Data I/O

Excel file format

Each sheet of the workbook must have exactly four columns and a header row:

Column	Content	Unit
1	Temperature T	°C
2	Frequency f	Hz
3	Complex modulus norm \|E*\|	Pa or MPa
4	Phase angle φ	degrees (°)

Isotherm layout (default): rows are grouped by temperature — all frequencies for one temperature appear consecutively.

Isochrone layout (Iso=true): rows are grouped by frequency — all temperatures for one frequency appear consecutively.

Multiple sheets in the same workbook are each loaded as an independent DataSeries.

Types

ViscoAnalysis.DataSeries — Type

DataSeries

Container for complex-modulus measurements read from one Excel sheet.

Fields

data — Dict keyed by temperature (°C) or frequency (Hz in Iso mode). Each entry contains vectors for "f", "omega", "|M*|", "delta", "M*", "M*_1", "M*_2".
MPa — true if modulus values are already in MPa (no unit conversion).
Iso — true if data is organised by frequency (isochrone layout).
name — Sheet name from the workbook.
nb_sheet — Total number of sheets in the workbook.
list_temp — Sorted vector of temperatures (°C).
list_omega — Sorted vector of angular frequencies (rad/s).
list_freq — Sorted vector of frequencies (Hz), or same as list_omega in non-Iso mode.
wlf — Fitted WLF function T -> log10(aT), set after calling build_shift_factors!.
C1, C2 — WLF coefficients at the chosen Tref.
_wlf, _C1, _C2 — Internal WLF fit at the first temperature (used when Tref is not in list_temp).

source

ViscoAnalysis.AbstractFitResult — Type

AbstractFitResult

Abstract supertype for all rheological model fit results. Every concrete subtype is callable: result(ω) returns E*(iω).

Concrete subtypes: FitResult2S2P1D, FitResult1S2P1D, FitResultHS.

source

ViscoAnalysis.FitResult2S2P1D — Type

FitResult2S2P1D <: AbstractFitResult

Result of fitting the 2S2P1D model (2 Springs, 2 Parabolic, 1 Dashpot).

Fields

Einf, E0 — Glassy / static modulus [MPa].
delta — δ shape parameter.
tauE — Relaxation time τ_E [s].
k, h — Exponents (0 < k < h ≤ 1).
beta — β dashpot coefficient.
residual — Weighted least-squares objective at optimum.
model — E*(p) function (callable).

r = fit2S2P1D(series[1], 10.0)
r(2π * 10.0)   # E* at 10 Hz

source

ViscoAnalysis.FitResult1S2P1D — Type

FitResult1S2P1D <: AbstractFitResult

Result of fitting the 1S2P1D model (no glassy spring; E∞ is implicitly ∞).

Fields

E0 — Static modulus [MPa].
delta — δ shape parameter.
tauE — Relaxation time τ_E [s].
k, h — Exponents (0 < k < h ≤ 1).
beta — β dashpot coefficient.
residual — Weighted least-squares objective at optimum.
model — E*(p) function (callable).

r = fit1S2P1D(series[1], 10.0)
r(2π * 10.0)

source

ViscoAnalysis.FitResultHS — Type

FitResultHS <: AbstractFitResult

Result of fitting the Huet-Sayegh model (no dashpot; β → ∞).

Fields

Einf, E0 — Glassy / static modulus [MPa].
delta — δ shape parameter.
tauE — Relaxation time τ_E [s].
k, h — Exponents (0 < k < h ≤ 1).
residual — Weighted least-squares objective at optimum.
model — E*(p) function (callable).

r = fitHS(series[1], 10.0)
r(2π * 10.0)

source

ViscoAnalysis.model_name — Function

model_name(r::AbstractFitResult) -> String

Return the name of the rheological model used for r.

source

Functions

ViscoAnalysis.load_data — Function

load_data(filename; MPa=false, Iso=false) -> Vector{DataSeries}

Read all sheets of an Excel workbook and return one DataSeries per sheet.

Arguments

filename — Path to the .xlsx file.
MPa — Set true if the modulus column is already in MPa; otherwise values are assumed to be in Pa and converted (÷ 10⁶).
Iso — Set true for an isochrone layout where rows are indexed by frequency instead of temperature.

Excel format expected

Column 1	Column 2	Column 3	Column 4
T (°C)	f (Hz)	E* norm (Pa/MPa)	φ (degrees)

All sheets in the workbook must share the same layout.

Example

using ViscoAnalysis
series = load_data("EBR_T0_T10.xlsx"; MPa=true)
series[1]          # first sheet
series[1].list_temp   # available temperatures

source

DataSeries — field reference

After calling load_data the returned DataSeries objects have the following fields:

Field	Type	Description
`name`	`String`	Sheet name from the workbook
`MPa`	`Bool`	`true` if modulus is stored in MPa
`Iso`	`Bool`	`true` if data is in isochrone layout
`list_temp`	`Vector{Float64}`	Sorted unique temperatures [°C]
`list_freq`	`Vector{Float64}`	Sorted unique frequencies [Hz]
`list_omega`	`Vector{Float64}`	Sorted unique angular frequencies [rad/s]
`wlf`	`Union{Nothing, Function}`	Fitted WLF function `T → log₁₀(aT)`
`C1`, `C2`	`Float64`	WLF coefficients at the chosen `Tref`

Each temperature entry d[T] is a Dict{String, Any} with keys:

Key	Description
`"f"`	Frequencies [Hz]
`"omega"`	Angular frequencies [rad/s]
`"\|M*\|"`	Modulus norm [MPa]
`"delta"`	Phase angle [°]
`"M*"`	Complex modulus [MPa]
`"M*_1"`	Storage modulus E₁ [MPa]
`"M*_2"`	Loss modulus E₂ [MPa]

Dict-like access

DataSeries supports a dict-like interface:

d = series[1]
d[10.0]              # entry at T = 10 °C
haskey(d, 10.0)      # check if temperature exists
keys(d)              # all temperatures
length(d)            # number of temperature entries
for (T, entry) in d  # iteration
    ...
end