Miscellaneous Operations

In [1]:

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

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

Load rocklea drillholes geolime objects:

In [3]:

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¶

In [4]:

dh.name

dh.name

Out[4]:

'rck_hs'

List all available methods and attribute of an object:

In [5]:

dh

dh

Out[5]:

Drillholes 
└─aggregate() 
└─bounds() 
└─builtins ⇨ {'X': 0, 'Y': 1, 'Z': 2, 'HOLEID': 3, 'FROM': 4, 'TO': 5} 
└─centroid() 
└─collar_coords() 
└─convert_to_gis_object() 
└─coords() 
└─data() 
└─default_support ⇨ AttributeSupportType.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() 
└─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() 

List all existing properties¶

In [6]:

dh.properties()

dh.properties()

Out[6]:

['__TOPO_LEVEL__',
 '__TOPO_TYPE__',
 '__TOPO_ELEMENT_V1__',
 '__TOPO_ELEMENT_V2__',
 'FROM',
 'TO',
 'HOLEID',
 'X_COLLAR',
 'Y_COLLAR',
 'Z_COLLAR',
 'X_M',
 'Y_M',
 'Z_M',
 'X_B',
 'Y_B',
 'Z_B',
 'X_E',
 'Y_E',
 'Z_E',
 'Fe_pct',
 'Al2O3',
 'SiO2_pct',
 'K2O_pct',
 'CaO_pct',
 'MgO_pct',
 'TiO2_pct',
 'P_pct',
 'S_pct',
 'Mn_pct',
 'LOI',
 'Fe_ox_ai',
 'hem_over_goe',
 'kaolin_abundance',
 'kaolin_composition',
 'wmAlsmai',
 'wmAlsmci',
 'carbai3pfit',
 'carbci3pfit',
 'Depth']

In [7]:

dh.describe()

dh.describe()

Out[7]:

	__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

List user defined properties¶

In [8]:

dh.user_properties()

dh.user_properties()

Out[8]:

['X_COLLAR',
 'Y_COLLAR',
 'Z_COLLAR',
 'X_M',
 'Y_M',
 'Z_M',
 'X_B',
 'Y_B',
 'Z_B',
 'X_E',
 'Y_E',
 'Z_E',
 'Fe_pct',
 'Al2O3',
 'SiO2_pct',
 'K2O_pct',
 'CaO_pct',
 'MgO_pct',
 'TiO2_pct',
 'P_pct',
 'S_pct',
 'Mn_pct',
 'LOI',
 'Fe_ox_ai',
 'hem_over_goe',
 'kaolin_abundance',
 'kaolin_composition',
 'wmAlsmai',
 'wmAlsmci',
 'carbai3pfit',
 'carbci3pfit',
 'Depth']

List existing regions¶

In [9]:

dh.regions()

dh.regions()

Out[9]:

[]

Spatial Information¶

In [10]:

dh.bounds()

dh.bounds()

Out[10]:

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

In [11]:

dh.centroid()

dh.centroid()

Out[11]:

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

Aggregate value along a drillhole¶

Drillhole properties can be aggregated to extract one value per drillholes. Aggregation methods are available

Get the smallest grade per drillhole¶

In [12]:

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

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

Out[12]:

	HOLEID	X	X_min	Y	Y_min	Z	Z_min	__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	453.5	406.5	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	453.4	407.4	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	453.8	403.8	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	453.4	416.4	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	456.0	449.0	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	465.5	410.5	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	452.7	405.7	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	452.4	406.4	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	452.4	407.4	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	470.0	469.0	2	1	7322	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	41.0

192 rows × 45 columns

Dealing with condition¶

Extract values where condition are required¶

In [13]:

dh["Z_M"].mean()

dh["Z_M"].mean()

Out[13]:

437.4488505747126

In [14]:

dh.data(names="Fe_pct", region="Z_M < 437")

dh.data(names="Fe_pct", region="Z_M < 437")

Out[14]:

array([48., 40., 56., ..., 39., 16., 12.])

In [15]:

dh.data(names="Fe_pct", region="(Z_M < 437) and (SiO2_pct < 20)")

dh.data(names="Fe_pct", region="(Z_M < 437) and (SiO2_pct < 20)")

Out[15]:

array([48., 56., 55., ..., 47., 44., 43.])

Property creation¶

Create property from numpy array¶

In [16]:

fe_gaussian_scores = geo.gauss_score(dh["Fe_pct"])

fe_gaussian_scores = geo.gauss_score(dh["Fe_pct"])

In [17]:

dh.set_property(name="fe_gauss", data=fe_gaussian_scores)

dh.set_property(name="fe_gauss", data=fe_gaussian_scores)

Create property from other properties¶

In [18]:

dh.set_property_expr(name="Composite_Thickness", expr="TO - FROM")

dh.set_property_expr(name="Composite_Thickness", expr="TO - FROM")

In [19]:

dh.set_property_expr(name="Fe_pct_acc", expr="Fe_pct * Composite_Thickness")

dh.set_property_expr(name="Fe_pct_acc", expr="Fe_pct * Composite_Thickness")

In [20]:

geo.scatter(
    geo_object=dh, 
    property_x="Fe_pct", 
    property_y="SiO2_pct",
    width=650, 
    height=650
)

geo.scatter( geo_object=dh, property_x="Fe_pct", property_y="SiO2_pct", width=650, height=650 )

In [21]:

geo.histogram_plot(
    [{"object": dh, "property": "fe_gauss"}], 
    width=650, 
    height=400
)

geo.histogram_plot( [{"object": dh, "property": "fe_gauss"}], width=650, height=400 )

Create empty property to be filled later¶

In [22]:

dh.set_property_expr(name="High_Fe", expr="nan")
dh.set_property_expr(name="High_Fe", expr="Fe_pct", region="Fe_pct > 30")

dh.set_property_expr(name="High_Fe", expr="nan") dh.set_property_expr(name="High_Fe", expr="Fe_pct", region="Fe_pct > 30")

In [23]:

geo.histogram_plot(
    [
        {"object": dh, "property": "Fe_pct"},
        {"object": dh, "property": "High_Fe"}
    ], 
    width=650, 
    height=400
)

geo.histogram_plot( [ {"object": dh, "property": "Fe_pct"}, {"object": dh, "property": "High_Fe"} ], width=650, height=400 )

Property deletion¶

In [24]:

dh.remove_property(name="fe_gauss")

dh.remove_property(name="fe_gauss")

Out[24]:

True

Region creation¶

Create region from external array¶

In [25]:

dh.set_region(name="fe_gaussian_postivie", data = fe_gaussian_scores > 0)

dh.set_region(name="fe_gaussian_postivie", data = fe_gaussian_scores > 0)

Create region from property condition¶

In [26]:

dh.set_region_condition(name="Ore", condition="Fe_pct > 30")

dh.set_region_condition(name="Ore", condition="Fe_pct > 30")

Region deletion¶

In [27]:

dh.remove_region(name="fe_new_values_gt_10")

dh.remove_region(name="fe_new_values_gt_10")

Out[27]:

False

Using numpy when condition thickens¶

In [28]:

dh.set_region_condition(name="Fe_pct_lt_5", condition="Fe_pct < 5")
dh.set_region_condition(name="SiO2_pct_gt_25", condition="SiO2_pct > 25")

dh.set_region_condition(name="Fe_pct_lt_5", condition="Fe_pct < 5") dh.set_region_condition(name="SiO2_pct_gt_25", condition="SiO2_pct > 25")

In [29]:

fe_high_silica_array = np.where(
    dh['SiO2_pct_gt_25'] & dh['Fe_pct_lt_5'],
    5,
    dh['Fe_pct']
)

fe_high_silica_array = np.where( dh['SiO2_pct_gt_25'] & dh['Fe_pct_lt_5'], 5, dh['Fe_pct'] )

In [30]:

dh.set_property(name="fe_high_silica", data=fe_high_silica_array)

dh.set_property(name="fe_high_silica", data=fe_high_silica_array)

Need to export to Pandas DataFrame for tabular like tasks¶

In [31]:

dh.to_dataframe()

dh.to_dataframe()

Out[31]:

	__TOPO_LEVEL__	__TOPO_TYPE__	__TOPO_ELEMENT_V1__	__TOPO_ELEMENT_V2__	FROM	TO	HOLEID	X_COLLAR	Y_COLLAR	Z_COLLAR	...	carbci3pfit	Depth	Composite_Thickness	Fe_pct_acc	High_Fe	fe_gaussian_postivie	Ore	Fe_pct_lt_5	SiO2_pct_gt_25	fe_high_silica
0	2	1	0	1	0.0	1.0	RKC278	548001.3	7474002.1	453.5	...	NaN	47.0	1.0	NaN	NaN	True	False	False	False	NaN
1	2	1	1	2	1.0	2.0	RKC278	548001.3	7474002.1	453.5	...	NaN	47.0	1.0	NaN	NaN	True	False	False	False	NaN
2	2	1	2	3	2.0	3.0	RKC278	548001.3	7474002.1	453.5	...	NaN	47.0	1.0	NaN	NaN	True	False	False	False	NaN
3	2	1	3	4	3.0	4.0	RKC278	548001.3	7474002.1	453.5	...	NaN	47.0	1.0	NaN	NaN	True	False	False	False	NaN
4	2	1	4	5	4.0	5.0	RKC278	548001.3	7474002.1	453.5	...	2312.89	47.0	1.0	NaN	NaN	True	False	False	False	NaN
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
7129	2	1	7319	7320	43.0	44.0	RKC485	547913.3	7473917.2	452.4	...	NaN	46.0	1.0	16.0	NaN	False	False	False	True	16.0
7130	2	1	7320	7321	44.0	45.0	RKC485	547913.3	7473917.2	452.4	...	NaN	46.0	1.0	12.0	NaN	False	False	False	True	12.0
7131	2	1	7322	7323	0.0	1.0	RKD015	547905.0	7476800.0	470.0	...	NaN	41.0	1.0	NaN	NaN	True	False	False	False	NaN
7132	2	1	7323	7324	0.0	1.0	RKD015	547905.0	7476800.0	470.0	...	NaN	41.0	1.0	NaN	NaN	True	False	False	False	NaN
7133	2	1	7324	7325	0.0	1.0	RKD015	547905.0	7476800.0	470.0	...	NaN	41.0	1.0	NaN	NaN	True	False	False	False	NaN

7134 rows × 47 columns

---

Last update: 2023-10-31