From 8e18a84404f633db867dba88aa61274704ed7b88 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Al=C3=A1n=20Mu=C3=B1oz?= <amuoz@ed.ac.uk>
Date: Thu, 14 Jul 2022 11:56:07 +0100
Subject: [PATCH] style(all): limit docstring length
---
core/functions/tracks.py | 23 ++--
core/processes/births.py | 6 +-
core/processes/knngraph.py | 5 +-
core/processes/leiden.py | 16 ++-
core/processes/peaks.py | 41 ------
core/processes/picker.py | 70 ++++++----
core/processes/savgol.py | 65 ++++++---
core/processes/standardscaler.py | 50 -------
examples/basic_processes.py | 15 ---
examples/group.py | 24 ----
examples/testing.py | 220 -------------------------------
grouper.py | 39 ++----
routines/heatmap.py | 4 +-
routines/mean_plot.py | 2 +-
routines/median_plot.py | 7 +-
15 files changed, 135 insertions(+), 452 deletions(-)
delete mode 100644 core/processes/peaks.py
delete mode 100644 core/processes/standardscaler.py
delete mode 100644 examples/basic_processes.py
delete mode 100644 examples/group.py
delete mode 100644 examples/testing.py
diff --git a/core/functions/tracks.py b/core/functions/tracks.py
index b5481329..fd91bf5c 100644
--- a/core/functions/tracks.py
+++ b/core/functions/tracks.py
@@ -245,13 +245,15 @@ def get_joinable(tracks, smooth=False, tol=0.1, window=5, degree=3) -> dict:
)
# 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
- ]
+ def idx_to_edge(preposts):
+ return [
+ (
+ [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
+ ]
+
# idx_to_means = lambda preposts: [
# (
# [get_means(smoothed_tracks.loc[pre], -window) for pre in pres],
@@ -311,7 +313,8 @@ def get_joinable(tracks, smooth=False, tol=0.1, window=5, degree=3) -> dict:
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 get_val(x, n):
+ return x[~np.isnan(x)][n] if len(x[~np.isnan(x)]) else np.nan
def get_means(x, i):
@@ -357,11 +360,11 @@ def localid_to_idx(local_ids, contig_trap):
def get_vec_closest_pairs(lst: List, **kwargs):
- return [get_closest_pairs(*l, **kwargs) for l in lst]
+ return [get_closest_pairs(*sublist, **kwargs) for sublist in lst]
def get_dMetric_wrap(lst: List, **kwargs):
- return [get_dMetric(*l, **kwargs) for l in lst]
+ return [get_dMetric(*sublist, **kwargs) for sublist in lst]
def solve_matrices_wrap(dMetric: List, edges: List, **kwargs):
diff --git a/core/processes/births.py b/core/processes/births.py
index 84a9e50c..9059b0bf 100644
--- a/core/processes/births.py
+++ b/core/processes/births.py
@@ -1,5 +1,6 @@
#!/usr/bin/env python3
+import typing as t
from itertools import product
import numpy as np
@@ -47,10 +48,9 @@ class births(LineageProcess):
if lineage is None:
lineage = self.lineage
- def fvi(signal):
- return signal.apply(lambda x: x.first_valid_index(), axis=1)
+ fvi = signal.apply(lambda x: x.first_valid_index(), axis=1)
- traps_mothers = {
+ traps_mothers: t.Dict[tuple, list] = {
tuple(mo): [] for mo in lineage[:, :2] if tuple(mo) in signal.index
}
for trap, mother, daughter in lineage:
diff --git a/core/processes/knngraph.py b/core/processes/knngraph.py
index d9532e0e..c3a165ab 100644
--- a/core/processes/knngraph.py
+++ b/core/processes/knngraph.py
@@ -1,5 +1,6 @@
#!/usr/bin/env python3
+import numpy as np
import pandas as pd
from sklearn.metrics.pairwise import euclidean_distances
import igraph as ig
@@ -41,7 +42,9 @@ class knngraph(PostProcessABC):
Feature matrix.
"""
distance_matrix = euclidean_distances(signal)
- distance_matrix_pruned = graph_prune(distance_matrix, self.n_neighbours)
+ distance_matrix_pruned = graph_prune(
+ distance_matrix, self.n_neighbours
+ )
graph = ig.Graph.Weighted_Adjacency(
distance_matrix_pruned.tolist(), mode="undirected"
)
diff --git a/core/processes/leiden.py b/core/processes/leiden.py
index 131985d0..9cc31050 100644
--- a/core/processes/leiden.py
+++ b/core/processes/leiden.py
@@ -2,7 +2,6 @@ from itertools import product
import numpy as np
import pandas as pd
-from itertools import product
import igraph as ig
import leidenalg
@@ -28,15 +27,22 @@ class leiden(PostProcessABC):
def run(self, features: pd.DataFrame):
# Generate euclidean distance matrix
- distances = np.linalg.norm(features.values - features.values[:, None], axis=2)
+ distances = np.linalg.norm(
+ features.values - features.values[:, None], axis=2
+ )
ind = [
- "_".join([str(y) for y in x[1:]]) for x in features.index.to_flat_index()
+ "_".join([str(y) for y in x[1:]])
+ for x in features.index.to_flat_index()
]
source, target = zip(*product(ind, ind))
df = pd.DataFrame(
- {"source": source, "target": target, "distance": distances.flatten()}
+ {
+ "source": source,
+ "target": target,
+ "distance": distances.flatten(),
+ }
)
df = df.loc[df["source"] != df["target"]]
g = ig.Graph.DataFrame(df, directed=False)
- part = leidenalg.find_partition(g, leidenalg.ModularityVertexPartition)
+ return leidenalg.find_partition(g, leidenalg.ModularityVertexPartition)
diff --git a/core/processes/peaks.py b/core/processes/peaks.py
deleted file mode 100644
index 99dff4eb..00000000
--- a/core/processes/peaks.py
+++ /dev/null
@@ -1,41 +0,0 @@
-from scipy.signal import argrelmax, argrelmin
-
-from agora.abc import ParametersABC
-from postprocessor.core.abc import PostProcessABC
-
-
-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.
- """
-
- _defaults = {"type": "minima", "order": 3}
-
-
-class Peaks(PostProcessABC):
- """
- 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/core/processes/picker.py b/core/processes/picker.py
index 14b3abe8..6811231c 100644
--- a/core/processes/picker.py
+++ b/core/processes/picker.py
@@ -71,7 +71,11 @@ class picker(PostProcessABC):
cell_gid = np.unique(idlist, axis=0)
last_lin_preds = [
- find_1st(((cell_label[::-1] == lbl) & (trap[::-1] == tr)), True, cmp_equal)
+ find_1st(
+ ((cell_label[::-1] == lbl) & (trap[::-1] == tr)),
+ True,
+ cmp_equal,
+ )
for tr, lbl in cell_gid
]
mother_assign_sorted = ma[::-1][last_lin_preds]
@@ -93,12 +97,18 @@ class picker(PostProcessABC):
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),
+ def search(a, b):
+ return 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":
@@ -109,7 +119,9 @@ class picker(PostProcessABC):
[
tuple(x)
for m in present_mothers
- for x in np.array(self.daughters)[search(self.mothers, m)]
+ for x in np.array(self.daughters)[
+ search(self.mothers, m)
+ ]
]
)
@@ -120,7 +132,9 @@ class picker(PostProcessABC):
[
tuple(x)
for d in present_daughters
- for x in np.array(self.mothers)[search(self.daughters, d)]
+ for x in np.array(self.mothers)[
+ search(self.daughters, d)
+ ]
]
)
elif how == "full_families":
@@ -171,13 +185,6 @@ class picker(PostProcessABC):
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))
@@ -197,6 +204,7 @@ class picker(PostProcessABC):
indices = set(signals.index)
self.mothers, self.daughters = self.get_mothers_daughters()
for alg, op, *params in self.sequence:
+ new_indices = tuple()
if indices:
if alg == "lineage":
param1 = params[0]
@@ -209,7 +217,7 @@ class picker(PostProcessABC):
signals.loc[list(indices)], param1, param2
)
- if op is "union":
+ if op == "union":
# new_indices = new_indices.intersection(set(signals.index))
new_indices = indices.union(new_indices)
@@ -228,7 +236,8 @@ class picker(PostProcessABC):
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,
+ "nonstoply_present": lambda s, thresh: s.apply(thresh, axis=1)
+ > thresh,
"growing": lambda s, thresh: s.diff(axis=1).sum(axis=1) > thresh,
"mb_guess": lambda s, p1, p2: self.mb_guess_wrap(s, p1, p2)
# "quantile": [np.quantile(signals.values[signals.notna()], threshold)],
@@ -258,16 +267,15 @@ class picker(PostProcessABC):
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.first_valid_index() <= df.loc[mother_id].last_valid_index(),
- axis=1,
- )
- ]
- )
+ nomother = nomother.loc[ # Clean short-lived cells outside our mother cell's timepoints
+ nomother.apply(
+ lambda x: x.first_valid_index()
+ >= df.loc[mother_id].first_valid_index()
+ and x.first_valid_index()
+ <= df.loc[mother_id].last_valid_index(),
+ axis=1,
+ )
+ ]
score = -nomother.apply( # Get slope of candidate daughters
lambda x: self.get_slope(x.dropna()), axis=1
@@ -299,8 +307,12 @@ class picker(PostProcessABC):
bud_candidates = pd.DataFrame()
else:
# Find the set with the highest number of growing cells and highest avg growth rate for this #
- mivs = self.max_ind_vertex_sets(cols_sorted.values, min_budgrowth_t)
- best_set = list(mivs[np.argmin([sum(score.iloc[list(s)]) for s in mivs])])
+ mivs = self.max_ind_vertex_sets(
+ cols_sorted.values, min_budgrowth_t
+ )
+ best_set = list(
+ mivs[np.argmin([sum(score.iloc[list(s)]) for s in mivs])]
+ )
best_indices = cols_sorted.index[best_set]
start = start.loc[best_indices]
diff --git a/core/processes/savgol.py b/core/processes/savgol.py
index 2584c414..40cbf3d6 100644
--- a/core/processes/savgol.py
+++ b/core/processes/savgol.py
@@ -31,16 +31,23 @@ class savgol(PostProcessABC):
def run(self, signal: pd.DataFrame):
try:
post_savgol = pd.DataFrame(
- savgol_filter(signal, self.parameters.window, self.parameters.polynom),
+ savgol_filter(
+ signal, self.parameters.window, self.parameters.polynom
+ ),
index=signal.index,
columns=signal.columns,
)
except Exception as e:
print(e)
- savgol_on_srs = lambda x: self.non_uniform_savgol(
- x.index, x.values, self.parameters.window, self.parameters.polynom
- )
+ def savgol_on_srs(x):
+ return self.non_uniform_savgol(
+ x.index,
+ x.values,
+ self.parameters.window,
+ self.parameters.polynom,
+ )
+
post_savgol = signal.apply(savgol_on_srs, 1).apply(pd.Series)
return post_savgol
@@ -72,23 +79,34 @@ class savgol(PostProcessABC):
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"')
+ _raiseif(
+ len(x) != len(y),
+ '"x" and "y" must be of the same size',
+ ValueError,
+ )
+ _raiseif(
+ len(x) < window,
+ "The data size must be larger than the window size",
+ ValueError,
+ )
+ _raiseif(
+ not isinstance(window, int),
+ '"window" must be an integer',
+ TypeError,
+ )
+ _raiseif(window % 2, 'The "window" must be an odd integer', ValueError)
+
+ _raiseif(
+ not isinstance(polynom, int),
+ '"polynom" must be an integer',
+ TypeError,
+ )
+
+ _raiseif(
+ polynom >= window,
+ '"polynom" must be less than "window"',
+ ValueError,
+ )
half_window = window // 2
polynom += 1
@@ -156,3 +174,8 @@ class savgol(PostProcessABC):
x_i *= x[i] - x[-half_window - 1]
return y_smoothed
+
+
+def _raiseif(cond, msg="", exc=AssertionError):
+ if cond:
+ raise exc(msg)
diff --git a/core/processes/standardscaler.py b/core/processes/standardscaler.py
deleted file mode 100644
index be2b3e73..00000000
--- a/core/processes/standardscaler.py
+++ /dev/null
@@ -1,50 +0,0 @@
-#!/usr/bin/env python3
-
-import pandas as pd
-from sklearn.preprocessing import StandardScaler
-
-from agora.abc import ParametersABC
-from postprocessor.core.abc import PostProcessABC
-
-
-class standardscalerParameters(ParametersABC):
- """
- Parameters for the 'scale' process.
- """
-
- _defaults = {}
-
-
-class standardscaler(PostProcessABC):
- """
- Process to scale a DataFrame of a signal using the standard scaler.
-
- Methods
- -------
- run(signal: pd.DataFrame)
- Scale values in a dataframe of time series.
- """
-
- def __init__(self, parameters: standardscalerParameters):
- super().__init__(parameters)
-
- def run(self, signal: pd.DataFrame):
- """Scale values in a dataframe of time series.
-
- Scale values in a dataframe of time series. This function is effectively a
- wrapper for sklearn.preprocessing.StandardScaler.
-
- Parameters
- ----------
- signal : pd.DataFrame
- Time series, with rows indicating individual time series (e.g. from
- each cell), and columns indicating time points.
- """
- signal_array = signal.to_numpy()
- scaler = StandardScaler().fit(signal_array.transpose())
- signal_scaled_array = scaler.transform(signal_array.transpose())
- signal_scaled_array = signal_scaled_array.transpose()
- signal_scaled = pd.DataFrame(
- signal_scaled_array, columns=signal.columns, index=signal.index
- )
- return signal_scaled
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 990f5616..00000000
--- a/examples/group.py
+++ /dev/null
@@ -1,24 +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/grouper.py b/grouper.py
index a3b947b3..982522b7 100644
--- a/grouper.py
+++ b/grouper.py
@@ -19,9 +19,7 @@ from agora.io.signal import Signal
class Grouper(ABC):
- """
- Base grouper class
- """
+ """Base grouper class."""
files = []
@@ -103,8 +101,6 @@ class Grouper(ABC):
Examples
--------
FIXME: Add docs.
-
-
"""
if not path.startswith("/"):
path = "/" + path
@@ -168,15 +164,13 @@ class Grouper(ABC):
class MetaGrouper(Grouper):
- """Group positions using metadata's 'group' number"""
+ """Group positions using metadata's 'group' number."""
pass
class NameGrouper(Grouper):
- """
- Group a set of positions using a subsection of the name
- """
+ """Group a set of positions using a subsection of the name."""
def __init__(self, dir, by=None):
super().__init__(dir=dir)
@@ -216,10 +210,8 @@ class NameGrouper(Grouper):
class phGrouper(NameGrouper):
- """
- Grouper for pH calibration experiments where all surveyed media pH values
- are within a single experiment.
- """
+ """Grouper for pH calibration experiments where all surveyed media pH
+ values are within a single experiment."""
def __init__(self, dir, by=(3, 7)):
super().__init__(dir=dir, by=by)
@@ -235,9 +227,7 @@ class phGrouper(NameGrouper):
return float(group_name.replace("_", "."))
def aggregate_multisignals(self, paths: list) -> pd.DataFrame:
- """
- Accumulate multiple signals
- """
+ """Accumulate multiple signals."""
aggregated = pd.concat(
[
@@ -269,9 +259,8 @@ def concat_signal_ind(
mode: str = "retained",
**kwargs,
) -> pd.DataFrame:
- """
- Core function that handles retrieval of an individual signal, applies filtering if requested and adjusts indices.
- """
+ """Core function that handles retrieval of an individual signal, applies
+ filtering if requested and adjusts indices."""
if mode == "retained":
combined = signal.retained(path, **kwargs)
if mode == "mothers":
@@ -291,9 +280,8 @@ def concat_signal_ind(
class MultiGrouper:
- """
- Wrap results from multiple experiments stored as folders inside a folder.
- """
+ """Wrap results from multiple experiments stored as folders inside a
+ folder."""
def __init__(self, source: Union[str, list]):
if isinstance(source, str):
@@ -312,9 +300,8 @@ class MultiGrouper:
@property
def sigtable(self) -> pd.DataFrame:
- """
- Generate a matrix containing the number of datasets for each signal and experiment
- """
+ """Generate a matrix containing the number of datasets for each signal
+ and experiment."""
def regex_cleanup(x):
x = re.sub(r"\/extraction\/", "", x)
@@ -360,7 +347,6 @@ class MultiGrouper:
Examples
--------
FIXME: Add docs.
-
"""
ax = sns.heatmap(self.sigtable, cmap="viridis")
ax.set_xticklabels(
@@ -393,7 +379,6 @@ class MultiGrouper:
Examples
--------
FIXME: Add docs.
-
"""
if isinstance(signals, str):
signals = [signals]
diff --git a/routines/heatmap.py b/routines/heatmap.py
index d2a8927c..e3616a47 100644
--- a/routines/heatmap.py
+++ b/routines/heatmap.py
@@ -91,12 +91,12 @@ class _HeatmapPlotter(BasePlotter):
births_array == 0, births_array
)
# Overlay
- births_heatmap = ax.imshow(
+ ax.imshow(
births_heatmap_mask,
interpolation="none",
)
# Draw colour bar
- colorbar = ax.figure.colorbar(
+ ax.figure.colorbar(
mappable=trace_heatmap, cax=cax, ax=ax, label=self.colorbarlabel
)
diff --git a/routines/mean_plot.py b/routines/mean_plot.py
index e2594483..1d4b13f9 100644
--- a/routines/mean_plot.py
+++ b/routines/mean_plot.py
@@ -70,7 +70,7 @@ def mean_plot(
error_color="lightblue",
mean_linestyle="-",
xlabel="Time (min)",
- ylabel=f"Normalised flavin fluorescence (AU)",
+ ylabel="Normalised flavin fluorescence (AU)",
plot_title="",
ax=None,
):
diff --git a/routines/median_plot.py b/routines/median_plot.py
index 573263b3..6e5a9f66 100644
--- a/routines/median_plot.py
+++ b/routines/median_plot.py
@@ -3,6 +3,7 @@
import numpy as np
import matplotlib.pyplot as plt
+
from postprocessor.routines.plottingabc import BasePlotter
@@ -71,11 +72,12 @@ def median_plot(
error_color="lightblue",
median_linestyle="-",
xlabel="Time (min)",
- ylabel=f"Normalised flavin fluorescence (AU)",
+ ylabel="Normalised flavin fluorescence (AU)",
plot_title="",
ax=None,
):
- """Plot median time series of a DataFrame, with interquartile range shading.
+ """Plot median time series of a DataFrame, with interquartile range
+ shading.
Parameters
----------
@@ -105,7 +107,6 @@ def median_plot(
Examples
--------
FIXME: Add docs.
-
"""
plotter = _MedianPlotter(
trace_df,
--
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