Data for paper "Quantitative and Qualitative Evaluation of IES TM-30 Indices for the Color Preference of Landscape Oil Paintings" published in LEUKOS

General Explanation This doi is reserved to store the data from a journal paper "Quantitative and Qualitative Evaluation of IES TM-30 Indices for the Color Preference of Landscape Oil Paintings" published in LEUKOSCurrently, I think a dataset with a published doi cannot be further updated even if I noticed an error after the publishing. In case if there is a necessary correction or new information should be added by a new version, the new doi of the new version will be offered in this description. Software used in this experiment: All analyses using Python were performed using Spyder with Python 3All analyses using MATLAB were performed using MATLAB 2020bAll analyses using R were performed using R Studio Statistical Software (v4.1.2; R Core Team 2021) Files Explaining the data: Explanation_of_the_Deposition_when_publishing.txt: same information as this Description until the time of publishing. But it is in a txt form and therefore without the format (such as bold or Header setting). Explanation_of_the_Deposition_when_publishing.docx: same information as this Description until the time of publishing. With the format that can be kept in a docx file. _README_to_explain_the_folders_after_unzip.txt: after unzipped all the zip file (except for code_Python3_module.zip) into the same folder. This file is the README file of this folder. As a result of unzipping files into one folder, this folder contains a file observer_order_paint_Ross_Munsell_20220520.xlsx (from observers_orders.zip) and these subfolders: - answer_sheets_from_observers: from answer_sheets_from_observers.zip- answer_sheets_researcher_summary_Excel: from answer_sheets_researcher_summary_Excel.zip- answer_sheets_researcher_summary_notes: from answer_sheets_researcher_summary_notes.zip- data_calibration: from analyzed_data_physical.zip + converted_raw_XYZ_pcf_to_img.zip + converted_raw_XYZ_pcf_to_mat.zip + raw_data_determine_spectra.zip + raw_data_physical.zip- data_visual: from raw_data_visual_perception.zip- instructions_and_answer_sheet: from instructions_and_answer_sheet.zip- literature: from literature.zip- Matlab: from code_MATLAB_and_output_results.zip- moo_fast2d: from spectra_moo_determine_the_intensity_of_each_channel.zip- moo_fast10d: from spectra_moo_determine_the_intensity_of_each_channel.zip- moo_res10d: from spectra_moo_determine_the_intensity_of_each_channel.zip- moo_result: from spectra_moo_determine_the_intensity_of_each_channel.zip- optm_brute_force: from spectra_simulation_determine_the_angle.zip + spectra_simulation_determine_the_angle_readme.zip- Python3_all: from code_Python3_all.zip- Python3_preliminary: from code_Python3_preliminary_test_lamp_decide_spd.zip- Python3_execute_exp: from code_Python3_execute_exp.zip- Python3_analyse: from from code_Python3_analyse_and_plot.zip- R: from code_R_and_output_results.zip- real_paintings: from real_paintings.zip- result_to_intens: from spectra_moo_determine_the_intensity_of_each_channel.zip- setup: from setup.zip Files of all README files in each folder after upzipped all_final_README_files_in_each_folder.zip: After correctly unzipped the file and correctly put into the folder structure, the unzipped file should follow the structure of this folder. (Except for the file code_Python3_module.zip). The latest README files in each unzipped folder is here. During unzipped process, if there is also a README in other zip file and conflicts with the files in this zip file, please use the README file in this zip file instead of those from other zip files because this is the latest one. Files related to the experimental design, the setup, the procedures: raw_data_physical.zip: all physical measurement raw data except for data included in raw_data_determine_spectra.zip * All the measured physical raw data. These files are independent of the analysis and any other process. Thus, they will never be changed in the future. = subfolders are: LMK, luxmeter, spd_lamp_monitor, spd_primaries, spd_white_tile, warmup - LMK: - raw_data: the LMK measurement raw_data - 20220330_CRend_3H - 20220415_CRend_FR_1H - 20220421_CRend_BM_KM - 20220511_CRend_RM_CT - 202204_CRend_FR_1H - 20240308_Paint_withWhiteTile - 20240318_LandsMCCTile_3000KRf95 - 20240318_Paint_3000KRf95withMCCTile - luxmeter: the illuminance measurement results including a csv file 20220331 that shows the measured results - spd_lamp_monitor: including a _README file and csv files generated, with three subfolders - quickCheck_before_removeCloth - raw_jeti - while_testcode - spd_primaries: including a _README file and the following subfolders - l1_2109 - l1_2109_testcode - l1_210910 - l1_210910_testcode - l1_211110gray - l1_21110gray_testcode - lamp_folder_template - lamp1- spd_white_tile: including files: Convert to csv.xlsx, SpecimIQWhite - comma.txt, SpecimIQWhite.csv, SpecimIQWhite.png, SpecimIQWhite.txt, SpecimIQWhite_wo_UVF.csv- warmup: including many csv file generated during the measurements and two subfolders - raw - when testing code raw_data_determine_spectra.zip: the data about how the nine spectra were decided. * This includes a huge amount of spectra generated by the lamp and measured by a JETI spectrometer. These huge amount of spectra were then selected (filter-in) and reached the final nine 3000 K spectra used in this experiment. = subfolders: - spd_sol_filt_combined - spd_sol_filt_combined_v## ##=00, v02, v03, v05, v07, v08, v09, v10, v12, v12_bgd, v12Cmpns, v12uvTmCmpns, v13bgd, v14bgd, v15, v15bgd - spd_sol_filt_combined_v##bgd ##=15,16,17,18,19,20,21,22,23,24,25 converted_raw_XYZ_pcf_to_mat.zip: convert the raw data from the colorimeter camera .pcf file to a .mat file by Matlab. * This contains all the paintings under all spectra in this experiments. converted_raw_XYZ_pcf_to_img.zip: the data to generate the images of paintings. * convert the XYZ image captured by a colorimetry camera under the 3000 K lighting condition to a sRGB image showing the color of paintings. A von Kries conversion was applied to let the appearance closer to the appearance under daylight instead of a yellowish image captured under the 3000 K spectra. = subfolders: - 20250610_CRend_bmp_devideToMCC_normToD65: with all images in the nine spectra. In fact, only the f95g100 is used in the paper. - PhotoEdit_202506 spectra_moo_determine_the_intensity_of_each_channel.zip: using a Python tool pymoo (a tool to use multi-objective optimization (MOO)) to decide the luminaire intensity from the spd_primaries. * There are four folder in the zip files, (1) moo_fast2d (2) moo_fast10d (3) moo_res10d (4) moo_result (5) result_to_intens. To run the python code smoothly, these folder should be in the same folder as Python3_all. = subfolders: - moo_fast2d: moo faster optimization with CIE 1931 2 degree - moo_fast10d: moo faster optimization with CIE 1976 10 degree - moo_res10d: moo normal optimization with CIE 1976 10 degree - moo_result: moo normal optimization with CIE 1931 2 degree - result_to_intens: from the results of pymoo optimization solutions, pick up solutions with the tolerance as Royer (Yuv to CCT based on CIE1931_2) * only used from 2021/05/13 to 2021/07/06. Afterwards, Royer tolerance is applied in pymoo optimization. spectra_simulation_determine_the_angle.zip: the simuation to determined the angle to be 22.5 degree. * the results from a brute force simulation according to the primiary of the lamp that detemined the angle of 22.5 for best-fitmaximized the Rf Rg combinations. spectra_simulation_determine_the_angle_readme.zip: the readme file in the folder explaining the spectra_simulation procedure and results. setup.zip: information of the setup (lighting spectra, gray cloth, photos and figures of the setup) = file: RoomSetup250610.pptx (a pptx describing the setup, the Fig, 1 in the published paper) = subfolders: - cloth_survey: data to select the cloth material among all available gray cloths in a local shop - cloth_using: the spectra of the purchased gray cloth after the cloth survey. However, the reflect spectrum is not identical to the cloth during the survey. - lamp_cond_formal: the lighting spectra of the formal visual experiment measured before the visual experiment started - lamp_cond_pilot: the lighting spectra of a pilot experiment to decide a proper experimental procedure. - photos_of_setup: photos of the setup real_paintings.zip: photos from a Canon camera (uncalibrated) to show the paintings with its original frame. The pictures in the questionnaire came from editing these photos. observers_orders.zip: an Excel file contain the observer orders, their anonymous information with gender and background. analyzed_data_physical.zip: all physical measurement raw data * The results came from analyzing the measured physical raw data. These files are dependent of the analysis and are usually the result from processing the raw data by a MATLAB code, a Python code, a R code, or an Excel file. * An _README_data_calibration.txt file is included to explain the folder of each subfolder in "data_calibration". All files unzipped from analyzed_data_physical.zip + raw_data_determine_spectra.zip + raw_data_physical.zip = the full data of "data_calibration" folder. * An Excel file generated the accpet_ind_plot of all results from all sol_filter_v## until 202203. From this result, the best-fit ellipse psi angles was set to 22.5 and -67.5 (112.5). = subfolders: - spd_lamp_monitor: contain the Excel file that analyzed the csv files and the subfolders that are color vector graphs generated from the csv data - cvg_2022-05-11-21-48-09_2s - ppFig_cvg_2022-05-11-21-48-09_2s - warmup: a powerpoint file as the report, the following subfolder - fig: contain figures and a _README txt filemany csv file generated during the measurements - LMK: contain a _README_lmk.txt file to explain the files. - jab_dist2024: analysis the color distribution of the test paintings. Two of the results are used in the paper. They are: (1) the color distribution of each huebin of each painting. It is located in data_calibration\LMK\jab_dist2024\xyzw_MCCinImg\bar_bin_amount (2) the color distribution of M' in cam02ucs of each painting. It is located in data_calibration\LMK\jab_dist2024\xyzw_MCCinImg\cam02uscM Other methods that were studied but not in the published article are also included in this zip files such as the color distribution of each pixel captured by LMK for each painting on the CAM02UCS ab plane. Files related to the questionaires (data of the qualitative results): answer_sheets_from_observers.zip: scan of all the answer sheet from observers. answer_sheets_researcher_summary_Excel.zip: the researcher's summary Excel file from the answer sheet of observers. answer_sheets_researcher_summary_notes.zip: the researcher's summary from the answer sheet of observers, the print from the Excel with his written summary. instructions_and_answer_sheet.zip: contains the instruction and answering sheet for the visual experiment. Files related to the 2AFC answers from participants (data of the quantitative results): raw_data_visual_perception.zip: all the quantitative visual raw data collected during the experiment and the preliminary/pilot experiments. After unzip it, the folder name is "data_visual". Its subfolder 202203_pairC contains the raw data for the experiment of this published paper Code used in this study: Python code (with Python3), MATLAB code, and R code used in this study.To run these code, all folder created from the zipped files should be in the same folder. As a result, that folder contains: data_calibration, data_visual, literature, Matlab, moo_fast2d, moo_fast10d,... Python3_all,... R, referance_white, result_to_intens, setup The only exception is the zip file: code_Python3_module.zip The upzipped folder should be located in another folder. The path relationship between the folder "common_py_module" and the folder that contains the other folders should be as show in code_Python3_module.zip: the folder "folder_contains_other_folders" contains the other folders. This is because those Python import functions from code_python3_modules by using code "sys.path.insert(1, '../../../common_py_module/v00')" The code_MATLAB_and_output_results.zip also contains several files generated from python code in the folder "Matlab\UC_software\pwcmp-master\cwlin_result". That is because these files are csv or png files that are either the input or output of MATLAB code. Details are described in a _README_pwcmp_master_code.txt located in "Matlab\UC_software\pwcmp-master\cwlin_result". The code_R_and_output_results.zip also contains the input file and otput file of these R code. That is because the R code put all inpit and output in this folder. The input are several files generated from python code. They can be either originally outputed in the folder (such as "raw_score_abc_to_ppABC_v##.py" and "ana_eachObsPScore_v##.py" directly generated some csv files in "R\RProjectDir\ANOVA") or being copied and pasted from the original python output folders (such as jod_P_v10.csv, jod_S_v10.csv, jod_B_v10.csv, jod_N_v10.csv were copied from "Matlab\UC_software\pwcmp-master\cwlin_result\output_v10_allPt\seed9"). Details are described in _README_....txt files in i the folder and subfolders. Python code code_Python3_module.zip : This is the module Author Ching-Wei Lin create to different research projects. Some of the other python code import functions in this module. To execure the other python code, functions in this module sometimes is necessary. To run the code, the other folder generated from other zip files (such as Python3_all, data_calibration,....) should be the folder "folder_contains_other_folders" in this zip file. This is because those Python import functions from code_python3_modules by using code "sys.path.insert(1, '../../../common_py_module/v00')" code_Python3_all.zip: all the raw Python codes related to this study, a _README file explain the files. Because the huge amount of files, these python codes are separated in the three zip files (code_Python3_analyse_and_plot.zip, code_Python3_execute_exp.zip, code_Python3_preliminary_test_lamp_decide_spd ) by the functions of these code. Draft for codes are included in Python3_all.zip but excluded from the the three separated zip files. code_Python3_preliminary_test_lamp_decide_spd.zip: Python code used in the preliminary study. Measuring the performance of the tunable LED lamp (warm up time, spectrum of each channel). A procedure to estimate and decide the final nine spectra including a Multi-objective-optimization simulation and a brute force mixing simulation, turn on the lamp with simulated results and selected the final nine spectra. A Pilot study shows that a step of 5 in Rf and Rg is too small. Another pilot experiment using scales instead of two-alternative-force-choice paired comparison. code_Python3_execute_exp.zip: Python code to execute the visual experiment and monitor the lamp spectra performance. code_Python3_analyse_and_plot.zip: Python code to analyze the data collected during the visual experiment and to generate plots from the collected data or the analyzed results. MATLAB code code_MATLAB_and_output_results.zip: MATLAB code and their output files (and output of some python code - remark: they are either the required input of the MATLAB codes or JOD plots generated from the MATLAB output. These python output files are located in "Matlab\UC_software\pwcmp-master\cwlin_result". R code code_R_and_output_results: R code, the required input data (such as JOD scores generated from Python code) and the output of these R code. These ouputs include figures and statisitcal test values. The tests are (1) Pearson Correlation (2) Spearman Test (3) repeated measure ANOVA (4) Sinusoidal Regression Other files literature.zip: compare the dEuv, Rf, Rg tolerance to three other articles (Royer 2016, Royer 2017, Houser 2017) at the very beginning of the research.通用说明 本DOI用于存储发表于《LEUKOS》的期刊论文《IES TM-30指数用于风景油画色彩偏好的定量与定性评价》的相关数据。目前笔者认为，即便在论文发表后发现错误，带有已发布DOI的数据集也无法进行更新。若需进行必要修正或通过新版本补充新信息，则需在本说明中提供新版本对应的新DOI。 本实验使用的软件： 所有基于Python的分析均通过Spyder与Python 3完成；所有基于MATLAB的分析均通过MATLAB 2020b完成；所有基于R的分析均通过R Studio统计软件（v4.1.2; R Core Team 2021）完成。 数据说明文件： Explanation_of_the_Deposition_when_publishing.txt：发布本数据集时的说明内容与本文档一致，仅以TXT格式存储，无格式排版（如加粗、标题设置等）。 Explanation_of_the_Deposition_when_publishing.docx：发布本数据集时的说明内容与本文档一致，保留了DOCX格式支持的排版样式。 _README_to_explain_the_folders_after_unzip.txt：将除code_Python3_module.zip外的所有压缩包解压至同一文件夹后，本文件为该文件夹的README说明文档。解压后该文件夹包含文件observer_order_paint_Ross_Munsell_20220520.xlsx（源自observers_orders.zip）以及以下子文件夹： - answer_sheets_from_observers：源自answer_sheets_from_observers.zip - answer_sheets_researcher_summary_Excel：源自answer_sheets_researcher_summary_Excel.zip - answer_sheets_researcher_summary_notes：源自answer_sheets_researcher_summary_notes.zip - data_calibration：源自analyzed_data_physical.zip + converted_raw_XYZ_pcf_to_img.zip + converted_raw_XYZ_pcf_to_mat.zip + raw_data_determine_spectra.zip + raw_data_physical.zip - data_visual：源自raw_data_visual_perception.zip - instructions_and_answer_sheet：源自instructions_and_answer_sheet.zip - literature：源自literature.zip - Matlab：源自code_MATLAB_and_output_results.zip - moo_fast2d：源自spectra_moo_determine_the_intensity_of_each_channel.zip - moo_fast10d：源自spectra_moo_determine_the_intensity_of_each_channel.zip - moo_res10d：源自spectra_moo_determine_the_intensity_of_each_channel.zip - moo_result：源自spectra_moo_determine_the_intensity_of_each_channel.zip - optm_brute_force：源自spectra_simulation_determine_the_angle.zip + spectra_simulation_determine_the_angle_readme.zip - Python3_all：源自code_Python3_all.zip - Python3_preliminary：源自code_Python3_preliminary_test_lamp_decide_spd.zip - Python3_execute_exp：源自code_Python3_execute_exp.zip - Python3_analyse：源自code_Python3_analyse_and_plot.zip - R：源自code_R_and_output_results.zip - real_paintings：源自real_paintings.zip - result_to_intens：源自spectra_moo_determine_the_intensity_of_each_channel.zip - setup：源自setup.zip all_final_README_files_in_each_folder.zip：将本压缩包正确解压并适配文件夹结构后，各解压后的文件夹应与本次数据集的文件夹结构保持一致（code_Python3_module.zip除外）。本压缩包包含各解压文件夹的最新README文件。若解压过程中其他压缩包内的README与本压缩包内的文件出现冲突，请以本压缩包内的README文件为准，因其为最新版本。 实验设计、装置与流程相关文件： raw_data_physical.zip：除raw_data_determine_spectra.zip包含的数据外的全部物理测量原始数据。* 所有实测物理原始数据，与分析及其他处理流程无关，因此后续不会进行修改。其子文件夹包括：LMK、luxmeter、spd_lamp_monitor、spd_primaries、spd_white_tile、warmup - LMK： - raw_data：LMK实测原始数据，包含子文件夹：20220330_CRend_3H、20220415_CRend_FR_1H、20220421_CRend_BM_KM、20220511_CRend_RM_CT、202204_CRend_FR_1H、20240308_Paint_withWhiteTile、20240318_LandsMCCTile_3000KRf95、20240318_Paint_3000KRf95withMCCTile - luxmeter：照度测量结果，包含20220331.csv等实测结果文件 - spd_lamp_monitor：包含_README文件与生成的CSV文件，其子文件夹包括：quickCheck_before_removeCloth、raw_jeti、while_testcode - spd_primaries：包含_README文件，其子文件夹包括：l1_2109、l1_2109_testcode、l1_210910、l1_210910_testcode、l1_211110gray、l1_21110gray_testcode、lamp_folder_template、lamp1 - spd_white_tile：包含文件：Convert to csv.xlsx、SpecimIQWhite - comma.txt、SpecimIQWhite.csv、SpecimIQWhite.png、SpecimIQWhite.txt、SpecimIQWhite_wo_UVF.csv - warmup：包含测量过程中生成的大量CSV文件与两个子文件夹：raw、when testing code raw_data_determine_spectra.zip：关于9组光谱如何确定的相关数据。* 包含灯具生成并经JETI光谱仪实测的海量光谱数据，经筛选（过滤保留）后得到本实验最终使用的9组3000K光谱。其子文件夹包括：spd_sol_filt_combined、spd_sol_filt_combined_v##（##=00、v02、v03、v05、v07、v08、v09、v10、v12、v12_bgd、v12Cmpns、v12uvTmCmpns、v13bgd、v14bgd、v15、v15bgd）、spd_sol_filt_combined_v##bgd（##=15、16、17、18、19、20、21、22、23、24、25） converted_raw_XYZ_pcf_to_mat.zip：通过MATLAB将色度相机采集的.pcf格式原始数据转换为.mat格式文件。* 包含本实验中所有光照条件下的全部油画图像数据。 converted_raw_XYZ_pcf_to_img.zip：用于生成油画图像的相关数据。* 将3000K光照条件下色度相机采集的XYZ图像转换为可呈现油画色彩的sRGB图像，过程中应用了von Kries转换，以使图像观感更接近日光照明下的效果，而非3000K光谱照明下偏黄的原始采集图像。其子文件夹包括：20250610_CRend_bmp_devideToMCC_normToD65（包含9组光谱下的全部图像，论文中仅使用了f95g100组数据）、PhotoEdit_202506 spectra_moo_determine_the_intensity_of_each_channel.zip：通过Python工具库pymoo（multi-objective optimization, MOO，多目标优化工具）基于spd_primaries确定灯具发光强度。* 本压缩包包含5个子文件夹：(1) moo_fast2d、(2) moo_fast10d、(3) moo_res10d、(4) moo_result、(5) result_to_intens。为顺利运行Python代码，这些子文件夹应与Python3_all处于同一文件夹层级下。其子文件夹说明如下： - moo_fast2d：基于CIE 1931 2度视场的快速多目标优化 - moo_fast10d：基于CIE 1976 10度视场的快速多目标优化 - moo_res10d：基于CIE 1976 10度视场的标准多目标优化 - moo_result：基于CIE 1931 2度视场的标准多目标优化结果 - result_to_intens：从pymoo优化求解结果中筛选符合Royer容差（基于CIE1931_2的Yuv到CCT转换）的解，仅在2021/05/13至2021/07/06期间使用，后续优化过程中直接应用了Royer容差 spectra_simulation_determine_the_angle.zip：用于确定最佳角度为22.5度的仿真数据。* 通过针对灯具基色的蛮力仿真（brute force simulation）得到结果，最终确定22.5度为最优角度，可最大化Rf与Rg的组合表现。 spectra_simulation_determine_the_angle_readme.zip：包含用于说明spectra_simulation_determine_the_angle.zip内仿真流程与结果的README文件。 setup.zip：实验装置相关信息（光照光谱、灰色背景布、装置照片与示意图）。* 包含文件RoomSetup250610.pptx（用于描述实验装置的PPTX文件，即已发表论文中的图1）。其子文件夹包括： - cloth_survey：从本地商店可选灰色背景布中筛选材料的相关数据 - cloth_using：经布料筛选后采购的灰色背景布的光谱数据，但其反射光谱与筛选阶段的样品不完全一致 - lamp_cond_formal：正式视觉实验开始前实测的实验照明光谱 - lamp_cond_pilot：用于确定合适实验流程的预实验照明光谱 - photos_of_setup：实验装置实拍照片 real_paintings.zip：使用佳能相机（未校准）拍摄的带原装画框的油画照片，问卷中使用的图片均由这些照片编辑得到。 observers_orders.zip：包含观察者实验顺序、匿名信息（性别与背景）的Excel文件。 问卷相关文件（定性结果数据）： answer_sheets_from_observers.zip：所有观察者填写的答卷扫描件。 answer_sheets_researcher_summary_Excel.zip：研究者基于观察者答卷整理的汇总Excel文件。 answer_sheets_researcher_summary_notes.zip：研究者基于观察者答卷整理的汇总内容，即Excel文件打印版附带手写备注。 instructions_and_answer_sheet.zip：包含视觉实验的指导说明与答卷模板。 二择一强制选择（2AFC）答题相关文件（定量结果数据）： raw_data_visual_perception.zip：实验及预实验中收集的全部定量视觉原始数据。解压后文件夹名为data_visual，其子文件夹202203_pairC包含本已发表论文对应实验的原始数据。 本研究使用的代码： 本研究使用基于Python3的Python代码、MATLAB代码与R代码。运行这些代码时，需将所有由压缩包解压得到的文件夹置于同一目录下，该目录应包含data_calibration、data_visual、literature、Matlab、moo_fast2d、moo_fast10d、Python3_all、R、referance_white、result_to_intens、setup等文件夹。 唯一例外的是code_Python3_module.zip，其解压后的文件夹应置于另一独立目录下。文件夹common_py_module与包含其他所有文件夹的父目录的路径关系应与code_Python3_module.zip内的结构一致：即包含其他所有文件夹的父目录应作为该压缩包内的folder_contains_other_folders。这是因为部分Python代码通过`sys.path.insert(1, '../../../common_py_module/v00')`语句导入该模块中的函数。 code_MATLAB_and_output_results.zip内的MatlabUC_softwarepwcmp-mastercwlin_result文件夹中还包含部分由Python代码生成的文件，这些文件要么是MATLAB代码的必需输入，要么是MATLAB输出生成的JOD绘图文件。相关细节可参见该文件夹内的_README_pwcmp_master_code.txt文件。 code_R_and_output_results.zip同样包含R代码的输入与输出文件：R代码将所有输入输出均置于该文件夹内，其中输入文件多为Python代码生成的结果，可分为两类：一类是由`raw_score_abc_to_ppABC_v##.py`与`ana_eachObsPScore_v##.py`直接在RRProjectDirANOVA目录下生成的CSV文件；另一类是从MatlabUC_softwarepwcmp-mastercwlin_resultoutput_v10_allPtseed9目录复制而来的jod_P_v10.csv、jod_S_v10.csv、jod_B_v10.csv、jod_N_v10.csv文件。相关细节可参见各文件夹及子文件夹内的_README_*.txt文件。 Python代码： code_Python3_module.zip：本研究作者Ching-Wei Lin为多项研究项目开发的通用Python模块，部分其他Python代码会导入该模块中的函数。运行其他Python代码时，该模块有时为必需依赖。运行代码时，由其他压缩包（如Python3_all、data_calibration等）解压得到的文件夹应置于code_Python3_module.zip内标注的folder_contains_other_folders目录下，这是因为部分Python代码通过`sys.path.insert(1, '../../../common_py_module/v00')`语句导入该模块。 code_Python3_all.zip：本研究相关的全部原始Python代码，附带README文件说明各文件用途。由于代码文件数量较多，本研究的Python代码按功能分为三个压缩包：code_Python3_analyse_and_plot.zip、code_Python3_execute_exp.zip、code_Python3_preliminary_test_lamp_decide_spd.zip。Python3_all.zip包含代码草稿，但未将其纳入上述三个功能拆分的压缩包中。 code_Python3_preliminary_test_lamp_decide_spd.zip：用于预实验的Python代码，包括可调光LED灯具性能测试（预热时间、各通道光谱）、通过多目标优化仿真与蛮力混合仿真估计并确定最终9组光谱的流程、基于仿真结果点亮灯具并筛选最终9组光谱。预实验结果显示，以Rf与Rg为步长5的间隔过小；另一预实验采用李克特量表替代二择一强制选择配对比较范式。 code_Python3_execute_exp.zip：用于执行视觉实验并监控灯具光谱性能的Python代码。 code_Python3_analyse_and_plot.zip：用于分析视觉实验采集的数据，并基于采集数据或分析结果生成图表的Python代码。 MATLAB代码： code_MATLAB_and_output_results.zip：MATLAB代码及其输出文件（以及部分Python代码的输出文件——注：这些文件要么是MATLAB代码的必需输入，要么是基于MATLAB输出生成的JOD绘图文件）。 R代码： code_R_and_output_results.zip：R代码、所需输入数据（如由Python代码生成的JOD得分）以及R代码的输出结果，包括图表与统计检验值，检验类型包括：(1) Pearson相关分析、(2) Spearman秩相关检验、(3) 重复测量方差分析、(4) 正弦回归分析。 其他文件： literature.zip：研究初期用于对比dEuv、Rf、Rg容差与另外三篇文献（Royer 2016、Royer 2017、Houser 2017）的相关数据。