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"worksheets": [
{
"cells": [
{
"cell_type": "heading",
"level": 2,
"metadata": {},
"source": [
"EE 123 iPython Notebook Tutorial Spring 2015\n",
"\n",
"\n",
" \n",
"
\n",
" modified from Berkeley Python Bootcamp 2013 https://github.com/profjsb/python-bootcamp\n",
"
and Python for Signal Processing http://link.springer.com/book/10.1007%2F978-3-319-01342-8\n",
" \n",
"
- Frank Ong \n",
"
"
]
},
{
"cell_type": "heading",
"level": 1,
"metadata": {},
"source": [
"Installing iPython Notebook"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Follow the instructions on the class website to install python:\n",
"\n",
"http://inst.eecs.berkeley.edu/~ee123/sp15/python_install.html"
]
},
{
"cell_type": "heading",
"level": 1,
"metadata": {},
"source": [
"iPython Notebook Navigation"
]
},
{
"cell_type": "heading",
"level": 3,
"metadata": {},
"source": [
"Run iPython notebook cell"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The ipython notebook is divided into cells. Each cell can contain texts, codes or html scripts. Running a non-code cell simply advances to the next cell. To run a code cell using Shift-Enter or pressing the play button in the toolbar above:"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"1+2"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 32,
"text": [
"3"
]
}
],
"prompt_number": 32
},
{
"cell_type": "heading",
"level": 3,
"metadata": {},
"source": [
"Interupting the kernel"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"For debugging, often we would like to interupt the current running process. This can be done by pressing the stop button. \n",
"\n",
"When a processing is running, the circle on the right upper corner is filled. When idle, the circle is empty."
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"import time\n",
"\n",
"while(1):\n",
" print \"error\"\n",
" time.sleep(1)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"error\n",
"error"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"error"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n"
]
},
{
"ename": "KeyboardInterrupt",
"evalue": "",
"output_type": "pyerr",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[0;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0;32mwhile\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0;32mprint\u001b[0m \u001b[0;34m\"error\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 5\u001b[0;31m \u001b[0mtime\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msleep\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;31mKeyboardInterrupt\u001b[0m: "
]
}
],
"prompt_number": 33
},
{
"cell_type": "heading",
"level": 3,
"metadata": {},
"source": [
"Restarting the kernels"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Interupting sometimes does not work. You can reset the state by restarting the kernel. This is done by clicking Kernel/Restart or the Refresh button in the toolbar above."
]
},
{
"cell_type": "heading",
"level": 3,
"metadata": {},
"source": [
"Saving the notebook"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"To save your notebook, either select `\"File->Save and Checkpoint\"` or hit `Command-s` for Mac and `Ctrl-s` for Windows"
]
},
{
"cell_type": "heading",
"level": 3,
"metadata": {},
"source": [
"Undoing"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"To undo changes in each cell, hit `Command-z` for Mac and `Ctrl-z` for Windows\n",
"\n",
"To undo `Delete Cell`, select `Edit->Undo Delete Cell`"
]
},
{
"cell_type": "heading",
"level": 3,
"metadata": {},
"source": [
"Tab Completion"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"One useful feature of iPython is tab completion "
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"one_plus_one = 1+1\n",
"\n",
"# type `one_` then hit TAB will auto-complete the variable\n",
"print one_\n"
],
"language": "python",
"metadata": {},
"outputs": [
{
"ename": "NameError",
"evalue": "name 'one_' is not defined",
"output_type": "pyerr",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 2\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0;31m# type `one_` then hit TAB will auto-complete the variable\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m \u001b[0;32mprint\u001b[0m \u001b[0mone_\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[0;31mNameError\u001b[0m: name 'one_' is not defined"
]
}
],
"prompt_number": 34
},
{
"cell_type": "heading",
"level": 3,
"metadata": {},
"source": [
"Help menu for functions"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Another useful feature is the help command. Type any function followed by `?` returns a help window. Hit the `x` button to close it."
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"abs?"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 35
},
{
"cell_type": "heading",
"level": 3,
"metadata": {},
"source": [
"Other iPython Notebook navigation tips"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"- To add a new cell, either select `\"Insert->Insert New Cell Below\"` or click the white plus button\n",
"- You can change the cell mode from code to text in the pulldown menu. I use `Markdown` for text\n",
"- You can change the texts in the `Markdown` cells by double-clicking them.\n",
"- `Help->Keyboard Shortcuts` has a list of keyboard shortcuts"
]
},
{
"cell_type": "heading",
"level": 1,
"metadata": {},
"source": [
"Libraries"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"These are the libraries that we will be using in this class:\n",
" \n",
"__Numpy__\n",
"\n",
"NumPy is the fundamental package for scientific computing with Python.\n",
"\n",
"__Scipy__\n",
"\n",
"The SciPy library is a collection of numerical algorithms and domain-specific toolboxes, including signal processing, optimization, statistics and much more.\n",
"\n",
"__matplotlib__\n",
"\n",
"matplotlib is a python 2D plotting library which produces publication quality figures in a variety of hardcopy formats and interactive environments across platforms.\n",
"\n",
"__bokeh__\n",
"\n",
"Bokeh is a Python interactive visualization library that targets modern web browsers for presentation. Allows real-time plotting\n",
"\n",
"__PyAudio__\n",
"\n",
"PyAudio provides Python bindings for PortAudio, the cross-platform audio I/O library. With PyAudio, you can easily use Python to play and record audio on a variety of platforms.\n",
"\n",
"__pyrtlsdr__\n",
"\n",
"pyrtlsdr is a simple Python interface to devices supported by the RTL-SDR project, which turns certain USB DVB-T dongles employing the Realtek RTL2832U chipset into low-cost, general purpose software-defined radio receivers. It wraps all the functions in the librtlsdr library (including asynchronous read support), and also provides a more Pythonic API."
]
},
{
"cell_type": "heading",
"level": 3,
"metadata": {},
"source": [
"Importing"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"To import a specific library `x`, simply type `import x`\n",
"\n",
"To access the library function `f`, type `x.f`\n",
"\n",
"If you want to change the library name to `y`, type `import x as y`\n",
"\n",
"\n",
" "
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"import numpy as np\n",
"\n",
"np.ones((3,1))\n"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 36,
"text": [
"array([[ 1.],\n",
" [ 1.],\n",
" [ 1.]])"
]
}
],
"prompt_number": 36
},
{
"cell_type": "heading",
"level": 1,
"metadata": {},
"source": [
"Data Types"
]
},
{
"cell_type": "heading",
"level": 3,
"metadata": {},
"source": [
"Floats and Integers"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Unlike MATLAB, there is a difference between `int` and `float` in Python. Mainly, integer division returns the floor. Always check this when debugging!"
]
},
{
"cell_type": "code",
"collapsed": true,
"input": [
"1 / 4"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 37,
"text": [
"0.25"
]
}
],
"prompt_number": 37
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"1 / 4.0"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 38,
"text": [
"0.25"
]
}
],
"prompt_number": 38
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"However, this will change in the future (Python 3.0)... If you import division from the future, then everything works fine"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"from __future__ import division\n",
"1 / 4"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 39,
"text": [
"0.25"
]
}
],
"prompt_number": 39
},
{
"cell_type": "heading",
"level": 3,
"metadata": {},
"source": [
"Strings"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Unlike MATLAB/C, doubles quotes and single quotes are the same thing. Both represent strings. `'+'` concatenates strings"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# This is a comment\n",
"\"123 \" + 'DSP'"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 40,
"text": [
"'123 DSP'"
]
}
],
"prompt_number": 40
},
{
"cell_type": "heading",
"level": 3,
"metadata": {},
"source": [
"Lists"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"A list is a mutable array of data. That is we can change it after we create it. They can be created using square brackets []\n",
"\n",
"\n",
"Important functions: \n",
"\n",
"- `'+'` appends lists. \n",
"\n",
"- `len(x)` to get length"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"x = [1, 2, 3] + [\"DSP\"]\n",
"\n",
"print x"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"[1, 2, 3, 'DSP']\n"
]
}
],
"prompt_number": 41
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"print len(x)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"4\n"
]
}
],
"prompt_number": 42
},
{
"cell_type": "heading",
"level": 3,
"metadata": {},
"source": [
"Tuples"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"A tuple is an unmutable list. They can be created using round brackets (). \n",
"\n",
"They are usually used as inputs and outputs to functions"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"t = (\"D\", \"S\", \"P\") + (1, 2, 3)\n",
"print t"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"('D', 'S', 'P', 1, 2, 3)\n"
]
}
],
"prompt_number": 43
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# cannot do assignment\n",
"t[0] = 10\n",
"\n",
"# errors in ipython notebook appear inline"
],
"language": "python",
"metadata": {},
"outputs": [
{
"ename": "TypeError",
"evalue": "'tuple' object does not support item assignment",
"output_type": "pyerr",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0;31m# cannot do assignment\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mt\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m10\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 3\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0;31m# errors in ipython notebook appear inline\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mTypeError\u001b[0m: 'tuple' object does not support item assignment"
]
}
],
"prompt_number": 44
},
{
"cell_type": "heading",
"level": 3,
"metadata": {},
"source": [
"Numpy Array"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Numpy array is like a list with multidimensional support and more functions. This will be the primary data structure in our class."
]
},
{
"cell_type": "heading",
"level": 3,
"metadata": {},
"source": [
"Creating a numpy array"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"x = np.array( [ [1, 2], [3 , 4] ] )\n",
"\n",
"print x"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"[[1 2]\n",
" [3 4]]\n"
]
}
],
"prompt_number": 45
},
{
"cell_type": "heading",
"level": 3,
"metadata": {},
"source": [
"Getting the shape of array"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"x.shape"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 46,
"text": [
"(2, 2)"
]
}
],
"prompt_number": 46
},
{
"cell_type": "heading",
"level": 3,
"metadata": {},
"source": [
"Elementwise operation"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"One major advantage of using numpy arrays is that arithmetic operations on numpy arrays correspond to elementwise operations. "
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"print x + 2"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"[[3 4]\n",
" [5 6]]\n"
]
}
],
"prompt_number": 47
},
{
"cell_type": "heading",
"level": 3,
"metadata": {},
"source": [
"Matrix multiplication"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"You can use np.matrix to do matrix multiplication"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"np.matrix(x) * np.matrix(x)\n"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 48,
"text": [
"matrix([[ 7, 10],\n",
" [15, 22]])"
]
}
],
"prompt_number": 48
},
{
"cell_type": "heading",
"level": 3,
"metadata": {},
"source": [
"Slicing numpy arrays"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Numpy uses pass-by-reference semantics so it creates views into the existing array, without implicit copying. This is particularly helpful with very large arrays because copying can be slow."
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"x = np.array([1,2,3,4,5,6])\n",
"print x"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"[1 2 3 4 5 6]\n"
]
}
],
"prompt_number": 49
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We slice an array from a to b-1 with `[a:b]`"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"y = x[0:4]\n",
"print y"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"[1 2 3 4]\n"
]
}
],
"prompt_number": 50
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Because slicing does not copy the array, changing `y` changes `x`"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"y[0] = 7\n",
"print x\n",
"print y"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"[7 2 3 4 5 6]\n",
"[7 2 3 4]\n"
]
}
],
"prompt_number": 51
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"To actually copy x, we should use .copy()"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"x = np.array([1,2,3,4,5,6])\n",
"y = x.copy()\n",
"y[0] = 7\n",
"print x\n",
"print y"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"[1 2 3 4 5 6]\n",
"[7 2 3 4 5 6]\n"
]
}
],
"prompt_number": 52
},
{
"cell_type": "heading",
"level": 3,
"metadata": {},
"source": [
"Useful Numpy function: r_"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We use `r_` to create integer sequences in numpy arrays\n",
"\n",
"`r_[0:N]` creates an array listing every integer from 0 to N-1\n",
"\n",
"`r_[0:N:m]` creates an array listing every `m` th integer from 0 to N-1 "
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"import numpy as np # by convention, import numpy as np\n",
"from numpy import r_ # import r_ function from numpy directly, so that we can call r_ directly instead of np.r_\n",
"\n",
"print np.r_[-5:5] # every integer from -5 ... 4\n",
"\n",
"print np.r_[0:5:2] # every other integer from 0 ... 4"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"[-5 -4 -3 -2 -1 0 1 2 3 4]\n",
"[0 2 4]\n"
]
}
],
"prompt_number": 53
},
{
"cell_type": "heading",
"level": 1,
"metadata": {},
"source": [
"Plotting"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"In this class, we will use `matplotlib.pyplot` to plot signals and images. \n",
"\n",
"By convention, we import `matplotlib.pyplot` as `plt`.\n",
"\n",
"To display the plots inside the browser, we use the command `%matplotlib inline`"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"\n",
"import matplotlib.pyplot as plt # by convention, we import pyplot as plt\n",
"\n",
"# plot in browser instead of opening new windows\n",
"%matplotlib inline\n"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 9
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# Generate signals\n",
"x = np.r_[:1:0.01] # if you don't specify a number before the colon, the starting index defaults to 0\n",
"y1 = np.exp( -x )\n",
"y2 = np.sin( x*10.0 )/4.0 + 0.5"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 12
},
{
"cell_type": "heading",
"level": 3,
"metadata": {},
"source": [
"Plotting one signal"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"plt.figure()\n",
"plt.plot( x, y1 )"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 13,
"text": [
"[]"
]
},
{
"metadata": {},
"output_type": "display_data",
"png": 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3VJYRX1q61MbFZ2TYHq6//rXriERKRmUZkXy6dLGNQTp2tHLNH/4A33/vOiqR2FFyF98q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\n",
"text": [
""
]
}
],
"prompt_number": 13
},
{
"cell_type": "heading",
"level": 3,
"metadata": {},
"source": [
"Plotting multiple signals in one figure"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"plt.figure()\n",
"plt.plot( x, y1 )\n",
"plt.plot( x, y2 )"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 14,
"text": [
"[]"
]
},
{
"metadata": {},
"output_type": "display_data",
"png": 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1y3gCYUBeIClwCHjVOMGGwMaX/EzrDVp1cpWRY0IOI/RmqNYcL7p496KRe2JubR9tLRUZ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\naBLLcb2B5cCN+AYQzil1stQ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"text": [
""
]
}
],
"prompt_number": 14
},
{
"cell_type": "heading",
"level": 3,
"metadata": {},
"source": [
"Plotting multiple signals in different figures"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"plt.figure()\n",
"plt.plot( x, y1 )\n",
"plt.figure()\n",
"plt.plot( x, y2 )"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 15,
"text": [
"[]"
]
},
{
"metadata": {},
"output_type": "display_data",
"png": 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"text": [
""
]
},
{
"metadata": {},
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"text": [
""
]
}
],
"prompt_number": 15
},
{
"cell_type": "heading",
"level": 3,
"metadata": {},
"source": [
"You can also add title and legends using `plt.title()` and `plt.legend()`"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"plt.figure()\n",
"plt.plot( x, y1 )\n",
"plt.plot( x, y2 )\n",
"plt.xlabel( \"x axis\" )\n",
"plt.ylabel( \"y axis\" )\n",
"\n",
"plt.title( \"Title\" )\n",
"\n",
"plt.legend( (\"blue\", \"red\") )"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 16,
"text": [
""
]
},
{
"metadata": {},
"output_type": "display_data",
"png": 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v21ba9Xi4exBQI4A+6/o45ciKt+HhAe3aqU2CGjd+dtu/X3cy/X7c9SN50uSh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\nNwwPHq41h0OtlSTs29IjSxk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9qTy7MqvbrLbpHg5Rpih6/N2DkLMhbGy/UYalCockhUE4\npV7le5E6SWp85/iytMVSymYta/X3fPD4Aa0Wt+LB4wcEdwwmZeKUVn9PIaxBLiUJp9WxREd+rPcj\n9X+rz5gtY6y6yc+x68eoMrsKqZOk5q/Wf0lREA5NRiUJp3fq1ilaLW6Fd1Jvfm74M+mSpbPYa5sM\nE1N3TMV/kz/+Vf3pWrqrLHUh7IaMShLiFXKmzklQhyAKpytM4amFmbBtgkX2czh45SB1fq3DL/t/\nIaRTCN3KdJOiIJyCo/wUS4tBWMS+S/vot74fR68fZWi1oTQr2IyEHgnj9Bp7L+1laNBQgs8E07tC\nb74s9yUJ3KW7Ttift20xSGEQLmnjyY0MDBzIoauHaJS/ER8W+pAqOarEummOYRgcuHKAVaGrWBW6\nirAbYXxT4Ru6lOoi8xOEXZPCIMRbOHP7DH8c+oOFBxey99Je0nim4Z3U75AuWTpuPrzJtQfXuHjv\nIl5JvKibuy5189TlvXfek13XhEOQwiBEPEWZorhw9wInb53k6v2rpPFMQ1rPtKTzTCfzEYRDksIg\nhBDiOTIqSQghhEVIYRBCCPEcKQxCCCGeI4VBCCHEc6QwCCGEeI4UBiGEEM+RwiCEEOI5tigMdYAj\nQCjQJ5bvfwTsA/YDIUBRG2QSQgjxCtYuDB7AJFRxKAi0Agq8cMwJoAqqIAwBfrRyJocWGBioO4Ld\nkHPxjJyLZ+RcxJ+1C0MZIAw4BTwGFgANXzjmX+B29P1tQFYrZ3Jo8kP/jJyLZ+RcPCPnIv6sXRiy\nADF3ZD8X/dirfAyssmoiIYQQr2XtReTjssBRNaATUNFKWYQQQpjB2ovolQP8UX0MAP0AExDwwnFF\ngT+jjwuL5XXCAB/rRBRCCKd1HMitO8SLEqCC5QQSAXt5ufM5O+qDv5xNkwkhhNCmLnAU9eHfL/qx\nLtE3gJnAdWBP9G27rQMKIYQQQgghHMSbJsMBTIj+/j6ghI1y6SATA58x5+cCoDQQCTSxRSgNzDkP\nvqiW939AoE1S6fGmc5EW+Ad1+fo/oIPNktneLOAycOA1xzjs56YH6nJTTiAhsfdHvM+z4axlga22\nCmdj5pyL8kCq6Pt1cO1z8eS4DcBKoKmtwtmQOechNXCQZ3OB0toqnI2Zcy78gRHR99OiLldbexSm\nLpVRH/avKgxx/ty0p7WSzJkM1wCYE31/G+o/gjNuxisTA58x51wA9AD+AK7aLJltmXMeWgOLUfOF\nAK7ZKpyNmXMuLgIpo++nRBWGSBvls7Vg4OZrvh/nz017KgzmTIaL7Rhn/ECUiYHPmPtz0RCYGv21\nM24Qbs55yAN4AxuBnUBb20SzOXPOxQygEHABdfnkC9tEs0tx/ty0p6aVuf+ZX5x74YwfAjIx8Blz\nzsU4oG/0sW5Yf36ODuach4RASaA64IlqVW5FXVt2Juaci/6oS0y+qDlQa4FiwF3rxbJrcfrctKfC\ncB7IFuPrbDxrEr/qmKzRjzkbc84FqA7nGag+htc1JR2ZOeeiFOpyAqjryXVRlxiWWz2d7ZhzHs6i\nLh89jL4FoT4Mna0wmHMuKgDDou8fB04C+VAtKVfj0J+b5kyGi9mJUg7n7XCViYHPmHMuYpqNc45K\nMuc85AfWoTpnPVGdkQVtF9FmzDkXY4GB0fczoAqHt43y6ZAT8zqfHfJz802T4UAt4x2Gum5Y0qbp\nbEsmBj5jzs/FE85aGMC88/ANamTSAaCnTdPZ1pvORVpgBepz4gCqY95Z/YbqS4lAtRo74bqfm0II\nIYQQQgghhBBCCCGEEEIIIYQQQgghhBBCCOHqQnQHEEIIIYQQQlhAadSs0MRAMtTmLrEtJ7EEtd7O\nf0Dn6MdyAMeANKgVi4OBGtHfuxf9ZybU2kV7UDNxK1n8byCEhTjjKpRCvK0hQBIgKWppgYBYjvFC\nLViYFLUMSZXorz8GagM7gFzA59HH3wVSAF+jis5w1P+7ZDwrGkIIIexUQlSrYSuv/qXJH7Vo215U\nQSgb43urUevRJIvx2JNlniujVjkdiFrxVAi7ZU8b9QihW1rUh3pyVIvgRb6ovQ7KAcVRxSFx9Pc8\nUcsZG6gWwouCUcXhPPAzzruJjnACUhiEeGY6MACYT+yXkVKiWgmPUEtcx1zyPACYi2oRzIjludlR\n247OjL451IbsQgjhitoBi6Lvu6MuJ/m+cEwi1Lr2h1Cd0BtQfQxVgS08u/y0GGgfff9O9J/tUZ3O\nu4FNqA5rIYQQQgghhBBCCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQQgjxxP8B6tMDz8EfLOAAAAAA\nSUVORK5CYII=\n",
"text": [
""
]
}
],
"prompt_number": 16
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# xkcd style\n",
"with plt.xkcd() :\n",
" plt.figure()\n",
" plt.plot( x, y1 )\n",
" plt.plot( x, y2 )\n",
" plt.xlabel( \"x axis\" )\n",
" plt.ylabel( \"y axis\" )\n",
"\n",
" plt.title( \"Title\" )\n",
"\n",
" plt.legend( (\"blue\", \"red\") )"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "display_data",
"png": 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cOnQIWVlZAEiRG41GGI1Gt4+n1+vLdSb7ojuDYhQcDgcMBoPHCSvFxcWIjo6u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\n2MHeeOMNduTIEfG1O+64g3377beMMcZ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kSkpKkJ6eXq/stGnT8Mknn0Amk2Hn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\ngg==\n",
"text": [
""
]
}
],
"prompt_number": 17
},
{
"cell_type": "heading",
"level": 3,
"metadata": {},
"source": [
"Showing images"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"`imshow` will be our default function to plot images"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# image\n",
"\n",
"image = np.outer( a, b ) # plotting the outer product of a and b\n",
"\n",
"plt.figure()\n",
"plt.imshow(image)\n"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 19,
"text": [
""
]
},
{
"metadata": {},
"output_type": "display_data",
"png": 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9ajLXWnshZ+lf4s3nxtwhw2dPWUAI6hiPP1tzoznAvfirSUShlTpKbCrtIFzL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\nHNMrGuipkmqmOG/sjqBhk23U+nukF7+OVm/djhTUffHmFsr0NbntXtg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"text": [
""
]
}
],
"prompt_number": 19
},
{
"cell_type": "heading",
"level": 2,
"metadata": {},
"source": [
"That's all!"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [],
"language": "python",
"metadata": {},
"outputs": []
}
],
"metadata": {}
}
]
}