From e34b6dd545c77cfa5cf8a85f3e776a3105b29648 Mon Sep 17 00:00:00 2001
From: Jacan Chaplais <jacan.chaplais@gmail.com>
Date: Fri, 31 May 2024 16:19:40 +0100
Subject: [PATCH] removed deprecated packages for 0.4.0 release

---
 graphicle/calculate.py | 236 -----------------------------------------
 1 file changed, 236 deletions(-)

diff --git a/graphicle/calculate.py b/graphicle/calculate.py
index f32ff96..d13c1d1 100644
--- a/graphicle/calculate.py
+++ b/graphicle/calculate.py
@@ -13,14 +13,11 @@
 import operator as op
 import typing as ty
 import warnings
-from functools import lru_cache, partial
 
-import networkx as nx
 import numba as nb
 import numpy as np
 import numpy.lib.recfunctions as rfn
 import scipy.optimize as spo
-from deprecation import deprecated
 
 from . import base
 
@@ -28,13 +25,9 @@
     from graphicle.data import *
 
 __all__ = [
-    "azimuth_centre",
-    "pseudorapidity_centre",
-    "rapidity_centre",
     "weighted_centroid",
     "resultant_coords",
     "combined_mass",
-    "flow_trace",
     "aggregate_momenta",
     "cluster_coeff_distbn",
     "thrust",
@@ -49,96 +42,6 @@
 )
 
 
-@deprecated(
-    deprecated_in="0.3.1",
-    removed_in="0.4.0",
-    details="Use ``weighted_centroid()`` or ``resultant_coords()`` instead.",
-)
-def azimuth_centre(pmu: "MomentumArray", pt_weight: bool = True) -> float:
-    """Calculates the central point in azimuth for a set of particles.
-
-    :group: calculate
-
-    .. versionadded:: 0.1.7
-
-    .. versionchanged:: 0.3.1
-       The ``pt_weight`` parameter implementation has been corrected.
-       Previous versions resulted in :math:`p_T`-weighting when
-       ``False``, and :math:`p_T^2`-weighting when ``True``.
-
-    Parameters
-    ----------
-    pmu : MomentumArray
-        Four-momenta of the particles.
-    pt_weight : bool
-        If ``True``, will weight the contributions of each particle by
-        transverse momentum. Similar to finding a centre-of-mass, but
-        for transverse momentum. Default is ``True``.
-
-    Returns
-    -------
-    float
-        The centre of the particle set in the azimuth dimension.
-    """
-    pol = pmu._xy_pol
-    if not pt_weight:
-        pol = pol / pmu.pt
-    return np.angle(pol.sum()).item()
-
-
-@deprecated(
-    deprecated_in="0.3.1",
-    removed_in="0.4.0",
-    details="Use ``weighted_centroid()`` or ``resultant_coords()`` instead.",
-)
-def pseudorapidity_centre(pmu: "MomentumArray") -> float:
-    """Calculates the central point in pseudorapidity for a set of
-    particles.
-
-    :group: calculate
-
-    .. versionadded:: 0.1.7
-
-    Parameters
-    ----------
-    pmu : MomentumArray
-        Four-momenta of the particles.
-
-    Returns
-    -------
-    float
-        The :math:`p_T` weighted centre of the particle set in the
-        pseudorapidity dimension.
-    """
-    return ((pmu.eta * pmu.pt).sum() / pmu.pt.sum()).item()
-
-
-@deprecated(
-    deprecated_in="0.3.1",
-    removed_in="0.4.0",
-    details="Use ``weighted_centroid()`` or ``resultant_coords()`` instead.",
-)
-def rapidity_centre(pmu: "MomentumArray") -> float:
-    """Calculates the central point in rapidity for a set of particles.
-
-    :group: calculate
-
-    .. versionadded:: 0.2.11
-
-    Parameters
-    ----------
-    pmu : MomentumArray
-        Four-momenta of the particles.
-
-    Returns
-    -------
-    float
-        The :math:`p_T` weighted centre of the particle set in the
-        rapidity dimension.
-    """
-    return (pmu.rapidity * pmu.pt).sum() / pmu.pt.sum()
-
-
 def weighted_centroid(
     pmu: "MomentumArray", pseudo: bool = True
 ) -> ty.Tuple[float, float]:
@@ -286,145 +189,6 @@ def combined_mass(
     return mass
 
 
-def _diffuse(colors: ty.List[base.AnyVector], feats: ty.List[base.AnyVector]):
-    color_shape = colors[0].shape
-    av_color = np.zeros((color_shape[0], color_shape[1]), dtype="<f8")
-    color_stack = np.dstack(colors)  # len_basis x feat_dim x num_in
-    feat_stack = np.vstack(feats).T  # feat_dim x num_in
-    feat_sum = np.sum(feat_stack, axis=1)
-    nonzero_mask = feat_sum != 0.0
-    av_color[:, nonzero_mask] = (
-        np.sum(
-            color_stack[:, nonzero_mask, :] * feat_stack[nonzero_mask], axis=2
-        )
-        / feat_sum[nonzero_mask]
-    )
-    return av_color
-
-
-@lru_cache(maxsize=None)
-def _trace_vector(
-    nx_graph: nx.DiGraph,
-    vertex: int,
-    basis: ty.Tuple[int, ...],
-    feat_dim: int,
-    is_structured: bool,
-    exclusive: bool = False,
-) -> base.AnyVector:
-    len_basis = len(basis)
-    feat_fmt = rfn.structured_to_unstructured if is_structured else lambda x: x
-    color = np.zeros((len_basis, feat_dim), dtype="<f8")
-    if vertex in basis:
-        color[basis.index(vertex)] = 1.0
-        if exclusive is True:
-            return color
-    in_edges = nx_graph.in_edges(vertex, data=True)
-    colors_in: list[base.AnyVector] = []
-    feats = []
-    for edge in in_edges:
-        feats.append(feat_fmt(edge[2]["feat"]))
-        in_vtx = edge[0]
-        colors_in.append(
-            _trace_vector(
-                nx_graph, in_vtx, basis, feat_dim, is_structured, exclusive
-            )
-        )
-    if colors_in:
-        color += _diffuse(colors_in, feats)
-    return color
-
-
-def flow_trace(
-    graph: "Graphicle",
-    mask: ty.Union[base.MaskBase, base.BoolVector],
-    prop: ty.Union[base.ArrayBase, base.AnyVector],
-    exclusive: bool = False,
-    target: ty.Optional[ty.Set[int]] = None,
-) -> ty.Dict[str, base.DoubleVector]:
-    """Performs flow tracing from specified particles in an event, back
-    to the hard partons.
-
-    :group: calculate
-
-    .. versionadded:: 0.1.0
-
-    Parameters
-    ----------
-    graph : Graphicle
-        Full particle event, containing hard partons, showering and
-        hadronisation.
-    mask : MaskArray or MaskGroup or ndarray[bool_]
-        Boolean mask identifying which particles should have their
-        ancestry traced.
-    prop : ArrayBase or ndarray[any]
-        Property to trace back, *eg.* 4-momentum, charge, *etc*. Must be
-        the same shape as arrays stored in graph. Can be structured,
-        unstructured, or a graphicle array, though unstructured arrays
-        must be 1D.
-    exclusive : bool
-        If ``True``, double counting from descendant particles in the
-        hard event will be switched off. *eg.* for event ``t > b W+``,
-        descendants of ``b`` will show no contribution from ``t``, as
-        ``b`` is a subset of ``t``. Default is ``False``.
-    target : set[int], optional
-        Highlights specific partons in the hard event to decompose
-        properties with respect to. If unset, will use all partons in
-        hard event, except for incoming partons.
-
-    Returns
-    -------
-    trace_array : dict[str, ndarray]
-        Keys are parton names, arrays represent the contributions of
-        hard partons traced down to the properties of the selected
-        subset of particles specified by mask.
-    """
-    if isinstance(prop, base.ArrayBase):
-        prop = prop.data
-    # encoding graph features onto NetworkX
-    nx_graph = nx.DiGraph()
-    graph_dict = graph.adj.to_dicts(edge_data={"feat": prop})
-    example_feat = graph_dict["edges"][0][2]["feat"]
-    try:
-        feat_dim = len(example_feat)
-        dtype = example_feat.dtype
-    except TypeError:
-        feat_dim = 1
-        dtype = np.dtype(type(example_feat))
-    is_structured = dtype.names is not None
-    nx_graph.add_edges_from(graph_dict["edges"])
-    # identify the hard ancestors to which we trace
-    hard_mask = graph.hard_mask.copy()
-    del hard_mask["incoming"]
-    hard_graph = graph[hard_mask.bitwise_or()]
-    if target:  # restrict hard partons to user specified pdgs
-        target_mask = hard_graph.pdg.mask(
-            list(target), blacklist=False, sign_sensitive=True
-        )
-        hard_graph = hard_graph[target_mask]
-    names, vtxs = tuple(hard_graph.pdg.name), tuple(hard_graph.edges["dst"])
-    # out vertices of user specified particles
-    focus_pcls = graph.edges[mask]["dst"]
-    trc = np.array(
-        [
-            _trace_vector(
-                nx_graph, pcl, vtxs, feat_dim, is_structured, exclusive
-            )
-            for pcl in focus_pcls
-        ]
-    )
-    _trace_vector.cache_clear()
-    traces = dict()
-    array_fmt: ty.Callable[[base.AnyVector], base.AnyVector] = (
-        partial(rfn.unstructured_to_structured, dtype=dtype)  # type: ignore
-        if is_structured
-        else lambda x: x.squeeze()
-    )
-
-    for i, name in enumerate(names):
-        traces[name] = array_fmt(trc[:, i, :])
-    return traces
-
-
 @nb.njit("float64[:](float64[:], float64[:], float64)", cache=True)
 def _rapidity(
     energy: base.DoubleVector, z: base.DoubleVector, zero_tol: float