rawdatautils.unpack.dataclasses Namespace Reference

Classes
class	RecordDataBase
class	SourceIDData
class	FragmentDataBase
class	TriggerRecordData
class	FragmentHeaderData
class	TriggerHeaderData
class	TriggerPrimitiveData
class	TriggerActivityData
class	TriggerCandidateData
class	DAQHeaderData
class	WIBEthHeaderData
class	WIBEthChannelDataBase
class	WIBEthAnalysisData
class	WIBEthWaveformData
class	TDEEthHeaderData
class	TDEEthChannelDataBase
class	TDEEthAnalysisData
class	TDEEthWaveformData
class	DAPHNEStreamHeaderData
class	DAPHNEChannelDataBase
class	DAPHNEStreamAnalysisData
class	DAPHNEStreamWaveformData
class	DAPHNEAnalysisData
class	DAPHNEWaveformData

Functions
	dts_to_seconds (dts)
	dts_to_datetime (dts_timestamp)
	sparsify_array_diff_locs_and_vals (arr)
	Sparsification and desparsifications for arrays.
	desparsify_array_diff_locs_and_vals (change_locations, change_values, arr_size)
	sparsify_array_diff_of_diff_locs_and_vals (arr)
	desparsify_array_diff_of_diff_locs_and_vals (arr_first, change_locations, change_values, arr_size)

Function Documentation

desparsify_array_diff_locs_and_vals()

rawdatautils.unpack.dataclasses.desparsify_array_diff_locs_and_vals	(		change_locations,
			change_values,
			arr_size )

Definition at line 32 of file dataclasses.py.

def desparsify_array_diff_locs_and_vals(change_locations, change_values, arr_size):
    # Create an empty array of the original size
    reconstructed_arr = np.empty(arr_size, dtype=np.uint)
    # Loop through each change location
    for i in range(len(change_locations)):
        # Apply the change value from the current change location to the end or next change location
        if (i + 1) == len(change_locations):
            reconstructed_arr[change_locations[i]:] = change_values[i]
        else:
            reconstructed_arr[change_locations[i]:change_locations[i + 1]] = change_values[i]
    return reconstructed_arr
 

desparsify_array_diff_of_diff_locs_and_vals()

rawdatautils.unpack.dataclasses.desparsify_array_diff_of_diff_locs_and_vals	(		arr_first,
			change_locations,
			change_values,
			arr_size )

Definition at line 54 of file dataclasses.py.

def desparsify_array_diff_of_diff_locs_and_vals(arr_first, change_locations, change_values, arr_size):
    # Reconstruct the differential array from sparse representation
    arr_diff = desparsify_array_diff_locs_and_vals(change_locations, change_values, arr_size - 1)
    # Reconstruct the original array by cumulatively summing the differences and adding the first value
    arr = np.concatenate((np.array([0], dtype=np.uint), arr_diff)).cumsum() + arr_first
    return arr
 
@dataclass(order=True)

dts_to_datetime()

rawdatautils.unpack.dataclasses.dts_to_datetime

(

dts_timestamp

)

Definition at line 16 of file dataclasses.py.

def dts_to_datetime(dts_timestamp):
    return datetime.fromtimestamp(dts_to_seconds(dts_timestamp), tz=pytz.timezone("UTC"))
 

dts_to_seconds()

rawdatautils.unpack.dataclasses.dts_to_seconds

(

dts

)

Definition at line 13 of file dataclasses.py.

def dts_to_seconds(dts):
     return dts*16 //1e9
 

sparsify_array_diff_locs_and_vals()

rawdatautils.unpack.dataclasses.sparsify_array_diff_locs_and_vals

(

arr

)

Sparsification and desparsifications for arrays.

Definition at line 21 of file dataclasses.py.

def sparsify_array_diff_locs_and_vals(arr):
    # Find indices where the value changes compared to the previous value, adjusting the first index to start from 0
    change_locations = np.insert(np.where(arr[1:] != arr[:-1])[0], 0, -1) + 1
    # Check if the array is not empty
    if len(arr) > 0:
        # Return locations of changes, values at these locations, and the length of the array
        return change_locations, arr[change_locations], len(arr)
    else:
        # Return empty results for an empty array
        return [], [], 0
 

sparsify_array_diff_of_diff_locs_and_vals()

rawdatautils.unpack.dataclasses.sparsify_array_diff_of_diff_locs_and_vals

(

arr

)

Definition at line 44 of file dataclasses.py.

def sparsify_array_diff_of_diff_locs_and_vals(arr):
    # Store the first value of the array for later reconstruction
    arr_first = arr[0]
    # Compute the difference of consecutive elements
    arr_diff = np.diff(arr)
    # Use sparsify function to find locations and values of changes in the diff array
    arr_diff_locs, arr_diff_vals, _ = sparsify_array_diff_locs_and_vals(arr_diff)
    # Return the first value, change locations, change values, and array size for reconstruction
    return arr_first, arr_diff_locs, arr_diff_vals, len(arr)