AMOF_dataformat_HVPS_v1_2.py

# -*- coding: utf-8 -*-



# Put psd in AMOF format
# Need to read in psd (with dimensions time and index) from separate file. Needs to include 
# DataFlag, Psd, Counts, Size_mid, Size_edge, Time_mid, Time_edge
# There are examples for .nc and .h5 files, but these need to altered for specific files.


import numpy as np
import h5py
import os
import datetime
import netCDF4
import matplotlib.pyplot as plt 
import pandas as pd


# v1.0 21/01/2021
# Original
# Generate AMOF file format for upload to CEDA.
# Needs processed data file and faam core file as input.

#v1.1 8/02/2021
# Apply UnitConversion before filling missing values.

#v1.2 4/2/2021
# Can accept pre AMOF .nc file.
# Change "_" to "-" as requested by CEDA


#_________________________________________________________________

#Load HVPS .nc that is preAMOFformat

def LoadHVPS_preAMOFformat(DataPath, DataFileName):
    Data_h5 = netCDF4.Dataset(DataPath+DataFileName)          
    DataFlag = np.array(Data_h5['Data_Flag']).astype(np.uint32)
    Psd= np.array(Data_h5['PSD_Num_All'])
    Counts= np.array(Data_h5['PSD_Counts_All'])
    Size_mid= np.array(Data_h5['Size_mid'])
    Size_edge= np.array(Data_h5['Size_edge'])
    Time_edge= np.array(Data_h5['Time_edge'])
    Time_mid= np.array(Data_h5['Time_mid'])
    Data_h5.close()
    return DataFlag, Psd, Counts, Size_mid, Size_edge, Time_mid, Time_edge 



#_________________________________________________________________
#load .h5 that has been generated using OASIS containing PSDs and data flags

def LoadOAPH5(DataPath, DataFileName):
    
    Data_h5 = h5py.File(DataPath+DataFileName, 'r')              
    DataFlag = np.array(Data_h5['DataFlag']).astype(np.uint32)
    Psd= np.array(Data_h5['PSD_All_accept_submit'])
    Counts= np.array(Data_h5['PSD_Counts_All_accept_submit'])
    Size_mid= np.array(Data_h5['Size_mid_submit'])
    Size_edge= np.array(Data_h5['Size_submit'])
    Time_edge= np.array(Data_h5['Time_edge_submit'])
    Time_mid= np.array(Data_h5['Time_mid_submit'])
    Data_h5.close()
    
    
    return DataFlag, Psd, Counts, Size_mid, Size_edge, Time_mid, Time_edge 

#_________________________________________________________________
# Change timebase of FAAM core variable to OAP timebase
# Also change data fill values to FillValueNew

def CovertTimeBase2NC(FAAMCore, VariableStr, TimeIndex, TimeDiff, FillValueOld,FillValueNew):
    tmp = np.array(FAAMCore[VariableStr][:])
    tmp_nc = tmp[TimeIndex]
    #tmp_nc[TimeDiff>=1] = FillValueNew
    NanIdx = TimeIndex[TimeDiff>=1]
    tmp_nc[NanIdx] = FillValueNew
    tmp_nc[tmp_nc == FillValueOld]= FillValueNew
    return tmp_nc

#_________________________________________________________________

# Load faam core file and change time base and format ready for AMOF data submisison

def LoadCoreVariables4AMOF(CorePath,CoreFileName,Time_mid):

    #Time_mid is the time base of returned array
    
    FAAMCore = netCDF4.Dataset(CorePath+CoreFileName)
    
    TimeCore=np.array(FAAMCore['Time'][:])
    FlightDate= datetime.datetime(int(CoreFileName[10:14]), int(CoreFileName[14:16]), int(CoreFileName[16:18]), 0, 0, 0)
    # index to change core timebase to Oap timebase
    TimeIndex = np.searchsorted(TimeCore,Time_mid)
    TimeDiff = np.absolute(Time_mid-TimeCore[TimeIndex])

    FillValueOld = -9999
    FillValueNew = -1E20
    
    LAT_GIN_nc = CovertTimeBase2NC(FAAMCore, 'LAT_GIN', TimeIndex, TimeDiff,FillValueOld,FillValueNew)
    LON_GIN_nc = CovertTimeBase2NC(FAAMCore, 'LON_GIN', TimeIndex, TimeDiff,FillValueOld,FillValueNew)
    ALT_GIN_nc = CovertTimeBase2NC(FAAMCore, 'ALT_GIN', TimeIndex, TimeDiff,FillValueOld,FillValueNew)
    TAS_RVSM_nc = CovertTimeBase2NC(FAAMCore, 'TAS_RVSM', TimeIndex, TimeDiff,FillValueOld,FillValueNew)
    TRCK_GIN_nc = CovertTimeBase2NC(FAAMCore, 'TRCK_GIN', TimeIndex, TimeDiff,FillValueOld,FillValueNew)
    GSPD_GIN_nc = CovertTimeBase2NC(FAAMCore, 'GSPD_GIN', TimeIndex, TimeDiff,FillValueOld,FillValueNew)
    PTCH_GIN_nc = CovertTimeBase2NC(FAAMCore, 'PTCH_GIN', TimeIndex, TimeDiff,FillValueOld,FillValueNew)
    PITR_GIN_nc = CovertTimeBase2NC(FAAMCore, 'PITR_GIN', TimeIndex, TimeDiff,FillValueOld,FillValueNew)
    ROLL_GIN_nc = CovertTimeBase2NC(FAAMCore, 'ROLL_GIN', TimeIndex, TimeDiff,FillValueOld,FillValueNew)
    ROLR_GIN_nc = CovertTimeBase2NC(FAAMCore, 'ROLR_GIN', TimeIndex, TimeDiff,FillValueOld,FillValueNew)
    HDG_GIN_nc = CovertTimeBase2NC(FAAMCore, 'HDG_GIN', TimeIndex, TimeDiff,FillValueOld,FillValueNew)
    HDGR_GIN_nc = CovertTimeBase2NC(FAAMCore, 'HDGR_GIN', TimeIndex, TimeDiff,FillValueOld,FillValueNew)
    
    FAAMCore.close()
    
    return LAT_GIN_nc, LON_GIN_nc, ALT_GIN_nc, TAS_RVSM_nc, TRCK_GIN_nc, GSPD_GIN_nc, PTCH_GIN_nc, PITR_GIN_nc, ROLL_GIN_nc, ROLR_GIN_nc, HDG_GIN_nc, HDGR_GIN_nc

#_________________________________________________________________


#FlightNumberStr = 'c174'
#FlightDate = datetime.datetime(2019,5,23)
#CorePath = 'C:/Users/mbexjso2/OneDrive - The University of Manchester/Documents/Work/Picasso/rawData/C174/' #Paths to core data file
#CoreFileName = 'core_faam_20190523_v004_r0_c174_1hz.nc'
#DataPath = 'C:/Users/mbexjso2/OneDrive - The University of Manchester/Documents/Work/Picasso/rawData/C174/' #Path to HVPS data file
#DataFileName = 'HVPS_C174.h5'

#FlightNumberStr = 'c174'
#FlightDate = datetime.datetime(2019,5,23)
#CoreFileName = 'core_faam_20190523_v004_r0_c174_1hz.nc'
#DataFileName = 'UMAN-HVPS_20190523_r1_C174.nc'

#FlightNumberStr = 'c172'
#FlightDate = datetime.datetime(2019,5,7)
#CoreFileName = 'core_faam_20190507_v004_r0_c172_1hz.nc'
#DataFileName = 'UMAN-HVPS_20190507_r1_C172.nc'

#FlightNumberStr = 'c171'
#FlightDate = datetime.datetime(2019,5,1)
#CoreFileName = 'core_faam_20190501_v004_r0_c171_1hz.nc'
#DataFileName = 'UMAN-HVPS_20190501_r1_C171.nc'

#FlightNumberStr = 'c170'
#FlightDate = datetime.datetime(2019,4,26)
#CoreFileName = 'core_faam_20190426_v004_r0_c170_1hz.nc'
#DataFileName = 'UMAN-HVPS_20190426_r1_C170.nc'

# FlightNumberStr = 'c169'
# FlightDate = datetime.datetime(2019,4,16)
# CoreFileName = 'core_faam_20190416_v004_r0_c169_1hz.nc'
# DataFileName = 'UMAN-HVPS_20190416_r1_C169.nc'

# FlightNumberStr = 'c098'
# FlightDate = datetime.datetime(2018,4,24)
# CoreFileName = 'core_faam_20180424_v004_r0_c098_1hz.nc'
# DataFileName = 'UMAN-HVPS_20180424_r2_C098.nc'

# FlightNumberStr = 'c097'
# FlightDate = datetime.datetime(2018,4,23)
# CoreFileName = 'core_faam_20180423_v004_r0_c097_1hz.nc'
# DataFileName = 'UMAN-HVPS_20180423_r1_C097.nc'

# FlightNumberStr = 'c082'
# FlightDate = datetime.datetime(2018,2,14)
# CoreFileName = 'core_faam_20180214_v004_r0_c082_1hz.nc'
# DataFileName = 'UMAN-HVPS_20180214_r2_C082.nc'

# FlightNumberStr = 'c081'
# FlightDate = datetime.datetime(2018,2,13)
# CoreFileName = 'core_faam_20180213_v004_r0_c081_1hz.nc'
# DataFileName = 'UMAN-HVPS_20180213_r1_C081.nc'

# FlightNumberStr = 'c080'
# FlightDate = datetime.datetime(2018,2,8)
# CoreFileName = 'core_faam_20180208_v004_r0_c080_1hz.nc'
# DataFileName = 'UMAN-HVPS_20180208_r1_C080.nc'

FlightNumberStr = 'c079'
FlightDate = datetime.datetime(2018,2,8)
CoreFileName = 'core_faam_20180208_v004_r0_c079_1hz.nc'
DataFileName = 'UMAN-HVPS_20180208_r1_C079.nc'

CorePath = 'D:/PICASSO/rawdata/FAAM_Data/AllCoreFaam/' #Paths to core data file
DataPath = 'D:/PICASSO/Processed/NewestVersionHVPS/' #Path to HVPS data file

rnumber = 0 # revision number
vnumber = '001'# Don't change relates to versions of processing software (currently not defined)
NormFlag = 0 #  is the data normalised, 0 = dN, 1 = dN/dD
UnitConversion =1E-3 # Factor to convert input PSD to cm-3 
FlagConversion = 0 # Conversion so that flag data is '0 = not used, 1 = valid data, 2 = reduced quality data. 3 = missing data'
FillValue = -1E20 # Fill value of output data file

#Load PSDs file
#DataFlag, Psd, Counts, Size_mid, Size_edge, Time_mid, Time_edge = LoadOAPH5(DataPath, DataFileName)
DataFlag, Psd, Counts, Size_mid, Size_edge, Time_mid, Time_edge = LoadHVPS_preAMOFformat(DataPath, DataFileName)

DataFlag += FlagConversion # Conversion so that flag data '0 = not used, 1 = valid data, 2 = reduced quality data. 3 = missing data'
DataFlag_2D = np.array([DataFlag,]*len(Size_mid)).transpose()

# Load variables from core files
LAT_GIN_nc, LON_GIN_nc, ALT_GIN_nc, TAS_RVSM_nc, TRCK_GIN_nc, GSPD_GIN_nc, PTCH_GIN_nc, PITR_GIN_nc, ROLL_GIN_nc, ROLR_GIN_nc, HDG_GIN_nc, HDGR_GIN_nc = LoadCoreVariables4AMOF(CorePath,CoreFileName,Time_mid)

#Convert time to seconds since 1970
DateTime_df = pd.to_datetime(FlightDate) + pd.to_timedelta(Time_mid, unit='s')

TimeSecondsSince = np.array((DateTime_df-datetime.datetime(1970,1,1)) / np.timedelta64(1,'s'))
year = np.array((DateTime_df.year)).astype(np.int32) 
month = np.array((DateTime_df.month)).astype(np.int32) 
day = np.array((DateTime_df.day)).astype(np.int32) 
hour = np.array((DateTime_df.hour)).astype(np.int32) 
minute = np.array((DateTime_df.minute)).astype(np.int32) 
second = np.array((DateTime_df.second))
day_of_year = np.array((DateTime_df.dayofyear)) + Time_mid/86400 # day of year with fraction of day

#Convert to cm-3
Psd *= UnitConversion

#Use chosen FillValue                    
Psd[Psd < 0] = FillValue
Counts[Counts < 0] = FillValue               
                    
#######################
#Save data as netcdf
Nc_filename='ncas-hvps3-1_faam_'+FlightDate.strftime("%Y%m%d")+'_v'+vnumber+'_r'+str(rnumber)+'_'+FlightNumberStr+'_particle-size-distribution.nc'
print(Nc_filename)
NcFile= netCDF4.Dataset(DataPath+Nc_filename,'w', format='NETCDF4') #'w' stands for write

# Global attributes
NcFile.conventions ='CF-1.6, NCAS-AMF-2.0.0'
NcFile.source = 'NCAS High Volume Precipitation Spectrometer unit 1'
NcFile.instrument_manufacturer = 'Spec Inc., USA'
NcFile.instrument_model = 'HVPS-3'
NcFile.instrument_serial_number = 'Not available'
NcFile.instrument_software = 'spec2d.exe'
NcFile.instrument_software_version ='Not available'
NcFile.creator_name = 'Sebastian OShea'
NcFile.creator_email = 'sebastian.oshea@manchester.ac.uk'
NcFile.url = 'https://orcid.org/0000-0002-0489-1723'
NcFile.institution = 'University of Manchester'
NcFile.processing_software_url = 'Not available'
NcFile.processing_software_version = 'OASIS_routines_v1.49962'
NcFile.calibration_sensitivity = 'The instrument counting uncertainty for each time and size bin is given by (100% / N^(1/2)), where N is the number of counts in the bin.'
NcFile.calibration_certification_date ='not applicable'
NcFile.calibration_certification_url ='not applicable'
NcFile.sampling_interval='1 second'
NcFile.averaging_interval='1 second'
NcFile.product_version='v'+str(rnumber+1)+'.0'
NcFile.processing_level= int(1)
NcFile.last_revised_date= datetime.datetime.now().strftime("%Y-%m-%dT%H:%M:%S")
NcFile.project='Parameterizing Ice Clouds using Airborne obServationS and triple-frequency dOppler radar (PICASSO)'
NcFile.project_principal_investigator='Jonathan Crosier'
NcFile.project_principal_investigator_email='jonathan.crosier@manchester.ac.uk'
NcFile.project_principal_investigator_url='https://orcid.org/0000-0002-3086-4729'
NcFile.licence='Data usage licence - UK Government Open Licence agreement: http://www.nationalarchives.gov.uk/doc/open-government-licence'
NcFile.acknowledgement='Acknowledgement of NCAS as the data provider is required whenever and wherever these data are used.'
NcFile.platform='faam'
NcFile.platform_type='mobile'
NcFile.deployment_mode='air'
NcFile.title='In situ measurement of particle size distributions from the FAAM BAe-146 research aircraft.' 
NcFile.featureType='timeSeries'
NcFile.time_coverage_start=FlightDate.strftime("%Y-%m-%dT%H:%M:%S")
NcFile.time_coverage_end=(FlightDate+datetime.timedelta(seconds=int(Time_mid[-1]))).strftime("%Y-%m-%dT%H:%M:%S")
NcFile.geospatial_bounds= str(np.around(np.min(LAT_GIN_nc[LAT_GIN_nc != FillValue]),2))+'N '+str(np.around(np.min(LON_GIN_nc[LON_GIN_nc != FillValue]),2))+'E, '+str(np.around(np.max(LAT_GIN_nc[LAT_GIN_nc != FillValue]),2))+'N '+str(np.around(np.max(LON_GIN_nc[LON_GIN_nc != FillValue]),2))+'E'
NcFile.platform_altitude='Not applicable'
NcFile.location_keywords = 'Chilbolton, UK'
NcFile.amf_vocabularies_release = 'https://github.com/ncasuk/AMF_CVs/releases/tag/v2.0.0'
NcFile.history = FlightDate.strftime("%Y-%m-%dT%H:%M:%SZ")+' - Data collected.'
NcFile.comment = 'Data processing was performed to derive size segregated number concentrations as in OShea et al. (https://doi.org/10.5194/amt-2020-265). Particle size has been calculated as the mean particle extent parallel and perpendicular to the probe optical array. Particles with inter-arrival times < 1E-5 s have been rejected. Particles in contact with the edge of the sample array have been rejected. Other aircraft variables have been extracted from the file '+CoreFileName+'. The instrument counting uncertainty for each time and size bin is given by (100% / N^(1/2)), where N is the number of counts in the bin.'
NcFile.measurement_technique = 'optical'

#Dimensions
time = NcFile.createDimension('time', len(TimeSecondsSince))
latitude = NcFile.createDimension('latitude', len(TimeSecondsSince))
longitude = NcFile.createDimension('longitude', len(TimeSecondsSince))
index = NcFile.createDimension('index', len(Size_mid))

# Probe variables
time = NcFile.createVariable(varname='time', dimensions=('time',),datatype='float64')
time[:] = TimeSecondsSince
time.units = 'seconds since 1970-01-01 00:00:00'
time.long_name = 'Time (seconds since 1970-01-01)'
time.calendar = 'standard'
time.axis = 'T'
time.valid_min= np.min(TimeSecondsSince)
time.valid_max= np.max(TimeSecondsSince)

ambient_particle_diameter= NcFile.createVariable(varname='ambient_particle_diameter', dimensions=('index',),datatype='float32')
ambient_particle_diameter[:] = Size_mid
ambient_particle_diameter.long_name = 'Ambient Particle Diameter'
ambient_particle_diameter.units = 'um'
ambient_particle_diameter.valid_min= np.min(Size_mid[Size_mid!=FillValue])
ambient_particle_diameter.valid_max= np.max(Size_mid[Size_mid!=FillValue])
ambient_particle_diameter.coordinates = 'latitude longitude'

measurement_channel_lower_limit = NcFile.createVariable(varname='measurement_channel_lower_limit', dimensions=('index',),datatype='float32')
measurement_channel_lower_limit[:] = Size_edge[:-1]
measurement_channel_lower_limit.long_name = 'Lower Limit of Spectrometer Measurement Channel'
measurement_channel_lower_limit.units = 'um'
measurement_channel_lower_limit.valid_min= np.min(Size_edge[:-1])
measurement_channel_lower_limit.valid_max= np.max(Size_edge[:-1])
measurement_channel_lower_limit.coordinates = 'latitude longitude'

measurement_channel_upper_limit = NcFile.createVariable(varname='measurement_channel_upper_limit', dimensions=('index',),datatype='float32')
measurement_channel_upper_limit[:] = Size_edge[1:]
measurement_channel_upper_limit.long_name = 'Upper Limit of Spectrometer Measurement Channel'
measurement_channel_upper_limit.units = 'um'
measurement_channel_upper_limit.valid_min= np.min(Size_edge[1:])
measurement_channel_upper_limit.valid_max= np.max(Size_edge[1:])
measurement_channel_upper_limit.coordinates = 'latitude longitude'

if NormFlag == 0 : 
    ambient_particle_number_per_channel = NcFile.createVariable(varname='ambient_particle_number_per_channel', dimensions=('time','index'),datatype='float32', fill_value = FillValue) 
    ambient_particle_number_per_channel[:,:] = Psd
    ambient_particle_number_per_channel.long_name = 'Ambient Particle Number per Channel (dN)'
    ambient_particle_number_per_channel.units = 'cm-3'
    ambient_particle_number_per_channel.valid_min= np.min(Psd[Psd!=FillValue])
    ambient_particle_number_per_channel.valid_max= np.max(Psd[Psd!=FillValue])
    ambient_particle_number_per_channel.coordinates = 'latitude longitude'
    ambient_particle_number_per_channel.cell_methods = 'time: mean'
if NormFlag == 1:
    ambient_particle_size_distribution = NcFile.createVariable(varname='ambient_particle_size_distribution', dimensions=('time','index'),datatype='float32', fill_value = FillValue) 
    ambient_particle_size_distribution[:,:] = Psd
    ambient_particle_size_distribution.long_name = 'Ambient Particle Size Distribution (dN\dD)'
    ambient_particle_size_distribution.units = 'cm-3 um-1'
    ambient_particle_size_distribution.valid_min= np.min(Psd[Psd!=FillValue])
    ambient_particle_size_distribution.valid_max= np.max(Psd[Psd!=FillValue])
    ambient_particle_size_distribution.coordinates = 'latitude longitude'
    ambient_particle_size_distribution.cell_methods = 'time: mean'  

number_of_instrument_counts_per_channel = NcFile.createVariable(varname='number_of_instrument_counts_per_channel', dimensions=('time','index'),datatype='float32', fill_value = FillValue)  
number_of_instrument_counts_per_channel[:,:] = Counts
number_of_instrument_counts_per_channel.long_name = 'Number of Instrument Counts per Channel'
number_of_instrument_counts_per_channel.units = '1'
number_of_instrument_counts_per_channel.valid_min= np.min(number_of_instrument_counts_per_channel[number_of_instrument_counts_per_channel!=FillValue])
number_of_instrument_counts_per_channel.valid_max= np.max(number_of_instrument_counts_per_channel[number_of_instrument_counts_per_channel!=FillValue])
number_of_instrument_counts_per_channel.coordinates = 'latitude longitude'
number_of_instrument_counts_per_channel.cell_methods = 'time: point'

qc_flag_ambient_particle_number_per_channel = NcFile.createVariable(varname='qc_flag_ambient_particle_number_per_channel', dimensions=('time','index'),datatype='u4')  
qc_flag_ambient_particle_number_per_channel[:,:] = DataFlag_2D
qc_flag_ambient_particle_number_per_channel.long_name = 'Data Quality flag: Ambient Particle Number per Channel'
qc_flag_ambient_particle_number_per_channel.flag_values = '0b, 1b, 2b, 3b'
qc_flag_ambient_particle_number_per_channel.flag_meanings = 'not_used \n\rvalid_data \n\rreduced_quality_data \n\rmissing_data'

# Core variables 
altitude = NcFile.createVariable(varname='altitude', dimensions=('time',),datatype='float32', fill_value = FillValue) 
altitude[:] = ALT_GIN_nc
altitude.long_name = 'Geometric height above geoid (WGS84)'
altitude.units = 'm'
altitude.axis = 'Z'
altitude.valid_min = np.min(ALT_GIN_nc[ALT_GIN_nc!=FillValue])
altitude.valid_max = np.max(ALT_GIN_nc[ALT_GIN_nc!=FillValue])
altitude.cell_methods = 'time:point'

latitude = NcFile.createVariable(varname='latitude', dimensions=('latitude',),datatype='float32', fill_value = FillValue) 
latitude[:] = LAT_GIN_nc
latitude.long_name = 'Latitude'
latitude.units = 'degrees_north'
latitude.axis = 'Y'
latitude.valid_min = np.min(LAT_GIN_nc[LAT_GIN_nc!=FillValue])
latitude.valid_max =  np.max(LAT_GIN_nc[LAT_GIN_nc!=FillValue])
latitude.cell_methods= 'time:point'

longitude = NcFile.createVariable(varname='longitude', dimensions=('longitude',),datatype='float32', fill_value = FillValue) 
longitude[:] = LON_GIN_nc
longitude.long_name = 'Longitude'
longitude.units = 'degrees_east'
longitude.axis= 'X'
longitude.valid_min=  np.min(LON_GIN_nc[LON_GIN_nc!=FillValue])
longitude.valid_max= np.min(LAT_GIN_nc[LAT_GIN_nc!=FillValue])
longitude.cell_methods= 'time:point'

platform_speed_wrt_air = NcFile.createVariable(varname='platform_speed_wrt_air', dimensions=('time',),datatype='float32', fill_value = FillValue)  
platform_speed_wrt_air[:] = TAS_RVSM_nc
platform_speed_wrt_air.long_name = 'Platform speed with respect to air (air speed)' 
platform_speed_wrt_air.units = 'm s-1'
platform_speed_wrt_air.valid_min= np.min(TAS_RVSM_nc[TAS_RVSM_nc!=FillValue])
platform_speed_wrt_air.valid_max= np.max(TAS_RVSM_nc[TAS_RVSM_nc!=FillValue])
platform_speed_wrt_air.cell_methods = 'time: mean'
platform_speed_wrt_air.coordinates = 'latitude longitude'

platform_course = NcFile.createVariable(varname='platform_course', dimensions=('time',),datatype='float32', fill_value = FillValue) 
platform_course[:] = TRCK_GIN_nc
platform_course.long_name = 'Direction in which the platform is travelling'
platform_course.units = 'degree'
platform_course.valid_min= np.min(TRCK_GIN_nc[TRCK_GIN_nc!=FillValue])
platform_course.valid_max= np.max(TRCK_GIN_nc[TRCK_GIN_nc!=FillValue])
platform_course.cell_methods = 'time: mean'
platform_course.coordinates = 'latitude longitude'

platform_speed_wrt_ground = NcFile.createVariable(varname='platform_speed_wrt_ground', dimensions=('time',),datatype='float32', fill_value = FillValue) 
platform_speed_wrt_ground[:] = GSPD_GIN_nc
platform_speed_wrt_ground.long_name = 'Platform speed with respect to ground (ground speed)'
platform_speed_wrt_ground.units = 'm s-1'
platform_speed_wrt_ground.valid_min= np.min(GSPD_GIN_nc[GSPD_GIN_nc!=FillValue])
platform_speed_wrt_ground.valid_max= np.max(GSPD_GIN_nc[GSPD_GIN_nc!=FillValue])
platform_speed_wrt_ground.cell_methods = 'time: mean'
platform_speed_wrt_ground.coordinates = 'latitude longitude'

platform_pitch_angle = NcFile.createVariable(varname='platform_pitch_angle', dimensions=('time',),datatype='float32', fill_value = FillValue) 
platform_pitch_angle[:] = PTCH_GIN_nc
platform_pitch_angle.long_name = 'Pitch Angle'
platform_pitch_angle.units = 'degree'
platform_pitch_angle.valid_min= np.min(PTCH_GIN_nc[PTCH_GIN_nc!=FillValue])
platform_pitch_angle.valid_max= np.max(PTCH_GIN_nc[PTCH_GIN_nc!=FillValue])
platform_pitch_angle.cell_methods = 'time: mean'
platform_pitch_angle.coordinates = 'latitude longitude'

platform_pitch_rate = NcFile.createVariable(varname='platform_pitch_rate', dimensions=('time',),datatype='float32', fill_value = FillValue) 
platform_pitch_rate[:] = PITR_GIN_nc
platform_pitch_rate.long_name = 'Pitch Angle Rate of Change'
platform_pitch_rate.units = 'degree s-1'
platform_pitch_rate.valid_min= np.min(PITR_GIN_nc[PITR_GIN_nc!=FillValue])
platform_pitch_rate.valid_max= np.max(PITR_GIN_nc[PITR_GIN_nc!=FillValue])
platform_pitch_rate.cell_methods = 'time: mean'
platform_pitch_rate.coordinates = 'latitude longitude'

platform_yaw_angle = NcFile.createVariable(varname='platform_yaw_angle', dimensions=('time',),datatype='float32', fill_value = FillValue) 
platform_yaw_angle[:] = HDG_GIN_nc
platform_yaw_angle.long_name = 'Yaw Angle'
platform_yaw_angle.units = 'degree'
platform_yaw_angle.valid_min= np.min(HDG_GIN_nc[HDG_GIN_nc!=FillValue])
platform_yaw_angle.valid_max= np.max(HDG_GIN_nc[HDG_GIN_nc!=FillValue])
platform_yaw_angle.cell_methods = 'time: mean'
platform_yaw_angle.coordinates = 'latitude longitude'

platform_yaw_rate = NcFile.createVariable(varname='platform_yaw_rate', dimensions=('time',),datatype='float32', fill_value = FillValue) 
platform_yaw_rate[:] = HDGR_GIN_nc
platform_yaw_rate.long_name = 'Yaw Angle Rate of Change'
platform_yaw_rate.units = 'degree s-1'
platform_yaw_rate.valid_min= np.min(HDGR_GIN_nc[HDGR_GIN_nc!=FillValue])
platform_yaw_rate.valid_max= np.max(HDGR_GIN_nc[HDGR_GIN_nc!=FillValue])
platform_yaw_rate.cell_methods = 'time: mean'
platform_yaw_rate.coordinates = 'latitude longitude'

DayOfYear = NcFile.createVariable(varname='day_of_year', dimensions=('time',),datatype='float32') 
DayOfYear[:] = day_of_year
DayOfYear.long_name = 'Day of Year'
DayOfYear.units = '1'
DayOfYear.valid_min = np.float32(np.nanmin(day_of_year))
DayOfYear.valid_max = np.float32(np.nanmax(day_of_year))

Year = NcFile.createVariable(varname='year', dimensions=('time',),datatype='u4') 
Year[:] = year
Year.long_name = 'Year'
Year.units = '1'
Year.valid_min = np.nanmin(year)
Year.valid_max = np.nanmax(year)

Month = NcFile.createVariable(varname='month', dimensions=('time',),datatype='u4') 
Month[:] = month
Month.long_name = 'Month'
Month.units = '1'
Month.valid_min = np.nanmin(month)
Month.valid_max = np.nanmax(month)

Day = NcFile.createVariable(varname='day', dimensions=('time',),datatype='u4') 
Day[:] = day
Day.long_name = 'Day'
Day.units = '1'
Day.valid_min = np.nanmin(day)
Day.valid_max = np.nanmax(day)

Hour = NcFile.createVariable(varname='hour', dimensions=('time',),datatype='u4') 
Hour[:] = hour
Hour.long_name = 'Hour'
Hour.units = '1'
Hour.valid_min = np.nanmin(hour)
Hour.valid_max = np.nanmax(hour)

Minute = NcFile.createVariable(varname='minute', dimensions=('time',),datatype='u4') 
Minute[:] = minute
Minute.long_name = 'Minute'
Minute.units = '1'
Minute.valid_min = np.nanmin(minute)
Minute.valid_max = np.nanmax(minute)

Second = NcFile.createVariable(varname='second', dimensions=('time',),datatype='float32') 
Second[:] = second
Second.long_name = 'Second'
Second.units = '1'
Second.valid_min = np.nanmin(second)
Second.valid_max = np.nanmax(second)

# LAT_GIN  # latitude
# LON_GIN  # longitude
# ALT_GIN # altitude
# TAS_RVSM # platform_speed_wrt_air
# TRCK_GIN # platform_course
# # platform_orientation
# GSPD_GIN # platform_speed_wrt_ground
# PTCH_GIN # platform_pitch_angle
# PITR_GIN # platform_pitch_rate
# ROLL_GIN # platform_roll_angle
# ROLR_GIN # platform_roll_rate
# HDG_GIN  # platform_yaw_angle
# HDGR_GIN # platform_yaw_rate


NcFile.close()