8. Annexes

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    These annexes allow this work to be used in other way for the lab or to go further.

• 8.1. Compute and save anisotropy factors
  • 8.1.1. Load data
  • 8.1.2. CI02
  • 8.1.3. CI04
  • 8.1.4. CI06
  • 8.1.5. CI09
  • 8.1.6. CI21
  • 8.1.7. Save new data
• 8.2. Compute Triple Junction dataset
  • 8.2.1. Loading data
  • 8.2.2. Compute variables
    • 8.2.2.1. Compute map of TJ and distance
    • 8.2.2.2. Filter TJ to close to border
    • 8.2.2.3. Compute RX of each TJ (10%)
    • 8.2.2.4. Compute schmid factor of each 3 grains
    • 8.2.2.5. Compute schmid diff for each 3 GB
    • 8.2.2.6. Compute misorientation angle of each 3 GB
    • 8.2.2.7. Compute volume ratio anisotropy of each TJ
    • 8.2.2.8. Compute mean of craft variables for each TJ
    • 8.2.2.9. Compute Distance to other TJ
    • 8.2.2.10. Compute number of pixel of the 3 grains of each TJ
  • 8.2.3. Building dataset
    • 8.2.3.1. Save New Dataset
• 8.3. Computation pipeline of Dataset for CNN and Mixuture NN
  • 8.3.1. Loadind data
  • 8.3.2. Compute TJ coordinates, grains and distance using xarrayaita
  • 8.3.3. Filter TJ too close to border
  • 8.3.4. Compute variables and maps
    • 8.3.4.1. Volume ratio anisotropy of TJ (single value)
    • 8.3.4.2. Distance to other TJ
    • 8.3.4.3. Number of pixel of TJ grains (sum)
    • 8.3.4.4. Mapped values (schmid, diff_schmid, misangle and CraFT data) with a \(10 \times 10\) px window around TJ
    • 8.3.4.5. RX value
  • 8.3.5. Building xarray dataset
    • 8.3.5.1. Save dataset
  • 8.3.6. Data view
• 8.4. Principal Components Analysis (PCA) on pixel dataset (with anisotropy)
  • 8.4.1. Import packages
  • 8.4.2. Loading data
  • 8.4.3. PCA with anisotropy factors
    • 8.4.3.1. Variables selection
    • 8.4.3.2. Normalization
    • 8.4.3.3. Apply PCA
  • 8.4.4. Results
    • 8.4.4.1. Eigen values / Explained variance ratio
    • 8.4.4.2. First Principal plan of individuals
    • 8.4.4.3. Individuals on 1-4 components
    • 8.4.4.4. Density maps
    • 8.4.4.5. Correlation cirle on components 1-2, 3-4 and 5-6
• 8.5. Principal Components Analysis (PCA) on TJ dataset
  • 8.5.1. Loading data
  • 8.5.2. PCA
    • 8.5.2.1. Variables selection
    • 8.5.2.2. Normalization and apply PCA
  • 8.5.3. Results
    • 8.5.3.1. Eigen values / Explained variance ratio
    • 8.5.3.2. First Principal plan of individuals
    • 8.5.3.3. Individuals on 1-4 components
    • 8.5.3.4. Density maps
    • 8.5.3.5. Correlation cirle on components 1-2 and 3-4
• 8.6. t-distribued Stochastic Neighbor Embleding (t-SNE) on TJ dataset
  • 8.6.1. Loading data
  • 8.6.2. t-SNE on CI02 geom and craft data
    • 8.6.2.1. Variables selection
    • 8.6.2.2. Normalization
    • 8.6.2.3. Apply t-SNE for class
    • 8.6.2.4. Projection of individuals on 2 dimensional space with labeling for pixel status
• 8.7. Tresholding data for class balance
  • 8.7.1. Loading data
  • 8.7.2. Description of variables
  • 8.7.3. Threshold on dist2GB
  • 8.7.4. Threshold on dist2TJ
  • 8.7.5. Subdatasets