IO Module

io

Classes:

Name	Description
ACY
DGN
DLY
DSL
DWC
OPC
SIT
SOL

ACY

Methods:

Name	Description
get_var	Extract variable from the ACY data.

get_var(varname)

Extract variable from the ACY data.

DGN

Methods:

Name	Description
get_var	Extract variable from the DGN data.

get_var(varname)

Extract variable from the DGN data.

DLY

Bases: DataFrame

Methods:

Name	Description
load	Load data from a DLY file into DataFrame.
save	Save DataFrame into a DLY file.
to_monthly	Save as monthly file
validate	Validate the DataFrame to ensure it contains a continuous range of dates

load(path) classmethod

Load data from a DLY file into DataFrame.

save(path=None)

Save DataFrame into a DLY file.

to_monthly(path=None)

Save as monthly file

validate(start_date, end_date)

Validate the DataFrame to ensure it contains a continuous range of dates between start_date and end_date, without duplicates.

DSL

Methods:

Name	Description
get_data	Return stored water data.

get_data()

Return stored water data.

DWC

Methods:

Name	Description
get_var	Extract variable from the DWC data.

get_var(varname)

Extract variable from the DWC data.

OPC

Bases: DataFrame

Methods:

Name	Description
append	Append another OPC or DataFrame to the current OPC instance.
edit_crop_season	Edit the planting and/or harvest dates for a given year and crop.
edit_fertilizer_rate	Edit the fertilizer rate for a given year.
edit_harvest_date	Edit the harvest date for a given year and crop.
edit_operation_date	Edit the operation date for a given year.
edit_operation_value	Edit the operation value for a given year.
edit_plantation_date	Edit the plantation date for a given year and crop.
get_harvest_date	Retrieve the harvest date(s) for a specific year and/or crop code.
get_plantation_date	Retrieve the plantation date(s) for a specific year and/or crop code.
iter_seasons	Iterate over OPC data, yielding dictionaries containing information for each growing season.
load	Load data from an OPC file into DataFrame.
new	Create a new OPC instance with an empty DataFrame with preset columns, a default header,
remove	Remove operation(s) from the OPC file that match all provided criteria.
save	Save DataFrame into an OPC file.
update	Add or update an operation in the OPC file.
update_phu	Update the OPV1 value with the calculated PHU from the DLY data for all plantation dates.
validate	Validate the OPC data to ensure it contains a continuous range of dates without duplicates.

IAUI property writable

Get the auto-irrigation implement ID from the OPC file header.

Returns:

Name	Type	Description
bool		True if auto-irrigation is enabled (72), False if disabled (0)

LUN property writable

Get the land use number from the OPC file header.

Returns:

Name	Type	Description
int		The land use number from the first 4 characters of header line 2

append(second_opc)

Append another OPC or DataFrame to the current OPC instance. Args: second_opc (pd.DataFrame or OPC): The data to append. Returns: OPC: A new OPC instance with combined data. Raises: ValueError: If second_opc is not a pandas DataFrame or OPC instance.

edit_crop_season(new_planting_date=None, new_harvest_date=None, crop_code=None)

Edit the planting and/or harvest dates for a given year and crop.

Parameters: year (int): Year. new_planting_date (datetime, optional): New planting date. If not provided, only harvest date will be updated. new_harvest_date (datetime, optional): New harvest date. If not provided, only planting date will be updated. crop_code (int, optional): Crop code. If not provided, changes the first crop found.

edit_fertilizer_rate(rate, year=2020, month=None, day=None)

Edit the fertilizer rate for a given year.

Parameters: rate (float): Fertilizer rate to be set. year (int, optional): Year for the fertilizer rate application. Defaults to 2020. month (int, optional): Month for the fertilizer rate application. If not provided, the first instance is changed. day (int, optional): Day for the fertilizer rate application. Defaults to None.

edit_harvest_date(date, crop_code)

Edit the harvest date for a given year and crop.

Parameters: date (datetime or str): Date of harvest in datetime object or 'YYYY-MM-DD' string format. crop_code (int): Crop code.

edit_operation_date(code, year, month, day, crop_code=None)

Edit the operation date for a given year.

Parameters: code (str): Operation code. year (int): Year. month (int): Month of operation. day (int): Day of operation. crop_code (int, optional): Crop code.

edit_operation_value(code, year, value, crop_code=None)

Edit the operation value for a given year.

Parameters: code (str): Operation code. year (int): Year. value (float): New operation value. crop_code (int, optional): Crop code.

edit_plantation_date(date, crop_code)

Edit the plantation date for a given year and crop.

Parameters: date (datetime or str): Date of plantation in datetime object or 'YYYY-MM-DD' string format. crop_code (int): Crop code.

get_harvest_date(year=None, crop_code=None)

Retrieve the harvest date(s) for a specific year and/or crop code.

Parameters: year (int, optional): Year. Defaults to None. crop_code (int, optional): Crop code. Defaults to None.

Returns: dict: A dictionary of crop codes and their harvest dates with row indices.

get_plantation_date(year=None, crop_code=None)

Retrieve the plantation date(s) for a specific year and/or crop code.

Parameters: year (int, optional): Year. Defaults to None. crop_code (int, optional): Crop code. Defaults to None.

Returns: dict: A dictionary of crop codes and their plantation dates with row indices.

iter_seasons(start_year=None, end_year=None)

Iterate over OPC data, yielding dictionaries containing information for each growing season.

Parameters: start_year (int, optional): The starting year to consider. Defaults to None. end_year (int, optional): The ending year to consider. Defaults to None.

dict: A dictionary containing: - plantation_date: The date of plantation - harvest_date: The date of harvest - crop_code: The crop code - operations: A subset of OPC rows for this season - plantation_index: The index of the plantation row

load(path, start_year=None) classmethod

Load data from an OPC file into DataFrame.

Parameters: path (str): Path to the OPC file. start_year (int, optional): Start year for the OPC file. If not provided, it will be read from the file header.

Returns: OPC: An instance of the OPC class containing the loaded data.

new(name, start_year) classmethod

Create a new OPC instance with an empty DataFrame with preset columns, a default header, and provided name and start year.

Parameters: name (str): The name for this OPC file/instance. start_year (int): The start year to assign in the OPC instance.

Returns: OPC: An OPC instance with no data but with the required metadata set.

remove(opID=None, date=None, cropID=None, XMTU=None, fertID=None, year=None)

Remove operation(s) from the OPC file that match all provided criteria.

Parameters:

Name	Type	Description	Default
opID	int	Operation ID to match	None
date	str	Date to match in format 'YYYY-MM-DD'	None
cropID	int	Crop ID to match	None
XMTU/LYR/pestID/fertID	int	Machine type/layer/pesticide ID/fertilizer ID to match	required
year	int	Year to match	None

save(path)

Save DataFrame into an OPC file.

Parameters: path (str): Path to save the OPC file.

update(operation)

Add or update an operation in the OPC file.

Parameters:

Name	Type	Description	Default
operation	dict	Dictionary containing operation details with keys: - opID: Operation ID (required) - cropID: Crop ID (required) - date: Operation date as string 'YYYY-MM-DD' (required) - XMTU/LYR/pestID/fertID: Machine type/years/pesticide ID/fertilizer ID (optional, default 0) - OPV1-OPV8: Additional operation values (optional, default 0)	required

update_phu(dly, cropcom)

Update the OPV1 value with the calculated PHU from the DLY data for all plantation dates.

Parameters: dly (DLY): DLY object containing weather data. cropcom (DataFrame): DataFrame containing crop code and TBS values.

validate(duration=None)

Validate the OPC data to ensure it contains a continuous range of dates without duplicates.

Parameters: duration (int, optional): Duration of the simulation in years. If None, uses the maximum Yid.

Returns: bool: True if the data is valid, False otherwise.

SIT

Methods:

Name	Description
load	Class method to load the .sit file and return a SiteFile instance.
save	Save the current site information to a .SIT file.

elevation property writable

Get elevation value.

lat property writable

Get latitude value.

lon property writable

Get longitude value.

slope property writable

Get slope steep value.

load(file_path) classmethod

Class method to load the .sit file and return a SiteFile instance.

Parameters: file_path (str): Path to the .sit file.

Returns: SiteFile: An instance of the SiteFile class with loaded data.

save(output_dir)

Save the current site information to a .SIT file.

Parameters: output_dir (str): Directory where the .SIT file will be saved, or the full path including the .SIT extension.

SOL

Methods:

Name	Description
from_sda	Create a Soil object from Soil Data Access using a query.
load	Load soil data from a file and return a Soil object.
save	Save the soil data to a file using a template.

from_sda(query) classmethod

Create a Soil object from Soil Data Access using a query.

Parameters:

Name	Type	Description	Default
query	int or str	Query string for SoilDataAccess. (mukey or WKT str) ( "POINT(-123.4567 45.6789)" )	required

Returns:

Name	Type	Description
Soil		A new Soil object populated with data from SDA.

load(filepath) classmethod

Load soil data from a file and return a Soil object.

Parameters:

Name	Type	Description	Default
filepath	str	Path to the soil file.	required

Returns:

Name	Type	Description
Soil		A new Soil object populated with data from the file.

save(filepath, template=None)

Save the soil data to a file using a template.

Parameters:

Name	Type	Description	Default
filepath	str	Path to save the soil file.	required
template	list	Optional list of template lines.	None

Raises:

Type	Description
ValueError	If soil properties DataFrame is empty.

config_parser

Classes:

Name	Description
ConfigParser

ConfigParser

Methods:

Name	Description
get	Retrieve a value from the configuration.
load	Load data from the YAML file.
save	Save data to the YAML file.
update	Update the current config with new values.

get(key, default=None)

Retrieve a value from the configuration.

load()

Load data from the YAML file.

save()

Save data to the YAML file.

update(updates)

Update the current config with new values.

cropcom

Classes:

Name	Description
CropCom	Class for handling CROPCOM.DAT file.

CropCom

Class for handling CROPCOM.DAT file.

Methods:

Name	Description
constraints	Returns the constraints (min, max ranges) for the parameters.
edit	Updates the parameters in the DataFrame with new values.
get_vars	Returns the vars DataFrame with an additional column containing current values.
save	Save DataFrame into an OPC file.
set_sensitive	Sets sensitive parameters based on a CSV path or list of parameter names.

current property

Returns the current values of parameters in the DataFrame.

constraints()

Returns the constraints (min, max ranges) for the parameters.

edit(values)

Updates the parameters in the DataFrame with new values.

get_vars()

Returns the vars DataFrame with an additional column containing current values.

save(path)

Save DataFrame into an OPC file.

set_sensitive(parms_input, crop_codes, all=False)

Sets sensitive parameters based on a CSV path or list of parameter names. If all is True, all parameters are considered sensitive.

Parameters:

Name	Type	Description	Default
parms_input	str or list	Either a CSV file path or list of parameter names to select	required
crop_codes	list	List of crop codes to apply parameters to	required
all	bool	If True, all parameters are considered sensitive regardless of input	False

data_logger

Classes:

Name	Description
DataLogger	A class to handle logging of data using different backends: Redis, SQL, or LMDB.
LMDBTableWriter	Minimal, efficient row store with auto-increment row_id.
RedisWriter

DataLogger

A class to handle logging of data using different backends: Redis, SQL, or LMDB. It supports logging dictionaries and retrieving logged data.

Attributes:

Name	Type	Description
output_folder	str	Directory where files are stored (if applicable).
delete_on_read	bool	Whether to delete the data after retrieving it.
backend	str	The backend to use ('redis', 'sql', 'lmdb').

Methods:

Name	Description
get	Retrieve logged data using the specified backend.
get_writer	Get the appropriate writer based on the backend.
log_dict	Log a dictionary of results using the specified backend.

get(func_name, keep=False)

Retrieve logged data using the specified backend.

Parameters:

Name	Type	Description	Default
func_name	str	The name of the function whose data needs to be retrieved.	required
keep	bool	If True, do not delete the table even if delete_on_read is True.	False

Returns:

Type	Description
	pandas.DataFrame: The DataFrame containing the logged data.

get_writer(func_name)

Get the appropriate writer based on the backend.

Parameters:

Name	Type	Description	Default
func_name	str	The name of the function to create a writer for.	required

Returns:

Name	Type	Description
Writer		An instance of the appropriate writer class.

Raises:

Type	Description
ValueError	If an unsupported backend is specified.

log_dict(func_name, result)

Log a dictionary of results using the specified backend.

Parameters:

Name	Type	Description	Default
func_name	str	The name of the function to log the data for.	required
result	dict	Dictionary of results to log.	required

Raises:

Type	Description
ValueError	If the result is not a dictionary.

LMDBTableWriter

Minimal, efficient row store with auto-increment row_id. - write_row(row_id=None, **kwargs) -> returns the row_id (string) - read_row(row_id) -> dict or None - query_rows() -> DataFrame with row_id index - delete_table(), open(), close(), context manager

Methods:

Name	Description
query_rows	Return all rows as a DataFrame with numeric-sorted row_id.
write_row	Atomically allocate/increment row ID (if None) and write the row.

query_rows()

Return all rows as a DataFrame with numeric-sorted row_id.

write_row(row_id=None, **kwargs)

Atomically allocate/increment row ID (if None) and write the row.

RedisWriter

Methods:

Name	Description
close	Close the connection to Redis (flag only; redis client is pooled).
delete_table	Delete all entries associated with the table name, including the counter.
open	Establish connection to Redis and initialize counter if needed.
query_rows	Retrieve all rows from the Redis hash as a DataFrame with row_id as index.
read_row	Read a row from Redis hash.
write_row	Write a row to Redis hash under the specified table name.

close()

Close the connection to Redis (flag only; redis client is pooled).

delete_table()

Delete all entries associated with the table name, including the counter.

open()

Establish connection to Redis and initialize counter if needed.

query_rows()

Retrieve all rows from the Redis hash as a DataFrame with row_id as index.

read_row(row_id)

Read a row from Redis hash.

write_row(row_id=None, **kwargs)

Write a row to Redis hash under the specified table name.

lmdb_writer

Classes:

Name	Description
LMDBTableWriter	Minimal, efficient row store with auto-increment row_id.

LMDBTableWriter

Minimal, efficient row store with auto-increment row_id. - write_row(row_id=None, **kwargs) -> returns the row_id (string) - read_row(row_id) -> dict or None - query_rows() -> DataFrame with row_id index - delete_table(), open(), close(), context manager

Methods:

Name	Description
query_rows	Return all rows as a DataFrame with numeric-sorted row_id.
write_row	Atomically allocate/increment row ID (if None) and write the row.

query_rows()

Return all rows as a DataFrame with numeric-sorted row_id.

write_row(row_id=None, **kwargs)

Atomically allocate/increment row ID (if None) and write the row.

main

Classes:

Name	Description
DataLogger	A class to handle logging of data using different backends: Redis, SQL, or LMDB.

DataLogger

A class to handle logging of data using different backends: Redis, SQL, or LMDB. It supports logging dictionaries and retrieving logged data.

Attributes:

Name	Type	Description
output_folder	str	Directory where files are stored (if applicable).
delete_on_read	bool	Whether to delete the data after retrieving it.
backend	str	The backend to use ('redis', 'sql', 'lmdb').

Methods:

Name	Description
get	Retrieve logged data using the specified backend.
get_writer	Get the appropriate writer based on the backend.
log_dict	Log a dictionary of results using the specified backend.

get(func_name, keep=False)

Retrieve logged data using the specified backend.

Parameters:

Name	Type	Description	Default
func_name	str	The name of the function whose data needs to be retrieved.	required
keep	bool	If True, do not delete the table even if delete_on_read is True.	False

Returns:

Type	Description
	pandas.DataFrame: The DataFrame containing the logged data.

get_writer(func_name)

Get the appropriate writer based on the backend.

Parameters:

Name	Type	Description	Default
func_name	str	The name of the function to create a writer for.	required

Returns:

Name	Type	Description
Writer		An instance of the appropriate writer class.

Raises:

Type	Description
ValueError	If an unsupported backend is specified.

log_dict(func_name, result)

Log a dictionary of results using the specified backend.

Parameters:

Name	Type	Description	Default
func_name	str	The name of the function to log the data for.	required
result	dict	Dictionary of results to log.	required

Raises:

Type	Description
ValueError	If the result is not a dictionary.

redis_writer

Classes:

Name	Description
RedisWriter

RedisWriter

Methods:

Name	Description
close	Close the connection to Redis (flag only; redis client is pooled).
delete_table	Delete all entries associated with the table name, including the counter.
open	Establish connection to Redis and initialize counter if needed.
query_rows	Retrieve all rows from the Redis hash as a DataFrame with row_id as index.
read_row	Read a row from Redis hash.
write_row	Write a row to Redis hash under the specified table name.

close()

Close the connection to Redis (flag only; redis client is pooled).

delete_table()

Delete all entries associated with the table name, including the counter.

open()

Establish connection to Redis and initialize counter if needed.

query_rows()

Retrieve all rows from the Redis hash as a DataFrame with row_id as index.

read_row(row_id)

Read a row from Redis hash.

write_row(row_id=None, **kwargs)

Write a row to Redis hash under the specified table name.

inputs

Classes:

Name	Description
DLY
OPC
SIT
SOL

DLY

Bases: DataFrame

Methods:

Name	Description
load	Load data from a DLY file into DataFrame.
save	Save DataFrame into a DLY file.
to_monthly	Save as monthly file
validate	Validate the DataFrame to ensure it contains a continuous range of dates

load(path) classmethod

Load data from a DLY file into DataFrame.

save(path=None)

Save DataFrame into a DLY file.

to_monthly(path=None)

Save as monthly file

validate(start_date, end_date)

Validate the DataFrame to ensure it contains a continuous range of dates between start_date and end_date, without duplicates.

OPC

Bases: DataFrame

Methods:

Name	Description
append	Append another OPC or DataFrame to the current OPC instance.
edit_crop_season	Edit the planting and/or harvest dates for a given year and crop.
edit_fertilizer_rate	Edit the fertilizer rate for a given year.
edit_harvest_date	Edit the harvest date for a given year and crop.
edit_operation_date	Edit the operation date for a given year.
edit_operation_value	Edit the operation value for a given year.
edit_plantation_date	Edit the plantation date for a given year and crop.
get_harvest_date	Retrieve the harvest date(s) for a specific year and/or crop code.
get_plantation_date	Retrieve the plantation date(s) for a specific year and/or crop code.
iter_seasons	Iterate over OPC data, yielding dictionaries containing information for each growing season.
load	Load data from an OPC file into DataFrame.
new	Create a new OPC instance with an empty DataFrame with preset columns, a default header,
remove	Remove operation(s) from the OPC file that match all provided criteria.
save	Save DataFrame into an OPC file.
update	Add or update an operation in the OPC file.
update_phu	Update the OPV1 value with the calculated PHU from the DLY data for all plantation dates.
validate	Validate the OPC data to ensure it contains a continuous range of dates without duplicates.

IAUI property writable

Get the auto-irrigation implement ID from the OPC file header.

Returns:

Name	Type	Description
bool		True if auto-irrigation is enabled (72), False if disabled (0)

LUN property writable

Get the land use number from the OPC file header.

Returns:

Name	Type	Description
int		The land use number from the first 4 characters of header line 2

append(second_opc)

Append another OPC or DataFrame to the current OPC instance. Args: second_opc (pd.DataFrame or OPC): The data to append. Returns: OPC: A new OPC instance with combined data. Raises: ValueError: If second_opc is not a pandas DataFrame or OPC instance.

edit_crop_season(new_planting_date=None, new_harvest_date=None, crop_code=None)

Edit the planting and/or harvest dates for a given year and crop.

Parameters: year (int): Year. new_planting_date (datetime, optional): New planting date. If not provided, only harvest date will be updated. new_harvest_date (datetime, optional): New harvest date. If not provided, only planting date will be updated. crop_code (int, optional): Crop code. If not provided, changes the first crop found.

edit_fertilizer_rate(rate, year=2020, month=None, day=None)

Edit the fertilizer rate for a given year.

Parameters: rate (float): Fertilizer rate to be set. year (int, optional): Year for the fertilizer rate application. Defaults to 2020. month (int, optional): Month for the fertilizer rate application. If not provided, the first instance is changed. day (int, optional): Day for the fertilizer rate application. Defaults to None.

edit_harvest_date(date, crop_code)

Edit the harvest date for a given year and crop.

Parameters: date (datetime or str): Date of harvest in datetime object or 'YYYY-MM-DD' string format. crop_code (int): Crop code.

edit_operation_date(code, year, month, day, crop_code=None)

Edit the operation date for a given year.

Parameters: code (str): Operation code. year (int): Year. month (int): Month of operation. day (int): Day of operation. crop_code (int, optional): Crop code.

edit_operation_value(code, year, value, crop_code=None)

Edit the operation value for a given year.

Parameters: code (str): Operation code. year (int): Year. value (float): New operation value. crop_code (int, optional): Crop code.

edit_plantation_date(date, crop_code)

Edit the plantation date for a given year and crop.

Parameters: date (datetime or str): Date of plantation in datetime object or 'YYYY-MM-DD' string format. crop_code (int): Crop code.

get_harvest_date(year=None, crop_code=None)

Retrieve the harvest date(s) for a specific year and/or crop code.

Parameters: year (int, optional): Year. Defaults to None. crop_code (int, optional): Crop code. Defaults to None.

Returns: dict: A dictionary of crop codes and their harvest dates with row indices.

get_plantation_date(year=None, crop_code=None)

Retrieve the plantation date(s) for a specific year and/or crop code.

Parameters: year (int, optional): Year. Defaults to None. crop_code (int, optional): Crop code. Defaults to None.

Returns: dict: A dictionary of crop codes and their plantation dates with row indices.

iter_seasons(start_year=None, end_year=None)

Iterate over OPC data, yielding dictionaries containing information for each growing season.

Parameters: start_year (int, optional): The starting year to consider. Defaults to None. end_year (int, optional): The ending year to consider. Defaults to None.

dict: A dictionary containing: - plantation_date: The date of plantation - harvest_date: The date of harvest - crop_code: The crop code - operations: A subset of OPC rows for this season - plantation_index: The index of the plantation row

load(path, start_year=None) classmethod

Load data from an OPC file into DataFrame.

Parameters: path (str): Path to the OPC file. start_year (int, optional): Start year for the OPC file. If not provided, it will be read from the file header.

Returns: OPC: An instance of the OPC class containing the loaded data.

new(name, start_year) classmethod

Create a new OPC instance with an empty DataFrame with preset columns, a default header, and provided name and start year.

Parameters: name (str): The name for this OPC file/instance. start_year (int): The start year to assign in the OPC instance.

Returns: OPC: An OPC instance with no data but with the required metadata set.

remove(opID=None, date=None, cropID=None, XMTU=None, fertID=None, year=None)

Remove operation(s) from the OPC file that match all provided criteria.

Parameters:

Name	Type	Description	Default
opID	int	Operation ID to match	None
date	str	Date to match in format 'YYYY-MM-DD'	None
cropID	int	Crop ID to match	None
XMTU/LYR/pestID/fertID	int	Machine type/layer/pesticide ID/fertilizer ID to match	required
year	int	Year to match	None

save(path)

Save DataFrame into an OPC file.

Parameters: path (str): Path to save the OPC file.

update(operation)

Add or update an operation in the OPC file.

Parameters:

Name	Type	Description	Default
operation	dict	Dictionary containing operation details with keys: - opID: Operation ID (required) - cropID: Crop ID (required) - date: Operation date as string 'YYYY-MM-DD' (required) - XMTU/LYR/pestID/fertID: Machine type/years/pesticide ID/fertilizer ID (optional, default 0) - OPV1-OPV8: Additional operation values (optional, default 0)	required

update_phu(dly, cropcom)

Update the OPV1 value with the calculated PHU from the DLY data for all plantation dates.

Parameters: dly (DLY): DLY object containing weather data. cropcom (DataFrame): DataFrame containing crop code and TBS values.

validate(duration=None)

Validate the OPC data to ensure it contains a continuous range of dates without duplicates.

Parameters: duration (int, optional): Duration of the simulation in years. If None, uses the maximum Yid.

Returns: bool: True if the data is valid, False otherwise.

SIT

Methods:

Name	Description
load	Class method to load the .sit file and return a SiteFile instance.
save	Save the current site information to a .SIT file.

elevation property writable

Get elevation value.

lat property writable

Get latitude value.

lon property writable

Get longitude value.

slope property writable

Get slope steep value.

load(file_path) classmethod

Class method to load the .sit file and return a SiteFile instance.

Parameters: file_path (str): Path to the .sit file.

Returns: SiteFile: An instance of the SiteFile class with loaded data.

save(output_dir)

Save the current site information to a .SIT file.

Parameters: output_dir (str): Directory where the .SIT file will be saved, or the full path including the .SIT extension.

SOL

Methods:

Name	Description
from_sda	Create a Soil object from Soil Data Access using a query.
load	Load soil data from a file and return a Soil object.
save	Save the soil data to a file using a template.

from_sda(query) classmethod

Create a Soil object from Soil Data Access using a query.

Parameters:

Name	Type	Description	Default
query	int or str	Query string for SoilDataAccess. (mukey or WKT str) ( "POINT(-123.4567 45.6789)" )	required

Returns:

Name	Type	Description
Soil		A new Soil object populated with data from SDA.

load(filepath) classmethod

Load soil data from a file and return a Soil object.

Parameters:

Name	Type	Description	Default
filepath	str	Path to the soil file.	required

Returns:

Name	Type	Description
Soil		A new Soil object populated with data from the file.

save(filepath, template=None)

Save the soil data to a file using a template.

Parameters:

Name	Type	Description	Default
filepath	str	Path to save the soil file.	required
template	list	Optional list of template lines.	None

Raises:

Type	Description
ValueError	If soil properties DataFrame is empty.

dly

Classes:

Name	Description
DLY

DLY

Bases: DataFrame

Methods:

Name	Description
load	Load data from a DLY file into DataFrame.
save	Save DataFrame into a DLY file.
to_monthly	Save as monthly file
validate	Validate the DataFrame to ensure it contains a continuous range of dates

load(path) classmethod

Load data from a DLY file into DataFrame.

save(path=None)

Save DataFrame into a DLY file.

to_monthly(path=None)

Save as monthly file

validate(start_date, end_date)

Validate the DataFrame to ensure it contains a continuous range of dates between start_date and end_date, without duplicates.

opc

Classes:

Name	Description
OPC

OPC

Bases: DataFrame

Methods:

Name	Description
append	Append another OPC or DataFrame to the current OPC instance.
edit_crop_season	Edit the planting and/or harvest dates for a given year and crop.
edit_fertilizer_rate	Edit the fertilizer rate for a given year.
edit_harvest_date	Edit the harvest date for a given year and crop.
edit_operation_date	Edit the operation date for a given year.
edit_operation_value	Edit the operation value for a given year.
edit_plantation_date	Edit the plantation date for a given year and crop.
get_harvest_date	Retrieve the harvest date(s) for a specific year and/or crop code.
get_plantation_date	Retrieve the plantation date(s) for a specific year and/or crop code.
iter_seasons	Iterate over OPC data, yielding dictionaries containing information for each growing season.
load	Load data from an OPC file into DataFrame.
new	Create a new OPC instance with an empty DataFrame with preset columns, a default header,
remove	Remove operation(s) from the OPC file that match all provided criteria.
save	Save DataFrame into an OPC file.
update	Add or update an operation in the OPC file.
update_phu	Update the OPV1 value with the calculated PHU from the DLY data for all plantation dates.
validate	Validate the OPC data to ensure it contains a continuous range of dates without duplicates.

IAUI property writable

Get the auto-irrigation implement ID from the OPC file header.

Returns:

Name	Type	Description
bool		True if auto-irrigation is enabled (72), False if disabled (0)

LUN property writable

Get the land use number from the OPC file header.

Returns:

Name	Type	Description
int		The land use number from the first 4 characters of header line 2

append(second_opc)

Append another OPC or DataFrame to the current OPC instance. Args: second_opc (pd.DataFrame or OPC): The data to append. Returns: OPC: A new OPC instance with combined data. Raises: ValueError: If second_opc is not a pandas DataFrame or OPC instance.

edit_crop_season(new_planting_date=None, new_harvest_date=None, crop_code=None)

Edit the planting and/or harvest dates for a given year and crop.

Parameters: year (int): Year. new_planting_date (datetime, optional): New planting date. If not provided, only harvest date will be updated. new_harvest_date (datetime, optional): New harvest date. If not provided, only planting date will be updated. crop_code (int, optional): Crop code. If not provided, changes the first crop found.

edit_fertilizer_rate(rate, year=2020, month=None, day=None)

Edit the fertilizer rate for a given year.

Parameters: rate (float): Fertilizer rate to be set. year (int, optional): Year for the fertilizer rate application. Defaults to 2020. month (int, optional): Month for the fertilizer rate application. If not provided, the first instance is changed. day (int, optional): Day for the fertilizer rate application. Defaults to None.

edit_harvest_date(date, crop_code)

Edit the harvest date for a given year and crop.

Parameters: date (datetime or str): Date of harvest in datetime object or 'YYYY-MM-DD' string format. crop_code (int): Crop code.

edit_operation_date(code, year, month, day, crop_code=None)

Edit the operation date for a given year.

Parameters: code (str): Operation code. year (int): Year. month (int): Month of operation. day (int): Day of operation. crop_code (int, optional): Crop code.

edit_operation_value(code, year, value, crop_code=None)

Edit the operation value for a given year.

Parameters: code (str): Operation code. year (int): Year. value (float): New operation value. crop_code (int, optional): Crop code.

edit_plantation_date(date, crop_code)

Edit the plantation date for a given year and crop.

Parameters: date (datetime or str): Date of plantation in datetime object or 'YYYY-MM-DD' string format. crop_code (int): Crop code.

get_harvest_date(year=None, crop_code=None)

Retrieve the harvest date(s) for a specific year and/or crop code.

Parameters: year (int, optional): Year. Defaults to None. crop_code (int, optional): Crop code. Defaults to None.

Returns: dict: A dictionary of crop codes and their harvest dates with row indices.

get_plantation_date(year=None, crop_code=None)

Retrieve the plantation date(s) for a specific year and/or crop code.

Parameters: year (int, optional): Year. Defaults to None. crop_code (int, optional): Crop code. Defaults to None.

Returns: dict: A dictionary of crop codes and their plantation dates with row indices.

iter_seasons(start_year=None, end_year=None)

Iterate over OPC data, yielding dictionaries containing information for each growing season.

Parameters: start_year (int, optional): The starting year to consider. Defaults to None. end_year (int, optional): The ending year to consider. Defaults to None.

dict: A dictionary containing: - plantation_date: The date of plantation - harvest_date: The date of harvest - crop_code: The crop code - operations: A subset of OPC rows for this season - plantation_index: The index of the plantation row

load(path, start_year=None) classmethod

Load data from an OPC file into DataFrame.

Parameters: path (str): Path to the OPC file. start_year (int, optional): Start year for the OPC file. If not provided, it will be read from the file header.

Returns: OPC: An instance of the OPC class containing the loaded data.

new(name, start_year) classmethod

Create a new OPC instance with an empty DataFrame with preset columns, a default header, and provided name and start year.

Parameters: name (str): The name for this OPC file/instance. start_year (int): The start year to assign in the OPC instance.

Returns: OPC: An OPC instance with no data but with the required metadata set.

remove(opID=None, date=None, cropID=None, XMTU=None, fertID=None, year=None)

Remove operation(s) from the OPC file that match all provided criteria.

Parameters:

Name	Type	Description	Default
opID	int	Operation ID to match	None
date	str	Date to match in format 'YYYY-MM-DD'	None
cropID	int	Crop ID to match	None
XMTU/LYR/pestID/fertID	int	Machine type/layer/pesticide ID/fertilizer ID to match	required
year	int	Year to match	None

save(path)

Save DataFrame into an OPC file.

Parameters: path (str): Path to save the OPC file.

update(operation)

Add or update an operation in the OPC file.

Parameters:

Name	Type	Description	Default
operation	dict	Dictionary containing operation details with keys: - opID: Operation ID (required) - cropID: Crop ID (required) - date: Operation date as string 'YYYY-MM-DD' (required) - XMTU/LYR/pestID/fertID: Machine type/years/pesticide ID/fertilizer ID (optional, default 0) - OPV1-OPV8: Additional operation values (optional, default 0)	required

update_phu(dly, cropcom)

Update the OPV1 value with the calculated PHU from the DLY data for all plantation dates.

Parameters: dly (DLY): DLY object containing weather data. cropcom (DataFrame): DataFrame containing crop code and TBS values.

validate(duration=None)

Validate the OPC data to ensure it contains a continuous range of dates without duplicates.

Parameters: duration (int, optional): Duration of the simulation in years. If None, uses the maximum Yid.

Returns: bool: True if the data is valid, False otherwise.

sit

Classes:

Name	Description
SIT

SIT

Methods:

Name	Description
load	Class method to load the .sit file and return a SiteFile instance.
save	Save the current site information to a .SIT file.

elevation property writable

Get elevation value.

lat property writable

Get latitude value.

lon property writable

Get longitude value.

slope property writable

Get slope steep value.

load(file_path) classmethod

Class method to load the .sit file and return a SiteFile instance.

Parameters: file_path (str): Path to the .sit file.

Returns: SiteFile: An instance of the SiteFile class with loaded data.

save(output_dir)

Save the current site information to a .SIT file.

Parameters: output_dir (str): Directory where the .SIT file will be saved, or the full path including the .SIT extension.

sol

Classes:

Name	Description
SOL

SOL

Methods:

Name	Description
from_sda	Create a Soil object from Soil Data Access using a query.
load	Load soil data from a file and return a Soil object.
save	Save the soil data to a file using a template.

from_sda(query) classmethod

Create a Soil object from Soil Data Access using a query.

Parameters:

Name	Type	Description	Default
query	int or str	Query string for SoilDataAccess. (mukey or WKT str) ( "POINT(-123.4567 45.6789)" )	required

Returns:

Name	Type	Description
Soil		A new Soil object populated with data from SDA.

load(filepath) classmethod

Load soil data from a file and return a Soil object.

Parameters:

Name	Type	Description	Default
filepath	str	Path to the soil file.	required

Returns:

Name	Type	Description
Soil		A new Soil object populated with data from the file.

save(filepath, template=None)

Save the soil data to a file using a template.

Parameters:

Name	Type	Description	Default
filepath	str	Path to save the soil file.	required
template	list	Optional list of template lines.	None

Raises:

Type	Description
ValueError	If soil properties DataFrame is empty.

outputs

Classes:

Name	Description
ACY
DGN
DSL
DWC

ACY

Methods:

Name	Description
get_var	Extract variable from the ACY data.

get_var(varname)

Extract variable from the ACY data.

DGN

Methods:

Name	Description
get_var	Extract variable from the DGN data.

get_var(varname)

Extract variable from the DGN data.

DSL

Methods:

Name	Description
get_data	Return stored water data.

get_data()

Return stored water data.

DWC

Methods:

Name	Description
get_var	Extract variable from the DWC data.

get_var(varname)

Extract variable from the DWC data.

all

Classes:

Name	Description
ACY
DGN
DWC

ACY

Methods:

Name	Description
get_var	Extract variable from the ACY data.

get_var(varname)

Extract variable from the ACY data.

DGN

Methods:

Name	Description
get_var	Extract variable from the DGN data.

get_var(varname)

Extract variable from the DGN data.

DWC

Methods:

Name	Description
get_var	Extract variable from the DWC data.

get_var(varname)

Extract variable from the DWC data.

sw

Classes:

Name	Description
DSL

DSL

Methods:

Name	Description
get_data	Return stored water data.

get_data()

Return stored water data.

parm

Classes:

Name	Description
Parm

Parm

Methods:

Name	Description
constraints	Returns the constraints (min, max ranges) for the parameters.
edit	Updates the parameters in the DataFrame with new values.
get_vars	Returns the vars DataFrame with an additional column containing current values.
read_parm	Reads and constructs a DataFrame from a .DAT file.
save	Saves the current DataFrame to a .DAT file.
set_sensitive	Sets sensitive parameters based on a CSV path or list of parameter names.

current property

Returns the current values of parameters in the DataFrame.

constraints()

Returns the constraints (min, max ranges) for the parameters.

edit(values)

Updates the parameters in the DataFrame with new values.

get_vars()

Returns the vars DataFrame with an additional column containing current values.

read_parm(file_name)

Reads and constructs a DataFrame from a .DAT file.

save(path)

Saves the current DataFrame to a .DAT file.

set_sensitive(parms_input, all=False)

Sets sensitive parameters based on a CSV path or list of parameter names. If all is True, all parameters are considered sensitive.

Parameters:

Name	Type	Description	Default
parms_input	str or list	Either a CSV file path or list of parameter names to select	required
all	bool	If True, all parameters are considered sensitive regardless of input	False