diff --git a/processes/align.py b/old_data_analysis/align.py
similarity index 100%
rename from processes/align.py
rename to old_data_analysis/align.py
diff --git a/processes/autoreg.py b/old_data_analysis/autoreg.py
similarity index 100%
rename from processes/autoreg.py
rename to old_data_analysis/autoreg.py
diff --git a/processes/crosscorr.py b/old_data_analysis/crosscorr.py
similarity index 100%
rename from processes/crosscorr.py
rename to old_data_analysis/crosscorr.py
diff --git a/processes/fft.py b/old_data_analysis/fft.py
similarity index 100%
rename from processes/fft.py
rename to old_data_analysis/fft.py
diff --git a/processes/fft_function.py b/old_data_analysis/fft_function.py
similarity index 100%
rename from processes/fft_function.py
rename to old_data_analysis/fft_function.py
diff --git a/processes/findpeaks.py b/old_data_analysis/findpeaks.py
similarity index 100%
rename from processes/findpeaks.py
rename to old_data_analysis/findpeaks.py
diff --git a/processes/standardscaler.py b/old_data_analysis/standardscaler.py
similarity index 100%
rename from processes/standardscaler.py
rename to old_data_analysis/standardscaler.py
diff --git a/routines/__init__.py b/old_plotting/__init__.py
similarity index 100%
rename from routines/__init__.py
rename to old_plotting/__init__.py
diff --git a/routines/boxplot.py b/old_plotting/boxplot.py
similarity index 100%
rename from routines/boxplot.py
rename to old_plotting/boxplot.py
diff --git a/routines/heatmap.py b/old_plotting/heatmap.py
similarity index 100%
rename from routines/heatmap.py
rename to old_plotting/heatmap.py
diff --git a/routines/histogram.py b/old_plotting/histogram.py
similarity index 100%
rename from routines/histogram.py
rename to old_plotting/histogram.py
diff --git a/routines/mean_plot.py b/old_plotting/mean_plot.py
similarity index 100%
rename from routines/mean_plot.py
rename to old_plotting/mean_plot.py
diff --git a/routines/median_plot.py b/old_plotting/median_plot.py
similarity index 100%
rename from routines/median_plot.py
rename to old_plotting/median_plot.py
diff --git a/routines/plot_utils.py b/old_plotting/plot_utils.py
similarity index 100%
rename from routines/plot_utils.py
rename to old_plotting/plot_utils.py
diff --git a/routines/plottingabc.py b/old_plotting/plottingabc.py
similarity index 100%
rename from routines/plottingabc.py
rename to old_plotting/plottingabc.py
diff --git a/routines/single_birth_plot.py b/old_plotting/single_birth_plot.py
similarity index 100%
rename from routines/single_birth_plot.py
rename to old_plotting/single_birth_plot.py
diff --git a/routines/single_plot.py b/old_plotting/single_plot.py
similarity index 100%
rename from routines/single_plot.py
rename to old_plotting/single_plot.py
diff --git a/plotting.py b/plotting.py
index db2a7d7313a54f71893bebcd4ecb3e42ebc88202..a229e308a8e8181c466002974e66e5f6dd35fd55 100644
--- a/plotting.py
+++ b/plotting.py
@@ -4,6 +4,7 @@ import matplotlib.cm
import matplotlib.pylab as plt
import numpy as np
import numpy.matlib
+from butterfilter import butterfilter
def kymograph(
@@ -536,6 +537,8 @@ def bud_to_bud_plot(
nbins=None,
return_signal=False,
df=None,
+ title=None,
+ filter=False,
):
"""
Plot the median and percentiles of a signal between consecutive buddings.
@@ -565,6 +568,10 @@ def bud_to_bud_plot(
bins.
df: pd.DataFrame, optional
If passed, use this data rather than dataloader's.
+ title: str, optional
+ Title for plot.
+ filter: boolean
+ If True, apply a butter filter to each time series.
Example
-------
@@ -577,6 +584,8 @@ def bud_to_bud_plot(
else:
t, signal_data = dl.get_time_series(signal, group=group, df=df)
t, buddings = dl.get_time_series("buddings", group=group, df=df)
+ if filter:
+ signal_data = butterfilter(signal_data)
if np.max(t) > 48:
# convert to hours
t = t / 60
@@ -590,43 +599,47 @@ def bud_to_bud_plot(
past_buddings = np.nonzero(buddings[i, :][:tpt_i])[0]
if np.any(past_buddings):
start_tpt_i = past_buddings[-1]
- local_signals.append(
- signal_data[i, start_tpt_i : end_tpt_i + 1]
- )
- local_times.append(t[start_tpt_i : end_tpt_i + 1])
- # find bins for normalised time, between 0 and 1
- nbins = int(np.median([len(local_time) for local_time in local_times]))
- ntbins = np.linspace(0, 1, nbins)
- # interpolate each local signal to make a new signal
- new_signal = np.nan * np.ones((len(local_signals), nbins))
- for i in range(len(local_signals)):
- s = local_signals[i]
- # normalise time between 0 and 1
- nt = local_times[i] - local_times[i][0]
- nt /= nt[-1]
- # interpolate into the bins
- new_signal[i, :] = np.interp(
- ntbins,
- nt[~np.isnan(s)],
- s[~np.isnan(s)],
- left=np.nan,
- right=np.nan,
- )
- # plot median and percentiles
- plt.figure()
- plt.plot(ntbins, np.nanmedian(new_signal, axis=0), f"{colour}.-")
- for lower, upper in zip([45, 40, 35], [55, 60, 65]):
- plt.fill_between(
- ntbins,
- np.nanpercentile(new_signal, lower, axis=0),
- np.nanpercentile(new_signal, upper, axis=0),
- alpha=0.06,
- color=colour,
- )
- plt.xlabel("position between budding events")
- plt.ylabel(signal.replace("_", " "))
- plt.grid()
- plt.title(f"{dl.dataname}: t={tpt}")
- plt.show(block=False)
- if return_signal:
- return new_signal
+ local_data = signal_data[i, start_tpt_i : end_tpt_i + 1]
+ if ~np.all(np.isnan(local_data)):
+ local_signals.append(local_data)
+ local_times.append(t[start_tpt_i : end_tpt_i + 1])
+ if local_times:
+ # find bins for normalised time, between 0 and 1
+ nbins = int(np.median([len(local_time) for local_time in local_times]))
+ ntbins = np.linspace(0, 1, nbins)
+ # interpolate each local signal to make a new signal
+ new_signal = np.nan * np.ones((len(local_signals), nbins))
+ for i in range(len(local_signals)):
+ s = local_signals[i]
+ # normalise time between 0 and 1
+ nt = local_times[i] - local_times[i][0]
+ nt /= nt[-1]
+ # interpolate into the bins
+ new_signal[i, :] = np.interp(
+ ntbins,
+ nt[~np.isnan(s)],
+ s[~np.isnan(s)],
+ left=np.nan,
+ right=np.nan,
+ )
+ # plot median and percentiles
+ plt.figure()
+ plt.plot(ntbins, np.nanmedian(new_signal, axis=0), f"{colour}.-")
+ for lower, upper in zip([45, 40, 35], [55, 60, 65]):
+ plt.fill_between(
+ ntbins,
+ np.nanpercentile(new_signal, lower, axis=0),
+ np.nanpercentile(new_signal, upper, axis=0),
+ alpha=0.06,
+ color=colour,
+ )
+ plt.xlabel("position between budding events")
+ plt.ylabel(signal.replace("_", " "))
+ plt.grid()
+ if title is None:
+ plt.title(f"{dl.dataname}: t={tpt}")
+ else:
+ plt.title(title)
+ plt.show(block=False)
+ if return_signal:
+ return new_signal