{
"cells": [
{
"cell_type": "markdown",
"id": "1bb66c07-5ab9-4621-a531-75711a01452b",
"metadata": {},
"source": [
"# `pystrat` Quickstart Guide"
]
},
{
"cell_type": "markdown",
"id": "22517350-43ca-430a-aecc-35c74c3754be",
"metadata": {},
"source": [
"This Jupyter notebook is intended to quickly demonstrate core `pystrat` functionality. \n",
"\n",
"The example data files used in this guide can be found [here](https://github.com/pystrat-maintainers/pystrat/tree/main/docs/example-data)."
]
},
{
"cell_type": "markdown",
"id": "5077b685-adbf-42c3-9047-f6cacd41f0a8",
"metadata": {},
"source": [
"## Installation"
]
},
{
"cell_type": "markdown",
"id": "02c24d7c-0e44-45c8-a1fc-71d322c0aedd",
"metadata": {},
"source": [
"To install `pystrat`, use `pip`:\n",
"```bash\n",
"pip install pystrat\n",
"```"
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "faba2776-e571-4756-a459-215fba01d2d9",
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd\n",
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"\n",
"import pystrat"
]
},
{
"cell_type": "markdown",
"id": "461f09b6-2119-4320-a5db-3bed58a5b08c",
"metadata": {},
"source": [
"## Input Data"
]
},
{
"cell_type": "markdown",
"id": "4a17a80c-62eb-4557-a9c0-24b54c699662",
"metadata": {},
"source": [
"To plot sections with `pystrat`, it is necessary to provide a spreadsheet for each section in which the rows (downwards) correspond to units/beds going stratigraphically upwards, and there are minimally the following columns (see [example](example-data/lithostratigraphy.csv)):\n",
"- unit thickness (numeric)\n",
"- facies (string)\n",
"\n",
"The facies column dictates how each unit will be visualized according to the styling."
]
},
{
"cell_type": "markdown",
"id": "3b425e38-ad5d-41a9-88df-4510b7be00d1",
"metadata": {},
"source": [
"The following code creates a `pystrat.Section`:"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "5a888315-87f0-43b9-951c-a3c3c6c8c112",
"metadata": {},
"outputs": [],
"source": [
"# read in your lithostratigraphy data\n",
"litho_df = pd.read_csv('example-data/lithostratigraphy.csv')\n",
"\n",
"# create a pystrat Section from the lithostratigraphy data\n",
"section = pystrat.Section(litho_df['THICKNESS'], # unit thicknesses\n",
" litho_df['FACIES']) # unit facies"
]
},
{
"cell_type": "markdown",
"id": "6504cca1-8cf4-4c78-9ac2-08eeefede8ca",
"metadata": {},
"source": [
"### Styling"
]
},
{
"cell_type": "markdown",
"id": "8f265940-365a-4ea2-bdf6-4ffd8f15f1ab",
"metadata": {},
"source": [
"Before plotting sections, it is necessary to define a `pystrat.Style` object. This object provides the styling information for widths, colors, and swatches for units within a section, as well as annotations that can be plotted alongside sections.\n",
"\n",
"The styling logic is based on the unit facies, whereby each facies has a unique style specified in `pystrat.Style`. "
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "36656870-06af-4346-b26b-e91739692cd3",
"metadata": {},
"outputs": [
{
"data": {
"application/vnd.microsoft.datawrangler.viewer.v0+json": {
"columns": [
{
"name": "index",
"rawType": "int64",
"type": "integer"
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{
"name": "facies",
"rawType": "object",
"type": "string"
},
{
"name": "R",
"rawType": "int64",
"type": "integer"
},
{
"name": "G",
"rawType": "int64",
"type": "integer"
},
{
"name": "B",
"rawType": "int64",
"type": "integer"
},
{
"name": "width",
"rawType": "float64",
"type": "float"
},
{
"name": "swatch",
"rawType": "int64",
"type": "integer"
}
],
"conversionMethod": "pd.DataFrame",
"ref": "886029f4-2b21-4cdb-89fb-0594efdfcaed",
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"cover",
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"85",
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"intraclast breccia",
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"marl-ribbonite",
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"shape": {
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"\n",
"\n",
"
\n",
" \n",
" \n",
" | \n",
" facies | \n",
" R | \n",
" G | \n",
" B | \n",
" width | \n",
" swatch | \n",
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" | 1 | \n",
" grainstone | \n",
" 248 | \n",
" 169 | \n",
" 85 | \n",
" 0.8 | \n",
" 627 | \n",
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" \n",
" | 2 | \n",
" intraclast breccia | \n",
" 200 | \n",
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" \n",
" | 3 | \n",
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