def main():
if (len(_sys.argv) > 1 and _os.path.exists(_sys.argv[1]) and _os.path.isfile(_sys.argv[1])):
filename = _sys.argv[1]
else:
filename = _gui.get_path("*.csv",defaultFile="odmanalysis.csv")
commonPath = _os.path.abspath(_os.path.split(filename)[0])
measurementName = _os.path.split(_os.path.split(filename)[0])[1]
try:
settings = _odm.CurveFitSettings.loadFromFile(commonPath + '/odmSettings.ini')
print "settings loaded from local odmSettings.ini"
except:
settings = _gui.getSettingsFromUser(None)
settings.saveToFile(commonPath + '/odmSettings.ini')
print "settings saved to local odmSettings.ini"
df = _odm.readAnalysisData(filename)
makeDisplacementPlots(df,commonPath, measurementName = measurementName, nmPerPx = settings.pxToNm)
_plt.show()
def processDataFrame(self,df):
"""
Processes a dataframe of raw odm data and analyzes the displacement. If this is the first dataframe
that is being processed, the gui will show to ask the user for fit-function details.
For subsequent calls to this method, the last fit-result of the previous dataframe
is used as the initial parameters for the fit in the current dataframe.
Parameters
----------
df: pandas.DataFrame
ODM dataframe that contains the raw ODM data
"""
if df is None:
return None
print "processing %s - %s" % (df.index.min(),df.index.max())
if not self.curveFitSettings:
globalSettings = _odm.CurveFitSettings.loadFromFileOrCreateDefault('./CurveFitScriptSettings.ini')
settings = _odm.CurveFitSettings.loadFromFileOrCreateDefault(self.commonPath + '/odmSettings.ini',prototype=globalSettings)
_gui.getSettingsFromUser(settings)
self.curveFitSettings = settings
settings.saveToFile()
if not self.movingPeakFitSettings:
q = _mp.Queue() #it is nescessary to host all gui elements in their own process, because the cannot be spawned from another thread than the main one.
p = _mp.Process(target=_getPeakFitSettingsFromUser, args=(q,df,settings))
p.start()
settingsDict = q.get()
p.join()
self.movingPeakFitSettings = settingsDict['movingPeakFitSettings']
self.referencePeakFitSettings = settingsDict['referencePeakFitSettings']
df_movingPeak = _odm.calculatePeakDisplacements(df.intensityProfile, self.movingPeakFitSettings, pInitial = self.popt_mp_previous, factor=100,maxfev=20000)
df_movingPeak.rename(columns = lambda columnName: columnName + "_mp",inplace=True)
df = df.join(df_movingPeak)
self.popt_mp_previous = df.curveFitResult_mp[-1].popt
if (self.referencePeakFitSettings is not None):
df_referencePeak = _odm.calculatePeakDisplacements(df.intensityProfile, self.referencePeakFitSettings, pInitial = self.popt_ref_previous, factor=100,maxfev=20000)
df_referencePeak.rename(columns = lambda columnName: columnName + "_ref",inplace=True)
df = df.join(df_referencePeak)
self.popt_ref_previous = df.curveFitResult_ref[-1].popt
df['displacement'] = df.displacement_mp - df.displacement_ref
else:
df['displacement'] = df.displacement_mp
if (self.lastDataFrame is not None):
tail = self.lastDataFrame.iloc[-2:]
dfC = _pd.concat([tail,df])
_odm.getActuationDirectionAndCycle(dfC,startDirection = tail.direction.iloc[1],startCycleNumber = tail.cycleNumber.iloc[1])
df = dfC.iloc[2:]
else:
_odm.getActuationDirectionAndCycle(df)
self.lastDataFrame = df
return df
def main():
if len(sys.argv) > 1 and os.path.exists(sys.argv[1]) and os.path.isfile(sys.argv[1]):
filename = sys.argv[1]
else:
filename = gui.get_path("*.csv", defaultFile="odmanalysis.csv")
commonPath = os.path.abspath(os.path.split(filename)[0])
try:
settings = odm.CurveFitSettings.loadFromFile(commonPath + "/odmSettings.ini")
print "settings loaded from local odmSettings.ini"
except:
settings = gui.getSettingsFromUser(None)
settings.saveToFile(commonPath + "/odmSettings.ini")
print "settings saved to local odmSettings.ini"
nmPerPx = settings.pxToNm
df = odm.readAnalysisData(filename)
plt.plot(np.arange(len(df.displacement)), df.displacement * nmPerPx)
plt.interactive(False)
coordinateGrabber = gui.InteractiveCoordinateGrabber(plt.gcf(), 2, "Select range for fitting a polynomial...")
coordinateGrabber.promptMessages = ["Select lower limit...", "Select upper limit..."]
coordinates = coordinateGrabber.getValuesFromUser()
xLimits = [int(c[0]) for c in coordinates]
dfs = df.iloc[slice(*xLimits)]
validInput = False
while validInput == False:
try:
deg = int(raw_input("Polynomial degree: "))
validInput = True
except:
pass
xValues = np.arange(len(dfs.displacement))
p = cp.polyfit(xValues, dfs.displacement, deg)
noise = (dfs.displacement - cp.polyval(p, xValues)) * nmPerPx
ax = plt.subplot(111)
ax.plot(xValues, dfs.displacement * nmPerPx)
ax.plot(xValues, cp.polyval(p, xValues) * nmPerPx, "r--")
ax.set_xlabel("Actuator Voltage (V)")
ax.set_ylabel("Displacement (nm)")
fig, (ax1, ax2) = plt.subplots(nrows=1, ncols=2, sharex=False, sharey=True, squeeze=True)
ax1.plot(np.arange(len(noise)), noise, "o")
ax2.hist(noise, bins=50, orientation="horizontal", facecolor="green", alpha=0.5)
sd = ODMStats.makeStatsDescriptionDict(nmPerPx * dfs.displacement)
peakDistanceToEdge = [abs(sd["mean"] - x * nmPerPx) for x in ax2.get_xlim()]
xtext = 0.1 if peakDistanceToEdge[0] > peakDistanceToEdge[1] else 0.7
ax2.text(xtext, 0.7, ODMStats.printStatsDescriptionDict(sd), transform=ax2.transAxes)
ax1.set_ylabel("Displacement (nm)")
ax2.set_xlabel("Count")
fig.savefig(os.path.join(commonPath, "Displacement noise.png"), dpi=150)
stats = noise.describe()
with open(os.path.join(commonPath, "position_noise_stats.txt"), "w") as f:
f.write(str(stats))
plt.show()
def fitRawODMData(filename,settingsFile=None,fitSettingsFile=None,referenceIPDataFile=None):
"""
This script opens and analyzes the target data.csv file produced by LabVIEW and
analyzes the optical displacement of a peak relative to another peak.
Results are saved to the same folder as the original data file.
Parameters
----------
filename: string
Path to the data.csv file produced by LabVIEW that contains the raw
Optical Displacement Measurement Data.
referenceIPDataFile: string
Path the data.csv file of which the first intensity profile will be used
as a reference for initializing the fit function.
Returns
-------
dataframe: pandas.DataFrame
A dataframe that contains the raw data, the calculated displacements,
curve fit results and other diagnostic data.
movingPeakFitSettings : CurveFitSettings instance
The curve fit settings used for the moving peak.
referencePeakFitSettings: CurveFitSettings instance
The curve fit settings used for the reference peak.
measurementName: string
The name of the measurement (deduced from the folder name of the csv file)
"""
commonPath = os.path.abspath(os.path.split(filename)[0])
measurementName = os.path.split(os.path.split(filename)[0])[1]
globalSettings = odm.CurveFitSettings.loadFromFileOrCreateDefault('./CurveFitScriptSettings.ini')
if (settingsFile is not None):
print "reading settings from %s" % settingsFile
settings = odm.CurveFitSettings.loadFromFile(settingsFile)
else:
settings = odm.CurveFitSettings.loadFromFileOrCreateDefault(commonPath + '/odmSettings.ini',prototype=globalSettings)
gui.getSettingsFromUser(settings)
df = odm.readODMData(filename)
if referenceIPDataFile is not None:
print "using the first profile from %s for initializing the fit functions" % referenceIPDataFile
for chunk in odm.getODMDataReader(referenceIPDataFile, chunksize=1):
referenceIntensityProfile = chunk.intensityProfile.iloc[0]
break
else:
referenceIntensityProfile = df.intensityProfile.iloc[0]
if (fitSettingsFile is not None):
with file(fitSettingsFile,'r') as f:
print "reading fit settings from %s" % fitSettingsFile
settingsDict = pickle.load(f)
movingPeakFitSettings = settingsDict['movingPeakFitSettings']
referencePeakFitSettings = settingsDict['referencePeakFitSettings']
else:
movingPeakFitFunction = ff.createFitFunction(settings.defaultFitFunction)
movingPeakFitSettings = gui.getPeakFitSettingsFromUser(referenceIntensityProfile,movingPeakFitFunction,
estimatorPromptPrefix="Moving peak:",
windowTitle="Moving Peak estimates")
try:
referencePeakFitFunction = ff.createFitFunction(settings.defaultFitFunction)
referencePeakFitSettings = gui.getPeakFitSettingsFromUser(referenceIntensityProfile,referencePeakFitFunction,
estimatorPromptPrefix="Reference peak:",
windowTitle="Reference Peak estimates")
except: #exception occurs if user cancels the 'dialog'
referencePeakFitFunction = None
referencePeakFitSettings = None
print 'no reference'
movingPeakFitSettings.referenceIntensityProfile = referenceIntensityProfile
referencePeakFitSettings.referenceIntensityProfile = referenceIntensityProfile
print "fitting a %s function..." % settings.defaultFitFunction
df_movingPeak = odm.calculatePeakDisplacements(df.intensityProfile, movingPeakFitSettings, factor=100,maxfev=20000)
df_movingPeak.rename(columns = lambda columnName: columnName + "_mp",inplace=True)
df = df.join(df_movingPeak)
if (referencePeakFitSettings is not None):
df_referencePeak = odm.calculatePeakDisplacements(df.intensityProfile, referencePeakFitSettings, factor=100,maxfev=20000)
df_referencePeak.rename(columns = lambda columnName: columnName + "_ref",inplace=True)
df = df.join(df_referencePeak)
df['displacement'] = df.displacement_mp - df.displacement_ref
else:
df['displacement'] = df.displacement_mp
#save settings
sys.stdout.write("saving defaults...")
globalSettings.saveToFile()
sys.stdout.write("done\r\n")
sys.stdout.write("saving local setting file...")
settings.saveToFile()
sys.stdout.write("done\r\n")
print "done"
#save calculated peak position data as csv
sys.stdout.write("saving dataframe as csv file...")
exportColumns = ['relativeTime','cycleNumber','direction','actuatorVoltage','displacement','displacement_mp','chiSquare_mp']
if ('displacement_ref' in df.columns):
exportColumns +=['displacement_ref','chiSquare_ref']
df[exportColumns].to_csv(os.path.join(commonPath,'odmanalysis.csv'),index_label='timestamp')
sys.stdout.write("done\r\n")
#save fit results as pickled dataframe
sys.stdout.write("pickling fit results dataframe as pcl file...")
fitResultColumns = ['curveFitResult_mp']
if 'curveFitResult_ref' in df.columns:
fitResultColumns += ['curveFitResult_ref']
df[fitResultColumns].to_pickle(commonPath + '/fitResults.pcl')
sys.stdout.write("done\r\n")
#save the used fit functions and fit settings as pickled objects
sys.stdout.write("pickling fit functions and settings...")
settingsDict = {'movingPeakFitSettings': movingPeakFitSettings,
'referencePeakFitSettings': referencePeakFitSettings if referencePeakFitSettings is not None else None}
with file(commonPath+'/fitSettings.pcl','w') as stream:
pickle.dump(settingsDict,stream)
sys.stdout.write("done\r\n")
print "ALL DONE"
return df,movingPeakFitSettings,referencePeakFitSettings,measurementName