# -*- coding: utf-8 -*-
"""
Created on Tue Jul  7 18:40:37 CEST 2015

@author: Elena Cuoco
simple starting script, without the use of MNE
Thanks to @author: alexandrebarachant for his wornderful starting script


"""

import numpy as np
import pandas as pd
from scipy.signal import butter, lfilter, boxcar
from numpy import convolve
from sklearn.linear_model import LogisticRegression
from glob import glob
import os
from sklearn.lda import LDA
from sklearn.preprocessing import StandardScaler
from sklearn.qda import QDA

 
#############function to read data###########

def prepare_data_train(fname):
    """ read and prepare training data """
    # Read data
    data = pd.read_csv(fname)
    # events file
    events_fname = fname.replace('_data','_events')
    # read event file
    labels= pd.read_csv(events_fname)
    clean=data.drop(['id' ], axis=1)#remove id
    labels=labels.drop(['id' ], axis=1)#remove id
    return  clean,labels

def butterworth_filter(X,t,k,l):
    if t==0:
        freq=[k, l]
        b,a = butter(5,np.array(freq)/300.0,btype='bandpass')
        X = lfilter(b,a,X)
    elif t==1:
        b,a = butter(5,k/300.0,btype='lowpass')
        X = lfilter(b,a,X)
    elif t==2:
        b,a = butter(5,l/300.0,btype='highpass')
        X = lfilter(b,a,X)      
    return X

def prepare_data_test(fname):
    """ read and prepare test data """
    # Read data
    data = pd.read_csv(fname)
    return data

scaler= StandardScaler()
def data_preprocess_train(X):
    X_prep_normal = scaler.fit_transform(X)
    X_prep_low = np.zeros((np.shape(X_prep_normal)[0],10))
    for i in range(10):
        X_prep_low[:,i] = butterworth_filter(X[:,0],1,2-(i*0.2),3)
        X_prep_low[:,i] = scaler.fit_transform(X_prep_low[:,i])
    X_prep_low_pow = X_prep_low ** 2
    X_prep = np.concatenate((X_prep_low,X_prep_normal,X_prep_low_pow),axis=1)
    #do here your preprocessing
    return X_prep

def data_preprocess_test(X):
    X_prep_normal = scaler.fit_transform(X)
    X_prep_low = np.zeros((np.shape(X_prep_normal)[0],10))
    for i in range(10):
        X_prep_low[:,i] = butterworth_filter(X[:,0],1,2-(i*0.2),3)
        X_prep_low[:,i] = scaler.fit_transform(X_prep_low[:,i])
    X_prep_low_pow = X_prep_low ** 2
    X_prep = np.concatenate((X_prep_low,X_prep_normal,X_prep_low_pow),axis=1)
    return X_prep

# training subsample.if you want to downsample the training data
subsample  = 150
subsample2 = 200
#######columns name for labels#############
cols = ['HandStart','FirstDigitTouch',
        'BothStartLoadPhase','LiftOff',
        'Replace','BothReleased']




#######number of subjects###############
subjects = range(1,13)
ids_tot = []
pred_tot = []

###loop on subjects and 8 series for train data + 2 series for test data
for subject in subjects:
    y_raw= []
    raw = []
    ################ READ DATA ################################################
    fnames =  glob('../input/train/subj%d_series*_data.csv' % (subject))
    for fname in fnames:
      data,labels=prepare_data_train(fname)
      raw.append(data)
      y_raw.append(labels)

    X = pd.concat(raw)
    y = pd.concat(y_raw)
    #transform in numpy array
    #transform train data in numpy array
    X_train =np.asarray(X.astype(float))
    y = np.asarray(y.astype(float))


    ################ Read test data #####################################
    #
    fnames =  glob('../input/test/subj%d_series*_data.csv' % (subject))
    test = []
    idx=[]
    for fname in fnames:
      data=prepare_data_test(fname)
      test.append(data)
      idx.append(np.array(data['id']))
    X_test= pd.concat(test)
    ids=np.concatenate(idx)
    ids_tot.append(ids)
    X_test=X_test.drop(['id' ], axis=1)#remove id
    #transform test data in numpy array
    X_test =np.asarray(X_test.astype(float))


    ################ Train classifiers ########################################
    lr1 = LDA()
    lr2=LogisticRegression()
    lr3 = LDA()
    
    pred1 = np.empty((X_test.shape[0],6))
    pred2 = np.empty((X_test.shape[0],6))
    pred3 = np.empty((X_test.shape[0],6))

    pred = np.empty((X_test.shape[0],6))
    
    X_train=data_preprocess_train(X_train)
    X_test=data_preprocess_test(X_test)
    for i in range(6):
        y_train= y[:,i]
        print('Train subject %d, class %s' % (subject, cols[i]))
        lr1.fit(X_train[::subsample,:],y_train[::subsample])
        lr2.fit(X_train[::subsample,:],y_train[::subsample])
        lr3.fit(X_train[::subsample2,:],y_train[::subsample2])
        pred1[:,i] = lr1.predict_proba(X_test)[:,1]
        pred2[:,i] = lr2.predict_proba(X_test)[:,1]
        pred3[:,i] = lr3.predict_proba(X_test)[:,1]
        pred[:,i]=0.4*pred1[:,i]+0.2*pred2[:,i]+0.4*pred3[:,i]

    pred_tot.append(pred)

# submission file
submission_file = 'grasp-sub-simple.csv'
# create pandas object for sbmission
submission = pd.DataFrame(index=np.concatenate(ids_tot),
                          columns=cols,
                          data=np.concatenate(pred_tot))

# write file
submission.to_csv(submission_file,index_label='id',float_format='%.3f')