gistfile1.txt

{"nbformat_minor": 0, "cells": [{"execution_count": 1, "cell_type": "code", "source": "%load_ext memory_profiler", "outputs": [], "metadata": {"collapsed": false, "trusted": true}}, {"execution_count": 2, "cell_type": "code", "source": "import numpy as np\nimport matplotlib\nimport matplotlib.pyplot as plt\n%matplotlib inline", "outputs": [], "metadata": {"collapsed": false, "trusted": true}}, {"execution_count": 3, "cell_type": "code", "source": "from dipy.data import read_stanford_labels\nimport nibabel as nib\nhardi_img, gtab, labels_img = read_stanford_labels()\ndata = hardi_img.get_data()\ncandidate_sl = [s[0] for s in nib.trackvis.read('./probabilistic_small_sphere.trk', points_space='voxel')[0]]", "outputs": [{"output_type": "stream", "name": "stdout", "text": "Dataset is already in place. If you want to fetch it again, please first remove the folder /home/arokem/.dipy/stanford_hardi \nAll files already in /home/arokem/.dipy/stanford_hardi.\n"}], "metadata": {"collapsed": false, "trusted": true}}, {"execution_count": 4, "cell_type": "code", "source": "import dipy.tracking.life as life\nfiber_model = life.FiberModel(gtab)", "outputs": [], "metadata": {"collapsed": false, "trusted": true}}, {"execution_count": 5, "cell_type": "code", "source": "len(candidate_sl)", "outputs": [{"execution_count": 5, "output_type": "execute_result", "data": {"text/plain": "2435680"}, "metadata": {}}], "metadata": {"collapsed": false, "trusted": true}}, {"execution_count": 6, "cell_type": "code", "source": "for i in np.arange(0, 19):\n    this = 2 ** i\n    print(\"Number of streamlines: %s\"%this)\n    %memit fiber_fit = fiber_model.fit(data, candidate_sl[:this], affine=np.eye(4))", "outputs": [{"output_type": "stream", "name": "stdout", "text": "Number of streamlines: 1\npeak memory: 3261.71 MiB, increment: 0.59 MiB\nNumber of streamlines: 2\npeak memory: 3261.73 MiB, increment: 0.00 MiB\nNumber of streamlines: 4\npeak memory: 3261.75 MiB, increment: 0.02 MiB\nNumber of streamlines: 8\npeak memory: 3261.75 MiB, increment: 0.00 MiB\nNumber of streamlines: 16\npeak memory: 3261.76 MiB, increment: 0.01 MiB\nNumber of streamlines: 32\npeak memory: 3261.76 MiB, increment: 0.00 MiB\nNumber of streamlines: 64\npeak memory: 3261.76 MiB, increment: 0.00 MiB\nNumber of streamlines: 128\npeak memory: 3261.76 MiB, increment: 0.00 MiB\nNumber of streamlines: 256\npeak memory: 3261.76 MiB, increment: 0.00 MiB\nNumber of streamlines: 512\npeak memory: 3261.76 MiB, increment: 0.00 MiB\nNumber of streamlines: 1024\npeak memory: 3261.76 MiB, increment: 0.00 MiB\nNumber of streamlines: 2048\npeak memory: 3276.14 MiB, increment: 14.38 MiB\nNumber of streamlines: 4096\npeak memory: 3321.05 MiB, increment: 44.91 MiB\nNumber of streamlines: 8192\npeak memory: 3414.68 MiB, increment: 93.63 MiB\nNumber of streamlines: 16384\npeak memory: 3617.24 MiB, increment: 202.57 MiB\nNumber of streamlines: 32768\npeak memory: 4721.73 MiB, increment: 1281.57 MiB\nNumber of streamlines: 65536\npeak memory: 8826.69 MiB, increment: 4939.83 MiB\nNumber of streamlines: 131072\npeak memory: 20247.30 MiB, increment: 14472.56 MiB\nNumber of streamlines: 262144\npeak memory: 55127.31 MiB, increment: 43989.15 MiB\n"}], "metadata": {"collapsed": false, "trusted": true}}, {"execution_count": 9, "cell_type": "code", "source": "m = [3261.71, 3261.73, 3261.75, 3261.76, 3261.76, 3261.76,\n     3261.76, 3261.76, 3261.76, 3261.76, 3261.76, 3276.14,\n     3321.05, 3414.68, 3617.24, 4721.73, 8826.69, 20247.30, 55127.31]     ", "outputs": [], "metadata": {"collapsed": false, "trusted": true}}, {"execution_count": 10, "cell_type": "code", "source": "fig, ax = plt.subplots()\nax.plot(2 ** np.arange(19), m, 'o-')\nax.set_ylabel('Memory usage (MiB)')\nax.set_xlabel('# streamlines')", "outputs": [{"execution_count": 10, "output_type": "execute_result", "data": {"text/plain": "<matplotlib.text.Text at 0x2578ded0>"}, "metadata": {}}, {"output_type": "display_data", "data": {"image/png": 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DnsbFEQ+mc8FQrZDXvrPdBXR0dF507969pnv37jVaWlr1FEVJdXV1q9tb7+7d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\n6+Tn538EACCRSDSqqqr0DQwMKuTZR1n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