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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,104 @@ | ||
| """Tool utilities""" | ||
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| from __future__ import annotations | ||
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| import random | ||
| import warnings | ||
| from collections.abc import Mapping, Sequence | ||
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| import numpy as np | ||
| import scipy as sp | ||
| import treedata as td | ||
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| def _check_previous_params(tdata: td.TreeData, params: Mapping, key: str, suffixes: Sequence[str]) -> None: | ||
| """When a function is updating previous results, check that the parameters are the same.""" | ||
| for suffix in suffixes: | ||
| if f"{key}_{suffix}" in tdata.uns: | ||
| prev_params = tdata.uns[f"{key}_{suffix}"]["params"] | ||
| for param, value in params.items(): | ||
| if param not in prev_params or prev_params[param] != value: | ||
| raise ValueError( | ||
| f"{param} value does not match previous call. " | ||
| f"Previous: {prev_params}. Current: {params}. " | ||
| f"Set `update=False` to avoid this error." | ||
| ) | ||
| return None | ||
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| def _csr_data_mask(csr): | ||
| """Boolean mask of explicit data in a csr matrix including zeros""" | ||
| return sp.sparse.csr_matrix((np.ones(len(csr.data), dtype=bool), csr.indices, csr.indptr), shape=csr.shape) | ||
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| def _set_random_state(random_state): | ||
| """Set random state""" | ||
| if random_state is not None: | ||
| random.seed(random_state) | ||
| np.random.seed(random_state) | ||
| return | ||
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| def _format_keys(keys, suffix): | ||
| """Ensures that keys are formatted correctly""" | ||
| if keys is None: | ||
| pass | ||
| elif isinstance(keys, str): | ||
| if not keys.endswith(suffix): | ||
| keys = f"{keys}_{suffix}" | ||
| elif isinstance(keys, Sequence): | ||
| keys = [f"{key}_{suffix}" if not key.endswith(suffix) else key for key in keys] | ||
| else: | ||
| raise ValueError("keys must be a string or a sequence of strings.") | ||
| return keys | ||
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| def _format_as_list(obj): | ||
| """Ensures that obj is a list""" | ||
| if obj is None: | ||
| pass | ||
| elif not isinstance(obj, Sequence): | ||
| obj = [obj] | ||
| return obj | ||
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| def _check_tree_overlap(tdata, tree_keys): | ||
| """If overlap is allowed there can only be one tree""" | ||
| n_trees = len(tdata.obst.keys()) | ||
| if (n_trees > 1) and tdata.allow_overlap and len(tree_keys) != 1: | ||
| raise ValueError("Must specify a singe tree if tdata.allow_overlap is True.") | ||
| return | ||
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| def _set_distances_and_connectivities(tdata, key_added, dist, connect, update): | ||
| """Set distances and connectivities in tdata""" | ||
| dist_key = f"{key_added}_distances" | ||
| connect_key = f"{key_added}_connectivities" | ||
| if update and (dist_key in tdata.obsp.keys()): | ||
| if isinstance(dist, np.ndarray): | ||
| tdata.obsp[dist_key] = dist | ||
| else: | ||
| with warnings.catch_warnings(): | ||
| warnings.simplefilter("ignore") | ||
| mask = _csr_data_mask(dist) | ||
| tdata.obsp[dist_key][mask] = dist[mask] | ||
| else: | ||
| if dist_key in tdata.obsp.keys(): | ||
| del tdata.obsp[dist_key] | ||
| if f"{key_added}_neighbors" in tdata.uns.keys(): | ||
| del tdata.uns[f"{key_added}_neighbors"] | ||
| tdata.obsp[dist_key] = dist | ||
| if connect is not None: | ||
| tdata.obsp[connect_key] = connect | ||
| return None | ||
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| def _assert_param_xor(params): | ||
| """Assert that only one of the parameters is set""" | ||
| n_set = sum([value is not None for key, value in params.items()]) | ||
| param_text = ", ".join(params.keys()) | ||
| if n_set > 1: | ||
| raise ValueError(f"Only one of {param_text} can be set.") | ||
| if n_set == 0: | ||
| raise ValueError(f"At least one of {param_text} must be set.") | ||
| return None |
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