From c94f7791d0bdeb5f7ca6c82fbd18a724cb8c0250 Mon Sep 17 00:00:00 2001 From: Kathryn Sutton Date: Tue, 29 Aug 2023 18:51:34 -0700 Subject: [PATCH 01/37] copied over CalibHitMerger as starting point for adding workflow --- data/proto_nd_flow/2x2.yaml | 55 + .../multi_tile_layout-2.4.16.yaml | 16509 ++++++++++++++++ src/proto_nd_flow/util/dummy.py | 324 + yamls/proto_nd_flow/util/dummy.yaml | 23 + .../workflows/charge/final_calibration.yaml | 2 +- 5 files changed, 16912 insertions(+), 1 deletion(-) create mode 100755 data/proto_nd_flow/2x2.yaml create mode 100755 data/proto_nd_flow/multi_tile_layout-2.4.16.yaml create mode 100644 src/proto_nd_flow/util/dummy.py create mode 100644 yamls/proto_nd_flow/util/dummy.yaml diff --git a/data/proto_nd_flow/2x2.yaml b/data/proto_nd_flow/2x2.yaml new file mode 100755 index 00000000..74bf6e0e --- /dev/null +++ b/data/proto_nd_flow/2x2.yaml @@ -0,0 +1,55 @@ +temperature: 87.17 # K +e_field: 0.50 # kV/cm +lifetime: 2.2e+3 # us +time_interval: [0, 200.] # us +long_diff: 4.0e-6 # cm * cm / us +tran_diff: 8.8e-6 # cm * cm / us +drift_length: 30.27225 # cm +response_sampling: 0.1 # us +reponse_bin_size: 0.04434 # cm +time_padding: 190 # us +time_window: 189.1 # us +tpc_offsets: # cm + - [33.5, -268, 1333.5] + - [33.5, -268, 1266.5] + - [-33.5, -268, 1333.5] + - [-33.5, -268, 1266.5] +tile_map: + - [[7,5,3,1],[8,6,4,2]] + - [[16,14,12,10],[15,13,11,9]] +module_to_io_groups: + 1: [1, 2] + 2: [3, 4] + 3: [5, 6] + 4: [7, 8] + +# Light geometry parameters +module_to_tpcs: + 1: [0, 1] + 2: [2, 3] + 3: [4, 5] + 4: [6, 7] +n_op_channel: 384 +tpc_to_op_channel: + - [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47] + - [48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95] + - [96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142, 143] + - [144, 145, 146, 147, 148, 149, 150, 151, 152, 153, 154, 155, 156, 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181, 182, 183, 184, 185, 186, 187, 188, 189, 190, 191] + - [192, 193, 194, 195, 196, 197, 198, 199, 200, 201, 202, 203, 204, 205, 206, 207, 208, 209, 210, 211, 212, 213, 214, 215, 216, 217, 218, 219, 220, 221, 222, 223, 224, 225, 226, 227, 228, 229, 230, 231, 232, 233, 234, 235, 236, 237, 238, 239] + - [240, 241, 242, 243, 244, 245, 246, 247, 248, 249, 250, 251, 252, 253, 254, 255, 256, 257, 258, 259, 260, 261, 262, 263, 264, 265, 266, 267, 268, 269, 270, 271, 272, 273, 274, 275, 276, 277, 278, 279, 280, 281, 282, 283, 284, 285, 286, 287] + - [288, 289, 290, 291, 292, 293, 294, 295, 296, 297, 298, 299, 300, 301, 302, 303, 304, 305, 306, 307, 308, 309, 310, 311, 312, 313, 314, 315, 316, 317, 318, 319, 320, 321, 322, 323, 324, 325, 326, 327, 328, 329, 330, 331, 332, 333, 334, 335] + - [336, 337, 338, 339, 340, 341, 342, 343, 344, 345, 346, 347, 348, 349, 350, 351, 352, 353, 354, 355, 356, 357, 358, 359, 360, 361, 362, 363, 364, 365, 366, 367, 368, 369, 370, 371, 372, 373, 374, 375, 376, 377, 378, 379, 380, 381, 382, 383] + +# Light simulation parameters +singlet_fraction: 0.3 +tau_s: 0.001 # us +tau_t: 1.530 # us +op_channel_efficiency: [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1] +light_gain: [-7.0, -7.0, -7.0, -7.0, -7.0, -7.0, -7.0, -7.0, -7.0, -7.0, -7.0, -7.0, -7.0, -7.0, -7.0, -7.0, -7.0, -7.0, -7.0, -7.0, -7.0, -7.0, -7.0, -7.0, -7.0, -7.0, -7.0, -7.0, -7.0, -7.0, -7.0, -7.0, -7.0, -7.0, -7.0, -7.0, -7.0, -7.0, -7.0, -7.0, -7.0, -7.0, 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-7.0, -7.0, -7.0, -7.0, -7.0, -7.0, -7.0, -7.0, -7.0, -7.0] # ADC us / PE +light_det_noise_sample_spacing: 0.016 # us +light_trig_threshold: [-4500, -2000, -4500, -2000, -4500, -2000, -4500, -2000, -4500, -2000, -4500, -2000, -4500, -2000, -4500, -2000, -4500, -2000, -4500, -2000, -4500, -2000, -4500, -2000, -4500, -2000, -4500, -2000, -4500, -2000, -4500, -2000, -4500, -2000, -4500, -2000, -4500, -2000, -4500, -2000, -4500, -2000, -4500, -2000, -4500, -2000, -4500, -2000, -4500, -2000, -4500, -2000, -4500, -2000, -4500, -2000, -4500, -2000, -4500, -2000, -4500, -2000, -4500, -2000] +light_trig_mode: 1 +light_window: [0, 16] # us +light_trig_window: [1.6, 14.4] # us +light_digit_sample_spacing: 0.016 # us +light_nbit: 14 diff --git a/data/proto_nd_flow/multi_tile_layout-2.4.16.yaml b/data/proto_nd_flow/multi_tile_layout-2.4.16.yaml new file mode 100755 index 00000000..e4e95186 --- /dev/null +++ b/data/proto_nd_flow/multi_tile_layout-2.4.16.yaml @@ -0,0 +1,16509 @@ 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and timestamp. Then, merge every pair of hits that fall within the merge cut. If an odd number of hits fall should be merged, the earliest hit of a group is excluded from the iteration. + - `'last-first'`: On each iteration, sort all hits by unique y-z and timestamp. Then, merge the last pair of hits that fall within the merge cut within each contiguous chunk of neighboring hits. + + Both algorithms should produce very similar results. + + Example config:: + + hit_merging: + classname: CalibHitMerger + path: module0_flow.reco.charge.hit_merger + requires: + - 'charge/hits' + params: + events_dset_name: 'charge/events' + hits_name: 'charge/hits' + hit_charge_name: 'charge/hits' # dataset to grab 'q' from + merged_name: 'charge/hits/merged' + mc_hit_frac_dset_name: ``str``, optional, output dataset path for hit charge fraction truth (if present) + merge_cut: 30 # merge hits with delta t < merge_cut [CRS ticks] + merge_mode: 'last-first' + ''' + class_version = '0.0.0' + defaults = dict( + events_dset_name = 'charge/events', + hits_name = 'charge/calib_prompt_hits', + hit_charge_name = 'charge/calib_prompt_hits', + merged_name = 'charge/hits/calib_merged_hits', + max_merge_steps = 5, + max_contrib_segments = 200, + merge_mode = 'last-first', + merge_cut = 50, # CRS ticks + mc_hit_frac_dset_name = 'mc_truth/calib_final_hit_backtrack' + ) + valid_merge_modes = ['last-first', 'pairwise'] + + print('running dummy.py!!!!!') + + merged_dtype = CalibHitBuilder.calib_hits_dtype + + sum_fields = ['Q','E'] + weighted_mean_fields = ['t_drift', 'ts_pps','x'] + + def __init__(self, **params): + print('running dummy.py!!!!!') + super(CalibHitMerger, self).__init__(**params) + for key in self.defaults: + setattr(self, key, params.get(key, self.defaults[key])) + self.merge_mode = self.merge_mode.lower() + assert self.merge_mode in self.valid_merge_modes, f'invalid merge mode: {self.merge_mode}' + + def init(self, source_name): + print('running dummy.py!!!!!') + super(CalibHitMerger, self).init(source_name) + + self.hit_frac_dtype = np.dtype([ + ('fraction', f'({self.max_contrib_segments},)f8'), + ('segment_id', f'({self.max_contrib_segments},)u8') + ]) + + self.data_manager.create_dset(self.merged_name, dtype=self.merged_dtype) + self.data_manager.create_dset(self.mc_hit_frac_dset_name, dtype=self.hit_frac_dtype) + self.data_manager.create_ref(self.hits_name, self.merged_name) + self.data_manager.create_ref(source_name, self.merged_name) + self.data_manager.create_ref(self.merged_name,self.mc_hit_frac_dset_name) + self.data_manager.create_ref(self.events_dset_name, self.merged_name) + + #@staticmethod + def merge_hits(self,hits, weights, seg_fracs, dt_cut, sum_fields=None, weighted_mean_fields=None, max_steps=-1, mode='last-first'): + ''' + Combines hits along the second axis on unique channels with a delta t less than dt_cut. Continues + until no hits (or merged hits) are within dt_cut of each other + + :param hits: original hits array, shape: (N,M) + + :param weights: values used for weighted mean, shape: (N,M) + + :param fracs: fractional contributions of true segments per packet + + :param dt_cut: delta t cut to merge hits (float) [CRS ticks] + + :sum_fields: list of fields in ``hits`` and that should be *summed* when combined, must not be in ``weighted_mean_fields`` + + :weighted_mean_fields: list of fields in ``hits`` and that should be averaged using the weights when combined, must not be in ``sum_fields`` + + :param max_steps: optional, maximum number of merges to apply to pairs of neighboring hits (<0 == no limit, 0 == skip merging, >0 == limit steps) + + :param mode: optional, merging strategy, either `'last-first'` (on each iteration merges the last hit pair) or `'pairwise'` (on each iteration merges each unique hit pair) + + :returns: new hit array, shape: (N,m), new hit charge array, shape: (N,m), and an index array with shape (L,2), [:,0] being the index into the original hit array and [:,1] being the flattened index into the compressed new array + + ''' + print('running dummy.py!!!!!') + new_seg_bt = np.array(seg_fracs[0]) + new_frac_bt = np.array(seg_fracs[1]) + iteration_count = 0 + mask = hits.mask['id'].copy() + new_hits = hits.data.copy() + weights = weights.data.copy() + old_ids = hits.data['id'].copy()[...,np.newaxis] + old_id_mask = hits.mask['id'].copy()[...,np.newaxis] + + hit_contributions = np.full(shape=weights.shape+(3,self.max_contrib_segments),fill_value=0.) + #print('weights shape',weights.shape) + #print('hit_contr shape',hit_contributions.shape) + for it, q in np.ndenumerate(weights): + #print('it',it) + #hit_contributions[it].append([]) + #print('---------------') + if len(new_frac_bt[it]) > 1: + print('!!!!!!!!!!!!!!!!!') + break + counter=0 + for entry_it, entry in enumerate(new_frac_bt[it][0]): + #print('frac =',entry) + if abs(entry) < 0.001: continue + hit_contributions[it][0][counter] = q + hit_contributions[it][1][counter] = new_frac_bt[it][0][entry_it] + hit_contributions[it][2][counter] = new_seg_bt[it][0][entry_it]['segment_id'] + counter+=1 + + #print('hit_contributions.shape =',hit_contributions.shape) + #print(hit_contributions) + + while new_hits.size > 0 and iteration_count != max_steps: + iteration_count += 1 + # algorithm is iterative, but typically only needs to loop a few (~2-3) times + # so we'll spit a warning if we reach the maximum number of steps + if iteration_count == max_steps: + logging.info(f'Hit merging algorithm reached max step limit {max_steps}') + + # sort array along last axis to find groups of hits on the same channel, use a stable sort with the aim of improving performance on later iterations + isort = np.argsort(ma.array(new_hits, mask=mask), axis=-1, order=['z','y','ts_pps','t_drift'], kind='stable') + mask = np.take_along_axis(mask, isort, axis=-1) + new_hits = np.take_along_axis(new_hits, isort, axis=-1) + weights = np.take_along_axis(weights, isort, axis=-1) + hit_contributions = np.take_along_axis(hit_contributions,isort[...,np.newaxis,np.newaxis],axis=-3) + old_ids = np.take_along_axis(old_ids, isort[...,np.newaxis], axis=-2) + old_id_mask = np.take_along_axis(old_id_mask, isort[...,np.newaxis], axis=-2) + N_new_hits = new_hits.shape[0]*new_hits.shape[1]-np.count_nonzero(mask) + print('current number of merged hits =',N_new_hits) + + # identify neighboring hits on the same channel + dt = np.abs(np.diff(new_hits['ts_pps'].astype(int), axis=-1)) + same_channel = ( + (new_hits['z'][..., :-1] == new_hits['z'][..., 1:]) + & (new_hits['y'][..., :-1] == new_hits['y'][..., 1:]) + ) + + # flag valid hits if they are on the same channel and are close in time + to_merge = (dt < dt_cut) & same_channel & ~mask[...,:-1] & ~mask[...,1:] + + if mode == 'last-first': + # only combine unambiguous pairs of hits on a channel on each iteration + to_merge[...,:-1] = ~to_merge[...,1:] & to_merge[...,:-1] + elif mode == 'pairwise': + # combine every available pair of hits on each iteration + to_merge[...,:-1] = to_merge[...,:-1] & (np.cumsum(to_merge, axis=-1) % 2 == 0)[...,:-1] + else: + raise RuntimeError(f'invalid merge mode: {mode}') + + print("merging:",np.count_nonzero(to_merge)) + + # exits loop if no remaining hits to combine + if np.any(to_merge): + # move 2nd hit into position of first hit, combining attributes along the way + hit0 = np.extract(to_merge, new_hits[...,:-1]) + hit1 = np.extract(to_merge, new_hits[...,1:]) + + # these fields will be summed hit[i][field] -> hit[i+1][field] + hit[i][field] + for field in sum_fields: + if field in new_hits.dtype.names: + np.place(new_hits[...,:-1][field], to_merge, hit0[field] + hit1[field]) + + # these fields will use the charge-weighted average hit[i][field] -> (hit[i+1][field] * q[i+1] + hit[i][field] * q[i]) / (q[i+1] + q[i]) + q0 = np.extract(to_merge, weights[...,:-1]) + q1 = np.extract(to_merge, weights[...,1:]) + qsum = np.abs(q0) + np.abs(q1) + # regularize so there are no nans + qsum = np.where(qsum == 0, 1e-300, qsum) + # it is not obvious how to treat the possibility of negative charge values (e.g. noise) + # this should(?) be rare, so we'll just spit out a warning + if np.any((q0 < 0) | (q1 < 0)): + logging.info(f'Hit merging encountered negative value(s) (count={((q0 < 0) | (q1 < 0)).sum()}) in charge weighting, results may be unreliable') + w0 = np.abs(q0)/qsum + w1 = np.abs(q1)/qsum + for field in weighted_mean_fields: + if field in new_hits.dtype.names: + base = np.minimum(hit0[field], hit1[field]) # improves precision of weighted sum if values are large (e.g. timestamps) + np.place(new_hits[...,:-1][field], to_merge, ((hit0[field]-base) * w0 + (hit1[field]-base) * w1).astype(new_hits.dtype[field]) + base) + # combine weights for next iteration + np.place(weights[...,:-1], to_merge, weights[...,:-1] + weights[...,1:]) + for hit_it, hit_cont in np.ndenumerate(weights[...,:-1]): + if (not to_merge[hit_it]) | mask[hit_it]: + #print('skipping') + continue + #if hit_contributions[hit_it][1].shape[0] < self.max_contrib_segments: print('a shape :',hit_contributions[hit_it][1].shape) + e = np.argwhere(hit_contributions[...,:-1][hit_it][1]==0)[0][0] + f = np.argwhere(hit_contributions[...,:][hit_it[0],hit_it[1]+1][1]==0)[0][0] + # merge the hit contributions: + for comb_it in range(f): + hit_contributions[...,:-1][hit_it][1][e+comb_it] = hit_contributions[...,:][hit_it[0],hit_it[1]+1][1][comb_it] + hit_contributions[...,:-1][hit_it][0][e+comb_it] = hit_contributions[...,:][hit_it[0],hit_it[1]+1][0][comb_it] + hit_contributions[...,:-1][hit_it][2][e+comb_it] = hit_contributions[...,:][hit_it[0],hit_it[1]+1][2][comb_it] + # and remove them from the hit that was merged in + hit_contributions[hit_it[0],hit_it[1]+1][1][comb_it] = 0 + hit_contributions[hit_it[0],hit_it[1]+1][0][comb_it] = 0. + hit_contributions[hit_it[0],hit_it[1]+1][2][comb_it] = 0. + + # now we mask off hits that have already been merged + mask[...,1:] = mask[...,1:] | to_merge + + # and track the hit ids of the hits that were merged by propogating the indices forward + if mode == 'last-first': + old_id_mask = np.concatenate([old_id_mask[...,0:1], old_id_mask], axis=-1) + old_ids = np.concatenate([old_ids[...,0:1], old_ids], axis=-1) + id_merge = np.broadcast_to(to_merge[...,np.newaxis], to_merge.shape + old_ids.shape[-1:]) + divider = 1 + elif mode == 'pairwise': + old_id_mask = np.concatenate([old_id_mask, old_id_mask], axis=-1) + old_ids = np.concatenate([old_ids, old_ids], axis=-1) + id_merge = np.broadcast_to(to_merge[...,np.newaxis], to_merge.shape + old_ids.shape[-1:]) + divider = old_ids.shape[-1]//2 + else: + raise RuntimeError(f'invalid mode {mode}') + # move ids from hit[i+1] to hit[i] (while keeping the ids for hit[i]) + np.place(old_ids[...,:-1,divider:], id_merge[...,divider:], np.extract(id_merge[...,divider:], old_ids[...,1:,divider:])) + # copy the id mask for hit[i+1] into hit[i] (while keeping the id mask for hit[i]) + np.place(old_id_mask[...,:-1,divider:], id_merge[...,divider:], np.extract(id_merge[...,divider:], old_id_mask[...,1:,divider:])) + # and clear the id mask for hit[i+1] + np.place(old_id_mask[...,1:,:], id_merge, True) + else: + break + + # calculate segment contributions for each merged hit + tmp_bt = np.full(shape=new_hits.shape+(2,self.max_contrib_segments),fill_value=0.) + back_track = np.full(shape=new_hits.shape,fill_value=0.,dtype=self.hit_frac_dtype) + # loop over hits + for hit_it, hit in np.ndenumerate(new_hits): + if mask[hit_it]: continue + hit_contr = hit_contributions[hit_it] + # renormalize the fractional contributions given the charge weighted average + norm = np.sum(np.multiply(hit_contr[0],hit_contr[1])) + if norm == 0.: norm = 1. + tmp_bt[hit_it][0] = np.multiply(hit_contr[0],hit_contr[1])/norm # fractional contributions + tmp_bt[hit_it][1] = hit_contr[2] # segment_ids + + # merge unique track contributions + track_dict = defaultdict(lambda:0) + for track in zip(tmp_bt[hit_it][0],tmp_bt[hit_it][1]): + track_dict[track[1]] += track[0] + track_dict = dict(track_dict) + bt_unique_segs = np.array(list(track_dict.keys())) + bt_unique_frac = np.array(list(track_dict.values())) + n_conts = bt_unique_frac.shape[0] + isort = np.flip(np.argsort(np.abs(bt_unique_frac), axis=-1, kind='stable')) + bt_unique_segs = np.take_along_axis(bt_unique_segs, isort, axis=-1) + bt_unique_frac = np.take_along_axis(bt_unique_frac, isort, axis=-1) + back_track[hit_it]['fraction'] = [0.]*self.max_contrib_segments + back_track[hit_it]['segment_id'] = [0]*self.max_contrib_segments + back_track[hit_it]['fraction'][:bt_unique_frac.shape[0]] = bt_unique_frac + back_track[hit_it]['segment_id'][:bt_unique_segs.shape[0]] = bt_unique_segs + + new_hit_idx = np.broadcast_to(np.cumsum(~mask.ravel(), axis=0).reshape(mask.shape + (1,)), old_ids.shape)-1 + + return ( + ma.array(new_hits, mask=mask), + np.c_[np.extract(~(old_id_mask | mask[...,np.newaxis]), old_ids), np.extract(~(old_id_mask | mask[...,np.newaxis]), new_hit_idx)], + ma.array(back_track, mask=mask) + ) + + def run(self, source_name, source_slice, cache): + super(CalibHitMerger, self).run(source_name, source_slice, cache) + + event_id = np.r_[source_slice] + packet_frac_bt = cache['packet_frac_backtrack'] + packet_seg_bt = cache['packet_seg_backtrack'] + hits = cache[self.hits_name] + + merged, ref, back_track = self.merge_hits(hits, weights=hits['Q'], seg_fracs=[packet_seg_bt,packet_frac_bt],dt_cut=self.merge_cut, sum_fields=self.sum_fields, weighted_mean_fields=self.weighted_mean_fields, max_steps=self.max_merge_steps, mode=self.merge_mode) + + merged_mask = merged.mask['id'] + + # first write the new merged hits to the file + new_nhit = int((~merged_mask).sum()) + merge_slice = self.data_manager.reserve_data(self.merged_name, new_nhit) + merge_idx = np.r_[merge_slice].astype(merged.dtype['id']) + if new_nhit > 0: + ref[:,1] += merge_idx[0] # offset references based on reserved region in output file + np.place(merged['id'], ~merged_mask, merge_idx) + + self.data_manager.write_data(self.merged_name, merge_idx, merged[~merged_mask]) + merge_bt_slice = self.data_manager.reserve_data(self.mc_hit_frac_dset_name, new_nhit) + self.data_manager.write_data(self.mc_hit_frac_dset_name, merge_idx, back_track[~merged_mask]) + + # HACK: Remove duplicate refs. Would be nice to actually understand and + # fix the origin of these duplicates. + ref = np.unique(ref, axis=0) + # sort based on the ID of the prompt hit, to make analysis more convenient + ref = ref[np.argsort(ref[:, 0])] + + # finally, write the references + self.data_manager.write_ref(self.hits_name, self.merged_name, ref) + self.data_manager.write_ref(self.merged_name,self.mc_hit_frac_dset_name,np.c_[merge_idx,merge_idx]) + ev_ref = np.c_[(np.indices(merged_mask.shape)[0] + source_slice.start)[~merged_mask], merge_idx] + self.data_manager.write_ref(source_name, self.merged_name, ev_ref) + self.data_manager.write_ref(self.events_dset_name, self.merged_name, ev_ref) diff --git a/yamls/proto_nd_flow/util/dummy.yaml b/yamls/proto_nd_flow/util/dummy.yaml new file mode 100644 index 00000000..ce14e482 --- /dev/null +++ b/yamls/proto_nd_flow/util/dummy.yaml @@ -0,0 +1,23 @@ +classname: CalibHitMerger # reco/charge/calib_hit_merger.py +path: proto_nd_flow.reco.charge.calib_hit_merger +requires: + - 'charge/events' + - 'charge/calib_prompt_hits' + - name: 'packet_frac_backtrack' + path: ['charge/calib_prompt_hits','charge/packets','mc_truth/packet_fraction'] + - name: 'packet_seg_backtrack' + path: ['charge/calib_prompt_hits','charge/packets','mc_truth/segments'] + + +params: + # inputs + events_dset_name: 'charge/events' + hits_name: 'charge/calib_prompt_hits' + mc_hit_frac_dset_name: 'mc_truth/calib_final_hit_backtrack' + merged_name: 'charge/calib_final_hits' + max_contrib_segments: 200 + merge_cut: 65 # merge hits with delta t < merge_cut [CRS ticks] + max_merge_steps: 50 # max number of iterations when merging + # adjacent packets in time on the same channel + + diff --git a/yamls/proto_nd_flow/workflows/charge/final_calibration.yaml b/yamls/proto_nd_flow/workflows/charge/final_calibration.yaml index bfd41731..a1ea1c4f 100644 --- a/yamls/proto_nd_flow/workflows/charge/final_calibration.yaml +++ b/yamls/proto_nd_flow/workflows/charge/final_calibration.yaml @@ -22,5 +22,5 @@ raw_events: chunk_size: 32 calib_hit_merger: - !include yamls/proto_nd_flow/reco/charge/CalibHitMerger.yaml + !include yamls/proto_nd_flow/util/dummy.yaml From 991669215e9508f58c60c7a0911e36bcb2e664ea Mon Sep 17 00:00:00 2001 From: Kathryn Sutton Date: Tue, 29 Aug 2023 21:02:15 -0700 Subject: [PATCH 02/37] this version runs Dummy module --- src/proto_nd_flow/util/dummy.py | 284 +----------------- yamls/proto_nd_flow/util/dummy.yaml | 4 +- .../workflows/charge/final_calibration.yaml | 4 +- 3 files changed, 9 insertions(+), 283 deletions(-) diff --git a/src/proto_nd_flow/util/dummy.py b/src/proto_nd_flow/util/dummy.py index f82ddf3b..76c201ec 100644 --- a/src/proto_nd_flow/util/dummy.py +++ b/src/proto_nd_flow/util/dummy.py @@ -8,35 +8,10 @@ from proto_nd_flow.reco.charge.calib_prompt_hits import CalibHitBuilder -class CalibHitMerger(H5FlowStage): - ''' - Merges the specified cached hits based on their unique channel id and timestamp: - - q -> sum(q) - - ts -> sum(ts * q) / sum(q) - - Two algorithms for selecting pairs of hits to merge have been implemented: - - - `'pairwise'`: On each iteration, sort all hits by unique y-z position and timestamp. Then, merge every pair of hits that fall within the merge cut. If an odd number of hits fall should be merged, the earliest hit of a group is excluded from the iteration. - - `'last-first'`: On each iteration, sort all hits by unique y-z and timestamp. Then, merge the last pair of hits that fall within the merge cut within each contiguous chunk of neighboring hits. - - Both algorithms should produce very similar results. - - Example config:: - - hit_merging: - classname: CalibHitMerger - path: module0_flow.reco.charge.hit_merger - requires: - - 'charge/hits' - params: - events_dset_name: 'charge/events' - hits_name: 'charge/hits' - hit_charge_name: 'charge/hits' # dataset to grab 'q' from - merged_name: 'charge/hits/merged' - mc_hit_frac_dset_name: ``str``, optional, output dataset path for hit charge fraction truth (if present) - merge_cut: 30 # merge hits with delta t < merge_cut [CRS ticks] - merge_mode: 'last-first' +class Dummy(H5FlowStage): ''' + this is a placeholder + ''' class_version = '0.0.0' defaults = dict( events_dset_name = 'charge/events', @@ -51,8 +26,6 @@ class CalibHitMerger(H5FlowStage): ) valid_merge_modes = ['last-first', 'pairwise'] - print('running dummy.py!!!!!') - merged_dtype = CalibHitBuilder.calib_hits_dtype sum_fields = ['Q','E'] @@ -60,7 +33,7 @@ class CalibHitMerger(H5FlowStage): def __init__(self, **params): print('running dummy.py!!!!!') - super(CalibHitMerger, self).__init__(**params) + super(Dummy, self).__init__(**params) for key in self.defaults: setattr(self, key, params.get(key, self.defaults[key])) self.merge_mode = self.merge_mode.lower() @@ -68,257 +41,10 @@ def __init__(self, **params): def init(self, source_name): print('running dummy.py!!!!!') - super(CalibHitMerger, self).init(source_name) + super(Dummy, self).init(source_name) self.hit_frac_dtype = np.dtype([ ('fraction', f'({self.max_contrib_segments},)f8'), ('segment_id', f'({self.max_contrib_segments},)u8') ]) - self.data_manager.create_dset(self.merged_name, dtype=self.merged_dtype) - self.data_manager.create_dset(self.mc_hit_frac_dset_name, dtype=self.hit_frac_dtype) - self.data_manager.create_ref(self.hits_name, self.merged_name) - self.data_manager.create_ref(source_name, self.merged_name) - self.data_manager.create_ref(self.merged_name,self.mc_hit_frac_dset_name) - self.data_manager.create_ref(self.events_dset_name, self.merged_name) - - #@staticmethod - def merge_hits(self,hits, weights, seg_fracs, dt_cut, sum_fields=None, weighted_mean_fields=None, max_steps=-1, mode='last-first'): - ''' - Combines hits along the second axis on unique channels with a delta t less than dt_cut. Continues - until no hits (or merged hits) are within dt_cut of each other - - :param hits: original hits array, shape: (N,M) - - :param weights: values used for weighted mean, shape: (N,M) - - :param fracs: fractional contributions of true segments per packet - - :param dt_cut: delta t cut to merge hits (float) [CRS ticks] - - :sum_fields: list of fields in ``hits`` and that should be *summed* when combined, must not be in ``weighted_mean_fields`` - - :weighted_mean_fields: list of fields in ``hits`` and that should be averaged using the weights when combined, must not be in ``sum_fields`` - - :param max_steps: optional, maximum number of merges to apply to pairs of neighboring hits (<0 == no limit, 0 == skip merging, >0 == limit steps) - - :param mode: optional, merging strategy, either `'last-first'` (on each iteration merges the last hit pair) or `'pairwise'` (on each iteration merges each unique hit pair) - - :returns: new hit array, shape: (N,m), new hit charge array, shape: (N,m), and an index array with shape (L,2), [:,0] being the index into the original hit array and [:,1] being the flattened index into the compressed new array - - ''' - print('running dummy.py!!!!!') - new_seg_bt = np.array(seg_fracs[0]) - new_frac_bt = np.array(seg_fracs[1]) - iteration_count = 0 - mask = hits.mask['id'].copy() - new_hits = hits.data.copy() - weights = weights.data.copy() - old_ids = hits.data['id'].copy()[...,np.newaxis] - old_id_mask = hits.mask['id'].copy()[...,np.newaxis] - - hit_contributions = np.full(shape=weights.shape+(3,self.max_contrib_segments),fill_value=0.) - #print('weights shape',weights.shape) - #print('hit_contr shape',hit_contributions.shape) - for it, q in np.ndenumerate(weights): - #print('it',it) - #hit_contributions[it].append([]) - #print('---------------') - if len(new_frac_bt[it]) > 1: - print('!!!!!!!!!!!!!!!!!') - break - counter=0 - for entry_it, entry in enumerate(new_frac_bt[it][0]): - #print('frac =',entry) - if abs(entry) < 0.001: continue - hit_contributions[it][0][counter] = q - hit_contributions[it][1][counter] = new_frac_bt[it][0][entry_it] - hit_contributions[it][2][counter] = new_seg_bt[it][0][entry_it]['segment_id'] - counter+=1 - - #print('hit_contributions.shape =',hit_contributions.shape) - #print(hit_contributions) - - while new_hits.size > 0 and iteration_count != max_steps: - iteration_count += 1 - # algorithm is iterative, but typically only needs to loop a few (~2-3) times - # so we'll spit a warning if we reach the maximum number of steps - if iteration_count == max_steps: - logging.info(f'Hit merging algorithm reached max step limit {max_steps}') - - # sort array along last axis to find groups of hits on the same channel, use a stable sort with the aim of improving performance on later iterations - isort = np.argsort(ma.array(new_hits, mask=mask), axis=-1, order=['z','y','ts_pps','t_drift'], kind='stable') - mask = np.take_along_axis(mask, isort, axis=-1) - new_hits = np.take_along_axis(new_hits, isort, axis=-1) - weights = np.take_along_axis(weights, isort, axis=-1) - hit_contributions = np.take_along_axis(hit_contributions,isort[...,np.newaxis,np.newaxis],axis=-3) - old_ids = np.take_along_axis(old_ids, isort[...,np.newaxis], axis=-2) - old_id_mask = np.take_along_axis(old_id_mask, isort[...,np.newaxis], axis=-2) - N_new_hits = new_hits.shape[0]*new_hits.shape[1]-np.count_nonzero(mask) - print('current number of merged hits =',N_new_hits) - - # identify neighboring hits on the same channel - dt = np.abs(np.diff(new_hits['ts_pps'].astype(int), axis=-1)) - same_channel = ( - (new_hits['z'][..., :-1] == new_hits['z'][..., 1:]) - & (new_hits['y'][..., :-1] == new_hits['y'][..., 1:]) - ) - - # flag valid hits if they are on the same channel and are close in time - to_merge = (dt < dt_cut) & same_channel & ~mask[...,:-1] & ~mask[...,1:] - - if mode == 'last-first': - # only combine unambiguous pairs of hits on a channel on each iteration - to_merge[...,:-1] = ~to_merge[...,1:] & to_merge[...,:-1] - elif mode == 'pairwise': - # combine every available pair of hits on each iteration - to_merge[...,:-1] = to_merge[...,:-1] & (np.cumsum(to_merge, axis=-1) % 2 == 0)[...,:-1] - else: - raise RuntimeError(f'invalid merge mode: {mode}') - - print("merging:",np.count_nonzero(to_merge)) - - # exits loop if no remaining hits to combine - if np.any(to_merge): - # move 2nd hit into position of first hit, combining attributes along the way - hit0 = np.extract(to_merge, new_hits[...,:-1]) - hit1 = np.extract(to_merge, new_hits[...,1:]) - - # these fields will be summed hit[i][field] -> hit[i+1][field] + hit[i][field] - for field in sum_fields: - if field in new_hits.dtype.names: - np.place(new_hits[...,:-1][field], to_merge, hit0[field] + hit1[field]) - - # these fields will use the charge-weighted average hit[i][field] -> (hit[i+1][field] * q[i+1] + hit[i][field] * q[i]) / (q[i+1] + q[i]) - q0 = np.extract(to_merge, weights[...,:-1]) - q1 = np.extract(to_merge, weights[...,1:]) - qsum = np.abs(q0) + np.abs(q1) - # regularize so there are no nans - qsum = np.where(qsum == 0, 1e-300, qsum) - # it is not obvious how to treat the possibility of negative charge values (e.g. noise) - # this should(?) be rare, so we'll just spit out a warning - if np.any((q0 < 0) | (q1 < 0)): - logging.info(f'Hit merging encountered negative value(s) (count={((q0 < 0) | (q1 < 0)).sum()}) in charge weighting, results may be unreliable') - w0 = np.abs(q0)/qsum - w1 = np.abs(q1)/qsum - for field in weighted_mean_fields: - if field in new_hits.dtype.names: - base = np.minimum(hit0[field], hit1[field]) # improves precision of weighted sum if values are large (e.g. timestamps) - np.place(new_hits[...,:-1][field], to_merge, ((hit0[field]-base) * w0 + (hit1[field]-base) * w1).astype(new_hits.dtype[field]) + base) - # combine weights for next iteration - np.place(weights[...,:-1], to_merge, weights[...,:-1] + weights[...,1:]) - for hit_it, hit_cont in np.ndenumerate(weights[...,:-1]): - if (not to_merge[hit_it]) | mask[hit_it]: - #print('skipping') - continue - #if hit_contributions[hit_it][1].shape[0] < self.max_contrib_segments: print('a shape :',hit_contributions[hit_it][1].shape) - e = np.argwhere(hit_contributions[...,:-1][hit_it][1]==0)[0][0] - f = np.argwhere(hit_contributions[...,:][hit_it[0],hit_it[1]+1][1]==0)[0][0] - # merge the hit contributions: - for comb_it in range(f): - hit_contributions[...,:-1][hit_it][1][e+comb_it] = hit_contributions[...,:][hit_it[0],hit_it[1]+1][1][comb_it] - hit_contributions[...,:-1][hit_it][0][e+comb_it] = hit_contributions[...,:][hit_it[0],hit_it[1]+1][0][comb_it] - hit_contributions[...,:-1][hit_it][2][e+comb_it] = hit_contributions[...,:][hit_it[0],hit_it[1]+1][2][comb_it] - # and remove them from the hit that was merged in - hit_contributions[hit_it[0],hit_it[1]+1][1][comb_it] = 0 - hit_contributions[hit_it[0],hit_it[1]+1][0][comb_it] = 0. - hit_contributions[hit_it[0],hit_it[1]+1][2][comb_it] = 0. - - # now we mask off hits that have already been merged - mask[...,1:] = mask[...,1:] | to_merge - - # and track the hit ids of the hits that were merged by propogating the indices forward - if mode == 'last-first': - old_id_mask = np.concatenate([old_id_mask[...,0:1], old_id_mask], axis=-1) - old_ids = np.concatenate([old_ids[...,0:1], old_ids], axis=-1) - id_merge = np.broadcast_to(to_merge[...,np.newaxis], to_merge.shape + old_ids.shape[-1:]) - divider = 1 - elif mode == 'pairwise': - old_id_mask = np.concatenate([old_id_mask, old_id_mask], axis=-1) - old_ids = np.concatenate([old_ids, old_ids], axis=-1) - id_merge = np.broadcast_to(to_merge[...,np.newaxis], to_merge.shape + old_ids.shape[-1:]) - divider = old_ids.shape[-1]//2 - else: - raise RuntimeError(f'invalid mode {mode}') - # move ids from hit[i+1] to hit[i] (while keeping the ids for hit[i]) - np.place(old_ids[...,:-1,divider:], id_merge[...,divider:], np.extract(id_merge[...,divider:], old_ids[...,1:,divider:])) - # copy the id mask for hit[i+1] into hit[i] (while keeping the id mask for hit[i]) - np.place(old_id_mask[...,:-1,divider:], id_merge[...,divider:], np.extract(id_merge[...,divider:], old_id_mask[...,1:,divider:])) - # and clear the id mask for hit[i+1] - np.place(old_id_mask[...,1:,:], id_merge, True) - else: - break - - # calculate segment contributions for each merged hit - tmp_bt = np.full(shape=new_hits.shape+(2,self.max_contrib_segments),fill_value=0.) - back_track = np.full(shape=new_hits.shape,fill_value=0.,dtype=self.hit_frac_dtype) - # loop over hits - for hit_it, hit in np.ndenumerate(new_hits): - if mask[hit_it]: continue - hit_contr = hit_contributions[hit_it] - # renormalize the fractional contributions given the charge weighted average - norm = np.sum(np.multiply(hit_contr[0],hit_contr[1])) - if norm == 0.: norm = 1. - tmp_bt[hit_it][0] = np.multiply(hit_contr[0],hit_contr[1])/norm # fractional contributions - tmp_bt[hit_it][1] = hit_contr[2] # segment_ids - - # merge unique track contributions - track_dict = defaultdict(lambda:0) - for track in zip(tmp_bt[hit_it][0],tmp_bt[hit_it][1]): - track_dict[track[1]] += track[0] - track_dict = dict(track_dict) - bt_unique_segs = np.array(list(track_dict.keys())) - bt_unique_frac = np.array(list(track_dict.values())) - n_conts = bt_unique_frac.shape[0] - isort = np.flip(np.argsort(np.abs(bt_unique_frac), axis=-1, kind='stable')) - bt_unique_segs = np.take_along_axis(bt_unique_segs, isort, axis=-1) - bt_unique_frac = np.take_along_axis(bt_unique_frac, isort, axis=-1) - back_track[hit_it]['fraction'] = [0.]*self.max_contrib_segments - back_track[hit_it]['segment_id'] = [0]*self.max_contrib_segments - back_track[hit_it]['fraction'][:bt_unique_frac.shape[0]] = bt_unique_frac - back_track[hit_it]['segment_id'][:bt_unique_segs.shape[0]] = bt_unique_segs - - new_hit_idx = np.broadcast_to(np.cumsum(~mask.ravel(), axis=0).reshape(mask.shape + (1,)), old_ids.shape)-1 - - return ( - ma.array(new_hits, mask=mask), - np.c_[np.extract(~(old_id_mask | mask[...,np.newaxis]), old_ids), np.extract(~(old_id_mask | mask[...,np.newaxis]), new_hit_idx)], - ma.array(back_track, mask=mask) - ) - - def run(self, source_name, source_slice, cache): - super(CalibHitMerger, self).run(source_name, source_slice, cache) - - event_id = np.r_[source_slice] - packet_frac_bt = cache['packet_frac_backtrack'] - packet_seg_bt = cache['packet_seg_backtrack'] - hits = cache[self.hits_name] - - merged, ref, back_track = self.merge_hits(hits, weights=hits['Q'], seg_fracs=[packet_seg_bt,packet_frac_bt],dt_cut=self.merge_cut, sum_fields=self.sum_fields, weighted_mean_fields=self.weighted_mean_fields, max_steps=self.max_merge_steps, mode=self.merge_mode) - - merged_mask = merged.mask['id'] - - # first write the new merged hits to the file - new_nhit = int((~merged_mask).sum()) - merge_slice = self.data_manager.reserve_data(self.merged_name, new_nhit) - merge_idx = np.r_[merge_slice].astype(merged.dtype['id']) - if new_nhit > 0: - ref[:,1] += merge_idx[0] # offset references based on reserved region in output file - np.place(merged['id'], ~merged_mask, merge_idx) - - self.data_manager.write_data(self.merged_name, merge_idx, merged[~merged_mask]) - merge_bt_slice = self.data_manager.reserve_data(self.mc_hit_frac_dset_name, new_nhit) - self.data_manager.write_data(self.mc_hit_frac_dset_name, merge_idx, back_track[~merged_mask]) - - # HACK: Remove duplicate refs. Would be nice to actually understand and - # fix the origin of these duplicates. - ref = np.unique(ref, axis=0) - # sort based on the ID of the prompt hit, to make analysis more convenient - ref = ref[np.argsort(ref[:, 0])] - - # finally, write the references - self.data_manager.write_ref(self.hits_name, self.merged_name, ref) - self.data_manager.write_ref(self.merged_name,self.mc_hit_frac_dset_name,np.c_[merge_idx,merge_idx]) - ev_ref = np.c_[(np.indices(merged_mask.shape)[0] + source_slice.start)[~merged_mask], merge_idx] - self.data_manager.write_ref(source_name, self.merged_name, ev_ref) - self.data_manager.write_ref(self.events_dset_name, self.merged_name, ev_ref) diff --git a/yamls/proto_nd_flow/util/dummy.yaml b/yamls/proto_nd_flow/util/dummy.yaml index ce14e482..4da598c5 100644 --- a/yamls/proto_nd_flow/util/dummy.yaml +++ b/yamls/proto_nd_flow/util/dummy.yaml @@ -1,5 +1,5 @@ -classname: CalibHitMerger # reco/charge/calib_hit_merger.py -path: proto_nd_flow.reco.charge.calib_hit_merger +classname: Dummy # reco/charge/calib_hit_merger.py +path: proto_nd_flow.util.dummy requires: - 'charge/events' - 'charge/calib_prompt_hits' diff --git a/yamls/proto_nd_flow/workflows/charge/final_calibration.yaml b/yamls/proto_nd_flow/workflows/charge/final_calibration.yaml index a1ea1c4f..c64de595 100644 --- a/yamls/proto_nd_flow/workflows/charge/final_calibration.yaml +++ b/yamls/proto_nd_flow/workflows/charge/final_calibration.yaml @@ -5,7 +5,7 @@ flow: source: raw_events #source: calib_prompt_hits #stages: [temp_hit_builder, calib_hit_merger] - stages: [calib_hit_merger] + stages: [Dummy] drop: [] @@ -21,6 +21,6 @@ raw_events: params: chunk_size: 32 -calib_hit_merger: +Dummy: !include yamls/proto_nd_flow/util/dummy.yaml From 141d3e2f32802313a462295eec7948944d0448b4 Mon Sep 17 00:00:00 2001 From: Kathryn Sutton Date: Wed, 30 Aug 2023 09:11:41 -0700 Subject: [PATCH 03/37] added separate workflow for dummy --- src/proto_nd_flow/util/dummy.py | 24 +++++++---------- .../workflows/charge/dummy_workflow.yaml | 26 +++++++++++++++++++ .../workflows/charge/final_calibration.yaml | 6 ++--- 3 files changed, 38 insertions(+), 18 deletions(-) create mode 100644 yamls/proto_nd_flow/workflows/charge/dummy_workflow.yaml diff --git a/src/proto_nd_flow/util/dummy.py b/src/proto_nd_flow/util/dummy.py index 76c201ec..0a7f631f 100644 --- a/src/proto_nd_flow/util/dummy.py +++ b/src/proto_nd_flow/util/dummy.py @@ -5,7 +5,7 @@ from h5flow.core import H5FlowStage, resources -from proto_nd_flow.reco.charge.calib_prompt_hits import CalibHitBuilder +from proto_nd_flow.reco.charge.calib_final_hits import CalibHitBuilder class Dummy(H5FlowStage): @@ -15,21 +15,11 @@ class Dummy(H5FlowStage): class_version = '0.0.0' defaults = dict( events_dset_name = 'charge/events', - hits_name = 'charge/calib_prompt_hits', - hit_charge_name = 'charge/calib_prompt_hits', - merged_name = 'charge/hits/calib_merged_hits', - max_merge_steps = 5, - max_contrib_segments = 200, - merge_mode = 'last-first', - merge_cut = 50, # CRS ticks - mc_hit_frac_dset_name = 'mc_truth/calib_final_hit_backtrack' + hits_name = 'charge/calib_final_hits', + hit_charge_name = 'charge/calib_final_hits', + hits_hough_name = 'charge/calib_hough_hits' ) - valid_merge_modes = ['last-first', 'pairwise'] - - merged_dtype = CalibHitBuilder.calib_hits_dtype - - sum_fields = ['Q','E'] - weighted_mean_fields = ['t_drift', 'ts_pps','x'] + hough_dtype = CalibHitBuilder.calib_hits_dtype def __init__(self, **params): print('running dummy.py!!!!!') @@ -48,3 +38,7 @@ def init(self, source_name): ('segment_id', f'({self.max_contrib_segments},)u8') ]) + self.data_manager.create_dset(self.hits_hough_name, dtype=self.hough_dtype) + self.data_manager.create_ref(self.hits_name, self.hits_hough_name) + self.data_manager.create_ref(source_name, self.hits_hough_name) + self.data_manager.create_ref(self.events_dset_name, self.hits_hough_name) diff --git a/yamls/proto_nd_flow/workflows/charge/dummy_workflow.yaml b/yamls/proto_nd_flow/workflows/charge/dummy_workflow.yaml new file mode 100644 index 00000000..c64de595 --- /dev/null +++ b/yamls/proto_nd_flow/workflows/charge/dummy_workflow.yaml @@ -0,0 +1,26 @@ +# Generates the mid-level event built data for charge data (i.e. hits and +# external triggers) + +flow: + source: raw_events + #source: calib_prompt_hits + #stages: [temp_hit_builder, calib_hit_merger] + stages: [Dummy] + drop: [] + + +resources: + - !include yamls/proto_nd_flow/resources/RunData.yaml + - !include yamls/proto_nd_flow/resources/LArData.yaml + - !include yamls/proto_nd_flow/resources/Geometry.yaml + +raw_events: + classname: H5FlowDatasetLoopGenerator + path: h5flow.modules + dset_name: 'charge/raw_events' + params: + chunk_size: 32 + +Dummy: + !include yamls/proto_nd_flow/util/dummy.yaml + diff --git a/yamls/proto_nd_flow/workflows/charge/final_calibration.yaml b/yamls/proto_nd_flow/workflows/charge/final_calibration.yaml index c64de595..bfd41731 100644 --- a/yamls/proto_nd_flow/workflows/charge/final_calibration.yaml +++ b/yamls/proto_nd_flow/workflows/charge/final_calibration.yaml @@ -5,7 +5,7 @@ flow: source: raw_events #source: calib_prompt_hits #stages: [temp_hit_builder, calib_hit_merger] - stages: [Dummy] + stages: [calib_hit_merger] drop: [] @@ -21,6 +21,6 @@ raw_events: params: chunk_size: 32 -Dummy: - !include yamls/proto_nd_flow/util/dummy.yaml +calib_hit_merger: + !include yamls/proto_nd_flow/reco/charge/CalibHitMerger.yaml From 1cdddcd7c93d402022f8a790bff4c35614489a03 Mon Sep 17 00:00:00 2001 From: Kathryn Sutton Date: Wed, 30 Aug 2023 14:17:06 -0700 Subject: [PATCH 04/37] committing before reverting dummy script --- src/proto_nd_flow/util/dummy.py | 20 +++++++++++--------- yamls/proto_nd_flow/util/dummy.yaml | 14 +++++++------- 2 files changed, 18 insertions(+), 16 deletions(-) diff --git a/src/proto_nd_flow/util/dummy.py b/src/proto_nd_flow/util/dummy.py index 0a7f631f..91b4732f 100644 --- a/src/proto_nd_flow/util/dummy.py +++ b/src/proto_nd_flow/util/dummy.py @@ -5,7 +5,7 @@ from h5flow.core import H5FlowStage, resources -from proto_nd_flow.reco.charge.calib_final_hits import CalibHitBuilder +from proto_nd_flow.reco.charge.calib_prompt_hits import CalibHitBuilder class Dummy(H5FlowStage): @@ -15,30 +15,32 @@ class Dummy(H5FlowStage): class_version = '0.0.0' defaults = dict( events_dset_name = 'charge/events', - hits_name = 'charge/calib_final_hits', - hit_charge_name = 'charge/calib_final_hits', + hits_name = 'charge/calib_prompt_hits', + hit_charge_name = 'charge/calib_prompt_hits', hits_hough_name = 'charge/calib_hough_hits' ) hough_dtype = CalibHitBuilder.calib_hits_dtype + ''' def __init__(self, **params): print('running dummy.py!!!!!') super(Dummy, self).__init__(**params) for key in self.defaults: setattr(self, key, params.get(key, self.defaults[key])) - self.merge_mode = self.merge_mode.lower() - assert self.merge_mode in self.valid_merge_modes, f'invalid merge mode: {self.merge_mode}' + # self.merge_mode = self.merge_mode.lower() + # assert self.merge_mode in self.valid_merge_modes, f'invalid merge mode: {self.merge_mode}' def init(self, source_name): print('running dummy.py!!!!!') super(Dummy, self).init(source_name) - self.hit_frac_dtype = np.dtype([ - ('fraction', f'({self.max_contrib_segments},)f8'), - ('segment_id', f'({self.max_contrib_segments},)u8') - ]) + # self.hit_frac_dtype = np.dtype([ + # ('fraction', f'({self.max_contrib_segments},)f8'), + # ('segment_id', f'({self.max_contrib_segments},)u8') + # ]) self.data_manager.create_dset(self.hits_hough_name, dtype=self.hough_dtype) self.data_manager.create_ref(self.hits_name, self.hits_hough_name) self.data_manager.create_ref(source_name, self.hits_hough_name) self.data_manager.create_ref(self.events_dset_name, self.hits_hough_name) + ''' diff --git a/yamls/proto_nd_flow/util/dummy.yaml b/yamls/proto_nd_flow/util/dummy.yaml index 4da598c5..a77bc945 100644 --- a/yamls/proto_nd_flow/util/dummy.yaml +++ b/yamls/proto_nd_flow/util/dummy.yaml @@ -4,20 +4,20 @@ requires: - 'charge/events' - 'charge/calib_prompt_hits' - name: 'packet_frac_backtrack' - path: ['charge/calib_prompt_hits','charge/packets','mc_truth/packet_fraction'] + path: ['charge/calib_final_hits','charge/packets','mc_truth/packet_fraction'] - name: 'packet_seg_backtrack' - path: ['charge/calib_prompt_hits','charge/packets','mc_truth/segments'] + path: ['charge/calib_final_hits','charge/packets','mc_truth/segments'] params: # inputs events_dset_name: 'charge/events' hits_name: 'charge/calib_prompt_hits' - mc_hit_frac_dset_name: 'mc_truth/calib_final_hit_backtrack' - merged_name: 'charge/calib_final_hits' - max_contrib_segments: 200 - merge_cut: 65 # merge hits with delta t < merge_cut [CRS ticks] - max_merge_steps: 50 # max number of iterations when merging + mc_hit_frac_dset_name: 'mc_truth/calib_prompt_hit_backtrack' + hits_hough_name: 'charge/calib_hough_hits' + # max_contrib_segments: 200 + #merge_cut: 65 # merge hits with delta t < merge_cut [CRS ticks] + #max_merge_steps: 50 # max number of iterations when merging # adjacent packets in time on the same channel From 9ef00ad995024e5668552007f4850be7522e8aa4 Mon Sep 17 00:00:00 2001 From: Kathryn Sutton Date: Wed, 30 Aug 2023 14:35:18 -0700 Subject: [PATCH 05/37] made merge hits run in dummy.py --- src/proto_nd_flow/util/dummy.py | 308 ++++++++++++++++++++++++++-- yamls/proto_nd_flow/util/dummy.yaml | 14 +- 2 files changed, 298 insertions(+), 24 deletions(-) diff --git a/src/proto_nd_flow/util/dummy.py b/src/proto_nd_flow/util/dummy.py index 91b4732f..8de6f4d7 100644 --- a/src/proto_nd_flow/util/dummy.py +++ b/src/proto_nd_flow/util/dummy.py @@ -10,37 +10,311 @@ class Dummy(H5FlowStage): ''' - this is a placeholder - ''' + Merges the specified cached hits based on their unique channel id and timestamp: + - q -> sum(q) + - ts -> sum(ts * q) / sum(q) + + Two algorithms for selecting pairs of hits to merge have been implemented: + + - `'pairwise'`: On each iteration, sort all hits by unique y-z position and timestamp. Then, merge every pair of hits that fall within the merge cut. If an odd number of hits fall should be merged, the earliest hit of a group is excluded from the iteration. + - `'last-first'`: On each iteration, sort all hits by unique y-z and timestamp. Then, merge the last pair of hits that fall within the merge cut within each contiguous chunk of neighboring hits. + + Both algorithms should produce very similar results. + + Example config:: + + hit_merging: + classname: CalibHitMerger + path: module0_flow.reco.charge.hit_merger + requires: + - 'charge/hits' + params: + events_dset_name: 'charge/events' + hits_name: 'charge/hits' + hit_charge_name: 'charge/hits' # dataset to grab 'q' from + merged_name: 'charge/hits/merged' + mc_hit_frac_dset_name: ``str``, optional, output dataset path for hit charge fraction truth (if present) + merge_cut: 30 # merge hits with delta t < merge_cut [CRS ticks] + merge_mode: 'last-first' + ''' class_version = '0.0.0' defaults = dict( events_dset_name = 'charge/events', hits_name = 'charge/calib_prompt_hits', hit_charge_name = 'charge/calib_prompt_hits', - hits_hough_name = 'charge/calib_hough_hits' + merged_name = 'charge/hits/calib_merged_hits', + max_merge_steps = 5, + max_contrib_segments = 200, + merge_mode = 'last-first', + merge_cut = 50, # CRS ticks + mc_hit_frac_dset_name = 'mc_truth/calib_final_hit_backtrack' ) - hough_dtype = CalibHitBuilder.calib_hits_dtype + valid_merge_modes = ['last-first', 'pairwise'] + + merged_dtype = CalibHitBuilder.calib_hits_dtype + + sum_fields = ['Q','E'] + weighted_mean_fields = ['t_drift', 'ts_pps','x'] - ''' def __init__(self, **params): - print('running dummy.py!!!!!') super(Dummy, self).__init__(**params) for key in self.defaults: setattr(self, key, params.get(key, self.defaults[key])) - # self.merge_mode = self.merge_mode.lower() - # assert self.merge_mode in self.valid_merge_modes, f'invalid merge mode: {self.merge_mode}' + self.merge_mode = self.merge_mode.lower() + assert self.merge_mode in self.valid_merge_modes, f'invalid merge mode: {self.merge_mode}' def init(self, source_name): - print('running dummy.py!!!!!') super(Dummy, self).init(source_name) - # self.hit_frac_dtype = np.dtype([ - # ('fraction', f'({self.max_contrib_segments},)f8'), - # ('segment_id', f'({self.max_contrib_segments},)u8') - # ]) + self.hit_frac_dtype = np.dtype([ + ('fraction', f'({self.max_contrib_segments},)f8'), + ('segment_id', f'({self.max_contrib_segments},)u8') + ]) - self.data_manager.create_dset(self.hits_hough_name, dtype=self.hough_dtype) - self.data_manager.create_ref(self.hits_name, self.hits_hough_name) - self.data_manager.create_ref(source_name, self.hits_hough_name) - self.data_manager.create_ref(self.events_dset_name, self.hits_hough_name) + self.data_manager.create_dset(self.merged_name, dtype=self.merged_dtype) + self.data_manager.create_dset(self.mc_hit_frac_dset_name, dtype=self.hit_frac_dtype) + self.data_manager.create_ref(self.hits_name, self.merged_name) + self.data_manager.create_ref(source_name, self.merged_name) + self.data_manager.create_ref(self.merged_name,self.mc_hit_frac_dset_name) + self.data_manager.create_ref(self.events_dset_name, self.merged_name) + + #@staticmethod + def merge_hits(self,hits, weights, seg_fracs, dt_cut, sum_fields=None, weighted_mean_fields=None, max_steps=-1, mode='last-first'): ''' + Combines hits along the second axis on unique channels with a delta t less than dt_cut. Continues + until no hits (or merged hits) are within dt_cut of each other + + :param hits: original hits array, shape: (N,M) + + :param weights: values used for weighted mean, shape: (N,M) + + :param fracs: fractional contributions of true segments per packet + + :param dt_cut: delta t cut to merge hits (float) [CRS ticks] + + :sum_fields: list of fields in ``hits`` and that should be *summed* when combined, must not be in ``weighted_mean_fields`` + + :weighted_mean_fields: list of fields in ``hits`` and that should be averaged using the weights when combined, must not be in ``sum_fields`` + + :param max_steps: optional, maximum number of merges to apply to pairs of neighboring hits (<0 == no limit, 0 == skip merging, >0 == limit steps) + + :param mode: optional, merging strategy, either `'last-first'` (on each iteration merges the last hit pair) or `'pairwise'` (on each iteration merges each unique hit pair) + + :returns: new hit array, shape: (N,m), new hit charge array, shape: (N,m), and an index array with shape (L,2), [:,0] being the index into the original hit array and [:,1] being the flattened index into the compressed new array + + ''' + + new_seg_bt = np.array(seg_fracs[0]) + new_frac_bt = np.array(seg_fracs[1]) + iteration_count = 0 + mask = hits.mask['id'].copy() + new_hits = hits.data.copy() + weights = weights.data.copy() + old_ids = hits.data['id'].copy()[...,np.newaxis] + old_id_mask = hits.mask['id'].copy()[...,np.newaxis] + + hit_contributions = np.full(shape=weights.shape+(3,self.max_contrib_segments),fill_value=0.) + #print('weights shape',weights.shape) + #print('hit_contr shape',hit_contributions.shape) + for it, q in np.ndenumerate(weights): + #print('it',it) + #hit_contributions[it].append([]) + #print('---------------') + if len(new_frac_bt[it]) > 1: + print('!!!!!!!!!!!!!!!!!') + break + counter=0 + for entry_it, entry in enumerate(new_frac_bt[it][0]): + #print('frac =',entry) + if abs(entry) < 0.001: continue + hit_contributions[it][0][counter] = q + hit_contributions[it][1][counter] = new_frac_bt[it][0][entry_it] + hit_contributions[it][2][counter] = new_seg_bt[it][0][entry_it]['segment_id'] + counter+=1 + + #print('hit_contributions.shape =',hit_contributions.shape) + #print(hit_contributions) + + while new_hits.size > 0 and iteration_count != max_steps: + iteration_count += 1 + # algorithm is iterative, but typically only needs to loop a few (~2-3) times + # so we'll spit a warning if we reach the maximum number of steps + if iteration_count == max_steps: + logging.info(f'Hit merging algorithm reached max step limit {max_steps}') + + # sort array along last axis to find groups of hits on the same channel, use a stable sort with the aim of improving performance on later iterations + isort = np.argsort(ma.array(new_hits, mask=mask), axis=-1, order=['z','y','ts_pps','t_drift'], kind='stable') + mask = np.take_along_axis(mask, isort, axis=-1) + new_hits = np.take_along_axis(new_hits, isort, axis=-1) + weights = np.take_along_axis(weights, isort, axis=-1) + hit_contributions = np.take_along_axis(hit_contributions,isort[...,np.newaxis,np.newaxis],axis=-3) + old_ids = np.take_along_axis(old_ids, isort[...,np.newaxis], axis=-2) + old_id_mask = np.take_along_axis(old_id_mask, isort[...,np.newaxis], axis=-2) + N_new_hits = new_hits.shape[0]*new_hits.shape[1]-np.count_nonzero(mask) + print('current number of merged hits =',N_new_hits) + + # identify neighboring hits on the same channel + dt = np.abs(np.diff(new_hits['ts_pps'].astype(int), axis=-1)) + same_channel = ( + (new_hits['z'][..., :-1] == new_hits['z'][..., 1:]) + & (new_hits['y'][..., :-1] == new_hits['y'][..., 1:]) + ) + + # flag valid hits if they are on the same channel and are close in time + to_merge = (dt < dt_cut) & same_channel & ~mask[...,:-1] & ~mask[...,1:] + + if mode == 'last-first': + # only combine unambiguous pairs of hits on a channel on each iteration + to_merge[...,:-1] = ~to_merge[...,1:] & to_merge[...,:-1] + elif mode == 'pairwise': + # combine every available pair of hits on each iteration + to_merge[...,:-1] = to_merge[...,:-1] & (np.cumsum(to_merge, axis=-1) % 2 == 0)[...,:-1] + else: + raise RuntimeError(f'invalid merge mode: {mode}') + + print("merging:",np.count_nonzero(to_merge)) + + # exits loop if no remaining hits to combine + if np.any(to_merge): + # move 2nd hit into position of first hit, combining attributes along the way + hit0 = np.extract(to_merge, new_hits[...,:-1]) + hit1 = np.extract(to_merge, new_hits[...,1:]) + + # these fields will be summed hit[i][field] -> hit[i+1][field] + hit[i][field] + for field in sum_fields: + if field in new_hits.dtype.names: + np.place(new_hits[...,:-1][field], to_merge, hit0[field] + hit1[field]) + + # these fields will use the charge-weighted average hit[i][field] -> (hit[i+1][field] * q[i+1] + hit[i][field] * q[i]) / (q[i+1] + q[i]) + q0 = np.extract(to_merge, weights[...,:-1]) + q1 = np.extract(to_merge, weights[...,1:]) + qsum = np.abs(q0) + np.abs(q1) + # regularize so there are no nans + qsum = np.where(qsum == 0, 1e-300, qsum) + # it is not obvious how to treat the possibility of negative charge values (e.g. noise) + # this should(?) be rare, so we'll just spit out a warning + if np.any((q0 < 0) | (q1 < 0)): + logging.info(f'Hit merging encountered negative value(s) (count={((q0 < 0) | (q1 < 0)).sum()}) in charge weighting, results may be unreliable') + w0 = np.abs(q0)/qsum + w1 = np.abs(q1)/qsum + for field in weighted_mean_fields: + if field in new_hits.dtype.names: + base = np.minimum(hit0[field], hit1[field]) # improves precision of weighted sum if values are large (e.g. timestamps) + np.place(new_hits[...,:-1][field], to_merge, ((hit0[field]-base) * w0 + (hit1[field]-base) * w1).astype(new_hits.dtype[field]) + base) + # combine weights for next iteration + np.place(weights[...,:-1], to_merge, weights[...,:-1] + weights[...,1:]) + for hit_it, hit_cont in np.ndenumerate(weights[...,:-1]): + if (not to_merge[hit_it]) | mask[hit_it]: + #print('skipping') + continue + #if hit_contributions[hit_it][1].shape[0] < self.max_contrib_segments: print('a shape :',hit_contributions[hit_it][1].shape) + e = np.argwhere(hit_contributions[...,:-1][hit_it][1]==0)[0][0] + f = np.argwhere(hit_contributions[...,:][hit_it[0],hit_it[1]+1][1]==0)[0][0] + # merge the hit contributions: + for comb_it in range(f): + hit_contributions[...,:-1][hit_it][1][e+comb_it] = hit_contributions[...,:][hit_it[0],hit_it[1]+1][1][comb_it] + hit_contributions[...,:-1][hit_it][0][e+comb_it] = hit_contributions[...,:][hit_it[0],hit_it[1]+1][0][comb_it] + hit_contributions[...,:-1][hit_it][2][e+comb_it] = hit_contributions[...,:][hit_it[0],hit_it[1]+1][2][comb_it] + # and remove them from the hit that was merged in + hit_contributions[hit_it[0],hit_it[1]+1][1][comb_it] = 0 + hit_contributions[hit_it[0],hit_it[1]+1][0][comb_it] = 0. + hit_contributions[hit_it[0],hit_it[1]+1][2][comb_it] = 0. + + # now we mask off hits that have already been merged + mask[...,1:] = mask[...,1:] | to_merge + + # and track the hit ids of the hits that were merged by propogating the indices forward + if mode == 'last-first': + old_id_mask = np.concatenate([old_id_mask[...,0:1], old_id_mask], axis=-1) + old_ids = np.concatenate([old_ids[...,0:1], old_ids], axis=-1) + id_merge = np.broadcast_to(to_merge[...,np.newaxis], to_merge.shape + old_ids.shape[-1:]) + divider = 1 + elif mode == 'pairwise': + old_id_mask = np.concatenate([old_id_mask, old_id_mask], axis=-1) + old_ids = np.concatenate([old_ids, old_ids], axis=-1) + id_merge = np.broadcast_to(to_merge[...,np.newaxis], to_merge.shape + old_ids.shape[-1:]) + divider = old_ids.shape[-1]//2 + else: + raise RuntimeError(f'invalid mode {mode}') + # move ids from hit[i+1] to hit[i] (while keeping the ids for hit[i]) + np.place(old_ids[...,:-1,divider:], id_merge[...,divider:], np.extract(id_merge[...,divider:], old_ids[...,1:,divider:])) + # copy the id mask for hit[i+1] into hit[i] (while keeping the id mask for hit[i]) + np.place(old_id_mask[...,:-1,divider:], id_merge[...,divider:], np.extract(id_merge[...,divider:], old_id_mask[...,1:,divider:])) + # and clear the id mask for hit[i+1] + np.place(old_id_mask[...,1:,:], id_merge, True) + else: + break + + # calculate segment contributions for each merged hit + tmp_bt = np.full(shape=new_hits.shape+(2,self.max_contrib_segments),fill_value=0.) + back_track = np.full(shape=new_hits.shape,fill_value=0.,dtype=self.hit_frac_dtype) + # loop over hits + for hit_it, hit in np.ndenumerate(new_hits): + if mask[hit_it]: continue + hit_contr = hit_contributions[hit_it] + # renormalize the fractional contributions given the charge weighted average + norm = np.sum(np.multiply(hit_contr[0],hit_contr[1])) + if norm == 0.: norm = 1. + tmp_bt[hit_it][0] = np.multiply(hit_contr[0],hit_contr[1])/norm # fractional contributions + tmp_bt[hit_it][1] = hit_contr[2] # segment_ids + + # merge unique track contributions + track_dict = defaultdict(lambda:0) + for track in zip(tmp_bt[hit_it][0],tmp_bt[hit_it][1]): + track_dict[track[1]] += track[0] + track_dict = dict(track_dict) + bt_unique_segs = np.array(list(track_dict.keys())) + bt_unique_frac = np.array(list(track_dict.values())) + n_conts = bt_unique_frac.shape[0] + isort = np.flip(np.argsort(np.abs(bt_unique_frac), axis=-1, kind='stable')) + bt_unique_segs = np.take_along_axis(bt_unique_segs, isort, axis=-1) + bt_unique_frac = np.take_along_axis(bt_unique_frac, isort, axis=-1) + back_track[hit_it]['fraction'] = [0.]*self.max_contrib_segments + back_track[hit_it]['segment_id'] = [0]*self.max_contrib_segments + back_track[hit_it]['fraction'][:bt_unique_frac.shape[0]] = bt_unique_frac + back_track[hit_it]['segment_id'][:bt_unique_segs.shape[0]] = bt_unique_segs + + new_hit_idx = np.broadcast_to(np.cumsum(~mask.ravel(), axis=0).reshape(mask.shape + (1,)), old_ids.shape)-1 + + return ( + ma.array(new_hits, mask=mask), + np.c_[np.extract(~(old_id_mask | mask[...,np.newaxis]), old_ids), np.extract(~(old_id_mask | mask[...,np.newaxis]), new_hit_idx)], + ma.array(back_track, mask=mask) + ) + + def run(self, source_name, source_slice, cache): + super(Dummy, self).run(source_name, source_slice, cache) + + event_id = np.r_[source_slice] + packet_frac_bt = cache['packet_frac_backtrack'] + packet_seg_bt = cache['packet_seg_backtrack'] + hits = cache[self.hits_name] + + merged, ref, back_track = self.merge_hits(hits, weights=hits['Q'], seg_fracs=[packet_seg_bt,packet_frac_bt],dt_cut=self.merge_cut, sum_fields=self.sum_fields, weighted_mean_fields=self.weighted_mean_fields, max_steps=self.max_merge_steps, mode=self.merge_mode) + + merged_mask = merged.mask['id'] + + # first write the new merged hits to the file + new_nhit = int((~merged_mask).sum()) + merge_slice = self.data_manager.reserve_data(self.merged_name, new_nhit) + merge_idx = np.r_[merge_slice].astype(merged.dtype['id']) + if new_nhit > 0: + ref[:,1] += merge_idx[0] # offset references based on reserved region in output file + np.place(merged['id'], ~merged_mask, merge_idx) + + self.data_manager.write_data(self.merged_name, merge_idx, merged[~merged_mask]) + merge_bt_slice = self.data_manager.reserve_data(self.mc_hit_frac_dset_name, new_nhit) + self.data_manager.write_data(self.mc_hit_frac_dset_name, merge_idx, back_track[~merged_mask]) + + # HACK: Remove duplicate refs. Would be nice to actually understand and + # fix the origin of these duplicates. + ref = np.unique(ref, axis=0) + # sort based on the ID of the prompt hit, to make analysis more convenient + ref = ref[np.argsort(ref[:, 0])] + + # finally, write the references + self.data_manager.write_ref(self.hits_name, self.merged_name, ref) + self.data_manager.write_ref(self.merged_name,self.mc_hit_frac_dset_name,np.c_[merge_idx,merge_idx]) + ev_ref = np.c_[(np.indices(merged_mask.shape)[0] + source_slice.start)[~merged_mask], merge_idx] + self.data_manager.write_ref(source_name, self.merged_name, ev_ref) + self.data_manager.write_ref(self.events_dset_name, self.merged_name, ev_ref) diff --git a/yamls/proto_nd_flow/util/dummy.yaml b/yamls/proto_nd_flow/util/dummy.yaml index a77bc945..4da598c5 100644 --- a/yamls/proto_nd_flow/util/dummy.yaml +++ b/yamls/proto_nd_flow/util/dummy.yaml @@ -4,20 +4,20 @@ requires: - 'charge/events' - 'charge/calib_prompt_hits' - name: 'packet_frac_backtrack' - path: ['charge/calib_final_hits','charge/packets','mc_truth/packet_fraction'] + path: ['charge/calib_prompt_hits','charge/packets','mc_truth/packet_fraction'] - name: 'packet_seg_backtrack' - path: ['charge/calib_final_hits','charge/packets','mc_truth/segments'] + path: ['charge/calib_prompt_hits','charge/packets','mc_truth/segments'] params: # inputs events_dset_name: 'charge/events' hits_name: 'charge/calib_prompt_hits' - mc_hit_frac_dset_name: 'mc_truth/calib_prompt_hit_backtrack' - hits_hough_name: 'charge/calib_hough_hits' - # max_contrib_segments: 200 - #merge_cut: 65 # merge hits with delta t < merge_cut [CRS ticks] - #max_merge_steps: 50 # max number of iterations when merging + mc_hit_frac_dset_name: 'mc_truth/calib_final_hit_backtrack' + merged_name: 'charge/calib_final_hits' + max_contrib_segments: 200 + merge_cut: 65 # merge hits with delta t < merge_cut [CRS ticks] + max_merge_steps: 50 # max number of iterations when merging # adjacent packets in time on the same channel From 8ce2be808f3b4c1e2ccd77826dc9bf82858e195a Mon Sep 17 00:00:00 2001 From: Kathryn Sutton Date: Wed, 30 Aug 2023 20:16:52 -0700 Subject: [PATCH 06/37] updated product name in yaml --- yamls/proto_nd_flow/util/dummy.yaml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/yamls/proto_nd_flow/util/dummy.yaml b/yamls/proto_nd_flow/util/dummy.yaml index 4da598c5..3ac60690 100644 --- a/yamls/proto_nd_flow/util/dummy.yaml +++ b/yamls/proto_nd_flow/util/dummy.yaml @@ -14,7 +14,7 @@ params: events_dset_name: 'charge/events' hits_name: 'charge/calib_prompt_hits' mc_hit_frac_dset_name: 'mc_truth/calib_final_hit_backtrack' - merged_name: 'charge/calib_final_hits' + merged_name: 'charge/calib_hough_hits' max_contrib_segments: 200 merge_cut: 65 # merge hits with delta t < merge_cut [CRS ticks] max_merge_steps: 50 # max number of iterations when merging From 0e406024c92479164607815b6af3625d41b1ec2c Mon Sep 17 00:00:00 2001 From: Kathryn Sutton Date: Thu, 31 Aug 2023 13:00:28 -0700 Subject: [PATCH 07/37] took out the bulk of the merge function --- src/proto_nd_flow/util/dummy.py | 94 +++++++++++++++++---------------- 1 file changed, 49 insertions(+), 45 deletions(-) diff --git a/src/proto_nd_flow/util/dummy.py b/src/proto_nd_flow/util/dummy.py index 8de6f4d7..246f5053 100644 --- a/src/proto_nd_flow/util/dummy.py +++ b/src/proto_nd_flow/util/dummy.py @@ -112,7 +112,54 @@ def merge_hits(self,hits, weights, seg_fracs, dt_cut, sum_fields=None, weighted_ weights = weights.data.copy() old_ids = hits.data['id'].copy()[...,np.newaxis] old_id_mask = hits.mask['id'].copy()[...,np.newaxis] + + new_hit_idx = np.broadcast_to(np.cumsum(~mask.ravel(), axis=0).reshape(mask.shape + (1,)), old_ids.shape)-1 + back_track = np.full(shape=new_hits.shape,fill_value=0.,dtype=self.hit_frac_dtype) + + return ( + ma.array(new_hits, mask=mask), + np.c_[np.extract(~(old_id_mask | mask[...,np.newaxis]), old_ids), np.extract(~(old_id_mask | mask[...,np.newaxis]), new_hit_idx)], + ma.array(back_track, mask=mask) + ) + + def run(self, source_name, source_slice, cache): + super(Dummy, self).run(source_name, source_slice, cache) + + event_id = np.r_[source_slice] + packet_frac_bt = cache['packet_frac_backtrack'] + packet_seg_bt = cache['packet_seg_backtrack'] + hits = cache[self.hits_name] + + merged, ref, back_track = self.merge_hits(hits, weights=hits['Q'], seg_fracs=[packet_seg_bt,packet_frac_bt],dt_cut=self.merge_cut, sum_fields=self.sum_fields, weighted_mean_fields=self.weighted_mean_fields, max_steps=self.max_merge_steps, mode=self.merge_mode) + + merged_mask = merged.mask['id'] + + # first write the new merged hits to the file + new_nhit = int((~merged_mask).sum()) + merge_slice = self.data_manager.reserve_data(self.merged_name, new_nhit) + merge_idx = np.r_[merge_slice].astype(merged.dtype['id']) + if new_nhit > 0: + ref[:,1] += merge_idx[0] # offset references based on reserved region in output file + np.place(merged['id'], ~merged_mask, merge_idx) + + self.data_manager.write_data(self.merged_name, merge_idx, merged[~merged_mask]) + merge_bt_slice = self.data_manager.reserve_data(self.mc_hit_frac_dset_name, new_nhit) + self.data_manager.write_data(self.mc_hit_frac_dset_name, merge_idx, back_track[~merged_mask]) + + # HACK: Remove duplicate refs. Would be nice to actually understand and + # fix the origin of these duplicates. + ref = np.unique(ref, axis=0) + # sort based on the ID of the prompt hit, to make analysis more convenient + ref = ref[np.argsort(ref[:, 0])] + + # finally, write the references + self.data_manager.write_ref(self.hits_name, self.merged_name, ref) + self.data_manager.write_ref(self.merged_name,self.mc_hit_frac_dset_name,np.c_[merge_idx,merge_idx]) + ev_ref = np.c_[(np.indices(merged_mask.shape)[0] + source_slice.start)[~merged_mask], merge_idx] + self.data_manager.write_ref(source_name, self.merged_name, ev_ref) + self.data_manager.write_ref(self.events_dset_name, self.merged_name, ev_ref) +''' hit_contributions = np.full(shape=weights.shape+(3,self.max_contrib_segments),fill_value=0.) #print('weights shape',weights.shape) #print('hit_contr shape',hit_contributions.shape) @@ -247,8 +294,7 @@ def merge_hits(self,hits, weights, seg_fracs, dt_cut, sum_fields=None, weighted_ # calculate segment contributions for each merged hit tmp_bt = np.full(shape=new_hits.shape+(2,self.max_contrib_segments),fill_value=0.) - back_track = np.full(shape=new_hits.shape,fill_value=0.,dtype=self.hit_frac_dtype) - # loop over hits + # loop over hits for hit_it, hit in np.ndenumerate(new_hits): if mask[hit_it]: continue hit_contr = hit_contributions[hit_it] @@ -274,47 +320,5 @@ def merge_hits(self,hits, weights, seg_fracs, dt_cut, sum_fields=None, weighted_ back_track[hit_it]['fraction'][:bt_unique_frac.shape[0]] = bt_unique_frac back_track[hit_it]['segment_id'][:bt_unique_segs.shape[0]] = bt_unique_segs - new_hit_idx = np.broadcast_to(np.cumsum(~mask.ravel(), axis=0).reshape(mask.shape + (1,)), old_ids.shape)-1 - - return ( - ma.array(new_hits, mask=mask), - np.c_[np.extract(~(old_id_mask | mask[...,np.newaxis]), old_ids), np.extract(~(old_id_mask | mask[...,np.newaxis]), new_hit_idx)], - ma.array(back_track, mask=mask) - ) - - def run(self, source_name, source_slice, cache): - super(Dummy, self).run(source_name, source_slice, cache) - - event_id = np.r_[source_slice] - packet_frac_bt = cache['packet_frac_backtrack'] - packet_seg_bt = cache['packet_seg_backtrack'] - hits = cache[self.hits_name] - - merged, ref, back_track = self.merge_hits(hits, weights=hits['Q'], seg_fracs=[packet_seg_bt,packet_frac_bt],dt_cut=self.merge_cut, sum_fields=self.sum_fields, weighted_mean_fields=self.weighted_mean_fields, max_steps=self.max_merge_steps, mode=self.merge_mode) - - merged_mask = merged.mask['id'] - - # first write the new merged hits to the file - new_nhit = int((~merged_mask).sum()) - merge_slice = self.data_manager.reserve_data(self.merged_name, new_nhit) - merge_idx = np.r_[merge_slice].astype(merged.dtype['id']) - if new_nhit > 0: - ref[:,1] += merge_idx[0] # offset references based on reserved region in output file - np.place(merged['id'], ~merged_mask, merge_idx) - - self.data_manager.write_data(self.merged_name, merge_idx, merged[~merged_mask]) - merge_bt_slice = self.data_manager.reserve_data(self.mc_hit_frac_dset_name, new_nhit) - self.data_manager.write_data(self.mc_hit_frac_dset_name, merge_idx, back_track[~merged_mask]) - - # HACK: Remove duplicate refs. Would be nice to actually understand and - # fix the origin of these duplicates. - ref = np.unique(ref, axis=0) - # sort based on the ID of the prompt hit, to make analysis more convenient - ref = ref[np.argsort(ref[:, 0])] + ''' - # finally, write the references - self.data_manager.write_ref(self.hits_name, self.merged_name, ref) - self.data_manager.write_ref(self.merged_name,self.mc_hit_frac_dset_name,np.c_[merge_idx,merge_idx]) - ev_ref = np.c_[(np.indices(merged_mask.shape)[0] + source_slice.start)[~merged_mask], merge_idx] - self.data_manager.write_ref(source_name, self.merged_name, ev_ref) - self.data_manager.write_ref(self.events_dset_name, self.merged_name, ev_ref) From be9cc8c251399e258f3972cf34a5705ad05163a0 Mon Sep 17 00:00:00 2001 From: Kathryn Sutton Date: Fri, 1 Sep 2023 16:58:53 -0700 Subject: [PATCH 08/37] added in some comments --- src/proto_nd_flow/util/dummy.py | 63 +++++++-------------------------- 1 file changed, 12 insertions(+), 51 deletions(-) diff --git a/src/proto_nd_flow/util/dummy.py b/src/proto_nd_flow/util/dummy.py index 246f5053..fbe33a1d 100644 --- a/src/proto_nd_flow/util/dummy.py +++ b/src/proto_nd_flow/util/dummy.py @@ -10,33 +10,12 @@ class Dummy(H5FlowStage): ''' - Merges the specified cached hits based on their unique channel id and timestamp: - - q -> sum(q) - - ts -> sum(ts * q) / sum(q) - - Two algorithms for selecting pairs of hits to merge have been implemented: - - - `'pairwise'`: On each iteration, sort all hits by unique y-z position and timestamp. Then, merge every pair of hits that fall within the merge cut. If an odd number of hits fall should be merged, the earliest hit of a group is excluded from the iteration. - - `'last-first'`: On each iteration, sort all hits by unique y-z and timestamp. Then, merge the last pair of hits that fall within the merge cut within each contiguous chunk of neighboring hits. - - Both algorithms should produce very similar results. - - Example config:: - - hit_merging: - classname: CalibHitMerger - path: module0_flow.reco.charge.hit_merger - requires: - - 'charge/hits' - params: - events_dset_name: 'charge/events' - hits_name: 'charge/hits' - hit_charge_name: 'charge/hits' # dataset to grab 'q' from - merged_name: 'charge/hits/merged' - mc_hit_frac_dset_name: ``str``, optional, output dataset path for hit charge fraction truth (if present) - merge_cut: 30 # merge hits with delta t < merge_cut [CRS ticks] - merge_mode: 'last-first' - ''' + This module was adapted from CalibHitMerger + The goal is to take the charge/calib_prompt_hits and perform a Hough transform + This could also be performed on calib_merged_hits that are in the "final" stage + The outputs are saved as a set of hits along the line that are a subset of the inital input hits + ksutton 8/30/23 + ''' class_version = '0.0.0' defaults = dict( events_dset_name = 'charge/events', @@ -81,28 +60,8 @@ def init(self, source_name): #@staticmethod def merge_hits(self,hits, weights, seg_fracs, dt_cut, sum_fields=None, weighted_mean_fields=None, max_steps=-1, mode='last-first'): ''' - Combines hits along the second axis on unique channels with a delta t less than dt_cut. Continues - until no hits (or merged hits) are within dt_cut of each other - - :param hits: original hits array, shape: (N,M) - - :param weights: values used for weighted mean, shape: (N,M) - - :param fracs: fractional contributions of true segments per packet - - :param dt_cut: delta t cut to merge hits (float) [CRS ticks] - - :sum_fields: list of fields in ``hits`` and that should be *summed* when combined, must not be in ``weighted_mean_fields`` - - :weighted_mean_fields: list of fields in ``hits`` and that should be averaged using the weights when combined, must not be in ``sum_fields`` - - :param max_steps: optional, maximum number of merges to apply to pairs of neighboring hits (<0 == no limit, 0 == skip merging, >0 == limit steps) - - :param mode: optional, merging strategy, either `'last-first'` (on each iteration merges the last hit pair) or `'pairwise'` (on each iteration merges each unique hit pair) - - :returns: new hit array, shape: (N,m), new hit charge array, shape: (N,m), and an index array with shape (L,2), [:,0] being the index into the original hit array and [:,1] being the flattened index into the compressed new array - - ''' + currently does nothing, need to add in Hough transform here +''' new_seg_bt = np.array(seg_fracs[0]) new_frac_bt = np.array(seg_fracs[1]) @@ -125,16 +84,18 @@ def merge_hits(self,hits, weights, seg_fracs, dt_cut, sum_fields=None, weighted_ def run(self, source_name, source_slice, cache): super(Dummy, self).run(source_name, source_slice, cache) + #get the event id, backtracking, and hits from the input file event_id = np.r_[source_slice] packet_frac_bt = cache['packet_frac_backtrack'] packet_seg_bt = cache['packet_seg_backtrack'] hits = cache[self.hits_name] + #get the new hits, references, and backtracking for the merged, ref, back_track = self.merge_hits(hits, weights=hits['Q'], seg_fracs=[packet_seg_bt,packet_frac_bt],dt_cut=self.merge_cut, sum_fields=self.sum_fields, weighted_mean_fields=self.weighted_mean_fields, max_steps=self.max_merge_steps, mode=self.merge_mode) - merged_mask = merged.mask['id'] + merged_mask = merged.mask['id'] #not sure what this does yet - # first write the new merged hits to the file + # first write the new hits to the file new_nhit = int((~merged_mask).sum()) merge_slice = self.data_manager.reserve_data(self.merged_name, new_nhit) merge_idx = np.r_[merge_slice].astype(merged.dtype['id']) From 3ea1e8b5f511a4443e12102d3c54b9ba99f6f291 Mon Sep 17 00:00:00 2001 From: Kathryn Sutton Date: Tue, 5 Sep 2023 19:01:39 -0700 Subject: [PATCH 09/37] renamed files to have better naming scheme --- src/proto_nd_flow/util/{dummy.py => hough.py} | 8 ++++---- yamls/proto_nd_flow/util/{dummy.yaml => hough.yaml} | 4 ++-- .../dummy_workflow.yaml => util/reco_workflow.yaml} | 6 +++--- 3 files changed, 9 insertions(+), 9 deletions(-) rename src/proto_nd_flow/util/{dummy.py => hough.py} (98%) rename yamls/proto_nd_flow/util/{dummy.yaml => hough.yaml} (89%) rename yamls/proto_nd_flow/workflows/{charge/dummy_workflow.yaml => util/reco_workflow.yaml} (88%) diff --git a/src/proto_nd_flow/util/dummy.py b/src/proto_nd_flow/util/hough.py similarity index 98% rename from src/proto_nd_flow/util/dummy.py rename to src/proto_nd_flow/util/hough.py index fbe33a1d..4b69074b 100644 --- a/src/proto_nd_flow/util/dummy.py +++ b/src/proto_nd_flow/util/hough.py @@ -8,7 +8,7 @@ from proto_nd_flow.reco.charge.calib_prompt_hits import CalibHitBuilder -class Dummy(H5FlowStage): +class hough(H5FlowStage): ''' This module was adapted from CalibHitMerger The goal is to take the charge/calib_prompt_hits and perform a Hough transform @@ -36,14 +36,14 @@ class Dummy(H5FlowStage): weighted_mean_fields = ['t_drift', 'ts_pps','x'] def __init__(self, **params): - super(Dummy, self).__init__(**params) + super(hough, self).__init__(**params) for key in self.defaults: setattr(self, key, params.get(key, self.defaults[key])) self.merge_mode = self.merge_mode.lower() assert self.merge_mode in self.valid_merge_modes, f'invalid merge mode: {self.merge_mode}' def init(self, source_name): - super(Dummy, self).init(source_name) + super(hough, self).init(source_name) self.hit_frac_dtype = np.dtype([ ('fraction', f'({self.max_contrib_segments},)f8'), @@ -82,7 +82,7 @@ def merge_hits(self,hits, weights, seg_fracs, dt_cut, sum_fields=None, weighted_ ) def run(self, source_name, source_slice, cache): - super(Dummy, self).run(source_name, source_slice, cache) + super(hough, self).run(source_name, source_slice, cache) #get the event id, backtracking, and hits from the input file event_id = np.r_[source_slice] diff --git a/yamls/proto_nd_flow/util/dummy.yaml b/yamls/proto_nd_flow/util/hough.yaml similarity index 89% rename from yamls/proto_nd_flow/util/dummy.yaml rename to yamls/proto_nd_flow/util/hough.yaml index 3ac60690..7ac9b0d6 100644 --- a/yamls/proto_nd_flow/util/dummy.yaml +++ b/yamls/proto_nd_flow/util/hough.yaml @@ -1,5 +1,5 @@ -classname: Dummy # reco/charge/calib_hit_merger.py -path: proto_nd_flow.util.dummy +classname: hough # reco/charge/calib_hit_merger.py +path: proto_nd_flow.util.hough requires: - 'charge/events' - 'charge/calib_prompt_hits' diff --git a/yamls/proto_nd_flow/workflows/charge/dummy_workflow.yaml b/yamls/proto_nd_flow/workflows/util/reco_workflow.yaml similarity index 88% rename from yamls/proto_nd_flow/workflows/charge/dummy_workflow.yaml rename to yamls/proto_nd_flow/workflows/util/reco_workflow.yaml index c64de595..6984ca93 100644 --- a/yamls/proto_nd_flow/workflows/charge/dummy_workflow.yaml +++ b/yamls/proto_nd_flow/workflows/util/reco_workflow.yaml @@ -5,7 +5,7 @@ flow: source: raw_events #source: calib_prompt_hits #stages: [temp_hit_builder, calib_hit_merger] - stages: [Dummy] + stages: [hough] drop: [] @@ -21,6 +21,6 @@ raw_events: params: chunk_size: 32 -Dummy: - !include yamls/proto_nd_flow/util/dummy.yaml +hough: + !include yamls/proto_nd_flow/util/hough.yaml From febd0a103e22942577ba6f227bcfdc035f6153b4 Mon Sep 17 00:00:00 2001 From: Kathryn Sutton Date: Wed, 6 Sep 2023 20:53:33 -0700 Subject: [PATCH 10/37] commented out a bit more of the backtracking --- src/proto_nd_flow/util/hough.py | 240 +++++--------------------------- 1 file changed, 36 insertions(+), 204 deletions(-) diff --git a/src/proto_nd_flow/util/hough.py b/src/proto_nd_flow/util/hough.py index 4b69074b..fe1cca26 100644 --- a/src/proto_nd_flow/util/hough.py +++ b/src/proto_nd_flow/util/hough.py @@ -14,6 +14,7 @@ class hough(H5FlowStage): The goal is to take the charge/calib_prompt_hits and perform a Hough transform This could also be performed on calib_merged_hits that are in the "final" stage The outputs are saved as a set of hits along the line that are a subset of the inital input hits + Currently no hough transform implemented!!! Just an empty module ksutton 8/30/23 ''' class_version = '0.0.0' @@ -21,44 +22,36 @@ class hough(H5FlowStage): events_dset_name = 'charge/events', hits_name = 'charge/calib_prompt_hits', hit_charge_name = 'charge/calib_prompt_hits', - merged_name = 'charge/hits/calib_merged_hits', - max_merge_steps = 5, - max_contrib_segments = 200, - merge_mode = 'last-first', - merge_cut = 50, # CRS ticks - mc_hit_frac_dset_name = 'mc_truth/calib_final_hit_backtrack' + output_name = 'charge/hits/calib_hough_hits', + #mc_hit_frac_dset_name = 'mc_truth/calib_hough_hit_backtrack' ) - valid_merge_modes = ['last-first', 'pairwise'] - merged_dtype = CalibHitBuilder.calib_hits_dtype - - sum_fields = ['Q','E'] - weighted_mean_fields = ['t_drift', 'ts_pps','x'] + output_dtype = CalibHitBuilder.calib_hits_dtype def __init__(self, **params): super(hough, self).__init__(**params) for key in self.defaults: setattr(self, key, params.get(key, self.defaults[key])) - self.merge_mode = self.merge_mode.lower() - assert self.merge_mode in self.valid_merge_modes, f'invalid merge mode: {self.merge_mode}' + # self.output_mode = self.output_mode.lower() + # assert self.output_mode in self.valid_output_modes, f'invalid output mode: {self.output_mode}' def init(self, source_name): super(hough, self).init(source_name) - self.hit_frac_dtype = np.dtype([ - ('fraction', f'({self.max_contrib_segments},)f8'), - ('segment_id', f'({self.max_contrib_segments},)u8') - ]) + # self.hit_frac_dtype = np.dtype([ + # ('fraction', f'({self.max_contrib_segments},)f8'), + # ('segment_id', f'({self.max_contrib_segments},)u8') + # ]) - self.data_manager.create_dset(self.merged_name, dtype=self.merged_dtype) - self.data_manager.create_dset(self.mc_hit_frac_dset_name, dtype=self.hit_frac_dtype) - self.data_manager.create_ref(self.hits_name, self.merged_name) - self.data_manager.create_ref(source_name, self.merged_name) - self.data_manager.create_ref(self.merged_name,self.mc_hit_frac_dset_name) - self.data_manager.create_ref(self.events_dset_name, self.merged_name) + self.data_manager.create_dset(self.output_name, dtype=self.output_dtype) + # self.data_manager.create_dset(self.mc_hit_frac_dset_name, dtype=self.hit_frac_dtype) + self.data_manager.create_ref(self.hits_name, self.output_name) + self.data_manager.create_ref(source_name, self.output_name) + # self.data_manager.create_ref(self.output_name,self.mc_hit_frac_dset_name) + self.data_manager.create_ref(self.events_dset_name, self.output_name) #@staticmethod - def merge_hits(self,hits, weights, seg_fracs, dt_cut, sum_fields=None, weighted_mean_fields=None, max_steps=-1, mode='last-first'): + def output_hits(self,hits, weights, seg_fracs): ''' currently does nothing, need to add in Hough transform here ''' @@ -73,12 +66,12 @@ def merge_hits(self,hits, weights, seg_fracs, dt_cut, sum_fields=None, weighted_ old_id_mask = hits.mask['id'].copy()[...,np.newaxis] new_hit_idx = np.broadcast_to(np.cumsum(~mask.ravel(), axis=0).reshape(mask.shape + (1,)), old_ids.shape)-1 - back_track = np.full(shape=new_hits.shape,fill_value=0.,dtype=self.hit_frac_dtype) + # back_track = np.full(shape=new_hits.shape,fill_value=0.,dtype=self.hit_frac_dtype) return ( ma.array(new_hits, mask=mask), np.c_[np.extract(~(old_id_mask | mask[...,np.newaxis]), old_ids), np.extract(~(old_id_mask | mask[...,np.newaxis]), new_hit_idx)], - ma.array(back_track, mask=mask) + # ma.array(back_track, mask=mask) ) def run(self, source_name, source_slice, cache): @@ -91,21 +84,23 @@ def run(self, source_name, source_slice, cache): hits = cache[self.hits_name] #get the new hits, references, and backtracking for the - merged, ref, back_track = self.merge_hits(hits, weights=hits['Q'], seg_fracs=[packet_seg_bt,packet_frac_bt],dt_cut=self.merge_cut, sum_fields=self.sum_fields, weighted_mean_fields=self.weighted_mean_fields, max_steps=self.max_merge_steps, mode=self.merge_mode) + # output, ref, back_track = self.output_hits(hits, weights=hits['Q'], seg_fracs=[packet_seg_bt,packet_frac_bt]) + output, ref = self.output_hits(hits, weights=hits['Q'], seg_fracs=[packet_seg_bt,packet_frac_bt]) + - merged_mask = merged.mask['id'] #not sure what this does yet + output_mask = output.mask['id'] #not sure what this does yet # first write the new hits to the file - new_nhit = int((~merged_mask).sum()) - merge_slice = self.data_manager.reserve_data(self.merged_name, new_nhit) - merge_idx = np.r_[merge_slice].astype(merged.dtype['id']) + new_nhit = int((~output_mask).sum()) + output_slice = self.data_manager.reserve_data(self.output_name, new_nhit) + output_idx = np.r_[output_slice].astype(output.dtype['id']) if new_nhit > 0: - ref[:,1] += merge_idx[0] # offset references based on reserved region in output file - np.place(merged['id'], ~merged_mask, merge_idx) + ref[:,1] += output_idx[0] # offset references based on reserved region in output file + np.place(output['id'], ~output_mask, output_idx) - self.data_manager.write_data(self.merged_name, merge_idx, merged[~merged_mask]) - merge_bt_slice = self.data_manager.reserve_data(self.mc_hit_frac_dset_name, new_nhit) - self.data_manager.write_data(self.mc_hit_frac_dset_name, merge_idx, back_track[~merged_mask]) + self.data_manager.write_data(self.output_name, output_idx, output[~output_mask]) + #output_bt_slice = self.data_manager.reserve_data(self.mc_hit_frac_dset_name, new_nhit) + #self.data_manager.write_data(self.mc_hit_frac_dset_name, output_idx, back_track[~output_mask]) # HACK: Remove duplicate refs. Would be nice to actually understand and # fix the origin of these duplicates. @@ -114,172 +109,9 @@ def run(self, source_name, source_slice, cache): ref = ref[np.argsort(ref[:, 0])] # finally, write the references - self.data_manager.write_ref(self.hits_name, self.merged_name, ref) - self.data_manager.write_ref(self.merged_name,self.mc_hit_frac_dset_name,np.c_[merge_idx,merge_idx]) - ev_ref = np.c_[(np.indices(merged_mask.shape)[0] + source_slice.start)[~merged_mask], merge_idx] - self.data_manager.write_ref(source_name, self.merged_name, ev_ref) - self.data_manager.write_ref(self.events_dset_name, self.merged_name, ev_ref) - -''' - hit_contributions = np.full(shape=weights.shape+(3,self.max_contrib_segments),fill_value=0.) - #print('weights shape',weights.shape) - #print('hit_contr shape',hit_contributions.shape) - for it, q in np.ndenumerate(weights): - #print('it',it) - #hit_contributions[it].append([]) - #print('---------------') - if len(new_frac_bt[it]) > 1: - print('!!!!!!!!!!!!!!!!!') - break - counter=0 - for entry_it, entry in enumerate(new_frac_bt[it][0]): - #print('frac =',entry) - if abs(entry) < 0.001: continue - hit_contributions[it][0][counter] = q - hit_contributions[it][1][counter] = new_frac_bt[it][0][entry_it] - hit_contributions[it][2][counter] = new_seg_bt[it][0][entry_it]['segment_id'] - counter+=1 - - #print('hit_contributions.shape =',hit_contributions.shape) - #print(hit_contributions) - - while new_hits.size > 0 and iteration_count != max_steps: - iteration_count += 1 - # algorithm is iterative, but typically only needs to loop a few (~2-3) times - # so we'll spit a warning if we reach the maximum number of steps - if iteration_count == max_steps: - logging.info(f'Hit merging algorithm reached max step limit {max_steps}') - - # sort array along last axis to find groups of hits on the same channel, use a stable sort with the aim of improving performance on later iterations - isort = np.argsort(ma.array(new_hits, mask=mask), axis=-1, order=['z','y','ts_pps','t_drift'], kind='stable') - mask = np.take_along_axis(mask, isort, axis=-1) - new_hits = np.take_along_axis(new_hits, isort, axis=-1) - weights = np.take_along_axis(weights, isort, axis=-1) - hit_contributions = np.take_along_axis(hit_contributions,isort[...,np.newaxis,np.newaxis],axis=-3) - old_ids = np.take_along_axis(old_ids, isort[...,np.newaxis], axis=-2) - old_id_mask = np.take_along_axis(old_id_mask, isort[...,np.newaxis], axis=-2) - N_new_hits = new_hits.shape[0]*new_hits.shape[1]-np.count_nonzero(mask) - print('current number of merged hits =',N_new_hits) - - # identify neighboring hits on the same channel - dt = np.abs(np.diff(new_hits['ts_pps'].astype(int), axis=-1)) - same_channel = ( - (new_hits['z'][..., :-1] == new_hits['z'][..., 1:]) - & (new_hits['y'][..., :-1] == new_hits['y'][..., 1:]) - ) - - # flag valid hits if they are on the same channel and are close in time - to_merge = (dt < dt_cut) & same_channel & ~mask[...,:-1] & ~mask[...,1:] - - if mode == 'last-first': - # only combine unambiguous pairs of hits on a channel on each iteration - to_merge[...,:-1] = ~to_merge[...,1:] & to_merge[...,:-1] - elif mode == 'pairwise': - # combine every available pair of hits on each iteration - to_merge[...,:-1] = to_merge[...,:-1] & (np.cumsum(to_merge, axis=-1) % 2 == 0)[...,:-1] - else: - raise RuntimeError(f'invalid merge mode: {mode}') - - print("merging:",np.count_nonzero(to_merge)) - - # exits loop if no remaining hits to combine - if np.any(to_merge): - # move 2nd hit into position of first hit, combining attributes along the way - hit0 = np.extract(to_merge, new_hits[...,:-1]) - hit1 = np.extract(to_merge, new_hits[...,1:]) - - # these fields will be summed hit[i][field] -> hit[i+1][field] + hit[i][field] - for field in sum_fields: - if field in new_hits.dtype.names: - np.place(new_hits[...,:-1][field], to_merge, hit0[field] + hit1[field]) - - # these fields will use the charge-weighted average hit[i][field] -> (hit[i+1][field] * q[i+1] + hit[i][field] * q[i]) / (q[i+1] + q[i]) - q0 = np.extract(to_merge, weights[...,:-1]) - q1 = np.extract(to_merge, weights[...,1:]) - qsum = np.abs(q0) + np.abs(q1) - # regularize so there are no nans - qsum = np.where(qsum == 0, 1e-300, qsum) - # it is not obvious how to treat the possibility of negative charge values (e.g. noise) - # this should(?) be rare, so we'll just spit out a warning - if np.any((q0 < 0) | (q1 < 0)): - logging.info(f'Hit merging encountered negative value(s) (count={((q0 < 0) | (q1 < 0)).sum()}) in charge weighting, results may be unreliable') - w0 = np.abs(q0)/qsum - w1 = np.abs(q1)/qsum - for field in weighted_mean_fields: - if field in new_hits.dtype.names: - base = np.minimum(hit0[field], hit1[field]) # improves precision of weighted sum if values are large (e.g. timestamps) - np.place(new_hits[...,:-1][field], to_merge, ((hit0[field]-base) * w0 + (hit1[field]-base) * w1).astype(new_hits.dtype[field]) + base) - # combine weights for next iteration - np.place(weights[...,:-1], to_merge, weights[...,:-1] + weights[...,1:]) - for hit_it, hit_cont in np.ndenumerate(weights[...,:-1]): - if (not to_merge[hit_it]) | mask[hit_it]: - #print('skipping') - continue - #if hit_contributions[hit_it][1].shape[0] < self.max_contrib_segments: print('a shape :',hit_contributions[hit_it][1].shape) - e = np.argwhere(hit_contributions[...,:-1][hit_it][1]==0)[0][0] - f = np.argwhere(hit_contributions[...,:][hit_it[0],hit_it[1]+1][1]==0)[0][0] - # merge the hit contributions: - for comb_it in range(f): - hit_contributions[...,:-1][hit_it][1][e+comb_it] = hit_contributions[...,:][hit_it[0],hit_it[1]+1][1][comb_it] - hit_contributions[...,:-1][hit_it][0][e+comb_it] = hit_contributions[...,:][hit_it[0],hit_it[1]+1][0][comb_it] - hit_contributions[...,:-1][hit_it][2][e+comb_it] = hit_contributions[...,:][hit_it[0],hit_it[1]+1][2][comb_it] - # and remove them from the hit that was merged in - hit_contributions[hit_it[0],hit_it[1]+1][1][comb_it] = 0 - hit_contributions[hit_it[0],hit_it[1]+1][0][comb_it] = 0. - hit_contributions[hit_it[0],hit_it[1]+1][2][comb_it] = 0. - - # now we mask off hits that have already been merged - mask[...,1:] = mask[...,1:] | to_merge - - # and track the hit ids of the hits that were merged by propogating the indices forward - if mode == 'last-first': - old_id_mask = np.concatenate([old_id_mask[...,0:1], old_id_mask], axis=-1) - old_ids = np.concatenate([old_ids[...,0:1], old_ids], axis=-1) - id_merge = np.broadcast_to(to_merge[...,np.newaxis], to_merge.shape + old_ids.shape[-1:]) - divider = 1 - elif mode == 'pairwise': - old_id_mask = np.concatenate([old_id_mask, old_id_mask], axis=-1) - old_ids = np.concatenate([old_ids, old_ids], axis=-1) - id_merge = np.broadcast_to(to_merge[...,np.newaxis], to_merge.shape + old_ids.shape[-1:]) - divider = old_ids.shape[-1]//2 - else: - raise RuntimeError(f'invalid mode {mode}') - # move ids from hit[i+1] to hit[i] (while keeping the ids for hit[i]) - np.place(old_ids[...,:-1,divider:], id_merge[...,divider:], np.extract(id_merge[...,divider:], old_ids[...,1:,divider:])) - # copy the id mask for hit[i+1] into hit[i] (while keeping the id mask for hit[i]) - np.place(old_id_mask[...,:-1,divider:], id_merge[...,divider:], np.extract(id_merge[...,divider:], old_id_mask[...,1:,divider:])) - # and clear the id mask for hit[i+1] - np.place(old_id_mask[...,1:,:], id_merge, True) - else: - break - - # calculate segment contributions for each merged hit - tmp_bt = np.full(shape=new_hits.shape+(2,self.max_contrib_segments),fill_value=0.) - # loop over hits - for hit_it, hit in np.ndenumerate(new_hits): - if mask[hit_it]: continue - hit_contr = hit_contributions[hit_it] - # renormalize the fractional contributions given the charge weighted average - norm = np.sum(np.multiply(hit_contr[0],hit_contr[1])) - if norm == 0.: norm = 1. - tmp_bt[hit_it][0] = np.multiply(hit_contr[0],hit_contr[1])/norm # fractional contributions - tmp_bt[hit_it][1] = hit_contr[2] # segment_ids - - # merge unique track contributions - track_dict = defaultdict(lambda:0) - for track in zip(tmp_bt[hit_it][0],tmp_bt[hit_it][1]): - track_dict[track[1]] += track[0] - track_dict = dict(track_dict) - bt_unique_segs = np.array(list(track_dict.keys())) - bt_unique_frac = np.array(list(track_dict.values())) - n_conts = bt_unique_frac.shape[0] - isort = np.flip(np.argsort(np.abs(bt_unique_frac), axis=-1, kind='stable')) - bt_unique_segs = np.take_along_axis(bt_unique_segs, isort, axis=-1) - bt_unique_frac = np.take_along_axis(bt_unique_frac, isort, axis=-1) - back_track[hit_it]['fraction'] = [0.]*self.max_contrib_segments - back_track[hit_it]['segment_id'] = [0]*self.max_contrib_segments - back_track[hit_it]['fraction'][:bt_unique_frac.shape[0]] = bt_unique_frac - back_track[hit_it]['segment_id'][:bt_unique_segs.shape[0]] = bt_unique_segs - - ''' + self.data_manager.write_ref(self.hits_name, self.output_name, ref) + #self.data_manager.write_ref(self.output_name,self.mc_hit_frac_dset_name,np.c_[output_idx,output_idx]) + ev_ref = np.c_[(np.indices(output_mask.shape)[0] + source_slice.start)[~output_mask], output_idx] + self.data_manager.write_ref(source_name, self.output_name, ev_ref) + self.data_manager.write_ref(self.events_dset_name, self.output_name, ev_ref) From b8eac60b50ce4945d21552106792a9578171e487 Mon Sep 17 00:00:00 2001 From: Kathryn Sutton Date: Wed, 20 Sep 2023 14:21:21 -0700 Subject: [PATCH 11/37] copied over tracklets macros from module0 --- src/proto_nd_flow/util/tracklet_merging.py | 757 ++++++++++++++++++ src/proto_nd_flow/util/tracklet_reco.py | 514 ++++++++++++ yamls/proto_nd_flow/util/TrackletMerger.yaml | 26 + .../util/TrackletReconstruction.yaml | 25 + 4 files changed, 1322 insertions(+) create mode 100644 src/proto_nd_flow/util/tracklet_merging.py create mode 100644 src/proto_nd_flow/util/tracklet_reco.py create mode 100644 yamls/proto_nd_flow/util/TrackletMerger.yaml create mode 100644 yamls/proto_nd_flow/util/TrackletReconstruction.yaml diff --git a/src/proto_nd_flow/util/tracklet_merging.py b/src/proto_nd_flow/util/tracklet_merging.py new file mode 100644 index 00000000..841efd38 --- /dev/null +++ b/src/proto_nd_flow/util/tracklet_merging.py @@ -0,0 +1,757 @@ +import numpy as np +import numpy.ma as ma +from scipy import ndimage + +from h5flow.core import H5FlowStage, resources + +from module0_flow.reco.combined.tracklet_reco import TrackletReconstruction +from module0_flow.util.func import condense_array + + +class TrackletMerger(H5FlowStage): + ''' + Merges existing tracks with neighbors based on a multi-dimensional + likelihood ratio metric. The observables used in the likelihood + estimation are: + + - ``sin^2(theta)``: angle between the two track segments + - transverse distance: maximum transverse displacement of track from the axis of the first track [mm] + - missing length: length of line segment between closer two endpoints that crosses active pixels [mm] + - overlap: quadrature sum of 1D overlap of tracks in x, y, and z [mm] + - delta-dQ/dx: difference in raw dQ/dx [mV] + + Requires an input histogram .npz file consisting of 4 arrays: + + - ``'{sig}'``: an array of shape: ``(N0, N1, ... N4)`` representing the number of signal events in each bin of the 5 observables + - ``'{sig}_bins'``: an array of 5 arrays each with shape: ``Ni+1`` representing the bin edges + - ``'{bkg}'``: an array of shape: ``(N0, N1, ... N4)`` representing the number of background events in each bin of the 5 observables + + The selection is performed by normalizing the input histograms to a PDF, + calculating the ``signal/background`` likelihood ratio, and rescaling + to a normalized metric between 0 and 1. The p-value (or inefficiency) + of this metric is calculated based on the signal histogram. The + track merging selection cut is applied on this p-value, e.g. a + ``pvalue_cut = 0.05`` will result in a 95% selection efficiency for + merging neighboring tracks (at least for the sample used to generate + the input histograms). + + Parameters: + - ``pdf_filename``: ``str``, path to .npz file containing multi-dimensional pdf (more details above) + - ``pdf_sig_name``: ``str``, name of array in .npz file containing the "signal" histogram + - ``pdf_bkg_name``: ``str``, name of array in .npz file containing the "background" histogram + - ``pvalue_cut``: ``float``, p-value/inefficiency used as cut for likelihood ratio + - ``max_neighbors``: ``int``, number of neighbor tracks to attempt merge procedure + - ``track_charge_dset_name``: ``str``, path to input charge dataset (1:1 with track hits, requires ``'q'`` field) + - ``hit_drift_dset_name``: ``str``, path to charge hit drift data + - ``hits_dset_name``: ``str``, path to input charge hits dataset + - ``track_hits_dset_name``: ``str``, path to input track-referred charge hits dataset + - ``tracks_dset_name``: ``str``, path to input track dataset + - ``merged_dset_name``: ``str``, path to output track dataset + + All of ``hits_dset_name``, ``hit_drift_dset_name``, ``track_hits_dset_name``, + and ``tracks_dset_name`` are required in the cache. + + Requires both Geometry and DisabledChannels resources in workflow. + + ``merged`` datatype is the same as the + ``TrackletReconstruction.tracklet_dtype``. + + Example config:: + + track_merge: + classname: TrackletMerger + requires: + - 'combined/tracklets' + - name: 'combined/track_hits + path: ['combined/tracklets', charge/hits'] + - name: 'combined/track_hit_drift + path: ['combined/tracklets', charge/hits', 'combined/hit_drift'] + params: + merged_dset_name: 'combined/tracklets/merged' + hit_drift_dset_name: 'combined/hit_drift' + hits_dset_name: 'charge/hits' + track_charge_dset_name: 'charge/hits' + tracks_dset_name: 'combined/tracklets' + pdf_filename: 'joint_pdf.npz' + pvalue_cut: 0.10 + max_neighbors: 5 + + ''' + class_version = '3.1.0' + + default_pdf_filename = 'joint_pdf-2_0_1.npz' + default_pdf_sig_name = 'rereco' + default_pdf_bkg_name = 'origin' + default_pvalue_cut = 0.10 + default_max_neighbors = 5 + + default_hit_drift_dset_name = 'combined/track_hit_drift' + default_hits_dset_name = 'charge/hits' + default_track_charge_dset_name = 'charge/hits' + default_tracks_dset_name = 'combined/tracklets' + default_track_hits_dset_name = 'combined/track_hits' + default_merged_dset_name = 'combined/tracklets/merged' + + merged_dtype = TrackletReconstruction.tracklet_dtype + + missing_track_segments = 150 + cathode_region = 15 + + def __init__(self, **params): + super(TrackletMerger, self).__init__(**params) + + self.pdf_filename = params.get('pdf_filename', self.default_pdf_filename) + self.pdf_sig_name = params.get('pdf_sig_name', self.default_pdf_sig_name) + self.pdf_bkg_name = params.get('pdf_bkg_name', self.default_pdf_bkg_name) + self.pvalue_cut = params.get('pvalue_cut', self.default_pvalue_cut) + self.max_neighbors = params.get('max_neighbors', self.default_max_neighbors) + + self.hit_drift_dset_name = params.get('hit_drift_dset_name', self.default_hit_drift_dset_name) + self.hits_dset_name = params.get('hits_dset_name', self.default_hits_dset_name) + self.track_charge_dset_name = params.get('track_charge_dset_name', self.default_track_charge_dset_name) + self.track_hits_dset_name = params.get('track_hits_dset_name', self.default_track_hits_dset_name) + self.tracks_dset_name = params.get('tracks_dset_name', self.default_tracks_dset_name) + self.merged_dset_name = params.get('merged_dset_name', self.default_merged_dset_name) + + def init(self, source_name): + super(TrackletMerger, self).init(source_name) + + self.r, self.r_bins, self.statistic_bins, self.p_bins = ( + self.load_r_values(self.pdf_filename, self.pdf_sig_name, + self.pdf_bkg_name)) + + self.data_manager.set_attrs(self.merged_dset_name, + classname=self.classname, + class_version=self.class_version, + hits_dset=self.hits_dset_name, + charge_dset=self.track_charge_dset_name, + hit_drift_dset=self.hit_drift_dset_name, + tracks_dset=self.tracks_dset_name, + max_neighbors=self.max_neighbors, + pvalue_cut=self.pvalue_cut, + pdf_filename=self.pdf_filename, + pdf_sig_name=self.pdf_sig_name, + pdf_bkg_name=self.pdf_bkg_name + ) + + self.trajectory_pts = self.data_manager.get_attrs(self.tracks_dset_name)['trajectory_pts'] + self.trajectory_dx = self.data_manager.get_attrs(self.tracks_dset_name)['trajectory_dx'] + self.trajectory_residual_mode = self.data_manager.get_attrs(self.tracks_dset_name).get('trajectory_residual_mode', 1) + + self.merged_dtype = TrackletMerger.merged_dtype(self.trajectory_pts) + self.data_manager.create_dset(self.merged_dset_name, self.merged_dtype) + self.data_manager.create_ref(self.merged_dset_name, self.hits_dset_name) + self.data_manager.create_ref(self.merged_dset_name, self.tracks_dset_name) + self.data_manager.create_ref(source_name, self.merged_dset_name) + + self.pixel_x = np.unique(resources['Geometry'].pixel_xy.compress((0,))) + self.pixel_y = np.unique(resources['Geometry'].pixel_xy.compress((1,))) + + def run(self, source_name, source_slice, cache): + super(TrackletMerger, self).run(source_name, source_slice, cache) + + track_hit_drift = cache[self.hit_drift_dset_name] + track_hits = cache[self.track_hits_dset_name] + track_hit_q = cache[self.track_charge_dset_name] + track_hit_q = track_hit_q.reshape(track_hits.shape) + tracks = cache[self.tracks_dset_name] + track_hit_drift = track_hit_drift.reshape(track_hits.shape) + + # ajacency matrix to represent if tracks should be merged or not (True == to merge) + track_merged = np.expand_dims(np.diagflat(np.ones(tracks.shape[-1], dtype=bool)), axis=0) + track_checked = (track_merged.copy() + | np.expand_dims(tracks['id'].mask, axis=1) + | np.expand_dims(tracks['id'].mask, axis=2)) + track_merged = np.broadcast_to(track_merged, tracks.shape + tracks.shape[-1:]).copy() + track_checked = np.broadcast_to(track_checked, tracks.shape + tracks.shape[-1:]).copy() + + if len(np.r_[source_slice]): + + # iterative approach + for _ in range(self.max_neighbors): + # find neighboring tracks that have not been checked + neighbor = self.find_k_neighbor(tracks, mask=~track_checked)['neighbor'] + + # calculate the p-value for neighbor pair + params = [ + self.calc_2track_deflection_angle(tracks, neighbor), + self.calc_2track_transverse_sin2theta(tracks, neighbor), + self.calc_2track_missing_length(tracks, neighbor, + self.missing_track_segments, + self.pixel_x, self.pixel_y, + resources['DisabledChannels'].disabled_channel_lut, + self.cathode_region), + self.calc_2track_overlap(tracks, neighbor), + self.calc_2track_sin2theta(tracks, neighbor) + ] + pvalue = np.expand_dims(self.score_neighbor(self.r, self.r_bins, self.statistic_bins, self.p_bins, *params), -1) + neighbor = np.expand_dims(neighbor, -1) + + # merge tracks that have large p-values + should_merge = (((pvalue >= self.pvalue_cut) + | np.take_along_axis(track_merged, neighbor, -1)) + & ~neighbor.mask + & ~tracks['id'][..., np.newaxis]) + np.put_along_axis(track_merged, neighbor, should_merge, axis=-1) + np.put_along_axis(track_checked, neighbor, True, axis=-1) + + if np.all(track_checked): + break + + # collect valid associations into track groups + axes = np.arange(track_merged.ndim).astype(int) + new_axes = axes.copy() + new_axes[-1] = axes[-2] + new_axes[-2] = axes[-1] + track_merged = track_merged | np.transpose(track_merged, axes=new_axes) + track_merged = self.create_groups(track_merged) + + # now, collect the hits from the original tracks into the track groups + # get unique track groups, shape: (n_ev, n_grp, n_track) + track_merged = np.unique(track_merged, axis=1) + track_merged_mask = np.ones(track_merged.shape, dtype=bool) + for ev in range(track_merged.shape[0]): + _, index = np.unique(track_merged[ev], axis=0, return_index=True) + track_merged_mask[ev, index] = False + track_grp = ma.array(track_merged, mask=track_merged_mask | ~track_merged, shrink=False) + track_grp_nhit = np.sum(np.expand_dims(tracks['nhit'], axis=1) * track_grp, axis=-1).filled(0) + + track_grp_hits_shape = track_grp.shape[:-1] + (np.max(track_grp_nhit),) + # (n_ev, n_grp, n_hit') + track_grp_hits = np.zeros(track_grp_hits_shape, dtype=track_hits.dtype) + track_grp_hit_drift = np.zeros(track_grp_hits_shape, dtype=track_hit_drift.dtype) + track_grp_hit_q = np.zeros(track_grp_hits_shape, dtype=track_hit_q.dtype) + track_grp_id = np.zeros(track_grp_hits_shape, dtype=int) + track_grp_hits_mask = np.ones(track_grp_hits_shape, dtype=bool) + for grp_idx in range(track_grp_hits_shape[-2]): + mask = np.indices(track_grp_hits[:, grp_idx].shape)[-1] < track_grp_nhit[:, grp_idx, np.newaxis] + + hit_mask = ~track_hits[track_grp[:, grp_idx].filled(False)]['id'].mask + np.place(track_grp_hits[:, grp_idx], mask, track_hits[track_grp[:, grp_idx].filled(0)][hit_mask]) + np.place(track_grp_hit_drift[:, grp_idx], mask, track_hit_drift[track_grp[:, grp_idx].filled(0)][hit_mask]) + np.place(track_grp_hit_q[:, grp_idx], mask, track_hit_q[track_grp[:, grp_idx].filled(0)][hit_mask]) + np.place(track_grp_id[:, grp_idx], mask, grp_idx) + np.place(track_grp_hits_mask[:, grp_idx], mask, False) + + track_grp_hits = ma.array(track_grp_hits, mask=track_grp_hits_mask, shrink=False) + track_grp_hit_q = ma.array(track_grp_hit_q, mask=track_grp_hits_mask, shrink=False) + track_grp_hit_drift = ma.array(track_grp_hit_drift, mask=track_grp_hits_mask, shrink=False) + track_grp_id = ma.array(track_grp_id, mask=track_grp_hits_mask, shrink=False) + + new_shape = track_grp.shape[0:1] + (-1,) + track_grp_hits = track_grp_hits.reshape(new_shape) + track_grp_hit_drift = track_grp_hit_drift.reshape(new_shape) + track_grp_hit_q = track_grp_hit_q.reshape(new_shape) + track_grp_id = track_grp_id.reshape(new_shape) + + # recalculate track parameters + calc_shape = (track_grp_id.shape[0], -1) + merged_tracks = TrackletReconstruction.calc_tracks( + track_grp_hits.reshape(calc_shape), track_grp_hit_q['q'].reshape(calc_shape), track_grp_hit_drift['z'].reshape(calc_shape), + track_grp_id.reshape(calc_shape), self.trajectory_pts, + self.trajectory_dx, self.trajectory_residual_mode) + else: + merged_tracks = ma.masked_all((0, 1), dtype=self.merged_dtype) + track_grp = ma.masked_all((0, 1, 1), dtype=bool) + track_grp_id = ma.masked_all((0, 1), dtype=int) + track_grp_hits = ma.masked_all((0, 1), dtype=track_hits.dtype) + track_grp_hit_drift = ma.masked_all((0, 1), dtype=track_hit_drift.dtype) + track_grp_hit_q = ma.masked_all((0, 1), dtype=track_hit_q.dtype) + + # save to merged track dataset + n_tracks = np.count_nonzero(~merged_tracks['id'].mask) + merged_tracks_mask = ~merged_tracks['id'].mask + + merged_tracks_slice = self.data_manager.reserve_data(self.merged_dset_name, n_tracks) + np.place(merged_tracks['id'], merged_tracks_mask, np.r_[merged_tracks_slice].astype('u4')) + self.data_manager.write_data(self.merged_dset_name, merged_tracks_slice, merged_tracks[merged_tracks_mask]) + + # merged -> tracklet ref + i_ev, i_grp, i_track = np.where(track_grp & np.expand_dims(~tracks['id'].mask, 1) & ~track_grp.mask) + ref = np.c_[merged_tracks['id'][i_ev, i_grp].compressed(), tracks['id'][i_ev, i_track].compressed()] + self.data_manager.write_ref(self.merged_dset_name, self.tracks_dset_name, ref) + + # merged -> hit ref + hit_mask = (np.expand_dims(track_grp_id, 1) + == np.expand_dims(np.indices(merged_tracks.shape)[-1], -1)) + i_ev, i_grp, i_hit = np.where(hit_mask) + ref = np.c_[merged_tracks['id'][i_ev, i_grp].compressed(), + track_grp_hits['id'][i_ev, i_hit].compressed()] + self.data_manager.write_ref(self.merged_dset_name, self.hits_dset_name, ref) + + # event -> merged ref + ev_id = np.broadcast_to(np.expand_dims(np.r_[source_slice], axis=-1), merged_tracks.shape) + ref = np.c_[ev_id[merged_tracks_mask], merged_tracks['id'][merged_tracks_mask]] + self.data_manager.write_ref(source_name, self.merged_dset_name, ref) + + @staticmethod + def create_groups(mask): + ''' + Combine masks of ``n x n`` ajacency matrix such that the mask of + row i is equal to the ``OR`` of the rows that can be reached from + ``i`` and the rows that can reach ``i``. E.g.:: + + arr = [[1,0,1], + [0,1,0], + [0,0,1]] + new_arr = create_groups(arr) + new_arr # [[1,0,1], + [0,1,0], + [1,0,1]] + + and:: + + arr = [[0,1,0], + [0,0,1], + [1,1,0]] + new_arr = create_groups(arr) + new_arr # [[1,1,1], + [1,1,1], + [0,1,1]] + + :param mask: ajacency matrix (``shape: (..., n, n)``) + + :returns: updated ajacency matrix (``shape: (..., n, n)``) + ''' + new_mask = np.zeros_like(mask) + + # get index of masks (starting with True values) + i_mask = np.indices(mask.shape)[-1] + j_mask = np.indices(mask.shape)[-2] + step = 0 + while (step < i_mask.shape[-1]): + # step through indices + # get other index (shape: (..., n, 1)) + ii_mask = np.expand_dims(i_mask[..., step], axis=-1) + jj_mask = np.expand_dims(j_mask[..., step], axis=-1) + # get other mask (shape: (..., n, n)) + other_mask = np.take_along_axis(mask, ii_mask, -2) + # get other matched to current (shape: (..., n, 1)) + other_matched = np.take_along_axis(mask, ii_mask, -1) + # get self matched to current (shape: (..., n, 1)) + self_matched = np.take_along_axis(other_mask, jj_mask, -1) + + # combine with current track(s) + new_mask[:] = (new_mask | (other_mask & other_matched) | (other_mask & self_matched)) + step += 1 + + if np.all(new_mask == mask): + return new_mask + return TrackletMerger.create_groups(new_mask) + + @staticmethod + def find_k_neighbor(tracks, mask=None, k=1): + ''' + Find ``k``-th neighbor based on endpoint distance and require no overlap: + + - ``tracks`` is an (N,M) array of tracks + - ``mask`` is boolean of same shape as ``tracks`` + - ``mask`` true indicates a valid track to search for neighbors + + ''' + ntracks = tracks.shape[-1] + if mask is None: + mask = np.ones(tracks.shape + tracks.shape[-1:], dtype=bool) + mask = (mask + & ~np.diagflat(np.ones(ntracks, dtype=bool)).reshape(1, ntracks, ntracks) + & np.expand_dims(~tracks['id'].mask, axis=1) + & np.expand_dims(~tracks['id'].mask, axis=2)) + + start1 = np.expand_dims(tracks['start'], axis=1) + start2 = np.expand_dims(tracks['start'], axis=2) + end1 = np.expand_dims(tracks['end'], axis=1) + end2 = np.expand_dims(tracks['end'], axis=2) + + endpoint_distance = ma.concatenate(( + ma.sum((start1 - end2)**2, axis=-1, keepdims=True), + ma.sum((end1 - end2)**2, axis=-1, keepdims=True), + ma.sum((start1 - start2)**2, axis=-1, keepdims=True), + ma.sum((end1 - start2)**2, axis=-1, keepdims=True), + ), axis=-1) + endpoint_distance = ma.sqrt(endpoint_distance) + endpoint_distance = ma.array(endpoint_distance.min(axis=-1), mask=~mask, shrink=False) + + neighbor = ma.argsort(endpoint_distance, axis=-1)[..., k - 1].reshape(tracks.shape) + neighbor = ma.array(neighbor, mask=tracks['id'].mask | np.all(~mask, axis=-1), shrink=False) + neighbor.fill_value = -1 + neighbor = ma.array(neighbor.filled(), mask=neighbor.mask, shrink=False) + neighbor.fill_value = -1 + return dict(neighbor=neighbor) + + @staticmethod + def poca(start_xyz0, end_xyz0, start_xyz1, end_xyz1): + ''' + Finds the scale factor to point of closest approach of two lines + each defined by 2 3D points. The scale factor is a number between 0 + and 1 representing the position along the line. To extract the + 3D point of closest approach on each line:: + + s0, s1 = poca(start0, end0, start1, end1) # shape: (N, 1) + poca0 = (1 - s0) * start0 + s0 * end0 # shape: (N, 3) + poca1 = (1 - s1) * start1 + s1 * end1 + + :param {start, end}_xyz(i): start/end point of line i, ``shape: (..., N, 3)`` + + :returns: ``tuple`` of line segment 0 and 1, ``shape: (..., N, 1)`` + ''' + orig_mask0 = start_xyz0.mask | end_xyz0.mask + orig_mask1 = start_xyz1.mask | end_xyz1.mask + orig_mask0, orig_mask1 = np.broadcast_arrays(orig_mask0, orig_mask1) + start_xyz0, end_xyz0, start_xyz1, end_xyz1 = np.broadcast_arrays( + start_xyz0, end_xyz0, start_xyz1, end_xyz1) + + d = start_xyz0 - start_xyz1 + v0, v1 = (end_xyz0 - start_xyz0, end_xyz1 - start_xyz1) + l0, l1 = (np.linalg.norm(v0, axis=-1, keepdims=True), + np.linalg.norm(v1, axis=-1, keepdims=True)) + with np.errstate(divide='ignore', invalid='ignore'): + v0 /= l0 + v1 /= l1 + v0[(l0 == 0)[..., 0]] = 0 + v1[(l1 == 0)[..., 0]] = 0 + v_dp = np.sum(v0 * v1, axis=-1, keepdims=True) + + with np.errstate(divide='ignore', invalid='ignore'): + s0 = (-np.sum(d * v0, axis=-1, keepdims=True) + + np.sum(d * v1, axis=-1, keepdims=True) * v_dp) / (1 - v_dp**2) + s1 = (np.sum(d * v1, axis=-1, keepdims=True) + - np.sum(d * v0, axis=-1, keepdims=True) * v_dp) / (1 - v_dp**2) + + s0 /= l0 + s1 /= l1 + + # handle 0 length line segment + s0[l0 == 0] = 0.5 + s1[l1 == 0] = 0.5 + + # handle parallel segments + parallel_mask = (1 - v_dp**2 == 0)[..., 0] + if np.any(parallel_mask): + # grab mean position + p = (start_xyz0 + end_xyz0 + start_xyz1 + end_xyz1) / 4 + # calculate perpendicular points on other segments + d0 = (start_xyz0 - p) - v0 * np.sum((start_xyz0 - p) * v0, + axis=-1, keepdims=True) + s0[parallel_mask] = np.sum((p + d0) * v0 / l0, axis=-1, + keepdims=True)[parallel_mask] + d1 = (start_xyz1 - p) - v1 * np.sum((start_xyz1 - p) * v1, + axis=-1, keepdims=True) + s1[parallel_mask] = np.sum((p + d1) * v1 / l1, axis=-1, + keepdims=True)[parallel_mask] + + mask0 = np.any(orig_mask0, axis=-1, keepdims=True) + mask1 = np.any(orig_mask1, axis=-1, keepdims=True) + s0 = ma.array(s0, mask=np.broadcast_to(mask0, s0.shape), shrink=False) + s1 = ma.array(s1, mask=np.broadcast_to(mask1, s1.shape), shrink=False) + return s0, s1 + + @staticmethod + def closest_trajectories(tracks0, tracks1): + ''' + :param tracks0: track dtype of shape: ``(..., M,)`` + + :param tracks1: track dtype of shape: ``(..., M,)`` + + :returns: start and end points of closest trajectory segments and points of closest approach, shape: ``(..., M, 3)`` + + ''' + start0 = tracks0['trajectory'][..., :-1, :] # (N, M, n0-1, 3) + end0 = tracks0['trajectory'][..., 1:, :] # (N, M, n0-1, 3) + start1 = tracks1['trajectory'][..., :-1, :] # (N, M, n1-1, 3) + end1 = tracks1['trajectory'][..., 1:, :] # (N, M, n1-1, 3) + + # reshape -> (N, M, n0-1, 1, 3) and (N, M, 1, n1-1, 3) + start0 = np.expand_dims(start0, -2) + end0 = np.expand_dims(end0, -2) + start1 = np.expand_dims(start1, -3) + end1 = np.expand_dims(end1, -3) + + # find point of closest approach + s0, s1 = TrackletMerger.poca(start0, end0, start1, end1) + s0 = ma.clip(s0, 0, 1) + s1 = ma.clip(s1, 0, 1) + + poca0 = (1 - s0) * start0 + s0 * end0 + poca1 = (1 - s1) * start1 + s1 * end1 + poca_d = np.linalg.norm(poca0 - poca1, axis=-1) + poca_d = ma.array(poca_d, mask=(s0.mask | s1.mask), shrink=False) + + # remove segments with 0 length + mask = ((np.linalg.norm(end0 - start0, axis=-1) == 0) + | (np.linalg.norm(end1 - start1, axis=-1) == 0)) + poca_d[mask] = poca_d.max() + + # minimize point of closest approach + min_poca_d0 = np.expand_dims(ma.argmin(poca_d, axis=-1), -1) # (n, M, n0-1, 1) + poca0 = np.take_along_axis(poca0, np.expand_dims(min_poca_d0, -1), -2) # (n, M, n0-1, 1, 3) + poca1 = np.take_along_axis(poca1, np.expand_dims(min_poca_d0, -1), -2) # (n, M, n0-1, 1, 3) + poca_d = np.take_along_axis(poca_d, min_poca_d0, -1) # (n, M, n0-1, 1) + + min_poca_d1 = np.expand_dims(ma.argmin(poca_d, axis=-2), -2) # (n, M, 1, 1) + poca0 = np.take_along_axis(poca0, np.expand_dims(min_poca_d1, -1), -3) # (n, M, 1, 1, 3) + poca1 = np.take_along_axis(poca1, np.expand_dims(min_poca_d1, -1), -3) # (n, M, 1, 1, 3) + poca_d = np.take_along_axis(poca_d, min_poca_d1, -2) # (n, M, 1, 1) + min_poca_d0 = np.take_along_axis(min_poca_d0, min_poca_d1, -2) # (n, M, 1, 1) + + start0 = np.take_along_axis(start0, np.expand_dims(min_poca_d1, -1), -3) # (n, M, 1, 1, 3) + end0 = np.take_along_axis(end0, np.expand_dims(min_poca_d1, -1), -3) # (n, M, 1, 1, 3) + start1 = np.take_along_axis(start1, np.expand_dims(min_poca_d0, -1), -2) # (n, M, 1, 1, 3) + end1 = np.take_along_axis(end1, np.expand_dims(min_poca_d0, -1), -2) # (n, M, 1, 1, 3) + + start0 = start0.reshape(tracks0.shape + (3,)) + end0 = end0.reshape(tracks0.shape + (3,)) + start1 = start1.reshape(tracks1.shape + (3,)) + end1 = end1.reshape(tracks1.shape + (3,)) + poca0 = poca0.reshape(tracks0.shape + (3,)) + poca1 = poca1.reshape(tracks1.shape + (3,)) + + mask = start0.mask | end0.mask | start1.mask | end1.mask | poca0.mask | poca1.mask + start0.mask[mask] = True + end0.mask[mask] = True + start1.mask[mask] = True + end1.mask[mask] = True + poca0.mask[mask] = True + poca1.mask[mask] = True + + return (start0, end0, start1, end1, poca0, poca1) + + @staticmethod + def calc_2track_deflection_angle(tracks, neighbor): + ntracks = tracks.shape[1] + neighbor_tracks = np.take_along_axis(tracks, neighbor, axis=1) + + start, end, neighbor_start, neighbor_end, poca, neighbor_poca = ( + TrackletMerger.closest_trajectories(tracks, neighbor_tracks)) + + orig_mask = poca.mask.copy() | neighbor_poca.mask.copy() + poca = (poca + neighbor_poca) / 2 + + # calculate deflection angle to farthest point on neighboring segment + neighbor_far = np.where( + np.linalg.norm(poca - neighbor_start, axis=-1, keepdims=True) + > np.linalg.norm(poca - neighbor_end, axis=-1, keepdims=True), + neighbor_start, neighbor_end) + ang1 = np.sum((neighbor_far - poca) * (poca - start), axis=-1) + ang1 /= np.linalg.norm((neighbor_far - poca), axis=-1) + 1e-15 + ang1 /= np.linalg.norm((poca - start), axis=-1) + 1e-15 + ang1 = np.arccos(np.clip(ang1, -1, 1)) + + mask = (tracks['id'].mask | neighbor.mask.reshape(ang1.shape) + | (neighbor == -1).reshape(ang1.shape)) + return ma.array(ang1 / np.pi, mask=mask, shrink=False) + + @staticmethod + def calc_2track_transverse_sin2theta(tracks, neighbor): + ntracks = tracks.shape[1] + neighbor_tracks = np.take_along_axis(tracks, neighbor, axis=-1) + + start1, end1, start2, end2, _, _ = TrackletMerger.closest_trajectories( + tracks, neighbor_tracks) + + d = ma.concatenate(( + np.expand_dims(start1 - end2, axis=-1), + np.expand_dims(end1 - end2, axis=-1), + np.expand_dims(start1 - start2, axis=-1), + np.expand_dims(end1 - start2, axis=-1) + ), axis=-1) + i_max = np.expand_dims(ma.argmax(np.sqrt(ma.sum(d * d, axis=-2, keepdims=True)), axis=-1), axis=-1) + d = np.take_along_axis(d, i_max, axis=-1)[..., 0] + d_norm = ma.sqrt(ma.sum(d**2, axis=-1, keepdims=True)) + d_norm[d_norm == 0] = 1 + d /= d_norm + + # transverse d + track_d = end1 - start1 + track_d_mask = np.all(track_d == 0, axis=-1) + track_d[track_d_mask] = (tracks['end'] - tracks['start'])[track_d_mask] + track_d /= ma.sqrt(ma.sum(track_d**2, axis=-1, keepdims=True)) + l_d = np.abs(ma.sum(d * track_d, axis=-1)) + l = np.sqrt(ma.sum(d * d, axis=-1)) + t_d = np.clip(l**2 - l_d**2, 0, 1) + + mask = (tracks['id'].mask | + neighbor.mask.reshape(t_d.shape) + | (neighbor == -1).reshape(t_d.shape)) + return ma.array(t_d, mask=mask, shrink=False) + + @staticmethod + def make_missing_segment(start1, end1, start2, end2): + track_d = np.concatenate(( + np.sum((start1 - end2)**2, axis=-1, keepdims=True), + np.sum((end1 - end2)**2, axis=-1, keepdims=True), + np.sum((start1 - start2)**2, axis=-1, keepdims=True), + np.sum((end1 - start2)**2, axis=-1, keepdims=True), + ), axis=-1) + i_min = np.expand_dims(np.argmin(track_d, axis=-1), axis=-1) + missing_track_start = np.select( + (i_min == 0, + i_min == 1, + i_min == 2, + i_min == 3), + (start1, end1, start1, end1)) + missing_track_end = np.select( + (i_min == 0, + i_min == 1, + i_min == 2, + i_min == 3), + (end2, end2, start2, start2)) + return missing_track_start, missing_track_end + + @staticmethod + def calc_2track_missing_length(tracks, neighbor, missing_track_segments, + pixel_x, pixel_y, disabled_channel_lut, + cathode_region, pixel_pitch=None): + # create missing track segment + _n_steps = missing_track_segments + neighbor_tracks = np.take_along_axis(tracks, neighbor, axis=-1) + start1, end1, start2, end2, poca1, poca2 = TrackletMerger.closest_trajectories( + tracks, neighbor_tracks) + + # _missing_start, _missing_end = TrackletMerger.make_missing_segment( + # start1, end1, start2, end2) + _missing_start, _missing_end = poca1, poca2 + + # interpolate + _missing_x, _dx = np.linspace(_missing_start[..., 0], _missing_end[..., 0], + _n_steps, axis=-1, retstep=True) + _missing_y, _dy = np.linspace(_missing_start[..., 1], _missing_end[..., 1], + _n_steps, axis=-1, retstep=True) + _missing_z, _dz = np.linspace(_missing_start[..., 2], _missing_end[..., 2], + _n_steps, axis=-1, retstep=True) + _ds = np.sqrt(_dx**2 + _dy**2 + _dz**2) + _missing_length = _ds * _n_steps + + pixel_pitch = pixel_pitch if pixel_pitch is not None else resources['Geometry'].pixel_pitch + _ix = np.clip(np.digitize(_missing_x, pixel_x + pixel_pitch / 2) - 1, + 0, len(pixel_x) - 1) + _iy = np.clip(np.digitize(_missing_y, pixel_y + pixel_pitch / 2) - 1, + 0, len(pixel_x) - 1) + + _missing_pixel_x = pixel_x[_ix] + _missing_pixel_y = pixel_y[_iy] + _missing_iogroup = (np.sign(_missing_z) / 2 + 1.5).astype(int) + + _hidden_length = _ds * ( + (disabled_channel_lut[_missing_iogroup, + _missing_pixel_x.astype(int), + _missing_pixel_y.astype(int)].reshape(_missing_iogroup.shape) + | (np.abs(_missing_z) < cathode_region)).sum(axis=-1)) + missing_length = _missing_length - _hidden_length + + mask = (tracks['id'].mask + | neighbor.mask.reshape(missing_length.shape) + | (neighbor == -1).reshape(missing_length.shape)) + return ma.array(missing_length, mask=mask, shrink=False) + + @staticmethod + def calc_2track_overlap(tracks, neighbor): + _ntracks = tracks.shape[1] + neighbor = neighbor.reshape(tracks.shape + (1,)) + _track1_min = np.minimum(tracks['start'], tracks['end']) + _track1_max = np.maximum(tracks['start'], tracks['end']) + _track2_min = np.take_along_axis(np.minimum(tracks['start'], tracks['end']), + neighbor, axis=1) + _track2_max = np.take_along_axis(np.maximum(tracks['start'], tracks['end']), + neighbor, axis=1) + + overlap = (np.minimum(_track2_max, _track1_max) + - np.maximum(_track2_min, _track1_min)) + overlap = np.clip(overlap, 0, None) + overlap = np.sqrt(np.sum(overlap**2, axis=-1)) + mask = (tracks['id'].mask + | neighbor.mask.reshape(overlap.shape) + | (neighbor == -1).reshape(overlap.shape)) + return ma.array(overlap, mask=mask, shrink=False) + + @staticmethod + def calc_2track_sin2theta(tracks, neighbor): + ntracks = tracks.shape[1] + neighbor_tracks = np.take_along_axis(tracks, neighbor, axis=-1) + start1, end1, start2, end2, _, _ = TrackletMerger.closest_trajectories( + tracks, neighbor_tracks) + dxyz = end1 - start1 + mask = np.all(dxyz == 0, axis=-1) + dxyz[mask] = (tracks['end'] - tracks['start'])[mask] + dxyz /= np.sqrt(np.sum((dxyz)**2, axis=-1, keepdims=True)) + + dxyz_neighbor = end2 - start2 + mask = np.all(dxyz_neighbor == 0, axis=-1) + dxyz_neighbor[mask] = (neighbor_tracks['end'] - neighbor_tracks['start'])[mask] + dxyz_neighbor /= np.sqrt(np.sum((dxyz_neighbor)**2, axis=-1, keepdims=True)) + sin2theta = 1 - np.sum(dxyz * dxyz_neighbor, axis=-1)**2 + mask = (tracks['id'].mask | neighbor.mask.reshape(sin2theta.shape) + | (neighbor == -1).reshape(sin2theta.shape)) + return ma.array(sin2theta, mask=mask, shrink=False) + + @staticmethod + def load_r_values(filename, sig_key, bkg_key): + ''' + Load the N-D pdf histogram from an .npz file. Loads and normalizes + the histograms stored under ``{sig_key}`` and ``{bkg_key}`` with + bins stored under ``{key}_bins`` to create a PDF. The likelihood + ratio (``R``) is then calculated and converted to a normalized + value between 0-1 (``r``) with the following transformation:: + + r = 1 - e^(-R) + + Bins with 0 entries are assigned an ``R``-value of 0. + + :param filename: path to .npz file with arrays + + :param sig_key: name of "signal" histogram in .npz file + + :param bkg_key: name of "background" histogram in .npz file + + :returns: ``tuple`` of r histogram (``shape: (N0, N1, ...)``), r bins in each dimension (``shape: (D, Ni)``), an array possible r values (``shape: (1001,)``, and corresponding p-values (``shape: (1001,)``) + + ''' + pdf = dict(np.load(filename, allow_pickle=True)) + + ndimage.gaussian_filter(pdf[sig_key], 1.5, output=pdf[sig_key], mode='nearest') + ndimage.gaussian_filter(pdf[bkg_key], 1.5, output=pdf[bkg_key], mode='nearest') + + sig_norm = np.sum(pdf[sig_key]) + bkg_norm = np.sum(pdf[bkg_key]) + with np.errstate(divide='ignore', invalid='ignore'): + r = 1 - np.exp(-(pdf[sig_key] / sig_norm) / (pdf[bkg_key] / bkg_norm)) + r_inf_mask = (pdf[bkg_key] == 0) & (pdf[sig_key] > 0) + r[r_inf_mask] = 1 + r_zero_mask = (pdf[sig_key] == 0) & (pdf[bkg_key] > 0) + r[r_zero_mask] = 0 + r_undef_mask = (pdf[sig_key] == 0) & (pdf[bkg_key] == 0) + r[r_undef_mask] = 0.5 + r_bins = pdf[sig_key + '_bins'] + + idx = np.where(pdf[sig_key]) + weights = pdf[sig_key][idx].flatten() + + statistic_bins = np.r_[0, np.geomspace(np.min(r[r > 0]), 1, 1000)] + statistic, statistic_bins = np.histogram(r[idx].flatten(), + bins=statistic_bins, weights=weights) + p_bins = 1 - np.cumsum(statistic[::-1])[::-1] / np.sum(statistic) + + return r, r_bins, statistic_bins, p_bins + + @staticmethod + def score_neighbor(r, r_bins, statistic_bins, p_bins, *params): + ''' + Calculates a p-value based on a binned, multi-dimensional PDF + + :param r: likelihood ratio, ``shape: (N,)*D`` + + :param r_bins: bin edge for each parameter, ``shape: (D, N+1)`` + + :param statistic_bins: bins for statistic, range 0-1, ``shape: (n,)`` + + :param p_bins: bins for p value range 0-1, ``shape: (n,)`` + + :param *params: array of parameters to use to calculate p-value, requires ``D`` parameters in the same sequence as listed in the bins, each with the same shape + + :returns: array of same shape as the ``params`` arrays with a p-value between 0-1 + + ''' + i_bin = [np.clip(np.digitize(np.clip(p, b[0], b[-1]), b) - 1, + 0, len(b) - 2) for b, p in zip(r_bins, params)] + statistic = r[tuple(i_bin)] + pvalue = p_bins[np.clip(np.digitize(statistic, statistic_bins), 0, len(statistic_bins) - 2)] + return pvalue diff --git a/src/proto_nd_flow/util/tracklet_reco.py b/src/proto_nd_flow/util/tracklet_reco.py new file mode 100644 index 00000000..8004b187 --- /dev/null +++ b/src/proto_nd_flow/util/tracklet_reco.py @@ -0,0 +1,514 @@ +import numpy as np +import numpy.ma as ma + +import sklearn.cluster as cluster +import sklearn.decomposition as dcomp +from skimage.measure import LineModelND, ransac + +from h5flow.core import H5FlowStage, resources + + +class TrackletReconstruction(H5FlowStage): + ''' + Reconstructs "tracklets" or short, collinear track segments from hit + data using DBSCAN and RANSAC. The track direction is estimated using + a PCA fit. + + Parameters: + - ``tracklet_dset_name``: ``str``, path to output dataset + - ``hits_dset_name``: ``str``, path to input charge hits dataset + - ``charge_dset_name``: ``str``, path to input charge dataset (1:1 with hits dataset, requires ``"q"`` field) + - ``hit_drift_dset_name``: ``str``, path to charge hits drift data + - ``dbscan_eps``: ``float``, dbscan epsilon parameter + - ``dbscan_min_samples``: ``int``, dbscan min neighbor points to consider as "core" point + - ``ransac_min_samples``: ``int``, min points to run ransac algorithm + - ``ransac_residual_threshold``: ``float``, max distance from trial axis + - ``ransac_max_trials``: ``int``, number of ransac trials per cluster + - ``max_iterations``: ``int``, max number of fitting iterations before giving up + - ``max_nhit``: ``int``, skip track fitting on events with greater number of hits, ``None`` to apply no cut + + Both ``hits_dset_name``, ``charge_dset_name``, and ``hits_drift_dset_name`` are required in the cache. + + Requires Geometry, RunData, and Units resource in workflow. + + ``tracklets`` datatype:: + + id u4, unique identifier + theta f8, track inclination w.r.t anode + phi f8, track orientation w.r.t anode + xp f8, intersection of track with ``x=0,y=0`` plane [mm] + yp f8, intersection of track with ``x=0,y=0`` plane [mm] + nhit i8, number of hits in track + q f8, charge sum [mV] + ts_start f8, PPS timestamp of track start [crs ticks] + ts_end f8, PPS timestamp of track end [crs ticks] + residual f8(3,) average track fit error in (x,y,z) [mm] + length f8 track length [mm] + start f8(3,) track start point (x,y,z) [mm] + end f8(3,) track end point (x,y,z) [mm] + trajectory f8(trajectory_pts, 3,) track approximation points (x,y,z) [mm] + trajectory_residual f8(trajectory_pts-1,) track approximation average error [mm] + dx f8(trajectory_pts-1, 3) track approximation displacement (dx,dy,dz) [mm] + dq f8(trajectory_pts-1,) charge along track displacement [mV] + dn i8(trajectory_pts-1,) nhit along track displacement + + ''' + class_version = '1.1.0' + + default_tracklet_dset_name = 'combined/tracklets' + default_hits_dset_name = 'charge/hits' + default_charge_dset_name = 'charge/hits' + default_hit_drift_dset_name = 'combined/hit_drift' + + default_dbscan_eps = 25 + default_dbscan_min_samples = 5 + default_ransac_min_samples = 2 + default_ransac_residual_threshold = 8 + default_ransac_max_trials = 100 + default_max_iterations = 100 + default_trajectory_pts = 5 + default_trajectory_dx = 10 + default_max_nhit = 3000 + default_trajectory_residual_mode = 1 + + @staticmethod + def tracklet_dtype(npts=default_trajectory_pts): + return np.dtype([ + ('id', 'u4'), + ('theta', 'f8'), ('phi', 'f8'), + ('xp', 'f8'), ('yp', 'f8'), + ('nhit', 'i8'), ('q', 'f8'), + ('ts_start', 'f8'), ('ts_end', 'f8'), + ('residual', 'f8', (3,)), ('length', 'f8'), + ('start', 'f8', (3,)), ('end', 'f8', (3,)), + ('trajectory', 'f8', (npts, 3)), + ('trajectory_residual', 'f8', (npts - 1,)), + ('dx', 'f8', (npts - 1, 3)), + ('dq', 'f8', (npts - 1,)), + ('dn', 'i8', (npts - 1,)) + ]) + + def __init__(self, **params): + super(TrackletReconstruction, self).__init__(**params) + + self.tracklet_dset_name = params.get('tracklet_dset_name', self.default_tracklet_dset_name) + self.hits_dset_name = params.get('hits_dset_name', self.default_hits_dset_name) + self.charge_dset_name = params.get('charge_dset_name', self.default_charge_dset_name) + self.hit_drift_dset_name = params.get('hit_drift_dset_name', self.default_hit_drift_dset_name) + + self._dbscan_eps = params.get('dbscan_eps', self.default_dbscan_eps) + self._dbscan_min_samples = params.get('dbscan_min_samples', self.default_dbscan_min_samples) + self._ransac_min_samples = params.get('ransac_min_samples', self.default_ransac_min_samples) + self._ransac_residual_threshold = params.get('ransac_residual_threshold', self.default_ransac_residual_threshold) + self._ransac_max_trials = params.get('ransac_max_trials', self.default_ransac_max_trials) + self.max_iterations = params.get('max_iterations', self.default_max_iterations) + self.max_nhit = params.get('max_nhit', self.default_max_nhit) + + self.trajectory_residual_mode = params.get('trajectory_residual_mode', self.default_trajectory_residual_mode) + self.trajectory_pts = params.get('trajectory_pts', self.default_trajectory_pts) + self.trajectory_dx = params.get('trajectory_dx', self.default_trajectory_dx) + self.tracklet_dtype = self.tracklet_dtype(self.trajectory_pts) + + self.dbscan = cluster.DBSCAN(eps=self._dbscan_eps, min_samples=self._dbscan_min_samples) + + def init(self, source_name): + super(TrackletReconstruction, self).init(source_name) + + self.data_manager.set_attrs(self.tracklet_dset_name, + classname=self.classname, + class_version=self.class_version, + hits_dset=self.hits_dset_name, + charge_dset=self.charge_dset_name, + hit_drift_dset=self.hit_drift_dset_name, + dbscan_eps=self._dbscan_eps, + dbscan_min_samples=self._dbscan_min_samples, + ransac_min_samples=self._ransac_min_samples, + ransac_residual_threshold=self._ransac_residual_threshold, + ransac_max_trials=self._ransac_max_trials, + max_iterations=self.max_iterations, + max_nhit=self.max_nhit, + trajectory_pts=self.trajectory_pts, + trajectory_dx=self.trajectory_dx, + trajectory_residual_mode=self.trajectory_residual_mode + ) + + self.data_manager.create_dset(self.tracklet_dset_name, self.tracklet_dtype) + self.data_manager.create_ref(self.tracklet_dset_name, self.hits_dset_name) + self.data_manager.create_ref(source_name, self.tracklet_dset_name) + + def run(self, source_name, source_slice, cache): + super(TrackletReconstruction, self).run(source_name, source_slice, cache) + + events = cache[source_name] # shape: (N,) + hits = cache[self.hits_dset_name] # shape: (N,M) + q = cache[self.charge_dset_name]['q'] + q = q.reshape(hits.shape) + hit_drift = cache[self.hit_drift_dset_name] # shape: (N,M,1) + hit_drift = hit_drift.reshape(hits.shape) + if self.max_nhit is not None: + hits = ma.array(hits, mask=(events['nhit'][..., np.newaxis] > self.max_nhit) | hits['id'].mask, + shrink=False) + hit_drift = ma.array(hit_drift, mask=(events['nhit'][..., np.newaxis] > self.max_nhit) | hits['id'].mask, + shrink=False) + + track_ids = self.find_tracks(hits, hit_drift['z']) + tracks = self.calc_tracks(hits, q, hit_drift['z'], track_ids, self.trajectory_pts, + self.trajectory_dx, self.trajectory_residual_mode) + n_tracks = np.count_nonzero(~tracks['id'].mask) + tracks_mask = ~tracks['id'].mask + + tracks_slice = self.data_manager.reserve_data(self.tracklet_dset_name, n_tracks) + np.place(tracks['id'], tracks_mask, np.r_[tracks_slice].astype('u4')) + self.data_manager.write_data(self.tracklet_dset_name, tracks_slice, tracks[tracks_mask]) + + # track -> hit ref + track_ref_id = np.take_along_axis(tracks['id'], track_ids, axis=-1) + mask = (~track_ref_id.mask) & (track_ids != -1) & (~hits['id'].mask) + ref = np.c_[track_ref_id[mask], hits['id'][mask]] + self.data_manager.write_ref(self.tracklet_dset_name, self.hits_dset_name, ref) + + # event -> track ref + ev_id = np.broadcast_to(np.expand_dims(np.r_[source_slice], axis=-1), tracks.shape) + ref = np.c_[ev_id[tracks_mask], tracks['id'][tracks_mask]] + self.data_manager.write_ref(source_name, self.tracklet_dset_name, ref) + + @staticmethod + def hit_xyz(hits, hit_z): + xyz = np.concatenate(( + np.expand_dims(hits['px'], axis=-1), + np.expand_dims(hits['py'], axis=-1), + np.expand_dims(hit_z, axis=-1), + ), axis=-1) + return xyz + + def find_tracks(self, hits, hit_z): + ''' + Extract tracks from a given hits array + + :param hits: masked array ``shape: (N, n)`` + + :param hit_z: masked array ``shape: (N, n)`` + + :returns: mask array ``shape: (N, n)`` of track ids for each hit, a value of -1 means no track is associated with the hit + ''' + xyz = self.hit_xyz(hits, hit_z) + + iter_mask = np.ones(hits.shape, dtype=bool) + iter_mask = iter_mask & (~hits['id'].mask) + track_id = np.full(hits.shape, -1, dtype='i8') + for i in range(hits.shape[0]): + + if not np.any(iter_mask[i]): + continue + + current_track_id = -1 + + for _ in range(self.max_iterations): + # dbscan to find clusters + track_ids = self._do_dbscan(xyz[i], iter_mask[i]) + + for id_ in np.unique(track_ids): + if id_ == -1: + continue + mask = track_ids == id_ + if np.sum(mask) <= self._ransac_min_samples: + continue + + # ransac for collinear hits + inliers = self._do_ransac(xyz[i], mask) + mask[mask] = inliers + + if np.sum(mask) < 1: + continue + + # and a final dbscan for re-clustering + final_track_ids = self._do_dbscan(xyz[i], mask) + + for id_ in np.unique(final_track_ids): + if id_ == -1: + continue + mask = final_track_ids == id_ + + current_track_id += 1 + track_id[i, mask] = current_track_id + iter_mask[i, mask] = False + + if np.all(track_ids == -1) or not np.any(iter_mask[i]): + break + + return ma.array(track_id, mask=hits['id'].mask, shrink=False) + + @classmethod + def calc_tracks(cls, hits, hit_q, hit_z, track_ids, trajectory_pts, trajectory_dx, trajectory_residual_mode): + ''' + Calculate track parameters from hits + + :param hits: masked array, ``shape: (N,M)`` + + :param hit_q: masked array, ``shape: (N,M)`` + + :param hit_z: masked array, ``shape: (N,M)`` + + :param track_ids: masked array, ``shape: (N,M)`` + + :param trajectory_pts: int + + :param trajectory_dx: float + + :returns: masked array, ``shape: (N,m)`` + ''' + xyz = cls.hit_xyz(hits, hit_z) + + n_tracks = np.clip(track_ids.max() + 1, 1, np.inf).astype(int) if np.count_nonzero(~track_ids.mask) \ + else 1 + tracks = np.empty((len(hits), n_tracks), dtype=cls.tracklet_dtype(trajectory_pts)) + tracks_mask = np.ones(tracks.shape, dtype=bool) + for i in range(tracks.shape[0]): + for j in range(tracks.shape[1]): + mask = ((track_ids[i] == j) & (~track_ids.mask[i]) + & (~hits['id'].mask[i])) + if np.count_nonzero(mask) < 2: + continue + + # PCA on central hits + centroid, axis = cls.do_pca(xyz[i], mask) + r_min, r_max = cls.projected_limits( + centroid, axis, xyz[i][mask]) + residual = cls.track_residual(centroid, axis, xyz[i][mask]) + xyp = cls.xyp(axis, centroid) + + # run trajectory approximation algo + traj = cls.trajectory_approx(centroid, axis, xyz[i][mask], trajectory_residual_mode, + npts=trajectory_pts, dx=trajectory_dx, + weights=hit_q[i][mask]) # (npts, 3) + d = cls.trajectory_residual(xyz[i][mask], traj, trajectory_residual_mode) # (npts-1, N) + + min_edge_mask = np.indices(d.shape)[0] != np.expand_dims(np.argmin(d, axis=0), 0) # (npts-1, N) + edge_q = ma.sum(ma.array( + np.broadcast_to(hit_q[i][mask][np.newaxis, :], + min_edge_mask.shape), + mask=min_edge_mask, shrink=False), axis=-1) # (npts-1,) + edge_res = ma.mean(ma.array(d, mask=min_edge_mask, + shrink=False), axis=-1) # (npts-1,) + + tracks[i, j]['theta'] = cls.theta(axis) + tracks[i, j]['phi'] = cls.phi(axis) + tracks[i, j]['xp'] = xyp[0] + tracks[i, j]['yp'] = xyp[1] + tracks[i, j]['nhit'] = np.count_nonzero(mask) + tracks[i, j]['q'] = np.sum(hit_q[i][mask]) + tracks[i, j]['ts_start'] = np.min(hits[i][mask]['ts']) + tracks[i, j]['ts_end'] = np.max(hits[i][mask]['ts']) + tracks[i, j]['residual'] = residual + tracks[i, j]['length'] = np.linalg.norm(r_max - r_min) + tracks[i, j]['start'] = r_min + tracks[i, j]['end'] = r_max + + tracks[i, j]['trajectory'] = traj + tracks[i, j]['trajectory_residual'] = edge_res + tracks[i, j]['dx'] = np.diff(traj, axis=0) + tracks[i, j]['dq'] = edge_q + tracks[i, j]['dn'] = np.sum(~min_edge_mask, axis=-1) + + tracks_mask[i, j] = False + + return ma.array(tracks, mask=tracks_mask, shrink=False) + + def _do_dbscan(self, xyz, mask): + ''' + :param xyz: ``shape: (N,3)`` array of precomputed 3D distances + + :param mask: ``shape: (N,)`` boolean array of valid positions (``True == valid``) + + :returns: ``shape: (N,)`` array of grouped track ids + ''' + clustering = self.dbscan.fit(xyz[mask]) + track_ids = np.full(len(mask), -1) + track_ids[mask] = clustering.labels_ + return track_ids + + def _do_ransac(self, xyz, mask): + ''' + :param xyz: ``shape: (N,3)`` array of 3D positions + + :param mask: ``shape: (N,)`` boolean array of valid positions (``True == valid``) + + :returns: ``shape: (N,)`` boolean array of colinear positions + ''' + model_robust, inliers = ransac(xyz[mask], LineModelND, + min_samples=self._ransac_min_samples, + residual_threshold=self._ransac_residual_threshold, + max_trials=self._ransac_max_trials) + return inliers + + @staticmethod + def trajectory_approx(centroid, axis, xyz, mode, npts, dx, weights=None): + ''' + :param centroid: ``shape: (3,)`` pre-calculated centroid of 3D positions + + :param axis: ``shape: (3,)`` pre-calculated PCA of 3D positions + + :param xyz: ``shape: (N, 3)`` array of 3D positions + + :returns: ``shape: (npts, 3)`` array of piecewise-linear approximation + ''' + # project hits onto PCA axis + s = np.sum((xyz - centroid[np.newaxis, :]) * axis[np.newaxis, :], + axis=-1, keepdims=True) # (N, 1) + + traj = np.empty((npts, 3)) # (M, 3) + traj_s = np.empty((npts, 1)) # (M, 1) + + start_pt = np.argmin(s, axis=0) + end_pt = np.argmax(s, axis=0) + + traj[0] = TrackletReconstruction.local_mean(xyz, xyz[start_pt], dx, weights=weights) + traj[1:] = TrackletReconstruction.local_mean(xyz, xyz[end_pt], dx, weights=weights) + traj_s[0] = s[start_pt] + traj_s[1:] = s[end_pt] + + for i in range(1, npts - 1): + # calculate residuals + d = TrackletReconstruction.trajectory_residual(xyz, traj, mode) # (M, N) + + # use smallest residual per point + i_res_min = np.expand_dims(np.argmin(d, axis=0), axis=0) # (1, N) + res = np.take_along_axis(d, i_res_min, axis=0) # (1, N) + node_d = np.take_along_axis(d, i_res_min, axis=0) # (1, N) + + # find farthest point + mask = node_d < dx # (1, N) + # important for short tracks + # the mask is to prevent breaking track segments into pieces smaller than trajectory_dx + if mask.all() == True: + break + max_pt = ma.argmax(ma.array(res, mask=mask, shrink=False), axis=1) # (1,) + + # update trajectory + new_pt = TrackletReconstruction.local_mean(xyz, xyz[max_pt].ravel(), dx, weights=weights) # (3,) + new_s = np.sum((new_pt - centroid) * axis, axis=-1) # (1,) + traj[i] = new_pt + traj_s[i] = new_s + + order = np.argsort(traj_s, axis=0) # (M, 1) + traj[:] = np.take_along_axis(traj, order, axis=0) + traj_s[:] = np.take_along_axis(traj_s, order, axis=0) + + return traj + + @staticmethod + def local_mean(xyz, pt, dx, weights=None): + ''' + :param xyz: ``shape: (N, 3)`` + + :param pt: ``shape: (3,)`` + + :param dx: ``float`` radius to include in mean + + :param weights: ``shape: (N,)`` relative weights for each pt, ``None`` applies same weights + + :returns: ``shape: (M, 3)`` + ''' + # calculate local mean + r = xyz - np.expand_dims(pt, axis=0) # (N,3) - (1,3) + d = np.linalg.norm(r, axis=-1, keepdims=True) # (N,1) + + mask = np.broadcast_to(d > dx, r.shape) # (N,3) + traj = ma.average(ma.array(np.expand_dims(xyz, axis=1), mask=mask, shrink=False), + axis=0, weights=weights) # (3,) + return traj + + @staticmethod + def do_pca(xyz, mask): + ''' + :param xyz: ``shape: (N,3)`` array of 3D positions + + :param mask: ``shape: (N,)`` boolean array of valid positions (``True == valid``) + + :returns: ``tuple`` of ``shape: (3,)``, ``shape: (3,)`` of centroid and central axis + ''' + centroid = np.mean(xyz[mask], axis=0) + pca = dcomp.PCA(n_components=1).fit(xyz[mask] - centroid) + axis = pca.components_[0] / np.linalg.norm(pca.components_[0]) + + # break degenerate pca axis direction by fixing y component to be negative + if axis[1] > 0: + axis = -axis + return centroid, axis + + @staticmethod + def projected_limits(centroid, axis, xyz): + s = np.dot((xyz - centroid), axis) + xyz_min, xyz_max = np.amin(xyz, axis=0), np.amax(xyz, axis=0) + r_max = np.clip(centroid + axis * np.max(s), xyz_min, xyz_max) + r_min = np.clip(centroid + axis * np.min(s), xyz_min, xyz_max) + return r_min, r_max + + @staticmethod + def track_residual(centroid, axis, xyz): + s = np.dot((xyz - centroid), axis) + res = np.abs(xyz - (centroid + np.outer(s, axis))) + return np.mean(res, axis=0) + + @staticmethod + # mode = 1, shortest distance to the segment ends + # mode = 2, shortest distance to the tractory + def trajectory_residual(xyz, traj, mode): + ''' + :param xyz: ``shape: (N, 3)``, 3D positions + + :param traj: ``shape: (npts, 3)```, trajectory 3D positions + + :returns: distance to nearest trajectory edge ``shape: (npts-1, N)`` + ''' + d0 = np.expand_dims(xyz, axis=0) - np.expand_dims(traj[:-1], axis=1) # (1, N, 3) - (npts-1, 1, 3) + d1 = np.expand_dims(xyz, axis=0) - np.expand_dims(traj[1:], axis=1) + if mode == 1: + dt = np.minimum(np.linalg.norm(d0, axis=-1), np.linalg.norm(d1, axis=-1)) + elif mode == 2: + d = np.expand_dims(np.diff(traj, axis=0), axis=1) # (npts-1, 1, 3) + with np.errstate(divide='ignore', invalid='ignore'): + n = d / np.linalg.norm(d, axis=-1, keepdims=True) + n[np.isnan(n) | np.isinf(n)] = 0 + + dl = np.linalg.norm(d0 * n, axis=-1) # (npts-1, N, 1) + dt = d0 - np.expand_dims(dl, -1) * n # (npts-1, N, 3) - (npts-1, N, 1) * (1, 1, 3) + dt = np.linalg.norm(dt, axis=-1) # (npts-1, N) + + non_overlap_mask = (dl < 0) | (dl > np.linalg.norm(d, axis=-1)) + dt[non_overlap_mask] = np.minimum(np.linalg.norm(d0, axis=-1), + np.linalg.norm(d1, axis=-1))[non_overlap_mask] + + return dt + + @staticmethod + def theta(axis): + ''' + :param axis: array, ``shape: (3,)`` + + :returns: angle of axis w.r.t z-axis + ''' + return np.arctan2(np.linalg.norm(axis[:2]), axis[-1]) + + @staticmethod + def phi(axis): + ''' + :param axis: array, ``shape: (3,)`` + + :returns: orientation of axis about z-axis + ''' + return np.arctan2(axis[1], axis[0]) + + @staticmethod + def xyp(axis, centroid): + ''' + :param axis: array, ``shape: (3,)`` + + :param centroid: array, ``shape: (3,)`` + + :returns: x,y coordinate where line intersects ``x=0,y=0`` plane + ''' + if axis[-1] == 0: + return centroid[:2] + s = -centroid[-1] / axis[-1] + return (centroid + axis * s)[:2] diff --git a/yamls/proto_nd_flow/util/TrackletMerger.yaml b/yamls/proto_nd_flow/util/TrackletMerger.yaml new file mode 100644 index 00000000..67b32bee --- /dev/null +++ b/yamls/proto_nd_flow/util/TrackletMerger.yaml @@ -0,0 +1,26 @@ +classname: TrackletMerger +path: module0_flow.reco.combined.tracklet_merging +requires: + - 'combined/tracklets' + - name: 'combined/track_hits' + path: ['combined/tracklets', 'charge/hits'] + - name: 'combined/track_hit_charge' + path: ['combined/tracklets', 'charge/hits', 'combined/q_calib_el'] + - name: 'combined/track_hit_drift' + path: ['combined/tracklets', 'charge/hits', 'combined/hit_drift'] +params: + # inputs + hits_dset_name: 'charge/hits' + track_charge_dset_name: 'combined/track_hit_charge' + track_hits_dset_name: 'combined/track_hits' + track_hit_drift_dset_name: 'combined/track_hit_drift' + tracks_dset_name: 'combined/tracklets' + + # output + merged_dset_name: 'combined/tracklets/merged' + + # configuration parameters + pdf_filename: 'data/module0_flow/joint_pdf-3_0_0.npz' + # download link: https://portal.nersc.gov/project/dune/data/Module0/merged/reco_data/joint_pdf-3_0_0.npz + pvalue_cut: 0.05 + max_neighbors: 5 diff --git a/yamls/proto_nd_flow/util/TrackletReconstruction.yaml b/yamls/proto_nd_flow/util/TrackletReconstruction.yaml new file mode 100644 index 00000000..c514e87e --- /dev/null +++ b/yamls/proto_nd_flow/util/TrackletReconstruction.yaml @@ -0,0 +1,25 @@ +classname: TrackletReconstruction +path: module0_flow.reco.combined.tracklet_reco +requires: + - 'charge/hits' + - name: 'combined/hit_drift' + path: ['charge/hits', 'combined/hit_drift'] + - name: 'combined/q_calib_el' + path: ['charge/hits', 'combined/q_calib_el'] +params: + # inputs + hits_dset_name: 'charge/hits' + charge_dset_name: 'combined/q_calib_el' + hit_drift_dset_name: 'combined/hit_drift' + + # output + tracklet_dset_name: 'combined/tracklets' + + # configuration parameters + dbscan_eps: 25 + dbscan_min_samples: 5 + ransac_min_samples: 2 + ransac_residual_threshold: 8 + ransac_max_trials: 10 + trajectory_pts: 16 + trajectory_residual_mode: 1 # 1: shortest distance to the segment ends # 2: shortest distance to the tractory From 43b9ad9bd622d993c2f36c60b74b145613c9e3f8 Mon Sep 17 00:00:00 2001 From: Kathryn Sutton Date: Wed, 20 Sep 2023 14:59:13 -0700 Subject: [PATCH 12/37] updated to util path --- yamls/proto_nd_flow/util/TrackletMerger.yaml | 2 +- yamls/proto_nd_flow/util/TrackletReconstruction.yaml | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/yamls/proto_nd_flow/util/TrackletMerger.yaml b/yamls/proto_nd_flow/util/TrackletMerger.yaml index 67b32bee..7753af71 100644 --- a/yamls/proto_nd_flow/util/TrackletMerger.yaml +++ b/yamls/proto_nd_flow/util/TrackletMerger.yaml @@ -1,5 +1,5 @@ classname: TrackletMerger -path: module0_flow.reco.combined.tracklet_merging +path: proto_nd_flow.util.tracklet_merging requires: - 'combined/tracklets' - name: 'combined/track_hits' diff --git a/yamls/proto_nd_flow/util/TrackletReconstruction.yaml b/yamls/proto_nd_flow/util/TrackletReconstruction.yaml index c514e87e..90d21dd3 100644 --- a/yamls/proto_nd_flow/util/TrackletReconstruction.yaml +++ b/yamls/proto_nd_flow/util/TrackletReconstruction.yaml @@ -1,5 +1,5 @@ classname: TrackletReconstruction -path: module0_flow.reco.combined.tracklet_reco +path: proto_nd_flow.util.tracklet_reco requires: - 'charge/hits' - name: 'combined/hit_drift' From 9b86a6ac828f604c496897f95dc55815020849f7 Mon Sep 17 00:00:00 2001 From: Elise Dianne Hinkle Date: Mon, 2 Oct 2023 12:16:16 -0700 Subject: [PATCH 13/37] Make tracklet reconstruction module compatible with proto_nd_flow datasets. --- .../run_proto_nd_tracklet_reco.sh | 41 +++++++++++++++++++ src/proto_nd_flow/util/tracklet_reco.py | 22 ++++++---- .../util/TrackletReconstruction.yaml | 12 ++---- .../workflows/util/tracklet_workflow.yaml | 23 +++++++++++ 4 files changed, 81 insertions(+), 17 deletions(-) create mode 100644 scripts/proto_nd_scripts/run_proto_nd_tracklet_reco.sh create mode 100644 yamls/proto_nd_flow/workflows/util/tracklet_workflow.yaml diff --git a/scripts/proto_nd_scripts/run_proto_nd_tracklet_reco.sh b/scripts/proto_nd_scripts/run_proto_nd_tracklet_reco.sh new file mode 100644 index 00000000..216fbe7d --- /dev/null +++ b/scripts/proto_nd_scripts/run_proto_nd_tracklet_reco.sh @@ -0,0 +1,41 @@ +#!/bin/bash +# Runs proto_nd_flow on an example file. +# Before using this script, use +# >> source get_proto_nd_input.sh +# to download all the necessary inputs into the correct directories +# +INPUT_FILE=$1 + +OUTPUT_DIR=`pwd` # !!change!! +OUTPUT_NAME=(${INPUT_FILE//"/"/ }) +OUTPUT_NAME=${OUTPUT_NAME[-1]} +OUTPUT_FILE="${OUTPUT_DIR}/${OUTPUT_NAME}" +OUTPUT_FILE=${OUTPUT_FILE//.h5/.proto_nd_flow.TRACKLETS.h5} +echo ${OUTPUT_FILE} + +# for running on a login node +H5FLOW_CMD='h5flow' +# for running on a single compute node with 32 cores +#H5FLOW_CMD='srun -n32 h5flow' + +# run all stages +WORKFLOW1='yamls/proto_nd_flow/workflows/util/tracklet_workflow.yaml' + +HERE=`pwd` +#cd ndlar_flow +# assumes this is being run from ndlar_flow/scripts/proto_nd_flow: +cd ../../ + +# avoid being asked if we want to overwrite the file if it exists. +# this is us answering "yes". +if [ -e $OUTPUT_FILE ]; then + rm -i $OUTPUT_FILE +fi + +$H5FLOW_CMD -c $WORKFLOW1 -i $INPUT_FILE -o $OUTPUT_FILE + +echo "Done!" +echo "Output can be found at $OUTPUT_FILE" + +cd ${HERE} + diff --git a/src/proto_nd_flow/util/tracklet_reco.py b/src/proto_nd_flow/util/tracklet_reco.py index 8004b187..7941980c 100644 --- a/src/proto_nd_flow/util/tracklet_reco.py +++ b/src/proto_nd_flow/util/tracklet_reco.py @@ -17,8 +17,10 @@ class TrackletReconstruction(H5FlowStage): Parameters: - ``tracklet_dset_name``: ``str``, path to output dataset - ``hits_dset_name``: ``str``, path to input charge hits dataset - - ``charge_dset_name``: ``str``, path to input charge dataset (1:1 with hits dataset, requires ``"q"`` field) + - ``charge_dset_name``: ``str``, path to input charge dataset (1:1 with hits dataset, requires ``"Q"`` field) + ** NOTE: change in charge field name from module0_flow datasets ("q") to proto_nd_flow calib datasets ("Q") - ``hit_drift_dset_name``: ``str``, path to charge hits drift data + ** NOTE: same as hits datasets when using proto_nd_flow calib datasets - ``dbscan_eps``: ``float``, dbscan epsilon parameter - ``dbscan_min_samples``: ``int``, dbscan min neighbor points to consider as "core" point - ``ransac_min_samples``: ``int``, min points to run ransac algorithm @@ -56,9 +58,9 @@ class TrackletReconstruction(H5FlowStage): class_version = '1.1.0' default_tracklet_dset_name = 'combined/tracklets' - default_hits_dset_name = 'charge/hits' - default_charge_dset_name = 'charge/hits' - default_hit_drift_dset_name = 'combined/hit_drift' + default_hits_dset_name = 'charge/calib_final_hits' + default_charge_dset_name = 'charge/calib_final_hits' + default_hit_drift_dset_name = 'combined/calib_final_hits' default_dbscan_eps = 25 default_dbscan_min_samples = 5 @@ -141,10 +143,12 @@ def run(self, source_name, source_slice, cache): events = cache[source_name] # shape: (N,) hits = cache[self.hits_dset_name] # shape: (N,M) - q = cache[self.charge_dset_name]['q'] + q = cache[self.charge_dset_name]['Q'] q = q.reshape(hits.shape) hit_drift = cache[self.hit_drift_dset_name] # shape: (N,M,1) hit_drift = hit_drift.reshape(hits.shape) + + if self.max_nhit is not None: hits = ma.array(hits, mask=(events['nhit'][..., np.newaxis] > self.max_nhit) | hits['id'].mask, shrink=False) @@ -175,8 +179,8 @@ def run(self, source_name, source_slice, cache): @staticmethod def hit_xyz(hits, hit_z): xyz = np.concatenate(( - np.expand_dims(hits['px'], axis=-1), - np.expand_dims(hits['py'], axis=-1), + np.expand_dims(hits['x'], axis=-1), + np.expand_dims(hits['y'], axis=-1), np.expand_dims(hit_z, axis=-1), ), axis=-1) return xyz @@ -297,8 +301,8 @@ def calc_tracks(cls, hits, hit_q, hit_z, track_ids, trajectory_pts, trajectory_d tracks[i, j]['yp'] = xyp[1] tracks[i, j]['nhit'] = np.count_nonzero(mask) tracks[i, j]['q'] = np.sum(hit_q[i][mask]) - tracks[i, j]['ts_start'] = np.min(hits[i][mask]['ts']) - tracks[i, j]['ts_end'] = np.max(hits[i][mask]['ts']) + tracks[i, j]['ts_start'] = np.min(hits[i][mask]['ts_pps']) + tracks[i, j]['ts_end'] = np.max(hits[i][mask]['ts_pps']) tracks[i, j]['residual'] = residual tracks[i, j]['length'] = np.linalg.norm(r_max - r_min) tracks[i, j]['start'] = r_min diff --git a/yamls/proto_nd_flow/util/TrackletReconstruction.yaml b/yamls/proto_nd_flow/util/TrackletReconstruction.yaml index 90d21dd3..6bfe664c 100644 --- a/yamls/proto_nd_flow/util/TrackletReconstruction.yaml +++ b/yamls/proto_nd_flow/util/TrackletReconstruction.yaml @@ -1,16 +1,12 @@ classname: TrackletReconstruction path: proto_nd_flow.util.tracklet_reco requires: - - 'charge/hits' - - name: 'combined/hit_drift' - path: ['charge/hits', 'combined/hit_drift'] - - name: 'combined/q_calib_el' - path: ['charge/hits', 'combined/q_calib_el'] + - 'charge/calib_final_hits' params: # inputs - hits_dset_name: 'charge/hits' - charge_dset_name: 'combined/q_calib_el' - hit_drift_dset_name: 'combined/hit_drift' + hits_dset_name: 'charge/calib_final_hits' + charge_dset_name: 'charge/calib_final_hits' + hit_drift_dset_name: 'charge/calib_final_hits' # output tracklet_dset_name: 'combined/tracklets' diff --git a/yamls/proto_nd_flow/workflows/util/tracklet_workflow.yaml b/yamls/proto_nd_flow/workflows/util/tracklet_workflow.yaml new file mode 100644 index 00000000..8b7a44aa --- /dev/null +++ b/yamls/proto_nd_flow/workflows/util/tracklet_workflow.yaml @@ -0,0 +1,23 @@ +# Generates higher-level event built data for charge data (i.e. tracklets) + +flow: + source: events + stages: [tracklet_reco] + drop: [] + + +resources: + - !include yamls/proto_nd_flow/resources/RunData.yaml + - !include yamls/proto_nd_flow/resources/LArData.yaml + - !include yamls/proto_nd_flow/resources/Geometry.yaml + +events: + classname: H5FlowDatasetLoopGenerator + path: h5flow.modules + dset_name: 'charge/events' + params: + chunk_size: 32 + +tracklet_reco: + !include yamls/proto_nd_flow/util/TrackletReconstruction.yaml + From 6483ea5fd7fc62939edee81a46220fceed020d81 Mon Sep 17 00:00:00 2001 From: Elise Dianne Hinkle Date: Tue, 3 Oct 2023 08:04:48 -0700 Subject: [PATCH 14/37] Update tracklet reconstruction script to reflect x- and z-coordinate swap for proto_nd_flow. --- src/proto_nd_flow/util/tracklet_reco.py | 52 ++++++++++++------------- 1 file changed, 26 insertions(+), 26 deletions(-) diff --git a/src/proto_nd_flow/util/tracklet_reco.py b/src/proto_nd_flow/util/tracklet_reco.py index 7941980c..97b13877 100644 --- a/src/proto_nd_flow/util/tracklet_reco.py +++ b/src/proto_nd_flow/util/tracklet_reco.py @@ -38,8 +38,8 @@ class TrackletReconstruction(H5FlowStage): id u4, unique identifier theta f8, track inclination w.r.t anode phi f8, track orientation w.r.t anode - xp f8, intersection of track with ``x=0,y=0`` plane [mm] - yp f8, intersection of track with ``x=0,y=0`` plane [mm] + yp f8, intersection of track with ``y=0,z=0`` plane [mm] + zp f8, intersection of track with ``y=0,z=0`` plane [mm] nhit i8, number of hits in track q f8, charge sum [mV] ts_start f8, PPS timestamp of track start [crs ticks] @@ -78,7 +78,7 @@ def tracklet_dtype(npts=default_trajectory_pts): return np.dtype([ ('id', 'u4'), ('theta', 'f8'), ('phi', 'f8'), - ('xp', 'f8'), ('yp', 'f8'), + ('yp', 'f8'), ('zp', 'f8'), ('nhit', 'i8'), ('q', 'f8'), ('ts_start', 'f8'), ('ts_end', 'f8'), ('residual', 'f8', (3,)), ('length', 'f8'), @@ -155,8 +155,8 @@ def run(self, source_name, source_slice, cache): hit_drift = ma.array(hit_drift, mask=(events['nhit'][..., np.newaxis] > self.max_nhit) | hits['id'].mask, shrink=False) - track_ids = self.find_tracks(hits, hit_drift['z']) - tracks = self.calc_tracks(hits, q, hit_drift['z'], track_ids, self.trajectory_pts, + track_ids = self.find_tracks(hits) + tracks = self.calc_tracks(hits, q, track_ids, self.trajectory_pts, self.trajectory_dx, self.trajectory_residual_mode) n_tracks = np.count_nonzero(~tracks['id'].mask) tracks_mask = ~tracks['id'].mask @@ -177,25 +177,25 @@ def run(self, source_name, source_slice, cache): self.data_manager.write_ref(source_name, self.tracklet_dset_name, ref) @staticmethod - def hit_xyz(hits, hit_z): + def hit_xyz(hits): xyz = np.concatenate(( np.expand_dims(hits['x'], axis=-1), np.expand_dims(hits['y'], axis=-1), - np.expand_dims(hit_z, axis=-1), + np.expand_dims(hits['z'], axis=-1), ), axis=-1) return xyz - def find_tracks(self, hits, hit_z): + def find_tracks(self, hits): ''' Extract tracks from a given hits array :param hits: masked array ``shape: (N, n)`` - :param hit_z: masked array ``shape: (N, n)`` + [[former input]] :param hit_drift_coord: masked array ``shape: (N, n)`` :returns: mask array ``shape: (N, n)`` of track ids for each hit, a value of -1 means no track is associated with the hit ''' - xyz = self.hit_xyz(hits, hit_z) + xyz = self.hit_xyz(hits) iter_mask = np.ones(hits.shape, dtype=bool) iter_mask = iter_mask & (~hits['id'].mask) @@ -243,7 +243,7 @@ def find_tracks(self, hits, hit_z): return ma.array(track_id, mask=hits['id'].mask, shrink=False) @classmethod - def calc_tracks(cls, hits, hit_q, hit_z, track_ids, trajectory_pts, trajectory_dx, trajectory_residual_mode): + def calc_tracks(cls, hits, hit_q, track_ids, trajectory_pts, trajectory_dx, trajectory_residual_mode): ''' Calculate track parameters from hits @@ -251,7 +251,7 @@ def calc_tracks(cls, hits, hit_q, hit_z, track_ids, trajectory_pts, trajectory_d :param hit_q: masked array, ``shape: (N,M)`` - :param hit_z: masked array, ``shape: (N,M)`` + [[former input]] :param hit_drift_coord: masked array, ``shape: (N,M)`` :param track_ids: masked array, ``shape: (N,M)`` @@ -261,7 +261,7 @@ def calc_tracks(cls, hits, hit_q, hit_z, track_ids, trajectory_pts, trajectory_d :returns: masked array, ``shape: (N,m)`` ''' - xyz = cls.hit_xyz(hits, hit_z) + xyz = cls.hit_xyz(hits) n_tracks = np.clip(track_ids.max() + 1, 1, np.inf).astype(int) if np.count_nonzero(~track_ids.mask) \ else 1 @@ -279,7 +279,7 @@ def calc_tracks(cls, hits, hit_q, hit_z, track_ids, trajectory_pts, trajectory_d r_min, r_max = cls.projected_limits( centroid, axis, xyz[i][mask]) residual = cls.track_residual(centroid, axis, xyz[i][mask]) - xyp = cls.xyp(axis, centroid) + yzp = cls.yzp(axis, centroid) # run trajectory approximation algo traj = cls.trajectory_approx(centroid, axis, xyz[i][mask], trajectory_residual_mode, @@ -297,8 +297,8 @@ def calc_tracks(cls, hits, hit_q, hit_z, track_ids, trajectory_pts, trajectory_d tracks[i, j]['theta'] = cls.theta(axis) tracks[i, j]['phi'] = cls.phi(axis) - tracks[i, j]['xp'] = xyp[0] - tracks[i, j]['yp'] = xyp[1] + tracks[i, j]['yp'] = yzp[0] + tracks[i, j]['zp'] = yzp[1] tracks[i, j]['nhit'] = np.count_nonzero(mask) tracks[i, j]['q'] = np.sum(hit_q[i][mask]) tracks[i, j]['ts_start'] = np.min(hits[i][mask]['ts_pps']) @@ -490,29 +490,29 @@ def theta(axis): ''' :param axis: array, ``shape: (3,)`` - :returns: angle of axis w.r.t z-axis + :returns: angle of axis w.r.t x-axis ''' - return np.arctan2(np.linalg.norm(axis[:2]), axis[-1]) + return np.arctan2(np.linalg.norm(axis[1:]), axis[0]) @staticmethod def phi(axis): ''' :param axis: array, ``shape: (3,)`` - :returns: orientation of axis about z-axis + :returns: orientation of axis about x-axis ''' - return np.arctan2(axis[1], axis[0]) + return np.arctan2(axis[1], axis[2]) @staticmethod - def xyp(axis, centroid): + def yzp(axis, centroid): ''' :param axis: array, ``shape: (3,)`` :param centroid: array, ``shape: (3,)`` - :returns: x,y coordinate where line intersects ``x=0,y=0`` plane + :returns: y,z coordinate where line intersects ``y=0,z=0`` plane ''' - if axis[-1] == 0: - return centroid[:2] - s = -centroid[-1] / axis[-1] - return (centroid + axis * s)[:2] + if axis[0] == 0: + return centroid[1:] + s = -centroid[0] / axis[0] + return (centroid + axis * s)[1:] From fe63f0d6802fbaf7102ec56d508ffc9803690f9a Mon Sep 17 00:00:00 2001 From: Elise Dianne Hinkle Date: Wed, 4 Oct 2023 05:03:57 -0700 Subject: [PATCH 15/37] Fix bug where arrays containing NaNs could be passed to DBSCAN. --- src/proto_nd_flow/util/tracklet_reco.py | 11 ++++++++++- 1 file changed, 10 insertions(+), 1 deletion(-) diff --git a/src/proto_nd_flow/util/tracklet_reco.py b/src/proto_nd_flow/util/tracklet_reco.py index 97b13877..dffbdd1f 100644 --- a/src/proto_nd_flow/util/tracklet_reco.py +++ b/src/proto_nd_flow/util/tracklet_reco.py @@ -197,8 +197,13 @@ def find_tracks(self, hits): ''' xyz = self.hit_xyz(hits) + # Adding masks where hit coordinate is recorded as nan to enable dbscan + hits['x'].mask = hits['x'].mask | ma.masked_invalid(hits['x']).mask + hits['y'].mask = hits['y'].mask | ma.masked_invalid(hits['y']).mask + hits['z'].mask = hits['z'].mask | ma.masked_invalid(hits['z']).mask + iter_mask = np.ones(hits.shape, dtype=bool) - iter_mask = iter_mask & (~hits['id'].mask) + iter_mask = iter_mask & (~hits['id'].mask) & (~hits['x'].mask) & (~hits['y'].mask) & (~hits['z'].mask) track_id = np.full(hits.shape, -1, dtype='i8') for i in range(hits.shape[0]): @@ -326,6 +331,10 @@ def _do_dbscan(self, xyz, mask): :returns: ``shape: (N,)`` array of grouped track ids ''' + + #print("XYZ:", xyz) + #print("Mask:", mask) + #print("XYZ Mask:", xyz[mask]) clustering = self.dbscan.fit(xyz[mask]) track_ids = np.full(len(mask), -1) track_ids[mask] = clustering.labels_ From 1e89d059edd89b3526d01bf4a8e65ce00cf14e76 Mon Sep 17 00:00:00 2001 From: Elise Dianne Hinkle Date: Thu, 5 Oct 2023 11:12:30 -0700 Subject: [PATCH 16/37] Allow tracklet plotting in proto_nd_flow event display with calib_final_hits hits dataset. --- .../module0flow_evd_example.ipynb | 26 ++-- .../proto_nd_flow/protondflow_evd.py | 136 ++++++------------ .../protondflow_evd_example.ipynb | 58 ++++++-- 3 files changed, 104 insertions(+), 116 deletions(-) diff --git a/event_display/module0_flow/module0flow_evd_example.ipynb b/event_display/module0_flow/module0flow_evd_example.ipynb index 73db9154..d5a466bf 100644 --- a/event_display/module0_flow/module0flow_evd_example.ipynb +++ b/event_display/module0_flow/module0flow_evd_example.ipynb @@ -18,7 +18,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 1, "id": "81557d67-5a69-4fc5-91d9-ae333bffe2b4", "metadata": { "tags": [] @@ -27,10 +27,10 @@ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 2, + "execution_count": 1, "metadata": {}, "output_type": "execute_result" } @@ -55,7 +55,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 2, "id": "00a3d01f-92be-4d86-8ecd-a68f7a423f69", "metadata": { "tags": [] @@ -84,7 +84,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 3, "id": "ab8119df-4892-425c-bd09-a68fa977ab72", "metadata": { "tags": [] @@ -92,9 +92,9 @@ "outputs": [ { "data": { - "image/png": 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\n", + "image/png": 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", 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\n", + "image/png": 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", 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" ] }, - "metadata": { - "needs_background": "light" - }, + "metadata": {}, "output_type": "display_data" } ], @@ -168,7 +166,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.7" + "version": "3.11.5" } }, "nbformat": 4, diff --git a/event_display/proto_nd_flow/protondflow_evd.py b/event_display/proto_nd_flow/protondflow_evd.py index d81b5df9..57cec267 100644 --- a/event_display/proto_nd_flow/protondflow_evd.py +++ b/event_display/proto_nd_flow/protondflow_evd.py @@ -24,6 +24,9 @@ class ProtoNDFlowEventDisplay: - nhits (int): hit threshold for events to be made available in interactive display - hits_dset (str): dataset of hits within the file that you want to display options are 'raw_hits', 'calib_prompt_hits', and 'calib_final_hits' + - tracklets (bool): bool denoting whether or not file contains 'combined/tracklets' dataset; + default is False. Right now, tracklets plotting is only set up to plot + with 'calib_final_hits' dataset In order to run the display, set up a Jupyter Notebook, import everything in this file, and execute the run() method, e.g.: @@ -36,12 +39,13 @@ class ProtoNDFlowEventDisplay: g = '/path/to/geometry/file/name_of_geometry_file' hd = 'hits_dataset_you_want_to_display' - evd = ProtoNDFlowEventDisplay(filedir=d, filename=f, geometry_file=g,nhits=1, hits_dset=hd) + evd = ProtoNDFlowEventDisplay(filedir=d, filename=f, geometry_file=g,nhits=1, hits_dset=hd, tracklets=False) test_evd.run() ''' - def __init__(self, filedir, filename, geometry_file=None, nhits=1, hits_dset='calib_final_hits'): + def __init__(self, filedir, filename, geometry_file=None, nhits=1, hits_dset='calib_final_hits', tracklets=False): f = h5py.File(filedir+filename, 'r') self.filename = filename + self.tracklets = tracklets # Set name of hits dataset to be used self.hits_dset = hits_dset @@ -49,14 +53,14 @@ def __init__(self, filedir, filename, geometry_file=None, nhits=1, hits_dset='ca # Load datasets events = f['charge/events/data'] self.events = events[events['nhit'] > nhits] - try: + if self.tracklets: self.tracks = f['combined/tracklets/data'] self.tracks_ref = f['charge/events/ref/combined/tracklets/ref'] self.tracks_region = f['charge/events/ref/combined/tracklets/ref_region'] - self.hits_trk_ref = f['combined/tracklets/ref/charge/hits/ref'] - self.hits_trk_region = f['combined/tracklets/ref/charge/hits/ref_region'] - self.hits_drift = f['combined/hit_drift/data'] - except KeyError: + self.hits_trk_ref = f['combined/tracklets/ref/charge/'+self.hits_dset+'/ref'] + self.hits_trk_region = f['combined/tracklets/ref/charge/'+self.hits_dset+'/ref_region'] + #self.hits_drift = f['combined/hit_drift/data'] + else: print("No tracklets found") self.hits = f['charge/'+self.hits_dset+'/data'] self.hits_ref = f['charge/events/ref/charge/'+self.hits_dset+'/ref'] @@ -472,100 +476,42 @@ def display_event(self, ev_id): self.ax_time_1.axvline(x=trig, c='g') self.ax_time_2.axvline(x=trig, c='g') - unassoc_hit_mask = np.ones(event['nhit']).astype(bool) + unassoc_hit_mask = np.ones(len(hits['id'])).astype(bool) - if 'ntracks' in event.dtype.name and event['ntracks']: - track_ref = event['track_ref'] + ev_id = event['id'] + + if self.tracklets and self.hits_dset == 'calib_final_hits': + track_ref = self.tracks_ref[self.tracks_region[ev_id,'start']:self.tracks_region[ev_id,'stop']] + track_ref = np.sort(track_ref[track_ref[:,0] == ev_id, 1]) tracks = self.tracks[track_ref] track_start = tracks['start'] track_end = tracks['end'] - for i, track in enumerate(tracks): - - hit_trk_ref = track['hit_ref'] - hits_trk = self.hits[hit_trk_ref] - - # Difference between the z coordinate using the event ts_start (used in the track fitter) - # and the start time found by get_event_start_time - z_correction = (self._get_z_coordinate(hits_trk['iogroup'][0], hits_trk['iochannel'][0], event_start_time) - - self._get_z_coordinate(hits_trk['iogroup'][0], hits_trk['iochannel'][0], event['ts_start'])) - - self.ax_zy.plot((track_start[i][0], track_end[i][0]), - (track_start[i][1], track_end[i][1]), - c='C{}'.format(i+1), alpha=0.75, lw=1) - - self.ax_xy.plot((track_start[i][2], track_end[i][2]), - (track_start[i][1], track_end[i][1]), - c='C{}'.format(i+1), alpha=0.75, lw=1) - - hits_anode1 = hits_trk[hits_trk[self.x_vals]*self.convert_to_mm <= 0] - hits_anode2 = hits_trk[hits_trk[self.x_vals]*self.convert_to_mm >0] - - if self.hits_dset == 'raw_hits': - self.ax_zy.scatter(hits_trk['px'], hits_trk['py'], lw=0.2, ec='C{}'.format( - i+1), c=cmap(norm(self.charge_from_ADC(hits_trk[self.charge]), self.vref_mv, self.vcm_mv, self.ped_mv)), s=5, alpha=0.75) - - hit_xvals = [self._get_z_coordinate(io_group, io_channel, time) for io_group, io_channel, time in zip( - hits_trk['iogroup'], hits_trk['iochannel'], hits_trk['ts']-track['t0'])] - - self.ax_xy.scatter(hit_xvals, hits_trk['py'], lw=0.2, ec='C{}'.format( - i+1), c=cmap(norm(self.charge_from_ADC(hits_trk[self.charge], self.vref_mv, self.vcm_mv, self.ped_mv))), s=5, alpha=0.75) - self.ax_zyx.scatter(hits_trk['px'], hit_xvals, hits_trk['py'], lw=0.2, ec='C{}'.format( - i+1), c=cmap(norm(self.charge_from_ADC(hits_trk[self.charge], self.vref_mv, self.vcm_mv, self.ped_mv))), s=5, alpha=0.75) - else: - self.ax_zy.scatter(hits_trk['px'], hits_trk['py'], lw=0.2, ec='C{}'.format( - i+1), c=cmap(norm(hits_trk[self.charge])), s=5, alpha=0.75) - - hit_xvals = [self._get_z_coordinate(io_group, io_channel, time) for io_group, io_channel, time in zip( - hits_trk['iogroup'], hits_trk['iochannel'], hits_trk['ts']-track['t0'])] - - self.ax_xy.scatter(hit_xvals, hits_trk['py'], lw=0.2, ec='C{}'.format( - i+1), c=cmap(norm(hits_trk[self.charge])), s=5, alpha=0.75) - self.ax_zyx.scatter(hits_trk['px'], hit_xvals, hits_trk['py'], lw=0.2, ec='C{}'.format( - i+1), c=cmap(norm(hits_trk[self.charge])), s=5, alpha=0.75) - - self.ax_zyx.plot((track_start[i][0], track_end[i][0]), - (track_start[i][2], track_end[i][2]), - (track_start[i][1], track_end[i][1]), - c='C{}'.format(i+1), alpha=0.5, lw=4) - - unassoc_hit_mask[np.in1d(hits['hid'], hits_trk['hid'])] = 0 - - - ev_id = event['id'] - - ''' For now, all tracklet plotting is just commented out''' - ''' - track_ref = self.tracks_ref[self.tracks_region[ev_id,'start']:self.tracks_region[ev_id,'stop']] - track_ref = np.sort(track_ref[track_ref[:,0] == ev_id, 1]) - tracks = self.tracks[track_ref] - track_start = tracks['start'] - track_end = tracks['end'] - for itrk, (ts, te) in enumerate(zip(track_start, track_end)): - hit_ref = self.hits_trk_ref[self.hits_trk_region[tracks[itrk]['id'],'start']:self.hits_trk_region[tracks[itrk]['id'],'stop']] - hit_ref = np.sort(hit_ref[hit_ref[:,0] == tracks[itrk]['id'], 1]) - hits_trk = self.hits[hit_ref] - hits_drift_trk = self.hits_drift[hit_ref] - self.ax_zyx.scatter(hits_trk['px'], hits_drift_trk[self.z_vals]*self.convert_to_mm, hits_trk['py'], lw=0.2, ec='C{}'.format( + for itrk, (ts, te) in enumerate(zip(track_start, track_end)): + hit_ref = self.hits_trk_ref[self.hits_trk_region[tracks[itrk]['id'],'start']:self.hits_trk_region[tracks[itrk]['id'],'stop']] + hit_ref = np.sort(hit_ref[hit_ref[:,0] == tracks[itrk]['id'], 1]) + hits_trk = self.hits[hit_ref] + self.ax_zyx.scatter(hits_trk[self.z_vals]*self.convert_to_mm, hits_trk[self.x_vals]*self.convert_to_mm, hits_trk[self.y_vals]*self.convert_to_mm+self.y_offset, lw=0.2, ec='C{}'.format( itrk+1), c=cmap(norm(hits_trk[self.charge])), s=5, alpha=0.75) - self.ax_xy.scatter(hits_drift_trk[self.z_vals]*self.convert_to_mm, hits_trk['py'], lw=0.2, ec='C{}'.format( + self.ax_xy.scatter(hits_trk[self.x_vals]*self.convert_to_mm, hits_trk[self.y_vals]*self.convert_to_mm+self.y_offset, lw=0.2, ec='C{}'.format( itrk+1), c=cmap(norm(hits_trk[self.charge])), s=5, alpha=0.75) - self.ax_zy.scatter(hits_trk['px'], hits_trk['py'], lw=0.2, ec='C{}'.format( + self.ax_zy.scatter(hits_trk[self.z_vals]*self.convert_to_mm, hits_trk[self.y_vals]*self.convert_to_mm+self.y_offset, lw=0.2, ec='C{}'.format( itrk+1), c=cmap(norm(hits_trk[self.charge])), s=5, alpha=0.75) - self.ax_zy.plot((ts[0], te[0]), - (ts[1], te[1]), + self.ax_zy.plot((ts[2]*self.convert_to_mm, te[2]*self.convert_to_mm), + (ts[1]*self.convert_to_mm+self.y_offset, te[1]*self.convert_to_mm+self.y_offset), c='C{}'.format(itrk+1), alpha=0.75, lw=1) - self.ax_xy.plot((ts[2], te[2]), - (ts[1], te[1]), + self.ax_xy.plot((ts[0]*self.convert_to_mm, te[0]*self.convert_to_mm), + (ts[1]*self.convert_to_mm+self.y_offset, te[1]*self.convert_to_mm+self.y_offset), c='C{}'.format(itrk+1), alpha=0.75, lw=1) - self.ax_zyx.plot((ts[0], te[0]), - (ts[2], te[2]), - (ts[1], te[1]), + self.ax_zyx.plot((ts[2]*self.convert_to_mm, te[2]*self.convert_to_mm), + (ts[0]*self.convert_to_mm, te[0]*self.convert_to_mm), + (ts[1]*self.convert_to_mm+self.y_offset, te[1]*self.convert_to_mm+self.y_offset), c='C{}'.format(itrk+1), alpha=0.5, lw=4) - unassoc_hit_mask[np.in1d(hits['id'], hits_trk['id'])] = 0 + unassoc_hit_mask[np.in1d(hits['id'], hits_trk['id'])] = 0 + if np.any(unassoc_hit_mask): - ''' - - unassoc_hits = hits#[unassoc_hit_mask] + unassoc_hits = hits[unassoc_hit_mask] + else: + unassoc_hits = hits BG = np.asarray([1., 1., 1., ]) my_cmap = cmap(np.arange(cmap.N)) alphas = np.linspace(0, 1, cmap.N) @@ -588,10 +534,14 @@ def display_event(self, ev_id): self.ax_xy.scatter(hit_xvals, unassoc_hits[self.y_vals]*self.convert_to_mm+self.y_offset, lw=0, ec='C0', c=cmap( norm(self.charge_from_ADC(unassoc_hits[self.charge], self.vref_mv, self.vcm_mv, self.ped_mv))), s=5, alpha=1) else: + if self.tracklets: + a = 0.75 + else: + a = 1.0 hit_xvals = unassoc_hits[self.x_vals]*self.convert_to_mm self.ax_zyx.scatter(unassoc_hits[self.z_vals]*self.convert_to_mm, hit_xvals, unassoc_hits[self.y_vals]*self.convert_to_mm+self.y_offset, lw=0, ec='C0', c=cmap( - norm(unassoc_hits[self.charge])), s=5, alpha=1) + norm(unassoc_hits[self.charge])), s=5, alpha=a) self.ax_zy.scatter(unassoc_hits[self.z_vals]*self.convert_to_mm, unassoc_hits[self.y_vals]*self.convert_to_mm+self.y_offset, lw=0, ec='C0', c=cmap( - norm(unassoc_hits[self.charge])), s=5, alpha=1) + norm(unassoc_hits[self.charge])), s=5, alpha=a) self.ax_xy.scatter(hit_xvals, unassoc_hits[self.y_vals]*self.convert_to_mm+self.y_offset, lw=0, ec='C0', c=cmap( - norm(unassoc_hits[self.charge])), s=5, alpha=1) \ No newline at end of file + norm(unassoc_hits[self.charge])), s=5, alpha=a) \ No newline at end of file diff --git a/event_display/proto_nd_flow/protondflow_evd_example.ipynb b/event_display/proto_nd_flow/protondflow_evd_example.ipynb index 35d9ef75..0e009739 100644 --- a/event_display/proto_nd_flow/protondflow_evd_example.ipynb +++ b/event_display/proto_nd_flow/protondflow_evd_example.ipynb @@ -27,7 +27,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 1, @@ -51,7 +51,8 @@ "- `filename` (str): name of file; must be flow file run through `proto_nd_flow`\n", "- `geometry_file` (str): full path and name of geometry file describing module to be displayed\n", "- `nhits` (int): hit threshold for events to be made available in interactive display (default=1)\n", - "- `hits_dset` (str): dataset of hits within the file that you want to display. Options are 'raw_hits', 'calib_prompt_hits', and 'calib_final_hits' (default)" + "- `hits_dset` (str): dataset of hits within the file that you want to display. Options are `raw_hits`, `calib_prompt_hits`, and `calib_final_hits` (default)\n", + "- `tracklets` (bool): boolean denoting whether or not the file contains the `combined/tracklets` dataset. Default is False. " ] }, { @@ -65,9 +66,13 @@ "source": [ "# This set of inputs allows you to look at a Module1 charge-only file\n", "# This file originates from the same raw data file as the input file in the Module0FlowEventDisplay example\n", - "directory = '/global/cfs/cdirs/dune/users/sfogarty/muon_samples/'\n", + "directory = '/global/cfs/cdirs/dune/www/data/Module1/TPC12/reflow-test'\n", "file = 'packet_2022_02_09_17_23_09_CET.module1_flow.h5'\n", - "geometry = '/global/cfs/cdirs/dune/www/data/Module1/TPC12/module1_layout-2.3.16.yaml'" + "geometry = '/global/cfs/cdirs/dune/www/data/Module1/TPC12/module1_layout-2.3.16.yaml'\n", + "\n", + "# Tracklet testing:\n", + "#directory = '/global/cfs/cdirs/dune/users/ehinkle/nd_prototypes_ana/2x2_tracklet_test/'\n", + "#file = 'packet_2022_02_09_17_23_09_CET.module1_flow.proto_nd_flow.TRACKLETS.h5'" ] }, { @@ -85,7 +90,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 5, "id": "b67d336e-f49f-448a-a580-c3affc5689a0", "metadata": { "tags": [] @@ -93,9 +98,44 @@ "outputs": [ { "data": { - "image/png": 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\n", 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdin", + "output_type": "stream", + "text": [ + "Next event (q to exit/s to save to pdf/enter for next/number to skip to position)?\n", + " q\n" + ] + }, + { + "ename": "SystemExit", + "evalue": "", + "output_type": "error", + "traceback": [ + "An exception has occurred, use %tb to see the full traceback.\n", + "\u001b[0;31mSystemExit\u001b[0m\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/global/common/software/nersc/pe/conda-envs/23.9.0/python-3.11/nersc-python/lib/python3.11/site-packages/IPython/core/interactiveshell.py:3516: UserWarning: To exit: use 'exit', 'quit', or Ctrl-D.\n", + " warn(\"To exit: use 'exit', 'quit', or Ctrl-D.\", stacklevel=1)\n" + ] + }, + { + "data": { + "image/png": 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", "text/plain": [ - "
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" ] }, "metadata": {}, @@ -103,7 +143,7 @@ } ], "source": [ - "evd = ProtoNDFlowEventDisplay(filedir=directory, filename=file, geometry_file=geometry, nhits=1, hits_dset='calib_final_hits')\n", + "evd = ProtoNDFlowEventDisplay(filedir=directory, filename=file, geometry_file=geometry, nhits=1, hits_dset='calib_final_hits', tracklets=True)\n", "evd.run()" ] }, @@ -132,7 +172,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.7" + "version": "3.11.5" } }, "nbformat": 4, From cba5368b7fd9a949d1ff2b6d58748792d84daa21 Mon Sep 17 00:00:00 2001 From: Elise Dianne Hinkle Date: Thu, 5 Oct 2023 12:22:40 -0700 Subject: [PATCH 17/37] Update TrackletReconstruction.yaml to run over charge/calib_prompt_hits dataset. --- yamls/proto_nd_flow/util/TrackletReconstruction.yaml | 9 +++++---- 1 file changed, 5 insertions(+), 4 deletions(-) diff --git a/yamls/proto_nd_flow/util/TrackletReconstruction.yaml b/yamls/proto_nd_flow/util/TrackletReconstruction.yaml index 6bfe664c..18ee60b3 100644 --- a/yamls/proto_nd_flow/util/TrackletReconstruction.yaml +++ b/yamls/proto_nd_flow/util/TrackletReconstruction.yaml @@ -1,12 +1,13 @@ classname: TrackletReconstruction path: proto_nd_flow.util.tracklet_reco requires: - - 'charge/calib_final_hits' + #- 'charge/calib_final_hits' + - 'charge/calib_prompt_hits' params: # inputs - hits_dset_name: 'charge/calib_final_hits' - charge_dset_name: 'charge/calib_final_hits' - hit_drift_dset_name: 'charge/calib_final_hits' + hits_dset_name: 'charge/calib_prompt_hits' #'charge/calib_final_hits' + charge_dset_name: 'charge/calib_prompt_hits' #'charge/calib_final_hits' + hit_drift_dset_name: 'charge/calib_prompt_hits' #'charge/calib_final_hits' # output tracklet_dset_name: 'combined/tracklets' From e4c9d64ff5a7b6cf041fa8095aec9b9be682b1bc Mon Sep 17 00:00:00 2001 From: Elise Dianne Hinkle Date: Fri, 6 Oct 2023 07:38:51 -0700 Subject: [PATCH 18/37] Add capability to plot tracklets dataset created from calib_prompt_hits. --- .../proto_nd_flow/protondflow_evd.py | 5 ++-- .../protondflow_evd_example.ipynb | 26 ++++++++++--------- 2 files changed, 17 insertions(+), 14 deletions(-) diff --git a/event_display/proto_nd_flow/protondflow_evd.py b/event_display/proto_nd_flow/protondflow_evd.py index 57cec267..49c7769f 100644 --- a/event_display/proto_nd_flow/protondflow_evd.py +++ b/event_display/proto_nd_flow/protondflow_evd.py @@ -26,7 +26,8 @@ class ProtoNDFlowEventDisplay: options are 'raw_hits', 'calib_prompt_hits', and 'calib_final_hits' - tracklets (bool): bool denoting whether or not file contains 'combined/tracklets' dataset; default is False. Right now, tracklets plotting is only set up to plot - with 'calib_final_hits' dataset + with hits dataset from which tracklets were made (either 'calib_prompt_hits' + OR 'calib_final_hits') In order to run the display, set up a Jupyter Notebook, import everything in this file, and execute the run() method, e.g.: @@ -480,7 +481,7 @@ def display_event(self, ev_id): ev_id = event['id'] - if self.tracklets and self.hits_dset == 'calib_final_hits': + if self.tracklets and (self.hits_dset == 'calib_final_hits' or self.hits_dset == 'calib_prompt_hits'): track_ref = self.tracks_ref[self.tracks_region[ev_id,'start']:self.tracks_region[ev_id,'stop']] track_ref = np.sort(track_ref[track_ref[:,0] == ev_id, 1]) tracks = self.tracks[track_ref] diff --git a/event_display/proto_nd_flow/protondflow_evd_example.ipynb b/event_display/proto_nd_flow/protondflow_evd_example.ipynb index 0e009739..f11e19b0 100644 --- a/event_display/proto_nd_flow/protondflow_evd_example.ipynb +++ b/event_display/proto_nd_flow/protondflow_evd_example.ipynb @@ -27,7 +27,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 1, @@ -52,12 +52,12 @@ "- `geometry_file` (str): full path and name of geometry file describing module to be displayed\n", "- `nhits` (int): hit threshold for events to be made available in interactive display (default=1)\n", "- `hits_dset` (str): dataset of hits within the file that you want to display. Options are `raw_hits`, `calib_prompt_hits`, and `calib_final_hits` (default)\n", - "- `tracklets` (bool): boolean denoting whether or not the file contains the `combined/tracklets` dataset. Default is False. " + "- `tracklets` (bool): boolean denoting whether or not the file contains the `combined/tracklets` dataset. Default is False. Only will work if `hits_dset` matches `hits_dset` used to build tracklets." ] }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 4, "id": "d3cc7962-6f70-446b-a4d1-f5f1da4ad23a", "metadata": { "tags": [] @@ -72,7 +72,7 @@ "\n", "# Tracklet testing:\n", "#directory = '/global/cfs/cdirs/dune/users/ehinkle/nd_prototypes_ana/2x2_tracklet_test/'\n", - "#file = 'packet_2022_02_09_17_23_09_CET.module1_flow.proto_nd_flow.TRACKLETS.h5'" + "#file = 'packet_2022_02_09_17_23_09_CET.module1_flow.proto_nd_flow.calib_prompt_hits.TRACKLETS.h5'" ] }, { @@ -98,9 +98,9 @@ "outputs": [ { "data": { - "image/png": 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", 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" + "
" ] }, "metadata": {}, @@ -127,23 +127,25 @@ "name": "stderr", "output_type": "stream", "text": [ - "/global/common/software/nersc/pe/conda-envs/23.9.0/python-3.11/nersc-python/lib/python3.11/site-packages/IPython/core/interactiveshell.py:3516: UserWarning: To exit: use 'exit', 'quit', or Ctrl-D.\n", + "/global/common/software/nersc/pm-2022q3/sw/python/3.9-anaconda-2021.11/lib/python3.9/site-packages/IPython/core/interactiveshell.py:3465: UserWarning: To exit: use 'exit', 'quit', or Ctrl-D.\n", " warn(\"To exit: use 'exit', 'quit', or Ctrl-D.\", stacklevel=1)\n" ] }, { "data": { - "image/png": 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", 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\n", 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" + "
" ] }, - "metadata": {}, + "metadata": { + "needs_background": "light" + }, "output_type": "display_data" } ], "source": [ - "evd = ProtoNDFlowEventDisplay(filedir=directory, filename=file, geometry_file=geometry, nhits=1, hits_dset='calib_final_hits', tracklets=True)\n", + "evd = ProtoNDFlowEventDisplay(filedir=directory, filename=file, geometry_file=geometry, nhits=1, hits_dset='calib_prompt_hits', tracklets=True)\n", "evd.run()" ] }, @@ -172,7 +174,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.11.5" + "version": "3.9.7" } }, "nbformat": 4, From 88b3103e15a17ed8c0fbad6a59373af046477cb7 Mon Sep 17 00:00:00 2001 From: Elise Dianne Hinkle Date: Fri, 6 Oct 2023 07:59:15 -0700 Subject: [PATCH 19/37] Update distance and charge units for proto_nd_flow tracklet reco script. --- src/proto_nd_flow/util/tracklet_reco.py | 30 ++++++++++++------------- 1 file changed, 15 insertions(+), 15 deletions(-) diff --git a/src/proto_nd_flow/util/tracklet_reco.py b/src/proto_nd_flow/util/tracklet_reco.py index dffbdd1f..23793758 100644 --- a/src/proto_nd_flow/util/tracklet_reco.py +++ b/src/proto_nd_flow/util/tracklet_reco.py @@ -21,10 +21,10 @@ class TrackletReconstruction(H5FlowStage): ** NOTE: change in charge field name from module0_flow datasets ("q") to proto_nd_flow calib datasets ("Q") - ``hit_drift_dset_name``: ``str``, path to charge hits drift data ** NOTE: same as hits datasets when using proto_nd_flow calib datasets - - ``dbscan_eps``: ``float``, dbscan epsilon parameter + - ``dbscan_eps``: ``float``, dbscan epsilon parameter [cm] - ``dbscan_min_samples``: ``int``, dbscan min neighbor points to consider as "core" point - ``ransac_min_samples``: ``int``, min points to run ransac algorithm - - ``ransac_residual_threshold``: ``float``, max distance from trial axis + - ``ransac_residual_threshold``: ``float``, max distance from trial axis [cm] - ``ransac_max_trials``: ``int``, number of ransac trials per cluster - ``max_iterations``: ``int``, max number of fitting iterations before giving up - ``max_nhit``: ``int``, skip track fitting on events with greater number of hits, ``None`` to apply no cut @@ -38,20 +38,20 @@ class TrackletReconstruction(H5FlowStage): id u4, unique identifier theta f8, track inclination w.r.t anode phi f8, track orientation w.r.t anode - yp f8, intersection of track with ``y=0,z=0`` plane [mm] - zp f8, intersection of track with ``y=0,z=0`` plane [mm] + yp f8, intersection of track with ``y=0,z=0`` plane [cm] + zp f8, intersection of track with ``y=0,z=0`` plane [cm] nhit i8, number of hits in track - q f8, charge sum [mV] + q f8, charge sum [ke-] ts_start f8, PPS timestamp of track start [crs ticks] ts_end f8, PPS timestamp of track end [crs ticks] - residual f8(3,) average track fit error in (x,y,z) [mm] - length f8 track length [mm] - start f8(3,) track start point (x,y,z) [mm] - end f8(3,) track end point (x,y,z) [mm] - trajectory f8(trajectory_pts, 3,) track approximation points (x,y,z) [mm] - trajectory_residual f8(trajectory_pts-1,) track approximation average error [mm] - dx f8(trajectory_pts-1, 3) track approximation displacement (dx,dy,dz) [mm] - dq f8(trajectory_pts-1,) charge along track displacement [mV] + residual f8(3,) average track fit error in (x,y,z) [cm] + length f8 track length [cm] + start f8(3,) track start point (x,y,z) [cm] + end f8(3,) track end point (x,y,z) [cm] + trajectory f8(trajectory_pts, 3,) track approximation points (x,y,z) [cm] + trajectory_residual f8(trajectory_pts-1,) track approximation average error [cm] + dx f8(trajectory_pts-1, 3) track approximation displacement (dx,dy,dz) [cm] + dq f8(trajectory_pts-1,) charge along track displacement [ke-] dn i8(trajectory_pts-1,) nhit along track displacement ''' @@ -62,10 +62,10 @@ class TrackletReconstruction(H5FlowStage): default_charge_dset_name = 'charge/calib_final_hits' default_hit_drift_dset_name = 'combined/calib_final_hits' - default_dbscan_eps = 25 + default_dbscan_eps = 2.5 default_dbscan_min_samples = 5 default_ransac_min_samples = 2 - default_ransac_residual_threshold = 8 + default_ransac_residual_threshold = 0.8 default_ransac_max_trials = 100 default_max_iterations = 100 default_trajectory_pts = 5 From 636c3137ca84e95e998eb82cc83d723af45dcade Mon Sep 17 00:00:00 2001 From: Elise Dianne Hinkle Date: Fri, 6 Oct 2023 10:10:50 -0700 Subject: [PATCH 20/37] Change proto_nd_flow tracklet reco yaml to reflect updated charge and distance units. --- yamls/proto_nd_flow/util/TrackletReconstruction.yaml | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/yamls/proto_nd_flow/util/TrackletReconstruction.yaml b/yamls/proto_nd_flow/util/TrackletReconstruction.yaml index 18ee60b3..f22395be 100644 --- a/yamls/proto_nd_flow/util/TrackletReconstruction.yaml +++ b/yamls/proto_nd_flow/util/TrackletReconstruction.yaml @@ -13,10 +13,10 @@ params: tracklet_dset_name: 'combined/tracklets' # configuration parameters - dbscan_eps: 25 + dbscan_eps: 2.5 dbscan_min_samples: 5 ransac_min_samples: 2 - ransac_residual_threshold: 8 + ransac_residual_threshold: 0.8 ransac_max_trials: 10 trajectory_pts: 16 trajectory_residual_mode: 1 # 1: shortest distance to the segment ends # 2: shortest distance to the tractory From 5087498220554ab00e1fcaca8d1bebbe0726a65c Mon Sep 17 00:00:00 2001 From: Elise Dianne Hinkle Date: Fri, 13 Oct 2023 11:06:53 -0700 Subject: [PATCH 21/37] Replace DBSCAN with HDBSCAN in tracklet reconstruction. --- .../run_proto_nd_tracklet_reco.sh | 2 +- src/proto_nd_flow/util/tracklet_reco.py | 36 ++++++++++--------- .../util/TrackletReconstruction.yaml | 5 +-- 3 files changed, 23 insertions(+), 20 deletions(-) diff --git a/scripts/proto_nd_scripts/run_proto_nd_tracklet_reco.sh b/scripts/proto_nd_scripts/run_proto_nd_tracklet_reco.sh index 216fbe7d..8702d244 100644 --- a/scripts/proto_nd_scripts/run_proto_nd_tracklet_reco.sh +++ b/scripts/proto_nd_scripts/run_proto_nd_tracklet_reco.sh @@ -10,7 +10,7 @@ OUTPUT_DIR=`pwd` # !!change!! OUTPUT_NAME=(${INPUT_FILE//"/"/ }) OUTPUT_NAME=${OUTPUT_NAME[-1]} OUTPUT_FILE="${OUTPUT_DIR}/${OUTPUT_NAME}" -OUTPUT_FILE=${OUTPUT_FILE//.h5/.proto_nd_flow.TRACKLETS.h5} +OUTPUT_FILE=${OUTPUT_FILE//.h5/.proto_nd_flow.TRACKLETS_HDBSCAN.h5} echo ${OUTPUT_FILE} # for running on a login node diff --git a/src/proto_nd_flow/util/tracklet_reco.py b/src/proto_nd_flow/util/tracklet_reco.py index 23793758..dea37fb9 100644 --- a/src/proto_nd_flow/util/tracklet_reco.py +++ b/src/proto_nd_flow/util/tracklet_reco.py @@ -11,7 +11,7 @@ class TrackletReconstruction(H5FlowStage): ''' Reconstructs "tracklets" or short, collinear track segments from hit - data using DBSCAN and RANSAC. The track direction is estimated using + data using HDBSCAN and RANSAC. The track direction is estimated using a PCA fit. Parameters: @@ -21,8 +21,9 @@ class TrackletReconstruction(H5FlowStage): ** NOTE: change in charge field name from module0_flow datasets ("q") to proto_nd_flow calib datasets ("Q") - ``hit_drift_dset_name``: ``str``, path to charge hits drift data ** NOTE: same as hits datasets when using proto_nd_flow calib datasets - - ``dbscan_eps``: ``float``, dbscan epsilon parameter [cm] - - ``dbscan_min_samples``: ``int``, dbscan min neighbor points to consider as "core" point + - DEPRECATED ``dbscan_eps``: ``float``, dbscan epsilon parameter [cm] + - ``hdbscan_min_samples``: ``int``, hdbscan min neighbor points to consider as "core" point + - ``hdbscan_min_cluster_size``: ``int``, hdbscan min number of points to form a cluster - ``ransac_min_samples``: ``int``, min points to run ransac algorithm - ``ransac_residual_threshold``: ``float``, max distance from trial axis [cm] - ``ransac_max_trials``: ``int``, number of ransac trials per cluster @@ -62,8 +63,9 @@ class TrackletReconstruction(H5FlowStage): default_charge_dset_name = 'charge/calib_final_hits' default_hit_drift_dset_name = 'combined/calib_final_hits' - default_dbscan_eps = 2.5 - default_dbscan_min_samples = 5 + #default_dbscan_eps = 2.5 + default_hdbscan_min_samples = 5 + default_hdbscan_min_cluster_size = 5 default_ransac_min_samples = 2 default_ransac_residual_threshold = 0.8 default_ransac_max_trials = 100 @@ -98,8 +100,8 @@ def __init__(self, **params): self.charge_dset_name = params.get('charge_dset_name', self.default_charge_dset_name) self.hit_drift_dset_name = params.get('hit_drift_dset_name', self.default_hit_drift_dset_name) - self._dbscan_eps = params.get('dbscan_eps', self.default_dbscan_eps) - self._dbscan_min_samples = params.get('dbscan_min_samples', self.default_dbscan_min_samples) + self._hdbscan_min_cluster_size = params.get('hdbscan_min_cluster_size', self.default_hdbscan_min_cluster_size) + self._hdbscan_min_samples = params.get('hdbscan_min_samples', self.default_hdbscan_min_samples) self._ransac_min_samples = params.get('ransac_min_samples', self.default_ransac_min_samples) self._ransac_residual_threshold = params.get('ransac_residual_threshold', self.default_ransac_residual_threshold) self._ransac_max_trials = params.get('ransac_max_trials', self.default_ransac_max_trials) @@ -111,7 +113,7 @@ def __init__(self, **params): self.trajectory_dx = params.get('trajectory_dx', self.default_trajectory_dx) self.tracklet_dtype = self.tracklet_dtype(self.trajectory_pts) - self.dbscan = cluster.DBSCAN(eps=self._dbscan_eps, min_samples=self._dbscan_min_samples) + self.hdbscan = cluster.HDBSCAN(min_cluster_size=self._hdbscan_min_cluster_size, min_samples=self._hdbscan_min_samples, allow_single_cluster=True) def init(self, source_name): super(TrackletReconstruction, self).init(source_name) @@ -122,8 +124,8 @@ def init(self, source_name): hits_dset=self.hits_dset_name, charge_dset=self.charge_dset_name, hit_drift_dset=self.hit_drift_dset_name, - dbscan_eps=self._dbscan_eps, - dbscan_min_samples=self._dbscan_min_samples, + hdbscan_min_cluster_size=self._hdbscan_min_cluster_size, + hdbscan_min_samples=self._hdbscan_min_samples, ransac_min_samples=self._ransac_min_samples, ransac_residual_threshold=self._ransac_residual_threshold, ransac_max_trials=self._ransac_max_trials, @@ -197,7 +199,7 @@ def find_tracks(self, hits): ''' xyz = self.hit_xyz(hits) - # Adding masks where hit coordinate is recorded as nan to enable dbscan + # Adding masks where hit coordinate is recorded as nan to enable hdbscan hits['x'].mask = hits['x'].mask | ma.masked_invalid(hits['x']).mask hits['y'].mask = hits['y'].mask | ma.masked_invalid(hits['y']).mask hits['z'].mask = hits['z'].mask | ma.masked_invalid(hits['z']).mask @@ -213,8 +215,8 @@ def find_tracks(self, hits): current_track_id = -1 for _ in range(self.max_iterations): - # dbscan to find clusters - track_ids = self._do_dbscan(xyz[i], iter_mask[i]) + # hdbscan to find clusters + track_ids = self._do_hdbscan(xyz[i], iter_mask[i]) for id_ in np.unique(track_ids): if id_ == -1: @@ -230,8 +232,8 @@ def find_tracks(self, hits): if np.sum(mask) < 1: continue - # and a final dbscan for re-clustering - final_track_ids = self._do_dbscan(xyz[i], mask) + # and a final hdbscan for re-clustering + final_track_ids = self._do_hdbscan(xyz[i], mask) for id_ in np.unique(final_track_ids): if id_ == -1: @@ -323,7 +325,7 @@ def calc_tracks(cls, hits, hit_q, track_ids, trajectory_pts, trajectory_dx, traj return ma.array(tracks, mask=tracks_mask, shrink=False) - def _do_dbscan(self, xyz, mask): + def _do_hdbscan(self, xyz, mask): ''' :param xyz: ``shape: (N,3)`` array of precomputed 3D distances @@ -335,7 +337,7 @@ def _do_dbscan(self, xyz, mask): #print("XYZ:", xyz) #print("Mask:", mask) #print("XYZ Mask:", xyz[mask]) - clustering = self.dbscan.fit(xyz[mask]) + clustering = self.hdbscan.fit(xyz[mask]) track_ids = np.full(len(mask), -1) track_ids[mask] = clustering.labels_ return track_ids diff --git a/yamls/proto_nd_flow/util/TrackletReconstruction.yaml b/yamls/proto_nd_flow/util/TrackletReconstruction.yaml index f22395be..d9093047 100644 --- a/yamls/proto_nd_flow/util/TrackletReconstruction.yaml +++ b/yamls/proto_nd_flow/util/TrackletReconstruction.yaml @@ -13,8 +13,9 @@ params: tracklet_dset_name: 'combined/tracklets' # configuration parameters - dbscan_eps: 2.5 - dbscan_min_samples: 5 + #dbscan_eps: 2.5 + hdbscan_min_cluster_size: 5 + hdbscan_min_samples: 5 ransac_min_samples: 2 ransac_residual_threshold: 0.8 ransac_max_trials: 10 From b156bc095204dc636af78df629fc470d7e01db97 Mon Sep 17 00:00:00 2001 From: Elise Dianne Hinkle Date: Fri, 13 Oct 2023 13:28:33 -0700 Subject: [PATCH 22/37] Remove use of RANSAC in proto_nd_flow tracklet reconstruction. --- src/proto_nd_flow/util/tracklet_reco.py | 32 ++++++++++++------- .../util/TrackletReconstruction.yaml | 8 ++--- 2 files changed, 25 insertions(+), 15 deletions(-) diff --git a/src/proto_nd_flow/util/tracklet_reco.py b/src/proto_nd_flow/util/tracklet_reco.py index dea37fb9..17aa017b 100644 --- a/src/proto_nd_flow/util/tracklet_reco.py +++ b/src/proto_nd_flow/util/tracklet_reco.py @@ -64,7 +64,7 @@ class TrackletReconstruction(H5FlowStage): default_hit_drift_dset_name = 'combined/calib_final_hits' #default_dbscan_eps = 2.5 - default_hdbscan_min_samples = 5 + default_hdbscan_min_samples = 2 default_hdbscan_min_cluster_size = 5 default_ransac_min_samples = 2 default_ransac_residual_threshold = 0.8 @@ -215,10 +215,17 @@ def find_tracks(self, hits): current_track_id = -1 for _ in range(self.max_iterations): + + if sum(iter_mask[i]) < self._hdbscan_min_samples: + continue # hdbscan to find clusters + + #print("Running first HDBSCAN...") track_ids = self._do_hdbscan(xyz[i], iter_mask[i]) - for id_ in np.unique(track_ids): + #print("First HDBSCAN successful.") + + '''for id_ in np.unique(track_ids): if id_ == -1: continue mask = track_ids == id_ @@ -229,20 +236,23 @@ def find_tracks(self, hits): inliers = self._do_ransac(xyz[i], mask) mask[mask] = inliers - if np.sum(mask) < 1: + if np.sum(mask) < self._hdbscan_min_samples: continue + #print("Running second HDBSCAN...") # and a final hdbscan for re-clustering final_track_ids = self._do_hdbscan(xyz[i], mask) - for id_ in np.unique(final_track_ids): - if id_ == -1: - continue - mask = final_track_ids == id_ + #print("Second HDBSCAN successful.")''' - current_track_id += 1 - track_id[i, mask] = current_track_id - iter_mask[i, mask] = False + final_track_ids = track_ids + for id_ in np.unique(final_track_ids): + if id_ == -1: + continue + mask = final_track_ids == id_ + current_track_id += 1 + track_id[i, mask] = current_track_id + iter_mask[i, mask] = False if np.all(track_ids == -1) or not np.any(iter_mask[i]): break @@ -336,7 +346,7 @@ def _do_hdbscan(self, xyz, mask): #print("XYZ:", xyz) #print("Mask:", mask) - #print("XYZ Mask:", xyz[mask]) + #print("XYZ Mask Shape:", xyz[mask].shape) clustering = self.hdbscan.fit(xyz[mask]) track_ids = np.full(len(mask), -1) track_ids[mask] = clustering.labels_ diff --git a/yamls/proto_nd_flow/util/TrackletReconstruction.yaml b/yamls/proto_nd_flow/util/TrackletReconstruction.yaml index d9093047..79fcb1b7 100644 --- a/yamls/proto_nd_flow/util/TrackletReconstruction.yaml +++ b/yamls/proto_nd_flow/util/TrackletReconstruction.yaml @@ -15,9 +15,9 @@ params: # configuration parameters #dbscan_eps: 2.5 hdbscan_min_cluster_size: 5 - hdbscan_min_samples: 5 - ransac_min_samples: 2 - ransac_residual_threshold: 0.8 - ransac_max_trials: 10 + hdbscan_min_samples: 2 + ransac_min_samples: 3 + ransac_residual_threshold: 1.222 + ransac_max_trials: 30 trajectory_pts: 16 trajectory_residual_mode: 1 # 1: shortest distance to the segment ends # 2: shortest distance to the tractory From aa2c58627ec4b82a19609593e976ed623056f13f Mon Sep 17 00:00:00 2001 From: Elise Dianne Hinkle Date: Mon, 16 Oct 2023 06:36:28 -0700 Subject: [PATCH 23/37] Add RANSAC and additional run of HDBSCAN back into proto_nd_flow tracklet reco. --- .../run_proto_nd_tracklet_reco.sh | 2 +- src/proto_nd_flow/util/tracklet_reco.py | 19 +++++++++---------- .../util/TrackletReconstruction.yaml | 7 ++++--- 3 files changed, 14 insertions(+), 14 deletions(-) diff --git a/scripts/proto_nd_scripts/run_proto_nd_tracklet_reco.sh b/scripts/proto_nd_scripts/run_proto_nd_tracklet_reco.sh index 8702d244..c91c49db 100644 --- a/scripts/proto_nd_scripts/run_proto_nd_tracklet_reco.sh +++ b/scripts/proto_nd_scripts/run_proto_nd_tracklet_reco.sh @@ -10,7 +10,7 @@ OUTPUT_DIR=`pwd` # !!change!! OUTPUT_NAME=(${INPUT_FILE//"/"/ }) OUTPUT_NAME=${OUTPUT_NAME[-1]} OUTPUT_FILE="${OUTPUT_DIR}/${OUTPUT_NAME}" -OUTPUT_FILE=${OUTPUT_FILE//.h5/.proto_nd_flow.TRACKLETS_HDBSCAN.h5} +OUTPUT_FILE=${OUTPUT_FILE//.h5/.proto_nd_flow.TRACKLETS_HDBSCAN_RANSAC.h5} echo ${OUTPUT_FILE} # for running on a login node diff --git a/src/proto_nd_flow/util/tracklet_reco.py b/src/proto_nd_flow/util/tracklet_reco.py index 17aa017b..ccfb9852 100644 --- a/src/proto_nd_flow/util/tracklet_reco.py +++ b/src/proto_nd_flow/util/tracklet_reco.py @@ -225,7 +225,7 @@ def find_tracks(self, hits): #print("First HDBSCAN successful.") - '''for id_ in np.unique(track_ids): + for id_ in np.unique(track_ids): if id_ == -1: continue mask = track_ids == id_ @@ -245,16 +245,15 @@ def find_tracks(self, hits): #print("Second HDBSCAN successful.")''' - final_track_ids = track_ids - for id_ in np.unique(final_track_ids): - if id_ == -1: - continue - mask = final_track_ids == id_ - current_track_id += 1 - track_id[i, mask] = current_track_id - iter_mask[i, mask] = False + for id_ in np.unique(final_track_ids): + if id_ < 0: + continue + mask = final_track_ids == id_ + current_track_id += 1 + track_id[i, mask] = current_track_id + iter_mask[i, mask] = False - if np.all(track_ids == -1) or not np.any(iter_mask[i]): + if np.all(track_ids < 0) or not np.any(iter_mask[i]): break return ma.array(track_id, mask=hits['id'].mask, shrink=False) diff --git a/yamls/proto_nd_flow/util/TrackletReconstruction.yaml b/yamls/proto_nd_flow/util/TrackletReconstruction.yaml index 79fcb1b7..770a5bd0 100644 --- a/yamls/proto_nd_flow/util/TrackletReconstruction.yaml +++ b/yamls/proto_nd_flow/util/TrackletReconstruction.yaml @@ -14,9 +14,10 @@ params: # configuration parameters #dbscan_eps: 2.5 - hdbscan_min_cluster_size: 5 - hdbscan_min_samples: 2 - ransac_min_samples: 3 + max_iterations: 10 + hdbscan_min_cluster_size: 10 + hdbscan_min_samples: 5 + ransac_min_samples: 2 ransac_residual_threshold: 1.222 ransac_max_trials: 30 trajectory_pts: 16 From 5a14bdf302c014d97d321cca067a6c292dd98bd9 Mon Sep 17 00:00:00 2001 From: Elise Dianne Hinkle Date: Tue, 17 Oct 2023 11:03:10 -0700 Subject: [PATCH 24/37] Update HDBSCAN parameters which can be customized in yaml for tracklet reco. --- .../run_proto_nd_tracklet_reco.sh | 2 +- src/proto_nd_flow/util/tracklet_reco.py | 71 ++++++++++--------- .../util/TrackletReconstruction.yaml | 11 +-- 3 files changed, 46 insertions(+), 38 deletions(-) diff --git a/scripts/proto_nd_scripts/run_proto_nd_tracklet_reco.sh b/scripts/proto_nd_scripts/run_proto_nd_tracklet_reco.sh index c91c49db..4f625df3 100644 --- a/scripts/proto_nd_scripts/run_proto_nd_tracklet_reco.sh +++ b/scripts/proto_nd_scripts/run_proto_nd_tracklet_reco.sh @@ -10,7 +10,7 @@ OUTPUT_DIR=`pwd` # !!change!! OUTPUT_NAME=(${INPUT_FILE//"/"/ }) OUTPUT_NAME=${OUTPUT_NAME[-1]} OUTPUT_FILE="${OUTPUT_DIR}/${OUTPUT_NAME}" -OUTPUT_FILE=${OUTPUT_FILE//.h5/.proto_nd_flow.TRACKLETS_HDBSCAN_RANSAC.h5} +OUTPUT_FILE=${OUTPUT_FILE//.h5/.proto_nd_flow.TRACKLETS_HDBSCAN_1_20_20_2488.h5} echo ${OUTPUT_FILE} # for running on a login node diff --git a/src/proto_nd_flow/util/tracklet_reco.py b/src/proto_nd_flow/util/tracklet_reco.py index ccfb9852..3625de44 100644 --- a/src/proto_nd_flow/util/tracklet_reco.py +++ b/src/proto_nd_flow/util/tracklet_reco.py @@ -24,6 +24,7 @@ class TrackletReconstruction(H5FlowStage): - DEPRECATED ``dbscan_eps``: ``float``, dbscan epsilon parameter [cm] - ``hdbscan_min_samples``: ``int``, hdbscan min neighbor points to consider as "core" point - ``hdbscan_min_cluster_size``: ``int``, hdbscan min number of points to form a cluster + - ``hdbscan_cluster_sel_eps``: ``int``, hdbscan threshold value [cm] clusters below this size may be merged using DBSCAN* algorithm - ``ransac_min_samples``: ``int``, min points to run ransac algorithm - ``ransac_residual_threshold``: ``float``, max distance from trial axis [cm] - ``ransac_max_trials``: ``int``, number of ransac trials per cluster @@ -66,6 +67,7 @@ class TrackletReconstruction(H5FlowStage): #default_dbscan_eps = 2.5 default_hdbscan_min_samples = 2 default_hdbscan_min_cluster_size = 5 + default_hdbscan_cluster_sel_eps = 5 default_ransac_min_samples = 2 default_ransac_residual_threshold = 0.8 default_ransac_max_trials = 100 @@ -102,6 +104,7 @@ def __init__(self, **params): self._hdbscan_min_cluster_size = params.get('hdbscan_min_cluster_size', self.default_hdbscan_min_cluster_size) self._hdbscan_min_samples = params.get('hdbscan_min_samples', self.default_hdbscan_min_samples) + self._hdbscan_cluster_sel_eps = params.get('hdbscan_cluster_sel_eps', self.default_hdbscan_cluster_sel_eps) self._ransac_min_samples = params.get('ransac_min_samples', self.default_ransac_min_samples) self._ransac_residual_threshold = params.get('ransac_residual_threshold', self.default_ransac_residual_threshold) self._ransac_max_trials = params.get('ransac_max_trials', self.default_ransac_max_trials) @@ -113,7 +116,10 @@ def __init__(self, **params): self.trajectory_dx = params.get('trajectory_dx', self.default_trajectory_dx) self.tracklet_dtype = self.tracklet_dtype(self.trajectory_pts) - self.hdbscan = cluster.HDBSCAN(min_cluster_size=self._hdbscan_min_cluster_size, min_samples=self._hdbscan_min_samples, allow_single_cluster=True) + self.hdbscan = cluster.HDBSCAN(min_cluster_size=self._hdbscan_min_cluster_size, \ + min_samples=self._hdbscan_min_samples, \ + cluster_selection_epsilon = self._hdbscan_cluster_sel_eps, \ + allow_single_cluster=True) def init(self, source_name): super(TrackletReconstruction, self).init(source_name) @@ -221,39 +227,40 @@ def find_tracks(self, hits): # hdbscan to find clusters #print("Running first HDBSCAN...") - track_ids = self._do_hdbscan(xyz[i], iter_mask[i]) - - #print("First HDBSCAN successful.") - - for id_ in np.unique(track_ids): - if id_ == -1: - continue - mask = track_ids == id_ - if np.sum(mask) <= self._ransac_min_samples: + #track_ids = self._do_hdbscan(xyz[i], iter_mask[i]) +# + ##print("First HDBSCAN successful.") +# + #for id_ in np.unique(track_ids): + # if id_ == -1: + # continue + # mask = track_ids == id_ + # if np.sum(mask) <= self._ransac_min_samples: + # continue +# + # # ransac for collinear hits + # inliers = self._do_ransac(xyz[i], mask) + # #mask[mask] = inliers + # iter_mask[i, mask] = inliers +# + #if np.sum(iter_mask[i]) < self._hdbscan_min_samples: + # continue + + #print("Running second HDBSCAN...") + # and a final hdbscan for re-clustering + final_track_ids = self._do_hdbscan(xyz[i], iter_mask[i]) + + #print("Second HDBSCAN successful.")''' + + for id_ in np.unique(final_track_ids): + if id_ < 0: continue + mask = final_track_ids == id_ + current_track_id += 1 + track_id[i, mask] = current_track_id + iter_mask[i, mask] = False - # ransac for collinear hits - inliers = self._do_ransac(xyz[i], mask) - mask[mask] = inliers - - if np.sum(mask) < self._hdbscan_min_samples: - continue - - #print("Running second HDBSCAN...") - # and a final hdbscan for re-clustering - final_track_ids = self._do_hdbscan(xyz[i], mask) - - #print("Second HDBSCAN successful.")''' - - for id_ in np.unique(final_track_ids): - if id_ < 0: - continue - mask = final_track_ids == id_ - current_track_id += 1 - track_id[i, mask] = current_track_id - iter_mask[i, mask] = False - - if np.all(track_ids < 0) or not np.any(iter_mask[i]): + if np.all(final_track_ids < 0) or not np.any(iter_mask[i]): break return ma.array(track_id, mask=hits['id'].mask, shrink=False) diff --git a/yamls/proto_nd_flow/util/TrackletReconstruction.yaml b/yamls/proto_nd_flow/util/TrackletReconstruction.yaml index 770a5bd0..8512a413 100644 --- a/yamls/proto_nd_flow/util/TrackletReconstruction.yaml +++ b/yamls/proto_nd_flow/util/TrackletReconstruction.yaml @@ -14,11 +14,12 @@ params: # configuration parameters #dbscan_eps: 2.5 - max_iterations: 10 - hdbscan_min_cluster_size: 10 - hdbscan_min_samples: 5 + max_iterations: 1 + hdbscan_min_cluster_size: 20 + hdbscan_min_samples: 20 + hdbscan_cluster_sel_eps: 2.488 ransac_min_samples: 2 - ransac_residual_threshold: 1.222 - ransac_max_trials: 30 + ransac_residual_threshold: 2.444 + ransac_max_trials: 5 trajectory_pts: 16 trajectory_residual_mode: 1 # 1: shortest distance to the segment ends # 2: shortest distance to the tractory From 4929458a33f78906661b3c0f11a14f1c544f731e Mon Sep 17 00:00:00 2001 From: Elise Dianne Hinkle Date: Thu, 19 Oct 2023 11:37:24 -0700 Subject: [PATCH 25/37] Final changes to proto_nd_flow tracklet display notebook. --- .../protondflow_evd_example.ipynb | 22 +++++++++---------- 1 file changed, 11 insertions(+), 11 deletions(-) diff --git a/event_display/proto_nd_flow/protondflow_evd_example.ipynb b/event_display/proto_nd_flow/protondflow_evd_example.ipynb index f11e19b0..e1d84e2d 100644 --- a/event_display/proto_nd_flow/protondflow_evd_example.ipynb +++ b/event_display/proto_nd_flow/protondflow_evd_example.ipynb @@ -18,7 +18,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 16, "id": "ab903276-e787-4142-bbb1-4becf42f76c1", "metadata": { "tags": [] @@ -27,10 +27,10 @@ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 1, + "execution_count": 16, "metadata": {}, "output_type": "execute_result" } @@ -57,7 +57,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 33, "id": "d3cc7962-6f70-446b-a4d1-f5f1da4ad23a", "metadata": { "tags": [] @@ -66,13 +66,13 @@ "source": [ "# This set of inputs allows you to look at a Module1 charge-only file\n", "# This file originates from the same raw data file as the input file in the Module0FlowEventDisplay example\n", - "directory = '/global/cfs/cdirs/dune/www/data/Module1/TPC12/reflow-test'\n", - "file = 'packet_2022_02_09_17_23_09_CET.module1_flow.h5'\n", + "#directory = '/global/cfs/cdirs/dune/www/data/Module1/TPC12/reflow-test'\n", + "#file = 'packet_2022_02_09_17_23_09_CET.module1_flow.h5'\n", "geometry = '/global/cfs/cdirs/dune/www/data/Module1/TPC12/module1_layout-2.3.16.yaml'\n", "\n", "# Tracklet testing:\n", - "#directory = '/global/cfs/cdirs/dune/users/ehinkle/nd_prototypes_ana/2x2_tracklet_test/'\n", - "#file = 'packet_2022_02_09_17_23_09_CET.module1_flow.proto_nd_flow.calib_prompt_hits.TRACKLETS.h5'" + "#directory = '/path/to/file/with/tracklets'\n", + "#file = 'file/with/tracklets'" ] }, { @@ -90,7 +90,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 34, "id": "b67d336e-f49f-448a-a580-c3affc5689a0", "metadata": { "tags": [] @@ -98,7 +98,7 @@ "outputs": [ { "data": { - "image/png": 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\n", + "image/png": 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\n", 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\n", + "image/png": 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\n", "text/plain": [ "
" ] From a6757b3e62b56d0efc2af97be77d512edb51fad6 Mon Sep 17 00:00:00 2001 From: Elise Dianne Hinkle Date: Mon, 6 Nov 2023 09:30:04 -0800 Subject: [PATCH 26/37] Add HIP selection code to proto_nd_flow. --- .../protondflow_evd_example.ipynb | 53 +- .../run_proto_nd_hip_selection.sh | 41 + .../run_proto_nd_tracklet_reco.sh | 2 +- src/proto_nd_flow/analysis/hip_selection.py | 1218 +++++++++++++++++ .../proto_nd_flow/analysis/hip_selection.yaml | 36 + .../util/TrackletReconstruction.yaml | 16 +- .../workflows/analysis/hip_sel_workflow.yaml | 22 + 7 files changed, 1334 insertions(+), 54 deletions(-) create mode 100644 scripts/proto_nd_scripts/run_proto_nd_hip_selection.sh create mode 100644 src/proto_nd_flow/analysis/hip_selection.py create mode 100644 yamls/proto_nd_flow/analysis/hip_selection.yaml create mode 100644 yamls/proto_nd_flow/workflows/analysis/hip_sel_workflow.yaml diff --git a/event_display/proto_nd_flow/protondflow_evd_example.ipynb b/event_display/proto_nd_flow/protondflow_evd_example.ipynb index e1d84e2d..a86caa47 100644 --- a/event_display/proto_nd_flow/protondflow_evd_example.ipynb +++ b/event_display/proto_nd_flow/protondflow_evd_example.ipynb @@ -18,7 +18,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 1, "id": "ab903276-e787-4142-bbb1-4becf42f76c1", "metadata": { "tags": [] @@ -27,10 +27,10 @@ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 16, + "execution_count": 1, "metadata": {}, "output_type": "execute_result" } @@ -57,7 +57,7 @@ }, { "cell_type": "code", - "execution_count": 33, + "execution_count": 4, "id": "d3cc7962-6f70-446b-a4d1-f5f1da4ad23a", "metadata": { "tags": [] @@ -71,8 +71,8 @@ "geometry = '/global/cfs/cdirs/dune/www/data/Module1/TPC12/module1_layout-2.3.16.yaml'\n", "\n", "# Tracklet testing:\n", - "#directory = '/path/to/file/with/tracklets'\n", - "#file = 'file/with/tracklets'" + "directory = '/global/cfs/cdirs/dune/users/ehinkle/nd_prototypes_ana/file_testing/2x2_tracklet_test/'\n", + "file = 'packet_2022_02_09_17_23_09_CET.module1_flow.proto_nd_flow.calib_prompt_hits.TRACKLETS_HDBSCAN_1_15_9_3421.h5'" ] }, { @@ -90,7 +90,7 @@ }, { "cell_type": "code", - "execution_count": 34, + "execution_count": null, "id": "b67d336e-f49f-448a-a580-c3affc5689a0", "metadata": { "tags": [] @@ -98,50 +98,13 @@ "outputs": [ { "data": { - "image/png": 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\n", 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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" - }, - { - "name": "stdin", - "output_type": "stream", - "text": [ - "Next event (q to exit/s to save to pdf/enter for next/number to skip to position)?\n", - " q\n" - ] - }, - { - "ename": "SystemExit", - "evalue": "", - "output_type": "error", - "traceback": [ - "An exception has occurred, use %tb to see the full traceback.\n", - "\u001b[0;31mSystemExit\u001b[0m\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/global/common/software/nersc/pm-2022q3/sw/python/3.9-anaconda-2021.11/lib/python3.9/site-packages/IPython/core/interactiveshell.py:3465: UserWarning: To exit: use 'exit', 'quit', or Ctrl-D.\n", - " warn(\"To exit: use 'exit', 'quit', or Ctrl-D.\", stacklevel=1)\n" - ] - }, - { - "data": { - "image/png": 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" } ], "source": [ diff --git a/scripts/proto_nd_scripts/run_proto_nd_hip_selection.sh b/scripts/proto_nd_scripts/run_proto_nd_hip_selection.sh new file mode 100644 index 00000000..537b29b7 --- /dev/null +++ b/scripts/proto_nd_scripts/run_proto_nd_hip_selection.sh @@ -0,0 +1,41 @@ +#!/bin/bash +# Runs proto_nd_flow HIP selection on an example file. +# Before using this script, use +# >> source get_proto_nd_input.sh +# to download all the necessary inputs into the correct directories +# +INPUT_FILE=$1 + +OUTPUT_DIR=`pwd` # !!change!! +OUTPUT_NAME=(${INPUT_FILE//"/"/ }) +OUTPUT_NAME=${OUTPUT_NAME[-1]} +OUTPUT_FILE="${OUTPUT_DIR}/${OUTPUT_NAME}" +OUTPUT_FILE=${OUTPUT_FILE//.h5/.proto_nd_flow.HIP_SEL.h5} +echo ${OUTPUT_FILE} + +# for running on a login node +H5FLOW_CMD='h5flow' +# for running on a single compute node with 32 cores +#H5FLOW_CMD='srun -n32 h5flow' + +# run all stages +WORKFLOW1='yamls/proto_nd_flow/workflows/analysis/hip_sel_workflow.yaml' + +HERE=`pwd` +#cd ndlar_flow +# assumes this is being run from ndlar_flow/scripts/proto_nd_flow: +cd ../../ + +# avoid being asked if we want to overwrite the file if it exists. +# this is us answering "yes". +if [ -e $OUTPUT_FILE ]; then + rm -i $OUTPUT_FILE +fi + +$H5FLOW_CMD -c $WORKFLOW1 -i $INPUT_FILE -o $OUTPUT_FILE + +echo "Done!" +echo "Output can be found at $OUTPUT_FILE" + +cd ${HERE} + diff --git a/scripts/proto_nd_scripts/run_proto_nd_tracklet_reco.sh b/scripts/proto_nd_scripts/run_proto_nd_tracklet_reco.sh index 4f625df3..f5941f8f 100644 --- a/scripts/proto_nd_scripts/run_proto_nd_tracklet_reco.sh +++ b/scripts/proto_nd_scripts/run_proto_nd_tracklet_reco.sh @@ -10,7 +10,7 @@ OUTPUT_DIR=`pwd` # !!change!! OUTPUT_NAME=(${INPUT_FILE//"/"/ }) OUTPUT_NAME=${OUTPUT_NAME[-1]} OUTPUT_FILE="${OUTPUT_DIR}/${OUTPUT_NAME}" -OUTPUT_FILE=${OUTPUT_FILE//.h5/.proto_nd_flow.TRACKLETS_HDBSCAN_1_20_20_2488.h5} +OUTPUT_FILE=${OUTPUT_FILE//.h5/.proto_nd_flow.TRACKLETS_HDBSCAN_1_15_9_3421.h5} echo ${OUTPUT_FILE} # for running on a login node diff --git a/src/proto_nd_flow/analysis/hip_selection.py b/src/proto_nd_flow/analysis/hip_selection.py new file mode 100644 index 00000000..d0d98d0c --- /dev/null +++ b/src/proto_nd_flow/analysis/hip_selection.py @@ -0,0 +1,1218 @@ +import numpy as np +import numpy.ma as ma +import logging +from scipy.interpolate import interp1d, pchip_interpolate +import scipy.integrate as integrate +import scipy.stats as stats +import scipy.ndimage as ndimage +import scipy.optimize as optimize +from copy import deepcopy + +# Need to have both h5flow and ndlar-flow installed +from h5flow.core import H5FlowStage, resources +from h5flow.data import dereference_chain + +from module0_flow.util.func import mode, condense_array +import proto_nd_flow.util.units as units + +class HIPSelection(H5FlowStage): + ''' + Perform a selection for highly ionizing particles. A HIP event is defined + using the following criteria: + - + - + - + Creates a boolean array of 1:1 with events indicating HIP events, and + creates a boolean array 1:1 with "tracklets if they individually meet the + HIP criteria. + + NOT CURRENTLY IMPLEMENTED: + A dQ/dx profile is generated per event and used to discriminate + stopping protons and muons, as well as through-going muons. + + If the file is a MC file, also generates boolean arrays with the true + value. + + ''' + class_version = '2.0.0' # change for getting around assertion error in geometry files + + default_params = dict( + fid_cut=1.0, # cm + cathode_fid_cut=1.0, # cm + anode_fid_cut=1.0, # cm + profile_dx=2.2, # cm + profile_max_range=200.0, # cm + larpix_gain=250, # e/mV + larpix_noise=5000, # e/cm + proton_classifier_cut=-1.0, + muon_classifier_cut=-1.0, + dqdx_peak_cut=5e4, # e/cm + profile_search_dx=2.2, # cm + remaining_e_cut=85e9, # keV + + curvature_rr_correction=22.6647 / 22, + density_dx_correction_params=[0.78497819, -3.41826874, 198.93022888], + + hits_dset_name='charge/calib_prompt_hits', # '/data' directory may not be necessary ... unclear + hit_drift_dset_name='charge/calib_prompt_hits', # TO DO: Calibrate for electron lifetime + tracklet_dset_name='combined/tracklets', #/merged', # no merged part? + t0_dset_name='combined/t0', # + ext_trigs_dset_name='charge/ext_trigs', + truth_trajectories_dset_name='mc_truth/trajectories', + charge_dset_name = 'charge/calib_prompt_hits', + path='high_purity_sel/hips') # path within hdf5 file vs. file path + + sel_dset_name = 'sel_reco' + sel_truth_dset_name = 'sel_truth' + #event_tracks_dset_name = 'event_tracks_reco' + #event_hits_dset_name = 'event_hits_reco' + + sel_dtype = np.dtype([('sel', 'u1'), + ('hip', 'f8'), + ('pdg_id', 'f8',(1000,)), + ('nhits_over_thresh', 'f8'), + ('event_id', 'f8'), + ('ntracks', 'f8')]) + #('max_dqdx', 'f4'), + #('muon_loglikelihood_mean', 'f8'), + #('proton_loglikelihood_mean', 'f8'), + #('mip_loglikelihood_mean', 'f8'), + #('pid_muon_proton', 'f8'), + #('pid_mip_proton', 'f8')]) + + + def __init__(self, **params): + super(HIPSelection, self).__init__(**params) + + for key,val in self.default_params.items(): + setattr(self, key, params.get(key, val)) + + self.curvature_rr_correction = params.get('curvature_rr_correction', dict()) + self.density_dx_correction_params = params.get('density_dx_correction_params', dict()) + self.larpix_gain = params.get('larpix_gain', dict()) + + + def init(self, source_name): + super(HIPSelection, self).init(source_name) + + self.is_mc = resources['RunData'].is_mc + correction_key = ('medm') + correction_key = ('mc' if self.is_mc + else 'medm') + correction_key = ('high' if (not self.is_mc + and resources['RunData'].charge_thresholds == 'high') + else correction_key) + self.curvature_rr_correction = self.curvature_rr_correction.get(correction_key, self.default_params['curvature_rr_correction']) + self.density_dx_correction_params = self.density_dx_correction_params.get(correction_key, self.default_params['density_dx_correction_params']) + self.larpix_gain = self.larpix_gain.get(correction_key, self.default_params['larpix_gain']) + + attrs = dict() + for key in self.default_params: + attrs[key] = getattr(self, key) + #print(attrs) + self.data_manager.set_attrs(self.path, + classname=self.classname, + class_version=self.class_version, + **attrs) + self.data_manager.create_dset(f'{self.path}/{self.sel_dset_name}', + self.sel_dtype) + #self.data_manager.create_dset(f'{self.path}/{self.event_tracks_dset_name}', + # self.event_tracks_dtype) + #self.data_manager.create_dset(f'{self.path}/{self.hit_profile_dset_name}', + # self.hit_profile_dtype) + #self.data_manager.create_ref(f'{self.path}/{self.hit_profile_dset_name}', self.hits_dset_name) + if self.is_mc: + self.data_manager.create_dset(f'{self.path}/{self.sel_truth_dset_name}', + self.sel_dtype) + + #self.create_dqdx_profile_templates() + #self.data_manager.set_attrs(self.path, + # proton_dqdx=self.proton_range_table['dqdx'], + # muon_dqdx=self.muon_range_table['dqdx'], + # proton_dqdx_width=self.proton_range_table['dqdx_width'], + # muon_dqdx_width=self.muon_range_table['dqdx_width'], + # proton_dedx=self.proton_range_table['dedx_mpv'], + # muon_dedx=self.muon_range_table['dedx_mpv'], + # proton_range=self.proton_range_table['range'], + # muon_range=self.muon_range_table['range'], + # proton_recom=self.proton_range_table['recomb'], + # muon_recom=self.muon_range_table['recomb']) + + def finish(self, source_name): + super(HIPSelection, self).finish(source_name) + sel_dset_name = f'{self.path}/{self.sel_dset_name}' + + if self.rank == 0: + total = len(self.data_manager.get_dset(sel_dset_name)) + #min_tracks = np.min(self.data_manager.get_dset(sel_dset_name)['ntracks']) + #print("Minimum tracks in an event:", min_tracks) + #max_tracks = np.max(self.data_manager.get_dset(sel_dset_name)['ntracks']) + #print("Maximum tracks in an event:", max_tracks) +# + #min_hits = np.min(self.data_manager.get_dset(sel_dset_name)['nhits']) + #print("Minimum hits in an event:", min_hits) + #max_hits = np.max(self.data_manager.get_dset(sel_dset_name)['nhits']) + #print("Maximum hits in an event:", max_hits) +# + #min_charge = np.min(self.data_manager.get_dset(sel_dset_name)['event_charge']) + #print("Minimum charge in an event:", min_charge) + #max_charge = np.max(self.data_manager.get_dset(sel_dset_name)['event_charge']) + #print("Maximum charge in an event:", max_charge) +# + #min_ext_trigs = np.min(self.data_manager.get_dset(sel_dset_name)['next_trigs']) + #print("Minimum ext_trigs in an event:", min_ext_trigs) + #max_ext_trigs = np.max(self.data_manager.get_dset(sel_dset_name)['next_trigs']) + #print("Maximum ext_trigs in an event:", max_ext_trigs) + + #nstopping = np.sum(self.data_manager.get_dset(sel_dset_name)['stop']) + nselected = np.sum(self.data_manager.get_dset(sel_dset_name)['sel']) + #print(f'Stopping: {nstopping} / {total} ({nstopping/total:0.03f})') + print(f'Selected: {nselected} / {total} ({nselected/total:0.03f})') + sel_events_mask = self.data_manager.get_dset(sel_dset_name)['sel'] == 1 + sel_events = self.data_manager.get_dset(sel_dset_name)[sel_events_mask]['event_id'] + print("Sample events:", sel_events) + + if self.is_mc: + sel_truth_dset_name = f'{self.path}/{self.sel_truth_dset_name}' + true_proton = np.sum(self.data_manager.get_dset(sel_truth_dset_name)['hip']) + true_contained_proton = np.sum(self.data_manager.get_dset(sel_truth_dset_name)['sel']) + print(f'True protons: {true_proton} / {total} ({true_proton/total:0.03f})') + print(f'True contained proton events: {true_contained_proton} / {total} ({true_contained_proton/total:0.03f})') + correct = np.sum(self.data_manager.get_dset(sel_truth_dset_name)['sel'] & + self.data_manager.get_dset(sel_dset_name)['sel']) + print(f'Purity: {correct} / {nselected} ({correct/nselected:0.03f})') + print(f'Efficiency: {correct} / {true_contained_proton} ({correct/true_contained_proton:0.03f})') +############ START OF CODE PORTED FROM STOPPING MUON CODE FOR PID + + + + + def create_dqdx_profile_templates(self): + # create range tables used for dQ/dx profile discrimination + self.muon_range_table = dict() + self.proton_range_table = dict() + + # only consider reasonable range values + muon_mask = resources['ParticleData'].muon_range_table['range'] > 0.1 + for key, val in deepcopy(resources['ParticleData'].muon_range_table).items(): + self.muon_range_table[key] = val[muon_mask] + proton_mask = resources['ParticleData'].proton_range_table['range'] > 0.1 + for key, val in deepcopy(resources['ParticleData'].proton_range_table).items(): + self.proton_range_table[key] = val[proton_mask] + + # convert mean dE/dx entries to MPV dE/dx + self.muon_range_table['dedx_mpv'] = resources['ParticleData'].landau_peak( + self.muon_range_table['t'], resources['ParticleData'].mu_mass, + self.profile_dx) / self.profile_dx + self.proton_range_table['dedx_mpv'] = resources['ParticleData'].landau_peak( + self.proton_range_table['t'], resources['ParticleData'].p_mass, + self.profile_dx) / self.profile_dx + + # calculate recombination correction + muon_r = resources['LArData'].ionization_recombination( + self.muon_range_table['dedx_mpv']) + proton_r = resources['LArData'].ionization_recombination( + self.proton_range_table['dedx_mpv']) + w = resources['LArData'].ionization_w + self.muon_range_table['recomb'] = muon_r + self.proton_range_table['recomb'] = proton_r + + self.muon_range_table['dqdx'] = (muon_r * self.muon_range_table['dedx_mpv'] / w) + self.proton_range_table['dqdx'] = (proton_r * self.proton_range_table['dedx_mpv'] / w) + self.muon_range_table['dqdx_width'] = ( + muon_r / w * resources['ParticleData'].landau_width(self.muon_range_table['t'], + resources['ParticleData'].mu_mass, + self.profile_dx) / self.profile_dx) + self.proton_range_table['dqdx_width'] = ( + proton_r / w * resources['ParticleData'].landau_width(self.proton_range_table['t'], + resources['ParticleData'].p_mass, + self.profile_dx) / self.profile_dx) + noise = (self.larpix_noise * np.sqrt(self.profile_dx / resources['Geometry'].pixel_pitch) + / resources['Geometry'].pixel_pitch) + post_dedx = resources['ParticleData'].landau_peak(50 * units.MeV, + resources['ParticleData'].e_mass, + self.profile_dx) / self.profile_dx + post_dedx_width = resources['ParticleData'].landau_width(50 * units.MeV, + resources['ParticleData'].e_mass, + self.profile_dx) / self.profile_dx + self.muon_range_table['post_dqdx'] = post_dedx * resources['LArData'].ionization_recombination(post_dedx) / w + self.proton_range_table['post_dqdx'] = 1 + self.muon_range_table['post_dqdx_width'] = post_dedx_width * resources['LArData'].ionization_recombination(post_dedx) / w + self.proton_range_table['post_dqdx_width'] = 1 + + self.muon_range_table['mcs_angle'] = resources['ParticleData'].mcs_angle(self.muon_range_table['t'], + resources['ParticleData'].mu_mass, + self.profile_dx) + self.proton_range_table['mcs_angle'] = resources['ParticleData'].mcs_angle(self.proton_range_table['t'], + resources['ParticleData'].p_mass, + self.profile_dx) + self.muon_range_table['post_mcs_angle'] = resources['ParticleData'].mcs_angle(50 * units.MeV, + resources['ParticleData'].e_mass, + self.profile_dx) + self.proton_range_table['post_mcs_angle'] = 1e-9 + + self.muon_range_table['dqdx_gaus_width'] = self.larpix_noise + self.proton_range_table['dqdx_gaus_width'] = self.larpix_noise + + #self.apply_position_resolution(self.muon_range_table, noise=noise) + #self.apply_position_resolution(self.proton_range_table, noise=noise) + + def apply_position_resolution(self, range_table, noise=0): + ''' Update the range table ``dqdx`` and ``dqdx_width`` by smearing the range values by a gaussian ``profile_dx`` ''' + # interpolate dQ/dx MPV and width to apply a gaussian smear + interpolation_pts, dx = np.linspace(-500, 2000, 10 * int(2500 / self.profile_dx), + retstep=True) + + # interpolate central value + rr = np.r_[-5000, 0, range_table['range']] + dqdx = np.r_[0, 0, range_table['dqdx']] + dqdx_width = np.r_[0, 0, range_table['dqdx_width']] + interp_rr = interp1d(rr, dqdx) + dqdx = interp_rr(interpolation_pts) + # apply a position resolution smearing + dqdx_smear = ndimage.uniform_filter(dqdx, int(self.profile_dx / dx), mode='nearest') +# dqdx_smear = ndimage.uniform_filter(dqdx, 1, mode='nearest') + + # interpolate width + interp_rr_width = interp1d(rr, dqdx_width) + dqdx_width = interp_rr_width(interpolation_pts) + # combine position resolution, intrinsic width, and noise contributions + dqdx_width = np.sqrt( + # ndimage.uniform_filtein_fid(self, xyz, cathode_fid=0.0, field_cage_fid=0.0)inr(np.abs(ndimage.convolve(dqdx * dx, [-1, 1], mode='nearest')), int(self.profile_dx / dx), mode='nearest')**2 + ndimage.uniform_filter(np.abs(ndimage.convolve(dqdx * dx, [0], mode='nearest')), 1, mode='nearest')**2 + + dqdx_width**2 + + noise**2) + + # re-align to max + high_val_align = interpolation_pts[np.argmax(dqdx_smear + dqdx_width)] + high_val_interp = interp1d(interpolation_pts - high_val_align, + dqdx_smear + dqdx_width) + low_val_align = interpolation_pts[np.argmax(dqdx_smear - dqdx_width)] + low_val_interp = interp1d(interpolation_pts - low_val_align, + dqdx_smear - dqdx_width) + high_val_interp, low_val_interp = (np.maximum(high_val_interp, low_val_interp), np.minimum(high_val_interp, low_val_interp)) + + # set values + _min, _max = (max(np.min(interpolation_pts - dx * low_val_align), np.min(interpolation_pts - dx * high_val_align)), + min(np.max(interpolation_pts - dx * low_val_align), np.max(interpolation_pts - dx * high_val_align))) + range_table['dqdx'] = 0.5 * (high_val_interp(np.clip(rr[2:], _min, _max)) + low_val_interp(np.clip(rr[2:], _min, _max))) + range_table['dqdx_width'] = 0.5 * (high_val_interp(np.clip(rr[2:], _min, _max)) - low_val_interp(np.clip(rr[2:], _min, _max))) + + @staticmethod + def density_dx_correction(rr, *params): + rr = np.clip(rr, 0, None) + rv = params[0] * np.exp(-rr / params[2]) + params[1] + return rv + + @staticmethod + def dx_estimate(profile_pos, hit_xyz, hit_idx, pixel_pitch, nsamples=10, tol=0.1): + ''' + Calculate the track dx to be associated with each profile point. + + First finds the furthest point along the line that falls on a hit pixel. + Then samples the track length between those points, checking to see if the sample point falls onto a + disabled channel. The track length is calculated as the length between the furthest points, minus the + approximate length on disabled channels + + :param profile_pos: xyz position of each profile point ``shape: (..., nprof, 3)`` + + :param hit_xyz: xyz position of each hit ``shape: (..., nhit, 3)`` + + :param hit_idx: index into ``profile_pos`` of each hit ``shape: (..., nhit)`` + + :param nsamples: number of sample points to estimate disabled fraction of track + + :returns: dx to be associated with each profile point ``shape: (..., nprof)`` + + ''' + dx = np.zeros(profile_pos.shape[:-1]) + for iprof in range(profile_pos.shape[-2]): + if ~np.any(hit_idx >= iprof): + break + hit_mask = hit_idx == iprof + if ~np.any(hit_mask): + continue + + xyz = ma.array(hit_xyz, mask=np.broadcast_to(~hit_mask[...,np.newaxis], hit_xyz.shape)) # (nev, nhit, 3) + valid = np.any(~xyz.mask[...,0], axis=-1) # (nev,) + + # get profile centroid + pos = profile_pos[...,iprof,:] # (nev, 3) + + # get profile trajectory segment directions + dirs = [profile_pos[...,iprof+1,:] - pos if iprof < profile_pos.shape[-2]-1 else profile_pos[...,-2,:] - pos, + profile_pos[...,iprof-1,:] - pos if iprof > 0 else profile_pos[...,1,:] - pos] + dirs = np.concatenate([dr[...,np.newaxis,np.newaxis,np.newaxis,:] for dr in dirs], axis=1) # (nev, ndirection, 1, 1, 3) + + for idr in range(dirs.shape[1]): + invalid_dir = np.all(dirs[:,idr] == 0., axis=-1) + dirs[:,idr][invalid_dir] = -dirs[:,(idr+1)%2][invalid_dir] + dirs = dirs / np.clip(np.linalg.norm(dirs, axis=-1, keepdims=True), 1e-15, None) + + # get active volume + min_xyz,max_xyz = np.min(xyz, axis=-2) - pixel_pitch/2, np.max(xyz, axis=-2) + pixel_pitch/2 + min_xyz = min_xyz.reshape(-1,1,1,1,3) + max_xyz = max_xyz.reshape(-1,1,1,1,3) + c = np.concatenate([min_xyz, max_xyz], axis=2) # (nev, 1, ncorner, 1, 3) + n = np.array([(1,0,0), (0,1,0), (0,0,1)]).reshape(1,1,1,3,3) # (1, 1, 1, naxes, 3) + + # find intersections with active volume planes + pos = pos.reshape(-1, 1, 1, 1, 3) + intersection = HIPSelection.intersection(pos, dirs, c, n) + alpha = np.sum(dirs * (intersection - pos), axis=-1) + + # only use intersections that are within active volume (and in the correct direction relative to the trajectory segment) + within_active_region = ((intersection[...,0] - max_xyz[...,0] <= tol) + & (intersection[...,0] - min_xyz[...,0] >= -tol) + & (intersection[...,1] - max_xyz[...,1] <= tol) + & (intersection[...,1] - min_xyz[...,1] >= -tol) + & (intersection[...,2] - max_xyz[...,2] <= tol) + & (intersection[...,2] - min_xyz[...,2] >= -tol) + & (alpha > 0) & valid.reshape(-1,1,1,1)) # (nev, ndirection, ncorner, naxes) + + intersection = np.take_along_axis(intersection, np.argmax(within_active_region[...,np.newaxis], axis=-2)[...,np.newaxis], axis=-2) # (nev, ndirection, ncorner, 1, 3) + within_active_region = np.take_along_axis(within_active_region[...,np.newaxis], np.argmax(within_active_region[...,np.newaxis], axis=-2)[...,np.newaxis], axis=-2) + intersection = np.take_along_axis(intersection, np.argmax(within_active_region, axis=-3)[...,np.newaxis], axis=-3) # (nev, ndirection, 1, 1, 3) + within_active_region = np.take_along_axis(within_active_region, np.argmax(within_active_region, axis=-3)[...,np.newaxis], axis=-3) + + # calculate track length in active volume + prof_dx = np.linalg.norm(intersection - pos, axis=-1) # (nev, ndirection, 1, 1) + + # correct for disabled channels + disabled_fraction = np.zeros_like(prof_dx) + if 'DisabledChannels' in resources: + sample_pts = np.linspace(pos, intersection, nsamples, axis=0) + sample_pt_disabled = ~resources['DisabledChannels'].is_active(sample_pts).reshape(sample_pts.shape[:-1]) + disabled_fraction = np.sum(sample_pt_disabled, axis=0) / nsamples + + prof_dx *= (1 - disabled_fraction) + + # collect result + dx[...,iprof] = (prof_dx * within_active_region[...,0]).sum(axis=(1,2,3)) # (nev,) + + return dx + + + @staticmethod + def profile_likelihood(profile_rr, profile_dqdx, profile_pos, range_table, type='', mcs_weight=0.0625): + ''' + Calculates the log-likelihood score of a given dqdx v. residual range profile + using a Moyal-distribution approximation. + + Likelihood data is passed via the ``range_table`` parameter which is + a ``dict`` with the following arrays: + + - ``range``: residual range values used in interpolation ``shape: (n_interp_pts,)`` + - ``dqdx``: dQ/dx values used in interpolation ``shape: (n_interp_pts,)`` + - ``dqdx_width``: dQ/dx sigma values ``shape: (n_interp_pts,)`` + + :param profile_rr: residual range ``shape: (..., n)`` + + :param profile_dqdx: dqdx ``shape: (..., n)`` + + :param profile_pos: bin position ``shape: (..., n, 3)`` + + :param range_table: ``dict``, see above. + + :param type: likelihood pdf name, one of ``'abs_exp'``, ``'moyal'``, ``'moyal_gaus'``, ``'gaus'`` + + :returns: likelihood ``shape: (..., n)`` + + ''' + profile_rr, profile_dqdx = np.broadcast_arrays(profile_rr, profile_dqdx) + profile_pos = np.broadcast_to(profile_pos, profile_rr.shape + (3,)) + + interp = interp1d(np.r_[0, range_table['range']], np.r_[range_table['post_dqdx'], range_table['dqdx']]) + interp_width = interp1d(np.r_[0, range_table['range']], np.r_[range_table['post_dqdx_width'], range_table['dqdx_width']]) + interp_angle_width = interp1d(np.r_[0, range_table['range']], np.r_[range_table['post_mcs_angle'], range_table['mcs_angle']]) + min_range = np.min(range_table['range']) + max_range = np.max(range_table['range']) + + # calculate dQ/dx log-likelihood + interp_dqdx = interp(np.clip(profile_rr, min_range, max_range)) + interp_dqdx_width = interp_width(np.clip(profile_rr, min_range, max_range)) + + if type == 'abs_exp': + dqdx_term = stats.expon.logpdf(np.abs(profile_dqdx - interp_dqdx), scale=interp_dqdx_width) + np.log(2) + #dqdx_term = -np.abs(profile_dqdx - interp_dqdx) / interp_dqdx_width - np.log(interp_dqdx_width / 2) + elif type == 'moyal': + dqdx_term = stats.moyal.logpdf(profile_dqdx, loc=interp_dqdx, scale=interp_dqdx_width) + elif type == 'moyal_gaus': + # interp_gaus_width = range_table['dqdx_gaus_width'] + interp_gaus_width = interp1d(range_table['range'], range_table['dqdx_gaus_width'])(np.clip(profile_rr, min_range, max_range)) + smear_values = np.linspace(-5 * interp_gaus_width, +5 * interp_gaus_width, 25).reshape(profile_rr.shape + (25,)) + smeared_profile_dqdx = profile_dqdx[..., np.newaxis] + smear_values + dqdx_term = np.log(np.sum(ma.maximum(stats.moyal.pdf( + smeared_profile_dqdx, loc=interp_dqdx[..., np.newaxis], scale=interp_dqdx_width[..., np.newaxis]), 1e-300) + * ma.maximum(stats.norm.pdf(smear_values, scale=interp_gaus_width[..., np.newaxis]), 1e-300), axis=-1)) + elif type == 'gaus': + dqdx_term = stats.norm.logpdf(profile_dqdx, loc=interp_dqdx, scale=interp_dqdx_width) + else: + dqdx_term = -np.abs(profile_dqdx - interp_dqdx) / np.abs(interp_dqdx) + + # calculate MCS log-likelihood + # pack profile pts + valid_mask = (profile_dqdx > 0) + any_valid = np.any(valid_mask) + npts = np.sum(valid_mask, axis=-1, keepdims=True) + if any_valid: + max_npts = npts.max() + else: + max_npts = 0 + + packed_pos = np.zeros(valid_mask.shape[:-1] + (max_npts, 3)) + packed_dqdx = np.zeros(valid_mask.shape[:-1] + (max_npts,)) + packed_rr = np.zeros(valid_mask.shape[:-1] + (max_npts,)) + place_mask = np.indices(packed_pos.shape)[-2] < npts[..., np.newaxis] + np.place(packed_pos, place_mask, profile_pos[valid_mask]) + place_mask = np.indices(packed_dqdx.shape)[-1] < npts + np.place(packed_dqdx, place_mask, profile_dqdx[valid_mask]) + np.place(packed_rr, place_mask, profile_rr[valid_mask]) + + #interp_angle_width = interp_angle_width(np.clip(packed_rr, min_range, max_range)) + interp_angle_width = interp_angle_width(np.clip(profile_rr, min_range, max_range)) + + d = packed_pos[..., 1:, :] - packed_pos[..., :-1, :] + d = d * place_mask[...,1:,np.newaxis] * place_mask[...,:-1,np.newaxis] + angle = np.zeros_like(packed_dqdx) + norm = np.linalg.norm(d[..., 1:, :], axis=-1) * np.linalg.norm(d[..., :-1, :], axis=-1) + if any_valid and angle.shape[-1] > 1: + angle[..., 2:] = np.sum(d[..., 1:, :] * d[..., :-1, :], axis=-1) / np.maximum(norm, 1e-15) + angle = np.arccos(angle) + angle[..., 0] = 0 + angle[..., 1] = 0 + #angle[..., 0] = angle[..., 1] + #angle[..., -1] = angle[..., -2] + + # and now unpack profile pts + rv_angle_term = np.zeros(valid_mask.shape) + np.place(rv_angle_term, valid_mask, angle[place_mask]) + + #angle_term = stats.norm.logpdf(angle, loc=0, scale=interp_angle_width) + np.log(2) + rv_angle_term = stats.expon.logpdf(rv_angle_term, scale=interp_angle_width) + np.log(2) +# angle_term = -np.abs(angle) / np.pi + #if any_valid: + # np.put_along_axis(angle_term, np.argmin(np.abs(packed_rr), axis=-1)[..., np.newaxis], -np.log(2), axis=-1) + if any_valid: + # don't count the last profile point towards score + np.put_along_axis(rv_angle_term, np.argmin(np.abs(profile_rr), axis=-1)[..., np.newaxis], -np.log(2), axis=-1) + + return dqdx_term, rv_angle_term * mcs_weight + + @staticmethod + def intersection(xyz, dxyz, pxyz, pnorm): + ''' + calculate the intersection of lines with planes + + :param xyz: (..., 3) array representing line origins + :param dxyz: (..., 3) array representing line directions (unit norm) + :param pxyz: (..., 3) array representing a point on the plane + :param pnorm: (..., 3) array representing plane normal (unit norm) + + :returns: (..., 3) array representing the intersection point + ''' + with np.errstate(divide='ignore', invalid='ignore'): + d = np.sum((pxyz - xyz) * pnorm, axis=-1) / np.sum(dxyz * pnorm, axis=-1) + return xyz + dxyz * d[..., np.newaxis] + + + @staticmethod + def profiled_dqdx_kalman(tracks, seed_pt, hit_xyz, hit_q, dx, max_range, search_dx, pixel_pitch, mask=None): + orig_len = len(tracks) + if mask is not None: + tracks = tracks[mask] + seed_pt = seed_pt[mask] + hit_xyz = hit_xyz[mask] + hit_q = hit_q[mask] + + n = len(tracks) + sample_points = int(max_range / dx) + + dq = np.zeros((n, sample_points)) + dn = np.zeros((n, sample_points), dtype=int) + ds = np.zeros((n, sample_points)) + pos = np.zeros((n, sample_points, 3)) + hit_prof_idx = np.full(hit_q.shape, -1, dtype=int) + hit_prof_s = np.full(hit_q.shape, 0, dtype=float) + + hit_mask = ~hit_q.mask + + # find initial point and direction + traj = np.zeros((n, sample_points, 3)) + start_pt = seed_pt[...,0:1,:] + end_pt = start_pt.copy() + traj[...,0:1,:] = seed_pt.copy() + local_mask = np.linalg.norm(hit_xyz - seed_pt, axis=-1, keepdims=True) < search_dx + local_mask = np.broadcast_to(hit_mask[...,np.newaxis] & local_mask, hit_xyz.shape) + curr_direction = ma.array(hit_xyz - seed_pt, mask=~local_mask).mean(axis=-2) + curr_direction /= np.clip(np.linalg.norm(curr_direction, axis=-1, keepdims=True),1e-15,None) + hit_mask = hit_mask & ~local_mask[...,0] + + disabled_channels = resources.get('DisabledChannels', None) + + i = 0 + while (i < sample_points-1) and np.any(hit_mask): + i += 1 + + # collect hits in local region + dr = (hit_xyz - traj[...,i-1,np.newaxis,:]) + dl = np.sum(dr * curr_direction[...,np.newaxis,:], axis=-1, keepdims=True) + forward = dl > 0 + dt = np.linalg.norm(dr - dl * curr_direction[...,np.newaxis,:], axis=-1, keepdims=True) + local_mask = (dl < dx) & (dt < dx/2) & hit_mask[...,np.newaxis] & forward + + # if none found, expand search + if np.any(~((local_mask[...,0]).any(axis=-1)) & hit_mask.any(axis=-1)): + r = np.linalg.norm(dr, axis=-1, keepdims=True) + + # if disabled channels list present and next step is a disabled region, search in a longer line first + if disabled_channels is not None: + proposed_step = traj[...,i-1,:] + curr_direction * dx + step_is_disabled = ~disabled_channels.is_active(proposed_step) + local_mask = local_mask | ( + (dl < 2*dx) & (dt < 3*dx/4) & hit_mask[...,np.newaxis] & forward + & step_is_disabled[...,np.newaxis,np.newaxis] + & ~(local_mask).any(axis=-2, keepdims=True)) + + # then search in a sphere in ever expanding circles + search_factor = 1 + while np.any(~(local_mask[...,0]).any(axis=-1) & hit_mask.any(axis=-1)): + local_mask = (local_mask | ( + (r < search_factor * search_dx) & hit_mask[...,np.newaxis] + & ~(local_mask).any(axis=-2, keepdims=True))) + search_factor += 1 + if search_factor > 5: + break + + # if no more hits found, continue + if not np.any(local_mask): + break + + # calculate new sample point (charge weighted average position) + traj[...,i,:] = ma.average(ma.array(hit_xyz, mask=~np.broadcast_to(local_mask, hit_xyz.shape)), + weights=np.broadcast_to(hit_q[...,np.newaxis], hit_xyz.shape), axis=-2) + end_pt = traj[...,i:i+1,:] + + # calculate new direction + curr_direction = traj[...,i,:] - traj[...,i-1,:] + curr_direction /= np.clip(np.linalg.norm(curr_direction, axis=-1, keepdims=True), 1e-15, None) + + # mask off used hits + hit_mask = hit_mask & ~local_mask[...,0] + + # project hits onto trajectory segments + dr = (hit_xyz[...,np.newaxis,:] - traj[...,np.newaxis,:-1,:]) # (ev, hit, traj-1, 3) + traj_dr = traj[...,np.newaxis,1:,:] - traj[...,np.newaxis,:-1,:] # (ev, 1, traj-1, 3) + traj_l = np.clip(np.linalg.norm(traj_dr, axis=-1, keepdims=True), 1e-15, None) # (ev, 1, traj-1, 1) + traj_dr /= traj_l + alpha = np.sum(dr * traj_dr, axis=-1) / traj_l[...,0] # (ev, hit, traj-1) + + # find closest segment + d = np.linalg.norm(dr - traj_dr * np.clip(alpha[...,np.newaxis], 0, 1) * traj_l, axis=-1) # (ev, hit, traj-1) + d = ma.array(d, mask=(hit_q.mask[...,np.newaxis] | (d > dx/2))) + d.mask[...,i-1:] = True # remove invalid segments + iseg_min = np.argmin(d, axis=-1) # (ev, hit) + iseg_min[np.take_along_axis(d.mask, iseg_min[...,np.newaxis], axis=-1).reshape(iseg_min.shape)] = -1 + + # calculate segment range + s = np.concatenate([np.zeros(traj_l.shape[:-2] + (1,1)), np.cumsum(traj_l, axis=-2)], axis=-2) # (ev, 1, traj-1, 1) + hit_s = np.take_along_axis(s, iseg_min[...,np.newaxis,np.newaxis], axis=-2) + hit_s = hit_s + np.take_along_axis(traj_l * alpha[...,np.newaxis], iseg_min[...,np.newaxis,np.newaxis], axis=-2) # (ev, hit, 1, 1) + hit_s = hit_s[...,0,0] + + # fill bins + bins = np.linspace(0, max_range, sample_points) + hit_prof_idx = np.clip(np.digitize(hit_s, bins=bins) - 1, 0, sample_points-1) + hit_prof_idx[hit_q.mask] = -1 + + sample_point_s = np.zeros_like(ds) + prev_pos = traj[...,0,:] + for i in range(sample_points): + #if not np.any(hit_prof_idx >= i): + # break + + # grab hits from current trajectory point + hit_mask = (hit_prof_idx == i) & (~hit_q.mask) + any_hit_mask = hit_mask.any(axis=-1) + #if not np.any(any_hit_mask): + # continue + + # re-estimate position and only use "local" hits + traj_hit_s = ma.array(hit_s, mask=~hit_mask) + local_pos = (ma.average(ma.array(hit_xyz, mask=~np.broadcast_to(hit_mask[...,np.newaxis], hit_xyz.shape)), + weights=np.broadcast_to(hit_q[...,np.newaxis], hit_xyz.shape), axis=-2) + * any_hit_mask[...,np.newaxis]) + local_pos[~any_hit_mask,:] = prev_pos[~any_hit_mask,:] + prev_pos = local_pos + + hit_mask = hit_mask & (np.linalg.norm(hit_xyz - local_pos[...,np.newaxis,:], axis=-1) < dx) + any_hit_mask = hit_mask.any(axis=-1) + + #if not np.any(any_hit_mask): + # continue + + # fill output arrays + pos[...,i,:] = local_pos + dq[...,i] = (np.sum(ma.array(hit_q, mask=~hit_mask), axis=-1)) * any_hit_mask + dn[...,i] = (np.sum(hit_mask, axis=-1)) * any_hit_mask + local_dir = pos[...,i,:] - pos[...,i-1,:] if i > 0 else traj[...,1,:] - traj[...,0,:] + if i > 0: + sample_point_s[...,i:] += np.linalg.norm(local_dir, axis=-1)[...,np.newaxis] + local_dir /= np.clip(np.linalg.norm(local_dir, axis=-1, keepdims=True), 1e-15, None) + local_s = ma.array(np.sum((hit_xyz - pos[...,i:i+1,:]) * local_dir[...,np.newaxis,:], axis=-1), mask=~hit_mask) + hit_prof_s[hit_mask] = (local_s + sample_point_s[...,i:i+1])[hit_mask] + ds[...,i] = (np.max(local_s, axis=-1) - np.min(local_s, axis=-1)) * any_hit_mask + + r_dq = np.zeros((orig_len,) + dq.shape[1:]) + r_dn = np.zeros((orig_len,) + dn.shape[1:], dtype=int) + r_start_pt = np.zeros((orig_len,) + start_pt.shape[1:]) + r_end_pt = np.zeros((orig_len,) + end_pt.shape[1:]) + r_pos = np.zeros((orig_len,) + pos.shape[1:]) + r_ds = np.zeros((orig_len,) + ds.shape[1:]) + r_hit_prof_idx = np.zeros((orig_len,) + hit_prof_idx.shape[1:], dtype=int) - 1 + r_hit_prof_s = np.zeros((orig_len,) + hit_prof_s.shape[1:], dtype=float) + + np.place(r_dq, np.broadcast_to(mask[..., np.newaxis], r_dq.shape), dq) + np.place(r_dn, np.broadcast_to(mask[..., np.newaxis], r_dn.shape), dn) + np.place(r_ds, np.broadcast_to(mask[..., np.newaxis], r_ds.shape), ds) + np.place(r_start_pt, np.broadcast_to(mask[..., np.newaxis, np.newaxis], r_start_pt.shape), start_pt) + np.place(r_end_pt, np.broadcast_to(mask[..., np.newaxis, np.newaxis], r_end_pt.shape), end_pt) + np.place(r_pos, np.broadcast_to(mask[..., np.newaxis, np.newaxis], r_pos.shape), pos) + np.place(r_hit_prof_idx, np.broadcast_to(mask[..., np.newaxis], r_hit_prof_idx.shape), hit_prof_idx) + np.place(r_hit_prof_s, np.broadcast_to(mask[..., np.newaxis], r_hit_prof_s.shape), hit_prof_s) + + return r_dq, r_dn, r_ds, r_start_pt, r_end_pt, r_pos, r_hit_prof_idx, r_hit_prof_s + + @staticmethod + def mean_neg_loglikelihood(r0, range_table, profile_n, profile_dqdx, profile_rr, profile_pos): + profile_rr = profile_rr - r0 + pt_likelihood_dqdx, pt_likelihood_mcs = HIPSelection.profile_likelihood( + profile_rr, profile_dqdx, profile_pos, range_table) + profile_n, profile_dqdx, profile_rr = np.broadcast_arrays(profile_n, profile_dqdx, profile_rr) + pt_likelihood_mcs = ma.masked_where((profile_n <= 0) | (profile_rr <= 0), pt_likelihood_mcs) + #pt_likelihood_dqdx = ma.masked_where((profile_rr <= 0), pt_likelihood_dqdx) + pt_likelihood_dqdx = ma.masked_where((profile_n <= 0) | (profile_rr <= 0), pt_likelihood_dqdx) + + mean_likelihood = -pt_likelihood_dqdx.mean(axis=-1) - pt_likelihood_mcs.mean(axis=-1) + return mean_likelihood + + + + + + def run(self, source_name, source_slice, cache): + super(HIPSelection, self).run(source_name, source_slice, cache) + + # load arrays of event-level, hit-level, and track-level info + events = cache[source_name] + t0 = cache[self.t0_dset_name].reshape(cache[source_name].shape) + hits = ma.array(cache[self.hits_dset_name], shrink=False) + q = ma.array(cache[self.charge_dset_name], shrink=False) + q = q.reshape(hits.shape) + tracks = ma.array(cache[self.tracklet_dset_name], shrink=False) + #hit_drift = ma.array(cache[self.hit_drift_dset_name].reshape(hits.shape), shrink=False) + #track_hits = ma.array(cache[self.track_hits_dset_name], shrink=False) + #track_hit_drift = ma.array(cache[self.track_hit_drift_dset_name], shrink=False) + #print("Track shape:", tracks.shape) + #print("Track hits shape:", track_hits.shape) + #print("Track hit drift shape:", track_hit_drift.shape) + + if events.shape[0]: + + ## EVENT-LEVEL CALCULATIONS + + # calculate hit positions and charge + hit_q = q['Q'] # convert mV -> ke + # filter out bad channel ids + hit_mask = (hits['y'] != 0.0) & (hits['z'] != 0.0) & ~hit_q.mask & ~hits['t_drift'].mask + hit_q.mask = hit_q.mask | ~hit_mask + hit_xyz = ma.array(np.concatenate([ + hits['x'][..., np.newaxis], hits['y'][..., np.newaxis], + hits['z'][..., np.newaxis]], axis=-1), shrink=False, mask=np.zeros(hits['y'].shape + (3,), dtype=bool) | hit_q.mask[...,np.newaxis] | ~hit_mask[...,np.newaxis]) + hit_in_fid = resources['Geometry'].in_fid( + hit_xyz.reshape(-1, 3), cathode_fid=2.0, field_cage_fid=2.0, anode_fid=2.0).reshape(hit_xyz.shape[:-1]) + hit_q.mask = hit_q.mask | ~hit_in_fid + + # find value for the most charge in one hit in each event + #print("HIt q", hit_q[~hit_q.mask]/self.larpix_gain) + #print("HIt q", hit_q.shape) + max_hit_charge = ma.array([int(ma.filled(hit_q[i,:].astype(float) > 25., False).sum()) for i in range(len(hits))]) + #print("test max hit charge:", ma.filled(hit_q[0,:].astype(float)/self.larpix_gain > 300., False)== True) + #print("NEW MAX HIT CHARGE SHAPE:", max_hit_charge.shape) + #print("Max Hit Charge:", max_hit_charge) + ## TRACK-LEVEL CALCULATIONS + + # find all tracks that end in the fiducial volume + track_start = tracks.ravel()['trajectory'][..., 0, :] + track_stop = tracks.ravel()['trajectory'][..., -1, :] + #print("Tracks trajectory:", tracks.ravel()['trajectory'][:5]) + #track_dqdx = tracks.ravel()['dq']/np.sqrt(np.sum(tracks.ravel()['dx']**2, axis=-1)) + #print("Tracks dq/dx:", track_dqdx[:5]) + #print("Tracks dn:", tracks.ravel()['dn'][:5]) + #track_dqdx_start = track_dqdx[track_dqdx != 0][..., 0] + #track_dqdx_stop = track_dqdx[track_dqdx != 0][..., -1] + #track_dn_start = tracks.ravel()['dn'][..., 0] + #track_dn_stop = tracks.ravel()['dn'][..., -1] + #print("Tracks start shape:", track_start.shape) + #print("Tracks dq shape:", track_dqdx_start.shape) + + + start_in_fid = resources['Geometry'].in_fid( + track_start, field_cage_fid=self.fid_cut, anode_fid=self.anode_fid_cut) + start_in_fid = start_in_fid.reshape(tracks.shape) + stop_in_fid = resources['Geometry'].in_fid( + track_stop, field_cage_fid=self.fid_cut, anode_fid=self.anode_fid_cut) + stop_in_fid = stop_in_fid.reshape(tracks.shape) + contained_in_fid = start_in_fid & stop_in_fid + #print("Track start:", track_start[:5,:]) + #print("Track stop:", track_stop[:5,:]) + #print("Track dq start:", track_dqdx_start[:5]) + #print("Track dq stop:", track_dqdx_stop[:5]) + #print("Track dnhits start:", track_dn_start[:5]) + #print("Track dnhits stop:", track_dn_stop[:5]) + #print("Shape of start_in_fid:", start_in_fid.shape) + #print("Start in fid:", start_in_fid) + #print("Stop in fid:", stop_in_fid) + #print("Contained in fid:", contained_in_fid) + event_ntracks_in_fid = np.zeros(len(tracks), dtype=int) + #print("Start in FID masked tracks:", np.array([int(start_in_fid[i].sum()) for i in range(len(tracks))])) + + # prep arrays to write to file + event_ids = events['id'] + event_next_trigs = events['n_ext_trigs'] + #print("Shape of one event's tracks:", tracks['id'][0].shape) + #print("One event's tracks's mask:", tracks['id'][0].mask) + #print("One event's tracks's ids:", tracks['id'][0]) + #print("Number of valid events for one event:", int((~tracks['id'][0].mask).sum())) + event_ntracks = np.array([int((~tracks['id'][i].mask).sum()) for i in range(len(tracks))]) + event_nhits = events['nhit'] + #event_charge = events['q'] + for i in range(len(tracks)): + if event_ntracks[i] > 0: + event_ntracks_in_fid[i] = int(contained_in_fid[i].sum()) + else: + event_ntracks_in_fid[i] = 0 + + nhits_cut = (event_nhits > 50) & (event_nhits < 5000) + #print("Number of hits:", nhits_cut) + hit_charge_threshold_cut = (max_hit_charge > 1) # cut on number of hits over threshold, which is currently 300 mV + external_trigger_cut = (event_next_trigs > 0) + ntracks_in_fid_cut = (event_ntracks_in_fid == event_ntracks) & (event_ntracks == 1) #& (event_ntracks <= 3) + event_level_cuts = nhits_cut & hit_charge_threshold_cut & external_trigger_cut & ntracks_in_fid_cut + + max_tracks = contained_in_fid.shape[1] + #print("Max tracks shape:",max_tracks) + #print("Event level cuts shape:", event_level_cuts.shape) + # make the array of all initial cuts the same length as the tracks array + #event_level_cuts_ext = np.array([np.full(max_tracks, event_level_cuts[i]) for i in range(len(event_level_cuts))]) + #print("Event level cuts extended shape:", event_level_cuts_ext.shape) + + #all_initial_cuts_ext = contained_in_fid & event_level_cuts_ext + contained_in_fid_red = np.logical_and.reduce(contained_in_fid, -1, dtype=bool) + + #print("All initial cuts shape:", all_initial_cuts.shape) + + # Look into unique channels: + #hits_with_channels = ma.array([hits['iogroup'], hits['iochannel'], hits['chipid'], hits['channelid']]) + #print("IO Group:", hits['iogroup']) + #print("IO Channel:", hits['iochannel']) + #print("Chip ID:", hits['chipid']) + #print("Channel ID:", hits['channelid']) + #print("Shape of hits with channels:", hits_with_channels.shape) + + + '''if self.is_mc: + # lookup the track's true trajectory + track_traj = cache[self.truth_trajectories_dset_name] + #print("True Trajectory PID situation:", track_traj['pdgId']) + + if track_traj.shape[0]: + #print("track ids pre-reshaping:", track_traj['trackID']) + track_traj = track_traj.reshape(tracks.shape[0:1] + (-1,)) + track_traj = condense_array(track_traj, track_traj['trackID'].mask) + track_pdg = condense_array(track_traj, track_traj['pdgId'].mask) + + #print("track ids post-reshaping:", track_traj['trackID']) + #print("pdg ids post-reshaping:", track_pdg['pdgId']) + proton_mask_true = track_pdg['pdgId'] == 2212 + proton_mask = np.tile(proton_mask_true[..., np.newaxis], (1,1,3)) + + + #print("Proton mask:", proton_mask) + true_xyz_start = ma.masked_where(~proton_mask, track_traj['xyz_start']) + true_xyz_end = ma.masked_where(~proton_mask, track_traj['xyz_end']) + + #n_protons = len(track_pdg[proton_mask_true]) + #true_xyz_start = true_xyz_start[~true_xyz_start.mask].reshape((n_protons,3)) + #true_xyz_end = true_xyz_end[~true_xyz_end.mask].reshape((n_protons,3)) + #print("True xyz start:", true_xyz_start) + #print("Proton trajectories:", proton_trajectories) + # Look at all possible proton trajectories + #i_primary_traj = proton_trajectories + #print("Track trajectory shape:", track_traj.shape) + #print("Proton trajectories axis shape:", i_primary_traj.shape) + #track_true_traj = d + #print("Track true trajectories only protons:", track_true_traj) + #track_true_traj = track_true_traj.reshape(-1) + #true_xyz_start = proton_trajectories['xyz_start'] #track_true_traj['xyz_start'] + #true_xyz_end = proton_trajectories['xyz_end']#track_true_traj['xyz_end'] + + # find if trajectory ends in the fiducial volume + #is_muon = ma.abs(track_true_traj['pdgId']) == 1 + #print("PDG ID shape:", track_traj['pdgId'].shape) + #is_proton = ma.array([int(((track_traj['pdgId'][i] == 2212).astype(float)).sum(axis=-1))>=1 for i in range(len(track_traj))]) + + is_proton = np.empty(tracks.shape[0], dtype=bool) + for i in range(len(tracks)): + all_pdg = track_traj['pdgId'][i].ravel() == 2212 + is_proton[i] = np.sum(all_pdg.astype(int)) + #print("What is is_proton?:", is_proton) + + #if len(is_proton): + #print("True start:", true_xyz_start[is_proton]) + #print("True start:", true_xyz_end[is_proton]) + else: + track_true_traj = np.empty(tracks.shape[0], dtype=track_traj.dtype) + is_muon = np.zeros(track_true_traj.shape, dtype=bool) + is_proton = np.zeros(track_true_traj.shape, dtype=bool) + true_xyz_start = track_true_traj['xyz_start'] + true_xyz_end = track_true_traj['xyz_end'] + # define seed point based on + start_in_fid = resources['Geometry'].in_fid( + track_start, field_cage_fid=self.fid_cut, anode_fid=self.anode_fid_cut) + seed_pt = track_start.reshape(tracks.shape + (3,)) + seed_track_mask = all_initial_cuts + max_seed_pts = int(max(np.sum((ma.filled(seed_track_mask.astype(float), 0.0)), axis=-1).max(), 1)) + seed_pt_idx = ma.argsort(ma.array(seed_track_mask, mask=~seed_track_mask | seed_track_mask.mask), axis=-1, fill_value=0)[..., ::-1, np.newaxis] + seed_pt = np.take_along_axis(seed_pt, seed_pt_idx, axis=1)[...,:max_seed_pts,:] + seed_pt = ma.array(seed_pt, mask=np.indices(seed_pt.shape)[1] >= np.sum(seed_track_mask, axis=-1, keepdims=True)[...,np.newaxis]) + #print("Seed point:", seed_pt[~seed_pt.mask]) + #print("Seed point shape:", seed_pt.shape) + + event_passes_initial_cuts = ((t0['type'] != 0) + #& (veto_q < self.veto_charge_cut) + #& (active_proj_length > self.projected_length_cut) + #& (ma.sum(is_throughgoing, axis=-1) == 0) + & (ma.sum(seed_track_mask, axis=-1) >= 1)) + + # now check the likelihood of a stopping muon + + # broadcast into appropriate shape for kalman fit + tracks_km = np.broadcast_to(tracks[:,np.newaxis], (tracks.shape[0], max_seed_pts, tracks.shape[1]), subok=True).reshape(-1, tracks.shape[1]) + tracks_km.mask = np.broadcast_to(tracks.mask[:,np.newaxis], (tracks.shape[0], max_seed_pts, tracks.shape[1]), subok=True).reshape(-1, tracks.shape[1]) + hit_xyz_km = np.broadcast_to(hit_xyz[:,np.newaxis], (hit_xyz.shape[0], max_seed_pts) + hit_xyz.shape[1:], subok=True).reshape(-1, *hit_xyz.shape[1:]) + hit_xyz_km.mask = np.broadcast_to(hit_xyz.mask[:,np.newaxis], (hit_xyz.shape[0], max_seed_pts) + hit_xyz.shape[1:], subok=True).reshape(-1, *hit_xyz.shape[1:]) + hit_q_km = np.broadcast_to(hit_q[:,np.newaxis], (hit_q.shape[0], max_seed_pts) + hit_q.shape[1:], subok=True).reshape(-1, *hit_q.shape[1:]) + hit_q_km.mask = np.broadcast_to(hit_q.mask[:,np.newaxis], (hit_q.shape[0], max_seed_pts) + hit_q.shape[1:], subok=True).reshape(-1, *hit_q.shape[1:]) + kalman_mask = (event_passes_initial_cuts[...,np.newaxis] & ~seed_pt.mask[...,0]).ravel() + + # first generate the dQ/dx profile + dq, dn, ds, start_pt, end_pt, pos, hit_prof_idx, hit_prof_s = self.profiled_dqdx_kalman( + tracks_km, seed_pt.reshape(-1, 1, 3), hit_xyz_km, hit_q_km, + mask=kalman_mask, + dx=self.profile_dx, search_dx=self.profile_search_dx, + max_range=self.profile_max_range, pixel_pitch=resources['Geometry'].pixel_pitch) + #ds += resources['Geometry'].pixel_pitch # correct for pixel edges + ds = self.dx_estimate(pos, hit_xyz_km, hit_prof_idx, resources['Geometry'].pixel_pitch) + profile_n = dn + profile_dqdx = dq / ma.maximum(ds, resources['Geometry'].pixel_pitch) * (dn > 0) + profile_dqdx[dn <= 0] = 0 + + # make an initial guess for the stopping point (maximum 2 dQ/dx bins) + profile_rr = np.linalg.norm(pos[...,1:,:] - pos[...,:-1,:], axis=-1) + profile_rr = np.concatenate((np.zeros(profile_rr.shape[:-1]+(1,)), profile_rr), axis=-1) + profile_rr = np.cumsum(profile_rr, axis=-1) + + i_max = np.argsort(profile_dqdx, axis=-1)[...,-2:] + profile_offset0 = np.take_along_axis(profile_rr, i_max[...,0:1], axis=-1) + profile_offset1 = np.take_along_axis(profile_rr, i_max[...,1:2], axis=-1) + + # refine guess by using the hit with the largest charge + hit_near_stop0 = (hit_prof_idx == i_max[...,0:1]) + hit_near_stop1 = (hit_prof_idx == i_max[...,1:2]) + profile_offset0[hit_near_stop0.any(axis=-1)] = np.take_along_axis( + hit_prof_s, np.argmax(ma.array(hit_q_km, mask=~hit_near_stop0), axis=-1)[...,np.newaxis], axis=-1)[hit_near_stop0.any(axis=-1)] + profile_offset1[hit_near_stop1.any(axis=-1)] = np.take_along_axis( + hit_prof_s, np.argmax(ma.array(hit_q_km, mask=~hit_near_stop1), axis=-1)[...,np.newaxis], axis=-1)[hit_near_stop1.any(axis=-1)] + + profile_rr0 = profile_offset0 - profile_rr + profile_rr1 = profile_offset1 - profile_rr + + # perform a fit for the stopping point assuming a muon or a proton + proton_score = np.full(profile_dqdx.shape[:-1], 1e+303) + muon_r0 = np.zeros(profile_dqdx.shape[:-1]) + proton_r0 = np.zeros(profile_dqdx.shape[:-1]) + max_range = 0 #self.profile_dx # within +/- 1 profile bins + sample_factor = 1 #20 # resolution is profile bin/10 + + for i in range(proton_r0.shape[0]): + if np.any((profile_n[i] > 0)): + valid_mask = profile_n[i] > 0 + + muon_offset = [] + proton_offset = [] + muon_likelihood = [] + proton_likelihood = [] + + for j,rr in enumerate([profile_rr0[i], profile_rr1[i]]): + rr_range = (np.maximum(-max_range, rr[valid_mask].min()), + np.minimum(+max_range, rr[valid_mask].max())) + rr_offset = np.expand_dims( + np.linspace(rr_range[0], rr_range[1], + np.clip(sample_factor * int(np.diff(rr_range) / self.profile_dx),1,None)), + axis=-1) + close_dqdx = np.take_along_axis(profile_dqdx[i:i + 1], np.argmin(np.abs(rr[np.newaxis,...] - rr_offset), axis=-1)[..., np.newaxis], axis=-1) + mask = np.ones_like((close_dqdx > self.dqdx_peak_cut)) # ignore dQ/dx mask + #if not np.any(mask): + # continue + + muon_likelihood.append(self.mean_neg_loglikelihood( + rr_offset + muon_r0[i], self.muon_range_table, profile_n[i:i + 1], profile_dqdx[i:i + 1], rr[np.newaxis,...], pos[i:i + 1])) + #muon_r0[i] = rr_offset[ma.argmin(ma.array(muon_likelihood, mask=~mask), axis=0)] + muon_r0[i] + muon_offset.append(rr_offset[ma.argmin(ma.array(muon_likelihood[j], mask=~mask), axis=0)]) + + proton_likelihood.append(self.mean_neg_loglikelihood( + rr_offset + proton_r0[i], self.proton_range_table, profile_n[i:i + 1], profile_dqdx[i:i + 1], rr[np.newaxis,...], pos[i:i + 1])) + #proton_r0[i] = rr_offset[ma.argmin(ma.array(proton_likelihood, mask=~mask), axis=0)] + proton_r0[i] + proton_offset.append(rr_offset[ma.argmin(ma.array(proton_likelihood[j], mask=~mask), axis=0)]) + + muon_j_min = np.argmin([np.min(ll) if ll is not np.nan else 1e+303 for ll in muon_likelihood]) + proton_j_min = np.argmin([np.min(ll) if ll is not np.nan else 1e+303 for ll in proton_likelihood]) + proton_score[i] = ma.filled(proton_likelihood[proton_j_min].astype(float), 1e+303) + muon_r0[i] = muon_offset[muon_j_min] + proton_r0[i] = proton_offset[proton_j_min] + profile_rr[i] = [profile_rr0[i], profile_rr1[i]][muon_j_min] + + # use only the dQ/dx profile from the most proton-like seed point + ibest_seed = ma.argmin(ma.array(proton_score, mask=np.all(profile_n == 0, axis=-1)).reshape(-1, max_seed_pts), axis=-1)[...,np.newaxis] + profile_dqdx = np.take_along_axis(profile_dqdx.reshape(ibest_seed.shape[0], max_seed_pts, -1), ibest_seed[...,np.newaxis], axis=1)[:,0] + profile_n = np.take_along_axis(profile_n.reshape(ibest_seed.shape[0], max_seed_pts, -1), ibest_seed[...,np.newaxis], axis=1)[:,0] + pos = np.take_along_axis(pos.reshape(ibest_seed.shape[0], max_seed_pts, -1, 3), ibest_seed[...,np.newaxis,np.newaxis], axis=1)[:,0] + profile_rr = np.take_along_axis(profile_rr.reshape(ibest_seed.shape[0], max_seed_pts, -1), ibest_seed[...,np.newaxis], axis=1)[:,0] + muon_r0 = np.take_along_axis(muon_r0.reshape(ibest_seed.shape[0], max_seed_pts), ibest_seed, axis=1)[:,0] + proton_r0 = np.take_along_axis(proton_r0.reshape(ibest_seed.shape[0], max_seed_pts), ibest_seed, axis=1)[:,0] + dq = np.take_along_axis(dq.reshape(ibest_seed.shape[0], max_seed_pts, -1), ibest_seed[...,np.newaxis], axis=1)[:,0] + dn = np.take_along_axis(dn.reshape(ibest_seed.shape[0], max_seed_pts, -1), ibest_seed[...,np.newaxis], axis=1)[:,0] + ds = np.take_along_axis(ds.reshape(ibest_seed.shape[0], max_seed_pts, -1), ibest_seed[...,np.newaxis], axis=1)[:,0] + start_pt = np.take_along_axis(start_pt.reshape(ibest_seed.shape[0], max_seed_pts, -1, 3), ibest_seed[...,np.newaxis,np.newaxis], axis=1)[:,0] + end_pt = np.take_along_axis(end_pt.reshape(ibest_seed.shape[0], max_seed_pts, -1, 3), ibest_seed[...,np.newaxis,np.newaxis], axis=1)[:,0] + hit_prof_idx = np.take_along_axis(hit_prof_idx.reshape(ibest_seed.shape[0], max_seed_pts, -1), ibest_seed[...,np.newaxis], axis=1)[:,0] + hit_prof_s = np.take_along_axis(hit_prof_s.reshape(ibest_seed.shape[0], max_seed_pts, -1), ibest_seed[...,np.newaxis], axis=1)[:,0] + + # calculate likelihood scores for refined dQ/dx profile + muon_likelihood_dqdx, muon_likelihood_mcs = self.profile_likelihood( + (profile_rr - muon_r0[..., np.newaxis]), profile_dqdx, pos, + self.muon_range_table) + proton_likelihood_dqdx, proton_likelihood_mcs = self.profile_likelihood( + (profile_rr - proton_r0[..., np.newaxis]), profile_dqdx, pos, + self.proton_range_table) + mip_likelihood_dqdx, mip_likelihood_mcs = self.profile_likelihood( + np.clip(profile_rr, 1500, 1500), profile_dqdx, pos, + self.muon_range_table) + + muon_likelihood_mcs = ma.masked_where( + (dn == 0) | (profile_rr - muon_r0[..., np.newaxis] <= 0), + muon_likelihood_mcs) + proton_likelihood_mcs = ma.masked_where( + (dn == 0) | (profile_rr - proton_r0[..., np.newaxis] <= 0), + proton_likelihood_mcs) + mip_likelihood_mcs = ma.masked_where( + (dn == 0) | (profile_rr - muon_r0[..., np.newaxis] <= 0), + mip_likelihood_mcs) + muon_likelihood_dqdx = ma.masked_where( + (dn == 0) | (profile_rr - muon_r0[..., np.newaxis] <= 0), + muon_likelihood_dqdx) + proton_likelihood_dqdx = ma.masked_where( + (dn == 0) | (profile_rr - proton_r0[..., np.newaxis] <= 0), + proton_likelihood_dqdx) + mip_likelihood_dqdx = ma.masked_where( + (dn == 0) | (profile_rr - muon_r0[..., np.newaxis] <= 0), + mip_likelihood_dqdx) + + # get end point (for stopping muon assumption) + profile_rr = ma.array(profile_rr - muon_r0[..., np.newaxis], mask=(profile_n <= 0)) + i_stop = np.argmin(np.abs(profile_rr), axis=-1)[..., np.newaxis, np.newaxis] + end_pt = np.take_along_axis(pos, i_stop, axis=-2) + + # correct for rounding error + stop_rr = np.take_along_axis(profile_rr, i_stop[...,0], axis=-1)[...,np.newaxis] + n = end_pt - np.take_along_axis(pos, np.clip(i_stop-1,0,None), axis=-2) + n /= np.clip(np.linalg.norm(n, axis=-1, keepdims=True), 1e-15, None) + end_pt_corr = stop_rr * n + + # check if endpoint in fiducial volume + end_pt_in_fid = resources['Geometry'].in_fid( + end_pt.reshape(-1, 3), cathode_fid=self.cathode_fid_cut, field_cage_fid=self.fid_cut, anode_fid=self.anode_fid_cut) + end_pt_in_fid = end_pt_in_fid.reshape(tracks.shape[0]) + + # estimate residual range for each hit + hit_prof_rr = profile_rr.max(axis=-1, keepdims=True) - hit_prof_s + + # calculate "additional" energy (all energy not associated to the parent muon) assuming nominal michel dE/dx + q_sum = hit_q.sum(axis=-1) - ma.array(dq, mask=(np.around(profile_rr/self.profile_dx) * self.profile_dx < 0) | (profile_n <= 0)).sum(axis=-1) + michel_dedx = resources['ParticleData'].landau_peak(50 * units.MeV, resources['ParticleData'].e_mass, resources['Geometry'].pixel_pitch) + e = q_sum * resources['LArData'].ionization_w / resources['LArData'].ionization_recombination(michel_dedx) + + # calculate active distance to exit detector + #active_proj_length = self.extrapolated_intersection(pos[...,0,:], end_pt.reshape(-1,3)) + + # apply a hit density correction + #profile_dqdx = profile_dqdx * ds / ma.maximum(ds - self.density_dx_correction(profile_rr, *self.density_dx_correction_params), resources['Geometry'].pixel_pitch) * (dn > 0) + # apply a curvature correction + profile_rr = profile_rr * self.curvature_rr_correction + + # find max dqdx + max_dqdx = profile_dqdx.max(axis=-1) + + # select stopping muons + #event_is_contained_muon = (event_is_contained & end_pt_in_fid # stops in fiducial volume + # & (e < self.remaining_e_cut) # has additional energy consistent with a Michel or less + # & (max_dqdx > self.dqdx_peak_cut) # has a prominent dQ/dx peak + # #& (ma.sum(is_stopping & ~is_near_edge, axis=-1) == 1) # only one track stopping in fiducial volume + # & (np.mean(muon_likelihood_dqdx, axis=-1) + # + np.mean(muon_likelihood_mcs, axis=-1) * 0 + # - np.mean(proton_likelihood_dqdx, axis=-1) + # - np.mean(proton_likelihood_mcs, axis=-1) * 0 < self.proton_classifier_cut) # dQ/dx profile more consistent with stopping muon than proton + # & (np.mean(muon_likelihood_dqdx, axis=-1) + # + np.mean(muon_likelihood_mcs, axis=-1) * 0 + # - np.mean(mip_likelihood_dqdx, axis=-1) + # - np.mean(mip_likelihood_mcs, axis=-1) * 0 > self.muon_classifier_cut)) # dQ/dx profile more consistent with stopping muon than MIP + + #PID score muon/proton = (2/pi)arctan((loglikelihood muon - loglikelihood proton) / 100) close to 1 = muon, close to -1 = proton + #print("MUON likelihood dqdx shape:", muon_likelihood_dqdx.shape) + #print("MUON likelihood dqdx:", muon_likelihood_dqdx) + #print("Mean MUON likelihood dqdx -1:", np.mean(muon_likelihood_dqdx, axis=-1)) + #print("Mean Muon likelihood dqdx general:", np.mean(muon_likelihood_dqdx)) + pid_muon_proton = (np.mean(muon_likelihood_dqdx, axis=-1) - (np.mean(proton_likelihood_dqdx, axis=-1))) + + #PID score mip/proton = (2/pi)arctan((loglikelihood mip - loglikelihood proton) / 100) close to 1 = mip, close to -1 = proton + pid_mip_proton = (np.mean(mip_likelihood_dqdx, axis=-1) - (np.mean(proton_likelihood_dqdx, axis=-1))) + + ''' + event_sel = (contained_in_fid_red & event_level_cuts) + #& (pid_muon_proton > -1.) + #& (pid_mip_proton > -1.)) # dQ/dx profile more consistent with stopping muon than MIP + #& (-np.mean(muon_likelihood_dqdx, axis=-1) + # + np.mean(proton_likelihood_dqdx, axis=-1)> self.proton_classifier_cut)) + + #track_nhits = tracks.ravel()['nhit'][~tracks['nhit'].mask] + #track_length = tracks.ravel()['length'][~tracks['length'].mask] + #track_theta = tracks.ravel()['theta'][~tracks['theta'].mask] + #track_phi = tracks.ravel()['phi'][~tracks['phi'].mask] + #track_q = tracks.ravel()['q'][~tracks['q'].mask] + + #passing_events = len(event_ids[event_sel]) + + #print("Max Track length:", max_track_length) + + '''if self.is_mc and len(is_proton): + # define true proton events contained in fid as any event with + # at least one proton contained in fid + event_is_true_proton = is_proton + #print("Shape of is_proton:", is_proton.shape) + true_contained = np.full_like(is_proton, False) + for i in range(len(true_contained)): + true_xyz_start_in_fid = resources['Geometry'].in_fid( + true_xyz_start[i], cathode_fid=self.cathode_fid_cut, field_cage_fid=self.fid_cut, anode_fid=self.anode_fid_cut) + true_xyz_end_in_fid = resources['Geometry'].in_fid( + true_xyz_end[i], cathode_fid=self.cathode_fid_cut, field_cage_fid=self.fid_cut, anode_fid=self.anode_fid_cut) + contained = true_xyz_start_in_fid & true_xyz_end_in_fid + true_contained[i] = bool(np.sum(contained)) + #print("Contained:", contained) + #print("Sum contained", bool(np.sum(contained))) + #true_contained.reshape(len(tracks)) + #print("True contained shape:", true_contained.shape)''' + + + sel = np.zeros(len(tracks), dtype=self.sel_dtype) + #print("Event selection:", event_sel) + + if len(sel): + #print("Selection identified:", str(len(sel))+"/32") + sel['sel'] = event_sel + #print("Selected:", sel['sel']) + sel['event_id'] = event_ids + sel['hip'] = event_sel #((pid_muon_proton > -1.)& (pid_mip_proton > -1.)) + sel['nhits_over_thresh'] = max_hit_charge + sel['pdg_id'] = np.zeros(1000) + #sel['muon_loglikelihood_mean'] = np.mean(muon_likelihood_mcs, axis=-1) * 0 + np.mean(muon_likelihood_dqdx, axis=-1) + #sel['proton_loglikelihood_mean'] = np.mean(proton_likelihood_mcs, axis=-1) * 0 + np.mean(proton_likelihood_dqdx, axis=-1) + #sel['mip_loglikelihood_mean'] = np.mean(mip_likelihood_mcs, axis=-1) * 0 + np.mean(mip_likelihood_dqdx, axis=-1) + #sel['max_dqdx'] = max_dqdx + sel['ntracks'] = event_ntracks_in_fid + #sel['pid_muon_proton'] = pid_muon_proton + #sel['pid_mip_proton'] = pid_mip_proton + #sel['next_trigs'] = event_next_trigs[event_sel] + #sel['ntracks'] = event_ntracks[event_sel] + #sel['nhits'] = event_nhits[event_sel] + #sel['event_charge'] = event_charge[event_sel] + + '''if self.is_mc: + event_true_sel = np.zeros(len(tracks), dtype=self.sel_dtype) + if len(event_true_sel): + event_true_sel['sel'] = event_is_true_proton & true_contained + event_true_sel['hip'] = event_is_true_proton + event_true_sel['event_id'] = event_ids + event_true_sel['pdg_id'] = np.concatenate((track_traj['pdgId'],np.zeros((len(tracks), 1000-len(track_traj['pdgId'][0])))), axis=-1) + #event_true_sel['muon_loglikelihood_mean'] = ma.sum(is_muon, axis=-1) >= 1 + #event_true_sel['proton_loglikelihood_mean'] = ma.sum(is_proton & is_true_stopping, axis=-1) >= 1 + #event_true_sel['mip_loglikelihood_mean'] = ma.sum(is_muon & ~is_true_stopping, axis=-1) >= 1''' +# + #event_tracks = np.zeros(len(track_length), dtype=self.event_tracks_dtype) + + #if len(event_tracks): + # event_tracks['nhits'] = track_nhits + # event_tracks['length'] = track_length + # event_tracks['theta'] = track_theta + # event_tracks['phi'] = track_phi + # event_tracks['track_q'] = track_q + # + #hit_profile = np.zeros(hits.shape, dtype=self.hit_profile_dtype) + #if len(hit_profile): + # hit_profile['idx'] -= 1 + # hit_profile['idx'][~hits['id'].mask] = hit_prof_idx[~hits['id'].mask] + # hit_profile['rr'][~hits['id'].mask] = hit_prof_rr[~hits['id'].mask] + + + # reserve data space + sel_slice = self.data_manager.reserve_data( + f'{self.path}/{self.sel_dset_name}', source_slice) + #event_tracks_slice = self.data_manager.reserve_data( + # f'{self.path}/{self.event_tracks_dset_name}', source_slice) + #event_hits_slice = self.data_manager.reserve_data( + # f'{self.path}/{self.hit_profile_dset_name}', int((~hits['id'].mask).sum())) + if self.is_mc: + sel_truth_slice = self.data_manager.reserve_data( + f'{self.path}/{self.sel_truth_dset_name}', + source_slice) + + # write + self.data_manager.write_data(f'{self.path}/{self.sel_dset_name}', + sel_slice, sel) + if self.is_mc: + self.data_manager.write_data( + f'{self.path}/{self.sel_truth_dset_name}', + sel_truth_slice, event_true_sel) + #self.data_manager.write_data(f'{self.path}/{self.event_tracks_dset_name}', + # event_tracks_slice, event_tracks) + #self.data_manager.write_data(f'{self.path}/{self.hit_profile_dset_name}', + # event_hits_slice, hit_profile[~hits['id'].mask]) + #self.data_manager.write_ref(f'{self.path}/{self.hit_profile_dset_name}', + # self.hits_dset_name, np.c_[event_hits_slice, hits['id'].compressed()]) + + ## calculate hit positions and charge + #hit_q = self.larpix_gain * q['q'] # convert mV -> ke +# + ## filter out bad channel ids + #hit_mask = (hits['px'] != 0.0) & (hits['py'] != 0.0) & ~hit_q.mask & ~hit_drift['t_drift'].mask + #hit_q.mask = hit_q.mask | ~hit_mask + #hit_xyz = ma.array(np.concatenate([ + # hits['px'][..., np.newaxis], hits['py'][..., np.newaxis], + # hit_drift['z'][..., np.newaxis]], axis=-1), shrink=False,\ + # mask=np.zeros(hits['px'].shape + (3,), dtype=bool) | hit_q.mask[...,np.newaxis] | ~hit_mask[...,np.newaxis]) + + #Event charge threshold selection + #HIP, MIP selection + #Track fitting + #PIDA + #Void analysis \ No newline at end of file diff --git a/yamls/proto_nd_flow/analysis/hip_selection.yaml b/yamls/proto_nd_flow/analysis/hip_selection.yaml new file mode 100644 index 00000000..27310395 --- /dev/null +++ b/yamls/proto_nd_flow/analysis/hip_selection.yaml @@ -0,0 +1,36 @@ +classname: HIPSelection # hip_selection.py +path: proto_nd_flow.analysis.hip_selection +requires: + - 'combined/tracklets' + - 'combined/t0' + - 'charge/calib_prompt_hits' + - name: 'mc_truth/trajectories' + path: ['charge/raw_events', 'mc_truth/events', 'mc_truth/trajectories'] + #- name: 'combined/track_hits' + # path: ['combined/tracklets', 'charge/hits'] + #- name: 'combined/track_hit_drift' + # path: ['combined/tracklets', 'charge/hits', 'combined/hit_drift'] + +params: + # inputs + hits_dset_name: 'charge/calib_prompt_hits' + ext_trigs_dset_name: 'charge/ext_trigs' + t0_dset_name: 'combined/t0' + tracklet_dset_name: 'combined/tracklets' + hit_drift_dset_name: 'charge/calib_prompt_hits' + truth_trajectories_dset_name: 'mc_truth/trajectories' + charge_dset_name: 'charge/calib_prompt_hits' + + # configuration parameters + fid_cut: 1.0 # cm + cathode_fid_cut: 0.0 # cm + anode_fid_cut: 1.0 # cm + profile_dx: 1.0 # cm + larpix_gain: + mc: 250 # e/mV + medm: 221 # e/mV + high: 221 # e/mV + curvature_rr_correction: + mc: 1.0 + medm: 1.0 + high: 1.0 \ No newline at end of file diff --git a/yamls/proto_nd_flow/util/TrackletReconstruction.yaml b/yamls/proto_nd_flow/util/TrackletReconstruction.yaml index 8512a413..8bdee173 100644 --- a/yamls/proto_nd_flow/util/TrackletReconstruction.yaml +++ b/yamls/proto_nd_flow/util/TrackletReconstruction.yaml @@ -1,13 +1,13 @@ classname: TrackletReconstruction path: proto_nd_flow.util.tracklet_reco requires: - #- 'charge/calib_final_hits' - - 'charge/calib_prompt_hits' + - 'charge/calib_final_hits' + #- 'charge/calib_prompt_hits' params: # inputs - hits_dset_name: 'charge/calib_prompt_hits' #'charge/calib_final_hits' - charge_dset_name: 'charge/calib_prompt_hits' #'charge/calib_final_hits' - hit_drift_dset_name: 'charge/calib_prompt_hits' #'charge/calib_final_hits' + hits_dset_name: 'charge/calib_final_hits' #'charge/calib_prompt_hits' + charge_dset_name: 'charge/calib_final_hits' #'charge/calib_prompt_hits' + hit_drift_dset_name: 'charge/calib_final_hits' #'charge/calib_prompt_hits' # output tracklet_dset_name: 'combined/tracklets' @@ -15,9 +15,9 @@ params: # configuration parameters #dbscan_eps: 2.5 max_iterations: 1 - hdbscan_min_cluster_size: 20 - hdbscan_min_samples: 20 - hdbscan_cluster_sel_eps: 2.488 + hdbscan_min_cluster_size: 15 + hdbscan_min_samples: 9 + hdbscan_cluster_sel_eps: 3.421 ransac_min_samples: 2 ransac_residual_threshold: 2.444 ransac_max_trials: 5 diff --git a/yamls/proto_nd_flow/workflows/analysis/hip_sel_workflow.yaml b/yamls/proto_nd_flow/workflows/analysis/hip_sel_workflow.yaml new file mode 100644 index 00000000..16918d56 --- /dev/null +++ b/yamls/proto_nd_flow/workflows/analysis/hip_sel_workflow.yaml @@ -0,0 +1,22 @@ +flow: + source: events + stages: [hip_sel] + drop: [] + + +resources: + - !include yamls/proto_nd_flow/resources/RunData.yaml + - !include yamls/proto_nd_flow/resources/LArData.yaml + - !include yamls/proto_nd_flow/resources/Geometry.yaml + #- !include yamls/proto_nd_flow/resources/ParticleData.yaml + #- !include yamls/module0_flow/resources/DisabledChannels.yaml + +events: + classname: H5FlowDatasetLoopGenerator + path: h5flow.modules + dset_name: 'charge/events' + params: + chunk_size: 32 + +hip_sel: + !include yamls/proto_nd_flow/analysis/hip_selection.yaml \ No newline at end of file From f51e4fa93c9e4f73bcedcd0590959f27f33a31be Mon Sep 17 00:00:00 2001 From: Elise Dianne Hinkle Date: Fri, 22 Dec 2023 11:36:03 -0800 Subject: [PATCH 27/37] Adding ParticleData resource source code in proto_nd_flow and yaml in module1_flow. --- src/proto_nd_flow/resources/particle_data.py | 245 ++++++++++++++++++ .../module1_flow/resources/ParticleData.yaml | 8 + 2 files changed, 253 insertions(+) create mode 100644 src/proto_nd_flow/resources/particle_data.py create mode 100644 yamls/module1_flow/resources/ParticleData.yaml diff --git a/src/proto_nd_flow/resources/particle_data.py b/src/proto_nd_flow/resources/particle_data.py new file mode 100644 index 00000000..2ff71699 --- /dev/null +++ b/src/proto_nd_flow/resources/particle_data.py @@ -0,0 +1,245 @@ +import numpy as np + +from h5flow.core import H5FlowResource, resources + +from proto_nd_flow.util.compat import assert_compat_version +import proto_nd_flow.util.units as units + + +class ParticleData(H5FlowResource): + ''' + Provides helper functions for calculating and accessing particle + properties. Range tables will be saved and/or loaded to/from metadata + within the output file. + + Requires ``LArData`` resource within workflow. + + Parameters: + - ``path``: ``str``, path to stored particle data within file + - ``muon_range_table_path``: ``str``, path to PDG text file of muon range in LAr + - ``proton_range_table_path``: ``str``, path to NIST text file of proton range in LAr + + Provides: + - ``muon_range_table``: Range, kinetic energy, and for muons in LAr + - ``proton_range_table``: Range, kinetic energy, and for protons in LAr + - ``landau_width``: 1-sigma width of Landau dE/dx distribution in LAr + - ``landau_peak``: MPV of Landau dE/dx distribution in LAr + - ``{}_mass``: for proton (``p``), neutron (``n``), muon (``mu``), electron (``e``), pion (``pi``), pi0 (``pi0``) + + Example usage:: + + from h5flow.core import resources + + resources['ParticleData'].muon_range_table['range'] + + Example config:: + + resources: + - classname: ParticleData + params: + path: 'particle_info' + + ''' + class_version = '0.0.0' + + default_path = 'particle_info' + default_muon_range_table_path = 'PDG_muon_range_table_Ar.txt' + default_proton_range_table_path = 'NIST_proton_range_table_Ar.txt' + + _K = 0.307075 * units.MeV * (units.cm)**2 + + #: electron mass + e_mass = 510.9989461 * units.keV + + #: muon mass + mu_mass = 105.6583745 * units.MeV + + #: proton mass + p_mass = 938.2720813 * units.MeV + + #: neutron mass + n_mass = 939.5654133 * units.MeV + + #: charged pion mass + pi_mass = 139.57039 * units.MeV + + #: neutral pion mass + pi0_mass = 134.9768 * units.MeV + + def __init__(self, **params): + super(ParticleData, self).__init__(**params) + + self.path = params.get('path', self.default_path) + self.muon_range_table_path = params.get('muon_range_table_path', + self.default_muon_range_table_path) + self.proton_range_table_path = params.get('proton_range_table_path', + self.default_proton_range_table_path) + + def init(self, source_name): + super(ParticleData, self).init(source_name) + + if not self.data_manager.attr_exists(self.path, 'classname'): + # no data stored in file, generate it + muon_table = self.load_pdg_range_table(self.muon_range_table_path) + proton_table = self.load_nist_range_table(self.proton_range_table_path) + + self.data = dict() + + # appropriate units from tables + self.data['muon_range'] = (muon_table['range'] * units.g / (units.cm)**2 + / resources['LArData'].density) + self.data['muon_t'] = muon_table['t'] * units.MeV + self.data['muon_dedx'] = (muon_table['dedx'] / units.g + * units.MeV * units.cm**2 + * resources['LArData'].density) + self.data['proton_range'] = (proton_table['range'] * units.g / (units.cm)**2 + / resources['LArData'].density) + self.data['proton_t'] = proton_table['t'] * units.MeV + self.data['proton_dedx'] = (proton_table['dedx'] / units.g + * units.MeV * units.cm**2 + * resources['LArData'].density) + + self.data['classname'] = self.classname + self.data['class_version'] = self.class_version + self.data_manager.set_attrs(self.path, **self.data) + else: + # data exists, check version compatibility + self.data = dict(self.data_manager.get_attrs(self.path)) + assert_compat_version(self.class_version, self.data['class_version']) + + @property + def muon_range_table(self): + ''' + Range v. kinetic energy v. dE/dx for a muon in LAr. ``dict`` with + keys: ``range``, ``t``, and ``dedx`` + + ''' + return dict(range=self.data['muon_range'], t=self.data['muon_t'], + dedx=self.data['muon_dedx']) + + @property + def proton_range_table(self): + ''' + Range v. kinetic energy v. dE/dx for a proton in LAr. ``dict`` with + keys: ``range``, ``t``, and ``dedx`` + + ''' + return dict(range=self.data['proton_range'], t=self.data['proton_t'], + dedx=self.data['proton_dedx']) + + def landau_width(self, t, mass, dx): + ''' Moyal scale factor for Landau dE/dx width in LAr ''' + e = t + mass + p = np.sqrt(e**2 - mass**2) + beta = p / e + + ksi = self._ksi(dx, beta) + + rv = (4 * ksi / 3.59) + return rv + + def landau_peak(self, t, mass, dx): + ''' Moyal peak location for Landau dE/dx distribution in LAr ''' + e = t + mass + p = np.sqrt(e**2 - mass**2) + beta = p / e + gamma = e / mass + + ksi = self._ksi(dx, beta) + I = 188.0 * units.eV # noqa: E741 + + t0 = np.log(2 * self.e_mass * beta**2 * gamma**2 / I) + t1 = np.log(ksi / I) + t2 = 0.200 - beta**2 - self._delta(beta * gamma) + + rv = ksi * (t0 + t1 + t2) + return rv + + def mcs_angle(self, t, mass, dx): + ''' Multiple coulomb scattering characteristic angle ''' + e = t + mass + p = np.sqrt(e**2 - mass**2) + beta = p / e + + x = dx / resources['LArData'].radiation_length # radiation lengths + f = (1 + 0.088 * np.log10(x / beta**2)) + theta0 = (13.6 * units.MeV) / (beta * p) * np.sqrt(x) * f + return theta0 + + def _ksi(self, x, beta): + Z = resources['LArData'].Z + A = resources['LArData'].A + ksi = (self._K / 2) * (Z / A) * (resources['LArData'].density * x) / (beta**2) + return ksi + + @staticmethod + def _delta(betagamma): + #: values from PDG LAr data + a = 0.1956 + x0 = 0.2 + x1 = 3.0 + cbar = 5.2146 + k = 3.00 + x = np.log10(betagamma) + + return (x < x0) * ( + (x < x1) * (2 * np.log(10) * x - cbar + a * (x1 - x)**k) + + (x > x1) * (2 * np.log(10) * x - cbar)) + + @staticmethod + def load_nist_range_table(path): + ''' + Loads particle range, kinetic energy, and dE/dx from a + NIST text file [https://physics.nist.gov/PhysRefData/Star/Text/PSTAR-t.html]. + + :param path: path to range table file + + :returns: ``dict`` with keys ``range``, ``t``, ``dedx`` + + ''' + with open(path, 'r') as fi: + _data = fi.readlines()[15:] + _r = np.empty(len(_data)) + _ke = np.empty(len(_data)) + _dedx = np.empty(len(_data)) + for i, line in enumerate(_data): + row_data = line.strip().split() + if row_data: + _ke[i] = float(row_data[0]) + _r[i] = float(row_data[4]) + _dedx[i] = float(row_data[3]) + + _table = dict(range=_r, + t=_ke, + dedx=_dedx) + + return _table + + @staticmethod + def load_pdg_range_table(path): + ''' + Loads particle range, kinetic energy, and dE/dx from a + PDG text file [https://pdg.lbl.gov/2021/AtomicNuclearProperties/]. + + :param path: path to range table file + + :returns: ``dict`` with keys ``range``, ``t``, ``dedx`` + + ''' + with open(path, 'r') as fi: + _data = fi.readlines()[10:] + _r = np.empty(len(_data)) + _ke = np.empty(len(_data)) + _dedx = np.empty(len(_data)) + for i, line in enumerate(_data): + row_data = line.strip().split() + if row_data: + _ke[i] = float(row_data[0]) + _r[i] = float(row_data[8]) + _dedx[i] = float(row_data[7]) + + _table = dict(range=_r, + t=_ke, + dedx=_dedx) + + return _table diff --git a/yamls/module1_flow/resources/ParticleData.yaml b/yamls/module1_flow/resources/ParticleData.yaml new file mode 100644 index 00000000..97935893 --- /dev/null +++ b/yamls/module1_flow/resources/ParticleData.yaml @@ -0,0 +1,8 @@ +classname: ParticleData +path: proto_nd_flow.resources.particle_data +params: + path: 'particle_info' + muon_range_table_path: 'data/module1_flow/PDG_muon_range_table_Ar.txt' + # download link: https://portal.nersc.gov/project/dune/data/Module0/merged/reco_data/PDG_muon_range_table_Ar.txt + proton_range_table_path: 'data/module1_flow/NIST_proton_range_table_Ar.txt' + # download link: https://portal.nersc.gov/project/dune/data/Module0/merged/reco_data/NIST_proton_range_table_Ar.txt \ No newline at end of file From 27ebd60e2307ccb8dd8adbc5e542ddc0f1cfae60 Mon Sep 17 00:00:00 2001 From: Elise Dianne Hinkle Date: Fri, 22 Dec 2023 14:15:09 -0800 Subject: [PATCH 28/37] Adding initial infrastructure for time-dependent version of Disabled Channels resource in proto_nd_flow. --- .../resources/disabled_channels.py | 269 ++++++++++++++++++ .../resources/DisabledChannels.yaml | 13 + .../combined/combined_reconstruction.yaml | 2 +- 3 files changed, 283 insertions(+), 1 deletion(-) create mode 100644 src/proto_nd_flow/resources/disabled_channels.py create mode 100644 yamls/module1_flow/resources/DisabledChannels.yaml diff --git a/src/proto_nd_flow/resources/disabled_channels.py b/src/proto_nd_flow/resources/disabled_channels.py new file mode 100644 index 00000000..ae3e6918 --- /dev/null +++ b/src/proto_nd_flow/resources/disabled_channels.py @@ -0,0 +1,269 @@ +import logging +import json +import numpy as np + +from h5flow.core import H5FlowResource, resources + +import proto_nd_flow.util.units as units +from proto_nd_flow.util.lut import LUT, write_lut, read_lut +from proto_nd_flow.util.compat import assert_compat_version + + +class DisabledChannels(H5FlowResource): + ''' + Provides helper functions for identifying the positions of disabled + channels. + + Requires ``RunData`` and ``Geometry`` resources within workflow. + + Parameters: + - ``path``: ``str``, path to stored geometry data within file + - ``disabled_channels_timestamp_dict``: ``str``, path to file mapping disabled channel file timestamps to data file timestamps + - ``disabled_channels_file_dir``: ``str``, path to directory with time dependent disabled channel files + - ``disabled_channels_common_filename``: ``str``, common beginning part of disabled channel file filenames + - ``disabled_channels_file_format``: ``str``, file format for disabled channel files + - ``missing_asic_list``: ``str``, path to file specifying disabled coordinates not in geometry file + + Provides: + - ``disabled_pixel_coords``: 2D coordinates of all disabled channels + - ``disabled_channel_lut``: lookup table to find if a pixel 2D coordinate is disabled + - ``is_active()``: helper function for determining if a 3D point in in an active region + + Example usage:: + + from h5flow.core import resources + + resources['DisabledChannels'].disabled_channel_lut[(io_group,z,y)] + + Example config:: + + resources: + - classname: DisabledChannels + params: + path: 'disabled_channels' + disabled_channels_timestamp_dict: 'data/module0_flow/module1_config_to_data_map.json' + disabled_channels_file_dir: '/global/cfs/cdirs/dune/www/data/Module1/TPC12/disabled/' + disabled_channels_common_filename: 'disabled_channels_' + disabled_channels_file_format: '.json' + missing_asic_list: 'data/module1_flow/module1-network-absent-ASICs.json' + + ''' + class_version = '0.0.0' + + default_path = 'disabled_channels' + + def __init__(self, **params): + super(DisabledChannels, self).__init__(**params) + + self.path = params.get('path', self.default_path) + self.disabled_channels_timestamp_dict = params.get('disabled_channels_timestamp_dict', None) + self.disabled_channels_file_dir = params.get('disabled_channels_file_dir', None) + self.disabled_channels_common_filename = params.get('disabled_channels_common_filename', None) + self.disabled_channels_file_format = params.get('disabled_channels_file_format', None) + self.missing_asic_list = params.get('missing_asic_list', None) + + def init(self, source_name): + super(DisabledChannels, self).init(source_name) + + # create group (if not present) + self.data_manager.set_attrs(self.path) + # load data (if present) + self.data = dict(self.data_manager.get_attrs(self.path)) + + if not self.data: + # no data stored in file, generate it + self._disabled_channel_lut, self._disabled_pixel_coords = self.load_disabled_channels_lut( + self.disabled_channels_list, self.missing_asic_list) + self.data['classname'] = self.classname + self.data['class_version'] = self.class_version + self.data['disabled_channels_list'] = (self.disabled_channels_list + if self.disabled_channels_list is not None + else '') + self.data['missing_asic_list'] = (self.missing_asic_list + if self.missing_asic_list is not None + else '') + self.data_manager.set_attrs(self.path, **self.data) + zy_dtype = np.dtype([('z', self._disabled_pixel_coords.dtype), ('y', self._disabled_pixel_coords.dtype)]) + self.data_manager.create_dset(self.path + '/zy', dtype=zy_dtype) + sl = self.data_manager.reserve_data(self.path + '/zy', slice(0, len(self._disabled_pixel_coords))) + self.data_manager.write_data(self.path + '/zy', sl, self._disabled_pixel_coords.view(zy_dtype).ravel()) + + write_lut(self.data_manager, self.path, self.disabled_channel_lut, + 'lut') + else: + assert_compat_version(self.class_version, self.data['class_version']) + + self._disabled_channel_lut = read_lut(self.data_manager, self.path, + 'lut') + self._disabled_pixel_coords = np.c_[self.data_manager[self.path+'/zy/data']['z'], self.data_manager[self.path+'/zy/data']['y']] + + if self.rank == 0: + logging.info(f'N disabled channels: {len(self.disabled_pixel_coords)}') + logging.info(f'Disabled channel LUT size: ' + f'{self.disabled_channel_lut.nbytes/1024/1024:0.02f}MB') + + self._pixel_pitch = resources['Geometry'].pixel_pitch + self._pixel_z_hi_edge = np.sort(np.unique(resources['Geometry'].pixel_coordinates_2D.compress((0,)))) + self._pixel_pitch/2 + self._pixel_y_hi_edge = np.sort(np.unique(resources['Geometry'].pixel_coordinates_2D.compress((1,)))) + self._pixel_pitch/2 + io_group,io_channel,_,_ = resources['Geometry'].pixel_coordinates_2D.keys() + tile_id = resources['Geometry'].tile_id[(io_group,io_channel)] + self._anode_drift_coordinate, idx = np.unique(resources['Geometry'].anode_drift_coordinate[(tile_id,)], return_index=True) + self._tpc_lookup = io_group[idx] + + @property + def disabled_pixel_coords(self): + return self._disabled_pixel_coords + + @property + def disabled_channel_lut(self): + return self._disabled_channel_lut + + def is_active(self, xyz): + ''' + Lookup a specific position to determine if it would fall onto an active pixel + + :param xyz: 3D position ``shape: (..., 3)`` + + :returns: boolean array with ``True == active``, ``shape: (...,)`` + + ''' + pixel_z = self._pixel_z_hi_edge[np.clip(np.digitize(xyz[...,2], bins=self._pixel_z_hi_edge), 0, len(self._pixel_z_hi_edge)-1)] - self._pixel_pitch/2 + pixel_y = self._pixel_y_hi_edge[np.clip(np.digitize(xyz[...,1], bins=self._pixel_y_hi_edge), 0, len(self._pixel_y_hi_edge)-1)] - self._pixel_pitch/2 + tpc = self._tpc_lookup[np.argmin(np.abs(xyz[...,2:3] - self._anode_drift_coordinate.reshape([1,]*(xyz.ndim-1)+[-1])), axis=-1)] + disabled = self.disabled_channel_lut[(tpc.astype(int), pixel_z.astype(int), pixel_y.astype(int))] + return ~disabled + + @staticmethod + def load_disabled_channels_lut(disabled_channels_list=None, + missing_asic_list=None): + ''' + Loads a disabled channels lookup-table from the json formatted filenames:: + + disabled_channels_*.json + missing_asic_list + + ``disabled_channels_*.json`` files contain ``chip-key: [channel_id]`` pairs of + disabled channels that are defined within the geometry, but should be + considered as disabled. The ``Geometry`` resource is used to find the 2D + locations of these pixels. + + ``missing_asic_list`` contains ``io_group: [[z,y], ...]`` pixel positions + that should be considered as disabled regions. + + Creates a boolean lookup table with keys of + ``(io_group, int(pixel_z), int(pixel_y))`` to determine if a given + pixel position falls onto a disabled channel. + + :returns: ``tuple`` of boolean ``proto_nd_flow.util.lut.LUT`` and ``list`` of pixel 2D coordinates for each disabled channel + + ''' + io_group = list() + zy = np.empty((0, 2)) + + if disabled_channels_list is not None: + # first load disabled channels list + with open(disabled_channels_list, 'r') as fi: + data = json.load(fi) + + # get disabled channels from file + io_channel = list() + chip_id = list() + channel_id = list() + for key in data: + if key == 'All': + continue + io_group_, io_channel_, chip_id_ = key.split('-') + for ch in data[key]: + io_group.append(int(io_group_)) + io_channel.append(int(io_channel_)) + chip_id.append(int(chip_id_)) + channel_id.append(int(ch)) + + if resources['Geometry'].network_agnostic == True: + # add additional entries for each io channel + n_io_channels_per_tile = resources['Geometry'].n_io_channels_per_tile + start_io_channel = ((io_channel_-1)//n_io_channels_per_tile)*n_io_channels_per_tile + 1 + for io_channel in range(start_io_channel, start_io_channel+n_io_channels_per_tile): + io_group.append(int(io_group_)) + io_channel.append(int(io_channel)) + chip_id.append(int(chip_id_)) + channel_id.append(int(ch)) + + pixel_coordinates_2D = resources['Geometry'].pixel_coordinates_2D + chip_key = (np.array(io_group), np.array(io_channel), + np.array(chip_id), np.array(channel_id)) + zy = pixel_coordinates_2D[chip_key] + + if missing_asic_list is not None: + # then load missing asic pixels + with open(missing_asic_list, 'r') as fi: + data = json.load(fi) + + # add to lists + for io_group_ in data: + for asic in data[io_group_]: + io_group.append(int(io_group_)) + zy = np.append(zy, np.array([asic]), axis=0) + + disable_channels_lut = LUT(bool, + (min(io_group), max(io_group)), + (min(zy[:, 0].astype(int)) - 1, + max(zy[:, 0].astype(int)) + 1), + (min(zy[:, 1].astype(int)) - 1, + max(zy[:, 1].astype(int)) + 1), + default=False) + # apply a fudge factor to account for any rounding errors + for dz in (+1, 0, -1): + for dy in (+1, 0, -1): + disable_channels_lut[(io_group, zy[:, 0].astype(int) + dz, + zy[:, 1].astype(int) + dy)] = True + + return disable_channels_lut, zy + + +""" TODO: Add version of the following code/methods for time dependent lookup functionality for dis. ch. list + + disabled_channels_config = self.disabled_channels_timestamp_dict + charge_filename = resources['RunData'].charge_filename + + def convert_ts_str_to_float(filename): + + filename = filename.strip('CET') + file_ts_arr = np.array([float(x)/100 for x in filename.split('_') if x and float(x)/100 < 1.]) + file_ts_float = 0. + len_file_ts_arr = len(file_ts_arr) + for i in range(len_file_ts_arr): + file_ts_float += file_ts_arr[i]*10**(len_file_ts_arr*2 - i*2) + + return file_ts_float + + print("File timestamp float:", convert_ts_to_float(charge_filename)) + file_ts = convert_ts_to_float(charge_filename) + + def lookup_disabled_channel_file_ts(self, filename): # use self + + dc_file_ts = '' + dc_config_file = open(self.disabled_channels_timestamp_dict) + dc_config = json.load(dc_config_file) + for ts in dc_config.keys(): + + dc_ts = convert_ts_to_float(ts) + + if file_ts > dc_ts: + dc_file_ts = ts + continue + else: + break + + if dc_file_ts == '': + raise ValueError("Disabled channel file timestamp not found.") + + return dc_file_ts + + print("Disabled Channel File Timestamp:", lookup_disabled_channel_file_ts(disabled_channels_config, charge_filename)) + + + + + +""" \ No newline at end of file diff --git a/yamls/module1_flow/resources/DisabledChannels.yaml b/yamls/module1_flow/resources/DisabledChannels.yaml new file mode 100644 index 00000000..8f1e7628 --- /dev/null +++ b/yamls/module1_flow/resources/DisabledChannels.yaml @@ -0,0 +1,13 @@ +classname: DisabledChannels # resources/disabled_channels.py +path: proto_nd_flow.resources.disabled_channels +params: + path: 'disabled_channels' + disabled_channels_timestamp_dict: 'data/module0_flow/module1_config_to_data_map.json' + # download link: https://portal.nersc.gov/project/dune/data/Module1/TPC12/disabled/module1_config_to_data_map.json + # format: key=ASIC config file timestamp + # value=[[row in run log spreadsheet, data file timestamp]] + disabled_channels_file_dir: '/global/cfs/cdirs/dune/www/data/Module1/TPC12/disabled/' + disabled_channels_common_filename: 'disabled_channels_' + disabled_channels_file_format: '.json' + missing_asic_list: 'data/module1_flow/module1-network-absent-ASICs.json' + # download link: https://portal.nersc.gov/project/dune/data/Module1/TPC12/module1-network-absent-ASICs.json diff --git a/yamls/module1_flow/workflows/combined/combined_reconstruction.yaml b/yamls/module1_flow/workflows/combined/combined_reconstruction.yaml index 7f4b2dd3..bf5881d2 100644 --- a/yamls/module1_flow/workflows/combined/combined_reconstruction.yaml +++ b/yamls/module1_flow/workflows/combined/combined_reconstruction.yaml @@ -19,7 +19,7 @@ resources: - !include yamls/module1_flow/resources/RunData.yaml - !include yamls/module1_flow/resources/Geometry.yaml - !include yamls/module1_flow/resources/LArData.yaml -# - !include yamls/proto_nd_flow/resources/DisabledChannels.yaml + - !include yamls/module1_flow/resources/DisabledChannels.yaml t0_reco: !include yamls/module1_flow/reco/combined/T0Reconstruction.yaml From f848e972198ef5208c371e7428332f73d985b6c0 Mon Sep 17 00:00:00 2001 From: Elise Dianne Hinkle Date: Thu, 4 Jan 2024 08:10:39 -0800 Subject: [PATCH 29/37] Allowing for time-based consideration of disabled channels in proto_nd_flow. --- .../resources/disabled_channels.py | 43 +++++++++++-------- 1 file changed, 26 insertions(+), 17 deletions(-) diff --git a/src/proto_nd_flow/resources/disabled_channels.py b/src/proto_nd_flow/resources/disabled_channels.py index ae3e6918..1ebbb692 100644 --- a/src/proto_nd_flow/resources/disabled_channels.py +++ b/src/proto_nd_flow/resources/disabled_channels.py @@ -60,6 +60,8 @@ def __init__(self, **params): self.disabled_channels_file_dir = params.get('disabled_channels_file_dir', None) self.disabled_channels_common_filename = params.get('disabled_channels_common_filename', None) self.disabled_channels_file_format = params.get('disabled_channels_file_format', None) + self.disabled_channels_list = self.disabled_channels_file_dir+self.disabled_channels_common_filename+ \ + self.lookup_disabled_channel_file_ts+self.disabled_channels_file_format self.missing_asic_list = params.get('missing_asic_list', None) def init(self, source_name): @@ -140,7 +142,7 @@ def load_disabled_channels_lut(disabled_channels_list=None, Loads a disabled channels lookup-table from the json formatted filenames:: disabled_channels_*.json - missing_asic_list + missing_asic_list (for Module 1, module1-network-absent-ASICs.json) ``disabled_channels_*.json`` files contain ``chip-key: [channel_id]`` pairs of disabled channels that are defined within the geometry, but should be @@ -220,34 +222,48 @@ def load_disabled_channels_lut(disabled_channels_list=None, return disable_channels_lut, zy + @staticmethod + def convert_ts_str_to_float(filename): -""" TODO: Add version of the following code/methods for time dependent lookup functionality for dis. ch. list + ''' + Convert timestamp in charge data file name to float so that timestamps can be compared - disabled_channels_config = self.disabled_channels_timestamp_dict - charge_filename = resources['RunData'].charge_filename + :param filename: charge filename ``str`` - def convert_ts_str_to_float(filename): + :returns: float with digits of form MMDDhhmmss (M=month, D=day, h=hour(24h), m=min, s=sec) + ''' filename = filename.strip('CET') + # Removes year from consideration in charge file timestamp bc year not in disabled channel file timestamp file_ts_arr = np.array([float(x)/100 for x in filename.split('_') if x and float(x)/100 < 1.]) file_ts_float = 0. len_file_ts_arr = len(file_ts_arr) for i in range(len_file_ts_arr): - file_ts_float += file_ts_arr[i]*10**(len_file_ts_arr*2 - i*2) + file_ts_float += file_ts_arr[i]*pow(10, 2*(len_file_ts_arr - i)) return file_ts_float - print("File timestamp float:", convert_ts_to_float(charge_filename)) - file_ts = convert_ts_to_float(charge_filename) - def lookup_disabled_channel_file_ts(self, filename): # use self + @staticmethod + def lookup_disabled_channel_file_ts(self): + + ''' + Find timestamp for relevant disabled channels file from charge filename + + :param [None] + + :returns: disabled channel dictionary file timestamp of form MM_DD_hh_mm_ss ``str'' + (M=month, D=day, h=hour(24h), m=min, s=sec) + ''' dc_file_ts = '' dc_config_file = open(self.disabled_channels_timestamp_dict) dc_config = json.load(dc_config_file) + file_ts = self.convert_ts_str_to_float(self.charge_filename) + for ts in dc_config.keys(): - dc_ts = convert_ts_to_float(ts) + dc_ts = self.convert_ts_str_to_float(ts) if file_ts > dc_ts: dc_file_ts = ts @@ -259,11 +275,4 @@ def lookup_disabled_channel_file_ts(self, filename): # use self raise ValueError("Disabled channel file timestamp not found.") return dc_file_ts - - print("Disabled Channel File Timestamp:", lookup_disabled_channel_file_ts(disabled_channels_config, charge_filename)) - - - - -""" \ No newline at end of file From 3e42379d93d6a5eb3aa8dc4e30b204b032a53ad0 Mon Sep 17 00:00:00 2001 From: Elise Dianne Hinkle Date: Thu, 4 Jan 2024 09:20:19 -0800 Subject: [PATCH 30/37] Adding MC case to disabled_channels resource in proto_nd_flow. --- .../resources/disabled_channels.py | 25 +++++++++++++------ 1 file changed, 18 insertions(+), 7 deletions(-) diff --git a/src/proto_nd_flow/resources/disabled_channels.py b/src/proto_nd_flow/resources/disabled_channels.py index 1ebbb692..02d2519f 100644 --- a/src/proto_nd_flow/resources/disabled_channels.py +++ b/src/proto_nd_flow/resources/disabled_channels.py @@ -1,6 +1,7 @@ import logging import json import numpy as np +import random from h5flow.core import H5FlowResource, resources @@ -63,12 +64,14 @@ def __init__(self, **params): self.disabled_channels_list = self.disabled_channels_file_dir+self.disabled_channels_common_filename+ \ self.lookup_disabled_channel_file_ts+self.disabled_channels_file_format self.missing_asic_list = params.get('missing_asic_list', None) + self.is_mc = False def init(self, source_name): super(DisabledChannels, self).init(source_name) # create group (if not present) self.data_manager.set_attrs(self.path) + self.is_mc = resources['RunData'].is_mc # load data (if present) self.data = dict(self.data_manager.get_attrs(self.path)) @@ -261,15 +264,23 @@ def lookup_disabled_channel_file_ts(self): dc_config = json.load(dc_config_file) file_ts = self.convert_ts_str_to_float(self.charge_filename) - for ts in dc_config.keys(): + # Choose random disabled channels file for MC files + if self.is_mc: + + dc_file_ts = random.choice(list(dc_config.keys())) + + # Choose disabled channels file based on timestamp for data files + else: + + for ts in dc_config.keys(): - dc_ts = self.convert_ts_str_to_float(ts) + dc_ts = self.convert_ts_str_to_float(ts) - if file_ts > dc_ts: - dc_file_ts = ts - continue - else: - break + if file_ts > dc_ts: + dc_file_ts = ts + continue + else: + break if dc_file_ts == '': raise ValueError("Disabled channel file timestamp not found.") From 4969e6d89cfeac4df52d26456c01a928fd414b38 Mon Sep 17 00:00:00 2001 From: Elise Dianne Hinkle Date: Fri, 5 Jan 2024 15:08:38 -0800 Subject: [PATCH 31/37] Adding bug fixes to allow flow to run over Module 1 Bern data. --- data/module1_flow/module0.yaml | 50 +++++++++++++++++++ .../proto_nd_flow/protondflow_evd.py | 6 +-- .../reco/charge/calib_prompt_hits.py | 8 +-- .../resources/disabled_channels.py | 3 +- .../reco/charge/CalibHitBuilder.yaml | 5 ++ .../combined/combined_reconstruction.yaml | 2 +- 6 files changed, 66 insertions(+), 8 deletions(-) create mode 100644 data/module1_flow/module0.yaml diff --git a/data/module1_flow/module0.yaml b/data/module1_flow/module0.yaml new file mode 100644 index 00000000..abddd9cf --- /dev/null +++ b/data/module1_flow/module0.yaml @@ -0,0 +1,50 @@ +# Argon properties +temperature: 87.17 # K +e_field: 0.50 # kV/cm +lifetime: 2.6e+3 # us +long_diff: 4.0e-6 # cm * cm / us +tran_diff: 8.8e-6 # cm * cm / us +singlet_fraction: 0.375 +tau_s: 0.001 # us +tau_t: 0.752 # us +#tau_t: 0.620 # us + +# Charge simulation parameters +drift_length: 30.27225 # cm +time_interval: [0, 200.] # us +response_sampling: 0.1 # us +#response_sampling: 0.05 # us +response_bin_size: 0.04434 # cm +time_padding: 190 # us +time_window: 189.1 # us + +# Charge geometry parameters +tpc_offsets: # cm + - [0, -21.8236, 0] +tile_map: + - [[7,5,3,1],[8,6,4,2]] + - [[16,14,12,10],[15,13,11,9]] +module_to_io_groups: + 1: [1,2] + +# Light simulation parameters +light_gain: [-5.2589, -5.1955, -5.1616, -5.0982, -5.6851, -5.6870, -58.5344, -58.1440, -58.7968, -59.8208, -55.1488, -57.2672, -5.5680, -5.2243, -5.4509, -5.4291, -5.2672, -5.4278, -57.8816, -54.9824, -54.2272, -54.5856, -56.7616, -58.1696, -5.1424, -6.1382, -6.2451, -5.9392, -4.9338, -5.0266, -51.6864, -50.1568, -49.3440, -51.5904, -48.4992, -46.8160, -6.0134, -6.3974, -6.0077, -6.1254, -6.1280, -6.2048, -0.0000, -0.0000, -0.0000, -0.0000, -0.0000, -0.0000, -5.8694, -5.9027, -5.9392, -6.0058, -6.0083, -6.0454, -59.9296, -61.7792, -63.2704, -60.4672, -61.1776, -60.7680, -6.7821, -6.8288, -6.7693, -6.9325, -6.7930, -6.7757, -55.9552, -57.2032, -56.1856, -53.5232, -59.9296, -0.0000, -6.2221, -6.0813, -6.0646, -6.2138, -6.2310, -6.3558, -42.9824, -41.6768, -51.2832, -49.4976, -42.1312, -44.9792, -6.1901, -0.0000, -5.9878, -5.8035, -6.1069, -6.4064, -0.0000, -55.6160, -56.4864, -55.6416, -54.8032, -55.4816] # PE/us / ADC +sipm_response_model: 1 # arbitrary impulse +impulse_model: 'larndsim/bin/sipm_impulse.npy' +impulse_tick_size: 0.01 # us + +light_det_noise_sample_spacing: 0.01 # us +light_trig_threshold: [ + -1500, -9e+9, -1500, -9e+9, -1500, -9e+9, -1500, -9e+9, -1500, -9e+9, -1500, -9e+9, -1500, -9e+9, -1500, -9e+9] # ArcLight=~no trigger, LCM=-1500 ADC, every 6 channels summed +light_trig_window: [0.9, 1.66] # us +light_nbit: 10 +op_channel_efficiency: [0.38, 0.38, 0.38, 0.38, 0.38, 0.38, 0.38, 0.38, 0.38, 0.38, 0.38, 0.38, 0.38, 0.38, 0.38, 0.38, 0.38, 0.38, 0.38, 0.38, 0.38, 0.38, 0.38, 0.38, 0.38, 0.38, 0.38, 0.38, 0.38, 0.38, 0.38, 0.38, 0.38, 0.38, 0.38, 0.38, 0.38, 0.38, 0.38, 0.38, 0.38, 0.38, 0.38, 0.38, 0.38, 0.38, 0.38, 0.38, 0.38, 0.38, 0.38, 0.38, 0.38, 0.38, 0.38, 0.38, 0.38, 0.38, 0.38, 0.38, 0.38, 0.38, 0.38, 0.38, 0.38, 0.38, 0.38, 0.38, 0.38, 0.38, 0.38, 0.38, 0.38, 0.38, 0.38, 0.38, 0.38, 0.38, 0.38, 0.38, 0.38, 0.38, 0.38, 0.38, 0.38, 0.38, 0.38, 0.38, 0.38, 0.38, 0.38, 0.38, 0.38, 0.38, 0.38, 0.38] # ad hoc PDE scale factor to better improve data/sim agreement + +# Light geometry parameters +n_op_channel: 96 +tpc_to_op_channel: + - [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47] + - [48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95] +module_to_tpcs: + 1: [0, 1] +lut_vox_div: [14, 26, 8] diff --git a/event_display/proto_nd_flow/protondflow_evd.py b/event_display/proto_nd_flow/protondflow_evd.py index b4346a1d..53da4302 100644 --- a/event_display/proto_nd_flow/protondflow_evd.py +++ b/event_display/proto_nd_flow/protondflow_evd.py @@ -390,9 +390,9 @@ def set_axes(self): self.ax_zyx.set_box_aspect((2, 2, 4)) self.ax_zyx.xaxis.set_major_locator(plt.MaxNLocator(3)) self.ax_zyx.yaxis.set_major_locator(plt.MaxNLocator(3)) - self.ax_zyx.w_xaxis.set_pane_color((1.0, 1.0, 1.0, 1.0)) - self.ax_zyx.w_yaxis.set_pane_color((1.0, 1.0, 1.0, 1.0)) - self.ax_zyx.w_zaxis.set_pane_color((1.0, 1.0, 1.0, 1.0)) + self.ax_zyx.xaxis.set_pane_color((1.0, 1.0, 1.0, 1.0)) + self.ax_zyx.yaxis.set_pane_color((1.0, 1.0, 1.0, 1.0)) + self.ax_zyx.zaxis.set_pane_color((1.0, 1.0, 1.0, 1.0)) self.ax_zyx.zaxis.labelpad = 20 def clear_axes(self): diff --git a/src/proto_nd_flow/reco/charge/calib_prompt_hits.py b/src/proto_nd_flow/reco/charge/calib_prompt_hits.py index ec336c3b..0d401eed 100644 --- a/src/proto_nd_flow/reco/charge/calib_prompt_hits.py +++ b/src/proto_nd_flow/reco/charge/calib_prompt_hits.py @@ -146,20 +146,22 @@ def run(self, source_name, source_slice, cache): mask = ~rfn.structured_to_unstructured(packets_data.mask).any(axis=-1) rh_mask = ~rfn.structured_to_unstructured(raw_hits.mask).any(axis=-1) + has_mc_truth = packet_seg_bt is not None # TODO: change to using RunData "is_mc" field? + # get event boundaries if np.count_nonzero(mask): raw_hits_arr = raw_hits.data[rh_mask] mask = (packets_data['packet_type'] == 0) & mask n = np.count_nonzero(mask) packets_arr = packets_data.data[mask] - packet_frac_bt_arr = packet_frac_bt.data[mask] - packet_seg_bt_arr = packet_seg_bt.data[mask] index_arr = packets_index.data[mask] + if has_mc_truth: + packet_frac_bt_arr = packet_frac_bt.data[mask] + packet_seg_bt_arr = packet_seg_bt.data[mask] else: n = 0 index_arr = np.zeros((0,), dtype=packets_index.dtype) - has_mc_truth = packet_seg_bt is not None # reserve new data calib_hits_slice = self.data_manager.reserve_data(self.calib_hits_dset_name, n) diff --git a/src/proto_nd_flow/resources/disabled_channels.py b/src/proto_nd_flow/resources/disabled_channels.py index 02d2519f..c5dcc44d 100644 --- a/src/proto_nd_flow/resources/disabled_channels.py +++ b/src/proto_nd_flow/resources/disabled_channels.py @@ -61,8 +61,9 @@ def __init__(self, **params): self.disabled_channels_file_dir = params.get('disabled_channels_file_dir', None) self.disabled_channels_common_filename = params.get('disabled_channels_common_filename', None) self.disabled_channels_file_format = params.get('disabled_channels_file_format', None) + self.disabled_channels_file_ts = self.lookup_disabled_channel_file_ts self.disabled_channels_list = self.disabled_channels_file_dir+self.disabled_channels_common_filename+ \ - self.lookup_disabled_channel_file_ts+self.disabled_channels_file_format + self.disabled_channels_file_ts+self.disabled_channels_file_format self.missing_asic_list = params.get('missing_asic_list', None) self.is_mc = False diff --git a/yamls/module1_flow/reco/charge/CalibHitBuilder.yaml b/yamls/module1_flow/reco/charge/CalibHitBuilder.yaml index 0800a28c..c45b9cad 100644 --- a/yamls/module1_flow/reco/charge/CalibHitBuilder.yaml +++ b/yamls/module1_flow/reco/charge/CalibHitBuilder.yaml @@ -10,6 +10,10 @@ requires: - name: 'charge/packets_index' path: ['charge/packets'] index_only: True + - name: 'packet_frac_backtrack' + path: ['charge/packets','mc_truth/packet_fraction'] + - name: 'packet_seg_backtrack' + path: ['charge/packets','mc_truth/segments'] params: # inputs events_dset_name: 'charge/events' @@ -17,6 +21,7 @@ params: packets_index_name: 'charge/packets_index' raw_hits_dset_name: 'charge/raw_hits' t0_dset_name: 'combined/t0' + max_contrib_segments: 10 # output calib_hits_dset_name: 'charge/calib_prompt_hits' diff --git a/yamls/module1_flow/workflows/combined/combined_reconstruction.yaml b/yamls/module1_flow/workflows/combined/combined_reconstruction.yaml index bf5881d2..be07d434 100644 --- a/yamls/module1_flow/workflows/combined/combined_reconstruction.yaml +++ b/yamls/module1_flow/workflows/combined/combined_reconstruction.yaml @@ -19,7 +19,7 @@ resources: - !include yamls/module1_flow/resources/RunData.yaml - !include yamls/module1_flow/resources/Geometry.yaml - !include yamls/module1_flow/resources/LArData.yaml - - !include yamls/module1_flow/resources/DisabledChannels.yaml + #- !include yamls/module1_flow/resources/DisabledChannels.yaml t0_reco: !include yamls/module1_flow/reco/combined/T0Reconstruction.yaml From 6b3339029808a1b5b68307a4cf6bb76852be1bdf Mon Sep 17 00:00:00 2001 From: Elise Dianne Hinkle Date: Mon, 8 Jan 2024 06:25:32 -0800 Subject: [PATCH 32/37] Updating event display notebook in proto_nd_flow. --- .../protondflow_evd_example.ipynb | 20 +++++++++---------- 1 file changed, 10 insertions(+), 10 deletions(-) diff --git a/event_display/proto_nd_flow/protondflow_evd_example.ipynb b/event_display/proto_nd_flow/protondflow_evd_example.ipynb index 35d9ef75..e146ea0a 100644 --- a/event_display/proto_nd_flow/protondflow_evd_example.ipynb +++ b/event_display/proto_nd_flow/protondflow_evd_example.ipynb @@ -18,7 +18,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 29, "id": "ab903276-e787-4142-bbb1-4becf42f76c1", "metadata": { "tags": [] @@ -27,10 +27,10 @@ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 1, + "execution_count": 29, "metadata": {}, "output_type": "execute_result" } @@ -56,7 +56,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 30, "id": "d3cc7962-6f70-446b-a4d1-f5f1da4ad23a", "metadata": { "tags": [] @@ -65,8 +65,8 @@ "source": [ "# This set of inputs allows you to look at a Module1 charge-only file\n", "# This file originates from the same raw data file as the input file in the Module0FlowEventDisplay example\n", - "directory = '/global/cfs/cdirs/dune/users/sfogarty/muon_samples/'\n", - "file = 'packet_2022_02_09_17_23_09_CET.module1_flow.h5'\n", + "directory = '/global/cfs/cdirs/dune/users/ehinkle/nd_prototypes_ana/flow_tests/Module1_Data/OUTPUT/'\n", + "file = 'packet_2022_02_11_11_39_26_CET.FLOW.h5'\n", "geometry = '/global/cfs/cdirs/dune/www/data/Module1/TPC12/module1_layout-2.3.16.yaml'" ] }, @@ -93,9 +93,9 @@ "outputs": [ { "data": { - "image/png": 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wIPBdIBdHYJ3LUem+AHgdmAd8CjTE3kTDcDD13wQichmwU1WXhntIkLGgneBU9TFVHa2qo7t3795qG43oYfcoCqg4YivYA7qJyBK/x63+h7q5WFcCfYESoL2I3Bj7izASgH7AUuBuIJ+j7c3bAQXu46vAf+OIrUrgPeBe4DQgO7bmGumOebaa5kzgChEZC+QBHUTkKWCHiBS736KLgZ3u/luB3n7Hl+KEOQzD8BK6EfVuVR3dxJFfBTao6i4AEXkROIPQv49G6iHAjcCjOJ/JzeXE5rkPgLOAk4EaHE/YJzihxznAIqA6CvYaBmCerSZR1ftVtVRVy3EScWer6o3Aq8Bkd7fJwCvu81eB8SKSKyJ9gYE4v8SGYbi0NkEeJ3x4moi0ExEBLsBZuBLq99FILToBL+EIrfY0FlqhJXxjcoEO7v+nAA8A/wYOAMuAnwMXuuc3jIhhnq3W8TAwXUSm4vwBuA5AVVeIyHRgJY5b+w5VtTwBw/DiDSO25lDVhSLyPPARzu/Xx8BjOCGjY34fjZTiHOAFnHud18y+LSGboyHFE3CS6/8DJxy5FngTmA18AOyP4LxGmmFiK0xUdS4w132+B+dbdbD9HgIeiplhhpFEqIK2oYK8qv4Y+HHAcA0hfh+NpCcb5/P0TpzcrGNoaGhg1qxZmQsXLqRjx46Ul5fTp08fysvL6dSpU0vnywI6us+HAoOAm3EE3hbgbWAmTv7X7pae3EhfTGwZhhFbrIK8ER4DgZeBckIIrT179vDCCy+Qn5+fkZGRwfXXX8/GjRtZu3Yts2bNIisriz59+vgeXbp0wYlAh00mTtgRoD9wOzABR3ztwBFeM4F3sfxcowlMbBmGETvcoqaG0QQCTAUewRE1x7xhVJVly5Yxa9Yszj33XEaOHFnzy1/+Mr+oqIiioiJOOeUUVJU9e/awadMmNm7cyNy5c1FVn+erT58+dOvWraXiSzgqvnoDN+GErXOAfTjJ9m/hiK+Nrbx+IwUxsWUYRgxpfc6WkRZ0BZ4CzsbJmzqGqqoqXnvtNXbv3s3kyZMpKiqivr4e1cY58iJCt27d6NatG6NGjUJV2bdvH5s2bWLTpk188MEH1NbW+oRXeXk5RUVFrRFf3hqMPYHrgctxPGJHcETXmzjlJ74g/ER+I8UwsWUYRkxRCyMawbkAmI6zEjA32A6bNm3ipZdeYvDgwVx11VVkZzu57SJyjNgKRETo0qULXbp04cQTTwTgwIEDbNy4kU2bNrFo0SKqqqooKyvzia8ePXqQkdHiLwcF7v/5wNXARe7repxE+zdwRNgKWtI7wUhqTGwZhhE7lLCbThtpQy7wC5xuDSGT4OfNm8fHH3/M5ZdfzqBBgxptFxFtTmwFo2PHjpxwwgmccMIJAFRWVvrCjh999BGVlZU+8dWnTx+Ki4vJzGxxu1v/MhJjga/giCwBFgKv4YivTzhanNVIMUxsGYYRO1QsZ8vwZyhOEnwpIYTWvn37vEnw3HbbbRQUFByzjzf0p6otDQM2orCwkBEjRjBixAgADh8+7BNf//73v9m/fz+9e/f2ia+SkhKyslr8Z9Q/PHo+TmHeOpyVl0txWgzNdZ/XtvpijITCxJZhGDHFwogGjlfnm8CvCJEED/Dpp58yY8YMzj77bE499dSQQipSYiuQ9u3bM2zYMIYNGwY4+WLenK+33nqLPXv2UFJS4gs79urVyxfabAH+Ve7PxKlyX+2OfcpR8bUQpyeokYSY2DIMI7aYZyvd6Q78C6dHYdAk+Orqat544w0qKiqYOHEiPXv2bO6cGk7eVlvJz89nyJAhDBkyxGfn5s2b2bRpE7NmzWLnzp0UFxf7PF+9e/cmJyenpdPkuA+A0TjFVr+DI77W4OR8zQY+BA5F4LKMGGBiyzCMmNHWoqaRxG35MwHop6o/FZEyoKeqWout6DEGR2i146igaMSWLVt48cUXGTBgALfeemvYnqJYiK1A8vLyGDRokC+HrLa2li1btrBx40bmzZvH9u3b6dGjh098lZWVkZsbNPe/Kfyr3B8HDMOp99UOWI9TauId4H2c8hNGAmJiyzCMGCKJFEb8E06i8vnAT4FKnJYwJ8fTqBQlD/gtMIkQ3iyPx8N7773H4sWLueyyy3zeo3CJh9gKJCcnh/79+9O/f38A6urq2Lp1K5s2beLDDz/kueeeo1u3br6wY1lZGfn5QVPVmiKTo1XuB+MUf70JJ+dtKzADmIWTdL8rApdlRAATW4ZhxI7EKmp6qqqeJCIfA6jqPhFpcczHaJbjcJqD9yCE0Nq/fz8vvvgiWVlZ3HbbbRQWFgbbrUkimasVKbKzs+nbty99+/YFoL6+ni+//NJXauLFF1+kc+fOjcRX+/Yt7oGdwdFCq32B24AbcATuThyv1wwc8fVlBC7LaAUmtgzDiCnqSRixVScimbiFJkWkO1b3KJIIcBfwc5w//EHV0Geffcabb77JGWecwRlnnNFq0ZQInq3m8G8fBE5Ji4qKCl+piVdeeYUOHTo0KrQabPVlM/hXuS/F8SZegxOKPIiT7zUDp9DqRqzQakwwsWUYRuxQSZicLeD3wEtAkYg8BHwN+H/xNSll6Ak8C4wiREmHmpoa3nzzTbZu3cqECRMoKSlp04TJILYCyczMpLS0lNLSUs466yw8Hg/bt29n48aNLF++nNdff5327ds36u/YsWPH5k/cGP8q93nAeOAynHBkFU5TbW+h1TWY+IoKJrYMw4gZSuKUflDVp0VkKU7lcgGuUtVVcTYrFbgc+CdOyDBodvuXX37JCy+8QHl5ObfeemtrVuwdQzKKrUAyMjIoKSmhpKSEM844A1Vlx44dbNq0idWrVzNjxgxycnIa9Xfs1KlTa7yB/lXurwS+6r724FS5fx1HfH2GeXsjgoktwzBiR2LlbKGqq4HV8bYjRWgH/C+O5yRkEvwHH3zAwoULGTt2rK9+VSRIBbEViIjQs2dPevbsyamnnoqqsmvXLjZt2sTatWt55513yMjIaBR27NKlS2vEl3+i2BjgXKABJx9sEU6V+3nAMqzKfaswsdUMItIbeBLHLe4BHlPVR0SkC46bvBwn7j1OVfe5x9yP07W+Afi2qs6Ig+mGkYBIIuVsGZHjRJxK8N0JETY8cOAAL730EgC33HJLa8JhTRGTOlvxRkQoKiqiqKiIk08+GVVl7969vv6O7777Lh6Pp5H46tatW2vEl/89/ApwOk41+2zgY46KryVATduvLPUxsdU89cD3VPUjESkElorITGAK8I6qPiwi9wH3AfeKyDCcb3bDgRJglogMUtWGONlvGImFJzHCiEZEyAC+D/yYJpLgV65cyRtvvMGpp57KmWee2Zrmzs2SDmIrEBGha9eudO3alVGjRqGq7N+/31fl/sMPP6S2trZRzlePHj1aI75yOdoc/HScXDxvlfvlODlfc3Cq3B+JzNWlFia2mkFVK4AK93mliKwCeuHEub/i7vYETjuFe93xaapaA2wQkbXAKcD82FpuGImHKnjiLLZcr3RzeFR1f7RtSXJ6Ac/jlHYI6s2qra3lrbfeYuPGjYwfP57S0tKoGZOOYisQEaFz58507tyZkSNHAo5H0dvfcdGiRRw5cqSR+OrZs2drxK9/lftROFXuv43zPvgceBNn1eMHOPXr0h4TWy1ARMpx3OULgR6uEENVK0SkyN2tF7DA77Ct7phhGCREI+pt7qMp1ZcJlMXGnKTkWuAfOJ6NoEnwFRUVvPDCC5SWlnLbbbe1pnJ6izCxFZyOHTty/PHHc/zxxwNQWVnp83x9/PHHHDx40Ndcu7y8nOLiYjIzM1s6TRZHC62OwGkwfhuO+NpA4yr3eyNwWUmHia0wEZECnOrSd6vqwSbcsME2HPMJICK3ArcClJXZZ3oiYvcoCmhCrEZcpaonNrWDt9CpcQwFwJ+BqwmRBK+qzJ8/nw8++IBLLrmEESNGxMQwE1vhUVhYyIgRI3z35fDhwz7x9dprr7Fv3z5KS0t9nq9evXqRldViqZDJ0Vpfg4ABOKk3eRMmTNj59NNP947Q5SQNJrbCQESycYTW06r6oju8Q0SKXa9WMU6lXnA8Wf5vpFKcb9GNUNXHgMcARo8ebZ8QCYjdo+iQAAnyp0don3TjZJwk+M6ECBsePHiQF198kYaGBm655RY6deoUM+NMbLWO9u3bM2zYMN/K0KqqKjZv3szGjRuZMWMGu3fvplevXj7xVVpaGna/Sj98Ve5XrlzZtoJqSYqJrWZwm9X+Defb8G/8Nr0KTAYedv9/xW/8GRH5DU6C/ECcpbOGYSRAI2pVrY7EPmlEJvAAziKgkI381qxZw6uvvkrv3r0ZNmwYe/bsoa6ujnbt2pGXl9ea0FSLMLEVGfLz8xk8eDCDBw8GoLq6mi1btrBp0yZmz57Njh076Nmzpy/s2Lt377DrpNXX15ORkZGWN8nEVvOcCUwElovIMnfshzgia7qITAU2A9cBqOoKEZkOrMRZyXiHrUQ0DAeNcyNqEbkdJ6H3HeBG4HVVfTRuBiU+fYAXcRoeBxVadXV1vP3226xdu5arrrqKmpoa2rdvT319PTt37sTjcWpitm/fng4dOvjEV6RXJJrYig55eXkMHDiQgQMHAs6iB6/4evfdd6moqKCoqKiR+MrLywt6rurqanJzc9OySKqJrWZQ1fcJnUh7QYhjHgIeippRhpHExDmMeD7wdeA9VT1LRP4cT2MSnPHAX3CS4IP+rdixYwcvvPACPXr04LbbbkNVWbduHSJCdna2L9ykqtTV1bF9+3bfsf7iKzc3t83iy8RWbMjJyaF///70798fcMT2l19+ycaNG/nwww/58ssv6datmy/sWFZW5hPXNTU15OTkpKXzwcSWYRixQ8ET39WIe1RVReQX7msryHgsHXBSJ8bSRBL8woULee+997jooos4/vjjERGqq4NHX0WEnJwcX7jJK74qKipQVTIyMigoKKCwsJD8/Hzy8vJa1ZDaxFbsyc7Opry8nPLycsAJFW7bto2NGzeycOFCpk+fzsiRI7niiiuorq42sWUYhhF9BG1DnS0R6QT8FWd5uQLfwGmeG7SbQxAeAVDVf7uvX2i1ManJGThhw444Hq1jOHToEK+88gpVVVVMnTqVLl0aly0LR/AEii+Px0N1dTWVlZWNxFeHDh3Iz88nNze3WfFlnq3EICsri7KyMgoKCvjss8/o3r073bt3B5wwYnZ2toktwzCMaBKBRtSPAG+p6tdEJAfH8/JDgnRzCDq/0wsRERmCU4C4l4hci7Ni+NU0bkSdBfwE+A5NJMF/8cUXvPrqq5x44omce+65xyS9i0irPFIZGRnk5ub6anF5xdfBgwcRkWPEV05OTuA8YmIrcVi/fj0vvvgi5557Ltu3byc7OxtVZdWqVWRlZdUF7t/SL1HJ2BIv6cWWiHw3jN0Oq+r/Rd0YwzCapg2NqEWkA3AOTr0eVLUWqBWRUN0cQp3nXuB6YBpHVwqXAv8SkWmq+nCrDExe+gEv4dRCCiq06uvrmTlzJmvWrOHaa6/1hYwCaY3QCkYw8XXkyBEOHjyIqpKZmUmHDh0oLCwkLy/PJ75MbMUXVWXx4sW8++67fO1rX6O8vJyXXnqJrKwsKisreeSRR1i4cGFHEXkOeFhVl7qHhv0lKllb4iW92MLpy/UoTVeDvh0wsWUYcUfwtD5Bvh+wC/iHiJwALAXuInQ3h1BMBYaraqNv2G65lhU4K43TAQEmAX/ECRkGrc2wc+dOXnjhBbp168Ztt91Gfn5Ix1fUyMjIaLTCzePxcOjQIfbtc6LFmZmZmfX19VRWVtKpUyeys7MjJvyM8GhoaOCNN95g69atTJ06lc6dO/vGs7Ky6NChAz/5yU/43//934pp06b9CDgArfoSlZQt8VJBbP1TVX/a1A4i0j5WxhiG0QTq9EcMQTcRWeL3+jG3sKyXLOAk4FuqulBEHsH5tttSPDjfiDcFjBe729KBzsDjOCuqg34+qipLlixh7ty5fPWrX2XkyJFh5U3FAq/48gqw+vp6PB4P27Zto6qqyvfH3ev5akURTqMFHD58mOnTp9OuXTu+8Y1vNGrNVF9f76tA7+Zs1QaE61v6JSopW+IlvdhS1R9EYh/DMKKP0mQYcbeqjm7i8K3AVlVd6L5+HkdshermEIq7gXdE5AtgiztWhhNG+1bzV5H0nIvzsysEgjYsPHz4MK+++iqVlZV84xvfoGvXrmGfPB6hvMzMTDIzM2nXrh0FBQU0NDRw4MAB9uzZAzjlCjp27Ej79u1NfEWY7du3M23aNE444QS+8pWvHCO46+vrfbl9NTU1ZGVlBa4AbumXqLBa4iUaSS+2vLgJdpNwkul816Wq346TSYZhBKK0OoyoqttFZIuIDFbVNThemZXuI1g3h1DneUtEBuGEHnrhfHhvBRYnet5HG8nGqf93J00kwa9bt45XXnmF4447jnHjxrWo8ntrE+QjhVfoZWZmNgp31tfXs3fvXnbv3o2qkpubS4cOHSgoKCAvL681vf8MYOXKlbz++uuMHTuW4cOHB93HG0YEx7OVmZlZFbBLS79EhdUSL9FIpXfYGziuxeWkTyjAMJKMNleQ/xbwtJtEux64Cafv2jHdHJpCVT00DkU41oncpKr/aIuBCcpAHBHahyaS4GfPns1nn33GVVddRb9+/WJqYFtpSuRlZWU1ElRe8bVr1y5EhLy8PF+B1fz8/Ki3Fkp2VJV58+axbNkybrzxRoqLi0PuGxhGzMzMbFSMrRVfopKyJV4qia08VQ1nZaJhGHGkLRXkVXUZECzUGLSbQyv4CZBKYktwFgQ8gpMEH/SHv3v3bl544QU6derE7bffTrt2QWuZNj9ZHL1aLVmNGCi+6urq2LVrl6+1UH5+Ph06dPCFHU18HaW2tpaXX36ZQ4cOcfPNN1NQUNDk/v5hxBCeLWjBl6hkbYmXSmLrnyJyC/AaflWhVXVv/EwyDMMfVfDEuRG1iHwaahPQI5a2RJmuwFPA2TRRCf6jjz5i9uzZnHfeeYwaNapNgimOYkvaMndga6H6+np27drFzp1O5Co/P5+OHTtGra9jsrB//36mTZtGcXEx11xzTVjh1wDPlmZmZh4J3KelX6KSsSVeKomtWuBXON3pvV9vFGelg2EYCUI8G1G79AAuBgKrzAvwYezNiQoXANNxVhoGTYI/cuQI//73v9m3bx9TpkzxVfluK/GsdRWJuZvq66iqiAjt27ensLAwrcTXpk2beP755znrrLM45ZRTwhbWATlbmp2dHcyzlfKkktj6LjBAVXfH2xDDMELRpjpbkeI1oMD9Nt0IEZkbc2siSy7wS+AWmkiC37BhAy+//DLDhg3j2muvjViCeLKEEVt63mB9HQPFV8eOHX2thVJNfC1dupQ5c+Zw9dVX+xpQh4u/Z6uqqsoTIoyY8qSS2FoBHOOeNAwjcVBtW85WZGzQqU1suyGWtkSYoThJxL0IIbQaGhqYM2cOn3zyCVdeeSUDBgyIuBFxElwxa9cTSnxt27bNt72lfR0TlYaGBmbMmMGGDRu46aabWlQCxEtAzpZmZ2en5d/pVBJbDcAyEZlD45wtK/1gGIlCAuRspSAC/AeORytkEvzevXt54YUXaN++Pbfffjvt20e+1rNXVHg9PjGkTTlbbZy42abahYWFFBYWJpX4OnLkCM899xzZ2dlMnTq1UQX/lhAYRszPzzexleS87D7ijoiMwVn9kwn8NQ17rRlGCNpc+qHtFoh8pKontXWfBOJR4EaaSIL/5JNPmDlzJueeey4nn3xyVP/YJ0KdrXgSqq/jgQMHAKcGmL/4CtJUO+7s3LmTadOmMXToUC644II2hUUDSz907NjxcKTsTCZSRmyp6hPxtgFARDJxeo1diFsoUUReVdWV8bXMMOKP0vqiphFkaBMrEsHxFHWMlTER4BAhWu5UV1fz2muvsWvXLiZNmkSPHtFfbOkN58VaQCRqI+rA1kIej4fDhw+zf/9+RMQnvjp06OCrbh9P8bVmzRpeffVVLr74Yo4//vg2ncvj8eDxeHxiraqqiszMTBNbyYyIXAb8DKdoXxbOB6aqaocYm3IKsFZV17t2TcNpnGliyzDaUEE+ggwJY5+Er9vjR9BFQZs2beKll15i0KBB3HzzzTFrUZPqOVttJVB8NTQ0UFlZ6WuqnZWVRceOHX3V7b3hyWijqrz//vssXryYG264gV692t5u0BtC9L4nampqyMnJOdTmEychKSO2gN8B1wDLNb6/cb042m8NHO/WqXGyxTASjPiHEVU1sAF1stOoIrfH42HevHl89NFHXH755QwaNCjmBsXhIzhuOVttJbC1kLev4969e1FVcnJyfGHHaPV1rKur45VXXmHfvn3ccsstFBYWRuS8/snxANXV1ZKbm2tiK8nZAnwWZ6EFYTbJFJFbgVsBysrKom2T0QrsHkWHJhpRG63DJ7Z2797NtGnTKCgo4Lbbbmu2unc0iFPZA4HEyNlqK6HE1549e3zJ+N7q9vn5+W0u23HgwAGeffZZunfvzpQpUyIq5vyT48HxbJnYSn5+ALwhIvNovBrxNzG2I6wmmar6GPAYwOjRo5P/EyIFsXsUeVTB40lOD0QC4xNbq1ev5sCBAxw6dIinnnqKfv360a9fP8rKymIWjornqsBUEFuBhGqqvWvXLgByc3Pp2LGjr7VQS8TXli1beO655zj11FM544wzIn7v/JPjwfFstWvXrjKikyQJqSS2HsJJFM0DYvOpEpzFwEAR6Qt8CYwHkrl2j2FElHiHEVMQn9jq1q0b/fv3Z9y4cWzbto3169fz/vvvs23bNkpKSujbty/9+vWjV69eUfNAxUP0iENKiq1AgvV13L17t098eZtqN9fXcdmyZcycOZOrrrqKgQMHRsXWwDBibW2tdOzY8WBUJktwUklsdVHVi+JthKrWi8idwAyc0g9/V9UVcTbLMBKEhKggD/iSfCYA/VT1pyJSBvRU1UVxNq2l+MRWVlYW9fX1ZGRkUFpaSmlpKeeccw61tbVs3ryZ9evX8/rrr7N//37Ky8t94qtbt24R82rEM0E+HQnV13HHjh2IiK+ptre1kIgwc+ZMPv/884i2aQpGMM9WcXGxia0kZ5aIXKSqb8fbEFV9A3gj3nYYRqLhNKJODLEF/AnwAOcDPwUqgReAk+NpVCvwia3MzEzq6+uP2SEnJ4cBAwb4KsYfPnyYDRs2sH79eubPn4/H46Ffv34+8dXWBOl4eZjSwbPVFMH6OtbX17Nz5048Hg+1tbUsXbqUvLw8br755kbhyWgQmLNVW1tLnz59LIyY5NwB/EBEaoA64lf6wTCMJkigMOKpqnqSiHwMoKr7RCSeKQit5RjPVnO0b9+eESNGMGLECFSVffv2sX79ej7//HNmzJhBQUGBT3iVl5f7CnSGQ0ZGBg0N8amcke5iKxB/8XXgwAHmzZtHUVERX/va11pdEb4lBHq2VFW6du1qvRGTGVWNzFpVwzCihyZOGBGoc4sQK4CIdMfxdCUbjcRWS4WOiNClSxe6dOnC6NGj8Xg8bN++nfXr17No0SJefPFFioqKfMn2paWlIfOAvOeLh+hJl5yt1rB161bef/99Ro0aRXFxcZP3L5IE5my5sd7q0EekLkkvtkSkp6pub+s+hmFEnwSpIO/l98BLQA8ReQj4GvD/4mtSq2ixZ6spMjIyKCkpoaSkhLPOOou6ujq2bNnC+vXrefvtt9m9ezdlZWU+8VVUVNQoTyuOqxGV4KV30hZVZcWKFaxYsYLzzjuPHj16cOhQ7CovBIYRXWqC7ZvqJL3YwsmNaq6HWTj7GIYRdRLHs6WqT4vIUuACd+gqVV0VT5taSUTFViDZ2dk+YQVOyxVvvteSJUuoqanxhRz79etnpR8ShIaGBubPn8/evXu59NJLG9Vci9U9Cgwjek2LyeQJRiqIrRNEpKnVDQKk5eoHw0g4FDRB6myJyHcDhi4RkTOApaq6LA4mtZaoiq1A8vPzGTZsGMOGDQNg//79PvE1a9YssrKyKCoqorS0lJ49e7Yo36utmNhyOHLkCHPmzKGgoICxY8cG5k3FzI7AMGICFB2PG0kvtlQ1NsFnwzDajBNGTAyxBYx2H/92X1+KUyfvdhF5TlV/GTfLWkazqxGjSadOnTjxxBM58cQTUVU+/fRTNm7cyBdffMEHH3xAhw4dKC4upqSkhKKiomjmC1kYEdi1axdz585l0KBBHH/88cd4sUQkbp4tE1uGYRixQKEhQcKIQFfgJFU9BCAiPwaeB84BlgJJJ7Zi4dlqCm+yfW5uLieccAINDQ3s2rWLiooKPv74Y/bt20f37t194qtz584RK65qYUR8ixrOOOOMJluMxUpsNTQ0+MS1W/8tbW+QiS3DMGKGJkAjaj/KgFq/13VAH1WtckvIJAvHrEZU1bjlTvmLp8zMTHr27EnPnj058cQTqa2tZfv27VRUVPDee+9RXV1Nz549KS4upri4mMLCwlbbna5FTcFpPv7xxx+zYcMGLrroIrp06RJy31iHEb2erZqaGnJycpJxtW9ESHqxJSJvAP+hqhvjbYthGM2TKAnywDPAAhF5xX19OfAvEWkPrIyfWS3GJwy9Qsfj8cRseX8gTXmYcnJyKCsr83ldDh8+zPbt29m2bRuffPIJmZmZPuFVXFzcolpQ6erZqq2t5d1336W+vp7LLrssrJ9ZPMKIrthKy+R4SAGxBTwOvC0iTwC/VNW6ONtjGEYoEqSCvFvv53Gclcpn4eT63K6qS9xdJsTJtNagOIIrF456t+IptsKlffv29O/fn/79+6OqHDhwgIqKCjZs2MD8+fMpLCykZ8+evnwvb2X0UKSb2Dp48CDvvPMOPXv25JRTTgnrnsfSA+hf+qG6uto8W8mMqk4XkdeBHwFLROSf+BUmVNXfxM04wzAaEYkwoluIdAnwpapeJiJdgGeBcmAjME5V9zVph6qKyMuqOgonPyvZqcZPbNXX15OTE59i+G0JA3bq1IlOnToxdOhQPB4Pu3fvpqKiguXLl7Nnzx66du3qy/fq2rVro5Blunm2tm3bxnvvvcfIkSMZPHhwWMd4fz6x9Gx5V6NWV1eTnZ0dv4TCOJP0YsulDjiM82FTSHJWgTaMtCACCfJ3AasAbyuu+4B3VPVhEbnPfX1vGOdZICInq+rithqUAFQDHSExkuQjIXoyMjIoKiqiqKiIE044gbq6Onbs2EFFRQXz58/n0KFDvnyvkpKSCFieHKgqq1atYvny5Zx77rn07NmzRcfH0rNVX19P+/btASeMaGIriRGRMcBvgFdxVhYdibNJhmGEQLVtOVsiUopTouEhwFsn60rgK+7zJ4C5hCe2zgNuE5FNOF/WvP1Uj2+1gfEjoVYkRsPDlJ2dTWlpKaWlpYBTXLWiooKKigpWrFhBdXV1xv79+8nKyqK4uJh27dpF3IZ409DQwIIFC9i9ezdjx45tccPwWC+c8M/ZqqqqIjs7O2iaT0u81SJyPzAVpzjqt1V1RrSvIxIkvdgCHgCuU9UV8TbEMIzm0bb5nX8H/ADHg+2lh6pWAKhqhYgUhXmuS9pkSWKRUGIrFuTn5/uq1qsq7777bgOQuWXLFhYtWkS7du18ifY9e/ZsNt8r0amqqmLOnDnk5eUxduzYVl9PrHO2MjMz2bt3L+PGjaOmpqZQRG4GXlHVXX67huWtFpFhwHhgOFACzBKRQaqa8In3SS+2VPXseNtgGEaYqDQVRuwmIkv8Xj+mqo95X4jIZcBOVV0qIl9psymqm0SkMzAQ8F/Ctamt544DcS1s6k88SjCICDk5OXTs2JFhw4bh8XjYu3cvFRUVrFq1infffZfOnTtTUlJCcXEx3bp1i9sCgtawd+9eZs+eTf/+/Rk5cmSrf8ax9mx5E+S7dOnC//3f//HrX/9679atW9sDPYBd0GJv9ZXANFWtATaIyFrgFGB+jC6p1SS92DIMI3lophH1blUd3cThZwJXiMhYHHHUQUSeAnaISLHr1SoGdoZji/sN+y6gFFgGnIbzoX1+OMcnGMfU2ooXGRkZcUlU9xcRGRkZdOvWjW7dunHcccdRX1/Pzp07qaioYPHixRw8eJCioiJfvlenTp0Stk7Xhg0bWLhwIaeeeip9+/Zt8/niFUasq6ujR48elar6SMBuvyN8b3UvYIHfflvdsYTHxFYTiMivcGrv1ALrgJtUdb+7LWjcWERG4Swpz8dZVn5XOrcoMIzGNOnZahJVvR+4H8D1bN2jqje6v6eTgYfd/18JdY4A7gJOBhao6nkiMgT4SauMiz8JE0aMF03limVlZVFSUuJLpK+urvYVV12zZg11dXWN6nv5N22OF6rKsmXLWLduHRdeeCFdu3aNyHljLba8HsTq6mqysrKq/be3wlsdzPik+PtqYqtpZgL3q2q9iPwC54O+ubjxo8CtOOr7DWAM8GZcrDeMBEPVeUSYh4HpIjIV2AxcF+Zx1apa7faKy1XV1SIS3hr6xCNhxFYcPVthf6/Ny8ujvLyc8vJyACorK6moqGDbtm189NFH5OTkNMr3imUzbXC8QN4K+5deein5+fkROW88E+RdsVUVsEtLvdVbgd5+x5cC26J6ERHCxFYTqOrbfi8XAF9znweNG4vIRqCDqs4HEJEngaswsWUYPiJR1FRV5+LkcaCqe4ALWnGarSLSCXgZmCki+0iSD+4gJIzYiietFXmFhYUUFhYyaNAgVJV9+/ZRUVHRqJm2N98rys20OXToEO+88w7dunXj3HPPjehc8crZAkdsZWRkNBJbrfBWvwo8IyK/wXF0DAQWRf9K2o6JrfD5Bs5SVAgdN65znweOG4aB49Vq8CRGboyqXu0+fVBE5uDUqXorjia1hYQRW/HKfYrUvN5m2l26dGH48OEhm2l7xVeXLl0iNvf27duZN28exx13HEOHDo3KzzJenq2amhoyMzPDLc0U1FutqitEZDpOO6164I5kWIkIJrYQkVlAsKpwD6jqK+4+D+Dc2Ke9hwXZX5sYDzbvrTjhxia7sxvxw+5RdEigRtQ+VHVevG1oIwm1GjFeKxKjEb4Mt5m2V3y1tPaVlzVr1rBs2TLOPvvsqBZpjVfOVlVVlSdIGNFHuN5qVX0IZ+ViUpH2YktVv9rUdhGZDFwGXOCXEBAqbrzVfR44Hmzex4DHAEaPHp0UCX7pht2jyKNIwni2RCQXuBancKLvs1BVfxovm9pAwqxGhLj1KIzJWqRgzbS9xVWXLVvma6ZdUlJCz549m20M3dDQwKJFi9i+fTtjxoyhY8eOUbM9nmHE5sRWqpP2Yqsp3Or09wLnBlSmDxo3VtUGEakUkdOAhcAk4H9jbbdhJCwakXY9keIV4ABOb8SaONvSVhIqjBivUGI8RF779u0ZMGAAAwYMQFXZv38/27dvZ926dXz44YcUFhb6ku179OjhEx/g5DHNnTuXrKwsLr300pj0s4xjgrxmZWWlbYcXE1tN8wecfosz3TfoAlW9vZm48Tc5WvrhTSw53jAa0cYK8pGkVFXHxNuICJEwYUSIj+hJhDpZIkLnzp3p3LnzMc20P/30U/bu3Uu3bt184calS5fSt29fTjzxxEZNtaNFPFYj+pd+yMvLM7FlHIuqDmhiW9C4saouAUZE0y7DSFa0DXW2osCHInKcqi6PtyERIO09W9HK2WoLTTXTXrZsGSeeeCLHHXdczG2KFYGercLCQhNbhmEY0SYRViOKyHKchStZwE0ish6/MGIqNKKuqUn2qGjLSUSxFYh/M+3t27dHNRE+GLH++QTkbJGVlXU4pgYkECa2DMOIKQnw9/AanN5sWwLG+5AidbYOH47f37R4hvMSXWz5E+uQnpdYebZUtVEYsaamhuzs7EMxmTwBSRh/vmEY6UGDR4I+YshvgYOqusn/ARxxtyUjCbMaMdnrbKUysRR4Ho+HjIwMn7irrq6W3NzcyphMnoCYZ8swjJiRCGFEoFxVPw0cVNUlIlIeB3siQcLkbMWLZBNbHo8n6WxuCf75WuB4tvLz89PWs2ViyzCMmOKJ/2rEpgofRaYJXexJmNWI8fRsJVMYEeLzs4pVGNE/hAhQU1MjeXl5aSu2LIxoGEbMUMCjEvQRQxaLyC2Bg25rkKWxNCSCJEwYEeKXO5VMYiseOVuxnNM/OR6cMGJhYeHBmEyegJhnyzCM2KHQEP+/h3cDL4nIBI6Kq9FADnB1qIMSnIQJI1rph/BIdbEVLIzYs2dPE1uGYRjRRhEa4twbUVV3AGeIyHkcrYn3uqrOjqNZbSWhxFY6zdta4iUMYym2AsOIAwYMMLFlGIYRCxLF96Cqc4A58bYjQiSM2IoXyebZgvgIxHiFEVVVunTpYr0RDcMwoo2SEGHEVCRhxJbV2QqPeIURY5kg7y+2XKqD7ZsOmNgyDCOmmNiKCgmzGhHi1xvRxFbiEBhGFOdi06+1gYuJLcMwYoYC8a/8kJIkzGrEeCbIJxNp6tlKv/i2i4ktwzBiSnyLEqQsCRNGjBfm2WqeWM4XJGcreW5OFDCxZRhGzLCcraiRMGLLcrYSl3iWfjCxZRiGESMU82xFibQXW+bZap5Yhnj9c7bc1kTJc3OigFWQDwMRuUdEVES6+Y3dLyJrRWSNiFzsNz5KRJa7234vyZZIYBhRxhPiYbQJX+JxvMVWvEi2j9p45WzFCv8wYk1NDTk5OWn9a25iqxlEpDdwIbDZb2wYMB4YDowB/iQi3mUXjwK3AgPdx5iYGmwYCYzXsxXsYbQJBWrB6X3n8XjiWjQzXqsRk4l08mxVV1eb2Iq3AUnAb4Ef0LgW45XANFWtUdUNwFrgFBEpBjqo6nw3Pv0kcFWsDTaMxEVpCPEw2kw1OH9Q09W7lWxhxHgQj5yt6upqcnNz0/o7lYmtJhCRK4AvVfWTgE29gC1+r7e6Y73c54HjhmG4aIiH0WYSIm/LcrbCJ9m8cS0hUGxlZ2enn/r3I+0T5EVkFtAzyKYHgB8CFwU7LMiYNjEebN5bccKNlJWVhWWrEVvsHkUeBRpC5ck283fSDek/ifP76gEeU9VHRKQL8CxQDmwExqnqvgiZnEwkRGFTa0QdHvEqahrLdj3eMGJNTU3ai62092yp6ldVdUTgA1gP9AU+EZGNQCnwkYj0xPFY9fY7TSmwzR0vDTIebN7HVHW0qo7u3r175C/MaDN2jyKP4lQ1DPYIg3rge6o6FDgNuMPNn7wPeEdVBwLvuK/TkYQpbAqxD5Ml22KkVE+QN89WY9JebIVCVZerapGqlqtqOY6QOklVtwOvAuNFJFdE+uIkwi9S1QqgUkROc3/xJwGvxOsaDCMR0RD/mj1OtUJVP3KfVwKrcML0VwJPuLs9QfrmSSZEGBHiFx4zz1bTxDpBPisriyNHjvDoo4+yb98+RMQXTROR3iIyR0RWicgKEbnLHe8iIjNF5Av3/85+xwStApAMmNhqBaq6ApgOrATeAu5QVe/XyG8Cf8VJml8HvBkXIw0jAXFWI4ZMkO8mIkv8HreGOo+IlAMnAguBHu4XHdz/i6J/JQlJQomtOHi2kkpsxYtYhhGzsrLIyMigR48erF+/Ph9YKiKnubu0yFPdTBWAhCftc7bCxfVu+b9+CHgoyH5LgBExMsswko6GUJ/1ym5VHd3c8SJSALwA3K2qB5MsehRNEkpsxWPOZBFbqpo2jajz8vI488wz2bJly5ePP/74SNzcZveLkfdLUqWI+Huqv+Ke5glgLnAvflUAgA0ishY4BZgfu6tqPebZMgwjZjiNqDXoIxxEJBtHaD2tqi+6wzvcsiu4/++Mhu1JQEKJLcvZap5UT5D3z9nKysqqVodj6m2F6akOVQUgKTCxZRhGDGl9nS33j+nfgFWq+hu/Ta8Ck93nk0nfPMmEWI0IlrPVHKnu1YJjE+QzMzOrgu0X6Klu4pRhr/ZPRCyMaBhGzHBWI7b68/FMYCKwXESWuWM/BB4GpovIVJxOD9e10cxkJWFWI1oYsWniKbbiUUG+pqaGjIyMY8RWU55qVa0I8FSHqgKQFJjYMgwjZjRZZ6u5Y1XfJ/i3W4ALWmtTCpHuYcSkEVvxJF5hxMzMzCMBdjTnqX6Yxp7qV4FnROQ3QAluFYCoXkQEMbFlGEZMCafMg9Eq0l5sJQvx8mzFq85WVVWVJzs7uzpglxZ5qlV1hYh4qwDU07gKQMJjYsswjJihaFvCiEbTJFTOVhy8TJIsnq14Vo+PRxixqqpKs7KyGnm2WuOpDlUFIBkwsWUYRkxpbRjRaJaE8mzFY04TW4lDQIK8J1SCfLpgYsswjJjhLf1gRIWEEluWsxWadEiQD8zZysnJORyTiVuIW6E+E2gI8vBEKlRpYsswjJgSTpkHo1Wk/WrEZCEdxFaAZ0vbt29/pJlD4sXPgYNAHU6Dew+u0AIaRKQBqFLVP7RlEquzZRhGzPCWfgj2MNqMebaSxLMVL2KdIO+Xs0VWVlZCerZwWv+sxymYuhPYDxzBScLPALKB77R1EvNsGYYRQ8JrOm20ikYJ8tXVgYu/YkcyeZniQTp4tvzDiDU1NeTk5ByKycQt50eq+s+mdhCRTm2dxMSWYRgxQwXq5ZhuHUZkSHfPlq1GbIZYr0b0CyNKbm5uQnq2VPWf7ntHRaQjjjerASesWK+qdar6g7bOY2LLMIyY0cYK8kbTJIzYysiIfYZKMoUR42VnrOb1eDx4PB7f+6Cmpoa8vLzKmEzeQvyE1snAVUAXjqZYVQF3R2IeE1uGYcQUCyNGjYRJkI8HydSIOh6eLe+csZjXG0L0zlVTUyO5ubkJKbb8PFrTgd/iVKXPxMnVitg3FhNbhmHEDKeoqYURo0TCeLbi5WVKFs8WpHZem39yPDilHwoLC5tqMh1vsoDVqvr7aE5gGIYRE5wwoomtKJFQYisecyaL2IqnZysW+CfHg+PZKioqSkjPlsth4G0ReQh4Eed3qRY4pKoVkZjASj80g4h8S0TWiMgKEfml3/j9IrLW3Xax3/goEVnubvt9Mrm2DSPaKFAvGvRhtJm0btfjpt7EdM7WEq8E+Vjl0vknx4OTIN+3b99E9mzlAoOBG4DHgeeA2cA/AUSkzT8482w1gYicB1wJHK+qNSJS5I4PA8YDw3G6j88SkUFupdlHgVuBBcAbODU83oyH/YaReKhVkI8eCZOzFa8E+WQh1dv1BIotVZXi4uJELWoKUA6MUtW+wTaqapvd8ebZappvAg+rag2Aqu50x68EpqlqjapuANYCp4hIMdBBVee7X7GexFndYBgGjmerDk/Qh9FmEiqMGAcvU9J4tuKBqsbUs+Wfs+USv8JvzVMDrBWRXiLSTUQ6ikg7EYmYQ8o8W00zCDjbjeNWA/eo6mKgF47nystWd6zOfR44bhgGToJ8naTXKrkYklBiKx4ki9iKZ52tWBCYs+Wm09TEZPLWUYvj3XoL+NAdywM+AX4jEYhRp73YEpFZQM8gmx7A+fl0Bk4DTgami0g/INg7VpsYDzbvrTjhRsrKylpuuBF17B5FHsV6I0aRhBFb8cAS5BNnzsAwonc4JpO3jv3AT3B6JBbilH4oAHZAZFymaS+2VPWrobaJyDeBF90f9CIR8QDdcDxWvf12LQW2ueOlQcaDzfsY8BjA6NGjk+MTIs2wexR5FKi1CvLRImHEVkZGRlx6IyYLqe7ZCgwjun9DE/YzVFX3isg8oAeOl6sGJ1IVsTyztBdbzfAycD4wV0QGATnAbuBV4BkR+Q1OgvxAYJGqNohIpYicBiwEJgH/29wkS5cu3S0im0Js7ubOGQ+63XTTTfGYu6lr7hNLQ7wk6D2K63sjxNxN3h+Pfjmjsvr+biE2x+taUoWEWY0YL8yzlRhzBoYRSWChBSAifYD7gAtxPFoZOJ9xTwJTRCTTXQDXakxsNc3fgb+LyGc4aneyq9BXiMh0YCWOa/QOvxvxTZylo/k4qxCbXYmoqt1DbRORJao6uk1X0UriNXc8rzkUiXiPkvG9oapjomGPASTYakQr/ZB4xKP0g8fjgQQVWyKS4a40vAToraoDgu3XVqEFJraaRFVrgRtDbHsIeCjI+BJgRJRNMwzDCERxvhTmxDuMGMuGx35zJo3YSnXPln8Ysba2luzs7ETNHfC+YTYAC0QkB2iH83vUANRG6k1lYsswDCN1qMZPbMWznpPlbIUmHXK2vJ6t6upqcnNzE1Js+QmpD4ELcCJRs3DCiO2Ad4DZkViNaHW2Ep/H0nDueF5za0jHn1Oy3aN0oRqcP6oZGRneEE7MiYdnCyxnqzlimbPl9WxVV1eTk5OTkPVeRMSbxX8jcBbwNlCF83vkwfFuRQTzbCU47oq4tJo7ntfcGtLx55Rs9yiNOGZFYpDikjHBcrZCE68m3fHybGVnZyek2OJoGDEXeDzU55qVfjAMwzD8OUZs5ebmxtyIOOVsmWcrQeb0F1s1NTVkZ2cn+tLYHcBFIrIeWI3j1aoB9kciOR4sjJgwiMivRGS1iHwqIi+JSCd3vFxEqkRkmfv4s98xUWl6LSJj3Abba0Xkvkic0+/cvUVkjoiscpt73+WOPygiX/pd51i/Y4I2/Y41do8S/x4ZiVNry2iaeIQRY7Ua0b/0g+vZqgv32Gh+tgWbzv2/N05rvSeBj4GlwC7gJtemNt8sE1uJw0xghKoeD3wO3O+3bZ2qjnQft/uNe5teD3QfbV5W78aw/4izFHYYcL04jbcjRT3wPVUdilOZ/w6/8//W7zrfcO3xb/o9BviTX5w91tg9Svx7lO4khNiKVz6SebYSA2/4urKyknHjxvHxxx+3F5GrRKSgqeNi8NnWCLc2pqjqL1W1s6qWqGp3VS1W1QxV/au7X5vfWCa2EgRVfVtVvZ+MC2hcif4YJHpNr08B1qrqerf0xTScxtsRQVUrVPUj93klsIqm+0cGbfodKXtagt2jkCTMPTISo7BpnMRWcigt0ieMWFhYyJ/+9Cf69et3EKflXb9mDo3qZ1sgInIy0KGZfbpG4sujia3E5Bs0LobaV0Q+FpF5InK2O9aL6DS97gVsicJ5j0FEyoETcartA9zphuj+LiKdY21PC7F7lPj3KB1JmMKm8SCZPFvxIB45Ww0NDZSWlh5U1QdU9dNmDo31Z8kvgB+JyBAROcn9v4+I9BCRru4+vweK2zqRJcjHEGmi6bWqvuLu8wBOGOdpd1sFUKaqe0RkFPCyiAynBU2vW2pmlM7beBLHnfwCcLeqHhSRR4GfuXP9DPg1jqCJiT1+dtk98k6SoPfIaJJ0DiMm1WrEVPZsBZZ+yMrKqm7mEC+x/iyZD3wduBTIw9FE2e7/Ge7PqyNwV1snMrEVQ7SJptcAIjIZuAy4wPupoao1OKsiUNWlIrIOGEQLml63kFBNtiOGiGTj/BF/WlVfBFDVHX7b/wK8Fit7/LF75JDI98hokoQQW2BFTZsjnRLks7KyqsI8NGafJW6u1gPAA9E4fyAWRkwQRGQMcC9whaoe8Rvv7o0Xi0g/nCTr9apaAVSKyGnuSolJwCsRMGUxMFBE+orTumA8TuPtiODa+jdglar+xm/c3017NfCZ+/xVYLyI5IpIX9ym35GypyXYPUr8e2QkhtgSK/3QJKnu2Qos/ZCRkRGuZyuqn23++LtBRSRLRDJFJMN9RPwHZZ6txOEPOIXVZrr3eYG7qu0c4KciUo9TzfZ2Vd3rHtPiptfNoar1InInMAPIBP6uqivael4/zgQmAstFZJk79kOcVScjcVzGG4HbXHuaavoda+weJf49SncSRmzFAxNbTRMPsVVdXU1mZubhcI6LwWdbI0TkeGC1m4wfVUxsJQgautv4CzjhnGDbotL02l3S/0akz+ue+32Cx+VDzqchmn7HGrtHiX+PjMRYjRgPki1nKx5zxlJseXO2qqqqPC3I2YrqZ1sQvgN8JiLTVPXLaE5kYUTDMIzUISFWI6ay1yZSpPLPyD9nq6qqSrOzs480c0i8+ClwLvATERkpIgUi0s5NiciO5ETm2TIMw0gdEiKMCPHz3iQD8QojxipBPiCM6MnMzAw3QT6muHUBr3BDl7OAfwJ7cFIlckTkF/75uW3BxJZhGEbqkBBhREuQb5p4JcjHCv8worsaMSE9W24S/iigM86inm1ALW7pB5wc3IhgYsswDCN1SBjPlhGaVE+QDyj9oF26dAkrQT4O3AFcB/xbVcc2t3NbMLFlGIaROiSE2IqnkEiGvoPxsDGW8wWuRszOzk5UsTUMuExV93rLPURrlYWJLcMwjNShkdiqqopPqkycxE5yxBDjRLxWI7phxIQUW6p6C/gKnEb1/WOrEVMMEektIhtEpIv7urP7uk/AfuUiUuVXRynSdjwtIntF5GvROH8yIyJXi8iygIdHRC4J2M/ukdFSEmI1YjxJhryteHm24lRnS3Jzcw/FZOJWEouaISa2UgxV3QI8CjzsDj0MPKaqm4Lsvk5VR0bJjglEqfJvsqOqL6nqSO8D+BPwHk4hv0DsHhktIW3DiN55TWyFnjNW+Ods1dTUkJeXl9BiKxZYGDE1+S2wVETuBs4CvtXcASJSDrwFvA+cBnwC/AP4CVAETFDVRSLyINAXpwv6IOC77v6XAF8Cl6tqXWQvJ3URkUHAj4AzVNXTzL7l2D0ymiYhViNCfDxMiZ6r5SWVPVuq2iiMWFNTI/n5+QejPnGCY56tFMT9Q/p9HNF1dwtaEQwAHgGOB4YAN+CItXtw2rV46Y/TJf1K4ClgjqoeB1S540YYuEXzngHuUdXNYR5m98hoioQII8ZT9CSLZysexOK+eDweMjIyfDW9qquradeuXWXUJ05wTGylLpcAFbSsVcwGVV3uelhWAO+4sezlQLnffm+6gm45Tv+qt9zxwP2MpvkZsEJVp7XgGLtHRlMkTBgxXisSk0VsJYsXrqX452uB49nq3r172ostCyOmIG6z4AtxQkfvu32fKsI4tMbvucfvtYfG75UaAFX1iEidX3Jh4H5GCETkK8C1wEktPNTukdEUCSG2IC7eG00WsQWp267HP4QIToJ87969LYwYbwOMyOLWCnkUJ3y4GfgV8D/xtcrwR0Q64+RaTVLVtP/GZ0SUhBBbXs9WrIVPsniLUjlB3j85HsDj8Uh5eXlCln6IJSa2Uo9bgM2qOtN9/SdgiIicG0ebjMbcjpPQ/mhA+Yevx9swI+lJCLEVJ7xFKeNtR7OkcoJ8YBjRpTrYvumEhRNSDFV9DHjM73UDTu+n5o7biF9+l6pOCbZNVR8MOK7A73mjbUZwVPW/gf9uxXEbsXtkNE3CrEb0erZiKSqSJYyYygnygWFEN9pSE/qI9MA8W+lLA9AxmgUzgXOxbzRtwe6R0VISYjUixC8nKRnEFiRPyLOlBIYR3etM+1Iz5tlKU9zip72jeP4J0Tp3umD3yGgFCRNGTNUE8EiQyo2oA8OIHo9HsVZK5tkyDMNIIRJGbGVkZMTFy5QMnq10Kv1gOJjYMgzDSB0UqIX4i614kCxhxFT3bHlzttx70WRnjHTBxJZhGEZqUQ3xF1vxKv2QLGIrHsRCbPnnbNXW1pKdnZ34NyQGmNgyDMNILarBWY2YjgnyyUKq1tnyDyNWV1eTk5MTvzdhAmFiyzAMI7WohqM5Ux5PfKI4sfYyuSUGksazlcp1trxhRFdsWRgRE1uGYRipRjU4f1zjWWvL24g4liRTGDGZvHAtwT+M6Iqt9EocDIGJLcMwjNQiYWptxRoTW00Ta8/W/v37yc7ODvkGFJFfichqEflURF4SkU5+2+4XkbUiskZELvYbHyUiy91tv5ckUa0mtgzDMFKLhCj/YL0RQ5PqYisrK4uamhomTZrE/Pnz24vI90SkZ5DdZwIjVPV44HPgftfOYcB4YDgwBviTiHjL0j8K3AoMdB9jonxJEcHElmEYRmqRMGLLSBxinSCfm5vLX/7yF04//fQ9wD6gMIhNb6uq9w26ACh1n18JTFPVGlXdAKwFThGRYqCDqs5X54KeBK6K9jVFAqs8ZhiGkVokjNiyoqbBSWXPVkNDgy+MWFNTQ4cOHapV9e9hHPoN4Fn3eS8c8eVlqztW5z4PHE94TGwZhmGkFgkhtuKQIC+WsxWaWK5GzM3NBZwE+ZkzZxaJyGdBdn1AVV9xbXsAqAee9pobZH9tYjzhsTCiYRhGauETW/FcjQix9zIlk9hK1fkC62xdfvnla1V1RJCHV2hNBi4DJuhRQ7fSuC9sKbDNHS8NMp7wmNgyDMNILRJiNWK8Sj8kC7G0NZaetMDSD5mZmdWh9hWRMcC9wBWqesRv06vAeBHJFZG+OInwi1S1AqgUkdPcVYiTgFeidS2RxMKIhmEYqUVChBEhPvlTyeDZ8ng8cQkjxgL/0g81NTVkZWUdaWL3PwC5wEzXvgWqeruqrhCR6cBKnPDiHarq/dbwTeBxIB94030kPFEXW65yfQTIBP6qqg8HbBd3+1jgCDBFVT9yt20EKoEGoF5VRzc3X7du3bS8vDxs+5ZtXwbAyJ4jwz7GMAwjkKVLl+5W1e7xtoMEEVtx8DIlTc4WxP7nEy/PloiEFFuqOqCJbQ8BDwUZXwKMiICpMSWqYsuti/FH4EKcWOtiEXlVVVf67XYJR+tlnIpTQ+NUv+3nqerucOcsLy9nyZIlYdvY6eFOACy5L/xjDMMwAhGRTfG2wSUhxJYlyIcm1gnyqhqz++Gfs1VVVaXZ2dkhw4jpRLR/+qcAa1V1varWAtNw6mf4cyXwpDosADq5tTQMwzCMlpMQYsuKmoYmldv1+IcRq6qqPFlZWVVxNikhiLbY6gVs8XsdrCZGU/so8LaILBWRW6NmpWEYRuqQEKsR4yUmksGzFWvilSBfU1Oj2dnZTeVspQ3RztkKpyZGU/ucqarbRKQIJ4Futaq+e8wkjhC7FaCsrKwt9hqGYSQ7Nd4n8VyNGAfPloURmyBeYcTMzEwTW0TfsxWqVkZY+6iq9/+dwEs4YcljUNXHVHW0qo7u3j0R8lMNwzDiRsKEEWM/pYmtUPPFiiA5Wya2iL7YWgwMFJG+IpKD01jy1YB9XgUmicNpwAFVrRCR9iJSCCAi7YGLgGBVaA3DMIyjJEQYEWIe0pNkyYNKdc+Wf+mH7OzsQzGZOMGJ6k/fbTB5JzADWAVMd+tn3C4it7u7vQGsx2k0+RfgP9zxHsD7IvIJsAh4XVXfiqa9RmqwZ88eRo4cyciRI+nZsye9evXyvRYRRo4cyYgRI7juuus4csT50rV9+3bGjx9P//79GTZsGGPHjuXzzz8/5tzf+MY3KCoqYsSIpFt5bKQPCeHZikdRU0iOnK1U9mwFln7Iyso6HLPJE5io19lS1TdwBJX/2J/9nitwR5Dj1gMnRNs+I/Xo2rUry5YtA+DBBx+koKCAe+65B4CCggLftgkTJvDnP/+Z73znO1x99dVMnjyZadOmAbBs2TJ27NjBoEGDGp17ypQp3HnnnUyaNClm12MYLaSR2Kqqis9isFiLLTc6kjRiK9bEsqipn9iS3NxcE1tYBXkjjTn77LP59NNPmTNnDtnZ2dx+++2+bSNHjgx6zDnnnMPGjRtjY6BhtI6E8GxBfMKIySC2IPY5bfEKI+bl5VXGZOIEx3ojGmlJfX09b775JscddxyfffYZo0aNirdJhhEpEqI3ooikbJX0thKPMGKcSj9Ifn6+iS1MbBlpRlVVFSNHjmT06NGUlZUxderUeJtkGJEmXT1bcZuzpcQjQT5OYUTatWtnYgsLIxppRn5+vi9ny8vw4cN5/vnn42OQYUSehFiNGA8vU7KEEVM1Qd7j8eDxeHwhy5qaGunatauJLcyzZRicf/751NTU8Je//MU3tnjxYubNmxdHqwyj1SREGBHiI7iSRWzFmljkbHlDiN77Xl1dLX379j0Q9YmTABNbRtojIrz00kvMnDmT/v37M3z4cB588EFKSkqO2ff666/n9NNPZ82aNZSWlvK3v/0tDhYbRpMkRBgxTp6txFdaLqmYs+WfHA+Op6tPnz62GhELIxopzoMPPtjo9aFDwevrlZSUMH369GbP969//SsSZhlGNElnsZU0nq1UzNnyT44HUFXB7/2YzphnyzAMI7VICLFlhCZVVyP6J8eDr6R/Tegj0gcTW4ZhGKlFQogt82yFJlWLmgYRWwC1UZ84CTCxZRiGkVokxGpEiIuo0GQQW5AeOVvuvUiOGxJlTGwZhmGkFh6gDqyoaaKSqo2og+RsmdByMbFlGIaRelSDhRETlXiIrVjgH0ZUVRNbfpjYMgzDSD3iLrbiRTL8fY9HgnwsPFv+YcS6ujpycnI8UZ80STCxZRiGkXrEXWyZZys06VD6obq6muzsbBNbLia2DMMwUo9qcBLkGxoa4iZAYj1vKobmIkE8Sj9UVVWRk5PTbMKgiNwjIioi3fzG7heRtSKyRkQu9hsfJSLL3W2/lyS64Sa2DMMwUo9qcMSHV3DFmnglyJtn61hidS+8Ysvj8TB27FhWrVqVLSJfF5EOIezqDVwIbPYbGwaMB4YDY4A/iYh3ieOjwK3AQPcxJoqXE1FMbBmGYaQeCVFrKx6Y2Ao+Xyzw5mxlZGTwzDPPMGDAgCpgKDAsxCG/BX5A4/IQVwLTVLVGVTcAa4FTRKQY6KCq893E+yeBq6J2MRHG2vUYhmGkHnEXW/HqjWhiKzixytnyJsjX1tbSvXv3GlV9MIQ9VwBfquonAbb1Ahb4vd7qjtW5zwPHkwITW4ZhGKlH3AubxitBPhmIRxgxFvjnbFVXV/P+++93EJHPguz6APBD4KIg24IZq02MJwUWRjQMw0g9Gnm24lXYNNZepkOHDklFRQU1NdaOL5BYJ8jX1NRw0UUXVajqiMAHsB7oC3wiIhuBUuAjEemJ47Hq7XfaUmCbO14aZDwpMLFlGIaReiREGDGW3pvVq1ezZ88eqaqq4vnnn2fGjBmsXLmSQ4cOxcyGcEnlMKK/ZysrKyuo6lXV5apapKrlqlqOI6ROUtXtwKvAeBHJFZG+OInwi1S1AqgUkdPcVYiTgFeiflERwsKIhmEYqUfcxRbExrOlqixbtowNGzZw6aWX1nfo0CG7rq6OiooKNm/ezKeffkq7du3o3bs3ZWVldOnSJe7hxlROkM/NzQUcsZWZmdliF6OqrhCR6cBKoB64Q1W9rtlvAo8D+cCb7iMpMLFlGIaResRdbMXCs+XxeFiwYAF79uxhzJgxtfn5+VkAWVlZnrKysoyysjI8Hg87d+5ky5YtzJs3j4aGBnr37k3v3r3p2bNno8bJsSKVSz+0b98e8Hm2qps5BADXu+X/+iHgoSD7LQFGtN3S2GNiyzAMI/VICLEVTerr63n33Xepr6/n4osvrsvOzs4SkQx37gxV9YhIhoh4evbsmdGzZ09Gjx7NgQMH2Lx5M8uWLePAgQP06tWLsrIyevXqRU5OTlRt9icVw4iBOVtZWVlVUZ80STCxZRiGkXrEfTUiRC98VVNTw+zZs2nXrh3nnHNOXZbzF76RmggmvABPp06dpFOnTnL88cdz5MgRtmzZwrp16/jwww8pKiryeb28Hppo4PGkZhcb/9IP1dXViMiROJuUMJjYMgzDSD3ivhoxWp6Uw4cPM2vWLIqLixk9enR9RkZGdhi2BAovATQ/P18HDx6cMXjwYOrq6vjyyy/ZsmULH3/8MQUFBb48r86dO0f8elLRs+WfIF9VVaXm2TqKiS3DMIzUI+5hxGiwf/9+Zs2axeDBg3X48OFhCa1AvMILxxOmXq9XVlaWp7y8PKO8vByPx8OOHTvYsmULs2fPBqCsrIzevXvTo0cPMjLatpA/lRPk/VYjerKzs01suZjYMgzDSD3iHkb0JmVHSljs3LmTOXPmMGrUKE///v0bRKTFQiuIjRl+z8U/z6u4uDijuLiYk08+mX379rF582aWLFnCoUOHKC0tpXfv3pSUlLQqzyuVE+T9xJZmZWVZGNEl6mJLRMYAjwCZwF9V9eGA7eJuHwscAaao6kfhHGsYhmEEJSE8W5H6A79lyxY++OADzjzzzLrS0lIVkWhksov79+iYPK8uXbpIly5dZOTIkRw+fJgtW7bwxRdf8MEHH1BUVOTzerVr1y6sieLRUihWYsubs1VVVaWZmZkmtlyiKrbcTt1/xOnqvRVYLCKvqupKv90u4WgH71NxunqfGuaxhmEYxrEkjNhqqxfniy++4KOPPuK8886rKyoq8ohIbgRNDEmoPK927drpkCFDMoYMGUJtbS1ffvklmzdvZunSpXTs2NGXYN+pU6cmrzvetb6iQWBR044dO5rYcom2Z+sUYK2qrgcQkWk4Hb39BdOVwJNu99AFItLJ7e5dHsaxhmEYxrEkjNhqLarK8uXL+fzzz7noootqXfESE6EVSGCel/uQ7OxsT9++fTP69u1LQ0MDO3bsYPPmzcyaNYuMjAxfgn1RUVGjPK9UrSAfGEbs1q3b4ahPmiREW2z1Arb4vd6K471qbp9eYR5rGIZhHEvcVyN6aU3ITFVZtGgR27dvZ8yYMbXt27eXSORoRYJQeV4ZGRmekpKSjJKSEk499VT27t3Lli1bWLRoEUeOHGmU5xUPsRUL/MOI1dXVZGdnJ16vpDgRbbEVTpfuNnf4FpFbgVvBWTHSUmrqPfx25ud858JBLT7WMAwjAWkkto4ciU80pzWr9hoaGnj//fepqqri4osvrsvNzc1000oSkZB5Xl27dpWuXbvKyJEjOXToEJs3b2b16tW8//77NDQ0sG7dOvr160d+fn6sDI36HAFhRMnOzjbPlku0xVao7t3h7JMTxrEAqOpjwGMAo0ePbvHXqNysDBNahmGkEnFfjdgaamtrmTNnDjk5OVxwwQV1WVlZmf6epEQnWJ6XqnoKCgpk2LBhMmzYMGpqanjuuefYsWMHn3zyCZ06dfKFGzt27BhN26J2bi+BFeTz8vLMs+USbbG1GBjodu7+EhgP3BCwz6vAnW5O1qnAAVWtEJFdYRxrGIZhHEvC5GyFG0asqqpi5syZdO/enVNPPbVVNbQSiSDCC0BycnI8GRkZGWeddRaZmZls376dzZs3M2PGDLKzs33Cq1u3bm2u5+UlHnW2ampqJD8/vzImEycBURVbqlovIncCM3DKN/zd7eh9u7v9z8AbOGUf1uKUfripqWOjaa9hGEaKkBA5W+GKhYMHDzJz5kz69++vxx9/fNILrUCC5HkhImRkZHh69eqV0atXL0477TR2797Nli1bmD9/PtXV1Y3yvLwippXzx7z0Q3V1NSa2jhL1Oluq+gaOoPIf+7PfcwXuCPdYwzAMo1kSwrMFzXtVdu/ezTvvvMPIkSM9gwYNikix0gTHJ7YC87y6d++e0b17d0466SQqKyvZvHkzK1eu5P3336dnz56UlZVRWlpKXl5evK8hKP45WzU1NdKlSxcTWy5WQd4wDCP1SAixlZGR0aRX7csvv+S9997j9NNPbygrK2uIUrHShMN/NWKwhtmq6iksLJThw4fL8OHDqa6uZuvWrb7VjZ07d/aFGzt06NCi+aJ5Tf6erZqaGikrKzsQ1UmTCBNbhmEYqUdCiC0I7dlav349ixcv5itf+Updjx49YlasNBEIJX5CFVLNzc3VAQMGZAwYMICGhga2bdvGli1bePPNN8nNzW2U5xXivFEXWx6Ph4yMDF/ouL6+npKSEkuQdzGxZRiGkXokxGrEUH/gV65cyYoVK7jwwgtrO3furOkktMIlVMPsjIwMT+/evTN69+6NqrJ79242b97M+++/T21trU94FRcX+7xMsUiQ90+Od+eU9u3bVzdxSFphYsswDCP1SMgEeVVl6dKlbNmyhTFjxtQWFBRIuoQO/WlpWC9UIVX88rxGjRrFgQMH2LJlC8uXL+fdd9+lpKSE0tJSunTpEnXPln8I0WsnUBPVSZMIE1uGYRipR0KEEf1LPzQ0NDB//nwOHDjAJZdcUpebm5swVeFjiffn0QbxE7SQqqp6OnbsKB07dpQRI0ZQVVXF1q1b2bRpEwsXLmT58uUMHjyYIUOG0KlTp8hcjB/+yfGubWBiy4eJLcMwjNTDA9QB2fEWWwB1dXXMnTsXEeGiiy5KumKlkSTSyeqh8rzy8vJ04MCBGf379+fw4cPk5OSwevVq3nvvPQoLCxk8eDCDBw+muLg4IvYECSN6e0gamNgyDMNIVaqJs9gCp97S+++/T8eOHTn99NPrMzMzswjeji1tiFZIL1TD7MzMzIbBgwdnDh48GI/Hw9atW1m9ejUvvPAC9fX1DBo0iCFDhlBeXt4oFNgSAsOIsSqkmiyY2DIMw0hNqoHCeIqtQ4cOMWvWLMrLyznxxBPrMzIy0v5vTqxEiFd4uZ60DBxvZ0ZGRkZDWVlZZllZGRdeeCG7d+9mzZo1zJ07l927d9O/f38GDx7MwIEDW1TPyz+MqKpez5bhkvZvfMMwjBSlGuK3GnHHjh28/vrr9O/fX/v3719fW1ubnZOTE7EWNMlKLGpeBZKRkSEc9SZmAg1Apog0uAn2ctZZZ3Ho0CHWrFnD8uXLee211ygtLfWFG5vr2+gfRvz1r3/NkSNHVESyVbUu2P4i8i3gTqAeeF1Vf+CO3w9MdW38tqrOcMdHAY8D+TjFzu9KJkFnYsswDCM1qYb4rEbcuHEjzz//PGPGjGkYMmRIfVVVVe6+ffuorKzE4/GQlZVFbm5uWgqveIitIPNl+v3f4P6vBQUFjBo1SkaNGkVtbS3r1q3zeb06duzoS7Dv0aPHMef0F1uDBg3i4MGDAiwVkR+q6msB9pwHXAkcr6o1IlLkjg/D6YM8HCgBZonIIFVtAB4FbgUW4IitMcCbEfkBxQATW4ZhGKmJT2zV19fH7I/8qlWreO2117jmmmtq+/fv7wHyCgsLKSwspKGhgaqqKg4cOMD+/fvxeDxkZmaSm5vb6lyhZCPWYiuM+bw/eMEJNYLTMLth6NChmUOHDsXj8bB582bWrFnD9OnT8Xg8PuFVVlbm85567+Hpp5/O0KFDa7dv334CEKy0xzeBh1W1xrVxpzt+JTDNHd8gImuBU0RkI9BBVecDiMiTwFWY2DIMwzDiTDUcrXXlFTbRZMmSJbz77rtMmDChpqSkxIMT8vGRmZlJQUEBBQUFFBcXU1VVxcGDB9m/fz/19fVkZGSQl5eXNsIrFrRQ3GUEPPfleZWXl2eWl5dz0UUXsWvXLlavXs2sWbPYt28fAwYMaJTfVVNTQ3Z2dr0b5gtW/mEQcLaIPITzPr1HVRcDvXA8V162umN17vPA8aTBxJZhGEZqckxh02iJGFVl7ty5fPbZZ0yZMqW6S5cuECC0AsnIyKB9+/a0b9+enj17Ul1dTWVlJXv37qWqqgqAvLy8RuUEUoEECSOGdRgh8ryKiooyi4qKOOecczh48CBr1qzxlZQAZwXq0qVL80TksyDnfQBHe3QGTgNOBqaLSD+Cr1LVJsaThtR6FxuGYRhejilsmpMT+WLtHo+H119/nYqKCm666aaqgoKCTIKHjkIiIuTn55Ofn0/37t2pqanh0KFD7N27l8rKSkSE3NxcsrOTvwZqEomtQILleXkKCwuloqJCunbtyg033AA4Yuucc87Z/+KLL44IYc83gRddz9ciEfEA3XA8Vr39di0FtrnjpUHGk4awxJaIdAljN4+q7m+bOYZhGEaEiHoV+bq6Ol588UVqa2uZPHlyTW5ubjZt/BIvIuTl5ZGXl0e3bt18wsubYC8i5OTkkJ2dHXPREgkSMGerNWS65854/fXXdffu3dx4442anZ3tATKrq6vJzs5u6g33MnA+MFdEBuGI893Aq8AzIvIbnAT5gcAiVW0QkUoROQ1YCEwC/jfSFxVNwv2l2OY+mrpjmUBZmy0yDMMwIkFUxVZVVRXTpk2jQ4cOXHvttbVZWVnZNM75iQi5ubnk5ubStWtXamtrOXz4MPv27ePw4cMAZGdnk5OTkzTCK4k9W41QVd566y22b98uEydOJCcnxxt29Hz22WcZW7ZsaSqM/Hfg726YsRaY7Hq5VojIdGAlTkmIO9yViOAk1T+OE55+kyRKjofwxdYqVT2xqR1E5OMI2GMYhmFEhqiJrYMHD/L000/Tt29fLrroorqMjIyYNJPOyckhJyeHzp07U19fz6FDhzhw4ACVlZWAc52JXssr1qWhoiHuVJWZM2eydetWJk6cSG5urndTxooVK/if//kfHTNmzH82cXwtcGOIbQ8BDwUZXwIEDUsmA+GKrdMjtI9hGIYRG3xiK5KFTXft2sXTTz/NySefrGeccUadiMREaAWSlZVFp06d6NSpEw0NDRw5coRkqeWVzJ4tVeWdd95hw4YNTJo0qdEqxDVr1nDDDTd4brnllsl33XXXUxGbNAUIS2ypajU4rcaBCUA/Vf2piJQBPVV1kXcfwzAMIyE4ZjViW9myZQvPPvssX/3qVxtGjhzZQAsT4aNFZmYm3lpeHo+HI0eOJGwtr2TP2Zo3bx5ffPEFkydPJj//aKRw3bp1jBs3znPTTTfdZkLrWFqayPgnnLob5wM/BSqBF3CWbhqGYRiJQ0TDiJ9//jmvvPIKV155Zd2gQYMagPAb58WQjIyMY2p5VVZWsm/fvoSo5RWndj0ROc+7777LihUrmDJlCu3atfONb9q0iWuvvVYnTZp019133/3XiEyWYrRUbJ2qqid587NUdV+8XMiGYRhGk0RMbH388cfMnj2b8ePH1/Tu3fuYYqWJin8trx49ejSq5XXkyJG4lJSIh2crEnzwwQd88sknTJkyhfbt2wPOIom9e/dy1VVX6fXXX/+D733ve3+IyGQpSEvlbp2IZOIWExOR7hwt728YhmEkDm0WW6rK+++/z7vvvsukSZOqe/furSSJ0ArEW8urqKiIwYMHM3DgQHr27ImqUllZyaFDh6irC9ozOaIk42rEBQsWsHTpUiZPnuwrXArwne98h0GDBlFfXz/nvvvu+3db7UxlWurZ+j3wElDkltn/GvD/Im6VYRiG0VbaJLa8S/s3btzITTfdVN2hQwcBcps9MAkIVctr//79KVHLK5C2hBEXL17MwoULmTx5Mh06dPCN79q1i6VLl3ruuOOO3/7qV79aglMTa03brU1NWiS2VPVpEVkKXIBTT+MqVV0VFcsMwzCMttDq1Yj19fW8/PLLHDp0iClTptTk5+dnkCDJ8NEgWC2v/fv3+2p5eVc2RkJ4JVOC/NKlS/nggw+YPHkynTp18o3v3buXSy+91HPFFVf87j//8z/v+eUvfxkha1OXFlf6VdXVwOoo2GIYhmFEjlatRqypqeHZZ58lLy+PCRMm1GRnZ2dxtFVLyhNYy8srvCorK1FVXxHV1nqLkkVsLVu2jHnz5jFlyhQ6d+7sGz9w4ACXXnqp59JLL/2///zP//xeJG1NZaw3omEYRmrS4jDioUOHePrpp+nVqxdjx471FitN/jhaK8nKyqJjx4507NjRV8tr//79HDhwAFVtVS2vZMjZ+vTTT5k9ezaTJk3CbSoOQGVlJZdddpnnwgsvfPLHP/7xf0TazlTGxJZhGEZq0qIw4t69e3nqqac44YQT9JxzzqkXkeTv+hxB/Gt59erVq021vBJZbK1YsYKZM2cyceJEunXr5hs/cuQIV1xxhefcc8997qc//elN0bAzlYlaaV0R6SIiM0XkC/f/ziH2GyMia0RkrYjc5zf+oIh8KSLL3MfYaNlqGIaRgoTt2dq2bRv/+Mc/OPPMM/Xcc8+tM6HVNN5aXr169WLo0KH069ePLl26UFtby6FDhzh8+HDIn3c82vWE63lbtWoVb775JhMmTKCoqMg3Xl1dzZVXXtlw+umnv/Zf//Vf46NlayrT7B0QkdtF5C8iMl5EXhORb4Z57vuAd1R1IPCO+zrw3JnAH4FLgGHA9SIyzG+X36rqSPfxRpjzGoZhGGGKrXXr1vH0008zduzY+lGjRtWSwonw0cBby6tnz54MGTKE/v370717dxoaGoKWlIhHGDEcPv/8c15//XUmTJhAz549feM1NTVcc801DSNHjpz185///Kr4WZjchBNGPB/4OvCeqp4lIn8O89xXAl9xnz8BzAXuDdjnFGCtqq4HEJFp7nErw5zDMAzDCE4jseVdWefP8uXLmTFjBtddd11teXm5hwStCp8seGt55efn0717d19Jib179/pKStTW1sY8Qb45z9batWt55ZVXuOGGGyguLvaN19XVMW7cuIYhQ4Z88Ktf/eoS3BqbRssJR2ztUVUVkV+4r2vCPHcPVa0AUNUKESkKsk8vYIvf663AqX6v7xSRScAS4Huqui/MuQ3DMNKdJlcjLliwgPnz5zNx4sTqHj16JG2x0kQlWC2vw4cPc+jQITweD5WVlb7q9dEUX82de/369bz00kuMHz+eXr16+cbr6+sZP358fXl5+dLf/OY3X8GEVpsIJ5D7CICqeqvDvuDdICKzROSzII8rw5w/2LvAe0MfBfoDI4EK4NchTyJyq4gsEZElu3btCnNqwzCMlCZoGFFVmTVrFkuXLuWmm26q7tGjB5jQijq5ubl06dKFkpIS2rVrR2lpKdnZ2T4BVl1dHZV8rqbClhs3buSFF15g3Lhx9O7d2zfe0NDAxIkT63v06LH8kUceOQMTWm2mWc+WW1cLERmCE+LrJSLXAtuAb4UqaioiO0Sk2PVqFQM7g+y2Fejt97rUPS+qusPvXH8BXmvCxseAxwBGjx5tbwrDMIwgqxEbGhr497//zZ49e7jpppuq2rVrl4nlaMUUb1ivc+fOUavl5Y+IBBVbmzdv5rnnnuNrX/saffr08Y17PB6mTp1aX1hY+Pmf/vSnk7GWfBEhrDspIvcC03A8UYuAxe7zf/mvIAzgVWCy+3wy8EqQfRYDA0Wkr9vQerx7HK5A83I18Fk4thqGYRhAgGerrq6OadOmceTIESZOnFjTrl27bExoxZxAT5O3llefPn0YOnQo5eXlFBQUcOTIESorK6mqqsLjab3eCeYt27p1K88++yzXXHMNffv2bbTvN7/5zXoR2XjrrbeeAIRXCddolnDrbE0Fhqtqoy6dIvIbYAXwcJBjHgami8hUYDNwnXtMCfBXVR2rqvUicicwA6dC8d9VdYV7/C9FZCSO+3IjcFtLLswwDCPN8YmthoYGNmzYwODBg7nyyivrMjMzs4li6R8jNE2F9fxreZWUlPhqeR04cICGhgYyMjLIy8sLq5aXl0DP1rZt25g2bRpXXnkl/fv3b2TX3XffXX/kyJEv77vvvmHDhw9veedyIyThii0PUAJsChgvJoSLUVX34PRQDBzfBoz1e/0GcExZB1WdGKZtjVi6dOluEQm0szm6yf2yuzXzJQDdgGS1HZLbfrM9fiSi/X2a3yWm+MRWhw4daN++PZ9//jkvvPBC9rBhwxg0aBA5OebYijXhln7w1vIqKCiguLiY6upqDh48yL59+6ivrycjI4Pc3FyyssKvTb59+3aeeeYZLrvsMgYNGtTIpnvvvbd+586dO//7v/97cHl5eV0TpzFaQbh36W7gHRH5gqOrB8uAAcCdUbCr1ahq95YeIyJLVHV0NOyJNslsOyS3/WZ7/Eh2+2OET2z16dNHv/3tb9dVVVXlrF69mk8++YTXXnuNfv36MWzYMAYOHEhubm48bU0bWlNnKyMjg3bt2tGuXTt69OhBdXU1lZWV7Nu3j6qqKkTEt7IxGCLCzp07vfXUGDJkSKPtP/rRj+rXr1+/97777htQXl4ebsUBowWEJbZU9S0RGYRTF6sXTr7WVmCxqlpM1zAMI/HwAHU4n9ceICc/P58TTzyRE088kaqqKvyFV9++ffF6vEx4RY+2FjUNp5ZXTk5Oo5ISe/bs4fnnn+eiiy5i2LBhjc730EMP1S9fvvzAPffc03/06NFVbbo4IyRh+x9V1QMsiKIthmEYRoSYPn16xrhx46qBbIIUKw0UXmvWrGH58uW8/vrrlJeXM2zYMAYPHmzCK8JEsoJ8qFpe+/bt4/Dhw6gqBw8e5K233uLCCy/kuOOOa3T8r3/96/oPP/zw0D333NP/rLPOOhQRo4ygtDlBUkRSoSHlY/E2oA0ks+2Q3Pab7fEj2e2POj/5yU/GnnHGGe1/9atfZW/a1HQaa35+PiNHjuSGG27g7rvvZujQoaxYsYLf/va3TJs2jU8//ZSaGosuRYJotuvx1vLq378/gwcPpqCggA8//JBzzjmHE044odG+//u//1v/9ttvV91zzz0DzjvvvAOtmU9EeovIHBFZJSIrROQudzxkb2MRud/thbxGRC5u0wUnEdLWImoisllVyyJkj2EYhhEhpk+ffub8+fPv//jjjy+ora3Nufrqq3XcuHGZ/nWVmqK6upo1a9awcuVKNm3aRJ8+fXwer7w86+zTGtasWcPHH3/M+PHR7ee8f/9+Hn/8cc466yxGj26c3vjYY4/VT58+vebuu+/uf9lll+0IcYpmcUs0FavqRyJSCCwFrgLGAYdU9X8C9h8G/AsnJakEmAUMSod0pLDEloh8GmoTzg/K/MyGYRgJTCSE1+eff87KlSvZuHGjCa9W4s2T+/rXvx61OQ4cOMATTzzBqaeeyqmnntpo2xNPPNHwj3/8o/bb3/72oGuuuWZrJOcVkVeAPwBnElxs3Q+gqv/tvp4BPKiq8yNpRyISrtjaAVwMBPYmFOBDVS2Jgm2tolu3blpeXh72/su2LwNgZM+RUbHHMIz0YOnSpbtbsxo6Dsi0adPOWrBgwX3Lli07v7a2Nufyyy/Xzz//PPP3v/89hYWFzZ6gpqbG5/HauHEjZWVlPuGVn2+df5pi5cqVfPbZZ4wbNy4q56+srOTxxx9n9OjRnH766Y22TZs2reEPf/hD/V133TX0uuuu2xDJeUWkHHgXGAF8F5gCHMSvt7GI/AFYoKpPucf8DXhTVZ+PpC2JSLhi62/AP1T1/SDbnlHVG6JhXGsYPXq0LlmyJOz9Oz3cCYD99+2PjkGGYaQFIrI0CctRyC9+8YsLf/vb3z5dWFjYqXv37hnXXHONjhs3LrOsLLzskJqaGp/Ha8OGDfTu3Zthw4YxZMgQE15BWLFiBStXruS6666L+LkPHTrE448/zsiRIznrrLN84y+99BJ/+9vfdMOGDfXf/e53j586derqSM4rIgXAPOAhVX1RRHrg1MFT4Gc4ocZviMgfgfkBYusNVX0h1LlThXBLP0xtYlvCCC3DMAyjReh99923Drh9+/btL3o9XpMmTbqgtrY2OxzhlZuby3HHHcdxxx1HTU0NX3zxBStXrmTGjBkmvIIQrQT5w4cP8+STT3Lcccc1Elouni+++MKzc+fOV26++ebfTZ06dUyk5hWRbOAF4GlVfRGa7G0csh9yqtPmBPlEwzxbhmHEgyT1bIXCF2r8+OOPL6irqwtLePlTW1vr83itX7+e0tJSn/Bq165dlM1PXJYvX87nn3/OtddeG7FzHjlyhCeffJJBgwZx3nnnNRJzb7/9tueBBx7wfPvb3z5l4sSJH4uIaIT+8Isz0RPAXlW922+8WFUr3OffAU5V1fEiMhx4hqMJ8u8AA9MhQT4sz5aIfKSqJ7V1H8MwjOYov+91Nj58abzNSHd0/Pjx740fP/49XOG1cOHC+ydOnHh+fX19tje5vinhlZOTw4gRIxgxYgS1tbU+j9fbb7+d1sIr0p6t6upqnnrqKfr373+M0Jo7d67ef//9+q1vfeusiRMnfuzOH0kPy5nARGC5iCxzx34IXB+st7GqrhCR6cBKoB64Ix2EFoRf1HRoEysSwUmU7xgBewzDMFqNCbWo0Eh4Pf3002cvXrz4vpYKr+HDhzN8+PBjhFevXr18wqt9+/axu6o4EUmx5RVaZWVlfPWrX2103g8++EC/+93veu64447zp0yZsjAiEwbg5nEHu5hj+h37HfMQ8FA07ElkwhVbQ5rfhbRQp4ZhGGmMTpgw4d0JEya8C8i//vWvcxYuXHjvpEmTzvcPNfbu3TvkCfyFV11dnU94zZw5k5KSEoYNG8bQoUNTVnhFSmzV1NTwzDPPUFxczMUXX9zonIsXL+aOO+7QO++885KpU6e+2+bJjDYTboJ80+WHDSOB2LNnDxdccAHgdLnPzMyke3dnRf4nn3zCCSecQH19PUOHDuWJJ56gXbt2bN++nbvvvpvFixeTm5tLeXk5v/vd7xg0aJDvvFu2bGHSpEls376djIwMbr31Vu666664XKNhJAB6/fXXz7v++uvn4Se8Jk6cGLbwys7OZtiwYQwbNswnvFatWsWsWbNSVnhFQmzV1tbyr3/9i+7duzN27NhG51u2bBk333yz5/bbb7/i5ptvntlWe43IEHZvRMNIFrp27cqyZcsAePDBBykoKOCee+4BoKCgwLdtwoQJ/PnPf+Y73/kOV199NZMnT2batGmA84G1Y8eORmIrKyuLX//615x00klUVlYyatQoLrzwwmMauxpGGhJx4bV27VpWrlzJrFmzKC4u9gmvgoKC2F1VFGir2Kqrq2PatGl07tyZyy67rNG5PvvsMyZNmuS59dZbx33zm998PRL2GpHBxJaRtpx99tl8+umnzJkzh+zsbG6//XbftpEjRx6zf3FxMcXFxQAUFhYydOhQvvzySxNbhtGYYMLrvhtvvPG8+vr6sIXX0KFDGTp0KHV1daxbt46VK1cye/ZsevbsmdTCqy1iq76+nmeffZaCggIuv/zyRudZvXo1EyZM8Nx8880T77zzzpSvW5VstEhsucs8JwD9VPWnIlIG9FTVRWEc2xt4EugJeIDHVPUREekCPAuU46xaGKeq+9xj7gem4uSDfVtVZ7TEXsMIRX19PW+++SZjxozhs88+Y9SoUS06fuPGjXz88cfHtMIwDKMRxwivBQsW3Ddx4sTz/JPrmxNeQ4YMYciQIdTX1/s8XrNnz6ZHjx4+4RVO5ftEoLViq6GhgenTp5Obm8tVV11FRkaGb9vatWv5+te/7rnppptu+fa3v/1MJO01IkNLPVt/whFK5wM/BSpxipmdHMax9Tgl+30NK0VkJk5J/3dU9WERuQ+4D7jXbVg5HhiO27BSRNKiYaURPaqqqnxeq7PPPpupU6fy5z//uUXnOHToENdeey2/+93v6NChQxSsNIyUpJHwmjZt2rnz58+/1yu8vB6v0tLSkCfIyspqJLy8Hq85c+ZQVFTkC0MmsvBqjdhqaGjg+eefJzMzk2uuuaaR0Nq4cSNf+9rXdPLkyXfefffdf4+0vUZkaKnYOlVVTxIRb72OfSKSE86BboGzCvd5pYisAnoBVwJfcXd7ApgL3OuOT1PVGmCDiKzFKYSW8g0rjeiRn5/vy9nyMnz4cJ5/PrzWXHV1dVx77bVMmDCBa665JgoWGkZaoOPHj587fvz4ufgJrwkTJpxXX1+ffe2114YlvAYPHszgwYN9wmvVqlXMnTvXJ7yGDh2acF+IWiq2PB4PL774Ih6Ph3HjxpGZmenbtnXrVq6++mqdMGHCPd/97ncfjYa9RmRoqdiqE5FMnEJliEh3HE9Xi3AbVp4ILAR6eCvNqmqFiBS5u/UCFvgdttUdM4yIcv755/PDH/6Qv/zlL9xyyy2As3T6yJEjnHvuub79VJWpU6cydOhQvvvd78bLXMNINUIKr4aGhuxrrrlGTzjhhMyamhrGjh0b9ASBwmv9+vWsXLmSuXPn0r17d5/HKxGEl8fjCVtseTweXnrpJWpra/n617/eSGhVVFRwxRVX6Pjx4//f97///d9Ey14jMrRUbP0eeAnoISIPAV8D/l9LTuA2rHwBuFtVDzbxpgu2IWjlWxG5FbgVINxWEobhRUR46aWXuPvuu3n44YfJy8vzlX7w54MPPuCf//wnxx13nC8U+fOf/zzkHwDDMFpMI+H19NNPn/viiy/+149+9KMzBg0apKtXrw7L4zVo0CAGDRpEQ0ODT3i9++67dOvWzefx6tgxPnW4w/VsqSqvvvoqhw8f5vrrrycr6+if6507d3L55Zd7rrvuuofuvffen0fTXiMytEhsqerTIrIUuMAdukpVV4V7fLCGlcAObx8lESkGdrrjYTesVNXHgMfA6Y0Y9gUZKc+DDz7Y6PWhQ4eC7ldSUsL06dObPNdZZ51FqvUSNYwERidMmDD3xhtvnJ+TkzP5e9/7Xu9FixbdN2HChK94PV7NCa/MzEwGDhzIwIEDjxFeXbt29Xm8Yim8whFbqsq///1v9u/fz4QJE8jOzvZt27NnD5dffrnnqquu+s3999//o2jba0SGlq5GDIydXCIiZwBLVXVZM8cK8Ddglar6uzxfBSYDD7v/v+I3/oyI/AYnQX4g0OyqR8MwDCN1UNXvu0/XTZgwYS6ux2vRokX3T5gw4dyGhobsa6+9Vq+77roWCa8NGzawYsUK3nvvPbp06eITXp06dYr29TQptlSVN954g927d3PjjTc2Elr79+/n0ksv9Vx66aWP/r//9/++H/IkRsLR0jDiaPfxb/f1pcBi4HYReU5Vf9nEsaEaVj4MTBeRqcBm4DpI74aVhmEYRkh0woQJc73C65lnnvnKwoUL77vhhhu+4vF4srzJ9b16hU7xzczMZMCAAQwYMMAnvFauXMlf/vIXOnfuHFXh1ZTYUlVmzJhBRUUFEydOJCfn6PqzyspKLrvsMs+YMWMe/9GPfnRnxA0zokpLxVZX4CRVPQQgIj8GngfOAZYCIcVWEw0r4WhYMvCYtGxYaRiGYYSF3nDDDXNuuOGGOfgJr+uvv/4rqprlDTW2RHht3LjRJ7w6derkE16dO3eOjMGqjUo3+I/PnDmTzZs3M2nSJHJzc33bDh8+zGWXXeY577zzpj344INTI2KIEVNaKrbKgFq/13VAH1WtEpGayJllGEY6cubDs+NtgpG8RER49e/fn/79+3PppZeyceNGVqxYwV//+teICa9gni1VZfbs2axfv57JkyeTl5fn21ZVVcWVV17ZcOaZZ776s5/9bEKrJzbiSkvF1jPAAhHx5lVdDvxLRNrjhPsMwzBazZf7q+JtgpEatFl4ZWRk0K9fP/r16+cTXitXruSvf/0rHTt29AmvLl26tMywIGJr3rx5fP7550yePJn8/HzfeE1NDddcc03DqFGjZv785z+/tkUTtQARGQM8AmQCf1XVh6M1V7rS0tWIPxORN4CzcEKCt6vqEnezKW7DMGKG1wv2wX3nx9mSxEZEfoXzxbgWWAfcpKr73XqHq4A17q4LVPV295hRwONAPvAGcJcm71LcRsLrqaeeOm/RokX3XX/99ee2RniNHTuWTZs2sWLFCv7+979TWFjIsGHDGD58eFjCK1Bsvffee6xYsYLJkyfTrl0733htbS3XXXddw7Bhw977xS9+MZYQpY/ails784/AhThVABaLyKuqag6UCBK22HJXE5aq6lKc/CzDMIwWEymR1BovWPl9r7Px4UvbNG8SMhO4X1XrReQXwP04XToA1qnqyCDHPIpTu3ABjtgaA7wZA1ujjd54442zb7zxxtk4qxrPX7hw4b3jx48/F8jyrmpsTnj17duXvn37+oTXypUrGwmvYcOG0bVr1+AG+OVsffjhhyxbtowpU6Y0aqpdX1/P+PHjG/r167fo17/+9flESWi5nAKsVdX1ACIyDaeDi4mtCBK22FJVFZGXgZZ17DUMw/DDXySZdyr6qOrbfi8X4BSjDolb77CDqs53Xz8JXEVqiC1/dMKECe9MmDDhHSIgvC655BI2b97MypUrefzxx2nfvr3P4+UvvLyerYULF7JkyRKmTJnSqJdjQ0MDEydOrC8pKfn0d7/73VlEV2iB05lli9/rrcCpUZ4z7WhpztYCETlZVRdHxRrDMNIKy9GKOd8AnvV73dftdXsQ+H+q+h7OH9+tfvukQ6u0iAiv8vJyysvLGTNmDFu2bGHFihWNhNewYcNQVb788kt27tzJ5MmTfS2EampqmDlzJtOmTavv2LHj6j/84Q8n04p2eK0g7G4tYZ1MpMBbscA4SkvF1nnAbSKyCTiMc5NUVY+PuGWGYUQV8yqlDiIyC+gZZNMDqvqKu88DODULn3a3VQBlqrrHzdF6WUSGE+E/vklISOElIlnXXHNNWMKrT58+9OnTp5HH68knn+TIkSNkZWVx2223NarjdfjwYe6//37Pli1bGg4ePPjx//3f//VS1S0hJ4kcYXdraQ63dd4QEfk/VV3T7AFpREvF1iVRscIwjJhy5sOzzauUQqjqV5vaLiKTgcuAC7yJ7qpaA9S4z5eKyDpgEM4fX/9S7K3+45sCBBNe940fP/4cr/AaN25cZklJCVVVVY1WEnoREZ/wGjNmDJ9++inFxcWNykeoKg8++GD9iSeeuPWOO+4Y/M1vfvMkHG9jLFgMDBSRvsCXwHjghpaexBXqt+GEJK8SkVdUdXVELU1ijq2s1gSqugnnDdAD6OP3MAwjiTChlT64y/rvBa5Q1SN+493dlWiISD+clmjrVbUCqBSR09yFUZM42kYtndEJEya88/vf//7C9957L+/222+/eP369XO+/vWv144aNcozePBg1q9f3+QJRIQTTjiBoqKioydV5Qc/+EH97t27d/znf/7n4Ntvv71WVReo6oFoX5A7fz1wJzADZ3XqdFVd0YrzrFDVUcAtwGBgnCvADFreG/Fm4C6cbzrLgNOA+YDFIQzDaESarvxLRP4A5AIz3ZID3hIP5wA/FZF6oAGnlM9e95hvcrT0w5ukXnJ8W9Ebbrhh1g033DArNze3X2Zm5tuXX375zsmTJ48SEV+OV0lJSbMn+tGPflS/cePG3ffee+/AgQMH1jZ7QBRQ1TdwVp22GBHJUFVfbpmq7hKRh4Hv4wiu51V1eYRMTVpaGka8CzgZ55f1PBEZAvwk8mYZhpFulN/3erxNSElUdUCI8ReAF0JsWwKMiKZdqUJtbe02YMyzzz67FpB//vOfX128ePEPxo0bd05GRkaTwuu//uu/6pcvX77/oYceGjB8+PCkczeLiHiFlogMA1YDWar6uSu4foAjuDJU9ZN42hpvWhRGBKpVtRpARHLdeOzgyJtlGIZhGImPqlar6lrvy4kTJ878/e9/f+H777+fd8stt4xZt27dnHHjxtWee+65DY888kjDtm1O+tuvfvWr+gULFlTeddddA4YPH344flfQerz5fyLyfeBl4B/AdSLSWVXXAT8HurtjJ8bN0ASgpZ6trSLSCeeHOlNE9pG+iZOGYRiGEQqdOHHizIkTJ87E9XgtWrTo3nHjxp29a9eu7KKiouof/ehHA84777yY5GZFEtej5RVa/YDhOJ1lrsSpxVkoIs+p6iZ3FewDwJ0i8n2/UHVa0dJ2PVe7Tx8UkTlARyyWbxhGgmP5Y0acaSS87rjjjqurqqoWXXjhhUkpPPyE1gSgCCd0uFNE/orTuu8k4Bsi8ne3tEgmzuKLpLzeSNDSBPlc4Fqg3O/YkcBPI2qVYRgpj7fOl2GkGfrHP/7xxXgb0VZE5ArgPuCvwNdF5DO3gfVTIpKNU6nAm4f2aLqXgWhpGPEV4ABOb8SayJtjGEa6YOUnjGC4pSoeATKBv7p/wI0EQkRG45QE+Z6qvi0iM4B5bi73T1T1HyKSr6pVAOkutKDlYqtUVcdExRLDMAwjrXHDTX8ELsQprrpYRF5VVWuKHEf8c7RcyoFC4BoRWaWqq0XkNGC1iHhU9WdeoWU4tHQ14ociclxULDEMwwiBlYVIG04B1qrqelWtBabhJF0bCYCIDBGRnjglQ36M0/7pGhHpraobgL7AM/G0MVEJS2yJyHIR+RRntcFHIrJGRD71GzcMI8VJJMHTGlsSyX4jJL1w2r14SYcm2AmPqqqIXAbMBX4FTAc+Bv4F9AMmikiZqm5T1XUi0lJHTsoTbhjxsqhaYRhG3AhsSJ2MK/cs2T5lSPcm2AmFN3woIu2BMuAq4CPgtzg53Ne6u34NqPYe519R3nAIS32q6ia3L+JPgQN+rw/iuBINw0hSvtxfFbVk9Vh5k6J5DUZM2Qr09nudzk2w44qf0DoH+DNwKZDrhne/BawE3gGWAw+o6s74WZv4tNTVd7yq7ve+UNV9QFpXhTUMwzAixmJgoIj0FZEcYDzwapxtSjtEJMsVWkOA+4ElOJGwc0RkqOu5+h7wPtDXv8G5EZyWiq0MEensfSEiXWj5ikbDMNIQy5kymkNV64E7gRnAKmC6qq6Ir1Xpg4jkg3MfRKQHTm7WO6r6CHAPTijxOhE5Xh3uUdVPxO1wboSmpWLr1zgrEn8mIj8FPgR+GXmzDMMwIoOJvORCVd9Q1UGq2l9VH4q3PemCiLQDVonI2a54OgDMB77jJr8vx0mOH4BTxLTAe2xAWQgjCC1t1/OkiCwBzsdJZLzG6p8YbaGuro6tW7dSXV3d/M5pRF5eHqWlpWRnZ8fblISipcn7JrSMZEVEfgVcDtQC64CbVHW/iJTjeP3WuLsuUNXb3WNGAY8D+cAbwF0tEEKnACWAt6ZWNXCbiPwXMF1EblTVz11HS3tVPRSJ60wXWhwCdMVVqwSWiPwdZ2XjTlUd4Y51AZ7FKZK2ERjn5oIhIvcDU4EG4NuqOqM18xqJy9atWyksLKS8vBzzRDuoKnv27GHr1q307ds3ZvMm4ypEw0hhZgL3uyG9X+DkTt3rblunqiODHPMocCuwAEdsjSHM/sWqOldErgP+V0R+oareelk/Bg4DL4vI1y2s2zpiXQvjcZyb7899ODHhgTgrG+4DEJFhOMmRw91j/uRWFzZSiOrqarp27WpCyw8RoWvXrmnl7bPSDYbRGFV9281hA0c8lTa1v4gUAx1Udb7rmXoSp1RDS+Z8BfghcK+I3OiONQC/wMnf6tOiizB8xFRsqeq7QGDX7yuBJ9znT3D0zXElME1Va9zKtGtx3JxGimFC61jS7WfSlrINJtSMNOAbNPZQ9RWRj0Vknoic7Y71wimd4aXZgrAiUuT+7/vAUdXXgR8A94jIVHfMAzykqm+0+UrSlESo8tpDVSsA3P+L3PGwKwmLyK0iskREluzatSuqxhqpx8aNGxkxYsQx4zfffDMrV0Y3JfHBBx/kf/7nf6I6R6oTSqi1Ol/L44EdO8ByfsNCRLq7ydVGCxGRWSLyWZDHlX77PIDTFudpd6gCKFPVE4HvAs+ISAdaWBBWRL4H/B2OTXB3U3a+A9wpIv/hjjW0+kKNhC7bEPYbR1UfAx4DGD16tH1CJjFnPjw7osUpe3XK91VGbyl//etfI2aH0XZikuzu8cB558GHH8IZZ8CcOZCRCN9JE5rTgYdEZDXw8v9v797Do6yvRY9/14RAAgSBcJGbcpEoCBQMj0XwAqfditpTtWA13sAbbotIz667ova0td0iW2hFWmUrLVWqwvYIHhU9irTwKIoCQe4oUYwSrklADCRcklnnj987w8xkhlzIZGbI+jyPT955r+t9w5NZ/n6/d/2A11K97pKI9MB1w50J+IHnVPUpEfktcDcQ+L/6hwOtPfUZY6yqP6whjnG4cc4/CCREqnoUOOot54vIl0AOrkEitKuxpoKwbwH9TxLbMhG5H5ghIkOBY4GB+KbukiHZ2isiXVR1t9fnHKhCa5WEm6Cd31Y06CDt2n5BV1ZWMm7cOD799FNycnKYN28eV111FTNmzGDo0KG0bt2ayZMns3jxYjIzM3n99dfp3Lkz48ePp02bNqxZs4Y9e/bwxBNPMHbsWACmT5/OK6+8wtGjR7nuuut49NFHAXjssceYN28ePXr0oGPHjuTm5jbY/SbSaTHAvrjYJVqVle5ncTF07pzoqJKaqr4hIu/ixtaOAR4XkU+A+1R1b2Kjq7dK4BequlZEsoB8EXnP2/akqoY1R0eMMe4KLBWRnFNpDRKR0bgB8ZeFJq8i0hHYr6pVItIb6AtsV9X9IlImIsOAT4DbgD9FOW8vXK/RDmCYiAxR1U+9bT5cQ1cgsftARH4OzAB+Ut97McnRjfgGMM5bHoebbymw/kYRaeH94+gLrEpAfKYJ+Pzzz5kwYQIbNmygTZs2PPPMM2HbDx8+zLBhw1i/fj2XXnopc+bMCW7bvXs3K1asYPHixUyZMgWAJUuWUFBQwKpVq1i3bh35+fm8//775Ofns2DBAj799FMWLVrE6tWrG/U+TQ06dYLhwznuS3MtW5061XxME+dVGz/qDa7+K64swV7g24j9kuH7plZUdbeqrvWWy3D3dLLxT/EYY/xnIAt4T0TWich/eesvBTaIyHrgVeBfVTUwFvpe4C/e9b8k4k1EEfmfwFJcBYA/4X5PA0TkLO9e/VG6FFfiEr5UTZyTQqO2bInIfGAk0EFEinCvlE7D1fC4E/gGuB5AVTeLyCu4MhOVwETrMzbx0qNHD0aMGAHALbfcwqxZs8K2N2/enB/9yM3Hnpuby3vvvRfcdu211+Lz+ejfvz9797q/R0uWLGHJkiUMGeJmszp06BAFBQWUlZVx3XXX0bKlG+Ly4x//OO73ZupABJYt46LJL7Nm1s3uszkprzSBAFNxExLf6b0MFZxfz9t1rogMAW5T1fUJCrfOvLpWQ3CtRSNw45huw01h8wuvVFE33BuDATUOTq+Jqp4TY/1CYGGMbWuA6gNQT2x/U0TW4obkXA20AWYCW0SkEDiAKxnxT28OxMBxldXPZuqiUZMtVc2LsekHMfZ/DLAKwibuIt/+i/ycnp4eXJeWlkZl5Ym/PS1atAguB75XVJWHHnqIe+65J+w8M2fOTOk3DQNv/tV3HFxK8PkoadXOEq1aEpGLgfFAR+B/qOoOEfGFtpJ4XV8X4MZB7fbWNQOqkrn6uFclfSHwc1X9TkRmA7/HJSu/x82qcgd1HJyeSKq601ucIyJ7cXnAjbjv4UuAHFV9J1Hxna5SplnXmHj65ptvWLlyJQDz58/n4osvPqXzXXHFFcydO5dDh1yR5Z07d7Jv3z4uvfRSXnvtNSoqKigrK+PNN9885dgb085vKxr0BQaT2rxaTNOBz3AVznd4rVn+iF0vwxWzngGUgGstCUnGku67SETScYnWS6q6CEBV96pqlXd/czjRVZiqY4zfA1oBGar6lqpOUdVZNR1k6i7p/oEbkwj9+vXjhRdeYNCgQezfv5977733lM53+eWXc9NNN3HRRRcxcOBAxo4dS1lZGRdccAE33HADgwcPZsyYMVxyySU1nywBek55y6a6MTGJiM97M28ebiD8jJBxQ9H8CFgCblyQiIwTkd+IyPcC60LOnfDi1V636F+Brar6x5D1XUJ2uw7Y5C2n3BhjL8GtAjKA4QkO57SXDG8jGhPUrW1mg37Jd2ubWeM+PXv2jFpPa/ny5cHlQAsVwNixY4NvHD7//PNhx4TuN3nyZCZPnlztvI888giPPPJIjXGZ+Dgt3ppMPMUlE+8Cf/bKDywEXo9s1fIKZw7GjdEN+Hfga1xxzj7ARKBEVXeFjs0NFNtMQFfjCOBWYKOIrPPWPQzkichg3P0XAvd48aXcGGPv93RMRJbiSlyYOLJkyySV03osUFPi99Ph8AFKWrZttEuK+skuP+iKkdp4q7jykp+3gbe90gjX4ubkGy0ij6pqaBfaZbhSBV8DiMh5uK6r21S11CupcA9Q5c3Nl6eqy0Oug3ecL0r3ZFyo6gqij8OKWUE9hccY/1lVSxMdxOnOki1jTEz1agXy+/m45/dYuXMr+d36weNXxb0wqKif+fMfJnfnVtgwxxUjNXEVGGfllUb4O/B3EWmO65oKdTXhJQhGA6u8RKsPUA58oapPishmXHK2XETuAb7DdeWti9LVWK1Mgak7S7Qah43ZMsY0rOJicnduJd1f5ZKfiCm0RF2rV22mw6ltl3J2+cHgNfnoI4ZOfrleoZva89429ENwDJdPVY9FdAN2Agbh6kEF3kAcDSzydhkBfAUE3hRpBvTylr+Pqxv1kIhsEZFeInK2d+3gW4wicr2I3OoNaDcmKVmyZYwJipXcRK6PnPw57HOnTuR368dxX5pr2QotDOp3LVArnxnPf788hXP3bXdT5JyikpZtg9dk+PBG7b404YlXhJG4N92+8lrCugBncSK5ysUV4Nzjfb4CeFNEBuC+n55W1RtwU8tMBaZ4cweOD7nGXmAWcFcyDK43JhrrRjTG1Flk+YewzyLk5U0lu/wgJS3bUhg6fiqk1evCos2887f7KWveksGTX8bvC/9zVKcXJUKuuWbWzfBQzKE1pnHl46Zku0NV5wI7RCRXVSu8N/s6AUtV9ZBXPLQHrsvxVtx0Mh965xkBrFXV+0RkMm7AfcAXwCvA5mQflG6aLmvZMsY0OJUYhUG9Vq9Kr6ySAFnHyjmntChst8iWs7pcc8R/2nitZKGqX6rqKOBv4MZaeYmWT1V344qhLvd2vxr3RmI5cB7wjaoWeS1i5wMPefv5gO9EpL33+Se4sV2FjXBLxtSLJVumySssLGTAgOozXPz6179m6dKlgKv8Xl5eXm0fU7OwFiqvBWrYvX+jLD0TBcqat2Rb9llhx4S2lNWmhSt0HJgVXU0+IRMbV3k//V7x06PeAHuAubiuwJ5AJq5QKMDNwDZVLfMq0XcE9obU9RqAK6pqc/eZpGXdiMbE8Lvf/S64PHPmTG655ZbgnIam/lR8lGRlM/jn8zmntMglWqfwtmLom4j53fqRlzcVTb6C5CZClAmPK3C1txCR+4HA+Ku7cWO2AAbiinBu8/YbBLTEJWNHGyFsY+rF/iIZA1RVVXH33Xdz/vnnc/nll1NRUcH48eN59dVXmTVrFrt27WLUqFGMGjWKqqoqxo8fz4ABAxg4cCBPPvlkosOvUbyqwY+Y9s+wLr+6XMfva8a2jj1PuSxE6JuIuTu30uHwgRrfdrTq+MnLa/E65iVf4LoXp3rL5wMt8JIt4GJci9bXjRulMXVjyZZJPX4/7N1bq9IBtVVQUMDEiRPZvHkzbdu2ZeHChcFt999/P127dmXZsmUsW7aMdevWsXPnTjZt2sTGjRu5/fbbGyyOuKrFc4talsEfu1TDyeZKrEuJh7rsGyn0TcT8rufxp9efYOUz41kw/yGkcWpgmgYUpcWrDFeVHeA5XBHOQHI1GOtCNCnAki2TWvx+GDUKuneHkSMbpGwAQK9evRg8eDAAubm5FBYWxty3d+/ebN++nUmTJvHOO+/Qpk2bBokhnkTDn1u0JCTQHReaqASOq2vyEnmuaL+nQIIl/qpq160Nn7+SnOJCUCUvbyoX/ex57rvmQXJ3fRZs5couP1irc5nkFigr4Y3x2ipOb2+zdSGapGfJlkktxcXw0UdQWel+RhTMrK8WLVoEl9PS0qisrIy5b7t27Vi/fj0jR47k6aef5q677mqQGOIpu/xg8LlVrvgwahIS2R2XXX6Q7PKDHF/xYZ2Tl+zyg+QWbXHHFW2JWtg0kGC9+tKDJ/at5TV8/krWPXUT7869jw1P3Yion5JW7Shp1S6sxldpZpt6t5iZpHYt8AHQHHsL0aQAGyBvUkunTjB8uEschg8PL5gZR1lZWZSVldGhQwdKSkpo3rw5Y8aMoU+fPowfP75RYjgVJS3bwvDhHF/xIfldz3PJR8QcgoHuuMBA80Bh0GjralKa2YaK9AyaHSunIj2D9Pbtw5Ke0MRu0J4CNnTJcT/P7EtJ5hk1nv+c0iKyjpWHlY7Y1rFnWL2t0sw2zF/wiA2cPw2p6msi8hbQDihJdDzG1MSSLZNaRNy8d8XFLtFqpAmHJ0yYwJVXXkmXLl2YOXMmt99+O36va+zxxx9vlBjqIzh43Xtuw+9/0Y1pmn17MAEB6HD4AIqQd+NjZFd855Iq79nm5U0Nbg918dSldDh8gNLMNtWOya74jszKowiQefwIXHYZKz9ZBRvmIMMeCEvsNpzZl+vzpvJ/5j/MoN3bWLDg4RoTo4L23akSH2nqp0p8HGjRKpg8BuptdTh8IJjQDS3aQodDB0BAEVcDzKQ0VT2GjdUyKcKSLZN6fD7o3LnBTtezZ082bdoU/PzAAw9U22fSpElMmjQp+Hnt2rUNdv14Chu87vOh4jsxpmnHJjp8V8KfFv+BC4s2A7Cq+/nk3fQ4gpJ9+FtKWrZF1M9/LZrKoD0FrO16Hr/5lwmUZp7BjJf+QO6urVQ0a0Hm8SPkd+vHpGt+Carsz2jNlo696L/3SzZ0ySF39ergvIU5fQopbdmWm3/6KItefJBBuz7jtRd/Sf+9X5AODN2xmeyyA5RmtQu2UGVXfMf+jNacs38nlJfzk03/QNWPAD7189Hs29nQuS9jb5uB+lzFgNLMNmzs3Ichu7fhUz//fHYCrarc0J6NHXrBY6Ohmf0JNMbEn5xuk6YPHTpU16xZU+v9205rC8C3U76NT0DmpLZu3Uq/fv0SHUZSaohnE1rioHDa1fR8cDELnv83vr+vAAA/gqLB/+s6Dkz88S+5Y/Ub5O7+jG3ZZ3HEl84FxV8iQOhfC8UN+lQIbqsCml1yCWUrV9O68giH0zMY8rO/UfBUXth+ChxOz6D18SNh6wJtZ1XA5uyzOb/0a440yyCj8gjgCi9FxkHIuk2de3PnT34FwOzXpjFkzzYkZHvg/ApI69awfz+kV5+/uOeUtyicdnXtHnIgBpF8VR1ap4OMMU2C/W+dMU1Is6qjwURLAJ+XtgSSl2bAs288Edzev/Sb4HJA6LIfOJSeSdbxiuDxfPABrb39Wh0/wtsv/FvYcYHkJzTRCt0GLqkaVPq1O0dl7P0i1w3Yu51PZt8RNdZqy4cOwbBhsHr1Kdf6MsaYk7G/MMacjryaWr6q45y3p4Dvf7UWioq4duNyoHriE/kfIduheotW6LE7sjqG7Rt5/nO+3e3GUkU5R7QEKvTckeeKFNrKFThX6H9K9VawsHXr1jXYG63GGBOLtWyZhFNVpJEGuqeKU+reD9QiW7GCbQppgbpVr/ya6aHX8H5G7WKLsj1a0iJAv/3fVDtf6HaADdlnk1ZVxfkHdoStj4wl9HrRYovcN3J/jTgm1r+qLe17kLO/iPSLL260N1qNMU2XtWyZhMrIyKC0tPTUkovTjKpSWlpKRkZG/U4QqEXm95PmDSKPbMmKtgyxu+qIWK9AuS+dSsJbkgAYMKBa0jOwpJB+B3aEnSd4v9HOQXgSFbn/6HEzGTbhOcp9zaslatH2D03G1nbqy4/u/DMX3TcPli9vtDdajTFNl7VsmYTq3r07RUVFFFtXTpiMjAy6d+9ev4O9WmTH3/8AETnRshVFZKtQOc1oRWVYonXjT3/PXxY9RlblEarEB+rnSHoGLY4fYXPH3gws3h48z6H0DLJat47a+gTRW6UC6yOTKz8nBtGHrl/d5VxKW7cHoIX/eNTk7TBuduLQ7sRKhPVdchhzy3TwufIQlmgZYxpD0idbIjIaeAo3ZvYvqjotwSGZBpSenk6vXr0SHcbpxaupddHkl9mf0Zqc4kLOqCjjq3Zdmf36Ewzas40NHXrTqvIIfb7dxZaOvblrzP9G09IQv59Vs8cDLkH59MwcVvUczOD/tYCc4q955e+/JKvqKK28we0Di7cH9wVcF+bHHwffUgwIbV2K3AYusfJ5P/1AOi45yvSuE3qejKpKVyfszBw3FiyiVfQQPgb9YiEdyw8wZ9Fj9N/3Ffnd+nHfNQ9agmWMSYikTrZEJA14GvgXoAhYLSJvqOqWxEZmTJILtNwAn53ZF3CFSwft+5J0YND+rxl+71xXADSkGCmqfNL9fIbu3Mr6Ln0Zc/N0EMEvzfD70siqOlqtlSq0/EPrqmNhYYSmNYH9IluxAiUkxPu5qfM59C/+yqt0Dxfu3Bw8z6HmmfTft510lNxdn5HmlVpVIO+nv2d/q7Zs63A2IjBr8R/pX1zIhi45rkiqV3/LGGMaW1InW8CFwBequh1ARBYA1wCWbBlTR5HT8RS3al+9lUeEvJseJ7v8YHgSBmzLPouy5i3JOlYOwNrOfTnerDm5uz9DVElTP2XNW9KmRRpaVkYVQpqXUgUSorVdzgVg0J4CVwy18ij5Xc6jf0khWcfKKWvekmtvnUH2kTJXUBX1Jqv20/bIIbZln8WC//am4OlyLv2LTxz38dnfC5ZwyA6pHj9oT4GrcG9V440xCZLsyVY3YEfI5yLg+wmKxZjUFjJvYGQiFSow3U01Ph+DJ79M35JvKM08g5LW7V2l+fKDweru27LPonDqlVxxz7MUtO9Ox8MHmLPwP+hf/BUbuuQw5uYnECGsMnxJy7b4tMrNb5h9VlirnCIUt84GYF8bV2Ii9B4ijwuINc+jMcYkQlJXkBeR64ErVPUu7/OtwIWqOilivwnABO/jucDndbxUB1J3MtNUjh1SO36LvRZ8QFdothMqG/CvTY3xx+m6J3O2qnZsnEsZY1JJsrdsFQE9Qj53B3ZF7qSqzwHP1fciIrImVafZSOXYIbXjt9gTJ9XjN8Y0LcleZ2s10FdEeolIc+BG4I0Ex2SMMcYYU2tJ3bKlqpUich/wLq70w1xV3ZzgsIwxxhhjai2pky0AVX0beDvOl6l3F2QSSOXYIbXjt9gTJ9XjN8Y0IUk9QN4YY4wxJtUl+5gtY4wxxpiU1qSTLREZLSKfi8gXIjIl0fHUhogUishGEVknImu8de1F5D0RKfB+JkX1RhGZKyL7RGRTyLqYsYrIQ97v4nMRuSIxUZ8QI/7fishO7/mvE5GrQrYlTfwi0kNElonIVhHZLCKTvfVJ//xPEntKPHtjjInUZLsRvamAthEyFRCQl+xTAYlIITBUVUtC1j0B7FfVaV7S2E5VH0xUjCFxXQocAuap6gBvXdRYRaQ/MB83a0BXYCmQo6pVCQo/Vvy/BQ6p6oyIfZMqfhHpAnRR1bUikgXkA9cC40ny53+S2H9KCjx7Y4yJ1JRbtoJTAanqMSAwFVAqugZ4wVt+AffFlHCq+j6wP2J1rFivARao6lFV/Qr4Avc7SpgY8ceSVPGr6m5VXestlwFbcTMyJP3zP0nssSRN7MYYE01TTraiTQV0sj/oyUKBJSKS71XOB+isqrvBfVEBnRIWXc1ixZpKv4/7RGSD180Y6IZL2vhFpCcwBPiEFHv+EbFDij17Y4yBpp1sRZsYLhX6VEeo6gXAlcBEr6vrdJAqv4/ZQB9gMLAb+IO3PinjF5HWwELg56r63cl2jbIuofFHiT2lnr0xxgQ05WSrVlMBJRtV3eX93Ae8husu2euNcwmMd9mXuAhrFCvWlPh9qOpeVa1SVT8whxPdVUkXv4ik45KVl1R1kbc6JZ5/tNhT6dkbY0yoppxspdxUQCLSyhswjIi0Ai4HNuHiHuftNg54PTER1kqsWN8AbhSRFiLSC+gLrEpAfCcVSFQ81+GePyRZ/CIiwF+Brar6x5BNSf/8Y8WeKs/eGGMiJX0F+XhJ0amAOgOvue8imgEvq+o7IrIaeEVE7gS+Aa5PYIxBIjIfGAl0EJEi4DfANKLEqqqbReQVYAtQCUxM9NtkMeIfKSKDcd1UhcA9kJTxjwBuBTaKyDpv3cOkxvOPFXteijx7Y4wJ02RLPxhjjDHGNIam3I1ojDHGGBN3lmwZY4wxxsSRJVvGGGOMMXFkyZYxxhhjTBxZsmWMMcYYE0eWbBljjDHGxJElW8YYY4wxcWTJlmlQItJWRH4W8vmjxrpWYxGRniJSEVJwExEZLSKfi8gXIjIlxnFzRWSfiGyKtr0ecWSKyDoROSYiHRrinMYYYxqeJVumobUFggmQqg5vrGs1si9VdTCAiKQBT+MmB++Pq3TeP8oxzwOjGyoAVa3wYrB5AI0xJolZsmUa2jSgj9fiMl1EDkGwNegzEfmLiGwSkZdE5Ici8qGIFIhIYFJhROQWEVnlneNZEUnz5oV8S0TWe8ffEHkt79j/KyL5IrJZRCbU5doh+70gIhtE5FURaVmLe74Q+EJVt6vqMWABcE3kTqr6PrD/ZCfyYtgU8vkBEfltjPs3xhiTAizZMg1tCl6rj6r+e8S2c4CngEHAecBNwMXAA7i57xCRfsANwAiv1aYKuBnXIrRLVb+nqgOAd2Jc6w5VzQWGAveLSHZtr+05F3hOVQcB31G7lrNuwI6Qz0XeuoYU7f6NMcakAEu2TGP6SlU3qqof2Az8Q93knBuBnt4+PwBygdXemKgfAL29fX4oIv8pIpeo6sEY17hfRNYDHwM9gL51uDbADlX90Ft+EZeQ1USirGvoSUdre//GGGOSTLNEB2CalKMhy/6Qz35O/FsU4AVVfSjyYBHJBa4CHheRJcC8iO0jgR8CF6lquYgsBzLqcG2oniTVJmkqwiV2Ad05tXFUoclbOoCqbou8f1X93SlcwxhjTCOxli3T0MqArFM4/h/AWBHpBCAi7UXkbBHpCpSr6ovADOCCKNc6AzjgJVrnAcPqcf2zROQibzkPWFGLY1YDfUWkl4g0B24E3qjHtQPOFpGOIuIDLgXSYty/McaYFGAtW6ZBqWqpN/B8E/D/6nH8FhH5FbDESzaOAxNxidR0EfF76+6Ncq1fAf8qIhuAz3FdiXW1FRgnIs8CBcDsWsRcKSL3Ae8CacBcVd0MICJvA3ep6i4RmQ+MBDqISBHwG1X9a5RTluJa7c4ElgK34VrPJobefz3uzRhjTAKIG7ZijBGRnsBibwD6Ke8XzxgijikEhqpqSUPHY4wx5tRZN6IxdVcFnBFa1DQRAkVNceO6/ImMxRhjTGzWsmWMMcYYE0fWsmWMMcYYE0eWbBljjDHGxJElW8YYY4wxcWTJljHGGGNMHFmyZYwxxhgTR5ZsGWOMMcbEkSVbxhhjjDFxZMmWMcYYY0wc/X91OvLyJUCmCgAAAABJRU5ErkJggg==\n", 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", "text/plain": [ - "
" + "
" ] }, "metadata": {}, @@ -103,7 +103,7 @@ } ], "source": [ - "evd = ProtoNDFlowEventDisplay(filedir=directory, filename=file, geometry_file=geometry, nhits=1, hits_dset='calib_final_hits')\n", + "evd = ProtoNDFlowEventDisplay(filedir=directory, filename=file, geometry_file=geometry, nhits=100, hits_dset='calib_prompt_hits')\n", "evd.run()" ] }, @@ -132,7 +132,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.7" + "version": "3.11.6" } }, "nbformat": 4, From 7ad7ce47184ee0f5fc0a024bffa290df53cb904f Mon Sep 17 00:00:00 2001 From: Elise Dianne Hinkle Date: Sat, 13 Jan 2024 16:44:46 -0800 Subject: [PATCH 33/37] Adding additional plotting capabilities for HIP selection. --- ...light_module_desc_single_module-2.0.0.yaml | 203 + data/module1_flow/module1_layout-2.3.16.yaml | 16403 ++++++++++++++++ .../data_mc_hit_level_metrics.py | 230 +- .../hip_selection/particlePDG_defs.py | 46 + .../hip_selection/plot_hit_level_metrics.py | 157 +- .../run_proto_nd_hip_selection.sh | 4 +- yamls/module1_flow/resources/Geometry.yaml | 2 +- 7 files changed, 16904 insertions(+), 141 deletions(-) create mode 100644 data/module1_flow/light_module_desc_single_module-2.0.0.yaml create mode 100644 data/module1_flow/module1_layout-2.3.16.yaml create mode 100644 scripts/proto_nd_scripts/analysis/hip_selection/particlePDG_defs.py diff --git a/data/module1_flow/light_module_desc_single_module-2.0.0.yaml b/data/module1_flow/light_module_desc_single_module-2.0.0.yaml new file mode 100644 index 00000000..cf6a3653 --- /dev/null +++ b/data/module1_flow/light_module_desc_single_module-2.0.0.yaml @@ -0,0 +1,203 @@ +format_version: "0.2.0" +geometry_version: "0.2.0" + +geom: + # ArcLight + 0: { min: [-15.14 , -15.51, 0], max: [+12.03, +15.51, 0] } + # LCM + 1: { min: [-15.14 , -5.17, 0], max: [+12.03, +5.17, 0] } + +tpc_center_offset: + 0: [+15.30, 0, 0] + 1: [-15.30, 0, 0] + +det_center: + 0: [0, -46.53, -31.49] + 1: [0, -25.85, -31.49] + 2: [0, -15.51, -31.49] + 3: [0, -5.16, -31.49] + 4: [0, 15.51, -31.49] + 5: [0, 36.19, -31.49] + 6: [0, 46.53, -31.49] + 7: [0, 56.87, -31.49] + 8: [0, -46.53, +31.49] + 9: [0, -25.84, +31.49] + 10: [0, -15.51, +31.49] + 11: [0, -5.17, +31.49] + 12: [0, 15.51, +31.49] + 13: [0, 36.19, +31.49] + 14: [0, 46.53, +31.49] + 15: [0, 56.87, +31.49] + +sipm_center: + 0: [15.14, -60.07, -31.49] + 1: [15.14, -55.37, -31.49] + 2: [15.14, -48.87, -31.49] + 3: [15.14, -44.17, -31.49] + 4: [15.14, -37.67, -31.49] + 5: [15.14, -32.97, -31.49] + 6: [15.14, -29.07, -31.49] + 7: [15.14, -24.37, -31.49] + 8: [15.14, -17.87, -31.49] + 9: [15.14, -13.17, -31.49] + 10: [15.14, -6.67, -31.49] + 11: [15.14, -1.97, -31.49] + 12: [15.14, 1.97, -31.49] + 13: [15.14, 6.67, -31.49] + 14: [15.14, 13.17, -31.49] + 15: [15.14, 17.87, -31.49] + 16: [15.14, 24.37, -31.49] + 17: [15.14, 29.07, -31.49] + 18: [15.14, 32.97, -31.49] + 19: [15.14, 37.67, -31.49] + 20: [15.14, 44.17, -31.49] + 21: [15.14, 48.87, -31.49] + 22: [15.14, 55.37, -31.49] + 23: [15.14, 60.07, -31.49] + 24: [15.14, -60.07, 31.49] + 25: [15.14, -55.37, 31.49] + 26: [15.14, -48.87, 31.49] + 27: [15.14, -44.17, 31.49] + 28: [15.14, -37.67, 31.49] + 29: [15.14, -32.97, 31.49] + 30: [15.14, -29.07, 31.49] + 31: [15.14, -24.37, 31.49] + 32: [15.14, -17.87, 31.49] + 33: [15.14, -13.17, 31.49] + 34: [15.14, -6.67, 31.49] + 35: [15.14, -1.97, 31.49] + 36: [15.14, 1.97, 31.49] + 37: [15.14, 6.67, 31.49] + 38: [15.14, 13.17, 31.49] + 39: [15.14, 17.87, 31.49] + 40: [15.14, 24.37, 31.49] + 41: [15.14, 29.07, 31.49] + 42: [15.14, 32.97, 31.49] + 43: [15.14, 37.67, 31.49] + 44: [15.14, 44.17, 31.49] + 45: [15.14, 48.87, 31.49] + 46: [15.14, 55.37, 31.49] + 47: [15.14, 60.07, 31.49] + +ch_to_vert_bin: # 0:ACL 1:LCM + 0: [-1,-1,-1,-1,0,1,2,3,4,5,12,13,14,15,16,17,-1,-1,-1,-1,0,1,2,3,4,5,12,13,14,15,16,17, -1,-1,-1,-1,0,1,2,3,4,5,12,13,14,15,16,17,-1,-1,-1,-1,0,1,2,3,4,5,12,13,14,15,16,17] + 1: [-1,-1,-1,-1,6,7,8,9,10,11,18,19,20,21,22,23,-1,-1,-1,-1,6,7,8,9,10,11,18,19,20,21,22,23,-1,-1,-1,-1,6,7,8,9,10,11,18,19,20,21,22,23,-1,-1,-1,-1,6,7,8,9,10,11,18,19,20,21,22,23] + +adc_to_det_type: + 0: 0 + 1: 1 + +det_side: #TPC side 0: -z direction 1: +z direction + 0: 0 + 1: 0 + 2: 0 + 3: 0 + 4: 0 + 5: 0 + 6: 0 + 7: 0 + 8: 1 + 9: 1 + 10: 1 + 11: 1 + 12: 1 + 13: 1 + 14: 1 + 15: 1 + +det_geom: + 0: 0 + 1: 1 + 2: 1 + 3: 1 + 4: 0 + 5: 1 + 6: 1 + 7: 1 + 8: 0 + 9: 1 + 10: 1 + 11: 1 + 12: 0 + 13: 1 + 14: 1 + 15: 1 + +det_adc: + 0: #TPC + # -z, increasing y (det-> adc) + 0: 0 + 1: 1 + 2: 1 + 3: 1 + 4: 0 + 5: 1 + 6: 1 + 7: 1 + # +z, increasing y + 8: 0 + 9: 1 + 10: 1 + 11: 1 + 12: 0 + 13: 1 + 14: 1 + 15: 1 + + 1: + # -z, increasing y + 0: 0 + 1: 1 + 2: 1 + 3: 1 + 4: 0 + 5: 1 + 6: 1 + 7: 1 + # +z, increasing y + 8: 0 + 9: 1 + 10: 1 + 11: 1 + 12: 0 + 13: 1 + 14: 1 + 15: 1 + + +det_chan: + 0: + 0: [4,5,6,7,8,9] + 1: [4,5] + 2: [6,7] + 3: [8,9] + 4: [10,11,12,13,14,15] + 5: [10,11] + 6: [12,13] + 7: [14,15] + 8: [20,21,22,23,24,25] + 9: [20,21] + 10: [22,23] + 11: [24,25] + 12: [26,27,28,29,30,31] + 13: [26,27] + 14: [28,29] + 15: [30,31] + + 1: + 0: [52,53,54,55,56,57] + 1: [52,53] + 2: [54,55] + 3: [56,57] + 4: [58,59,60,61,62,63] + 5: [58,59] + 6: [60,61] + 7: [62,63] + 8: [36,37,38,39,40,41] + 9: [36,37] + 10: [38,39] + 11: [40,41] + 12: [42,43,44,45,46,47] + 13: [42,43] + 14: [44,45] + 15: [46,47] \ No newline at end of file diff --git a/data/module1_flow/module1_layout-2.3.16.yaml b/data/module1_flow/module1_layout-2.3.16.yaml new file mode 100644 index 00000000..84687300 --- /dev/null +++ b/data/module1_flow/module1_layout-2.3.16.yaml @@ -0,0 +1,16403 @@ +chip_channel_to_position: + 11000: + - 3 + - 68 + 11001: + - 2 + - 69 + 11002: + - 1 + - 69 + 11003: + - 0 + - 69 + 11004: + - 2 + - 68 + 11005: + - 1 + - 68 + 11010: + - 0 + - 68 + 11011: + - 2 + - 67 + 11012: + - 1 + - 67 + 11013: + - 0 + - 67 + 11014: + - 3 + - 66 + 11015: + - 2 + - 66 + 11016: + - 1 + - 66 + 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a/scripts/proto_nd_scripts/analysis/hip_selection/data_mc_hit_level_metrics.py +++ b/scripts/proto_nd_scripts/analysis/hip_selection/data_mc_hit_level_metrics.py @@ -10,9 +10,10 @@ import matplotlib.pyplot as plt import sys import file_parsing +import json from plot_hit_level_metrics import plot_event_hit_summ_metrics, plot_channel_metrics -def main(file_dir, is_sim, hits_dset): +def main(file_dir, is_sim, hits_dset, sel_event_dict): is_sim = bool(is_sim == 'True') # initialize plotting datasets @@ -39,18 +40,21 @@ def main(file_dir, is_sim, hits_dset): tracks_region = f['charge/events/ref/combined/tracklets/ref_region'] hits_trk_ref = f['combined/tracklets/ref/charge/'+hits_dset+'/ref'] hits_trk_region = f['combined/tracklets/ref/charge/'+hits_dset+'/ref_region'] - hits_drift = f['combined/hit_drift/data'] - hits = f['charge/'+hits_dset+'/data'] - hits_ref = f['charge/events/ref/charge/'+hits_dset+'/ref'] - hits_region = f['charge/events/ref/charge/'+hits_dset+'/ref_region'] - if not is_sim: - charge_hits = hits#f['combined/q_calib_el/data'] - charge_hits_ref = hits_ref#f['charge/events/ref/combined/q_calib_el/ref'] - charge_hits_region = hits_region#f['charge/events/ref/combined/q_calib_el/ref_region'] - else: - charge_hits = hits - charge_hits_ref = hits_ref - charge_hits_region = hits_region + #hits_drift = f['combined/hit_drift/data'] + hits_dsets = ['calib_final_hits', 'calib_prompt_hits'] + hits = [f['charge/calib_final_hits/data'], f['charge/calib_prompt_hits/data']] + hits_ref = [f['charge/events/ref/charge/calib_final_hits/ref'], \ + f['charge/events/ref/charge/calib_prompt_hits/ref']] + hits_region = [f['charge/events/ref/charge/calib_final_hits/ref_region'], \ + f['charge/events/ref/charge/calib_prompt_hits/ref_region']] + #if not is_sim: + # charge_hits = hits#f['combined/q_calib_el/data'] + # charge_hits_ref = hits_ref#f['charge/events/ref/combined/q_calib_el/ref'] + # charge_hits_region = hits_region#f['charge/events/ref/combined/q_calib_el/ref_region'] + #else: + # charge_hits = hits + # charge_hits_ref = hits_ref + # charge_hits_region = hits_region ext_trigs = f['charge/ext_trigs/data'] ext_trigs_ref = f['charge/events/ref/charge/ext_trigs/ref'] ext_trigs_region = f['charge/events/ref/charge/ext_trigs/ref_region'] @@ -61,101 +65,115 @@ def main(file_dir, is_sim, hits_dset): mc_truth_events = f['mc_truth/events/data'] print("File:", file) - sel_mask = (sel_reco['sel'] == True) - sel_event_ids = sel_reco[sel_mask]['event_id'] - print("Selected Event Ids:", sel_event_ids) - if is_sim==True: - sel_truth_mask = (sel_truth['sel'] == True) - sel_truth_protons = sel_truth[sel_mask]['hips'] - sel_truth_sel = sel_truth[sel_truth_mask]['event_id'] - sel_pdg_mask = (sel_truth[sel_truth_mask]['pdg_id'] != 0) - sel_truth_pdg = sel_truth[sel_truth_mask]['pdg_id'][sel_pdg_mask] - print("Selected Proton?:", sel_truth_protons) - print("Selected True?:", sel_truth_sel) - print("Selected PDG IDs:", sel_truth_pdg) - for event in sel_event_ids: - event_sel_mask = f['high_purity_sel']['hips']['sel_truth']['data']['event_id'] == event - zero_mask = f['high_purity_sel']['hips']['sel_truth']['data'][event_sel_mask]['pdg_id'] != 0. - print('Selected event true PID:', f['high_purity_sel']['hips']['sel_truth']['data'][event_sel_mask]['pdg_id'][zero_mask], "| Event ID:", event) + #sel_mask = (sel_reco['sel'] == True) + #sel_event_ids = sel_reco[sel_mask]['event_id'] + #print("Selected Event Ids:", sel_event_ids) + #if is_sim==True: + #sel_truth_mask = (sel_truth['sel'] == True) + #sel_truth_protons = sel_truth[sel_mask]['hips'] + #sel_truth_sel = sel_truth[sel_truth_mask]['event_id'] + #sel_pdg_mask = (sel_truth[sel_truth_mask]['pdg_id'] != 0) + #sel_truth_pdg = sel_truth[sel_truth_mask]['pdg_id'][sel_pdg_mask] + #print("Selected Proton?:", sel_truth_protons) + #print("Selected True?:", sel_truth_sel) + #print("Selected PDG IDs:", sel_truth_pdg) + #for event in sel_event_ids: + #event_sel_mask = f['high_purity_sel']['hips']['sel_truth']['data']['event_id'] == event + #zero_mask = f['high_purity_sel']['hips']['sel_truth']['data'][event_sel_mask]['pdg_id'] != 0. + #print('Selected event true PID:', f['high_purity_sel']['hips']['sel_truth']['data'][event_sel_mask]['pdg_id'][zero_mask], "| Event ID:", event) ### partition file by selected events - sel_event_mask = np.isin(events['id'], sel_event_ids) + #sel_event_mask = np.isin(events['id'], sel_event_ids) #print("Events:", events[sel_event_mask]) - for event_id in sel_event_ids: - - # Get hit information related to given event_id - charge_hit_ref = charge_hits_ref[charge_hits_region[int(event_id),'start']:charge_hits_region[int(event_id),'stop']] - charge_hit_ref = np.sort(charge_hit_ref[charge_hit_ref[:,0] == event_id, 1]) - - # Event-level hit metrics - charge_hits_data = charge_hits[charge_hit_ref]['Q'] - ts_hits_data = charge_hits[charge_hit_ref]['ts_pps'] - num_charge_hits = len(charge_hits_data) - - # Channel-level hit metrics - iogroup_hits = charge_hits[charge_hit_ref]['io_group'] - iochannel_hits = charge_hits[charge_hit_ref]['io_channel'] - chipid_hits = charge_hits[charge_hit_ref]['chip_id'] - channelid_hits = charge_hits[charge_hit_ref]['channel_id'] - - channel_id = np.array([int(str(iogroup_hits[i])+str(iochannel_hits[i])+str(chipid_hits[i])+str(channelid_hits[i])) for i in range(num_charge_hits)]) - unique_channels, unique_channel_hit_counts = np.unique(channel_id, return_counts=True) - num_channels = len(unique_channels) - - #print("String of channels:", channel_id) - #print("Number of unique channels:", num_channels) - #print("Hits per channel:", unique_channel_hit_counts) - #print("Length of hits per channel:", len(unique_channel_hit_counts)) - for i in range(num_channels): - - channel = unique_channels[i] - hits_per_channel = unique_channel_hit_counts[i] - channel_mask = np.argwhere(channel_id == channel).flatten() - channel_hit_amps = charge_hits_data[channel_mask] - channel_hit_ts = ts_hits_data[channel_mask] / 10. # convert to us - - max_hit_amp = max(channel_hit_amps) - min_hit_amp = min(channel_hit_amps) - - first_hit_idx = np.argmin(channel_hit_ts) - last_hit_idx = np.argmax(channel_hit_ts) - first_hit_amp = channel_hit_amps[first_hit_idx] - last_hit_amp = channel_hit_amps[last_hit_idx] - first_last_hit_delta_t = abs(channel_hit_ts[last_hit_idx] - channel_hit_ts[first_hit_idx]) - - #print("Channel hit amplitudes:", channel_hit_amps) - #print("Channel hit timestamps:", channel_hit_ts) - #print("Maximum hit amplitude:", max_hit_amp) - #print("Minimum hit amplitude:", min_hit_amp) - #print("First hit amplitude:", first_hit_amp) - #print("Last hit amplitude:", last_hit_amp) - #print("First/Last hit delta t:", first_last_hit_delta_t) - - - - channel_metric_dict[(file, event_id, channel)]=dict( - hit_mult = int(hits_per_channel), - max_hit_amp = float(max_hit_amp), - min_hit_amp = float(min_hit_amp), - first_hit_amp = float(first_hit_amp), - last_hit_amp = float(last_hit_amp), - first_last_hit_delta_t = float(first_last_hit_delta_t) - ) - - event_hit_summ_dict[(file, event_id)]=dict( - total_charge=float(sum(charge_hits_data)), - num_hits=int(num_charge_hits), - num_channels=int(num_channels) - ) - - ## Save all Python dictionaries to JSON files - file_parsing.save_dict_to_json(event_hit_summ_dict, sample_type+"event_hit_summ_dict", True) - file_parsing.save_dict_to_json(channel_metric_dict, sample_type+"channel_metric_dict", True) - - # PLOT: Signal Event Info - plot_event_hit_summ_metrics(event_hit_summ_dict, is_sim==True) - plot_channel_metrics(channel_metric_dict, is_sim==True) + # TO DO: Make this variable based on input file + sel_event_id_file = open(file_dir+'/'+sel_event_dict) + sel_event_id_data = json.load(sel_event_id_file) + sel_event_pdgs = sel_event_id_data.keys() + for pdg in sel_event_pdgs: + sel_event_ids = sel_event_id_data[pdg] + for event_id in sel_event_ids: + for x in range(len(hits_dsets)): + charge_hits_dset = hits_dsets[x] + charge_hits = hits[x] + charge_hits_ref = hits_ref[x] + charge_hits_region = hits_region[x] + + # Get hit information related to given event_id + charge_hit_ref = charge_hits_ref[charge_hits_region[int(event_id),'start']:charge_hits_region[int(event_id),'stop']] + charge_hit_ref = np.sort(charge_hit_ref[charge_hit_ref[:,0] == event_id, 1]) + + # Event-level hit metrics + charge_hits_data = charge_hits[charge_hit_ref]['Q'] + ts_hits_data = charge_hits[charge_hit_ref]['ts_pps'] + num_charge_hits = len(charge_hits_data) + + # Channel-level hit metrics + iogroup_hits = charge_hits[charge_hit_ref]['io_group'] + iochannel_hits = charge_hits[charge_hit_ref]['io_channel'] + chipid_hits = charge_hits[charge_hit_ref]['chip_id'] + channelid_hits = charge_hits[charge_hit_ref]['channel_id'] + + channel_id = np.array([int(str(iogroup_hits[i])+str(iochannel_hits[i])+str(chipid_hits[i])+str(channelid_hits[i])) for i in range(num_charge_hits)]) + unique_channels, unique_channel_hit_counts = np.unique(channel_id, return_counts=True) + num_channels = len(unique_channels) + + #print("String of channels:", channel_id) + #print("Number of unique channels:", num_channels) + #print("Hits per channel:", unique_channel_hit_counts) + #print("Length of hits per channel:", len(unique_channel_hit_counts)) + for i in range(num_channels): + + channel = unique_channels[i] + hits_per_channel = unique_channel_hit_counts[i] + channel_mask = np.argwhere(channel_id == channel).flatten() + channel_hit_amps = charge_hits_data[channel_mask] + channel_hit_ts = ts_hits_data[channel_mask] / 10. # convert to us + + max_hit_amp = max(channel_hit_amps) + min_hit_amp = min(channel_hit_amps) + + first_hit_idx = np.argmin(channel_hit_ts) + last_hit_idx = np.argmax(channel_hit_ts) + first_hit_amp = channel_hit_amps[first_hit_idx] + last_hit_amp = channel_hit_amps[last_hit_idx] + first_last_hit_delta_t = abs(channel_hit_ts[last_hit_idx] - channel_hit_ts[first_hit_idx]) + + #print("Channel hit amplitudes:", channel_hit_amps) + #print("Channel hit timestamps:", channel_hit_ts) + #print("Maximum hit amplitude:", max_hit_amp) + #print("Minimum hit amplitude:", min_hit_amp) + #print("First hit amplitude:", first_hit_amp) + #print("Last hit amplitude:", last_hit_amp) + #print("First/Last hit delta t:", first_last_hit_delta_t) + + channel_metric_dict[(file, pdg, charge_hits_dset, event_id, channel)]=dict( + hit_mult = int(hits_per_channel), + max_hit_amp = float(max_hit_amp), + min_hit_amp = float(min_hit_amp), + first_hit_amp = float(first_hit_amp), + last_hit_amp = float(last_hit_amp), + first_last_hit_delta_t = float(first_last_hit_delta_t), + event_pdg = int(pdg), + hits_dset = str(charge_hits_dset) + ) + + event_hit_summ_dict[(file, pdg, charge_hits_dset, event_id)]=dict( + event_pdg = int(pdg), + total_charge=float(sum(charge_hits_data)), + num_hits=int(num_charge_hits), + num_channels=int(num_channels), + hits_dset = str(charge_hits_dset) + ) + + ## Save all Python dictionaries to JSON files + file_parsing.save_dict_to_json(event_hit_summ_dict, sample_type+"_event_hit_summ_dict", True) + file_parsing.save_dict_to_json(channel_metric_dict, sample_type+"_channel_metric_dict", True) + + + # PLOT: Signal Event Info + plot_event_hit_summ_metrics(event_hit_summ_dict, is_sim) + plot_channel_metrics(channel_metric_dict, is_sim) if __name__=='__main__': parser = argparse.ArgumentParser() @@ -164,6 +182,8 @@ def main(file_dir, is_sim, hits_dset): parser.add_argument('-mc', '--is_sim', default=False, required=True, type=str, \ help='''str corresponding to bool whether files are simulation (MC) or data''') parser.add_argument('-hd', '--hits_dset', default='calib_final_hits', required=True, type=str,\ - help='''str corresponding to bool of hits dataset name''') + help='''str corresponding to hits dataset name associated with tracklets''') + parser.add_argument('-sed', '--sel_event_dict', default=None, required=True, type=str,\ + help='''str corresponding name of json file containing selected event ids''') args = parser.parse_args() main(**vars(args)) \ No newline at end of file diff --git a/scripts/proto_nd_scripts/analysis/hip_selection/particlePDG_defs.py b/scripts/proto_nd_scripts/analysis/hip_selection/particlePDG_defs.py new file mode 100644 index 00000000..96776eb5 --- /dev/null +++ b/scripts/proto_nd_scripts/analysis/hip_selection/particlePDG_defs.py @@ -0,0 +1,46 @@ +################################################################################ +## ## +## CONTAINS: Definitions of particle information from PDG (e.g. particle ## +## PDG IDs, masses, etc.) ## +## ## +################################################################################ + +####-------------------------- PDG ID DEFINITIONS --------------------------#### + +neutral_pdg=[111] #, 22] #, 2112] # add K0, rho0, eta0? +meson_pdg={111,211,-211,130,310,311,321,-321,221,331,421,-421,411,-411, 431,-431} +nu_mu_pdg=14 + + +####------------------ PDG ID/PARTICLE LABEL DICTIONARIES ------------------#### + +hadron_pdg_dict ={2112:'n', + -2112:r'$\bar{n}$', + 2212:'p', + -2212:r'$\bar{p}$', + 3112:r'$\Sigma^-$', + 3122:r'$\Lambda^0$', + -3122:r'$\bar{\Lambda}^0$', + 3212:r'$\Sigma^0$', + 3222:r'$\Sigma^+$', + 4212:r'$\Sigma_c^+$', + 4222:r'$\Sigma_c^{++}$', + 4112:r'$\Sigma_c^0$', + 4122:r'$\Lambda_c^+$'} + +neutral_hadron_pdg_dict ={2112:'n', + -2112:r'$\bar{n}$', + 3122:r'$\Lambda^0$', + -3122:r'$\bar{\Lambda}^0$', + 3212:r'$\Sigma^0$', + 4112:r'$\Sigma_c^0$'} + +selection_pdg_dict ={2212:'Protons', + 13:'Muons'} + + + +####---------------- PDG ID/PARTICLE PROPERTY DICTIONARIES -----------------#### + +rest_mass_dict ={2212: 938.27208816, + 13: 105.6583755 } # Masses in MeV (from PDG) \ No newline at end of file diff --git a/scripts/proto_nd_scripts/analysis/hip_selection/plot_hit_level_metrics.py b/scripts/proto_nd_scripts/analysis/hip_selection/plot_hit_level_metrics.py index 0fb15a2b..7e8b6a8d 100644 --- a/scripts/proto_nd_scripts/analysis/hip_selection/plot_hit_level_metrics.py +++ b/scripts/proto_nd_scripts/analysis/hip_selection/plot_hit_level_metrics.py @@ -7,6 +7,7 @@ import matplotlib.pyplot as plt import numpy as np +import particlePDG_defs as pdg_defs def plot_event_hit_summ_metrics(d, is_mc): @@ -17,37 +18,70 @@ def plot_event_hit_summ_metrics(d, is_mc): mc_title = '[Data]' sample_type = "Data" + sel_pdg = np.unique([d[key]['event_pdg'] for key in d.keys()]) + hits_dsets = np.unique([d[key]['hits_dset'] for key in d.keys()]) + alpha_options = [1.0, 0.8] + color_options = ['#4daf4a', '#ff7f00'] + linestyle_options = ['--', '-'] + linewidth_options = [1.5, 1.5] + fill_options = [False, False] + print("hits_dsets:",hits_dsets) + # PLOT: total charge in an event fig0, ax0 = plt.subplots(figsize=(6,4)) - data0tot = np.array([d[key]['total_charge'] for key in d.keys()]) - counts0tot, bins0tot = np.histogram(data0tot, bins=np.linspace(0,20,21)) - ax0.hist(bins0tot[:-1], bins=bins0tot, weights = counts0tot) + for pdg in sel_pdg: + for hits_dset in hits_dsets: + idx = hits_dsets.tolist().index(hits_dset) + data0 = np.array([d[key]['total_charge'] for key in d.keys() if d[key]['event_pdg']==pdg and d[key]['hits_dset']==hits_dset]) + counts0, bins0 = np.histogram(data0, bins=np.linspace(0,20000,21)) + ax0.hist(bins0[:-1], bins=bins0, weights = counts0, \ + label=pdg_defs.selection_pdg_dict[pdg]+", "+hits_dset, \ + linewidth=linewidth_options[idx], alpha=alpha_options[idx], \ + color=color_options[idx], edgecolor=color_options[idx], \ + linestyle=linestyle_options[idx], fill = fill_options[idx]) ax0.set_xlabel('Total Charge [ke-]') - ax0.set_ylabel('Count / ke-') + ax0.set_ylabel('Count / 1000 ke-') ax0.set_title(r'Total Charge Per Selected Event '+mc_title) + ax0.legend() plt.savefig(sample_type+"_selected_events_total_charge.png") plt.close(fig0) # PLOT: number of hits in an event fig1, ax1 = plt.subplots(figsize=(6,4)) - data1tot = np.array([d[key]['num_hits'] for key in d.keys()]) - counts1tot, bins1tot = np.histogram(data1tot, bins=np.linspace(50,5000,101)) - ax1.hist(bins1tot[:-1], bins=bins1tot, weights = counts1tot) + for pdg in sel_pdg: + for hits_dset in hits_dsets: + idx = hits_dsets.tolist().index(hits_dset) + data1 = np.array([d[key]['num_hits'] for key in d.keys() if d[key]['event_pdg']==pdg and d[key]['hits_dset']==hits_dset]) + counts1, bins1 = np.histogram(data1, bins=np.linspace(0,500,26)) + ax1.hist(bins1[:-1], bins=bins1, weights = counts1, \ + label=pdg_defs.selection_pdg_dict[pdg]+", "+hits_dset, \ + linewidth=linewidth_options[idx], alpha=alpha_options[idx], \ + color=color_options[idx], edgecolor=color_options[idx], \ + linestyle=linestyle_options[idx], fill = fill_options[idx]) ax1.set_xlabel('Number of Hits') - ax1.set_ylabel('Event Count / 50 Hits') + ax1.set_ylabel('Event Count / 20 Hits') ax1.set_title(r'Number of Hits Per Selected Event '+mc_title) + ax1.legend() plt.savefig(sample_type+"_selected_events_total_hits_per_event.png") plt.close(fig1) # PLOT: number of separate pixels triggered in an event fig2, ax2 = plt.subplots(figsize=(6,4)) - data2tot = np.array([d[key]['num_channels'] for key in d.keys()]) - counts2tot, bins2tot = np.histogram(data2tot, bins=np.linspace(0,5000,251)) - ax2.hist(bins2tot[:-1], bins=bins2tot, weights = counts2tot) + for pdg in sel_pdg: + for hits_dset in hits_dsets: + idx = hits_dsets.tolist().index(hits_dset) + data2 = np.array([d[key]['num_channels'] for key in d.keys() if d[key]['event_pdg']==pdg and d[key]['hits_dset']==hits_dset]) + counts2, bins2 = np.histogram(data2, bins=np.linspace(0,100,21)) + ax2.hist(bins2[:-1], bins=bins2, weights = counts2, \ + label=pdg_defs.selection_pdg_dict[pdg]+", "+hits_dset,\ + linewidth=linewidth_options[idx], alpha=alpha_options[idx], \ + color=color_options[idx], edgecolor=color_options[idx], \ + linestyle=linestyle_options[idx], fill = fill_options[idx]) ax2.set_xlabel('Number of Unique Channels Triggered') ax2.set_ylabel('Event Count / 20 Channels') ax2.set_title("Number of Unique Channels Triggered \nPer Selected Event "+mc_title) + ax2.legend() plt.savefig(sample_type+"_selected_events_total_unique_channels_per_event.png") plt.close(fig2) @@ -62,69 +96,126 @@ def plot_channel_metrics(d, is_mc): mc_title = '[Data]' sample_type = "Data" + sel_pdg = np.unique([d[key]['event_pdg'] for key in d.keys()]) + hits_dsets = np.unique([d[key]['hits_dset'] for key in d.keys()]) + alpha_options = [1.0, 0.8] + color_options = ['#4daf4a', '#ff7f00'] + linestyle_options = ['--', '-'] + linewidth_options = [1.5, 1.5] + fill_options = [False, False] + # PLOT: hits per channel per event fig0, ax0 = plt.subplots(figsize=(6,4)) - data0tot = np.array([d[key]['hit_mult'] for key in d.keys()]) - counts0tot, bins0tot = np.histogram(data0tot, bins=np.linspace(0,20,21)) - ax0.hist(bins0tot[:-1], bins=bins0tot, weights = counts0tot) + for pdg in sel_pdg: + for hits_dset in hits_dsets: + idx = hits_dsets.tolist().index(hits_dset) + data0 = np.array([d[key]['hit_mult'] for key in d.keys() if d[key]['event_pdg']==pdg and d[key]['hits_dset']==hits_dset]) + counts0, bins0 = np.histogram(data0, bins=np.linspace(0,10,11)) + ax0.hist(bins0[:-1], bins=bins0, weights = counts0, \ + label=pdg_defs.selection_pdg_dict[pdg]+", "+hits_dset, \ + linewidth=linewidth_options[idx], alpha=alpha_options[idx], \ + color=color_options[idx], edgecolor=color_options[idx], \ + linestyle=linestyle_options[idx], fill = fill_options[idx]) ax0.set_xlabel('Hit Multiplicity / Channel / Event') ax0.set_ylabel('Channel Count / Hit') ax0.set_title(r'Hit Multiplicity Per Channel in Selected Events '+mc_title) + ax0.legend() plt.savefig(sample_type+"_selected_events_hits_per_channel_per_event.png") plt.close(fig0) # PLOT: max hit amplitude per channel per event fig1, ax1 = plt.subplots(figsize=(6,4)) - data1tot = np.array([d[key]['max_hit_amp'] for key in d.keys()]) - counts1tot, bins1tot = np.histogram(data1tot, bins=np.linspace(0,500,26)) - ax1.hist(bins1tot[:-1], bins=bins1tot, weights = counts1tot) + for pdg in sel_pdg: + for hits_dset in hits_dsets: + idx = hits_dsets.tolist().index(hits_dset) + data1 = np.array([d[key]['max_hit_amp'] for key in d.keys() if d[key]['event_pdg']==pdg and d[key]['hits_dset']==hits_dset]) + counts1, bins1 = np.histogram(data1, bins=np.linspace(0,200,41)) + ax1.hist(bins1[:-1], bins=bins1, weights = counts1, \ + label=pdg_defs.selection_pdg_dict[pdg]+", "+hits_dset, \ + linewidth=linewidth_options[idx], alpha=alpha_options[idx], \ + color=color_options[idx], edgecolor=color_options[idx], \ + linestyle=linestyle_options[idx], fill = fill_options[idx]) ax1.set_xlabel('Max Hit Amplitude / Channel / Event [ke-]') - ax1.set_ylabel('Channel Count / 20 ke-') + ax1.set_ylabel('Channel Count / 5 ke-') ax1.set_title(r'Maximum Hit Amplitiude Per Channel in Selected Events '+mc_title) + ax1.legend() plt.savefig(sample_type+"_selected_events_max_hit_amp_per_channel_per_event.png") plt.close(fig1) # PLOT: min hit amplitude per channel per event fig2, ax2 = plt.subplots(figsize=(6,4)) - data2tot = np.array([d[key]['min_hit_amp'] for key in d.keys()]) - counts2tot, bins2tot = np.histogram(data2tot, bins=np.linspace(0,500,26)) - ax2.hist(bins2tot[:-1], bins=bins2tot, weights = counts2tot) + for pdg in sel_pdg: + for hits_dset in hits_dsets: + idx = hits_dsets.tolist().index(hits_dset) + data2 = np.array([d[key]['min_hit_amp'] for key in d.keys() if d[key]['event_pdg']==pdg and d[key]['hits_dset']==hits_dset]) + counts2, bins2 = np.histogram(data2, bins=np.linspace(0,200,41)) + ax2.hist(bins2[:-1], bins=bins2, weights = counts2, \ + label=pdg_defs.selection_pdg_dict[pdg]+", "+hits_dset, \ + linewidth=linewidth_options[idx], alpha=alpha_options[idx], \ + color=color_options[idx], edgecolor=color_options[idx], \ + linestyle=linestyle_options[idx], fill = fill_options[idx]) ax2.set_xlabel('Min Hit Amplitude / Channel / Event [ke-]') - ax2.set_ylabel('Channel Count / 20 ke-') + ax2.set_ylabel('Channel Count / 5 ke-') ax2.set_title(r'Minimum Hit Amplitiude Per Channel in Selected Events '+mc_title) + ax2.legend() plt.savefig(sample_type+"_selected_events_min_hit_amp_per_channel_per_event.png") plt.close(fig2) # PLOT: first hit amplitude per channel per event fig3, ax3 = plt.subplots(figsize=(6,4)) - data3tot = np.array([d[key]['first_hit_amp'] for key in d.keys()]) - counts3tot, bins3tot = np.histogram(data3tot, bins=np.linspace(0,500,26)) - ax3.hist(bins3tot[:-1], bins=bins3tot, weights = counts3tot) + for pdg in sel_pdg: + for hits_dset in hits_dsets: + idx = hits_dsets.tolist().index(hits_dset) + data3 = np.array([d[key]['first_hit_amp'] for key in d.keys() if d[key]['event_pdg']==pdg and d[key]['hits_dset']==hits_dset]) + counts3, bins3 = np.histogram(data3, bins=np.linspace(0,200,41)) + ax3.hist(bins3[:-1], bins=bins3, weights = counts3, \ + label=pdg_defs.selection_pdg_dict[pdg]+", "+hits_dset, \ + linewidth=linewidth_options[idx], alpha=alpha_options[idx], \ + color=color_options[idx], edgecolor=color_options[idx], \ + linestyle=linestyle_options[idx], fill = fill_options[idx]) ax3.set_xlabel('First Hit Amplitude / Channel / Event [ke-]') - ax3.set_ylabel('Channel Count / 20 ke-') + ax3.set_ylabel('Channel Count / 5 ke-') ax3.set_title(r'First Hit Amplitiude Per Channel in Selected Events '+mc_title) + ax3.legend() plt.savefig(sample_type+"_selected_events_first_hit_amp_per_channel_per_event.png") plt.close(fig3) # PLOT: last hit amplitude per channel per event fig4, ax4 = plt.subplots(figsize=(6,4)) - data4tot = np.array([d[key]['last_hit_amp'] for key in d.keys()]) - counts4tot, bins4tot = np.histogram(data4tot, bins=np.linspace(0,500,26)) - ax4.hist(bins4tot[:-1], bins=bins4tot, weights = counts4tot) + for pdg in sel_pdg: + for hits_dset in hits_dsets: + idx = hits_dsets.tolist().index(hits_dset) + data4 = np.array([d[key]['last_hit_amp'] for key in d.keys() if d[key]['event_pdg']==pdg and d[key]['hits_dset']==hits_dset]) + counts4, bins4 = np.histogram(data4, bins=np.linspace(0,200,41)) + ax4.hist(bins4[:-1], bins=bins4, weights = counts4, \ + label=pdg_defs.selection_pdg_dict[pdg]+", "+hits_dset, \ + linewidth=linewidth_options[idx], alpha=alpha_options[idx], \ + color=color_options[idx], edgecolor=color_options[idx], \ + linestyle=linestyle_options[idx], fill = fill_options[idx]) ax4.set_xlabel('Last Hit Amplitude / Channel / Event [ke-]') - ax4.set_ylabel('Channel Count / 20 ke-') + ax4.set_ylabel('Channel Count / 5 ke-') ax4.set_title(r'Last Hit Amplitiude Per Channel in Selected Events '+mc_title) + ax4.legend() plt.savefig(sample_type+"_selected_events_last_hit_amp_per_channel_per_event.png") plt.close(fig4) # PLOT: first/last hit delta(t) per channel per event fig4, ax4 = plt.subplots(figsize=(6,4)) - data4tot = np.array([d[key]['first_last_hit_delta_t'] for key in d.keys()]) - counts4tot, bins4tot = np.histogram(data4tot, bins=np.linspace(0,20,41)) - ax4.hist(bins4tot[:-1], bins=bins4tot, weights = counts4tot) + for pdg in sel_pdg: + for hits_dset in hits_dsets: + idx = hits_dsets.tolist().index(hits_dset) + data4 = np.array([d[key]['first_last_hit_delta_t'] for key in d.keys() if d[key]['event_pdg']==pdg and d[key]['hits_dset']==hits_dset]) + counts4, bins4 = np.histogram(data4, bins=np.linspace(0,20,41)) + ax4.hist(bins4[:-1], bins=bins4, weights = counts4, \ + label=pdg_defs.selection_pdg_dict[pdg]+", "+hits_dset, \ + linewidth=linewidth_options[idx], alpha=alpha_options[idx], \ + color=color_options[idx], edgecolor=color_options[idx], \ + linestyle=linestyle_options[idx], fill = fill_options[idx]) ax4.set_xlabel(r'First/Last Hit $\Delta$t / Channel / Event [$\mu$s]') ax4.set_ylabel(r'Channel Count / 0.5 $\mu$s') + ax4.set_yscale('log') ax4.set_title("Difference in Time between First and Last Hit\nPer Channel in Selected Events "+mc_title) + ax4.legend() plt.savefig(sample_type+"_selected_events_first_last_hit_deltat_per_channel_per_event.png") plt.close(fig4) diff --git a/scripts/proto_nd_scripts/analysis/hip_selection/run_proto_nd_hip_selection.sh b/scripts/proto_nd_scripts/analysis/hip_selection/run_proto_nd_hip_selection.sh index 57834e2f..aa20c219 100644 --- a/scripts/proto_nd_scripts/analysis/hip_selection/run_proto_nd_hip_selection.sh +++ b/scripts/proto_nd_scripts/analysis/hip_selection/run_proto_nd_hip_selection.sh @@ -23,8 +23,8 @@ WORKFLOW1='yamls/proto_nd_flow/workflows/analysis/hip_sel_workflow.yaml' HERE=`pwd` #cd ndlar_flow -# assumes this is being run from ndlar_flow/scripts/proto_nd_flow: -cd ../../ +# assumes this is being run from ndlar_flow/scripts/proto_nd_flow/analysis/hip_selection/: +cd ../../../../ # avoid being asked if we want to overwrite the file if it exists. # this is us answering "yes". diff --git a/yamls/module1_flow/resources/Geometry.yaml b/yamls/module1_flow/resources/Geometry.yaml index 5cbe955c..dddd2d8d 100644 --- a/yamls/module1_flow/resources/Geometry.yaml +++ b/yamls/module1_flow/resources/Geometry.yaml @@ -6,4 +6,4 @@ params: path: 'geometry_info' det_geometry_file: 'data/module1_flow/module0.yaml' crs_geometry_files: ['/global/cfs/cdirs/dune/www/data/Module1/TPC12/module1_layout-2.3.16.yaml'] - lrs_geometry_file: 'data/proto_nd_flow/light_module_desc-1.0.0.yaml' + lrs_geometry_file: 'data/module1_flow/light_module_desc_single_module-2.0.0.yaml' From 8157a439fb31cb2915841f7349935cc2949eaa0c Mon Sep 17 00:00:00 2001 From: Elise Dianne Hinkle Date: Mon, 15 Jan 2024 15:48:54 -0800 Subject: [PATCH 34/37] Updating hit level metrics plotting script for HIP selection. --- .../hip_selection/plot_hit_level_metrics.py | 127 ++++++++++-------- 1 file changed, 68 insertions(+), 59 deletions(-) diff --git a/scripts/proto_nd_scripts/analysis/hip_selection/plot_hit_level_metrics.py b/scripts/proto_nd_scripts/analysis/hip_selection/plot_hit_level_metrics.py index 7e8b6a8d..497a74d9 100644 --- a/scripts/proto_nd_scripts/analysis/hip_selection/plot_hit_level_metrics.py +++ b/scripts/proto_nd_scripts/analysis/hip_selection/plot_hit_level_metrics.py @@ -20,25 +20,26 @@ def plot_event_hit_summ_metrics(d, is_mc): sel_pdg = np.unique([d[key]['event_pdg'] for key in d.keys()]) hits_dsets = np.unique([d[key]['hits_dset'] for key in d.keys()]) - alpha_options = [1.0, 0.8] - color_options = ['#4daf4a', '#ff7f00'] - linestyle_options = ['--', '-'] - linewidth_options = [1.5, 1.5] - fill_options = [False, False] + alpha_options = [[0.8, 0.8], [0.8, 0.8]] + color_options = [['#4daf4a', '#ff7f00'], ['#377eb8', '#e41a1c']] + linestyle_options = [['--', '--'], ['-', '-']] + linewidth_options = [[1.5, 1.5], [1.5, 1.5]] + fill_options = [[False, False], [False, False]] print("hits_dsets:",hits_dsets) # PLOT: total charge in an event fig0, ax0 = plt.subplots(figsize=(6,4)) for pdg in sel_pdg: + pdg_idx = sel_pdg.tolist().index(pdg) for hits_dset in hits_dsets: - idx = hits_dsets.tolist().index(hits_dset) + dset_idx = hits_dsets.tolist().index(hits_dset) data0 = np.array([d[key]['total_charge'] for key in d.keys() if d[key]['event_pdg']==pdg and d[key]['hits_dset']==hits_dset]) counts0, bins0 = np.histogram(data0, bins=np.linspace(0,20000,21)) - ax0.hist(bins0[:-1], bins=bins0, weights = counts0, \ + ax0.hist(bins0[:-1], bins=bins0, weights = counts0, histtype='stepfilled',\ label=pdg_defs.selection_pdg_dict[pdg]+", "+hits_dset, \ - linewidth=linewidth_options[idx], alpha=alpha_options[idx], \ - color=color_options[idx], edgecolor=color_options[idx], \ - linestyle=linestyle_options[idx], fill = fill_options[idx]) + linewidth=linewidth_options[pdg_idx][dset_idx], alpha=alpha_options[pdg_idx][dset_idx], \ + color=color_options[pdg_idx][dset_idx], edgecolor=color_options[pdg_idx][dset_idx], \ + linestyle=linestyle_options[pdg_idx][dset_idx], fill = fill_options[pdg_idx][dset_idx]) ax0.set_xlabel('Total Charge [ke-]') ax0.set_ylabel('Count / 1000 ke-') ax0.set_title(r'Total Charge Per Selected Event '+mc_title) @@ -49,15 +50,16 @@ def plot_event_hit_summ_metrics(d, is_mc): # PLOT: number of hits in an event fig1, ax1 = plt.subplots(figsize=(6,4)) for pdg in sel_pdg: + pdg_idx = sel_pdg.tolist().index(pdg) for hits_dset in hits_dsets: - idx = hits_dsets.tolist().index(hits_dset) + dset_idx = hits_dsets.tolist().index(hits_dset) data1 = np.array([d[key]['num_hits'] for key in d.keys() if d[key]['event_pdg']==pdg and d[key]['hits_dset']==hits_dset]) - counts1, bins1 = np.histogram(data1, bins=np.linspace(0,500,26)) - ax1.hist(bins1[:-1], bins=bins1, weights = counts1, \ + counts1, bins1 = np.histogram(data1, bins=np.linspace(0,600,31)) + ax1.hist(bins1[:-1], bins=bins1, weights = counts1, histtype='stepfilled',\ label=pdg_defs.selection_pdg_dict[pdg]+", "+hits_dset, \ - linewidth=linewidth_options[idx], alpha=alpha_options[idx], \ - color=color_options[idx], edgecolor=color_options[idx], \ - linestyle=linestyle_options[idx], fill = fill_options[idx]) + linewidth=linewidth_options[pdg_idx][dset_idx], alpha=alpha_options[pdg_idx][dset_idx], \ + color=color_options[pdg_idx][dset_idx], edgecolor=color_options[pdg_idx][dset_idx], \ + linestyle=linestyle_options[pdg_idx][dset_idx], fill = fill_options[pdg_idx][dset_idx]) ax1.set_xlabel('Number of Hits') ax1.set_ylabel('Event Count / 20 Hits') ax1.set_title(r'Number of Hits Per Selected Event '+mc_title) @@ -69,15 +71,16 @@ def plot_event_hit_summ_metrics(d, is_mc): # PLOT: number of separate pixels triggered in an event fig2, ax2 = plt.subplots(figsize=(6,4)) for pdg in sel_pdg: + pdg_idx = sel_pdg.tolist().index(pdg) for hits_dset in hits_dsets: - idx = hits_dsets.tolist().index(hits_dset) + dset_idx = hits_dsets.tolist().index(hits_dset) data2 = np.array([d[key]['num_channels'] for key in d.keys() if d[key]['event_pdg']==pdg and d[key]['hits_dset']==hits_dset]) - counts2, bins2 = np.histogram(data2, bins=np.linspace(0,100,21)) - ax2.hist(bins2[:-1], bins=bins2, weights = counts2, \ + counts2, bins2 = np.histogram(data2, bins=np.linspace(0,600,31)) + ax2.hist(bins2[:-1], bins=bins2, weights = counts2, histtype='stepfilled',\ label=pdg_defs.selection_pdg_dict[pdg]+", "+hits_dset,\ - linewidth=linewidth_options[idx], alpha=alpha_options[idx], \ - color=color_options[idx], edgecolor=color_options[idx], \ - linestyle=linestyle_options[idx], fill = fill_options[idx]) + linewidth=linewidth_options[pdg_idx][dset_idx], alpha=alpha_options[pdg_idx][dset_idx], \ + color=color_options[pdg_idx][dset_idx], edgecolor=color_options[pdg_idx][dset_idx], \ + linestyle=linestyle_options[pdg_idx][dset_idx], fill = fill_options[pdg_idx][dset_idx]) ax2.set_xlabel('Number of Unique Channels Triggered') ax2.set_ylabel('Event Count / 20 Channels') ax2.set_title("Number of Unique Channels Triggered \nPer Selected Event "+mc_title) @@ -98,24 +101,25 @@ def plot_channel_metrics(d, is_mc): sel_pdg = np.unique([d[key]['event_pdg'] for key in d.keys()]) hits_dsets = np.unique([d[key]['hits_dset'] for key in d.keys()]) - alpha_options = [1.0, 0.8] - color_options = ['#4daf4a', '#ff7f00'] - linestyle_options = ['--', '-'] - linewidth_options = [1.5, 1.5] - fill_options = [False, False] + alpha_options = [[0.8, 0.8], [0.8, 0.8]] + color_options = [['#4daf4a', '#ff7f00'], ['#377eb8', '#e41a1c']] + linestyle_options = [['--', '--'], ['-', '-']] + linewidth_options = [[1.5, 1.5], [1.5, 1.5]] + fill_options = [[False, False], [False, False]] # PLOT: hits per channel per event fig0, ax0 = plt.subplots(figsize=(6,4)) for pdg in sel_pdg: + pdg_idx = sel_pdg.tolist().index(pdg) for hits_dset in hits_dsets: - idx = hits_dsets.tolist().index(hits_dset) + dset_idx = hits_dsets.tolist().index(hits_dset) data0 = np.array([d[key]['hit_mult'] for key in d.keys() if d[key]['event_pdg']==pdg and d[key]['hits_dset']==hits_dset]) counts0, bins0 = np.histogram(data0, bins=np.linspace(0,10,11)) - ax0.hist(bins0[:-1], bins=bins0, weights = counts0, \ + ax0.hist(bins0[:-1], bins=bins0, weights = counts0, histtype='stepfilled',\ label=pdg_defs.selection_pdg_dict[pdg]+", "+hits_dset, \ - linewidth=linewidth_options[idx], alpha=alpha_options[idx], \ - color=color_options[idx], edgecolor=color_options[idx], \ - linestyle=linestyle_options[idx], fill = fill_options[idx]) + linewidth=linewidth_options[pdg_idx][dset_idx], alpha=alpha_options[pdg_idx][dset_idx], \ + color=color_options[pdg_idx][dset_idx], edgecolor=color_options[pdg_idx][dset_idx], \ + linestyle=linestyle_options[pdg_idx][dset_idx], fill = fill_options[pdg_idx][dset_idx]) ax0.set_xlabel('Hit Multiplicity / Channel / Event') ax0.set_ylabel('Channel Count / Hit') ax0.set_title(r'Hit Multiplicity Per Channel in Selected Events '+mc_title) @@ -126,15 +130,16 @@ def plot_channel_metrics(d, is_mc): # PLOT: max hit amplitude per channel per event fig1, ax1 = plt.subplots(figsize=(6,4)) for pdg in sel_pdg: + pdg_idx = sel_pdg.tolist().index(pdg) for hits_dset in hits_dsets: - idx = hits_dsets.tolist().index(hits_dset) + dset_idx = hits_dsets.tolist().index(hits_dset) data1 = np.array([d[key]['max_hit_amp'] for key in d.keys() if d[key]['event_pdg']==pdg and d[key]['hits_dset']==hits_dset]) counts1, bins1 = np.histogram(data1, bins=np.linspace(0,200,41)) - ax1.hist(bins1[:-1], bins=bins1, weights = counts1, \ + ax1.hist(bins1[:-1], bins=bins1, weights = counts1, histtype='stepfilled',\ label=pdg_defs.selection_pdg_dict[pdg]+", "+hits_dset, \ - linewidth=linewidth_options[idx], alpha=alpha_options[idx], \ - color=color_options[idx], edgecolor=color_options[idx], \ - linestyle=linestyle_options[idx], fill = fill_options[idx]) + linewidth=linewidth_options[pdg_idx][dset_idx], alpha=alpha_options[pdg_idx][dset_idx], \ + color=color_options[pdg_idx][dset_idx], edgecolor=color_options[pdg_idx][dset_idx], \ + linestyle=linestyle_options[pdg_idx][dset_idx], fill = fill_options[pdg_idx][dset_idx]) ax1.set_xlabel('Max Hit Amplitude / Channel / Event [ke-]') ax1.set_ylabel('Channel Count / 5 ke-') ax1.set_title(r'Maximum Hit Amplitiude Per Channel in Selected Events '+mc_title) @@ -145,15 +150,16 @@ def plot_channel_metrics(d, is_mc): # PLOT: min hit amplitude per channel per event fig2, ax2 = plt.subplots(figsize=(6,4)) for pdg in sel_pdg: + pdg_idx = sel_pdg.tolist().index(pdg) for hits_dset in hits_dsets: - idx = hits_dsets.tolist().index(hits_dset) + dset_idx = hits_dsets.tolist().index(hits_dset) data2 = np.array([d[key]['min_hit_amp'] for key in d.keys() if d[key]['event_pdg']==pdg and d[key]['hits_dset']==hits_dset]) counts2, bins2 = np.histogram(data2, bins=np.linspace(0,200,41)) - ax2.hist(bins2[:-1], bins=bins2, weights = counts2, \ + ax2.hist(bins2[:-1], bins=bins2, weights = counts2, histtype='stepfilled',\ label=pdg_defs.selection_pdg_dict[pdg]+", "+hits_dset, \ - linewidth=linewidth_options[idx], alpha=alpha_options[idx], \ - color=color_options[idx], edgecolor=color_options[idx], \ - linestyle=linestyle_options[idx], fill = fill_options[idx]) + linewidth=linewidth_options[pdg_idx][dset_idx], alpha=alpha_options[pdg_idx][dset_idx], \ + color=color_options[pdg_idx][dset_idx], edgecolor=color_options[pdg_idx][dset_idx], \ + linestyle=linestyle_options[pdg_idx][dset_idx], fill = fill_options[pdg_idx][dset_idx]) ax2.set_xlabel('Min Hit Amplitude / Channel / Event [ke-]') ax2.set_ylabel('Channel Count / 5 ke-') ax2.set_title(r'Minimum Hit Amplitiude Per Channel in Selected Events '+mc_title) @@ -164,15 +170,16 @@ def plot_channel_metrics(d, is_mc): # PLOT: first hit amplitude per channel per event fig3, ax3 = plt.subplots(figsize=(6,4)) for pdg in sel_pdg: + pdg_idx = sel_pdg.tolist().index(pdg) for hits_dset in hits_dsets: - idx = hits_dsets.tolist().index(hits_dset) + dset_idx = hits_dsets.tolist().index(hits_dset) data3 = np.array([d[key]['first_hit_amp'] for key in d.keys() if d[key]['event_pdg']==pdg and d[key]['hits_dset']==hits_dset]) counts3, bins3 = np.histogram(data3, bins=np.linspace(0,200,41)) - ax3.hist(bins3[:-1], bins=bins3, weights = counts3, \ + ax3.hist(bins3[:-1], bins=bins3, weights = counts3, histtype='stepfilled',\ label=pdg_defs.selection_pdg_dict[pdg]+", "+hits_dset, \ - linewidth=linewidth_options[idx], alpha=alpha_options[idx], \ - color=color_options[idx], edgecolor=color_options[idx], \ - linestyle=linestyle_options[idx], fill = fill_options[idx]) + linewidth=linewidth_options[pdg_idx][dset_idx], alpha=alpha_options[pdg_idx][dset_idx], \ + color=color_options[pdg_idx][dset_idx], edgecolor=color_options[pdg_idx][dset_idx], \ + linestyle=linestyle_options[pdg_idx][dset_idx], fill = fill_options[pdg_idx][dset_idx]) ax3.set_xlabel('First Hit Amplitude / Channel / Event [ke-]') ax3.set_ylabel('Channel Count / 5 ke-') ax3.set_title(r'First Hit Amplitiude Per Channel in Selected Events '+mc_title) @@ -183,15 +190,16 @@ def plot_channel_metrics(d, is_mc): # PLOT: last hit amplitude per channel per event fig4, ax4 = plt.subplots(figsize=(6,4)) for pdg in sel_pdg: + pdg_idx = sel_pdg.tolist().index(pdg) for hits_dset in hits_dsets: - idx = hits_dsets.tolist().index(hits_dset) + dset_idx = hits_dsets.tolist().index(hits_dset) data4 = np.array([d[key]['last_hit_amp'] for key in d.keys() if d[key]['event_pdg']==pdg and d[key]['hits_dset']==hits_dset]) counts4, bins4 = np.histogram(data4, bins=np.linspace(0,200,41)) - ax4.hist(bins4[:-1], bins=bins4, weights = counts4, \ + ax4.hist(bins4[:-1], bins=bins4, weights = counts4, histtype='stepfilled',\ label=pdg_defs.selection_pdg_dict[pdg]+", "+hits_dset, \ - linewidth=linewidth_options[idx], alpha=alpha_options[idx], \ - color=color_options[idx], edgecolor=color_options[idx], \ - linestyle=linestyle_options[idx], fill = fill_options[idx]) + linewidth=linewidth_options[pdg_idx][dset_idx], alpha=alpha_options[pdg_idx][dset_idx], \ + color=color_options[pdg_idx][dset_idx], edgecolor=color_options[pdg_idx][dset_idx], \ + linestyle=linestyle_options[pdg_idx][dset_idx], fill = fill_options[pdg_idx][dset_idx]) ax4.set_xlabel('Last Hit Amplitude / Channel / Event [ke-]') ax4.set_ylabel('Channel Count / 5 ke-') ax4.set_title(r'Last Hit Amplitiude Per Channel in Selected Events '+mc_title) @@ -202,17 +210,18 @@ def plot_channel_metrics(d, is_mc): # PLOT: first/last hit delta(t) per channel per event fig4, ax4 = plt.subplots(figsize=(6,4)) for pdg in sel_pdg: + pdg_idx = sel_pdg.tolist().index(pdg) for hits_dset in hits_dsets: - idx = hits_dsets.tolist().index(hits_dset) + dset_idx = hits_dsets.tolist().index(hits_dset) data4 = np.array([d[key]['first_last_hit_delta_t'] for key in d.keys() if d[key]['event_pdg']==pdg and d[key]['hits_dset']==hits_dset]) - counts4, bins4 = np.histogram(data4, bins=np.linspace(0,20,41)) - ax4.hist(bins4[:-1], bins=bins4, weights = counts4, \ + counts4, bins4 = np.histogram(data4, bins=np.linspace(0,150,76)) + ax4.hist(bins4[:-1], bins=bins4, weights = counts4, histtype='stepfilled',\ label=pdg_defs.selection_pdg_dict[pdg]+", "+hits_dset, \ - linewidth=linewidth_options[idx], alpha=alpha_options[idx], \ - color=color_options[idx], edgecolor=color_options[idx], \ - linestyle=linestyle_options[idx], fill = fill_options[idx]) + linewidth=linewidth_options[pdg_idx][dset_idx], alpha=alpha_options[pdg_idx][dset_idx], \ + color=color_options[pdg_idx][dset_idx], edgecolor=color_options[pdg_idx][dset_idx], \ + linestyle=linestyle_options[pdg_idx][dset_idx], fill = fill_options[pdg_idx][dset_idx]) ax4.set_xlabel(r'First/Last Hit $\Delta$t / Channel / Event [$\mu$s]') - ax4.set_ylabel(r'Channel Count / 0.5 $\mu$s') + ax4.set_ylabel(r'Channel Count / 2 $\mu$s') ax4.set_yscale('log') ax4.set_title("Difference in Time between First and Last Hit\nPer Channel in Selected Events "+mc_title) ax4.legend() From 845365fcfc718c0a88ce142ac8bb6afd43ce2949 Mon Sep 17 00:00:00 2001 From: Elise Dianne Hinkle Date: Tue, 16 Jan 2024 07:58:27 -0800 Subject: [PATCH 35/37] Adding tracklet metrics to HIP selection. --- .../protondflow_evd_example.ipynb | 6 +- .../hip_selection/data_mc_all_metrics.py | 243 +++++++++++++++ .../hip_selection/plot_all_metrics.py | 279 ++++++++++++++++++ 3 files changed, 525 insertions(+), 3 deletions(-) create mode 100644 scripts/proto_nd_scripts/analysis/hip_selection/data_mc_all_metrics.py create mode 100644 scripts/proto_nd_scripts/analysis/hip_selection/plot_all_metrics.py diff --git a/event_display/proto_nd_flow/protondflow_evd_example.ipynb b/event_display/proto_nd_flow/protondflow_evd_example.ipynb index 50ee138f..0f3479e7 100644 --- a/event_display/proto_nd_flow/protondflow_evd_example.ipynb +++ b/event_display/proto_nd_flow/protondflow_evd_example.ipynb @@ -27,7 +27,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 1, @@ -104,7 +104,7 @@ }, { "data": { - "image/png": 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", + "image/png": 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", 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" ] @@ -114,7 +114,7 @@ } ], "source": [ - "evd = ProtoNDFlowEventDisplay(filedir=directory, filename=file, geometry_file=geometry, nhits=1, hits_dset='raw_hits', tracklets=False)\n", + "evd = ProtoNDFlowEventDisplay(filedir=directory, filename=file, geometry_file=geometry, nhits=1, hits_dset='calib_final_hits', tracklets=True)\n", "evd.run()" ] }, diff --git a/scripts/proto_nd_scripts/analysis/hip_selection/data_mc_all_metrics.py b/scripts/proto_nd_scripts/analysis/hip_selection/data_mc_all_metrics.py new file mode 100644 index 00000000..5f35df9a --- /dev/null +++ b/scripts/proto_nd_scripts/analysis/hip_selection/data_mc_all_metrics.py @@ -0,0 +1,243 @@ +################################################################################ +## ## +## CONTAINS: Script to plot contents in output file from proton selection ## +## being run over Bern Module Data. ## +## ## +################################################################################ + +import h5py, glob, argparse +import numpy as np +import matplotlib.pyplot as plt +import sys +import file_parsing +import json +from plot_all_metrics import plot_event_hit_summ_metrics, plot_channel_metrics, plot_track_metrics + +def main(file_dir, is_sim, hits_dset, sel_event_dict): + + is_sim = bool(is_sim == 'True') + # initialize plotting datasets + event_hit_summ_dict = dict() + channel_metric_dict = dict() + track_summ_dict = dict() + print("Is MC?:", is_sim) + if is_sim: + sample_type = 'MC' + else: + sample_type = 'data' + + count = 0 + + for file in glob.glob(file_dir+'/*.h5'): # Loop over files files + + if count > 10: break + count+=1 + f = h5py.File(file,'r') + + # Prepare datasets for plotting + events = f['charge/events/data'] + tracks = f['combined/tracklets/data'] + tracks_ref = f['charge/events/ref/combined/tracklets/ref'] + tracks_region = f['charge/events/ref/combined/tracklets/ref_region'] + hits_trk_ref = f['combined/tracklets/ref/charge/'+hits_dset+'/ref'] + hits_trk_region = f['combined/tracklets/ref/charge/'+hits_dset+'/ref_region'] + #hits_drift = f['combined/hit_drift/data'] + hits_dsets = ['calib_final_hits', 'calib_prompt_hits'] + hits = [f['charge/calib_final_hits/data'], f['charge/calib_prompt_hits/data']] + hits_ref = [f['charge/events/ref/charge/calib_final_hits/ref'], \ + f['charge/events/ref/charge/calib_prompt_hits/ref']] + hits_region = [f['charge/events/ref/charge/calib_final_hits/ref_region'], \ + f['charge/events/ref/charge/calib_prompt_hits/ref_region']] + #if not is_sim: + # charge_hits = hits#f['combined/q_calib_el/data'] + # charge_hits_ref = hits_ref#f['charge/events/ref/combined/q_calib_el/ref'] + # charge_hits_region = hits_region#f['charge/events/ref/combined/q_calib_el/ref_region'] + #else: + # charge_hits = hits + # charge_hits_ref = hits_ref + # charge_hits_region = hits_region + ext_trigs = f['charge/ext_trigs/data'] + ext_trigs_ref = f['charge/events/ref/charge/ext_trigs/ref'] + ext_trigs_region = f['charge/events/ref/charge/ext_trigs/ref_region'] + print("Available datasets:",f.keys(),'\n') + sel_reco = f['high_purity_sel']['hips']['sel_reco']['data'] + if is_sim: + sel_truth = f['high_purity_sel']['hips']['sel_truth']['data'] + mc_truth_events = f['mc_truth/events/data'] + + print("File:", file) + #sel_mask = (sel_reco['sel'] == True) + #sel_event_ids = sel_reco[sel_mask]['event_id'] + #print("Selected Event Ids:", sel_event_ids) + #if is_sim==True: + #sel_truth_mask = (sel_truth['sel'] == True) + #sel_truth_protons = sel_truth[sel_mask]['hips'] + #sel_truth_sel = sel_truth[sel_truth_mask]['event_id'] + #sel_pdg_mask = (sel_truth[sel_truth_mask]['pdg_id'] != 0) + #sel_truth_pdg = sel_truth[sel_truth_mask]['pdg_id'][sel_pdg_mask] + #print("Selected Proton?:", sel_truth_protons) + #print("Selected True?:", sel_truth_sel) + #print("Selected PDG IDs:", sel_truth_pdg) + #for event in sel_event_ids: + #event_sel_mask = f['high_purity_sel']['hips']['sel_truth']['data']['event_id'] == event + #zero_mask = f['high_purity_sel']['hips']['sel_truth']['data'][event_sel_mask]['pdg_id'] != 0. + #print('Selected event true PID:', f['high_purity_sel']['hips']['sel_truth']['data'][event_sel_mask]['pdg_id'][zero_mask], "| Event ID:", event) + + ### partition file by selected events + #sel_event_mask = np.isin(events['id'], sel_event_ids) + #print("Events:", events[sel_event_mask]) + + # TO DO: Make this variable based on input file + sel_event_id_file = open(file_dir+'/'+sel_event_dict) + sel_event_id_data = json.load(sel_event_id_file) + sel_event_pdgs = sel_event_id_data.keys() + for pdg in sel_event_pdgs: + #if pdg == '13': continue + sel_event_ids = sel_event_id_data[pdg] + for event_id in sel_event_ids: + + # Get track information related to given event_id + track_ref = tracks_ref[tracks_region[int(event_id),'start']:tracks_region[int(event_id),'stop']] + track_ref = np.sort(track_ref[track_ref[:,0] == event_id, 1]) + track_charge_data = tracks[track_ref]['q'][0] + track_length_data = tracks[track_ref]['length'][0] + track_num_hits_data = tracks[track_ref]['nhit'][0] + track_theta_data = tracks[track_ref]['theta'][0] + track_phi_data = tracks[track_ref]['phi'][0] + track_ts_start_data = tracks[track_ref]['ts_start'][0] + track_ts_end_data = tracks[track_ref]['ts_end'][0] + track_dx_data = tracks[track_ref]['dx'][0] + track_dq_data = tracks[track_ref]['dq'][0] + track_start_pt_data = tracks[track_ref]['start'][0] + track_end_pt_data = tracks[track_ref]['end'][0] + + zero_dq_mask = track_dq_data != 0. + + track_dx_dist = np.array([np.sqrt(i[0]**2 + i[1]**2 + i[2]**2) for i in list(track_dx_data)]) + track_dx_dist = track_dx_dist[zero_dq_mask][::-1] + track_dq_data = track_dq_data[zero_dq_mask][::-1] + track_dqdx = track_dq_data / track_dx_dist + track_rr = np.zeros(len(track_dqdx)) + track_rr = np.cumsum(track_dx_dist[::-1])[::-1]-0.5*track_dx_dist + #print("Residual range:", track_rr) + #print("Track dqdx:", track_dqdx) + #print("PDG:", pdg) + #print("Track dx:", track_dx_data) + #print("Track dx dist:", track_dx_dist) + #print("Track dq:", track_dq_data) + #print("Track start pt:", track_start_pt_data) + + for x in range(len(hits_dsets)): + charge_hits_dset = hits_dsets[x] + charge_hits = hits[x] + charge_hits_ref = hits_ref[x] + charge_hits_region = hits_region[x] + + # Get hit information related to given event_id + charge_hit_ref = charge_hits_ref[charge_hits_region[int(event_id),'start']:charge_hits_region[int(event_id),'stop']] + charge_hit_ref = np.sort(charge_hit_ref[charge_hit_ref[:,0] == event_id, 1]) + + + # Event-level hit metrics + charge_hits_data = charge_hits[charge_hit_ref]['Q'] + ts_hits_data = charge_hits[charge_hit_ref]['ts_pps'] + num_charge_hits = len(charge_hits_data) + + # Channel-level hit metrics + iogroup_hits = charge_hits[charge_hit_ref]['io_group'] + iochannel_hits = charge_hits[charge_hit_ref]['io_channel'] + chipid_hits = charge_hits[charge_hit_ref]['chip_id'] + channelid_hits = charge_hits[charge_hit_ref]['channel_id'] + + channel_id = np.array([int(str(iogroup_hits[i])+str(iochannel_hits[i])+str(chipid_hits[i])+str(channelid_hits[i])) for i in range(num_charge_hits)]) + unique_channels, unique_channel_hit_counts = np.unique(channel_id, return_counts=True) + num_channels = len(unique_channels) + + #print("String of channels:", channel_id) + #print("Number of unique channels:", num_channels) + #print("Hits per channel:", unique_channel_hit_counts) + #print("Length of hits per channel:", len(unique_channel_hit_counts)) + for i in range(num_channels): + + channel = unique_channels[i] + hits_per_channel = unique_channel_hit_counts[i] + channel_mask = np.argwhere(channel_id == channel).flatten() + channel_hit_amps = charge_hits_data[channel_mask] + channel_hit_ts = ts_hits_data[channel_mask] / 10. # convert to us + + max_hit_amp = max(channel_hit_amps) + min_hit_amp = min(channel_hit_amps) + + first_hit_idx = np.argmin(channel_hit_ts) + last_hit_idx = np.argmax(channel_hit_ts) + first_hit_amp = channel_hit_amps[first_hit_idx] + last_hit_amp = channel_hit_amps[last_hit_idx] + first_last_hit_delta_t = abs(channel_hit_ts[last_hit_idx] - channel_hit_ts[first_hit_idx]) + + #print("Channel hit amplitudes:", channel_hit_amps) + #print("Channel hit timestamps:", channel_hit_ts) + #print("Maximum hit amplitude:", max_hit_amp) + #print("Minimum hit amplitude:", min_hit_amp) + #print("First hit amplitude:", first_hit_amp) + #print("Last hit amplitude:", last_hit_amp) + #print("First/Last hit delta t:", first_last_hit_delta_t) + + channel_metric_dict[(file, pdg, charge_hits_dset, event_id, channel)]=dict( + hit_mult = int(hits_per_channel), + max_hit_amp = float(max_hit_amp), + min_hit_amp = float(min_hit_amp), + first_hit_amp = float(first_hit_amp), + last_hit_amp = float(last_hit_amp), + first_last_hit_delta_t = float(first_last_hit_delta_t), + event_pdg = int(pdg), + hits_dset = str(charge_hits_dset) + ) + + event_hit_summ_dict[(file, pdg, charge_hits_dset, event_id)]=dict( + event_pdg = int(pdg), + total_charge=float(sum(charge_hits_data)), + num_hits=int(num_charge_hits), + num_channels=int(num_channels), + hits_dset = str(charge_hits_dset) + ) + + track_summ_dict[(file, pdg, charge_hits_dset, event_id)]=dict( + event_pdg = int(pdg), + total_charge = float(track_charge_data), + length = float(track_length_data), + hits_in_track = int(track_num_hits_data), + theta = float(track_theta_data), + phi = float(track_phi_data), + ts_start = float(track_ts_start_data), + ts_end = float(track_ts_end_data), + dx = [float(i) for i in list(track_dx_dist)], + dq = [float(i) for i in list(track_dq_data)], + start_pt = [float(i) for i in list(track_start_pt_data)], + end_pt = [float(i) for i in list(track_end_pt_data)], + dqdx = [float(i) for i in list(track_dqdx)], + rr = [float(i) for i in list(track_rr)], + hits_dset = str(hits_dset) + ) + + ## Save all Python dictionaries to JSON files + file_parsing.save_dict_to_json(event_hit_summ_dict, sample_type+"_event_hit_summ_dict", True) + file_parsing.save_dict_to_json(channel_metric_dict, sample_type+"_channel_metric_dict", True) + file_parsing.save_dict_to_json(track_summ_dict, sample_type+"_track_summ_dict", True) + + # PLOT: Signal Event Info + plot_event_hit_summ_metrics(event_hit_summ_dict, is_sim) + plot_channel_metrics(channel_metric_dict, is_sim) + plot_track_metrics(track_summ_dict, is_sim) + +if __name__=='__main__': + parser = argparse.ArgumentParser() + parser.add_argument('-d', '--file_dir', default=None, required=True, type=str, \ + help='''string corresponding to the path of the directory containing processed files for plotting''') + parser.add_argument('-mc', '--is_sim', default=False, required=True, type=str, \ + help='''str corresponding to bool whether files are simulation (MC) or data''') + parser.add_argument('-hd', '--hits_dset', default='calib_final_hits', required=True, type=str,\ + help='''str corresponding to hits dataset name associated with tracklets''') + parser.add_argument('-sed', '--sel_event_dict', default=None, required=True, type=str,\ + help='''str corresponding name of json file containing selected event ids''') + args = parser.parse_args() + main(**vars(args)) \ No newline at end of file diff --git a/scripts/proto_nd_scripts/analysis/hip_selection/plot_all_metrics.py b/scripts/proto_nd_scripts/analysis/hip_selection/plot_all_metrics.py new file mode 100644 index 00000000..f6511961 --- /dev/null +++ b/scripts/proto_nd_scripts/analysis/hip_selection/plot_all_metrics.py @@ -0,0 +1,279 @@ +################################################################################ +## ## +## CONTAINS: Script to create plots describing data/MC metrics ## +## events using a dictionary ## +## ## +################################################################################ + +import matplotlib.pyplot as plt +import numpy as np +import particlePDG_defs as pdg_defs + +def plot_event_hit_summ_metrics(d, is_mc): + + if is_mc: + mc_title = '[Simulation]' + sample_type = "MC" + else: + mc_title = '[Data]' + sample_type = "Data" + + sel_pdg = np.unique([d[key]['event_pdg'] for key in d.keys()]) + hits_dsets = np.unique([d[key]['hits_dset'] for key in d.keys()]) + alpha_options = [[0.8, 0.8], [0.8, 0.8]] + color_options = [['#4daf4a', '#ff7f00'], ['#377eb8', '#e41a1c']] + linestyle_options = [['--', '--'], ['-', '-']] + linewidth_options = [[1.5, 1.5], [1.5, 1.5]] + fill_options = [[False, False], [False, False]] + print("hits_dsets:",hits_dsets) + + # PLOT: total charge in an event + fig0, ax0 = plt.subplots(figsize=(6,4)) + for pdg in sel_pdg: + pdg_idx = sel_pdg.tolist().index(pdg) + for hits_dset in hits_dsets: + dset_idx = hits_dsets.tolist().index(hits_dset) + data0 = np.array([d[key]['total_charge'] for key in d.keys() if d[key]['event_pdg']==pdg and d[key]['hits_dset']==hits_dset]) + counts0, bins0 = np.histogram(data0, bins=np.linspace(0,20000,21)) + ax0.hist(bins0[:-1], bins=bins0, weights = counts0, histtype='stepfilled',\ + label=pdg_defs.selection_pdg_dict[pdg]+", "+hits_dset, \ + linewidth=linewidth_options[pdg_idx][dset_idx], alpha=alpha_options[pdg_idx][dset_idx], \ + color=color_options[pdg_idx][dset_idx], edgecolor=color_options[pdg_idx][dset_idx], \ + linestyle=linestyle_options[pdg_idx][dset_idx], fill = fill_options[pdg_idx][dset_idx]) + ax0.set_xlabel('Total Charge [ke-]') + ax0.set_ylabel('Count / 1000 ke-') + ax0.set_title(r'Total Charge Per Selected Event '+mc_title) + ax0.legend() + plt.savefig(sample_type+"_selected_events_total_charge.png") + plt.close(fig0) + + # PLOT: number of hits in an event + fig1, ax1 = plt.subplots(figsize=(6,4)) + for pdg in sel_pdg: + pdg_idx = sel_pdg.tolist().index(pdg) + for hits_dset in hits_dsets: + dset_idx = hits_dsets.tolist().index(hits_dset) + data1 = np.array([d[key]['num_hits'] for key in d.keys() if d[key]['event_pdg']==pdg and d[key]['hits_dset']==hits_dset]) + counts1, bins1 = np.histogram(data1, bins=np.linspace(0,600,31)) + ax1.hist(bins1[:-1], bins=bins1, weights = counts1, histtype='stepfilled',\ + label=pdg_defs.selection_pdg_dict[pdg]+", "+hits_dset, \ + linewidth=linewidth_options[pdg_idx][dset_idx], alpha=alpha_options[pdg_idx][dset_idx], \ + color=color_options[pdg_idx][dset_idx], edgecolor=color_options[pdg_idx][dset_idx], \ + linestyle=linestyle_options[pdg_idx][dset_idx], fill = fill_options[pdg_idx][dset_idx]) + ax1.set_xlabel('Number of Hits') + ax1.set_ylabel('Event Count / 20 Hits') + ax1.set_title(r'Number of Hits Per Selected Event '+mc_title) + ax1.legend() + plt.savefig(sample_type+"_selected_events_total_hits_per_event.png") + plt.close(fig1) + + + # PLOT: number of separate pixels triggered in an event + fig2, ax2 = plt.subplots(figsize=(6,4)) + for pdg in sel_pdg: + pdg_idx = sel_pdg.tolist().index(pdg) + for hits_dset in hits_dsets: + dset_idx = hits_dsets.tolist().index(hits_dset) + data2 = np.array([d[key]['num_channels'] for key in d.keys() if d[key]['event_pdg']==pdg and d[key]['hits_dset']==hits_dset]) + counts2, bins2 = np.histogram(data2, bins=np.linspace(0,600,31)) + ax2.hist(bins2[:-1], bins=bins2, weights = counts2, histtype='stepfilled',\ + label=pdg_defs.selection_pdg_dict[pdg]+", "+hits_dset,\ + linewidth=linewidth_options[pdg_idx][dset_idx], alpha=alpha_options[pdg_idx][dset_idx], \ + color=color_options[pdg_idx][dset_idx], edgecolor=color_options[pdg_idx][dset_idx], \ + linestyle=linestyle_options[pdg_idx][dset_idx], fill = fill_options[pdg_idx][dset_idx]) + ax2.set_xlabel('Number of Unique Channels Triggered') + ax2.set_ylabel('Event Count / 20 Channels') + ax2.set_title("Number of Unique Channels Triggered \nPer Selected Event "+mc_title) + ax2.legend() + plt.savefig(sample_type+"_selected_events_total_unique_channels_per_event.png") + plt.close(fig2) + + return + +def plot_channel_metrics(d, is_mc): + + if is_mc: + mc_title = '[Simulation]' + sample_type = "MC" + else: + mc_title = '[Data]' + sample_type = "Data" + + sel_pdg = np.unique([d[key]['event_pdg'] for key in d.keys()]) + hits_dsets = np.unique([d[key]['hits_dset'] for key in d.keys()]) + alpha_options = [[0.8, 0.8], [0.8, 0.8]] + color_options = [['#4daf4a', '#ff7f00'], ['#377eb8', '#e41a1c']] + linestyle_options = [['--', '--'], ['-', '-']] + linewidth_options = [[1.5, 1.5], [1.5, 1.5]] + fill_options = [[False, False], [False, False]] + + # PLOT: hits per channel per event + fig0, ax0 = plt.subplots(figsize=(6,4)) + for pdg in sel_pdg: + pdg_idx = sel_pdg.tolist().index(pdg) + for hits_dset in hits_dsets: + dset_idx = hits_dsets.tolist().index(hits_dset) + data0 = np.array([d[key]['hit_mult'] for key in d.keys() if d[key]['event_pdg']==pdg and d[key]['hits_dset']==hits_dset]) + counts0, bins0 = np.histogram(data0, bins=np.linspace(0,10,11)) + ax0.hist(bins0[:-1], bins=bins0, weights = counts0, histtype='stepfilled',\ + label=pdg_defs.selection_pdg_dict[pdg]+", "+hits_dset, \ + linewidth=linewidth_options[pdg_idx][dset_idx], alpha=alpha_options[pdg_idx][dset_idx], \ + color=color_options[pdg_idx][dset_idx], edgecolor=color_options[pdg_idx][dset_idx], \ + linestyle=linestyle_options[pdg_idx][dset_idx], fill = fill_options[pdg_idx][dset_idx]) + ax0.set_xlabel('Hit Multiplicity / Channel / Event') + ax0.set_ylabel('Channel Count / Hit') + ax0.set_title(r'Hit Multiplicity Per Channel in Selected Events '+mc_title) + ax0.legend() + plt.savefig(sample_type+"_selected_events_hits_per_channel_per_event.png") + plt.close(fig0) + + # PLOT: max hit amplitude per channel per event + fig1, ax1 = plt.subplots(figsize=(6,4)) + for pdg in sel_pdg: + pdg_idx = sel_pdg.tolist().index(pdg) + for hits_dset in hits_dsets: + dset_idx = hits_dsets.tolist().index(hits_dset) + data1 = np.array([d[key]['max_hit_amp'] for key in d.keys() if d[key]['event_pdg']==pdg and d[key]['hits_dset']==hits_dset]) + counts1, bins1 = np.histogram(data1, bins=np.linspace(0,200,41)) + ax1.hist(bins1[:-1], bins=bins1, weights = counts1, histtype='stepfilled',\ + label=pdg_defs.selection_pdg_dict[pdg]+", "+hits_dset, \ + linewidth=linewidth_options[pdg_idx][dset_idx], alpha=alpha_options[pdg_idx][dset_idx], \ + color=color_options[pdg_idx][dset_idx], edgecolor=color_options[pdg_idx][dset_idx], \ + linestyle=linestyle_options[pdg_idx][dset_idx], fill = fill_options[pdg_idx][dset_idx]) + ax1.set_xlabel('Max Hit Amplitude / Channel / Event [ke-]') + ax1.set_ylabel('Channel Count / 5 ke-') + ax1.set_title(r'Maximum Hit Amplitiude Per Channel in Selected Events '+mc_title) + ax1.legend() + plt.savefig(sample_type+"_selected_events_max_hit_amp_per_channel_per_event.png") + plt.close(fig1) + + # PLOT: min hit amplitude per channel per event + fig2, ax2 = plt.subplots(figsize=(6,4)) + for pdg in sel_pdg: + pdg_idx = sel_pdg.tolist().index(pdg) + for hits_dset in hits_dsets: + dset_idx = hits_dsets.tolist().index(hits_dset) + data2 = np.array([d[key]['min_hit_amp'] for key in d.keys() if d[key]['event_pdg']==pdg and d[key]['hits_dset']==hits_dset]) + counts2, bins2 = np.histogram(data2, bins=np.linspace(0,200,41)) + ax2.hist(bins2[:-1], bins=bins2, weights = counts2, histtype='stepfilled',\ + label=pdg_defs.selection_pdg_dict[pdg]+", "+hits_dset, \ + linewidth=linewidth_options[pdg_idx][dset_idx], alpha=alpha_options[pdg_idx][dset_idx], \ + color=color_options[pdg_idx][dset_idx], edgecolor=color_options[pdg_idx][dset_idx], \ + linestyle=linestyle_options[pdg_idx][dset_idx], fill = fill_options[pdg_idx][dset_idx]) + ax2.set_xlabel('Min Hit Amplitude / Channel / Event [ke-]') + ax2.set_ylabel('Channel Count / 5 ke-') + ax2.set_title(r'Minimum Hit Amplitiude Per Channel in Selected Events '+mc_title) + ax2.legend() + plt.savefig(sample_type+"_selected_events_min_hit_amp_per_channel_per_event.png") + plt.close(fig2) + + # PLOT: first hit amplitude per channel per event + fig3, ax3 = plt.subplots(figsize=(6,4)) + for pdg in sel_pdg: + pdg_idx = sel_pdg.tolist().index(pdg) + for hits_dset in hits_dsets: + dset_idx = hits_dsets.tolist().index(hits_dset) + data3 = np.array([d[key]['first_hit_amp'] for key in d.keys() if d[key]['event_pdg']==pdg and d[key]['hits_dset']==hits_dset]) + counts3, bins3 = np.histogram(data3, bins=np.linspace(0,200,41)) + ax3.hist(bins3[:-1], bins=bins3, weights = counts3, histtype='stepfilled',\ + label=pdg_defs.selection_pdg_dict[pdg]+", "+hits_dset, \ + linewidth=linewidth_options[pdg_idx][dset_idx], alpha=alpha_options[pdg_idx][dset_idx], \ + color=color_options[pdg_idx][dset_idx], edgecolor=color_options[pdg_idx][dset_idx], \ + linestyle=linestyle_options[pdg_idx][dset_idx], fill = fill_options[pdg_idx][dset_idx]) + ax3.set_xlabel('First Hit Amplitude / Channel / Event [ke-]') + ax3.set_ylabel('Channel Count / 5 ke-') + ax3.set_title(r'First Hit Amplitiude Per Channel in Selected Events '+mc_title) + ax3.legend() + plt.savefig(sample_type+"_selected_events_first_hit_amp_per_channel_per_event.png") + plt.close(fig3) + + # PLOT: last hit amplitude per channel per event + fig4, ax4 = plt.subplots(figsize=(6,4)) + for pdg in sel_pdg: + pdg_idx = sel_pdg.tolist().index(pdg) + for hits_dset in hits_dsets: + dset_idx = hits_dsets.tolist().index(hits_dset) + data4 = np.array([d[key]['last_hit_amp'] for key in d.keys() if d[key]['event_pdg']==pdg and d[key]['hits_dset']==hits_dset]) + counts4, bins4 = np.histogram(data4, bins=np.linspace(0,200,41)) + ax4.hist(bins4[:-1], bins=bins4, weights = counts4, histtype='stepfilled',\ + label=pdg_defs.selection_pdg_dict[pdg]+", "+hits_dset, \ + linewidth=linewidth_options[pdg_idx][dset_idx], alpha=alpha_options[pdg_idx][dset_idx], \ + color=color_options[pdg_idx][dset_idx], edgecolor=color_options[pdg_idx][dset_idx], \ + linestyle=linestyle_options[pdg_idx][dset_idx], fill = fill_options[pdg_idx][dset_idx]) + ax4.set_xlabel('Last Hit Amplitude / Channel / Event [ke-]') + ax4.set_ylabel('Channel Count / 5 ke-') + ax4.set_title(r'Last Hit Amplitiude Per Channel in Selected Events '+mc_title) + ax4.legend() + plt.savefig(sample_type+"_selected_events_last_hit_amp_per_channel_per_event.png") + plt.close(fig4) + + # PLOT: first/last hit delta(t) per channel per event + fig4, ax4 = plt.subplots(figsize=(6,4)) + for pdg in sel_pdg: + pdg_idx = sel_pdg.tolist().index(pdg) + for hits_dset in hits_dsets: + dset_idx = hits_dsets.tolist().index(hits_dset) + data4 = np.array([d[key]['first_last_hit_delta_t'] for key in d.keys() if d[key]['event_pdg']==pdg and d[key]['hits_dset']==hits_dset]) + counts4, bins4 = np.histogram(data4, bins=np.linspace(0,150,76)) + ax4.hist(bins4[:-1], bins=bins4, weights = counts4, histtype='stepfilled',\ + label=pdg_defs.selection_pdg_dict[pdg]+", "+hits_dset, \ + linewidth=linewidth_options[pdg_idx][dset_idx], alpha=alpha_options[pdg_idx][dset_idx], \ + color=color_options[pdg_idx][dset_idx], edgecolor=color_options[pdg_idx][dset_idx], \ + linestyle=linestyle_options[pdg_idx][dset_idx], fill = fill_options[pdg_idx][dset_idx]) + ax4.set_xlabel(r'First/Last Hit $\Delta$t / Channel / Event [$\mu$s]') + ax4.set_ylabel(r'Channel Count / 2 $\mu$s') + ax4.set_yscale('log') + ax4.set_title("Difference in Time between First and Last Hit\nPer Channel in Selected Events "+mc_title) + ax4.legend() + plt.savefig(sample_type+"_selected_events_first_last_hit_deltat_per_channel_per_event.png") + plt.close(fig4) + + return + +def plot_track_metrics(d, is_mc): + + if is_mc: + mc_title = '[Simulation]' + sample_type = "MC" + else: + mc_title = '[Data]' + sample_type = "Data" + + sel_pdg = np.unique([d[key]['event_pdg'] for key in d.keys()]) + hits_dsets = np.unique([d[key]['hits_dset'] for key in d.keys()]) + alpha_options = [[0.8, 0.8], [0.8, 0.8]] + color_options = [['#4daf4a', '#ff7f00'], ['#ff7f00', '#e41a1c']] + linestyle_options = [['--', '--'], ['-', '-']] + linewidth_options = [[1.5, 1.5], [1.5, 1.5]] + fill_options = [[False, False], [False, False]] + print("hits_dsets:",hits_dsets) + + # PLOT: track dq/dx vs resid range + fig0, ax0 = plt.subplots(figsize=(6,4)) + for pdg in sel_pdg: + pdg_idx = sel_pdg.tolist().index(pdg) + first_track = True + for hits_dset in hits_dsets: + dset_idx = hits_dsets.tolist().index(hits_dset) + for key in d.keys(): + if d[key]['event_pdg']==pdg and d[key]['hits_dset']==hits_dset: + if first_track: + ax0.scatter(d[key]['rr'], d[key]['dqdx'], \ + color=color_options[pdg_idx][dset_idx], \ + label=pdg_defs.selection_pdg_dict[pdg]+", "+hits_dset,\ + s=1.) + first_track = False + else: + ax0.scatter(d[key]['rr'], d[key]['dqdx'], \ + color=color_options[pdg_idx][dset_idx],\ + s=1.) + ax0.set_xlabel('Residual Range [cm]') + ax0.set_ylabel('dQ/dx [ke- / cm]') + ax0.set_xlim(0, 40) + ax0.set_title(r'Selected Event dQ/dx vs. Residual Range '+mc_title) + ax0.legend() + plt.savefig(sample_type+"_selected_events_dqdx_vs_resid_range.png") + plt.close(fig0) + + + return \ No newline at end of file From e01d174df91669acc09cdcc1a18dadf419d92753 Mon Sep 17 00:00:00 2001 From: Elise Dianne Hinkle Date: Wed, 17 Jan 2024 05:54:35 -0800 Subject: [PATCH 36/37] Associating all hits with tracks in HIP selection plotting. --- .../hip_selection/data_mc_all_metrics.py | 246 ++++++++++-------- .../run_proto_nd_tracklet_reco.sh | 2 +- .../proto_nd_flow/analysis/hip_selection.yaml | 8 +- .../util/TrackletReconstruction.yaml | 10 +- 4 files changed, 141 insertions(+), 125 deletions(-) diff --git a/scripts/proto_nd_scripts/analysis/hip_selection/data_mc_all_metrics.py b/scripts/proto_nd_scripts/analysis/hip_selection/data_mc_all_metrics.py index 5f35df9a..f9fa7fed 100644 --- a/scripts/proto_nd_scripts/analysis/hip_selection/data_mc_all_metrics.py +++ b/scripts/proto_nd_scripts/analysis/hip_selection/data_mc_all_metrics.py @@ -13,7 +13,7 @@ import json from plot_all_metrics import plot_event_hit_summ_metrics, plot_channel_metrics, plot_track_metrics -def main(file_dir, is_sim, hits_dset, sel_event_dict): +def main(file_dir, is_sim, sel_event_dict): is_sim = bool(is_sim == 'True') # initialize plotting datasets @@ -34,6 +34,13 @@ def main(file_dir, is_sim, hits_dset, sel_event_dict): count+=1 f = h5py.File(file,'r') + if 'calib_final_hits' in file: + hits_dset = 'calib_final_hits' + elif 'calib_prompt_hits' in file: + hits_dset = 'calib_prompt_hits' + else: + print("No hits dataset detected.") + # Prepare datasets for plotting events = f['charge/events/data'] tracks = f['combined/tracklets/data'] @@ -42,12 +49,9 @@ def main(file_dir, is_sim, hits_dset, sel_event_dict): hits_trk_ref = f['combined/tracklets/ref/charge/'+hits_dset+'/ref'] hits_trk_region = f['combined/tracklets/ref/charge/'+hits_dset+'/ref_region'] #hits_drift = f['combined/hit_drift/data'] - hits_dsets = ['calib_final_hits', 'calib_prompt_hits'] - hits = [f['charge/calib_final_hits/data'], f['charge/calib_prompt_hits/data']] - hits_ref = [f['charge/events/ref/charge/calib_final_hits/ref'], \ - f['charge/events/ref/charge/calib_prompt_hits/ref']] - hits_region = [f['charge/events/ref/charge/calib_final_hits/ref_region'], \ - f['charge/events/ref/charge/calib_prompt_hits/ref_region']] + hits = f['charge/'+hits_dset+'/data'] + hits_ref = f['charge/events/ref/charge/'+hits_dset+'/ref'] + hits_region = f['charge/events/ref/charge/'+hits_dset+'/ref_region'] #if not is_sim: # charge_hits = hits#f['combined/q_calib_el/data'] # charge_hits_ref = hits_ref#f['charge/events/ref/combined/q_calib_el/ref'] @@ -96,20 +100,35 @@ def main(file_dir, is_sim, hits_dset, sel_event_dict): sel_event_ids = sel_event_id_data[pdg] for event_id in sel_event_ids: + # Prepare datasets for plotting + events = f['charge/events/data'] + tracks = f['combined/tracklets/data'] + tracks_ref = f['charge/events/ref/combined/tracklets/ref'] + tracks_region = f['charge/events/ref/combined/tracklets/ref_region'] + hits_trk_ref = f['combined/tracklets/ref/charge/'+hits_dset+'/ref'] + hits_trk_region = f['combined/tracklets/ref/charge/'+hits_dset+'/ref_region'] + #hits_drift = f['combined/hit_drift/data'] + hits = f['charge/'+hits_dset+'/data'] + hits_ref = f['charge/events/ref/charge/'+hits_dset+'/ref'] + hits_region = f['charge/events/ref/charge/'+hits_dset+'/ref_region'] + # Get track information related to given event_id track_ref = tracks_ref[tracks_region[int(event_id),'start']:tracks_region[int(event_id),'stop']] track_ref = np.sort(track_ref[track_ref[:,0] == event_id, 1]) - track_charge_data = tracks[track_ref]['q'][0] - track_length_data = tracks[track_ref]['length'][0] - track_num_hits_data = tracks[track_ref]['nhit'][0] - track_theta_data = tracks[track_ref]['theta'][0] - track_phi_data = tracks[track_ref]['phi'][0] - track_ts_start_data = tracks[track_ref]['ts_start'][0] - track_ts_end_data = tracks[track_ref]['ts_end'][0] - track_dx_data = tracks[track_ref]['dx'][0] - track_dq_data = tracks[track_ref]['dq'][0] - track_start_pt_data = tracks[track_ref]['start'][0] - track_end_pt_data = tracks[track_ref]['end'][0] + tracks = tracks[track_ref] + track_start = tracks['start'] + track_end = tracks['end'] + track_charge_data = tracks['q'][0] + track_length_data = tracks['length'][0] + track_num_hits_data = tracks['nhit'][0] + track_theta_data = tracks['theta'][0] + track_phi_data = tracks['phi'][0] + track_ts_start_data = tracks['ts_start'][0] + track_ts_end_data = tracks['ts_end'][0] + track_dx_data = tracks['dx'][0] + track_dq_data = tracks['dq'][0] + track_start_pt_data = tracks['start'][0] + track_end_pt_data = tracks['end'][0] zero_dq_mask = track_dq_data != 0. @@ -127,116 +146,113 @@ def main(file_dir, is_sim, hits_dset, sel_event_dict): #print("Track dq:", track_dq_data) #print("Track start pt:", track_start_pt_data) - for x in range(len(hits_dsets)): - charge_hits_dset = hits_dsets[x] - charge_hits = hits[x] - charge_hits_ref = hits_ref[x] - charge_hits_region = hits_region[x] - - # Get hit information related to given event_id - charge_hit_ref = charge_hits_ref[charge_hits_region[int(event_id),'start']:charge_hits_region[int(event_id),'stop']] - charge_hit_ref = np.sort(charge_hit_ref[charge_hit_ref[:,0] == event_id, 1]) - - - # Event-level hit metrics - charge_hits_data = charge_hits[charge_hit_ref]['Q'] - ts_hits_data = charge_hits[charge_hit_ref]['ts_pps'] - num_charge_hits = len(charge_hits_data) - - # Channel-level hit metrics - iogroup_hits = charge_hits[charge_hit_ref]['io_group'] - iochannel_hits = charge_hits[charge_hit_ref]['io_channel'] - chipid_hits = charge_hits[charge_hit_ref]['chip_id'] - channelid_hits = charge_hits[charge_hit_ref]['channel_id'] - - channel_id = np.array([int(str(iogroup_hits[i])+str(iochannel_hits[i])+str(chipid_hits[i])+str(channelid_hits[i])) for i in range(num_charge_hits)]) - unique_channels, unique_channel_hit_counts = np.unique(channel_id, return_counts=True) - num_channels = len(unique_channels) - - #print("String of channels:", channel_id) - #print("Number of unique channels:", num_channels) - #print("Hits per channel:", unique_channel_hit_counts) - #print("Length of hits per channel:", len(unique_channel_hit_counts)) - for i in range(num_channels): - - channel = unique_channels[i] - hits_per_channel = unique_channel_hit_counts[i] - channel_mask = np.argwhere(channel_id == channel).flatten() - channel_hit_amps = charge_hits_data[channel_mask] - channel_hit_ts = ts_hits_data[channel_mask] / 10. # convert to us - - max_hit_amp = max(channel_hit_amps) - min_hit_amp = min(channel_hit_amps) - - first_hit_idx = np.argmin(channel_hit_ts) - last_hit_idx = np.argmax(channel_hit_ts) - first_hit_amp = channel_hit_amps[first_hit_idx] - last_hit_amp = channel_hit_amps[last_hit_idx] - first_last_hit_delta_t = abs(channel_hit_ts[last_hit_idx] - channel_hit_ts[first_hit_idx]) - - #print("Channel hit amplitudes:", channel_hit_amps) - #print("Channel hit timestamps:", channel_hit_ts) - #print("Maximum hit amplitude:", max_hit_amp) - #print("Minimum hit amplitude:", min_hit_amp) - #print("First hit amplitude:", first_hit_amp) - #print("Last hit amplitude:", last_hit_amp) - #print("First/Last hit delta t:", first_last_hit_delta_t) - - channel_metric_dict[(file, pdg, charge_hits_dset, event_id, channel)]=dict( - hit_mult = int(hits_per_channel), - max_hit_amp = float(max_hit_amp), - min_hit_amp = float(min_hit_amp), - first_hit_amp = float(first_hit_amp), - last_hit_amp = float(last_hit_amp), - first_last_hit_delta_t = float(first_last_hit_delta_t), - event_pdg = int(pdg), - hits_dset = str(charge_hits_dset) - ) - - event_hit_summ_dict[(file, pdg, charge_hits_dset, event_id)]=dict( + charge_hits_dset = hits_dset + charge_hits = hits + charge_hits_ref = hits_ref + charge_hits_region = hits_region + + for itrk, (ts, te) in enumerate(zip(track_start, track_end)): + hit_ref = hits_trk_ref[hits_trk_region[tracks[itrk]['id'],'start']:hits_trk_region[tracks[itrk]['id'],'stop']] + hit_ref = np.sort(hit_ref[hit_ref[:,0] == tracks[itrk]['id'], 1]) + hits_trk = charge_hits[hit_ref] + # Get hit information related to given event_id + #charge_hit_ref = charge_hits_ref[charge_hits_region[int(event_id),'start']:charge_hits_region[int(event_id),'stop']] + #charge_hit_ref = np.sort(charge_hit_ref[charge_hit_ref[:,0] == event_id, 1]) + + # Event-level hit metrics + charge_hits_data = hits_trk['Q'] + ts_hits_data = hits_trk['ts_pps'] + num_charge_hits = len(charge_hits_data) + + # Channel-level hit metrics + iogroup_hits = hits_trk['io_group'] + iochannel_hits = hits_trk['io_channel'] + chipid_hits = hits_trk['chip_id'] + channelid_hits = hits_trk['channel_id'] + channel_id = np.array([int(str(iogroup_hits[i])+str(iochannel_hits[i])+str(chipid_hits[i])+str(channelid_hits[i])) for i in range(num_charge_hits)]) + unique_channels, unique_channel_hit_counts = np.unique(channel_id, return_counts=True) + num_channels = len(unique_channels) + #print("String of channels:", channel_id) + #print("Number of unique channels:", num_channels) + #print("Hits per channel:", unique_channel_hit_counts) + #print("Length of hits per channel:", len(unique_channel_hit_counts)) + + for i in range(num_channels): + channel = unique_channels[i] + hits_per_channel = unique_channel_hit_counts[i] + channel_mask = np.argwhere(channel_id == channel).flatten() + channel_hit_amps = charge_hits_data[channel_mask] + channel_hit_ts = ts_hits_data[channel_mask] / 10. # convert to us + max_hit_amp = max(channel_hit_amps) + min_hit_amp = min(channel_hit_amps) + first_hit_idx = np.argmin(channel_hit_ts) + last_hit_idx = np.argmax(channel_hit_ts) + first_hit_amp = channel_hit_amps[first_hit_idx] + last_hit_amp = channel_hit_amps[last_hit_idx] + first_last_hit_delta_t = abs(channel_hit_ts[last_hit_idx] - channel_hit_ts[first_hit_idx]) + #print("Channel hit amplitudes:", channel_hit_amps) + #print("Channel hit timestamps:", channel_hit_ts) + #print("Maximum hit amplitude:", max_hit_amp) + #print("Minimum hit amplitude:", min_hit_amp) + #print("First hit amplitude:", first_hit_amp) + #print("Last hit amplitude:", last_hit_amp) + #print("First/Last hit delta t:", first_last_hit_delta_t) + + channel_metric_dict[(file, pdg, charge_hits_dset, event_id, channel)]=dict( + hit_mult = int(hits_per_channel), + max_hit_amp = float(max_hit_amp), + min_hit_amp = float(min_hit_amp), + first_hit_amp = float(first_hit_amp), + last_hit_amp = float(last_hit_amp), + first_last_hit_delta_t = float(first_last_hit_delta_t), event_pdg = int(pdg), - total_charge=float(sum(charge_hits_data)), - num_hits=int(num_charge_hits), - num_channels=int(num_channels), hits_dset = str(charge_hits_dset) ) - track_summ_dict[(file, pdg, charge_hits_dset, event_id)]=dict( - event_pdg = int(pdg), - total_charge = float(track_charge_data), - length = float(track_length_data), - hits_in_track = int(track_num_hits_data), - theta = float(track_theta_data), - phi = float(track_phi_data), - ts_start = float(track_ts_start_data), - ts_end = float(track_ts_end_data), - dx = [float(i) for i in list(track_dx_dist)], - dq = [float(i) for i in list(track_dq_data)], - start_pt = [float(i) for i in list(track_start_pt_data)], - end_pt = [float(i) for i in list(track_end_pt_data)], - dqdx = [float(i) for i in list(track_dqdx)], - rr = [float(i) for i in list(track_rr)], - hits_dset = str(hits_dset) - ) + event_hit_summ_dict[(file, pdg, charge_hits_dset, event_id)]=dict( + event_pdg = int(pdg), + total_charge=float(sum(charge_hits_data)), + num_hits=int(num_charge_hits), + num_channels=int(num_channels), + hits_dset = str(charge_hits_dset) + ) - ## Save all Python dictionaries to JSON files - file_parsing.save_dict_to_json(event_hit_summ_dict, sample_type+"_event_hit_summ_dict", True) - file_parsing.save_dict_to_json(channel_metric_dict, sample_type+"_channel_metric_dict", True) - file_parsing.save_dict_to_json(track_summ_dict, sample_type+"_track_summ_dict", True) + track_summ_dict[(file, pdg, charge_hits_dset, event_id)]=dict( + event_pdg = int(pdg), + total_charge = float(track_charge_data), + length = float(track_length_data), + hits_in_track = int(track_num_hits_data), + theta = float(track_theta_data), + phi = float(track_phi_data), + ts_start = float(track_ts_start_data), + ts_end = float(track_ts_end_data), + dx = [float(i) for i in list(track_dx_dist)], + dq = [float(i) for i in list(track_dq_data)], + start_pt = [float(i) for i in list(track_start_pt_data)], + end_pt = [float(i) for i in list(track_end_pt_data)], + dqdx = [float(i) for i in list(track_dqdx)], + rr = [float(i) for i in list(track_rr)], + hits_dset = str(hits_dset) + ) - # PLOT: Signal Event Info - plot_event_hit_summ_metrics(event_hit_summ_dict, is_sim) - plot_channel_metrics(channel_metric_dict, is_sim) - plot_track_metrics(track_summ_dict, is_sim) + ## Save all Python dictionaries to JSON files + file_parsing.save_dict_to_json(event_hit_summ_dict, sample_type+"_event_hit_summ_dict", True) + file_parsing.save_dict_to_json(channel_metric_dict, sample_type+"_channel_metric_dict", True) + file_parsing.save_dict_to_json(track_summ_dict, sample_type+"_track_summ_dict", True) + # PLOT: Signal Event Info + plot_event_hit_summ_metrics(event_hit_summ_dict, is_sim) + plot_channel_metrics(channel_metric_dict, is_sim) + plot_track_metrics(track_summ_dict, is_sim) + if __name__=='__main__': parser = argparse.ArgumentParser() parser.add_argument('-d', '--file_dir', default=None, required=True, type=str, \ help='''string corresponding to the path of the directory containing processed files for plotting''') parser.add_argument('-mc', '--is_sim', default=False, required=True, type=str, \ help='''str corresponding to bool whether files are simulation (MC) or data''') - parser.add_argument('-hd', '--hits_dset', default='calib_final_hits', required=True, type=str,\ - help='''str corresponding to hits dataset name associated with tracklets''') + #parser.add_argument('-hd', '--hits_dset', default='calib_final_hits', required=True, type=str,\ + # help='''str corresponding to hits dataset name associated with tracklets''') parser.add_argument('-sed', '--sel_event_dict', default=None, required=True, type=str,\ help='''str corresponding name of json file containing selected event ids''') args = parser.parse_args() diff --git a/scripts/proto_nd_scripts/run_proto_nd_tracklet_reco.sh b/scripts/proto_nd_scripts/run_proto_nd_tracklet_reco.sh index c3807f67..3ca39097 100644 --- a/scripts/proto_nd_scripts/run_proto_nd_tracklet_reco.sh +++ b/scripts/proto_nd_scripts/run_proto_nd_tracklet_reco.sh @@ -10,7 +10,7 @@ OUTPUT_DIR=`pwd` OUTPUT_NAME=(${INPUT_FILE//"/"/ }) OUTPUT_NAME=${OUTPUT_NAME[-1]} OUTPUT_FILE="${OUTPUT_DIR}/${OUTPUT_NAME}" -OUTPUT_FILE=${OUTPUT_FILE//.h5/.proto_nd_flow.calib_final_hits.TRACKLETS_HDBSCAN_1_15_9_3421.h5} +OUTPUT_FILE=${OUTPUT_FILE//.h5/.proto_nd_flow.calib_prompt_hits.TRACKLETS_HDBSCAN_1_15_9_3421.h5} echo ${OUTPUT_FILE} # for running on a login node diff --git a/yamls/proto_nd_flow/analysis/hip_selection.yaml b/yamls/proto_nd_flow/analysis/hip_selection.yaml index 868d0fcf..b2ddb353 100644 --- a/yamls/proto_nd_flow/analysis/hip_selection.yaml +++ b/yamls/proto_nd_flow/analysis/hip_selection.yaml @@ -3,7 +3,7 @@ path: proto_nd_flow.analysis.hip_selection requires: - 'combined/tracklets' - 'combined/t0' - - 'charge/calib_final_hits' + - 'charge/calib_prompt_hits' - name: 'mc_truth/trajectories' path: ['charge/raw_events', 'mc_truth/events', 'mc_truth/trajectories'] #- name: 'combined/track_hits' @@ -13,13 +13,13 @@ requires: params: # inputs - hits_dset_name: 'charge/calib_final_hits' + hits_dset_name: 'charge/calib_prompt_hits' ext_trigs_dset_name: 'charge/ext_trigs' t0_dset_name: 'combined/t0' tracklet_dset_name: 'combined/tracklets' - hit_drift_dset_name: 'charge/calib_final_hits' + hit_drift_dset_name: 'charge/calib_prompt_hits' truth_trajectories_dset_name: 'mc_truth/trajectories' - charge_dset_name: 'charge/calib_final_hits' + charge_dset_name: 'charge/calib_prompt_hits' # configuration parameters fid_cut: 5.0 # cm diff --git a/yamls/proto_nd_flow/util/TrackletReconstruction.yaml b/yamls/proto_nd_flow/util/TrackletReconstruction.yaml index 8bdee173..b8a1e5e7 100644 --- a/yamls/proto_nd_flow/util/TrackletReconstruction.yaml +++ b/yamls/proto_nd_flow/util/TrackletReconstruction.yaml @@ -1,13 +1,13 @@ classname: TrackletReconstruction path: proto_nd_flow.util.tracklet_reco requires: - - 'charge/calib_final_hits' - #- 'charge/calib_prompt_hits' + #- 'charge/calib_final_hits' + - 'charge/calib_prompt_hits' params: # inputs - hits_dset_name: 'charge/calib_final_hits' #'charge/calib_prompt_hits' - charge_dset_name: 'charge/calib_final_hits' #'charge/calib_prompt_hits' - hit_drift_dset_name: 'charge/calib_final_hits' #'charge/calib_prompt_hits' + hits_dset_name: 'charge/calib_prompt_hits' #'charge/calib_final_hits' + charge_dset_name: 'charge/calib_prompt_hits' #'charge/calib_final_hits' + hit_drift_dset_name: 'charge/calib_prompt_hits' #'charge/calib_final_hits' # output tracklet_dset_name: 'combined/tracklets' From 900dd3e117b1eb9ff051077880b7693613c4c35c Mon Sep 17 00:00:00 2001 From: Elise Dianne Hinkle Date: Fri, 19 Jan 2024 09:02:35 -0800 Subject: [PATCH 37/37] Update event display, tracklet reconstruction phi angle, and RunData module1_flow input. --- .../proto_nd_flow/protondflow_evd.py | 4 +- .../protondflow_evd_example.ipynb | 25 ++++-- .../hip_selection/data_mc_all_metrics.py | 9 +- .../hip_selection/plot_all_metrics.py | 90 ++++++++++++++++--- src/proto_nd_flow/util/tracklet_reco.py | 2 +- yamls/module1_flow/resources/RunData.yaml | 2 +- .../proto_nd_flow/analysis/hip_selection.yaml | 8 +- 7 files changed, 109 insertions(+), 31 deletions(-) diff --git a/event_display/proto_nd_flow/protondflow_evd.py b/event_display/proto_nd_flow/protondflow_evd.py index 8c47f498..e43cb74b 100644 --- a/event_display/proto_nd_flow/protondflow_evd.py +++ b/event_display/proto_nd_flow/protondflow_evd.py @@ -443,8 +443,8 @@ def display_event(self, ev_id): vmin=min(self.hits[hit_ref][self.charge]), vmax=max(self.hits[hit_ref][self.charge])) mcharge = plt.cm.ScalarMappable(norm=norm, cmap=cmap) - hits_anode1 = hits[hits[self.x_vals]*self.convert_to_mm <= 0] - hits_anode2 = hits[hits[self.x_vals]*self.convert_to_mm > 0] + hits_anode1 = hits[hits['io_group']== 1] + hits_anode2 = hits[hits['io_group']== 2] if self.hits_dset == 'raw_hits': q_anode1 = self.charge_from_ADC(hits_anode1[self.charge], self.vref_mv, self.vcm_mv, self.ped_mv) diff --git a/event_display/proto_nd_flow/protondflow_evd_example.ipynb b/event_display/proto_nd_flow/protondflow_evd_example.ipynb index 0f3479e7..88067ec2 100644 --- a/event_display/proto_nd_flow/protondflow_evd_example.ipynb +++ b/event_display/proto_nd_flow/protondflow_evd_example.ipynb @@ -18,7 +18,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 23, "id": "ab903276-e787-4142-bbb1-4becf42f76c1", "metadata": { "tags": [] @@ -27,10 +27,10 @@ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 1, + "execution_count": 23, "metadata": {}, "output_type": "execute_result" } @@ -60,7 +60,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 32, "id": "d3cc7962-6f70-446b-a4d1-f5f1da4ad23a", "metadata": { "tags": [] @@ -69,8 +69,9 @@ "source": [ "# This set of inputs allows you to look at a Module1 charge-only file\n", "# This file originates from the same raw data file as the input file in the Module0FlowEventDisplay example\n", - "directory = '/global/cfs/cdirs/dune/users/ehinkle/nd_prototypes_ana/flow_tests/Module1_Data/TRACKLET_OUTPUT/'\n", - "file = 'packet_2022_02_11_11_39_26_CET.FLOW.proto_nd_flow.calib_final_hits.TRACKLETS_HDBSCAN_1_15_9_3421.h5'\n", + "directory = '/global/cfs/cdirs/dune/users/ehinkle/nd_prototypes_ana/flow_tests/Module1_Data/TRACKLET_OUTPUT/NOMINAL_E_FIELD/'\n", + "#directory = '/global/cfs/cdirs/dune/users/ehinkle/nd_prototypes_ana/flow_tests/Module1_Data/OUTPUT/'\n", + "file = 'packet_2022_02_11_07_40_23_CET.EDH_FLOW.proto_nd_flow.calib_final_hits.TRACKLETS_HDBSCAN_1_15_9_3421.h5'\n", "geometry = '/global/cfs/cdirs/dune/www/data/Module1/TPC12/module1_layout-2.3.16.yaml'" ] }, @@ -87,6 +88,14 @@ " - `n`+`Enter` : proceed to the `n`th available event (may not correspond with event ID number if `nhits` > 1)" ] }, + { + "cell_type": "code", + "execution_count": null, + "id": "af6a3dc8-97df-4000-81ef-d5fc38a55c77", + "metadata": {}, + "outputs": [], + "source": [] + }, { "cell_type": "code", "execution_count": null, @@ -104,7 +113,7 @@ }, { "data": { - "image/png": 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", + "image/png": 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OBx980GNbzT10botAjaKiIhRFISwsrNa27777jptuuomxY8fywQcf1PtQXTNu5P3332fp0qXccMMNHtvvvfdej9c1ZsyYNotr/fr1td5XLS1j31KNva5zW21q9j0/qRk/fjyKorBjx44Gr9Wc+zgyMrLOmCorK7HZbHW2JtXn1KlTjB49Gp1Ox5o1axo8tqCggBUrVjBhwoRa3fjqU5PM1vcedDgc6PX6JscLuL9gOnHiRLOOE0L4LkmMhPBh48ePJyEhgSVLlgBnS3wPGzbMo6R3VFQUkZGRbN++vc6lLQshwNmHlDNnzvDiiy8SHh7uXj766CMqKysJDw/n9ttvb/Q8qqo2qQWoZmD24cOHLzj2prBYLKxevZquXbs22I1u3759lJaW0r17d/fPoKZE75///GfCw8PZu3dvg9eqaZlq7Oeg0+m45JJLGv0Z/Pa3v/W4F7744osG96/xzDPPMHfuXObOnVtnyeWuXbtiNpvrfD179+6lW7dumEwmj/XfffcdkyZNYuTIkSxbtgyDwVDntWuSoiVLlvDmm29yxx131Npn7ty5Hq/r9ddfb7O4Bg8eXOs91VCC3BZqup3W97pqtkPd45DO1di91Zz7uF+/fuTn55Obm1srpnPjbsypU6cYNWoUqqqybt26Rscovvfee9hstmYVXYiNjQX+19XwXKqqkpOT496nqWqSz88++6xZxwkhfJgXKuEJ0aE1t1z3H/7wB9VoNKobNmxQAfX111/32P7++++rgLp169ZGz1VXqWpVVdWRI0d6lKpdsWKFCqhff/11o+e0WCzqunXrai3jx49XTSaTum7dOnXv3r0NnuOTTz5RAXXhwoWNXu+tt95SAfU///lPo/vWpaXluhcvXtzgeQ8ePFjrZ/DRRx+pgPrAAw+o69atU8vLy+s93mazqQMHDlSjoqJUh8PR4LUsFovao0cPNS0trcH9WmLevHkqoP7pT39qcL9bbrlFjYmJUcvKytzrTp06pRoMBvUPf/iDx77fffedajKZ1Kuvvlq1WCz1ntPlcqlTp05VFUVp9OfdnnE1RWPlus/VknLdl156qZqWluZxb2zZskUF1Ndee8297uDBg6qiKOr999/vcfzy5ctVQH3vvfcavE5z7uOact1//etfPc4xbdq0JpXrVtWzv5uUlBQ1KSmpySX4+/btqyYkJDT6PjnX0aNHVUVRPEq21/j6669VQF2yZEmdx15Iue7t27dLuW4h/IiMMRLCS/bt21dnn/uuXbu6S1LD2W5Dzz//PJMnT8ZsNnPrrbd67H/bbbfxwQcf8Mtf/pJHHnmESy+9FL1ez+nTp1m3bh033HADN954Y7Niq/mmd/Hixe7S0KmpqXV2wzOZTHWOc1m6dClardZj26lTp5g8eTK33XYb3bp1Q1EU1q9fz8KFC+nbt6/HN8Cvv/46GzduZNy4cSQlJVFZWcnGjRtZtGgRw4cP9+hedfLkSVJTU7n77rubVFa8LmfOnGHr1q2oqkp5ebl7gtfdu3fz6KOPcv/99zd4fK9evWpN2FvT5apr164eP4fHHnsMu93OiBEjiIuLIzMzk0WLFrFr1y6WLFniMV5m+PDhXH/99fTu3ZvQ0FBOnjzJa6+9xrFjxzzKNLeGF198kaeeeoprrrmGCRMm1OraeG4hjmeeeYahQ4cyceJE/vjHP7onUo2KimLWrFnu/TZt2sSkSZOIi4vjiSeeYNeuXR7n7NOnj7u09sMPP8xbb73FvffeS79+/TyubzQaGTRoUKOvoS3iasg333xDZWUl5eXlwNnJkf/zn/8A8Mtf/tJd6CA/P5/169cD/2tR+eabb4iOjiY6OrrRymrPP/88Y8eO5eabb2b69Onk5eXxxz/+kbS0NI9y2b169WLGjBm8+uqrBAcHc+2113L48GH+9Kc/MWjQIG655Rb3vuvXr2fMmDE89dRT7gltm3Mf9+3bl6lTp/L000+j1WoZOnQoK1euZPHixTz77LMe3eHqulZeXh6jR48mJyeHt956i7y8PPLy8tzHJCYm1mo9+vHHH9m/fz9PPPFEs8bLde3alQcffJC//e1vlJSU8Mtf/hKz2cz27dv561//ypAhQ2pNttwU7777Ltdccw3XXnst9957L9deey3h4eHk5OTwxRdf8NFHH5Geni4lu4XwF97OzIToaGq+faxveeONN2odM3z4cBVQb7/99jrPabfb1b///e/qgAEDVJPJpAYFBam9evVSp02bph45csS9X1NbjFRVVRcuXKimpqaqWq22wW9T61PXRK5FRUXqjTfeqKakpKhms1k1GAxq9+7d1Tlz5qglJSUe+/7www/qxIkT1YSEBNVgMKgBAQHqgAED1L/85S9qZWWlx7579+5VAfWPf/xjo3HV12JUs2g0GjUkJETt16+f+tvf/lbdsmVLs173ueqbyPWtt95SL730UjUiIkLV6XRqeHi4On78ePW7776rdY5Zs2apAwYMUENDQ1WdTqfGxcWpN954o/rDDz+0OK76jBw5ssF783w//fSTOmbMGDUgIEANCQlRJ02apB49etRjn5qJNetbzp1EODk5ud79kpOTm/w6WjuuhjQU87ktQuvWrat3v/omFj3fypUr1V/84heqyWRSIyIi1LvuuqvOSXEdDof617/+Ve3WrZuq1+vV+Ph49Xe/+51aXFzssV9NTI21cjU0IbHNZlOffvpptXPnzqrBYFB79Oih/t///V+t/eq6VkM/k/riuv/++1VFUVo0wbPL5VJfe+01dciQIWpAQID78+cPf/hDg625jbXyWywW9f/+7//Uyy67TA0JCVF1Op2akJCg3nTTTepXX33V7DiFEN6jqGoTyi0JIYQPe/XVV5kzZw7Hjh1r9jgBIYQQQgiQ4gtCiIvAunXrePjhhyUpEkIIIUSLSYuREEIIIYQQosOTFiMhhBBCCCFEhyeJkRBCCCGEEKLDk8RICCGEEEII0eFJYiSEEEIIIYTo8CQxEkIIIYQQQnR4khgJIYQQQgghOjxJjIQQQgghhBAdniRGQgghhBBCiA5PEiMhhBBCCCFEhyeJkRBCCCGEEKLDk8RICCGEEEII0eFJYiSEEEIIIYTo8CQxEkIIIYQQQnR4khgJIYQQQgghOjydtwMQnrKysvjDH/7AN998g8VioUePHrz11lsMHjwYAFVVeeaZZ1i8eDHFxcUMGzaMf/zjH/Tt29d9DqvVyuzZs/noo4+wWCyMGTOGV199lcTExCbH4XK5yM7OJjg4GEVRWv11igujqirl5eUkJCSg0fjm9xtyD/m25txD1dXV2Gy2Rs9pMBgwmUytFaIQQgjRriQx8iHFxcWMGDGC0aNH88033xATE8OxY8cICwtz7/PCCy/w0ksvsXTpUnr06MGzzz7L2LFjOXToEMHBwQDMnDmTL774go8//pjIyEhmzZrFxIkTSU9PR6vVNimW7OxskpKS2uJlilaUmZnZrIS3Pck95B8au4eqq6tJSQ3iTK6z0XPFxcVx4sQJSY6EEEL4JUVVVdXbQYiz/vjHP/LDDz+wcePGOrerqkpCQgIzZ87kD3/4A3C2dSg2Npbnn3+eadOmUVpaSnR0NO+99x633nor8L8H1K+//prx48c3KZbS0lLCwsLIzMwkJCSkdV6gl9hsNl588UUAZs2ahcFg8HJEF66srIykpCRKSkoIDQ31djh1upjuoYtRU++hsrIyQkNDOXA4heDg+luWystd9OlxktLSUvl9CyGE8EvSYuRDVqxYwfjx47n55ptZv349nTp1Yvr06dx///0AnDhxgtzcXMaNG+c+xmg0MnLkSDZv3sy0adNIT0/Hbrd77JOQkEBaWhqbN2+uNzGyWq1YrVb3v8vLywEICQnx+4ccm83m/gY7JCTkokiMavhSF7WL+R66mDX1HgoO0hIS3ECLs9p4i5IQQgjhy3xzcEIHdfz4cV577TW6d+/Od999xwMPPMDDDz/Mu+++C0Bubi4AsbGxHsfFxsa6t+Xm5mIwGAgPD693n7osWLCA0NBQ9yJdoERzyT10cVNcSqOLEEII4c8kMfIhLpeLSy65hPnz5zNo0CCmTZvG/fffz2uvveax3/nf8Kqq2ui3vo3t8/jjj1NaWupeMjMzW/5CRIck99DFTXE1vgghhBD+TBIjHxIfH0+fPn081vXu3ZuMjAzg7MBmoFbLT15enrsVKS4uDpvNRnFxcb371MVoNLq7PEnXJ9EScg9d3BRn44sQQgjhzyQx8iEjRozg0KFDHusOHz5McnIyAKmpqcTFxbFq1Sr3dpvNxvr16xk+fDgAgwcPRq/Xe+yTk5PDvn373PsIIURzKWojLUZSxkcIIYSfk+ILPuTRRx9l+PDhzJ8/n1tuuYVt27axePFiFi9eDJztQjdz5kzmz59P9+7d6d69O/PnzycgIIDJkycDEBoaytSpU5k1axaRkZFEREQwe/Zs+vXrx9VXX+3NlyeE8Gcu9ezS0HYhhBDCj0li5EOGDh3Kp59+yuOPP868efNITU1l4cKF3H777e595syZg8ViYfr06e4JXleuXOmewwjg5ZdfRqfTccstt7gneF26dGmT5zASQojzKWrDrULSYiSEEMLfyTxGok41c5dcDHOS2Gw2FixYAJwtEHAxlOv2h9+PP8TYkTX191OzX+6hrg2W6y4rdxLX85j8voUQQvgtaTESQgjRKMWhojjq/x6toW1CCCGEP5DESAghRKOkK50QQoiLnSRGQgghGuf679LQdiGEEMKPSWIkhBCiUY1N4ioTvAohhPB3khgJIYRolOICxdnAGCNJjIQQQvg5SYyEEEI0TrrSCSGEuMhJYiRazm4BlxOMQd6ORAjRXE57s3aX4gtCCCEudpIYiRYp3LiS7/62Gbsuimt+P5jYyy7zdkhCiCaqzjzMN3Peat5B0mIkhBDiIqfxdgDC/1hOH+fYqy8zIPxb9m9R+PnzdG+HJIRoKns1m558idyDpc07ztWERQghhPBj0mIkmq1493aMqoMgUyVB+goSQoq9HZIQoonUklPY80rpl5oDu5t+nOIExak0uF0IIYTwZ9JiJJotqncXysoDCQus4tpLfiS2k7cjEkI0lWqrxm530SU2s3kHSouREEKIi5y0GIlm08d2JSSwgmqbDrtDh90V7u2QhBBNdOrjL0iJKsNFM6slqP9dGtouhBBC+DFpMRLNZs/Yx+ncOHSKgq3KBLYcb4ckhGiiop+Oo1UqsVqCm3Wc4lIaXYQQQgh/JomRaDZD5zQcWidWl0qpy4Ytt+HEKC+zgsoyWztFJ4SoT+n+PeDKxxRcTFZBZPMOVpuwCCGEEH5MEiPRfIERJEeXotc5SY0v5sSJiHp3/f7fx/nrPRuYf/d6CnOq2jFIIcT5Vv59PZ/u6osdhezy6OYd7FTA0cDSQGEGIYQQwh9IYiRapKwqFJdDT1Z2NFpD/V8VH95ZCICl3E7WsbL2Ck8IUYczx4roFJGLxWrieE7n5h0sLUZCCCEuclJ8QbSI2WxAo3EQGVpEqKH+2+jq33SlONdCVGIgvYY28xtqIUSrcZYW0C/0ODrzSXKKohk+MB+ONuMELuXs0tB2IYQQwo9JYiRaRGuyolG06BUzZ3IVetazX5d+Efzh7SvbNTYhRG1nVq7EYbXRLzWX48cH4ugU0rwTqMrZpaHtQgghhB+TrnSiRewODdUOhTwrqFqZ2VEIX2fZuxmtxkqVQ8OR4lBCR1/VrOMVh9LoIoQQQvgzaTESzaaW56NUOTBFqXSJLMBpNHs7JCFEA9TSHE7vzadbpyw0dhN9OhXSqZmJkcxjJIQQ4mInLUai2YoP7Gf36QQs1QFUVwfilErcQvi2qgLKyhSM5jKyc7tQao9q/jlqxhg1tAghhBB+TBIj0WzB3XuhDdRSatWjM5ehKtLwKIRPU110jS7CgZP92fGEXprYgnMojS9CCCGEH5PESDSbPjyWtK55hBqr0akKRaUmb4ckhGhAVfoGdFoL2E0khZfQ56bRzT+JqwmLEEII4cckMfJhCxYsQFEUZs6c6V6nqipz584lISEBs9nMqFGj2L9/v8dxVquVhx56iKioKAIDA7n++us5ffp06wVmq2DPqQDOlIZTWBmA3ljSeucWQrQuh428r9ZhCCpm3/EeHC+KRjUHNvs0qkuD6mxgccmfEyGEEP5N/pL5qO3bt7N48WL69+/vsf6FF17gpZde4pVXXmH79u3ExcUxduxYysvL3fvMnDmTTz/9lI8//phNmzZRUVHBxIkTcTpbr3pcgK6SShUCQkqIDJJBRkL4qsMfLMNeVoXJZOFwSQRhKRUY4ns1/0TSlU4IIcRFThIjH1RRUcHtt9/OG2+8QXh4uHu9qqosXLiQJ598kptuuom0tDTeeecdqqqq+PDDDwEoLS3lrbfe4sUXX+Tqq69m0KBBvP/+++zdu5fVq1e3ToBOOxH6IBKDqogy2agsC2id8wohWl3+5r3YHHb0TgMDossYfc9o0LegkqR0pRNCCHGRk8TIB82YMYMJEyZw9dVXe6w/ceIEubm5jBs3zr3OaDQycuRINm/eDEB6ejp2u91jn4SEBNLS0tz71MVqtVJWVuax1Csggm6XBmKxBOJ0asGhgVZsjRL+qVn3kGg3kc5MQkKLOJMfT3CIgchf3tyyE0mLkRBCiIucJEY+5uOPP2bHjh0sWLCg1rbc3FwAYmNjPdbHxsa6t+Xm5mIwGDxams7fpy4LFiwgNDTUvSQlJTUYpykyDKNWg06j4LCBWtqKY5iEX2ruPSTaXuWuH7EUVhFsrqK6MhZ9RCho9S07mVPT+CKEEEL4MflL5kMyMzN55JFHeP/99zGZ6q/0piie38yqqlpr3fka2+fxxx+ntLTUvWRmZjZ4PguhlFn0oLVSVq3FWZjV4P7i4tfce0i0vcofV+FwWXG4FPKLQ3Cl9Gj5yWQeIyGEEBc5mYDGh6Snp5OXl8fgwYPd65xOJxs2bOCVV17h0KFDwNlWofj4ePc+eXl57lakuLg4bDYbxcXFHq1GeXl5DB8+vN5rG41GjEZjk2MNCVWp1DpB4yAitBxtQs8mHysuTs29h0Qbs1dzetsBOsVlU1QaiWK20uPeX7X8fOp/l4a2CyGEEH5MWox8yJgxY9i7dy+7du1yL0OGDOH2229n165ddOnShbi4OFatWuU+xmazsX79enfSM3jwYPR6vcc+OTk57Nu3r8HEqLmc1mqqysPRKVBeacaRJ13phPAlzoJjHD1qwqCvpKwokfDoAAxRCS0+n+pSGl2EEEIIfyYtRj4kODiYtLQ0j3WBgYFERka618+cOZP58+fTvXt3unfvzvz58wkICGDy5MkAhIaGMnXqVGbNmkVkZCQRERHMnj2bfv361SrmcCFiJ04k79s1KFoHTpeCPesI+tQBrXZ+IcSFUayVJEWcQdW4qKoy0ufWPhd2wsYKLEjxBSGEEH5OWoz8zJw5c5g5cybTp09nyJAhZGVlsXLlSoKDg937vPzyy0yaNIlbbrmFESNGEBAQwBdffIFWq221OFTVgFY1okXBpNWiOC2tdm4hxIU7sfxbOoUVUV0RgcGgJXLkpRd2QqfSSPGF5idGWVlZ3HHHHURGRhIQEMDAgQNJT093b2/KhNZCCCFEa5EWIx/3/fffe/xbURTmzp3L3Llz6z3GZDKxaNEiFi1a1GZxVWSeoFI9+yWxzlxBVXYOLZgZRQjRRo6sO8PQrgXk5PSAADuqIbjxgxqiNlJgoZktRsXFxYwYMYLRo0fzzTffEBMTw7FjxwgLC3PvUzOh9dKlS+nRowfPPvssY8eO5dChQx5fBgnh67Kzs/nggw/o1KkTffr0ISUlxeNeF0L4BkmMRIuE9h1MiUWH1eWi2OIia18Vkd4OSggBQNmh/YRwGrsDjuaHEpUSiDb2AirSAap6dmloe3M8//zzJCUlsWTJEve6lJSUc87nOaE1wDvvvENsbCwffvgh06ZNa94FhfACVVXZunUrq1evxuVycfr0aY4cOQKc7fqenJzsXiIiIhqtMCuEaFuSGIkWKTu0nzCDE61Li6U8CqdLemUK4Sv2vfcdoSFFVFtCcJZH0m1UN9BcYFfaJo4xOn9i3/qqFa5YsYLx48dz8803s379ejp16sT06dO5//77gcYntJbESPi6iooKPvvsM44dO8bQoUPZvn07EyZMoEuXLpw6dcq97N27F1VVCQ4O9kiUoqKiJFESop1JYiRaxBwSgMNlQKNViYspoPvQbt4OSQgB4HRgytpGWGgRBQWJJHYqI3LkuMaPa4Tq1KA2MIlrzbbzJ/Z9+umn6+z6e/z4cV577TUee+wxnnjiCbZt28bDDz+M0WjkrrvuanBC61OnTl3gqxGibR0+fJjPP/8cRVG44447SEpKYvv27aiqitlsplevXvTq1QuA6upqMjIy3InS/v37UVWVwMBAj0QpJiZGEiUh2pgkRqJFDJ37EhdejNlYTXx4EcU7DxN0k7ejEkLkrP0Wk7WIkMgqbGYrUUkW9Mm9L/zEjU3i+t9tmZmZhISEuFfXN7eVy+ViyJAhzJ8/H4BBgwaxf/9+XnvtNe666y73fi2Z0FoIb7Hb7axatYrt27fTvXt3brjhBgIDA7Hb7cDZ+/d8JpOJHj160KPH2e6uNpuNzMxMTp48yalTp/juu+9wuVyYzWY6d+5McnIyKSkpxMbGotFIbw0hWpMkRqJlXDbOFEQRHZlBQVEkeiwkNX6UEKKNVf+8D7vDgc1qIjurJ+HR4XRqjRM3sStdSEiIR2JUn/j4ePr08Swh3rt3b5YtWwacnawaGp7QWghfkpeXx7JlyygsLOTaa69l6NCh7iS+5r91JUbnMxgMdO3ala5duwJnk63Tp09z8uRJMjIyWLNmDU6nE6PR6E6UkpOTiY+Pb9Xqs0J0RJIYiZYxBFOtBGCp1mN32QgyBHg7IiEEEKK3ogst5HRuAtlFgXS5+spWOa+qKqgNJEYNbavLiBEjOHTokMe6w4cPk5ycDEBqaqp7QutBgwYB/5vQ+vnnn29m9EK0HVVV2b59OytXriQyMpLf/va3xMTEeOzTnMTofHq9ntTUVFJTUwFwOBxkZWW5u96tX78eu92OXq/3SJQ6deokiZIQzSSJkWgZjYaeA/QEVFcTG1GGqgvydkRCCFWF3N2Eh5RjrYqif79TxI+/tnXO7frv0tD2Znj00UcZPnw48+fP55ZbbmHbtm0sXryYxYsXA2cfJBub0FoIb6usrOTzzz/nyJEjXHrppVx99dXo9fpa+11IYnQ+nU7nTn4AnE4nOTk57q53mzZtYu3ateh0OhITE91d7xITE9Hp5LFPiIbIO0S0mMFgwVlpQsWA3nThH/ZCiAuj5h9Ga8lFVTVUlicRNigBWmk8TlOLLzTV0KFD+fTTT3n88ceZN28eqampLFy4kNtvv929z5w5c7BYLEyfPp3i4mKGDRtWa0JrIbzl6NGjfPbZZ6iqym9+8xv3GKG6tGZidD6tVktiYiKJiYlcfvnluFwucnNz3YnSjz/+yPr169FqtXTq1MkjUTIYDK0ejxD+TBIj0WI6rROD0U5QaAX6sLoHWAsh2k/10V0YlGIcGjOhUcUk/Po3rXfyJo4xao6JEycyceLEerc3ZUJrIdqbw+FgzZo1bN26la5duzJp0iSCgprWa6ItEqPzaTQaEhISSEhIYPjw4aiqypkzZ9xd79LT09m4caN7v5rWp86dO9dbLEWIjkISI9FiljI9epcOrWKhIC+OMG8HJEQHd+rjH0kyVmO1xqHoDegS+7bauVt7jJEQ/ig/P59ly5ZRUFDA+PHjGTZsWJOqJLZli1FTrh0XF0dcXBzDhg1DVVXy8/PdidKuXbv44YcfUBSF+Ph4j0TJbDa3e7xCeJMkRqLFHEoQ5aVBWCwh2DVllB3/mZAuvbwdlhAdk9OBkn8cW7SRE4d6kzg8ACU8sfXOrzZSrlsSI3ERU1WV9PR0vvvuO8LCwrjvvvvclRObSlEUryRGdcURExNDTEwMQ4cORVVVioqK3F3v9u/fz5YtW4Cz84bVdL1LTk4mIEAKLYmLmyRGosWqSvIIVTToDVYKKh18O3cLD/2/MHThzftjIYS4cCc/Wk5o+AmstoCzn+whrTy/SRt0pWtvGzdu5PXXX+fYsWP85z//oVOnTrz33nukpqZy+eWXezs84aOqqqpYsWIFhw4dYvDgwYwfP77OAguN8ZXE6HyKohAZGUlkZCSDBw9GVVVKSkrc5cEPHz7Mtm3bAIiOjna3KKWkpDS5C6EQ/kISI9Fiu4+FMaL7UfQGK+mH+pAVEoCttFASIyG84MyGXXQ2VWKpiqVEU42+JIiEVjy/6lRQnQ10pWtgmy9YtmwZd955J7fffjs7d+7EarUCUF5ezvz58/n666+9HKHwRcePH+ezzz7D4XBw66230qtXy3tF+GpidD5FUQgPDyc8PNxdKr+0tJRTp05x8uRJjh8/zk8//QRAZGSkR6LUlDnMhPBlkhiJFouOcXAsN4bIsHwUjZ277zEQkNyn8QOFEK0uXr8PjeJi9cFkHE4TQ669oVXP7+9jjJ599ln++c9/ctddd/Hxxx+71w8fPpx58+Z5MTLhi5xOJ2vXrmXz5s2kpqYyadKkC37o95fEqC6hoaH079+f/v37A2e/UKgZo3Tq1Cl27NgBQFhYmLvbXXJyMmFhYU0agyWEr5DESLRYn0siKdt7mCCTk5HJVfS59UZvhyREh2TZtx2sxRi0BpKNJuLTAoj4xVWtexE/70p36NAhrryy9mS3ISEhlJSUtH9AwmcVFhaybNkyzpw5w9VXX83w4cNb5eHenxOj8wUHB5OWlkZaWhpwdj6ncxOlXbt2AWffX+cmShEREZIoCZ8miZG4ABa0LgNaVYPVagSXCzStPK5BCNEwVeXMx69j0FgoLoklzAhdp97TavMXuS/jUlAbKL7Q0DZfEB8fz9GjR0lJSfFYv2nTJrp06eKdoIRPUVWVXbt28c033xAcHMzUqVNJSGi9DqkXU2J0vsDAQPr06UOfPmd7jVgsFjIyMtwFHfbu3YuqqgQFBXl0vYuKipJESfgUSYxEi2UfrCIupBqDoZrIuLxWfxATQjTOkXuI7IOF9O9aSFl2HIl9CwkcMLjVr3M2MWpgglcfT4ymTZvGI488wttvv42iKGRnZ7NlyxZmz57NU0895e3whJdZLBa+/PJLDhw4wKBBg7jmmmtaffLTizkxOp/ZbKZnz5707NkTgOrqajIzM90FHb799ltcLhcBAQHuRCk5OZnY2FhJlIRXSWIkWsyqhpOfF0nnxGLycoNIqi5FMYd5OywhOhS1shCbxYXVqqekPJKgASltcyFXI+W6fTwxmjNnDqWlpYwePZrq6mquvPJKjEYjs2fP5sEHH/R2eMKLTp06xfLly7HZbNx8883uVo/W1pEf+E0mE927d6d79+4A2Gw2MjMz3V3vVq1ahdPpxGQyeSRKcXFxaKQnimhHkhiJFut1yyCOvr4fp8NFfnZn1KIMlE5h3g5LiA6lev9OkmJzyCuMpLrKScJt17XJdVT17NLQdl/33HPP8eSTT3LgwAFcLhd9+vSRcsMdmNPpZP369WzcuJHk5GRuvPFGQkND2+x6HanFqDEGg4GuXbvStWtXAOx2O6dPn3YnSmvXrsXhcGA0GklKSnJ3vYuPj0er1Xo5enExk8RItJjZ6CQiQEWnVYiPzkMTGOHtkIToWFxOsj//kfiwIjIz+hKTYEIT1bVNLuXvVelqBAQEMGTIEG+HIbysqKiI5cuXk52dzVVXXcWIESPavGVCEqP66fV6UlNTSU1NBcDhcJCdne0eo7RhwwbWrFmDXq/3SJQSEhLQ6eRRVrQeuZtEi5X8fBjQo9WoBAaUoSo6/OPRSIiLw6FPlmNwZqOqWlRXEAlDAsEc3jYX8/OudELA2QILe/bs4euvvyYwMJB7772XxMTEdrm2JEZNp9Pp6Ny5M507dwbOtu7l5OS4W5Q2b97MunXr0Ol0JCYmurveJSYmtmjyXSFqSGIkWiw3s5JQnQ2t1o5L48SR/TP6UJncVYj2cvy7vQwKzMfp0qMx2HGExLTZtVRV03DxBVXGAQjfVl1dzVdffcW+ffsYMGAA1157LUajsd2uL4lRy2m1WhITE0lMTGTEiBG4XC5yc3PdidKPP/7I+vXr0Wq1dOrUyZ0oJSUltXoRDXFxk8RItFhwchJ7fywgLOIEpRYNGmOAt0MSokOJU0+haqrJKujM6eJg4vtc0WbXuli60omOKTMzk+XLl2OxWLjpppvo169fu8cgiVHr0Wg0JCQkkJCQwGWXXYaqquTl5bm73qWnp7Nx40Y0Gg3x8fHurnedO3du12RY+B9JjHzIggULWL58OT///DNms5nhw4fz/PPPu8tdwtluAM888wyLFy+muLiYYcOG8Y9//IO+ffu697FarcyePZuPPvoIi8XCmDFjePXVV1u9u0Ds0EEUfvs9TpeCWaNSfuhnwrpc2qrXEELUrXTPT5icWZh0LnLyYojtXE34kLZLjKQrnfBHLpeLDRs2sGHDBhITE7n77rsJCwvzWjySGLUNRVGIjY0lNjaWYcOGoaoqBQUF7kRpz549bN68GUVRiIuL80iUzGazt8MXPkQSIx+yfv16ZsyYwdChQ3E4HDz55JOMGzeOAwcOEBgYCMALL7zASy+9xNKlS+nRowfPPvssY8eO5dChQwQHBwMwc+ZMvvjiCz7++GMiIyOZNWsWEydOJD09vVWrueTu/pmqahM6RaGkPIjo4spWO7cQomFFm9ZgdVZjs5rROIKxGoPbdC4xf6xKt2LFimYfM3bsWHlQukiUlJSwfPlyTp8+zZVXXsmVV17p1dLP0mLUfhRFITo6mujoaIYOHYqqqhQVFbm73h04cICtW7cCEBsb61EivOZ5S3RMkhj5kG+//dbj30uWLCEmJob09HSuvPJKVFVl4cKFPPnkk9x0000AvPPOO8TGxvLhhx8ybdo0SktLeeutt3jvvfe4+uqrAXj//fdJSkpi9erVjB8/vtXiDe/ZleXZB+nWZR9WbSVBAVWtdm4hRAMcVqr2biMyvJADp5M5UBjAyEG92/SSqquRMUYNbPOWSZMmNWt/RVE4cuQIXbp0aZuARLvZt28fX375JSaTiSlTprgH8XuTJEbeoygKkZGRREZGcskll6CqKiUlJe5E6ciRI2zbtg2A6OhoOnfuTEpKCsnJye4vnUXHIImRDystLQUgIuJsGewTJ06Qm5vLuHHj3PsYjUZGjhzJ5s2bmTZtGunp6djtdo99EhISSEtLY/PmzfUmRlarFavV6v53WVlZo/EZgoIZ1vk0Rr2TuCAHFdnFyIwgHVdL7iHRMtbMfWRluEjsYUOjmumVnEP/u//Sthd1Kah+2JUuNzeXmJimFaWQByD/Z7Va+eabb9i9ezdpaWlMmDABk8nk7bAASYx8iaIohIeHEx4ezsCBA4Gzz1w1idLJkydJT08Hzj6D1XS9S05ObtO5roT3SWLko1RV5bHHHuPyyy8nLS0NOPsHHs42+54rNjaWU6dOufcxGAyEh4fX2qfm+LosWLCAZ555plkxlpzO5diZONJSD2J1IpOudXAtuYdEyxRs30xoUCFVVgOVRQmYe+hQjG37tYQ/Fl+4++67m9Ut7o477iAkJKQNIxJtKSsri2XLllFZWcmkSZPo378/Sht2L20uSYx8W2hoKP3796d///4AlJeXk5GR4R6ntHPnTgDCwsLc3e5SUlIICwtr9/vs/fffp2/fvgwaNKhdr9sRSGLkox588EH27NnDpk2bam07/w2oqmqjb8rG9nn88cd57LHH3P8uKysjKSmpwXPmHjhJZFgxVlXFZKzCYJIP/I6sJfeQaJldn+3i0qgizpQHU6wr45Jbr2v7i6rK2aWh7T5myZIlzdr/tddea6NIRFtyuVz88MMPfP/998THx3PHHXe4e1r4EkmM/EtwcDB9+/Z1F7eqrKz0SJR2794NQEhIiEeiFBER0eaJ0pIlS7jrrrskMWoDkhj5oIceeogVK1a4q+jUiIs7O0dQbm4u8fHx7vV5eXnuVqS4uDhsNhvFxcUerUZ5eXkMHz683msajcZml7AsKdZiqwpHjwGjVkNFsQFpYO64WnIPiZbRVpSgianGXtGTToE6YocObfNr+mOLkbj4lZaW8u9//5usrCwuu+wyrrrqKnQ633y0kcTIvwUGBtK7d2969z47ntNisZCRkeHufrdv3z5UVSUoKMijmEN0dHSrJ0rV1dU+00X0YuObnx4dlKqqPPTQQ3z66ad8//33pKamemxPTU0lLi6OVatWub8lsNlsrF+/nueffx6AwYMHo9frWbVqFbfccgsAOTk57Nu3jxdeeKFV4x1w/TDe/eI4FTYNNhtoy6qbdNzXbx7m1P5ixt/TnS79fe9bPSF8We6uA5SUmim2GPnxZBw9BjhQwtq+ZU51KqjOBhKjBrZ5Q3FxMaqqEhERQX5+Phs2bKBnz57ursnC/x04cIAVK1agKAqXXXYZCQkJnDx5kqCgIAICAjCZTOj1ep/pTieJ0cXFbDbTs2dP95QqVqvVI1H69ttvcblcBAQEkJyc7C7oEBsbe8H3ZHV1tXwR2UYkMfIhM2bM4MMPP+Tzzz8nODjYPSYoNDQUs9mMoijMnDmT+fPn0717d7p37878+fMJCAhg8uTJ7n2nTp3KrFmziIyMJCIigtmzZ9OvXz93lbrWEt45kgHxhRhcWipdBlyukkaPOX24lHUfHQdgxas/M/Of9bdiCSFqW/an70kJLuFEbhK26hB6jIwFbdt/lPtTi9Gbb77JggULcLlczJkzhw8++ID+/fvz9NNP8/DDD/Pb3/7W2yGKC2Cz2fj222/ZuXMnvXv3pkePHhiNRrRaLdXV1ZSXlwOg1+sxm80EBQVhNpsxmUwYDAavxS2J0cXNaDS6n83g7H16+vRpd9e71atX43Q6MZlMdO7c2d31Li4urtll5KXFqO1IYuRDavq3jxo1ymP9kiVLmDJlCgBz5szBYrEwffp09wSvK1eu9Kim9PLLL6PT6bjlllvcE7wuXbq01Ysj6MKiOVNtItYSiF1jI5jGH4zCYkwEBOupKrcT30UqQAnRLC4XUdYDJHXKYd3eobgMTsIuH9sul/anxGjRokXs37+fqqoqOnfuzIkTJ4iOjqasrIwrr7xSEiM/lp2dzfLlyykrK+O6665jwIABHD58GACDweBOfFRVxW63Y7FY3BUy9Xo9AQEBHomSXq9vt9glMepYDAYDXbp0cZf/t9vtZGVluVuU1q1bh8PhwGAwuBOl5ORkEhISGn1es1qtkhi1EUmMfEhTPjAVRWHu3LnMnTu33n1MJhOLFi1i0aJFrRhdbRpzGD1iCwgNqERxGrEVNT7Ba1CYkcfeHEHeqQq6DJRudEI0R/b6lfSIPEp8SBndIspI6W0nqFvf9rm4HxVf0Gq1mEwmTCYT3bp1Izo6Gjg7SNpXulWJ5lFVlc2bN7N27VpiY2P57W9/S1RUVL1/NxVF8UiUXC4XdrudyspKSkpK0Gg07hal4OBgd6LUluOTJDHq2PR6PSkpKaSkpADgdDo9EqUNGzZgt9vR6/UkJSV5JEoajcYjWZIWo7YjiZG4IBklMYSaCgGVijILqCo08uARGmUiNEre0EI0V8nmjVicVqqqgnFZwjGkDW63a7tcCi5n/d09XD40j5FOp3M/OKxfv969vqaLlfAv5eXlfPrpp5w4cYLhw4dz1VVXNbsHhEaj8SgQ43K5sNlsVFRUUFpa6k6kalqUahLr1kyUJDES59JqtXTu3JnOnTtzxRVX4HQ6yc3NdXe927x5M+vWrUNRFKKjo/nd737nPlbGGLUdSYxEyykKXWJyqLTpCDZXYS2MAocV9JL0CNHaVEsxebtO0i0yn4wznTAGlNP1rpvbMYD/Lg1t9xFr1651PzScOxmjxWLhrbfe8lZYogV+/vlnVqxYgVar5c4773R3S6qhKAqKouByuZp1Xo1G405+4H+JUllZGUVFRWg0GgwGA4GBgR6J0oV0SZfESDREq9XSqVMnOnXqxIgRIygpKeGDDz6goKCg1tg4aTFqO5IYiZZTVYpLwugcXoZO1WLQF4BOvsEQoi1YT+7DaS3BpLdTXdiJ8FgnGlP7jdPzpzFGQUF1T3YbExNDTExMO0cjWsJut/Pdd9+Rnp5Oz549uf766wkICKhz39ZIOOpLlEpKSmolSoGBgS1KlCQxEk2VmZnJJ598gk6nIz4+vta8XDLGqO1IYvRf505M2VR/+tOffHISufbispRRbFWJdhiICi7E6Gy8G50QomUyV60kMqgYm0uDXe/CkRTf+EGtyJ8So/OdPn2a1157jc2bN5Obm4uiKMTGxjJ8+HAeeOABmYjYx+Tm5rJs2TJKSkqYMGECgwcPbnBsWFuMGzs/UXI6ne45AgsKCtBqtRgMBoKCgjwSpYaqi0liJJpi165dfPnllyQkJHDrrbfy0UcfuRNwm82GzWbD4XBIV7o20rz6gBexhQsX8uOPP7Jz584mLYsWLaKkpMTbYXtV2ZH96IFKqw5UDVXl8iYVoq3sWJ1JVICFouIYii16Btwzvl2vr7qURhdftGnTJnr37s2nn37KgAEDuOuuu7jjjjsYMGAAn332GX379uWHH37wdpiCswUWtm7dyptvvolWq+W3v/0tQ4YMaTTxaW6p45bQarWYzWZCQ0OJiIhwt0oWFRVx8uRJjhw5wuHDhzl9+jTFxcVYLJZa3fskMRINcblcfPfdd3z++ef079+fu+++m8DAQBwOhzsxWrduHZGRkQC89NJLrF69mspKz8JXWVlZ3HHHHURGRhIQEMDAgQNJT093b1dVlblz55KQkIDZbGbUqFHs37/f4xxWq5WHHnqIqKgoAgMDuf766zl9+nQb/wR8g7QYnePTTz9tcjeLc8tjd1TB3XpjV01YykJQo7MoqPTNByMhLgbBrlK0xipOHRyIXmMgILlXu15fdWlQXfU/gDa0zZseffRR7rvvPl5++eV6t8+cOZPt27e3c2TiXBUVFXz++eccPXqUX/ziF4wZM6ZZhQ/aO+GoSZTMZjMADocDm81GQUEB+fn5aLVajEajx2SzQtSnurqa//znPxw/fpxrrrmGSy+91P2FgNPpdL8Xxo0bx6ZNm7j00kvJysri3nvvZcqUKcybNw84O7H1iBEjGD16NN988w0xMTEcO3aMsLAw97VeeOEFXnrpJZYuXUqPHj149tlnGTt2LIcOHXI/286cOZMvvviCjz/+mMjISGbNmsXEiRNJT09v9alffI0kRv+1ZMkSj0G6jXn99deJjY1tw4h8nzYwnEuGa8ncWY6isxFwkb9ZhPAaVSXBbEFRXBTbtfS9QgfBce0cgn92pdu3bx/vv/9+vdunTZvGP//5z3aMSJzvyJEjfPbZZyiKwu233063bt2adbwvlGDX6XTodDr3OKiaRCkvLw9VVd2VEmvKhZtMJoxGo0/ELryroKCAjz/+mMrKSu64445aBUYcDoc7MVIUhU6dOgHw/vvvo9FocDgc7n2ff/55kpKSWLJkiXtdTXlwOPsFwsKFC3nyySe56aabAHjnnXeIjY3lww8/ZNq0aZSWlvLWW2/x3nvvcfXVV7uvlZSUxOrVqxk/vn17K7Q33/yKzwvuvvvuZvXXnDx5MoGBgW0YkX+wljoJVIPRoyUipMLb4QhxUSr+8t/EReZQWphEtDaI5IEJ7T6eT1UbX3xRfHw8mzdvrnf7li1biI9v3/Fa4iyHw8E333zDhx9+SKdOnfjd737X7KQIfCMxOl9NkhQeHk54eLi7ZclisXDixAmOHDnCkSNHyMnJobS0FKvVKt3sOqCjR4/y5ptvoigK999/f62kCDwTIzjbuqTT6dBqtSiK4jFJ8YoVKxgyZAg333wzMTExDBo0iDfeeMO9/cSJE+Tm5jJu3Dj3OqPRyMiRI92fk+np6djtdo99EhISSEtLa/Cz9GIhLUaNqKioqNVPOCQkxEvR+J7MPDNxAfkYjDaMhvImzWMkhGieglWbiTKX4yhJJDCqGHvUiHaPwV9bjGbPns0DDzxAeno6Y8eOJTY2FkVRyM3NZdWqVbz55pssXLjQ22F2OHl5eSxbtozCwsJaXYeay9fH7tQ8vGq1WvR6PWFhYe4WpTNnzqCqKnq9HqPR6DHZrMFg8MmkT1w4VVXZsmULq1evplu3bvzqV7+q98v5c8cYQcOluo8fP85rr73GY489xhNPPMG2bdt4+OGHMRqN3HXXXeTm5gLU6vEUGxvLqVOngLPFTwwGA+Hh4bX2qTn+YiaJUR1OnDjBgw8+yPfff091dbV7vaqqKIqC0+n0YnS+JSbOxukDZhI7uXBajeB0gE7f+IFCiCaxn8mAkgOo4RqO5EYQGA4xY37Z7nGoTg1qAxO8NrTNm6ZPn05kZCQvv/wyr7/+uvvzW6vVMnjwYN59911uueUWL0fZcaiqyvbt21m1ahXh4eHcf//9F9wt3dcToxo1cdYkSjXf9Kuqit1ux2q1UlFRgaqqGAwGTCaTR6Kk1+slUboIOBwOvvzyS3bv3s2IESO46qqrGiwgcu4YI2i4VLfL5WLIkCHMnz8fgEGDBrF//35ee+017rrrLvd+599HNfdlQ5qyz8VAEqM63H777QC8/fbb7m8XRd3ytXGYnVacdhNllQZcxRloort6OywhLhr5367CSTmlpdGoFQmEXx6AYmj/brz+2mIEcOutt3Lrrbdit9spKCgAICoqyqMLimh7lZWVrFixgsOHDzN06FDGjh3bKr8Df/kbXV+ciqJgMBjck3jWJErV1dWUlZW5Eymz2exRzEHuX/9TXl7OJ598wpkzZ7jpppvo169fo8fU1ZWuvsQoPj6ePn36eKzr3bs3y5YtAyAu7uzY1NzcXI8uxHl5ee4vKOLi4tyl6c9tNcrLy2P48OFNfKX+SxKjOuzZs8c9qZxoWFyClu/XG+iJE5diQ6NRwOngp3eXcfKohUuv6UrnK67wdphC+CeXi4w1q0gKLGVfZirZSimXXT/JK6H4c2JUQ6/Xy3giLzl27BifffYZLpeL3/zmN/To0aPVzu0viRE0rXpeXYmSzWajqqqqVqIUHBzsnkNJEiXflpWVxSeffALAlClT3EUUGuJyuVBVtVZXuvq63Y0YMYJDhw55rDt8+DDJyckApKamEhcXx6pVqxg0aBBwdm6k9evX8/zzzwMwePBg9Ho9q1atcrem5+TksG/fPl544YVmvmr/I4lRHYYOHUpmZqYkRk3gLCmie3AFesWFy6lF1Zoo2v0jSz804FIDKC08wNQRI6Ad5pkQ4mJTnv4NxYVlpAS5UCtC6R1bQdyll3kpmoYTI/Cfh9NzZWZm8vTTT/P22297O5SLksPhYM2aNWzdupWuXbtyww03tPp0F/7Wla4lxxmNRvfDsMvlwm63uyvcaTQa9Ho9AQEBHolSc8qdi7a1d+9eVqxYQWxsLLfeemuT3wM1Feea2mL06KOPMnz4cObPn88tt9zCtm3bWLx4MYsXLwbO3kszZ85k/vz5dO/ene7duzN//nwCAgKYPHkyAKGhoUydOpVZs2YRGRlJREQEs2fPpl+/fu4qdRczedfU4c033+SBBx4gKyuLtLS0Wt/C9O/f30uR+Z5KVyA/F0XT12GgSmelfP8uzEndiAs/QXZRKNExGinGIEQL2fMyCAgqodRqZn92Kp37F3svGFU5uzS03Q8VFRXxzjvvSGLUBgoKCli2bBl5eXmMGzeOX/ziF23SunOxtRg1RqPR1EqUbDYb5eXllJSUuFucAgICCAoKwmQyYTabL/r5Z3yRy+Vi7dq1/PDDDwwYMICJEyc2K2GtKzFqaIzR0KFD+fTTT3n88ceZN28eqampLFy40D1EBGDOnDlYLBamT59OcXExw4YNY+XKlR7J2ssvv4xOp+OWW27BYrEwZswYli5d2iHuIUmM6pCfn8+xY8e455573OvOHTQpxRf+J3l4P7Z+txqH4kTrBOvpE4SM+CUz5lnJP5ZFyvDrJTESooXSV+ymZ1gJhwuiKTeU0eea3l6LxeVUcGnqfy+7nL75Pl+xYkWD248fP95OkXQcqqqyY8cOvv32W8LCwrjvvvvatAujP7UYtQWNRuNuJYL/JUplZWUUFRW5E6lzEyWTydQhHnK9yWq1snz5co4cOdLiLwZqnjfPbzFqaHqZiRMnMnHixHq3K4rC3LlzmTt3br37mEwmFi1axKJFi5oV78VAEqM63HvvvQwaNIiPPvpIii80whzXmWSjgyCdA43TiLWwEoDwXv0I79X4oEIhRP0qTpeiT3biLIpjRGIZfX9ze+MHtRF/HWM0adKkRh+c5TO+9VRVVfHFF1/w888/M3jwYMaPH9/mY1/85ffXXgnc+YmS0+nEZrNRUlJCYWEhWq0Wg8FAYGCgR6LUUGU00TxFRUV89NFHlJeXM3ny5BbNzwX/azFqarluceEkMarDqVOnWLFiRYtv5I4kqv8QIkIWYncYCQu0YpCxn0K0Cld1Ob3Dc3GhobQ8hkvHBkFghNfi8dfEKD4+nn/84x9MmjSpzu27du1i8ODB7RvURerEiRN8+umnOBwObrnlFnr3bp8WTo1G4zctRt6IU6vVYjab3ZPM1iRKxcXFFBYWotFoMBgMBAcHuyveSaLUcsePH+ff//43gYGB3HfffURFRbX4XM3tSicunNz1dbjqqqvYvXu3t8PwCwffew1bRQhaVYfZYMeUkuTtkIS4KBx680OCAwupKoknzKCgj430ajyq+r/kqO7lws6/YMEC98Dg/11TZe7cuSQkJGA2mxk1ahT79+9v1nkHDx7Mjh076t3uL92wfJnT6WT16tW8++67REZG8sADD7RbUuRvfOFeq0mUQkNDCQ8PJygoCDg7JuzkyZMcOXKEI0eOkJWVRXFxMRaLpdZE96I2VVX58ccfef/99+nUqRNTp069oKQI6u9KJ4lR25EWozpcd911PProo+zdu5d+/frV6gZw/fXXeyky35O5cxebc2Pp1ROs2koC+w3ydkhC+D3LmQwKdn9DqLmKjFPhnLCopOrr71PeLtqw+ML27dtZvHhxrcI2L7zwAi+99BJLly6lR48ePPvss4wdO5ZDhw41uarT73//eyorK+vd3q1bN9atW9fi2Du6wsJCli9fTm5uLmPGjGH48OHt3tLgLy0bvpqEn9+i5HA4sNls5Ofnu0tFG41GgoKCCAwMxGQyYTQa/aYLY3twOp189dVX7Ny5k1/84heMHTu2Ve7L+rrSNTTGSFwYSYzq8MADDwAwb968Wtuk+IKnLr31bNlaQYFdj8lcTfGab4i6vZe3wxLCrx38YiVxoZloXE7W58QTFmwn7hejvBqTy6XB5ar/D31D2xpSUVHB7bffzhtvvMGzzz7rXq+qKgsXLuTJJ5/kpptuAuCdd94hNjaWDz/8kGnTpjXp/Fc0Mo9aYGAgI0eObFHsHZmqquzatYtvvvmG4OBg7r333ibNy9KR+UsiodPp0Ol0BAQEAGC327HZbOTl5bm31yRKNV3vOnKiVFlZyb/+9S+ysrK44YYbGDhwYKudu7nlusWF84+vWdqZy+Wqd5GkyFPcyCmM7X2YmCAbakUo+cfKvB2SEH7PlXMKa54WpzOEy5ILuerKSIJ7eLeYiepqfAEoKyvzWKxWa4PnnTFjBhMmTKg1P8aJEyfIzc1l3Lhx7nVGo5GRI0eyefPmJsW8Z8+eZnUB2r9/v/tBRNTPYrGwbNkyVqxYQd++fZk2bZpXkyJfbYk5n7/EeT69Xk9gYCDh4eGEhYVhMpmw2+3k5eVx4sQJjhw5wtGjR8nNzaW0tBSr1eqXr7MlcnNzeeONNygsLOTuu+9u1aQIZIyRN0iLkbggQb2GUZwfRaRSjKKxYqwu8HZIQvi9SFcFhuASTp5MwVlqpue0exo/qI01tfhCUpLnOMOnn3663rKwH3/8MTt27GD79u21tuXm5gIQGxvrsT42NpZTp041KeZBgwaRm5tLdHR0k/a/7LLL2LVrF126dGnS/h3RqVOnWL58OVarlV//+tf07dvX2yEB/tMa4+8Jg6Io6PV69xADVVVxOBxYrVYqKiqAs4mUyWQiKCjI3UVPr9f7ze+oqQ4cOMBnn31GVFQUt912GyEhIa1+DRlj1P4kMarHtm3b+P7778nLy6v1jeNLL73kpaia59VXX+Vvf/sbOTk59O3bl4ULFzbaraTZtDpiQzS4qkMx620UZ9la9/xCdDBqVQm2Uz8TEqihLLcnUZ0qMCd7v0JmUxOjzMxMjweE+vrCZ2Zm8sgjj7By5coG/8if/zBVM59c02JW+fOf/+zuEtQYm00+v+rjdDpZv349mzZtIikpiZtuuonQ0FBvhwX4T0uMv8TZHHUlSna7nerqasrLy4GziZLZbHYnSiaTCYPB4M2wL4iqqnz//fds2LCBtLQ0rr/++jYrSV/fGCNfee9djCQxqsP8+fP505/+RM+ePWvNY+Qv33h88sknzJw5k1dffZURI0bw+uuvc+2113LgwAE6d+7cqtc6VW6gSyA4XVBSVd2q5xaio7Ed24nNWkWxGsiZikDUIN/4mG7qGKOQkJAmfXOanp5OXl6eR6lsp9PJhg0beOWVVzh06BBwtuXo3MlB8/LyarUi1efKK690n6cpLrvsMvcAdPE/xcXFLF++nKysLEaNGsXll1/uUwUPFEXxi7/N/hDjhVIUBYPB4E58ahIli8VCWdnZrvZ6vd492WxNotTWc121FpvNxmeffcbBgwe56qqruPzyy9v091rfGKOmfgaK5vONv7g+5v/9v//H22+/zZQpU7wdSou99NJLTJ06lfvuuw+AhQsX8t133/Haa6+xYMGCVr1WRKCdcuzEmMsIdkhJTyEuRMHa74kOKeBIQTTVodmk/cY3qmC29jxGY8aMYe/evR7r7rnnHnr16sUf/vAHunTpQlxcHKtWrWLQoLPVLm02G+vXr+f5559v0jW+//77ZsUkatuzZw9fffUVAQEB3HvvvSQmJno7JL91MbYYNeb8RMnlcmG326msrKSkpASNRuNuUQoODsZkMmE2mz0SAV9RUlLCxx9/THFxMbfddhs9e/Zs82vWdKU7t8VIxhi1Ld+783yARqNhxIgR3g6jxWw2G+np6fzxj3/0WD9u3LgmD1puMpcLuy2UQGc4hgArtgoVHFbQSSlJIVri9MZDdIlzUnyiH8mBTjpfdpm3QwJaPzEKDg4mLS3NY11gYCCRkZHu9TNnzmT+/Pl0796d7t27M3/+fAICApg8eXLzX4Bolurqar7++mv27t1L//79+eUvf+mzJYL9qSWmoyVG59NoNBiNRve95HK5sNlsVFRUUFpa6k6kalqUaiab9XaidOrUKf71r39hNBqZOnUqMTEx7XJdh8OBVqv1uMelXHfbksSoDo8++ij/+Mc/WLhwobdDaZGCggKcTmedg5ZrBjSfz2q1elSPqmnybpRGgzHaSNlpLbGGagrLgsBSAsHSzNvRtPgeEh7MugIsNh2ny0Lp3bvYd95LqgKutpnHqD5z5szBYrEwffp0iouLGTZsGCtXrmzyHEaiZTIzM1m+fDlVVVXceOONteaXEi3X0ROj82k0GnfyA/9LlMrKyiguLvZIlGpalEwmk0cLSltLT0/n66+/pnPnztx8881NHrPYGhwOR62kUIovtC1JjOowe/ZsJkyYQNeuXenTp0+tvq/Lly/3UmTN05xBywsWLOCZZ55p0XUSkkyUFpaj09pRdApo5LbqiC7kHhJnVe7cRlhwASXFkXSKzqXn2DTQ+sb7qbVbjOpyftc3RVGYO3duvVXtROtbuXIlW7ZsISwsjNtuu42UlBRvh9Qof2kx8pc4vamxREmj0WAwGAgMDHRPNttWiZLT6eTbb7/lp59+YujQoYwfP75dEzL4X4vRuaQrXdvyjb+4Puahhx5i3bp1jB49msjISL/7MIuKikKr1dZqHWpo0PLjjz/OY4895v53WVlZrbK7dVHzf0ZbepIqawiqmoMzIAL0Mni5I2rpPST+p+DzTwgwV7Fz31CqiCDkF2O8HZKby6XgaqDFqKFtwn/k5OSgKAqlpaW8++67hIWFkZqaSpcuXUhNTSUwMNDbIdbiL3+jO+IYowt1fqLkdDqx2WwUFxdTUFCAVqvFYDAQFBTkkShdaHGQqqoq/v3vf5ORkcHEiRM9isS0J6fTKS1G7UwSozq8++67LFu2jAkTJng7lBYxGAwMHjyYVatWceONN7rXr1q1ihtuuKHOY87t89scrqydGAPKCTKaMJtsdE8pAUP7NTML39HSe0icpVorCSjfhAYdJpOG/sNOEuTlSV09NNJi1BZd6UT7i42NpbKykqlTp3Ly5EmOHz/OiRMn2LlzJwBxcXHuRKlz585+XXa5vUlidOG0Wq17biT4X6JUVFREQUGBewxTUFAQAQEBmM1mjEZjsxKlvLw8PvroI2w2G3fddRfJyclt9XIaVV9XOvlb23YkMapDREQEXbt29XYYF+Sxxx7jzjvvZMiQIVx22WUsXryYjIwMHnjggVa9zqlDDiqLq4nrko8OBxWZ/lFyUwif4rSz+49/Irq6CnOQi5SemVTavPfHuC7t0ZWurW3cuJHXX3+dY8eO8Z///IdOnTrx3nvvkZqayuWXX+7t8HyCTqfD4XBgNBrp2bOnu/JWeXm5O0nat28fW7ZsQavVkpiYSJcuXejSpQsJCQleKePtLy1GIGOMWtv5iZLD4cBms1FQUICqqmi1Wo9EqaZFqb575ueff+bTTz8lPDycu+++m7CwsHZ8NbXV1ZVOWozaliRGdZg7dy5PP/00S5YsaddBdq3p1ltvpbCwkHnz5pGTk0NaWhpff/11q3/z4SzK4e8fTGBsr71cPaSIgJC9qJZiFHN4q15HiItZ5cFtlJzIJLZzJZvSh3E8K5XeoytJa/zQduPvidGyZcu48847uf3229m5c6e7UEh5eTnz58/n66+/9nKEvqEmMTpfcHAwAwYMYMCAAaiqSkFBgTtR+uGHH1i3bh1Go5GUlBR3ouSPXdHbkvws2p5Op0On07mf3WoSpby8PFRVRafTYTKZPBKlmtaXjRs3sm7dOnr37s2kSZN8ojW0rq50MsaobUliVIf/+7//49ixY8TGxpKSklKr+MKOHTu8FFnzTJ8+nenTp7fpNVLGXMbwfq+SWRlEpQsqYquh7BiYh7TpdYW4mDgrColOPIWic7IjPxyLror+Sb41X4zLqcGlNDDBq9N3Jvysy7PPPss///lP7rrrLj7++GP3+uHDhzNv3jwvRuZb6kuMzqUoCtHR0URHRzNs2DBcLhdZWVmcOHGC48eP89133+FyuQgODnYnSampqW1WTdBfuqj5S5wXk3MTJVVV3YnSmTNn3Nu1Wi3p6ekcP36ckSNHMnLkSJ9JYqUqXfuTxKgOkyZN8nYIfqP4ZB4FxZEkJZ/AFGAlzlSNEimlXYVoDp3TibbajitES3xKDk6bgSG/ad1urxdKVRtuFfL1571Dhw5x5ZVX1lofEhJCSUlJ+wfko3Q6nXtSyabSaDQkJSWRlJTElVdeic1mIyMjg+PHj3P8+HF2794NQHR0tDtRSk5ObtVxEr7yINsQSYy8S1EU9Ho9er2ewMBAVFWlpKSENWvWUF5e7tUiC/Wpr/iCjDFqO5IY1eHpp5/2dgh+wxDbmfCgYApPdkfb5RA5B1JJrq5AGxTh7dCE8BuHvllFgtFC+o40Co735KqbQwnp0svbYXnw96508fHxHD16tFb56U2bNtGlSxfvBOWDtFptoy1GjTEYDHTr1o1u3boBUFlZ6W5N+vnnn/nxxx9RFIXExER3IYfExMQWl0L2p4TDX+LsCPLz81m3bh1arZYrrriCHj16eDukWmSMUfuTxEhckLABQxkx9mMyNhShcelw6aupPn2cwF6SGAnRJKpKzv5cEpNc6GwhDErJ4tKH7vV2VLX4e2I0bdo0HnnkEd5++20URSE7O5stW7Ywe/ZsnnrqKW+H5zN0Oh0ulwuXy9VqhRQCAwNJS0sjLS0NVVUpLi52tyZt27aNDRs2oNfrSUlJcSdKMTExTW4FUhTFnRz5csuRL8fW0Rw5coStW7cSFRXFyJEjPSYn9yV1daWTMUZtSxKj/4qIiODw4cNERUU1af/OnTuzceNGr5Zx9BWhWgslVi0OjROnxk5gSndvhySE3yhI30Yncz5lVj1ZFWb6XW5EG+t77yF/T4zmzJlDaWkpo0ePprq6miuvvBKj0cjs2bN58MEHvR2ez6h5CHM6nW1SYU5RFCIiIoiIiGDIkCG4XC5yc3PdhRzWrFnDypUrCQwMdI9N6tKlC6Ghoa0eS3vzp5ati5XL5eKnn37i4MGDdO/enWHDhqHRaLBarT6ZuDqdTo8kyOl0YrfbpStdG5LE6L9KSkr45ptvmvzhW1hY2Ox+2BerM7l24rXBGFQtsUYVXPJzEaKpfnpvFZeEF5Kfl0SXQC1dR14CPvgH2uXS4HI1UHyhgW2+4rnnnuPJJ5/kwIEDuFwu+vTpQ1BQkLfD8ik1iZHD4ahVeKgtaDQaEhISSEhI4PLLL8dut5OZmelOlPbu3QtAZGSkO0lKSUlxl2cG/2oxksTIe6xWKxs2bCA7O5tLL72UXr16oSgKLpfLfQ/5mvO70tW0bEmLUduRxOgcd999t7dD8Eu6mFCqlVIcTi2K4gK9f5Y4F8IbEmx7censnClMQGdSCfrF1d4OqU7+3mJUIyAggCFDpGpmfc5NjLxBr9e7CzQAWCwW9/ik48eP89NPP6EoCvHx8e79IiIifPKhVviO0tJS1qxZg9VqZdy4ccTHx3ts99X75/yudJIYtT1JjP7L5XJ5OwS/1b1fOOZjBzCbrRiMReCwgV7etEI0xlWURaLpCDqNk7iYfKJ7B6GP7eztsOqkqgqqy38To8cee6zO9YqiYDKZ6NatGzfccAMRER17fKS3E6Pzmc1m+vTpQ58+fYCzvTtqWpN27NjBpk2b0Ol0hIeHk5iYSKdOnXw2UZIWI+84ffo0GzZsICAggAkTJhASEuKxveZ34ov3zPktRtXV1QDSla4NSWIkLljmSScVlZG4VD1VFj3hFXko5pDGDxSigzvz0VtgtWFVIrC6QilQUon2dlD18PcWo507d7Jjxw6cTic9e/ZEVVWOHDmCVqulV69evPrqq8yaNYtNmza5H8I7opqHMF9JjM4XFhbGJZdcwiWXXIKqquTl5XHo0CEOHDjAnj172LlzJ0ajkbi4OBISEoiPj2+z+ZOayxcfvC9mqqqyf/9+0tPTSUxM5IorrvCJSVub4/xy3dXV1Wi12loFGUTrkZ+suGCVJXmkn4okLDwIHSqqpQT5+BeiYbbCLA6v3UtaUjXbD/UirzSEsY+M9XZY9XK6FJwNPNg5G2hN8gU1rUFLlixxf2NcVlbG1KlTufzyy7n//vuZPHkyjz76KN99952Xo/UeX2sxaoiiKMTGxhISEkJoaChms5mioiJycnLIyclh69atqKpKUFAQ8fHxJCQkEBcX57VuSNJi1H6cTiebN2/m+PHjpKWlcckllzSamPpi4np+Vzop1d32JDESF6yk1EBKYBEBOhtVVcFkbDlESmfpwy9EQyyH0nE6S3E5tBjtelLjK4kfMdLbYdXL31uM/va3v7Fq1SqPbjQhISHMnTuXcePG8cgjj/DUU08xbtw4L0bpfedWpfMXNQPnNRoNcXFxxMXFMWjQIGw2G7m5ue5E6ciRI8DZKrTx8fHEx8cTGxvbrt++S2LU9qqqqli3bh1FRUVcccUVjc5TVlO0wx8SIynV3fYkMTrH6dOnSUxM9HYYfidpSB8+X3uQ+PJQFI0DQ2WJt0MSwveVFRAeVEx2SQQ/5yQyYLiPj21pJDHCxxOj0tJS8vLyanWTy8/Pp6ysDDjbTctms3kjPJ/hTy1G56qrNcZgMNC5c2c6dz47bq+yspLc3Fyys7M5fvw4+/fvR6PREBMT406UIiMj26RMeQ1JjNpWzaStANdee22Tp2DxxaQIzn5Bcf4YI0mM2pYkRudIS0tj0aJF3Hnnnd4Oxa/0/vUNHHxzAyouTIqKWV/l7ZCE8Hl5X28iNriSnzO6EBGez6XTfLsqpr+3GN1www3ce++9vPjiiwwdOhRFUdi2bRuzZ89m0qRJAGzbto0ePXp4N1Av88fEqKkPtYGBgXTt2pWuXbuiqiqlpaXu1qR9+/axc+dO9Hq9x/ikkJCQVnto9tWH74vFsWPH2Lx5MxEREYwePZqAgKZVyPX14gvnd6WTwgttSxKjc8yfP58ZM2bw2WefsXjxYiIjI70dkl/I2buLQpuOFIceh86Bs7zY2yEJ4dtcLuzFGRDioCA/HosGdEG+/Xnj74nR66+/zqOPPsptt93mfujX6XTcfffdvPzyywD06tWLN99805thep2/JkbNfahVFIWwsDDCwsLo3bs3LpeLgoICd6K0fft2XC4XAQEB7tak+Pj4Jj9s13dNaTFqfS6Xix07drB//366du3KZZdd5tHK0hS+mBSpqlqr+IJ0pWt7khidY/r06Vx77bVMnTqVvn37snjxYq6//npvh+XzDEFB7LNriLFr6R5eQVgnH+8SJISXnfjqayKC8zljNbC1wkzXnlUQEO7tsBrkcmpw0cAEr07fneDVbrdz3XXX8frrr/Pyyy9z/PhxVFWla9euHhO8Dhw40HtB+ghfr0rXkAtJOmq61MXExDBgwADsdjtnzpxxJ0rHjh0Dzna3rEmS4uLimjUJriRGrc9ms7FhwwaysrIYMmQIffr0aXaS46stRjXj/KQrXfuSxOg8qamprF27lldeeYVf/epX9O7du9bAzB07dngpOt8U2edSbuo7lyB9BTHBVirKqwn0dlBC+LDDX/3AJWYLQa4wRvXPYMTvH/T5ub/8ucVIr9ezb98+FEUhKCiI/v37ezskn+WvxRdam16vJzEx0T3u2GKxuJOkjIwMDh48iKIoREdHuxOl6OjoRscnSWLUekpLS1m7di0Wi4Wrr76aTp06XdD5fC0xOrdlu4Z0pWt7khjV4dSpUyxbtoyIiAhuuOEGqRffGI0Gg92E02XDZtdirijzdkRC+Cx7US7J9nSsOoXsgmgG9wkkcvDV3g6rUf6cGAHcddddvPXWW/z1r3/1dig+TaPRoCiKX7YYtSWz2UyXLl3o0qULqqpSXl7uTpQOHjzI7t270el0xMXFuROlsLAwj4dtX3vw9mdZWVmsX78es9nMhAkTCA0NbfG5aqrS+ZqaLyekXHf7kif+87zxxhvMmjWLq6++mn379hEd7avTLfoOW8YeTFY9UZEKQXoXlnKLt0MSwmed/M+3mFzlhBhUQl3hOJ1BjR/kA1wquBpIflw+/kW4zWbjzTffZNWqVQwZMoTAQM927ZdeeslLkfkWRVHQarV+lRjVjDFqr9YYRVEICQkhJCSEnj174nK5POZPSk9Px+VyYTKZ3EUc4uPjpStdK1BVlYMHD7J9+3YSEhIYOXJkq03a6mvJUV0tRjLGqO1JYnSOa665hm3btvHKK69w1113eTscv1F2upBTJWFoNE6CQzLA2PJvboS42GnyjqExVFBQGEdJeSDG8Ibn2PAV/t5itG/fPi655BIADh8+7LHN1x6IvE2n0/lVYgTeHb+j0WiIiooiKiqKfv364XA4yMvLcydKx48fB8BkMuF0Ojl16hTx8fGt9kDfUTidTrZu3crRo0fp06cPgwcPbpXS6r46j1HNe1DGGLUvSYzO4XQ62bNnj8xl1Ezhgy7BpVlKoc1BsuJAW5Xj7ZCE8E1OB7qCvQSbK9mZlUKZq5qBN1zn7aiaxOVScNFAi5HLtx4qzlczt4lonL8lRr72QKvT6UhISCAhIQE4+zCbm5vLgQMHqK6u5vvvv0dRFCIjI92tSTExMc2upNaRWCwW1q1bR2FhISNGjKBbt26ten5fu4egdovRv//9b7Zt29akYxcsWMATTzzBI488wsKFC4GzCeAzzzzD4sWLKS4uZtiwYfzjH/+gb9++7uOsViuzZ8/mo48+wmKxMGbMGF599dUO9VwsidE5Vq1a5e0Q/JLWGETXmGJMGgdajYYzWVU0bUo1IToWy4EtWAqKCY9ViDQEEh9XQUj33t4Oq0n8vcVINJ0/Jka++GBbw2QykZKSgsViobCwkBtvvJGcnByys7M5cuQIe/fuRavVEhsb606UIiIifPo1tafCwkLWrl2Ly+XimmuuabMhDr728z5/jFFGRgarV68mLy+PgQMHMnr0aObNm0dwcLDHcdu3b2fx4sW1isy88MILvPTSSyxdupQePXrw7LPPMnbsWA4dOuQ+x8yZM/niiy/4+OOPiYyMZNasWUycOJH09PQOk7hLYiQunKWY3afiiA6qIjRSh8shE7wKURfrwW3o9BVk5seyLyuO6CAN/pEWnR1f1PAYI996qKjPgQMHyMjIwGazeayXqRn+R6fT+VVVuhq+Pn6nprtfUFAQ3bt3p3v37qiqSnFxsbvb3e7du0lPT8doNHrMn3T+w29HceLECTZt2kR4eDijR4+uNTawNfjqfXN+V7pZs2ZRVFREVlYWEyZMYNOmTbXm1aqoqOD222/njTfe4Nlnn3WvV1WVhQsX8uSTT3LTTTcB8M477xAbG8uHH37ItGnTKC0t5a233uK9997j6qvPFgR6//33SUpKYvXq1YwfP749XrbXSWIkLlxAJAkxZZSVadHrbJg0Zm9HJITvUVV+/vIofaKL2H28O05tJb1v85+HcX9vMTp+/Dg33ngje/fu9RiPUvMtsT8mAm3FH1uM/EFd46AURSEiIoKIiAj69u2L0+kkPz/fnSht3brVnUzFx8eTkJBAXFzcRT/ORFVVdu3axZ49e0hNTWX48OFtXiHY1+6j+sp1h4eHc/PNN3PzzTfXOmbGjBlMmDCBq6++2iMxOnHiBLm5uYwbN869zmg0MnLkSDZv3sy0adNIT0/Hbrd77JOQkEBaWhqbN2+WxEiIJtNoyKsMJ1xTgd1uorKy2NsRCeF77Fb0luNUVBspLuxMWJRC0lUjvR1Vk7lcjUzw6vLdCV4BHnnkEVJTU1m9ejVdunRh27ZtFBYWMmvWLP7+9797Ozyf4m9V6eDsQ63L5fJ2GBdMq9USFxdHXFwcgwYNwmazkZub606Ujhw5AkBERIS7NSk2NvaimlbEbrezceNGMjMzueSSS0hLS2vTpEVVVXeZel9SX7nu+loPP/74Y3bs2MH27dtrbcvNzQUgNjbWY31sbCynTp1y72MwGAgPD6+1T83xHYFv/yXrQE6ePMnUqVNJTU3FbDbTtWtXnn766VrdPTIyMrjuuusIDAwkKiqKhx9+uNY+e/fuZeTIkZjNZjp16sS8efPavKk4LqqMDcd6YHFo0WKlOvt4m15PCH+z4833SYrNpqgihJU/p6GNCAN9QKPH+Qr1v13p6lt8vcVoy5YtzJs3zz0Jp0aj4fLLL2fBggU8/PDD3g7Pp/hbVzpfe6CtT02czfl7bDAY6Ny5M8OGDWPSpEn8+te/5vLLLycsLIzjx4+zevVqPvroI7777jv27NlDfn6+XyeI5eXlfP311+Tk5HDVVVfRr1+/dvn9+uI9VFdVuvrKdWdmZvLII4/w/vvvN9iaeP7rbMocTr46z1NbuXi+YvBzP//8My6Xi9dff51u3bqxb98+7r//fiorK93fZjqdTiZMmEB0dDSbNm2isLCQu+++G1VVWbRoEQBlZWWMHTuW0aNHs337dg4fPsyUKVMIDAxk1qxZbRO8w4pq0XH9wB1EBlbyU04XEgrzMCX4RxliIdpDzpZdJIVXE6gL5qahP9BjzGWg9Z+PYFU9uzS03Zc5nU6Cgs7OGRUVFUV2djY9e/YkOTmZQ4cOeTk63+JvXengbMlsX0/mWuPhMjAwkK5du9K1a1dUVaW0tNRdyGHfvn3s3LkTg8HgMdFsSEiIXzzY5ubmsm7dOgwGAxMmTCAsLKxdruurD/71daWrK/FJT08nLy+PwYMHu9c5nU42bNjAK6+84v6My83NJT4+3r1PXl6euxUpLi4Om81GcXGxR6tRXl4ew4cPb90X58P856/yRe6aa67hmmuucf+7S5cuHDp0iNdee82dGK1cuZIDBw6QmZnpLgP64osvMmXKFJ577jlCQkL44IMPqK6uZunSpRiNRtLS0jh8+DAvvfQSjz32WNu8+RUN5dXBHMmOJCnhZ2LCCwntNbD1ryOEnyo7vJ8exj04VZUNe/viIoBhQ0d5O6xmUV0KagPlulUfL9edlpbGnj176NKlC8OGDeOFF17AYDCwePFiunSRL3HO5Y+JkT84t8WoNf4WK4pCWFgYYWFh9O7dG5fLRUFBgTtR2rZtG6qqEhAQ4B6fFB8fj9nse+OADx06xI8//khsbCyjRo3CaDS26/X9KTGq62czZswY9u7d67HunnvuoVevXvzhD3+gS5cuxMXFsWrVKgYNGgScnfR6/fr1PP/88wAMHjwYvV7PqlWruOWWWwDIyclh3759vPDCC23yGn2RJEY+rLS0lIiICPe/t2zZQlpamjspAhg/fjxWq5X09HRGjx7Nli1bGDlypMcbZ/z48Tz++OOcPHmS1NTU1g9Uq6fHgALCDQphQRbKSwNw5BxC13lA619LCD90dMU6wjVVBBghJbaCiFQ7gb0u8XZYzeLvxRf+9Kc/UVlZCcCzzz7LxIkTueKKK4iMjOSTTz7xcnS+RafTYbFYvB1Gs7TGRJ/tpa26tms0GmJiYoiJiWHAgAHY7XbOnDnjHp907NgxAMLCwtyJUmxsLHq9vk3iaQqn08m2bds4fPgwvXr1YsiQIe1eFtpXW4ycTieKonjc2/W1GAUHB5OWluaxLjAwkMjISPf6mTNnMn/+fHdFxPnz5xMQEMDkyZMBCA0NZerUqcyaNYvIyEgiIiKYPXs2/fr1c1ep6wgkMfJRx44dY9GiRbz44ovudbm5ubUGzoWHh2MwGNwD43Jzc0lJSfHYp+aY3NzcehMjq9WK1Wp1/7usrKxZ8VqjB1H0489UVAZQWqnHZa1u1vHC/13oPXQxMxafwqVaychOpLgwjMhrBno7pGZzuhScDbQYOX28xejcikpdunThwIEDFBUVER4e7pMPRd7kr8UXfLXsco32vs/0ej2JiYnuyTktFos7ScrIyODgwYMoikJ0dLQ7UYqKimq3JLNmstu8vDx+8Ytf0LNnz3a5bl188TPA4XDUKqpR3xijppgzZw4Wi4Xp06e7J3hduXKlRzGHl19+GZ1Oxy233OKe4HXp0qUdZg4jkMSozc2dO5dnnnmmwX22b9/OkCFD3P/Ozs7mmmuu4eabb+a+++7z2LeuN+/533bUNbiuvmNrLFiwoNE4G9Lzyu4c/PQwVqdCsNmKPiS0xecS/ulC76GLlWopw5h3jDBzNQdORuEKsdNj8iRvh9Vs/t5iVJdzW+TF/0hXurbRkuILrclsNtOlSxe6dOmCqqqUl5eTnZ1NTk4OBw8eZPfu3eh0Oo/xSWFhYW2SNBQVFbF27VocDgfjxo0jLi6u1a/RHP6SGNXXYlSX77//3uPfiqIwd+5c5s6dW+8xJpOJRYsWucetd0SSGLWxBx98kNtuu63Bfc5t4cnOzmb06NFcdtllLF682GO/uLg4fvzxR491xcXF2O12j8Fz55dVzMvLA2qXaTzX448/zmOPPeb+d1lZGUlJSQ3Gfa7jK3cQ6AxAYw/EqHWgGPyn2pZoHRd6D12sSla+B9XFOPR6HAVdSbnUgiG8/veir7oYJnhds2YNa9asIS8vr1blrrfffttLUfkef6tKB/7VYuQLcSqKQkhICCEhIfTq1QuXy0VRUZF7fFJ6ejoulwuz2ewx0WxrTLCakZHBhg0bCAkJ4ZprrnEXRfEWX+5Kd35LTX1jjETrkcSojUVFRREVFdWkfbOyshg9ejSDBw9myZIltZqzL7vsMp577jlycnLcVUVWrlyJ0Wh0VyK57LLLeOKJJ7DZbBgMBvc+CQkJtbrYnctoNF7Qmy1zVx77c7vSo2c6AeZKqjJPExDeucXnE/7nQu+hi5KqsuOzTaSFneFkdiJnbDqCKv3zSwN/r0r3zDPPMG/ePIYMGUJ8fLxPPgj5Cn/tSucvfCExOp9Go3E/r/Tr1w+Hw0FeXp67693x42en4AgJCfGYaLbmOaMpVFVlz5497Nq1i86dO3P55Zd7dXzTuXH54v1zoS1GomUkMfIR2dnZjBo1is6dO/P3v/+d/Px897aaJuZx48bRp08f7rzzTv72t79RVFTE7Nmzuf/++wkJCQFg8uTJPPPMM0yZMoUnnniCI0eOMH/+fJ566qk2feNHhZfQMykDk9GOUauiluY3fpAQFzuXi5LcagL7VqBRDESFldHnEv/sZup0aXA2MPWd08cneP3nP//J0qVLufPOO70dis/zx650Go3GJxMOf6XT6UhISHAXe6qurnZPNJudnc2hQ4dQFIXIyEh3ohQdHV3vWBS73c4PP/zAqVOnGDBgAAMGDPCpZMQXi3e09hgj0TSSGPmIlStXcvToUY4ePeoeKFmj5sNeq9Xy1VdfMX36dEaMGIHZbGby5Mkes7aHhoayatUqZsyYwZAhQwgPD+exxx7z6OLUFuLGT6DkxFp0KtgcKtWHDxJ4xQ3NPo/D5mTLvzLQm7QM+1WST31wCtFcNqsTrVNPUXkwltJQ+nTPJ/7uOd4Oq0VUFdQG5o309WdSm83WoebiuBD+mBj5A1/qStdcJpOJlJQUd8+T8vJyd2vS4cOH2bt3L1qtltjYWHe3u4iICBRFoaKigrVr11JWVsaoUaNITk727ovxEw6Ho86udJIYtS1JjHzElClTmDJlSqP7de7cmS+//LLBffr168eGDRtaKbKmSbp5CsWfrUDRuNBhoHB/LpEtOM+mD0+y/t0TABgDtAz6ZafWDVSIdrT27xsI0leTkZ/AmfJw+o1PQRPpn+OuVLWReYx8fIzRfffdx4cffsif//xnb4fi8/wxMfKHL9H8OTE6X3BwMMHBwfTo0QNVVSkuLnYXcti1axfp6ekYjUbCw8MpLCzEYDDwy1/+0icLnqiq6pMtRk6ns86udNJlvW1JYiRajdlsproyBIPBiqW6ZQM0Ndr//XHT6Hzvg0qI5qje8xM9o7P56eAlaB3hmAaO8nZILeZSFVwNJEa+WHzh3JZyl8vF4sWLWb16Nf379681tuGll15q7/B8lj8mRv7Qle5iSozOpSgKERERREREkJaWhtPpJD8/n5ycHA4cOICiKEyYMMEnJ5b1ZXUlRtKVru1JYiRajdnsxOUEo8lKYqfsFp1jxG9SMAboMJi19B/r3fKdQlwQh41h8avQqFWEaI0MuiIDc+8hjR/nq1Ro8HHOB5/1du7c6fHvgQMHArBv3z6P9f7Q2tCeaqrS+eqgdOHbtFotcXFxxMXFUVFRQXl5uU8nRb7aYiTFF7xDEiPRarSBCkZnNUaThaICpUVd6bQ6DcN+JdXshP8r/PoLsFcREOikf49swgZf7u2QLojTBc4GHpKdPpgYrVu3ztsh+KWacQ11fWPtq6TFyDcpiuIXybUvxnj+GCOXy4XNZpOudG3M91Jk4bdcejOVlWbsdg1VJd6ORgjvsuxYDy4bxYWxuFwKQdc0PJ+Zr6uZ4LWhpTkWLFjA0KFDCQ4OJiYmhkmTJnHo0KHzrqkyd+5cEhISMJvNjBo1iv379zfrOmvXrqVPnz6UlZXV2lZaWkrfvn3ZuHFjs855satJhvytO52v64iJkT+0OvpqjOe3GFmtVgBpMWpjkhiJ1qGqVBSVo6JDo3ERaCrwdkRCeI2jMIuqjMMEBZdQWh6LLkRFEx7v7bAuiEttfGmO9evXM2PGDLZu3cqqVatwOByMGzeOyspK9z4vvPACL730Eq+88grbt28nLi6OsWPHUl5e3uTrLFy40GNKg3OFhoYybdo0GV90npqHMX+a5NWfWow6El9NOs7nizGe32IriVH7kMRItA5V5WBGHHbViVZno7jY+5O2CeEtlt0/UFqi4HBpcaChOKCLt0O6YDUTvDa0NMe3337LlClT6Nu3LwMGDGDJkiVkZGSQnp7+3+upLFy4kCeffJKbbrqJtLQ03nnnHaqqqvjwww+bfJ3du3dzzTXX1Lt93Lhx7muKs/yxxcgXH2zr4+sJXGvyh8TIV2M8vytddXU1IIlRW5PESLQOjYZefe2cPBOG1QHYFN+f2ESINlK48Xs6ReWQkRvL8bwAkm9t/pxevsbpUhpdAMrKyjyWmm85G1NaWgrgLud74sQJcnNzGTdunHsfo9HIyJEj2bx5c5PjPnPmTK0KdOfS6XQeE2oL/02MfD3h8MWH77bm678T8N1xUOd3pauurkaj0fjNuD9/JYmRaDUOh4tOoXYMGi2qxgb2Km+HJET7c9rJ2JFPYFA5GlsMqdGVhPfu6+2oLlhTW4ySkpIIDQ11LwsWLGjCuVUee+wxLr/8ctLS0gDIzc0FIDY21mPf2NhY97am6NSpE3v37q13+549e4iP9+9ujq3NHxMj8P3EoyOOMQLf/734KqfTWavFSFqL2p6knaLVlFh1FOSGEhmt4UyZHldFIZqIls1nJIS/Kv55L+EBuVRU69h3shP6SAuXaA3eDuuCNXUeo8zMTI/xPE2poPTggw+yZ88eNm3aVGvb+Q9Vze328stf/pKnnnqKa6+9ttZDhcVi4emnn2bixIlNPl9HUPMw5k+JkT+1GPl6nK3JV7upnctXY6yr+IIkRm1PEiPRaiIjDRQHVIHWTkSAFWtuFuYIKb0tOpbNr61iUHAFVbYA7AGl9BuTBEb//4JApeGpimq2hYSE1FnooD4PPfQQK1asYMOGDSQmJrrXx8WdnccsNzfXo0UnLy+vVitSQ/70pz+xfPlyevTowYMPPkjPnj1RFIWDBw/yj3/8A6fTyZNPPtnk83UE/thi5KvdoeoiiZFv8dX46upKJ4lR25OudKLVBAwdRSA6FJeB3Pwoyg8cavwgIS4yStYp0FeRm9uVWGMA3e+6z9shtYrWrkqnqioPPvggy5cvZ+3ataSmpnpsT01NJS4ujlWrVrnX2Ww21q9fz/Dhw5t8ndjYWDZv3kxaWhqPP/44N954I5MmTeKJJ54gLS2NH374oVmJVkfgj1XpwPcTDl99AG9L/pAYgW/+buoqviBzGLU9aTESrSZ5/ASy31iCgpMAjQal6JS3QxKiXall+aSYM9ACZeUBdBmswxif4u2wWoVLVXA2oStdU82YMYMPP/yQzz//nODgYPe4odDQUMxmM4qiMHPmTObPn0/37t3p3r078+fPJyAggMmTJzfrWsnJyXz99dcUFxdz9OhRVFWle/fuhIeHN+s8HYW0GLUN6Urnu3wxxrrKdUuLUduTxEi0Hp2e4KBQ9FqIjKhEUaVBUnQsZeuWER5aiMseRIAmksjhQ7wdUqtpale6pnrttdcAGDVqlMf6JUuWMGXKFADmzJmDxWJh+vTpFBcXM2zYMFauXElwcHAzr3ZWeHg4Q4cObdGxHYk/Jkb+oCMmRuCbSYc/kK503iGJkWg1zopCFGMhWo1KSEg5pmD/H3AuRFPZCrPZ/p8fuDS6mKLCTiT2Libqhpu8HVarcangamR7czTl4VBRFObOncvcuXObd3JxQfwxMfKnh++OlBhJi1HLuFwuVFWtlRhJV7q2J1/pi1Zjzc/h5NFEHC4NZeUBFO7P8HZIQrSbst2bsOZXY3co5OYnUmGPQ9GbvR1Wq1GbsIiLg79WpfN1/hBja/OXxMjX1Lz3pFx3+5PESLSagM69QWPDbtfhdDmw/3fCRiE6ArOznMSIHKxWPQWFkWjiLq7xK61dfEH4LkVR0Gq1fpUY+YOO2JXOX16rryVvNe89GWPU/iQxEq3HZScmsgKXSyHAXAmBiY0fI8RFwl6YR3xoGWXl4ZgCiug17UZvh9SqnE1YxMVDp9P5VVU6X3uwrUtHTIwANBrff9T0tfun5r0nY4zan+/frcJvqOW5/Hg4BadLh82uxxRY/t8NKlTkg8Pq3QCFaCsuF5Xpa0BXTV5eMlZFiyEqvvHj/Ih0petYpMWo7XSkxMjlamhkoqhPfV3pZIxR25PiC6LVVGQXEBlaSEm1AYe+Ep0rC4CD7y9nzb9t9OhdzZg/T0IbdHF1MRJi3yuvEGsvwOrUcLAohJhOKugvrm/2XDRSfKG9AhHtQqfT+VVi5Gvf+NfFH2JsC77+un1xHFRdXemkxah9SIuRaDWZu/Lo3T2TkKBqOsUVc/KwGVSVvRvKKKyIZsv2JIr37vF2mEK0ut1fnkBnqECv19ElMZ/BE2NAq/d2WK1K5Wzjb72LtwMUrcrfEiN/0pFajOpMOgqOQtaOsz1JfOBn4YtzYMkYI++RFiPRahIGJPDVaz24rE8RBYUhWMp1oCj0GmLixKkKevYsIbTXtd4OU4hWlxqWgc0ChzK6UVwYQXC/Yd4OqdU1No7If0ajiKbwt8RIURSfTzg64hijOhOjwiOoRVkc+2ETdqeBHtemoE0dCMFxXonRF8kYI++RxEi0mrB+Axk4IA9buZ6wwDKCTWUA9L//FnpNOIo+Kh7FFOLlKIVoXY7TP9M5NB+T3k5lmZnEFAvhw8Z4O6xWp9Jwd7mO86jXMfhjYuRr3/qfr6MmRh6sFVhPHifjx2LWbRgAWg06zRG632jwSmJUE5+v3Tv1jTEKDAz0VkgdhnSlE60qoygSo1aPQaPD4fxvHxuNFkNiT0mKxEWp4MM3cCkVVFTEEKQNI+KSnhddNzr43xijhhZx8fC3qnTQsRIOf3Ju0uE4up0vl0aw/6dwKktUXJUKgWoRhCVRXeXg55/yKS2sbrfYfHF8EUhXOm+SxMgHWa1WBg4ciKIo7Nq1y2NbRkYG1113HYGBgURFRfHwww9js9k89tm7dy8jR47EbDbTqVMn5s2b125/MMwmC6crjDi01Ri0VrCWt8t1hfAGy/EDFOw+QlBgGcfyozjttBF7zcXZXVSq0nUs/laVrqbFyJeTo47aYuSRGJ06yNHDYZzO7cTQ7vv45SU/0TnVCSGJfPXWIdZ8dIz/LNyPzdq+SbmvJUfSlc57pCudD5ozZw4JCQns3r3bY73T6WTChAlER0ezadMmCgsLufvuu1FVlUWLFgFQVlbG2LFjGT16NNu3b+fw4cNMmTKFwMBAZs2a1baBqyp6WzWp0UUEmCwUWAw+MbBSiLZi278Rq82KUeckIcROp+QKwvsO9HZYbUKq0nUs/taVDv43zsjXHnJrdPjEyGbBGOSif5cDuCx6osx2nA4T1rBotAYzFSVnv+SttjiwW50YjNoGztx6fLEbppTr9h5JjHzMN998w8qVK1m2bBnffPONx7aVK1dy4MABMjMzSUhIAODFF19kypQpPPfcc4SEhPDBBx9QXV3N0qVLMRqNpKWlcfjwYV566SUee+yxtn3z2y0UWELQWmPoHGenxKKAQfrDiouUqnLk+50EBZRSXBHEodNd6Dp4gLejajNOVJwNtAs1tE34H51Oh91u93YYTeZrD7Z16fCJUdlpHGUq/ZNyycsPIycnkW4DstH2/QUAV0/uyt5NuST3DicwxNBu8fkiKdftPdKVzoecOXOG+++/n/fee4+AgIBa27ds2UJaWpo7KQIYP348VquV9PR09z4jR470+FZh/PjxZGdnc/LkybZ9AXozeiNYXAo6nYWomCpwWNr2mkJ4i9OOJb+UoOBS8srCMQcX0PPeW70dVZuRrnQdi7+1GPnit/7ivMQjYw/7/hOItTCUMK2J7sln6HSZGSK7A9CpawjX3N2D3pdGt3ucvnbv1HSlO7fFSMYYtQ9pMfIRqqoyZcoUHnjgAYYMGVJnEpObm0tsbKzHuvDwcAwGA7m5ue59UlJSPPapOSY3N5fU1NQ6r2+1WrFare5/l5WVNf9FKAoJKaHosw9jMrgoLNWCIrl3R9Eq95Afyd+wiXA1lyBjNZUmPZqgUBRzqLfDajPSla5j8bcxRjV8tQUAfO/hu70oigIOG5Rlo612sTs7hVCTg9CoPJJSk1CNQV6LzVe7XjocDrRarUds0mLUPuSptY3NnTvX/U1WfctPP/3EokWLKCsr4/HHH2/wfHW9gc9/Y5+/T1PKUS5YsIDQ0FD3kpSU1JyX6WbSVOGyBeB0aNG7DJRsXNmi8wj/01r3kL/Yt2QD0cFVqC4Dmae6EDe0y0VZja6GioqqNLBIm9FFxR9bjHxdh+5KV5ZFVV4VPbodIDIyj8zyAEpVA5ijvB0i4Hv3j8Ph8OhGBzLGqL1IYtTGHnzwQQ4ePNjgkpaWxtq1a9m6dStGoxGdTke3bt0AGDJkCHfffTcAcXFx7pahGsXFxdjtdnerUF375OXlAdRqbTrX448/TmlpqXvJzMxs0eu1FVXgdGrQalRKywyU5sgt1lG01j3kD5wVxeiKjuFSK6mqjCDIbCMgbYi3w2pTjiYs4uLhb4kR+P4krx06MSrNpGBfOZbyaKKjSgkPLyK5rwU1YaBPxOeLidG53ehAWozai3Sla2NRUVFERTX+jcj//d//8eyzz7r/nZ2dzfjx4/nkk08YNmwYAJdddhnPPfccOTk5xMfHA2cLMhiNRgYPHuze54knnsBms2EwGNz7JCQk1Opidy6j0dgq30REBlZwKkfB6lCx2+1EReVf8DmFf2ite8gfHPvX58RF5mI0VvLziWR0EQ5Chlx8k7p6aqxVqOM87HUE/jaPka892DakwyVGqgrZP3PiQCeCsBJoVOmXfIYuYzujBkZ6O0SfvHecTqdHi1FxcbGMMWon8nW+j+jcuTNpaWnupUePHgB07dqVxMREAMaNG0efPn2488472blzJ2vWrGH27Nncf//9hIScnTx18uTJGI1GpkyZwr59+/j000+ZP39+21ekq6HXkZpQTXCAnaRkC44DW9v+mkK0M0P2bkJCCwgwqaTEu+g+OAB07VNFyVtkgteOxR/HGPlLi1FHoqoq2Cugqpzi/BhKyoPILTYRPsCEmjjQ2+E1aaiBN5zble7MmTNERUVx4sQJ3n77bVavXo3F8r/CVgsWLGDo0KEEBwcTExPDpEmTOHTokMf5VFVl7ty5JCQkYDabGTVqFPv37/fYx2q18tBDDxEVFUVgYCDXX389p0+fbvsX62MkMfIjWq2Wr776CpPJxIgRI7jllluYNGkSf//73937hIaGsmrVKk6fPs2QIUOYPn06jz32GI899li7xJhlvJbyKg0ulwZUFXtZcbtcV4h2o6poS7LAZafaFkSl1UBlQH9vR9Xm1Cb8T1w8/K0rna892NalI3alA1Cs5VTlVNM78QRmnYbEqCqiuqgQ1tnbofmscxOj2NhYsrOzMZvNlJeXc8899/Dggw+6912/fj0zZsxg69atrFq1CofDwbhx46isrHTv88ILL/DSSy/xyiuvsH37duLi4hg7dizl5eXufWbOnMmnn37Kxx9/zKZNm6ioqGDixIl+1XLcGqQrnY9KSUmp88Ozc+fOfPnllw0e269fPzZs2NBWoTWo6y/7sHVOIjERh7GUO8koCsU3hlYK0TrKd23kyH4dg3tVcSorlYLycC6/bpK3w2pzUpWuY/HHxMjXk6OOmBipqopSVcjWFcn0iv4Z1RmARq9gKXWiD4jwdnhuvnbvOJ1OjzFGsbGxKIrC/Pnz6du3r0eL0bfffutx7JIlS4iJiSE9PZ0rr7wSVVVZuHAhTz75JDfddBMA77zzDrGxsXz44YdMmzaN0tJS3nrrLd577z2uvvpqAN5//32SkpJYvXo148ePb4dX7RukxUi0qshhw4jpXI7VZuZwRipl5e0zc7UQ7aV671YsVhWLzcjJjO4kXD0UQ+zFXYEPwKmAU1EbWLwdoWhN/pYY1fCHpMMfYmwtqsuBxmVHb6/m5zNR5NlVMp16bJHdwQeSEV8uvlBXVTqTyYSiKHXOdVmjtLQUgIiIs4nniRMnyM3NZdy4ce59jEYjI0eOZPPmzQCkp6djt9s99klISCAtLc29T0chLUai1QX/f/bOPDyq8uz/n1mSmcm+7xtrWIKAgKxlkd2lWq0bClLR4itiecFaxf4sWsFWBa1U0b5tBRGFWrWuVYKyCghEUDbDIiEhCwkkmcky+5zfH3HGTDIJSZjMnEmez3XlyizPOc99Zs7MPN9zbxoVQWorsZGVRISpQZJk8QUoEFw2dhulW4/QL7UYq0VDQmw12b++3d9W+QThMepeqNVqJEnC4XCgVMr/GqrcFraeCAQbvY1kt2O6WE9WrJHqOi0mdQ1ZY+yEjvilv00D5NvHqGnxBUmSMJvNlyxwJEkSixcvZty4ceTk5AC4KhU3rUycmJjI2bNnXWOCg4OJjo5uNqZppeOujvy/7QQBh1kdQ5BKIibMhMOmxlZ60t8mCQTewVpPWYGamOhyJGskcSnqLt3UtTEix6h74VyUBZLXKFCKL8jZRq/icOCwWvn+SAxmu5IIrURWpETCgEgIbbl9iD+QmzhqWq7b2Tz9UlXpHnzwQb777jvefvvtZs956nF5qeOWq3DsTIQwEngXuxW7vRK7Q4FDkqg1RKLUhvrbKoHg8nHY2bfiOeLCK0FhR2+IJW5Mb1B1D8e7qErXvXAuygJFGAXC4q3bCaP6C0gSYLFRaIR6ZR1mpQMiM0FGXshACKVrizBauHAhH374IVu3bnVVM4aG/paAxx6XjXtgWiwWqqqqWhzTXZDPmSnoGpj0FP+gxGbV4LAHo9VVYq83Xno7gUDmFH70MUdzK4iKqsBm1VJjiEGRkOlvs3xG6/lFDX+CrkOgeYyci9tAEB2BYKNX0BehUEF0qIF9Jwby3+8GY4qVkDKu9LdlLuT6XjQVRiaTCfAsjCRJ4sEHH+S9997jyy+/pEePHm7P9+jRg6SkJHJzc12PWSwWtm/fzpgxYwAYNmwYQUFBbmNKS0s5cuSIa0x3oXtc6hT4DKn2HI4IO7XmYCwOM5W18TiMtf42SyC4bIp3HaRnxnmUmloqqmIpN2kZkjXA32b5DAcSjlbC5Vp7ThB4OBdl3a1Ub2ciN69EpyJJoD+HhIIwtUS4xopabSWhjwLCkvxtXTPk9t40zTEymUwoFIpmBRkAFixYwFtvvcUHH3xAeHi4yzMUGRmJTqdDoVCwaNEiVqxYQZ8+fejTpw8rVqwgJCSEWbNmucbOmzePJUuWEBsbS0xMDA8//DCDBg1yVanrLghhJPAu6lAG/eIk6u8saBUGUrSVaHoP9rdVAsFlExdyEVV2ETG2aoqNafT5WTUh/a/yt1k+5FJ5REIYdSUC1WMUCMjVS+FVjFVgqkFyOLCZgxib8QNKjZnIQf2RVPKpVitJkiyLizTNMWpcka4pa9asAWDixIluj7/++uvMnTsXgEceeQSj0cgDDzxAVVUVI0eOZPPmzYSHh7vGv/DCC6jVam699VaMRiOTJ09m7dq1bnZ0B4QwEngVh13i+496cmWP71HYNFgURkylhWhTuk/IkaBrkpRazZntWmIyVKAKwaLO6FbVFkVVuu5FoAkjJ3IWHd0qx0hfhMpUjeQI5UKdjtqLoSSlX4RkeXrZ5SaqPeUYtZRf1JbzSaFQsGzZMpYtW9biGK1Wy+rVq1m9enW77e1KyE8mCwIaVWQykg5KS8Oot1n4oTKTE+8c8LdZAsFlYwy2Ex5VzZlzCXz7fRxh2fL8ge8snKF0rf0Jug6BJozktrD1RHcSRtL5E1QePo9DArPCjkFtJSyrHim2l79Nc0OuHqOmDV6dHiNB5yO/s0EQ2GjC6T0qk9AYM0EaBwWFVxESIU4zQeBTXtiXyMg6apURpKfY6X3Ljf42yaeI4gvdi0CrSgfyL9fdbTAZOL25hC0f9gWFApsE9Qo7qVelgyrI39Y1Q46i2lPxhUv1MBJ4B7FiFXidsD7XUlLWF4dNy/gbi+l153R/myQQdJi6gpPsfOZt1FIFVnMYxeXjGHjndBSaMH+b5lOEx6h7EYgeIzkucJvSLcRbdRHmKhtlpQlIKBgQWcMvh58nZtRYf1vmETmeN+0JpRN4F5FjJPA6Nn0ZVrMVcxAUFukYEhzib5MEgg6zc9XHfPivKK4bcYEx4xK4/tfXE9p3iL/N8jkSrZdX6OJLvW5HIFalCwTREQg2Xjb6IrKHVFN76jjfK6JQAXFjwiAyw9+WNUOuDUy7QyjdhQsXKCsrQ6vVolarXX8qlcrttvNPqVT6JOxRCCOB1zl/spIL1eHoVDXUnj+HreQ06hR5xRULBG3FcLqQuuooUqPPUlWTRo/sof42yS+Ict3di0DzGIE8r/x7oksLI0st1JVTd0ZBSpiJ7xWKhoVtWiIOtTxDweR43nSHULr33nuPv/71r6SlpSFJUjNR1PhPo9FQV1fHDTfcwC9+8YtOtUsII4HXiYtToA+rIiHqIuZaidqtHxB152J/myUQdIj0iItMGbqL2PBq0Azytzl+w3YJn5FNCKMuRaAKI7mLDjkuwr2KvhilqYaS4xGY64IBsCIhJfT3s2GekaPHSJIkj32MuprHaPv27VRXV3PbbbdRWVmJ1WrFYrFgsVhct+vr67FYLOh0Oj799FOio6OFMBIEGHUXqTtspV9yOeGhtRhDalFoPCdb1labeX3ZQeoNFmb/fggpPSN8bKxA0DqmotNY6uyERdRRXR9OhKNrXbFrD9Il+hi13uNIEGgolUoUCkXACSO5Ewji7bKoLkRpqycqvJKtJ4YBdThCFUixffxtWYvI7bxxhq929Ryj1NRUHn/8cebPn9+m8b/61a988n0khJHAu6iDwVpN2fkeRPauoNpkoeqchUgPQw9uK+XMkUoAdv7nLLct7r5X4wXypPTfn7P9+yxmjNjK4aKehGqzkF+UvG+QLhFKJ4RR10OtVgth5GUCwcYOYzXhqCjg0If1xNi0jLriKDvJIu6KaJBprrEcPUbOz1xXzzFauHChWy6jJEmu96NxMRVJklCpVMybN4+QkM4/j4QwEngXTTiZs0dx5NlvqKoNoaQ8jqzifI9De+bEEKxVYTE76HtlnI8NFQgugSQh/bCblNAoQrX15Jekc+dvx/nbKr/hUEgoWinJLXKMuh6BKIwCwRsTCDZ2CMM5DOf0nD8XSnFhD4JDLDDGQlCivBu8y1UYdfUco/T0dNftxiKwJcaN883vrxBGAu8THUdQiJGYCAMD0kvBEupxWGrvCH6/YRJWs53oBJ2PjRQIWsd4MJeqEguTrzhCRHgt148qo8/0Uf42y284gNaWDw5fGSLwGWq1OqCq0gUKXVYYVZ8jMsKGyqEmNL6cIJUC0EGofC98ytFj5CmUrit6jJpSWlpKSUkJ9fX1qFQqtFotOp0OpVJJVlaWz4ShEEYCr6MOCWNPfi9Sos6htyrR1rf8IxAWGexDywSCNiJJfL/2bUrPxxAedYayykQUQ0d2eHdVpUbOHa6m9+g4dOHya3DYFuw4aE3+2IU06nKoVKqA8hgplUrZiw65LcK9ht0CNaVgsdA7woFCqkcVZuEIfVDINIzOidzeE0+hdF0xx6gxx44d47HHHmP79u3YbDbsdrurZLnZbObzzz9n6tSpOByOTi/ZLYSRwOt883E5ZkMM9WYtFReiiUup9bdJAkH7MOkp+aGOMLWC6NBqCs6nM+y+Ozu0K4vJztoH9lOvt5DaP5K5L4/wsrG+wYGEQpTr7lYEYiid3AmUcL92oy/BUVvDiW0qtNYgLlzIIr3/CUD+74vc7GsplK4rCiOn+Hn00Ue5cOECb7/9NgMGDMBms7kEUn19Pf37N1Q1FH2MBAFJ+YHjVNeHoVBIKDUWCOmCPwKCro3NQoiqFk1sDSqlRFVdPEpdx6omWk12jAYrAIYKkzet9ClCGHU/Ak0YgfzD1LquMCrCVl5NwYk4UuMvkJhRgCPMAshPeDQmkELpdLqum3JQUFDA//t//4+ZM2f62xQ6X3oJuh0T5/QgLvoiksJBmcVCzXkldMUfAkGXpfA/nxIuaZCCDJw4n4A+PB6Ul04O9URoVDDX/24A/SckcsPjOV621Hc4G7y29ifoWgSaMPLF1WRv0OWEkd0OhhKCg80kRV5k+/E+bM1PpkLpqR6tvJAkSXbnjSePUVcNpXO+9nfeeScnTpzg/PnzfrZIeIwEnUDMhCkM7vMXIkLq6BVuw1IbDHZrQylvgUDmOIwGDr+RhxodQ7MqOFmawfh5o+EyrioOmpbMoGnJXrTS9zQUX2jNY9QxXnnlFZ577jlKS0sZOHAgL774Ij/72c86uDeBNwm04gtyu/LviUCwsd3UllJfVE7uO2n0DypkbHohdo2Z9KHJHKyU/zHLzb7uUq4bfnrtf/e73zFjxgyOHj3K+PHjiYqKchVfAJg4caLPii/ISyYL+OSTTxg5ciQ6nY64uDhuuukmt+cLCwu5/vrrCQ0NJS4ujoceegiLxeI25vDhw0yYMAGdTkdqaipPPfWUT69QXTi4m11nsqizBKEIqQSFyDESBA5HN/yHYKkCg6IWkx2OVyQS1qufv83yO3aFhK2VP3srpbxbYtOmTSxatIjHH3+cgwcP8rOf/YyZM2dSWFjYCUcgaC+B5jEKhDC1QLCx3VQXcfFUPbXn1Tgc4ViVwcREqwjJaehNKDfh0RS52dddynU35j//+Q/nz59n69atLFu2jAceeIA5c+bwy1/+kpkzZ/rUkySEkYx49913mT17Nr/61a/49ttv+eqrr5g1a5brebvdzrXXXktdXR27du1i48aNvPvuuyxZssQ1xmAwMHXqVFJSUti/fz+rV6/m+eefZ9WqVT47DnVIKGW1MZhtQVgcKmoVQeiP5vlsfoHgcqj47nuCIkzERxo4Vx3LD6a+qMOi/W2W3+mMULpVq1Yxb9487r33Xvr378+LL75Ieno6a9as6YQjELSXQKtKJ7cFrie6nDByOEBfRJziPAmheoJ1evok1tBjONjjsoHAeF/kRHcq1+38LCxatIgrr7ySvXv3UlxcTFlZGefPn6e0tJSLFy+69TzqbEQonUyw2Wz85je/4bnnnmPevHmux7Ozs123N2/ezLFjxygqKiIlJQWAlStXMnfuXJYvX05ERAQbNmzAZDKxdu1aNBoNOTk5nDhxglWrVrF48WKffEFF9R/K3TeuI9JaR6jGysHTCQyrqu70eQUCbxCisHC4LIIZVx6nsCqee5b0QpPc099m+R07ElIbii8YDAa3xzUajccrnRaLhby8PB599FG3x6dNm8bu3bu9YLHgclGr1dTX1/vbjDYTKKIjEGxsM3Xl1J0p5WR+FKOzzoNdTUh0KYpxNyH9mJcpZ2Ekx+IL3bFct81m46GHHiIzs6EZcFsavnYWwmMkE7755huKi4tRKpUMHTqU5ORkZs6cydGjR11j9uzZQ05OjksUAUyfPh2z2UxeXp5rzIQJE9wWItOnT6ekpISCggKfHc/AX85EYdUQIjnQ2jWY60SPE4H8kSz1mEurSFAo0SjtBKlT6H3LXf42Sxa01WOUnp5OZGSk6++ZZ57xuL8LFy5gt9tJTEx0ezwxMZGysrJOPx7BpRE5Rt4nEGxsF9VFWGosHPo+k3qjAotNycULSSiDowJCACoUCtm9J92pXLfztf/Vr37Ff/7zH8rLy11luh0Oh1/OIeExkgk//PADAMuWLWPVqlVkZWWxcuVKJkyYwIkTJ4iJiaGsrKzZIiI6Oprg4GDXQqKsrIysrCy3Mc5tysrK6NGjh8f5zWYzZrPZdb/pVd/2og6WMJtCqauLJDLYRPIVvnODCvyDt88hf1D91RYqfogjLj6femMoUYMm+Nsk2WBV2FEqWl4kO2h4rqioiIiIn0qbXyouvumiRI5XcLsrgRhKJ/fFeCDY2GYkqSGMrqeWQalnKagII9iuIzarGkfKYDAagS4oBjsZu92OQqFwq5bX1XOM9Ho9f/3rX9m7dy/Dhw8nLCwMrVaLRqMhKCiIe+65x2fnkRBGncyyZct48sknWx2zf/9+HI4Gj8rjjz/OzTffDMDrr79OWloa77zzDvPnzwc8f8E0XUh4Wmi0tK2TZ5555pJ2tgeFZEelchCqkojR2FGHhntt3wJ54u1zyB9Yj31NnSGCIQPKcVjD6H3/Hf42STa0NZQuIiLCTRi1RFxcHCqVqpl3qLy8vNkFIIF/CMTiC3JfhHcpYVR3AaxGUCjpFWfi0PcpFBuiybxGApWqTWsPOSA3+2w2m5u3CLqux8jJnj17GDt2LIWFhRw/fhyz2YzFYsFms2G1Wt1STDobIYw6mQcffJDbb7+91TFZWVnU1NQAMGDAANfjGo2Gnj17uio0JSUl8fXXX7ttW1VVhdVqdS0kkpKSPC40gFYXG4899hiLFy923TcYDJeV7KZOSEcTBPGxlaiV0ViKfyA4xrO3StA18PY55HOsRiq/t5KTnU98zAWqrQNRhkT52yrZ4LiEMGrtOU8EBwczbNgwcnNz+cUvfuF6PDc3lxtuuKHDdgq8R6AJIwiM/J1AsLFN6IsA0FwoQCnpuKLneYJDThI55XrstO2irKA5noRRV88x2r9/v79NcCGEUScTFxdHXFzcJccNGzYMjUZDfn4+48aNA8BqtVJQUOBKRhs9ejTLly+ntLSU5OSGniibN29Go9EwbNgw15ilS5disVgIDg52jUlJSWkWYteYpgnSzi+0DodDhaZSpUyhxqyjPjSeGnUUQX4KrbJYLJhMJqDheJyvSyDjfF/k9APr9XPI19jMGIISsehqKa1JInTGNRhqu26p+faeQzaFqdU+RpLC3OJzLbF48WJmz57N8OHDGT16NH/7298oLCzk/vvvb/e+BN4n0IRRoHiMugSSBPpzKOxmVBdqkRSh2CUNFikSKTLrxyGBIYzkZp/NZmtWfKCre4yg4bgrKytxOByo1Wq3P18euxBGMiEiIoL777+fP/zhD6Snp5OZmclzzz0HwC233AI0VGsaMGAAs2fP5rnnnqOyspKHH36Y++67zxW6MmvWLJ588knmzp3L0qVLOXnyJCtWrOCJJ55o14ff6cHy2hX/Tc96Zz+XyZ/+9Cd/m+BVampqiIyUZ3dxr59DvuafO4Ff+9uKTudS51BwcPCPnuhLf3aSkpLadeHhtttu4+LFizz11FOUlpaSk5PDp59+6roYJPAvgSaMAoEuE0pnqgZzDWpDBZJRIjqxhroaM9oR6Tii09yGyk14NEVu9tntdo+hdF05x+js2bNs2LCB77//HpPJhEqlQq1Wo1AoiI6O5i9/+YvPbBHCSEY899xzqNVqZs+ejdFoZOTIkXz55ZdERzf0UFGpVHzyySc88MADjB07Fp1Ox6xZs3j++edd+4iMjCQ3N5cFCxYwfPhwoqOjWbx4sVuIU1tISUmhqKiI8PDwFr80nKFSTZOt5Uig2NpWOyVJoqamxq1CodxoyzkEXe+98TfePoe0Wi1nzpxp1kjaE8HBwe2+svfAAw/wwAMPtGsbgW9QqVSiKp2X6TLCqLoIJAeOE8VUn09Eo60jPMaA7WfTXUOcudOC9tHdQulqa2tZsGABe/bsYfLkyYSEhGC1WrFYLNTX17s+L74qzCOEkYwICgri+eefdxM6TcnIyODjjz9udT+DBg1ix44dl2WLUqkkLS3t0gNpe7K1HAgUW9tip1w9RU7acw5B13pv5IA3zyGtVttlf5QFLRNoHqNAEEYgrxDoDqMvgvPlnNrfl7jIcizGUBSREejCm+cyy/19kZt93a34QkVFBdu2bePo0aOtRgv46n0SfYwEAoFAIBA0w9nHqEss5GWC3BbhHcJkAGM1SmMtJkM45WWJ1NeCOS0EgsNcwwIlx0hu2O12txwjSZK6fChddna2bDyMQhgJBAKBQCBohvOqdaCE0wXCArxLhNLpz4HVzMV8I/FxxYSE1qPWmflwTwqOi0WuYYEijORmX1OPkTOMuat6jNLT0/nNb37DqlWrKCgooLCwkPLycqqqqjAYDG79EX2BCKUTdBiNRsMf/vCHgLiKESi2Boqd3iRQjlnYKehuOBdnnkJ7BB2jawijIup+qOLbA6kMyChBbddhC7Ki1ISgUAde1Ve5CaOmxRecwqCrCiO1Wk1GRgZLlixh48aNDBgwAIVCQVBQEEajkSuvvJKXXnoJh8Ph1vS20+zp9BkEXRaNRsOyZcv8bUabCBRbA8VObxIoxyzsFHQ3GgujQEBuC9wuiaUe6i6gU9cSrA7mnd1X0iOpgqQUGzc81BtF5E85RoHiMZIbTct1O9uNdDVh5CymcPToUe677z769u3LjBkzcDgcWCwW7HY7BoOBnj17+tQuIYwEAoFAIBA0w7k4CxRhFAgEvMfox6auQUo7qbHVnKuqosakJjMznfD+OW5DA0EY+arSWXuw2WxuIsgpjIKCgvxlUqfgfO1PnDiB2Wzmq6++anW8L7xFIHKMBAKBQCAQeEB4jATN0J9DYTFRe8SIxhpL/+QKeiZdoNcvrmo2VO7CyLkwl5t9TUPpnBXp5Gbn5eI8nsTEREaNGkVJSYmfLWpACCOBQCAQCATNEMLI+wS0x8hqgtrzUFFO8Q+9qDMGE+oIo9dAHdq+VzQbLndhBPK0rWkoXVftYeR87VNTU1EoFCxevJjjx49z8uRJioqKKC8v5+LFi6L4gkAgEAgEAv8TaFXpQP49ggJaGBmKqT97kcp9ehIS6zBcjCIh7SyaG29G8iAw5H6cchVuTYuddNUeRs5iCkeOHGHr1q0AfP755yQmNuSpqdVqLly4wLx581i+fHmzMuadhfAYCS5JQUEB8+bNo0ePHuh0Onr16sUf/vAHVwlJJ06XdOO/V1991W3M4cOHmTBhAjqdjtTUVJ566qlO//J85ZVX6NGjB1qtlmHDhrFz585Ona8pzzzzDCNGjCA8PJyEhARuvPFG8vPz3cbMnTu32Ws3atQotzFms5mFCxcSFxdHaGgoP//5zzl37pwvD6XDiHPo8hDnkMAfCI9R5yB3wdAi+nOc2evg+HeZKILN9Bh6Bm2shJQ+tNXN5Py+yNG2xh6jd955h9/97ndYLJbL+q72929YUyRJcn0OcnJyeOGFF3jttdd49dVXeeyxx3j44Yd56KGH+N///V+uvvpqwHc5RsJjJLgk33//PQ6Hg9dee43evXtz5MgR7rvvPurq6nj++efdxr7++uvMmDHDdT8yMtJ122AwMHXqVCZNmsT+/fs5ceIEc+fOJTQ0lCVLlnSK7Zs2bWLRokW88sorjB07ltdee42ZM2dy7NgxMjIyOmXOpmzfvp0FCxYwYsQIbDYbjz/+ONOmTePYsWOEhoa6xs2YMYPXX3/ddT842L3s6aJFi/joo4/YuHEjsbGxLFmyhOuuu468vDyfXEW5HMQ5dHmIc0jgDwJNGMFPHhk5LnhBngvxNmG3gqEUnaqGsop+fLM3jFCtjezr9ITrIj1uIlePjBO52tc4x+iKK64gPT2dPXv2kJWVRXZ2Nps2bSInJ+cSe/kJOfyGNUWhUKBSqXA4HGRmZpKZmdmmbXyBEEaCSzJjxgy3hWrPnj3Jz89nzZo1zRa1UVFRJCUledzPhg0bMJlMrF27Fo1GQ05ODidOnGDVqlUsXry4U076VatWMW/ePO69914AXnzxRT7//HPWrFnDM8884/X5PPHZZ5+53X/99ddJSEggLy+P8ePHux7XaDQtvnZ6vZ5//OMfrF+/nilTpgDw5ptvkp6ezpYtW5g+fXrnHYAXEOfQ5SHOIYE/CLSqdHJb4HoiYEPpDCUg2UlL15OVWIpCkkhOLSdy6mQcLWwiV+HRFLnZ1ziULjs7m1tuuYVvv/2Wbdu28cUXX7RbzMjhN6wpeXl52O12rrqqedEOTzgcDp8VyhChdIIOodfriYmJafb4gw8+SFxcHCNGjODVV1/F4fjpK3PPnj1MmDDBrfHk9OnTKSkpoaCgwOs2WiwW8vLymDZtmtvj06ZNY/fu3V6fr63o9XqAZq/ftm3bSEhIoG/fvtx3332Ul5e7nsvLy8NqtbodS0pKCjk5OX49lstBnEMdR5xDAl8QaB4j58JJzsJD7va1SHURisoL1BdE0S+1kqz4esJj1UixfVrcJBCEkRxt85RjpNFoiIqK4uabbyYiIqLN+5Lrb9jLL7/Mn/70JwCMRiMWiwWbzYbdbsfhcOBwOJAkCavVCsDvfvc7fv3rX/vENuExErSb06dPs3r1alauXOn2+B//+EcmT56MTqfjiy++YMmSJVy4cIHf//73AJSVlZGVleW2jTPJrqysjB49enjVzgsXLmC3211zNJ6zrKzMq3O1FUmSWLx4MePGjXNzhc+cOZNbbrmFzMxMzpw5w//7f/+Pq6++mry8PDQaDWVlZQQHBxMdHe22P38ey+UgzqGOI84hga8IxOILgUDACSO7HfSFmE+cpercQGKiSwgNMxJ6ZThSeEqLm8n9OOUq3JoWGbic4gty/A1z8tlnnzF//nzsdjtarRadTodGo0Gj0aDVatFqtahUKjIzM9mzZw+DBg3yiV1CGHVjli1bxpNPPtnqmP379zN8+HDX/ZKSEmbMmMEtt9zicss6cS5eAYYMGQLAU0895fZ40y8gX3wxeZrTX1+EDz74IN999x27du1ye/y2225z3c7JyWH48OFkZmbyySefcNNNN7W4P3/H0otzyPd0tXNIIF8CMZRO7uey3O3zSG0ZxfvLKPg6mV4RJdglJZFR1UgjroU2JMTL9Zjl+t3X1GPkjXLdcvoNg4ZIj+LiYs6cOYPRaMRoNGIymbBYLJjNZiwWi8uLJEkStbW1TJ482Se2CWHUjXnwwQe5/fbbWx3T+Op8SUkJkyZNYvTo0fztb3+75P5HjRqFwWDg/PnzJCYmkpSU1OwKhTPUp+nVDG8QFxeHSqXyOGdnzHcpFi5cyIcffsiOHTtIS0trdWxycjKZmZmcPHkSgKSkJCwWC1VVVW5X/MvLyxkzZkyn2t0a4hzyLV3xHBLIF2eCdKAIIydy9lTIcSF+SfRFGMqsHD6TSnSfIlKiDDgUalQRKbT2SsvVI9MUOdnnDCHzVrluuf2GQcN5cdttt7ldzJMTIseoGxMXF0e/fv1a/XN+GIuLi5k4cSJXXnklr7/+epvKJh48eBCtVktUVBQAo0ePZseOHW4lmjdv3kxKSkqz8ChvEBwczLBhw8jNzXV7PDc316cLQUmSePDBB3nvvff48ssv2xTudfHiRYqKikhOTgZg2LBhBAUFuR1LaWkpR44c8euiVpxDvqErn0MCeaNWqwNGGMlpgdsSAZdj5HCA/hx90qvIiK6gqFpNcVUIFapopIjUVjcNBGEkNy+j87PWNJSucV5te5DLb1hjFAoFDocDq9WKzWZz/dntdtdf4zwjX39ehMdIcElKSkqYOHEiGRkZPP/881RUVLiec1bA+uijjygrK2P06NHodDq2bt3K448/zq9//WvXB3rWrFk8+eSTzJ07l6VLl3Ly5ElWrFjBE0880WlfTIsXL2b27NkMHz7c5aUoLCzk/vvv75T5PLFgwQLeeustPvjgA8LDw11XbiIjI9HpdNTW1rJs2TJuvvlmkpOTKSgoYOnSpcTFxfGLX/zCNXbevHksWbKE2NhYYmJiePjhhxk0aJCrwpicEefQ5SHOIYG/CCRhBIEhPORunxt15Uj1BkyVVgYnVvN1QTon6hyk3xoJP37nWkx26mssRMXr3DaVuzCS4/vg/Kx5s8GrHH7DmqJUKn3Wl6i9CGEkuCSbN2/m1KlTnDp1qln4jvOLJSgoiFdeeYXFixfjcDjo2bMnTz31FAsWLHCNjYyMJDc3lwULFjB8+HCio6NZvHgxixcv7jTbb7vtNi5evMhTTz1FaWkpOTk5fPrpp22qme8t1qxZA8DEiRPdHn/99deZO3cuKpWKw4cP88Ybb1BdXU1ycjKTJk1i06ZNhIeHu8a/8MILqNVqbr31VoxGI5MnT2bt2rUB0X9GnEOXhziHBP4ikISRXBfgjQkE4eaG/hwlB/Uc2N6fwbEXGJNVikJjJurKO3EAxloL/155lDq9lSunpXDVjOYhvnJ/X+Rkn7PQiTdzjOTwGxZIKKSA+oQKBAKBQCDwFX/961/Jzs5m6tSp/jblkpjNZk6cOIFWqyUoKMjf5nhk27ZtWK3WgHg9kSQ4+h/Kcw+xaeOVJIZV0jP+AsowJcNevg9JE0HxKQMfvfI9APEZody8aKBr84KCArZv384dd9zRrNm0HDCbzdhsNvr27Sub86WqqoqXXnqJOXPmuEKmH3nkEYxGI6tXr/azdd7BWfhh7dq1DB06lMGDB/vbJDfk6ccSCAQCgUDgdwKx+IKcCSiPUf1FbNXlmKoMjM/JQxNeRUGtBk2fICRNQy+dpB5h9BocQ3hsMMOmupfulnsonRM52ddSjtHlVqWTE87z4v3332f+/PnN8p/8jRBGAoFAIBAIPBJooXSBIDzkbp+L6iIuntKTfzKRCElLmMZGREQ9WdN+usKvUimZendv7nx8CFkD3XukyV0YOT0XcrLPU46RN8p1ywlnbtHLL79M7969mT9/Pi+++CLV1dX+NexHRI6RQCAQCAQCj6jVatHg1YsEgnADGsLo9EWEK+oBFeX6aGIVKlJ6lRM6qG3VzOQujEB+tnnKMepqHiMnaWlpvPnmm6xZs4a//OUvnD59mnvuuYfo6GjUajUajQa1Wt2sKXlnI4SRQCAQCAQCjwSax0juBIKNAJgMGE4WcOFUHRHqcL4tCyUpspaI9BgS1PLIx7lc5CjcvF2uW87Y7XZUKhX/8z//Q3JyMjfddBPvvPMOMTExOBwO1Go1lZWVHDhwgJSUlEvv0EsIYSQQCAQCgcAjgSaM5O6Rkbt9LvSFHNwlcfxgP67NOYpaa0MZJJEx4+dt3oUchYfc6Yxy3XJFpVJRV1fHe++9x1//+lfGjx/PrFmz0Gq1mM1m7HY7NTU1rj6GvkIII4FAIBAIBB5Rq9WYzWZ/m9GlCAxhVESspppgVTCvfDmBoRmFzJxajjqj7RXEAkUYycm+zijXLVfy8vJYvXo1W7Zs4bbbbuPZZ5+VResIIYwEAoFAIBB4JJCq0gWCxwgCQBiZa6C+igG9ayk4FkKJWqLgQhL00YKq7WW35S6MJElCqVTKyr7u5DG65557iIiI4N///jejRo0C5HHOiKp0goAmKyvL9WPoj4om27Ztc81/4403+nx+weUjziGBoGUCKZQO5LsIdyJ3+wDQn8OQf4GDOyPpEWJhaFoh0wcdIfzKUe3ajbPqm1yRo33dKcforrvuYufOnYwaNQqHwwEgiyqBQhgJ/I7dbmfMmDHcfPPNbo/r9XrS09P5/e9/3+r2zm7OkZGRnWmmR8aMGUNpaSm33nqrz+cW/ERjceHpb9KkSa1uL84hgcAzgVSVzrmgkrNHJhA8WlQXUfGDneLz8ZTWRBOt1KCLDoLY3u3ajeyPE/kJ1e7Qx8jJb3/7W9dtZwlvOSAfSwTdFpVKxbp16/jss8/YsGGD6/GFCxcSExPDE0880er24eHhJCUl+eULLjg4mKSkJHQ6nc/nFvyEU1w0/XvttddQKBQ88MADrW4vziGBwDOBFkond2QvjCxGqDtPrLaQWG0d501KDtdoCBsUC0Ht/46S+3sipwU5/FSprfHr1lVzjOSKvM4IQbelT58+PPPMMyxcuJCSkhI++OADNm7cyLp16wgObntMM8DatWuJiori448/Jjs7m5CQEH75y19SV1fHunXryMrKIjo6moULF7pdCc3KyuLpp59mzpw5hIWFkZmZyQcffEBFRQU33HADYWFhDBo0iAMHDnj78AWXiVNcNP6rqqrit7/9LUuXLuWWW25p1/7EOSQQNBBooXSBgKyFkb6I80cq2HMwmbRIMxMzKrljxGFSpoxr967kGKrWGDnaZ7PZ3PKLoOuG0skVIYwEsmHhwoUMHjyYOXPm8Otf/5onnniCIUOGdGhf9fX1vPTSS2zcuJHPPvuMbdu2cdNNN/Hpp5/y6aefsn79ev72t7/x73//2227F154gbFjx3Lw4EGuvfZaZs+ezZw5c7jrrrv45ptv6N27N3PmzJH3D5uA6upqbrzxRiZMmMAf//jHDu1DnEMCQWAJo0AoviC3hXgz9OewXKjjfHkM3xXHca4ykgriILZHu3clR+Ehd1oSRsJj5DtEVTqBbFAoFKxZs4b+/fszaNAgHn300Q7vy2q1smbNGnr16gXAL3/5S9avX8/58+cJCwtjwIABTJo0ia1bt3Lbbbe5trvmmmuYP38+AE888QRr1qxhxIgRLo/D7373O0aPHs358+dJSkq6jKMVdBYOh4NZs2ahUql48803O/zDLM4hgSCwhBHIX3jIWrjZzDgqzuCw1NM3qgq7A0x2DfH91RAc1u7dyV0YydE+ZyhdY0QonW8RHiOBrPjnP/9JSEgIZ86c4dy5cx3eT0hIiGtBC5CYmEhWVhZhYWFuj5WXl7ttd8UVV7g9DzBo0KBmjzXdTiAfli5dyp49e/jggw+IiIjo8H7EOSQQBKYwkq3wQOb2GUo4faiKfd8kkhFhJ0On4YqeF4kY3b5qdE5ke5yNkJswEh4j/yOEkUA27NmzhxdeeIEPPviA0aNHM2/evA5/sQYFBbndVygUHh9zloj0tJ3zC9PTY023E8iDTZs28fzzz7Nx40b69OlzWfsS55BA8FNVukBY5IL8FrqekO1rqS8k1GYAs8TZC2mY7FrUkQ5Iv+LS27aAnN8PZx8jOdFUGEmSJHKMfIy8zghBt8VoNHL33Xczf/58pkyZwt///nf279/Pa6+95m/TBAHCoUOHuOeee/jTn/7E9OnT/W2OQNAlUKlUSJIUMEJe1h4ZZCwU7FbqTx3DUmshM+oimsgzVEu1XNSFga5jbQzkGKrWFLnZ11QYWa1WJEkSHiMfIoSRQBY8+uijOBwO/vznPwOQkZHBypUr+e1vf0tBQYF/jRPIngsXLnDjjTcyceJE7rrrLsrKytz+Kioq/G2iQBCQOBdpgRJOJ7eFblNkK9xqSvl6s4l1Hw/FYdRRbQijzmEnedLgDu9S7sJIju9D0xwjs9kMIISRDxHFFwR+Z/v27bz88sts27aN0NBQ1+P33Xcf//73v5k3bx5btmyR9ReswL988sknnD17lrNnz5KcnNzs+czMTCGwBYIO4BRGgdbkVa7I1r7qIhQWM3V1Gj4/1ZfxyaVEx1jR9B/Z4V3KUXg0RW6hdHa73c1jZDKZACGMfIkQRgK/M2HChBavRn7++eft3t/cuXOZO3eu22PLli1j2bJlbo+tXbvW7b6nhXPTL/asrKyA+LLvbtx9993cfffdXtufOIcEggYC0WMk98+X7Oxz2MFQzOhRNZjPfY/DocBQG0Naphl00Ze1a9kKwR+Rm31NQ+mcwqi9/RwFHUdeUlkg6AC/+93vCAsLQ6/X+3zunTt3EhYWxoYNG3w+t8B7iHNIIPCMEEbeR3b21ZSB3YpOAT11SjJ1dgZkFdLnlkFwGcIhEELp5GafzWZzC6VzFl6Qm51dGeExEgQ027dvx2q1AhAeHu7z+YcPH86hQ4cA3Mo4CwIHcQ4JBC3jXKQFkjCSM7K0T38ORX0NhiM2QnUOqqoSSOn1A6QMuazdylF4NEVu9tlsNrewOdHDyPcIYSQIaDIzM/06v06no3fv3n61QXB5iHNIIGiZQPQYyRnZebQcDtAXodRf5IfCaDRBCkKiDJjUJrR2UF16Dy0id2EkR/s85RiJUt2+RYTSCQQCgUAg8EggCiNZCY8myM6++gqwmbEW6lErrXxxpBd7C8P574l+7F2/97J2LavjbAG5CSNPoXTCY+RbhDASCAQCgUDgEVGVzvvISjBUn0NprqH4iA6VRcfP+pxkcHohJYosqquAy7RVzu+HHD1GTYsviFA63yNC6QQCgUAgEHgkED1GckZW9kkS6IuQTEZOlodRWRNOiEJJRv8zjM60kjMhq0sXX1AoFLKzr2kfI+Ex8j1CGAkEAoFAIPBIIAojWXlkmiAr+4xVSHVVlBy6gEaK4LRBh6SQGDk+i5wbrr3s3ctdGMkRT+W6RY6RbxHCSCAQCAQCgUcCsSqdbISHB2RlX3UhpUeq2LUzgRRbEOPiq0hMvEjcmLvwhoVyF0ZytM+TMBIeI98ihJFAIBAIBAKPBKLHSO7IRhjpzxGsrKe6OpFKaxBxIVbsNgO9o7O8sns5Co/GyDGUTuQY+R9RfEEgEAgEAoFHlEolSqUyYISRUinvZY1sFuJGPbaLxZw65uCK1B+Iji7nos1KcIIS1MFemUI2AjCAEDlG/kfe3yACgUAgEAj8ikqlCpiqdCDvBblsQun0RVQV1fPVN72w6ePQ2dXEhpoZOC7Fq9PIRgi2gJzskyRJ9DGSAUIYCQQCgUAgaBG1Wh0wHiM5hkc1Rk7CKCopiPSwi9RISkIs4VzRw0bY+J97bQq5h9LJDefFB5Fj5F9EjpFAIBAIBIIWCSRhFAj4XRhZaqG+ElV9HaNjrdjtQei0haTcnoU9PNZr0wSCMJKTfc7PWONQOpFj5HuEMBIIBAKBQNAigSSMZOORaQFZLMSrz+Ew6DnwoRat1YJkCkGpkZDie3t9KlkcbyvIyT7nZ0yE0vkXEUonEAgEAoGgRQJNGMlpsdsUWQi36kJqTx2nrNrK+qP9eTM/jTKdBUfqMK9O4/fjDDCahtL985//5PDhw206nwsKCpg3bx49evRAp9PRq1cv/vCHP2CxWNzGFRYWcv311xMaGkpcXBwPPfRQszGHDx9mwoQJ6HQ6UlNTeeqpp7rVeyk8RgKBQCAQCFokkISR3PG7MLIaMeQfpQQLaRmF6I/1BcARGgTaMK9OJULp2kdTj9H58+fZu3cvn3/+OYcPH+aGG27gf//3fz1u+/333+NwOHjttdfo3bs3R44c4b777qOuro7nn38eaBBe1157LfHx8ezatYuLFy9y9913I0kSq1evBsBgMDB16lQmTZrE/v37OXHiBHPnziU0NJQlS5b44FXwP0IYCQQCgUAgaJFAqkonp4WuLNEXY7qop/BYJEOurGBCyQks+mCS00O9PlUgCCM50TTH6LHHHiM/P5+4uDh69+5NUVFRi9vOmDGDGTNmuO737NmT/Px81qxZ4xJGmzdv5tixYxQVFZGS0lB9cOXKlcydO5fly5cTERHBhg0bMJlMrF27Fo1GQ05ODidOnGDVqlUsXry4W7yfIpROIBAIBAJBiwiPkfdwLiz95jXSF6HVqPjhaCaFO+MYFlHKFYk1pE24yutTyV0Yyc2+lqrSJSYmcv/997N8+fJ27U+v1xMTE+O6v2fPHnJyclyiCGD69OmYzWby8vJcYyZMmOCW1zR9+nRKSkooKCjoyGEFHEIYCQQCgUAgaBG1Wi08Rl7Cr/bZLFBTRlRGNFfG1HC+OBNjZRz9RztQ9Rvp9enknpcit3y0loovdKQq3enTp1m9ejX333+/67GysjISExPdxkVHRxMcHExZWVmLY5z3nWO6OkIYCQQCgUAgaBHhMfIefvUYGYpBclD3XT2pYbWkqTWEqnWoEtOhkwSCnISH3PFUrvvIkSM89NBDLhHn6e/AgQNu+ykpKWHGjBnccsst3HvvvW7PeXo/mnrOmo5xnqvd5b0UOUYCgUAgEAhaRK1WYzab/W1GmwiUxZtfhJG+IUel+riDE1UhmGtVYNXQIzm5U6aTW6haY+S42PfkMUpMTOS+++7jhhtuaHG7rKws1+2SkhImTZrE6NGj+dvf/uY2Likpia+//trtsaqqKqxWq8srlJSU1MwzVF5e7rKlOyCEkUAgEAgEghYJJI+RnBa6ssJuA0MJjpoaTLUWSk3h1ChMxCbUoOo9pFOmlLswkpttnnKMHA4HPXr0oF+/fpfcvri4mEmTJjFs2DBef/11lEr3oLDRo0ezfPlySktLSf5RDG/evBmNRsOwYcNcY5YuXYrFYiE4ONg1JiUlxU2AdWVEKJ1AIBAIBIIWUalUASOM5I7fQulqSsFhp2SbCatFRZIpjAExembOCEEZ1TmeADmKj6bIyb7LyTEqKSlh4sSJpKen8/zzz1NRUUFZWZmb92fatGkMGDCA2bNnc/DgQb744gsefvhh7rvvPiIiIgCYNWsWGo2GuXPncuTIEd5//31WrFjRbSrSgfAYCQQCgUAgaAXhMfIefhNGP4bR2aoucvJ8FjEaBxFBSkInTu+0KUXxhfbhKcfIbDa3SRht3ryZU6dOcerUKdLS0tyec74PKpWKTz75hAceeICxY8ei0+mYNWuWq5w3QGRkJLm5uSxYsIDhw4cTHR3N4sWLWbx4sTcOMSAQwqgVHA4HJSUlhIeHy+rDIxAIBAL5IkkSNTU1pKSkNAtnCUQCqSqd3PGLMHLYQX8OALXSit6k4XR5NOMyyyGud6dOLde1kxxFm91uR6FQuH1nmEwmt9LZLTF37lzmzp17yXEZGRl8/PHHrY4ZNGgQO3bsuOS+uipCGLVCSUkJ6enp/jZDIBAIBAFIUVFRs6u3gUgghdIpFApZLnr9Sm052K04ii9SWZpIeqSBrIRyMrKV0InCXZIk2V8YkJNws9lsbmF00PFy3YKOI0thtGbNGtasWeNqJjVw4ECeeOIJZs6cCTR82J588kn+9re/UVVVxciRI3n55ZcZOHCgax9ms5mHH36Yt99+G6PRyOTJk3nllVfa9SMVHh4ONPy4OeMv20tFbQW9VzdckTm18BTxYfEd2o9AIBAIAgODwUB6errrNyTQCbRQOqc4ktOi14lfPEb6IhR2M+d3KtCGGCktj0UbokZK71xvkVzfA/jJNjnZ50kYtTWUTuA9ZCmM0tLS+NOf/kTv3g0f2nXr1nHDDTdw8OBBBg4cyLPPPsuqVatYu3Ytffv25emnn2bq1Knk5+e7fogWLVrERx99xMaNG4mNjWXJkiVcd9115OXlucVvtobzAxMREdFhYWRSmuDHczo8IpyIsI7tRyAQCASBhZwWXZdDIAkjueNzYSRJUF2Etq6QPoOKMBuD0GiNhA8NJ+a6OZ08tXyFEcjv8yk8RvJAlj7O66+/nmuuuYa+ffvSt29fli9fTlhYGHv37kWSJF588UUef/xxbrrpJnJycli3bh319fW89dZbAOj1ev7xj3+wcuVKpkyZwtChQ3nzzTc5fPgwW7Zs8fPRCQQCgUAQOASSMGrsMZIjPhdGdRfAZkJ5/jwKtQq1TkVyei0R6akQ1H0X3HLsY2S3290u3EuS1OYcI4H3kKUwaozdbmfjxo3U1dUxevRozpw5Q1lZGdOmTXON0Wg0TJgwgd27dwOQl5eH1Wp1G5OSkkJOTo5rjEDQUYqrjRRXG/1thkAgEPgEtVqNw+GQrdhoipwWu/7Gce4w+blVnP5WSW2tDWO9maoLkSiTMjp9buExah9NPUY2mw2HwyE8Rj5GlqF0AIcPH2b06NGYTCbCwsJ4//33GTBggEvYNO3Am5iYyNmzZwEoKysjODiY6OjoZmOadvRtjNlsduvubTAYvHU4gi5CcbWRKSu3A7BlyQRSo3R+tkggEAg6F+dizWazERQU5GdrWsdv5bDbiE/tkyTObj7GZx9kkhIBpz9PQK1UcM3P9pA4cIwPppevMJKjx6ipMHKuR4Uw8i2yFUbZ2dkcOnSI6upq3n33Xe6++262b9/uer7pydyWD+ClxjzzzDM8+eSTl2e4oEtTVWfBaLW7bgthJPAnkiRhs9lEKWU/EBQU1OZ81UDHeZyBIIzkjk8X4sYqLPpqqqr70jupFr2xF2G6evIvRDDQWoOkiurU6eUsjJzIyb6moXQmkwkQwsjXyFYYBQcHu4ovDB8+nP379/OXv/yF3/3ud0CDVyg5Odk1vry83OVFSkpKwmKxUFVV5eY1Ki8vZ8yYlq+SPPbYY25NrJyVhQQCgUBuWCwWSktLqa+v97cp3RKFQkFaWhphYWH+NqXTaewxkjtyWuh6wqceI30R6YOCGbjvGEHqeiYO205xTSTG4Egkq8VVGKqzkKvXzonczpWmHiOnMAoODvaXSd0S2QqjpkiShNlspkePHiQlJZGbm8vQoUOBhgXC9u3b+fOf/wzAsGHDCAoKIjc3l1tvvRWA0tJSjhw5wrPPPtviHBqNRiS5CQQC2eNwODhz5gwqlYqUlBSCg4Nl9yPflZEkiYqKCs6dO0efPn26vOfIuVgLBM+k3IsvOPGJfdVFKENDGZB8kh35g7ErbaSkFTP2nvEQntD584Ns+xjJMZTObrc3E0bBwcGyfQ27KrIURkuXLmXmzJmkp6dTU1PDxo0b2bZtG5999hkKhYJFixaxYsUK+vTpQ58+fVixYgUhISHMmjULgMjISObNm8eSJUuIjY0lJiaGhx9+mEGDBjFlyhQ/H51AIBBcHhaLBYfDQXp6OiEhIf42p1sSHx9PQUEBVqu12wijQPAYyR2fLcRNBjDpOb63HktNDJHBNuwOBcP7KEgcPdYnJshZnMoxzM9TjpEIo/M9spSh58+fZ/bs2WRnZzN58mS+/vprPvvsM6ZOnQrAI488wqJFi3jggQcYPnw4xcXFbN682a2Z3gsvvMCNN97IrbfeytixYwkJCeGjjz7q8j9gAkFLOK+ktvQ3d+7cZuPCw8MZPnw47733ntu+DAYDjz/+OP369UOr1ZKUlMSUKVN47733WvwxLC0tZdasWWRnZ6NUKlm0aFEnH3HXR1xJ9B9yW1R1JoEkjOTuMfJZKF11EQDqi/WYgWiNhZQoA7EjMjt33kbIUXw0RU722Wy2ZjlGIorJ98jSY/SPf/yj1ecVCgXLli1j2bJlLY7RarWsXr2a1atXe9k6gSAwKS0tdd3etGkTTzzxBPn5+a7HdLqfCkm8/vrrzJgxg+rqap577jluueUWdu3axejRo6murmbcuHHo9XqefvppRowYgVqtZvv27TzyyCNcffXVREVFNZvfbDYTHx/P448/zgsvvNCpxyoQCLxH4+ILgYCcFrtN8Z0wKqT6ZDERdSpq7cHU2ZRkDa4jdOI9nTtvI+QujJwiWi54CqUTHiPfI0thJBAIvE9SUpLrdmRkJAqFwu2xxkRFRZGUlERSUhKvvvoqGzdu5MMPP2T06NEsXbqUgoICTpw4QUpKimubvn37cscdd7T4RZ6VlcVf/vIXAP75z3968cgEToqrjVTVWXw2X3RocLsqM06cOJEhQ4bw4osvenw+KyuLRYsWycKbqFAoeP/997nxxhv9bYrfCTSPEcg7jAs62T5LHfU/5PPupjjGhFUSF6wiPc1A4uBw0EV13rxNkPN7IEfbmnqMRCidfxDCSCAQtEpQUBBqtRqr1YrD4WDjxo3ceeedbqLISXeo0CVXnD22nOXkfYEuSOXVfl779+8nNDTUK/sSeI9AFEZyxSf26c/hsDiorAjjlKOOvtE1mO0hSEm9On/uJoj3o+14qkonhJHvEcJIIBC0iNls5rnnnsNgMDB58mQuXLhAVVUV/fr187dpgiY4e2y9eNsQeid0vkA9VV7Lok2HvNrPKz4+3iv7EXiXQKpKJ3d84tHSFxGWFsudo3ZxtiSdMG0tSTmVqEbe13lzekDOoXSSJMkuR9OTMBI5Rr5HXmeFQCCQBXfccQdhYWGEhISwatUqnn/+eWbOnCnLEqcCd3onhJGTGtnpfx0VXzabjQcffJCoqChiY2P5/e9/7zqvsrKy3MLsFAoFf//73/nFL35BSEgIffr04cMPP3Q9v23bNhQKBV988QXDhw8nJCSEMWPGuOXOAXz00UcMGzYMrVZLz549efLJJ928HydPnmT8+PFotVoGDBhAbm5uh46tqxJoHqNuXXzBaoLacpT5Z0kOtzAq+zzpGUY06XGg8a03Vs7CCOSZY9S0+ILwGPkeIYwEAkEzXnjhBQ4dOkRpaSmVlZUsWbIEaLiiHx0dzfHjx/1soSBQWbduHWq1mq+//pqXXnqJF154gb///e8tjn/yySe59dZb+e6777jmmmu48847qaysdBvz+OOPs3LlSg4cOIBareaee35KMP/888+56667eOihhzh27BivvfYaa9euZfny5UBDT6ibbroJlUrF3r17efXVV12NxAUNBJIwAmQtjJx0mn2Gc0imeoyn9Xx3Kp0Tp1M5cTIJe3LfzpmvFeQsjORomyjXLQ+EMBIIBM1ISkqid+/eJCS4NwFUKpXcdtttbNiwgZKSkmbb1dXVBcziSeAf0tPTeeGFF8jOzubOO+9k4cKFrVYpnDt3LnfccQe9e/dmxYoV1NXVsW/fPrcxy5cvZ8KECQwYMIBHH32U3bt3u7rGL1++nEcffZS7776bnj17MnXqVP74xz/y2muvAbBlyxaOHz/O+vXrGTJkCOPHj2fFihWd9wIEIM6Qo0D4bMttsetr7OeO8sEGEyd/CONUZQTHLgZT6AB6j/CLPXJ+P+Rmm8gxkgdCGAkEgnaxYsUK0tPTGTlyJG+88QbHjh3j5MmT/POf/2TIkCHU1ta2uO2hQ4c4dOgQtbW1VFRUcOjQIY4dO+ZD6wX+ZtSoUW4LktGjR3Py5MkW81euuOIK1+3Q0FDCw8MpLy9vcUxycjKAa0xeXh5PPfUUYWFhrr/77ruP0tJS6uvrOX78OBkZGaSlpbnZJPgJhUKBWq0OGGEktwVvYzo1lM5uof7sD+w/nMTX55JJ0dSTqKtj+FgrUlS69+e7BA6Hw+dzthU5eow8lesWOUa+RxRfEAgE7SI6Opq9e/fypz/9iaeffpqzZ88SHR3NoEGDeO6554iMjGxx26FDh7pu5+Xl8dZbb5GZmUlBQYEPLBcEIkFBQW73FQpFswVX4zHOxY5zjMPh4Mknn+Smm25qtm+tVutxgSq3BZMcCBRh5ESuoXSdKoz0JYQlRTK513d8czqVWiS0UjBRYwbiL4ki58+S3Gzz1OBVeIx8jxBGAkE3ZO7cucydO9fjc235wY6MjOSZZ57hmWeeade8cl2sCHzH3r17m93v06eP24LAm1x55ZXk5+fTu3dvj88PGDCAwsJCSkpKXCXo9+zZ0ym2BDIqlSogqtLJbbHblM4VRkXYS2tJt6mpiajjy/yBXDPmEI4M/4TRydEr0xg52eZwOJAkSeQYyQAhjAQCgUDgM4qKili8eDHz58/nm2++YfXq1axcubLT5nviiSe47rrrSE9P55ZbbkGpVPLdd99x+PBhnn76aaZMmUJ2djZz5sxh5cqVGAwGHn/88U6zJ1AJJI9RIBRf8Dp2OxhK0H9jod4OocZwbuj7Pem97BDqnzL4chZGcrPN+dkSoXT+RwgjgUAg6EKcKm85x0sO88yZMwej0chVV12FSqVi4cKF/PrXv/aydT8xffp0Pv74Y5566imeffZZgoKC6NevH/feey/QUFjg/fffZ968eVx11VVkZWXx0ksvMWPGjE6zKRAJFGHkkz5Bl0Gn2VdbBnYLOqmUc/ZQymwKgmrDyEoNBz8JALmJj8bIzTbnZ0uE0vkfIYwEAoGgCxAdGowuSMWiTYd8NqcuSEV0aHCbx2/bts11e82aNc2eb5pr5mnxWF1d7bo9ceLEZmOGDBnS7LHp06czffr0Fu3q27cvO3fuvOTc3ZlAEUYgb49RpwkjfSFFB86jtdopsYMhvJIgrY2IK0Z5d54uhJyEkTNMtWkoXUxMjL9M6rYIYSQQCARdgNQoHVuWTKCqzuKzOaNDg0mN0vlsPoH/CDRhJFc6RRg5HKA/x4XTFqxV8UzIrOSYwUz/wTpCBk703jztRG5emabIybaWQumEx8j3CGEkEAgEXYTUKJ0QKoJOQa1WB0TxBfip71K3oa4ch76KXmHF7C/qSYxOxa1Dq7Bc+0vw42shZ2EkSZKszhORYyQfhDASCAQCgUDQKiqVKmA8RiDfUMhO8RhVF3Fys5HQ8hhGJNWiVIZQb1Si9kPvosbIWRjJDZFjJB/kI5cFAoFAIBDIEhFK5x28LowkCfTnqCu1Y0OisjIcg16HKkYLoQnemaPDpslTnIL8RFtLOUZCGPkeIYwEAoFAIBC0SqAJI7kuyr2+GK+7ANZ6+iSd43hZHNU2uGhX4EhL9msYnRM5iY+myMk2kWMkH/z/qREIBAKBQCBrAinHSE4L3pbwmnDTF2Erq0YyKUmJrGH3qUzqVVZUORO8s//LQG5emcbINceoaSidyDHyPSLHSCAQCAQCQasEUo6RUqnsHh6jH8Po6vMN1FshLdrKrPF70KZKENfHe/N02Dz5CiOQl4D2FEonPEb+QQgjgUAgEAgErRJIoXRyxqs5RiY9mAzoTBUcL4+kujaeILWdfulWUhp5HvyFnIWR3GzzFEoncoz8g3z8iAKBQCAQCGRJIAkjOS14m+JVYaQvourQBSrOhWJ3hFJrVWOwqYjI6dVsqCRJ2K2+DYWUq9cO5HeONA2l+/TTT6mvr293KJ3ZbGbIkCEoFAoOHTrk9lxhYSHXX389oaGhxMXF8dBDD2GxuPe9O3z4MBMmTECn05GamspTTz0l6/exMxDCSCAQCAQ+YeLEiSxatKjF5xUKBf/5z398Zo+g7QSSMJJzKJ0Tr9hXXUjtWSN5R/qTGlFJemw5w64qI2zcVLdhxlor7604wobHDnE67+Llz9sO5CZAnMjVY6RSqaiurmbJkiUUFBTw6KOPsnr1agoLC9u0n0ceeYSUlJRmj9vtdq699lrq6urYtWsXGzdu5N1332XJkiWuMQaDgalTp5KSksL+/ftZvXo1zz//PKtWrfLOQQYIQhgJBII2U1xtpLja6G8zBF2U0tJSZs6cCUBBQYHHq54C/xBIwkjOeG0xbjJAfSXhqkKiY85z8ExPvr+QjDW5JwSFuA09f7qG2gsWJDucyav0zvxtQG7iozEKhUJWttntdlQqFQqFgqioKI4fP05UVBTjx4/ngw8+4O23377kPv773/+yefNmnn/++WbPbd68mWPHjvHmm28ydOhQpkyZwsqVK/m///s/DAYDABs2bMBkMrF27VpycnK46aabWLp0KatWrZL9hQZvIjth9MwzzzBixAjCw8NJSEjgxhtvJD8/322MJEksW7aMlJQUdDodEydO5OjRo25jzGYzCxcuJC4ujtDQUH7+859z7tw5Xx6KQNClKK42MmXldqas3C7EkaBTSEpKElWYZIpKpQqoqnRyXch5LZROfw6l0YBkVJGVWkaZJQQJifj+Sc2GJveJICpJizJIQZ9RcZc3bxtxHp+cxIecsdlsbvlFAFarlXvuuYctW7bwu9/9rtXtz58/z3333cf69esJCQlp9vyePXvIyclx8yZNnz4ds9lMXl6ea8yECRPcvoOnT59OSUkJBQUFl3F0gYXshNH27dtZsGABe/fuJTc3F5vNxrRp06irq3ONefbZZ1m1ahV//etf2b9/P0lJSUydOpWamhrXmEWLFvH++++zceNGdu3aRW1tLdddd13AfLELBHKjqs6C0WrHaLVTVWe59AYCgQccDgePPPIIMTExJCUlsWzZMtdzjUPpevToAcDQoUNRKBRMnDgRgG3btnHVVVcRGhpKVFQUY8eO5ezZsz4+iu5HIHmM5LwY954wKkI6U0JlVTjnLyQytf9Rpo4+SfiIic2GakLU3PC7gdz156FkXhF9efO2kUAQRnKyzZMwamu5bkmSmDt3Lvfffz/Dhw/3OKasrIzExES3x6KjowkODqasrKzFMc77zjHdAdlVpfvss8/c7r/++uskJCSQl5fH+PHjkSSJF198kccff5ybbroJgHXr1pGYmMhbb73F/Pnz0ev1/OMf/2D9+vVMmTIFgDfffJP09HS2bNnC9OnTfX5cAkGg4fQKpUbp/GyJoCuxbt06Fi9ezNdff82ePXuYO3cuY8eOZepU97yIffv2cdVVV7FlyxYGDhxIcHAwNpuNG2+8kfvuu4+3334bi8XCvn37ZLXA6ao4hZGcw6OcdPkcI0s9xh9OUp1fS0VVDw6d6otCaeeWhwogqOUqZv543+R8rsjJNrvd7iaMbDYbdrudnj17trrd/v372b17NwaDgccee6zVsZ6Ot+nnuemYQBC43kZ2wqgper0egJiYGADOnDlDWVkZ06ZNc43RaDRMmDCB3bt3M3/+fPLy8rBarW5jUlJSyMnJYffu3S0KI7PZjNlsdt13xl0KBP7EHwLFGTYHsGXJBCGOBF7jiiuu4A9/+AMAffr04a9//StffPFFM2EUHx8PQGxsLElJDeFBlZWV6PV6rrvuOnr1aqi81b9/fx9a331xLtocDodbE0pBx7gsYaQv4uI5M6fOJjCox1nqLEFEhtUQc+UQr9l3uchdmMoNm83m9rlyrkX37t1LZGRki9tlZWXx9NNPs3fv3mbepeHDh3PnnXeybt06kpKS+Prrr92er6qqwmq1urxCSUlJzTxD5eXlAM08SV0ZWQsjSZJYvHgx48aNIycnB/jJnefJ3ecMpygrKyM4OJjo6OhmY1pzBz7zzDM8+eST3jwEgeCy8CRQfCGUnGFzzttCGHVRbDZYsQJ27YJx42DpUlB37s/CFVdc4XY/OTnZ9eN7KWJiYpg7dy7Tp09n6tSpTJkyhVtvvZXk5OTOMFXQCKcwarqAkyNKpeyyBFw4czX27NlDSUkJGRkZpKSkNAujahX9OZKSTOyt0lGh1ZEVW4FNB/Qa0yk2d4RA8DTIybamoXQmkwlo+L7U6Vr//X3ppZd4+umnXfdLSkqYPn06mzZtYuTIkQCMHj2a5cuXU1pa6vq+3Lx5MxqNhmHDhrnGLF26FIvFQnBwsGtMSkoKWVlZXjtWuSPfbw/gwQcf5LvvvvNYjcOTu+9SJ/mlxjz22GPo9XrXX1FRUccMFwi8RNO8HlEAQeBVVqyAZcsgN7fh/4oVnT5lUFCQ232FQoHD4Wjz9q+//jp79uxhzJgxbNq0ib59+7J3715vmyloQmNhJOgYR44cYc+ePaSmppKdnU1FRQVbt27l7bff5ssvv+TkyZMYjZf4XreZoaaM4AvVDI6tZWd+Xz75vjfBAyJBG+GbA2kDgSCM5ERLwqgtOUYZGRnk5OS4/vr27QtAr169SEtLA2DatGkMGDCA2bNnc/DgQb744gsefvhh7rvvPiIiGs6bWbNmodFomDt3LkeOHOH9999nxYoVLF68uFu9j7L1GC1cuJAPP/yQHTt2uN5YwBVSUVZW5naVsLy83M0daLFYqKqqcvMalZeXM2ZMy1dUNBqNqIgkkDXCkyPwKrt2gTPkRZIa7ssE5xVLTwVzhg4dytChQ3nssccYPXo0b731FqNGjfK1id0Kp5coEISR3KrSSZLEgQMHOHbsGDk5OVx55ZUoFAqGDx+OXq+nsLCQoqIidu/eDTSEkWZkZJCent48jEp/DpX+AheOxoBVy8SMcoKja+lx7TW0/fJC5xMIwkhOtjnLdTsxmUwEBQV5zfupUqn45JNPeOCBBxg7diw6nY5Zs2a5lfaOjIwkNzeXBQsWMHz4cKKjo1m8eDGLFy/2ig2BguyEkSRJLFy4kPfff59t27a5KhM56dGjB0lJSeTm5jJ06FAALBYL27dv589//jMAw4YNIygoiNzcXG699VagoT/GkSNHePbZZ317QAKBQCBXxo2DLVsaRJFC0XBfJiQkJKDT6fjss89IS0tDq9VSWVnJ3/72N37+85+TkpJCfn4+J06cYM6cOf42t8sTSB4jOfWosdvt7N69mx9++IERI0YwYMAAt+cjIyMZNGgQgwYNwmg0UlRURFFREQcPHiQvL4+IiAgyMjLIyMggLi4OR8kxSvMKUVl1VFmDwKah/5h6HAkDWrDAv8jlffCEnGxr6jEym81otS0X0miNrKwsjxcGMjIy+Pjjj1vddtCgQezYsaND83YVZCeMFixYwFtvvcUHH3xAeHi4KycoMjISnU6HQqFg0aJFrFixgj59+tCnTx9WrFhBSEgIs2bNco2dN28eS5YsITY2lpiYGB5++GEGDRrkqlInEHgDUblNENAsXdrwv3GOkUxQq9W89NJLPPXUUzzxxBP87Gc/Y9OmTXz//fesW7eOixcvkpyczIMPPsj8+fP9bW6Xx7loC5SWF3LwGFmtVrZt20ZpaSnjx49vdqG3KTqdjr59+9K3b1+sViulpaUUFhZy8uRJjhw5glarJfF0AUf39GVyegHfFfZBQkVC4QHCZRRGB/J4/QOJplXp2lqqW+B9ZCeM1qxZA+DqWeHk9ddfZ+7cuQA88sgjGI1GHnjgAaqqqhg5ciSbN28mPDzcNf6FF15ArVZz6623YjQamTx5MmvXrpV90qjAN3hD0BRXG7nl1T1A2yu3CSElkBVqNTzxhM+m27ZtW7PHnH2LoPli6t577+Xee+91e+z999/vDNMEl0B4jNqH0Wjkiy++QK/XM3Xq1HYXCAkKCnJ5ihwOB+Xl5RSdOobh2+OcPteX0Uk/EBtZjSQpiPVNz9Z2IULp2ofNZnPLvzSZTB32GAkuD9kJo7ZcZVAoFCxbtsytMWBTtFotq1evZvXq1V60TtAV8FYp6vbm+4gS2AKBIFAJJGHkb2pqasjNzcVqtTJjxgxiY2Mva39KpZKkpCSSTKeouSoCx5nDlBYnM77ft2jD7BwKS6bms89IT08nIyPD7SKxvwgEYSQnbDabmxC6nFA6weUhO2EkEHQ2/ipgECiFE4qrjZwqr/W3GQKBQEYEWvEFf1FZWUlubi5BQUFcc8013hMpdjsYionUSQxNNGCKrEOvjyUorZ64ib/AWFTEN998w4EDB4iKiiI9PZ309HTi4uL88nrIXRjJrVGxp1A6IYz8gxBGAoHAhdOr5RRwAoFAAMJj1BbKysr48ssvCQ8PZ8qUKZfsP9MuasvAZsH8bQ0qSYehIo2gcAuKtAyys7PJzs7GarVSXFxMUVER33//PYcPH0an07k8SUlJST5LJ5BzjpFTFMlJGDXtDyZyjPyHEEYCQScTSP2GnF6tF28bgi5Yxfz1eZ06n8i5EggCg0ASRv5Y8BYUFLBz504SExOZOHGiq9y819AXgSShrK3DYc0kMbmM8N61BM/8hWtIUFAQWVlZZGVlufKSCgsLKSws5MSJE6jValJTU0lPTyctLc0nC285iY/GyM0uT32MhMfIPwhhJBB0AsXVRpfImPOPfQC8ePsQ/xrVDnonhHX6HCLnSiAIHAKpKp2vF73Hjx9n3759ZGVlMW7cOO97ZRwO0J/DdPwCwdQTFl6BKsiCLkWHNTLV4yauvKSkJEaMGEFVVZWrFPiuXbtQKBQkJia6vElhYd79zpdzKJ0cbWsaSidyjPyHEEYCgZdpKRytqs7iJ4vkSaDkXMkZOYerdHW622sfSDlGvkKSJA4ePMjhw4fp378/I0aM6JzFdl052MyYC2qpuZCELlhCEQSOHjkN/ccugUKhICYmhpiYGAYPHkxdXZ1LJB04cID9+/cTHR3tEkkxMTGXfRxyFB+NkZtdnkLphDDyD0IYCQRexrngX3h1b1Z/eeqy9nWxiZhqel/QPXGWda2vr/duHoOgzVgsDZ/F7tICQqlUolQqA0IY+WLR63A42LNnD6dOneLKK68kJyen8+bVn0OqryeMCs5b09BqqglNrsSa/bMO7S40NJR+/frRr18/LBYLxcXFFBYWcvz4cb777jtCQ0NJS0sjIyODxMTEDp3jchZGcrTNUyidyDHyD0IYCQSdxOV6QIqrjdzfKMenuNrIoo2HLtMqQVdApVIRFRVFeXk5ACEhIbL6ke/qOBwOKioqCAkJcVvMdHXUanVACKPOxmazsWPHDoqKihg7diy9e/fuvMkkCfRFXDhgwFyuRRVSD1obRMdBWMJl7z44OJgePXrQo0cP7HY758+fp7CwkKKiIvLz8wkKCiI1NZWMjAxSU1PbnDsVCB5VOX1nihwj+dB9vtEFggDBWZDA6Xn67fRsnvs8v9l9QfcmKSkJwCWOBL5FqVSSkZEhq8VVZxMowkihUHTawtxsNvPFF19QWVnJ5MmTSUtL65R5XNRXgqWeIMN5Pjs4hMq6KEJ0dcybpkep9K63UqVSkZKSQkpKCiNHjqSyspKioiIKCwvZsWMHCoWC5ORkVynw0NDQS+5Trp8Pudllt9vdPHMix8h/CGEkEMiIxgUJnMUaYkPdr9A1ve8PTpXXEh0aLPKC/IhzkZKQkIDVavW3Od2O4OBglEqlv83wKYEkjDpj4VtbW8uWLVswGo1Mnz6d+Ph4r8/RDH0RpuIqbFV2+qcX8vWJMK7ocxrFgF9cetvLQKFQEBsbS2xsLEOGDKG2ttblSdq3bx9ff/01MTExrryk6Ohot9dcjuFqTuRmmyRJHvsYiVA6/yCEkUAgI5oWJJArizYdQhekEtXkZIBKpeo2eS4C/6JSqQKiKh14P5Srurqa3NxcFAoF11xzDZGRkV7dv0ckCaoLqco3UXkxgvQoC1lj9hKSbEMR14nhex4ICwtjwIABDBgwALPZ7OqXdPToUb799ltCQ0PJyMggPT2dxMRE2YmPxsixuSsgQulkghBGgm5Pa7102vKcr5BLP6TXZg/DaLGzaNMhUU1OIOhGBJrHyFsL4PLycrZs2UJoaChTp04lJCTEC1a2AZMBTHrC7CX8YI7mh6J0tBoLfVP1ZKr8t3zTaDT07NmTnj17YrfbKSsro7CwkIKCAo4fP05QUBAJCQ35T3I+X+QijpyvUdNy3VFRUX6yqHsjhJGgW1NcbeSWV/cAzXvpXKrPzuVWnGsr0aHB6IJUrP7yFLogFdF+DqUTQkgg6J4EijAC7y16CwsL2b59O/Hx8Vx99dXeb9zaGvpCCveXU/Z9PEFBFqrUZrRqCyGDfestag2VSkVqaiqpqamMGjWKixcvUlhYyJkzZwDIzc0lOTnZ5U3ymai8BJ0VbtkRnJ8pUa5bHghhJOjWtNZLp7XnXps9jNQoHcXVRuY3qRzXHs9Oax4pJ6lROrYsmUBVnYXo0GBZh9gJBIKui1qtDohQOueC93I9RidOnGDPnj1kZGQwfvx434es6ou4WCJxujSWIUl6hmaUEBxhI/7qeb61o40oFAri4uKIi4sjMzOTjz/+mP79+3Px4kX27t3L3r17iYuLcxVviIqK8os4kVvFvJZC6USOkX8Qwkgg6ACpUTpyUt1jzFtq7NoSl/JINZ3P+XygCKO2iD6BQBA4dBePkSRJfPfddxw6dIi+ffsycuRI3xfaMNdCfRX9s8opzAum2GAnLlhNxk09IFgeXpfWcIqPXr16MWLECEwmE+fOnaOoqIjvvvuOgwcPEhYW5irekJCQ4PPXWG4eI5FjJA+EMBIIvERjD1N7x3e1XJ32iD6BQBAYBIowauwxai8Oh4N9+/aRn5/P4MGDGTx4sH8W0PoikByEmerprQ3GagkjNqac0CtycPjemnbTtPiCVquld+/e9O7dG7vdTklJCUVFRZw5c4bjx4+j0WhcTWWTk5NdTaw7yza5h9KJct3+QwgjgUDgdbqy6BMIuisqlSqghFF7sdvt7Ny5k7NnzzJq1Ciys7O9bFk7qC6itqQcXU0wcQkXMFTHEJ5mwRHX1382tYPWRKlKpXKF00mSxIULFygsLKSwsJDTp0+jVCpJSUlxjdHpvP/7IRdRBMJjJDeEMBIIvEBxtRGjRf6x9wKBQNBR1Go1JpPJ32Z0ChaLhS+//JKKigomTZpERkaGH40xUnv6e/72ejQ3p1QSF1pHbOIFgq69Ckkb4T+7OsClwuMUCgXx8fHEx8czbNgw9Ho9RUVFFBUVsWfPHvbs2UN8fLwr5M4bZdLlVkpc5BjJCyGMBAIv0LgAg0AgEHRFAimUzlmuuy3U19ezZcsWamtrmTZtGomJiZ1s4SUwnKO+2oihPIpzZFJsiiQh1kB2aCyBcvmtowUOIiMjiYyMJCcnB6PR6MpLOnToEN988w0REREukRQXF9fhvCS5iCIQHiO5IYSRQOCB4mojp8pr27XNi7cNQResEiJJIBB0SQKpwWtbhZFeryc3NxeHw8HMmTOJjo72gXWXoLqIxJ7RzOiTj6U+jNjgOmyKWs4djiG5v7+Naxve8MrodDr69OlDnz59sNlslJSUUFhYyKlTpzh69CharZa0tDTS09NJSUlxExadbZs3ETlG8kIII4GgCe2tLuekd0KY231n/6H27kcgEAjkSCB5jNpCRUUFX3zxBRqNhhkzZhAWFnbpjTobmwVqy5CUwfSLNlFmCeNCtYawxDrOfh1G8s0WUPm3l11b8Lb4UKvVZGRkkJGRgcPhoKKiwhVyd+rUKVQqFSkpKWRkZJCWltYmUSEXYSRC6eSFEEYCQROchQMu1wPk7D90pFjfaV4kURJbIBD4ikASRpda9BYXF7N161aio6OZPHmyfK7OG4rB4UA6+C2ffzuI8GA7CdHnOWvtxxWDbaDsvGpt3qQzvTJKpZLExEQSExMZPnw4er3eVbzhq6++AiAhIcEVchcR0TwvSy6iCEQondwQwkgg+JGLTfoDNfYAOQVIe5q3QoNg6ay+Q8XVRm55dQ/gm5LYTV+fluhIGKJAIJA/gSKMnLQUSnf69Gm++uorUlJSmDhxYptDsHyCvhCFzYipvJ6C8mQckpIREXqufyoTZY8hIKMFfWv4solqZGQkgwYNYtCgQRiNRgoLCykqKuLgwYPk5eURGRnplpckynULWsPHHcvaxo4dO7j++utJSUlBoVDwn//8x+15SZJYtmwZKSkp6HQ6Jk6cyNGjR93GmM1mFi5cSFxcHKGhofz85z/n3LlzPjwKQaBx//q8FoXP/PV5XLd6V6d6fo4U69slvJyeLaPV3i7x1d65iquNbD9Rwf3r89AFqYgObTmMwxmGuGjToTbbIxAIAoNAEUatLXiPHDnCrl276NmzJ5MmTZKXKLLbMJ08zsFNVYSF1jC41wn6pBUQE1OPsufwgPEWNcbX4kOn05Gdnc2UKVO4/fbbmThxInFxcZw4cYJPP/2Uf/3rXxw4cIDS0lLZnMt2ux2lUulWSEJ4jPyHjL4RfqKuro7Bgwfzq1/9iptvvrnZ888++yyrVq1i7dq19O3bl6effpqpU6eSn59PeHg4AIsWLeKjjz5i48aNxMbGsmTJEq677jry8vLcVLlAAPDb6dk893l+qwJj4dW9Wf3lqU6ZvzMLNhRXG6mqs7gEjTN/ShekapOnyWmbLkjFO/ePbtUL5q0wRIFAID8CRRhB8+ILkiRx4MABjh07Rk5ODldeeaVsPAYuako4d9jOgQP9CeMiY0Yc42xRHEmD4wPGU+REDgUOgoKCyMzMJDMzE4fDQXl5OYWFhZw9e5YzZ86wd+9eevfuTXZ2Nn369OmUfkltwWazNVuXihwj/yFLYTRz5kxmzpzp8TlJknjxxRd5/PHHuemmmwBYt24diYmJvPXWW8yfPx+9Xs8//vEP1q9fz5QpUwB48803SU9PZ8uWLUyfPt1nxyIIDGIbeUFaChnr7FA1bwmvxnlHjQtJ6IJUvHj7EIxWu2uutjZfffG2IYzoEdPm16BpIQqBQBD4BEpVuqaLcbvdzu7du/nhhx8YMWIEAwYM8JNll6C6iNgkC1plLbnbR3Jlr+MYTFH0nx8gpegaIQdh1BilUklSUhJJSUkMGDAAo9GI0WgkPz+f999/H4VCQWZmJtnZ2fTr14+oqCif2Waz2dw8l3a7HZvNJjxGfkKWwqg1zpw5Q1lZGdOmTXM9ptFomDBhArt372b+/Pnk5eVhtVrdxqSkpJCTk8Pu3buFMBK0SHG1kUUbD7lCxjorP8gT3hBeTiEEDXlHTg9OYyHUkbl6J4SJAg8CQTdHrVbjcDhwOBwd7h/jK5weI6vVyrZt2ygtLWX8+PH06NHD36Z5xmEHQzFxIfXccNUu9IZQTp7rSd8Bpahz7vW3de1GbsKoKTExMfTq1Yvx48djMBg4ceIE+fn5bNmyhc8//5zExESys7PJzs4mOTm5U4+jqTAym80AQhj5iYATRmVlZQDNGrAlJiZy9uxZ15jg4OBm/QgSExNd23vCbDa7TkgAg8HgLbMFAcKRYj1Gq51191zVqYUTvI3Ty+UUQs7bTloSNW0tqHCpeQUCQdfHuXiz2WwEB8u3ZLRzEWsymdi1axd6vZ4pU6aQkpLiZ8taoaYMjDVYT56nsCIejT2SXskXyZzRE0ktQqo6k4iICIYPH87w4cMxm82cOnWK/Px89u3bx44dO4iIiKBv377069ePrKwsr6dj2O32ZhXpQAgjfxFwwshJU/XurDLSGpca88wzz/Dkk096xT5BYOHsObT6y1PoglQBFwp2//o8tiyZ0ObxzuN1btdeb9Dlbi8QCAIP5+JN7uF0CoWCuro6tm/fjt1uZ8aMGcTGxvrbrNapPsfJT/WEVmrJK04lNtRIGJA5aJS/LesQcvYYtbYW1Gg0DBw4kIEDB2K32yksLOT7778nPz+fAwcOoNFo3PKSvCFemuYYOYWRyDHyDwEnjJKSkoAGr1BycrLr8fLycpcXKSkpCYvFQlVVlZvXqLy8nDFjxrS478cee4zFixe77hsMBtLT0719CAIZ4uw55CxSEEgL/bYUjmhKapSOV2cP4+5/7mtznlFL2x8p1rsVd7gUzmIQ7S19LhAI/Etjj5GcKSsr48svv0ShUHD11Vc3ix6RHQ4H6AupOBPM92X9GZZeQJ1DSd++lUgxffxtXYeQszCCttmlUqno0aMHPXr0YMaMGZw/f94lkt577z2USiVZWVmukLvIyMgO2dI4lM5kMnHhwgXUarUoFOYn5B0k7IEePXqQlJREbm6u6zGLxcL27dtdomfYsGEEBQW5jSktLeXIkSOtCiONRkNERITbn6D7kBqlIyc1MqBEEbgXjvDFdk23d5Yyn7Jy+yXFjjMHqjNLnwsEgs4hEITRmTNnWLduHREREdx8882EhYVhMBioqqqirq5Ont6u+gvUni5EqapAqa7DbleSEKbAkdwPZJ7L1RJyFkaSJLU7R06hUJCUlMTEiROZP38+ixYtcuWrf/7557z44ou89tprbNu2jbKysnb1cWocSpebm8vw4cORJIk1a9ZQXFx8ye0/+eQTRo4ciU6nIy4uzlWYzElhYSHXX389oaGhxMXF8dBDD2GxuF9IPXz4MBMmTECn05GamspTTz3l015UckKWHqPa2lpOnfqpOteZM2c4dOgQMTExZGRksGjRIlasWEGfPn3o06cPK1asICQkhFmzZgENzb7mzZvHkiVLiI2NJSYmhocffphBgwa5qtQJBJ2BM8TMebu70LS4Q0s0LQYhEAgCB+cVbLkKo6NHj/L++++TmZnJrbfeikajwW63U19fT21tLQaDgZqaGhwOBxqNBo1GI48+Rvoizn1nYueJHJLDq3BcjKFPrJ6MCYEZRgfyFkZw+XZFRkZy1VVXcdVVV2EymTh58iT5+fns3buX7du3ExkZ6apwl5GR0ar3p3Eo3fXXX88777zDrFmz2LhxIwsXLuSrr75i5MiRHrd99913ue+++1ixYgVXX301kiRx+PBh1/N2u51rr72W+Ph4du3axcWLF7n77ruRJInVq1cDDdFRU6dOZdKkSezfv58TJ04wd+5cQkNDWbJkyWW9ToGIDL4RmnPgwAEmTZrkuu8Mb7v77rtZu3YtjzzyCEajkQceeICqqipGjhzJ5s2bXT2MAF544QXUajW33norRqORyZMns3btWuGaFHQqzpA85+3ugvNY21qMoTu9NgJBV0HOHqN9+/bx3//+l5ycHG688UbXb71KpSI8PJzw8HASExOpr6+nrq4OvV5PXV0dDoeDoKAgNBoNQUF+aKAqSVBVQFxoNeP6fMfZCxn063WeAVeasGZe4Xt7vIwchZG3PSFarZZBgwYxaNAg7HY7BQUFfP/993z//ffs27cPrVZLnz59yM7Opnfv3s1yh5oWX0hISCA6Oprt27dz4cKFFkuH22w2fvOb3/Dcc88xb9481+PZ2dmu25s3b+bYsWMUFRW5io+sXLmSuXPnsnz5ciIiItiwYQMmk4m1a9ei0WjIycnhxIkTrFq1isWLF8vyPexMZCmMJk6c2OqJq1AoWLZsGcuWLWtxjFarZfXq1S5FLBD4Cn8t+k+V16IL7mThb7PB00/D+vX0sTn4Tfpo/jrmdpen7LnP8zt3foFA4DfkKIwkSWLr1q3s3LmTUaNGMW3atBYXckqlkrCwMMLCwoiPj8doNFJXV4fBYKC+vp6amho3keSTBaGxioqthejzYwmzO+gbWY1RH44+JpsQZeBeyJV7GFZnlZtXqVT06tWLXr16cc0111BaWkp+fj75+fkcPnzYlbfkzEsKDw/3WK7bWdQhLi6uxbm++eYbiouLUSqVDB06lLKyMoYMGcLzzz/PwIEDAdizZw85OTluFRmnT5+O2WwmLy+PSZMmsWfPHiZMmOAm2KZPn85jjz1GQUGBfEvcdxKyFEYCQZfAZoMVK8jcspWHpFQUP/+pSd/FOkvbc3xsNnjqKdi1C8aNg7sXAD81cnWKkkWbDnn7CJqzYgX8WLlRA/ymsABJoST1NxN45/7R3PLqHpdNgVLqXCAQtA25VaVzOBx8/PHHHDx4kClTpjBmzJg2ixmlUkloaCihoaHEx8djMplcniRn6J1arUar1XauSKoupPx0MBeKwxnQ5yy1JguRcTWEjLq1c+bzEV09lK6tc6SkpJCSksKkSZOoqqpyiaRPP/2UTz75hJSUFPR6vVsxMZPJ1KZqdz/88AMAy5YtY9WqVWRlZbFy5UomTJjAiRMniImJoaysrFl7m+joaIKDg13ta8rKysjKynIb49ymrKxMCCOBQOAlVqyAZcsIlyQWoeDo2kSIuRpoKK/94u1D2rSb+NUrYdWfGkIutmwhw2RFFzTWVVo8JzWSLUsmsP9MZTNx5PU+Q7t2ud1VAiPOHQVw2QEEVA8ogUDQNuTkMbJarbz77rucOHGCG2+8kcGDB3d4XwqFAp1Oh06nIzY2FrPZTH19vSvcrra2FpVK5cpL8uai2vrDERI1p6mQsikpDyUy3AzxURCR5rU5/IGchVFb2rt0BtHR0YwaNYpRo0ZhNBo5efIkhw4dcp1jTtauXcvRo0dbtXH//v04HA4AHn/8cW6++WYAXn/9ddLS0njnnXeYP38+4Pk9aPoaeGqB09K2XR0hjASCzmLXrgYxAyiRiPt2H0y6ut3ltUP27XHtB0ki4sDXbPnX0malxaua9F4qrjayaOMhdEEq7xWCGDcOGlV7dACHMnK46cf9i9whgaDrIhdhZDQaefvttykrK+OOO+6gTx/vlbRWKBRotVq0Wi0xMTEukWQwGKitraW6uhqFQuESSZcVkmXUc2ZXJZkaJVcMPMe+7zPRK2oYOSwVVH7Id/Iicg6l85cwaoxOpyM+Pp7S0lJSU1OZPXu267mrr76a/Px83n777Ra3z8rKoqamBoABAwa4HtdoNPTs2ZPCwkKgoX3N119/7bZtVVUVVqvVrcWN03vkpLy8HKCZt6k7IISRoNvQGf1zWq1CN24cbNkCkoQDBRcGXwW0v0x2/VWjCd+1vUEcKRQwbhypUbpLihBnBbh191zVpjmLq42Xfm2WLm3oubF+PQC1t9zOTQ8/6jVB5JxfCCyBQH7IoSqdXq9nw4YN1NbWMmfOHNLSOtez4hRA0dHRWK1W19V9g8GAXq+/PJGkLyIspBalyUS4TsP1Y/ejjTVSM+D6zjkYH6NQKPwuQFrC33adP3+e9evXExMTw1133eWW36PRaIiJiaFfv36t7mPYsGFoNBry8/MZN24c0OBJLSgoIDMzE4DRo0ezfPlySktLXeF6mzdvRqPRMGzYMNeYpUuXYrFYCA4Odo1JSUlpFmLXHeiQMPrwww/bvc3UqVPR6cRiR+AfnP1zjFbvxsa3WoVu6VIAarZs5f+kVPjFPbC9oN1zVCxcQmKE9qccox/32xSnSGt6jG0VRW16fdRqWLas4Q+I+PHPGzhtANiyZIIQRwKBzPC3x6i8vJwNGzagUCi45557Wk1M7wyCgoKIiooiKioKm83mKthgMBgwGAxIkkRwcDBarbZtFXD154gOqcJaYUGBhYv6UOKS1BAR+I3l5ewxgs4rvtAWKioqeOONN4iMjOSuu+5qlk/U1hyjiIgI7r//fv7whz+Qnp5OZmYmzz33HAC33HILANOmTWPAgAHMnj2b5557jsrKSh5++GHuu+8+V6/OWbNm8eSTTzJ37lyWLl3KyZMnWbFiBU888YTfBaQ/6JAwuvHGG9s1XqFQcPLkSXr27NmR6QSCy8bpPXniugE893m+VwVSiwt4tRqeeALDQ7/ln899yT0rnmFD8TESa6egChnXds/Vj/tpix1blkzgSLG+3c1Tna+PymFn0dfv0PvrVeiHjUSlHIXdR5WRnDY4bwthJBDIC+di0h/CqKioiLfeeouIiAjuuusut/Yc/kCtVrsawSclJbmVAW9TryRLLVQWsG9bCtq6NIrLMrA5JK7pfYSwIE3z8QGGHMLV5MiFCxdYt24dYWFhzJ4926PD4MyZM1RWVrZpf8899xxqtZrZs2djNBoZOXIkX375JdHR0UCDl/eTTz7hgQceYOzYseh0OmbNmsXzzz/v2kdkZCS5ubksWLCA4cOHEx0dzeLFi12tcrobHQ6lKysrIyEhoU1j/f0FJhA4uapHTIfFQzN+rDrn5snx8AOYGqVjt7SX8K/eQiFJSK8cYtHPSlmpvP3y5vfA5RY9+Lx+F712bkAhSWi2b2XB2Fm8NPYOL1rYQEveLYFAIF8UCgVqtdrnVeny8/P597//TWpqKrfffnubrqb7ksa9khISEtrWK0lfjEIB9YYQEqKLMVk0REZXE9yv40Uk5ISchZG/bKusrOSNN94gJCSEOXPmEBIS0mxMfn4+r732GqNGta25b1BQEM8//7yb0GlKRkYGH3/8cav7GTRoEDt27GjTnF2dDgmju+++u11hcXfddZfLZScQ+BuvVUz7seqcs1oc0KJnJ+LA164CCgpJ4j5lCZMWNniNOizQ2ijM2kPid/tRNLLTWXHO21yOd0sgEPgPtVrtU4/RN998w8cff0y/fv246aabPHtfZERbeiWp1Wq0ZSc494mJIcnllF1MIjG2kswrqwgeMdXfh+AV5CyM/GFXdXU169atIzg4mDlz5hAaGtpszOnTp7n66qv5n//5H5YvX+5zGwUNdOgb5vXXX2/X+DVr1nRkGoFAvthssG6dW7W4pqWs3WhUiAGFAu2kCeSkRrY6hcJm46Gv3iZz9/MwZVJz4dNUmDkcoFR67Jvk5FLluxsXepAUCvanDXQ/5ksJsUZj4q8YjkrZ8lWv1LAg1K+/xPp3/0u8bSYVC5e0aptAIPA/vhJGkiSxc+dOtm7dyrBhw7jmmmv8mhfSETz1Sqqvr0d/4TzGwlMc+qoP2Ql24iOMhIbYCElPQNI09yIEInLOMfK1aNPr9axbtw6VSsWcOXMICwtrNubs2bNcffXV3HbbbSxfvly2orI7IO9LLwJBZ2CzEf/Cn1n/7n+JqrwaVdSEdu8ifvVK+LG5mosfq8J4xFkw4RIFFBozYO3L3LLrLZRIsGs7bNvWIESc2zcqB44kNVSKO3PGY98kJ899nu8q3+3Ja1bxP78h8Zu98O231PUbyKsjb/7pyRaEmNsxNRqTsGULC8bOgt+08PquWEHCqj+RKElIq75teEw99pKvi0Ag8B++EEYOh4PPPvuM/fv3M3HiRMaPHx/wC8XGvZJiTIVUaGFAxrfk5/djxBV5BIfZqe01EpXJ5PVeSf5Crsfgy2p5BoOBdevWAQ3RVp6ip86dO8fVV1/Nddddx8qVK2X7unUXvCKMdu7cyWuvvcbp06ddccDr16+nR48erhKCAoFsaLwg//u3LBh7vuXFO6By2Fmw519M2PonHgrO5OXRtzb0FmpMz56ti502FFBomncT9+2+BlEEDWJk69aG27m58OKL7hs7v0g99E1qyquzh7UYThi/5i+ueUJ372TtOT2zb/tjw5Pbt7sLsTfegIICd6G0fr1byGDTUDw3j9WuXW5heyH79sAYIYwEAjnT2cLIZrPx/vvvc/z4ca677jpXSeEugyRx9LX1bHoricEJhYwa8i04QgnLNMCAcdRa7d7tleQn5BxK5ytqa2t54403sNvtzJ07l8jI5lEiZWVlTJ48mauvvprVq1d3+9dMDly2MHr33XeZPXs2d955JwcPHsRsNgNQU1PDihUr+PTTTy/bSIHAq2zb5rYgH1l0xL0HUZNwsAf3FvCbrzaiRGIRXwFQP2Ik4Tu3/bTN7NkN4ucy8n6a5t1cGHwVifu++kkcNaaq6qfbPXtCZmaDSPkRZ98klcNO/At/JuTr3bxVWIVdoaCv7Rr401MebWgs+BTA6MLvWPjVWw3CsamHrLS0uceq0RgJ2J82kGE/iqHiaiP3r8/7qeHsuHFIW7Y0FKRQKKi/arRr21PltW7NawUCgTxQqVSdJoxMJhObNm3i3Llz3HrrrZfs4xKImH/4hh2fqimtDKe+pgdBfU+jIz5kPwAAagVJREFUsYbSMyOG+L4Dvd8ryU/IXRh1tm11dXW88cYbWCwW5s6d66oS15iKigomT57MyJEjefXVVwPmve3qXLYwevrpp3n11VeZM2cOGzdudD0+ZswYnnrK8+JLIPArZ864bkrAVVI1QWFB8NRTDYLGZnN5TRJyc3kIhUucKPnRCzIsxX2fW7c2CIOqqp9ES0vhZq0IpcaenGNzF7DlWCkPFO1BU6N3F0ONKSlxEyTm9ExezhxH6twFPPjI70n46u0GkUOD2JFWfgt/XUWfhETWK6KIt13ryu+pv2q0m+BTAHPzPsH8wp/h3LlLvLDu2LN68M/xd/B/6/PYsmQCVXUWLGYLX5i+IvXWVTBmDOWLHuHE+5/T9+Yfc4zW7AVg0aZD6IJUop+RQCAzOqsqXU1NDRs2bECv13PXXXe5GlR2NUw/fEtk7AVunXCUY9v6YrKDWakkeuRQ4NK9kpxlwNvcK8mPyFkYdSb19fW88cYbGI1G7r77bmJiYpqNqaysZOrUqeTk5PDPf/5T9u9ld+KyhVF+fj7jx49v9nhERATV1dWXu3uBwGs4vSeNF/gKIKi2xj1/phEKQIWE9ONtB6ByOIh/bbX7zj2VuZQkWL68QWhBi5XrnKF6mV89B0FKMq12HiIdfv4UkkJJcNHZZna5YTK531coeGnsHfwZ+FXeRzh/mtz+m80EFxXyMwqRVh1ueEI9tqGZ7F+e+8lmINRiJHLlM83n1Wjc505PbxCdP9qq1lezf/0CXs0YQ9W9I0Ct/vE432oYk5tLbEQkRLkvgF6bPQyjxc6iTYdEPyOBQGZ0RijdxYsXefPNN7Hb7fzqV79qcyuQQESrsZOeUUHCDRVEJ5go2tWDAf2rUfeb3Gxsa72SamtrsdvtroaycqvWJ+fiC9B5os1oNLJ+/Xpqa2uZO3dusybE7733Hu+88w779++nf//+vPnmm7J777o7l/1uJCcnc+rUKbKystwe37Vrl2joKpAVC/b8iwTnorwxBgM880yr4kNBg3fJrlAyuvA72vyV2ngB0ULlugV7/sUiZ5EFIBxYhIKKh34g+OhhV9hfW1Hpq1m/6ff03aIj0lTb4jiXUJIkVLu/gvFjG7xZY8c25BPRcMxKyeH5ePV69/vffQfjx8PXXzcIpqoqdFVVLCo6S/nqLCoWLuHmI1+4HY/KoOdnhu/cxFljIXSpKnoCgcC3eNtjVFxczFtvvUVISEiLeRhdBrsVjeMiWanFGHcHExFmJzO6mt4TBkJoHKZ6GxqdyuOi/VK9kpwiya1Xkh/pjqF0JpOJN998E71ez9133018fHyzMenp6ezYsYPq6mqKi4v55S9/yf/93/916YsBgcZlC6P58+fzm9/8hn/+858oFApKSkrYs2cPDz/8ME9cItlcIPAlI84d9SwybDZ3AdMCCkDdkkhoK/v3N3imfv97V0jdiKIjzfKIlEgk5O1xCbL2zKnSV/Mz/SGkAvftWtqPAyjNGd5wx2ZziRvJZELxoy1toqrKJagao6Ahdyls5bNkVJc1ew4axFnsP9bwVvjH9HnLgFKl5OH00SxQwee/vVp4jQQCmeBNj9GpU6f417/+RWJiInfccYfHhpddiqoCHPWnUYxQ8PffzCBZZ0ITVEfKRCX5/z7Dh699T3KPcBb+ZTTakJaXZ017JZlMJldOkluvJK2WoKAgvwgUuQsjb2M2m9mwYQOVlZXMmTOHxMTEZmPq6up4+OGHGTBgAB9++CEFBQV8/PHHHkPtBP7jsoXRI488gl6vZ9KkSZhMJsaPH49Go+Hhhx/mwQcf9IaNAoFX2J82kHFnv/1JHCmVDTlAl8ApKNorUDxSXQ1PPtkgIAoL6XfhIpKhptm+G993Pe4s7uDEk/1aLYofw9ua2qzIzIQePeDQoQY7fnx+b8YVFM1dAB8eJ/4vz8ELf3abt9Vj1mqbh/I1QQJUZ86gPPx9i/uSAHV1NaOrq11jFhSc4cqzR6j69ZdCGAkEMkGtVmM0Gi97P9999x0ffPABvXr14pZbbpGFl6PTqchHoY5DbVcydvR3HP6mL+FBED3qavJeKgGg9EwNpT/U0COnebK+J5RKJSEhIYSEhLj3SvrRk1RbW4tarUaj0RAcHOwzsSJ3YeRN2ywWC2+99RYVFRXMnj2b5OTkZmOMRiM33HADCoWCDz74AJ1OR//+/enfv3m/QYF/8Upg4/Lly3n88cc5duwYDoeDAQMGeGxgJRD4k5dH38qdI9JIXPu3Bu9GG0QRNAgDq0KFWvJiwvG2bcBlfgAdjubCpLUv+7lzGzxV2dkuYaQAUg3lpD54F29dqCW++FizzVoVhMnJbsUsPG0LEHLuLOmt7MuTCHNWxStfvRKeX9HiHAKBwHd4oyrd7t27yc3NZciQIVx33XXdIvHcbrjA4bd2gsNKedFVWE0qkqIlrrtNQtt/HGNvOMd7q4+R0S+StL7Ne920BbdeSTExmM1m6uvrMRgM1NbWUldXh0qlclW460zh0l2EkdVq5e2336asrIy77rqL1NTUZmPMZjM333wzRqORzz//vOt7RgMcr2V8hYSEMHz4cG/tTiDwOnalqsHL0lJ1t1ZQS/bL9xa1A49zeVqMWBrl4CgUMGIE0o4dnj1cu3c3FJloUlI7vboM5cEyMjzM6+Yty8qCw4dh1aqfquw5HA0esBZwREai+jEXybmfpp4vp3jyZLMzDE8gEMiDywmlkySJ3Nxc9uzZw7hx47j66qtlvXj2Jof/8Q5vb8hCpZK4YepBrPZ4eo6qJ2nKDFAoGDUznVEz0702n0KhQKvVotVqPYokZ3EsrVYbUGXA5YTNZmPjxo0UFxdz5513kp7e/P2zWq3cdtttXLhwgdzcXOE0CABEKQxBt+Civp6Hvnqb2CMd66vltVA6b+P0ejl7GX3zTctjx41rVvxBQcshc83C+fr0gbAw96p6NltDRT5n89kmqPT65vvxsKhq6fV18GMJ8ZaPSiAQ+JCOCiO73c4HH3zA4cOHmTFjBiNHjuwE6+RL2TffU1o6BIUkYTPUcu29PYFwSBrkk/mdXqLo6GisVqtbGXBnryRnhTtviKSu7jGy2Wz861//orCwkFmzZnksL2+z2bjzzjs5e/YsX375ZdcuLNKFEJcIBF2eI8V6Dt//MIt2bUBd3cRb1MYfAAlQTJjQELrWFLmU2ty2DfR6z0KnZ8+GHkrjxrV5d5JK5fLmSABWK0yb1tDvyVmwYsUK+PZb9w2bvB6KqKhW52lNmO3NuMLVY0kgEPgflUrV7qp0FouFt99+m6NHj3LzzTd3O1GEJKHASk7maQZk/oCqDrBZIDgEIr3nJWorQUFBREZGkpaWRt++fenVqxcJCQkoFAoMBgOVlZWuSncdRa7CyBtlxO12O//+97/54YcfuP322+nRo4fHMXPnzuXYsWPk5uZ6bPAqkCcyWdEJBJ1DcbWR21/Zxa59H3i+CtDWPCOtFgoKmufzjBsHhYVw9qw3zO04paUtlhuXAMXddzfcsdvbXHRCYW8SPvhjXhRbtjTcLijwnF/UtEjEkCEwaVKz5rmXnB+wK5XyEZ4CgaDdHqO6ujreeustLly4wJ133tk923gYisnpUY7pXD1qNMRFOsBhgdg+bb4411k07pWUmJjotV5JchVGTjpqm8Ph4N133+XUqVPcdttt9OrVy+OYX//61xw4cIDt27c362UkkDdixSHouphMRF47nf1ff43Wfpn9cEym5uJHkhrKb1+iKptPaFIlqllu0COPNHh3/vjH1pvFNqJprs9PO5daFzdNX4+Cgob5oSHsbtKklkVVk2PIrCol+vafw9TJDR4vb4kkp7fLmSvlzX0LAhNxTrSJ9gijqqoq3nzzTcxmM3PnzvVYras74DjxJYVfxxNqiiMh6SRRWXYICoX4bH+b5oY3eyXJtcGrU7B1RBg5HA7ef/998vPzufXWW+nTp4/H/T/44INs376d7du3eyzbLZA37f7Wr6qqQpIkYmJiqKioYMeOHWRnZ5OTk9MZ9l02r7zyCs899xylpaUMHDiQF198kZ/97Gf+NkvgC665htDdOzs3L0gOoqgx0dGwcCGKbdvgxyIMFBQ0NLDdubPNosirFBTAjBnuPY4yMxv6Je3Y0eJmCiBdfx7FrvPw1c6GB1vrjdaehe2KFUjLljWUbt+y5dL79idyWLDLwYbOJpDOCT/SVmFUVlbGhg0bCAoKYt68ed03lEiSsJ3ZT3VVHBf1MdTZemJJLSYuSANRzfNS5IKnXklOkdTWXkly9Rh1VBR98MEHHD16lF/+8pdkZzcXtZIksXjxYj799FN27NjhsUKdQP6065ft73//O8888wwOh4NHHnmEDRs2cMUVV/CHP/yBhx56iF//+tedZWeH2LRpE4sWLeKVV15h7NixvPbaa8ycOZNjx46RkZHhb/MEnc2337YuiiZOhL175SduLgdJaqgS19S9/+abzcPn1GrIyIDiYjCbO9euvXvd75892yCOJk1y9z45w2x+rJznev8kqVnhiGY0WthKW7Y0bNvCwta0dTtap0hsy779iRwW7O14bQOVgDon/EhbhNGZM2fYtGkTMTEx3HnnnYSGhvrIOhlSW87FozUkpxZhU/WgXrISEWqF2N6gCoyLC417JcXFxbn1Sqqvr6e2thaVSoVWq3X1SupKoXSSJPHRRx9x+PBhbrrpJgYMGOBxzGOPPcY777zDjh07xBozgGlXcOvq1as5evQoeXl5/Pa3v+X999/nlVdeYffu3bzyyiudZWOHWbVqFfPmzePee++lf//+vPjii6Snp7NmzRp/mybwBYMHuxcPaMz48Q0L8t/+1sdGdTI1NS0/ZzC434+IgLvvblkUqdWQleX+mKeqOp4KUjTFUxLv/v3NvQ69ejXY9OOPluv9c+ZztYJp63ZX816FJGHaur3FsfphI3H8KLvasm9/0vi4/LVgb89rG6gE0jnhT9RqNXa7vcVQqaNHj7JhwwZSU1O5++67u7coAmoOfUn+ARsff9+XGpWBlNQieo5Kgzh5hdG1FWevpNjYWHr06EHv3r3JysoiMjISq9VKdXU1er0em80mS2HU3hA/SZL45JNPOHToEDfeeGOL0VFPPvkka9eu5csvv+yeeXRdiHZdrnBeEdBqtfTu3Zv4+HgAIiIiZPcBsFgs5OXl8eijj7o9Pm3aNHbv3u1xG7PZjLnRItHQdCEpCCw+/ZS6ydOwfnOQCMmKqrEAcMZHP/FEQ/Lr+vUN99PS4LvvGsRCQkKDB2PcuIaQr337muXydDpqdUOJ7B97Trho2tjVibOPwuzZ7v2FZs9uXlZ78OCWF9kKBfy//9cQMtU4hOqRRxrC8t5886f9/v73DaFW11zTUKFu0KCGuRr/AKnVDX+NbdZqG/a5ZUvDWOdidOnShud37qSu3szBYgNxM6dgv3sBFOtbfKlU/YaSvW0rSiQcKDjTbyj2Fsafvm0+p78uZMS5o8TNmHzJffuTxsclKRSUXzGcCh/b2p7XNlBpfE70vXkmic7zUOCGsxmr3W5vloy/b98+/vvf/5KTk8ONN97YLRq3toajrpI3/3SETLODU2VpnL2YyPUph1HHxENM4C+em/ZKslgs1NXVUVNTg8PhwG63U1VVJcteSW1Zs0qSxGeffUZeXh4///nPueKKKzyOe+aZZ3j55ZfZtm0bffv29bapAh/TLmGkVqsxmUxotVq2N8oXqGntKrWfuHDhAna7vVniW2JiImVlZR63eeaZZ3iylWaVggBDq6X+v5vZ8HUh9217k7Bnnv5pAT5+fMMYtRqWLWv4uxQ2Gzz9dIOIcjga9lVe3rDA/5//aRAPreTMtAtn5TibrUEUTZzYUP0O4K674LHH4NlnG4TOoUNQW9sg6g4fbhjz+9837KNxTkhj8TJ4MHz6acM+GguTiRMbXpPGeSRNQ6aefLJ5U1e1Gr788qf7Eye65xSNHNnwmv/xjz899uCDP4mgprkrP86przby65XbMVrtsKZJOF4TVOHjWTT+PHMc53hDmcaL4eOxr27FuzL2jp9uX2Lf/kQVPp4F48oYce4o+9MG8rJyVOvH1Uk2tOu1DVTG3sEtw9L47fTsrpdD5SWcYshms7luS5LE1q1b2blzJyNHjmT69Omyu1jqD2wVBVQU2smMl5jQ5yiSWmL0WCCmB6iD/W2e1wkODiY4OJjo6GgiIiKoq6sjPj6+03oldYS2Fl+QJInNmzezb98+rrvuOoYOHepx3AsvvMDzzz/Pl19+ycCBAzvDZIGPadc3/5dffolGowFwa1RlNBr5xz/+4V3LvETTk7+1uNfHHnuMxYsXu+4bDAaPnYwFgUNChJb/ndoXJj0BGrX7Ary9XEpEORPUd+78qSz2uHENt996q2HMnXc2/H/zzQbBEx3d8JjDAa+80uBRueqqhm0be3eCguD0aff5nnii5TwPT4KmqXiBloXJ5fLZZ81FmNNr1III8kRqlI4tSyZQVde2qoLRoVOJjNJxU7WRSZfYJjq0YWHS1n37k+ilUxv+11mY5i8b2vHaBirRocGkRun8bYasaSyMoCEp/eOPP+bgwYNMmTKFMWPGCFH0I45TBxmScI5PDo1CpYDROd8RPyAT4rq+V0GhUKBWq0lLS8Nms7lykQwGAwaDAYfDgUajQavV+tyz2BZR9MUXX7B3715mzpzJsGHDPI57+eWXefLJJ9myZQuDBw/uDFMFfqBdK6CwsDCPjyckJJCQkOAVg7xFXFwcKpWqmXeovLy8xfKJzs7Qgi7IJRbgnT5HY08JNDRJbcrTT7s/v22be4hZZ9BZr4tW21yEQYfmSo3StXux2p5tAmkhLAdbO/J+CLoOTmFkt9uxWq28++67nDhxghtvvFEsDptQ8tkBtLZIglUKai1KSk06HFYbyrjmZZ67Go0vQndWr6SO2gWti6Nt27bx1VdfMW3aNK666iqPY/7+97+zdOlSPv/8c4YPH94ptgr8w2WdgefOnWPNmjXs3r2bsrIyFAoFiYmJjBkzhvvvv9+v3pbg4GCGDRtGbm4uv/jFL1yP5+bmcsMNN/jNLoGgTXjy5AgEAoGfcS5ca2treffddykrK+OOO+7w2NOlO2M+d4Kqc+WYbWlMH/gtZXYTvQdKKJNHQ1DXv7AgSZLHcLmmvZKMRiO1tbVuvZKCgoJcZcA7g9ZE0Y4dO9ixYweTJ09m9OjRHse88cYb/O///i+ffvopo0aN6hQbBf6jw8Jo165dzJw5k/T0dKZNm8a0adOQJIny8nL+85//sHr1av773/8yduxYb9rbLhYvXszs2bMZPnw4o0eP5m9/+xuFhYXcf//9frNJIGgTvvBwCQQCQTtxCqN3330Xk8nEnDlzSEtL87NVMsNSxwePvofx+xSKbFBSlUFmciWzrw+HhH7+ts4ntKVct1KpJDQ0lNDQUJdIqq+vp7q6us29kjpiF3gWR1999RVbt25l4sSJjGshSmPTpk088MADfPDBB6InZhelw8Lof//3f7n33nt54YUXWnx+0aJF7N+/v8PGXS633XYbFy9e5KmnnqK0tJScnBw+/fRTMjPb1lTN+QG6nOp0NbU18GMhrhpDDVpHG0obCwQCgSBgcf5mtLc0cCBQW1sLQF1dHTfffLMQRR6w11zg2/1K0iKsXDREERZWQ6+Uc6h0w7tFfhG0TRg1RqFQuHolxcbGtqlX0uXQdPu9e/eyZcsWxo8fz4QJEzxu8/7773PPPffw7rvvMnny5MuaXyBfOiyMjhw5wpvOkr0emD9/Pq+++mpHd+81HnjgAR544IEObeustuetkMDef+rtlf0IBAKBQP7U1NS4FSrqCiQkJBAZGYnRaOTtt98mPj6eAQMGMGDAANnlGvsLVVgcI/t+x5kyLcW1kQSp4aKlBiJTQRPub/N8wuU0eHX2StLpdMTExGA2m6mvr8dgMFBbW0t9fT1KpdKVF97eeZqO37dvH59//jljx45l4sSJHrf55JNPuOuuu3j77beZMWNGh45LEBh0WBglJyeze/dusrM9Nynbs2cPycnJHTZMDqSkpFBUVER4ePhlXZ1wVrcrKioiIiLCixYGBuL4u/fxg3gNxPF3r+OXJImamhpSUlL8bYrXiY6OZtGiRdhsNk6fPs2xY8fYu3cv27dvJy4ujgEDBjBw4EDi4+O7b3W6iyfpEWfh3Ik0JmedxKRUctMtwQHb1LUjXI4wakxrvZJqamqo/rHHX1t7JTUNpcvLy+O///0vo0aNYvLkyR5t3rx5M7fddhvr1q3j5z//+WUfk0DedFgYPfzww9x///3k5eUxdepUEhMTUSgUlJWVkZuby9///ndefPFFL5rqe5RKpVfDBJxVWbor4vi79/GDeA3E8Xef4+9qnqKmqNVqsrOzyc7OdhNJX3/9NTt27HCJJKcnqTuJpKqdmzlxpCfJkTXUO0IZ2MdKfM906IRqdIc3l7Lv34X0m5DA2Dt7eH3/HcVbwqgpjXslWa1WVy5STU0Nen1D02mnJ8lTGfDGfYwOHTrExx9/zIgRI/j/7d15XE354z/w122PkiVtVMoaWUYZEopI9m1sQ8luiEmMGWNfG2NfRjQfE9nKOoYxZrKULSIhspNoFSmStnt+f/je8+uqUN321/PxuI/HdM77nPN+3zLdV+/N0dExz/oGBgZi4MCB8Pb2xjfffKPw9lDZU+hgNHnyZNSqVQtr167F1q1bkZ2dDeDDiiNWVlbw9fXFkCFDFFZRIiIiKns+DkmPHz+WC0m1atUSQ5Lsj6gVVlY63t+9jaSXjaEKHaipZ6JJu9eAlh5QpabCH/fvxntIT81C3MM3aNWrDqpWLxsbxxZXMMpJVVUVOjo60NHRybVX0ps3bz65V1J4eDiOHDmC1q1bo0ePHvkuxtC3b19s3LgR3377bbG2hcqOIi3XPXToUAwdOhSZmZlITEwE8GH/oOJaYpGIiIjKLhUVFTRq1AiNGjWSC0lXrlzBuXPnULNmTXG4XUUMSdJnYUiNTIWx3itkQ0C9hs+ga/lVsS26UMdCB4+vvkQtk6rQ0CrePYAKoiSCUU5fulcSAERHR+PSpUto1aoVevfunWc9Q0JC0LNnT/z6668YPXp0ibWDSp9C/hWpqqqW+/lExUldXR0LFiyotJvHsv2Vu/0A3wO2v3K3vzy7du0aAgMD8c0338DExKRA1+YMSdnZ2WJIunr1Ks6fPy+GpKZNm8LAwKBChKSM2+fxKrketKtmQIASquj932ILtYtnftHgZS0Rey8FeuZaUFb59PyaklTSwSinT+2V9OjRI4SEhKB58+bo06dPnnUMCwuDk5MTFi9ezO1dKiGJUEzriT579gwLFizAH3/8URy3JyIiomJ24cIFLFq0CGfOnIG1tTUGDx5cqJCUU3Z2Np48eYLbt2/j7t27eP/+PWrUqCH2JJXbkJSdBSHoVzw9HoPXz6SoVvsdTLrXgUptY6DtRKA8tqmQ9u7dC4lEgmHDhpV2VUR3797Fvn370LhxYwwePDjPhRpu3boFe3t7zJo1C7NmzVJ4HTw9PXHo0CHcvXsXmpqaaN++PVasWCG3kJmrqyt27Nghd13btm1x6dIl8ev09HTMnDkTe/fuRVpaGhwcHLB582Yun68AxRaMbty4gdatW4tzj4iIiKh8SkxMxOHDh7Fv3z4EBgYqPCRFRETg7t27SEtLE0NS06ZNYWhoWH5CUuJDIHw/IEiBrPeAiuaHMGTSFqjfpbRrV6L27NkDZWVlDB06tLSrAgB4+PAh/Pz80KhRIwwaNCjPhRnu3r0LOzs7uLm5Yd68ecVSDycnJwwbNgxt2rRBVlYW5syZg/DwcERERKBq1aoAPgSj+Ph4+Pj4iNepqamhZs3/P0ftu+++w9GjR7F9+3bUqlULM2bMwKtXrxAaGppn2+jLFToY/fXXX588//jxY8yYMYPBiIiIqAIpzpAUGRkp9iSlpaWhevXqYkgyMjIq2yEp/Q2QcAd4cQ9IiQZkH69au3zYw6gS2b17N1RVVcvEIlyPHz/Gnj17UL9+fQwZMiTP4PDw4UN06tQJo0ePxtKlS0vs5+zFixfQ09NDUFAQOnXqBOBDMHr9+jX+/PPPPK9JTk5G7dq1sXPnTjF4xsTEwNjYGMePH0f37t1LpO4VVaGDkZKSEiQSySd39pZIJOU6GEmlUsTExBR5HyMiIqo8cu5j9Ll9Vcq7Fy9e4PDhw9i/f78YkoYMGYJvvvkGxsbGEAQBL1++hK6uboHuKwtJsp6kd+/eoXr16rCwsECzZs3KQUh6C7x8ALyOAiz6VqphdACwc+dOaGhoYPDgwaVaj8jISOzevRv16tXD0KFDoaKSe2p9ZGQkOnXqhCFDhmDlypUl+nP18OFDNGzYEOHh4bC0tATwIRj9+eefUFNTQ/Xq1WFnZ4dly5aJGyifPn0aDg4OePXqFWrUqCHeq2XLlujfvz8WLVpUYvWviAodjOrUqYPffvsN/fv3z/P89evXYWVlVa6D0fPnz2FsbFza1SAionLo2bNnlWrM/8chycrKCqqqqkhLS8OVK1cK/YFTKpWKIenOnTt49+4ddHR0xJ6kOnXqlO2QVAn5+vqiSpUqpbr3T1RUFHbt2gVjY2MMGzYszxWTnz9/jk6dOqFXr17YsGFDif4cCYKAfv36ISkpCefOnROP+/v7Q0tLC6ampnjy5AnmzZuHrKwshIaGQl1dHXv27MHo0aORnp4udz9HR0eYmZlh69atJdaGiqjQq9JZWVnh2rVr+Qajz/UmfWzz5s1YuXIlYmNj0axZM6xbtw4dO3bMt3xQUBA8PDxw+/ZtGBkZYdasWXKrh2zfvj3PJRbT0tKgoaHxRXXS1v6wmkxRdmt/8fYFGmxsAAB4OPUhamvVLtR9iIiofEhJSYGxsbH4O6SyqF27NiZMmIAJEyYgOjoaAwYMwLVr15Ceng5bW1txuF1B/+CopKQEc3NzmJubo2fPnnj69Clu376NmzdvIjg4GDo6OmJPEkNS2SAIQqn2lj5//hy7d+9GnTp18gxFmzZtws2bN/HPP/+ge/fuWL9+fYn/3Li5ueHmzZs4f/683PGc87IsLS1hbW0NU1NT/P333xg4cGC+9yvNlQArkkIHox9++AGpqan5nm/QoAHOnDnzRffy9/eHu7s7Nm/eDFtbW2zduhU9evRAREREnuOVnzx5gp49e2L8+PHYtWsXLly4gMmTJ6N27doYNGiQWK5atWq4d++e3LVfGooAiD9gRdmt/b3Se+D/HqldTRvVtCrHru9ERJVdZf6QcvToUXFhBYlEIs5JmjVrFtq0aVOkkGRmZgYzMzMxJEVERCA8PByXLl1CtWrVxJ6kunXrVurvQWkqzQ/pMTEx2LVrFwwMDDB8+PA8e4pMTEwwd+5cvH//HseOHcPkyZOxdu1aaGpqlkgdp06dir/++gtnz579bK+yoaEhTE1N8eDBAwCAgYEBMjIykJSUJDeULiEhAe3bty/WelcGxbYqXUG0bdsWrVu3hpeXl3jMwsIC/fv3h6enZ67yP/74I/766y/cuXNHPDZp0iTcuHEDwcHBAD70GLm7u+P169eFrldKSgp0dHSQnJxc6GCU8DYB+qv1AQDxM+Khp6VX6PoQEVHZp4jfHeWdVCrFu3fvoKWlJXdcNtxu3759CAoKKlJI+vh5UVFRuH37Nu7cuYPU1FRUq1ZN7EliSCpZPj4+qFGjRr6jiopLXFwcduzYAV1dXYwcOTLPvdNevnyJLl26oGnTpti+fTsuXryIM2fOYNGiRcX+MyIIAqZOnYrDhw8jMDAQDRs2/Ow1L1++RJ06deDt7Q0XFxdx8YVdu3aJi1vExsaibt26XHxBAQrVz3nz5k1IpdIvLn/79m1kZWXleS4jIwOhoaFwdHSUO+7o6IiLFy/meU1wcHCu8t27d8fVq1eRmZkpHnv79i1MTU1Rt25d9O7dG2FhYZ+sZ3p6OlJSUuReREREVDBKSkq5QhHw/4fbnTx5EtHR0Rg1ahT+/vtvmJubo3379li7di2ePXtWqOfVq1cPvXr1goeHB0aNGoXGjRvj9u3b+OOPP7B27Vr8888/iIqKKtAwfyqc0ugxSkhIgK+vL2rWrIkRI0bkGYpev36N7t27o379+vD19YW6ujo6d+6MxYsXl0h9p0yZgl27dmHPnj3Q1tZGXFwc4uLikJaWBuDD59aZM2ciODgYkZGRCAwMRJ8+faCrq4sBAwYAAHR0dDB27FjMmDEDp06dQlhYGEaOHInmzZuja9euxd6Giq5Qweirr77Cy5cvv7i8jY0NoqKi8jyXmJiI7Oxs6Ovryx3X19dHXFxcntfExcXlWT4rKwuJiYkAgCZNmmD79u3466+/sHfvXmhoaMDW1lbsisyLp6cndHR0xBcXXiAiIioeenp6mDhxYr4had26dXj+/HmB7ysLST179sT06dPh6uqKJk2aICIiAj4+PmJIevr0KUNSMSnp9/XFixfw9fWFjo4ORo4cmee0iTdv3qBHjx4wMDCAn59fnkPsipuXlxeSk5Nhb28PQ0ND8eXv7w8AUFZWRnh4OPr164dGjRph1KhRaNSoEYKDg+XmLK5duxb9+/fHkCFDYGtriypVquDo0aPcw0gBCjXHSBAEzJs3D1WqVPmi8hkZGZ8t83FS/9xfG/Iqn/N4u3bt0K5dO/G8ra0tWrdujY0bN2LDhg153nP27Nnw8PAQv5ZNoCUiIqLiIwtJEydOREJCgri63Q8//ICvv/5aHG5X0FX+lJSUYGpqClNTU/To0QPPnj0Th9uFhIRAS0tLHG5nbGxc4ZdXLykl2WP08uVL+Pr6omrVqnB2ds5znlBqaip69uyJatWq4cCBA1BTUyuRun3sc4FRU1MT//7772fvo6GhgY0bN2Ljxo2Kqhr9n0IFo06dOuVa1OBTbGxs8p3QpqurC2Vl5Vy9QwkJCbl6hWQMDAzyLK+iooJatWrleY2SkhLatGnzyR4jdXX1PLteiYiIqGQUV0iSSCQwMTGBiYkJnJyc8OzZM0RERCAiIgJXrlwRQ1LTpk1hYmLCkFQEJRWMXr16hR07dkBDQwMuLi55/sE+LS0Nffv2hYqKCg4fPlygRbio8ilUMAoMDFRYBdTU1GBlZYWAgABx/CQABAQEoF+/fnleY2Njg6NHj8od+++//2BtbZ1v16ggCLh+/TqaN2+usLoTERFR8ckrJO3bt0+hIal79+54/vy52JN05coVVK1aVexJYkgquJJYrvv169fw9fWFmpoaXFxcULVq1Vxl0tPTMXDgQKSnp+PEiRNfPNKJKq9CL9etSB4eHnB2doa1tTVsbGzg7e2NqKgocV+i2bNnIzo6Gr6+vgA+rEC3adMmeHh4YPz48QgODsa2bduwd+9e8Z6LFi1Cu3bt0LBhQ6SkpGDDhg24fv06fvvtt1JpIxERERXexyHp0KFDCutJMjY2hrGxsRiSZD1JV69eFUNS06ZNYWpqypD0BYq7xyg5ORk7duyAkpISXFxc8twzLCMjA4MHD8arV68QEBCQ52IgRB8rE/+6hw4dinXr1mHx4sVo1aoVzp49i+PHj8PU1BTAh2UIcy7eYGZmhuPHjyMwMBCtWrXCkiVLsGHDBrk9jF6/fo0JEybAwsICjo6OiI6OxtmzZ/H111+XePuIygKJRPLJl6ura65y2trasLa2xqFDh+TulZKSgjlz5qBJkybQ0NCAgYEBunbtikOHDuU7hvrQoUPo1q0bateujWrVqsHGxuaLxlITEX1MT08PkyZNwqlTpxAdHQ1nZ2ccPXoUZmZmsLW1xfr16wu1cIMsJHXv3h3u7u4YO3YsmjdvjgcPHsDX1xerV6/GsWPH8Pjx4wKtzlvZFGcwevPmjfiH8lGjRuW5JH5WVha+/fZbPH/+HCdOnKi0y+ZTwZWJfYzKKu5jRBVJznl5/v7+mD9/vtxcQU1NTejo6EAikcDHxwdOTk54/fo1Vq5cie3bt+P8+fOwsbHB69ev0aFDByQnJ2Pp0qVo06YNVFRUEBQUhBUrVuDq1auoXr16rue7u7vDyMgInTt3RvXq1eHj44NVq1bh8uXL+Oqrr0riLSAqEdzHqPTk7EmS/TF0yJAh+Oabb1CnTp1C31cQBMTExOD27duIiIhAcnIyqlSpgiZNmqBZs2aoV68ee5Jy8PLygpmZGZycnBR637dv32L79u3IzMyEq6ur3AanMtnZ2XBxccHNmzdx5swZ6OrqKrQOVLExGH0CgxFVVJ/aAFm2S71sY77MzExUr14d06ZNg6enJyZPngxfX1/cv38fRkZGcte+ffsWGhoaUFH5slG6zZo1w9ChQzF//vyiNomozGAwKhs+Dklt27YVh9spIiTJhtu9fv1aDElNmzaFmZlZpQ9JmzdvRv369RW62Whqaip27NiBtLQ0jB49GjVr1sxVRiqVYuzYsbh06RICAwPzXcSLKD9lYo4REZVdqqqqUFFRQWZmJqRSKfz8/DBixIhcoQhAgcZwS6VSvHnzJs9fbkRERSUbbjdp0iTEx8eLCzfMnDmzSCFJIpGgTp06qFOnDrp27YrY2FixJ+natWvQ1NSU60mqjHvLSKVShQ6le/fuHXbu3Il3797B1dU1z98bgiBg8uTJOHfuHM6ePctQRIWikGB07tw5bN26FY8ePcKBAwdQp04d7Ny5E2ZmZujQoYMiHkFEpSA9PR0rV65ESkoKHBwckJiYiKSkJDRp0qTI9169ejVSU1MxZMgQBdSUiCh/+vr6xRaSjIyMYGRkJIYkWU9SWFiYGJJkPUmVJSQpco5RWloadu7ciTdv3mDUqFF5Do0TBAHu7u74999/ERQUlOcf7oi+RJGD0cGDB+Hs7IwRI0YgLCwM6enpAD5Mjlu+fDmOHz9e5EoSUckaPnw4lJWVkZaWBh0dHaxatQo9evRAfHw8gNwbLBfU3r17sXDhQhw5cgR6ehxeSkQl5+OQJBtuJwtJQ4YMwaBBg4oUkhwcHBAXFyf2JMlCUuPGjdG0aVOYm5tX6JCkqGD0/v177Nq1C8nJyRg1alSevy8EQcCPP/6IQ4cO4ezZszAxMSnyc6nyKnIwWrp0KbZs2QIXFxf4+fmJx9u3b4/FixcX9fZEVArWrl2Lrl27olq1anK/iGrXro0aNWrgzp07hb63v78/xo4di/3796Nr166KqC4RUaHo6+vju+++w3fffScXkmbMmFHkkGRoaAhDQ0M4ODggPj5eDEnXr1+HhoaG2JNUEUOSIoJReno69uzZg1evXsHFxSXfoXELFizAzp07cfbsWZiZmRXpmURFDkb37t1Dp06dch2vVq1anhO7iajsMzAwQIMGDXIdV1JSwtChQ7Fz504sWLAg13CF1NRUqKur57v4wt69ezFmzBjs3bsXvXr1Kpa6ExEVxqdCUrt27TB48OBcIelLAoBEIoGBgQEMDAzQpUsXxMfHi8PtZCFJ1pNUv379ChGSihqMMjIysHfvXiQkJMDZ2RmGhoZ5llu2bBm2bNmCwMBANGzYsNDPI5IpcjAyNDTEw4cPUa9ePbnj58+fh7m5eVFvT0RlzPLlyxEYGIi2bdti2bJlsLa2hqqqKs6dOwdPT09cuXIlz+W69+7dCxcXF6xfvx7t2rUTlw+XLRNORFRW5BWS9u3bJxeSoqOjUbVq1QKtqpkzJHXu3BkJCQliT9KNGzegrq4u15P0pSt8ljVFCUaZmZnw8/NDbGwsRo4cmW9v3erVq7FmzRqcOXMGTZs2LUp1iURF/hc3ceJEfP/99/jjjz8gkUgQExOD4OBgzJw5k0vwElVANWrUwKVLl/DLL79g6dKlePr0KWrUqIHmzZtj5cqV+YacrVu3IisrC1OmTMGUKVPE46NGjcL27dtLqPZERAXzcUg6ePAgfv31Vzx9+hRfffUVqlevXujhdvr6+tDX1xdDkqwnSRaScvYklaeQVNhglJWVBX9/fzx//hwjRoyAsbFxnuU2bdqEJUuW4NSpU2jRokVRq0skUsg+RnPmzMHatWvx/v17AIC6ujpmzpyJJUuWFLmCpYn7GBERUUFxH6OKbenSpfDy8oKfnx/Cw8Oxf/9+nD9/XuxJ+uabb4q0KpogCHjx4oXYk5SYmCiGJAsLCzRo0KDMh6TVq1fD2toadnZ2X3xNVlYW9u3bhydPnuDbb7/Nd76Qt7c3fvjhB/z7779o166doqpMBECBG7y+e/cOERERkEqlaNq0aYH2MymrGIyIiKigGIwqtoiICKirq6N+/frisbi4OHFOkiwkyRZuKOrS0TlD0osXL6Cmpib2JJXVkLRq1Sq0bdsWHTt2/KLy2dnZ2L9/Px4+fIjhw4fLvbc57dixA25ubjh+/PgX35uoIBQWjCoiBiMiIiooBqPK7eOQZGNjIy7coIiQJBtul5CQADU1NTRq1EgMSaqqqgpqRdGsXLkSNjY2X7SXpVQqxcGDB3H37l0MGzYs30UU/Pz8MG7cOPz111/o0qWLoqtMBEABc4w8PDzyPC6RSKChoYEGDRqgX79+3N2eiIiIKjwDAwNMnjwZkydPlgtJ06dPR/v27YsUkmrXrg07OzvY2dkhMTFR7Em6detWmQpJXzrHSCqV4vDhw7h79y4GDx6cbyg6dOgQxo4di4MHDzIUUbEqco9R586dce3aNWRnZ6Nx48YQBAEPHjyAsrIymjRpgnv37kEikeD8+fPlbtUQ9hgREVFBsceI8iILSfv27cOFCxcU2pOUmJgo9iTFx8dDVVVVDEkNGzYs8ZC0YsUKdOzYEe3bt8+3jCAIOHLkCG7evIlvvvkm38+Ix44dw9ChQ+Hn54c+ffoUV5WJAABKRb1Bv3790LVrV8TExCA0NBTXrl1DdHQ0unXrhuHDhyM6OhqdOnXC9OnTFVFfIiIionJH1pMUGBiIZ8+eYejQoTh48CBMTU3RqVMnbNy4ETExMYW6t66uLjp16oRJkybBzc0NHTp0QGJiIvbv34+VK1fiwIEDiIiIQGZmpoJblbfP9RgJgoCjR4/i5s2bGDBgQL6h6N9//8XQoUPh6+vLUEQlosg9RnXq1EFAQECuH+rbt2/D0dER0dHRuHbtGhwdHZGYmFikypY09hgREVFBsceICiIuLg4HDx7E/v37xZ4k2cIN+W1s+qVevnwp9iTFxcVBVVUVDRs2FHuS1NTUFNQKeZ6enujcuXOeq8YJgoC///4boaGh6N+/P1q2bJnnPc6cOYM+ffrg999/x/Dhw4ulnkQfK/Ico+TkZCQkJOQKRi9evEBKSgoAoHr16sjIyCjqo4iIiIgqFAMDA3F/t9jYWHFOkru7O9q3b1+kkFSrVi107NgRHTt2xKtXr8SQdODAAaioqIghqVGjRgoNSVKpNM8eI0EQcOLECYSGhqJv3775hqLz58+jb9++2LRpE0MRlSiFDKUbM2YMDh8+jOfPnyM6OhqHDx/G2LFj0b9/fwBASEgIGjVqVNRHEREREVVYhoaGmDJlitxwuwMHDsDY2Bh2dnbYtGkTYmNjC3XvmjVrokOHDpgwYQKmTp0KOzs7vH79GgcPHsTKlSuxb98+3Lp1SyF/yM5rKJ0gCAgICEBISAh69eqFr776Ks9rL1++jF69emH16tVwdXUtcl2KavPmzTAzM4OGhgasrKxw7ty50q4SFaMiB6OtW7fCwcEBw4YNg6mpKUxMTDBs2DA4ODhgy5YtAIAmTZrgf//7X5ErS0RERKUjMjISY8eOhZmZGTQ1NVG/fn0sWLAg1wdpiUSS6yX7PCATHh4OOzs7aGpqok6dOli8eDG4e4i8j0PSkCFDsH//foWHpGnTpsHe3h7JycliSPL390d4eDjS09MLdf+Pg5EgCDh9+jSCg4Ph5OQEa2vrPK+7du0anJycsHTpUkyYMKFQz1Ykf39/uLu7Y86cOQgLC0PHjh3Ro0cPREVFlXbVqJgobB+jt2/f4vHjxxAEAfXr1+cGr/+Hc4yIiCqXijrH6MSJE/D398fw4cPRoEED3Lp1C+PHj4ezszNWrVollpNIJPDx8YGTk5N4TEdHB5qamgA+vD+NGjVC586dMWfOHNy/fx+urq5YsGABZsyYUeLtKm9kw+327duHixcvyi0BXtQ5SUlJSeJwu5iYGCgrK8sNt1NXV/+i+yxevBg9e/YUA1BgYCCCgoLg6OgIGxubPK8JDw+Hvb09Zs+ejZkzZxapHYrStm1btG7dGl5eXuIxCwsL9O/fH56enqVYMyouRZpjlJmZCUdHR2zduhWNGjVCixYtFFUvIiIiKkOcnJzkwo65uTnu3bsHLy8vuWAEfJhbbGBgkOd9du/ejffv32P79u1QV1eHpaUl7t+/jzVr1sDDw+OL9r+pzGQ9SbI5SbKFG2T7JMnmJOX3/n9KjRo1YGtrC1tbW7x+/VoMSYcOHYKysjIaNGiApk2bonHjxp8MSTl7jM6dO4egoCA4ODjkG4ru3LkDBwcHTJ8+vcyEooyMDISGhuKnn36SO+7o6IiLFy+WUq2ouBVpKJ2qqipu3brF/4kRERFVQsnJyXlu4O7m5gZdXV20adMGW7ZsgVQqFc8FBwfDzs5O7oN19+7dERMTg8jIyJKodoVhaGgINzc3BAUFISoqCoMHD8a+fftQt25d2NnZ4bfffkNcXFyh7l29enW0b98e48aNg7u7OxwcHJCamorDhw9j5cqV2Lt3L27cuIH379/LXScbiCSRSHDhwgWcPn0a9vb26NChQ57PefDgARwcHDBx4kTMnTu3UHUtDomJicjOzoa+vr7ccX19/UK/p597HpW+Is8xcnFxwbZt2xRRFyIiIionHj16hI0bN2LSpElyx5csWYL9+/fj5MmTGDZsGGbMmIHly5eL5+Pi4vL8sCk7R4WTMyQ9e/ZMLiTZ29sXKSTp6OjAxsYGY8eOFUPSu3fv8Oeff2LVqlVyIUkWjJ48eYKTJ0+iY8eO6NSpk9z9BEFAYGAgHjx4gC5dumDEiBFYvHhxkd+D4pDXIhKK7hDw9fVF3759ERgYqND7UsEVebnujIwM/O9//0NAQACsra1RtWpVufNr1qwp6iOIiIiomCxcuBCLFi36ZJkrV67ITZiPiYmBk5MTBg8ejHHjxsmVzflX/1atWgH4MOck5/G8PmzmdZwKRxaS3NzcEBMTg0OHDsHf3x/ff/89OnToIM5JKsxwO1lIsrGxQXJyMu7cuYOIiAj8+eefUFJSgrm5OQDg1q1baN++PTp37pzr+xoVFYUhQ4bg5cuXsLCwQLdu3ZCdnQ0VlSJ/LFUYXV1dKCsr5wqTCQkJuYJ9Ufj6+sLV1RUtW7bEihUrkJ2dDQcHB4XdnwqmyD+Bt27dQuvWrQEA9+/flzvH/8ERERGVbW5ubhg2bNgny9SrV0/875iYGHTu3Bk2Njbw9vb+7P3btWuHlJQUxMfHQ19fHwYGBnl+2ASg0A+c9IGRkVGeIcnd3R22trZFDknt2rUTv8cREREIDw8HADRr1gxdu3bN87OgqqoqqlWrhrZt28LU1BSurq4YM2YMli5dWuT2KoqamhqsrKwQEBCAAQMGiMcDAgLQr18/hTxDEATUrFkTFy5cgJKSEpYsWQJPT09kZWWhe/fuCnkGFZBA+UpOThYACMnJyYW+R/ybeAELIWAhhPg38QqsHRERlUWK+N1RVj1//lxo2LChMGzYMCErK+uLrtm4caOgoaEhvH//XhAEQdi8ebNQvXp1IT09XSzzyy+/CEZGRoJUKi2WelNu0dHRwoYNG4SOHTsKKioqgp2dnbBp0yYhNjZWLPPq1atC3TsuLi7f72V8fLzQpEkTwdXVVcjOzhYEQRCys7OFN2/eFOpZxcnPz09QVVUVtm3bJkRERAju7u5C1apVhcjISIU9I+e/o9OnTwt9+/YVOnfuLBw9elRhz6Avp7DluiMiIhAVFSW3n4FEIkGfPn0UcftSweW6iYiooCrqct0xMTGws7ODiYkJfH19oaysLJ6T9TYcPXoUcXFxsLGxgaamJs6cOYMZM2bA1dUV69evB/BhwYbGjRujS5cu+Pnnn/HgwQO4urpi/vz5XK67lMTExIir2wUHB8PW1hY6Ojp49OgRbt68CSWlIk9JBwC8fPkSnTt3hqWlJXbu3Cn3M1RWbd68Gb/++itiY2NhaWmJtWvX5pozVVTCR6v4rVmzBklJSXB3d0f//v0V+iz6tCIHo8ePH2PAgAEIDw+HRCLJNU44Ozu76LUsJQxGRERUUBU1GG3fvh2jR4/O85zsd/+JEycwe/ZsPHz4EFKpFObm5hg3bhymTJkiN38kPDwcU6ZMQUhICGrUqIFJkyZh/vz5HIJfBsTExGDy5Mn4+++/IQgCOnbsiMGDB2PgwIGFGm4n8/r1a3Tp0gXm5ubw8/MrU/OJSoPw0SIOWVlZ4nty8eJFrFmzBi9evICbmxsGDx5cWtWsdIocjPr06QNlZWX8/vvvMDc3R0hICF6+fIkZM2Zg1apV6Nixo6LqWuIYjIiIqKAqajCiyuH06dP45ptvcOrUKejr68v1JMkWbihoSEpJSUG3bt2gr6+PAwcOQE1NrRhbUPbJQlBaWhoyMjJQpUoVqKqqIjs7W+xFCwkJwapVqxAXF4fvvvsOw4cPL+VaVw5FDka6uro4ffo0WrRoAR0dHYSEhKBx48Y4ffo0ZsyYgbCwMEXVtcQxGBERUUExGFF5JggCoqKiYGpqKndcNtxu3759uHTp0heHpLdv38LJyQlaWlo4cuTIJzeGrQxkPUVxcXHo06cPJBIJsrKysH37drRo0UKu7LVr17BixQrExMRg4sSJGDlyZCnVuvIo8qDR7OxsaGlpAfgQkmJiYgAApqamuHfvXlFvT0REREQlRCKR5ApFwIfV7aZOnYpz584hMjISAwYMwN69e2FsbIzOnTtj8+bNiI+Pl7vm3bt36Nu3L9TU1HD48OFKH4qAD+9veno6Bg0ahMaNG2Ps2LEwNjaGra0tjh49Kle2devW+Pnnn9GsWTMsXboUBw8eLKVaVx5FDkaWlpa4efMmAKBt27b49ddfceHCBSxevFhcy56IiIiIKoY6depg2rRpuUJS3bp10blzZ3h5eeHp06cYMGAAMjMzcfToUWhqapZ2tUtVzjn36urq6NChAzZu3IiJEyfiyJEjGDlyJIYOHYrDhw/LXdeyZUuoqqoiMTERxsbGJV3tSqfIM9/mzp2L1NRUAMDSpUvRu3dvdOzYEbVq1YK/v3+RK0hEREREZZMsJE2bNg3R0dE4ePAg9uzZgylTpsDU1BQ3btxA1apVS7uapUo2d+j9+/fw9vZGVFQULl++jFevXqFGjRoAAC8vL6iqqsLFxQVeXl7isLnExERERUVhy5Yt+Prrr0uzGZWCwpbrzkn2jS7vq8twjhERERUU5xgRAcHBwdDR0UHTpk1LuyqlSjanKDs7G82bN8f79++hpaWFW7duYcOGDRg9erRccJw2bRo2bdqEyMhImJiYQBAE8f8pVPwUszD9R2rWrFnuQxERERERFY6NjU2lD0VSqVT8PLx9+3ZYW1sjNDQUISEhmDZtGmbOnAl/f39x5BUAbNiwAdeuXYOJiQmAD3OSGIpKjkIWkT916hROnTqFhIQESKVSuXN//PGHIh5BRERERFRuyDbGnTBhAp48eQIbGxtx6Ny6deugoqKCSZMmITs7G8OHDxcXM2vVqhWA3HsdUfErcjBatGgRFi9eDGtraxgaGvIbSERERET0f7Kzs3Hq1CloaGjg5cuXqFWrFgBg1apV0NDQwMSJE5GcnAx3d3e5jW/5mbrkFTkYbdmyBdu3b4ezs7Mi6kNERERU6jZv3oyVK1ciNjYWzZo1w7p168r1pvVUMnJu0iqzbds2GBgYwMfHBz4+PnBxcYGe3oc550uXLkVWVhbU1dXlQhGVjiJ/BzIyMtC+fXtF1IWIiIio1Pn7+8Pd3R2bN2+Gra0ttm7dih49eiAiIkKc+0H0sZyh6NixY6hWrRrq1q0Lc3NzLFu2DJmZmdi4cSOysrIwevRo6Ot/WJzrl19+Kc1qUw5FXnxh3Lhx2LNnjyLqQkRERFTq1qxZg7Fjx2LcuHGwsLDAunXrYGxsDC8vr9KuGpVhslDUu3dvTJkyBY6Ojpg+fTp8fX0BAL/++iucnZ2xdetWeHt74/nz56VZXcpDoXqMPDw8xP+WSqXw9vbGyZMn0aJFC6iqqsqVXbNmTdFqSERERFRCMjIyEBoaip9++knuuKOjIy5evFhKtaKyLOciCceOHcObN29w6dIlhIeHw8fHB97e3nj37h0mTZqEpUuXQkVFBcuWLUPHjh1Rt27dUq495VSoHqOwsDDxdePGDbRq1QpKSkq4deuW3LmwsLAvvufmzZthZmYGDQ0NWFlZ4dy5c58sHxQUBCsrK2hoaMDc3BxbtmzJVebgwYNo2rQp1NXV0bRp01y7CRMRERHllJiYiOzsbHGYk4y+vj7i4uJKqVZUVmVnZ8stkiCRSMQFyRwdHTFv3jw0aNAAvr6+2Lx5MwBg4cKFOHHiBOzt7Uup1pSfQvUYnTlzRqGVKOhY3idPnqBnz54YP348du3ahQsXLmDy5MmoXbs2Bg0aBODDxmJDhw7FkiVLMGDAABw+fBhDhgzB+fPn0bZtW4XWn4iIiCqWj1cE49LJlBfZ8LlZs2YhJCQEWVlZaNKkiXi+adOmmDNnDn799Vfs3LkTr169wty5c2FnZwfgw8gr2bLeVPqK/J3w9PTMc6+iP/74AytWrPiiexR0LO+WLVtgYmKCdevWwcLCAuPGjcOYMWOwatUqscy6devQrVs3zJ49G02aNMHs2bPh4OCAdevWFaqdREREVPHp6upCWVk5V+9QQkJCrl4kqrxy7tv5888/Y/fu3WjTpg2UlJTg5+eHtWvXiucbNmyI2bNnw9jYGBoaGgD+f/BmKCpbivzd2Lp1q1wylmnWrFmew9s+JhvL6+joKHf8U2N5g4ODc5Xv3r07rl69iszMzE+W+dT44PT0dKSkpMi9iIiIqPJQU1ODlZUVAgIC5I4HBARwFV4CIN/LExwcDF1dXezfvx8rV66Er68vvvvuO3h7e2PlypXiNbJpHzNnzgTwoQeSyp4iB6O4uDgYGhrmOl67dm3ExsZ+9vrCjOWNi4vLs3xWVhYSExM/WeZT44M9PT2ho6MjvoyNjT9bfyIiIqpYPDw88L///Q9//PEH7ty5g+nTpyMqKgqTJk0q7apRKVq2bJlcKAoMDIStrS2WLl0qDqmrV68epkyZgv79+8PHxweenp7i9TVr1gTAYZllWZGDkbGxMS5cuJDr+IULF2BkZPTF9ynoWN68yn98vKD3nD17NpKTk8XXs2fPvrj+REREVDEMHToU69atw+LFi9GqVSucPXsWx48fh6mpaWlXjUrJkCFDsGrVKrnPkQ0aNMDixYuRkZGBEydOiMfr1auHyZMnY9CgQVi5ciUOHTokdy+GorKryBu8jhs3Du7u7sjMzESXLl0AAKdOncKsWbMwY8aMz15fmLG8BgYGeZZXUVFBrVq1PlnmU+OD1dXVoa6u/tk6ExERUcU2efJkTJ48ubSrQWXAiRMncODAAcybNw+pqanQ0tICANStWxcTJkyARCLB8uXLoaKigjlz5gD40HEwfvx4WFpaYuDAgaVZfSqAIgejWbNm4dWrV5g8eTIyMjIAABoaGvjxxx8xe/bsz16fcyzvgAEDxOMBAQHo169fntfY2Njg6NGjcsf+++8/WFtbi/so2djYICAgANOnT5crw/HBRERERPSlnJycMG/ePOzcuROqqqoYM2aMOCpKT08PEydOhKqqKjw9PZGZmYmFCxcCAExMTMTVlbOzs8XhdlR2FTkYSSQSrFixAvPmzcOdO3egqamJhg0bFqjnxcPDA87OzrC2toaNjQ28vb3lxvLOnj0b0dHR4s7BkyZNwqZNm+Dh4YHx48cjODgY27Ztw969e8V7fv/99+jUqRNWrFiBfv364ciRIzh58iTOnz9f1CYTERERUSUgCzSLFi2CiooKtm7dCqlUirFjx6JOnToAPox+Gjt2LFRVVbF8+XIkJCSIexbJMBSVDwpbI1BLSwtt2rSBpaVlgYejfW4sb2xsLKKiosTyZmZmOH78OAIDA9GqVSssWbIEGzZsEPcwAoD27dvDz88PPj4+aNGiBbZv3w5/f3/uYURERERUBkVGRmLs2LEwMzODpqYm6tevjwULFogjkmQkEkmu18crIYeHh8POzg6ampqoU6cOFi9eXKiV4JSVlcWluefNmwc3Nzd4e3tjy5YtcnPRa9WqBVdXV8yYMQPPnz8vROupLJAIXC8wXykpKdDR0UFycjKqVatWqHskvE2A/uoP85riZ8RDT0tPkVUkIqIyRhG/O4gqoxMnTsDf3x/Dhw9HgwYNcOvWLYwfPx7Ozs5ye1VKJBL4+PjAyclJPKajowNNTU0AH/4NNmrUCJ07d8acOXNw//59uLq6YsGCBV80/10m56JdOVejW7t2LVavXg0XFxdMmDAB9erVE695+/atOAeJm7eWP0UeSkdEREREVFROTk5yYcfc3Bz37t2Dl5eXXDACgOrVq8PAwCDP++zevRvv37/H9u3boa6uDktLS9y/fx9r1qyBh4dHvqvCyYJMYmIitLW15UZAKSkpieenT58ONTU1eHp6IisrC5MmTYK5uTkAiKFIEASGonKI3zEiIiIqN7Kzs7k5ZiWSnJws7v+Tk5ubG3R1ddGmTRts2bJFHO4GfNh01c7OTi7YdO/eHTExMYiMjMz3WUpKSoiLi0PPnj3x9OnTPM/LnjNlyhTMnz8fu3fvxvr16xEfHw8A4n6aXJK7fGKPEREREZV5GRkZUFNTk5vEnp2dDSUlJX4IraAePXqEjRs3YvXq1XLHlyxZAgcHB2hqauLUqVOYMWMGEhMTMXfuXABAXFyc3PA2AOJ2LXFxcTAzM8v3mVpaWnj48CEePnyIRo0a5Tqfs+dowoQJUFdXx7x586Cqqop///0XjRs3xr59+9hbVE7xu0ZERERl3rFjx6ChoYFRo0YhODgYwIeJ8TnngFDZtHDhwjwXTMj5unr1qtw1MTExcHJywuDBgzFu3Di5c3PnzoWNjQ1atWqFGTNmYPHixVi5cqVcmY/DsqyXMa8QLTuXkZEBZWVlmJiY4PHjx/m2R0lJSbxm1KhR+OWXX7B27Vpoa2tj/fr1DEXlGL9zREREVOYNGDAA+/fvR2ZmJrp37w4dHR24uLjgypUrAMAPo//H09MTbdq0gba2NvT09NC/f3/cu3dProyrq2uuYNKuXTu5Munp6Zg6dSp0dXVRtWpV9O3bt9Crrbm5ueHOnTuffFlaWorlY2Ji0LlzZ3ELl89p164dUlJSxOFsBgYGiIuLkyuTkJAA4P/3HAH/P0zLQo6amho0NTXRoUMHBAUFQSqVIisrK89nSiQS8bpvv/0WFy5cwIEDB8QlvKl84lA6IiIiKvMkEgn69OmDPn36ICMjA2fOnIGPjw/atm2Lhg0bYv369XIT9yuroKAgTJkyBW3atEFWVhbmzJkDR0dHREREoGrVqmI5Jycn+Pj4iF+rqanJ3cfd3R1Hjx6Fn58fatWqhRkzZqB3794IDQ0t8J48urq60NXV/aKy0dHR6Ny5M6ysrODj4/NFgTcsLAwaGhqoXr06AMDGxgY///yzOPwSAP777z8YGRnJDbFTUlJCcnKyGABbtGgBW1tbpKamIjExEUpKSrmen3OlOVk4yitYUvnEYERERETlRnZ2NtTU1NC9e3fcvXsXp0+fRrNmzcR5I7IPrrKNOW/fvo2kpCR06NChlGteMk6cOCH3tY+PD/T09BAaGopOnTqJx9XV1fNd1S05ORnbtm3Dzp070bVrVwDArl27YGxsjJMnT6J79+7FUveYmBjY29vDxMQEq1atwosXL8RzsroePXoUcXFxsLGxgaamJs6cOYM5c+aI832ADz04ixYtgqurK37++Wc8ePAAy5cvx/z583MNpQsJCYG6ujpCQkJw5swZ/Pzzz6hWrRru37+Pfv36oVOnTjA3N0fDhg1haWmJ1NRUaGtri9dzflvFwmBEREREZZ4s8CgrK+PcuXOYNWsW7t27h+XLl2PMmDFiz0DOv+YDH+ajHDlyBMCHZZyHDx9eOg0oJcnJyQCQa2W3wMBA6OnpoXr16rCzs8OyZcugp/dhr8XQ0FBkZmbC0dFRLG9kZARLS0tcvHix2ILRf//9Jy58ULduXblzsmFrqqqq2Lx5Mzw8PCCVSmFubo7FixdjypQpYlkdHR0EBARgypQpsLa2Ro0aNeDh4QEPD49cz+zWrRu6desm/nzdunULcXFx6NevH+Li4nDs2DFcu3YNNWrUQEZGBtq0aYN9+/ZBTU2NoagC4gavn8ANXomIqKC4wWvxiY+Px/z583Hw4EF069YN8+fPh4WFBQD5zThl0tPT0bx5c4wbNw7W1tawsLCAoaFhaVS9VAiCgH79+iEpKQnnzp0Tj/v7+0NLSwumpqZ48uQJ5s2bh6ysLISGhkJdXR179uzB6NGjkZ6eLnc/R0dHmJmZYevWrSXdlGL18c+ObI6Tt7c37OzsEB0djdTUVJw+fRpdu3ZFgwYNSrG2VJzYY0RERERlWlZWFk6ePInJkydDRUUFu3fvztVrkfODrWwY3bFjx5CVlYWWLVuiS5cucuVlZSoyNzc33Lx5E+fPn5c7PnToUPG/LS0tYW1tDVNTU/z9998YOHBgvvfLK3xWBB+3ycjICA0bNsSff/4JOzs7GBoaQklJSVy+O+c8I6pY+F0lIiKiMuvu3btwc3ODs7MzHBwccPXqVXTv3h3Z2dn5XiP7oHvgwAFYWlqiSZMm4jnZQBlZKJJKpZ+8V3k1depU/PXXXzhz5kyuYWkfMzQ0hKmpKR48eADgw3yejIwMJCUlyZVLSEiQW9WtIpL9fJiamiIsLAxA7hUPGYoqLn5niYiIqEy6ceMGbG1t8e+//+LgwYP4/fffUa1aNXFj1/woKSkhPT0dly9fRvv27eWWUA4ODka/fv1w584dpKamivOWZARBQHmeZSAIAtzc3HDo0CGcPn36k5uZyrx8+RLPnj0ThxlaWVlBVVUVAQEBYpnY2FjcunUL7du3L7a6lyX9+vVDfHw8UlNTS7sqVIIYjIiIiKhMUlNTQ4cOHRAdHY2JEydi7ty5uHv3rtzGrh+T9f4cO3YMEokEVlZWUFFREc/dvHkTR48exfbt29GlSxc0a9ZMXJwBgLivT3kNR1OmTMGuXbuwZ88eaGtrIy4uDnFxcUhLSwMAvH37FjNnzkRwcDAiIyMRGBiIPn36QFdXFwMGDADwYfGCsWPHYsaMGTh16hTCwsIwcuRING/eXFylrqKS/Vxpamri4cOHuXrNqGJjMCIiIqIyycLCAkeOHMG7d+/www8/4MyZM7C0tESDBg0wZ84cPHnyJNc1OYfRNWvWDI0bNxbPJSUl4Z9//oGxsTFMTExw8OBB2NraYunSpUhJScGZM2ewefNmxMfH5wpess1AyzovLy8kJyfD3t4ehoaG4svf3x/AhyGE4eHh6NevHxo1aoRRo0ahUaNGCA4OlluGeu3atejfvz+GDBkCW1tbVKlSBUePHq3w87Jk2rZti9u3b392GCJVLFyV7hO4Kh0RERUUV6VTHFnvT84P41lZWdi1axd+++032NraYt26dbkWBUhPT4eFhQUmTJiAmTNnij1GISEh6N27N9asWYORI0cCAA4dOgQXFxdxkv3Dhw9x//59eHl5oV+/frnqJAiCOPm+Ii5EQFSZcVU6IiIiKpNyBiJZSFJRUYGrqytcXV3Fc7JglHM1OolEgtatW4uhSCqV4urVq8jKysKQIUPEa9++fYvs7GyMGTMGvXr1goaGBrp06YKDBw+ib9++kEgkOH/+PGJiYtC/f3+oqalVml4TosqGwYiIiIjKvLxCkuzYx5u6HjhwABYWFnL7zbx8+RInT55E+/btxc1g3759i9DQULRq1QqDBg0SyxoaGkIqlSIpKQk1a9aEt7c3Tp06hdu3b+PmzZto27Yt3NzcoKWlJV4jG2qXc1GIN2/eyA1PI6KyjXOMiIiIqFxRVlbOs9dGSUkJ79+/x+XLl9G3b1+Ym5uL5+7du4cLFy7A2dlZPPbgwQNcv34dnTp1Eo89e/YMr1+/hra2NmrWrAmpVIorV64gIyMDVatWRbdu3bBu3TqsWLECABAREYGEhAQoKSnJhaLs7Gz4+flBSUkJEydOxKNHj4rjrSAiBWKPEREREVUYV69eRWRkJDZv3oxWrVrh66+/BvCh90YqlaJ///5i2Vu3buHly5dyc4muXLmCV69eieWOHTuGlJQUrFixAmPGjAEApKWlYdGiRXj79i0SExNx9OhR2NraYseOHdDV1QXwIaSNHz8e0dHR2LhxI1q2bInJkyeXzJtARIXCHiMiIiKqMNq2bYugoCBYWlri4sWL4vEePXogPj4e6urqAD4MowsICIBUKpXbm+fSpUtQU1ODnZ0dAGD//v1o2bKl+DXwYZ5TRkYGTExMsH79epw6dQqPHj3CsWPHxDKyYX2CIKBly5ZwcHAo1nYTUdGxx4iIiIgqDFVVVXTs2BEdO3YUj8lWkss5/E5LSwvjx4+X25cnKioKt27dQqNGjdCoUSNkZ2cjODgYo0aNgrGxsVjO19cXgwYNwrhx46CtrY2qVatCV1cXZ8+ehaurK7KysqCiooL4+HiEhYXB2NhYbtlwIiqb2GNEREREFZpEIslzTlLHjh3h4uIifh0TE4Pk5GRYWloCAI4ePQqJRII2bdqICzYkJSUhLCwMgwYNklt84cGDB2LPk2wnlJCQELx48UKuR4qIyi72GBEREVGlJNuPSKZdu3Y4e/Ys3r9/DwA4fPgwatSoIbe63Z49e2BoaIiWLVuKw+WCg4ORlJQk9lKpqqqKx1VVVWFvb19CLSKiomAwIiIiokopZygCIA63q1q1KgBg27ZtePjwIczMzMQyq1evRteuXWFoaCge27NnD1q3bo369euLx+Lj43H79m2YmZlxGB1ROcGhdERERETIHZRUVFTQpEkTcRheVlYWmjdvjn79+qFKlSoAgIyMDJw4cQJDhw6FioqK3DC6hIQEDqMjKkfYY0RERET0BVRUVHDkyBG5Y8HBwXj+/DkcHR0BQG54HYfREZUv7DEiIiIi+gJSqVTsEZLp2LEjTp8+jSZNmojHoqOjOYyOqBxiMCIiIiL6AkpKSmKPUM5j9vb2UFZWFkPTkydPkJCQAFtb29KoJhEVEofSERERESnA9evX4eXlhfj4eLlNYomofGCPEREREZEC1K5dG2lpaXj8+DHq1q3LYXRE5Qx7jIiIiIgUoG7duti5cyfS0tLw9u3b0q4OERUQgxERERGRAmlqakJTU7O0q0FEBcShdEREREREVOkxGBERERERUaXHYERERERERJUegxEREREREVV6DEZERERERFTpMRgREREREVGlx2BERERERESVXqkHo6SkJDg7O0NHRwc6OjpwdnbG69evP3mNIAhYuHAhjIyMoKmpCXt7e9y+fVuujL29PSQSidxr2LBhxdgSIiIiIiIqr0o9GH377be4fv06Tpw4gRMnTuD69etwdnb+5DW//vor1qxZg02bNuHKlSswMDBAt27d8ObNG7ly48ePR2xsrPjaunVrcTaFiIiIiIjKKZXSfPidO3dw4sQJXLp0CW3btgUA/P7777CxscG9e/fQuHHjXNcIgoB169Zhzpw5GDhwIABgx44d0NfXx549ezBx4kSxbJUqVWBgYFAyjSEiIiIionKrVHuMgoODoaOjI4YiAGjXrh10dHRw8eLFPK958uQJ4uLi4OjoKB5TV1eHnZ1drmt2794NXV1dNGvWDDNnzszVo0RERERERASUco9RXFwc9PT0ch3X09NDXFxcvtcAgL6+vtxxfX19PH36VPx6xIgRMDMzg4GBAW7duoXZs2fjxo0bCAgIyLc+6enpSE9PF79OSUkpUHuIiIiIiKh8KpYeo4ULF+Za+ODj19WrVwEAEokk1/WCIOR5PKePz398zfjx49G1a1dYWlpi2LBhOHDgAE6ePIlr167le09PT09xEQgdHR0YGxsXpNlERERERFROFUuPkZub22dXgKtXrx5u3ryJ+Pj4XOdevHiRq0dIRjZnKC4uDoaGhuLxhISEfK8BgNatW0NVVRUPHjxA69at8ywze/ZseHh4iF+npKQwHBERERERVQLFEox0dXWhq6v72XI2NjZITk5GSEgIvv76awDA5cuXkZycjPbt2+d5jWx4XEBAAL766isAQEZGBoKCgrBixYp8n3X79m1kZmbKhamPqaurQ11d/bP1JiIiIiKiiqVUF1+wsLCAk5MTxo8fj0uXLuHSpUsYP348evfuLbciXZMmTXD48GEAH4bQubu7Y/ny5Th8+DBu3boFV1dXVKlSBd9++y0A4NGjR1i8eDGuXr2KyMhIHD9+HIMHD8ZXX30FW1vbUmkrERERERGVXaW6+ALwYeW4adOmiavM9e3bF5s2bZIrc+/ePSQnJ4tfz5o1C2lpaZg8eTKSkpLQtm1b/Pfff9DW1gYAqKmp4dSpU1i/fj3evn0LY2Nj9OrVCwsWLICysvIX100QBABFW4Thzds3wPv/+++UN9CQahT6XkREVPbJfmfIfocQEVH5IBH4f+58PX/+nHOMiIioUJ49e4a6deuWdjWIiOgLMRh9glQqRUxMDLS1tT+7St6nyBZxePbsGapVq6bAGpYPbH/lbj/A94Dtr1ztFwQBb968gZGREZSUSnXEOhERFUCpD6Ury5SUlBT6175q1apVig8F+WH7K3f7Ab4HbH/lab+Ojk5pV4GIiAqIf8oiIiIiIqJKj8GIiIiIiIgqPQajEqCuro4FCxZU2j2S2P7K3X6A7wHbX7nbT0RE5QMXXyAiIiIiokqPPUZERERERFTpMRgREREREVGlx2BERERERESVHoMRERERERFVegxGxWzz5s0wMzODhoYGrKyscO7cudKukkJ4enqiTZs20NbWhp6eHvr374979+7JlREEAQsXLoSRkRE0NTVhb2+P27dvy5VJT0/H1KlToauri6pVq6Jv3754/vx5STZFITw9PSGRSODu7i4eq+jtj46OxsiRI1GrVi1UqVIFrVq1QmhoqHi+Irc/KysLc+fOhZmZGTQ1NWFubo7FixdDKpWKZSpa+8+ePYs+ffrAyMgIEokEf/75p9x5RbU3KSkJzs7O0NHRgY6ODpydnfH69etibh0REREAgYqNn5+foKqqKvz+++9CRESE8P333wtVq1YVnj59WtpVK7Lu3bsLPj4+wq1bt4Tr168LvXr1EkxMTIS3b9+KZX755RdBW1tbOHjwoBAeHi4MHTpUMDQ0FFJSUsQykyZNEurUqSMEBAQI165dEzp37iy0bNlSyMrKKo1mFUpISIhQr149oUWLFsL3338vHq/I7X/16pVgamoquLq6CpcvXxaePHkinDx5Unj48KFYpiK3f+nSpUKtWrWEY8eOCU+ePBH2798vaGlpCevWrRPLVLT2Hz9+XJgzZ45w8OBBAYBw+PBhufOKaq+Tk5NgaWkpXLx4Ubh48aJgaWkp9O7du6SaSURElRiDUTH6+uuvhUmTJskda9KkifDTTz+VUo2KT0JCggBACAoKEgRBEKRSqWBgYCD88ssvYpn3798LOjo6wpYtWwRBEITXr18Lqqqqgp+fn1gmOjpaUFJSEk6cOFGyDSikN2/eCA0bNhQCAgIEOzs7MRhV9Pb/+OOPQocOHfI9X9Hb36tXL2HMmDFyxwYOHCiMHDlSEISK3/6Pg5Gi2hsRESEAEC5duiSWCQ4OFgAId+/eLeZWERFRZcehdMUkIyMDoaGhcHR0lDvu6OiIixcvllKtik9ycjIAoGbNmgCAJ0+eIC4uTq796urqsLOzE9sfGhqKzMxMuTJGRkawtLQsN+/RlClT0KtXL3Tt2lXueEVv/19//QVra2sMHjwYenp6+Oqrr/D777+L5yt6+zt06IBTp07h/v37AIAbN27g/Pnz6NmzJ4CK3/6PKaq9wcHB0NHRQdu2bcUy7dq1g46OTrl7T4iIqPxRKe0KVFSJiYnIzs6Gvr6+3HF9fX3ExcWVUq2KhyAI8PDwQIcOHWBpaQkAYhvzav/Tp0/FMmpqaqhRo0auMuXhPfLz88O1a9dw5cqVXOcqevsfP34MLy8veHh44Oeff0ZISAimTZsGdXV1uLi4VPj2//jjj0hOTkaTJk2grKyM7OxsLFu2DMOHDwdQ8b//H1NUe+Pi4qCnp5fr/np6euXuPSEiovKHwaiYSSQSua8FQch1rLxzc3PDzZs3cf78+VznCtP+8vAePXv2DN9//z3+++8/aGho5FuuorZfKpXC2toay5cvBwB89dVXuH37Nry8vODi4iKWq6jt9/f3x65du7Bnzx40a9YM169fh7u7O4yMjDBq1CixXEVtf34U0d68ypfn94SIiMoPDqUrJrq6ulBWVs71V86EhIRcf1Utz6ZOnYq//voLZ86cQd26dcXjBgYGAPDJ9hsYGCAjIwNJSUn5limrQkNDkZCQACsrK6ioqEBFRQVBQUHYsGEDVFRUxPpX1PYbGhqiadOmcscsLCwQFRUFoOJ//3/44Qf89NNPGDZsGJo3bw5nZ2dMnz4dnp6eACp++z+mqPYaGBggPj4+1/1fvHhR7t4TIiIqfxiMiomamhqsrKwQEBAgdzwgIADt27cvpVopjiAIcHNzw6FDh3D69GmYmZnJnTczM4OBgYFc+zMyMhAUFCS238rKCqqqqnJlYmNjcevWrTL/Hjk4OCA8PBzXr18XX9bW1hgxYgSuX78Oc3PzCt1+W1vbXMuz379/H6ampgAq/vf/3bt3UFKS/9+nsrKyuFx3RW//xxTVXhsbGyQnJyMkJEQsc/nyZSQnJ5e794SIiMqh0ljxobKQLde9bds2ISIiQnB3dxeqVq0qREZGlnbViuy7774TdHR0hMDAQCE2NlZ8vXv3Tizzyy+/CDo6OsKhQ4eE8PBwYfjw4Xku31u3bl3h5MmTwrVr14QuXbqU2eWKPyfnqnSCULHbHxISIqioqAjLli0THjx4IOzevVuoUqWKsGvXLrFMRW7/qFGjhDp16ojLdR86dEjQ1dUVZs2aJZapaO1/8+aNEBYWJoSFhQkAhDVr1ghhYWHi9gOKaq+Tk5PQokULITg4WAgODhaaN2/O5bqJiKhEMBgVs99++00wNTUV1NTUhNatW4vLWZd3APJ8+fj4iGWkUqmwYMECwcDAQFBXVxc6deokhIeHy90nLS1NcHNzE2rWrCloamoKvXv3FqKiokq4NYrxcTCq6O0/evSoYGlpKairqwtNmjQRvL295c5X5PanpKQI33//vWBiYiJoaGgI5ubmwpw5c4T09HSxTEVr/5kzZ/L8Nz9q1ChBEBTX3pcvXwojRowQtLW1BW1tbWHEiBFCUlJSCbWSiIgqM4kgCELp9FURERERERGVDZxjRERERERElR6DERERERERVXoMRkREREREVOkxGBERERERUaXHYERERERERJUegxEREREREVV6DEZERERERFTpMRgREREREVGlx2BERERERESVHoMRkQLZ29vD3d29tKtR5tnb20MikUAikeD69eulXZ0Cc3V1Fev/559/lnZ1iIiISAEYjIgKKa8QdOjQISxZsqR0KvR/yks4Gz9+PGJjY2FpaSke27x5M8zMzKChoQErKyucO3fus/c5e/Ys+vTpAyMjoxILKuvXr0dsbGyxP4eIiIhKDoMRkQLVrFkT2trapV2NcqFKlSowMDCAiooKAMDf3x/u7u6YM2cOwsLC0LFjR/To0QNRUVGfvE9qaipatmyJTZs2lUS1AQA6OjowMDAosecRERFR8WMwIioEV1dXBAUFYf369eKQqsjIyFy9Nfb29pg6dSrc3d1Ro0YN6Ovrw9vbG6mpqRg9ejS0tbVRv359/PPPP+I1giDg119/hbm5OTQ1NdGyZUscOHBA7vkHDhxA8+bNoampiVq1aqFr165ITU3Nt14AcOLECXTo0AHVq1dHrVq10Lt3bzx69KhIdZVd5+bmBjc3N/Hec+fOhSAIBXpP16xZg7Fjx2LcuHGwsLDAunXrYGxsDC8vr09e16NHDyxduhQDBw784mfVq1cP69atkzvWqlUrLFy4UPw6v/eYiIiIKiYGI6JCWL9+PWxsbMThYLGxsTA2Ns6z7I4dO6Crq4uQkBBMnToV3333HQYPHoz27dvj2rVr6N69O5ydnfHu3TsAwNy5c+Hj4wMvLy/cvn0b06dPx8iRIxEUFAQAiI2NxfDhwzFmzBjcuXMHgYGBGDhwIARB+GS9UlNT4eHhgStXruDUqVNQUlLCgAEDIJVKC13XnNepqKjg8uXL2LBhA9auXYv//e9/X/x+ZmRkIDQ0FI6OjnLHHR0dcfHixS++j6J86j0mIiKiikmltCtAVB7p6OhATU1NHA72KS1btsTcuXMBALNnz8Yvv/wCXV1djB8/HgAwf/58eHl54ebNm2jevDnWrFmD06dPw8bGBgBgbm6O8+fPY+vWrbCzs0NsbCyysrIwcOBAmJqaAgCaN28uPi+/eg0aNEju623btkFPTw8RERHiPJ+C1LVdu3bivYyNjbF27VpIJBI0btwY4eHhWLt2rXjd5yQmJiI7Oxv6+vpyx/X19REXF/dF91Ckz73HREREVPGwx4iomLVo0UL8b2VlZdSqVUvuQ7YsDCQkJCAiIgLv379Ht27doKWlJb58fX3FYW8tW7aEg4MDmjdvjsGDB+P3339HUlLSZ+vx6NEjfPvttzA3N0e1atVgZmYGAHJzeApS15zatWsHiUQifm1jY4MHDx4gOzv7829QDjnvAXwYVvjxsZJQ2PeYiIiIyi8GI6JipqqqKve1RCKROyb74C+VSsVhbX///TeuX78uviIiIsR5RsrKyggICMA///yDpk2bYuPGjWjcuDGePHnyyXr06dMHL1++xO+//47Lly/j8uXLAD4MYytMXRVJV1cXysrKuXqHEhIScvUiFZecIa6w7zERERGVXwxGRIWkpqZW4B6Rz2natCnU1dURFRWFBg0ayL1yzmGSSCSwtbXFokWLEBYWBjU1NRw+fDjfer18+RJ37tzB3Llz4eDgAAsLC4X2gFy6dCnX1w0bNoSysvIXXa+mpgYrKysEBATIHQ8ICED79u0VVs+ccoawzMxMPHv2TO78p95jIiIiqng4x4iokOrVq4fLly8jMjISWlpaqFmzZpHvqa2tjZkzZ2L69OmQSqXo0KEDUlJScPHiRWhpaWHUqFG4fPkyTp06BUdHR+jp6eHy5ct48eIFLCws8q1XjRo1UKtWLXh7e8PQ0BBRUVH46aefilxfmWfPnsHDwwMTJ07EtWvXsHHjRqxevbpA9/Dw8ICzszOsra1hY2MDb29vREVFYdKkSWKZTZs24fDhwzh16pR47O3bt3j48KH49ZMnT3D9+nXUrFkTJiYm+T7Px8cHXbt2hampKdavX4/k5GQ8evQI8fHxiIyM/OR7TERERBUPgxFRIc2cOROjRo1C06ZNkZaWprBhVkuWLIGenh48PT3x+PFjVK9eHa1bt8bPP/8MAKhWrRrOnj2LdevWISUlBaampli9ejV69OiRb73q1asHPz8/TJs2DZaWlmjcuDE2bNgAe3t7hdTZxcUFaWlp+Prrr6GsrIypU6diwoQJBbrH0KFD8fLlSyxevFjc+PX48ePi4gfAh0Uaci4xDgBXr15F586dxa89PDwAAKNGjcL27dvzfV6fPn0wbdo0PH78GAMHDsSSJUvg6ekJJycntG7d+pPvMREREVU8EoHrzxJREdjb26NVq1a59gVS9DWKVK9ePbi7u8vtOVUYEokEhw8fRv/+/RVSLyIiIio9nGNERKVi8+bN0NLSQnh4eGlXpcAmTZoELS2t0q4GERERKRCH0hFRidu9ezfS0tIA4JPzgMqqxYsXY+bMmQAAQ0PDUq4NERERKQKH0hERERERUaXHoXRERERERFTpMRgREREREVGlx2BERERERESVHoMRERERERFVegxGRERERERU6TEYERERERFRpcdgRERERERElR6DERERERERVXoMRkREREREVOkxGBERERERUaXHYERERERERJXe/wNX+/X2CdzAZgAAAABJRU5ErkJggg==", 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" ] @@ -143,7 +152,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.11.6" + "version": "3.11.7" } }, "nbformat": 4, diff --git a/scripts/proto_nd_scripts/analysis/hip_selection/data_mc_all_metrics.py b/scripts/proto_nd_scripts/analysis/hip_selection/data_mc_all_metrics.py index f9fa7fed..7b937023 100644 --- a/scripts/proto_nd_scripts/analysis/hip_selection/data_mc_all_metrics.py +++ b/scripts/proto_nd_scripts/analysis/hip_selection/data_mc_all_metrics.py @@ -64,10 +64,10 @@ def main(file_dir, is_sim, sel_event_dict): ext_trigs_ref = f['charge/events/ref/charge/ext_trigs/ref'] ext_trigs_region = f['charge/events/ref/charge/ext_trigs/ref_region'] print("Available datasets:",f.keys(),'\n') - sel_reco = f['high_purity_sel']['hips']['sel_reco']['data'] - if is_sim: - sel_truth = f['high_purity_sel']['hips']['sel_truth']['data'] - mc_truth_events = f['mc_truth/events/data'] + #sel_reco = f['high_purity_sel']['hips']['sel_reco']['data'] + #if is_sim: + # sel_truth = f['high_purity_sel']['hips']['sel_truth']['data'] + # mc_truth_events = f['mc_truth/events/data'] print("File:", file) #sel_mask = (sel_reco['sel'] == True) @@ -222,6 +222,7 @@ def main(file_dir, is_sim, sel_event_dict): total_charge = float(track_charge_data), length = float(track_length_data), hits_in_track = int(track_num_hits_data), + avg_q_per_unit_length = float(track_charge_data/track_length_data), theta = float(track_theta_data), phi = float(track_phi_data), ts_start = float(track_ts_start_data), diff --git a/scripts/proto_nd_scripts/analysis/hip_selection/plot_all_metrics.py b/scripts/proto_nd_scripts/analysis/hip_selection/plot_all_metrics.py index f6511961..0c5c1d75 100644 --- a/scripts/proto_nd_scripts/analysis/hip_selection/plot_all_metrics.py +++ b/scripts/proto_nd_scripts/analysis/hip_selection/plot_all_metrics.py @@ -20,8 +20,8 @@ def plot_event_hit_summ_metrics(d, is_mc): sel_pdg = np.unique([d[key]['event_pdg'] for key in d.keys()]) hits_dsets = np.unique([d[key]['hits_dset'] for key in d.keys()]) - alpha_options = [[0.8, 0.8], [0.8, 0.8]] - color_options = [['#4daf4a', '#ff7f00'], ['#377eb8', '#e41a1c']] + alpha_options = [[0.8, 0.9], [0.8, 0.9]] + color_options = [['#D62728', '#FF9896'], ['#1F77B4', '#AEC7E8']] linestyle_options = [['--', '--'], ['-', '-']] linewidth_options = [[1.5, 1.5], [1.5, 1.5]] fill_options = [[False, False], [False, False]] @@ -101,8 +101,8 @@ def plot_channel_metrics(d, is_mc): sel_pdg = np.unique([d[key]['event_pdg'] for key in d.keys()]) hits_dsets = np.unique([d[key]['hits_dset'] for key in d.keys()]) - alpha_options = [[0.8, 0.8], [0.8, 0.8]] - color_options = [['#4daf4a', '#ff7f00'], ['#377eb8', '#e41a1c']] + alpha_options = [[0.8, 0.9], [0.8, 0.9]] + color_options = [['#D62728', '#FF9896'], ['#1F77B4', '#AEC7E8']] linestyle_options = [['--', '--'], ['-', '-']] linewidth_options = [[1.5, 1.5], [1.5, 1.5]] fill_options = [[False, False], [False, False]] @@ -122,6 +122,8 @@ def plot_channel_metrics(d, is_mc): linestyle=linestyle_options[pdg_idx][dset_idx], fill = fill_options[pdg_idx][dset_idx]) ax0.set_xlabel('Hit Multiplicity / Channel / Event') ax0.set_ylabel('Channel Count / Hit') + ax0.set_yscale('log') + ax0.set_xlim(0,8) ax0.set_title(r'Hit Multiplicity Per Channel in Selected Events '+mc_title) ax0.legend() plt.savefig(sample_type+"_selected_events_hits_per_channel_per_event.png") @@ -134,14 +136,16 @@ def plot_channel_metrics(d, is_mc): for hits_dset in hits_dsets: dset_idx = hits_dsets.tolist().index(hits_dset) data1 = np.array([d[key]['max_hit_amp'] for key in d.keys() if d[key]['event_pdg']==pdg and d[key]['hits_dset']==hits_dset]) - counts1, bins1 = np.histogram(data1, bins=np.linspace(0,200,41)) - ax1.hist(bins1[:-1], bins=bins1, weights = counts1, histtype='stepfilled',\ + counts1, bins1 = np.histogram(data1, bins=np.linspace(0,200,21)) + ax1.hist(bins1[:-1], bins=bins1, weights = counts1/sum(counts1), histtype='stepfilled',\ label=pdg_defs.selection_pdg_dict[pdg]+", "+hits_dset, \ linewidth=linewidth_options[pdg_idx][dset_idx], alpha=alpha_options[pdg_idx][dset_idx], \ color=color_options[pdg_idx][dset_idx], edgecolor=color_options[pdg_idx][dset_idx], \ linestyle=linestyle_options[pdg_idx][dset_idx], fill = fill_options[pdg_idx][dset_idx]) ax1.set_xlabel('Max Hit Amplitude / Channel / Event [ke-]') - ax1.set_ylabel('Channel Count / 5 ke-') + ax1.set_ylabel('Channel Count / 10 ke- [Area Normalized]') + ax1.set_xlim(0,150) + ax1.set_yscale('log') ax1.set_title(r'Maximum Hit Amplitiude Per Channel in Selected Events '+mc_title) ax1.legend() plt.savefig(sample_type+"_selected_events_max_hit_amp_per_channel_per_event.png") @@ -214,15 +218,16 @@ def plot_channel_metrics(d, is_mc): for hits_dset in hits_dsets: dset_idx = hits_dsets.tolist().index(hits_dset) data4 = np.array([d[key]['first_last_hit_delta_t'] for key in d.keys() if d[key]['event_pdg']==pdg and d[key]['hits_dset']==hits_dset]) - counts4, bins4 = np.histogram(data4, bins=np.linspace(0,150,76)) + counts4, bins4 = np.histogram(data4, bins=np.linspace(0,25,51)) ax4.hist(bins4[:-1], bins=bins4, weights = counts4, histtype='stepfilled',\ label=pdg_defs.selection_pdg_dict[pdg]+", "+hits_dset, \ linewidth=linewidth_options[pdg_idx][dset_idx], alpha=alpha_options[pdg_idx][dset_idx], \ color=color_options[pdg_idx][dset_idx], edgecolor=color_options[pdg_idx][dset_idx], \ linestyle=linestyle_options[pdg_idx][dset_idx], fill = fill_options[pdg_idx][dset_idx]) ax4.set_xlabel(r'First/Last Hit $\Delta$t / Channel / Event [$\mu$s]') - ax4.set_ylabel(r'Channel Count / 2 $\mu$s') + ax4.set_ylabel(r'Channel Count / 0.5 $\mu$s') ax4.set_yscale('log') + ax4.set_xlim(0,21) ax4.set_title("Difference in Time between First and Last Hit\nPer Channel in Selected Events "+mc_title) ax4.legend() plt.savefig(sample_type+"_selected_events_first_last_hit_deltat_per_channel_per_event.png") @@ -241,8 +246,8 @@ def plot_track_metrics(d, is_mc): sel_pdg = np.unique([d[key]['event_pdg'] for key in d.keys()]) hits_dsets = np.unique([d[key]['hits_dset'] for key in d.keys()]) - alpha_options = [[0.8, 0.8], [0.8, 0.8]] - color_options = [['#4daf4a', '#ff7f00'], ['#ff7f00', '#e41a1c']] + alpha_options = [[0.8, 0.9], [0.8, 0.9]] + color_options = [['#D62728', '#FF9896'], ['#1F77B4', '#AEC7E8']] linestyle_options = [['--', '--'], ['-', '-']] linewidth_options = [[1.5, 1.5], [1.5, 1.5]] fill_options = [[False, False], [False, False]] @@ -275,5 +280,68 @@ def plot_track_metrics(d, is_mc): plt.savefig(sample_type+"_selected_events_dqdx_vs_resid_range.png") plt.close(fig0) + # PLOT: track theta (inclination w.r.t. anode) + fig1, ax1 = plt.subplots(figsize=(6,4)) + for pdg in sel_pdg: + pdg_idx = sel_pdg.tolist().index(pdg) + for hits_dset in hits_dsets: + dset_idx = hits_dsets.tolist().index(hits_dset) + data1 = np.array([d[key]['theta'] for key in d.keys() if d[key]['event_pdg']==pdg and d[key]['hits_dset']==hits_dset]) + counts1, bins1 = np.histogram(data1, bins=np.linspace(0,3.0,16)) + ax1.hist(bins1[:-1], bins=bins1, weights = counts1, histtype='stepfilled',\ + label=pdg_defs.selection_pdg_dict[pdg]+", "+hits_dset, \ + linewidth=linewidth_options[pdg_idx][dset_idx], alpha=alpha_options[pdg_idx][dset_idx], \ + color=color_options[pdg_idx][dset_idx], edgecolor=color_options[pdg_idx][dset_idx], \ + linestyle=linestyle_options[pdg_idx][dset_idx], fill = fill_options[pdg_idx][dset_idx]) + ax1.set_xlabel('Selected Event Track Inclination w.r.t Anode [rad]') + ax1.set_ylabel('Event Count / 0.2 rad') + ax1.set_title(r'Selected Event Inclination w.r.t. Anode '+mc_title) + ax1.legend() + ax1.set_xlim(0,3.2) + plt.savefig(sample_type+"_selected_events_theta_angle.png") + plt.close(fig1) + + # PLOT: track phi (orientation w.r.t. anode) + fig2, ax2 = plt.subplots(figsize=(8,4)) + for pdg in sel_pdg: + pdg_idx = sel_pdg.tolist().index(pdg) + for hits_dset in hits_dsets: + dset_idx = hits_dsets.tolist().index(hits_dset) + data2 = np.array([d[key]['phi'] for key in d.keys() if d[key]['event_pdg']==pdg and d[key]['hits_dset']==hits_dset]) + counts2, bins2 = np.histogram(data2, bins=np.linspace(-6,6.0,61)) + ax2.hist(bins2[:-1], bins=bins2, weights = counts2, histtype='stepfilled',\ + label=pdg_defs.selection_pdg_dict[pdg]+", "+hits_dset, \ + linewidth=linewidth_options[pdg_idx][dset_idx], alpha=alpha_options[pdg_idx][dset_idx], \ + color=color_options[pdg_idx][dset_idx], edgecolor=color_options[pdg_idx][dset_idx], \ + linestyle=linestyle_options[pdg_idx][dset_idx], fill = fill_options[pdg_idx][dset_idx]) + ax2.set_xlabel('Selected Event Track Orientation w.r.t Anode [rad]') + ax2.set_ylabel('Event Count / 0.2 rad') + ax2.set_xlim(-3.3, 3.3) + ax2.set_title(r'Selected Event Orientation w.r.t. Anode '+mc_title) + ax2.legend() + plt.savefig(sample_type+"_selected_events_phi_angle.png") + plt.close(fig2) + + fig3, ax3 = plt.subplots(figsize=(8,4)) + for pdg in sel_pdg: + pdg_idx = sel_pdg.tolist().index(pdg) + for hits_dset in hits_dsets: + dset_idx = hits_dsets.tolist().index(hits_dset) + data3 = np.array([d[key]['avg_q_per_unit_length'] for key in d.keys() if d[key]['event_pdg']==pdg and d[key]['hits_dset']==hits_dset]) + counts3, bins3 = np.histogram(data3, bins=np.linspace(0,500,26)) + ax3.hist(bins3[:-1], bins=bins3, weights = counts3, histtype='stepfilled',\ + label=pdg_defs.selection_pdg_dict[pdg]+", "+hits_dset, \ + linewidth=linewidth_options[pdg_idx][dset_idx], alpha=alpha_options[pdg_idx][dset_idx], \ + color=color_options[pdg_idx][dset_idx], edgecolor=color_options[pdg_idx][dset_idx], \ + linestyle=linestyle_options[pdg_idx][dset_idx], fill = fill_options[pdg_idx][dset_idx]) + ax3.set_xlabel('Average Charge per Unit Length [ke-/cm]') + ax3.set_ylabel('Count / 20 ke-/cm') + ax3.set_xlim(0,500) + ax3.set_yscale('log') + ax3.set_title(r'Average Charge per Unit Length for Selected Event Tracks '+mc_title) + ax3.legend() + plt.savefig(sample_type+"_selected_events_avg_q_per_unit_length.png") + plt.close(fig3) + return \ No newline at end of file diff --git a/src/proto_nd_flow/util/tracklet_reco.py b/src/proto_nd_flow/util/tracklet_reco.py index 4d223855..c9c2c326 100644 --- a/src/proto_nd_flow/util/tracklet_reco.py +++ b/src/proto_nd_flow/util/tracklet_reco.py @@ -528,7 +528,7 @@ def phi(axis): :returns: orientation of axis about x-axis ''' - return np.arctan2(axis[1], axis[2]) + return np.arctan2(axis[2], axis[1]) @staticmethod def yzp(axis, centroid): diff --git a/yamls/module1_flow/resources/RunData.yaml b/yamls/module1_flow/resources/RunData.yaml index 708379bf..adcf9f23 100644 --- a/yamls/module1_flow/resources/RunData.yaml +++ b/yamls/module1_flow/resources/RunData.yaml @@ -4,7 +4,7 @@ classname: RunData path: proto_nd_flow.resources.run_data params: path: 'run_info' - runlist_file: '/global/cfs/cdirs/dune/www/data/Module1/runlist.txt' + runlist_file: '/global/cfs/cdirs/dune/www/data/Module1/runlist_BR.txt' # download link: https://portal.nersc.gov/project/dune/data/Module0/runlist.txt # download link: https://portal.nersc.gov/project/dune/data/Module0-Run2/runlist.txt defaults: diff --git a/yamls/proto_nd_flow/analysis/hip_selection.yaml b/yamls/proto_nd_flow/analysis/hip_selection.yaml index b2ddb353..868d0fcf 100644 --- a/yamls/proto_nd_flow/analysis/hip_selection.yaml +++ b/yamls/proto_nd_flow/analysis/hip_selection.yaml @@ -3,7 +3,7 @@ path: proto_nd_flow.analysis.hip_selection requires: - 'combined/tracklets' - 'combined/t0' - - 'charge/calib_prompt_hits' + - 'charge/calib_final_hits' - name: 'mc_truth/trajectories' path: ['charge/raw_events', 'mc_truth/events', 'mc_truth/trajectories'] #- name: 'combined/track_hits' @@ -13,13 +13,13 @@ requires: params: # inputs - hits_dset_name: 'charge/calib_prompt_hits' + hits_dset_name: 'charge/calib_final_hits' ext_trigs_dset_name: 'charge/ext_trigs' t0_dset_name: 'combined/t0' tracklet_dset_name: 'combined/tracklets' - hit_drift_dset_name: 'charge/calib_prompt_hits' + hit_drift_dset_name: 'charge/calib_final_hits' truth_trajectories_dset_name: 'mc_truth/trajectories' - charge_dset_name: 'charge/calib_prompt_hits' + charge_dset_name: 'charge/calib_final_hits' # configuration parameters fid_cut: 5.0 # cm