In [1]:

    

%load http://pmb-bordeaux.fr/scripts/PyODAM.py


    

In [2]:

    

%load http://pmb-bordeaux.fr/scripts/PyODAM_pca.py


    

In [3]:

    

## API Call to retrieve data
dataset = 'frim1'
subset = 'qNMR_metabo'
d = getSubsetFromODAM(dataset, subset)

## Retrieve factors
d['factor']['Attribute']


    

    
Out[3]:





0    Treatment
1     DevStage
2     FruitAge
Name: Attribute, dtype: object

In [4]:

    

# Matrix X
X = d['data'][d['numvars']]

# Choose 'DevStage' as Factor (index=1)
facname = d['factor'].Attribute[1]
Y = d['data'][facname]

# Factor levels
factorlevels = []
for f in Y:
    if f not in factorlevels:
        factorlevels.append(f)
factorlevels


    

    
Out[4]:





['FF.01', 'FF.03', 'FR.01', 'FR.04', 'FR.02', 'FF.04', 'FF.02', 'FR.03']

In [5]:

    

# Compute PCA
res_pca = PCA_compute(X, Y, n=3, scale=True)

# Factor level selection
FacLevSel = ['FF.01', 'FF.02', 'FF.04', 'FR.02', 'FR.04']

# Plot PCA Scores
plotPCA(res_pca, 1, 3, FacLevSel)


    

In [6]:

    

# Explained Variance (%)
res_pca['EV']


    

    
Out[6]:





array([38.27177796, 23.22130007, 14.47418228])