Scientific Data Analysis with LabPlot in Python: Signal Processing, Spectral Peak Fitting, Visualization, and Batch Automation

In this tutorial, we explore a LabPlot-inspired scientific data analysis workflow in Python while preserving the structure and terminology of LabPlot’s aspect tree, analysis kernels, plotting system, and project model.

We build reusable components to import tabular data, compute descriptive statistics, smooth and differentiate signals, perform Fourier analysis and filtering, detect peaks, integrate curves, reduce data, and fit nonlinear models with detailed statistical diagnostics.

We then apply these tools to a realistic spectroscopy example: removing periodic interference, identifying overlapping peaks, fitting a multi-Gaussian model, inspecting residuals, visualizing results through themed worksheets, exporting figures, and saving project data in LabPlot-compatible .

lml-style files.Finally, we extend the same workflow to batch processing so we can analyze multiple temperature-dependent spectra and fit secondary trends across the resulting measurements.

Copy CodeCopiedUse a different Browserimport os, sys, gzip, bz2, lzma, time, math, textwrap, warnings import xml.etree.ElementTree as ET from dataclasses import dataclass, field from enum import Enum import numpy as np, pandas as pd, matplotlib, matplotlib.pyplot as plt from matplotlib.

ticker import AutoMinorLocator import scipy from scipy import signal, stats, optimize warnings.filterwarnings("ignore", category=RuntimeWarning) np.random.seed(20260815) INCOLAB = "google.colab" in sys.modules OUT = "/content/labplotout" if INCOLAB else os.path.join(os.getcwd(), "labplotout") os.

makedirs(OUT, existok=True) try: from pylabplot import ; HAVESDK = True except Exception: HAVESDK = False banner = lambda t: print("\n" + "=" 76 + f"\n {t}\n" + "=" 76) banner("environment") print(f" numpy {np.version} | scipy {scipy.version} | mpl {matplotlib.

version} | " f"colab={INCOLAB} | pylabplot={'yes' if HAVESDK else 'no -> emulation'}\n -> {OUT}") class PlotDesignation(Enum): NoDesignation = 0; X = 1; Y = 2; Z = 3 XError = 4; XErrorMinus = 5; XErrorPlus = 6 YError = 7; YErrorMinus = 8; YErrorPlus = 9 class ColumnMode(Enum): Double = 0; Text = 1; Integer = 2; BigInt = 3; DateTime = 4 class AbstractAspect: def init(self, name, comment=""): self.

name, self.comment, self.parent, self.children = name, comment, None, [] def name(self): return self.name def addChild(self, a): a.parent = self; self.children.append(a); return a def tree(self, d=0): s = " " d + f"{'|- ' if d else ''}{type(self).name:<20} {self.

name}" if isinstance(self, Column): s += f" [{self.columnMode.name}, {self.rowCount()} rows, {self.plotDesignation.name}]" return "\n".join([s] + [c.tree(d+1) for c in self.children]) class Column(AbstractAspect): """LabPlot's fundamental data source: a typed vector + a plot designation.

""" def init(self, name, values=None, mode=ColumnMode.Double, designation=PlotDesignation.NoDesignation): super().init(name) self.columnMode, self.plotDesignation = mode, designation self.d = np.asarray([] if values is None else values, float) def values(self): return self.

d def rowCount(self): return len(self.d) def clean(self): return self.d[np.isfinite(self.d)] def statistics(self): """The 20 quantities in LabPlot's Column Statistics dialog.""" x = self.clean(); n = x.size if not n: return {} q1, med, q3 = np.

percentile(x, [25, 50, 75]); iqr, pos = q3 - q1, x[x > 0] h = 2 iqr / n(1/3) if iqr > 0 else 0 c, = np.histogram(x, bins=int(np.clip(np.ptp(x)/h, 1, 1000)) if h else 10) p = c[c > 0] / c.sum(); v, k = np.unique(np.round(x, 12), returncounts=True) return {"Count": n, "Minimum": x.min(), "Maximum": x.

max(), "Arithmetic mean": x.mean(), "Geometric mean": stats.gmean(pos) if pos.size else np.nan, "Harmonic mean": stats.hmean(pos) if pos.size else np.nan, "Contraharmonic mean": (x2).sum() / x.sum() if x.sum() else np.nan, "Mode": v[k.argmax()] if k.max() > 1 else np.

nan, "First quartile": q1, "Median": med, "Third quartile": q3, "Interquartile range": iqr, "Trimean": (q1 + 2med + q3) / 4, "Variance": x.var(ddof=1), "Standard deviation": x.std(ddof=1), "Skewness": stats.skew(x), "Mean absolute deviation": np.abs(x - x.mean()).

mean(), "Median absolute deviation": np.median(np.abs(x - med)), "Kurtosis": stats.kurtosis(x, fisher=False), "Entropy": float(-(p np.log2(p)).sum())} def sparkline(self, w=26): """LabPlot 2.11+ draws these in the column header; text version.""" b, x = ".-~^", self.clean() s = x[np.linspace(0, x.

size-1, min(w, x.size)).astype(int)] if x.size > 1 else x return "" if s.size < 2 or np.ptp(s) == 0 else "".join( b[i] for i in ((s - s.min()) / np.ptp(s) 4).round().astype(int)) class Spreadsheet(AbstractAspect): def columns(self): return [c for c in self.

children if isinstance(c, Column)] def column(self, k): cs = self.columns() return cs[k] if isinstance(k, int) else next(c for c in cs if c.name() == k) def columnCount(self): return len(self.columns()) def rowCount(self): return max([c.rowCount() for c in self.

columns()], default=0) def appendColumn(self, n, v, d=PlotDesignation.Y): return self.addChild(Column(n, v, designation=d)) def toDataFrame(self): return pd.DataFrame({c.name(): c.values() for c in self.columns()}) def info(self): print(f" Spreadsheet '{self.name}': {self.rowCount()} rows x {self.

columnCount()} cols") for c in self.columns(): s = c.statistics() print(f" {c.name():<12}{c.plotDesignation.name:<6}min {s['Minimum']:>9.4g} max " f"{s['Maximum']:>9.4g} mean {s['Arithmetic mean']:>9.4g} {c.

sparkline()}") class Project(AbstractAspect): XMLVERSION = 15 def init(self, name="project", author=""): super().init(name); self.author, self.version = author, "2.12.1" def spreadsheets(self): return [c for c in self.

children if isinstance(c, Spreadsheet)] class AsciiFilter: """LabPlot's text import: separator auto-detect, comments, row/col limits.""" def init(self, separator="auto", commentCharacter="#", headerEnabled=True, startRow=1, endRow=-1, startColumn=1, endColumn=-1): self.separator, self.

commentCharacter = separator, commentCharacter self.headerEnabled, self.startRow, self.endRow = headerEnabled, startRow, endRow self.startColumn, self.

endColumn = startColumn, endColumn def readDataFromFile(self, path, dataSource): with open(path, encoding="utf-8", errors="replace") as fh: lines = [l.rstrip("\n") for l in fh if l.strip() and not l.lstrip().startswith(self.commentCharacter)] lines = lines[self.startRow - 1: None if self.

endRow < 0 else self.endRow] if not lines: raise ValueError("AsciiFilter: nothing to import") sep = (next((s for s in (",", ";", "\t", "|") if s in lines[0]), None) if self.separator == "auto" else self.separator) split = lambda l: [p.strip() for p in (l.split(sep) if sep else l.split()) if p.

strip()] header = split(lines[0]) if self.headerEnabled else None rows = [split(l) for l in (lines[1:] if self.headerEnabled else lines)] ncol = max(map(len, rows)) c0, c1 = self.startColumn - 1, ncol if self.endColumn < 0 else self.

endColumn for j in range(c0, min(c1, ncol)): vals = [] for r in rows: try: vals.append(float(r[j])) except (IndexError, ValueError): vals.append(np.nan) dataSource.addChild(Column( header[j] if header and j < len(header) else f"Column {j+1}", vals, designation=PlotDesignation.

X if j == c0 else PlotDesignation.Y)) return dataSource We set up the Python environment, configure reproducibility, and establish the output directory for the tutorial.We recreate LabPlot’s core aspect-tree structure using projects, spreadsheets, columns, plot designations, and column modes.

We also implement the AsciiFilter workflow to import structured text data into our LabPlot-style data model.Copy CodeCopiedUse a different Browserclass nslsmooth: """Analysis -> Smooth (Savitzky-Golay; LabPlot also offers moving average/percentile).

""" @staticmethod def savitzkygolay(y, points=11, order=3, deriv=0): points += points % 2 == 0 return signal.savgol_filter(y, points, min(order, points-1), deriv=deriv, mode="interp") cla