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Designs/Measuring_instruments/ISMS01A/SW/average.py
Nový soubor

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#!/usr/bin/python
import pandas as pd
import sys
import os
import time
import datetime
from mlabutils import ejson
parser = ejson.Parser()
#### Script Arguments ###############################################
if len(sys.argv) != 2:
sys.stderr.write("Invalid number of arguments.\n")
sys.stderr.write("Usage: %s CONFIG_FILE\n" % (sys.argv[0], ))
sys.exit(1)
value = parser.parse_file(sys.argv[1])
dataSource = value['data_path'] # raw data
dataArchive = value['data_archive'] # archive for row data
dataUpload = value['data_upload'] # computed mean values for upload
stationName = value['origin']
loop = 1
csvHeader = "Date;LevelMeter;Temperature1;Conductivity;Salinity;TDSKcl;Temperature2;pH;Redox" #csv header
sleepTime = 1000 # sleep time in seconds
while True:
try:
print("Start")
## Create sorted list of csv files
listOfDataFiles = list() #empty list
listOfSpecDataFiles = list() #empty list
files = list() #empty list
flag = False # is computation needed
files = sorted(os.listdir(dataSource)) # list of all files and folders in directory
for idx, val in enumerate(files): #goes through files
if val.endswith("data.csv"): # in case of *data.csv
listOfDataFiles.append(val) #add file to listOfFiles
## Find the newest and oldest and compare them. If they are from different day, compute the average of all measurement from oldest day
if len(listOfDataFiles)>=2: # if there are more than 2 data files
first = listOfDataFiles[0] # get first of them
last = listOfDataFiles[-1] # get last of them
if time.mktime(datetime.datetime.strptime(last[:8], "%Y%m%d").timetuple()) > time.mktime(datetime.datetime.strptime(first[:8], "%Y%m%d").timetuple()): # if the last is older than first
flag = True # computation needed
print("Computing...")
print(loop)
loop +=1
listOfSpecDataFiles = list() # empty list
for file in listOfDataFiles: # go through data files and create lis of data files measured on same day
# if the day is same like the first one
if time.mktime(datetime.datetime.strptime(first[:8], "%Y%m%d").timetuple()) == time.mktime(datetime.datetime.strptime(file[:8], "%Y%m%d").timetuple()):
listOfSpecDataFiles.append(file)
filename = dataUpload + first[:8]+'000000_' + stationName + '_data_mean.csv'
for file in listOfSpecDataFiles:
df=pd.read_csv(dataSource + file, sep=';', header=None) # read current csv
dim=df.shape # gets data file dimensions
rowsInd=dim[0] # maximal index of rows
columnsInd=dim[1] # maximal index of columns
values=pd.DataFrame() # empty DataFrame
for x in range(0,columnsInd): # for each column
values = values.set_value(0,x,round(df[x].mean(),3),0) #calculates mean value for all cloumns and round it by 3
outfile = open(filename, 'a')
values.to_csv(filename, sep=';', header=False, index=False, mode='a') # save (add) DataFrame to csv
outfile.close()
#adding header
with open(filename, 'r+') as f:
content = f.read()
f.seek(0, 0)
f.write(csvHeader.rstrip('\r\n') + '\n' + content)
# move files to archive structure
for file in listOfSpecDataFiles:
year = file[:4]
month = file[4:6]
day = file[6:8]
directory = dataArchive + year + "/" + month + "/" + day + "/"
if not os.path.exists(directory):
os.makedirs(directory)
os.rename(dataSource + file, directory + file) # move file
else:
flag = False # computation is not needed
else:
flag = False # computation is not needed
if flag == False:
time.sleep(sleepTime) #long sleep, because is nothing to process
except ValueError:
print ValueError
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