Add analysis component #44
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reader() needed to allow writing while also being read. writer() was not allowing 'Overwrite = False' to function correctly when fixed_size was False
packs/ana/analysis_utils.py
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| This file holds all the relevant functions for analysing data from h5 files. | ||
| """ | ||
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| def remove_secondaries(threshold, wf_data, time, event_number, verbose, WINDOW_END, bin_size): |
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Best practices (that I want to implement somewhat retroactively) is to include type-checking for all functions.
In your case that would look like (assuming your time and wf_data arrays are numpy based):
def remove_secondaries(threshold : int,
wf_data : np.ndarray,
time : np.ndarray,
event_number : int,
etc, etc) -> np.ndarray
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Also, general good practice is to pass the waveform data in first.
packs/ana/analysis_utils.py
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| def remove_secondaries(threshold, wf_data, time, event_number, verbose, WINDOW_END, bin_size): | ||
| ''' | ||
| Removes events with large secondary peaks |
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Give more context about what this function does. Step by step.
packs/ana/analysis_utils.py
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| after_window_wf = wf_data[collect_index(time, WINDOW_END) : len(wf_data)-1] | ||
| time_after = np.linspace(WINDOW_END, 2e6, num=len(after_window_wf), dtype=int) * bin_size # After WINDOW_END |
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perhaps add a comment explaining these two lines, I understand that its the waveform after the window, but it is a bit dense.
packs/ana/analysis_utils.py
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| if verbose > 1: | ||
| plt.plot(time, wf_data) | ||
| plt.xlabel('Time (ns)') | ||
| plt.ylabel('ADCs') | ||
| plt.yscale('log') | ||
| plt.axhline(second_peak, 0, 2e5, c = 'r', ls = '--') | ||
| plt.axvline(WINDOW_END, c = 'r', ls = '--') | ||
| plt.title(f'Event {event_number} subtracted waveform') | ||
| plt.show() | ||
| print(f'Event {event_number} excluded due to large secondary peak') |
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The docstring suggests that putting verbose = 0 will omit the print statement, but this isn't the case in the code.
I dislike the use of a verbosity argument, but there isn't a good alternative implemented within MULE at the moment (logging to be implemented in the future).
packs/ana/analysis_utils.py
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| return wf_data | ||
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| def suppress_baseline(wf_data, threshold): |
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Same comment as above wrt the types.
packs/ana/analysis_utils.py
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| # Return subtracted waveforms | ||
| return sub_data | ||
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| def average_waveforms(files, bin_size, window_args, chunk_size = 5, negative=False, baseline_mode='median', verbose=1, peak_threshold=800): |
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same as above wrt type checking
packs/ana/analysis_utils.py
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| def average_waveforms(files, bin_size, window_args, chunk_size = 5, negative=False, baseline_mode='median', verbose=1, peak_threshold=800): | ||
| ''' | ||
| Averages waveforms |
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expand please :) since its an encapsulating function (does many things within it), you either need each function within this one to have docstrings fully explaining their functionality, or a more extensive docstring here (preferably both).
| if os.path.exists(filepath): | ||
| print(f"Processing file: {filepath}") | ||
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| x = io.load_rwf_info(filepath, samples=2) |
| # Process the data in chunks to avoid memory overload, cooks data in chunks also | ||
| for start_idx in range(0, total_waveforms, chunk_size): | ||
| end_idx = min(start_idx + chunk_size, total_waveforms) | ||
| waveform_chunk = waveforms[start_idx:end_idx] |
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Lazy loading would avoid this roughness, although this may be a quicker method!
| [required] | ||
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| files = ['run19.h5'] | ||
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| window_args = {'WINDOW_START' : 4e2, | ||
| 'WINDOW_END' : 3e4, | ||
| 'BASELINE_POINT_1' : 1e6, | ||
| 'BASELINE_POINT_2' : 1.5e6, | ||
| 'BASELINE_RANGE_1' : 40e3, | ||
| 'BASELINE_RANGE_2' : 40e3} | ||
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| bin_size = 4 | ||
| chunk_size = 5 | ||
| negative = True | ||
| baseline_mode = 'median' | ||
| verbose = 1 | ||
| peak_threshold = 1000 | ||
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| save_path = 'test.csv' No newline at end of file |
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Would any of these be considered optional? You could set the save_path to be optional if it wrote out to a h5 file, and it would be stored within the same h5 from which it takes the data.
2 participants
Includes waveform averaging code in the form of ana.py, moves some functions around for cleanliness.