diff --git a/src/agora/io/signal.py b/src/agora/io/signal.py
index 8a3957c35fcf81744abdceaab91c7ad556a7d4b9..876424d12e038707156454381eff7808c1cacc66 100644
--- a/src/agora/io/signal.py
+++ b/src/agora/io/signal.py
@@ -11,7 +11,7 @@ import pandas as pd
from agora.io.bridge import BridgeH5
from agora.io.decorators import _first_arg_str_to_raw_df
-from agora.utils.indexing import validate_association
+from agora.utils.indexing import validate_lineage
from agora.utils.kymograph import add_index_levels
from agora.utils.merge import apply_merges
@@ -295,13 +295,13 @@ class Signal(BridgeH5):
# assume that df is sorted
mother_label = np.zeros(len(df), dtype=int)
lineage = self.lineage()
- valid_association, valid_indices = validate_association(
+ # information on buds
+ valid_lineage, valid_indices = validate_lineage(
lineage,
np.array(df.index.to_list()),
- #: are mothers not match_column=0?
- match_column=1,
+ "daughters",
)
- mother_label[valid_indices] = lineage[valid_association, 1]
+ mother_label[valid_indices] = lineage[valid_lineage, 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 a36f7300bdd71971e3ca201b8cf59e85f579fa7f..9a07a6d52faa9e5c5a577b1e2776456a39174e7a 100644
--- a/src/agora/utils/indexing.py
+++ b/src/agora/utils/indexing.py
@@ -14,7 +14,11 @@ def validate_lineage(
lineage: np.ndarray, indices: np.ndarray, how: str = "families"
):
"""
- Identify mother-bud pairs that exist both in lineage and a Signal's indices.
+ Identify mother-bud pairs that exist both in lineage and a Signal's
+ indices.
+
+ We expect the lineage information to be unique: a bud should not have
+ two mothers.
Parameters
----------
@@ -71,28 +75,33 @@ def validate_lineage(
c_index = 0
elif how == "daughters":
c_index = 1
- # dtype links together trap and cell ids
+ # data type to link together trap and cell ids
dtype = {"names": ["trap_id", "cell_id"], "formats": [np.int64, np.int64]}
lineage = np.ascontiguousarray(lineage, dtype=np.int64)
# find (trap, cell_ids) in intersection
inboth = np.intersect1d(lineage.view(dtype), indices.view(dtype))
# find valid lineage
- valid_lineage = np.isin(lineage.view(dtype), inboth)
+ valid_lineages = np.isin(lineage.view(dtype), inboth)
if how == "families":
# both mother and bud must be in indices
- valid_lineage = valid_lineage.all(axis=1)
+ valid_lineage = valid_lineages.all(axis=1)
else:
- valid_lineage = valid_lineage[:, c_index, :]
+ valid_lineage = valid_lineages[:, c_index, :]
# find valid indices
- possible_indices = lineage[valid_lineage.flatten(), ...]
+ selected_lineages = lineage[valid_lineage.flatten(), ...]
if how == "families":
# select only pairs of mother and bud indices
valid_indices = np.isin(
- indices.view(dtype), possible_indices.view(dtype)
+ indices.view(dtype), selected_lineages.view(dtype)
)
else:
valid_indices = np.isin(
- indices.view(dtype), possible_indices.view(dtype)[:, c_index, :]
+ indices.view(dtype), selected_lineages.view(dtype)[:, c_index, :]
+ )
+ if valid_indices[valid_indices].size != valid_lineage[valid_lineage].size:
+ raise Exception(
+ "Error in validate_lineage: "
+ "lineage information is likely not unique."
)
return valid_lineage.flatten(), valid_indices.flatten()
diff --git a/src/postprocessor/core/functions/tracks.py b/src/postprocessor/core/functions/tracks.py
index 5fa04a1506996909f1cccb18ddb3e9c80f6eb0c3..d49a4cfbbfea06f1554aaae290f54bf358478484 100644
--- a/src/postprocessor/core/functions/tracks.py
+++ b/src/postprocessor/core/functions/tracks.py
@@ -197,23 +197,26 @@ def get_joinable(tracks, smooth=False, tol=0.1, window=5, degree=3) -> dict:
tolerance. If there is a track that can be assigned to two or more other
ones, choose the one with lowest error.
- :param tracks: (m x n) dataframe where rows are cell tracks and
- columns are timepoints
- :param tol: float or int threshold of average (prediction error/std) necessary
+ Parameters
+ ----------
+ tracks: (m x n) Signal
+ A Signal, usually area, dataframe where rows are cell tracks and
+ columns are time points.
+ tol: float or int
+ threshold of average (prediction error/std) necessary
to consider two tracks the same. If float is fraction of first track,
if int it is absolute units.
- :param window: int value of window used for savgol_filter
- :param degree: int value of polynomial degree passed to savgol_filter
-
+ window: int
+ value of window used for savgol_filter
+ degree: int
+ value of polynomial degree passed to savgol_filter
"""
+ # only consider time series with more than two non-NaN data points
tracks = tracks.loc[tracks.notna().sum(axis=1) > 2]
- # Commented because we are not smoothing in this step yet
- # candict = {k:v for d in contig.values for k,v in d.items()}
-
# smooth all relevant tracks
-
- if smooth: # Apply savgol filter TODO fix nans affecting edge placing
+ if smooth:
+ # Apply savgol filter TODO fix nans affecting edge placing
clean = clean_tracks(
tracks, min_len=window + 1, min_gr=0.9
) # get useful tracks
@@ -243,14 +246,6 @@ def get_joinable(tracks, smooth=False, tol=0.1, window=5, degree=3) -> dict:
for pres, posts in preposts
]
- # idx_to_means = lambda preposts: [
- # (
- # [get_means(smoothed_tracks.loc[pre], -window) for pre in pres],
- # [get_means(smoothed_tracks.loc[post], window) for post in posts],
- # )
- # for pres, posts in preposts
- # ]
-
def idx_to_pred(preposts):
result = []
for pres, posts in preposts:
@@ -274,15 +269,6 @@ def get_joinable(tracks, smooth=False, tol=0.1, window=5, degree=3) -> dict:
# edges_dMetric = edges.apply(get_dMetric_wrap, tol=tol)
# edges_dMetric_mean = edges_mean.apply(get_dMetric_wrap, tol=tol)
edges_dMetric_pred = pre_pred.apply(get_dMetric_wrap, tol=tol)
-
- # combined_dMetric = pd.Series(
- # [
- # [np.nanmin((a, b), axis=0) for a, b in zip(x, y)]
- # for x, y in zip(edges_dMetric, edges_dMetric_mean)
- # ],
- # index=edges_dMetric.index,
- # )
- # closest_pairs = combined_dMetric.apply(get_vec_closest_pairs, tol=tol)
solutions = []
# for (i, dMetrics), edgeset in zip(combined_dMetric.items(), edges):
for (i, dMetrics), edgeset in zip(edges_dMetric_pred.items(), edges):
@@ -479,25 +465,25 @@ def plot_joinable(tracks, joinable_pairs):
def get_contiguous_pairs(tracks: pd.DataFrame) -> list:
"""
- Get all pair of contiguous track ids from a tracks dataframe.
+ Get all pair of contiguous track ids from a tracks data frame.
- :param tracks: (m x n) dataframe where rows are cell tracks and
- columns are timepoints
- :param min_dgr: float minimum difference in growth rate from
- the interpolation
+ Parameters
+ ----------
+ tracks: pd.Dataframe
+ A dataframe for one trap where rows are cell tracks and columns
+ are time points.
"""
- mins, maxes = [
+ # time points bounding a tracklet of non-NaN values
+ mins, maxs = [
tracks.notna().apply(np.where, axis=1).apply(fn)
for fn in (np.min, np.max)
]
mins_d = mins.groupby(mins).apply(lambda x: x.index.tolist())
mins_d.index = mins_d.index - 1 # make indices equal
# TODO add support for skipping time points
- maxes_d = maxes.groupby(maxes).apply(lambda x: x.index.tolist())
- common = sorted(
- set(mins_d.index).intersection(maxes_d.index), reverse=True
- )
- return [(maxes_d[t], mins_d[t]) for t in common]
+ maxs_d = maxs.groupby(maxs).apply(lambda x: x.index.tolist())
+ common = sorted(set(mins_d.index).intersection(maxs_d.index), reverse=True)
+ return [(maxs_d[t], mins_d[t]) for t in common]
# def fit_track(track: pd.Series, obj=None):
diff --git a/src/postprocessor/core/reshapers/merger.py b/src/postprocessor/core/reshapers/merger.py
index b5115c4df9b8b08a0c85c828c65359773eec5748..5c035040d4f7c559e4b7ecf996f665a3b80370da 100644
--- a/src/postprocessor/core/reshapers/merger.py
+++ b/src/postprocessor/core/reshapers/merger.py
@@ -14,7 +14,7 @@ class MergerParameters(ParametersABC):
Whether or not to smooth with a savgol_filter.
tol: float or int
The threshold of average prediction error/std necessary to
- consider two tracks the same.
+ consider two tracks to be the same.
If float, the threshold is the fraction of the first track;
if int, the threshold is in absolute units.
window: int