In [17]:
import pandas as pd
import numpy as np
time_zero = 0
filename = input("Filename? ")
df =pd.read_csv(filename, header = 2)
array_num = int(input("Did you use 1 array or 2?"))
if array_num== 1:
columns = np.arange(0,32)
df.columns = columns
if array_num == 2:
columns = np.arange(0,64)
df.columns = columns
print(df)
In [18]:
#determine the zero time point and subtract it from the entire column
time_zero = float(input("Which time would you like to be set to 0? (Must be a valid time)"))
df[0]= df[0]-time_zero
In [19]:
#Where is the index value for the zero location?
zero_location_series = df[df[0] == min(filter(lambda x:x>0, df[0]))]
if array_num ==2:#using that index, divide each row by the value at that index to normalize the data to 1
r1 = zero_location_series[2]
df["Res1"] = df[2].apply(lambda x: x/r1, 0)
df["Res1"]
r2 = zero_location_series[6]
df["Res2"] = df[6].apply(lambda x: x/r2, 0)
r3 = zero_location_series[10]
df["Res3"] = df[10].apply(lambda x: x/r3, 0)
r4 = zero_location_series[14]
df["Res4"] = df[14].apply(lambda x: x/r4, 0)
r5 = zero_location_series[18]
df["Res5"] = df[18].apply(lambda x: x/r5, 0)
r6 = zero_location_series[22]
df["Res6"] = df[22].apply(lambda x: x/r6, 0)
r7 = zero_location_series[26]
df["Res7"] = df[26].apply(lambda x: x/r7, 0)
r8 = zero_location_series[30]
df["Res8"] = df[30].apply(lambda x: x/r8, 0)
r9 = zero_location_series[34]
df["Res9"] = df[34].apply(lambda x: x/r9, 0)
r10 = zero_location_series[38]
df["Res10"] = df[38].apply(lambda x: x/r10, 0)
r11 = zero_location_series[42]
df["Res11"] = df[42].apply(lambda x: x/r11, 0)
r12 = zero_location_series[46]
df["Res12"] = df[46].apply(lambda x: x/r12, 0)
r13 = zero_location_series[50]
df["Res13"] = df[50].apply(lambda x: x/r13, 0)
r14 = zero_location_series[54]
df["Res14"] = df[54].apply(lambda x: x/r14, 0)
r15 = zero_location_series[58]
df["Res15"] = df[58].apply(lambda x: x/r15, 0)
r16 = zero_location_series[62]
df["Res16"] = df[62].apply(lambda x: x/r16, 0)
if array_num ==1:
r1 = zero_location_series[2]
df["Res1"] = df[2].apply(lambda x: x/r1, 0)
df["Res1"]
r2 = zero_location_series[6]
df["Res2"] = df[6].apply(lambda x: x/r2, 0)
r3 = zero_location_series[10]
df["Res3"] = df[10].apply(lambda x: x/r3, 0)
r4 = zero_location_series[14]
df["Res4"] = df[14].apply(lambda x: x/r4, 0)
r5 = zero_location_series[18]
df["Res5"] = df[18].apply(lambda x: x/r5, 0)
r6 = zero_location_series[22]
df["Res6"] = df[22].apply(lambda x: x/r6, 0)
r7 = zero_location_series[26]
df["Res7"] = df[26].apply(lambda x: x/r7, 0)
r8 = zero_location_series[30]
df["Res8"] = df[30].apply(lambda x: x/r8, 0)
In [20]:
#get rid of all the extraneous data we don't need now
if array_num ==2:
dropcolumns = np.arange(1,64)
if array_num==1:
dropcolumns = np.arange(1,32)
df.drop(dropcolumns, axis = 1, inplace = True)
In [21]:
#output the data to a filename - can use excel and probably should in the future who likes csv files anyway
output = input("Filename to export to? ")
df.to_csv(output)
In [ ]: