gempy-simple-interactive.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\", category=RuntimeWarning) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "params = {'legend.fontsize': 'x-large',\n",
    "          'figure.figsize': (15, 5),\n",
    "         'axes.labelsize': 'x-large',\n",
    "         'axes.titlesize':'x-large',\n",
    "         'xtick.labelsize':'x-large',\n",
    "         'ytick.labelsize':'x-large'}\n",
    "plt.rcParams.update(params)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<module 'gempy_simple' from '/Users/flow/git/teaching-geomodeling/gempy_simple.py'>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import gempy_simple\n",
    "import importlib\n",
    "importlib.reload(gempy_simple)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "intp = gempy_simple.interp_with_drift()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "layer1 = np.array([[0, 0], [2, 0], [3, 0], [4, 0]])\n",
    "layer2 = np.array([[0, 2], [2, 2], [3, 3]])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "gempy_simple.plot_interp_with_drift(point_1_y = 2.,\n",
    "                                    point_2_y = 2.4,\n",
    "                                    G_1_x=0,\n",
    "                                    show_contours=False, \n",
    "                                    show_field=False,\n",
    "                                    pick_contour=False,\n",
    "                                    contour_val=2.8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "from ipywidgets import interactive                      # widgets and interactivity\n",
    "from ipywidgets import widgets                            \n",
    "from ipywidgets import Layout\n",
    "from ipywidgets import Label\n",
    "from ipywidgets import VBox, HBox"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "from ipywidgets import interact, interactive, fixed, interact_manual\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "b4007f50fed54d42ada19371e4798dfa",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "interactive(children=(FloatSlider(value=2.0, description='point_1_y', max=3.0, min=1.0), FloatSlider(value=3.0…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "interact(gempy_simple.plot_interp_with_drift,\n",
    "         point_1_y=(1.,3.,0.1),\n",
    "         point_2_y=(1.,3.,0.1),\n",
    "         contour_val=(1.,3.,0.1));"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}