makePst

makePst: A Reproducible and Auditable Spreadsheet Workflow for PEST Model Calibration

makePst builds PEST / PEST_HP / PEST++ control files (.pst) from Excel or CSV tables, reads control files back into editable workbooks, and returns calibration results (.par, .res/.rei, PESTPP-IES ensembles) into the workbook you already maintain.

(nothing)                              ->  starter workbook       makepst init
.pst or workbook + tpl/ins files       ->  pestchek-style report  makepst validate
Excel / CSV tables                     ->  .pst                   makepst build
.pst                                   ->  editable workbook      makepst dump
.par / .res / .rei / IES ensemble      ->  existing workbook      makepst update
.par or IES realization (+ tweaks)     ->  new .pst               makepst parrep
.pst                                   ->  values moved to SCALE  makepst rescale
.res/.rei or IES obs + group targets   ->  reweighted .pst        makepst reweight
.res/.rei or IES obs + existing .hp    ->  new .hp                makepst hpstart
two .pst / workbooks                   ->  what changed           makepst diff
manifests in a folder                  ->  run ledger             makepst log
.pst + everything it references        ->  zip for review         makepst bundle

See What round-tripping preserves for the exact guarantees and the compatibility table for which PEST dialects and sections are covered.

Why makePst?

A PEST setup of a few thousand parameters and tens of thousands of observations is easier to review in a spreadsheet than in a 40,000-line text file. In practice most calibration teams already keep one: the workbook holds the parameter tables per group, weights computed by formulas, helper columns (layer, pilot-point index, native values), and a run history. The control file is derived from it.

makePst makes that derivation deterministic and safe:

• The workbook is the source of truth. The control file is rebuilt from it with one command; the build command itself can live in the workbook (BUILD sheet).
• Counts are computed, never typed. npar, nobs, npargp, nprior, nobsgp, ntplfle, ninsfle always match the tables.
• Validation before PEST sees the file. Duplicate names, undefined parameter groups, tie targets that don't exist, regularisation equations that reference fixed or tied parameters, initial values outside bounds — reported by name, not as a PEST run-time error.
• Results flow back. Best parameters, residuals or an IES realization are written into the existing workbook by name, across however many sheets the tables are split over, without touching formulas, macros or helper columns.
• PEST_HP tokens (win_mrun_hours=, uptestmin=, absparmax(1)=…) and ++ PEST++ options are first-class, so no post-editing of the generated file.
• Fits existing workflows. The build command line is the same one the original single-file script accepted; batch files keep working.

Install

pip install makepst                 # adds the makepst command
pip install "makepst[excel]"        # + xlwings, for Excel-faithful workbook updates (see update)
pip install "makepst[pyemu]"        # + pyEMU, for to_pyemu / from_pyemu (see the bridge)

The development version: pip install git+https://github.com/ougx/makePst.

Requires Python ≥ 3.9, pandas ≥ 2.0, numpy, openpyxl ≥ 3.1. Installing adds the makepst command; python -m makepst is equivalent, and python makepst.py works from a checkout without installing.

Starting from nothing

makepst init project.xlsx

writes a workbook that already builds a valid control file: every sheet with the headers build expects and a few example rows, a CONTROL sheet listing every PEST variable with its default and a one-line description, drop-down lists where PEST allows only fixed words (PARTRANS, INCTYP, FORCEN, DERMTHD, IO!TYPE, the on/off control variables), header comments explaining each column, frozen header rows, and a BUILD sheet with the build command. Replace the example rows with your own and run that command.

Two-minute tutorial

examples/minimal/ holds a four-parameter, four-observation setup as CSV tables, its template / instruction files, a toy model.py, and a fake model.par and model.res (this is also run by the test suite):

cd examples/minimal            # on Windows cmd, replace the trailing \ with ^

# 1. tables -> control file (also writes dump.tpl, a template that echoes every parameter)
makepst build model.pst regul --set_ctl_csv control.csv --add_pargp_csv pargp.csv \
    --add_par_csv par.csv --add_obs_csv obs.csv --add_io_csv io.csv --add_comment "minimal example"

# 2. check it against the template / instruction / output files, like pestchek
makepst validate model.pst --outputs

# 2b. what PEST will do with them: write the model input file, read the model output files
makepst tempchek model.pst --dry_run
makepst inschek model.pst

# 3. control file -> workbook (CONTROL / PARGP / PAR / OBS / PRIOR / IO sheets + a BUILD sheet)
makepst dump model.pst model.xlsx

# 4. results -> a copy of the workbook: PARVAL1 from .par, MODELLED / RESIDUAL from .res
makepst update model.xlsx --par model.par --res model.res --out model-results.xlsx

# 5. best parameters -> a new control file for a final run
makepst parrep model.pst model.par model-final.pst --set noptmax=0

Open model.pst to see what the tables became: hk3 is tied to hk1 (the TIETO column), hk1 and hk2 got regularisation equations from their PRIOR/WEIGHT columns (log(hk1) = log10(25), log(hk2) − log(hk1) = 0), lamforgive and maxsing 3 came from control.csv, and every count on the control-data lines was computed.

Commands

init — starter workbook

makepst init project.xlsx [--name case] [--force]

See Starting from nothing. --name sets the control-file name in the BUILD command (default: the workbook's name). Existing files are not overwritten without --force.

validate — pestchek-style checks

makepst validate model.pst --outputs
makepst validate book.xlsx --strict

See Validation. Template and instruction paths are resolved the way PEST resolves them, from the directory PEST is run in: makePst tries the control file's folder, the current directory and the control file's parent, uses the one where the files are, and says which; --base_dir overrides that. --strict also fails on warnings, --quiet hides the informational lines.

A workbook is accepted in place of a control file when it carries a BUILD sheet (one that dump or init wrote), which is built first and then checked. For tables that have no BUILD sheet — a hand-made workbook, or a set of CSV files — name them on a build command line and add --dry_run, which runs this same report and writes nothing.

build — tables → .pst

makepst build out.pst [estimation|regularisation|prediction|pareto]
    --set_ctl_xls book.xlsx,CONTROL          (or --set_ctl_csv file.csv)
    --add_pargp_xls book.xlsx,PARGP
    --add_par_xls   book.xlsx,PAR_*          (repeatable; sheet names may be globs)
    --add_obs_xls   book.xlsx,OBS_*
    --add_io_xls    book.xlsx,IO  --add_pp_xls book.xlsx,PPcntl
    [--add_tied_xls ...] [--add_prior_xls ...] [--add_comment "text"] [--add_comment_xls ...]
    [--fill_parval run.par [--real NAME]] [--ss] [--no_dump_tpl] [--v2]

Each --add_* takes book,SHEET (_xls) or a csv path (_csv) and may be repeated; tables split across sheets are concatenated. The sheet name may be a glob: --add_par_xls tr13.xlsx,PAR_* adds every PAR_… sheet in workbook order. Headers are case-insensitive. makepst build book.xlsx [--out x.pst] runs the command stored in the workbook's BUILD sheet instead. The mode argument overrides pestmode in the CONTROL sheet; adding prior information switches to regularisation. --ss drops ss*/sy* parameters and groups (steady-state runs of a transient setup).

--dry_run builds the control file in memory, runs the full validation report on it — including the template, instruction and model files it references — and writes nothing (no .pst, no dump.tpl, no manifest), exiting 1 when there are errors. This is how to check a workbook or a set of CSV tables that has no BUILD sheet; for one that has a BUILD sheet, makepst validate book.xlsx does the same in one argument.

makepst build case.pst --add_par_xls book.xlsx,PAR_* --add_obs_xls book.xlsx,OBS_* ... --dry_run

Classic PEST numeric control settings are positional. If a later setting is supplied, all earlier numeric settings on that line must have values after built-in defaults are applied. For example, upvecbend requires iboundstick, and regsingthresh requires both noptregadj and regweightrat. Export raises an error naming any missing predecessors instead of shifting settings into the wrong slots. Supply those values explicitly; trailing optional fields may remain blank. Text flags and named tokens such as absparmax(1)=0.1 do not require numeric fillers. This positional requirement does not apply to the version-2 keyword format (--v2).

Regularisation from the parameter table. In regularisation mode each adjustable parameter with a PRIOR and a positive WEIGHT gets an equation: a numeric PRIOR gives 1.0 * log(p) = log10(v) (or 1.0 * p = v for untransformed parameters); a PRIOR naming another parameter gives 1.0 * log(p) − 1.0 * log(q) = 0. The group is regul + parameter group, truncated to 12 characters.

dump — .pst → workbook

makepst dump run.pst run.xlsx [--split]

Writes CONTROL, PARGP, PAR (with TIETO), OBS, PRIOR, IO, PP, NOTES (the header comments) and BUILD — the command that rebuilds the control file from this workbook. --split writes one PAR_<group> and OBS_<group> sheet per group instead of PAR and OBS. Use it to bring an inherited control file into the spreadsheet workflow, or to recover a workbook that was lost.

update — results → existing workbook

makepst update book.xlsx --par run.par                          # PARVAL1
makepst update book.xlsx --res run.res                          # adds MODELLED / RESIDUAL
makepst update book.xlsx --pst run.pst --par_cols PARVAL1,PARTRANS,PARLBND,PARUBND
makepst update book.xlsx --par run.3.par.csv --real best        # PESTPP-IES ensemble
makepst update book.xlsx --obs_csv run.3.obs.csv --pst run.pst  # IES simulated values
makepst update book.xlsx --par run.par --dry_run               # preview; write nothing
    [--sheet "PAR_*"] [--group hk,sy] [--overwrite_formulas] [--out copy.xlsx] [--backend openpyxl|xlwings]

update saves in place unless --out is given. Use --out the first time, and keep the workbook closed in Excel while it runs.

Every worksheet whose header row contains PARNME (or OBSNME) is matched row by row on the name, so tables spread over several sheets are all found; only the requested columns are written and only for names present in both: names in the results with no row in the workbook, and workbook rows absent from the results, are left alone and reported as a warning with counts and examples. Cells that hold formulas are left alone unless --overwrite_formulas; cells inside an array formula or a dynamic-array spill range are never written (Excel would refuse), only counted. --sheet (glob, case-insensitive) restricts the worksheets; --group restricts the parameter / observation groups (a .par file carries no groups, so add --pst to supply them). --pst on its own writes the control file's parameter and observation columns; next to --par, --res or --obs_csv it only supplies groups and measured values, and the observation sheets are not touched by a parameter update. Likewise, --res or --obs_csv with --pst leaves parameter cells unchanged unless --par is also supplied.

The xlwings backend reads only each sheet's header, name column and target columns, writes contiguous runs of cells in one call each and recalculates once before saving: the demonstration workbook (27 sheets, 1,068 parameters over eight sheets) updates in about 9 s.

PESTPP-IES ensembles (case.N.par.csv, case.N.obs.csv) are recognised by their real_name header. --real picks the realization: a name (base, 17) or best — the lowest phi for that iteration in case.phi.actual.csv next to the file. Default base. An observation ensemble writes MODELLED; with --pst also RESIDUAL (measured − modelled).

With residuals (--res, or --obs_csv plus --pst) update also writes a PHI sheet — the objective function by observation group as PEST reports it at the end of a run: number of observations and of weighted ones, phi (Σ(w·r)²) and its fraction of the total, RMS / mean / max-abs residual of the weighted observations in their own units, and the worst observation, with a TOTAL row. For an IES ensemble with case.phi.actual.csv beside it, a PHI_IES sheet lists every realization's phi for that iteration, sorted, with mean / std / min / max. Both sheets are rewritten on each run (--no_phi to skip).

Two backends: xlwings (used when installed; drives Excel invisibly, so everything in the workbook is preserved and formulas recalculate; Windows/macOS with Excel) or openpyxl (no Excel needed; keeps VBA macros; drops charts and images; does not recalculate formulas). Before building from a workbook updated with openpyxl, recalculate and save it in Excel; otherwise the build may read missing or stale cached formula values. The backend used is printed. --dry_run uses openpyxl to preview matched names, target columns, skipped formulas and unmatched names without saving a workbook or manifest. It does not test whether Excel will refuse individual writes. During build, missing cached formula results in referenced tables produce a warning; existing cached values may still be stale and cannot be verified.

reweight — balance observation-group weights

makepst reweight run.pst balanced.pst --res run.res --equal
makepst reweight run.pst balanced.pst --res run.res --targets group_shares.csv
makepst reweight run.pst scaled.pst --factor heads=0.5 --factor flow=2
makepst reweight run.pst balanced.pst --obs_csv run.3.obs.csv --real base --equal
makepst reweight run.pst adjusted.pst --res run.res --discrepancy group
makepst reweight run.pst adjusted.pst --obs_csv run.0.obs.csv --discrepancy obs

reweight writes a new control file; it never overwrites the input. --equal gives each eligible measurement group the same initial objective-function contribution; a group whose current phi is zero cannot be scaled to a share, so it is skipped with an INFO line and its weights are left unchanged. --targets reads a CSV or BOOK,SHEET table with OBGNME,TARGET_SHARE columns. Shares are relative, so 4,3,2 means 44.44%, 33.33% and 22.22%; the selected groups' total phi is preserved. Direct --factor GROUP=NUMBER specifications multiply the existing positive weights in named groups.

--discrepancy makes weights consistent with the current misfit, so that each weighted observation contributes about 1 to phi. --discrepancy group scales each measurement group by one factor so its phi equals its number of weighted observations (as PEST's PWTADJ2); relative weights inside a group are kept and zero-phi groups are skipped. It raises the weights of groups that already fit better than that, so pair it with --clip when some groups fit very closely. --discrepancy obs sets w = min(w, 1/|residual|) for each observation (pyEMU's adjust_weights_discrepancy with its original ceiling): observations the model cannot fit are lowered to a contribution of 1, weights are never raised, and zero residuals keep their weight. It is commonly applied to the prior mean or base realization before a PESTPP-IES run so that observation noise is not smaller than the misfit the prior can reach. Total phi is not preserved in either variant.

For equal, target and discrepancy modes, residuals are joined by observation name while weights and group membership come from the input control file. Groups backed by covariance matrices, predict, and regul* groups are not measurement groups and are rejected or skipped as appropriate.

Use --clip MIN MAX to constrain final positive weights, for example --clip 0 10. Original zero weights remain zero. Clipping is included in the balancing calculation; if a requested target cannot be reached under the bounds, the command fails instead of reporting a misleading result. --report FILE writes the before/after group table, including phi, percentages, factors, clipping counts and the number of weights lowered (N_REDUCED). --dry_run prints that table without writing a control file, report or manifest.

The workbook remains the source of truth. If the control file is rebuilt from a workbook later, formula-generated workbook weights will replace weights changed by reweight unless the change is deliberately transferred back to the workbook.

parrep — .par values → new .pst

makepst parrep run.pst run.par next.pst [--set noptmax=0] [--set NAME=VALUE ...] [--v1|--v2]
makepst parrep run.pst run.3.par.csv next.pst --real best
makepst parrep book.xlsx run.par next.pst                       # base is the workbook

Like PEST's PARREP: PARVAL1 is replaced from a .par file or an IES realization and a new control file is written; --set changes control values on the way. All supported content of the input control file is preserved semantically (see below); adjustable parameters that end up outside their bounds are reported. A .par file records the SCALE and OFFSET each value was written with; where those differ from the control file's (a run from before a rescale), the value is converted so the model sees the same number (PARVAL1 × SCALE + OFFSET). An IES ensemble carries no scales and is copied as is.

With a workbook as the base, the control file is first built from the command in the workbook's BUILD sheet (dump writes one; for a hand-made workbook paste the build command into column A, one option per row). File names in it that match the workbook's own name refer to that workbook; other relative names resolve against the workbook's folder. Note that this route rebuilds from the workbook's current state, so any edits made since the original control file was written are picked up too.

rescale — parameter values → SCALE

makepst rescale run.pst run_unit.pst                            # every parameter starts at 1
makepst rescale run.pst run_unit.pst --par "hk*,sy*" --exclude_fixed
makepst rescale run.pst run_unit.pst --pargp kh --pargp kv
makepst rescale run_unit.pst run_back.pst --undo                # SCALE folded back into PARVAL1

PEST applies SCALE and OFFSET only when it writes a model input file, so a parameter at value v can start at 1 without the model seeing any difference: PARVAL1 = 1, SCALE = SCALE × v, bounds divided by v (swapped when v < 0), OFFSET unchanged. --undo does the reverse (PARVAL1 × SCALE, bounds likewise, SCALE = 1).

• Selection: all parameters by default, fixed ones included; --par (names or globs) and --pargp narrow it, and both must match when both are given; --exclude_fixed leaves fixed parameters alone. Tied children follow a selected parent. Parameters at zero are skipped and listed.
• Prior information is written in PEST's own units, so every equation naming a rescaled parameter is rewritten exactly: a linear term takes v into its coefficient, a log term moves log10(v) to the right-hand side. Each equation's residual is the same number as before, so its weight is unchanged.
• Warned, not converted: settings in parameter units that one factor per parameter can't convert — INCTYP absolute and DERINCLB of groups holding rescaled parameters, absolute(n) change limits — and files named in the control file that were written for the old values (++parcov, ++base_jacobian, ++ies_par_en, SVD-assist basepestfile / basejacfile). A Jacobian given to PEST another way (a restart, a command-line switch) can't be seen from the control file; don't reuse one across a rescale.

--set, --eol, --v1|--v2 and --no_manifest work as for parrep.

hpstart — modeled observations → new PEST_HP .hp file

makepst hpstart case.pst start.hp --template case.hp --res case.res
makepst hpstart case.pst start.hp --template case.hp --obs_csv case.3.obs.csv --real best
makepst hpstart case.pst start.hp --template case.hp --res case.rei --par case.par

By default, parameter values are copied unchanged from the template .hp, not from PARVAL1 in the .pst. Use --par to replace them with values from a PEST .par file or a selected IES parameter realization. The .pst supplies parameter and observation names and order; the template supplies PEST_HP instruction metadata that a residual or IES observation file cannot provide. hpstart replaces the template's reference modeled-observation array in .pst observation order. Every required observation must be present with a finite value; extra result rows are ignored. The template must come from the same control-file layout and observation/parameter order. The binary format stores counts but not names, so matching counts alone cannot establish that the template has the right order. The original .hp is never modified, and the new file receives a provenance manifest by default.

tempchek — templates → model input files

makepst tempchek model.tpl                          # check the template, list its parameters
makepst tempchek model.tpl model.in [model.par]     # write model.in (values from model.par by default)
makepst tempchek model.pst [--par run.par] [--real best] [--dry_run]   # every template of the case

Like PEST's TEMPCHEK: a template is checked, then its model input file is written with parameter values from a .par file (or, given a control file / workbook, from PARVAL1 with SCALE and OFFSET applied, or from --par — a .par file or an IES realization). Numbers are written the way PEST writes them (its WRTSIG): into the parameter space with as much precision as the space allows, fixed or exponential notation, right-justified, one word per parameter sized for its narrowest space, PRECIS / DPOINT honoured (double, nopoint). Checked against tempchek.exe on a grid of values and widths from 3 to 23 characters: the same fields are refused, and the written value is never further from the true value than PEST's. A value that cannot be written (1e-10 in a five-character space, say) is an error and nothing is written; parameters in the .par file that the template does not cite are warnings. --dry_run checks without writing.

inschek — model output files → observation values

makepst inschek heads.ins                           # check the instruction file, list its observations
makepst inschek heads.ins heads.out [--out heads.obf]
makepst inschek model.pst [--out model.obf]          # every instruction file of the case, one .obf

Like PEST's INSCHEK: the instruction file is checked (the syntax rules in the validation table below, including that a line advance can only start an instruction line and that fixed / tab instructions must move right), then read against the model output file with the interpreter that validate --outputs uses, and the values go to an observation value file (name value per line, INSCHEK's .obf). With a control file, all instruction files are read and observations the control file lists but no file produced are reported.

diff — what changed

makepst diff tr12.pst tr13.pst [--xlsx changes.xlsx] [--file changes.txt] [--rtol 1e-9] [--max_rows 50]
makepst diff tr13.xlsx tr13.pst                # is the control file still what the workbook says?

Compares the tables, not the text: parameters and observations added, removed or changed (column by column), prior-information equations, parameter groups, effective control values (defaults filled in, so a file and a workbook compare fairly), ++ options, template / instruction pairs, command lines and header comments. Numbers are compared with a relative tolerance, so 49.7377 vs 49.73775 shows up at the default 1e-9 and disappears at --rtol 1e-5. Prints a table per section and a one-line summary; --xlsx writes the same tables to a workbook for review. Exits 1 when there are differences, like diff. --file writes the complete text report, including rows beyond the console's --max_rows limit.

log — the run ledger

makepst log [FOLDER ...] [--file tr13.xlsx] [--command build] [--last 10] [--check] [--no_recursive]

Every manifest under the folders (default: the current one, recursively), oldest first, one line each: when, which command, which output, from which sources, the dimensions, the makePst version. --file keeps the entries whose output or sources include a file — by name, path or sha256 prefix, so --file b43fc509 answers "which runs used that version of the workbook?" even after the file was renamed or overwritten. --check adds a column from provenance: unchanged / changed / missing.

2026-09-19 21:14 build      pest/tr13.pst <- tr13.xlsx  [1068 par 41747 obs 159 prior]  makepst 0.4.0
2026-09-21 08:10 update     tr13-xlwings.xlsx <- tr13.xlsx, tr13.par  [1068 rows in 8 sheets]  makepst 0.4.0

bundle — a run, zipped for review

makepst bundle model.pst [run.zip] [--sources] [--outputs] [--extra "bin/**"] [--list] [--strict]

Zips the control file with its manifest, every template and instruction file, the model input files the templates write, and any file named on the model command line, with paths relative to the run directory so the archive unzips runnable. --sources adds the manifest's sources (the workbook, the .par ...), --outputs the model output files, --extra any glob relative to the run directory (the model binaries, say). Files the control file references but that don't exist are listed as MISSING (exit 1 with --strict); --list shows what would be bundled without writing. The zip gets its own manifest, so log shows who bundled what and when.

What round-tripping preserves

dump → build and read_pst → write_pst are semantically lossless for supported content, not byte-identical: read → write → read is an identity, and dump → build reproduces the same text as read → write. Concretely:

Preserved: every parameter, group, observation, prior-information equation, tied pair, template/instruction pair, command line, ++ option, header comment, control-data value (including PEST_HP keyed tokens), SVD / LSQR / AUI / SVD-assist / regularisation values, and the verbatim text of * sensitivity reuse, * derivatives command line, * predictive analysis and * pareto. Any section unknown to this version of makePst is also retained verbatim at its original position, so newer or site-specific PEST content survives a round trip.

Normalised: parameter, observation and group names are lower-cased (PEST is case-insensitive); numbers are written with 11 significant digits; whitespace and column alignment in understood sections is makePst's own; comments inside understood sections are dropped; observation groups are listed in first-use order (or the order of the source file when reading a .pst).

Also preserved: covariance-file references in * observation groups (an OBSGP sheet on dump), and the control-file format version: a PEST++ version-2 file (pcf version=2, * control data keyword, * … external csv tables) reads into the same tables and is written back as version 2 unless --v1 is given; --v2 writes any control file in that format, with case.par_data.csv / obs_data / pargp_data / prior_data beside it. Both versions of the same content are identical after reading (makepst diff says so).

Compatibility

Validation

Pst.validate() is a read-only audit of the in-memory tables. It returns a report with errors, warnings, and fixes_available; inspecting it never changes the Pst. Apply reviewed fixes explicitly with pst.apply_fixes(report), or use pst.normalize() to apply all currently available normalizations. Writers refuse validation errors and serialize the current tables without modifying them. The CLI's file-building commands call normalize() explicitly before writing.

makepst validate uses the same table checks and adds the pestchek-style report: it changes nothing, looks beyond the tables at the files the control file points to, and exits 1 on errors so a batch file can stop:

makepst validate model.pst [--outputs] [--strict] [--quiet] [--base_dir DIR]
makepst validate tr13.xlsx            # a workbook with a BUILD sheet is built first, in memory

The ++ checks use the option list in makepst/pestpp.py, read off PEST++ 5.2.29's option parser (250 options and 34 aliases, with their types). They point at where each option came from — PP!A17 for a workbook cell, pp.csv:17, line 42 of a control file — and build prints them too, without refusing to write: plain PEST ignores ++ lines, and a newer PEST++ may know an option this list does not. PEST++ itself stops on an option it does not accept, so fix or remove what is flagged before a run.

The --outputs interpreter follows the PEST manual (primary and secondary markers, l, w, t, !name!, [name]c1:c2, (name)c1:c2, dum, & continuation) and catches the classic mistakes — a w too few so a label is read instead of a number, a marker that never appears — but it is not PEST. Keep running PEST's own checker before a long run:

pestchek model            # PEST / PEST_HP
pestpp-glm model.pst      # PEST++ reports problems on start-up

Audit trail: the manifest

build, parrep, dump and update each write a sidecar <output>.manifest.json next to what they produce (--no_manifest to skip). It answers "where did this control file come from?" after the fact:

{
  "makepst": "0.4.0",
  "command": "build",
  "argv": ["build", "tr13.pst", "regul", "--set_ctl_xls", "tr13.xlsx,CONTROL", "..."],
  "created": "2026-09-19T13:51:39-04:00",
  "user": "hydro", "host": "OU13700", "cwd": "D:\\...\\0023-makePst",
  "python": "3.12.12", "pandas": "3.0.5", "platform": "Windows-11-10.0.26200-SP0",
  "sources": [
    {"path": "D:\\...\\tr13.xlsx", "sha256": "9d641a2f...", "size": 3089317,
     "modified": "2026-07-12T03:32:10-04:00", "role": "control",
     "sheets": ["CONTROL", "PARGP", "PAR_HK", "PAR_VK", "...", "PPglm"]}
  ],
  "output": {"path": "D:\\...\\tr13.pst", "sha256": "d93f317d...", "size": 2163331,
             "npar": 1068, "nobs": 41747, "npargp": 12, "nprior": 159, "nobsgp": 12,
             "ntplfle": 18, "ninsfle": 7, "pestmode": "regularisation"}
}

Every input file appears once with its SHA-256, size, modification time, role and the sheets read from it; the output carries its own hash and the section counts. parrep adds the realization and --set values (and, from a workbook, the resolved build arguments); update records which sheets were written, how many rows each, how many formula cells were left alone, and the backend. A hash mismatch between a manifest and the workbook on disk is the signal that the control file no longer corresponds to the spreadsheet.

Run makepst provenance model.pst (or pass model.pst.manifest.json) to compare every recorded input and output hash with the files at their recorded paths. It reports unchanged, changed, missing, or not recorded and exits with status 1 unless all records are unchanged. This checks file identity, not the validity of PEST tables; use makepst validate for the latter. Moving files requires updating their recorded paths. makepst log lists the manifests of a whole project folder as a ledger (with --check, each one verified), and makepst bundle zips a run with everything its control file references — see the two commands above.

Relationship to pyEMU

pyEMU is the broader toolkit: it constructs PEST++ interfaces programmatically (PstFrom), runs linear and ensemble-based uncertainty analysis, and provides geostatistics. makePst does one thing pyEMU does not: it treats a spreadsheet the modeler already maintains as the source of a control file and keeps the two in sync in both directions, including PEST_HP keywords that pyEMU's control-data model does not carry. They are complementary — a control file written by makePst loads in pyEMU, and pyEMU's outputs (.par, .res, ensembles) load into makepst update.

There is a direct bridge (the pyemu extra):

from makepst import read_pst, to_pyemu, from_pyemu

ppst = to_pyemu(read_pst('tr13.pst'))      # a pyemu.Pst: use pyEMU's Schur, ensembles, plotting, ...
pst = from_pyemu(ppst)                     # back to a makePst Pst, e.g. to dump into a workbook

Both directions go through a temporary classic control file, so they depend only on the file format the two agree on. The tables, ++ options and standard control values survive the round trip; what does not is exactly what pyEMU's control-data model lacks — PEST_HP keyed tokens (absparmax(n)=, uptestmin=, win_mrun_hours=), header comments, and the regularisation section, which pyEMU rewrites with its own defaults. makepst diff shows the difference, and the test suite pins it.

Python API

from makepst import Pst, read_pst, write_pst, to_workbook, update_workbook, load_table, read_par

pst = read_pst('run.pst')                    # Pst: .par/.obs/.pargp/.prior DataFrames, .control dict, ...
pst.fill_parval('run.3.par.csv', real='best')
pst.set_control({'noptmax': 0})
pst.rescale_par(patterns=['hk*'], include_fixed=False)   # values -> SCALE; returns a report dict
write_pst(pst, 'run_final.pst', dump_tpl=False)

to_workbook(pst, 'run.xlsx', split=True)
update_workbook('book.xlsx', par='run.par', res='run.res', out='book_results.xlsx',
                sheets=['PAR_*'], groups=['hk'])

Testing

python -m pytest tests -q

106 tests, no external data needed: a synthetic workbook in the real project layout (tests/data/demo.xlsx, generated by tests/make_fixture.py) with a golden control file, .par, .res and IES ensemble files. They cover golden-file builds from Excel and CSV, the read → write → read identity, dump → build byte equality, workbook updates (multi-sheet matching, formula protection, sheet/group filters, ensembles), parrep from .par, ensembles and workbooks, the init starter workbook, validate (table checks, template / instruction cross-checks, the instruction interpreter), the control-section parser, validation errors, and the README tutorial above. Two extra tests run against a real 1,000-parameter / 40,000-observation project when its files are present locally, and two pyEMU bridge tests run when pyemu is installed. CI runs everything on Linux and Windows.

Project layout

makepst.py          entry point for a checkout (same arguments as `makepst`)
makepst/
  sections.py       control-style sections: field order, defaults, PEST_HP keyed tokens; one table drives render and parse
  pst.py            Pst data model and validate(); .par / .res / ensemble readers
  writer.py         Pst -> .pst text (+ dump.tpl)
  reader.py         .pst text -> Pst
  excel.py          sheets -> Pst; Pst -> workbook; results -> existing workbook
  cli.py            init / validate / build / dump / update / parrep / set / rescale / reweight / diff / tempchek / inschek / log / bundle
  diff.py           semantic comparison of two Pst objects
  phi.py            objective function by group from residuals; IES realization phis
  reweight.py       observation-group balancing, scaling and bounded weight adjustment
  pyemu_bridge.py   to_pyemu / from_pyemu
  checks.py         the validate report: table checks, template / instruction cross-checks, instruction interpreter
  rules.py          pestchek's rules (parameters, groups, observations, prior information, control variables)
  model_files.py    PEST's number writer, template filling, .obf files (tempchek / inschek)
  provenance.py     the <output>.manifest.json sidecar; provenance / log / bundle
  starter.py        the `init` workbook: control-variable descriptions, example rows, drop-downs
examples/minimal/   the tutorial inputs
tests/              suite + fixture generator

Releasing

Releases are published to PyPI by GitHub Actions through trusted publishing, so no token is stored anywhere. To release: bump the version in pyproject.toml and the fallback in makepst/__init__.py (a test keeps them equal), add a CHANGELOG.md entry, commit, then

git tag v0.4.0 && git push origin main v0.4.0

The publish workflow checks that the tag matches the package version, runs the tests, builds the sdist and wheel, uploads them to PyPI, and attaches them to a GitHub release. PyPI never accepts the same version twice, so a mistake means a new version, not a re-upload.

Contributing, issues, citation

Bug reports and feature requests: GitHub issues. A control file that makePst misreads, plus the command used, is the most useful report. Pull requests should keep python -m pytest tests -q green and regenerate the fixture with python tests/make_fixture.py when the writer's output changes.

Changes are listed in CHANGELOG.md. To cite, use CITATION.cff (GitHub's "Cite this repository" button).

License: MIT.