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-## NIfTI Files
-Neuroimaging Informatics Technology Initiative is a data format for storage. Functional Magnetic Resonance Imaging (fMRI) and other medical images.
-## In this, we'll be using different packages:
-1. Matplotlib
-2. Nibabel: It gives access to a variety of imaging formats, it provides a common interface for various formats produced by different scanners.
-
-## Various Methods for viewing a NIfTI file
-### 1. Load NIfTI file
-```
-brain_vol = nib.load("") #Enter the path to NIfTI file here
-type(brain_vol)
-```
-### 2. View Metadata
-```
-print(brain_vol.header)
-```
-### Output
-```
-<class 'nibabel.nifti1.Nifti1Header'> object, endian='<'
-sizeof_hdr      : 348
-data_type       : b''
-db_name         : b''
-extents         : 0
-session_error   : 0
-regular         : b'r'
-dim_info        : 54
-dim             : [  3 192 256 256   1   1   1   1]
-intent_p1       : 0.0
-intent_p2       : 0.0
-intent_p3       : 0.0
-intent_code     : none
-datatype        : int16
-bitpix          : 16
-slice_start     : 0
-pixdim          : [1.  1.  1.  1.  2.3 0.  0.  0. ]
-vox_offset      : 0.0
-scl_slope       : nan
-scl_inter       : nan
-slice_end       : 0
-slice_code      : unknown
-xyzt_units      : 10
-cal_max         : 0.0
-cal_min         : 0.0
-slice_duration  : 0.0
-toffset         : 0.0
-glmax           : 0
-glmin           : 0
-descrip         : b'TE=2.7;Time=150734.828;phase=1'
-aux_file        : b''
-qform_code      : scanner
-sform_code      : scanner
-quatern_b       : 0.00042526424
-quatern_c       : -0.027039066
-quatern_d       : 0.01571919
-qoffset_x       : -81.5222
-qoffset_y       : -130.72974
-qoffset_z       : -144.47054
-srow_x          : [ 9.9804670e-01 -3.1445995e-02 -5.4038301e-02 -8.1522202e+01]
-srow_y          : [ 3.1400096e-02  9.9950546e-01 -1.7001767e-03 -1.3072974e+02]
-srow_z          : [ 5.4065209e-02  4.7863640e-08  9.9853742e-01 -1.4447054e+02]
-intent_name     : b''
-magic           : b'n+1'
-```
-### 3. Access data in the NIfTI object
-```
-brain_vol_data = brain_vol.get_fdata()
-type(brain_vol_data)
-```
-### Output
-```
-numpy.ndarray
-```
-### Dimensions of the image
-```
-brain_vol_data.shape
-```
-### Output
-```
-(192, 256, 256)
-```
-### 4. Visualize a Slice
-```
-plt.imshow(brain_vol_data[96], cmap='bone')
-plt.axis('off')
-plt.show()
-```
-The image can be rotated
-```
-plt.imshow(ndi.rotate(brain_vol_data[96], 90), cmap='bone')
-plt.axis('off')
-plt.show()
-```
-### Plot a series of slices
-```
-fig_rows = 4
-fig_cols = 4
-n_subplots = fig_rows * fig_cols
-n_slice = brain_vol_data.shape[0]
-step_size = n_slice // n_subplots
-plot_range = n_subplots * step_size
-start_stop = int((n_slice - plot_range) / 2)
-
-fig, axs = plt.subplots(fig_rows, fig_cols, figsize=[10, 10])
-
-for idx, img in enumerate(range(start_stop, plot_range, step_size)):
-    axs.flat[idx].imshow(ndi.rotate(brain_vol_data[img, :, :], 90), cmap='gray')
-    axs.flat[idx].axis('off')
-        
-plt.tight_layout()
-plt.show()
-```
-### Multiples slices at once
-```
-plotting.plot_img(brain_vol, display_mode='mosaic', cmap='gray')
-plt.show()
-```
-
-
-
-
-