{ "cells": [ { "cell_type": "raw", "metadata": { "raw_mimetype": "text/restructuredtext" }, "source": [ "Plotting\n", "********\n", "\n", "Pymaid contains functions for 2D and 3D plotting of neurons, synapses and networks. These functions represent wrappers for `matplotlib `_ for 2D, `Vispy `_ and `Plotly `_ for 3D.\n", "\n", ".. note::\n", " If you are experiencing issues when using vispy\n", " as backend, you should try installing the dev\n", " version (currently 0.6.0dev0) directly from\n", " `Github `_.\n", " The version installed from PIP is 0.5.2.\n", " \n", "Plotting Neurons\n", "================\n", "\n", "Neuron objects, :class:`~pymaid.CatmaidNeuron` and :class:`~pymaid.CatmaidNeuronList`, as well as nblast results, :class:`~pymaid.rmaid.NBLASTresults`, have built-in methods that call :func:`~pymaid.plot3d` or :func:`~pymaid.plot2d`.\n", "\n", "2D Plotting\n", "-----------\n", "This uses matplotlib to generate 2D plots. The big advantage is that you can save these plots as vector graphics. Unfortunately, matplotlib's capabilities regarding 3D data are limited. The main problem is that depth (z) is at best simulated by trying to layer objects according to their z-order rather than doing proper rendering. You have several options to deal with this: see `method` parameter in :func:`pymaid.plot2d`. It is important to be aware of this issue as e.g. neuron A might be plotted in front of neuron B even though it is actually spatially behind it. The more busy your plot and the more neurons intertwine, the more likely this is to happen." ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import pymaid\n", "import matplotlib.pyplot as plt\n", "\n", "# Connect to CATMAID\n", "rm = pymaid.CatmaidInstance('https://www.your.catmaid-server.org',\n", " 'HTTP_USER' ,\n", " 'HTTP_PASSWORD',\n", " 'TOKEN')\n", "\n", "# Get two example neurons by their skeleton ID\n", "nl = pymaid.get_neurons(['57311', '27295'])\n", "\n", "# Plot using default settings\n", "fig, ax = nl.plot2d()\n", "plt.show()" ] }, { "cell_type": "raw", "metadata": { "raw_mimetype": "text/restructuredtext" }, "source": [ "Above plot used the default matplotlib 2D plot. This is very fast and convenient to get an impression of a neuron's morphology.\n", "\n", "Matplotlib has some basic 3D plotting capabilities that are perfectly sufficient for simple visualizations but are still limited when it comes to perspectively correct z-ordering. We can switch from plain 2D to this \"2.5D\" by changing the ``method`` parameter:" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Plot using matplotlib's 3D capabilities\n", "fig, ax = nl.plot2d(method='3d_complex')\n", "# Change from default frontal view to lateral view\n", "ax.azim = 0\n", "# Zoom in a bit\n", "ax.dist = 6\n", "plt.show()" ] }, { "cell_type": "raw", "metadata": { "raw_mimetype": "text/restructuredtext" }, "source": [ "Noticed how we changed the perspective by adjusting the azimuth (``.azim``)? You can also change the elevation (``.elev``) to get a top view. \n", "\n", "This can even be used to generate small animations:" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Render 3D rotation\n", "for i in range(0, 360, 10):\n", " # Change rotation\n", " ax.azim = i\n", " # Save each incremental rotation as frame\n", " plt.savefig('frame_{0}.png'.format(i), dpi=200)" ] }, { "cell_type": "raw", "metadata": { "raw_mimetype": "text/restructuredtext" }, "source": [ "Plotting volumes\n", "+++++++++++++++" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Retrieve volume\n", "lh = pymaid.get_volume('LH_R')\n", "# Set color and alpha\n", "lh.color = (0, 1, 0, .1)\n", "# Plot\n", "fig, ax = pymaid.plot2d([nl ,lh], method='3d_complex')\n", "ax.dist = 6\n", "plt.show()" ] }, { "cell_type": "raw", "metadata": { "raw_mimetype": "text/restructuredtext" }, "source": [ "Interactive 3D Plotting\n", "-----------------------\n", "For 3D plots, we are using either Vispy (default) or Plotly to render neurons and volumes. \n", "\n", ".. important:: \n", " In general, you want to use Vispy if you are using the terminal or an IDE, and Plotly \n", " if you are working with Jupyter notebooks. Please note that Vispy currently simply does\n", " NOT work within Jupyter notebooks.\n", " \n", "By default, pymaid will detect whether you are working in a Jupyter notebook or not, and choose the correct backend automatically: Vispy for terminal, Plotly for Jupyter. For demonstration purposes, we will override this by using the ``backend`` parameter of :func:`~pymaid.plot3d`.\n", " \n", "Our first two example use Vispy:" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Plot using Vispy (will open 3D viewer)\n", "viewer = nl.plot3d(backend='vispy')\n", "# Save screenshot\n", "viewer.screenshot('screenshot.png', alpha=True)" ] }, { "cell_type": "raw", "metadata": { "raw_mimetype": "text/restructuredtext" }, "source": [ "The :class:`pymaid.Viewer` is persistent and survives simply closing the window. Calling :func:`~pymaid.plot3d` again will add objects to the canvas and open it again." ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Add another set of neurons to existing canvas\n", "nl2 = pymaid.get_neurons([987675, 543210])\n", "nl2.plot3d(backend='vispy')\n", "\n", "# To clear canvas either pass parameter when plotting...\n", "nl2.plot3d(clear3d=True)\n", "\n", "# ... or call function to clear\n", "pymaid.clear3d()\n", "\n", "# To wipe canvas from memory\n", "pymaid.close3d()" ] }, { "cell_type": "raw", "metadata": {}, "source": [ "If working with multiple viewers, you can specify which :class:`pymaid.Viewer` to add the neurons to." ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [], "source": [ "# Open 2 iewers\n", "v1 = pymaid.Viewer()\n", "v2 = pymaid.Viewer()\n", "\n", "# Add neurons to each one separately\n", "v1.add(nl)\n", "v2.add(nl2)\n", "\n", "# Clear one viewer\n", "v1.clear()\n", "\n", "# Close the second viewer\n", "v2.close()" ] }, { "cell_type": "raw", "metadata": {}, "source": [ "If you've lost track of your viewer, simply use :func:`~pymaid.get_viewer` to get it back:" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "v = pymaid.get_viewer()" ] }, { "cell_type": "raw", "metadata": { "raw_mimetype": "text/restructuredtext" }, "source": [ "Now let's have a look at Plotly as backend:" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Using plotly as backend generates \"inline\" plots by default (i.e. they are rendered right away)\n", "fig = nl.plot3d(backend='plotly', connectors=True, width=1000)" ] }, { "cell_type": "raw", "metadata": { "raw_mimetype": "text/restructuredtext" }, "source": [ ".. raw:: html\n", " :file: 3d_plot.html\n", "\n", "|\n", "|\n", "|\n", "|" ] }, { "cell_type": "raw", "metadata": { "raw_mimetype": "text/restructuredtext" }, "source": [ ".. note::\n", " Vispy itself uses either one of these backends:\n", " Qt, GLFW,SDL2, Wx, or Pyglet. By default, pymaid\n", " installs and sets PyQt5 as vispy's backend. If\n", " you need to change that use e.g. ``vispy.use(app='PyQt4')``\n", " \n", "Navigating the 3D viewer\n", "++++++++++++++++++++++++\n", "\n", "1. Rotating: Hold left mousebutton\n", "2. Zooming: Use the mousewheel or hold left+right mousebutton and drag\n", "3. Panning: Hold shift + left mousebutton\n", "4. Perspective: Hold shift + left and right mousbutton\n", "5. Hide/unhide: Click legend (Vispy only)\n", "\n", "Adding volumes\n", "++++++++++++++ \n", "\n", ":func:`~pymaid.plot3d` allows plotting of volumes (e.g. neuropil meshes). It's very straight forward to use meshes directly from you Catmaid Server:\n", "there is a custom class for Catmaid Volumes, :class:`pymaid.Volume` which has some neat methods - check out its reference." ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Provide colors\n", "vols = [pymaid.get_volume('LH_R', color=(255, 0, 0, .2)),\n", " pymaid.get_volume('LH_L', color=(0, 255, 0, .2))]\n", "fig = pymaid.plot3d([nl, *vols], backend='plotly', width=1000)" ] }, { "cell_type": "raw", "metadata": { "raw_mimetype": "text/restructuredtext" }, "source": [ ".. raw:: html\n", " :file: 3d_volumes.html\n", "\n", "|\n", "|\n", "|\n", "|" ] }, { "cell_type": "raw", "metadata": { "raw_mimetype": "text/restructuredtext" }, "source": [ "You can also pass your own custom volumes:" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": true }, "outputs": [], "source": [ "cust_vol = pymaid.Volume(vertices=[[1, 2, 1],\n", " [5, 6, 7],\n", " [8, 6, 4]],\n", " faces=[(0, 1, 2)],\n", " name='custom volume',\n", " color=(255, 0, 0))\n", "fig = pymaid.plot3d(cust_vol, backend='plotly', width=1000)" ] }, { "cell_type": "raw", "metadata": { "raw_mimetype": "text/restructuredtext" }, "source": [ ".. raw:: html\n", " :file: 3d_custom.html\n", " \n", "|\n", "|\n", "|\n", "| " ] }, { "cell_type": "raw", "metadata": { "raw_mimetype": "text/restructuredtext" }, "source": [ "Plotting Networks\n", "=================\n", "I highly recommend having a look at the excellent NetworkX library -> you can convert CATMAID connectivity to NetworkX Graphs using :func:`~pymaid.network2nx`.\n", "\n", "For quick-n-dirty plots, :func:`~pymaid.plot_network` is a wrapper to plot networks using plotly. It's rather slow for large-ish graphs though!" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": true, "scrolled": true }, "outputs": [], "source": [ "import plotly.offline as pyoff\n", "\n", "# Get some PNs\n", "pns = pymaid.find_neurons(annotations='glomerulus DA1')\n", "# Get their connectivity table\n", "partners = pymaid.get_partners(pns)\n", "# Get the 10 strongest downstream partners\n", "top_partners = partners[ partners.relation == 'downstream'].iloc[:10]\n", "\n", "all_skeleton_ids = list(pns.skeleton_id) + list(top_partners.skeleton_id)\n", "fig = pymaid.plot_network(all_skeleton_ids, \n", " label_nodes=False, \n", " label_hover=False,\n", " width=700,\n", " height=700, \n", " layout='circular_layout')\n", "poff.iplot(fig, filename='network_plot.html')" ] }, { "cell_type": "raw", "metadata": { "raw_mimetype": "text/restructuredtext" }, "source": [ ".. raw:: html\n", " :file: network_plot.html" ] }, { "cell_type": "raw", "metadata": { "raw_mimetype": "text/restructuredtext" }, "source": [ "Alternatively, you can generate adjacency matrices using :func:`~pymaid.adjacency_matrix` and use seaborn to plot neat connectivity heatmaps:" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "data": { "image/png": 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V9rh8/hopfemonMu1xnCynpPtabbvBt6r62oN4E7bb2Ypj5tJwXk1TgW+U+un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\ni0qlUqkMSO0sKpVKpTIg/wd9Q90Pke86twAAAABJRU5ErkJggg==\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import seaborn as sns\n", "\n", "adj_mat = pymaid.adjacency_matrix(pns, top_partners)\n", "\n", "hm = sns.heatmap(adj_mat, cbar_kws=dict(label='# of synapses'))\n", "\n", "plt.show()" ] }, { "cell_type": "raw", "metadata": { "raw_mimetype": "text/restructuredtext" }, "source": [ "Reference\n", "=========\n", "\n", ".. autosummary::\n", " :toctree: generated/\n", "\n", " ~pymaid.plot3d\n", " ~pymaid.plot2d\n", " ~pymaid.plot1d\n", " ~pymaid.plot_network\n", " ~pymaid.clear3d\n", " ~pymaid.close3d\n", " ~pymaid.get_viewer\n", " ~pymaid.screenshot\n", " ~pymaid.Volume\n", " ~pymaid.Viewer" ] } ], "metadata": { "celltoolbar": "Raw Cell Format", "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": "3.6.4" } }, "nbformat": 4, "nbformat_minor": 2 }