diff --git a/src/wela/dataloader.py b/src/wela/dataloader.py
index 9ef2ff06a799be1d845d1f05f86e4490b2dd8dc3..4a66f512e6f2a42cac7e23ccb36e1e3a9f5ec6d8 100644
--- a/src/wela/dataloader.py
+++ b/src/wela/dataloader.py
@@ -600,6 +600,7 @@ class dataloader:
duration_threshold=None,
tmin=None,
tmax=None,
+ group=None,
):
"""
Find a sub data frame of dataloader's main data frame.
@@ -617,8 +618,13 @@ class dataloader:
Only include data for times greater than tmin
tmax: float (optional)
Only include data for times less than tmax
+ group: str (optional)
+ Group to specialise to.
"""
- sdf = self.df
+ if group is None:
+ sdf = self.df
+ else:
+ sdf = self.df[self.df.group == group]
selected_ids = []
# drop signals that are all NaN
if dropna and signal:
diff --git a/src/wela/plotting.py b/src/wela/plotting.py
index d57ec6b20991f83d72ec0cfb37ed7f11319ecd3a..5c95d8ca4ec8a8e6d70b2309fdb3630c7cfdaa5a 100644
--- a/src/wela/plotting.py
+++ b/src/wela/plotting.py
@@ -1,9 +1,12 @@
+"""Plotting routines to work with dataloader."""
+
from copy import copy
import matplotlib.cm
import matplotlib.pylab as plt
import numpy as np
import numpy.matlib
+from scipy.stats import binned_statistic
try:
from sklearn.preprocessing import StandardScaler
@@ -289,7 +292,6 @@ def plot_lineage(
raise Exception("idx not part of dataframe")
if isinstance(signals, str):
signals = [signals]
- nosubplots = len(signals)
# show buddings if possible
if "buddings" in df.columns:
buddings = df[df.id == idx]["buddings"].to_numpy()
@@ -301,38 +303,34 @@ def plot_lineage(
# find time
t = df[df.id == idx]["time"].to_numpy()
# generate figure
- fig = plt.figure(figsize=figsize)
+ no_subplots = len(signals)
+ fig, ax = plt.subplots(no_subplots, 1, figsize=figsize, sharex=True)
# index for subplot
- splt = 1
if title is None:
plt.suptitle(idx)
else:
plt.suptitle(title)
# plot signals
- for signal in signals:
+ for i, signal in enumerate(signals):
if "bud_" + signal in df.columns:
bud_sig = df[df.id == idx]["bud_" + signal].to_numpy()
if signal == "growth_rate" and "growth_rate" not in df.columns:
signal = "mother_" + signal
m_sig = df[df.id == idx][signal].to_numpy()
- plt.subplot(nosubplots, 1, splt)
for start, end in zip(nb_pts, nb_pts[1:]):
# plot bud signal
- plt.plot(t[start:end], bud_sig[start:end], ".-")
+ ax[i].plot(t[start:end], bud_sig[start:end], ".-")
# plot mother signal
- plt.plot(t, m_sig, "k-")
- plt.ylabel(signal)
+ ax[i].plot(t, m_sig, "k-")
+ ax[i].set_ylabel(signal)
add_shading(shade_times, shade_colour)
- plt.grid()
- splt += 1
+ ax[i].grid(True)
else:
m_sig = df[df.id == idx][signal].to_numpy()
- plt.subplot(nosubplots, 1, splt)
- plt.plot(t, m_sig)
- plt.ylabel(signal)
+ ax[i].plot(t, m_sig)
+ ax[i].set_ylabel(signal)
add_shading(shade_times, shade_colour)
- plt.grid()
- splt += 1
+ ax[i].grid(True)
# plot buddings
if (
plot_budding_pts
@@ -341,10 +339,7 @@ def plot_lineage(
):
for bpt in b_pts:
plt.plot(t[bpt], m_sig[bpt], "k.")
- plt.xlabel("time (hours)")
- # share the x axis
- ax_list = fig.axes
- ax_list[0].get_shared_x_axes().join(ax_list[0], *ax_list)
+ ax[-1].set_xlabel("time (hours)")
if show:
plt.show(block=False)
@@ -762,3 +757,67 @@ def get_bud_to_bud_data(
local_signals.append(local_data)
local_times.append(t[start_tpt_i : end_tpt_i + 1])
return local_signals, local_times
+
+
+def plot_binned_mean(df, x_signal, y_signal, bins=10, groups=None, fmt="o-"):
+ """
+ Plot the mean of y_signal found for bins of x_signal against x_signal.
+
+ Use scipy's binned_statistic.
+
+ Parameters
+ ----------
+ df: pd.DataFrame
+ Dataframe with the data, typically dl.df.
+ x_signal: str
+ Name of the signal to bin and plot on the x-axis.
+ y_signal: str
+ Name of the signal to be averaged in bins of x_signal.
+ bins: int
+ Number of bins.
+ groups: list of str (optional)
+ Specific groups to plot.
+ fmt: str (optional)
+ Formatting for points and lines, passed to plt.errorbar.
+
+ Example
+ -------
+ >>> plot_binned_mean(dl.df, "median_GFP", "bud_growth_rate",
+ bins=10, groups=["2pc_raf", "2pc_glc"])
+ """
+ stats_dict = {}
+ if groups is None:
+ groups = df.group.unique()
+ for group in groups:
+ sdf = df[df.group == group][[x_signal, y_signal]].dropna()
+ stats = ["mean", "median", "std", "count"]
+ for stat in stats:
+ stats_dict[f"{stat}_{group}"], bin_edges, _ = binned_statistic(
+ sdf.median_GFP.values,
+ values=sdf.bud_growth_rate.values,
+ statistic=stat,
+ bins=bins,
+ )
+ stats_dict[f"stderr_{group}"] = stats_dict[f"std_{group}"] / np.sqrt(
+ stats_dict[f"count_{group}"]
+ )
+ stats_dict[f"bin_midpoints_{group}"] = np.array(
+ [
+ np.mean([bin_edges[i], bin_edges[i + 1]])
+ for i in range(len(bin_edges) - 1)
+ ]
+ )
+ # plot using errorbar
+ plt.figure()
+ for group in groups:
+ plt.errorbar(
+ stats_dict[f"bin_midpoints_{group}"],
+ stats_dict[f"mean_{group}"],
+ yerr=stats_dict[f"stderr_{group}"],
+ fmt=fmt,
+ label=group,
+ )
+ plt.xlabel(x_signal.replace("_", " "))
+ plt.ylabel(y_signal.replace("_", " "))
+ plt.legend()
+ plt.show(block=False)