def readODMData(dataFilePath,progressReporter=_StdOutProgressReporter()):
"""
Reads a data.csv file that has been written by a LabVIEW ODM Measurement and returns
it as a dataframe. It also determines the cyclenumber and direction.
The data is read from the source file in chunks of 2005 lines. This ensures that also extremely large
files can be read without memory problems.
Parameters
----------
dataFilePath : string
Path string to the data.csv file that contains the raw odm data or a stream object.
progressReporter : ODMAnalysisGui.ProgressReporter
The ProgressReporter to use. If None (default) a StdOutProgressReporter is used.
Returns
-------
dataframe: pandas.DataFrame,
A pandas dataframe with the data in data.csv. The index is set to the timestamp. The columns
'cycleNumber' and 'direction' have been determined from the 'actuatorVoltage' column.
"""
progressReporter.message('loading data from %s ...' % dataFilePath)
reader = getODMDataReader(dataFilePath)
chunks=[]
for chunk in reader:
chunks.append(chunk)
print "%s - %s" % (chunk.index.min(),chunk.index.max())
df = _pd.concat(chunks)
df = df[df.intensityProfile.map(len) != 0]
getActuationDirectionAndCycle(df)
progressReporter.done()
return df
def calculatePeakDisplacements(intensityProfiles, peakFitSettings, progressReporter = None, pInitial = None, **curveFitKwargs):
"""
Fits an ODM FitFunction to the target Series of intensity profiles.
Parameters
----------
intensityProfiles : pandas.Series of 1D numpy.ndarray
A series of intensityProfiles that will be curve fit
peakFitSettings : ODAFitSettings instance
The curve fit settings to use for curve fitting
progressReporter : ProgressReporter instance
The ProgressReporter to use for displaying progress information.
A StdOutProgressReporter is used by default.
curveFitKwargs : Keyword arguments that will be passed to the curve_fit
function (scipy.optimization).
Returns
-------
A dataframe with the calculated displacements that has the same index as the input
intensity profile Series.
"""
if not progressReporter:
progressReporter = _StdOutProgressReporter()
fitFunction = peakFitSettings.fitFunction
index=intensityProfiles.index
if pInitial is not None:
p0 = pInitial
else:
templateProfile = peakFitSettings.referenceIntensityProfile if peakFitSettings.referenceIntensityProfile is not None else intensityProfiles.iloc[0]
estimatesDict = fitFunction.estimateInitialParameters(templateProfile, **peakFitSettings.estimatorValuesDict)
p0 = estimatesDict.values()
xmin = peakFitSettings.xminBound
xmax = peakFitSettings.xmaxBound
xdata = _np.arange(len(intensityProfiles.iloc[0]))[xmin:xmax]
progress = 0.0
total = len(index)
curveFitResults = total*[None]
for i in range(total):
ydata = intensityProfiles.iloc[i][xmin:xmax]
popt,pcov = _curve_fit(fitFunction,\
xdata = xdata,\
ydata = ydata,\
p0 = p0,**curveFitKwargs)
p0 = popt
curveFitResult = {}
curveFitResult['popt'] = popt
curveFitResult['pcov'] = pcov
curveFitResult['chiSquare'] = _chisquare(ydata,fitFunction(xdata,*popt))[0]
curveFitResult['curveFitResult'] = attrdict.AttrDict(curveFitResult)
curveFitResult['displacement'] = fitFunction.getDisplacement(*popt)
curveFitResults[i] = curveFitResult
progress += 1
progressReporter.progress(progress / total * 100)
df = _pd.DataFrame(index=index,data=curveFitResults)
progressReporter.done()
return df
