grainsize 0.1.1

Sedimentological data‐analysis tools (grain‐size, XRF, forams, bryozoans)

grainsize

Sedimentological Data Analysis Made Easy

The grainsize Python package provides a suite of tools for processing, analyzing, and visualizing grain size distributions, elemental data, and micropaleontological proxies (foraminifera and bryozoans) from sediment cores.

---

Features

• Grain Size Analysis:

  • Clean and normalize grain size distributions.
  • Calculate statistical parameters (mean, median, mode, std, skewness, kurtosis).
  • Create grain size categories (clay, silt, sand, gravel).
  • Visualize statistics and fine fraction (<63 µm).

• XRF Data Analysis:

  • Import and clean XRF elemental data.
  • Convert percentages to ppm.
  • Plot elemental abundances and elemental ratios.

• Cluster Analysis:

  • Perform Bray-Curtis dissimilarity analysis.
  • Create dendrograms for stratigraphic clustering.

• Foraminifera Data Analysis:

  • Calculate normalized abundance per 1cc.
  • Plot planktic and benthic foraminifera abundance with color scaling.

• Bryozoans Data Analysis:

  • Compare abundance categories across cores.
  • Perform statistical tests (Chi-square, Mann-Whitney U).
  • Visualize depth distribution of bryozoan types.

• Integrated Core Analysis:

  • Plot combined grain size and XRF results side-by-side for stratigraphic interpretation.

---

Installation

Manually clone the repository and install dependencies listed in requirements

---

Quickstart Example

from grainsize import GrainSize, XRF, Forams, Bryozoans, Stratigraphy, BCD

# Grain Size Example
gs = GrainSize(fname="grainsize_data.xlsx")
gs.clean_data()
stats = gs.create_stats_df()
stats.plot_stats(core_name="Core A")

# XRF Example
xrf = XRF(fname="xrf_data.xlsx")
xrf_clean = xrf.clean_data()
xrf_clean.plot_elements(core_name="Core A")

# Bray-Curtis Clustering
bcd = BCD.from_grain_size(gs)
bcd.plot_dendrogram(core_name="Core A")

# Foraminifera Example
forams = Forams(fname="forams.csv")
forams.plot_forams(core_name="Core A")

# Bryozoans Example
bryo = Bryozoans(fname="bryozoans.xlsx")
bryo.plot_corr_matrix(core_name="Core A")

---

Main Classes and Functionalities

GrainSize

• clean_data() → Clean raw MasterSizer data.
• normalize_gs() → Normalize grain size percentages.
• create_stats_df() → Calculate median, mode, mean, std, skewness, kurtosis.
• create_categories() → Create clay, silt, sand, gravel summaries.
• plot_stats() → Plot statistics.
• compare_gs() → Compare multiple cores.

XRF

• clean_data() → Clean depth intervals.
• to_ppm() → Convert % values to ppm.
• plot_elements() → Plot elemental concentrations.
• plot_ratios() → Plot elemental ratios.
• compare_ratios() → Compare elemental ratios across cores.

Forams

• Automatically calculates normalized abundance, p/b ratios, and percentages.
• plot_forams() → Plot total abundance colored by planktic %.
• compare_forams_plot() → Compare cores.
• plot_benthic() → Plot benthic abundance.

Bryozoans

• calc_chi2() → Chi-square test for presence/absence.
• calc_mann_whitney() → Mann-Whitney U test.
• calc_corr() + plot_corr_matrix() → Correlation analysis.
• plot_large_bryo() → Compare large bryozoans categories.
• plot_depth_bars() → Bryozoan types vs. depth.

BCD (Bray-Curtis Dissimilarity)

• compute_BCD() → Compute Bray-Curtis distances.
• plot_dendrogram() → Create a stratigraphic dendrogram.
• merge_interp() → Merge and interpolate two cores.

---

Requirements

• Python 3.8+
• pandas
• numpy
• matplotlib
• seaborn
• scipy

---

License

MIT License

---

Acknowledgements

Developed to assist sedimentological and micropaleontological research workflows, with a special focus on making complex laboratory datasets ready for visualization and statistical analysis.

---

Author

Jarden Aaltonen Feel free to report issues or suggest improvements!