├── Exploring_decompaction_with_Python.ipynb ├── Forest_cover_change_in_Eastern_Europe.ipynb ├── License.txt ├── README.md ├── Read_and_plot_SVG_file.ipynb ├── channel_sinuosities.ipynb ├── diffusion_equation.ipynb ├── grain_settling.ipynb └── torkeal1.svg /License.txt: -------------------------------------------------------------------------------- 1 | Apache License 2 | Version 2.0, January 2004 3 | http://www.apache.org/licenses/ 4 | 5 | TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION 6 | 7 | 1. Definitions. 8 | 9 | "License" shall mean the terms and conditions for use, reproduction, 10 | and distribution as defined by Sections 1 through 9 of this document. 11 | 12 | "Licensor" shall mean the copyright owner or entity authorized by 13 | the copyright owner that is granting the License. 14 | 15 | "Legal Entity" shall mean the union of the acting entity and all 16 | other entities that control, are controlled by, or are under common 17 | control with that entity. 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-------------------------------------------------------------------------------- 1 | IPython notebooks 2 | ======================= 3 | This repository is a collection of IPython notebooks that deal with aspects of sedimentology / sedimentary geology. 4 | 5 | - [Grain settling](https://github.com/zsylvester/notebooks/blob/master/grain_settling.ipynb) - Plotting and comparing settling velocities calculated with different methods (Stokes, turbulent, and Ferguson-Church) 6 | 7 | - [Submarine channel sinuosities](https://github.com/zsylvester/notebooks/blob/master/channel_sinuosities.ipynb) - A critical look at how submarine channel sinuosities change with latitude and slope 8 | 9 | - [Diffusion equation](https://github.com/zsylvester/notebooks/blob/master/diffusion_equation.ipynb) - Solving the diffusion equation numerically, following Chapter 2 in Slingerland and Kump (2011), Mathematical Modeling of Earth's Dynamical Systems, Princeton University Press. 10 | 11 | - [Decompaction](https://github.com/zsylvester/notebooks/blob/master/Exploring_decompaction_with_Python.ipynb) 12 | 13 | - [Forest cover change in the Carpathian area since 1985](https://github.com/zsylvester/notebooks/blob/master/Forest_cover_change_in_Eastern_Europe.ipynb) -------------------------------------------------------------------------------- /Read_and_plot_SVG_file.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "code", 5 | "execution_count": 10, 6 | "metadata": {}, 7 | "outputs": [], 8 | "source": [ 9 | "import numpy as np\n", 10 | "import matplotlib.pyplot as plt\n", 11 | "%matplotlib inline\n", 12 | "\n", 13 | "def get_next_target(page,string_to_find):\n", 14 | " start_line = page.find(string_to_find)\n", 15 | " if start_line == -1:\n", 16 | " return None, 0\n", 17 | " start_quote = page.find('\"', start_line)\n", 18 | " end_quote = page.find('\"', start_quote + 1)\n", 19 | " line = page[start_quote + 1:end_quote]\n", 20 | " return line, end_quote\n", 21 | "\n", 22 | "def get_all_lines(page,string_to_find,lines):\n", 23 | " while True:\n", 24 | " line, endpos = get_next_target(page,string_to_find)\n", 25 | " if line:\n", 26 | " lines.append(line.split())\n", 27 | " page = page[endpos:]\n", 28 | " else:\n", 29 | " break\n", 30 | " return lines\n" 31 | ] 32 | }, 33 | { 34 | "cell_type": "code", 35 | "execution_count": 11, 36 | "metadata": {}, 37 | "outputs": [ 38 | { 39 | "data": { 40 | "text/plain": [ 41 | "(20.31225, 769.1207499999999, 218.34044999999998, 160.51455)" 42 | ] 43 | }, 44 | "execution_count": 11, 45 | "metadata": {}, 46 | "output_type": "execute_result" 47 | }, 48 | { 49 | "data": { 50 | "image/png": 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" 53 | ] 54 | }, 55 | "metadata": {}, 56 | "output_type": "display_data" 57 | } 58 | ], 59 | "source": [ 60 | "fname = 'torkeal1.svg'\n", 61 | "f = open(fname)\n", 62 | "t = f.read()\n", 63 | "f.close()\n", 64 | "\n", 65 | "lines=[]\n", 66 | "get_all_lines(t,'points=',lines)\n", 67 | "\n", 68 | "all_lines = []\n", 69 | "for i in range(0,np.size(lines)):\n", 70 | " temp = np.zeros([np.size(lines[i]),2])\n", 71 | " for j in range(0,np.size(lines[i])):\n", 72 | " temp[j,0] = float(lines[i][j].split(',')[0])\n", 73 | " temp[j,1] = float(lines[i][j].split(',')[1])\n", 74 | " all_lines.append(temp)\n", 75 | "\n", 76 | "fig1 = plt.figure(figsize=(20, 10))\n", 77 | "for i in range(0,np.size(all_lines)):\n", 78 | " plt.plot(all_lines[i][:,0],all_lines[i][:,1],'k')\n", 79 | "plt.gca().invert_yaxis()\n", 80 | "plt.axis('equal')\n" 81 | ] 82 | }, 83 | { 84 | "cell_type": "code", 85 | "execution_count": null, 86 | "metadata": {}, 87 | "outputs": [], 88 | "source": [] 89 | } 90 | ], 91 | "metadata": { 92 | "kernelspec": { 93 | "display_name": "Python 3", 94 | "language": "python", 95 | "name": "python3" 96 | }, 97 | "language_info": { 98 | "codemirror_mode": { 99 | "name": "ipython", 100 | "version": 3 101 | }, 102 | "file_extension": ".py", 103 | "mimetype": "text/x-python", 104 | "name": "python", 105 | "nbconvert_exporter": "python", 106 | "pygments_lexer": "ipython3", 107 | "version": "3.5.5" 108 | } 109 | }, 110 | "nbformat": 4, 111 | "nbformat_minor": 2 112 | } 113 | -------------------------------------------------------------------------------- /torkeal1.svg: -------------------------------------------------------------------------------- 1 | 2 | 3 | 4 | 6 | 28 | 46 | 48 | 77 | 108 | 125 | 134 | 139 | 140 | 141 | 142 | 143 | 144 | 145 | 146 | 149 | 150 | 151 | 152 | 153 | 154 | 155 | 156 | 161 | 169 | 176 | 187 | 199 | 201 | 203 | 205 | 207 | 209 | 218 | 225 | 228 | 236 | 239 | 242 | 247 | 249 | 255 | 260 | 262 | 265 | 267 | ? 268 | 271 | 274 | 276 | 283 | 290 | 304 | 318 | 326 | 331 | 334 | 337 | 340 | 345 | 349 | 351 | 353 | 356 | 358 | 376 | 391 | 401 | 415 | 427 | 429 | 431 | 435 | 437 | 438 | --------------------------------------------------------------------------------