motus/resampling_monthly.py
ecdc9d54a5d9master

resampling_monthly.py
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			#!/usr/bin/env python
			# coding: utf-8

			# In[2]:


			from py7zr import unpack_7zarchive
			import shutil
			import os
			import pandas as pd
			import numpy as np
			from IPython.core import display as ICD
			import glob
			from os import listdir

			TEMP = r'data/5'
			temp_files=os.listdir(TEMP)

			print(temp_files)

			wind_motus=pd.DataFrame()

			months=os.listdir(TEMP)
			print(months)
			for files in months:
			df_temp=pd.read_csv(TEMP+'/'+files, header=None, comment=',', error_bad_lines=False, sep=',',keep_default_na=False,na_values=[' '],names = ["Q", "Dir", "U", "w", "M", "sonicsp", "sonictemp","Time","COM"],low_memory=False)
			wind_motus=wind_motus.append(df_temp)
			df_temp=[]

			print('Done reading month 5')

			wind_motus_test=wind_motus
			wind_motus_test=wind_motus_test[wind_motus_test.COM == 'COM14'] #removing bad lines

			formatmotus = '%d.%m.%Y %H:%M:%S'

			#Setting index and datetimes
			wind_motus_test['Time']=pd.to_datetime(wind_motus_test['Time'], format=formatmotus)
			wind_motus_test=wind_motus_test.set_index('Time')

			wind_motus_test['U']=wind_motus_test['U'].apply(pd.to_numeric)
			wind_motus_resam=pd.DataFrame()
			wind_motus_resam['U']=wind_motus_test['U'].resample('5min').mean()

			wind_motus_resam.to_csv('resampled_motus_5_2019.csv')

			print('finished averaging Month 5')

			wind_motus=pd.DataFrame()

			print('Done with data')