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Reorganization of the repository structure in anticipation of the release of the new version of the script (expected in late 2023 or early 2024).
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@marcoalopez
marcoalopez committed Nov 14, 2023
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3 changes: 3 additions & 0 deletions .gitignore
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Expand Up @@ -237,3 +237,6 @@ grain_size_tools/.ipynb_checkpoints/notebook_example-checkpoint.ipynb
grain_size_tools/.ipynb_checkpoints/notebook_example-checkpoint.ipynb
grain_size_tools/example_notebooks/2step_plot.png
grain_size_tools/example_notebooks/saltykov_plot.png
templates/.jupyter/desktop-workspaces/default-37a8.jupyterlab-workspace
DOCS/test_distribution.png
DOCS/.jupyter/desktop-workspaces/default-37a8.jupyterlab-workspace
265 changes: 0 additions & 265 deletions DOCS/JN_example.ipynb
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158 changes: 81 additions & 77 deletions ...w_describing_grain_size_populations.ipynb → DOCS/apparent_grain_sizes.ipynb
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Expand Up @@ -30,7 +30,7 @@
"A free open-source cross-platform script to visualize and characterize grain size\n",
"population and estimate differential stress via paleopizometers.\n",
"\n",
"Version: 2023.11.xx\n",
"Version: v3.0.2 (2020-12-30)\n",
"Documentation: https://marcoalopez.github.io/GrainSizeTools/\n",
"\n",
"Type get.functions_list() to get a list of the main methods\n",
Expand Down Expand Up @@ -517,7 +517,7 @@
"============================================================================\n",
"Shapiro-Wilk test warnings:\n",
"Data is not normally distributed!\n",
"Normality test: 0.89, 0.00 (test statistic, p-value)\n",
"Normality test: 0.92, 0.00 (test statistic, p-value)\n",
"============================================================================\n"
]
}
Expand Down Expand Up @@ -583,7 +583,7 @@
"Data is not normally distributed!\n",
"Normality test: 0.95, 0.00 (test statistic, p-value)\n",
"Data is not lognormally distributed!\n",
"Lognormality test: 0.99, 0.02 (test statistic, p-value)\n",
"Lognormality test: 0.98, 0.01 (test statistic, p-value)\n",
"============================================================================\n"
]
}
Expand Down Expand Up @@ -685,75 +685,77 @@
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\u001b[1;31mSignature:\u001b[0m\n",
"\u001b[0mplot\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdistribution\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m\n",
"\u001b[0m \u001b[0mdata\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\n",
"\u001b[0m \u001b[0mplot\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'hist'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'kde'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\n",
"\u001b[0m \u001b[0mavg\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'amean'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'gmean'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'median'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'mode'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\n",
"\u001b[0m \u001b[0mbinsize\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'auto'\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\n",
"\u001b[0m \u001b[0mbandwidth\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'silverman'\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\n",
"\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mfig_kw\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\n",
"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;31mDocstring:\u001b[0m\n",
"Return a plot with the ditribution of (apparent or actual) grain sizes\n",
"in a dataset.\n",
"\n",
"Parameters\n",
"----------\n",
"data : array_like\n",
" the size of the grains\n",
"\n",
"plot : string, tuple or list; optional\n",
" the type of plot, either histogram ('hist'), kernel density estimate\n",
" ('kde') or both ('hist', 'kde'). Default is both.\n",
"\n",
"avg : string, tuple or list; optional\n",
" the central tendency measures o show, either the arithmetic ('amean')\n",
" or geometric ('gmean') means, the median ('median'), and/or the\n",
" KDE-based mode ('mode'). Default all averages.\n",
"\n",
"binsize : string or positive scalar; optional\n",
" If 'auto', it defines the plug-in method to calculate the bin size.\n",
" When integer or float, it directly specifies the bin size.\n",
" Default: the 'auto' method.\n",
"\n",
" | Available plug-in methods:\n",
" | 'auto' (fd if sample_size > 1000 or Sturges otherwise)\n",
" | 'doane' (Doane's rule)\n",
" | 'fd' (Freedman-Diaconis rule)\n",
" | 'rice' (Rice's rule)\n",
" | 'scott' (Scott rule)\n",
" | 'sqrt' (square-root rule)\n",
" | 'sturges' (Sturge's rule)\n",
"\n",
"bandwidth : string {'silverman' or 'scott'} or positive scalar; optional\n",
" the method to estimate the bandwidth or a scalar directly defining the\n",
" bandwidth. It uses the Silverman plug-in method by default.\n",
"\n",
"**fig_kw :\n",
" additional keyword arguments to control the size (figsize) and\n",
" resolution (dpi) of the plot. Default figsize is (6.4, 4.8).\n",
" Default resolution is 100 dpi.\n",
"\n",
"Call functions\n",
"--------------\n",
"- gaussian_kde (from Scipy stats)\n",
"\n",
"Examples\n",
"--------\n",
">>> distribution(data['diameters'])\n",
">>> distribution(data['diameters'], figsize=(6.4, 4.8))\n",
"\n",
"Returns\n",
"-------\n",
"A plot showing the distribution of (apparent) grain sizes and\n",
"the location of the averages defined.\n",
"\u001b[1;31mFile:\u001b[0m c:\\users\\marco\\documents\\github\\grainsizetools\\grain_size_tools\\plot.py\n",
"\u001b[1;31mType:\u001b[0m function"
]
"data": {
"text/plain": [
"\u001b[1;31mSignature:\u001b[0m\n",
"\u001b[0mplot\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdistribution\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m\n",
"\u001b[0m \u001b[0mdata\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\n",
"\u001b[0m \u001b[0mplot\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'hist'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'kde'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\n",
"\u001b[0m \u001b[0mavg\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'amean'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'gmean'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'median'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'mode'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\n",
"\u001b[0m \u001b[0mbinsize\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'auto'\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\n",
"\u001b[0m \u001b[0mbandwidth\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'silverman'\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\n",
"\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mfig_kw\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\n",
"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;31mDocstring:\u001b[0m\n",
"Return a plot with the ditribution of (apparent or actual) grain sizes\n",
"in a dataset.\n",
"\n",
"Parameters\n",
"----------\n",
"data : array_like\n",
" the size of the grains\n",
"\n",
"plot : string, tuple or list; optional\n",
" the type of plot, either histogram ('hist'), kernel density estimate\n",
" ('kde') or both ('hist', 'kde'). Default is both.\n",
"\n",
"avg : string, tuple or list; optional\n",
" the central tendency measures o show, either the arithmetic ('amean')\n",
" or geometric ('gmean') means, the median ('median'), and/or the\n",
" KDE-based mode ('mode'). Default all averages.\n",
"\n",
"binsize : string or positive scalar; optional\n",
" If 'auto', it defines the plug-in method to calculate the bin size.\n",
" When integer or float, it directly specifies the bin size.\n",
" Default: the 'auto' method.\n",
"\n",
" | Available plug-in methods:\n",
" | 'auto' (fd if sample_size > 1000 or Sturges otherwise)\n",
" | 'doane' (Doane's rule)\n",
" | 'fd' (Freedman-Diaconis rule)\n",
" | 'rice' (Rice's rule)\n",
" | 'scott' (Scott rule)\n",
" | 'sqrt' (square-root rule)\n",
" | 'sturges' (Sturge's rule)\n",
"\n",
"bandwidth : string {'silverman' or 'scott'} or positive scalar; optional\n",
" the method to estimate the bandwidth or a scalar directly defining the\n",
" bandwidth. It uses the Silverman plug-in method by default.\n",
"\n",
"**fig_kw :\n",
" additional keyword arguments to control the size (figsize) and\n",
" resolution (dpi) of the plot. Default figsize is (6.4, 4.8).\n",
" Default resolution is 100 dpi.\n",
"\n",
"Call functions\n",
"--------------\n",
"- gaussian_kde (from Scipy stats)\n",
"\n",
"Examples\n",
"--------\n",
">>> distribution(data['diameters'])\n",
">>> distribution(data['diameters'], figsize=(6.4, 4.8))\n",
"\n",
"Returns\n",
"-------\n",
"A plot showing the distribution of (apparent) grain sizes and\n",
"the location of the averages defined.\n",
"\u001b[1;31mFile:\u001b[0m c:\\users\\marco\\documents\\github\\grainsizetools\\grain_size_tools\\plot.py\n",
"\u001b[1;31mType:\u001b[0m function"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
Expand All @@ -780,7 +782,7 @@
"text": [
"=======================================\n",
"Shapiro-Wilk test (lognormal):\n",
"0.99, 0.01 (test statistic, p-value)\n",
"0.98, 0.00 (test statistic, p-value)\n",
"It doesnt look like a lognormal distribution (p-value < 0.05)\n",
"(╯°□°）╯︵ ┻━┻\n",
"=======================================\n"
Expand Down Expand Up @@ -952,13 +954,15 @@
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"metadata": {
"tags": []
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Notebook tested in 2023-11-13 using:\n",
"Notebook tested in 2023-11-14 using:\n",
"Python 3.10.13 | packaged by Anaconda, Inc. | (main, Sep 11 2023, 13:24:38) [MSC v.1916 64 bit (AMD64)]\n",
"Numpy 1.26.0\n",
"Matplotlib 3.8.0\n"
Expand All @@ -980,7 +984,7 @@
],
"metadata": {
"kernelspec": {
"display_name": "main",
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
Expand All @@ -998,5 +1002,5 @@
}
},
"nbformat": 4,
"nbformat_minor": 2
"nbformat_minor": 4
}
43 changes: 43 additions & 0 deletions DOCS/paleopiezometry_module.ipynb
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@@ -0,0 +1,43 @@
{
"cells": [
{
"cell_type": "markdown",
"id": "10bf9962-b18d-47a5-80d4-b66348dd8956",
"metadata": {},
"source": [
"# The paleopiezometry module\n",
"\n",
"TODO"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "93287c25-1187-44c0-985d-3f293cf99762",
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.10.13"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
45 changes: 45 additions & 0 deletions DOCS/stereology_module.ipynb
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{
"cells": [
{
"cell_type": "markdown",
"id": "7f085718-4204-45b0-a268-6d4df51d81ef",
"metadata": {
"tags": []
},
"source": [
"# The Stereology module\n",
"\n",
"TODO"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "315f5820-a286-4277-a4a5-e1a24f6940d6",
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.10.13"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
43 changes: 43 additions & 0 deletions templates/grain_size_analysis.ipynb
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{
"cells": [
{
"cell_type": "markdown",
"id": "f45741ac-0aa1-44c0-917f-b0d3ee2990fd",
"metadata": {},
"source": [
"# Grain Size Analysis Template\n",
"\n",
"**Goal**: TODO"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "f6d4b773-f3cd-4cb5-8b63-027062c090c6",
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.10.13"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
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