competitions.py

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
#import seaborn as sns
import os
import sys

def main():
    '''Produces a simple plot

       `python competitions.py data/datafile.xlsx`

    '''
    # Import data
    dataset = sys.argv[1]
    results_basename = os.path.basename(dataset).split('.')[0]
    df_inoculum = pd.read_excel(dataset, sheetname='input')
    df = pd.read_excel(dataset, sheetname='output')

    #create way to differentiate B and C dilutions - code it in or put on different sheets
    #ignore apo squid - Competition 1 or A dilutions - delete this from working table?

    # Calculate input ratios
    df_inoculum['ratioWB'] = df_inoculum['white']/df_inoculum['blue']
    # Calculate output ratios
    df['ratioWB'] = df['white']/df['blue']
    # Mean of duplicate input plates
    df_inoculum_summary = df_inoculum.groupby(['competition'], as_index=False).mean()
    # Merge input data & calculate competitive index
    df_summary = pd.merge(df, df_inoculum_summary[['competition', 'ratioWB']], on=['competition'])
    df_summary.rename(columns={'ratioWB_x': 'ratioWB_o', 'ratioWB_y': 'ratioWB_i'}, inplace=True)
    df_summary['CI'] = np.log10(df_summary['ratioWB_o'] / df_summary['ratioWB_i'])
    # Mean of replicate plates per animal
    df_summary = df_summary.groupby(['competition', 'squid'], as_index=False).mean()

    # Save dataframe to csv
    df_summary.to_csv('results/' + results_basename + '.csv')

    # Set up plot
    # Create a new figure of size 8x6 points, using 100 dots per inch
    plt.figure(figsize=(8,6), dpi=100)
    # Create a new subplot from a grid of 1x1
    plt.subplot(111)

    # Data
    y = df_summary['CI']
    x = df_summary['competition']
    x = np.random.normal(x, 0.05, size=len(y)) # jitter

    # Scale ([xmin, xmax, ymin, ymax])
    xmin = min(x) - 1
    xmax = max(x) + 1
    plt.axis([xmin, xmax, -3, 3])

    # Plot
    plt.scatter(x, y, marker='o', s=75,
                color='gray', linewidths=2, alpha=0.7)
    plt.scatter(df_summary.groupby('competition', as_index=False).median()['competition'],
                df_summary.groupby('competition', as_index=False).median()['CI'], marker='_', s=500,
                color='black', linewidths=2, alpha=1.0)

    # Save plot to png, pdf
    plt.savefig('results/' + results_basename + '.png')
    plt.savefig('results/' + results_basename + '.pdf')


if __name__ == '__main__':
    main()