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Commit 0cc34a45 authored by Mikhail Karnevskiy's avatar Mikhail Karnevskiy
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Add plotting Cal. constants for Jungfrau

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%% Cell type:markdown id: tags:
# Statistical analysis of calibration factors#
Author: Mikhail Karnevskiy, Steffen Hauf, Version 0.1
Calibration constants for JungFrau detector from the data base with injection time between start_date and end_date are considered.
To be visualized, calibration constants are averaged per group of pixels. Plots shows calibration constant over time for each constant.
Values shown in plots are saved in h5 files.
%% Cell type:code id: tags:
``` python
cluster_profile = "noDB" # The ipcluster profile to use
start_date = "2019-06-30" # date to start investigation interval from
end_date = "2019-09-01" # date to end investigation interval at, can be "now"
dclass="jungfrau" # Detector class
db_modules = ["Jungfrau_M125", "Jungfrau_M260"] # detector entry in the DB to investigate
constants = ["Noise", "Offset"] # constants to plot
nconstants = 10 # Number of time stamps to plot. If not 0, overcome start_date.
bias_voltage = [90, 180]
memory_cells = [1]
pixels_x = [1024]
pixels_y = [512, 1024]
temperature = [291]
integration_time = [50, 250]
gain_setting = [0]
parameter_names = ['bias_voltage', 'integration_time', 'pixels_x', 'pixels_y', 'gain_setting',
'temperature', 'memory_cells'] # names of parameters
max_time = 15
photon_energy = 9.2 # Photon energy of the beam
out_folder = "/gpfs/exfel/data/scratch/karnem/test_JF2/" # output folder
use_existing = "" # If not empty, constants stored in given folder will be used
cal_db_interface = "tcp://max-exfl016:8016" # the database interface to use
cal_db_timeout = 180000 # timeout on caldb requests",
range_offset = [1000., 2200] # plotting range for offset: high gain l, r, medium gain l, r
range_noise = [15, 20, 3, 7, 1, 6] # plotting range for noise: high gain l, r, medium gain l, r
plot_range = 3 # range for plotting in units of median absolute deviations
spShape = [256, 64] # Shape of superpixel
```
%% Cell type:code id: tags:
``` python
import copy
import datetime
import dateutil.parser
import numpy as np
import os
import sys
import warnings
warnings.filterwarnings('ignore')
import matplotlib.pyplot as plt
%matplotlib inline
from iCalibrationDB import Constants, Conditions, Detectors, ConstantMetaData
from cal_tools.tools import get_from_db, get_random_db_interface
from cal_tools.ana_tools import (save_dict_to_hdf5, load_data_from_hdf5,
HMType, hm_combine,
combine_lists, get_range)
```
%% Cell type:code id: tags:
``` python
# Prepare variables
parameters = [globals()[x] for x in parameter_names]
constantsDark = {'Noise': 'BadPixelsDark',
'Offset': 'BadPixelsDark'}
print('Bad pixels data: ', constantsDark)
# Define parameters in order to perform loop over time stamps
start = datetime.datetime.now() if start_date.upper() == "NOW" else dateutil.parser.parse(
start_date)
end = datetime.datetime.now() if end_date.upper() == "NOW" else dateutil.parser.parse(
end_date)
# Create output folder
os.makedirs(out_folder, exist_ok=True)
# Get getector conditions
dconstants = getattr(Constants, dclass)
print('CalDB Interface: {}'.format(cal_db_interface))
print('Start time at: ', start)
print('End time at: ', end)
```
%% Cell type:code id: tags:
``` python
parameter_list = combine_lists(*parameters, names = parameter_names)
print(parameter_list)
```
%% Cell type:code id: tags:
``` python
# Retrieve list of meta-data
constant_versions = []
constant_parameters = []
constantBP_versions = []
# Loop over constants
for c, const in enumerate(constants):
for db_module in db_modules:
det = getattr(Detectors, db_module)
if use_existing != "":
break
# Loop over parameters
for pars in parameter_list:
if (const in ["Offset", "Noise", "SlopesPC"] or "DARK" in const.upper()):
dcond = Conditions.Dark
mcond = getattr(dcond, dclass)(**pars)
else:
dcond = Conditions.Illuminated
mcond = getattr(dcond, dclass)(**pars,
photon_energy=photon_energy)
print('Request: ', const, 'with paramters:', pars)
# Request Constant versions for given parameters and module
data = get_from_db(det,
getattr(dconstants,
const)(),
copy.deepcopy(mcond), None,
cal_db_interface,
creation_time=start,
verbosity=0,
timeout=cal_db_timeout,
meta_only=True,
version_info=True)
if not isinstance(data, list):
continue
if const in constantsDark:
# Request BP constant versions
print('constantDark:', constantsDark[const], )
dataBP = get_from_db(det,
getattr(dconstants,
constantsDark[const])(),
copy.deepcopy(mcond), None,
cal_db_interface,
creation_time=start,
verbosity=0,
timeout=cal_db_timeout,
meta_only=True,
version_info=True)
if not isinstance(data, list) or not isinstance(dataBP, list):
continue
found_BPmatch = False
for d in data:
# Match proper BP constant version
# and get constant version within
# requested time range
if d is None:
print('Time or data is not found!')
continue
dt = dateutil.parser.parse(d['begin_at'])
if dt.replace(tzinfo=None) > end or dt.replace(tzinfo=None) < start:
continue
closest_BP = None
closest_BPtime = None
for dBP in dataBP:
if dBP is None:
print("Bad pixels are not found!")
continue
dt = dateutil.parser.parse(d['begin_at'])
dBPt = dateutil.parser.parse(dBP['begin_at'])
if dt == dBPt:
found_BPmatch = True
else:
if np.abs(dBPt-dt).seconds < (max_time*60):
if closest_BP is None:
closest_BP = dBP
closest_BPtime = dBPt
else:
if np.abs(dBPt-dt) < np.abs(closest_BPtime-dt):
closest_BP = dBP
closest_BPtime = dBPt
if dataBP.index(dBP) == len(dataBP)-1:
if closest_BP:
dBP = closest_BP
dBPt = closest_BPtime
found_BPmatch = True
else:
print('Bad pixels are not found!')
if found_BPmatch:
print("Found constant {}: begin at {}".format(const, dt))
print("Found bad pixels at {}".format(dBPt))
constantBP_versions.append(dBP)
constant_versions.append(d)
constant_parameters.append(copy.deepcopy(pars))
found_BPmatch = False
break
else:
constant_versions += data
constant_parameters += [copy.deepcopy(pars)]*len(data)
# Remove dublications
constant_versions_tmp = []
constant_parameters_tmp = []
for i, x in enumerate(constant_versions):
if x not in constant_versions_tmp:
constant_versions_tmp.append(x)
constant_parameters_tmp.append(constant_parameters[i])
constant_versions=constant_versions_tmp
constant_parameters=constant_parameters_tmp
print('Number of stored constant versions is {}'.format(len(constant_versions)))
```
%% Cell type:code id: tags:
``` python
def get_rebined(a, rebin):
return a.reshape(
int(a.shape[0] / rebin[0]),
rebin[0],
int(a.shape[1] / rebin[1]),
rebin[1],
a.shape[2],
a.shape[3])
def modify_const(const, data, isBP = False):
return data
ret_constants = {}
constant_data = ConstantMetaData()
constant_BP = ConstantMetaData()
# sort over begin_at
idxs, _ = zip(*sorted(enumerate(constant_versions),
key=lambda x: x[1]['begin_at'], reverse=True))
for i in idxs:
const = constant_versions[i]['data_set_name'].split('/')[-2]
qm = constant_versions[i]['physical_device']['name']
if not const in ret_constants:
ret_constants[const] = {}
if not qm in ret_constants[const]:
ret_constants[const][qm] = []
if nconstants>0 and len(ret_constants[const][qm])>=nconstants:
continue
print("constant: {}, module {}".format(const,qm))
constant_data.retrieve_from_version_info(constant_versions[i])
cdata = constant_data.calibration_constant.data
ctime = constant_data.calibration_constant_version.begin_at
cdata = modify_const(const, cdata)
if len(constantBP_versions)>0:
constant_BP.retrieve_from_version_info(constantBP_versions[i])
cdataBP = constant_BP.calibration_constant.data
cdataBP = modify_const(const, cdataBP, True)
if cdataBP.shape != cdata.shape:
print('Wrong bad pixel shape! {}, expected {}'.format(cdataBP.shape, cdata.shape))
continue
# Apply bad pixel mask
cdataABP = np.copy(cdata)
cdataABP[cdataBP > 0] = np.nan
# Create superpixels for constants with BP applied
cdataABP = get_rebined(cdataABP, spShape)
toStoreBP = np.nanmean(cdataABP, axis=(1, 3))
toStoreBPStd = np.nanstd(cdataABP, axis=(1, 3))
# Prepare number of bad pixels per superpixels
cdataBP = get_rebined(cdataBP, spShape)
cdataNBP = np.nansum(cdataBP > 0, axis=(1, 3))
else:
toStoreBP = 0
toStoreBPStd = 0
cdataNBP = 0
# Create superpixels for constants without BP applied
cdata = get_rebined(cdata, spShape)
toStoreStd = np.nanstd(cdata, axis=(1, 3))
toStore = np.nanmean(cdata, axis=(1, 3))
# Convert parameters to dict
dpar = {p.name: p.value for p in constant_data.detector_condition.parameters}
print("Store values in dict", const, qm, ctime)
ret_constants[const][qm].append({'ctime': ctime,
'nBP': cdataNBP,
'dataBP': toStoreBP,
'dataBPStd': toStoreBPStd,
'data': toStore,
'dataStd': toStoreStd,
'mdata': dpar})
```
%% Cell type:code id: tags:
``` python
if use_existing == "":
print('Save data to /CalDBAna_{}_{}.h5'.format(dclass, db_module))
save_dict_to_hdf5(ret_constants,
'{}/CalDBAna_{}_{}.h5'.format(out_folder, dclass, db_module))
```
%% Cell type:code id: tags:
``` python
if use_existing == "":
fpath = '{}/CalDBAna_{}_*.h5'.format(out_folder, dclass)
else:
fpath = '{}/CalDBAna_{}_*.h5'.format(use_existing, dclass)
print('Load data from {}'.format(fpath))
ret_constants = load_data_from_hdf5(fpath)
```
%% Cell type:code id: tags:
``` python
# Parameters for plotting
# Define range for plotting
rangevals = {
"OffsetEPix100": [range_offset[0:2], range_offset[2:4]],
"Noise10Hz": [range_noise[0:2], range_noise[2:4], range_noise[4:6]],
}
keys = {
'Mean': ['data', '', 'Mean over pixels'],
'std': ['dataStd', '', '$\sigma$ over pixels'],
'MeanBP': ['dataBP', 'Good pixels only', 'Mean over pixels'],
'NBP': ['nBP', 'Fraction of BP', 'Number of BP'],
'stdBP': ['dataBPStd', 'Good pixels only', '$\sigma$ over pixels'],
}
gain_name = ['High', 'Medium', 'Low']
```
%% Cell type:code id: tags:
``` python
print('Plot calibration constants')
# loop over constat type
for const, modules in ret_constants.items():
for gain in range(3):
print('Const: {}'.format(const))
# summary over modules
mod_data = {}
mod_names = []
mod_times = []
# Loop over modules
for mod, data in modules.items():
print(mod)
ctimes = np.array(data["ctime"])
ctimes_ticks = [x.strftime('%y-%m-%d') for x in ctimes]
if ("mdata" in data):
cmdata = np.array(data["mdata"])
for i, tick in enumerate(ctimes_ticks):
ctimes_ticks[i] = ctimes_ticks[i] + \
', V={:1.0f}'.format(cmdata[i]['Sensor Temperature']) + \
', T={:1.0f}'.format(
cmdata[i]['Integration Time'])
sort_ind = np.argsort(ctimes_ticks)
ctimes_ticks = list(np.array(ctimes_ticks)[sort_ind])
# Create sorted by data dataset
rdata = {}
for key, item in keys.items():
if item[0] in data:
rdata[key] = np.array(data[item[0]])[sort_ind]
nTimes = rdata['Mean'].shape[0]
nPixels = rdata['Mean'].shape[1] * rdata['Mean'].shape[2]
nBins = nPixels
# Select gain
if const not in ["Gain", "Noise-e"]:
for key in rdata:
if len(rdata[key].shape)<5:
continue
rdata[key] = rdata[key][..., 0, gain]
# Avoid to low values
if const in ["Noise10Hz", "Offset10Hz"]:
rdata['Mean'][rdata['Mean'] < 0.1] = np.nan
if 'MeanBP' in rdata:
rdata['MeanBP'][rdata['MeanBP'] < 0.1] = np.nan
if 'NBP' in rdata:
rdata['NBP'] = rdata['NBP'].astype(float)
rdata['NBP'][rdata['NBP'] == spShape[0]*spShape[1]] = np.nan
# Reshape: ASICs over cells for plotting
pdata = {}
for key in rdata:
if len(rdata[key].shape)<3:
continue
pdata[key] = rdata[key].reshape(nTimes, nBins).swapaxes(0, 1)
# Summary over ASICs
adata = {}
for key in rdata:
if len(rdata[key].shape)<3:
continue
adata[key] = np.nansum(rdata[key], axis=(1, 2))
# Summary information over modules
for key in pdata:
if key not in mod_data:
mod_data[key] = []
if key == 'NBP':
mod_data[key].append(np.nansum(pdata[key], axis=0))
else:
mod_data[key].append(np.nanmean(pdata[key], axis=0))
mod_names.append(mod)
mod_times.append(ctimes[sort_ind])
# Plotting
for key in pdata:
if len(pdata[key].shape)<2:
continue
vmin,vmax = get_range(pdata[key][::-1].flatten(), plot_range)
#if const in rangevals and key in ['Mean', 'MeanBP']:
# vmin = rangevals[const][0][0]
# vmax = rangevals[const][0][1]
if key == 'NBP':
unit = '[%]'
else:
unit = '[ADU]'
if const == 'Noise-e':
unit = '[$e^-$]'
title = '{}, module {}, {}'.format(
const, mod, keys[key][1])
cb_label = '{}, {} {}'.format(const, keys[key][2], unit)
hm_combine(pdata[key][::-1], htype=HMType.mro,
x_label='Creation Time', y_label='ASIC ID',
x_ticklabels=ctimes_ticks,
x_ticks=np.arange(len(ctimes_ticks))+0.3,
title=title, cb_label=cb_label,
vmin=vmin, vmax=vmax,
fname='{}/{}_{}_g{}_ASIC_{}.png'.format(
out_folder, const, mod.replace('_', ''), gain, key),
pad=[0.125, 0.125, 0.12, 0.185])
# Summary over modules
for key in mod_data:
if key == 'NBP':
unit = ''
else:
unit = '[ADU]'
title = '{}, All modules, {} gain, {}'.format(
const, gain_name[gain], keys[key][1])
fig = plt.figure(figsize=(12,12) )
for i in range(len(mod_data[key])):
plt.scatter(mod_times[i], mod_data[key][i], label=mod_names[i])
plt.grid()
plt.xlabel('Creation Time')
plt.ylabel('{}, {} {}'.format(const, keys[key][2], unit))
plt.legend(loc='best guess')
plt.title(title)
fig.savefig('{}/{}_{}_g{}_ASIC_{}.png'.format(
out_folder, const, 'All', gain, key))
```
xfel_calibrate/notebooks.py
+ 7
− 0
View file @ 0cc34a45
......@@ -150,6 +150,13 @@ notebooks = {
"use function": "balance_sequences",
"cluster cores": 4},
},
"STATS_FROM_DB": {
"notebook": "notebooks/Jungfrau/PlotFromCalDB_Jungfrau_NBC.ipynb",
"concurrency": {"parameter": None,
"default concurrency": None,
"cluster cores": 1},
},
},
"EPIX": {
"DARK": {
......
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