agroecometrics 0.1.0b2

Agricultural ecology metrics and visualization tools.

AgroEcoMetrics

AgroEcoMetrics is a useful tool for manipulating agricultural and Ecological data

Source code: (https://github.com/wiscbicklab/AgroEcoMetrics)

Bug reports: (https://github.com/wiscbicklab/AgroEcoMetrics/issues)

Documentation: (https://wiscbicklab.github.io/AgroEcoMetrics/)

It provides:

• Methods for calulating agricultural and ecological data
• Methods for manipulating and cleaning agriculture and ecological data
• Methods for visualizing agricultural and ecological data

Submodules Overview

agroecometrics.data

Provides utilities for loading, cleaning, interpolating, and saving agricultural datasets. Includes functions to:

• Check CSV file validity
• Load and filter data by date range
• Interpolate missing data
• Save processed DataFrames

agroecometrics.equations

Contains models and equations for ecological and agricultural analysis, including:

• Evapotranspiration Models: Dalton, Penman, Hargreaves, etc.
• Solar Radiation Calculations: Compute extraterrestrial radiation (Ra)
• Vapor Pressure Calculations: Estimate saturation vapor pressure (esat)
• Water Movement Models: Infiltration and hydraulic conductivity
• Crop Models: Calculate Growing Degree Days (GDD)

agroecometrics.visualizations

Provides methods for creating plots from the calulations made in equations.

• Rainfall and Runoff plot
• Air Temperature plots
• Soil Temperature plots and 3d mesh graphs
• Photoperiod Prediction plots

agroecometrics.settings

Used to have consistent references to data within the dataframe. The example below shows how to print the current labels and to change labels

data_labels = AEM.settings.get_labels()
print(data_labels)
AEM.settings.set_labels({'temp_avg': 'Temperature', 'date_time': 'Date'})

Getting Started

Installation

Install via pip:

pip install AgroEcoMetrics

Quick Example

from pathlib import Path
from agroecometrics as AEM
import pandas as pd

# Load your data
file_path = Path("your_weather_data.csv")
df = AEM.data.load_data(file_path)

# Create a Graph of air temperature on a particular day
air_temp_pred = AEM.equations.model_air_temp(df)
AEM.visualizations.plot_air_temp(df, air_temp_pred, Path("your_saved_plot.png"))

# Add Growing Degree Days (GDD) to your dataset
gdd_to_df(df, temp_avg=df["TempAvg"].values, temp_base=10)

# Save your CSV file with new data
AEM.data.save_data(df, Path('your_updated_data.csv'))

This script loads weather data, computes growing degree days, and adds the results to your DataFrame.