From 9a7ec445e3649a172c8109a8c6ccf46bf000ee0e Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Al=C3=A1n=20Mu=C3=B1oz?= <amuoz@ed.ac.uk>
Date: Mon, 18 Oct 2021 14:08:37 +0100
Subject: [PATCH] rename postprocessor
Former-commit-id: 184f18f71b1d0ce12f1ef25e4a3a9b040f3cd828
---
__init__.py | 0
examples/basic_processes.py | 15 -
examples/group.py | 21 -
examples/testing.py | 220 ---------
.../extraction/joinable_pairs.gz | Bin 15488 -> 0 bytes
.../extraction/phl_ura8_024.gz.REMOVED.git-id | 1 -
postprocessor/__init__.py | 1 -
.../__pycache__/processor.cpython-37.pyc | Bin 9941 -> 0 bytes
postprocessor/functions/__init__.py | 1 -
.../__pycache__/tracks.cpython-37.pyc | Bin 13256 -> 0 bytes
postprocessor/functions/test_hdf.py | 19 -
postprocessor/functions/tracks.py | 429 ------------------
postprocessor/group.py | 130 ------
postprocessor/old/cell.py | 23 -
postprocessor/old/ph.py | 364 ---------------
postprocessor/old/postprocessor.py | 106 -----
postprocessor/processes/__init__.py | 1 -
.../__pycache__/aggregate.cpython-37.pyc | Bin 2881 -> 0 bytes
.../processes/__pycache__/base.cpython-37.pyc | Bin 228 -> 0 bytes
.../__pycache__/bud_metric.cpython-37.pyc | Bin 2986 -> 0 bytes
.../__pycache__/davol.cpython-37.pyc | Bin 2827 -> 0 bytes
.../__pycache__/dsignal.cpython-37.pyc | Bin 1462 -> 0 bytes
.../__pycache__/merger.cpython-37.pyc | Bin 1940 -> 0 bytes
.../__pycache__/picker.cpython-37.pyc | Bin 11532 -> 0 bytes
postprocessor/processes/aggregate.py | 74 ---
postprocessor/processes/base.py | 1 -
postprocessor/processes/births.py | 1 -
postprocessor/processes/bud_metric.py | 65 ---
postprocessor/processes/dsignal.py | 28 --
postprocessor/processes/gpsignal.py | 97 ----
postprocessor/processes/merger.py | 60 ---
postprocessor/processes/peaks.py | 44 --
postprocessor/processes/picker.py | 339 --------------
postprocessor/processes/savgol.py | 152 -------
postprocessor/processes/template.py | 31 --
postprocessor/processor.py | 316 -------------
36 files changed, 2539 deletions(-)
delete mode 100644 __init__.py
delete mode 100644 examples/basic_processes.py
delete mode 100644 examples/group.py
delete mode 100644 examples/testing.py
delete mode 100644 gluStarv_2_0_x2_dual_phl_ura8_00/extraction/joinable_pairs.gz
delete mode 100644 gluStarv_2_0_x2_dual_phl_ura8_00/extraction/phl_ura8_024.gz.REMOVED.git-id
delete mode 100644 postprocessor/__init__.py
delete mode 100644 postprocessor/__pycache__/processor.cpython-37.pyc
delete mode 100644 postprocessor/functions/__init__.py
delete mode 100644 postprocessor/functions/__pycache__/tracks.cpython-37.pyc
delete mode 100644 postprocessor/functions/test_hdf.py
delete mode 100644 postprocessor/functions/tracks.py
delete mode 100644 postprocessor/group.py
delete mode 100644 postprocessor/old/cell.py
delete mode 100644 postprocessor/old/ph.py
delete mode 100644 postprocessor/old/postprocessor.py
delete mode 100644 postprocessor/processes/__init__.py
delete mode 100644 postprocessor/processes/__pycache__/aggregate.cpython-37.pyc
delete mode 100644 postprocessor/processes/__pycache__/base.cpython-37.pyc
delete mode 100644 postprocessor/processes/__pycache__/bud_metric.cpython-37.pyc
delete mode 100644 postprocessor/processes/__pycache__/davol.cpython-37.pyc
delete mode 100644 postprocessor/processes/__pycache__/dsignal.cpython-37.pyc
delete mode 100644 postprocessor/processes/__pycache__/merger.cpython-37.pyc
delete mode 100644 postprocessor/processes/__pycache__/picker.cpython-37.pyc
delete mode 100644 postprocessor/processes/aggregate.py
delete mode 100644 postprocessor/processes/base.py
delete mode 100644 postprocessor/processes/births.py
delete mode 100644 postprocessor/processes/bud_metric.py
delete mode 100644 postprocessor/processes/dsignal.py
delete mode 100644 postprocessor/processes/gpsignal.py
delete mode 100644 postprocessor/processes/merger.py
delete mode 100644 postprocessor/processes/peaks.py
delete mode 100644 postprocessor/processes/picker.py
delete mode 100644 postprocessor/processes/savgol.py
delete mode 100644 postprocessor/processes/template.py
delete mode 100644 postprocessor/processor.py
diff --git a/__init__.py b/__init__.py
deleted file mode 100644
index e69de29b..00000000
diff --git a/examples/basic_processes.py b/examples/basic_processes.py
deleted file mode 100644
index c322fd4b..00000000
--- a/examples/basic_processes.py
+++ /dev/null
@@ -1,15 +0,0 @@
-from postprocessor.core.processor import PostProcessor, PostProcessorParameters
-
-params = PostProcessorParameters.default()
-pp = PostProcessor(
- "/shared_libs/pipeline-core/scripts/pH_calibration_dual_phl__ura8__by4741__01/ph_5_29_025store.h5",
- params,
-)
-tmp = pp.run()
-
-import h5py
-
-# f = h5py.File(
-# "/shared_libs/pipeline-core/scripts/pH_calibration_dual_phl__ura8__by4741__01/ph_5_29_025store.h5",
-# "a",
-# )
diff --git a/examples/group.py b/examples/group.py
deleted file mode 100644
index 1e59978a..00000000
--- a/examples/group.py
+++ /dev/null
@@ -1,21 +0,0 @@
-from pathlib import Path
-
-from postprocessor.core.group import GroupParameters, Group
-
-poses = [
- x.name.split("store")[0]
- for x in Path(
- "/shared_libs/pipeline-core/scripts/data/ph_calibration_dual_phl_ura8_5_04_5_83_7_69_7_13_6_59__01"
- ).rglob("*")
- if x.name != "images.h5"
-]
-
-gr = Group(
- GroupParameters(
- signals=["/extraction/general/None/area", "/extraction/mCherry/np_max/median"]
- )
-)
-gr.run(
- central_store="/shared_libs/pipeline-core/scripts/data/ph_calibration_dual_phl_ura8_5_04_5_83_7_69_7_13_6_59__01",
- poses=poses,
-)
diff --git a/examples/testing.py b/examples/testing.py
deleted file mode 100644
index d5420d83..00000000
--- a/examples/testing.py
+++ /dev/null
@@ -1,220 +0,0 @@
-from typing import Dict, List, Union
-import re
-
-import numpy as np
-import pandas as pd
-from pandas import Series, DataFrame
-from sklearn.cluster import KMeans
-from matplotlib import pyplot as plt
-import seaborn as sns
-
-import compress_pickle
-
-from postprocessor.core.postprocessor import PostProcessor
-from postprocessor.core.tracks import get_avg_grs, non_uniform_savgol
-from postprocessor.core.tracks import clean_tracks, merge_tracks, join_tracks
-from postprocessor.core.ph import *
-
-sns.set_context("talk", font_scale=1.8)
-sns.set_theme(style="whitegrid")
-
-# pp_c = PostProcessor(source=19920) # 19916
-# pp_c.load_tiler_cells()
-# # f = "/home/alan/Documents/libs/extraction/extraction/examples/gluStarv_2_0_x2_dual_phl_ura8_00/extraction"
-# # f = "/home/alan/Documents/tmp/pH_calibration_dual_phl__ura8__by4741__01/extraction/"
-# f = "/home/alan/Documents/tmp/pH_calibration_dual_phl__ura8__by4741_Alan4_00/extraction/"
-# pp_c.load_extraction(f)
-
-# calib = process_phs(pp_c)
-# # c = calib.loc[(5.4 < calib["ph"]) & (calib["ph"] < 8)].groupby("ph").mean()
-# sns.violinplot(x="ph", y="em_ratio_mean", data=calib)
-# plt.show()
-
-
-# bring timelapse data and convert it to pH
-
-pp = PostProcessor(source=19831) # 19831
-pp.load_tiler_cells()
-f = "/home/alan/Documents/tmp/gluStarv_2_0_x2_dual_phl_ura8_00/extraction/"
-# f = "/home/alan/Documents/tmp/downUpshift_2_0_2_glu_dual_phluorin__glt1_psa1_ura7__thrice_00/extraction/"
-pp.load_extraction(f)
-
-import compress_pickle
-
-compress_pickle.dump(pp.extraction, "/home/alan/extraction_example.pkl")
-
-if True: # Load extracted data or pkld version
- new_dfs = compress_pickle.load("/home/alan/Documents/tmp/new_dfs.gz")
-# Combine dataframes
-else:
- new_dfs = combine_dfs(get_dfs(pp))
- # t = [x.index for x in new_dfs.values()]
- # i = set(t[0])
- # for j in t:
- # i = i.intersection(j)
- new_dfs = {
- k: v
- for k, v in new_dfs.items()
- if k[2] != "imBackground"
- and k[2] != "median"
- and ~(((k[0] == "GFPFast") | (k[0] == "pHluorin405")) and k[2] == "max2p5pc")
- }
-
-del pp
-compress_pickle.dump(new_dfs, "/home/alan/Documents/tmp/new_dfs.gz")
-
-
-def get_clean_dfs(dfs=None):
- if dfs is None:
- clean_dfs = compress_pickle.load("/home/alan/Documents/tmp/clean_dfs.gz")
- else:
-
- # Clean timelapse
- clean = clean_tracks(new_dfs[("general", None, "area")])
- tra, joint = merge_tracks(clean)
- clean_dfs = new_dfs
- i_ids = set(clean.index).intersection(
- clean_dfs[("general", None, "area")].index
- )
- clean_dfs = {k: v.loc[i_ids] for k, v in clean_dfs.items()}
- clean_dfs = {k: join_tracks(v, joint, drop=True) for k, v in clean_dfs.items()}
-
- del new_dfs
- compress_pickle.dump(clean_dfs, "/home/alan/Documents/tmp/clean_dfs.gz")
-
-
-def plot_ph_hmap(clean_dfs):
- GFPFast = clean_dfs[("GFPFast", np.maximum, "mean")]
- phluorin = clean_dfs[("pHluorin405", np.maximum, "mean")]
- ph = GFPFast / phluorin
- ph = ph.loc[ph.notna().sum(axis=1) > 0.7 * ph.shape[1]]
- ph = 1 / ph
-
- fig, ax = plt.subplots()
- hmap(ph, cbar_kws={"label": r"emission ratio $\propto$ pH"})
- plt.xlabel("Time (hours)")
- plt.ylabel("Cells")
- xticks = plt.xticks(fontsize=15)[0]
- ax.set(yticklabels=[], xticklabels=[str(round(i * 5 / 60, 1)) for i in xticks])
- # plt.setp(ax.get_xticklabels(), Rotation=90)
- plt.show()
-
-
-def fit_calibs(c, h):
- h = process_phs(pp_c)
- h["ratio"] = h["GFPFast_bgsub_median"] / h["pHluorin405_bgsub_median"]
- sns.lineplot(x="ph", y="ratio", data=h, err_style="bars")
- plt.show()
-
- calibs = fit_calibration(c)
- for k, params in calibs.items():
- i, j = ("_".join(k.split("_")[:-1]), k.split("_")[-1])
- if j == "mean" and "k2" not in k:
- clean_dfs[k] = objective(clean_dfs[i, np.maximum, j], *params)
-
-
-# max 2.5% / med
-def plot_ratio_vs_max2p5(h):
- fig, ax = plt.subplots()
- sns.regplot(
- x="em_ratio_median",
- y="mCherry_max2p5pc",
- data=h,
- scatter=False,
- ax=ax,
- color="teal",
- )
- sns.scatterplot(x="em_ratio_median", y="max5_d_med", data=h, hue="ph", ax=ax)
- plt.xlabel(r"Fluorescence ratio $R \propto (1/pH)$")
- plt.ylabel("Max 2.5% px / median")
- plt.show()
-
-
-em = clean_dfs[("em_ratio", np.maximum, "mean")]
-area = clean_dfs[("general", None, "area")]
-
-
-def get_grs(clean_dfs):
- area = clean_dfs[("general", None, "area")]
- area = area.loc[area.notna().sum(axis=1) > 10]
- return area.apply(growth_rate, axis=1)
-
-
-def get_agg(dfs, rng):
- # df dict of DataFrames containing an area/vol one TODO generalise this beyond area
- # rng tuple of section to use
- grs = get_grs(dfs)
- smooth = grs.loc(axis=1)[list(range(rng[0], rng[1]))].dropna(how="all")
-
- aggregate_mean = lambda dfs, rng: pd.concat(
- {
- k[0] + "_" + k[2]: dfs[k].loc[smooth.index, rng[0] : rng[1]].mean(axis=1)
- for k in clean_dfs.keys()
- },
- axis=1,
- )
- # f_comp_df = comp_df.loc[(comp_df["gr"] > 0) & (area.notna().sum(axis=1) > 50)]
-
- agg = aggregate_mean(dfs, rng)
- agg["max2_med"] = agg["mCherry_max2p5pc"] / agg["mCherry_mean"]
-
- for c in agg.columns:
- agg[c + "_log"] = np.log(agg[c])
-
- agg["gr_mean"] = smooth.loc[set(agg.index).intersection(smooth.index)].mean(axis=1)
- agg["gr_max"] = smooth.loc[set(agg.index).intersection(smooth.index)].max(axis=1)
-
- return agg
-
-
-def plot_scatter_fit(x, y, data, hue=None, xlabel=None, ylabel=None, ylim=None):
- fig, ax = plt.subplots()
- sns.regplot(x=x, y=y, data=data, scatter=False, ax=ax)
- sns.scatterplot(x=x, y=y, data=data, ax=ax, alpha=0.1, hue=hue)
- # plt.show()
- if xlabel is not None:
- plt.xlabel(xlabel)
- if ylabel is not None:
- plt.ylabel(ylabel)
- if ylim is not None:
- plt.ylim(ylim)
-
- fig.savefig(
- "/home/alan/Documents/sync_docs/drafts/third_year_pres/figs/"
- + str(len(data))
- + "_"
- + x
- + "_vs_"
- + y
- + ".png",
- dpi=200,
- )
-
-
-from extraction.core.argo import Argo, annot_from_dset
-
-
-def additional_feats(aggs):
- aggs["gr_mean_norm"] = aggs["gr_mean"] * 12
- aggs["log_ratio_r"] = np.log(1 / aggs["em_ratio_mean"])
- return aggs
-
-
-def compare_methods_ph_calculation(dfs):
- GFPFast = dfs[("GFPFast", np.maximum, "mean")]
- phluorin = dfs[("pHluorin405", np.maximum, "mean")]
- ph = GFPFast / phluorin
-
- sns.scatterplot(
- dfs["em_ratio", np.maximum, "mean"].values.flatten(),
- ph.values.flatten(),
- alpha=0.1,
- )
- plt.xlabel("ratio_median")
- plt.ylabel("median_ratio")
- plt.title("Comparison of ph calculation")
- plt.show()
-
-
-# get the number of changes in a bool list
-nchanges = lambda x: sum([i for i, j in zip(x[:-2], x[1:]) if operator.xor(i, j)])
diff --git a/gluStarv_2_0_x2_dual_phl_ura8_00/extraction/joinable_pairs.gz b/gluStarv_2_0_x2_dual_phl_ura8_00/extraction/joinable_pairs.gz
deleted file mode 100644
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index 7081c4b1..00000000
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diff --git a/postprocessor/functions/__init__.py b/postprocessor/functions/__init__.py
deleted file mode 100644
index e5a0d9b4..00000000
--- a/postprocessor/functions/__init__.py
+++ /dev/null
@@ -1 +0,0 @@
-#!/usr/bin/env python3
diff --git a/postprocessor/functions/__pycache__/tracks.cpython-37.pyc b/postprocessor/functions/__pycache__/tracks.cpython-37.pyc
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diff --git a/postprocessor/functions/test_hdf.py b/postprocessor/functions/test_hdf.py
deleted file mode 100644
index 5bd3b1a1..00000000
--- a/postprocessor/functions/test_hdf.py
+++ /dev/null
@@ -1,19 +0,0 @@
-#!/usr/bin/env python3
-# c=CellsHDF.from_source("/home/alan/Documents/sync_docs/PhD/tmp/DO6MS2_003store.h5")
-import h5py
-from core.cells import Cells
-import pandas as pd
-
-# f = h5py.File("/home/alan/Documents/sync_docs/PhD/tmp/DO6MS2_003store.h5")
-fname = "/shared_libs/pipeline-core/scripts/data/20191026_ss_experiments_01/DO6MS2_003store.h5"
-f = h5py.File(fname)
-tracks = f["/extraction/general/None/area"][()]
-cell = Cells.from_source(fname)
-from postprocessor.core.processes.picker import Picker, PickerParameters
-
-picker = Picker(cells=cell, parameters=PickerParameters.default())
-
-from postprocessor.core.processor import PostProcessor, PostProParameters
-
-pp = PostProcessor(filename=fname, parameters=PostProParameters.default())
-pp.run()
diff --git a/postprocessor/functions/tracks.py b/postprocessor/functions/tracks.py
deleted file mode 100644
index e47e6023..00000000
--- a/postprocessor/functions/tracks.py
+++ /dev/null
@@ -1,429 +0,0 @@
-"""
-Functions to process, filter and merge tracks.
-"""
-
-# from collections import Counter
-
-from copy import copy
-from typing import Union, List
-
-import numpy as np
-import pandas as pd
-from utils_find_1st import find_1st, cmp_larger
-
-import more_itertools as mit
-from scipy.signal import savgol_filter
-
-# from scipy.optimize import linear_sum_assignment
-# from scipy.optimize import curve_fit
-
-from matplotlib import pyplot as plt
-
-
-def load_test_dset():
- # Load development dataset to test functions
- return pd.DataFrame(
- {
- ("a", 1, 1): [2, 5, np.nan, 6, 8] + [np.nan] * 5,
- ("a", 1, 2): list(range(2, 12)),
- ("a", 1, 3): [np.nan] * 8 + [6, 7],
- ("a", 1, 4): [np.nan] * 5 + [9, 12, 10, 14, 18],
- },
- index=range(1, 11),
- ).T
-
-
-def max_ntps(track: pd.Series) -> int:
- # Get number of timepoints
- indices = np.where(track.notna())
- return np.max(indices) - np.min(indices)
-
-
-def max_nonstop_ntps(track: pd.Series) -> int:
- nona_tracks = track.notna()
- consecutive_nonas_grouped = [
- len(list(x)) for x in mit.consecutive_groups(np.flatnonzero(nona_tracks))
- ]
- return max(consecutive_nonas_grouped)
-
-
-def get_tracks_ntps(tracks: pd.DataFrame) -> pd.Series:
- return tracks.apply(max_ntps, axis=1)
-
-
-def get_avg_gr(track: pd.Series) -> int:
- """
- Get average growth rate for a track.
-
- :param tracks: Series with volume and timepoints as indices
- """
- ntps = max_ntps(track)
- vals = track.dropna().values
- gr = (vals[-1] - vals[0]) / ntps
- return gr
-
-
-def get_avg_grs(tracks: pd.DataFrame) -> pd.DataFrame:
- """
- Get average growth rate for a group of tracks
-
- :param tracks: (m x n) dataframe where rows are cell tracks and
- columns are timepoints
- """
- return tracks.apply(get_avg_gr, axis=1)
-
-
-def clean_tracks(tracks, min_len: int = 15, min_gr: float = 1.0) -> pd.DataFrame:
- """
- Clean small non-growing tracks and return the reduced dataframe
-
- :param tracks: (m x n) dataframe where rows are cell tracks and
- columns are timepoints
- :param min_len: int number of timepoints cells must have not to be removed
- :param min_gr: float Minimum mean growth rate to assume an outline is growing
- """
- ntps = get_tracks_ntps(tracks)
- grs = get_avg_grs(tracks)
-
- growing_long_tracks = tracks.loc[(ntps >= min_len) & (grs > min_gr)]
-
- return growing_long_tracks
-
-
-def merge_tracks(tracks, drop=False, **kwargs) -> pd.DataFrame:
- """
- Join tracks that are contiguous and within a volume threshold of each other
-
- :param tracks: (m x n) dataframe where rows are cell tracks and
- columns are timepoints
- :param kwargs: args passed to get_joinable
-
- returns
-
- :joint_tracks: (m x n) Dataframe where rows are cell tracks and
- columns are timepoints. Merged tracks are still present but filled
- with np.nans.
- """
-
- # calculate tracks that can be merged until no more traps can be merged
- joinable_pairs = get_joinable(tracks, **kwargs)
- if joinable_pairs:
- tracks = join_tracks(tracks, joinable_pairs, drop=drop)
- joint_ids = get_joint_ids(joinable_pairs)
-
- return (tracks, joinable_pairs)
-
-
-def get_joint_ids(merging_seqs) -> dict:
- """
- Convert a series of merges into a dictionary where
- the key is the cell_id of destination and the value a list
- of the other track ids that were merged into the key
-
- :param merging_seqs: list of tuples of indices indicating the
- sequence of merging events. It is important for this to be in sequential order
-
- How it works:
-
- The order of merging matters for naming, always the leftmost track will keep the id
-
- For example, having tracks (a, b, c, d) and the iterations of merge events:
-
- 0 a b c d
- 1 a b cd
- 2 ab cd
- 3 abcd
-
- We shold get:
-
- output {a:a, b:a, c:a, d:a}
-
- """
- if not merging_seqs:
- return {}
-
- targets, origins = list(zip(*merging_seqs))
- static_tracks = set(targets).difference(origins)
-
- joint = {track_id: track_id for track_id in static_tracks}
- for target, origin in merging_seqs:
- joint[origin] = target
-
- moved_target = [
- k for k, v in joint.items() if joint[v] != v and v in joint.values()
- ]
-
- for orig in moved_target:
- joint[orig] = rec_bottom(joint, orig)
-
- return {
- k: v for k, v in joint.items() if k != v
- } # remove ids that point to themselves
-
-
-def rec_bottom(d, k):
- if d[k] == k:
- return k
- else:
- return rec_bottom(d, d[k])
-
-
-def join_tracks(tracks, joinable_pairs, drop=True) -> pd.DataFrame:
- """
- Join pairs of tracks from later tps towards the start.
-
- :param tracks: (m x n) dataframe where rows are cell tracks and
- columns are timepoints
-
- returns (copy)
-
- :param joint_tracks: (m x n) Dataframe where rows are cell tracks and
- columns are timepoints. Merged tracks are still present but filled
- with np.nans.
- :param drop: bool indicating whether or not to drop moved rows
-
- """
-
- tmp = copy(tracks)
- for target, source in joinable_pairs:
- tmp.loc[target] = join_track_pair(tmp.loc[target], tmp.loc[source])
-
- if drop:
- tmp = tmp.drop(source)
-
- return tmp
-
-
-def join_track_pair(target, source):
- tgt_copy = copy(target)
- end = find_1st(target.values[::-1], 0, cmp_larger)
- tgt_copy.iloc[-end:] = source.iloc[-end:].values
- return tgt_copy
-
-
-def get_joinable(tracks, smooth=False, tol=0.1, window=5, degree=3) -> dict:
- """
- Get the pair of track (without repeats) that have a smaller error than the
- tolerance. If there is a track that can be assigned to two or more other
- ones, it chooses the one with a 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
- 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
-
- """
-
- # 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
- clean = clean_tracks(
- tracks, min_len=window + 1, min_gr=0.9
- ) # get useful tracks
- savgol_on_srs = lambda x: non_uniform_savgol(x.index, x.values, window, degree)
- contig = clean.groupby(["trap"]).apply(get_contiguous_pairs)
- contig = contig.loc[contig.apply(len) > 0]
- flat = set([k for v in contig.values for i in v for j in i for k in j])
- smoothed_tracks = clean.loc[flat].apply(savgol_on_srs, 1)
- else:
- contig = tracks.groupby(["trap"]).apply(get_contiguous_pairs)
- contig = contig.loc[contig.apply(len) > 0]
- flat = set([k for v in contig.values for i in v for j in i for k in j])
- smoothed_tracks = tracks.loc[flat].apply(lambda x: np.array(x.values), axis=1)
-
- # fetch edges from ids TODO (IF necessary, here we can compare growth rates)
- idx_to_edge = lambda preposts: [
- (
- [get_val(smoothed_tracks.loc[pre], -1) for pre in pres],
- [get_val(smoothed_tracks.loc[post], 0) for post in posts],
- )
- for pres, posts in preposts
- ]
- edges = contig.apply(idx_to_edge)
-
- closest_pairs = edges.apply(get_vec_closest_pairs, tol=tol)
-
- # match local with global ids
- joinable_ids = [
- localid_to_idx(closest_pairs.loc[i], contig.loc[i]) for i in closest_pairs.index
- ]
-
- return [pair for pairset in joinable_ids for pair in pairset]
-
-
-get_val = lambda x, n: x[~np.isnan(x)][n] if len(x[~np.isnan(x)]) else np.nan
-
-
-def localid_to_idx(local_ids, contig_trap):
- """Fetch then original ids from a nested list with joinable local_ids
-
- input
- :param local_ids: list of list of pairs with cell ids to be joint
- :param local_ids: list of list of pairs with corresponding cell ids
-
- return
- list of pairs with (experiment-level) ids to be joint
- """
- lin_pairs = []
- for i, pairs in enumerate(local_ids):
- if len(pairs):
- for left, right in pairs:
- lin_pairs.append((contig_trap[i][0][left], contig_trap[i][1][right]))
- return lin_pairs
-
-
-def get_vec_closest_pairs(lst: List, **kwags):
- return [get_closest_pairs(*l, **kwags) for l in lst]
-
-
-def get_closest_pairs(pre: List[float], post: List[float], tol: Union[float, int] = 1):
- """Calculate a cost matrix the Hungarian algorithm to pick the best set of
- options
-
- input
- :param pre: list of floats with edges on left
- :param post: list of floats with edges on right
- :param tol: int or float if int metrics of tolerance, if float fraction
-
- returns
- :: list of indices corresponding to the best solutions for matrices
-
- """
- if len(pre) > len(post):
- dMetric = np.abs(np.subtract.outer(post, pre))
- else:
- dMetric = np.abs(np.subtract.outer(pre, post))
- # dMetric[np.isnan(dMetric)] = tol + 1 + np.nanmax(dMetric) # nans will be filtered
- # ids = linear_sum_assignment(dMetric)
- dMetric[np.isnan(dMetric)] = tol + 1 + np.nanmax(dMetric) # nans will be filtered
-
- ids = solve_matrix(dMetric)
- if not len(ids[0]):
- return []
-
- norm = (
- np.array(pre)[ids[len(pre) > len(post)]] if tol < 1 else 1
- ) # relative or absolute tol
- result = dMetric[ids] / norm
- ids = ids if len(pre) < len(post) else ids[::-1]
-
- return [idx for idx, res in zip(zip(*ids), result) if res <= tol]
-
-
-def solve_matrix(dMetric):
- """
- Solve cost matrix focusing on getting the smallest cost at each iteration.
-
- input
- :param dMetric: np.array cost matrix
-
- returns
- tuple of np.arrays indicating picks with lowest individual value
- """
- glob_is = []
- glob_js = []
- if (~np.isnan(dMetric)).any():
- tmp = copy(dMetric)
- std = sorted(tmp[~np.isnan(tmp)])
- while (~np.isnan(std)).any():
- v = std[0]
- i_s, j_s = np.where(tmp == v)
- i = i_s[0]
- j = j_s[0]
- tmp[i, :] += np.nan
- tmp[:, j] += np.nan
- glob_is.append(i)
- glob_js.append(j)
-
- std = sorted(tmp[~np.isnan(tmp)])
-
- return (np.array(glob_is), np.array(glob_js))
-
-
-def plot_joinable(tracks, joinable_pairs, max=64):
- """
- Convenience plotting function for debugging and data vis
- """
-
- nx = 8
- ny = 8
- _, axes = plt.subplots(nx, ny)
- for i in range(nx):
- for j in range(ny):
- if i * ny + j < len(joinable_pairs):
- ax = axes[i, j]
- pre, post = joinable_pairs[i * ny + j]
- pre_srs = tracks.loc[pre].dropna()
- post_srs = tracks.loc[post].dropna()
- ax.plot(pre_srs.index, pre_srs.values, "b")
- # try:
- # totrange = np.arange(pre_srs.index[0],post_srs.index[-1])
- # ax.plot(totrange, interpolate(pre_srs, totrange), 'r-')
- # except:
- # pass
- ax.plot(post_srs.index, post_srs.values, "g")
-
- plt.show()
-
-
-def get_contiguous_pairs(tracks: pd.DataFrame) -> list:
- """
- Get all pair of contiguous track ids from a tracks dataframe.
-
- :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
- """
- # indices = np.where(tracks.notna())
-
- mins, maxes = [
- 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]
-
-
-# def fit_track(track: pd.Series, obj=None):
-# if obj is None:
-# obj = objective
-
-# x = track.dropna().index
-# y = track.dropna().values
-# popt, _ = curve_fit(obj, x, y)
-
-# return popt
-
-# def interpolate(track, xs) -> list:
-# '''
-# Interpolate next timepoint from a track
-
-# :param track: pd.Series of volume growth over a time period
-# :param t: int timepoint to interpolate
-# '''
-# popt = fit_track(track)
-# # perr = np.sqrt(np.diag(pcov))
-# return objective(np.array(xs), *popt)
-
-
-# def objective(x,a,b,c,d) -> float:
-# # return (a)/(1+b*np.exp(c*x))+d
-# return (((x+d)*a)/((x+d)+b))+c
-
-# def cand_pairs_to_dict(candidates):
-# d={x:[] for x,_ in candidates}
-# for x,y in candidates:
-# d[x].append(y)
-# return d
diff --git a/postprocessor/group.py b/postprocessor/group.py
deleted file mode 100644
index 99cf00cf..00000000
--- a/postprocessor/group.py
+++ /dev/null
@@ -1,130 +0,0 @@
-"""
-Class to group multiple positions into one using one of several criteria.
-"""
-
-from pathlib import Path
-import re
-
-import h5py
-import pandas as pd
-
-from postprocessor.core.io.base import groupsort
-from postprocessor.core.io.signal import Signal
-
-from postprocessor.core.processes.base import ParametersABC, ProcessABC
-
-
-class GroupParameters(ParametersABC):
- def __init__(self, by="name", processes=[], signals=[]):
- self.by = by
- self.signals = signals
- self.processes = processes
-
- @classmethod
- def default(cls):
- return cls.from_dict({"by": "name", "signals": [], "processes": []})
-
-
-class Group(ProcessABC):
- def __init__(self, parameters):
- super().__init__(parameters)
-
- def get_position_filenames(self, exp_root, poses):
- """
- Get filenames as a dictionary where the key is the position and value the filename.
- """
- central_store = Path(exp_root) / "store.h5"
- if central_store.exists():
- hdf = h5py.File(central_store, "r")
- self.filenames = [pos.attrs["filename"] for pos in hdf["/positions/"]]
- hdf.close()
- else: # If no central store just list position files in expt root folder
- fullfiles = [x for x in Path(exp_root).glob("*store.h5")]
- files = [x.name for x in Path(exp_root).glob("*store.h5")]
- filenames = [False for _ in poses]
- for i, pos in enumerate(poses):
- matches = [
- True if re.match(pos + ".*.h5", fname) else False for fname in files
- ]
- if any(matches):
- assert sum(matches) == 1, "More than one match"
- filenames[i] = (pos, fullfiles[matches.index(True)])
-
- self.filenames = {fname[0]: fname[1] for fname in filenames if fname}
-
- self.positions = list(self.filenames.keys())
- return self.filenames
-
- def get_signals(self):
- # hdf = h5py.File(central_store, "r")
- # keys_d = groupsort(keys)
- self.signals = {pos: {} for pos in self.filenames.keys()}
- for pos, fname in self.filenames.items():
- for signal in self.parameters.signals:
- self.signals[pos][signal] = pd.read_hdf(fname, signal)
-
- return self.signals
-
- def gen_groups(self):
- if self.by == "group": # Use group names in metadata
- pass
- elif self.by == "name": # Infer groups from signal concatenation
- # Remove last four characters and find commonalities larger than 4
- # characters between posnames and group them that way.
- groupnames = list(set([x[:-3] for x in self.positions]))
- self.group_signal_tree = {group: [] for group in groupnames}
- self.poses_grouped = {group: [] for group in groupnames}
- for pos in self.positions:
- group = groupnames[groupnames.index(pos[:-3])]
- self.group_signal_tree[group].append(self.signals[pos])
- self.poses_grouped[group].append(pos)
-
- elif (
- type(self.by) == tuple
- ): # Manually give groups as tuple or list of positions
- pass
-
- def concat_signals(self):
- self.concated_signals = {group: {} for group in self.group_signal_tree}
- for k, group in self.group_signal_tree.items():
- for signal in self.parameters.signals:
- self.concated_signals[k][signal] = pd.concat(
- [g[signal] for g in group], keys=self.poses_grouped[k]
- )
-
- return self.concated_signals
-
- def process_signals(self, grouped_signals):
- pass
-
- def run(self, central_store, poses):
-
- self.get_position_filenames(central_store, poses)
- self.get_signals()
- self.gen_groups()
- self.concat_signals()
- # processed_signals = self.process_signals(grouped_signals)
-
- return self.concated_signals
- # return processed_signals
-
-
-poses = [
- x.name.split("store")[0]
- for x in Path(
- "/shared_libs/pipeline-core/scripts/data/ph_calibration_dual_phl_ura8_5_04_5_83_7_69_7_13_6_59__01"
- ).rglob("*")
- if x.name != "images.h5"
-]
-gr = Group(
- GroupParameters(
- signals=["/extraction/general/None/area", "/extraction/mCherry/np_max/median"]
- )
-)
-gr.run(
- central_store="/shared_libs/pipeline-core/scripts/data/ph_calibration_dual_phl_ura8_5_04_5_83_7_69_7_13_6_59__01",
- poses=poses,
-)
-signal = Signal(
- "/shared_libs/pipeline-core/scripts/data/ph_calibration_dual_phl_ura8_5_04_5_83_7_69_7_13_6_59__01/ph_5_04_001store.h5"
-)
diff --git a/postprocessor/old/cell.py b/postprocessor/old/cell.py
deleted file mode 100644
index f96f30d5..00000000
--- a/postprocessor/old/cell.py
+++ /dev/null
@@ -1,23 +0,0 @@
-"""
-Main data processing functions. The inputs for this functions must be data series.
-"""
-
-import numpy as np
-from pandas import Series, DataFrames
-
-def bg_sub(data:Series, background:float):
- return data - background
-
-def ratio(data1:Series, data2:Series):
- return data1 / data2
-
-def norm_tps(data:Series, tps:list, fun=np.nanmean):
- return data / fun(data.loc(axis=1)[tps])
-
-def growth_rate(data:Series, alg=None, filt = 'savgol'):
- if alg is None:
- alg='standard'
-
-
-
-
diff --git a/postprocessor/old/ph.py b/postprocessor/old/ph.py
deleted file mode 100644
index a28a8055..00000000
--- a/postprocessor/old/ph.py
+++ /dev/null
@@ -1,364 +0,0 @@
-from typing import Dict, List, Union
-import re
-
-import numpy as np
-import pandas as pd
-from pandas import Series, DataFrame
-from sklearn.cluster import KMeans
-from matplotlib import pyplot as plt
-import seaborn as sns
-
-import compress_pickle
-
-from postprocessor.core.postprocessor import PostProcessor
-from postprocessor.core.tracks import get_avg_grs, non_uniform_savgol
-from postprocessor.core.ph import *
-
-
-def filter_by_gfp(dfs):
- gfps = pd.concat([t[("GFPFast_bgsub", np.maximum, "median")] for t in dfs])
- avgs_gfp = gfps.mean(axis=1)
- high_gfp = get_high_k2(avgs_gfp)
- # high_gfp = avgs_gfp[avgs_gfp > 200]
-
- return high_gfp
-
-
-def filter_by_area(dfs, min=50):
- areas = pd.concat([t[("general", None, "area")] for t in dfs])
- avgs_areas = areas[(areas.notna().sum(axis=1) > areas.shape[1] // (1.25))].mean(
- axis=1
- )
- avgs_areas = avgs_areas[(avgs_areas > min)]
-
- return avgs_areas
-
-
-def get_high_k2(df):
- kmeans = KMeans(n_clusters=2)
- vals = df.values.reshape(-1, 1)
- kmeans.fit(vals)
- high_clust_id = kmeans.cluster_centers_.argmax()
-
- return df.loc[kmeans.predict(vals) == high_clust_id]
-
-
-def get_concats(dfs, keys):
- return pd.concat([t.get((keys), pd.DataFrame()) for t in dfs])
-
-
-def get_dfs(pp):
- dfs = [pp.extraction[pp.expt.positions[i]] for i in range(len(pp.expt.positions))]
- return dfs
-
-
-combine_dfs = lambda dfs: {k: get_concats(dfs, k) for k in dfs[0].keys()}
-
-
-def merge_channels(pp, min_area=50):
- dfs = get_dfs(pp)
- # rats = get_concats(dfs, ("em_ratio_bgsub", np.maximum, "median"))
- # gfps = filter_by_gfp(dfs)
-
- avgs_area = filter_by_area(dfs, min=50)
- # ids = [x for x in set(gfps.index).intersection(avgs_area.index)]
- ids = avgs_area.index
-
- new_dfs = combine_dfs(dfs)
-
- h = pd.DataFrame(
- {
- k[0] + "_" + k[2]: v.loc[ids].mean(axis=1)
- for k, v in new_dfs.items()
- if k[-1] != "imBackground"
- }
- )
- return h
-
-
-def process_phs(pp, min_area=200):
- h = merge_channels(pp, min_area)
- h.index.names = ["pos", "trap", "cell"]
- ids = h.index
- h = h.reset_index()
-
- h["ph"] = h["pos"].apply(lambda x: float(x[3:7].replace("_", ".")))
- h["max5_d_med"] = h["mCherry_max2p5pc"] / h["mCherry_median"]
-
- h = h.set_index(ids)
- h = h.drop(["pos", "trap", "cell"], axis=1)
- return h
-
-
-def growth_rate(
- data: Series, alg=None, filt={"kind": "savgol", "window": 5, "degree": 3}
-):
- window = filt["window"]
- degree = filt["degree"]
- if alg is None:
- alg = "standard"
-
- if filt: # TODO add support for multiple algorithms
- data = Series(
- non_uniform_savgol(
- data.dropna().index, data.dropna().values, window, degree
- ),
- index=data.dropna().index,
- )
-
- return Series(np.convolve(data, diff_kernel, "same"), index=data.dropna().index)
-
-
-# import numpy as np
-
-# diff_kernel = np.array([1, -1])
-# gr = clean.apply(growth_rate, axis=1)
-# from postprocessor.core.tracks import non_uniform_savgol, clean_tracks
-
-
-def sort_df(df, by="first", rev=True):
- nona = df.notna()
- if by == "len":
- idx = nona.sum(axis=1)
- elif by == "first":
- idx = nona.idxmax(axis=1)
- idx = idx.sort_values().index
-
- if rev:
- idx = idx[::-1]
-
- return df.loc[idx]
-
-
-# test = tmp[("GFPFast", np.maximum, "median")]
-# test2 = tmp[("pHluorin405", np.maximum, "median")]
-# ph = test / test2
-# ph = ph.stack().reset_index(1)
-# ph.columns = ["tp", "fl"]
-
-
-def m2p5_med(ext, ch, red=np.maximum):
- m2p5pc = ext[(ch, red, "max2p5pc")]
- med = ext[(ch, red, "median")]
-
- result = m2p5pc / med
-
- return result
-
-
-def plot_avg(df):
- df = df.stack().reset_index(1)
- df.columns = ["tp", "val"]
-
- sns.relplot(x=df["tp"], y=df["val"], kind="line")
- plt.show()
-
-
-def split_data(df: DataFrame, splits: List[int]):
- dfs = [df.iloc[:, i:j] for i, j in zip((0,) + splits, splits + (df.shape[1],))]
- return dfs
-
-
-def growth_rate(
- data: Series, alg=None, filt={"kind": "savgol", "window": 7, "degree": 3}
-):
- if alg is None:
- alg = "standard"
-
- if filt: # TODO add support for multiple algorithms
- window = filt["window"]
- degree = filt["degree"]
- data = Series(
- non_uniform_savgol(
- data.dropna().index, data.dropna().values, window, degree
- ),
- index=data.dropna().index,
- )
-
- diff_kernel = np.array([1, -1])
-
- return Series(np.convolve(data, diff_kernel, "same"), index=data.dropna().index)
-
-
-# pp = PostProcessor(source=19831)
-# pp.load_tiler_cells()
-# f = "/home/alan/Documents/sync_docs/libs/postproc/gluStarv_2_0_x2_dual_phl_ura8_00/extraction"
-# pp.load_extraction(
-# "/home/alan/Documents/sync_docs/libs/postproc/postprocessor/"
-# + pp.expt.name
-# + "/extraction/"
-# )
-# tmp = pp.extraction["phl_ura8_002"]
-
-
-def _check_bg(data):
- for k in list(pp.extraction.values())[0].keys():
- for p in pp.expt.positions:
- if k not in pp.extraction[p]:
- print(p, k)
-
-
-# data = {
-# k: pd.concat([pp.extraction[pos][k] for pos in pp.expt.positions[:-3]])
-# for k in list(pp.extraction.values())[0].keys()
-# }
-
-
-def hmap(df, **kwargs):
- g = sns.heatmap(sort_df(df), robust=True, cmap="mako_r", **kwargs)
- plt.xlabel("")
- return g
-
-
-# from random import randint
-# x = randint(0, len(smooth))
-# plt.plot(clean.iloc[x], 'b')
-# plt.plot(smooth.iloc[x], 'r')
-# plt.show()
-
-
-# data = tmp
-# df = data[("general", None, "area")]
-# clean = clean_tracks(df, min_len=160)
-# clean = clean.loc[clean.notna().sum(axis=1) > 9]
-# gr = clean.apply(growth_rate, axis=1)
-# splits = (72, 108, 180)
-# gr_sp = split_data(gr, splits)
-
-# idx = gr.index
-
-# bg = get_bg(data)
-# test = data[("GFPFast", np.maximum, "median")]
-# test2 = data[("pHluorin405", np.maximum, "median")]
-# ph = (test / test2).loc[idx]
-# c = pd.concat((ph.mean(1), gr.max(1)), axis=1)
-# c.columns = ["ph", "gr_max"]
-# # ph = ph.stack().reset_index(1)
-# # ph.columns = ['tp', 'fl']
-
-# ph_sp = split_data(gr, splits)
-
-
-def get_bg(data):
- bg = {}
- fl_subkeys = [
- x
- for x in data.keys()
- if x[0] in ["GFP", "GFPFast", "mCherry", "pHluorin405"]
- and x[-1] != "imBackground"
- ]
- for k in fl_subkeys:
- nk = list(k)
- bk = tuple(nk[:-1] + ["imBackground"])
- nk = tuple(nk[:-1] + [nk[-1] + "_BgSub"])
- tmp = []
- for i, v in data[bk].iterrows():
- if i in data[k].index:
- newdf = data[k].loc[i] / v
- newdf.index = pd.MultiIndex.from_tuples([(*i, c) for c in newdf.index])
- tmp.append(newdf)
- bg[nk] = pd.concat(tmp)
-
- return bg
-
-
-def calc_ph(bg):
- fl_subkeys = [x for x in bg.keys() if x[0] in ["GFP", "GFPFast", "pHluorin405"]]
- chs = list(set([x[0] for x in fl_subkeys]))
- assert len(chs) == 2, "Too many channels"
- ch1 = [x[1:] for x in fl_subkeys if x[0] == chs[0]]
- ch2 = [x[1:] for x in fl_subkeys if x[0] == chs[1]]
- inter = list(set(ch1).intersection(ch2))
- ph = {}
- for red_fld in inter:
- ph[tuple(("ph",) + red_fld)] = (
- bg[tuple((chs[0],) + red_fld)] / bg[tuple((chs[1],) + red_fld)]
- )
-
-
-def get_traps(pp):
- t0 = {}
- for pos in pp.tiler.positions:
- pp.tiler.current_position = pos
- t0[pos] = pp.tiler.get_traps_timepoint(
- 0, channels=[0, pp.tiler.channels.index("mCherry")], z=[0, 1, 2, 3, 4]
- )
-
- return t0
-
-
-def get_pos_ph(pp):
- pat = re.compile(r"ph_([0-9]_[0-9][0-9])")
- return {
- pos: float(pat.findall(pos)[0].replace("_", ".")) for pos in pp.tiler.positions
- }
-
-
-def plot_sample_bf_mch(pp):
- bf_mch = get_traps(pp)
- ts = [{i: v[:, j, ...] for i, v in bf_mch.items()} for j in [0, 1]]
- tsbf = {i: v[:, 0, ...] for i, v in bf_mch.items()}
-
- posdict = {k: v for k, v in get_pos_ph(pp).items()}
- posdict = {v: k for k, v in posdict.items()}
- posdict = {v: k for k, v in posdict.items()}
- ph = np.unique(list(posdict.values())).tolist()
- counters = {ph: 0 for ph in ph}
- n = [np.random.randint(ts[0][k].shape[0]) for k in posdict.keys()]
-
- fig, axes = plt.subplots(2, 5)
- for k, (t, name) in enumerate(zip(ts, ["Bright field", "mCherry"])):
- for i, (pos, ph) in enumerate(posdict.items()):
- # i = ph.index(posdict[pos])
- axes[k, i].grid(False)
- axes[k, i].set(
- xticklabels=[],
- yticklabels=[],
- )
- axes[k, i].set_xlabel(posdict[pos] if k else None, fontsize=28)
- axes[k, i].imshow(
- np.maximum.reduce(t[pos][n[i], 0], axis=2),
- cmap="gist_gray" if not k else None,
- )
- # counters[posdict[pos]] += 1
- plt.tick_params(
- axis="x", # changes apply to the x-axis
- which="both", # both major and minor ticks are affected
- bottom=False, # ticks along the bottom edge are off
- top=False, # ticks along the top edge are off
- labelbottom=False,
- ) # labels along the bottom edge are off
- axes[k, 0].set_ylabel(name, fontsize=28)
- plt.tight_layout()
- plt.show()
-
-
-# Plotting calibration curve
-from scipy.optimize import curve_fit
-
-
-def fit_calibration(h):
- ycols = [x for x in h.columns if "em_ratio" in x]
- xcol = "ph"
-
- def objective(x, a, b):
- return a * x + b
-
- # fig, axes = plt.subplots(1, len(ycols))
- # for i, ycol in enumerate(ycols):
- # d = h[[xcol, ycol]]
- # params, _ = curve_fit(objective, *[d[col].values for col in d.columns])
- # sns.lineplot(x=xcol, y=ycol, data=h, alpha=0.5, err_style="bars", ax=axes[i])
- # # sns.lineplot(d[xcol], objective(d[xcol].values, *params), ax=axes[i])
- # plt.show()
-
- ycol = "em_ratio_mean"
- d = h[[xcol, *ycols]]
- tmp = d.groupby("ph").mean()
- calibs = {ycol: curve_fit(objective, tmp.index, tmp[ycol])[0] for ycol in ycols}
- # sns.lineplot(x=xcol, y=ycol, data=d, alpha=0.5, err_style="bars")
- # plt.xlabel("pH")
- # plt.ylabel("pHluorin emission ratio")
- # sns.lineplot(d[xcol], objective(d[xcol], *params))
-
- return calibs
diff --git a/postprocessor/old/postprocessor.py b/postprocessor/old/postprocessor.py
deleted file mode 100644
index 7e024d26..00000000
--- a/postprocessor/old/postprocessor.py
+++ /dev/null
@@ -1,106 +0,0 @@
-import pkg_resources
-from pathlib import Path, PosixPath
-from typing import Union, List, Dict
-from datetime import datetime
-
-from compress_pickle import load, dump
-
-from core.experiment import Experiment
-from core.cells import Cells
-from core.segment import Tiler
-from core.io.matlab import matObject
-from core.experiment import Experiment
-
-
-class PostProcessor:
- '''
- Base class to perform feature extraction.
- :param parameters: Parameters class with channels, reduction functions and
- extraction functions to use.
- :param source: Origin of experiment, if int it is assumed from Omero, if str
- or Path
- '''
- def __init__(self, parameters=None, source: Union[int, str, Path] = None):
- # self.params = parameters
- if source is not None:
- if type(source) is int:
- self.expt_id = source
- self.load_expt(source, omero=True)
- elif type(source) is str or PosixPath:
- self.load_expt(source, omero=False)
-
- @property
- def channels(self):
- if not hasattr(self, '_channels'):
- if type(self.params.tree) is dict:
- self._channels = tuple(self.params.tree.keys())
-
- return self._channels
-
- @property
- def current_position(self):
- assert hasattr(self, 'expt'), 'No experiment loaded.'
-
- if hasattr(self, 'tiler'):
- assert self.expt.current_position.name == self.tiler.current_position
-
- return self.expt.current_position
-
- def load_expt(self, source: Union[int, str], omero: bool = False) -> None:
- if omero:
- self.expt = Experiment.from_source(
- self.expt_id, #Experiment ID on OMERO
- 'upload', #OMERO Username
- '***REMOVED***', #OMERO Password
- 'islay.bio.ed.ac.uk', #OMERO host
- port=4064 #This is default
- )
- else:
- self.expt = Experiment.from_source(source)
- self.expt_id = self.expt.exptID
-
- def load_tiler_cells(self) -> None:
- self.tiler = Tiler(self.expt)
- self.cells = Cells()
- self.cells = self.cells.from_source(
- self.expt.current_position.annotation)
-
- def get_pos_mo_bud(self):
- annot = self.expt._get_position_annotation(
- self.expt.current_position.name)
- matob = matObject(annot)
- m = matob["timelapseTrapsOmero"].get("cellMothers", None)
- if m is not None:
- ids = np.nonzero(m.todense())
- d = {(self.expt.current_position.name, i, int(m[i, j])): []
- for i, j in zip(*ids)}
- for i, j, k in zip(*ids, d.keys()):
- d[k].append((self.expt.current_position.name, i, j + 1))
- else:
- print("Pos {} has no mother matrix".format(
- self.expt.current_position.name))
- d = {}
-
- return d
-
- def get_exp_mo_bud(self):
- d = {}
- for pos in self.expt.positions:
- self.expt.current_position = pos
- d = {**d, **self.get_pos_mo_bud()}
-
- self.expt.current_position = self.expt.positions[0]
-
- return d
-
- def load_extraction(self, folder=None) -> None:
- if folder is None:
- folder = Path(self.expt.name + '/extraction')
-
- self.extraction = {}
- for pos in self.expt.positions:
- try:
- self.extraction[pos] = load(folder / Path(pos + '.gz'))
-
- except:
- print(pos, ' not found')
diff --git a/postprocessor/processes/__init__.py b/postprocessor/processes/__init__.py
deleted file mode 100644
index e5a0d9b4..00000000
--- a/postprocessor/processes/__init__.py
+++ /dev/null
@@ -1 +0,0 @@
-#!/usr/bin/env python3
diff --git a/postprocessor/processes/__pycache__/aggregate.cpython-37.pyc b/postprocessor/processes/__pycache__/aggregate.cpython-37.pyc
deleted file mode 100644
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diff --git a/postprocessor/processes/aggregate.py b/postprocessor/processes/aggregate.py
deleted file mode 100644
index cfe8e8f0..00000000
--- a/postprocessor/processes/aggregate.py
+++ /dev/null
@@ -1,74 +0,0 @@
-from itertools import cycle
-
-import numpy as np
-import pandas as pd
-
-from agora.base import ParametersABC, ProcessABC
-
-
-class aggregateParameters(ParametersABC):
- """
- Parameters
- reduction: str to be passed to a dataframe for collapsing across columns
- """
-
- def __init__(self, reductions):
- super().__init__()
- self.reductions = reductions
-
- @classmethod
- def default(cls):
- return cls.from_dict({"reductions": ["mean", "median", "max"]})
-
-
-class aggregate(ProcessABC):
- """
- aggregate multiple datasets
- """
-
- def __init__(self, parameters: aggregateParameters):
- super().__init__(parameters)
-
- def run(self, signals):
- names = np.array([signal.index.names for signal in signals])
- index = signals[0].index
- for s in signals[0:]:
- index = index.intersection(s.index)
-
- tmp_signals = [s.loc[index] for s in signals]
- for i, s in enumerate(signals):
- tmp_signals[i].name = s.name
- signals = tmp_signals
-
- assert len(signals), "Signals is empty"
-
- bad_words = {
- "postprocessing",
- "extraction",
- "None",
- "np_max",
- "",
- }
- get_keywords = lambda df: [
- ind
- for item in df.name.split("/")
- for ind in item.split("/")
- if ind not in bad_words
- ]
- colnames = [
- "_".join(get_keywords(s) + [red])
- for s in signals
- for red in self.parameters.reductions
- ]
- concat = pd.concat(
- [
- getattr(signal, red)(axis=1)
- for signal in signals
- for red in self.parameters.reductions
- ],
- names=signals[0].index.names,
- axis=1,
- )
- concat.columns = colnames
-
- return concat
diff --git a/postprocessor/processes/base.py b/postprocessor/processes/base.py
deleted file mode 100644
index 142bd6c3..00000000
--- a/postprocessor/processes/base.py
+++ /dev/null
@@ -1 +0,0 @@
-from agora.base import ParametersABC, ProcessABC
\ No newline at end of file
diff --git a/postprocessor/processes/births.py b/postprocessor/processes/births.py
deleted file mode 100644
index e5a0d9b4..00000000
--- a/postprocessor/processes/births.py
+++ /dev/null
@@ -1 +0,0 @@
-#!/usr/bin/env python3
diff --git a/postprocessor/processes/bud_metric.py b/postprocessor/processes/bud_metric.py
deleted file mode 100644
index 518ddf52..00000000
--- a/postprocessor/processes/bud_metric.py
+++ /dev/null
@@ -1,65 +0,0 @@
-import numpy as np
-import pandas as pd
-from agora.base import ParametersABC, ProcessABC
-
-
-class bud_metricParameters(ParametersABC):
- """
- Parameters
- """
-
- def __init__(self, mode="longest"):
- super().__init__()
- self.mode = mode
-
- @classmethod
- def default(cls):
- return cls.from_dict({"mode": "longest"})
-
-
-class bud_metric(ProcessABC):
- """
- Obtain the volume of daughter cells
- if 'longest' assumes a single mother per trap.
- """
-
- def __init__(self, parameters: bud_metricParameters):
- super().__init__(parameters)
-
- def run(self, signal: pd.DataFrame):
- if self.parameters.mode is "longest":
- result = self.get_bud_metric_wrap(signal)
-
- return result
-
- @staticmethod
- def get_bud_metric(signal):
- mother_id = signal.index[signal.notna().sum(axis=1).argmax()]
-
- nomother = signal.drop(mother_id)
-
- starts = nomother.apply(pd.Series.first_valid_index, axis=1).sort_values()
-
- ranges = [np.arange(i, j) for i, j in zip(starts[:-1], starts[1:])]
- ranges.append(np.arange(starts.iloc[-1], signal.columns[-1]))
-
- bud_metric = pd.concat(
- [signal.loc[i, rng] for i, rng in zip(starts.index, ranges)]
- )
- srs = pd.Series(bud_metric, index=signal.columns, name=mother_id)
-
- return srs
-
- def get_bud_metric_wrap(self, signals):
- srs = [
- self.get_bud_metric(signals.loc[trap])
- for trap in signals.index.unique(level="trap")
- ]
- index = [
- (trap, mother.name)
- for trap, mother in zip(signals.index.unique(level="trap"), srs)
- ]
-
- concatenated = pd.concat(srs, keys=index, axis=1, sort=True).T.sort_index()
- concatenated.index.names = ["trap", "cell_label"]
- return concatenated
diff --git a/postprocessor/processes/dsignal.py b/postprocessor/processes/dsignal.py
deleted file mode 100644
index 484ad599..00000000
--- a/postprocessor/processes/dsignal.py
+++ /dev/null
@@ -1,28 +0,0 @@
-import pandas as pd
-
-from postprocessor.core.processes.base import ParametersABC, ProcessABC
-
-
-class dsignalParameters(ParametersABC):
- """
- :window: Number of timepoints to consider for signal.
- """
-
- def __init__(self, window: int):
- self.window = window
-
- @classmethod
- def default(cls):
- return cls.from_dict({"window": 3})
-
-
-class dsignal(ProcessABC):
- """
- Calculate the change in a signal depending on a window
- """
-
- def __init__(self, parameters: dsignalParameters):
- super().__init__(parameters)
-
- def run(self, signal: pd.DataFrame):
- return signal.rolling(window=self.parameters.window, axis=1).mean().diff(axis=1)
diff --git a/postprocessor/processes/gpsignal.py b/postprocessor/processes/gpsignal.py
deleted file mode 100644
index f6cdb45f..00000000
--- a/postprocessor/processes/gpsignal.py
+++ /dev/null
@@ -1,97 +0,0 @@
-"""Gaussian process fit of a Signal."""
-import logging
-
-from postprocessor.core.processes.base import ParametersABC, ProcessABC
-import numpy as np
-import pandas as pd
-
-import gaussian_processes.gaussianprocess as gp
-
-def estimate_gr(volume, dt, noruns, bounds, verbose):
- """
- Parameters
- ----------
-
- volume : pd.Series
- The volume series of a given cell
- dt : float
- The time interval in hours
- noruns : int
- The number of runs for optimisation
- bounds : dict
- The hyperparameter bounds used for optimisation
- verbose : bool
- If True, prints results
-
- Returns
- -------
- """
- volume = volume.values
- n = len(volume)
- idx = np.arange(n)
- t = idx * dt
- y = volume[volume > 0]
- x = t[volume > 0]
- idx = idx[volume > 0]
- # Fit the GP
- mg = gp.maternGP(bounds, x, y)
- mg.findhyperparameters(noruns=noruns)
- if verbose:
- mg.results() # Prints out the hyperparameters
- mg.predict(x, derivs=2) # Saves the predictions to object
- # Save the predictions to a csv file so they can be reused
- results = dict(time=mg.x, volume=mg.y, fit_time=mg.xnew, fit_volume=mg.f,
- growth_rate=mg.df, d_growth_rate=mg.ddf,
- volume_var=mg.fvar, growth_rate_var=mg.dfvar,
- d_growth_rate_var=mg.ddfvar)
- for name, value in results.items():
- results[name] = np.full((n, ), np.nan)
- results[name][idx] = value
- return results
-
-# Give that to a writer: NOTE the writer needs to learn how to write the
-# output of a process that returns multiple signals like this one does.
-
-class gpParameters(ParametersABC):
- default_dict = dict(dt=5,
- noruns=5,
- bounds={0: (-2, 3),
- 1: (-2, 1),
- 2: (-4, -1)},
- verbose=False)
- def __init__(self, dt, noruns, bounds, verbose):
- """
- Parameters
- ----------
- dt : float
- The time step between time points, in minutes
- noruns : int
- The number of times the optimisation is tried
- bounds : dict
- Hyperparameter bounds for the Matern Kernel
- verbose : bool
- Determines whether to print hyperparameter results
- """
- self.dt = dt
- self.noruns = noruns
- self.bounds = bounds
- self.verbose = verbose
-
- @classmethod
- def default(cls):
- return cls.from_dict(cls.default_dict)
-
-
-class GPSignal(ProcessABC):
- """Gaussian process fit of a Signal.
- """
- def __init__(self, parameters: gpParameters):
- super().__init__(parameters)
-
- def run(self, signal: pd.DataFrame):
- results = signal.apply(lambda x: estimate_gr(x,
- **self.parameters.to_dict()),
- result_type='expand').T
- multi_signal = {name: pd.DataFrame(np.vstack(results[name]))
- for name in results.columns}
- return multi_signal
diff --git a/postprocessor/processes/merger.py b/postprocessor/processes/merger.py
deleted file mode 100644
index a79227fa..00000000
--- a/postprocessor/processes/merger.py
+++ /dev/null
@@ -1,60 +0,0 @@
-from agora.base import ParametersABC, ProcessABC
-from postprocessor.core.functions.tracks import get_joinable
-
-
-class mergerParameters(ParametersABC):
- """
- :param 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
- """
-
- def __init__(
- self,
- tolerance: float,
- smooth: bool = False,
- window: int = 5,
- degree: int = 3,
- min_avg_delta: float = 0.9,
- ):
-
- self.tolerance = tolerance
-
- self.smooth = smooth
-
- self.window = window
-
- self.degree = degree
-
- self.min_avg_delta = min_avg_delta
-
- @classmethod
- def default(cls):
- return cls.from_dict(
- {
- "smooth": False,
- "tolerance": 0.2,
- "window": 5,
- "degree": 3,
- "min_avg_delta": 0.9,
- }
- )
-
-
-class merger(ProcessABC):
- """
- TODO check why it needs to be run a few times to complete the merging
- """
-
- def __init__(self, parameters):
- super().__init__(parameters)
-
- def run(self, signal):
- joinable = get_joinable(signal, tol=self.parameters.tolerance)
- # merged, _ = merge_tracks(signal) # , min_len=self.window + 1)
- # indices = (*zip(*merged.index.tolist()),)
- # names = merged.index.names
- # return {name: ids for name, ids in zip(names, indices)}
- return joinable
diff --git a/postprocessor/processes/peaks.py b/postprocessor/processes/peaks.py
deleted file mode 100644
index 15ddf950..00000000
--- a/postprocessor/processes/peaks.py
+++ /dev/null
@@ -1,44 +0,0 @@
-from scipy.signal import argrelmax, argrelmin
-from postprocessor.core.processes.base import ParametersABC, ProcessABC
-
-
-class PeaksParameters(ParametersABC):
- """
- Parameters
- type : str {minima, maxima, "all"}. Determines which type of peaks to identify
- order : int Parameter to pass to scipy.signal.argrelextrema indicating
- how many points to use for comparison.
- """
-
- def __init__(self, type):
- self.type = type
-
- @classmethod
- def default(cls):
- return cls.from_dict({"type": "minima", "order": 3})
-
-
-class Peaks(ProcessABC):
- """
- Identifies a signal sharply dropping.
- """
-
- def __init__(self, parameters: PeaksParameters):
- super().__init__(parameters)
-
- def run(self, signal: pd.DataFrame):
- """
- Returns a boolean dataframe with the same shape as the
- original signal but with peaks as true values.
- """
- peaks_mat = np.zeros_like(signal, dtype=bool)
-
- comparator = np.less if self.parameters.type is "minima" else np.greater
- peaks_ids = argrelextrema(new_df, comparator=comparator, order=order)
- peaks_mat[peak_ids] = True
-
- return pd.DataFrame(
- peaks_mat,
- index=signal.index,
- columns=signal.columns,
- )
diff --git a/postprocessor/processes/picker.py b/postprocessor/processes/picker.py
deleted file mode 100644
index a7221e6f..00000000
--- a/postprocessor/processes/picker.py
+++ /dev/null
@@ -1,339 +0,0 @@
-import seaborn as sns
-from matplotlib import pyplot as plt # TODO DELETE THIS
-
-from typing import Tuple, Union, List
-from abc import ABC, abstractmethod
-
-from itertools import groupby
-
-from utils_find_1st import find_1st, cmp_equal
-import numpy as np
-import pandas as pd
-
-from pcore.cells import CellsHDF
-
-from agora.base import ParametersABC, ProcessABC
-from postprocessor.core.functions.tracks import max_ntps, max_nonstop_ntps
-
-
-class pickerParameters(ParametersABC):
- def __init__(
- self,
- sequence: List[str] = ["lineage", "condition"],
- ):
- self.sequence = sequence
-
- @classmethod
- def default(cls):
- return cls.from_dict(
- {
- "sequence": [
- # ("lineage", "intersection", "families"),
- ("condition", "intersection", "any_present", 0.8),
- ("condition", "intersection", "growing", 40),
- ("condition", "intersection", "present", 8),
- ("condition", "intersection", "mother_buds", 5, 0.8),
- # ("lineage", "full_families", "intersection"),
- ],
- }
- )
-
-
-class picker(ProcessABC):
- """
- :cells: Cell object passed to the constructor
- :condition: Tuple with condition and associated parameter(s), conditions can be
- "present", "nonstoply_present" or "quantile".
- Determines the thersholds or fractions of signals/signals to use.
- :lineage: str {"mothers", "daughters", "families" (mothers AND daughters), "orphans"}. Mothers/daughters picks cells with those tags, families pick the union of both and orphans the difference between the total and families.
- """
-
- def __init__(
- self,
- parameters: pickerParameters,
- cells: CellsHDF,
- ):
- super().__init__(parameters=parameters)
-
- self._cells = cells
-
- @staticmethod
- def mother_assign_to_mb_matrix(ma: List[np.array]):
- # Convert from list of lists to mother_bud sparse matrix
- ncells = sum([len(t) for t in ma])
- mb_matrix = np.zeros((ncells, ncells), dtype=bool)
- c = 0
- for cells in ma:
- for d, m in enumerate(cells):
- if m:
- mb_matrix[c + d, c + m - 1] = True
-
- c += len(cells)
-
- return mb_matrix
-
- @staticmethod
- def mother_assign_from_dynamic(ma, label, trap, ntraps: int):
- """
- Interpolate the list of lists containing the associated mothers from the mother_assign_dynamic feature
- """
- idlist = list(zip(trap, label))
- cell_gid = np.unique(idlist, axis=0)
-
- last_lin_preds = [
- find_1st(((label[::-1] == lbl) & (trap[::-1] == tr)), True, cmp_equal)
- for tr, lbl in cell_gid
- ]
- mother_assign_sorted = ma[last_lin_preds]
-
- traps = cell_gid[:, 0]
- iterator = groupby(zip(traps, mother_assign_sorted), lambda x: x[0])
- d = {key: [x[1] for x in group] for key, group in iterator}
- nested_massign = [d.get(i, []) for i in range(ntraps)]
-
- return nested_massign
-
- def pick_by_lineage(self, signals, how):
-
- idx = signals.index
-
- if how:
- mothers = set(self.mothers)
- daughters = set(self.daughters)
- # daughters, mothers = np.where(mother_bud_mat)
-
- search = lambda a, b: np.where(
- np.in1d(
- np.ravel_multi_index(np.array(a).T, np.array(a).max(0) + 1),
- np.ravel_multi_index(np.array(b).T, np.array(a).max(0) + 1),
- )
- )
- if how == "mothers":
- idx = mothers
- elif how == "daughters":
- idx = daughters
- elif how == "daughters_w_mothers":
- present_mothers = idx.intersection(mothers)
- idx = set(
- [
- tuple(x)
- for m in present_mothers
- for x in np.array(self.daughters)[search(self.mothers, m)]
- ]
- )
-
- print("associated daughters: ", idx)
- elif how == "mothers_w_daughters":
- present_daughters = idx.intersection(daughters)
- idx = set(
- [
- tuple(x)
- for d in present_daughters
- for x in np.array(self.mothers)[search(self.daughters, d)]
- ]
- )
- elif how == "full_families":
- present_mothers = idx.intersection(mothers)
- dwm_idx = set(
- [
- tuple(x)
- for m in present_mothers
- for x in np.array(self.daughters)[
- search(np.array(self.mothers), m)
- ]
- ]
- )
- present_daughters = idx.intersection(daughters)
- mwd_idx = set(
- [
- tuple(x)
- for d in present_daughters
- for x in np.array(self.mothers)[
- search(np.array(self.daughters), d)
- ]
- ]
- )
- idx = mwd_idx.union(dwm_idx)
- elif how == "families" or how == "orphans":
- families = mothers.union(daughters)
- if how == "families":
- idx = families
- elif how == "orphans":
- idx = idx.diference(families)
-
- idx = idx.intersection(signals.index)
-
- return idx
-
- def pick_by_condition(self, signals, condition, thresh):
- idx = self.switch_case(signals, condition, thresh)
- return idx
-
- def get_mothers_daughters(self):
- ma = self._cells["mother_assign_dynamic"]
- trap = self._cells["trap"]
- label = self._cells["cell_label"]
- nested_massign = self.mother_assign_from_dynamic(
- ma, label, trap, self._cells.ntraps
- )
- # mother_bud_mat = self.mother_assign_to_mb_matrix(nested_massign)
-
- 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))
- for tid, trapcells in enumerate(nested_massign)
- for d, m in enumerate(trapcells, 1)
- if m
- ]
- )
- return mothers, daughters
-
- def run(self, signals):
- indices = set(signals.index)
- self.mothers, self.daughters = self.get_mothers_daughters()
- for alg, op, *params in self.sequence:
- if alg is "lineage":
- param1 = params[0]
- new_indices = getattr(self, "pick_by_" + alg)(
- signals.loc[list(indices)], param1
- )
- else:
- param1, *param2 = params
- new_indices = getattr(self, "pick_by_" + alg)(
- signals.loc[list(indices)], param1, param2
- )
-
- if op is "union":
- # new_indices = new_indices.intersection(set(signals.index))
- new_indices = indices.union(new_indices)
-
- indices = indices.intersection(new_indices)
-
- return np.array(list(indices))
-
- @staticmethod
- def switch_case(
- signals: pd.DataFrame,
- condition: str,
- threshold: Union[float, int, list],
- ):
- if len(threshold) == 1:
- threshold = [_as_int(*threshold, signals.shape[1])]
- case_mgr = {
- "any_present": lambda s, thresh: any_present(s, thresh),
- "present": lambda s, thresh: s.notna().sum(axis=1) > thresh,
- "nonstoply_present": lambda s, thresh: s.apply(thresh, axis=1) > thresh,
- "growing": lambda s, thresh: s.diff(axis=1).sum(axis=1) > thresh,
- "mother_buds": lambda s, p1, p2: mother_buds_wrap(s, p1, p2)
- # "quantile": [np.quantile(signals.values[signals.notna()], threshold)],
- }
- return set(signals.index[case_mgr[condition](signals, *threshold)])
-
-
-def any_present(signals, threshold):
- """
- Returns a mask for cells, True if there is a cell in that trap that was present for more than :threshold: timepoints.
- """
- any_present = pd.Series(
- np.sum(
- [
- np.isin([x[0] for x in signals.index], i) & v
- for i, v in (signals.notna().sum(axis=1) > threshold)
- .groupby("trap")
- .any()
- .items()
- ],
- axis=0,
- ).astype(bool),
- index=signals.index,
- )
- return any_present
-
-
-from copy import copy
-
-
-def mother_buds(df, min_budgrowth_t, min_mobud_ratio):
- """
- Parameters
- ----------
- signals : pd.DataFrame
- min_budgrowth_t: Minimal number of timepoints we lock reassignment after assigning bud
- min_initial_size: Minimal mother-bud ratio at the assignment
- #TODO incorporate bud-assignment data?
-
- # If more than one bud start in the same time point pick the smallest one
- """
-
- ntps = df.notna().sum(axis=1)
- mother_id = df.index[ntps.argmax()]
- nomother = df.drop(mother_id)
- if not len(nomother):
- return []
- nomother = ( # Clean short-lived cells outside our mother cell's timepoints
- nomother.loc[
- nomother.apply(
- lambda x: x.first_valid_index() >= df.loc[mother_id].first_valid_index()
- and x.last_valid_index() <= df.loc[mother_id].last_valid_index(),
- axis=1,
- )
- ]
- )
-
- start = nomother.apply(pd.Series.first_valid_index, axis=1)
-
- # clean duplicates
- duplicates = start.duplicated(False)
- if duplicates.any():
- dup_tps = np.unique(start[duplicates])
- idx, tps = zip(
- *[(nomother.loc[start == tp, tp].idxmin(), tp) for tp in dup_tps]
- )
- start = start[~duplicates]
- start = pd.concat(
- (start, pd.Series(tps, index=idx, dtype="int", name="cell_label"))
- )
- nomother = nomother.loc[start.index]
- nomother.index = nomother.index.astype("int")
-
- d_to_mother = nomother[start] - df.loc[mother_id, start] * min_mobud_ratio
- size_filter = d_to_mother[
- d_to_mother.apply(lambda x: x.dropna().iloc[0], axis=1) < 0
- ]
- cols_sorted = (
- size_filter.sort_index(axis=1)
- .apply(pd.Series.first_valid_index, axis=1)
- .sort_values()
- )
- if not len(cols_sorted):
- return []
- bud_candidates = cols_sorted.loc[
- [True, *(np.diff(cols_sorted.values) > min_budgrowth_t)]
- ]
-
- return [mother_id] + [int(i) for i in bud_candidates.index.tolist()]
-
-
-def mother_buds_wrap(signals, *args):
- ids = []
- for trap in signals.index.unique(level="trap"):
- df = signals.loc[trap]
- selected_ids = mother_buds(df, *args)
- ids += [(trap, i) for i in selected_ids]
-
- idx_srs = pd.Series(False, signals.index).astype(bool)
- idx_srs.loc[ids] = True
- return idx_srs
-
-
-def _as_int(threshold: Union[float, int], ntps: int):
- if type(threshold) is float:
- threshold = ntps * threshold
- return threshold
diff --git a/postprocessor/processes/savgol.py b/postprocessor/processes/savgol.py
deleted file mode 100644
index d0256736..00000000
--- a/postprocessor/processes/savgol.py
+++ /dev/null
@@ -1,152 +0,0 @@
-import numpy as np
-import pandas as pd
-
-from postprocessor.core.processes.base import ParametersABC, ProcessABC
-
-
-class savgolParameters(ParametersABC):
- """
- Parameters
-
- window : int (odd)
- Window length of datapoints. Must be odd and smaller than x
- polynom : int
- The order of polynom used. Must be smaller than the window size
- """
-
- def __init__(self, window, polynom):
- self.window = window
- self.polynom = polynom
-
- @classmethod
- def default(cls):
- return cls.from_dict({"window": 3, "polynom": 2})
-
-
-class savgol(ProcessABC):
- """
- Apply Savitzky-Golay filter (works with NaNs, but it might return
- NaN regions).
- """
-
- def __init__(self, parameters: savgolParameters):
- super().__init__(parameters)
-
- def run(self, signal: pd.DataFrame):
- savgol_on_srs = lambda x: self.non_uniform_savgol(
- x.index, x.values, self.parameters.window, self.parameters.polynom
- )
- return signal.apply(savgol_on_srs, 1)
-
- @staticmethod
- def non_uniform_savgol(x, y, window: int, polynom: int):
- """
- Applies a Savitzky-Golay filter to y with non-uniform spacing
- as defined in x
-
- This is based on https://dsp.stackexchange.com/questions/1676/savitzky-golay-smoothing-filter-for-not-equally-spaced-data
- The borders are interpolated like scipy.signal.savgol_filter would do
-
- source: https://dsp.stackexchange.com/a/64313
-
- Parameters
- ----------
- x : array_like
- List of floats representing the x values of the data
- y : array_like
- List of floats representing the y values. Must have same length
- as x
- window : int (odd)
- Window length of datapoints. Must be odd and smaller than x
- polynom : int
- The order of polynom used. Must be smaller than the window size
-
- Returns
- -------
- np.array of float
- The smoothed y values
- """
- if len(x) != len(y):
- raise ValueError('"x" and "y" must be of the same size')
-
- if len(x) < window:
- raise ValueError("The data size must be larger than the window size")
-
- if type(window) is not int:
- raise TypeError('"window" must be an integer')
-
- if window % 2 == 0:
- raise ValueError('The "window" must be an odd integer')
-
- if type(polynom) is not int:
- raise TypeError('"polynom" must be an integer')
-
- if polynom >= window:
- raise ValueError('"polynom" must be less than "window"')
-
- half_window = window // 2
- polynom += 1
-
- # Initialize variables
- A = np.empty((window, polynom)) # Matrix
- tA = np.empty((polynom, window)) # Transposed matrix
- t = np.empty(window) # Local x variables
- y_smoothed = np.full(len(y), np.nan)
-
- # Start smoothing
- for i in range(half_window, len(x) - half_window, 1):
- # Center a window of x values on x[i]
- for j in range(0, window, 1):
- t[j] = x[i + j - half_window] - x[i]
-
- # Create the initial matrix A and its transposed form tA
- for j in range(0, window, 1):
- r = 1.0
- for k in range(0, polynom, 1):
- A[j, k] = r
- tA[k, j] = r
- r *= t[j]
-
- # Multiply the two matrices
- tAA = np.matmul(tA, A)
-
- # Invert the product of the matrices
- tAA = np.linalg.inv(tAA)
-
- # Calculate the pseudoinverse of the design matrix
- coeffs = np.matmul(tAA, tA)
-
- # Calculate c0 which is also the y value for y[i]
- y_smoothed[i] = 0
- for j in range(0, window, 1):
- y_smoothed[i] += coeffs[0, j] * y[i + j - half_window]
-
- # If at the end or beginning, store all coefficients for the polynom
- if i == half_window:
- first_coeffs = np.zeros(polynom)
- for j in range(0, window, 1):
- for k in range(polynom):
- first_coeffs[k] += coeffs[k, j] * y[j]
- elif i == len(x) - half_window - 1:
- last_coeffs = np.zeros(polynom)
- for j in range(0, window, 1):
- for k in range(polynom):
- last_coeffs[k] += coeffs[k, j] * y[len(y) - window + j]
-
- # Interpolate the result at the left border
- for i in range(0, half_window, 1):
- y_smoothed[i] = 0
- x_i = 1
- for j in range(0, polynom, 1):
- y_smoothed[i] += first_coeffs[j] * x_i
- x_i *= x[i] - x[half_window]
-
- # Interpolate the result at the right border
- for i in range(len(x) - half_window, len(x), 1):
- y_smoothed[i] = 0
- x_i = 1
- for j in range(0, polynom, 1):
- y_smoothed[i] += last_coeffs[j] * x_i
- x_i *= x[i] - x[-half_window - 1]
-
- return y_smoothed
diff --git a/postprocessor/processes/template.py b/postprocessor/processes/template.py
deleted file mode 100644
index 6fad758f..00000000
--- a/postprocessor/processes/template.py
+++ /dev/null
@@ -1,31 +0,0 @@
-import numpy as np
-import pandas as pd
-
-from agora.base import ParametersABC, ProcessABC
-
-
-class TemplateParameters(ParametersABC):
- """
- Parameters
- """
-
- def __init__(
- self,
- ):
- super().__init__()
-
- @classmethod
- def default(cls):
- return cls.from_dict({})
-
-
-class Template(ProcessABC):
- """
- Template for process class.
- """
-
- def __init__(self, parameters: TemplateParameters):
- super().__init__(parameters)
-
- def run(self):
- pass
diff --git a/postprocessor/processor.py b/postprocessor/processor.py
deleted file mode 100644
index 81c54b6f..00000000
--- a/postprocessor/processor.py
+++ /dev/null
@@ -1,316 +0,0 @@
-import h5py
-from typing import List, Dict, Union
-from pydoc import locate
-
-import numpy as np
-import pandas as pd
-
-from tqdm import tqdm
-
-from agora.base import ParametersABC
-from pcore.io.writer import Writer
-from pcore.io.signal import Signal
-
-from pcore.cells import Cells
-from postprocessor.core.processes.merger import mergerParameters, merger
-from postprocessor.core.processes.picker import pickerParameters, picker
-
-
-class PostProcessorParameters(ParametersABC):
- """
- Anthology of parameters used for postprocessing
- :merger:
- :picker: parameters for picker
- :processes: Dict processes:[objectives], 'processes' are defined in ./processes/
- while objectives are relative or absolute paths to datasets. If relative paths the
- post-processed addresses are used.
-
- """
-
- def __init__(
- self,
- targets={},
- parameters={},
- outpaths={},
- ):
- self.targets: Dict = targets
- self.parameters: Dict = parameters
- self.outpaths: Dict = outpaths
-
- def __getitem__(self, item):
- return getattr(self, item)
-
- @classmethod
- def default(cls, kind=None):
- if kind == "defaults" or kind == None:
- return cls(
- targets={
- "prepost": {
- "merger": "/extraction/general/None/area",
- "picker": ["/extraction/general/None/area"],
- },
- "processes": (
- (
- "bud_metric",
- [
- "/extraction/general/None/volume",
- "/extraction/em_ratio/np_max/mean",
- "/extraction/em_ratio/np_max/median",
- ],
- ),
- (
- "dsignal",
- [
- "/extraction/general/None/volume",
- "/extraction/em_ratio/np_max/mean",
- "/extraction/em_ratio/np_max/median",
- "/extraction/em_ratio_bgsub/np_max/mean",
- "/extraction/em_ratio_bgsub/np_max/median",
- "/postprocessing/bud_metric/extraction_general_None_volume",
- "/postprocessing/bud_metric/extraction_em_ratio_np_max_mean",
- "/postprocessing/bud_metric/extraction_em_ratio_np_max_median",
- ],
- ),
- (
- "aggregate",
- [
- [
- "/extraction/general/None/volume",
- "/extraction/em_ratio/np_max/mean",
- "/extraction/em_ratio/np_max/median",
- "/extraction/em_ratio_bgsub/np_max/mean",
- "/extraction/em_ratio_bgsub/np_max/median",
- "/extraction/gsum/np_max/median",
- "/extraction/gsum/np_max/mean",
- "postprocessing/bud_metric/extraction_general_None_volume",
- "postprocessing/bud_metric/extraction_em_ratio_np_max_mean",
- "postprocessing/bud_metric/extraction_em_ratio_np_max_median",
- "postprocessing/dsignal/extraction_general_None_volume",
- "postprocessing/dsignal/postprocessing_bud_metric_extraction_general_None_volume",
- "postprocessing/dsignal/postprocessing_bud_metric_extraction_em_ratio_np_max_median",
- "postprocessing/dsignal/postprocessing_bud_metric_extraction_em_ratio_np_max_mean",
- ]
- ],
- ),
- # "savgol": ["/extraction/general/None/area"],
- ),
- },
- parameters={
- "prepost": {
- "merger": mergerParameters.default(),
- "picker": pickerParameters.default(),
- }
- },
- outpaths={"aggregate": "/postprocessing/experiment_wide/aggregated/"},
- )
-
- def to_dict(self):
- return {k: _if_dict(v) for k, v in self.__dict__.items()}
-
-
-class PostProcessor:
- def __init__(self, filename, parameters):
- self.parameters = parameters
- self._filename = filename
- self._signal = Signal(filename)
- self._writer = Writer(filename)
-
- # self.outpaths = parameters["outpaths"]
- self.merger = merger(parameters["parameters"]["prepost"]["merger"])
-
- self.picker = picker(
- parameters=parameters["parameters"]["prepost"]["picker"],
- cells=Cells.from_source(filename),
- )
- self.classfun = {
- process: self.get_process(process)
- for process, _ in parameters["targets"]["processes"]
- }
- self.parameters_classfun = {
- process: self.get_parameters(process)
- for process, _ in parameters["targets"]["processes"]
- }
- self.targets = parameters["targets"]
-
- @staticmethod
- def get_process(process):
- """
- Dynamically import a process class from the 'processes' folder.
- Assumes process filename and class name are the same
- """
- return locate("postprocessor.core.processes." + process + "." + process)
-
- @staticmethod
- def get_parameters(process):
- """
- Dynamically import a process class from the 'processes' folder.
- Assumes process filename and class name are the same
- """
- return locate(
- "postprocessor.core.processes." + process + "." + process + "Parameters"
- )
-
- def run_prepost(self):
- """Important processes run before normal post-processing ones"""
-
- merge_events = self.merger.run(self._signal[self.targets["prepost"]["merger"]])
-
- with h5py.File(self._filename, "r") as f:
- prev_idchanges = self._signal.get_merges()
-
- changes_history = list(prev_idchanges) + [np.array(x) for x in merge_events]
- self._writer.write("modifiers/merges", data=changes_history)
-
- with h5py.File(self._filename, "a") as f: # TODO Remove this once done tweaking
- if "modifiers/picks" in f:
- del f["modifiers/picks"]
-
- indices = self.picker.run(self._signal[self.targets["prepost"]["picker"][0]])
- from collections import Counter
-
- mothers, daughters = np.array(self.picker.mothers), np.array(
- self.picker.daughters
- )
- self.tmp = [y for y in Counter([tuple(x) for x in mothers]).items() if y[1] > 2]
- self._writer.write(
- "postprocessing/lineage",
- data=pd.MultiIndex.from_arrays(
- np.append(mothers, daughters[:, 1].reshape(-1, 1), axis=1).T,
- names=["trap", "mother_label", "daughter_label"],
- ),
- overwrite="overwrite",
- )
-
- # apply merge to mother-daughter
- moset = set([tuple(x) for x in mothers])
- daset = set([tuple(x) for x in daughters])
- picked_set = set([tuple(x) for x in indices])
- with h5py.File(self._filename, "a") as f:
- merge_events = f["modifiers/merges"][()]
- merged_moda = set([tuple(x) for x in merge_events[:, 0, :]]).intersection(
- set([*moset, *daset, *picked_set])
- )
- search = lambda a, b: np.where(
- np.in1d(
- np.ravel_multi_index(a.T, a.max(0) + 1),
- np.ravel_multi_index(b.T, a.max(0) + 1),
- )
- )
-
- for target, source in merge_events:
- if (
- tuple(source) in moset
- ): # update mother to lowest positive index among the two
- mother_ids = search(mothers, source)
- mothers[mother_ids] = (
- target[0],
- self.pick_mother(mothers[mother_ids][0][1], target[1]),
- )
- if tuple(source) in daset:
- daughters[search(daughters, source)] = target
- if tuple(source) in picked_set:
- indices[search(indices, source)] = target
-
- self._writer.write(
- "postprocessing/lineage_merged",
- data=pd.MultiIndex.from_arrays(
- np.append(mothers, daughters[:, 1].reshape(-1, 1), axis=1).T,
- names=["trap", "mother_label", "daughter_label"],
- ),
- overwrite="overwrite",
- )
-
- self._writer.write(
- "modifiers/picks",
- data=pd.MultiIndex.from_arrays(
- indices.T,
- names=["trap", "cell_label"],
- ),
- overwrite="overwrite",
- )
-
- @staticmethod
- def pick_mother(a, b):
- """Update the mother id following this priorities:
-
- The mother has a lower id
- """
- x = max(a, b)
- if min([a, b]):
- x = [a, b][np.argmin([a, b])]
- return x
-
- def run(self):
- self.run_prepost()
-
- for process, datasets in tqdm(self.targets["processes"]):
- if process in self.parameters["parameters"].get(
- "processes", {}
- ): # If we assigned parameters
- parameters = self.parameters_classfun[process](self.parameters[process])
-
- else:
- parameters = self.parameters_classfun[process].default()
-
- loaded_process = self.classfun[process](parameters)
- for dataset in datasets:
- # print("Processing", process, "for", dataset)
-
- if isinstance(dataset, list): # multisignal process
- signal = [self._signal[d] for d in dataset]
- elif isinstance(dataset, str):
- signal = self._signal[dataset]
- else:
- raise ("Incorrect dataset")
-
- result = loaded_process.run(signal)
-
- if process in self.parameters.to_dict()["outpaths"]:
- outpath = self.parameters.to_dict()["outpaths"][process]
- elif isinstance(dataset, list):
- # If no outpath defined, place the result in the minimum common
- # branch of all signals used
- prefix = "".join(
- prefix + c[0]
- for c in takewhile(
- lambda x: all(x[0] == y for y in x), zip(*dataset)
- )
- )
- outpath = (
- prefix
- + "_".join( # TODO check that it always finishes in '/'
- [d[len(prefix) :].replace("/", "_") for d in dataset]
- )
- )
- elif isinstance(dataset, str):
- outpath = dataset[1:].replace("/", "_")
- else:
- raise ("Outpath not defined", type(dataset))
-
- if process not in self.parameters.to_dict()["outpaths"]:
- outpath = "/postprocessing/" + process + "/" + outpath
-
- if isinstance(result, dict): # Multiple Signals as output
- for k, v in result:
- self.write_result(
- outpath + f"/{k}",
- v,
- metadata={},
- )
- else:
- self.write_result(
- outpath,
- result,
- metadata={},
- )
-
- def write_result(
- self, path: str, result: Union[List, pd.DataFrame, np.ndarray], metadata: Dict
- ):
- self._writer.write(path, result, meta=metadata)
-
-
-def _if_dict(item):
- if hasattr(item, "to_dict"):
- item = item.to_dict()
- return item
--
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