API reference#

Temperature Response Functions classes#

`Site(uid, attributes, params=None)` #

Bases: object

Site class

Object containing attributes and functions related to the nursery site.

Attributes:

Name	Type	Description
`uid`	`integer`	The unique identifier for the site.
`attributes`	`object`	The default attributes for each location in IWIN dataset.
`params`	`dictionary`	The parameters to use during calculations.
`pheno_dates`	`array`	The phenology dates of the trial.
`weather`	`array`	Table with daily weather data for each location.
`raw_ndvi`	`array`	NDVI values during growing period.
`inputWPN`	`object`	A dataframe with phenology, NDVI and weather data for each site.
`errors`	`dictionary`	Dictionary with different errors presented during the model processing.

Methods:

Name	Description
`getAttr`	Get the attibutes of the site

`getTDay(m=None, tminFactor=None)` #

Calculate day time temperature for the selected site.

Parameters:

Name	Type	Description	Default
`m`	`object`	Model with information to estimate grain yield	`None`
`tminFactor`	`float`	Minimum Temperature factor	`None`

Returns:

Type	Description
`array`	A number or array of Day Temperature

`getPRFT(m=None, TDay=None, TOpt=None)` #

Estimate Photosynthesis reduction factor (PRFT) for each site.

Parameters:

Name	Type	Description	Default
`m`	`object`	A tfunct model	`None`
`TDay`	`float`	Number or array of Day Temperatures	`None`
`TOpt`	`float`	Optimum Temperature. Default value 18	`None`

Returns:

Type	Description
`float`	A number or array of PRFT

`getSFvpd(m=None, VPDMAX=None, Lvpd=None, Uvpd=None, SFvpd_Lthres=None, SFvpd_Uthres=None)` #

Calculation of Vapor pressure deficit (VPD) stress factor for each site

Parameters:

Name	Type	Description	Default
`m`	`object`	A tfunct model	`None`
`VPDMAX`	`array`	Array of daily temperature values	`None`
`Lvpd`	`float`	A number for threshold of lower VPD. Default is 1	`None`
`Uvpd`	`array`	A number for threshold of upper VPD. Default is 4	`None`
`SFvpd_Lthres`	`array`	A number for threshold of stress factor of lower VPD. Default is 0.2	`None`
`SFvpd_Uthres`	`array`	A number for threshold of stress factor of upper VPD. Default is 1	`None`

Returns:

Type	Description
`array`	A number or array of stressed factors of VPD

`getGPP(m=None, SolRad=None, PRFT=None, iPAR=None, RUE=3.0, stressFactor=1.0, SFvpd=None, is_VPDStress=False)` #

Estimate the Gross primary production (GPP) for each site

Parameters:

Name	Type	Description	Default
`m`	`object`	A tfunct model	`None`
`SolRad`	`float`	Solar Radiation	`None`
`PRFT`	`float`	Photosynthesis reduction factor	`None`
`iPAR`	`float`	the photosynthetically active radiation (PAR) intercepted by a plant or crop	`None`
`RUE`	`float`	Radiation-use efficiency. Default value is 3.0	`3.0`
`stressFactor`	`float`	Stress Factor	`1.0`
`SFvpd`	`float`	Stress Factor for VPD	`None`
`is_VPDStress`	`bool`	Vapor pressure deficit stress. Default is `False`.	`False`

Returns:

Type	Description
`float`	the gross primary production (GPP)

`fit(m=None, ft='PRFT', is_VPDStress=False, verbose=False)` #

Run a model to fit yield for a selected site using a specific temperature function

Deprecated.

This function was depreciated on Dec, 2022.

Parameters:

Name	Type	Description	Default
`m`	`object`	Model to run	`None`
`ft`	`str`	Name of the temperature response function: Default is 'PRFT'	`'PRFT'`
`is_VPDStress`	`bool`	Vapor pressure deficit stress. Default is `False`	`False`
`verbose`	`bool`	Display comments during processing	`False`

Returns:

Type	Description
`array`	An array of Sites with intermediate results

`main` #

`PathType` #

Bases: Path

A Click path argument that returns a pathlib Path, not a string

`data` #

`load_dataset()` #

Load example (Phenology, NDVI, and Weather) datasets from International Wheat Improvement Network (IWIN) sites and the other locations across the globe to estimate yield under non-stressed and Vapor pressure Deficit (VPD) stressed conditions as a function of temperature.

Return

A data dictionary with all raw data and information needed to carry out the demo.

Examples:

    >>> from tfunct.data import load_dataset
    >>> # Load example dataset (Phenology, NDVI and Weather data for each site)
    >>> data = load_dataset()
    >>> print(data.keys()) # dict_keys(['Pheno', 'NDVI', 'Weather'])
    >>> # Display Pheno data
    >>> data['Pheno']
    >>> # Display NDVI data
    >>> data['NDVI']
    >>> # Display Weather data
    >>> data['Weather']

`model` #

`Model(config, params=None)` #

Bases: object

`load_raw_datasets()` #

Load raw phenology and AgERA5 datasets

Return

A existing dataset including WeatherFile, PhenoFile, NDVIFile. The raw data can be seen in config object. eg. config['PhenoFile']

`preprocess_raw_datasets(data)` #

Preprocess raw datasets

Parameters:

Name	Type	Description	Default
`data`	`dictionary`	The phenology, ndvi and weather data for each location in example dataset.	required

Return

A dataset in a specific format for the tfunct package

`getTDay(m=None, w=None, tminFactor=None)` #

Calculate day time temperature

Parameters:

Name	Type	Description	Default
`m`	`object`	A tfunct model with sites and the necessary information	`None`
`w`	`array`	Table of weather data with minimum and maximum temperature records	`None`
`tminFactor`	`float`	Minimum Temperature factor. Default is usually 0.25. It can be reviewed in configuration parameters such as `TMIN_PERC_FACTOR`.	`None`

Returns:

Type	Description
`array`	A number or array of Day Temperature

`getIPAR(m=None, ndvi=None)` #

Total light interception - iPAR

Reference

iPAR = NDVI * 1.25 - 0.19 # between heading and maturity (Campos et al. 2018)

iPAR = NDVI * 1.25 - 0.21 Daughtry et al. (1992)

Asrar, G., Fuchs, M., Kanemasu, E.T., Hatfield, J.L., 1984. Estimating absorbed photosynthetic radiation and leaf area index from spectral reflectance in wheat. Agron. J. 76, 30-306.
Campos 2018 Remote sensing-based crop biomass with water or light-driven crop growth models in wheat commercial fields.

Parameters:

Name	Type	Description	Default
`m`	`object`	A tfunct model	`None`
`ndvi`	`array`	Array of float values	`None`

Returns:

Type	Description
`array`	An array of Total light interception values

`getYield(tfun='PRFT', sites=None, is_VPDStress=False, expCSV=True)` #

Get yield using a defined temperature function and default parameters

Parameters:

Name	Type	Description	Default
`tfun`	`str`	Name of the temperature function	`'PRFT'`
`sites`	`dictionary`	List of sites with the basic information to estimate yield.	`None`
`is_VPDStress`	`bool`	Vapor Pressure Deficit stress. Default is False.	`False`
`expCSV`	`bool`	Export grain yield results in an individual file. Default is True.	`True`

Returns:

Type	Description
`array`	A dataframe with simulated grain yield values

`runCombinations_PRFT(data=None, sites=None, comb=None, is_VPDStress=False, expCSV=True, individualCSV=False, target=None)` #

Run several combinations using different parameters for PRFT model

Deprecated

Stop using this function.

Parameters:

Name	Type	Description	Default
`data`	`object`	Array of base data.	`None`
`sites`	`object`	Array of sites	`None`
`comb`	`object`	Array of combinations	`None`
`is_VPDStress`	`bool`	Vapor Pressure Deficit stress. Default is False	`False`
`expCSV`	`bool`	Save results in CSV format. Default is True	`True`
`individualCSV`	`bool`	Default is False	`False`
`target`	`str`	Name of the target device. Available values are `cpu`, `parallel` and `cuda`. Default is `cpu`	`None`

Returns:

Type	Description
`object`	A dataframe with estimated grain yield of all combinations.

`fit(data=None, sites=None, comb=None, ft='PRFT', is_VPDStress=False, expCSV=True, individualCSV=False, n_jobs=4, verbose=False)` #

Run a model to fit yield

Deprecated on Dec, 2022.

Stop using this function.

Parameters:

Name	Type	Description	Default
`data`	`object`	Array of base data.	`None`
`sites`	`object`	Array of sites	`None`
`comb`	`object`	Array of combinations	`None`
`ft`	`str`	Name of the temperature response function. Default is 'PRFT'.	`'PRFT'`
`is_VPDStress`	`bool`	Vapor Pressure Deficit stress. Default is False	`False`
`expCSV`	`bool`	Save results in CSV format. Default is True	`True`
`individualCSV`	`bool`	Default is False	`False`
`n_jobs`	`int`	Number of CPU cores to use in paralell processing. Default is 4.	`4`
`verbose`	`bool`	Display comments during processing. Default is False	`False`

Returns:

Type	Description
`array`	An array of sites with intermediate results

`setup_dataInput_forCombinations(sites)` #

Set up data for each sites to estimate grain yield from different combinations

Parameters:

Name	Type	Description	Default
`sites`	`dictionary`		required

Return

Array of arrays with weather, iPAR and GPP data

`getCombinations(functype='PRFT', cols=None, RUE=None, Tmin=None, Toptmin=None, Topt=None, Toptmax=None, Tmax=None, TminFactor=None, Lvpd=None, Uvpd=None, SFvpd_Lthres=None, SFvpd_Uthres=None, isVPDStress=False)` #

Generate combinations to establish the optimum temperature response for grain-filling period and estimate grain yield.

Parameters:

Name	Type	Description	Default
`functype`	`str`	Type of temperature response function (eg. PRFT, WETF, TPF)	`'PRFT'`
`cols`	`int`	Number of daily records in growing period	`None`
`RUE`	`array`	Array of RUE values	`None`
`Tmin`	`array`	Array of minimum temperature values	`None`
`Toptmin`	`array`	Array of Optimun minimum temperature values	`None`
`Topt`	`array`	Array of Optimun temperature values	`None`
`Toptmax`	`array`	Array of Optimun maximum temperature values	`None`
`Tmax`	`array`	Array of maximum temperature values	`None`
`TminFactor`	`array`	Array of minimum temperature factor	`None`
`Lvpd`	`array`	Array of lower VPD values	`None`
`Uvpd`	`array`	Array of lower VPD values	`None`
`SFvpd_Lthres`	`array`	Array of lower VPD stress factor values	`None`
`SFvpd_Uthres`	`array`	Array of lower VPD stress factor values	`None`
`isVPDStress`	`bool`	True/False value for using VPD stress condition	`False`

Return

Array of combinations and an array to save results

`getGYield_forCombinations(functype, df_GYield, data_input, array_params_to_run, isVPDStress, array_results, saveFile=True, returnDF=True, fmt='parquet')` #

Estimate grain yield using parameters for each combinations

Parameters:

Name	Type	Description	Default
`df_GYield`	`array`	Dataframe with observed grain yield	required
`data_input`	`array`	Array of inputs containing weather, iPAR, PRFT and GPP data for each site.	required
`array_params_to_run`	`array`	Array of combinations (RUE, TOpt and TminFactor) to simulate	required
`isVPDStress`	`bool`	True/False value for using VPD stress condition	required
`array_results`	`array`	Array used to save results	required
`saveFile`	`bool`	True if save file in results folder	`True`
`returnDF`	`bool`	True if return a pandas dataframe. Use False when the combinations are too large.	`True`
`fmt`	`str`	Format to save file. Comma separate value (csv) or Apache parquet (parquet). Default is `parquet`	`'parquet'`

Return

A dataframe or table with results

`getCombinations_Metrics(functype, isVPDStress, df_GYield, array_params_to_run, array_results, saveFile=True, fmt='parquet')` #

Get evaluation metrics for each simulation

Parameters:

Name	Type	Description	Default
`functype`	`str`	Type of temperature response function (eg. PRFT, WETF, TPF)	required
`isVPDStress`	`bool`	True/False value for using VPD stress condition	required
`df_GYield`	`array`	Dataframe with observed grain yield	required
`array_params_to_run`	`array`	Array of combinations (RUE, TOpt and TminFactor) to simulate	required
`array_results`	`array`	Array used to save results	required
`saveFile`	`bool`	True if save file in results folder	`True`
`fmt`	`str`	Format to save file. Comma separate value (csv) or Apache parquet (parquet). Default is `parquet`	`'parquet'`

Return

A dataframe with several metrics

Returns:

Name	Type	Description
`MAE`	`float`	Mean Absolute Error
`MSE`	`float`	Mean Squared Error
`RMSE`	`float`	Root Mean Squared Error
`RMSRE`	`float`	Root Mean Squared Relative Error
`MAPE`	`float`	Mean Absolute Percentage Error
`pvalue`	`float`	p-value
`R2`	`float`	R Squared metric
`EF`	`float`	Nash-Sutcliffe metric
`intercept`	`float`	Intercept of the regression model
`slope`	`float`	Slope of the regression model
`Cb`	`float`	A bias correction factor
`CCC`	`float`	Concordance correlation coefficient
`Accuracy`	`float`	Accuracy in percentage

`gpp` #

`calcGPP(SolRad, TF, iPAR, RUE=3.0, stressFactor=1.0)` #

Estimate the Gross primary production

Parameters:

Name	Type	Description	Default
`SolRad`	`float`	Solar Radiation	required
`TF`	`float`	Temperature function result (PRFT, WETF, TPF)	required
`iPAR`	`float`	the photosynthetically active radiation (PAR) intercepted by a plant or crop estimated from NDVI	required
`RUE`	`float`	Radiation-use efficiency. Default value is 3.0 gMJ^−1	`3.0`
`stressFactor`	`float`	Stress Factor (eg. VPD stress factor)	`1.0`

Deprecated.

This function only use one value for each paramters. it is not optimized for run models in parallel.

Returns:

Type	Description
`float`	the gross primary production (GPP)

`apply_GPP_v0(SolRad, PRFT, iPAR, RUE=3.0, stressFactor=1.0)` #

`calculateGPP(SolRad, PRFT, iPAR, RUE=3.0, stressFactor=1.0)` #

Estimate the Gross primary production. The accumulated dry matter from heading to maturity

# GPP = Solar Radiation × TemFun × VPD_StressFactor × iPAR_fromNDVI × RUE 
GPP = SolRad * 0.5 * RUE * PRFT * iPAR * stressFactor

Parameters:

Name	Type	Description	Default
`SolRad`	`float`	Solar Radiation	required
`PRFT`	`float`	Photosynthesis reduction factor	required
`iPAR`	`float`	the photosynthetically active radiation (PAR) intercepted by a plant or crop	required
`RUE`	`float`	Radiation-use efficiency. Default value is 3.0 gMJ^-1	`3.0`
`stressFactor`	`float`	Stress Factor	`1.0`

Returns:

Type	Description
`float`	a number or array of values with the gross primary production (GPP)

`applyGPP_VPDStress(SolRad, PRFT, iPAR, SFvpd, RUE=3.0)` #

`calculateGPP_VPDStress(SolRad, PRFT, iPAR, SFvpd, RUE=3.0)` #

Estimate the Gross primary production with stressed VPD

GPP = SolRad * 0.5 * RUE * PRFT * iPAR * SFvpd

Parameters:

Name	Type	Description	Default
`SolRad`	`float`	Solar Radiation	required
`PRFT`	`float`	Photosynthesis reduction factor	required
`iPAR`	`float`	the photosynthetically active radiation (PAR) intercepted by a plant or crop	required
`RUE`	`float`	Radiation-use efficiency. Default value is 3.0	`3.0`
`SFvpd`	`float`	Vapor Pressure Deficit (VPD) stress factor	required

Returns:

Type	Description
`float`	a number or array of values with the gross primary production (GPP)

`estimate(data, params, is_VPDStress, tf, results)` #

An optimized function for estimating GPP for all observations using one of the three Temperature Function (TF) such as PRFT, WETF and TPF.

Parameters:

Name	Type	Description	Default
`data`	`object`	Array of arrays containing tn, tx, ndvi, solrad, VPDx, ipar, GPP datasets	required
`params`	`object`	An array with RUE, tminFactor, Topt, Lvpd, Uvpd, SFvpd_Lthres, SFvpd_Uthres values	required
`is_VPDStress`	`array`	Array of one value with T/F if Vapor Pressure Deficit stress affecting grain yield. Default is [False].	required
`tf`	`array`	Array of one value representing the type of function. [1]: PRFT; [2]: WETF; [3]: TPF.	required
`results`	`array`	Empty array for outputs.	required

Returns:

Name	Type	Description
`results`	`array`	An array with estimated yield for each site.

`gyield` #

`estimate(data, params, is_VPDStress, tf, results)` #

An optimized function for estimating grain yield in one step for all observations using one of the three Temperature Function (TF) such as PRFT, WETF and TPF.

Parameters:

Name	Type	Description	Default
`data`	`object`	Array of arrays containing tn, tx, ndvi, solrad, VPDx, ipar, GPP datasets	required
`params`	`object`	An array with RUE, tminFactor, Topt, Lvpd, Uvpd, SFvpd_Lthres, SFvpd_Uthres values	required
`is_VPDStress`	`array`	Array of one value with T/F if Vapor Pressure Deficit stress affecting grain yield. Default is [False].	required
`tf`	`array`	Array of one value representing the type of function. [1]: PRFT; [2]: WETF; [3]: TPF.	required
`results`	`array`	Empty array for outputs.	required

Returns:

Name	Type	Description
`results`	`array`	An array with estimated yield for each site.

`ipar` #

`calcIPAR(ndvi)` #

Total light interception - iPAR

Reference

iPAR = NDVI * 1.25 - 0.19 # between heading and maturity (Campos et al. 2018)

iPAR = NDVI * 1.25 - 0.21 Daughtry et al. (1992)

Asrar, G., Fuchs, M., Kanemasu, E.T., Hatfield, J.L., 1984. Estimating absorbed photosynthetic radiation and leaf area index from spectral reflectance in wheat. Agron. J. 76, 30-306.
Campos 2018 Remote sensing-based crop biomass with water or light-driven crop growth models in wheat commercial fields.

Deprecated.

Stop using this function. This function estimate iPAR using only one value for each parameters.

Parameters:

Name	Type	Description	Default
`ndvi`	`array`	Array of float values	required

Returns:

Type	Description
`array`	An array of Total light interception values

`estimate_IPAR(ndvi=None)` #

Total light interception - iPAR.

It is assumed that NDVI at maturity is 0.25.

Reference

iPAR = NDVI * 1.25 - 0.19 # between heading and maturity (Campos et al. 2018)

iPAR = NDVI * 1.25 - 0.21 Daughtry et al. (1992)

Asrar, G., Fuchs, M., Kanemasu, E.T., Hatfield, J.L., 1984. Estimating absorbed photosynthetic radiation and leaf area index from spectral reflectance in wheat. Agron. J. 76, 30-306.
Campos 2018 Remote sensing-based crop biomass with water or light-driven crop growth models in wheat commercial fields.

Parameters:

Name	Type	Description	Default
`ndvi`	`array`	Array of float values	`None`

Returns:

Type	Description
`array`	An array of Total light interception values

`prft` #

`calcPRFT(TDay, TOpt=18)` #

Estimate Photosynthesis reduction factor (PRFT)

PRFT = 1 - 0.0025 * (TDay - TOpt)^2

Deprecated.

Stop using this function. This function estimate PRFT using only one value for each parameters.

Parameters:

Name	Type	Description	Default
`TDay`	`float`	Number or array of Day Temperatures	required
`TOpt`	`float`	Optimum Temperature. Default value 18	`18`

Returns:

Type	Description
`float`	A number or array of PRFT

`calculatePRFT(Tday, Topt=18)` #

Estimate Photosynthesis reduction factor (PRFT) in parallel.

PRFT = 1 - 0.0025 * (TDay - TOpt)^2

Parameters:

Name	Type	Description	Default
`Tday`	`float`	Number or array of Day Temperatures	required
`Topt`	`float`	Optimum Temperature. Default value 18	`18`

Returns:

Type	Description
`float`	A number or array of PRFT

`sfvpd` #

`calcSFvpd(VPDx, Lvpd, Uvpd, SFvpd_Lthres, SFvpd_Uthres)` #

`getSFvpd(VPDx, Lvpd, Uvpd, SFvpd_Lthres, SFvpd_Uthres)` #

`apply_SFvpd(VPDMAX, Lvpd=1, Uvpd=4, SFvpd_Lthres=0.2, SFvpd_Uthres=1)` #

`calculateSFvpd(VPDMAX, Lvpd=1, Uvpd=4, SFvpd_Lthres=0.2, SFvpd_Uthres=1)` #

Calculation of Vapor pressure deficit (VPD) stress factor

Parameters:

Name	Type	Description	Default
`VPDMAX`	`array`	Array of daily temperature values	required
`Lvpd`	`float`	A number for threshold of lower VPD. Default is 1	`1`
`Uvpd`	`array`	A number for threshold of upper VPD. Default is 4	`4`
`SFvpd_Lthres`	`array`	A number for threshold of stress factor of lower VPD. Default is 0.2	`0.2`
`SFvpd_Uthres`	`array`	A number for threshold of stress factor of upper VPD. Default is 1	`1`

Returns:

Type	Description
`array`	A number or array of stressed factors of VPD

`tday` #

`calcTDay(Tmin, Tmax, tminFactor=0.25)` #

Calculate day time temperature. TDay is a function of weighted Tmin and weighted Tmax.

 TDay = 0.75 * Tmax + 0.25 * TMin

Deprecated.

Stop using this function. This function estimate day time temperature (TDay) using only one value for each parameters.

Parameters:

Name	Type	Description	Default
`Tmin`	`float`	Number or array of Minimum Temperatures	required
`Tmax`	`float`	Number or array of Maximum Temperatures	required
`tminFactor`	`float`	Minimum Temperature factor	`0.25`

Returns:

Type	Description
`float`	A number of Day Temperatures

`getTDay(Tmin, Tmax, tminFactor=0.25)` #

`apply_TDay(Tmin, Tmax, tminFactor)` #

`estimate_TDay(Tmin=None, Tmax=None, tminFactor=0.25)` #

An optimized function to calculate day time temperature in parallel.

 TDay = 0.75 * Tmax + 0.25 * TMin

Parameters:

Name	Type	Description	Default
`Tmin`	`float`	Number or array of Minimum Temperatures	`None`
`Tmax`	`float`	Number or array of Maximum Temperatures	`None`
`tminFactor`	`float`	Minimum Temperature factor	`0.25`

Returns:

Type	Description
`float`	A number of Day Temperatures

`util` #

`find_nearest_value(array, value)` #

Find nearest value to a user define value from array

Parameters:

Name	Type	Description	Default
`array`	`array`	Array of values	required
`value`	`int`	value to find into the array	required

Returns:

Type	Description
`int`	a number with the nearest value found

`find_nearest_index(array, values)` #

Find nearest index to a user define value from array

Parameters:

Name	Type	Description	Default
`array`	`array`	Array of values	required
`values`	`int`	value to find into the array	required

Returns:

Type	Description
`int`	a number with the nearest index found

`find_nearest(array, value)` #

Find nearest index and value to a user define value from array

Parameters:

Name	Type	Description	Default
`array`	`array`	Array of values	required
`value`	`int`	value to find into the array	required

Returns:

Type	Description
`int`	a number with the nearest value found

`CCC(y_true, y_pred)` #

Lin's Concordance correlation coefficient

Computes Lin's (1989, 2000) concordance correlation coefficient for agreement on a continuous measure obtained by two methods. The concordance correlation coefficient combines measures of both precision and accuracy to determine how far the observed data deviate from the line of perfect concordance (that is, the line at 45 degrees on a square scatter plot).

Parameters:

Name	Type	Description	Default
`y_true`	`array`	Array of observed values	required
`y_pred`	`array`	Array of predicted values	required

Returns:

Type	Description
`float`	Concordance correlation coefficient

`Cb(x, y)` #

A bias correction factor that measures how far the best-fit line deviates from a line at 45 degrees (a measure of accuracy).

No deviation from the 45 degree line occurs when Cb = 1. See Lin (1989 page 258).

Parameters:

Name	Type	Description	Default
`x`	`array`	Array of observed values	required
`y`	`array`	Array of predicted values	required

Returns:

Type	Description
`float`	Bias correction factor

`getScores(df, fld1=None, fld2=None)` #

Get stats for model results

Parameters:

Name	Type	Description	Default
`df`	`array`	A pandas dataframe with Observed and Simulated values	required
`fld1`	`str`	Name of the columns or field with observed values	`None`
`fld2`	`str`	Name of the columns or field with predicted values	`None`

Returns:

Name	Type	Description
`r2score`	`float`	R squared metric
`mape`	`float`	Mean absolute percentage error
`rmse`	`float`	Root mean squared error
`n_rmse`	`float`	Normalized RMSE
`d_index`	`float`	d-index metric
`ef`	`float`	Nash-Sutcliffe metric
`ccc`	`float`	Concordance correlation coefficient
`cb`	`float`	A bias correction factor
`accuracy`	`float`	Accuracy in percentage

`filterTopCombinations(df_m, df_cmb, fnct='PRFT', VPDstress=False, top=3, Cb=1.0, ccc=0.86, rmsre=100)` #

Filter combinations for selecting Top 3 of the best simulations

Deprecated

Stop using this function, instead use filterSimulations.

Parameters:

Name	Type	Description	Default
`df_m`	`array`	A dataframe with metrics from each simulations	required
`df_cmb`	`array`	A dataframe with combinations results	required
`fnct`	`str`	Temperature response function. default 'PRFT'	`'PRFT'`
`VPDstress`	`bool`	Stressed VPD. default False	`False`
`top`	`int`	Number of selected records. default 3	`3`
`Cb`	`float`	A threshold for Cb metric. default 1.0	`1.0`
`ccc`	`float`	A threshold for CCC metric. default 0.86	`0.86`
`rmsre`	`float`	A threshold for RMSRE metric. default 100	`100`

Returns:

Type	Description
`object`	A dataframe with all filtered combinations

`filterSimulations(functype='PRFT', VPDstress=False, cmb=None, met=None, cmb_filters=None, met_filters=None, pdFormat=False, dispFig=True, saveFig=False, figname='Fig_topComb_avgYield', figfmt='jpg', saveResults=True, outputPath='./', fmt='parquet')` #

Filter a table with several combinations results from the three temperature functions. This function is similar to util.filterTopCombinations function but optimized to work only with Apache parquet files.

Parameters:

Name	Type	Description	Default
`functype`	`str`	Name of the temperature response function. Default 'PRFT'	`'PRFT'`
`VPDstress`	`bool`	Stressed VPD. default False	`False`
`cmb`	`str`	Full path of the combinations result file	`None`
`met`	`str`	Full path of the metrics result file	`None`
`cmb_filters`	`dict`	Dictionary with the parameters to use as a constraints in combonation filters. it must match with the respective temperature function parameters. Default filters: dict( RUE = ('RUE', '=', 3.0 ), TminFactor = ('TminFactor', '=', 0.25 ) )	`None`
`met_filters`	`dict`	Dictionary with the parameters to use as a constraints in metrics filters. Default values: metfilters = dict( Cb = ('>=', 0.9), CCC = ('>=', 0.8) )	`None`
`pdFormat`	`bool`	Export filtered dataset in pandas format. Default `False`	`False`
`dispFig`	`bool`	Display figure of grain yield correlation. Default is `True`	`True`
`saveFig`	`bool`	Save figure. Default is `False`	`False`
`figname`	`str`	Name of the figure file to be saved	`'Fig_topComb_avgYield'`
`figfmt`	`str`	Format of the figure file. `JPEG` or `PDF` formats are the available options. Default is `pdf`.	`'jpg'`
`saveResults`	`bool`	Save filtered data. Default `True`	`True`
`outputPath`	`str`	Output folder to save the data	`'./'`
`fmt`	`str`	File format to save in csv or parquet. Default is `parquet`	`'parquet'`

Returns:

Type	Description
`object`	A dataframe or parquet file with the filtered dataset

`figures` #

`chart_compareResults(df_result=None, fld1=None, fld2=None, alpha=0.75, s=15, xy_lim=2, hue=None, loc_leg=2, ncol=2, ha='left', va='top', title='Observed vs Simulated grain yield', xlabel='Observed Yield (tha$^{-1}$)', ylabel='Simulated Yield (tha$^{-1}$)', dirname='Figures_tFunt', fname='Fig_model_', dispScore=True, dispLegend=True, saveFig=False, showFig=True, fmt='pdf')` #

Display a scatter plot to compare two variables in the results

Parameters:

Name	Type	Description	Default
`df_result`	`array`	A pandas DataFrame with the results and variables to compare	`None`
`fld1`	`str`	Variable or column name to compare	`None`
`fld2`	`str`	Variable or column name to compare	`None`
`alpha`	`float`	Transparency of the points in chart	`0.75`
`s`	`float`	Size of the points in chart	`15`
`xy_lim`	`int`	Used to extend the x-axis limit. Default 2 units	`2`
`hue`	`str`	Variable to classify or discriminate the results in colors	`None`
`title`	`str`	Title of the figure	`'Observed vs Simulated grain yield'`
`xlabel`	`str`	Label of the x-axis	`'Observed Yield (tha$^{-1}$)'`
`ylabel`	`str`	Label of the y-axis	`'Simulated Yield (tha$^{-1}$)'`
`dirname`	`str`	Folder name to save results	`'Figures_tFunt'`
`fname`	`str`	File name to save the figure	`'Fig_model_'`
`dispScore`	`bool`	Display the accurracy and others stats of the model	`True`
`dispLegend`	`bool`	Display the legend of the chart	`True`
`saveFig`	`bool`	Save file in JPG or PDF format	`False`
`fmt`	`str`	Format of the output	`'pdf'`

Returns:

Type	Description
`object`	A figure in JPG or PDF format with the filename specified into the folder name

`plot_TempFunct(fld1='ObsYield', fld2='SimYield', hue='location', ncol=6, s=80, alpha=0.45, xy_lim=1, fonts_axes=12, fonts_titles=14, dispScore=False, errorbar=False, saveFig=True, showFig=True, path_to_save_results='./', dirname='Figures', fname='Fig_1', fmt='pdf')` #

API reference#

Temperature Response Functions classes#

Site(uid, attributes, params=None) #

getTDay(m=None, tminFactor=None) #

getPRFT(m=None, TDay=None, TOpt=None) #

getSFvpd(m=None, VPDMAX=None, Lvpd=None, Uvpd=None, SFvpd_Lthres=None, SFvpd_Uthres=None) #

getGPP(m=None, SolRad=None, PRFT=None, iPAR=None, RUE=3.0, stressFactor=1.0, SFvpd=None, is_VPDStress=False) #

fit(m=None, ft='PRFT', is_VPDStress=False, verbose=False) #

__main__ #

PathType #

data #

load_dataset() #

model #

Model(config, params=None) #

load_raw_datasets() #

preprocess_raw_datasets(data) #

getTDay(m=None, w=None, tminFactor=None) #

getIPAR(m=None, ndvi=None) #

getYield(tfun='PRFT', sites=None, is_VPDStress=False, expCSV=True) #

runCombinations_PRFT(data=None, sites=None, comb=None, is_VPDStress=False, expCSV=True, individualCSV=False, target=None) #

fit(data=None, sites=None, comb=None, ft='PRFT', is_VPDStress=False, expCSV=True, individualCSV=False, n_jobs=4, verbose=False) #

setup_dataInput_forCombinations(sites) #

getCombinations(functype='PRFT', cols=None, RUE=None, Tmin=None, Toptmin=None, Topt=None, Toptmax=None, Tmax=None, TminFactor=None, Lvpd=None, Uvpd=None, SFvpd_Lthres=None, SFvpd_Uthres=None, isVPDStress=False) #

getGYield_forCombinations(functype, df_GYield, data_input, array_params_to_run, isVPDStress, array_results, saveFile=True, returnDF=True, fmt='parquet') #

getCombinations_Metrics(functype, isVPDStress, df_GYield, array_params_to_run, array_results, saveFile=True, fmt='parquet') #

gpp #

calcGPP(SolRad, TF, iPAR, RUE=3.0, stressFactor=1.0) #

apply_GPP_v0(SolRad, PRFT, iPAR, RUE=3.0, stressFactor=1.0) #

calculateGPP(SolRad, PRFT, iPAR, RUE=3.0, stressFactor=1.0) #

applyGPP_VPDStress(SolRad, PRFT, iPAR, SFvpd, RUE=3.0) #

calculateGPP_VPDStress(SolRad, PRFT, iPAR, SFvpd, RUE=3.0) #

estimate(data, params, is_VPDStress, tf, results) #

gyield #

estimate(data, params, is_VPDStress, tf, results) #

ipar #

calcIPAR(ndvi) #

estimate_IPAR(ndvi=None) #

prft #

calcPRFT(TDay, TOpt=18) #

calculatePRFT(Tday, Topt=18) #

sfvpd #

calcSFvpd(VPDx, Lvpd, Uvpd, SFvpd_Lthres, SFvpd_Uthres) #

getSFvpd(VPDx, Lvpd, Uvpd, SFvpd_Lthres, SFvpd_Uthres) #

apply_SFvpd(VPDMAX, Lvpd=1, Uvpd=4, SFvpd_Lthres=0.2, SFvpd_Uthres=1) #

calculateSFvpd(VPDMAX, Lvpd=1, Uvpd=4, SFvpd_Lthres=0.2, SFvpd_Uthres=1) #

tday #

calcTDay(Tmin, Tmax, tminFactor=0.25) #

getTDay(Tmin, Tmax, tminFactor=0.25) #

apply_TDay(Tmin, Tmax, tminFactor) #

estimate_TDay(Tmin=None, Tmax=None, tminFactor=0.25) #

util #

find_nearest_value(array, value) #

find_nearest_index(array, values) #

find_nearest(array, value) #

CCC(y_true, y_pred) #

Cb(x, y) #

getScores(df, fld1=None, fld2=None) #

filterTopCombinations(df_m, df_cmb, fnct='PRFT', VPDstress=False, top=3, Cb=1.0, ccc=0.86, rmsre=100) #

filterSimulations(functype='PRFT', VPDstress=False, cmb=None, met=None, cmb_filters=None, met_filters=None, pdFormat=False, dispFig=True, saveFig=False, figname='Fig_topComb_avgYield', figfmt='jpg', saveResults=True, outputPath='./', fmt='parquet') #

figures #

plot_TempFunct(fld1='ObsYield', fld2='SimYield', hue='location', ncol=6, s=80, alpha=0.45, xy_lim=1, fonts_axes=12, fonts_titles=14, dispScore=False, errorbar=False, saveFig=True, showFig=True, path_to_save_results='./', dirname='Figures', fname='Fig_1', fmt='pdf') #

`Site(uid, attributes, params=None)` #

`getTDay(m=None, tminFactor=None)` #

`getPRFT(m=None, TDay=None, TOpt=None)` #

`getSFvpd(m=None, VPDMAX=None, Lvpd=None, Uvpd=None, SFvpd_Lthres=None, SFvpd_Uthres=None)` #

`getGPP(m=None, SolRad=None, PRFT=None, iPAR=None, RUE=3.0, stressFactor=1.0, SFvpd=None, is_VPDStress=False)` #

`fit(m=None, ft='PRFT', is_VPDStress=False, verbose=False)` #

`main` #

`PathType` #

`data` #

`load_dataset()` #

`model` #

`Model(config, params=None)` #

`load_raw_datasets()` #

`preprocess_raw_datasets(data)` #

`getTDay(m=None, w=None, tminFactor=None)` #

`getIPAR(m=None, ndvi=None)` #

`getYield(tfun='PRFT', sites=None, is_VPDStress=False, expCSV=True)` #

`runCombinations_PRFT(data=None, sites=None, comb=None, is_VPDStress=False, expCSV=True, individualCSV=False, target=None)` #

`fit(data=None, sites=None, comb=None, ft='PRFT', is_VPDStress=False, expCSV=True, individualCSV=False, n_jobs=4, verbose=False)` #

`setup_dataInput_forCombinations(sites)` #

`getCombinations(functype='PRFT', cols=None, RUE=None, Tmin=None, Toptmin=None, Topt=None, Toptmax=None, Tmax=None, TminFactor=None, Lvpd=None, Uvpd=None, SFvpd_Lthres=None, SFvpd_Uthres=None, isVPDStress=False)` #

`getGYield_forCombinations(functype, df_GYield, data_input, array_params_to_run, isVPDStress, array_results, saveFile=True, returnDF=True, fmt='parquet')` #

`getCombinations_Metrics(functype, isVPDStress, df_GYield, array_params_to_run, array_results, saveFile=True, fmt='parquet')` #

`gpp` #

`calcGPP(SolRad, TF, iPAR, RUE=3.0, stressFactor=1.0)` #

`apply_GPP_v0(SolRad, PRFT, iPAR, RUE=3.0, stressFactor=1.0)` #

`calculateGPP(SolRad, PRFT, iPAR, RUE=3.0, stressFactor=1.0)` #

`applyGPP_VPDStress(SolRad, PRFT, iPAR, SFvpd, RUE=3.0)` #

`calculateGPP_VPDStress(SolRad, PRFT, iPAR, SFvpd, RUE=3.0)` #

`estimate(data, params, is_VPDStress, tf, results)` #

`gyield` #

`estimate(data, params, is_VPDStress, tf, results)` #

`ipar` #

`calcIPAR(ndvi)` #

`estimate_IPAR(ndvi=None)` #

`prft` #

`calcPRFT(TDay, TOpt=18)` #

`calculatePRFT(Tday, Topt=18)` #

`sfvpd` #

`calcSFvpd(VPDx, Lvpd, Uvpd, SFvpd_Lthres, SFvpd_Uthres)` #

`getSFvpd(VPDx, Lvpd, Uvpd, SFvpd_Lthres, SFvpd_Uthres)` #

`apply_SFvpd(VPDMAX, Lvpd=1, Uvpd=4, SFvpd_Lthres=0.2, SFvpd_Uthres=1)` #

`calculateSFvpd(VPDMAX, Lvpd=1, Uvpd=4, SFvpd_Lthres=0.2, SFvpd_Uthres=1)` #

`tday` #

`calcTDay(Tmin, Tmax, tminFactor=0.25)` #

`getTDay(Tmin, Tmax, tminFactor=0.25)` #

`apply_TDay(Tmin, Tmax, tminFactor)` #

`estimate_TDay(Tmin=None, Tmax=None, tminFactor=0.25)` #

`util` #

`find_nearest_value(array, value)` #

`find_nearest_index(array, values)` #

`find_nearest(array, value)` #

`CCC(y_true, y_pred)` #

`Cb(x, y)` #

`getScores(df, fld1=None, fld2=None)` #

`filterTopCombinations(df_m, df_cmb, fnct='PRFT', VPDstress=False, top=3, Cb=1.0, ccc=0.86, rmsre=100)` #

`filterSimulations(functype='PRFT', VPDstress=False, cmb=None, met=None, cmb_filters=None, met_filters=None, pdFormat=False, dispFig=True, saveFig=False, figname='Fig_topComb_avgYield', figfmt='jpg', saveResults=True, outputPath='./', fmt='parquet')` #

`figures` #

`plot_TempFunct(fld1='ObsYield', fld2='SimYield', hue='location', ncol=6, s=80, alpha=0.45, xy_lim=1, fonts_axes=12, fonts_titles=14, dispScore=False, errorbar=False, saveFig=True, showFig=True, path_to_save_results='./', dirname='Figures', fname='Fig_1', fmt='pdf')` #