start2013, stop2013 = datetime.datetime(2013,1,1,0,0), datetime.datetime(2013,12,31,11,59) start2014, stop2014 = datetime.datetime(2014,1,1,0,0), datetime.datetime(2014,12,31,11,59) PT1 = PT1.reindex(pd.date_range(start2012,stop2014,freq='15Min')) PT3 = PT3.reindex(pd.date_range(start2012,stop2014,freq='15Min')) Fagaalu_stage_data = Fagaalu_stage_data.reindex(pd.date_range(start2012,stop2014,freq='15Min')) #### Import T and SSC Data from load_from_MASTER_XL import TS3000,YSI,OBS,loadTSS ## Turbidimeter Data DAM DAM_TS3K = TS3000(XL,'DAM-TS3K') DAM_YSI = YSI(XL,'DAM-YSI') ## Turbidimeter Data LBJ LBJ_YSI = YSI(XL,'LBJ-YSI') LBJ_OBSa = OBS(XL,'LBJ-OBSa').truncate(after=dt.datetime(2013,4,1)) LBJ_OBSb = OBS(XL,'LBJ-OBSb') LBJ_OBSa=LBJ_OBSa.rename(columns={'Turb_SS_Avg':'NTU'}) LBJ_OBSb=LBJ_OBSb.rename(columns={'Turb_SS_Mean':'NTU'}) LBJ_OBS=LBJ_OBSa.append(LBJ_OBSb) ## TSS Data, equivalent to SSC but I don't want to change all the code and file names TSSXL = pd.ExcelFile(datadir+'SSC/TSS_grab_samples.xlsx') TSS = loadTSS(TSSXL,'ALL_MASTER') TSS= TSS[TSS['TSS (mg/L)']>0] #### Import NUTRIENT Data from load_from_MASTER_XL import loadNUTES1,loadNUTES2and3 ##NUTRIENTS1 (first field season) NUTESXL = pd.ExcelFile(datadir+'NUTRIENTS/NUTRIENTS1.xlsx')
start2014, stop2014 = datetime.datetime(2014, 1, 1, 0, 0), datetime.datetime( 2014, 12, 31, 11, 59) PT1 = PT1.reindex(pd.date_range(start2012, stop2014, freq='15Min')) PT3 = PT3.reindex(pd.date_range(start2012, stop2014, freq='15Min')) Fagaalu_stage_data = Fagaalu_stage_data.reindex( pd.date_range(start2012, stop2014, freq='15Min')) #### Import T and SSC Data from load_from_MASTER_XL import TS3000, YSI, OBS, loadTSS ## Turbidimeter Data DAM DAM_TS3K = TS3000(XL, 'DAM-TS3K') DAM_YSI = YSI(XL, 'DAM-YSI') ## Turbidimeter Data LBJ LBJ_YSI = YSI(XL, 'LBJ-YSI') LBJ_OBSa = OBS(XL, 'LBJ-OBSa').truncate(after=dt.datetime(2013, 4, 1)) LBJ_OBSb = OBS(XL, 'LBJ-OBSb') LBJ_OBSa = LBJ_OBSa.rename(columns={'Turb_SS_Avg': 'NTU'}) LBJ_OBSb = LBJ_OBSb.rename(columns={'Turb_SS_Mean': 'NTU'}) LBJ_OBS = LBJ_OBSa.append(LBJ_OBSb) ## TSS Data, equivalent to SSC but I don't want to change all the code and file names TSSXL = pd.ExcelFile(datadir + 'SSC/TSS_grab_samples.xlsx') TSS = loadTSS(TSSXL, 'ALL_MASTER') TSS = TSS[TSS['TSS (mg/L)'] > 0] #### Import NUTRIENT Data from load_from_MASTER_XL import loadNUTES1, loadNUTES2and3 ##NUTRIENTS1 (first field season) NUTESXL = pd.ExcelFile(datadir + 'NUTRIENTS/NUTRIENTS1.xlsx') NUTES1 = loadNUTES1(NUTESXL, 'Data')
## STAGE DATA FOR PT's stage_data = pd.DataFrame({'N1':PT1['stage'],'N2':PT2['stage']}) ## Year Interval Times start2012, stop2012 = dt.datetime(2012,1,7,0,0), dt.datetime(2012,12,31,11,59) start2013, stop2013 = dt.datetime(2013,1,1,0,0), dt.datetime(2013,12,31,11,59) start2014, stop2014 = dt.datetime(2014,1,1,0,0), dt.datetime(2014,12,31,11,59) PT1 = PT1.reindex(pd.date_range(start2012,stop2014,freq='15Min')) PT2 = PT2.reindex(pd.date_range(start2012,stop2014,freq='15Min')) stage_data = stage_data.reindex(pd.date_range(start2012,stop2014,freq='15Min')) #### Import T and SSC Data from load_from_MASTER_XL import OBS,loadTSS N1_OBS = OBS(XL,sheet='N1-OBS') N2_OBS = OBS(XL,sheet='N2-OBS').truncate(after=dt.datetime(2014,6,7,23,59)) N1_OBS = N1_OBS.rename(columns={'Turb_SS_Min':'NTU'}) N2_OBS = N2_OBS.rename(columns={'Turb_SS_Min':'NTU'}) ## TSS Data, equivalent to SSC but I don't want to change all the code and file names TSSXL = pd.ExcelFile(datadir+'SSC/TSS_grab_samples.xlsx') TSS = loadTSS(TSSXL,'ALL_MASTER') TSS= TSS[TSS['TSS (mg/L)']>0] #### Import NUTRIENT Data from load_from_MASTER_XL import loadNUTES1,loadNUTES2and3 ##NUTRIENTS1 (first field season)
## Year Interval Times start2012, stop2012 = dt.datetime(2012, 1, 7, 0, 0), dt.datetime(2012, 12, 31, 11, 59) start2013, stop2013 = dt.datetime(2013, 1, 1, 0, 0), dt.datetime(2013, 12, 31, 11, 59) start2014, stop2014 = dt.datetime(2014, 1, 1, 0, 0), dt.datetime(2014, 12, 31, 11, 59) PT1 = PT1.reindex(pd.date_range(start2012, stop2014, freq='15Min')) PT2 = PT2.reindex(pd.date_range(start2012, stop2014, freq='15Min')) stage_data = stage_data.reindex( pd.date_range(start2012, stop2014, freq='15Min')) #### Import T and SSC Data from load_from_MASTER_XL import OBS, loadTSS N1_OBS = OBS(XL, sheet='N1-OBS') N2_OBS = OBS(XL, sheet='N2-OBS') N1_OBS = N1_OBS.rename(columns={'Turb_SS_Min': 'NTU'}) N2_OBS = N2_OBS.rename(columns={'Turb_SS_Min': 'NTU'}) ## TSS Data, equivalent to SSC but I don't want to change all the code and file names TSSXL = pd.ExcelFile(datadir + 'SSC/TSS_grab_samples.xlsx') TSS = loadTSS(TSSXL, 'ALL_MASTER') TSS = TSS[TSS['TSS (mg/L)'] > 0] #### Import NUTRIENT Data from load_from_MASTER_XL import loadNUTES1, loadNUTES2and3 ##NUTRIENTS1 (first field season) NUTESXL = pd.ExcelFile(datadir + 'NUTRIENTS/NUTRIENTS1.xlsx') NUTES1 = loadNUTES1(NUTESXL, 'Data')
## Year Interval Times start2012, stop2012 = dt.datetime(2012, 1, 7, 0, 0), dt.datetime(2012, 12, 31, 11, 59) start2013, stop2013 = dt.datetime(2013, 1, 1, 0, 0), dt.datetime(2013, 12, 31, 11, 59) start2014, stop2014 = dt.datetime(2014, 1, 1, 0, 0), dt.datetime(2014, 12, 31, 11, 59) PT1 = PT1.reindex(pd.date_range(start2012, stop2014, freq='15Min')) PT2 = PT2.reindex(pd.date_range(start2012, stop2014, freq='15Min')) stage_data = stage_data.reindex( pd.date_range(start2012, stop2014, freq='15Min')) #### Import T and SSC Data from load_from_MASTER_XL import OBS, loadTSS N1_OBS = OBS(XL, sheet='N1-OBS') N2_OBS = OBS(XL, sheet='N2-OBS').truncate(after=dt.datetime(2014, 6, 7, 23, 59)) N1_OBS = N1_OBS.rename(columns={'Turb_SS_Min': 'NTU'}) N2_OBS = N2_OBS.rename(columns={'Turb_SS_Min': 'NTU'}) ## TSS Data, equivalent to SSC but I don't want to change all the code and file names TSSXL = pd.ExcelFile(datadir + 'SSC/TSS_grab_samples.xlsx') TSS = loadTSS(TSSXL, 'ALL_MASTER') TSS = TSS[TSS['TSS (mg/L)'] > 0] #### Import NUTRIENT Data from load_from_MASTER_XL import loadNUTES1, loadNUTES2and3 ##NUTRIENTS1 (first field season) NUTESXL = pd.ExcelFile(datadir + 'NUTRIENTS/NUTRIENTS1.xlsx')
Fagaalu_stage_data = pd.DataFrame({'LBJ':PT1['stage'],'DT':PT2['stage'],'Dam':PT3['stage']}) Fagaalu_stage_data = Fagaalu_stage_data.reindex(pd.date_range(start2012,stop2014,freq='15Min')) #### Import T and SSC Data from load_from_MASTER_XL import TS3000,YSI,OBS,loadSSC ## Turbidimeter Data DAM DAM_TS3K = TS3000(XL,'DAM-TS3K') DAM_TS3K = DAM_TS3K[DAM_TS3K>=0] DAM_YSI = YSI(XL,'DAM-YSI') ## Correct negative NTU values DAM_YSI['NTU'][dt.datetime(2013,6,1):dt.datetime(2013,12,31)]=DAM_YSI['NTU'][dt.datetime(2013,6,1):dt.datetime(2013,12,31)]+6 ## Turbidimeter Data LBJ LBJ_YSI = YSI(XL,'LBJ-YSI') LBJ_OBSa = OBS(XL,'LBJ-OBSa').truncate(after=dt.datetime(2013,4,1)) LBJ_OBSb = OBS(XL,'LBJ-OBSb') LBJ_OBSa=LBJ_OBSa.rename(columns={'Turb_SS_Avg':'FNU'}) LBJ_OBSb=LBJ_OBSb.rename(columns={'Turb_SS_Mean':'FNU'}) LBJ_OBS=LBJ_OBSa.append(LBJ_OBSb) LBJ_OBS['FNU'] = LBJ_OBS['FNU'][LBJ_OBS['FNU']<=4000] LBJ_OBS['FNU'] = LBJ_OBS['FNU'].interpolate(limit=2) ## Turbidimeter Data LBJ #QUARRYxl = pd.ExcelFile(datadir+'QUARRY-OBS.xlsx') #QUARRY_OBS = QUARRYxl.parse('QUARRY-OBS',header=4,parse_cols='A:L',parse_dates=True,index_col=0) QUARRY_OBS = OBS(XL,'QUARRY-OBS') QUARRY_OBS=QUARRY_OBS.rename(columns={'Turb_SS_Mean':'FNU'}) QUARRY_OBS['FNU'] = QUARRY_OBS['FNU'][QUARRY_OBS['FNU']<=4000] QUARRY_OBS['FNU'] = QUARRY_OBS['FNU']