M6 minor code fix

ecmatz · ecmatz · commit 70435a5d0b71 · 2024-07-10T21:01:30.000-04:00
diff --git a/build/doctrees/content/m6/data_analysis.doctree b/build/doctrees/content/m6/data_analysis.doctree
diff --git a/build/doctrees/environment.pickle b/build/doctrees/environment.pickle
diff --git a/build/html/_sources/content/m6/data_analysis.rst.txt b/build/html/_sources/content/m6/data_analysis.rst.txt
@@ -59,7 +59,9 @@ There are options to access the data with column names, but this becomes inconve
    df = np.loadtxt('sample_semg.csv', delimiter=',', dtype=float, skiprows = 1500, max_rows=6000)
    emg_channels = df[:,[1,2]]
    time = df[:,0]
+   nrows = emg_channels.shape[0]
    print(df.shape, emg_channels.shape, time.shape)
+   
 
 Notice that we have 6,000 rows and 3 columns. Each row represents the signal values read from the sensor at a particular time, and the columns denote time (seconds), right bicep EMG (volts), and left bicep EMG (volts). Checking the shape is always a good step to ensure there wasn't an error in the data loading process.
 
diff --git a/build/html/content/m6/data_analysis.html b/build/html/content/m6/data_analysis.html
@@ -168,7 +168,8 @@ <h3>Raw Data: Load and Plot<a class="headerlink" href="#raw-data-load-and-plot"
 <span class="linenos">3</span><span class="n">df</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">loadtxt</span><span class="p">(</span><span class="s1">&#39;sample_semg.csv&#39;</span><span class="p">,</span> <span class="n">delimiter</span><span class="o">=</span><span class="s1">&#39;,&#39;</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="nb">float</span><span class="p">,</span> <span class="n">skiprows</span> <span class="o">=</span> <span class="mi">1500</span><span class="p">,</span> <span class="n">max_rows</span><span class="o">=</span><span class="mi">6000</span><span class="p">)</span>
 <span class="linenos">4</span><span class="n">emg_channels</span> <span class="o">=</span> <span class="n">df</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="linenos">5</span><span class="n">time</span> <span class="o">=</span> <span class="n">df</span><span class="p">[:,</span><span class="mi">0</span><span class="p">]</span>
-<span class="linenos">6</span><span class="nb">print</span><span class="p">(</span><span class="n">df</span><span class="o">.</span><span class="n">shape</span><span class="p">,</span> <span class="n">emg_channels</span><span class="o">.</span><span class="n">shape</span><span class="p">,</span> <span class="n">time</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span>
+<span class="linenos">6</span><span class="n">nrows</span> <span class="o">=</span> <span class="n">emg_channels</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="linenos">7</span><span class="nb">print</span><span class="p">(</span><span class="n">df</span><span class="o">.</span><span class="n">shape</span><span class="p">,</span> <span class="n">emg_channels</span><span class="o">.</span><span class="n">shape</span><span class="p">,</span> <span class="n">time</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span>
 </pre></div>
 </div>
 <p>Notice that we have 6,000 rows and 3 columns. Each row represents the signal values read from the sensor at a particular time, and the columns denote time (seconds), right bicep EMG (volts), and left bicep EMG (volts). Checking the shape is always a good step to ensure there wasn’t an error in the data loading process.</p>
diff --git a/docs/_sources/content/m6/data_analysis.rst.txt b/docs/_sources/content/m6/data_analysis.rst.txt
@@ -59,7 +59,9 @@ There are options to access the data with column names, but this becomes inconve
    df = np.loadtxt('sample_semg.csv', delimiter=',', dtype=float, skiprows = 1500, max_rows=6000)
    emg_channels = df[:,[1,2]]
    time = df[:,0]
+   nrows = emg_channels.shape[0]
    print(df.shape, emg_channels.shape, time.shape)
+   
 
 Notice that we have 6,000 rows and 3 columns. Each row represents the signal values read from the sensor at a particular time, and the columns denote time (seconds), right bicep EMG (volts), and left bicep EMG (volts). Checking the shape is always a good step to ensure there wasn't an error in the data loading process.
 
diff --git a/docs/content/m6/data_analysis.html b/docs/content/m6/data_analysis.html
@@ -168,7 +168,8 @@ <h3>Raw Data: Load and Plot<a class="headerlink" href="#raw-data-load-and-plot"
 <span class="linenos">3</span><span class="n">df</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">loadtxt</span><span class="p">(</span><span class="s1">&#39;sample_semg.csv&#39;</span><span class="p">,</span> <span class="n">delimiter</span><span class="o">=</span><span class="s1">&#39;,&#39;</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="nb">float</span><span class="p">,</span> <span class="n">skiprows</span> <span class="o">=</span> <span class="mi">1500</span><span class="p">,</span> <span class="n">max_rows</span><span class="o">=</span><span class="mi">6000</span><span class="p">)</span>
 <span class="linenos">4</span><span class="n">emg_channels</span> <span class="o">=</span> <span class="n">df</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="linenos">5</span><span class="n">time</span> <span class="o">=</span> <span class="n">df</span><span class="p">[:,</span><span class="mi">0</span><span class="p">]</span>
-<span class="linenos">6</span><span class="nb">print</span><span class="p">(</span><span class="n">df</span><span class="o">.</span><span class="n">shape</span><span class="p">,</span> <span class="n">emg_channels</span><span class="o">.</span><span class="n">shape</span><span class="p">,</span> <span class="n">time</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span>
+<span class="linenos">6</span><span class="n">nrows</span> <span class="o">=</span> <span class="n">emg_channels</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="linenos">7</span><span class="nb">print</span><span class="p">(</span><span class="n">df</span><span class="o">.</span><span class="n">shape</span><span class="p">,</span> <span class="n">emg_channels</span><span class="o">.</span><span class="n">shape</span><span class="p">,</span> <span class="n">time</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span>
 </pre></div>
 </div>
 <p>Notice that we have 6,000 rows and 3 columns. Each row represents the signal values read from the sensor at a particular time, and the columns denote time (seconds), right bicep EMG (volts), and left bicep EMG (volts). Checking the shape is always a good step to ensure there wasn’t an error in the data loading process.</p>
diff --git a/source/content/m6/data_analysis.rst b/source/content/m6/data_analysis.rst
@@ -59,7 +59,9 @@ There are options to access the data with column names, but this becomes inconve
    df = np.loadtxt('sample_semg.csv', delimiter=',', dtype=float, skiprows = 1500, max_rows=6000)
    emg_channels = df[:,[1,2]]
    time = df[:,0]
+   nrows = emg_channels.shape[0]
    print(df.shape, emg_channels.shape, time.shape)
+   
 
 Notice that we have 6,000 rows and 3 columns. Each row represents the signal values read from the sensor at a particular time, and the columns denote time (seconds), right bicep EMG (volts), and left bicep EMG (volts). Checking the shape is always a good step to ensure there wasn't an error in the data loading process.
 
