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dl-ingest.py
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dl-ingest.py
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import numpy as np
from netCDF4 import Dataset
import os
import datetime
import xarray as xr
import pandas as pd
import matplotlib.dates as mdates
import argparse
import matplotlib.pyplot as plt
import cmweather
from glob import glob
from datetime import datetime, timedelta
DEFAULT_SOURCE_PATH = '/nfs/gce/projects/crocus/data/staging_raw_data/NEIU-AGES-plus-deployment/Proc'
DEFAULT_OUTPUT_PATH = '/nfs/gce/projects/crocus/data/staging_raw_data/NEIU-AGES-plus-deployment/netcdf'
DEFAULT_QUICKLOOKS_PATH = '/nfs/gce/projects/crocus/data/staging_raw_data/NEIU-AGES-plus-deployment/quicklooks'
neiu_lat = 41.98054
neiu_lon = -87.71700
neiu_alt = 176.5
'''
Import of StreamLine .hpl (txt) files and save locally in directory. Therefore
the data is converted into matrices with dimension "number of range gates" x "time stamp/rays".
In newer versions of the StreamLine software, the spectral width can be
stored as additional parameter in the .hpl files.
'''
def hpl2dict(file_path):
#import hpl files into intercal storage
with open(file_path, 'r') as text_file:
lines=text_file.readlines()
#write lines into Dictionary
data_temp=dict()
header_n=17 #length of header
data_temp['filename']=lines[0].split()[-1]
data_temp['system_id']=int(lines[1].split()[-1])
data_temp['number_of_gates']=int(lines[2].split()[-1])
data_temp['range_gate_length_m']=float(lines[3].split()[-1])
data_temp['gate_length_pts']=int(lines[4].split()[-1])
data_temp['pulses_per_ray']=int(lines[5].split()[-1])
data_temp['number_of_waypoints_in_file']=int(lines[6].split()[-1])
rays_n=(len(lines)-header_n)/(data_temp['number_of_gates']+1)
'''
number of lines does not match expected format if the number of range gates
was changed in the measuring period of the data file (especially possible for stare data)
'''
if not rays_n.is_integer():
print('Number of lines does not match expected format')
return np.nan
data_temp['no_of_rays_in_file']=int(rays_n)
data_temp['scan_type']=' '.join(lines[7].split()[2:])
data_temp['focus_range']=lines[8].split()[-1]
data_temp['start_time']=pd.to_datetime(' '.join(lines[9].split()[-2:]))
data_temp['resolution']=('%s %s' % (lines[10].split()[-1],'m s-1'))
data_temp['range_gates']=np.arange(0,data_temp['number_of_gates'])
data_temp['center_of_gates']=(data_temp['range_gates']+0.5)*data_temp['range_gate_length_m']
#dimensions of data set
gates_n=data_temp['number_of_gates']
rays_n=data_temp['no_of_rays_in_file']
# item of measurement variables are predefined as symetric numpy arrays filled with NaN values
data_temp['radial_velocity'] = np.full([gates_n,rays_n],np.nan) #m s-1
data_temp['intensity'] = np.full([gates_n,rays_n],np.nan) #SNR+1
data_temp['beta'] = np.full([gates_n,rays_n],np.nan) #m-1 sr-1
data_temp['spectral_width'] = np.full([gates_n,rays_n],np.nan)
data_temp['elevation'] = np.full(rays_n,np.nan) #degrees
data_temp['azimuth'] = np.full(rays_n,np.nan) #degrees
data_temp['decimal_time'] = np.full(rays_n,np.nan) #hours
data_temp['pitch'] = np.full(rays_n,np.nan) #degrees
data_temp['roll'] = np.full(rays_n,np.nan) #degrees
for ri in range(0,rays_n): #loop rays
lines_temp = lines[header_n+(ri*gates_n)+ri+1:header_n+(ri*gates_n)+gates_n+ri+1]
header_temp = np.asarray(lines[header_n+(ri*gates_n)+ri].split(),dtype=float)
data_temp['decimal_time'][ri] = header_temp[0]
data_temp['azimuth'][ri] = header_temp[1]
data_temp['elevation'][ri] = header_temp[2]
data_temp['pitch'][ri] = header_temp[3]
data_temp['roll'][ri] = header_temp[4]
for gi in range(0,gates_n): #loop range gates
line_temp=np.asarray(lines_temp[gi].split(),dtype=float)
data_temp['radial_velocity'][gi,ri] = line_temp[1]
data_temp['intensity'][gi,ri] = line_temp[2]
data_temp['beta'][gi,ri] = line_temp[3]
if line_temp.size>4:
data_temp['spectral_width'][gi,ri] = line_temp[4]
return data_temp
def convert_to_hours_minutes_seconds(decimal_hour, initial_time):
delta = timedelta(hours=decimal_hour)
return datetime(initial_time.year, initial_time.month, initial_time.day) + delta
def read_as_netcdf(file, lat, lon, alt, n_sweeps=1):
field_dict = hpl2dict(file)
initial_time = pd.to_datetime(field_dict['start_time'])
time = pd.to_datetime([convert_to_hours_minutes_seconds(x, initial_time) for x in field_dict['decimal_time']])
ds = xr.Dataset(coords={'range': field_dict['center_of_gates'],
'time': time,
'azimuth': ('time', field_dict['azimuth']),
'elevation': ('time', field_dict['elevation']),
'pitch': ('time', field_dict['pitch']),
'roll': ('time', field_dict['roll'])} ,
data_vars={'radial_velocity':(['time', 'range'],
field_dict['radial_velocity'].T),
'beta': (('time', 'range'),
field_dict['beta'].T),
'intensity': (('time', 'range'),
field_dict['intensity'].T),
'spectral_width': (('time', 'range'),
field_dict['spectral_width'].T)
}
)
ds['azimuth'] = xr.where(ds['azimuth'] < 360., ds['azimuth'], ds['azimuth'] - 360.)
ds["radial_velocity"].attrs["long_name"] = "Radial velocity of scatterers away from instrument."
ds["radial_velocity"].attrs["standard_name"] = "radial_velocity_of_scatterers_away_from_instrument"
ds["radial_velocity"].attrs["units"] = "m s-1"
ds["spectral_width"].attrs["long_name"] = "Spectral width"
ds["spectral_width"].attrs["standard_name"] = "spectral_width_of_radio_wave_in_air_scattered_by_air"
ds["spectral_width"].attrs["units"] = "m s-1"
ds["beta"].attrs["long_name"] = 'Backscatter coefficient'
ds["beta"].attrs["units"] = "m-1 sr-1"
ds["beta"].attrs["standard_name"] = 'backscattering_ratio'
ds["latitude"] = lat
ds["latitude"].attrs["long_name"] = 'latitude'
ds["latitude"].attrs["standard_name"] = 'latitude'
ds["latitude"].attrs["units"] = "degrees_north"
ds["latitude"].attrs["_FillValue"] = np.nan
ds["longitude"] = lon
ds["longitude"].attrs["long_name"] = 'longitude'
ds["longitude"].attrs["standard_name"] = 'longitude'
ds["longitude"].attrs["units"] = "degrees_east"
ds["longitude"].attrs["_FillValue"] = np.nan
ds["pitch"].attrs["long_name"] = 'Pitch angle'
ds["pitch"].attrs["standard_name"] = 'platform_pitch'
ds["pitch"].attrs["units"] = "degrees"
ds["pitch"].attrs["_FillValue"] = np.nan
ds["roll"].attrs["long_name"] = 'Roll angle'
ds["roll"].attrs["standard_name"] = 'platform_roll'
ds["roll"].attrs["units"] = "degrees"
ds["roll"].attrs["_FillValue"] = np.nan
ds["azimuth"].attrs["long_name"] = 'Azimuth angle'
ds["azimuth"].attrs["standard_name"] = 'sensor_azimuth_angle'
ds["azimuth"].attrs["units"] = "degrees"
ds["azimuth"].attrs["_FillValue"] = np.nan
ds["elevation"].attrs["long_name"] = 'Elevation angle'
ds["elevation"].attrs["units"] = "degrees"
ds["elevation"].attrs["_FillValue"] = np.nan
ds["range"].attrs["long_name"] = "Range from lidar"
ds["range"].attrs["units"] = "meters"
ds["range"].attrs["_FillValue"] = np.nan
ds["altitude"] = alt
ds["altitude"].attrs["long_name"] = "altitude"
ds["altitude"].attrs["standard_name"] = "altitude"
ds["altitude"].attrs["units"] = "meters"
ds["altitude"].attrs["_FillValue"] = np.nan
num_rays = ds.dims['time']
ds.attrs["Conventions"] = "CF-1.7"
ds.attrs["version"] = "R0"
ds.attrs["mentor"] = "Bobby Jackson"
ds.attrs['mentor_email'] = "rjackson@anl.gov"
ds.attrs['mentor_institution'] = 'Argonne National Laboratory'
ds.attrs['mentor_orcid'] = "0000-0003-2518-1234"
ds.attrs['contributors'] = "Bobby Jackson, Scott Collis, Paytsar Muradyan, Max Grover, Joseph O'Brien"
ds = ds.sortby('time')
return ds
if __name__ == "__main__":
parser = argparse.ArgumentParser(
prog='HaloIngest',
description='Halo photonics Doppler lidar R1-level ingest')
parser.add_argument('--source_path', default=DEFAULT_SOURCE_PATH,
help="Source path for .hpl files")
parser.add_argument('--dest_path', default=DEFAULT_OUTPUT_PATH,
help="Destination path for .nc files")
parser.add_argument('--date', default=None, help="Date in YYYYMMDD, default is today")
parser.add_argument('--quicklooks_path', default=DEFAULT_QUICKLOOKS_PATH,
help="Destination path for quicklooks")
args = parser.parse_args()
if args.date == None:
inp_date = datetime.now().strftime("%Y%m%d")
else:
inp_date = args.date
input_list = glob(args.source_path + '/**/*' + inp_date + "*.hpl", recursive=True)
for fi in input_list:
print("Processing %s" % fi)
base, name = os.path.split(fi)
scan_type = name.split("_")[0]
if scan_type == "Processed":
continue
ds = read_as_netcdf(fi, neiu_lat, neiu_lon, neiu_alt)
if scan_type == "Wind":
scan_type = "Wind_Profile"
date = name.split("_")[3]
time = name.split("_")[4][:-4]
elif scan_type == "Processed":
continue
else:
date = name.split("_")[2]
time = name.split("_")[3][:-4]
if(len(time) == 2):
time = time + "0000"
ds.attrs["scan_type"] = scan_type
out_name = 'dl.ages.%s.%s.r0.nc' % (date, time)
dest_path = os.path.join(args.dest_path, date)
if not os.path.exists(dest_path):
os.makedirs(dest_path)
out_name = os.path.join(dest_path, out_name)
print("Saving to %s" % out_name)
ds.to_netcdf(out_name)
quick_dir_path = os.path.join(args.quicklooks_path, date)
if not os.path.exists(quick_dir_path):
os.makedirs(quick_dir_path)
quick_dir_path = os.path.join(quick_dir_path, '%s.%s.%s.png' % (scan_type, date, time))
if scan_type == "Stare" or scan_type == "VAD":
fig, ax = plt.subplots(3, 1, figsize=(10, 7))
ds['intensity'].T.plot(ax=ax[0], cmap='ChaseSpectral', vmin=0, vmax=10)
ds['radial_velocity'].T.plot(ax=ax[1], cmap='balance', vmin=-5, vmax=5)
ds['spectral_width'].T.plot(ax=ax[2], cmap='balance', vmin=0, vmax=10)
ax[0].set_ylim([0, 3000])
ax[1].set_ylim([0, 3000])
ax[2].set_ylim([0, 3000])
fig.tight_layout()
fig.savefig(quick_dir_path, bbox_inches='tight')
elif scan_type == "RHI":
fig, ax = plt.subplots(3, 1, subplot_kw=dict(projection="polar"), figsize=(10, 11))
r, theta = np.meshgrid(ds.range.values / 1e3, np.deg2rad(ds["elevation"].values), indexing='xy')
c = ax[0].pcolormesh(theta, r, ds.intensity.values, vmin=0, vmax=10, cmap='ChaseSpectral')
ax[0].set_xlim([0, np.pi])
ax[0].set_rlim([0, 4.0])
ax[0].set_xlabel('Range [km]')
plt.colorbar(c, ax=ax[0], label='Intensity [SNR+1]')
c = ax[1].pcolormesh(theta, r, ds.radial_velocity.values, vmin=-10, vmax=10, cmap='balance')
ax[1].set_xlim([0, np.pi])
ax[1].set_rlim([0, 4.0])
ax[1].set_xlabel('Range [km]')
plt.colorbar(c, ax=ax[1], label='Radial velocity [m/s]')
c = ax[2].pcolormesh(theta, r, ds.spectral_width.values, vmin=0, vmax=10, cmap='balance')
ax[2].set_xlim([0, np.pi])
ax[2].set_rlim([0, 4.0])
ax[2].set_xlabel('Range [km]')
plt.colorbar(c, ax=ax[2], label='Spectral width [m/s]')
fig.tight_layout()
fig.savefig(quick_dir_path, bbox_inches='tight')
print("Quicklook for %s saved in %s!" % (out_name, quick_dir_path))