Coverage for hiphive/cluster_space_data.py: 100%
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1from typing import Any, Dict, List
2import numpy as np
3from .input_output.pretty_table_prints import print_table
4from pandas import DataFrame
7class ClusterSpaceData:
9 """Container class that holds information concerning a cluster space, including,
10 e.g., the number of cluster, orbits, and parameters by order and number of bodies.
12 Parameters
13 ----------
14 cs : ClusterSpace
15 cluster space
16 """
18 def __init__(self, cs) -> None:
19 self.max_nbody = cs.cutoffs.max_nbody
20 self.max_order = cs.cutoffs.max_order
22 # collect cutoff matrix
23 self._cutoff_matrix = cs.cutoffs.cutoff_matrix
25 # collect cluster, orbit, eigentensor counts
26 self._cluster_counts = np.zeros((self.max_nbody, self.max_order - 1), dtype=int)
27 self._orbit_counts = np.zeros((self.max_nbody, self.max_order - 1), dtype=int)
28 self._eigentensor_counts = np.zeros((self.max_nbody, self.max_order - 1), dtype=int)
29 for orbit in cs.orbits:
30 proto_cluster = cs.cluster_list[orbit.prototype_index]
31 order = len(proto_cluster)
32 nbody = len(set(proto_cluster))
33 self._cluster_counts[nbody-1, order-2] += len(orbit.orientation_families)
34 self._orbit_counts[nbody-1, order-2] += 1
35 self._eigentensor_counts[nbody-1, order-2] += len(orbit.eigentensors)
37 # collect number of parameters after sum rule constraint
38 self.ndofs_by_order = {o: cs.get_n_dofs_by_order(o) for o in cs.cutoffs.orders}
40 def print_tables(self) -> None:
41 """ Prints information concerning the underlying cluster space to stdout, including,
42 e.g., the number of cluster, orbits, and parameters by order and number of bodies. """
44 # print table data
45 print('Cutoff Matrix')
46 print_table(self._cutoff_matrix_padded)
47 print('\nCluster counts')
48 print_table(self._cluster_counts, include_sum=True)
49 print('\nOrbit counts')
50 print_table(self._orbit_counts, include_sum=True)
51 print('\nEigentensor counts')
52 print_table(self._eigentensor_counts, include_sum=True)
54 @property
55 def _cutoff_matrix_padded(self) -> np.array:
56 """ Padded cutoff matrix with None for nbody=1 terms """
57 return np.vstack(([[None] * (self.max_order - 1)], self._cutoff_matrix))
59 def to_list(self) -> List[Dict[str, Any]]:
60 """ Returns cluster space data in the form of a list of dicts. """
61 records = []
62 for order in range(2, self.max_order+1):
63 for nbody in range(1, self.max_nbody+1):
64 if nbody > order:
65 continue
66 row = dict(order=order, nbody=nbody)
67 row['cutoff'] = self._cutoff_matrix_padded[nbody-1, order-2]
68 row['cluster_counts'] = self._cluster_counts[nbody-1, order-2]
69 row['orbit_counts'] = self._orbit_counts[nbody-1, order-2]
70 row['eigentensor_counts'] = self._eigentensor_counts[nbody-1, order-2]
71 records.append(row)
72 return records
74 def to_dataframe(self) -> DataFrame:
75 """ Returns cluster space data in the form of a pandas DataFrame. """
76 return DataFrame.from_dict(self.to_list())
78 def __str__(self) -> str:
79 s = f'Cutoff matrix: \n {self._cutoff_matrix}\n'
80 s += f'Cluster counts: \n{self._cluster_counts}\n'
81 s += f'Orbit counts: \n{self._orbit_counts}\n'
82 s += f'Eigentensor counts: \n{self._eigentensor_counts}\n'
83 s += f'Degrees of freedoms: \n{self.ndofs_by_order}\n'
84 return s
86 def __repr__(self) -> str:
87 return str(self)