Introduction: how to extract NVCL borehole data

1. Create a ‘SimpleNamespace’ object, fill it with parameters, instantiate class

from nvcl_kit.reader import NVCLReader
from types import SimpleNamespace
param = SimpleNamespace()

# URL of the GeoSciML v4.1 BoreHoleView Web Feature Service
param.WFS_URL = "http://blah.blah.blah/nvcl/geoserver/wfs"

# URL of NVCL service
param.NVCL_URL = "https://blah.blah.blah/nvcl/NVCLDataServices"

# Optional bounding box to search for boreholes using WFS, default units are EPSG:4326 degrees
param.BBOX = {"west": 132.76, "south": -28.44, "east": 134.39, "north": -26.87 }

# Optional maximum number of boreholes to fetch, default is no limit
param.MAX_BOREHOLES = 20

# Instantiate class and search for boreholes
reader = NVCLReader(param)

2. Check if ‘wfs’ is not ‘None’ to see if this instance initialised properly

if not reader.wfs:
    print("ERROR!")

3. Call get_boreholes_list() to get list of WFS borehole data for NVCL boreholes

# Returns a list of python dictionaries
# Each dict has fields from GeoSciML v4.1 BoreholeView
bh_list = reader.get_boreholes_list()

4. Call get_nvcl_id_list() to get a list of NVCL borehole ids

nvcl_id_list = reader.get_nvcl_id_list()

5. Using an NVCL borehole id from previous step, call get_imagelog_data() to get the NVCL log ids

# Get list of NVCL log ids
nvcl_id_list = reader.get_nvcl_id_list()

# Get NVCL log id for first borehole in list
nvcl_id = nvcl_id_list[0]

# Get image log data for first borehole
imagelog_data_list = reader.get_imagelog_data(nvcl_id)
for ild in imagelog_data_list:
    print(ild.log_id,
          ild.log_name,
          ild.log_type,
          ild.algorithmout_id)

6. Using image log data, call get_borehole_data() to get borehole data

# Analysis class has 2 parts:
# 1. Min1,2,3 = 1st, 2nd, 3rd most common mineral
#    OR Grp1,2,3 = 1st, 2nd, 3rd most common group of minerals
# 2. uTSAV = visible light, uTSAS = shortwave IR, uTSAT = thermal IR
#
# These combine to give us a class name such as 'Grp1 uTSAS'
#
# Here we extract data for log type '1' and 'Grp1 uTSAS'
HEIGHT_RESOLUTION = 20.0
ANALYSIS_CLASS = 'Grp1 uTSAS'
LOG_TYPE = '1'
for ild in imagelog_data_list:
    if ild.log_type == LOG_TYPE and ild.log_name == ANALYSIS_CLASS:
        # Get top 5 minerals at each depth
        bh_data = reader.get_borehole_data(ild.log_id, HEIGHT_RESOLUTION, ANALYSIS_CLASS, top_n=5)
        for depth in bh_data:
            for meas in bh_data[depth]:
                print("At {} metres: class={}, abundance={}, mineral={}, colour={}".format(depth, meas.className,
                  meas.classCount, meas.classText, meas.colour))
            print()

7. Using the NVCL ids from Step 5, you can also call get_spectrallog_data() and get_profilometer_data()

spectrallog_data_list = reader.get_spectrallog_data(nvcl_id)
for sld in spectrallog_data_list:
    print(sld.log_id,
          sld.log_name,
          sld.wavelength_units,
          sld.sample_count,
          sld.script,
          sld.script_raw,
          sld.wavelengths)

profilometer_data_list = reader.get_profilometer_data(nvcl_id)
for pdl in profilometer_data_list:
    print(pdl.log_id,
          pdl.log_name,
          pdl.max_val,
          pdl.min_val,
          pdl.floats_per_sample,
          pdl.sample_count)

8. Option: get a list of dataset ids

datasetid_list = reader.get_datasetid_list(nvcl_id)

9. Option: Get a list of datasets

dataset_list = reader.get_dataset_list(nvcl_id)
for ds in dataset_list:
    print(ds.dataset_id,
          ds.dataset_name,
          ds.borehole_uri,
          ds.tray_id,
          ds.section_id,
          ds.domain_id)

10. Using an element from ‘datasetid_list’ in Step 8 or ‘ds.dataset_id’ from Step 9, can retrieve log data

# Scalar log data
log_list = reader.get_scalar_logs(ds.dataset_id)
for log in log_list:
    print(log.log_id,
          log.log_name,
          log.is_public,
          log.log_type,
          log.algorithm_id)
# Different types of image log data
ilog_list = reader.get_all_imglogs(ds.dataset_id)
ilog_list = reader.get_mosaic_imglogs(ds.dataset_id)
ilog_list = reader.get_tray_thumb_imglogs(ds.dataset_id)
ilog_list = reader.get_tray_imglogs(ds.dataset_id)
ilog_list = reader.get_imagery_imglogs(ds.dataset_id)

for ilog in ilog_list:
    print(ilog.log_id,
          ilog.log_name,
          ilog.sample_count)

11. Using the scalar log ids, can get scalar data and plots of scalar data

# Scalar data in CSV format
log_id_list = [l.log_id for l in log_list]
data = reader.get_scalar_data(log_id_list)

# Sampled scalar data in JSON (or CSV) format
samples = reader.get_sampled_scalar_data(log.log_id,
                                         outputformat='json',
                                         startdepth=0,
                                         enddepth=2000,
                                         interval=100)

# A data plot in PNG
plot_data = reader.plot_scalar_png(log_id)

# Data plots in HTML, only plots the first 6 log ids
plot_data = reader.plot_scalars_html(log_id_list)

12. Using the image log ids can produce images of NVCL cores

ilog_list = reader.get_mosaic_imglogs(ds.dataset_id)
for ilog in ilog_list:
    img = reader.get_mosaic_image(ilog.log_id)

ilog_list = reader.get_tray_thumb_imglogs(ds.dataset_id)
for ilog in ilog_list:
    # Either HTML or JPG
    img = reader.get_tray_thumb_html(ds.dataset_id, ilog.log_id)
    img = reader.get_tray_thumb_jpg(ilog.log_id)

# Use either 'get_tray_thumb_imglogs()' or 'get_tray_imglogs()'
ilog_list = reader.get_tray_thumb_imglogs(ds.dataset_id)
ilog_list = reader.get_tray_imglogs(ds.dataset_id)
for ilog in ilog_list:
    depth_list = reader.get_tray_depths(ilog.log_id)
    for depth in depth_list:
        print(depth.sample_no,
              depth.start_value,
              depth.end_value)