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KnockdownFeatures_class.py
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#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Thu Feb 7 11:19:25 2019
@author: max
"""
import os
import re
import functools
import pandas as pd
import numpy as np
class KnockdownFeatures:
'''
class that holds all the features of one knockdown
Usage: Initialize with the path to the experiment and a list of the folder names
you want to search for features
either use find_csv() and then load_features(feature) to load an
individual feature, or use load_all() to load all features.
'''
def __init__(self, kd_path, KD):
self.kd_path=kd_path
self.KD=KD
self.KD_pattern=re.compile('({}{}[0-9]+)'.format(self.KD, os.sep))
#use this if you have uniform path architecture, but different group namings
#self.experiment_identifier=kd_path.split(os.sep)[-2]
#use this if you have different path architectures in your folders
self.Experiment_pattern=re.compile('SiRNA_[0-9]+')
self.experiment_identifier=re.search(self.Experiment_pattern, self.kd_path).group()
def info(self):
'''
prints info about this instance of the class
'''
print('experiment folder: \n', self.kd_path)
print('experiment name: \n', self.experiment_identifier)
print('Knockdown: \n', self.KD)
def find_csv(self):
'''
returns a list with all csv files in the KD folder
'''
#pattern: must end with '.csv'
csv_find=re.compile('\.csv$')
#finds the directory with that specific name
find_dir='GCAFeatureExtraction'
self.i_dirs=[]
#Knockdown pattern, must match inserted variable self.__KD followed by '/'
#and one or more digits
for root, dirs, files in os.walk(self.kd_path):
#looks in each folder from given path
#if folder is matching the KD pattern, the find_dir pattern and
#if there are csv files
if re.search(self.KD_pattern, root) and find_dir in root and len([x for x in files if re.search(csv_find, x)])!=0:
#finds the csv files in the folder and adds them to a list
csv_files=[x for x in files if re.search(csv_find, x)]
for csv in csv_files:
#each csv file is added to the path and appended to a list
self.i_dirs.append(os.path.join(root, csv))
return self.i_dirs
def get_features(self):
'''
creates a list of all the feature names
calls find_csv to get i_dirs variable
for interactive feature selection, change here!
'''
#pattern: must start with a '/' followed by characters that are not '/'
#and end with '.csv'
self.features=[]
csv_pattern=re.compile('[{}][^{}]+\.csv$'.format(os.sep, os.sep))
self.i_dirs=self.find_csv()
for file in self.i_dirs:
#making tuples for what to replace with what
repls = ('.csv', ''), (os.sep, '')
filename=re.search(csv_pattern, file).group()
#applies repls to replace name parts
filename=functools.reduce(lambda a, kv:a.replace(*kv), repls, filename)
if filename not in self.features:
self.features.append(filename)
return self.features
def load_feature(self, feature):
'''
loads all csvs of a single feature
needs to be called by load_all
'''
GC_list=[]
time_pattern=re.compile('(n[0-9]+)')
for file in self.i_dirs:
if feature in file:
identifier=re.search(self.KD_pattern, file).group()
try:
temp=pd.read_csv(file, header=None)
except:
break
rows, columns=temp.shape
num_identifier=[]
#creates a list of identfiers to match the columns
for i in range(0, columns):
num_identifier.append(self.experiment_identifier+'/'+identifier+'n'+str(i))
#renames columns with identifier
temp.columns=num_identifier
#adding each loaded file to a list
GC_list.append(temp)
#concatonating the list to a dataframe
full_feature = pd.concat(GC_list, axis=1, sort=True)
#creating an index for melting
rows, columns=full_feature.shape
full_feature['meltid']= range(0, rows)
#melting to long format
long_feature=pd.melt(full_feature, id_vars='meltid')
#dropping NAs
long_feature=long_feature.dropna()
long_feature=long_feature.reset_index(drop=True)
#dropping all values that cannot be converted to a float
for n, i in enumerate(long_feature['value']):
try:
long_feature.loc[n, 'value']=float(i)
except ValueError:
long_feature=long_feature.drop(n)
#reset index again
long_feature=long_feature.reset_index(drop=True)
long_feature['experiment']=self.experiment_identifier
long_feature['KD']=self.KD
long_feature['value']=long_feature['value'].astype('float')
long_feature['item']=long_feature['variable'].str.extract(self.KD_pattern)
long_feature['timepoint']=long_feature['variable'].str.extract(time_pattern)
return long_feature
# =============================================================================
# long_feature=long_feature.reset_index(drop=True)
# long_feature['experiment']=self.experiment_identifier
# long_feature['KD']=self.KD
# long_feature['item']='placeholder'
# long_feature['timepoint']='placeholder'
# long_feature['value']=long_feature['value'].astype('float')
# for n, var in enumerate(long_feature['variable']):
# long_feature.loc[[n],['item']]=re.search(self.KD_pattern, var).group()
# long_feature.loc[[n],['timepoint']]=re.search(time_pattern, var).group()
# return long_feature
# =============================================================================
def load_all(self):
'''
loops over load_feature for each feature
calls get_features to get the features and i_dirs
'''
self.get_features()
self.all_features={}
for i in self.features:
#for each element in feature the load_feature function is called
#and its output is added to a dictionary with the feature as a key
feature=self.load_feature(i)
self.all_features.update({i:feature})
return self.all_features