diff --git a/src/precise_multiple_templates_prog.py b/src/precise_multiple_templates_prog.py index ba1336a..a1e6e0e 100644 --- a/src/precise_multiple_templates_prog.py +++ b/src/precise_multiple_templates_prog.py @@ -1,1171 +1,1171 @@ import multiprocessing import os import pickle from re import template import pandas as pd import shutil import sys from multiprocessing import Pool from time import time import math import matplotlib import matplotlib.pyplot as plt from numpy.core.numeric import base_repr # configure backend here matplotlib.use('SVG') import numpy as np from dtaidistance import dtw from numpy.core.fromnumeric import argmin scripts = '../helper_scripts' if scripts not in sys.path: sys.path.insert(0,scripts) import warnings from copy import copy from dtw import dtw_std, ddtw_tv from csv2mit_with_dir_out import csv2mit_with_dir_out from scipy import stats from scipy.interpolate import CubicSpline, interp1d from scipy.signal.signaltools import fftconvolve import wfdb from build_template import build_template, multi_template data_beats_dir = "../data/beats/" log_dir = "../results/beat_recon_logs_multi_prog_" # Signal freqeuncy variables FREQ = 128 MULTIPLIER_FREQ = 20 #Final freq: 128*20 = 2.56 KHz ADC_GAIN = 200 # Parameters fixed for result evaluation PERC_BINS = 10 PERCENTILE_TO_PLOT = [1,25,50,75,99] # Global constants # Variable fixed by arguments (eg. arguments) CLUSTER_PERCENTAGE = 3 NUM_BEAT_ANALYZED = 50 INTERPOLATION_TYPE = 'flat' FILES_SELECTED = ["17052.pickle"] PARALLELIZE_ALONG = 'files' LEVELS = [5]#[3,4,5,6,7,8,9,10,11] USE_DIFFERENTIAL_DTW = False SEC_FOR_INITIAL_TEMPLATES = 3*60 #5*60 LEN_DISTANCE_VECTOR = 60 #80 LEN_DISTANCE_VECTOR_REF = 400 #500 SEC_FOR_NEW_TEMPLATES = 40 #2*60 TIME_MODE = 'short' def delineate(log_dir_this_lvl_resampling, signal_type, QRS_pos): #TODO: problem here: /!\ level 3- spline does not with many samples, maybe a random beats trigger a condition that breake ecgpwuave (not halting) # IT IS SOLVED BY TERMINATING (CTRL-C ing) ONCE WE USE THE LEVEL 3 --> arresting ecgpuwave stop only the delineator and everything else works # TO ANALYZE BY NOT DELETING LEVELS AND TEST FROM TERMINAL --> there are annotations where level 3 fails to have any reconizable peak around the given annotation ! dir_to_use = os.path.join(log_dir_this_lvl_resampling,signal_type) annotations_tot = pd.DataFrame({"time": [], "idx": [], "type": [], "0": [], "1": [], "2": []}) end_record = False start_from = 0 singal = os.path.join(dir_to_use,"data.csv") #Trasform the signal in the MIT format csv2mit_with_dir_out(singal,fs = FREQ, units = 'mV', header = False, fmt = '212', adc_gain=ADC_GAIN, baseline = 0) os.remove(singal) #Run the delineator wfdb.wrann("data","atrN",np.array(QRS_pos), symbol= ['N']*len(QRS_pos),write_dir=dir_to_use) while not end_record: - cmd_delineator = f"(cd {dir_to_use} ; timeout 10 ecgpuwave -r data -a atrTemp -i atrN -f {start_from} > /dev/null 2>&1)" + cmd_delineator = f"(cd {dir_to_use} ; timeout 20 ecgpuwave -r data -a atrTemp -i atrN -f {start_from} > /dev/null 2>&1)" os.system(cmd_delineator) #Write the new annotations back # THIS METHOD FAIL IF THE DELINEATOR DIDN'T TERMINATE (I.E. IT FAILED ANNOTITING AFTER A CERTAIN TIMESTAMP) # SOLUTION: SUBSTITUTE WITH A SYS CALL AND READ THE STDOUT cmd_read_ann = f"(cd {dir_to_use} ; rdann -r data -a atrTemp >annotations_text.txt 2>error.txt)" os.system(cmd_read_ann) annotations_this_chunk = pd.read_csv(os.path.join(dir_to_use,"annotations_text.txt"),header=None, delim_whitespace=True,names=["time", "idx", "type", "0", "1", "2"]) annotations_tot = annotations_tot.append(annotations_this_chunk, ignore_index = True) if len(annotations_this_chunk) > 0: - start_from = int(annotations_this_chunk.idx.values[-1]/FREQ+1) + start_from = int(annotations_this_chunk.idx.values[-1]/FREQ+300) #skip 300 seconds (5 minutes) else: - start_from += 1 + start_from += 300 #skip 300 seconds (5 minutes) with open(f"{dir_to_use}/error.txt","r") as error_file: if len(error_file.readlines()) == 0: end_record = True else: print(f"/!\/!\/!\/!\/!\/!\ dir: {dir_to_use}: ecgpuwave needed a second pass at idx: {start_from-1} /!\/!\/!\/!\/!\ ") os.remove(os.path.join(dir_to_use,"data.atrTemp")) os.remove(os.path.join(dir_to_use,"annotations_text.txt")) os.remove(os.path.join(dir_to_use,"error.txt")) # annotations_tot_pt = annotations_tot[(annotations_tot['type'] == "p") | (annotations_tot['type'] == "t")] if len(annotations_tot) == 0: annotations_tot = pd.DataFrame({"time": [0], "idx": [0], "type": ['p'], "0": [0], "1": [0], "2": [0]}) if len(annotations_tot_pt) == 0: annotations_tot_pt = pd.DataFrame({"time": [0], "idx": [0], "type": ['p'], "0": [0], "1": [0], "2": [0]}) annotations_tot = annotations_tot.sort_values(by = 'idx') annotations_tot_pt = annotations_tot_pt.sort_values(by = 'idx') wfdb.wrann("data","atrDelinAll",annotations_tot["idx"].values.astype(np.int32), symbol = annotations_tot['type'].values, write_dir=dir_to_use)#, num=annotations_tot['2'].values.astype(np.int32)) wfdb.wrann("data","atrDelinPT",annotations_tot_pt["idx"].values.astype(np.int32), symbol = annotations_tot_pt['type'].values, write_dir=dir_to_use)#, num=annotations_tot_pt['2'].values.astype(np.int32)) return annotations_tot def compare(log_dir_this_lvl_resampling, resampling_type, annot_full_O, annot_full_r): ''' bxb: The -o option produces an output annotation file with annotator name bxb. The output annotation file contains exact copies of all of the test annotator’s beat labels that match those of the reference annotator, as well as NOTE annotations that describe all mismatches. Mismatched annotation types are mapped into the AAMI ‘test label’ mnemonics. The ‘aux’ field of each NOTE annotation indicates the element of the confusion matrix in which the mismatch is tallied: e.g., Nv represents an eventcalled a normal beat by the reference annotator and a ventricular ectopic beat by the test annotator) NOTE annotations that correspond to beats missed by the test annotator are placed at the sample indicated by the reference annotation; all others are placed at that indicated by the test annotation. bxb output: ... 4:59.672 38358 " 0 0 0 O/p 5:00.031 38404 t 0 0 0 5:00.328 38442 p 0 0 0 5:00.688 38488 t 0 0 0 5:00.938 38520 p 0 0 0 5:01.297 38566 " 0 0 5 t[0,0,0]/t ---> NOT PRESENT ANYMORE: here the reference say a normal t (code 0) while the test say it's a t but with code 5 5:01.367 38575 " 0 0 5 O/t ---> Nothing in the reference, "t" in test 5:01.523 38595 p 0 0 0 5:01.875 38640 t 0 0 0 5:02.102 38669 " 0 0 0 p/O ---> "p" in the reference, nothing in test ... ''' dir_dest = os.path.join(log_dir_this_lvl_resampling,resampling_type) shutil.copy(os.path.join(log_dir_this_lvl_resampling,"original","data.atrDelinPT"),os.path.join(dir_dest,"data.atrOrigPT")) shutil.copy(os.path.join(log_dir_this_lvl_resampling,"original","data.atrDelinAll"),os.path.join(dir_dest,"data.atrDelinOrig")) #This can compare all the annotations in one go: bxb -r data -a atrDelinTotOrig atrDelinTot -f 0 -v -O cmd_bxb = f"(cd {dir_dest}; bxb -r data -a atrOrigPT atrDelinPT -O -f 0 2>/dev/null)" os.system(cmd_bxb) annots_pt = wfdb.rdann(os.path.join(dir_dest,"data"),"bxb") p_wave_true_pos = 0 p_wave_false_pos = 0 p_wave_false_negative = 0 p_wave_sens = 0 p_wave_pp = 0 p_wave_f1 = 0 t_wave_true_pos = 0 t_wave_false_pos = 0 t_wave_false_negative = 0 t_wave_sens = 0 t_wave_pp = 0 t_wave_f1 = 0 for pos,sym,aux in zip(annots_pt.sample,annots_pt.symbol,annots_pt.aux_note): if sym == 'p': p_wave_true_pos += 1 elif aux == "p/O": p_wave_false_negative += 1 elif aux == "O/p": p_wave_false_pos += 1 if sym == 't': t_wave_true_pos += 1 elif aux == "t/O": t_wave_false_negative += 1 elif aux == "O/t": t_wave_false_pos += 1 groud_truth_p = p_wave_true_pos+p_wave_false_negative found_p = p_wave_true_pos+p_wave_false_pos if groud_truth_p == 0: p_wave_sens = 0 else: p_wave_sens = p_wave_true_pos/(p_wave_true_pos+p_wave_false_negative) if found_p == 0: p_wave_pp = 0 else: p_wave_pp = p_wave_true_pos/(p_wave_true_pos+p_wave_false_pos) if p_wave_sens+p_wave_pp == 0: p_wave_f1 = 0 else: p_wave_f1 = 2*(p_wave_sens*p_wave_pp)/(p_wave_sens+p_wave_pp) groud_truth_t = t_wave_true_pos+t_wave_false_negative found_t = t_wave_true_pos+t_wave_false_pos if groud_truth_t == 0: t_wave_sens = 0 else: t_wave_sens = t_wave_true_pos/(t_wave_true_pos+t_wave_false_negative) if found_t == 0: t_wave_pp = 0 else: t_wave_pp = t_wave_true_pos/(t_wave_true_pos+t_wave_false_pos) if t_wave_sens+t_wave_pp == 0: t_wave_f1 = 0 else: t_wave_f1 = 2*(t_wave_sens*t_wave_pp)/(t_wave_sens+t_wave_pp) print("----------------------------------------") print(f"delineation_score: {dir_dest}: \n\tp_s:{p_wave_sens}\n\tp_pp:{p_wave_pp}\n\tp_f1:{p_wave_f1}\n\tt_s:{t_wave_sens}\n\tt_pp:{t_wave_pp}\n\tt_f1:{t_wave_f1}") return {"p":{'sens':p_wave_sens,'ppv':p_wave_pp,'f1':p_wave_f1},"t":{'sens':t_wave_sens,'ppv':t_wave_pp,'f1':t_wave_f1}} def delineate_and_compare(log_dir_this_lvl, QRS_pos): annots_orig = delineate(log_dir_this_lvl, "original", QRS_pos) annots_warp = delineate(log_dir_this_lvl, "warped", QRS_pos) annots_resamp = delineate(log_dir_this_lvl, "resampled", QRS_pos) results_warp = compare(log_dir_this_lvl, "warped", annots_orig,annots_warp) results_resamp = compare(log_dir_this_lvl, "resampled", annots_orig,annots_resamp) return {"resamp":results_resamp,"warp":results_warp} def add_beat_to_csv(base_path_name,beat): ''' base_path_name is the path to use to save the data in the form: 'results/$log_dir/$sample_name/$lvl/$resampling_type/$signal_type/ ''' mode = '' data_path = os.path.join(base_path_name,"data.csv") first_beat = True if os.path.exists(data_path): mode = 'a' else: mode = 'w' with open(data_path,mode) as f: for pt in beat: if mode == 'w' and first_beat: f.write(str(pt/ADC_GAIN)) first_beat = False else: f.write('\n'+str(pt/ADC_GAIN)) def decimate(v,factor): return v[::factor] def revert_normaliztion(data,params): un_norm_data = [] saturate = lambda x: x if abs(x)<2**11 else (2**11-1)*np.sign(x) for pt in data: un_norm_pt = saturate((pt + params['avg'])*(params['max']-params['min']) + params['min']) un_norm_data.append(un_norm_pt) return un_norm_data def un_norma_and_save_back_beat(beat,log_dir,signal_type,norm_params): dir_to_save_to = os.path.join(log_dir,signal_type) os.makedirs(dir_to_save_to, exist_ok=True) data_v_un_norm = revert_normaliztion(beat['v'],norm_params) add_beat_to_csv(dir_to_save_to,data_v_un_norm) return data_v_un_norm def z_score_filter(vector,threshold = 3): out = None z = stats.zscore(vector) out = [vector[idx] for idx in range(len(vector)) if z[idx] Beat_annot: | --> "t": [] | --> "v": [] ''' file_name_full = os.path.join(data_beats_dir, os.path.basename(file_name)) data = {} with open(file_name_full,"rb") as f: data = pickle.load(f) for k in data.keys(): if k <= FREQ*start_after: data.pop(k) return data def upsample_uniform_beat(beat,multiplier): out_beat = {} upsampled = [] v = beat['v'] last_new_t = (len(v)-1)*multiplier t = np.arange(0,last_new_t+1,multiplier,dtype = np.int64) new_base = np.arange(0,last_new_t,1,dtype = np.int64) f = interp1d(t, v) upsampled = f(new_base) out_beat = {'t':new_base,'v':upsampled} return out_beat def get_beats_in_time_span(data, t_start_seconds = 0, t_stop_seconds = SEC_FOR_INITIAL_TEMPLATES): beats = {} for annot in data.keys(): if annot >= int(t_start_seconds*FREQ) and annot <= int(t_stop_seconds*FREQ): beats[annot] = data[annot] elif annot > int(t_stop_seconds*FREQ): break return beats def min_max_normalization_one_beat(beat,param = None): params_out = {} normalized_beat = {'t':beat['t'],'v':[]} vector = beat['v'] if param is not None: mi_v = param['min'] ma_v = param['max'] avg = param['avg'] norm = (np.array(vector)-mi_v)/(ma_v-mi_v) norm -= avg else: mi_v = min(vector) ma_v = max(vector) norm = (np.array(vector)-mi_v)/(ma_v-mi_v) avg = np.average(norm) norm -= avg normalized_beat['v'] = norm.tolist() params_out['min'] = mi_v params_out['max'] = ma_v params_out['avg'] = avg return normalized_beat,params_out def min_max_normalization_beats_chunk(beats, params = None): params_out = {} normalized_beats = {} for beat in beats: normalized_beats[beat],params_out[beat] = min_max_normalization_one_beat(beats[beat],params) return normalized_beats,params_out def resamp_one_signal(t,v,resample_type = INTERPOLATION_TYPE, min_t = None, max_t = None): if min_t is None: min_t = t[0] if max_t is None: max_t = t[-1] t_extended = copy(t) v_extended = copy(v) if max_t not in t: t_extended.insert(len(t_extended), max_t) v_extended.insert(len(v_extended), 0) if min_t not in t: # This is not needed in this implementation as the first sample is always an events for each beat t_extended.insert(0, min_t) # Still, we write it for clearity and consistency v_extended.insert(0, 0) if resample_type == "linear": f = interp1d(t_extended,v_extended, bounds_error = False, fill_value = (v[0],v[-1])) elif resample_type == "flat": f = interp1d(t_extended,v_extended, kind = 'previous', bounds_error = False, fill_value = (v[0],v[-1])) elif resample_type == "spline": f = CubicSpline(t_extended,v_extended, bc_type="natural") t_new = list(range(min_t,max_t+1)) v_new = f(t_new) return t_new,v_new def percentile_idx(vector,perc): pcen=np.percentile(np.array(vector),perc,interpolation='nearest') i_near=abs(np.array(vector)-pcen).argmin() return i_near def get_template_and_beat_at_idx(all_beats,all_templates,look_up_beat_template,idx_sel): beat_id = look_up_beat_template['beats'][idx_sel] template_id = look_up_beat_template['template_id'][idx_sel] beat = all_beats[beat_id] template = all_templates[template_id] return beat,beat_id,template def find_all_connceted_template(id_temp,templates_info): id_collected = [id_temp] for id_connected in id_collected: for id_to_add in templates_info[id_connected]["connected_template"]: if id_to_add not in id_collected: id_collected.append(id_to_add) return id_collected def plot_perc(orig,warp,resamp,template,title,out_file_name): fig, (ax1, ax2) = plt.subplots(2) fig.suptitle(title) ax1.plot(orig[0],orig[1]) ax1.plot(warp[0],warp[1]) ax1.plot(resamp[0],resamp[1]) ax1.legend(['original','warped template','resampled']) ax2.plot(template) ax2.legend(['template used']) fig.savefig(out_file_name) plt.close(fig) def reconstruct_plot_and_save_beat(beat,interpolation_type,template,lvl,title,save_fig_to): beat_norm_timing = copy(beat) beat_norm_timing['t'] = list(range(len(beat['t']))) events = ADC(beat,lvl,MULTIPLIER_FREQ) beat_norm,params = min_max_normalization_one_beat(beat_norm_timing) events_norm,_ = min_max_normalization_one_beat(events,params) resampled = resample(events_norm, resample_type = interpolation_type, min_t = beat_norm['t'][0], max_t = beat_norm['t'][-1]) template_with_envelope = {"tempalte":template} #TODO: rework this warp function with the new one _, reconstructed, _, _ = warp_events(template_with_envelope,events_norm, start_t = beat_norm['t'][0], end_t = beat_norm['t'][-1]) orig = beat_norm['t'],beat_norm['v'] recon = reconstructed['t'],reconstructed['v'] resamp = resampled['t'],resampled['v'] plot_perc(orig,recon,resamp,template['point'],title,save_fig_to) #-------------------------------------- WORK ON THIS -------------------------------------- def stitch_segments(segments): stitched = {'t':[],'v':[]} for i,segment in enumerate(segments): if i == len(segments)-1: stitched['t'].extend(segment[0]) stitched['v'].extend(segment[1]) else: stitched['t'].extend(segment[0][0:-1]) stitched['v'].extend(segment[1][0:-1]) f = interp1d(stitched['t'],stitched['v'], bounds_error = False, fill_value = (stitched['v'][0],stitched['v'][-1])) t_new = np.arange(math.floor(stitched['t'][0]),math.ceil(stitched['t'][-1])+1) v_new = f(t_new) stitched['t'] = t_new stitched['v'] = v_new return stitched def segment_warp(segment): v = segment['segment'] t = [] if len(v) == 1: v = np.array([v[0],v[0]]) v = np.array(v) t = np.linspace(segment['t_start'],segment['t_stop'],len(v)) v += (segment['v_start'] - v[0]) m = (segment['v_stop'] - v[-1])/(len(v)-1) v += [m*x for x in range(len(v))] return t,v #TODO: rework of the warp class to use timing informations def warp_events(templates,events,start_t,end_t): warped = {} segments = [] # --> {"segment":v, "length_to_warp":length, "v_start":v_eb_0, "v_stop":v_eb_1} segment = {"segment":None, "length_to_warp":None, "v_start":None, "v_stop":None} segment_start = None event_start = None #ad the first and last points of the resampled and truncated beat if not already in event if start_t not in events['t']: events['t'].insert(0, start_t) events['v'].insert(0, 0) if end_t not in events['t']: events['t'].insert(len(events['t']),end_t) events['v'].insert(len(events['v']),0) #This is how its actualy done on the library when calling 'warping_path' dist = float('inf') path = [] selected_template = [] disatances_vector = [] template_id = None for id in templates: template_v = templates[id]['point'] template_t = list(range(len(template_v))) events_v = events['v'] events_t = events['t'] #dist_this_template, paths_this_template = dtw.warping_paths(v_src, t) with warnings.catch_warnings(): warnings.simplefilter("ignore") dist_this_template, _, path_this_template = ddtw_tv(events_t, events_v, template_t, template_v, use_diff = USE_DIFFERENTIAL_DTW, dist_only=False) disatances_vector.append(dist_this_template) if dist_this_template < dist: dist = dist_this_template path = path_this_template selected_template = template_v template_id = id path = [(event_idx,templ_idx) for event_idx,templ_idx in zip(path[0],path[1])] #Find matching events and warp the templates between those points path.append(path[0]) #Add a fake sample at the end so to reach the actual last sample in the loop for path_idx in range(len(path)-1): idx_events = path[path_idx][0] idx_next_event = path[path_idx+1][0] idx_template = path[path_idx][1] if idx_events != idx_next_event: if segment_start == None: segment_start = idx_template event_start = idx_events else: segment['segment'] = np.array(selected_template[segment_start:idx_template+1], dtype=np.float64) segment['t_start'] = events['t'][event_start] segment['t_stop'] = events['t'][idx_events] segment['v_start'] = events['v'][event_start] segment['v_stop'] = events['v'][idx_events] #TODO: segment warp is the previous implementation tw,vw = segment_warp(segment) segments.append((tw,vw)) segment_start = idx_template event_start = idx_events segment_stitched = stitch_segments(segments) warped = {'t':segment_stitched['t'],'v':segment_stitched['v']} return dist, warped, disatances_vector, template_id #------------------------------------------------------------------------------------------ def dtw_dist(v1,v2): with warnings.catch_warnings(): warnings.simplefilter("ignore") dist = dtw_std(v1, v2) return dist def ddtw_tv_dist(t1,v1,t2,v2): with warnings.catch_warnings(): warnings.simplefilter("ignore") dist = ddtw_tv(t1, v1, t2, v2, use_diff = USE_DIFFERENTIAL_DTW) return dist def compute_new_templates(data, t_start, t_stop, old_templates, templates_info): beats_for_new_template = get_beats_in_time_span(data, t_start_seconds=t_start,t_stop_seconds=t_stop) beats_for_new_template,_ = min_max_normalization_beats_chunk(beats_for_new_template) _, new_templates_descriptor = multi_template(beats_for_new_template, percentage_each_cluster = CLUSTER_PERCENTAGE*2, freq= FREQ, use_diff_dtw = USE_DIFFERENTIAL_DTW) lbls_new_templates = list(new_templates_descriptor.keys()) old_ids = list(old_templates.keys()) if old_ids == []: next_id = 0 else: next_id = max(old_ids) + 1 cluster_representatives = {} new_template_set = {} print(f"\n\tNew template built, number of new templates:{len(lbls_new_templates)}\n") old_templates_kept = 0 old_templates_substituted = 0 new_templates_kept = 0 new_templates_substituted = 0 # We search which of the old templates can be considered clustered with the newly founded ones # TODO: /!\ WRONG BACK PROJECTION!!!! need to send back the function to template prediction and measure distance from THAT template if len(lbls_new_templates) > 0: for id_old_template in old_templates: t_o = old_templates[id_old_template]['point'] dists = np.zeros((len(lbls_new_templates))) for k,t_n in enumerate(lbls_new_templates): t1 = list(range(len(t_o))) v1 = t_o t2 = list(range(len(new_templates_descriptor[t_n]['center']))) v2 = new_templates_descriptor[t_n]['center'] #TODO: what distance do we want to use here? the templates ar not event-based acquired dists[k] = ddtw_tv_dist(t1,v1,t2,v2) min_dist_pos = np.argmin(dists) min_val = dists[min_dist_pos] this_lbls_new_templates = lbls_new_templates[min_dist_pos] distances_intra_cluster = new_templates_descriptor[this_lbls_new_templates]['dist_all_pt_cluster'] dist_threshold = np.average(distances_intra_cluster)+np.std(distances_intra_cluster) if min_val < dist_threshold: if this_lbls_new_templates not in cluster_representatives.keys(): cluster_representatives[this_lbls_new_templates] = {"ids": [], "dists": []} cluster_representatives[this_lbls_new_templates]["ids"].append(id_old_template) cluster_representatives[this_lbls_new_templates]["dists"].append(min_val) else: #This templates minimum distances to the newly founded templates are too big to be considered the same cluster new_template_set[id_old_template] = old_templates[id_old_template] old_templates_kept += 1 # Now we check if the old templates, clustered with the newly founded one, are representative of the cluster of if we should use the new templates for local_new_id in lbls_new_templates: if local_new_id in cluster_representatives.keys(): connected_ids = cluster_representatives[local_new_id]["ids"] arg_min_dist = np.argmin(cluster_representatives[local_new_id]["dists"]) old_template_id = cluster_representatives[local_new_id]["ids"][arg_min_dist] old_template_dist = cluster_representatives[local_new_id]["dists"][arg_min_dist] # The old template is nearer to the found cluster center than the beat found by the clustering alg if old_template_dist < new_templates_descriptor[local_new_id]["dist"]: new_template_set[old_template_id] = old_templates[old_template_id] connected_ids.remove(old_template_id) templates_info[old_template_id]["connected_template"].extend(connected_ids) new_templates_substituted += 1 # The beat found by the clustering alg is nearer to the found cluster center than the old template else: new_template_set[next_id] = new_templates_descriptor[local_new_id] templates_info[next_id] = {"used": 0, "deceased": False, "connected_template": connected_ids} next_id += 1 old_templates_substituted += len(cluster_representatives[local_new_id]["ids"]) # The templates in this cluster that are not the center are now deceased for id_removed in connected_ids: templates_info[id_removed]["deceased"] = True else: new_template_set[next_id] = new_templates_descriptor[local_new_id] templates_info[next_id] = {"used": 0, "deceased": False, "connected_template": []} next_id += 1 new_templates_kept += 1 print(f"\tOld templates kept untuched: {old_templates_kept}/{len(old_templates)}") print(f"\tOld templates kept, representative of new clusters (but already present): {new_templates_substituted}/{len(old_templates)}") print(f"\tOld templates removed for old clusters (with new defined params): {len(old_templates) - (old_templates_kept+old_templates_substituted+new_templates_substituted)}/{len(old_templates)}") print(f"\tOld templates removed for new clusters: {old_templates_substituted}/{len(old_templates)}") print(f"\n\tNew templates kept untuched: {new_templates_kept}/{len(lbls_new_templates)}") print(f"\tNew templates kept, representative of old clusters (with new params): {len(lbls_new_templates) - (new_templates_kept+new_templates_substituted)}/{len(lbls_new_templates)}") print(f"\tNew templates removed for old clusters: {new_templates_substituted}/{len(lbls_new_templates)}") print(f"\n\tFinal lenght of the new tempalte set: {len(new_template_set)}") else: new_template_set = old_templates print("\tNo new template found, keeping the previously computed ones") return new_template_set def resample(data, resample_type = INTERPOLATION_TYPE, min_t = None, max_t = None): resampled_data = {"t":None,"v":None} t = data['t'] v = data['v'] t_r,v_r = resamp_one_signal(t,v,resample_type = resample_type, min_t = min_t, max_t = max_t) resampled_data['t'] = t_r resampled_data['v'] = v_r return resampled_data def ADC(beat, nBits, frequency_multiplier ,hist = 5, original_bits = 11): #ADC stats up_samp_beat = upsample_uniform_beat(beat, frequency_multiplier) delta = 2**original_bits dV = (delta)/(2**nBits) hist = (hist/100)*dV min_val = -delta//2 events = {'t':[],'v':[]} #init value, first sample (we assume we always sample the nearest level at start) and ADC status v_0 = up_samp_beat['v'][0] lowTh = min_val+((v_0-min_val)//dV)*dV highTh = lowTh + dV events['t'].append(up_samp_beat['t'][0]/frequency_multiplier) events['v'].append(int(lowTh if v_0-lowTh < highTh - v_0 else highTh)) for val,time in zip(up_samp_beat['v'],up_samp_beat['t']): #print(f"Value: {val}, time: {time}, low_th = {lowTh - hist}, high_th = { highTh + hist}") if val > highTh + hist or val < lowTh - hist: direction = 1 if val > highTh else -1 lowTh = min_val+((val-min_val)//dV)*dV #Delta from the bottom: (val-min_val)//dV*dV then compute the actual level summin min_val highTh = lowTh + dV events['t'].append(time/frequency_multiplier) events['v'].append(int(lowTh if direction == 1 else highTh)) return events def reconstruct_beats(data_orig, lvl_number, init_templates = None, start_after = SEC_FOR_INITIAL_TEMPLATES, resample_type = INTERPOLATION_TYPE, num_beats_analyzed = None, verbose = False, log_dir = None): beat_seq_number = 0 prev_len = 0 all_new_QRS_pos = [] distances = [] distances_ref = [] num_distances_out = 0 skip_until = start_after time_info = {'beats_low_res_num': 0, 'tot_beats_num': 0} # COMPUTE INITIAL TEMPLATES #Init templet info templates_info = {} #{$templet_id: {used: 0, deceased: False, "connected_template": []}} all_templates_collection = {} if init_templates is None: print(f"\n################################################################") print(f"\nLEVELS:{lvl_number}, resample type: {resample_type}: Starting templates ... ") templates = compute_new_templates(data_orig, 0, start_after, {}, templates_info) else: templates = init_templates for id_new in templates: templates_info[id_new] = {"used": 0, "deceased": False, "connected_template": []} all_templates_collection = copy (templates) init_templates = copy(templates) measurements_id_coupling = {"ids":\ {"template_id":[], "beats":[]},\ "measures":\ {"dtw":{"warp":[],"resamp":[]},\ "prd":{"warp":[],"resamp":[]},\ "prd_left":{"warp":[],"resamp":[]},\ "prd_right":{"warp":[],"resamp":[]}}} dist_vector = [0]*len(templates) for beat in data_orig.keys(): t_beat = beat/FREQ time_info['tot_beats_num'] += 1 if t_beat < skip_until: continue if beat_seq_number >= num_beats_analyzed: break analyzed_beat = copy(data_orig[beat]) #print(f"Reconstructing beat {beat} ({beat_seq_number}/{num_beats_analyzed}: {100*beat_seq_number /num_beats_analyzed}%, LEVELS:{lvl_number}, resample type: {resample_type})") #print(f"\tmax_v = {max(analyzed_beat['v'])},\n\tmax_v = {min(analyzed_beat['v'])}\n\tmax_t = {max(analyzed_beat['t'])}\n\tmin_t = {min(analyzed_beat['t'])}") QRS_idx = analyzed_beat['t'].index(beat) #Define a new time-base for each beat analyzed_beat['t'] = list(range(len(analyzed_beat['t']))) time_info['beats_low_res_num'] += 1 beat_seq_number +=1 if (beat_seq_number %(num_beats_analyzed/20)==0 or beat_seq_number == 1) and verbose: print(f"Reconstructing beat {beat} ({beat_seq_number}/{num_beats_analyzed}: {100*beat_seq_number /num_beats_analyzed}%, LEVELS:{lvl_number}, resample type: {resample_type})") events = ADC(analyzed_beat,lvl_number,MULTIPLIER_FREQ) # For the warping to work, both signal need to be in the same form (wether normalized or un-normailized), otherwise, the distance matrix # express non coherent distances between points. Because the templates NEED to be normalized (to better express a topology, instead of raw magnitude) # This oblige us to normalize also the input signals analyzed_beat_norm, min_max_params = min_max_normalization_one_beat(analyzed_beat) events_norm,_ = min_max_normalization_one_beat(events, min_max_params) # Re-sample and warp resampled = resample(events_norm, resample_type = resample_type, min_t = analyzed_beat_norm['t'][0], max_t = analyzed_beat_norm['t'][-1]) dist, reconstructed, dist_all_template, local_template_id = warp_events(templates,events_norm, start_t = analyzed_beat_norm['t'][0], end_t = analyzed_beat_norm['t'][-1]) # Save-back beat un_norm_resamp = un_norma_and_save_back_beat(resampled,log_dir,"resampled",min_max_params) un_norm_warped = un_norma_and_save_back_beat(reconstructed,log_dir,"warped",min_max_params) un_norm_original = un_norma_and_save_back_beat(analyzed_beat_norm,log_dir,"original",min_max_params) #Update templates usage info template_id = list(templates_info.keys())[-len(templates)+local_template_id] templates_info[template_id]["used"] += 1 dist_vector = [dist_vector[i]+dist_all_template[i] for i in range(len(dist_vector))] #Compute PRD #{'warp':None,'resamp':None,'warp_left':None,'resamp_left':None,'warp_right':None,'resamp_right':None} prd_this_beat = PRD_one_beat(analyzed_beat_norm,resampled,reconstructed,QRS_idx) measurements_id_coupling["ids"]["template_id"].append(local_template_id) measurements_id_coupling["ids"]["beats"].append(beat) #Do we want to modify this dtw into ddtw_tv? measurements_id_coupling[ "measures"]["dtw"]["warp"].append(dtw_dist(un_norm_warped,un_norm_original)) measurements_id_coupling[ "measures"]["dtw"]["resamp"].append(dtw_dist(un_norm_resamp,un_norm_original)) measurements_id_coupling[ "measures"]["prd"]["warp"].append(prd_this_beat['warp']) measurements_id_coupling[ "measures"]["prd"]["resamp"].append(prd_this_beat['resamp']) measurements_id_coupling[ "measures"]["prd_left"]["warp"].append(prd_this_beat['warp_left']) measurements_id_coupling[ "measures"]["prd_left"]["resamp"].append(prd_this_beat['resamp_left']) measurements_id_coupling[ "measures"]["prd_right"]["warp"].append(prd_this_beat['warp_right']) measurements_id_coupling[ "measures"]["prd_right"]["resamp"].append(prd_this_beat['resamp_right']) # check if we need to re-compute the templates if len(distances_ref) < LEN_DISTANCE_VECTOR_REF: distances_ref.append(dist) if len(distances_ref) >= LEN_DISTANCE_VECTOR_REF: distances.append(dist) if len(distances) >= LEN_DISTANCE_VECTOR: with warnings.catch_warnings(): warnings.simplefilter("ignore") p = stats.anderson_ksamp([distances_ref,distances])[2] distances = [] if (p>= 0.05): num_distances_out = 0 else: # p value les than 0.05: null hypothesis (same distribution) rejected (please Kolmogorov forgive me) num_distances_out += 1 if num_distances_out > 2: # The acquired vector of distances was out for 2 times max_accum_dist = max(dist_vector) print(f"\n################################################################") print(f"\nLEVELS:{lvl_number}, resample type: {resample_type}: New template needed ... ") print(f"Beat number:{beat_seq_number} ({beat_seq_number}/{num_beats_analyzed}: {100*beat_seq_number /num_beats_analyzed}%)") print(f"\t p-value: {p}") for j in range(len(dist_vector)): print(f"\tTemplate {j}, dist: {dist_vector[j]}:\t","|"*int(20*dist_vector[j]/max_accum_dist)) print("\n") templates = compute_new_templates(data_orig, t_beat, t_beat+SEC_FOR_NEW_TEMPLATES, templates, templates_info) for t in templates: all_templates_collection[t] = templates[t] dist_vector = [0]*len(templates) print(f"\n################################################################\n") distances_ref = [] skip_until = t_beat + SEC_FOR_NEW_TEMPLATES num_distances_out = 0 #Save the used beats: this_new_QRS_pos = prev_len+QRS_idx prev_len += len(analyzed_beat_norm['t']) all_new_QRS_pos.append(this_new_QRS_pos) return all_templates_collection,measurements_id_coupling,init_templates,time_info,all_new_QRS_pos def measurments_reuslts(measurements_id_coupling,interpolation_type,data_orig,all_templates_collection,time_info,file,lvl,log_dir_this_lvl,log_dir_this_file): """ measurements_id_coupling = {"ids":\ {"template_id":[], "beats":[]},\ "measures":\ {"dtw":{"warp":[],"resamp":[]},\ "prd":{"warp":[],"resamp":[]},\ "prd_left":{"warp":[],"resamp":[]},\ "prd_right":{"warp":[],"resamp":[]}}} need to modify stats so to accept vectors """ for measurement_type in measurements_id_coupling['measures']: warp = np.array(measurements_id_coupling['measures'][measurement_type]['warp']) resamp = np.array(measurements_id_coupling['measures'][measurement_type]['resamp']) avg_warp = np.average(warp) std_warp = np.std(warp) avg_resamp = np.average(resamp) std_resamp = np.std(resamp) log_dir_this_measurement = os.path.join(log_dir_this_lvl,measurement_type) os.makedirs(log_dir_this_measurement,exist_ok=True) file_name_to_save = "L_"+measurement_type+"_"+interpolation_type+"_"+file.split(".")[0]+".log" # Particular log for each lvl with open(os.path.join(log_dir_this_measurement,file_name_to_save),"a") as f: f.write(f"({measurement_type}) Lvl: {lvl}, using the {interpolation_type} interpolation:\n") f.write(f"\tWarp: {avg_warp}, +-{std_warp}\n") f.write(f"\tInterpolation: {avg_resamp}, +-{std_resamp}\n") f.write(f"\tTime (percentage) passed in low-sampling mode: {time_info['beats_low_res_num']/time_info['tot_beats_num']*100}%\n") f.write(f"\n\n") # General log (the same but all toghether: more confusing but with all infos) with open(os.path.join(log_dir_this_file,file_name_to_save),"a") as f: f.write(f"({measurement_type}) Lvl: {lvl}, using the {interpolation_type} interpolation:\n") f.write(f"\tWarp: {avg_warp}, +-{std_warp}\n") f.write(f"\tInterpolation: {avg_resamp}, +-{std_resamp}\n") f.write(f"\tTime (percentage) passed in low-sampling mode: {time_info['beats_low_res_num']/time_info['tot_beats_num']*100}%\n") f.write(f"\n\n") print("\n-------------------------------------------------------------------------") print(f"({measurement_type}) File:{file_name_to_save}, using the {interpolation_type} interpolation:") print(f"\tLvl: {lvl}") print(f"\t\twarp: {avg_warp}, +-{std_warp}") print(f"\t\tinterpolation: {avg_resamp}, +-{std_resamp}") print(f"\t\tTime (percentage) passed in low-sampling mode: {time_info['beats_low_res_num']/time_info['tot_beats_num']*100}%") print("\n") for perc in PERCENTILE_TO_PLOT: idx_perc_abs = percentile_idx(warp,perc) beat, beat_id, template = get_template_and_beat_at_idx(data_orig,all_templates_collection,measurements_id_coupling['ids'],idx_perc_abs) title = f'File: {file}, Lvl: {lvl}, Beat time (samples): {beat_id}, {str(perc)} percentile, Absolute' file_name_to_save_fig = os.path.join(log_dir_this_measurement,file.split(".")[0]+"_"+str(perc)+"_perc"+str(lvl)+"_absolute.svg") reconstruct_plot_and_save_beat(beat,interpolation_type,template,lvl,title,file_name_to_save_fig) idx_perc_rel = percentile_idx(warp-resamp,perc) beat,beat_id,template = get_template_and_beat_at_idx(data_orig,all_templates_collection,measurements_id_coupling['ids'],idx_perc_rel) title = f'File: {file}, Lvl: {lvl}, Beat time (samples): {beat_id}, {str(perc)} percentile, Absolute' file_name_to_save_fig = os.path.join(log_dir_this_measurement,file.split(".")[0]+"_"+str(perc)+"_perc"+str(lvl)+"_absolute.svg") reconstruct_plot_and_save_beat(beat,interpolation_type,template,lvl,title,file_name_to_save_fig) #Filter and save back the values for better plots (the filtered data is still accounted for before) warp = z_score_filter(warp) resamp = z_score_filter(resamp) file_name_to_save_fig_hist = os.path.join(log_dir_this_measurement,file.split(".")[0]+"_hist"+str(lvl)+".svg") n_bins = min(len(warp),len(resamp))*PERC_BINS//100 min_bin = min(min(warp),min(resamp)) max_bin = max(max(warp),max(resamp)) delta = (max_bin-min_bin)/n_bins bins = np.arange(min_bin,max_bin+delta,delta) plt.figure() plt.hist(warp, bins = bins, alpha=0.5) plt.hist(resamp, bins = bins, alpha=0.5) plt.title(f'File: {file}, Lvl: {lvl}, {measurement_type} histogram') plt.legend([f'{measurement_type} warp',f'{measurement_type} resampled']) plt.savefig(file_name_to_save_fig_hist) plt.close() def delinetaion_results_save(delineation_results,log_dir_this_lvl): log_dir_this_measurement = os.path.join(log_dir_this_lvl,"delineation") os.makedirs(log_dir_this_measurement,exist_ok=True) file_name_to_save = "L_delineation.log" with open(os.path.join(log_dir_this_measurement,file_name_to_save), "w") as f: f.write(f"Delineation_score:") f.write(f"\n\twarping p-wave sensitivity:{delineation_results['warp']['p']['sens']}") f.write(f"\n\tresampling p-wave sensitivity:{delineation_results['resamp']['p']['sens']}\n") f.write(f"\n\twarping p-wave positive predicitivity:{delineation_results['warp']['p']['ppv']}") f.write(f"\n\tresampling p-wave positive predicitivity:{delineation_results['resamp']['p']['ppv']}\n") f.write(f"\n\twarping p-wave f1 score:{delineation_results['warp']['p']['f1']}") f.write(f"\n\tresampling p-wave f1 score:{delineation_results['resamp']['p']['f1']}\n\n") f.write(f"\n\twarping t-wave sensitivity:{delineation_results['warp']['t']['sens']}") f.write(f"\n\tresampling t-wave sensitivity:{delineation_results['resamp']['t']['sens']}\n") f.write(f"\n\twarping t-wave positive predicitivity:{delineation_results['warp']['t']['ppv']}") f.write(f"\n\tresampling t-wave positive predicitivity:{delineation_results['resamp']['t']['ppv']}\n") f.write(f"\n\twarping t-wave f1 score:{delineation_results['warp']['t']['f1']}") f.write(f"\n\tresampling t-wave f1 score:{delineation_results['resamp']['t']['f1']}") def reconstruction_one_lvl_one_file_and_compare(data_orig,lvl,file,initial_templates,verbose = True): log_dir_this_file = os.path.join(log_dir,file.split(".")[0]) interpolation_type_list = None if INTERPOLATION_TYPE == 'all': interpolation_type_list = ['flat','spline','linear'] else: interpolation_type_list = [INTERPOLATION_TYPE] measurements_each_interpolation_type = {} measurements_id_coupling = {} for interpolation_type in interpolation_type_list: if verbose: print(f"Level:{lvl}, Using interpolation: {interpolation_type}") log_dir_this_lvl = os.path.join(log_dir_this_file,str(lvl),interpolation_type) os.makedirs(log_dir_this_lvl, exist_ok=True) all_templates_collection, measurements_id_coupling, initial_templates, time_info, QRS_pos_new = reconstruct_beats(data_orig, lvl, init_templates = initial_templates, start_after = SEC_FOR_INITIAL_TEMPLATES, resample_type = interpolation_type, num_beats_analyzed = NUM_BEAT_ANALYZED, verbose = True, log_dir = log_dir_this_lvl) #resample_type = flat vs linear ############################################################################# # RESULTS EVALUATION # ############################################################################# delineation_results = delineate_and_compare(log_dir_this_lvl,QRS_pos_new) measurements_each_interpolation_type[interpolation_type] = measurements_id_coupling['measures'] measurments_reuslts(measurements_id_coupling,interpolation_type,data_orig,all_templates_collection,time_info,file,lvl,log_dir_this_lvl,log_dir_this_file) delinetaion_results_save(delineation_results,log_dir_this_lvl) if INTERPOLATION_TYPE == 'all': for measure in measurements_id_coupling['measures']: log_dir_this_lvl_combined = os.path.join(log_dir_this_file,str(lvl),"combined_results") os.makedirs(log_dir_this_lvl_combined, exist_ok=True) file_name_to_save = "L_"+measure+"_"+file.split(".")[0]+".log" global_file_name_to_save = "L_"+measure+"_all_interpolations_"+file.split(".")[0]+".log" with open(os.path.join(log_dir_this_lvl_combined,file_name_to_save),"a") as f: f.write(f"Lvl: {lvl}\n") with open(os.path.join(log_dir_this_file,global_file_name_to_save),"a") as f: f.write(f"Lvl: {lvl}\n") # Histograms plt.figure() file_name_to_save_fig_hist = os.path.join(log_dir_this_lvl_combined,file.split(".")[0]+"_"+measure+"_hist"+str(lvl)+".svg") n_bins = np.inf min_bin = np.inf max_bin = -np.inf legend_str = [] #Compute the bins for interp_type in measurements_each_interpolation_type: r_w = measurements_each_interpolation_type[interp_type][measure]["warp"] r_r = measurements_each_interpolation_type[interp_type][measure]["resamp"] n_bins = min(min(len(r_w),len(r_r))*PERC_BINS//100,n_bins) min_bin = min(min(r_w),min(r_r),min_bin) max_bin = max(max(r_w),max(r_r),max_bin) legend_str.extend([f'{measure} warp {interp_type}',f'{measure} resampled {interp_type}']) delta = (max_bin-min_bin)/n_bins bins = np.arange(min_bin,max_bin+delta,delta) #write results and plot for interp_type in measurements_each_interpolation_type: r_w = measurements_each_interpolation_type[interp_type][measure]["warp"] r_r = measurements_each_interpolation_type[interp_type][measure]["resamp"] avg_warp = np.average(r_w) std_warp = np.std(r_w) avg_resamp = np.average(r_r) std_resamp = np.std(r_r) # Particular log for each lvl with open(os.path.join(log_dir_this_lvl_combined,file_name_to_save),"a") as f: f.write(f"\tWarp using the {interp_type} interpolation: {avg_warp}, +-{std_warp}\n") f.write(f"\tInterpolation using the {interp_type} interpolation: {avg_resamp}, +-{std_resamp}\n") # General log (the same but all toghether: more confusing but with all infos) with open(os.path.join(log_dir_this_file,global_file_name_to_save),"a") as f: f.write(f"\tWarp using the {interp_type} interpolation: {avg_warp}, +-{std_warp}\n") f.write(f"\tInterpolation using the {interp_type} interpolation: {avg_resamp}, +-{std_resamp}\n") plt.hist(r_w, bins = bins, alpha=0.3) plt.hist(r_r, bins = bins, alpha=0.3) plt.legend(legend_str) plt.title(f'File: {file}, Lvl: {lvl}, {measure} histogram') plt.savefig(file_name_to_save_fig_hist) plt.close() with open(os.path.join(log_dir_this_lvl_combined,file_name_to_save),"a") as f: f.write(f"\n\n") with open(os.path.join(log_dir_this_file,global_file_name_to_save),"a") as f: f.write(f"\n\n") return initial_templates def reconstruct_and_compare_level_parallel(lvl): for file in FILES_SELECTED: verbose = True log_dir_this_file = os.path.join(log_dir,file.split(".")[0]) os.makedirs(log_dir_this_file,exist_ok=True) init_templates = None if verbose: print(f"(Level: {lvl}): Extracting original data") data_orig = open_file(file, start_after = 0) init_templates = reconstruction_one_lvl_one_file_and_compare(data_orig,lvl,file,init_templates,verbose = True) def recontruct_and_compare_file_parallel(file): verbose = True log_dir_this_file = os.path.join(log_dir,file.split(".")[0]) os.mkdir(log_dir_this_file) init_templates = None if verbose: print(f"(File: {file}): Extracting original data") data_orig = open_file(file, start_after = 0) for lvl in LEVELS: init_templates = reconstruction_one_lvl_one_file_and_compare(data_orig,lvl,file,init_templates,verbose = True) def process(files, levels, parallelize_along = PARALLELIZE_ALONG, cores=1): # ------------ INIT ------------ global log_dir for i in range(1,10000): tmp_log_dir = log_dir+str(i) if not os.path.isdir(tmp_log_dir): log_dir = tmp_log_dir break os.makedirs(log_dir, exist_ok=True) with open(os.path.join(log_dir,"specs.txt"), "w") as f: f.write(f"Results generated by script: {sys.argv[0]}\n") f.write(f"Time: {time.ctime(time.time())}\n\n") f.write(f"Files: {files}\n") f.write(f"Levels: {levels}\n") f.write(f"Parallelize along: {parallelize_along}\n") f.write(f"Cores: {cores}\n") f.write(f"Beats: {NUM_BEAT_ANALYZED}\n") f.write(f"Cluster percentage: {CLUSTER_PERCENTAGE}\n") f.write(f"Interpolation type: {INTERPOLATION_TYPE}\n") f.write(f"Timing mode: {TIME_MODE}\n") f.write(f"Differential dtw: {USE_DIFFERENTIAL_DTW}\n") # ------------ Extract DATA & ANNOTATIONS ------------ if cores == 1: print("Single core") if parallelize_along == 'levels': for lvl in levels: reconstruct_and_compare_level_parallel(lvl) elif parallelize_along == 'files': for f in files: recontruct_and_compare_file_parallel(f) else: with Pool(cores) as pool: if parallelize_along == 'levels': print(f"parallelizing along levels: {levels}") pool.map(reconstruct_and_compare_level_parallel, levels) elif parallelize_along == 'files': print("parallelizing along files") pool.map(recontruct_and_compare_file_parallel, files) if __name__ == "__main__": import argparse import time seconds_start = time.time() local_time_start = time.ctime(seconds_start) print("\nStarted at:", local_time_start,"\n\n") #global NUM_BEAT_ANALYZED parser = argparse.ArgumentParser() parser.add_argument("--file", help="Force to analyze one specific file instead of default one (first found)") parser.add_argument("--levels", help="Decide how many bits to use in the ADC: options:\n\t->1: [3]\n\t->2: [3,4]\n\t->3: [3,4,5]\n\t->4: ...") parser.add_argument("--cores", help="Force used number of cores (default, half of the available ones") parser.add_argument("--parallelize_along", help="describe if to parallelize on the number of 'files'\n or on the number of 'levels'") parser.add_argument("--beats", help="Number of used beats, default: 5000") parser.add_argument("--cluster_opt", help="Percentage of points for a cluster to be considered") parser.add_argument("--interpolation_type", help="Chose between: spline, flat, linear, and all. Default: flat") parser.add_argument("--use_differential_dtw", action="store_true", help="define if to use the differentail form of dtw or the original one") parser.add_argument("--acquisition_time_mode", help="Menage the time length the algorithm acquire the data at \ full speed ant the time horizon used for template recomputation. \nChose between: short, normal, and long. Default: normal") args = parser.parse_args() files = os.listdir(data_beats_dir) if args.file is not None: if args.file == 'all': FILES_SELECTED = files else: FILES_SELECTED = list(filter(lambda string: True if args.file in string else False, files)) else: FILES_SELECTED = [files[0]] if args.cores is not None: used_cores = int(args.cores) else: used_cores = multiprocessing.cpu_count()//3 if args.beats is not None: NUM_BEAT_ANALYZED = int(args.beats) else: NUM_BEAT_ANALYZED = 5000 if args.cluster_opt is not None: CLUSTER_PERCENTAGE = int(args.cluster_opt) else: CLUSTER_PERCENTAGE = 3 if args.interpolation_type is not None: INTERPOLATION_TYPE = args.interpolation_type else: INTERPOLATION_TYPE = "flat" if args.parallelize_along is not None: PARALLELIZE_ALONG = args.parallelize_along else: PARALLELIZE_ALONG = "levels" if args.levels is not None: LEVELS = LEVELS[:int(args.levels)] else: LEVELS = [3,4,5,6,7,8] if args.use_differential_dtw: USE_DIFFERENTIAL_DTW = True else: USE_DIFFERENTIAL_DTW = False if args.acquisition_time_mode is not None: if args.acquisition_time_mode == "short": SEC_FOR_INITIAL_TEMPLATES = 3*60 #5*60 LEN_DISTANCE_VECTOR = 60 #80 LEN_DISTANCE_VECTOR_REF = 400 #500 SEC_FOR_NEW_TEMPLATES = 40 #2*60 TIME_MODE = 'short' elif args.acquisition_time_mode == "long": SEC_FOR_INITIAL_TEMPLATES = 8*60 LEN_DISTANCE_VECTOR = 110 LEN_DISTANCE_VECTOR_REF = 650 SEC_FOR_NEW_TEMPLATES = 3*60 TIME_MODE = 'long' else: SEC_FOR_INITIAL_TEMPLATES = 5*60 LEN_DISTANCE_VECTOR = 80 LEN_DISTANCE_VECTOR_REF = 500 SEC_FOR_NEW_TEMPLATES = 2*60 TIME_MODE = 'medium' else: SEC_FOR_INITIAL_TEMPLATES = 5*60 LEN_DISTANCE_VECTOR = 80 LEN_DISTANCE_VECTOR_REF = 500 SEC_FOR_NEW_TEMPLATES = 2*60 TIME_MODE = 'medium' print(f"Analyzing files: {FILES_SELECTED}") print(f"Extracting data with {used_cores} cores...") process(files = FILES_SELECTED, levels = LEVELS, parallelize_along = PARALLELIZE_ALONG, cores=used_cores) seconds_stop = time.time() local_time_stop = time.ctime(seconds_stop) elapsed = seconds_stop - seconds_start hours = elapsed//60//60 minutes = (elapsed - hours * 60 * 60) // 60 seconds = (elapsed - hours * 60 * 60 - minutes * 60) // 1 print("\n\n\n-----------------------------------------------------------------------------------------------------------------") print(f"Finished at: {local_time_stop}, elapsed: {elapsed} seconds ({hours} hours, {minutes} minutes, {seconds} seconds)")