def allAvailableDataSources(runDir, forceUpdate=False):
if runDataDict["runDir"] is not None and runDataDict[
"runData"] is not None and not forceUpdate and runDataDict[
"runDir"] == runDir:
# no action needed the dictionary runDataDict contains already the data we are interested in and user does not neccesarily desires the update
pass
else:
runDataDict["runData"] = kd.RunDirectory(runDir)
runDataDict["runDir"] = runDir
return runDataDict["runData"].all_sources
def allImages(runNumber, path='/gpfs/exfel/exp/SQS/201921/p002430/raw'):
'''
finds mean scattering and plots and returns that image
input:
runNumber: to analyze
path: to experiment H5s
'''
run = runFormat(runNumber)
runData = kd.RunDirectory(path + run)
trainIds = runData.train_ids
scatImages = np.asarray(
runData.get_array('SQS_DPU_LIC/CAM/YAG_UPSTR:daqOutput',
'data.image.pixels'))
return scatImages, trainIds
def analyzeMaximumImages(runNumber,
path='/gpfs/exfel/exp/SQS/201921/p002430/raw',
maskRadius=100,
nlargest=10):
'''
finds maximum scattering images and plots that image, saving to saveDir
input:
runNumber: to analyze
path: to experiment H5s
maskRadius: radius below which to ignore for determining max scattering
nlargest: number of largest images to plot
'''
run = runFormat(runNumber)
runData = kd.RunDirectory(path + run)
trainIds = runData.train_ids
scatImages = np.asarray(
runData.get_array('SQS_DPU_LIC/CAM/YAG_UPSTR:daqOutput',
'data.image.pixels'))
NT, NX, NY = scatImages.shape
xs = np.arange(NX)
ys = np.arange(NY)
YY, XX = np.meshgrid(ys, xs)
RR = np.sqrt((XX - NX / 2.)**2 + (YY - NY / 2.)**2)
MASK = RR < maskRadius
maskedScats = np.copy(scatImages)
maskedScats[:, MASK] = 0.
scaledScat = np.sum(maskedScats, (1, 2))
indMaxToMin = np.argsort(scaledScat)[-nlargest:]
fig, axes = plt.subplots(nlargest, 1, figsize=(10, 100))
for idx, ax in enumerate(axes):
realInd = indMaxToMin[idx]
plotme = scatImages[realInd, :, :].squeeze()
im = ax.imshow(plotme.astype(float))
ax.set_title('run %d and tid %d' % (runNumber, trainIds[realInd]))
plt.show()
plt.pause(2)
del scatImages
del maskedScats
def getData(runDir, devicePath, dataPath, forceUpdate=False):
'''
Returns Data from given adress / device using Karabo Data or using already loaded data if no update forced and run already loaded
run_dir - directory of run files (hdf5)
device_path - path for device in hdf5 file eg "SQS_AQS_MOV/MOTOR/Y_DOWNSTR"
data_path - path to data within hdf5 device path eg "actualPosition.value"
'''
if runDataDict["runDir"] is not None and runDataDict[
"runData"] is not None and not forceUpdate and runDataDict[
"runDir"] == runDir:
# no action needed the dictionary runDataDict contains already the data we are interested in and user does not neccesarily desires the update
pass
else:
runDataDict["runData"] = kd.RunDirectory(runDir)
runDataDict["runDir"] = runDir
data = runDataDict["runData"].get_array(devicePath, dataPath)
return data
def getTrainIds(runDir, forceUpdate=False):
'''
Gets train IDs of specified run
inputs
runNumber = number of run of interest
path = path for data; defined at the top
outputs
array of train IDs
'''
if runDataDict["runDir"] is not None and runDataDict[
"runData"] is not None and not forceUpdate and runDataDict[
"runDir"] == runDir:
# no action needed the dictionary runDataDict contains already the data we are interested in and user does not neccesarily desires the update
pass
else:
runDataDict["runData"] = kd.RunDirectory(runDir)
runDataDict["runDir"] = runDir
return runDataDict["runData"].train_ids
def analyzeAverageImage(
runNumber,
path='/gpfs/exfel/exp/SQS/201921/p002430/raw', ###imagePath
maskRadius=100):
'''
finds mean scattering and plots and returns that image
input:
runNumber: to analyze
path: to experiment H5s
maskRadius: radius below which to ignore for determining max scattering
saveDir: path to save picture to
'''
run = runFormat(runNumber)
runData = kd.RunDirectory(path + run)
trainIds = runData.train_ids
scatImages = np.asarray(
runData.get_array('SQS_DPU_LIC/CAM/YAG_UPSTR:daqOutput',
'data.image.pixels'))
scatMean = np.mean(scatImages, 0).astype(float)
return scatMean
def analyzeMaximumImage(runNumber,
path='/gpfs/exfel/exp/SQS/201921/p002430/raw',
maskRadius=100):
'''
finds maximum scattering and plots that image
input:
runNumber: to analyze
path: to experiment H5s
maskRadius: radius below which to ignore for determining max scattering
saveDir: path to save picture to
'''
run = runFormat(runNumber)
runData = kd.RunDirectory(path + run)
trainIds = runData.train_ids
scatImages = np.asarray(
runData.get_array('SQS_DPU_LIC/CAM/YAG_UPSTR:daqOutput',
'data.image.pixels'))
NT, NX, NY = scatImages.shape
xs = np.arange(NX)
ys = np.arange(NY)
YY, XX = np.meshgrid(ys, xs)
RR = np.sqrt((XX - NX / 2.)**2 + (YY - NY / 2.)**2)
MASK = RR < maskRadius
maskedScats = np.copy(scatImages)
maskedScats[:, MASK] = 0.
scaledScat = np.sum(maskedScats, (1, 2))
indMax = np.argmax(scaledScat)
indMin = np.argmin(scaledScat)
scatMax = scatImages[indMax, :, :].astype(float)
scatMean = np.mean(scatImages, 0).astype(float)
plt.figure(5)
plt.imshow(scatMax)
plt.title('Max scattering from run %d' % runNumber)
plt.colorbar()
plt.show()
plt.pause(2)
plt.figure(5)
plt.imshow(
np.log(
np.abs(scatMax / np.sum(scatMax.flatten()) -
scatMean / np.sum(scatMean.flatten()))))
plt.title(
'Log of difference between scatMax/intensity-scatMean/intensity from run %d'
% runNumber)
plt.colorbar()
plt.show()
plt.pause(2)
plt.figure(5)
plt.imshow(scatMean)
plt.title('Mean from run %d' % runNumber)
plt.colorbar()
plt.show()
plt.pause(2)
del scatImages
del maskedScats
return scatMax, trainIds[indMax]
def correlationPlots(run, Path, roi1, offset):
'''
outputs a scatter plot correlating FEL pulse intensity to light peak
inputs
run = run number of interest
Path = path to data
roi = region of interest in TOF for integration
offset = offset for +/- 1 error
returns the correlation number and scatter plot
'''
runf = xfel.runFormat(run)
runData = kd.RunDirectory(Path + runf)
pixels, tofs, trainIds = xfel.getRunTOF(run,
Path,
tofrange=(265000,
285000)) ###tofrange
pulse_E = np.asarray(
runData.get_array('SA3_XTD10_XGM/XGM/DOOCS:output',
'data.intensityTD'))
scatImagesini = np.asarray(
runData.get_array('SQS_DPU_LIC/CAM/YAG_UPSTR:daqOutput',
'data.image.pixels'))
integratedROI1s = []
pulseEnergies = []
if offset > 0:
for ind, elem in enumerate(trainIds[0:len(trainIds) - offset]):
train_pos = np.where(trainIds == elem)[0][0]
temp_E = pulse_E[1, :]
pos = max(enumerate(temp_E), key=(lambda a: a[1]))
run_E = pulse_E[:, pos[0]]
train_E = run_E[train_pos + offset]
pulseEnergies.append(train_E)
integratedROI1s.append(
np.sum((tofs[train_pos])[(roi1[0] < pixels)
& (pixels < roi1[1])]))
else:
for ind, elem in enumerate(trainIds):
train_pos = np.where(trainIds == elem)[0][0]
#print (train_pos)
temp_E = pulse_E[1, :]
#print (temp_E)
pos = max(enumerate(temp_E), key=(lambda a: a[1]))
run_E = pulse_E[:, pos[0]]
#print (run_E)
train_E = run_E[train_pos + offset]
pulseEnergies.append(train_E)
integratedROI1s.append(
np.sum((tofs[train_pos])[(roi1[0] < pixels)
& (pixels < roi1[1])]))
plt.scatter(pulseEnergies, integratedROI1s)
plt.xlabel('pulse energy')
plt.ylabel('integrated ROI from TOF')
return
#!/usr/bin/env python3
import karabo_data
run_path = '/gpfs/exfel/exp/SPB/201701/p002012/proc/r0243'
run = karabo_data.RunDirectory(run_path)
run = run.select("*/DET/*", "*")
run = run.select_trains(
karabo_data.by_id[[76961506, 76961507, 76961508, 76961509]])
run.write("data_subset.h5")