From 9f9d2ba0ecf8a1e9dd61f8b354ed9af7b521d3db Mon Sep 17 00:00:00 2001
From: Swainlab <peter.swain@ed.ac.uk>
Date: Sun, 2 Jul 2023 18:08:43 +0100
Subject: [PATCH] docs for budding; re-write of budmetric
---
src/agora/io/signal.py | 2 +-
src/agora/utils/lineage.py | 5 -
.../core/reshapers/bud_metric.py | 100 +++++++++++-------
3 files changed, 61 insertions(+), 46 deletions(-)
diff --git a/src/agora/io/signal.py b/src/agora/io/signal.py
index 20b6d8ba..7a7f871f 100644
--- a/src/agora/io/signal.py
+++ b/src/agora/io/signal.py
@@ -70,7 +70,7 @@ class Signal(BridgeH5):
@staticmethod
def add_name(df, name):
- """Add column of identical strings to a dataframe."""
+ """TODO"""
df.name = name
return df
diff --git a/src/agora/utils/lineage.py b/src/agora/utils/lineage.py
index 52fb552b..05c161a5 100644
--- a/src/agora/utils/lineage.py
+++ b/src/agora/utils/lineage.py
@@ -1,12 +1,8 @@
#!/usr/bin/env python3
-import re
-import typing as t
import numpy as np
-import pandas as pd
from agora.io.bridge import groupsort
-from itertools import groupby
def mb_array_to_dict(mb_array: np.ndarray):
@@ -19,4 +15,3 @@ def mb_array_to_dict(mb_array: np.ndarray):
for trap, mo_da in groupsort(mb_array).items()
for mo, daughters in groupsort(mo_da).items()
}
-
diff --git a/src/postprocessor/core/reshapers/bud_metric.py b/src/postprocessor/core/reshapers/bud_metric.py
index b8952288..ee239221 100644
--- a/src/postprocessor/core/reshapers/bud_metric.py
+++ b/src/postprocessor/core/reshapers/bud_metric.py
@@ -20,8 +20,9 @@ class BudMetricParameters(LineageProcessParameters):
class BudMetric(LineageProcess):
"""
- Requires mother-bud information to create a new dataframe where the indices are mother ids and
- values are the daughters' values for a given signal.
+ Requires mother-bud information to create a new dataframe where the
+ indices are mother ids and values are the daughters' values for a
+ given signal.
"""
def __init__(self, parameters: BudMetricParameters):
@@ -38,7 +39,6 @@ class BudMetric(LineageProcess):
else:
assert "mother_label" in signal.index.names
lineage = signal.index.to_list()
-
return self.get_bud_metric(signal, mb_array_to_dict(lineage))
@staticmethod
@@ -48,7 +48,9 @@ class BudMetric(LineageProcess):
"""
signal: Daughter-inclusive dataframe
- md: Mother-daughters dictionary where key is mother's index and its values are a list of daughter indices
+ md: dictionary where key is mother's index,
+ defined as (trap, cell_label), and its values are a list of
+ daughter indices, as (trap, cell_label).
Get fvi (First Valid Index) for all cells
Create empty matrix
@@ -61,63 +63,81 @@ class BudMetric(LineageProcess):
Convert matrix into dataframe using mother indices
"""
- mothers_mat = np.zeros((len(md), signal.shape[1]))
- cells_were_dropped = 0 # Flag determines if mothers (1), daughters (2) or both were missing (3)
-
md_index = signal.index
- if (
- "mother_label" not in md_index.names
- ): # Generate mother label from md dict if unavailable
+ # md_index should only comprise (trap, cell_label)
+ if "mother_label" not in md_index.names:
+ # dict with daughter indices as keys
d = {v: k for k, values in md.items() for v in values}
+ # generate mother_label in signal using the mother's cell_label
signal["mother_label"] = list(
map(lambda x: d.get(x, [0])[-1], signal.index)
)
signal.set_index("mother_label", append=True, inplace=True)
- related_items = set(
- [*md.keys(), *[y for x in md.values() for y in x]]
- )
- md_index = md_index.intersection(related_items)
- elif "mother_label" in md_index.names:
- md_index = md_index.droplevel("mother_label")
+ # combine mothers and daughter indices
+ mothers_index = md.keys()
+ daughters_index = [y for x in md.values() for y in x]
+ relations = set([*mothers_index, *daughters_index])
+ # keep from md_index only mother and daughters
+ md_index = md_index.intersection(relations)
else:
- raise ("Unavailable relationship information")
-
+ md_index = md_index.droplevel("mother_label")
if len(md_index) < len(signal):
- print("Dropped cells before bud_metric") # TODO log
-
+ print("Dropped cells before applying bud_metric") # TODO log
+ # restrict signal to the cells in md_index, moving mother_label to do so
signal = (
signal.reset_index("mother_label")
.loc(axis=0)[md_index]
.set_index("mother_label", append=True)
)
-
- names = list(signal.index.names)
- del names[-2]
-
- output_df = (
- signal.loc[signal.index.get_level_values("mother_label") > 0]
- .groupby(names)
- .apply(lambda x: _combine_daughter_tracks(x))
+ # restrict to daughters: cells with a mother
+ mother_labels = signal.index.get_level_values("mother_label")
+ daughter_df = signal.loc[mother_labels > 0]
+ # join data for daughters with the same mother
+ output_df = daughter_df.groupby(["trap", "mother_label"]).apply(
+ lambda x: _combine_daughter_tracks(x)
)
+
output_df.columns = signal.columns
output_df["padding_level"] = 0
output_df.set_index("padding_level", append=True, inplace=True)
-
if len(output_df):
output_df.index.names = signal.index.names
return output_df
-def _combine_daughter_tracks(tracks: t.Collection[pd.Series]):
+def _combine_daughter_tracks(tracks: pd.DataFrame):
"""
- Combine multiple time series of cells into one, overwriting values
- prioritising the most recent entity.
+ Combine multiple time series of daughter cells into one time series.
+
+ At any one time, a mother cell should have only one daughter.
+
+ Two daughters are still sometimes present at the same time point, and we
+ then choose the daughter that appears first.
+
+ TODO We need to fix examples with more than one daughter at a time point.
+
+ Parameters
+ ----------
+ tracks: a Signal
+ Data for all daughters, which are distinguished by different cell_labels,
+ for a particular trap and mother_label.
"""
- sorted_da_ids = tracks.sort_index(level="cell_label")
- sorted_da_ids.index = range(len(sorted_da_ids))
- tp_fvt = sorted_da_ids.apply(lambda x: x.first_valid_index(), axis=0)
- tp_fvt = sorted_da_ids.columns.get_indexer(tp_fvt)
- tp_fvt[tp_fvt < 0] = len(sorted_da_ids) - 1
-
- _metric = np.choose(tp_fvt, sorted_da_ids.values)
- return pd.Series(_metric, index=tracks.columns)
+ # sort by daughter IDs
+ bud_df = tracks.sort_index(level="cell_label")
+ # remove multi-index
+ bud_df.index = range(len(bud_df))
+ # find which row of sorted_df has the daughter for each time point
+ tp_fvt: pd.Series = bud_df.apply(lambda x: x.first_valid_index(), axis=0)
+ # combine data for all daughters
+ combined_tracks = np.nan * np.ones(tracks.columns.size)
+ for bud_row in np.unique(tp_fvt.dropna().values).astype(int):
+ ilocs = np.where(tp_fvt.values == bud_row)[0]
+ combined_tracks[ilocs] = bud_df.values[bud_row, ilocs]
+ # TODO delete old version
+ tp_fvt = bud_df.columns.get_indexer(tp_fvt)
+ tp_fvt[tp_fvt == -1] = len(bud_df) - 1
+ old = np.choose(tp_fvt, bud_df.values)
+ assert (
+ (combined_tracks == old) | (np.isnan(combined_tracks) & np.isnan(old))
+ ).all(), "yikes"
+ return pd.Series(combined_tracks, index=tracks.columns)
--
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