"""
Histograms from Drill Holes Data
================================

This example shows how to create histograms from drill holes data.
"""

import os
import numpy as np

import geoassistant

# Path to example data
test_resources_dir = os.path.join("./resources/read_drillholes/")
collar_path = os.path.join(test_resources_dir, 'collar.csv')
survey_path = os.path.join(test_resources_dir, 'survey.csv')
geotech_log_path = os.path.join(test_resources_dir, 'drillhole_log.xlsx')

# %%
# Step 1 - Load drillhole positional data
# ---------------------------------------
# The geoassistant library provides convenient methods for reading collar and survey data.
# These classes allow flexible column mapping, so the CSV structure can be user-defined.
# The `readCollarsCsvFile` and `readSurveysCsvFile` functions return structured collections
# that will be used to build the full 3D drillhole geometry.

cc = geoassistant.readCollarsCsvFile(filepath=collar_path, id_key="HOLEID",
                                     x_key="X", y_key="Y", z_key="Z")

sc = geoassistant.readSurveysCsvFile(filepath=survey_path, id_key="HOLEID",
                                     dip_key="DIP", azimuth_key="AZIMUTH", depth_key="DEPTH")

# %%
# Step 2 - Create drillhole geometry
# ----------------------------------
# Using collars and surveys, the full trajectory of each drillhole can be reconstructed.
# This is done through the `createDrillholesFromCollarsAndSurveys` method.
# The result is a `DrillholesCollection` object, which supports spatial queries, data linking,
# visualization, and parameter operations.

drillholes = geoassistant.createDrillholesFromCollarsAndSurveys(collars=cc, surveys=sc)

# %%
# Basic info about the drillholes can now be queried directly from the object:
print(f"Number of drillholes: {len(drillholes)}")
print(f"Total length: {int(drillholes.getTotalLength()):,}")

# %%
# See how there is no length defined? That is because geoassitant doesn't have any reference
# for the length of the drillholes. This can come in various ways:
#
# - By manually setting the length of each drill hole
# - By setting the length from a log file
# - By assigning the highest "To" value from a log file

# %%
# Step 3 - Load geotechnical interval data
# ----------------------------------------
# Interval-based parameters (e.g., RQD, FF) can be imported from Excel or CSV using
# `IntervalsCollection`. Each interval has a "from-to" depth and is linked to a hole ID.
# The column names in the spreadsheet must be specified when loading.

ic = geoassistant.readIntervalsExcelSheet(filepath=geotech_log_path, sheetname="GeotechLog",
                                          id_col='B', from_col="C", to_col="D")

# %%
# Step 4 - Map parameters from the file
# -------------------------------------
# The raw intervals don’t contain metadata until parameter definitions are explicitly set.
# Here, we declare that column "L" from the Excel sheet corresponds to the "RQD" parameter.

ic.setParameterColumn(parameter_id="RQD", column="L")

# %%
# Step 5 - Define computed parameters
# -----------------------------------
# If a parameter is derived (rather than directly mapped from a column),
# it can be computed manually. In this case, we define FF = n_joints / interval_length,
# and assign it to the interval object using `setParameterValues`.

n_joints = ic.getColumnValues(column="V")
lengths = ic.getLengths()
ff = np.array(n_joints) / np.array(lengths)
ic.setParameterValues(parameter_id="FF", values=ff)

# %%
# Step 6 - Link intervals to drillholes
# -------------------------------------
# The geotechnical intervals can now be linked to the positional drillholes.
# This operation matches intervals to drillholes by hole ID and assigns the data accordingly.
# After this step, each drillhole object may contain its own set of parameters (if available).

drillholes.addIntervalsCollection(intervals=ic)

# %%
# Note:
# Not all drillholes in the project may have matching geotechnical data.
# You can filter the collection to obtain only those drillholes that have a given parameter defined.
# Here, we get only those with FF values.

ff_drillholes = drillholes.getSubsetByParameterDefinition(parameter_id="FF")

# %%
# Step 7 - Visualize the data
# ----------------------------
# Using the filtered collection, we can easily generate histograms or other statistical views.
# In this example, we show a histogram of RQD values, but only for drillholes where FF is defined.

ff_drillholes.createParameterHistogram(parameter_id="RQD")