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# NetCDF to CDF converter for AMDADB

- File: [./converter/nc2cdf.py](./converter/nc2cdf.py)
- Python interpreter: 2.7 or 3.6

## Converting a Net-CDF file

### Without output path:

Convert a NetCDF file and save the CDF file in a temp directory, then display its path:

**CLI usage**

```bash
./converter/nc2cdf.py <input_netcdf_file>
```

**Python usage**

```python
from nc2cdf import NetCdf
netcdf = NetCdf('<input_netcdf_file>')
netcdf.get_cdf()
print('CDF path: ' + netcdf.get_cdf_path())
```

- `<input_netcdf_file>`: the NetCDF file you want to convert to CDF (can be a gzip archive containing the CDF file).

> Working example:
> 
> ```bash
> ./converter/nc2cdf.py examples/skr151150000.nc.gz
> File stored in "/tmp/skr151150000.cdf".
> ```

### With output path

Convert a Net-CDF file and save the CDF file it in the specified path:

**CLI usage**

```bash
./nc2cdf.py <input_netcdf_file> <output_cdf_file>
```

**Python usage**

```python
from nc2cdf import NetCdf
netcdf = NetCdf('<input_netcdf_file>')
netcdf.get_cdf('<output_cdf_file>')
```

- `<input_netcdf_file>`: the NetCDF file you want to convert (can be a gzip archive containing the CDF file);
- `<output_cdf_file>`: the path where you want to save the CDF file.

> Working example:
> 
> ```bash
> ./converter/nc2cdf.py examples/skr151150000.nc.gz ./skr151150000.ncf
> ```

## Describing a NetCDf file

Display information about a Net-CDF file, such as global attributes and variables information:

**CLI usage**

```bash
./nc2cdf.py -i <input_netcdf_file>
```

**Python usage**

```python
from nc2cdf import NetCdf
netcdf = NetCdf('<input_netcdf_file>')
netcdf.describe()
```

- `<input_netcdf_file>`: the NetCDF file you want to display (can be a gzip archive containing the CDF file).

> Working example:
> 
> ```bash
> ./converter/nc2cdf.py -i examples/skr151150000.nc.gz
> == Time ==
>   - numpy type: |S1 ^ 2 
>   - dimension(s): Time, TimeLength
>   - size: 481x17 = 8177
> == Received ==
>   - numpy type: float32 ^ 2 
>   - dimension(s): Time, Data
>   - size: 481x4 = 1924
>   - Attributes:
>       - Order: RH 100-400kHz;  LH 100-400kHz; RH 10-1000kHz;  RH 10-1000kHz
>       - Units: W/m^2
> == Emitted ==
>   - numpy type: float32 ^ 2 
>   - dimension(s): Time, Data
>   - size: 481x4 = 1924
>   - Attributes:
>       - Order: RH 100-400kHz;  LH 100-400kHz; RH 10-1000kHz;  RH 10-1000kHz
>       - Units: W/sr
> == RH ==
>   - numpy type: float32 ^ 2 
>   - dimension(s): Time, Spec
>   - size: 481x48 = 23088
>   - Attributes:
>       - Units: W/m^2/Hz
> == VR ==
>   - numpy type: float32 ^ 2 
>   - dimension(s): Time, Spec
>   - size: 481x48 = 23088
>   - Attributes:
>       - desc: circular polarization degree; valid range: -1.1 - -0.2
> == LH ==
>   - numpy type: float32 ^ 2 
>   - dimension(s): Time, Spec
>   - size: 481x48 = 23088
>   - Attributes:
>       - Units: W/m^2/Hz
> == VL ==
>   - numpy type: float32 ^ 2 
>   - dimension(s): Time, Spec
>   - size: 481x48 = 23088
>   - Attributes:
>       - desc: circular polarization degree; valid range: -1.1 - -0.2
> == StartTime ==
>   - numpy type: |S1 ^ 1 
>   - dimension(s): TimeLength
>   - size: 17 = 17
>   - values: '2015115000000000', ...
> == StopTime ==
>   - numpy type: |S1 ^ 1 
>   - dimension(s): TimeLength
>   - size: 17 = 17
>   - values: '2015115235959000', ...
> == Global attributes ==
>   - Source: http://www.lesia.obspm.fr/kronos/ 
>   - note: Only fluxes and powers with |V| > 0.2 are taken into account
>   - Created: Wed Jul 22 11:09:30 2015
> ```