startDateTuple = xlrd.xldate.xldate_as_tuple(r[4].value,workbook.datemode)
endDateTuple = xlrd.xldate.xldate_as_tuple(r[5].value,workbook.datemode)
timeZone = r[6].value
sensorType = r[7].value
condition = r[8].value
climate = r[9].value
coord = (r[10].value,r[11].value)
PERdict[serial] = [site,location,project,startDateTuple,endDateTuple,
timeZone,sensorType,condition,climate,coord]
except Exception as e:
print e
raise Exception("Cannot find/open 'PER Logger Data.xlsx' in the parent directory, or the file format is faulty")
#Create madgetech objects from all valid .csv files in folder. create a dictionary of these objects with serial as key
#eliminate sensors that do not have a listed gps coordinate and csv files that are not for TOW sensors.
mts = mt2.mt2folder(r'..\2 TOW Sensor Analysis\_raw')
mtdict = dict([(mt.serial,mt) for mt in mts if PERdict[mt.serial][9]!=('','') and PERdict[mt.serial][6]=='TOW'])
keys = sorted(mtdict.keys(),key = lambda x:PERdict[x][0])
#create lists for the cross site Q4 plots
overallPlotT = []
overallQ4inc = []
overallQ4time = []
#create TOW data processor
TOWproc = TOWprocessor()
with open('TOW bucketing.csv','wb') as csvfile:
csvwrite = csv.writer(csvfile,delimiter = ',')
csvwrite.writerow(['Site','Location','minimun Voltage','maximum Voltage','Readings','Mean','Q1','Q2+Q3','Q4']+['Bucket ' + str(n) for n in range(bucketCount)])
for key in keys:
import numpy as np
from matplotlib.ticker import MaxNLocator
from pylab import get_cmap
from madgetech import madgetech_2 as mt2
from madgetech.madgetech_2_rht import rhtProcessor
from perdb import perdb
from math import sqrt
try:
#load meta data into a dictionary
PERdict = perdb.loadPERdict()
invPERdict = perdb.inversePERdict()
#Create madgetech objects from all valid .csv files in folder. create a dictionary of these objects with serial as key
#eliminate sensors that do not have a listed gps coordinate and csv files that are not for corrosion sensors.
mts = mt2.mt2folder(r'..\3 RHT and TC Sensor Analysis\_raw')
mtdict = dict([(mt.serial,mt) for mt in mts if PERdict[mt.serial][6]=='RHT'])
keys = sorted(mtdict.keys())
#create rht data processor
rhtproc = rhtProcessor()
tempAggregates = []
rhAggregates = []
for key in keys:
mt = mtdict[key]
mt.loaddata()
secondInterval = mt.timeinterval.total_seconds()
site,location,project,startDateTuple,endDateTuple,timeZone,sensorType,condition,climate,coord = PERdict[key]
condition = r[8].value
climate = r[9].value
coord = (r[10].value, r[11].value)
PERdict[serial] = [
site, location, project, startDateTuple, endDateTuple,
timeZone, sensorType, condition, climate, coord
]
except Exception as e:
print e
raise Exception(
"Cannot find/open 'PER Logger Data.xlsx' in the parent directory, or the file format is faulty"
)
#Create madgetech objects from all valid .csv files in folder. create a dictionary of these objects with serial as key
#eliminate sensors that do not have a listed gps coordinate and csv files that are not for TOW sensors.
mts = mt2.mt2folder(r'..\2 TOW Sensor Analysis\_raw')
mtdict = dict([
(mt.serial, mt) for mt in mts
if PERdict[mt.serial][9] != ('', '') and PERdict[mt.serial][6] == 'TOW'
])
keys = sorted(mtdict.keys(), key=lambda x: PERdict[x][0])
#create lists for the cross site Q4 plots
overallPlotT = []
overallQ4inc = []
overallQ4time = []
#create TOW data processor
TOWproc = TOWprocessor()
with open('TOW bucketing.csv', 'wb') as csvfile:
import matplotlib.pyplot as plt
from matplotlib.ticker import MaxNLocator
from pylab import get_cmap
from googleEarth import plotGoogleColorMap as pltGE
from perdb import perdb
from madgetech import madgetech_2 as mt2
from madgetech.madgetech_2_cs import CorrosionSensorProcessor
try:
#load meta data into a dictionary
PERdict = perdb.loadPERdict()
invPERdict = perdb.inversePERdict()
#Create madgetech objects from all valid .csv files in folder. create a dictionary of these objects with serial as key
#eliminate sensors that do not have a listed gps coordinate and csv files that are not for corrosion sensors.
mts = mt2.mt2folder(r'..\1 Corrosion Sensor Analysis\_raw')
mtdict = dict([(mt.serial,mt) for mt in mts if PERdict[mt.serial][9]!=('','') and PERdict[mt.serial][6]=='CS'])
keys = sorted(mtdict.keys())
#create lists for the cross site cumulative corrosion index and the Google Earth color plot
overallPlotT=[]
overallPlotY = []
googleEarthList = []
#create a corrosion sensor data processing object (Contains functions for processing corrosion sensor data)
csProc = CorrosionSensorProcessor(useDefaultInput = True)
#Iterate through serial numbers
for key in keys:
try:
#load data from the mt object and truncate data to the dates listed in the PER meta data excel spreadsheet
import numpy as np
from matplotlib.ticker import MaxNLocator
from pylab import get_cmap
from madgetech import madgetech_2 as mt2
from madgetech.madgetech_2_rht import rhtProcessor
from perdb import perdb
from math import sqrt
try:
#load meta data into a dictionary
PERdict = perdb.loadPERdict()
invPERdict = perdb.inversePERdict()
#Create madgetech objects from all valid .csv files in folder. create a dictionary of these objects with serial as key
#eliminate sensors that do not have a listed gps coordinate and csv files that are not for corrosion sensors.
mts = mt2.mt2folder(r'..\3 RHT and TC Sensor Analysis\_raw')
mtdict = dict([(mt.serial, mt) for mt in mts
if PERdict[mt.serial][6] == 'RHT'])
keys = sorted(mtdict.keys())
#create rht data processor
rhtproc = rhtProcessor()
tempAggregates = []
rhAggregates = []
for key in keys:
mt = mtdict[key]
mt.loaddata()
secondInterval = mt.timeinterval.total_seconds()
site, location, project, startDateTuple, endDateTuple, timeZone, sensorType, condition, climate, coord = PERdict[