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# Doppler LiDAR HPL to NetCDF Conversion

This notebook demonstrates the full pipeline for converting `.hpl` files from Halo Photonics Doppler LiDAR systems into CF-compliant NetCDF format.

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``` python

# Install required packages (uncomment and run if needed)

# !pip install xarray netCDF4 numpy

```

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## Step 1: Load and Parse HPL Header

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``` python

def parse_hpl_header(lines):

    header = {}

    for line in lines:

        if line.strip() == "****":

            break

        if ":" in line:

            key, value = line.split(":", 1)

            header[key.strip()] = value.strip()

    return header

with open("Stare_115_20240423_00.hpl", "r") as file:

    lines = file.readlines()

header = parse_hpl_header(lines)

header

```

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## Step 2: Convert Decimal Time to Epoch Seconds

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``` python

import datetime

import numpy as np

base_date = datetime.datetime(2024, 4, 23)

decimal_time = 1.5  # Example: 1.5 hours

measured_time = base_date + datetime.timedelta(hours=decimal_time)

epoch_seconds = (measured_time - datetime.datetime(1970, 1, 1)).total_seconds()

epoch_seconds

```

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## Step 3: Create and Populate xarray Dataset

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``` python

import xarray as xr

# Dummy data

time = np.array([epoch_seconds])

range_gate = np.arange(10) * 30.0 + 15.0

doppler = np.random.randn(1, 10)

ds = xr.Dataset(

    data_vars={

        "doppler": ("time", "range", doppler)

    },

    coords={

        "time": time,

        "range": range_gate

    },

    attrs={"title": "Sample Doppler LiDAR Data"}

)

ds["doppler"].attrs = {

    "units": "m s-1",

    "long_name": "radial_velocity_of_scatterers_away_from_instrument",

    "standard_name": "radial_velocity_of_scatterers_away_from_instrument"

}

ds

```

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## Step 4: Save as CF-Compliant NetCDF

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``` python

ds.to_netcdf("sample_lidar_output.nc", format="NETCDF4", engine="netcdf4")

```