'axis': 1,
'thr_low': thr,
})
an.add_analysis("get_mean_std", args={'thr_low': thr})
bins = np.arange(-150, 300, 5)
an.add_analysis("get_histo_counts", args={'bins': bins})
an.add_analysis(get_line_histos, args={'axis': 0, 'bins': bins})
# set the dataset
an.set_dataset(dataset_name)
# add preprocess steps
#an.add_preprocess("image_set_roi", args={'roi': roi})
#an.add_preprocess("image_set_thr", thr_low=thr)
# run the analysis
results = an.analyze_images(fname, n=1000)
# plot
plt.figure(figsize=(7, 7))
plt.plot(np.nansum(results["spectra"], axis=0), label="ADU > " + str(thr))
plt.legend(loc='best')
#plt.show()
plt.figure(figsize=(7, 7))
plt.bar(bins[:-1], results["histo_counts"], log=True, width=5)
#plt.show()
plt.figure(figsize=(15, 10))
plt.subplot(121)
plt.imshow(results["histo_counts_line"],
vmin=0,
selection=sel)
# get laser on/off tags
is_laser_on_tags = df[df.is_laser == 1].index.tolist()
is_laser_off_tags = df[df.is_laser == 0].index.tolist()
# get spectra from Von Hamos, using laser on / off tags
#roi = [[0, 1024], [325, 335]] # X, Y
ap = ImagesProcessor(facility="SACLA")
ap.add_analysis('get_projection', args={"axis": 1})
ap.add_analysis('get_mean_std')
ap.set_dataset('/run_%s/detector_2d_1' % run)
ap.add_preprocess("set_thr", args={"thr_low": 65})
# get the total spectra
results_on = ap.analyze_images(fname, tags=is_laser_on_tags)
spectrum_on = results_on["get_projection"]["spectra"].sum(axis=0)
results_off = ap.analyze_images(fname, tags=is_laser_off_tags)
spectrum_off = results_off["get_projection"]["spectra"].sum(axis=0)
spectrum_off = spectrum_off / spectrum_off.sum()
spectrum_on = spectrum_on / spectrum_on.sum()
# this is the average image from the Von Hamos
sum_image_on = results_on["get_mean_std"]["images_mean"]
# Plot!
plt.subplot(1, 2, 1)
plt.imshow(sum_image_on)
plt.subplot(1, 2, 2)
plt.plot(spectrum_off, label="laser off")
for run in runs:
rname = str(run)
fname = DIR + rname + ".h5"
print('\nAnalyzing run ' + rname + '\n')
"""
Analyze images and integrate roi and bkgRoi. Can take a lot of time
The results are saved in a pickle file in the folder analyzed_runs/imgAna.
To add prepocess, analysis functions, open the dataAna.imgAna function.
"""
if imgAna:
dataset_name = "/run_" + rname + "/detector_2d_1"
ip.set_dataset(dataset_name, remove_preprocess=False)
# run the analysis
results = ip.analyze_images(fname, n=n)
# plot results
imgs = results["images_mean"]
plt.figure(figsize=(8, 8))
plt.subplot2grid((2, 2), (0, 0), rowspan=2)
plt.imshow(imgs)
# plt.imshow(imgs[bkgRoi[0][0]:bkgRoi[0][1], bkgRoi[1][0]:bkgRoi[1][1]], aspect=0.5,
# extent=(bkgRoi[1][0], bkgRoi[1][1], bkgRoi[0][0], bkgRoi[0][1]), interpolation="none")
plt.subplot2grid((2, 2), (0, 1))
plt.imshow(imgs[roi[0][0]:roi[0][1], roi[1][0]:roi[1][1]], aspect=0.5,
extent=(roi[1][0], roi[1][1], roi[0][1], roi[0][0]), interpolation="none")
plt.title('ROI')
# FOR DARKFRAME save to npy:
# np.save('/home/esposiv/python_scripts2016/analysis/dark_2016_388662_testsave.npy', imgs)
for run in runs:
rname = str(run)
fname = DIR + rname + ".h5"
print(('\nAnalyzing run ' + rname + '\n'))
"""
Analyze images and integrate roi and bkgRoi. Can take a lot of time
The results are saved in a pickle file in the folder analyzed_runs/imgAna.
To add prepocess, analysis functions, open the dataAna.imgAna function.
"""
if imgAna:
dataset_name = "/run_" + rname + "/detector_2d_1"
ip.set_dataset(dataset_name, remove_preprocess=False)
# run the analysis
results = ip.analyze_images(fname, n=n)
# plot results
imgs = results["images_mean"]
plt.figure(figsize=(8, 8))
plt.subplot2grid((2, 2), (0, 0), rowspan=2)
plt.imshow(imgs)
# plt.imshow(imgs[bkgRoi[0][0]:bkgRoi[0][1], bkgRoi[1][0]:bkgRoi[1][1]], aspect=0.5,
# extent=(bkgRoi[1][0], bkgRoi[1][1], bkgRoi[0][0], bkgRoi[0][1]), interpolation="none")
plt.subplot2grid((2, 2), (0, 1))
plt.imshow(imgs[roi[0][0]:roi[0][1], roi[1][0]:roi[1][1]], aspect=0.5,
extent=(roi[1][0], roi[1][1], roi[0][1], roi[0][0]), interpolation="none")
plt.title('ROI')
# FOR DARKFRAME save to npy:
# np.save('/home/esposiv/python_scripts2016/analysis/dark_2016_388662_testsave.npy', imgs)
def bin_tt_COM(df, bin_edges, rname, fname, calibration=0.01, roi=[[235, 270], [500, 540]]):
"""
Bin data according to the timing tool and perform a center of mass analysis of the roi
This script is somewhat redundant with the image analysis, as it loops again through all the images.
"""
# create corrected delay
df["dl_corr"] = df.delay + calibration * df.tt
bin_size = bin_edges[1] - bin_edges[0]
df_xon = df[df.x_status == 1]
df_lon = df_xon[df.laser_status == 1]
df_loff = df_xon[df.laser_status == 0]
bin_center = bin_edges[:-1] + 0.5 * bin_size
df_out = pd.DataFrame(bin_center, columns=["time"])
if len(df_lon) != 0:
binned_int_lon = stats.binned_statistic(df_lon.dl_corr, df_lon.intensity, bins=bin_edges, statistic="mean")
binned_bkg_lon = stats.binned_statistic(df_lon.dl_corr, df_lon.bkg, bins=bin_edges, statistic="mean")
binned_I0_lon = stats.binned_statistic(df_lon.dl_corr, df_lon.I0, bins=bin_edges, statistic="mean")
df_out["intensity_lon"] = binned_int_lon.statistic
df_out["bkg_lon"] = binned_bkg_lon.statistic
df_out["I0_lon"] = binned_I0_lon.statistic
else:
print ("No laser ON shots")
if len(df_loff) != 0:
binned_int_loff = stats.binned_statistic(df_loff.dl_corr, df_loff.intensity, bins=bin_edges, statistic="mean")
binned_bkg_loff = stats.binned_statistic(df_loff.dl_corr, df_loff.bkg, bins=bin_edges, statistic="mean")
binned_I0_loff = stats.binned_statistic(df_loff.dl_corr, df_loff.I0, bins=bin_edges, statistic="mean")
df_out["I0_loff"] = binned_I0_loff.statistic
df_out["bkg_loff"] = binned_bkg_loff.statistic
df_out["intensity_loff"] = binned_int_loff.statistic
else:
print ("No laser OFF shots")
"""
COM analysis
COM analysis loops through the bins and load the images corresponding for each bin.
The COM of the averaged images in the bin is taken and written in the df_out dataframe
"""
binnumber = binned_int_lon.binnumber
peakCOM = np.zeros([len(df_out.time), 2])
dataset_name = "/run_" + rname + "/detector_2d_1"
ip = ImagesProcessor(facility="SACLA")
ip.flatten_results = True
ip.set_dataset(dataset_name)
ip.add_preprocess("set_roi", args={"roi": roi})
ip.add_analysis("get_mean_std")
for ii in range(len(df_out.time)):
n = ii + 1
ismember = binnumber == n
tagList = df.index[ismember]
results = ip.analyze_images(fname, n=-1, tags=tagList)
if "images_mean" in results:
peakCOM[ii, :] = ndimage.measurements.center_of_mass(results["images_mean"])
else:
peakCOM[ii, :] = np.NaN
del results
print ("bin number %s" % n)
df_out["COMx"] = peakCOM[:, 0]
df_out["COMy"] = peakCOM[:, 1]
return df_out
def bin_tt_COM(df,
bin_edges,
rname,
fname,
calibration=0.01,
roi=[[235, 270], [500, 540]]):
"""
Bin data according to the timing tool and perform a center of mass analysis of the roi
This script is somewhat redundant with the image analysis, as it loops again through all the images.
"""
# create corrected delay
df['dl_corr'] = df.delay + calibration * df.tt
bin_size = bin_edges[1] - bin_edges[0]
df_xon = df[df.x_status == 1]
df_lon = df_xon[df.laser_status == 1]
df_loff = df_xon[df.laser_status == 0]
bin_center = bin_edges[:-1] + 0.5 * bin_size
df_out = pd.DataFrame(bin_center, columns=['time'])
if len(df_lon) != 0:
binned_int_lon = stats.binned_statistic(df_lon.dl_corr,
df_lon.intensity,
bins=bin_edges,
statistic='mean')
binned_bkg_lon = stats.binned_statistic(df_lon.dl_corr,
df_lon.bkg,
bins=bin_edges,
statistic='mean')
binned_I0_lon = stats.binned_statistic(df_lon.dl_corr,
df_lon.I0,
bins=bin_edges,
statistic='mean')
df_out['intensity_lon'] = binned_int_lon.statistic
df_out['bkg_lon'] = binned_bkg_lon.statistic
df_out['I0_lon'] = binned_I0_lon.statistic
else:
print('No laser ON shots')
if len(df_loff) != 0:
binned_int_loff = stats.binned_statistic(df_loff.dl_corr,
df_loff.intensity,
bins=bin_edges,
statistic='mean')
binned_bkg_loff = stats.binned_statistic(df_loff.dl_corr,
df_loff.bkg,
bins=bin_edges,
statistic='mean')
binned_I0_loff = stats.binned_statistic(df_loff.dl_corr,
df_loff.I0,
bins=bin_edges,
statistic='mean')
df_out['I0_loff'] = binned_I0_loff.statistic
df_out['bkg_loff'] = binned_bkg_loff.statistic
df_out['intensity_loff'] = binned_int_loff.statistic
else:
print('No laser OFF shots')
"""
COM analysis
COM analysis loops through the bins and load the images corresponding for each bin.
The COM of the averaged images in the bin is taken and written in the df_out dataframe
"""
binnumber = binned_int_lon.binnumber
peakCOM = np.zeros([len(df_out.time), 2])
dataset_name = "/run_" + rname + "/detector_2d_1"
ip = ImagesProcessor(facility="SACLA")
ip.flatten_results = True
ip.set_dataset(dataset_name)
ip.add_preprocess("set_roi", args={'roi': roi})
ip.add_analysis("get_mean_std")
for ii in range(len(df_out.time)):
n = ii + 1
ismember = (binnumber == n)
tagList = df.index[ismember]
results = ip.analyze_images(fname, n=-1, tags=tagList)
if 'images_mean' in results:
peakCOM[ii, :] = ndimage.measurements.center_of_mass(
results['images_mean'])
else:
peakCOM[ii, :] = np.NaN
del results
print(('bin number %s' % n))
df_out['COMx'] = peakCOM[:, 0]
df_out['COMy'] = peakCOM[:, 1]
return df_out
