diff --git a/ipynb/tutorial.ipynb b/ipynb/tutorial.ipynb deleted file mode 100644 index 939add0b2..000000000 --- a/ipynb/tutorial.ipynb +++ /dev/null @@ -1,918 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "
If you use NeuroM for your (published) work, please add a reference to our work:\n", - "\n", - "Juan Palacios, Lida Kanari, Eleftherios Zisis, Mike Gevaert, Guy Atenekeng, Liesbeth Vanherpe, Julian Shillcock, EPFL,\n", - "NeuroM - https://github.com/BlueBrain/NeuroM.git.
" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "collapsed": true - }, - "source": [ - "\n",
- "$ virtualenv --system-site-packages nrm # creates a virtualenv called \"nrm\" in nrm directory\n",
- "$ source nrm/bin/activate # activates virtualenv\n",
- "(nrm)$ # now we are in the nrm virtualenv\n",
- "
\n",
- "\n",
- "The prompt indicates that the virtualenv has been activated. To de-activate it,\n",
- "\n",
- "\n",
- "(nrm)$ deactivate\n",
- "
\n",
- "\n",
- "\n",
- "(nrm)$ morph_check -h # shows help for morphology checking script\n",
- "
\n",
- "\n",
- "Try it yourself! You can go to [NeuroMorpho](http://neuromorpho.org) to download a neuronal morphology and perform the semantic checks.\n",
- "\n",
- "\n",
- "(nrm)$ morph_check path/to/files/filename\n",
- "\n",
- "\n",
- "2.2 Extract basic morphometrics of a sample morphology
\n",
- "\n",
- "The application [morph_stats](http://neurom.readthedocs.io/en/latest/morph_stats.html) extracts various morphometrics for one or many morphologies.\n",
- "Its contents can be easily configured via a configuration file, as shown in the [online documentation](http://neurom.readthedocs.io/en/latest/morph_stats.html)\n",
- "\n",
- "\n",
- "(nrm)$ morph_stats -h # shows help for the morphometrics extraction script\n",
- "(nrm)$ morph_stats path/to/files/filename # analyze single morphology file\n",
- "(nrm)$ morph_stats path/to/files # analyze many morphology files\n",
- "
\n",
- "\n",
- "2.3 Basic morphometrics with the neurom and neurom.viewer modules
\n",
- "\n",
- "The neurom module contains helper functions that allow to easily load neuron morphologies from files into NeuroM data structures. It also provides convenient methods to query various properties of the neurons, and an easy way to visualize morphological objects.\n",
- "\n",
- "Open the interactive python terminal:\n",
- "\n",
- "\n",
- "(nrm)$ ipython\n",
- "
\n",
- "\n",
- "If this gives a warning \"Attempting to work in a virtualenv …\", ipython can be pip-installed inside the virtualenv but this must override the system ipython:\n",
- "\n",
- "\n",
- "(nrm)$ pip install -I ipython\n",
- "\n",
- "\n",
- "Now you can try and use the basic neurom functionality! We will start by loading and visualizing neuronal morphologies. Next, we will extract basic morphometrics."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 1,
- "metadata": {
- "collapsed": false
- },
- "outputs": [
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "/home/palacios/code/bbp/NeuroM/neurom/utils.py:81: DeprecationWarning: Module neurom.point_neurite.segments is deprecated. \n",
- " _warn_deprecated('Module %s is deprecated. %s' % (mod_name, msg))\n"
- ]
- }
- ],
- "source": [
- "%matplotlib inline\n",
- "# import NeuroM module\n",
- "import neurom as nm\n",
- "# import neurom visualization module\n",
- "from neurom import viewer"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 2,
- "metadata": {
- "collapsed": false
- },
- "outputs": [],
- "source": [
- "# load a neuron from an swc, h5 or NL ascii (Warning: Neurolucida ascii reader is experimental! There are no guarantees regarding correctness of loading output.) file \n",
- "neuron = nm.load_neuron('../test_data/valid_set/Neuron.swc')"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 3,
- "metadata": {
- "collapsed": false
- },
- "outputs": [
- {
- "data": {
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00D597OvPP7f9BK66Cv7v/6yLaNs2mx5ataqzsQa58jhYLGXtwAFbBTp0qNV8adMmv/jX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\nAAAAAAAAAAAAAAAAAAAAAABg3f0DJQfPukwnw3QAAAAASUVORK5CYII=\n",
- "text/plain": [
- ""
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "# visualize a neuron in two dimensions\n",
- "fig, ax = viewer.draw(neuron)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 4,
- "metadata": {
- "collapsed": false
- },
- "outputs": [
- {
- "data": {
- "image/png": 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17mxRSgRZci52JuSaxWbrCmkUQT7XoQf1lEG1WGk+cdddd+Gf//wnpk6dilGj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\nQYMGDRo0aNCgQYMGDRo0aNBQXvH/SNX0wKyrjNYAAAAASUVORK5CYII=\n",
- "text/plain": [
- ""
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "# visualize a neuron in three dimensions\n",
- "fix, ax = viewer.draw(neuron, mode='3d')"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 5,
- "metadata": {
- "collapsed": false
- },
- "outputs": [
- {
- "data": {
- "image/png": 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1Yvr06QwaNIgRI0Zw6tQpXnnlFWJiYjQrX++++y7r1q2jR48e1KpVi4sXLzJz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- "text/plain": [
- ""
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "# visualize the dendrogram of a neuron\n",
- "fig, ax = viewer.draw(neuron, mode='dendrogram')"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Feature Extraction"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 6,
- "metadata": {
- "collapsed": false
- },
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Neuron id : Neuron \n",
- "Number of neurites : [4] \n",
- "Soma Radius : 0.17 \n",
- "Number of sections : [84]\n",
- "\n",
- "Neurite type \t\t\t| Number of sections\n",
- "NeuriteType.axon | 21\n",
- "NeuriteType.basal_dendrite | 21\n",
- "NeuriteType.basal_dendrite | 21\n",
- "NeuriteType.apical_dendrite | 21\n"
- ]
- }
- ],
- "source": [
- "# Extract the number of all neurites (basal and apical dendrites, and axons)\n",
- "number_of_neurites = nm.get('number_of_neurites', neuron)\n",
- "\n",
- "# Extract the number of sections\n",
- "number_of_sections = nm.get('number_of_sections', neuron)\n",
- "\n",
- "# Extract soma radius\n",
- "soma_radius = neuron.soma.radius\n",
- "\n",
- "# Extract the number of sections per neurite\n",
- "number_of_sections_per_neurite = nm.get('number_of_sections_per_neurite', neuron)\n",
- "\n",
- "# printing\n",
- "print \"Neuron id : {0} \\n\\\n",
- "Number of neurites : {1} \\n\\\n",
- "Soma Radius : {2:.2f} \\n\\\n",
- "Number of sections : {4}\".format(neuron.name, number_of_neurites, soma_radius, number_of_neurites, number_of_sections)\n",
- "\n",
- "print\n",
- "print \"Neurite type \\t\\t\\t| Number of sections\"\n",
- "\n",
- "for i, neurite in enumerate(neuron.neurites):\n",
- " \n",
- " print \"{0:31} | {1}\".format(str(neurite.type), number_of_sections_per_neurite[i])"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "collapsed": false
- },
- "source": [
- "In following block, features with list output are calculated,"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 7,
- "metadata": {
- "collapsed": false,
- "scrolled": true
- },
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "|sg_len|sc_len|lc_bif_angles|rm_bif_angles|sc_path_dists|sc_rad_dists|\n",
- "| 0.10 | 9.58 | 2.09 | 0.34 | 9.58 | 8.84 |\n",
- "| 0.65 | 9.65 | 2.09 | 0.57 | 19.23 | 15.75 |\n",
- "| 1.01 |10.26 | 2.09 | 0.59 | 19.84 | 16.74 |\n",
- "| 1.06 | 9.19 | 2.09 | 0.49 | 29.03 | 23.23 |\n",
- "| 1.15 | 9.28 | 2.09 | 0.16 | 29.12 | 23.07 |\n",
- "| 0.94 |10.73 | 2.09 | 0.52 | 39.85 | 30.58 |\n",
- "| 1.30 | 9.59 | 2.09 | 0.76 | 38.71 | 30.18 |\n",
- "| 1.09 |10.45 | 2.09 | 0.47 | 49.17 | 37.80 |\n",
- "| 1.18 | 8.93 | 2.09 | 0.72 | 47.64 | 36.63 |\n",
- "| 1.09 |10.05 | 2.09 | 0.34 | 57.70 | 44.10 |\n",
- "| 1.41 | 9.97 | 2.09 | 0.58 | 57.61 | 43.97 |\n",
- "| 0.93 |10.72 | 2.09 | 0.26 | 68.33 | 51.29 |\n",
- "| 0.80 |10.55 | 2.09 | 0.12 | 68.16 | 51.92 |\n",
- "| 1.12 | 9.11 | 2.09 | 0.21 | 77.28 | 57.79 |\n",
- "| 1.38 |10.09 | 2.09 | 0.51 | 78.26 | 59.43 |\n",
- "| 0.81 |10.33 | 2.09 | 0.36 | 88.59 | 66.61 |\n",
- "| 0.49 | 9.18 | 2.09 | 0.24 | 87.43 | 66.25 |\n",
- "| 1.13 | 8.86 | 2.09 | 0.26 | 96.29 | 71.36 |\n",
- "| 0.72 |10.37 | 2.09 | 0.41 | 97.81 | 74.05 |\n",
- "| 0.85 | 9.95 | 2.09 | 0.19 | 107.76 | 80.70 |\n",
- "| 1.41 |11.02 | 2.09 | 0.25 | 108.83 | 82.44 |\n",
- "| 0.54 | 7.97 | 2.09 | 0.28 | 7.97 | 7.31 |\n",
- "| 1.33 | 8.73 | 2.09 | 0.20 | 16.70 | 14.54 |\n",
- "| 1.25 |10.71 | 2.09 | 0.37 | 18.68 | 16.26 |\n",
- "| 0.82 |10.52 | 2.09 | 0.14 | 29.20 | 25.07 |\n",
- "| 1.23 | 9.63 | 2.09 | 0.06 | 28.32 | 23.94 |\n",
- "| 0.32 |10.13 | 2.09 | 0.46 | 38.45 | 32.50 |\n",
- "| 1.53 |10.10 | 2.09 | 0.15 | 38.42 | 32.21 |\n",
- "| 1.40 |10.90 | 2.09 | 0.06 | 49.33 | 41.32 |\n",
- "| 0.44 |11.65 | 2.09 | 0.36 | 50.07 | 42.47 |\n",
- "| 1.41 |10.20 | 2.09 | 0.16 | 60.28 | 50.59 |\n",
- "| 0.97 | 9.54 | 2.09 | 0.37 | 59.61 | 50.36 |\n",
- "| 1.18 | 9.53 | 2.09 | 0.23 | 69.14 | 58.20 |\n",
- "| 0.53 |10.80 | 2.09 | 0.21 | 70.41 | 59.19 |\n",
- "| 1.25 |10.25 | 2.09 | 0.08 | 80.66 | 67.45 |\n",
- "| 0.85 |11.61 | 2.09 | 0.17 | 82.02 | 69.32 |\n",
- "| 0.74 | 8.93 | 2.09 | 0.25 | 90.95 | 76.34 |\n",
- "| 0.88 | 8.23 | 2.09 | 0.20 | 90.25 | 76.26 |\n",
- "| 0.40 | 9.67 | 2.09 | 0.16 | 99.92 | 83.73 |\n",
- "| 0.97 |10.13 | 2.09 | 0.18 | 100.38 | 84.86 |\n",
- "| 1.41 |10.97 | | | 111.35 | 94.04 |\n",
- "| 0.94 |10.89 | | | 111.28 | 94.43 |\n",
- "| 0.97 | 8.22 | | | 8.22 | 7.46 |\n",
- "| 0.95 | 9.59 | | | 17.82 | 15.43 |\n",
- "| 0.69 |11.02 | | | 19.24 | 15.37 |\n",
- "| 0.76 |10.26 | | | 29.50 | 24.71 |\n",
- "| 0.83 |10.76 | | | 30.00 | 22.84 |\n",
- "| 0.97 |10.38 | | | 40.38 | 31.87 |\n",
- "| 0.78 |10.62 | | | 40.62 | 30.50 |\n",
- "| 0.96 |11.05 | | | 51.67 | 40.23 |\n"
- ]
- }
- ],
- "source": [
- "# Extract the lengths of the sections\n",
- "section_lengths = nm.get('section_lengths', neuron)\n",
- "\n",
- "# Extract the lengths of the segments\n",
- "segment_lengths = nm.get('segment_lengths', neuron)\n",
- "\n",
- "# Extract the local bifurcation angles\n",
- "local_bif_angles = nm.get('local_bifurcation_angles', neuron)\n",
- "\n",
- "# Extract the remote bifurcation angles\n",
- "remote_bif_angles = nm.get('remote_bifurcation_angles', neuron)\n",
- "\n",
- "# Extract the radial distances of sections\n",
- "section_radial_distances = nm.get('section_radial_distances', neuron)\n",
- "\n",
- "# Extract the path distances of sections\n",
- "section_path_distances = nm.get('section_path_distances', neuron)\n",
- "\n",
- "# printing\n",
- "\n",
- "features = (segment_lengths, section_lengths, local_bif_angles, \n",
- " remote_bif_angles, section_path_distances, section_radial_distances)\n",
- "\n",
- "def check(feature_list, n): \n",
- " return '{0:.2f}'.format(feature_list[n]) if n < len(feature_list) else ''\n",
- "\n",
- "print '|sg_len|sc_len|lc_bif_angles|rm_bif_angles|sc_path_dists|sc_rad_dists|'\n",
- "\n",
- "n = 0\n",
- "finished = False\n",
- "while not finished:\n",
- " \n",
- " args = (check(f, n) for f in features)\n",
- " \n",
- " print '|{0:^6}|{1:^6}|{2:^13}|{3:^13}|{4:^13}|{5:^12}|'.format(*args)\n",
- " \n",
- " n += 1\n",
- " if n == 50: finished = True\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 8,
- "metadata": {
- "collapsed": false
- },
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Available features:\n",
- "\n",
- "\tlocal_bifurcation_angles\n",
- "\tnumber_of_bifurcations\n",
- "\tnumber_of_forking_points\n",
- "\tnumber_of_neurites\n",
- "\tnumber_of_sections\n",
- "\tnumber_of_sections_per_neurite\n",
- "\tnumber_of_segments\n",
- "\tnumber_of_terminations\n",
- "\tpartition\n",
- "\tprincipal_direction_extents\n",
- "\tremote_bifurcation_angles\n",
- "\tsection_areas\n",
- "\tsection_branch_orders\n",
- "\tsection_lengths\n",
- "\tsection_path_distances\n",
- "\tsection_radial_distances\n",
- "\tsection_tortuosity\n",
- "\tsection_volumes\n",
- "\tsegment_lengths\n",
- "\tsegment_meander_angles\n",
- "\tsegment_midpoints\n",
- "\tsegment_radial_distances\n",
- "\tsegment_radii\n",
- "\tsegment_taper_rates\n",
- "\ttotal_length\n",
- "\ttotal_length_per_neurite\n"
- ]
- }
- ],
- "source": [
- "print 'Available features:\\n'\n",
- "from neurom import fst\n",
- "for f in sorted(fst.NEURITEFEATURES.keys()):\n",
- " print '\\t', f"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "In order to visualize the output of the features above, we will need a histogram function,"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "collapsed": true
- },
- "source": [
- "3. More Examples
\n",
- "\n",
- " 3.1 Analyze different type of trees
\n",
- "\n",
- "The previous examples treated all neurites in the same way. NeuroM allows you to extract morphometrics for a selected type of trees."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 9,
- "metadata": {
- "collapsed": false
- },
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\n",
- "axonal [ 9.57911737 9.64901212 10.26444194 9.18963499 9.28095558\n",
- " 10.72637819 9.58862945 10.45414656 8.92750196 10.05466932\n",
- " 9.96815205 10.72221858 10.55440382 9.11262954 10.09303133\n",
- " 10.33071556 9.17709438 8.86068767 10.37491982 9.95295124\n",
- " 11.01846074]\n",
- "\n",
- "basal [ 7.97232242 8.73002814 10.71154672 10.51683552 9.63361814\n",
- " 10.1348335 10.1034446 10.90464832 11.65250813 10.20352358\n",
- " 9.54012263 9.53084499 10.79778536 10.25222844 11.60598013\n",
- " 8.92943746 8.23366666 9.66996901 10.13395757 10.96762258\n",
- " 10.89245052 8.22452877 9.59239376 11.0190682 10.25855549\n",
- " 10.75631381 10.38491293 10.62047288 11.05192629 10.06943611\n",
- " 10.10998146 10.55534081 10.58562592 10.74722939 8.23176374\n",
- " 9.8508199 8.93049233 10.73839347 9.48292967 8.58137852\n",
- " 9.0358861 8.48759244]\n",
- "\n",
- "apical [ 9.21270799 11.05092479 11.02994892 10.7541096 10.17670693\n",
- " 9.36444805 10.49054247 9.52925566 9.49194374 10.36496319\n",
- " 8.42121218 10.92441795 10.34721651 8.99513591 11.75828156\n",
- " 11.56005879 10.38431278 9.97008743 10.85399109 9.87885774\n",
- " 9.81392252]\n"
- ]
- }
- ],
- "source": [
- "# Extract the section lengths of axonal trees\n",
- "ax_section_lengths = nm.get('section_lengths', neuron, neurite_type=nm.AXON)\n",
- "\n",
- "# Extract the section lengths of basal dendrite trees\n",
- "ba_section_lengths = nm.get('section_lengths', neuron, neurite_type=nm.BASAL_DENDRITE)\n",
- "\n",
- "# Extract the section lengths of apical dendrite trees\n",
- "ap_section_lengths = nm.get('section_lengths', neuron, neurite_type=nm.APICAL_DENDRITE)\n",
- "\n",
- "print '\\naxonal ', ax_section_lengths\n",
- "print '\\nbasal ', ba_section_lengths\n",
- "print '\\napical ', ap_section_lengths"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- " 3.2 Perform statistical analysis on extracted measurements
\n",
- "\n",
- "Now we are ready to extract basic statistical measurements, using common Python functions. For this, we will use [numpy](http://www.numpy.org/), which is a package for scientific computing with Python.\n",
- "\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 10,
- "metadata": {
- "collapsed": false
- },
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Section Lengths stats : \n",
- "\n",
- "\tmean = 10.01 +- 0.86\n",
- "\t[min, max] : [7.97, 11.76]\n"
- ]
- }
- ],
- "source": [
- "import numpy as np\n",
- "\n",
- "# Now we can get the mean section length\n",
- "mean_sl = np.mean(section_lengths)\n",
- "\n",
- "# We can get the standard deviation of section lengths\n",
- "std_sl = np.std(section_lengths)\n",
- "\n",
- "# We can get the minimum section length\n",
- "min_sl = np.min(section_lengths)\n",
- "\n",
- "# … and the maximum section length\n",
- "max_sl = np.max(section_lengths)\n",
- "\n",
- "print 'Section Lengths stats : \\n'\n",
- "print '\\tmean = {0:.2f} +- {1:.2f}'.format(mean_sl, std_sl)\n",
- "print '\\t[min, max] : [{0:.2f}, {1:.2f}]'.format(min_sl, max_sl)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- " 3.3 Generate plots from the extracted morphometrics
\n",
- "\n",
- "The distribution of the extracted measurements can be plotted with [`matplotlib`](http://matplotlib.org/), which is a Python library for plot generation. We shall be making use of the [`matplotlib.pyplot`](http://matplotlib.org/api/pyplot_api.html) sub module, together with NeuroM's own matplotlib-based visualization wrappers.\n",
- "\n",
- "First we will create two functions, histogram and boxplot, which can be found in the examples folder"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 11,
- "metadata": {
- "collapsed": false
- },
- "outputs": [],
- "source": [
- "from neurom.view import common\n",
- "\n",
- "def histogram(neuron, feature_values, bins=25, normed=False, cumulative=False, new_fig=True, subplot=False, **kwargs):\n",
- "\n",
- " fig, ax = common.get_figure(new_fig=new_fig, subplot=subplot)\n",
- "\n",
- " # generate histogram\n",
- " ax.hist(feature_values, bins=bins, cumulative=cumulative, normed=normed)\n",
- "\n",
- " return common.plot_style(fig=fig, ax=ax, **kwargs)\n",
- "\n",
- "def boxplot(neuron, feature_values, new_fig=True, subplot=False, **kwargs):\n",
- "\n",
- " # create figure or use the existing one\n",
- " fig, ax = common.get_figure(new_fig=new_fig, subplot=subplot)\n",
- "\n",
- " # boxplot function\n",
- " ax.boxplot(feature_values)\n",
- " \n",
- " return common.plot_style(fig=fig, ax=ax, **kwargs)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": []
- },
- {
- "cell_type": "code",
- "execution_count": 12,
- "metadata": {
- "collapsed": false
- },
- "outputs": [
- {
- "data": {
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MDSlFmLVsAWFx4oLs6tsayukCagAjSZKkYuwLpM97NRuYTxiU/yq7d/U9A/gRYXr7Z5mY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\nKKtWrWLbtm27pTvORfVk69a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- "text/plain": [
- ""
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "from matplotlib import pyplot as plt\n",
- "\n",
- "# select the feature of choice\n",
- "feature = nm.get('segment_lengths', neuron)\n",
- "\n",
- "# create empty figure\n",
- "plt.figure(figsize=(11,3))\n",
- "\n",
- "# kwargs dictionary is used by plot_style to propagate the options\n",
- "# in the figure. These kwargs can also be passed directly in kwargs)\n",
- "xlabel = ''\n",
- "ylabel = ''\n",
- "title = ''\n",
- "\n",
- "# figure is automatically used from the plot function if and only if new_fig=False\n",
- "# otherwise a new figure will be created\n",
- "histogram(neuron, feature, final=False, normed=True, new_fig=False, subplot=131, xlabel=xlabel, ylabel=ylabel, title=title)\n",
- "\n",
- "# toggle cumulative=True for cumulative histogram\n",
- "histogram(neuron, feature, final=False, cumulative=True, normed=True, new_fig=False, subplot=132, xlabel=xlabel, ylabel=ylabel, title=title)\n",
- "\n",
- "# create boxplot\n",
- "ax, fig = boxplot(neuron, feature, final=False, new_fig=False, subplot=133, xlabel=xlabel, ylabel=ylabel, title=title)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "3.4 Fit the extracted data with a statistical distribution
\n",
- "\n",
- "Now we are ready to fit the extracted data using common Python functions. For this, we will use [scipy](http://www.scipy.org/), which is a package for numerical routines for scientific computing with Python."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 13,
- "metadata": {
- "collapsed": false
- },
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Fit output type : \n",
- "[mu, sigma] : [1.00, 0.31]\n",
- "\n",
- "Kolmogorov-Smirnof distance : 0.05\n",
- "P-value : 0.02\n"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "/usr/lib/python2.7/dist-packages/numpy/oldnumeric/__init__.py:11: ModuleDeprecationWarning: The oldnumeric module will be dropped in Numpy 1.9\n",
- " warnings.warn(_msg, ModuleDeprecationWarning)\n"
- ]
- }
- ],
- "source": [
- "from neurom import stats\n",
- "\n",
- "data = nm.get('segment_lengths', neuron)\n",
- "\n",
- "# Let’s start with a normal distribution. We will fit the data (here we use section_lengths) that we\n",
- "# computed above with a normal distribution\n",
- "p = stats.fit(data, distribution='norm')\n",
- "\n",
- "# The output of the function is a named tuple called FitResults\n",
- "print 'Fit output type : ', type(p)\n",
- "\n",
- "# the parameters are stored in the variable params which in the case of the normal distribution\n",
- "# stores the mu and sigma of the normal distribution\n",
- "mu, sigma = p.params\n",
- "ks_dist, pvalue = p.errs\n",
- "\n",
- "# Print the results \n",
- "print '[mu, sigma] : [{0:.2f}, {1:.2f}]\\n'.format(mu, sigma)\n",
- "\n",
- "# We need to check the statistical error of the performed fit to evaluate the accuracy of the \n",
- "# selected model. To do so we use the errors variable of FitResults, namely,\n",
- "print 'Kolmogorov-Smirnof distance : {0:.2f}'.format(ks_dist)\n",
- "print 'P-value : {0:.2f}'.format(pvalue)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Which can be visualized:"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 14,
- "metadata": {
- "collapsed": false,
- "scrolled": true
- },
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "[-0.54286824 -0.54186824 -0.54086824 ..., 2.54213176 2.54313176\n",
- " 2.54413176]\n"
- ]
- },
- {
- "data": {
- "text/plain": [
- "[]"
- ]
- },
- "execution_count": 14,
- "metadata": {},
- "output_type": "execute_result"
- },
- {
- "data": {
- "image/png": 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- "text/plain": [
- ""
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "from scipy.stats import norm\n",
- "\n",
- "data = nm.get('segment_lengths', neuron)\n",
- "\n",
- "# make a histogram as above\n",
- "fig, ax = histogram(neuron, data, final=False, new_fig=True, normed=True, xlabel='', ylabel='', title='')\n",
- "\n",
- "# normal range 5 standard deviations aroung its mean\n",
- "norm_range = np.arange(mu - 5.*sigma, mu + 5.*sigma, 0.001)\n",
- "print norm_range\n",
- "\n",
- "# plot the normal pdf with the given range, mu and sigma\n",
- "ax.plot(norm_range, norm.pdf(norm_range, mu, sigma), linewidth=3., c='r', alpha=0.8)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "It is also possible to find the optimal distribution that best fits the data, among a number of distributions that are\n",
- "supported by scipy. For instance,"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 15,
- "metadata": {
- "collapsed": false
- },
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "FitResults(params=(1.0008157314553809, 0.30873679377786722), errs=(0.053380962543575161, 0.016059728228706938), type='norm')\n"
- ]
- }
- ],
- "source": [
- "p = stats.optimal_distribution(data, distr_to_check=('lognorm', 'logistic', 'norm'))\n",
- "print p"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "3.5 Apply more advanced manipulation on extracted data
\n",
- "\n",
- "For example extract the section lengths above a selected threshold (>10)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 16,
- "metadata": {
- "collapsed": false
- },
- "outputs": [
- {
- "data": {
- "text/plain": [
- "array([ 10.26444194, 10.72637819, 10.45414656, 10.05466932,\n",
- " 10.72221858, 10.55440382, 10.09303133, 10.33071556,\n",
- " 10.37491982, 11.01846074, 10.71154672, 10.51683552,\n",
- " 10.1348335 , 10.1034446 , 10.90464832, 11.65250813,\n",
- " 10.20352358, 10.79778536, 10.25222844, 11.60598013,\n",
- " 10.13395757, 10.96762258, 10.89245052, 11.0190682 ,\n",
- " 10.25855549, 10.75631381, 10.38491293, 10.62047288,\n",
- " 11.05192629, 10.06943611, 10.10998146, 10.55534081,\n",
- " 10.58562592, 10.74722939, 10.73839347, 11.05092479,\n",
- " 11.02994892, 10.7541096 , 10.17670693, 10.49054247,\n",
- " 10.36496319, 10.92441795, 10.34721651, 11.75828156,\n",
- " 11.56005879, 10.38431278, 10.85399109])"
- ]
- },
- "execution_count": 16,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "# Get the ids of section lengths above the threshold\n",
- "selected_ids = np.where(section_lengths > 10)\n",
- "\n",
- "# Get the values of section lengths above the threshold\n",
- "section_lengths[selected_ids]"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "3.6 Combine morphometrics
\n",
- "\n",
- "We can study relations between different morphometrics. For example, we can combine section length and path length to soma,"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 17,
- "metadata": {
- "collapsed": false
- },
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "[ 9.57911737 9.64901212 10.26444194 9.18963499 9.28095558\n",
- " 10.72637819 9.58862945 10.45414656 8.92750196 10.05466932\n",
- " 9.96815205 10.72221858 10.55440382 9.11262954 10.09303133\n",
- " 7.97232242 8.73002814 10.71154672 10.51683552 9.63361814\n",
- " 10.1348335 10.1034446 10.90464832 11.65250813 10.20352358\n",
- " 9.54012263 9.53084499 10.79778536 8.22452877 9.59239376\n",
- " 11.0190682 10.25855549 10.75631381 10.38491293 10.62047288\n",
- " 11.05192629 10.06943611 10.10998146 10.55534081 10.58562592\n",
- " 10.74722939 8.23176374 9.8508199 9.21270799 11.05092479\n",
- " 11.02994892 10.7541096 10.17670693 9.36444805 10.49054247\n",
- " 9.52925566 9.49194374 10.36496319 8.42121218 10.92441795]\n"
- ]
- }
- ],
- "source": [
- "# Get the length of all sections with a radial distance between 0.0 and 60.0\n",
- "section_indices = np.where((section_radial_distances < 60.0) & (section_radial_distances >= 0.0))\n",
- "selected_section_lengths = section_lengths[section_indices]\n",
- "print selected_section_lengths"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "collapsed": true
- },
- "outputs": [],
- "source": []
- }
- ],
- "metadata": {
- "kernelspec": {
- "display_name": "Python 2",
- "language": "python",
- "name": "python2"
- },
- "language_info": {
- "codemirror_mode": {
- "name": "ipython",
- "version": 2
- },
- "file_extension": ".py",
- "mimetype": "text/x-python",
- "name": "python",
- "nbconvert_exporter": "python",
- "pygments_lexer": "ipython2",
- "version": "2.7.6"
- }
- },
- "nbformat": 4,
- "nbformat_minor": 0
-}
diff --git a/package.json b/package.json
new file mode 100644
index 000000000..3e8fab113
--- /dev/null
+++ b/package.json
@@ -0,0 +1,16 @@
+{
+ "name": "NeuroM",
+ "description": "NeuroM is a Python toolkit for the analysis and processing of neuron morphologies.",
+ "version": "1.4.2",
+ "scripts": {
+ "build_doc": "echo 'neurom.readthedocs.io/en/latest'"
+ },
+ "repository": {
+ "type": "git",
+ "url": "https://github.com/BlueBrain/NeuroM/releases",
+ "issuesurl": "https://github.com/BlueBrain/NeuroM/issues"
+ },
+ "author": "Michael Gevaert, Lida Kanari, Juan Palacios, Eleftherios Zisis",
+ "contributors": ["Oren Amsalem", "Jean-Denis Courcol", "Michael Gevaert", "Lida Kanari", "Juan Palacios", "Arseny Povolotsky", "Liesbeth Vanherpe", "Eleftherios Zisis"],
+ "license": "BSD 3-Clause"
+}
diff --git a/tutorial/neurom_tutorial.ipynb b/tutorial/neurom_tutorial.ipynb
new file mode 100644
index 000000000..974473861
--- /dev/null
+++ b/tutorial/neurom_tutorial.ipynb
@@ -0,0 +1,677 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# NeuroM Tutorial Notebook\n",
+ "\n",
+ "NeuroM contains helper functions that allow to easily load neuronal morphologies from files into NeuroM data structures. It also provides convenient methods to query various properties of the morphologies, as well as an easy way to visualize morphological objects."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {
+ "collapsed": true
+ },
+ "outputs": [],
+ "source": [
+ "%matplotlib inline\n",
+ "\n",
+ "# Import neurom module\n",
+ "import neurom as nm\n",
+ "# Import neurom visualization module\n",
+ "from neurom import viewer"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## 1. Loading a morphology or a population\n",
+ "\n",
+ "NeuroM can load morphologies from swc, h5 or NL ascii files. Please note that the Neurolucida ascii reader is experimental! There are no guarantees regarding correctness of loading data from files in this format."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {
+ "collapsed": true
+ },
+ "outputs": [],
+ "source": [
+ "# Load a single morphology \n",
+ "neuron = nm.load_neuron('../test_data/valid_set/Neuron.swc')\n",
+ "\n",
+ "# Load a population of morphologies from a set of files\n",
+ "pop = nm.load_neurons('../test_data/valid_set/')\n",
+ "\n",
+ "# Get a single morphology from the population\n",
+ "single_neuron = pop.neurons[0]"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## 2. Morphology visualization"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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47TaYOdM2zBVm5eqU4+L3L6ZG2xpkpGcgqcLo20cz+YXJPooJiCeXBLB6Ndx1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\nAElFTkSuQmCC\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# Visualize a morphology in two dimensions\n",
+ "fig, ax = viewer.draw(neuron)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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XBXy6zvXvHSbC6ER+ZmpqikatyUzeeC1uPPdtt9vXve4Y2GCkDQexWEyjjqHg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\nRK5CYII=\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# Visualize a morphology in three dimensions\n",
+ "fig, ax = viewer.draw(neuron, mode='3d')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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nZ01zTaL9io1IEJLv39TUhIaGBlAUhb6+PnR2dmJoaAh2u50VESYnJ3HmmWei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\nAAAASUVORK5CYII=\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# Visualize a single tree in three dimensions\n",
+ "fig, ax = viewer.draw(neuron.neurites[0], mode='3d')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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/WIPlwKxKVQsWLVqEUootMXqLTZ48mYKCglp9BsBzzz3HNZExMFHo2bMnudF+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+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# Visualize the dendrogram of a morphology\n",
+ "fig, ax = viewer.draw(neuron, mode='dendrogram')"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## 3. Morphology analysis\n",
+ "\n",
+ "### 3.1 Morphometrics extraction"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "metadata": {
+ "scrolled": true
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Neuron id : Neuron \n",
+ "Number of neurites : 4 \n",
+ "Soma radius : 0.13 \n",
+ "Number of sections : 84\n",
+ "\n",
+ "Neurite type \t\t\t| Number of sections\n",
+ "NeuriteType.axon | 21\n",
+ "NeuriteType.basal_dendrite | 21\n",
+ "NeuriteType.basal_dendrite | 21\n",
+ "NeuriteType.apical_dendrite | 21\n"
+ ]
+ }
+ ],
+ "source": [
+ "# Extract the total number of neurites (basal and apical dendrites, and axons)\n",
+ "number_of_neurites = nm.get('number_of_neurites', neuron)\n",
+ "\n",
+ "# Extract the total number of sections\n",
+ "number_of_sections = nm.get('number_of_sections', neuron)\n",
+ "\n",
+ "# Extract the soma radius\n",
+ "soma_radius = neuron.soma.radius\n",
+ "\n",
+ "# Extract the number of sections per neurite\n",
+ "number_of_sections_per_neurite = nm.get('number_of_sections_per_neurite', neuron)\n",
+ "\n",
+ "# Print result\n",
+ "print \"Neuron id : {0} \\n\\\n",
+ "Number of neurites : {1} \\n\\\n",
+ "Soma radius : {2:.2f} \\n\\\n",
+ "Number of sections : {3}\".format(neuron.name, number_of_neurites[0], soma_radius, number_of_sections[0])\n",
+ "print\n",
+ "print \"Neurite type \\t\\t\\t| Number of sections\"\n",
+ "for i, neurite in enumerate(neuron.neurites): \n",
+ " print \"{0:31} | {1}\".format(str(neurite.type), number_of_sections_per_neurite[i])"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "metadata": {
+ "scrolled": true
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "|sg_len|sc_len|lc_bif_angles|rm_bif_angles|sc_path_dists|sc_rad_dists|\n",
+ "| 0.10 | 9.58 | 2.09 | 0.34 | 9.58 | 8.84 |\n",
+ "| 0.65 | 9.65 | 2.09 | 0.57 | 19.23 | 15.75 |\n",
+ "| 1.01 |10.26 | 2.09 | 0.59 | 19.84 | 16.74 |\n",
+ "| 1.06 | 9.19 | 2.09 | 0.49 | 29.03 | 23.23 |\n",
+ "| 1.15 | 9.28 | 2.09 | 0.16 | 29.12 | 23.07 |\n",
+ "| 0.94 |10.73 | 2.09 | 0.52 | 39.85 | 30.58 |\n",
+ "| 1.30 | 9.59 | 2.09 | 0.76 | 38.71 | 30.18 |\n",
+ "| 1.09 |10.45 | 2.09 | 0.47 | 49.17 | 37.80 |\n",
+ "| 1.18 | 8.93 | 2.09 | 0.72 | 47.64 | 36.63 |\n",
+ "| 1.09 |10.05 | 2.09 | 0.34 | 57.70 | 44.10 |\n",
+ "| 1.41 | 9.97 | 2.09 | 0.58 | 57.61 | 43.97 |\n",
+ "| 0.93 |10.72 | 2.09 | 0.26 | 68.33 | 51.29 |\n",
+ "| 0.80 |10.55 | 2.09 | 0.12 | 68.16 | 51.92 |\n",
+ "| 1.12 | 9.11 | 2.09 | 0.21 | 77.28 | 57.79 |\n",
+ "| 1.38 |10.09 | 2.09 | 0.51 | 78.26 | 59.43 |\n",
+ "| 0.81 |10.33 | 2.09 | 0.36 | 88.59 | 66.61 |\n",
+ "| 0.49 | 9.18 | 2.09 | 0.24 | 87.43 | 66.25 |\n",
+ "| 1.13 | 8.86 | 2.09 | 0.26 | 96.29 | 71.36 |\n",
+ "| 0.72 |10.37 | 2.09 | 0.41 | 97.81 | 74.05 |\n",
+ "| 0.85 | 9.95 | 2.09 | 0.19 | 107.76 | 80.70 |\n",
+ "| 1.41 |11.02 | 2.09 | 0.25 | 108.83 | 82.44 |\n",
+ "| 0.54 | 7.97 | 2.09 | 0.28 | 7.97 | 7.31 |\n",
+ "| 1.33 | 8.73 | 2.09 | 0.20 | 16.70 | 14.54 |\n",
+ "| 1.25 |10.71 | 2.09 | 0.37 | 18.68 | 16.26 |\n",
+ "| 0.82 |10.52 | 2.09 | 0.14 | 29.20 | 25.07 |\n",
+ "| 1.23 | 9.63 | 2.09 | 0.06 | 28.32 | 23.94 |\n",
+ "| 0.32 |10.13 | 2.09 | 0.46 | 38.45 | 32.50 |\n",
+ "| 1.53 |10.10 | 2.09 | 0.15 | 38.42 | 32.21 |\n",
+ "| 1.40 |10.90 | 2.09 | 0.06 | 49.33 | 41.32 |\n",
+ "| 0.44 |11.65 | 2.09 | 0.36 | 50.07 | 42.47 |\n",
+ "| 1.41 |10.20 | 2.09 | 0.16 | 60.28 | 50.59 |\n",
+ "| 0.97 | 9.54 | 2.09 | 0.37 | 59.61 | 50.36 |\n",
+ "| 1.18 | 9.53 | 2.09 | 0.23 | 69.14 | 58.20 |\n",
+ "| 0.53 |10.80 | 2.09 | 0.21 | 70.41 | 59.19 |\n",
+ "| 1.25 |10.25 | 2.09 | 0.08 | 80.66 | 67.45 |\n",
+ "| 0.85 |11.61 | 2.09 | 0.17 | 82.02 | 69.32 |\n",
+ "| 0.74 | 8.93 | 2.09 | 0.25 | 90.95 | 76.34 |\n",
+ "| 0.88 | 8.23 | 2.09 | 0.20 | 90.25 | 76.26 |\n",
+ "| 0.40 | 9.67 | 2.09 | 0.16 | 99.92 | 83.73 |\n",
+ "| 0.97 |10.13 | 2.09 | 0.18 | 100.38 | 84.86 |\n",
+ "| 1.41 |10.97 | | | 111.35 | 94.04 |\n",
+ "| 0.94 |10.89 | | | 111.28 | 94.43 |\n",
+ "| 0.97 | 8.22 | | | 8.22 | 7.46 |\n",
+ "| 0.95 | 9.59 | | | 17.82 | 15.43 |\n",
+ "| 0.69 |11.02 | | | 19.24 | 15.37 |\n",
+ "| 0.76 |10.26 | | | 29.50 | 24.71 |\n",
+ "| 0.83 |10.76 | | | 30.00 | 22.84 |\n",
+ "| 0.97 |10.38 | | | 40.38 | 31.87 |\n",
+ "| 0.78 |10.62 | | | 40.62 | 30.50 |\n",
+ "| 0.96 |11.05 | | | 51.67 | 40.23 |\n"
+ ]
+ }
+ ],
+ "source": [
+ "# Extract the lengths of the sections\n",
+ "section_lengths = nm.get('section_lengths', neuron)\n",
+ "\n",
+ "# Extract the lengths of the segments\n",
+ "segment_lengths = nm.get('segment_lengths', neuron)\n",
+ "\n",
+ "# Extract the local bifurcation angles\n",
+ "local_bif_angles = nm.get('local_bifurcation_angles', neuron)\n",
+ "\n",
+ "# Extract the remote bifurcation angles\n",
+ "remote_bif_angles = nm.get('remote_bifurcation_angles', neuron)\n",
+ "\n",
+ "# Extract the radial distances of the sections\n",
+ "section_radial_distances = nm.get('section_radial_distances', neuron)\n",
+ "\n",
+ "# Extract the path distances of the sections\n",
+ "section_path_distances = nm.get('section_path_distances', neuron)\n",
+ "\n",
+ "# Print result\n",
+ "features = (segment_lengths, section_lengths, local_bif_angles, \n",
+ " remote_bif_angles, section_path_distances, section_radial_distances)\n",
+ "\n",
+ "def check(feature_list, n): \n",
+ " return '{0:.2f}'.format(feature_list[n]) if n < len(feature_list) else ''\n",
+ "\n",
+ "print '|sg_len|sc_len|lc_bif_angles|rm_bif_angles|sc_path_dists|sc_rad_dists|'\n",
+ "for n in range(0, 50):\n",
+ " args = (check(f, n) for f in features)\n",
+ " print '|{0:^6}|{1:^6}|{2:^13}|{3:^13}|{4:^13}|{5:^12}|'.format(*args)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "collapsed": true
+ },
+ "source": [
+ "### 3.2 Analyze different types of trees\n",
+ "\n",
+ "The previous examples treated all neurites in the same way. NeuroM allows you to extract morphometrics for a selected type of trees."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\n",
+ "Axonal section lengths = [ 9.57911737 9.64901212 10.26444194 9.18963499 9.28095558\n",
+ " 10.72637819 9.58862945 10.45414656 8.92750196 10.05466932\n",
+ " 9.96815205 10.72221858 10.55440382 9.11262954 10.09303133\n",
+ " 10.33071556 9.17709438 8.86068767 10.37491982 9.95295124\n",
+ " 11.01846074]\n",
+ "\n",
+ "Basal section lengths = [ 7.97232242 8.73002814 10.71154672 10.51683552 9.63361814\n",
+ " 10.1348335 10.1034446 10.90464832 11.65250813 10.20352358\n",
+ " 9.54012263 9.53084499 10.79778536 10.25222844 11.60598013\n",
+ " 8.92943746 8.23366666 9.66996901 10.13395757 10.96762258\n",
+ " 10.89245052 8.22452877 9.59239376 11.0190682 10.25855549\n",
+ " 10.75631381 10.38491293 10.62047288 11.05192629 10.06943611\n",
+ " 10.10998146 10.55534081 10.58562592 10.74722939 8.23176374\n",
+ " 9.8508199 8.93049233 10.73839347 9.48292967 8.58137852\n",
+ " 9.0358861 8.48759244]\n",
+ "\n",
+ "Apical section lengths = [ 9.21270799 11.05092479 11.02994892 10.7541096 10.17670693\n",
+ " 9.36444805 10.49054247 9.52925566 9.49194374 10.36496319\n",
+ " 8.42121218 10.92441795 10.34721651 8.99513591 11.75828156\n",
+ " 11.56005879 10.38431278 9.97008743 10.85399109 9.87885774\n",
+ " 9.81392252]\n"
+ ]
+ }
+ ],
+ "source": [
+ "# Extract the section lengths of axonal trees\n",
+ "ax_section_lengths = nm.get('section_lengths', neuron, neurite_type=nm.AXON)\n",
+ "\n",
+ "# Extract the section lengths of basal dendrite trees\n",
+ "ba_section_lengths = nm.get('section_lengths', neuron, neurite_type=nm.BASAL_DENDRITE)\n",
+ "\n",
+ "# Extract the section lengths of apical dendrite trees\n",
+ "ap_section_lengths = nm.get('section_lengths', neuron, neurite_type=nm.APICAL_DENDRITE)\n",
+ "\n",
+ "print '\\nAxonal section lengths = ', ax_section_lengths\n",
+ "print '\\nBasal section lengths = ', ba_section_lengths\n",
+ "print '\\nApical section lengths = ', ap_section_lengths"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### 3.3 Perform statistical analysis on extracted measurements\n",
+ "\n",
+ "Now we are ready to extract basic statistical measurements, using common Python functions. For this, we will use [`numpy`](http://www.numpy.org/), which is a package for scientific computing with Python."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 10,
+ "metadata": {
+ "scrolled": true
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Section length statistics:\n",
+ " mean = 10.01 +- 0.86\n",
+ " [min, max]: [7.97, 11.76]\n"
+ ]
+ }
+ ],
+ "source": [
+ "import numpy as np\n",
+ "\n",
+ "# We can get the mean section length\n",
+ "mean_sl = np.mean(section_lengths)\n",
+ "\n",
+ "# We can get the standard deviation of the section lengths\n",
+ "std_sl = np.std(section_lengths)\n",
+ "\n",
+ "# We can get the minimum section length\n",
+ "min_sl = np.min(section_lengths)\n",
+ "\n",
+ "# ... and the maximum section length\n",
+ "max_sl = np.max(section_lengths)\n",
+ "\n",
+ "print 'Section length statistics:'\n",
+ "print ' mean = {0:.2f} +- {1:.2f}'.format(mean_sl, std_sl)\n",
+ "print ' [min, max]: [{0:.2f}, {1:.2f}]'.format(min_sl, max_sl)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### 3.4 Generate plots from the extracted morphometrics\n",
+ "\n",
+ "The distribution of the extracted measurements can be plotted with [`matplotlib`](http://matplotlib.org/), which is a Python library for plot generation. We will use the [`matplotlib.pyplot`](http://matplotlib.org/api/pyplot_api.html) sub module."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 11,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "import matplotlib.pyplot as plt\n",
+ "\n",
+ "# Select the feature of choice\n",
+ "feature = nm.get('segment_lengths', neuron)\n",
+ "\n",
+ "# Create empty figure\n",
+ "fig = plt.figure(figsize=(11,3))\n",
+ "\n",
+ "# Create histogram\n",
+ "ax = fig.add_subplot('131')\n",
+ "ax.hist(feature, bins=25, edgecolor='black')\n",
+ "\n",
+ "# Create cumulative histogram\n",
+ "ax = fig.add_subplot('132')\n",
+ "ax.hist(feature, bins=25, cumulative=True, edgecolor='black')\n",
+ "\n",
+ "# Create boxplot; flier points are indicated with green dots\n",
+ "ax = fig.add_subplot('133')\n",
+ "_ = ax.boxplot(feature, sym='g.')"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### 3.5 Fit the extracted data with a statistical distribution\n",
+ "\n",
+ "Now we are ready to fit the extracted data using common Python functions. For this, we will use [`scipy`](http://www.scipy.org/), which is a package for numerical routines for scientific computing with Python."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 12,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Fit output type : \n",
+ "[mu, sigma] : [1.00, 0.31]\n",
+ "\n",
+ "Kolmogorov-Smirnov distance : 0.05\n",
+ "P-value : 0.02\n"
+ ]
+ }
+ ],
+ "source": [
+ "from neurom import stats\n",
+ "\n",
+ "data = nm.get('segment_lengths', neuron)\n",
+ "\n",
+ "# Let’s start with a normal distribution. We will fit the data that we extracted above with a normal distribution\n",
+ "p = stats.fit(data, distribution='norm')\n",
+ "\n",
+ "# The output of the function is a named tuple of type FitResults\n",
+ "print 'Fit output type : ', type(p)\n",
+ "\n",
+ "# The parameters are stored in the variable params, which in the case of the normal distribution stores the mu and sigma\n",
+ "# of the normal distribution\n",
+ "mu, sigma = p.params\n",
+ "ks_dist, pvalue = p.errs\n",
+ "\n",
+ "# Print result\n",
+ "print '[mu, sigma] : [{0:.2f}, {1:.2f}]\\n'.format(mu, sigma)\n",
+ "\n",
+ "# We need to check the statistical error of the performed fit to evaluate the accuracy of the \n",
+ "# selected model. To do so we use the errors variable of FitResults:\n",
+ "print 'Kolmogorov-Smirnov distance : {0:.2f}'.format(ks_dist)\n",
+ "print 'P-value : {0:.2f}'.format(pvalue)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "The result of the fitting can be visualized:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 13,
+ "metadata": {
+ "scrolled": true
+ },
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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+yV5ZOkRDfV4ccGyiMFtDW0n6KfFLdvr006D/PgTt+mPGRBtPGr3a9yjK8loFheXLNWKn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\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "from scipy.stats import norm\n",
+ "\n",
+ "# Create a histogram as above\n",
+ "fig = plt.figure()\n",
+ "plt.hist(data, bins=25, normed=True, edgecolor='black')\n",
+ "\n",
+ "# Plot range: 5 standard deviations around the mean\n",
+ "norm_range = np.arange(mu - 5.*sigma, mu + 5.*sigma, 0.001)\n",
+ "\n",
+ "# Plot the normal pdf with the given range, mu and sigma\n",
+ "_ = plt.plot(norm_range, norm.pdf(norm_range, mu, sigma), linewidth=3., c='r', alpha=0.8)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "It is also possible to find the optimal distribution that best fits the data, among a number of distributions that are\n",
+ "supported by `scipy`:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 14,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Fit results: FitResults(params=(1.0008157314553803, 0.30873679377786745), errs=KstestResult(statistic=0.053380962543574939, pvalue=0.016059728228707604), type='norm')\n"
+ ]
+ }
+ ],
+ "source": [
+ "p = stats.optimal_distribution(data, distr_to_check=('lognorm', 'logistic', 'norm'))\n",
+ "print 'Fit results:', p"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### 3.6 Apply more advanced manipulation on extracted data\n",
+ "\n",
+ "In this example, we extract all section lengths that exceed a selected threshold."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 15,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "array([ 10.26444194, 10.72637819, 10.45414656, 10.05466932,\n",
+ " 10.72221858, 10.55440382, 10.09303133, 10.33071556,\n",
+ " 10.37491982, 11.01846074, 10.71154672, 10.51683552,\n",
+ " 10.1348335 , 10.1034446 , 10.90464832, 11.65250813,\n",
+ " 10.20352358, 10.79778536, 10.25222844, 11.60598013,\n",
+ " 10.13395757, 10.96762258, 10.89245052, 11.0190682 ,\n",
+ " 10.25855549, 10.75631381, 10.38491293, 10.62047288,\n",
+ " 11.05192629, 10.06943611, 10.10998146, 10.55534081,\n",
+ " 10.58562592, 10.74722939, 10.73839347, 11.05092479,\n",
+ " 11.02994892, 10.7541096 , 10.17670693, 10.49054247,\n",
+ " 10.36496319, 10.92441795, 10.34721651, 11.75828156,\n",
+ " 11.56005879, 10.38431278, 10.85399109])"
+ ]
+ },
+ "execution_count": 15,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# Threshold value\n",
+ "threshold = 10\n",
+ "\n",
+ "# Get the ids of sections which length exceeds the threshold\n",
+ "selected_ids = np.where(section_lengths > threshold)\n",
+ "\n",
+ "# Get the values of section lengths that exceed the threshold\n",
+ "section_lengths[selected_ids]"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### 3.7 Combine morphometrics\n",
+ "\n",
+ "We can study relations between different morphometrics. For example, we can combine section length and path length to soma:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 16,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "[ 9.57911737 9.64901212 10.26444194 9.18963499 9.28095558\n",
+ " 10.72637819 9.58862945 10.45414656 8.92750196 10.05466932\n",
+ " 9.96815205 10.72221858 10.55440382 9.11262954 10.09303133\n",
+ " 7.97232242 8.73002814 10.71154672 10.51683552 9.63361814\n",
+ " 10.1348335 10.1034446 10.90464832 11.65250813 10.20352358\n",
+ " 9.54012263 9.53084499 10.79778536 8.22452877 9.59239376\n",
+ " 11.0190682 10.25855549 10.75631381 10.38491293 10.62047288\n",
+ " 11.05192629 10.06943611 10.10998146 10.55534081 10.58562592\n",
+ " 10.74722939 8.23176374 9.8508199 9.21270799 11.05092479\n",
+ " 11.02994892 10.7541096 10.17670693 9.36444805 10.49054247\n",
+ " 9.52925566 9.49194374 10.36496319 8.42121218 10.92441795]\n"
+ ]
+ }
+ ],
+ "source": [
+ "# Get the length of all sections with a radial distance between 0.0 and 60.0\n",
+ "section_indices = np.where((section_radial_distances >= 0.0) & (section_radial_distances < 60.0))\n",
+ "selected_section_lengths = section_lengths[section_indices]\n",
+ "print selected_section_lengths"
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 2",
+ "language": "python",
+ "name": "python2"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 2
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython2",
+ "version": "2.7.6"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 1
+}
diff --git a/tutorial/neurom_tutorial.rst b/tutorial/neurom_tutorial.rst
new file mode 100644
index 000000000..e780b96c1
--- /dev/null
+++ b/tutorial/neurom_tutorial.rst
@@ -0,0 +1,135 @@
+===============
+NeuroM Tutorial
+===============
+
+1. Installation instructions
+============================
+
+`NeuroM `__ is a
+Python-based toolkit for the analysis and processing of neuron
+morphologies. It is supported for Linux and OS X. Windows users are
+advised to use `VirtualBox `__. More
+detailed installation instructions can be found
+`here `__.
+
+1.1 Requirements
+----------------
+
+It is assumed that the following packages are installed on your system:
+
+- Python >= 2.7
+- pip >= 8.1.0
+- ipython
+- virtualenv
+
+These are available as packages in most Linux distributions. For OS X,
+please refer to `MacPorts `__ if you don't
+already use a different package manager.
+
+The following Python packages will be installed automatically with
+NeuroM if not pre-installed in your system:
+
+- numpy >= 1.8.0
+- scipy >= 0.17.0
+- h5py >= 2.2.1 (optional)
+- matplotlib >= 1.3.1
+- pyyaml >= 3.10
+- enum34 >= 1.0.4
+- tqdm >= 4.8.4
+- future >= 0.16.0
+- pylru >= 1.0
+
+1.2 Virtual environment set-up
+------------------------------
+
+It is recommended that you install NeuroM into a virtual environment.
+
+::
+
+ $ virtualenv nrm # creates a virtualenv called "nrm" in nrm directory
+ $ source nrm/bin/activate # activates virtualenv
+ (nrm)$ # now we are in the nrm virtualenv
+
+The prompt indicates that the virtual environment has been activated. To
+de-activate it:
+
+::
+
+ (nrm)$ deactivate
+
+
+1.3 Installation from source
+----------------------------
+
+Clone the NeuroM repository and install it:
+
+::
+
+ (nrm)$ git clone https://github.com/BlueBrain/NeuroM.git
+ (nrm)$ pip install --upgrade pip # install newest pip inside virtualenv if version too old
+ (nrm)$ pip install -e ./NeuroM # the -e flag makes source changes immediately effective
+
+2. Applications using NeuroM
+============================
+
+NeuroM ships with configurable command line applications for commonly
+needed functionality.
+
+2.1 morph_check: check the validity of a morphology file
+--------------------------------------------------------
+
+The application
+`morph_check `__
+allows you to apply semantic checks to a morphology file before loading
+it into NeuroM:
+
+::
+
+ (nrm)$ morph_check -h # shows help for morphology checking script
+
+Try it yourself! You can go to
+`NeuroMorpho.Org `__ to download a neuronal
+morphology and perform the semantic checks:
+
+::
+
+ (nrm)$ morph_check path/to/files/filename
+
+2.2 morph_stats: extract basic morphometrics of a sample morphology
+-------------------------------------------------------------------
+
+The application
+`morph_stats `__
+extracts various morphometrics for one or many morphologies. Its
+contents can be easily configured via a configuration file, as shown in
+the `online
+documentation `__.
+
+::
+
+ (nrm)$ morph_stats -h # shows help for the morphometrics extraction script
+ (nrm)$ morph_stats path/to/files/filename # analyze single morphology file
+ (nrm)$ morph_stats path/to/files # analyze many morphology files
+
+3. The NeuroM Tutorial Notebook
+===============================
+
+In the NeuroM repository, you will find a folder ``tutorial``, which
+contains a tutorial notebook on NeuroM. For a detailed explanation on
+installing and running Jupyter/IPython notebooks, we refer to `the
+Jupyter/IPython Notebook Quick Start
+Guide `__.
+
+First, install ``jupyter`` in the virtual environment. Second, launch
+the Jupyter Notebook App. Make sure that you launch the application from
+a folder that contains the NeuroM Tutorial notebook.
+
+::
+
+ (nrm)$ pip install jupyter
+ (nrm)$ cd /path/to/dir/containing/notebook
+ (nrm)$ jupyter notebook # launch the Jupyter Notebook App
+
+Next, you can select the notebook that you want to open. Now, you can go
+through the tutorial and learn about loading, viewing, and analyzing
+neuronal morphologies!