PhoTorch

PhoTorch is a robust and generalized photosynthesis biochemical model fitting package based on PyTorch. Read more about PhoTorch in our paper: https://baileylab.ucdavis.edu/publications/Lei_etal_PhoTorch.pdf

Installation of dependencies

pip install torch
pip install numpy
pip install scipy
pip install pandas

1. FvCB model usage

After installing the dependencies, download the package and import it into your Python script.

import fitaci
import pandas as pd
import torch

Load data

Load the example CSV file. Then, specify the ID of the light response curve. If there is no light response curve in the dataset, ignore it. The loaded data frame should have columns with titles 'CurveID', 'FittingGroup', 'Ci', 'A', 'Qin', and 'Tleaf'. Each A/Ci curve should have a unique 'CurveID'. If no 'Qin' and 'Tleaf' are available, it will be automatically set to 2000 and 25, respectively.

The data to be loaded should be:

CurveID	FittingGroup	Ci	A	Qin	Tleaf
1	1	200	20	2000	25
1	1	400	30	2000	25
1	1	600	40	2000	25
2	1	200	25	2000	30
2	1	400	35	2000	30
2	1	700	55	2000	30

dftest = pd.read_csv('dfMAGIC043_lr.csv')
# initialize the data, and preprocess the data
# specify the list of light response curve IDs, if no light response curve, input "lightresp_id = None"
lcd = fitaci.initD.initLicordata(dftest, preprocess=True, lightresp_id = [118])

Define the device

'cuda' means an NVIDIA GPU will be used. If you want to use the CPU, set 'device_fit' to 'cpu'.

device_fit = 'cpu'
lcd.todevice(torch.device(device_fit)) # if device is cuda, then execute this line

Initialize FvCB model

If 'onefit' is set to 'True', all curves in a fitting group will share the same set of Vcmax25, Jmax25, TPU25, and Rd25. Otherwise, each curve will have its own set of these four main parameters but share the same light and temperature response parameters for the fitting group.

If no light response curve is specified, set 'LightResp_type' to 0.

LightResp_type 0: J is equal to Jmax.

LightResp_type 1: using equation $J = \frac{\alpha Q J_{max}}{\alpha Q + J_{max}}$ and fitting $\alpha$.

LightResp_type 2: using equation $J = \frac{\alpha Q + J_{max} - \sqrt{(\alpha Q + J_{max})^2 - 4 \theta \alpha Q J_{max}}}{2 \theta}$ and fitting $\alpha$ and $\theta$.

TempResp_type 0: Vcmax, Jmax, TPU, and Rd are equal to the Vcmax25, Jmax25, TPU25, and Rd25, respectively.

TempResp_type 1: using equation $k = k_{25} \exp{\left[\frac{\Delta{H_a}}{R}\left(\frac{1}{298}-\frac{1}{T_{leaf}}\right)\right]}$ and fitting $\Delta{H_a}$ for Vcmax, Jmax, and TPU.

TempResp_type 2: using equation $k = k_{25} \exp\left[\frac{\Delta H_a}{R} \left(\frac{1}{298}-\frac{1}{T_{leaf}}\right)\right] \frac{f\left(298\right)}{f\left(T_{leaf}\right)}$, where $f(T) = 1+\exp \left[\frac{\Delta H_d}{R}\left(\frac{1}{T_{opt}}-\frac{1}{T} \right)-\ln \left(\frac{\Delta H_d}{\Delta H_a}-1 \right) \right]$, and fitting $\Delta{H_a}$ and $T_{opt}$ for Vcmax, Jmax, and TPU.

# initialize the model
fvcbm = fitaci.initM.FvCB(lcd, LightResp_type = 2, TempResp_type = 2, onefit = False)

(Alternatively) Specify default fixed or learnable parameters, and set whether to fit Kc25, Ko25, gamma25, and gm (all defaults are False)

allparamas = fitaci.initM.allparameters()
allparamas.dHa_Vcmax = torch.tensor(40.0)
fvcbm = fitaci.initM.FvCB(lcd, LightResp_type = 0, TempResp_type = 1, onefit = False, fitgm= False, fitgamma=True, fitKo=False, fitKc=True, allparams=allparamas)

Fit A/Ci curves

fitresult = fitaci.run(fvcbm, learn_rate= 0.08, device=device_fit, maxiteration = 20000, minloss= 1, recordweightsTF=False)
fvcbm = fitresult.model

Get fitted parameters by ID

The main parameters are stored in the 'fvbm'. The temperature response parameters are in 'fvcbm.TempResponse', just like the light response parameters.

id_index = 0
id = int(lcd.IDs[id_index]) # target curve ID
fg_index =  int(lcd.FGs[id_index]) # index of the corresponding fitting group
if not fvcbm.onefit:
    Vcmax25_id = fvcbm.Vcmax25[id_index]
    Jmax25_id = fvcbm.Jmax25[id_index]
else:
    Vcmax25_id = fvcbm.Vcmax25[fg_index]
    Jmax25_id = fvcbm.Jmax25[fg_index]

dHa_Vcmax_id = fvcbm.TempResponse.dHa_Vcmax[fg_index]
alpha_id = fvcbm.LightResponse.alpha[fg_index]

Get fitted A/Ci curves

A, Ac, Aj, Ap = fvcbm()

Get fitted A/Ci curves by ID

id_index = 0
id = lcd.IDs[id_index]
indices_id = lcd.getIndicesbyID(id)
A_id = A[indices_id]
Ac_id = Ac[indices_id]

Get the (preprocessed) photosynthesis data by ID

A_id_mea, Ci_id, Q_id, Tlf_id = lcd.getDatabyID(lcd.IDs[id_index])

2. Stomatal conductance model usage

The stomatal conductance model is under development.

import fitstomat

Initialize stomatal conductance models

Four stomatal conductance models are available: Ball Woodrow Berry (BWB), Ball Berry Leuning (BBL), Medlyn (MED), and Buckley Mott Farquhar (BMF). More details about these four models can be found at: https://baileylab.ucdavis.edu/software/helios/_stomatal_doc.html.

All input variables should be torch vector (1-D tensor) of the same length for the model. Abbriviations: rh = relative humidity, VPD = vapor pressure deficit, Gamma = CO2 compensation point.

# scm = fitstomat.stomat.BWB(A, rh)
scm = fitstomat.stomat.BMF(Qin, VPD)
# scm = fitstomat.stomat.MED(A, VPD)
# scm = fitstomat.stomat.BBL(A, Gamma,VPD)

Fit the parameters of target stomatal conductance model

scm = fitstomat.run(scm, gsw, learnrate = 0.01, maxiteration = 8000) # gsw is the stomatal conductance data (same length as above variables).
scm.Em # the fitted parameter of BMF model
scm.i0 # the fitted parameter of BMF model
scm.k # the fitted parameter of BMF model
scm.b # the fitted parameter of BMF model

GEMINI-Breeding/photorch