import csv
import numpy as np





# Avaiable Variable:
#1) no_rep = number of repatitions for each position
#2) no_of_pos = the number of positions in the scan
#3) headers() = the headers in the file



#Opens and reads the file.
filename = "C:/Users/Tim/Documents/Phd/rhul_thz_from_Raspberry/data/2015_09_18/20150918_123033_scan.csv"
data_file = open(filename, "rb")
data = csv.reader(data_file, delimiter = '\t')


################################################################## Arrays ##############################################################################

mir_pos = []
chan_1 = []
chan_2 = []
chan_1_area_norm = []
chan_2_mean = []
mean_amplitude_for_position_chan_1 =[]

################################################################### Functions ########################################################################


# Prints the headers in the file.
def headers():
	for rows in data:
		header_1 = rows[0]
		print header_1
# Prints out the number of position in the mirror
def no_of_pos_mir():
	del mir_pos[0]
	no_of_pos = len(mir_pos)
	return no_of_pos
# A genera function to return the number of position/elements in a list
def no_of_pos(axis):
	no_of_pos_1 = len(axis)
	return no_of_pos_1
#Coverts the list of lists for each channe to seperate np array for each positions.
def convert_list_to_array_of_float(chan,i):
	chan[i] = chan[i].split(",")[1:-1] #removes problem with [ attachd for first and last entry
	chan[i] = map(float,chan[i]) # converts every elemenet in the list to a float
	chan[i] = np.array(chan[i]) # Converts each list in the total chan_1 list to a seperate np array
	return chan[i]
# Calculates the mean for a givem channel and position index
def mean_calc(chan,i):
	chan_avg_i = np.mean(chan[i])
	return chan_avg_i
# Finds the data for channels 1 and 2.
def channel_data_to_list():
	for thing in data:
		header = thing[0]
		        # Creates an array of ALL channel_1 data
		if header == "CH1_DAT":
                	chan_1.append(thing[1])
        # Creates an array of All Channe_2 data
	        if header == "CH2_DAT":
        	        chan_2.append(thing[1])
		if header == "INT_MIR":
                	mir_pos.append(thing[1])


##################################################### Code ##################################################################################

channel_data_to_list()  # files the lists with the data from the respective channels.
print len(chan_1)
		
# converts each list to an array
a = np.arange(len(chan_1))
for i in a:
	convert_list_to_array_of_float(chan_1,i)

	convert_list_to_array_of_float(chan_2,i)




# calculates the normaised area for each chan_1 repetision and position
b = np.arange(len(chan_1))
for j in b:
	chan_1_avg_i = np.trapz(chan_1[i], dx=1.0/len(chan_1[0])/np.trapz(chan_2[i], dx=1.0/len(chan_1[i])))
	chan_1_area_norm.append(chan_1_avg_i)

# Removes the extra vaue at the beginning
mir_pos = mir_pos[1:]
print mir_pos




print chan_1_mean


#values = 0 
#for values in chan_1_mean
	


































#for rows in data:
#	header = rows[0]
# Defines the numer of repatitions at each position
#	if header == "SCN_REP":
#		no_rep = rows[1]
# Creates an array of mirror positions
#	if header == "INT_MIR":
#		mir_pos.append(rows[1])
# Creates an array of ALL channel_1 data
#	if header == "CH1_DAT":
#		chan_1.append(rows[1])
# Creates an array of All Channe_2 data
#	if header == "CH2_DAT":
#		chan_2.append(rows[1])
	


#irint no_rep

# Fixes problem with extra value at start of mirror position.

#a = np.arange(len(chan_1))

#for i in a:
	#chan_1[i] = chan_1[i].split(",")[1:len(chan_1[i]-1)]

#print chan_1[0], len(chan_1[0])

# len(chan_1[0].split(','))

#for i in a:
#	chan_1[i] = chan_1[i].split(",")[1:-1] #removes problem with [ attachd for first and last entry
#	chan_1[i] = map(float,chan_1[i]) # converts every elemenet in the list to a float
#	chan_1[i] = np.array(chan_1[i])

#chan_1_fl = map(float,chan_1[0])
#a = np.array(chan_1_fl)
#print a
#print np.mean(chan_1[0].split(","))