diff --git a/abc.py b/abc.py
index 3c20f39cb3dab83231bbc6e4807fd8ab8af359e0..03290383214289e334b98de19262f17c81c64e89 100644
--- a/abc.py
+++ b/abc.py
@@ -15,10 +15,12 @@ class ParametersABC(ABC):
"""
def __init__(self, **kwargs):
- '''
+ """
Defines parameters as attributes
- '''
- assert "parameters" not in kwargs, "No attribute should be named parameters"
+ """
+ assert (
+ "parameters" not in kwargs
+ ), "No attribute should be named parameters"
for k, v in kwargs.items():
setattr(self, k, v)
@@ -38,7 +40,9 @@ class ParametersABC(ABC):
]
):
return {
- k: v.to_dict() if hasattr(v, "to_dict") else self.to_dict(v)
+ k: v.to_dict()
+ if hasattr(v, "to_dict")
+ else self.to_dict(v)
for k, v in iterable.items()
}
else:
@@ -76,16 +80,12 @@ class ParametersABC(ABC):
Returns instance from a yaml filename or stdin
"""
is_buffer = True
- try:
- if Path(source).exists():
- is_buffer = False
- except:
- pass
+ if Path(source).exists():
+ is_buffer = False
if is_buffer:
params = safe_load(source)
- else:
- with open(source) as f:
- params = safe_load(f)
+ with open(source) as f:
+ params = safe_load(f)
return cls(**params)
@classmethod
@@ -95,6 +95,7 @@ class ParametersABC(ABC):
overriden_defaults[k] = v
return cls.from_dict(overriden_defaults)
+
###
diff --git a/io/bridge.py b/io/bridge.py
index c8b0f5308768825ba31f4381e9cfc4d317ee1137..bf1c626a13b985d0121e83a73ec0144a2eab2bda 100644
--- a/io/bridge.py
+++ b/io/bridge.py
@@ -29,24 +29,10 @@ class BridgeH5:
def close(self):
self._hdf.close()
- def max_ncellpairs(self, nstepsback):
- """
- Get maximum number of cell pairs to be calculated
- """
-
- dset = self._hdf["cell_info"][()]
- # attrs = self._hdf[dataset].attrs
- pass
-
@property
def cell_tree(self):
return self.get_info_tree()
- def get_n_cellpairs(self, nstepsback=2):
- cell_tree = self.cell_tree
- # get pair of consecutive trap-time points
- pass
-
@staticmethod
def get_consecutives(tree, nstepsback):
# Receives a sorted tree and returns the keys of consecutive elements
@@ -83,7 +69,8 @@ class BridgeH5:
npairs = []
for t in self._hdf["cell_info"]["processed_timepoints"][()]:
tmp_tree = {
- k: {k2: v2 for k2, v2 in v.items() if k2 <= t} for k, v in tree.items()
+ k: {k2: v2 for k2, v2 in v.items() if k2 <= t}
+ for k, v in tree.items()
}
npairs.append(self.get_npairs(tree=tmp_tree))
@@ -122,14 +109,18 @@ def groupsort(iterable: Union[tuple, list]):
# Sorts iterable and returns a dictionary where the values are grouped by the first element.
iterable = sorted(iterable, key=lambda x: x[0])
- grouped = {k: [x[1:] for x in v] for k, v in groupby(iterable, lambda x: x[0])}
+ grouped = {
+ k: [x[1:] for x in v] for k, v in groupby(iterable, lambda x: x[0])
+ }
return grouped
def recursive_groupsort(iterable):
# Recursive extension of groupsort
if len(iterable[0]) > 1:
- return {k: recursive_groupsort(v) for k, v in groupsort(iterable).items()}
+ return {
+ k: recursive_groupsort(v) for k, v in groupsort(iterable).items()
+ }
else: # Only two elements in list
return [x[0] for x in iterable]
diff --git a/io/cells.py b/io/cells.py
index aee65562d914487786341c18419c58c5b30cafea..45ce385f42c585692ad79d2b5408cdb0a24d4ef5 100644
--- a/io/cells.py
+++ b/io/cells.py
@@ -69,14 +69,18 @@ class CellsHDF(Cells):
@property
def max_labels(self) -> t.List[int]:
- with h5py.File(self.filename, mode="r") as f:
- return [max(self.labels_in_trap(i)) for i in range(self.ntraps)]
+ return [max(self.labels_in_trap(i)) for i in range(self.ntraps)]
@property
def ntraps(self) -> int:
with h5py.File(self.filename, mode="r") as f:
return len(f["/trap_info/trap_locations"][()])
+ @property
+ def tinterval(self):
+ with h5py.File(self.filename, mode="r") as f:
+ return f.attrs["time_settings/timeinterval"]
+
@property
def traps(self) -> t.List[int]:
return list(set(self["trap"]))
@@ -306,28 +310,18 @@ class CellsLinear(CellsHDF):
"""
Return nested list with final prediction of mother id for each cell
"""
- with h5py.File(self.filename, "r") as f:
-
- return self.mother_assign_from_dynamic(
- self["mother_assign_dynamic"],
- self["cell_label"],
- self["trap"],
- self.ntraps,
- )
+ return self.mother_assign_from_dynamic(
+ self["mother_assign_dynamic"],
+ self["cell_label"],
+ self["trap"],
+ self.ntraps,
+ )
@property
def mothers_daughters(self):
nested_massign = self.mothers
if sum([x for y in nested_massign for x in y]):
-
- idx = set(
- [
- (tid, i + 1)
- for tid, x in enumerate(nested_massign)
- for i in range(len(x))
- ]
- )
mothers, daughters = zip(
*[
((tid, m), (tid, d))
diff --git a/io/reader.py b/io/reader.py
index bbd7631d32eb20a4dcc375106f47a13cc0232d5a..a8d5d369fffb51c60e1bc3a2006941f242c68d2f 100644
--- a/io/reader.py
+++ b/io/reader.py
@@ -67,13 +67,13 @@ class StateReader(DynamicReader):
def read_all(self):
self.raw_data = {
- key: self.read_raw(key, dtype) for key, (_, dtype) in self.datatypes.items()
+ key: self.read_raw(key, dtype)
+ for key, (_, dtype) in self.datatypes.items()
}
return self.raw_data
def reconstruct_states(self, data: dict):
- ntraps = len(data["max_lbl"])
ntps_back = max(data["tp_back"]) + 1
from copy import copy
@@ -88,14 +88,19 @@ class StateReader(DynamicReader):
states[k][val_name] = [[] for _ in range(ntps_back)]
else:
states[k][val_name] = [
- np.zeros((0, data[val_name].shape[1]), dtype=np.float64)
+ np.zeros(
+ (0, data[val_name].shape[1]), dtype=np.float64
+ )
for _ in range(ntps_back)
]
- data[val_name] = list(zip(trap_as_idx, tpback_as_idx, data[val_name]))
+ data[val_name] = list(
+ zip(trap_as_idx, tpback_as_idx, data[val_name])
+ )
for k, v in groupsort(data[val_name]).items():
states[k][val_name] = [
- np.array([w[0] for w in val]) for val in groupsort(v).values()
+ np.array([w[0] for w in val])
+ for val in groupsort(v).values()
]
for val_name in ("lifetime", "p_was_bud", "p_is_mother"):
diff --git a/io/signal.py b/io/signal.py
index b486737d6a33961bd0e5007119c025ce4ab05941..77b27e5ec5be78687b9e563ee6fc9385b12a6ef3 100644
--- a/io/signal.py
+++ b/io/signal.py
@@ -34,11 +34,9 @@ class Signal(BridgeH5):
assert sum(is_bgd) == 0 or sum(is_bgd) == len(
dsets
), "Trap data and cell data can't be mixed"
- with h5py.File(self.filename, "r") as f:
- return [
- self.add_name(self.apply_prepost(dset), dset)
- for dset in dsets
- ]
+ return [
+ self.add_name(self.apply_prepost(dset), dset) for dset in dsets
+ ]
# return self.cols_in_mins(self.add_name(df, dsets))
return self.add_name(df, dsets)
@@ -183,12 +181,6 @@ class Signal(BridgeH5):
def n_merges(self):
print("{} merge events".format(len(self.merges)))
- @property
- def merges(self):
- with h5py.File(self.filename, "r") as f:
- dsets = f.visititems(self._if_merges)
- return dsets
-
@property
def picks(self):
with h5py.File(self.filename, "r") as f:
diff --git a/io/utils.py b/io/utils.py
index 25413613d633424f4177d6eeb8a64b33ae610b31..83a70c6d814513fc8fda78b8e7f3b49aa17c0585 100644
--- a/io/utils.py
+++ b/io/utils.py
@@ -4,11 +4,14 @@ Utility functions and classes
import itertools
import logging
import operator
+from functools import partial, wraps
from pathlib import Path
+from time import perf_counter
from typing import Callable
+import typing as t
-import h5py
import cv2
+import h5py
import numpy as np
@@ -89,9 +92,10 @@ class Cache:
self._queue.clear()
-def accumulate(l: list):
- l = sorted(l)
- it = itertools.groupby(l, operator.itemgetter(0))
+def accumulate(list_: list) -> t.Generator:
+ """Accumulate list based on the first value"""
+ list_ = sorted(list_)
+ it = itertools.groupby(list_, operator.itemgetter(0))
for key, sub_iter in it:
yield key, [x[1] for x in sub_iter]
@@ -125,11 +129,6 @@ def parametrized(dec):
return layer
-from functools import wraps, partial
-from time import perf_counter
-import logging
-
-
@parametrized
def timed(f, name=None):
@wraps(f)
diff --git a/io/writer.py b/io/writer.py
index 19cf7e6227001e3054f4983430c2823a0bb7433a..c53ab8a032372846d3a97b7673cb7417bbbc102c 100644
--- a/io/writer.py
+++ b/io/writer.py
@@ -37,8 +37,10 @@ class DynamicWriter:
"""Append data to existing dataset."""
try:
n = len(data)
- except:
- # Attributes have no length
+ except Exception as e:
+ logging.debug(
+ "DynamicWriter:Attributes have no length: {}".format(e)
+ )
n = 1
if key not in hgroup:
# TODO Include sparsity check
@@ -62,9 +64,11 @@ class DynamicWriter:
dset = hgroup[key]
dset.resize(dset.shape[0] + n, axis=0)
dset[-n:] = data
- except:
+ except Exception as e:
logging.debug(
- "DynamicWriter:Inconsistency between dataset shape and new empty data"
+ "DynamicWriter:Inconsistency between dataset shape and new empty data: {}".format(
+ e
+ )
)
return
@@ -228,7 +232,7 @@ class BabyWriter(DynamicWriter):
save_complex(current_indices, ix_dset)
def __append_edgemasks(self, hgroup, edgemasks, current_indices):
- key = "edgemasks"
+ # key = "edgemasks"
val_dset = hgroup["values"]
ix_dset = hgroup["indices"]
existing_indices = load_complex(ix_dset)
@@ -252,7 +256,11 @@ class BabyWriter(DynamicWriter):
try:
val_dset[ix, n_tps - 1] = mask
except Exception as e:
- logging.debug(f"{ix}, {n_tps}, {val_dset.shape}")
+ logging.debug(
+ "Exception: {}:{}, {}, {}".format(
+ e, ix, n_tps, val_dset.shape
+ )
+ )
# Save the index values
save_complex(missing, ix_dset)
@@ -262,7 +270,7 @@ class BabyWriter(DynamicWriter):
# DATA is TRAP_IDS, CELL_LABELS, EDGEMASKS in a structured array
key = "edgemasks"
val_key = "values"
- idx_key = "indices"
+ # idx_key = "indices"
# Length of edgemasks
traps, cell_labels, edgemasks = data
n_cells = len(cell_labels)
@@ -503,7 +511,6 @@ class Writer(BridgeH5):
data : Iterable, default = None
meta : Dict, default = {}
-
"""
self.id_cache = {}
with h5py.File(self.filename, "a") as f:
@@ -644,7 +651,7 @@ class Writer(BridgeH5):
if (
not hasattr(self, "id_cache")
- or not df.index.nlevels in self.id_cache
+ or df.index.nlevels not in self.id_cache
): # Use cache dict to store previously-obtained indices
self.id_cache[df.index.nlevels] = {}
existing_ids = self.get_existing_ids(
diff --git a/utils/example.py b/utils/example.py
index 1089ae40331154f504c5c735a611f25e8468fa4a..e3ff571acc0cb780e2feb92007111dbe041a91fb 100644
--- a/utils/example.py
+++ b/utils/example.py
@@ -48,5 +48,6 @@ def example_function(parameter: Union[int, str]):
return ExampleClass(int(parameter))
except ValueError as e:
raise ValueError(
- f"The parameter {parameter} could not be turned " f"into an integer."
+ f"The parameter {parameter} could not be turned "
+ f"into an integer."
) from e