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haddock3 / data /src /haddock /libs /libclust.py

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"""
Library of functions related to the clustering modules.
Main functions
--------------
* :py:func:`write_unclustered_list`
* :py:func:`plot_cluster_matrix`
"""

import os
from pathlib import Path

import numpy as np
from scipy.spatial.distance import squareform
from typing import List, Tuple

from haddock import log
from haddock.core.typing import FilePath, Optional, ParamDictT, Union
from haddock.libs.libontology import PDBFile
from haddock.libs.libplots import heatmap_plotly

MAX_NB_ENTRY_HTML_MATRIX = 3100


def write_structure_list(
    input_models: list[PDBFile], clustered_models: list[PDBFile], out_fname: FilePath
) -> None:
    """
    Get the list of unclustered structures.
    Parameters
    ----------
    input_models : list
        list of input models
    clustered_models : list
        list of clustered models
    """
    output_fname = Path(out_fname)
    output_str = f"rank\tmodel_name\tscore\tcluster_id{os.linesep}"
    structure_list: list[PDBFile] = []
    # checking which input models have not been clustered
    for model in input_models:
        if model not in clustered_models:
            model.clt_id = "-"
            structure_list.append(model)
    # extending and sorting
    structure_list.extend(clustered_models)
    structure_list.sort(key=lambda model: model.score)
    # adding models to output string
    for mdl_rank, mdl in enumerate(structure_list, start=1):
        output_str += (
            f"{mdl_rank}\t{mdl.file_name}\t{mdl.score:.2f}\t{mdl.clt_id}{os.linesep}"
        )
    output_str += os.linesep
    log.info(f"Saving structure list to {out_fname}")
    with open(output_fname, "w") as out_fh:
        out_fh.write(output_str)


def plot_cluster_matrix(
    matrix_path: Union[Path, FilePath, str],
    final_order_idx: list[int],
    labels: list[str],
    dttype: str = "",
    diag_fill: Union[int, float] = 1,
    color_scale: str = "Blues",
    reverse: bool = False,
    output_fname: Union[str, Path, FilePath] = "clust_matrix",
    matrix_cluster_dt: Optional[list[list[list[int]]]] = None,
    cluster_limits: Optional[list[dict[str, float]]] = None,
) -> Optional[str]:
    """Plot a plotly heatmap of a matrix file.
    Parameters
    ----------
    matrix_path : Union[Path, FilePath, str]
        Path to a half-matrix
    final_order_idx : list[int]
        Index orders
    labels : list[str]
        Ordered labels
    dttype : str
        Name of the data type, by default ``
    color_scale : str, optional
        Color scale for the plot, by default "Blues"
    reversed : bool, optional
        Should the color scale be reversed ?, by default False
    output_fname : Union[str, Path, FilePath], optional
        Name of the output file to generate, by default 'clust_matrix.html'
    matrix_cluster_dt: Optional[list[list[list[int]]]]
        A matrix of cluster ids, used for extra hover annotation in plotly.
    cluster_limits: Optional[list[dict[str, float]]]
        A list of dict enabling to draw lines separating cluster ids.
    Return
    ------
    output_fname_ext : str
        Path to the generated file containing the figure.
    """
    # Check that we will be able to generate a functional interactive plot
    if len(final_order_idx) > MAX_NB_ENTRY_HTML_MATRIX:
        return None

    upper_diag, lower_diag = [], []
    # Read matrix
    with open(matrix_path, "r") as f:
        # Loop over lines
        for _ in f:
            # Split line
            s_ = _.strip().split()
            # Point first value
            uv = float(s_[2])
            # Point second value (if exists)
            lv = float(s_[3]) if len(s_) == 4 else uv
            # Hold them
            upper_diag.append(uv)
            lower_diag.append(lv)

    # Genereate full matrix from N*(N-1)/2 vector
    upper_matrix = squareform(upper_diag)
    lower_matrix = squareform(lower_diag)
    # Update diagonal with data
    np.fill_diagonal(upper_matrix, diag_fill)

    # Full matrix (lower triangle + upper triangle)
    full_matrix = np.tril(lower_matrix, k=-1) + np.triu(upper_matrix)

    # Extract submatrix of selected models and re-order them
    submat = full_matrix[np.ix_(final_order_idx, final_order_idx)]

    # Check if must reverse the colorscale
    if reverse:
        if color_scale[-2:] == "_r":
            color_scale = color_scale[:-2]
        else:
            color_scale += "_r"

    # Define hovering tempalte string
    if matrix_cluster_dt:
        hovertemplate = (
            f"                                {dttype}: %{{z}} <br>"
            f" Model1: %{{x}}    ClusterID: %{{customdata[0]}} <br>"
            f" Model2: %{{y}}    ClusterID: %{{customdata[1]}} "
            "<extra></extra>"
        )
    else:
        hovertemplate = (
            f"                       {dttype}: %{{z}} <br>"
            f" Model1: %{{x}} <br>"
            f" Model2: %{{y}} "
            "<extra></extra>"
        )

    # Generate file name
    output_fname_ext = f"{output_fname}.html"
    # Draw heatmap
    heatmap_plotly(
        submat,
        labels={"color": dttype},
        xlabels=labels,
        ylabels=labels,
        color_scale=color_scale,
        title=f"{dttype} clustering matrix",
        output_fname=output_fname_ext,
        hovertemplate=hovertemplate,
        customdata=matrix_cluster_dt,
        delineation_traces=cluster_limits,
    )
    # Return generated filepath
    return output_fname_ext


def get_cluster_matrix_plot_clt_dt(
    cluster_ids: list[int],
) -> tuple[list[list[list[int]]], list[dict[str, float]]]:
    """Generate cluster matrix data for plotly.
    Parameters
    ----------
    cluster_ids : list[int]
        List containing ordered cluster ids.
    Returns
    -------
    matrix_cluster_dt: list[list[list[int]]]
        A matrix of cluster ids, used for plotly.
    cluster_limits: list[dict[str, float]]]
        Boundaries to draw lines between clusters with plotly.
    """
    # Set custom data
    matrix_cluster_dt = [[[clix, cliy] for clix in cluster_ids] for cliy in cluster_ids]
    # Build delineation lines
    del_ind = -0.5
    del_posi = []
    current_clid = cluster_ids[0]
    for clid in cluster_ids:
        if clid != current_clid:
            del_posi.append(del_ind)
            current_clid = clid
        del_ind += 1
    cluster_limits = [
        {
            "x0": delpos,
            "x1": delpos,
            "y0": -0.5,
            "y1": len(cluster_ids) - 0.5,
        }
        for delpos in del_posi
    ] + [
        {
            "y0": delpos,
            "y1": delpos,
            "x0": -0.5,
            "x1": len(cluster_ids) - 0.5,
        }
        for delpos in del_posi
    ]
    return matrix_cluster_dt, cluster_limits


def rank_clusters(clt_dic, threshold):
    """
    Rank the clusters by their average score.
    Parameters
    ----------
    clt_dic : :obj:`dict`
        Dictionary with the clusters.
    threshold : int
        Number of models to consider for the average score.
    Returns
    -------
    score_dic : :obj:`dict`
        Dictionary with the cluster ID as key and the average score as value.
    sorted_score_dic : :obj:`list`
        List of tuples with the cluster ID and the average score, sorted by
        the average score.
    """
    score_dic = {}
    for clt_id in clt_dic:
        score_l = [p.score for p in clt_dic[clt_id]]
        score_l.sort()
        denom = float(min(threshold, len(score_l)))
        top4_score = sum(score_l[:threshold]) / denom
        score_dic[clt_id] = top4_score

    sorted_score_dic = sorted(score_dic.items(), key=lambda k: k[1])
    return score_dic, sorted_score_dic


def add_cluster_info(
    sorted_score: List[Tuple[int, float]], clt_dic: dict[int, List[PDBFile]]
) -> List[PDBFile]:
    """
    Rank clusters and models, then attach a cluster ID and rank info to each model.
    Parameters
    ----------
    sorted_score : :obj:`list`
        List of tuples with the cluster ID and the average score, sorted by
        the average score. ex: ``[(1, -341.2), (2, -347.5)]``
    clt_dic : :obj:`dict`
        Dictionary with the clusters.
        ex: ``{1: [PDBFile, PDBFile, ...], 2: [PDBFile, PDBFile, ...]}``
    Returns
    -------
    output_models : :obj:`list`
        List of models with the cluster information attached, ordered by cluster rank,
        then by model rank within each cluster.
    """
    # Add this info to the models
    output_models = []
    log.warning("Reordering by cluster rank and model rank")
    for cluster_rank, _e in enumerate(sorted_score, start=1):
        cluster_id, _ = _e
        # sort the models by score
        clt_dic[cluster_id].sort()
        # rank the models
        for model_ranking, pdb in enumerate(clt_dic[cluster_id], start=1):
            pdb.clt_id = int(cluster_id)
            pdb.clt_rank = cluster_rank
            pdb.clt_model_rank = model_ranking
            output_models.append(pdb)
    return output_models


def clustrmsd_tolerance_params(
    parameters: ParamDictT,
) -> tuple[str, Union[int, float]]:
    """Provide parameters of interest for clust rmsd.
    Parameters
    ----------
    parameters : ParamDictT
        The clustrmsd module parameters
    Returns
    -------
    tuple[str, Union[int, float]]
        Name of the tolerance parameter and its value.
    """
    # adjust the parameters
    if parameters["criterion"] == "maxclust":
        tolerance_param_name = "n_clusters"
        tolerance = parameters[tolerance_param_name]
    else:  # Expected to be parameters["criterion"] == "distance"
        tolerance_param_name = "clust_cutoff"
        tolerance = parameters[tolerance_param_name]
    return tolerance_param_name, tolerance

	"""
	Library of functions related to the clustering modules.

	Main functions
	--------------

	* :py:func:`write_unclustered_list`
	* :py:func:`plot_cluster_matrix`
	"""

	import os
	from pathlib import Path

	import numpy as np
	from scipy.spatial.distance import squareform
	from typing import List, Tuple

	from haddock import log
	from haddock.core.typing import FilePath, Optional, ParamDictT, Union
	from haddock.libs.libontology import PDBFile
	from haddock.libs.libplots import heatmap_plotly

	MAX_NB_ENTRY_HTML_MATRIX = 3100


	def write_structure_list(
	input_models: list[PDBFile], clustered_models: list[PDBFile], out_fname: FilePath
	) -> None:
	"""
	Get the list of unclustered structures.

	Parameters
	----------
	input_models : list
	list of input models
	clustered_models : list
	list of clustered models
	"""
	output_fname = Path(out_fname)
	output_str = f"rank\tmodel_name\tscore\tcluster_id{os.linesep}"
	structure_list: list[PDBFile] = []
	# checking which input models have not been clustered
	for model in input_models:
	if model not in clustered_models:
	model.clt_id = "-"
	structure_list.append(model)
	# extending and sorting
	structure_list.extend(clustered_models)
	structure_list.sort(key=lambda model: model.score)
	# adding models to output string
	for mdl_rank, mdl in enumerate(structure_list, start=1):
	output_str += (
	f"{mdl_rank}\t{mdl.file_name}\t{mdl.score:.2f}\t{mdl.clt_id}{os.linesep}"
	)
	output_str += os.linesep
	log.info(f"Saving structure list to {out_fname}")
	with open(output_fname, "w") as out_fh:
	out_fh.write(output_str)


	def plot_cluster_matrix(
	matrix_path: Union[Path, FilePath, str],
	final_order_idx: list[int],
	labels: list[str],
	dttype: str = "",
	diag_fill: Union[int, float] = 1,
	color_scale: str = "Blues",
	reverse: bool = False,
	output_fname: Union[str, Path, FilePath] = "clust_matrix",
	matrix_cluster_dt: Optional[list[list[list[int]]]] = None,
	cluster_limits: Optional[list[dict[str, float]]] = None,
	) -> Optional[str]:
	"""Plot a plotly heatmap of a matrix file.

	Parameters
	----------
	matrix_path : Union[Path, FilePath, str]
	Path to a half-matrix
	final_order_idx : list[int]
	Index orders
	labels : list[str]
	Ordered labels
	dttype : str
	Name of the data type, by default ``
	color_scale : str, optional
	Color scale for the plot, by default "Blues"
	reversed : bool, optional
	Should the color scale be reversed ?, by default False
	output_fname : Union[str, Path, FilePath], optional
	Name of the output file to generate, by default 'clust_matrix.html'
	matrix_cluster_dt: Optional[list[list[list[int]]]]
	A matrix of cluster ids, used for extra hover annotation in plotly.
	cluster_limits: Optional[list[dict[str, float]]]
	A list of dict enabling to draw lines separating cluster ids.

	Return
	------
	output_fname_ext : str
	Path to the generated file containing the figure.
	"""
	# Check that we will be able to generate a functional interactive plot
	if len(final_order_idx) > MAX_NB_ENTRY_HTML_MATRIX:
	return None

	upper_diag, lower_diag = [], []
	# Read matrix
	with open(matrix_path, "r") as f:
	# Loop over lines
	for _ in f:
	# Split line
	s_ = _.strip().split()
	# Point first value
	uv = float(s_[2])
	# Point second value (if exists)
	lv = float(s_[3]) if len(s_) == 4 else uv
	# Hold them
	upper_diag.append(uv)
	lower_diag.append(lv)

	# Genereate full matrix from N*(N-1)/2 vector
	upper_matrix = squareform(upper_diag)
	lower_matrix = squareform(lower_diag)
	# Update diagonal with data
	np.fill_diagonal(upper_matrix, diag_fill)

	# Full matrix (lower triangle + upper triangle)
	full_matrix = np.tril(lower_matrix, k=-1) + np.triu(upper_matrix)

	# Extract submatrix of selected models and re-order them
	submat = full_matrix[np.ix_(final_order_idx, final_order_idx)]

	# Check if must reverse the colorscale
	if reverse:
	if color_scale[-2:] == "_r":
	color_scale = color_scale[:-2]
	else:
	color_scale += "_r"

	# Define hovering tempalte string
	if matrix_cluster_dt:
	hovertemplate = (
	f" {dttype}: %{{z}} <br>"
	f" Model1: %{{x}} ClusterID: %{{customdata[0]}} <br>"
	f" Model2: %{{y}} ClusterID: %{{customdata[1]}} "
	"<extra></extra>"
	)
	else:
	hovertemplate = (
	f" {dttype}: %{{z}} <br>"
	f" Model1: %{{x}} <br>"
	f" Model2: %{{y}} "
	"<extra></extra>"
	)

	# Generate file name
	output_fname_ext = f"{output_fname}.html"
	# Draw heatmap
	heatmap_plotly(
	submat,
	labels={"color": dttype},
	xlabels=labels,
	ylabels=labels,
	color_scale=color_scale,
	title=f"{dttype} clustering matrix",
	output_fname=output_fname_ext,
	hovertemplate=hovertemplate,
	customdata=matrix_cluster_dt,
	delineation_traces=cluster_limits,
	)
	# Return generated filepath
	return output_fname_ext


	def get_cluster_matrix_plot_clt_dt(
	cluster_ids: list[int],
	) -> tuple[list[list[list[int]]], list[dict[str, float]]]:
	"""Generate cluster matrix data for plotly.

	Parameters
	----------
	cluster_ids : list[int]
	List containing ordered cluster ids.

	Returns
	-------
	matrix_cluster_dt: list[list[list[int]]]
	A matrix of cluster ids, used for plotly.

	cluster_limits: list[dict[str, float]]]
	Boundaries to draw lines between clusters with plotly.
	"""
	# Set custom data
	matrix_cluster_dt = [[[clix, cliy] for clix in cluster_ids] for cliy in cluster_ids]
	# Build delineation lines
	del_ind = -0.5
	del_posi = []
	current_clid = cluster_ids[0]
	for clid in cluster_ids:
	if clid != current_clid:
	del_posi.append(del_ind)
	current_clid = clid
	del_ind += 1
	cluster_limits = [
	{
	"x0": delpos,
	"x1": delpos,
	"y0": -0.5,
	"y1": len(cluster_ids) - 0.5,
	}
	for delpos in del_posi
	] + [
	{
	"y0": delpos,
	"y1": delpos,
	"x0": -0.5,
	"x1": len(cluster_ids) - 0.5,
	}
	for delpos in del_posi
	]
	return matrix_cluster_dt, cluster_limits


	def rank_clusters(clt_dic, threshold):
	"""
	Rank the clusters by their average score.

	Parameters
	----------
	clt_dic : :obj:`dict`
	Dictionary with the clusters.

	threshold : int
	Number of models to consider for the average score.

	Returns
	-------
	score_dic : :obj:`dict`
	Dictionary with the cluster ID as key and the average score as value.

	sorted_score_dic : :obj:`list`
	List of tuples with the cluster ID and the average score, sorted by
	the average score.
	"""
	score_dic = {}
	for clt_id in clt_dic:
	score_l = [p.score for p in clt_dic[clt_id]]
	score_l.sort()
	denom = float(min(threshold, len(score_l)))
	top4_score = sum(score_l[:threshold]) / denom
	score_dic[clt_id] = top4_score

	sorted_score_dic = sorted(score_dic.items(), key=lambda k: k[1])
	return score_dic, sorted_score_dic


	def add_cluster_info(
	sorted_score: List[Tuple[int, float]], clt_dic: dict[int, List[PDBFile]]
	) -> List[PDBFile]:
	"""
	Rank clusters and models, then attach a cluster ID and rank info to each model.

	Parameters
	----------
	sorted_score : :obj:`list`
	List of tuples with the cluster ID and the average score, sorted by
	the average score. ex: ``[(1, -341.2), (2, -347.5)]``

	clt_dic : :obj:`dict`
	Dictionary with the clusters.
	ex: ``{1: [PDBFile, PDBFile, ...], 2: [PDBFile, PDBFile, ...]}``


	Returns
	-------
	output_models : :obj:`list`
	List of models with the cluster information attached, ordered by cluster rank,
	then by model rank within each cluster.
	"""
	# Add this info to the models
	output_models = []
	log.warning("Reordering by cluster rank and model rank")
	for cluster_rank, _e in enumerate(sorted_score, start=1):
	cluster_id, _ = _e
	# sort the models by score
	clt_dic[cluster_id].sort()
	# rank the models
	for model_ranking, pdb in enumerate(clt_dic[cluster_id], start=1):
	pdb.clt_id = int(cluster_id)
	pdb.clt_rank = cluster_rank
	pdb.clt_model_rank = model_ranking
	output_models.append(pdb)
	return output_models


	def clustrmsd_tolerance_params(
	parameters: ParamDictT,
	) -> tuple[str, Union[int, float]]:
	"""Provide parameters of interest for clust rmsd.

	Parameters
	----------
	parameters : ParamDictT
	The clustrmsd module parameters

	Returns
	-------
	tuple[str, Union[int, float]]
	Name of the tolerance parameter and its value.
	"""
	# adjust the parameters
	if parameters["criterion"] == "maxclust":
	tolerance_param_name = "n_clusters"
	tolerance = parameters[tolerance_param_name]
	else: # Expected to be parameters["criterion"] == "distance"
	tolerance_param_name = "clust_cutoff"
	tolerance = parameters[tolerance_param_name]
	return tolerance_param_name, tolerance