Use NumPy 2.0 API

pyproject.toml

@@ -20,9 +20,7 @@ classifiers = [
 ]
 
 dependencies = [
-    # Wheels compiled with NumPy 2.0 are backward compatible with NumPy 1.x
-    # https://numpy.org/devdocs/dev/depending_on_numpy.html#numpy-2-0-specific-advice
-    "numpy >= 1.25",
+    "numpy >= 2.1",
     "biotraj >= 1.0, < 2.0",
     "requests >= 2.12",
     "msgpack >= 0.5.6",

src/biotite/application_v2/sra/app.py

@@ -20,7 +20,6 @@
 from os.path import join
 from pathlib import Path
 from tempfile import TemporaryDirectory
-from typing import Literal, TypeAlias
 import numpy as np
 from biotite.application_v2.localapp import (
     CLIArgument,
@@ -37,10 +36,6 @@
 from biotite.sequence.io.fastq.file import FastqFile
 from biotite.sequence.seqtypes import NucleotideSequence
 
-_OffsetFormat: TypeAlias = Literal[
-    "Sanger", "Solexa", "Illumina-1.3", "Illumina-1.5", "Illumina-1.8"
-]
-
 
 @dataclass(frozen=True)
 class PrefetchDirectory:
@@ -85,13 +80,15 @@ class FastqResult:
         default=None, repr=False, compare=False
     )
 
-    def get_files(self, offset: int | _OffsetFormat = "Sanger") -> list[FastqFile]:
+    def get_files(
+        self, offset: int | FastqFile.Offset = FastqFile.Offset.SANGER
+    ) -> list[FastqFile]:
         """
         Parse the extracted FASTQ files.
 
         Parameters
         ----------
-        offset : int or {'Sanger', 'Solexa', 'Illumina-1.3', 'Illumina-1.5', 'Illumina-1.8'}, optional
+        offset : int or FastqFile.Offset, optional
             This value is subtracted from the FASTQ ASCII code to obtain
             the quality score.
 
@@ -103,14 +100,14 @@ def get_files(self, offset: int | _OffsetFormat = "Sanger") -> list[FastqFile]:
         return [FastqFile.read(path, offset=offset) for path in self.file_paths]
 
     def get_sequences(
-        self, offset: int | _OffsetFormat = "Sanger"
+        self, offset: int | FastqFile.Offset = FastqFile.Offset.SANGER
     ) -> list[dict[str, NucleotideSequence]]:
         """
         Get the reads from the extracted FASTQ files.
 
         Parameters
         ----------
-        offset : int or {'Sanger', 'Solexa', 'Illumina-1.3', 'Illumina-1.5', 'Illumina-1.8'}, optional
+        offset : int or FastqFile.Offset, optional
             This value is subtracted from the FASTQ ASCII code to obtain
             the quality score.
 
@@ -127,14 +124,14 @@ def get_sequences(
         ]
 
     def get_sequences_and_scores(
-        self, offset: int | _OffsetFormat = "Sanger"
+        self, offset: int | FastqFile.Offset = FastqFile.Offset.SANGER
     ) -> list[dict[str, tuple[NucleotideSequence, np.ndarray]]]:
         """
         Get the reads and quality scores from the extracted FASTQ files.
 
         Parameters
         ----------
-        offset : int or {'Sanger', 'Solexa', 'Illumina-1.3', 'Illumina-1.5', 'Illumina-1.8'}, optional
+        offset : int or FastqFile.Offset, optional
             This value is subtracted from the FASTQ ASCII code to obtain
             the quality score.
 

src/biotite/database/rcsb/query.py

@@ -233,7 +233,7 @@ class FieldQuery(SingleQuery):
 
     >>> query = FieldQuery("reflns.d_resolution_high", less_or_equal=0.5)
     >>> print(sorted(search(query)))
-    ['3NIR', '4JLJ', '5D8V', '7R0H']
+    ['30OH', '30OR', '3NIR', '4JLJ', '5D8V', '7R0H']
     """
 
     # Overloads expose the mutually exclusive operator keywords.
@@ -886,10 +886,10 @@ def count(
 
     >>> query = FieldQuery("reflns.d_resolution_high", less_or_equal=0.5)
     >>> print(count(query))
-    4
+    6
     >>> ids = search(query)
     >>> print(sorted(ids))
-    ['3NIR', '4JLJ', '5D8V', '7R0H']
+    ['30OH', '30OR', '3NIR', '4JLJ', '5D8V', '7R0H']
     """
     query_dict = _initialize_query_dict(query, return_type, group_by, content_types)
 
@@ -1029,20 +1029,20 @@ def search(
 
     >>> query = FieldQuery("reflns.d_resolution_high", less_or_equal=0.5)
     >>> print(sorted(search(query)))
-    ['3NIR', '4JLJ', '5D8V', '7R0H']
+    ['30OH', '30OR', '3NIR', '4JLJ', '5D8V', '7R0H']
     >>> print(search(query, sort_by="rcsb_accession_info.initial_release_date"))
-    ['7R0H', '5D8V', '4JLJ', '3NIR']
+    ['30OH', '30OR', '7R0H', '5D8V', '4JLJ', '3NIR']
     >>> print(search(
     ...     query, range=(1,4), sort_by="rcsb_accession_info.initial_release_date"
     ... ))
-    ['5D8V', '4JLJ', '3NIR']
+    ['30OR', '7R0H', '5D8V']
     >>> print(sorted(search(query, return_type="polymer_instance")))
-    ['3NIR.A', '4JLJ.A', '4JLJ.B', '5D8V.A', '7R0H.A']
+    ['30OH.A', '30OR.A', '3NIR.A', '4JLJ.A', '4JLJ.B', '5D8V.A', '7R0H.A']
     >>> print(search(
     ...     query, return_type="polymer_entity", return_groups=True,
     ...     group_by=UniprotGrouping(sort_by="rcsb_accession_info.initial_release_date"),
     ... ))
-    {'P27707': ['4JLJ_1'], 'P80176': ['5D8V_1'], 'O29777': ['7R0H_1'], 'P01542': ['3NIR_1']}
+    {'Q9V099': ['30OR_1', '30OH_1'], 'P27707': ['4JLJ_1'], 'P80176': ['5D8V_1'], 'O29777': ['7R0H_1'], 'P01542': ['3NIR_1']}
     """
     query_dict = _initialize_query_dict(query, return_type, group_by, content_types)
 

src/biotite/interface/openmm/system.py

@@ -20,7 +20,7 @@
 from biotite.structure.chains import get_chain_starts
 from biotite.structure.error import BadStructureError
 from biotite.structure.filter import filter_amino_acids, filter_nucleotides
-from biotite.structure.info.masses import mass
+from biotite.structure.info.masses import masses
 from biotite.structure.residues import get_residue_starts
 from biotite.typing import XYZ, K, NDArray1, NDArray2
 
@@ -56,8 +56,8 @@ def to_system(
     """
     system = System()
 
-    for element in atoms.element.tolist():
-        system.addParticle(mass(element))
+    for atom_mass in masses(atoms).tolist():
+        system.addParticle(atom_mass)
 
     if atoms.box is not None:
         if isinstance(atoms, AtomArrayStack):

src/biotite/interface/pymol/shapes.py

@@ -85,7 +85,7 @@ def draw_arrows(
     color = _arrayfy(color, expected_length, 2)
     head_color = _arrayfy(head_color, expected_length, 2)
 
-    normal = (end - start) / np.linalg.norm(end - start, axis=-1)[:, np.newaxis]
+    normal = (end - start) / np.linalg.vector_norm(end - start, axis=-1)[:, np.newaxis]
     middle = end - normal * head_length[:, np.newaxis]
 
     cgo_list = []

src/biotite/sequence/io/fasta/convert.py

@@ -382,7 +382,7 @@ def set_a3m_alignments(
         if not isinstance(alphabet, LetterAlphabet):
             raise ValueError("A3M requires a letter sequence")
         # Keep gaps in the query sequence as lower case letters
-        a3m_string_array[query_gaps] = np.char.lower(
+        a3m_string_array[query_gaps] = np.strings.lower(
             query.alphabet.decode_multiple(target_code[query_gaps], as_bytes=True)  # pyright: ignore[reportCallIssue,reportArgumentType]
         )
         # Matches/mismatches are indicated with upper case letters

src/biotite/structure/alphabet/encoder.py

@@ -282,7 +282,7 @@ def _calc_conformation_descriptors(
         desc[i, 4] = numpy.sum(u1 * u4, axis=-1)
         desc[i, 5] = numpy.sum(u2 * u3, axis=-1)
         desc[i, 6] = numpy.sum(u1 * u3, axis=-1)
-        desc[i, 7] = numpy.linalg.norm(ca[i] - ca[j], axis=-1)
+        desc[i, 7] = numpy.linalg.vector_norm(ca[i] - ca[j], axis=-1)
         desc[i, 8] = numpy.clip(j - i, -4, 4)
         desc[i, 9] = numpy.copysign(numpy.log(numpy.abs(j - i) + 1), j - i)
         return desc
@@ -380,5 +380,5 @@ def encode(
 
 
 def _normalize(x: numpy.ndarray, *, inplace: bool = False) -> numpy.ndarray:
-    norm = numpy.linalg.norm(x, axis=-1).reshape(*x.shape[:-1], 1)
+    norm = numpy.linalg.vector_norm(x, axis=-1).reshape(*x.shape[:-1], 1)
     return numpy.divide(x, norm, out=x if inplace else None, where=norm != 0)

src/biotite/structure/basepairs.py

@@ -948,7 +948,7 @@ def base_pairs(
         to_remove = []
 
         # Get all bases that have non-unique pairing interactions
-        base_indices, occurrences = np.unique(basepairs, return_counts=True)
+        base_indices, occurrences = np.unique_counts(basepairs)
         for base_index, occurrence in zip(base_indices, occurrences):
             if occurrence > 1:
                 # Write the non-unique base pairs to a dictionary as

src/biotite/structure/box.py

@@ -230,9 +230,9 @@ def unitcell_from_vectors(
     a = box[0]
     b = box[1]
     c = box[2]
-    len_a = linalg.norm(a)
-    len_b = linalg.norm(b)
-    len_c = linalg.norm(c)
+    len_a = linalg.vector_norm(a)
+    len_b = linalg.vector_norm(b)
+    len_c = linalg.vector_norm(c)
     alpha = np.arccos(np.dot(b, c) / (len_b * len_c))
     beta = np.arccos(np.dot(a, c) / (len_a * len_c))
     gamma = np.arccos(np.dot(a, b) / (len_a * len_b))

src/biotite/structure/connect.py

@@ -39,6 +39,7 @@
 from biotite.structure.bonds import BondList, BondType
 from biotite.structure.error import BadStructureError, InconsistentBondTypeWarning
 from biotite.typing import K, N, NDArray1
+from biotite.util import map_unique
 
 # Covalent radii (in Å) taken from Cordero et al.
 # For carbon the sp3 radius is used, for manganese, iron and cobalt the low-spin radius
@@ -240,10 +241,9 @@ def connect_via_distances(
 
     # NaN for unknown elements ensures that they never form bonds,
     # as any comparison with NaN is false
-    radii = np.array(
-        [_COVALENT_RADII.get(element.upper(), np.nan) for element in atoms.element],
-        dtype=np.float32,
-    )
+    radii = map_unique(
+        lambda element: _COVALENT_RADII.get(element.upper(), np.nan), atoms.element
+    ).astype(np.float32)
 
     residue_starts = get_residue_starts(atoms, add_exclusive_stop=True)
 
@@ -559,7 +559,7 @@ def infer_bond_types(
     else:
         known_charges = None
     bond_list, charges, converged = rust_infer_bond_types(
-        [element.upper() for element in atoms.element],
+        atoms.element.tolist(),
         atoms.bonds,
         total_charge,
         known_charges,
@@ -668,9 +668,7 @@ def _connect_inter_residue(
     # Avoid circular import
     from biotite.structure.info.misc import link_type
 
-    link_types = [
-        link_type(res_name) for res_name in atoms.res_name[residue_starts[:-1]]
-    ]
+    link_types = map_unique(link_type, atoms.res_name[residue_starts[:-1]]).tolist()
     is_disconnected = (
         # Residues are not inside the same chain
         (atoms.chain_id[residue_starts[1:-1]] != atoms.chain_id[residue_starts[:-2]])

src/biotite/structure/filter.py

@@ -376,7 +376,7 @@ def filter_linear_bond_continuity(
     Note that this function purely uses distances between consecutive atoms.
     A potential ``BondList`` is not considered here.
     """
-    dist = np.linalg.norm(np.diff(array.coord, axis=0), axis=1)
+    dist = np.linalg.vector_norm(np.diff(array.coord, axis=0), axis=1)
     mask = (dist >= min_len) & (dist <= max_len)
     return np.append(mask, True)  # pyright: ignore[reportReturnType]
 

src/biotite/structure/graphics/rna.py

@@ -279,14 +279,16 @@ def plot_nucleotide_secondary_structure(
                     coordinates[i - 1][1] - coordinates[i][1],
                 ]
             )
-            vector_to_previous = vector_to_previous / np.linalg.norm(vector_to_previous)
+            vector_to_previous = vector_to_previous / np.linalg.vector_norm(
+                vector_to_previous
+            )
             vector_to_next = np.array(
                 [
                     coordinates[i][0] - coordinates[i + 1][0],
                     coordinates[i][1] - coordinates[i + 1][1],
                 ]
             )
-            vector_to_next = vector_to_next / np.linalg.norm(vector_to_next)
+            vector_to_next = vector_to_next / np.linalg.vector_norm(vector_to_next)
             vector = (vector_to_next + vector_to_previous) / 2
         elif i > 0:
             # For the last base get the direction vector to the previous
@@ -307,7 +309,7 @@ def plot_nucleotide_secondary_structure(
                 ]
             )
         # Normalize the vector
-        vector = vector / np.linalg.norm(vector)
+        vector = vector / np.linalg.vector_norm(vector)
         # Get the perpendicular vector
         vector = np.array([vector[1], -vector[0]])
         # The annotations are offset in the direction of the

src/biotite/structure/hbond.py

@@ -19,6 +19,7 @@
 from biotite.structure.bonds import BondList
 from biotite.structure.filter import filter_heavy
 from biotite.structure.geometry import angle, distance
+from biotite.structure.residues import get_all_residue_positions
 from biotite.typing import C3, XYZ, K, M, N, NDArray1, NDArray2, NDArray3
 
 
@@ -413,7 +414,7 @@ def _get_bonded_h_via_distance(
     CUTOFF = 1.5
 
     coord = array.coord
-    res_id = array.res_id
+    residue_positions = get_all_residue_positions(array)
     hydrogen_mask = ~filter_heavy(array)
 
     donor_hydrogen_mask = np.zeros(len(array), dtype=bool)
@@ -427,15 +428,12 @@ def _get_bonded_h_via_distance(
             associated_donor_indices,
         )  # pyright: ignore[reportReturnType]
 
-    # Sort the hydrogen atoms by residue ID, so that the candidates for each
-    # donor can be found via binary search instead of scanning all atoms
-    hydrogen_indices = hydrogen_indices[
-        np.argsort(res_id[hydrogen_indices], kind="stable")
-    ]
-    hydrogen_res_ids = res_id[hydrogen_indices]
-    donor_res_ids = res_id[donor_indices]
-    candidate_start = np.searchsorted(hydrogen_res_ids, donor_res_ids, side="left")
-    candidate_stop = np.searchsorted(hydrogen_res_ids, donor_res_ids, side="right")
+    # The residue positions are already sorted in atom order, so the candidates
+    # for each donor can be found via binary search instead of scanning all atoms
+    hydrogen_residues = residue_positions[hydrogen_indices]
+    donor_residues = residue_positions[donor_indices]
+    candidate_start = np.searchsorted(hydrogen_residues, donor_residues, side="left")
+    candidate_stop = np.searchsorted(hydrogen_residues, donor_residues, side="right")
 
     # Flatten the ragged donor-to-candidate mapping into plain index arrays,
     # so that all distances can be computed in a single vectorized call
@@ -447,7 +445,7 @@ def _get_bonded_h_via_distance(
             associated_donor_indices,
         )  # pyright: ignore[reportReturnType]
     donor_of_pair = np.repeat(np.arange(len(donor_indices)), candidate_count)
-    offsets = np.cumsum(candidate_count) - candidate_count
+    offsets = np.cumulative_sum(candidate_count, include_initial=True)[:-1]
     index_in_candidates = np.arange(total_count) - offsets[donor_of_pair]
     pair_hydrogen_i = hydrogen_indices[
         candidate_start[donor_of_pair] + index_in_candidates

src/biotite/structure/info/groups.py

@@ -129,7 +129,7 @@ def ion_names() -> list[str]:
     formulas = chem_comp["formula"].as_array()
 
     # Simple filter: The formula must contain only one element
-    mask = np.char.isalpha(formulas)
+    mask = np.strings.isalpha(formulas)
     comp_ids = comp_ids[mask]
     formulas = formulas[mask]
 
@@ -164,4 +164,6 @@ def _get_group_members(match_types: list[str]) -> list[str]:
     comp_ids = category["id"].as_array()
     types = category["type"].as_array()
     # Ignore case
-    return comp_ids[np.isin(np.char.lower(types), np.char.lower(match_types))].tolist()
+    return comp_ids[
+        np.isin(np.strings.lower(types), np.strings.lower(match_types))
+    ].tolist()