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Add a new Rust article!
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Bradlee Speice
@ -4,7 +4,7 @@
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<meta charset="utf-8">
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<meta http-equiv="X-UA-Compatible" content="IE=edge">
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<meta name="viewport" content="width=device-width, initial-scale=1">
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<meta name="description" content="In [1]: import pickle import pandas as pd import numpy as np from bokeh.plotting import output_notebook, figure, show from bokeh.palettes import RdBu4 as Palette from datetime import datetime import ...">
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<meta name="description" content="In [1]: import pickle import pandas as pd import numpy as np from bokeh.plotting import output_notebook, figure, show from bokeh.palettes import RdBu4 as Palette from datetime import datetime ...">
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<meta name="keywords" content="data science, weather">
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<link rel="icon" href="https://bspeice.github.io/favicon.ico">
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@ -359,7 +359,7 @@
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<div class="prompt input_prompt">In [2]:</div>
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<div class="inner_cell">
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<div class="input_area">
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<div class=" highlight hl-ipython3"><pre><span class="n">city_forecasts</span> <span class="o">=</span> <span class="n">pickle</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="nb">open</span><span class="p">(</span><span class="s">'city_forecasts.p'</span><span class="p">,</span> <span class="s">'rb'</span><span class="p">))</span>
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<div class=" highlight hl-ipython3"><pre><span class="n">city_forecasts</span> <span class="o">=</span> <span class="n">pickle</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="nb">open</span><span class="p">(</span><span class="s1">'city_forecasts.p'</span><span class="p">,</span> <span class="s1">'rb'</span><span class="p">))</span>
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<span class="n">forecasts_df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="o">.</span><span class="n">from_dict</span><span class="p">(</span><span class="n">city_forecasts</span><span class="p">)</span>
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</pre></div>
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@ -373,24 +373,24 @@
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<div class="prompt input_prompt">In [3]:</div>
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<div class="inner_cell">
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<div class="input_area">
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<div class=" highlight hl-ipython3"><pre><span class="n">cities</span> <span class="o">=</span> <span class="p">[</span><span class="s">'binghamton'</span><span class="p">,</span> <span class="s">'cary'</span><span class="p">,</span> <span class="s">'nyc'</span><span class="p">,</span> <span class="s">'seattle'</span><span class="p">]</span>
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<div class=" highlight hl-ipython3"><pre><span class="n">cities</span> <span class="o">=</span> <span class="p">[</span><span class="s1">'binghamton'</span><span class="p">,</span> <span class="s1">'cary'</span><span class="p">,</span> <span class="s1">'nyc'</span><span class="p">,</span> <span class="s1">'seattle'</span><span class="p">]</span>
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<span class="n">city_colors</span> <span class="o">=</span> <span class="p">{</span><span class="n">cities</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">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">0</span><span class="p">,</span> <span class="mi">4</span><span class="p">)}</span>
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<span class="k">def</span> <span class="nf">safe_cover</span><span class="p">(</span><span class="n">frame</span><span class="p">):</span>
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<span class="k">if</span> <span class="n">frame</span> <span class="ow">and</span> <span class="s">'cloudCover'</span> <span class="ow">in</span> <span class="n">frame</span><span class="p">:</span>
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<span class="k">return</span> <span class="n">frame</span><span class="p">[</span><span class="s">'cloudCover'</span><span class="p">]</span>
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<span class="k">if</span> <span class="n">frame</span> <span class="ow">and</span> <span class="s1">'cloudCover'</span> <span class="ow">in</span> <span class="n">frame</span><span class="p">:</span>
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<span class="k">return</span> <span class="n">frame</span><span class="p">[</span><span class="s1">'cloudCover'</span><span class="p">]</span>
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<span class="k">else</span><span class="p">:</span>
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<span class="k">return</span> <span class="n">np</span><span class="o">.</span><span class="n">NaN</span>
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<span class="k">def</span> <span class="nf">monthly_avg_cloudcover</span><span class="p">(</span><span class="n">city</span><span class="p">,</span> <span class="n">year</span><span class="p">,</span> <span class="n">month</span><span class="p">):</span>
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<span class="n">dates</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DatetimeIndex</span><span class="p">(</span><span class="n">start</span><span class="o">=</span><span class="n">datetime</span><span class="p">(</span><span class="n">year</span><span class="p">,</span> <span class="n">month</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">12</span><span class="p">),</span>
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<span class="n">end</span><span class="o">=</span><span class="n">datetime</span><span class="p">(</span><span class="n">year</span><span class="p">,</span> <span class="n">month</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">12</span><span class="p">),</span>
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<span class="n">freq</span><span class="o">=</span><span class="s">'D'</span><span class="p">,</span> <span class="n">closed</span><span class="o">=</span><span class="s">'left'</span><span class="p">)</span>
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<span class="n">cloud_cover_vals</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="nb">map</span><span class="p">(</span><span class="k">lambda</span> <span class="n">x</span><span class="p">:</span> <span class="n">safe_cover</span><span class="p">(</span><span class="n">forecasts_df</span><span class="p">[</span><span class="n">city</span><span class="p">][</span><span class="n">x</span><span class="p">][</span><span class="s">'currently'</span><span class="p">]),</span> <span class="n">dates</span><span class="p">))</span>
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<span class="n">freq</span><span class="o">=</span><span class="s1">'D'</span><span class="p">,</span> <span class="n">closed</span><span class="o">=</span><span class="s1">'left'</span><span class="p">)</span>
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<span class="n">cloud_cover_vals</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="nb">map</span><span class="p">(</span><span class="k">lambda</span> <span class="n">x</span><span class="p">:</span> <span class="n">safe_cover</span><span class="p">(</span><span class="n">forecasts_df</span><span class="p">[</span><span class="n">city</span><span class="p">][</span><span class="n">x</span><span class="p">][</span><span class="s1">'currently'</span><span class="p">]),</span> <span class="n">dates</span><span class="p">))</span>
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<span class="n">cloud_cover_samples</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="nb">filter</span><span class="p">(</span><span class="k">lambda</span> <span class="n">x</span><span class="p">:</span> <span class="n">x</span> <span class="ow">is</span> <span class="ow">not</span> <span class="n">np</span><span class="o">.</span><span class="n">NaN</span><span class="p">,</span> <span class="n">cloud_cover_vals</span><span class="p">)))</span>
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<span class="c"># Ignore an issue with nanmean having all NaN values. We'll discuss the data issues below.</span>
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<span class="c1"># Ignore an issue with nanmean having all NaN values. We'll discuss the data issues below.</span>
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<span class="k">with</span> <span class="n">warnings</span><span class="o">.</span><span class="n">catch_warnings</span><span class="p">():</span>
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<span class="n">warnings</span><span class="o">.</span><span class="n">simplefilter</span><span class="p">(</span><span class="s">'ignore'</span><span class="p">)</span>
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<span class="n">warnings</span><span class="o">.</span><span class="n">simplefilter</span><span class="p">(</span><span class="s1">'ignore'</span><span class="p">)</span>
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<span class="k">return</span> <span class="n">np</span><span class="o">.</span><span class="n">nanmean</span><span class="p">(</span><span class="n">cloud_cover_vals</span><span class="p">),</span> <span class="n">cloud_cover_samples</span>
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</pre></div>
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@ -409,18 +409,18 @@
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<span class="k">return</span> <span class="p">[</span><span class="n">monthly_avg_cloudcover</span><span class="p">(</span><span class="n">city</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="n">month</span><span class="p">)</span> <span class="k">for</span> <span class="n">y</span> <span class="ow">in</span> <span class="n">years</span><span class="p">]</span>
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<span class="n">months</span> <span class="o">=</span> <span class="p">[</span>
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<span class="p">(</span><span class="s">'July'</span><span class="p">,</span> <span class="mi">7</span><span class="p">),</span>
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<span class="p">(</span><span class="s">'August'</span><span class="p">,</span> <span class="mi">8</span><span class="p">),</span>
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<span class="p">(</span><span class="s">'September'</span><span class="p">,</span> <span class="mi">9</span><span class="p">),</span>
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<span class="p">(</span><span class="s">'October'</span><span class="p">,</span> <span class="mi">10</span><span class="p">),</span>
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<span class="p">(</span><span class="s">'November'</span><span class="p">,</span> <span class="mi">11</span><span class="p">)</span>
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<span class="p">(</span><span class="s1">'July'</span><span class="p">,</span> <span class="mi">7</span><span class="p">),</span>
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<span class="p">(</span><span class="s1">'August'</span><span class="p">,</span> <span class="mi">8</span><span class="p">),</span>
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<span class="p">(</span><span class="s1">'September'</span><span class="p">,</span> <span class="mi">9</span><span class="p">),</span>
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<span class="p">(</span><span class="s1">'October'</span><span class="p">,</span> <span class="mi">10</span><span class="p">),</span>
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<span class="p">(</span><span class="s1">'November'</span><span class="p">,</span> <span class="mi">11</span><span class="p">)</span>
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<span class="p">]</span>
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<span class="k">for</span> <span class="n">month</span><span class="p">,</span> <span class="n">month_id</span> <span class="ow">in</span> <span class="n">months</span><span class="p">:</span>
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<span class="n">month_averages</span> <span class="o">=</span> <span class="p">{</span><span class="n">city</span><span class="p">:</span> <span class="n">city_avg_cc</span><span class="p">(</span><span class="n">city</span><span class="p">,</span> <span class="n">month_id</span><span class="p">)</span> <span class="k">for</span> <span class="n">city</span> <span class="ow">in</span> <span class="n">cities</span><span class="p">}</span>
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<span class="n">f</span> <span class="o">=</span> <span class="n">figure</span><span class="p">(</span><span class="n">title</span><span class="o">=</span><span class="s">"{} Average Cloud Cover"</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">month</span><span class="p">),</span>
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<span class="n">x_axis_label</span><span class="o">=</span><span class="s">'Year'</span><span class="p">,</span>
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<span class="n">y_axis_label</span><span class="o">=</span><span class="s">'Cloud Cover Percentage'</span><span class="p">)</span>
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<span class="n">f</span> <span class="o">=</span> <span class="n">figure</span><span class="p">(</span><span class="n">title</span><span class="o">=</span><span class="s2">"{} Average Cloud Cover"</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">month</span><span class="p">),</span>
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<span class="n">x_axis_label</span><span class="o">=</span><span class="s1">'Year'</span><span class="p">,</span>
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<span class="n">y_axis_label</span><span class="o">=</span><span class="s1">'Cloud Cover Percentage'</span><span class="p">)</span>
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<span class="k">for</span> <span class="n">city</span> <span class="ow">in</span> <span class="n">cities</span><span class="p">:</span>
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<span class="n">f</span><span class="o">.</span><span class="n">line</span><span class="p">(</span><span class="n">years</span><span class="p">,</span> <span class="p">[</span><span class="n">x</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="n">month_averages</span><span class="p">[</span><span class="n">city</span><span class="p">]],</span>
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<span class="n">legend</span><span class="o">=</span><span class="n">city</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="n">city_colors</span><span class="p">[</span><span class="n">city</span><span class="p">])</span>
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@ -610,20 +610,20 @@
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<div class="inner_cell">
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<div class="input_area">
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<div class=" highlight hl-ipython3"><pre><span class="k">def</span> <span class="nf">safe_precip</span><span class="p">(</span><span class="n">frame</span><span class="p">):</span>
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<span class="k">if</span> <span class="n">frame</span> <span class="ow">and</span> <span class="s">'precipProbability'</span> <span class="ow">in</span> <span class="n">frame</span><span class="p">:</span>
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<span class="k">return</span> <span class="n">frame</span><span class="p">[</span><span class="s">'precipProbability'</span><span class="p">]</span>
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<span class="k">if</span> <span class="n">frame</span> <span class="ow">and</span> <span class="s1">'precipProbability'</span> <span class="ow">in</span> <span class="n">frame</span><span class="p">:</span>
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<span class="k">return</span> <span class="n">frame</span><span class="p">[</span><span class="s1">'precipProbability'</span><span class="p">]</span>
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<span class="k">else</span><span class="p">:</span>
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<span class="k">return</span> <span class="n">np</span><span class="o">.</span><span class="n">NaN</span>
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<span class="k">def</span> <span class="nf">monthly_avg_precip</span><span class="p">(</span><span class="n">city</span><span class="p">,</span> <span class="n">year</span><span class="p">,</span> <span class="n">month</span><span class="p">):</span>
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<span class="n">dates</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DatetimeIndex</span><span class="p">(</span><span class="n">start</span><span class="o">=</span><span class="n">datetime</span><span class="p">(</span><span class="n">year</span><span class="p">,</span> <span class="n">month</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">12</span><span class="p">),</span>
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<span class="n">end</span><span class="o">=</span><span class="n">datetime</span><span class="p">(</span><span class="n">year</span><span class="p">,</span> <span class="n">month</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">12</span><span class="p">),</span>
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<span class="n">freq</span><span class="o">=</span><span class="s">'D'</span><span class="p">,</span> <span class="n">closed</span><span class="o">=</span><span class="s">'left'</span><span class="p">)</span>
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<span class="n">precip_vals</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="nb">map</span><span class="p">(</span><span class="k">lambda</span> <span class="n">x</span><span class="p">:</span> <span class="n">safe_precip</span><span class="p">(</span><span class="n">forecasts_df</span><span class="p">[</span><span class="n">city</span><span class="p">][</span><span class="n">x</span><span class="p">][</span><span class="s">'currently'</span><span class="p">]),</span> <span class="n">dates</span><span class="p">))</span>
|
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<span class="n">freq</span><span class="o">=</span><span class="s1">'D'</span><span class="p">,</span> <span class="n">closed</span><span class="o">=</span><span class="s1">'left'</span><span class="p">)</span>
|
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<span class="n">precip_vals</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="nb">map</span><span class="p">(</span><span class="k">lambda</span> <span class="n">x</span><span class="p">:</span> <span class="n">safe_precip</span><span class="p">(</span><span class="n">forecasts_df</span><span class="p">[</span><span class="n">city</span><span class="p">][</span><span class="n">x</span><span class="p">][</span><span class="s1">'currently'</span><span class="p">]),</span> <span class="n">dates</span><span class="p">))</span>
|
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<span class="n">precip_samples</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="nb">filter</span><span class="p">(</span><span class="k">lambda</span> <span class="n">x</span><span class="p">:</span> <span class="n">x</span> <span class="ow">is</span> <span class="ow">not</span> <span class="n">np</span><span class="o">.</span><span class="n">NaN</span><span class="p">,</span> <span class="n">precip_vals</span><span class="p">)))</span>
|
||||
<span class="c"># Ignore an issue with nanmean having all NaN values. We'll discuss the data issues below.</span>
|
||||
<span class="c1"># Ignore an issue with nanmean having all NaN values. We'll discuss the data issues below.</span>
|
||||
<span class="k">with</span> <span class="n">warnings</span><span class="o">.</span><span class="n">catch_warnings</span><span class="p">():</span>
|
||||
<span class="n">warnings</span><span class="o">.</span><span class="n">simplefilter</span><span class="p">(</span><span class="s">'ignore'</span><span class="p">)</span>
|
||||
<span class="n">warnings</span><span class="o">.</span><span class="n">simplefilter</span><span class="p">(</span><span class="s1">'ignore'</span><span class="p">)</span>
|
||||
<span class="k">return</span> <span class="n">np</span><span class="o">.</span><span class="n">nanmean</span><span class="p">(</span><span class="n">precip_vals</span><span class="p">),</span> <span class="n">precip_samples</span>
|
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|
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<span class="k">def</span> <span class="nf">city_avg_precip</span><span class="p">(</span><span class="n">city</span><span class="p">,</span> <span class="n">month</span><span class="p">):</span>
|
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@ -631,9 +631,9 @@
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|
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<span class="k">for</span> <span class="n">month</span><span class="p">,</span> <span class="n">month_id</span> <span class="ow">in</span> <span class="n">months</span><span class="p">:</span>
|
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<span class="n">month_averages</span> <span class="o">=</span> <span class="p">{</span><span class="n">city</span><span class="p">:</span> <span class="n">city_avg_cc</span><span class="p">(</span><span class="n">city</span><span class="p">,</span> <span class="n">month_id</span><span class="p">)</span> <span class="k">for</span> <span class="n">city</span> <span class="ow">in</span> <span class="n">cities</span><span class="p">}</span>
|
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<span class="n">f</span> <span class="o">=</span> <span class="n">figure</span><span class="p">(</span><span class="n">title</span><span class="o">=</span><span class="s">"{} Average Precipitation Chance"</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">month</span><span class="p">),</span>
|
||||
<span class="n">x_axis_label</span><span class="o">=</span><span class="s">'Year'</span><span class="p">,</span>
|
||||
<span class="n">y_axis_label</span><span class="o">=</span><span class="s">'Precipitation Chance Percentage'</span><span class="p">)</span>
|
||||
<span class="n">f</span> <span class="o">=</span> <span class="n">figure</span><span class="p">(</span><span class="n">title</span><span class="o">=</span><span class="s2">"{} Average Precipitation Chance"</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">month</span><span class="p">),</span>
|
||||
<span class="n">x_axis_label</span><span class="o">=</span><span class="s1">'Year'</span><span class="p">,</span>
|
||||
<span class="n">y_axis_label</span><span class="o">=</span><span class="s1">'Precipitation Chance Percentage'</span><span class="p">)</span>
|
||||
<span class="k">for</span> <span class="n">city</span> <span class="ow">in</span> <span class="n">cities</span><span class="p">:</span>
|
||||
<span class="n">f</span><span class="o">.</span><span class="n">line</span><span class="p">(</span><span class="n">years</span><span class="p">,</span> <span class="p">[</span><span class="n">x</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="n">month_averages</span><span class="p">[</span><span class="n">city</span><span class="p">]],</span>
|
||||
<span class="n">legend</span><span class="o">=</span><span class="n">city</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="n">city_colors</span><span class="p">[</span><span class="n">city</span><span class="p">])</span>
|
||||
|
||||
Reference in New Issue
Block a user