From a9179378e589ccd4612ba3fe3f043c0b38f1f9b7 Mon Sep 17 00:00:00 2001
From: Michiel Cottaar <MichielCottaar@gmail.com>
Date: Fri, 16 Feb 2018 11:47:35 +0000
Subject: [PATCH] Added packages/speed talks
---
talks/packages/packages.ipynb | 870 ++++++++++++++
talks/packages/packages.md | 579 +++++++++
talks/speed/speed.ipynb | 2060 +++++++++++++++++++++++++++++++++
3 files changed, 3509 insertions(+)
create mode 100644 talks/packages/packages.ipynb
create mode 100644 talks/packages/packages.md
create mode 100644 talks/speed/speed.ipynb
diff --git a/talks/packages/packages.ipynb b/talks/packages/packages.ipynb
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+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Main scientific python libraries\n",
+ "See https://scipy.org/\n",
+ "\n",
+ "Most of these packages have or are in thr progress of dropping support for python2.\n",
+ "So use python3!\n",
+ "\n",
+ "## [Numpy](http://www.numpy.org/): arrays\n",
+ "This is the main library underlying (nearly) all of the scientific python ecosystem.\n",
+ "See the tutorial in the beginner session or [the official numpy tutorial](https://docs.scipy.org/doc/numpy-dev/user/quickstart.html) for usage details.\n",
+ "\n",
+ "The usual nickname of numpy is np:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import numpy as np"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Numpy includes support for:\n",
+ "- N-dimensional arrays with various datatypes\n",
+ "- basic functions (e.g., polynomials)\n",
+ "- basic linear algebra\n",
+ "- random number generation\n",
+ "\n",
+ "## [Scipy](https://scipy.org/scipylib/index.html): most general scientific tools\n",
+ "At the top level this module includes all of the basic functionality from numpy.\n",
+ "You could import this as, but you might as well import numpy directly."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import scipy as sp"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "The main strength in scipy lies in its sub-packages:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from scipy import optimize\n",
+ "def costfunc(params):\n",
+ " return params[0] ** 2 * (params[1] - 3) ** 2 + (params[0] - 2) ** 2\n",
+ "optimize.minimize(costfunc, x0=[0, 0], method='l-bfgs-b')"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Tutorials for all sub-packages can be found [here](https://docs.scipy.org/doc/scipy-1.0.0/reference/).\n",
+ "\n",
+ "## [Matplotlib](https://matplotlib.org/): Main plotting library"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import matplotlib as mpl\n",
+ "mpl.use('nbagg')\n",
+ "import matplotlib.pyplot as plt"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "The matplotlib tutorials are [here](https://matplotlib.org/tutorials/index.html)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "x = np.linspace(0, 2, 100)\n",
+ "\n",
+ "plt.plot(x, x, label='linear')\n",
+ "plt.plot(x, x**2, label='quadratic')\n",
+ "plt.plot(x, x**3, label='cubic')\n",
+ "\n",
+ "plt.xlabel('x label')\n",
+ "plt.ylabel('y label')\n",
+ "\n",
+ "plt.title(\"Simple Plot\")\n",
+ "\n",
+ "plt.legend()\n",
+ "\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Alternatives:\n",
+ "- [Mayavi](http://docs.enthought.com/mayavi/mayavi/): 3D plotting (hard to install)\n",
+ "- [Bokeh](https://bokeh.pydata.org/en/latest/) among many others: interactive plots in the browser (i.e., in javascript)\n",
+ "\n",
+ "## [Ipython](http://ipython.org/)/[Jupyter](https://jupyter.org/) notebook: interactive python environments\n",
+ "There are many [useful extensions available](https://github.com/ipython-contrib/jupyter_contrib_nbextensions).\n",
+ "\n",
+ "## [Pandas](https://pandas.pydata.org/): Analyzing \"clean\" data\n",
+ "Once your data is in tabular form (e.g. Biobank IDP's), you want to use pandas dataframes to analyze them.\n",
+ "This brings most of the functionality of R into python.\n",
+ "Pandas has excellent support for:\n",
+ "- fast IO to many tabular formats\n",
+ "- accurate handling of missing data\n",
+ "- Many, many routines to handle data\n",
+ " - group by categorical data (i.e., male/female, or age groups)\n",
+ " - joining/merging data\n",
+ " - time series support\n",
+ "- statistical models through [statsmodels](http://www.statsmodels.org/stable/index.html)\n",
+ "- plotting though seaborn [seaborn](https://seaborn.pydata.org/)\n",
+ "- Use [dask](https://dask.pydata.org/en/latest/) if your data is too big for memory (or if you want to run in parallel)\n",
+ "\n",
+ "You should also install `numexpr` and `bottleneck` for optimal performance.\n",
+ "\n",
+ "For the documentation check [here](http://pandas.pydata.org/pandas-docs/stable/index.html)\n",
+ "\n",
+ "### Adjusted example from statsmodels tutorial"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import statsmodels.api as sm\n",
+ "import statsmodels.formula.api as smf\n",
+ "import numpy as np"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "df = sm.datasets.get_rdataset(\"Guerry\", \"HistData\").data\n",
+ "df"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "df.describe()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "df.groupby('Region').mean()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "results = smf.ols('Lottery ~ Literacy + np.log(Pop1831)', data=df).fit()\n",
+ "results.summary()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "df['log_pop'] = np.log(df.Pop1831)\n",
+ "df"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "results = smf.ols('Lottery ~ Literacy + log_pop', data=df).fit()\n",
+ "results.summary()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "results = smf.ols('Lottery ~ Literacy + np.log(Pop1831) + Region', data=df).fit()\n",
+ "results.summary()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "results = smf.ols('Lottery ~ Literacy + np.log(Pop1831) + Region + Region * Literacy', data=df).fit()\n",
+ "results.summary()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "%matplotlib nbagg\n",
+ "import seaborn as sns\n",
+ "sns.pairplot(df, hue=\"Region\", vars=('Lottery', 'Literacy', 'log_pop'))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## [Sympy](http://www.sympy.org/en/index.html): Symbolic programming"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import sympy as sym # no standard nickname"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "x, a, b, c = sym.symbols('x, a, b, c')\n",
+ "sym.solve(a * x ** 2 + b * x + c, x)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "sym.integrate(x/(x**2+a*x+2), x)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "f = sym.utilities.lambdify((x, a), sym.integrate((x**2+a*x+2), x))\n",
+ "f(np.random.rand(10), np.random.rand(10))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Other topics\n",
+ "## [Argparse](https://docs.python.org/3.6/howto/argparse.html): Command line arguments"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "%%writefile test_argparse.py\n",
+ "import argparse\n",
+ "\n",
+ "def main():\n",
+ " parser = argparse.ArgumentParser(description=\"calculate X to the power of Y\")\n",
+ " parser.add_argument(\"-v\", \"--verbose\", action=\"store_true\")\n",
+ " parser.add_argument(\"x\", type=int, help=\"the base\")\n",
+ " parser.add_argument(\"y\", type=int, help=\"the exponent\")\n",
+ " args = parser.parse_args()\n",
+ " answer = args.x**args.y\n",
+ "\n",
+ " if args.verbose:\n",
+ " print(\"{} to the power {} equals {}\".format(args.x, args.y, answer))\n",
+ " else:\n",
+ " print(\"{}^{} == {}\".format(args.x, args.y, answer))\n",
+ "\n",
+ "if __name__ == '__main__':\n",
+ " main()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "%run test_argparse.py 3 8 -v"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "%run test_argparse.py -h"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "%run test_argparse.py 3 8.5 -q"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### [Gooey](https://github.com/chriskiehl/Gooey): GUI from command line tool"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "%%writefile test_gooey.py\n",
+ "import argparse\n",
+ "from gooey import Gooey\n",
+ "\n",
+ "@Gooey\n",
+ "def main():\n",
+ " parser = argparse.ArgumentParser(description=\"calculate X to the power of Y\")\n",
+ " parser.add_argument(\"-v\", \"--verbose\", action=\"store_true\")\n",
+ " parser.add_argument(\"x\", type=int, help=\"the base\")\n",
+ " parser.add_argument(\"y\", type=int, help=\"the exponent\")\n",
+ " args = parser.parse_args()\n",
+ " answer = args.x**args.y\n",
+ "\n",
+ " if args.verbose:\n",
+ " print(\"{} to the power {} equals {}\".format(args.x, args.y, answer))\n",
+ " else:\n",
+ " print(\"{}^{} == {}\".format(args.x, args.y, answer))\n",
+ "\n",
+ "if __name__ == '__main__':\n",
+ " main()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "%run test_gooey.py"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "!gcoord_gui"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## [Jinja2](http://jinja.pocoo.org/docs/2.10/): HTML generation"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "%%writefile image_list.jinja2\n",
+ "<!DOCTYPE html>\n",
+ "<html lang=\"en\">\n",
+ "<head>\n",
+ " {% block head %}\n",
+ " <title>{{ title }}</title>\n",
+ " {% endblock %}\n",
+ "</head>\n",
+ "<body>\n",
+ " <div id=\"content\">\n",
+ " {% block content %}\n",
+ " {% for description, filenames in images %}\n",
+ " <p>\n",
+ " {{ description }}\n",
+ " </p>\n",
+ " {% for filename in filenames %}\n",
+ " <a href=\"{{ filename }}\">\n",
+ " <img src=\"{{ filename }}\">\n",
+ " </a>\n",
+ " {% endfor %}\n",
+ " {% endfor %}\n",
+ " {% endblock %}\n",
+ " </div>\n",
+ " <footer>\n",
+ " Created on {{ time }}\n",
+ " </footer>\n",
+ "</body>\n",
+ "</html>"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def plot_sine(amplitude, frequency):\n",
+ " x = np.linspace(0, 2 * np.pi, 100)\n",
+ " y = amplitude * np.sin(frequency * x)\n",
+ " plt.plot(x, y)\n",
+ " plt.xticks([0, np.pi, 2 * np.pi], ['0', '$\\pi$', '$2 \\pi$'])\n",
+ " plt.ylim(-1.1, 1.1)\n",
+ " filename = 'plots/A{:.2f}_F{:.2f}.png'.format(amplitude, frequency)\n",
+ " plt.title('A={:.2f}, F={:.2f}'.format(amplitude, frequency))\n",
+ " plt.savefig(filename)\n",
+ " plt.close(plt.gcf())\n",
+ " return filename\n",
+ "\n",
+ "!mkdir plots\n",
+ "amplitudes = [plot_sine(A, 1.) for A in [0.1, 0.3, 0.7, 1.0]]\n",
+ "frequencies = [plot_sine(1., F) for F in [1, 2, 3, 4, 5, 6]]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from jinja2 import Environment, FileSystemLoader\n",
+ "from datetime import datetime\n",
+ "loader = FileSystemLoader('.')\n",
+ "env = Environment(loader=loader)\n",
+ "template = env.get_template('image_list.jinja2')\n",
+ "\n",
+ "images = [\n",
+ " ('Varying the amplitude', amplitudes),\n",
+ " ('Varying the frequency', frequencies),\n",
+ "]\n",
+ "\n",
+ "with open('image_list.html', 'w') as f:\n",
+ " f.write(template.render(title='Lots of sines',\n",
+ " images=images, time=datetime.now()))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "!open image_list.html"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Neuroimage packages\n",
+ "The [nipy](http://nipy.org/) ecosystem covers most of these.\n",
+ "\n",
+ "### [CIFTI](https://github.com/MichielCottaar/cifti): easy creation/manipulation"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import nibabel\n",
+ "thickness = nibabel.load('100307/fsaverage_LR32k/100307.L.thickness.32k_fs_LR.shape.gii').darrays[0].data\n",
+ "thickness"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import cifti\n",
+ "ctx = thickness != 0\n",
+ "arr = np.random.rand(ctx.sum())\n",
+ "\n",
+ "bm_ctx = cifti.BrainModel.from_mask(ctx, name='CortexLeft')\n",
+ "sc = cifti.Scalar.from_names(['random'])\n",
+ "cifti.write('random_ctx.dscalar.nii', arr[None, :], (sc, bm_ctx))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "!wb_view 100307/fsaverage_LR32k/100307.*.32k_fs_LR.surf.gii random_ctx.dscalar.nii"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "img = nibabel.load('100307/aparc+aseg.nii.gz')\n",
+ "cerebellum = img.get_data() == 8\n",
+ "\n",
+ "bm = bm_ctx + cifti.BrainModel.from_mask(cerebellum, name='CerebellumLeft', affine=img.affine)\n",
+ "sc = cifti.Scalar.from_names(['random'])\n",
+ "arr = np.random.rand(len(bm))\n",
+ "cifti.write('random_ctx_cerebellum.dscalar.nii', arr[None, :], (sc, bm))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "!wb_view 100307/fsaverage_LR32k/100307.*.32k_fs_LR.surf.gii 100307/aparc+aseg.nii.gz random_ctx_cerebellum.dscalar.nii"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "arr = abs(thickness[ctx, None] - thickness[None, ctx])\n",
+ "cifti.write('diff_thickness.dconn.nii', arr, (bm_ctx, bm_ctx))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "!wb_view 100307/fsaverage_LR32k/100307.*.32k_fs_LR.surf.gii diff_thickness.dconn.nii"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## [networkx](https://networkx.github.io/): graph theory\n",
+ "\n",
+ "## GUI\n",
+ "- [tkinter](https://docs.python.org/3.6/library/tkinter.html): thin wrapper around Tcl/Tk; included in python\n",
+ "- [wxpython](https://www.wxpython.org/): Wrapper around the C++ wxWidgets library"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "%%writefile wx_hello_world.py\n",
+ "#!/usr/bin/env python\n",
+ "\"\"\"\n",
+ "Hello World, but with more meat.\n",
+ "\"\"\"\n",
+ "\n",
+ "import wx\n",
+ "\n",
+ "class HelloFrame(wx.Frame):\n",
+ " \"\"\"\n",
+ " A Frame that says Hello World\n",
+ " \"\"\"\n",
+ "\n",
+ " def __init__(self, *args, **kw):\n",
+ " # ensure the parent's __init__ is called\n",
+ " super(HelloFrame, self).__init__(*args, **kw)\n",
+ "\n",
+ " # create a panel in the frame\n",
+ " pnl = wx.Panel(self)\n",
+ "\n",
+ " # and put some text with a larger bold font on it\n",
+ " st = wx.StaticText(pnl, label=\"Hello World!\", pos=(25,25))\n",
+ " font = st.GetFont()\n",
+ " font.PointSize += 10\n",
+ " font = font.Bold()\n",
+ " st.SetFont(font)\n",
+ "\n",
+ " # create a menu bar\n",
+ " self.makeMenuBar()\n",
+ "\n",
+ " # and a status bar\n",
+ " self.CreateStatusBar()\n",
+ " self.SetStatusText(\"Welcome to wxPython!\")\n",
+ "\n",
+ "\n",
+ " def makeMenuBar(self):\n",
+ " \"\"\"\n",
+ " A menu bar is composed of menus, which are composed of menu items.\n",
+ " This method builds a set of menus and binds handlers to be called\n",
+ " when the menu item is selected.\n",
+ " \"\"\"\n",
+ "\n",
+ " # Make a file menu with Hello and Exit items\n",
+ " fileMenu = wx.Menu()\n",
+ " # The \"\\t...\" syntax defines an accelerator key that also triggers\n",
+ " # the same event\n",
+ " helloItem = fileMenu.Append(-1, \"&Hello...\\tCtrl-H\",\n",
+ " \"Help string shown in status bar for this menu item\")\n",
+ " fileMenu.AppendSeparator()\n",
+ " # When using a stock ID we don't need to specify the menu item's\n",
+ " # label\n",
+ " exitItem = fileMenu.Append(wx.ID_EXIT)\n",
+ "\n",
+ " # Now a help menu for the about item\n",
+ " helpMenu = wx.Menu()\n",
+ " aboutItem = helpMenu.Append(wx.ID_ABOUT)\n",
+ "\n",
+ " # Make the menu bar and add the two menus to it. The '&' defines\n",
+ " # that the next letter is the \"mnemonic\" for the menu item. On the\n",
+ " # platforms that support it those letters are underlined and can be\n",
+ " # triggered from the keyboard.\n",
+ " menuBar = wx.MenuBar()\n",
+ " menuBar.Append(fileMenu, \"&File\")\n",
+ " menuBar.Append(helpMenu, \"&Help\")\n",
+ "\n",
+ " # Give the menu bar to the frame\n",
+ " self.SetMenuBar(menuBar)\n",
+ "\n",
+ " # Finally, associate a handler function with the EVT_MENU event for\n",
+ " # each of the menu items. That means that when that menu item is\n",
+ " # activated then the associated handler function will be called.\n",
+ " self.Bind(wx.EVT_MENU, self.OnHello, helloItem)\n",
+ " self.Bind(wx.EVT_MENU, self.OnExit, exitItem)\n",
+ " self.Bind(wx.EVT_MENU, self.OnAbout, aboutItem)\n",
+ "\n",
+ "\n",
+ " def OnExit(self, event):\n",
+ " \"\"\"Close the frame, terminating the application.\"\"\"\n",
+ " self.Close(True)\n",
+ "\n",
+ "\n",
+ " def OnHello(self, event):\n",
+ " \"\"\"Say hello to the user.\"\"\"\n",
+ " wx.MessageBox(\"Hello again from wxPython\")\n",
+ "\n",
+ "\n",
+ " def OnAbout(self, event):\n",
+ " \"\"\"Display an About Dialog\"\"\"\n",
+ " wx.MessageBox(\"This is a wxPython Hello World sample\",\n",
+ " \"About Hello World 2\",\n",
+ " wx.OK|wx.ICON_INFORMATION)\n",
+ "\n",
+ "\n",
+ "if __name__ == '__main__':\n",
+ " # When this module is run (not imported) then create the app, the\n",
+ " # frame, show it, and start the event loop.\n",
+ " app = wx.App()\n",
+ " frm = HelloFrame(None, title='Hello World 2')\n",
+ " frm.Show()\n",
+ " app.MainLoop()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "%run wx_hello_world.py"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Machine learning\n",
+ "- scikit-learn\n",
+ "- theano/tensorflow/pytorch\n",
+ " - keras\n",
+ "\n",
+ "## [Pycuda](https://documen.tician.de/pycuda/): Programming the GPU"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import pycuda.autoinit\n",
+ "import pycuda.driver as drv\n",
+ "\n",
+ "from pycuda.compiler import SourceModule\n",
+ "mod = SourceModule(\"\"\"\n",
+ "__global__ void multiply_them(double *dest, double *a, double *b)\n",
+ "{\n",
+ " const int i = threadIdx.x;\n",
+ " dest[i] = a[i] * b[i];\n",
+ "}\n",
+ "\"\"\")\n",
+ "\n",
+ "multiply_them = mod.get_function(\"multiply_them\")\n",
+ "\n",
+ "a = np.random.randn(400)\n",
+ "b = np.random.randn(400)\n",
+ "\n",
+ "dest = np.zeros_like(a)\n",
+ "multiply_them(\n",
+ " drv.Out(dest), drv.In(a), drv.In(b),\n",
+ " block=(400,1,1), grid=(1,1))\n",
+ "\n",
+ "print(dest-a*b)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Testing\n",
+ "- [unittest](https://docs.python.org/3.6/library/unittest.html): python built-in testing\n",
+ "> ```\n",
+ "> import unittest\n",
+ ">\n",
+ "> class TestStringMethods(unittest.TestCase):\n",
+ ">\n",
+ "> def test_upper(self):\n",
+ "> self.assertEqual('foo'.upper(), 'FOO')\n",
+ ">\n",
+ "> def test_isupper(self):\n",
+ "> self.assertTrue('FOO'.isupper())\n",
+ "> self.assertFalse('Foo'.isupper())\n",
+ ">\n",
+ "> def test_split(self):\n",
+ "> s = 'hello world'\n",
+ "> self.assertEqual(s.split(), ['hello', 'world'])\n",
+ "> # check that s.split fails when the separator is not a string\n",
+ "> with self.assertRaises(TypeError):\n",
+ "> s.split(2)\n",
+ ">\n",
+ "> if __name__ == '__main__':\n",
+ "> unittest.main()\n",
+ "> ```\n",
+ "- [doctest](https://docs.python.org/3.6/library/doctest.html): checks the example usage in the documentation\n",
+ "> ```\n",
+ "> def factorial(n):\n",
+ "> \"\"\"Return the factorial of n, an exact integer >= 0.\n",
+ ">\n",
+ "> >>> [factorial(n) for n in range(6)]\n",
+ "> [1, 1, 2, 6, 24, 120]\n",
+ "> >>> factorial(30)\n",
+ "> 265252859812191058636308480000000\n",
+ "> >>> factorial(-1)\n",
+ "> Traceback (most recent call last):\n",
+ "> ...\n",
+ "> ValueError: n must be >= 0\n",
+ ">\n",
+ "> Factorials of floats are OK, but the float must be an exact integer:\n",
+ "> >>> factorial(30.1)\n",
+ "> Traceback (most recent call last):\n",
+ "> ...\n",
+ "> ValueError: n must be exact integer\n",
+ "> >>> factorial(30.0)\n",
+ "> 265252859812191058636308480000000\n",
+ ">\n",
+ "> It must also not be ridiculously large:\n",
+ "> >>> factorial(1e100)\n",
+ "> Traceback (most recent call last):\n",
+ "> ...\n",
+ "> OverflowError: n too large\n",
+ "> \"\"\"\n",
+ ">\n",
+ "> import math\n",
+ "> if not n >= 0:\n",
+ "> raise ValueError(\"n must be >= 0\")\n",
+ "> if math.floor(n) != n:\n",
+ "> raise ValueError(\"n must be exact integer\")\n",
+ "> if n+1 == n: # catch a value like 1e300\n",
+ "> raise OverflowError(\"n too large\")\n",
+ "> result = 1\n",
+ "> factor = 2\n",
+ "> while factor <= n:\n",
+ "> result *= factor\n",
+ "> factor += 1\n",
+ "> return result\n",
+ ">\n",
+ ">\n",
+ "> if __name__ == \"__main__\":\n",
+ "> import doctest\n",
+ "> doctest.testmod()\n",
+ "> ```\n",
+ "Two external packages provide more convenient unit tests:\n",
+ "- [py.test](https://docs.pytest.org/en/latest/)\n",
+ "- [nose2](http://nose2.readthedocs.io/en/latest/usage.html)\n",
+ "> ```\n",
+ "> # content of test_sample.py\n",
+ "> def inc(x):\n",
+ "> return x + 1\n",
+ ">\n",
+ "> def test_answer():\n",
+ "> assert inc(3) == 5\n",
+ "> ```\n",
+ "\n",
+ "- [coverage](https://coverage.readthedocs.io/en/coverage-4.5.1/): measures which part of the code is covered by the tests\n",
+ "\n",
+ "## Linters\n",
+ "Linters check the code for any syntax errors, [style errors](https://www.python.org/dev/peps/pep-0008/), unused variables, unreachable code, etc.\n",
+ "- [pylint](https://pypi.python.org/pypi/pylint): most extensive linter\n",
+ "- [pyflake](https://pypi.python.org/pypi/pyflakes): if you think pylint is too strict\n",
+ "- [pep8](https://pypi.python.org/pypi/pep8): just checks for style errors\n",
+ "- [mypy](http://mypy-lang.org/): adding explicit typing to python"
+ ]
+ }
+ ],
+ "metadata": {},
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/talks/packages/packages.md b/talks/packages/packages.md
new file mode 100644
index 0000000..8da72e4
--- /dev/null
+++ b/talks/packages/packages.md
@@ -0,0 +1,579 @@
+# Main scientific python libraries
+See https://scipy.org/
+
+Most of these packages have or are in thr progress of dropping support for python2.
+So use python3!
+
+## [Numpy](http://www.numpy.org/): arrays
+This is the main library underlying (nearly) all of the scientific python ecosystem.
+See the tutorial in the beginner session or [the official numpy tutorial](https://docs.scipy.org/doc/numpy-dev/user/quickstart.html) for usage details.
+
+The usual nickname of numpy is np:
+```
+import numpy as np
+```
+
+Numpy includes support for:
+- N-dimensional arrays with various datatypes
+- basic functions (e.g., polynomials)
+- basic linear algebra
+- random number generation
+
+## [Scipy](https://scipy.org/scipylib/index.html): most general scientific tools
+At the top level this module includes all of the basic functionality from numpy.
+You could import this as, but you might as well import numpy directly.
+```
+import scipy as sp
+```
+
+The main strength in scipy lies in its sub-packages:
+```
+from scipy import optimize
+def costfunc(params):
+ return params[0] ** 2 * (params[1] - 3) ** 2 + (params[0] - 2) ** 2
+optimize.minimize(costfunc, x0=[0, 0], method='l-bfgs-b')
+```
+
+Tutorials for all sub-packages can be found [here](https://docs.scipy.org/doc/scipy-1.0.0/reference/).
+
+## [Matplotlib](https://matplotlib.org/): Main plotting library
+```
+import matplotlib as mpl
+mpl.use('nbagg')
+import matplotlib.pyplot as plt
+```
+The matplotlib tutorials are [here](https://matplotlib.org/tutorials/index.html)
+
+```
+x = np.linspace(0, 2, 100)
+
+plt.plot(x, x, label='linear')
+plt.plot(x, x**2, label='quadratic')
+plt.plot(x, x**3, label='cubic')
+
+plt.xlabel('x label')
+plt.ylabel('y label')
+
+plt.title("Simple Plot")
+
+plt.legend()
+
+plt.show()
+```
+
+Alternatives:
+- [Mayavi](http://docs.enthought.com/mayavi/mayavi/): 3D plotting (hard to install)
+- [Bokeh](https://bokeh.pydata.org/en/latest/) among many others: interactive plots in the browser (i.e., in javascript)
+
+## [Ipython](http://ipython.org/)/[Jupyter](https://jupyter.org/) notebook: interactive python environments
+There are many [useful extensions available](https://github.com/ipython-contrib/jupyter_contrib_nbextensions).
+
+## [Pandas](https://pandas.pydata.org/): Analyzing "clean" data
+Once your data is in tabular form (e.g. Biobank IDP's), you want to use pandas dataframes to analyze them.
+This brings most of the functionality of R into python.
+Pandas has excellent support for:
+- fast IO to many tabular formats
+- accurate handling of missing data
+- Many, many routines to handle data
+ - group by categorical data (i.e., male/female, or age groups)
+ - joining/merging data
+ - time series support
+- statistical models through [statsmodels](http://www.statsmodels.org/stable/index.html)
+- plotting though seaborn [seaborn](https://seaborn.pydata.org/)
+- Use [dask](https://dask.pydata.org/en/latest/) if your data is too big for memory (or if you want to run in parallel)
+
+You should also install `numexpr` and `bottleneck` for optimal performance.
+
+For the documentation check [here](http://pandas.pydata.org/pandas-docs/stable/index.html)
+
+### Adjusted example from statsmodels tutorial
+```
+import statsmodels.api as sm
+import statsmodels.formula.api as smf
+import numpy as np
+```
+
+```
+df = sm.datasets.get_rdataset("Guerry", "HistData").data
+df
+```
+
+```
+df.describe()
+```
+
+```
+df.groupby('Region').mean()
+```
+
+```
+results = smf.ols('Lottery ~ Literacy + np.log(Pop1831)', data=df).fit()
+results.summary()
+```
+
+```
+df['log_pop'] = np.log(df.Pop1831)
+df
+```
+
+```
+results = smf.ols('Lottery ~ Literacy + log_pop', data=df).fit()
+results.summary()
+```
+
+```
+results = smf.ols('Lottery ~ Literacy + np.log(Pop1831) + Region', data=df).fit()
+results.summary()
+```
+
+```
+results = smf.ols('Lottery ~ Literacy + np.log(Pop1831) + Region + Region * Literacy', data=df).fit()
+results.summary()
+```
+
+```
+%matplotlib nbagg
+import seaborn as sns
+sns.pairplot(df, hue="Region", vars=('Lottery', 'Literacy', 'log_pop'))
+```
+
+## [Sympy](http://www.sympy.org/en/index.html): Symbolic programming
+```
+import sympy as sym # no standard nickname
+```
+
+```
+x, a, b, c = sym.symbols('x, a, b, c')
+sym.solve(a * x ** 2 + b * x + c, x)
+```
+
+```
+sym.integrate(x/(x**2+a*x+2), x)
+```
+
+```
+f = sym.utilities.lambdify((x, a), sym.integrate((x**2+a*x+2), x))
+f(np.random.rand(10), np.random.rand(10))
+```
+
+# Other topics
+## [Argparse](https://docs.python.org/3.6/howto/argparse.html): Command line arguments
+```
+%%writefile test_argparse.py
+import argparse
+
+def main():
+ parser = argparse.ArgumentParser(description="calculate X to the power of Y")
+ parser.add_argument("-v", "--verbose", action="store_true")
+ parser.add_argument("x", type=int, help="the base")
+ parser.add_argument("y", type=int, help="the exponent")
+ args = parser.parse_args()
+ answer = args.x**args.y
+
+ if args.verbose:
+ print("{} to the power {} equals {}".format(args.x, args.y, answer))
+ else:
+ print("{}^{} == {}".format(args.x, args.y, answer))
+
+if __name__ == '__main__':
+ main()
+```
+
+```
+%run test_argparse.py 3 8 -v
+```
+
+```
+%run test_argparse.py -h
+```
+
+```
+%run test_argparse.py 3 8.5 -q
+```
+
+### [Gooey](https://github.com/chriskiehl/Gooey): GUI from command line tool
+```
+%%writefile test_gooey.py
+import argparse
+from gooey import Gooey
+
+@Gooey
+def main():
+ parser = argparse.ArgumentParser(description="calculate X to the power of Y")
+ parser.add_argument("-v", "--verbose", action="store_true")
+ parser.add_argument("x", type=int, help="the base")
+ parser.add_argument("y", type=int, help="the exponent")
+ args = parser.parse_args()
+ answer = args.x**args.y
+
+ if args.verbose:
+ print("{} to the power {} equals {}".format(args.x, args.y, answer))
+ else:
+ print("{}^{} == {}".format(args.x, args.y, answer))
+
+if __name__ == '__main__':
+ main()
+```
+
+```
+%run test_gooey.py
+```
+
+```
+!gcoord_gui
+```
+
+## [Jinja2](http://jinja.pocoo.org/docs/2.10/): HTML generation
+```
+%%writefile image_list.jinja2
+<!DOCTYPE html>
+<html lang="en">
+<head>
+ {% block head %}
+ <title>{{ title }}</title>
+ {% endblock %}
+</head>
+<body>
+ <div id="content">
+ {% block content %}
+ {% for description, filenames in images %}
+ <p>
+ {{ description }}
+ </p>
+ {% for filename in filenames %}
+ <a href="{{ filename }}">
+ <img src="{{ filename }}">
+ </a>
+ {% endfor %}
+ {% endfor %}
+ {% endblock %}
+ </div>
+ <footer>
+ Created on {{ time }}
+ </footer>
+</body>
+</html>
+```
+
+```
+def plot_sine(amplitude, frequency):
+ x = np.linspace(0, 2 * np.pi, 100)
+ y = amplitude * np.sin(frequency * x)
+ plt.plot(x, y)
+ plt.xticks([0, np.pi, 2 * np.pi], ['0', '$\pi$', '$2 \pi$'])
+ plt.ylim(-1.1, 1.1)
+ filename = 'plots/A{:.2f}_F{:.2f}.png'.format(amplitude, frequency)
+ plt.title('A={:.2f}, F={:.2f}'.format(amplitude, frequency))
+ plt.savefig(filename)
+ plt.close(plt.gcf())
+ return filename
+
+!mkdir plots
+amplitudes = [plot_sine(A, 1.) for A in [0.1, 0.3, 0.7, 1.0]]
+frequencies = [plot_sine(1., F) for F in [1, 2, 3, 4, 5, 6]]
+```
+
+```
+from jinja2 import Environment, FileSystemLoader
+from datetime import datetime
+loader = FileSystemLoader('.')
+env = Environment(loader=loader)
+template = env.get_template('image_list.jinja2')
+
+images = [
+ ('Varying the amplitude', amplitudes),
+ ('Varying the frequency', frequencies),
+]
+
+with open('image_list.html', 'w') as f:
+ f.write(template.render(title='Lots of sines',
+ images=images, time=datetime.now()))
+```
+
+```
+!open image_list.html
+```
+
+## Neuroimage packages
+The [nipy](http://nipy.org/) ecosystem covers most of these.
+
+### [CIFTI](https://github.com/MichielCottaar/cifti): easy creation/manipulation
+```
+import nibabel
+thickness = nibabel.load('100307/fsaverage_LR32k/100307.L.thickness.32k_fs_LR.shape.gii').darrays[0].data
+thickness
+```
+
+```
+import cifti
+ctx = thickness != 0
+arr = np.random.rand(ctx.sum())
+
+bm_ctx = cifti.BrainModel.from_mask(ctx, name='CortexLeft')
+sc = cifti.Scalar.from_names(['random'])
+cifti.write('random_ctx.dscalar.nii', arr[None, :], (sc, bm_ctx))
+```
+
+```
+!wb_view 100307/fsaverage_LR32k/100307.*.32k_fs_LR.surf.gii random_ctx.dscalar.nii
+```
+
+```
+img = nibabel.load('100307/aparc+aseg.nii.gz')
+cerebellum = img.get_data() == 8
+
+bm = bm_ctx + cifti.BrainModel.from_mask(cerebellum, name='CerebellumLeft', affine=img.affine)
+sc = cifti.Scalar.from_names(['random'])
+arr = np.random.rand(len(bm))
+cifti.write('random_ctx_cerebellum.dscalar.nii', arr[None, :], (sc, bm))
+```
+
+```
+!wb_view 100307/fsaverage_LR32k/100307.*.32k_fs_LR.surf.gii 100307/aparc+aseg.nii.gz random_ctx_cerebellum.dscalar.nii
+```
+
+```
+arr = abs(thickness[ctx, None] - thickness[None, ctx])
+cifti.write('diff_thickness.dconn.nii', arr, (bm_ctx, bm_ctx))
+```
+
+```
+!wb_view 100307/fsaverage_LR32k/100307.*.32k_fs_LR.surf.gii diff_thickness.dconn.nii
+```
+
+## [networkx](https://networkx.github.io/): graph theory
+
+## GUI
+- [tkinter](https://docs.python.org/3.6/library/tkinter.html): thin wrapper around Tcl/Tk; included in python
+- [wxpython](https://www.wxpython.org/): Wrapper around the C++ wxWidgets library
+```
+%%writefile wx_hello_world.py
+#!/usr/bin/env python
+"""
+Hello World, but with more meat.
+"""
+
+import wx
+
+class HelloFrame(wx.Frame):
+ """
+ A Frame that says Hello World
+ """
+
+ def __init__(self, *args, **kw):
+ # ensure the parent's __init__ is called
+ super(HelloFrame, self).__init__(*args, **kw)
+
+ # create a panel in the frame
+ pnl = wx.Panel(self)
+
+ # and put some text with a larger bold font on it
+ st = wx.StaticText(pnl, label="Hello World!", pos=(25,25))
+ font = st.GetFont()
+ font.PointSize += 10
+ font = font.Bold()
+ st.SetFont(font)
+
+ # create a menu bar
+ self.makeMenuBar()
+
+ # and a status bar
+ self.CreateStatusBar()
+ self.SetStatusText("Welcome to wxPython!")
+
+
+ def makeMenuBar(self):
+ """
+ A menu bar is composed of menus, which are composed of menu items.
+ This method builds a set of menus and binds handlers to be called
+ when the menu item is selected.
+ """
+
+ # Make a file menu with Hello and Exit items
+ fileMenu = wx.Menu()
+ # The "\t..." syntax defines an accelerator key that also triggers
+ # the same event
+ helloItem = fileMenu.Append(-1, "&Hello...\tCtrl-H",
+ "Help string shown in status bar for this menu item")
+ fileMenu.AppendSeparator()
+ # When using a stock ID we don't need to specify the menu item's
+ # label
+ exitItem = fileMenu.Append(wx.ID_EXIT)
+
+ # Now a help menu for the about item
+ helpMenu = wx.Menu()
+ aboutItem = helpMenu.Append(wx.ID_ABOUT)
+
+ # Make the menu bar and add the two menus to it. The '&' defines
+ # that the next letter is the "mnemonic" for the menu item. On the
+ # platforms that support it those letters are underlined and can be
+ # triggered from the keyboard.
+ menuBar = wx.MenuBar()
+ menuBar.Append(fileMenu, "&File")
+ menuBar.Append(helpMenu, "&Help")
+
+ # Give the menu bar to the frame
+ self.SetMenuBar(menuBar)
+
+ # Finally, associate a handler function with the EVT_MENU event for
+ # each of the menu items. That means that when that menu item is
+ # activated then the associated handler function will be called.
+ self.Bind(wx.EVT_MENU, self.OnHello, helloItem)
+ self.Bind(wx.EVT_MENU, self.OnExit, exitItem)
+ self.Bind(wx.EVT_MENU, self.OnAbout, aboutItem)
+
+
+ def OnExit(self, event):
+ """Close the frame, terminating the application."""
+ self.Close(True)
+
+
+ def OnHello(self, event):
+ """Say hello to the user."""
+ wx.MessageBox("Hello again from wxPython")
+
+
+ def OnAbout(self, event):
+ """Display an About Dialog"""
+ wx.MessageBox("This is a wxPython Hello World sample",
+ "About Hello World 2",
+ wx.OK|wx.ICON_INFORMATION)
+
+
+if __name__ == '__main__':
+ # When this module is run (not imported) then create the app, the
+ # frame, show it, and start the event loop.
+ app = wx.App()
+ frm = HelloFrame(None, title='Hello World 2')
+ frm.Show()
+ app.MainLoop()
+```
+
+```
+%run wx_hello_world.py
+```
+
+## Machine learning
+- scikit-learn
+- theano/tensorflow/pytorch
+ - keras
+
+## [Pycuda](https://documen.tician.de/pycuda/): Programming the GPU
+```
+import pycuda.autoinit
+import pycuda.driver as drv
+
+from pycuda.compiler import SourceModule
+mod = SourceModule("""
+__global__ void multiply_them(double *dest, double *a, double *b)
+{
+ const int i = threadIdx.x;
+ dest[i] = a[i] * b[i];
+}
+""")
+
+multiply_them = mod.get_function("multiply_them")
+
+a = np.random.randn(400)
+b = np.random.randn(400)
+
+dest = np.zeros_like(a)
+multiply_them(
+ drv.Out(dest), drv.In(a), drv.In(b),
+ block=(400,1,1), grid=(1,1))
+
+print(dest-a*b)
+```
+
+## Testing
+- [unittest](https://docs.python.org/3.6/library/unittest.html): python built-in testing
+> ```
+> import unittest
+>
+> class TestStringMethods(unittest.TestCase):
+>
+> def test_upper(self):
+> self.assertEqual('foo'.upper(), 'FOO')
+>
+> def test_isupper(self):
+> self.assertTrue('FOO'.isupper())
+> self.assertFalse('Foo'.isupper())
+>
+> def test_split(self):
+> s = 'hello world'
+> self.assertEqual(s.split(), ['hello', 'world'])
+> # check that s.split fails when the separator is not a string
+> with self.assertRaises(TypeError):
+> s.split(2)
+>
+> if __name__ == '__main__':
+> unittest.main()
+> ```
+- [doctest](https://docs.python.org/3.6/library/doctest.html): checks the example usage in the documentation
+> ```
+> def factorial(n):
+> """Return the factorial of n, an exact integer >= 0.
+>
+> >>> [factorial(n) for n in range(6)]
+> [1, 1, 2, 6, 24, 120]
+> >>> factorial(30)
+> 265252859812191058636308480000000
+> >>> factorial(-1)
+> Traceback (most recent call last):
+> ...
+> ValueError: n must be >= 0
+>
+> Factorials of floats are OK, but the float must be an exact integer:
+> >>> factorial(30.1)
+> Traceback (most recent call last):
+> ...
+> ValueError: n must be exact integer
+> >>> factorial(30.0)
+> 265252859812191058636308480000000
+>
+> It must also not be ridiculously large:
+> >>> factorial(1e100)
+> Traceback (most recent call last):
+> ...
+> OverflowError: n too large
+> """
+>
+> import math
+> if not n >= 0:
+> raise ValueError("n must be >= 0")
+> if math.floor(n) != n:
+> raise ValueError("n must be exact integer")
+> if n+1 == n: # catch a value like 1e300
+> raise OverflowError("n too large")
+> result = 1
+> factor = 2
+> while factor <= n:
+> result *= factor
+> factor += 1
+> return result
+>
+>
+> if __name__ == "__main__":
+> import doctest
+> doctest.testmod()
+> ```
+Two external packages provide more convenient unit tests:
+- [py.test](https://docs.pytest.org/en/latest/)
+- [nose2](http://nose2.readthedocs.io/en/latest/usage.html)
+> ```
+> # content of test_sample.py
+> def inc(x):
+> return x + 1
+>
+> def test_answer():
+> assert inc(3) == 5
+> ```
+
+- [coverage](https://coverage.readthedocs.io/en/coverage-4.5.1/): measures which part of the code is covered by the tests
+
+## Linters
+Linters check the code for any syntax errors, [style errors](https://www.python.org/dev/peps/pep-0008/), unused variables, unreachable code, etc.
+- [pylint](https://pypi.python.org/pypi/pylint): most extensive linter
+- [pyflake](https://pypi.python.org/pypi/pyflakes): if you think pylint is too strict
+- [pep8](https://pypi.python.org/pypi/pep8): just checks for style errors
+- [mypy](http://mypy-lang.org/): adding explicit typing to python
\ No newline at end of file
diff --git a/talks/speed/speed.ipynb b/talks/speed/speed.ipynb
new file mode 100644
index 0000000..c8c9cde
--- /dev/null
+++ b/talks/speed/speed.ipynb
@@ -0,0 +1,2060 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Numpy vectorizing"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 17,
+ "metadata": {
+ "collapsed": true
+ },
+ "outputs": [],
+ "source": [
+ "import numpy as np"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 18,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "((nan, nan), (-1.0, 4.0), (0.0, 0.0))"
+ ]
+ },
+ "execution_count": 18,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "def root(a, b, c):\n",
+ " D = b ** 2 - 4 * a * c\n",
+ " if D < 0:\n",
+ " return np.nan, np.nan\n",
+ " x1 = (-b + np.sqrt(D)) / (2 * a)\n",
+ " x2 = (-b - np.sqrt(D)) / (2 * a)\n",
+ " return x1, x2\n",
+ "root(1, 3, 4), root(-1, 3, 4), root(1, 0, 0)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 19,
+ "metadata": {},
+ "outputs": [
+ {
+ "ename": "ValueError",
+ "evalue": "The truth value of an array with more than one element is ambiguous. Use a.any() or a.all()",
+ "output_type": "error",
+ "traceback": [
+ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+ "\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)",
+ "\u001b[0;32m<ipython-input-19-5d1fed3ed2df>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 2\u001b[0m \u001b[0mb\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrandom\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrandn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m1e6\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0mc\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrandom\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrandn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m1e6\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m \u001b[0mroot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mb\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mc\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+ "\u001b[0;32m<ipython-input-18-54b500cd66b1>\u001b[0m in \u001b[0;36mroot\u001b[0;34m(a, b, c)\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mroot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mb\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mc\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2\u001b[0m \u001b[0mD\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mb\u001b[0m \u001b[0;34m**\u001b[0m \u001b[0;36m2\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0;36m4\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0ma\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0mc\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0;32mif\u001b[0m \u001b[0mD\u001b[0m \u001b[0;34m<\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 4\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnan\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnan\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0mx1\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0mb\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msqrt\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mD\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m/\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;36m2\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0ma\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+ "\u001b[0;31mValueError\u001b[0m: The truth value of an array with more than one element is ambiguous. Use a.any() or a.all()"
+ ]
+ }
+ ],
+ "source": [
+ "a = np.random.randn(int(1e6))\n",
+ "b = np.random.randn(int(1e6))\n",
+ "c = np.random.randn(int(1e6))\n",
+ "root(a, b, c)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 20,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "CPU times: user 3.64 s, sys: 65.9 ms, total: 3.71 s\n",
+ "Wall time: 3.71 s\n"
+ ]
+ },
+ {
+ "data": {
+ "text/plain": [
+ "array([[-0.55797188, 1.11121928],\n",
+ " [ nan, nan],\n",
+ " [ 0.76166857, -1.70637551],\n",
+ " ...,\n",
+ " [-0.56859783, 0.98659831],\n",
+ " [ nan, nan],\n",
+ " [-0.53531175, 0.18339357]])"
+ ]
+ },
+ "execution_count": 20,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "%%time\n",
+ "np.array([root(av, bv, cv) for av, bv, cv in zip(a, b, c)])"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 21,
+ "metadata": {
+ "collapsed": true
+ },
+ "outputs": [],
+ "source": [
+ "root_vec = np.vectorize(root)\n",
+ "np.vectorize?"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 22,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "CPU times: user 1.85 s, sys: 71.9 ms, total: 1.92 s\n",
+ "Wall time: 1.92 s\n"
+ ]
+ },
+ {
+ "data": {
+ "text/plain": [
+ "(array([-0.55797188, nan, 0.76166857, ..., -0.56859783,\n",
+ " nan, -0.53531175]),\n",
+ " array([ 1.11121928, nan, -1.70637551, ..., 0.98659831,\n",
+ " nan, 0.18339357]))"
+ ]
+ },
+ "execution_count": 22,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "%%time\n",
+ "root_vec(a, b, c)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 23,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "CPU times: user 1.66 s, sys: 62.7 ms, total: 1.73 s\n",
+ "Wall time: 1.73 s\n"
+ ]
+ },
+ {
+ "data": {
+ "text/plain": [
+ "(array([-1.72246968, -2.63787563, nan, ..., -2.20722406,\n",
+ " nan, -1.84415698]),\n",
+ " array([2.27571708, 2.04353033, nan, ..., 2.62522454, nan,\n",
+ " 1.4922388 ]))"
+ ]
+ },
+ "execution_count": 23,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "%%time\n",
+ "root_vec(a, b, 7)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Numba"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 24,
+ "metadata": {
+ "collapsed": true
+ },
+ "outputs": [],
+ "source": [
+ "import numba"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 25,
+ "metadata": {
+ "collapsed": true
+ },
+ "outputs": [],
+ "source": [
+ "root_jit = numba.jit(root)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 26,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "3.08 µs ± 192 ns per loop (mean ± std. dev. of 7 runs, 100000 loops each)\n"
+ ]
+ }
+ ],
+ "source": [
+ "%timeit root(23, 78, 19.0)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 27,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "The slowest run took 11.09 times longer than the fastest. This could mean that an intermediate result is being cached.\n",
+ "786 ns ± 1.08 µs per loop (mean ± std. dev. of 7 runs, 1 loop each)\n"
+ ]
+ }
+ ],
+ "source": [
+ "%timeit root_jit(23, 78, 19.0)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 28,
+ "metadata": {},
+ "outputs": [
+ {
+ "ename": "SystemError",
+ "evalue": "CPUDispatcher(<function root at 0x114035620>) returned a result with an error set",
+ "output_type": "error",
+ "traceback": [
+ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+ "\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)",
+ "\u001b[0;31mValueError\u001b[0m: The truth value of an array with more than one element is ambiguous. Use a.any() or a.all()",
+ "\nThe above exception was the direct cause of the following exception:\n",
+ "\u001b[0;31mSystemError\u001b[0m Traceback (most recent call last)",
+ "\u001b[0;32m<timed eval>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n",
+ "\u001b[0;31mSystemError\u001b[0m: CPUDispatcher(<function root at 0x114035620>) returned a result with an error set"
+ ]
+ }
+ ],
+ "source": [
+ "%%time\n",
+ "root_jit(a, b, 7)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 29,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "CPU times: user 406 ms, sys: 57.2 ms, total: 464 ms\n",
+ "Wall time: 464 ms\n"
+ ]
+ },
+ {
+ "data": {
+ "text/plain": [
+ "(array([-1.72246968, -2.63787563, nan, ..., -2.20722406,\n",
+ " nan, -1.84415698]),\n",
+ " array([2.27571708, 2.04353033, nan, ..., 2.62522454, nan,\n",
+ " 1.4922388 ]))"
+ ]
+ },
+ "execution_count": 29,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "root_jit_vec = np.vectorize(root_jit)\n",
+ "%time root_jit_vec(a, b, 7)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 30,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "CPU times: user 391 ms, sys: 58.2 ms, total: 449 ms\n",
+ "Wall time: 448 ms\n"
+ ]
+ },
+ {
+ "data": {
+ "text/plain": [
+ "(array([-1.72246968, -2.63787563, nan, ..., -2.20722406,\n",
+ " nan, -1.84415698]),\n",
+ " array([2.27571708, 2.04353033, nan, ..., 2.62522454, nan,\n",
+ " 1.4922388 ]))"
+ ]
+ },
+ "execution_count": 30,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "@np.vectorize\n",
+ "@numba.jit\n",
+ "def fast_root(a, b, c):\n",
+ " D = b ** 2 - 4 * a * c\n",
+ " if D < 0:\n",
+ " return np.nan, np.nan\n",
+ " x1 = (-b + np.sqrt(D)) / (2 * a)\n",
+ " x2 = (-b - np.sqrt(D)) / (2 * a)\n",
+ " return x1, x2\n",
+ "%time fast_root(a, b, 7)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 31,
+ "metadata": {},
+ "outputs": [
+ {
+ "ename": "NotImplementedError",
+ "evalue": "(float64 x 2) cannot be represented as a Numpy dtype",
+ "output_type": "error",
+ "traceback": [
+ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+ "\u001b[0;31mNotImplementedError\u001b[0m Traceback (most recent call last)",
+ "\u001b[0;32m<timed eval>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n",
+ "\u001b[0;32m~/miniconda3/lib/python3.6/site-packages/numba/npyufunc/dufunc.py\u001b[0m in \u001b[0;36m_compile_for_args\u001b[0;34m(self, *args, **kws)\u001b[0m\n\u001b[1;32m 166\u001b[0m \u001b[0margty\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0margty\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 167\u001b[0m \u001b[0margtys\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0margty\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 168\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_compile_for_argtys\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtuple\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0margtys\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 169\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 170\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m_compile_for_argtys\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0margtys\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mreturn_type\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+ "\u001b[0;32m~/miniconda3/lib/python3.6/site-packages/numba/npyufunc/dufunc.py\u001b[0m in \u001b[0;36m_compile_for_argtys\u001b[0;34m(self, argtys, return_type)\u001b[0m\n\u001b[1;32m 188\u001b[0m cres, argtys, return_type)\n\u001b[1;32m 189\u001b[0m dtypenums, ptr, env = ufuncbuilder._build_element_wise_ufunc_wrapper(\n\u001b[0;32m--> 190\u001b[0;31m cres, actual_sig)\n\u001b[0m\u001b[1;32m 191\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_add_loop\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mutils\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlongint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mptr\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtypenums\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 192\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_keepalive\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mptr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcres\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlibrary\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0menv\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+ "\u001b[0;32m~/miniconda3/lib/python3.6/site-packages/numba/npyufunc/ufuncbuilder.py\u001b[0m in \u001b[0;36m_build_element_wise_ufunc_wrapper\u001b[0;34m(cres, signature)\u001b[0m\n\u001b[1;32m 163\u001b[0m \u001b[0;31m# Get dtypes\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 164\u001b[0m \u001b[0mdtypenums\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mas_dtype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnum\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0ma\u001b[0m \u001b[0;32min\u001b[0m \u001b[0msignature\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 165\u001b[0;31m \u001b[0mdtypenums\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mas_dtype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msignature\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreturn_type\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnum\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 166\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mdtypenums\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mptr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0menv\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 167\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
+ "\u001b[0;32m~/miniconda3/lib/python3.6/site-packages/numba/numpy_support.py\u001b[0m in \u001b[0;36mas_dtype\u001b[0;34m(nbtype)\u001b[0m\n\u001b[1;32m 134\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mas_dtype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnbtype\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 135\u001b[0m raise NotImplementedError(\"%r cannot be represented as a Numpy dtype\"\n\u001b[0;32m--> 136\u001b[0;31m % (nbtype,))\n\u001b[0m\u001b[1;32m 137\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 138\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
+ "\u001b[0;31mNotImplementedError\u001b[0m: (float64 x 2) cannot be represented as a Numpy dtype"
+ ]
+ }
+ ],
+ "source": [
+ "@numba.vectorize\n",
+ "def fast_root(a, b, c):\n",
+ " D = b ** 2 - 4 * a * c\n",
+ " if D < 0:\n",
+ " return np.nan, np.nan\n",
+ " x1 = (-b + np.sqrt(D)) / (2 * a)\n",
+ " x2 = (-b - np.sqrt(D)) / (2 * a)\n",
+ " return x1, x2\n",
+ "%time fast_root(a, b, 7)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# But run the profiler first"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 32,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "CPU times: user 1.56 s, sys: 3.5 ms, total: 1.56 s\n",
+ "Wall time: 1.56 s\n"
+ ]
+ },
+ {
+ "data": {
+ "text/plain": [
+ "5702887"
+ ]
+ },
+ "execution_count": 32,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "def fib(n):\n",
+ " if n <= 1:\n",
+ " return 1\n",
+ " else:\n",
+ " return fib(n-2) + fib(n-1)\n",
+ "%time fib(33)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 35,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "CPU times: user 63.4 ms, sys: 1.79 ms, total: 65.2 ms\n",
+ "Wall time: 64.2 ms\n"
+ ]
+ },
+ {
+ "data": {
+ "text/plain": [
+ "5702887"
+ ]
+ },
+ "execution_count": 35,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "@numba.jit\n",
+ "def fib_jit(n):\n",
+ " if n <= 1:\n",
+ " return 1\n",
+ " else:\n",
+ " return fib_jit(n-2) + fib_jit(n-1)\n",
+ "%time fib_jit(33)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 34,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "CPU times: user 1.28 s, sys: 5.26 ms, total: 1.28 s\n",
+ "Wall time: 1.28 s\n"
+ ]
+ },
+ {
+ "data": {
+ "text/plain": [
+ "267914296"
+ ]
+ },
+ "execution_count": 34,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "%time fib_jit(41)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 103,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ " "
+ ]
+ }
+ ],
+ "source": [
+ "%prun fib(33)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 104,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ " "
+ ]
+ }
+ ],
+ "source": [
+ "%prun fib_jit(41)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 112,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "CPU times: user 20 µs, sys: 1 µs, total: 21 µs\n",
+ "Wall time: 24.1 µs\n"
+ ]
+ }
+ ],
+ "source": [
+ "from functools import lru_cache\n",
+ "@lru_cache(None)\n",
+ "def fib_cache(n):\n",
+ " if n <= 1:\n",
+ " return 1\n",
+ " else:\n",
+ " return fib_cache(n-2) + fib_cache(n-1)\n",
+ "%time fib_cache(33)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "collapsed": true
+ },
+ "outputs": [],
+ "source": [
+ "lru_cache?"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 111,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "CPU times: user 261 µs, sys: 1e+03 ns, total: 262 µs\n",
+ "Wall time: 267 µs\n"
+ ]
+ },
+ {
+ "data": {
+ "text/plain": [
+ "225591516161936330872512695036072072046011324913758190588638866418474627738686883405015987052796968498626"
+ ]
+ },
+ "execution_count": 111,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "%time fib_cache(500)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Optimizing numpy algebra"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Multiplying/adding arrays incurs a lot of overhead, because lots of intermediate arrays get created and discarded again"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 48,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "139 ms ± 1.16 ms per loop (mean ± std. dev. of 7 runs, 10 loops each)\n"
+ ]
+ }
+ ],
+ "source": [
+ "a = np.random.randn(int(1e7))\n",
+ "b = np.random.randn(int(1e7))\n",
+ "%timeit a * b + 4.1 * a < 3 * b"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 49,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "11.4 ms ± 484 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n"
+ ]
+ }
+ ],
+ "source": [
+ "import numexpr as ne\n",
+ "%timeit ne.evaluate('a * b + 4.1 * a < 3 * b')"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Efficiency gain reduces once the operations become more complex"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 54,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "141 ms ± 1 ms per loop (mean ± std. dev. of 7 runs, 10 loops each)\n"
+ ]
+ }
+ ],
+ "source": [
+ "%timeit a * np.sqrt(abs(b)) < 3 * b"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 55,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "12.6 ms ± 986 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n"
+ ]
+ }
+ ],
+ "source": [
+ "%timeit ne.evaluate('a * sqrt(abs(b)) < 3 * b')"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Cython"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "http://docs.cython.org/en/latest/\n",
+ "\n",
+ "Compiles most python programs to C code for extra speed (but less introspection). Also allows direct calling of C library code.\n",
+ "\n",
+ "Can include python classes and much more."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 69,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "The cython extension is already loaded. To reload it, use:\n",
+ " %reload_ext cython\n"
+ ]
+ }
+ ],
+ "source": [
+ "%load_ext cython"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 70,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "[2, 3, 5, 7, 11, 13, 17, 19, 23, 29]\n",
+ "CPU times: user 67.4 ms, sys: 1.22 ms, total: 68.6 ms\n",
+ "Wall time: 68 ms\n"
+ ]
+ }
+ ],
+ "source": [
+ "def primes(kmax):\n",
+ " p = {}\n",
+ " result = []\n",
+ " if kmax > 1000:\n",
+ " kmax = 1000\n",
+ " k = 0\n",
+ " n = 2\n",
+ " while k < kmax:\n",
+ " i = 0\n",
+ " while i < k and n % p[i] != 0:\n",
+ " i = i + 1\n",
+ " if i == k:\n",
+ " p[k] = n\n",
+ " k = k + 1\n",
+ " result.append(n)\n",
+ " n = n + 1\n",
+ " return result"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 91,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "<!DOCTYPE html>\n",
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+ " \n",
+ "body.cython { font-family: courier; font-size: 12; }\n",
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+ "metadata": {},
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+ "source": [
+ "%%cython -a\n",
+ "def cprimes(int kmax):\n",
+ " cdef int[1000] p\n",
+ " result = []\n",
+ " if kmax > 1000:\n",
+ " kmax = 1000\n",
+ " cdef int k, n, i\n",
+ " k = 0\n",
+ " n = 2\n",
+ " while k < kmax:\n",
+ " i = 0\n",
+ " while i < k and n % p[i] != 0:\n",
+ " i = i + 1\n",
+ " if i == k:\n",
+ " p[k] = n\n",
+ " k = k + 1\n",
+ " result.append(n)\n",
+ " n = n + 1\n",
+ " return result"
+ ]
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+ {
+ "cell_type": "code",
+ "execution_count": 92,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "[2, 3, 5, 7, 11, 13, 17, 19, 23, 29]\n",
+ "CPU times: user 2.2 ms, sys: 10 µs, total: 2.21 ms\n",
+ "Wall time: 2.22 ms\n"
+ ]
+ }
+ ],
+ "source": [
+ "print(cprimes(10))\n",
+ "%time _ = cprimes(10000)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Defining C-style function"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 47,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "#%%cython -a\n",
+ "def fib(n):\n",
+ " if n <= 1:\n",
+ " return 1\n",
+ " else:\n",
+ " return fib(n-2) + fib(n-1)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 48,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "CPU times: user 1.48 s, sys: 1.79 ms, total: 1.48 s\n",
+ "Wall time: 1.48 s\n"
+ ]
+ },
+ {
+ "data": {
+ "text/plain": [
+ "5702887"
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+ "execution_count": 48,
+ "metadata": {},
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+ "source": [
+ "%time fib(33)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 49,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "CPU times: user 27.5 ms, sys: 73 µs, total: 27.5 ms\n",
+ "Wall time: 27.5 ms\n"
+ ]
+ },
+ {
+ "data": {
+ "text/plain": [
+ "5702887"
+ ]
+ },
+ "execution_count": 49,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "%time fib_jit(33)"
+ ]
+ },
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+ "execution_count": 56,
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+ "<pre class='cython code score-10 '> <span class='pyx_macro_api'>__Pyx_XDECREF</span>(__pyx_r);\n",
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+ " __pyx_t_2 = <span class='pyx_c_api'>__Pyx_PyInt_From_int</span>(__pyx_f_46_cython_magic_ed1fcd3356d03e7c12842ade7d34bd4b_cfib(__pyx_t_1)); if (unlikely(!__pyx_t_2)) __PYX_ERR(0, 8, __pyx_L1_error)\n",
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+ "\n",
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+ "execution_count": 57,
+ "metadata": {},
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+ "name": "stdout",
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+ "text": [
+ "CPU times: user 10.2 ms, sys: 138 µs, total: 10.4 ms\n",
+ "Wall time: 10.4 ms\n"
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+ ],
+ "source": [
+ "%time fib(33)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Cython in production code"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "collapsed": true
+ },
+ "outputs": [],
+ "source": [
+ "def root(a, b, c):\n",
+ " D = b ** 2 - 4 * a * c\n",
+ " if D < 0:\n",
+ " return NAN, NAN\n",
+ " x1 = (-b + sqrt(D)) / (2 * a)\n",
+ " x2 = (-b - sqrt(D)) / (2 * a)\n",
+ " return x1, x2"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 68,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "CPU times: user 236 ms, sys: 53.7 ms, total: 290 ms\n",
+ "Wall time: 288 ms\n"
+ ]
+ },
+ {
+ "data": {
+ "text/plain": [
+ "(array([-1.72246969, -2.6378758 , nan, ..., -2.20722389,\n",
+ " nan, -1.84415698]),\n",
+ " array([2.27571702, 2.04353046, nan, ..., 2.62522459, nan,\n",
+ " 1.49223876]))"
+ ]
+ },
+ "execution_count": 68,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "%time np.vectorize(root)(a, b, 7)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 66,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "CPU times: user 353 ms, sys: 57.8 ms, total: 411 ms\n",
+ "Wall time: 410 ms\n"
+ ]
+ },
+ {
+ "data": {
+ "text/plain": [
+ "(array([-1.72246968, -2.63787563, nan, ..., -2.20722406,\n",
+ " nan, -1.84415698]),\n",
+ " array([2.27571708, 2.04353033, nan, ..., 2.62522454, nan,\n",
+ " 1.4922388 ]))"
+ ]
+ },
+ "execution_count": 66,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "%time root_jit_vec(a, b, 7)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "collapsed": true
+ },
+ "outputs": [],
+ "source": [
+ "%%cython -a\n",
+ "from libc.math cimport sqrt, NAN\n",
+ "\n",
+ "def root(float a, float b, float c):\n",
+ " cdef float D, x1, x2\n",
+ " D = b ** 2 - 4 * a * c\n",
+ " if D < 0:\n",
+ " return NAN, NAN\n",
+ " x1 = (-b + sqrt(D)) / (2 * a)\n",
+ " x2 = (-b - sqrt(D)) / (2 * a)\n",
+ " return x1, x2\n",
+ "print(root(1, 3, 4), root(-1, 3, 4), root(1, 0, 0))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Cython in production code"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "http://docs.cython.org/en/latest/src/tutorial/cython_tutorial.html"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Steps to optimizing code:\n",
+ "- Profile where the bottleneck is (%prun)\n",
+ "- Is there a faster algorithm for the bottleneck?\n",
+ "- If the bottleneck is vectorized: can we optimize with numexpr?\n",
+ "- If the internal part of the loop can not be vectorized:\n",
+ " - numba jit if simple enough otherwise cython\n",
+ " - the loop itself can be sped up with numpy.vectorize\n",
+ "- If the bottleneck gets called too often: cache the result\n",
+ "- If even that fails: pycuda\n",
+ "- If that is too slow: learn to optimize cuda code or find an easier problem"
+ ]
+ },
+ {
+ "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": "3.6.2"
+ },
+ "toc": {
+ "colors": {
+ "hover_highlight": "#DAA520",
+ "running_highlight": "#FF0000",
+ "selected_highlight": "#FFD700"
+ },
+ "moveMenuLeft": true,
+ "nav_menu": {
+ "height": "179px",
+ "width": "252px"
+ },
+ "navigate_menu": true,
+ "number_sections": true,
+ "sideBar": true,
+ "threshold": 4,
+ "toc_cell": false,
+ "toc_section_display": "block",
+ "toc_window_display": true
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
--
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