This notebook demonstrate how to use the PlateModelManager to access plate model files.¶
In [1]:
import os, warnings
from pathlib import Path
import cartopy.crs as ccrs
import matplotlib.pyplot as plt
from plate_model_manager import PlateModelManager, PresentDayRasterManager
from gplately import PlateReconstruction, PlotTopologies
from gplately.commands.list_models import get_model_names
warnings.filterwarnings("ignore", category=UserWarning, module="gplately")
pm_manager = PlateModelManager()
Get the names of all available models in the PlateModelManager¶
In [2]:
gplately_model_names = get_model_names()
for name in pm_manager.get_available_model_names():
# the pm_manager.get_available_model_names() returns a superset of GPlately models.
# we need to check the the model name agaist a list of GPlately officially supported models.
# https://gplates.github.io/gplately/latest/sphinx/html/plate_models.html
if name in gplately_model_names:
print(name)
shirmard2025 alfonso2024 cao2024 muller2022 zahirovic2022 merdith2021 clennett2020 clennett2020_m2019 clennett2020_s2013 muller2019 young2018 torsvikcocks2017 matthews2016 matthews2016_pmag_ref muller2016 scotese2016 zahirovic2016 gibbons2015 zahirovic2014 shephard2013 gurnis2012 seton2012 muller2008
Download model "Muller2019" and put the files in folder "plate-model-repo"¶
In [3]:
model = pm_manager.get_model("Muller2019")
assert model
model.set_data_dir("plate-model-repo")
for layer in model.get_avail_layers():
model.get_layer(layer)
# now let's see what are inside the "plate-model-repo/muller2019" folder
print(os.listdir("plate-model-repo/muller2019"))
['Johansson2018LIPs', 'ContinentalPolygons', 'SeafloorFabric', '.metadata.json', 'Whittaker2015LIPs', 'COBs', 'StaticPolygons', 'Coastlines', 'readme.txt', 'Hotspots', 'Topologies']
List all vailable layers in model Muller2019¶
In [4]:
for layer in model.get_avail_layers():
print(layer)
Coastlines StaticPolygons ContinentalPolygons Topologies COBs Hotspots SeafloorFabric Whittaker2015LIPs Johansson2018LIPs
Download rotation files¶
In [5]:
rotation_files = model.get_rotation_model()
print(rotation_files)
['plate-model-repo/muller2019/Rotations/Muller_etal_2019_CombinedRotations.rot']
Download static polygons¶
In [6]:
static_polygon_files = model.get_layer("StaticPolygons")
print(static_polygon_files)
['plate-model-repo/muller2019/StaticPolygons/Muller_etal_2019_Global_StaticPlatePolygons.gpmlz']
Download Coastlines¶
In [7]:
coasts_files = model.get_layer("Coastlines")
print(coasts_files)
['plate-model-repo/muller2019/Coastlines/Muller_etal_2019_Global_Coastlines.gpmlz']
Download all layers¶
In [8]:
for layer in model.get_avail_layers():
print(model.get_layer(layer))
['plate-model-repo/muller2019/Coastlines/Muller_etal_2019_Global_Coastlines.gpmlz'] ['plate-model-repo/muller2019/StaticPolygons/Muller_etal_2019_Global_StaticPlatePolygons.gpmlz'] ['plate-model-repo/muller2019/ContinentalPolygons/Global_PresentDay_ContPolygons_2019_v1.shp'] ['plate-model-repo/muller2019/Topologies/Muller_etal_2019_PlateBoundaries_DeformingNetworks.gpmlz'] ['plate-model-repo/muller2019/COBs/Global_COBLineSegments_2019_v1.gpmlz'] ['plate-model-repo/muller2019/Hotspots/Hotspots_Compilation_Whittaker_etal.gpmlz'] ['plate-model-repo/muller2019/SeafloorFabric/FZ_cookiecut.gpmlz', 'plate-model-repo/muller2019/SeafloorFabric/FZLC_cookiecut.gpmlz', 'plate-model-repo/muller2019/SeafloorFabric/UNCV_cookiecut.gpmlz', 'plate-model-repo/muller2019/SeafloorFabric/DZ_cookiecut.gpmlz', 'plate-model-repo/muller2019/SeafloorFabric/VANOM_cookiecut.gpmlz', 'plate-model-repo/muller2019/SeafloorFabric/ExtinctRidges_cookiecut.gpmlz'] ['plate-model-repo/muller2019/Whittaker2015LIPs/Whittaker_etal_2015_LIPs.gpmlz'] ['plate-model-repo/muller2019/Johansson2018LIPs/Johansson_etal_2018_VolcanicProvinces_v2.gpmlz']
Get a list of time dependent rasters¶
In [9]:
for raster in model.get_avail_time_dependent_raster_names():
print(raster)
AgeGrids SedimentThickness
Download AgeGrids rasters¶
In [10]:
import warnings
warnings.filterwarnings("ignore")
print(model.get_rasters("AgeGrids", times=[10, 20, 30]))
print(model.get_raster("AgeGrids", time=100))
['plate-model-repo/muller2019/Rasters/AgeGrids/Muller_etal_2019_Tectonics_v2.0_AgeGrid-10.nc', 'plate-model-repo/muller2019/Rasters/AgeGrids/Muller_etal_2019_Tectonics_v2.0_AgeGrid-20.nc', 'plate-model-repo/muller2019/Rasters/AgeGrids/Muller_etal_2019_Tectonics_v2.0_AgeGrid-30.nc']
plate-model-repo/muller2019/Rasters/AgeGrids/Muller_etal_2019_Tectonics_v2.0_AgeGrid-100.nc
Download AgeGrids rasters for all available times¶
This function will take a while to finish and download a large volume data. Uncomment the code in the code cell below to try it.
In [11]:
# model.download_time_dependent_rasters("AgeGrids")
List the names of all present-day rasters¶
In [12]:
print(PresentDayRasterManager().list_present_day_rasters())
['etopo1_grd', 'etopo1_tif', 'topography', 'agegrid', 'vgg']
Get "topography" present-day raster¶
In [13]:
print(PresentDayRasterManager().get_raster("topography"))
present-day-rasters/topography/topo15-3601x1801.nc
In [14]:
# use `PlateModelManager` to create `PlateReconstruction` and `PlotTopologies` objects
model = PlateModelManager().get_model("Zahirovic2022")
model.set_data_dir("plate-model-repo") # type: ignore
age = 55
test_model = PlateReconstruction(
model.get_rotation_model(), # type: ignore
topology_features=model.get_layer("Topologies"), # type: ignore
static_polygons=model.get_layer("StaticPolygons"), # type: ignore
)
gplot = PlotTopologies(
test_model,
coastlines=model.get_layer("Coastlines"), # type: ignore
COBs=model.get_layer("COBs"), # type: ignore
time=age,
)
fig = plt.figure(figsize=(12, 6), dpi=72)
ax = fig.add_subplot(111, projection=ccrs.Robinson(central_longitude=180))
ax.set_global() # type: ignore
# now use PlotTopologies object to plot some model data
gplot.plot_continent_ocean_boundaries(ax, color="cornflowerblue")
gplot.plot_coastlines(ax, color="black")
gplot.plot_all_topological_sections(
ax,
plot_subduction_teeth=True,
other_kwargs={
"color": "grey",
"linewidth": 0.5,
},
ridge_kwargs={
"color": "black",
"linewidth": 0.7,
},
transform_kwargs={
"color": "green",
"linewidth": 0.7,
},
trench_kwargs={
"color": "blue",
"linewidth": 0.7,
},
)
plt.title(f"{age} Ma")
# save the map as a .png file
data_dir = Path("./gplately-example-data")
data_dir.mkdir(parents=True, exist_ok=True)
output_file = data_dir / "02-PlateModelManager.png"
fig.savefig(output_file, dpi=120, bbox_inches="tight") # transparent=True)
print(f"Done! The output file {output_file} has been saved.")
plt.show()
plt.close(fig)
Done! The output file gplately-example-data/02-PlateModelManager.png has been saved.
