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Som plugin (#1269)
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* som peaklet classification

* Updating plugin, this needs straxen PR Waveform attributes #745

* update plugin, this works now

* remove som type and propagate som classification up to peaks

* do not classify type 0

* Fix issue with array sisize

* add som_type

* Update peaklet_classification_som.py

fix error in data_to_log_deciles

* fix som_type issue

* update model + some function details

* new file + new type 3 added

* separate som_plugin from main pipeline

* Add tests to produce input vectors

* current status of plugin, detach plugin before we are ready fro review

* start making new context

* fix plugin and context

* clean up plugin

* Update peaklet_classification_som.py

Comply with some of the pep8 suggestions

* conform to pep8 style

* Update peaklet_classification_som.py

missed a function with upper cases

* Update peaklet_classification_som.py

pep8 space

---------

Co-authored-by: ahiguera-mx <ahiguera-mx@fried.rice.edu>
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LuisSanchez25 and ahiguera-mx authored Oct 9, 2023
1 parent 8c70f8f commit 02621e1
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13 changes: 13 additions & 0 deletions straxen/contexts.py
Original file line number Diff line number Diff line change
Expand Up @@ -8,6 +8,7 @@
import typing as ty
from pandas.util._decorators import deprecate_kwarg
import socket
from straxen.plugins.peaklets.peaklet_classification_som import PeakletClassificationSOM

common_opts = dict(
register_all=[],
Expand Down Expand Up @@ -111,6 +112,18 @@ def xenonnt(cmt_version='global_ONLINE', xedocs_version=None,
return st


def xenonnt_som(cmt_version='global_ONLINE', xedocs_version=None,
_from_cutax=False, **kwargs):
"""XENONnT context for the SOM"""

st = straxen.contexts.xenonnt(cmt_version=cmt_version, xedocs_version=xedocs_version,
_from_cutax=_from_cutax, **kwargs)
del st._plugin_class_registry['peaklet_classification']
st.register(PeakletClassificationSOM)

return st


def find_rucio_local_path(include_rucio_local, _rucio_local_path):
"""
Check the hostname to determine which rucio local path to use. Note that access to
Expand Down
297 changes: 297 additions & 0 deletions straxen/plugins/peaklets/peaklet_classification_som.py
Original file line number Diff line number Diff line change
@@ -0,0 +1,297 @@
import numpy as np
import numpy.lib.recfunctions as rfn
from scipy.spatial.distance import cdist
from straxen.plugins.peaklets.peaklet_classification import PeakletClassification
import numba

import strax
import straxen

export, __all__ = strax.exporter()


@export
class PeakletClassificationSOM(PeakletClassification):

"""
Self-Organizing Maps (SOM)
https://xe1t-wiki.lngs.infn.it/doku.php?id=xenon:xenonnt:lsanchez:unsupervised_neural_network_som_methods
For peaklet classification. We this pluggin will provide 2 data types, the 'type' we are
already familiar with, classifying peaklets as s1, s2 (using the new classification) or
unknown (from the previous classification). As well as a new data type, SOM type, which
will be assigned numbers based on the cluster in the SOM in which they are found. For
each version I will make some documentation in the corrections repository explaining
what I believe each cluster represents.
This correction/plugin is currently on the testing phase, feel free to use it if you are
curious or just want to test it or try it out but note this is note ready to be used in
analysis.
"""

__version__ = '0.0.1'

depends_on = ('peaklets')

provides = ('peaklet_classification', 'som_peaklet_data')
data_kind = dict(peaklet_classification='peaklets',
som_peaklet_data='peaklets')

parallel = True

som_files = straxen.URLConfig(
default='resource://xedocs://som_classifiers?attr=value&version=v1&run_id=045000&fmt=npy'
)

def infer_dtype(self):
dtype = {}
dtype['peaklet_classification'] = (
strax.peak_interval_dtype +
[('type', np.int8, 'Classification of the peak(let)')]
)
dtype['som_peaklet_data'] = (
strax.peak_interval_dtype +
[('som_type', np.int8, 'SOM type of the peak(let)')] +
[('loc_x_som', np.int16, 'x location of the peak(let) in the SOM')] +
[('loc_y_som', np.int16, 'y location of the peak(let) in the SOM')]
)

return dtype

def setup(self):

self.som_weight_cube = self.som_files['weight_cube']
self.som_img = self.som_files['som_img']
self.som_norm_factors = self.som_files['norm_factors']
self.som_s1_array = self.som_files['s1_array']
self.som_s2_array = self.som_files['s2_array']
self.som_s3_array = self.som_files['s3_array']
self.som_s0_array = self.som_files['s0_array']

def compute(self, peaklets):
# Current classification
ptype = np.zeros(len(peaklets), dtype=np.int8)

# Properties needed for classification:
rise_time = -peaklets['area_decile_from_midpoint'][:, 1]
n_channels = (peaklets['area_per_channel'] > 0).sum(axis=1)
n_top = self.n_top_pmts
area_top = peaklets['area_per_channel'][:, :n_top].sum(axis=1)
area_total = peaklets['area_per_channel'].sum(axis=1)
area_fraction_top = area_top / area_total

is_large_s1 = (peaklets['area'] >= 100)
is_large_s1 &= (rise_time <= self.s1_max_rise_time_post100)
is_large_s1 &= peaklets['tight_coincidence'] >= self.s1_min_coincidence

is_small_s1 = peaklets["area"] < 100
is_small_s1 &= rise_time < self.upper_rise_time_area_boundary(
peaklets["area"],
*self.s1_risetime_area_parameters,
)

is_small_s1 &= rise_time < self.upper_rise_time_aft_boundary(
area_fraction_top,
*self.s1_risetime_aft_parameters,
*self.s1_flatten_threshold_aft,
)

is_small_s1 &= peaklets['tight_coincidence'] >= self.s1_min_coincidence

ptype[is_large_s1 | is_small_s1] = 1

is_s2 = n_channels >= self.s2_min_pmts
is_s2[is_large_s1 | is_small_s1] = False
ptype[is_s2] = 2

# SOM classification
peaklets_w_type = peaklets.copy()
peaklets_w_type['type'] = ptype
mask_non_zero = peaklets_w_type['type'] != 0
peaklets_w_type = peaklets_w_type[mask_non_zero]
result = np.zeros(len(peaklets), dtype=self.dtype['peaklet_classification'])
som_info = np.zeros(len(peaklets), dtype=self.dtype['som_peaklet_data'])
som_type, x_som, y_som = recall_populations(peaklets_w_type, self.som_weight_cube,
self.som_img,
self.som_norm_factors)

som_info['time'] = peaklets['time']
som_info['length'] = peaklets['length']
som_info['dt'] = peaklets['dt']
som_info['som_type'][mask_non_zero] = som_type
som_info['loc_x_som'][mask_non_zero] = x_som
som_info['loc_y_som'][mask_non_zero] = y_som

strax_type = som_type_to_type(som_type,
self.som_s1_array,
self.som_s2_array,
self.som_s3_array,
self.som_s0_array)
result['time'] = peaklets['time']
result['length'] = peaklets['length']
result['dt'] = peaklets['dt']
result['type'][mask_non_zero] = strax_type

return dict(peaklet_classification=result, som_peaklet_data=som_info)



def recall_populations(dataset, weight_cube, som_cls_img, norm_factors):

"""
Master function that should let the user provide a weightcube,
a reference img as a np.array, a dataset and a set of normalization factors.
In theory, if these 5 things are provided, this function should output
the original data back with one added field with the name "SOM_type"
weight_cube: SOM weight cube (3D array)
som_cls_img: SOM reference image as a numpy array
dataset: Data to preform the recall on (Should be peaklet level data)
normfactos: A set of 11 numbers to normalize the data so we can preform a recall
"""

xdim, ydim, zdim = weight_cube.shape
img_xdim, img_ydim, img_zdim = som_cls_img.shape
unique_colors = np.unique(np.reshape(som_cls_img, [xdim * ydim, 3]), axis=0)
# Checks that the reference image matches the weight cube
assert xdim == img_xdim, f'Dimensions mismatch between SOM weight cube ({xdim}) and reference image ({img_xdim})'
assert ydim == img_ydim, f'Dimensions mismatch between SOM weight cube ({ydim}) and reference image ({img_ydim})'

assert all(dataset['type'] != 0), 'Dataset contains unclassified peaklets'
# Get the deciles representation of data for recall
decile_transform_check = data_to_log_decile_log_area_aft(dataset, norm_factors)
# preform a recall of the dataset with the weight cube
# assign each population color a number (can do from previous function)
ref_map = generate_color_ref_map(som_cls_img, unique_colors, xdim, ydim)
som_cls_array = np.empty(len(dataset['area']))
som_cls_array[:] = np.nan
# Make new numpy structured array to save the SOM cls data
data_with_SOM_cls = rfn.append_fields(dataset, 'SOM_type', som_cls_array)
# preforms the recall and assigns SOM_type label
output_data, x_som, y_som = som_cls_recall(data_with_SOM_cls, decile_transform_check, weight_cube, ref_map)
return output_data['SOM_type'], x_som, y_som


def generate_color_ref_map(color_image, unique_colors, xdim, ydim):
ref_map = np.zeros((xdim, ydim))
for color in np.arange(len(unique_colors)):
mask = np.all(np.equal(color_image, unique_colors[color, :]), axis=2)
indices = np.argwhere(mask) # generates a 2d mask
for loc in np.arange(len(indices)):
ref_map[indices[loc][0], indices[loc][1]] = color
return ref_map


def som_cls_recall(array_to_fill, data_in_som_fmt, weight_cube, reference_map):
som_xdim, som_ydim, _ = weight_cube.shape
# for data_point in data_in_SOM_fmt:
distances = cdist(weight_cube.reshape(-1, weight_cube.shape[-1]), data_in_som_fmt, metric='euclidean')
w_neuron = np.argmin(distances, axis=0)
x_idx, y_idx = np.unravel_index(w_neuron, (som_xdim, som_ydim))
array_to_fill['SOM_type'] = reference_map[x_idx, y_idx]
return array_to_fill, x_idx, y_idx


def som_type_to_type(som_type, s1_array, s2_array, s3_array, s0_array):
"""
Converts the SOM type into either S1 or S2 type (1, 2)
som_type: array with integers corresponding to the different SOM types
s1_array: array containing the number corresponding to the SOM types which should
be converted to S1's
"""
som_type_copy = som_type.copy()
som_type_copy[np.isin(som_type_copy, s1_array)] = 1234
som_type_copy[np.isin(som_type_copy, s2_array)] = 5678
som_type_copy[np.isin(som_type_copy, s3_array)] = -5
som_type_copy[np.isin(som_type_copy, s0_array)] = -250
som_type_copy[som_type_copy == 1234] = 1
som_type_copy[som_type_copy == 5678] = 2
som_type_copy[som_type_copy == -5] = 3
som_type_copy[som_type_copy == -250] = 0

return som_type_copy


def data_to_log_decile_log_area_aft(peaklet_data, normalization_factor):
"""
Converts peaklet data into the current best inputs for the SOM,
log10(deciles) + log10(area) + AFT
Since we are dealing with logs, anything less than 1 will be set to 1
"""
# turn deciles into approriate 'normalized' format (maybe also consider L1 normalization of these inputs)
decile_data = compute_quantiles(peaklet_data, 10)
data = peaklet_data.copy()
decile_data[decile_data < 1] = 1

decile_log = np.log10(decile_data)
decile_log_over_max = np.divide(decile_log, normalization_factor[:10])
# Now lets deal with area
data['area'] = data['area'] + normalization_factor[11] + 1
peaklet_log_area = np.log10(data['area'])
peaklet_aft = np.sum(data['area_per_channel'][:, :straxen.n_top_pmts], axis=1) / peaklet_data['area']
peaklet_aft = np.where(peaklet_aft > 0, peaklet_aft, 0)
peaklet_aft = np.where(peaklet_aft < 1, peaklet_aft, 1)
deciles_area_aft = np.concatenate((decile_log_over_max,
np.reshape(peaklet_log_area, (len(peaklet_log_area), 1)) / normalization_factor[
10],
np.reshape(peaklet_aft, (len(peaklet_log_area), 1))), axis=1)
return deciles_area_aft


def compute_quantiles(peaks: np.ndarray, n_samples: int):

"""
Compute waveforms and quantiles for a given number of nodes(attributes)
:param peaks:
:param n_samples: number of nodes or attributes
:return:quantiles
"""

data = peaks['data'].copy()
data[data < 0.0] = 0.0
dt = peaks['dt']
q = compute_wf_attributes(data, dt, n_samples)
return q


@export
@numba.jit(nopython=True, cache=True)
def compute_wf_attributes(data, sample_length, n_samples: int):

"""
Compute waveform attribures
Quantiles: represent the amount of time elapsed for
a given fraction of the total waveform area to be observed in n_samples
i.e. n_samples = 10, then quantiles are equivalent deciles
Waveforms: downsampled waveform to n_samples
:param data: waveform e.g. peaks or peaklets
:param n_samples: compute quantiles for a given number of samples
:return: waveforms and quantiles of size n_samples
"""

assert data.shape[0] == len(sample_length), "ararys must have same size"

num_samples = data.shape[1]

quantiles = np.zeros((len(data), n_samples), dtype=np.float64)

# Cannot compute with with more samples than actual waveform sample
assert num_samples > n_samples, "cannot compute with more samples than the actual waveform"
assert num_samples % n_samples == 0, "number of samples must be a multiple of n_samples"

# Compute quantiles
inter_points = np.linspace(0., 1. - (1. / n_samples), n_samples)
cumsum_steps = np.zeros(n_samples + 1, dtype=np.float64)
frac_of_cumsum = np.zeros(num_samples + 1)
sample_number_div_dt = np.arange(0, num_samples + 1, 1)
for i, (samples, dt) in enumerate(zip(data, sample_length)):
if np.sum(samples) == 0:
continue
# reset buffers
frac_of_cumsum[:] = 0
cumsum_steps[:] = 0
frac_of_cumsum[1:] = np.cumsum(samples)
frac_of_cumsum[1:] = frac_of_cumsum[1:] / frac_of_cumsum[-1]
cumsum_steps[:-1] = np.interp(inter_points, frac_of_cumsum, sample_number_div_dt * dt)
cumsum_steps[-1] = sample_number_div_dt[-1] * dt
quantiles[i] = cumsum_steps[1:] - cumsum_steps[:-1]

return quantiles
2 changes: 1 addition & 1 deletion straxen/plugins/peaks/peaks.py
Original file line number Diff line number Diff line change
Expand Up @@ -56,4 +56,4 @@ def compute(self, peaklets, merged_s2s):

assert np.all(peaks['time'][to_check][1:]
>= strax.endtime(peaks)[to_check][:-1]), "Peaks not disjoint"
return peaks
return peaks
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