First version of the slides for the GridKA school

295a6a70 · Thomas Keck · 295a6a70 · 295a6a70 · 295a6a70 · 295a6a70
Commit 295a6a70 authored 7 years ago by Thomas Keck
--- a/Introduction.ipynb
+++ b/Introduction.ipynb
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "# Introduction to the SciPy stack and IPython Notebooks\n",
+    "\n",
+    "**GridKA School 2017**\n",
+    "\n",
+    "Thomas Keck"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "## SciPy\n",
+    "\n",
+    "**Python-based ecosystem of open-source software for mathematics, science, and engineering**\n",
+    "\n",
+    "\n",
+    "### The SciPy stack includes:\n",
+    "<div style=\"position:relative;top:-15px\">\n",
+    "<img src=images/ipython.png style=\"width: 40px; float: left\" />\n",
+    "</div>\n",
+    "&nbsp;&nbsp; **IPython** Interactive python shell\n",
+    "<br />\n",
+    "<br />\n",
+    "\n",
+    "<div style=\"position:relative;top:-15px\">\n",
+    "<img src=images/numpylogo_med.png style=\"width: 40px; float: left\" />\n",
+    "</div>\n",
+    " &nbsp;&nbsp;  **NumPy** N-dimensional array package and data format used in SciPy\n",
+    "<br />\n",
+    "<br />\n",
+    "\n",
+    "<div style=\"position:relative;top:-15px\">\n",
+    "<img src=images/matplotlib_med.png style=\"width: 40px; float: left\" />\n",
+    "</div>\n",
+    "&nbsp;&nbsp; **Matplotlib** Plotting and data visualization\n",
+    "<br />\n",
+    "<br />\n",
+    "\n",
+    "<div style=\"position:relative;top:-15px\">\n",
+    "<img src=images/scipy_med.png style=\"width: 40px; float: left\" />\n",
+    "</div>\n",
+    "&nbsp;&nbsp; **SciPy** Scientific computing: Integration, Optimization, Statistics, ...\n",
+    "<br />\n",
+    "<br />\n",
+    "\n",
+    "<div style=\"position:relative;top:-15px\">\n",
+    "<img src=images/pandas_badge2.jpg style=\"width: 40px; float: left\" />\n",
+    "</div>\n",
+    "&nbsp;&nbsp; **Pandas** Data structures & data analysis\n",
+    "<br />\n",
+    "<br />\n",
+    "\n",
+    "<div style=\"position:relative;top:-15px\">\n",
+    "<img src=images/sympy_logo.png style=\"width: 40px; float: left\" />\n",
+    "</div>\n",
+    "&nbsp;&nbsp; **Sympy** Symbolic mathematics\n",
+    "<br />\n",
+    "<br />"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "## IPython is an interactive Python shell\n",
+    "\n",
+    "The project was split up into:\n",
+    "<div style=\"position:relative;top:-15px\">\n",
+    "<img src=images/ipython.png style=\"width: 40px; float: left\" />\n",
+    "</div>\n",
+    "&nbsp;&nbsp; **IPython** focuses on interactive Python\n",
+    "<br />\n",
+    "<br />\n",
+    "\n",
+    "<div style=\"position:relative;top:-15px\">\n",
+    "<img src=images/jupyter.png style=\"width: 40px; float: left\" />\n",
+    "</div>\n",
+    "&nbsp;&nbsp; **Jupyter** contains the language agnostic parts\n",
+    "<br />\n",
+    "<br />\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "collapsed": true,
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "## Useful IPython Features\n",
+    "\n",
+    "see http://ipython.readthedocs.io/en/stable/interactive/index.html for a complete list"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "subslide"
+    }
+   },
+   "source": [
+    "Documentation"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "collapsed": false,
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "from IPython.core import page\n",
+    "from IPython.display import display\n",
+    "import functools\n",
+    "page.page = functools.partial(display, raw=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "collapsed": false,
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "\u001b[0;31mDocstring:\u001b[0m\n",
+       "sqrt(x)\n",
+       "\n",
+       "Return the square root of x.\n",
+       "\u001b[0;31mType:\u001b[0m      builtin_function_or_method\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import math\n",
+    "math.sqrt?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "subslide"
+    }
+   },
+   "source": [
+    "Profiling"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The slowest run took 23.18 times longer than the fastest. This could mean that an intermediate result is being cached.\n",
+      "1000000 loops, best of 3: 259 ns per loop\n"
+     ]
+    }
+   ],
+   "source": [
+    "%timeit math.sqrt(2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The slowest run took 24.61 times longer than the fastest. This could mean that an intermediate result is being cached.\n",
+      "10000000 loops, best of 3: 179 ns per loop\n"
+     ]
+    }
+   ],
+   "source": [
+    "from math import sqrt\n",
+    "%timeit sqrt(2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The slowest run took 76.18 times longer than the fastest. This could mean that an intermediate result is being cached.\n",
+      "10000000 loops, best of 3: 24.8 ns per loop\n"
+     ]
+    }
+   ],
+   "source": [
+    "%timeit 2**0.5"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "subslide"
+    }
+   },
+   "source": [
+    "Debugging"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Automatic pdb calling has been turned ON\n"
+     ]
+    },
+    {
+     "ename": "ValueError",
+     "evalue": "math domain error",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-6-4c17f9ae18c9>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0mget_ipython\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmagic\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'pdb'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0ma\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmath\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msqrt\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      3\u001b[0m \u001b[0mprint\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: math domain error"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "> \u001b[0;32m<ipython-input-6-4c17f9ae18c9>\u001b[0m(2)\u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n",
+      "\u001b[0;32m      1 \u001b[0;31m\u001b[0mget_ipython\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmagic\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'pdb'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0m\u001b[0;32m----> 2 \u001b[0;31m\u001b[0ma\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmath\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msqrt\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0m\u001b[0;32m      3 \u001b[0;31m\u001b[0mprint\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[0m\n",
+      "ipdb> c\n"
+     ]
+    }
+   ],
+   "source": [
+    "%pdb\n",
+    "a = math.sqrt(-1)\n",
+    "print(a)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "subslide"
+    }
+   },
+   "source": [
+    "Plotting"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x7fb78a68bc50>]"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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kfSG3TskgsUsHr+OIiISUgMrdzC4zs+1mlmNmP2nm+Q5m9krD86vNLCPYQZt6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\n/hdumquacPPpnQAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7fb7b318bda0>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "%matplotlib inline\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "plt.plot(np.arange(10), np.sqrt(np.arange(10)))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "subslide"
+    }
+   },
+   "source": [
+    "Execute Shell Commands"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "root:x:0:0:root:/root:/bin/bash\r\n",
+      "daemon:x:1:1:daemon:/usr/sbin:/usr/sbin/nologin\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!cat /etc/passwd | head -n 2"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "## Exercise 1\n",
+    "\n",
+    "  1. Create a new cell below, change the cell type to markdown and write your favorite formula using latex\n",
+    "  1. Change the plot above so it uses blue circle markers instead of a line, look into the documentation of plt.plot to do so.\n",
+    "  1. Get a list of all users registered on this system by reading in the file /etc/passwd using a shell command\n",
+    "  1. Is there a difference between the speed of different arithmetic operations in pure Python?"
+   ]
+  }
+ ],
+ "metadata": {
+  "celltoolbar": "Slideshow",
+  "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": "3.5.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
+%% Cell type:markdown id: tags:
+
+# Introduction to the SciPy stack and IPython Notebooks
+
+**GridKA School 2017**
+
+Thomas Keck
+
+%% Cell type:markdown id: tags:
+
+## SciPy
+
+**Python-based ecosystem of open-source software for mathematics, science, and engineering**
+
+
+### The SciPy stack includes:
+<div style="position:relative;top:-15px">
+<img src=images/ipython.png style="width: 40px; float: left" />
+</div>
+&nbsp;&nbsp; **IPython** Interactive python shell
+<br />
+<br />
+
+<div style="position:relative;top:-15px">
+<img src=images/numpylogo_med.png style="width: 40px; float: left" />
+</div>
+ &nbsp;&nbsp;  **NumPy** N-dimensional array package and data format used in SciPy
+<br />
+<br />
+
+<div style="position:relative;top:-15px">
+<img src=images/matplotlib_med.png style="width: 40px; float: left" />
+</div>
+&nbsp;&nbsp; **Matplotlib** Plotting and data visualization
+<br />
+<br />
+
+<div style="position:relative;top:-15px">
+<img src=images/scipy_med.png style="width: 40px; float: left" />
+</div>
+&nbsp;&nbsp; **SciPy** Scientific computing: Integration, Optimization, Statistics, ...
+<br />
+<br />
+
+<div style="position:relative;top:-15px">
+<img src=images/pandas_badge2.jpg style="width: 40px; float: left" />
+</div>
+&nbsp;&nbsp; **Pandas** Data structures & data analysis
+<br />
+<br />
+
+<div style="position:relative;top:-15px">
+<img src=images/sympy_logo.png style="width: 40px; float: left" />
+</div>
+&nbsp;&nbsp; **Sympy** Symbolic mathematics
+<br />
+<br />
+
+%% Cell type:markdown id: tags:
+
+## IPython is an interactive Python shell
+
+The project was split up into:
+<div style="position:relative;top:-15px">
+<img src=images/ipython.png style="width: 40px; float: left" />
+</div>
+&nbsp;&nbsp; **IPython** focuses on interactive Python
+<br />
+<br />
+
+<div style="position:relative;top:-15px">
+<img src=images/jupyter.png style="width: 40px; float: left" />
+</div>
+&nbsp;&nbsp; **Jupyter** contains the language agnostic parts
+<br />
+<br />
+
+%% Cell type:markdown id: tags:
+
+## Useful IPython Features
+
+see http://ipython.readthedocs.io/en/stable/interactive/index.html for a complete list
+
+%% Cell type:markdown id: tags:
+
+Documentation
+
+%% Cell type:code id: tags:
+
+``` python
+from IPython.core import page
+from IPython.display import display
+import functools
+page.page = functools.partial(display, raw=True)
+```
+
+%% Cell type:code id: tags:
+
+``` python
+import math
+math.sqrt?
+```
+
+%% Output
+
+
+%% Cell type:markdown id: tags:
+
+Profiling
+
+%% Cell type:code id: tags:
+
+``` python
+%timeit math.sqrt(2)
+```
+
+%% Output
+
+    The slowest run took 23.18 times longer than the fastest. This could mean that an intermediate result is being cached.
+    1000000 loops, best of 3: 259 ns per loop
+
+%% Cell type:code id: tags:
+
+``` python
+from math import sqrt
+%timeit sqrt(2)
+```
+
+%% Output
+
+    The slowest run took 24.61 times longer than the fastest. This could mean that an intermediate result is being cached.
+    10000000 loops, best of 3: 179 ns per loop
+
+%% Cell type:code id: tags:
+
+``` python
+%timeit 2**0.5
+```
+
+%% Output
+
+    The slowest run took 76.18 times longer than the fastest. This could mean that an intermediate result is being cached.
+    10000000 loops, best of 3: 24.8 ns per loop
+
+%% Cell type:markdown id: tags:
+
+Debugging
+
+%% Cell type:code id: tags:
+
+``` python
+%pdb
+a = math.sqrt(-1)
+print(a)
+```
+
+%% Output
+
+    Automatic pdb calling has been turned ON
+
+    ---------------------------------------------------------------------------
+    ValueError                                Traceback (most recent call last)
+    <ipython-input-6-4c17f9ae18c9> in <module>()
+          1 get_ipython().magic('pdb')
+    ----> 2 a = math.sqrt(-1)
+          3 print(a)
+    ValueError: math domain error
+
+    > [0;32m<ipython-input-6-4c17f9ae18c9>[0m(2)[0;36m<module>[0;34m()[0m
+    [0;32m      1 [0;31m[0mget_ipython[0m[0;34m([0m[0;34m)[0m[0;34m.[0m[0mmagic[0m[0;34m([0m[0;34m'pdb'[0m[0;34m)[0m[0;34m[0m[0m
+    [0m[0;32m----> 2 [0;31m[0ma[0m [0;34m=[0m [0mmath[0m[0;34m.[0m[0msqrt[0m[0;34m([0m[0;34m-[0m[0;36m1[0m[0;34m)[0m[0;34m[0m[0m
+    [0m[0;32m      3 [0;31m[0mprint[0m[0;34m([0m[0ma[0m[0;34m)[0m[0;34m[0m[0m
+    [0m
+    ipdb> c
+
+%% Cell type:markdown id: tags:
+
+Plotting
+
+%% Cell type:code id: tags:
+
+``` python
+%matplotlib inline
+import numpy as np
+import matplotlib.pyplot as plt
+plt.plot(np.arange(10), np.sqrt(np.arange(10)))
+```
+
+%% Output
+
+    [<matplotlib.lines.Line2D at 0x7fb78a68bc50>]
+
+
+
+%% Cell type:markdown id: tags:
+
+Execute Shell Commands
+
+%% Cell type:code id: tags:
+
+``` python
+!cat /etc/passwd | head -n 2
+```
+
+%% Output
+
+    root:x:0:0:root:/root:/bin/bash
+    daemon:x:1:1:daemon:/usr/sbin:/usr/sbin/nologin
+
+%% Cell type:markdown id: tags:
+
+## Exercise 1
+
+  1. Create a new cell below, change the cell type to markdown and write your favorite formula using latex
+  1. Change the plot above so it uses blue circle markers instead of a line, look into the documentation of plt.plot to do so.
+  1. Get a list of all users registered on this system by reading in the file /etc/passwd using a shell command
+  1. Is there a difference between the speed of different arithmetic operations in pure Python?
--- a/Numpy.ipynb
+++ b/Numpy.ipynb
--- a/Performance.ipynb
+++ b/Performance.ipynb
--- a/images/ipython.png
+++ b/images/ipython.png
--- a/images/jupyter.png
+++ b/images/jupyter.png
--- a/images/matplotlib_med.png
+++ b/images/matplotlib_med.png
--- a/images/nav_logo.svg
+++ b/images/nav_logo.svg
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--- a/images/numpylogo_med.png
+++ b/images/numpylogo_med.png
--- a/images/pandas_badge2.jpg
+++ b/images/pandas_badge2.jpg
--- a/images/scipy_med.png
+++ b/images/scipy_med.png
--- a/images/sympy_logo.png
+++ b/images/sympy_logo.png