Miscellaneous Operations

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import geolime as geo
from pyproj import CRS
import numpy as np
import geolime as geo
from pyproj import CRS
import numpy as np

GeoLime python library consists in objects suited for geoscience analyses and specifically resource modelling and estimation.

Load rocklea drillholes geolime objects using existing dataset included in the library.

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dh = geo.datasets.load(data_name="rocklea_dome/dh_hyper.geo")
dh = geo.datasets.load(data_name="rocklea_dome/dh_hyper.geo")

Explore Loaded objects¶

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dh.name
dh.name

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'rck_hs'

We can list the different holes using the holeids() method.

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dh.holeids()
dh.holeids()

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array(['RKC278', 'RKC279', 'RKC280', 'RKC281', 'RKC282', 'RKC283',
       'RKC284', 'RKC285', 'RKC286', 'RKC287', 'RKC288', 'RKC289',
       'RKC290', 'RKC291', 'RKC292', 'RKC293', 'RKC294', 'RKC295',
       'RKC296', 'RKC297', 'RKC298', 'RKC299', 'RKC300', 'RKC301',
       'RKC302', 'RKC303', 'RKC304', 'RKC305', 'RKC306', 'RKC307',
       'RKC308', 'RKC309', 'RKC310', 'RKC311', 'RKC312', 'RKC313',
       'RKC314', 'RKC315', 'RKC316', 'RKC317', 'RKC318', 'RKC319',
       'RKC320', 'RKC321', 'RKC322', 'RKC323', 'RKC324', 'RKC325',
       'RKC326', 'RKC327', 'RKC328', 'RKC329', 'RKC330', 'RKC331',
       'RKC332', 'RKC333', 'RKC334', 'RKC335', 'RKC336', 'RKC337',
       'RKC338', 'RKC339', 'RKC340', 'RKC341', 'RKC342', 'RKC344',
       'RKC345', 'RKC346', 'RKC347', 'RKC348', 'RKC358', 'RKC359',
       'RKC360', 'RKC361', 'RKC362', 'RKC363', 'RKC364', 'RKC365',
       'RKC366', 'RKC367', 'RKC368', 'RKC369', 'RKC370', 'RKC371',
       'RKC372', 'RKC373', 'RKC374', 'RKC375', 'RKC376', 'RKC377',
       'RKC378', 'RKC379', 'RKC380', 'RKC381', 'RKC382', 'RKC383',
       'RKC384', 'RKC385', 'RKC386', 'RKC387', 'RKC388', 'RKC389',
       'RKC390', 'RKC391', 'RKC392', 'RKC393', 'RKC394', 'RKC395',
       'RKC396', 'RKC397', 'RKC398', 'RKC399', 'RKC400', 'RKC401',
       'RKC402', 'RKC403', 'RKC404', 'RKC405', 'RKC406', 'RKC407',
       'RKC408', 'RKC409', 'RKC410', 'RKC411', 'RKC412', 'RKC413',
       'RKC414', 'RKC415', 'RKC416', 'RKC417', 'RKC418', 'RKC419',
       'RKC420', 'RKC421', 'RKC422', 'RKC423', 'RKC424', 'RKC425',
       'RKC426', 'RKC427', 'RKC428', 'RKC429', 'RKC430', 'RKC431',
       'RKC432', 'RKC433', 'RKC437', 'RKC438', 'RKC443', 'RKC444',
       'RKC445', 'RKC446', 'RKC447', 'RKC448', 'RKC449', 'RKC450',
       'RKC451', 'RKC452', 'RKC453', 'RKC454', 'RKC455', 'RKC456',
       'RKC457', 'RKC458', 'RKC459', 'RKC460', 'RKC461', 'RKC462',
       'RKC463', 'RKC464', 'RKC465', 'RKC466', 'RKC467', 'RKC468',
       'RKC469', 'RKC470', 'RKC471', 'RKC472', 'RKC473', 'RKC474',
       'RKC475', 'RKC476', 'RKC477', 'RKC478', 'RKC479', 'RKC480',
       'RKC481', 'RKC482', 'RKC483', 'RKC484', 'RKC485', 'RKD015'],
      dtype=object)

List all available methods and attribute of an object:

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dh
dh

Out[6]:

Drillholes 
└─aggregate() 
└─bounds() 
└─builtins ⇨ {'X': 0, 'Y': 1, 'Z': 2, 'HOLEID': 3, 'FROM': 4, 'TO': 5} 
└─centroid() 
└─collar_coords() 
└─convert_to_gis_object() 
└─coords() 
└─copy() 
└─data() 
└─default_support ⇨ ELEMENT 
└─delete_composites() 
└─delete_holes() 
└─describe() 
└─element_count() 
└─generate_attribute_name() 
└─holeids() 
└─internals() 
└─keep_composites() 
└─keep_holes() 
└─mid_coords() 
└─name ⇨ rck_hs 
└─node_coords() 
└─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() 
└─to_csv() 
└─to_dataframe() 
└─to_file() 
└─to_pyvista() 
└─transform() 
└─translate_by() 
└─user_properties()

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dh.describe()
dh.describe()

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	__TOPO_LEVEL__	__TOPO_TYPE__	__TOPO_ELEMENT_V1__	__TOPO_ELEMENT_V2__	FROM	TO	X_COLLAR	Y_COLLAR	Z_COLLAR	X_M	...	LOI	Fe_ox_ai	hem_over_goe	kaolin_abundance	kaolin_composition	wmAlsmai	wmAlsmci	carbai3pfit	carbci3pfit	Depth
count	7134.0	7134.0	7134.000000	7134.000000	7134.000000	7134.000000	7134.000000	7.134000e+03	7134.000000	7134.000000	...	4979.000000	6920.000000	6663.000000	3850.000000	3850.000000	1693.000000	1693.000000	343.000000	343.000000	7134.000000
mean	2.0	1.0	3662.478133	3663.478133	20.547939	21.547939	547414.513625	7.475480e+06	458.496790	547414.513625	...	11.303017	0.175605	910.741882	0.162095	1.023877	0.053175	2207.759327	0.070173	2319.090816	42.666526
std	0.0	0.0	2118.998485	2118.998485	13.833730	13.833730	686.760995	1.585644e+03	7.466562	686.760995	...	4.642987	0.083948	8.857894	0.082429	0.046523	0.038166	3.046365	0.024599	9.787246	11.263359
min	2.0	1.0	0.000000	1.000000	0.000000	1.000000	545494.900000	7.472793e+06	441.000000	545494.900000	...	0.710000	0.001300	869.780000	0.017200	0.937000	0.020000	2186.280000	0.040100	2296.430000	7.000000
25%	2.0	1.0	1823.250000	1824.250000	9.000000	10.000000	546904.700000	7.474003e+06	453.800000	546904.700000	...	9.670000	0.107000	907.705000	0.091025	0.991000	0.028100	2206.370000	0.052750	2312.235000	37.000000
50%	2.0	1.0	3663.500000	3664.500000	19.000000	20.000000	547586.100000	7.475604e+06	458.200000	547586.100000	...	11.200000	0.185500	911.130000	0.161000	1.007000	0.039600	2206.910000	0.063600	2321.900000	46.000000
75%	2.0	1.0	5499.750000	5500.750000	31.000000	32.000000	547947.600000	7.476004e+06	463.600000	547947.600000	...	11.900000	0.246000	914.480000	0.226000	1.049750	0.064200	2208.140000	0.080500	2324.615000	51.000000
max	2.0	1.0	7324.000000	7325.000000	60.000000	61.000000	548498.100000	7.479595e+06	478.600000	548498.100000	...	45.280000	0.355000	974.080000	0.413000	1.192000	0.285000	2233.120000	0.182000	2338.390000	61.000000

8 rows × 38 columns

Spatial Information¶

Bounds of an object represent the coordinates of the most furthest points of the bounding box of the object.

No description has been provided for this image

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dh.bounds()
dh.bounds()

Out[8]:

array([[5.4549490e+05, 7.4727934e+06, 3.8970000e+02],
       [5.4849810e+05, 7.4795953e+06, 4.7810000e+02]])

Centroid correspond to the mean coordinates of the data along each axis. It can be also be seen as the "center of mass" or barycentre.

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dh.centroid()
dh.centroid()

Out[9]:

array([5.47414514e+05, 7.47548047e+06, 4.37448851e+02])

Aggregate value along a drillhole¶

Aggregation operations aims to reduce the number of spatial dimension of a GeoLime object. This will enable to display object on map with a selected property with a specific aggregation method. Drillholes object are aggregated along a specific hole, meaning that all values from a given hole are aggregated using the specific aggregation method. Aggregation methods are available.

Note that the aggregation is not weighted by the composite size, meaning that irregular composite may lead to biased aggregation value.

Get the smallest grade per drillhole¶

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dh.aggregate(properties=["Fe"], agg_methods=["min"])
dh.aggregate(properties=["Fe"], agg_methods=["min"])

Out[10]:

	HOLEID	X_min	X	Y_min	Y	Z_min	Z	__TOPO_LEVEL___min	__TOPO_TYPE___min	__TOPO_ELEMENT_V1___min	...	LOI_min	Fe_ox_ai_min	hem_over_goe_min	kaolin_abundance_min	kaolin_composition_min	wmAlsmai_min	wmAlsmci_min	carbai3pfit_min	carbci3pfit_min	Depth_min
0	RKC278	548001.3	548001.3	7474002.1	7474002.1	406.5	453.5	2	1	0	...	7.30	0.01950	891.13	0.0269	0.958	0.0210	2206.08	0.0582	2301.30	47.0
1	RKC279	547804.6	547804.6	7474004.5	7474004.5	407.4	453.4	2	1	48	...	8.16	0.01950	893.42	0.0243	0.974	0.0217	2205.88	0.0450	2301.66	46.0
2	RKC280	547310.8	547310.8	7474002.2	7474002.2	403.8	453.8	2	1	95	...	8.53	0.03920	892.49	0.0254	0.946	0.0240	2205.81	0.0420	2323.12	51.0
3	RKC281	547100.8	547100.8	7474003.4	7474003.4	416.4	453.4	2	1	146	...	8.27	0.03150	895.25	0.1160	0.971	0.0222	2205.89	0.0626	2314.86	37.0
4	RKC282	547199.0	547199.0	7474801.2	7474801.2	449.0	456.0	2	1	184	...	NaN	0.11500	908.45	0.1520	1.065	NaN	NaN	NaN	NaN	7.0
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
187	RKC482	547748.8	547748.8	7475997.8	7475997.8	410.5	465.5	2	1	7125	...	7.42	0.05810	892.78	0.0229	0.986	0.0215	2206.20	0.0436	2322.69	55.0
188	RKC483	547825.6	547825.6	7473716.0	7473716.0	405.7	452.7	2	1	7181	...	5.24	0.01510	889.28	0.0321	0.965	0.0299	2205.54	0.0526	2310.33	47.0
189	RKC484	547866.5	547866.5	7473820.9	7473820.9	406.4	452.4	2	1	7229	...	8.00	0.00686	884.35	0.0353	0.976	0.0237	2205.70	0.0479	2305.67	46.0
190	RKC485	547913.3	547913.3	7473917.2	7473917.2	407.4	452.4	2	1	7276	...	8.75	0.03780	888.81	0.0306	0.985	0.0219	2205.02	NaN	NaN	46.0
191	RKD015	547905.0	547905.0	7476800.0	7476800.0	469.0	470.0	2	1	7322	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	41.0

192 rows × 45 columns