Kubke Lab:Research/ABR/Notebook/2013/10/28: Difference between revisions

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==Fabiana==
==Fabiana==
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<p>#libraries</p>
<p>#test setting directory and set to analysis file</p>
<pre><code class="r">rootdir &lt;- getwd()
basedir &lt;- paste(rootdir, &quot;Dropbox/OrisABR/Sandbox&quot;, sep = &quot;/&quot;)
setwd(basedir)
</code></pre>
<pre><code>## Error: cannot change working directory
</code></pre>
<pre><code class="r">getwd()
</code></pre>
<pre><code>## [1] &quot;/Users/mkub003/Dropbox/OrisABR/Sandbox&quot;
</code></pre>
<p>#Get info from the text file</p>
<pre><code class="r">metadata &lt;- readLines(&quot;Copy224l0a.txt&quot;)
n = length(metadata)
print(metadata[1:n])
</code></pre>
<pre><code>## [1] &quot;MOUSEABR version 6.00&quot;                                     
## [2] &quot;Date: 10/07/97&quot;                                             
## [3] &quot;Time: 21:35:23&quot;                                             
## [4] &quot;Comment: D=9700 Tek 5+  Lux-46 Loud Norm&quot;                   
## [5] &quot;Stimulus was 1 cycles of 5000 Hz at interval of 1000 msec&quot; 
## [6] &quot;delay-0: 0 usec    delay-1: 60 usec    delay-2: 400 usec    &quot;
## [7] &quot;Number of repetitions: 30&quot;                                 
## [8] &quot;Sample Clock Rate:  50000 Hz&quot;
</code></pre>
<p>#read data file and pass it to a new file so I can modify without loosing the
#original file. Tried write.csv but it adds a number column to the left -
#write table copies without adding a 6th variable</p>
<pre><code class="r">mydata &lt;- read.table(&quot;Copy233L0B.ABR.txt&quot;, header = T)
head(mydata)  #passed test
</code></pre>
<pre><code>##  msec left right spont  bin
## 1 0.00 1000  1000  1000 1000
## 2 0.02 1000  1000  1000 1000
## 3 0.04 1000  1000  1000 1000
## 4 0.06 1000  1000  1000 1000
## 5 0.08 1000  1000  1000 1000
## 6 0.10 1000  1000  1000 1000
</code></pre>
<pre><code class="r">write.table(mydata, &quot;mydata_new&quot;)
mydata_new &lt;- read.table(&quot;mydata_new&quot;, header = T)
head(mydata_new)  #passed test
</code></pre>
<pre><code>##  msec left right spont  bin
## 1 0.00 1000  1000  1000 1000
## 2 0.02 1000  1000  1000 1000
## 3 0.04 1000  1000  1000 1000
## 4 0.06 1000  1000  1000 1000
## 5 0.08 1000  1000  1000 1000
## 6 0.10 1000  1000  1000 1000
</code></pre>
<p>#Plot the 4 variables (left, right, spont and bin)</p>
<pre><code class="r">par(mfrow = c(2, 2))  #puts the 4 graphs in a page
plot(mydata_new[c(1, 2)], type = &quot;l&quot;)
plot(mydata_new[c(1, 3)], type = &quot;l&quot;)
plot(mydata_new[c(1, 4)], type = &quot;l&quot;)
plot(mydata_new[c(1, 5)], type = &quot;l&quot;)
</code></pre>
<p><img 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" alt="plot of chunk plot1"/> </p>
<p>I will ignore spont for now, although will need to look into it to determine the reasonable level of noise.</p>
<p>#write a column that is bin - (left + right)</p>
<pre><code class="r">head(mydata_new)
</code></pre>
<pre><code>##  msec left right spont  bin
## 1 0.00 1000  1000  1000 1000
## 2 0.02 1000  1000  1000 1000
## 3 0.04 1000  1000  1000 1000
## 4 0.06 1000  1000  1000 1000
## 5 0.08 1000  1000  1000 1000
## 6 0.10 1000  1000  1000 1000
</code></pre>
<pre><code class="r">mydata_new$bincomp[1:1000] &lt;- (mydata_new$bin[1:1000] - (mydata_new$left[1:1000] +
    mydata_new$right[1:1000]))
</code></pre>
<p>I get an inversion of the stim artifact (duh!)</p>
<pre><code class="r">par(mfrow = c(2, 2))  #puts the 4 graphs in a page
plot(mydata_new[c(1, 2)], type = &quot;l&quot;)
plot(mydata_new[c(1, 3)], type = &quot;l&quot;)
plot(mydata_new[c(1, 5)], type = &quot;l&quot;)
plot(mydata_new[c(1, 6)], type = &quot;l&quot;)
</code></pre>
<p><img 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" alt="plot of chunk plot2"/> </p>
<p>#well, bin component is different from spont - may be something at the 5 msec?</p>
</body>
</html>


==Andy==
==Andy==

Revision as of 04:23, 27 October 2013

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