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%run basics


    

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aws_name=qcio.get_filename_dialog(path="../Sites")


    

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ds_aws_30minute = qcio.nc_read_series(aws_name)
has_gaps = qcutils.CheckTimeStep(ds_aws_30minute)
if has_gaps:
    print "Problems found with time step"
    qcutils.FixTimeStep(ds_aws_30minute)
    qcutils.get_ymdhmsfromdatetime(ds_aws_30minute)
dt_aws_30minute = ds_aws_30minute.series["DateTime"]["Data"]
ddt=[dt_aws_30minute[i+1]-dt_aws_30minute[i] for i in range(0,len(dt_aws_30minute)-1)]
print "Minimum time step is",min(ddt)," Maximum time step is",max(ddt)


    

    




Minimum time step is 0:30:00  Maximum time step is 0:30:00

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dt_aws_30minute = ds_aws_30minute.series["DateTime"]["Data"]
start_date = dt_aws_30minute[0]
end_date = dt_aws_30minute[-1]
si_wholehour = qcutils.GetDateIndex(dt_aws_30minute,str(start_date),ts=30,match="startnexthour")
ei_wholehour = qcutils.GetDateIndex(dt_aws_30minute,str(end_date),ts=30,match="endprevioushour")
start_date = dt_aws_30minute[si_wholehour]
end_date = dt_aws_30minute[ei_wholehour]
dt_aws_30minute_array = numpy.array(dt_aws_30minute[si_wholehour:ei_wholehour+1])
nRecs_30minute = len(dt_aws_30minute_array)
dt_aws_2d = numpy.reshape(dt_aws_30minute_array,(nRecs_30minute/2,2))
dt_aws_60minute = list(dt_aws_2d[:,1])
nRecs_60minute = len(dt_aws_60minute)


    

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series_list = ds_aws_30minute.series.keys()
for item in ["DateTime","Ddd","Day","Minute","xlDateTime","Hour","time","Month","Second","Year"]:
    if item in series_list: series_list.remove(item)


    

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# get the 60 minute data structure
ds_aws_60minute = qcio.DataStructure()
# get the global attributes
for item in ds_aws_30minute.globalattributes.keys():
    ds_aws_60minute.globalattributes[item] = ds_aws_30minute.globalattributes[item]
# overwrite with 60 minute values as appropriate
ds_aws_60minute.globalattributes["nc_nrecs"] = str(nRecs_60minute)
ds_aws_60minute.globalattributes["time_step"] = str(60)
# put the Python datetime into the data structure
ds_aws_60minute.series["DateTime"] = {}
ds_aws_60minute.series["DateTime"]["Data"] = dt_aws_60minute
ds_aws_60minute.series["DateTime"]["Flag"] = numpy.zeros(nRecs_60minute,dtype=numpy.int32)
ds_aws_60minute.series["DateTime"]["Attr"] = qcutils.MakeAttributeDictionary(long_name="DateTime in local time zone",units="None")
# add the Excel datetime, year, month etc
qcutils.get_xldatefromdatetime(ds_aws_60minute)
qcutils.get_ymdhmsfromdatetime(ds_aws_60minute)
# loop over the series and take the average (every thing but Precip) or sum (Precip)
for item in series_list:
    if "Precip" in item:
        data_30minute,flag_30minute,attr = qcutils.GetSeriesasMA(ds_aws_30minute,item,si=si_wholehour,ei=ei_wholehour)
        data_2d = numpy.reshape(data_30minute,(nRecs_30minute/2,2))
        flag_2d = numpy.reshape(flag_30minute,(nRecs_30minute/2,2))
        data_60minute = numpy.ma.sum(data_2d,axis=1)
        flag_60minute = numpy.ma.max(flag_2d,axis=1)
        qcutils.CreateSeries(ds_aws_60minute,item,data_60minute,Flag=flag_60minute,Attr=attr)
    elif "Wd" in item:
        Ws_30minute,flag_30minute,attr = qcutils.GetSeriesasMA(ds_aws_30minute,item,si=si_wholehour,ei=ei_wholehour)
        Wd_30minute,flag_30minute,attr = qcutils.GetSeriesasMA(ds_aws_30minute,item,si=si_wholehour,ei=ei_wholehour)
        U_30minute,V_30minute = qcutils.convert_WsWdtoUV(Ws_30minute,Wd_30minute)
        U_2d = numpy.reshape(U_30minute,(nRecs_30minute/2,2))
        V_2d = numpy.reshape(V_30minute,(nRecs_30minute/2,2))
        flag_2d = numpy.reshape(flag_30minute,(nRecs_30minute/2,2))
        U_60minute = numpy.ma.sum(U_2d,axis=1)
        V_60minute = numpy.ma.sum(V_2d,axis=1)
        Ws_60minute,Wd_60minute = qcutils.convert_UVtoWsWd(U_60minute,V_60minute)
        flag_60minute = numpy.ma.max(flag_2d,axis=1)
        qcutils.CreateSeries(ds_aws_60minute,item,Wd_60minute,Flag=flag_60minute,Attr=attr)
    else:
        data_30minute,flag_30minute,attr = qcutils.GetSeriesasMA(ds_aws_30minute,item,si=si_wholehour,ei=ei_wholehour)
        data_2d = numpy.reshape(data_30minute,(nRecs_30minute/2,2))
        flag_2d = numpy.reshape(flag_30minute,(nRecs_30minute/2,2))
        data_60minute = numpy.ma.average(data_2d,axis=1)
        flag_60minute = numpy.ma.max(flag_2d,axis=1)
        qcutils.CreateSeries(ds_aws_60minute,item,data_60minute,Flag=flag_60minute,Attr=attr)
# write out the 60 minute data
nc_60minute = aws_name.replace('.nc','_60minute.nc')
ncfile = qcio.nc_open_write(nc_60minute)
qcio.nc_write_series(ncfile, ds_aws_60minute, ndims=1)


    

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