merge_tables.py

import pandas as pd
import fire
import seaborn as sns
import matplotlib.pyplot as plt


def combine(table_day1_path, table_day2_path, table_output_path, swarmplot_output_path, prob_plot_path, threshold:int=20):
    day1, day2 = [pd.read_csv(t) for t in [table_day1_path, table_day2_path]]
    day1.loc[:,'n_cells_final'] = day2.n_cells
    day1.loc[:,'intensity_final'] = day2.intensity

    day1.loc[:,'final_state'] = day1.n_cells_final > threshold

    day1.to_csv(table_output_path, index=None)
    n_max = int(day1.n_cells.mean() * 3)
    plot_swarm_counts(day1, n_max=n_max, path=swarmplot_output_path)
    plot_swarm_intensity(day1, n_max=n_max, path=swarmplot_output_path.replace('.png', '_intensities.png'))
    plot_probs(day1, n_max=n_max,path=prob_plot_path)
    plot_probs_log(day1, n_max=n_max,path=prob_plot_path.replace('.png', '_log.png'))
    
def plot_swarm_counts(table:pd.DataFrame, n_max:int=5, path=None):
    fig, ax = plt.subplots(dpi=300)
    sns.swarmplot(ax=ax, data=table.query(f'n_cells < {n_max}'), x='[AB]', y='n_cells_final', hue='n_cells', dodge=True, size=1 )
    fig.savefig(path)

def plot_swarm_intensity(table:pd.DataFrame, n_max:int=5, path=None):
    fig, ax = plt.subplots(dpi=300)
    sns.swarmplot(ax=ax, data=table.query(f'n_cells < {n_max}'), x='[AB]', y='intensity_final', hue='n_cells', dodge=True, size=1 )
    fig.savefig(path)

def plot_probs(table:pd.DataFrame, n_max:int=5, path=None):
    
    fig, ax = plt.subplots(dpi=300)
    sns.lineplot(ax=ax, data=table.query(f'n_cells < {n_max}'), x='[AB]', y='final_state', hue='n_cells')
    fig.savefig(path)

def plot_probs_log(table:pd.DataFrame, n_max:int=5, path=None):
    fig, ax = plt.subplots(dpi=300)
    sns.lineplot(ax=ax, data=table.query(f'n_cells < {n_max}'), x='[AB]', y='final_state', hue='n_cells')
    ax.set_xscale('log')
    fig.savefig(path)

if __name__ == "__main__":
    fire.Fire(combine)