def plot(out,
fontsize=12,
savepath='',
width=10,
height=6,
cmap='Set1',
cii_alpha=0.05,
cii_lines='dense',
methodtype='lifeline',
title='Survival function',
full_ylim=False,
y_percentage=False):
"""Make plot.
Parameters
----------
out : dict
Results from the fit function.
fontsize : int, optional
Font size for the graph. The default is 12.
savepath : String, optional
Path to store the figure. The default is ''.
width : int, optional
Width of the figure. The default is 10.
height : int, optional
height of the figure. The default is 6.
cmap : String, optional
Specify your own colors for each class-label or use a colormap: https://matplotlib.org/examples/color/colormaps_reference.html. The default is 'Set1'.
[(1, 0, 0),(0, 0, 1),(..)]
'Set1' (default)
'Set2' Discrete colors
'Pastel1' Discrete colors
'Paired' Discrete colors
'rainbow'
'bwr' Blue-white-red
'binary' or 'binary_r'
'seismic' Blue-white-red
'Blues' white-to-blue
'Reds' white-to-red
cii_alpha : float, optional
Confidence interval (works only when methodtype='lifelines'). The default is 0.05.
cii_lines : String, optional
Confidence lines (works only when methodtype='lifelines'). The default is 'dense'.
'lifelines' (default)
'custom'
methodtype : String, optional
Implementation type. The default is 'lifeline'.
'dense' (dense/filled lines)
'line'
None (no lines)
title : TYPE, optional
DESCRIPTION. The default is 'Survival function'.
Returns
-------
None.
"""
KMcoord = {}
Param = {}
Param['width'] = width
Param['height'] = height
Param['fontsize'] = fontsize
Param['savepath'] = savepath
labx = out['labx']
# Combine data and gather class labels
data = np.vstack((out['time_event'], out['censoring'])).T
# Make colors and legend-names for class-labels
[class_colors, classlabel] = make_class_color_names(data,
out['labx'],
out['uilabx'],
cmap=cmap)
if methodtype == 'lifeline':
# Init
kmf_all = []
# Startup figure
fig = plt.figure(figsize=(Param['width'], Param['height']))
ax = fig.add_subplot(111)
if full_ylim:
ax.set_ylim([0.0, 1.05])
if y_percentage:
ax.yaxis.set_major_formatter(PercentFormatter(1.0))
if out['logrank'] != []:
plt.title('%s, Logrank Test P-Value = %.5f' %
(title, out['logrank_P']))
# Compute KM survival coordinates per class
if cii_lines == 'dense':
cii_lines = False
if cii_lines == 'line':
cii_lines = True
if cii_lines == '' or cii_lines == None or cii_alpha == None:
cii_lines = False
cii_alpha = 0
for i in range(0, len(out['uilabx'])):
kmf = KaplanMeierFitter()
idx = np.where(labx == out['uilabx'][i])[0]
# Fit
kmf.fit(out['time_event'][idx],
event_observed=out['censoring'][idx],
label=classlabel[i],
ci_labels=None,
alpha=(1 - cii_alpha))
# Plot
kmf.plot(ax=ax,
ci_force_lines=cii_lines,
color=class_colors[i],
show_censors=True)
# Store
kmf_all.append(
kmf.fit(out['time_event'][idx],
event_observed=out['censoring'][idx],
label=classlabel[i],
ci_labels=None,
alpha=(1 - cii_alpha)))
add_at_risk_counts(*kmf_all, ax=ax)
ax.tick_params(axis='x',
length=15,
width=1,
direction='out',
labelsize=Param['fontsize'])
ax.tick_params(axis='y',
length=15,
width=1,
direction='out',
labelsize=Param['fontsize'])
ax.spines['bottom'].set_position(['outward', Param['fontsize']])
ax.spines['left'].set_position(['outward', Param['fontsize']])
# ax.rc('font', size= Param['fontsize']) # controls default text sizes
# ax.rc('axes', labelsize = Param['fontsize']) # fontsize of the x and y labels
if Param['savepath'] != '':
savefig(fig, Param['savepath'])
if methodtype == 'custom':
# Compute KM survival coordinates per class
for i in range(0, len(out['uilabx'])):
idx = np.where(labx == out['uilabx'][i])[0]
tmpdata = data[idx, :].tolist()
KMcoord[i] = compute_coord(tmpdata)
# Plot KM survival lines
plotkm(KMcoord,
classlabel,
cmap=class_colors,
width=Param['width'],
height=Param['height'],
fontsize=Param['fontsize'])
def plot(out,
fontsize=12,
savepath='',
width=10,
height=6,
cmap='Set1',
cii_alpha=0.05,
cii_lines='dense',
methodtype='lifeline',
title='Survival function'):
'''
Parameters
----------
out : [dict] Dictionary derived from the fit function.
fontsize : [INT], Font size for the graph
default is 12.
savepath: [STRING], Path to store the figure
width: [INT], Width of the figure
10 (default)
height: [INT], Width of the figure
6 (default)
cmap: [STRING], Specify your own colors for each class-label or use a colormap: https://matplotlib.org/examples/color/colormaps_reference.html
[(1, 0, 0),(0, 0, 1),(..)]
'Set1' (default)
'Set2' Discrete colors
'Pastel1' Discrete colors
'Paired' Discrete colors
'rainbow'
'bwr' Blue-white-red
'binary' or 'binary_r'
'seismic' Blue-white-red
'Blues' white-to-blue
'Reds' white-to-red
cii_alpha: [FLOAT], Confidence interval (works only when methodtype='lifelines')
0.05 (default)
cii_lines: [STRING], Confidence lines (works only when methodtype='lifelines')
'lifelines' (default)
'custom'
methodtype: [STRING], Implementation type
'dense' (dense/filled lines)
'line'
None (no lines)
Returns
-------
None.
'''
KMcoord = {}
Param = {}
Param['width'] = width
Param['height'] = height
Param['fontsize'] = fontsize
Param['savepath'] = savepath
labx = out['labx']
# Combine data and gather class labels
data = np.vstack((out['time_event'], out['censoring'])).T
# Make colors and legend-names for class-labels
[class_colors, classlabel] = make_class_color_names(data,
out['labx'],
out['uilabx'],
cmap=cmap)
if methodtype == 'lifeline':
# Init
kmf_all = []
# Startup figure
fig = plt.figure(figsize=(Param['width'], Param['height']))
ax = fig.add_subplot(111)
# ax.grid(True)
# ax.ylabel('Percentage survival')
if out['logrank'] != []:
plt.title('%s, Logrank Test P-Value = %.5f' %
(title, out['logrank_P']))
# Compute KM survival coordinates per class
if cii_lines == 'dense':
cii_lines = False
if cii_lines == 'line':
cii_lines = True
if cii_lines == '' or cii_lines == None or cii_alpha == None:
cii_lines = False
cii_alpha = 0
for i in range(0, len(out['uilabx'])):
kmf = KaplanMeierFitter()
idx = np.where(labx == out['uilabx'][i])[0]
# Fit
kmf.fit(out['time_event'][idx],
event_observed=out['censoring'][idx],
label=classlabel[i],
ci_labels=None,
alpha=1 - cii_alpha)
# Plot
kmf.plot(ax=ax,
ci_force_lines=cii_lines,
color=class_colors[i],
show_censors=True)
# Store
kmf_all.append(
kmf.fit(out['time_event'][idx],
event_observed=out['censoring'][idx],
label=classlabel[i],
ci_labels=None,
alpha=1 - cii_alpha))
if len(kmf_all) == 1:
add_at_risk_counts(kmf_all[0], ax=ax)
elif len(kmf_all) == 2:
add_at_risk_counts(kmf_all[0], kmf_all[1], ax=ax)
elif len(kmf_all) == 3:
add_at_risk_counts(kmf_all[0], kmf_all[1], kmf_all[2], ax=ax)
elif len(kmf_all) == 4:
add_at_risk_counts(kmf_all[0],
kmf_all[1],
kmf_all[2],
kmf_all[3],
ax=ax)
elif len(kmf_all) == 5:
add_at_risk_counts(kmf_all[0],
kmf_all[1],
kmf_all[2],
kmf_all[3],
kmf_all[4],
ax=ax)
elif len(kmf_all) == 6:
add_at_risk_counts(kmf_all[0],
kmf_all[1],
kmf_all[2],
kmf_all[3],
kmf_all[4],
kmf_all[5],
ax=ax)
elif len(kmf_all) == 7:
add_at_risk_counts(kmf_all[0],
kmf_all[1],
kmf_all[2],
kmf_all[3],
kmf_all[4],
kmf_all[5],
kmf_all[6],
ax=ax)
elif len(kmf_all) == 8:
add_at_risk_counts(kmf_all[0],
kmf_all[1],
kmf_all[2],
kmf_all[3],
kmf_all[4],
kmf_all[5],
kmf_all[6],
kmf_all[7],
ax=ax)
elif len(kmf_all) == 9:
add_at_risk_counts(kmf_all[0],
kmf_all[1],
kmf_all[2],
kmf_all[3],
kmf_all[4],
kmf_all[5],
kmf_all[6],
kmf_all[7],
kmf_all[8],
ax=ax)
elif len(kmf_all) == 10:
add_at_risk_counts(kmf_all[0],
kmf_all[1],
kmf_all[2],
kmf_all[3],
kmf_all[4],
kmf_all[5],
kmf_all[6],
kmf_all[7],
kmf_all[8],
kmf_all[9],
ax=ax)
else:
print('[KM] Maximum of 10 classes is reached.')
ax.tick_params(axis='x',
length=15,
width=1,
direction='out',
labelsize=Param['fontsize'])
ax.tick_params(axis='y',
length=15,
width=1,
direction='out',
labelsize=Param['fontsize'])
ax.spines['bottom'].set_position(['outward', Param['fontsize']])
ax.spines['left'].set_position(['outward', Param['fontsize']])
# ax.rc('font', size= Param['fontsize']) # controls default text sizes
# ax.rc('axes', labelsize = Param['fontsize']) # fontsize of the x and y labels
if Param['savepath'] != '':
savefig(fig, Param['savepath'])
if methodtype == 'custom':
# Compute KM survival coordinates per class
for i in range(0, len(out['uilabx'])):
idx = np.where(labx == out['uilabx'][i])[0]
tmpdata = data[idx, :].tolist()
KMcoord[i] = compute_coord(tmpdata)
# Plot KM survival lines
plotkm(KMcoord,
classlabel,
cmap=class_colors,
width=Param['width'],
height=Param['height'],
fontsize=Param['fontsize'])