PEP-8 compliance.
This commit is contained in:
Roger Labbe committed
1 parent
4755c46714
commit
43e6d101e9
1 file changed
+83
-52
@@ -1,7 +1,7 @@
|
||||
{
|
||||
"metadata": {
|
||||
"name": "",
|
||||
"signature": "sha256:2e309940166b97d5add0292b35ffd44bf21b1fc4427c233aed7521269e0805b1"
|
||||
"signature": "sha256:e15041853bd034fbae3600dbd7c0acc9dff195a71aa98cf1ec267baca0b5dcb0"
|
||||
},
|
||||
"nbformat": 3,
|
||||
"nbformat_minor": 0,
|
||||
@@ -32,7 +32,7 @@
|
||||
"from __future__ import division, print_function\n",
|
||||
"import matplotlib.pyplot as plt\n",
|
||||
"import sys\n",
|
||||
"sys.path.insert(0,'../code') # allow us to format the book\n",
|
||||
"sys.path.insert(0, '../code') # allow us to format the book\n",
|
||||
"import book_format\n",
|
||||
"book_format.load_style()"
|
||||
],
|
||||
@@ -259,13 +259,13 @@
|
||||
],
|
||||
"metadata": {},
|
||||
"output_type": "pyout",
|
||||
"prompt_number": 2,
|
||||
"prompt_number": 1,
|
||||
"text": [
|
||||
"<IPython.core.display.HTML at 0x7f99b0d79550>"
|
||||
"<IPython.core.display.HTML at 0x7fd679b4a4e0>"
|
||||
]
|
||||
}
|
||||
],
|
||||
"prompt_number": 2
|
||||
"prompt_number": 1
|
||||
},
|
||||
{
|
||||
"cell_type": "heading",
|
||||
@@ -300,13 +300,13 @@
|
||||
{
|
||||
"metadata": {},
|
||||
"output_type": "display_data",
|
||||
"png": "iVBORw0KGgoAAAANSUhEUgAAAtIAAAGJCAYAAACny9QDAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
|
||||
"png": "iVBORw0KGgoAAAANSUhEUgAAAtIAAAGJCAYAAACny9QDAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
|
||||
"text": [
|
||||
"<matplotlib.figure.Figure at 0x7f999856b978>"
|
||||
"<matplotlib.figure.Figure at 0x7fd656243668>"
|
||||
]
|
||||
}
|
||||
],
|
||||
"prompt_number": 3
|
||||
"prompt_number": 2
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
@@ -346,13 +346,13 @@
|
||||
{
|
||||
"metadata": {},
|
||||
"output_type": "display_data",
|
||||
"png": "iVBORw0KGgoAAAANSUhEUgAAAtIAAAGJCAYAAACny9QDAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAIABJREFUeJzs3XlAVOXixvHvsDPgigqKG4oiSYg7KmG2aJi2mFaaaNe0\nRcuyzRa3tM0WbdNu3RalLLulVuZS7uaehpZGKiiiKKi4sO/z+0Mv9/pTURF4gXk+fwln5pxneB3m\nmeGc97Xs3r3bhoiIiIiIXBEH0wFERERERCojFWkRERERkRJQkRYRERERKQEVaRERERGRElCRFhER\nEREpARVpEREREZESUJEWERERESmBSxbppKQkIiMjCQkJoV+/fuzdu/eydhwVFUW3bt3o1KkT06ZN\nK/p+fHw8DzzwAJ07d6Zr166MHTuW9PT0ou2RkZEEBwfTtm1b2rZty9ixY0vwsEREREREytYli/T4\n8eMJCAhgy5YtREREMGbMmEvudMeOHcyYMYOoqCgWLlzIokWLWLJkCQAZGRn07duXFStWsHLlSnJy\ncnj99dfPuf+ECROIjo4mOjqaqVOnlvChiYiIiIiUnWKLdHp6Ohs2bGDEiBG4uLgwdOhQEhMT2bNn\nT7E7Xbp0KT179qR58+Z4e3szYMAAFi9eDEDr1q2544478PT0xM3NjT59+rB9+/Zz7m+zabFFERER\nEanYii3SBw4cwMXFBavVyqBBgzh06BCNGzdm3759xe40Pj4ePz8/Zs+ezdSpU/H392f//v0XvG10\ndDQBAQHnfG/atGmEhoYybNgw4uLirvAhiYiIiIiUvWKLdFZWFh4eHmRkZBAXF0dqaioeHh5kZWUV\nu9OsrCysVisHDx7kwIEDeHh4kJmZed7tdu7cyYIFC3jiiSeKvjd27FjWrFnDqlWraN26NSNHjiQ/\nP7+ED09EREREpGw4FbfR3d2djIwMfHx82Lx5M3DmHGer1VrsTt3d3cnMzGTcuHEALFu27Lz7HDp0\niNGjR/PGG2/QqFGjou8HBQUV/fvJJ59kzpw57Nu3j5YtW55z/4MHD17GwxMRERERuTr/21X/V7FF\nukmTJuTk5JCcnIy3tze5ubkkJCTg5+dX7MGaNm16zukfsbGxNGvWrOjrlJQUhg8fzpgxYwgLCyt2\nXxaL5aLnTAcGBhZ7X7kwLy8v5s+fT/fu3U1HkXKmsbdvGn/7pbG3bxr/qxMTE3PRbcWe2uHp6UlY\nWBgff/wxOTk5zJo1C19f33M+HY6MjOStt946534REREsW7aM2NhYkpOTmTdvHhEREQCkpaUxfPhw\nBg4cSN++fc+5X1paGmvWrCE3N5fc3Fw++OAD6tSpg7+//xU/aBERERGRslTsJ9IAkydP5plnnqFT\np040b96c6dOnn7M9MTGRhg0bnvO94OBgRo0axZAhQ8jPz+fee+8tKtLLly8nJiaG+Ph43nnnHeDM\np86///47eXl5vPPOOzzxxBM4Oztz7bXX8uGHH+Lo6Fhaj1dEREREpFRYdu/eXSnnmjt48KBO7Sgh\nLy8vYmJiqFevnukoUs409vZN42+/NPb2TeN/dWJiYi56jrSWCLdTehNivzT29k3jb7809vZN4182\nVKRFREREREpARVpEREREpARUpEVERERESkBFWkRERESkBFSkRURERERKQEVaRERERKQEVKRFRERE\nREpARVpEREREpARUpEVERERESkBFWkRERESkBFSkRURERERKQEVaRERERKQEVKRFRERERErAyXQA\nEREpXYWFNpJOZpBwNI2UtGxOpmeTnpWHh4cVgILcHGpVc6OWpyu+dTxpXLc6zk76XEVE5EqpSIuI\nVHIHj6Wx4a8jRMcdZXvcMfYePkl2bsFl39/RwUKjutVo06wubZvXpXMrH4Ka1MHBwVKGqUVEKj8V\naRGRSsZms/HH/uP8sDGOldsPsvfwqfNu41Xdjabe1albw51anm54uDnjYXXHZoOTqemcSs8hJS2L\nhKNpJKakE5+cSnxyKj9sjAOgTnV3ugf70rujHz3aNMLV2bG8H6aISIWnIi0iUkkknczg27V7+W7d\nXmL/pzxXc3emW+sGtPf3pq1/Pa5pXJsaHq7n3d/LywuAlJSUc76fnZtP7OHTbN93lN9jj7Ju52ES\nU9KZty6Weetiqenhyq2d/Rh60zW0buJVtg9SRKQSUZEWEang/kpI4aPFf/LDhjjyCgqBM58Y3961\nOREdmtKhhfdVnePs5uJEUFMvgpp6MfiGQGw2G3sTT7Es+gALNsQRk3CCOSv/Zs7Kv+kSWJ8HerWm\nZ/smODrovGoRsW8q0iIiFdQf+4/xxr+3suqPQwA4WCz07tiUe68PIDyoYZldIGixWGjZsBYtG9Zi\nVN8Q/j54gq9W/c3cNXvYGHOEjTFH8G9Qk6f7t+fWjn46l1pE7JaKtIhIBbM/6TRvfLuVHzftA8Dq\n6sTA6wMYfksQjetVL/c8rRrVZvKQrjzTvwPfrN3DJ0v/JPbwKR5+bwVBTb147u6O9GjTqNxziYiY\npiItIlJBZGbnMW3+7/xr6Z/kF9hwdXbkHz1bM6pvG2pXczMdj2pWF4bfEsSQmwKZu3o3734fzc74\nFAa/sZSe7ZoweUgXGtWtZjqmiEi5UZEWEakAlv1+gHGzN3DoeDoWC9zTvSVP3dUeXy9P09HO4+Lk\nyJCbrmFAeEtm/bKL6Qui+eX3A6zdeYjRt7flkT7BuDhplg8RqfpUpEVEDDqZns0Ln68vOo2jdRMv\npj4QRtvm9QwnuzR3Fyce6dOGO7v5M2XOZr7fGMcb325l0Zb9vPvw9QQ2rm06oohImdIl1yIihvy6\nM5GbnpvPj5v2YXV1YuLgUBZPuaNSlOj/5VPLgxmP3sA3L/Smcd1q7DqQQu/xC/jwpx0UFBaajici\nUmZUpEVEyllufgGT52zi3tcWk3Qyg/Yt6rHstbt4MOJanBwr76/lsNa+LHutH/fd0Irc/EJe/noL\n97y6mKOnMk1HExEpE5X3N7aISCWUdDKDAS8v4qPFf+LoYOHp/u2ZP74vTb3LfzaOsuDp7sIbD1xH\n1DO9qFvDnY0xR7jlxQVs/vuI6WgiIqVORVpEpJxs/vsIEeMWsHVvMj61PJg/oS9j7mxXqT+Fvpgb\nQxrz8yv9CG3lQ/KpTAa8soh/LvoDm81mOpqISKmper+9RUQqoKjlf3H3q4s4eiqLLoH1+fmVO+nQ\nwtt0rDLlXcvKNy/cysg+wRQU2pjy1WbGfLSG3PwC09FEREqFirSISBkqLLQxec4mnv98PfkFNh6+\nNZi5z/emTg1309HKhZOjAy8O7My/nrgJd1cnvv11LwNfW8yJtGzT0URErpqKtIhIGcnKyeeh95bz\n0eI/cXK0MO3BcMYP6lwlT+W4lN4d/Vgwvi8+taxs+juJvhN/YF/SadOxRESuiv39NhcRKQenMnK4\n57VFLP4tnupWF758NoJ7ugeYjmXUtX51+GnyHQQ19SI+OZU7X1rIzvjjpmOJiJSYirSISCk7fjqL\nAS//xLa9R/H18uSHibdxXZCv6VgVQv3aHswf35fu1/pyPDWL/i//xKYYzeghIpWTirSISClKTEnn\nzikL+SvhBM3q12DBxL60bFjLdKwKxcPNmVlP96Jv52akZeVx39Ql/PL7AdOxRESumIq0iEgp2Z90\nmn6TF7LvyGmuaVyb+eP74OvlaTpWheTi5MiMR3sw+IZWZOcV8OA7y/llm8q0iFQuKtIiIqUgPjmV\n/i//xKHj6bTzr8e34/pQt4bVdKwKzdHBgdeHhfFQ72vJKyjkwXeX65NpEalUVKRFRK7SoWNp3P3K\nIpJOZhLayoe5z/empoer6ViVgsViYfygzjwYcbZMv6MyLSKVh4q0iMhVOHIig3teW0xiSjrtW9Rj\n9tO98HBzNh2rUrFYLEy4rzMjIoKKyvQylWkRqQRUpEVESujY6UzueXUR8cmpBPvV4ctnI/B0dzEd\nq1KyWCxMvC+U4becKdMPvbeCjZrNQ0QqOBVpEZESOJ2Rw8DXlxB35DSBjWszZ2wE1a0q0VfDYrEw\naXAokTcGkpNXwD/e/lnzTItIhaYiLSJyhXLyCnhg+jJiEk7QvH4N5j7Xm9rV3EzHqhIsFguv3N/1\nf6bGW8p+rYAoIhWUirSIyBUoLLQx+sNVbIw5gndNK1+NjaBODXfTsaoURwcH3n3keq4LOrNoy6DX\nl5B8MtN0LBGR86hIi4hcJpvNxqQvN/LT5v1Uc3fmy7G30LBuNdOxqiRXZ0c+HXMzIc3qknAsjfve\nWEJ6Vq7pWCIi51CRFhG5TB/+9Aef/rwLFycHPh3Tk2sae5mOVKV5uDnzxbO30Kx+DWISTvDIByvJ\nLyg0HUtEpIiKtIjIZVj8235embsFiwXefeR6urVuYDqSXahdzY2op3tRy9OVldsPMvGLjdhsNtOx\nREQAFWkRkUv6c/9xRn+4GoAX7unEbaHNzQayM34+NfjsyZ64ODkwa9lffPrzLtORREQAFWkRkWIl\nnczg/rd/ISsnn7vDW/JIn2DTkexSpwAfpj3YHYBJX27U6ociUiGoSIuIXERWTj7Dpv1C0skMOgV4\n8/qwMCwWi+lYduvObv48fVd7bDZ4dMYq9hw6aTqSiNg5FWkRkQuw2Ww8+fEaduw7TqO6nnzyxM24\nOjuajmX3nrizLbd3aU5Gdh7Dpv/C6Ywc05FExI6pSIuIXMBHi//kx0378HRzZtZTvfCqrrmiKwKL\nxcJbw68jsHFt9iel8tjMVRQW6uJDETFDRVpE5P/Z8NdhXp27BYB3Hu5Oq0a1DSeS/2V1c+azMTdT\n09OVFdsP8vb8baYjiYidUpEWEfkfh1PSefj9FRQU2ni0bxsiOvqZjiQX0LhedT589AYcLBLine truncated
|
||||
"png": "iVBORw0KGgoAAAANSUhEUgAAAtIAAAGJCAYAAACny9QDAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
|
||||
"text": [
|
||||
"<matplotlib.figure.Figure at 0x7f9994166198>"
|
||||
"<matplotlib.figure.Figure at 0x7fd655a4cb70>"
|
||||
]
|
||||
}
|
||||
],
|
||||
"prompt_number": 5
|
||||
"prompt_number": 3
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
@@ -423,13 +423,13 @@
|
||||
{
|
||||
"metadata": {},
|
||||
"output_type": "display_data",
|
||||
"png": "iVBORw0KGgoAAAANSUhEUgAAAssAAAGKCAYAAAD65AyPAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
|
||||
"png": "iVBORw0KGgoAAAANSUhEUgAAAssAAAGKCAYAAAD65AyPAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
|
||||
"text": [
|
||||
"<matplotlib.figure.Figure at 0x7fdfc764c940>"
|
||||
"<matplotlib.figure.Figure at 0x7fd65572d630>"
|
||||
]
|
||||
}
|
||||
],
|
||||
"prompt_number": 5
|
||||
"prompt_number": 4
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
@@ -474,7 +474,7 @@
|
||||
]
|
||||
}
|
||||
],
|
||||
"prompt_number": 6
|
||||
"prompt_number": 5
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
@@ -517,7 +517,7 @@
|
||||
]
|
||||
}
|
||||
],
|
||||
"prompt_number": 7
|
||||
"prompt_number": 6
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
@@ -535,10 +535,10 @@
|
||||
"import numpy as np\n",
|
||||
"import matplotlib.pyplot as plt\n",
|
||||
"\n",
|
||||
"xs = np.arange(15,30,0.05)\n",
|
||||
"p1, = plt.plot (xs,[gaussian(x, 23, .2) for x in xs], label='var=0.2')\n",
|
||||
"p2, = plt.plot (xs,[gaussian(x, 23, 1) for x in xs], label='var=1')\n",
|
||||
"p3, = plt.plot (xs,[gaussian(x, 23, 5) for x in xs], label='var=5')\n",
|
||||
"xs = np.arange(15, 30, 0.05)\n",
|
||||
"p1, = plt.plot(xs, [gaussian(x, 23, .2) for x in xs], label='var=0.2')\n",
|
||||
"p2, = plt.plot(xs, [gaussian(x, 23, 1) for x in xs], label='var=1')\n",
|
||||
"p3, = plt.plot(xs, [gaussian(x, 23, 5) for x in xs], label='var=5')\n",
|
||||
"plt.legend()\n",
|
||||
"plt.show()"
|
||||
],
|
||||
@@ -548,13 +548,13 @@
|
||||
{
|
||||
"metadata": {},
|
||||
"output_type": "display_data",
|
||||
"png": "iVBORw0KGgoAAAANSUhEUgAAAsoAAAF2CAYAAAB3dr7TAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
|
||||
"png": "iVBORw0KGgoAAAANSUhEUgAAAsoAAAF2CAYAAAB3dr7TAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
|
||||
"text": [
|
||||
"<matplotlib.figure.Figure at 0x7fdfc759fa58>"
|
||||
"<matplotlib.figure.Figure at 0x7fd655902860>"
|
||||
]
|
||||
}
|
||||
],
|
||||
"prompt_number": 8
|
||||
"prompt_number": 7
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
@@ -590,13 +590,13 @@
|
||||
{
|
||||
"metadata": {},
|
||||
"output_type": "display_data",
|
||||
"png": "iVBORw0KGgoAAAANSUhEUgAAAqwAAAFwCAYAAAB91in5AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
|
||||
"png": "iVBORw0KGgoAAAANSUhEUgAAAqwAAAFwCAYAAAB91in5AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAIABJREFUeJzs3XdY1vXi//HnzQZBcaACigsHiBv3zm2W5SizZcPWOVan\nMm2oLcuyvTMbajnKXcc9cytOVETFBS5ciGy47/v3Bx6/nd9pOID3PV6P6zpXXmqnpxTcL28/n8/b\nYrfb7YiIiIiIOCgP0wEiIiIiIn9Fg1VEREREHJoGq4iIiIg4NA1WEREREXFoGqwiIiIi4tA0WEVE\nRETEoXn91Q8uX768pDpERERExM116dLlD7//LwcrQFRUVJHHiEjxKl++PLNnz6Zjx46mU9yOPvbm\n6GMv4twSEhL+9Md0SYCIiIiIODQNVhERERFxaBqsIi5Kl/OYo4+9OfrYi7gmDVYRF6UXbnP0sTdH\nH3sR16TBKiIiIiIOTYNVRERERByaBquIiIiIODQNVhERERFxaBqsIiIiIuLQNFhFRERExKFpsIqI\niIiIQ9NgFRERERGHpsEqIiIiIg5Ng1VEREREHJoGq4iIiIg4NA1WEREREXFoGqwiIiIi4tA0WEVE\nRETEoWmwioiIiIhD02AVEREREYemwSoiIiIiDk2DVUREREQcmgariIiIiDg0DVYRERERcWgarCIi\nIiLi0DRYRURERMShabCKiIiIiEPTYBURERERh6bBKiIiIiIOTYNVRERERByaBquIiIiIODQNVhER\nERFxaBqsIiIiIuLQNFhFRERExKFpsIqIiIiIQ9NgFRERERGH5mU6QETEmRVYbaSczSAtI5dL2XlY\nvM4CYC/IJcjfh+BAX6pUCMTLU+8PiIhcLw1WEZGrZLfbSTp5kd/iU4g7kMr+4xdIOpFGXoHt/37S\nqjGFf+306pXv8vX2pGZoGeqElyW2dkU6NqxCzcplsFgsJfwrEBFxThqsIiJ/wW63s+vwWX76bT/L\nth8j5WzG//yc0HKlCCnjT6C/N+tXFX5fm+hQMrLzOXMxm5PnM0k4dp6EY+eZtyEJgCoVAunaJII7\nOtShYY0KGq8iIn9Bg1VE5A9kZOcxbVUiM1bvJyH5/JXvLxvoS4cGVWgbHUZ0tXJEhgYTFOBz5cfD\nPy/8688v9bnyfZey8jh4Mo29R8+zbu8JfotPIeVsBt8v3cv3S/cSVbUcd3asw12d6hLo/3//XyIi\nUkiDVUTkdy5m5vLtkj1MXLibtMxcAMoF+dGvbSS3t4mkYY0KeHhc27uhQQE+NKlVkSa1KnL3TfWw\n2QrftZ2z/iCz1h4gIfk8r/ywkQ/nbufhnjE82L0+ZUr5FscvT0TEKWmwiogAuflWvlqwi89/2cml\n7HwAWtatzEM9Y+jWNAIfL88i+2d5eFhoXCuExrVCeOmuFizddoyJi+LZnHiad2duZcKCeB7v05BH\nezfE17vo/rkiIs5Kg1VE3N7yHccYPXkDR06nA9C2fhj/ur0praNCi/2f7ePlyc0tatC7eXU2JJzk\nw7nbWbfnBG//FMeM1ft57b7WdGkcUewdIiKOTINVRNxWaloWI79dy+KtRwGoEx7M6/e3oV398BJv\nsVgstIkOo010GGv3HGfUpPXsP57GfeMX06NZNcY92I6KwQEl3iUi4gg0WEXELS3ccpjhE9dwISOX\nQD9vnh3QjAe61cfby/zzUtvVD2fJm/35buke3pu5lcVbj7Jl/2nGP9yenrHVTeeJiJQ481+ZRURK\nUGZOPs9MWM3DHy7jQkYuHWLCWTV+II/0auAQY/U/vL08eKRXA1a+M4D2MeGcv5TDQx8s5ZkJq8nM\nyTedJyJSohznq7OISDE7dOoit4yZx4zV+/Hz9uS1e1vz44hehJYrZTrtT4WVD2TqiF68dm9rfL09\nmbF6P7eMmcfhUxdNp4mIlBgNVhFxCyt2JHPzqLkkplwgMiyYBW/cxkM9Y675EVUmeHhYeKhnDAvf\nuI3IsGASUy5w86i5rNiRbDpNRKREaLCKiEuz2+18/stO7nt3EelZefSMrcavr/albpVyptOuWd0q\n5fj11b70aFaNi1l53PfuIj7/ZSd2u910mohIsdJgFRGXZbXZeHnSesZO3wzAcwOa8fVT3f7rZCpn\nExTgw8Snu/HcgGYAjJ2+mVGT12O12QyXiYgUHz0lQERcUk5eAcM+X8WCLYfx8fLgkyc606dlTdNZ\nRcLDw8K/bm9KZFgwT36+ku+W7CU1LZuPH++En4++rIuI69FXNhFxOZey8hjy3mI27jtF6QAfvn2m\ne4kcAlDSbmlZk/JBfjz0wVL+vfkw59Kz+f7ZHk79DrKIyB/RJQEi4lLSs/IY/PZCNu47ReWypZg9\n6haXHKv/0SY6jNmjbqFy2QA27jvF3e8sJD0rz3SWiEiR0mAVEZdxMTOXweMWsO1gKlUqBDJndB+i\nIpzv5qprFRVRjtmjbyG8fCBbD6QyeJxGq4i4Fg1WEXEJaZm53DVuAduTzlA1JJCZL/chomJp01kl\nplrF0swa1YeqIYFsT0rlrrcWcDEz13SWiEiR0GAVEaeXlZPPfeMXsfPQWapVDGLWy7dQNSTIdFaJ\nqxoSxMyX+hAREsSOQ2e4b/xisnQqloi4AA1WEXFqeQVWhn60jK0HUgkrX4qfX+pDeIVA01nGVAkJ\nYubLfQgrX4q4A6d55KNl5BVYTWeJiNwQDVYRcVpWm42nvljFql0plC/tx7SRvd16rP5HeIVApo3s\nTbkgP1buSuHpL1frOa0i4tQ0WEXEKdntdkZP3sD8jYcI9PPmx+d7ERkWbDrLYUSGBfPjiJ4E+nkz\nb0MSoydv0IlYIuK0NFhFxClNWBjP90v34uvtyXfPdqdBjQqmkxxOwxohfPdsd3y9Pfl+6V6+XrTb\ndJKIyHXRYBURp7Mo7givT90EwAePdqRNdJjhIsfVJjqMDx7tCMBrP25kcdwRs0EiItdBg1VEnMrO\nQ2f4x2crsNthxB2x9G1dy3SSw+vbuhbPD4zFbod/fL6SXYfPmE4SEbkmGqwi4jROns9kyHuLycmz\nckeHOgy7tbHpJKfxZN/GDGxfm+zcAu5/dzEnz2eaThIRuWoarCLiFHLyChj64VJS07JpHRXK2w+1\nw2KxmM5yGhaLhXcebk/rqFBS07IZ+uEycvP1uCsRcQ4arCLi8Ox2Oy99v47tSWeoUiGQCU91xcfL\n03SW0/Hx8uSrJ7sQXr7wNKyXvl+nJweIiFPQYBURhzdpWQLTV+/Hz8eTb/7VjXJBfqaTnFb50v58\n869u+Hl7Mm1VIpOXJ5hOEhH5WxqsIuLQtiSeYsyU9QC8+3AHYqrr8VU3qkGNCowf2gGA0ZPXsyXx\nlOEiEZG/psEqIg7rXHo2j32ynAKrnUd6NeD2tpGmk1xGv7aRDO0VQ4HVzmOfrOBcerbpJBGRP6XB\nKiIOyWaz8+QXqzh1IYvmdSrx4qAWppNczkuDWhJbuxKnLmTy5BersNl0PauIOCYNVhFxSJ/M38Gq\nXSmUC/Lj83/ehLeXvlwVNW8vD74YdhNlA31ZtSuFT3/ZYTpJROQP6RVARBzO+r0neHfmVgA+frwT\nYeUDDRe5rrDygXz8eGcAxv+8lQ0JJw0XiYj8Lw1WEXEo5y/l8M/PVmKz2xnWtzGdG1U1neTybmpc\nlX/e2hib3c4/Pl3B+Us5ppNERP6LBquIOAy73c5zX//G6bTC61af69/MdJLbGD6gGc3rVOJ0WhbP\nT1yj57OKiEPRYBURh/Hjyn0s3nqUIH9vPnmiM16e+hJVUrw8Pfjkic4E+XuzMO4IU1cmmk4SEblC\nrwYi4hAOnkhjzJQNAIx7sB1VQ4IMF7mfqiFBvPVAOwDG/LCBgyfSDBeJiBTSYBUR4/IKrPzjsxXk\n5Fnp3y6S29roeaum3N42kn5tI8nOLeCfn60kr8BqOklERINVRMz7YPY2dh85R0RIEGPvb2s6x+2N\nHdKWqiGBxB85y4dztpvOERHRYBURs7YdTOXT+TuxWODDxzoSFOBjOsntlQ7w4aPHOmGxwKfzd7A9\nKdV0koi4OQ1WETEmO6+Ap79chc1u59HeDWlZL9R0klzWsl4oj/RqgNVm5+kvV5OdV2A6SUTcmAar\niBjz9k9bSDp5kdphwQwfoEdYOZrnB8ZSOyyYgyfSeOenONM5IuLGNFhFxIgNCSeZuGg3nh4WPnq8\nE34+XqaT5P/j5+PFR493wtPDwteL4tmoU7BExBANVhEpcdm5BTz39W/Y7TCsb2Ma1QwxnSR/olHN\nEJ7s2wS7HZ6b+JsuDRARIzRYRaTEvfNzHEdOpxNVtRxP3dbEdI78jSdva0zdKmU5fCpdTw0QESM0\nWEWkRG07mMrERbvxsFh4d2gHfLw8TSfJ3/Dx8uSdh9tjscAXv+5kz9FzppNExM1osIpIicnNt/Ls\nhNWXnwrQgMa1dCmAs4itXYkHutXHarMzfOJvWG0200ki4kY0WEWkxHw8bzv7j6dRo3JpntVTAZzO\niDtiCStfip2HzvLN4j2mc0TEjWiwikiJ2J9ygc/m7wTg3Yc74K+nAjidQH8f3nqgHVB4HfKx1HTD\nRSLiLjRYRaTY2e12XvhuLflWG3d3rkerKB0Q4Ky6Nomgb+taZOcWMPLbtdjtdtNJIuIGNFhFpNj9\nvOYAG/edonxpP14Y1Nx0jtyg1+5tTXCgL6vjjzNr7UHTOSLiBjRYRaRYnb+Uw+tTNwEwenArygb6\nGS6SG1WhjD9j7m4FwCs/bOBcerbhIhFxdRqsIlKs3pq+mfOXcmgdFUr/dpGmc6SIDGxfm/Yx4VzI\nyOWVHzaazhERF6fBKiLFZnPiKaauSsTb04NxD7bDYrGYTpIiYrFYGPdgO/x8PJm97iCrd6Line truncated
|
||||
"text": [
|
||||
"<matplotlib.figure.Figure at 0x7fdfc759f390>"
|
||||
"<matplotlib.figure.Figure at 0x7fd65572d940>"
|
||||
]
|
||||
}
|
||||
],
|
||||
"prompt_number": 9
|
||||
"prompt_number": 8
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
@@ -630,15 +630,15 @@
|
||||
"from IPython.html.widgets import interact, interactive, fixed\n",
|
||||
"import IPython.html.widgets as widgets\n",
|
||||
"\n",
|
||||
"def plt_g (mu,variance):\n",
|
||||
" xs = np.arange(2,8,0.1)\n",
|
||||
" ys = [gaussian (x, mu,variance) for x in xs]\n",
|
||||
" plt.plot (xs, ys)\n",
|
||||
" plt.ylim((0,1))\n",
|
||||
"def plt_g(mu,variance):\n",
|
||||
" xs = np.arange(2, 8, 0.1)\n",
|
||||
" ys = [gaussian (x, mu, variance) for x in xs]\n",
|
||||
" plt.plot(xs, ys)\n",
|
||||
" plt.ylim((0, 1))\n",
|
||||
" plt.show()\n",
|
||||
"\n",
|
||||
"interact (plt_g, mu=(0,10), \n",
|
||||
" variance=widgets.FloatSliderWidget(value=0.6,min=0.2,max=4.5))"
|
||||
"interact (plt_g, mu=(0, 10), \n",
|
||||
" variance=widgets.FloatSliderWidget(value=0.6, min=0.2, max=4.5))"
|
||||
],
|
||||
"language": "python",
|
||||
"metadata": {},
|
||||
@@ -646,21 +646,21 @@
|
||||
{
|
||||
"metadata": {},
|
||||
"output_type": "display_data",
|
||||
"png": "iVBORw0KGgoAAAANSUhEUgAAAscAAAF2CAYAAACCiDVjAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAIABJREFUeJzs3XlYlOXixvF7ZmCAYRFFBcUd10TTTM0Nl9wos7LU0lyy\n7LSXWdYprU5mZZna+R1bNEuz7FimZqa4ZKJm7nsuICgqigsuyLAz8/uDkZOVQIq+DHw/18XFMPPO\n601PzNzzzjPPa9q/f79TAAAAAGQ2OgAAAABQUlCOAQAAABfKMQAAAOBCOQYAAABcKMcAAACAC+UY\nAAAAcKEcAwAAAC6FluMVK1aof//+atKkif75z38WecdffPGF2rVrp1atWmnixIlXFRIAAAC4HjwK\n2yAgIEAPP/yw1q1bp4yMjCLtdMeOHZoyZYpmz54tPz8/DRgwQI0aNVJkZORVBwYAAACulUKPHLdq\n1UrdunVTuXLlirzTqKgode/eXWFhYQoODlbfvn21ePHiqwoKAAAAXGuFHjm+yOks+lmmDx06pJYt\nW2rmzJlKSkpSixYttGjRoisKCAAAAFwvRf5AnslkKvJO09PTZbPZdOTIESUkJMjX11dpaWlXFBAA\nAAC4Xq7JkWMfHx+lpaVp9OjRkqTly5fLZrP95bZHjhwp8n4BAACAK1W9evVCtylyOf47R45r1aql\n+Pj4/J8PHDigOnXqXHb7Ro0aFXnfKBmCgoI0b948dezY0egouAKMn/ti7Nwb4+feGD/3FRQUpLVr\n1xZp20KnVTgcDmVmZio3N1e5ubnKyspSbm5u/u2DBg3ShAkTLrlPZGSkli9frgMHDujEiRP67rvv\nWKkCAAAAJV6hR44XLFigl19+Of/nhQsX6sknn9STTz4pSUpMTFS1atUuuU/Tpk31xBNPaPDgwcrJ\nydF9991HOQYAAECJV2g57tOnj/r06XPZ21euXPmX1w8ePFiDBw++8mQo8ZgO494YP/fF2Lk3xs+9\nMX6lH6ePxhXjAcK9MX7ui7Fzb4yfe2P8Sj/KMQAAAOBCOQYAAABcKMcAAACAC+UYAAAAcKEcAwAA\nAC6UYwAAAMCFcgwAAAC4UI4BAAAAF8oxAAAA4EI5BgAAAFwoxwAAAIAL5RgAAABwoRwDAAAALpRj\nAAAAwIVyDAAAALhQjgEAAAAXyjEAAADgQjkGAAAAXCjHAAAAgAvlGAAAAHChHAMAAAAulGMAAADA\nhXIMAAAAuFCOAQAAABfKMQAAAOBCOQYAAABcKMcAAACAC+UYAAAAcKEcAwAAAC6UYwAAAMCFcgwA\nAAC4UI4BAAAAF8oxAAAA4EI5BgAAAFwoxwAAAIAL5RgAAABwoRwDAAAALpRjAAAAwIVyDAAAALhQ\njgEAAAAXyjEAAADgQjkGAAAAXCjHAAAAgAvlGAAAAHChHAMAAAAulGMAAADAhXIMAAAAuFCOAQAA\nABfKMQAAAOBCOQYAAABcKMcAAACAC+UYAAAAcKEcAwAAAC6UYwAAAMCFcgwAAAC4UI4BAAAAl0LL\ncVJSkgYNGqRmzZqpT58+io2NLdKOJ0+erPbt26t169YaOXKkUlNTrzosAAAAcC0VWo7HjBmjBg0a\naOPGjYqMjNSIESMK3emKFSv0/fffa968eVq1apXOnTunDz/8sFgCAwAAANdKgeU4NTVV69at0/Dh\nw2W1WjVkyBAlJiYqJiamwJ3Gx8erefPmqly5snx8fNSpUyfFxcUVa3AAAACguBVYjhMSEmS1WmWz\n2TRgwAAdPXpUNWrUUHx8fIE7bdOmjXbt2qWkpCTZ7XatWrVKnTp1Ks7cAAAAQLHzKOjG9PR0+fr6\nym63Ky4uTikpKfL19VV6enqBO23SpIluv/12derUSWazWR06dFC/fv0uu31QUNCVpYdhPD09JTF2\n7orxc1+MnXtj/Nwb4+e+Lo5dURRYjn18fGS32xUSEqINGzZIkux2u2w2W4E7/eqrr7RlyxatX79e\nVqtVo0eP1ptvvqnXXnvtL7cfO3Zs/uWIiAh17NixyL8AAAAA8EfR0dFavXq1JMlisSgiIqJI9yuw\nHNesWVOZmZk6ceKEgoODlZWVpcOHD6t27doF7nT16tXq0aOHAgMDJUm9e/fWO++8c9ntH3/88Ut+\nTk5OLlJ4GOfiq2bGyj0xfu6LsXNvjJ97Y/zcS3h4uMLDwyXljd3atWuLdL8C5xz7+fmpffv2mjp1\nqjIzMzVjxgyFhoaqfv36+dsMGjRIEyZMuOR+derU0bJly5SSkqLMzEwtWbJE9erV+7u/EwAAAHBd\nFbqU2xtvvKGYmBi1atVKUVFRmjRp0iW3JyYm/ukV1JNPPqkqVaqoR48eioiIUEpKikaPHl28yQEA\nAIBiVuC0CkkKCQnRrFmzLnv7ypUr/3Sdr6+vxo8ff3XJAAAAgOuM00cDAAAALpRjAAAAwIVyDAAA\nALhQjgEAAAAXyjEAAADgQjkGAAAAXCjHAAAAgAvlGAAAAHChHAMAAAAulGMAAADAhXIMAAAAuFCO\nAQAAABfKMQAAAOBCOQYAAABcKMcAAACAC+UYAAAAcKEcAwAAAC6UYwAAAMCFcgwAAAC4UI4BAAAA\nF8oxAAAA4EI5BgAAAFwoxwAAAIAL5RgAAABwoRwDAAAALpRjAAAAwIVyDAAAALhQjgEAAAAXyjEA\nAADgQjkGAAAAXCjHAAAAgAvlGAAAAHChHAMAAAAulGMAAADAhXIMAAAAuFCOAQAAABfKMQAAAOBC\nOQYAAABcKMcAAACAC+UYAAAAcKEcAwAAAC6UYwAAAMCFcgwAAAC4UI4BAAAAF8oxAAAA4EI5BgAA\nAFwoxwAAAIAL5RgAAABwoRwDAAAALpRjAAAAwIVyDAAAALhQjgEAAAAXyjEAAADgQjkGAAAAXCjH\nAAAAgAvlGAAAAHChHAMAAAAuhZbjpKQkDRo0SM2aNVOfPn0UGxtbpB2vW7dOvXv3VvPmzdWtWzft\n27fvqsMCAAAA11Kh5XjMmDFq0KCBNm7cqMjISI0YMaLQnR49elRPPfWUHnvsMW3ZskVff/21Kleu\nXCyBAQAAgGulwHKcmpqqdevWafjw4bJarRoyZIgSExMVExNT4E7nz5+viIgIRUZGymw2q2LFiqpQ\noUKxBgcAAACKW4HlOCEhQVarVTabTQMGDNDRo0dVo0YNxcfHF7jT/fv3KyAgQP369VO7du00cuRI\npaamFmtwAAAAoLh5FHRjenq6fH19ZbfbFRcXp5SUFPn6+io9Pb3AnV64cEGbNm3SzJkzVbNmTY0Y\nMUIffPCBXnnllb/cPigo6Mp/AxjC09NTEmPnrhg/98XYuTfGz70xfu7r4tgVRYHl2MfHR3a7XSEh\nIdqwYYMkyW63y2azFbhTHx8ftWvXTg0bNpQk9evXTx988MFltx87dmz+5YiICHXs2LHIvwAAAADw\nR9HR0Vq9erUkyWKxKCIiokj3K7Ac16xZU5mZmTpx4oSCg4OVlZWlw4cPq3bt2gXutEaNGjp9+nT+\nz06nU06n87LbP/7445f8nJycXJTsMNDFV82MlXti/NwXY+feGD/3xvi5l/DwcIWHh0vKG7u1a9cW\n6X4Fzjn28/NT+/btNXXqVGVmZmrGjBkKDQ1V/fr187cZNGiQJkyYcMn9unXrpujoaMXExCgzM1Nz\n587VLbfc8nd/JwAAAOC6KnQptzfeeEMxMTFq1aqVoqKiNGnSpEtuT0xM/NMrqJYtW+qJJ57QsGHD\nFBERIZvNpqeffrp4kwMAAADFrMBpFZIUEhKiWbNmXfb2lStX/uX1w4YN07Bhw648GQAAAHCdcfpo\nAAAAwIVyDAAAALhQjgEAAAAXyjEAAADgQjkGAAAAXCjHAAAAgAvlGAAAAHChHAMAAAAulGMAAADA\nhXIMAAAAuFCOAQAAABfKMQAAAOBCOQYAAABcKMcAAACAC+UYAAAAcKEcAwAAAC6UYwAAAMCFcgwA\nAAC4UI4BAAAAF8oxAAAA4EI5BgAAAFwoxwAAAICLh9EBAACXl5GVo6OnU3X45AUdPnVByak5Opua\noSBfD1Wv5K/qlfxUvZK/Qsr7ymw2GR0XANwe5RgADJaSlqUd8ad09PQFHT55QUdO5RXhI6cu6OS5\n9CLtw+phVmhFP9Wo5K9qlfxVo5K/qlfyV+2QAIXXrEhxBoAiohwDgAFyHQ6t3X1Mc1bHKGrzIWVm\n5/7ldh4Wk0KD8o4O16jkr/o1g1Xez1uxh0+4CnSqjpy6oNMp6TqYlKKDSSl/2kdokJ/6RtRTv4j6\nqlk54Fr/agDg1ijHAHAdHUw6r29Wx+jbNbE6fsYuSTKZpOZhlVWnSoBqVArIK8KV88pwcHmbPCz/\n+3hIUFCQJCk5OfmS/aZlZOuI68jzxWkYR05d0M6Dp5WYnKrJ87dp8vxtatOoivpF1FevVrVl8/a8\nfr84ALgJyjEAXGOp6Vn6ceNBzYmO0Yb9SfnX16zsr74R9dW3fT1Vq+R/Vf+GzdtTDapVUINqFS65\n3uFw6te9xzVn9X79uPGgft17XL/uPa7RM9fpjta11S+ivlo1CJHJxLQLAJAoxwBwTTidTq3fl6Q5\n0XmlNC0zR5Lk4+WhXq1qq3/HBmrdIOSazwU2m01q17iq2jWuqnFD2umHDfGaEx2jzbEn9N/oGP03\nOka1ggPUL6K+7u1QT6FBftc0DwCUdJRjAChmv+49rtEzftG+o2fzr2vVIFj9IxqoV+va8vOxGpLL\n32bVgM4NNaBzQ8UdP6dvVsdq7poYHTqRone/3az35m5W3w71Nfr+VgoK8DEkIwAYjXIMAMXk1Pk0\njZ29Qd+tPSBJCinvm/9BuDoh5QxOd6mwKoH6Z/+WGtW3hVbvStSc6LwPBn6zOkbLtiTopfLine truncated
|
||||
"png": "iVBORw0KGgoAAAANSUhEUgAAAscAAAF2CAYAAACCiDVjAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
|
||||
"text": [
|
||||
"<matplotlib.figure.Figure at 0x7fdfc7551f28>"
|
||||
"<matplotlib.figure.Figure at 0x7fd65574eb38>"
|
||||
]
|
||||
},
|
||||
{
|
||||
"metadata": {},
|
||||
"output_type": "pyout",
|
||||
"prompt_number": 10,
|
||||
"prompt_number": 9,
|
||||
"text": [
|
||||
"<function __main__.plt_g>"
|
||||
]
|
||||
}
|
||||
],
|
||||
"prompt_number": 10
|
||||
"prompt_number": 9
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
@@ -712,8 +712,8 @@
|
||||
"collapsed": false,
|
||||
"input": [
|
||||
"from scipy.stats import norm\n",
|
||||
"print (norm(2,3).pdf(1.5))\n",
|
||||
"print (gaussian(x=1.5, mean=2, var=3*3))"
|
||||
"print(norm(2, 3).pdf(1.5))\n",
|
||||
"print(gaussian(x=1.5, mean=2, var=3*3))"
|
||||
],
|
||||
"language": "python",
|
||||
"metadata": {},
|
||||
@@ -727,7 +727,7 @@
|
||||
]
|
||||
}
|
||||
],
|
||||
"prompt_number": 11
|
||||
"prompt_number": 10
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
@@ -741,9 +741,9 @@
|
||||
"collapsed": false,
|
||||
"input": [
|
||||
"n23 = norm(2,3)\n",
|
||||
"print ('probability density of 1.5 is %.4f' % n23.pdf(1.5))\n",
|
||||
"print ('probability density of 2.5 is also %.4f' % n23.pdf(2.5))\n",
|
||||
"print ('whereas probability density of 2 is %.4f' % n23.pdf(2))"
|
||||
"print('probability density of 1.5 is %.4f' % n23.pdf(1.5))\n",
|
||||
"print('probability density of 2.5 is also %.4f' % n23.pdf(2.5))\n",
|
||||
"print('whereas probability density of 2 is %.4f' % n23.pdf(2))"
|
||||
],
|
||||
"language": "python",
|
||||
"metadata": {},
|
||||
@@ -758,7 +758,7 @@
|
||||
]
|
||||
}
|
||||
],
|
||||
"prompt_number": 12
|
||||
"prompt_number": 11
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
@@ -773,11 +773,22 @@
|
||||
"cell_type": "code",
|
||||
"collapsed": false,
|
||||
"input": [
|
||||
"print (n23.rvs(size=15))"
|
||||
"print(n23.rvs(size=15))"
|
||||
],
|
||||
"language": "python",
|
||||
"metadata": {},
|
||||
"outputs": []
|
||||
"outputs": [
|
||||
{
|
||||
"output_type": "stream",
|
||||
"stream": "stdout",
|
||||
"text": [
|
||||
"[ 0.8890914 -1.21963858 -0.36342745 -1.69175021 1.04580929 -4.03699521\n",
|
||||
" 0.62282703 2.86612581 3.95733631 0.29548548 4.90591126 1.03687335\n",
|
||||
" -0.75739343 -3.71850042 1.8192876 ]\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"prompt_number": 12
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
@@ -791,11 +802,20 @@
|
||||
"collapsed": false,
|
||||
"input": [
|
||||
"# probability that a random value is less than the mean 2\n",
|
||||
"print (n23.cdf (2))"
|
||||
"print(n23.cdf(2))"
|
||||
],
|
||||
"language": "python",
|
||||
"metadata": {},
|
||||
"outputs": []
|
||||
"outputs": [
|
||||
{
|
||||
"output_type": "stream",
|
||||
"stream": "stdout",
|
||||
"text": [
|
||||
"0.5\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"prompt_number": 13
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
@@ -808,13 +828,24 @@
|
||||
"cell_type": "code",
|
||||
"collapsed": false,
|
||||
"input": [
|
||||
"print ('variance is', n23.var())\n",
|
||||
"print ('standard deviation is', n23.std())\n",
|
||||
"print ('mean is', n23.mean())"
|
||||
"print('variance is', n23.var())\n",
|
||||
"print('standard deviation is', n23.std())\n",
|
||||
"print('mean is', n23.mean())"
|
||||
],
|
||||
"language": "python",
|
||||
"metadata": {},
|
||||
"outputs": []
|
||||
"outputs": [
|
||||
{
|
||||
"output_type": "stream",
|
||||
"stream": "stdout",
|
||||
"text": [
|
||||
"variance is 9.0\n",
|
||||
"standard deviation is 3.0\n",
|
||||
"mean is 2.0\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"prompt_number": 14
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
|
||||
Reference in new issue
Block a user