Data

The main entry point for the data is the Measurement class. These can be loaded from a .pssession file or directly returned as a result from an expirement.

These classes are wrappers for the underlying .NET code. Each Python wrapper in this module holds a reference to the underlying .NET SDK object, usually prefixed ps... like Measurement.psmeasurement. These .NET classes are instantiated by the measurement or data loading code. These wrappers are intended to be used for data processing and exploration and not to be directly instantiated.

The raw data is stored in a DataSet under Measurement.dataset. A dataset in turn consist of a series of `DataArray’`s. These would be analogous to the ’Data’ tab in the PSTrace software. A data array would be the equivalent of a column, with a title, array type, and units.

The Curve objects (retrieved via Measurement.curves) are interpretations of the data, much like the plots in the PSTrace software. These can be used for plots or data processing like smoothing the data and peak finding.

`pypalmsens.data`

This module contains the public api for classes representing measurement data.

Classes:

ArrayType – Data array type for standard arrays.
DataArray – Python wrapper for .NET DataArray class.
Peak – Python wrapper for .NET Peak class.
DeviceInfo – Dataclass for device information.
Curve – Python wrapper for .NET Curve class.
Measurement – Python wrapper for .NET Measurement class.
DataSet – Python wrapper for .NET DataSet class.
EISData – Python wrapper for .NET EISdata class.

`ArrayType`

Data array type for standard arrays.

Attributes:

Unspecified – Unspecified
Time – Time / s
Potential – Potential / V
Current – Current / μA
Charge – Charge
ExtraValue – ExtraValue
Frequency – Frequency
Phase – Phase
ZRe – Z real
ZIm – Z imaginary
Iac – I AC values
Z – Z
Y – Y
YRe – Y real
YIm – Y imaginary
Cs – Cs
CsRe – Cs real
CsIm – Cs imaginary
Index – Index
Admittance – Admittance
Concentration – Concentration
Signal – Signal
Func – Func
Integral – Integral
AuxInput – Auxillary input
BipotCurrent – Bipot current
BipotPotential – Bipot potential
ReverseCurrent – Reverse current
CEPotential – CE potential
DCCurrent – DC current
ForwardCurrent – Forward current
PotentialExtraRE – Potential setpoint measured back on RE
CurrentExtraWE – Current setpoint measured back on WE
InverseDerative_dtdE – Inverse derivative dt/dE
mEdc – Measured applied DC
Eac – E AC values
MeasuredStepStartIndex – MeasuredStepStartIndex
miDC – Measured I DC values
SE2vsXPotential – SE2 vs XPotential

`Unspecified`

Unspecified = -1

Unspecified

`Time`

Time = 0

Time / s

`Potential`

Potential = 1

Potential / V

`Current`

Current = 2

Current / μA

`Charge`

Charge = 3

Charge

`ExtraValue`

ExtraValue = 4

ExtraValue

`Frequency`

Frequency = 5

Frequency

`Phase`

Phase = 6

Phase

`ZRe`

ZRe = 7

Z real

`ZIm`

ZIm = 8

Z imaginary

`Iac`

Iac = 9

I AC values

`Z`

Z = 10

`Y`

Y = 11

`YRe`

YRe = 12

Y real

`YIm`

YIm = 13

Y imaginary

`Cs`

Cs = 14

`CsRe`

CsRe = 15

Cs real

`CsIm`

CsIm = 16

Cs imaginary

`Index`

Index = 17

Index

`Admittance`

Admittance = 18

Admittance

`Concentration`

Concentration = 19

Concentration

`Signal`

Signal = 20

Signal

`Func`

Func = 21

Func

`Integral`

Integral = 22

Integral

`AuxInput`

AuxInput = 23

Auxillary input

`BipotCurrent`

BipotCurrent = 24

Bipot current

`BipotPotential`

BipotPotential = 25

Bipot potential

`ReverseCurrent`

ReverseCurrent = 26

Reverse current

`CEPotential`

CEPotential = 27

CE potential

`DCCurrent`

DCCurrent = 28

DC current

`ForwardCurrent`

ForwardCurrent = 29

Forward current

`PotentialExtraRE`

PotentialExtraRE = 30

Potential setpoint measured back on RE

`CurrentExtraWE`

CurrentExtraWE = 31

Current setpoint measured back on WE

`InverseDerative_dtdE`

InverseDerative_dtdE = 32

Inverse derivative dt/dE

`mEdc`

mEdc = 33

Measured applied DC

`Eac`

Eac = 34

E AC values

`MeasuredStepStartIndex`

MeasuredStepStartIndex = 35

MeasuredStepStartIndex

`miDC`

miDC = 36

Measured I DC values

`SE2vsXPotential`

SE2vsXPotential = 37

SE2 vs XPotential

`DataArray`

DataArray(*, psarray)

Python wrapper for .NET DataArray class.

Parameters:

psarray – Reference to .NET DataArray object.

Functions:

copy – Return a copy of the array.
min – Return min value.
max – Return max value.
savitsky_golay – Smooth the array using a Savitsky-Golay filter with the window size.
to_numpy – Export data array to numpy.
to_list – Export data array to list.

Attributes:

name (str) – Name of the array.
type (ArrayType) – ArrayType enum.
unit (str) – Unit for array.
quantity (str) – Quantity for array.
ocp_value (float) – OCP Value.

`name`

name: str

Name of the array.

`type`

type: ArrayType

ArrayType enum.

`unit`

unit: str

Unit for array.

`quantity`

quantity: str

Quantity for array.

`ocp_value`

ocp_value: float

OCP Value.

`copy`

copy()

Return a copy of the array.

`min`

min()

Return min value.

`max`

max()

Return max value.

`savitsky_golay`

savitsky_golay(window_size=3)

Smooth the array using a Savitsky-Golay filter with the window size.

(i.e. window size 2 will filter points based on the values of the next/previous 2 points)

Parameters:

window_size (int) – Size of the window

`to_numpy`

to_numpy()

Export data array to numpy.

`to_list`

to_list()

Export data array to list.

`Peak`

Peak(*, pspeak)

Python wrapper for .NET Peak class.

Parameters:

pspeak (Peak) – Reference to .NET Peak object.

Attributes:

curve (Curve) – Parent curve associated with Peak.
curve_title (str) – Title of parent curve.
x_unit (str) – Units of X axis
y_unit (str) – Units for Y axis
analyte_name (str) – Name of analyte.
area (float) – Area of the peak.
label (str) – Formatted label for the peak value.
left_index (int) – Left side of the peaks baseline as index number of the curve.
left_x (float) – X of the left side of the peak baseline.
left_y (float) – Y of the left side of the peak baseline.
maximum_of_derivative_neg (float) – Maximum derivative of the negative slope of the peak.
maximum_of_derivative_pos (float) – Maximum derivative of the positive slope of the peak.
maximum_of_derivative_sum (float) – Sum of the absolute values for both the positive and negative maximum derivative.
notes (str) – User notes stored on this peak.
y_offset (float) – Offset of Y.
index (int) – Location of the peak as index number of the curve.
type (str) – Used to determine if a peak is auto found.
value (float) – Value of the peak in units of the curve.
x (float) – X value of the peak.
y (float) – Y value of the peak.
right_index (int) – Left side of the peaks baseline as index number of the curve.
right_x (float) – X of the right side of the peak baseline.
right_y (float) – Returns the Y of the right side of the peak baseline.
width (float) – Full width at half-height of the peak.

`curve`

curve: Curve

Parent curve associated with Peak.

`curve_title`

curve_title: str

Title of parent curve.

`x_unit`

x_unit: str

Units of X axis

`y_unit`

y_unit: str

Units for Y axis

`analyte_name`

analyte_name: str

Name of analyte.

`area`

area: float

Area of the peak.

`label`

label: str

Formatted label for the peak value.

`left_index`

left_index: int

Left side of the peaks baseline as index number of the curve.

`left_x`

left_x: float

X of the left side of the peak baseline.

`left_y`

left_y: float

Y of the left side of the peak baseline.

`maximum_of_derivative_neg`

maximum_of_derivative_neg: float

Maximum derivative of the negative slope of the peak.

`maximum_of_derivative_pos`

maximum_of_derivative_pos: float

Maximum derivative of the positive slope of the peak.

`maximum_of_derivative_sum`

maximum_of_derivative_sum: float

Sum of the absolute values for both the positive and negative maximum derivative.

`notes`

notes: str

User notes stored on this peak.

`y_offset`

y_offset: float

Offset of Y.

`index`

index: int

Location of the peak as index number of the curve.

`type`

type: str

Used to determine if a peak is auto found.

`value`

value: float

Value of the peak in units of the curve. This is the value of the peak height relative to the baseline of the peak.

`x`

x: float

X value of the peak.

`y`

y: float

Y value of the peak.

`right_index`

right_index: int

Left side of the peaks baseline as index number of the curve.

`right_x`

right_x: float

X of the right side of the peak baseline.

`right_y`

right_y: float

Returns the Y of the right side of the peak baseline.

`width`

width: float

Full width at half-height of the peak.

`DeviceInfo`

DeviceInfo(type, firmware, serial, id)

Dataclass for device information.

Attributes:

type (str) – Device type
firmware (str) – Firmware version
serial (str) – Serial number
id (int) – Device ID

`type`

type: str

Device type

`firmware`

firmware: str

Firmware version

`serial`

serial: str

Serial number

`id`

id: int

Device ID

`Curve`

Curve(*, pscurve)

Python wrapper for .NET Curve class.

Parameters:

pscurve (Curve) – Reference to .NET curve object.

Functions:

copy – Return a copy of this curve.
smooth – Smooth the .y_array using a Savitsky-Golay filter with the specified smooth
savitsky_golay – Smooth the .y_array using a Savitsky-Golay filter with the specified window
find_peaks – Find peaks in a curve in all directions.
find_peaks_semiderivative – Find peaks in a curve using the semi-derivative algorithm.
clear_peaks – Clear peaks stored on object.
linear_slope – Calculate linear line parameters for this curve between two indexes.
plot – Generate simple plot for this curve using matplotlib.

Attributes:

max_x (float) – Maximum X value found in this curve.
max_y (float) – Maximum Y value found in this curve.
min_x (float) – Minimum X value found in this curve.
min_y (float) – Minimum Y value found in this curve.
mux_channel (int) – The corresponding MUX channel number with the curve starting at 0.
n_points (int) – Number of points for this curve.
ocp_value (float) – OCP value for curve.
reference_electrode_name (link:#typing.Union[None, link:#str]) – The name of the reference electrode. Return None if not set.
reference_electrode_potential (link:#typing.Union[None, link:#str]) – The reference electrode potential offset. Return None if not set.
x_unit (str) – Units for X dimension.
x_label (str) – Label for X dimension.
y_unit (str) – Units for Y dimension.
y_label (str) – Label for Y dimension.
z_unit (link:#typing.Union[None, link:#str]) – Units for Z dimension. Returns None if not set.
z_label (link:#typing.Union[None, link:#str]) – Units for Z dimension. Returns None if not set.
title (str) – Title for the curve.
peaks (link:#list[link:#pypalmsens._data.peak.Peak]) – Return peaks stored on object.
x_array (DataArray) – Y data for the curve.
y_array (DataArray) – Y data for the curve.

`max_x`

max_x: float

Maximum X value found in this curve.

`max_y`

max_y: float

Maximum Y value found in this curve.

`min_x`

min_x: float

Minimum X value found in this curve.

`min_y`

min_y: float

Minimum Y value found in this curve.

`mux_channel`

mux_channel: int

The corresponding MUX channel number with the curve starting at 0. Return -1 when no MUX channel used.

`n_points`

n_points: int

Number of points for this curve.

`ocp_value`

ocp_value: float

OCP value for curve.

`reference_electrode_name`

reference_electrode_name: Union[None, str]

The name of the reference electrode. Return None if not set.

`reference_electrode_potential`

reference_electrode_potential: Union[None, str]

The reference electrode potential offset. Return None if not set.

`x_unit`

x_unit: str

Units for X dimension.

`x_label`

x_label: str

Label for X dimension.

`y_unit`

y_unit: str

Units for Y dimension.

`y_label`

y_label: str

Label for Y dimension.

`z_unit`

z_unit: Union[None, str]

Units for Z dimension. Returns None if not set.

`z_label`

z_label: Union[None, str]

Units for Z dimension. Returns None if not set.

`title`

title: str

Title for the curve.

`peaks`

peaks: list[Peak]

Return peaks stored on object.

`x_array`

x_array: DataArray

Y data for the curve.

`y_array`

y_array: DataArray

Y data for the curve.

`copy`

copy()

Return a copy of this curve.

`smooth`

smooth(smooth_level=0)

Smooth the .y_array using a Savitsky-Golay filter with the specified smooth level.

Parameters:

smooth_level (int) – The smooth level to be used. -1 = none, 0 = no smooth (spike rejection only), 1 = 5 points, 2 = 9 points, 3 = 15 points, 4 = 25 points

`savitsky_golay`

savitsky_golay(window_size=3)

Smooth the .y_array using a Savitsky-Golay filter with the specified window size.

(i.e. window size 2 will filter points based on the values of the next/previous 2 points)

Parameters:

window_size (int) – Size of the window

`find_peaks`

find_peaks(min_peak_width=0.1, min_peak_height=0.0, peak_shoulders=False, merge_overlapping_peaks=True)

Find peaks in a curve in all directions.

CV can have 1 or 2 direction changes.

Parameters:

min_peak_width (float) – Minimum width of the peak in V
min_peak_height (float) – Minimum height of the peak in uA
peak_shoulders (bool) – Use alternative peak search algorithm optimized for finding peaks on slopes
merge_overlapping_peaks (bool) – Two or more peaks that overlap will be identified as a single base peak and as shoulder peaks on the base peak.

Returns:

peak_list (link:#list[link:#pypalmsens._data.peak.Peak]) –

`find_peaks_semiderivative`

find_peaks_semiderivative(min_peak_height=0.0)

Find peaks in a curve using the semi-derivative algorithm.

Used for detecting non-overlapping peaks in LSV and CV curves. The peaks are also assigned to the curve, updating Curve.peaks. Existing peaks are overwritten.

For more info, see this Wikipedia page.

Parameters:

min_peak_height (float) – Minimum height of the peak in uA

Returns:

peak_list (link:#list[link:#pypalmsens._data.peak.Peak]) –

`clear_peaks`

clear_peaks()

Clear peaks stored on object.

`linear_slope`

linear_slope(start=None, stop=None)

Calculate linear line parameters for this curve between two indexes.

current = a + b * x

Parameters:

start (int) – begin index
stop (int) – end index

Returns:

a (float) –
b (float) –
coefdet (float) – Coefficient of determination (R2)

`plot`

plot(ax=None, legend=True, **plot_kwargs)

Generate simple plot for this curve using matplotlib.

Parameters:

ax (link:#typing.Optional[link:#matplotlib.axes.Axes]) – Add plot to this ax if specified.
legend (bool) – If True, add legend.
plot_kwargs – These keyword arguments are passed to ax.plot.

Returns:

fig (Figure) – Matplotlib figure. Use fig.show() to render plot.

`Measurement`

Measurement(*, psmeasurement)

Python wrapper for .NET Measurement class.

Parameters:

psmeasurement (Measurement) – Reference to .NET measurement object.

Attributes:

title (str) – Title for the measurement.
timestamp (str) – Date and time of the start of this measurement..
device (DeviceInfo) – Return dataclass with measurement device information.
blank_curve (Curve | None) – Blank curve.
has_blank_subtracted_curves (bool) – Return True if the curve collection contains a blank subtracted curve.
has_eis_data (bool) – Return True if EIS data are is available.
dataset (DataSet) – Dataset containing multiple arrays of values.
eis_data (link:#list[link:#pypalmsens._data.eisdata.EISData]) – EIS data in measurement.
method (Method) – Method related with this Measurement.
channel (float) – Get the channel that the measurement was measured on.
ocp_value (float) – First OCP Value from either curves or EISData.
n_curves (int) – Number of curves that are part of the Measurement class.
n_eis_data (int) – Number of EISdata curves (channels) that are part of the Measurement class.
peaks (link:#list[link:#pypalmsens._data.peak.Peak]) – Get peaks from all curves.
eis_fit (link:#list[link:#pypalmsens._fitting.FitResult]) – Get all EIS fits from measurement
curves (link:#list[link:#pypalmsens._data.curve.Curve]) – Get all curves in measurement.

`title`

title: str

Title for the measurement.

`timestamp`

timestamp: str

Date and time of the start of this measurement..

`device`

device: DeviceInfo

Return dataclass with measurement device information.

`blank_curve`

blank_curve: Curve | None

Blank curve.

if Blank curve is present (not null) a new curve will be added after each measurement containing the result of the measured curve subtracted with the Blank curve.

`has_blank_subtracted_curves`

has_blank_subtracted_curves: bool

Return True if the curve collection contains a blank subtracted curve.

`has_eis_data`

has_eis_data: bool

Return True if EIS data are is available.

`dataset`

dataset: DataSet

Dataset containing multiple arrays of values.

All values are related by means of their indices. Data arrays in a dataset should always have an equal amount of entries.

`eis_data`

eis_data: list[EISData]

EIS data in measurement.

`method`

method: Method

Method related with this Measurement.

The information from the Method is used when saving Curves.

`channel`

channel: float

Get the channel that the measurement was measured on.

`ocp_value`

ocp_value: float

First OCP Value from either curves or EISData.

`n_curves`

n_curves: int

Number of curves that are part of the Measurement class.

`n_eis_data`

n_eis_data: int

Number of EISdata curves (channels) that are part of the Measurement class.

`peaks`

peaks: list[Peak]

Get peaks from all curves.

Returns:

peaks (link:#list[link:#pypalmsens._data.peak.Peak]) – List of peaks

`eis_fit`

eis_fit: list[FitResult]

Get all EIS fits from measurement

Returns:

eis_fits (link:#list[link:#EISFitResults]) – Return list of EIS fits

`curves`

curves: list[Curve]

Get all curves in measurement.

Returns:

curves (link:#list[link:#pypalmsens._data.curve.Curve]) – List of curves

`DataSet`

DataSet(*, psdataset)

Python wrapper for .NET DataSet class.

Parameters:

psdataset (DataSet) – Reference to .NET DataSet object.

Functions:

curve – Construct a custom curve from x and y keys.
arrays – Return list of all arrays. Alias for .to_list()
hidden_arrays – Return ‘hidden’ arrays used for debugging.
arrays_by_name – Get arrays by name.
arrays_by_quantity – Get arrays by quantity.
arrays_by_type – Get arrays by data type.
current_arrays – Return all Current arrays.
potential_arrays – Return all Potential arrays.
time_arrays – Return all Time arrays.
freq_arrays – Return all Frequency arrays.
zre_arrays – Return all ZRe arrays.
zim_arrays – Return all ZIm arrays.
aux_input_arrays – Return all AuxInput arrays.
current_range – Return current range as list of strings.
reading_status – Return reading status as list of strings.
timing_status – Return timing status as list of strings.
to_dataframe – Return dataset as pandas dataframe.

Attributes:

array_types (link:#set[link:#pypalmsens._data._shared.ArrayType]) – Return unique set of array type (enum) for arrays in dataset.
array_names (link:#set[link:#str]) – Return unique set of names for arrays in dataset.
array_quantities (link:#set[link:#str]) – Return unique set of quantities for arrays in dataset.

`array_types`

array_types: set[ArrayType]

Return unique set of array type (enum) for arrays in dataset.

`array_names`

array_names: set[str]

Return unique set of names for arrays in dataset.

`array_quantities`

array_quantities: set[str]

Return unique set of quantities for arrays in dataset.

`curve`

curve(x, y, title=None)

Construct a custom curve from x and y keys.

Parameters:

x (str) – Key identifying the x array
y (str) – Key identifying the y array
title (str) – Set the title. If None, use the \(x-\)y as title

Returns:

curve (Curve) – New Curve with plotting x against y

`arrays`

arrays()

Return list of all arrays. Alias for .to_list()

`hidden_arrays`

hidden_arrays()

Return ‘hidden’ arrays used for debugging.

`arrays_by_name`

arrays_by_name(name)

Get arrays by name.

Parameters:

name (str) – Name of the array.

Returns:

arrays (link:#list[link:#pypalmsens._data.data_array.DataArray]) –

`arrays_by_quantity`

arrays_by_quantity(quantity)

Get arrays by quantity.

Parameters:

quantity (str) – Quantity of the array.

Returns:

arrays (link:#list[link:#pypalmsens._data.data_array.DataArray]) –

`arrays_by_type`

arrays_by_type(array_type)

Get arrays by data type.

Parameters:

array_type (str) – Type of the array.

Returns:

arrays (link:#list[link:#pypalmsens._data.data_array.DataArray]) –

`current_arrays`

current_arrays()

Return all Current arrays.

`potential_arrays`

potential_arrays()

Return all Potential arrays.

`time_arrays`

time_arrays()

Return all Time arrays.

`freq_arrays`

freq_arrays()

Return all Frequency arrays.

`zre_arrays`

zre_arrays()

Return all ZRe arrays.

`zim_arrays`

zim_arrays()

Return all ZIm arrays.

`aux_input_arrays`

aux_input_arrays()

Return all AuxInput arrays.

`current_range`

current_range()

Return current range as list of strings.

`reading_status`

reading_status()

Return reading status as list of strings.

`timing_status`

timing_status()

Return timing status as list of strings.

`to_dataframe`

to_dataframe()

Return dataset as pandas dataframe.

`EISData`

EISData(*, pseis)

Python wrapper for .NET EISdata class.

Parameters:

pseis – Reference to .NET EISdata object.

Functions:

get_data_for_frequency – Returns dictionary with data per frequency.
arrays – Complete list of data arrays.
current_range – Current ranges for the measurement.

Attributes:

title (str) – Tite for EIS data.
frequency_type (str) – Frequency type.
scan_type (str) – Scan type.
dataset (DataSet) – Dataset which contains multiple arrays of values.
subscans (link:#list[link:#pypalmsens._data.eisdata.EISData]) – Get list of subscans.
n_points (int) – Number of points (including subscans).
n_frequencies (int) – Number of frequencies.
n_subscans (int) – Number of subscans.
x_unit (str) – Unit for array.
x_quantity (str) – Quantity for array.
ocp_value (float) – OCP Value.
has_subscans (bool) – Return True if data contains subscans.
mux_channel (int) – Mux channel.
cdc (str) – Gets the CDC circuit for fitting.
cdc_values (link:#list[link:#float]) – Return values for circuit description code (CDC).

`title`

title: str

Tite for EIS data.

`frequency_type`

frequency_type: str

Frequency type.

`scan_type`

scan_type: str

Scan type.

`dataset`

dataset: DataSet

Dataset which contains multiple arrays of values.

`subscans`

subscans: list[EISData]

Get list of subscans.

`n_points`

n_points: int

Number of points (including subscans).

`n_frequencies`

n_frequencies: int

Number of frequencies.

`n_subscans`

n_subscans: int

Number of subscans.

`x_unit`

x_unit: str

Unit for array.

`x_quantity`

x_quantity: str

Quantity for array.

`ocp_value`

ocp_value: float

OCP Value.

`has_subscans`

has_subscans: bool

Return True if data contains subscans.

`mux_channel`

mux_channel: int

Mux channel.

`cdc`

cdc: str

Gets the CDC circuit for fitting.

`cdc_values`

cdc_values: list[float]

Return values for circuit description code (CDC).

`get_data_for_frequency`

get_data_for_frequency(frequency)

Returns dictionary with data per frequency.

Parameters:

frequency (int) – Index of the frequency to retrieve the data for.

Returns:

[link:#str">str, link:#pypalmsens._data.data_array.DataArray] – Data are returned as a dictionary keyed by the data type.

`arrays`

arrays()

Complete list of data arrays.

`current_range`

current_range()

Current ranges for the measurement.