diff --git a/examples/Example.ipynb b/examples/Example.ipynb
index 0a66ce9..b575a77 100644
--- a/examples/Example.ipynb
+++ b/examples/Example.ipynb
@@ -9,7 +9,7 @@
},
{
"cell_type": "code",
- "execution_count": 1,
+ "execution_count": null,
"metadata": {},
"outputs": [],
"source": [
@@ -18,7 +18,8 @@
"from pyGMS import GMS\n",
"import matplotlib.pyplot as plt\n",
"from matplotlib import tri\n",
- "import numpy as np"
+ "import numpy as np\n",
+ "%matplotlib inline"
]
},
{
@@ -30,111 +31,27 @@
},
{
"cell_type": "code",
- "execution_count": 2,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Loading ./model.fem\n",
- "Done!\n",
- "Triangulating layers\n",
- "Done!\n"
- ]
- }
- ],
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
"source": [
"model = GMS('./model.fem')"
]
},
{
"cell_type": "code",
- "execution_count": 3,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/html": [
- "
\n",
- " \n",
- " \n",
- " | \n",
- " ./model.fem | \n",
- "
\n",
- " \n",
- " \n",
- " \n",
- " Number of layers | \n",
- " 46 | \n",
- "
\n",
- " \n",
- " Number of unique layers | \n",
- " 6 | \n",
- "
\n",
- " \n",
- " Points per layer | \n",
- " 1881 | \n",
- "
\n",
- " \n",
- " Number of points | \n",
- " 86526 | \n",
- "
\n",
- " \n",
- " xlim | \n",
- " (200000.0, 1000000.0) | \n",
- "
\n",
- " \n",
- " ylim | \n",
- " (6200000.0, 7600000.0) | \n",
- "
\n",
- " \n",
- " zlim | \n",
- " (-102500.104684, 4736.80127) | \n",
- "
\n",
- " \n",
- "
"
- ],
- "text/plain": [
- ""
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
"source": [
"model.info"
]
},
{
"cell_type": "code",
- "execution_count": 4,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- ""
- ]
- },
- "execution_count": 4,
- "metadata": {},
- "output_type": "execute_result"
- },
- {
- "data": {
- "image/png": "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\n",
- "text/plain": [
- "