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Commit 74beb5c9 authored by Karim Ahmed's avatar Karim Ahmed
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send constant to database notebook

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notebooks/Jungfrau/gainCal_JF_Fit_sendDB_NBC.ipynb 0 → 100755
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%% Cell type:markdown id: tags:
## Send constants from file to the DB
%% Cell type:code id: tags:
``` python
in_folder = '/gpfs/exfel/data/user/mramilli/jungfrau/module_PSI_gainmaps/M109'
raw_folder = '/gpfs/exfel/exp/SPB/202330/900322/raw'
gain_map_file = '/gainMaps_M109_Burst_Fix_20230523.h5' # path
g0_run = 94
# Detector module parameters.
karabo_id = 'SPB_CFEL_JF1M'
da_name = 'JNGFR09'
db_module = "Jungfrau_M109" # ID of module in calibration database
# Parameter conditions
bias_voltage = 180 # detector bias voltage
integration_time = 10 # the detector acquisition rate, use 0 to try to auto-determine
gain_setting = 0
gain_mode = 0
memory_cells = 0 # number of memory cells used, use 0 to auto-derive
# Condition limits
bias_voltage_lims = [0, 200]
integration_time_lims = [0.1, 1000]
gain_map_name = 'gain_map_g0'
db_output = True
send_bpix = False
# CALCAT API parameters
cal_db_interface = "tcp://max-exfl016:8020" # the database interface to use
creation_time = "" # To overwrite the measured creation_time. Required Format: YYYY-MM-DD HR:MN:SC e.g. "2022-06-28 13:00:00"\
cal_db_timeout = 180000
```
%% Cell type:code id: tags:
``` python
# imports, usually no need to change anything here
import os
from h5py import File as h5file
import numpy as np
import datetime
import dateutil.parser
from iCalibrationDB import (
Constants,
Conditions,
)
from cal_tools.tools import (
send_to_db,
calcat_creation_time
)
from pathlib import Path
import matplotlib.pyplot as plt
from XFELDetAna.plotting.heatmap import heatmapPlot
%matplotlib inline
```
%% Cell type:code id: tags:
``` python
if gain_setting == 1:
gain_map_name = 'gain_map_hg0'
in_folder = Path(in_folder)
raw_folder = Path(raw_folder)
in_file = in_folder / gain_map_file
```
%% Cell type:code id: tags:
``` python
# Read constants from file
constant_data = {}
# Run's creation time:
creation_time = calcat_creation_time(in_folder, g0_run, creation_time)
print(f"Creation time: {creation_time}")
gain_map = None
bad_pixel_ff = None
with h5file(in_file, 'r') as f:
gain_map = np.array(f[gain_map_name])
if memory_cells == 0:
memory_cells = int(gain_map.shape[-2])
print(f'Memory Cells: {memory_cells}')
if 'bad_pixel_fit' in f.keys():
bad_pixel_ff = np.array(f['bad_pixel_fit'])
print(bad_pixel_ff.shape)
else:
print('BadPixelsFF not found')
if 'integration_time' in f.attrs.keys():
integration_time = np.float32(f.attrs['integration_time'])
print(f'Integration time: {integration_time} us')
else:
print('integration_time not found, using default {} us'.format(integration_time))
if 'bias_voltage' in f.attrs.keys():
bias_voltage = np.float32(f.attrs['bias_voltage'])
print(f'Bias voltage: {bias_voltage} V')
else:
print('bias_voltage not found, using default {} V'.format(bias_voltage))
if 'dir_date_iso' in f.attrs.keys():
dir_date_iso = str(f.attrs['dir_date_iso'])
creation_time = dateutil.parser.isoparse(dir_date_iso)
print(f'Acquisition Time: {creation_time}')
else:
print(f'Acquisition date not found!\nUsing this date: {creation_time}')
if 'karabo_id' in f.attrs.keys():
if isinstance(f.attrs['karabo_id'], str):
karabo_id = str(f.attrs['karabo_id'])
print(f'Karabo ID: {karabo_id}')
else:
print(f'karabo id not a string, using {karabo_id}')
else:
print(f'karabo id not found, using {karabo_id}')
if 'da_name' in f.attrs.keys():
if isinstance(f.attrs['da_name'], str):
da_name = str(f.attrs['da_name'])
print(f'DA name: {da_name}')
else:
print(f'DA name not a string, using {da_name}')
else:
print(f'DA name not found, using {da_name}')
if 'gain_mode' in f.attrs.keys():
gain_mode = np.int(f.attrs['gain_mode'])
print(f'Gain mode: {gain_mode}')
else:
print(f'gain mode not found, using default {gain_mode}')\
f.close()
```
%% Cell type:code id: tags:
``` python
gain = Constants.jungfrau.RelativeGain()
gain.data = gain_map
condition = Conditions.Dark.jungfrau(
memory_cells=memory_cells,
bias_voltage=bias_voltage,
integration_time=integration_time,
gain_mode=gain_mode,
gain_setting=gain_setting)
for parm in condition.parameters:
if parm.name == "Integration Time":
print('setting integration time limits')
parm.lower_deviation = integration_time - integration_time_lims[0]
parm.upper_deviation = integration_time_lims[1] - integration_time
for parm in condition.parameters:
if parm.name == "Sensor Bias Voltage":
print('setting bias voltage limits')
parm.lower_deviation = bias_voltage - bias_voltage_lims[0]
parm.upper_deviation = bias_voltage_lims[1] - bias_voltage
```
%% Cell type:code id: tags:
``` python
print('Creating time: ', creation_time)
if db_output:
send_to_db(
db_module=db_module,
karabo_id=karabo_id,
constant=gain,
condition=condition,
file_loc=in_file,
report_path='',
cal_db_interface=cal_db_interface,
creation_time=creation_time,
)
if bad_pixel_ff is not None and send_bpix:
bpix_ff = Constants.jungfrau.BadPixelsFF()
# WHY CONDITION DEVIATIONS ARE NOT CONSIDERED FOR THE BADPIXELS
condition = Conditions.Dark.jungfrau(
memory_cells=memory_cells,
bias_voltage=bias_voltage,
integration_time=integration_time,
gain_setting=gain_setting,
)
bpix_ff.data = bad_pixel_ff
send_to_db(
db_module=db_module,
karabo_id=karabo_id,
constant=bpix_ff,
condition=condition,
file_loc=in_file,
report_path='',
cal_db_interface=cal_db_interface,
creation_time=creation_time,
)
```
%% Cell type:code id: tags:
``` python
for g in range(0, gain_map.shape[3]):
f_im = heatmapPlot(
np.swapaxes(gain_map[..., 0, g], 0, 1),
y_label="Row",
x_label="Column",
lut_label="G{:01d}[ADCu/keV]".format(g),
aspect=1.,
vmin=np.min(gain_map[..., 0, g].ravel()),
vmax=np.max(gain_map[..., 0, g].ravel()),
)
plt.show()
```
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