def plotCrossCorrelation(events1, events2, **kwargs):
if (len(events1['ts']) == 0) or (len(events2['ts']) == 0):
return
# use other library functions to do spatiotemporal cropping
# If this is called from plotautocorrelation then this has already been done,
# But do it here as well so that this function can be independent.
events1 = cropSpace(events1, **kwargs)
events1 = cropTime(events1, zeroTime=False, **kwargs)
events2 = cropSpace(events2, **kwargs)
events2 = cropTime(events2, zeroTime=False, **kwargs)
boundaries, densities, centres, widths = defineBoundariesAndDensities(
**kwargs)
# Spatial cropping has happened; now iterate through defacto spatial range
minX = min(np.min(events1['x']), np.min(events2['x']))
maxX = max(np.max(events1['x']), np.max(events2['x']))
minY = min(np.min(events1['y']), np.min(events2['y']))
maxY = max(np.max(events1['y']), np.max(events2['y']))
hist = np.zeros((len(densities)), dtype=np.float64)
# TODO: This loop could be much more efficient, following the pattern in plotInterSpikeInterval, above
for currX in trange(minX, maxX + 1, leave=True, position=0):
for currY in range(minY, maxY + 1):
ts1 = events1['ts'][np.logical_and(events1['x'] == currX,
events1['y'] == currY)]
ts2 = events2['ts'][np.logical_and(events2['x'] == currX,
events2['y'] == currY)]
if np.any(ts1) and np.any(ts2):
hist = hist + crossCorrelation(ts1, ts2, boundaries, densities,
**kwargs)
# Normalise
weightedSum = np.sum(hist / densities)
if weightedSum > 0:
hist = hist / np.sum(weightedSum)
axes = kwargs.get('axes')
if axes is None:
fig, axes = plt.subplots()
kwargs['axes'] = axes
axes.bar(centres, hist, widths, antialiased=False)
#axes.set_ylim(minX + minY*numX - 1, maxX + maxY*numX + 1)
#plt.xticks(theRange, labels)
title = kwargs.get('title', '')
if kwargs.get('minX', False) or kwargs.get('maxX', False):
title = title + ' ' + str(minX) + '<=X<=' + str(maxX)
if kwargs.get('minY', False) or kwargs.get('maxY', False):
title = title + ' ' + str(minY) + '<=Y<=' + str(maxY)
axes.set_title(title)
callback = kwargs.get('callback')
if callback is not None:
callback(**kwargs)
def plotInterSpikeInterval(inDict, **kwargs):
# Boilerplate for descending container hierarchy
if isinstance(inDict, list):
for inDictInst in inDict:
plotInterSpikeInterval(inDictInst, **kwargs)
return
if 'info' in inDict: # Top level container
fileName = inDict['info'].get('filePathOrName', '')
print('plotInterSpikeInterval was called for file ' + fileName)
if not inDict['data']:
print('The import contains no data.')
return
for channelName in inDict['data']:
channelData = inDict['data'][channelName]
if 'dvs' in channelData and len(channelData['dvs']['ts']) > 0:
kwargs['title'] = ' '.join([fileName, str(channelName)])
plotInterSpikeInterval(channelData['dvs'], **kwargs)
else:
print('Channel ' + channelName +
' skipped because it contains no polarity data')
return
print('plotInterSpikeInterval working: ' + kwargs.get('title', 'unnamed'))
# use other library functions to do spatiotemporal cropping
inDict = cropSpace(inDict, **kwargs)
inDict = cropTime(inDict, zeroTime=False, **kwargs)
if kwargs.get('splitByPol', False):
splitDict = splitByPolarity(inDict)
fig, axes = plt.subplots(1, 3)
kwargs['axes'] = axes[0]
kwargs['title'] = 'on + off'
print(kwargs['title'])
plotInterSpikeIntervalSingle(inDict, **kwargs)
kwargs['axes'] = axes[1]
kwargs['title'] = 'off'
print(kwargs['title'])
plotInterSpikeIntervalSingle(splitDict['0'], **kwargs)
kwargs['axes'] = axes[2]
kwargs['title'] = 'on'
print(kwargs['title'])
plotInterSpikeIntervalSingle(splitDict['1'], **kwargs)
else:
axes = kwargs.get('axes')
if axes is None:
fig, axes = plt.subplots()
kwargs['axes'] = axes
kwargs['title'] = 'auto'
plotInterSpikeIntervalSingle(inDict, **kwargs)
def plotInterSpikeIntervalSingle(events, **kwargs):
if (len(events['ts']) == 0):
return
# use other library functions to do spatiotemporal cropping
# If this is called from plotInterSpikeInterval then this has already been done,
# But do it here as well so that this function can be independent.
events = cropSpace(events, **kwargs)
events = cropTime(events, zeroTime=False, **kwargs)
kwargs['mirrored'] = False
boundaries, densities, centres, widths = defineBoundariesAndDensities(
**kwargs)
# Spatial cropping has happened; now iterate through defacto spatial range
minX = np.min(events['x'])
maxX = np.max(events['x'])
minY = np.min(events['y'])
maxY = np.max(events['y'])
hist = np.zeros((len(densities)), dtype=np.float64)
for currX in trange(minX, maxX + 1, leave=True, position=0):
currXLogical = events['x'] == currX
tsForCurrX = events['ts'][currXLogical]
yForCurrX = events['y'][currXLogical]
for currY in range(minY, maxY + 1):
tsXY = tsForCurrX[yForCurrX == currY]
if np.any(tsXY):
hist = hist + interSpikeInterval(tsXY, boundaries, densities,
**kwargs)
# Normalise
weightedSum = np.sum(hist / densities)
if weightedSum > 0:
hist = hist / np.sum(weightedSum)
axes = kwargs.get('axes')
if axes is None:
fig, axes = plt.subplots()
kwargs['axes'] = axes
axes.bar(centres, hist, widths, antialiased=False)
#axes.set_ylim(minX + minY*numX - 1, maxX + maxY*numX + 1)
#plt.xticks(theRange, labels)
title = kwargs.get('title', '')
if kwargs.get('minX', False) or kwargs.get('maxX', False):
title = title + ' ' + str(minX) + '<=X<=' + str(maxX)
if kwargs.get('minY', False) or kwargs.get('maxY', False):
title = title + ' ' + str(minY) + '<=Y<=' + str(maxY)
axes.set_title(title)
callback = kwargs.get('callback')
if callback is not None:
callback(**kwargs)
def plotPose(inDicts, **kwargs):
if isinstance(inDicts, list):
for inDict in inDicts:
plotPose(inDict, **kwargs)
return
else:
inDict = inDicts
if not isinstance(inDict, dict):
return
if 'ts' not in inDict:
title = kwargs.pop('title', '')
if 'info' in inDict and isinstance(inDict, dict):
fileName = inDict['info'].get('filePathOrName')
if fileName is not None:
print('plotPose was called for file ' + fileName)
title = (title + ' ' + fileName).lstrip()
for key in inDict.keys():
kwargs['title'] = (title + ' ' + key).lstrip()
plotPose(inDict[key], **kwargs)
return
# From this point onwards, it's a data-type container
if 'point' not in inDict:
return
# From this point onwards, it's a pose or point data-type container
ts = inDict['ts']
minTime = kwargs.get(
'minTime', kwargs.get('startTime', kwargs.get('beginTime', ts.min())))
maxTime = kwargs.get(
'maxTime', kwargs.get('stopTime', kwargs.get('endTime', ts.max())))
if minTime > ts.min() or maxTime < ts.max():
inDict = cropTime(inDict,
minTime=minTime,
maxTime=maxTime,
zeroTime=False)
ts = inDict['ts']
if 'rotation' in inDict:
fig, allAxes = plt.subplots(2, 1)
fig.suptitle(kwargs.get('title', ''))
axesT = allAxes[0]
axesR = allAxes[1]
rotation = inDict['rotation']
axesR.plot(ts, rotation[:, 0], 'k')
axesR.plot(ts, rotation[:, 1], 'r')
axesR.plot(ts, rotation[:, 2], 'g')
axesR.plot(ts, rotation[:, 3], 'b')
axesR.legend(['r_w', 'r_x', 'r_y', 'r_z'])
axesR.set_xlabel('Time (s)')
axesR.set_ylabel('Quaternion components')
axesR.set_ylim([-1, 1])
if 'point' in inDict:
if not 'axesT' in locals():
fig, axesT = plt.subplots()
if kwargs.get('zeroT', False):
point = np.copy(inDict['point'])
firstPoint = inDict['point'][0, :]
point = point - firstPoint
else:
point = inDict['point']
axesT.plot(ts, point[:, 0], 'r')
axesT.plot(ts, point[:, 1], 'g')
axesT.plot(ts, point[:, 2], 'b')
axesT.legend(['x', 'y', 'z'])
axesT.set_xlabel('Time (s)')
axesT.set_ylabel('Coords (m)')
callback = kwargs.get('callback')
if callback is not None:
kwargs['axesT'] = axesT
kwargs['axesR'] = axesR
kwargs['minTime'] = minTime
kwargs['maxTime'] = maxTime
callback(**kwargs)
#%% Import realsense
from importIntelRealsense import importIntelRealsense
filePathOrName = os.path.join(prefix,
'/data/2019_10_23_static/20191023_165520.bag')
imported = importIntelRealsense(filePathOrName=filePathOrName)
#%% MANIPULATION FUNCTIONS
#%% Cropping a dataset to a desired time range
from split import cropTime
cropped = cropTime(imported, minTime=35, maxTime=38.5)
#%% Cropping a dataset to a desired spatial range
# works for dvs and derived data types 2020_01 doesn't yet work for frame datatype
from split import cropSpace
# This example takes all events with x in 9-19 inclusive, and with y in 0-9 inclusive
cropped = cropSpace(imported, minX=9, maxX=19, maxY=9)
#%% Splitting a dataset by labels
from split import splitByLabel, selectByLabel
# select your labelled data from an import (alternatively label it using some processing)
labelledData = imported['data']['right']['dvslbl']