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Commit dd58ef97 authored by Marco Ramilli's avatar Marco Ramilli
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cleared notebook outputs

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notebooks/Jungfrau/Jungfrau_dark_analysis_all_gains_burst_mode_NBC.ipynb
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%% Cell type:markdown id: tags:
# Jungfrau Dark Image Characterization #
Version: 0.1, Author: M. Ramilli, S. Hauf
Analyzes Jungfrau dark image data to deduce offset, noise and resulting bad pixel maps
%% Cell type:code id: tags:
``` python
cluster_profile = 'noDB' # the ipcluster profile name
in_folder = '/gpfs/exfel/exp/FXE/202030/p900121/raw/' # folder under which runs are located, required
out_folder = '/gpfs/exfel/exp/HSLAB/201831/p900053/proc/proc_data/FXE/p900121/test_dark/' # path to place reports at, required
sequences = 1 # number of sequence files in that run
run_high = 130 # run number for G0 dark run, required
run_med = 131 # run number for G1 dark run, required
run_low = 132 # run number for G2 dark run, required
karabo_da = ['JNGFR03'] # list of data aggregators, which corresponds to different JF modules
karabo_id = "FXE_XAD_JF500K" # bla karabo prefix of Jungfrau devices
karabo_id_control = "" # if control is on a different ID, set to empty string if it is the same a karabo-id
receiver_id = 'JNGFR{:02d}' # inset for receiver devices
receiver_control_id = "CONTROL" # inset for control devices
path_template = 'RAW-R{:04d}-{}-S{{:05d}}.h5' # template to use for file name, double escape sequence number
h5path = '/INSTRUMENT/{}/DET/{}:daqOutput/data' # path in H5 file under which images are located
h5path_run = '/RUN/{}/DET/{}' # path to run data
h5path_cntrl = '/CONTROL/{}/DET/{}' # path to control data
karabo_da_control = "JNGFRCTRL00" # file inset for control data
use_dir_creation_date = True # use dir creation date
cal_db_interface = 'tcp://max-exfl016:8016' # calibrate db interface to connect to
cal_db_timeout = 300000 # timeout on caldb requests
local_output = True # output constants locally
db_output = False # output constants to database
integration_time = 1000 # integration time in us, will be overwritten by value in file
bias_voltage = 90 # sensor bias voltage in V, will be overwritten by value in file
badpixel_threshold_sigma = 5. # bad pixels defined by values outside n times this std from median
offset_abs_threshold_low = [1000, 10000, 10000] # absolute bad pixel threshold in terms of offset, lower values
offset_abs_threshold_high = [8000, 15000, 15000] # absolute bad pixel threshold in terms of offset, upper values
chunkSize = 10 # iteration chunk size, needs to match or be less than number of images in a sequence file
imageRange = [0, 500] # image range in which to evaluate
memoryCells = 16 # number of memory cells
db_module = ["Jungfrau_M260"] # ID of module in calibration database
manual_slow_data = False # if true, use manually entered bias_voltage and integration_time values
time_limits = 0.025 # to find calibration constants later on, the integration time is allowed to vary by 0.5 us
```
%% Cell type:code id: tags:
``` python
import warnings
warnings.filterwarnings('ignore')
import matplotlib
matplotlib.use('agg')
import numpy as np
import h5py
from h5py import File as h5file
from cal_tools.enums import BadPixels
from cal_tools.tools import get_dir_creation_date, get_random_db_interface
from cal_tools.ana_tools import save_dict_to_hdf5
from iCalibrationDB import ConstantMetaData, Constants, Conditions, Detectors, Versions
from XFELDetAna.util import env
env.iprofile = cluster_profile
from XFELDetAna.detectors.jungfrau import readerPSI as jfreaderPSI
from XFELDetAna.detectors.jungfrau import reader as jfreader
from XFELDetAna.detectors.jungfrau.jf_chunk_reader import JFChunkReader
from XFELDetAna.detectors.jungfrau.util import count_n_files, rollout_data, sanitize_data_cellid
import glob
import matplotlib.pyplot as plt
%matplotlib inline
from XFELDetAna.plotting.histogram import histPlot
from XFELDetAna.plotting.heatmap import heatmapPlot
import os
os.makedirs(out_folder, exist_ok=True)
```
%% Cell type:code id: tags:
``` python
path_inset = karabo_da[0] # karabo_da is a concurrency parameter
receiver_id = receiver_id.format(int(path_inset[-2:]))
proposal = list(filter(None, in_folder.strip('/').split('/')))[-2]
file_loc = 'proposal:{} runs:{} {} {}'.format(proposal, run_high, run_med, run_low)
# TODO
# this trick is needed until proper mapping is introduced
if len(db_module)>1:
db_module = db_module[int(path_inset[-2:])-1]
else:
db_module = db_module[0]
# Constants relevant for the analysis
run_nums = [run_high, run_med, run_low] # run number for G0/HG0, G1, G2
sensorSize = [1024, 512]
blockSize = [1024, 512]
xRange = [0, 0+sensorSize[0]]
yRange = [0, 0+sensorSize[1]]
gains = [0, 1, 2]
h5path = h5path.format(karabo_id, receiver_id)
creation_time = None
if use_dir_creation_date:
creation_time = get_dir_creation_date(in_folder, run_high)
print("Using {} as creation time".format(creation_time))
cal_db_interface = get_random_db_interface(cal_db_interface)
print('Calibration database interface: {}'.format(cal_db_interface))
offset_abs_threshold = [offset_abs_threshold_low, offset_abs_threshold_high]
if karabo_id_control == "":
karabo_id_control = karabo_id
print('Path inset ', path_inset)
print('Receiver Id ', receiver_id)
```
%% Output
Using 2020-03-04 11:22:16.791550 as creation time
Calibration database interface: tcp://max-exfl016:8016
Path inset JNGFR03
Receiver Id JNGFR03
%% Cell type:code id: tags:
``` python
def check_memoryCells(file_name, path):
with h5file(file_name, 'r') as f:
t_stamp = np.array(f[path + '/storageCells/timestamp'])
st_cells = np.array(f[path + '/storageCells/value'])
sc_start = np.array(f[path + '/storageCellStart/value'])
valid_train = t_stamp > 0
n_scs = st_cells[valid_train][0] + 1
sc_s = sc_start[valid_train][0]
return n_scs, sc_s
```
%% Cell type:code id: tags:
``` python
chunkSize = 100
filep_size = 1000
noiseCal = None
noise_map = None
offset_map = None
memoryCells = None
for i, r_n in enumerate(run_nums):
gain = i
print(f"Gain stage {gain}, run {r_n}")
valid_data = []
valid_cellids = []
if r_n is not None:
n_tr = 0
n_empty_trains = 0
n_empty_sc = 0
ped_dir = "{}/r{:04d}/".format(in_folder, r_n)
fp_name = path_template.format(r_n, karabo_da_control)
fp_path = '{}/{}'.format(ped_dir, fp_name)
n_files = len(glob.glob("{}/*{}*.h5".format(ped_dir, path_inset)))
myRange = range(0, n_files)
control_path = h5path_cntrl.format(karabo_id_control, receiver_control_id)
this_run_mcells, sc_start = check_memoryCells(fp_path.format(0).format(myRange[0]), control_path)
if noise_map is None:
if not manual_slow_data:
with h5py.File(fp_path.format(0), 'r') as f:
run_path = h5path_run.format(karabo_id_control, receiver_control_id)
integration_time = float(f[f'{run_path}/exposureTime/value'][()]*1e6)
bias_voltage = int(np.squeeze(f[f'{run_path}/vHighVoltage/value'])[0])
print("Integration time is {} us".format(integration_time))
print("Bias voltage is {} V".format(bias_voltage))
if this_run_mcells == 1:
memoryCells = 1
print('Dark runs in single cell mode\n storage cell start: {:02d}'.format(sc_start))
else:
memoryCells = 16
print('Dark runs in burst mode\n storage cell start: {:02d}'.format(sc_start))
noise_map = np.zeros(sensorSize+[memoryCells, 3])
offset_map = np.zeros(sensorSize+[memoryCells, 3])
fp_name = path_template.format(r_n, path_inset)
fp_path = '{}/{}'.format(ped_dir, fp_name)
myRange_P = range(0, sequences)
path = h5path
print("Reading data from {}".format(fp_path))
print("Run is: {}".format(r_n))
print("HDF5 path: {}".format(h5path))
imageRange = [0, filep_size*len(myRange)]
reader = JFChunkReader(filename = fp_path, readFun = jfreader.readData, size = filep_size, chunkSize = chunkSize,
path = h5path, image_range=imageRange, pixels_x = sensorSize[0], pixels_y = sensorSize[1],
x_range = xRange, y_range = yRange, imagesPerChunk=chunkSize, filesRange = myRange,
memoryCells=this_run_mcells, blockSize=blockSize)
for data in reader.readChunks():
images = np.array(data[0], dtype=np.float)
gainmaps = np.array(data[1], dtype=np.uint16)
trainId = np.array(data[2])
fr_num = np.array(data[3])
acelltable = np.array(data[4])
n_tr += acelltable.shape[-1]
this_tr = acelltable.shape[-1]
idxs = np.nonzero(trainId)[0]
images = images[..., idxs]
gainmaps = gainmaps[..., idxs]
fr_num = fr_num[..., idxs]
acelltable = acelltable[..., idxs]
if memoryCells == 1:
acelltable -= sc_start
n_empty_trains += this_tr - acelltable.shape[-1]
n_empty_sc += len(acelltable[acelltable > 15])
if i > 0 and memoryCells == 16: ## throwing away the second memory cell entry in lower gains
acelltable[1] = 255
images, gainmaps, acelltable = rollout_data([images, gainmaps, acelltable]) # makes 4-dim vecs into 3-dim
# makes 2-dim into 1-dim
# leaves 1-dim and 3-dim vecs
images, gainmaps, acelltable = sanitize_data_cellid([images, gainmaps], acelltable) # removes entries with cellID 255
valid_data.append(images)
valid_cellids.append(acelltable)
valid_data = np.concatenate(valid_data, axis=2)
valid_cellids = np.concatenate(valid_cellids, axis=0)
for cell in range(memoryCells):
thiscell = valid_data[...,valid_cellids == cell]
noise_map[...,cell,gain] = np.std(thiscell, axis=2)
offset_map[...,cell,gain] = np.mean(thiscell, axis=2)
print('G{:01d} dark calibration'.format(i))
print('Missed {:d} out of {:d} trains'.format(n_empty_trains, n_tr))
print('Lost {:d} images out of {:d}'.format(n_empty_sc, this_run_mcells * (n_tr - n_empty_trains)))
else:
print('missing G{:01d}'.format(i))
```
%% Output
Gain stage 0, run 126
Integration time is 250.00001525878906 us
Bias voltage is 90 V
Dark runs in single cell mode
storage cell start: 15
Reading data from /gpfs/exfel/exp/FXE/202030/p900121/raw//r0126//RAW-R0126-JNGFR03-S{:05d}.h5
Run is: 126
HDF5 path: /INSTRUMENT/FXE_XAD_JF500K/DET/JNGFR03:daqOutput/data
---------------------------------------------------------------------------
KeyboardInterrupt Traceback (most recent call last)
<ipython-input-5-2cdbfae5a631> in <module>
72
73 idxs = np.nonzero(trainId)[0]
---> 74 images = images[..., idxs]
75 gainmaps = gainmaps[..., idxs]
76 fr_num = fr_num[..., idxs]
KeyboardInterrupt:
%% Cell type:markdown id: tags:
## Offset and Noise Maps ##
Below offset and noise maps for the high ($g_0$) gain stage are shown, alongside the distribution of these values. One expects block-like structures mapping to the ASICs of the detector
%% Cell type:code id: tags:
``` python
import matplotlib.pyplot as plt
from XFELDetAna.core.util import remove_nans
%matplotlib inline
#%matplotlib notebook
from XFELDetAna.plotting.histogram import histPlot
from XFELDetAna.plotting.heatmap import heatmapPlot
g_name = ['G0', 'G1', 'G2']
g_range = [(0, 8000), (8000, 16000), (8000, 16000)]
n_range = [(0., 50.), (0., 50.), (0., 50.)]
unit = '[ADCu]'
```
%% Cell type:code id: tags:
``` python
for g_idx in gains:
for cell in range(0, memoryCells):
f_o0 = heatmapPlot(np.swapaxes(offset_map[..., cell, g_idx], 0, 1),
y_label="Row",
x_label="Column",
lut_label="Pedestal {} [ADCu]".format(g_name[g_idx]),
aspect=1.,
vmin=g_range[g_idx][0],
vmax=g_range[g_idx][1])
fo0, ax_o0 = plt.subplots()
res_o0 = histPlot(ax_o0, offset_map[..., cell, g_idx],
bins=800,
range=g_range[g_idx],
facecolor='b',
histotype='stepfilled')
ax_o0.tick_params(axis='both',which='major',labelsize=15)
ax_o0.set_title('Module pedestal distribution Cell {:02d}'.format(cell), fontsize=15)
ax_o0.set_xlabel('Pedestal {} [ADCu]'.format(g_name[g_idx]),fontsize=15)
ax_o0.set_yscale('log')
f_n0 = heatmapPlot(np.swapaxes(noise_map[..., cell, g_idx], 0, 1),
y_label="Row",
x_label="Column",
lut_label="RMS noise {} ".format(g_name[g_idx]) + unit,
aspect=1.,
vmin=n_range[g_idx][0],
vmax=n_range[g_idx][1])
fn0, ax_n0 = plt.subplots()
res_n0 = histPlot(ax_n0, noise_map[..., cell, g_idx],
bins=100,
range=n_range[g_idx],
facecolor='b',
histotype='stepfilled')
ax_n0.tick_params(axis='both',which='major',labelsize=15)
ax_n0.set_title('Module noise distribution Cell {:02d}'.format(cell), fontsize=15)
ax_n0.set_xlabel('RMS noise {} '.format(g_name[g_idx]) + unit,fontsize=15)
#ax_n0.set_yscale('log')
plt.show()
```
%% Cell type:markdown id: tags:
## Bad Pixel Map ###
The bad pixel map is deduced by comparing offset and noise of each pixel ($v_i$) and each gain ($g$) against the median value for that gain stage:
$$
v_i > \mathrm{median}(v_{k,g}) + n \sigma_{v_{k,g}}
$$
or
$$
v_i < \mathrm{median}(v_{k,g}) - n \sigma_{v_{k,g}}
$$
Values are encoded in a 32 bit mask, where for the dark image deduced bad pixels the following non-zero entries are relevant:
%% Cell type:code id: tags:
``` python
def print_bp_entry(bp):
print("{:<30s} {:032b}".format(bp.name, bp.value))
print_bp_entry(BadPixels.OFFSET_OUT_OF_THRESHOLD)
print_bp_entry(BadPixels.NOISE_OUT_OF_THRESHOLD)
print_bp_entry(BadPixels.OFFSET_NOISE_EVAL_ERROR)
```
%% Cell type:code id: tags:
``` python
bad_pixels_map = np.zeros(noise_map.shape, np.uint32)
def eval_bpidx(d):
mdn = np.nanmedian(d, axis=(0, 1))[None, None, :, :]
std = np.nanstd(d, axis=(0, 1))[None, None, :, :]
idx = (d > badpixel_threshold_sigma*std+mdn) | (d < (-badpixel_threshold_sigma)*std+mdn)
return idx
offset_abs_threshold = np.array(offset_abs_threshold)
bad_pixels_map[eval_bpidx(offset_map)] = BadPixels.OFFSET_OUT_OF_THRESHOLD.value
bad_pixels_map[~np.isfinite(offset_map)] |= BadPixels.OFFSET_NOISE_EVAL_ERROR.value
bad_pixels_map[eval_bpidx(noise_map)] |= BadPixels.NOISE_OUT_OF_THRESHOLD.value
bad_pixels_map[~np.isfinite(noise_map)] |= BadPixels.OFFSET_NOISE_EVAL_ERROR.value
bad_pixels_map[(offset_map < offset_abs_threshold[0][None, None, None, :]) | (offset_map > offset_abs_threshold[1][None, None, None, :])] |= BadPixels.OFFSET_OUT_OF_THRESHOLD.value
for g_idx in gains:
for cell in range(0, memoryCells):
bad_pixels = bad_pixels_map[:, :, cell, g_idx]
fn_0 = heatmapPlot(np.swapaxes(bad_pixels, 0, 1),
y_label="Row",
x_label="Column",
lut_label="Badpixels {} [ADCu]".format(g_name[g_idx]),
aspect=1.,
vmin=0)
```
%% Cell type:code id: tags:
``` python
constants = {'Offset': np.moveaxis(offset_map, 0, 1),
'Noise': np.moveaxis(noise_map, 0, 1),
'BadPixelsDark': np.moveaxis(bad_pixels_map, 0, 1)}
for key, const_data in constants.items():
metadata = ConstantMetaData()
const = getattr(Constants.jungfrau, key)()
const.data = const_data
metadata.calibration_constant = const
# set the operating condition
condition = Conditions.Dark.jungfrau(memory_cells=memoryCells, bias_voltage=bias_voltage,
integration_time=integration_time)
for parm in condition.parameters:
if parm.name == "Integration Time":
parm.lower_deviation = time_limits
parm.upper_deviation = time_limits
device = getattr(Detectors, db_module)
metadata.detector_condition = condition
# specify the version for this constant
if creation_time is None:
metadata.calibration_constant_version = Versions.Now(device=device)
else:
metadata.calibration_constant_version = Versions.Timespan(device=device,
start=creation_time)
metadata.calibration_constant_version.raw_data_location = file_loc
if db_output:
metadata.send(cal_db_interface, timeout=cal_db_timeout)
print('Constants {} is sent to the data base'.format(key))
# save everything to file.
# one file - one entrt in the DB
if local_output:
dpar = {}
for parm in metadata.detector_condition.parameters:
dpar[parm.name] = {'lower_deviation': parm.lower_deviation,
'upper_deviation': parm.upper_deviation,
'value': parm.value}
data_to_store = {}
data_to_store['condition'] = dpar
data_to_store['db_module'] = db_module
data_to_store['constant'] = key
data_to_store['data'] = const_data
data_to_store['creation_time'] = creation_time
data_to_store['dclass'] = 'jungfrau'
data_to_store['file_loc'] = file_loc
ofile = "{}/const_{}_{}.h5".format(out_folder, key, db_module)
save_dict_to_hdf5(data_to_store, ofile)
```
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