agroecometrics 0.1.1

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, manipulating, and saving agricultural datasets. Includes functions to:

• Check CSV file validity
• Load and filter data by date range
• Interpolate missing data
• Save processed DataFrames
• Match date times between numpy arrays
• Get a pandas DataFrame as a dictionary

agroecometrics.equations

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

• TEMPERATURE MODELS: Soil and Air Temperature Prediction Models.
• Evapotranspiration Models: Dalton, Penman, Hargreaves, etc.
• Crop Models: Calculate Growing Degree Days (GDD)
• Photoperiod Models: Photoperiod Predictions.
• Water Movement Models: Infiltration and hydraulic conductivity

agroecometrics.visualizations

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

• Air Temperature plots
• Soil temperature plots and 3d mesh graphs
• Rainfall and runoff plot
• Growing degree day plots
• Photoperiod prediction plots

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
data_path = Path("**your_weather_data.csv**")
image_path = Path("**your_saved_plot.png**")
df = AEM.data.load_data_csv(data_path, "**date_time_col_name**", start_date='2024-01-01', end_date='2024-12-31')

# Create a Graph of air temperature on a particular day
date_times = df["**date_time_col_name**"]
avg_air_temp = df['**avg_air_temp_col_name**']
air_temp_pred = AEM.equations.model_air_temp(avg_air_temp)
AEM.visualizations.plot_air_temp(avg_air_temp, air_temp_pred, date_times, image_path)

This script loads weather data filtered to only 2024, creates an air temperature models from the data, and saves a plot of the predicted temperatures from the model against the actual temperatures.