Estimating grain yield using TPF¶

Here we use the temperature Trapezoid temperature function with different cardinal temperature combinations with and without VPD stress function for the period from heading to maturity (grain filling period).

Authors:

• Azam Lashkari (CIMMYT-China, azam.lashkari@jic.ac.uk)
• Urs Christoph Schulthess (CIMMYT-China, U.Schulthess@cgiar.org)
• Ernesto Giron Echeverry (Independent Researcher, e.giron.e@gmail.com)

Last updated: October 01, 2023

Load libraries¶

In [1]:

import os, sys, gc
import pandas as pd
#import datetime as dt
pd.set_option('display.max_columns', None)

# Import Temperature Response Functions package
import tfunct
from tfunct.data import load_dataset # Function to load existing dataset
from tfunct.model import Model # Load module to create a model
from tfunct.model import tday, gpp  # Module to calculate GPP and estimated yield
from tfunct.util import figures # module to create figures

print(tfunct.__version__)

import os, sys, gc import pandas as pd #import datetime as dt pd.set_option('display.max_columns', None) # Import Temperature Response Functions package import tfunct from tfunct.data import load_dataset # Function to load existing dataset from tfunct.model import Model # Load module to create a model from tfunct.model import tday, gpp # Module to calculate GPP and estimated yield from tfunct.util import figures # module to create figures print(tfunct.__version__)

tfunct version 1.0.0

Load Phenology, NDVI and Weather example datasets¶

In [2]:

%%time
# Load example dataset (Phenology, NDVI and Weather data for each site)
data = load_dataset()

%%time # Load example dataset (Phenology, NDVI and Weather data for each site) data = load_dataset()

CPU times: user 71 ms, sys: 48.3 ms, total: 119 ms
Wall time: 57.2 ms

Note: For details how to display phenology, ndvi and weather tables, please see Getting Started

Create a model¶

The model contains all of the required functions to analyse the data

In [3]:

%%time 

# Define the Path where the output data will be stored
PATH_PRJ = '/Users/ernestogiron/Desktop/TemperatureFunctions/'
RESULTS_PATH = PATH_PRJ + 'results/'

config = {
    "PROJECT_PATH": PATH_PRJ,
    "RESULTS_PATH": RESULTS_PATH, #'./', # Results will be put in the same folder where the model is running
}

# create model to estimate grain yield
model = Model(config)
model.preprocess_raw_datasets(data) # Preprocess datasets
# Prepare dataset to run all process in parallel using NDVIA GPU if available
sites = model.prepareData()

%%time # Define the Path where the output data will be stored PATH_PRJ = '/Users/ernestogiron/Desktop/TemperatureFunctions/' RESULTS_PATH = PATH_PRJ + 'results/' config = { "PROJECT_PATH": PATH_PRJ, "RESULTS_PATH": RESULTS_PATH, #'./', # Results will be put in the same folder where the model is running } # create model to estimate grain yield model = Model(config) model.preprocess_raw_datasets(data) # Preprocess datasets # Prepare dataset to run all process in parallel using NDVIA GPU if available sites = model.prepareData()

50it [00:01, 38.20it/s]

CPU times: user 1.62 s, sys: 78.4 ms, total: 1.69 s
Wall time: 1.69 s

Trapezoid temperature function (TPF)¶

No stress conditions¶

In [4]:

%%time
TPF_noStress = model.getYield(tfun='TPF', sites=sites, is_VPDStress=False, expCSV=True)
TPF_noStress.head()

%%time TPF_noStress = model.getYield(tfun='TPF', sites=sites, is_VPDStress=False, expCSV=True) TPF_noStress.head()

CPU times: user 704 ms, sys: 31.4 ms, total: 736 ms
Wall time: 742 ms

Out[4]:

	UID	country	location	loc_code	cycle	ObsYield	RUE	Tmin	Toptmin	Toptmax	Tmax	TminFactor	SimYield
0	1	Nepal	Bhairahawa	BHR	2019	2.97	3.0	9.0	15.0	25.0	34.0	0.25	2.73
1	2	Pakistan	Faisalabad	FAS	2014	4.88	3.0	9.0	15.0	25.0	34.0	0.25	4.74
2	3	Pakistan	Faisalabad	FAS	2015	3.59	3.0	9.0	15.0	25.0	34.0	0.25	3.27
3	4	Pakistan	Faisalabad	FAS	2016	2.86	3.0	9.0	15.0	25.0	34.0	0.25	3.94
4	5	Pakistan	Faisalabad	FAS	2017	3.93	3.0	9.0	15.0	25.0	34.0	0.25	4.34

Stressed VPD¶

In [5]:

%%time
TPF_SFvpd = model.getYield(tfun='TPF', sites=sites, is_VPDStress=True, expCSV=True)
TPF_SFvpd.head()

%%time TPF_SFvpd = model.getYield(tfun='TPF', sites=sites, is_VPDStress=True, expCSV=True) TPF_SFvpd.head()

CPU times: user 129 ms, sys: 3.43 ms, total: 132 ms
Wall time: 133 ms

Out[5]:

	UID	country	location	loc_code	cycle	ObsYield	RUE	TminFactor	Tmin	Toptmin	Toptmax	Tmax	Lvpd	Uvpd	SFvpd_Lthres	SFvpd_Uthres	SimYield
0	1	Nepal	Bhairahawa	BHR	2019	2.97	3.0	9.0	15.0	25.0	34.0	0.25	1.0	4.0	0.2	1.0	1.77
1	2	Pakistan	Faisalabad	FAS	2014	4.88	3.0	9.0	15.0	25.0	34.0	0.25	1.0	4.0	0.2	1.0	3.48
2	3	Pakistan	Faisalabad	FAS	2015	3.59	3.0	9.0	15.0	25.0	34.0	0.25	1.0	4.0	0.2	1.0	2.73
3	4	Pakistan	Faisalabad	FAS	2016	2.86	3.0	9.0	15.0	25.0	34.0	0.25	1.0	4.0	0.2	1.0	2.50
4	5	Pakistan	Faisalabad	FAS	2017	3.93	3.0	9.0	15.0	25.0	34.0	0.25	1.0	4.0	0.2	1.0	2.52

Display Grain Yield¶

Create a figure to compare simulated grain yield against observed.

In [6]:

%%time
dirname=os.path.join(config['RESULTS_PATH'], 'TPF', 'Figures')
figures.chart_compareResults(df_result=TPF_noStress, fld1="ObsYield", fld2="SimYield", alpha=.75, s=45, xy_lim=2, hue='loc_code', 
                             loc_leg=2, ncol=2, ha='left', va='top',
                             title='TPF\nNo streess condition', #xlabel='', ylabel='', 
                             dirname=dirname, fname='TPF_noStress', 
                             dispScore=True, dispLegend=True, saveFig=True, showFig=True, fmt='jpg')

%%time dirname=os.path.join(config['RESULTS_PATH'], 'TPF', 'Figures') figures.chart_compareResults(df_result=TPF_noStress, fld1="ObsYield", fld2="SimYield", alpha=.75, s=45, xy_lim=2, hue='loc_code', loc_leg=2, ncol=2, ha='left', va='top', title='TPF\nNo streess condition', #xlabel='', ylabel='', dirname=dirname, fname='TPF_noStress', dispScore=True, dispLegend=True, saveFig=True, showFig=True, fmt='jpg')

CPU times: user 1.09 s, sys: 116 ms, total: 1.2 s
Wall time: 695 ms

In [7]:

%%time
dirname=os.path.join(config['RESULTS_PATH'], 'TPF', 'Figures')
figures.chart_compareResults(df_result=TPF_SFvpd, fld1="ObsYield", fld2="SimYield", alpha=.75, s=45, xy_lim=2, hue='loc_code', 
                             loc_leg=2, ncol=2, ha='left', va='top',
                             title='TPF\nVPD streess condition', #xlabel='', ylabel='', 
                             dirname=dirname, fname='TPF_SFvpd', 
                             dispScore=True, dispLegend=True, saveFig=True, showFig=True, fmt='jpg')

%%time dirname=os.path.join(config['RESULTS_PATH'], 'TPF', 'Figures') figures.chart_compareResults(df_result=TPF_SFvpd, fld1="ObsYield", fld2="SimYield", alpha=.75, s=45, xy_lim=2, hue='loc_code', loc_leg=2, ncol=2, ha='left', va='top', title='TPF\nVPD streess condition', #xlabel='', ylabel='', dirname=dirname, fname='TPF_SFvpd', dispScore=True, dispLegend=True, saveFig=True, showFig=True, fmt='jpg')

CPU times: user 860 ms, sys: 79.5 ms, total: 939 ms
Wall time: 547 ms

In [8]:

%%time
path_to_save_results=os.path.join(config['RESULTS_PATH'], 'TPF')
figures.plot_corrTempFunct(cmb_noStress=TPF_noStress, cmb_noStress_filtered=None, 
                           cmb_SFvpd=TPF_SFvpd, cmb_SFvpd_filtered=None,
                   functype='TPF',fld1='ObsYield',fld2='SimYield',hue='location', ncol=6, s=80, alpha=0.95, xy_lim=1, 
                   fonts_axes=10, fonts_titles=12, dispScore=True, errorbar=True, saveFig=True, showFig=True,
                   path_to_save_results=path_to_save_results, dirname='Figures', fname='Fig_1_errorbar', fmt='pdf')

%%time path_to_save_results=os.path.join(config['RESULTS_PATH'], 'TPF') figures.plot_corrTempFunct(cmb_noStress=TPF_noStress, cmb_noStress_filtered=None, cmb_SFvpd=TPF_SFvpd, cmb_SFvpd_filtered=None, functype='TPF',fld1='ObsYield',fld2='SimYield',hue='location', ncol=6, s=80, alpha=0.95, xy_lim=1, fonts_axes=10, fonts_titles=12, dispScore=True, errorbar=True, saveFig=True, showFig=True, path_to_save_results=path_to_save_results, dirname='Figures', fname='Fig_1_errorbar', fmt='pdf')

CPU times: user 1.66 s, sys: 222 ms, total: 1.88 s
Wall time: 894 ms

In [ ]:

Combinations¶

To establish the optimum temperature response for grain-filling period, you can run several models using a wide range of cardinal temperatures.

The optimum temperature for photosynthesis depends on the choosen temperature function.

TPF combinations¶

No stress conditions¶

In [9]:

%%time

functype='TPF'
isVPDStress=False
df_GYield, data_input, cols = model.setup_dataInput_forCombinations(sites) # Setup input data
# 1) Combinations for TPF (no stress)
RUE = [3.0]
Tmin = [x for x in range(0, 5)]
Toptmin = [x for x in range(14, 20)]
Toptmax = [x for x in range(15, 20)]
Tmax = [x for x in range(30, 40)]
TminFactor = [0.25]
# No stress conditions
array_params_to_run, array_results = model.getCombinations(functype=functype, cols=cols, RUE=RUE, 
                                                           Tmin=Tmin, Toptmin=Toptmin, Toptmax=Toptmax, Tmax=Tmax, 
                                                           TminFactor=TminFactor, isVPDStress=isVPDStress)
cmb_TPF_noStress = model.getGYield_forCombinations(functype, df_GYield, data_input, array_params_to_run, 
                                                    isVPDStress, array_results, saveFile=True)
cmb_TPF_noStress

%%time functype='TPF' isVPDStress=False df_GYield, data_input, cols = model.setup_dataInput_forCombinations(sites) # Setup input data # 1) Combinations for TPF (no stress) RUE = [3.0] Tmin = [x for x in range(0, 5)] Toptmin = [x for x in range(14, 20)] Toptmax = [x for x in range(15, 20)] Tmax = [x for x in range(30, 40)] TminFactor = [0.25] # No stress conditions array_params_to_run, array_results = model.getCombinations(functype=functype, cols=cols, RUE=RUE, Tmin=Tmin, Toptmin=Toptmin, Toptmax=Toptmax, Tmax=Tmax, TminFactor=TminFactor, isVPDStress=isVPDStress) cmb_TPF_noStress = model.getGYield_forCombinations(functype, df_GYield, data_input, array_params_to_run, isVPDStress, array_results, saveFile=True) cmb_TPF_noStress

CPU times: user 1.86 s, sys: 33.6 ms, total: 1.89 s
Wall time: 1.9 s

Out[9]:

	UID	country	location	loc_code	cycle	ObsYield	RUE	Tmin	Toptmin	Toptmax	Tmax	TminFactor	SimYield
0	1	Nepal	Bhairahawa	BHR	2019	2.97	3.0	0.0	14.0	15.0	30.0	0.25	1.29
1	2	Pakistan	Faisalabad	FAS	2014	4.88	3.0	0.0	14.0	15.0	30.0	0.25	2.65
2	3	Pakistan	Faisalabad	FAS	2015	3.59	3.0	0.0	14.0	15.0	30.0	0.25	2.15
3	4	Pakistan	Faisalabad	FAS	2016	2.86	3.0	0.0	14.0	15.0	30.0	0.25	1.54
4	5	Pakistan	Faisalabad	FAS	2017	3.93	3.0	0.0	14.0	15.0	30.0	0.25	2.15
...	...	...	...	...	...	...	...	...	...	...	...	...	...
37495	46	India	Pusa	PUS-LS	2018	1.44	3.0	4.0	18.0	19.0	39.0	0.25	1.17
37496	47	India	Pusa	PUS-LS	2019	3.10	3.0	4.0	18.0	19.0	39.0	0.25	2.50
37497	48	India	Ludhiana	LDH-NS	2022	6.09	3.0	4.0	18.0	19.0	39.0	0.25	3.84
37498	49	India	Ludhiana	LDH-LS	2022	4.77	3.0	4.0	18.0	19.0	39.0	0.25	2.09
37499	50	Spain	Spain	SPN	2018	8.35	3.0	4.0	18.0	19.0	39.0	0.25	7.17

37500 rows × 13 columns

No stress Metrics¶

In [10]:

%%time

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

%%time m_TPF_noStress = model.getCombinations_Metrics(functype, isVPDStress, df_GYield, array_params_to_run, array_results, saveFile=True) #, fmt='parquet') m_TPF_noStress

100%|████████████████████████████████████████████████████████████████████████████████████████████| 750/750 [00:30<00:00, 24.77it/s]

CPU times: user 28 s, sys: 2.56 s, total: 30.6 s
Wall time: 30.3 s

Out[10]:

	RUE	Tmin	Toptmin	Toptmax	Tmax	TminFactor	MAE	MSE	RMSE	RMSRE	MAPE	pvalue	R2	EF	intercept	slope	Cb	CCC	Accuracy
0	3.0	0.0	14.0	15.0	30.0	0.25	2.386	6.935	2.633	57.047	53.493	5.649380e-09	0.510	-1.696	-0.2431	0.5328	0.39	0.29	46.51
1	3.0	0.0	14.0	15.0	31.0	0.25	2.198	5.954	2.440	52.386	49.026	7.032547e-10	0.551	-1.315	-0.1588	0.5562	0.43	0.33	50.97
2	3.0	0.0	14.0	15.0	32.0	0.25	2.025	5.118	2.262	48.047	44.864	8.950862e-11	0.587	-0.990	-0.0748	0.5764	0.47	0.37	55.14
3	3.0	0.0	14.0	15.0	33.0	0.25	1.868	4.419	2.102	44.164	41.087	1.341391e-11	0.618	-0.718	0.0015	0.5948	0.52	0.42	58.91
4	3.0	0.0	14.0	15.0	34.0	0.25	1.726	3.839	1.959	40.700	37.646	2.653647e-12	0.643	-0.492	0.0767	0.6102	0.56	0.46	62.35
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
745	3.0	4.0	18.0	19.0	35.0	0.25	0.986	1.555	1.247	26.291	21.532	1.849601e-13	0.680	0.396	0.1290	0.7911	0.88	0.74	78.47
746	3.0	4.0	18.0	19.0	36.0	0.25	0.909	1.347	1.161	24.309	19.924	6.301395e-14	0.694	0.476	0.1983	0.7994	0.90	0.77	80.08
747	3.0	4.0	18.0	19.0	37.0	0.25	0.844	1.186	1.089	22.720	18.560	2.559257e-14	0.705	0.539	0.2643	0.8059	0.92	0.79	81.44
748	3.0	4.0	18.0	19.0	38.0	0.25	0.797	1.062	1.031	21.552	17.671	1.294678e-14	0.713	0.587	0.3239	0.8117	0.94	0.81	82.33
749	3.0	4.0	18.0	19.0	39.0	0.25	0.759	0.968	0.984	20.738	17.005	7.516897e-15	0.720	0.624	0.3755	0.8174	0.96	0.83	83.00

750 rows × 19 columns

Stressed Vapor pressure deficit (VPD)¶

In [11]:

%%time

functype='TPF'
isVPDStress=True
df_GYield, data_input, cols = model.setup_dataInput_forCombinations(sites) # Setup input data
# Combinations
RUE = [3.0]
Tmin = [x for x in range(0, 5)]
Toptmin = [x for x in range(14, 20)]
Toptmax = [x for x in range(15, 20)]
Tmax = [x for x in range(30, 40)]
TminFactor = [0.25]
Lvpd = [1.5, 2, 2.5, 3]
Uvpd = [2, 2.5, 3, 3.5]
SFvpd_Lthres = [0.6, 0.8] 
SFvpd_Uthres = [1]
# No stress conditions
array_params_to_run, array_results = model.getCombinations(functype=functype, cols=cols, RUE=RUE, Tmin=Tmin, 
                                                           Toptmin=Toptmin, Toptmax=Toptmax,
                                                           Tmax=Tmax, TminFactor=TminFactor,  
                                                           Lvpd=Lvpd, Uvpd=Uvpd, SFvpd_Lthres=SFvpd_Lthres, SFvpd_Uthres=SFvpd_Uthres,
                                                           isVPDStress=isVPDStress)

# If combinations are not large
#cmb_TPF_SFvpd = model.getGYield_forCombinations(functype, df_GYield, data_input, array_params_to_run, 
#                                           isVPDStress, array_results, saveFile=True) # fmt='parquet' #csv
# cmb_TPF_SFvpd

# Use returnDF=False when the number of combinations is too large. 
# This avoid memory issues and save the file in parquet format by default
model.getGYield_forCombinations(functype, df_GYield, data_input, array_params_to_run, 
                                           isVPDStress, array_results, saveFile=True, returnDF=False)

%%time functype='TPF' isVPDStress=True df_GYield, data_input, cols = model.setup_dataInput_forCombinations(sites) # Setup input data # Combinations RUE = [3.0] Tmin = [x for x in range(0, 5)] Toptmin = [x for x in range(14, 20)] Toptmax = [x for x in range(15, 20)] Tmax = [x for x in range(30, 40)] TminFactor = [0.25] Lvpd = [1.5, 2, 2.5, 3] Uvpd = [2, 2.5, 3, 3.5] SFvpd_Lthres = [0.6, 0.8] SFvpd_Uthres = [1] # No stress conditions array_params_to_run, array_results = model.getCombinations(functype=functype, cols=cols, RUE=RUE, Tmin=Tmin, Toptmin=Toptmin, Toptmax=Toptmax, Tmax=Tmax, TminFactor=TminFactor, Lvpd=Lvpd, Uvpd=Uvpd, SFvpd_Lthres=SFvpd_Lthres, SFvpd_Uthres=SFvpd_Uthres, isVPDStress=isVPDStress) # If combinations are not large #cmb_TPF_SFvpd = model.getGYield_forCombinations(functype, df_GYield, data_input, array_params_to_run, # isVPDStress, array_results, saveFile=True) # fmt='parquet' #csv # cmb_TPF_SFvpd # Use returnDF=False when the number of combinations is too large. # This avoid memory issues and save the file in parquet format by default model.getGYield_forCombinations(functype, df_GYield, data_input, array_params_to_run, isVPDStress, array_results, saveFile=True, returnDF=False)

100%|██████████████████████████████████████████████████████████████████████████████████████████████| 16/16 [00:14<00:00,  1.08it/s]

<pyarrow._parquet.FileMetaData object at 0x13bec8c70>
  created_by: parquet-cpp-arrow version 13.0.0
  num_columns: 17
  num_rows: 749600
  num_row_groups: 1
  format_version: 2.6
  serialized_size: 3350
<pyarrow._parquet.FileMetaData object at 0x13bec8a40>
  created_by: parquet-cpp-arrow version 13.0.0
  num_columns: 23
  num_rows: 14992
  num_row_groups: 1
  format_version: 2.6
  serialized_size: 4382
CPU times: user 31.4 s, sys: 12.4 s, total: 43.8 s
Wall time: 33.5 s

In [12]:

%%time

# The above command export metrics automatically, if not used the following command to get evaluations
m_TPF_SFvpd = model.getCombinations_Metrics(functype, isVPDStress, df_GYield, 
                                             array_params_to_run, array_results, saveFile=True)
m_TPF_SFvpd

%%time # The above command export metrics automatically, if not used the following command to get evaluations m_TPF_SFvpd = model.getCombinations_Metrics(functype, isVPDStress, df_GYield, array_params_to_run, array_results, saveFile=True) m_TPF_SFvpd

100%|██████████████████████████████████████████████████████████████████████████████████████| 15000/15000 [00:10<00:00, 1427.37it/s]

CPU times: user 8.64 s, sys: 12 s, total: 20.7 s
Wall time: 10.6 s

Out[12]:

	RUE	Tmin	Toptmin	Toptmax	Tmax	TminFactor	Lvpd	Uvpd	SFvpd_Lthres	SFvpd_Uthres	MAE	MSE	RMSE	RMSRE	MAPE	pvalue	R2	EF	intercept	slope	Cb	CCC	Accuracy
0	3.0	0.0	14.0	15.0	30.0	0.25	1.5	2.0	0.6	1.0	2.890	9.955	3.155	67.285	64.302	1.891892e-06	0.380	-2.870	-0.1922	0.4106	0.28	0.18	35.70
1	3.0	0.0	14.0	15.0	30.0	0.25	1.5	2.0	0.8	1.0	2.688	8.644	2.940	62.968	59.915	1.045183e-07	0.449	-2.360	-0.2051	0.4576	0.32	0.22	40.09
2	3.0	0.0	14.0	15.0	30.0	0.25	1.5	2.5	0.6	1.0	2.859	9.759	3.124	66.654	63.624	1.467053e-06	0.386	-2.794	-0.1917	0.4173	0.29	0.18	36.38
3	3.0	0.0	14.0	15.0	30.0	0.25	1.5	2.5	0.8	1.0	2.753	9.031	3.005	64.317	61.360	1.814225e-07	0.436	-2.511	-0.2123	0.4449	0.30	0.21	38.64
4	3.0	0.0	14.0	15.0	30.0	0.25	1.5	3.0	0.6	1.0	2.881	9.904	3.147	67.132	64.099	1.961385e-06	0.379	-2.850	-0.1945	0.4132	0.28	0.18	35.90
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
14995	3.0	4.0	18.0	19.0	39.0	0.25	2.5	3.0	0.8	1.0	1.061	1.777	1.333	27.686	22.973	1.163686e-12	0.655	0.309	0.2340	0.7468	0.84	0.70	77.03
14996	3.0	4.0	18.0	19.0	39.0	0.25	2.5	3.5	0.6	1.0	1.321	2.652	1.629	34.206	28.945	3.840658e-10	0.562	-0.031	0.2013	0.6935	0.76	0.59	71.06
14997	3.0	4.0	18.0	19.0	39.0	0.25	2.5	3.5	0.8	1.0	1.208	2.227	1.492	31.131	26.252	2.021537e-11	0.612	0.134	0.2130	0.7160	0.79	0.64	73.75
14998	3.0	4.0	18.0	19.0	39.0	0.25	3.0	3.5	0.6	1.0	1.088	1.900	1.378	29.054	23.862	2.278331e-11	0.610	0.262	0.3101	0.7331	0.85	0.68	76.14
14999	3.0	4.0	18.0	19.0	39.0	0.25	3.0	3.5	0.8	1.0	0.976	1.545	1.243	26.043	21.209	8.038264e-13	0.660	0.400	0.3231	0.7555	0.88	0.73	78.79

15000 rows × 23 columns

Display grain yield comparison with and without VPD stress¶

In [13]:

%%time
# If the process above used "returnDF=False", it's necessary to load the data to create figures

# Defined path of the combinations and metrics files
c_TPF = os.path.join(config['RESULTS_PATH'], "TPF", "TPF_noStress", "combinations_Yield_TPF_noStress.parquet")
c_TPF_SFvpd = os.path.join(config['RESULTS_PATH'], "TPF", "TPF_SFvpd", "combinations_Yield_TPF_SFvpd.parquet")

cmb_TPF_noStress = pd.read_parquet(c_TPF)
cmb_TPF_SFvpd = pd.read_parquet(c_TPF_SFvpd)

%%time # If the process above used "returnDF=False", it's necessary to load the data to create figures # Defined path of the combinations and metrics files c_TPF = os.path.join(config['RESULTS_PATH'], "TPF", "TPF_noStress", "combinations_Yield_TPF_noStress.parquet") c_TPF_SFvpd = os.path.join(config['RESULTS_PATH'], "TPF", "TPF_SFvpd", "combinations_Yield_TPF_SFvpd.parquet") cmb_TPF_noStress = pd.read_parquet(c_TPF) cmb_TPF_SFvpd = pd.read_parquet(c_TPF_SFvpd)

CPU times: user 195 ms, sys: 95.1 ms, total: 290 ms
Wall time: 85.3 ms

In [14]:

%%time
path_to_save_results=os.path.join(config['RESULTS_PATH'], 'TPF')
figures.plot_corrTempFunct(cmb_noStress=cmb_TPF_noStress, cmb_noStress_filtered=cmb_TPF_noStress, 
                   cmb_SFvpd=cmb_TPF_SFvpd, cmb_SFvpd_filtered=cmb_TPF_SFvpd,
                   functype='TPF',fld1='ObsYield',fld2='SimYield',hue='location', ncol=6, s=20, alpha=0.65, xy_lim=1, 
                   fonts_axes=10, fonts_titles=12, dispScore=True, errorbar=False, saveFig=True, showFig=True,
                   path_to_save_results=path_to_save_results, dirname='Figures', fname='Fig_2_nofilters', fmt='jpg')

%%time path_to_save_results=os.path.join(config['RESULTS_PATH'], 'TPF') figures.plot_corrTempFunct(cmb_noStress=cmb_TPF_noStress, cmb_noStress_filtered=cmb_TPF_noStress, cmb_SFvpd=cmb_TPF_SFvpd, cmb_SFvpd_filtered=cmb_TPF_SFvpd, functype='TPF',fld1='ObsYield',fld2='SimYield',hue='location', ncol=6, s=20, alpha=0.65, xy_lim=1, fonts_axes=10, fonts_titles=12, dispScore=True, errorbar=False, saveFig=True, showFig=True, path_to_save_results=path_to_save_results, dirname='Figures', fname='Fig_2_nofilters', fmt='jpg')

CPU times: user 22.3 s, sys: 729 ms, total: 23 s
Wall time: 22.6 s

In [15]:

%%time
path_to_save_results=os.path.join(config['RESULTS_PATH'], 'TPF')
figures.plot_corrTempFunct(cmb_noStress=cmb_TPF_noStress, cmb_noStress_filtered=cmb_TPF_noStress, 
                   cmb_SFvpd=cmb_TPF_SFvpd, cmb_SFvpd_filtered=cmb_TPF_SFvpd,
                   functype='TPF',fld1='ObsYield',fld2='SimYield',hue='location', ncol=6, s=80, alpha=0.95, xy_lim=1, 
                   fonts_axes=10, fonts_titles=12, dispScore=True, errorbar=True, saveFig=True, showFig=True,
                   path_to_save_results=path_to_save_results, dirname='Figures', fname='Fig_2_nofilters_errorbar', fmt='jpg')

%%time path_to_save_results=os.path.join(config['RESULTS_PATH'], 'TPF') figures.plot_corrTempFunct(cmb_noStress=cmb_TPF_noStress, cmb_noStress_filtered=cmb_TPF_noStress, cmb_SFvpd=cmb_TPF_SFvpd, cmb_SFvpd_filtered=cmb_TPF_SFvpd, functype='TPF',fld1='ObsYield',fld2='SimYield',hue='location', ncol=6, s=80, alpha=0.95, xy_lim=1, fonts_axes=10, fonts_titles=12, dispScore=True, errorbar=True, saveFig=True, showFig=True, path_to_save_results=path_to_save_results, dirname='Figures', fname='Fig_2_nofilters_errorbar', fmt='jpg')

CPU times: user 1.29 s, sys: 153 ms, total: 1.44 s
Wall time: 888 ms

Select the best results using filters¶

After running several models you can filter results to see which one look better.

The following table is an example of one filter used to evaluate the best models, it only use three (3) evaluation metrics (Cb, CCC, and RMSRE) to select the top ones.

Temperature Function	Cb	CCC	RMSRE
TPF	0.94	> 0.8	< 20.9
TPF vpd stress	0.86	> 0.7	< 28

In [16]:

# Load module to filter combinations in an easy way
from tfunct.util import filterSimulations

# Load module to filter combinations in an easy way from tfunct.util import filterSimulations

filter TPF manually¶

In [17]:

m_TPF_noStress[
    (m_TPF_noStress['Cb']>0.94) & (m_TPF_noStress['CCC']>0.8) & (m_TPF_noStress['RMSRE']<20.9)
]

m_TPF_noStress[ (m_TPF_noStress['Cb']>0.94) & (m_TPF_noStress['CCC']>0.8) & (m_TPF_noStress['RMSRE']<20.9) ]

Out[17]:

	RUE	Tmin	Toptmin	Toptmax	Tmax	TminFactor	MAE	MSE	RMSE	RMSRE	MAPE	pvalue	R2	EF	intercept	slope	Cb	CCC	Accuracy
589	3.0	3.0	17.0	19.0	39.0	0.25	0.769	0.989	0.995	20.786	17.151	4.626587e-15	0.725	0.615	0.3798	0.8058	0.95	0.82	82.85
599	3.0	3.0	18.0	19.0	39.0	0.25	0.760	0.971	0.985	20.632	16.959	3.673087e-15	0.728	0.622	0.3699	0.8108	0.95	0.83	83.04
749	3.0	4.0	18.0	19.0	39.0	0.25	0.759	0.968	0.984	20.738	17.005	7.516897e-15	0.720	0.624	0.3755	0.8174	0.96	0.83	83.00

In [18]:

m_TPF_SFvpd[(m_TPF_SFvpd['Cb']>0.86) & (m_TPF_SFvpd['CCC']>0.7) & (m_TPF_SFvpd['RMSRE']<28.0)]

m_TPF_SFvpd[(m_TPF_SFvpd['Cb']>0.86) & (m_TPF_SFvpd['CCC']>0.7) & (m_TPF_SFvpd['RMSRE']<28.0)]

Out[18]:

	RUE	Tmin	Toptmin	Toptmax	Tmax	TminFactor	Lvpd	Uvpd	SFvpd_Lthres	SFvpd_Uthres	MAE	MSE	RMSE	RMSRE	MAPE	pvalue	R2	EF	intercept	slope	Cb	CCC	Accuracy
11999	3.0	3.0	18.0	19.0	39.0	0.25	3.0	3.5	0.8	1.0	0.986	1.566	1.251	26.065	21.338	3.842510e-13	0.670	0.391	0.3172	0.7489	0.87	0.73	78.66
14799	3.0	4.0	17.0	19.0	39.0	0.25	3.0	3.5	0.8	1.0	0.995	1.574	1.255	26.244	21.588	7.230819e-13	0.661	0.388	0.3345	0.7475	0.87	0.72	78.41
14999	3.0	4.0	18.0	19.0	39.0	0.25	3.0	3.5	0.8	1.0	0.976	1.545	1.243	26.043	21.209	8.038264e-13	0.660	0.400	0.3231	0.7555	0.88	0.73	78.79

No stress conditions¶

In [19]:

%%time
# path of the combinations and metrics files
c_TPF = os.path.join(config['RESULTS_PATH'], "TPF", "TPF_noStress", "combinations_Yield_TPF_noStress.parquet")
m_TPF = os.path.join(config['RESULTS_PATH'], "TPF", "TPF_noStress", "metrics_Yield_TPF_noStress.parquet")

# Filters
cmb_filters = dict(
                RUE = ('RUE', '=', 3.0 ),
                TminFactor = ('TminFactor', '=', 0.25 )
            )
met_filters = dict(
        Cb = ('>=', 0.94),
        CCC = ('>=', 0.8),
        Accuracy = ('>=', 82),
        RMSRE = ('<=', 20.9),
    )
outputPath = os.path.join(config['RESULTS_PATH'], "TPF")

# Use pdFormat=True to display filtered table. Use when the results are small to avoid freeze you computer session
# Use fmt='parquet' and avoid fmt='csv' when the results are quite large.
# Use dispFig=True if the filtered table is small.
filtered_c_TPF = filterSimulations(functype='TPF', VPDstress=False, cmb=c_TPF, met=m_TPF, 
                                 cmb_filters=cmb_filters, met_filters=met_filters, pdFormat=True, 
                                 dispFig=True, saveFig=True, figname='Fig_topComb_avgYield', figfmt='pdf',
                                 saveResults=True, outputPath=outputPath, fmt='parquet')

filtered_c_TPF

%%time # path of the combinations and metrics files c_TPF = os.path.join(config['RESULTS_PATH'], "TPF", "TPF_noStress", "combinations_Yield_TPF_noStress.parquet") m_TPF = os.path.join(config['RESULTS_PATH'], "TPF", "TPF_noStress", "metrics_Yield_TPF_noStress.parquet") # Filters cmb_filters = dict( RUE = ('RUE', '=', 3.0 ), TminFactor = ('TminFactor', '=', 0.25 ) ) met_filters = dict( Cb = ('>=', 0.94), CCC = ('>=', 0.8), Accuracy = ('>=', 82), RMSRE = ('<=', 20.9), ) outputPath = os.path.join(config['RESULTS_PATH'], "TPF") # Use pdFormat=True to display filtered table. Use when the results are small to avoid freeze you computer session # Use fmt='parquet' and avoid fmt='csv' when the results are quite large. # Use dispFig=True if the filtered table is small. filtered_c_TPF = filterSimulations(functype='TPF', VPDstress=False, cmb=c_TPF, met=m_TPF, cmb_filters=cmb_filters, met_filters=met_filters, pdFormat=True, dispFig=True, saveFig=True, figname='Fig_topComb_avgYield', figfmt='pdf', saveResults=True, outputPath=outputPath, fmt='parquet') filtered_c_TPF

Loading combinations...
Loading metrics...
750 combinations found
13 combinations found after applied filter
Filtered data set saved at /Users/ernestogiron/Desktop/TemperatureFunctions/results/TPF/TPF_filtered/filtered_metrics_combinations_Yield_TPF_noStress_20231001.parquet
CPU times: user 1.21 s, sys: 164 ms, total: 1.37 s
Wall time: 748 ms

Out[19]:

	UID	country	location	loc_code	cycle	ObsYield	RUE	Tmin	Toptmin	Toptmax	Tmax	TminFactor	SimYield	MAE	MSE	RMSE	RMSRE	MAPE	pvalue	R2	EF	intercept	slope	Cb	CCC	Accuracy
0	1	Nepal	Bhairahawa	BHR	2019	2.97	3.0	0.0	14.0	19.0	39.0	0.25	2.29	0.772	1.001	1.000	20.832	17.193	3.050376e-15	0.73	0.611	0.3912	0.7970	0.94	0.82	82.81
1	2	Pakistan	Faisalabad	FAS	2014	4.88	3.0	0.0	14.0	19.0	39.0	0.25	4.08	0.772	1.001	1.000	20.832	17.193	3.050376e-15	0.73	0.611	0.3912	0.7970	0.94	0.82	82.81
2	3	Pakistan	Faisalabad	FAS	2015	3.59	3.0	0.0	14.0	19.0	39.0	0.25	3.01	0.772	1.001	1.000	20.832	17.193	3.050376e-15	0.73	0.611	0.3912	0.7970	0.94	0.82	82.81
3	4	Pakistan	Faisalabad	FAS	2016	2.86	3.0	0.0	14.0	19.0	39.0	0.25	3.23	0.772	1.001	1.000	20.832	17.193	3.050376e-15	0.73	0.611	0.3912	0.7970	0.94	0.82	82.81
4	5	Pakistan	Faisalabad	FAS	2017	3.93	3.0	0.0	14.0	19.0	39.0	0.25	3.84	0.772	1.001	1.000	20.832	17.193	3.050376e-15	0.73	0.611	0.3912	0.7970	0.94	0.82	82.81
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
645	46	India	Pusa	PUS-LS	2018	1.44	3.0	4.0	18.0	19.0	39.0	0.25	1.17	0.759	0.968	0.984	20.738	17.005	7.516897e-15	0.72	0.624	0.3755	0.8174	0.96	0.83	83.00
646	47	India	Pusa	PUS-LS	2019	3.10	3.0	4.0	18.0	19.0	39.0	0.25	2.50	0.759	0.968	0.984	20.738	17.005	7.516897e-15	0.72	0.624	0.3755	0.8174	0.96	0.83	83.00
647	48	India	Ludhiana	LDH-NS	2022	6.09	3.0	4.0	18.0	19.0	39.0	0.25	3.84	0.759	0.968	0.984	20.738	17.005	7.516897e-15	0.72	0.624	0.3755	0.8174	0.96	0.83	83.00
648	49	India	Ludhiana	LDH-LS	2022	4.77	3.0	4.0	18.0	19.0	39.0	0.25	2.09	0.759	0.968	0.984	20.738	17.005	7.516897e-15	0.72	0.624	0.3755	0.8174	0.96	0.83	83.00
649	50	Spain	Spain	SPN	2018	8.35	3.0	4.0	18.0	19.0	39.0	0.25	7.17	0.759	0.968	0.984	20.738	17.005	7.516897e-15	0.72	0.624	0.3755	0.8174	0.96	0.83	83.00

650 rows × 26 columns

VPD stress conditions¶

In [20]:

%%time

# Path of the combinations and metrics files
c_TPF_SFvpd = os.path.join(config['RESULTS_PATH'], "TPF", "TPF_SFvpd", "combinations_Yield_TPF_SFvpd.parquet")
m_TPF_SFvpd = os.path.join(config['RESULTS_PATH'], "TPF", "TPF_SFvpd", "metrics_Yield_TPF_SFvpd.parquet")

cmb_filters = dict(
                RUE = ('RUE', '=', 3.0 ),
                TminFactor = ('TminFactor', '=', 0.25 )
            )
met_filters = dict(
        Cb = ('>=', 0.86),
        CCC = ('>=', 0.7),
        #Accuracy = ('>=', 78),
        RMSRE = ('<=', 28),
    )
outputPath = os.path.join(config['RESULTS_PATH'], "TPF") 

# Use pdFormat=True to display filtered table. Use when the results are small to avoid freeze you computer session
# Use fmt='parquet' and avoid fmt='csv' when the results are quite large.
# Use dispFig=True if the filtered table is small.
filtered_c_TPF_SFvpd = filterSimulations(functype='TPF', VPDstress=True, cmb=c_TPF_SFvpd, met=m_TPF_SFvpd, 
                                 cmb_filters=cmb_filters, met_filters=met_filters, pdFormat=True, 
                                 dispFig=True, saveFig=True, figname='Fig_topComb_avgYield', figfmt='pdf',
                                 saveResults=True, outputPath=outputPath, fmt='parquet')

filtered_c_TPF_SFvpd

%%time # Path of the combinations and metrics files c_TPF_SFvpd = os.path.join(config['RESULTS_PATH'], "TPF", "TPF_SFvpd", "combinations_Yield_TPF_SFvpd.parquet") m_TPF_SFvpd = os.path.join(config['RESULTS_PATH'], "TPF", "TPF_SFvpd", "metrics_Yield_TPF_SFvpd.parquet") cmb_filters = dict( RUE = ('RUE', '=', 3.0 ), TminFactor = ('TminFactor', '=', 0.25 ) ) met_filters = dict( Cb = ('>=', 0.86), CCC = ('>=', 0.7), #Accuracy = ('>=', 78), RMSRE = ('<=', 28), ) outputPath = os.path.join(config['RESULTS_PATH'], "TPF") # Use pdFormat=True to display filtered table. Use when the results are small to avoid freeze you computer session # Use fmt='parquet' and avoid fmt='csv' when the results are quite large. # Use dispFig=True if the filtered table is small. filtered_c_TPF_SFvpd = filterSimulations(functype='TPF', VPDstress=True, cmb=c_TPF_SFvpd, met=m_TPF_SFvpd, cmb_filters=cmb_filters, met_filters=met_filters, pdFormat=True, dispFig=True, saveFig=True, figname='Fig_topComb_avgYield', figfmt='pdf', saveResults=True, outputPath=outputPath, fmt='parquet') filtered_c_TPF_SFvpd

Loading combinations...
Loading metrics...
15000 combinations found
13 combinations found after applied filter
Filtered data set saved at /Users/ernestogiron/Desktop/TemperatureFunctions/results/TPF/TPF_filtered/filtered_metrics_combinations_Yield_TPF_SFvpd_20231001.parquet
CPU times: user 1.54 s, sys: 217 ms, total: 1.76 s
Wall time: 814 ms

Out[20]:

	UID	country	location	loc_code	cycle	ObsYield	RUE	Tmin	Toptmin	Toptmax	Tmax	TminFactor	Lvpd	Uvpd	SFvpd_Lthres	SFvpd_Uthres	SimYield	MAE	MSE	RMSE	RMSRE	MAPE	pvalue	R2	EF	intercept	slope	Cb	CCC	Accuracy
0	1	Nepal	Bhairahawa	BHR	2019	2.97	3.0	4.0	16.0	19.0	39.0	0.25	3.0	3.5	0.8	1.0	2.12	1.019	1.613	1.270	26.564	22.086	1.068685e-12	0.656	0.373	0.3621	0.7380	0.86	0.72	77.91
1	2	Pakistan	Faisalabad	FAS	2014	4.88	3.0	4.0	16.0	19.0	39.0	0.25	3.0	3.5	0.8	1.0	4.04	1.019	1.613	1.270	26.564	22.086	1.068685e-12	0.656	0.373	0.3621	0.7380	0.86	0.72	77.91
2	3	Pakistan	Faisalabad	FAS	2015	3.59	3.0	4.0	16.0	19.0	39.0	0.25	3.0	3.5	0.8	1.0	3.04	1.019	1.613	1.270	26.564	22.086	1.068685e-12	0.656	0.373	0.3621	0.7380	0.86	0.72	77.91
3	4	Pakistan	Faisalabad	FAS	2016	2.86	3.0	4.0	16.0	19.0	39.0	0.25	3.0	3.5	0.8	1.0	2.83	1.019	1.613	1.270	26.564	22.086	1.068685e-12	0.656	0.373	0.3621	0.7380	0.86	0.72	77.91
4	5	Pakistan	Faisalabad	FAS	2017	3.93	3.0	4.0	16.0	19.0	39.0	0.25	3.0	3.5	0.8	1.0	3.55	1.019	1.613	1.270	26.564	22.086	1.068685e-12	0.656	0.373	0.3621	0.7380	0.86	0.72	77.91
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
595	46	India	Pusa	PUS-LS	2018	1.44	3.0	2.0	15.0	19.0	39.0	0.25	3.0	3.5	0.8	1.0	0.93	1.018	1.614	1.271	26.407	21.984	4.441160e-13	0.668	0.372	0.3486	0.7355	0.86	0.72	78.02
596	47	India	Pusa	PUS-LS	2019	3.10	3.0	2.0	15.0	19.0	39.0	0.25	3.0	3.5	0.8	1.0	1.74	1.018	1.614	1.271	26.407	21.984	4.441160e-13	0.668	0.372	0.3486	0.7355	0.86	0.72	78.02
597	48	India	Ludhiana	LDH-NS	2022	6.09	3.0	2.0	15.0	19.0	39.0	0.25	3.0	3.5	0.8	1.0	3.57	1.018	1.614	1.271	26.407	21.984	4.441160e-13	0.668	0.372	0.3486	0.7355	0.86	0.72	78.02
598	49	India	Ludhiana	LDH-LS	2022	4.77	3.0	2.0	15.0	19.0	39.0	0.25	3.0	3.5	0.8	1.0	1.75	1.018	1.614	1.271	26.407	21.984	4.441160e-13	0.668	0.372	0.3486	0.7355	0.86	0.72	78.02
599	50	Spain	Spain	SPN	2018	8.35	3.0	2.0	15.0	19.0	39.0	0.25	3.0	3.5	0.8	1.0	6.69	1.018	1.614	1.271	26.407	21.984	4.441160e-13	0.668	0.372	0.3486	0.7355	0.86	0.72	78.02

600 rows × 30 columns

In [21]:

path_to_save_results=os.path.join(config['RESULTS_PATH'],'TPF', 'TPF_filtered')
figures.plot_corrTempFunct(cmb_noStress=filtered_c_TPF, cmb_noStress_filtered=filtered_c_TPF, 
                           cmb_SFvpd=filtered_c_TPF_SFvpd, cmb_SFvpd_filtered=filtered_c_TPF_SFvpd,
                   functype='TPF',fld1='ObsYield',fld2='SimYield',hue='location', ncol=6, s=40, alpha=0.95, xy_lim=1, 
                   fonts_axes=10, fonts_titles=12, dispScore=True, errorbar=False, saveFig=True, showFig=True,
                   path_to_save_results=path_to_save_results, dirname='Figures', fname='Fig_2_filtered', fmt='jpg')

path_to_save_results=os.path.join(config['RESULTS_PATH'],'TPF', 'TPF_filtered') figures.plot_corrTempFunct(cmb_noStress=filtered_c_TPF, cmb_noStress_filtered=filtered_c_TPF, cmb_SFvpd=filtered_c_TPF_SFvpd, cmb_SFvpd_filtered=filtered_c_TPF_SFvpd, functype='TPF',fld1='ObsYield',fld2='SimYield',hue='location', ncol=6, s=40, alpha=0.95, xy_lim=1, fonts_axes=10, fonts_titles=12, dispScore=True, errorbar=False, saveFig=True, showFig=True, path_to_save_results=path_to_save_results, dirname='Figures', fname='Fig_2_filtered', fmt='jpg')

In [22]:

path_to_save_results=os.path.join(config['RESULTS_PATH'], 'TPF', 'TPF_filtered')
figures.plot_corrTempFunct(cmb_noStress=filtered_c_TPF, cmb_noStress_filtered=filtered_c_TPF, 
                           cmb_SFvpd=filtered_c_TPF_SFvpd, cmb_SFvpd_filtered=filtered_c_TPF_SFvpd,
                   functype='TPF',fld1='ObsYield',fld2='SimYield',hue='location', ncol=6, s=80, alpha=0.95, xy_lim=1, 
                   fonts_axes=10, fonts_titles=12, dispScore=True, errorbar=True, saveFig=True, showFig=True,
                   path_to_save_results=path_to_save_results, dirname='Figures', fname='Fig_2_filtered_errorbar', fmt='jpg')

path_to_save_results=os.path.join(config['RESULTS_PATH'], 'TPF', 'TPF_filtered') figures.plot_corrTempFunct(cmb_noStress=filtered_c_TPF, cmb_noStress_filtered=filtered_c_TPF, cmb_SFvpd=filtered_c_TPF_SFvpd, cmb_SFvpd_filtered=filtered_c_TPF_SFvpd, functype='TPF',fld1='ObsYield',fld2='SimYield',hue='location', ncol=6, s=80, alpha=0.95, xy_lim=1, fonts_axes=10, fonts_titles=12, dispScore=True, errorbar=True, saveFig=True, showFig=True, path_to_save_results=path_to_save_results, dirname='Figures', fname='Fig_2_filtered_errorbar', fmt='jpg')

Comparison of the best combinations¶

In [25]:

%%time
# Prepare data as a input for create comparison figures by site and/or best combinations
path_to_save_results=os.path.join(config['RESULTS_PATH'], 'TPF', 'TPF_filtered')
df_TDays_vs_TempResponse, df_TDays_vs_TempResponse_mean_allSites = \
figures.createFigure_Type_III_TPF(sites, cmb=filtered_c_TPF, roundVal=3, maxTDay=50, saveTable=True,
                        path_to_save_results=path_to_save_results, fmt='parquet')
df_TDays_vs_TempResponse.head()

%%time # Prepare data as a input for create comparison figures by site and/or best combinations path_to_save_results=os.path.join(config['RESULTS_PATH'], 'TPF', 'TPF_filtered') df_TDays_vs_TempResponse, df_TDays_vs_TempResponse_mean_allSites = \ figures.createFigure_Type_III_TPF(sites, cmb=filtered_c_TPF, roundVal=3, maxTDay=50, saveTable=True, path_to_save_results=path_to_save_results, fmt='parquet') df_TDays_vs_TempResponse.head()

CPU times: user 24.3 s, sys: 2.03 s, total: 26.4 s
Wall time: 26.9 s

Out[25]:

	TDay	TempResponse	UID	country	location	loc_code	cycle	RUE	TminFactor	Tmin	Toptmin	Toptmax	Tmax	ObsYield	SimYield
0	0.0	0.000	1	Nepal	Bhairahawa	BHR	2019	3.0	0.25	0.0	14.0	19.0	39.0	2.97	2.29
1	0.1	0.007	1	Nepal	Bhairahawa	BHR	2019	3.0	0.25	0.0	14.0	19.0	39.0	2.97	2.29
2	0.2	0.014	1	Nepal	Bhairahawa	BHR	2019	3.0	0.25	0.0	14.0	19.0	39.0	2.97	2.29
3	0.3	0.021	1	Nepal	Bhairahawa	BHR	2019	3.0	0.25	0.0	14.0	19.0	39.0	2.97	2.29
4	0.4	0.029	1	Nepal	Bhairahawa	BHR	2019	3.0	0.25	0.0	14.0	19.0	39.0	2.97	2.29

In [26]:

%%time
figures.diplay_Figure_Type_II_TPF_InOneFig(cmb=filtered_c_TPF, fnct='TPF', 
                                           df_tdays=df_TDays_vs_TempResponse, 
                                           df_tdays_mean=df_TDays_vs_TempResponse_mean_allSites,
                                           saveFig=True,showFig=True, fmt='jpg', leg_ncol=2,
                                           path_to_save_results=path_to_save_results)

%%time figures.diplay_Figure_Type_II_TPF_InOneFig(cmb=filtered_c_TPF, fnct='TPF', df_tdays=df_TDays_vs_TempResponse, df_tdays_mean=df_TDays_vs_TempResponse_mean_allSites, saveFig=True,showFig=True, fmt='jpg', leg_ncol=2, path_to_save_results=path_to_save_results)

100%|██████████████████████████████████████████████████████████████████████████████████████████████| 50/50 [00:32<00:00,  1.53it/s]
100%|██████████████████████████████████████████████████████████████████████████████████████████████| 13/13 [00:00<00:00, 21.11it/s]

CPU times: user 39.3 s, sys: 696 ms, total: 40 s
Wall time: 39.7 s

In [28]:

%%time
figures.display_FigTDay_vs_TempResponseBySiteYrs_TPF(cmb=filtered_c_TPF, df_tdays=df_TDays_vs_TempResponse, 
                                         roundVal=2, maxTDay=50, fnct='TPF', 
                                         saveFig=True, showFig=True, fmt='jpg', cols=5, leg_ncol=2,
                                         path_to_save_results=path_to_save_results)

%%time figures.display_FigTDay_vs_TempResponseBySiteYrs_TPF(cmb=filtered_c_TPF, df_tdays=df_TDays_vs_TempResponse, roundVal=2, maxTDay=50, fnct='TPF', saveFig=True, showFig=True, fmt='jpg', cols=5, leg_ncol=2, path_to_save_results=path_to_save_results)

100%|██████████████████████████████████████████████████████████████████████████████████████████████| 10/10 [00:52<00:00,  5.22s/it]

CPU times: user 1min 1s, sys: 898 ms, total: 1min 2s
Wall time: 1min 2s

In [27]:

uid = 1
df0 = filtered_c_TPF[filtered_c_TPF['UID']==uid].reset_index(drop=True)
figures.diplay_Figure_Type_II_TPF_InOneFig(cmb=df0, fnct='TPF', 
                                           df_tdays=df_TDays_vs_TempResponse, 
                                           df_tdays_mean=df_TDays_vs_TempResponse_mean_allSites,
                                           saveFig=True,showFig=True, fmt='jpg', leg_ncol=2,
                                           path_to_save_results=path_to_save_results) #fname = "Figure_TPF_SiteYrs_Comparison"

uid = 1 df0 = filtered_c_TPF[filtered_c_TPF['UID']==uid].reset_index(drop=True) figures.diplay_Figure_Type_II_TPF_InOneFig(cmb=df0, fnct='TPF', df_tdays=df_TDays_vs_TempResponse, df_tdays_mean=df_TDays_vs_TempResponse_mean_allSites, saveFig=True,showFig=True, fmt='jpg', leg_ncol=2, path_to_save_results=path_to_save_results) #fname = "Figure_TPF_SiteYrs_Comparison"

100%|██████████████████████████████████████████████████████████████████████████████████████████████| 13/13 [00:00<00:00, 14.76it/s]

Conclusion¶

Congratulations! You have run a simulation using a prebuilt dataset and the Temperature Functions API.

Type 3- Four cardinal temperatures¶

Model Parameters	No stress	VPD stress
RUE	3.0	3.0
TminFactor	0.25	0.25
Tmin	0-4 °C	2-4 °C
Toptmin	14-18 °C	14-18 °C
Toptmax	19 °C	19 °C
Tmax	39 °C	38-39 °C
SFvpdL		0.8
SFvpdU		1
VPDL		3 kPa
VPDU		3.5 kPa

In [24]:

print(filtered_c_TPF['Tmin'].sort_values().unique())
print(filtered_c_TPF['Toptmin'].sort_values().unique())
print(filtered_c_TPF['Toptmax'].sort_values().unique())
print(filtered_c_TPF['Tmax'].sort_values().unique())
print(filtered_c_TPF_SFvpd['Tmin'].sort_values().unique())
print(filtered_c_TPF_SFvpd['Toptmin'].sort_values().unique())
print(filtered_c_TPF_SFvpd['Toptmax'].sort_values().unique())
print(filtered_c_TPF_SFvpd['Tmax'].sort_values().unique())
print(filtered_c_TPF_SFvpd['Lvpd'].sort_values().unique())
print(filtered_c_TPF_SFvpd['Uvpd'].sort_values().unique())
print(filtered_c_TPF_SFvpd['SFvpd_Lthres'].sort_values().unique())
print(filtered_c_TPF_SFvpd['SFvpd_Uthres'].sort_values().unique())

print(filtered_c_TPF['Tmin'].sort_values().unique()) print(filtered_c_TPF['Toptmin'].sort_values().unique()) print(filtered_c_TPF['Toptmax'].sort_values().unique()) print(filtered_c_TPF['Tmax'].sort_values().unique()) print(filtered_c_TPF_SFvpd['Tmin'].sort_values().unique()) print(filtered_c_TPF_SFvpd['Toptmin'].sort_values().unique()) print(filtered_c_TPF_SFvpd['Toptmax'].sort_values().unique()) print(filtered_c_TPF_SFvpd['Tmax'].sort_values().unique()) print(filtered_c_TPF_SFvpd['Lvpd'].sort_values().unique()) print(filtered_c_TPF_SFvpd['Uvpd'].sort_values().unique()) print(filtered_c_TPF_SFvpd['SFvpd_Lthres'].sort_values().unique()) print(filtered_c_TPF_SFvpd['SFvpd_Uthres'].sort_values().unique())

[0. 1. 2. 3. 4.]
[14. 15. 16. 17. 18.]
[19.]
[39.]
[2. 3. 4.]
[14. 15. 16. 17. 18.]
[19.]
[38. 39.]
[3.]
[3.5]
[0.8]
[1.]

In [ ]:

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