DSSC Configuraiton Files Generation 
===================================

Environment Setup
----------------

Create a folder in your office PC :code:`mkdir dsscConfigFileGenerator` go into the folder :code:`cd dsscConfigFileGenerator` and clone the following libraries:

 :code:`git clone https://git.xfel.eu/samartse/dsscdetlib`
       
 :code:`git clone https://git.xfel.eu/samartse/dssccaldata`
       
 :code:`git clone https://git.xfel.eu/samartse/dssccalibratio`
       
go to the dsscdetlib and run

 :code:`pip install . --upgrade -r requirements.txt`

once the installation is done, go back to the head folder (:code:`dssfConfigGenerator`) and launch jupyter by:

 :code:`jupyter notebook` 


in case if you work remotely open ssh tunnel from your home office pc to your office pc to access jupyter:

 :code:`ssh -L XXXX:localhost:XXXX exflqr****`

where :code:`exflqr***` hostname of your office PC.


now on your office pc run jupyer with port forwarding:
 :code:`jupyter notebook --port=XXXX`
       

Open this file by the jupyter notebook:
 :code:`dssccalibration/notebooks/GeneratePixelGainConfiguration.ipynb`

In the first cell, the main input parameters are defined, inline comments are explaining the meaninng of the variables:

 .. code:: bash

       
       PhotonEnergy = 2200.0  # this is photon energy in electron volts
       
       photons_per_bin = (1.0/5.0) # this is gain constant, 1/5 means that a single \
                                   # photon with 2200 electron volt energy will produce 5 bins.

       op_frequency = 2.25 # options 2.25 or 1.125 (MHz)

       integration_time = 105  # Integration time in terms 700 MHz clock ticks, 
                               # i.e. price of one thick is by 1.44 ns
                                   # options at 4.5   MHz: 35
                                   # options at 2.25  MHz: 35, 70, 80, 105
                                   # options at 1.125 MHz: 35, 70, 105, 140, 210

       Trimmed = True   # All pixels will be trimmed according to the gain obtained by Pulxar
                        # callibration data. In case of False flat gain, configuration will be
                # produced, i.e. setting of all pixels will be equal.

       fine_trim_targ = 12  # Iramp parameter, possible range is (0-63), but should not exceed 20 due to ADC limits

       RmpCurrDouble_settings = [0] # current dounle parameteres, possible lalues are 0 or 1.
                                    # 1 works for low gain and 0 for high gain
       
       CSA_Resistor_settings = [3,5] #[6] # all settings: [1,2,3,4,5,6,7]

       # Rest of the variables bellow should not be changed:
       proc_channels = [0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15]
       
       FCF_EnCap_settings = [1,2,3,4,5,6,7,8,9,11,15] # all settings: [1,2,3,4,5,6,7,8,9,11,15]
       
       CSA_FbCap_settings = [0,1,4,5,6,7] # all settings: [0,1,4,5,6,7]
      
       bins_dynamic_range = {4.5:250, 2.25:500, 1.125:500} #required for CSA max charge estimation

       default_integr_time = {4.5: 35, 2.25:80, 1.125:80}

       CSA_Vlim = 0.3
       
       FCF_Vlim = 1.6
       

Config file generation
----------------------

After adopting the above parameters, run the notebook. It takes up to 2 minutes. A new folder will be created :code:`pyGenConfigData/` under the main folder :code:`dsscConfigFileGenerator`.

The :code:`pyGenConfigData/` will contain any newly generated configuration file under a specific folder. These specific folder names contain photon energy, gain contact and integration time. For example: :code:`GenConf_TG2.2727_nG29_trimm_f2.25_intgr150/`. Under the gain folder, pixel registry and sequencer files are organized by quadrants. The folder now can be copied on the instrument Karabo server and deployed for every PPT device.