Pymepix postprocessing

The raw data acquired from the camera could be processed from command line with the command. The processing can also be triggered from the PymepixViewer.

Doing:

pymepix post-process -f FILE -o OUTPUT_FILE [-t TIMEWALK_FILE] [-c CENT_TIMEWALK_FILE] [-n NUMBER_OF_PROCESSES]

The generated output file has HDF data format may contain the following datagroups in its root:

• centroided
• raw
• timing/timepix
• triggers

The centroided datagroups contains the data after centroiding processing. It consists of several datasets : “trigger nr”, “x”, “y”, “tof”, “tot avg”, “tot max”, “clustersize”. Where “trigger nr” is event number, “x”/”y” - coordinates of centroid, tof is time-of-flight (time-of-arrival corrected to the timewalk effect), “tot avg” average value of tot for all voxels in the cluster, “tot max” - max tot value, “clustersize” - the number of voxels in the detected cluster.

The raw datagroups contains event data - voxel data with tof synchronized to first triigger. it consists of following datasets: “trigger nr”, “x”, “y”, “tof”, “tot”.

The timing/timepix datagroup has only two datasets: “trigger nr”, “timestamp”. Where “trigger nr” contains triggering event numbers from first trigger, while dataset “time” contains the timestamps for the corresponding trigger event in nanosecond in absolute time from the timer of the camera.

Datagroup triggers may contain two subgroups “trigger1” and “trigger2” corresponding to the first and second trigger of the camera. Each subgroup consists of only one dataset “time”. These are firing times of the corresponding trigger starting from acquisition in seconds. In case of first trigger these are the times of rising front of the detected trigger pulse. For the second trigger both rising and falling pulse edges are detected. Negative values corresponf to the falling edge.

Here’s an example to retrieve the data from the HDF5 file into a Pandas DataFrame: