From 90a06eeca6b8b60383be228df57207f24f4dc54c Mon Sep 17 00:00:00 2001
From: pswain <peter.swain@ed.ac.uk>
Date: Tue, 23 May 2023 15:31:22 +0100
Subject: [PATCH] some more docs focused on picker
---
src/agora/io/signal.py | 17 +++++++++--------
src/agora/utils/indexing.py | 11 ++++++-----
src/postprocessor/chainer.py | 8 +++++---
src/postprocessor/core/reshapers/picker.py | 5 +++--
src/postprocessor/grouper.py | 11 ++++++-----
5 files changed, 29 insertions(+), 23 deletions(-)
diff --git a/src/agora/io/signal.py b/src/agora/io/signal.py
index ae6ae71e..4611663f 100644
--- a/src/agora/io/signal.py
+++ b/src/agora/io/signal.py
@@ -60,10 +60,10 @@ class Signal(BridgeH5):
def get(self, dsets: t.Union[str, t.Collection], **kwargs):
"""Get and potentially pre-process data from h5 file and return as a dataframe."""
- if isinstance(dsets, str): # no pre-processing
- df = self.get_raw(dsets, **kwargs)
+ if isinstance(dsets, str):
+ # no pre-processing
+ dsets = self.get_raw(dsets, **kwargs)
prepost_applied = self.apply_prepost(dsets, **kwargs)
-
return self.add_name(prepost_applied, dsets)
@staticmethod
@@ -73,7 +73,7 @@ class Signal(BridgeH5):
return df
def cols_in_mins(self, df: pd.DataFrame):
- # Convert numerical columns in a dataframe to minutes
+ """Convert numerical columns in a dataframe to minutes."""
try:
df.columns = (df.columns * self.tinterval // 60).astype(int)
except Exception as e:
@@ -141,7 +141,6 @@ class Signal(BridgeH5):
if lineage_location not in f:
lineage_location = "postprocessing/lineage"
tile_mo_da = f[lineage_location]
-
if isinstance(tile_mo_da, h5py.Dataset):
lineage = tile_mo_da[()]
else:
@@ -288,15 +287,17 @@ class Signal(BridgeH5):
self.get_raw(dset, in_minutes=in_minutes, lineage=lineage)
for dset in dataset
]
- if lineage: # assume that df is sorted
+ if lineage:
+ # assume that df is sorted
mother_label = np.zeros(len(df), dtype=int)
lineage = self.lineage()
- a, b = validate_association(
+ valid_association, valid_indices = validate_association(
lineage,
np.array(df.index.to_list()),
+ #: are mothers not match_column=0?
match_column=1,
)
- mother_label[b] = lineage[a, 1]
+ mother_label[valid_indices] = lineage[valid_association, 1]
df = add_index_levels(df, {"mother_label": mother_label})
return df
except Exception as e:
diff --git a/src/agora/utils/indexing.py b/src/agora/utils/indexing.py
index a86fce66..5eedeabe 100644
--- a/src/agora/utils/indexing.py
+++ b/src/agora/utils/indexing.py
@@ -28,12 +28,13 @@ def validate_association(
association : np.ndarray
2D array of lineage associations where columns are (trap, mother, daughter)
or
- a 3D array, which is an array of 2 X 2 arrays comprising [[trap_id, mother_label], [trap_id, daughter_label]].
+ a 3D array, which is an array of 2 X 2 arrays comprising [[trap_id, mother_label], [trap_id, daughter_label]].
indices : np.ndarray
- a 2D array where each column is a different level, such as (trap_id, cell_label). This should not include mother_label.
+ A 2D array where each column is a different level, such as (trap_id, cell_label). This array should not include mother_label.
match_column: int
- If 0, match mothers; if 1 match daughters.
- If None, match both mothers and daughters.
+ If 0, matches indicate mothers from mother-bud pairs;
+ If 1, matches indicate daughters from mother-bud pairs;
+ If None, matches indicate either mothers or daughters in mother-bud pairs.
Returns
-------
@@ -100,7 +101,7 @@ def validate_association(
# make True comparisons have both trap_ids and cell labels matching
valid_cell_ids_va = valid_ndassociation[valid_association].all(axis=2)
if match_column is None:
- # make True comparisons match at least one mother or bud in association
+ # make True comparisons match either a mother or a bud in association
valid_indices = valid_cell_ids_va.any(axis=1)[0]
else:
valid_indices = valid_cell_ids_va[:, match_column][0]
diff --git a/src/postprocessor/chainer.py b/src/postprocessor/chainer.py
index b834fb5d..027e02ee 100644
--- a/src/postprocessor/chainer.py
+++ b/src/postprocessor/chainer.py
@@ -14,9 +14,10 @@ from postprocessor.core.abc import get_process
class Chainer(Signal):
"""
Extend Signal by applying post-processes and allowing composite signals that combine basic signals.
- It "chains" multiple processes upon fetching a dataset to produce the desired datasets.
- Instead of reading processes previously applied, it executes
+ Chainer "chains" multiple processes upon fetching a dataset.
+
+ Instead of reading processes previously applied, Chainer executes
them when called.
"""
@@ -25,6 +26,7 @@ class Chainer(Signal):
}
def __init__(self, *args, **kwargs):
+ """Initialise chainer."""
super().__init__(*args, **kwargs)
def replace_path(path: str, bgsub: bool = ""):
@@ -34,7 +36,7 @@ class Chainer(Signal):
path = re.sub(channel, f"{channel}{suffix}", path)
return path
- # Add chain with and without bgsub for composite statistics
+ # add chain with and without bgsub for composite statistics
self.common_chains = {
alias
+ bgsub: lambda **kwargs: self.get(
diff --git a/src/postprocessor/core/reshapers/picker.py b/src/postprocessor/core/reshapers/picker.py
index 168b022a..f3c35852 100644
--- a/src/postprocessor/core/reshapers/picker.py
+++ b/src/postprocessor/core/reshapers/picker.py
@@ -30,7 +30,8 @@ class PickerParameters(ParametersABC):
class Picker(LineageProcess):
"""
- Picker selects cells from a signal using lineage information and by how long and by how they are retained in the data set.
+ Picker selects cells from a signal using lineage information and
+ by how and for how long they are retained in the data set.
"""
def __init__(
@@ -143,7 +144,7 @@ def _as_int(threshold: t.Union[float, int], ntps: int):
def any_present(signal, threshold):
"""Return pd.Series for cells where True indicates that cell was present for more than threshold time points."""
- #: isn't full_traps all we need?
+ #: why isn't full_traps all we need?
full_traps = (signal.notna().sum(axis=1) > threshold).groupby("trap")
any_present = pd.Series(
np.sum(
diff --git a/src/postprocessor/grouper.py b/src/postprocessor/grouper.py
index 27f905c3..08440093 100644
--- a/src/postprocessor/grouper.py
+++ b/src/postprocessor/grouper.py
@@ -86,8 +86,9 @@ class Grouper(ABC):
**kwargs,
):
"""
- Concatenate data for one signal from different h5 files, with
- one h5 file per position, into a dataframe.
+ Concatenate data for one signal from different h5 files into a dataframe.
+
+ Each h5 file corresponds to one position
Parameters
----------
@@ -267,17 +268,17 @@ class Grouper(ABC):
@property
def stages_span(self):
- # FAILS on my example
+ # TODO: fails on my example
return self.fsignal.stages_span
@property
def max_span(self):
- # FAILS on my example
+ # TODO: fails on my example
return self.fsignal.max_span
@property
def stages(self):
- # FAILS on my example
+ # TODO: fails on my example
return self.fsignal.stages
@property
--
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