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Basics of Matplot lib part 1
{
"metadata": {
"name": "",
"signature": "sha256:e721f8ef22152aa75a00d48b4b30476adf91438aca3ed880ae7035a672589a71"
},
"nbformat": 3,
"nbformat_minor": 0,
"worksheets": [
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#Simple Plotting example"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"%matplotlib inline \n",
"import matplotlib.pyplot as plt #importing matplot lib library\n",
"import numpy as np \n",
"x = range(100) \n",
"#print x, print and check what is x\n",
"y =[val**2 for val in x] \n",
"#print y\n",
"plt.plot(x,y) #plotting x and y"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 113,
"text": [
"[<matplotlib.lines.Line2D at 0x7857bb0>]"
]
},
{
"metadata": {},
"output_type": "display_data",
"png": 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xL05Vsb+TBwsd45drcSwn+91PeF28nhwysU7ZAjU4Ovv5QUksMQzHmpXAvhFc\n4OxXwT44E9mNQBvgOyAVuA27Hn67DmD//7dgE1SCdUw3Arbhv2txNTAf2AnkAuOwpmg/XosCx/ub\nKPxZWt0pOy6vJ4clQF1CN8h1ItQZ6QdJwGBsRMpbYeUTsI43nMc0EtsL2H/s2kBn4HPgIfx3HcA+\n+H4A6jnPm2GjdSbiv2uxGms3Pxv7W2mG/a348VoUON7fxATsb6cU9ndUF1hU7NHFmJ9vkGuCtbEv\nx5pUlmFDeytgnbOJPlTvWJoS+oLg1+vQAKs5rMC+LZfDv9fiWUJDWYdiNW2/XItUrK8lG/vC8AdO\n/Lu/gH2OrgZaFGukIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIisfT/aY4DA839B3AAAAAASUVO\nRK5CYII=\n",
"text": [
"<matplotlib.figure.Figure at 0x4f3ecf0>"
]
}
],
"prompt_number": 113
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<b>See how [np.linspace](http://docs.scipy.org/doc/numpy/reference/generated/numpy.linspace.html) works. </b>"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#Using Numpy"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"x = np.linspace(0, 2*np.pi, 100)\n",
"y =np.sin(x)\n",
"plt.plot(x,y)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 17,
"text": [
"[<matplotlib.lines.Line2D at 0x579aef0>]"
]
},
{
"metadata": {},
"output_type": "display_data",
"png": 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bkqF9e9s3f+ZMKFEics8noQml0H8NXAssPsE1hYHnsGJfG+gO1ArhOT0rxYvT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\niIiIiIiIiIiIiIiIiEjs+3+0mgoiOtCM+QAAAABJRU5ErkJggg==\n",
"text": [
"<matplotlib.figure.Figure at 0x5ad52b0>"
]
}
],
"prompt_number": 17
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"x= np.linspace(-3,2, 200)\n",
"Y = x ** 2 - 2 * x + 1.\n",
"plt.plot(x,Y)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 24,
"text": [
"[<matplotlib.lines.Line2D at 0x6ffb310>]"
]
},
{
"metadata": {},
"output_type": "display_data",
"png": 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"text": [
"<matplotlib.figure.Figure at 0x5d06190>"
]
}
],
"prompt_number": 24
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# plotting multiple plots\n",
"x =np.linspace(0, 2 * np.pi, 100)\n",
"y = np.sin(x)\n",
"z = np.cos(x)\n",
"plt.plot(x,y) \n",
"plt.plot(x,z)\n",
"plt.show()\n",
"\n",
"# Matplot lib picks different colors for different plot. "
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "display_data",
"png": 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spKXScps2Qbp05inylkpooY8EfIHNwAlgGRAK9Ix5A9gEnEfdtJ0FfJTAa7o8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\nQmCC\n",
"text": [
"<matplotlib.figure.Figure at 0x7438350>"
]
}
],
"prompt_number": 32
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"cd C:\\Users\\tk\\Desktop\\Matplot"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"C:\\Users\\tk\\Desktop\\Matplot\n"
]
}
],
"prompt_number": 35
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"data = np.loadtxt('numpy.txt')\n",
"plt.plot(data[:,0], data[:,1]) # plotting column 1 vs column 2\n",
"# The text in the numpy.txt should look like this\n",
"# 0 0\n",
"# 1 1\n",
"# 2 4\n",
"# 4 16\n",
"# 5 25\n",
"# 6 36"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 39,
"text": [
"[<matplotlib.lines.Line2D at 0x740f090>]"
]
},
{
"metadata": {},
"output_type": "display_data",
"png": 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"text": [
"<matplotlib.figure.Figure at 0x5dabd10>"
]
}
],
"prompt_number": 39
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"data1 = np.loadtxt('scipy.txt') # load the file\n",
"print data1.T\n",
"\n",
"for val in data1.T: #loop over each and every value in data1.T\n",
" plt.plot(data1[:,0], val) #data1[:,0] is the first row in data1.T\n",
" \n",
"# data in scipy.txt looks like this:\n",
"# 0 0 6\n",
"# 1 1 5\n",
"# 2 4 4 \n",
"# 4 16 3\n",
"# 5 25 2\n",
"# 6 36 1\n"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"[[ 0. 1. 2. 4. 5. 6.]\n",
" [ 0. 1. 4. 16. 25. 36.]\n",
" [ 6. 5. 4. 3. 2. 1.]]\n"
]
},
{
"metadata": {},
"output_type": "display_data",
"png": 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"text": [
"<matplotlib.figure.Figure at 0x763ccb0>"
]
}
],
"prompt_number": 56
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Scatter Plots and Bar Graphs"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"sct = np.random.rand(20, 2)\n",
"print sct\n",
"plt.scatter(sct[:,0], sct[:,1]) # I am plotting a scatter plot."
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"[[ 0.51454542 0.61859101]\n",
" [ 0.45115993 0.69774873]\n",
" [ 0.29051205 0.28594808]\n",
" [ 0.73240446 0.41905186]\n",
" [ 0.23869394 0.5238878 ]\n",
" [ 0.38422814 0.31108919]\n",
" [ 0.52218967 0.56526379]\n",
" [ 0.60760426 0.80247073]\n",
" [ 0.37239096 0.51279078]\n",
" [ 0.45864677 0.28952167]\n",
" [ 0.8325996 0.28479446]\n",
" [ 0.14609382 0.8275477 ]\n",
" [ 0.86338279 0.87428696]\n",
" [ 0.55481585 0.24481165]\n",
" [ 0.99553336 0.79511137]\n",
" [ 0.55025277 0.67267026]\n",
" [ 0.39052024 0.65924857]\n",
" [ 0.66868207 0.25186664]\n",
" [ 0.64066313 0.74589812]\n",
" [ 0.20587731 0.64977807]]\n"
]
},
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 64,
"text": [
"<matplotlib.collections.PathCollection at 0x78a7110>"
]
},
{
"metadata": {},
"output_type": "display_data",
"png": 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"text": [
"<matplotlib.figure.Figure at 0x77c55b0>"
]
}
],
"prompt_number": 64
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"ghj =[5, 10 ,15, 20, 25]\n",
"it =[ 1, 2, 3, 4, 5]\n",
"plt.bar(ghj, it) # simple bar graph"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 65,
"text": [
"<Container object of 5 artists>"
]
},
{
"metadata": {},
"output_type": "display_data",
"png": "iVBORw0KGgoAAAANSUhEUgAAAWwAAAEACAYAAACXqUyYAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nrkJggg==\n",
"text": [
"<matplotlib.figure.Figure at 0x77cb070>"
]
}
],
"prompt_number": 65
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"ghj =[5, 10 ,15, 20, 25]\n",
"it =[ 1, 2, 3, 4, 5]\n",
"plt.bar(ghj, it, width =5)# you can change the thickness of a bar, by default the bar will have a thickness of 0.8 units"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 74,
"text": [
"<Container object of 5 artists>"
]
},
{
"metadata": {},
"output_type": "display_data",
"png": "iVBORw0KGgoAAAANSUhEUgAAAWwAAAEACAYAAACXqUyYAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
"text": [
"<matplotlib.figure.Figure at 0x8343290>"
]
}
],
"prompt_number": 74
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"ghj =[5, 10 ,15, 20, 25]\n",
"it =[ 1, 2, 3, 4, 5]\n",
"plt.barh(ghj, it) # barh is a horizontal bar graph"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 75,
"text": [
"<Container object of 5 artists>"
]
},
{
"metadata": {},
"output_type": "display_data",
"png": "iVBORw0KGgoAAAANSUhEUgAAAW4AAAEACAYAAACTXJylAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAdKJePsjbYL9AAAAAElFTkSuQmCC\n",
"text": [
"<matplotlib.figure.Figure at 0x83fc670>"
]
}
],
"prompt_number": 75
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<b><font size = \"5\">Multiple bar charts </font>"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"new_list = [[5., 25., 50., 20.], [4., 23., 51., 17.], [6., 22., 52., 19.]]\n",
"x = np.arange(4) \n",
"plt.bar(x + 0.00, new_list[0], color ='b', width =0.25)\n",
"plt.bar(x + 0.25, new_list[1], color ='r', width =0.25)\n",
"plt.bar(x + 0.50, new_list[2], color ='g', width =0.25)\n",
"\n",
"#plt.show()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "display_data",
"png": 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oJaQzGy7kOi6envYe2tfTKnW2oadvIQ1VHAe+QRr/hfb1s0qdbejnRe/i8lkubeulJEmS\nJEmSJEmSJEmSJEmSJEmSpLb7f444yPpRvfOlAAAAAElFTkSuQmCC\n",
"text": [
"<matplotlib.figure.Figure at 0x8d35250>"
]
}
],
"prompt_number": 95
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"#Stacked Bar charts\n",
"p = [5., 30., 45., 22.]\n",
"q = [5., 25., 50., 20.]\n",
"x =range(4)\n",
"plt.bar(x, p, color ='b')\n",
"plt.bar(x, q, color ='y', bottom =p) "
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 100,
"text": [
"<Container object of 4 artists>"
]
},
{
"metadata": {},
"output_type": "display_data",
"png": 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AAElFTkSuQmCC\n",
"text": [
"<matplotlib.figure.Figure at 0x9169d10>"
]
}
],
"prompt_number": 100
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# plotting more than 2 values\n",
"A = np.array([5., 30., 45., 22.])\n",
"B = np.array([5., 25., 50., 20.])\n",
"C = np.array([1., 2., 1., 1.])\n",
"X = np.arange(4)\n",
"plt.bar(X, A, color = 'b')\n",
"plt.bar(X, B, color = 'g', bottom = A)\n",
"plt.bar(X, C, color = 'r', bottom = A + B) # for the third argument, I use A+B\n",
"plt.show()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "display_data",
"png": "iVBORw0KGgoAAAANSUhEUgAAAXkAAAEACAYAAABWLgY0AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nTkSuQmCC\n",
"text": [
"<matplotlib.figure.Figure at 0x565a410>"
]
}
],
"prompt_number": 35
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"black_money = np.array([5., 30., 45., 22.]) \n",
"white_money = np.array([5., 25., 50., 20.])\n",
"z = np.arange(4)\n",
"plt.barh(z, black_money, color ='g')\n",
"plt.barh(z, -white_money, color ='r')# - notation is needed for generating, back to back charts\n"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 94,
"text": [
"<Container object of 4 artists>"
]
},
{
"metadata": {},
"output_type": "display_data",
"png": 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"text": [
"<matplotlib.figure.Figure at 0x565a110>"
]
}
],
"prompt_number": 94
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#Other Plots "
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"#Pie charts\n",
"y = [5, 25, 45, 65]\n",
"plt.pie(y)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 114,
"text": [
"([<matplotlib.patches.Wedge at 0x7a19d50>,\n",
" <matplotlib.patches.Wedge at 0x7a252b0>,\n",
" <matplotlib.patches.Wedge at 0x7a257b0>,\n",
" <matplotlib.patches.Wedge at 0x7a25cb0>],\n",
" [<matplotlib.text.Text at 0x7a25070>,\n",
" <matplotlib.text.Text at 0x7a25550>,\n",
" <matplotlib.text.Text at 0x7a25a50>,\n",
" <matplotlib.text.Text at 0x7a25f50>])"
]
},
{
"metadata": {},
"output_type": "display_data",
"png": 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MUen6x7GkUtdj26dwzjlhLrggSseOpjNJUK1fD48+WuLvf7exrPk0NNyAdwqY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\nSUVORK5CYII=\n",
"text": [
"<matplotlib.figure.Figure at 0x7070d70>"
]
}
],
"prompt_number": 114
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"#Histograms\n",
"d = np.random.randn(100)\n",
"plt.hist(d, bins = 20)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 115,
"text": [
"(array([ 2., 3., 2., 1., 2., 6., 5., 7., 10., 12., 9.,\n",
" 12., 11., 5., 6., 4., 1., 0., 1., 1.]),\n",
" array([-2.9389701 , -2.64475645, -2.35054281, -2.05632916, -1.76211551,\n",
" -1.46790186, -1.17368821, -0.87947456, -0.58526092, -0.29104727,\n",
" 0.00316638, 0.29738003, 0.59159368, 0.88580733, 1.18002097,\n",
" 1.47423462, 1.76844827, 2.06266192, 2.35687557, 2.65108921,\n",
" 2.94530286]),\n",
" <a list of 20 Patch objects>)"
]
},
{
"metadata": {},
"output_type": "display_data",
"png": "iVBORw0KGgoAAAANSUhEUgAAAW4AAAEACAYAAACTXJylAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAAAASUVORK5CYII=\n",
"text": [
"<matplotlib.figure.Figure at 0x6e2cc70>"
]
}
],
"prompt_number": 115
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"d = np.random.randn(100)\n",
"plt.boxplot(d)\n",
"#1) The red bar is the median of the distribution\n",
"#2) The blue box includes 50 percent of the data from the lower quartile to the upper quartile. \n",
"# Thus, the box is centered on the median of the data.\n"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 116,
"text": [
"{'boxes': [<matplotlib.lines.Line2D at 0x7cca090>],\n",
" 'caps': [<matplotlib.lines.Line2D at 0x7c02d70>,\n",
" <matplotlib.lines.Line2D at 0x7cc2c90>],\n",
" 'fliers': [<matplotlib.lines.Line2D at 0x7cca850>,\n",
" <matplotlib.lines.Line2D at 0x7ccae10>],\n",
" 'medians': [<matplotlib.lines.Line2D at 0x7cca470>],\n",
" 'whiskers': [<matplotlib.lines.Line2D at 0x7c02730>,\n",
" <matplotlib.lines.Line2D at 0x7cc24b0>]}"
]
},
{
"metadata": {},
"output_type": "display_data",
"png": "iVBORw0KGgoAAAANSUhEUgAAAW0AAAEACAYAAAB4ayemAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nRU5ErkJggg==\n",
"text": [
"<matplotlib.figure.Figure at 0x70a4f90>"
]
}
],
"prompt_number": 116
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"d = np.random.randn(100, 5) # generating multiple box plots\n",
"plt.boxplot(d)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 118,
"text": [
"{'boxes': [<matplotlib.lines.Line2D at 0x7f49d70>,\n",
" <matplotlib.lines.Line2D at 0x7ea1c90>,\n",
" <matplotlib.lines.Line2D at 0x7eafb90>,\n",
" <matplotlib.lines.Line2D at 0x7ebea90>,\n",
" <matplotlib.lines.Line2D at 0x7ece990>],\n",
" 'caps': [<matplotlib.lines.Line2D at 0x7f2b3b0>,\n",
" <matplotlib.lines.Line2D at 0x7f49990>,\n",
" <matplotlib.lines.Line2D at 0x7ea14d0>,\n",
" <matplotlib.lines.Line2D at 0x7ea18b0>,\n",
" <matplotlib.lines.Line2D at 0x7eaf3d0>,\n",
" <matplotlib.lines.Line2D at 0x7eaf7b0>,\n",
" <matplotlib.lines.Line2D at 0x7ebe2d0>,\n",
" <matplotlib.lines.Line2D at 0x7ebe6b0>,\n",
" <matplotlib.lines.Line2D at 0x7ece1d0>,\n",
" <matplotlib.lines.Line2D at 0x7ece5b0>],\n",
" 'fliers': [<matplotlib.lines.Line2D at 0x7e98550>,\n",
" <matplotlib.lines.Line2D at 0x7e98930>,\n",
" <matplotlib.lines.Line2D at 0x7ea8470>,\n",
" <matplotlib.lines.Line2D at 0x7ea8a10>,\n",
" <matplotlib.lines.Line2D at 0x7eb6370>,\n",
" <matplotlib.lines.Line2D at 0x7eb6730>,\n",
" <matplotlib.lines.Line2D at 0x7ec6270>,\n",
" <matplotlib.lines.Line2D at 0x7ec6810>,\n",
" <matplotlib.lines.Line2D at 0x8030170>,\n",
" <matplotlib.lines.Line2D at 0x8030710>],\n",
" 'medians': [<matplotlib.lines.Line2D at 0x7e98170>,\n",
" <matplotlib.lines.Line2D at 0x7ea8090>,\n",
" <matplotlib.lines.Line2D at 0x7eaff70>,\n",
" <matplotlib.lines.Line2D at 0x7ebee70>,\n",
" <matplotlib.lines.Line2D at 0x7eced70>],\n",
" 'whiskers': [<matplotlib.lines.Line2D at 0x7f2bb50>,\n",
" <matplotlib.lines.Line2D at 0x7f491b0>,\n",
" <matplotlib.lines.Line2D at 0x7e98cf0>,\n",
" <matplotlib.lines.Line2D at 0x7ea10f0>,\n",
" <matplotlib.lines.Line2D at 0x7ea8bf0>,\n",
" <matplotlib.lines.Line2D at 0x7ea8fd0>,\n",
" <matplotlib.lines.Line2D at 0x7eb6cd0>,\n",
" <matplotlib.lines.Line2D at 0x7eb6ed0>,\n",
" <matplotlib.lines.Line2D at 0x7ec6bd0>,\n",
" <matplotlib.lines.Line2D at 0x7ec6dd0>]}"
]
},
{
"metadata": {},
"output_type": "display_data",
"png": 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"text": [
"<matplotlib.figure.Figure at 0x75684d0>"
]
}
],
"prompt_number": 118
}
],
"metadata": {}
}
]
}
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