def plot_mean_abs_derivative(inputfile, outputfolder, group='Group', cutoff=None, id_col='TrackID'):
"""
Plot the derivative of each spectral component of different groups over time.
Parameters
----------
inputfile : str
Path to the file with spectral data.
outputfolder : str
Directory to save the plotted derivative.
group : str, optional
Column in the input data sheet to use for grouping.
Default is 'Group'.
cutoff : int, optional
The number of degrees to display.
If None, all degrees will be displayed.
id_col : str
Column in the input data sheet to group connected time points.
Default is 'TrackID'
"""
filelib.make_folders([os.path.dirname(outputfolder)])
if not os.path.exists(inputfile[:-4] + '_mean_abs_derivative.csv'):
stat = pd.read_csv(inputfile, sep='\t', index_col=0)
if id_col == 'CellID':
stat.loc[:, 'Time'] = np.int_(np.round_(stat['Time'] / 10.)) * 10
nstat = pd.DataFrame()
if cutoff is not None:
stat = stat[stat['degree'] <= cutoff]
for gr in stat[group].unique():
substat = stat[stat[group] == gr]
for id in substat[id_col].unique():
subsubstat = substat[substat[id_col] == id]
subsubstat = subsubstat.sort_values('Time')
time_spectrum = TimeSpectrum()
for t in subsubstat['Time'].unique():
sp = Spectrum()
sp.harmonics_csv = subsubstat[subsubstat['Time'] == t]
time_spectrum.add_spectrum(sp, timepoint=t)
time_spectrum.compute_derivative()
meanderivstat = time_spectrum.mean_abs_derivative
meanderivstat['Group'] = gr
meanderivstat['TrackID'] = id
nstat = pd.concat([nstat, meanderivstat], ignore_index=True)
nstat.to_csv(inputfile[:-4] + '_mean_abs_derivative.csv', sep='\t')
nstat = pd.read_csv(inputfile[:-4] + '_mean_abs_derivative.csv', sep='\t', index_col=0)
nstat = nstat.sort_values(['harmonic', group])
plt.clf()
plt.figure(figsize=(20, 5))
sns.barplot(x='harmonic', y='absolute amplitude', data=nstat, hue=group)
plt.ylabel('Mean absolute derivative of amplitude')
labels = nstat['harmonic'].unique()
plt.xticks(np.arange(len(labels)) + 0.6, labels, rotation='vertical')
margins = {'left': 0.07, 'right': 0.98, 'top': 0.93, 'bottom': 0.25}
plt.subplots_adjust(**margins)
plt.savefig(outputfolder + 'mean_abs_derivative.png')
plt.close()
def plot_average_heatmaps(inputfile, outputfolder, **kwargs):
"""
Plot heatmaps for group-averaged SPHARM spectra.
Parameters
----------
inputfile : str
Path to the file with spectral data.
outputfolder : str
Output directory to save the heatmaps.
kwargs : key, value pairings
Arbitrary keyword arguments to pass to the Spectrum.heatmap function.
"""
filelib.make_folders([os.path.dirname(outputfolder), outputfolder + 'timepoints/'])
stat = pd.read_csv(inputfile, sep='\t', index_col=0)
stat.loc[:, 'Time'] = np.int_(np.round_(stat['Time'] / 10.)) * 10
if 'Group' not in stat.columns:
for name in stat['Name'].unique():
group = name.split('/')[0]
stat = stat.set_value(stat[stat['Name'] == name].index, 'Group', group)
data = stat.groupby(['degree', 'order', 'Group']).mean().reset_index()
for gr in data['Group'].unique():
curdata = data[data['Group'] == gr]
s = Spectrum()
s.harmonics_csv = curdata
pl = s.heatmap(title=gr + ' average', **kwargs)
pl.savefig(outputfolder + gr + '.png')
pl.clf()
# plot separate time points
stat = stat.groupby(['Time', 'Group', 'degree', 'order']).mean().reset_index()
for t in stat['Time'].unique():
for gr in stat['Group'].unique():
curdata = stat[(stat['Group'] == gr) & (stat['Time'] == t)]
if len(curdata) > 0:
s = Spectrum()
s.harmonics_csv = curdata
pl = s.heatmap(title=gr + ' average, time point ' + str(t), **kwargs)
pl.savefig(outputfolder + 'timepoints/' + gr + '_time=' + str(t) + '.png')
pl.clf()
def plot_average_frequency_heatmaps(inputfile, outputfolder, group='Group', cutoff=None,
logscale=False, id_col='TrackID'):
"""
Plot the Fourier frequencies of spectral components of different groups as a heatmap.
Parameters
----------
inputfile : str
Path to the file with spectral data.
outputfolder : str
Directory to save the plotted heat maps.
group : str, optional
Column in the input data sheet to use for grouping.
Default is 'Group'.
cutoff : int, optional
The number of degrees to display.
If None, all degrees will be displayed.
logscale : bool, optional
If True, the natural logarithm of the value will be displayed.
Default is False.
id_col : str
Column in the input data sheet to group connected time points.
Default is 'TrackID'
"""
filelib.make_folders([os.path.dirname(outputfolder)])
stat = pd.read_csv(inputfile, sep='\t', index_col=0)
stat.loc[:, 'Time'] = np.int_(np.round_(stat['Time'] / 10.)) * 10
if cutoff is not None:
stat = stat[stat['degree'] <= cutoff]
frequency_stat = pd.DataFrame()
for gr in stat[group].unique():
substat = stat[stat[group] == gr]
for id in substat[id_col].unique():
subsubstat = substat[substat[id_col] == id]
time_spectrum = TimeSpectrum()
for t in subsubstat['Time'].unique():
sp = Spectrum()
sp.harmonics_csv = subsubstat[subsubstat['Time'] == t]
time_spectrum.add_spectrum(sp, timepoint=t)
time_spectrum.fourier_analysis(value='amplitude')
time_spectrum.frequencies['Group'] = gr
frequency_stat = pd.concat([frequency_stat, time_spectrum.frequencies], ignore_index=True)
frequency_stat = frequency_stat.groupby(['Group', 'frequency', 'harmonic']).mean().reset_index()
for gr in stat[group].unique():
time_spectrum = TimeSpectrum()
time_spectrum.frequencies = frequency_stat[frequency_stat['Group'] == gr]
pl = time_spectrum.frequency_heatmap(value='amplitude', logscale=logscale)
if pl is not None:
pl.savefig(outputfolder + gr + '.png')
def plot_individual_time_heatmaps(inputfile, outputfolder, group='Group', cutoff=None,
logscale=False, id_col='TrackID'):
"""
Plot the amplitude of spectral components over time for different groups as a heatmap.
Parameters
----------
inputfile : str
Path to the file with spectral data.
outputfolder : str
Directory to save the plotted heat maps.
group : str, optional
Column in the input data sheet to use for grouping.
Default is 'Group'.
cutoff : int, optional
The number of degrees to display.
If None, all degrees will be displayed.
logscale : bool, optional
If True, the natural logarithm of the value will be displayed.
Default is False.
id_col : str
Column in the input data sheet to group connected time points.
Default is 'TrackID'
"""
filelib.make_folders([os.path.dirname(outputfolder)])
stat = pd.read_csv(inputfile, sep='\t', index_col=0)
stat['Time'] = np.int_(np.round_(stat['Time'] / 10.)) * 10
if cutoff is not None:
stat = stat[stat['degree'] <= cutoff]
for gr in stat[group].unique():
substat = stat[stat[group] == gr]
for id in substat[id_col].unique():
subsubstat = substat[substat[id_col] == id]
subsubstat = subsubstat.sort_values('Time').reset_index()
time_spectrum = TimeSpectrum()
for t in subsubstat['Time'].unique():
sp = Spectrum()
sp.harmonics_csv = subsubstat[subsubstat['Time'] == t]
time_spectrum.add_spectrum(sp, timepoint=t)
pl = time_spectrum.time_heatmap(value='amplitude', logscale=logscale)
if pl is not None:
pl.savefig(outputfolder + '_' + gr + '_' + 'track_' + str(id) + '.png')
def plot_inverse_shapes(inputfile, outputfolder, group='Group'):
"""
Plot average cells shapes obtained by inverse SPHARM.
Parameters
----------
inputfile : str
Path to the file with spectral data.
outputfolder : str
Directory to save the plotted distributions.
group : str, optional
Column in the input data sheet to use for grouping.
Default is 'Group'.
"""
filelib.make_folders([os.path.dirname(outputfolder)])
stat = pd.read_csv(inputfile, sep='\t', index_col=0)
stat['value'] = stat['real'] + stat['imag']*1j
if 'Group' not in stat.columns:
for name in stat['Name'].unique():
group = name.split('/')[0]
stat = stat.set_value(stat[stat['Name'] == name].index, 'Group', group)
data = stat.groupby(['degree', 'order', 'Group']).mean().reset_index()
groups = data[group].unique()
for gr in groups:
curdata = data[data[group] == gr]
sp = Spectrum()
sp.harmonics_csv = curdata
sp.convert_to_shtools_array()
surf = Surface()
surf.spharm = sp
maxdegree = np.max(sp.harmonics_csv['degree'])
for lmax in np.arange(5, maxdegree + 1, 5):
surf.inverse_spharm(lmax=lmax)
surf.plot_surface(points=False).save(outputfolder + '_' + gr + '_inverse_lmax=' + str(lmax) + '.png',
size=(200, 200))
surf.inverse_spharm(lmax=None)
surf.plot_surface(points=False).save(outputfolder + '_' + gr + '_inverse_full.png',
size=(200, 200))
