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/* the real stuff */
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<div class=" highlight hl-ipython2"><pre><span class="o">%</span><span class="k">load_ext</span> load_style
<span class="o">%</span><span class="k">load_style</span> talk.css
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<h1 id="IPython-notebook-widgets">IPython notebook widgets<a class="anchor-link" href="#IPython-notebook-widgets">¶</a></h1>
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<p>from <code>version 2.0</code>, IPython includes an architecture for <strong>interactive widgets</strong> that tie together Python code running in the kernel and JavaScript/HTML/CSS running in the browser. These widgets enable users to <strong>explore their code and data interactively</strong>.</p>
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<div class=" highlight hl-ipython2"><pre><span class="kn">from</span> <span class="nn">IPython.html.widgets</span> <span class="kn">import</span> <span class="n">interactive</span><span class="p">,</span> <span class="n">interact</span><span class="p">,</span> <span class="n">fixed</span>
<span class="kn">from</span> <span class="nn">IPython.html</span> <span class="kn">import</span> <span class="n">widgets</span>
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<pre>:0: FutureWarning: IPython widgets are experimental and may change in the future.
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<h3 id="simple-function-that-just-prints-its-argument">simple function that just prints its argument<a class="anchor-link" href="#simple-function-that-just-prints-its-argument">¶</a></h3>
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<div class=" highlight hl-ipython2"><pre><span class="k">def</span> <span class="nf">f</span><span class="p">(</span><span class="n">x</span><span class="p">):</span>
<span class="k">print</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
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<h3 id="if-x-is-a-integer,-a-widget-slider-will-be-created">if x is a integer, a widget slider will be created<a class="anchor-link" href="#if-x-is-a-integer,-a-widget-slider-will-be-created">¶</a></h3>
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<div class=" highlight hl-ipython2"><pre><span class="n">interact</span><span class="p">(</span><span class="n">f</span><span class="p">,</span> <span class="n">x</span><span class="o">=</span><span class="mi">10</span><span class="p">);</span>
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<pre>10
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<h4 id="you-can-also-use-a-python-decorator-syntax">you can also use a python <code>decorator</code> syntax<a class="anchor-link" href="#you-can-also-use-a-python-decorator-syntax">¶</a></h4>
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<div class=" highlight hl-ipython2"><pre><span class="kn">from</span> <span class="nn">datetime</span> <span class="kn">import</span> <span class="n">datetime</span>
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<div class=" highlight hl-ipython2"><pre><span class="nd">@interact</span><span class="p">(</span><span class="n">x</span><span class="o">=</span><span class="mi">10</span><span class="p">)</span>
<span class="k">def</span> <span class="nf">f</span><span class="p">(</span><span class="n">x</span><span class="p">):</span>
<span class="k">print</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
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<pre>10
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<h3 id="If-x-is-Boolean-(True-/-False),-a-checkbox-is-created">If x is Boolean (True / False), a checkbox is created<a class="anchor-link" href="#If-x-is-Boolean-(True-/-False),-a-checkbox-is-created">¶</a></h3>
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<div class=" highlight hl-ipython2"><pre><span class="n">interact</span><span class="p">(</span><span class="n">f</span><span class="p">,</span> <span class="n">x</span><span class="o">=</span><span class="bp">True</span><span class="p">);</span>
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<pre>True
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<h3 id="If-x-is-a-string:-text-box">If x is a string: text box<a class="anchor-link" href="#If-x-is-a-string:-text-box">¶</a></h3>
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<div class=" highlight hl-ipython2"><pre><span class="n">interact</span><span class="p">(</span><span class="n">f</span><span class="p">,</span> <span class="n">x</span><span class="o">=</span><span class="s">'Hi there!'</span><span class="p">);</span>
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<pre>Hi there!
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<h3 id="Widgets-can-be-called-with-arguments-to-customize-them">Widgets can be called with arguments to customize them<a class="anchor-link" href="#Widgets-can-be-called-with-arguments-to-customize-them">¶</a></h3>
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<div class=" highlight hl-ipython2"><pre><span class="n">interact</span><span class="p">(</span><span class="n">f</span><span class="p">,</span> <span class="n">x</span><span class="o">=</span><span class="n">widgets</span><span class="o">.</span><span class="n">IntSliderWidget</span><span class="p">(</span><span class="nb">min</span><span class="o">=-</span><span class="mi">20</span><span class="p">,</span><span class="nb">max</span><span class="o">=</span><span class="mi">40</span><span class="p">,</span><span class="n">step</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span><span class="n">value</span><span class="o">=</span><span class="mi">10</span><span class="p">));</span>
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<pre>10
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<h3 id="An-example-with-plot">An example with plot<a class="anchor-link" href="#An-example-with-plot">¶</a></h3>
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<div class=" highlight hl-ipython2"><pre><span class="kn">import</span> <span class="nn">numpy</span> <span class="kn">as</span> <span class="nn">np</span>
<span class="kn">from</span> <span class="nn">matplotlib</span> <span class="kn">import</span> <span class="n">pyplot</span> <span class="k">as</span> <span class="n">plt</span>
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<div class=" highlight hl-ipython2"><pre><span class="o">%</span><span class="k">matplotlib</span> inline
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<div class=" highlight hl-ipython2"><pre><span class="nd">@interact</span><span class="p">(</span><span class="n">degree</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span>
<span class="k">def</span> <span class="nf">f</span><span class="p">(</span><span class="n">degree</span><span class="p">):</span>
<span class="n">x</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">linspace</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span><span class="mi">1</span><span class="p">,</span><span class="mi">100</span><span class="p">)</span>
<span class="n">f</span><span class="p">,</span> <span class="n">ax</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">10</span><span class="p">,</span><span class="mi">5</span><span class="p">))</span>
<span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">x</span><span class="o">**</span><span class="n">degree</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
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<h3 id="An-example-using-sympy-to-factor-polynomials">An example using <a href="http://sympy.org/en/index.html">sympy</a> to factor polynomials<a class="anchor-link" href="#An-example-using-sympy-to-factor-polynomials">¶</a></h3>
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<div class=" highlight hl-ipython2"><pre><span class="kn">from</span> <span class="nn">IPython.display</span> <span class="kn">import</span> <span class="n">display</span>
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<div class=" highlight hl-ipython2"><pre><span class="kn">from</span> <span class="nn">sympy</span> <span class="kn">import</span> <span class="n">Symbol</span><span class="p">,</span> <span class="n">Eq</span><span class="p">,</span> <span class="n">factor</span><span class="p">,</span> <span class="n">init_printing</span>
<span class="n">init_printing</span><span class="p">(</span><span class="n">use_latex</span><span class="o">=</span><span class="s">'mathjax'</span><span class="p">)</span>
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<div class=" highlight hl-ipython2"><pre><span class="n">x</span> <span class="o">=</span> <span class="n">Symbol</span><span class="p">(</span><span class="s">'x'</span><span class="p">)</span>
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<div class=" highlight hl-ipython2"><pre><span class="k">def</span> <span class="nf">factorit</span><span class="p">(</span><span class="n">n</span><span class="p">):</span>
<span class="n">display</span><span class="p">(</span><span class="n">Eq</span><span class="p">(</span><span class="n">x</span><span class="o">**</span><span class="n">n</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="n">factor</span><span class="p">(</span><span class="n">x</span><span class="o">**</span><span class="n">n</span><span class="o">-</span><span class="mi">1</span><span class="p">)))</span>
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<div class=" highlight hl-ipython2"><pre><span class="n">factorit</span><span class="p">(</span><span class="mi">12</span><span class="p">)</span>
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$$x^{12} - 1 = \left(x - 1\right) \left(x + 1\right) \left(x^{2} + 1\right) \left(x^{2} - x + 1\right) \left(x^{2} + x + 1\right) \left(x^{4} - x^{2} + 1\right)$$
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<div class=" highlight hl-ipython2"><pre><span class="n">interact</span><span class="p">(</span><span class="n">factorit</span><span class="p">,</span> <span class="n">n</span><span class="o">=</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span><span class="mi">40</span><span class="p">));</span>
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$$x^{21} - 1 = \left(x - 1\right) \left(x^{2} + x + 1\right) \left(x^{6} + x^{5} + x^{4} + x^{3} + x^{2} + x + 1\right) \left(x^{12} - x^{11} + x^{9} - x^{8} + x^{6} - x^{4} + x^{3} - x + 1\right)$$
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<h3 id="Exploring-the-Lorenz-equations-using-IPython-widgets">Exploring the Lorenz equations using IPython widgets<a class="anchor-link" href="#Exploring-the-Lorenz-equations-using-IPython-widgets">¶</a></h3>
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<p>The Lorenz system is a system of ordinary differential equations (the Lorenz equations) first studied by <a href="http://en.wikipedia.org/wiki/Edward_Norton_Lorenz">Edward Lorenz</a>. It is notable for having chaotic solutions for certain parameter values and initial conditions. In particular, the Lorenz attractor is a set of chaotic solutions of the Lorenz system which, when plotted, resemble a butterfly or figure eight.</p>
<p>The system is described by:</p>
$$
\begin{align}
\frac{\mathrm{d}x}{\mathrm{d}t} &= \sigma (y - x), \\
\frac{\mathrm{d}y}{\mathrm{d}t} &= x (\rho - z) - y, \\
\frac{\mathrm{d}z}{\mathrm{d}t} &= x y - \beta z.
\end{align}
$$<p>$x$, $y$, and $z$ make up the system state, $t$ is time, and $\sigma$, $\rho$, $\beta$ are the system parameters</p>
<p>The small animation below shows a sample solution for when when ρ = 28, σ = 10, and β = 8/3 (Values used by Lorenz)</p>
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<p><img src="http://upload.wikimedia.org/wikipedia/commons/1/13/A_Trajectory_Through_Phase_Space_in_a_Lorenz_Attractor.gif" alt=""></p>
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<p>The IPython widget infrastructure is a convenient way to visualise what happens to the solutions as we vary the parameters</p>
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<div class=" highlight hl-ipython2"><pre><span class="kn">from</span> <span class="nn">IPython.display</span> <span class="kn">import</span> <span class="n">clear_output</span><span class="p">,</span> <span class="n">display</span><span class="p">,</span> <span class="n">HTML</span>
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<div class=" highlight hl-ipython2"><pre><span class="kn">import</span> <span class="nn">numpy</span> <span class="kn">as</span> <span class="nn">np</span>
<span class="kn">from</span> <span class="nn">scipy</span> <span class="kn">import</span> <span class="n">integrate</span>
<span class="kn">from</span> <span class="nn">matplotlib</span> <span class="kn">import</span> <span class="n">pyplot</span> <span class="k">as</span> <span class="n">plt</span>
<span class="kn">from</span> <span class="nn">mpl_toolkits.mplot3d</span> <span class="kn">import</span> <span class="n">Axes3D</span>
<span class="kn">from</span> <span class="nn">matplotlib.colors</span> <span class="kn">import</span> <span class="n">cnames</span>
<span class="kn">from</span> <span class="nn">matplotlib</span> <span class="kn">import</span> <span class="n">animation</span>
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<p>We define a function that can integrate the system of differential equations numerically and then plot the solutions. This function has arguments that control the parameters of the differential equation ($\sigma$, $\beta$, $\rho$), the numerical integration (N, max_time) and the visualization (angle).</p>
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<div class=" highlight hl-ipython2"><pre><span class="k">def</span> <span class="nf">solve_lorenz</span><span class="p">(</span><span class="n">N</span><span class="o">=</span><span class="mi">10</span><span class="p">,</span> <span class="n">angle</span><span class="o">=</span><span class="mf">0.0</span><span class="p">,</span> <span class="n">max_time</span><span class="o">=</span><span class="mf">10.0</span><span class="p">,</span> <span class="n">sigma</span><span class="o">=</span><span class="mf">10.0</span><span class="p">,</span> <span class="n">beta</span><span class="o">=</span><span class="mf">8.</span><span class="o">/</span><span class="mi">3</span><span class="p">,</span> <span class="n">rho</span><span class="o">=</span><span class="mf">28.0</span><span class="p">):</span>
<span class="n">fig</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">()</span>
<span class="n">ax</span> <span class="o">=</span> <span class="n">fig</span><span class="o">.</span><span class="n">add_axes</span><span class="p">([</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">],</span> <span class="n">projection</span><span class="o">=</span><span class="s">'3d'</span><span class="p">)</span>
<span class="n">ax</span><span class="o">.</span><span class="n">axis</span><span class="p">(</span><span class="s">'off'</span><span class="p">)</span>
<span class="c"># prepare the axes limits</span>
<span class="n">ax</span><span class="o">.</span><span class="n">set_xlim</span><span class="p">((</span><span class="o">-</span><span class="mi">25</span><span class="p">,</span> <span class="mi">25</span><span class="p">))</span>
<span class="n">ax</span><span class="o">.</span><span class="n">set_ylim</span><span class="p">((</span><span class="o">-</span><span class="mi">35</span><span class="p">,</span> <span class="mi">35</span><span class="p">))</span>
<span class="n">ax</span><span class="o">.</span><span class="n">set_zlim</span><span class="p">((</span><span class="mi">5</span><span class="p">,</span> <span class="mi">55</span><span class="p">))</span>
<span class="k">def</span> <span class="nf">lorenz_deriv</span><span class="p">((</span><span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="n">z</span><span class="p">),</span> <span class="n">t0</span><span class="p">,</span> <span class="n">sigma</span><span class="o">=</span><span class="n">sigma</span><span class="p">,</span> <span class="n">beta</span><span class="o">=</span><span class="n">beta</span><span class="p">,</span> <span class="n">rho</span><span class="o">=</span><span class="n">rho</span><span class="p">):</span>
<span class="sd">"""Compute the time-derivative of a Lorentz system."""</span>
<span class="k">return</span> <span class="p">[</span><span class="n">sigma</span> <span class="o">*</span> <span class="p">(</span><span class="n">y</span> <span class="o">-</span> <span class="n">x</span><span class="p">),</span> <span class="n">x</span> <span class="o">*</span> <span class="p">(</span><span class="n">rho</span> <span class="o">-</span> <span class="n">z</span><span class="p">)</span> <span class="o">-</span> <span class="n">y</span><span class="p">,</span> <span class="n">x</span> <span class="o">*</span> <span class="n">y</span> <span class="o">-</span> <span class="n">beta</span> <span class="o">*</span> <span class="n">z</span><span class="p">]</span>
<span class="c"># Choose random starting points, uniformly distributed from -15 to 15</span>
<span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">seed</span><span class="p">(</span><span class="mi">1</span><span class="p">)</span>
<span class="n">x0</span> <span class="o">=</span> <span class="o">-</span><span class="mi">15</span> <span class="o">+</span> <span class="mi">30</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">random</span><span class="p">((</span><span class="n">N</span><span class="p">,</span> <span class="mi">3</span><span class="p">))</span>
<span class="c"># Solve for the trajectories</span>
<span class="n">t</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">linspace</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">max_time</span><span class="p">,</span> <span class="nb">int</span><span class="p">(</span><span class="mi">250</span><span class="o">*</span><span class="n">max_time</span><span class="p">))</span>
<span class="n">x_t</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">asarray</span><span class="p">([</span><span class="n">integrate</span><span class="o">.</span><span class="n">odeint</span><span class="p">(</span><span class="n">lorenz_deriv</span><span class="p">,</span> <span class="n">x0i</span><span class="p">,</span> <span class="n">t</span><span class="p">)</span>
<span class="k">for</span> <span class="n">x0i</span> <span class="ow">in</span> <span class="n">x0</span><span class="p">])</span>
<span class="c"># choose a different color for each trajectory</span>
<span class="n">colors</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">cm</span><span class="o">.</span><span class="n">jet</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">linspace</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="n">N</span><span class="p">))</span>
<span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">N</span><span class="p">):</span>
<span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="n">z</span> <span class="o">=</span> <span class="n">x_t</span><span class="p">[</span><span class="n">i</span><span class="p">,:,:]</span><span class="o">.</span><span class="n">T</span>
<span class="n">lines</span> <span class="o">=</span> <span class="n">ax</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="n">z</span><span class="p">,</span> <span class="s">'-'</span><span class="p">,</span> <span class="n">c</span><span class="o">=</span><span class="n">colors</span><span class="p">[</span><span class="n">i</span><span class="p">])</span>
<span class="n">plt</span><span class="o">.</span><span class="n">setp</span><span class="p">(</span><span class="n">lines</span><span class="p">,</span> <span class="n">linewidth</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span>
<span class="n">ax</span><span class="o">.</span><span class="n">view_init</span><span class="p">(</span><span class="mi">30</span><span class="p">,</span> <span class="n">angle</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
<span class="k">return</span> <span class="n">t</span><span class="p">,</span> <span class="n">x_t</span>
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<div class=" highlight hl-ipython2"><pre><span class="n">t</span><span class="p">,</span> <span class="n">x_t</span> <span class="o">=</span> <span class="n">solve_lorenz</span><span class="p">(</span><span class="n">angle</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">N</span><span class="o">=</span><span class="mi">10</span><span class="p">)</span>
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<div class="prompt input_prompt">In [23]:</div>
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<div class=" highlight hl-ipython2"><pre><span class="n">w</span> <span class="o">=</span> <span class="n">interactive</span><span class="p">(</span><span class="n">solve_lorenz</span><span class="p">,</span> <span class="n">N</span><span class="o">=</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span><span class="mi">50</span><span class="p">),</span> <span class="n">max_time</span><span class="o">=</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span><span class="mi">30</span><span class="p">),</span> <span class="n">angle</span><span class="o">=</span><span class="p">(</span><span class="mf">0.</span><span class="p">,</span><span class="mf">360.</span><span class="p">),</span> <span class="n">sigma</span><span class="o">=</span><span class="p">(</span><span class="mf">0.0</span><span class="p">,</span><span class="mf">50.0</span><span class="p">),</span> <span class="n">rho</span><span class="o">=</span><span class="p">(</span><span class="mf">0.0</span><span class="p">,</span><span class="mf">50.0</span><span class="p">),</span> <span class="n">beta</span><span class="o">=</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span><span class="mi">30</span><span class="p">)</span> <span class="p">)</span>
<span class="n">display</span><span class="p">(</span><span class="n">w</span><span class="p">)</span>
</pre></div>
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"
>
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<div class="input">
<div class="prompt input_prompt">In [24]:</div>
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<div class=" highlight hl-ipython2"><pre><span class="kn">import</span> <span class="nn">pandas</span> <span class="kn">as</span> <span class="nn">pd</span>
</pre></div>
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<div class="prompt input_prompt">In [25]:</div>
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<div class=" highlight hl-ipython2"><pre><span class="n">xdf</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">({</span><span class="s">'A'</span><span class="p">:</span><span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="mi">5</span><span class="p">),</span> <span class="s">'B'</span><span class="p">:</span><span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="mi">5</span><span class="p">)})</span>
</pre></div>
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<div class="prompt input_prompt">In [26]:</div>
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<div class=" highlight hl-ipython2"><pre><span class="n">xdf</span><span class="o">.</span><span class="n">plot</span><span class="p">()</span>
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<div class="output_area"><div class="prompt output_prompt">Out[26]:</div>
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<pre><matplotlib.axes._subplots.AxesSubplot at 0x10b57dd90></pre>
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"
>
</div>
</div>
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</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In [27]:</div>
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<div class=" highlight hl-ipython2"><pre><span class="k">def</span> <span class="nf">plot_df</span><span class="p">(</span><span class="n">label</span><span class="p">):</span>
<span class="n">xdf</span><span class="p">[</span><span class="n">label</span><span class="p">]</span><span class="o">.</span><span class="n">plot</span><span class="p">()</span>
</pre></div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In [28]:</div>
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<div class=" highlight hl-ipython2"><pre><span class="n">w</span> <span class="o">=</span> <span class="n">interactive</span><span class="p">(</span><span class="n">plot_df</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="p">[</span><span class="s">'A'</span><span class="p">,</span><span class="s">'B'</span><span class="p">])</span>
</pre></div>
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<div class="prompt input_prompt">In [29]:</div>
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<div class=" highlight hl-ipython2"><pre><span class="n">display</span><span class="p">(</span><span class="n">w</span><span class="p">)</span>
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<div class=" highlight hl-ipython2"><pre><span class="o">!</span>ipython nbconvert IPython_widgets.ipynb --to html
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<pre>This application is used to convert notebook files (*.ipynb) to various other
formats.
WARNING: THE COMMANDLINE INTERFACE MAY CHANGE IN FUTURE RELEASES.
Options
-------
Arguments that take values are actually convenience aliases to full
Configurables, whose aliases are listed on the help line. For more information
on full configurables, see '--help-all'.
--init
Initialize profile with default config files. This is equivalent
to running `ipython profile create <profile>` prior to startup.
--execute
Execute the notebook prior to export.
--stdout
Write notebook output to stdout instead of files.
--debug
set log level to logging.DEBUG (maximize logging output)
--quiet
set log level to logging.CRITICAL (minimize logging output)
--inplace
Run nbconvert in place, overwriting the existing notebook (only
relevant when converting to notebook format)
--profile=<Unicode> (BaseIPythonApplication.profile)
Default: u'default'
The IPython profile to use.
--reveal-prefix=<Unicode> (RevealHelpPreprocessor.url_prefix)
Default: 'reveal.js'
The URL prefix for reveal.js. This can be a a relative URL for a local copy
of reveal.js, or point to a CDN.
For speaker notes to work, a local reveal.js prefix must be used.
--ipython-dir=<Unicode> (BaseIPythonApplication.ipython_dir)
Default: u''
The name of the IPython directory. This directory is used for logging
configuration (through profiles), history storage, etc. The default is
usually $HOME/.ipython. This option can also be specified through the
environment variable IPYTHONDIR.
--nbformat=<Enum> (NotebookExporter.nbformat_version)
Default: 4
Choices: [1, 2, 3, 4]
The nbformat version to write. Use this to downgrade notebooks.
--writer=<DottedObjectName> (NbConvertApp.writer_class)
Default: 'FilesWriter'
Writer class used to write the results of the conversion
--log-level=<Enum> (Application.log_level)
Default: 30
Choices: (0, 10, 20, 30, 40, 50, 'DEBUG', 'INFO', 'WARN', 'ERROR', 'CRITICAL')
Set the log level by value or name.
--to=<CaselessStrEnum> (NbConvertApp.export_format)
Default: 'html'
Choices: ['custom', 'html', 'latex', 'markdown', 'notebook', 'pdf', 'python', 'rst', 'script', 'slides']
The export format to be used.
--template=<Unicode> (TemplateExporter.template_file)
Default: u'default'
Name of the template file to use
--output=<Unicode> (NbConvertApp.output_base)
Default: ''
overwrite base name use for output files. can only be used when converting
one notebook at a time.
--post=<DottedOrNone> (NbConvertApp.postprocessor_class)
Default: u''
PostProcessor class used to write the results of the conversion
--config=<Unicode> (BaseIPythonApplication.extra_config_file)
Default: u''
Path to an extra config file to load.
If specified, load this config file in addition to any other IPython config.
--profile-dir=<Unicode> (ProfileDir.location)
Default: u''
Set the profile location directly. This overrides the logic used by the
`profile` option.
To see all available configurables, use `--help-all`
Examples
--------
The simplest way to use nbconvert is
> ipython nbconvert mynotebook.ipynb
which will convert mynotebook.ipynb to the default format (probably HTML).
You can specify the export format with `--to`.
Options include ['custom', 'html', 'latex', 'markdown', 'notebook', 'pdf', 'python', 'rst', 'script', 'slides']
> ipython nbconvert --to latex mynotebook.ipynb
Both HTML and LaTeX support multiple output templates. LaTeX includes
'base', 'article' and 'report'. HTML includes 'basic' and 'full'. You
can specify the flavor of the format used.
> ipython nbconvert --to html --template basic mynotebook.ipynb
You can also pipe the output to stdout, rather than a file
> ipython nbconvert mynotebook.ipynb --stdout
PDF is generated via latex
> ipython nbconvert mynotebook.ipynb --to pdf
You can get (and serve) a Reveal.js-powered slideshow
> ipython nbconvert myslides.ipynb --to slides --post serve
Multiple notebooks can be given at the command line in a couple of
different ways:
> ipython nbconvert notebook*.ipynb
> ipython nbconvert notebook1.ipynb notebook2.ipynb
or you can specify the notebooks list in a config file, containing::
c.NbConvertApp.notebooks = ["my_notebook.ipynb"]
> ipython nbconvert --config mycfg.py
[NbConvertApp] CRITICAL | Bad config encountered during initialization:
[NbConvertApp] CRITICAL | Unrecognized flag: '-to-html'
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