vvmtools#

Contents#

VVMTools#

VVMTools is a Python class designed to extract and process variables from simulation output files, especially NetCDF files. It includes functions to handle spatial dimensions, time intervals, topographic variables, and supports parallel processing to optimize data handling. The class also provides logging for debugging.

Features#

Variable Extraction: Extract variable data from NetCDF files using specified time steps and domain ranges.
Topographic Variable Handling: Load topographic variables from TOPO.nc files.
Parallel Processing: Efficiently extract data across multiple time steps using multiprocessing.
Time Array Generation: Create a time array from 05:00 to 05:00 the next day at 2-minute intervals.
Initial Profile Parsing: Parse and store atmospheric profiles (e.g., RHO, THBAR, PBAR) from fort.98.
Debugging: Enable debug mode for logging and troubleshooting.

Requirements#

Python 3.x
Required libraries: - xarray - numpy - multiprocessing - logging

Installation#

Install the package from PyPI:

pip install git+https://github.com/Aaron-Hsieh-0129/VVMTools.git

You can also clone this repository and install the required dependencies:

pip install xarray numpy

Usage#

Initialization#

To initialize the VVMTools class, provide the path to the directory containing the case files. Optionally, enable debug_mode to log more detailed information:

import vvmtools

case_path = "/path/to/case/files"
mytools = vvmtools.analyze.DataRetriever(case_path, debug_mode=True)

Example Operations#

Extract a Variable

To retrieve a variable at a specific time step, use the get_var method:
```
var_data = mytools.get_var("th", time=0, numpy=True)
```

Parallel Data Extraction

For faster processing of data over multiple time steps, use get_var_parallel:

time_steps = range(0, 10)
domain_range = (0, 10, None, None, 10, 20) # (k1, k2, j1, j2, i1, i2)
var_data_parallel = mytools.get_var_parallel("th", time_steps, domain_range=domain_range, cores=4)

Parallelize Function in Time

For faster processing of data over multiple time steps, use get_var_parallel:

def user_define_function(t):
    # some complex analysis
    return # some results

time_steps = range(0, 10)
var_data_parallel = mytools.func_time_parallel(user_define_function, time_steps=time_steps, cores=4)

Debugging#

Enable debugging by setting debug_mode=True when initializing VVMTools. This will provide detailed logging to help trace errors and issues.

import vvmtools
vvm_tools = vvmtools.analyze.DataRetriever(case_path, debug_mode=True)

This will display warnings, errors, and status information during execution.