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timeline_plotting.py
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timeline_plotting.py
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import matplotlib.pyplot as plt
import matplotlib.cm
from scipy.signal import medfilt
import numpy as np
def splitSignal(array):
split_indices = []
labelorder = []
sizes = []
previous_element = array[0]
sizes.append(0)
labelorder.append(previous_element)
for element in array:
if(previous_element == element):
sizes[-1] = sizes[-1] + 1
previous_element = element
else:
sizes.append(1)
labelorder.append(element)
previous_element = element
return sizes,labelorder
def plotlabelpositions(array,timestamps):
sizes,labelorder = splitSignal(array)
label_indices = []
start = []
stop = []
sum = 0
for size in sizes:
label_indices.append(timestamps[sum+size/2])
start.append(timestamps[sum])
sum = size + sum
print sum,len(timestamps)
stop.append(timestamps[sum-1])
return label_indices,labelorder,start,stop
def plotResult(testPredict,timestamps,signal,labelsgiven = True,plotmeans = False,showplot = True,ax = None,labelNames = [],legend = False,line = False,medfiltwidth = 101,labelnumfilts= None,actuallabels = None):
if ax is None:
ax = plt.gca()
if len(labelNames) ==0:
labelNamesSet = set(testPredict)
labelNames = list(labelNamesSet)
testPredictIds = [labelNames.index(label) for idx, label in enumerate(testPredict)]
testPredictIds = medfilt(testPredictIds,medfiltwidth)
testPredictIds = (testPredictIds.astype(int)).tolist()
testPredict = [labelNames[id] for id in testPredictIds]
if labelnumfilts !=None:
out_testPredict = []
sizes, labelorder = splitSignal(testPredict)
for size,label in zip(sizes,labelorder):
if size < labelnumfilts[labelNames.index(label)]:
out_testPredict = out_testPredict + ['']*size
else: out_testPredict = out_testPredict + [label]*size
testPredict = out_testPredict
print splitSignal(testPredict)
testPredictIds = [labelNames.index(label) for idx, label in enumerate(testPredict)]
for jj,label in enumerate(labelNames):
testPredictIds[jj] = jj
color_map_rgba = matplotlib.cm.ScalarMappable(cmap="Vega20").to_rgba(testPredictIds)
if labelsgiven == True:
for label_id in set(testPredictIds):
if line == False:
ax.scatter([timestamp for idx, timestamp in enumerate(timestamps) if testPredictIds[idx] == label_id], \
[signal_point for idx, signal_point in enumerate(signal) if testPredictIds[idx] == label_id], \
c= [color for idx, color in enumerate(color_map_rgba) if testPredictIds[idx] == label_id], lw=0,\
label = labelNames[label_id],s = 1)
else:
ax.plot([timestamp for idx, timestamp in enumerate(timestamps) if testPredictIds[idx] == label_id], \
[id for idx, id in enumerate(testPredictIds) if testPredictIds[idx] == label_id] ,\
'.k', label = labelNames[label_id],linewidth = 1, markersize=2,color = 'black')
# ax.set_yticklabels(labelNames)
labelNames_plot = [label.split('_')[0] for label in labelNames]
print labelNames_plot
labelNames_plot[0] = "unlabelled"
ax.yaxis.set(ticks=np.arange(0, len(labelNames)), ticklabels=labelNames_plot)
ax.set_ylim([-0.5,len(labelNames) + 0.25])
if actuallabels != None:
label_indices ,label_order,starts,stops = plotlabelpositions(actuallabels,timestamps)
label_order_plot = [label.split('_')[0] for label in label_order]
ax.xaxis.set(ticks=label_indices, ticklabels=label_order_plot)
for start,stop,label in zip(starts,stops,label_order):
color = color_map_rgba[labelNames.index(label)]
ax.axvspan(start, stop, alpha=0.2, color=color)
ax.axvline(start,linewidth=0.5, color='k')
# ax.xaxis.grid(False)
ax.yaxis.grid(True)
# ax.axvline(start, linewidth=4, color='k')
# else: ax.xaxis.set(ticks = np.arange(0.5,np.max(timestamps)))
else: ax.scatter(timestamps, signal, c=testPredict, cmap=plt.cm.RdYlGn, lw=0,s = 1)
if legend == True:
ax.legend()
return ax
def plotResult_colorbars(testPredict,timestamps,signal,labelsgiven = True,plotmeans = False,showplot = True,ax = None,labelNames = [],legend = False,line = False,medfiltwidth = 101,labelnumfilts= None,actuallabels = None):
if ax is None:
ax = plt.gca()
if len(labelNames) ==0:
labelNamesSet = set(testPredict)
labelNames = list(labelNamesSet)
testPredictIds = [labelNames.index(label) for idx, label in enumerate(testPredict)]
testPredictIds = medfilt(testPredictIds,medfiltwidth)
testPredictIds = (testPredictIds.astype(int)).tolist()
testPredict = [labelNames[id] for id in testPredictIds]
if labelnumfilts !=None:
out_testPredict = []
sizes, labelorder = splitSignal(testPredict)
for size,label in zip(sizes,labelorder):
if size < labelnumfilts[labelNames.index(label)]:
out_testPredict = out_testPredict + ['']*size
else: out_testPredict = out_testPredict + [label]*size
testPredict = out_testPredict
print splitSignal(testPredict)
testPredictIds = [labelNames.index(label) for idx, label in enumerate(testPredict)]
for jj,label in enumerate(labelNames):
testPredictIds[jj] = jj
color_map_rgba = matplotlib.cm.ScalarMappable(cmap="RdYlGn").to_rgba(testPredictIds)
if labelsgiven == True:
for label_id in set(testPredictIds):
if line == False:
ax.set_ylim([-0.5, 0.25])
if actuallabels != None:
label_indices ,label_order,starts,stops = plotlabelpositions(actuallabels,timestamps)
label_order_plot = [label.split('_')[0] for label in label_order]
ax.xaxis.set(ticks=label_indices, ticklabels=label_order_plot)
for start,stop,label in zip(starts,stops,label_order):
color = color_map_rgba[labelNames.index(label)]
ax.axvspan(start, stop, alpha=0.2, color=color)
ax.axvline(start,linewidth=0.5, color='k')
# ax.xaxis.grid(False)
ax.yaxis.grid(True)
else:
ax.plot([timestamp for idx, timestamp in enumerate(timestamps) if testPredictIds[idx] == label_id], \
[id for idx, id in enumerate(testPredictIds) if testPredictIds[idx] == label_id] ,\
'.k', label = labelNames[label_id],linewidth = 1, markersize=2,color = 'black')
# ax.set_yticklabels(labelNames)
labelNames_plot = [label.split('_')[0] for label in labelNames]
print labelNames_plot
labelNames_plot[0] = "unlabelled"
ax.yaxis.set(ticks=np.arange(0, len(labelNames)), ticklabels=labelNames_plot)
ax.set_ylim([-0.5,len(labelNames) + 0.25])
if actuallabels != None:
label_indices ,label_order,starts,stops = plotlabelpositions(actuallabels,timestamps)
label_order_plot = [label.split('_')[0] for label in label_order]
ax.xaxis.set(ticks=label_indices, ticklabels=label_order_plot)
for start,stop,label in zip(starts,stops,label_order):
color = color_map_rgba[labelNames.index(label)]
ax.axvspan(start, stop, alpha=0.2, color=color)
ax.axvline(start,linewidth=0.5, color='k')
# ax.xaxis.grid(False)
ax.yaxis.grid(True)
else: ax.scatter(timestamps, signal, c=testPredict, cmap=plt.cm.RdYlGn, lw=0,s = 1)
if legend == True:
ax.legend()
return ax