metdatapy 1.0.0

Source-agnostic toolkit for ingesting, cleaning, QC, and preparing meteorological time-series data for ML.

MetDataPy

Source-agnostic toolkit for ingesting, cleaning, QC-flagging, enriching, and preparing meteorological time-series data for machine learning.

Statement of Need

Modern ML pipelines require clean, unit-consistent, well-flagged meteorological time series. MetDataPy provides a canonical schema, robust ingestion (with autodetection and an interactive mapping wizard), quality control, derived metrics, time-safe ML preparation, and reproducible exports.

Quickstart

# Install MetDataPy
pip install metdatapy

# Detect column mappings
mdp ingest detect --csv path/to/file.csv --save mapping.yml

# Apply mapping and ingest data
mdp ingest apply --csv path/to/file.csv --map mapping.yml --out raw.parquet

# Run quality control
mdp qc run --in raw.parquet --out clean.parquet --report qc_report.json \
  --config qc_config.yml

For detailed installation options (including optional features), see the Installation section below.

Installation

Basic Installation

pip install metdatapy

This installs MetDataPy with core dependencies only. The core package is compatible with both NumPy 1.x and 2.x.

Installation with Optional Features

# For machine learning features
pip install "metdatapy[ml]"

# For NetCDF export functionality
pip install "metdatapy[netcdf]"

# For visualization (examples/notebooks)
pip install "metdatapy[viz]"

# For all optional features
pip install "metdatapy[all]"

# Or combine specific features
pip install "metdatapy[ml,netcdf]"

Development Installation

For developers or contributors who want to install from source:

git clone https://github.com/kkartas/MetDataPy.git
cd MetDataPy
pip install -e .

Requirements

Python: 3.9+

Core dependencies: pandas ≥2.0, numpy ≥1.23, pyarrow ≥13.0, click ≥8.1, pydantic ≥2.4, PyYAML ≥6.0

Optional dependencies:

• ML: scikit-learn ≥1.2, statsmodels ≥0.13
• NetCDF: xarray ≥2023.6.0, netCDF4 ≥1.6, cftime ≥1.6
• Visualization: matplotlib ≥3.5, seaborn ≥0.12
• Extras: astral ≥3.2, holidays ≥0.36

NumPy 2.x Compatibility

Core MetDataPy package: Fully compatible with both NumPy 1.x and 2.x. All functionality works with either version.

Visualization dependencies: Some visualization packages (matplotlib, seaborn) may have compatibility issues with NumPy 2.x on certain platforms. If you encounter errors like:

A module that was compiled using NumPy 1.x cannot be run in NumPy 2.x

Solutions:

1. For core usage (data processing, QC, ML prep): No action needed - works with any NumPy version
2. For visualization (running examples with plots):

   pip install 'numpy<2.0' matplotlib seaborn
   

3. Alternative: Wait for matplotlib/seaborn to release NumPy 2.x compatible builds

Note: This issue only affects optional visualization features. The core MetDataPy functionality (ingestion, QC, derived metrics, ML preparation, NetCDF export) works perfectly with NumPy 2.x.

Documentation

Full documentation is available on Read the Docs.

To build documentation locally:

pip install metdatapy[all]
pip install mkdocs mkdocs-material
mkdocs serve
# Then open http://localhost:8000

Features

• Canonical schema with UTC index and metric units
• Ingestion from CSV with mapping autodetection and interactive mapping wizard
• Unit normalization, rain accumulation fix-up, gap insertion with gap flag
• WeatherSet resampling/aggregation, calendar features, exogenous joins
• Derived: dew point, VPD, heat index, wind chill
• ML prep: supervised table builder (lags, horizons), time-safe split, scaling (Standard/MinMax/Robust)
• Export: Parquet and CF-compliant NetCDF with metadata
• Performance: Processes 1 year of 10-min data in <0.5s (see benchmarks/)

Quality Control

• Range checks with boolean flags (qc_<var>_range)
• Spike detection (rolling MAD z-score) and flatline detection (rolling variance)
• Cross-variable consistency checks with aggregate qc_any
• CLI supports a config file for thresholds:

mdp qc run --in raw.parquet --out clean.parquet \
  --config qc_config.yml --report qc_report.json

Example qc_config.yml:

spike:
  window: 9
  thresh: 6.0
flatline:
  window: 5
  tol: 0.0

Python API example

import pandas as pd
from metdatapy.mapper import Mapper
from metdatapy.core import WeatherSet
from metdatapy.mlprep import make_supervised, time_split, fit_scaler, apply_scaler

mapping = Mapper.load("mapping.yml")
df = pd.read_csv("path/to/file.csv")
ws = WeatherSet.from_mapping(df, mapping).to_utc().normalize_units(mapping)
ws = ws.insert_missing().fix_accum_rain().qc_range().qc_spike().qc_flatline().qc_consistency()
ws = ws.derive(["dew_point", "vpd", "heat_index", "wind_chill"]).resample("1H").calendar_features()
clean = ws.to_dataframe()

# Export to CF-compliant NetCDF
ws.to_netcdf("weather_data.nc", metadata={"title": "Weather Station Data"}, 
             station_metadata={"station_id": "AWS001", "lat": 40.7, "lon": -74.0})

sup = make_supervised(clean, targets=["temp_c"], horizons=[1,3], lags=[1,2,3])
splits = time_split(sup, train_end=pd.Timestamp("2025-01-15T00:00Z"))
scaler = fit_scaler(splits["train"], method="standard")
train_scaled = apply_scaler(splits["train"], scaler)

Examples

See the examples/ directory for:

Jupyter Notebook:

• metdatapy_tutorial.ipynb - Publication-quality interactive tutorial
  

  Comprehensive tutorial with:

  • Step-by-step workflow with scientific references
  • Publication-ready visualizations (QC flags, derived metrics)
  • Mathematical formulas and physical validation
  • Complete reproducible pipeline

Python Scripts:

• complete_workflow.py - Automated batch processing script
• netcdf_export_example.py - CF-compliant NetCDF export demonstration

Additional Resources:

• README.md - Detailed usage guide
• Sample weather data - data/sample_weather_2024.csv contains a full year (2024) of synthetic 10-minute weather station data (52,561 records) with realistic meteorological patterns. This dataset includes temperature (°F), relative humidity (%), pressure (mbar), wind speed/direction (mph/degrees), rainfall (mm), solar radiation (W/m²), and UV index. The data is used in all examples and can be used to test the full MetDataPy workflow.

Try the notebooks:

• 📁 View on GitHub - Click GitHub links above for native rendering (works immediately)
• 🔍 View on nbviewer - Better rendering with MathJax support
  • Note: nbviewer may show 400 errors for new/recently updated files due to caching. If this happens, use GitHub view or try again in a few minutes.
• 🚀 Run interactively - Click Binder badge for live Jupyter environment (takes ~2 min to launch)

Or run locally:

# Install MetDataPy with all optional features
pip install metdatapy[all]

# Clone the repository for examples
git clone https://github.com/kkartas/MetDataPy.git
cd MetDataPy/examples
jupyter notebook metdatapy_tutorial.ipynb

Automated workflow:

# Install MetDataPy with all optional features
pip install metdatapy[all]

# Clone the repository for examples
git clone https://github.com/kkartas/MetDataPy.git
cd MetDataPy/examples
python complete_workflow.py

Citation

If you use MetDataPy in your research, please cite it:

@software{metdatapy,
  title = {MetDataPy: A Source-Agnostic Toolkit for Meteorological Time-Series Data},
  author = {Kyriakos Kartas},
  year = {2025},
  url = {https://github.com/kkartas/MetDataPy},
  version = {1.0.0}
}

See CITATION.cff for machine-readable citation metadata.

License

MIT License. See LICENSE for details.