Lux grapher

graph.py

import matplotlib.pyplot as plt
import numpy as np
import pandas

def char_range(c1, c2):
  for c in xrange(ord(c1), ord(c2) + 1):
    yield chr(c)

def make_plate_range(a, b, number):
  for c in char_range(a, b):
    yield c + str(number).zfill(2)

def munge_data(results):
  grouped_results = {}
  agged = {}
  for k in results:
    results[k]['CPS_norm'] = results[k]['CPS'] / results[k]['Abs']
    grouped_results[k] = results[k].groupby('Repeat')
  for k in grouped_results:
    mean = grouped_results[k].agg({'Abs': np.mean, 'CPS': np.mean, 'CPS_norm': np.mean})
    mean = mean.rename(columns = {
      'Abs': 'Abs_mean',
      'CPS': 'CPS_mean',
      'CPS_norm': 'CPS_norm_mean'
    })

    stddev = grouped_results[k].agg({'Abs': np.std, 'CPS': np.std, 'CPS_norm': np.std})
    stddev = stddev.rename(columns = {
      'Abs': 'Abs_std',
      'CPS': 'CPS_std',
      'CPS_norm': 'CPS_norm_std'
    })

    agged[k] = mean.join(stddev)

  return agged

def plot_normalized(agged, show_errorbars, filename):
  combined = {}
  for k in agged:
    combined[k] = agged[k]['CPS_norm_mean']
  combined = pandas.DataFrame(combined)

  plt.figure()
  ax = combined.plot(legend=False)

  if show_errorbars:
    for k in agged:
      plt.errorbar(
        x=agged[k].index,
        y=agged[k]['CPS_norm_mean'],
        yerr=agged[k]['CPS_norm_std'],
        label = k
      )

  if show_errorbars:
    # Below is hack to make legend's labels correspond to correct lines.
    # See http://stackoverflow.com/q/13555607/1691611.
    labels, handles = ax.get_legend_handles_labels()
    half_count = len(handles) / 2
    plt.legend(labels[:half_count], handles[half_count:])
  else:
    plt.legend()

  plt.xlabel('Time')
  plt.ylabel('CPS/Absorbency')
  plt.title('CPS/Absorbency vs. Time')
  plt.savefig(filename)

def plot_raw(agged, show_errorbars, filename):
  cps = {}
  for k in agged:
    cps[k] = agged[k]['CPS_mean']
  cps = pandas.DataFrame(cps)

  absor = {}
  for k in agged:
    absor[k] = agged[k]['Abs_mean']
  absor = pandas.DataFrame(absor)
  plt.figure()

  ax = cps.plot(linestyle='-', legend=False)
  plt.ylabel('CPS')
  if show_errorbars:
    for k in agged:
      plt.errorbar(
        x=agged[k].index,
        y=agged[k]['CPS_mean'],
        yerr=agged[k]['CPS_std'],
        label = k
      )
  if show_errorbars:
    labels, handles = ax.get_legend_handles_labels()
    half_count = len(handles) / 2
    plt.legend(labels[:half_count], handles[half_count:])
  else:
    plt.legend()

  # For whatever reason, xlabel() call must be made here to take effect.
  plt.xlabel('Time')

  ax = absor.plot(secondary_y=True, ax=ax, style='--', legend=False)
  plt.ylabel('Absorbency')
  if show_errorbars:
    for k in agged:
      plt.errorbar(
        x=agged[k].index,
        y=agged[k]['Abs_mean'],
        yerr=agged[k]['Abs_std'],
        label = k
      )


  plt.title('CPS and Absorbency vs. Time')
  plt.savefig(filename)

def main():
  df = pandas.read_csv('lux.csv')
  results = {}

  # Surface mutations
  results['R162N'] = df[df['Well'].isin(make_plate_range('E', 'H', 7))]
  results['Q180K'] = df[df['Well'].isin(make_plate_range('E', 'H', 6))]
  results['V177T'] = df[df['Well'].isin(make_plate_range('E', 'H', 5))]
  results['K174G'] = df[df['Well'].isin(make_plate_range('E', 'H', 4))]
  results['T138S'] = df[df['Well'].isin(make_plate_range('E', 'H', 3))]
  results['K34G'] = df[df['Well'].isin(make_plate_range('C', 'H', 10))]
  results['K10E'] = df[df['Well'].isin(make_plate_range('E', 'H', 9))]

  # Iron coordination
  results['H9Q'] = df[df['Well'].isin(make_plate_range('A', 'E', 2))]
  results['H9A'] = df[df['Well'].isin(make_plate_range('A', 'E', 1))]
  results['Y196A'] = df[df['Well'].isin(make_plate_range('A', 'D', 8))]
  results['Y195A'] = df[df['Well'].isin(make_plate_range('A', 'D', 7))]
  results['N193L'] = df[df['Well'].isin(make_plate_range('A', 'D', 6))]
  results['N175L'] = df[df['Well'].isin(make_plate_range('A', 'D', 5))]
  results['Q58L'] = df[df['Well'].isin(make_plate_range('A', 'D', 4))]
  results['E57A'] = df[df['Well'].isin(make_plate_range('A', 'D', 3))]

  results['Wildtype'] = df[df['Well'].isin(make_plate_range('A', 'C', 9))]
  results['Blank'] = df[df['Well'].isin(make_plate_range('A', 'B', 10))]

  # WTF is this? Label on wells schematic not clear, and all mutations listed
  # for students in spreadsheet are already accounted for.
  #results['KVQ'] = df[df['Well'].isin(make_plate_range('E', 'H', 8))]

  desired_plots = {
    'Q180K_vs_H9A': ('Q180K', 'H9A', 'Wildtype', 'Blank'),
    'all': results.keys(),
  }

  for plot_name in desired_plots:
    results_to_plot = {}
    for result_name in desired_plots[plot_name]:
      results_to_plot[result_name] = results[result_name]

    agged = munge_data(results_to_plot)
    plot_normalized(agged, False, '%s_normalized.png' % plot_name)
    plot_normalized(agged, True, '%s_normalized_errorbars.png' % plot_name)
    plot_raw(agged, False, '%s_raw.png' % plot_name)
    plot_raw(agged, True, '%s_raw_errorbars.png' % plot_name)

main()

lux.csv