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lgvaz/graph.ipynb

Created September 7, 2018 22:08
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graph.ipynb
{
"cells": [
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"with open('points.dat') as f:\n",
" data_raw = f.read().splitlines()"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
"d = data_raw[0]"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"['15.55', '28.65', '2']"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"d.split()"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[15.55, 28.65, 2.0]"
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"[float(valor) for valor in d.split()]"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [],
"source": [
"novo_valor = []\n",
"for valor in d.split():\n",
" novo_valor.append(float(valor))"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[15.55, 28.65, 2.0]"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"novo_valor"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [],
"source": [
"data = []\n",
"for d in data_raw:\n",
" data.append([float(valor) for valor in d.split()])"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [
{
"data": {
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]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [],
"source": [
"import numpy as np"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {},
"outputs": [],
"source": [
"data_np = np.array(data)"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([[15.55, 28.65, 2. ],\n",
" [14.9 , 27.55, 2. ],\n",
" [14.45, 28.35, 2. ],\n",
" ...,\n",
" [ 8.5 , 3.25, 5. ],\n",
" [ 8.1 , 3.55, 5. ],\n",
" [ 8.15, 4. , 5. ]])"
]
},
"execution_count": 21,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data_np"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {
"scrolled": true
},
"outputs": [
{
"data": {
"text/plain": [
"(788, 3)"
]
},
"execution_count": 23,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data_np.shape"
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(3, 788)"
]
},
"execution_count": 29,
"metadata": {},
"output_type": "execute_result"
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],
"source": [
"data_np.T.shape"
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {},
"outputs": [],
"source": [
"x, y, k = data_np.T"
]
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {},
"outputs": [],
"source": [
"import matplotlib.pyplot as plt"
]
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.collections.PathCollection at 0x7f40753ab668>"
]
},
"execution_count": 32,
"metadata": {},
"output_type": "execute_result"
},
{
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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.scatter(x=x, y=y)"
]
},
{
"cell_type": "code",
"execution_count": 33,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.collections.PathCollection at 0x7f4075309748>"
]
},
"execution_count": 33,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.scatter(x=x, y=y, marker='+')"
]
},
{
"cell_type": "code",
"execution_count": 34,
"metadata": {},
"outputs": [],
"source": [
"COLORS = ['blue', 'green', 'red', 'magenta', 'cyan', 'yellow', 'black']"
]
},
{
"cell_type": "code",
"execution_count": 37,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"'black'"
]
},
"execution_count": 37,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"COLORS[-1]"
]
},
{
"cell_type": "code",
"execution_count": 40,
"metadata": {},
"outputs": [],
"source": [
"cores = []\n",
"for n in k:\n",
" cor_string = COLORS[int(n - 1)]\n",
" cores.append(cor_string)"
]
},
{
"cell_type": "code",
"execution_count": 41,
"metadata": {},
"outputs": [
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},
"execution_count": 41,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"cores"
]
},
{
"cell_type": "code",
"execution_count": 42,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
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},
"execution_count": 42,
"metadata": {},
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},
{
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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.scatter(x=x, y=y, marker='+', c=cores)"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.5"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
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