This repository contains code for training Phenotype Risk Score models (PheRS) based on the INTERVENE data formats. Below you can find a description of the data files needed and instructions on how to run the analysis. The commands used to replicate the analyses done in FinnGen are listed in section Calls to replicate the FinnGen analyses .
The scripts have been run in the FinnGen sandbox using Python version 3.9.18 and the following packages:
- matplotlib (version 3.8.2)
- numpy (1.26.2)
- scipy (1.11.4)
- pandas (2.1.4)
- scikit-learn (1.3.2)
The file data/requirements.yml can be used to create a conda environment with the above mentioned packages and versions:
conda env create -f INTERVENE_PheRS/data/requirements.yml
The environment can then be activated with
conda activate phers-intervene
Note that in order for the scripts to work, you will need to add the package src/ directory to your PATH:
export PATH="/full/path/to/INTERVENE_PheRS/src/:$PATH"
and to give executable rights to the Python scripts:
chmod u+x /full/path/to/INTERVENE_PheRS/src/*.py
This section is under construction. The Python script
src/PheRSwrapper.py
converts ICD10 codes input int the INTERVENE longitudinal file format into phecodes and preprocesses data for PheRS model fitting. It then fits the PheRS-model (elastic net) and makes predictions on a test set also reporting prediction performance. The script also writes several log files that list all the files and parameter settings of the run. Help can be viewed by typing
python /finngen/red/thartone/git/INTERVENE_PheRS/src/PheRSwrapper.py -h
usage: PheRSwrapper.py [-h] [--ICDfile ICDFILE]
[--phenotypefile PHENOTYPEFILE]
[--includevars INCLUDEVARS [INCLUDEVARS ...]]
[--phecodefile PHECODEFILE]
[--ICD9tophecodefile ICD9TOPHECODEFILE]
[--ICD10tophecodefile ICD10TOPHECODEFILE]
[--ICD10CMtophecodefile ICD10CMTOPHECODEFILE]
[--targetphenotype TARGETPHENOTYPE]
[--excludephecodes EXCLUDEPHECODES] [--outdir OUTDIR]
[--testfraction TESTFRACTION] [--testidfile TESTIDFILE]
[--washout_window WASHOUT_WINDOW WASHOUT_WINDOW]
[--exposure_window EXPOSURE_WINDOW EXPOSURE_WINDOW]
[--observation_window OBSERVATION_WINDOW OBSERVATION_WINDOW]
[--minage MINAGE] [--maxage MAXAGE] [--seed SEED]
[--dateformatstr DATEFORMATSTR]
[--excludeICDfile EXCLUDEICDFILE] [--missing MISSING]
[--frequency FREQUENCY]
[--controlfraction CONTROLFRACTION] [--nproc NPROC]
[--paramgridfile PARAMGRIDFILE]
optional arguments:
-h, --help show this help message and exit
--ICDfile ICDFILE Full path to an INTERVENE longitudinal ICD code file.
If last two letters of file name are gz, gzipped file
is assumed.
--phenotypefile PHENOTYPEFILE
Full path to an INTERVENE phenotype file. If last two
letters of file name are gz, gzipped file is assumed
--includevars INCLUDEVARS [INCLUDEVARS ...]
List of variable names to include from the phenotype
file (default=nothing).
--phecodefile PHECODEFILE
Phecode definition file (NOTE: comma-separated).
--ICD9tophecodefile ICD9TOPHECODEFILE
Full path to the ICD9 to phecode map (NOTE: comma-
separated).
--ICD10tophecodefile ICD10TOPHECODEFILE
Full path to the ICD10 to phecode map (NOTE: comma-
separated).
--ICD10CMtophecodefile ICD10CMTOPHECODEFILE
Full path to the ICD10CM to phecode map (NOTE: comma-
separated).
--targetphenotype TARGETPHENOTYPE
Name of the target phenotype.
--excludephecodes EXCLUDEPHECODES
Path to a file containing the Phecode(s) corresponding
to and related to the target phenotype that should be
excluded from the analysis.
--outdir OUTDIR Output directory (must exist, default=./).
--testfraction TESTFRACTION
Fraction of IDs used in test set (default=0.15).
Sampling at random.
--testidfile TESTIDFILE
Full path to a file containing the IDs used in the
test set. If given, overrides --testfraction.
--washout_window WASHOUT_WINDOW WASHOUT_WINDOW
Start and end dates for washout (default= 2009-01-01
2010-12-31).
--exposure_window EXPOSURE_WINDOW EXPOSURE_WINDOW
Start and end dates for exposure (default = 1999-01-01
2008-12-31).
--observation_window OBSERVATION_WINDOW OBSERVATION_WINDOW
Start and end dates for observation (default=
2011-01-01 2019-01-01).
--minage MINAGE Minimum age at the start of the observation period to
include (default=32).
--maxage MAXAGE Maximum age at the start of the observation period to
include (default=100).
--seed SEED Random number generator seed (default=42).
--dateformatstr DATEFORMATSTR
Date format used (default='%Y-%m-%d')
--excludeICDfile EXCLUDEICDFILE
Full path to a file containing the ICD codes to be
excluded. Format per row: ICD_version, ICD_code.
--missing MISSING Fraction of missing values allowed for a feature to be
included into the analysis (default=0.5).
--frequency FREQUENCY
Minimum frequency for a predictor to be included into
the analysis (default=0.01).
--controlfraction CONTROLFRACTION
How many controls to use, number of cases multiplied
by this flag value (default=None, meaning all controls
are used).
--nproc NPROC Number of parallel processes used (default=1).
--paramgridfile PARAMGRIDFILE
File containing the parameter grid explored using
cross-validation. One parameter per row, first value
is key, rest are possible values.
There are two types of input files needed, data files that are biobank-specific, and general input files detailing phecode definitions and parameters for elastic net model fitting. In the following, I will first go through the biobank-specific files.
--phenotypefile PHENOTYPEFILE
Full path to an INTERVENE phenotype file. If last two
letters of file name are gz, gzipped file is assumed
This file must follow the INTERVENE phenotype file format described here: https://docs.google.com/document/d/1GbZszpPeyf-hyb0V_YDx828YbM7woh8OBJhvzkEwo2g/edit .
--ICDfile ICDFILE Full path to an INTERVENE longitudinal ICD code file.
If last two letters of file name are gz, gzipped file
is assumed.
This file must follow the INTERVENE ICD longitudinal file format described here: https://docs.google.com/document/d/1E2Jc72CmMItEchgQaCvfA4MhZUkQYjALwTu3dCl7qd8/edit .
These two above are the only files that need to be provided by the biobanks. The remaining required input files are provided with this repository and can be used as such. In the following I will indicate where to find these files.
--phecodefile PHECODEFILE
Phecode definition file (NOTE: comma-separated).
This file can be found from data/phecode_definitions1.2_manual_additions_v1.csv.
--ICD9tophecodefile ICD9TOPHECODEFILE
Full path to the ICD9 to phecode map (NOTE: comma-
separated).
This file can be found from data/phecode_icd9_map_unrolled.csv.
--ICD10tophecodefile ICD10TOPHECODEFILE
Full path to the ICD10 to phecode map (NOTE: comma-
separated).
This file can be found from data/Phecode_map_v1_2_icd10_beta_manual_additions_v1.csv.
--ICD10CMtophecodefile ICD10CMTOPHECODEFILE
Full path to the ICD10CM to phecode map (NOTE: comma-
separated).
This file can be found from data/Phecode_map_v1_2_icd10cm_beta.csv.
--excludephecodes EXCLUDEPHECODES
Path to a file containing the Phecode(s) corresponding
to and related to the target phenotype that should be
excluded from the analysis.
These files are specific to endpoints, and have been pre-created based on FinnGen endpoint definitions. The files are in data/ENDPOINTNAME-excluded-phecodes.txt. Insert name of the endpoint in place of ENDPOINTNAME.
--paramgridfile PARAMGRIDFILE
File containing the parameter grid explored using
cross-validation. One parameter per row, first value
is key, rest are possible values.
By default, you can use the file data/INTERVENE_logreg_paramgrid_C3_breast.txt for all endpoints.
All output is written into path defined by the following flag:
--outdir OUTDIR Output directory (must exist, default=./).
A file named ENDPOINTNAME_PheRSwrapper.log will be written to the root of --outdir. This file records the call string of PheRSwrapper.py as well as call strings of subsequent function calls within the script. Subsequent output is written into a directory created by PheRSwrapper.py, whose name is of format ENDPOINTNAME_exposure=1999-01-01-2008-12-31-washoutend=2010-12-31-observationend=2019-01-01 (with the actual dates corresponding to the dates specified as input for PheRSwrapper.py). This folder contains more log files (*.log), the fitted model (best_model.pkl) and its coefficients (best_model_coefficients.txt) and hyperparameters (best_model_hyperparameters.txt). The predicted PheRS scores for the test set individuals are in file pred_probas.txt.gz, and the *.png files contain some classification performance measures computed on the test set. The file target-ENDPOINTNAME-PheRS-ML-input.txt.gz contains the preprocessed input data used for the actual model fitting and scoring.
Here, I will give examples to replicate the analyses done in FinnGen. These should be directly replicable in ohter biobanks given that one replaces the file paths with file paths specific to your system. It is worth noting that the command line flag --nproc only affects the training of the elastic net models, and can be changed according to what type of computational resources are available.
for e in {C3_COLORECTAL,C3_PROSTATE,AUD_SWEDISH,C3_BREAST,C3_BRONCHUS_LUNG,C3_CANCER,C3_MELANOMA_SKIN,C3_PROSTATE,COX_ARTHROSIS,E4_HYTHYNAS,E4_THYTOXGOITDIF,F5_DEPRESSIO,FE_STRICT,G6_AD_WIDE,G6_EPLEPSY,G6_SLEEPAPNO,GE_STRICT,GOUT,H7_GLAUCOMA,I9_ABAORTANEUR,I9_AF,I9_CHD,I9_HEARTFAIL_NS,I9_SAH,I9_STR,I9_THAORTANEUR,I9_VTE,ILD,IPF,J10_ASTHMA,K11_APPENDACUT,K11_IBD_STRICT,KNEE_ARTHROSIS,M13_OSTEOPOROSIS,N14_CHRONKIDNEYDIS,RHEUMA_SEROPOS_OTH,T1D,T2D}; do python /finngen/red/thartone/git/INTERVENE_PheRS/src/PheRSwrapper.py --ICDfile data/finngen_R8_detailed_longituinal_INTERVENE_format_ICD10_only_262022.txt --phenotypefile /finngen/red/Zhiyu/Share/Pheno_R8_HighEdu --phecodefile /finngen/red/thartone/git/INTERVENE_PheRS/data/phecode_definitions1.2_manual_additions.csv --ICD9tophecodefile /finngen/red/thartone/git/INTERVENE_PheRS/data/phecode_icd9_map_unrolled.csv --ICD10tophecodefile /finngen/red/thartone/git/INTERVENE_PheRS/data/Phecode_map_v1_2_icd10_beta_manual_additions.csv --ICD10CMtophecodefile /finngen/red/thartone/git/INTERVENE_PheRS/data/Phecode_map_v1_2_icd10cm_beta.csv --targetphenotype $e --excludephecodes /finngen/red/thartone/INTERVENE_pheRS/data/"$e"-excluded-phecodes.txt --outdir results_250123_with_age_no_downsampling/"$e"_ --nproc 6 --paramgridfile /finngen/red/thartone/git/INTERVENE_PheRS/data/INTERVENE_logreg_paramgrid_C3_breast.txt --testfraction 0.5 --includevars age --minage 32 --maxage 70; done;
for e in {C3_COLORECTAL,C3_PROSTATE,AUD_SWEDISH,C3_BREAST,C3_BRONCHUS_LUNG,C3_CANCER,C3_MELANOMA_SKIN,C3_PROSTATE,COX_ARTHROSIS,E4_HYTHYNAS,E4_THYTOXGOITDIF,F5_DEPRESSIO,FE_STRICT,G6_AD_WIDE,G6_EPLEPSY,G6_SLEEPAPNO,GE_STRICT,GOUT,H7_GLAUCOMA,I9_ABAORTANEUR,I9_AF,I9_CHD,I9_HEARTFAIL_NS,I9_SAH,I9_STR,I9_THAORTANEUR,I9_VTE,ILD,IPF,J10_ASTHMA,K11_APPENDACUT,K11_IBD_STRICT,KNEE_ARTHROSIS,M13_OSTEOPOROSIS,N14_CHRONKIDNEYDIS,RHEUMA_SEROPOS_OTH,T1D,T2D}; do python /finngen/red/thartone/git/INTERVENE_PheRS/src/PheRSwrapper.py --ICDfile data/finngen_R8_detailed_longituinal_INTERVENE_format_ICD10_only_262022.txt --phenotypefile /finngen/red/Zhiyu/Share/Pheno_R8_HighEdu --phecodefile /finngen/red/thartone/git/INTERVENE_PheRS/data/phecode_definitions1.2_manual_additions.csv --ICD9tophecodefile /finngen/red/thartone/git/INTERVENE_PheRS/data/phecode_icd9_map_unrolled.csv --ICD10tophecodefile /finngen/red/thartone/git/INTERVENE_PheRS/data/Phecode_map_v1_2_icd10_beta_manual_additions.csv --ICD10CMtophecodefile /finngen/red/thartone/git/INTERVENE_PheRS/data/Phecode_map_v1_2_icd10cm_beta.csv --targetphenotype $e --excludephecodes /finngen/red/thartone/INTERVENE_pheRS/data/"$e"-excluded-phecodes.txt --outdir results_250123_with_age/"$e"_ --controlfraction 4.0 --nproc 6 --paramgridfile /finngen/red/thartone/git/INTERVENE_PheRS/data/INTERVENE_logreg_paramgrid_C3_breast.txt --testfraction 0.5 --includevars age --minage 32 --maxage 70; done;
for e in {C3_COLORECTAL,C3_PROSTATE,AUD_SWEDISH,C3_BREAST,C3_BRONCHUS_LUNG,C3_CANCER,C3_MELANOMA_SKIN,C3_PROSTATE,COX_ARTHROSIS,E4_HYTHYNAS,E4_THYTOXGOITDIF,F5_DEPRESSIO,FE_STRICT,G6_AD_WIDE,G6_EPLEPSY,G6_SLEEPAPNO,GE_STRICT,GOUT,H7_GLAUCOMA,I9_ABAORTANEUR,I9_AF,I9_CHD,I9_HEARTFAIL_NS,I9_SAH,I9_STR,I9_THAORTANEUR,I9_VTE,ILD,IPF,J10_ASTHMA,K11_APPENDACUT,K11_IBD_STRICT,KNEE_ARTHROSIS,M13_OSTEOPOROSIS,N14_CHRONKIDNEYDIS,RHEUMA_SEROPOS_OTH,T1D,T2D}; do python /finngen/red/thartone/git/INTERVENE_PheRS/src/PheRSwrapper.py --ICDfile data/finngen_R8_detailed_longituinal_INTERVENE_format_ICD10_only_262022.txt --phenotypefile /finngen/red/Zhiyu/Share/Pheno_R8_HighEdu --phecodefile /finngen/red/thartone/git/INTERVENE_PheRS/data/phecode_definitions1.2_manual_additions.csv --ICD9tophecodefile /finngen/red/thartone/git/INTERVENE_PheRS/data/phecode_icd9_map_unrolled.csv --ICD10tophecodefile /finngen/red/thartone/git/INTERVENE_PheRS/data/Phecode_map_v1_2_icd10_beta_manual_additions.csv --ICD10CMtophecodefile /finngen/red/thartone/git/INTERVENE_PheRS/data/Phecode_map_v1_2_icd10cm_beta.csv --targetphenotype $e --excludephecodes /finngen/red/thartone/INTERVENE_pheRS/data/"$e"-excluded-phecodes.txt --outdir results_250123_with_age_young_no_downsampling/"$e"_ --nproc 6 --paramgridfile /finngen/red/thartone/git/INTERVENE_PheRS/data/INTERVENE_logreg_paramgrid_C3_breast.txt --testfraction 0.5 --includevars age --minage 32 --maxage 50; done;
for e in {C3_COLORECTAL,C3_PROSTATE,AUD_SWEDISH,C3_BREAST,C3_BRONCHUS_LUNG,C3_CANCER,C3_MELANOMA_SKIN,C3_PROSTATE,COX_ARTHROSIS,E4_HYTHYNAS,E4_THYTOXGOITDIF,F5_DEPRESSIO,FE_STRICT,G6_AD_WIDE,G6_EPLEPSY,G6_SLEEPAPNO,GE_STRICT,GOUT,H7_GLAUCOMA,I9_ABAORTANEUR,I9_AF,I9_CHD,I9_HEARTFAIL_NS,I9_SAH,I9_STR,I9_THAORTANEUR,I9_VTE,ILD,IPF,J10_ASTHMA,K11_APPENDACUT,K11_IBD_STRICT,KNEE_ARTHROSIS,M13_OSTEOPOROSIS,N14_CHRONKIDNEYDIS,RHEUMA_SEROPOS_OTH,T1D,T2D}; do python /finngen/red/thartone/git/INTERVENE_PheRS/src/PheRSwrapper.py --ICDfile data/finngen_R8_detailed_longituinal_INTERVENE_format_ICD10_only_262022.txt --phenotypefile /finngen/red/Zhiyu/Share/Pheno_R8_HighEdu --phecodefile /finngen/red/thartone/git/INTERVENE_PheRS/data/phecode_definitions1.2_manual_additions.csv --ICD9tophecodefile /finngen/red/thartone/git/INTERVENE_PheRS/data/phecode_icd9_map_unrolled.csv --ICD10tophecodefile /finngen/red/thartone/git/INTERVENE_PheRS/data/Phecode_map_v1_2_icd10_beta_manual_additions.csv --ICD10CMtophecodefile /finngen/red/thartone/git/INTERVENE_PheRS/data/Phecode_map_v1_2_icd10cm_beta.csv --targetphenotype $e --excludephecodes /finngen/red/thartone/INTERVENE_pheRS/data/"$e"-excluded-phecodes.txt --outdir results_250123_with_age_young/"$e"_ --controlfraction 4.0 --nproc 6 --paramgridfile /finngen/red/thartone/git/INTERVENE_PheRS/data/INTERVENE_logreg_paramgrid_C3_breast.txt --testfraction 0.5 --includevars age --minage 32 --maxage 50; done;
for e in {C3_COLORECTAL,C3_PROSTATE,AUD_SWEDISH,C3_BREAST,C3_BRONCHUS_LUNG,C3_CANCER,C3_MELANOMA_SKIN,C3_PROSTATE,COX_ARTHROSIS,E4_HYTHYNAS,E4_THYTOXGOITDIF,F5_DEPRESSIO,FE_STRICT,G6_AD_WIDE,G6_EPLEPSY,G6_SLEEPAPNO,GE_STRICT,GOUT,H7_GLAUCOMA,I9_ABAORTANEUR,I9_AF,I9_CHD,I9_HEARTFAIL_NS,I9_SAH,I9_STR,I9_THAORTANEUR,I9_VTE,ILD,IPF,J10_ASTHMA,K11_APPENDACUT,K11_IBD_STRICT,KNEE_ARTHROSIS,M13_OSTEOPOROSIS,N14_CHRONKIDNEYDIS,RHEUMA_SEROPOS_OTH,T1D,T2D}; do python /finngen/red/thartone/git/INTERVENE_PheRS/src/PheRSwrapper.py --ICDfile data/finngen_R8_detailed_longituinal_INTERVENE_format_ICD10_only_262022.txt --phenotypefile /finngen/red/Zhiyu/Share/Pheno_R8_HighEdu --phecodefile /finngen/red/thartone/git/INTERVENE_PheRS/data/phecode_definitions1.2_manual_additions.csv --ICD9tophecodefile /finngen/red/thartone/git/INTERVENE_PheRS/data/phecode_icd9_map_unrolled.csv --ICD10tophecodefile /finngen/red/thartone/git/INTERVENE_PheRS/data/Phecode_map_v1_2_icd10_beta_manual_additions.csv --ICD10CMtophecodefile /finngen/red/thartone/git/INTERVENE_PheRS/data/Phecode_map_v1_2_icd10cm_beta.csv --targetphenotype $e --excludephecodes /finngen/red/thartone/INTERVENE_pheRS/data/"$e"-excluded-phecodes.txt --outdir results_250123_with_age_old_no_downsampling/"$e"_ --nproc 6 --paramgridfile /finngen/red/thartone/git/INTERVENE_PheRS/data/INTERVENE_logreg_paramgrid_C3_breast.txt --testfraction 0.5 --includevars age --minage 51 --maxage 70; done;
for e in {C3_COLORECTAL,C3_PROSTATE,AUD_SWEDISH,C3_BREAST,C3_BRONCHUS_LUNG,C3_CANCER,C3_MELANOMA_SKIN,C3_PROSTATE,COX_ARTHROSIS,E4_HYTHYNAS,E4_THYTOXGOITDIF,F5_DEPRESSIO,FE_STRICT,G6_AD_WIDE,G6_EPLEPSY,G6_SLEEPAPNO,GE_STRICT,GOUT,H7_GLAUCOMA,I9_ABAORTANEUR,I9_AF,I9_CHD,I9_HEARTFAIL_NS,I9_SAH,I9_STR,I9_THAORTANEUR,I9_VTE,ILD,IPF,J10_ASTHMA,K11_APPENDACUT,K11_IBD_STRICT,KNEE_ARTHROSIS,M13_OSTEOPOROSIS,N14_CHRONKIDNEYDIS,RHEUMA_SEROPOS_OTH,T1D,T2D}; do python /finngen/red/thartone/git/INTERVENE_PheRS/src/PheRSwrapper.py --ICDfile data/finngen_R8_detailed_longituinal_INTERVENE_format_ICD10_only_262022.txt --phenotypefile /finngen/red/Zhiyu/Share/Pheno_R8_HighEdu --phecodefile /finngen/red/thartone/git/INTERVENE_PheRS/data/phecode_definitions1.2_manual_additions.csv --ICD9tophecodefile /finngen/red/thartone/git/INTERVENE_PheRS/data/phecode_icd9_map_unrolled.csv --ICD10tophecodefile /finngen/red/thartone/git/INTERVENE_PheRS/data/Phecode_map_v1_2_icd10_beta_manual_additions.csv --ICD10CMtophecodefile /finngen/red/thartone/git/INTERVENE_PheRS/data/Phecode_map_v1_2_icd10cm_beta.csv --targetphenotype $e --excludephecodes /finngen/red/thartone/INTERVENE_pheRS/data/"$e"-excluded-phecodes.txt --outdir results_250123_with_age_old/"$e"_ --controlfraction 4.0 --nproc 6 --paramgridfile /finngen/red/thartone/git/INTERVENE_PheRS/data/INTERVENE_logreg_paramgrid_C3_breast.txt --testfraction 0.5 --includevars age --minage 51 --maxage 70; done;
Tuomo Hartonen 31.1.2023
Main updates to previous version
-
Greatly extended documentation, aimed at replication of the PheRS training in other biobanks.
-
Some bug fixes, for example fixed a bug that lead to wrong IDs being written for the pred_probas-files that contain predicted PheRS scores for test set individuals.
Tuomo Hartonen 2.12.2022
Main updates to previous version
-
ICD10 to phecode mapping was updated so that the ICD10 code M50.8 with no matching phecode was by manual curation set to map to phecode 714.1: Rheumatoid arthritis.
-
PheRS models have been fitted in FinnGen for all endpoints and the results can be viewed from the INTERVENE flagship Google Drive under folder
PheRS-project. -
Several minor changes to the study setup were made so that everything is comparable with the setup used by Kira. Detailed description of the study setup can also be found from INTERVENE flagship Google Drive in the same folder.
Tuomo Hartonen 5.4.2022
Main updates to previous version
-
The preprocessing, fitting and scoring scripts have now been tested with one endpoint on FinnGen data, and seem to work for that.
-
folder data/ contains files mapping ICD codes to phecodes, a list of ICD codes that map to multiple phecodes and a file listing all related endpoints for each FinnGen endpoint made by Lisa Eick. This file is used to define what predictors are excluded from the models, by mapping the FinnGen endpoints back to ICD10 codes and from there to phecodes.
There were only three one-to-many mapping ICD10 codes with more than 100 longitudinal entries in FinnGen. We have decided to represent these as new custom phecodes that are added to the file ´phecode_definitions1.2_manual_additions.csv´. These added entries are:
"phecode","phenotype","phecode_exclude_range","sex","rollup","leaf","category_number","category" "1200","Exfoliative dermatitis","816-819.99","Both","","","","" "1201","Gonarthrosis [arthrosis of knee]","710-716.99","Both","","","","" "1202","Sequelae of injuries of head","816-819.99","Both","","","",""
Notice that at this point, only ICD10 codes have been used in the analyses and other phecode maps have not been utilized.
Tuomo Hartonen 21.2.2022
This repository is under construction and the scripts are not guaranteed to work. For each script, get help by running python script_name.py -h.