def __init__(self, thisData, thisConfig, thisExpName, thisDebug=True):
Format.__init__(self, thisDebug)
self.data = thisData
self.config = thisConfig
self._printToFilename = thisExpName
self.debug = thisDebug
self.colorMap = plt.get_cmap(self.config.getMatrixColorMap())
self.SPECIAL_SCORE = 12345
self.plotType = 'matrix_plot'
self.title = self.data.getTitle()
self.event = None
def __init__(self, thisConfig, thisDebug=True):
self.debug = thisDebug
Format.__init__(self, self.debug)
self._config = thisConfig
# N must be bigger than 3 otherwise the matrix will be all white.
self._N = 4
self._metaValues = ['condA', 'condB', 'condC', 'condD', 'condE', 'condF']
#self._metaValues = ['condA']
self.getMaximum4ThisType = self._config.getMaximum4Type3
self.getMinimum4ThisType = self._config.getMinimum4Type3
self._ma = self.getMinimum4ThisType()
self._mi = self.getMaximum4ThisType()
self._data = collections.defaultdict(list)
self._title = 'dummy test title for matrix data'
def __init__(self, thisConfig, thisDebug=True):
self.debug = thisDebug
Format.__init__(self, self.debug)
self._config = thisConfig
# N must be bigger than 3 otherwise the matrix will be all white.
self._N = 4
self._metaValues = [
'condA', 'condB', 'condC', 'condD', 'condE', 'condF'
]
#self._metaValues = ['condA']
self.getMaximum4ThisType = self._config.getMaximum4Type3
self.getMinimum4ThisType = self._config.getMinimum4Type3
self._ma = self.getMinimum4ThisType()
self._mi = self.getMaximum4ThisType()
self._data = collections.defaultdict(list)
self._title = 'dummy test title for matrix data'
def __init__(self, thisPattern, thisAgmsv, thisAimsv, thisNumberOfTargets, thisNumberOfNonTargets, thisDebug=True):
Format.__init__(self, thisDebug)
self._pattern = thisPattern
self._aimsv = thisAimsv
self._agmsv = thisAgmsv
self._numberOfTargets = thisNumberOfTargets
self._numberOfNonTargets = thisNumberOfNonTargets
self.debug = thisDebug
self._agmStdDev = 0.0
self._aimStdDev = 0.0
self._agmNormStdDev = 0.0
self._aimNormStdDev = 0.0
self._singleTargetScore = False
self._singleNonTargetScore = False
# wasLimited can be used to display points that were limited in stdev in a
# different way than other points.
self._wasLimited = False
def __init__(self,
thisData,
thisConfig,
thisExpName,
thisType='normal',
thisDebug=True,
thisUseMeta=False):
Format.__init__(self, thisDebug)
self.data = thisData
self.config = thisConfig
self._printToFilename = thisExpName
self.type = thisType
self.debug = thisDebug
self.title = self.data.getTitle()
self.useMeta = thisUseMeta
self.plotType = "histogram_plot"
self.fig = None
self.event = None
self.colors = None
self.nrColors = None
def __init__(self):
Format.__init__(self, "[:;|]", 2,
'''CREATE TABLE data (user text, password text)''',
'''INSERT INTO data VALUES (?, ?)''')
def __init__(self,
thisConfig,
thisTitle,
thisThreshold,
thisDataType,
maxNrTargetSamplesPerLabel,
maxNrNonTargetSamplesPerLabel,
thisDebug=True,
thisSources='database'):
Format.__init__(self, thisDebug)
self.config = thisConfig
self._title = thisTitle
self._defaultThreshold = thisThreshold
self._dataType = thisDataType
# Annotate _doves, _phantoms, _worms and _chameleons
self._maxNrTargetSamplesPerLabel = maxNrTargetSamplesPerLabel
self._maxNrNonTargetSamplesPerLabel = maxNrNonTargetSamplesPerLabel
self.debug = thisDebug
self._sources = thisSources
self._format = Format(self.debug)
self._plotType = None
# Target scores per label and meta value pattern.
self._targetScores = collections.defaultdict(list)
# Number of targets per label.
self._targetCnt = collections.Counter()
# Non target scores per label and meta value pattern.
self._nonTargetScores = collections.defaultdict(list)
# Number of non targets per label.
self._nonTargetCnt = collections.Counter()
# Target scores per label.
self._targetScores4Label = collections.defaultdict(list)
self._targetScores4MetaValue = collections.defaultdict(list)
# Non target scores per label.
self._nonTargetScores4Label = collections.defaultdict(list)
self._nonTargetScores4MetaValue = collections.defaultdict(list)
self._results = collections.defaultdict(list)
# Count which labels + condition exceed the maxNrTargetSamplesPerLabel
# and maxNrNonTargetSamplesPerLabel
self._targetScoresInExcess = collections.Counter()
self._nonTargetScoresInExcess = collections.Counter()
self._nrDistinctMetaDataValues = 0
# Contains: { speakerId: metaDataValue }
self._metaDataValues = collections.defaultdict(set)
self._LabelsToShowAlways = []
self._minimumScore = collections.defaultdict(dict)
self._maximumScore = collections.defaultdict(dict)
# Keep track of labels.
self._targetLabels = set()
self._nonTargetLabels = set()
# Do we allow both scores (A vs B and B vs A) in a symmetric tests or only the first read?
self._allowDups = self.config.getAllowDups()
if self.debug:
print('Data._source(s):')
for el in self._sources:
print(el)
# If the user did not specify a filename, we assume a database as the source.
if self._sources == 'database':
print("You need to add some code for this to work!")
# And remove the sys.exit(1) statement.
#res = self._readFromDatabase()
sys.exit(1)
else:
res = self._readFromFiles(self._sources)
#
# Choose between decoder for type of results.
#
if self._dataType == 'type3':
self._decodeType3Results(res)
elif self._dataType == 'type2':
print("Type2 data is not supported anymore. Convert it to type3!")
sys.exit(1)
elif self._dataType == 'type1':
self._decodeType1Results(res)
else:
print("Unknown data type, must be 'type1' or 'type3'.")
sys.exit(1)
def __init__(self, thisConfig, thisTitle, thisThreshold, thisDataType, thisDebug=True, thisSources='database'):
Format.__init__(self, thisDebug)
self.config = thisConfig
self._title = thisTitle
self._defaultThreshold = thisThreshold
self._dataType = thisDataType
# Annotate _doves, _phantoms, _worms and _chameleons
self.debug = thisDebug
self._sources = thisSources
self._format = Format(self.debug)
self._plotType = None
# Target scores per label and meta value pattern.
self._targetScores = collections.defaultdict(list)
# Number of targets per label.
self._targetCnt = collections.Counter()
# Non target scores per label and meta value pattern.
self._nonTargetScores = collections.defaultdict(list)
# Number of non targets per label.
self._nonTargetCnt = collections.Counter()
# Target scores per label.
self._targetScores4Label = collections.defaultdict(list)
self._targetScores4MetaValue = collections.defaultdict(list)
# Non target scores per label.
self._nonTargetScores4Label = collections.defaultdict(list)
self._nonTargetScores4MetaValue = collections.defaultdict(list)
self._results = collections.defaultdict(list)
self._results4Subject = collections.defaultdict(list)
self._nrDistinctMetaDataValues = 0
# Contains: { speakerId: metaDataValue }
self._metaDataValues = collections.defaultdict(set)
self._LabelsToShowAlways = []
self._minimumScore = collections.defaultdict(dict)
self._maximumScore = collections.defaultdict(dict)
# Keep track of labels.
self._targetLabels = set()
self._nonTargetLabels = set()
# Do we allow both scores (A vs B and B vs A) in a symmetric tests or only the first read?
self._allowDups = self.config.getAllowDups()
if self.debug:
print('Data._source(s):')
for el in self._sources:
print(el)
# If the user did not specify a filename, we assume a database as the source.
if self._sources == 'database':
print("You need to add some code for this to work!")
# And remove the sys.exit(1) statement.
sys.exit(1)
res = self._readFromDatabase()
else:
res = self._readFromFiles(self._sources)
#
# Choose between decoder for type of results.
#
if self._dataType == 'type3':
self._decodeType3Results(res)
elif self._dataType == 'type2':
print("Type2 data is not supported anymore. Convert it to type3!")
sys.exit(1)
elif self._dataType == 'type1':
self._decodeType1Results(res)
else:
print("Unknown data type, must be 'type1' or 'type3'.")
sys.exit(1)
