Metadata-Version: 2.3
Name: cavapy
Version: 0.4.0
Summary: CAVA Python package. Retrive climate data.
License: MIT
Author: Riccardo Soldan
Author-email: riccardosoldan@hotmail.it
Requires-Python: >=3.11,<4.0
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.11
Classifier: Programming Language :: Python :: 3.12
Classifier: Programming Language :: Python :: 3.13
Requires-Dist: bottleneck
Requires-Dist: cartopy
Requires-Dist: dask
Requires-Dist: llvmlite
Requires-Dist: matplotlib
Requires-Dist: netcdf4
Requires-Dist: seaborn
Requires-Dist: xclim
Requires-Dist: xsdba
Project-URL: Repository, https://github.com/Risk-Team/cavapy
Description-Content-Type: text/markdown

<h1 align="center">cavapy: CORDEX-CORE Climate Data Access Simplified</h1>

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**Check GitHub issues for known servers' downtimes**

**We will release bias-corrected CORDEX-CORE simulations with the ISIMIP methodology in 2025. This will allow non-expert users to directly use these datasets and avoid the need for custom bias-correction**

--------------------------------------------------------------------------------------------------


## Introduction

`cavapy` is a Python library designed to streamline the retrieval of CORDEX-CORE climate models hosted on THREDDS servers at the University of Cantabria. Using the Open-source Project for a Network Data Access Protocol (**OPeNDAP**), users can directly access and subset datasets without the need to download large NetCDF files. This capability is part of the Climate and Agriculture Risk Visualization and Assessment (CAVA) [project](https://risk-team.github.io/CAVAanalytics/articles/CAVA.html), which focuses on providing high-resolution climate data for scientific, environmental, and agricultural applications.

With `cavapy`, users can efficiently integrate CORDEX-CORE data into their workflows, making it an ideal resource for hydrological and crop modeling, among other climate-sensitive analyses. Additionally, `cavapy` enables bias correction, potentially enhancing the precision and usability of the data for a wide range of applications.


## Data Source

The climate data provided by `cavapy` is hosted on the THREDDS data server of the University of Cantabria as part of the CAVA project. CAVA is a collaborative effort by FAO, the University of Cantabria, the University of Cape Town, and Predictia, aimed at democratising accessibility and usability of climate information.

### Available Datasets via capapy:
- **CORDEX-CORE Simulations**: Dynamically downscaled high-resolution (25 km) climate models, used in the IPCC AR5 report, featuring simulations from:
  - 3 Global Climate Models (GCMs)
  - 2 Regional Climate Models (RCMs)
  - Two Representative Concentration Pathways (RCPs: RCP2.6 and RCP8.5)
- **Reanalyses Dataset**:
  - ERA5 (used for the optional bias correction of the CORDEX-CORE projections)

---

## Available Variables

`cavapy` grants access to critical climate variables, enabling integration into diverse modeling frameworks. The variables currently available include:

- **Daily Maximum Temperature (tasmax)**: °C  
- **Daily Minimum Temperature (tasmin)**: °C  
- **Daily Precipitation (pr)**: mm  
- **Daily Relative Humidity (hurs)**: %  
- **Daily Wind Speed (sfcWind)**: 2 m level, m/s  
- **Daily Solar Radiation (rsds)**: W/m²  

---

## Installation
cavapy can be installed with pip. 

```
conda create -n test "python>=3.11,<3.13"
conda activate test
pip install cavapy
```

## Process

The get_climate_data function performs automatically:
- Data retrieval in parallel
- Unit conversion
- Convert into a Gregorian calendar (CORDEX-CORE models do not have a full 365 days calendar) through linear interpolation
- Bias correction using the empirical quantile mapping (optional)

## Example usage

Depending on the interest, downloading climate data can be done in a few different ways. Note that GCM stands for General Circulation Model while RCM stands for Regional Climate Model. As the climate data comes from the CORDEX-CORE initiative, users can choose between 3 different GCMs downscaled with two RCMs. In total, there are six simulations for any given domain (except for CAS-22 where only three are available).
Since bias-correction requires both the historical run of the CORDEX model and the observational dataset (in this case ERA5), even when the historical argument is set to False, the historical run will be used for learning the bias correction factor.


### Bias-corrected climate projections
**By default all available climate variables are used. You can specify a subset with the variable argument**

Note that bias correction is automatically performed with empirical quantile mapping on a monthly basis to account for seasonality.
```
import cavapy
Togo_climate_data = cavapy.get_climate_data(country="Togo", variables=["tasmax", "pr"], cordex_domain="AFR-22", rcp="rcp26", gcm="MPI", rcm="REMO", years_up_to=2030, obs=False, bias_correction=True, historical=False)
```
### Non bias-corrected climate projections

```
import cavapy
Togo_climate_data = cavapy.get_climate_data(country="Togo",variables=["tasmax", "pr"], cordex_domain="AFR-22", rcp="rcp26", gcm="MPI", rcm="REMO", years_up_to=2030, obs=False, bias_correction=False, historical=False)
```
### Bias-corrected climate projections plus the historical run

This is useful when assessing changes in crop yield from the historical period. In this case, we provide the bias-corrected historical run of the climate models plus the bias-corrected projections. 

```
import cavapy
Togo_climate_data = cavapy.get_climate_data(country="Togo", variables=["tasmax", "pr"], cordex_domain="AFR-22", rcp="rcp26", gcm="MPI", rcm="REMO", years_up_to=2030, obs=False, bias_correction=True, historical=True)
```
### Observations only (ERA5)

```
import cavapy
Togo_climate_data = cavapy.get_climate_data(country="Togo", variables=["tasmax", "pr"], obs=True,  years_obs=range(1980,2019))
```

## Plotting Functionality

`cavapy` now includes built-in plotting functions to easily visualize your climate data as maps and time series. The plotting functions work seamlessly with the data returned by `get_climate_data()`.

### Available Plotting Functions

- **`plot_spatial_map()`**: Create spatial maps of climate variables
- **`plot_time_series()`**: Generate time series plots with trend analysis

### Plotting Examples

#### Spatial Maps
```python
import cavapy

# Get climate data
data = cavapy.get_climate_data(country="Togo", obs=True, years_obs=range(1990, 2011))

# Plot mean temperature map for a specific period
fig = cavapy.plot_spatial_map(
    data['tasmax'], 
    time_period=(2000, 2010),
    title="Mean Max Temperature 2000-2010",
    cmap="Reds"
)
```

<div align="center">
  <img src="figures/spatial_map_temperature.png" alt="Spatial Temperature Map" width="600">
  <br><em>Example spatial map showing mean maximum temperature in Togo (2000-2010)</em>
</div>

#### Time Series Analysis
```python
# Plot precipitation time series with trend analysis
fig_precip = cavapy.plot_time_series(
    data['pr'],
    title="Precipitation Time Series - Togo (1990-2000)",
    trend_line=True,
    ylabel="Annual Precipitation (mm)",
    aggregation="sum",
    figsize=(12, 6)
)
```

<div align="center">
  <img src="figures/time_series_precipitation.png" alt="Precipitation Trends" width="600">
  <br><em>Example time series plot showing precipitation trends in Togo (1990-2011) with trend line</em>
</div>



