![](\"../images/tutorial15_cover.png\")
"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Set File Paths and download Tutorial Data\n",
"\n",
"If you downloaded the latest `GemGIS` version from the Github repository, append the path so that the package can be imported successfully. Otherwise, it is recommended to install `GemGIS` via `pip install gemgis` and import `GemGIS` using `import gemgis as gg`. In addition, the file path to the folder where the data is being stored is set. The tutorial data is downloaded using Pooch (https://www.fatiando.org/pooch/latest/index.html) and stored in the specified folder. Use `pip install pooch` if Pooch is not installed on your system yet."
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"ExecuteTime": {
"end_time": "2021-03-17T11:11:12.890845Z",
"start_time": "2021-03-17T11:11:10.328249Z"
}
},
"outputs": [],
"source": [
"import gemgis as gg\n",
"\n",
"file_path ='data/15_opening_leapfrog_meshes_and_gocad_tsurfaces/'"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"ExecuteTime": {
"end_time": "2021-03-17T11:11:13.302340Z",
"start_time": "2021-03-17T11:11:10.322Z"
}
},
"outputs": [],
"source": [
"gg.download_gemgis_data.download_tutorial_data(filename=\"15_opening_leapfrog_meshes_and_gocad_tsurfaces.zip\", dirpath=file_path)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Reading Leapfrog Meshes\n",
"\n",
"### Loading the Mesh Data\n",
"\n",
"The Leapfrog mesh (.msh) is loaded and parsed with ``read_msh(..)``. A dictionary containing the face and vertex data will be returned."
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"ExecuteTime": {
"end_time": "2021-01-14T08:19:58.119712Z",
"start_time": "2021-01-14T08:19:55.049878Z"
}
},
"outputs": [],
"source": [
"import numpy as np\n",
"\n",
"data = gg.raster.read_msh(file_path + 'GM_Granodiorite.msh')"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"ExecuteTime": {
"end_time": "2021-01-14T08:19:58.135713Z",
"start_time": "2021-01-14T08:19:58.121711Z"
}
},
"outputs": [
{
"data": {
"text/plain": [
"{'Tri': array([[ 0, 1, 2],\n",
" [ 0, 3, 1],\n",
" [ 4, 3, 0],\n",
" ...,\n",
" [53677, 53672, 53680],\n",
" [53679, 53677, 53680],\n",
" [53673, 53672, 53677]]),\n",
" 'Location': array([[ 1.44625109e+06, 5.24854344e+06, -1.12743862e+02],\n",
" [ 1.44624766e+06, 5.24854640e+06, -1.15102216e+02],\n",
" [ 1.44624808e+06, 5.24854657e+06, -1.15080548e+02],\n",
" ...,\n",
" [ 1.44831008e+06, 5.24896679e+06, -1.24755449e+02],\n",
" [ 1.44830385e+06, 5.24896985e+06, -1.33694397e+02],\n",
" [ 1.44829874e+06, 5.24897215e+06, -1.42506587e+02]])}"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"ExecuteTime": {
"end_time": "2021-01-14T08:19:58.150726Z",
"start_time": "2021-01-14T08:19:58.136716Z"
}
},
"outputs": [
{
"data": {
"text/plain": [
"dict_keys(['Tri', 'Location'])"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data.keys()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Converting Mesh Data to PyVista PolyData\n",
"\n",
"The loaded data will now be converted to PyVista PolyData using ``create_polydata_from_msh(..)``."
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"ExecuteTime": {
"end_time": "2021-01-14T08:19:58.437798Z",
"start_time": "2021-01-14T08:19:58.152727Z"
}
},
"outputs": [
{
"data": {
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