labtex 0.6.1

Lab report data analysis and LaTeX file generation

labtex

New extensive Documentation!

Purpose

This package provides a single solution to repetitive analysis tasks in a lab environment. If you are doing error propagation, linear regression or LaTeX tables/figures manually, this package automates the process.

Features

• Measurement and MeasurementList classes with automatic:
  • Error propagation
  • Printing to correct significant figures
  • Unit parsing, propagation and conversion
• Linear regression and nonlinear curve fitting
• A Document class with automatic:
  • LaTeX table generation in two different styles
  • Plot generation with matplotlib
  • Insertion/updating of tables and figures into a LaTeX file

Installation

Using pip or pip3 the latest release can be installed with

pip3 install labtex

Usage

For ease of use, you can import the package into your file's global namespace with

from labtex import *

The rest of this section will assume the package is imported in this way. Alternatively do import labtex as lt.

Single measurements can be instantiated with Measurement(value,uncertainty,unit) where unit is a string that will be parsed.

x = Measurement(1.1,0.3,"m")
y = Measurement(2.22,0.4,"m")
z = M(314,10,"V")

where M is an equivalent shorthand for Measurement. Note that the unit parsing supports all combinations of common units, prefixes and powers of units, eg. any of "nm^2", "C^-1", "kg m^2 s^-2", "J^3" etc. are supported.

Measurement instances support all operations (+-*/ and **) as well as math functions with the error and units automatically propagated. Some examples are shown below.

print(x)
# 1.1 ± 0.3 m

print(x + y)
# 3.3 ± 0.5 m

print(x * z)
# (35 ± 9) × 10^{1} V m

print(x ** 2)
# 1.2 ± 0.7 m^2

print(Measurement.tan(x / y))
# 0.5 ± 0.2  

Notice also that Measurements are rounded to the significant figures as dictated by the uncertainty.

For a list of measurements, the MeasurementList class functions identically to the Measurement class, only now taking a list of values. The uncertainty can be a list or a single value for all measurements.

heights = MeasurementList([185,183,182,194,184,177],5,"cm")
# Note: One could use the shorthand class ML(values,uncertainty,unit)

print(heights)
# [185 ± 5, 183 ± 5, 182 ± 5, 194 ± 5, 184 ± 5, 177 ± 5] cm

print(200 - heights)
# [15 ± 5, 17 ± 5, 18 ± 5, 6 ± 5, 16 ± 5, 23 ± 5] cm

MeasurementLists also support all operations (+-*/ and **) with themselves and with Measurements.

With two MeasurementList instances, they can be linearly regressed with the LinearRegression class.

voltages = MeasurementList([1.3,3,5,7,8.5,10],1,"V")
temperatures = MeasurementList([23,55,67,82,88,96],[5,3,7,10,5,6],"K")

reg = LinearRegression(voltages,temperatures)

print(lobf)
# m = 7 ± 1 V^{-1} C
# c = 27 ± 7 C

Observe that printing all Measurement and MeasurementList instances rounds the value to the largest significant figure of the error, as is convention.

To visualise this linear regression, the plot method can be used.

fig = linreg.plot(xlabel="Voltage, V", ylabel="Temperature, T", title="Voltage and Temperature Plot")
fig.show()

This generates the following plot, in which the line of best fit is shown, as well as the region of uncertainty (in the linear regression parameters). Notice that the units are automatically included in the axis labels.

In order to include such a figure or a table of the data used in a LaTeX document, the Document class can be used. Argument names are not required, they are shown here only for demonstration.

doc = Document(title="Lab Report Template",author="CianLM", filename="report.tex")

Note that filename here can be a preexisting .tex file or a new one to be created by labtex.

Once doc has been instantiated, tables and figures may be added to the document with their respective methods. Once again argument names are for demonstration and most are optional anyway.

doc.add_table(
    nameandsymbol = ["Voltage, V","Temperature, T"],
    data = [voltages,temperatures],
    caption = "Voltage and Temperature Correlation"
)

This inserts the following into the doc instance.

\begin{table}[ht]
    \centering
    \caption{Voltage and Temperature Correlation}
    \label{tab:1}
    \begin{tabular}{c|cccccc}
        \toprule
            Voltage, V, ($\pm 1$ V) & 1 & 3 & 5 & 7 & 8 & 10 \\ 
            Temperature, T& $23 \pm 5 $ & $55 \pm 3 $ & $67 \pm 7 $ & $(8 \pm 1) \times 10^{1} $ & $88 \pm 5 $ & $96 \pm 6 $ \\ 
        \bottomrule
    \end{tabular}
\end{table}

which results in

Alternatively if an upright table is preferred, this may be specified through the style argument.

doc.add_table(
    nameandsymbol = ["Voltage, V", "Temperature, T"],
    data = [voltages,temperatures**2],
    caption = "Voltage and Temperature Squared Correlation",
    style = "upright"
)

Once again, this inserts the following into the doc instance.

\begin{table}[ht]
    \centering
    \caption{Voltage and Temperature Squared Correlation}
    \label{tab:2}
    \begin{tabular}{*{2}c}
        \toprule
        Voltage, V, ($\pm 1$ V)  & Temperature, T \\ 
            \midrule
              1 & $(5 \pm 2) \times 10^{2} $  \\
              3 & $(30 \pm 3) \times 10^{2} $  \\
              5 & $(45 \pm 9) \times 10^{2} $  \\
              7 & $(7 \pm 2) \times 10^{3} $  \\
              8 & $(77 \pm 9) \times 10^{2} $  \\
              10& $(9 \pm 1) \times 10^{3} $ \\
        \bottomrule
    \end{tabular}
\end{table}

which results in

For a figure, a similar process occurs. Given any figure object, such as the one generated by linreg.plot() above, it can be added with the doc.add_figure method. For example, one could do

squarereg = LinearRegression(voltages,temperatures**2)
fig = squarereg.plot(xlabel="Voltage, V", ylabel="Temperature Squared, $T^2$", title="Voltage and Temperature Squared Plot")
doc.add_figure(fig, caption="Linear Regression of Voltage and Temperature Squared")
# labtex: Wrote to 'figures/graph1.png'.

This generates the figure below and saves it to figures/graph1.png where the 1 will increment with each figure you add.

If you want a different filename, you can specify with the filename=... argument and if you want your graphs elsewhere than figures/, you may change Document.graphfolder at your convenience.

Once you have added all your tables and graphs to the doc object, you may save this file as shown below. The default write directory is tex/ relative to root. This directory is also customizable with Document.texfolder.

doc.save()
# labtex: Wrote to 'tex/report.tex'.

You can then edit the generated .tex file freely, and run the python file again at any point to add/edit/remove tables and figures. labtex will automatically update the .tex file with your changes.

By default, tables will be inserted after a line starting with %labtex-tables and figures will be inserted after a line starting with %labtex-figures. If you want to change the comments labtex looks for, you can edit the Document.tablemarker and Document.figuremarker attributes respectively. If such comments are not found, tables/figures will be inserted at the very end of the document.

For more advanced usage and guides (such as the creation of the plot below) see the Documentation.

Contributions

This package is under active development. Feel free to submit a pull request or reach out with feature suggestions.