def run(features, attributeName):
#stats = {
# type: 'number',
# populatedCount: 1462,
# totalCount: 0,
# uniqueValues: {
# '10.5': 4,
# '9.7': 1,
# },
# min: 36.618,
# max: 86.2747,
# range: 49.6567,
# sum: 106165,
# mean: 72.6165,
# median: 74.2404,
# stdDev: 15.324,
# variance: 0.0123
#}
ds = DescriptiveStatistics()
ss = SummaryStatistics()
stats = {
'type': 'number',
'populatedCount': 0,
'totalCount': 0,
'uniqueValues': {}
}
for f in features.features():
stats['totalCount'] += 1
val = f.attributes[attributeName]
if str(val) in stats['uniqueValues']:
stats['uniqueValues'][str(val)] += 1
else:
stats['uniqueValues'][str(val)] = 1
if val is not None and val != '':
stats['populatedCount'] += 1
try:
val_float = float(val)
ds.addValue(val_float)
ss.addValue(val_float)
except ValueError:
stats['type'] = 'string'
except:
# might be a date object
stats['type'] = 'string'
if stats['type'] == 'number':
if ss.getN():
stats['min'] = ss.getMin()
stats['max'] = ss.getMax()
stats['range'] = stats['max'] - stats['min']
stats['sum'] = ss.getSum()
stats['mean'] = ss.getMean()
stats['median'] = ds.getPercentile(50)
stats['stdDev'] = ds.getStandardDeviation()
stats['variance'] = ss.getPopulationVariance()
stats['uniqueValueCount'] = len(stats['uniqueValues'])
return json.dumps(stats, allow_nan=False)
def run(features, attributeName):
#stats = {
# type: 'number',
# populatedCount: 1462,
# totalCount: 0,
# uniqueValues: {
# '10.5': 4,
# '9.7': 1,
# },
# min: 36.618,
# max: 86.2747,
# range: 49.6567,
# sum: 106165,
# mean: 72.6165,
# median: 74.2404,
# stdDev: 15.324,
# variance: 0.0123
#}
ds = DescriptiveStatistics()
ss = SummaryStatistics()
stats = {
'type': 'number',
'populatedCount': 0,
'totalCount': 0,
'uniqueValues': {}
}
for f in features.features():
stats['totalCount'] += 1
val = f.attributes[attributeName]
if str(val) in stats['uniqueValues']:
stats['uniqueValues'][str(val)] += 1
else:
stats['uniqueValues'][str(val)] = 1
if val is not None and val != '':
stats['populatedCount'] += 1
try:
val_float = float(val)
ds.addValue(val_float)
ss.addValue(val_float)
except ValueError:
stats['type'] = 'string'
except:
# might be a date object
stats['type'] = 'string'
if stats['type'] == 'number':
if ss.getN():
stats['min'] = ss.getMin()
stats['max'] = ss.getMax()
stats['range'] = stats['max'] - stats['min']
stats['sum'] = ss.getSum()
stats['mean'] = ss.getMean()
stats['median'] = ds.getPercentile(50)
stats['stdDev'] = ds.getStandardDeviation()
stats['variance'] = ss.getPopulationVariance()
stats['uniqueValueCount'] = len(stats['uniqueValues'])
return json.dumps(stats, allow_nan=False)
print(fi)
orig = ImagePlus(fi)
strName = os.path.basename(fi)
strName = strName.split('.')[0]
lStr = strName.split('-')
ti = orig.getShortTitle()
orig.setTitle(ti)
IJ.run(orig, "Measure","")
time.sleep(1)
orig.close()
rt = ResultsTable.getResultsTable()
nMeas = rt.getCounter()
minC = rt.getColumnIndex("Min")
maxC = rt.getColumnIndex("Max")
minStats = DSS()
maxStats = DSS()
for i in range(nMeas):
minVal = rt.getValueAsDouble(minC, i)
minStats.addValue(minVal)
maxVal = rt.getValueAsDouble(maxC, i)
maxStats.addValue(maxVal)
meanMinVal = minStats.getMean()
meanMaxVal = maxStats.getMean()
strOut = "mean minimum gray = %.2f, mean maximum gray = %.2f" % (meanMinVal, meanMaxVal)
print(strOut)
rt.reset()
def getDStat(values): dStat = DescriptiveStatistics(values) return dStat