From b905961bfbd803b8290ca164a10169c1d88d94d4 Mon Sep 17 00:00:00 2001
From: pswain <peter.swain@ed.ac.uk>
Date: Fri, 17 May 2024 14:39:49 +0100
Subject: [PATCH] change: updated plot_kymograph for new sorting
---
examples/plot_kymograph.py | 33 +++++++++++++++++++++++++--------
src/wela/sorting.py | 10 ++++++----
2 files changed, 31 insertions(+), 12 deletions(-)
diff --git a/examples/plot_kymograph.py b/examples/plot_kymograph.py
index 0a31406..e32cb7d 100644
--- a/examples/plot_kymograph.py
+++ b/examples/plot_kymograph.py
@@ -1,25 +1,42 @@
import matplotlib.cm
+import numpy as np
from wela.figs2pdf import figs2pdf
-from wela.dataloader import dataloader, sort_df
+from wela.dataloader import dataloader
+from wela.sorting import sort_by_maximum
+
from wela.plotting import kymograph, plot_lineage
-datasets = ["Pdr5_3_11_22", "Pdr5_flc_10ugml_1676"]
+datasets = ["Pdr5_3_11_22"]
for dataname in datasets:
+ # load data
dl = dataloader(".", "/Users/pswain/wip/tsv_data/pdr_with_gr/")
- dl.load(dataname, use_tsv=True)
- sdf = sort_df(dl.df, "median_GFP")
-
+ dl.load(dataname, use_tsv=True, hours=False)
+ dl.df.time = dl.df.time / 60
title = dataname.split(".")[0]
- kymograph(sdf, hue="buddings", title=title)
- kymograph(sdf, hue="median_GFP", title=title)
+ # although only one group for this data
+ groups = dl.df.group.unique()
+ # standard kymographs
+ kymograph(dl.df, hue="buddings", title=title)
+ kymograph(dl.df, hue="median_GFP", title=title, buddings=True)
kymograph(
- sdf,
+ dl.df,
hue="bud_growth_rate",
title=title,
vmin=-50,
vmax=50,
cmap=matplotlib.cm.Spectral,
)
+ # sorted kymograph
+ t, data = dl.get_time_series("median_GFP", group=groups[0])
+ sort_order = sort_by_maximum(data[:, t < 5], np.nanmean)
+ kymograph(
+ dl.df,
+ "median_GFP",
+ group=groups[0],
+ buddings=True,
+ sort_order=sort_order,
+ title=title,
+ )
if False:
diff --git a/src/wela/sorting.py b/src/wela/sorting.py
index 11694aa..a44a7de 100644
--- a/src/wela/sorting.py
+++ b/src/wela/sorting.py
@@ -4,14 +4,16 @@ import numpy as np
def sort_by_budding(buddings, bud_number=0):
"""Return indices for each cell when a particular budding occurs."""
bud_indices = np.transpose(np.nonzero(buddings))
- sort_order = []
+ particular_bud_indices = []
for cell_i in range(buddings.shape[0]):
+ # find particular bud for this cell
cell_buds = bud_indices[bud_indices[:, 0] == cell_i, :]
if bud_number < cell_buds.shape[0]:
- sort_order.append(cell_buds[bud_number][1])
+ particular_bud_indices.append(cell_buds[bud_number][1])
else:
- sort_order.append(np.nan)
- sort_order = np.argsort(sort_order)
+ particular_bud_indices.append(np.nan)
+ # arrange by earliest bud
+ sort_order = np.argsort(particular_bud_indices)
return sort_order
--
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