqalwOBywWq3MlZDijnbv3o2ioiLU19cjMzNT4FZJc0gxggS5Ge7ZswdTpkxhCRq8Xq9AYKf06JQkJBgMQqPRsDba29tZAXi+sE3xidRP/pyTEOz3+1myHYPBgL6+PpSXl7MkPaTodHZ2ssyU/NhQcnUmBYfgj1sMJfahREIEKRmkJJCVpL6+XrbAd3d3N3JzcyXWJVL45Aq3i/tHiUjIYkUui/RbKmxOLtI0bofDwWIEBxvvRkokXZtKCTmo7WTdBWlxRk6ZIPdWcduJFFlKIMX/PhwOy/aZn+BmsAxGiaZ7ANhrwRsu+Iru4QA9Zw4kZF0YLPz5l+Ng9FlFRUVFReVAkHQCmSeeeOIAdOPwoKenB1arVfDSF7/gKYmF0WiE2WyGy+ViFkK+65zT6WRxD2Tp0mg0aGtrQ25uLhPASJGkpB5arRYul4vtT6UuKAEMpdSnGLVgMIj8/HyWhZQf22U0GuF2u1FTU4Mrr7xSUL8RiGfYu+mmm3Dttdfim2++QUFBAZqbm5miRvFRALBlyxaMHDmS9dfn86GgoACpqakIh8OIRCKwWq3o6emB1+vFhAkT0NbWxpQvSlbBR6PRMPdAsXWI4m6cTidisRhzV6RyDCUlJbBYLGycWq0WDoeDuaHyFTVKcmMwGJhiTJZLUtj5gnAgEGC/3bNnDyorK+HxeFBQUAC/3y+wGPp8PqSlpaGvrw8ZGRmCulakuMol6CCFnWK3CFLgAoEAywza19eHsrIyNDY2YsyYMWxfp9OJ1NRU9Pf3Iy8vj8XyUZbTgYEBWK1W2Rg++ptfyJnfNsXF0WIH3wJI80OLBRaLhV3n+4I/bnL3TATFTfL7r6SI0f4DAwOyRbnpeqa2CfGijBh+bB61HQqFZK1AckpxomPvq22CX8iZSnsMB5TN93BK1HGgrYJk6U+GRAXE6boaajZtFRUVFRWVQ0nSyuCll156IPpxWODxeJCRkcGEX7kkGEBcuEtPT0cwGGQWlfb2duaeRwoQP2kIucOFQiFkZGQwt0J+KQsgXq/M7XYzwWLnzp0oLi6GRqNhbqQWiwUOh4O5q+7cuRMZGRksC5/H48HAwAAef/xxPP/88yy9OlFRUYEzzzwT1157LcaNG8dqu2VnZ6Ouro4Jhj09PcjKykIsFmMZK2k7x3HIyclBd3c30tPTMTAwgMrKSuzYsQM5OTks0yIlZ6CVdbHgS5Y3sdJNmVLz8vLQ2trKvnc6nYhEIigsLERzczNycnIk1lc6VwaDAbFYDC6XC6mpqSzbZ3p6Oouh0+v1MJvNLGMpALS1tbFSEpS4xefzob29HVlZWdDr9aykAbkCixOgkIXI6XTKZt+kcYsVIiq7YLVaYbfb2fWxZ88e5OTkwGQyoa+vD1lZWez88Eu8kHLMT+qSyLWNL3hTwiNS+OhcORwOtsjBd5WlItr7EoRJQRWPezBQKQdgr6WQSniIIYsgAFllIhwOS6yw1IZSf8jlkz9uSgwkJpEVONH4kskgOpRSCIk43Fwb+ef7QOFwOJJSphMpj3KWfRUVFRUVlR8TQyo6H41G8d5776G2thYajQYTJkzA6aef/qNfGc3MzGQCXTQaFWQwJFdD2kZFuCmOhNLKG41GeL1eZg0k6x1ZoHJycphySG6OVLqCBF1SRMmaRtkWnU4nDAYDU2q0Wi1MJhN6enpYzNlnn32Gv/71r1i1apWgoDgAjB49Gtdffz0uv/xybNy4Eenp6QiFQnC5XEzgdblcKC0tRTgcZgIO36ICxAVIqonGt6JRcefc3Fw0NTUhLS2NKZKU5ZIv6FFtRL41DQArv2CxWJibZGFhIYC9pQN0Oh0reQCA1U+MxWLo6upCRkYGU3J1Oh3bz+l0YsSIEairq2OWPOpLZmamRDGg80tZZPnF1AsLC2G32zEwMACdTof09HR0dXUhOzsbwWCQWbTEgiKVJuDHePIXC+SU5kAgwKxddG5isRhz0+QrCG63m2UD3FfdOr7SHAqFZAt8U8F08f3tdDphNptZVlOltkKhELPi8l2E+dlIlRC7+yZ6xvT390Oj0ShaBeWsO2Kro/g3pOTLjYdPJBJhCz37gj/uZNP3ezyeYbMKkuJ1uNQXBPZ9jveXoVgF5c43EQgEDitlWkVFRUVFJVmS9sVpaGjA+PHjcckll+Cdd97BW2+9hYsuuggTJ05EY2PjgejjQcNisbDU9wAEwq3b7WZKH7muUcIREuRJcYhEIsyKwbdkkMJGFiOKM6F/yfWNEoo0NTVh1qxZzIWSX7yeXB6pRMHGjRsxf/58LFmyBJ988glTBLVaLU466SR8+umneOGFF3DaaacxN1QgXpRZXFOurKwMDocDRUVFAPbGNxoMBvT29iI9PV2QBdFqtTIlzGKxMNfGvLw85gpLAicpnlT3zWw2S4RxcX0wsizZ7XaYzWZBAhuj0Qin08lcGslqmpGRAZ/PB4PBIIilJOuZy+WC1WplbVA/+/r6UFlZyfpCAnt3dzebDz6UnTISiSArK4u5qorHTYsF0WiUjZs/DrII87eTotLR0SEbK0iWPLFVlVw6ycLrdDoFwqzH4xG4AhuNRmYBEyt0NOdia00wGBRYRAZTiNvj8SAlJYXtx7/u5OC7sA4Gt9vNEjsBwrp/VJNPrAhQsh4xVD9U3D7fms+H3EfFipXc/jTuRBZJOQKBwLDF9dHCVrJF7g8kB8sqKGepV0LpfANx9+FkrLoqKioqKiqHI0mXlliyZAk4jsOrr77KEsYMDAzgoosuglarxcqVKw9IRw8klEzgwQcfhMlkYqv8Pwai0Sg+//xzrFu3TrA9NTUVM2bMwIwZM1hSFZWfF6SYiGOwyBJH1imKeRUnvqFFD7mEOPwYT7KW07/iR4o4oQq/PqP4t2JliizS4r7KWXeovAu/0D2Nm+/SyodqKPIVRwCSv/lzp6Q8ylmIyJVbnCiKFMfBKmOklAxnKvXDqdg8ANmFjeEm2TlUKnxOi4GHkzKtovJzQC0toaIy/CStDFqtVnz//feYPHmyYPu2bdswb948ZlX7MUEPF6oz19PTg40bN7KV32uuuQadnZ3YtWsXNm/eDK1Wi/Lycpx99tn473//i7y8PGzfvh1msxmFhYU48cQTsWHDBgBxIa6xsRFpaWk488wz0dnZiZSUFGzfvh1OpxNdXV1YunQpOjs70d7ejq6uLgSDQYRCIcyePRujR4/G559/jqqqKjQ2NkKr1SInJwczZ87EE088gRUrVmDLli1sLMceeyzOOOMMnHTSSfD7/fB4PLDb7TjttNPw2WefYcaMGaivr0dJSQl27tyJJUuWYM2aNSgoKMDAwABzOxwzZgwKCwuZO+W3336LhQsXYvPmzTj55JOxZs0aTJ06Fdu3b8fs2bPR1dWFUaNG4cMPP8TChQtRW1sLo9GIkpIS5ObmorGxESkpKXC5XBgxYgRqa2sxY8YM+P1+1NXVIRQKYerUqSwbKdUyrKiowObNmzFz5kwWf7dmzRosWrQI3377LaZPnw6bzYbS0lLs3r0bI0aMwM6dO5Gfn89q+NXW1qK8vByBQAAOh4PV8Ovs7ERWVhb8fj+LscvKyoLBYGA19bZu3YpZs2axEg1Un2/r1q2YOnUqamtrMWHCBGzduhXTpk1DbW0txo8fj4aGBowaNQp79uxBVVUVi2mk5CaUnZVKjlDikZSUFNTX12Ps2LHMGkkWpM7OThQXF7N/CSqjQMlAfD4fa8NkMqGmpgYTJ05k+/NrDPITwPDLOChZreiYNpuNtcGv+yeXvIWfVMVmswGIJy+iZEwpKSnQaDQJ20xUW5AQty1O5qL0e/F4E41/ONjXOOT2F7tSq6ioqPwcUZVBFZXhJ2k3UcpQKYZcwH7MkMWErBsul0uQvXFgYIDV1ONbAciVTpyVEtjryie2OpC7ID9+iiA305EjR7J5HRgYYJk0y8rK8MMPP+DBBx9kiqBer8dDDz2Ezz//HCeccAL0ej16e3uRn58vcFUkd0q+eyaNm5LehEIhFBYWMte62tpaphjy54Pg16Qjiwop0uLafGLXQH7GUnJXzM3Nhd1uR2ZmJgYGBlitRnE8FtXYE7uTytV/02q1zMVXfL7535Nbb0pKClpbW1FcXMzcOsXIZUiVQ6msBD/rKMUlUV1HcnlMpqyC3L5ybYtj9MSWw325Z+4rzb742Pw+iM+BuObigURp3UvcdrKlLA4GqiKooqKioqKiciBIWhk89dRT8etf/xrr169nStP333+Pa6+9FqeffvqB6ONBh8oaAMCsWbMAgGX/BOJCJT+JA2V39Pl8rAYdEQgEEIlEBCtYlD2SXzze4/GwLI7kuqbX61nWRgCs8Pnbb7+N+fPnw263A4gnvlm+fDmWLl0qEBpDoZAg+QElZ6D6hHyBXqPRsGQrAARZUAOBANLT0+Hz+VgiFyCesCM1NRU2mw35+fmCcVMZCHHyD4qvo7bJ+ieG4iz5sWh8haWtrY1lThW3TccXKywDAwPIy8uT9FEMf56o+Dt/v7a2NuTk5KCtrU0QX0g4HI59puqn1U2xgkJp/vmlMfjn1GazJVwNTSbOLhwOy7pC7iu1P2XdFSMX8yVWrNxut+y495U4ZF9Fwvcn3kyu7QOlfCXpiKGioqKioqKickBJWhn861//ipEjR+Koo46CyWSCyWTCvHnzMGrUKDz55JMHoo8HDYpHohqAAFhNOYfDIUhMkZWVxTJpUmIXAIL4PCp0znGcQIDnZwul4xOU3INvMSQ3MbfbjX/84x9YunQpgsEgAGDEiBF45plnMGXKFPYbcjvU6/Xo7++XKKgAmIskjRuI1+MbOXKkoJ8EzYvZbIbNZoPJZGLJY8RF5Ht6elgpAjlrT1tbG3NzlLOmUnviPgDxZChGo1FRIenv74fJZJLEulFb/Pb4CV7k4FvQNBoN/H4/SzCUm5vLrMT8tqnuH19pJkjhJ7RarUTRpHGLlQa6ZvZVE87r9e5TEaXz7fF4hsXNhjI0yrUtFwcoN24laNx85ViOwYz7cLCuDXbcKioqKioqKioHg6Sj3zMzM/H++++jvr4eu3btAsdxmDBhAkaNGnUg+ndIiEQiEnc2yi5IiS5ycnJgt9uZwkGKBQmskUiEKRNixcTv97NU9/wYJyo54Ha7WfF0St+/e/du3H///aitrWX7n3XWWVi0aBEriUEWwEgkAr/fj9TUVOZuSHg8HlagXawkRiIRdHR0sBp7TqcTJSUlAquMRqNBR0cHCgsL4XQ6AQgtSdFoFL29vSguLhYkGYnFYkx5djqdAqWTaGpqYuOmDJvUB1KOuru7kZubK1ESqW99fX0SSyGNjeLS5LIJ2mw2yXxQQXlyi6ZMhEpxsX19fSxekA9lJOVn+CQ4joPb7RYUNU9JSWGxjZTIiM4BLQIMFloUkEPsZjtU+GVIBstg26YSLVR/cbAMpWwD9etAIqccq6ioqKioqKgcKpK2DBKjR4/GaaedhtNPP/0npQgCcRc+OVc5ftF5Euqp0DU/jioajTJhlGL++Bahjo4OViydjsNxHDweDzQaDcuKCMQF7U2bNuHqq69miqDRaMR1112HO++8ExqNBhaLhbmQUhkIsqRQDB6xZ88elJWVSepjBYNBcBwHv98vUMgoFrK1tZWdZ1KUKSmKuGg59YMv7IfDYXR3d8u6VZKCEwgEmOJKrnvRaBSBQIBZXEnBJHdavhsqISdsU+F5UraIWCzGFHc5hYZiHekc6XQ6VjidrwAnQikGjRQPcsflt0Hzx//dYOL/xDQ1NbFkMXIkU9ctGAxCq9UKspTuD4NteyjK01Dj/g63eEEqUaOioqKioqKiciAYlGXw5ptvxkMPPQSr1Yqbb7454b5/+ctfhqVjh5KBgQFBdkVSfsh9lGL6fD4f+43L5WKKjNPpRGpqKgYGBpgLHV/YBwCDwSBIpR4Oh9Hf34+8vDy0tbUhPT0d0WgUTzzxBP7xj3+w35WUlOCDDz5ATU0N26bRaJi7H6Wtt9vtKCgoYLUO+ZDSyld2W1tbkZWVJVuEmlxE+bXpUlJSmIWTXCIbGhqQm5uLQCAgW6Sc3zaf3bt3Y9y4cejo6JAtS8BXzJ1OJ8aNG4f+/n5WExEAS8bjdruh0+kkpRSUiny7XC4AEGSdpELs/PkF9tbSo5qKZEl0Op1IS0tDKBSCx+ORrT0mp9BQfB8p4nLQ78glV2who4yZpFDLudwOV226WCwGv9/PFHO61sLh8D5j+g4kSm0P1QJ3OFnuDnQRdhUVFRUVFZWfN4NSBrds2YJwOMz+/1OGlD69Xs+EwlAoBIfDwZRC+o4ScFANMIPBwFwbjUYjXC4XUx74lj6O42Cz2aDX6wUFuCmhDMdx8Hq9WLJkCT777DPWt1mzZuHZZ5/F9OnTsXPnTkFcHd8qSWNwu92ChCnUPrme8jOAxmIxWCwWgWsnAGzdupW5oZJSSRYraouURIfDgdzcXPT29rKSAWJ6e3tZfUoiFAohPT0dzc3NAqHeZrPBarUKFDtSRkgBIEGZspT29/dDr9cLYhjD4TC8Xi+sVqvE+kfj5ScNCoVCsklx+Ilv+LGHlJ01FAqhu7tb1lIul5FUKVGLOLYQiCuO4pIN1NfU1FT4/X5WUmJ/GIzrJn+RJCUlBeFwGG63W9L2wcgQCkC2bRUVFRUVFRUVlX0zKGVwzZo1sv//qUKCPt9C4PP5WKFh/kq92+0WbOOXVQiHw8xlMiUlhSkcFD8nru3GcRyCwSC6urrw0EMPoaurC0Bc+L766quxePFipkhptVp0dHTAYrEIFFePx8MSqPBjFR0OByvtoNPpkJmZKVBs/H4/OI7DiBEjBMqg3W5Heno63G63YDsdj6/M8N0dgb0WFrLIRaNROJ1O2fIUbW1tyM7OFiiDgUBAYFUlenp6YLVaBdtJIQf2WvaAuEKi1+sRDAaRlZUlq5w4HA6J4uf1elFQUCA4FrWdmZnJkvoA8WsjOzsbTqdTMB/UNhBXYsUKSzgcRjQalSiJlMVUjNPplC1sTiRj0SJXV7FFjRS8ZJFrOxKJyFpJqe1ksp7+FIp7/1TGoaKioqKiovLTIemYwSuuuEK2zqDX68UVV1wxLJ06VJBCxi9nQFBsGbmIAmAFvg0GA6uvZzAY4HA4YDAYWEwbx3HMhZHKBlB74pp769evx5///GemCGZlZeHxxx/HySefjKKiIrZvNBplBeLNZjPrE50bysBIx6+trUVVVZVsMW1SQmOxGHO75Pepq6sLeXl5gng+srTx3RuplAWNnaA6hIkyQpJ7JT9eixLhiDNe2mw2pKWlCY4ViUTYeeNb8AYGBljWUaXkHXKJXeh4fMsntS1W6jweD7MK82P4BgYGJFZQcRs0f+Jx891yCa/XK5tBlUgmho+SFSWjnMjFJ5Kin0zb4vtoMPsfDBfU/SlPMRgO1jhUVFRUVFRUVAZL0srgSy+9xOrk8fH7/VixYsWwdOpQQcK5OFYMiAu9ZMnhx85RhkqdTidQUPr6+mAymQTZRqPRKHO39Xq9LD6PlMNVq1bh0UcfZfM7e/Zs/P3vf8eECROYeyoJk9SfSCQicX/s6upCRkaGwLpJ8V3icgh+v5+5rppMJomyRLUH+RlJya3U5/MhMzNTIEB3dHSgrKxMouRSiQA+cvGB/PaVlAZShCnRDX/cpaWlgnb41hh+cpB9JX0hi6C4zxQvSBlXifb2dpSUlAjGTQsEYgYzbr1en7DWnxi+Akz95Lu+8vcDki9xQG7H/NIpdBy5mo5y7CvZzVAQjxuQV4oHo6zuq77iwYbijVVUVFRUVFRUDhSDlnxcLhecTidLhe9yudjHbrfjo48+kk3p/2OCMmOSoiUWDEnwJVc9shzRh7/qH4vF4Ha7mRsnEFdMGhsbkZqaipSUFOj1eqSnp8PhcOD111/H888/z4T1k046CWvWrEFOTo4gNpGv9IXDYdYXvrtpIBBAX18fSkpKmJseuZ9mZWWxYvB6vR51dXXsvMlZzTiOk7hK0vHIzZU/T9FolMXHUV/tdjuLJ+Rb+fblNkdxgGJIAeALypRFlcpBEH6/H9FoVJBxFdib9EUJpcQd1Ge5fnd2dgosq6FQiCl64hjG4bQQRaNRidUwGAyira1N4pLb39+P7OzspEscUBZWOWXJ5XIltFgScnGT+4tc23LW7+Ge84OBOPOtioqKioqKispwM2gfsczMTKb0yMV8aTQaPPDAA8PauYON1WplsXOkgAF7LRqxWAwmk4kJleSm6XQ6JZYvUib5xbBJsfL5fEhPT2eukddffz1efvll9tvjjz8eK1asYIoev+QCtSHOjEl9ItdKi8UCn8/HLFjk/urxeDBt2jS0tLSwrKNUzkBsaSPkyi7IuTFGo1FmOeU4jv2G5s/hcGDGjBnM8hkIBNDR0YGCggJkZ2dLlJNAICBbFJ36K+4PAImSSBYyceyh3++HXq9HOByWVWSG06VPnNiFLMwcxyE1NXW/s1cqJWrhOE4yBp/Ph8LCQvT29h7Q+DV+6Q2CLLNDqUuohJIVUDynQ42FVFFRUVFRUVH5KTNoy+CaNWvwxRdfgOM4vPXWW1i9ejX7rF27Fq2trbjnnnuSajwSieB3v/sdqqqqYDabMWLECDz44IMCdzKPx4MbbrgBpaWlMJvNGD9+PJYvXy44TjAYxI033ojc3FxYrVacfvrpaG9vT6ovBLkP8q1hbW1tAPYWlidlg/alWnRiNz2y0vHj0fg1CEOhEH75y18KFMGLLroI119/PUs44/V6EQwGWaF1fokKvhslPxkMsFdp5AvFZM0TC9AejwexWAxFRUVMkI5EIujs7EQ4HEZ+fr5EOZKzyAwmfoz/XWtrK1JTU8FxnEARDYVCrGahXMIUfmIW/rwqufiRVVJ8DJ1OB7/fLyhCH4vFMDAwAIvFIqs8kNV4f2LL3G43zGYzwuGwIMFKIBBgc3ggFReqQylWFBNZapN18ZRzQ6UsqeJraagF4ocDavtA1/M7lGNUUVFRUVFRUVFi0KaBhQsXAogXsS4vLx8WwebRRx/Fs88+i5deegkTJ07EDz/8gMsvvxwZGRn47W9/CwC46aabsGbNGrzyyiuorKzEZ599hqVLl6K4uBhnnHEGAGDZsmX44IMP8MYbbyAnJwe33HILTj31VGzatCnpmBvKdEjxcEA8/o9iuPgZM6nmW0pKCnQ6nUBBsNvt0Ol0stkwY7EYQqEQHn/8cdTU1ACIZyH9/+3deZxU5ZU//k/te1V3Ve/QDc2+tYqoUcSocR031JkYNYlm9DtxiSCKBJy4xYTgikacDDMZIwbGqDOJRrMY0REMoomyKYKgCN3QdNP0VnvVrar7/P7o3/NY1VWNXdDQLJ/369Wv0Ldu3XurLs7cwznPObfccgvOPvtsWCwWeDwedHZ2qqyW0+nM6Zgp1yk6HA61nlDSNA1OpzOvQQ3QEyT2Lj8LBoPq+PI9HR0dqoPo0KFDc5q/aJqGdDqNqqoqtQYyW6E5hdnXJs+dHRRkP4zv3btXrXfs/d0lEgkYDIa88Q0yyO5vNk+uD+udRUomkwiHw6isrMz7uxMKhRCJRDB69Oi8wLN3Ce++yGCs971pbm6Gx+PJ61gL9J0llccr5r9H2dCnt319hlAoVDBzXMy5+/rc+1q/WChozT7ega7xk+eOx+MHtXlMsWs0iYiIiA6FouvEGhsb0djY2OfrX//61/t9rPfeew/Tp0/HxRdfDAAYPnw4fvOb3+DDDz/M2ef666/HWWedBQD4/ve/j//4j//Ahx9+iOnTpyMYDOKZZ57B0qVLce655wIAli1bhtraWrz55pu44IILivp8wWAQJpMJyWRSPXxnrw2UMwMzmQxCoRBqa2sRiURyAjUhBLq6ulBSUpI3ty8ejyORSOBHP/qRCgRdLhd++MMfYuzYseoYJpMJwWBQdeGU5we+DEpqa2tVhil7ZmB3dzdGjRqV87CcTqeh6zr8fr9a+5hNCAGn0wmDwQCj0ajGJMj3Z49qiEQiCAQCBcsAHQ5H3kO17I7pcDgKrsWT36s8nhzJ0fs4ckZiWVlZXiMTuTawd1dQ4MtS0UKZn0JZPnmt2Q1gMpkMOjs7UV1d3WdDm/Hjx+cdf1/D2LP/vshrsVqteUGDEAKhUAgjRowoeJxgMIiqqqqCr/WlmCBKNlUq1ABooM7d13pVOUexkO7u7ry/B/sj+7+tg6mYcxTTnZWIiIhofxUdDMqgLFv2Q85XdWnMNm3aNCxevBhbt27FmDFjsGHDBqxatQpPPvlkzj6vvvoqbrjhBtTU1GDFihXYunUrfv7znwMA1qxZg1QqhfPPP1+9p6amBpMmTcLq1asLBoPJZBLJZFL9HgqFAPQEMzITIYMj4MtMmMwiOZ1OFSyZzWYVNMggQc4klJlE+TCraRoaGxuxaNEifPrppwB6RkcsXrw4p6wzOygymUw5TUuMRiM++OAD2O122Gy2vLVQra2tsNlscDgciEajarscvD516lS0t7fnZJmi0SgCgUBOZk2eO5PJIJlMqiYzu3btgs1mg9PpzCut6+zsxGmnnZYX/CQSCbS3t+Oss85S2VSpu7sbo0ePztkmv7ve3ThbW1vhcrlyhs1LLS0tGDduXMGspGw40/s1OZNxX4GIzJQFg0FYrdaCAWVraytGjx5dMMCORCJ5nUfld9I701doLSSw7/JNGTT0DrAKZWyzXysmY97XyAW5vZjAMpVK9TtD1p+mL1/Voba/DrfgK7vzLREREdHBUnSNVVdXV85PW1sbXn/9dZx88sl44403ijrW3Llzcc0112DcuHGwWCyYPHkyZs2ahWuuuUbt89RTT2HChAkYOnQorFYrLrzwQvziF7/AtGnTAPQ8iFut1px1XwBQWVmJ1tbWguddsGABfD6f+pEdICORiFoTmN0AJRKJ5ASJbrcbnZ2dsNvtqtlM9kOxDHhkdkmue9uwYQMeeeQRFQh6vV68/fbbaGhoQCgUgtfrVesPgZ7AsLu7W3XmlNmttrY2eDweFTBlPzSGQiG43e68h2SZRZGNX7Jfj8ViGD58OIxGY87Dtxw4n1062N7err7rRCKRs+YtnU7D5/PlBS6tra0IBAIFg65QKIThw4fnPfjLBi/ZwdeuXbsKNpqR5+4991B+Npk97X1dkUikYPOYQs1GZGfYvrI7hYK1fa0tjMfjcLvdeZ+7UDMYWcZbSDKZLHiOpqYm1NfXF3xP7/vWH3197mIzasV0yNzXXMq+7E85Zl8NeAYb1xgSERHRwVZ0MJgdRPl8PpSVleG8887DI488gh/+8IdFHevFF1/EsmXL8Pzzz2Pt2rV47rnn8Nhjj+G5555T+zz11FN4//338eqrr2LNmjV4/PHHceutt+LNN9/c57H31Tr/7rvvRjAYVD+yQYzMmMjmLzJ7IstE5fgCOdhcPlTLwFE2hZEldfF4HEajES6XC83NzZgxY4YqsS0tLcWjjz6K448/Hrt371YjK+LxeE7pWzKZhNPpVA08ZEmlLEvNzmzJ7WazGV1dXXmNUbLL7eR72traYLFYYLfbC47SKCkpyckiaZqGeDwOn8+X8x13d3ercrveAY4M1Hrbs2cPSktLVcOa3kFR7yBHZm57B3Cyk6hsBJL9vmAwiPLy8rzj7yvzUigIkaWgFRUVed9HX1mlvubWZX8/hZqp9D53X9m07M9d6Bx9BXzFdkqV/1CRTf43Usi+hrcPVIDTV0Ob/VlHuD9BJxEREdHRYMCegMrLy7Fly5ai3jNnzhzMmzcPV199NQCgoaEBjY2NWLBgAa6//nrE43H867/+K15++WW1rvC4447D+vXr8dhjj+Hcc89FVVUVNE3LC37a2towderUgue12Wx9zq+TpZGyXLO9vR0WiwWapqkHdY/Hg1gspvaXD7hWqxVNTU0qSJMPmTt37sSVV16pgs7q6mrMnj0bJ510EoCeQMrr9SKRSMBoNKqgLRqNQtM02O32vOYesoQy+3N0d3erBifxeDxnjl52B05N01TA2dnZmRfwAT2BWl/BkqZpecHdxo0b4fF44HA4VHMbqdDMRqAnMyfXwWUHFvF4HJqm9Ttr193dDYvFApvNBk3Tcs6VTCZVVrX3Z5Clttk6OjpgNptht9vzSloLjahobm6Gruuoq6sreK2FPncikVB/V7M/dyqV6nN9XCHhcBi6rhe1bq6vc+xrwHmhYCmZTCKRSKC6ujpv//4EwQdqoNYLAge/RPRgdyolIiIi2l9FZwY/+uijnJ8NGzbg9ddfxy233ILjjz++qGPFYrG8h0aZmQN6Hh4LBRLZ+0yZMgUWiwXLly9Xr7e0tGDjxo19BoN9kXMAZXbBaDSiubkZZrNZZQANBoPqPth72LrFYkFnZyd8Pp8KWiKRCL75zW/iiy++AAAMHToUTz31FGpqalSwIztkxuPxnABlz549cDqd8Pl8OY1XYrGY6iBqNBpzsnNmszlnnaKUTCYxfPjwvGYgMmjs/bDf3NyMmpqavIBQfj+9s06xWEzNkOsdKIbDYYwcOTLv+87uepotGAzC7/cX7NgJ5JdkykC3UJMYeT29Ax1ZGtj7HC0tLaitrc3LYMXj8YLBUjAYhMvlKphZisVieeXLQN9ZyUgkUjDA6WuNn7yPxa7Z66uRTrEBS/b61gM5d1++qlPpgXYSPVQKNU0iIiIiOhwUnRk84YQTCs6SO/XUU/GrX/2qqGNdeumlmD9/Purq6jBx4kSsW7cOCxcuxA033ACgZ03dmWeeiTlz5sDhcGDYsGFYuXIlfv3rX2PhwoUAespWb7zxRsyePRuBQAB+vx933XUXGhoaVHfR/pJBoBwvUVpaqsooZdZOPtQ1NzejpKQkp1GH0WhU5XO6rkPXdcyfPx+fffYZAKCqqgr/8R//Ab/fj6amJvWgKx+SZVZLPuR2dHTA5/PBarXmNFOR3ULlw7h8wJYZzd7Dxjdu3AiHw1EwA5JOp1FTU4NUKpUTiEQiEZSVlamAU2pvb0d5ebnKAGYfR16D1WpVn02uwywkFouhrq4ub51XMpksWFba3t6O4447rmDAB+Q/dO/duxcOh6NgyalsmtObXMvXW0tLC2pqavK295X96+joKPgZ5OeTZZfZ3032etFsXV1daGhoKHisQvYVRPXVVbUvhWY6AvuXTZPrVfsrmUwWDKYHUiqV6vdIkENlXx1oiYiIiAZS0cHg9u3bc343Go0oLy/fr853ixYtwr333otbb70VbW1tqKmpwU033YT77rtP7fPCCy/g7rvvxre//W10dnZi2LBhmD9/Pm6++Wa1zxNPPAGz2YyrrroK8Xgc55xzDpYsWbJfMwZlUxiDwYCSkhLs3LkTVqsVu3fvzuliGQwGUVlZqbKbct1gOByG0WiEw+HA448/jo8//hgAEAgEcM8996CkpEQ9XGc/UMtMZ3bXS9lWXw5H9/v9AKBGKMgspdz/888/Vw/P2WV/u3fvVmMu+sqmyNfkNckSwObm5pz5ctFotOAIhT179mDSpEnqWPIztrW1feXDdu+ywr7WyCUSCfh8vrzXdu7cCb/fn7e9d9fU/pDBbO+yyVAohFNPPTVv/9bWVgwfPjxvezAYLFjm+lXn7isIKPR3OZlMFryfhUaH7K++yjHj8fghCVgKBbX7Wq9YrGLXTx4KhUqhiYiIiA6Gop8Yhw0bNmAn93g8ePLJJ3NGSfRWVVWFZ599dp/HsdvtWLRoERYtWnRA1yPn25nN5pwOgzLzlB1syIxCc3MzXC6XyuqEw2HU1dXh2WefVaWrdrsdP/3pTzFy5EiYzeacJieyRFQGRLquq/PKoFSWxWbPpZMZxOw1i3v27MHZZ5+NPXv25IxFaG9vR1lZGYxGY05mprOzM2/GXzbZHMXlckHTNABfBqK9H8blOkS5bk9mxWQGtdCas2KbicjMX+/sUjweR11dXd7xOjs7UVZWlnccGUTtq3yvd8ZIlpv2Xke4rxLY7DWbkmxGNBBCoVDBczc1NRWc/SfXchZz/r4yieFwuODIjL7WHvZey3kgQqFQwUC7r7V5X1VuSkRERHSs6lcw+NRTT/X7gDNnztzvixlsMstXqJOkrutqhqLRaFTlc7LRiVy7J4TAa6+9hv/+7/8G0JPZWLp0KZLJJEaMGIFIJIJkMqnGMKxevRpVVVWIRqNqdp98mJbBYDKZhMViyZvnKDMI2QPnrVarWj8nt3d1deH000+H3W5XD+UmkwmvvfYaAoEAEolEnyWNQG67fpn1K9QxUgao2V1Gm5ub8bWvfS0va7dixQo10qMYhbrE9tWwpLu7G5MnT87bvnXrVtTU1CCRSKhsa38UWuun63rBLGYoFMKYMWPytu/YsQPl5eX9Pue+FOo8CvT8PR47dmze9qamJgwZMuSgnruvssumpqaCZbYDee5MJlOwDHV/xk0MlH015iEiIiIabAbRj3qrvuaV5R3MYFCNUo4kMtMwb968A35o/OKLL7Bs2TJV9nnBBRfgtNNOG4jLJMrTV8arUIZMCKG2F3pfoW2F/nFEbi90jr6ayui6nlNunC27NDqbxWIpWMJpMpkKBpwGg6HPz93Xf9cul+uglokKIYqe6Wg2mw+70lUiosOBfF4LBoNFLwMhosL6lRnsvU7waHXeeefBarViw4YNCIVCqjRy5MiRyGQyat1eXV0d/vKXv+Daa6/FX//6V5SXl8Pv92Pbtm1YsGCBCgS/853v4Fvf+hbq6+vx6quvYvr06bBYLGhra0NjYyPOO+88LFmyBGeccQbS6TR27tyJIUOGYOjQofj973+PqVOnIhKJYMKECdi7dy9KS0vx+9//Hh6PB2VlZXA4HDj++OPhdDrx7rvvwmQyoa6uDi0tLRgyZAjq6+uxZs0a7NixA6eeeqrKDMp5hBs3bkR9fT2SySTGjRsHIQTS6TTWrl2LadOmoampSXUhtdlsaGxshNPpVJnSsrIy2O12dHd3Y9euXSpDWlpaCqfTiffeew+jR49GKBTCiBEjVDZR13V8/PHHOOGEExCPxxGPx1FaWop4PI69e/eivLw8p3lIOp3Grl27UFNTg/b29pwMUzQahdlsRigUUo1fHA4HotGoyqr6fD41YiKRSKgGHdkP6i0tLUgkEggEAnA6nTkZrlgsBqfTiXg8PqDNRmT32P0pYYxGo/vM5vZFfhYiIiIiogNaxCPHFBwtzGYzdF3Pacxx7rnnqnJQg8GATCajsp9yFIXFYlEzEZPJJADgpJNOwty5cxGPx9HR0aEewOWstUwmg87OTlitVsRiMVUGmkqlsGvXLrjdblX2JsvM1q9fD6AnOJVruQwGAz7++GPouo5hw4ap8lWr1Ypt27YhHA7nBE9CCASDQaRSKXg8HtUNUwiBRCKBRCIBh8OBSCSS01UzFArBYDDA6XSqjquyJE+WAMrvzGAwYPPmzaiqqsoJPGSJZ0dHh1pPJ//+ZDIZxGIxeDyevHLMzs5OuN1uWCyWnKxTLBaDw+FQn0GuDUskEioblF1Wmr0mNHt7LBZDe3s76uvrkU6nczJhqVTqoA4k359AUH5uIiIiIqIDsV/B4K9//Ws0NDTA4XDA4XDguOOOw9KlSwf62g45o9GIdDqNdDqtgg6v1wubzaYCRJfLhV27dqnh9EBP5urVV19Fc3MzAGDEiBGYOXMmdu7cCa/Xi71796qOjAaDAel0GpqmwWQywWQyqfMZjUa0tbWpkQ5yVISmaSpA1HUdXq8XdrsddrsdTU1NsNlssNvtKCkpQSaTQTqdVmsT5fgGuc4qHo+ju7tbNRjJXgeZSCSg67pqlGK322E0GtVaSjk30ePx5ARTcmSAzIi2tLRA0zRUVFTkrXVMpVJob29Xw8pl04+Ojo6CzV7a29tRUlICo9GIcDisgqBEIqGCQxlQZgfx2SM65JrLvubcffrpp32Obtif+Xv9tT//kJL9ufcHG6kQERERkVT0E+XChQtxyy234KKLLsJLL72EF198ERdeeCFuvvlmPPHEEwfjGg+pWCyWk/GMx+M53UWtVivi8Th8Ph8sFgt0XUc4HM4Jhm+77TaUlJQgHA6rBi0yE5ZKpRCLxWAymbB7924VWKXTaYTDYZhMJlRUVMBkMiGVSsFoNGLXrl0AeoIaOSLCYDAgEomozp3y+OFwGE6nE3v37kVNTY0aeSHXi7W2tqKyshLbt2/H6NGjAfQEJbLcUmbOdF1HJBJRMw4BqONkD7pvbW1FaWmpCiTlGI5Ro0apADM7yycb2MigTgiB7u5uBAKBvK6PoVAITqcT6XQaDodDZUPlTEO5rioajapGOjJjmX0cIQSSySScTqfKfEoff/wxxo0bl/N3QGYDE4mECgQPh9lvvT83EREREdGBKDoYXLRoEf793/8dDz/8MC677DJMnz4djzzyCH7xi18U1XX0cJRIJNSQdRkAyWHzAFR5IQDU1taqss4//vGP6O7uBgBMmTIFJ598suoO2tXVpbJ4QE/wZLVaYbPZVNYpnU6rwNDpdOLTTz9FWVkZNE2DpmnqGmSQI8dBdHZ2IhAIwG63w2q1qhEYu3fvRn19PbZv344hQ4YglUpBCIFdu3bB7/fnnBuAykxaLJacDF00GlXZQRkAynNLkUgEXq8X8XgcJpMJmzdvxrhx4+B0OlUQJj97MplEJBKBy+XKGUrvcDhgNpvR3d0Nn8+nSj11XYfT6VRBovzf3t0hs4O37KymlL3WLzuj2dTUhLKyspxSVpm5laW8skQ0+88DpZgsXe/vkoiIiIjoQBUdDLa0tGDq1Kl526dOnYqWlpYBuajBIuftZY9lSKfTiEajMJlMMBqNqhQ0EAigo6MDwWAQv/vd7wD0lCbeeOON6OjoUEPhZWAjx07I9XhyJqHX64WmaWo9m8vlgtlsVuMhOjo6YDabYbfbEY1GUVpaCl3X0dbWhvLyctjtdgSDQZXVa2trQ0lJicriyeApHo/D6XTCbDarwEjXdTXAWzbIcbvdqvRSrn+0WCwqKyeziL3XQBoMBnz22WcYMWJETuZQ0zS1tjCTySAajSIQCABATsAnyUA8HA6jpKREle3K+YWpVKpgA5RCAV8qlVKzBHuXVXZ1dSGVSqlyVUkG+4fburzs7/xAHE1rfImIiIjowBQdDI4aNQovvfRS3vYXX3xRlR0eqWRAJbttAj1BgQw0ZHAHfDk8/C9/+Qvi8TgA4OSTT0ZVVZUajK7rOmKxmArmstd7yeHzXV1dAKCaswBAR0cHAoEA9uzZo2bxdXd3qyYtjY2NqtNmLBaDpmmoq6vD7t27YTAYUFpaikwmk5Mpk2vznE6nCky7urpU4ClLXmOxGLxeLxKJBDKZDFwul5pfKL8buf5w586dcLlc8Hq92LVrF+x2u1rPKBuxyMBMds6UdF1HKBSC1+vNC1BkxhOAKs+VQa28/mzZ6y9lQA9Albn2XvMXi8VUKWsh8noGakh6X/obmGX//TsQ8rskIiIiIgL6OWcw229/+1t861vfwrnnnovTTz8dBoMBq1atwltvvYWXXnoJV1xxxcG61oNGzq157LHHVEDUHx0dHfi3f/s36LoOi8WCmTNnqrV7h5rsppn9Z7n+Twafclsmk1GBiFwDaDKZIITICaZkN1N5XBmMyP+V+8osnNlsVlnA7L9W2fvIc8n/ldckM7LZ15zdpEcGe72HiqfTaZVxlWQ5p+yIKgNTeS6DwYCqqqqCAZbT6UQsFoPP58vZLoPmgdTfURUDNXduf2beERERHS44Z5Bo4BX9dPuP//iP+Nvf/oYnnngCr7zyCoQQmDBhAv7+979j8uTJB+MaD5mrrroKb7/9NrZv3w6Px4NwOIyxY8cilUqpUsVoNIrdu3dj0qRJeOKJJ1SgdMUVV8Dv96OyshJDhw6F3W7H559/jqqqKgghEIlEMHToUJSUlODjjz9W2bkhQ4ZA0zR0dXXBYrGo9XMOhwN2u12tOYxGo0gkEnC73XA6nejo6EBtba0aO1FaWgpN07BlyxZMmjRJdR+tr6+HxWJBe3s7rFYrkskkdu7cibq6OgSDQbS2tmLy5MmIRCKqZNRkMiEQCODTTz/F6NGjVWYvex1gKpVCMBiE3++HpmmIx+NIJpMYOXIk4vE4UqkUAoGAmiPo9/uxadMm1NfXq4BEZjXdbrdaF5lIJOD1enMCUFkaKoMnXddV6a3D4UAsFlNBnQx44vE4IpEIysvL1fuyZ+z1FYjJsuDeweBA0zQNXq/3oI6tICIiIiLal/16Ep0yZQqWLVs20Ncy6GSwBHzZudHj8UDTNHR3d6OtrQ0jRozA7t27sXnzZrz77rsAgJKSElx00UXYvXu36jwqG6UYjUZ0dnbC5/NB13VEo1G1xk4GVbKTp81mU+sV/X4/Pv/8cwwbNgyNjY2w2WwoKytT3U7lmIja2lrs3btXdem02+1qQH12Exw5cw/oCZhkaWU6nYbX61WdSW02mxraLktbXS4XbDabKvlMp9NoaWnB+PHj0dLSorJWcvyD3+9Xw91llq9QiWMymYTNZlPfmTy3HPguR1pkZxuj0SiMRmPBDJcM2DOZjBoyX6xDVUopS2+JiIiIiAZL0WWia9euhcViUXPZfv/73+PZZ5/FhAkT8MADDxyRD7iy7OD5559HY2OjCggBwO12q/JJ2WAlHA7j17/+NbZv3w4AuPzyyzF16lRVXirLILMHqttsNtUcJXs+odVqVTMF5Xtk2aNsrhKNRgFAjU5wOBzQNA12uz2n46bFYkE0GkU6nUZVVRXC4TA8Hk9OQ5p0Oo2uri643W7VxGbYsGGIRqOIx+Ow2Wyqa6kMqLJLFJPJpBpOL4fWy2swm83weDywWq3QNA0AVCAXDAZht9vV8eX3Ln/v7OyE1+tV342c7Zh9DJPJlFeGm8lkVBfVRCKBkpISlSm02+05GcX+ZAblCI6DnbHrb4koERER9WCZKNHAK/qJ96abbsK8efPQ0NCAL774At/61rdw5ZVX4n/+538Qi8Xw5JNPHoTLPDTGjx+P7u5udHR0qAxYdXU1nE4nIpEIWltb4XQ6sXbtWhUIlpeX49prr0VJSQlWr16NsWPHIp1Oo6KiAo2NjTjxxBOxadMmDB8+HF6vF3v27EFnZyesVivC4TBGjhwJq9WKlpYWeDwelSk87bTT0NzcjGg0Crfbjba2NsRiMYwcOVI1XyktLUUoFMIpp5yClpYWBAIBrF27FpqmoaamBmPGjMkpsYxEIuo4brcbQ4YMwUcffYTx48fDbrdjx44dKC0tRUlJCbq7u6FpGgKBAEwmE5LJJHw+Hzo7OxEOh9UYjuHDh6O9vR0ejwfBYFA1tnE4HKq0NB6PY9euXRgyZIjKXgI9jWLk2A2bzYYhQ4aoZjxyzaCmaX2uc5MBurw/TqdTNbHp/T4Z4GY3sSkUjMm5kgcbu3oSERER0WArul3i1q1bccIJJwAA/ud//gdnnnkmnn/+eSxZsgS//e1vB/r6Dild15FMJpHJZFSZqMyqyTVsuq7jzTffVO/55je/CbPZrAbDu91uaJqGkpISNVYinU7D6XSqrGlXVxeqqqpgMBjU2jQ5NkFm/uQYBxnsyI6acgh8JBJRw+nlrL9QKIRQKISysjI1NzCbpmnYuXMnTjzxRMTj8Zx/VZNrFrOblWTP1pPjJ+R8w/LycjWv0GazqSwk0LPOT5ZbyqydpmkQQhSckxcMBlWWUZaTftVcPxkIulwuJJNJNZheXmf2DEWTyZQ3e5CIiIiI6FhXdDAoSyYB4M0338RFF10EoGcIe3t7+8Be3SEm1+HJ7puSwWBAKBSC2WzGli1b0NbWBgAYOXIkpkyZApPJhK6uLpV9ymQyKtslZ+m5XC50d3ejpKQkZ+SEHN0gZwq6XC6UlpaqYwkh0NbWBpvNBpPJBIvFotbuZZc9Aj2ZNgCoqakp+PnkaIvu7m4VCMp7KefqWa3WnCBSBlMyUyrHb8iSWXmNcm1h72BOri+sq6sruG5QBsFerzdvqLo8X2/y+3O5XAC+/DspA8LsQDKVSh2WpcsHOiaCiIiIiOhAFR0MnnTSSfjpT3+KpUuXYuXKlbj44osBANu3b0dlZeWAX+ChVFpaqgIt2aUT+HKdnNPpxN/+9je1/eKLL0YqlYLdbldjE+R8PFmC2NXVpRqyyAHosqkK0BOAhsNhNdpBrqEzmUzYvn07ampqVKdPj8ej1izK93m9XhWsyfV2MpjTNE39WV6fPK8cP6FpmhrnAEAFTrK5i+wUKrN+0WhUNXuRWUo5IiL7O8sm5xL2JoTICfhkgCSzeIWauQghVJlrtuzh9kRERERE9NWKDgaffPJJrF27Frfddht+9KMfqcHd//u//4upU6cO+AUeStlZN1neCHxZ4phMJrF+/XoAPfPopk+fjlQqpdaZGQwGdHV1wel0YseOHbBarapbZyqVUmWeuq6jq6sLVqsV0WhUdR7NDkLlejm3263KVOX1dXV1qSDRZrOhpKQEQgjs2rULFRUVaG5uRk1NjepMCnw5cy+7gc3evXvVOjo5UkKS8weB3GBOHjuVSqlrzC43lU1f5HkTiUTBzJwsgZWlndmyZx32fo9cQ5ktFoupIDs7AO5LdjnqYOGaQSIiIiIabEV3E+1LIpFQZYxHGtmdasGCBTmdRHtbv349XnnlFQA94zUuvfTSQ3SF+yZLNQGojqTZoyBkh1LZ4VMGrrquw2q1qixhduZQdjeVQZPM/gkh4PF4VIAog9R0Oq3GcMgB8UBPRlI2hrHb7ep4chSGHG/hdDqRSqVgsVjU6IhCmUG5djJbKpVCWVkZ7HZ7XpdOOY9QHlfu7/F4CgaE2aMsDha5NpXdRImIiPqP3USJBt6AtU08GppznHrqqfjrX/8K4Mu1dBUVFaipqcFnn32GTz/9VO07ffp0nHTSSWhsbITX60VLSwsMBgPKysoQCATQ1dUFk8mE6upqBINBCCEwduxYbNmyBXv27IHD4cDJJ5+MPXv2IBqNIhqNwuVywe/3w+fzqVLSZDKJ7du3w+12w2KxoKSkBK2traioqEAikcCQIUMQCASwZ88eJJNJCCHQ3d2NU045BbFYLKfUc9OmTfD7/bDb7Rg+fDiam5vhcrkQDAbhcrkghEAgEEB7e7sKxtLpNNxuN9xuNyKRiBpfEYvF1FB5v9+v1i92dHTA4XDkdQDt7OyE3+9Xv0ejURV4ZzfbkQGSXIfY34Cp0PgIuV0Ooj9c9F4bSUREREQ0GIouEz2a6bqufqTKykq0tbUhEomoYDAQCGDy5MmqA6fFYlGlpFarVc0GlDMKo9EoysrK1DHj8bgawWA2m1VWL51OI5VKoaKiArFYDFarFc3NzSgrK1NBWDKZRHl5OWKxGFKpFEpLS9WMQJPJlPcvZYUCKl3X0dHRoa5Blo7KtYOZTAYWiwXxeBwWi0Vl+eS6PNnwRgY02Rk8md3bl+yATZaj9u72KTOJxejPuQ8Xg12mSkREREQ0qMFgOp3GPffcg/r6ejgcDowYMQIPPvhgTjAGAJs3b8Zll10Gn88Hj8eDU089FU1NTer1ZDKJGTNmoKysDC6XC5dddpka9VCM3ueVZYUGgwEffvih2n7iiSfCaDQiHo8jEAioIeuy+UssFoMQAqWlpSpTlr12T5Y6ptNp1fhFCIFwOIzy8nK1Pk82V4lGo6pZiwzswuEwKisrYbfb0dzcjKqqKgC5w8wTiUROyaMMUOW1Zq8JTCaTqjunJJvjyGuRawTlWApZhprdOVQIsc+ATK41LBQM9d5WbMB0JAWDRERERESDbVCDwYcffhiLFy/G008/jc2bN+ORRx7Bo48+ikWLFql9tm3bhmnTpmHcuHFYsWIFNmzYgHvvvTcnazRr1iy8/PLLeOGFF7Bq1SpEIhFccsklqlFKf8mB55Jcf5dKpfD+++8D6AlQTj31VNjtdjV3LxaLAejJsAUCAXR2dsLpdKoRDCUlJbBYLGhtbVVNYQCgvb1dZQxlsxeZKcvOxkWjUXR2dmLkyJFqmwx6uru71XgKq9WKvXv3YsiQIWp9n8w8tra2wmazweVyIZFI5GQQ0+m0ClDl8HZ5HrneTo5okN+p0WhEd3e3mpMIIGfWYDYZAMpjZgeoBztDlkqlDskQeSIiIiKiI81+PyVrmobt27dj5MiR+/2w/d5772H69OlqPMXw4cPxm9/8JicL96Mf/QgXXXQRHnnkEbVtxIgR6s/BYBDPPPMMli5dinPPPRcAsGzZMtTW1uLNN9/EBRdc0O/r6V2WKAOYrVu3oqOjAwAwevRo+P1++P1+xONx2Gw27Nq1CwaDQZU7yqCuubkZ5eXlCAaDmDRpkuowmt0UxWQyIRQKqZJS2clUNmeR5Z+y+YrD4UA4HEYymVSLqGVAlkqlVGYuO0OoaRqCwSDKysoQDAZVMGowGNTAdymZTMLpdKpgUpaOyjWNMjgdOnQouru7czp/apqWl12U12W1WvOau8RisZzy2f0lg9ZCoy3kDEMiIiIiIspVdGYwFovhxhtvhNPpxMSJE1W55syZM/HQQw8Vdaxp06bhrbfewtatWwEAGzZswKpVq9Qge13X8cc//hFjxozBBRdcgIqKCnzta19THT0BYM2aNUilUjj//PPVtpqaGkyaNAmrV68ueN5kMolQKJTzAyAn6wX0rOfr6OjICU6nTJmimrtkMhlEIhFVTllWVoZMJqMGzcs1ebL8NBqNqsBZjlaoqqpSjV5kB1C5f2NjIwKBADRNg8/nUxk8ac+ePaitrYWmaaqTpizBzN5P13UkEgk116+0tFSVkMqZfTJjJ9cvhsPhnMAue5C7/I5kh1KZ6dvXgHdZEps9MkJed++GtnLGYX/JDG2hmYRcm0dEREREVFjRweDdd9+NDRs2YMWKFTmZtHPPPRcvvvhiUceaO3currnmGowbNw4WiwWTJ0/GrFmzcM011wCAatzy0EMP4cILL8Qbb7yBK664AldeeSVWrlwJAGhtbYXValWBl1RZWYnW1taC512wYAF8Pp/6qa2tBQC0tLQA+DJ4czqd6OrqwgcffACgJ3M4YcIEmM1mtLW1qREKcnyDLB3VNE0FRh6PR2X/UqmUCvpkVtHtdqu1gXLQent7O6qrqxEKhWA2m/M6Ye7duxdOpxNWq1UNq5dr93RdV9cjS0llgxhd1+F2u3NGSchAVtd1tXbRZrMhEokULPmUnUclGWzta0JJOp1GJpPJy7z2HjIv7WuAfTGSyWSfwSkRERER0bGu6GDwlVdewdNPP41p06blZF0mTJiAbdu2FXWsF198EcuWLcPzzz+PtWvX4rnnnsNjjz2G5557DsCXQdn06dNxxx134IQTTsC8efNwySWXYPHixfs89r6yQnfffTeCwaD62blzJwDkzNkDALfbjXXr1qkRCMcffzyMRiMsFosaHeFwONTMPZPJhJaWFtjtdvh8PhWMyE6g2WWkuq6jpKQE3d3dKhso1+bJ1yKRCHw+H6xWK5LJJFKplBrx4HA4EAgEcj5XS0sLKioq4HK58gbOyzmA2RKJBMxms2q8IgfTG41GlbXr/V0Gg0GUlpbmNduJRCIFS0T7GhQvM4VS9p+LHX0pm9v0NlBB5UDjwHkiIiIiOhwUHQzu3bsXFRUVeduj0WjRJXlz5szBvHnzcPXVV6OhoQHf/e53cccdd2DBggUAgLKyMpjNZkyYMCHnfePHj1flqVVVVdA0DV1dXTn7tLW1obKysuB5bTYbvF5vzg/QUwKbXQpptVpVVhDo6SKq6zoCgYBq3CLLL4GeYDIUCmHUqFEqEOnq6oLP58NHH32EUaNGIZVKIRqNqnl/q1atgs/ng67r8Hq9atg70BOslZaWwmw2IxgMwul0wuv1Ih6Pq7V2yWRSDZGXw9t7W7duHUaNGqXmFsqgsKurCy6XS5Vl9hWkZHcllYFvKBSC3W5XAaPMevYWiURgs9ny1pVmr108UL2b0kj7M57iYMsew0FERERENJiK7vxy8skn449//CNmzJgB4MuMzi9/+UucdtppRR0rFovlZOKAnoYqMutktVpx8sknY8uWLTn7bN26FcOGDQPQs4bPYrFg+fLluOqqqwD0ZMg2btyY03SmP5qamnKCh/fffx9ffPEFAMDv96OiogKZTAYbNmwo+P41a9YAAJYvX17w9X1lTuV5evvlL3+p/vzee++pPy9ZsiRnP1n6uWHDBrWeT96bdDqNzz//XDWYyS4LlVlEg8Gg1vTJMtO///3v6rjy9VQqhfXr1yOTyagyWQCqXLZQMyGz2Zw38iGTyaC6uhq7du3KC5AsFkve34t9MZvNKnvbWyKR6PdxDgVd1wtmUImIiIiIDrWig8EFCxbgwgsvxKZNm5BOp/Hzn/8cn3zyCd577z21jq+/Lr30UsyfPx91dXWYOHEi1q1bh4ULF+KGG25Q+8yZMwff+ta38PWvfx1nn302Xn/9dbz22mtYsWIFAMDn8+HGG2/E7NmzEQgE4Pf7cdddd6GhoUF1F+0v2dFTNpLJDvrOOussWCwWGAwGVcI5ZMgQmEwmbNu2DS6XCxMmTEBHRwdOOOEEfPjhh6itrUVnZycmTZqETz75BKNHj8bq1atht9sxefJktUbQYDBg06ZNGDduHEpKShAOh5FIJDBy5EisWbMGEyZMwJo1a3DCCSeoLJjFYlEdR4GeLJjRaITNZoPf71eD3bu7uwH0BCF79uyB3+9HZWUlYrEY4vE4nE4n9u7dC6vVioqKCiSTSQghVBDl9/vR2dmJ0tJS1WgnuzNo7+6ghTKTvbuIFvs6ERERERENvKLLRKdOnYp3330XsVgMI0eOxBtvvIHKykq89957mDJlSlHHWrRoEf7pn/4Jt956K8aPH4+77roLN910E37yk5+ofa644gosXrwYjzzyCBoaGvBf//Vf+O1vf4tp06apfZ544glcfvnluOqqq3D66afD6XTitddeK3q9mMx2yWzY+vXrAfRkP4877jiYTCZkMhkEg0FUV1cjkUjA4/GoBjDZ6+lMJpNakyeEgNVqVUPZrVarGichyxszmYxqOiMbqsjuoO3t7arRTCwWQ0lJidpXrjfUNK1gIBYKhVBSUqJKQM1mc14mTq4ZlJm/7CyeHHORTqcBQL0ve9bhgcguQSUiIiIiokNnvwYENjQ0qCYvB8Lj8eDJJ5/Ek08+uc/9brjhhpxsYW92ux2LFi3KGVa/vzRNAwA0NzerrNqECRNgt9vh8Xiwd+9eGAwG+P1+7N69W2XQ5ID3mpoahMNh2Gw2GI1GpNNptLW1oby8XJVjypEMXq8XsVhMlWGm02l0dHSgpKRElXEajUbs3r0bY8eOVQ1l5Pk0TYPRaER7ezsqKyvVfEGps7MTJSUleZ+xr7WFyWQSJpNJNbJxOByqEU3vALL3qIn9JYQoqiSUiIiIiIgGxn49zeu6js8//xxtbW15XSW//vWvD8iFDQYZlAHAjh071PYpU6aoIA7oCabKy8uxc+dOdHV1qfV02YGU2WxWnTojkQhGjhyJLVu2qHV5shzV4XCgq6tLlXyGw2EMGTIEe/fuVefKHvre+3plhi6dTsPtducEg9FoVI3NAHI7dsrun6lUCslkUg2Ul2smM5mMyvwlk0k1i9BgMCAUCnGQOxERERHREa7oYPD999/Htddei8bGxrzuk3LW3ZEqexSB7FYKAKNHj4YQQnUbzc5mRSIRWK1W1Y3TZDIhGo3CZrOpLKOu62rAO9CTyTSZTGrGXywWU+eV5ykrK0MwGFTZxc7OTowaNUoFiVJnZ6caPJ+dYWtvb1cdR6Xs+yUDvHA4DIPBALvdjmg0WjDIk1lA+XmSyWRexlEGrUREREREdGQouj7v5ptvxkknnYSNGzeis7MTXV1d6qezs/NgXOMhI4MpXddVMOj3+1FaWqqyYul0Ws0WlD9Az7iKdDoNn8+H9vZ22Gw2ZDIZuN1uFejJjpe9Sy+7urrUmAXZnMXj8SAUCqnj2+12tLe3o7y8HMCXQSMAlTGUwV4sFoMQIqcUVM4m7C0cDucMvpdkExld12G323PW9hUaIyHXMBIRERER0ZGh6FTOZ599hv/93//FqFGjDsb1DCqz2YxMJoO2tjYVuB133HGqhFMGi7JpTCqVUusAw+EwxowZkzPKIJ1Oo7S0FNFoFKlUCiaTCclkEk6nExaLRTWskaWjyWQSXq9XZR6j0Sg8Hg9aW1sxbNgwNZg+nU6rIfR+v1+Vd8p5gYVmQabT6YKNWmR5aV9NZYAvx1bIoLbQPMn9GaSeTCYZQBIRERERDZKiM4Nf+9rX8Pnnnx+MazlsZJeITpgwQXXSlOTQdaAnmJJlonKUgyzttFqt2LNnD+rr65FOp5FIJJDJZOD1ehEIBFRQJYRQw+OzZQ+gl+sSJTm0XXYUzW5Mo+t6wSxgLBaD3W7PKSeNxWJwOBx5TVx0XUcymVQlpNL+BH19yQ4wiYiIiIjo0OpXZvCjjz5Sf54xYwZmz56N1tZWNDQ05I0XOO644wb2Cg8hGZBlB4MjR47MG2judDoRj8chhIAQQo1lMJvNiEQiMBqNOd02rVarmh0o1xK63W7Y7XZkMhnouo54PA6v1wu3263el0qlVGMauSZRkqWkvQPVQs1dZIAoB8pHo1GUlpbmvG42m3OCPlk22jsIld1O+2ugRlAQEREREdHA6lcweMIJJ+R00wSQM+pBvnakN5BJp9MwmUwqGLRaraisrFTjJFKpFAAgEAhg165dOWWUco1eU1MTrFYrLBYLjEajyrjFYjE1C1BmxOTawUwmo0pFjUajGh8hxztkMhkIIVSQ19raCpfLpUpGAaggz2Qy5WUFZcCZvYZQlqXKpjfyGFIsFoPP50M8Hs8pkZXX1F+ZTIaloEREREREh6F+BYPbt28/2Ndx2AgGg6oEdMiQIWhsbFRBoFwPuGPHDjUOQnb6LCkpwYYNG9DS0qI6cwI94x22bNmC1tZWJJNJ6LqOrq4ufPLJJ8hkMuju7kZzczMcDgdisRiam5vhcrlgNBrR2NgIp9MJTdOwdu1a2Gw2GAwG1Tims7NTrROUAbnFYsHu3btzAjCZlTSbzWhpaVFD7ROJBDwej2r8k50ZTCaT6vhCCNUNNTvLJ4RAPB5XgSwRERERER05+hUMDhs2TP35nXfewdSpU/PWt6XTaaxevTpn3yNRdoloXV2dCgSz7d69O+f3TCaDjo4OdHR0FDzmnj17cn5vbW3F1q1bVXYOALq7u2EymdSsQnnczs5OmEwmRCIRldlLJBIwmUxob29XzWZkaapsSJO9Fi+VSsFoNMJqtcJkMkEIAYfDAU3TEIvF1LiK3uWc3d3d6vpk8xkZ/Nrt9pz1hDIw7E3TtIKZRDmLkYiIiIiIBkfRT+Nnn302Wlpa8rpVBoNBnH322Ud0mSiQHwwGAgGEQiGYzWYkEgn4fD5UVVWp7Ft3dzfsdjvOP/98BAIBbNq0SXX0TKVSGDlyJEaMGIHVq1cjk8kgkUigpqYGY8eOhd1ux+uvv476+nqEw2GUlpYiEAigrq4O3d3dcLvd+OMf/4jRo0fD6/Vi6NChyGQy2LVrF1wulwrs7HY7nE6nyhgCyCkJBYBt27ahpKQEBoMBVqtVjbIAoIK4QkGbXBuZfbx4PN7vUtG+9pMjOoiIiIiIaHAUXdsn1wb21tHRoebdHckaGxsB9KyfGzp0KM444wy1pg8ATCYT2tra4Ha7c2bvmUwmbNmyBcOHDwcA1TnU7/cjGo2qrJtsCuN2u7Fz5074fD41nkLTNNWcRXYSles0ZdlnNBqF3W6HruvqmgwGA+LxuMrWFSLXc8ph85LMGvZ3XER2h1MiIiIiIjpy9TszeOWVVwLoCSq+973v5cysy2Qy+OijjzB16tSBv8JDKB6Pq5LJ6upq2Gw2VcqYTCZzSjHtdjvC4TA0TYPf71elnEOGDMGmTZvgcDjU2rzm5mYEAgE0NTUhkUiowfEywxqLxRCJRFBZWQmLxYJEIgGr1YpQKASj0Yh4PK66f8rh7vF4HDabTQWZslxU07Q+g/JCJZuyAU32esHs/bOb4MjzF5pXWAzOFyQiIiIiGnz9TvH4fD74fD4IIeDxeNTvsmzy+9//PpYtW3Ywr/Wga25uVn+uq6uD2WxGMBhU2VCr1aoCqrKyMiSTSaTTaQQCAXR2dqpun5FIBA6HQ3X7TCQScLlcCIVCsNvtagRFLBZDTU0N0uk0MpkM3G636grq8XgQi8VgMBhU0CczkbK7ZyKRUGv/ZHAlR0L0puv6Pj97ocygruvIZDJ5TWP6yj72lwyoiYiIiIho8PQ7M/jss88CAIYPH4677rrrqCgJ7W3nzp3qz3V1dTCZTEilUirrJstFnU6nGglhs9lgNBoRDocxduxYFTC2t7dj9OjR6OzshM1mg8PhQDKZhN/vB9BTjlpWVgaz2axKNVOplBpJAfRk5tLptPqu5UgKGfzJuYgyAMwuHe0tHo+joqKiYEOcfTnQwK+37DEVREREREQ0eIp+Kr///vuPykAQAHbt2qX+LINBmVGTa/ocDgdMJhPi8bjKcAkhkEwmVVMXGXB5PB5omqY6hKbTaQgh4HK50NHRgaqqKgBfjqzweDwIh8PweDwAerJwuq6rbKAMzIQQKgDMXsMng8lC0uk0YrGYOnZ/FVo3eCCSyWTBklQiIiIiIjq0mKLJIkdG+P1+uN1u2Gw2hMPhnH3Kyspgs9nUWjugJ8ApLS2F0+mEEEKVVgaDQTXs3ePxqAHskUgEZrMZDodDBZlGoxEulwuZTEaVeaZSKaRSKXi9XlUSKsn9IpGIagjzVeWX6XS6qLV6Ax0IEhERERHR4YPBYBYZ/NTV1QHo6RDa2toKADkZNZmpAwCj0Qhd19V6v1gshkwmoxrIeDweJBIJuN1upNNpWCwW7Ny5E1VVVfD5fGrtn81mUw1nJDl7UGZisxuvCCFUhrCvbGDvz1aoPHNfpaW9A9ADpWlav66ViIiIiIgOPgaDBchgUDaQAb4MFM1mM4xGo8oMWq1WNcQd6MkuylENsoxTlohmMhmUlpYimUzC6XSqMQ/JZBI+n081mpHa2trg9/tV+adcMwj0BFZA33P8sslsZaEuoPsq2xyIZjHZ+mpuQ0REREREh16/gkG/34/29nYAwA033JBXOnm0GTZsGICeQCuTyQCA+swyGOzo6AAAlRWUpZrd3d1IJBKqOYwQAjabTTWiaW9vh9frVWsNzWYzkskkHA5H3qD4eDyOkpKSvOyfzCQaDIacYK2vwE0GrsV08BzoLN5AB5ZERERERHRg+hUMapqGUCgEAHjuuedUw5OjkcvlUh0/A4EANE1TA9tNJpP66erqgsVigclkgtPpVIGOzNh5vV61ri97REUsFkNVVZUaQyHf4/V6c4bBA1AZw97kNfU3WEulUojH4znn/CrZaxcHQu8SWCIiIiIiGlz9eto/7bTTcPnll2PKlCkQQmDmzJl9lif+6le/GtALPNTq6upUYDd8+HB8+OGHAACLxQKDwYB0Og2Px4N0Oq2G0md/F83NzbBYLPB6vejq6lIlnps3b1aBIdAz5D0ej0PXddUxtHdwl06nYTKZCg6KNxqNecFaXwPno9Eo7HZ7USMdes8XPFDMDBIRERERHV76FR0sW7YMF110ESKRCAwGA4LBILq6ugr+FCOdTuOee+5BfX09HA4HRowYgQcffLDPAek33XQTDAYDnnzyyZztyWQSM2bMQFlZGVwuFy677LKcMRHFkOsFDQYDAoEAgJ51gQaDAUajEZqmoby8HABUZtBisag1hZ2dnfB4PKqEU5aV7ty5EwaDAbW1tTldOsPhcJ9ZPtl91GAw5LwnkUjklZQCfa/JC4fDBfffF03TCq4x3B/7GnlBRERERESDo1+ZwcrKSjz00EMAgPr6eixdulQFSgfi4YcfxuLFi/Hcc89h4sSJ+PDDD/HP//zP8Pl8uP3223P2feWVV/C3v/0NNTU1eceZNWsWXnvtNbzwwgsIBAKYPXs2LrnkEqxZs6aodXLAl8Gg0WhUnTttNhvi8bgqB5Wf3eVyIZ1Ow+fzqUYskUgEEyZMUNk4ef5wOAyr1QqPx5PTmfTTTz9VQ+l7y2Qyat/sMkvZpTQWi/Ur2xYOh1FSUlLw+IdiAHwqlSo6GCUiIiIiooOr6EVh27dvH7CTv/fee5g+fTouvvhiAD1lmb/5zW9UaabU3NyM2267DX/5y1/UvlIwGMQzzzyDpUuX4txzzwXQk8msra3Fm2++iQsuuKDf12M2m9Ug+Ewmg7feegtATyOXdDqNUCiESCSCZ555BgCwZ88eGAwGvP766zAajbBYLIhGo1i/fj10XVeNW4xGowoG//CHP6ggUtd1RKNROJ1OvP322zCbzXnzC3fs2AGDwYBkMqkydVarFaFQCJqmweFwIB6PA+gJEuWfs3V2dmLo0KF5r6VSqYJlpcDAzhhkeSgRERER0eFnvzqErFy5Eo899hg2b94Mg8GA8ePHY86cOTjjjDOKOs60adOwePFibN26FWPGjMGGDRuwatWqnDJQXdfx3e9+F3PmzMHEiRPzjrFmzRqkUimcf/75altNTQ0mTZqE1atXFwwGk8kkksmk+l02xxkyZEhOJnHTpk0AkBOg6bqO7u5u9bsQIq+7al/lspqm5TTjkcfTNA1btmxR22RZaDqdxsaNGwHkDpRPJBJq/+zrFUIUbNLS3d2NYcOGqTEZ0r6auvTuVJp9jmKkUqmcTCgRERERER0eig4Gly1bhn/+53/GlVdeiZkzZ0IIgdWrV+Occ87BkiVLcO211/b7WHPnzkUwGMS4ceNgMpmQyWQwf/58XHPNNWqfhx9+GGazGTNnzix4jNbWVlitVpSWluZsr6ysVAPje1uwYAF+/OMf522vra1Vf7bZbCpg9Hg8SKVScLvdqK6uRiAQwIoVKzBq1ChYrVYcf/zxKC0txbZt27B27Vr8wz/8A5qbm2Gz2VBSUoKqqiq88847qK2thc1mw9ixY1VA2NbWBqPRiBEjRqhz67qOxsZGuN1utT4xHo/D4XAgnU5j7969quxzf4fCJ5PJPoPHgTSQQ+uJiIiIiGjgFB0Mzp8/H4888gjuuOMOte3222/HwoUL8ZOf/KSoYPDFF1/EsmXL8Pzzz2PixIlYv349Zs2ahZqaGlx//fVYs2YNfv7zn2Pt2rVFlxruq3vl3XffjTvvvFP9HgqFUFtbi6FDh6ptci1dTU0NotGoGtyeyWTQ2NgIh8MBTdPUEPnPP/8c4XAYlZWVan2hruuw2+3Yu3cvPB4PEomECloTiQSMRiNcLldO+aau69izZw/KysqQSqXyrj0UChUcN1GMWCwGi8XCpi5ERERERMeworuHfPHFF7j00kvztl922WVFryecM2cO5s2bh6uvvhoNDQ347ne/izvuuAMLFiwAAPz1r39FW1sb6urq1FiGxsZGzJ49G8OHDwcAVFVVQdO0vNLMtrY2VFZWFjyvzWaD1+vN+QGQ05xGDps3Go0wmUxIJpOwWq0wmUxob2+Hz+eDyWRCKpVCZ2cnhg0bhnA4DL/fr2YR6rqu9i8tLUUmk4HT6VRBnuwiKoNWGQh6vd6ctXxyxIS0v2vw5BpFu93OQJCIiIiI6BhXdDBYW1urGqtke+utt3LKLPsjFovldbOUQRQAfPe738VHH32E9evXq5+amhrMmTMHf/nLXwAAU6ZMgcViwfLly9UxWlpasHHjRkydOrWo65HdQ4Ev1wlGo1EVGPp8PiQSCZjNZjidTkSjURgMBpSVlaGtrQ2ZTAZ1dXXQdV0dKxwOqwyhwWCAzWbLGQERi8VUY5i9e/eisrJSjbGQUqkUrFZrnyM3+kPTNCSTSbhcrkPSQZSIiIiIiA5vRZeJzp49GzNnzsT69esxdepUGAwGrFq1CkuWLMHPf/7zoo516aWXYv78+airq8PEiROxbt06LFy4EDfccAMAIBAI5I2wsFgsqKqqwtixYwH0BGg33ngjZs+ejUAgAL/fj7vuugsNDQ2qu+j+sFgsSCaT6O7uVk1Tdu3apWYNyoBN13VEIhF0dnZC13V88cUXsNvtak1gc3MzrFYrNE1De3s7vF4vNE1TXUTD4TBGjhyJzz77DKWlpdizZw8sFgsSiQQ0TUM8HldlpNFotOhRGcCXJalcv0dERERERFLRweAtt9yCqqoqPP7443jppZcAAOPHj8eLL76I6dOnF3WsRYsW4d5778Wtt96KtrY21NTU4KabbsJ9991X1HGeeOIJmM1mXHXVVYjH46qZzf4ETpLX68XevXvV2j8hBLxeL6qrq9HW1oZAIIDOzk4EAgGk02nU19dj27ZtcLvdGDNmDD799FN4PB50d3ejtLQULpcLDocDEyZMQDweR3NzM6qqqmA2m9HW1ob6+nqYzWbEYjE1k082jQF6GrHEYjG15rC/gaGmaTkzE4mIiIiIiADAIAZyoNwRSjZlmTdvXl53TVmemU6nYbPZUF1djdbWVpSUlCCZTKrMpcfjQUdHB+rr61FZWYkvvvgCTqcTsVgMdrsd1dXV6OzsxIQJE5BIJNDa2gqfz4eSkhI1jD6RSMBisaggTwaD8XgcdrtdBZ9Az/iKkpKSfa4fzGQySCaTHPhORERERzz5vBYMBlW/ByI6MFw89hUCgYBq5uLz+TB16lQ4nU7U19ejvLwcJSUlGDduHBoaGlBdXY1JkyahoqICdrsdbrcbVVVVcDgcGD58OPx+P/x+vwoCS0pKEAgE1PpB2XCmEBkQSvvqlpr9HgaCRERERERUCIPBfTAajUin02rNndFoxNatW+FyuVQn0EwmoxrhGAwGOBwOlZFLJBIwmUw5WT+gJ6vX+1+0EomEaiSTLZlMwmazIZPJqKCxP+SaRCIiIiIiokIYDO6D3W6HpmnIZDKwWCwQQqj1fUIIRCIRWCwWeDwe7Nq1CzU1NdA0TQWH8XgcFosFJSUlOR1E0+l0XjlqX1lBXddhMBigaVrBYLEQOQaDXUOJiIiIiKgvjBYKkEGZxWJRwZjRaFTNYuRaQKPRiEwmA7fbjXg8jqqqKui6jmAwCCEETCYTDAaDygzKTF0qlVJZQl3X1eiIviQSiZzgMZVK9ZklTKfTEEJwjiAREREREe3TfgeDmqZhy5Ytah7f0cjtdqsxEplMBiaTCWazGWazWc0EzGQycDgcKrOXSCSQSqXUej2Px6PKPC0WC0KhECwWi2pIo2maOm5feq8PTKfTBYM9IQSSyWRe1pGIiIiIiKi3okdLxGIxzJgxA8899xwAYOvWrRgxYgRmzpyJmpoazJs3b8Av8lCTQ+ZbWlpyBr03NjYiHo8jlUqhu7sbdrsdqVQK69evR2trKwwGg2r00tHRAZPJhEgkgrKyMmiaBiGEygS2traitLRUdQWVswQlIQQSiQSEEMhkMrDZbGqf3vtmX7fb7T5I3woRERERER1Nig4G7777bmzYsAErVqzAhRdeqLafe+65uP/++4+KYFDyeDxwu91qEPzUqVOxbt06TJkyBZ9++imqqqpgMpkwYcIEfPDBBxgzZgwcDgcSiQT27t2L8ePHw2QyYeTIkchkMgiHw4jFYjCbzTCZTHC73XC5XAXXAsbjcfh8PoRCIdjtdtXRFPhyzSFLQYmIiIiIaH8VXSb6yiuv4Omnn8a0adNyShcnTJiAbdu2DejFDTY5AzCTycBoNKKjowNCCAghUFZWhkgkAofDoRq8VFZWwmQyYefOnXA4HDkBXDKZVKWiZrNZZQr7agojhICmabBarXkjJPoqEyUiIiIiIuqvooPBvXv3oqKiIm97NBr9yrl3Rxq73a4ashiNRnR1daGsrExl9jKZDAwGA7Zs2YKamhoAPUPq5XeU3UFU0zSk02m1DhFAv7qDFvpOhRAD+CmJiIiIiOhYVHQwePLJJ+OPf/yj+l0GK7/85S9x2mmnDdyVDTIZqMkg12g0IpVKIRAIAOjJzhmNRsRiMQCA3+9X4yaEEPB4PNA0Ta3h03VdZQQB7LN7qCTnG/ZnXyIiIiIiomIUvWZwwYIFuPDCC7Fp0yak02n8/Oc/xyeffIL33nsPK1euPBjXOCgMBgN8Ph9aW1sB9ARv6XRalYXGYjGk02mUl5ejra1NjY3YvXs3PB4PhBBqDAXQ09xFdgWVA+v7kk6nYTKZoOu6CiJ7XxsREREREdGBKDoYnDp1Kt5991089thjGDlyJN544w2ceOKJeO+999DQ0HAwrnFQJBIJfPTRR0gkEgCA9vZ2GAwGBINBuFwuhEIhmM1mJJNJdHd3o6OjQ71eUlKCaDQKu92OcDgMq9WKjo4ODB8+HJqmIZFIoLKyEpFIpM9z2+121cm00JpBIiIiIiKiA1F0MAgADQ0NarTE0crr9aKiogIGgwHbtm1DWVkZAGDEiBGYOnUq/v73v6OzsxNf+9rX8PHHH+Mb3/gGAKCzsxNNTU1wOp2wWq0YM2YMTCYTOjs74ff70dHRgUQiodYYFhKLxVRGMRaLweFwqNd0XWdmkIiIiIiIDljRawa/8Y1v4Mc//nHe9q6uLhUQHS2i0SjKy8thMBhUB0+bzYaNGzdi7NixqjuowWBAKpVCKpWC2WxW8wQtFgtMJlPOMZPJZL8axwA9GcLe+8qsIRERERER0YEoOhhcsWIFnn76aVx++eWIRqNqu6ZpR9WaQbvdDpPJhFQqhUwmozqIWiwWhMNhlJSUwGAwoKmpCYFAQG2X4yRMJpPK4MVisZwArr+ZPV3X84JJIiIiIiKigVB0MAgAb775JlpbW3Hqqadix44dA3xJhwchBEpKSpDJZAD0BHButxsej0ftYzQa1XB4Sa4JNBqNat9EIqHKPuU6wL7sK3PIkRJERERERDRQ9isYrK6uxsqVK3Hcccfh5JNPxooVKwb4sgafwWCA3W5XTV7kzMCOjg64XC7s2bMHyWQSkUgEmqahpaUF8XhcdRmNRqMwm82Ix+NIJBLqtXg8jmQyiXg8XvAnGo1C0zTE43EYjbm3Jx6P56wfJCIiIiIi2l9FN5CRJY42mw3//d//jZ/+9Ke48MILMXfu3AG/uMFitVphsVhQXl6OPXv2AOj53E6nE2azGT6fDyaTCR6PB2azGdXV1Ugmk/D7/TAYDDCZTLDZbLDb7ep/HQ4HEokEhBCoqqrKC/QkIcQ+Az42jyEiIiIiooFQdDDYu1Txnnvuwfjx43H99dcP2EUNNpkV9Pl8iEQiMJvN0HUd8XgcTqcTDocDRqNRbZc/drsdyWQS6XQaVqs1Z9QE0PPdmUymPgPBfZEzComIiIiIiAZC0cHg9u3b1ZgF6R//8R8xduxYrFmzZsAubDDpug6XywWPxwNN09RcP4vFAo/HA6vVimQyqdb/yWH0qVRKBYGyrFQIoYK/dDq9z4BuXwEfu4gSEREREdFAKjoYHDZsWMHtkyZNwqRJkw74gg4HJpMJPp9PBXMmk0mVfgI9JbLd3d2IRqOoqamBrutwu92q+6ecBWixWHKOq2la3rZs+1oTyMwgERERERENpH7VK1555ZUIhULqz/v6KUY6ncY999yD+vp6OBwOjBgxAg8++KDKuKVSKcydOxcNDQ1wuVyoqanBddddh927d+ccJ5lMYsaMGSgrK4PL5cJll12GXbt2FXUt2eQ8QVkGmslkVCAohIDb7UY4HIYQAhUVFXlln8lkEk6nE4lEAlarNee1rwroGPAREREREdGh0K/MoM/nU0FK9hiFA/Xwww9j8eLFeO655zBx4kR8+OGH+Od//mf4fD7cfvvtiMViWLt2Le69914cf/zx6OrqwqxZs3DZZZfhww8/VMeZNWsWXnvtNbzwwgsIBAKYPXs2LrnkEqxZs2a/5vTJMRBerxepVEptd7lcsFqtcDgciMfjMBgMMBgMKlhMJpNIpVJIJBIoLS1FLBaD3+9X74/H43nBYX/E43GWiBIRERER0YDqVzD47LPPFvzzgXrvvfcwffp0XHzxxQCA4cOH4ze/+Y0K9Hw+H5YvX57znkWLFuGUU05BU1MT6urqEAwG8cwzz2Dp0qU499xzAQDLli1DbW0t3nzzTVxwwQVFX5ccGG82m9WcQbvdDl3X4fV6YTAY1OiIZDKJ6upqlR1sb2+H1WqF3W5HIpHIOW44HMbYsWOLvp7sdYdEREREREQDoegIQ87LkxobG/Hkk0/ijTfeKPrk06ZNw1tvvYWtW7cCADZs2IBVq1bhoosu6vM9wWAQBoNBdehcs2YNUqkUzj//fLVPTU0NJk2ahNWrVxc8RjKZRCgUyvmRZKavsrIy5z1utxsAVIZOru8TQsBisaj1gJ2dnX1m8fYV1KVSqX2uJyQiIiIiIhpIRTeQmT59Oq688krcfPPN6O7uximnnAKr1Yr29nYsXLgQt9xyS7+PNXfuXASDQYwbNw4mkwmZTAbz58/HNddcU3D/RCKBefPm4dprr4XX6wUAtLa2wmq1orS0NGffyspKtLa2FjzOggUL8OMf/7jga3a7HSaTKa+Ri9frVV1GgZ7ATo6TAIBMJgOr1QpN02Cz2RAOh/OuaV9SqZQqT+39mVkiSkREREREA63ozODatWtxxhlnAAD+93//F1VVVWhsbMSvf/1rPPXUU0Ud68UXX8SyZcvw/PPPY+3atXjuuefw2GOP4bnnnsvbN5VK4eqrr4au6/jFL37xlcfeV/fNu+++G8FgUP3s3LlTvZbJZFBeXp73HqvVCpfLpY4Zi8VgMBjUOkIpHo+rLqT7sz6wN13XWSJKREREREQDrujMYCwWg8fjAQC88cYbuPLKK2E0GnHqqaeisbGxqGPNmTMH8+bNw9VXXw0AaGhoQGNjIxYsWJAzxD6VSuGqq67C9u3b8X//938qKwgAVVVV0DQNXV1dOZm4trY2TJ06teB5bTab6g7am9lszisRlcFd9nljsRjKy8vhcDhymtREIhGUlpbmBYKZTIadQomIiIiI6LBRdMpp1KhReOWVV7Bz50785S9/UWv12tracoKl/ojFYnlZLzmnT5KB4GeffYY333wTgUAgZ/8pU6bAYrHkNJppaWnBxo0b+wwG90WePzuY83g80HU959wy89i7W2koFEIgECi4fV9rCQtJJpN9Bq1EREREREQHoujM4H333Ydrr70Wd9xxB8455xycdtppAHqyhJMnTy7qWJdeeinmz5+Puro6TJw4EevWrcPChQtxww03AOiZQ/hP//RPWLt2Lf7whz8gk8modYB+vx9WqxU+nw833ngjZs+ejUAgAL/fj7vuugsNDQ2qu2gxHA4HjEZjTpDqcDhgt9thsVhUkJfJZOB0OmGz2ZBOp2E293yVuq4jnU7njeAIBoMoKysreM5kMlkwUMyeb0hERERERDSQig4G/+mf/gnTpk1DS0sLjj/+eLX9nHPOwRVXXFHUsRYtWoR7770Xt956K9ra2lBTU4ObbroJ9913HwBg165dePXVVwEAJ5xwQs573377bZx11lkAgCeeeAJmsxlXXXUV4vE4zjnnHCxZsmS/Zgzquq46lWaz2+2qSQzwZTbPYrEglUqphjNCiILnzWQyKmAsdE6uCyQiIiIiokPJIPqqUTyGhEIh+Hw+zJs3r89STqfTqdYOJpNJGAwGlJWVobS0NCczmEqlMHToUNTU1OS8P5lMYsiQIQXHR8RisYKdRG02G4NEIiIiInz5vBYMBotemkREhRWdGTyamc1muFwujBo1CpqmIZVK4fPPP8e0adMwdOhQGI1GOJ1ONDY2wmazwefzYdSoUWrmoKZp+Pjjj1FbW4uKigp13FgsBiGEGkvRmxAib5QFERERERHRwcS0U5Z0Og273Q4hBAKBAIYPHw4AqqFNWVkZ2traMHHiRESj0bxsXjqdRjqdzvnXqnQ6nbcGMVsmk2H2j4iIiIiIDjlmBnvp6OhAKpUC0BPIAT0BW1NTE3bs2AEAaG9vx549ewAATU1N0HUdZrMZ0WgUJpMJ0WgUsVgMQN8loFL2IHsiIiIiIqJDhcFgFrPZjNLSUlRUVMBkMqG6uhorV65EfX09rFYrmpubMWTIELjdbnz22Wc47bTTEI/HAfSsCfR4PAgGg2oERTweh8/n269GNkRERERERAcT6xOzyEYww4YNU2WimUwG6XQaTU1NqK6uhtFoRHl5OYCeYE+uF7TZbDndQuWQeQaCRERERER0OGJmsJfOzk6sXLkSRqMRH374IUwmE3bt2oVIJAKj0Qi/34/Vq1cjFoshkUhA0zR0dXUhkUggmUyq4C+RSLD8k4iIiIiIDlsMBns5//zz8cUXX6C2thZCCHR0dAAAxowZA7fbDbPZjJqaGui6Dr/fr8pES0tLYTAYAPQEghwWT0REREREhzOWiWYxmUxqXWBlZSVKSkqwZ88eGAwGWCwWlJaWoqamBg6HAwaDAbquw2AwQAihAkFd1yGE6HPAPBERERER0eGAEUuWTCaDHTt2IBKJ4LPPPgPQ0xhm2LBhqhFMd3c37Ha76hZqt9uRSCRygkGWhxIRERER0eGOwWAWr9eLoUOHoqGhASaTCY2NjaioqIDX64XFYoHdbofNZkN3dzdqamqQTCYhhMgpESUiIiIiIjoSMBjMous6ysrKEI/Hoes6AoEAdF2H1WqFwWCApmnQNA0lJSU5g+IZCBIRERER0ZGGawZ7sdlsMBqN8Hq9sFqtAABN02AwGJBKpWA2m9X2TCaTExQSEREREREdKRjJZDGZTEin0/B6vTAajbDb7QB6gr5EIoFMJgOPxwMAEEIgmUyqfYiIiIiIiI4kDAazGI1GVFVVwWq1Ip1Ow+l0AgDC4TCEEAgEAmrfeDzOQJCIiIiIiI5YDAaz2Gw2xGIxhEIhtLW1wWKxIJFIIB6Po6KiImffVCoFi8UySFdKRERERER0YBgMZvH7/dA0DUIIWK1WmEwmOBwOlJaW5s0NzGQyau0gERERERHRkYbdRLPE43G0t7cjkUgglUqhqakJyWQSiUQCnZ2dOfuycQwRERERER3JGAxmGTp0KIYMGYKysjIkEgnYbDbU1NSgra0Nfr9/sC+PiIiIiIhowDC9lSUajaKqqgrd3d1wu92w2+0IBoOqgygREREREdHRgsFgFpfLBaPRiHQ6DaPRCKfTCSEES0KJiIiIiOiowyinF4PBAE3TYLFYYLPZOFieiIiIiIiOSoMa5aTTadxzzz2or6+Hw+HAiBEj8OCDD0LXdbWPEAIPPPAAampq4HA4cNZZZ+GTTz7JOU4ymcSMGTNQVlYGl8uFyy67DLt27Sr6eoxGI0wmEwCobqKpVIpdQ4mIiIiI6KgzqMHgww8/jMWLF+Ppp5/G5s2b8cgjj+DRRx/FokWL1D6PPPIIFi5ciKeffhoffPABqqqqcN555yEcDqt9Zs2ahZdffhkvvPACVq1ahUgkgksuuQSZTKao67FarWhvb0dpaSkMBoPanv1nIiIiIiKio4FBCCEG6+SXXHIJKisr8cwzz6ht//iP/win04mlS5dCCIGamhrMmjULc+fOBdCTBaysrMTDDz+Mm266CcFgEOXl5Vi6dCm+9a1vAQB2796N2tpa/OlPf8IFF1yQd95kMolkMql+D4VCqK2txbvvvgtd1zFlyhSYTCZYrVbs3r0bNTU1B/mbICIiIqJ9CYVC8Pl8CAaD8Hq9g305REeFQc0MTps2DW+99Ra2bt0KANiwYQNWrVqFiy66CACwfft2tLa24vzzz1fvsdlsOPPMM7F69WoAwJo1a5BKpXL2qampwaRJk9Q+vS1YsAA+n0/91NbWAgBMJhPcbrcaOk9ERERERHS0GtQ5g3PnzkUwGMS4ceNgMpmQyWQwf/58XHPNNQCA1tZWAEBlZWXO+yorK9HY2Kj2sVqtKC0tzdtHvr+3u+++G3feeaf6XWYGrVYrzGaOXiQiIiIioqPfoEY+L774IpYtW4bnn38eEydOxPr16zFr1izU1NTg+uuvV/v1XrMnhPjKdXz72sdms8Fms+Vtt1gsMJvN7B5KRERERERHvUENBufMmYN58+bh6quvBgA0NDSgsbERCxYswPXXX4+qqioAPdm/6upq9b62tjaVLayqqoKmaejq6srJDra1tWHq1KlFXU9HRwfKysoQi8UQi8Wg6zozhUREREREdFQa1BRYLBbLy8KZTCY1WqK+vh5VVVVYvny5el3TNKxcuVIFelOmTIHFYsnZp6WlBRs3biw6GDSZTKivr4ff74ff74fD4UBFRcX+fjwiIiIiIqLD1qCmvS699FLMnz8fdXV1mDhxItatW4eFCxfihhtuANBTHjpr1iz87Gc/w+jRozF69Gj87Gc/g9PpxLXXXgsA8Pl8uPHGGzF79mwEAgH4/X7cddddaGhowLnnnrvf15ZMJtlEhoiIiIiIjlqDGgwuWrQI9957L2699Va0tbWhpqYGN910E+677z61zw9/+EPE43Hceuut6Orqwte+9jW88cYb8Hg8ap8nnngCZrMZV111FeLxOM455xwsWbJEDZDvr1QqBbvdDiEEMplMwXWFRERERERER4NBnTN4uJBza1atWoXTTz8d0WgULpdrsC+LiIiIiP5/nDNINPDYNjPLkCFDoGkay0OJiIiIiOiox2Awi8vlQiqVgsViGexLISIiIiIiOqgYDGZJp9NwOp2DfRlEREREREQHHYPBLBaL5SuH2RMRERERER0NGAxm4VpBIiIiIiI6VjAYJCIiIiIiOgYxGCQiIiIiIjoGMRgkIiIiIiI6BjEYJCIiIiIiOgYxGCQiIiIiIjoGMRgkIiIiIiI6BjEYJCIiIiIiOgYxGCQiIiIiIjoGMRgkIiIiIiI6BjEYJCIiIiIiOgaZB/sCDgdCCABAKBQa5CshIiIiokLkc5p8biOiA8dgEEBHRwcAoLa2dpCvhIiIiIj2paOjAz6fb7Avg+iowGAQgN/vBwA0NTXx/7gcA0KhEGpra7Fz5054vd7Bvhw6yHi/jy2838cW3u9jSzAYRF1dnXpuI6IDx2AQgNHYs3TS5/Px/5kcQ7xeL+/3MYT3+9jC+31s4f0+tsjnNiI6cPyviYiIiIiI6BjEYJCIiIiIiOgYxGAQgM1mw/333w+bzTbYl0KHAO/3sYX3+9jC+31s4f0+tvB+Ew08g2B/XiIiIiIiomMOM4NERERERETHIAaDRERERERExyAGg0RERERERMcgBoNERERERETHoGM+GPzFL36B+vp62O12TJkyBX/9618H+5JoACxYsAAnn3wyPB4PKioqcPnll2PLli05+wgh8MADD6CmpgYOhwNnnXUWPvnkk0G6YhpICxYsgMFgwKxZs9Q23u+jS3NzM77zne8gEAjA6XTihBNOwJo1a9TrvN9Hj3Q6jXvuuQf19fVwOBwYMWIEHnzwQei6rvbh/T5yvfPOO7j00ktRU1MDg8GAV155Jef1/tzbZDKJGTNmoKysDC6XC5dddhl27dp1CD8F0ZHrmA4GX3zxRcyaNQs/+tGPsG7dOpxxxhn4h3/4BzQ1NQ32pdEBWrlyJX7wgx/g/fffx/Lly5FOp3H++ecjGo2qfR555BEsXLgQTz/9ND744ANUVVXhvPPOQzgcHsQrpwP1wQcf4D//8z9x3HHH3HKTngAAEkBJREFU5Wzn/T56dHV14fTTT4fFYsGf//xnbNq0CY8//jhKSkrUPrzfR4+HH34YixcvxtNPP43NmzfjkUcewaOPPopFixapfXi/j1zRaBTHH388nn766YKv9+fezpo1Cy+//DJeeOEFrFq1CpFIBJdccgkymcyh+hhERy5xDDvllFPEzTffnLNt3LhxYt68eYN0RXSwtLW1CQBi5cqVQgghdF0XVVVV4qGHHlL7JBIJ4fP5xOLFiwfrMukAhcNhMXr0aLF8+XJx5plnittvv10Iwft9tJk7d66YNm1an6/zfh9dLr74YnHDDTfkbLvyyivFd77zHSEE7/fRBIB4+eWX1e/9ubfd3d3CYrGIF154Qe3T3NwsjEajeP311w/ZtRMdqY7ZzKCmaVizZg3OP//8nO3nn38+Vq9ePUhXRQdLMBgEAPj9fgDA9u3b0dramnP/bTYbzjzzTN7/I9gPfvADXHzxxTj33HNztvN+H11effVVnHTSSfjmN7+JiooKTJ48Gb/85S/V67zfR5dp06bhrbfewtatWwEAGzZswKpVq3DRRRcB4P0+mvXn3q5ZswapVCpnn5qaGkyaNIn3n6gfzIN9AYOlvb0dmUwGlZWVOdsrKyvR2to6SFdFB4MQAnfeeSemTZuGSZMmAYC6x4Xuf2Nj4yG/RjpwL7zwAtauXYsPPvgg7zXe76PLF198gX//93/HnXfeiX/913/F3//+d8ycORM2mw3XXXcd7/dRZu7cuQgGgxg3bhxMJhMymQzmz5+Pa665BgD/+z6a9efetra2wmq1orS0NG8fPs8RfbVjNhiUDAZDzu9CiLxtdGS77bbb8NFHH2HVqlV5r/H+Hx127tyJ22+/HW+88Qbsdnuf+/F+Hx10XcdJJ52En/3sZwCAyZMn45NPPsG///u/47rrrlP78X4fHV588UUsW7YMzz//PCZOnIj169dj1qxZqKmpwfXXX6/24/0+eu3PveX9J+qfY7ZMtKysDCaTKe9fjdra2vL+BYqOXDNmzMCrr76Kt99+G0OHDlXbq6qqAID3/yixZs0atLW1YcqUKTCbzTCbzVi5ciWeeuopmM1mdU95v48O1dXVmDBhQs628ePHq+Zf/O/76DJnzhzMmzcPV199NRoaGvDd734Xd9xxBxYsWACA9/to1p97W1VVBU3T0NXV1ec+RNS3YzYYtFqtmDJlCpYvX56zffny5Zg6deogXRUNFCEEbrvtNvzud7/D//3f/6G+vj7n9fr6elRVVeXcf03TsHLlSt7/I9A555yDjz/+GOvXr1c/J510Er797W9j/fr1GDFiBO/3UeT000/PGxWzdetWDBs2DAD/+z7axGIxGI25jysmk0mNluD9Pnr1595OmTIFFoslZ5+WlhZs3LiR95+oPwatdc1h4IUXXhAWi0U888wzYtOmTWLWrFnC5XKJHTt2DPal0QG65ZZbhM/nEytWrBAtLS3qJxaLqX0eeugh4fP5xO9+9zvx8ccfi2uuuUZUV1eLUCg0iFdOAyW7m6gQvN9Hk7///e/CbDaL+fPni88++0z893//t3A6nWLZsmVqH97vo8f1118vhgwZIv7whz+I7du3i9/97neirKxM/PCHP1T78H4fucLhsFi3bp1Yt26dACAWLlwo1q1bJxobG4UQ/bu3N998sxg6dKh48803xdq1a8U3vvENcfzxx4t0Oj1YH4voiHFMB4NCCPFv//ZvYtiwYcJqtYoTTzxRjR6gIxuAgj/PPvus2kfXdXH//feLqqoqYbPZxNe//nXx8ccfD95F04DqHQzyfh9dXnvtNTFp0iRhs9nEuHHjxH/+53/mvM77ffQIhULi9ttvF3V1dcJut4sRI0aIH/3oRyKZTKp9eL+PXG+//XbB/399/fXXCyH6d2/j8bi47bbbhN/vFw6HQ1xyySWiqalpED4N0ZHHIIQQg5OTJCIiIiIiosFyzK4ZJCIiIiIiOpYxGCQiIiIiIjoGMRgkIiIiIiI6BjEYJCIiIiIiOgYxGCQiIiIiIjoGMRgkIiIiIiI6BjEYJCIiIiIiOgYxGCQiIiIiIjoGMRgkoqPeWWedhVmzZg32ZShCCHz/+9+H3++HwWDA+vXrB/uSsGLFChgMBnR3dw/2pRAREdEhwmCQiOgQe/3117FkyRL84Q9/QEtLCyZNmjTYl3TUGT58OJ588skBO57BYMArr7wyYMcjIiI6HJgH+wKIiI5EmUwGBoMBRmPx/6a2bds2VFdXY+rUqYfkfEeaVCoFi8Uy2JcBANA0DVardbAvg4iI6KA4+p8qiOiwcNZZZ2HmzJn44Q9/CL/fj6qqKjzwwAPq9R07duSVTHZ3d8NgMGDFihUAvixl/Mtf/oLJkyfD4XDgG9/4Btra2vDnP/8Z48ePh9frxTXXXINYLJZz/nQ6jdtuuw0lJSUIBAK45557IIRQr2uahh/+8IcYMmQIXC4Xvva1r6nzAsCSJUtQUlKCP/zhD5gwYQJsNhsaGxsLftaVK1filFNOgc1mQ3V1NebNm4d0Og0A+N73vocZM2agqakJBoMBw4cPL3iMvs73wQcf4LzzzkNZWRl8Ph/OPPNMrF27Nue9BoMB//Vf/4UrrrgCTqcTo0ePxquvvpqzz5/+9CeMGTMGDocDZ599Nnbs2JF3Db/97W8xceJE2Gw2DB8+HI8//njO68OHD8dPf/pTXHfddXC73Rg2bBh+//vfY+/evZg+fTrcbjcaGhrw4YcfFvyM2de7ePFiTJ8+HS6XCz/96U8BAK+99hqmTJkCu92OESNG4Mc//rH6HgHggQceQF1dHWw2G2pqajBz5kwAPX/XGhsbcccdd8BgMMBgMAAAOjo6cM0112Do0KFwOp1oaGjAb37zm5xrOeuss3DbbbfhzjvvRFlZGc477zx1j6644oqce7ZhwwacffbZ8Hg88Hq9mDJlyld+ViIiosOKICI6BM4880zh9XrFAw88ILZu3Sqee+45YTAYxBtvvCGEEGL79u0CgFi3bp16T1dXlwAg3n77bSGEEG+//bYAIE499VSxatUqsXbtWjFq1Chx5plnivPPP1+sXbtWvPPOOyIQCIiHHnoo59xut1vcfvvt4tNPPxXLli0TTqdT/Od//qfa59prrxVTp04V77zzjvj888/Fo48+Kmw2m9i6dasQQohnn31WWCwWMXXqVPHuu++KTz/9VEQikbzPuWvXLuF0OsWtt94qNm/eLF5++WVRVlYm7r//fiGEEN3d3eLBBx8UQ4cOFS0tLaKtra3g99XX+d566y2xdOlSsWnTJrFp0yZx4403isrKShEKhdR7AYihQ4eK559/Xnz22Wdi5syZwu12i46ODiGEEE1NTcJms+V8H5WVlQKA6OrqEkII8eGHHwqj0SgefPBBsWXLFvHss88Kh8Mhnn32WXWeYcOGCb/fLxYvXiy2bt0qbrnlFuHxeMSFF14oXnrpJbFlyxZx+eWXi/Hjxwtd1/v8uwFAVFRUiGeeeUZs27ZN7NixQ7z++uvC6/WKJUuWiG3btok33nhDDB8+XDzwwANCCCH+53/+R3i9XvGnP/1JNDY2ir/97W/qfnZ0dIihQ4eKBx98ULS0tIiWlhZ1bx599FGxbt06sW3bNvHUU08Jk8kk3n///by/K3PmzBGffvqp2Lx5s2hraxMAxLPPPptzzyZOnCi+853viM2bN4utW7eKl156Saxfv77Pz0lERHS4YTBIRIfEmWeeKaZNm5az7eSTTxZz584VQhQXDL755ptqnwULFggAYtu2bWrbTTfdJC644IKcc/cOSObOnSvGjx8vhBDi888/FwaDQTQ3N+dc3znnnCPuvvtuIURPcAbgKx/2//Vf/1WMHTs251z/9m//Jtxut8hkMkIIIZ544gkxbNiwfR6nv+dLp9PC4/GI1157TW0DIO655x71eyQSEQaDQfz5z38WQghx9913F/w+soPBa6+9Vpx33nk555ozZ46YMGGC+n3YsGHiO9/5jvq9paVFABD33nuv2vbee+8JACogKwSAmDVrVs62M844Q/zsZz/L2bZ06VJRXV0thBDi8ccfF2PGjBGaphU85rBhw8QTTzzR5zmliy66SMyePVv9fuaZZ4oTTjih4DW+/PLLOds8Ho9YsmTJV56DiIjocMUyUSI6ZI477ric36urq9HW1nZAx6msrITT6cSIESNytvU+7qmnnqrKBQHgtNNOw2effYZMJoO1a9dCCIExY8bA7Xarn5UrV2Lbtm3qPVarNe8z9LZ582acdtppOec6/fTTEYlEsGvXrqI+Z6HztbW14eabb8aYMWPg8/ng8/kQiUTQ1NSUs1/2+1wuFzwej/pONm/eXPD76P05Tj/99Jxtp59+uvrOCp2nsrISANDQ0JC37avu80knnZTz+5o1a/Dggw/m3I9/+Zd/QUtLC2KxGL75zW8iHo9jxIgR+Jd/+Re8/PLLOSWkhWQyGcyfPx/HHXccAoEA3G433njjjbzvrve19OXOO+/E//t//w/nnnsuHnrooZy/K0REREcCNpAhokOmd1MQg8EAXdcBQDVGEVnr+FKp1Fcex2Aw7PO4/aHrOkwmE9asWQOTyZTzmtvtVn92OBw5AVQhQoi8feRn+qr39lbofN/73vewd+9ePPnkkxg2bBhsNhtOO+00aJqWs9++vpPs73h/Pkdf55H7F9r2VffD5XLl/K7rOn784x/jyiuvzNvXbrejtrYWW7ZswfLly/Hmm2/i1ltvxaOPPoqVK1f22Xzm8ccfxxNPPIEnn3wSDQ0NcLlcmDVrVt531/ta+vLAAw/g2muvxR//+Ef8+c9/xv33348XXngBV1xxRb/eT0RENNgYDBLRYaG8vBwA0NLSgsmTJwPAgM7fe//99/N+Hz16NEwmEyZPnoxMJoO2tjacccYZB3SeCRMm4Le//W1OMLV69Wp4PB4MGTLkgI4NAH/961/xi1/8AhdddBEAYOfOnWhvby/6GnuPSej9/UyYMAGrVq3K2bZ69WqMGTMmL2A+GE488URs2bIFo0aN6nMfh8OByy67DJdddhl+8IMfYNy4cfj4449x4oknwmq15mQwgZ7vbvr06fjOd74DoCfg/OyzzzB+/PivvB6LxZJ3PAAYM2YMxowZgzvuuAPXXHMNnn32WQaDRER0xGCZKBEdFhwOB0499VQ89NBD2LRpE9555x3cc889A3b8nTt34s4778SWLVvwm9/8BosWLcLtt98OoOeB/tvf/jauu+46/O53v8P27dvxwQcf4OGHH8af/vSnos5z6623YufOnZgxYwY+/fRT/P73v8f999+PO++8c0DGQowaNQpLly7F5s2b8be//Q3f/va34XA4ijrGzTffjG3btqnv4/nnn8eSJUty9pk9ezbeeust/OQnP8HWrVvx3HPP4emnn8Zdd911wJ+hP+677z78+te/xgMPPIBPPvkEmzdvxosvvqj+TixZsgTPPPMMNm7ciC+++AJLly6Fw+HAsGHDAPR0On3nnXfQ3NysguVRo0Zh+fLlWL16NTZv3oybbroJra2t/bqe4cOH46233kJrayu6uroQj8dx2223YcWKFWhsbMS7776LDz74oF+BJRER0eGCwSARHTZ+9atfIZVK4aSTTsLtt9+uRgwMhOuuuw7xeBynnHIKfvCDH2DGjBn4/ve/r15/9tlncd1112H27NkYO3YsLrvsMvztb39DbW1tUecZMmQI/vSnP+Hvf/87jj/+eNx888248cYbByyw/dWvfoWuri5MnjwZ3/3udzFz5kxUVFQUdYy6ujr89re/xWuvvYbjjz8eixcvxs9+9rOcfU488US89NJLeOGFFzBp0iTcd999ePDBB/G9731vQD7HV7ngggvwhz/8AcuXL8fJJ5+MU089FQsXLlTBXklJCX75y1/i9NNPx3HHHYe33noLr732GgKBAADgwQcfxI4dOzBy5EiVdb733ntx4okn4oILLsBZZ52FqqoqXH755f26nscffxzLly9HbW0tJk+eDJPJhI6ODlx33XUYM2YMrrrqKvzDP/wDfvzjHx+U74OIiOhgMIj+LB4hIiIiIiKiowozg0RERERERMcgBoNERERERETHIAaDRERERERExyAGg0RERERERMcgBoNERERERETHIAaDRERERERExyAGg0RERERERMcgBoNERERERETHIAaDRERERERExyAGg0RERERERMcgBoNERERERETHoP8Py52nzRBL6K8AAAAASUVORK5CYII=", "text/plain": [ "
" ] @@ -1173,7 +1182,7 @@ "outputs": [ { "data": { - "image/png": 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+ "image/png": "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", "text/plain": [ "
" ] @@ -1197,7 +1206,7 @@ "outputs": [ { "data": { - "image/png": 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+ "image/png": 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"text/plain": [ "
" ] @@ -1221,7 +1230,7 @@ "outputs": [ { "data": { - "image/png": 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+ "image/png": "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", "text/plain": [ "
" ] @@ -1245,7 +1254,7 @@ "outputs": [ { "data": { - "image/png": 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+ "image/png": "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", "text/plain": [ "
" ] @@ -1269,7 +1278,7 @@ "outputs": [ { "data": { - "image/png": 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+ "image/png": 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" ] @@ -1305,8 +1314,8 @@ "name": "stdout", "output_type": "stream", "text": [ - "CPU times: total: 0 ns\n", - "Wall time: 7.89 ms\n" + "CPU times: total: 15.6 ms\n", + "Wall time: 7.22 ms\n" ] }, { @@ -1337,7 +1346,7 @@ "output_type": "stream", "text": [ "CPU times: total: 0 ns\n", - "Wall time: 6.96 ms\n" + "Wall time: 6 ms\n" ] }, { @@ -1367,8 +1376,8 @@ "name": "stdout", "output_type": "stream", "text": [ - "CPU times: total: 609 ms\n", - "Wall time: 615 ms\n" + "CPU times: total: 781 ms\n", + "Wall time: 777 ms\n" ] }, { diff --git a/optimask/__init__.py b/optimask/__init__.py index eb1f135..b865720 100644 --- a/optimask/__init__.py +++ b/optimask/__init__.py @@ -7,4 +7,4 @@ __all__ = ['OptiMask'] -__version__ = '1.2.5' +__version__ = '1.3' diff --git a/optimask/_optimask.py b/optimask/_optimask.py index 9845f36..111a3ce 100644 --- a/optimask/_optimask.py +++ b/optimask/_optimask.py @@ -7,65 +7,119 @@ import numpy as np import pandas as pd +from numba import bool_, float64, njit, prange, uint32 +from numba.types import UniTuple from ._misc import check_params, warning -from .optimask_cython import groupby_max, is_decreasing, permutation_index __all__ = ["OptiMask"] class OptiMask: """ - OptiMask is a class for calculating the optimal rows and columns to retain in a 2D array or DataFrame - to remove NaN values and preserve the maximum number of non-NaN cells. - The class uses a heuristic optimization approach, and increasing the value of `n_tries` generally leads to better results, - potentially reaching or closely approaching the optimal quantity. + A class to solve the optimal problem of removing NaNs for a 2D array or DataFrame. - Parameters: - n_tries (int): The number of optimization attempts. Higher values may lead to better results. - max_steps (int): The maximum number of steps to perform in each optimization attempt. - random_state (Union[int, None]): Seed for the random number generator. - verbose (bool): If True, print verbose information during optimization. + Attributes: + n_tries (int): Number of tries to find the optimal solution. + max_steps (int): Maximum number of steps for the optimization process. + random_state (int, optional): Seed for the random number generator to ensure reproducibility. + verbose (bool): If True, prints detailed information about the optimization process. - .. code-block:: python - - from optimask import OptiMask - import numpy as np - - # Create a matrix with NaN values - m = 120 - n = 7 - data = np.zeros(shape=(m, n)) - data[24:72, 3] = np.nan - data[95, :5] = np.nan - - # Solve for the largest submatrix without NaN values - rows, cols = OptiMask().solve(data) - - # Calculate the ratio of non-NaN values in the result - coverage_ratio = len(rows) * len(cols) / data.size - - # Check if there are any NaN values in the selected submatrix - has_nan_values = np.isnan(data[rows][:, cols]).any() - - # Print or display the results - print(f"Coverage Ratio: {coverage_ratio:.2f}, Has NaN Values: {has_nan_values}") - # Output: Coverage Ratio: 0.85, Has NaN Values: False """ - def __init__(self, n_tries=5, max_steps=32, random_state=None, verbose=False): + def __init__(self, n_tries=5, max_steps=16, random_state=None, verbose=False): self.n_tries = n_tries self.max_steps = max_steps self.random_state = random_state self.verbose = bool(verbose) + @staticmethod + @njit(uint32[:](uint32[:], uint32[:], uint32), boundscheck=False) + def groupby_max(a, b, n): + size_a = len(a) + ret = np.zeros(n, dtype=np.uint32) + for k in range(size_a): + ak = a[k] + ret[ak] = max(ret[ak], b[k]+1) + return ret + + @staticmethod + @njit(UniTuple(uint32[:], 2)(uint32[:], uint32[:], uint32, uint32), boundscheck=False) + def cross_groupby_max(a, b, m, n): + size_a = len(a) + size_b = len(b) + s = max(size_a, size_b) + ret_a = np.zeros(m, dtype=np.uint32) + ret_b = np.zeros(n, dtype=np.uint32) + for k in range(s): + ak = a[k] + bk = b[k] + if k < size_a: + ret_a[ak] = max(ret_a[ak], bk+1) + if k < size_b: + ret_b[bk] = max(ret_b[bk], ak+1) + return ret_a, ret_b + def _verbose(self, msg): if self.verbose: warning(msg) @staticmethod - def _sort_by_na_max_index(height: np.ndarray) -> np.ndarray: - return np.argsort(-height, kind='mergesort') + @njit(bool_(uint32[:]), boundscheck=False) + def is_decreasing(h): + for i in range(len(h) - 1): + if h[i] < h[i + 1]: + return False + return True + + @classmethod + def is_pareto_ordered(cls, hy, hx): + return cls.is_decreasing(hx) and cls.is_decreasing(hy) + + @staticmethod + @njit(uint32[:](uint32[:], uint32[:]), parallel=True, boundscheck=False) + def numba_apply_permutation(p, x): + n = p.size + m = x.size + rank = np.empty(n, dtype=np.uint32) + result = np.empty(m, dtype=np.uint32) + + for i in prange(n): + rank[p[i]] = i + + for i in prange(m): + result[i] = rank[x[i]] + return result + + @staticmethod + @njit((uint32[:], uint32[:]), parallel=True, boundscheck=False) + def numba_apply_permutation_inplace(p, x): + n = p.size + rank = np.empty(n, dtype=np.uint32) + + for i in prange(n): + rank[p[i]] = i + + for i in prange(x.size): + x[i] = rank[x[i]] + + @classmethod + def apply_permutation(cls, p, x, inplace: bool): + if inplace: + cls.numba_apply_permutation_inplace(p, x) + else: + return cls.numba_apply_permutation(p, x) + + @staticmethod + @njit(UniTuple(uint32[:], 2)(uint32[:], uint32[:], uint32[:]), parallel=True, boundscheck=False) + def apply_p_step(p_step, a, b): + ret_a = np.empty(a.size, dtype=np.uint32) + ret_b = np.empty(b.size, dtype=np.uint32) + for k in prange(a.size): + pk = p_step[k] + ret_a[k] = a[pk] + ret_b[k] = b[pk] + return ret_a, ret_b @staticmethod def _get_largest_rectangle(heights, m, n): @@ -73,42 +127,73 @@ def _get_largest_rectangle(heights, m, n): i0 = np.argmax(areas) return i0, heights[i0], areas[i0] - @classmethod - def _is_pareto_ordered(cls, hx, hy): - return is_decreasing(hx) and is_decreasing(hy) + @staticmethod + @njit(UniTuple(uint32[:], 4)(float64[:, :],), boundscheck=False) + def _preprocess(x): + m, n = x.shape + iy, ix = [], [] + cols_index_mapper = -np.ones(n) + rows_with_nan = np.zeros(m, dtype=np.bool_) + n_rows_with_nan = 0 + n_cols_with_nan = 0 + for i in range(m): + for j in range(n): + if np.isnan(x[i, j]): + rows_with_nan[i] = True + + iy.append(n_rows_with_nan) + + if cols_index_mapper[j] >= 0: + ix.append(cols_index_mapper[j]) + else: + ix.append(n_cols_with_nan) + cols_index_mapper[j] = n_cols_with_nan + n_cols_with_nan += 1 + + if rows_with_nan[i]: + n_rows_with_nan += 1 + + iy, ix = np.array(iy).astype(np.uint32), np.array(ix).astype(np.uint32) + rows_with_nan = np.flatnonzero(rows_with_nan).astype(np.uint32) + cols_with_nan = np.flatnonzero(cols_index_mapper >= 0)[cols_index_mapper[cols_index_mapper >= 0].argsort()].astype(np.uint32) + return iy, ix, rows_with_nan, cols_with_nan def _trial(self, rng, m_nan, n_nan, iy, ix, m, n): - p_rows = rng.permutation(m_nan).astype(np.int64) - p_cols = rng.permutation(n_nan).astype(np.int64) - iy_trial = permutation_index(p_rows)[iy] - ix_trial = permutation_index(p_cols)[ix] + p_rows = rng.permutation(m_nan).astype(np.uint32) + p_cols = rng.permutation(n_nan).astype(np.uint32) + iy_trial = self.apply_permutation(p_rows, iy, inplace=False) + ix_trial = self.apply_permutation(p_cols, ix, inplace=False) + hy, hx = self.cross_groupby_max(iy_trial, ix_trial, m_nan, n_nan) step = 0 - h0, h1 = groupby_max(iy_trial, ix_trial, m_nan), groupby_max(ix_trial, iy_trial, n_nan) - while (not self._is_pareto_ordered(h0, h1)) and (step < self.max_steps): - axis = (step % 2) + is_pareto_ordered = False + while not is_pareto_ordered and step < self.max_steps: + axis = step % 2 step += 1 if axis == 0: - p_step = self._sort_by_na_max_index(h0) - iy_trial = permutation_index(p_step)[iy_trial] - p_rows = p_rows[p_step] - h0, h1 = h0[p_step], groupby_max(ix_trial, iy_trial, n_nan) - if axis == 1: - p_step = self._sort_by_na_max_index(h1) - ix_trial = permutation_index(p_step)[ix_trial] - p_cols = p_cols[p_step] - h0, h1 = groupby_max(iy_trial, ix_trial, m_nan), h1[p_step] - - if not self._is_pareto_ordered(h0, h1): + p_step = (-hy).argsort().astype(np.uint32) + self.apply_permutation(p_step, iy_trial, inplace=True) + p_rows, hy = self.apply_p_step(p_step, p_rows, hy) + hx = self.groupby_max(ix_trial, iy_trial, n_nan) + is_pareto_ordered = self.is_decreasing(hx) + else: + p_step = (-hx).argsort().astype(np.uint32) + self.apply_permutation(p_step, ix_trial, inplace=True) + hy = self.groupby_max(iy_trial, ix_trial, m_nan) + p_cols, hx = self.apply_p_step(p_step, p_cols, hx) + is_pareto_ordered = self.is_decreasing(hy) + + if not self.is_pareto_ordered(hy, hx): raise ValueError("An error occurred while calculating optimal permutations. " "You can try again with a larger `max_steps` value.") else: - i0, j0, area = self._get_largest_rectangle(h1, m, n) + i0, j0, area = self._get_largest_rectangle(hx, m, n) return area, i0, j0, p_rows, p_cols def _solve(self, x): m, n = x.shape - iy, ix = np.unravel_index(np.flatnonzero(np.isnan(x)), x.shape) + iy, ix, rows_with_nan, cols_with_nan = self._preprocess(x) + m_nan, n_nan = len(rows_with_nan), len(cols_with_nan) if len(iy) == 0: return np.arange(m), np.arange(n) elif m == 1: @@ -117,10 +202,6 @@ def _solve(self, x): return np.flatnonzero(np.isfinite(x.ravel())), np.arange(n) else: rng = np.random.default_rng(seed=self.random_state) - rows_with_nan, iy = np.unique(iy, return_inverse=True) - cols_with_nan, ix = np.unique(ix, return_inverse=True) - m_nan, n_nan = len(rows_with_nan), len(cols_with_nan) - area_max = -1 for k in range(self.n_tries): area, i0, j0, p_rows, p_cols = self._trial(rng, m_nan, n_nan, iy, ix, m, n) @@ -132,8 +213,8 @@ def _solve(self, x): i0, j0, p_rows, p_cols = opt self._verbose(f"Result: the largest submatrix found is of size {m-j0}x{n-i0} ({area_max} elements) found.") - rows_to_keep = np.setdiff1d(np.arange(m), rows_with_nan[p_rows[:j0]]) - cols_to_keep = np.setdiff1d(np.arange(n), cols_with_nan[p_cols[:i0]]) + rows_to_keep = np.setdiff1d(np.arange(m, dtype=np.uint32), rows_with_nan[p_rows[:j0]]) + cols_to_keep = np.setdiff1d(np.arange(n, dtype=np.uint32), cols_with_nan[p_cols[:i0]]) return rows_to_keep, cols_to_keep def solve(self, X: Union[np.ndarray, pd.DataFrame], return_data: bool = False) -> Union[Tuple[np.ndarray, np.ndarray], Tuple[pd.Index, pd.Index]]: diff --git a/optimask/optimask_cython.pyx b/optimask/optimask_cython.pyx deleted file mode 100644 index c1b29cf..0000000 --- a/optimask/optimask_cython.pyx +++ /dev/null @@ -1,28 +0,0 @@ -# cython: boundscheck=False, wraparound=False, nonecheck=False - -import numpy as np -cimport numpy as np - -ctypedef np.int64_t DTYPE_INT_t - -cpdef np.ndarray[DTYPE_INT_t, ndim=1] groupby_max(np.ndarray[DTYPE_INT_t, ndim=1] a, np.ndarray[DTYPE_INT_t, ndim=1] b, int size): - cdef np.ndarray[DTYPE_INT_t, ndim=1] ret = np.zeros(size, dtype=np.int64) - cdef int i - for i in range(a.shape[0]): - ret[a[i]] = max(ret[a[i]], b[i] + 1) - return ret - -cpdef bint is_decreasing(np.ndarray[DTYPE_INT_t, ndim=1] x): - cdef int n = x.shape[0] - cdef int i - for i in range(n - 1): - if x[i] < x[i + 1]: - return False - return True - -cpdef np.ndarray[DTYPE_INT_t, ndim=1] permutation_index(np.ndarray[DTYPE_INT_t, ndim=1] p): - cdef int i - cdef np.ndarray[DTYPE_INT_t, ndim=1] s = np.empty(p.size, dtype=np.int64) - for i in range(p.size): - s[p[i]] = i - return s diff --git a/readthedocs.yaml b/readthedocs.yaml index 18daa94..b12f16e 100644 --- a/readthedocs.yaml +++ b/readthedocs.yaml @@ -4,9 +4,6 @@ build: os: ubuntu-lts-latest tools: python: "mambaforge-22.9" - jobs: - pre_install: - - cython optimask/optimask_cython.pyx conda: environment: docs/environment.yml diff --git a/setup.py b/setup.py index 0a59346..aef79d5 100644 --- a/setup.py +++ b/setup.py @@ -1,8 +1,7 @@ import os import re -import numpy as np -from setuptools import Extension, find_packages, setup +from setuptools import find_packages, setup def read_version(): @@ -24,19 +23,11 @@ def read_version(): version=read_version(), packages=find_packages(), url='https://optimask.readthedocs.io', - install_requires=['numpy', 'pandas'], - setup_requires=['numpy'], + install_requires=['numpy', 'pandas', 'numba'], description="OptiMask: extracting the largest (non-contiguous) submatrix without NaN", long_description_content_type='text/markdown', long_description=long_description, author='Cyril Joly', license='MIT', classifiers=['License :: OSI Approved :: MIT License'], - ext_modules=[ - Extension( - "optimask.optimask_cython", - sources=["optimask/optimask_cython.c"], - include_dirs=[np.get_include()] - ) - ] )