science pack

50 nodes, in noodlelab[science] and above.

Output

Save CSV

science.save_csv

Write a table into this run’s output folder.

Inputs

Name	Type	Description
table	DataFrame
filename	str	Default 'table.csv'.

Outputs

Name	Type	Description
result	Path

Science/Plot

Bar Chart

science.bar_chart

One bar per row: a value per category, with optional error bars (e.g. mean ± standard error from Group Summary).

Inputs

Name	Type	Description
table	DataFrame
category	str	Default ''.
value	str	Default ''.
error	str	Error bar column Default ''.
horizontal	bool	Default False.
title	str	Default ''.
y_label	str	Default ''.
color_by_sign	bool	Negative bars in another colour Default False.

Outputs

Name	Type	Description
result	Figure

Box Plot

science.box_plot

Distribution per group: the box spans the quartiles, the line is the median, whiskers reach 1.5 IQR; individual values are drawn on top.

Inputs

Name	Type	Description
table	DataFrame
value	str	Default ''.
group	str	One box per group Default ''.
show_points	bool	Default True.
title	str	Default ''.
y_label	str	Default ''.

Outputs

Name	Type	Description
result	Figure

Residual Plot

science.residual_plot

Residuals of a fit against x, with the zero line and a ±2σ band: patterns (curvature, funnels) mean the model misses something.

Inputs

Name	Type	Description
x	NDArray[number]
residuals	NDArray[number]
title	str	Default 'Residuals'.
x_label	str	Default 'x'.
y_label	str	Default 'residual'.

Outputs

Name	Type	Description
result	Figure

Scatter Plot

science.scatter_plot

Two columns of a table against each other, optionally coloured by a third (numbers get a colour bar, categories a legend) and with a least-squares line.

Inputs

Name	Type	Description
table	DataFrame
x	str	Default ''.
y	str	Default ''.
color	str	Colour points by this column Default ''.
fit_line	bool	Default False.
colormap	Literal['viridis', 'plasma', 'cividis', 'magma', 'coolwarm', 'RdBu_r', 'YlOrRd', 'Blues']	Default 'viridis'.
title	str	Default ''.
x_label	str	Default ''.
y_label	str	Default ''.

Outputs

Name	Type	Description
result	Figure

Time Series Plot

science.time_series_plot

One or more columns against time; the first is drawn boldest.

Inputs

Name	Type	Description
table	DataFrame
time	str	Default 'date'.
columns	str	Comma-separated columns to draw Default ''.
style	Literal['line', 'bars', 'steps']	Default 'line'.
title	str	Default ''.
y_label	str	Default ''.
zero_line	bool	Default False.
color_by_sign	bool	Bars above zero red, below blue (for anomalies) Default False.

Outputs

Name	Type	Description
result	Figure

Science/Signal

Butterworth Filter

science.butterworth_filter

A Butterworth filter applied forwards and backwards (zero phase shift, scipy.signal.sosfiltfilt): keep frequencies below, above, between or outside the cutoffs.

Inputs

Name	Type	Description
y	NDArray[number]
sample_rate	float	Samples per second (Hz) Default 100.0.
kind	Literal['lowpass', 'highpass', 'bandpass', 'bandstop']	Default 'lowpass'.
cutoff	float	Hz; the lower edge for band filters Default 10.0.
cutoff_high	float	Hz; band filters only Default 20.0.
order	int	Default 4.

Outputs

Name	Type	Description
result	NDArray[floating]

Detrend

science.detrend

Remove a straight-line trend (or only the mean, constant), such as sensor drift, before computing spectra.

Inputs

Name	Type	Description
y	NDArray[number]
kind	Literal['linear', 'constant']	Default 'linear'.

Outputs

Name	Type	Description
result	NDArray[floating]

Envelope

science.envelope

The amplitude envelope (magnitude of the analytic signal, via the Hilbert transform). The spectrum of the envelope of a band-passed signal reveals how often impacts repeat: the classic bearing-fault diagnosis.

Inputs

Name	Type	Description
y	NDArray[number]

Outputs

Name	Type	Description
result	NDArray[floating]

Fft

science.fft

One-sided amplitude spectrum of a real signal. Fourier Transform (in Math/Fourier) does more: units, phase, windows, padding and the inverse.

Inputs

Name	Type	Description
y	NDArray[number]
sample_spacing	float	Default 1.0.

Outputs

Name	Type	Description
frequency	NDArray[float64]
amplitude	NDArray[float64]

Find Peaks

science.find_peaks

Local maxima of y (scipy.signal.find_peaks), as a table with position, height, prominence and width at half prominence (in x units), sorted by prominence, largest first.

Inputs

Name	Type	Description
x	NDArray[number]
y	NDArray[number]
min_prominence	float	How far a peak stands out Default 0.0.
min_distance	int	Samples between peaks Default 1.
max_peaks	int	Keep the most prominent N; 0: all Default 0.

Outputs

Name	Type	Description
table	DataFrame
positions	NDArray[float64]
heights	NDArray[float64]
count	int

Savitzky-Golay Filter

science.savgol_filter

Smooth a signal while preserving peak shape (scipy.signal.savgol_filter).

Inputs

Name	Type	Description
y	NDArray[floating]
window	int	Default 21.
order	int	Default 3.

Outputs

Name	Type	Description
result	NDArray[floating]

Signal Statistics

science.signal_statistics

Root mean square, peak absolute value, peak-to-peak range, crest factor (peak / RMS: about 1.41 for a pure sine) and kurtosis (3 for Gaussian noise). Impacts, such as from a damaged bearing, raise the last two.

Inputs

Name	Type	Description
y	NDArray[number]

Outputs

Name	Type	Description
rms	float
peak	float
peak_to_peak	float
crest_factor	float
kurtosis	float
summary	dict[str, float]

Welch PSD

science.welch_psd

Power spectral density by Welch’s method: the average spectrum of overlapping windowed segments, much less noisy than a single FFT. density gives units²/Hz, spectrum units² (for reading tone amplitudes).

Inputs

Name	Type	Description
y	NDArray[number]
sample_rate	float	Samples per second (Hz) Default 100.0.
segment	int	Samples per segment Default 1024.
scaling	Literal['density', 'spectrum']	Default 'density'.

Outputs

Name	Type	Description
frequency	NDArray[float64]
power	NDArray[float64]

Science/Statistics

Bootstrap CI

science.bootstrap_ci

A confidence interval for a statistic by resampling (percentile-BCa, scipy.stats.bootstrap), with no assumption about the distribution.

Inputs

Name	Type	Description
x	NDArray[number]
statistic	Literal['mean', 'median', 'std']	Default 'mean'.
confidence	float	Default 0.95.
resamples	int	Default 5000.
seed	int	Default 0.

Outputs

Name	Type	Description
estimate	float
low	float
high	float

Compare Groups

science.compare_groups

Do the groups differ? One-way ANOVA (or the rank-based Kruskal-Wallis test) across all groups, then every pair: Welch’s t-test (or Mann-Whitney U), with Holm’s correction for the number of comparisons.

Inputs

Name	Type	Description
table	DataFrame
value	str	The measured variable Default ''.
group	str	The column naming each row’s group Default ''.
test	Literal['anova', 'kruskal-wallis']	Default 'anova'.
alpha	float	Default 0.05.

Outputs

Name	Type	Description
groups	DataFrame
pairs	DataFrame
statistic	float
p_value	float
effect_size	float
summary	dict[str, Any]

Correlation Matrix

science.correlation_matrix

Pairwise correlation coefficients between numeric columns. The first column, variable, names the rows (Heatmap uses it as labels).

Inputs

Name	Type	Description
table	DataFrame
method	Literal['pearson', 'spearman', 'kendall']	Default 'pearson'.
columns	str	Comma-separated; empty: every numeric column Default ''.

Outputs

Name	Type	Description
result	DataFrame

Detect Outliers

science.detect_outliers

Flag unusual values in a column. iqr: outside [Q1 − t·IQR, Q3 + t·IQR] (t = 1.5 is Tukey’s rule); z-score: more than t standard deviations from the mean; mad: robust z-score from the median absolute deviation. Outputs the table with an outlier column, the table without them, and how many there were.

Inputs

Name	Type	Description
table	DataFrame
column	str	Default ''.
method	Literal['iqr', 'z-score', 'mad']	Default 'iqr'.
threshold	float	Default 1.5.

Outputs

Name	Type	Description
flagged	DataFrame
clean	DataFrame
count	int

Trend Test

science.trend_test

Is there a monotonic trend? The Mann-Kendall test (non-parametric, robust to outliers) with Sen’s slope, the median of all pairwise slopes, as is usual for climate and hydrology series. Dates are converted to decimal years, so with per = 10 the slope is per decade. The rows need not be in time order, and several values may share a time. table: the input with Sen’s line added as <value>_trend, for plotting.

Inputs

Name	Type	Description
table	DataFrame
time	str	Dates, or numbers such as years Default ''.
value	str	Default ''.
per	float	Express the slope per this many time units (years for dates) Default 10.0.
alpha	float	Default 0.05.

Outputs

Name	Type	Description
slope	float
intercept	float
tau	float
p_value	float
trend	str
table	DataFrame
summary	dict[str, Any]

Two-Way ANOVA

science.two_way_anova

Analysis of variance with two factors, such as treatment and block in a randomised block trial: does each factor explain variation once the other is accounted for? Type II sums of squares (valid for unbalanced data); partial η² as the effect size.

Inputs

Name	Type	Description
table	DataFrame
value	str	The measured variable Default ''.
factor_a	str	e.g. the treatment Default ''.
factor_b	str	e.g. the block or site Default ''.
interaction	bool	Also test A × B (needs replicates) Default False.

Outputs

Name	Type	Description
table	DataFrame
p_a	float
p_b	float
summary	dict[str, Any]

Science/Studies

Files Design

science.files_design

One row per file matching the pattern, so a Sweep zone processes each file in turn (column file, and name without folder or suffix).

Inputs

Name	Type	Description
folder	Path	Default 'data'.
pattern	str	Which files, e.g. .csv or run_/*.tif Default '*.csv'.

Outputs

Name	Type	Description
result	DataFrame

Grid Design

science.grid_design

Every combination of evenly spaced values: levels per range (a normal parameter spans mean ± 2 sd), and each listed value.

Inputs

Name	Type	Description
parameters	str	One per line: name = low .. high [unit], normal(mean, sd) or [a, b, c] Default 'g = 9.7 .. 9.9\nL = 0.9 .. 1.1 m'.
levels	int	Values per range Default 5.

Outputs

Name	Type	Description
result	DataFrame

Latin Hypercube

science.latin_hypercube

A space-filling random design: each parameter’s range is cut into samples slices and each slice is used once (scipy.stats.qmc).

Inputs

Name	Type	Description
parameters	str	One per line: name = low .. high [unit], normal(mean, sd) or [a, b, c] Default 'g = 9.7 .. 9.9\nL = 0.9 .. 1.1 m'.
samples	int	Default 50.
seed	int	The same seed gives the same design Default 1.

Outputs

Name	Type	Description
result	DataFrame

Morris Design

science.morris_design

Morris elementary-effects screening (SALib): cheap, it tells which parameters matter at all. Runs (parameters + 1) × trajectories times. Analyse the sweep’s results with Sensitivity Analysis.

Inputs

Name	Type	Description
parameters	str	One per line: name = low .. high [unit], normal(mean, sd) or [a, b, c] Default 'g = 9.7 .. 9.9\nL = 0.9 .. 1.1 m'.
trajectories	int	Default 20.
levels	int	Default 4.
seed	int	The same seed gives the same design Default 1.

Outputs

Name	Type	Description
result	DataFrame

Random Design

science.random_design

Parameter sets drawn at random: uniformly within ranges, from normal distributions, or among listed values.

Inputs

Name	Type	Description
parameters	str	One per line: name = low .. high [unit], normal(mean, sd) or [a, b, c] Default 'g = 9.7 .. 9.9\nk = normal(2, 0.1)'.
samples	int	Default 100.
seed	int	The same seed gives the same design Default 1.

Outputs

Name	Type	Description
result	DataFrame

Sensitivity Analysis

science.sensitivity_analysis

How much each parameter influences a result, from a Sweep zone over a Morris Design (μ*: mean absolute effect, σ: non-linearity and interactions) or a Sobol Design (S1: variance share alone, ST: with interactions, with 95 % confidence half-widths).

Inputs

Name	Type	Description
results	DataFrame
output	str	The result column to explain Default ''.

Outputs

Name	Type	Description
indices	DataFrame
chart	Any

Sobol Design

science.sobol_design

Sobol variance-based sensitivity (SALib, Saltelli’s sampling): how much of the output’s variance each parameter causes, alone (S1) and with its interactions (ST). Analyse the sweep’s results with Sensitivity Analysis.

Inputs

Name	Type	Description
parameters	str	One per line: name = low .. high [unit], normal(mean, sd) or [a, b, c] Default 'g = 9.7 .. 9.9\nL = 0.9 .. 1.1 m'.
base	int	A power of 2; runs = base × (parameters + 2) Default 256.
seed	int	The same seed gives the same design Default 1.

Outputs

Name	Type	Description
result	DataFrame

Science/Tables

Add Column

science.add_column

Compute a new column from others (pandas DataFrame.eval). Supports

• • • / ** , comparisons, pi and sqrt, exp, log, log10, sin, cos, tan and abs; put names with spaces in back quotes. Attribute access, indexing and other Python are refused. When columns have units, the arithmetic is done with them: the new column gets its unit, and adding metres to seconds is an error.

Inputs

Name	Type	Description
table	DataFrame
name	str	Default 'result'.
expression	str	Arithmetic on columns: length_cm / 100, or (a + b) / 2 Default ''.

Outputs

Name	Type	Description
result	DataFrame

Column As Text

science.column_as_text

The values of a column joined into one text, e.g. the names of the top three samples for a report sentence.

Inputs

Name	Type	Description
table	DataFrame
column	str	Default ''.
separator	str	Default ', '.
max_items	int	0: all Default 0.
format	str	A format spec: .2f for numbers, %Y for dates; empty: as is Default ''.

Outputs

Name	Type	Description
result	str

Describe

science.describe

Descriptive statistics per column.

Inputs

Name	Type	Description
table	DataFrame

Outputs

Name	Type	Description
result	DataFrame

Describe Columns

science.describe_columns

Say what each column holds. The descriptions travel with the table and show in the inspector.

Inputs

Name	Type	Description
table	DataFrame
descriptions	str	column = what it holds, one per line Default ''.

Outputs

Name	Type	Description
result	DataFrame

Drop Missing

science.drop_missing

Remove rows with missing values (NaN or empty).

Inputs

Name	Type	Description
table	DataFrame
columns	str	Only look at these columns; empty: all Default ''.

Outputs

Name	Type	Description
result	DataFrame

Filter Rows

science.filter_rows

Keep the rows matching a condition (pandas DataFrame.query). The index is renumbered. The condition may use column names, constants, lists of constants (region in ["North", "South"]), arithmetic, comparisons, and/or/not and sqrt, exp, log, log10, sin, cos, tan and abs; attribute access, indexing and other Python are refused.

Inputs

Name	Type	Description
table	DataFrame
condition	str	A pandas query: magnitude >= 2.5 and region == “North”. Put names with spaces in back quotes. Default ''.

Outputs

Name	Type	Description
result	DataFrame

Get Column

science.get_column

One column of a table as an array. Its type follows the column: numbers (NDArray[float64], or int64, which links anywhere floats do), text (NDArray[str_]), dates (NDArray[datetime64]) or true/false (NDArray[bool_]).

Inputs

Name	Type	Description
table	DataFrame
column	str

Outputs

Name	Type	Description
result	NDArray

Get Quantity Column

science.get_quantity_column

One numeric column as an array with its unit (see Set Column Units), so what follows converts and checks units.

Inputs

Name	Type	Description
table	DataFrame
column	str
unit	str	The column’s unit, if the table does not give one Default ''.

Outputs

Name	Type	Description
result	Quantity

Group Summary

science.group_summary

Summary statistics of one column for each group: one row per group, in the order the groups first appear. ci95 is the half-width of the 95 % confidence interval of the mean (Student’s t).

Inputs

Name	Type	Description
table	DataFrame
by	str	The column that defines the groups Default ''.
value	str	The numeric column to summarise Default ''.
statistics	str	Comma-separated: count, mean, std, sem, ci95, min, median, max, sum Default 'count, mean, std, sem, min, max'.

Outputs

Name	Type	Description
result	DataFrame

Join Tables

science.join_tables

Combine two tables on a shared key column (a database join). Columns with the same name in both get the suffixes _left and _right.

Inputs

Name	Type	Description
left	DataFrame
right	DataFrame
on	str	A column both tables have Default ''.
how	Literal['left', 'inner', 'outer', 'right']	Default 'left'.

Outputs

Name	Type	Description
result	DataFrame

Load CSV

science.load_csv

Read a CSV file into a table: from the workspace (relative paths) or from remote storage (e.g. lab-s3://2026/run1.csv). Headers such as “length [m]” give the column a unit, which it keeps through the table nodes.

Inputs

Name	Type	Description
path	FileRef
separator	str	Default ','.
units	bool	Read “length [m]” or “length (m)” as a column “length” in metres Default True.

Outputs

Name	Type	Description
result	DataFrame

Lookup Value

science.lookup_value

One cell: the value in value_column of the first row whose key_column equals key (compared as text), e.g. the mean yield of treatment “N120” from a Group Summary. Without a key column, the first row’s value, e.g. after Sort Rows.

Inputs

Name	Type	Description
table	DataFrame
key_column	str	Empty: the first row Default ''.
key	str	The row whose key column holds this Default ''.
value_column	str	Default ''.

Outputs

Name	Type	Description
result	Any

Make Table

science.make_table

Combine two arrays of the same length (numbers, text or dates) into a two-column table.

Inputs

Name	Type	Description
x	NDArray
y	NDArray
x_name	str	Default 'x'.
y_name	str	Default 'y'.

Outputs

Name	Type	Description
result	DataFrame

Parse Dates

science.parse_dates

Turn a text column into dates and times, for the time series nodes.

Inputs

Name	Type	Description
table	DataFrame
column	str	Default 'date'.
format	str	strftime format, e.g. %d.%m.%Y; empty: detect ISO dates Default ''.

Outputs

Name	Type	Description
result	DataFrame

Pivot Table

science.pivot_table

Cross-tabulate: one row per value of rows, one column per value of columns, cells aggregated from values.

Inputs

Name	Type	Description
table	DataFrame
rows	str	Default ''.
columns	str	Default ''.
values	str	Default ''.
aggregate	Literal['mean', 'sum', 'count', 'min', 'max', 'median']	Default 'mean'.

Outputs

Name	Type	Description
result	DataFrame

Rename Columns

science.rename_columns

Give columns readable names, e.g. before adding a table to a report.

Inputs

Name	Type	Description
table	DataFrame
mapping	str	old=new pairs, comma-separated: temp_c=Temperature (°C) Default ''.

Outputs

Name	Type	Description
result	DataFrame

Select Columns

science.select_columns

Keep only some columns, in the given order.

Inputs

Name	Type	Description
table	DataFrame
columns	str	Comma-separated, in the order wanted Default ''.

Outputs

Name	Type	Description
result	DataFrame

Set Column Units

science.set_column_units

Give columns their units. They stay with the table through filtering, sorting, grouping and joining, show in previews and report tables as “length (cm)”, and make the table’s column pins quantities.

Inputs

Name	Type	Description
table	DataFrame
units	str	column = unit, one per line or comma-separated: length_cm = cm Default ''.

Outputs

Name	Type	Description
result	DataFrame

Sort Rows

science.sort_rows

Sort by a column; with a limit, the top N rows (e.g. the largest events).

Inputs

Name	Type	Description
table	DataFrame
column	str	Default ''.
descending	bool	Default False.
limit	int	Keep the first N rows; 0: all Default 0.

Outputs

Name	Type	Description
result	DataFrame

Table Info

science.table_info

The number of rows and columns, e.g. for “n = 120” in a report.

Inputs

Name	Type	Description
table	DataFrame

Outputs

Name	Type	Description
rows	int
columns	int

Science/Time Series

Climatology

science.climatology

The typical value for each month (or season, or day of the year) over a baseline period, and each row’s anomaly: its departure from that normal.

Inputs

Name	Type	Description
table	DataFrame
time	str	Default 'date'.
value	str	Default ''.
period	Literal['month', 'season', 'day of year']	Default 'month'.
baseline_start	int	First year of the baseline; 0: all Default 0.
baseline_end	int	Last year of the baseline; 0: all Default 0.

Outputs

Name	Type	Description
climatology	DataFrame
anomalies	DataFrame

Resample

science.resample

Aggregate to regular periods: daily to monthly means, hourly to daily sums. Each period is labelled by its start. Adds a count column with the number of values in each period.

Inputs

Name	Type	Description
table	DataFrame
time	str	Default 'date'.
every	Literal['hour', 'day', 'week', 'month', 'quarter', 'year']	Default 'month'.
aggregate	Literal['mean', 'sum', 'min', 'max', 'median', 'count']	Default 'mean'.
columns	str	Comma-separated; empty: every numeric column Default ''.
min_count	int	Periods with fewer values become empty (NaN) Default 1.

Outputs

Name	Type	Description
result	DataFrame

Rolling Window

science.rolling_window

Add a moving-window statistic of a column, e.g. a 7-day mean or a 5-year running average. Windows at the ends use the values they have.

Inputs

Name	Type	Description
table	DataFrame
column	str	Default ''.
window	int	Rows per window Default 7.
statistic	Literal['mean', 'median', 'sum', 'std', 'min', 'max']	Default 'mean'.
center	bool	Default True.
name	str	The new column; empty: _ Default ''.

Outputs

Name	Type	Description
result	DataFrame