polypoint

#!/usr/bin/env python3

efile = open('efile', 'w')

import logging
log = logging.getLogger(__name__)

import os
if 'DEBUG' in os.environ:
	logging.basicConfig(level=logging.DEBUG)

try:
	import coloredlogs
	#coloredlogs.DEFAULT_LOG_FORMAT = '%(asctime)s %(hostname)s %(name)s[%(process)d] %(levelname)s %(message)s'
	coloredlogs.DEFAULT_LOG_FORMAT = '%(message)s'
	coloredlogs.DEFAULT_LEVEL_STYLES['debug'] = {'color': 'cyan'}
	if 'DEBUG' in os.environ:
		coloredlogs.install(level=logging.DEBUG)
	else:
		coloredlogs.install()

except ImportError:
	pass


import argparse
import binascii
import pprint
import random
import struct
import sys
import time

import serial

import numpy as np
import scipy.io as sio
from scipy.optimize import fmin_bfgs, least_squares

import dataprint


parser = argparse.ArgumentParser()
parser.add_argument('-a', '--anchor-history', action='store_true')
parser.add_argument('-s', '--serial',   default='/dev/tty.usbserial-AL00EZAS')
parser.add_argument('-f', '--file',     default=None)
parser.add_argument('-b', '--baudrate', default=3000000, type=int)
parser.add_argument('-o', '--outfile',  default='out')
parser.add_argument('-t', '--trilaterate', action='store_true')
parser.add_argument('-g', '--ground-truth', default=None)
parser.add_argument('-m', '--subsample', action='store_true')
parser.add_argument('-n', '--no-iter', action='store_true')
parser.add_argument('-e', '--exact', default=None, type=int)
parser.add_argument('-d', '--dump-full', action='store_true')
parser.add_argument('-v', '--diversity', default=None)
#parser.add_argument('-t', '--textfiles',action='store_true',
#		help="Generate ASCII text files with the data")
#parser.add_argument('-m', '--matfile',  action='store_true',
#		help="Generate Matlab-compatible .mat file of the data")
#parser.add_argument('-n', '--binfile',  action='store_true',
#		help="Generate binary file of the data")

args = parser.parse_args()

if args.subsample and (args.exact is not None):
	raise NotImplementedError("Illegal flags -m + -e")

#if not (args.textfiles or args.matfile or args.binfile):
#	print("Error: Must specify at least one of -t, -m, or -n")
#	print("")
#	parser.print_help()
#	sys.exit(1)

if args.file is not None:
	dev = open(args.file, 'rb')
	print("Reading data back from file:", args.file)
else:
	dev = serial.Serial(args.serial, args.baudrate)
	if dev.isOpen():
		print("Connected to device at " + dev.portstr)
	else:
		raise NotImplementedError("Failed to connect to serial device " + args.serial)


##########################################################################

# ; 4.756-2.608+.164
# 	2.312
# ; 14.125-2.640+.164
# 	11.649
# ; -4.685+.164+15.819-2.640+0.164
# 	8.822
# ; 3.193-2.64+.164
# 	0.717
ANCHORS = {
		'22': (  .212,  8.661, 4.047),
		'3f': ( 7.050,  0.064, 3.295),
		'28': (12.704,  9.745, 3.695),
		'2c': ( 2.312,  0.052, 1.369),
		'24': (11.649,  0.058, 0.333),
		'23': (12.704,  3.873, 2.398),
		'2e': ( 8.822, 15.640, 3.910),
		'2b': ( 0.717,  3.870, 2.522),
		'26': (12.704, 15.277, 1.494),
		'30': (12.610,  9.768, 0.064),
		'27': ( 0.719,  3.864, 0.068),
		}

if args.ground_truth:
	GT = np.array(list(map(float, args.ground_truth.split(','))))
	GT_RANGE = {}
	for a in ANCHORS:
		GT_RANGE[a] = np.sqrt(sum( (np.array(ANCHORS[a]) - GT)**2 ))




def location_optimize(x,anchor_ranges,anchor_locations):
	if(x.size == 2):
		x = np.append(x,2)
	x = np.expand_dims(x, axis=0)
	x_rep = np.tile(x, [anchor_ranges.size,1])
	r_hat = np.sqrt(np.sum(np.power((x_rep-anchor_locations),2),axis=1))
	r_hat = np.reshape(r_hat,(anchor_ranges.size,))
	ret = np.sum(np.power(r_hat-anchor_ranges,2))
	return ret



def _trilaterate2(lr, lp, last_pos):
	log.debug('------------------- tri2')
	lsq = least_squares(location_optimize, last_pos, args=(lr, lp))
	pos = lsq['x']
	if args.ground_truth:
		error = np.sqrt(sum( (pos - GT)**2 ))
		log.debug('lsq: {} err {:.4f} opt {:.8f}'.format(pos, error, lsq['optimality']))

	if lsq['optimality'] > 1e-5:
		best = 1
		nextp = None
		dropping = None
		for i in range(len(lr)):
			lr2 = np.delete(np.copy(lr), i)
			lp2 = np.delete(np.copy(lp), i, axis=0)
			lsq2 = least_squares(location_optimize, last_pos, args=(lr2, lp2))
			pos2 = lsq2['x']
			opt2 = lsq2['optimality']
			if args.ground_truth:
				error2 = np.sqrt(sum( (pos2 - GT)**2 ))
				log.debug("lsq w/out {:>8.4f}: {} err {:.4f} opt {:.8f}".format(
					lr[i], pos2, error2, opt2))
			if opt2 < best:
				best = opt2
				nextp = (lr2, lp2, last_pos)
				dropping = lr[i]
		log.debug('dropping {:.4f}'.format(dropping))
		return _trilaterate2(*nextp)

	if args.ground_truth:
		efile.write(str(error)+'\t'+str(lsq['optimality'])+'\t'+str(lsq['status'])+'\n')
	return pos, lr, lp, lsq['optimality']


def trilaterate(ranges, last_position):
	efile.write('\n')
	loc_anchor_positions = []
	loc_anchor_ranges = []
	for eui,arange in ranges.items():
		try:
			loc_anchor_positions.append(ANCHORS[eui])
			loc_anchor_ranges.append(arange)
		except KeyError:
			log.warn("Skipping anchor {} with unknown location".format(eui))
	loc_anchor_positions = np.array(loc_anchor_positions)
	loc_anchor_ranges = np.array(loc_anchor_ranges)

	pos, lr, lp = _trilaterate(loc_anchor_ranges, loc_anchor_positions, last_position)
	return pos
	pos2, lr2, lp2, opt2 = _trilaterate2(loc_anchor_ranges, loc_anchor_positions, last_position)

	if args.ground_truth:
		error  = np.sqrt(sum( (pos  - GT)**2 ))
		error2 = np.sqrt(sum( (pos2 - GT)**2 ))
	if len(lr) == len(lr2):
		if args.ground_truth:
			efile.flush()
			if (error - error2) > .05: raise
		return pos

	log.debug("")
	log.debug("Aggregate")
	# np.intersect1d sorts and requires flattening lp; roll our own
	j = 0
	lr3 = []
	lp3 = []
	for i in range(len(lr)):
		if lr[i] in lr2:
			lr3.append(lr[i])
			lp3.append(lp[i])
	lr3 = np.array(lr3)
	lp3 = np.array(lp3)
	pos3, _, _ = _trilaterate(lr3, lp3, pos)
	pos4, _, _, opt4 = _trilaterate2(lr3, lp3, pos)
	#pos5 = (pos3+pos4)/2
	#if args.ground_truth:
	#	error5 = np.sqrt(sum( (pos5 - GT)**2 ))
	#	efile.write(str(error5)+'\n')
	#	log.debug('pos5 {} err {}'.format(pos5, error5))

	if args.ground_truth:
		error3 = np.sqrt(sum( (pos3 - GT)**2 ))
		error4 = np.sqrt(sum( (pos4 - GT)**2 ))
		efile.flush()
		if (error3 - error4) > .10: raise
		if (error3 - error2) > .10: raise

	if opt2 < opt4:
		return pos2
	else:
		return pos3
	return pos5

nodiversity_drop_count = 0

t1_errs = []
t1_iter_drop_count = 0

total_ranges_count = 0

def _trilaterate(loc_anchor_ranges, loc_anchor_positions, last_position, last_error=None):
	if loc_anchor_ranges.size < 3:
		raise NotImplementedError("Not enough ranges")
	#elif(num_valid_anchors == 2):
	#	if args.always_report_range:
	#		log.debug("WARNING: ONLY TWO ANCHORS...")
	#		loc_anchor_positions = ANCHOR_POSITIONS[sorted_range_idxs[first_valid_idx:last_valid_idx]]
	#		loc_anchor_ranges = sorted_ranges[first_valid_idx:last_valid_idx]
	#		log.debug("loc_anchor_ranges = {}".format(loc_anchor_ranges))
	#		tag_position = fmin_bfgs(
	#				f=location_optimize,
	#				x0=last_position[0:2],
	#				args=(loc_anchor_ranges, loc_anchor_positions)
	#				)
	#		tag_position = np.append(tag_position,2)
	#	else:
	#		return None
	else:
		disp = False #True if log.isEnabledFor(logging.DEBUG) else False
		tag_position, root_fopt, gopt, Bopt, func_calls, grad_calls, warnflag = fmin_bfgs(
				disp=disp,
				f=location_optimize, 
				x0=last_position,
				args=(loc_anchor_ranges, loc_anchor_positions),
				full_output=True,
				)

	if args.no_iter:
		return tag_position, loc_anchor_ranges, loc_anchor_positions

	#print("{} {} {}".format(tag_position[0], tag_position[1], tag_position[2]))

	log.debug("root_fopt: {}".format(root_fopt))
	if root_fopt > 0.1 and loc_anchor_ranges.size > 3:
		fopts = {}
		fopts_dbg_r = {}
		for i in range(len(loc_anchor_ranges)):
			lr = np.delete(np.copy(loc_anchor_ranges), i)
			lp = np.copy(loc_anchor_positions)
			lp = np.delete(lp, i, axis=0)
			tag_position, fopt, gopt, Bopt, func_calls, grad_calls, warnflag = fmin_bfgs(
					disp=disp,
					f=location_optimize, 
					x0=last_position,
					args=(lr, lp),
					full_output=True,
					)
			fopts[fopt] = (lr, lp)
			fopts_dbg_r[fopt] = i

		s = list(sorted(fopts.keys()))

		global t1_iter_drop_count
		t1_iter_drop_count += 1
		log.warn("--------------------------iter drop (count {})".format(t1_iter_drop_count))
		log.debug(fopts.keys())
		log.debug("dropping range {:.4f}".format(loc_anchor_ranges[fopts_dbg_r[min(fopts)]]))
		lr, lp = fopts[min(fopts)]
		error = None
		if args.ground_truth:
			error = np.sqrt(sum( (tag_position - GT)**2 ))
			log.debug("pos before drop {} Err {:.4f}".format(tag_position, error))
		else:
			log.debug("pos before drop {}".format(tag_position))
		return _trilaterate(lr, lp, last_position, last_error=error)


	if args.ground_truth:
		error = np.sqrt(sum( (tag_position - GT)**2 ))
		efile.write(str(error)+'\t'+str(root_fopt)+'\n')
		t1_errs.append(error)
		log.debug("tag_position {} Err {}".format(tag_position, error))
	else:
		log.debug("tag_position {}".format(tag_position))
	return tag_position, loc_anchor_ranges, loc_anchor_positions

##########################################################################

def useful_read(length):
	b = dev.read(length)
	while len(b) < length:
		r = dev.read(length - len(b))
		if len(r) == 0:
			raise EOFError
		b += r
	assert len(b) == length
	return b


HEADER      = (0x80018001).to_bytes(4, 'big')
DATA_HEADER = (0x8080).to_bytes(2, 'big')
FOOTER      = (0x80FE).to_bytes(2, 'big')
DWT_TIME_UNITS = 1/499.2e6/128;
SPEED_OF_LIGHT = 2.99792458e8;
AIR_N = 1.0003;
good = 0
bad = 0
NUM_RANGING_CHANNELS = 3
NUM_RANGING_BROADCASTS = 30
EUI_LEN = 8
data_section_length = 8*NUM_RANGING_CHANNELS + 8+1+1+8+8+30*8


if args.dump_full:
	dffiles = {}
	for a in ANCHORS:
		dffiles[a] = open(args.outfile + '.df.' + a, 'w')
		dffiles[a].write('# ts\t\t' + '\t'.join(list(map(str,
			range(NUM_RANGING_BROADCASTS)))) + '\n')


def antenna_and_channel_to_subsequence_number(tag_antenna_index, anchor_antenna_index, channel_index):
	anc_offset = anchor_antenna_index * NUM_RANGING_CHANNELS
	tag_offset = tag_antenna_index * NUM_RANGING_CHANNELS * NUM_RANGING_CHANNELS
	base_offset = anc_offset + tag_offset + channel_index
	
	ret = base_offset
	return ret

def oneway_get_ss_index_from_settings(anchor_antenna_index, window_num):
	tag_antenna_index = 0
	channel_index = window_num % NUM_RANGING_CHANNELS
	ret = antenna_and_channel_to_subsequence_number(tag_antenna_index, anchor_antenna_index, channel_index)
	return ret

def find_header():
	b = useful_read(len(HEADER))
	while b != HEADER:
		b = b[1:len(HEADER)] + useful_read(1)

def dwtime_to_millimeters(dwtime):
	ret = dwtime*DWT_TIME_UNITS*SPEED_OF_LIGHT/AIR_N
	ret = ret * 1000;
	return ret

#if args.textfiles:
#	tsfile  = open(args.outfile + '.timestamps', 'w')
#	datfile = open(args.outfile + '.data', 'w')
#if args.matfile:
#	allts   = []
#	alldata = []
#if args.binfile:
#	binfile = open(args.outfile + '.bin', 'wb')

data_array = []

anc_seen_hist = [5]
anc_seen_hist_ids = []

anc_seen_counts = {}
for a in ANCHORS:
	anc_seen_counts[a] = 0

windows = [0,0,0]

start = 1459998187.496
first_time = None

last_position = np.array((0,0,0))

try:
	while True:
		#if good >= 38:
		#	break
		if args.anchor_history:
			for _aid in sorted(ANCHORS):
				cnt = 0
				for l in anc_seen_hist_ids:
					for k in l:
						if k == _aid:
							cnt += 1
				print('{} {}'.format(_aid, cnt))

		#sys.stdout.write("\rGood {}    Bad {}\t\t".format(good, bad))
		log.info("Good {}    Bad {}    Avg {:.1f}    Last {}\t\t".format(
				good, bad, np.mean(anc_seen_hist), anc_seen_hist[-1]))

		try:
			log.debug("")
			log.debug("")
			log.debug("")
			find_header()

			num_anchors, = struct.unpack("<B", useful_read(1))

			ranging_broadcast_ss_send_times = np.array(struct.unpack("<30Q", useful_read(8*NUM_RANGING_BROADCASTS)))

			if first_time is None:
				first_time = ranging_broadcast_ss_send_times[15]
			DWT_TIME_UNITS = (1.0/499.2e6/128.0)
			ts = start +  (ranging_broadcast_ss_send_times[15] - first_time) * DWT_TIME_UNITS

			ranges = {}
			
			for x in range(num_anchors):
				b = useful_read(len(DATA_HEADER))
				if b != DATA_HEADER:
					log.warn("missed DATA_HEADER")
					raise AssertionError
				anchor_eui = useful_read(EUI_LEN)
				anchor_eui = anchor_eui[::-1] # reverse bytes
				anchor_eui = binascii.hexlify(anchor_eui).decode('utf-8')
				anchor_final_antenna_index, = struct.unpack("<B", useful_read(1))
				window_packet_recv, = struct.unpack("<B", useful_read(1))
				anc_final_tx_timestamp, = struct.unpack("<Q", useful_read(8))
				anc_final_rx_timestamp, = struct.unpack("<Q", useful_read(8))
				tag_poll_first_idx, = struct.unpack("<B", useful_read(1))
				tag_poll_first_TOA, = struct.unpack("<Q", useful_read(8))
				tag_poll_last_idx, = struct.unpack("<B", useful_read(1))
				tag_poll_last_TOA, = struct.unpack("<Q", useful_read(8))
				tag_poll_TOAs = np.array(struct.unpack("<"+str(NUM_RANGING_BROADCASTS)+"H", useful_read(2*NUM_RANGING_BROADCASTS)))

				if tag_poll_first_idx >= NUM_RANGING_BROADCASTS or tag_poll_last_idx >= NUM_RANGING_BROADCASTS:
					log.warn("tag_poll outside of range; skpping")
					continue
			
				# Perform ranging operations with the received timestamp data
				tag_poll_TOAs[tag_poll_first_idx] = tag_poll_first_TOA
				tag_poll_TOAs[tag_poll_last_idx] = tag_poll_last_TOA

				approx_clock_offset = (tag_poll_last_TOA - tag_poll_first_TOA)/(ranging_broadcast_ss_send_times[tag_poll_last_idx] - ranging_broadcast_ss_send_times[tag_poll_first_idx])

				# Interpolate betseen the first and last to find the high 48 bits which fit best
				for jj in range(tag_poll_first_idx+1,tag_poll_last_idx):
					estimated_toa = tag_poll_first_TOA + (approx_clock_offset*(ranging_broadcast_ss_send_times[jj] - ranging_broadcast_ss_send_times[tag_poll_first_idx]))
					actual_toa = (int(estimated_toa) & 0xFFFFFFFFFFF0000) + tag_poll_TOAs[jj]

					if(actual_toa < estimated_toa - 0x7FFF):
						actual_toa = actual_toa + 0x10000
					elif(actual_toa > estimated_toa + 0x7FFF):
						actual_toa = actual_toa - 0x10000

					tag_poll_TOAs[jj] = actual_toa

				# Get the actual clock offset calculation
				num_valid_offsets = 0
				offset_cumsum = 0
				for jj in range(NUM_RANGING_CHANNELS):
					if(tag_poll_TOAs[jj] & 0xFFFF > 0 and tag_poll_TOAs[27+jj] & 0xFFFF > 0):
						offset_cumsum = offset_cumsum + (tag_poll_TOAs[27+jj] - tag_poll_TOAs[jj])/(ranging_broadcast_ss_send_times[27+jj] - ranging_broadcast_ss_send_times[jj])
						num_valid_offsets = num_valid_offsets + 1

				if num_valid_offsets == 0:
					continue
				offset_anchor_over_tag = offset_cumsum/num_valid_offsets;

				# Figure out what broadcast the received response belongs to
				ss_index_matching = oneway_get_ss_index_from_settings(anchor_final_antenna_index, window_packet_recv)
				if int(tag_poll_TOAs[ss_index_matching]) & 0xFFFF == 0:
					log.warn("no bcast ss match, ss_index_matching {}, TOAs[{}] = {}".format(
						ss_index_matching, ss_index_matching, tag_poll_TOAs[ss_index_matching]))
					continue
		
				matching_broadcast_send_time = ranging_broadcast_ss_send_times[ss_index_matching]
				matching_broadcast_recv_time = tag_poll_TOAs[ss_index_matching]
				response_send_time = anc_final_tx_timestamp
				response_recv_time = anc_final_rx_timestamp
		
				two_way_TOF = ((response_recv_time - matching_broadcast_send_time)*offset_anchor_over_tag) - (response_send_time - matching_broadcast_recv_time)
				one_way_TOF = two_way_TOF/2

				if args.dump_full:
					dffiles[anchor_eui[-2:]].write('{:.2f}\t'.format(ts))
		
				# Declare an array for sorting ranges
				distance_millimeters = []
				d_mm_all = []
				for jj in range(NUM_RANGING_BROADCASTS):
					broadcast_send_time = ranging_broadcast_ss_send_times[jj]
					broadcast_recv_time = tag_poll_TOAs[jj]
					if int(broadcast_recv_time) & 0xFFFF == 0:
						if args.dump_full:
							dffiles[anchor_eui[-2:]].write('nan\t')
						d_mm_all.append(-111)
						continue
		
					broadcast_anchor_offset = broadcast_recv_time - matching_broadcast_recv_time
					broadcast_tag_offset = broadcast_send_time - matching_broadcast_send_time
					TOF = broadcast_anchor_offset - broadcast_tag_offset*offset_anchor_over_tag + one_way_TOF
		
					mm = dwtime_to_millimeters(TOF)
					mm -= 121.591
					distance_millimeters.append(mm)
					d_mm_all.append(mm)

					if args.dump_full:
						dffiles[anchor_eui[-2:]].write('{:.2f}\t'.format(mm/1000))

				if args.dump_full:
					dffiles[anchor_eui[-2:]].write('\n')


				# Allow for experiments that ignore diversity
				if args.diversity is not None:
					if args.diversity[0] == 'r':
						d = []
						for i in range(int(args.diversity[1:])):
							cand = d_mm_all.pop(random.randrange(len(d_mm_all)))
							if cand != -111:
								d.append(cand)
						if len(d) == 0:
							nodiversity_drop_count += 1
							print("Dropping, cnt", nodiversity_drop_count)
							continue
						range_mm = np.percentile(d,10)
					else:
						range_mm = d_mm_all[int(args.diversity)]
				else:
					range_mm = np.percentile(distance_millimeters,10)


				if range_mm < 0 or range_mm > (1000*30):
					log.warn('Dropping impossible range %d', range_mm)
					continue

				if args.ground_truth:
					log.debug('Anchor {} Range {:.4f} Error {:.4f}'.format(anchor_eui,
						range_mm/1000, range_mm/1000 - GT_RANGE[anchor_eui[-2:]]))
				else:
					log.debug('Anchor {} Range {:.4f}'.format(anchor_eui, range_mm/1000))

				ranges[anchor_eui[-2:]] = range_mm / 1000
				windows[window_packet_recv] += 1

			footer = useful_read(len(FOOTER))
			if footer != FOOTER:
				raise AssertionError

			if len(anc_seen_hist) > 20:
				anc_seen_hist.pop(0)
			anc_seen_hist.append(len(ranges))

			if len(anc_seen_hist_ids) > 30:
				anc_seen_hist_ids.pop(0)
			anc_seen_hist_ids.append(list(ranges.keys()))

			if args.subsample:
				while len(ranges) > 3:
					del ranges[list(ranges.keys())[random.randrange(0, len(ranges))]]

			if args.exact is not None:
				if len(ranges) < args.exact:
					continue
				while len(ranges) > args.exact:
					del ranges[list(ranges.keys())[random.randrange(0, len(ranges))]]

			total_ranges_count += len(ranges)

			for r in ranges:
				anc_seen_counts[r] += 1

			if args.trilaterate:# and good > 35:
				position = trilaterate(ranges, last_position)
				last_position = position

				if args.ground_truth:
					error = np.sqrt(sum( (position - GT)**2 ))
					efile.write(str(error)+'\n')
					s = "{:.3f} {:1.4f} {:1.4f} {:1.4f} Err {:1.4f}".format(ts, *position, error)
				else:
					s = "{:.3f} {:1.4f} {:1.4f} {:1.4f}".format(ts, *position)
				print(s)

				aa = []
				for a in sorted(ANCHORS.keys()):
					if a in ranges:
						aa.append(ranges[a])
					else:
						aa.append(0)

				data_array.append([ts, *position, *aa])

			good += 1

			#if args.binfile:
			#	binfile.write(bline)

		except (AssertionError, NotImplementedError):
			bad += 1

except KeyboardInterrupt:
	pass
except EOFError:
	pass

print("\nGood {}\nBad  {}".format(good, bad))
if sum(windows):
	print("Windows {} ({:.1f}) {} ({:.1f}) {} ({:.1f})".format(
		windows[0], 100* windows[0] / sum(windows),
		windows[1], 100* windows[1] / sum(windows),
		windows[2], 100* windows[2] / sum(windows),
		))
print("Iteration dropped {} times".format(t1_iter_drop_count))
print("Total ranges {}".format(total_ranges_count))
print("Anchors Seen:")
pprint.pprint(anc_seen_counts)
#if args.textfiles:
#	print("Wrote ASCII outputs to " + args.outfile + ".{timestamps,data}")
#if args.matfile:
#	sio.savemat(args.outfile+'.mat', {
#		'timestamps': allts,
#		'data': alldata,
#		})
#	print('Wrote Matlab-friendly file to ' + args.outfile + '.mat')
#if args.binfile:
#	print('Wrote binary output to ' + args.outfile + '.bin')
#	print('\tBinary data is formatted as:')
#	print('\t<uint64_t><int16_t><int16_t><int16_t><int16_t>... all little endian')
#	print('\ttimestmap real0    imag0    real1    imag1    ...')
#	print('\tFor 1024 total complex numbers')

#dataprint.to_newfile(args.outfile, data_array,
#		overwrite=True,
#		#comments=("Time", "X", "Y", "Z"),
#		)

if args.trilaterate:
	ofile = open(args.outfile, 'w')
	aa = []
	for a in sorted(ANCHORS.keys()):
		aa.append(':'.join((a, *map(str, ANCHORS[a]))))
	ofile.write("#" + '\t'.join(("Time", "X", "Y", "Z", *aa)) + '\n')
	dataprint.to_file(ofile, data_array)
	ofile.write('#windows {} {} {}\n'.format(*windows))
	print("Saved to {}".format(args.outfile))


if args.ground_truth:
	t1_errs = np.array(t1_errs)
	print("t1:  min {:.4f} max {:.4f} mean {:.4f} med {:.4f}".format(
		np.min   (t1_errs),
		np.max   (t1_errs),
		np.mean  (t1_errs),
		np.median(t1_errs),
		))

print(nodiversity_drop_count)