<p><span style="padding: 3px 10px; border-radius: 5px; color: #ffffff; font-weight: bold; display: inline-block; background-color: #ff0000;"> External Email - Use Caution </span></p><p></p><div dir="ltr"><div>This works for me:</div><div><br></div><div><div style="color:rgb(212,212,212);background-color:rgb(30,30,30);font-weight:normal;font-size:14px;line-height:19px;white-space:pre"><div><span style="color:rgb(197,134,192)">import</span><span style="color:rgb(212,212,212)"> os</span></div><div><span style="color:rgb(197,134,192)">import</span><span style="color:rgb(212,212,212)"> numpy </span><span style="color:rgb(197,134,192)">as</span><span style="color:rgb(212,212,212)"> np</span></div><div><span style="color:rgb(197,134,192)">import</span><span style="color:rgb(212,212,212)"> matplotlib.pyplot </span><span style="color:rgb(197,134,192)">as</span><span style="color:rgb(212,212,212)"> plt</span></div><div><span style="color:rgb(197,134,192)">import</span><span style="color:rgb(212,212,212)"> mne</span></div><div><span style="color:rgb(197,134,192)">from</span><span style="color:rgb(212,212,212)"> mne.preprocessing </span><span style="color:rgb(197,134,192)">import</span><span style="color:rgb(212,212,212)"> ICA</span></div><br><div><span style="color:rgb(212,212,212)">sample_data_folder </span><span style="color:rgb(212,212,212)">=</span><span style="color:rgb(212,212,212)"> mne.datasets.sample.data_path()</span></div><div><span style="color:rgb(212,212,212)">sample_data_raw_file </span><span style="color:rgb(212,212,212)">=</span><span style="color:rgb(212,212,212)"> os.path.join(sample_data_folder, </span><span style="color:rgb(206,145,120)">'MEG'</span><span style="color:rgb(212,212,212)">, </span><span style="color:rgb(206,145,120)">'sample'</span><span style="color:rgb(212,212,212)">,</span></div><div><span style="color:rgb(212,212,212)"> </span><span style="color:rgb(206,145,120)">'sample_audvis_raw.fif'</span><span style="color:rgb(212,212,212)">)</span></div><div><span style="color:rgb(212,212,212)">raw </span><span style="color:rgb(212,212,212)">=</span><span style="color:rgb(212,212,212)"> mne.io.read_raw_fif(sample_data_raw_file)</span></div><div><span style="color:rgb(212,212,212)">raw.crop(</span><span style="color:rgb(156,220,254)">tmax</span><span style="color:rgb(212,212,212)">=</span><span style="color:rgb(181,206,168)">60</span><span style="color:rgb(212,212,212)">.).load_data().filter(</span><span style="color:rgb(156,220,254)">l_freq</span><span style="color:rgb(212,212,212)">=</span><span style="color:rgb(181,206,168)">1</span><span style="color:rgb(212,212,212)">., </span><span style="color:rgb(156,220,254)">h_freq</span><span style="color:rgb(212,212,212)">=</span><span style="color:rgb(86,156,214)">None</span><span style="color:rgb(212,212,212)">)</span></div><div><span style="color:rgb(212,212,212)">ica </span><span style="color:rgb(212,212,212)">=</span><span style="color:rgb(212,212,212)"> ICA(</span><span style="color:rgb(156,220,254)">n_components</span><span style="color:rgb(212,212,212)">=</span><span style="color:rgb(181,206,168)">30</span><span style="color:rgb(212,212,212)">, </span><span style="color:rgb(156,220,254)">random_state</span><span style="color:rgb(212,212,212)">=</span><span style="color:rgb(181,206,168)">97</span><span style="color:rgb(212,212,212)">)</span></div><div><span style="color:rgb(212,212,212)">ica.fit(raw)</span></div><br><div><span style="color:rgb(212,212,212)">sources_raw </span><span style="color:rgb(212,212,212)">=</span><span style="color:rgb(212,212,212)"> ica.get_sources(raw)</span></div><div><span style="color:rgb(212,212,212)">sources_array, times </span><span style="color:rgb(212,212,212)">=</span><span style="color:rgb(212,212,212)"> sources_raw.get_data(</span><span style="color:rgb(156,220,254)">return_times</span><span style="color:rgb(212,212,212)">=</span><span style="color:rgb(86,156,214)">True</span><span style="color:rgb(212,212,212)">)</span></div><br><div><span style="color:rgb(106,153,85)"># PLOT THE NUMPY ARRAY OF SOURCES</span></div><div><span style="color:rgb(212,212,212)">fig, ax </span><span style="color:rgb(212,212,212)">=</span><span style="color:rgb(212,212,212)"> plt.subplots()</span></div><div><span style="color:rgb(106,153,85)"># this will let us plot the sources without overlapping:</span></div><div><span style="color:rgb(212,212,212)">offsets </span><span style="color:rgb(212,212,212)">=</span><span style="color:rgb(212,212,212)"> </span><span style="color:rgb(181,206,168)">15</span><span style="color:rgb(212,212,212)"> </span><span style="color:rgb(212,212,212)">*</span><span style="color:rgb(212,212,212)"> np.linspace(</span><span style="color:rgb(181,206,168)">29</span><span style="color:rgb(212,212,212)">, </span><span style="color:rgb(181,206,168)">0</span><span style="color:rgb(212,212,212)">, </span><span style="color:rgb(220,220,170)">len</span><span style="color:rgb(212,212,212)">(sources_array))</span></div><div><span style="color:rgb(212,212,212)">ax.plot(times, sources_array.T </span><span style="color:rgb(212,212,212)">+</span><span style="color:rgb(212,212,212)"> offsets, </span><span style="color:rgb(156,220,254)">linewidth</span><span style="color:rgb(212,212,212)">=</span><span style="color:rgb(181,206,168)">0.5</span><span style="color:rgb(212,212,212)">, </span><span style="color:rgb(156,220,254)">color</span><span style="color:rgb(212,212,212)">=</span><span style="color:rgb(206,145,120)">'k'</span><span style="color:rgb(212,212,212)">)</span></div><br><div><span style="color:rgb(106,153,85)"># COMPARE WITH THE MNE-PYTHON PLOT OF SOURCES</span></div><div><span style="color:rgb(212,212,212)">ica.plot_sources(raw)</span></div><br></div></div></div><br><div class="gmail_quote"><div dir="ltr" class="gmail_attr">On Wed, Aug 28, 2019 at 5:16 PM Brown Amumbwe <<a href="mailto:bamumbwe@gmail.com">bamumbwe@gmail.com</a>> wrote:<br></div><blockquote class="gmail_quote" style="margin:0px 0px 0px 0.8ex;border-left:1px solid rgb(204,204,204);padding-left:1ex"><p><span style="padding:3px 10px;border-radius:5px;color:rgb(255,255,255);font-weight:bold;display:inline-block;background-color:rgb(255,0,0)"> External Email - Use Caution </span></p><p></p><div dir="ltr"><div>Hi,</div><div>Basically I have an MNE RawArray object and I would like to convert it to an ndarray.</div><div><br></div><div>I am doing an ICA of an EEG signal with 22 channels. I need an ndarray with the corresponding time courses of the the Independent Components from the ICA.</div><div><br></div><div>Code Snippet</div><div><div style="color:rgb(212,212,212);background-color:rgb(30,30,30);font-family:Consolas,"Courier New",monospace;font-size:14px;line-height:19px;white-space:pre-wrap"><div>ica = mne.preprocessing.ICA(<span style="color:rgb(156,220,254)">method</span>=<span style="color:rgb(206,145,120)">"infomax"</span>, <span style="color:rgb(156,220,254)">random_state</span>=<span style="color:rgb(181,206,168)">1</span>)</div><div>ica.fit(raw_tmp)</div><br><div>data = ica.get_sources(<span style="color:rgb(156,220,254)">inst</span>=raw_tmp)</div></div></div><div><br></div><div>data is a RawArray object</div><div>Using </div><div><div style="color:rgb(212,212,212);background-color:rgb(30,30,30);font-family:Consolas,"Courier New",monospace;font-size:14px;line-height:19px;white-space:pre-wrap"><div>cpt = data.get_data()</div></div></div><div><br></div><div>gives an ndarray but without the time courses of the ICs</div><div><br></div><div>Thank you for any help.</div></div>
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