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Cleanup #71

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merged 4 commits into from
Mar 22, 2024
Merged

Cleanup #71

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16782ec
Cleanup
mgeplf Feb 14, 2024
a9d3638
rename descendant_id_set -> descendant_ids
mgeplf Feb 14, 2024
f302ca6
asdf
mgeplf Feb 20, 2024
9a6b90e
remove junk
mgeplf Mar 21, 2024
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10 changes: 5 additions & 5 deletions atlas_densities/densities/fitting.py
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Original file line number Diff line number Diff line change
Expand Up @@ -147,11 +147,11 @@ def fill_in_homogenous_regions(
inhibitory_mask = homogenous_regions["cell_type"] == "inhibitory"

for region_name in homogenous_regions[inhibitory_mask]["brain_region"]:
desc_id_set = hierarchy_info.at[region_name, "descendant_id_set"]
desc_id_set = hierarchy_info.at[region_name, "descendant_ids"]
id_mask = [id_ in desc_id_set for id_ in hierarchy_info["id"]]
for child_region_name in hierarchy_info[id_mask].index:
region_mask = np.isin(
annotation, list(hierarchy_info.at[child_region_name, "descendant_id_set"])
annotation, list(hierarchy_info.at[child_region_name, "descendant_ids"])
)
density = np.mean(neuron_density[region_mask])
densities.at[child_region_name, "inhibitory_neuron"] = density
Expand All @@ -165,7 +165,7 @@ def fill_in_homogenous_regions(
excitatory_mask = homogenous_regions["cell_type"] == "excitatory"

for region_name in homogenous_regions[excitatory_mask]["brain_region"]:
desc_id_set = hierarchy_info.at[region_name, "descendant_id_set"]
desc_id_set = hierarchy_info.at[region_name, "descendant_ids"]
id_mask = [id_ in desc_id_set for id_ in hierarchy_info["id"]]
for child_region_name in hierarchy_info[id_mask].index:
densities.at[child_region_name, "inhibitory_neuron"] = 0.0
Expand Down Expand Up @@ -232,7 +232,7 @@ def compute_average_intensities(
}
where each intensity array is of shape (W, H, D) and where the items of each slice
list range in [0, W - 1].
hierarchy_info: data frame with colums "descendant_id_set" (set[int]) and "brain_region"
hierarchy_info: data frame with colums "descendant_ids" (set[int]) and "brain_region"
(str) and whose index is a list of region ids.
See :fun:`atlas_densities.densities.utils.get_hierarchy_info`.

Expand Down Expand Up @@ -260,7 +260,7 @@ def compute_average_intensities(
region_count,
)
for region_name in tqdm(result.index):
region_mask = np.isin(annotation, list(hierarchy_info.at[region_name, "descendant_id_set"]))
region_mask = np.isin(annotation, list(hierarchy_info.at[region_name, "descendant_ids"]))
for marker, intensity in gene_marker_volumes.items():
result.at[region_name, marker.lower()] = compute_average_intensity(
intensity["intensity"], region_mask, intensity["slices"]
Expand Down
123 changes: 1 addition & 122 deletions atlas_densities/densities/inhibitory_neuron_densities_helper.py
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Original file line number Diff line number Diff line change
Expand Up @@ -2,16 +2,12 @@
"""

import logging
from typing import List, Optional, Set, Tuple
from typing import List

import numpy as np
import pandas as pd
from atlas_commons.typing import FloatArray

from atlas_densities.exceptions import AtlasDensitiesError

L = logging.getLogger(__name__)
MinMaxPair = Tuple[float, Optional[float]]


def average_densities_to_cell_counts(
Expand All @@ -34,11 +30,6 @@ def average_densities_to_cell_counts(
:func:`atlas_densities.densities.utils.compute_region_volumes`.
The volumes to be used are stored in the "volume" column.


Note: Volumes can refer to volumes of entire regions, including all descendants
subregions, or volumes of single region identifiers, depending on the value of the
option `with_descendants` used when creating `region_volumes`.

Returns:
data frame containing the cell counts of brain regions and their associated standard
deviations. The data frame index is `average_densities.index` (brain region names) and
Expand All @@ -54,35 +45,6 @@ def average_densities_to_cell_counts(
return result


def resize_average_densities(
average_densities: pd.DataFrame, hierarchy_info: pd.DataFrame
) -> pd.DataFrame:
"""
Resize the `average_densities` data frame in such a way that the resized index coincides with
`hierarchy_info["brain_region"]`.

Missing entries are set to ``np.nan``.

average_densities: a data frame whose columns are described in
:func:`atlas_densities.densities.fitting.linear_fitting` containing the average
cell densities of brain regions and their associated standard deviations. Columns are
labelled by T and T_standard_deviation for various cell types T. The index of
`average_densities` is a list of region names.
hierarchy_info: data frame returned by
:func:`atlas_densities.densities.utils.get_hierarchy_info`.

Returns: a data frame containing all the entries of `average_densities` and whose index is
`hierarchy_info["brain_region"]`. New entries are set to ``np.nan``.

"""
resized = pd.DataFrame(
np.nan, index=hierarchy_info["brain_region"], columns=average_densities.columns
)
resized.loc[average_densities.index] = average_densities

return resized


def get_cell_types(data_frame: pd.DataFrame) -> List[str]:
"""
Extract cell types from column labels.
Expand All @@ -96,86 +58,3 @@ def get_cell_types(data_frame: pd.DataFrame) -> List[str]:
"""

return sorted({column.replace("_standard_deviation", "") for column in data_frame.columns})


def check_region_counts_consistency(
region_counts: pd.DataFrame, hierarchy_info: pd.DataFrame, tolerance: float = 0.0
) -> None:
"""
Check that cell counts considered as certain are consistent across the brain regions hierarchy.

Args:
region_counts: data frame returned by
:fun:`densities.inhibitory_densities_helper.average_densities_to_cell_counts`.
A region is understood as a region of the brain hierarchy and includes all descendant
subregions.
hierarchy_info: data frame returned by
:func:`atlas_densities.densities.utils.get_hierarchy_info`.
tolerance: non-negative float number used as tolerance when comparing counts.
Defaults to 0.0.

Raises:
AtlasDensitiesError if the sum of the cell counts of some leaf regions, all given with
certainty, exceeds the cell count of an ancestor, also given with certainty.

"""

def _check_descendants_consistency(
region_counts, hierarchy_info, region_name: str, id_set: Set[int], cell_type: str
):
if region_counts.at[region_name, f"{cell_type}_standard_deviation"] == 0.0:
count = region_counts.at[region_name, cell_type]
descendants_names = hierarchy_info.loc[list(id_set), "brain_region"]
zero_std_mask = (
region_counts.loc[descendants_names, f"{cell_type}_standard_deviation"] == 0.0
)
mask = zero_std_mask & (
region_counts.loc[descendants_names, cell_type] > count + tolerance
)
descendants_counts = region_counts.loc[descendants_names, cell_type][mask]
if not descendants_counts.empty:
raise AtlasDensitiesError(
f"Counts of {cell_type} cells in regions {list(descendants_names)} all exceed "
f"the count of its ancestor region {region_name} and each count is given "
f"for certain. The counts {descendants_counts} are all larger than "
f"{count}."
)
names_with_certainty = descendants_names[zero_std_mask.to_list()].to_list()
leaf_names = [
hierarchy_info.at[id_, "brain_region"]
for id_ in id_set
if len(hierarchy_info.at[id_, "descendant_id_set"]) == 1
and hierarchy_info.at[id_, "brain_region"] in names_with_certainty
]
leaves_count_sum = np.sum(region_counts.loc[leaf_names, cell_type])
if leaves_count_sum > count + tolerance:
raise AtlasDensitiesError(
f"The sum of the counts of {cell_type} cells in leaf regions",
f" which are given with certainty, exceeds the count of the ancestor"
f" region {region_name}, also given with certainty: "
f"{leaves_count_sum} > {count}.",
)

cell_types = get_cell_types(region_counts)
for region_name, id_set in zip(
hierarchy_info["brain_region"], hierarchy_info["descendant_id_set"]
):
for cell_type in cell_types:
_check_descendants_consistency(
region_counts, hierarchy_info, region_name, id_set, cell_type
)


def replace_inf_with_none(bounds: FloatArray) -> List[MinMaxPair]:
"""
Replace the upper bounds equal to ``np.inf`` by None so as to comply with
the `bounds` interface of scipy.optimize.linprog.

Args:
bounds: float array of shape (N, 2). Values in `bounds[..., 1]` can be
``np.inf`` to indicate the absence of a constraining upper bound.

Return:
list of pairs (min, max) where min is a float and max either a float or None.
"""
return [(float(min_), None if np.isinf(max_) else float(max_)) for min_, max_ in bounds]
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