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Commit 747cecdc authored by pswain's avatar pswain
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more extractor

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aliby/tile/tiler.py
+ 24
− 5
View file @ 747cecdc
......@@ -15,7 +15,7 @@ One key method is Tiler.run.
The image-processing is performed by traps/segment_traps.
The experiment is stored as an array wuth a standard indexing order of (Time, Channels, Z-stack, Y, X).
The experiment is stored as an array with a standard indexing order of (Time, Channels, Z-stack, Y, X).
"""
import warnings
from functools import lru_cache
......@@ -519,6 +519,24 @@ class Tiler(ProcessABC):
"""
Get a multidimensional array with all tiles for a set of channels
and z-stacks.
Used by extractor.
Parameters
---------
tp: int
Index of time point
tile_shape: int or tuple of two ints
Size of tile in x and y dimensions
channels: string or list of strings
Names of channels of interest
z: int
Index of z-channel of interest
Returns
-------
res: array
Data arranged as (traps, channels, timepoints, X, Y, Z)
"""
# FIXME add support for subtiling trap
# FIXME can we ignore z(always give)
......@@ -526,12 +544,13 @@ class Tiler(ProcessABC):
channels = [0]
elif isinstance(channels, str):
channels = [channels]
# get the data
res = []
for c in channels:
val = self.get_tp_data(tp, c)[:, z] # Only return requested z
# positions
# Starts at traps, z, y, x
# Turn to Trap, C, T, X, Y, Z order
# only return requested z
val = self.get_tp_data(tp, c)[:, z]
# starts with the order: traps, z, y, x
# returns the order: trap, C, T, X, Y, Z
val = val.swapaxes(1, 3).swapaxes(1, 2)
val = np.expand_dims(val, axis=1)
res.append(val)
......
extraction/core/extractor.py
+ 57
− 23
View file @ 747cecdc
......@@ -221,7 +221,11 @@ class Extractor(ProcessABC):
self.meta = load_attributes(self.local)
def get_traps(
self, tp: int, channels: list = None, z: list = None, **kwargs
self,
tp: int,
channels: list = None,
z: list = None,
**kwargs,
) -> tuple:
"""
Finds traps for a given time point and given channels and z-stacks.
......@@ -250,7 +254,7 @@ class Extractor(ProcessABC):
# a list of the indices of the z stacks
if z is None:
z = list(range(self.tiler.shape[-1]))
# find the appropiate traps from tiler
# gets the data via tiler
traps = (
self.tiler.get_traps_timepoint(
tp, channels=channel_ids, z=z, **kwargs
......@@ -258,6 +262,7 @@ class Extractor(ProcessABC):
if channel_ids
else None
)
# data arranged as (traps, channels, timepoints, X, Y, Z)
return traps
def extract_traps(
......@@ -270,14 +275,23 @@ class Extractor(ProcessABC):
"""
Apply a function for a whole position.
:traps: List[np.array] list of images
:masks: List[np.array] list of masks
:metric:str metric to extract
:labels: List[int] cell Labels to use as indices for output DataFrame
:pos_info: bool Whether to add the position as index or not
Parameters
----------
traps: List[np.array]
List of images
masks: List[np.array]
List of masks
metric: str
Metric to extract
labels: List[int]
Cell labels to use as indices in output dataFrame
pos_info: bool
Whether to add the position as an index or not
returns
:d: Dictionary of dataframe
Returns
-------
d: dict
A dictionary of dataframes
"""
if labels is None:
......@@ -311,7 +325,7 @@ class Extractor(ProcessABC):
**kwargs,
) -> dict:
"""
Extract multiple metrics from a timepoint
Returns dict with metrics as key and metrics applied to data as values for data from one timepoint.
"""
d = {
metric: self.extract_traps(
......@@ -333,16 +347,21 @@ class Extractor(ProcessABC):
Parameters
----------
param red_metrics: dict
dict for which keys are reduction funcions and values are strings indicating the metric function
traps: array
An array of image data arranged as (traps, X, Y, Z)
masks: list of arrays
An array of masks for each trap: one per cell at the trap
red_metrics: dict
dict for which keys are reduction functions and values are either a list or a set of strings giving the metric functions.
For example: {'np_max': {'max5px', 'mean', 'median'}}
**kwargs: dict
All other arguments and must include masks and traps.
All other arguments and must include masks and traps. Alan: stll true?
Returns
------
Dictionary of dataframes with the corresponding reductions and metrics nested.
"""
# create dict of traps with reduction in the z-direction
# create dict with keys naming the reduction in the z-direction and the reduced data as values
reduced_traps = {}
if traps is not None:
for red_fun in red_metrics.keys():
......@@ -364,7 +383,15 @@ class Extractor(ProcessABC):
def reduce_dims(self, img: np.array, method=None) -> np.array:
"""
Collapse a z-stack into 2d array. It may perform a null operation.
Collapse a z-stack into 2d array using method.
If method is None, return the original data.
Parameters
----------
img: array
An array of the image data arranged as (X, Y, Z)
method: function
The reduction function
"""
if method is None:
return img
......@@ -390,14 +417,16 @@ class Extractor(ProcessABC):
tree : dict
Nested dictionary indicating channels, reduction functions and
metrics to be used.
For example: {'general': {'None': ['area', 'volume', 'eccentricity']}}
tile_size : int
size of the tile to be extracted.
Size of the tile to be extracted.
masks : np.ndarray
A 3-D boolean numpy array with dimensions (ncells, tile_size,
A 3d boolean numpy array with dimensions (ncells, tile_size,
tile_size).
labels : t.List[t.List[int]]
List of lists of ints indicating the ids of masks.
**kwargs : Additional keyword arguments to be passed to extractor.reduce_extract.
**kwargs : keyword arguments
Passed to extractor.reduce_extract.
Returns
-------
......@@ -421,7 +450,7 @@ class Extractor(ProcessABC):
for trap_id in range(cells.ntraps)
}
# find the cell masks as a dict with trap_ids as keys
# find the cell masks for a given trap as a dict with trap_ids as keys
if masks is None:
raw_masks = cells.at_time(tp, kind="mask")
masks = {trap_id: [] for trap_id in range(cells.ntraps)}
......@@ -431,12 +460,14 @@ class Extractor(ProcessABC):
# convert to a list of masks
masks = [np.array(v) for v in masks.values()]
# find traps at the time point
# find image data at the time point
# stored as an array arranged as (traps, channels, timepoints, X, Y, Z)
# Alan: traps does not appear the best name here!
traps = self.get_traps(tp, tile_shape=tile_size, channels=tree_chs)
self.img_bgsub = {}
if self.params.sub_bg:
# generate boolean masks for background
# generate boolean masks for background as a list with one mask per trap
bg = [
~np.sum(m, axis=2).astype(bool)
if np.any(m)
......@@ -446,10 +477,13 @@ class Extractor(ProcessABC):
d = {}
for ch, red_metrics in tree.items():
img = None
# ch != is necessary for threading
# image data for all traps and z sections for a particular channel
# as an array arranged as (traps, X, Y, Z)
# NB ch != is necessary for threading
if ch != "general" and traps is not None and len(traps):
img = traps[:, tree_chs.index(ch), 0]
else:
img = None
d[ch] = self.reduce_extract(
red_metrics=red_metrics,
......
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