From fe3fe73c48a1e9e42a0ab0c986996cecd0cce181 Mon Sep 17 00:00:00 2001 From: bushuhui Date: Thu, 8 Oct 2020 12:33:22 +0800 Subject: [PATCH] Improve python lib tutorials --- .../1-numpy_tutorial.ipynb | 623 ++++++++++---------- .../2-matplotlib_tutorial.ipynb | 27 +- .../3-ipython_notebook.ipynb | 240 ++++++-- .../4-scipy_tutorial.ipynb | 350 +++++------ .../5-sympy_tutorial.ipynb | 649 +++++++++------------ 1_numpy_matplotlib_scipy_sympy/test.txt | 1 + References.md | 2 + 7 files changed, 953 insertions(+), 939 deletions(-) create mode 100644 1_numpy_matplotlib_scipy_sympy/test.txt diff --git a/1_numpy_matplotlib_scipy_sympy/1-numpy_tutorial.ipynb b/1_numpy_matplotlib_scipy_sympy/1-numpy_tutorial.ipynb index 7a53ac7..2dcf94b 100644 --- a/1_numpy_matplotlib_scipy_sympy/1-numpy_tutorial.ipynb +++ b/1_numpy_matplotlib_scipy_sympy/1-numpy_tutorial.ipynb @@ -47,11 +47,20 @@ }, { "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "# 不建议用这种方式导入库\n", + "from numpy import *" + ] + }, + { + "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ - "from numpy import *\n", "import numpy as np" ] }, @@ -139,7 +148,7 @@ ], "source": [ "# 矩阵:数组函数的参数是一个嵌套的Python列表\n", - "M = array([[1, 2], [3, 4], [5, 6]])\n", + "M = np.array([[1, 2], [3, 4], [5, 6]])\n", "\n", "print(M)\n", "print(M.shape)" @@ -697,7 +706,7 @@ ], "source": [ "# 从主对角线偏移的对角线\n", - "diag([1,2,3], k=1) " + "np.diag([1,2,3], k=1) " ] }, { @@ -1002,35 +1011,37 @@ }, { "cell_type": "code", - "execution_count": 42, + "execution_count": 4, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "float64\n", + "int64\n", "8\n" ] } ], "source": [ + "M = np.array([[1, 2], [3, 4], [5, 6]])\n", + "\n", "print(M.dtype)\n", "print(M.itemsize) # 每个元素的字节数\n" ] }, { "cell_type": "code", - "execution_count": 39, + "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "72" + "48" ] }, - "execution_count": 39, + "execution_count": 5, "metadata": {}, "output_type": "execute_result" } @@ -1041,7 +1052,7 @@ }, { "cell_type": "code", - "execution_count": 40, + "execution_count": 6, "metadata": {}, "outputs": [ { @@ -1050,7 +1061,7 @@ "2" ] }, - "execution_count": 40, + "execution_count": 6, "metadata": {}, "output_type": "execute_result" } @@ -1082,7 +1093,7 @@ }, { "cell_type": "code", - "execution_count": 43, + "execution_count": 7, "metadata": {}, "outputs": [ { @@ -1091,7 +1102,7 @@ "1" ] }, - "execution_count": 43, + "execution_count": 7, "metadata": {}, "output_type": "execute_result" } @@ -1104,16 +1115,16 @@ }, { "cell_type": "code", - "execution_count": 44, + "execution_count": 8, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "0.21286523712879435\n", - "0.21286523712879435\n", - "[0.77573698 0.21286524 0.68518057]\n" + "4\n", + "4\n", + "[3 4]\n" ] } ], @@ -1134,18 +1145,18 @@ }, { "cell_type": "code", - "execution_count": 43, + "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "array([[0.73171836, 0.46544202, 0.72372739],\n", - " [0.32390603, 0.09679475, 0.95467059],\n", - " [0.36051701, 0.78361037, 0.00716923]])" + "array([[1, 2],\n", + " [3, 4],\n", + " [5, 6]])" ] }, - "execution_count": 43, + "execution_count": 9, "metadata": {}, "output_type": "execute_result" } @@ -1156,16 +1167,16 @@ }, { "cell_type": "code", - "execution_count": 45, + "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "array([0.77573698, 0.21286524, 0.68518057])" + "array([3, 4])" ] }, - "execution_count": 45, + "execution_count": 10, "metadata": {}, "output_type": "execute_result" } @@ -1183,16 +1194,16 @@ }, { "cell_type": "code", - "execution_count": 46, + "execution_count": 11, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "array([0.77573698, 0.21286524, 0.68518057])" + "array([3, 4])" ] }, - "execution_count": 46, + "execution_count": 11, "metadata": {}, "output_type": "execute_result" } @@ -1203,16 +1214,16 @@ }, { "cell_type": "code", - "execution_count": 47, + "execution_count": 12, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "array([0.96924573, 0.21286524, 0.70297393])" + "array([2, 4, 6])" ] }, - "execution_count": 47, + "execution_count": 12, "metadata": {}, "output_type": "execute_result" } @@ -1230,7 +1241,7 @@ }, { "cell_type": "code", - "execution_count": 48, + "execution_count": 13, "metadata": {}, "outputs": [], "source": [ @@ -1239,18 +1250,18 @@ }, { "cell_type": "code", - "execution_count": 49, + "execution_count": 14, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "array([[1. , 0.96924573, 0.53434945],\n", - " [0.77573698, 0.21286524, 0.68518057],\n", - " [0.32862765, 0.70297393, 0.39513101]])" + "array([[1, 2],\n", + " [3, 4],\n", + " [5, 6]])" ] }, - "execution_count": 49, + "execution_count": 14, "metadata": {}, "output_type": "execute_result" } @@ -1261,13 +1272,13 @@ }, { "cell_type": "code", - "execution_count": 50, + "execution_count": 16, "metadata": {}, "outputs": [], "source": [ "# 对行和列也同样有用\n", "M[1,:] = 0\n", - "M[:,2] = -1" + "M[:,1] = -1" ] }, { @@ -1308,7 +1319,7 @@ }, { "cell_type": "code", - "execution_count": 53, + "execution_count": 17, "metadata": {}, "outputs": [ { @@ -1317,7 +1328,7 @@ "array([1, 2, 3, 4, 5])" ] }, - "execution_count": 53, + "execution_count": 17, "metadata": {}, "output_type": "execute_result" } @@ -1329,7 +1340,7 @@ }, { "cell_type": "code", - "execution_count": 54, + "execution_count": 18, "metadata": {}, "outputs": [ { @@ -1338,7 +1349,7 @@ "array([2, 3])" ] }, - "execution_count": 54, + "execution_count": 18, "metadata": {}, "output_type": "execute_result" } @@ -1356,7 +1367,7 @@ }, { "cell_type": "code", - "execution_count": 55, + "execution_count": 19, "metadata": {}, "outputs": [ { @@ -1365,7 +1376,7 @@ "array([ 1, -2, -3, 4, 5])" ] }, - "execution_count": 55, + "execution_count": 19, "metadata": {}, "output_type": "execute_result" } @@ -1387,7 +1398,7 @@ }, { "cell_type": "code", - "execution_count": 56, + "execution_count": 20, "metadata": {}, "outputs": [ { @@ -1396,7 +1407,7 @@ "array([ 1, -2, -3, 4, 5])" ] }, - "execution_count": 56, + "execution_count": 20, "metadata": {}, "output_type": "execute_result" } @@ -1550,7 +1561,7 @@ }, { "cell_type": "code", - "execution_count": 64, + "execution_count": 24, "metadata": {}, "outputs": [ { @@ -1563,7 +1574,7 @@ " [40, 41, 42, 43, 44]])" ] }, - "execution_count": 64, + "execution_count": 24, "metadata": {}, "output_type": "execute_result" } @@ -1576,7 +1587,7 @@ }, { "cell_type": "code", - "execution_count": 65, + "execution_count": 25, "metadata": {}, "outputs": [ { @@ -1587,7 +1598,7 @@ " [31, 32, 33]])" ] }, - "execution_count": 65, + "execution_count": 25, "metadata": {}, "output_type": "execute_result" } @@ -1636,7 +1647,7 @@ }, { "cell_type": "code", - "execution_count": 68, + "execution_count": 26, "metadata": {}, "outputs": [ { @@ -1655,6 +1666,8 @@ } ], "source": [ + "A = np.array([[n+m*10 for n in range(5)] for m in range(5)])\n", + "\n", "row_indices = [1, 2, 3]\n", "print(A[row_indices])\n", "print(A)" @@ -1662,22 +1675,22 @@ }, { "cell_type": "code", - "execution_count": 69, + "execution_count": 27, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "array([11, 22, 34])" + "array([11, 21, 34])" ] }, - "execution_count": 69, + "execution_count": 27, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "col_indices = [1, 2, -1] # 索引-1 代表最后一个元素\n", + "col_indices = [1, 1, -1] # 索引-1 代表最后一个元素\n", "A[row_indices, col_indices]" ] }, @@ -1690,7 +1703,7 @@ }, { "cell_type": "code", - "execution_count": 70, + "execution_count": 28, "metadata": {}, "outputs": [ { @@ -1699,7 +1712,7 @@ "array([0, 1, 2, 3, 4])" ] }, - "execution_count": 70, + "execution_count": 28, "metadata": {}, "output_type": "execute_result" } @@ -1711,7 +1724,7 @@ }, { "cell_type": "code", - "execution_count": 71, + "execution_count": 29, "metadata": {}, "outputs": [ { @@ -1720,7 +1733,7 @@ "array([0, 2])" ] }, - "execution_count": 71, + "execution_count": 29, "metadata": {}, "output_type": "execute_result" } @@ -1868,7 +1881,7 @@ }, { "cell_type": "code", - "execution_count": 76, + "execution_count": 32, "metadata": {}, "outputs": [ { @@ -1877,12 +1890,15 @@ "(array([11, 12, 13, 14]),)" ] }, - "execution_count": 76, + "execution_count": 32, "metadata": {}, "output_type": "execute_result" } ], "source": [ + "x = np.arange(0, 10, 0.5)\n", + "mask = (5 < x) * (x < 7.5)\n", + "\n", "indices = np.where(mask)\n", "\n", "indices" @@ -1890,7 +1906,7 @@ }, { "cell_type": "code", - "execution_count": 77, + "execution_count": 33, "metadata": {}, "outputs": [ { @@ -1899,7 +1915,7 @@ "array([5.5, 6. , 6.5, 7. ])" ] }, - "execution_count": 77, + "execution_count": 33, "metadata": {}, "output_type": "execute_result" } @@ -1924,7 +1940,7 @@ }, { "cell_type": "code", - "execution_count": 78, + "execution_count": 34, "metadata": {}, "outputs": [ { @@ -1933,18 +1949,18 @@ "array([ 0, 11, 22, 33, 44])" ] }, - "execution_count": 78, + "execution_count": 34, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "diag(A)" + "np.diag(A)" ] }, { "cell_type": "code", - "execution_count": 79, + "execution_count": 35, "metadata": {}, "outputs": [ { @@ -1953,13 +1969,13 @@ "array([10, 21, 32, 43])" ] }, - "execution_count": 79, + "execution_count": 35, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "diag(A, -1)" + "np.diag(A, -1)" ] }, { @@ -1978,7 +1994,7 @@ }, { "cell_type": "code", - "execution_count": 80, + "execution_count": 36, "metadata": {}, "outputs": [ { @@ -1987,19 +2003,19 @@ "array([-3, -2, -1, 0, 1, 2])" ] }, - "execution_count": 80, + "execution_count": 36, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "v2 = arange(-3,3)\n", + "v2 = np.arange(-3,3)\n", "v2" ] }, { "cell_type": "code", - "execution_count": 81, + "execution_count": 37, "metadata": {}, "outputs": [ { @@ -2008,7 +2024,7 @@ "array([-2, 0, 2])" ] }, - "execution_count": 81, + "execution_count": 37, "metadata": {}, "output_type": "execute_result" } @@ -2020,7 +2036,7 @@ }, { "cell_type": "code", - "execution_count": 82, + "execution_count": 38, "metadata": {}, "outputs": [ { @@ -2029,7 +2045,7 @@ "array([-2, 0, 2])" ] }, - "execution_count": 82, + "execution_count": 38, "metadata": {}, "output_type": "execute_result" } @@ -2047,7 +2063,7 @@ }, { "cell_type": "code", - "execution_count": 83, + "execution_count": 39, "metadata": {}, "outputs": [ { @@ -2056,13 +2072,13 @@ "array([-2, 0, 2])" ] }, - "execution_count": 83, + "execution_count": 39, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "take([-3, -2, -1, 0, 1, 2], row_indices)" + "np.take([-3, -2, -1, 0, 1, 2], row_indices)" ] }, { @@ -2132,7 +2148,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 40, "metadata": {}, "outputs": [], "source": [ @@ -2143,7 +2159,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 41, "metadata": {}, "outputs": [ { @@ -2152,7 +2168,7 @@ "array([0, 2, 4, 6, 8])" ] }, - "execution_count": 2, + "execution_count": 41, "metadata": {}, "output_type": "execute_result" } @@ -2163,7 +2179,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 42, "metadata": {}, "outputs": [ { @@ -2172,7 +2188,7 @@ "array([2, 3, 4, 5, 6])" ] }, - "execution_count": 3, + "execution_count": 42, "metadata": {}, "output_type": "execute_result" } @@ -2183,7 +2199,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 43, "metadata": {}, "outputs": [ { @@ -2201,7 +2217,7 @@ " [42, 43, 44, 45, 46]]))" ] }, - "execution_count": 5, + "execution_count": 43, "metadata": {}, "output_type": "execute_result" } @@ -2228,17 +2244,17 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 44, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "array([[0.56313066, 0.29002953, 0.61502088],\n", - " [0.1022442 , 0.26811919, 0.01595774]])" + "array([[0.01114895, 0.05665904, 0.29848178],\n", + " [0.05093977, 0.00040534, 0.14069349]])" ] }, - "execution_count": 6, + "execution_count": 44, "metadata": {}, "output_type": "execute_result" } @@ -2251,7 +2267,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 45, "metadata": {}, "outputs": [ { @@ -2260,7 +2276,7 @@ "array([ 0, 1, 4, 9, 16])" ] }, - "execution_count": 7, + "execution_count": 45, "metadata": {}, "output_type": "execute_result" } @@ -2278,7 +2294,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 46, "metadata": {}, "outputs": [ { @@ -2287,7 +2303,7 @@ "((2, 3), (5,))" ] }, - "execution_count": 8, + "execution_count": 46, "metadata": {}, "output_type": "execute_result" } @@ -2298,7 +2314,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 47, "metadata": {}, "outputs": [ { @@ -2306,9 +2322,9 @@ "evalue": "operands could not be broadcast together with shapes (2,3) (5,) ", "output_type": "error", "traceback": [ - "\u001b[0;31m-----------------------------------------------------------\u001b[0m", - "\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mA\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0mv1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mA\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0mv1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", "\u001b[0;31mValueError\u001b[0m: operands could not be broadcast together with shapes (2,3) (5,) " ] } @@ -2333,20 +2349,20 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 48, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "array([[0.72376121, 1.02741179, 1.84320475, 1.4644025 , 0.93766737],\n", - " [0.93371216, 1.36026092, 1.81889819, 1.83440464, 1.04760436],\n", - " [1.09260054, 1.80363181, 2.27237106, 2.28907943, 1.42251772],\n", - " [0.53394728, 1.08528537, 1.22967423, 1.23649788, 0.90293134],\n", - " [0.71121961, 1.11261141, 1.74864104, 1.46510406, 0.95440664]])" + "array([[1.903065 , 1.67919746, 2.3622027 , 1.88412044, 1.11105008],\n", + " [1.41054459, 1.8120306 , 1.88727633, 1.43014072, 0.86699677],\n", + " [1.66542157, 1.50549264, 1.92165524, 1.68084913, 0.96953227],\n", + " [2.60415277, 2.45432125, 3.10136235, 2.57384453, 1.3456306 ],\n", + " [1.2318307 , 1.12068957, 1.38086198, 1.18571816, 0.5442449 ]])" ] }, - "execution_count": 10, + "execution_count": 48, "metadata": {}, "output_type": "execute_result" } @@ -2360,16 +2376,16 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 49, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "array([5.346923 , 6.32008278, 7.66667311, 3.19229347, 4.84861051])" + "array([5.14498999, 4.21899366, 6.02247961, 7.06795814, 2.91510261])" ] }, - "execution_count": 11, + "execution_count": 49, "metadata": {}, "output_type": "execute_result" } @@ -2380,7 +2396,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 50, "metadata": {}, "outputs": [ { @@ -2389,7 +2405,7 @@ "30" ] }, - "execution_count": 12, + "execution_count": 50, "metadata": {}, "output_type": "execute_result" } @@ -2407,7 +2423,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 51, "metadata": {}, "outputs": [], "source": [ @@ -2417,7 +2433,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 52, "metadata": {}, "outputs": [ { @@ -2430,7 +2446,7 @@ " [4]])" ] }, - "execution_count": 14, + "execution_count": 52, "metadata": {}, "output_type": "execute_result" } @@ -2618,20 +2634,20 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 53, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "[[0.02229536 0.67037896 0.40832169 0.50556638]\n", - " [0.86868781 0.82208649 0.9197217 0.30321799]\n", - " [0.98529543 0.59989498 0.85806822 0.26069024]]\n", - "[[0.02229536 0.86868781 0.98529543]\n", - " [0.67037896 0.82208649 0.59989498]\n", - " [0.40832169 0.9197217 0.85806822]\n", - " [0.50556638 0.30321799 0.26069024]]\n" + "[[0.45130455 0.64330743 0.28059702 0.37347175]\n", + " [0.88485087 0.9022088 0.6700072 0.10678579]\n", + " [0.98276964 0.05115262 0.29053376 0.40809875]]\n", + "[[0.45130455 0.88485087 0.98276964]\n", + " [0.64330743 0.9022088 0.05115262]\n", + " [0.28059702 0.6700072 0.29053376]\n", + " [0.37347175 0.10678579 0.40809875]]\n" ] } ], @@ -2643,7 +2659,7 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 54, "metadata": {}, "outputs": [ { @@ -2653,7 +2669,7 @@ " [0.+3.j, 0.+4.j]])" ] }, - "execution_count": 23, + "execution_count": 54, "metadata": {}, "output_type": "execute_result" } @@ -2665,7 +2681,7 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 55, "metadata": {}, "outputs": [ { @@ -2675,7 +2691,7 @@ " [0.-3.j, 0.-4.j]])" ] }, - "execution_count": 25, + "execution_count": 55, "metadata": {}, "output_type": "execute_result" } @@ -2693,7 +2709,7 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": 56, "metadata": {}, "outputs": [ { @@ -2703,7 +2719,7 @@ " [0.-2.j, 0.-4.j]])" ] }, - "execution_count": 26, + "execution_count": 56, "metadata": {}, "output_type": "execute_result" } @@ -2721,7 +2737,7 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": 57, "metadata": {}, "outputs": [ { @@ -2731,7 +2747,7 @@ " [0., 0.]])" ] }, - "execution_count": 28, + "execution_count": 57, "metadata": {}, "output_type": "execute_result" } @@ -2742,7 +2758,7 @@ }, { "cell_type": "code", - "execution_count": 29, + "execution_count": 58, "metadata": {}, "outputs": [ { @@ -2752,7 +2768,7 @@ " [3., 4.]])" ] }, - "execution_count": 29, + "execution_count": 58, "metadata": {}, "output_type": "execute_result" } @@ -2770,7 +2786,7 @@ }, { "cell_type": "code", - "execution_count": 30, + "execution_count": 59, "metadata": {}, "outputs": [ { @@ -2780,7 +2796,7 @@ " [1.24904577, 1.32581766]])" ] }, - "execution_count": 30, + "execution_count": 59, "metadata": {}, "output_type": "execute_result" } @@ -2791,7 +2807,7 @@ }, { "cell_type": "code", - "execution_count": 32, + "execution_count": 60, "metadata": {}, "outputs": [ { @@ -2801,7 +2817,7 @@ " [3., 4.]])" ] }, - "execution_count": 32, + "execution_count": 60, "metadata": {}, "output_type": "execute_result" } @@ -2826,7 +2842,7 @@ }, { "cell_type": "code", - "execution_count": 33, + "execution_count": 61, "metadata": {}, "outputs": [ { @@ -2836,7 +2852,7 @@ " [0.-1.5j, 0.+0.5j]])" ] }, - "execution_count": 33, + "execution_count": 61, "metadata": {}, "output_type": "execute_result" } @@ -2847,17 +2863,17 @@ }, { "cell_type": "code", - "execution_count": 133, + "execution_count": 62, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "matrix([[1.00000000e+00+0.j, 0.00000000e+00+0.j],\n", - " [2.22044605e-16+0.j, 1.00000000e+00+0.j]])" + " [1.11022302e-16+0.j, 1.00000000e+00+0.j]])" ] }, - "execution_count": 133, + "execution_count": 62, "metadata": {}, "output_type": "execute_result" } @@ -2875,7 +2891,7 @@ }, { "cell_type": "code", - "execution_count": 34, + "execution_count": 63, "metadata": {}, "outputs": [ { @@ -2884,7 +2900,7 @@ "(2.0000000000000004+0j)" ] }, - "execution_count": 34, + "execution_count": 63, "metadata": {}, "output_type": "execute_result" } @@ -2895,7 +2911,7 @@ }, { "cell_type": "code", - "execution_count": 36, + "execution_count": 64, "metadata": {}, "outputs": [ { @@ -2904,7 +2920,7 @@ "(0.49999999999999967+0j)" ] }, - "execution_count": 36, + "execution_count": 64, "metadata": {}, "output_type": "execute_result" } @@ -2931,7 +2947,7 @@ }, { "cell_type": "code", - "execution_count": 38, + "execution_count": 65, "metadata": {}, "outputs": [ { @@ -2940,7 +2956,7 @@ "(77431, 7)" ] }, - "execution_count": 38, + "execution_count": 65, "metadata": {}, "output_type": "execute_result" } @@ -2962,7 +2978,7 @@ }, { "cell_type": "code", - "execution_count": 39, + "execution_count": 66, "metadata": {}, "outputs": [ { @@ -2978,7 +2994,7 @@ "6.197109684751585" ] }, - "execution_count": 39, + "execution_count": 66, "metadata": {}, "output_type": "execute_result" } @@ -2991,16 +3007,16 @@ }, { "cell_type": "code", - "execution_count": 40, + "execution_count": 67, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "0.6281761069770216" + "0.536617668205844" ] }, - "execution_count": 40, + "execution_count": 67, "metadata": {}, "output_type": "execute_result" } @@ -3026,7 +3042,7 @@ }, { "cell_type": "code", - "execution_count": 41, + "execution_count": 68, "metadata": {}, "outputs": [ { @@ -3035,7 +3051,7 @@ "(8.282271621340573, 68.59602320966341)" ] }, - "execution_count": 41, + "execution_count": 68, "metadata": {}, "output_type": "execute_result" } @@ -3053,7 +3069,7 @@ }, { "cell_type": "code", - "execution_count": 42, + "execution_count": 69, "metadata": {}, "outputs": [ { @@ -3062,7 +3078,7 @@ "-25.8" ] }, - "execution_count": 42, + "execution_count": 69, "metadata": {}, "output_type": "execute_result" } @@ -3074,7 +3090,7 @@ }, { "cell_type": "code", - "execution_count": 43, + "execution_count": 70, "metadata": {}, "outputs": [ { @@ -3083,7 +3099,7 @@ "28.3" ] }, - "execution_count": 43, + "execution_count": 70, "metadata": {}, "output_type": "execute_result" } @@ -3760,20 +3776,19 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## 9. 添加新的维度：newaxis" + "## 9. 添加、删除维度：newaxis、squeeze" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "有了`newaxis`，我们可以在数组中插入新的维度，例如将一个向量转换为列或行矩阵：\n", - "FIXME: 加一个例子说明为什么需要增加纬度、删除纬度" + "当矩阵乘法的时候，需要两个矩阵的对应的纬度保持一致才可以正确执行，有了`newaxis`，我们可以在数组中插入新的维度，例如将一个向量转换为列或行矩阵：" ] }, { "cell_type": "code", - "execution_count": 85, + "execution_count": 78, "metadata": {}, "outputs": [], "source": [ @@ -3782,44 +3797,26 @@ }, { "cell_type": "code", - "execution_count": 86, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(3,)" - ] - }, - "execution_count": 86, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "np.shape(v)" - ] - }, - { - "cell_type": "code", - "execution_count": 87, + "execution_count": 79, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ + "(3,)\n", "[1 2 3]\n" ] } ], "source": [ + "print(np.shape(v))\n", "print(v)" ] }, { "cell_type": "code", - "execution_count": 88, + "execution_count": 80, "metadata": {}, "outputs": [ { @@ -3837,7 +3834,7 @@ }, { "cell_type": "code", - "execution_count": 89, + "execution_count": 81, "metadata": {}, "outputs": [ { @@ -3858,7 +3855,7 @@ }, { "cell_type": "code", - "execution_count": 91, + "execution_count": 82, "metadata": {}, "outputs": [ { @@ -3867,7 +3864,7 @@ "(3, 1)" ] }, - "execution_count": 91, + "execution_count": 82, "metadata": {}, "output_type": "execute_result" } @@ -3879,7 +3876,7 @@ }, { "cell_type": "code", - "execution_count": 92, + "execution_count": 83, "metadata": {}, "outputs": [ { @@ -3888,7 +3885,7 @@ "(1, 3)" ] }, - "execution_count": 92, + "execution_count": 83, "metadata": {}, "output_type": "execute_result" } @@ -3902,35 +3899,113 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## 10. 叠加和重复数组" + "也可以通过`np.expand_dims`来实现类似的操作" ] }, { - "cell_type": "markdown", + "cell_type": "code", + "execution_count": 85, "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(3, 1)\n", + "[[1]\n", + " [2]\n", + " [3]]\n" + ] + } + ], "source": [ - "利用函数`repeat`, `tile`, `vstack`, `hstack`, 和`concatenate` 我们可以用较小的向量和矩阵来创建更大的向量和矩阵：" + "v = np.array([1,2,3])\n", + "v3 = np.expand_dims(v, 1)\n", + "print(v3.shape)\n", + "print(v3)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "### 10.1 tile and repeat" + "在某些情况，需要将纬度为1的那个纬度删除掉，可以使用`np.squeeze`实现" ] }, { "cell_type": "code", - "execution_count": 93, + "execution_count": 86, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(1, 2, 3)\n", + "[[[1 2 3]\n", + " [2 3 4]]]\n" + ] + } + ], "source": [ - "a = np.array([[1, 2], [3, 4]])" + "arr = np.array([[[1, 2, 3], [2, 3, 4]]])\n", + "print(arr.shape)\n", + "print(arr)" ] }, { "cell_type": "code", - "execution_count": 94, + "execution_count": 87, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(2, 3)\n", + "[[1 2 3]\n", + " [2 3 4]]\n" + ] + } + ], + "source": [ + "# 实际上第一个纬度为`1`，我们不需要\n", + "arr2 = np.squeeze(arr, 0)\n", + "print(arr2.shape)\n", + "print(arr2)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "需要注意：只有数组长度在该纬度上为1，那么该纬度才可以被删除；否则会报错。" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 10. 叠加和重复数组" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "利用函数`repeat`, `tile`, `vstack`, `hstack`, 和`concatenate` 我们可以用较小的向量和矩阵来创建更大的向量和矩阵：" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 10.1 tile and repeat" + ] + }, + { + "cell_type": "code", + "execution_count": 89, "metadata": {}, "outputs": [ { @@ -3940,28 +4015,37 @@ "[[1 2]\n", " [3 4]]\n" ] - }, + } + ], + "source": [ + "a = np.array([[1, 2], [3, 4]])\n", + "print(a)" + ] + }, + { + "cell_type": "code", + "execution_count": 90, + "metadata": {}, + "outputs": [ { "data": { "text/plain": [ "array([1, 1, 1, 2, 2, 2, 3, 3, 3, 4, 4, 4])" ] }, - "execution_count": 94, + "execution_count": 90, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "print(a)\n", - "\n", "# 重复每一个元素三次\n", "np.repeat(a, 3)" ] }, { "cell_type": "code", - "execution_count": 95, + "execution_count": 91, "metadata": {}, "outputs": [ { @@ -3971,7 +4055,7 @@ " [3, 4, 3, 4, 3, 4]])" ] }, - "execution_count": 95, + "execution_count": 91, "metadata": {}, "output_type": "execute_result" } @@ -3983,7 +4067,7 @@ }, { "cell_type": "code", - "execution_count": 196, + "execution_count": 92, "metadata": {}, "outputs": [ { @@ -3993,7 +4077,7 @@ " [3, 4, 3, 4, 3, 4]])" ] }, - "execution_count": 196, + "execution_count": 92, "metadata": {}, "output_type": "execute_result" } @@ -4005,7 +4089,7 @@ }, { "cell_type": "code", - "execution_count": 34, + "execution_count": 93, "metadata": {}, "outputs": [ { @@ -4019,7 +4103,7 @@ " [3, 4]])" ] }, - "execution_count": 34, + "execution_count": 93, "metadata": {}, "output_type": "execute_result" } @@ -4037,7 +4121,7 @@ }, { "cell_type": "code", - "execution_count": 96, + "execution_count": 94, "metadata": {}, "outputs": [], "source": [ @@ -4046,7 +4130,7 @@ }, { "cell_type": "code", - "execution_count": 97, + "execution_count": 95, "metadata": {}, "outputs": [ { @@ -4057,7 +4141,7 @@ " [5, 6]])" ] }, - "execution_count": 97, + "execution_count": 95, "metadata": {}, "output_type": "execute_result" } @@ -4068,7 +4152,7 @@ }, { "cell_type": "code", - "execution_count": 98, + "execution_count": 96, "metadata": {}, "outputs": [ { @@ -4078,7 +4162,7 @@ " [3, 4, 6]])" ] }, - "execution_count": 98, + "execution_count": 96, "metadata": {}, "output_type": "execute_result" } @@ -4096,7 +4180,7 @@ }, { "cell_type": "code", - "execution_count": 99, + "execution_count": 97, "metadata": {}, "outputs": [ { @@ -4107,7 +4191,7 @@ " [5, 6]])" ] }, - "execution_count": 99, + "execution_count": 97, "metadata": {}, "output_type": "execute_result" } @@ -4118,7 +4202,7 @@ }, { "cell_type": "code", - "execution_count": 100, + "execution_count": 98, "metadata": {}, "outputs": [ { @@ -4128,7 +4212,7 @@ " [3, 4, 6]])" ] }, - "execution_count": 100, + "execution_count": 98, "metadata": {}, "output_type": "execute_result" } @@ -4153,7 +4237,7 @@ }, { "cell_type": "code", - "execution_count": 110, + "execution_count": 99, "metadata": {}, "outputs": [ { @@ -4163,7 +4247,7 @@ " [3, 4]])" ] }, - "execution_count": 110, + "execution_count": 99, "metadata": {}, "output_type": "execute_result" } @@ -4176,7 +4260,7 @@ }, { "cell_type": "code", - "execution_count": 111, + "execution_count": 100, "metadata": {}, "outputs": [], "source": [ @@ -4186,7 +4270,7 @@ }, { "cell_type": "code", - "execution_count": 112, + "execution_count": 101, "metadata": {}, "outputs": [ { @@ -4196,7 +4280,7 @@ " [ 3, 4]])" ] }, - "execution_count": 112, + "execution_count": 101, "metadata": {}, "output_type": "execute_result" } @@ -4210,7 +4294,7 @@ }, { "cell_type": "code", - "execution_count": 113, + "execution_count": 102, "metadata": {}, "outputs": [ { @@ -4220,7 +4304,7 @@ " [ 3, 4]])" ] }, - "execution_count": 113, + "execution_count": 102, "metadata": {}, "output_type": "execute_result" } @@ -4238,7 +4322,7 @@ }, { "cell_type": "code", - "execution_count": 114, + "execution_count": 103, "metadata": {}, "outputs": [], "source": [ @@ -4247,7 +4331,7 @@ }, { "cell_type": "code", - "execution_count": 115, + "execution_count": 104, "metadata": {}, "outputs": [ { @@ -4257,7 +4341,7 @@ " [ 3, 4]])" ] }, - "execution_count": 115, + "execution_count": 104, "metadata": {}, "output_type": "execute_result" } @@ -4271,7 +4355,7 @@ }, { "cell_type": "code", - "execution_count": 116, + "execution_count": 105, "metadata": {}, "outputs": [ { @@ -4281,7 +4365,7 @@ " [ 3, 4]])" ] }, - "execution_count": 116, + "execution_count": 105, "metadata": {}, "output_type": "execute_result" } @@ -4308,7 +4392,7 @@ }, { "cell_type": "code", - "execution_count": 117, + "execution_count": 106, "metadata": {}, "outputs": [ { @@ -4331,7 +4415,7 @@ }, { "cell_type": "code", - "execution_count": 118, + "execution_count": 107, "metadata": {}, "outputs": [ { @@ -4367,7 +4451,7 @@ }, { "cell_type": "code", - "execution_count": 119, + "execution_count": 108, "metadata": {}, "outputs": [ { @@ -4396,7 +4480,7 @@ }, { "cell_type": "code", - "execution_count": 163, + "execution_count": 109, "metadata": {}, "outputs": [ { @@ -4406,7 +4490,7 @@ " [ 9, 16]])" ] }, - "execution_count": 163, + "execution_count": 109, "metadata": {}, "output_type": "execute_result" } @@ -4804,76 +4888,11 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "* http://numpy.scipy.org\n", - "* http://scipy.org/Tentative_NumPy_Tutorial\n", - "* http://scipy.org/NumPy_for_Matlab_Users - 一个针对MATLAB使用者的Numpy教程." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 版本" - ] - }, - { - "cell_type": "code", - "execution_count": 178, - "metadata": {}, - "outputs": [ - { - "data": { - "application/json": { - "Software versions": [ - { - "module": "Python", - "version": "2.7.10 64bit [GCC 4.2.1 (Apple Inc. build 5577)]" - }, - { - "module": "IPython", - "version": "3.2.1" - }, - { - "module": "OS", - "version": "Darwin 14.1.0 x86_64 i386 64bit" - }, - { - "module": "numpy", - "version": "1.9.2" - } - ] - }, - "text/html": [ - "
SoftwareVersion
Python2.7.10 64bit [GCC 4.2.1 (Apple Inc. build 5577)]
IPython3.2.1
OSDarwin 14.1.0 x86_64 i386 64bit
numpy1.9.2
Sat Aug 15 11:02:09 2015 JST
" - ], - "text/latex": [ - "\\begin{tabular}{|l|l|}\\hline\n", - "{\\bf Software} & {\\bf Version} \\\\ \\hline\\hline\n", - "Python & 2.7.10 64bit [GCC 4.2.1 (Apple Inc. build 5577)] \\\\ \\hline\n", - "IPython & 3.2.1 \\\\ \\hline\n", - "OS & Darwin 14.1.0 x86\\_64 i386 64bit \\\\ \\hline\n", - "numpy & 1.9.2 \\\\ \\hline\n", - "\\hline \\multicolumn{2}{|l|}{Sat Aug 15 11:02:09 2015 JST} \\\\ \\hline\n", - "\\end{tabular}\n" - ], - "text/plain": [ - "Software versions\n", - "Python 2.7.10 64bit [GCC 4.2.1 (Apple Inc. build 5577)]\n", - "IPython 3.2.1\n", - "OS Darwin 14.1.0 x86_64 i386 64bit\n", - "numpy 1.9.2\n", - "Sat Aug 15 11:02:09 2015 JST" - ] - }, - "execution_count": 178, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "%reload_ext version_information\n", - "\n", - "%version_information numpy" + "* [NumPy 简易教程](https://www.runoob.com/numpy/numpy-tutorial.html)\n", + "* [NumPy 官方用户指南](https://www.numpy.org.cn/user/)\n", + "* [NumPy 官方参考手册](https://www.numpy.org.cn/reference/)\n", + "* [NumPy Tutorial](http://scipy.org/Tentative_NumPy_Tutorial)\n", + "* [一个针对MATLAB使用者的Numpy教程](http://scipy.org/NumPy_for_Matlab_Users)" ] } ], diff --git a/1_numpy_matplotlib_scipy_sympy/2-matplotlib_tutorial.ipynb b/1_numpy_matplotlib_scipy_sympy/2-matplotlib_tutorial.ipynb index 8ed7696..40cb41d 100644 --- a/1_numpy_matplotlib_scipy_sympy/2-matplotlib_tutorial.ipynb +++ b/1_numpy_matplotlib_scipy_sympy/2-matplotlib_tutorial.ipynb @@ -18,8 +18,10 @@ }, { "cell_type": "code", - "execution_count": 11, - "metadata": {}, + "execution_count": 1, + "metadata": { + "scrolled": true + }, "outputs": [ { "data": { @@ -41,6 +43,7 @@ "%matplotlib inline\n", "\n", "import matplotlib.pyplot as plt\n", + "\n", "plt.plot([1,2,3,4], '-*')\n", "plt.ylabel('some numbers')\n", "plt.show()" @@ -48,7 +51,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 2, "metadata": { "lines_to_next_cell": 2 }, @@ -56,10 +59,10 @@ { "data": { "text/plain": [ - "[]" + "[]" ] }, - "execution_count": 12, + "execution_count": 2, "metadata": {}, "output_type": "execute_result" }, @@ -435,18 +438,14 @@ "source": [ "## 3. References\n", "\n", - "\n", + "* [Matplotlib 教程](https://www.runoob.com/w3cnote/matplotlib-tutorial.html)\n", "* [Pyplot tutorial](https://matplotlib.org/users/pyplot_tutorial.html)\n", "* [Image tutorial](https://matplotlib.org/users/image_tutorial.html)\n", - "* [手把手教你用Python做数据可视化](https://mp.weixin.qq.com/s/3Gwdjw8trwTR5uyr4G7EOg)" + "* [手把手教你用Python做数据可视化](https://mp.weixin.qq.com/s/3Gwdjw8trwTR5uyr4G7EOg)\n", + "* matplotlib Gallery\n", + " - https://matplotlib.org/gallery.html\n", + " - https://github.com/rasbt/matplotlib-gallery" ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] } ], "metadata": { diff --git a/1_numpy_matplotlib_scipy_sympy/3-ipython_notebook.ipynb b/1_numpy_matplotlib_scipy_sympy/3-ipython_notebook.ipynb index a036950..841d053 100644 --- a/1_numpy_matplotlib_scipy_sympy/3-ipython_notebook.ipynb +++ b/1_numpy_matplotlib_scipy_sympy/3-ipython_notebook.ipynb @@ -9,7 +9,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 3, "metadata": { "podoc": { "output_text": "Screenshot of a Jupyter notebook" @@ -22,16 +22,6 @@ "text": [ "Hello world!\n" ] - }, - { - "data": { - "image/png": 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NeZer243DUL5lQ01uydq8Sz2TaxVT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ILXjfV1YnItLlMBKcMEQhiFelUxg6bBbafsPWDtoDovUC213nPf0+3naGcflpiGazNx3SQQxl2S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\nLOu9733vnXfeWVFxl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NDZs2crKyvNXSuIRSGTyU6dOmUw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LC2v07sbnesIu9VCSnf/0MC0zSUr2j04O6Z2yPrno7/X8o7EJ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\nMC4urub2DRs2fPLJJxKJpOlLMhpm/w7rlAm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LLjh88uFd\n53/43A+fevep3hSZVOXOyrtX3t3xeEH5gksXXXpCxQmTSya3R9vX167/w5t/+Mnyn+xs3Blb39Le\ncs2T19x1zl2pTnjm7DP3HbNv1/k3tr1x+V8vjw1njZl18wdvTl/bvLJ5PftiQqFQKLRsWadw2OGH\nJ3kx91rb/vrXyu9/P2Fy1AEHTPzYx0qPOmrYhAmhUCja1tZQWbl92bKNv/pVy/b386xVDz88+tBD\nJ557br9WnFNVDz9c9cgjCZPjTjpp4rnnjj7ssI6QTXN19fbHH9905531azvF9dZec82ogw4aNmlS\n/5UbZ/TBB9e98kpsWLdiRdE++/TmhHUrViScv+uaPW+//eYNNyRMluy33+SPf3zM4sWRceNCoVBr\nXV3t8uVb7r13+xNPxC/bfM89pcccU/bBD/amyKqHH65ZujQ2LBg1qvSoo0YtWlQ4dmz+yJFtu3Y1\nbd6884UXal94IdZKKofevuWW+tWr42fyi4unXnxx+RlnFE2fHsrLa9uzp/b55zfdddeOp54KhUKh\naHTDL3+Zm1oHtnA4fNhhh/3hD3+IzSxbtuzQQw/NYUkAAAAAAGRCLAmGjo5MUmFh3o1XHnfy8RWx\n+eHDC8aOmXjIgRNPPbHiK5f/ubGpLfbU75e+KZbEIBXLJH3mwM/c/MGbI/nvtzOZWDLxcwd/7oIF\nF3zq4U/9qfJPsfk/Vf7p92t/f+bsM7O7Ymt767VPXtvxeFzRuG8d/a3/dfD/KswrTFj2f476P1vr\nt/7nC/95y/Jb0pxt3zH7Jo31/Pq1X2dXXnpPvvNkx4NvHPmNaz5wTUHe+28ASoeXLpqw6JP7f/LU\nu099c8f7LT2WrFuys3Fn6fDSpCecNmratFHTus6XRErih6OGjTpl5ikBfAFdJPTJWLgwsM3yimbO\nnPfDH8aGXTdUmvGVr7Ts/GeEa+Dtd9a0Zcvaq66Kn8mLRGZde+2Ec86Jnwzn5xfPnl08e/akCy5Y\n9Y1vvBeJCIVCoVDl978/5uijh02e3E8V51TL9u0JEa5wYeHc732v/PTT4ycj5eUTzztvwtlnV/7f\n/7vprvfjem319euuv37Brbf2U7mdjTrkkFBchGXXihW93FQuvltSuKBg5AEHJK6IRtdec01757jP\nlIsv3ufrXw8XvH9XKRg5ctyJJ4478cStDz209tvfjra9/8aj8oYbSo84omuXoAxFW1vfvuW9W2th\naem0z39+8sc+Fi5MvA9PC4Waa2o2/frXG+7ItEVcX6hfs2bzb34TP1NUUbHwv/5reFxrt/yiorGL\nF49dvHjjnXdW3nRTKBSKtrT0d6E9FH+HLBw7NuHZ8WeeWbLf++8ti2bMCOq6CxcujI8l6ZYEAAAA\nADAoiCXBUPPdby8+6bjknwAddtCkz11y0I//64XYzMrVNZu31k+aUNxf1UHATtv3tJ+c8pO8cJJo\nyOhho+88+87jfnXcqm2rYpNXP3l11rGkJ995sqMH0qwxs5Z8bMn0USm3DJtQPOH6xddfsOCCWHZq\ngPjCIV/4zuLkmyhNGTnl9rNuP+5Xx0VD7/VUa25rfvTNRy9ccGE/FtgDL3Xe5yjAWFKkrKz8Qx9K\ns6D06KN7f5W2urrm6upMF+/Z0/2if1p33XXvp6ZCoXBh4f7/7/+NSt1TpGDkyAX/8R8rLr5418sv\nv3e5+vqNv/rVzMsvT3XIULLh9ttbduyIn5l97bUJmaSYcCSy75VXNm/fXvPHP8Ymtz/xxK4XX0y1\n31mfGnXQQfHDus5bsPVUc01N06ZNseHI+fPzhifuibnjmWdqly+Pnxl/9tkz/+3fUp1zwjnntO/Z\nE793W1NV1abf/Gb65z6XXZE7ly/v6IFUNGPG/rfdliY8Fykrq/ja18afdVZ77lI+62+5JT6SlV9S\nsuBnPxueYrvJKZ/4RMu2be/+/Of9VV320t8hi+fOLZ47ty+uu//++8cPX3jhhVQrAQAAAAAYOMSS\nYEg56bgZqTJJHT76kf1+fscre/a0xmZWrdkmlsQgVZhXeMsptyTNJHUYGRn5vRO/d8697zeJeaPm\njZe2vHTwxCQ7E3WrI5M0rmjcny7806SS7vds2q9sYLUimzZqWqpMUodDJx26eMbix9c/Hpt5ecvL\nAzOWtGHDhu1x+46FQqE5c+bkqpjsrO6b0E/96tUJ22bte/nlaTJJHcKFhXNuuumFM84Itbd3zGx9\n8MEZX/lKx/5lQ1i0ra364YfjZ0qPOmrCRz6S7phweNbVV+94+um23btjc1seeCAnsaTC0tIR++7b\n8OZ7Tc7qV69ub2zsmiXKUN1rr8UPRybbwW3L/ffHDwtGj973iivSn3bSBRdULVmyKy5HuPWBB6Z/\n9rOhcDiLIjsySYWlpfvfccew8eO7Xd+121m/adm+ffuTT8bPTPv0p4sqKtIcMuMLX6h65JH4cBjx\n5nZOO23cuHHbtm3jxo3LVT0AAAAAAGRiwO08AvTGRR/rpl/I8GH5Rx7aqbXAurd2pFoMA9wFCy5I\nuo9YvFNnnrr/+E79FX77+m97c9EfnfyjTDJJA9BnD/7siMJuUiZnzurUSmpl9cq+rCh769evT5iZ\nvHfsONatDbffHj8snj170kc/msmBRTNmjDvhhNiwta5u+7JlwdY2AO145pmmqqr4mRlf/GK3RxWW\nlk6+sFNcr2bp0raGhoCLy8youPBQtK1t9+uvZ32quhUrUp25Q2tt7bbOPxVTL7ook+3Ypn/hC/HD\nxg0banvX52bfq67KJJOUW1WPPhptfT8Fnl9UNOnjH09/SLiwcNqllwZYQ/zOeqFQKC8SSbVyUJgy\nZUrCTNd/CwAAAAAAGGjEkmDomDyxZP/55d0um73PmPjh5q27U62EAe4jc9M2Nfmns2afFT/8U+Wf\nsr7i7LGzz9vvvKwPz61MKl84vlO08Z1d7/RZOb2S8FH02LFji4qKclXMwBFtbq75wx/iZ6ZcfHEo\nL9M3e+Wnnho/3PXKK4FVNlDtfO65+OGwCRMStkVLpey00+KHbQ0NCZmefjO6c5emVGWsueKK17/0\npY7/bf6f/0m6JjGW1OWlqH3ppY5mRTEJr0MqpUccUTim03uPnX/7WyYHJlVUUZF+E7EBorbz11h6\n5JGZRLjGnXxygDUkXDGTAgaycePGDe/cDEwsCQAAAABg4LOJGwwdC/cry2TZxAkl8cP6hpa+KYcc\n+PcbTmpuaUuYLMjPZqOcgS+SH1k8Y3EmKz+4zwdveuam2HDd9nV7WvcUFWSTYrlk0SVZHDUQjC8e\nP33U9G6XJXSf2t08QGOLmzpvcjR+wPdN6R91//hHe1xqJJyfP+7EEzM/fOSBB8YP94ZYUkJvoTEf\n+ECGO4uVzJsXmTCheevW2Ez9G2+UHnlkwPVlIKGn0a5XX03sJxMK7Vm/futDD8WGjRs3Tjr//MRF\n0ejuf/wjNiqqqIh02Rur/o034ofDp09PvyVZTDg/v/SYY6p///vYzO7Op+qRieeem/Wx/Snhp6v0\n6KMzOSpSVlayYMHulcF0qsvvnEPKH+SxpFAoNH78+HfeeT8vu8mGdwAAAAAAA55YEgwd+3Zug5TK\niKJOv/gNDa2pVjLozJs9Ntcl9J+ZpTMzjBbtN26/+GFbtG1l9cpDJx2axUVPnNGDkMeAMr9sfibL\niguL44d1zXV9U05v7d7dKS81YkQ3m9MNQONOOGH41KkZLt75/PP1q1d3u6z2xRfjh8Vz5hSMHp15\nScMnTw7l5YXa2zuGDevWZX7sIJWQsxkxZ07mxxbPmRMfS6oLKEfSU8OnTh02fnxsK7q6117rumbH\n00/HD+tXr26qqkrYBK2hsrK17v3f9647uIW65GxK5s7NvM6SuXM7xZJ6sdlc6VFHZX1sv2netq0p\n7scjFAoVz56d4bEjZs0KKpaU2C2ppCTVysEi4W5fX1+fq0oAAAAAAMiQWBIMHaNKIpksixTmxw9b\nWhOb68CgMHtsph/xlg4vLRtRVtNQE5tZt2NdFrGk4QXDF5Qv6OlRA8SY4RnFFoflD4sfNrc1p1qZ\nWw0NDfHDhG19BoUJ55477oQTMlz85g03ZBJLSuhAUzRzZrS1NRQKhaLR2P9HO56Ln4m+NxeKRgtH\nj27ZsaNj1N7U1N7cnBfJ6F+Wwai1tjY+iBMKhUbMnJn54UX77LPjqadiw6YNGwKrrIdGHnRQ09Kl\n75WxaVNzTU2krFP3xJ3PPptwyM5nn51wzjnxMwk7uI1OFktq3Lgxfli0zz6ZF5mwuLm6ur2pKW/Y\nsFTrU8mLRDLP9+RQY5efh+EzZmR47IievLDp5XfOIeUP/lhSwn6dYkkAAAAAAAOfWBIMHcUjCrM4\nKvZ5NAwuE0smZr54/Ijx8bGk2sbaLK64T+k+BXmD9d/Nkkg2n0ZHB+oNorm5U16qsDCbu9/Q07pz\nZ/ywesmS6iVLenXC2tpIeXnvihq4Wncn7lGYkOZJb1jnV6alLmetxUYdfHDNP2NJoVCobsWK+M37\noi0tO//+94RDdjz1VPpYUtJuSQkprh79bBR2eW1b6+oiPY8lDZ82LVwwCO7Dbb346erRz2F6id2S\nBv8mbpHOQcmmpqZcVQIAAAAAQIbycl0AEJi8vHCuS4D+M6KgB/t2FUc67U22q3lXFlccFRmVxVED\nRH44v/tFg8ewzmkGn0x3aK3NJm+XRtuQ7kTS2iVIlN+T3QDzOvfoas/da5XQ2SghYLTr5Zfb/tld\nrPif267t/NvfYrv1vXfUq6/GHkfKyooqKrpeqK3zK5ZflNE2mqkWdw3uZHSeQdLvJ+GnKxyJhPMz\nvQn36Oewm1MNuU3cEu72g7FVHgAAAADA3kYsCYBBaURhDz64LSro9Il4XdP/Z+/Ow6uq7v0Bn8y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- "text/plain": [ - "Screenshot of a Jupyter notebook" - ] - }, - "metadata": {}, - "output_type": "display_data" } ], "source": [ @@ -40,7 +30,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 4, "metadata": {}, "outputs": [ { @@ -49,7 +39,7 @@ "4" ] }, - "execution_count": 2, + "execution_count": 4, "metadata": {}, "output_type": "execute_result" } @@ -60,7 +50,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 5, "metadata": {}, "outputs": [ { @@ -69,7 +59,7 @@ "12" ] }, - "execution_count": 3, + "execution_count": 5, "metadata": {}, "output_type": "execute_result" } @@ -80,14 +70,24 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 6, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "my_notebook.ipynb\n" + "1-numpy_tutorial_EN.ipynb\texample.png\r\n", + "1-numpy_tutorial.ipynb\t\tmatplotlib_ani.ipynb\r\n", + "2-matplotlib_tutorial_EN.ipynb\tmatplotlib_full.ipynb\r\n", + "2-matplotlib_tutorial.ipynb\trandom-matrix.csv\r\n", + "3-ipython_notebook_EN.ipynb\trandom-matrix.npy\r\n", + "3-ipython_notebook.ipynb\tREADME.md\r\n", + "4-scipy_tutorial_EN.ipynb\tstockholm_td_adj.dat\r\n", + "4-scipy_tutorial.ipynb\t\tutils_git_advanced.ipynb\r\n", + "5-sympy_tutorial_EN.ipynb\tutils_git.ipynb\r\n", + "5-sympy_tutorial.ipynb\t\tutils_shell.ipynb\r\n", + "bokeh_tutorial.ipynb\r\n" ] } ], @@ -97,14 +97,144 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 7, "metadata": {}, "outputs": [ { "data": { + "application/json": { + "cell": { + "!": "OSMagics", + "HTML": "Other", + "SVG": "Other", + "bash": "Other", + "capture": "ExecutionMagics", + "debug": "ExecutionMagics", + "file": "Other", + "html": "DisplayMagics", + "javascript": "DisplayMagics", + "js": "DisplayMagics", + "latex": "DisplayMagics", + "markdown": "DisplayMagics", + "perl": "Other", + "prun": "ExecutionMagics", + "pypy": "Other", + "python": "Other", + "python2": "Other", + "python3": "Other", + "ruby": "Other", + "script": "ScriptMagics", + "sh": "Other", + "svg": "DisplayMagics", + "sx": "OSMagics", + "system": "OSMagics", + "time": "ExecutionMagics", + "timeit": "ExecutionMagics", + "writefile": "OSMagics" + }, + "line": { + "alias": "OSMagics", + "alias_magic": "BasicMagics", + "autoawait": "AsyncMagics", + "autocall": "AutoMagics", + "automagic": "AutoMagics", + "autosave": "KernelMagics", + "bookmark": "OSMagics", + "cat": "Other", + "cd": "OSMagics", + "clear": "KernelMagics", + "colors": "BasicMagics", + "conda": "PackagingMagics", + "config": "ConfigMagics", + "connect_info": "KernelMagics", + "cp": "Other", + "debug": "ExecutionMagics", + "dhist": "OSMagics", + "dirs": "OSMagics", + "doctest_mode": "BasicMagics", + "ed": "Other", + "edit": "KernelMagics", + "env": "OSMagics", + "gui": "BasicMagics", + "hist": "Other", + "history": "HistoryMagics", + "killbgscripts": "ScriptMagics", + "ldir": "Other", + "less": "KernelMagics", + "lf": "Other", + "lk": "Other", + "ll": "Other", + "load": "CodeMagics", + "load_ext": "ExtensionMagics", + "loadpy": "CodeMagics", + "logoff": "LoggingMagics", + "logon": "LoggingMagics", + "logstart": "LoggingMagics", + "logstate": "LoggingMagics", + "logstop": "LoggingMagics", + "ls": "Other", + "lsmagic": "BasicMagics", + "lx": "Other", + "macro": "ExecutionMagics", + "magic": "BasicMagics", + "man": "KernelMagics", + "matplotlib": "PylabMagics", + "mkdir": "Other", + "more": "KernelMagics", + "mv": "Other", + "notebook": "BasicMagics", + "page": "BasicMagics", + "pastebin": "CodeMagics", + "pdb": "ExecutionMagics", + "pdef": "NamespaceMagics", + "pdoc": "NamespaceMagics", + "pfile": "NamespaceMagics", + "pinfo": "NamespaceMagics", + "pinfo2": "NamespaceMagics", + "pip": "PackagingMagics", + "popd": "OSMagics", + "pprint": "BasicMagics", + "precision": "BasicMagics", + "prun": "ExecutionMagics", + "psearch": "NamespaceMagics", + "psource": "NamespaceMagics", + "pushd": "OSMagics", + "pwd": "OSMagics", + "pycat": "OSMagics", + "pylab": "PylabMagics", + "qtconsole": "KernelMagics", + "quickref": "BasicMagics", + "recall": "HistoryMagics", + "rehashx": "OSMagics", + "reload_ext": "ExtensionMagics", + "rep": "Other", + "rerun": "HistoryMagics", + "reset": "NamespaceMagics", + "reset_selective": "NamespaceMagics", + "rm": "Other", + "rmdir": "Other", + "run": "ExecutionMagics", + "save": "CodeMagics", + "sc": "OSMagics", + "set_env": "OSMagics", + "store": "StoreMagics", + "sx": "OSMagics", + "system": "OSMagics", + "tb": "ExecutionMagics", + "time": "ExecutionMagics", + "timeit": "ExecutionMagics", + "unalias": "OSMagics", + "unload_ext": "ExtensionMagics", + "who": "NamespaceMagics", + "who_ls": "NamespaceMagics", + "whos": "NamespaceMagics", + "xdel": "NamespaceMagics", + "xmode": "BasicMagics" + } + }, "text/plain": [ "Available line magics:\n", - "%alias %alias_magic %autocall %automagic %autosave %bookmark %cat %cd %clear %colors %config %connect_info %cp %debug %dhist %dirs %doctest_mode %ed %edit %env %gui %hist %history %killbgscripts %ldir %less %lf %lk %ll %load %load_ext %loadpy %logoff %logon %logstart %logstate %logstop %ls %lsmagic %lx %macro %magic %man %matplotlib %mkdir %more %mv %notebook %page %pastebin %pdb %pdef %pdoc %pfile %pinfo %pinfo2 %popd %pprint %precision %profile %prun %psearch %psource %pushd %pwd %pycat %pylab %qtconsole %quickref %recall %rehashx %reload_ext %rep %rerun %reset %reset_selective %rm %rmdir %run %save %sc %set_env %store %sx %system %tb %time %timeit %unalias %unload_ext %who %who_ls %whos %xdel %xmode\n", + "%alias %alias_magic %autoawait %autocall %automagic %autosave %bookmark %cat %cd %clear %colors %conda %config %connect_info %cp %debug %dhist %dirs %doctest_mode %ed %edit %env %gui %hist %history %killbgscripts %ldir %less %lf %lk %ll %load %load_ext %loadpy %logoff %logon %logstart %logstate %logstop %ls %lsmagic %lx %macro %magic %man %matplotlib %mkdir %more %mv %notebook %page %pastebin %pdb %pdef %pdoc %pfile %pinfo %pinfo2 %pip %popd %pprint %precision %prun %psearch %psource %pushd %pwd %pycat %pylab %qtconsole %quickref %recall %rehashx %reload_ext %rep %rerun %reset %reset_selective %rm %rmdir %run %save %sc %set_env %store %sx %system %tb %time %timeit %unalias %unload_ext %who %who_ls %whos %xdel %xmode\n", "\n", "Available cell magics:\n", "%%! %%HTML %%SVG %%bash %%capture %%debug %%file %%html %%javascript %%js %%latex %%markdown %%perl %%prun %%pypy %%python %%python2 %%python3 %%ruby %%script %%sh %%svg %%sx %%system %%time %%timeit %%writefile\n", @@ -112,7 +242,7 @@ "Automagic is ON, % prefix IS NOT needed for line magics." ] }, - "execution_count": 5, + "execution_count": 7, "metadata": {}, "output_type": "execute_result" } @@ -123,7 +253,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 8, "metadata": {}, "outputs": [ { @@ -141,14 +271,15 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 9, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Hello world!\n" + "Hello world!\n", + "\n" ] } ], @@ -160,31 +291,20 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 10, "metadata": { "podoc": { "output_text": "Screenshot of the pager" } }, - "outputs": [ - { - "data": { - "image/png": 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wcXEZM2aMl5dXZGTk7NmzxWiezZs3T5kyJavdGrhZ0yO9H+FM5b4dNtEjnJaWJgZU2dnZdejQ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\nDw9p8uHDh9lucv/+fX1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- "text/plain": [ - "Screenshot of the pager" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "%run?" ] }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 11, "metadata": {}, "outputs": [], "source": [ @@ -193,7 +313,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 12, "metadata": { "podoc": { "output_text": "" @@ -202,13 +322,19 @@ "outputs": [ { "data": { - "image/png": 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zP9NN+jz9rTq2F3+XoM/H7n5l5mf7JYL88xfrtb9qnslgvzzF+u1v2qwPczP9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\n6V+g20X1ZY8i8irarWuXqWvY17g1w1G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- "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "YouTubeVideo('VQBZ2MqWBZI')" ] diff --git a/1_numpy_matplotlib_scipy_sympy/4-scipy_tutorial.ipynb b/1_numpy_matplotlib_scipy_sympy/4-scipy_tutorial.ipynb index 4c42c6d..c8542f6 100644 --- a/1_numpy_matplotlib_scipy_sympy/4-scipy_tutorial.ipynb +++ b/1_numpy_matplotlib_scipy_sympy/4-scipy_tutorial.ipynb @@ -11,8 +11,6 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "J.R. Johansson (jrjohansson at gmail.com)\n", - "\n", "最新的[IPython notebook](http://ipython.org/notebook.html) 课程可以在这里找到： [http://github.com/jrjohansson/scientific-python-lectures](http://github.com/jrjohansson/scientific-python-lectures).\n", "\n", "其他的这个课程系列的笔记在这里[http://jrjohansson.github.io](http://jrjohansson.github.io)." @@ -20,7 +18,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 7, "metadata": {}, "outputs": [], "source": [ @@ -63,15 +61,6 @@ ] }, { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "from scipy import *" - ] - }, - { "cell_type": "markdown", "metadata": {}, "source": [ @@ -105,7 +94,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 8, "metadata": {}, "outputs": [], "source": [ @@ -113,12 +102,13 @@ "# 在这里我们将会运用函数jn和yn，它们是第一类和第二类实值阶的贝塞尔函数。 \n", "# 我们同样包含了函数jn_zeros和yn_zeros，它们给出了函数jn和yn的零点\n", "#\n", - "from scipy.special import jn, yn, jn_zeros, yn_zeros" + "from scipy.special import jn, yn, jn_zeros, yn_zeros\n", + "import numpy as np" ] }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 9, "metadata": {}, "outputs": [ { @@ -135,31 +125,33 @@ "x = 0.0\n", "\n", "# 第一类贝塞尔函数\n", - "print \"J_%d(%f) = %f\" % (n, x, jn(n, x))\n", + "print(\"J_%d(%f) = %f\" % (n, x, jn(n, x)))\n", "\n", "x = 1.0\n", "# 第二类贝塞尔函数\n", - "print \"Y_%d(%f) = %f\" % (n, x, yn(n, x))" + "print(\"Y_%d(%f) = %f\" % (n, x, yn(n, x)))" ] }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 10, "metadata": {}, "outputs": [ { "data": { - "image/png": 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rBas9V+Ny3GWdCqmJwa+/sqKUpTZZuX0bePdd4KOPWLiPDtE52vAo/xG6OXXD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\nTkSuQmCC\n", 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/KN4YnEtA3qJgYQXJGt3zIXfg/lHd19llcOp3KNEE6g6GUs1BCFO9REXJ1S5dusRbb731yuczW+KwR48e2Q9KSpkjv7y8vOSb7veDt6Xn6K3yun+klMkaKY/NlXK6p5TfOEq59iMpH56WUqvNeIPRQVIe/EnKGeV0bazqImXIXYPFryg5zdWrV00dwlN169aVp0+fTvP5e/fuPf25SZMmcvfu3VJKKcePHy8HDx4spZTywoULsnXr1qm2kdrrRXehnWpeVZunmVBnLw9m7LrJ9iOnKBv1s+4qveTb0GwiFK6e+Qbz5IdGI6D+UDj5OxyYBvPq6B5rMAzM0rlXoChvkICpU0m4pt8Sh9bly1Fo3Lh0j5M5tMShSvgm5JzHitFFr9HlygyklUB0XABVu2d/GMbcEuoNhorvwa6xsG8K+ByB95dAHhf9BK8oyis9W+Lw2rVrzJ49m+DgYJo1a8aAAQPSLXF44MCBVEscvlgJK7NUwjcVKeHQT/Tx/55z2lKENJ5Di2p19duHUxHougLOrYBtI2BhY+i6HIp46bcfRcmBMnIlbijPljgsX748CxYsQKvV0qtXLwYMGKBKHOYq2mTYNgz2f4+s0p1heaax9KoBb67W6AWfppRMW9Iarm83XF+KorxU4nDLli20bduWNm3aAKrEYe6hSYR1veDMEmjwFeK9BXSuVZxjd0K4HxJjuH6L1IC+B6FQJVjbEy6vN1xfipLLPVviEKB9+/bs2LGD1atXP31MlTh802mTYePncH0rtJoOzb8FIejs5YGZgHVnHqbbRLbkcYFem3ULtTZ8BmeXG7Y/RcmFXixxeODAAb744gv69ev39AofVInDN5uUumGcq//AO1OgzoCnTxVysqFp2QL8fcaXr5qXwcLcgO/D1g7Q42/dp4x/vwAzC6iuh/m9iqKkWuKwSZMmNGnS5KVjTVHiUF3hG8t/3+kWRjUcDvWGvPR0t1oeBEYlsP+GEfYQsrKD7mugRFPYMgRu7jJ8n4qSC4wYMYLz588/dyWflk8//TTdhVe9evXSV3gq4RvF2WVwZBZ4fQJvf53qIU3LFcDVwdrwwzpPWFhBt5VQqDKs6w0P9TPPV1GUnEslfEN7eEo3JbJkM2j78yvn2Fuam9GpRhH2XQ8kMMpINW+tHaDHenB0gzVddNs0ZJJGqyEwNhD/aH9C40OJTYpFt9hPUZScRo3hG1KkP6z9SDcf/v1F6a507eLlwe8H7/LP+Uf0bVTSODHau0LPjbCoGfz1IXy2V/dGkIqAmADOPT7HxaCLXA6+zKPoR4TFhyF5PsHbWthS0qkkJfOWpGL+ijRyb0QR+xdLJCiKYUgpEblgD6msXFiJnHo1VrNmTXnmzBlTh5F1mgRY1g4ee8Nne6Bg6kusX9Rp3lGi4jXs/qqRcX9p7x6ElR2hXFvouvLpJ5HA2EB2++xmh88OLgVdAnQJvaJLRYo5FSO/bX7y2+TH0tySeE08CckJPI59zO3w29wJv0NwXDAApfKWorlnc7qU6UIBuwLGe11KrnLv3j0cHBxwcXF5o5O+lJKQkBCioqIoXrz4c88JIc5KKWumdp66wjeUfZPB9xR0WZbhZA/QtaYHYzZe5sLDcKoXzWe4+F5UojG0mAy7x8Phn7leqR1Lryxll88ukmUy5ZzLMbTGUOoXrk/pfKWxMMvYr45PhA8HfQ9y0Pcgv1/8nUWXFtHCswUfVfiIyq6VDfyilNzG3d0dX19f3uQCSk/Y2Njg7p7K1uppUFf4hnD3AKzooNubvt2sTJ0aFZ9Ere/30qmGO1PfM3JClJJrf3/I7JDTHLWzxc7Cjs5lOvN+mfcp4VQi280/jHrIX9f/YtOtTUQlRdHCswVf1fgKD0eP9E9WFCVD0rrCVwlf32JDYX59sMoD/Q7ppkBm0rB1F9jj/ZhT45tja2WcHS6D44L57fxvbLq1CScp6R2TRNcPduDoXDz9kzMpNimW5VeXs/TKUpK0SfQo14OB1QZiZ5n5vytFUZ6XVsJXs3T0SUr4dyjEBML7f2Qp2YNuWCcqQcNOb389B/gyKSXrbqyj3aZ2bLm9hY8qfMS25ov5LDQUxx2jdK9Jz+ws7RhQdQDb3ttG+5LtWX51OV3+7cKFwAt670tRlP9TCV+fLq2Da1vg7QlZ288+xVvFnSnqbMffZ3z1GNzLAmIC6LenH5NPTKZS/kps6rCJkbVG4uheW7ca+NZu3b76BuJq58p39b5j8TuL0Wg19N7Zm1/P/YpGqzFYn4qSm6mbtvoSEww7x4B7Laj3RbaaEkLQ2cudWXtv4hsWi3s+/Q917PLZxbfHviVZJjPhrQl0Ldv1+VkNtT+HO/tgz9dQrIFu0zUDqe1Wmw3tN/Dj6R/54/IfXAq6xIzGM8hrk/eV52gTEki4eZP4q9dI8vNDExCAJigQbWIiJGtBq8XMwQGL/PmxyO+CVbFiWJcvj3Xp0phZWRnstShKTqbG8PVlY1+4shH6H4YC5bPdnG9YLA1+2M+wFmX4ollpPQSok5ScxMyzM1l1bRVVXKswveF0PBxecdM0JkRXMcuhIHy+X1dYxcA2397MpOOTcLVzZXbT2ZR1LguA1GiIu3iR6IOHiDlyhPibN0GT8knA3ByLAgWwKOCKmY0tmAmEMCM5KgpNSDDJQcHIpJQi8JaW2FaujH3jxtg3aYJ1mdJv9PQ9JfdRN20N7fZeWPU+NBoFb4/XW7Mf/nEC37A4Do5sopekFBgbyLADw7gYdJGe5XsyzGsYlukl8WtbYW0PaDoeGo/KdgwZcTnoMl/u/5KoxEhmFxpCsaP3iNq+g+SICLCwwK56dWyrV8emYkVsKlbE0q0QIo39SKRWS5KvL/FXrxLv7U3MsePEe3sDYFWsGHm7dMGp03tY5DPiNFhFMRCDJ3whRCtgNmAOLJJSTn/Fce8D64FaUso0s/lrk/ATomFeXbC0gf5HwMJab01vPOfLsHUXWdevLrWLO2errRuhNxj03yAiEyOZVH8SrYq1yvjJ6/vA1c3Q72Cm1hRklTY+Ht91q7i3eA4FHiegtbIg7zutcGjenDz162GezTJvAEmPA4k+eICIfzYTd+4cwtISxzatcenfH+vi+p+ZpCjGYtBZOkIIc2Au0BqoAHwghKiQynEOwFDgZHb7zFEO/QQRD+DdX/Wa7AFaVSpEHitz1p/N3oZqh3wP0WtHLySSla1XZi7ZA7T+EWzzwj8DIDkpW7GkJTkigqC5c7n9djNipv5Mkfwl2NmtOJ8OgsOfeeHYqqVekj2AZcEC5OvalWJrVlN8y2bydutG5O493G3bDr/Ro0m8f18v/ShKTqKPWTq1gdtSyrtSykTgL6BDKsdNBn4AjLQzmBGE3IHjc6FaD/DUcz1awM7KgrZV3Nh2yZ/YxKzNXPn75t8M2TcET0dP1rRZ83RMPFPyuEDbmeB/EY79mqU40qKNiyP4jz+43eIdgn+bg22VKhRdsZyS6zcw4OuN1C7dhO9Pfs8K7xV67xvApkwZCk0YT6m9e3Du3ZvIXbu50+5dAn+eiTY21iB9Koop6CPhFwGevQT1TXnsKSFEDcBDSrktrYaEEH2FEGeEEGdei6XRO8eChQ00+8ZgXXT28iAmMZkdlwMyfe6iy4uYdHwS9QvXZ1mrZRTMUzDrgVRoD+XfhYM/Qui9rLfzDKnVEr5hI3feaUnQzzOxq16d4pv/wWPBfPLUro0QAhsLG2Y1mUULzxb8dOYn1l5fq5e+U2Ph4kLB0aMotWc3Tu3aEfLHH9xp247IPXsM1qeiGJPB5+ELIcyAmcDw9I6VUi6UUtaUUtZ0dXU1dGjZc3MX3NoFTUbrZrEYSK1i+fB0sWP92YzPyZdSMvPsTGafm02b4m2Y/fZs/axibfWDrkLW9pHZXpAV5+3N/Q8+xH/8eCwLF8Zz1Uo8fl+ATdmXP4FYmFnwQ8MfaOzemCknp7Dp1qZs9Z0eC1dXCk+biufqVZg7OvJoyBf4jR5NclSUQftVFEPTR8J/BDw7r8895bEnHIBKwAEhhA9QB9gihEj1psJrQZOgm3PvUhpq66fW5KsIIXi/hjvH74bgG5b+8IJWavn+5PcsvbKUbmW7Ma3hNCzN9DSd0qmIbrbO7T26m7hZoI2NJWDqVHw6dyHR1xe3adPw/HMNdjXT/nWwNLfk5yY/U9etLt8c+4Y99w1/1W3n5UXxDevJP2gQEVu3ca9DR2Jfh4kEivIK+kj4p4HSQojiQggroDuw5cmTUsoIKWV+KWUxKWUx4ATQPr1ZOjnayQUQeldXiNzC8It43quuGyHbdO5RmsdppZbvT3zP2htr+aTSJ4x/azxmQs8f4mr31VXJ2jkG4iMzdWrMiZPcbd+BsBUryfdBd0ru2E7e9zoizDIWo7W5NbPfnk1V16qMOTSG84Hns/IKMkVYWOA6ZDCeq1aChQX3e/UmZMlSVeRFeS1lOxtIKTXAYGAXcA1YJ6X0FkJMEkK0z277OU5sKBz+GUq1gNLNjdKlh7MddUo4s+Gc7ysTjVZqmXJiCuturuOzyp/xVY2vDLOgyNwC2s2GqAA4MC1Dp2jj4wmYNJkHH38M5mZ4rlxBoYkTMXd0zHT3tha2/Pr2r7jZuzFk3xDuRejnfkJ67KpXp/jGjTi0aEHgjz/iN3yEuqGrvHb0cvknpdwupSwjpSwppfw+5bGJUsotqRzb5LW+uj/8s+7KtsV3Ru32/Rru+ITEcvZ+2EvPSSmZenIqf9/8m88qf8YX1b8w7OpRdy/w+li3z07g9TQPjb92jXvvdyZszRqce/eixD//YFerVra6z2eTj/nN5mMuzBmwdwAhcSHZai+jzO3zUOSXWbgOH0bkjh34fPAhSf6G3+BOUfRFbZ6WGWH34dRC3TRMIyxAelbrym7YWpqz4dzzN2+f3KB9Moxj8GT/xNtfg7U97Byd6g1cKSWhy5fj07Ub2shIPBYvouDYsZjZ2uqlew9HD+a8PYeQuBCGHRhGkgHXBzxLCEH+zz/HY+FCkh49wqf7B8TfuGmUvhUlu1TCz4z934Mwg6bjjN61vbUFrSsXYutFf+KTkp8+vuDSApZ5L6N72e6GG8ZJTR4X3Q3cuwfg+vOzbZMjI/EdMoTH06aTp2FDim/ZjH39+noPobJrZSbVn8S5wHNMP5Xq4m6DsW/YAM/Vq0BK7vfoQcyJN2s9ofJmUgk/o/wvwqW1UGeAbraKCXSu4U5UgoZd3ro5+Su8VzDvwjw6lOzA2LfGGn8TsJp9wLU87BoLSXEAxF3x5l6n94k+cJCCY8fgPneOQfeoaV28NZ9W+pR1N9ex7sY6g/WTGpuyZSn2159YFCrIw88/J2rvXqP2ryiZpRJ+Rv03GWzzQf0vTRZCnRIuFMlry4Zzj9hyZws/nfmJFp4t+Lbet/qfjZMR5hbQ+gcIfwDH5hC+YSP3P/wQmZxMsVUrce7d2yhvQl9U/4IGRRow7eQ0zj0+Z/D+nmVZuDDFVq/GukJ5fL/8isidO43av6Jkhkr4GfHgpG7uef2huj1lTMTMTPBe9SIc9zvM10cn8pbbW0xvOD3DBcUNokRjZOm2BPw8H//x47GtUYPiGzdgW62a0UIwNzPnh0Y/UNi+MCMPjjTaTdyn/Ts5UXTxYmyrVuXRsOFE/PuvUftXlIxSCT8j9k+BPK66OegmVr54CNZFVuNiWYzZTWdjZW7aYh6a4GDub04g7KY1zo08KbroD5NsM+xo5cjMJjMJTwhnzOExJGuT0z9Jj8zt7Sn6x0LsatXCb9RolfSVHEkl/PTcOwz3DkGDYbrC5CZ0N/wuU8+OxIp8EPAZdhamLfodf+0a97p0Jf7mXQr3qknBIicRwWlP0zSkss5lGffWOE74n2Dh5YVG79/Mzg6PBfOxq10bvzFj1Zi+kuOoEodpkVI3M8fBDWp+atJQAmMD6b+3P5ZmlnxYaio/bQvm8qMIqrjnNUk8kbt34zd6DOZOTniuWoVtCTf49RDsngAfbQITVZHqVLoTZx+fZf6F+VQvUJ06bnWM2r+ZrS3uc+fyoM+nPPpqGO4L5htkhpIh+YXHcfR2MLeDonkYGsuD0Fjik7SYCTATAuc8VhTLn4fiLnmo4u5EDc98WJqra8fXgap4lZbb/8GqTtBmhq7Gq4lEJ0bz8c6PeRD1gKWtluJuV4Za3+/lg1oefNfBcLVmUyOlJGThHwTNmoVt1aq4z/kNiycb3R2fp5ux02M9lG5h1LieFZsUS/dt3YlOjGZD+w3kszH+EFNyRAT3e39Moo8PRZcswa5G1ovaG8PtwCjWnn7I/htB3A6MBsDK3Ax3Z1s88tmRx9ocKSFZKwmMSuBecAwRcbq1D/bWFtQr6ULLioVoW8UNG8tXVx9TDE+VOMwKKWFxC90WAkPO6r24SUYlaZMYtHcQpwNO81uz32hQpAEAg9ec4+jtYE6Oa46VhXGurmRiIv4TvyHin39wbNsWt6nfY2b9zN+LJhHmvQXm1jDgKJiZ7j/+9dDrfLjtQ+oXqc+vTX81Sd1aTUgI9z/sQXJ4OJ5//ol1iZxVSUurley++pgVx304dicES3NBnRIuNC7jSsPSrpQqYI+52av/3kJjEjntE8rBm0EcvBHEo/A4nGwt6eLlzkd1PfF0Me0QaG6VVsJHSpkjv7y8vKRJ3dkv5TeOUp5aZLIQtFqtHH94vKy0rJLcdGvTc8/tu/5Yeo7eKndc9jdKLJqwMOnTo6e8WracDPxtjtRqtakfeGWj7u/t3CqjxJWWFd4rZKVlleTa62tNFkPCgwfyRr368tbbzWRSYKDJ4njR8TvBst2vh6Xn6K2y3rT/5Jx9t2RQVHyW29NqtfLY7WA5cPVZWXLsNlli7DY5buMl+TgiTo9RZ5ImUcq4iP9/aZJMF4sRAWfkK/KqusJ/laVtIfQOfHFBV6/WBOZfmM+8i/MYUHUAA6sNfO45TbKWutP3UdU9L4t6G3an6cT793nYrz9Jjx7hNnUqTu+2e/XBUsKiZv//ZGSpn60UskIrtQzcO5Azj8+wtt1aSuYtaZI44i5f4X7v3lgV88RzxUrM7U135esXHsc3W7zZc/Uxbk42jGxZlg7ViqR5JZ9ZgZHxzN1/m9UnH2BpbsbnjUowsElJww71RD0Gn8PgcwSCb0H4fYh8BFL7/2OEOTgWgbxFwbUMeNbXfTm6GS4uE1BDOpl1/xgsba3b/rjOAJOEsPn2ZiYcnUD7ku2ZUn9KqkMSU7dfY8mRe5wc1wwXe8MMOcWeO4fvwEEAuM+dg52XV/on3TsMy9tBi0m6tQsmFBwXzPtb3qegXUFWt1mNpbmeagNkUvShQzwcMBD7Bg1wnzcXYW7c4S4pJf9ceMTEzd4kayWDmpaiT4PiBk3C90NimLH7Jv9e9KOkax5+7FwVL0893k+JDtStfr/4Fzy+onvM2hEKVIB8npDXE2ycUg6WEB+hWyQYdh8ee0NiSkEb1/JQpQtU7gp5PVLt6nWiEn5mreio+wUaegmsjD/18YT/CQbsGYBXIS/mN5v/yiR1IyCKlr8cYmK7CnzaQP/jw5Hbt+M3ZiyWbm54LPwdK0/PjJ+8qjP4noKhF3UrlE1o34N9DN0/lH5V+jG4+mCTxRH2558EfDcJ508+oeDoUUbrNyIuiXEbL7Ptsj9envmY2bWqUcfXD90MYuzGy/hFxPFp/eKMalUWa4tsvNH4nYdDM+DmTtBqwL0WlGsHxRtCoaq6FeDpSdZAwCW4f1S3F9SD44CAEo11q+lLNDH4TDMpJREJETyMesjDqIdEJEaQlJyERmpwsXGhQ6nUSoOnTyX8zPA9oxuSaP4dNPjS6N3fCrtFrx29KJSnECtar8DByiHN49/97QjJWsn2oQ31FoOUkpBFiwj6eSa2Xl66mTiZXUwVcAUWNIB6Q+CdyXqLLavGHxnPtrvbWNl6JZVdK5ssjoDJUwhbvRq3KZPJ27mzwfu7ExTN58vP8CA0lmHvlKFfo5J6Hb7JqOgEDT/suM7KE/ep6u7EnA9r4OGcyYspvwtwYDrc3AE2eaFGL6jeE1xfLouZaaH3dJ8WziyF6AAo4gWNRkGZlnpN/A8jH3Lc/zinA05zOuA0IfGprwqvnL8ya9quyVIfKuFnxpru8PAEfHkZrNNOtvoWFBtEj+090Gg1rG6zGjf79McWlx/z4Zst3mz/oiEVCme+oMiLZFISAZOnEL5uHY5t2uA2berzM3EyY1N/8N4EX5wHx8LZji07ohKj6LSlEzbmNvz97t/YWJjmvozUaHjYfwAxJ05QdMli8tSubbC+Dt4MYvCac1iZmzG/pxe1izsbrK+M2nnFn5F/X8LMTDCrW1XeLpeBetBxYbD3Wzi7TDdEU3cIvNUPbLL/+/6SpHi4uAaO/KK7D1Ciia6Wc4FyWW4yPD6cnT47+ffuv1wKugRAAdsC1HKrRQXnCng4eODh4EFem7xYmVthaWaJpZlllrdMUbN0MirAWzfDZP80o3cdkxgju2zpImutqiW9g70zfF5odIIsNW6bnPRvxs95FU1UlLzf5zN5tWw5+XjGz1KbnJy9BkN9pPzORcotX2Q7Nn047ndcVlpWSU4/Od2kcWgiI+Xt1m3kjbr1ZOKjRwbpY/WJ+7L4mK2y5ayD8mFojEH6yKp7QdGy9S+HZLExW+XCg3dePeNLq5Xy4jopfywp5bf5pNw5Tsq4cOMEqUmS8uRCKacV1fW9Y4yUCdGZasI3yldOOT5Feq30kpWWVZId/+kol1xeIu+F33v1a9YD0pilY/LE/qovkyT8DX2lnFJIypgQo3ablJwkB+4dKKssryIPPjyY6fP7rTgja0zaLRM1WU/Qif7+8k77DvJqhYoydK0epzFuG6H7DxN8W39tZsP3J76XlZdVlucenzNpHPF37srrXjXl3U7vy+Q4/U5d/P3gbek5eqv8eMlJGR2fM6cixiVq5MBVZ6Xn6K1ywqbLMunF3924cCnX9dZdgC1sKqXfRZPEKaODpdwyVMpvnKScXU3K+yfSPcU/2l+OOzxOVl1eVVZbUU1+c/QbeS3kmkGT/LNUws+IsPv/fyc3Iq1WKycfn5yt+eJ7vAOk5+itco93QJbOj7t2Td5s1Fher+Elow4dzlIbrxQZoHsT/fsT/babRTGJMbLl+pay3cZ2Mi7JhHPEpZSR/+2TV8uWk49GjdZLMtBqtfLn3Tek5+itcuCqszIhKZuf0AwsOVkrp26/Kj1Hb5WfLD0lYxJS3px8z0r5SxXd/8dDM6RM1pg2UCmlvHdYypmVpPw2r5R7vtXN8X9BvCZeLriwQNZaVUvWWFFDTj85XfpHG2edzLMMnvCBVsAN4DYwJpXnhwFXgUvAf4Bnem0aPeFvHyXld85Shj80areLLy+WlZZVkj+f+TnLbSRqkmWNSbtl3xWnM31u1IED8nr1GvJm4yYy7tq1LMeQpj3f6q7UTHWV9oInQzszTs8wdSgy8Lc58mrZcjJkxcpst/XjzmvSc/RWOWLdBalJNs7VpD6sOuEji4/ZKrvMPybjTizRDQP+XEHK+8dNHdrz4iKk/Geg7nd5cUspI/+fzE/6nZQt17eUlZZVkl/t/0r6RvmaLEyDJnzAHLgDlACsgItAhReOaQrYpXw/AFibXrtGTfjRwVJOLijlpgHG61NKue3ONllpWSU54sAImazN3tXYpH+9Zalx22RIdEKGzwlZtUpeLV9B3nnvPZkY8Dhb/acpNlTKaR5SrupsuD4y6dtj38oqy6vIi4GmfRPSJifLB/0HyKsVK8mYc1kfZpq3XzeMM2bDRZn8GiX7J/698FD+PuFDKb9xlIlL2xt9WDVTLv2t+9T6U2kZd2e/nH5yuqy0rJJsu7GtPOGX/pCPoaWV8PWxCUtt4LaU8q6UMhH4C3huAqmUcr+UMjblxxOAux761Z9Tv4MmzqiLhE75n2L80fF4FfRiSoMp2a5Y1dnLnaRkyeYLj9I9Vmo0BHw/lceTp2DfuDHFVq7EsmCBbPWfpieVwm7t1hWTyQGGew2ngF0Bvj76NYnJiSaLQ5iZUXj6NCwLFeLRV8PQhIZmuo1VJ+7zw87rvFu1MFM6VsbMBNMusyUxlnbXRtPXfCurk1vQIexLQrQ5eB+eyp3hs/+4YZOHLvv6s+raKj4s9yF/v/s3b7m9Zero0qSPhF8EePjMz74pj71KH2BHak8IIfoKIc4IIc4EBQXpIbQMSIyBUwuhbBv9zOfNgJthN/ly/5d4Ongyu+lsrM2zv0q2vJsjlYo48vcZ3zSPS46O5uHAgYStXIlz7164z/kNszxG+M/1Vj/IUwD2TdZtv2Bi9lb2fF3na+5G3GXx5cUmjcXcyQn3X2eTHBqK34gRyOSMF2/596IfX2++wtvlCjCza1WTzLHPlvgI3Y60N7ZDq+kU/Wged0Li6bn4FOGxpnsjTs+/0XfomdecGEsb/vB/zNjIeGzNTLPBYmYYdRNrIURPoCbwU2rPSykXSilrSilruj7ZctfQLqzRzfM10tV9QEwAA/cOxMbChvnN5+Nk7ZT+SRnUxcuDq/6RePtFpPp8ou8j7n/wITFHj1Ho228oOHas8Zb4W+WBhsN1+53cPWCcPtPRyL0RbYq3YeHlhdwOu23SWGwqVKDg1xOIOXac4LnzMnTOGZ9Qhq+7SC1PZ+b1qPH67UkfGwrL24PvaXh/MdQZQMMyBVjYqyZ3AqPpveQUkfFJpo7yOUnaJKaenMq4I+OomL8S697fSZ1KPeDoL7DhU908/hxMH78hj4BnN6BwT3nsOUKI5sB4oL2UMkEP/WafNhmOz9EtzfYw/Eex8Phw+u3pR0xSDPObz8/QwqrM6FCtMFbmZqw/+/JVfuzp0/h06UJSQABF/1hIvu7d9dp3htT8BBzdYd+UHHGVDzC69mjsLe355vg3Ri+L+KK8nTvj9N57BM+fT8yxY2ke+yAklr4rz1Ikny2/f+T1+u1BH/UYlrWFwGvQfQ1U6vT0qcZlXJnXowbefpF8uvQ0sYkaEwb6f1GJUQzcO5A/r/9Jrwq9+OOdP8hvXwjazoQWk3WLDNd0gYQoU4f6SvpI+KeB0kKI4kIIK6A7sOXZA4QQ1YHf0SX7QD30qR/Xt0KYj275v4H3zYhNimXQvkH4Rvny69u/UtZZ/8NHee2saFGhIJsv+JGo+f8ugWHr1nH/k08xd3Ki2Lq15KlXT+99Z4iFNTQeBY/O6PZByQGcbZwZVWsUl4Iu8deNv0waixCCQl9PwKpkCR6NGo3mFcOaEXFJfLLsFFopWfJxLfLlMW1d40yLCYEVHXSbmPX4W7d9wQuaVyjI7O7VOfcgjIGrz5GUrE2lIeMJiAmg987enAk4w+T6kxlZaySWZil7XAkB9b+A9xaCz1HdXlyxmb8XYwzZTvhSSg0wGNgFXAPWSSm9hRCThBDtUw77CbAH/hZCXBBCbHlFc8YjJRz9FfIV0228ZEBJ2iRGHhrJleAr/NDoB2oVqmWwvjrXdCc0JpF91x8jExMJmDSJgInfkKdOHYqtW4t1cRMX4aj2ITiX0F3la037n/iJdiXaUb9wfX499ysBMQEmjcXMzg73WbPQRkfzaOSol8bzk7WSIX+e50FoLAt6elE8fw6+uZmauHBY9R6E3YMP/9JtVvYKbau48f17lTlwI4jRGy49mfFndDfDbtJjWw/8ov2Y13weHUt1TP3Aqt2g20rdpmzL2uo+xeQ0r5q+Y+ovg0/LvH9cN5/25EKDdqNJ1siRB0carRCHJlkra3+/Rw7+bbe898GH8mrZcjLghx+lVpMDFq88cXGd7u/+8gZTR/LUw8iHsubKmnLwf4ONtiIyLWHr1+uKzcyZ89zjP+28Lj1Hb5VrTt43UWTZEB8l5R/NdfPsb+7O8Gmz996UnqO3yqnbrxowuNRdCboi662pJ99e+7a8HnI9Yyfd2S/lFDcpf6upW3hoZBh4Wubr6dhvYOsM1XoYrAspJVNOTmHHvR18WeNLupbtarC+njA3E/TJF023RROI875K4Z9nUHDUSKPvv56mSp10e5Dvn6rbpjYHcHdwZ2C1gRx4eID/Hvxn6nBw6tQJx/bvEjx3HrEpmwju9g5gzv7bdK/lwQe1i5o4wkxKToJ1H8Gjs9B5SaZqHg95uxQf1fHk94N3WXr0ngGDfN65x+fos7sPDlYOLGu9LOPDsCWa6IaqIh7B8nd1+/bnELkz4Yfc0e2BXauPwfa7l1Iy8+xM1t9cz2eVP6NP5T4G6efFPkOXL6fhnAkkmllyeuSPOLVta/B+M83MHJqOg5BbcPlvU0fzVM8KPSmbryzTTk4jKtG0N96EEBSa+A2WHu48GjmKO3ceMXzdRaq4O/Ft+4omjS3TpIQtX8CdfdD+V6jQPv1zniGE4Nv2FXmnQkEmb73K3quGHyo56X+S/nv742rryrJWy/BwyGRhlGL1U5L+Q1jWLsck/dyZ8E8uADMLqPWZwbqYe2Euy7yX0b1sd76o/oXB+nkiOSqKR0O/5PG06dg3acKyXt+xLMDSZOOe6Sr/LhSqAgen667+cgBLM0u+rfctQXFBzD4329ThYG6fhyI/z0QTFMSZQSOwNBfM7/kazsjZ/71uy+Em43T712eBuZngl+7VqFTEiSF/nueyb+pTj/Xh7OOzDNk3hCL2RVjaaimF8hTKWkPPJv0cciM39yX8uHA4vxoqdwGHLP5DpmP+hfn8ful3OpbqyNi3xqZanlCf4i5e5N57nYj67z8KjByJ+5zfaN+wLHeDYzjtE2bQvrNMCHj7a90sqQurTR3NU5XyV+LD8h+y7sa6p3uXm5JtpYqcfedDqvhcYL7DPYrkNV2N4Cw5uwwO/aQrVtI4e1W+7KwsWNS7Js55rOiz/DR+4XH6ifEZF4MuMnDvQArlKaSbdmmbP3sNFmugm3YacgtWm37KZu5L+OeWQ1IM1B2Y/rFZMP+irvB4h5Id+K7ed9neMiEtUqsl+I8/8OnRE6lNxnPlClz6fIoQgjaVC2FvbcHa0w/Tb8hUSrcA99pw8McctWBlcLXBuNq58t3x70jSmvbTx7ZL/nxtWYmA8jVwWjaf+Js3TRpPptw7DNuGQ8lm0HaWXqY+F3CwYcnHtYhLTOaz5Wf0Okf/ashVBuwZgIutC4veWZT9ZP9EyabQZZmuNOOfH0CS/t+oMip3JfzkJDj5OxRrCIX0W+ZOSsncC3OZd2Ee7Uu2N3iyT3r0iAeffErQzzNxaN6cEv/8g12NGk+ft7Oy4N2qbmy/7E9UDlut+JQQ8PYEiHykeyPOIeyt7Blbeyw3w26y+qrpPn08DI1lzIZLVC3qTJ3ff8HM3h6/4SPQxuecN8dXCr2nu0nrXBK6LM1YndkMKlvIgV8/rM71gEiGr7uIVpv9YUufCB8G7B2Ag5UDi99ZTAE7Pe8tVa4tvLcAfI7A35+YbLJC7kr4VzfrkkvdQXptVkrJj6d/ZMHFBXQs1ZFJ9SZhbmaYcVYpJeEbN3G3fQfiL1/Gbcpkisyaibnjy+Xeutb0IC4pmX8v+hskFr0o0Vj3Bnxohm5foxyiWdFmNHFvwryL8/CL9jN6/5pkLV/8dR6A3z6ojm0BVwpPn0bCrVsEzvjZ6PFkSnwk/JmykvuDP3VlCfWsadkCjGtTnh1XAvjlv1vZaiswNpB+e/ohpeT3Fr/rfQX8U1W6QpufdDV5tw41yWrz3JPwpYQT83RXHKVfXtmXVcnaZL47/h2rrq2iR/kefFfvO4Ml+6SAAHwHDsJ/3Dhsypen+JbN5O3c+ZX3CKp55KVMQXvWnsnBwzqgu8qPCYRTf5g6kqeEEIx9aywAU09ONfrN73kH7nD+QThT3qv0tNi3fcOGOPfuRdiqVUQdOGDUeDJMq4WNfSHkNnRdAS4lDdZVnwbF6VrTnV//u8XWS1l7U45IiKDfnn6EJ4Qzv/l8ijkV02+QL6r9ua44+vlV8N8kw/aVityT8H1P6+YA1xkAZvp52fGaeEYcHMGGWxvoW6Uvo2uNNsgwjtRqCV2zhrtt2xFz/DgFxoym6IrlWLmnvcu0EIKuNT24+DCc6wGReo9Lb4rWgVItdBtQxeecOAvbF2ZQtUEc9D1o1Ln5Fx+GM/u/W7SvWpgO1Z7feNZ1+HCsy5XDf+y4V269YFKHUq5gW06D4o0M2pUQgskdK1HTMx8j/r7IVb/M/e4kJicydP9Q7kfeZ/bbs6mY30jTXZuOA6+P4chMODHfOH2myD0J/8Q8sHaCqh/opbnQ+FD67O7Dfw/+Y1StUQypPsQgs3HivL25/2EPHk+ajG3VqpT4dwsuH3+MyOCbVqca7liZm/HXqZx+lT9et2upkf8DpKdH+R66ufmnphGTZPghp7jEZL5ad4ECDtZM7lDppefNrKwoMuMntLGx+I0dh8wh21MAcHMXHJim+z9W+3OjdGltYc68njXIa2tF35VnCI3J2JbKWqllwtEJnH18lin1p1DHrY6BI32GELoN18q1g51jwfsfo3WdOxJ+hC9c3QJevcDaPtvN+UT40HN7T26E3mBmk5l8VOEjPQT5PE1YGP7ffotP5y4kPnyI2/RpeCxehJVH5haAOOexolWlQmw850t8kml3g0xT4eq6/wDH5+SI+cpPWJhZMLHuRIJig5hzfo7B+5u24xp3g2L4uUtVnOwsUz3GulQpCo4ZTcyRI4StWmXwmDIk5A5s/BwKVYJ2+pmRk1EFHGz4/SMvAqMSGLzmHJoMbLQ25/wcdtzbwdAaQ2lToo0RonyBmTm8v0i3U+/GvnD/uHG6NUovpnbqD0BC7b7Zburgw4N8sO0DohOjWfTOIpp7Ns9+fM/QJiQQsngxd1q2Ivzv9Tj3+oiSO3eQt2PHLH+C6F7bg8h4Ddsv5+CbtwBNx+vmKR81/aKnZ1VxrULXsl1Zc30NV0OuGqyfY7eDWXH8Pp/WL069UmlPCczbvTv2TZsS+NMM4m/cMFhMGZIUB+t6AQK6rQJL468VqOqRl2nvVebYnRCmbr+e5rEbb23kj8t/8H7p9+lTyfAr4F/J0hY+XAt5PXQ3uYMM/+/45if8xFjd4o9y7SBv1vcf0Uot8y/OZ/C+wXg4ePBXu7+oVqCa3sKUGg3hGzdxp1VrAn+agW21qhTftJGCY8di7uCQrbbrlnChmItdzh/WKVhBVz7u5O85bqfBL2p8gbONM5OOTzLIvvnRCRpGrr9E8fx5GNky/T1bhBC4fT8FMycn/EaYeKrmjlHw+IruijVfMZOF8b6XO5/UL8aSo/f453zqpT5PB5xm8vHJ1Ctcj/F1xht8UWS67Jyh5wYwt4LVnSE6EJmcbLChujc/4V/6C+LDoU7WF1oFxwUz8L+BT+fYr2i9gsL2hfUSnkxKInzDRu60aYv/uHFYuLhQdNkyii5ciE2ZMnrpQwhB99pFOeUTyu3AnFucAYAmYyE5EQ7PMHUkz3G0cmRUrVF4h3gbZN/8aduv4RcRx0+dq2BrlbFZXhbOzhSeNo2EW7cJ/MlEf18X/oRzK6DBsExtiGYo49qU563izozZeIkrj57ffuFB5AO+OvAVRR2LMqPxjP/vZ29q+YrprvRjgpGruxLw7Tf4pbI1tj682QlfSt3VoltV3UyQLNj3YB+dNnfiTMAZJrw1gSn1p2BjYZPt0JKjoghZtow7LVvhP3485vb2uM+doytQUkf/1bc6e7ljaS5y/lW+S0ndfitnlkL4A1NH85xWxVpRv3B9fjv/G49j9PcJ5OjtYFaffECf+sWpWcw5U+faN2ygm6q5erXxp2oGXodtw8Czvm44LgewNDdjbo8a5LOzov+qs4Sl3MSNTIxk8L7BAMx5ew4OVtn71Kx3RWrA+4sJ2Xub8L83YOlWyCA73L7ZCf/ufgi6Dm8NyPRNpND4UCYcmcDQ/UMplKcQ69qto1u5btn+CJhw6xYB30/ldpOmBE7/AYvCbrjPn0exDetxaNYsw7NvMiu/vTUtKhRkQ06/eQu6PVeEGRz4wdSRPEcIwfg649FoNfxwWj+xxSRoGLX+EiXy52FEBoZyUuM6bBjWZcviP248muBgvcSVrsRY+Lu3rlbx+4v1upI2u/LbWzO/pxeBkQkM+fM8iRoNow6O4mHkQ2Y1mYWHYyZ3vjSSsMsxBF12wKlYLK4VDfPv+GYn/BMLIE+B5+plpkcrtfx982/e3fQu2+5u47PKn7G6zWpK5C2R5TCSIyII+/tvfLp15+677Qn76y/s336bYuvXU2zVKhyaNjXKWOKHtT0Ji01i5xXTVnVKl5O7buvqi2sgKGftHePh4EH/qv3Zc38PBx4eyHZ7P+26gV9EHD92rpLlXTDNrK11UzVjYvAbN844i8R2jtbdZOy0EBwNtDI1G6p55GVKx0ocuR1Mz00TOep3lPF1xhu02lx2RO3fT8A335KnQQPchnRHJETqam7rWc55W9a3kDtwaxc0HqOrpZoOKSWHfA8x98JcroVeo1ahWox/azwl82ZtpaAmLIzogweJ2rGT6GPHICkJq1IlKTB6NE4d2mPhnLmP7vpQr6Tu5u2qE/fpWL1I+ieYUsPhurHh/VN0KzZzkN4Ve7Pt7jamnpxK7UK1sbPMWk2Fs/dDWX7ch951i2V6KOdF1qVLU2DkSB5PmULYylU499L/VOGnLq///7h9ybcN1082da3lwda7Wzkf+y/1XNvTuUxnU4eUqrhLl3j01TBsypfHffYvCFtb3YiEAS4C39yEf2ohmFlCzU/TPEyj1XDY9zALLy3kSsgV3O3dmd5wOm2Kt8nUVbc2MZH4K1eIOXGCmEOHibt4EaTEws0N5549cWzTGptKlUw6K8DMTNCzjidTtl3jmn8k5d1e3n8nx8iTH+oO1u2X/+gsFPEydURPWZpZMrHuRHrt6MWcC3MYVSvz2/7GJyUzav0lCjvZZmhWTkbk6/EhMYcPE/jTT9i9VRubsvpp9zmh9+DfL3W7nDYdp//29cg72BvvxEXYJJfmyIl63PKKonTBnDV2n/jwIQ/7D8Aif348fl+AWR7D1igW+vj4J4RoBcwGzIFFUsrpLzxvDawAvIAQoJuU0ietNmvWrCnPpJR2y7T4SJhZAcq10X3kTIVftB//3P6Hjbc28jj2MUXsi9CvSj/alWyX7t17qdWS5OtLvLc38d7exF26TNzFi8iEBABsKlfGvlEj7Bs30iV5A43LZ0V4bCJvTf2Pzl7ufP+efncM1bv4SPi1mm5n016bTR3NSyYfn8z6W+tZ03YNFV0ytyz/5903+G3fbZZ9UosmZfW3M6MmNJS7HTpg7uRE8fXrMbPJ/gSDp5KTYElL3T45/Y9ka5qzoQXHBdNtazcshAW/NFrKRwu9cbSx5J/B9XG0yRmzczRhYdz/4EOSw8Lw/PNPrEsU10u7QoizUsqaqT2X7St8IYQ5MBdoAfgCp4UQW6SUz65Q6QOESSlLCSG6Az8A3bLb9ytdWA2JUfBW/6cPBccFczPsJif8T3DY9zC3w28jENQrXI+xtcfSyKPR00QvNRqSw8PRhISgCQwkyc+fJD8/kh4+JOHePRLv3UM+mfdsaYlNmTLk694N25o1sfPyMslwTUbltbOiXZXC/HP+EWPblMfeOgd/yLNxhIYjYNdYuLNft694DjLUayj7H+7nu2PfsabtGizMMvZ3eT0gkvkH7tCpehG9Jnt4MlVzOg8/+4zAH3+k0MSJ+mt8//e6T1tdV+ToZJ+kTWL4geFEJkSyss1KyjkXZu6H1vRYdJJhay+y8CMvzMxMO/9em5CA76DBJPn5UXTpEr0l+/Rk+wpfCFEX+FZK2TLl57EAUsppzxyzK+WY40IICyAAcJVpdJ7VK/zwEF+ODmhJkpkZifYFSUpOJDoxiiRNAkKChRS4WjlTwCo/BSzyYpMs0MYnoI2JQRsVRXJ0NNqoqJe3LrWwwNLNDasSxbEuXgKrkiWwqVABm9KlEVZWmY7TlM4/COO9eceY3LESH9XxNHU4aUuKhzk1dUM8n+836pL9jNjts5vhB4czouYIelfsne7xyVrJ+/OP8SA0lr3DGuOcxzC/O49/+JHQpUtxnzsHh2bNst/g3YOwooOuclX7X7PfngFNOzmNNdfXML3hdNqW+H9N52VH7/Htv1f5qnkZhjYvbbL4pFbLo+HDidqxkyKzZuLYurVe2zfoFT5QBHh2crcv8OJE8qfHSCk1QogIwAV4bu6REKIv0BegaNGsXUHI4Pvkv61FCIkQjxEIzMzNMTfPg4WZJZZWNphZWiLMExHWEWitrRE2NljmzYu5gz1m9g6YOzlhnt8FC2cXLFzzY1m4MBYFChhkXqwpVPPIS8XCjqw+cZ+ebxU1/WrDtFja6BZjbR4IV/+Biu+ZOqLntPBsQRP3Jsy9MJdmRZvh7pD2DqarT97nwsNwfulWzWDJHsD1qy+JPXkS/3HjsdlcEctC2SjnGRMCm/qBSyloNS39401o8+3NrLm+hl4Vej2X7AF61yvGJd8IfvnvJpWKONKsfEGTxBg06xeiduykwIjhek/26ZJSZusL6Ixu3P7Jzx8Bc1445grg/szPd4D8abXr5eUls0yTKKUmKevn5wJ/nrwvPUdvlSfvhpg6lPQla6Sc85aUs6vp/m1zGP9of1l7VW352a7PpFarfeVxfuGxsuLEnbLnohNpHqcv8XfvymvVa0ifnh9JrUaTtUa0WinXfCDlpPxS+l3Qb4B6diX4iqyxoobss7OPTEpO/f9/XKJGtv31kKz0zU55JzDKyBFKGbpunbxatpz0m/iNwX4HgDPyFXlVH3cTHwHPrmRwT3ks1WNShnSc0N28NQxzyxy1ECQnal+tME62liw/5mPqUNJnZg4tvoPQu7p9kXKYQnkK8ZXXV5zwP8HmO6++ufzNZm80Wi3fd6xslE9V1sWLU2ji18SePk3w779nrZGzS+HGNmj2jW7Feg4VFh/GV/u/wsXWhR8b//jK+yk2luYs6OmFpbkZn684Y9Tyn9FHjxLw7XfkadiQQl9PMMkna30k/NNAaSFEcSGEFdAd2PLCMVuAJwOcnYF9Ke9EionYWVnQvZYHO70D8As3XVHlDCv9jq4U4oHpOapIyhNdy3alRoEa/HT6J4LjXl4lucs7gN1XH/Nl8zIUdcnavP2scOrQAcd33yV4zlxiM3tPLOgm7Bynm2ufjb2oDE2j1TDy0EhC4kKY1WQWzjZpT5pwz2fHvB418AmJ5au1+qmJm574mzd5NPRLrEuVosismQgL01yQZjvhSyk1wGBgF3ANWCel9BZCTBJCtE85bDHgIoS4DQwDxmS3XyX7etbxRErJqhP3TR1K+oTQXeXHBsOx30wdzUvMhBnf1vuWeE08004+P84dFZ/EN5u9Ke/mSJ8GxpmN8YQQgkLffIOlhzuPho9AExaWsRM1CbChD1jZQcf5eqsSZwi/nv+Vk/4nmVBnQoarVtUp4cLEdhXYe+1xtmvipicpMJCH/ftjZmeHx+8LMLfPfk2OrNLLv6KUcruUsoyUsqSU8vuUxyZKKbekfB8vpewipSwlpawtpbyrj36V7PFwtqNFhYL8eepBzt9fB3SLryp20hVJicx5e/sXdypO/6r92X1/N//d/39JxJ933+RxVDxT36uEpbnxE6e5fR7cZ80iOTQUv9GjM7b17r4pEHAJ2s8Bh2zc8DWwXT67WHplKV3LdOW90pm7od+rriddvHQ1cbddMszvkzY2Ft+Bg0gOj8Bjwfzs3TzXg5z7tq0YRe96xQiLTWLLhawVgTa6Zl/rFgDtn2LqSFL1caWPKe9cnsknJhMeH87Fh+EsP+7DR3U8qV40n8nisqlQgQJjxxBz6DChS5akffDdA3DsV90q9XImqAaVQbfCbvH10a+p6lqVMbUzP2gghGDKe5Xw8szH8L8vvLSdcnbJ5GQejRhJ/NWrFPl5BjYVKui1/axQCT+Xq1vChbIFHVh6zMc4m25ll3MJeKsfnF8N/hdNHc1LLM0smVx/MhEJEUw7OY1xmy7jam+d5Z0w9SnfBx/g0LIlgbN+IfbcudQPig2FTf0hf1l453vjBpgJkYmRfHXgK+ws7JjZZCaW5llbPWttobuJ65LHms9XnCEwUn+FZB5P/4HoffsoOH4cDk1zxqJBlfBzOSEEH9cvxjX/SE7czTm1ZNPUaCTY5oNd419eIJcDlHUuS98qfdnus50bkcf5tn3FHLGcXwiB25TJWBYpwqMvv0IT8sJEOSlhyxCICdZVr7Iy3s3lzNBKLeMPj+dR1CNmNplJAbvsrVZ2dbDmj141CY9Nou/Ks3oZ3gxdsYKwlStx7t0b5x49st2evqiEr/Be9SI457Fi8ZHX5NaKbV7dxl0+h+HGdlNHk6rWRXsgE9xwdN9M3VJ63M8mm8wdHHD/dTbJERE8GjHi+apK55bD9a3Q/Btwq2K6INOx4OICDvgeYGStkdQoWEMvbVYo7Mgv3atx0Tec4euyN3Mncs8eHk+bjkOL5hQYNVIv8emLSvgKNpbmfFTHk73XArkdGG3qcDLG62PIXwZ2fw2aRFNH8xwpJVP+vUHy425ozWKYdipnrU61KVeOQhMnEnv8BEFz5ugeDL4FO8dCiaZQZ5BpA0zD/gf7mX9xPu1LtueDch/ote2WFQsxtnU5tl3256fdWSsoHnfhAn4jRmJTpTKFf/wxx63OVwlfAeCjup5YW5i9Plf55pa6MebQO7qtsHOQXd6P2XstkK8aN2FA1QHs8NnBjns7TB3Wc/K+3wmn9zsRMn8BUXv3wPpPwcImR0/BvBtxl7FHxlLRpSIT6040yMKlzxuW4IPaRZl/4A7rTmeuHGji/fs8HDAQi4IF8Zg/HzNbW73Hl105819WMbr89ta87+XOhnOPCI5OMHU4GVO6BZRqoVuMFaW/GrPZEZ2g4dstujn3n9QvTp/KfajiWoXJJybrtQ6uPhT6+mtsKlTAb8QwEm56Q8d5ObJ6FUBUYhRD9w3F2tyaX5r+grV5+kWNskIIwaQOFWlYOj/jNl3mwI3ADJ2nCQ3lQd++ICVFF/6eY3fMVQlfeapPg+IkarSsOP4aLMQC3WKs1j9AcgLs/cbU0QAwY9eN5+bcW5hZMLXBVDRaDROPTcxRM6HMbGxwH9ENIRPwPVuS5CKNTB1SqpK1yYw5PAbfKF9mNJ5BoTyGnctuaW7GvB41KFPQgYGrz3HxYXiax2tjYnjYrz+agMe4z5uHVbFiBo0vO1TCV54q6WpP8/IFWXnch7jE12AhFoBLSag7CC7+CQ9OmjSUCylz7nu9MOfe09GTETVHcMzvGGuurzFhhC+IDsTy6HiKtHEkMTgO/7FjMrYoy8h+O/8bh3wPMbr2aKPVpHWwsWTZp7Vwsbfi02WnuRcck+pxMikJ3y+/It7bmyKzZmJXo7pR4ssqlfCV5/RtVIKw2CTWncnc+KVJNRwBDoVh+wiDFH7OiKRkLWM3Xqagg02qc+67lOlCI/dGzDwzkxuhWbshqFdarW6+fUIkeb5cToGRI4jas5fgefNNHdlztt/dzuIri+lcpjPdyhquZlJqCjjYsPyT2kig15KTPH5hjr6UEv8JXxNz+DCFvv0Gh7dzbn3fJ1TCV55Tq1g+anrmY+GhuyRqct7VXqqs7aFlylYAZ9JZRWogS47c45p/JN+2r4hDKnPuhRBMrj8ZR2tHRh0aRZzGxBvWHf8N7vyn29++YEWce/fGqWNHgufMIXJHzrjB7B3szcRjE6lRoAbjao8zye6SJVztWfJxLUKjE+m56CQhKfe3pJQE/jSDiM2byT94MPm6djV6bFmhEr7yHCEEg94uxaPwOP45/+Iu1zlYxU5QvBH8NxmiAoza9cPQWGbtvUmLCgVpVenV48vONs5MbTCVexH3+PH0j0aM8AW+Z+C/SVChA3h9AqRssjbpO2y9vPAbM5a4S5dMFx8QEBPAkH1DcLFxydZKWn2o5pGXxR/X4kFoLL2WnCIiLomQPxYRumQJ+T78kPyDcu5Ooi9SCV95SZMyrlQq4sj8g3dINsLWsXohBLSdBZp43XxyI5FSMm7TZSzMzJjUIf2dGusWrssnlT5h/c317PbZbYQIXxAXDus/0Q2BvfvrcyUjzayscP/tVyxcXXk4aBBJ/qbZoC42KZYv9n1BTFIMvzX7DRdbF5PE8aw6JVz4/SMvbj6OYt7wnwiaORPHdu0oOGF8zq4Y9wKV8JWXCCEY1KQU94Jj2HY55+1K+Ur5S0GjEeC9EW7tMUqXG8894vCtYEa3KoubU8bmXQ+uPpgq+asw8dhEfCJ8DBvgs6SEzYMg0g86L9atWH6BhbMzHvPnIePiedi3H8mRxq09oJVaxh8Zz42wG/zU+CfK5Ctj1P7T0qRsARZ7hNN+3yquF6tCnonfIXLomoVXeb2iVYymZcVClCpgz9x9t41SIEJv6g/Vbfy1dRgkpj6zQl+CoxOYvO0qXp756PFWxovBW5pZMqPxDCzNLBl2cJjxxvNPzE/ZOuE78Kj9ysOsS5fG/bdfSfDxwXfQYLSJxlvJ/MvZX9j7YC/DvYbTyD1nTRON3LUb19+mklihChOqfsBHy88RHpuzVnmnRyV8JVVmZoJBTUty43EUe67lrAVDabKwhnazIOIB7J9q0K4m/XuV2IRkpneqjJlZ5j7Wu9m7Mb3hdG6H3Wby8cmGn5//8DTs+RrKttVNY01Hnrp1KTx1KrGnT+M/xjjTNf+8/idLvZfSvWx3PqrwkcH7y4yofft5NHw4tlWqUHXlYn79uC43AqL44I+Tr89CRVTCV9LwbpXCFHOxY9aem6/XVX6x+rqbkcfnGmxu/r7rj9ly0Y9BTUtRuqBDltqoX6Q+/av259+7//L3zb/1HOEzYkN14/aOhaHj3OfG7dPi9G47CowcSeT2HTyeOs2gb0r7Huxj+qnpNHFvwpjaY3LUuHj0oUM8GjoUm3Ll8Fj4O2Z58tCsfEEW9a7JveBo3p9/DJ9XzNPPaVTCV17JwtyMr1qU4XpA1Os1lg/wzmRw8oDNAyFJv0MmEXFJjN14mbIFHRjQpGS22upXpR8NijRg2slpnA44racIn6FN1pUqjH4MXZbptpXOBOdPP8H5448JW7WKoFm/6D8+4GLQRUYfGk0F5wr80OgHzM1yzoZjUfv24TtoMFalS1F00R+YO/z/zb1RGVf+/LwOUfEa3p9/jAvprMjNCVTCV9L0bpXClC3owKw9N9Ekvybz8gGsHaDDbxByW1euT4++33aV4OhEfupSBSuL7P0XMjcz54dGP+Du4M6wA8PwjfLVU5Qp9n8Pd/ZBmxm6EpGZJISgwOhR5O3WjZCFCwlesECv4d0Ou83AvQNxtXPlt2a/YWeZc/bgj9y5C98vhmJdvjyeS5dinjfvS8dUL5qP9f3rYmdtzgcLT7DzSs6+MMrWb6sQwlkIsUcIcSvlz5cuH4QQ1YQQx4UQ3kKIS0II4y6XU7LFzEzwVYsy3A2OYePrNC8foEQTqNknZWjnhF6aPHQziHVnfOnbqARV3PPqpU1HK0fmNJtDskxmyL4hxCTpaXjg2r9w+Geo0Ru8eme5GV0h9Ik4tn+XoF9mE7JkqV7CexT9iH57+mFtbs3CFgvJb5tfL+3qQ8SWLbox+8qVKbpkMeZOTq88toSrPRsH1KdsIQf6rzrHjF03cux05uxe4Y8B/pNSlgb+S/n5RbFALyllRaAV8IsQIm82+1WMqGXFglQu4sTsvbden9W3T7SYBHmLwsa+EJ+9mqXRCRrGbrxMSdc8DG1WWk8B6ng6ejKj8QzuRdxj5MGRaLSa7DUYdBM2DdBd1bf5KdvxCTMzCk+dikOrVgT++GO2r/SD44Lpu7svcclxLGixAHcH92zHqC8hS5fhN2o0djVr6oZx7O3TPcfVwZq1/erQraYHc/bf5rPlp4mITTJCtJmT3YTfAVie8v1yoOOLB0gpb0opb6V87wcEAq7Z7FcxIiEEw98pw6PwOP489cDU4WSOtb2uXF+EL2z9KlslEb/fdhW/iDh+7FwVG0v9jzPXK1yPcW+N4/Cjw0w6PinrN0ljQ2FNV7C0ga4rdDOX9EBYWFBkxk84vqu70g+c9UuWYgyJC+Hz3Z8TFBfEvGbzcsxceyklgTNmEPjDDzi0bPn0Bm1GWVuYM/39ykzpWIkjt4NpNfsQx24HGzDizMtuwi8opXwyaBUAFEzrYCFEbcAKuPOK5/sKIc4IIc4EBQVlMzRFnxqXcaVOCWdm/3eLiLicd+WSJo/a0HQsXNkAF7K2W+W+64/589RD+jUqiZdn5m58ZkbXsl3pX7U/m25vYs6FOZlvQJMI63pB5CPovgac9HvlLCwsKDx9Gnm7dCbk9995/P3U58skpiMsPozP93yOb5Qvc96eQ7UC1fQaX1ZpExLwGz2akEWLyftBd4rM/Bkz68y/UQoh6FnHkw0D6mFrZc6Hi04yZetVvdTJ1QspZZpfwF7gSipfHYDwF44NS6MdN+AGUCe9PqWUeHl5SSVnuewbLouN2Sqnbrtq6lAyL1kj5dK2Uk5xkzLoZqZODY1OkDWn7JEtZx2U8UkaAwX4f1qtVn5z9BtZaVklufrq6sycKOXmIVJ+4yjlhb8MF6CUUpucLAOmTpNXy5aTDwcPlsmxsemeExYXJt/f/L70Wuklj/sdN2h8mZEUGCjvde0mr5YtJ4Pmz5darVYv7cYmaOSETZel5+itsvGP++S+a4/10m56gDPyVXn4VU9k5CslgbvJZxL6K45zBM4BnTPatkr4OdPwdRdk6XHb5f3gGFOHknkRj6Sc7inlvHpSJkRn6BStVisHrjorS43bJr0fRRg2vmckJSfJIf8NkZWWVZLrbqzL2ElHf9Ml+z3fGja4Z4QsXy6vlisv73btKpOCg1953OOYx7LjPx1ljRU15FHfo0aLLz1x3t7yZpOm8lq16jJi5y6D9HH4ZpBsOmO/9By9VfZZdkreCYwySD9PpJXwszukswV4cvu/N7D5xQOEEFbAJmCFlHJ9NvtTTGxky7KYmwmm77xm6lAyz7Gwbjz/sTdsHpyh8fwtF/3YdtmfL5uXoUJhRyMEqWNhZsGMxjNoWKQhk45PYsPNDWmfcHk97B4P5dvD218bJ0jAuVcvivw6m4TrN7jXpQtxly+/dMzDqIf02tELv2g/5jWfR70i9YwW36tIKQlbuw6f7h+AVovnqpU4tnzHIH01KJ2fnUMbMbZ1OY7dCaH5zIMM/es8tx5HGaS/NL3qnSAjX4ALutk5t9AN/TinPF4TWJTyfU8gCbjwzFe19NpWV/g51y97bkrP0Vvlybshpg4law79rLsSPjI7zcPuB8fIShN3yk7zjsokTbKRgntevCZe9tvTT1ZaVkmuv7E+9YPu7JfyOxcpl7SWMjHOqPE9EXvpsrzZtKm8VqmyDP3zr6fDIjdCb8ima5vK+n/Wl5cCL5kkthclR0dL3+Ej5NWy5eT9T/uk+clE3wIj4+XUbVdl+a93yGJjtsreS07KrRf99DpUSBpX+ELmoBqbz6pZs6Y8c+aMqcNQUhGXmMzbPx/AydaSf4c0wNL8NVu/JyX83Vs3T73nBij5cqWipGQtXRYc505QNDuGNsQ9n+kWBCUkJzB031CO+h1laI2h9KnU5/9bD/hfgqVtdDdnP92R6ZW0+qQJC8Nv1GhiDh/G8d13uffp24w4/y15LPKw8J2FlMybvVXJ+hBz6hT+E74mydcX1y+G4NK3r0l2vAyNSWTZMR/WnX5IQGQ8TraWvFOhIPVL5adeSRcKONpkuW0hxFkpZc1Un1MJX8mKXd4B9Ft5lnFtytG3ken/I2daQjQsbgERj+DTnVCwwnNP/7jzOvMO3GHuhzVoW8XNREH+X1JyEhOOTmD7ve10L9udMbXHYB58E5a1BQsb6LMHnIqYOkykVkvw/AUEzZ1DmJ1ke1dPhgxaZvDC4+nRxsQQOHMWYatXY1m0KIW/n4JdLePUx01LslZy9HYw68/6cvBm0NMZcA1K5WfVZ29lqc20Er5F1kNVcrOWFQvRvHxBZu25RetKbng455wl8RlibQ8froVFLWDV+/DZnqdTGI/dDmb+wTt0q+mRI5I9gKW5JdMaTqOAXQGWeS8jMPwuU68cIo+ZJfT+N0cke4B4bQJzqj/mfC8zRu22peeS+2hDZpE0bDiWBQsYPR6p1RK5bRuBM2ehCQggX6+PKPDll5jZ5YzfV3MzQaMyrjQq40qyVnLNP5Kjt4MxM9DmceoKX8myR+FxtJh5kLeKO7Pk41o5aofDDAu4rBsScSwMn+4kINGWdr8dfjpcZWeV866JVp+ZzU9X/sAjWTKr6WxKlWhu6pAAuBN+hxEHR3A7/DafV/6cgRX7Ejp/ASGLlyAsLHD5rA8un3xitGQbe/Ysj3/4kfhLl7CpUIGCE8ZjV6OGUfo2JTWkoxjMosN3mbLtWo4Z+siSuwdh1ftoC1enV8Iozj1OZvOg+lne9tigAq7Aqk6cNktmpJsbsdpEJtadSNvibU32hiulZMOtDfx4+kdsLWyZ2mAq9YvUf/p84oMHBP48k6hduzB3zY9zj57k694t1c3Ish2LVkv0wYOELFpM3NmzWLi64jpsGE4d2r921amySiV8xWA0yVo6zjuKX3g8O79sSAGHrN9sMqmrm9Gu+4QL2uI8fnc1rWuVM3VEL3twImXLhDzw0SYC7Z0ZeXAk5wLP0axoM8a/NR5XO+PuWnI/8j7fHf+O0wGnqV2o9tNhp9TEnj1L8Lz5xBw9irC1xaljB5zefRfbatWynYwTfR8RuXUrEZs3k3jvHhaF3XD5+GPydu6cY4ZvjEUlfMWgbj2Oot1vR6hX0uW1HdrZctGPbWsXMs/6N8zdKkPPjWDnbOqw/u/GTvj7Y91Y/UebdBvCARqthhVXVzDvwjyszK0Y7jWcjqU6GnxP+ZikGFZ4r2DR5UVYm1szrOYwOpXuhJlIP3HH37hJ6LJlRG7bhkxMxKJgQRyaN8euphe2Vati4eaW7u+QNjaW2PPniT19mtjjJ4i7eBEA25pe5OvSBcc2bRCWlnp5ra8blfAVg1t69B7f/XuVKR0r0bNOxuu75gQXH4bT9ffjVC7ixF9NwrFY3xvyl4EP/oK8HqYNTko4Mgv+mwRuVaDHBrB/+SreJ8KHb49/y9nHZynpVJLB1QfTrGgzvb/5JiQnsPb6WhZdXkRYQhgti7VkdK3RWfpkkRwdTfT+A0Tu3EnMkSPIBF2pQHMXFyzd3LAoUAALFxcAZHIyMjGRJH9/kh4+RBMYqGvE3BybihVxePttHNu1w8o9Z9y8NiWV8BWD02olvZee4oxPGNu+aEAJ1/S3lM0J/MLj6DD3KDaWZvwzsD4u9ta6giHreut2mey2GopmbXpctiXGwOZB4L0JKnaCDnPB6tXDE1qpZe/9vcy5MId7Efco71yebmW70ap4K/JYZnzXx9Q8jHrIhpsb2HR7E6HxodR1q8uQ6kOo7Fo5W+0+IZOSiL9xk7iLF4i/ehXN40A0QUFoQkJAgLCwRFhYYFmwIJYeHlh6uGNbuQq21atjbp+91/amUQlfMYrHkfG0/OUQhZ1sn+4WmJPFJGjovOA4vqGxbBhYjzLP3qQNugFruul2nWz3C1TvYdzg/C7Apn66OJp/A/W/zHAtWo1Ww7a721h6ZSl3Iu5ga2HLO57v0KBIA2oWqpmhQiNSSm6G3eSY3zEOPzrM6YDTmAkzGrk34qPyH1HbrXb2Xp9iMCrhK0az/3ogny4/TfuqhfmlW7UcO56flKyl74ozHLoVzJKPa9G4TCpDErGhuhW59w5BhY7QdibkcTFsYMlJcHgmHPoR7PJDx3lQqlmWmpJScin4EhtvbWS3z26ik6IBKO5UHE8HT9zs3ShoVxBzYY5GakjSJuEX7ceDyAfci7hHWEIYAKXyluIdz3d4r/R7Jl9ApaRPJXzFqObuv81Pu24woW15PmtYwtThvCRZKxn613m2XvJnWqfKfFC7aBoHa+DYbNg/DWzz6pJ++XczfLWdKXcPwO4JurUBlbtA6x/1duNYo9VwLeQapwJOcSHoAo+iH+Ef7f/0TeAJV1tXijoWxdPRk2qu1ahbuK5K8q8ZlfAVo5JSMmjNOXZeCWD5p7VpWDrnFDiTUjJ242X+Ov2Qsa3L0a9xBreFeOwNm/pDwCUoWhfengDFGugnqIDLsPdbuL0XnIpCyylQoYN+2k5HbFIsoCumbi7MsTDLeQvNlMxRCV8xupgEDZ3mHcMvPI41n9ehsvuri0Abi5SSKduusfjIPQY3LcWIlmUz10ByEpxbAYd+gih/KN5YVxy8bBuwtM1cW5pEuLYFziyB+0fBxgkajoDafXWlCRUli1TCV0zCLzyOLguOE5uoYW2/us/fFDWyZK1k/Cbdlf0n9YsxsV2FrN9fSIqD04vh+FyI8gMrB90wT9E6ULg6FCgP5i/MAU+MgfAHuuR+96DuvkB8OOT1hJqfQo1eOWvev/LaUglfMRmf4Bi6/H4cAfzdvy6eLsafQpegSWbY2otsu+zPkLdLMaxFGf3cTNYmg88RuLQOrv8L8RG6x80swdoBrPLopnbGBP3/OQBHdyjRBCq+p9uaOZcs+VeMQyV8xaRuPo6i2+/HsbE0Z+kntShXyHiVo8JiEhny53mO3A427E1krRbC7oHfed14f0Kk7qo+KQ7y5AfHIrov95rgXMIwN30VBZXwlRzgql8knyw7RUxCMnN71Eh9GqSeXXkUQf9VZwmMTOD79yrRpaaJV80qihGklfDVZ0nFKCoUduSfQfXxcLbj02WnWXncB0NdbEgpWXv6Ae/PP4ZWK1nXv65K9oqCSviKEbk52fJ3/7o0Kp2frzd78+my0wRExOu1j/shMfRacorRGy7j5ZmPf4c0oJpHXr32oSivq2wlfCGEsxBijxDiVsqfryyoKYRwFEL4CiHmZKdP5fVmb23B4t61mNiuAsfvhtBi1kH+OvUATbI2W+1GJ2iYu/8278w6xPkH4XzXviIr+7yl2xtHURQgm2P4QogfgVAp5XQhxBggn5Ry9CuOnQ24phw/OL221Rj+m88nOIZR6y9xyicUD2db+jYqSRcvd2wsM74HT1BUAsuO3WPl8ftExmtoWbEg37aviJtTJufFK8obwmA3bYUQN4AmUkp/IYQbcEBK+dJqFiGEFzAS2AnUVAlfeUKrley59ph5B+5w8WE4+ewsdTU+S7tSt6QLBR1tMDf7/4yWqPgk7ofEcuR2MAduBHLGJ4xkKWlVsRD9GpdUwzdKrmfIIuYFpZT+Kd8HAAVT6dwM+BnoCeSM4ptKjmFmJmhZsRDvVCjIibuhrD39gCO3g9l8wQ/QzV7Ma2uJg40lIdEJxCQmPz23XCEHPmtYgm61PCieX22RqyjpSTfhCyH2AqntnjT+2R+klFIIkdrHhYHAdimlb3qLXYQQfYG+AEWLprGhlfLGEUJQt6QLdUu6oNVKrgVEcu5+GEHRiYTGJBAZp8E5jxVuTja45bWlVrF8athGUTLJ4EM6QojVQENAC9gDVsA8KeWYtNpWQzqKoiiZZ8ghnS1Ab2B6yp+bXzxASvm0coQQ4mN0Y/hpJntFURRF/7I7D3860EIIcQvd+Px0ACFETSHEouwGpyiKouiP2lpBURTlDaK2VlAURVFUwlcURcktVMJXFEXJJVTCVxRFySVUwlcURcklcuwsHSFEEHA/G03kB4L1FM7rIre95tz2ekG95twiO6/ZU0qZaoWhHJvws0sIceZVU5PeVLntNee21wvqNecWhnrNakhHURQll1AJX1EUJZd4kxP+QlMHYAK57TXnttcL6jXnFgZ5zW/sGL6iKIryvDf5Cl9RFEV5hkr4iqIoucQbl/CFEK2EEDeEELdTCqu/0YQQHkKI/UKIq0IIbyHEUFPHZCxCCHMhxHkhxFZTx2IMQoi8Qoj1QojrQohrQoi6po7J0IQQX6X8Xl8RQvwphLAxdUz6JoRYIoQIFEJceeYxZyHEHiHErZQ/8+mjrzcq4QshzIG5QGugAvCBEKKCaaMyOA0wXEpZAagDDMoFr/mJocA1UwdhRLOBnVLKckBV3vDXLoQoAnyBrmhSJcAc6G7aqAxiGdDqhcfGAP9JKUsD/6X8nG1vVMIHagO3pZR3pZSJwF9ABxPHZFBSSn8p5bmU76PQJYEipo3K8IQQ7kBbIFcU2hFCOAGNgMUAUspEKWW4SYMyDgvAVghhAdgBfiaOR++klIeA0Bce7gAsT/l+OdBRH329aQm/CPDwmZ99yQXJ7wkhRDGgOnDSxKEYwy/AKHS1knOD4kAQsDRlGGuRECKPqYMyJCnlI2AG8ADwByKklLtNG5XRFJRS+qd8HwAU1Eejb1rCz7WEEPbABuBLKWWkqeMxJCFEOyBQSnnW1LEYkQVQA5gvpawOxKCnj/k5Vcq4dQd0b3aFgTxCiJ6mjcr4pG7uvF7mz79pCf8R4PHMz+4pj73RhBCW6JL9ainlRlPHYwT1gfZCCB90w3ZvCyFWmTYkg/MFfKWUTz69rUf3BvAmaw7ck1IGSSmTgI1APRPHZCyPhRBuACl/Buqj0Tct4Z8GSgshigshrNDd4Nli4pgMSggh0I3rXpNSzjR1PMYgpRwrpXSXUhZD92+8T0r5Rl/5SSkDgIdCiLIpDzUDrpowJGN4ANQRQtil/J434w2/Uf2MLUDvlO97A5v10aiFPhrJKaSUGiHEYGAXujv6S6SU3iYOy9DqAx8Bl4UQF1IeGyel3G66kBQDGQKsTrmYuQt8YuJ4DEpKeVIIsR44h2422nnewG0WhBB/Ak2A/EIIX+AbYDqwTgjRB9028V310pfaWkFRFCV3eNOGdBRFUZRXUAlfURQll1AJX1EUJZdQCV9RFCWXUAlfURQll1AJX1EUJZdQCV9RFCWX+B/AJ6KXw7oh/wAAAABJRU5ErkJggg==\n", 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" ] }, - "metadata": {}, + "metadata": { + "needs_background": "light" + }, "output_type": "display_data" } ], "source": [ - "x = linspace(0, 10, 100)\n", + "x = np.linspace(0, 10, 100)\n", "\n", "fig, ax = plt.subplots()\n", "for n in range(4):\n", @@ -169,16 +161,16 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 11, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "array([ 2.40482556, 5.52007811, 8.65372791, 11.79153444])" + "array([ 2.40482556, 5.52007811, 8.65372791, 11.79153444])" ] }, - "execution_count": 7, + "execution_count": 11, "metadata": {}, "output_type": "execute_result" } @@ -217,7 +209,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 12, "metadata": {}, "outputs": [], "source": [ @@ -235,7 +227,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 13, "metadata": {}, "outputs": [], "source": [ @@ -246,14 +238,14 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 14, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "integral value = 0.5 , absolute error = 5.55111512313e-15\n" + "integral value = 0.5 , absolute error = 5.551115123125783e-15\n" ] } ], @@ -263,7 +255,7 @@ "\n", "val, abserr = quad(f, x_lower, x_upper)\n", "\n", - "print \"integral value =\", val, \", absolute error =\", abserr " + "print(\"integral value =\", val, \", absolute error =\", abserr)" ] }, { @@ -275,14 +267,14 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 15, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "0.736675137081 9.3891268825e-13\n" + "0.7366751370811073 9.389126882496403e-13\n" ] } ], @@ -299,7 +291,7 @@ "\n", "val, abserr = quad(integrand, x_lower, x_upper, args=(3,))\n", "\n", - "print val, abserr " + "print(val, abserr)" ] }, { @@ -311,25 +303,25 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 18, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "numerical = 1.77245385091 1.42026367809e-08\n", - "analytical = 1.77245385091\n" + "numerical = 1.7724538509055159 1.4202636780944923e-08\n", + "analytical = 1.7724538509055159\n" ] } ], "source": [ - "val, abserr = quad(lambda x: exp(-x ** 2), -Inf, Inf)\n", + "val, abserr = quad(lambda x: np.exp(-x ** 2), -Inf, Inf)\n", "\n", - "print \"numerical =\", val, abserr\n", + "print(\"numerical =\", val, abserr)\n", "\n", - "analytical = sqrt(pi)\n", - "print \"analytical =\", analytical" + "analytical = np.sqrt(pi)\n", + "print(\"analytical =\", analytical)" ] }, { @@ -343,20 +335,20 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 20, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "0.785398163397 1.63822994214e-13\n" + "0.7853981633974476 1.3753098510218528e-08\n" ] } ], "source": [ "def integrand(x, y):\n", - " return exp(-x**2-y**2)\n", + " return np.exp(-x**2-y**2)\n", "\n", "x_lower = 0 \n", "x_upper = 10\n", @@ -365,7 +357,7 @@ "\n", "val, abserr = dblquad(integrand, x_lower, x_upper, lambda x : y_lower, lambda x: y_upper)\n", "\n", - "print val, abserr " + "print(val, abserr)" ] }, { @@ -397,7 +389,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 21, "metadata": {}, "outputs": [], "source": [ @@ -445,7 +437,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 22, "metadata": {}, "outputs": [ { @@ -457,7 +449,7 @@ "" ] }, - "execution_count": 15, + "execution_count": 22, "metadata": {}, "output_type": "execute_result" } @@ -493,7 +485,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 23, "metadata": {}, "outputs": [], "source": [ @@ -517,7 +509,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 24, "metadata": {}, "outputs": [], "source": [ @@ -527,19 +519,34 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 25, "metadata": {}, "outputs": [], "source": [ "# 解决常微分方程的坐标：从0到10秒\n", - "t = linspace(0, 10, 250)" + "t = np.linspace(0, 10, 250)" ] }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 26, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/bushuhui/virtualenv/dl/lib/python3.6/site-packages/ipykernel_launcher.py:11: DeprecationWarning: scipy.cos is deprecated and will be removed in SciPy 2.0.0, use numpy.cos instead\n", + " # This is added back by InteractiveShellApp.init_path()\n", + "/home/bushuhui/virtualenv/dl/lib/python3.6/site-packages/ipykernel_launcher.py:12: DeprecationWarning: scipy.cos is deprecated and will be removed in SciPy 2.0.0, use numpy.cos instead\n", + " if sys.path[0] == '':\n", + "/home/bushuhui/virtualenv/dl/lib/python3.6/site-packages/ipykernel_launcher.py:13: DeprecationWarning: scipy.sin is deprecated and will be removed in SciPy 2.0.0, use numpy.sin instead\n", + " del sys.path[0]\n", + "/home/bushuhui/virtualenv/dl/lib/python3.6/site-packages/ipykernel_launcher.py:14: DeprecationWarning: scipy.sin is deprecated and will be removed in SciPy 2.0.0, use numpy.sin instead\n", + " \n" + ] + } + ], "source": [ "# 解决常微分方程\n", "x = odeint(dx, x0, t)" @@ -547,17 +554,33 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 27, "metadata": {}, "outputs": [ { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/bushuhui/virtualenv/dl/lib/python3.6/site-packages/ipykernel_launcher.py:8: DeprecationWarning: scipy.sin is deprecated and will be removed in SciPy 2.0.0, use numpy.sin instead\n", + " \n", + "/home/bushuhui/virtualenv/dl/lib/python3.6/site-packages/ipykernel_launcher.py:9: DeprecationWarning: scipy.cos is deprecated and will be removed in SciPy 2.0.0, use numpy.cos instead\n", + " if __name__ == '__main__':\n", + "/home/bushuhui/virtualenv/dl/lib/python3.6/site-packages/ipykernel_launcher.py:11: DeprecationWarning: scipy.sin is deprecated and will be removed in SciPy 2.0.0, use numpy.sin instead\n", + " # This is added back by InteractiveShellApp.init_path()\n", + "/home/bushuhui/virtualenv/dl/lib/python3.6/site-packages/ipykernel_launcher.py:12: DeprecationWarning: scipy.cos is deprecated and will be removed in SciPy 2.0.0, use numpy.cos instead\n", + " if sys.path[0] == '':\n" + ] + }, + { "data": { - "image/png": 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FFcDTT5OI/vxzW8ZpBLF4op0kohVPfUIMn/TUVOr6d+qU8eOKlWgi0YmJtIqwZ4+5Y7Ia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\n6CNyrXz5pS8mp2b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/kV8wGs4+2zuNTrvHU51j5EgycVVXW1Y0P6iI/vlnmsSlpUV8rF69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\nI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HbTplo0hxNBF9FKqfcA3AMgHsBeAE+KyHmL7VoB+BBABICJImLhatInZI1Gk3nJDCJa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\n/B/NCqfN4Rm/ZwAAAABJRU5ErkJggg==\n", 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\n", 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" ] }, - "metadata": {}, + "metadata": { + "needs_background": "light" + }, "output_type": "display_data" } ], @@ -590,7 +613,7 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 28, "metadata": {}, "outputs": [], "source": [ @@ -600,14 +623,14 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 29, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", 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\n", "text/plain": [ - "" + "
" ] }, - "metadata": {}, + "metadata": { + "needs_background": "light" + }, "output_type": "display_data" } ], @@ -675,7 +700,7 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 30, "metadata": {}, "outputs": [], "source": [ @@ -693,7 +718,7 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 31, "metadata": {}, "outputs": [], "source": [ @@ -703,18 +728,18 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 33, "metadata": {}, "outputs": [], "source": [ "# 为了求解常微分方程的时间坐标\n", - "t = linspace(0, 10, 1000)\n", + "t = np.linspace(0, 10, 1000)\n", "w0 = 2*pi*1.0" ] }, { "cell_type": "code", - "execution_count": 26, + "execution_count": 34, "metadata": {}, "outputs": [], "source": [ @@ -769,7 +794,7 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": 35, "metadata": {}, "outputs": [], "source": [ @@ -786,7 +811,7 @@ }, { "cell_type": "code", - "execution_count": 29, + "execution_count": 36, "metadata": {}, "outputs": [], "source": [ @@ -803,17 +828,19 @@ }, { "cell_type": "code", - "execution_count": 30, + "execution_count": 37, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", "text/plain": [ - "" + "
" ] }, - "metadata": {}, + "metadata": { + "needs_background": "light" + }, "output_type": "display_data" } ], @@ -1269,7 +1296,7 @@ }, { "cell_type": "code", - "execution_count": 50, + "execution_count": 38, "metadata": {}, "outputs": [], "source": [ @@ -1278,7 +1305,7 @@ }, { "cell_type": "code", - "execution_count": 51, + "execution_count": 40, "metadata": {}, "outputs": [ { @@ -1290,29 +1317,29 @@ " [1, 0, 0, 1]])" ] }, - "execution_count": 51, + "execution_count": 40, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# 稠密矩阵\n", - "M = array([[1,0,0,0], [0,3,0,0], [0,1,1,0], [1,0,0,1]]); M" + "M = np.array([[1,0,0,0], [0,3,0,0], [0,1,1,0], [1,0,0,1]]); M" ] }, { "cell_type": "code", - "execution_count": 52, + "execution_count": 41, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "<4x4 sparse matrix of type ''\n", + "<4x4 sparse matrix of type ''\n", "\twith 6 stored elements in Compressed Sparse Row format>" ] }, - "execution_count": 52, + "execution_count": 41, "metadata": {}, "output_type": "execute_result" } @@ -1324,7 +1351,7 @@ }, { "cell_type": "code", - "execution_count": 53, + "execution_count": 42, "metadata": {}, "outputs": [ { @@ -1333,10 +1360,10 @@ "matrix([[1, 0, 0, 0],\n", " [0, 3, 0, 0],\n", " [0, 1, 1, 0],\n", - " [1, 0, 0, 1]])" + " [1, 0, 0, 1]], dtype=int64)" ] }, - "execution_count": 53, + "execution_count": 42, "metadata": {}, "output_type": "execute_result" } @@ -1355,17 +1382,17 @@ }, { "cell_type": "code", - "execution_count": 54, + "execution_count": 43, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "<4x4 sparse matrix of type ''\n", - "\twith 6 stored elements in LInked List format>" + "<4x4 sparse matrix of type ''\n", + "\twith 6 stored elements in List of Lists format>" ] }, - "execution_count": 54, + "execution_count": 43, "metadata": {}, "output_type": "execute_result" } @@ -1381,19 +1408,19 @@ }, { "cell_type": "code", - "execution_count": 55, + "execution_count": 44, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "matrix([[ 1., 0., 0., 0.],\n", - " [ 0., 3., 0., 0.],\n", - " [ 0., 1., 1., 0.],\n", - " [ 1., 0., 0., 1.]])" + "matrix([[1., 0., 0., 0.],\n", + " [0., 3., 0., 0.],\n", + " [0., 1., 1., 0.],\n", + " [1., 0., 0., 1.]])" ] }, - "execution_count": 55, + "execution_count": 44, "metadata": {}, "output_type": "execute_result" } @@ -1411,17 +1438,17 @@ }, { "cell_type": "code", - "execution_count": 56, + "execution_count": 45, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "<4x4 sparse matrix of type ''\n", - "\twith 6 stored elements in LInked List format>" + "<4x4 sparse matrix of type ''\n", + "\twith 6 stored elements in List of Lists format>" ] }, - "execution_count": 56, + "execution_count": 45, "metadata": {}, "output_type": "execute_result" } @@ -1432,17 +1459,17 @@ }, { "cell_type": "code", - "execution_count": 57, + "execution_count": 46, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "<4x4 sparse matrix of type ''\n", + "<4x4 sparse matrix of type ''\n", "\twith 6 stored elements in Compressed Sparse Row format>" ] }, - "execution_count": 57, + "execution_count": 46, "metadata": {}, "output_type": "execute_result" } @@ -1453,17 +1480,17 @@ }, { "cell_type": "code", - "execution_count": 58, + "execution_count": 47, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "<4x4 sparse matrix of type ''\n", + "<4x4 sparse matrix of type ''\n", "\twith 6 stored elements in Compressed Sparse Column format>" ] }, - "execution_count": 58, + "execution_count": 47, "metadata": {}, "output_type": "execute_result" } @@ -1481,19 +1508,19 @@ }, { "cell_type": "code", - "execution_count": 59, + "execution_count": 48, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "matrix([[ 1., 0., 0., 0.],\n", - " [ 0., 3., 0., 0.],\n", - " [ 0., 1., 1., 0.],\n", - " [ 1., 0., 0., 1.]])" + "matrix([[1., 0., 0., 0.],\n", + " [0., 3., 0., 0.],\n", + " [0., 1., 1., 0.],\n", + " [1., 0., 0., 1.]])" ] }, - "execution_count": 59, + "execution_count": 48, "metadata": {}, "output_type": "execute_result" } @@ -1504,19 +1531,19 @@ }, { "cell_type": "code", - "execution_count": 60, + "execution_count": 49, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "matrix([[ 1., 0., 0., 0.],\n", - " [ 0., 9., 0., 0.],\n", - " [ 0., 4., 1., 0.],\n", - " [ 2., 0., 0., 1.]])" + "matrix([[1., 0., 0., 0.],\n", + " [0., 9., 0., 0.],\n", + " [0., 4., 1., 0.],\n", + " [2., 0., 0., 1.]])" ] }, - "execution_count": 60, + "execution_count": 49, "metadata": {}, "output_type": "execute_result" } @@ -1660,7 +1687,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 50, "metadata": {}, "outputs": [], "source": [ @@ -1683,7 +1710,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 51, "metadata": {}, "outputs": [], "source": [ @@ -1693,7 +1720,7 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 52, "metadata": {}, "outputs": [ { @@ -1852,7 +1879,7 @@ }, { "cell_type": "code", - "execution_count": 30, + "execution_count": 53, "metadata": {}, "outputs": [], "source": [ @@ -1864,7 +1891,7 @@ }, { "cell_type": "code", - "execution_count": 32, + "execution_count": 54, "metadata": {}, "outputs": [ { @@ -2323,85 +2350,6 @@ "* http://docs.scipy.org/doc/scipy/reference/tutorial/index.html - 关于如何开始使用SciPy的教程. \n", "* https://github.com/scipy/scipy/ - SciPy源码. " ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 版本" - ] - }, - { - "cell_type": "code", - "execution_count": 93, - "metadata": {}, - "outputs": [ - { - "data": { - "application/json": { - "Software versions": [ - { - "module": "Python", - "version": "2.7.10 64bit [GCC 4.2.1 (Apple Inc. build 5577)]" - }, - { - "module": "IPython", - "version": "3.2.1" - }, - { - "module": "OS", - "version": "Darwin 14.1.0 x86_64 i386 64bit" - }, - { - "module": "numpy", - "version": "1.9.2" - }, - { - "module": "matplotlib", - "version": "1.4.3" - }, - { - "module": "scipy", - "version": "0.16.0" - } - ] - }, - "text/html": [ - "
SoftwareVersion
Python2.7.10 64bit [GCC 4.2.1 (Apple Inc. build 5577)]
IPython3.2.1
OSDarwin 14.1.0 x86_64 i386 64bit
numpy1.9.2
matplotlib1.4.3
scipy0.16.0
Sat Aug 15 11:13:18 2015 JST
" - ], - "text/latex": [ - "\\begin{tabular}{|l|l|}\\hline\n", - "{\\bf Software} & {\\bf Version} \\\\ \\hline\\hline\n", - "Python & 2.7.10 64bit [GCC 4.2.1 (Apple Inc. build 5577)] \\\\ \\hline\n", - "IPython & 3.2.1 \\\\ \\hline\n", - "OS & Darwin 14.1.0 x86\\_64 i386 64bit \\\\ \\hline\n", - "numpy & 1.9.2 \\\\ \\hline\n", - "matplotlib & 1.4.3 \\\\ \\hline\n", - "scipy & 0.16.0 \\\\ \\hline\n", - "\\hline \\multicolumn{2}{|l|}{Sat Aug 15 11:13:18 2015 JST} \\\\ \\hline\n", - "\\end{tabular}\n" - ], - "text/plain": [ - "Software versions\n", - "Python 2.7.10 64bit [GCC 4.2.1 (Apple Inc. build 5577)]\n", - "IPython 3.2.1\n", - "OS Darwin 14.1.0 x86_64 i386 64bit\n", - "numpy 1.9.2\n", - "matplotlib 1.4.3\n", - "scipy 0.16.0\n", - "Sat Aug 15 11:13:18 2015 JST" - ] - }, - "execution_count": 93, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "%reload_ext version_information\n", - "\n", - "%version_information numpy, matplotlib, scipy" - ] } ], "metadata": { diff --git a/1_numpy_matplotlib_scipy_sympy/5-sympy_tutorial.ipynb b/1_numpy_matplotlib_scipy_sympy/5-sympy_tutorial.ipynb index e6ef6d5..24c4ed2 100644 --- a/1_numpy_matplotlib_scipy_sympy/5-sympy_tutorial.ipynb +++ b/1_numpy_matplotlib_scipy_sympy/5-sympy_tutorial.ipynb @@ -11,8 +11,6 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "J.R. Johansson (jrjohansson at gmail.com)\n", - "\n", "这门课的最新版本[IPython notebook](http://ipython.org/notebook.html) 可以在这里获得[http://github.com/jrjohansson/scientific-python-lectures](http://github.com/jrjohansson/scientific-python-lectures).\n", "\n", "这节课其他的参考书可以在这里找到[http://jrjohansson.github.io](http://jrjohansson.github.io) 。" @@ -53,11 +51,11 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 11, "metadata": {}, "outputs": [], "source": [ - "from sympy import *" + "import sympy as sp" ] }, { @@ -69,11 +67,11 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 13, "metadata": {}, "outputs": [], "source": [ - "init_printing()\n", + "sp.init_printing()\n", "\n", "# 或者使用较旧版本的sympy/ipython，加载ipython扩展\n", "#%load_ext sympy.interactive.ipythonprinting\n", @@ -97,16 +95,16 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 14, "metadata": {}, "outputs": [], "source": [ - "x = Symbol('x')" + "x = sp.Symbol('x')" ] }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 15, "metadata": {}, "outputs": [ { @@ -120,7 +118,7 @@ "(x + π) " ] }, - "execution_count": 6, + "execution_count": 15, "metadata": {}, "output_type": "execute_result" } @@ -131,17 +129,17 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 16, "metadata": {}, "outputs": [], "source": [ "# 定义符号的可替代的方式\n", - "a, b, c = symbols(\"a, b, c\")" + "a, b, c = sp.symbols(\"a, b, c\")" ] }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 17, "metadata": {}, "outputs": [ { @@ -150,7 +148,7 @@ "sympy.core.symbol.Symbol" ] }, - "execution_count": 8, + "execution_count": 17, "metadata": {}, "output_type": "execute_result" } @@ -168,16 +166,16 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 18, "metadata": {}, "outputs": [], "source": [ - "x = Symbol('x', real=True)" + "x = sp.Symbol('x', real=True)" ] }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 19, "metadata": {}, "outputs": [ { @@ -186,7 +184,7 @@ "False" ] }, - "execution_count": 10, + "execution_count": 19, "metadata": {}, "output_type": "execute_result" } @@ -197,16 +195,16 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 20, "metadata": {}, "outputs": [], "source": [ - "x = Symbol('x', positive=True)" + "x = sp.Symbol('x', positive=True)" ] }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 21, "metadata": {}, "outputs": [ { @@ -219,7 +217,7 @@ "True" ] }, - "execution_count": 12, + "execution_count": 21, "metadata": {}, "output_type": "execute_result" } @@ -244,7 +242,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 23, "metadata": {}, "outputs": [ { @@ -257,18 +255,18 @@ "1 + ⅈ" ] }, - "execution_count": 13, + "execution_count": 23, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "1+1*I" + "1+1*sp.I" ] }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 24, "metadata": {}, "outputs": [ { @@ -281,18 +279,18 @@ "-1" ] }, - "execution_count": 14, + "execution_count": 24, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "I**2" + "sp.I**2" ] }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 25, "metadata": {}, "outputs": [ { @@ -306,13 +304,13 @@ "(ⅈ⋅x + 1) " ] }, - "execution_count": 15, + "execution_count": 25, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "(x * I + 1)**2" + "(x * sp.I + 1)**2" ] }, { @@ -331,17 +329,17 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 27, "metadata": {}, "outputs": [], "source": [ - "r1 = Rational(4,5)\n", - "r2 = Rational(5,4)" + "r1 = sp.Rational(4,5)\n", + "r2 = sp.Rational(5,4)" ] }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 28, "metadata": {}, "outputs": [ { @@ -354,7 +352,7 @@ "4/5" ] }, - "execution_count": 17, + "execution_count": 28, "metadata": {}, "output_type": "execute_result" } @@ -365,7 +363,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 29, "metadata": {}, "outputs": [ { @@ -380,7 +378,7 @@ "20" ] }, - "execution_count": 18, + "execution_count": 29, "metadata": {}, "output_type": "execute_result" } @@ -391,7 +389,7 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 30, "metadata": {}, "outputs": [ { @@ -406,7 +404,7 @@ "25" ] }, - "execution_count": 19, + "execution_count": 30, "metadata": {}, "output_type": "execute_result" } @@ -433,7 +431,7 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 31, "metadata": {}, "outputs": [ { @@ -446,27 +444,27 @@ "3.1415926535897932384626433832795028841971693993751" ] }, - "execution_count": 20, + "execution_count": 31, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "pi.evalf(n=50)" + "sp.pi.evalf(n=50)" ] }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 32, "metadata": {}, "outputs": [], "source": [ - "y = (x + pi)**2" + "y = (x + sp.pi)**2" ] }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 33, "metadata": {}, "outputs": [ { @@ -480,13 +478,13 @@ "9.8696⋅(0.31831⋅x + 1) " ] }, - "execution_count": 22, + "execution_count": 33, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "N(y, 5) # 和evalf 一样" + "sp.N(y, 5) # 和evalf 一样" ] }, { @@ -498,7 +496,7 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 34, "metadata": {}, "outputs": [ { @@ -512,7 +510,7 @@ "(1.5 + π) " ] }, - "execution_count": 23, + "execution_count": 34, "metadata": {}, "output_type": "execute_result" } @@ -523,7 +521,7 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 35, "metadata": {}, "outputs": [ { @@ -536,13 +534,13 @@ "21.5443823618587" ] }, - "execution_count": 24, + "execution_count": 35, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "N(y.subs(x, 1.5))" + "sp.N(y.subs(x, 1.5))" ] }, { @@ -554,7 +552,7 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 37, "metadata": {}, "outputs": [ { @@ -568,13 +566,13 @@ "(a + 2⋅π) " ] }, - "execution_count": 25, + "execution_count": 37, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "y.subs(x, a+pi)" + "y.subs(x, a+sp.pi)" ] }, { @@ -586,34 +584,34 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": 38, "metadata": {}, "outputs": [], "source": [ - "import numpy" + "import numpy as np" ] }, { "cell_type": "code", - "execution_count": 28, + "execution_count": 39, "metadata": {}, "outputs": [], "source": [ - "x_vec = numpy.arange(0, 10, 0.1)" + "x_vec = np.arange(0, 10, 0.1)" ] }, { "cell_type": "code", - "execution_count": 29, + "execution_count": 40, "metadata": {}, "outputs": [], "source": [ - "y_vec = numpy.array([N(((x + pi)**2).subs(x, xx)) for xx in x_vec])" + "y_vec = np.array([N(((x + pi)**2).subs(x, xx)) for xx in x_vec])" ] }, { "cell_type": "code", - "execution_count": 30, + "execution_count": 42, "metadata": {}, "outputs": [ { @@ -630,6 +628,7 @@ } ], "source": [ + "import matplotlib.pyplot as plt\n", "fig, ax = plt.subplots()\n", "ax.plot(x_vec, y_vec);" ] @@ -643,17 +642,17 @@ }, { "cell_type": "code", - "execution_count": 31, + "execution_count": 43, "metadata": {}, "outputs": [], "source": [ - "f = lambdify([x], (x + pi)**2, 'numpy') # 第一个参数是一个变量列表\n", + "f = sp.lambdify([x], (x + pi)**2, 'numpy') # 第一个参数是一个变量列表\n", " # f将是以下函数的函数:本例中仅为x -> f(x)" ] }, { "cell_type": "code", - "execution_count": 32, + "execution_count": 44, "metadata": {}, "outputs": [], "source": [ @@ -669,34 +668,33 @@ }, { "cell_type": "code", - "execution_count": 31, + "execution_count": 47, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "10 loops, best of 3: 28.2 ms per loop\n" + "11.4 ms ± 82.5 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n" ] } ], "source": [ "%%timeit\n", "\n", - "y_vec = numpy.array([N(((x + pi)**2).subs(x, xx)) for xx in x_vec])" + "y_vec = np.array([sp.N(((x + pi)**2).subs(x, xx)) for xx in x_vec])" ] }, { "cell_type": "code", - "execution_count": 32, + "execution_count": 48, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "The slowest run took 8.86 times longer than the fastest. This could mean that an intermediate result is being cached \n", - "100000 loops, best of 3: 2.93 µs per loop\n" + "1.41 µs ± 17.3 ns per loop (mean ± std. dev. of 7 runs, 1000000 loops each)\n" ] } ], @@ -736,7 +734,7 @@ }, { "cell_type": "code", - "execution_count": 33, + "execution_count": 49, "metadata": {}, "outputs": [ { @@ -749,7 +747,7 @@ "(x + 1)⋅(x + 2)⋅(x + 3)" ] }, - "execution_count": 33, + "execution_count": 49, "metadata": {}, "output_type": "execute_result" } @@ -760,7 +758,7 @@ }, { "cell_type": "code", - "execution_count": 34, + "execution_count": 50, "metadata": {}, "outputs": [ { @@ -774,13 +772,13 @@ "x + 6⋅x + 11⋅x + 6" ] }, - "execution_count": 34, + "execution_count": 50, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "expand((x+1)*(x+2)*(x+3))" + "sp.expand((x+1)*(x+2)*(x+3))" ] }, { @@ -792,7 +790,7 @@ }, { "cell_type": "code", - "execution_count": 35, + "execution_count": 51, "metadata": {}, "outputs": [ { @@ -805,18 +803,18 @@ "sin(a + b)" ] }, - "execution_count": 35, + "execution_count": 51, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "sin(a+b)" + "sp.sin(a+b)" ] }, { "cell_type": "code", - "execution_count": 36, + "execution_count": 52, "metadata": {}, "outputs": [ { @@ -829,13 +827,13 @@ "sin(a)⋅cos(b) + sin(b)⋅cos(a)" ] }, - "execution_count": 36, + "execution_count": 52, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "expand(sin(a+b), trig=True)" + "sp.expand(sp.sin(a+b), trig=True)" ] }, { @@ -854,7 +852,7 @@ }, { "cell_type": "code", - "execution_count": 37, + "execution_count": 53, "metadata": {}, "outputs": [ { @@ -867,13 +865,13 @@ "(x + 1)⋅(x + 2)⋅(x + 3)" ] }, - "execution_count": 37, + "execution_count": 53, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "factor(x**3 + 6 * x**2 + 11*x + 6)" + "sp.factor(x**3 + 6 * x**2 + 11*x + 6)" ] }, { @@ -894,7 +892,7 @@ }, { "cell_type": "code", - "execution_count": 38, + "execution_count": 54, "metadata": {}, "outputs": [ { @@ -907,19 +905,19 @@ "(x + 1)⋅(x + 2)⋅(x + 3)" ] }, - "execution_count": 38, + "execution_count": 54, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# simplify 展开乘积\n", - "simplify((x+1)*(x+2)*(x+3))" + "sp.simplify((x+1)*(x+2)*(x+3))" ] }, { "cell_type": "code", - "execution_count": 39, + "execution_count": 55, "metadata": {}, "outputs": [ { @@ -932,19 +930,19 @@ "1" ] }, - "execution_count": 39, + "execution_count": 55, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# simplify 使用三角恒等式\n", - "simplify(sin(a)**2 + cos(a)**2)" + "sp.simplify(sin(a)**2 + cos(a)**2)" ] }, { "cell_type": "code", - "execution_count": 40, + "execution_count": 56, "metadata": {}, "outputs": [ { @@ -959,13 +957,13 @@ "tan(x)" ] }, - "execution_count": 40, + "execution_count": 56, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "simplify(cos(x)/sin(x))" + "sp.simplify(cos(x)/sin(x))" ] }, { @@ -984,7 +982,7 @@ }, { "cell_type": "code", - "execution_count": 41, + "execution_count": 57, "metadata": {}, "outputs": [], "source": [ @@ -993,7 +991,7 @@ }, { "cell_type": "code", - "execution_count": 42, + "execution_count": 58, "metadata": {}, "outputs": [ { @@ -1008,7 +1006,7 @@ "(a + 1)⋅(a + 2)" ] }, - "execution_count": 42, + "execution_count": 58, "metadata": {}, "output_type": "execute_result" } @@ -1019,7 +1017,7 @@ }, { "cell_type": "code", - "execution_count": 43, + "execution_count": 59, "metadata": {}, "outputs": [ { @@ -1034,18 +1032,18 @@ " a + 2 a + 1" ] }, - "execution_count": 43, + "execution_count": 59, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "apart(f1)" + "sp.apart(f1)" ] }, { "cell_type": "code", - "execution_count": 44, + "execution_count": 60, "metadata": {}, "outputs": [], "source": [ @@ -1054,14 +1052,14 @@ }, { "cell_type": "code", - "execution_count": 45, + "execution_count": 61, "metadata": {}, "outputs": [ { "data": { - "image/png": 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"text/latex": [ - "$$\\frac{1}{a + 3} + \\frac{1}{a + 2}$$" + "$\\displaystyle \\frac{1}{a + 3} + \\frac{1}{a + 2}$" ], "text/plain": [ " 1 1 \n", @@ -1069,7 +1067,7 @@ "a + 3 a + 2" ] }, - "execution_count": 45, + "execution_count": 61, "metadata": {}, "output_type": "execute_result" } @@ -1080,14 +1078,14 @@ }, { "cell_type": "code", - "execution_count": 46, + "execution_count": 62, "metadata": {}, "outputs": [ { "data": { - "image/png": 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+ "image/png": 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"text/latex": [ - "$$\\frac{2 a + 5}{\\left(a + 2\\right) \\left(a + 3\\right)}$$" + "$\\displaystyle \\frac{2 a + 5}{\\left(a + 2\\right) \\left(a + 3\\right)}$" ], "text/plain": [ " 2⋅a + 5 \n", @@ -1095,13 +1093,13 @@ "(a + 2)⋅(a + 3)" ] }, - "execution_count": 46, + "execution_count": 62, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "together(f2)" + "sp.together(f2)" ] }, { @@ -1113,23 +1111,28 @@ }, { "cell_type": "code", - "execution_count": 40, + "execution_count": 63, "metadata": {}, "outputs": [ { - "ename": "NameError", - "evalue": "name 'f2' is not defined", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0msimplify\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mf2\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", - "\u001b[0;31mNameError\u001b[0m: name 'f2' is not defined" - ] + "data": { + "image/png": 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+ "text/latex": [ + "$\\displaystyle \\frac{2 a + 5}{\\left(a + 2\\right) \\left(a + 3\\right)}$" + ], + "text/plain": [ + " 2⋅a + 5 \n", + "───────────────\n", + "(a + 2)⋅(a + 3)" + ] + }, + "execution_count": 63, + "metadata": {}, + "output_type": "execute_result" } ], "source": [ - "simplify(f2)" + "sp.simplify(f2)" ] }, { @@ -1162,7 +1165,7 @@ }, { "cell_type": "code", - "execution_count": 45, + "execution_count": 64, "metadata": {}, "outputs": [ { @@ -1176,7 +1179,7 @@ "(x + π) " ] }, - "execution_count": 45, + "execution_count": 64, "metadata": {}, "output_type": "execute_result" } @@ -1187,7 +1190,7 @@ }, { "cell_type": "code", - "execution_count": 46, + "execution_count": 65, "metadata": {}, "outputs": [ { @@ -1201,13 +1204,13 @@ "4⋅(x + π) " ] }, - "execution_count": 46, + "execution_count": 65, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "diff(y**2, x)" + "sp.diff(y**2, x)" ] }, { @@ -1219,7 +1222,7 @@ }, { "cell_type": "code", - "execution_count": 47, + "execution_count": 66, "metadata": {}, "outputs": [ { @@ -1233,18 +1236,18 @@ "12⋅(x + π) " ] }, - "execution_count": 47, + "execution_count": 66, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "diff(y**2, x, x)" + "sp.diff(y**2, x, x)" ] }, { "cell_type": "code", - "execution_count": 48, + "execution_count": 67, "metadata": {}, "outputs": [ { @@ -1258,13 +1261,13 @@ "12⋅(x + π) " ] }, - "execution_count": 48, + "execution_count": 67, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "diff(y**2, x, 2) # 和上面的一样" + "sp.diff(y**2, x, 2) # 和上面的一样" ] }, { @@ -1276,7 +1279,7 @@ }, { "cell_type": "code", - "execution_count": 49, + "execution_count": 68, "metadata": {}, "outputs": [], "source": [ @@ -1285,11 +1288,11 @@ }, { "cell_type": "code", - "execution_count": 50, + "execution_count": 69, "metadata": {}, "outputs": [], "source": [ - "f = sin(x*y) + cos(y*z)" + "f = sp.sin(x*y) + sp.cos(y*z)" ] }, { @@ -1301,7 +1304,7 @@ }, { "cell_type": "code", - "execution_count": 51, + "execution_count": 70, "metadata": {}, "outputs": [ { @@ -1314,13 +1317,13 @@ "-x⋅(x⋅y⋅cos(x⋅y) + 2⋅sin(x⋅y))" ] }, - "execution_count": 51, + "execution_count": 70, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "diff(f, x, 1, y, 2)" + "sp.diff(f, x, 1, y, 2)" ] }, { @@ -1339,7 +1342,7 @@ }, { "cell_type": "code", - "execution_count": 52, + "execution_count": 71, "metadata": {}, "outputs": [ { @@ -1352,7 +1355,7 @@ "sin(x⋅y) + cos(y⋅z)" ] }, - "execution_count": 52, + "execution_count": 71, "metadata": {}, "output_type": "execute_result" } @@ -1363,7 +1366,7 @@ }, { "cell_type": "code", - "execution_count": 53, + "execution_count": 72, "metadata": {}, "outputs": [ { @@ -1380,13 +1383,13 @@ " ⎝⎩ 0 otherwise⎠" ] }, - "execution_count": 53, + "execution_count": 72, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "integrate(f, x)" + "sp.integrate(f, x)" ] }, { @@ -1398,7 +1401,7 @@ }, { "cell_type": "code", - "execution_count": 54, + "execution_count": 73, "metadata": {}, "outputs": [ { @@ -1411,13 +1414,13 @@ "2⋅cos(y⋅z)" ] }, - "execution_count": 54, + "execution_count": 73, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "integrate(f, (x, -1, 1))" + "sp.integrate(f, (x, -1, 1))" ] }, { @@ -1429,7 +1432,7 @@ }, { "cell_type": "code", - "execution_count": 55, + "execution_count": 77, "metadata": {}, "outputs": [ { @@ -1444,13 +1447,13 @@ "⎝ℯ , (x, -∞, ∞)⎠" ] }, - "execution_count": 55, + "execution_count": 77, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "(exp(-x**2), (x, -oo, oo))" + "(sp.exp(-x**2), (x, -sp.oo, sp.oo))" ] }, { @@ -1476,7 +1479,7 @@ }, { "cell_type": "code", - "execution_count": 56, + "execution_count": 78, "metadata": {}, "outputs": [], "source": [ @@ -1485,7 +1488,7 @@ }, { "cell_type": "code", - "execution_count": 57, + "execution_count": 80, "metadata": {}, "outputs": [ { @@ -1507,61 +1510,61 @@ "n = 1 " ] }, - "execution_count": 57, + "execution_count": 80, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "Sum(1/n**2, (n, 1, 10))" + "sp.Sum(1/n**2, (n, 1, 10))" ] }, { "cell_type": "code", - "execution_count": 61, + "execution_count": 81, "metadata": {}, "outputs": [ { "data": { - "image/png": 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+ "image/png": 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"text/latex": [ - "$$1.54976773116654$$" + "$\\displaystyle 1.54976773116654$" ], "text/plain": [ "1.54976773116654" ] }, - "execution_count": 61, + "execution_count": 81, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "Sum(1/n**2, (n,1, 10)).evalf()" + "sp.Sum(1/n**2, (n,1, 10)).evalf()" ] }, { "cell_type": "code", - "execution_count": 62, + "execution_count": 82, "metadata": {}, "outputs": [ { "data": { - "image/png": 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+ "image/png": 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"text/latex": [ - "$$1.64493406684823$$" + "$\\displaystyle 1.64493406684823$" ], "text/plain": [ "1.64493406684823" ] }, - "execution_count": 62, + "execution_count": 82, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "Sum(1/n**2, (n, 1, oo)).evalf()" + "sp.Sum(1/n**2, (n, 1, oo)).evalf()" ] }, { @@ -1573,30 +1576,30 @@ }, { "cell_type": "code", - "execution_count": 63, + "execution_count": 83, "metadata": {}, "outputs": [ { "data": { - "image/png": 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"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\n", "text/latex": [ - "$$\\prod_{n=1}^{10} n$$" + "$\\displaystyle \\prod_{n=1}^{10} n$" ], "text/plain": [ " 10 \n", - "┬───┬ \n", - "│ │ n\n", - "│ │ \n", + "─┬─┬─ \n", + " │ │ n\n", + " │ │ \n", "n = 1 " ] }, - "execution_count": 63, + "execution_count": 83, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "Product(n, (n, 1, 10)) # 10!" + "sp.Product(n, (n, 1, 10)) # 10!" ] }, { @@ -1615,26 +1618,26 @@ }, { "cell_type": "code", - "execution_count": 40, + "execution_count": 84, "metadata": {}, "outputs": [ { "data": { - "image/png": "iVBORw0KGgoAAAANSUhEUgAAAAgAAAAPBAMAAAArJJMAAAAAJFBMVEX///8AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAADHJj5lAAAAC3RSTlMAzRAiu5mrdu/dZmiL4QAAAAAjSURBVAgdY2BgEGJgYDDZxMCgEgYkGNhJJVgzdmYB9TEwAACPpQrvlUCHcAAAAABJRU5ErkJggg==\n", + "image/png": "iVBORw0KGgoAAAANSUhEUgAAAAgAAAAOCAYAAAASVl2WAAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAXUlEQVQYGWP8//8/Aww0NDQYAdmrgdgYyP4AEmcBMgSA9GwgfgfEJkCsBMRwAFIAUhkKEgGyy4AUyBQ4YIKzcDBGFUAChmA4MIIiCxjEoAgCxQkoLkD0PSA+B8SnAeDgGGsGGU5UAAAAAElFTkSuQmCC\n", "text/latex": [ - "$$1$$" + "$\\displaystyle 1$" ], "text/plain": [ "1" ] }, - "execution_count": 40, + "execution_count": 84, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "limit(sin(x)/x, x, 0)" + "sp.limit(sp.sin(x)/x, x, 0)" ] }, { @@ -1646,20 +1649,20 @@ }, { "cell_type": "code", - "execution_count": 65, + "execution_count": 85, "metadata": {}, "outputs": [ { "data": { - "image/png": "iVBORw0KGgoAAAANSUhEUgAAAKAAAAAUBAMAAAD4uit9AAAAMFBMVEX///8AAAAAAAAAAAAAAAAA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\nXd6gLq79C/gHf4qzfkFAyyAAAAAASUVORK5CYII=\n", + "image/png": "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\n", "text/latex": [ - "$$\\sin{\\left (x y \\right )} + \\cos{\\left (y z \\right )}$$" + "$\\displaystyle \\sin{\\left(x y \\right)} + \\cos{\\left(y z \\right)}$" ], "text/plain": [ "sin(x⋅y) + cos(y⋅z)" ] }, - "execution_count": 65, + "execution_count": 85, "metadata": {}, "output_type": "execute_result" } @@ -1670,26 +1673,26 @@ }, { "cell_type": "code", - "execution_count": 66, + "execution_count": 86, "metadata": {}, "outputs": [ { "data": { - "image/png": "iVBORw0KGgoAAAANSUhEUgAAAFMAAAAUBAMAAAADwRznAAAAMFBMVEX///8AAAAAAAAAAAAAAAAA\nAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAv3aB7AAAAD3RSTlMAEHaZIu+JVM27RDKr\nZt2dj8xZAAAACXBIWXMAAA7EAAAOxAGVKw4bAAABlUlEQVQoFXWTPUvDUBSG31jbfDUxFBQEh9BK\nXYRmEkXEiBRRUDo46RJaEXEKgrMZHDo6uAiKXXS0/gMFN0EIRZyLCK5qQWg76IlJ9V6jBxLOc86T\n+8G9Af6Lfp/t3LPwO1/kCmmbQx72ORQ8DjnQLA5R5pEl3WYJGOSRpRUWKJdqEHImigYgTFRcDGW3\noY41PGrl6MmML+XNSEhYSIklFKhcdNVV9QmyOQw0iU/oa0d71L1I0DwsJDwcUasKsas7ULtnBkzi\nZyBlpF5FOxLEJoykiTcaoUXtKxd419ujlGKNapAO0BPUF6BuqF1AIR17NaAtbH1YoQoMmJSFQqBO\noq/EjNqag9YhgRZA01zSKxSUJnAM+ZoKVUBMWlA6O8AdMW1LNAroMyKBtoUp1H1qFX1sqOuQnF0D\nh8QVmtk9xXJPSDjASHnWppZwk7cxv3mL88ZDjfiCOtnMDI0SClKwFlrMH8EebCDQ6aU9IdhFLL6v\nSyTQddF92Yx5QSFYcBChEFxCJTv9VYm9elc7FGQ7JvwUYj/MJ3CTXM0O35peAAAAAElFTkSuQmCC\n", + "image/png": 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"text/latex": [ - "$$y \\cos{\\left (x y \\right )}$$" + "$\\displaystyle y \\cos{\\left(x y \\right)}$" ], "text/plain": [ "y⋅cos(x⋅y)" ] }, - "execution_count": 66, + "execution_count": 86, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "diff(f, x)" + "sp.diff(f, x)" ] }, { @@ -1701,7 +1704,7 @@ }, { "cell_type": "code", - "execution_count": 67, + "execution_count": 88, "metadata": {}, "outputs": [], "source": [ @@ -1710,33 +1713,26 @@ }, { "cell_type": "code", - "execution_count": 68, + "execution_count": 89, "metadata": {}, "outputs": [ { "data": { - "image/png": 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"text/latex": [ - "$$y \\cos{\\left (x y \\right )}$$" + "$\\displaystyle y \\cos{\\left(x y \\right)}$" ], "text/plain": [ "y⋅cos(x⋅y)" ] }, - "execution_count": 68, + "execution_count": 89, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "limit((f.subs(x, x+h) - f)/h, h, 0)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "OK!" + "sp.limit((f.subs(x, x+h) - f)/h, h, 0)" ] }, { @@ -1748,50 +1744,50 @@ }, { "cell_type": "code", - "execution_count": 69, + "execution_count": 90, "metadata": {}, "outputs": [ { "data": { - "image/png": "iVBORw0KGgoAAAANSUhEUgAAABMAAAALBAMAAABv+6sJAAAAMFBMVEX///8AAAAAAAAAAAAAAAAA\nAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAv3aB7AAAAD3RSTlMAEKvvZom7mXYyzVQi\n3UQ6SGZXAAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAaklEQVQIHWNgYBBgAAIQwaj82YGBIayogYGB\nbQLHLwapDQxTGRg8GRj2J6xkYGA5wACUYP0LJBgcQEyGfBDRAGYm/wNqd2BwZGDgiDE+wMBxgIGd\ngSGcYb4dgytQolxtAwNjvXEAUDncNgBJUBUwaYAbUgAAAABJRU5ErkJggg==\n", + "image/png": "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\n", "text/latex": [ - "$$\\infty$$" + "$\\displaystyle \\infty$" ], "text/plain": [ "∞" ] }, - "execution_count": 69, + "execution_count": 90, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "limit(1/x, x, 0, dir=\"+\")" + "sp.limit(1/x, x, 0, dir=\"+\")" ] }, { "cell_type": "code", - "execution_count": 70, + "execution_count": 91, "metadata": {}, "outputs": [ { "data": { - "image/png": "iVBORw0KGgoAAAANSUhEUgAAACMAAAALBAMAAAAHCCkxAAAAMFBMVEX///8AAAAAAAAAAAAAAAAA\nAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAv3aB7AAAAD3RSTlMAEM3dMqvvZom7mXZU\nIkRJD0iWAAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAfklEQVQIHWNggAMBEAtMQIUYw74VMDB0Lt0A\nV8LA6cD9iUHoAIMHQqiEgeH8BBUGBvYLDELGIKDCANTA8RmkC6gdCkC8+SAChCEAxJr2j4GBsQAm\nwlDIwMDdm3aBgfsCXIiLgaGLwT+PoQIuwsC4KvIAA+P6tAaEENThAgwMAMSLGqu/gFQwAAAAAElF\nTkSuQmCC\n", + "image/png": "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\n", "text/latex": [ - "$$-\\infty$$" + "$\\displaystyle -\\infty$" ], "text/plain": [ "-∞" ] }, - "execution_count": 70, + "execution_count": 91, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "limit(1/x, x, 0, dir=\"-\")" + "sp.limit(1/x, x, 0, dir=\"-\")" ] }, { @@ -1810,7 +1806,7 @@ }, { "cell_type": "code", - "execution_count": 58, + "execution_count": 94, "metadata": {}, "outputs": [ { @@ -1826,13 +1822,13 @@ " 2 6 24 120 " ] }, - "execution_count": 58, + "execution_count": 94, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "series(exp(x), x)" + "sp.series(sp.exp(x), x)" ] }, { @@ -1844,7 +1840,7 @@ }, { "cell_type": "code", - "execution_count": 59, + "execution_count": 95, "metadata": {}, "outputs": [ { @@ -1865,13 +1861,13 @@ " " ] }, - "execution_count": 59, + "execution_count": 95, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "series(exp(x), x, 1)" + "sp.series(sp.exp(x), x, 1)" ] }, { @@ -1883,7 +1879,7 @@ }, { "cell_type": "code", - "execution_count": 60, + "execution_count": 96, "metadata": {}, "outputs": [ { @@ -1904,13 +1900,13 @@ " 5040 40320 362880 " ] }, - "execution_count": 60, + "execution_count": 96, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "series(exp(x), x, 1, 10)" + "sp.series(sp.exp(x), x, 1, 10)" ] }, { @@ -1922,14 +1918,14 @@ }, { "cell_type": "code", - "execution_count": 74, + "execution_count": 97, "metadata": {}, "outputs": [ { "data": { - "image/png": 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+ "image/png": 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"text/latex": [ - "$$1 - \\frac{x^{2}}{2} + \\frac{x^{4}}{24} + \\mathcal{O}\\left(x^{5}\\right)$$" + "$\\displaystyle 1 - \\frac{x^{2}}{2} + \\frac{x^{4}}{24} + O\\left(x^{5}\\right)$" ], "text/plain": [ " 2 4 \n", @@ -1938,59 +1934,59 @@ " 2 24 " ] }, - "execution_count": 74, + "execution_count": 97, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "s1 = cos(x).series(x, 0, 5)\n", + "s1 = sp.cos(x).series(x, 0, 5)\n", "s1" ] }, { "cell_type": "code", - "execution_count": 75, + "execution_count": 98, "metadata": {}, "outputs": [ { "data": { - "image/png": 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"text/latex": [ - "$$x + \\mathcal{O}\\left(x^{2}\\right)$$" + "$\\displaystyle x + O\\left(x^{2}\\right)$" ], "text/plain": [ " ⎛ 2⎞\n", "x + O⎝x ⎠" ] }, - "execution_count": 75, + "execution_count": 98, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "s2 = sin(x).series(x, 0, 2)\n", + "s2 = sp.sin(x).series(x, 0, 2)\n", "s2" ] }, { "cell_type": "code", - "execution_count": 76, + "execution_count": 99, "metadata": {}, "outputs": [ { "data": { - "image/png": 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"text/latex": [ - "$$x + \\mathcal{O}\\left(x^{2}\\right)$$" + "$\\displaystyle x + O\\left(x^{2}\\right)$" ], "text/plain": [ " ⎛ 2⎞\n", "x + O⎝x ⎠" ] }, - "execution_count": 76, + "execution_count": 99, "metadata": {}, "output_type": "execute_result" } @@ -2008,14 +2004,14 @@ }, { "cell_type": "code", - "execution_count": 77, + "execution_count": 100, "metadata": {}, "outputs": [ { "data": { - "image/png": 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+ "image/png": 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"text/latex": [ - "$$\\frac{x^{5}}{24} - \\frac{x^{3}}{2} + x$$" + "$\\displaystyle \\frac{x^{5}}{24} - \\frac{x^{3}}{2} + x$" ], "text/plain": [ " 5 3 \n", @@ -2024,13 +2020,13 @@ "24 2 " ] }, - "execution_count": 77, + "execution_count": 100, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "expand(s1.removeO() * s2.removeO())" + "sp.expand(s1.removeO() * s2.removeO())" ] }, { @@ -2042,14 +2038,14 @@ }, { "cell_type": "code", - "execution_count": 78, + "execution_count": 101, "metadata": {}, "outputs": [ { "data": { - "image/png": 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+ "image/png": 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"text/latex": [ - "$$x - \\frac{2 x^{3}}{3} + \\frac{2 x^{5}}{15} + \\mathcal{O}\\left(x^{6}\\right)$$" + "$\\displaystyle x - \\frac{2 x^{3}}{3} + \\frac{2 x^{5}}{15} + O\\left(x^{6}\\right)$" ], "text/plain": [ " 3 5 \n", @@ -2058,13 +2054,13 @@ " 3 15 " ] }, - "execution_count": 78, + "execution_count": 101, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "(cos(x)*sin(x)).series(x, 0, 6)" + "(sp.cos(x)*sp.sin(x)).series(x, 0, 6)" ] }, { @@ -2090,24 +2086,24 @@ }, { "cell_type": "code", - "execution_count": 42, + "execution_count": 103, "metadata": {}, "outputs": [], "source": [ - "m11, m12, m21, m22 = symbols(\"m11, m12, m21, m22\")\n", - "b1, b2 = symbols(\"b1, b2\")" + "m11, m12, m21, m22 = sp.symbols(\"m11, m12, m21, m22\")\n", + "b1, b2 = sp.symbols(\"b1, b2\")" ] }, { "cell_type": "code", - "execution_count": 43, + "execution_count": 104, "metadata": {}, "outputs": [ { "data": { - "image/png": "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\n", + "image/png": "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\n", "text/latex": [ - "$$\\left[\\begin{matrix}m_{11} & m_{12}\\\\m_{21} & m_{22}\\end{matrix}\\right]$$" + "$\\displaystyle \\left[\\begin{matrix}m_{11} & m_{12}\\\\m_{21} & m_{22}\\end{matrix}\\right]$" ], "text/plain": [ "⎡m₁₁ m₁₂⎤\n", @@ -2115,26 +2111,26 @@ "⎣m₂₁ m₂₂⎦" ] }, - "execution_count": 43, + "execution_count": 104, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "A = Matrix([[m11, m12],[m21, m22]])\n", + "A = sp.Matrix([[m11, m12],[m21, m22]])\n", "A" ] }, { "cell_type": "code", - "execution_count": 44, + "execution_count": 105, "metadata": {}, "outputs": [ { "data": { - "image/png": "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\n", + "image/png": "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\n", "text/latex": [ - "$$\\left[\\begin{matrix}b_{1}\\\\b_{2}\\end{matrix}\\right]$$" + "$\\displaystyle \\left[\\begin{matrix}b_{1}\\\\b_{2}\\end{matrix}\\right]$" ], "text/plain": [ "⎡b₁⎤\n", @@ -2142,13 +2138,13 @@ "⎣b₂⎦" ] }, - "execution_count": 44, + "execution_count": 105, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "b = Matrix([[b1], [b2]])\n", + "b = sp.Matrix([[b1], [b2]])\n", "b" ] }, @@ -2161,14 +2157,14 @@ }, { "cell_type": "code", - "execution_count": 45, + "execution_count": 106, "metadata": {}, "outputs": [ { "data": { - "image/png": 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+ "image/png": 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"text/latex": [ - "$$\\left[\\begin{matrix}m_{11}^{2} + m_{12} m_{21} & m_{11} m_{12} + m_{12} m_{22}\\\\m_{11} m_{21} + m_{21} m_{22} & m_{12} m_{21} + m_{22}^{2}\\end{matrix}\\right]$$" + "$\\displaystyle \\left[\\begin{matrix}m_{11}^{2} + m_{12} m_{21} & m_{11} m_{12} + m_{12} m_{22}\\\\m_{11} m_{21} + m_{21} m_{22} & m_{12} m_{21} + m_{22}^{2}\\end{matrix}\\right]$" ], "text/plain": [ "⎡ 2 ⎤\n", @@ -2178,7 +2174,7 @@ "⎣m₁₁⋅m₂₁ + m₂₁⋅m₂₂ m₁₂⋅m₂₁ + m₂₂ ⎦" ] }, - "execution_count": 45, + "execution_count": 106, "metadata": {}, "output_type": "execute_result" } @@ -2189,14 +2185,14 @@ }, { "cell_type": "code", - "execution_count": 83, + "execution_count": 107, "metadata": {}, "outputs": [ { "data": { - "image/png": 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+ "image/png": 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"text/latex": [ - "$$\\left[\\begin{matrix}b_{1} m_{11} + b_{2} m_{12}\\\\b_{1} m_{21} + b_{2} m_{22}\\end{matrix}\\right]$$" + "$\\displaystyle \\left[\\begin{matrix}b_{1} m_{11} + b_{2} m_{12}\\\\b_{1} m_{21} + b_{2} m_{22}\\end{matrix}\\right]$" ], "text/plain": [ "⎡b₁⋅m₁₁ + b₂⋅m₁₂⎤\n", @@ -2204,7 +2200,7 @@ "⎣b₁⋅m₂₁ + b₂⋅m₂₂⎦" ] }, - "execution_count": 83, + "execution_count": 107, "metadata": {}, "output_type": "execute_result" } @@ -2222,20 +2218,20 @@ }, { "cell_type": "code", - "execution_count": 84, + "execution_count": 108, "metadata": {}, "outputs": [ { "data": { - "image/png": "iVBORw0KGgoAAAANSUhEUgAAAKYAAAAMBAMAAAAaIdvMAAAAMFBMVEX///8AAAAAAAAAAAAAAAAA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\n9AyNtKqM6qgiUQAAAABJRU5ErkJggg==\n", + "image/png": "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\n", "text/latex": [ - "$$m_{11} m_{22} - m_{12} m_{21}$$" + "$\\displaystyle m_{11} m_{22} - m_{12} m_{21}$" ], "text/plain": [ "m₁₁⋅m₂₂ - m₁₂⋅m₂₁" ] }, - "execution_count": 84, + "execution_count": 108, "metadata": {}, "output_type": "execute_result" } @@ -2246,14 +2242,14 @@ }, { "cell_type": "code", - "execution_count": 46, + "execution_count": 109, "metadata": {}, "outputs": [ { "data": { - "image/png": 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+ "image/png": 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"text/latex": [ - "$$\\left[\\begin{matrix}\\frac{m_{22}}{m_{11} m_{22} - m_{12} m_{21}} & - \\frac{m_{12}}{m_{11} m_{22} - m_{12} m_{21}}\\\\- \\frac{m_{21}}{m_{11} m_{22} - m_{12} m_{21}} & \\frac{m_{11}}{m_{11} m_{22} - m_{12} m_{21}}\\end{matrix}\\right]$$" + "$\\displaystyle \\left[\\begin{matrix}\\frac{m_{22}}{m_{11} m_{22} - m_{12} m_{21}} & - \\frac{m_{12}}{m_{11} m_{22} - m_{12} m_{21}}\\\\- \\frac{m_{21}}{m_{11} m_{22} - m_{12} m_{21}} & \\frac{m_{11}}{m_{11} m_{22} - m_{12} m_{21}}\\end{matrix}\\right]$" ], "text/plain": [ "⎡ m₂₂ -m₁₂ ⎤\n", @@ -2265,7 +2261,7 @@ "⎣m₁₁⋅m₂₂ - m₁₂⋅m₂₁ m₁₁⋅m₂₂ - m₁₂⋅m₂₁⎦" ] }, - "execution_count": 46, + "execution_count": 109, "metadata": {}, "output_type": "execute_result" } @@ -2290,7 +2286,7 @@ }, { "cell_type": "code", - "execution_count": 61, + "execution_count": 110, "metadata": {}, "outputs": [ { @@ -2303,18 +2299,18 @@ "[-1, 1]" ] }, - "execution_count": 61, + "execution_count": 110, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "solve(x**2 - 1, x)" + "sp.solve(x**2 - 1, x)" ] }, { "cell_type": "code", - "execution_count": 62, + "execution_count": 111, "metadata": {}, "outputs": [ { @@ -2330,13 +2326,13 @@ "⎣ ╲╱ 2 2 ╲╱ 2 2 ╲╱ 2 2 ╲╱ 2 2 ⎦" ] }, - "execution_count": 62, + "execution_count": 111, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "solve(x**4 - x**2 - 1, x)" + "sp.solve(x**4 - x**2 - 1, x)" ] }, { @@ -2348,26 +2344,26 @@ }, { "cell_type": "code", - "execution_count": 88, + "execution_count": 112, "metadata": {}, "outputs": [ { "data": { - "image/png": "iVBORw0KGgoAAAANSUhEUgAAAH4AAAAVBAMAAAByPkciAAAAMFBMVEX///8AAAAAAAAAAAAAAAAA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\n/1sDfgDNr2XH9LJoSQAAAABJRU5ErkJggg==\n", + "image/png": "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\n", "text/latex": [ - "$$\\left \\{ x : 1, \\quad y : 0\\right \\}$$" + "$\\displaystyle \\left\\{ x : 1, \\ y : 0\\right\\}$" ], "text/plain": [ "{x: 1, y: 0}" ] }, - "execution_count": 88, + "execution_count": 112, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "solve([x + y - 1, x - y - 1], [x,y])" + "sp.solve([x + y - 1, x - y - 1], [x,y])" ] }, { @@ -2379,14 +2375,14 @@ }, { "cell_type": "code", - "execution_count": 89, + "execution_count": 113, "metadata": {}, "outputs": [ { "data": { - "image/png": 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+ "image/png": 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"text/latex": [ - "$$\\left \\{ x : \\frac{a}{2} + \\frac{c}{2}, \\quad y : \\frac{a}{2} - \\frac{c}{2}\\right \\}$$" + "$\\displaystyle \\left\\{ x : \\frac{a}{2} + \\frac{c}{2}, \\ y : \\frac{a}{2} - \\frac{c}{2}\\right\\}$" ], "text/plain": [ "⎧ a c a c⎫\n", @@ -2394,13 +2390,13 @@ "⎩ 2 2 2 2⎭" ] }, - "execution_count": 89, + "execution_count": 113, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "solve([x + y - a, x - y - c], [x,y])" + "sp.solve([x + y - a, x - y - c], [x,y])" ] }, { @@ -2418,85 +2414,6 @@ "* https://github.com/sympy/sympy - The source code of SymPy.\n", "* http://live.sympy.org - Online version of SymPy for testing and demonstrations." ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 版本" - ] - }, - { - "cell_type": "code", - "execution_count": 90, - "metadata": {}, - "outputs": [ - { - "data": { - "application/json": { - "Software versions": [ - { - "module": "Python", - "version": "2.7.10 64bit [GCC 4.2.1 (Apple Inc. build 5577)]" - }, - { - "module": "IPython", - "version": "3.2.1" - }, - { - "module": "OS", - "version": "Darwin 14.1.0 x86_64 i386 64bit" - }, - { - "module": "numpy", - "version": "1.9.2" - }, - { - "module": "matplotlib", - "version": "1.4.3" - }, - { - "module": "sympy", - "version": "0.7.6" - } - ] - }, - "text/html": [ - "
SoftwareVersion
Python2.7.10 64bit [GCC 4.2.1 (Apple Inc. build 5577)]
IPython3.2.1
OSDarwin 14.1.0 x86_64 i386 64bit
numpy1.9.2
matplotlib1.4.3
sympy0.7.6
Sat Aug 15 11:37:37 2015 JST
" - ], - "text/latex": [ - "\\begin{tabular}{|l|l|}\\hline\n", - "{\\bf Software} & {\\bf Version} \\\\ \\hline\\hline\n", - "Python & 2.7.10 64bit [GCC 4.2.1 (Apple Inc. build 5577)] \\\\ \\hline\n", - "IPython & 3.2.1 \\\\ \\hline\n", - "OS & Darwin 14.1.0 x86\\_64 i386 64bit \\\\ \\hline\n", - "numpy & 1.9.2 \\\\ \\hline\n", - "matplotlib & 1.4.3 \\\\ \\hline\n", - "sympy & 0.7.6 \\\\ \\hline\n", - "\\hline \\multicolumn{2}{|l|}{Sat Aug 15 11:37:37 2015 JST} \\\\ \\hline\n", - "\\end{tabular}\n" - ], - "text/plain": [ - "Software versions\n", - "Python 2.7.10 64bit [GCC 4.2.1 (Apple Inc. build 5577)]\n", - "IPython 3.2.1\n", - "OS Darwin 14.1.0 x86_64 i386 64bit\n", - "numpy 1.9.2\n", - "matplotlib 1.4.3\n", - "sympy 0.7.6\n", - "Sat Aug 15 11:37:37 2015 JST" - ] - }, - "execution_count": 90, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "%reload_ext version_information\n", - "\n", - "%version_information numpy, matplotlib, sympy" - ] } ], "metadata": { diff --git a/1_numpy_matplotlib_scipy_sympy/test.txt b/1_numpy_matplotlib_scipy_sympy/test.txt new file mode 100644 index 0000000..cd08755 --- /dev/null +++ b/1_numpy_matplotlib_scipy_sympy/test.txt @@ -0,0 +1 @@ +Hello world! diff --git a/References.md b/References.md index 10532fa..af2369e 100644 --- a/References.md +++ b/References.md @@ -18,3 +18,5 @@ * 一款图像转卡通的Python项目，超级值得你练手 - https://www.toutiao.com/a6821299115175969287/ - https://github.com/minivision-ai/photo2cartoon + +* [Awesome Deep Learning Project Ideas](https://github.com/NirantK/awesome-project-ideas) \ No newline at end of file