Import BlockModel Data

In [1]:

import geolime as geo
from pyproj import CRS

geo.Project().set_crs(CRS("EPSG:20350"))

import geolime as geo from pyproj import CRS geo.Project().set_crs(CRS("EPSG:20350"))

Create a BlockModel from a file¶

In [3]:

block_model = geo.block_model_from_file(
    name="block_model",
    file_path="../../../geolime/datasets/rocklea_dome/bm_panel.csv"
)

block_model = geo.block_model_from_file( name="block_model", file_path="../../../geolime/datasets/rocklea_dome/bm_panel.csv" )

In [4]:

block_model.axis()

block_model.axis()

Out[4]:

array([100., 100.,   1.])

In [5]:

block_model.user_properties()

block_model.user_properties()

Out[5]:

['in_channel_prop']

In [6]:

geo.plot_2d(block_model, property='in_channel_prop', agg_method='mean')

geo.plot_2d(block_model, property='in_channel_prop', agg_method='mean')

Create a BlockModel from a DataFrame¶

In [7]:

import pandas as pd

df = pd.read_csv("../../../geolime/datasets/rocklea_dome/bm_panel.csv")
block_model = geo.block_model_from_dataframe(name="my_block_model", dataframe=df)

block_model

import pandas as pd df = pd.read_csv("../../../geolime/datasets/rocklea_dome/bm_panel.csv") block_model = geo.block_model_from_dataframe(name="my_block_model", dataframe=df) block_model

Out[7]:

BlockModel 
└─aggregate() 
└─axis ⇨ 
   [[100.   0.   0.]
    [  0. 100.   0.]
    [  0.   0.   1.]] 
└─bounds() 
└─builtins ⇨ {'X': 0, 'Y': 1, 'Z': 2, 'U': 3, 'V': 4, 'W': 5} 
└─centroid() 
└─check_coords_bounds() 
└─convert_to_gis_object() 
└─coords() 
└─coords2idx() 
└─coords2xyz() 
└─copy() 
└─data() 
└─default_support ⇨ NODE 
└─delete_cells() 
└─describe() 
└─dimension() 
└─downscale() 
└─element_count() 
└─generate_attribute_name() 
└─idx2coords() 
└─indexed_coords() 
└─internals() 
└─keep_only_cells() 
└─n_cells() 
└─name ⇨ my_block_model 
└─origin ⇨ [5.468700e+05 7.473568e+06 4.090000e+02] 
└─plot_2d() 
└─properties() 
└─property() 
└─read_file() 
└─read_manifest() 
└─refresh_attributes() 
└─region() 
└─region_mask() 
└─regions() 
└─remove_attribute() 
└─remove_property() 
└─remove_region() 
└─rename_attribute() 
└─rename_property() 
└─rename_region() 
└─sample_count() 
└─set_property() 
└─set_property_expr() 
└─set_property_value() 
└─set_region() 
└─set_region_condition() 
└─set_region_value() 
└─shape ⇨ [16 36 55] 
└─to_csv() 
└─to_dataframe() 
└─to_file() 
└─to_pyvista() 
└─transform() 
└─translate_by() 
└─user_properties() 
└─volume() 

In [8]:

block_model.axis()

block_model.axis()

Out[8]:

array([100., 100.,   1.])

In [9]:

block_model.user_properties()

block_model.user_properties()

Out[9]:

['in_channel_prop']

In [10]:

geo.plot_2d(block_model, property='in_channel_prop', agg_method='mean')

geo.plot_2d(block_model, property='in_channel_prop', agg_method='mean')