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class="document" itemscope="itemscope" itemtype="http://schema.org/Article"> + <div itemprop="articleBody"> + + <h1>Source code for pathflowai.visualize</h1><div class="highlight"><pre> +<span></span><span class="sd">"""</span> +<span class="sd">visualize.py</span> +<span class="sd">=======================</span> +<span class="sd">Plots SHAP outputs, UMAP embeddings, and overlays predictions on top of WSI.</span> +<span class="sd">"""</span> + +<span class="kn">import</span> <span class="nn">plotly.graph_objs</span> <span class="k">as</span> <span class="nn">go</span> +<span class="kn">import</span> <span class="nn">plotly.offline</span> <span class="k">as</span> <span class="nn">py</span> +<span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span><span class="o">,</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span> +<span class="kn">import</span> <span class="nn">networkx</span> <span class="k">as</span> <span class="nn">nx</span> +<span class="kn">import</span> <span class="nn">dask.array</span> <span class="k">as</span> <span class="nn">da</span> +<span class="kn">from</span> <span class="nn">PIL</span> <span class="k">import</span> <span class="n">Image</span> +<span class="kn">import</span> <span class="nn">matplotlib</span><span class="o">,</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span> +<span class="kn">import</span> <span class="nn">seaborn</span> <span class="k">as</span> <span class="nn">sns</span> +<span class="kn">import</span> <span class="nn">sqlite3</span> +<span class="kn">import</span> <span class="nn">seaborn</span> <span class="k">as</span> <span class="nn">sns</span> +<span class="kn">from</span> <span class="nn">os.path</span> <span class="k">import</span> <span class="n">join</span> +<span class="n">sns</span><span class="o">.</span><span class="n">set</span><span class="p">()</span> + +<div class="viewcode-block" id="PlotlyPlot"><a class="viewcode-back" href="../../index.html#pathflowai.visualize.PlotlyPlot">[docs]</a><span class="k">class</span> <span class="nc">PlotlyPlot</span><span class="p">:</span> + <span class="sd">"""Creates plotly html plots."""</span> + <span class="k">def</span> <span class="nf">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span> + <span class="bp">self</span><span class="o">.</span><span class="n">plots</span><span class="o">=</span><span class="p">[]</span> + +<div class="viewcode-block" id="PlotlyPlot.add_plot"><a class="viewcode-back" href="../../index.html#pathflowai.visualize.PlotlyPlot.add_plot">[docs]</a> <span class="k">def</span> <span class="nf">add_plot</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">t_data_df</span><span class="p">,</span> <span class="n">G</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">color_col</span><span class="o">=</span><span class="s1">'color'</span><span class="p">,</span> <span class="n">name_col</span><span class="o">=</span><span class="s1">'name'</span><span class="p">,</span> <span class="n">xyz_cols</span><span class="o">=</span><span class="p">[</span><span class="s1">'x'</span><span class="p">,</span><span class="s1">'y'</span><span class="p">,</span><span class="s1">'z'</span><span class="p">],</span> <span class="n">size</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">opacity</span><span class="o">=</span><span class="mf">1.0</span><span class="p">,</span> <span class="n">custom_colors</span><span class="o">=</span><span class="p">[]):</span> + <span class="sd">"""Adds plotting data to be plotted.</span> + +<span class="sd"> Parameters</span> +<span class="sd"> ----------</span> +<span class="sd"> t_data_df:dataframe</span> +<span class="sd"> 3-D transformed dataframe.</span> +<span class="sd"> G:nx.Graph</span> +<span class="sd"> Networkx graph.</span> +<span class="sd"> color_col:str</span> +<span class="sd"> Column to use to color points.</span> +<span class="sd"> name_col:str</span> +<span class="sd"> Column to use to name points.</span> +<span class="sd"> xyz_cols:list</span> +<span class="sd"> 3 columns that denote x,y,z coords.</span> +<span class="sd"> size:int</span> +<span class="sd"> Marker size.</span> +<span class="sd"> opacity:float</span> +<span class="sd"> Marker opacity.</span> +<span class="sd"> custom_colors:list</span> +<span class="sd"> Custom colors to supply.</span> +<span class="sd"> """</span> + <span class="n">plots</span> <span class="o">=</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="nb">tuple</span><span class="p">(</span><span class="n">xyz_cols</span><span class="p">)</span> + <span class="k">if</span> <span class="n">t_data_df</span><span class="p">[</span><span class="n">color_col</span><span class="p">]</span><span class="o">.</span><span class="n">dtype</span> <span class="o">==</span> <span class="n">np</span><span class="o">.</span><span class="n">float64</span><span class="p">:</span> + <span class="n">plots</span><span class="o">.</span><span class="n">append</span><span class="p">(</span> + <span class="n">go</span><span class="o">.</span><span class="n">Scatter3d</span><span class="p">(</span><span class="n">x</span><span class="o">=</span><span class="n">t_data_df</span><span class="p">[</span><span class="n">x</span><span class="p">],</span> <span class="n">y</span><span class="o">=</span><span class="n">t_data_df</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">t_data_df</span><span class="p">[</span><span class="n">z</span><span class="p">],</span> + <span class="n">name</span><span class="o">=</span><span class="s1">''</span><span class="p">,</span> <span class="n">mode</span><span class="o">=</span><span class="s1">'markers'</span><span class="p">,</span> + <span class="n">marker</span><span class="o">=</span><span class="nb">dict</span><span class="p">(</span><span class="n">color</span><span class="o">=</span><span class="n">t_data_df</span><span class="p">[</span><span class="n">color_col</span><span class="p">],</span> <span class="n">size</span><span class="o">=</span><span class="n">size</span><span class="p">,</span> <span class="n">opacity</span><span class="o">=</span><span class="n">opacity</span><span class="p">,</span> <span class="n">colorscale</span><span class="o">=</span><span class="s1">'Viridis'</span><span class="p">,</span> + <span class="n">colorbar</span><span class="o">=</span><span class="nb">dict</span><span class="p">(</span><span class="n">title</span><span class="o">=</span><span class="s1">'Colorbar'</span><span class="p">)),</span> <span class="n">text</span><span class="o">=</span><span class="n">t_data_df</span><span class="p">[</span><span class="n">color_col</span><span class="p">]</span> <span class="k">if</span> <span class="n">name_col</span> <span class="ow">not</span> <span class="ow">in</span> <span class="nb">list</span><span class="p">(</span><span class="n">t_data_df</span><span class="p">)</span> <span class="k">else</span> <span class="n">t_data_df</span><span class="p">[</span><span class="n">name_col</span><span class="p">]))</span> + <span class="k">else</span><span class="p">:</span> + <span class="n">colors</span> <span class="o">=</span> <span class="n">t_data_df</span><span class="p">[</span><span class="n">color_col</span><span class="p">]</span><span class="o">.</span><span class="n">unique</span><span class="p">()</span> + <span class="n">c</span> <span class="o">=</span> <span class="n">sns</span><span class="o">.</span><span class="n">color_palette</span><span class="p">(</span><span class="s1">'hls'</span><span class="p">,</span> <span class="nb">len</span><span class="p">(</span><span class="n">colors</span><span class="p">))</span> + <span class="n">c</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="s1">'rgb(</span><span class="si">{}</span><span class="s1">)'</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="s1">','</span><span class="o">.</span><span class="n">join</span><span class="p">(((</span><span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">c_i</span><span class="p">)</span><span class="o">*</span><span class="mi">255</span><span class="p">)</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="nb">int</span><span class="p">)</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="nb">str</span><span class="p">)</span><span class="o">.</span><span class="n">tolist</span><span class="p">())))</span> <span class="k">for</span> <span class="n">c_i</span> <span class="ow">in</span> <span class="n">c</span><span class="p">])</span><span class="c1">#c = ['hsl(' + str(h) + ',50%' + ',50%)' for h in np.linspace(0, 360, len(colors) + 2)]</span> + <span class="k">if</span> <span class="n">custom_colors</span><span class="p">:</span> + <span class="n">c</span> <span class="o">=</span> <span class="n">custom_colors</span> + <span class="n">color_dict</span> <span class="o">=</span> <span class="p">{</span><span class="n">name</span><span class="p">:</span> <span class="n">c</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="k">for</span> <span class="n">i</span><span class="p">,</span><span class="n">name</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="nb">sorted</span><span class="p">(</span><span class="n">colors</span><span class="p">))}</span> + + <span class="k">for</span> <span class="n">name</span><span class="p">,</span><span class="n">col</span> <span class="ow">in</span> <span class="n">color_dict</span><span class="o">.</span><span class="n">items</span><span class="p">():</span> + <span class="n">plots</span><span class="o">.</span><span class="n">append</span><span class="p">(</span> + <span class="n">go</span><span class="o">.</span><span class="n">Scatter3d</span><span class="p">(</span><span class="n">x</span><span class="o">=</span><span class="n">t_data_df</span><span class="p">[</span><span class="n">x</span><span class="p">][</span><span class="n">t_data_df</span><span class="p">[</span><span class="n">color_col</span><span class="p">]</span><span class="o">==</span><span class="n">name</span><span class="p">],</span> <span class="n">y</span><span class="o">=</span><span class="n">t_data_df</span><span class="p">[</span><span class="n">y</span><span class="p">][</span><span class="n">t_data_df</span><span class="p">[</span><span class="n">color_col</span><span class="p">]</span><span class="o">==</span><span class="n">name</span><span class="p">],</span> + <span class="n">z</span><span class="o">=</span><span class="n">t_data_df</span><span class="p">[</span><span class="n">z</span><span class="p">][</span><span class="n">t_data_df</span><span class="p">[</span><span class="n">color_col</span><span class="p">]</span><span class="o">==</span><span class="n">name</span><span class="p">],</span> + <span class="n">name</span><span class="o">=</span><span class="nb">str</span><span class="p">(</span><span class="n">name</span><span class="p">),</span> <span class="n">mode</span><span class="o">=</span><span class="s1">'markers'</span><span class="p">,</span> + <span class="n">marker</span><span class="o">=</span><span class="nb">dict</span><span class="p">(</span><span class="n">color</span><span class="o">=</span><span class="n">col</span><span class="p">,</span> <span class="n">size</span><span class="o">=</span><span class="n">size</span><span class="p">,</span> <span class="n">opacity</span><span class="o">=</span><span class="n">opacity</span><span class="p">),</span> <span class="n">text</span><span class="o">=</span><span class="n">t_data_df</span><span class="o">.</span><span class="n">index</span><span class="p">[</span><span class="n">t_data_df</span><span class="p">[</span><span class="n">color_col</span><span class="p">]</span><span class="o">==</span><span class="n">name</span><span class="p">]</span> <span class="k">if</span> <span class="s1">'name'</span> <span class="ow">not</span> <span class="ow">in</span> <span class="nb">list</span><span class="p">(</span><span class="n">t_data_df</span><span class="p">)</span> <span class="k">else</span> <span class="n">t_data_df</span><span class="p">[</span><span class="n">name_col</span><span class="p">][</span><span class="n">t_data_df</span><span class="p">[</span><span class="n">color_col</span><span class="p">]</span><span class="o">==</span><span class="n">name</span><span class="p">]))</span> + <span class="k">if</span> <span class="n">G</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span> + <span class="c1">#pos = nx.spring_layout(G,dim=3,iterations=0,pos={i: tuple(t_data.loc[i,['x','y','z']]) for i in range(len(t_data))})</span> + <span class="n">Xed</span><span class="p">,</span> <span class="n">Yed</span><span class="p">,</span> <span class="n">Zed</span> <span class="o">=</span> <span class="p">[],</span> <span class="p">[],</span> <span class="p">[]</span> + <span class="k">for</span> <span class="n">edge</span> <span class="ow">in</span> <span class="n">G</span><span class="o">.</span><span class="n">edges</span><span class="p">():</span> + <span class="k">if</span> <span class="n">edge</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="ow">in</span> <span class="n">t_data_df</span><span class="o">.</span><span class="n">index</span><span class="o">.</span><span class="n">values</span> <span class="ow">and</span> <span class="n">edge</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="ow">in</span> <span class="n">t_data_df</span><span class="o">.</span><span class="n">index</span><span class="o">.</span><span class="n">values</span><span class="p">:</span> + <span class="n">Xed</span> <span class="o">+=</span> <span class="p">[</span><span class="n">t_data_df</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="n">edge</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span><span class="n">x</span><span class="p">],</span> <span class="n">t_data_df</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="n">edge</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span><span class="n">x</span><span class="p">],</span> <span class="kc">None</span><span class="p">]</span> + <span class="n">Yed</span> <span class="o">+=</span> <span class="p">[</span><span class="n">t_data_df</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="n">edge</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span><span class="n">y</span><span class="p">],</span> <span class="n">t_data_df</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="n">edge</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span><span class="n">y</span><span class="p">],</span> <span class="kc">None</span><span class="p">]</span> + <span class="n">Zed</span> <span class="o">+=</span> <span class="p">[</span><span class="n">t_data_df</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="n">edge</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span><span class="n">z</span><span class="p">],</span> <span class="n">t_data_df</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="n">edge</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span><span class="n">z</span><span class="p">],</span> <span class="kc">None</span><span class="p">]</span> + <span class="n">plots</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">go</span><span class="o">.</span><span class="n">Scatter3d</span><span class="p">(</span><span class="n">x</span><span class="o">=</span><span class="n">Xed</span><span class="p">,</span> + <span class="n">y</span><span class="o">=</span><span class="n">Yed</span><span class="p">,</span> + <span class="n">z</span><span class="o">=</span><span class="n">Zed</span><span class="p">,</span> + <span class="n">mode</span><span class="o">=</span><span class="s1">'lines'</span><span class="p">,</span> + <span class="n">line</span><span class="o">=</span><span class="n">go</span><span class="o">.</span><span class="n">scatter3d</span><span class="o">.</span><span class="n">Line</span><span class="p">(</span><span class="n">color</span><span class="o">=</span><span class="s1">'rgb(210,210,210)'</span><span class="p">,</span> <span class="n">width</span><span class="o">=</span><span class="mi">2</span><span class="p">),</span> + <span class="n">hoverinfo</span><span class="o">=</span><span class="s1">'none'</span> + <span class="p">))</span> + <span class="bp">self</span><span class="o">.</span><span class="n">plots</span><span class="o">.</span><span class="n">extend</span><span class="p">(</span><span class="n">plots</span><span class="p">)</span></div> + +<div class="viewcode-block" id="PlotlyPlot.plot"><a class="viewcode-back" href="../../index.html#pathflowai.visualize.PlotlyPlot.plot">[docs]</a> <span class="k">def</span> <span class="nf">plot</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">output_fname</span><span class="p">,</span> <span class="n">axes_off</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span> + <span class="sd">"""Plot embedding of patches to html file.</span> + +<span class="sd"> Parameters</span> +<span class="sd"> ----------</span> +<span class="sd"> output_fname:str</span> +<span class="sd"> Output html file.</span> +<span class="sd"> axes_off:bool</span> +<span class="sd"> Remove axes.</span> + +<span class="sd"> """</span> + <span class="k">if</span> <span class="n">axes_off</span><span class="p">:</span> + <span class="n">fig</span> <span class="o">=</span> <span class="n">go</span><span class="o">.</span><span class="n">Figure</span><span class="p">(</span><span class="n">data</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">plots</span><span class="p">,</span><span class="n">layout</span><span class="o">=</span><span class="n">go</span><span class="o">.</span><span class="n">Layout</span><span class="p">(</span><span class="n">scene</span><span class="o">=</span><span class="nb">dict</span><span class="p">(</span><span class="n">xaxis</span><span class="o">=</span><span class="nb">dict</span><span class="p">(</span><span class="n">title</span><span class="o">=</span><span class="s1">''</span><span class="p">,</span><span class="n">autorange</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span><span class="n">showgrid</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span><span class="n">zeroline</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span><span class="n">showline</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span><span class="n">ticks</span><span class="o">=</span><span class="s1">''</span><span class="p">,</span><span class="n">showticklabels</span><span class="o">=</span><span class="kc">False</span><span class="p">),</span> + <span class="n">yaxis</span><span class="o">=</span><span class="nb">dict</span><span class="p">(</span><span class="n">title</span><span class="o">=</span><span class="s1">''</span><span class="p">,</span><span class="n">autorange</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span><span class="n">showgrid</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span><span class="n">zeroline</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span><span class="n">showline</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span><span class="n">ticks</span><span class="o">=</span><span class="s1">''</span><span class="p">,</span><span class="n">showticklabels</span><span class="o">=</span><span class="kc">False</span><span class="p">),</span> + <span class="n">zaxis</span><span class="o">=</span><span class="nb">dict</span><span class="p">(</span><span class="n">title</span><span class="o">=</span><span class="s1">''</span><span class="p">,</span><span class="n">autorange</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span><span class="n">showgrid</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span><span class="n">zeroline</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span><span class="n">showline</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span><span class="n">ticks</span><span class="o">=</span><span class="s1">''</span><span class="p">,</span><span class="n">showticklabels</span><span class="o">=</span><span class="kc">False</span><span class="p">))))</span> + <span class="k">else</span><span class="p">:</span> + <span class="n">fig</span> <span class="o">=</span> <span class="n">go</span><span class="o">.</span><span class="n">Figure</span><span class="p">(</span><span class="n">data</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">plots</span><span class="p">)</span> + <span class="n">py</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">fig</span><span class="p">,</span> <span class="n">filename</span><span class="o">=</span><span class="n">output_fname</span><span class="p">,</span> <span class="n">auto_open</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span></div></div> + +<div class="viewcode-block" id="to_pil"><a class="viewcode-back" href="../../index.html#pathflowai.visualize.to_pil">[docs]</a><span class="k">def</span> <span class="nf">to_pil</span><span class="p">(</span><span class="n">arr</span><span class="p">):</span> + <span class="sd">"""Numpy array to pil.</span> + +<span class="sd"> Parameters</span> +<span class="sd"> ----------</span> +<span class="sd"> arr:array</span> +<span class="sd"> Numpy array.</span> + +<span class="sd"> Returns</span> +<span class="sd"> -------</span> +<span class="sd"> Image</span> +<span class="sd"> PIL Image.</span> + +<span class="sd"> """</span> + <span class="k">return</span> <span class="n">Image</span><span class="o">.</span><span class="n">fromarray</span><span class="p">(</span><span class="n">arr</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="s1">'uint8'</span><span class="p">),</span> <span class="s1">'RGB'</span><span class="p">)</span></div> + +<div class="viewcode-block" id="blend"><a class="viewcode-back" href="../../index.html#pathflowai.visualize.blend">[docs]</a><span class="k">def</span> <span class="nf">blend</span><span class="p">(</span><span class="n">arr1</span><span class="p">,</span> <span class="n">arr2</span><span class="p">,</span> <span class="n">alpha</span><span class="o">=</span><span class="mf">0.5</span><span class="p">):</span> + <span class="sd">"""Blend 2 arrays together, mixing with alpha.</span> + +<span class="sd"> Parameters</span> +<span class="sd"> ----------</span> +<span class="sd"> arr1:array</span> +<span class="sd"> Image 1.</span> +<span class="sd"> arr2:array</span> +<span class="sd"> Image 2.</span> +<span class="sd"> alpha:float</span> +<span class="sd"> Higher alpha makes image more like image 1.</span> + +<span class="sd"> Returns</span> +<span class="sd"> -------</span> +<span class="sd"> array</span> +<span class="sd"> Resulting image.</span> + +<span class="sd"> """</span> + <span class="k">return</span> <span class="n">alpha</span><span class="o">*</span><span class="n">arr1</span> <span class="o">+</span> <span class="p">(</span><span class="mf">1.</span><span class="o">-</span><span class="n">alpha</span><span class="p">)</span><span class="o">*</span><span class="n">arr2</span></div> + +<div class="viewcode-block" id="prob2rbg"><a class="viewcode-back" href="../../index.html#pathflowai.visualize.prob2rbg">[docs]</a><span class="k">def</span> <span class="nf">prob2rbg</span><span class="p">(</span><span class="n">prob</span><span class="p">,</span> <span class="n">palette</span><span class="p">,</span> <span class="n">arr</span><span class="p">):</span> + <span class="sd">"""Convert probability score to rgb image.</span> + +<span class="sd"> Parameters</span> +<span class="sd"> ----------</span> +<span class="sd"> prob:float</span> +<span class="sd"> Between 0 and 1 score.</span> +<span class="sd"> palette:palette</span> +<span class="sd"> Pallet converts between prob and color.</span> +<span class="sd"> arr:array</span> +<span class="sd"> Original array.</span> + +<span class="sd"> Returns</span> +<span class="sd"> -------</span> +<span class="sd"> array</span> +<span class="sd"> New image colored by prediction score.</span> + +<span class="sd"> """</span> + <span class="n">col</span> <span class="o">=</span> <span class="n">palette</span><span class="p">(</span><span class="n">prob</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="mi">3</span><span class="p">):</span> + <span class="n">arr</span><span class="p">[</span><span class="o">...</span><span class="p">,</span><span class="n">i</span><span class="p">]</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="n">col</span><span class="p">[</span><span class="n">i</span><span class="p">]</span><span class="o">*</span><span class="mi">255</span><span class="p">)</span> + <span class="k">return</span> <span class="n">arr</span></div> + +<div class="viewcode-block" id="seg2rgb"><a class="viewcode-back" href="../../index.html#pathflowai.visualize.seg2rgb">[docs]</a><span class="k">def</span> <span class="nf">seg2rgb</span><span class="p">(</span><span class="n">seg</span><span class="p">,</span> <span class="n">palette</span><span class="p">,</span> <span class="n">n_segmentation_classes</span><span class="p">):</span> + <span class="sd">"""Color each pixel by segmentation class.</span> + +<span class="sd"> Parameters</span> +<span class="sd"> ----------</span> +<span class="sd"> seg:array</span> +<span class="sd"> Segmentation mask.</span> +<span class="sd"> palette:palette</span> +<span class="sd"> Color to RGB map.</span> +<span class="sd"> n_segmentation_classes:int</span> +<span class="sd"> Total number segmentation classes.</span> + +<span class="sd"> Returns</span> +<span class="sd"> -------</span> +<span class="sd"> array</span> +<span class="sd"> Returned segmentation image.</span> +<span class="sd"> """</span> + <span class="c1">#print(seg.shape)</span> + <span class="c1">#print((seg/n_segmentation_classes))</span> + <span class="n">img</span><span class="o">=</span><span class="p">(</span><span class="n">palette</span><span class="p">(</span><span class="n">seg</span><span class="o">/</span><span class="n">n_segmentation_classes</span><span class="p">)[</span><span class="o">...</span><span class="p">,:</span><span class="mi">3</span><span class="p">]</span><span class="o">*</span><span class="mi">255</span><span class="p">)</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="nb">int</span><span class="p">)</span> + <span class="c1">#print(img.shape)</span> + <span class="k">return</span> <span class="n">img</span></div> + +<div class="viewcode-block" id="annotation2rgb"><a class="viewcode-back" href="../../index.html#pathflowai.visualize.annotation2rgb">[docs]</a><span class="k">def</span> <span class="nf">annotation2rgb</span><span class="p">(</span><span class="n">i</span><span class="p">,</span><span class="n">palette</span><span class="p">,</span><span class="n">arr</span><span class="p">):</span> + <span class="sd">"""Go from annotation of patch to color.</span> + +<span class="sd"> Parameters</span> +<span class="sd"> ----------</span> +<span class="sd"> i:int</span> +<span class="sd"> Annotation index.</span> +<span class="sd"> palette:palette</span> +<span class="sd"> Index to color mapping.</span> +<span class="sd"> arr:array</span> +<span class="sd"> Image array.</span> + +<span class="sd"> Returns</span> +<span class="sd"> -------</span> +<span class="sd"> array</span> +<span class="sd"> Resulting image.</span> + +<span class="sd"> """</span> + <span class="n">col</span> <span class="o">=</span> <span class="n">palette</span><span class="p">[</span><span class="n">i</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="mi">3</span><span class="p">):</span> + <span class="n">arr</span><span class="p">[</span><span class="o">...</span><span class="p">,</span><span class="n">i</span><span class="p">]</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="n">col</span><span class="p">[</span><span class="n">i</span><span class="p">]</span><span class="o">*</span><span class="mi">255</span><span class="p">)</span> + <span class="k">return</span> <span class="n">arr</span></div> + +<div class="viewcode-block" id="plot_image_"><a class="viewcode-back" href="../../index.html#pathflowai.visualize.plot_image_">[docs]</a><span class="k">def</span> <span class="nf">plot_image_</span><span class="p">(</span><span class="n">image_file</span><span class="p">,</span> <span class="n">compression_factor</span><span class="o">=</span><span class="mf">2.</span><span class="p">,</span> <span class="n">test_image_name</span><span class="o">=</span><span class="s1">'test.png'</span><span class="p">):</span> + <span class="sd">"""Plots entire SVS/other image.</span> + +<span class="sd"> Parameters</span> +<span class="sd"> ----------</span> +<span class="sd"> image_file:str</span> +<span class="sd"> Image file.</span> +<span class="sd"> compression_factor:float</span> +<span class="sd"> Amount to shrink each dimension of image.</span> +<span class="sd"> test_image_name:str</span> +<span class="sd"> Output image file.</span> + +<span class="sd"> """</span> + <span class="kn">from</span> <span class="nn">pathflowai.utils</span> <span class="k">import</span> <span class="n">svs2dask_array</span><span class="p">,</span> <span class="n">npy2da</span> + <span class="kn">import</span> <span class="nn">cv2</span> + <span class="n">arr</span><span class="o">=</span><span class="n">svs2dask_array</span><span class="p">(</span><span class="n">image_file</span><span class="p">,</span> <span class="n">tile_size</span><span class="o">=</span><span class="mi">1000</span><span class="p">,</span> <span class="n">overlap</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">remove_last</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">allow_unknown_chunksizes</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span> <span class="k">if</span> <span class="p">(</span><span class="ow">not</span> <span class="n">image_file</span><span class="o">.</span><span class="n">endswith</span><span class="p">(</span><span class="s1">'.npy'</span><span class="p">))</span> <span class="k">else</span> <span class="n">npy2da</span><span class="p">(</span><span class="n">image_file</span><span class="p">)</span> + <span class="n">arr2</span><span class="o">=</span><span class="n">to_pil</span><span class="p">(</span><span class="n">cv2</span><span class="o">.</span><span class="n">resize</span><span class="p">(</span><span class="n">arr</span><span class="o">.</span><span class="n">compute</span><span class="p">(),</span> <span class="n">dsize</span><span class="o">=</span><span class="nb">tuple</span><span class="p">((</span><span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">arr</span><span class="o">.</span><span class="n">shape</span><span class="p">[:</span><span class="mi">2</span><span class="p">])</span><span class="o">/</span><span class="n">compression_factor</span><span class="p">)</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="nb">int</span><span class="p">)</span><span class="o">.</span><span class="n">tolist</span><span class="p">()),</span> <span class="n">interpolation</span><span class="o">=</span><span class="n">cv2</span><span class="o">.</span><span class="n">INTER_CUBIC</span><span class="p">))</span> + <span class="n">arr2</span><span class="o">.</span><span class="n">save</span><span class="p">(</span><span class="n">test_image_name</span><span class="p">)</span></div> + +<span class="c1"># for now binary output</span> +<div class="viewcode-block" id="PredictionPlotter"><a class="viewcode-back" href="../../index.html#pathflowai.visualize.PredictionPlotter">[docs]</a><span class="k">class</span> <span class="nc">PredictionPlotter</span><span class="p">:</span> + <span class="sd">"""Plots predictions over entire image.</span> + +<span class="sd"> Parameters</span> +<span class="sd"> ----------</span> +<span class="sd"> dask_arr_dict:dict</span> +<span class="sd"> Stores all dask arrays corresponding to all of the images.</span> +<span class="sd"> patch_info_db:str</span> +<span class="sd"> Patch level information, eg. prediction.</span> +<span class="sd"> compression_factor:float</span> +<span class="sd"> How much to compress image by.</span> +<span class="sd"> alpha:float</span> +<span class="sd"> Low value assigns higher weight to prediction over original image.</span> +<span class="sd"> patch_size:int</span> +<span class="sd"> Patch size.</span> +<span class="sd"> no_db:bool</span> +<span class="sd"> Don't use patch information.</span> +<span class="sd"> plot_annotation:bool</span> +<span class="sd"> Plot annotations from patch information.</span> +<span class="sd"> segmentation:bool</span> +<span class="sd"> Plot segmentation mask.</span> +<span class="sd"> n_segmentation_classes:int</span> +<span class="sd"> Number segmentation classes.</span> +<span class="sd"> input_dir:str</span> +<span class="sd"> Input directory.</span> +<span class="sd"> annotation_col:str</span> +<span class="sd"> Annotation column to plot.</span> +<span class="sd"> scaling_factor:float</span> +<span class="sd"> Multiplies the prediction scores to make them appear darker on the images when predicting.</span> +<span class="sd"> """</span> + <span class="c1"># some patches have been filtered out, not one to one!!! figure out</span> + <span class="k">def</span> <span class="nf">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">dask_arr_dict</span><span class="p">,</span> <span class="n">patch_info_db</span><span class="p">,</span> <span class="n">compression_factor</span><span class="o">=</span><span class="mi">3</span><span class="p">,</span> <span class="n">alpha</span><span class="o">=</span><span class="mf">0.5</span><span class="p">,</span> <span class="n">patch_size</span><span class="o">=</span><span class="mi">224</span><span class="p">,</span> <span class="n">no_db</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">plot_annotation</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">segmentation</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">n_segmentation_classes</span><span class="o">=</span><span class="mi">4</span><span class="p">,</span> <span class="n">input_dir</span><span class="o">=</span><span class="s1">''</span><span class="p">,</span> <span class="n">annotation_col</span><span class="o">=</span><span class="s1">'annotation'</span><span class="p">,</span> <span class="n">scaling_factor</span><span class="o">=</span><span class="mf">1.</span><span class="p">):</span> + + <span class="bp">self</span><span class="o">.</span><span class="n">segmentation</span> <span class="o">=</span> <span class="n">segmentation</span> + <span class="bp">self</span><span class="o">.</span><span class="n">scaling_factor</span><span class="o">=</span><span class="n">scaling_factor</span> + <span class="bp">self</span><span class="o">.</span><span class="n">segmentation_maps</span> <span class="o">=</span> <span class="kc">None</span> + <span class="bp">self</span><span class="o">.</span><span class="n">n_segmentation_classes</span><span class="o">=</span><span class="nb">float</span><span class="p">(</span><span class="n">n_segmentation_classes</span><span class="p">)</span> + <span class="bp">self</span><span class="o">.</span><span class="n">pred_palette</span> <span class="o">=</span> <span class="n">sns</span><span class="o">.</span><span class="n">cubehelix_palette</span><span class="p">(</span><span class="n">start</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span><span class="n">as_cmap</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span> + <span class="k">if</span> <span class="ow">not</span> <span class="n">no_db</span><span class="p">:</span> + <span class="bp">self</span><span class="o">.</span><span class="n">compression_factor</span><span class="o">=</span><span class="n">compression_factor</span> + <span class="bp">self</span><span class="o">.</span><span class="n">alpha</span> <span class="o">=</span> <span class="n">alpha</span> + <span class="bp">self</span><span class="o">.</span><span class="n">patch_size</span> <span class="o">=</span> <span class="n">patch_size</span> + <span class="n">conn</span> <span class="o">=</span> <span class="n">sqlite3</span><span class="o">.</span><span class="n">connect</span><span class="p">(</span><span class="n">patch_info_db</span><span class="p">)</span> + <span class="n">patch_info</span><span class="o">=</span><span class="n">pd</span><span class="o">.</span><span class="n">read_sql</span><span class="p">(</span><span class="s1">'select * from "</span><span class="si">{}</span><span class="s1">";'</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">patch_size</span><span class="p">),</span><span class="n">con</span><span class="o">=</span><span class="n">conn</span><span class="p">)</span> + <span class="n">conn</span><span class="o">.</span><span class="n">close</span><span class="p">()</span> + <span class="bp">self</span><span class="o">.</span><span class="n">annotations</span> <span class="o">=</span> <span class="p">{</span><span class="nb">str</span><span class="p">(</span><span class="n">a</span><span class="p">):</span><span class="n">i</span> <span class="k">for</span> <span class="n">i</span><span class="p">,</span><span class="n">a</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">patch_info</span><span class="p">[</span><span class="s1">'annotation'</span><span class="p">]</span><span class="o">.</span><span class="n">unique</span><span class="p">()</span><span class="o">.</span><span class="n">tolist</span><span class="p">())}</span> + <span class="bp">self</span><span class="o">.</span><span class="n">plot_annotation</span><span class="o">=</span><span class="n">plot_annotation</span> + <span class="bp">self</span><span class="o">.</span><span class="n">palette</span><span class="o">=</span><span class="n">sns</span><span class="o">.</span><span class="n">color_palette</span><span class="p">(</span><span class="n">n_colors</span><span class="o">=</span><span class="nb">len</span><span class="p">(</span><span class="nb">list</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">annotations</span><span class="o">.</span><span class="n">keys</span><span class="p">())))</span> + <span class="c1">#print(self.palette)</span> + <span class="k">if</span> <span class="s1">'y_pred'</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">patch_info</span><span class="o">.</span><span class="n">columns</span><span class="p">:</span> + <span class="n">patch_info</span><span class="p">[</span><span class="s1">'y_pred'</span><span class="p">]</span> <span class="o">=</span> <span class="mf">0.</span> + <span class="bp">self</span><span class="o">.</span><span class="n">patch_info</span><span class="o">=</span><span class="n">patch_info</span><span class="p">[[</span><span class="s1">'ID'</span><span class="p">,</span><span class="s1">'x'</span><span class="p">,</span><span class="s1">'y'</span><span class="p">,</span><span class="s1">'patch_size'</span><span class="p">,</span><span class="s1">'annotation'</span><span class="p">,</span><span class="n">annotation_col</span><span class="p">]]</span> <span class="c1"># y_pred</span> + <span class="k">if</span> <span class="mi">0</span><span class="p">:</span> + <span class="k">for</span> <span class="n">ID</span> <span class="ow">in</span> <span class="n">predictions</span><span class="p">:</span> + <span class="n">patch_info</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="n">patch_info</span><span class="p">[</span><span class="s2">"ID"</span><span class="p">]</span><span class="o">==</span><span class="n">ID</span><span class="p">,</span><span class="s1">'y_pred'</span><span class="p">]</span> <span class="o">=</span> <span class="n">predictions</span><span class="p">[</span><span class="n">ID</span><span class="p">]</span> + <span class="bp">self</span><span class="o">.</span><span class="n">patch_info</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">patch_info</span><span class="p">[</span><span class="n">np</span><span class="o">.</span><span class="n">isin</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">patch_info</span><span class="p">[</span><span class="s1">'ID'</span><span class="p">],</span><span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="nb">list</span><span class="p">(</span><span class="n">dask_arr_dict</span><span class="o">.</span><span class="n">keys</span><span class="p">())))]</span> + <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">segmentation</span><span class="p">:</span> + <span class="bp">self</span><span class="o">.</span><span class="n">segmentation_maps</span> <span class="o">=</span> <span class="p">{</span><span class="n">slide</span><span class="p">:</span><span class="n">da</span><span class="o">.</span><span class="n">from_array</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="n">join</span><span class="p">(</span><span class="n">input_dir</span><span class="p">,</span><span class="s1">'</span><span class="si">{}</span><span class="s1">_mask.npy'</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">slide</span><span class="p">)),</span><span class="n">mmap_mode</span><span class="o">=</span><span class="s1">'r+'</span><span class="p">))</span> <span class="k">for</span> <span class="n">slide</span> <span class="ow">in</span> <span class="n">dask_arr_dict</span><span class="o">.</span><span class="n">keys</span><span class="p">()}</span> + <span class="c1">#self.patch_info[['x','y','patch_size']]/=self.compression_factor</span> + <span class="bp">self</span><span class="o">.</span><span class="n">dask_arr_dict</span> <span class="o">=</span> <span class="p">{</span><span class="n">k</span><span class="p">:</span><span class="n">v</span><span class="p">[</span><span class="o">...</span><span class="p">,:</span><span class="mi">3</span><span class="p">]</span> <span class="k">for</span> <span class="n">k</span><span class="p">,</span><span class="n">v</span> <span class="ow">in</span> <span class="n">dask_arr_dict</span><span class="o">.</span><span class="n">items</span><span class="p">()}</span> + +<div class="viewcode-block" id="PredictionPlotter.add_custom_segmentation"><a class="viewcode-back" href="../../index.html#pathflowai.visualize.PredictionPlotter.add_custom_segmentation">[docs]</a> <span class="k">def</span> <span class="nf">add_custom_segmentation</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">basename</span><span class="p">,</span> <span class="n">npy</span><span class="p">):</span> + <span class="sd">"""Replace segmentation mask with new custom segmentation.</span> + +<span class="sd"> Parameters</span> +<span class="sd"> ----------</span> +<span class="sd"> basename:str</span> +<span class="sd"> Patient ID</span> +<span class="sd"> npy:str</span> +<span class="sd"> Numpy mask.</span> +<span class="sd"> """</span> + <span class="bp">self</span><span class="o">.</span><span class="n">segmentation_maps</span><span class="p">[</span><span class="n">basename</span><span class="p">]</span> <span class="o">=</span> <span class="n">da</span><span class="o">.</span><span class="n">from_array</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="n">npy</span><span class="p">,</span><span class="n">mmap_mode</span><span class="o">=</span><span class="s1">'r+'</span><span class="p">))</span></div> + +<div class="viewcode-block" id="PredictionPlotter.generate_image"><a class="viewcode-back" href="../../index.html#pathflowai.visualize.PredictionPlotter.generate_image">[docs]</a> <span class="k">def</span> <span class="nf">generate_image</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">ID</span><span class="p">):</span> + <span class="sd">"""Generate the image array for the whole slide image with predictions overlaid.</span> + +<span class="sd"> Parameters</span> +<span class="sd"> ----------</span> +<span class="sd"> ID:str</span> +<span class="sd"> patient ID.</span> + +<span class="sd"> Returns</span> +<span class="sd"> -------</span> +<span class="sd"> array</span> +<span class="sd"> Resulting overlaid whole slide image.</span> + +<span class="sd"> """</span> + <span class="n">patch_info</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">patch_info</span><span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">patch_info</span><span class="p">[</span><span class="s1">'ID'</span><span class="p">]</span><span class="o">==</span><span class="n">ID</span><span class="p">]</span> + <span class="n">dask_arr</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">dask_arr_dict</span><span class="p">[</span><span class="n">ID</span><span class="p">]</span> + <span class="n">arr_shape</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">dask_arr</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="nb">float</span><span class="p">)</span> + + <span class="c1">#image=da.zeros_like(dask_arr)</span> + + <span class="n">arr_shape</span><span class="p">[:</span><span class="mi">2</span><span class="p">]</span><span class="o">/=</span><span class="bp">self</span><span class="o">.</span><span class="n">compression_factor</span> + + <span class="n">arr_shape</span><span class="o">=</span><span class="n">arr_shape</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="nb">int</span><span class="p">)</span><span class="o">.</span><span class="n">tolist</span><span class="p">()</span> + + <span class="n">img</span> <span class="o">=</span> <span class="n">Image</span><span class="o">.</span><span class="n">new</span><span class="p">(</span><span class="s1">'RGB'</span><span class="p">,</span><span class="n">arr_shape</span><span class="p">[:</span><span class="mi">2</span><span class="p">],</span><span class="s1">'white'</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">patch_info</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]):</span> + <span class="n">ID</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">patch_size</span><span class="p">,</span><span class="n">annotation</span><span class="p">,</span><span class="n">pred</span> <span class="o">=</span> <span class="n">patch_info</span><span class="o">.</span><span class="n">iloc</span><span class="p">[</span><span class="n">i</span><span class="p">]</span><span class="o">.</span><span class="n">tolist</span><span class="p">()</span> + <span class="c1">#print(x,y,annotation)</span> + <span class="n">x_new</span><span class="p">,</span><span class="n">y_new</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="n">x</span><span class="o">/</span><span class="bp">self</span><span class="o">.</span><span class="n">compression_factor</span><span class="p">),</span><span class="nb">int</span><span class="p">(</span><span class="n">y</span><span class="o">/</span><span class="bp">self</span><span class="o">.</span><span class="n">compression_factor</span><span class="p">)</span> + <span class="n">image</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">((</span><span class="n">patch_size</span><span class="p">,</span><span class="n">patch_size</span><span class="p">,</span><span class="mi">3</span><span class="p">))</span> + <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">segmentation</span><span class="p">:</span> + <span class="n">image</span><span class="o">=</span><span class="n">seg2rgb</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">segmentation_maps</span><span class="p">[</span><span class="n">ID</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">patch_size</span><span class="p">,</span><span class="n">y</span><span class="p">:</span><span class="n">y</span><span class="o">+</span><span class="n">patch_size</span><span class="p">]</span><span class="o">.</span><span class="n">compute</span><span class="p">(),</span><span class="bp">self</span><span class="o">.</span><span class="n">pred_palette</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">n_segmentation_classes</span><span class="p">)</span> + <span class="k">else</span><span class="p">:</span> + <span class="n">image</span><span class="o">=</span><span class="n">prob2rbg</span><span class="p">(</span><span class="n">pred</span><span class="o">*</span><span class="bp">self</span><span class="o">.</span><span class="n">scaling_factor</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">pred_palette</span><span class="p">,</span> <span class="n">image</span><span class="p">)</span> <span class="k">if</span> <span class="ow">not</span> <span class="bp">self</span><span class="o">.</span><span class="n">plot_annotation</span> <span class="k">else</span> <span class="n">annotation2rgb</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">annotations</span><span class="p">[</span><span class="nb">str</span><span class="p">(</span><span class="n">pred</span><span class="p">)],</span><span class="bp">self</span><span class="o">.</span><span class="n">palette</span><span class="p">,</span><span class="n">image</span><span class="p">)</span> <span class="c1"># annotation</span> + <span class="n">arr</span><span class="o">=</span><span class="n">dask_arr</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">patch_size</span><span class="p">,</span><span class="n">y</span><span class="p">:</span><span class="n">y</span><span class="o">+</span><span class="n">patch_size</span><span class="p">]</span><span class="o">.</span><span class="n">compute</span><span class="p">()</span> + <span class="c1">#print(image.shape)</span> + <span class="n">blended_patch</span><span class="o">=</span><span class="n">blend</span><span class="p">(</span><span class="n">arr</span><span class="p">,</span><span class="n">image</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">alpha</span><span class="p">)</span><span class="o">.</span><span class="n">transpose</span><span class="p">((</span><span class="mi">1</span><span class="p">,</span><span class="mi">0</span><span class="p">,</span><span class="mi">2</span><span class="p">))</span> + <span class="n">blended_patch_pil</span> <span class="o">=</span> <span class="n">to_pil</span><span class="p">(</span><span class="n">blended_patch</span><span class="p">)</span> + <span class="n">patch_size</span><span class="o">/=</span><span class="bp">self</span><span class="o">.</span><span class="n">compression_factor</span> + <span class="n">patch_size</span><span class="o">=</span><span class="nb">int</span><span class="p">(</span><span class="n">patch_size</span><span class="p">)</span> + <span class="n">blended_patch_pil</span><span class="o">=</span><span class="n">blended_patch_pil</span><span class="o">.</span><span class="n">resize</span><span class="p">((</span><span class="n">patch_size</span><span class="p">,</span><span class="n">patch_size</span><span class="p">))</span> + <span class="n">img</span><span class="o">.</span><span class="n">paste</span><span class="p">(</span><span class="n">blended_patch_pil</span><span class="p">,</span> <span class="n">box</span><span class="o">=</span><span class="p">(</span><span class="n">x_new</span><span class="p">,</span><span class="n">y_new</span><span class="p">),</span> <span class="n">mask</span><span class="o">=</span><span class="kc">None</span><span class="p">)</span> + <span class="k">return</span> <span class="n">img</span></div> + +<div class="viewcode-block" id="PredictionPlotter.return_patch"><a class="viewcode-back" href="../../index.html#pathflowai.visualize.PredictionPlotter.return_patch">[docs]</a> <span class="k">def</span> <span class="nf">return_patch</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">ID</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">patch_size</span><span class="p">):</span> + <span class="sd">"""Return one single patch instead of entire image.</span> + +<span class="sd"> Parameters</span> +<span class="sd"> ----------</span> +<span class="sd"> ID:str</span> +<span class="sd"> Patient ID</span> +<span class="sd"> x:int</span> +<span class="sd"> X coordinate.</span> +<span class="sd"> y:int</span> +<span class="sd"> Y coordinate.</span> +<span class="sd"> patch_size:int</span> +<span class="sd"> Patch size.</span> + +<span class="sd"> Returns</span> +<span class="sd"> -------</span> +<span class="sd"> array</span> +<span class="sd"> Image.</span> +<span class="sd"> """</span> + <span class="n">img</span><span class="o">=</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">dask_arr_dict</span><span class="p">[</span><span class="n">ID</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">patch_size</span><span class="p">,</span><span class="n">y</span><span class="p">:</span><span class="n">y</span><span class="o">+</span><span class="n">patch_size</span><span class="p">]</span><span class="o">.</span><span class="n">compute</span><span class="p">()</span> <span class="k">if</span> <span class="ow">not</span> <span class="bp">self</span><span class="o">.</span><span class="n">segmentation</span> <span class="k">else</span> <span class="n">seg2rgb</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">segmentation_maps</span><span class="p">[</span><span class="n">ID</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">patch_size</span><span class="p">,</span><span class="n">y</span><span class="p">:</span><span class="n">y</span><span class="o">+</span><span class="n">patch_size</span><span class="p">]</span><span class="o">.</span><span class="n">compute</span><span class="p">(),</span><span class="bp">self</span><span class="o">.</span><span class="n">pred_palette</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">n_segmentation_classes</span><span class="p">))</span> + <span class="k">return</span> <span class="n">to_pil</span><span class="p">(</span><span class="n">img</span><span class="p">)</span></div> + +<div class="viewcode-block" id="PredictionPlotter.output_image"><a class="viewcode-back" href="../../index.html#pathflowai.visualize.PredictionPlotter.output_image">[docs]</a> <span class="k">def</span> <span class="nf">output_image</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">img</span><span class="p">,</span> <span class="n">filename</span><span class="p">,</span> <span class="n">tif</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span> + <span class="sd">"""Output calculated image to file.</span> + +<span class="sd"> Parameters</span> +<span class="sd"> ----------</span> +<span class="sd"> img:array</span> +<span class="sd"> Image.</span> +<span class="sd"> filename:str</span> +<span class="sd"> Output file name.</span> +<span class="sd"> tif:bool</span> +<span class="sd"> Store in TIF format?</span> +<span class="sd"> """</span> + <span class="k">if</span> <span class="n">tif</span><span class="p">:</span> + <span class="kn">from</span> <span class="nn">tifffile</span> <span class="k">import</span> <span class="n">imwrite</span> + <span class="n">imwrite</span><span class="p">(</span><span class="n">filename</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">img</span><span class="p">),</span> <span class="n">photometric</span><span class="o">=</span><span class="s1">'rgb'</span><span class="p">)</span> + <span class="k">else</span><span class="p">:</span> + <span class="n">img</span><span class="o">.</span><span class="n">save</span><span class="p">(</span><span class="n">filename</span><span class="p">)</span></div></div> + +<div class="viewcode-block" id="plot_shap"><a class="viewcode-back" href="../../index.html#pathflowai.visualize.plot_shap">[docs]</a><span class="k">def</span> <span class="nf">plot_shap</span><span class="p">(</span><span class="n">model</span><span class="p">,</span> <span class="n">dataset_opts</span><span class="p">,</span> <span class="n">transform_opts</span><span class="p">,</span> <span class="n">batch_size</span><span class="p">,</span> <span class="n">outputfilename</span><span class="p">,</span> <span class="n">n_outputs</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">method</span><span class="o">=</span><span class="s1">'deep'</span><span class="p">,</span> <span class="n">local_smoothing</span><span class="o">=</span><span class="mf">0.0</span><span class="p">,</span> <span class="n">n_samples</span><span class="o">=</span><span class="mi">20</span><span class="p">,</span> <span class="n">pred_out</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span> + <span class="sd">"""Plot shapley attributions overlaid on images for classification tasks.</span> + +<span class="sd"> Parameters</span> +<span class="sd"> ----------</span> +<span class="sd"> model:nn.Module</span> +<span class="sd"> Pytorch model.</span> +<span class="sd"> dataset_opts:dict</span> +<span class="sd"> Options used to configure dataset</span> +<span class="sd"> transform_opts:dict</span> +<span class="sd"> Options used to configure transformers.</span> +<span class="sd"> batch_size:int</span> +<span class="sd"> Batch size for training.</span> +<span class="sd"> outputfilename:str</span> +<span class="sd"> Output filename.</span> +<span class="sd"> n_outputs:int</span> +<span class="sd"> Number of top outputs.</span> +<span class="sd"> method:str</span> +<span class="sd"> Gradient or deep explainer.</span> +<span class="sd"> local_smoothing:float</span> +<span class="sd"> How much to smooth shapley map.</span> +<span class="sd"> n_samples:int</span> +<span class="sd"> Number shapley samples to draw.</span> +<span class="sd"> pred_out:bool</span> +<span class="sd"> Label images with binary prediction score?</span> + +<span class="sd"> """</span> + <span class="kn">import</span> <span class="nn">torch</span> + <span class="kn">from</span> <span class="nn">torch.nn</span> <span class="k">import</span> <span class="n">functional</span> <span class="k">as</span> <span class="n">F</span> + <span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span> + <span class="kn">from</span> <span class="nn">torch.utils.data</span> <span class="k">import</span> <span class="n">DataLoader</span> + <span class="kn">import</span> <span class="nn">shap</span> + <span class="kn">from</span> <span class="nn">pathflowai.datasets</span> <span class="k">import</span> <span class="n">DynamicImageDataset</span> + <span class="kn">import</span> <span class="nn">matplotlib</span> + <span class="kn">from</span> <span class="nn">matplotlib</span> <span class="k">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">pathflowai.sampler</span> <span class="k">import</span> <span class="n">ImbalancedDatasetSampler</span> + + <span class="n">out_transform</span><span class="o">=</span><span class="nb">dict</span><span class="p">(</span><span class="n">sigmoid</span><span class="o">=</span><span class="n">F</span><span class="o">.</span><span class="n">sigmoid</span><span class="p">,</span><span class="n">softmax</span><span class="o">=</span><span class="n">F</span><span class="o">.</span><span class="n">softmax</span><span class="p">,</span><span class="n">none</span><span class="o">=</span><span class="k">lambda</span> <span class="n">x</span><span class="p">:</span> <span class="n">x</span><span class="p">)</span> + <span class="n">binary_threshold</span><span class="o">=</span><span class="n">dataset_opts</span><span class="o">.</span><span class="n">pop</span><span class="p">(</span><span class="s1">'binary_threshold'</span><span class="p">)</span> + <span class="n">num_targets</span><span class="o">=</span><span class="n">dataset_opts</span><span class="o">.</span><span class="n">pop</span><span class="p">(</span><span class="s1">'num_targets'</span><span class="p">)</span> + + <span class="n">dataset</span> <span class="o">=</span> <span class="n">DynamicImageDataset</span><span class="p">(</span><span class="o">**</span><span class="n">dataset_opts</span><span class="p">)</span> + + <span class="k">if</span> <span class="n">dataset_opts</span><span class="p">[</span><span class="s1">'classify_annotations'</span><span class="p">]:</span> + <span class="n">binarizer</span><span class="o">=</span><span class="n">dataset</span><span class="o">.</span><span class="n">binarize_annotations</span><span class="p">(</span><span class="n">num_targets</span><span class="o">=</span><span class="n">num_targets</span><span class="p">,</span><span class="n">binary_threshold</span><span class="o">=</span><span class="n">binary_threshold</span><span class="p">)</span> + <span class="n">num_targets</span><span class="o">=</span><span class="nb">len</span><span class="p">(</span><span class="n">dataset</span><span class="o">.</span><span class="n">targets</span><span class="p">)</span> + + <span class="n">dataloader_val</span> <span class="o">=</span> <span class="n">DataLoader</span><span class="p">(</span><span class="n">dataset</span><span class="p">,</span><span class="n">batch_size</span><span class="o">=</span><span class="n">batch_size</span><span class="p">,</span> <span class="n">num_workers</span><span class="o">=</span><span class="mi">10</span><span class="p">,</span> <span class="n">shuffle</span><span class="o">=</span><span class="kc">True</span> <span class="k">if</span> <span class="n">num_targets</span><span class="o">></span><span class="mi">1</span> <span class="k">else</span> <span class="kc">False</span><span class="p">,</span> <span class="n">sampler</span><span class="o">=</span><span class="n">ImbalancedDatasetSampler</span><span class="p">(</span><span class="n">dataset</span><span class="p">)</span> <span class="k">if</span> <span class="n">num_targets</span><span class="o">==</span><span class="mi">1</span> <span class="k">else</span> <span class="kc">None</span><span class="p">)</span> + <span class="c1">#dataloader_test = DataLoader(dataset,batch_size=batch_size,num_workers=10, shuffle=False)</span> + + <span class="n">background</span><span class="p">,</span><span class="n">y_background</span><span class="o">=</span><span class="nb">next</span><span class="p">(</span><span class="nb">iter</span><span class="p">(</span><span class="n">dataloader_val</span><span class="p">))</span> + <span class="k">if</span> <span class="n">method</span><span class="o">==</span><span class="s1">'gradient'</span><span class="p">:</span> + <span class="n">background</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">cat</span><span class="p">([</span><span class="n">background</span><span class="p">,</span><span class="nb">next</span><span class="p">(</span><span class="nb">iter</span><span class="p">(</span><span class="n">dataloader_val</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="n">X_test</span><span class="p">,</span><span class="n">y_test</span><span class="o">=</span><span class="nb">next</span><span class="p">(</span><span class="nb">iter</span><span class="p">(</span><span class="n">dataloader_val</span><span class="p">))</span> + + <span class="k">if</span> <span class="n">torch</span><span class="o">.</span><span class="n">cuda</span><span class="o">.</span><span class="n">is_available</span><span class="p">():</span> + <span class="n">background</span><span class="o">=</span><span class="n">background</span><span class="o">.</span><span class="n">cuda</span><span class="p">()</span> + <span class="n">X_test</span><span class="o">=</span><span class="n">X_test</span><span class="o">.</span><span class="n">cuda</span><span class="p">()</span> + + <span class="k">if</span> <span class="n">pred_out</span><span class="o">!=</span><span class="s1">'none'</span><span class="p">:</span> + <span class="k">if</span> <span class="n">torch</span><span class="o">.</span><span class="n">cuda</span><span class="o">.</span><span class="n">is_available</span><span class="p">():</span> + <span class="n">model2</span><span class="o">=</span><span class="n">model</span><span class="o">.</span><span class="n">cuda</span><span class="p">()</span> + <span class="n">y_test</span><span class="o">=</span><span class="n">out_transform</span><span class="p">[</span><span class="n">pred_out</span><span class="p">](</span><span class="n">model2</span><span class="p">(</span><span class="n">X_test</span><span class="p">))</span><span class="o">.</span><span class="n">detach</span><span class="p">()</span><span class="o">.</span><span class="n">cpu</span><span class="p">()</span> + + <span class="n">y_test</span><span class="o">=</span><span class="n">y_test</span><span class="o">.</span><span class="n">numpy</span><span class="p">()</span> + + <span class="k">if</span> <span class="n">method</span><span class="o">==</span><span class="s1">'deep'</span><span class="p">:</span> + <span class="n">e</span> <span class="o">=</span> <span class="n">shap</span><span class="o">.</span><span class="n">DeepExplainer</span><span class="p">(</span><span class="n">model</span><span class="p">,</span> <span class="n">background</span><span class="p">)</span> + <span class="n">s</span><span class="o">=</span><span class="n">e</span><span class="o">.</span><span class="n">shap_values</span><span class="p">(</span><span class="n">X_test</span><span class="p">,</span> <span class="n">ranked_outputs</span><span class="o">=</span><span class="n">n_outputs</span><span class="p">)</span> + <span class="k">elif</span> <span class="n">method</span><span class="o">==</span><span class="s1">'gradient'</span><span class="p">:</span> + <span class="n">e</span> <span class="o">=</span> <span class="n">shap</span><span class="o">.</span><span class="n">GradientExplainer</span><span class="p">(</span><span class="n">model</span><span class="p">,</span> <span class="n">background</span><span class="p">,</span> <span class="n">batch_size</span><span class="o">=</span><span class="n">batch_size</span><span class="p">,</span> <span class="n">local_smoothing</span><span class="o">=</span><span class="n">local_smoothing</span><span class="p">)</span> + <span class="n">s</span><span class="o">=</span><span class="n">e</span><span class="o">.</span><span class="n">shap_values</span><span class="p">(</span><span class="n">X_test</span><span class="p">,</span> <span class="n">ranked_outputs</span><span class="o">=</span><span class="n">n_outputs</span><span class="p">,</span> <span class="n">nsamples</span><span class="o">=</span><span class="n">n_samples</span><span class="p">)</span> + + <span class="k">if</span> <span class="n">y_test</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">></span><span class="mi">1</span><span class="p">:</span> + <span class="n">y_test</span><span class="o">=</span><span class="n">y_test</span><span class="o">.</span><span class="n">argmax</span><span class="p">(</span><span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span> + + <span class="k">if</span> <span class="n">n_outputs</span><span class="o">></span><span class="mi">1</span><span class="p">:</span> + <span class="n">shap_values</span><span class="p">,</span> <span class="n">idx</span> <span class="o">=</span> <span class="n">s</span> + <span class="k">else</span><span class="p">:</span> + <span class="n">shap_values</span><span class="p">,</span> <span class="n">idx</span> <span class="o">=</span> <span class="n">s</span><span class="p">,</span> <span class="n">y_test</span> + + <span class="c1">#print(shap_values) # .detach().cpu()</span> + + <span class="k">if</span> <span class="n">num_targets</span> <span class="o">==</span> <span class="mi">1</span><span class="p">:</span> + <span class="n">shap_numpy</span> <span class="o">=</span> <span class="p">[</span><span class="n">np</span><span class="o">.</span><span class="n">swapaxes</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">swapaxes</span><span class="p">(</span><span class="n">shap_values</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="o">-</span><span class="mi">1</span><span class="p">),</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">)]</span> + <span class="k">else</span><span class="p">:</span> + <span class="n">shap_numpy</span> <span class="o">=</span> <span class="p">[</span><span class="n">np</span><span class="o">.</span><span class="n">swapaxes</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">swapaxes</span><span class="p">(</span><span class="n">s</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="o">-</span><span class="mi">1</span><span class="p">),</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">)</span> <span class="k">for</span> <span class="n">s</span> <span class="ow">in</span> <span class="n">shap_values</span><span class="p">]</span> + <span class="c1">#print(shap_numpy.shape)</span> + <span class="n">X_test_numpy</span><span class="o">=</span><span class="n">X_test</span><span class="o">.</span><span class="n">detach</span><span class="p">()</span><span class="o">.</span><span class="n">cpu</span><span class="p">()</span><span class="o">.</span><span class="n">numpy</span><span class="p">()</span> + <span class="n">X_test_numpy</span><span class="o">=</span><span class="n">X_test_numpy</span><span class="o">.</span><span class="n">transpose</span><span class="p">((</span><span class="mi">0</span><span class="p">,</span><span class="mi">2</span><span class="p">,</span><span class="mi">3</span><span class="p">,</span><span class="mi">1</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">X_test_numpy</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]):</span> + <span class="n">X_test_numpy</span><span class="p">[</span><span class="n">i</span><span class="p">,</span><span class="o">...</span><span class="p">]</span><span class="o">*=</span><span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">transform_opts</span><span class="p">[</span><span class="s1">'std'</span><span class="p">])</span> + <span class="n">X_test_numpy</span><span class="p">[</span><span class="n">i</span><span class="p">,</span><span class="o">...</span><span class="p">]</span><span class="o">+=</span><span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">transform_opts</span><span class="p">[</span><span class="s1">'mean'</span><span class="p">])</span> + <span class="n">X_test_numpy</span><span class="o">=</span><span class="n">X_test_numpy</span><span class="o">.</span><span class="n">transpose</span><span class="p">((</span><span class="mi">0</span><span class="p">,</span><span class="mi">3</span><span class="p">,</span><span class="mi">1</span><span class="p">,</span><span class="mi">2</span><span class="p">))</span> + <span class="n">test_numpy</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">swapaxes</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">swapaxes</span><span class="p">(</span><span class="n">X_test_numpy</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="o">-</span><span class="mi">1</span><span class="p">),</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">)</span> + <span class="k">if</span> <span class="n">pred_out</span><span class="o">!=</span><span class="s1">'none'</span><span class="p">:</span> + <span class="n">labels</span><span class="o">=</span><span class="n">y_test</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="nb">str</span><span class="p">)</span> + <span class="k">else</span><span class="p">:</span> + <span class="n">labels</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([[(</span><span class="n">dataloader_val</span><span class="o">.</span><span class="n">dataset</span><span class="o">.</span><span class="n">targets</span><span class="p">[</span><span class="n">i</span><span class="p">[</span><span class="n">j</span><span class="p">]]</span> <span class="k">if</span> <span class="n">num_targets</span><span class="o">></span><span class="mi">1</span> <span class="k">else</span> <span class="nb">str</span><span class="p">(</span><span class="n">i</span><span class="p">))</span> <span class="k">for</span> <span class="n">j</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">n_outputs</span><span class="p">)]</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="n">idx</span><span class="p">])</span><span class="c1">#[:,np.newaxis] # y_test</span> + <span class="k">if</span> <span class="mi">0</span> <span class="ow">and</span> <span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">labels</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span><span class="o"><</span><span class="mi">2</span> <span class="ow">or</span> <span class="n">labels</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">==</span><span class="mi">1</span><span class="p">):</span> + <span class="n">labels</span><span class="o">=</span><span class="n">labels</span><span class="o">.</span><span class="n">flatten</span><span class="p">()</span><span class="c1">#[:np.newaxis]</span> + + <span class="c1">#print(labels.shape,shap_numpy.shape[0])</span> + <span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">()</span> + <span class="n">shap</span><span class="o">.</span><span class="n">image_plot</span><span class="p">(</span><span class="n">shap_numpy</span><span class="p">,</span> <span class="n">test_numpy</span><span class="p">,</span> <span class="n">labels</span><span class="p">)</span><span class="c1"># if num_targets!=1 else shap_values -test_numpy , labels=dataloader_test.dataset.targets)</span> + <span class="n">plt</span><span class="o">.</span><span class="n">savefig</span><span class="p">(</span><span class="n">outputfilename</span><span class="p">,</span> <span class="n">dpi</span><span class="o">=</span><span class="mi">300</span><span class="p">)</span></div> + +<div class="viewcode-block" id="plot_umap_images"><a class="viewcode-back" href="../../index.html#pathflowai.visualize.plot_umap_images">[docs]</a><span class="k">def</span> <span class="nf">plot_umap_images</span><span class="p">(</span><span class="n">dask_arr_dict</span><span class="p">,</span> <span class="n">embeddings_file</span><span class="p">,</span> <span class="n">ID</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">cval</span><span class="o">=</span><span class="mf">1.</span><span class="p">,</span> <span class="n">image_res</span><span class="o">=</span><span class="mf">300.</span><span class="p">,</span> <span class="n">outputfname</span><span class="o">=</span><span class="s1">'output_embedding.png'</span><span class="p">,</span> <span class="n">mpl_scatter</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">remove_background_annotation</span><span class="o">=</span><span class="s1">''</span><span class="p">,</span> <span class="n">max_background_area</span><span class="o">=</span><span class="mf">0.01</span><span class="p">,</span> <span class="n">zoom</span><span class="o">=</span><span class="mf">0.05</span><span class="p">,</span> <span class="n">n_neighbors</span><span class="o">=</span><span class="mi">10</span><span class="p">,</span> <span class="n">sort_col</span><span class="o">=</span><span class="s1">''</span><span class="p">,</span> <span class="n">sort_mode</span><span class="o">=</span><span class="s1">'asc'</span><span class="p">):</span> + <span class="sd">"""Make UMAP embedding plot, overlaid with images.</span> + +<span class="sd"> Parameters</span> +<span class="sd"> ----------</span> +<span class="sd"> dask_arr_dict:dict</span> +<span class="sd"> Stored dask arrays for each WSI.</span> +<span class="sd"> embeddings_file:str</span> +<span class="sd"> Embeddings pickle file stored from running using after trainign the model.</span> +<span class="sd"> ID:str</span> +<span class="sd"> Patient ID.</span> +<span class="sd"> cval:float</span> +<span class="sd"> Deprecated</span> +<span class="sd"> image_res:float</span> +<span class="sd"> Image resolution.</span> +<span class="sd"> outputfname:str</span> +<span class="sd"> Output image file.</span> +<span class="sd"> mpl_scatter:bool</span> +<span class="sd"> Recommended: Use matplotlib for scatter plot.</span> +<span class="sd"> remove_background_annotation:str</span> +<span class="sd"> Remove the background annotations. Enter for annotation to remove.</span> +<span class="sd"> max_background_area:float</span> +<span class="sd"> Maximum backgrund area in each tile for inclusion.</span> +<span class="sd"> zoom:float</span> +<span class="sd"> How much to zoom in on each patch, less than 1 is zoom out.</span> +<span class="sd"> n_neighbors:int</span> +<span class="sd"> Number of neighbors for UMAP embedding.</span> +<span class="sd"> sort_col:str</span> +<span class="sd"> Patch info column to sort on.</span> +<span class="sd"> sort_mode:str</span> +<span class="sd"> Sort ascending or descending.</span> + +<span class="sd"> Returns</span> +<span class="sd"> -------</span> +<span class="sd"> type</span> +<span class="sd"> Description of returned object.</span> + +<span class="sd"> Inspired by: https://gist.github.com/lukemetz/be6123c7ee3b366e333a</span> +<span class="sd"> WIP!! Needs testing."""</span> + <span class="kn">import</span> <span class="nn">torch</span> + <span class="kn">import</span> <span class="nn">dask</span> + <span class="kn">from</span> <span class="nn">dask.distributed</span> <span class="k">import</span> <span class="n">Client</span> + <span class="kn">from</span> <span class="nn">umap</span> <span class="k">import</span> <span class="n">UMAP</span> + <span class="kn">from</span> <span class="nn">pathflowai.visualize</span> <span class="k">import</span> <span class="n">PlotlyPlot</span> + <span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span><span class="o">,</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span> + <span class="kn">import</span> <span class="nn">skimage.io</span> + <span class="kn">from</span> <span class="nn">skimage.transform</span> <span class="k">import</span> <span class="n">resize</span> + <span class="kn">import</span> <span class="nn">matplotlib</span> + <span class="n">matplotlib</span><span class="o">.</span><span class="n">use</span><span class="p">(</span><span class="s1">'Agg'</span><span class="p">)</span> + <span class="kn">from</span> <span class="nn">matplotlib</span> <span class="k">import</span> <span class="n">pyplot</span> <span class="k">as</span> <span class="n">plt</span> + <span class="n">sns</span><span class="o">.</span><span class="n">set</span><span class="p">(</span><span class="n">style</span><span class="o">=</span><span class="s1">'white'</span><span class="p">)</span> + + <span class="k">def</span> <span class="nf">min_resize</span><span class="p">(</span><span class="n">img</span><span class="p">,</span> <span class="n">size</span><span class="p">):</span> + <span class="sd">"""</span> +<span class="sd"> Resize an image so that it is size along the minimum spatial dimension.</span> +<span class="sd"> """</span> + <span class="n">w</span><span class="p">,</span> <span class="n">h</span> <span class="o">=</span> <span class="nb">map</span><span class="p">(</span><span class="nb">float</span><span class="p">,</span> <span class="n">img</span><span class="o">.</span><span class="n">shape</span><span class="p">[:</span><span class="mi">2</span><span class="p">])</span> + <span class="k">if</span> <span class="nb">min</span><span class="p">([</span><span class="n">w</span><span class="p">,</span> <span class="n">h</span><span class="p">])</span> <span class="o">!=</span> <span class="n">size</span><span class="p">:</span> + <span class="k">if</span> <span class="n">w</span> <span class="o"><=</span> <span class="n">h</span><span class="p">:</span> + <span class="n">img</span> <span class="o">=</span> <span class="n">resize</span><span class="p">(</span><span class="n">img</span><span class="p">,</span> <span class="p">(</span><span class="nb">int</span><span class="p">(</span><span class="nb">round</span><span class="p">((</span><span class="n">h</span><span class="o">/</span><span class="n">w</span><span class="p">)</span><span class="o">*</span><span class="n">size</span><span class="p">)),</span> <span class="nb">int</span><span class="p">(</span><span class="n">size</span><span class="p">)))</span> + <span class="k">else</span><span class="p">:</span> + <span class="n">img</span> <span class="o">=</span> <span class="n">resize</span><span class="p">(</span><span class="n">img</span><span class="p">,</span> <span class="p">(</span><span class="nb">int</span><span class="p">(</span><span class="n">size</span><span class="p">),</span> <span class="nb">int</span><span class="p">(</span><span class="nb">round</span><span class="p">((</span><span class="n">w</span><span class="o">/</span><span class="n">h</span><span class="p">)</span><span class="o">*</span><span class="n">size</span><span class="p">))))</span> + <span class="k">return</span> <span class="n">img</span> + + <span class="c1">#dask_arr = dask_arr_dict[ID]</span> + + <span class="n">embeddings_dict</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="n">embeddings_file</span><span class="p">)</span> + <span class="n">embeddings</span><span class="o">=</span><span class="n">embeddings_dict</span><span class="p">[</span><span class="s1">'embeddings'</span><span class="p">]</span> + <span class="n">patch_info</span><span class="o">=</span><span class="n">embeddings_dict</span><span class="p">[</span><span class="s1">'patch_info'</span><span class="p">]</span> + <span class="k">if</span> <span class="n">sort_col</span><span class="p">:</span> + <span class="n">idx</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">argsort</span><span class="p">(</span><span class="n">patch_info</span><span class="p">[</span><span class="n">sort_col</span><span class="p">]</span><span class="o">.</span><span class="n">values</span><span class="p">)</span> + <span class="k">if</span> <span class="n">sort_mode</span> <span class="o">==</span> <span class="s1">'desc'</span><span class="p">:</span> + <span class="n">idx</span><span class="o">=</span><span class="n">idx</span><span class="p">[::</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span> + <span class="n">patch_info</span> <span class="o">=</span> <span class="n">patch_info</span><span class="o">.</span><span class="n">iloc</span><span class="p">[</span><span class="n">idx</span><span class="p">]</span> + <span class="n">embeddings</span><span class="o">=</span><span class="n">embeddings</span><span class="o">.</span><span class="n">iloc</span><span class="p">[</span><span class="n">idx</span><span class="p">]</span> + <span class="k">if</span> <span class="n">ID</span><span class="p">:</span> + <span class="n">removal_bool</span><span class="o">=</span><span class="p">(</span><span class="n">patch_info</span><span class="p">[</span><span class="s1">'ID'</span><span class="p">]</span><span class="o">==</span><span class="n">ID</span><span class="p">)</span><span class="o">.</span><span class="n">values</span> + <span class="n">patch_info</span> <span class="o">=</span> <span class="n">patch_info</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="n">removal_bool</span><span class="p">]</span> + <span class="n">embeddings</span><span class="o">=</span><span class="n">embeddings</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="n">removal_bool</span><span class="p">]</span> + <span class="k">if</span> <span class="n">remove_background_annotation</span><span class="p">:</span> + <span class="n">removal_bool</span><span class="o">=</span><span class="p">(</span><span class="n">patch_info</span><span class="p">[</span><span class="n">remove_background_annotation</span><span class="p">]</span><span class="o"><=</span><span class="p">(</span><span class="mf">1.</span><span class="o">-</span><span class="n">max_background_area</span><span class="p">))</span><span class="o">.</span><span class="n">values</span> + <span class="n">patch_info</span><span class="o">=</span><span class="n">patch_info</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="n">removal_bool</span><span class="p">]</span> + <span class="n">embeddings</span><span class="o">=</span><span class="n">embeddings</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="n">removal_bool</span><span class="p">]</span> + + <span class="n">umap</span><span class="o">=</span><span class="n">UMAP</span><span class="p">(</span><span class="n">n_components</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span><span class="n">n_neighbors</span><span class="o">=</span><span class="n">n_neighbors</span><span class="p">)</span> + <span class="n">t_data</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="n">umap</span><span class="o">.</span><span class="n">fit_transform</span><span class="p">(</span><span class="n">embeddings</span><span class="o">.</span><span class="n">iloc</span><span class="p">[:,:</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">values</span><span class="p">),</span><span class="n">columns</span><span class="o">=</span><span class="p">[</span><span class="s1">'x'</span><span class="p">,</span><span class="s1">'y'</span><span class="p">],</span><span class="n">index</span><span class="o">=</span><span class="n">embeddings</span><span class="o">.</span><span class="n">index</span><span class="p">)</span> + + <span class="n">images</span><span class="o">=</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">patch_info</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]):</span> + <span class="n">ID</span><span class="o">=</span><span class="n">patch_info</span><span class="o">.</span><span class="n">iloc</span><span class="p">[</span><span class="n">i</span><span class="p">][</span><span class="s1">'ID'</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">patch_size</span><span class="o">=</span><span class="n">patch_info</span><span class="o">.</span><span class="n">iloc</span><span class="p">[</span><span class="n">i</span><span class="p">][[</span><span class="s1">'x'</span><span class="p">,</span><span class="s1">'y'</span><span class="p">,</span><span class="s1">'patch_size'</span><span class="p">]]</span><span class="o">.</span><span class="n">values</span><span class="o">.</span><span class="n">tolist</span><span class="p">()</span> + <span class="n">arr</span><span class="o">=</span><span class="n">dask_arr_dict</span><span class="p">[</span><span class="n">ID</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">patch_size</span><span class="p">,</span><span class="n">y</span><span class="p">:</span><span class="n">y</span><span class="o">+</span><span class="n">patch_size</span><span class="p">]</span><span class="c1">#.transpose((2,0,1))</span> + <span class="n">images</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">arr</span><span class="p">)</span> + + <span class="n">c</span><span class="o">=</span><span class="n">Client</span><span class="p">()</span> + <span class="n">images</span><span class="o">=</span><span class="n">dask</span><span class="o">.</span><span class="n">compute</span><span class="p">(</span><span class="n">images</span><span class="p">)</span> + <span class="n">c</span><span class="o">.</span><span class="n">close</span><span class="p">()</span> + + <span class="k">if</span> <span class="n">mpl_scatter</span><span class="p">:</span> + <span class="kn">from</span> <span class="nn">matplotlib.offsetbox</span> <span class="k">import</span> <span class="n">OffsetImage</span><span class="p">,</span> <span class="n">AnnotationBbox</span> + <span class="k">def</span> <span class="nf">imscatter</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">ax</span><span class="p">,</span> <span class="n">imageData</span><span class="p">,</span> <span class="n">zoom</span><span class="p">):</span> + <span class="n">images</span> <span class="o">=</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="nb">len</span><span class="p">(</span><span class="n">x</span><span class="p">)):</span> + <span class="n">x0</span><span class="p">,</span> <span class="n">y0</span> <span class="o">=</span> <span class="n">x</span><span class="p">[</span><span class="n">i</span><span class="p">],</span> <span class="n">y</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> + <span class="n">img</span> <span class="o">=</span> <span class="n">imageData</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> + <span class="c1">#print(img.shape)</span> + <span class="n">image</span> <span class="o">=</span> <span class="n">OffsetImage</span><span class="p">(</span><span class="n">img</span><span class="p">,</span> <span class="n">zoom</span><span class="o">=</span><span class="n">zoom</span><span class="p">)</span> + <span class="n">ab</span> <span class="o">=</span> <span class="n">AnnotationBbox</span><span class="p">(</span><span class="n">image</span><span class="p">,</span> <span class="p">(</span><span class="n">x0</span><span class="p">,</span> <span class="n">y0</span><span class="p">),</span> <span class="n">xycoords</span><span class="o">=</span><span class="s1">'data'</span><span class="p">,</span> <span class="n">frameon</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span> + <span class="n">images</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">ax</span><span class="o">.</span><span class="n">add_artist</span><span class="p">(</span><span class="n">ab</span><span class="p">))</span> + + <span class="n">ax</span><span class="o">.</span><span class="n">update_datalim</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">column_stack</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">ax</span><span class="o">.</span><span class="n">autoscale</span><span class="p">()</span> + + <span class="n">fig</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">imscatter</span><span class="p">(</span><span class="n">t_data</span><span class="p">[</span><span class="s1">'x'</span><span class="p">]</span><span class="o">.</span><span class="n">values</span><span class="p">,</span> <span class="n">t_data</span><span class="p">[</span><span class="s1">'y'</span><span class="p">]</span><span class="o">.</span><span class="n">values</span><span class="p">,</span> <span class="n">imageData</span><span class="o">=</span><span class="n">images</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">ax</span><span class="o">=</span><span class="n">ax</span><span class="p">,</span> <span class="n">zoom</span><span class="o">=</span><span class="n">zoom</span><span class="p">)</span> + <span class="n">sns</span><span class="o">.</span><span class="n">despine</span><span class="p">()</span> + <span class="n">plt</span><span class="o">.</span><span class="n">savefig</span><span class="p">(</span><span class="n">outputfname</span><span class="p">,</span><span class="n">dpi</span><span class="o">=</span><span class="mi">300</span><span class="p">)</span> + + + <span class="k">else</span><span class="p">:</span> + <span class="n">xx</span><span class="o">=</span><span class="n">t_data</span><span class="o">.</span><span class="n">iloc</span><span class="p">[:,</span><span class="mi">0</span><span class="p">]</span> + <span class="n">yy</span><span class="o">=</span><span class="n">t_data</span><span class="o">.</span><span class="n">iloc</span><span class="p">[:,</span><span class="mi">1</span><span class="p">]</span> + + <span class="n">images</span> <span class="o">=</span> <span class="p">[</span><span class="n">min_resize</span><span class="p">(</span><span class="n">image</span><span class="p">,</span> <span class="n">img_res</span><span class="p">)</span> <span class="k">for</span> <span class="n">image</span> <span class="ow">in</span> <span class="n">images</span><span class="p">]</span> + <span class="n">max_width</span> <span class="o">=</span> <span class="nb">max</span><span class="p">([</span><span class="n">image</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="k">for</span> <span class="n">image</span> <span class="ow">in</span> <span class="n">images</span><span class="p">])</span> + <span class="n">max_height</span> <span class="o">=</span> <span class="nb">max</span><span class="p">([</span><span class="n">image</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="k">for</span> <span class="n">image</span> <span class="ow">in</span> <span class="n">images</span><span class="p">])</span> + + <span class="n">x_min</span><span class="p">,</span> <span class="n">x_max</span> <span class="o">=</span> <span class="n">xx</span><span class="o">.</span><span class="n">min</span><span class="p">(),</span> <span class="n">xx</span><span class="o">.</span><span class="n">max</span><span class="p">()</span> + <span class="n">y_min</span><span class="p">,</span> <span class="n">y_max</span> <span class="o">=</span> <span class="n">yy</span><span class="o">.</span><span class="n">min</span><span class="p">(),</span> <span class="n">yy</span><span class="o">.</span><span class="n">max</span><span class="p">()</span> + <span class="c1"># Fix the ratios</span> + <span class="n">sx</span> <span class="o">=</span> <span class="p">(</span><span class="n">x_max</span><span class="o">-</span><span class="n">x_min</span><span class="p">)</span> + <span class="n">sy</span> <span class="o">=</span> <span class="p">(</span><span class="n">y_max</span><span class="o">-</span><span class="n">y_min</span><span class="p">)</span> + <span class="k">if</span> <span class="n">sx</span> <span class="o">></span> <span class="n">sy</span><span class="p">:</span> + <span class="n">res_x</span> <span class="o">=</span> <span class="n">sx</span><span class="o">/</span><span class="nb">float</span><span class="p">(</span><span class="n">sy</span><span class="p">)</span><span class="o">*</span><span class="n">res</span> + <span class="n">res_y</span> <span class="o">=</span> <span class="n">res</span> + <span class="k">else</span><span class="p">:</span> + <span class="n">res_x</span> <span class="o">=</span> <span class="n">res</span> + <span class="n">res_y</span> <span class="o">=</span> <span class="n">sy</span><span class="o">/</span><span class="nb">float</span><span class="p">(</span><span class="n">sx</span><span class="p">)</span><span class="o">*</span><span class="n">res</span> + + <span class="n">canvas</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">ones</span><span class="p">((</span><span class="n">res_x</span><span class="o">+</span><span class="n">max_width</span><span class="p">,</span> <span class="n">res_y</span><span class="o">+</span><span class="n">max_height</span><span class="p">,</span> <span class="mi">3</span><span class="p">))</span><span class="o">*</span><span class="n">cval</span> + <span class="n">x_coords</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="n">x_min</span><span class="p">,</span> <span class="n">x_max</span><span class="p">,</span> <span class="n">res_x</span><span class="p">)</span> + <span class="n">y_coords</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="n">y_min</span><span class="p">,</span> <span class="n">y_max</span><span class="p">,</span> <span class="n">res_y</span><span class="p">)</span> + <span class="k">for</span> <span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="n">image</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="n">xx</span><span class="p">,</span> <span class="n">yy</span><span class="p">,</span> <span class="n">images</span><span class="p">):</span> + <span class="n">w</span><span class="p">,</span> <span class="n">h</span> <span class="o">=</span> <span class="n">image</span><span class="o">.</span><span class="n">shape</span><span class="p">[:</span><span class="mi">2</span><span class="p">]</span> + <span class="n">x_idx</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">argmin</span><span class="p">((</span><span class="n">x</span> <span class="o">-</span> <span class="n">x_coords</span><span class="p">)</span><span class="o">**</span><span class="mi">2</span><span class="p">)</span> + <span class="n">y_idx</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">argmin</span><span class="p">((</span><span class="n">y</span> <span class="o">-</span> <span class="n">y_coords</span><span class="p">)</span><span class="o">**</span><span class="mi">2</span><span class="p">)</span> + <span class="n">canvas</span><span class="p">[</span><span class="n">x_idx</span><span class="p">:</span><span class="n">x_idx</span><span class="o">+</span><span class="n">w</span><span class="p">,</span> <span class="n">y_idx</span><span class="p">:</span><span class="n">y_idx</span><span class="o">+</span><span class="n">h</span><span class="p">]</span> <span class="o">=</span> <span class="n">image</span> + + <span class="n">skimage</span><span class="o">.</span><span class="n">io</span><span class="o">.</span><span class="n">imsave</span><span class="p">(</span><span class="n">outputfname</span><span class="p">,</span> <span class="n">canvas</span><span class="p">)</span></div> +</pre></div> + + </div> + + </div> + <footer> + + + <hr/> + + <div role="contentinfo"> + <p> + © Copyright 2019, Joshua Levy + + </p> + </div> + Built with <a href="http://sphinx-doc.org/">Sphinx</a> using a <a href="https://github.com/rtfd/sphinx_rtd_theme">theme</a> provided by <a href="https://readthedocs.org">Read the Docs</a>. + +</footer> + + </div> + </div> + + </section> + + </div> + + + + <script type="text/javascript"> + jQuery(function () { + SphinxRtdTheme.Navigation.enable(true); + }); + </script> + + + + + + +</body> +</html> \ No newline at end of file