nc.py

import os
import sys
import logging
import pandas as pd
from datetime import datetime as dt
from aosstower.l00 import parser
from netCDF4 import Dataset
import numpy as np 
import platform
from aosstower import station
from datetime import timedelta as delta
import calc

LOG = logging.getLogger(__name__)


#create the '_mean','_low','_high' file structure
def make_mean_dict(source_dict):
    dest_dict = {}
    for key in source_dict:
        dest_dict[key+'_high'] = source_dict[key]
        dest_dict[key+'_mean'] = source_dict[key]
        dest_dict[key+'_low'] = source_dict[key]
    return dest_dict

mean_database = make_mean_dict(parser.database)

def filterArray(array, valid_min, valid_max):

    qcControl = []

    for value in array:
        if value == float(-99999):
            qcControl.append(np.byte(0b1))

        elif valid_min != '' and value < float(valid_min):
            qcControl.append(np.byte(0b10))
 
        elif valid_max != '' and value > float(valid_max):
            qcControl.append(np.byte(0b100))
 
        else:
            qcControl.append(np.byte(0b0))

    return np.array(qcControl)

# The purpose of this function is to write the dimensions
# for the nc file
# no parameters
# no returns

def writeDimensions(ncFile):
    ncFile.createDimension('time', None)
    ncFile.createDimension('max_len_station_name', 32)

    return ncFile

def createVariables(ncFile, firstStamp, chunksizes, zlib, database=parser.database):
    #base_time long name
    btln = 'base time as unix timestamp'

    #base time units
    btu = 'seconds since 1970-01-01 00:00:00'

    #base time string
    bts = firstStamp.strftime('%Y-%m-%d 00:00:00Z')

    #time long name
    tln = 'time offset from base_time'

    #time units
    tu = 'seconds since ' + firstStamp.strftime('%Y-%m-%d 00:00:00Z')

    

    coordinates = {
                      #fields: type, dimension, fill, valid_min, std_name, longname, units, valid_max, cf_role, axis
                      'lon': [np.float32, None, float(-999), '-180L', 'longitude', None, 'degrees_east', '180L', None],
                      'lat': [np.float32, None, float(-999), '-90L', 'latitude', None, 'degrees_north', '90L', None],
                      'alt': [np.float32, None, float(-999), None, 'height', 'vertical distance', 'm', None, None],
                      'base_time': [np.int32, None, float(-999), None, 'time', btln, btu, None, None],
                      'time_offset': [np.float64, 'time', float(-999), None, 'time', tln, tu, None, None],
                      'station_name': ['c', 'max_len_station_name', '-', None, None, 'station name', None, None, 'timeseries_id'],   
                      'time': [np.float32, 'time', float(-999), None, None, "Time offset from epoch", "seconds since 1970-01-01 00:00:00Z", None, None, None]
                  }

    for key in coordinates:
        attr = coordinates[key]

        if(attr[1]):
            if attr[1] == 'max_len_station_name':
                if (chunksizes) and chunksizes[0] > 32:
                    variable = ncFile.createVariable(key, attr[0], dimensions=(attr[1]), fill_value=attr[2], zlib=zlib, chunksizes=[32])

                else:
                    variable = ncFile.createVariable(key, attr[0], dimensions=(attr[1]), fill_value=attr[2], zlib=zlib, chunksizes=chunksizes)

            else:
                variable = ncFile.createVariable(key, attr[0], dimensions=(attr[1]), fill_value=attr[2], zlib=zlib, chunksizes=chunksizes)
        else:
            variable = ncFile.createVariable(key, attr[0], fill_value=attr[1], zlib=zlib, chunksizes=chunksizes)

        #create var attributes
        if key == 'alt':
            variable.positive = 'up'
            variable.axis = 'Z'

        if(attr[3]):
            variable.valid_min = attr[3]
            variable.valid_max = attr[7]

        if(attr[4]):
            variable.standard_name = attr[4]

        if(attr[5]):
            variable.long_name = attr[5]

        if(attr[6]):
            variable.units = attr[6]

        if(attr[8]):
             variable.cf_role = attr[8]

        if key == 'base_time':
            variable.string = bts

        if 'time' in key:
            variable.calendar = 'gregorian' 

    for entry in database:
        if(entry == 'stamp'):
            continue

        varTup = database[entry]
        
        variable = ncFile.createVariable(entry, np.float32,
        dimensions=('time'), fill_value=float(-99999), zlib=zlib, chunksizes=chunksizes)

        variable.standard_name = varTup[1]
        variable.description = varTup[3]
        variable.units = varTup[4]

        if(varTup[5] != ''):
            variable.valid_min = float(varTup[5])
            variable.valid_max = float(varTup[6])

        qcVariable = ncFile.createVariable('qc_' + entry, 'b',
        dimensions=('time'), fill_value=0b0,  zlib=zlib, chunksizes=chunksizes)

        qcVariable.long_name = 'data quality'
        qcVariable.valid_range = np.byte(0b1), np.byte(0b1111)
        qcVariable.flag_masks = np.byte(0b1), np.byte(0b10), np.byte(0b100), np.byte(0b1000)

        flagMeaning = ['value is equal to missing_value', 
                       'value is less than the valid min', 
                       'value is greater than the valid max',
                       'difference between current and previous values exceeds valid_delta.']

        qcVariable.flag_meaning = ', '.join(flagMeaning)

    #create global attributes
    ncFile.source = 'surface observation'
    ncFile.conventions = 'ARM-1.2 CF-1.6'
    ncFile.institution = 'UW SSEC'
    ncFile.featureType = 'timeSeries'
    ncFile.data_level = 'b1'

    #monthly files end with .month.nc
    #these end with .day.nc

    ncFile.datastream = 'aoss.tower.nc-1d-1m.b1.v00'
    ncFile.software_version = '00'

    #generate history
    ncFile.history = ' '.join(platform.uname()) + " " + os.path.basename(__file__)
    
    return ncFile

def getGust(rollingAvg, speeds):
    averages = rollingAvg.tolist()
    maxSpeed = speeds['wind_speed'].tolist()

    gust = []

    for idx, average in enumerate(averages):
        if not average:
             gust.append(np.nan)
             continue

        elif average >= 4.63 and maxSpeed[idx] > average + 2.573:
              gust.append(maxSpeed[idx])

        else:
            gust.append(np.nan)
            continue

    return gust

#gets the rolling mean closest to the nearest minute
def getRolling(series, minutes):
    returnSeries = series.rolling(25, win_type='boxcar').mean()

    data = {}

    for minute in minutes:

        #doesn't go past the minute
        closestStamp = returnSeries.index.asof(minute)
        data[minute] = returnSeries[returnSeries.index.asof(minute)]

    returnSeries = pd.Series(data)
    
    return returnSeries

def getNewWindDirection(wind_dir, wind_speed, stamps):
    newWindDir = {}

    for stamp in stamps:
        before = stamp - delta(minutes=1)

        if before not in wind_speed.index:
            newWindDir[stamp] = None

        else:
            speed = wind_speed[before: stamp].tolist()
            dire = wind_dir[before: stamp].tolist()

            wind_dire = calc.mean_wind_vector(speed, dire)[0]
            
            newWindDir[stamp] = wind_dire
    
    return pd.Series(newWindDir)

def minuteAverages(frame):
    frame['minute'] = [(ts + delta(minutes=1)).replace(second=0) for ts in frame.index]
    newFrame = frame.groupby('minute').mean()
    newFrame.index.names = ['']

    columns = list(newFrame.columns.values)
    if 'wind_speed' in columns:
        del newFrame['wind_speed']

        windSeries = frame['wind_speed']

        windSeries = getRolling(windSeries, list(newFrame.index))

        newFrame['wind_speed'] = windSeries
  
        rollingAvg = newFrame['wind_speed']

        maxSpeed = pd.DataFrame()
        maxSpeed['minute'] = frame['minute']
        maxSpeed['speed'] = frame['wind_speed']

        maxSpeed = frame.groupby('minute').max()

        gust = getGust(rollingAvg, maxSpeed)

        newFrame['gust'] = gust
    
    if 'wind_dir' in columns:
        del newFrame['wind_dir']

        dupFrame = frame.set_index('minute')

        stamps = newFrame.index

        windDirSeries = dupFrame['wind_dir']

        windSeries = dupFrame['wind_speed']

        windDirSeries = getNewWindDirection(windDirSeries, windSeries, stamps)

        newFrame['wind_dir'] = windDirSeries

    del frame['minute']

    return newFrame.fillna(-99999)

def averageOverInterval(frame,interval_width):
    """takes a frame and an interval to average it over, and returns a minimum,
    maximum, and average dataframe for that interval"""
    ts = frame.index
    #round each timestamp to the nearest n minutes
    frame['interval'] = (ts.astype(int)-ts.astype(int)%(interval_width*60e9)).astype('datetime64[ns]')
    outFrames = {}
    outFrames['low'] = frame.groupby('interval').min()
    outFrames['high'] = frame.groupby('interval').max()
    outFrames['mean'] = frame.groupby('interval').mean()
    del frame['interval']
    for key in outFrames:
        #append the appropriate suffix to each column 
        columns = outFrames[key].columns 
        outFrames[key].columns = ['_'.join([col,key]) for col in columns]
    outFrames = pd.concat(outFrames.values(),axis=1)
    return outFrames 

def getData(inputFiles):
    dictData = {}

    for filename in inputFiles:
        getFrames = list(parser.read_frames(filename))

        for frame in getFrames:
            if 'stamp' not in frame:
                continue

            stamp = frame['stamp']
            del frame['stamp']

            dictData[stamp] = frame

    return pd.DataFrame(dictData).transpose().replace(-99999, np.nan)

def writeVars(ncFile, frame, database=parser.database):
    stamps = list(frame.index)
    baseDTObj = dt.strptime(str(stamps[0]).split(' ')[0], '%Y-%m-%d')

    #find out how much time elapsed
    #since the origin to the start of the day
    #in seconds
    baseTimeValue = baseDTObj - dt(1970,1,1)
    baseTimeValue = baseTimeValue.total_seconds()

    #create time numpy
    timeNumpy = np.empty(len(stamps), dtype='float64')

    counter = 0

    #write stamps in, yo

    for stamp in stamps:
        stampObj = dt.strptime(str(stamp), '%Y-%m-%d %H:%M:%S')
        timeValue = (stampObj - baseDTObj).total_seconds()

        timeNumpy[counter] = timeValue
        counter += 1

    fileVar = ncFile.variables
    fileVar['base_time'].assignValue(baseTimeValue)
    fileVar['time_offset'][:] = timeNumpy
    fileVar['time'][:] = timeNumpy 

    #write coordinate var values to file
    #alt might not be right, need to verify
    fileVar['lon'].assignValue(station.LONGITUDE)
    fileVar['lat'].assignValue(station.LATITUDE)
    fileVar['alt'].assignValue(328)

    #might change
    stationName = ("AOSS Tower")
    
    #transfer station name into array of chars
    statChars = list(stationName)
    statNumpy = np.asarray(statChars)

    #write station name to file
    fileVar['station_name'][0:len(statNumpy)] = statNumpy

    #writes data into file
    for varName in frame:
        if varName not in fileVar:
            logging.warn('Extraneous key: %s in frame'%varName)
            continue
        dataList = frame[varName].tolist()

        dataArray = np.asarray(dataList)
        fileVar[varName][:] = dataArray

        valid_min = database[varName][5]
        valid_max = database[varName][6]

        fileVar['qc_' + varName][:] = filterArray(dataArray, valid_min, valid_max)

    coordinates = ['lon', 'lat', 'alt', 'base_time', 'time_offset', 'station_name', 'time']

    for varName in fileVar:
        if varName.startswith('qc_'):
            continue

        elif varName in frame:
            continue

        elif varName in coordinates:
            continue

        else:
            fileVar['qc_' + varName][:] = np.full(len(list(frame.index)), np.byte(0b1), dtype='b')
    
    return ncFile

#The purpose of this method is to take a begin date, and end date
# input filenames and output filename and create a netCDF file 
# based upon that
# @param start time - a start datetime object
# @param end time - an end datetime object
# @param input filenames - list of filenames
# @param output filename - filename of the netcdf file

def createGiantNetCDF(start, end, inputFiles, outputName, zlib, chunkSize,
                      interval_width = None,  database=parser.database):
    default = False

    if(chunkSize):
        chunksizes = [chunkSize]

    else:
        default = True

    frame = getData(inputFiles)

    if(frame.empty):
        return False

    else:

        frame = minuteAverages(frame)
        if interval_width:
            frame = averageOverInterval(frame,interval_width) 

        if(start and end):
            frame = frame[start.strftime('%Y-%m-%d %H:%M:%S'): end.strftime('%Y-%m-%d %H:%M:%S')]

        if(default):
            chunksizes = [len(list(frame.index))]

        firstStamp = dt.strptime(str(list(frame.index)[0]), '%Y-%m-%d %H:%M:%S')

        ncFile = Dataset(outputName, 'w', format='NETCDF4_CLASSIC')

        ncFile = writeDimensions(ncFile)

        ncFile = createVariables(ncFile, firstStamp, chunksizes, zlib,database)
 
        ncFile.inputFiles = ', '.join(inputFiles)

        ncFile = writeVars(ncFile, frame,database)

        ncFile.close()
        
        return True

def createMultiple(filenames, outputFilenames, zlib, chunkSize):
    if(outputFilenames and len(filenames) != len(outputFilenames)):
        print('USAGE: number of output filenames must equal number of input filenames when start and end times are not specified')
        exit(0)
    
    results = []

    for idx, filename in enumerate(filenames):
        results.append(createGiantNetCDF(None, None, [filename], outputFilenames[idx], zlib, chunkSize))

    allFalse = True

    for result in results:
        if result == True:
            allFalse = False

    if allFalse == True:
        raise IOError('All ASCII files were empty')

#The purpose of this method is to take a string in the format
# YYYY-mm-ddTHH:MM:SS and convert that to a datetime object
# used in coordination with argparse -s and -e params
# @param datetime string
# @return datetime object

def _dt_convert(datetime_str):
    #parse datetime string, return datetime object
    try: 
        return dt.strptime(datetime_str, '%Y-%m-%dT%H:%M:%S')
    except:
        return dt.strptime(datetime_str, '%Y-%m-%d')

def main():
    import argparse

    #argparse description
    argparser = argparse.ArgumentParser(description="Convert level_00 aoss tower data to level_a0",
                                        fromfile_prefix_chars='@')

    #argparse verbosity info
    argparser.add_argument('-v', '--verbose', action="count", default=int(os.environ.get("VERBOSITY", 2)),
                         dest='verbosity',
                         help='each occurrence increases verbosity 1 level through ERROR-WARNING-INFO-DEBUG (default INFO)')

    #argparse start and end times
    argparser.add_argument('-s', '--start-time', type=_dt_convert, 
        help="Start time of massive netcdf file, if only -s is given, a netcdf file for only that day is given" + 
        ". Formats allowed: \'YYYY-MM-DDTHH:MM:SS\', \'YYYY-MM-DD\'")
    argparser.add_argument('-e', '--end-time', type=_dt_convert, help='End time of massive netcdf file. Formats allowed:' +
        "\'YYYY-MM-DDTHH:MM:SS\', \'YYYY-MM-DD\'")
    argparser.add_argument('-i', '--interval', type=float, 
            help='Width of the interval to average input data over in minutes.'+
        " If not specified, 1 is assumed. (Use 60 for one hour and 1440 for 1 day)")
    argparser.add_argument('-cs', '--chunk-size', type=int, help='chunk Size for the netCDF file')
    argparser.add_argument('-z', '--zlib', action='store_true', help='compress netCDF file with zlib')

    argparser.add_argument("input_files", nargs="+",
                         help="aoss_tower level_00 paths. Use @filename to red a list of paths from that file.")

    argparser.add_argument('-o', '--output', required=True, nargs="+", help="filename pattern or filename. " +
    "Should be along the lines of <filepath>/aoss_tower.YYYY-MM-DD.nc")
    args = argparser.parse_args()

    levels = [logging.ERROR, logging.WARN, logging.INFO, logging.DEBUG]
    level=levels[min(3, args.verbosity)]
    logging.basicConfig(level=level)


    database = mean_database if args.interval else parser.database
    if(args.start_time and args.end_time):
        result = createGiantNetCDF(args.start_time, args.end_time, args.input_files, args.output[0], args.zlib, args.chunk_size,
                                   args.interval, database)
        if(result == False):
            raise IOError('An empty ASCII file was found')

    elif(args.start_time):
        end_time = args.start_time.replace(hour=23, minute=59, second=59)
        result = createGiantNetCDF(args.start_time, end_time, args.input_files, args.output[0], args.zlib, args.chunk_size,
                                   args.interval, database)
        if(result == False):
            raise IOError('An empty ASCII file was found')

    elif(args.end_time):
        print('USAGE: start time must be specified when end time is specified')

    else:
        createMultiple(args.input_files, args.output, args.zlib, args.chunk_size)

if __name__ == "__main__":
    main()