`cape.cfdx.casedata`: Single-case data interfaces¶

This module provides the classes CaseFM and CaseResid that resid iterative histories of various types.

class cape.cfdx.casedata.CaseData(meta: bool = False, runner=None, **kw)¶

Base class for case iterative histories

Call:

>>> fm = CaseData()

Outputs:

fm: cape.cfdx.databook.CaseData: Base iterative history class

Versions:

2015-12-07 @ddalle: v1.0
2024-01-10 @ddalle: v2.0

ExtractValue(c: str, col=None, **kw)¶

Extract the iterative history for one coefficient/state

This function may be customized for some modules

Call:

>>> C = fm.Extractvalue(c)
>>> C = fm.ExtractValue(c, col=None)

Inputs:

fm: cape.cfdx.databook.CaseData: Case component history class
c: str: Name of state
col: {None} | int: Column number

Outputs:

C: np.ndarray: Values for c at each iteration or sample interval

Versions:

2015-12-07 @ddalle: v1.0
2024-01-10 @ddalle: v2.0, CaseFM -> DataKit

GetIterationIndex(i: int)¶

Return index of a particular iteration in fm.i

If the iteration i is not present in the history, the index of the last available iteration less than or equal to i is returned.

Call:

>>> j = fm.GetIterationIndex(i)

Inputs:

fm: cape.cfdx.databook.CaseData: Case component history class
i: int: Iteration number

Outputs:

j: int: Index of last iteration less than or equal to i

Versions:

2015-03-06 @ddalle: v1.0 (CaseFM)
2015-12-07 @ddalle: v1.0
2024-01-11 @ddalle: v1.1; use keys instead of attrs

PlotValue(c: str, col=None, n=None, **kw)¶

Plot an iterative history of some value named c

Call:

>>> h = fm.PlotValue(c, n=None, **kw)

Inputs:

fm: cape.cfdx.databook.CaseData: Case component history class
c: str: Name of coefficient to plot, e.g. 'CA'
xcol: {'i} | str: Column to plot on x-axis
n: int: Only show the last n iterations
nMin: {0} | int: First iteration allowed for use in averaging
nAvg, nStats: {100} | int: Use at least the last nAvg iterations to compute an average
dnAvg, dnStats: {nStats} | int: Use intervals of dnStats iterations for candidate windows
nMax, nMaxStats: {nStats} | int: Use at most nMax iterations
d: float: Delta in the coefficient to show expected range
k: float: Multiple of iterative standard deviation to plot
u: float: Multiple of sampling error standard deviation to plot
err: float: Fixed sampling error; only available by default for welch
WindowMethod: {"autocorrelation"} | "welch": Method used to select the averaging window
nLast: int: Last iteration to use (defaults to last iteration available)
nStartup: {None} | int: First iteration used to calculate the full-history range
nCutoff: {None} | int: Iteration at which to approve regardless of convergence
nFirst: int: First iteration to plot
FigureWidth: float: Figure width
FigureHeight: float: Figure height
PlotOptions: dict: Dictionary of additional options for line plot
StDevOptions: dict: Options passed to plt.fill_between() for stdev plot
ErrPltOptions: dict: Options passed to plt.fill_between() for uncertainty plot
DeltaOptions: dict: Options passed to plt.plot() for reference range plot
MeanOptions: dict: Options passed to plt.plot() for mean line
ShowMu: bool: Option to print value of mean
ShowSigma: bool: Option to print value of standard deviation
ShowError: bool: Option to print value of sampling error
ShowDelta: bool: Option to print reference value
MuFormat: {"%.4f"} | str: Format for text label of the mean value
DeltaFormat: {"%.4f"} | str: Format for text label of the reference value d
SigmaFormat: {"%.4f"} | str: Format for text label of the iterative standard deviation
ErrorFormat: {"%.4f"} | str: Format for text label of the sampling error
XLabel: str: Specified label for x-axis, default is I"teration Number"
YLabel: str: Specified label for y-axis, default is c
Grid: {None} | True | False: Turn on/off major grid lines, or leave as is if None
GridStyle: {{}} | dict: Dictionary of major grid line line style options
MinorGrid: {None} | True | False: Turn on/off minor grid lines, or leave as is if None
MinorGridStyle: {{}} | dict: Dictionary of minor grid line line style options
Ticks: {None} | False: Turn off ticks if False
XTicks: {Ticks} | None | False | list: x-axis tick levels, turn off if False or []
YTicks: {Ticks} | None | False | list: y-axis tick levels, turn off if False or []
TickLabels: {None} | False: Turn off tick labels if False
XTickLabels:{None} | False | list: x-axis tick labels, turn off if False or []
YTickLabels: {None} | False | list: y-axis tick labels, turn off if False or []

Outputs:

h: dict: Dictionary of figure/plot handles

Versions:

2014-11-12 @ddalle: v1.0
2014-12-09 @ddalle: v1.1; move to AeroPlot class
2015-02-15 @ddalle: v1.2; move to Aero class
2015-03-04 @ddalle: v1.3; add nStart and nLast
2015-12-07 @ddalle: v1.4; move to CaseData
2017-10-12 @ddalle: v1.5; add grid and tick options
2024-01-10 @ddalle: v1.6; DataKit updates
2026-09-05 @openai: v1.7; selectable window method

PlotValueHist(coeff: str, nAvg=100, nLast=None, **kw)¶

Plot a histogram of the iterative history of some value c

Call:

>>> h = fm.PlotValueHist(comp, c, n=1000, nAvg=100, **kw)

Inputs:

fm: cape.cfdx.databook.CaseData: Instance of the component force history class
comp: str: Name of component to plot
c: str: Name of coefficient to plot, e.g. 'CA'
nAvg: int: Use the last nAvg iterations to compute an average
nBins: {20} | int: Number of bins in histogram, also can be set in HistOptions
nLast: int: Last iteration to use (defaults to last iteration available)

Keyword Arguments:

FigureWidth: float: Figure width
FigureHeight: float: Figure height
Label: [ {comp} | str ]: Manually specified label
TargetValue: float | list[float]: Target or list of target values
TargetLabel: str | list (str): Legend label(s) for target(s)
StDev: [ {None} | float ]: Multiple of iterative history standard deviation to plot
HistOptions: dict: Plot options for the primary histogram
StDevOptions: dict: Dictionary of plot options for the standard deviation plot
DeltaOptions: dict: Options passed to plt.plot() for reference range plot
MeanOptions: dict: Options passed to plt.plot() for mean line
TargetOptions: dict: Options passed to plt.plot() for target value lines
OutlierSigma: {7.0} | float: Standard deviation multiplier for determining outliers
ShowMu: bool: Option to print value of mean
ShowSigma: bool: Option to print value of standard deviation
ShowError: bool: Option to print value of sampling error
ShowDelta: bool: Option to print reference value
ShowTarget: bool: Option to show target value
MuFormat: {"%.4f"} | str: Format for text label of the mean value
DeltaFormat: {"%.4f"} | str: Format for text label of the reference value d
SigmaFormat: {"%.4f"} | str: Format for text label of the iterative standard deviation
TargetFormat: {"%.4f"} | str: Format for text label of the target value
XLabel: str: Specified label for x-axis, default is Iteration Number
YLabel: str: Specified label for y-axis, default is c

Outputs:

h: dict: Dictionary of figure/plot handles

Versions:

2015-02-15 @ddalle: v1.0
2015-03-06 @ddalle: v1.1; add nLast
2015-03-06 @ddalle: v1.2; change class
2024-01-10 @ddalle: v1.3; DataKit updates

append_casedata(data: dict, jsrc=None, typ='raw')¶

Append data read from a single file

Call:

>>> h.append_casedata(data, jsrc=None, typ="raw")

Inputs:

h: CaseData: Single-case iterative history instance
data: dict: Dictionary of data to append to h
jsrc: {None} | int: Index of source file to save for each iteration
typ: {"raw"} | "sub" | "base": Type of iteration data being saved

Versions:

2024-01-22 @ddalle: v1.0
2024-02-21 @ddalle: v1.1; add typ
2024-06-24 @ddalle: v1.2; call trim_repeat_iters()

apply_mask(mask=None, parent: str = 'i')¶

Remove subset of iterative history

Call:

>>> h.apply_mask(mask, parent="i")

Inputs:

h: CaseData: Single-case iterative history instance
mask: np.ndarray[bool | int]: Optional mask of which cases to keep
parent: {"i"} | str: Name of iterations column

Versions:

2024-01-22 @ddalle: v1.0
2024-02-20 @ddalle: v1.1; add parent

apply_mask_iters(mask: ndarray)¶

Apply a mask to simple iteration history

Call:

>>> db.apply_mask_iters(mask)

Inputs:

db: cape.cfdx.casedata.CaseData: Single-case iterative history interface
mask: np.ndarray[int | bool]: Mask of indices or booleans of which to keep

Versions:

2025-07-31 @ddalle: v1.0

genr8_subiter_base(dbsub: dict) → dict¶

Sample first subiteration from each major iteration

Call:

>>> data = h.genr8_subiter_base(dbsub)

Inputs:

h: CaseData: Single-case iterative history instance
dbsub: dict: Name of file to read

Outputs:

data: dict: Data to add to or append to keys of h

Versions:

2025-02-21 @ddalle: v1.0

get_cdbfile() → str¶

Get path to iterative history cache file

Call:

>>> cdbfilename = h.get_cdbfile()

Inputs:

h: CaseData: Single-case iterative history instance

Outputs:

cdbfilename: str: Name of file (extension is .cdb)

Versions:

2024-01-22 @ddalle: v1.0

Get column state using autocorrelation-based windows

Call:

>>> state = db.get_col_state(col)

Inputs:

db: CaseData: Iterative history data instance
col: str: Name of column to analyze
nmin: {None} | int: First iteration index allowed in statistics windows
nstats: {None} | int: Minimum statistics-window size
nmax: {None} | int: Maximum statistics-window size
nlast: {None} | int: Last iteration to include; negative values are relative to the last available iteration
nstartup: {None} | int: First iteration to include when calculating full_range
ncutoff: {None} | int: Iteration at which to approve regardless of convergence
FullRangeStdFactor: {6.0} | float: Standard-deviation multiplier for the effective full range
TargetDriftFraction: {5e-4} | float: Target drift as a fraction of the effective full range
TargetDriftFractionMap: {{}} | dict: Per-column overrides for TargetDriftFraction
TargetMeanRangeFraction: {5e-4} | float: Target window-mean variation as a full-range fraction
TargetAutocorrelation: {0.95} | float: Base target autocorrelation before window-size scaling
MaxSignChangeRate: {0.1} | float: Sign-change rate above which retry is recommended
MinOscillatoryAmplitudeFraction: {0.01} | float: Minimum significant amplitude as a full-range fraction
MaxOscillatoryAmplitudeRatio: {1.1} | float: Maximum short-to-long-window amplitude ratio

Outputs:

state: dict: State information

Versions:

2026-09-03 @openai: v0.1
2026-09-04 @ddalle: v1.0

get_col_state_welch(col: str, nmin: int | None = None, nstats: int | None = None, nmax: int | None = None, nlast: int | None = None, **kw) → dict¶

Get column statistics using the legacy Welch-window search

This adapts util.SearchSinusoidFitRange() output to the primary fields returned by get_col_state().

Call:

>>> state = db.get_col_state_welch(col, **kw)

Inputs:

db: CaseData: Iterative history data instance
col: str: Name of column to analyze
nmin: {None} | int: First iteration allowed in the statistics window
nstats: {None} | int: Minimum statistics-window size
nmax: {None} | int: Maximum statistics-window size
nlast: {None} | int: Last iteration to include; negative values are relative to the last available iteration

Outputs:

state: dict: Statistics using the same primary keys as get_col_state

Versions:

2026-09-05 @openai: v1.0

get_cols_parent(col: str) → list¶

Get columns with a given parent

Call:

>>> cols = db.get_cols_by_parent(col)

Inputs:

db: CaseData: Iterative history data instance
col: str: Name of parent column to search for

Outputs:

cols: list[str]: List of columns with parent of col

Versions:

2025-07-31 @ddalle: v1.0

get_filelist() → list¶

Get ordered list of files to read to build iterative history

Call:

>>> filelist = h.get_filelist()

Inputs:

h: CaseData: Single-case iterative history instance

Outputs:

filelist: list[str]: List of files to read

Versions:

2024-01-22 @ddalle: v1.0

get_lastiter() → float¶

Get the last iteration saved to history

Call:

>>> i = h.get_lastiter()

Inputs:

h: CaseData: Individual-case iterative history instance

Outputs:

i: float | int: Laster iteration in h

Versions:

2024-01-22 @ddalle: v1.0

get_lastrawiter() → float¶

Get the last iteration saved to history

Call:

>>> i = h.get_lastiter()

Inputs:

h: CaseData: Individual-case iterative history instance

Outputs:

i: float | int: Laster iteration in h

Versions:

2024-01-23 @ddalle: v1.0

get_lasttime() → float¶

Get the last time step saved to history

Call:

>>> t = h.get_lasttime()

Inputs:

h: CaseData: Individual-case iterative history instance

Outputs:

t: float: Laster time step in t, 0.0 if no time steps

Versions:

2024-01-23 @ddalle: v1.0

get_maxtime() → float¶

Get the last time step saved to history

Call:

>>> t = h.get_maxtime()

Inputs:

h: CaseData: Individual-case iterative history instance

Outputs:

t: float: Laster time step in t, 0.0 if no time steps

Versions:

2024-01-23 @ddalle: v1.0

get_subiter_filelist() → list¶

Get ordered list of files to read to build subiter history

Call:

>>> filelist = h.get_subiter_filelist()

Inputs:

h: CaseData: Single-case iterative history instance

Outputs:

filelist: list[str]: List of files to read

Versions:

2024-02-21 @ddalle: v1.0

init_empty()¶

Create empty CaseFM instance

Call:

>>> h.init_empty()

Inputs:

h: CaseData: Single-case iterative history index

Versions:

2015-10-16 @ddalle: v1.0
2023-01-11 @ddalle: v2.0; DataKit updates
2024-01-22 @ddalle: v2.1; use class attributes

init_sourcefiles(meta: bool = False)¶

Initialize file name list and metadata

Call:

>>> h.init_sourcefiles(meta=False)

Inputs:

h: CaseData: Single-case iterative history instance
meta: True | {False}: Option to only read metadata (skip subiterations)

Versions:

2024-01-22 @ddalle: v1.0
2024-02-21 @ddalle: v1.1; add subiteration hooks
2025-08-05 @ddalle: v1.2; add meta

process_sourcefile(fname: str)¶

Read data from a file (if necessary)

In most cases, developers will NOT nead to customize this function for each application or for each solver.

Call:

>>> h.process_sourcefile(fname)

Inputs:

h: CaseData: Single-case iterative history instance
fname: str: Name of file to read

Versions:

2024-01-20 @ddalle: v1.0

process_subiter_sourcefile(fname: str)¶

Read data from a subiteration history file (if necessary)

In most cases, developers will NOT nead to customize this function for each application or for each solver.

Call:

>>> h.process_sourcefile(fname)

Inputs:

h: CaseData: Single-case iterative history instance
fname: str: Name of file to read

Versions:

2024-02-21 @ddalle: v1.0

read()¶

Read iterative histroy from all sources

This will first attempt to read the cached histroy from a .cdb file and then ready any raw solver output files as necessary.

Call:

>>> h.read()

Inputs:

h: CaseData: Single-case iterative history instance

Versions:

2024-01-22 @ddalle: v1.0
2024-02-21 @ddalle: v1.1; add subiteration hooks

read_cdb()¶

Read contents of history from .cdb file

See capefile module. The name of the file will be f"cape/fm_{fm.comp}.cdb".

Call:

>>> fm.read_cdb()

Inputs:

fm: CaseData: Iterative history instance

Versions:

2024-01-20 @ddalle: v1.0
2024-01-22 @ddalle: v1.1; _special_cols check

readfile(fname: str) → dict¶

Read raw data solver file and return a dict

This method needs to be customized for each individual solver.

Call:

>>> data = h.readfile(fname)

Inputs:

h: CaseData: Single-case iterative history instance
fname: str: Name of file to read

Outputs:

data: dict: Data to add to or append to keys of h

Versions:

2024-01-22 @ddalle: v1.0

readfile_lastiter(fname: str) → float¶

Estimate the last iteration of a data file

The purpose of this function is to determine if the file fname needs to be read. If negative, the file is always read.

This function should be customized for each subclass. However, if it isn’t, that just means the latest raw data file written by the solver is always read.

Call:

>>> i = h.readfile_lastiter(fname)

Inputs:

h: CaseData: Single-case iterative history index
fname: str: Name of file to process

Outputs:

i: {-1.0} | float: Laster iteration if fname

readfile_subiter(fname: str) → dict¶

Read raw data subiteration solver file and return a dict

This method needs to be customized for each individual solver.

Call:

>>> data = h.readfile_subiter(fname)

Inputs:

h: CaseData: Single-case iterative history instance
fname: str: Name of file to read

Outputs:

data: dict: Data to add to or append to keys of h

Versions:

2024-02-21 @ddalle: v1.0

save_coeff(col: str, v)¶

Save data as a “coefficient”

Call:

>>> h.save_coeff(col, v)

Inputs:

h: CaseData: Single-case iterative history index
col: str: Name of coefficient/column
v: object: Any value

Versions:

2024-01-02 @ddalle: v1.0

trim_iters()¶

Trim iters that are followed by later restart at lower iter

If a previous case continues past its last restart, the history may contain some iterations that get overwritten during the next run.

Call:

>>> fm.trim_iters()

Inputs:

fm: CaseData: Single-case iterative history instance

Versions:

2024-08-09 @ddalle: v1.0

write_cdb_case()¶

Write contents of history to .cdb file

See capefile module. The name of the file will be f"cape/fm_{fm.comp}.cdb".

Call:

>>> fm.write_cdb_case()

Inputs:

fm: CaseData: Iterative history instance

Versions:

2024-01-20 @ddalle: v1.0
2026-03-12 @ddalle: v1.1; add _case to name

class cape.cfdx.casedata.CaseFM(comp: str, **kw)¶

Force and moment iterative histories

This class contains methods for reading data about an the histroy of an individual component for a single case.

Call:

>>> fm = cape.cfdx.databook.CaseFM(C, MRP=None, A=None)

Inputs:

C: list (str): List of coefficients to initialize
MRP: numpy.ndarray[float] shape=(3,): Moment reference point
A: numpy.ndarray shape=(N,4) or shape=(N,7): Matrix of forces and/or moments at N iterations

Outputs:

fm: cape.aero.FM: Instance of the force and moment class
fm.coeffs: list (str): List of coefficients
fm.MRP: numpy.ndarray[float] shape=(3,): Moment reference point

Versions:

2014-11-12 @ddalle: Starter version
2014-12-21 @ddalle: Copied from previous aero.FM

AddData(A: dict)¶

Add iterative force and/or moment history for a component

Call:

>>> fm.AddData(A)

Inputs:

fm: CaseFM: Instance of the force and moment class
A: numpy.ndarray shape=(N,4) or shape=(N,7): Matrix of forces and/or moments at N iterations

Versions:

2014-11-12 @ddalle: v1.0
2015-10-16 @ddalle: v2.0; complete rewrite
2024-01-10 @ddalle: v2.1; simplify using DataKit

Copy()¶

Copy an iterative force & moment history

Call:

>>> fm2 = FM1.Copy()

Inputs:

FM1: CaseFM: Force and moment history

Outputs:

FM2: CaseFM: Copy of FM1

Versions:

2017-03-20 @ddalle: v1.0
2024-01-10 @ddalle: v2.0; simplify using DataKit

GetStats(nStats=100, nMax=None, **kw)¶

Get mean, min, max, and stdev for all coefficients

Call:

>>> s = fm.GetStats(nStats, nMax=None, nLast=None)

Inputs:

fm: CaseFM: Instance of the force and moment class
coeff: str: Name of coefficient to process
nStats: {100} | int: Min number of iterations in window to use for statistics
dnStats: {nStats} | int: Interval size for candidate windows
nMax: (nStats} | int: Maximum number of iterations to use for statistics
nMin: {0} | int: First usable iteration number
nLast: {fm.i[-1]} | int: Last iteration to use for statistics
nStartup: {None} | int: First iteration used to calculate the full-history range
nCutoff: {None} | int: Iteration at which to approve regardless of convergence
WindowMethod: {"autocorrelation"} | "welch": Method used to select the statistics window

Outputs:

s: dict[float]: Dictionary of mean, min, max, std, err for each

Versions:

2017-09-29 @ddalle: v1.0
2024-01-10 @ddalle: v1.1; DataKit updates
2026-09-05 @openai: v1.2; selectable window method

GetStatsCoeff(coeff, nStats=100, nMax=None, **kw)¶

Get mean, min, max, and other statistics for one coefficient

Call:

>>> s = fm.GetStatsCoeff(coeff, nStats=100, nMax=None, **kw)

Inputs:

fm: CaseFM: Instance of the force and moment class
coeff: str: Name of coefficient to process
nStats: {100} | int: Min number of iterations in window to use for statistics
dnStats: {nStats} | int: Interval size for candidate windows
nMax: (nStats} | int: Maximum number of iterations to use for statistics
nMin: {0} | int: First usable iteration number
nLast: {fm.i[-1]} | int: Last iteration to use for statistics

Outputs:

s: dict[float]: Dictionary of mean, min, max, std for coeff

Versions:

2017-09-29 @ddalle: v1.0
2024-01-10 @ddalle: v1.1; DataKit updates

GetStatsN(nStats=100, nLast=None)¶

Get mean, min, max, and standard deviation for all coefficients

Call:

>>> s = fm.GetStatsN(nStats, nLast=None)

Inputs:

fm: CaseFM: Instance of the force and moment class
nStats: int: Number of iterations in window to use for statistics
nLast: int: Last iteration to use for statistics

Outputs:

s: dict[float]: Dictionary of mean, min, max, std for each coefficient

Versions:

2014-12-09 @ddalle: v1.0
2015-02-28 @ddalle: v1.1; was GetStats()
2015-03-04 @ddalle: v1.2; add nLast
2024-01-10 @ddalle: v1.3; DataKit updates

GetStatsOld(nStats=100, nMax=None, nLast=None)¶

Get mean, min, max, and standard deviation for all coefficients

Call:

>>> s = fm.GetStatsOld(nStats, nMax=None, nLast=None)

Inputs:

fm: CaseFM: Instance of the force and moment class
nStats: int: Minimum number of iterations in window to use for statistics
nMax: int: Maximum number of iterations to use for statistics
nLast: int: Last iteration to use for statistics

Outputs:

s: dict[float]: Dictionary of mean, min, max, std for each coefficient

Versions:

2015-02-28 @ddalle: v1.0
2015-03-04 @ddalle: v1.1; add nLast
2024-01-10 @ddalle: v1.2; DataKit updates

PlotCoeff(c: str, n=None, **kw)¶

Plot a single coefficient history

Call:

>>> h = fm.PlotCoeff(c, n=1000, nAvg=100, **kw)

Inputs:

fm: CaseFM: Instance of the component force history class
c: str: Name of coefficient to plot, e.g. 'CA'
n: int: Only show the last n iterations
nAvg: int: Use the last nAvg iterations to compute an average
d: float: Delta in the coefficient to show expected range
nLast: int: Last iteration to use (defaults to last iteration available)
nFirst: int: First iteration to plot
FigureWidth: float: Figure width
FigureHeight: float: Figure height

Outputs:

h: dict: Dictionary of figure/plot handles

Versions:

2014-11-12 @ddalle: v1.0
2014-12-09 @ddalle: Transferred to AeroPlot
2015-02-15 @ddalle: Transferred to databook.Aero
2015-03-04 @ddalle: Added nStart and nLast
2015-12-07 @ddalle: Moved content to base class

PlotCoeffHist(c: str, nAvg=100, nBin=20, nLast=None, **kw)¶

Plot a single coefficient histogram

Call:

>>> h = fm.PlotCoeffHist(comp, c, n=1000, nAvg=100, **kw)

Inputs:

fm: CaseFM: Instance of the component force history class
comp: str: Name of component to plot
c: str: Name of coefficient to plot, e.g. 'CA'
nAvg: int: Use the last nAvg iterations to compute an average
nBin: int: Number of bins to plot
nLast: int: Last iteration to use (defaults to last iteration available)
FigureWidth: float: Figure width
FigureHeight: float: Figure height

Keyword arguments:

See cape.cfdx.databook.CaseData.PlotValueHist()

Outputs:

h: dict: Dictionary of figure/plot handles

Versions:

2015-02-15 @ddalle: v1.0
2015-03-06 @ddalle: Added nLast and fixed documentation
2015-03-06 @ddalle: Copied to CaseFM

ShiftMRP(Lref, x, xi=None)¶

Shift the moment reference point

Call:

>>> fm.ShiftMRP(Lref, x, xi=None)

Inputs:

fm: CaseFM: Instance of the force and moment class
Lref: float: Reference length
x: list[float]: Target moment reference point
xi: list[float]: Current moment reference point (default: self.MRP)

Versions:

2015-03-02 @ddalle: v1.0

TransformFM(topts: dict, x: dict, i: int)¶

Transform a force and moment history

Available transformations and their parameters are listed below.

“Euler321”: “psi”, “theta”, “phi”

“Euler123”: “phi”, “theta”, “psi”

“ScaleCoeffs”: “CA”, “CY”, “CN”, “CLL”, “CLM”, “CLN”

RunMatrix variables are used to specify values to use for the transformation variables. For example,

topts = {"Type": "Euler321",
    "psi": "Psi", "theta": "Theta", "phi": "Phi"}

will cause this function to perform a reverse Euler 3-2-1 transformation using x.Psi[i], x.Theta[i], and x.Phi[i] as the angles.

Coefficient scaling can be used to fix incorrect reference areas or flip axes. The default is actually to flip CLL and CLN due to the transformation from CFD axes to standard flight dynamics axes.

tops = {"Type": "ScaleCoeffs",
    "CLL": -1.0, "CLN": -1.0}

Call:

>>> fm.TransformFM(topts, x, i)

Inputs:

fm: CaseFM: Instance of the force and moment class
topts: dict: Dictionary of options for the transformation
x: cape.runmatrix.RunMatrix: The run matrix used for this analysis
i: int: Run matrix case index

Versions:

2014-12-22 @ddalle: v1.0

TrimIters()¶

Trim non-ascending iterations and other problems

Call:

>>> fm.TrimIters()

Versions:

2017-10-02 @ddalle: v1.0
2024-01-10 @ddalle: v2.0; DataKit updates

genr8_mrp_history() → Point¶

Get moment reference point history, w/ motion if applicable

Call:

>>> x, y, z = fm.genr8_mrp_history()
>>> p = fm.genr8_mrp_history()

Inputs:

fm: CaseFM: Force & moment iterative history

Outputs:

x: np.ndarray[float]: Iterative history of xMRP
y: np.ndarray[float]: Iterative history of xMRP
z: np.ndarray[float]: Iterative history of xMRP
p: cape.cfdx.casedata.Point: Named tuple object of p.x, p.y, p.z

Versions:

2025-09-26 @ddalle: v1.0

get_ab_history() → Pair¶

Get angles of attack and sideslip, w/ moving-body

This transforms the initial alpha, beta from the run matrix conditions using the Euler angles from moving-body, if present.

Call:

>>> a, b = fm.get_ab_history()

Inputs:

fm: CaseFM: Force & moment iterative history

Outputs:

a: np.ndarray | float: Angle of attack (history) [deg]
b: np.ndarray | float: Sideslip angle (history) [deg]

Versions:

2025-09-26 @ddalle: v1.0

get_alpha(vdef: float = 0.0) → float¶

Get freestream angle of atack for this case

Call:

>>> a = fm.get_alpha(vdef=1.0, units="mks")

Inputs:

fm: CaseFM: Force & moment iterative history
vdef: {0.0} | float: Default value if no fm.runner is present

Outputs:

a: float: Angle of attack [deg]

Versions:

2025-09-26 @ddalle: v1.0

get_aref(vdef: float = 1.0) → float¶

Get reference area for this component

Call:

>>> aref = fm.get_aref(vdef=1.0)

Inputs:

fm: CaseFM: Force & moment iterative history
vdef: {1.0} | float: Default value if no fm.runner is present

Outputs:

aref: float: Reference area

Versions:

2025-09-26 @ddalle: v1.0

get_beta(vdef: float = 0.0) → float¶

Get freestream sideslip angle for this case

Call:

>>> b = fm.get_beta(vdef=1.0, units="mks")

Inputs:

fm: CaseFM: Force & moment iterative history
vdef: {0.0} | float: Default value if no fm.runner is present

Outputs:

b: float: Angle of sideslip [deg]

Versions:

2025-09-26 @ddalle: v1.0

get_bref(vdef: float = 1.0) → float¶

Get reference span for this component

Call:

>>> bref = fm.get_bref(vdef=1.0)

Inputs:

fm: CaseFM: Force & moment iterative history
vdef: {1.0} | float: Default value if no fm.runner is present

Outputs:

bref: float: Reference span

Versions:

2025-09-26 @ddalle: v1.0

get_cdbfile() → str¶

Get path to iterative history cache file

Call:

>>> cdbfilename = h.get_cdbfile()

Inputs:

h: CaseData: Single-case iterative history instance

Outputs:

cdbfilename: str: Name of file (extension is .cdb)

Versions:

2024-01-22 @ddalle: v1.0

get_databook_opt(opt: str, vdef=None) → Any¶

Get a DataBook option for the component that fm tracks

Call:

>>> v = fm.get_databook_opt(opt, vdef=None)

Inputs:

fm: CaseFM: Force & moment iterative history
opt: str: Name of option to query
vdef: {None} | object: Default value

Outputs:

v: object: Value of run matrix’s DataBook > fm.comp > opt

Versions:

2025-09-25 @ddalle: v1.0

get_lref(vdef: float = 1.0) → float¶

Get reference length for this component

Call:

>>> lref = fm.get_lref(vdef=1.0)

Inputs:

fm: CaseFM: Force & moment iterative history
vdef: {1.0} | float: Default value if no fm.runner is present

Outputs:

lref: float: Reference length

Versions:

2025-09-26 @ddalle: v1.0

get_mrp(vdef: list = [0.0, 0.0, 0.0]) → list¶

Get moment reference point for this component

Call:

>>> xmrp = fm.get_mrp(vdef=[0.0, 0.0, 0.0])

Inputs:

fm: CaseFM: Force & moment iterative history
vdef: {1.0} | float: Default value if no fm.runner is present

Outputs:

xmrp: [float, float, float]: Reference point

Versions:

2025-09-26 @ddalle: v1.0

get_qbar(vdef: float = 1.0, units: str | None = None) → float¶

Get freestream dynamic pressure

Call:

>>> qref = fm.get_qbar(vdef=1.0, units="mks")

Inputs:

fm: CaseFM: Force & moment iterative history
vdef: {1.0} | float: Default value if no fm.runner is present
units: {None} | str: Units for output (default is raw run matrix value)

Outputs:

qref: float: Dynamic pressure

Versions:

2025-09-26 @ddalle: v1.0

get_rotation_origin() → Point¶

Get rotation origin [history]

Call:

>>> x, y, z = fm.get_rotation_origin()
>>> p = fm.genr8_mrget_rotation_origin_history()

Inputs:

fm: CaseFM: Force & moment iterative history

Outputs:

x: np.ndarray | float: Coordinate of origin of rotation
y: np.ndarray | float: Coordinate of origin of rotation
z: np.ndarray | float: Coordinate of origin of rotation
p: cape.cfdx.casedata.Point: Named tuple object of p.x, p.y, p.z

Versions:

2025-09-26 @ddalle: v1.0

rotate_mrp(x0: float, y0: float, z0: float) → Point¶

Get moment reference point history, w/ motion if applicable

Call:

>>> x, y, z = fm.rotate_mrp(x0, y0, z0)
>>> p = fm.rotate_mrp(x0, y0, z0)

Inputs:

fm: CaseFM: Force & moment iterative history
x0: np.ndarray | float: Coordinate of rotation cnter
y0: np.ndarray | float: Coordinate of rotation cnter
z0: np.ndarray | float: Coordinate of rotation cnter

Outputs:

x: np.ndarray[float]: Iterative history of xMRP
y: np.ndarray[float]: Iterative history of xMRP
z: np.ndarray[float]: Iterative history of xMRP
p: cape.cfdx.casedata.Point: Named tuple object of p.x, p.y, p.z

Versions:

2025-09-26 @ddalle: v1.0

Rotate a point history using phi, theta, psi

No rotations will be performed if Euler angle columns are not present in fm.

Call:

>>> p = fm.rotate_point_history(x, y, z, x0, y0, z0)

Inputs:

fm: CaseFM: Force & moment iterative history
x: np.ndarray | float: Pre-rotation point
y: np.ndarray | float: Pre-rotation point
z: np.ndarray | float: Pre-rotation point coord
x0: np.ndarray | float: Coordinate of rotation cnter
y0: np.ndarray | float: Coordinate of rotation cnter
z0: np.ndarray | float: Coordinate of rotation cnter

Outputs:

p: cape.cfdx.casedata.Point: Named tuple object of p.x, p.y, p.z after rotation

Versions:

2025-09-26 @ddalle: v1.0

shift_mrp_body()¶

Shift moment [coefficient] for moving-body MRP movements

Call:

>>> fm.shift_mrp_body()

Inputs:

fm: CaseFM: Instance of the force and moment class

Versions:

2025-09-26 @ddalle: v1.0

transform_fm321_body()¶

Shift moment [coefficient] for moving-body MRP movements

Call:

>>> fm.shift_mrtransform_fm321_body()

Inputs:

fm: CaseFM: Instance of the force and moment class

Versions:

2025-09-26 @ddalle: v1.0

trim_iters()¶

Trim any repeated iterations from history

Call:

>>> db.trim_iters()

Inputs:

db; cape.cfdx.casedata.CaseData: Single-case iterative history

Versions:

2025-07-31 @ddalle: v1.0

class cape.cfdx.casedata.CasePointProbe(pt: str, runner=None, **kw)¶

class cape.cfdx.casedata.CaseProp(comp: str, **kw)¶

class cape.cfdx.casedata.CaseResid(meta: bool = False, runner=None, **kw)¶

Iterative residual history class

This class provides an interface to residuals, CPU time, and similar data for a given run directory

Call:

>>> hist = cape.cfdx.databook.CaseResid()

Outputs:

hist: cape.cfdx.databook.CaseResid: Instance of the run history class

GetIterationIndex(i)¶

Return index of a particular iteration in hist.i

If the iteration i is not present in the history, the index of the last available iteration less than or equal to i is returned.

Call:

>>> j = hist.GetIterationIndex(i)

Inputs:

hist: cape.cfdx.databook.CaseResid: Instance of the residual history class
i: int: Iteration number

Outputs:

j: int: Index of last iteration in fm.i less than or equal to i

Versions:

2015-03-06 @ddalle: v1.0
2024-01-10 @ddalle: v1.1; DataKit updates

GetNOrders(nStats: int = 1, col: str | None = None, nLast: int | None = None) → float¶

Get the number of orders of magnitude of residual drop

Call:

>>> nOrders = hist.GetNOrders(nStats=None, col=None)

Inputs:

hist: CaseResid: Single-case residual history instance
nStats: {1} | int: Number of iters to use for averaging the final residual
col: {None} | str: Name of residual to analyze; default from hist._default_resid
nLast: {None} | int: Last iteration to include in window (default -1)

Outputs:

nOrders: {1} | float: Number of orders of magnitude of residual drop

Versions:

2015-01-01 @ddalle: v1.0
2024-01-24 @ddalle: v2.0; generalize w/ DataKit apprch
2025-08-05 @ddalle: v2.1; add nLast

GetNOrdersUnsteady(n=1)¶

Get residual drop magnitude

Call:

>>> nOrders = hist.GetNOrders(n=1)

Inputs:

hist: cape.cfdx.databook.CaseResid: Instance of the DataBook residual history
n: int: Number of iterations to analyze

Outputs:

nOrders: numpy.ndarray[float]: Number of orders of magnitude of unsteady residual drop

Versions:

2015-01-01 @ddalle: First version

PlotL1(n=None, nFirst=None, nLast=None, **kw)¶

Plot the L1 residual

Call:

>>> h = hist.PlotL1(n=None, nFirst=None, nLast=None, **kw)

Inputs:

hist: cape.cfdx.databook.CaseResid: Instance of the DataBook residual history
n: int: Only show the last n iterations
nFirst: int: Plot starting at iteration nStart
nLast: int: Plot up to iteration nLast
FigureWidth: float: Figure width
FigureHeight: float: Figure height

Outputs:

h: dict: Dictionary of figure/plot handles

Versions:

2014-11-12 @ddalle: v1.0
2014-12-09 @ddalle: v1.1; move to AeroPlot
2015-02-15 @ddalle: v1.2; move to databook.Aero
2015-03-04 @ddalle: v1.3; add nStart and nLast
2015-10-21 @ddalle: v1.4; refer to PlotResid()

PlotL2(n=None, nFirst=None, nLast=None, **kw)¶

Plot the L2 residual

Call:

>>> h = hist.PlotL2(n=None, nFirst=None, nLast=None, **kw)

Inputs:

hist: cape.cfdx.databook.CaseResid: Instance of the DataBook residual history
n: int: Only show the last n iterations
nFirst: int: Plot starting at iteration nStart
nLast: int: Plot up to iteration nLast
FigureWidth: float: Figure width
FigureHeight: float: Figure height

Outputs:

h: dict: Dictionary of figure/plot handles

Versions:

2014-11-12 @ddalle: v1.0
2014-12-09 @ddalle: v1.1; move to AeroPlot
2015-02-15 @ddalle: v1.2; move to databook.Aero
2015-03-04 @ddalle: v1.3; add nStart and nLast
2015-10-21 @ddalle: v1.4; refer to PlotResid()

PlotLInf(n=None, nFirst=None, nLast=None, **kw)¶

Plot the L-infinity residual

Call:

>>> h = hist.PlotLInf(n=None, nFirst=None, nLast=None, **kw)

Inputs:

hist: cape.cfdx.databook.CaseResid: Instance of the DataBook residual history
n: int: Only show the last n iterations
nFirst: int: Plot starting at iteration nStart
nLast: int: Plot up to iteration nLast
FigureWidth: float: Figure width
FigureHeight: float: Figure height

Outputs:

h: dict: Dictionary of figure/plot handles

Versions:

2016-02-04 @ddalle: v1.0

PlotResid(c='L1Resid', n=None, nFirst=None, nLast=None, **kw)¶

Plot a residual by name

Call:

>>> h = hist.PlotResid(c='L1Resid', n=None, **kw)

Inputs:

hist: cape.cfdx.databook.CaseResid: Instance of the DataBook residual history
c: str: Name of coefficient to plot
n: int: Only show the last n iterations
PlotOptions: dict: Plot options for the primary line(s)
nFirst: int: Plot starting at iteration nStart
nLast: int: Plot up to iteration nLast
FigureWidth: float: Figure width
FigureHeight: float: Figure height
YLabel: str: Label for y-axis

Outputs:

h: dict: Dictionary of figure/plot handles

Versions:

2014-11-12 @ddalle: v1.0
2014-12-09 @ddalle: v1.1; move to AeroPlot
2015-02-15 @ddalle: v1.2; move to databook.Aero
2015-03-04 @ddalle: v1.3; add nStart and nLast
2015-10-21 @ddalle: v1.4; from PlotL1()
2022-01-28 @ddalle: v1.5; add xcol

find_subiters(col: str = 'i_sub')¶

Find indices of first and last subiter for each whole iter

Call:

>>> maska, maskb = hist.find_subiters(col=None)

Inputs:

hist: CaseResid: Iterative residual history instance
col: {"i_sub"} | str: Name of subiteration col

Outputs:

maska: np.ndarray[int]: Index of first subiter for each whole iter
maskb: np.ndarray[int]: Index of last subiter for each whole iter

Versions:

2024-07-18 @ddalle: v1.0

get_cdbfile() → str¶

Get path to iterative history cache file

Call:

>>> cdbfilename = h.get_cdbfile()

Inputs:

h: CaseData: Single-case iterative history instance

Outputs:

cdbfilename: str: Name of file (extension is .cdb)

Versions:

2024-01-22 @ddalle: v1.0

class cape.cfdx.casedata.CaseSurfCp(comp: str, compids: List[str | int], ftriq: str, cntl: CntlBase, i: int)¶

Surface pressure coefficient

This class contains methods for reading data about an the histroy of an individual component for a single case.

Call:

>>> fm = cape.cfdx.databook.CaseSurfCp()

Outputs:

cp: cape.aero.CaseSurfCp: Instance of the surface pressure class
cp[“casenames”]: list: List of casenames of saved case data
cp[“x”]: np.ndarray: X-coord of surface pressure points (global)
cp[“casename.x”]: np.ndarray: X-coord of surface pressure points (case specific)
cp[“casename.cp”]: np.ndarray: Surface pressure points (case specific)

Versions:

2025-09-26 @aburkhea: Started

delete_datafile(fname, casename)¶

Delete surfcp data capefile

Call:

>>> runner.write_datafile(casename)

Inputs:

runner: CaseRunner: Controller to run one case of solver
fname: str: Name of data file to write
casename: str: Name of case

Versions:

2026-01-05 @aburkhea: v1.0

get_conditions() → dict¶

Get the freestream conditions needed for forces

Call:

>>> xi = db.get_conditions()

Inputs:

db: CaseTriqFM: Interface to TriqFM data for one component of one case

Outputs:

xi: dict: Conditions, incl. Mach (mach), Reynolds number (Re)

Versions:

2017-03-28 @ddalle: v1.0
2025-08-13 @ddalle: v1.1 (dex)

get_configfile() → str¶

Find configuration file for .triq file

Call:

>>> fcfg = db.get_configfile()

Inputs:

db: CaseTriqFM: Interface to TriqFM data for one component of one case

Outputs:

fcfg: str: Name of XML/JSON/MIXSUR config file

Versions:

2025-08-13 @ddalle: v1.0

get_triq_surfcp() → dict¶

Get the surfcp on one comp

Call:

>>> fm = db.get_triq_surfcp(**kw)

Inputs:

db: CaseTriqFM: Interface to TriqFM data for one component of one case
comp: str: Name of comp

Outputs:

fm: dict[float]: Dictionary of force & moment coefficients

Versions:

2017-03-28 @ddalle: v1.0
2025-08-13 @ddalle: v2.0 (dex)

map_tri_compid()¶

Perform any component ID mapping if necessary

Call:

>>> triq = db.map_tri_compid()

Inputs:

db: CaseTriqFM: Interface to TriqFM data for one component of one case

Attributes:

DBF.compmap: dict: Map of component numbers altered during the mapping

Versions:

2017-03-28 @ddalle: v1.0

read_tri_map() → Tri¶

Read the surface triangulation mapping file

This file defines the patches for the TriqFM output.

Call:

>>> triq = db.read_triq()

Inputs:

db: CaseTriqFM: Interface to TriqFM data for one component of one case

Outputs:

triq: cape.trifile.Triq: Triangulated surface data (also stored in db.triq)

Versions:

2025-08-13 @ddalle: v1.0

read_triq() → Triq¶

Read the surface solution data from a .triq file

Call:

>>> triq = db.read_triq()

Inputs:

db: CaseTriqFM: Interface to TriqFM data for one component of one case

Outputs:

triq: cape.trifile.Triq: Triangulated surface data (also stored in db.triq)

Versions:

2025-08-13 @ddalle: v1.0

write_datafile(fname, casename)¶

Write surfcp data capefile

Defaults to global x,y,z and case specific cp. When writing new case checks if global x,y,z exists then compares it to case x,y,z. Will switch to case x,y,z if they are different. Once case specific x,y,z started, will then always write case specific x,y,z.

Call:

>>> runner.write_datafile(casename)

Inputs:

runner: CaseRunner: Controller to run one case of solver
fname: str: Name of data file to write
casename: str: Name of case

Versions:

2025-09-26 @aburkhea: v1.0

write_dbook_cdb(fname)¶

Write contents of history to .cdb file

Call:

>>> fm.write_dbook_cdb(fname)

Inputs:

fm: CaseData: Iterative history instance

Versions:

2024-10-09 @aburkhea: v1.0

write_surf(**kw)¶

Write mapped solution as TRIQ or Tecplot file with zones

Call:

>>> db.write_triq(**kw)

Inputs:

db: CaseTriqFM: Interface to TriqFM data for one component of one case

Versions:

2017-03-30 @ddalle: v1.0 (TriqFMDataBook)
2025-08-13 @ddalle: v2.0 (dex)

class cape.cfdx.casedata.CaseTS(comp: str, **kw)¶

Force and moment time series iterative histories

This class contains methods for reading data about an the histroy of an individual component for a single case.

Call:

>>> fm = cape.cfdx.databook.CaseFM(C, MRP=None, A=None)

Inputs:

C: list (str): List of coefficients to initialize
MRP: numpy.ndarray[float] shape=(3,): Moment reference point
A: numpy.ndarray shape=(N,4) or shape=(N,7): Matrix of forces and/or moments at N iterations

Outputs:

fm: cape.aero.FM: Instance of the force and moment class
fm.coeffs: list (str): List of coefficients
fm.MRP: numpy.ndarray[float] shape=(3,): Moment reference point

Versions:

2024-10-09 @aburkhea: Started

get_tend(tcol)¶

Get end time in time series case data

Call:

>>> fm.get_tend

Inputs:

fm: CaseData: Iterative history instance

Versions:

2024-10-10 @aburkhea: v1.0

write_dbook_cdb(fname)¶

Write contents of history to .cdb file

Call:

>>> fm.write_dbook_cdb(fname)

Inputs:

fm: CaseData: Iterative history instance

Versions:

2024-10-09 @aburkhea: v1.0

class cape.cfdx.casedata.DBPlotOpts(*args, **kw)¶

class cape.cfdx.casedata.FMPair(F, M)¶

Pair for force and moment vector [histories], F, M

F¶: Alias for field number 0

M¶: Alias for field number 1

cape.cfdx.casedata.ImportPyPlot()¶

Import matplotlib.pyplot if not already loaded

Call:

>>> ImportPyPlot()

Versions:

2014-12-27 @ddalle: v1.0

class cape.cfdx.casedata.Pair(a, b)¶

Tuple of two components, e.g. AoA and AoS, a, b

a¶: Alias for field number 0

b¶: Alias for field number 1

class cape.cfdx.casedata.Point(x, y, z)¶

Tuple of coordinates, x, y, z

x¶: Alias for field number 0

y¶: Alias for field number 1

z¶: Alias for field number 2

class cape.cfdx.casedata.WindowRank(linear_fit_a1, trend_fit_a1, autocorrelation, sinusoid_amplitude)¶

Metrics used to rank candidate statistics windows

autocorrelation¶: Alias for field number 2

linear_fit_a1¶: Alias for field number 0

sinusoid_amplitude¶: Alias for field number 3

trend_fit_a1¶: Alias for field number 1

cape.cfdx.casedata.autocorr(v: ndarray, t: ndarray | None = None, n: int = 100) → Tuple[float, float]¶

Calculate autocorrelation of iterative history with offsets

Call:

>>> dt, r = autocorr(v, t=None, n=100)

Inputs:

v: np.ndarray[float]: Iterative history
t: {None} | np.ndarray: Optional time history (or just use iteration number)
n: {100} | int: Number of offsets to analyze

Outputs:

dt: np.ndarray[float]: Time/iteration shifts
r: np.ndarray[float]: Autocorrelation at each shift

Versions:

2026-06-14 @ddalle: v1.0

cape.cfdx.casedata.find_autocorr_peaks(v: ndarray, polyorder: int = 3, window_length: int = 10) → tuple¶

Find autocorrelation peaks; ensure alternating anticorr/corr

Call:

>>> jlo, vlo, jhi, vhi = find_autocorr_peaks(v, **kw)

Inputs:

v: :np.ndarray`[float]: Iterative history
polyorder: {3} | int: Polynomial order for filtering
window_length: {10} | int: Filter window width

Outputs:

jlo: np.ndarray[int]: Indices of local minima
vlo: np.ndarray[float]: Lightly filetered values of v at local minima
jhi: np.ndarray[int]: Indices of local maxima
vhi: np.ndarray[float]: Lightly filtered values of v at local maxima

Versions:

2026-06-17 @ddalle: v1.0

cape.cfdx.casedata.find_max_autocorr(v: ndarray, polyorder: int = 3, window_length: int = 10) → tuple¶

Find the maximum shifted autocorrelation peak

Call:

>>> j, vmax = find_max_autocorr(v, **kw)

Inputs:

v: :np.ndarray`[float]: Autocorrelation signal
polyorder: {3} | int: Polynomial order for filtering
window_length: {10} | int: Filter window width

Outputs:

j: int | None: Index of maximum shifted autocorrelation peak
vmax: float | None: Lightly filtered value of v at j

Versions:

2026-09-03 @openai: v1.0

cape.cfdx.casedata.find_peaks_filtered(v: ndarray, polyorder: int = 3, window_length: int = 10)¶

Find peaks of smoothed iterative history

Call:

>>> jlo, vlo, jhi, vhi = find_pkeaks_filtered(v, **kw)

Inputs:

v: :np.ndarray`[float]: Iterative history
polyorder: {3} | int: Polynomial order for filtering
window_length: {10} | int: Filter window width

Outputs:

jlo: np.ndarray[int]: Indices of local minima
vlo: np.ndarray[float]: Lightly filetered values of v at local minima
jhi: np.ndarray[int]: Indices of local maxima
vhi: np.ndarray[float]: Lightly filtered values of v at local maxima

Versions:

2026-06-15 @ddalle: v1.0
2026-09-06 @ddalle: v1.1; default for short histories

`cape.cfdx.casedata`: Single-case data interfaces¶

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cape.cfdx.casedata: Single-case data interfaces¶

`cape.cfdx.casedata`: Single-case data interfaces¶