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Given the following text description, write Python code to implement the functionality described below step by step
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Description:
 Masimo Analysis
For Pulse Ox. Analysis, make sure the data file is the right .csv format
Step2:  Import File into Python
Change File Name!
Step3:  Set Date and Time of ROP Exam and Eye Drops
Step4:  Baseline Averages
Step5:  Average q 5 Min for 1 hour after 1st Eye Drops
Step6:  Average Every 10 Sec During ROP Exam for first 4 minutes
Step7:  Average Every 5 Mins Hour 1-2 After ROP Exam
Step8:  Average Every 15 Mins Hour 2-3 After ROP Exam
Step9:  Average Every 30 Mins Hour 3-4 After ROP Exam
Step10:  Average Every Hour 4-24 Hours Post ROP Exam
Step11:  Mild, Moderate, and Severe Desaturation Events
Step12:  Export to CSV
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Python Code:
#the usual beginning
import pandas as pd
import numpy as np
from pandas import Series, DataFrame
from datetime import datetime, timedelta
from pandas import concat
#define any string with 'C' as NaN
def readD(val):
    if 'C' in val:
        return np.nan
    return val
Explanation: Masimo Analysis
For Pulse Ox. Analysis, make sure the data file is the right .csv format:
a) Headings on Row 1
b) Open the csv file through Notepad or TextEdit and delete extra 
row commas (non-printable characters)
c) There are always Dates in Column A and Time in Column B. 
d) There might be a row that says "Time Gap Present". Delete this row from Notepad 
or TextEdit
End of explanation
df = pd.read_csv('/Users/John/Dropbox/LLU/ROP/Pulse Ox/ROP007PO.csv',
            parse_dates={'timestamp': ['Date','Time']},
            index_col='timestamp',
            usecols=['Date', 'Time', 'SpO2', 'PR', 'PI', 'Exceptions'],
            na_values=['0'],
            converters={'Exceptions':  readD}
            )
#parse_dates tells the read_csv function to combine the date and time column 
#into one timestamp column and parse it as a timestamp.
#    pandas is smart enough to know how to parse a date in various formats
#index_col sets the timestamp column to be the index.
#usecols tells the read_csv function to select only the subset of the columns.
#na_values is used to turn 0 into NaN
#converters: readD is the dict that means any string with 'C' with be NaN (for PI)
#dfclean = df[27:33][df[27:33].loc[:, ['SpO2', 'PR', 'PI', 'Exceptions']].apply(pd.notnull).all(1)]
#clean the dataframe to get rid of rows that have NaN for PI purposes
df_clean = df[df.loc[:, ['PI', 'Exceptions']].apply(pd.notnull).all(1)]
Pulse ox date/time is 1 mins and 32 seconds faster than phone. Have to correct for it.
TC = timedelta(minutes=1, seconds=32)
Explanation: Import File into Python
Change File Name!
End of explanation
df_first = df.first_valid_index() #get the first number from index
Y = datetime(2015, 6, 30, 19, 2, 48)
#Y = pd.to_datetime(df_first) #convert index to datetime
# Y = TIME DATA COLLECTION BEGAN / First data point on CSV
# SYNTAX: 
# datetime(year, month, day[, hour[, minute[, second[, microsecond[,tzinfo]]]]])
W = datetime(2015, 7, 1, 8, 30)+TC
# W = first eye drop starts
X = datetime(2015, 7, 1, 9, 49)+TC
# X = ROP Exam Started
Z = datetime(2015, 7, 1, 9, 53)+TC
# Z = ROP Exam Ended
df_last = df.last_valid_index() #get the last number from index
Q = datetime(2015, 7, 1, 13, 51, 19)
#Q = pd.to_datetime(df_last) 
# Q = TIME DATA COLLECTION ENDED / Last Data point on CSV
df_clean[W:X]
Explanation: Set Date and Time of ROP Exam and Eye Drops
End of explanation
avg0PI = df_clean.PI[Y:W].mean()
avg0O2 = df.SpO2[Y:W].mean()
avg0PR = df.PR[Y:W].mean()
print 'Baseline Averages\n', 'PI :\t',avg0PI, '\nSpO2 :\t',avg0O2,'\nPR :\t',avg0PR,
#df.std() for standard deviation
str(avg0PI)
Explanation: Baseline Averages
End of explanation
# Every 5 min Average from start of eye drops to start of exam
def perdeltadrop(start, end, delta):
    rdrop = []
    curr = start
    while curr < end:
        rdrop.append(curr)
        curr += delta
    return rdrop
    
dfdropPI = df_clean.PI[W:W+timedelta(hours=1)]
dfdropO2 = df.SpO2[W:W+timedelta(hours=1)]
dfdropPR = df.PR[W:W+timedelta(hours=1)]
windrop = timedelta(minutes=5)#make the range
rdrop = perdeltadrop(W, W+timedelta(minutes=15), windrop)
avgdropPI = Series(index = rdrop, name = 'PI DurEyeD')
avgdropO2 = Series(index = rdrop, name = 'SpO2 DurEyeD')
avgdropPR = Series(index = rdrop, name = 'PR DurEyeD')
for i in rdrop:
    avgdropPI[i] = dfdropPI[i:(i+windrop)].mean()
    avgdropO2[i] = dfdropO2[i:(i+windrop)].mean()
    avgdropPR[i] = dfdropPR[i:(i+windrop)].mean()
    
resultdrops = concat([avgdropPI, avgdropO2, avgdropPR], axis=1, join='inner')
print resultdrops
Explanation: Average q 5 Min for 1 hour after 1st Eye Drops
End of explanation
#AVERAGE DURING ROP EXAM FOR FIRST FOUR MINUTES
def perdelta1(start, end, delta):
    r1 = []
    curr = start
    while curr < end:
        r1.append(curr)
        curr += delta
    return r1
df1PI = df_clean.PI[X:X+timedelta(minutes=4)]
df1O2 = df.SpO2[X:X+timedelta(minutes=4)]
df1PR = df.PR[X:X+timedelta(minutes=4)]
win1 = timedelta(seconds=10) #any unit of time & make the range
r1 = perdelta1(X, X+timedelta(minutes=4), win1)
#make the series to store
avg1PI = Series(index = r1, name = 'PI DurEx')
avg1O2 = Series(index = r1, name = 'SpO2 DurEx')
avg1PR = Series(index = r1, name = 'PR DurEX')
#average!
for i1 in r1:
    avg1PI[i1] = df1PI[i1:(i1+win1)].mean()
    avg1O2[i1] = df1O2[i1:(i1+win1)].mean()
    avg1PR[i1] = df1PR[i1:(i1+win1)].mean()
result1 = concat([avg1PI, avg1O2, avg1PR], axis=1, join='inner')
print result1
Explanation: Average Every 10 Sec During ROP Exam for first 4 minutes
End of explanation
#AVERAGE EVERY 5 MINUTES ONE HOUR AFTER ROP EXAM
def perdelta2(start, end, delta):
    r2 = []
    curr = start
    while curr < end:
        r2.append(curr)
        curr += delta
    return r2
# datetime(year, month, day, hour, etc.)
df2PI = df_clean.PI[Z:(Z+timedelta(hours=1))]
df2O2 = df.SpO2[Z:(Z+timedelta(hours=1))]
df2PR = df.PR[Z:(Z+timedelta(hours=1))]
win2 = timedelta(minutes=5) #any unit of time, make the range
r2 = perdelta2(Z, (Z+timedelta(hours=1)), win2) #define the average using function
#make the series to store
avg2PI = Series(index = r2, name = 'PI q5MinHr1')
avg2O2 = Series(index = r2, name = 'O2 q5MinHr1')
avg2PR = Series(index = r2, name = 'PR q5MinHr1')
#average!
for i2 in r2:
    avg2PI[i2] = df2PI[i2:(i2+win2)].mean()
    avg2O2[i2] = df2O2[i2:(i2+win2)].mean()
    avg2PR[i2] = df2PR[i2:(i2+win2)].mean()
result2 = concat([avg2PI, avg2O2, avg2PR], axis=1, join='inner')
print result2
Explanation: Average Every 5 Mins Hour 1-2 After ROP Exam
End of explanation
#AVERAGE EVERY 15 MINUTES TWO HOURS AFTER ROP EXAM
def perdelta3(start, end, delta):
    r3 = []
    curr = start
    while curr < end:
        r3.append(curr)
        curr += delta
    return r3
# datetime(year, month, day, hour, etc.)
df3PI = df_clean.PI[(Z+timedelta(hours=1)):(Z+timedelta(hours=2))]
df3O2 = df.SpO2[(Z+timedelta(hours=1)):(Z+timedelta(hours=2))]
df3PR = df.PR[(Z+timedelta(hours=1)):(Z+timedelta(hours=2))]
win3 = timedelta(minutes=15) #any unit of time, make the range
r3 = perdelta3((Z+timedelta(hours=1)), (Z+timedelta(hours=2)), win3)
#make the series to store
avg3PI = Series(index = r3, name = 'PI q15MinHr2')
avg3O2 = Series(index = r3, name = 'O2 q15MinHr2')
avg3PR = Series(index = r3, name = 'PR q15MinHr2')
#average!
for i3 in r3:
    avg3PI[i3] = df3PI[i3:(i3+win3)].mean()
    avg3O2[i3] = df3O2[i3:(i3+win3)].mean()
    avg3PR[i3] = df3PR[i3:(i3+win3)].mean()
    
result3 = concat([avg3PI, avg3O2, avg3PR], axis=1, join='inner')
print result3
Explanation: Average Every 15 Mins Hour 2-3 After ROP Exam
End of explanation
#AVERAGE EVERY 30 MINUTES THREE HOURS AFTER ROP EXAM
def perdelta4(start, end, delta):
    r4 = []
    curr = start
    while curr < end:
        r4.append(curr)
        curr += delta
    return r4
# datetime(year, month, day, hour, etc.)
df4PI = df_clean.PI[(Z+timedelta(hours=2)):(Z+timedelta(hours=3))]
df4O2 = df.SpO2[(Z+timedelta(hours=2)):(Z+timedelta(hours=3))]
df4PR = df.PR[(Z+timedelta(hours=2)):(Z+timedelta(hours=3))]
win4 = timedelta(minutes=30) #any unit of time, make the range
r4 = perdelta4((Z+timedelta(hours=2)), (Z+timedelta(hours=3)), win4)
#make the series to store
avg4PI = Series(index = r4, name = 'PI q30MinHr3')
avg4O2 = Series(index = r4, name = 'O2 q30MinHr3')
avg4PR = Series(index = r4, name = 'PR q30MinHr3')
#average!
for i4 in r4:
    avg4PI[i4] = df4PI[i4:(i4+win4)].mean()
    avg4O2[i4] = df4O2[i4:(i4+win4)].mean()
    avg4PR[i4] = df4PR[i4:(i4+win4)].mean()
    
result4 = concat([avg4PI, avg4O2, avg4PR], axis=1, join='inner')
print result4
Explanation: Average Every 30 Mins Hour 3-4 After ROP Exam
End of explanation
#AVERAGE EVERY 60 MINUTES 4-24 HOURS AFTER ROP EXAM
def perdelta5(start, end, delta):
    r5 = []
    curr = start
    while curr < end:
        r5.append(curr)
        curr += delta
    return r5
# datetime(year, month, day, hour, etc.)
df5PI = df_clean.PI[(Z+timedelta(hours=3)):(Z+timedelta(hours=24))]
df5O2 = df.SpO2[(Z+timedelta(hours=3)):(Z+timedelta(hours=24))]
df5PR = df.PR[(Z+timedelta(hours=3)):(Z+timedelta(hours=24))]
win5 = timedelta(minutes=60) #any unit of time, make the range
r5 = perdelta5((Z+timedelta(hours=3)), (Z+timedelta(hours=24)), win5)
#make the series to store
avg5PI = Series(index = r5, name = 'PI q60MinHr4+')
avg5O2 = Series(index = r5, name = 'O2 q60MinHr4+')
avg5PR = Series(index = r5, name = 'PR q60MinHr4+')
#average!
for i5 in r5:
    avg5PI[i5] = df5PI[i5:(i5+win5)].mean()
    avg5O2[i5] = df5O2[i5:(i5+win5)].mean()
    avg5PR[i5] = df5PR[i5:(i5+win5)].mean()
result5 = concat([avg5PI, avg5O2, avg5PR], axis=1, join='inner')
print result5
Explanation: Average Every Hour 4-24 Hours Post ROP Exam
End of explanation
df_O2_pre = df[Y:W]
#Find count of these ranges
below = 0 # v <=80
middle = 0 #v >= 81 and v<=84
above = 0 #v >=85 and v<=89
ls = []
b_dict = {}
m_dict = {}
a_dict = {}
for i, v in df_O2_pre['SpO2'].iteritems():
    
    if v <= 80: #below block
        
        if not ls: 
            ls.append(v)
        else:
            if ls[0] >= 81: #if the range before was not below 80
                if len(ls) >= 5: #if the range was greater than 10 seconds, set to 5 because data points are every 2
                    if ls[0] <= 84: #was it in the middle range?
                        m_dict[middle] = ls
                        middle += 1
                        ls = [v]
                    elif ls[0] >= 85 and ls[0] <=89: #was it in the above range?
                        a_dict[above] = ls
                        above += 1
                        ls = [v]
                else: #old list wasn't long enough to count
                    ls = [v]
            else: #if in the same range
                ls.append(v)
                
    elif v >= 81 and v<= 84: #middle block
        
        if not ls:
            ls.append(v)
        else:
            if ls[0] <= 80 or (ls[0]>=85 and ls[0]<= 89): #if not in the middle range
                if len(ls) >= 5: #if range was greater than 10 seconds
                    if ls[0] <= 80: #was it in the below range?
                        b_dict[below] = ls
                        below += 1
                        ls = [v]
                    elif ls[0] >= 85 and ls[0] <=89: #was it in the above range?
                        a_dict[above] = ls
                        above += 1
                        ls = [v]
                else: #old list wasn't long enough to count
                    ls = [v]
            else:
                ls.append(v)
    
    elif v >= 85 and v <=89: #above block
        
        if not ls:
            ls.append(v)
        else:
            if ls[0] <=84 : #if not in the above range
                if len(ls) >= 5: #if range was greater than 
                    if ls[0] <= 80: #was it in the below range?
                        b_dict[below] = ls
                        below += 1
                        ls = [v]
                    elif ls[0] >= 81 and ls[0] <=84: #was it in the middle range?
                        m_dict[middle] = ls
                        middle += 1
                        ls = [v]
                else: #old list wasn't long enough to count
                    ls = [v]
            else:
                ls.append(v)
    
    else: #v>90 or something else weird. start the list over
        ls = []
#final list check
if len(ls) >= 5:
    if ls[0] <= 80: #was it in the below range?
        b_dict[below] = ls
        below += 1
        ls = [v]
    elif ls[0] >= 81 and ls[0] <=84: #was it in the middle range?
        m_dict[middle] = ls
        middle += 1
        ls = [v]
    elif ls[0] >= 85 and ls[0] <=89: #was it in the above range?
        a_dict[above] = ls
        above += 1
        
b_len = 0.0
for key, val in b_dict.iteritems():
    b_len += len(val)
m_len = 0.0
for key, val in m_dict.iteritems():
    m_len += len(val)
    
a_len = 0.0
for key, val in a_dict.iteritems():
    a_len += len(val)
 
 
df_O2_post = df[Z:Q]
#Find count of these ranges
below2 = 0 # v <=80
middle2= 0 #v >= 81 and v<=84
above2 = 0 #v >=85 and v<=89
ls2 = []
b_dict2 = {}
m_dict2 = {}
a_dict2 = {}
for i2, v2 in df_O2_post['SpO2'].iteritems():
    
    if v2 <= 80: #below block
        
        if not ls2: 
            ls2.append(v2)
        else:
            if ls2[0] >= 81: #if the range before was not below 80
                if len(ls2) >= 5: #if the range was greater than 10 seconds, set to 5 because data points are every 2
                    if ls2[0] <= 84: #was it in the middle range?
                        m_dict2[middle2] = ls2
                        middle2 += 1
                        ls2 = [v2]
                    elif ls2[0] >= 85 and ls2[0] <=89: #was it in the above range?
                        a_dict2[above2] = ls2
                        above2 += 1
                        ls2 = [v2]
                else: #old list wasn't long enough to count
                    ls2 = [v2]
            else: #if in the same range
                ls2.append(v2)
                
    elif v2 >= 81 and v2<= 84: #middle block
        
        if not ls2:
            ls2.append(v2)
        else:
            if ls2[0] <= 80 or (ls2[0]>=85 and ls2[0]<= 89): #if not in the middle range
                if len(ls2) >= 5: #if range was greater than 10 seconds
                    if ls2[0] <= 80: #was it in the below range?
                        b_dict2[below2] = ls2
                        below2 += 1
                        ls2 = [v2]
                    elif ls2[0] >= 85 and ls2[0] <=89: #was it in the above range?
                        a_dict2[above2] = ls2
                        above2 += 1
                        ls2 = [v2]
                else: #old list wasn't long enough to count
                    ls2 = [v2]
            else:
                ls2.append(v2)
    
    elif v2 >= 85 and v2 <=89: #above block
        
        if not ls2:
            ls2.append(v2)
        else:
            if ls2[0] <=84 : #if not in the above range
                if len(ls2) >= 5: #if range was greater than 
                    if ls2[0] <= 80: #was it in the below range?
                        b_dict2[below2] = ls2
                        below2 += 1
                        ls2 = [v2]
                    elif ls2[0] >= 81 and ls2[0] <=84: #was it in the middle range?
                        m_dict2[middle2] = ls2
                        middle2 += 1
                        ls2 = [v2]
                else: #old list wasn't long enough to count
                    ls2 = [v2]
            else:
                ls2.append(v2)
    
    else: #v2>90 or something else weird. start the list over
        ls2 = []
#final list check
if len(ls2) >= 5:
    if ls2[0] <= 80: #was it in the below range?
        b_dict2[below2] = ls2
        below2 += 1
        ls2= [v2]
    elif ls2[0] >= 81 and ls2[0] <=84: #was it in the middle range?
        m_dict2[middle2] = ls2
        middle2 += 1
        ls2 = [v2]
    elif ls2[0] >= 85 and ls2[0] <=89: #was it in the above range?
        a_dict2[above2] = ls2
        above2 += 1
        
b_len2 = 0.0
for key, val2 in b_dict2.iteritems():
    b_len2 += len(val2)
m_len2 = 0.0
for key, val2 in m_dict2.iteritems():
    m_len2 += len(val2)
    
a_len2 = 0.0
for key, val2 in a_dict2.iteritems():
    a_len2 += len(val2)
#print results from count and min
print "Desat Counts for X mins\n"    
print "Pre Mild Desat (85-89) Count: %s\t" %above, "for %s min" %((a_len*2)/60.)
print "Pre Mod Desat (81-84) Count: %s\t" %middle, "for %s min" %((m_len*2)/60.) 
print "Pre Sev Desat (=< 80) Count: %s\t" %below, "for %s min\n" %((b_len*2)/60.)
print "Post Mild Desat (85-89) Count: %s\t" %above2, "for %s min" %((a_len2*2)/60.) 
print "Post Mod Desat (81-84) Count: %s\t" %middle2, "for %s min" %((m_len2*2)/60.) 
print "Post Sev Desat (=< 80) Count: %s\t" %below2, "for %s min\n" %((b_len2*2)/60.) 
print "Data Recording Time!"
print '*' * 10
print "Pre-Exam Data Recording Length\t", X - Y # start of exam - first data point
print "Post-Exam Data Recording Length\t", Q - Z #last data point - end of exam
print "Total Data Recording Length\t", Q - Y #last data point - first data point
Pre = ['Pre',(X-Y)]
Post = ['Post',(Q-Z)]
Total = ['Total',(Q-Y)]
RTL = [Pre, Post, Total]
PreMild = ['Pre Mild Desats \t',(above), 'for', (a_len*2)/60., 'mins']
PreMod = ['Pre Mod Desats \t',(middle), 'for', (m_len*2)/60., 'mins']
PreSev = ['Pre Sev Desats \t',(below), 'for', (b_len*2)/60., 'mins']
PreDesats = [PreMild, PreMod, PreSev]
PostMild = ['Post Mild Desats \t',(above2), 'for', (a_len2*2)/60., 'mins']
PostMod = ['Post Mod Desats \t',(middle2), 'for', (m_len2*2)/60., 'mins']
PostSev = ['Post Sev Desats \t',(below2), 'for', (b_len2*2)/60., 'mins']
PostDesats = [PostMild, PostMod, PostSev]
#creating a list for recording time length
#did it count check sort correctly? get rid of the ''' if you want to check your values
'''
print "Mild check"
for key, val in b_dict.iteritems():
    print all(i <=80 for i in val)
print "Moderate check"
for key, val in m_dict.iteritems():
    print all(i >= 81 and i<=84 for i in val)
    
print "Severe check"
for key, val in a_dict.iteritems():
    print all(i >= 85 and i<=89 for i in val)
'''
print Q
print Z
Explanation: Mild, Moderate, and Severe Desaturation Events
End of explanation
leest = [(avg0PI), 'PI Start']
print leest[:1]
import csv
class excel_tab(csv.excel):
        delimiter = '\t'
csv.register_dialect("excel_tab", excel_tab)
with open('test.csv', 'w') as f:    #CHANGE CSV FILE NAME, saves in same directory
    writer = csv.writer(f, dialect=excel_tab)
    #writer.writerow(['PI, O2, PR']) accidently found this out but using commas = gives me columns YAY! fix this
    #to make code look nice ok nice
    writer.writerow([leest])
Explanation: Export to CSV
End of explanation
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