ninetysix

import ninetysix as ns

import pandas as pd
import numpy as np

# Seed
np.random.seed(8675309)

# A1-H12 wells
wells = [row+str(col)
         for row in list('ABCDEFGH')
         for col in range(1, 13)]

# Create a list of five plates
pt_list = []
for i in range(1, 6):
    
    # Generate random distribution of values and make Plate
    values = np.random.normal(10+i, 2, size=len(wells))
    pt_ = ns.Plate(zip(wells, values))
    
    # Add the annotation `plate` = i
    pt_['plate'] = i
    
    # Set `plate` as a location (not necessary)
    pt_ = pt_.set_as_location('plate', idx=0)
    
    # Add control annotations
    controls = {
        '[A-D]10': 'standard',
        'E10': 'empty',
        '[F-H]10': 'negative',
        'other': 'experiment',
    }
    pt_ = pt_.annotate_wells({'controls': controls})
    
    # Append only the DataFrame attribute (Plate.df) to work with pd.concat
    pt_list.append(pt_.df)
    
    # Store the location and values info
    locations = pt_.locations
    values = pt_.values

# Create new Plate object
plates = ns.Plate(pd.concat(pt_list).reset_index(drop=True))

# Update attributes
plates.locations = locations
plates.values = values

# Check the Plate's MultiIndexed DataFrame to see attributes
plates.mi_df

# Normalize each plate on its own and change prefix
plates = plates.normalize(
    to='controls=standard',
    zero=True,
    groupby='plate',
    prefix='norm '
)

plates

# Show that the mean of `norm value` for `controls == standard` in each plate is now 1
plates[plates['controls']=='standard'].groupby(['plate']).mean()

# Generate layout of unnormalized and normalized plots
p = plates.plot_rof(groupby='plate') +\
    plates.plot_rof(value_name='norm value', groupby='plate')

# Set plot-specific axis scaling
p = p.opts({'Scatter': {'framewise': True}})

# The data is translated and scaled, but otherwise the same
p.cols(1)

cmap = {
    'standard': ns.Colors.green,
    'negative': ns.Colors.orange,
    'empty': ns.Colors.gray,
    'experiment': ns.Colors.blue,
}

plates.plot_bar(
    variable='controls',
    color='controls',
    cmap=cmap,
    groupby='plate',
    jitter=0.5,
)