{
"cells": [
{
"cell_type": "markdown",
"metadata": {
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"source": [
"\n",
"IPYTHON E MATPLOTLIB
\n",
"
\n",
"\n",
" Python fo computational science
\n",
" CINECA, 16-18 October 2017
\n",
"\n",
" m.cestari@cineca.it
\n",
"\n",
""
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"source": [
"## Introduction\n",
"* plotting data give us __visual feedback__\n",
"* Typical workflow:\n",
" * write a python program to parse data\n",
" * pass the data to a plot tool to show the results\n",
"* with __Matplotlib__ we can achieve the same result in a single script and with more flexibility\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"source": [
"## Premises/1\n",
"\n",
"* During this course we have extensively used __Jupyter__\n",
"\n",
"* So why we are still talking about __ipython__?\n",
"\n",
"* jupyter provides a common user interface for different _language specific kernels_"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"source": [
"## Premises/2: widely used kernels\n",
" * __python__\n",
" * IPython \n",
" * __R__\n",
" * IRkernel \n",
" * IRdisplay \n",
" * repr \n",
" * __Julia__\n",
" * IJulia Kernel \n",
" * Interactive Widgets \n",
" * __Bash__ "
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"source": [
"## Premises/3: widely used kernels\n",
"\n",
"more kernels here: \n",
"\n",
"https://github.com/ipython/ipython/wiki/IPython-kernels-for-other-languages"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"source": [
"## Ipython/1\n",
" \n",
"* improves the interactive mode usage\n",
" * tab completion for functions, modules, variables, files\n",
" * Introspection, accessibile with “?”, of objects and function\n",
" * %run, to execute a python script\n",
" * filesystem navigation (cd, ls, pwd) and bash like behaviour (cat) \n",
" * `!cmd` execute command in the shell\n",
" * Debugging and profiling\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"source": [
"## Ipython/2\n",
" \n",
"improves the interactive mode usage\n",
"\n",
" * Search commands Ctrl-n, Ctrl-p, Ctrl-r (don't work with notebook) \n",
" \n",
" * magic commands:\n",
" * `%magic` (list them all)\n",
" * `%whos`\n",
" * `%timeit`\n",
" * `%logstart`\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"source": [
"## Ipython/3\n",
"\n",
"__Ipython__ is recommended over python for interactive\n",
"usage:\n",
"* Has a matplotlib support mode\n",
"```\n",
" $ ipython --pylab\n",
"``` \n",
"\n",
"* no need to import any modules; merges __`matplotlib.pyplot`__ (for plotting) and __`numpy`__ (for mathematical functions)\n",
"* spawn a thread to handle the GUI and another one to handle the user inputs\n",
" * every plot command triggers a plot update\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"source": [
"## Ipython/4\n",
" \n",
"* notebook\n",
" ``` \n",
" $ jupyter/ipython notebook\n",
" ```\n",
" * documents that contain live code, equations, visualizations and explanatory text. \n",
"\n",
" * You can share notebooks (.ipynb files)\n",
"\n",
" * can be converted to static formats such as HTML, Markdown, LaTeX/PDF, and reStrucuredText\n"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": true,
"slideshow": {
"slide_type": "subslide"
}
},
"outputs": [],
"source": [
"# Well integrates with matplotlib\n",
"%matplotlib inline"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"source": [
"## Ipython/5\n",
"* QT GUI console \n",
"```\n",
"$ jupyter/ipython qtconsole\n",
"``` "
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"## Matplotlib:\n",
"* makes use of Numpy to provide __good performance__ with __large__ data arrays\n",
"* allows __publication quality__ plots\n",
"* since it's a Python module can be easily __integrated__ in a Python program"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"source": [
"## Module import\n",
"\n",
"Let us be consistent with the official documentation\n",
"\n",
"```\n",
"$ (i)python\n",
">>> import matplotlib.pyplot as plt\n",
"```"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"source": [
"## Matplotlib first example\n"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"slideshow": {
"slide_type": "fragment"
}
},
"outputs": [
{
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v8xjjCpFhIdx5aSe+Tj3Eml1HnI5jHDA/o4DCYuXBkV2djvIjUtlVmCKyCGhV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\nUMzPXvmWb1IPOR2pQsUlyh/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"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# following imports are not necessary if --pylab\n",
"import matplotlib.pyplot as plt\n",
"import numpy as np\n",
"\n",
"x = np.linspace(0, 2*np.pi, 10**4)\n",
"y = np.sin(x)\n",
"plt.plot(x,y) \n",
"plt.grid()"
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": true,
"slideshow": {
"slide_type": "subslide"
}
},
"source": [
"## Matplotlib: multiple line plot"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"slideshow": {
"slide_type": "fragment"
}
},
"outputs": [
{
"data": {
"image/png": 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0JXJnKeglppTVNPHCqr08t6KItXuqSUqI44KpQ7lizkgKRg7QOjS9JdgKlbu9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\n",
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"x = np.arange(0,7,0.00001)\n",
"plt.plot(x,x**3)\n",
"plt.plot(x,x**2)\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"source": [
"Or by passing multiple (x,y) arguments to the plot function "
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"slideshow": {
"slide_type": "fragment"
}
},
"outputs": [
{
"data": {
"image/png": 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0JXJnKeglppTVNPHCqr08t6KItXuqSUqI44KpQ7lizkgKRg7QOjS9JdgKlbu9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\n",
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"x = np.arange(0,7,0.00001)\n",
"plt.plot(x,x**3, x,x**2)\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"source": [
"## Plot control commands\n",
"Classic plot interaction is available"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(-0.34999950000000002, 7.3499895000000004, -17.149926500105003, 360.14845650220502)\n"
]
},
{
"data": {
"image/png": 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TqE/4JOoT3J3ceXPEm4z2Hi2jemEVkvDrMkOJaf6+971VaubL3afJLSxl6g0y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\nNhgNV93HaDTq938/qtu98IN+fFWELi4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"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.plot(x,x**2, x, x**3, x, x)\n",
"plt.grid()\n",
"print(plt.axis()) # shows the current axis limits values\n",
"plt.axis([0,5,0,5]) #[xmin,xmax,ymin,ymax]\n",
"plt.xlabel('This is the X axis')\n",
"plt.ylabel('y / s')\n",
"plt.title('Outstanding title here') \n",
"plt.legend(['Fast', 'SuperFast', 'Ideal'],loc=4) \n",
"plt.savefig('plot123.png', dpi=350) "
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"source": [
""
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"## Object-oriented interface/1\n",
"A figure is composed by a hierarchical series of Matplotlib objects:\n",
"* __Figure__: Container for one or more Axes instances\n",
"* __Subplot (Axes)__: The rectangular areas that holds the basic elements, such as lines, text, and so on\n",
"* __Lines__: real plotting objects (lines, histograms, ...)\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"source": [
"## Object-oriented interface/2"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "fragment"
}
},
"source": [
""
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [
{
"data": {
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"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"fig = plt.figure()\n",
"ax = fig.add_subplot(1,1,1)"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"source": [
"## Object-oriented interface/3\n",
"* OO use of matplotlib makes the code more explicit and allows a lot more customizations\n",
"* it is also consistent with all the examples found here \n",
"http://matplotlib.sourceforge.net/gallery.html\n"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"outputs": [
{
"data": {
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/sGWoy7VYRg2dnSLsOTnyPdFZMakmZOZ4JFrYbeWpn0RlxXwduN+XEbMH+HyClmuxpD5O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\nZoXjrW8YGBXCXr9sI5s5gYc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BdBH2PR0hwu6LYZ+ZtZYHHwRvm4d28oIM2sQKuXmbDviu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\nXqNdXWnc3PwD7uVSznhtL10qPF+5ri4HOIPa2m1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KbS8eOyB5ud/8ZvnHLF8uJ1YxYS+1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\nChaNc+wEsDn392YAvwjqwM1BHShsmHmKiL4K4AnIyvn3mHkgYrPC5mM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"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"x = np.arange(0, 10, 0.1) # ; print(x)\n",
"y = np.random.randn(x.size) # ; print(y)\n",
"fig = plt.figure() # instance of the fig obj \n",
"ax = fig.add_subplot(111) # instance of the axes obj\n",
"l, m = ax.plot(x, y, x, y**2) # returns a tuple of obj \n",
"l.set_color('blue') \n",
"m.set_color('red')\n",
"t = ax.set_title('random numbers')\n",
"ax.grid()\n",
"l.set_label('mylabel')\n",
"#plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"source": [
"## Object-oriented interface: multiple plot"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"outputs": [],
"source": [
"x = np.arange(0, 10, 0.001)\n",
"y = np.random.randn(len(x))\n",
"fig = plt.figure(figsize=(6,8)) # instance of the fig obj \n",
"ax = fig.add_subplot(211) # two axes instances in \n",
"ax2 = fig.add_subplot(212) # the same column \n",
"l, = ax.plot(x, y) # returns a tuple of obj \n",
"m, = ax2.plot(x, y**2) # returns a tuple of obj \n",
"l.set_color('blue') \n",
"m.set_color('red')\n",
"t = ax.set_title('random numbers')\n",
"ax.set_ylabel('my y / my values')\n",
"type(l)\n",
"#ax.grid()"
]
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" \n",
" ## Object-oriented interface\n",
"* Multiple figures are allowed\n"
]
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"source": [
"fig1 = plt.figure()\n",
"ax1 = fig1.add_subplot(111)\n",
"ax1.plot([1, 2, 3], [1, 2, 3]);\n",
"fig2 = plt.figure(figsize=(7,3))\n",
"ax2 = fig2.add_subplot(111)\n",
"ax2.plot([1, 2, 3], [3, 2, 1]);"
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"## To summarize\n",
"* This was a very brief introduction to Matplotlib.\n",
"* We don't need to cover everything; just go with your needs\n",
"* It can be used interactively, a la Matlab, or Object-oriented\n",
"* It can be fully integrated in a Python program;\n",
" * your analysis code can be integrated with a plot tool, tailored to the application needs\n"
]
},
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"source": [
"## Bibliography\n",
"\n",
"http://matplotlib.sourceforge.net/contents.html\n",
" \n",
"Matplotlib for Python developers (Sandro Tosi, Packt Publishing Ltd., 2009)\n"
]
}
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