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@Phlya
Last active July 21, 2016 09:14
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{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"hi\n"
]
}
],
"source": [
"%matplotlib inline\n",
"import numpy as np\n",
"from mirnylib.numutils import completeIC\n",
"import matplotlib.pyplot as plt\n",
"import cooler"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"serum10K = cooler.Cooler('Hi-C/raw_data/main_data_rep3/serum_rep3_10K.cool')"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def get_IC_data(data, coords):\n",
" bias = data.bintable().fetch(coords)['weight'].values\n",
" mat = data.matrix().fetch(coords)\n",
" mat.data = bias[mat.row] * bias[mat.col] * mat.data\n",
" return mat.toarray()"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"chr19 = get_IC_data(serum10K, 'chr19')"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false,
"scrolled": true
},
"outputs": [
{
"data": {
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sRJFURHXHHcljge2/f/D1pEnluYdFKbS16DPMbbCwaVP6j89C1FzOITwOjwjw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\nimBwICKiCAYHIiKK+H8htMV1EK38XgAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f6969a47f98>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.plot(chr19.sum(axis=0))\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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EtJiIBrrCexDRIiJaSkT3qL8V9bitknpVcuVhultCOitad9rc3Zcj6hJgmcl5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\nTAaZ9KPm2717+tZDo7oW9Vp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jRLSOiBa5wloR0XQiWkJE04iopevcSCKqJ6LFRDTQFd6D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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f6969a2f7f0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.plot(serum10K.matrix().fetch('chr19').toarray().sum(axis=0))\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/usr/local/lib/python3.4/dist-packages/mirnylib/numutils.py:1360: VisibleDeprecationWarning: using a boolean instead of an integer will result in an error in the future\n",
" diag=skipDiags)\n"
]
},
{
"data": {
"image/png": 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"text/plain": [
"<matplotlib.figure.Figure at 0x7f6957a71cc0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.plot(completeIC(serum10K.matrix().fetch('chr19').toarray()).sum(axis=0))\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"chr19[np.isnan(chr19)]=0"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/usr/local/lib/python3.4/dist-packages/mirnylib/numutils.py:1360: VisibleDeprecationWarning: using a boolean instead of an integer will result in an error in the future\n",
" diag=skipDiags)\n"
]
},
{
"data": {
"image/png": 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9zCdv9rM6Ta2rDl2et2F0JhwkSfUxHPrM+96C1Eaut/UzHCS1nudv6mc4SCVN3chY1/R1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\niv8PDddkjStjTDsAAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f69578f6a20>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.plot(completeIC(chr19).sum(axis=0))\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
"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": "3.4.3"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
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