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data_processing.py
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import pandas as pd
from numpy.core.numeric import NaN
import pandas as pd
from pandas.core import series
import talib as tl
from matplotlib import pyplot as plt
import numpy as np
class DataProcessing(object):
def __init__(self, data):
self.data = data
def get_datetime_from_twelve(self):
data = pd.DataFrame(self.data)
data.to_csv('batch.csv')
data = pd.read_csv('batch.csv')
print(data)
try:
t = data.datetime
except KeyError:
t = data.date
return t
def get_ticker_from_twelve(self):
data = pd.DataFrame(self.data)
print(data)
data.to_csv('batch.csv')
data0 = pd.read_csv('batch.csv')
data = data.columns
return data0[data[0]]
def real_data_to_double(self,real_data):
float_data = [float(x) for x in real_data]
np_float_data = np.array(float_data)
return np_float_data
def generate_n_lenght_list(self, n):
t = []
null = [t.append(i) for i in range(n)]
return t
def list_of_zeros(self, value):
list_of_zeroes = []
empty_container = [list_of_zeroes.append(0) for x in range(len(value) + 1)]
return list_of_zeroes, empty_container
def series_to_list(self, series):
list1 = []
for i in series:
list1.append(i)
return list1
def remove_repeats(self, list1):
list2 = []
for i in list1:
if i in list2:
pass
else:
list2.append(i)
return list2
def find_location(self, value):
i = self.data.isin([value])
c = 0
t = 0
for p in i:
c += 1
if p == True:
t = c
else:
pass
return t
def Convert(self, tup, di):
for a, b in tup:
di.setdefault(a, []).append(b)
return di
def seperate_even_location_odd_location(self):
x = self.data
even = []
odd = []
for i in range(len(x)):
if i % 2 == 1:
even.append(x[i])
else:
odd.append(x[i])
df1 = pd.DataFrame(odd)
df2 = pd.DataFrame(even)
return df1, df2
if __name__ == '__main__':
number_of_gestures = 7
gestures = { '0': 'Clench-Fist',
'1': 'Spider-Man',
'2': 'Thumb-to-pinky',
'3': 'Wrist-side-to-side-horizontal',
'4': 'Wrist-up-and-down',
'5': 'Wrist-rotate-inwards',
'6': 'Wrist-side-to-side-vertical',
'7': 'Pointer-finger'}
#import all the data
data0 = pd.read_csv(r'C:\Users\Uchek\OneDrive\Documents\Projects\learningpython\bionic_society\data_final-mark_emgData-G0.csv')
data1 = pd.read_csv(r'C:\Users\Uchek\OneDrive\Documents\Projects\learningpython\bionic_society\data_final-mark_emgData-G1.csv')
data2 = pd.read_csv(r'C:\Users\Uchek\OneDrive\Documents\Projects\learningpython\bionic_society\data_final-mark_emgData-G2.csv')
data3 = pd.read_csv(r'C:\Users\Uchek\OneDrive\Documents\Projects\learningpython\bionic_society\data_final-mark_emgData-G3.csv')
data4 = pd.read_csv(r'C:\Users\Uchek\OneDrive\Documents\Projects\learningpython\bionic_society\data_final-mark_emgData-G4.csv')
data5 = pd.read_csv(r'C:\Users\Uchek\OneDrive\Documents\Projects\learningpython\bionic_society\data_final-mark_emgData-G5.csv')
data6 = pd.read_csv(r'C:\Users\Uchek\OneDrive\Documents\Projects\learningpython\bionic_society\data_final-mark_emgData-G6.csv')
classes_data0 = []
for i in range(len(data0)):
classes_data0.append(0)
classes_data1 = []
for i in range(len(data1)):
classes_data1.append(1)
classes_data2 = []
for i in range(len(data2)):
classes_data2.append(2)
classes_data3 = []
for i in range(len(data3)):
classes_data3.append(3)
classes_data4 = []
for i in range(len(data4)):
classes_data4.append(4)
classes_data5 = []
for i in range(len(data5)):
classes_data5.append(5)
classes_data6 = []
for i in range(len(data6)):
classes_data6.append(6)
data0['Classes'] = pd.DataFrame(classes_data0)
data1['Classes'] = pd.DataFrame(classes_data1)
data2['Classes'] = pd.DataFrame(classes_data2)
data3['Classes'] = pd.DataFrame(classes_data3)
data4['Classes'] = pd.DataFrame(classes_data4)
data5['Classes'] = pd.DataFrame(classes_data5)
data6['Classes'] = pd.DataFrame(classes_data6)
big = data0.append(data1)
data = data2.append(data3)
big_data = big.append(data)
t = data4.append(data5)
g = big_data.append(t)
p = g.append(data6)
p = p.fillna(0)
p.replace('Repeat 1', 0, inplace=True)
p.replace('Repeat 2', 0, inplace=True)
p.replace('Repeat 3', 0, inplace=True)
p.replace('Repeat 4', 0, inplace=True)
p.replace('Repeat 5', 0, inplace=True)
p.replace('Repeat 6', 0, inplace=True)
p.replace('Repeat 7', 0, inplace=True)
p.replace('Repeat 8', 0, inplace=True)
p.replace('Repeat 9', 0, inplace=True)
p.replace('Repeat 10', 0, inplace=True)
p.replace('Repeat 11', 0, inplace=True)
p.replace('Repeat 12', 0, inplace=True)
p.replace('Repeat 13', 0, inplace=True)
print(p)
#p.to_excel('clean_data_mark.xlsx')