\n", "
\n", " \n", "For a good overview of the history and key user guide tips, see \n", "1.
![](\"images/convertfile.png\")
"
],
"text/plain": [
"')"
]
},
{
"cell_type": "markdown",
"metadata": {
"run_control": {
"frozen": false,
"read_only": false
}
},
"source": [
"### Command-line conversion"
]
},
{
"cell_type": "markdown",
"metadata": {
"run_control": {
"frozen": false,
"read_only": false
}
},
"source": [
"On the command line IPython has a `convert` subcommand to convert from the IPython format to other formats. In the command window, where the file is located type: \n",
"`ipython nbconvert filename.ipynb` \n",
"This will create a fully self-contained HTML file that you can print or mail as a single file. The only problem is that HTML does not print very well, especially with large figures.\n",
"\n",
"The notebook can be converted to LaTeX and then compiled to a PDF format with the following command: \n",
"`ipython nbconvert --to latex --post filename.ipynb` \n",
"\n",
"The LaTeX document style can be specified as follows: \n",
"`ipython nbconvert --to latex filename.ipynb --template=article` \n",
"once the tex document is ready run pdflatex: \n",
"`pdflatex filename.tex` \n",
"\n",
""
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Stored 'table' (ndarray)\n"
]
}
],
"source": [
"%run ./PlayStats.ipynb"
]
},
{
"cell_type": "markdown",
"metadata": {
"run_control": {
"frozen": false,
"read_only": false
}
},
"source": [
"The %store command lets you pass variables between two different notebooks.\n",
"In the PlayStats.ipynb the `table` variable is stored as follows:\n",
"\n",
" # store the variable in the server for another notebook to read it\n",
" %store table\n",
"\n",
"If the other notebook has been executed in the present Jupyter session, the data can be retrieved in this notebook by \n",
"\n",
" %store -r table\n",
" print(table)\n",
"\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"run_control": {
"frozen": false,
"read_only": false
}
},
"source": [
"The %timeit magic can time the execution of a an expression.\n",
"It can be one line or multiline statement. In a one liner we can pass through multiple ones separated by semicolon. \n",
""
]
},
{
"cell_type": "code",
"execution_count": 40,
"metadata": {
"run_control": {
"frozen": false,
"read_only": false
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"129 ns ± 1.61 ns per loop (mean ± std. dev. of 7 runs, 10000000 loops each)\n"
]
}
],
"source": [
"%timeit range(100)"
]
},
{
"cell_type": "markdown",
"metadata": {
"run_control": {
"frozen": false,
"read_only": false
}
},
"source": [
"`% pastebin 'file.py'` to upload code to pastebin and get the url returned."
]
},
{
"cell_type": "markdown",
"metadata": {
"run_control": {
"frozen": false,
"read_only": false
}
},
"source": [
"`% bash` to run cell with bash in a subprocess. "
]
},
{
"cell_type": "markdown",
"metadata": {
"run_control": {
"frozen": false,
"read_only": false
}
},
"source": [
"`%mprun & %memit`: See how much memory a script uses (line-by-line, or averaged over a bunch of runs)"
]
},
{
"cell_type": "markdown",
"metadata": {
"run_control": {
"frozen": false,
"read_only": false
}
},
"source": [
"`%prun statement_name` will give you an ordered table showing you the number of times each internal function was called within the statement, the time each call took as well as the cumulative time of all runs of the function."
]
},
{
"cell_type": "markdown",
"metadata": {
"run_control": {
"frozen": false,
"read_only": false
}
},
"source": [
"`%% HTML`: to render the cell as HTML. So you can even embed an image or other media in your notebook"
]
},
{
"cell_type": "markdown",
"metadata": {
"run_control": {
"frozen": false,
"read_only": false
}
},
"source": [
" %%HTML\n",
" ![](\"http://storage.proboards.com/6578018/thumbnailer/cQSQRzgbljQLzGPJ3hfZ.jpg\")
"
]
},
{
"cell_type": "markdown",
"metadata": {
"run_control": {
"frozen": false,
"read_only": false
}
},
"source": [
"`%%latex` to render cell contents as LaTeX, see [here](http://nbviewer.ipython.org/github/ipython/ipython/blob/1.x/examples/notebooks/Part%205%20-%20Rich%20Display%20System.ipynb#LaTeX)"
]
},
{
"cell_type": "markdown",
"metadata": {
"run_control": {
"frozen": false,
"read_only": false
}
},
"source": [
"$$\n",
"\\begin{aligned}\n",
"\\nabla \\times \\vec{\\mathbf{B}} -\\, \\frac1c\\, \\frac{\\partial\\vec{\\mathbf{E}}}{\\partial t} & = \\frac{4\\pi}{c}\\vec{\\mathbf{j}} \\\\\n",
"\\nabla \\cdot \\vec{\\mathbf{E}} & = 4 \\pi \\rho \\\\\n",
"\\nabla \\times \\vec{\\mathbf{E}}\\, +\\, \\frac1c\\, \\frac{\\partial\\vec{\\mathbf{B}}}{\\partial t} & = \\vec{\\mathbf{0}} \\\\\n",
"\\nabla \\cdot \\vec{\\mathbf{B}} & = 0\n",
"\\end{aligned}\n",
"$$"
]
},
{
"cell_type": "code",
"execution_count": 104,
"metadata": {
"run_control": {
"frozen": false,
"read_only": false
}
},
"outputs": [
{
"data": {
"text/latex": [
"\\begin{eqnarray}\n",
"\\nabla \\times \\vec{\\mathbf{B}} -\\, \\frac1c\\, \\frac{\\partial\\vec{\\mathbf{E}}}{\\partial t} & = \\frac{4\\pi}{c}\\vec{\\mathbf{j}} \\\\\n",
"\\nabla \\cdot \\vec{\\mathbf{E}} & = 4 \\pi \\rho \\\\\n",
"\\nabla \\times \\vec{\\mathbf{E}}\\, +\\, \\frac1c\\, \\frac{\\partial\\vec{\\mathbf{B}}}{\\partial t} & = \\vec{\\mathbf{0}} \\\\\n",
"\\nabla \\cdot \\vec{\\mathbf{B}} & = 0 \n",
"\\end{eqnarray}"
],
"text/plain": [
""
]
},
"execution_count": 104,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from IPython.display import Latex\n",
"Latex(r\"\"\"\\begin{eqnarray}\n",
"\\nabla \\times \\vec{\\mathbf{B}} -\\, \\frac1c\\, \\frac{\\partial\\vec{\\mathbf{E}}}{\\partial t} & = \\frac{4\\pi}{c}\\vec{\\mathbf{j}} \\\\\n",
"\\nabla \\cdot \\vec{\\mathbf{E}} & = 4 \\pi \\rho \\\\\n",
"\\nabla \\times \\vec{\\mathbf{E}}\\, +\\, \\frac1c\\, \\frac{\\partial\\vec{\\mathbf{B}}}{\\partial t} & = \\vec{\\mathbf{0}} \\\\\n",
"\\nabla \\cdot \\vec{\\mathbf{B}} & = 0 \n",
"\\end{eqnarray}\"\"\")"
]
},
{
"cell_type": "markdown",
"metadata": {
"run_control": {
"frozen": false,
"read_only": false
}
},
"source": [
"## Magics for using IPython for numeric and scientific work"
]
},
{
"cell_type": "markdown",
"metadata": {
"run_control": {
"frozen": false,
"read_only": false
}
},
"source": [
"Early tutorials advised that you start the IPython kernel with the `--pylab=inline` option to load a bunch of packages and see Matplotlib graphs in the browser. As from IPython 2.0, the server advises you not to use this option, because it pre-loads a number of modules and packages that may not be required and may even interfere with your intended work.\n",
"\n",
"**Don't use `%pylab` either:**\n",
"Then there is the `%pylab` magic command, which essentially does the same as the `--pylab=inline` option. The full command is [`%pylab [--no-import-all] [gui]`](http://ipython.org/ipython-doc/stable/api/generated/IPython.core.magics.pylab.html). \n",
"When using this magic, IPython loads numpy and matplotlib. The following libraries are imported in this magic: \n",
"\n",
" import numpy\n",
" import matplotlib\n",
" from matplotlib import pylab, mlab, pyplot\n",
" np = numpy\n",
" plt = pyplot\n",
"\n",
" from IPython.display import display\n",
" from IPython.core.pylabtools import figsize, getfigs\n",
"\n",
" from pylab import *\n",
" from numpy import *\n",
"\n",
"Clearly the last two imports could potentially cause namespace conflicts with other modules. This is because `import *` is [not good practice](https://stackoverflow.com/questions/2386714/why-is-import-bad). If you use the form `%pylab -–no-import-all` the last two * imports will not be executed.\n",
"So the `%pylab` magic command could be something like `%pylab -–no-import-all inline` to get inline plots in the notebook.\n",
"\n",
"But this method still clutters the IPython interactive namespace with global pylab names, potentially causing problems.\n",
"\n",
"**Use `%matplotlib` instead:**\n",
"Do use the `%matplotlib [gtk|gtk3|inline|osx|qt|qt4|tk|wx]` magic to define the Matplotlib plotting backend without importing anything into the IPython interactive namespace. For the full discussion [see here](https://carreau.github.io/posts/10-No-PyLab-Thanks/). So the preferred method to get Matplotlib graphics inline in the notebook is to use the magic command \n",
" `%matplotlib inline` \n",
"After using this magic, you still have to import numpy manually. "
]
},
{
"cell_type": "markdown",
"metadata": {
"run_control": {
"frozen": false,
"read_only": false
}
},
"source": [
"Note that the file format to which Matplotlib renders a graphic can be set by the following magic (svg, png or high resolution png): \n",
"\n",
"`%config InlineBackend.figure_format = 'svg'` \n",
"`%config InlineBackend.figure_format = 'png'` \n",
"`%config InlineBackend.figure_format = 'retina'` \n",
" \n",
" \n"
]
},
{
"cell_type": "markdown",
"metadata": {
"run_control": {
"frozen": false,
"read_only": false
}
},
"source": [
"## Results display in Results Cell\n",
"\n",
"Normally IPython will display the _last_ unassigned result from the cell in the result cell. You can modify the `ast_note_interactivity` kernel option to make jupyter do this for all unassigned variables.\n",
"\n",
"If you want to set this behaviour for all instances of Jupyter (Notebook and Console), simply create a file `~/.ipython/profile_default/ipython_config.py` with the lines below.\n",
"\n",
" c = get_config()\n",
" # Run all nodes interactively\n",
" c.InteractiveShell.ast_node_interactivity = \"all\"\n",
"\n",
"https://www.dataquest.io/blog/jupyter-notebook-tips-tricks-shortcuts/"
]
},
{
"cell_type": "code",
"execution_count": 44,
"metadata": {
"run_control": {
"frozen": false,
"read_only": false
}
},
"outputs": [],
"source": [
"from IPython.core.interactiveshell import InteractiveShell\n",
"InteractiveShell.ast_node_interactivity = \"all\""
]
},
{
"cell_type": "code",
"execution_count": 45,
"metadata": {
"run_control": {
"frozen": false,
"read_only": false
}
},
"outputs": [
{
"data": {
"text/plain": [
"5"
]
},
"execution_count": 45,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"text/plain": [
"7"
]
},
"execution_count": 45,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"5\n",
"a = 6\n",
"7"
]
},
{
"cell_type": "markdown",
"metadata": {
"run_control": {
"frozen": false,
"read_only": false
}
},
"source": [
"## Writing functions in other languages\n",
"This example is taken from [Josh Devlin](https://www.dataquest.io/blog/jupyter-notebook-tips-tricks-shortcuts/), thanks Josh!"
]
},
{
"cell_type": "markdown",
"metadata": {
"run_control": {
"frozen": false,
"read_only": false
}
},
"source": [
"You can write functions in cython or fortran and use those directly from python code. First you’ll need to install:\n",
" pip install cython fortran-magic \n",
"Then\n",
"\n",
" %load_ext Cython\n",
" \n",
" %%cython\n",
" def myltiply_by_2(float x):\n",
" return 2.0 * x\n",
" \n",
"Then in some further-down cell:\n",
" myltiply_by_2(23.)\n",
"\n",
"Or \n",
"\n",
" %load_ext fortranmagic\n",
"\n",
" %%fortran\n",
" subroutine compute_fortran(x, y, z)\n",
" real, intent(in) :: x(:), y(:)\n",
" real, intent(out) :: z(size(x, 1))\n",
"\n",
" z = sin(x + y)\n",
"\n",
" end subroutine compute_fortran\n",
" \n",
"Then in some further-down cell:\n",
"\n",
" compute_fortran([1, 2, 3], [4, 5, 6])\n",
" \n",
" "
]
},
{
"cell_type": "markdown",
"metadata": {
"run_control": {
"frozen": false,
"read_only": false
}
},
"source": [
"## IPython notebook extensions"
]
},
{
"cell_type": "markdown",
"metadata": {
"run_control": {
"frozen": false,
"read_only": false
}
},
"source": [
"### contrib nbextensions"
]
},
{
"cell_type": "markdown",
"metadata": {
"run_control": {
"frozen": false,
"read_only": false
}
},
"source": [
"The following repository contains a collection of extensions that add functionality to the Jupyter notebook. There are several extensions, best visit the repo for more information.\n",
"\n",
"https://github.com/ipython-contrib/jupyter_contrib_nbextensions \n",
"https://github.com/ipython-contrib/IPython-notebook-extensions \n",
"https://github.com/ipython/ipython/wiki/Extensions-Index \n",
"\n",
"The following commands will install the extensions, as well as a menu based configurator that will help you browse and enable the extensions from the main Jupyter notebook screen.\n",
"\n",
"Method 1 \n",
"\n",
"https://github.com/Jupyter-contrib/jupyter_nbextensions_configurator\n",
"\n",
"\n",
"Method 2\n",
"The install instructions are taken from [Josh Devlin](https://www.dataquest.io/blog/jupyter-notebook-tips-tricks-shortcuts/). There is a risk that the following installation may not succeed on your Jupyter installation, depending on software version status. \n",
" pip install --upgrade https://github.com/ipython-contrib/jupyter_contrib_nbextensions/tarball/master\n",
" pip install --upgrade jupyter_nbextensions_configurator\n",
" jupyter contrib nbextension install --user\n",
" jupyter nbextensions_configurator enable --user"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"You can install Nbextensions any time from your command line like this\n",
"\n",
" conda install -c conda-forge jupyter_contrib_nbextensions\n",
" conda install -c conda-forge jupyter_nbextensions_configurator\n",
" jupyter contrib nbextension install --user\n",
" \n",
"Once they’re installed, you’ll see an Nbextensions tab. Explore away! "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {
"run_control": {
"frozen": false,
"read_only": false
}
},
"source": [
"### General notes"
]
},
{
"cell_type": "markdown",
"metadata": {
"run_control": {
"frozen": false,
"read_only": false
}
},
"source": [
"The IPython architecture supports the installation of extension packages to add new functionality to notebooks. \n",
"\n",
" and modified [here](http://stackoverflow.com/questions/32046241/how-to-add-automatically-extension-to-jupiter-ipython-notebook). The installation requires at least the first two steps: \n",
"\n",
"1. Download the extension (do this once only) - I am not sure which to use for the different combinations of Jupyter and repos: \n",
" - Let Jupyter download it for you:\n",
" \n",
" !jupyter nbextension install https://github.com/Calysto/notebook-extensions/archive/master.zip\n",
" \n",
" or \n",
" \n",
" !jupyter nbextension install https://github.com/Calysto/notebook-extensions\n",
" \n",
"\n",
" - Download manually:\n",
" Clone the repo at `https://github.com/Calysto/notebook-extensions` into the local directory `C:\\ProgramData\\jupyter\\nbextensions\\notebook-extensions-master`. Note that you need to clone to a different directory name than is the repo name.\n",
" Make a copy of the files shown below, to one level lower than where you cloned, in `C:\\ProgramData\\jupyter\\nbextensions`\n",
" \n",
" \n",
" \n",
" \n",
"2. Activate it by executing the following: \n",
" !jupyter nbextension enable calico-document-tools\n",
" \n",
"3. To activate the spell checker for Jupyer 4.x notebooks, edit the file \n",
"`C:\\Users\\YourUserName\\.jupyter\\nbconfig\\notebook.json` in your Jupyter profile and conform that the following load_extensions commands are present (add in the appropriate place if necessary): \n",
" {\n",
" \"load_extensions\": {\"calico-spell-check\":true,\n",
" \"calico-document-tools\": true,\n",
" \"calico-cell-tools\":true\n",
" }\n",
" }\n",
"\n",
"The ICalico spell checker discussion takes place here: \n",
" \n"
]
},
{
"cell_type": "code",
"execution_count": 46,
"metadata": {
"run_control": {
"frozen": false,
"read_only": false
}
},
"outputs": [
{
"data": {
"image/jpeg": 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"text/html": [
"\n",
" \n",
" "
],
"text/plain": [
""
]
},
"execution_count": 46,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from IPython.display import YouTubeVideo\n",
"# a talk about the ICalico spell checker extension\n",
"YouTubeVideo('Km3AtRynWFQ')"
]
},
{
"cell_type": "markdown",
"metadata": {
"run_control": {
"frozen": false,
"read_only": false
}
},
"source": [
"### UK English spell checker\n",
"\n",
" I have not yet had the time to figure out how to do this for Jupyter 4\n",
"\n",
"\n",
"[Marco Pinto](http://marcoagpinto.cidadevirtual.pt/proofingtoolgui.html) maintains a UK English hunspell-style list [here](https://github.com/marcoagpinto/aoo-mozilla-en-dict/tree/master/en_GB%20%28Marco%20Pinto%29). To implement the UK dictionary in an Anaconda Jupyter installation on Windows:\n",
"\n",
"1. Download the two files `en-GB.aff` and `en-GB.dic` into the (new) folder `C:\\Anaconda\\share\\jupyter\\nbextensions\\typo\\dictionaries\\en_GB\\`. \n",
"\n",
"2. Rename the two files to use underscore instead of dash/hyphen (look at the en_US equivalent). Change `en-GB.aff` to `en_GB.aff` and change `en-GB.dic` to `en_GB.dic`.\n",
"\n",
"3. Edit the file `C:\\Anaconda\\share\\jupyter\\nbextensions\\calico-spell-check.js` to replace `var lang = \"en_US\";` with `var lang = \"en_GB\";`.\n",
"\n",
"It should be a simple matter to find the equivalent directories in Linux.\n",
"\n",
"So now at least we have a UK dictionary, the remaining work is to add new words. Marco Pinto's GUI-based tool at is exceptionally suitable tool for this purpose. Doug Blank also pointed out: \"Also, one can add words to the `calico-spell-check extension` by making a JSON object in a file named `words.json` and putting it next to `calico-spell-check.js`.\""
]
},
{
"cell_type": "markdown",
"metadata": {
"run_control": {
"frozen": false,
"read_only": false
}
},
"source": [
"## Multicursor editor support\n",
"\n",
"Jupyter supports mutiple cursors, similar to Sublime Text. Simply click and drag your mouse while holding down `Alt`."
]
},
{
"cell_type": "markdown",
"metadata": {
"run_control": {
"frozen": false,
"read_only": false
}
},
"source": [
"## Optimising with IPython"
]
},
{
"cell_type": "markdown",
"metadata": {
"run_control": {
"frozen": false,
"read_only": false
}
},
"source": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"from collections import defaultdict\n",
"# defaultdict?\n",
"display(Image(filename='images/introspection.png'))"
]
},
{
"cell_type": "markdown",
"metadata": {
"run_control": {
"frozen": false,
"read_only": false
}
},
"source": [
"You can also access the built in Python help system, by typing help(objectname), and then execute the cell. "
]
},
{
"cell_type": "code",
"execution_count": 48,
"metadata": {
"run_control": {
"frozen": false,
"read_only": false
}
},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# help(defaultdict)\n",
"display(Image(filename='images/python-help.png'))"
]
},
{
"cell_type": "markdown",
"metadata": {
"run_control": {
"frozen": false,
"read_only": false
}
},
"source": [
"Tab-completion help is also available, just press TAB after the period"
]
},
{
"cell_type": "code",
"execution_count": 49,
"metadata": {
"run_control": {
"frozen": false,
"read_only": false
}
},
"outputs": [
{
"data": {
"image/png": 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13yWaVjZw87F4Vm44XziejcllMqPJwHB1034AAKC1IV+xSe1BsTKtNifwWi+E1Vm7LSrY16e/gh/xhsbE5EWIcSNVZU8AANDykK/o8Q3Kl1BJZkzpGXHzAS5k7y6aYJ+c0OSEWKRoTXY6R5fz/oi1uuQt6JwVMQyjOE0PjJmbIs4YYQ6nrH5O+1g8PcagHtI+ameJtDyVDSgZSCI0ok1MSSW9g2qiJ3axpDLoXAEAAGPIV3T5wmlOHcfxc9FoIqRMxWBWMJND9efbusW+A2X0KMkFuYhfWTNIMh8PXR6IR5UV8pRTqSZPySRasS6PXFVglnbsGMVpWI8vLE6j0bZIax1EevWbNJB+PXyMWUyWj6iTUYJRb1JdUZKa8COc34/OFQAAMOQyeu+8a7+rsK9lX0lPjnp9fX1tbS2fz6+urq6srCwtLU1OTg4NDTkdWmsSeJ6LGc+VyYzy08PVTaVpefR9h0ePHrXyvkP6fuaJiYmBgYHe3t6enp7u7u6urq7Ozs6Ojg6SKTp9NAAAhswTD/SvgPMwcwUAAMwhX4G6E3iXPDBV8mwV5REu0qNf7L+BAAAA2gaebwt1xwsF/YfAiTdCN9QDgQEAoEGhfwUAAAAaHfIVAAAAaHTIVwAAAKDRIV8BAACARod8BQAAABod8pWKpJtu9e9vKSU/ub7kwbT29ybt0O7dvTbibMv2qU88AABwViBfqcgXtvqmwsxYyJu29CJE070VCrovWhZ480uqTpzk4m79ul6p/mZvn/rEU327AQCADchXaic3nVXfbtgYyMW9gR5y32jt02jxAACAMeQrBpihgqKvz+wQAjMIov++Q04bv2CHMGgdtIi83vwrulRIeghsaVWGcRqPmzARjfK8tN6k/mZvH5MtLMdTXbtpDVHSDlKx4t+P/Knkg6UTFQCgPSBf0ZUZld5RLElzfn9EWSGMqMsLs4Gkep0Thylm48FgvGx0YVAdcQgkPcprmwV1REPcsOJwijQIEg9G1aqUXhPDOI3GTci1UHyZsrJFImFef7O3j+kWluOpot1IsqI2hNgOSvJBDycR8ii1jfRNS4vJMUbTdHNSRqxT4DkAAFAgX9GTSWXjMeXS4wurF8+ckKTvwZGQ7+dcJFVp7kJK/fIdSpytOE030K7N4sWzqvGiZmmfaujGY7PdSDt402oJkn6lvUm2syYYn1Vq8/nk/4bj2ZhcJjOaDAxjoAoAgIV8xSb1G7mVaZo5gde+lVudldrcmrp9HI2HH/GGxsTsDm+rBgAoh3xFj29QvnRIMmPKN203H+BC9u4GUWd05oRYpGhNdjpHl/P+iLW65C3o/A4xDKM4TQ+MmWMhzpRgDqesfk77WFSwWdqnCvrx2Gw30g5qZ4lUPpUNVM5AxC6WVAadKwAAugoGuP9suKoFrK+v5/P55eXlxcXFubm5kydPzszMjI+PayXYL9fRqPhB7jko/tatdCeUfhdXpjQwy4PBILNGG7sJxuNRZUX5V3ptbgSzUhxPMI3TrB520Iip3aB+LdSisk3TPgZsx2O33YrH5nSqL69HXl+6bF4iCMI/VkLK0MLkTCbnMzmryblNznBynpOznZzzdfr/JgCAmjBPPFxiCT2u/a7CPv1VLaAgpSxra2vk7/jq6urKysrS0tLk5OTQ0JDToUEby4zy08MlU2MWFhbIv0ePHt22bZv51qdOndqxYwf5YWJiYmBgoLe3t6enp7u7u6urq7Ozs6Ojw+VyOX2EAACGzBMPjAcBNArMXAEAMIJ8BcBpysNYpEe51ORNAwAArQb5CrQo9sl1JRrtAfrSc1wUDfREYgCAxtHldAAA9eHmhQKeEQsA0CLQvwIAAACNDvkKAAAANDrkKwAAANDokK8AAABAozPMV97S85bTZ047HV4jkG42tTZxk70lpcp7UJRbW+3f1WojzlqpwfEakCrGnb0AAO1i6vTUZW+5zKSAYb4y2DcYy8Scjr8R+MJW33yXGQt51Yeph6t7B4x0a6vui5YF3jwl0ImTpDDWr/mV6q/P8RrghULFl00DAEDLGMuOed1ekwKG9zPHPhbzftO7dfPWW668pX9rv9MH0gxy09lgX9jpKFgkhaljf0vjHS8AADSdqdNTDx1/KJaJZf88a1LMMF8hOQrZcuR/jTyYfnBmYcbpw6mlDldHB9fR6ersdnX3dvSe23nuW7resqVzyx3vuEMrlBN4j/za4Wg6rbtcfJmfwNMfM6Mu+hbhRCJEt5E7HNQV0tvw6LPAaB20iLxeLa9Lq720KsM4lU20kmWVkS2CwWwgJvDTxvVXjKj4eOlysvnItKdk/wLvkgOl7UYjDwaDiUSCi8bj2ZC4uqQhtGDlFfrtLxejO1M/0LXMfoNBLiAov7LWdtv3b5tfm1/ILyyuLS6tL60WVtcKa+vc+nph3enQAACKXPaWy7xuL0k5zDtHzJ4XR7b8YeiHTh9IXRi971BZnxn1JAOzBcHNydfCYJx2JAgj6nJp9sZoH728isM4wwI/wpVdDgfThQK9AKvF3bwwy3Fj0kJxw8FRV8o0XLFQWOBH+4SSpMYwTrpJToyoZAOXn1Mikj4EYib1m0akd7zy4fg9Yjom7iWTyXCcWzx2NVCx3XiOJBNiK0jxx1I8+W+hIJCIUpmwTwvC1xfkggEteTJof184HY2kBmkxEkM8S46Fp2WKGigbaJMnyD1y6yN43yEAtBLcH6Qnk8rGY8ol0hdWp5PkhGQiEfLIM0zF7/mRVKU5Hym/MiNV6Raof5ymG2j9F2JyUZenvwfjs8pefD6pUyTpTat7Iula2puU59VE5Rf8BQODZdWITZ1kkhWz9veF49mYXGdmNBkY9sn7GvEqG/gj0XR7dK4AALQe5Cs2BeOFIqYdEuTbvdSbQVmctQuKYDwd50IjRTOGjdtfzEzGxOyl+C3HzLt50pwfD+gHAGhOyFf0+AblS58kM6b0jLj5ABeydxdNsE++oOaEWKRoTXY6R5fz/oi1uuQt6G3EYhhGcZoeGHPHkHgLNHM4ZfVz2scN3bBM2k3t/JD2m8oGLPXr9IldMSGPsmvz9he7WFIZpnOFE8fgcFM0AEArQL6iyxcm38WVcRw/F40mQvTRJuT6qa1gnnciXvk9oQQpxj6NRBqNUEYvklyQi/iVNYPkyuuhywPxqLJCfvqKVJOn5LEm2siGNOdDnjajH6dhPb5wIZD0aFsws1v16jekf7x0v/6IOmajRs/HmN2S/Y4o822VFiGb0MyCLtAmyXJ0mVyXUfsrh8D5/WznCq2YOVyMBwEANCdXoVBwOgYHGM23HRoacjo02IDMKD89XJcpOc5ZWFgg/x49enTbtm3mJU+dOrVjxw7yw8TExMDAAObbAkArQf8KtI7imSsAANA6kK9A81NeYiANL2HCCgBAC+raeBUADpOeH+N0EAAAUEfoXwEAAIBGh3wFAAAAGh3yFQAAAGh0yFcA2kIuh3nIANDEkK8AtLzXxnef+9c/djoKAIANQL5SkXSzrLX3zshPri95MK39vUk7tPtt2EacAAAAzQX5SkW+sNU3FWbGQt60pRchmu6tUNB90bLAm6dAOnGSFMZ63lOp/o2qd/1g4OSj/3778Pe58V2XOR0JAED18PyV2slNZ4N9DfUYEJLCoL+l7b3tk985wa2/88ifzDgdCQBA9dC/YoAZ2inqFmCHfJjBF4P3/2njO+wQD62DFpHXm3c9SIWkh7eWVmUYp/G4EhPRKE9fX2xSv3lMtKwwqu1et32qqR8AAECDfEVXZlR6R7Ekzfn9EWWFMKIuL8wGkmqOIA7jzMaDwXjZaNCgOkIUSHqU1zYL6oiPuGHF4SZpkCgejKpVKa/0M4zTaFyJZA7iS5mVLRIJ8/oNCbxSDWmFUISLyi961m8f+/UDAACwkK/oyaSy8ZhySfWF1ct+TkgmEiGP3EngCSW4SKrSnIyU2qkQSpytOE030HIpMY2oKnMg7eBNy9W4+Zi622raBwAAoDLkKzapPShWptXmtG6IgoVulCbl7vNW2T4AAADWIF/R4xv0hsbUjoHMmNIz4uYDXMjeXS7BPvmCnRNikaI12ekcXc77I9bqkregc0TEMIziND0wZu6IOK+EOZyy+jntY1FB0g7ZmFqNdmAV2segfgAAgEqQr+jyhdOcOo7j56LRRIjOHuUFZgUzpVR/vq2bH/EqSzxJLshF/MqaQXJl99DlgXhUWSFPYpVq8pRMohXr8shVBWZpx4VRnIb1+MLiNBpti7TWAaJXvyE+plUjHpi63KB97NYPNXXxB//kVtzPDABNzVUoFJyOwQHkqNfX19fW1vL5/Orq6srKytLS0uTk5NDQkNOhNSGSHaUGkYDUycLCAvn36NGj27ZtMy956tSpHTt2kB8mJiYGBgZ6e3t7enq6u7u7uro6Ozs7OjpIruj00QAAVAnPX4GNyqQiwb5Zp6MAAIBWhvEgqI72GBc/l8b9yQAAUFfoX4HqiDdDN9TDfAEAoIUhXwFoDqdOnXI6BAAAxyBfAWgCdCItAEDbwvwVAAAAaHTIVwAAAKDRIV8BAACARod8pSLpxl3mOa0m5CfXlzyY1v7epB3mqtnSWpwbRA+zrg/Ul/Zgvw0AAKBFIV+pyBe2+qbCzFjIm97gi/7E24QLui9aFnjzDEEnTpLCWL/mV6pf4+aFyq+C3hheKNR5DwAA0Exwf1Dt5Kazwb6GeiQJSWHORn8LAABAnaF/xQAztFPU7cAO+TCDL/rvO+TYx8BqXR3seIq83rxrQyoUSkT8ZVUZxmk8rsRENMrz0nqT+k1Mq7tm9qx3vJQWKC/u1tqvQatO3od++8vF6B7VD3U4LQAAwBnIV3RlRqV3CEvSnN8fUVYII+rywmwgqV6pxWGc2XgwGC8bDRpUR4gCSY/y2mZtPEXcsOJwkzRIFA9G1aqU598bxmk0rkSu5eJLmZUtEgnz+s1EQvKuZ+PZmJaa6BwvJ6UZWqBeebcW+PqCXDA+q7aoQfv7wqQNo/JrAcjRiMci8HU+SQAA4OxBvqInk8rGY8ol2xdWL/s5IZlIhDzy13tPKMFFUpXmfKTUTouQ5av0RuM03UDLpcQ0pfo3/yjpAefu8yamp82P182PeJWG80csJhNiU5P0RAvRpP19YS1ryowmA8N4WzQAQCtBvmKT2oNiZVptTuC13gyrs3abmNnxSn04ak+QpcGaYDwd50IjRcNKxu0vZkRjYvYixLgRvH8RAKC1IF/R4xuUL32SzJjSU+DmA1zI3n28wT75gpoTYpGiNdnpHF3O+yPW6pK3oHM4xDCM4jQ9MGZaiTjTgzmcsvo57aPV+5cNjlfgq7o5uY8X0t6QOqxk3v5iF0sqg84VAICWVGhL6+vr+Xx+eXl5cXFxbm7u5MmTMzMz4+PjWgm2cyAaFT/I3+yLew2Ur/ulfSfKVBBmeTAYZNZoYzfBeDyqrCjvgtHmlDAr6YwOkzjN6mEHjZjaDerXQtXKqrEXhSytNzre4pGqot3qkspLYRTXb9T+zCFUrry5zNtHzmRyPpOzmpzb5Awn5zk528k57/ShAABUz0X+dzbTowZRkFKWtbU18nd8dXV1ZWVlaWlpcnJyaGjI6dBgAzKj/PRw9VNyGtLCwoLdTSYmJgYGBnp7e3t6erq7u7u6ujo7Ozs6Olwul9NHAwBQJYwHQevAzBUAgFaFfAWan/KUFukRMniKPwBAC0K+Ag5hn/xWwu6riZh7jzZyhzYAADQsPI8fHOLmhQKeQQsAAJagfwUAAAAaHfIVAAAAaHTIVwAAAKDRIV8BAACARod8pSLpZllrE0PZW17s3uPC7k3aod27cm3ECQAA0FyQr1TkC1t9U2FmLORVHwYfru4dNtKtubovWhZ48xRIJ06SwljPeyrVr3fAevVXUQ8AAIA53M9cO7npbLAv7HQULJLC1LW/pd71AwAAUOhfMcAM7RR1F7BDPszgizgY4wklEqHSbZTxHXaIh9ZBi8jrzbskpELSw1tLqzKM03hciYlolKevTTap3zSk0pLW4pTbjS4hEUiRCPJ67SC0DXgxTKdPCAAAcBLyFV2ZUU8yIL+jOM35/RFlhTCiLi/MBpLq5VUcxpmNB9XXBWujQYPqCFEg6aHF3bygjviIG1YcbpIGieJB7c3DyjNcDeM0GlciGYWfU6pJc4mEef0VQiqt37genXajrZDgyPLZeDaUDIgRRSMpqYlIMqMdmFcOEwAA2hbyFT2ZVDYeUy61vrB6Wc4JyUQi5JG/9ntCCU6+vJpIqZ0NoZpfdY3iNN1Ay6XE9OKsPL3epN2i8gsKg4FBdgs3P+JVNvBHommBr3+UAADQuJCv2KT2oFiZVpsTeK03w+qs3dZkq91EzDuB0pwf40EAAO0N+Yoe36A3NKZ2nGTGlJ4RNx/gQvbufgn2yRfmnBCLFK3JTufoct4fsVaXvAWd+SGGYRSn6YExM07E+SbM4ZTVz2kf7dygXVaP/XYTeLxmGQAAGIW2tL6+ns/nl5eXFxcX5+bmTp48OTMzMz4+rpVgO0OiUfGD3ENQ3EuidBuU9p0oUziY5cFgkFmjjd0E4/GosqK8C0abC8KsDMZnzeM0q4cdNGJqN6hfC5Utaz/O8nZTWyCapj+S0mmlJYpHtorCbDfz9pEzmZzP5Kwm5zY5w8l5Ts52cs47fSgAANVzkf+dtdyocRSklGVtbY38HV9dXV1ZWVlaWpqcnBwaGnI6NIAiCwsLdjeZmJgYGBjo7e3t6enp7u7u6urq7Ozs6OhwuVxOHw0AQJUwHgQAAACNDvkKAAAANDrkKwAAANDokK8AAABAo0O+AgAAAI0O+QpAC7rttq89+Tq7IJf4dI/TQQEAVA/5CkBLyty386a7j55yOgwAgNpAvgLQgvZKj1V+PPzlIyedDgUAoBaQr1QkPrTeZe39NfKT6132Hl9fvjdph3YfR28jTmh579u9d1D87+OfO/yc07EAANQA8pWKfGGrbyrMjIW86pPjK77Qz3Bv7MP6GQJvngLpxElSGOt5T6X69Q5Yr/4q6nGq/lZ2wUVX0B/++ZXXnI4FAGDjupwOoIXkprPBvrDTUbBIClPX/pZmr7+F3Xbbvnn6049e+Q3HXeJ0PAAAG4T+FQPM0E7R13l2yIcZfBEHYzyhRCJUuo0yvsMO8dA6aBF5vXmXgVQolIj4y6oyjNN4XImJaJSnr0E2qd80pNKS1uIsbTepWGl9G6+fLiFHKB2pIK/XGknbgBeb4aydWQAAUAXkK7oyo55kQH63cJrz+yPKCmFEXV6YDSTVy584jDMbDyqva2ZGgwbVEaJA0kOLu3lBHfERN6w43CQNEsWD2muKBd5tHqfRuBK54vs5pZo0l0iY118hpNL6jesxa7d0NBHyKFGN9E3XqH7aygmOLJ+NZ0PJgHjE0UhK2jNJZrSG88rN0EoeeeSRJ5544vjx4y+/vOcGp4MBANg45Ct6MqlsPKZcCn1h9bKZE5IJcm2Vv5Z7QglOvvyZSKmdAaGaXxWN4jTdQMulxMt/5cykBiq1WzA+q0Tl81Ux78ek/ugIPcBgYJDdws2PeJUN/JFoWuDr3wpn14tHbrrppmuvvfbyg5hvCwCtAPmKTWoPipVpteRbvNabYXXWbmuy1W5no36pr0btmWq58aBnjtIj+ujXh65zOhYAgBpAvqLHN+gNjakdAJkxpWfEzQe4kL27U4J98oUzJ8QiRWuy0zm6nPdHrNUlb0FnZohhGMVpemDMjBBxPghzOGX1c9pHOzdol9VTRbvVuX6Bt3/DeFM5ILXGR0fvvvltTocCAFAThba0vr6ez+eXl5cXFxfn5uZOnjw5MzMzPj6ulWA7Q6JR8YP8Db64l0T5Wl/ad6JMsWCWB4NBZo02dhOMx6PKivIuGG2uBrNSHD8xjdOsHnbQiKndoH4tVLas/Tj1281Cs1Vdv9rC0TT9kZROKy1dPHJW1AyNZt4+ciZ/4AN3Cs+fJOc2OcPJeZ7PvxIPdTl9KAAA1XOR/20w42lGBSllWVtbI1nL6urqysrK0tLS5OTk0NCQ06EBFFlYWLC7ycTExMDAQG9vb09PT3d3d1dXV2dnZ0dHh8vlcvpoAACqhPEgAAAAaHTIVwAAAKDRIV8BAACARod8BQAAABod8hUAAABodMhXAAAAoNEhXwEAAIBGh3wFAAAAGh3ylYrEh9a7rL1fRn5yvcve4+vL9ybt0O7j4m3E2fikhmzxR+YDAIB1yFcq8oWtvqkwMxbyqk92r/KFftJr+HRftCzw5imQTpwkhbF+za9Uv94B69VfRT3leKFQ8WXTAADQPrqcDqCF5Kazwb6w01GwSApT1/6WetcPAABAoX/FADO0U9RdwA75MIMv4mCMJ5RIhEq3UcZ32OENWgctIq8375KQCoUSEX9ZVYZxGo8rMRGN8vQ1xSb1m4ZUWtJanKXtJhUziFcLVj46/XrkYnRj9QNdp23Ai4d71s4gAACoIeQrujKjnmRAfvdvmvP7I8oKYURdXpgNJNUcQRzGmY0Hldc1M6NBg+oIUSDpocXdvKCO+IgbVhxukgaJ4kHtNcIC7zaP02hciVzL/ZxSTZpLJMzrrxBSaf3G9Zi1WzqaCHmUqEb6potq7AvKb2GmLWpQjy9M2jCaprsjdYoxCDwnpTdaA3nlwwUAgKaDfEVPJpWNx5RLrS+sXpZzQjJBrq3y13VPKMFFUpXmaqTUzoZQza+WRnGabqDlUmKyUDkzqYFK7SZmJHJUPp8270fchGQbWogm9fjC8WxM7pvJjCYDw7QaNz/iVTbwR+QkBgAAmg7yFZvUHhQr02rJt3utN8PqrN3WZKvd1G3ScS40UjRCZFyPmJmMidmLEONGtCxM6vNRe6AwHgQA0JyQr+jxDcqXPklmTOkZcfMBLmTv7pdgn3xBzQmxSNGa7HSOLuf9EWt1yVvQORxiGEZxmh4YM0NEnOnBHE5Z/Zz20c4N2mX1VNFuVB8vpL0hj7KpeT1iF0sqw3SucOLkFdwUDQDQEgptaX19PZ/PLy8vLy4uzs3NnTx5cmZmZnx8XCvBdoZEo+IH+Zt9cS+J8nW/tO9EmcLBLA8Gg8wabewmGI9HlRXlXTDaXBBmJZ3RYRKnWT3soBFTu0H9WqhsWftx6rebUbPRIKXN1SLyOv32Z3ZSdEzFI2RFq5rFvH3kTCbnMzmryblNznBynpOznZzzTh8KAED1XOR/9UiDGlxBSlnW1tbI3/HV1dWVlZWlpaXJycmhoSGnQ4MNyIzy08NnZUrO2bOwsGB3k4mJiYGBgd7e3p6enu7u7q6urs7Ozo6ODpfL5fTRAABUCeNB0DqKZ64AAEDrQL4CzU95Sov06BdMWAEAaEF4vi00P/EeoIZ6sDAAANQY+lcAAACg0SFfAWhZp06dcjoEAIDaQL4C0JLmnrz3pi//jP787APnnvvlSacjAgDYAOQrAAAA0OiQrwC0oGe+tue+FPd4+KbLDz4nL5rG2x4BoIkhX6lIulnW2ntn5CfXu+w9vr58b9IO7d6VayPOFlR9u7Wm9/3VwS8Nch8dfeLlPdfRJf9x18tOBwUAUD3kKxX5wlbfVJgZC3nVJ75beaGf/t7Yh/UzBN48BdKJk1zErV+/K9Wvd8B69VdRTw0YtxtQf3PsbqdDAACoHp6/Uju56Wywr6EeA0Iu4nXtb6l3/VDu9ttvL1/40EMPOR0XAEB9oX/FADO0U9RdwA75MIMv4nCEJ5RIhEq3UcYp2KEKWgctIq8375KQCkkPby2tyjBO4/ERJqJRnr6+2KR+05BKS1qLs7TdpGJsfVrZ0Qz7s1F7mv8O9dvZIB5O7B9Sl4vNU/tTa2PKUxMkKwDQDpCv6MqMepIB+d3Cac7vjygrhBF1eWE2kFSvf+JwxGw8qL4uWBsNGlRHiAJJDy3u5gV15ELcsOJwkzTYEQ9qrxdWXulnGKfR+Ai5bPs5pZo0l0iY118hpNL6jesxa7d0NBHyKFGN9E2L7SO2Cakm7KNtJb6pWW5SnfY0YtLORvGQNEZrUG+iMWeosgkKkhUAaBPIV/RkUtl4TLnU+sLqRS8nJBPk2ip//faEElwkVWmuRkrtbAjV/OpnFKfpBlouJV7Dz8rLjCu1G5OO+Hzyf8PxrFwmlfQybzGsRXsax+PmR7zKCn8kmhb4+rdONWiagmQFANoH8hWb1B4UK9Nqybd1rTfD6qzd1mSr3SSDgWxMyHGZ0ZB3UC5cw/Y0jEfqI1J7rBpvPEhVKVm5wPv+waL7mQEAmhnyFT2+QW9oTO0AyIwp3+TdfIAL2bv7JdinXmtjkaI12ekcXc77I9bqkregcy/EMIziND0wZtqHOKmDOZyy+jnto50btMvqqaLd5K6OMT6WjQ8zuY1xe1aIhm1n43gEvnVuiH7rh+85fvy4dD/zDXcuLt494HRAAAAbUWhL6+vr+Xx+eXl5cXFxbm7u5MmTMzMz4+PjWgn2y3s0Kn6Qv5EXf6tXvqaXftdXpnAwy4PBILNGG7sJxuNRZUV5l4E2F4RZKY6fmMZpVg87aMTUblC/Fipb1n6c+u1m1GzMnosXGbSncTz67Wz4eyweUSsJxxnz9pEzmZzP5Kwm5zY5w8l5Ts52cs47fSgAANVzkf9tJN1pUgUpZVlbWyN/x1dXV1dWVpaWliYnJ2+99VaXy+V0dACahYUFu5tMTEz4fL5Nmzb19PR0d3d3dXV1dnZ2dHTg3AaA5tXu40F0ZiX9gfxNJ0mM0xEBbNTmzZvpD+rpDQDQ7No6Xyn5U37hhRe+8MIL7dnhBC1jfn7+/PPPZ5cgZQGAFtDW+YqKfg11u90kX3nppZdWV1eRtUAzIsnKs88+S85k9KwAQItp6/krdNYtsbKysry8vLS0dObMmVOnTr3xxhvkB6djBLBn8+bN55133rZt28gPvb29dP5Kl6RDggwGAJpX+74/SJ22QtE/6OQv+9atW8855xySwdBUZm1tjc5M5qQsx+moAUTs2dvZ2UmTEpKdkByFnV3LTs9yOmQAgA1p33yFK0tWOhXkLz5NTdRJuMhXoKGoWYh66nZL2N6UkpQFAKCptXW+wikpi/pHn/ytV7MTsnxtbY19aoXTwQJo2Gyb5is0WaE5N5uvOB0pAEANtHW+UvIXn01W6BI2X+HQvwJn0dLSUm9vr9FadjxIPXsJNmshy3/7299ecsklyFoAoAW0ab6i/vmmf+5JIkLHfcife4578ehn7usIf/PGSzvpnNyadq7M/vS/PsLd9td/6Nl4NcfIf6/8+J9/4qpz69NINQrVcY4dB/0lfbB414vP/+ibj73wQdN4xDIvXUF+s5tNKp984Nkte27efp429YpmLeQclnpYTj92h/9//ptnvzNEzvGffrnn65f/5pGz8W5LAID6aNN8RcV+Q1WGgTrWfv/7QuemTZvq0bnSkT99musw+eZsyctTR077P/fAhy+vZ9vUJlTnOXYcZMeXXXbtkanXPtSv/qJ+/y+/PHPZZafzpvGsFs6cPlPoMQ25Y+30UqGbFHGps1VovqL828Gt5wuc3LOCrkEAaHZtna/QP+XkDz0n/UEnf+Vp7rL+xhuFk8987e4HnhE71f/tfd8LvFMs8Osjt+3j9j9y8zuljV9UP5Gfjm7bf8Oz+w5Ib9Dz7T34V++7QCoz98zX9kgLtWUd+ddf53733N/tLyusT61CK0v2t198bfD+P/0ur4ZTrCQkphhTX9EKo1DpdZWupaWNapBaRKCB7u07cEBtKp1DMFxqoRmYnRq3fHEw6nFYbWKzAK0HThrwzLve9ZEDL/zmj69Umv+5n138rhumnzolx6NzXGTRHeKip+54SvzMGfwqSTK0JKU05Jyd+98PDP/tk+JrJVb/aN/j/2XHxZ2v/8NfXv8fHl3NJ/4g8fITS3e7OOQrANDk2jdfIX/m1Xm1NGVR+lpc64uL3/jS7H/76eNfKhR+94/7Pvmx73/jqdu2c51r8/Ncp3Ld0z6Rnx6/b+TSrz/x6N0cdyJ+0+f+/obxez78VvLjp+8jS+/e/vqT9+78+2ekZdJm377vtbLCusiGn77vCrEKTir86fu/RAq/+1OP/s3aTZ/jvv5EaLvBwRWFJO7+4z+QSqshcbTCz4384Jqi5aWhikf4ZnEURjXcNPLSl8YfJRuKddw3z/2Z1DjiITz9/vFH734rPZ6d93Ni7Ww14oH9wORgjHdq1PJkPyMv7JWCkRYrwVSqW4vEIGzD5Ua/hWXOe83Oncf/76feLcX++omnL73mL7kX5tekePSP68N3f5u7dyfZi1TxCYNfZWd+7gzXvWnTptNP7Bv6T+/81mTqX5PT+J++vd3/ee5n37jlk3+X5dauOvLxF7/7KQ/HTZKz2+n/hwMA2JC2fr6ti8Hc0uxae/PNvxj7zHU9IveO4J/+/ivPvUAuDJ35hYV8R4+sQ/0k/jT8jdu90mLve4YXjrx6kvz0/M+/sjD8HnGp+2P3Hrv3Y+4ek8K6Fn7x5BG1bI/39m8MH/nCY88X71xf0V7cH9s5vPCVnz8v1XHsmFKftHdai3Go+Y7nD330C8tf/IEWhVEN/i/eQTd0f+yOL/qV5Y994cjwTrlCac2ydAjsARRVqctgp4Ytz+5UbDazxtKNxChso+WGVS8XukhctPGlX2jfe7xdhWV5j0bHJZYgW5r8KsnZuP77FZKv9Cw898SRz3znz66jM22v/4vEnd//rPCceDJza/J4kNiPiP4VAGhy/x+mS42ElgPNPgAAAABJRU5ErkJggg==\n",
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# defaultdict.\n",
"display(Image(filename='images/tabcompletion.png'))"
]
},
{
"cell_type": "markdown",
"metadata": {
"run_control": {
"frozen": false,
"read_only": false
}
},
"source": [
"You can also obtain the docstring by prepending a function with a question mark and then executing.\n",
"\n",
" ?str.replace()"
]
},
{
"cell_type": "markdown",
"metadata": {
"run_control": {
"frozen": false,
"read_only": false
}
},
"source": [
"The IPython *Help* menu item has links to IPython and the scientific packages"
]
},
{
"cell_type": "code",
"execution_count": 50,
"metadata": {
"run_control": {
"frozen": false,
"read_only": false
}
},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"display(Image(filename='images/ipythonhelp.png'))"
]
},
{
"cell_type": "markdown",
"metadata": {
"run_control": {
"frozen": false,
"read_only": false
}
},
"source": [
" "
]
},
{
"cell_type": "markdown",
"metadata": {
"run_control": {
"frozen": false,
"read_only": false
}
},
"source": [
"## Notebooks remember, but not the way you might think "
]
},
{
"cell_type": "markdown",
"metadata": {
"run_control": {
"frozen": false,
"read_only": false
}
},
"source": [
"\n",
"\n",
"The notebook visible in the browser is not really the notebook, it is only a rendering of the notebook. The actual notebook and its data resides in the IPython kernel. The browser is connected to the kernel via a zmq channel, for the purpose of rendering and input. You can close all the browser windows, but the code and data still resides in the kernel process. As long as the kernel is running you can open a new browser window at 127.0.0.1:8888 and the notebook will be displayed.\n",
"\n",
"The results (including graphs and images) from previous runs are recorded in the notebook, so when it is rendered the previously stored results are shown. The previous results are overwritten only if the cell is executed again.\n",
"\n",
"Most important, the results from a previous cell execution remains in the kernel, even if the cell is removed or moved up in the notebook. It happens from time to time that you execute a cell in a given location, creating some data in the kernel. The next cell uses this information in a calculation. So far, so good. Now you decide to re-organise the notebook and move the 'next cell' to a position earlier in the notebook, even before the cell that created the information in the first place. The newly moved cell still works in its new location, because the information is still in memory. At the end of work, you save and close the notebook and exit the kernel.\n",
"\n",
"Tomorrow you start a new kernel and load the notebook. To ensure that all is fresh and well, you decide to execute all cells in the notebook. But now the moved cell does not work, because its input information is not yet created - it is only created a few cells into the future. Yet it did work yesterday (because of results still remained in memory after moving the cell). But today it fails because there is no memory of something that is yet to come.\n",
"\n",
"It is important to understand that the concept of the non-existence of data prior to cell execution does not apply if you move cells around in the notebook. All results are remembered and accessible by cells _linearly before_ the cell that created the information.\n",
"\n",
"Similarly, remember that a cell changes information for prosperity, also for subsequent runs of the same cell. For example, suppose we assign a value to a variable in the next cell and in the cell thereafter increment the value. If the first cell is executed once and the second cell is executed repeatedly, the value will increase much more than it would be if the program was executed from start to finish with each cell executed once only."
]
},
{
"cell_type": "code",
"execution_count": 51,
"metadata": {
"run_control": {
"frozen": false,
"read_only": false
}
},
"outputs": [],
"source": [
"#run this cell once\n",
"a = 5"
]
},
{
"cell_type": "code",
"execution_count": 52,
"metadata": {
"run_control": {
"frozen": false,
"read_only": false
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"6\n"
]
}
],
"source": [
"#run this cell several times\n",
"a = a + 1\n",
"print(a)"
]
},
{
"cell_type": "markdown",
"metadata": {
"run_control": {
"frozen": false,
"read_only": false
}
},
"source": [
"A similar error occurs in Pandas if the first cell creates a dataframe, and the second cell adds a new column to the dataframe. If the second cell is executed a number of times, many columns will be added, which was not the intention with the code _if executed linearly from start to end_.\n",
"\n",
"Therefore be careful with cells that modifies its own input data - such data is not static and changes with each invocation of the cell."
]
},
{
"cell_type": "markdown",
"metadata": {
"run_control": {
"frozen": false,
"read_only": false
}
},
"source": [
"## Reloading imports\n",
"\n",
"Python's import facility loads a file only once, if the `import` is encountered again for the same module, the import is ignored. If you are actively developing the the module to be imported the module contents changes all the time and these changes must be imported to see their effect.\n",
"In this case you want to force another import execution. This can be done with the `%load_ext autoreload` magic command. If the extension is already loaded, Ipython may complain, so I use `%reload_ext autoreload`, which attempts to load or reload, as appropriate.\n",
"\n",
""
]
},
{
"cell_type": "code",
"execution_count": 53,
"metadata": {
"run_control": {
"frozen": false,
"read_only": false
}
},
"outputs": [],
"source": [
"%reload_ext autoreload\n",
"%autoreload 2\n",
"import numpy as np"
]
},
{
"cell_type": "markdown",
"metadata": {
"run_control": {
"frozen": false,
"read_only": false
}
},
"source": [
"## Clearing the IPython memory\n",
"\n",
"If for some reason you need to clear out all memory in your notebook this can be done by selecting the `Kernel Restart` menu option. This will clear the kernel memory, including the memory of all open notebooks. So use this avenue with care. After restarting the kernel, all notebooks must be re-run from start to build the information again.\n",
"\n",
"You can use the Cell | All Output | Clear menu option to *remove all output* from a notebook. It will be much smaller, but also empty of any output or embedded media. To see the full notebook with all calculation results, you would have to run all cells again."
]
},
{
"cell_type": "markdown",
"metadata": {
"run_control": {
"frozen": false,
"read_only": false
}
},
"source": [
"## Markdown (MD) language syntax\n",
"\n",
"Markdown is a simplified syntax for simple text layout. It allows you to embed simple formatting commands in a regular text file. In an ASCII text editor you will see the markup but not formatted. You can see the formatted version in a local or online markdown editor.\n",
"\n",
"1. https://en.wikipedia.org/wiki/Markdown \n",
"2. https://daringfireball.net/projects/markdown/\n",
"3. https://help.github.com/articles/markdown-basics/\n",
"\n",
"You can write markdown in any text editor or in a dedicated markdown editor:\n",
"\n",
"1. https://notepad-plus-plus.org/\n",
"3. http://www.sitepoint.com/best-markdown-editors-windows/\n",
"4. http://www.sublimetext.com/ (paid software)\n",
"5. http://markdownpad.com/ (paid software)\n",
"\n",
"IPython uses the markdown syntax for text in its non-code cells. When IPython creates a new cell it is a code cell by default, you must remember to change it to a markdown cell on the menu.\n",
"\n",
"You can also edit markdown online here:\n",
"\n",
"1. https://github.com/benweet/stackedit\n",
"2. http://dillinger.io/\n",
"3. http://markdownlivepreview.com/\n",
"\n",
"\n",
"One confusing matter is the fact there are different variants of markdown. For more details on the syntax see \n",
" \n",
" \n",
" \n",
" \n",
" \n",
" \n",
"\n",
"\n",
"You can also use embedded raw HTML into a markdown page, but you need to type a lot more than in pure markdown.\n",
"\n",
"IPython uses the Pandoc converter to convert between different markup languages, so my guess is that it is prudent to work with the Pandoc variant of MD syntax (which is close to the original). The other major MD variant is the github MD syntax - which is somewhat different.\n",
"\n",
"To force a newline you must end the current line with two spaces.\n",
"\n",
"If your markdown does not want to work right, first try to leave a blank line before the offending text."
]
},
{
"cell_type": "markdown",
"metadata": {
"run_control": {
"frozen": false,
"read_only": false
}
},
"source": [
"### Markdown Cells"
]
},
{
"cell_type": "markdown",
"metadata": {
"run_control": {
"frozen": false,
"read_only": false
}
},
"source": [
"Type this to get the output shown below:\n",
"\n",
" Markdown basics: lists, markup and code\n",
" \n",
" * list item \n",
" * list item\n",
" * nested list item\n",
" \n",
" \n",
" * *italics*\n",
" * **bold**\n",
" * `fixed_font`\n",
"\n",
" You can embed code meant for illustration instead of execution in Python - use four spaces before:\n",
" \n",
" def hello_ipython():\n",
" print \"Hello IPython!\"\n",
" \n"
]
},
{
"cell_type": "markdown",
"metadata": {
"run_control": {
"frozen": false,
"read_only": false
}
},
"source": [
"Markdown basics: lists, markup and code\n",
"\n",
"* list item \n",
"* list item\n",
" * nested list item\n",
"\n",
"\n",
"* *italics*\n",
"* **bold**\n",
"* `fixed_font` \n",
"\n",
"You can embed code meant for illustration instead of execution in Python - use four spaces before:\n",
"\n",
" def hello_ipython():\n",
" print \"Hello IPython!\"\n",
" \n"
]
},
{
"cell_type": "markdown",
"metadata": {
"run_control": {
"frozen": false,
"read_only": false
}
},
"source": [
"A key and important point is that IPython markdown can take embedded HTML of any form.
Therefore in applications where the markdown syntax it too weak, use HTML. Be aware however that not all of the HTML constructs can be converted to some of the conversion output formats (e.g., embedding video in a LaTeX document).\n", "\n", "Type this to get the output shown below:\n", "\n", " Markdown cells can also contain HTML\n", " \n", " \n",
"\n",
" - LaTeX citations in the notebook. \n",
"\n",
"The LaTeX docs with template control script provides full citation support when creating PDF documents."
]
},
{
"cell_type": "markdown",
"metadata": {
"run_control": {
"frozen": false,
"read_only": false
}
},
"source": [
"### Cross linking and table of contents"
]
},
{
"cell_type": "markdown",
"metadata": {
"run_control": {
"frozen": false,
"read_only": false
}
},
"source": [
"IPython does not yet support the automatic creation of a table of contents, but it can be added manually by placing an HTML anchor immediately before the section heading and then linking to the anchor. Note that in IPYthon the browser's 'back arrow' won't navigate back to the previous location in the page.\n",
"\n",
" # Table of Contents\n",
"\n",
" - [Overview](#Overview)\n",
" - [Learning Python and hints and tips](#LearningPythonandhintsandtips)\n",
" - [Introducing Python for scientific work](#IntroducingPythonforscientificwork)\n",
"\n",
" \n",
" ##Overview\n",
"\n",
" This notebook provides a brief summary ....\n",
"\n",
" \n",
" ##Learning Python, and hints and tips\n",
"\n",
" There are many free ....\n",
"\n",
" \n",
" ##Introducing Python for scientific work\n",
"\n",
" A very good introduction to Python for scientific work ...."
]
},
{
"cell_type": "markdown",
"metadata": {
"run_control": {
"frozen": false,
"read_only": false
}
},
"source": [
"\n",
"Table of Contents\n",
"\n",
"- [Overview](#Overview)\n",
"- [Learning Python and hints and tips](#LearningPythonandhintsandtips)\n",
"- [Introducing Python for scientific work](#IntroducingPythonforscientificwork)\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"run_control": {
"frozen": false,
"read_only": false
}
},
"source": [
"## IPython's rich display system"
]
},
{
"cell_type": "markdown",
"metadata": {
"run_control": {
"frozen": false,
"read_only": false
}
},
"source": [
"IPython notebook can use all of the modern browsers' capabilities. This is called the rich display system, see [here.](http://nbviewer.ipython.org/urls/raw.github.com/ipython/ipython/1.x/examples/notebooks/Part%205%20-%20Rich%20Display%20System.ipynb)"
]
},
{
"cell_type": "markdown",
"metadata": {
"run_control": {
"frozen": false,
"read_only": false
}
},
"source": [
"### Plotting"
]
},
{
"cell_type": "markdown",
"metadata": {
"run_control": {
"frozen": false,
"read_only": false
}
},
"source": [
"__Plotting packages and options__\n",
"\n",
"[Scientific Plotting in Python](http://www.pycon.se/2014/assets/slides/Plotly-Pycon-Sweden.pdf) \n",
"[Interactive Plotting in IPython Notebook (Part 1/2): Bokeh](https://pyscience.wordpress.com/2014/09/01/interactive-plotting-in-ipython-notebook-part-12-bokeh/) \n",
"[Interactive Plotting in IPython Notebook (Part 2/2): Plotly](https://pyscience.wordpress.com/2014/09/02/interactive-plotting-in-ipython-notebook-part-22-plotly-2/) \n",
"[Overview of Python Visualization Tools ](http://pbpython.com/visualization-tools-1.html) \n",
" \n",
"\n",
"__matplotlib__\n",
"[matplotlib](http://matplotlib.org/)\n",
"[Gallery](http://matplotlib.org/gallery.html)\n",
"[Examples](http://matplotlib.org/examples/index.html)\n",
"[docs](http://matplotlib.org/contents.html#)\n",
"\n",
"__bokeh__\n",
"[Welcome to bokeh](http://bokeh.pydata.org/en/latest/)\n",
"[Gallery](http://bokeh.pydata.org/en/latest/docs/gallery.html)\n",
"[Quickstart](http://bokeh.pydata.org/en/latest/docs/quickstart.html)\n",
"[Tutorials](http://nbviewer.ipython.org/github/bokeh/bokeh-notebooks/blob/master/index.ipynb#Tutorial)\n",
"[User Guide](http://bokeh.pydata.org/en/latest/docs/user_guide.html)\n",
"\n",
"__plotly__\n",
"[What is Plotly.js](https://plot.ly/javascript/)\n",
"[Plotly.js Open-Source Announcement](https://plot.ly/javascript/open-source-announcement/)\n",
"[Getting Started: Plotly for Python](https://plot.ly/python/getting-started/)\n",
"[Plotly Offline](https://plot.ly/python/offline/)\n",
"[User Guide](https://plot.ly/python/user-guide/)\n",
"[Plotly's Python API User Guide](https://plotly.com/python-api-reference/\n",
"[Example 3-D plot](https://plot.ly/~chriddyp/1780.embed)\n",
"\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"run_control": {
"frozen": false,
"read_only": false
}
},
"source": [
"### Matplotlib in IPython notebook\n",
"\n",
"IPython can inline `matplotlib plots`. \n",
"\n",
" - animations \n",
" - interactivity \n",
" - multi-colour lines "
]
},
{
"cell_type": "code",
"execution_count": 54,
"metadata": {
"run_control": {
"frozen": false,
"read_only": false
}
},
"outputs": [
{
"data": {
"text/plain": [
"[]"
]
},
"execution_count": 54,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"text/plain": [
"Text(0.5, 0, 'time (s)')"
]
},
"execution_count": 54,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"text/plain": [
"Text(0, 0.5, 'voltage (mV)')"
]
},
"execution_count": 54,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"text/plain": [
"Text(0.5, 1.0, 'About as simple as it gets, folks')"
]
},
"execution_count": 54,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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\n",
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]
},
{
"cell_type": "markdown",
"metadata": {
"run_control": {
"frozen": false,
"read_only": false
}
},
"source": [
"`%%latex` to render cell contents as LaTeX, see [here](http://nbviewer.ipython.org/github/ipython/ipython/blob/1.x/examples/notebooks/Part%205%20-%20Rich%20Display%20System.ipynb#LaTeX)"
]
},
{
"cell_type": "markdown",
"metadata": {
"run_control": {
"frozen": false,
"read_only": false
}
},
"source": [
"$$\n",
"\\begin{aligned}\n",
"\\nabla \\times \\vec{\\mathbf{B}} -\\, \\frac1c\\, \\frac{\\partial\\vec{\\mathbf{E}}}{\\partial t} & = \\frac{4\\pi}{c}\\vec{\\mathbf{j}} \\\\\n",
"\\nabla \\cdot \\vec{\\mathbf{E}} & = 4 \\pi \\rho \\\\\n",
"\\nabla \\times \\vec{\\mathbf{E}}\\, +\\, \\frac1c\\, \\frac{\\partial\\vec{\\mathbf{B}}}{\\partial t} & = \\vec{\\mathbf{0}} \\\\\n",
"\\nabla \\cdot \\vec{\\mathbf{B}} & = 0\n",
"\\end{aligned}\n",
"$$"
]
},
{
"cell_type": "code",
"execution_count": 104,
"metadata": {
"run_control": {
"frozen": false,
"read_only": false
}
},
"outputs": [
{
"data": {
"text/latex": [
"\\begin{eqnarray}\n",
"\\nabla \\times \\vec{\\mathbf{B}} -\\, \\frac1c\\, \\frac{\\partial\\vec{\\mathbf{E}}}{\\partial t} & = \\frac{4\\pi}{c}\\vec{\\mathbf{j}} \\\\\n",
"\\nabla \\cdot \\vec{\\mathbf{E}} & = 4 \\pi \\rho \\\\\n",
"\\nabla \\times \\vec{\\mathbf{E}}\\, +\\, \\frac1c\\, \\frac{\\partial\\vec{\\mathbf{B}}}{\\partial t} & = \\vec{\\mathbf{0}} \\\\\n",
"\\nabla \\cdot \\vec{\\mathbf{B}} & = 0 \n",
"\\end{eqnarray}"
],
"text/plain": [
"Therefore in applications where the markdown syntax it too weak, use HTML. Be aware however that not all of the HTML constructs can be converted to some of the conversion output formats (e.g., embedding video in a LaTeX document).\n", "\n", "Type this to get the output shown below:\n", "\n", " Markdown cells can also contain HTML\n", " \n", "
Markdown basics: lists, markup and code
\n", " \n", "- \n",
"
- list item \n", "
list item
\n", " \n", "- \n",
"
- nested list item \n", "
\n",
" italics
\n",
" - bold \n", " \n", "
fixed_font\n",
"
Code examples:
\n", " \n", "def hello_ipython():\n",
" print \"Hello IPython!\"\n",
" Markdown basics: lists, markup and code
\n", "\n", "- \n",
"
- list item \n", "
list item
\n", "\n", "- \n",
"
- nested list item \n", "
\n",
"italics
\n",
"- bold \n", "\n", "
fixed_font\n",
"
Code examples:
\n", "\n", "def hello_ipython():\n",
" print \"Hello IPython!\"\n",
""
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"%matplotlib inline\n",
"\n",
"import pylab as pl\n",
"import numpy as np\n",
"\n",
"t = np.arange(0.0, 2.0, 0.01)\n",
"s = np.sin(2*np.pi*t)\n",
"pl.plot(t, s)\n",
"pl.xlabel('time (s)')\n",
"pl.ylabel('voltage (mV)')\n",
"pl.title('About as simple as it gets, folks')\n",
"pl.grid(True)\n",
"# savefig(\"test.png\")\n",
"# show()"
]
},
{
"cell_type": "markdown",
"metadata": {
"run_control": {
"frozen": false,
"read_only": false
}
},
"source": [
"By just importing seaborn, the Matplotlib graphs is given a different style. If seaborn is not installed do `conda install seaborn`."
]
},
{
"cell_type": "code",
"execution_count": 55,
"metadata": {
"run_control": {
"frozen": false,
"read_only": false
}
},
"outputs": [
{
"data": {
"text/plain": [
"[]"
]
},
"execution_count": 55,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"text/plain": [
"Text(0.5, 0, 'time (s)')"
]
},
"execution_count": 55,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"text/plain": [
"Text(0, 0.5, 'voltage (mV)')"
]
},
"execution_count": 55,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"text/plain": [
"Text(0.5, 1.0, 'About as simple as it gets, folks')"
]
},
"execution_count": 55,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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\n",
"text/plain": [
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]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"import seaborn as sns\n",
"\n",
"t = np.arange(0.0, 2.0, 0.01)\n",
"s = np.sin(2*np.pi*t)\n",
"pl.plot(t, s)\n",
"pl.xlabel('time (s)')\n",
"pl.ylabel('voltage (mV)')\n",
"pl.title('About as simple as it gets, folks')\n",
"pl.grid(True)\n",
"# savefig(\"test.png\")\n",
"# show()"
]
},
{
"cell_type": "markdown",
"metadata": {
"run_control": {
"frozen": false,
"read_only": false
}
},
"source": [
"### Matplotlib in qt window\n",
"\n",
"Use the ipython magic command pylab to control the graphing backend, switch between inline and qt as required.
\n", " "
]
},
{
"cell_type": "markdown",
"metadata": {
"run_control": {
"frozen": false,
"read_only": false
}
},
"source": [
"### Seaborn distribution plotting\n",
"The [seaborn](https://github.com/mwaskom/seaborn) package provides special means to plot distributions\n",
"\n",
""
]
},
{
"cell_type": "markdown",
"metadata": {
"run_control": {
"frozen": false,
"read_only": false
}
},
"source": [
"### Including images into the notebook"
]
},
{
"cell_type": "markdown",
"metadata": {
"run_control": {
"frozen": false,
"read_only": false
}
},
"source": [
"There are (at least) two ways to include images in the notebook. \n",
"\n",
" display(Image(filename='images/portalpage.png'))\n",
" HTML('![](\"files/images/portalpage.png\")
')\n",
" \n",
"The first form includes the image in its natural size, but the size can be adjusted by width and height function parameters. The second form injects HTML code and, likewise, allows you to set the image size."
]
},
{
"cell_type": "code",
"execution_count": 56,
"metadata": {
"run_control": {
"frozen": false,
"read_only": false
}
},
"outputs": [
{
"data": {
"text/html": [
"![](\"images/ipythonhelp.png\")
"
],
"text/plain": [
""
]
},
"execution_count": 56,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"HTML('')"
]
},
{
"cell_type": "code",
"execution_count": 57,
"metadata": {
"run_control": {
"frozen": false,
"read_only": false
}
},
"outputs": [
{
"data": {
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\n",
"text/plain": [
""
]
},
"metadata": {
"image/png": {
"height": 250,
"width": 250
}
},
"output_type": "display_data"
}
],
"source": [
"display(Image(filename='images/ipythonhelp.png', width=250, height=250))"
]
},
{
"cell_type": "markdown",
"metadata": {
"run_control": {
"frozen": false,
"read_only": false
}
},
"source": [
"Images can also be included as markdown by using the following format \n",
"``\n",
"\n",
"\n",
"\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Images can also be included as markdown by using the following format \n",
"``\n",
"\n",
"\n",
""
]
},
{
"cell_type": "markdown",
"metadata": {
"run_control": {
"frozen": false,
"read_only": false
}
},
"source": [
"### Embedded vs non-embedded images"
]
},
{
"cell_type": "markdown",
"metadata": {
"run_control": {
"frozen": false,
"read_only": false
}
},
"source": [
"As of IPython 0.13, images are embedded by default for compatibility with QtConsole, and the ability to still be displayed offline.\n",
"\n",
""
]
},
{
"cell_type": "code",
"execution_count": 58,
"metadata": {
"run_control": {
"frozen": false,
"read_only": false
}
},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# by default Image data are embedded\n",
"picUrl = 'https://raw.githubusercontent.com/NelisW/pyradi/master/pyradi/doc/_images/pyradi.png'\n",
"Embed = Image(picUrl)\n",
"display(Embed)\n",
"\n",
"# if kwarg `url` is given, the embedding is assumed to be false\n",
"# SoftLinked = Image(url=picUrl)\n",
"\n",
"# In each case, embed can be specified explicitly with the `embed` kwarg\n",
"# ForceEmbed = Image(url=picUrl, embed=True)"
]
},
{
"cell_type": "markdown",
"metadata": {
"run_control": {
"frozen": false,
"read_only": false
}
},
"source": [
"### Embedding other media"
]
},
{
"cell_type": "markdown",
"metadata": {
"run_control": {
"frozen": false,
"read_only": false
}
},
"source": [
"SVG graphic."
]
},
{
"cell_type": "code",
"execution_count": 59,
"metadata": {
"run_control": {
"frozen": false,
"read_only": false
}
},
"outputs": [
{
"data": {
"image/svg+xml": [
""
],
"text/plain": [
""
]
},
"execution_count": 59,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from IPython.display import SVG\n",
"SVG(filename='images/solidangleflatplate.svg')"
]
},
{
"cell_type": "markdown",
"metadata": {
"run_control": {
"frozen": false,
"read_only": false
}
},
"source": [
"### Embed a video from YouTube."
]
},
{
"cell_type": "code",
"execution_count": 60,
"metadata": {
"run_control": {
"frozen": false,
"read_only": false
}
},
"outputs": [],
"source": [
"from IPython.display import YouTubeVideo\n",
"# a talk about IPython at Sage Days at U. Washington, Seattle.\n",
"# Video credit: William Stein.\n",
"if False:\n",
" YouTubeVideo('1j_HxD4iLn8')"
]
},
{
"cell_type": "markdown",
"metadata": {
"run_control": {
"frozen": false,
"read_only": false
}
},
"source": [
"### Embed an external web page."
]
},
{
"cell_type": "code",
"execution_count": 61,
"metadata": {
"run_control": {
"frozen": false,
"read_only": false
}
},
"outputs": [],
"source": [
"if False:\n",
" HTML('')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Embed a video from local file system\n",
"\n",
"The following shell shows a recording of the Mayavi display. The file is locally saved in the notebook. It seems that the format must be webm format, the other formats (avi, mp4) did not work."
]
},
{
"cell_type": "code",
"execution_count": 62,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"\n",
""
]
},
"execution_count": 62,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# display a locally saved video file.\n",
"# it seems that only webm format works here\n",
"import io\n",
"import base64\n",
"from IPython.core.display import HTML\n",
"\n",
"filename = './images/interpolationSphere.webm'\n",
"\n",
"# video = io.open(filename, 'r+b').read()\n",
"# encoded = base64.b64encode(video)\n",
"# HTML(data=''''''.format(encoded.decode('ascii')))\n",
"\n",
"HTML(\"\"\"\n",
" \n",
" \n",
"\n",
" \n",
" \n",
" \n",
" \n",
"\n",
"\n",
"\n",
"The following examples are taken from \n",
" \n"
]
},
{
"cell_type": "markdown",
"metadata": {
"run_control": {
"frozen": false,
"read_only": false
}
},
"source": [
"The widget ecosystem changes frequently, the following code may not work...."
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"run_control": {
"frozen": false,
"read_only": false
}
},
"outputs": [
{
"data": {
"text/plain": [
"['Accordion',\n",
" 'AppLayout',\n",
" 'Audio',\n",
" 'BoundedFloatText',\n",
" 'BoundedIntText',\n",
" 'Box',\n",
" 'Button',\n",
" 'ButtonStyle',\n",
" 'CallbackDispatcher',\n",
" 'Checkbox',\n",
" 'Color',\n",
" 'ColorPicker',\n",
" 'ColorsInput',\n",
" 'Combobox',\n",
" 'Controller',\n",
" 'CoreWidget',\n",
" 'DOMWidget',\n",
" 'DatePicker',\n",
" 'Datetime',\n",
" 'DatetimePicker',\n",
" 'Dropdown',\n",
" 'FileUpload',\n",
" 'FloatLogSlider',\n",
" 'FloatProgress',\n",
" 'FloatRangeSlider',\n",
" 'FloatSlider',\n",
" 'FloatText',\n",
" 'FloatsInput',\n",
" 'GridBox',\n",
" 'GridspecLayout',\n",
" 'HBox',\n",
" 'HTML',\n",
" 'HTMLMath',\n",
" 'Image',\n",
" 'IntProgress',\n",
" 'IntRangeSlider',\n",
" 'IntSlider',\n",
" 'IntText',\n",
" 'IntsInput',\n",
" 'Label',\n",
" 'Layout',\n",
" 'NaiveDatetimePicker',\n",
" 'NumberFormat',\n",
" 'Output',\n",
" 'Password',\n",
" 'Play',\n",
" 'RadioButtons',\n",
" 'Select',\n",
" 'SelectMultiple',\n",
" 'SelectionRangeSlider',\n",
" 'SelectionSlider',\n",
" 'SliderStyle',\n",
" 'Stack',\n",
" 'Style',\n",
" 'Tab',\n",
" 'TagsInput',\n",
" 'Text',\n",
" 'Textarea',\n",
" 'TimePicker',\n",
" 'ToggleButton',\n",
" 'ToggleButtons',\n",
" 'ToggleButtonsStyle',\n",
" 'TwoByTwoLayout',\n",
" 'TypedTuple',\n",
" 'VBox',\n",
" 'Valid',\n",
" 'ValueWidget',\n",
" 'Video',\n",
" 'Widget']"
]
},
"execution_count": 1,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# import IPython.html.widgets as widgets\n",
"from IPython.display import display\n",
"import ipywidgets\n",
"from ipywidgets import widgets \n",
"\n",
"[n for n in dir(ipywidgets) if n[0] == n[0].upper() and not n[0] == '_']\n"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"run_control": {
"frozen": false,
"read_only": false
}
},
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "32e8140c336c4af7bc3ff05fd0040578",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"FloatSlider(value=7.5, description='Test:', max=10.0, min=5.0)"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "b5745a53893e4bf59203d1f52573f824",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Checkbox(value=True, description='Check me')"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "d9a837bcd0a6439dbc3bb5ae0d7aca0a",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Dropdown(description='Number:', index=1, options=('test 1', 'option 2', 'selection 3'), value='option 2')"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "fdb4dcfc2aad495fa156159281738f32",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Dropdown(description='Focal length:', index=1, options=OrderedDict([('0.3', '0.3'), ('0.4', '0.4'), ('0.5', '0…"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"xx = widgets.FloatSlider(\n",
" value=7.5,\n",
" min=5.0,\n",
" max=10.0,\n",
" step=0.1,\n",
" description='Test:',\n",
")\n",
"\n",
"y = ipywidgets.Checkbox(\n",
" description='Check me',\n",
" value=True,\n",
")\n",
"\n",
"w = ipywidgets.Dropdown(\n",
" options = [ 'test 1', 'option 2', 'selection 3',],\n",
" value='option 2',\n",
" description='Number:',\n",
")\n",
"\n",
"#use ordered dic to get required sorting sequence\n",
"from collections import OrderedDict\n",
"foclens = [ 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1., 1.1, 1.2, 1.3, 1.4, 1.5]\n",
"m = ipywidgets.Dropdown(\n",
" options = OrderedDict([(str(x), str(x)) for x in foclens]) ,\n",
" value='0.4',\n",
" description='Focal length:',\n",
")\n",
"\n",
"\n",
"\n",
"from IPython.display import display\n",
"\n",
"display(xx)\n",
"display(y)\n",
"display(w)\n",
"display(m)\n"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"run_control": {
"frozen": false,
"read_only": false
}
},
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "22e35f231d5e47419fbf84d40cf9d632",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"interactive(children=(Select(description='country', options=('USA', 'Russia'), value='USA'), Output()), _dom_c…"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "9be7e6e2305144e8aa19d86d828d9dfc",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"interactive(children=(Select(description='city', options=('CHI', 'NYC'), value='CHI'), Output()), _dom_classes…"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# http://stackoverflow.com/questions/28529157/dynamically-changing-dropdowns-in-ipython-notebook-widgets-and-spyre\n",
"# from IPython.html import widgets\n",
"from IPython.display import display\n",
"\n",
"geo={'USA':['CHI','NYC'],'Russia':['MOW','LED']}\n",
"\n",
"def print_city(city):\n",
" print(city)\n",
"\n",
"def select_city(country):\n",
" cityW.options = geo[country]\n",
"\n",
"\n",
"scW = ipywidgets.Select(options=geo.keys())\n",
"init = scW.value\n",
"cityW = ipywidgets.Select(options=geo[init])\n",
"j = ipywidgets.interactive(print_city, city=cityW)\n",
"i = ipywidgets.interactive(select_city, country=scW)\n",
"display(i)\n",
"display(j)"
]
},
{
"cell_type": "markdown",
"metadata": {
"run_control": {
"frozen": false,
"read_only": false
}
},
"source": [
"The following two cells illustrate how a slider is used in the `widgets.interactive` function to test the value of the slider and then do something with the value. The example below shows how to pass 'fixed' or non-widget parameters to the function. Any number of such widgets may be passed, but they must all be named.\n",
"\n",
"For more examples see the links shown above. An example of interactive image segmentation is shown in notebook '10-ImageUtilities' in this series."
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"run_control": {
"frozen": false,
"read_only": false
}
},
"outputs": [],
"source": [
"def doSomething(scale, thx):\n",
" print('scale={} thx={} product={}'.format(scale, thx, scale * thx))\n",
" return (scale, thx)"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"run_control": {
"frozen": false,
"read_only": false
}
},
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "3e79e2e0daba41c29306f05065f05762",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"interactive(children=(FloatSlider(value=128.0, description='thx', max=255.0, step=1.0), Output()), _dom_classe…"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"scale = 5.0\n",
"v = ipywidgets.interactive(doSomething, scale=ipywidgets.fixed(scale), \n",
" thx=ipywidgets.FloatSlider(value=128, min=0.0, max=255.0, step=1))\n",
"display(v)"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"run_control": {
"frozen": false,
"read_only": false
}
},
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "2f962ee103b74348b85b337d23564c8a",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"VBox(children=(Text(value='', description='First Name:'), Text(value='', description='Last Name:'), Checkbox(v…"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"form = widgets.VBox()\n",
"first = widgets.Text(description=\"First Name:\")\n",
"last = widgets.Text(description=\"Last Name:\")\n",
"\n",
"students = widgets.VBox(visible=True, children=[\n",
" widgets.Checkbox(description=\"Student1:\", value=False),\n",
" widgets.Checkbox(description=\"Student2:\", value=False),\n",
" ])\n",
"\n",
"student = widgets.Checkbox(description=\"Student:\", value=False)\n",
"\n",
"school_info = widgets.VBox(visible=False, children=[\n",
" widgets.Text(description=\"School:\"),\n",
" widgets.IntText(description=\"Grade:\", min=0, max=12)\n",
" ])\n",
"\n",
"pet = widgets.Text(description=\"Pet's Name:\")\n",
"\n",
"form.children = [first, last, student, students, school_info, pet]\n",
"\n",
"display(form)\n",
"\n",
"def on_student_toggle(name, value):\n",
" if value:\n",
" school_info.visible = True\n",
" else:\n",
" school_info.visible = False\n",
" \n",
"student.observe (on_student_toggle, 'value')\n",
"students.children[0].observe(on_student_toggle, 'value')\n",
"students.children[1].observe(on_student_toggle, 'value')\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"run_control": {
"frozen": false,
"read_only": false
}
},
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "835395c09813420fa2cd930b811f4eed",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"VBox(children=(Text(value='', description='First Name:'), Text(value='', description='Last Name:'), Checkbox(v…"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"form = widgets.VBox()\n",
"first = widgets.Text(description=\"First Name:\")\n",
"last = widgets.Text(description=\"Last Name:\")\n",
"\n",
"student = widgets.Checkbox(description=\"Student:\", value=False)\n",
"\n",
"school_info = widgets.VBox(visible=False, children=[\n",
" widgets.Text(description=\"School:\"),\n",
" widgets.IntText(description=\"Grade:\", min=0, max=12)\n",
" ])\n",
"\n",
"pet = widgets.Text(description=\"Pet's Name:\")\n",
"form.children = [first, last, student, school_info, pet]\n",
"display(form)\n",
"\n",
"def on_student_toggle(name, value):\n",
" if value:\n",
" school_info.visible = True\n",
" else:\n",
" school_info.visible = False\n",
"student.observe(on_student_toggle, 'value')"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"run_control": {
"frozen": false,
"read_only": false
}
},
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "e92da3e7e7004b4983160894fa7f3763",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Box(children=(FloatSlider(value=0.0), Text(value='hi')))"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\nwillers\\AppData\\Local\\Temp\\ipykernel_47532\\2542408873.py:16: DeprecationWarning: on_submit is deprecated. Instead, set the .continuous_update attribute to False and observe the value changing with: mywidget.observe(callback, 'value').\n",
" string.on_submit(on_string_change,'value')\n"
]
}
],
"source": [
"from IPython.display import display\n",
"\n",
"float_range = widgets.FloatSlider()\n",
"string = widgets.Text(value='hi')\n",
"container = widgets.Box(children=[float_range, string])\n",
"\n",
"container.border_color = 'red'\n",
"container.border_style = 'dotted'\n",
"container.border_width = 3\n",
"display(container) # Displays the `container` and all of it's children.\n",
"\n",
"def on_string_change(name, value):\n",
" print(value)\n",
"\n",
"# string.on_trait_change(on_string_change,'value')\n",
"string.on_submit(on_string_change,'value')\n",
"\n",
"\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"run_control": {
"frozen": false,
"read_only": false
}
},
"source": [
"The following is an example by Ketcheson, Ahmadia and Granger taken from \n",
" \n",
"It demonstrates aliasing during sampling of a signal. To see the effects of aliasing:\n",
"\n",
"1. Run the next cell, then set the `grid_points` slider to `13`.\n",
"2. Move the `frequency` slider to values above `10`.\n",
"3. As the frequency increases, the measured signal (blue) has a lower frequency than the real one (red)."
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"run_control": {
"frozen": false,
"read_only": false
}
},
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "7e1c6a5304b6465dabe3494a3f1273f6",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"interactive(children=(FloatSlider(value=4.0, description='frequency', max=22.0, min=1.0, step=0.5), IntSlider(…"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Import matplotlib (plotting) and numpy (numerical arrays).\n",
"# This enables their use in the Notebook.\n",
"%matplotlib inline\n",
"import matplotlib.pyplot as plt\n",
"import numpy as np\n",
"\n",
"# Import IPython's interact function which is used below to\n",
"# build the interactive widgets\n",
"from ipywidgets import interact#, interactive, fixed, interact_manual\n",
"# import ipywidgets as widgets\n",
"\n",
"\n",
"def plot_sine(frequency=4.0, grid_points=12, plot_original=True):\n",
" \"\"\"\n",
" Plot discrete samples of a sine wave on the interval ``[0, 1]``.\n",
" \"\"\"\n",
" x = np.linspace(0, 1, grid_points + 2)\n",
" y = np.sin(2 * frequency * np.pi * x)\n",
"\n",
" xf = np.linspace(0, 1, 1000)\n",
" yf = np.sin(2 * frequency * np.pi * xf)\n",
"\n",
" fig, ax = plt.subplots(figsize=(8, 6))\n",
" ax.set_xlabel('x')\n",
" ax.set_ylabel('signal')\n",
" ax.set_title('Aliasing in discretely sampled periodic signal')\n",
"\n",
" if plot_original:\n",
" ax.plot(xf, yf, color='red', linestyle='solid', linewidth=2)\n",
"\n",
" ax.plot(x, y, marker='o', linewidth=2)\n",
"\n",
"# The interact function automatically builds a user interface for exploring the\n",
"# plot_sine function.\n",
"interact(plot_sine, frequency=(1.0, 22.0, 0.5), grid_points=(10, 60, 1), plot_original=True);"
]
},
{
"cell_type": "markdown",
"metadata": {
"run_control": {
"frozen": false,
"read_only": false
}
},
"source": [
"An example at shows how to use interactive widgets when segmenting an image."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"https://stackoverflow.com/questions/47102564/how-to-set-interact-arguments-programmatically \n"
]
},
{
"cell_type": "code",
"execution_count": 72,
"metadata": {},
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "19e046edc8ee4e1283bb9bdcf66f8170",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"interactive(children=(FloatSlider(value=0.0, continuous_update=False, description='s0', max=2.0, min=-2.0), Fl…"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/plain": [
""
]
},
"execution_count": 72,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": "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\n",
"text/plain": [
"
\n", "
')\n",
" \n",
"The first form includes the image in its natural size, but the size can be adjusted by width and height function parameters. The second form injects HTML code and, likewise, allows you to set the image size."
]
},
{
"cell_type": "code",
"execution_count": 56,
"metadata": {
"run_control": {
"frozen": false,
"read_only": false
}
},
"outputs": [
{
"data": {
"text/html": [
""
],
"text/plain": [
"')"
]
},
{
"cell_type": "code",
"execution_count": 57,
"metadata": {
"run_control": {
"frozen": false,
"read_only": false
}
},
"outputs": [
{
"data": {
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\n",
"text/plain": [
"`\n",
"\n",
"\n",
""
]
},
{
"cell_type": "markdown",
"metadata": {
"run_control": {
"frozen": false,
"read_only": false
}
},
"source": [
"### Embedded vs non-embedded images"
]
},
{
"cell_type": "markdown",
"metadata": {
"run_control": {
"frozen": false,
"read_only": false
}
},
"source": [
"As of IPython 0.13, images are embedded by default for compatibility with QtConsole, and the ability to still be displayed offline.\n",
"\n",
"\n",
"
"
],
"text/plain": [
"\n",
"
\"\"\".format(filename))"
]
},
{
"cell_type": "markdown",
"metadata": {
"run_control": {
"frozen": false,
"read_only": false
}
},
"source": [
"## Interactive widgets"
]
},
{
"cell_type": "markdown",
"metadata": {
"run_control": {
"frozen": false,
"read_only": false
}
},
"source": [
"IPython includes an architecture for interactive widgets that tie together Python code running in the kernel and JavaScript/HTML/CSS running in the browser. These widgets enable users to explore their code and data interactively. For details see \n",
"\n",
""
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"n_weights = 10\n",
"weight_sliders = [widgets.FloatSlider(value=0,min=-2,max=2,step=0.1,description=f's{i}',\n",
" disabled=False,continuous_update=False,orientation='horizontal',\n",
" readout=True,readout_format='.2f') for i in range(n_weights)]\n",
"\n",
"def PlotSuper(**kwargs):\n",
" def f(x):\n",
" y=0\n",
" for i,weight in enumerate(kwargs.values()):\n",
" if i==0:\n",
" y+=weight\n",
" else:\n",
" y+=weight*np.sin(x*i)\n",
" return y\n",
" vf = np.vectorize(f)\n",
" xx= np.arange(0,6,0.1)\n",
" plt.plot(xx,vf(xx))\n",
" plt.gca().set_ylim(-5,5)\n",
" \n",
"kwargs = {f's{i}':slider for i,slider in enumerate(weight_sliders)}\n",
"interact(PlotSuper,**kwargs)\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"https://github.com/jupyter-widgets/ipywidgets/blob/master/docs/source/examples/Widget%20Styling.ipynb \n",
"shows how to style the widgets using css."
]
},
{
"cell_type": "code",
"execution_count": 73,
"metadata": {},
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "3be72706080c4f79854212e340eeb0ff",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Button(description='(50% width, 80px height) button', layout=Layout(height='80px', width='50%'), style=ButtonS…"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"from ipywidgets import Button, Layout\n",
"\n",
"b = Button(description='(50% width, 80px height) button',\n",
" layout=Layout(width='50%', height='80px'))\n",
"b\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 74,
"metadata": {},
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "3716113a623843718d42e52e87e23bd9",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Button(description='Another button with the same layout', layout=Layout(height='80px', width='50%'), style=But…"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"c = Button(description='Another button with the same layout', layout=b.layout)\n",
"\n",
"c"
]
},
{
"cell_type": "code",
"execution_count": 75,
"metadata": {},
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "0655b228c7844a1da2db659664eb8da3",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"HBox(children=(VBox(children=(Button(description='correct', style=ButtonStyle()), Button(description='horse', …"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"from ipywidgets import Button, HBox, VBox\n",
"\n",
"words = ['correct', 'horse', 'battery', 'staple']\n",
"items = [Button(description=w) for w in words]\n",
"left_box = VBox([items[0], items[1]])\n",
"right_box = VBox([items[2], items[3]])\n",
"HBox([left_box, right_box])"
]
},
{
"cell_type": "code",
"execution_count": 76,
"metadata": {},
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "c1e4021b829b45b4b191a930b4151401",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"IntSlider(value=0, description='\\\\(\\\\int_0^t f\\\\)')"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"from ipywidgets import IntSlider, Label\n",
"IntSlider(description=r'\\(\\int_0^t f\\)')"
]
},
{
"cell_type": "code",
"execution_count": 77,
"metadata": {},
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "cb8cf06b9a1649299bd1eea77e2a7714",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Box(children=(Button(button_style='danger', description='correct', layout=Layout(width='auto'), style=ButtonSt…"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"from ipywidgets import Layout, Button, Box\n",
"\n",
"items_layout = Layout( width='auto') # override the default width of the button to 'auto' to let the button grow\n",
"\n",
"box_layout = Layout(display='flex',\n",
" flex_flow='column', \n",
" align_items='stretch', \n",
" border='solid',\n",
" width='50%')\n",
"\n",
"words = ['correct', 'horse', 'battery', 'staple']\n",
"items = [Button(description=word, layout=items_layout, button_style='danger') for word in words]\n",
"box = Box(children=items, layout=box_layout)\n",
"box"
]
},
{
"cell_type": "code",
"execution_count": 78,
"metadata": {},
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "0af67dcdb65448188bacea70af5ba64d",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"VBox(children=(Box(children=(Button(button_style='danger', description='weight=1; auto', layout=Layout(flex='1…"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"from ipywidgets import Layout, Button, Box, VBox\n",
"\n",
"# Items flex proportionally to the weight and the left over space around the text \n",
"items_auto = [\n",
" Button(description='weight=1; auto', layout=Layout(flex='1 1 auto', width='auto'), button_style='danger'),\n",
" Button(description='weight=3; auto', layout=Layout(flex='3 1 auto', width='auto'), button_style='danger'),\n",
" Button(description='weight=1; auto', layout=Layout(flex='1 1 auto', width='auto'), button_style='danger'),\n",
" ]\n",
"\n",
"# Items flex proportionally to the weight \n",
"items_0 = [\n",
" Button(description='weight=1; 0%', layout=Layout(flex='1 1 0%', width='auto'), button_style='danger'),\n",
" Button(description='weight=3; 0%', layout=Layout(flex='3 1 0%', width='auto'), button_style='danger'),\n",
" Button(description='weight=1; 0%', layout=Layout(flex='1 1 0%', width='auto'), button_style='danger'),\n",
" ]\n",
"box_layout = Layout(display='flex',\n",
" flex_flow='row', \n",
" align_items='stretch', \n",
" width='70%')\n",
"box_auto = Box(children=items_auto, layout=box_layout)\n",
"box_0 = Box(children=items_0, layout=box_layout)\n",
"VBox([box_auto, box_0])\n"
]
},
{
"cell_type": "code",
"execution_count": 79,
"metadata": {},
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "c895d30bca324c289a7d900e2484115c",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Box(children=(Box(children=(Label(value='Age of the captain'), IntSlider(value=40, max=60, min=40)), layout=La…"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"from ipywidgets import Layout, Button, Box, FloatText, Textarea, Dropdown, Label, IntSlider\n",
"\n",
"form_item_layout = Layout(\n",
" display='flex',\n",
" flex_flow='row',\n",
" justify_content='space-between'\n",
")\n",
"\n",
"form_items = [\n",
" Box([Label(value='Age of the captain'), IntSlider(min=40, max=60)], layout=form_item_layout),\n",
" Box([Label(value='Egg style'), \n",
" Dropdown(options=['Scrambled', 'Sunny side up', 'Over easy'])], layout=form_item_layout),\n",
" Box([Label(value='Ship size'), \n",
" FloatText()], layout=form_item_layout),\n",
" Box([Label(value='Information'), \n",
" Textarea()], layout=form_item_layout)\n",
"]\n",
"\n",
"form = Box(form_items, layout=Layout(\n",
" display='flex',\n",
" flex_flow='column',\n",
" border='solid 2px',\n",
" align_items='stretch',\n",
" width='50%'\n",
"))\n",
"form\n"
]
},
{
"cell_type": "code",
"execution_count": 80,
"metadata": {},
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "57ef280ba9074d2e85567ea86fcf6879",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"VBox(children=(Label(value='Scroll horizontally:'), Box(children=(Button(button_style='warning', description='…"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"from ipywidgets import Layout, Button, Box\n",
"\n",
"item_layout = Layout(height='100px', min_width='40px')\n",
"items = [Button(layout=item_layout, description=str(i), button_style='warning') for i in range(40)]\n",
"box_layout = Layout(overflow_x='scroll',\n",
" border='3px solid black',\n",
" width='500px',\n",
" height='',\n",
" flex_flow='row',\n",
" display='flex')\n",
"carousel = Box(children=items, layout=box_layout)\n",
"VBox([Label('Scroll horizontally:'), carousel])"
]
},
{
"cell_type": "code",
"execution_count": 81,
"metadata": {},
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "cb747a7ae5ed4ed38c746866a50376c7",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"VBox(children=(HBox(children=(VBox(children=(Text(value='Hello World', description='Title:', placeholder='Type…"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"def makeplot(title,display_trend,marker,amplitude,step_size,periods,noise_scale,offset,trend):\n",
" pass\n",
"\n",
"def interact_hookup(f, controls):\n",
" from ipywidgets import Output\n",
" out = Output()\n",
" def observer(change):\n",
" out.clear_output()\n",
" kwargs = {k:v.value for k,v in controls.items()}\n",
" with out:\n",
" f(**kwargs)\n",
" for k,w in controls.items():\n",
" w.observe(observer, 'value')\n",
" observer(None)\n",
" return out\n",
"\n",
"w = dict(\n",
" title=widgets.Text(value='Hello World', placeholder='Type something', description='Title:', disabled=False),\n",
" display_trend=widgets.ToggleButton(value=False, description='Display Trend', icon='check'),\n",
" marker=widgets.RadioButtons(options=['x', 'o', '.'], value='x', description='Marker:'),\n",
" amplitude=widgets.FloatSlider(value=1, min=-5, max=5, description='Amplitude:'),\n",
" step_size=widgets.FloatSlider(value=0.1, min=0.01, max=0.1, step=0.01, description='Step size:'),\n",
" periods=widgets.FloatSlider(value=5, min=1, max=20, description='Periods:'),\n",
" noise_scale=widgets.FloatSlider(value=0.1, min=0.01, max=2, description='Noise:'),\n",
" offset=widgets.FloatSlider(value=0, min=-5, max=5, description='Offset:'),\n",
" trend=widgets.FloatSlider(value=1, min=-5, max=5, description='Trend:'),\n",
" )\n",
"\n",
"output = interact_hookup(makeplot, w)\n",
"\n",
"UI = VBox([\n",
" HBox([\n",
" VBox([\n",
" w['title'],\n",
" w['display_trend'],\n",
" w['marker'],\n",
" ]),\n",
" VBox([\n",
" w['amplitude'],\n",
" w['step_size'],\n",
" w['periods'],\n",
" w['noise_scale'],\n",
" w['offset'],\n",
" w['trend'],\n",
" ])\n",
" ]),\n",
" output\n",
"])\n",
"\n",
"\n",
"display(UI)\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Interdependent widgets\n",
"\n",
"The softmax function is used in neural networks.\n",
"Suppose we have a network with four neurons, and four corresponding weighted inputs, which we'll denote $z_{1}^{L}, z_{2}^{L}, z_{3}^{L}$, and $z_{4}^{L}$. \n",
"\n",
"According to this function, the activation $a^L_j$ of the $j$ output neuron is \n",
"\\begin{equation}\n",
"a_{j}^{L}=\\frac{e^{z_{j}^{L}}}{\\sum_{k} e^{z_{k}^{L}}}\n",
"\\label{eq:c03-78} \n",
"\\end{equation}\n",
"where in the denominator we sum over all the inputs $z^L_j$.\n",
"\n",
"As you increase any one component, its output will increase\n",
"\n",
"Shown below are adjustable sliders showing possible values for the weighted inputs, and a graph of the corresponding output activations. A good place to start exploration is by using the bottom slider to increase $z_{4}^{L}$. \n",
"As you increase $z_{4}^{L}$, you'll see an increase in the corresponding output activation, $a_{4}^{L}$, and a decrease in the other output activations. Similarly, if you decrease $z_{4}^{L}$ then $a_{4}^{L}$ will decrease, and all the other output activations will increase. In fact, if you look closely, you'll see that in both cases the total change in the other activations exactly compensates for the change in $a_{4}^{L}$. The reason is that the output activations are guaranteed to always sum up to 1."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"In the code below there is a direct match between each slider and progress bar next to it.\n",
"The sliders and progress bars are created in a dict comprehension, using the same keys for sliders and progress bars.\n",
"These widgets have global scope and are available inside the softmax function.\n",
"\n",
"The widgets are displayed manually (not automatically in interact) with the idea that these will be updated later. \n",
"\n",
"`ipywidgets.interact` automatically displays the widgets when invoked, but we already displayed the widgets. Hence, the `interactive` function is called rather than `interact`, because `interactive` does not display/show the widgets.\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 82,
"metadata": {},
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "4c245248e46d4a768c61f43b0d9b131c",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"VBox(children=(HBox(children=(FloatSlider(value=2.5, description='$z^L_1$', max=5.0, min=-5.0, step=0.05), Flo…"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"import numpy as np\n",
"from ipywidgets import HBox,VBox,Button,FloatSlider,FloatProgress,interactive\n",
"\n",
"# set up the widgets with precalculated values\n",
"# these sliders and prog bars are visible and are updated below in the softmax function\n",
"sliders = {'1':[2.5,0.31], '2':[-1.,0.009], '3':[3.2,0.633], '4':[0.5,0.043]}\n",
"sld = {key:FloatSlider(min=-5.0, max=+5.0, value=f'{sliders[key][0]}', step=0.05,description=f'$z^L_{key}$') for key in sliders}\n",
"prb = {key:FloatProgress(value=f'{sliders[key][1]}',min=0,max=1.0,step=0.01,description=f'$a^L_{key}$',bar_style='info',orientation='horizontal') for key in sliders}\n",
"\n",
"# build and display the widget grid in pairs of sliders and prog bars\n",
"lstD = [HBox([sld[key], prb[key]]) for key in sld]\n",
"display(VBox(lstD))\n",
"\n",
"# function is invoked if any of the sliders change\n",
"# and the result is used to change the progress bar\n",
"def softmax(**lstZ):\n",
" sum = 0\n",
" for key in lstZ:\n",
" sum += np.exp(lstZ[key])\n",
" for key in lstZ:\n",
" prb[key].value = np.exp(lstZ[key])/sum\n",
"\n",
"# `interactive` does not display/show the widgets, already done above.\n",
"w = interactive(softmax, **sld )"
]
},
{
"cell_type": "markdown",
"metadata": {
"run_control": {
"frozen": false,
"read_only": false
}
},
"source": [
"# The following information is somewhat esoteric, you need not go into this"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Simple progress bar\n",
"\n",
"Note that clear_output wipes the entire cell output, including previous output\n",
"\n",
"https://mikulskibartosz.name/how-to-display-a-progress-bar-in-jupyter-notebook-47bd4c2944bf"
]
},
{
"cell_type": "code",
"execution_count": 83,
"metadata": {},
"outputs": [],
"source": [
"def update_progress(progress, bar_length=20):\n",
"\n",
" from IPython.display import clear_output\n",
" \n",
" if isinstance(progress, int):\n",
" progress = float(progress)\n",
" if not isinstance(progress, float):\n",
" progress = 0\n",
" if progress < 0:\n",
" progress = 0\n",
" if progress >= 1:\n",
" progress = 1\n",
" block = int(round(bar_length * progress))\n",
" clear_output(wait = True)\n",
" text = \"Progress: [{0}] {1:.1f}%\".format( \"#\" * block + \"-\" * (bar_length - block), progress * 100)\n",
" print(text)\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Test:"
]
},
{
"cell_type": "code",
"execution_count": 84,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Progress: [########################################] 100.0%\n",
"after\n"
]
}
],
"source": [
"import time\n",
"\n",
"print('before')\n",
"\n",
"#Replace this with a real computation\n",
"number_of_elements = 10\n",
"for i in range(number_of_elements):\n",
" time.sleep(0.1) \n",
" \n",
" # progress must be a float between 0 and 1\n",
" update_progress((i+1) / number_of_elements,bar_length=40)\n",
" \n",
"print('after')\n"
]
},
{
"cell_type": "code",
"execution_count": 85,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Progress: [########################################] 100.0%\n",
"after\n"
]
}
],
"source": [
"import pyradi.ryutils as ryutils\n",
"\n",
"import time\n",
"\n",
"print('before')\n",
"\n",
"#Replace this with a real computation\n",
"number_of_elements = 10\n",
"for i in range(number_of_elements):\n",
" time.sleep(0.1) \n",
" \n",
" # progress must be a float between 0 and 1\n",
" ryutils.update_progress((i+1) / number_of_elements,bar_length=40)\n",
" \n",
"print('after')\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Alternative progress bar \n",
"\n",
""
]
},
{
"cell_type": "markdown",
"metadata": {
"run_control": {
"frozen": false,
"read_only": false
}
},
"source": [
"### Notebook file format"
]
},
{
"cell_type": "markdown",
"metadata": {
"run_control": {
"frozen": false,
"read_only": false
}
},
"source": [
"From \n",
"\n",
"Binary data such as figures are also saved directly in the JSON file. This provides convenient single-file portability, but means that the files can be large; a diff of binary data is also not very meaningful. Since the binary blobs are encoded in a single line, they affect only one line of the diff output, but they are typically very long lines. You can use the Cell | All Output | Clear menu option to remove all output from a notebook prior to committing it to version control, if this is a concern.\n",
"\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"run_control": {
"frozen": false,
"read_only": false
}
},
"source": [
"### Reading json files in IPython"
]
},
{
"cell_type": "markdown",
"metadata": {
"run_control": {
"frozen": false,
"read_only": false
}
},
"source": [
"The following code reads this file and prints the first five cells."
]
},
{
"cell_type": "code",
"execution_count": 86,
"metadata": {
"run_control": {
"frozen": false,
"read_only": false
}
},
"outputs": [],
"source": [
"import nbformat\n",
" \n",
"nb = nbformat.read('01-IPythonHintsAndTips.ipynb', as_version=4) \n"
]
},
{
"cell_type": "code",
"execution_count": 87,
"metadata": {
"run_control": {
"frozen": false,
"read_only": false
}
},
"outputs": [
{
"data": {
"text/plain": [
"[{'cell_type': 'markdown',\n",
" 'metadata': {'run_control': {'frozen': False, 'read_only': False}},\n",
" 'source': 'This notebook forms part of a series on [computational optical radiometry](https://github.com/NelisW/ComputationalRadiometry#computational-optical-radiometry-with-pyradi). The notebooks can be downloaded from [Github](https://github.com/NelisW/ComputationalRadiometry#computational-optical-radiometry-with-pyradi). These notebooks are constantly revised and updated, please revisit from time to time. \\n\\nThis notebook was written several Ipython/Jupyter generations ago, some information may be no longer applicable.\\n'},\n",
" {'cell_type': 'markdown',\n",
" 'metadata': {'run_control': {'frozen': False, 'read_only': False}},\n",
" 'source': '# 1 Jupyter / IPython notebook hints and tips '},\n",
" {'cell_type': 'markdown',\n",
" 'metadata': {'run_control': {'frozen': False, 'read_only': False}},\n",
" 'source': 'The date of this document and module versions used in this document are given at the end of the file. \\nFeedback is appreciated: neliswillers at gmail dot com. '},\n",
" {'cell_type': 'markdown',\n",
" 'metadata': {'run_control': {'frozen': False, 'read_only': False}},\n",
" 'source': '# Jupyter and IPython'},\n",
" {'cell_type': 'markdown',\n",
" 'metadata': {'run_control': {'frozen': False, 'read_only': False}},\n",
" 'source': 'From \\n \\n'}]"
]
},
"execution_count": 87,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"nb.cells[0:5]"
]
},
{
"cell_type": "markdown",
"metadata": {
"run_control": {
"frozen": false,
"read_only": false
}
},
"source": [
"### Running notebook servers"
]
},
{
"cell_type": "markdown",
"metadata": {
"run_control": {
"frozen": false,
"read_only": false
}
},
"source": [
"This document describes how you can secure a notebook server and how to run it on a public interface: \n",
"\n",
"\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"run_control": {
"frozen": false,
"read_only": false
},
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"### Markdown formatting in dynamic output display\n",
"\n",
" \n",
"\n",
"For this to work, you first have to install the Python markdown package (assuming you have the pip python package installer): \n",
"`pip install markdown`\n",
"\n",
"Use the following function to render a Python string in markdown syntax to display in IPython:"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"run_control": {
"frozen": false,
"read_only": false
}
},
"outputs": [],
"source": [
"import markdown\n",
"class MD(str):\n",
" def _repr_html_(self):\n",
" return markdown.markdown(self)"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"run_control": {
"frozen": false,
"read_only": false
}
},
"outputs": [
{
"data": {
"text/html": [
"
\\nThe square root of 2 used to be somewhere near 1.4142135623730951'" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import math\n", "a = 2\n", "MD(\"\"\"\n", "Dynamic demonstration\n", "--------------\n", "This is a mixture of markdown **and** html:
\n", "The square root of {0} used to be somewhere near {1}\"\"\".format(a,math.sqrt(a)))" ] }, { "cell_type": "markdown", "metadata": { "run_control": { "frozen": false, "read_only": false }, "slideshow": { "slide_type": "slide" } }, "source": [ "### HTML formatting in dynamic output display\n", "\n", "Use HTML to format the output of your code\n" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "run_control": { "frozen": false, "read_only": false } }, "outputs": [ { "data": { "text/html": [ "
Dynamic demonstration
\n", "This is a mixture of markdown and html:
\n",
"The square root of 2 used to be somewhere near 1.4142135623730951
\\nThe square root of 2 used to be somewhere near 1.4142135623730951'" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import math\n", "a = 2\n", "MD(\"\"\"\n", "Dynamic demonstration\n", "--------------\n", "This is a mixture of markdown **and** html:
\n", "The square root of {0} used to be somewhere near {1}\"\"\".format(a,math.sqrt(a)))" ] }, { "cell_type": "markdown", "metadata": { "run_control": { "frozen": false, "read_only": false }, "slideshow": { "slide_type": "slide" } }, "source": [ "### HTML formatting in dynamic output display\n", "\n", "Use HTML to format the output of your code\n" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "run_control": { "frozen": false, "read_only": false } }, "outputs": [ { "data": { "text/html": [ "
Length 0"
],
"text/plain": [
" Length 1"
],
"text/plain": [
" Length 2"
],
"text/plain": [
" Length \" + str(x) + \"\")) "
]
},
{
"cell_type": "markdown",
"metadata": {
"run_control": {
"frozen": false,
"read_only": false
},
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"### Displaying tables in HTML\n",
"\n",
"\"]\n",
" for row in self:\n",
" html.append(\"
\")\n",
" return ''.join(html)"
]
},
{
"cell_type": "code",
"execution_count": 92,
"metadata": {
"run_control": {
"frozen": false,
"read_only": false
}
},
"outputs": [
{
"data": {
"text/html": [
"\")\n",
" \n",
" for col in row:\n",
" html.append(\" \")\n",
" html.append(\"{0} \".format(col))\n",
" \n",
" html.append(\"
"
],
"text/plain": [
"[['x', 'y', 'x-y', '(x-y)**2'],\n",
" [7.961474434798401, 6.865343639904734, 1.096130794893667, 1.201502719514222],\n",
" [5.201977519140058, 7.346595757886568, -2.14461823874651, 4.599387389964183],\n",
" [4.696815945296278, 3.42530669581821, 1.2715092494780675, 1.6167357715082786],\n",
" [6.2854048732677565,\n",
" 9.328224476424943,\n",
" -3.042819603157186,\n",
" 9.258751137357656],\n",
" [1.337242255262987, 6.186116388698062, -4.848874133435075, 23.51158036189575],\n",
" [7.040166772972675, 3.984930885436305, 3.0552358875363703, 9.334466328490153],\n",
" [3.941562299040963, 1.4756150197404327, 2.46594727930053, 6.080895984289687]]"
]
},
"execution_count": 92,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import random\n",
"table = ListTable()\n",
"table.append(['x', 'y', 'x-y', '(x-y)**2'])\n",
"for i in range(7):\n",
" x = random.uniform(0, 10)\n",
" y = random.uniform(0, 10)\n",
" table.append([x, y, x-y, (x-y)**2])\n",
" \n",
"table"
]
},
{
"cell_type": "markdown",
"metadata": {
"run_control": {
"frozen": false,
"read_only": false
},
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"## Fine-tuning IPython typographic output appearance\n",
"\n",
"Changing the fonts, colours and layout to suit your own style. \n",
"x y x-y (x-y)**2 7.961474434798401 6.865343639904734 1.096130794893667 1.201502719514222 5.201977519140058 7.346595757886568 -2.14461823874651 4.599387389964183 4.696815945296278 3.42530669581821 1.2715092494780675 1.6167357715082786 6.2854048732677565 9.328224476424943 -3.042819603157186 9.258751137357656 1.337242255262987 6.186116388698062 -4.848874133435075 23.51158036189575 7.040166772972675 3.984930885436305 3.0552358875363703 9.334466328490153 3.941562299040963 1.4756150197404327 2.46594727930053 6.080895984289687
\n",
"`ipython nbconvert --to slides --post serve filename`
\n",
"where `filename` is the name of the ipython notebook you want to convert to a slide show.\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"run_control": {
"frozen": false,
"read_only": false
}
},
"source": [
"## Blogging with IPython\n",
"\n",
"IPython is also used to created blogging pages: \n",
" \n",
"This is an autogenerated header
This is some text in a box"
],
"text/plain": [
"' + str(text) + '
'\n",
" return raw_html\n",
"\n",
"def box(text):\n",
" raw_html = ''+str(text)+''\n",
" return raw_html\n",
"\n",
"def addContent(raw_html):\n",
" global htmlContent\n",
" htmlContent += raw_html\n",
"\n",
"\n",
"# Example\n",
"addContent( header(\"This is an autogenerated header\") )\n",
"addContent( box(\"This is some text in a box\") )\n",
"\n",
"from IPython.core.display import HTML\n",
"HTML(htmlContent)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Class Descriptors\n",
"\n",
"https://twitter.com/jakevdp/status/1121873857973870592/photo/1 \n",
"\n",
"1. The instance method (aka plain function) remains unbound when retrieved from the class so `C.method.__get__` just returns itself. By contrast, the class method gets bound to the class in that case, so it returns a new bound method object. In the first case both calls point to the same thing. In the second case since it is a classmethod, every call call creates a new instance of the method. I think this kind of explains the whole idea behind classmethods.\n",
"\n",
"1. In the first cell, `C.method` evaluates to `C.__dict__[\"method\"]` ; in second cell it evaluates to `C.__dict__[\"method\"].__get__(C, None)` which turns new objects each time\n",
"\n",
"1. In the first case, `C.method` points to the `method()` function.\n",
"In the second case, because the `method()` function is decorated with a `@classmethod` decorator, `C.method` returns an instance of bound method object. Every time you write `C.method` a new object is created.\n",
"\n",
"\n",
"To add to your confusion, `id(Example.clsmethod) == id(Example.clsmethod)`, because method objects use a free list. After the first id() call, the object gets deallocated and the second call can reuse the same object.\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 98,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"True"
]
},
"execution_count": 98,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"class C:\n",
" def method(self):\n",
" pass\n",
" \n",
"C.method is C.method"
]
},
{
"cell_type": "code",
"execution_count": 99,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"False\n",
"True\n",
"False\n"
]
}
],
"source": [
"class C:\n",
" @classmethod\n",
" def method(cls):\n",
" pass\n",
" \n",
"print(C.method is C.method)\n",
"\n",
"print(id(C.method)==id(C.method))\n",
"\n",
"a = C.method\n",
"b = C.method\n",
"print(id(a)==id(b))"
]
},
{
"cell_type": "code",
"execution_count": 100,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"mappingproxy({'__module__': '__main__',\n",
" 'method':