code: import pandas as pd
  import numpy as np
  from glob import glob
  from natsort import natsorted
  
  # TODO: from ops.constants import *
  from . import utils
  
  def load_hist(filename, threshold):
      try:
          return (pd.read_csv(filename, sep='\s+', header=None)
              .rename(columns={0: 'count', 1: 'seq'})
              .query('count > @threshold')
              .assign(fraction=lambda x: x['count']/x['count'].sum())
              .assign(log10_fraction=lambda x: np.log10(x['fraction']))
              .assign(file=filename)
             )
      except pd.errors.EmptyDataError:
          return None
  
  
  def load_sgRNA_hists(histogram_files, threshold=3):
      pat = '(?P<plate>T.)_(?P<well>(?P<row>.)(?P<col>..))_S'
      cols = ['dataset', 'plate', 'well', 'row', 'col', 
              'count', 'log10_fraction', 'fraction', 'sgRNA']
      arr = []
      for dataset, search in histogram_files.items():
          files = natsorted(glob(search))
          (pd.concat([load_hist(f, threshold) for f in files])
           .rename(columns={'seq': 'sgRNA'})
           .pipe(lambda x: pd.concat([x['file'].str.extract(pat), x], 
                                     axis=1))
           .pipe(utils.cast_cols, int_cols=['col'])
           .drop(['file'], axis=1)
           .assign(dataset=dataset)
           [cols]
           .pipe(arr.append)
           )
  
      return pd.concat(arr)
  
  def calc_stats(df_hist, df_design, extra_cols=[]):
      sample_cols = ['dataset', 'plate', 'well', 'subpool'] + extra_cols
      sizes = df_design.groupby('subpool').size()
      fractions = (df_hist
       .groupby(sample_cols)
           ['fraction'].sum()
       .apply('{0:.1%}'.format)
      )
  
      cols = {'NGS_count': 'sgRNA_detected', 
              'NGS_missing': 'sgRNA_missing', 
              'NGS_designed': 'sgRNA_designed'}
  
      final_cols = ['NGS_fraction', 'NGS_Q10', 'NGS_Q50', 'NGS_Q90', 'NGS_Q90_10',
          'NGS_mean', 'NGS_std', 'NGS_max', 'NGS_min', 'sgRNA_designed', 
          'sgRNA_detected', 'sgRNA_missing']
  
      return (df_hist
       .groupby(sample_cols)['count']
       .describe(percentiles=[0.1, 0.5, 0.9])
       .rename(columns={'10%': 'Q10', 
                        '50%': 'Q50', 
                        '90%': 'Q90'})
       .join(sizes.rename('designed'), on='subpool')
       .assign(Q90_10=lambda x: x.eval('Q90 / Q10'))
       .assign(missing=lambda x: x.eval('designed - count').astype(int))
       .pipe(utils.cast_cols, int_cols=['count', 'max', 'min'])
       .join(fractions)
       .rename(columns=lambda x: 'NGS_' + x)
       .rename(columns=cols)
       [final_cols]
       .sort_values(['dataset', 'plate', 'well', 'sgRNA_detected'],
          ascending=[True, True, True, False])
      )     
  
  
  def identify_pool(df_hist, df_design):
      cols = ['subpool', 'spots_per_oligo']
      return (df_hist
             .join(df_design.set_index('sgRNA')[cols], on='sgRNA')
             .pipe(add_design_rank, df_design)
             .sort_values(['dataset', 'plate', 'well', 'sgRNA', 'design_rank'])
             .groupby(['dataset', 'plate', 'well', 'sgRNA']).head(1)
             .sort_values(['dataset', 'plate', 'well', 'fraction'], 
                ascending=[True, True, True, False])
             .assign(mapped=lambda x: 1 - x['subpool'].isnull())
             .assign(mapped_fraction=lambda x: x.eval('fraction * mapped'))  
             )
  
  
  def add_design_rank(df_hist, df_design):
      """For one file
      """
      a = df_design.groupby('subpool').size()
      b = df_hist.groupby('subpool').size()
      ranked = (((b / a) * np.log10(a))
                .dropna().sort_values(ascending=False))
      designs = {k: v for v, k in enumerate(list(ranked.index))}
      get_design = lambda x: designs.get(x, 1e10)
      return (df_hist.assign(design_rank=lambda x: 
                             x['subpool'].apply(get_design)))
apis: pandas.read_csv numpy.log10 pandas.concat