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Mario Chirinos Colunga
tap1012
Commits
369ad78c
Commit
369ad78c
authored
Mar 23, 2019
by
Victor Hugo Pacheco Flores
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Respuesta del segundo ejercicio del segundo parcial
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71499d6f
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Test2-2.ipynb
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369ad78c
{
"cells": [
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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khFivpT0HjAdmCyEGAvsAu5ONhUBnIA04B/S30uAe335pmFe1VBkru1IoAo67RT6F/1g5oo9kPAq9lHIVGB427KBTXgKP+mmXIeE+F6ZQ5MdokRDcL/L5yuoHH6alwVRGNNL003dDbULYo+YoDLi2bLlQm6CIEl5Y9VNQ+0soUdIwL1L2fXvDyYsXQm1C2BPRLhACycDGSQVyTlNhPeG0CPqfX5aw+N5+oTYjaBSEbaVmUCN6A25QEagUUcj2E8dCbYIiBESN0DcoX9HS9mrFX2VpewqFQhEqokboS8cZ+/pQKBSKgkzUCH32ZfduXxUKhaKgEjWLsQdOnwq1CQqFIsxQLshtRM2I/ui5s5a2Fy0xMxUKZ+pZ4OBLEXlEjdBbzc8H0kNtgiJK6F2/SahNcDC48fWhNkERApTQG/D6mlWhNkERJYxr0zGo/aVlHTfM61GnfhAtUYQLSugNyJVeeV0OKeHgMEvhG08snW95m92//cLyNsOZ19p1CrUJYU/ECf0nyT0M8y7lXg6iJeFDgzB3kaowJiVtu+VtnsnOtrzNcOae6xoa5jUvYDFzjYg4ob+1ek3DvDpT3wyiJeFD5Pz2KLg8WL9pqE3g4aY3hNqEoHPiwvlQmxAWmPFHX1QI8acQYoMQYosQYqyWXkMIsVoIkSaEmCWEiNPSi2jP07T8xMC+hCtYJXjXTnndopagdZXqlrWliFxebnObYZ6V/ljctTWqZVvL+gk3Fvyrj2Fev4Vzg2hJeGJmRH8RaC+lbAI0BTppAUUmAG9KKWsBWcBArfxAIEtLf1MrZym3J9ayusk8XLZwfv6d2+4yzNthgd+R4csX+t2GQhHpuHOBsuLA3iBaEp54FHppwx6QsbD2kEB7YI6WPh3orl13056j5XfQolRZxpQ7uhvm+Ts6mrtzi1/181OuaDHDvDu+/tTv9uft3Op3G4rgEOjg3e7aCER4zCdb3Gh5m/5Q3E3s3YLuxdJszNhYLbrUUWAJsBs4KaW0+x3IAOzuHqsABwC0/FNAUD2E+fNPHbF8kYWWeCb7su8LyAX9zRtt3DVvhs91PQXfCER4zKeTWhvmrQzBOZStHmLvBuLzkhEhJ/JN/fellJellE2xBfq+Aajrb8dCiCFCiFQhRGpmpnEcWCN2Dnrabf6ktb953WaghPPx5q0M8+p85NsCshL5yMTdqH5T5hE+2bzO6zZH/fyD2+AboXAD0GfhHM+FAsDqBx92m2/V56bG5NdInPwaN3851ZL2Ao1Xvm6klCeFEMuBG4GyQohC2qi9KmAPXXMQqAZkCCEKAWUAlxMcUsopwBSApKQkryfF42JjqXtVBbYbBAuflPobk1J/Y8uAJyhROM6wnezLl30WW7OMuP5m3ln3h2G+/c1n5gOpBD7yWXr/ADrM+lg3b+yvyxj76zLGtG5Pv4bN3bbzxppfeXvd727LhHJ6JXHya/Rp0IyXbnaJOBowEkqUpHRcEf7JvujWLoDyxYqT2meYxzaX7d/DgEXzLLMxFAjpYeFRCFEBuKSJfDHgR2wLrH2BuVLKmUKID4GNUsr3hRCPAo2klA8LIXoCPaSU9xn3YBP61NRUn15AoIQvfehIw7Z9GSE1/fTdgIU8e6VNRy7l5vLir0td8rrXrsek9l18avfw2TMs27+HtYcPsmz/HrIs3KoWIwRtqybSolIV2lWrQaMKCZa17S3f7NrK08v0F7UDNRp+8delTN/8V0DatmNWyPwhV0pqerFLrXBMrMfzLlbc8xqTXwvatuNQOk4TQqyVUiZ5KmdmRF8ZmC6EiMU21TNbSjlfCLEVmCmEeBn4C5imlZ8GzBBCpAEngJ4+vQKTuBNkf9q0mvX9HgvYl9IDmi8VPaH3hWD8asiVkhUH9rLiwN487iYKirfBsa07MLZ1h4Dd6+qly7Cy1+CAtO1MjBA8UL8JX27dYKp8sA417h06ko6zP2GXG3cQBQkzu242SimbSSkbSykbSilf0tL3SClvkFLWklLeK6W8qKVf0J7X0vL3BPpFWCkOnub+/cFqEYsVosAIY7SyY9BTlrc5t/sDQRF5O68E2ZePWZbc15/N/Z8ItRlhQcSdjDUifehImiVU9rl+5ZKlSB86krjYWAutciV96Ei3bhy8aWf3kBEWWKQIJUViC5E+dCR1LXAfXCquCOlDR9Ii4WoLLPOO9KEj+bpbr6D364mScXGkDx3JCzfdannb9vsdCURN4BGAb7r3Bryfegj2P+vW6jVJHzqS62d8QKaXfvRHt7qFoU2Uq9loY/E9fQG47qNJXPQyWlqhmJiA7JP3lusrVSF96EhqTX2DnNzcUJuThwGNWjCgUQtmbt/EqJ9/8KutrQOfdLtnPxzxuBgbDPxZjPXErqzjvL3ud5bt20NObi43VanO4MZJ3GTCNYGVi7FmWL5/D++s+4P9/5zk3KVL1C5XnseataJj4rUB6U9hIxSLsWb5attG5u7cwoajh4mLjaXV1dXoWbdxRL0nvtm1lakbUjl45h8uXs6haqky3HbNtXSrVS8sAqHM27mVz7euZ2fWMS7nSioUL06bqom0r16TDteE9322cjE2oqkdfxXvdLgz1GaY4tbqNd06bVMUPHrVa0yveo39bueOkn3zPB/x4SBuf7CN3+2a4e7a9bm7dvj6we9Rp37U++mPmjl6hUKhUOijhF6hUCiiHCX0igLPJT/8DSkUkYASekWBZ+2Rv0NtgkIRUJTQKwo8s7ZvCrUJCkVAUUKvUCgUUY4SekWBxp1DrlDvoVcorEIJvaJAkxsGBwYVikAT9QemFAo92s/6mD0nTxjmv9fRONavQhFpmBZ6zU1xKnBQSnmnEKIGMBNbmMC1wENSymwhRBHgM6AFtoAj90sp0y23XFGgeWHVUj7bYvPnfkPlqky5oztlixT1WO/9v1Yz8c9fPJbrUvM6v21UKMIFb0b0TwLbgNLa8wnAm06BRwYCH2h/s6SUtbTAIxOA+y20WaHIw5+HMjzGTPWGTzv/y7K2AkV+lwbO/HBmekj7L0g2RAqmhF4IURXoAowDhgshBNAeeEArMh0Yg03ou2nXAHOAd4UQQoaD9zSFwgNbBjxJicLh65nQk7g5l/n28GSKlfT8K8cs3ROGcv6suShpdhsm/zmOxPpVve7L+XWWLFuCuRnv6+aZaeOFL5+gddcWXtsQTZhdjJ0EPAPYfY9eBZzU4sUCZABVtOsqwAEALf+UVl6hCGsW39M3bEV+//aDpgXOTvdKQ5n0mH5sWm+5o2Rf0yLvzNAbnvfa7vycOWlz5S2l9Kmtlx54m8Pp+rGlCwoehV4IcSdwVEq51sqOhRBDhBCpQojUzMyC/U9QhJbEMmUtC/4RKAYnPedTvUWf/szW1bv86ttfobaqjU6l+vlct2/DkZw/E5iYzZGAmamb1kBXIURnoCi2Ofq3gLJCiELaqL0qcFArfxCoBmQIIQoBZbAtyuZBSjkFmAI2f/T+vhBFweKlmzuw7fhR1hw+6LmwAb3rN2FcmIbBc8ZIJBecmEahOP2PsHOdpzu8bHnfry95noY31tHN27p6l26fd5Ts6/O8eX47GrSqzRs//Z9u2We7TGD9z1td0rtXGlpg5+09Cr2UcjQwGkAI0Q4YKaXsLYT4GrgH286bvsB3WpUU7fnvWv6ySJ2fVwdmwptwDF1nNUZC60mwfjgznTE93+L3+euC3nf9lrX54cx03fpnTp2jZJniPttkpv8JC54FrPkVES34c2DqWWwLs2nY5uCnaenTgKu09OHAKP9MVJhh3f7qhg9FdGF2VDpm5pMh69uo7L+qPBK0/vVYPvt3v+pHKl4JvZRyhZTyTu16j5TyBillLSnlvVLKi1r6Be15LS1/TyAMVyiinQtnL7qkvfjVE1614aswvnDvm5a09ekm13Cc7zzlm00vff20V+X17B0/4EOf+o50lAuEKKF59f15HuVL9g61SQo/6ZYwxCXtpruCs01w9aL1lrRTuYbrAvf8j5b51FbL5Kb+mlNgUUKvUCg84s9+/DrNa1hoicIXlNArFFFOcr9bvCq/8ps/XdI+2TDR5/7fWTnG57oKa1BCr1BECCXLlvCp3hNv9fOq/LiH3nNJi08o41PfRmz8ZbtX5bsMvNXS/gsaSugVijAk+8Ill7T299/oU1sxseH3MZ/33g9elQ/W2kS0EnZuiu3bAZtX35/nuRH2cmbb9aUd57pm+9ud2Y9T522LThVLDaBq/BiDkrms259o2E6JuGZcV+k7w/xAcPrCr+w66nmPeuOqGygUEx8EiwoeO9a6blZr3KZuCCwJDOtXuB5ockdivSqeCykMCb+veifM7AEPRpmSRZIc18fPzvbYFuAQecBQ5HPlRbciD3A2+6+g74U3I/IAGzOaBNiSgkvW0VMuaaXLlQyBJYHBW3cEhYuGpw+iSCHsRvR2PI2infP/PjmBq8s+67GdiqUGUzX+P3nyj56eSkbWfx1l9fqqkzDP0c6+4yO5qsR9bm3PlWfd5gNcuLSTrYduczzX6/ev/TWR5Li1LZDUSfiakkVauqRvzGhCTm5WyOwqCFROrOiSdvzQyRBYEhgqJYavX6FoJKxH9GA8VVKv0iLH9eF/XBePANYfuBI8on7lpS4iDzbxb1J1i+O5FaPn9QfqOa6N7Pck8gDNqofmrJl9L76eyINtykYRWGo1vcYlbfNvO0JgSWC4sXOzUJtQoAhroXc3UiwW18Bj/Vx53nFdtHBtw3KxMaW8suXgSd+dROWnSVX3c5VWfwlZRfG4ho5r+68OhXXYQj7kZcG05T61lXPpslflm7St57mQnwz8r4pFFEzCWuj94diZzx3X7kTeToVS/R3XB7JedFv2yD9TTNlQt9L3uunrD1zx+hcb437e1cyXUCgoX/JBx3V2TkYILQkdH++8md3/eLd7JBR8OnaOV+XHf/+MS9qSL1b53H/6Vtf3R+EiYTtrHJVErdDvP3HFf3e9yj96LF8tfqzjOvP0Jz736zzqLh6nv1iZKyPfL7YQanEsUvh60kKvyuttx3xt6FSf+x96w/M+11VYQ4H4WhXEWtJO8+r7HUJu1SJkOE3H5GdjRjNycl1CCSiCRExsDLmXcz0XVCg8ELUj+lCQkXXlV0Htil+G0BL/sLs3ViIfWhadcv1l+Urf93VKWs/Ck64hCC/neDfXD/qnbEdOHuyTTQrfMRscPB04DVwGcqSUSUKIcsAsIBFIB+6TUmZpgcPfAjoD54B+Ukrfox+EGY2qrGHTwet1846enua4LlX0ZlPthdvWxPy/MIzs+/vkqxz+5x2/+srJvcDnuzvRr/YKR9o3+/pSuVhzWlXU96V+4uIuFmcMJ0eep9lVA2gU/4Buufyk7B9I1sW9tKn0HDVL3eax/PoT09mSNZvCMcVpUPYeGsS7Xzw8dmE7izIep0ShBHokfu627ImLu/j58Mucyt5HrdLJ3JygvzVYj5/nrua56cNMl/c1+EZsIddfwZ3LDvDaVbGe35yOvc19NhTW4c3Uza1SymNOz0cBS6WU44UQo7TnzwLJQG3t0RL4QPsbFRSOTXBcG03fFClUM5gmWcbl3LyHdNx9CUmyfe7n4503M6DOKj5Luy3P84932gQg6+Jutp78mgF1riwAfrKzLZK80xhrMt9nTeb73FV9ChWK1nfpA6BobBkuXL7yulYcGsOKQ2PytO3M52mdyM4943h+8fIpVme+w+rMd3TrlCtax9EXwMnsdMfz/OX1XsPOU9+z89T3xMWU5MFai13aL391PMf+zsqTZjYkX69a/gUembnnbXrWzOv/3ptwgHpfMgU1lF+o8WeOvhvQTrueDqzAJvTdgM+08IF/CCHKCiEqSykP+WOot9SuOJNdR3sCsPnvG2l4tfvIMvtPXBlVXVWyp9f9HTr1huO6wdUrvK4fDmzIaOS4rlfJVXScMbPzaPSYufy1YT9PPdqRTrc1zJNnF3f7df7n+elfZyXHLmyjfNF6Lu18v3+IoXBfuHwqT17WxT18s69Pnv7szEjryKVc25bc/HkHz7mOTAG+SX+Iplf1o/lVg/LYpEf/OitZdeR/3Jww2uU1ZOee4UzOEUoWSsiT98XOSbqCeUfJvsRXLMPMPW+75M179wen8zqCAAAfIElEQVQmj/J/6jC+Yhn+7/PHePnBd136BmPRNvoVUaZ8eO4gKwiYnaOXwI9CiLVCCHs0hAQn8T4M2N+hVYADTnUztLSgUqroTY7r7BzPAaSPnfnKcX1NOfcuWetXvrKf+dT5JQAcOjXJtG3Nq+9zXO880sN0vWBSLK6+hxLuwwC3S57I76t3c+HCJca/vpB2yebd3N5SWX97a36RB2hRfqjbtvILdnwR419bRiIPUKX4DYb1nEXeuf5Pf7tG0cwv8s7lv9vXT7d9I0HNOnqKO0r2dXnkF3l/RtFtuutPUwK6fbubKpqd/q5hniKwmBX6m6WUzbFNyzwqhGjrnKmN3r0KAC6EGCKESBVCpGZmZnpT1TTO8+RnL641LHc597RX7RYtfK3jenfmwDx5CaXNxMS8chjmzMVUTl/4zav+A4Vw+oF3IMv1FLGd85e2uW3HSNS/mPWH47p+2XsM61cqZt6HTpNyD9lsyjlhuo4eq45MAKBf7Z+9qlc6rqph3v4z3u09v3jZ+H3oq1jb6xUtUcSn+vY23ls11nNBA5q3b6imbEKMKaGXUh7U/h4FvgFuAI4IISoDaH+PasUPAtWcqlfV0vK3OUVKmSSlTKpQITB+L5x3vuw4cjf7TrgeBMk8/QkbMq6csnUebbujVNHWjuv041fmMauUdR2x6dG02hWx3HW0J3uPGX9BbP77pqBsw2xafZfjOvO0/gdz77FH2XboDp/an/rpSsd16bhqbkoa8/vRNxxTPfaHFew8ZTvcFiO824rbsKz3Jzx9fQ3eiqVz+XHfjPCqbn5qNU30Sax/ODOd/6X826++Ff7jcY5eCFECiJFSntaubwdeAlKAvsB47a/dl24K8JgQYia2RdhTwZ6fd8Z57/vxMzM5fmam27JmqV3xK0e7J85+q6W6Hls3IkaUyGNb1rkFZPko5p6+BIzy879eQSxCFEbKSx7bdbbdF7wVVGcxbFLuIRqXe4jCMcVd8oKN3QYzONtZtUQrWlV8itKFq7rkucMutktn/sbEQZNd8qeseYVrdFz6NrzpOktG1fY2so6c4sF6w8nJzuv+IrZQLJP/HEe1OpUt6cdf1C8JG2YWYxOAbzTfG4WAL6WUi4UQa4DZQoiBwD7A7tJxIbatlWnYtlf2d20yuJgRJSu2OZr9NZC/XzOCWaZYB19M8ppm1XYH7F7d2ck3t8bnL9umZQQx9K+zMk9eTq41p4xLFa7C6Uue13KswGjh2Bs69LyJDj1v8lwwQMQnlGHBiWmeCyrCAo9CL6XcA7h8QqWUxwEX9dHm6x/11SBvRCRQZYPdpr/tWP3azLZnVO6tib148pmvXNJHPunblM+8dJtfnfwiDzB77798ajM/99aY5bL7JxDULdPNJS1Xen8QSaHwBnUyVmE5TRpVo+8DeUebKxa5ro+YpU6ZuwDIOPuHS57zHnmr2HDiM8vbtLP9lGu0sE93eRe8W6HwlgLh60YRfPo/dDP9H7Jm7vz68o+w6cQX/HhwpEue80Erf7G3tfbYFNYecz0nYNVIP7+9t1R6gZ8Pv2RJ2wqFHmpEr4gI9EQ2EFMsA+qsIrmqq2uHFuWH6JT2vu2EYo1d0q4tfbvfbSsU7hC2KfXQkpSUJFNTU0NthkKhUEQUQoi1UsokT+XUiF6hUCiiHCX0CoVCEeUooVcoFIooR+268YCU8N/xKaT+lU5OTi4N6l3Ns8M7U/4q97FeQ8X589m8Omkxf23cz/nzl0i8pjwP3NuStjfX8Vw5CklZuJ6pn6wkPr4EI564nSYNzbleeH/qclIWrqdK5bIMG9yeFs2uCbClNia9t4Tf/9xN5rHTVKxQmq5dmvLAveHt5furr1fzzffrOHbsDJUqleGmlrV4bGj7oPR9+MgpPvvyNzZszuDQoZOULFmERg2r0eam2i4eUwsyBWoxVs/Zlt7+7oHDPmH3XvOO1vzZI67HkmVbGPfqAtN9eOMZ0s6cz4f5/GXlS39g7X3Kb0P+tj3ZaGSLr/V8xdt7GRdXiB+/G26pDUZ2GL3WWXPX8MFHy3XzjHhsaHvu6e5xzdAtx0+c4V+9fY+w9Z9n76JDO1cPqJGMWoz1kXbJE70SeXudvzYEP1LUmnXpPovufQ99YLE1ocVZeLr8y7PLaL37ZuZe+nq/9drxpa3s7BzaJU9kzreh2aXWLnmi1yIP8O7kZT73+chTM2iXPNEvkQf474TvLfv/RRpK6DWefOYrv94ET4+aSceur1toUV5On8nr06Vd8kT+/fxsn9tbtjC6PArOmrsGgGFPf87Zc+aiX/Xsd8UpmDf/e3/eJ1knz1kiNu9OXhZU0fptdZpf/d2V7L2fI/uX4bYd1vpEbJc8kaOZ3rkmj3SU0Gts2HTAcyEPXLp0ma73uUb8sYJ5KVfC7nbq/mZA+ogGtm7/23TZw0ds7hP+OW2NYzRP/L56N3f3sjb4RjDEfsu2v3luzDy/2hjxhPd+jqyeJnPmvj7R9YvWEwV+MfaPP3cz6sW5LunVqpRjxkeDXNKlhFs7G3+4/jl9gVP/nKdM6WKW2vnJjFX0feAmtx/sTrc1ZFC/to6593Pns/lg6nK+X7QhT7lSJYv6ZYunD+ADA6bw96GTfvXhCwt/3OS4nvnpUCollHE8d3ffnL+cG9avwruv9/ZYr13yRK+E6MLFS4we4/o+syMELFvwDELH0/WS5VsZN3G+YV1vbfGWR4frBzuPEYJ3Xu9Ng3pXO9Jmz1vD+1O9n9oxIiZGkJurv454y83XMfZ5Vydxdjx9CQb6voUTphZjhRBlgY+AhtgiSQ0AdgCzgEQgHbhPSpklbP6M38Lmqvgc0E9KuU6nWQehXIzVw8w/PzdX0r7Lq361YYTeYqy9TW8WzfKzeetBHhvxRcDf3EZCH8jFWGeWzh9JbKzrj1VP//8bkmow8b/3mq7nzetx17fZdpb9vI2Xxn+vm9ekYTXeerWXaXvyY/S+8uf95tyuP/97Zxu6dm7K8Me9cxlhxb0PV6xejH0LWCylrIvNZfE2YBSwVEpZG1iqPQdbuMHa2mMIEDG/kRbMedL0Pz4mRli+E8YTejtNvHmjNqxfJeLf2J7ofX8rXZEHGDb4Vrd19UQe/BcDo/dCQsXSXrXd/pZ6huU3bPZ/6jE//r7ffK2TnxbNrnG0463I220w4v6+H/pjWsTgUeiFEGWAtsA0AClltpTyJNANsIdvmQ501667AZ9JG38AZe0hB8OZSgllKOFDXM0lKf6FaPOVaBdsXxncr61h3n09jANdh+J+zpr+sE/1Hup5o256IOfrQ7l4//or3odrzM/3Xz+hm37k6D9+tx0JmBnR1wAygU+EEH8JIT7SQgomOIUIPIwtEhVAFcB5eJGhpYU1Mz8d6lO9woWNQ+JduhSYgBJK5CMHK6Z98jOwbxuf6/rCkpQRxOgtHkQQ/q5LRTpmhL4Q0Bz4QErZDDjLlWkawBFVyquTV0KIIUKIVCFEamamd/vWw42nHu2omx6I7Zb3/8t4VKoILufPm9vGmZ+G9QM37gnE4MLdYCaSiIsruHtPzAh9BpAhpVytPZ+DTfiP2KdktL9HtfyDgPM586paWh6klFOklElSyqQKFSr4ar8ljH/pHr/qd7+zmUWWeOaRQe7nmRWBoWKFUi5pm7e6jzFrtIXPeVePr8yY6rojDKwfXIwe2cXS9kLJ2OeMd+hEOx6FXkp5GDgghLhOS+oAbAVSgL5aWl/AHiMtBegjbLQCTjlN8YQlra6vGWoTFCHEzKxE5YSyLmlZJ8+5rRPIQznVqpYLWNvO3NGhQVD6CQY1a4R2QBlKzP6WeRz4QggRB+wB+mP7kpgthBgI7APu08ouxLa1Mg3b9sr+llocptSpXYmduw67pG/akkGjBlUt6aNsmeKWtBOttG5Vy6d6d5hwflWseJxLWqDWYBSBIVqmoHzB1PZKKeV6bZqlsZSyu5QyS0p5XErZQUpZW0p5m5TyhFZWSikflVJeK6VsJKUsEKGjhg1qp5s+5ZOVlvXxn2fvsqytaKT2tQmeC1lYT6GIFAru6oTFNG1cXTd905YMy/oIlqvcSCXxmvI+1ataJd5iS9wTKY61nE+8hiOvvrWYBYs3htqMiEAJvSJqKF3aty10xYu5TssoyONCIlyIlC/JcEMJvSJqKFqkcKhNiCpKFPf+AGEgSFmwnjfe/THUZkQ0SugVigBwKUct1FpBco9JPp9XUFxBCb1CEQAKFyq4Ozyswuw0TYwQjBrRmds9bAVdvGQz499YaIVpEYcSeoUiyCgXFp4Z6SGozscf9KdmYsHdF+8tSugVCkXYkbou3TBPfVF6j4owZREz5/ypm96tS/DcIygU0UCg/MefyDrjc91IRwm9RXw4bYVu+rAhyjeNQmEF/o7k167fZ5ElkYcSegK7Q6JIAfaYp1CEE2v/UkJfoLnr3sAE9FYUbIzcEX8x648gW6Io6CihBy5cuORX/XsefF833dcj+YrowMgd8dRPrfN/pDCHJ5fS0Y4Segs4dlx/kefTDwcE2RJFpBCIoDQKYx4b8UWoTQgpZmLGXieEWO/0+EcI8ZQQopwQYokQYpf2N14rL4QQbwsh0oQQG4UQzQP/MvzHVx8ao8fMtdgSRTRhtIB46dJlut6npgyDwQvjvg21CSHHTOCRHVLKplLKpkALbD7mv8EWTnCplLI2sJQr4QWTgdraYwigH2YnDPFW7E9kneX31bt187p0amyFSYoo5p/TF3h54nxL2ioIzr4mf/yzT3VWrtoZAGsiC2+nbjoAu6WU+4BuwHQtfTrQXbvuBnym+aX/AyhrDzkYCbRLnkiu9Bz+dsAjn9DjgfcM8//9ZCcrzVJEMO62Bf60fCvtkify9gc/edXmt/P/ol3yRMcjmnjjf/frpn/19WrGefHF2C55Il99vdpzwQKAt3v/egJfadcJTiECDwP26A1VgANOdTK0tLAMJzj2+W68OO67PGntO78K2EIMjnm+m8Mr4tmzFxkw7BOOHP3HbZsF6eTe4SOn2LQlgz/W7GHTlgy34fPsgnRd7Uo0rF+Flkk1aVC/CiV0ojdFGysWPeNWkOelrGNeyjrH8xqJ5alSOZ7jJ86wN/0YFy76t2Egkmje1DjuwpLlW1myfCvLFz6jGwIy89hp7n1IfxLh25mP0b3nu1aZGVGYFnotjGBXYHT+PCmlFEJ4HgbnbW8ItqkdqlfXD9oRDMrFlzD8EP6xZg+dur/pVXtfz3jEKtPCirsfeI+srLOWtLVj12F27DrM3O/WGpZ5/ZX7oy7Qiiexd2Zv+jH2ph8LsEXhi6d7dWtn737FFKTBlx7eTN0kA+uklEe050fsUzLa36Na+kGgmlO9qlpaHqSUU7TwhEkVKoTOOdHSFdsAeGF0V7/b+vLjIVQoX8rvdhTRS0EXHG8witrmLW9N7OW4jokxEQk+CvFG6HtxZdoGIAXoq133Bb5zSu+j7b5pBZxymuIJO1IWrgegfdu6fn0IVyx6hqsrl7XKLEUUs2LRM6xY9IylohONXyCTJvT063XFCMGKRc/QpNGVceeyBf+2wrSIw9TUjRCiBNARGOqUPB6YLYQYCOwD7tPSFwKdgTRsO3T6W2ZtAMjNzTvjtGLRM2Rn53B7tzdM1Z80oadlIw9FwcIuOsOe/pyt2//2uv6gvm15sGcrq80KO1Yseoapn6706kRxNH7x+YOQJnaYBJqkpCSZmpoa8H6M5vzUm0KhUEQiQoi1UsokT+WUxy2FIoC0WTyBk9nnANjUdWyIrQlPGqW8CKj7E0iUCwQF5y5dosY7r1PjHXUs32rsIq9QhBI1olcoAogapXpG3aPAo0b0CoVCEeUooVcoFIooR03dKBQWY19czI+7KYoJmxfx+Z4/HGX02ljT5f8oGlvYsI13ti9lyk73vu5fbtadbtVc4xh/tvt3Xt2yOE9ah8r1mHR9T9128i+g5re3ZOGi/J7scojepb4zZqdwzuVk03LhOJ/qJy14mYuXXd1J1CtTmdm3PByQPsMBNaJXKCym37WtuabEVT7VnZ+xwfCL4voFL3Mq+7xuXqOUFx0i/2PH4Wy4a4xLmYeva6cr8o1SXnQReYClh7YZ2mLnzmVv65Y5c+mC27q3JNRB4P2BsWbzX8ojuLHiioQ1SnmR59bNM6zbKOVFXZEH2HbK+EynP32GC2pEr1BYzIgGtzOiwe0ATNv1C5O2mfdMOVoTjfwjRbto3rx4vNtRpHPepq5j6bVyCptPHqRGyfI8ep1roPrGKWMAiBExbLgrrzDb+2yU8qJhn/vOHOf2q+vzelJej5P2ujm5lykUE+tS792WvV3KemJBxkZyci87Xlt+GqW8yPcZG3ileQ+XvJsW/Q+ABmWvZmbboS75G7IOuKR50+fLzboTI8J33KyEPopwtz2yepky/NxnkKl2kj76gOPn9bcF7n18hCkb3k++i+RadTh27hzXT9P3Jvjdfb1pnFDJJf3bHdt4+seFbvtpVDGBlPsfdFsmUtETlE1dxxoKYqefJgHwQI2WLnlftR1Co5QX2XtG30GaxHZgMr/Ie+rTmfwiD1CzVAX2nM6k2fyXLJveGLVursMuPe6u3pxv9q/T/WI6fekCYLsfejSJr6ab7qlP+z1q8v3YsJ7GCd+vIIVXeNoDv//UKZpP1Y9t68zqgxmGIm+mHzsfb7C53DUSeUBX5AGPIg+w6egRTmdfNGVLtHPwXBYAiSW9i1HcdrHtpPgtCXU8lv1m/zrddCNxG1H/dq9ssYKXmnYzzBtcuy1w5RdMQUON6KMAZ/HVG3FvyTzKnTNnkHXhPLXefYO0x4YbttVz3izeuqMLXevUNeynxjuvexzZp/59kBrvvE5sTAxpjz5t9qUAsPTB/mzKPEK3OvV08+12NJ78rkc7CgJftBlM71+m8sqmBfSqcUOePKM5fYCsbJvb6Z+P7PQ4cp+zby13VzcfFbRsXHHTZYPBE/U6MGffWrKyzzpea9m44vzS6dkQWxYc1Ig+itg89HHd9AYVKtI+sSYAl034NtITeYCHW9ygm+4Ob0UeoGZ8OUORB2hR+Wqv24xmGsdXdVxn5TuJe/Pi8QC6i7PekCNz/aofDqzs9AxLb78yMDiZfY5GKS+aXiOIZNSIPsJxHs2XiDOO1DTtrrsdZX/au5vbalyrWy65lvHP+GdvasOHa/8E4OP1axnQtIVb2wI12p5zTy/lriEf9rnitosn6Oa548eOw6lcrEygTAsrKhYt7bgfn+/5gwmbFwHuF5yjATWiL4AMnv+tYd77yXeZauO/v6ywyBqFlRSNLcymrmPzPDwxdkNKECwLPx6s2SrP/Ynmkb1Zf/RPA4MACWzC5mO+MjATuApYCzwkpcwWQhQBPgNaAMeB+6WU6dab7j3KHXFkMW7VCj76yzjcoOIKdpFa0+X/TNex/wr49WhaoMwKKr7uZze7uyiS8TiiF0JUAZ4AkqSUDYFYbEHCJwBvSilrAVnAQK3KQCBLS39TK6dQmGbYwhRqvPO6Evkg4k7ojA4ZBZu22u4gI1u/z9gAeH9a1V7Plz7t6XpbW8MJs3P0hYBiQohLQHHgENAeeEDLnw6MAT4AumnXAHOAd4UQQoZDhBNF2PPIwhQW794FQOHYWHYOe0q3XLjO0S8/vIOPdq1kY1aGS55dFBrHV6Vpuer8u8EdlvU7/eaB9F01za1ge9qf721dXxmzIYWVR3aSeeF0nnR7/8ULxdE0vhqTb+yTJ/+9lr3pseJ9dv1zxFG2UEys40ATQKP4Krp95n9t+euB/ms02+foRp3dvuZQ43FEL6U8CLwG7Mcm8KewTdWclFLmaMUyAPsdrgIc0OrmaOV9Ow+uCDpmduUEErvIA4YiH86sOLxdV+Sd2ZiVwWe7f7O0376rpnks484HT4lCRQzrPVa3vc926TF331oXkXfmXE42v2Xu1s2b125Ynl1GzoK76Lan+LKN/oGo/JgReTN93lThWtN9hhKPI3ohRDy2UXoN4CTwNdDJ346FEEOAIQDVq6uYq77SsWYtluzxbo513aBhhnnXvfem2332dn7o3c+rPr3l6ZY3GeaNWvZjQPv2h7FNuzHWzcEdI55tmMyzDZPdltETo5PZ52izeAICwcauYwzrepqD/qPzc6bsdGeLM43jq7o9TeoPX7QZ7HWdUPQZTpjZdXMbsFdKmSmlvATMA1oDZYUQ9i+KqsBB7fogUA1Ayy+DbVE2D1LKKVLKJCllUoUKFfx8GQWXKV2uiMqJ88aHY/p+N9dxHV+smGE5dyP6/6y44rOlTrnA/kg7fPaMYd6sLZsC2nck0UbbTulO5BUKM0K/H2glhCguhBBAB2ArsBy4RyvTF/hOu07RnqPlL1Pz88GhxUf6Lg62Hctk5f500+0889MPuumfbzJetLKarzZv1E3vNW920GxQKKIFj1M3UsrVQog5wDogB/gLmAIsAGYKIV7W0uyThNOAGUKINOAEth06igCy9/ERedwTGBEjBLs9TMu83jGZEUsW8fW2zW77CxT/a9+R0cuWAMavJe2x4dR6942A2RBJDKrdho92/eL2wE+0bx1UeMbUrhsp5YtA/nfLHsDlTLyU8gJwr/+mKbzBWez1KBVXhI1DH/PYTo+69Xn19184fEZ/6iTQvmV6NmjsEHo9pt7ZnVjhvR/zaOXJerfx0a5fAM+CHs0nPxXuEeEwq5KUlCRTU1NDbYZCEbFIpK5nxutKV2JOu0eCb5AiKAgh1kopkzyVU75uFIooQCDUiF1hiPJ1o1AoFFGOEnqFQqGIcpTQKxQKRZSjhF6hUCiinLDYdSOEOA3sCLUdXlIe0I+4HJ5Emr2gbA4GkWYvKJuduUZK6dG1QLjsutlhZotQOCGESI0kmyPNXlA2B4NIsxeUzb6gpm4UCoUiylFCr1AoFFFOuAj9lFAb4AORZnOk2QvK5mAQafaCstlrwmIxVqFQKBSBI1xG9AqFQqEIECEXeiFEJyHEDiFEmhBiVKjtARBCVBNCLBdCbBVCbBFCPKmllxNCLBFC7NL+xmvpQgjxtvYaNgohmofQ9lghxF9CiPna8xpCiNWabbOEEHFaehHteZqWnxgCW8sKIeYIIbYLIbYJIW4M93sshHhae09sFkJ8JYQoGm73WAjxsRDiqBBis1Oa1/dVCNFXK79LCNFXr68A2/yq9t7YKIT4RghR1ilvtGbzDiHEHU7pQdETPXud8kYIIaQQorz2PPT3WEoZsgcQC+wGagJxwAagfiht0uyqDDTXrksBO4H6wERglJY+CpigXXcGFgECaAWsDqHtw4Evgfna89lAT+36Q+AR7XoY8KF23ROYFQJbpwODtOs4oGw432Ns8ZD3AsWc7m2/cLvHQFugObDZKc2r+wqUw+aKvBwQr13HB9nm24FC2vUEJ5vra1pRBFuI092algRNT/Ts1dKrAT8A+4Dy4XKPg/pB0blZNwI/OD0fDYwOpU0Gdn4HdMR2qKuyllYZ2/5/gMlAL6fyjnJBtrMqsBRoD8zX3ljHnD4sjvutvRlv1K4LaeVEEG0to4mmyJcetveYK4Hvy2n3bD5wRzjeYyAxn2h6dV+BXsBkp/Q85YJhc768u4EvtOs8OmG/z8HWEz17gTlAEyCdK0If8nsc6qkb+wfHToaWFjZoP7ebAauBBCnlIS3rMJCgXYfL65gEPAPkas+vAk5KKXN07HLYrOWf0soHixpAJvCJNtX0kRCiBGF8j6WUB4HXsIXXPITtnq0lfO+xM97e15Df73wMwDYqhjC1WQjRDTgopcwfczPk9oZa6MMaIURJYC7wlJTyH+c8afsKDpstS0KIO4GjUsq1obbFJIWw/fT9QErZDDiLbUrBQRje43igG7YvqauBEkCnkBrlA+F2Xz0hhHgeWxjTL0JtixFCiOLAc8ALobZFj1AL/UFsc1p2qmppIUcIURibyH8hpZynJR8RQlTW8isDR7X0cHgdrYGuQoh0YCa26Zu3gLJCCLurC2e7HDZr+WWA40G0NwPIkFKu1p7PwSb84XyPbwP2SikzpZSXgHnY7nu43mNnvL2v4XC/EUL0A+4EemtfUBCeNl+LbQCwQfsMVgXWCSEqubEraPaGWujXALW1XQtx2BasUkJsE0IIgS3I+TYppXMU6hTAvjLeF9vcvT29j7a63go45fQzOShIKUdLKatKKROx3cdlUsrewHLgHgOb7a/lHq180EZ5UsrDwAEhxHVaUgdgK2F8j7FN2bQSQhTX3iN2m8PyHufD2/v6A3C7ECJe+yVzu5YWNIQQnbBNRXaVUp5zykoBemq7mmoAtYE/CaGeSCk3SSkrSikTtc9gBrYNHYcJh3scqIUKLxY0OmPb1bIbeD7U9mg23Yztp+1GYL326IxtfnUpsAv4CSinlRfAe9pr2AQkhdj+dlzZdVMT24cgDfgaKKKlF9Wep2n5NUNgZ1MgVbvP32LbeRDW9xgYC2wHNgMzsO38CKt7DHyFbQ3hEjbBGejLfcU2L56mPfqHwOY0bHPY9s/gh07ln9ds3gEkO6UHRU/07M2Xn86VxdiQ32N1MlahUCiinFBP3SgUCoUiwCihVygUiihHCb1CoVBEOUroFQqFIspRQq9QKBRRjhJ6hUKhiHKU0CsUCkWUo4ReoVAoopz/B/KvLyi5EhsIAAAAAElFTkSuQmCC\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"from os import path \n",
"from scipy.misc import imread \n",
"import matplotlib.pyplot as plt\n",
"import numpy as np\n",
"#import random \n",
"from wordcloud import WordCloud, STOPWORDS \n",
"\n",
"class NubePalabras():\n",
" text=\"\"\n",
" wordcloud=\"\"\n",
" \n",
" def __init__(self,diccionario,stopwords={'a','ante','cabe','con','contra','de','desde','en','entre','el','hacia','para',\n",
" 'por','segun','si','sobre','tras'}):\n",
" self.stopwords=stopwords\n",
" #print(diccionario)\n",
" for i in diccionario:\n",
" #print(i[0])\n",
" for j in range(len(diccionario[0])):\n",
" if(type(i[j]) == int):\n",
" #print(i[j])\n",
" for k in range(i[j]):\n",
" self.text +=i[0]+' '\n",
" #print(self.text)\n",
" self.wordcloud = WordCloud( \n",
" background_color=\"white\",\n",
" max_words=50, \n",
" width=1500, \n",
" height=850 , \n",
" prefer_horizontal = 1 ,\n",
" #relative_scaling = .5, \n",
" stopwords=self.stopwords\n",
" ).generate(self.text)\n",
" \n",
" def plot_cloud(self):\n",
" wordcloud=self.wordcloud\n",
" plt.imshow(wordcloud) \n",
" plt.show()\n",
" #self.wordcloud=wordcloud\n",
"\n",
" def store_cloud(self):\n",
" wordcloud=self.wordcloud\n",
" wordcloud.to_file(\"nubepalabra2.jpg\")\n",
" \n",
"text =[('quijote', 5),\n",
" ('primera', 4),\n",
" ('don', 3),\n",
" ('novela', 3),\n",
" ('parte', 3),\n",
" ('obra', 3),\n",
" ('título', 2),\n",
" ('ingenioso', 2),\n",
" ('mancha', 2),\n",
" ('1605', 2)]\n",
"\n",
"\n",
"c=NubePalabras(text)\n",
"c.plot_cloud()\n",
"c.store_cloud()"
]
}
],
"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": "3.6.7"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
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