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--- analysis:nsb2015:week3short [2015/07/16 19:02]
mvdm
+++ analysis:nsb2015:week3short [2018/07/07 10:19] (current)
@@ Line 46: / Line 46: @@
 <code matlab>
 %% cd to data folder -- replace this with yours
-fd = 'D:\data\DataAnalysisTutorial\R042-2013-08-18';
+fc = 'D:\data\DataAnalysisTutorial\R042-2013-08-18';
 cd(fc);
@@ Line 103: / Line 103: @@
 ''csc'' is the common Neuralynx designation for "continuously sampled channel" and typically is an EEG or LFP type signal sampled and filtered so that high-frequency components such as spikes are not accessible. It is possible to have wide-band, 32kHz CSCs suitable for spike extraction, but these are not included in the current dataset. As discussed in [[analysis:nsb2015:week2|Module 3]] a LFP **tsd** is defined by matching sets of sample timestamps (''csc.tvec'') and sampled data (''csc.data'').
-==== plotting spike rasters ====
+==== Plotting basic spike rasters ====
 The ''S'' variable that contains our spike times is a MATLAB struct that conforms to the ''ts'' ("timestamp") format introduced in [[analysis:nsb2015:week2|Module 2]]. Its ''.t'' field contains spike trains as lists of numbers, corresponding to the timestamps of the spikes. So, to plot a raster with the spikes of the first cell in our data set:
@@ Line 118: / Line 118: @@
 You should find that it takes a significant amount of time to plot that many spikes. In practice we often want to plot spikes for specific time intervals; we will do this below. Alternatively, there exists code that is much faster at plotting rasters (albeit more obscure; implemented in ''PlotSpikeRaster2()'' if you want to see an example).
-==== restricting ts and tsd objects ====
+==== Temporally restricting ts and tsd objects ====
 A useful function that works on ''ts'', ''tsd'' and ''iv'' objects is ''restrict()''.
@@ Line 134: / Line 134: @@
 This high-frequency osillation (the "ripple") superimposed on a slower oscillation (the "sharp wave") is the signature associated with "replay" events in the hippocampus. We will explore this phenomenon in detail in later modules.
-==== quick figure export ====
+==== Exporting figures ====
 A good way to save figures you may want to keep is:
@@ Line 144: / Line 144: @@
 Notice that the first argument of ''print'' is the current figure (''gcf''). The other arguments specify that we want a ''PNG'' file, with 300dpi resolution. PNG format is a good choice for saving figures because it uses lossless compression, JPG images, which use lossy compression, can have ugly artifacts (but more colors). Other useful save formats are ''-dill'' which saves ''.ai'' Illustrator files, and ''-depsc'' which saves an embedded %%PostScript%% file (both vector formats).
-==== a fully featured plotting function ====
+☛ Critically evaluate the formatting of figure shown above. What are some missing elements you would want to see if this figure were to be used in a talk or paper? What changes could you make to improve legibility?
+==== A fully featured plotting function ====
+''MultiRaster()'' is a versatile and fast plotting function, with many different options and features.
+The most basic usage is simply:
+<code matlab>
+MultiRaster([],S)
+</code>
+Note that the first argument in this case is the empty matrix ''[]''. For more features, this argument is used as a ''cfg'' variable with several options, for instance:
+<code matlab>
+plot_cfg = [];
+plot_cfg.lfp = csc;
+plot_cfg.spkColor = 'hsv';
+MultiRaster(plot_cfg,S)
+</code>
+This particular data set also has some experimenter annotations associated with it, for example in the %%ExpKeys%% file:
+<code matlab>
+>> LoadExpKeys
+>> ExpKeys
+ExpKeys =
+                Behavior: 'MotivationalT'
+         RestrictionType: 'water'
+                 Session: 'standard'
+                  Layout: 'foodLeft'
+                Pedestal: 'R'
+                nPellets: 5
+             waterVolume: []
+                 nTrials: 18
+            forcedTrials: []
+    nonConsumptionTrials: []
+               badTrials: []
+              pathlength: 318
+                patharms: 369
+           realTrackDims: [139 185]
+                convFact: [2.9176 2.3794]
+             TimeOnTrack: 3240
+            TimeOffTrack: 5645
+               prerecord: [2.1266e+03 3.2141e+03]
+                    task: [3.2387e+03 5.6452e+03]
+              postrecord: [5.6564e+03 6.5635e+03]
+                 goodSWR: {1x2 cell}
+               goodTheta: {'R042-2013-08-18-CSC07a.ncs'}
+</code>
+The meaning of these fields is explained in more detail in the full [[task description]], but for now we will just use the start and end times for the 'prerecord' segment of the data to highlight it visually:
+<code matlab>
+plot_cfg = [];
+plot_cfg.evt = iv(ExpKeys.prerecord);
+plot_cfg.evtColor = [0 0 0.5];
+MultiRaster(plot_cfg,S)
+</code>
+''MultiRaster()'' is interactive, allowing the user to scroll, zoom, move from one event to another, et cetera. To see the full list of keybindings, type ''help navigate''.
+The [[analysis:nsb2015:week3long|long version]] of this module challenges you to write a version of this visualization function yourself, so you can understand how it is done, and customize it for your application.

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