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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,10 @@ | ||
| ### Navigation | ||
| This directory comports the `utils` directory including three subdirectories: | ||
| - `scripts`: find all the scripts written for the data analysis in this directory | ||
| in addition to ploting function that were not included in the `functions` because | ||
| not testable. | ||
| - `functions`: The functions have been writtent to be used in the scripts for the | ||
| data analysis. Each function in this directory has a test associated with it | ||
| - `test`: this directory includes the tests written for the functions from the | ||
| `functions` directory. | ||
|
|
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| Original file line number | Diff line number | Diff line change |
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| @@ -0,0 +1,101 @@ | ||
| """ | ||
| This script plots some exploratory analysis plots for the raw and filtered data: | ||
| - Moisaic of the mean voxels values for each brain slices | ||
| Run with: | ||
| python eda.py | ||
| from this directory | ||
| """ | ||
| from __future__ import print_function, division | ||
| import sys, os, pdb | ||
| import numpy as np | ||
| import matplotlib.pyplot as plt | ||
| import nibabel as nib | ||
| sys.path.append(os.path.join(os.path.dirname(__file__), "./")) | ||
| from plot_mosaic import * | ||
| from mask_filtered_data import * | ||
|
|
||
| # Locate the paths | ||
| project_path = '../../../' | ||
| data_path = project_path+'data/ds005/' | ||
| path_dict = {'data_filtered':{ | ||
| 'type' : 'filtered', | ||
| 'feat' : '.feat', | ||
| 'bold_img_name' : 'filtered_func_data_mni.nii.gz', | ||
| 'run_path' : 'model/model001/' | ||
| }, | ||
| 'data_original':{ | ||
| 'type' : '', | ||
| 'feat': '', | ||
| 'bold_img_name' : 'bold.nii.gz', | ||
| 'run_path' : 'BOLD/' | ||
| }} | ||
|
|
||
| #subject_list = [str(i) for i in range(1,17)] | ||
| #run_list = [str(i) for i in range(1,4)] | ||
|
|
||
| # Run only for subject 1 and 5 - run 1 | ||
| run_list = [str(i) for i in range(1,2)] | ||
| subject_list = ['1', '5'] | ||
|
|
||
| # set gray colormap and nearest neighbor interpolation by default | ||
| plt.rcParams['image.cmap'] = 'gray' | ||
| plt.rcParams['image.interpolation'] = 'nearest' | ||
|
|
||
| # Create the needed directories if they do not exist | ||
| dirs = [project_path+'fig/',\ | ||
| project_path+'fig/BOLD'] | ||
| for d in dirs: | ||
| if not os.path.exists(d): | ||
| os.makedirs(d) | ||
|
|
||
| # Template to plot the unmasked filetered data | ||
| template_path = project_path+'data/mni_icbm152_t1_tal_nlin_asym_09c_2mm.nii' | ||
|
|
||
| # Loop through the data type - raw or filtered | ||
| for dat in path_dict: | ||
| d_path = path_dict[dat] | ||
| # Set the data name and paths | ||
| images_paths = [('ds005' + '_sub' + s.zfill(3) + '_t1r' + r, \ | ||
| data_path + 'sub%s/'%(s.zfill(3)) + d_path['run_path'] \ | ||
| + 'task001_run%s%s/%s' %(r.zfill(3),d_path['feat'],\ | ||
| d_path['bold_img_name'])) \ | ||
| for r in run_list \ | ||
| for s in subject_list] | ||
| for image_path in images_paths: | ||
| name = image_path[0] | ||
| # Plot | ||
| if d_path['type']=='filtered': | ||
| data = nib.load(image_path[1]).get_data() | ||
| data = data.astype(float) | ||
| mean_data = np.mean(data, axis=-1) | ||
| Transpose=False | ||
| template_data = nib.load(template_path).get_data() | ||
| plt.imshow(\ | ||
| plot_mosaic(template_data, transpose=Transpose), \ | ||
| cmap='gray', alpha=1) | ||
| else: | ||
| img = nib.load(image_path[1]) | ||
| data = img.get_data() | ||
| data = data.astype(float) | ||
| mean_data = np.mean(data, axis=-1) | ||
| in_brain_mask = mean_data > 375 | ||
| mean_data = np.mean(data, axis=-1) | ||
| Transpose=True | ||
| plt.contour(\ | ||
| plot_mosaic(in_brain_mask, transpose=Transpose), \ | ||
| cmap='gray' , alpha=1) | ||
| plt.imshow(\ | ||
| plot_mosaic(mean_data, transpose=Transpose), cmap='gray', alpha=1) | ||
| plt.colorbar() | ||
| plt.title('Voxels mean values' + '\n' + (d_path['type'] + str(name))) | ||
| plt.savefig(project_path+'fig/BOLD/%s_mean_voxels.png'\ | ||
| %(d_path['type'] + str(name))) | ||
| #plt.show() | ||
| plt.clf() | ||
|
|
||
|
|
||
| print("======================================") | ||
| print("\nEDA analysis done") | ||
| print("Mosaic plots in project_epsilon/fig/BOLD/ \n\n") | ||
|
|
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