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Commit 6d1aa45d authored by Mikhail Karnevskiy's avatar Mikhail Karnevskiy
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Add plotting constants of FCCD

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cal_tools/cal_tools/ana_tools.py
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......@@ -352,6 +352,20 @@ class HMType(Enum):
INSET_AXIS = 2
def get_range(data, scale):
"""
Get range, which includes most of the data points.
Range is calculated in units of median absolute deviations
:param data: numpy.array of data points
:param scale: range in units of median absolute deviations
:return:
"""
med = np.nanmedian(data)
mad = np.nanmedian(np.abs(data.flatten() - med))
return med - scale * mad, med + scale * mad
def hm_combine(data, fname=None, htype=None, **kwargs):
"""
Plot heatmap for calibration report
......
notebooks/FastCCD/PlotFromCalDB_FastCCD_NBC.ipynb 0 → 100644
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%% Cell type:markdown id: tags:
# Statistical analysis of calibration factors#
Author: Mikhail Karnevskiy, Steffen Hauf, Version 0.1
A description of the notebook.
%% Cell type:code id: tags:
``` python
cluster_profile = "noDB" # The ipcluster profile to use
start_date = "2019-01-30" # date to start investigation interval from
end_date = "2019-08-30" # date to end investigation interval at, can be "now"
nconstants = 10 # Number of time stamps to plot. If not 0, overcome start_date.
dclass="CCD" # Detector class
db_module = "fastCCD1" # detector entry in the DB to investigate
constants = ["Noise"]#, "Offset"] # constants to plot
gain_setting = [0,1,2,8] # gain stages
bias_voltage = [79] # Bias voltage
temperature = [235]#, 216, 245] # Operation temperature
integration_time = [1, 50] # Integration time
pixels_x=[1934]
pixels_y=[960]
max_time = 15
parameter_names = ['bias_voltage', 'integration_time', 'temperature',
'gain_setting', 'pixels_x', 'pixels_y'] # names of parameters
photon_energy = 9.2 # Photon energy of the beam
out_folder = "/gpfs/exfel/data/scratch/karnem/test_FCCD5/" # output folder
use_existing = "" # If not empty, constants stored in given folder will be used
cal_db_interface = "tcp://max-exfl016:8015#8025" # the database interface to use
cal_db_timeout = 180000 # timeout on caldb requests",
plot_range = 3 # range for plotting in units of median absolute deviations
```
%% Cell type:code id: tags:
``` python
import copy
import datetime
import dateutil.parser
import numpy as np
from operator import itemgetter
import os
import sys
import warnings
warnings.filterwarnings('ignore')
import h5py
import matplotlib
%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,
combine_constants, HMType,
hm_combine, combine_lists, get_range)
```
%% Cell type:code id: tags:
``` python
# Prepare variables
spShape = (967, 10) # Shape of superpixel
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
det = getattr(Detectors, db_module)
dconstants = getattr(Constants, dclass)(det.detector_type)
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):
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
data = sorted(data, key=itemgetter('begin_at'))
print('Number of retrieved constants: {}'.format(len(data)) )
if const in constantsDark:
# Request BP constant versions
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
print('Number of retrieved darks: {}'.format(len(dataBP)) )
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
(nconstants==0 and dt.replace(tzinfo=None) < start)):
continue
if nconstants>0 and constant_parameters.count(pars)>nconstants-1:
break
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[:,:,0].reshape(
int(a.shape[0] / rebin[0]),
rebin[0],
int(a.shape[1] / rebin[1]),
rebin[1])
def modify_const(const, data, isBP = False):
return data
ret_constants = {}
constand_data = ConstantMetaData()
constant_BP = ConstantMetaData()
for i, constant_version in enumerate(constant_versions):
const = constant_version['data_set_name'].split('/')[-2]
qm = db_module
print("constant: {}, module {}".format(const,qm))
constand_data.retrieve_from_version_info(constant_version)
# Convert parameters to dict
dpar = {p.name: p.value for p in constand_data.detector_condition.parameters}
const = "{}_{}_{}_{}".format(const,
constant_parameters[i]['gain_setting'],
constant_parameters[i]['temperature'],
constant_parameters[i]['integration_time'])
if not const in ret_constants:
ret_constants[const] = {}
if not qm in ret_constants[const]:
ret_constants[const][qm] = []
cdata = constand_data.calibration_constant.data
ctime = constand_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 constand_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
keys = {
'Mean': ['data', '', 'Mean over pixels'],
'std': ['dataStd', '', '$\sigma$ over pixels'],
'MeanBP': ['dataBP', 'Good pixels only', 'Mean over pixels'],
'NBP': ['nBP', 'Fraction of BP', 'Fraction of BP'],
'stdBP': ['dataBPStd', 'Good pixels only', '$\sigma$ over pixels'],
'stdASIC': ['', '', '$\sigma$ over ASICs'],
'stdCell': ['', '', '$\sigma$ over Cells'],
}
```
%% Cell type:code id: tags:
``` python
print('Plot calibration constants')
# loop over constat type
for const, modules in ret_constants.items():
const, gain, temp, int_time = const.split("_")
print('Const: {}'.format(const))
# loop over modules
mod_data = {}
mod_data['stdASIC'] = []
mod_data['stdCell'] = []
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
# Avoid to low values
if const in ["Noise", "Offset", "Noise-e"]:
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"] / spShape[0] / spShape[1] * 100
# 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.nanmean(rdata[key], axis=(1, 2))
# Summary information over modules
for key in pdata:
if key not in mod_data:
mod_data[key] = []
mod_data[key].append(np.nanmean(pdata[key], axis=0))
mod_data['stdASIC'].append(np.nanstd(rdata['Mean'], axis=(1, 2)))
mod_names.append(mod)
mod_times.append(ctimes_ticks)
# Plotting
for key in pdata:
if len(pdata[key].shape)<2:
continue
if key == 'NBP':
unit = '[%]'
else:
unit = '[ADU]'
if const == 'Noise-e':
unit = '[$e^-$]'
title = '{}, module {}, gain {} {}'.format(
const, mod, gain, keys[key][1])
cb_label = '{}, {} {}'.format(const, keys[key][2], unit)
vmin,vmax = get_range(pdata[key][::-1].flatten(), plot_range)
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{}_t{}_t{}_ASIC_{}.png'.format(
out_folder, const, mod.replace('_', ''), gain, temp, int_time, key),
pad=[0.125, 0.125, 0.12, 0.185])
```
xfel_calibrate/notebooks.py
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......@@ -129,6 +129,12 @@ notebooks = {
"use function": "balance_sequences",
"cluster cores": 4},
},
"STATS_FROM_DB": {
"notebook": "notebooks/FastCCD/PlotFromCalDB_FastCCD_NBC.ipynb",
"concurrency": {"parameter": None,
"default concurrency": None,
"cluster cores": 1},
},
},
"JUNGFRAU": {
"DARK": {
......
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