apsimNGpy: The Next Generation Agroecosytem Simulation Library

Our cutting-edge open-source framework, apsimNGpy, empowers advanced agroecosystem modeling through the utilization of object-oriented principles. It features fast batch file simulation, model prediction, evaluation, apsimx file editing, seamless weather data retrieval, and efficient soil profile development

1. Dotnet, install from https://learn.microsoft.com/en-us/dotnet/core/install/
2. Python3
3. APSIM: Add the directory containing the models executable to the system's PATH or python path (to locate the required .dll files). This can be achieved in either of the following ways:
4. Utilize the APSIM installer provided for this purpose.
5. Build APSIM from its source code. This is comming soon
6. Minimum; 8GM RAM, CPU Core i7

Installation

All versions are currently in development, phase and they can be installed as follows:

• Method 1. install from PyPI

pip install apsimNGpy

• Method 1. clone the current development repository

git clone https://github.com/MAGALA-RICHARD/apsimNGpy.git
cd apsimNGpy
pip install .

• Method 2. Use pip straight away and install from github

pip install git+https://github.com/MAGALA-RICHARD/apsimNGpy.git@dev

Before using apsimNGpy, it is necessary to install APSIM. Please follow the instructions provided at the following link to complete the installation: https://www.apsim.info/download-apsim/downloads/ for MAcOS or Linux users see: https://apsimnextgeneration.netlify.app/install/ model documentation and tutorial are also available via; https://docs.apsim.info/ we expect that by accepting to use apsimNGpy, you have a basic understanding of APSIM process-based model, therefore, our focus is to make sure you are able to use apsimNGpy

In addition, make sure that the APSIM installation binaries folder is added to the system path. if you run the following code and returns None you need to do something as explained below.

• 1. Use command line interface

apsim_bin_path -s

• 2. Use apsimNGpy config module

from apsimNGpy.core import config
   print(config.get_apsim_bin_path())

You can also try to check if automatic search will be successful as follows

apsim_bin_path --auto_search

The short cut

apsim_bin_path -a

By default the APSIM binaries are located automatically. The process for determining the APSIM binary path is as follows:

In apsimNGpy, priority is first given to the user-supplied binary path. If no path is supplied, the module searches through the Python global environment using the os module. If that fails, it searches through other folders. If all approaches are exhausted and no valid path is found, a ValueError will be raised.

If the automatic search fails, please follow one of the steps below to resolve the issue:

In your home folder you could look for folder named apsimNGpy_meta_info './APSIMNGpy_meta_data'

1. Locate the folder named APSIMNGpy_meta_info in your home directory (e.g., ./APSIMNGpy_meta_data).
2. Open the file apsimNGpy_config.ini within this folder.
3. Modify the apsim_location entry to reflect your desired APSIM binary path.

Alternatively, you can use the code at the top of your script as follows

# Search for the APSIM binary installation path and add it to os.environ as follows:
import os
os.environ['APSIM'] = r'path/to/your/apsim/binary/folder/bin'

• Note:

This approach may not work consistently in all scenarios, but you can try it. The above script line should always be placed at the beginning of your simulation script. However, why follow this approach when you can achieve the same result more efficiently? See the approach below:

from apsimNGpy.config import set_apsim_bin_path

# Set the path to the APSIM binaries:
set_apsim_bin_path(path=r'path/to/your/apsim/binary/folder/bin')

After installing apsimNGpy, navigate to your terminal and run the following

apsim_bin_path -u 'path/to/your/apsim/binary/folder/bin'

Or

apsim_bin_path --update 'path/to/your/apsim/binary/folder/bin'

# For example, try importing the ApsimModel class:
from apsimNGpy.core.apsim import ApsimModel

The above code is also applicable for running different versions of APSIM models. The set_apsim_bin_path function can be called once and retained unless you uninstall apsimNGpy or the APSIM application itself. This implies that you can switch between apsim versions easily if you have more than one versions installed on your computer

Please note that apsimNGpy is tested on Python 3. We are not aware of its performance in Python 2 because it utilizes some of the new libraries Usage

This example demonstrates how to use apsimNGpy to load a default simulation, run it, retrieve results, and visualize the output.

# Import necessary modules
import apsimNGpy
from apsimNGpy.core.base_data import load_default_simulations
from apsimNGpy.core.apsim import ApsimModel as SoilModel
from pathlib import Path
import os
from apsimNGpy.validation.visual import plot_data

The above code imports the necessary modules for running APSIM simulations. This includes apsimNGpy modules for loading default simulations and managing results, as well as standard Python libraries for file handling and visualization.

# Load the default simulation
soybean_model = load_default_simulations(crop='soybean')  # Case-insensitive crop specification

The load_default_simulations function loads a default APSIM simulation for the specified crop. In this example, the crop is set to soybean, but you can specify other crops as needed.

# Load the simulation path without initializing the object
soybean_path_model = load_default_simulations(crop='soybean', simulation_object=False)

If you prefer not to initialize the simulation object immediately, you can load only the simulation path by setting simulation_object=False.

# Initialize the APSIM model with the simulation file path
apsim = SoilModel(soybean_path_model)

This code initializes the APSIM model using the previously loaded simulation file path.

# Run the simulation
apsim.run(report_name='Report')

The run method executes the simulation. The report_name parameter specifies which data table from the simulation will be used for results.

# Retrieve and save the results
df = apsim.results
df.to_csv('apsim_df_res.csv')  # Save the results to a CSV file
print(apsim.results)  # Print all DataFrames in the storage domain

After the simulation runs, results are stored in the apsim.results attribute as pandas DataFrames. Please see note above. These results can be saved to a CSV file or printed to the console.

The code below retrieves the names of simulations from the APSIM model and examines the management modules used in the specified simulations.

# Examine management modules in the simulation
sim_name = apsim.simulation_names  # Retrieve simulation names
apsim.examine_management_info(simulations=sim_name)

You can preview the current simulation in the APSIM graphical user interface (GUI) using the preview_simulation method.

# Preview the current simulation in the APSIM GUI
apsim.preview_simulation()

Visualise the results. please note that python provide very many plotting libraries below is just a basic description of your results

# Visualize the simulation results
res = apsim.results['MaizeR']  # Replace with the appropriate report name
plot_data(df['Clock.Today'], df.Yield, xlabel='Date', ylabel='Soybean Yield (kg/ha)')

Finally, the plot_data function is used to visualize the simulation results. Replace 'df['Clock.Today']' and df.Yield with the appropriate report name and column from your simulation results.

A graph similar to the example below should appear

import apsimNGpy
from apsimNGpy.core.base_data import load_default_simulations
from apsimNGpy.core.apsim  import ApsimModel as SoilModel
from pathlib import Path
import os
from apsimNGpy.validation.visual import plot_data
cwd = Path.cwd().home() # sending this to your home folder
wd = cwd.joinpath("apsimNGpy_demo")
if not wd.exists():
  os.mkdir(wd)
# change directory
os.chdir(wd)
# Get maize model
maize_model = load_default_simulations(crop = 'maize')

maize_model.change_simulation_dates(start_date='01/01/1998', end_date='12/31/2010')

# First, examine the manager scripts in the simulation node
apsim.examine_management_info()
# now create dictionary holding the parameters. the key to this is that the name of the script manage must be
passed in the dictionary.

# in this node we if have a script named the Simple Rotation,we want to change the rotation to maybe Maize, Wheat or
something else
rotation  = {'Name': "Simple Rotation", "Crops": 'Maize, Wheat, Soybean'}, # the crops must be seperated my commas
apsim.update_mgt(management = rotation)
# now you cans see we passed rotation as a tuple. That means you can add other scripts as your needs suggest. They will all be changed at the
#same time

from apsimNGpy.core.weather import daymet_bylocation_nocsv
lonlat = -93.08, 42.014
start_year, end_year = 2000, 2002
wf = daymet_bylocation_nocsv(lonlat, startyear, endyear, filename="mymet.met")
# you may need to first see what file currently exists in the model
mis = apsim.show_met_file_in_simulation()
print(mis)
# change
maize_model.replace_met_file(weather_file=wf)
# check again if you want to
mis = maize_model.show_met_file_in_simulation()
print(mis)

The apsimNGpy Python package provides a convenient way to validate model simulations against measured data. Below is a step-by-step guide on how to use the validation.evaluator module from apsimNGpy.

# Start by importing the required libraries
from apsimNGpy.validation.evaluator import validate
import pandas as pd

# Load the data if external. Replace with your own data
df = pd.read_csv('evaluation.csv')
apsim_results = apsim.results  # Assuming 'apsim' is a predefined object from aopsimNGpy.core.core.APSIMN class and contains your simualted results

# Preparing Data for Validation
# Extract the relevant columns from your DataFrame for comparison. In this example, we use
# 'Measured' for observed values and compare them with different model outputs:
measured = df['Measured']
predicted = apsim_results['MaizeR'].Yield

# Now we need to pass both the measured and the observed in the validate class
val = validate(measured, predicted)

# Both variables should be the same length, and here we are assuming that they are sorted in the corresponding order

# There are two options:
# 1. Evaluate all
metrics = val.evaluate_all(verbose=True)
# Setting verbose=True prints all the results on the go; otherwise, a dictionary is returned with the value for each metric

# 2. Select or pass your desired metric
RMSE = val.evaluate("RMSE")
print(RMSE)

# If you want to see the available metrics, use the code below
available_metrics = metrics.keys()
print(available_metrics)
# Then select your choice from the list

We welcome contributions from the community, whether they are bug fixes, enhancements, documentation updates, or new features. Here's how you can contribute to apsimNGpy:

If you find a bug or have a suggestion for improving apsimNGpy, please first check the Issue Tracker to see if it has already been reported. If it hasn't, feel free to submit a new issue. Please provide as much detail as possible, including steps to reproduce the issue, the expected outcome, and the actual outcome.

We accept code contributions via Pull Requests (PRs). Here are the steps to contribute:

Start by forking the apsimNGpy repository on GitHub. This creates a copy of the repo under your GitHub account.

Clone your fork to your local machine:

git clone https://github.com/MAGALA-RICHARD/apsimNGpy.git
cd apsimNGpy

Create a New Branch

Create a new branch for your changes:

git checkout -b your-branch-name

Make Your Changes

Make the necessary changes or additions to the codebase. Please try to adhere to the coding style already in place.

Test Your Changes

Run any existing tests, and add new ones if necessary, to ensure your changes do not break existing functionality.

Commit Your Changes

Commit your changes with a clear commit message that explains what you've done:

git commit -m "A brief explanation of your changes"

Push to GitHub

Push your changes to your fork on GitHub:

git push origin your-branch-name

Submit a Pull Request

Go to the apsimNGpy repository on GitHub, and you'll see a prompt to submit a pull request based on your branch. Click on "Compare & pull request" and describe the changes you've made. Finally, submit the pull request.

Improvements or updates to documentation are greatly appreciated. You can submit changes to documentation with the same process used for code contributions.

Feel free to join in discussions on issues or pull requests. Your feedback and insights are valuable to the community!

Dynamic handling of simulations and their properties

replacements made easier

object oriented factorial experiment set ups and simulations

This project, ApsimNGpy, greatly appreciates the support and contributions from various organizations and initiatives that have made this research possible. We extend our gratitude to Iowa State University's C-CHANGE Presidential Interdisciplinary Research Initiative, which has played a pivotal role in the development of this project. Additionally, our work has been significantly supported by a generous grant from the USDA-NIFA Sustainable Agricultural Systems program (Grant ID: 2020-68012-31824), underscoring the importance of sustainable agricultural practices and innovations.

We would also like to express our sincere thanks to the APSIM Initiative. Their commitment to quality assurance and the structured innovation program for APSIM's modelling software has been invaluable. APSIM's software, which is available for free for research and development use, represents a cornerstone for agricultural modeling and simulation. For further details on APSIM and its capabilities, please visit www.apsim.info.

Our project stands on the shoulders of these partnerships and support systems, and we are deeply thankful for their contribution to advancing agricultural research and development. Please not that that this library is designed as a bridge to APSIM software, and we hope that by using this library, you have the appropriate APSIM license to do so whether free or commercial.

Lastly but not least, ApsimNGpy is not created in isolation but draws inspiration from apsimx, an R package (https://cran.r-project.org/web/packages/apsimx/vignettes/apsimx.html). We acknowledge and appreciate the writers and contributors of apsimx for their foundational work. ApsimNGpy is designed to complement apsimx by offering similar functionalities and capabilities in the Python ecosystem.