Merge pull request #312 from soazig/soazig-master

Updating master  from Soazig master

data/data.py

@@ -7,7 +7,8 @@
    To create the json file relative to a specific
    folder, see project-epsilon/data/get_data_hashes.py
 
-Assuming the ds005_hashes.json file is present:
+Assuming the ds005_hashes.json file is present
+in the directory:
    python data.py
 """
 

data/get_ds005_hashes_from_txt.py

@@ -32,6 +32,6 @@ def create_dict(filename):
 
 #For our data ds005
 newDict = create_dict('ds005_raw_checksums.txt')
-with open('ds005_hashes_checksums.json', 'w') as file_out:
+with open('ds005_hashes.json', 'w') as file_out:
     json.dump(newDict, file_out)
 #    print(check_hashes(newDict))

paper/final_sections/4_image.tex

@@ -8,8 +8,6 @@ \subsection{Introduction}
   apply noise modeling and PCA to compare the MRSS so that we can finally decide our design matrix.
 
 \subsection{Methods}
-Here are some of the methods (this needs to be updated soon as well.)
-
 \subsubsection {Convolution}
 Our experiment is event-oriented. The subject is shown with the different conditions 
 such as gain and loss amounts over random time. After being provided with the conditions,

paper/final_sections/4_noise-inbrain.tex

@@ -27,8 +27,6 @@ \subsubsection{In brain voxels}
 time of 375. Our further analysis with the raw data uses a mask on the mean data accross
 time that select the ones with a higher value than the threshold.
 
-\subsubsection{Selecting the voxels in the brain - filtered data}
-
 \par For the filtered data, we applied the mask provided by the Montreal Neurological 
 Institute's website. The filter has 1 for the voxels inside the brain and 0 outside.
 We created a function in the 'project-epsilon/code/utils/scripts' directory to 

paper/final_sections/4_noise-pca.tex

@@ -101,27 +101,26 @@ \subsubsection{Noise modeling results}
 principal components.
 
 \begin{figure}[H]
-\begin{subfigure}{.45\textwidth}
-    \centering
-    \includegraphics[width=.9\linewidth]{../fig/mosaic/ds005_sub001_t1r1_withdrift_Task.png}
-    \caption{Design matrix includes linear and quadratic drifts regressors}
-    \label{fig:betas1}
-\end{subfigure}%
-\begin{subfigure}{.5\textwidth}
-    \centering
-    \includegraphics[width=.9\linewidth]{../fig/mosaic/ds005_sub001_t1r1_withPCA_Task.png}
-    \caption{Design matrix includes drifts and PC regressors}
-    \label{fig:betas2}
-\end{subfigure}
-\caption{Mean values of the Betas coefficients on the slice 18 of in brain voxels}
-\label{fig:betas}
+  \centering
+  \includegraphics[scale=0.75]{../fig/mosaic/ds005_sub001_t1r1_withdrift_Task.png}
+  \caption{Mean values of the Betas coefficients with drifts}
+  \label{fig:betas1}
+\end{figure}
+
+\begin{figure}[H]
+  \centering
+  \includegraphics[scale=0.85]{../fig/mosaic/ds005_sub001_t1r1_withPCA_Task.png}
+  \caption{Mean values of the Betas coefficients with PCs}
+  \label{fig:betas2}
 \end{figure}
 
 \noindent
 \par Betas coefficients of our linear model related to the brain activation with the task 
-are plotted on the brain image in Figure \ref{fig:betas}. We can see on both model 
+are plotted on the brain image in Figures \ref{fig:betas1} and \ref{fig:betas2}. We can see on both model 
 (with or without the PC) we can localized the region of the brain related to the task 
 activation thanks to the values of the betaghest the values are in red, the lowest in blue. 
 From the journal article, we know the regions of interest are the inferior/middle frontal 
-(see (100,200:400) and (150,0:300)), the ventral stratium (275, (150:400)) for example. Further analysis and statistical tests are needed to locate the activated voxels with task, which is developped in the next section.
+(see (100,200:400) and (150,0:300)), the ventral stratium (275, (150:400)) for example. 
+Further analysis and statistical tests are needed to locate the activated voxels with task,
+which is developped in the next section.