Convert the source image file into a pycroscopy compatible hierarchical data format (HDF or .h5) file. This simple translation gives you access to the powerful data functions within pycroscopy
#### H5 files:
* are like smart containers that can store matrices with data, folders to organize these datasets, images, metadata like experimental parameters, links or shortcuts to datasets, etc.
* are readily compatible with high-performance computing facilities
* scale very efficiently from few kilobytes to several terabytes
* can be read and modified using any language including Python, Matlab, C/C++, Java, Fortran, Igor Pro, etc.
%% Cell type:code id: tags:
``` python
# Check if an HDF5 file with the chosen image already exists.
print('HDF5 file with Raw_Data found. No need to translate.')
exceptKeyError:
print('Raw Data not found.')
else:
print('No HDF5 file found.')
ifneed_translation:
# Initialize the Image Translator
tl=px.ImageTranslator()
# create an H5 file that has the image information in it and get the reference to the dataset
h5_raw=tl.translate(image_path)
# create a reference to the file
h5_file=h5_raw.file
print('HDF5 file is located at {}.'.format(h5_file.filename))
```
%% Cell type:markdown id: tags:
### Inspect the contents of this h5 data file
The file contents are stored in a tree structure, just like files on a contemporary computer.
The data is stored as a 2D matrix (position, spectroscopic value) regardless of the dimensionality of the data.
In the case of these 2D images, the data is stored as a N x 1 dataset
The main dataset is always accompanied by four ancillary datasets that explain the position and spectroscopic value of any given element in the dataset.
In the case of the 2d images, the positions will be arranged as row0-col0, row0-col1.... row0-colN, row1-col0....
The spectroscopic information is trivial since the data at any given pixel is just a scalar value
## Performing Singular Value Decompostion (SVD) on the windowed data
SVD decomposes data (arranged as position x value) into a sequence of orthogonal components arranged in descending order of variance. The first component contains the most significant trend in the data. The second component contains the next most significant trend orthogonal to all previous components (just the first component). Each component consists of the trend itself (eigenvector), the spatial variaion of this trend (eigenvalues), and the variance (statistical importance) of the component.
Since the data consists of the large sequence of small windows, SVD essentially compares every single window with every other window to find statistically significant trends in the image
%% Cell type:code id: tags:
``` python
# check to make sure number of components is correct:
The plot below shows the variance or statistical significance of the SVD components. The first few components contain the most significant information while the last few components mainly contain noise.
Note also that the plot below is a log-log plot. The importance of each subsequent component drops exponentially.
%% Cell type:code id: tags:
``` python
fig_S,ax_S=px.plot_utils.plotScree(h5_S[()]);
```
%% Cell type:markdown id: tags:
#### V (Eigenvectors or end-members)
The V dataset contains the end members for each component
We will attempt to find the positions and the identities of atoms in the image now
## Perform clustering on the dataset
Clustering divides data into k clusters such that the variance within each cluster is minimized.<br>
Here, we will be performing k-means clustering on a set of components in the U matrix from SVD.<br>
We want a large enough number of clusters so that K-means identifies fine nuances in the data. At the same time, we want to minimize computational time by reducing the number of clusters. We recommend 32 - 64 clusters.
