def make_plot3d(x, y, z, title=None, orthogonal_fig=True):
cmean = np.mean([x, y, z], axis=1)
cmed = np.median([x, y, z], axis=1)
ce90 = geolib.CE90(x, y)
le90 = geolib.LE90(z)
coefs = [ce90, ce90, le90]
maxdim = np.ceil(np.max([np.max(np.abs([x, y, z])), ce90, le90]))
if orthogonal_fig:
from matplotlib.patches import Ellipse
fig_ortho = plt.figure(figsize=(10, 4))
#fig_ortho = plt.figure()
title = 'Co-registration Translation Vector Components\nn=%i, mean: (%0.2f, %0.2f, %0.2f)\nCE90: %0.2f, LE90: %0.2f' % (
x.shape[0], cmean[0], cmean[1], cmean[2], ce90, le90)
plt.suptitle(title)
m = '.'
ax = fig_ortho.add_subplot(131)
ax.plot(x,
y,
color='b',
linestyle='None',
marker=m,
label='ICP correction vector')
ax.plot(cmean[0],
cmean[1],
color='r',
linestyle='None',
marker='s',
label='Mean')
#ax.scatter(x, y)
#ax.scatter(cmean[0], cmean[1], color='r', marker='s')
ax.set_xlim(-maxdim, maxdim)
ax.set_ylim(-maxdim, maxdim)
ax.minorticks_on()
ax.set_aspect('equal')
ax.set_xlabel('X offset (m)')
ax.set_ylabel('Y offset (m)')
e = Ellipse((0, 0), 2 * ce90, 2 * ce90, linewidth=0, alpha=0.1)
ax.add_artist(e)
plt.legend(prop={'size': 8}, numpoints=1, loc='upper left')
ax = fig_ortho.add_subplot(132)
ax.plot(x,
z,
color='b',
linestyle='None',
marker=m,
label='ICP correction vector')
ax.plot(cmean[0],
cmean[2],
color='r',
linestyle='None',
marker='s',
label='Mean')
#ax.scatter(x, z)
#ax.scatter(cmean[0], cmean[2], color='r', marker='s')
ax.set_xlim(-maxdim, maxdim)
ax.set_ylim(-maxdim, maxdim)
ax.minorticks_on()
ax.set_aspect('equal')
ax.set_xlabel('X offset (m)')
ax.set_ylabel('Z offset (m)')
e = Ellipse((0, 0), 2 * ce90, 2 * le90, linewidth=0, alpha=0.1)
ax.add_artist(e)
ax = fig_ortho.add_subplot(133)
ax.plot(y,
z,
color='b',
linestyle='None',
marker=m,
label='ICP correction vector')
ax.plot(cmean[1],
cmean[2],
color='r',
linestyle='None',
marker='s',
label='Mean')
#ax.scatter(y, z)
#ax.scatter(cmean[1], cmean[2], color='r', marker='s')
ax.set_xlim(-maxdim, maxdim)
ax.set_ylim(-maxdim, maxdim)
ax.minorticks_on()
ax.set_aspect('equal')
ax.set_xlabel('Y offset (m)')
ax.set_ylabel('Z offset (m)')
e = Ellipse((0, 0), 2 * ce90, 2 * le90, linewidth=0, alpha=0.1)
ax.add_artist(e)
plt.tight_layout()
#Note: postscript doesn't properly handle tansparency
#fig_fn = 'icp_translation_vec_proj_meters_orthogonal.pdf'
fig_fn = 'icp_translation_vec_local_meters_orthogonal.pdf'
plt.savefig(fig_fn, dpi=600, bbox_inches='tight')
def make_plot3d(x, y, z, title=None, orthogonal_fig=True):
fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')
ax.set_aspect('equal')
ax.set_xlabel('X offset (m)')
ax.set_ylabel('Y offset (m)')
ax.set_zlabel('Z offset (m)')
if title is not None:
plt.suptitle(title)
ax.plot(x, y, z, 'o')
cmean = np.mean([x,y,z], axis=1)
cmed = np.median([x,y,z], axis=1)
ax.scatter(cmean[0], cmean[1], cmean[2], color='r', marker='s')
ce90 = geolib.CE90(x,y)
le90 = geolib.LE90(z)
coefs = [ce90, ce90, le90]
ax.set_title("CE90: %0.2f, LE90: %0.2f, n=%i" % (ce90, le90, x.shape[0]))
maxdim = np.ceil(np.max([np.max(np.abs([x, y, z])), ce90, le90]))
ax.set_xlim(-maxdim, maxdim)
ax.set_ylim(-maxdim, maxdim)
ax.set_zlim(-maxdim, maxdim)
rx, ry, rz = coefs
u = np.linspace(0, 2 * np.pi, 100)
v = np.linspace(0, np.pi, 100)
ex = rx * np.outer(np.cos(u), np.sin(v))
ey = ry * np.outer(np.sin(u), np.sin(v))
ez = rz * np.outer(np.ones_like(u), np.cos(v))
ax.plot_surface(ex, ey, ez, rstride=2, cstride=2, linewidth=0, color='b', alpha=0.1)
#max_radius = max(rx, ry, rz)
#for axis in 'xyz':
# getattr(ax, 'set_{}lim'.format(axis))((-max_radius, max_radius))
if orthogonal_fig:
from matplotlib.patches import Ellipse
fig_ortho = plt.figure(figsize=(10,4))
#fig_ortho = plt.figure()
title='ICP Alignment Translation Vectors\nn=%i, mean: (%0.2f, %0.2f, %0.2f)\nCE90: %0.2f, LE90: %0.2f' % (x.shape[0], cmean[0], cmean[1], cmean[2], ce90, le90)
plt.suptitle(title)
ax = fig_ortho.add_subplot(131)
ax.plot(x, y, color='b', linestyle='None', marker='o', label='ICP correction vector')
ax.plot(cmean[0], cmean[1], color='r', linestyle='None', marker='s', label='Mean')
#ax.scatter(x, y)
#ax.scatter(cmean[0], cmean[1], color='r', marker='s')
ax.set_xlim(-maxdim, maxdim)
ax.set_ylim(-maxdim, maxdim)
ax.minorticks_on()
ax.set_aspect('equal')
ax.set_xlabel('X offset (m)')
ax.set_ylabel('Y offset (m)')
e = Ellipse((0,0), 2*ce90, 2*ce90, linewidth=0, alpha=0.1)
ax.add_artist(e)
plt.legend(prop={'size':8}, numpoints=1, loc='upper left')
ax = fig_ortho.add_subplot(132)
ax.plot(x, z, color='b', linestyle='None', marker='o', label='ICP correction vector')
ax.plot(cmean[0], cmean[2], color='r', linestyle='None', marker='s', label='Mean')
#ax.scatter(x, z)
#ax.scatter(cmean[0], cmean[2], color='r', marker='s')
ax.set_xlim(-maxdim, maxdim)
ax.set_ylim(-maxdim, maxdim)
ax.minorticks_on()
ax.set_aspect('equal')
ax.set_xlabel('X offset (m)')
ax.set_ylabel('Z offset (m)')
e = Ellipse((0,0), 2*ce90, 2*le90, linewidth=0, alpha=0.1)
ax.add_artist(e)
ax = fig_ortho.add_subplot(133)
ax.plot(y, z, color='b', linestyle='None', marker='o', label='ICP correction vector')
ax.plot(cmean[1], cmean[2], color='r', linestyle='None', marker='s', label='Mean')
#ax.scatter(y, z)
#ax.scatter(cmean[1], cmean[2], color='r', marker='s')
ax.set_xlim(-maxdim, maxdim)
ax.set_ylim(-maxdim, maxdim)
ax.minorticks_on()
ax.set_aspect('equal')
ax.set_xlabel('Y offset (m)')
ax.set_ylabel('Z offset (m)')
e = Ellipse((0,0), 2*ce90, 2*le90, linewidth=0, alpha=0.1)
ax.add_artist(e)
plt.tight_layout()
#Note: postscript doesn't properly handle tansparency
#fig_fn = 'icp_translation_vec_proj_meters_orthogonal.pdf'
fig_fn = 'icp_translation_vec_local_meters_orthogonal.pdf'
plt.savefig(fig_fn, dpi=600, bbox_inches='tight')
#Set back to original figure
plt.figure(fig.number)
def main():
#filenames = !ls *align/*reference-DEM.tif
#run ~/src/demtools/error_analysis.py $filenames.s
if len(sys.argv) < 1:
sys.exit('No input files provided')
fn_list = sys.argv[1:]
n_samp = len(fn_list)
error_dict_list = []
for fn in fn_list:
ed = parse_pc_align_log(fn)
if 'Translation vector (North-East-Down, meters)' in ed.keys():
error_dict_list.append(ed)
import matplotlib.dates as mdates
#This is used for interactive display of x-value in plot window
date_str = '%Y-%m-%d %H:%M'
date_fmt = mdates.DateFormatter(date_str)
#ax.fmt_xdata = mdates.DateFormatter(date_fmt)
months = mdates.MonthLocator()
months_int = mdates.MonthLocator(interval=6) # every n months
years = mdates.YearLocator() # every year
yearsFmt = mdates.DateFormatter('%Y')
#ax.xaxis.set_major_formatter(yearsFmt)
#ax.xaxis.set_major_locator(months_int3)
print
print "n:", len(error_dict_list)
"""
#ECEF translations
#key = 'Translation vector (ECEF meters)'
key = 'Translation vector (Cartesian, meters)'
#key = 'Translation vector (meters)'
val = np.array([e[key] for e in error_dict_list])
#make_plot3d(val[:,0], val[:,1], val[:,2], title=key)
ce90 = geolib.CE90(val[:,0], val[:,1])
le90 = geolib.LE90(val[:,2])
print
print key
print "CE90:", ce90
print "LE90:", le90
print
#Proj translation
key = 'Translation vector (Proj meters)'
val = np.array([e[key] for e in error_dict_list])
ce90 = geolib.CE90(val[:,0], val[:,1])
le90 = geolib.LE90(val[:,2])
print
print key
print "CE90:", ce90
print "LE90:", le90
print
print 'Centroid (mean) of offsets (Proj meters): ', np.mean(val, axis=0)
print 'Centroid (median) of offsets (Proj meters): ', np.median(val, axis=0)
"""
#NOTE: changed default to N-E-D on 9/18/15
#Can have significant differences for local proj vs. polar stereographic proj
#Should regenerate all previous figures
#Local translation on ellipsoid
#This appears to be local stereographic projection on ellipsoid
key = 'Translation vector (North-East-Down, meters)'
val = np.array([e[key] for e in error_dict_list])
#Reformat (n, e, +d) for (x, y, +z) coord sys
val[:,[0,1]] = val[:,[1,0]]
val[:,2] *= -1
ce90 = geolib.CE90(val[:,0], val[:,1])
le90 = geolib.LE90(val[:,2])
print
print key
print "CE90:", ce90
print "LE90:", le90
print
print 'Centroid (mean) of offsets (local ned meters): ', np.mean(val, axis=0)
print 'Centroid (median) of offsets (local ned meters): ', np.median(val, axis=0)
#Remove vertical bias
remove_vertbias = False
if remove_vertbias:
print "Removing vertical bias: %0.2f" % np.mean(val, axis=0)[2]
val[:,2] -= np.mean(val, axis=0)[2]
remove_outliers = False
#Flag outliers
x_mag = val[:,0]
y_mag = val[:,1]
h_mag = np.sqrt(val[:,0]**2 + val[:,1]**2)
v_mag = val[:,2]
mag = np.sqrt(val[:,0]**2 + val[:,1]**2 + val[:,2]**2)
abs_thresh = 10.0
p = 98.0
p_thresh = np.percentile(h_mag, p)
#print "Outliers with horiz error >= %0.2f (%0.1f%%)" % (p_thresh, p)
print "Outliers:"
#idx = (h_mag >= p_thresh).nonzero()[0]
idx = (h_mag >= ce90).nonzero()[0]
idx = np.unique(np.hstack([idx, ((np.abs(v_mag) >= le90).nonzero()[0])]))
#Print all
#idx = np.arange(h_mag.size)
#idx_sort = np.argsort(mag[idx])
#idx = idx[idx_sort]
print 'name, m, h, v, x, y, z'
for i in idx:
print error_dict_list[i]['File'], mag[i], h_mag[i], v_mag[i], val[i,0:3]
#Delete from list
if remove_outliers:
print "Removing from calculation"
del error_dict_list[i]
if remove_vertbias or remove_outliers:
print
print "Updated values"
print key
print "CE90:", geolib.CE90(val[:,0], val[:,1])
print "LE90:", geolib.LE90(val[:,2])
print
print 'Centroid (mean) of offsets (local ned meters): ', np.mean(val, axis=0)
print 'Centroid (median) of offsets (local ned meters): ', np.median(val, axis=0)
#Extract dates
date_vec = np.array([e['Date'] for e in error_dict_list])
x = date_vec
make_plot3d(val[:,0], val[:,1], val[:,2], title=key)
#Note: there is a bug in pdf that displayes surface lines
#fig_fn = 'icp_translation_vec_proj_meters.png'
fig_fn = 'icp_translation_vec_local_meters.png'
#plt.savefig(fig_fn, dpi=600, bbox_inches='tight')
fig, ax = plt.subplots(1)
key = 'Translation vector (lat,lon,z)'
plt.title('ICP translation vector (lat,lon,z): Z component')
val = np.array([e[key] for e in error_dict_list])
y = val[:,2]
make_plot(x,y,c='b',label=key, abs=False)
fig.autofmt_xdate()
ax.xaxis.set_minor_locator(months)
#ax.xaxis.set_major_locator(months_int)
#ax.xaxis.set_major_formatter(date_fmt)
ax.fmt_xdata = date_fmt
ax.set_ylabel('Z offset (m)')
fig, ax = plt.subplots(1)
key = 'Translation vector magnitude (meters)'
plt.title('ICP Translation vector magnitude (meters)')
y = np.array([e[key] for e in error_dict_list])
make_plot(x,y,c='b',label=key, abs=True)
fig.autofmt_xdate()
ax.xaxis.set_minor_locator(months)
#ax.xaxis.set_major_locator(months_int)
#ax.xaxis.set_major_formatter(date_fmt)
ax.fmt_xdata = date_fmt
ax.set_ylabel('Offset (m)')
fig, ax = plt.subplots(1)
key = 'Number of errors'
plt.title('Number of error samples')
nerr = np.array([e[key] for e in error_dict_list])
make_plot(x,nerr,c='b',label=key)
fig.autofmt_xdate()
ax.xaxis.set_minor_locator(months)
#ax.xaxis.set_major_locator(months_int)
#ax.xaxis.set_major_formatter(date_fmt)
ax.fmt_xdata = date_fmt
ax.set_ylabel('N samples')
"""
fig, ax = plt.subplots(1)
plt.title('ICP Standard Deviation')
key = 'Input Std Error'
in_std = np.array([e[key] for e in error_dict_list])
make_plot(x,in_std,c='r',label=key)
key = 'Output Std Error'
out_std = np.array([e[key] for e in error_dict_list])
make_plot(x,out_std,c='b',label=key)
fig.autofmt_xdate()
ax.xaxis.set_minor_locator(months)
#ax.xaxis.set_major_locator(months_int)
#ax.xaxis.set_major_formatter(date_fmt)
ax.fmt_xdata = date_fmt
plt.legend(scatterpoints=1)
"""
fig, ax = plt.subplots(1)
plt.title('ICP Mean Error')
key = 'Input Mean Error'
in_mean = np.array([e[key] for e in error_dict_list])
#make_plot(x,in_mean,c='r',label=key,yerr=in_std)
make_plot(x,in_mean,c='r',label=key, abs=True)
key = 'Output Mean Error'
out_mean = np.array([e[key] for e in error_dict_list])
#make_plot(x,out_mean,c='b',label=key,yerr=out_std)
make_plot(x,out_mean,c='b',label=key, abs=True)
fig.autofmt_xdate()
ax.xaxis.set_minor_locator(months)
#ax.xaxis.set_major_locator(months_int)
#ax.xaxis.set_major_formatter(date_fmt)
ax.fmt_xdata = date_fmt
ax.set_ylabel('Mean error (m)')
plt.legend(scatterpoints=1, loc='upper left', prop={'size':8})
fig, ax = plt.subplots(1)
plt.title('ICP Median Error')
key = 'Input 16th Percentile Error'
in_16p = np.array([e[key] for e in error_dict_list])
key = 'Input 84th Percentile Error'
in_84p = np.array([e[key] for e in error_dict_list])
key = 'Input Median Error'
in_med = np.array([e[key] for e in error_dict_list])
make_plot(x,in_med,c='r',label=key,yerr=[in_med - in_16p, in_84p - in_med], abs=True)
key = 'Output 16th Percentile Error'
out_16p = np.array([e[key] for e in error_dict_list])
key = 'Output 84th Percentile Error'
out_84p = np.array([e[key] for e in error_dict_list])
key = 'Output Median Error'
out_med = np.array([e[key] for e in error_dict_list])
make_plot(x,out_med,c='b',label=key,yerr=[out_med - out_16p, out_84p - out_med], abs=True)
fig.autofmt_xdate()
ax.fmt_xdata = mdates.DateFormatter(date_fmt)
ax.xaxis.set_minor_locator(months)
#ax.xaxis.set_major_locator(months_int)
#ax.xaxis.set_major_formatter(date_fmt)
ax.fmt_xdata = date_fmt
ax.set_ylabel('Median error (m)')
plt.legend(scatterpoints=1, loc='upper left', prop={'size':8})
fig_fn = 'icp_median_error.pdf'
plt.savefig(fig_fn, dpi=600, bbox_inches='tight')
fig, ax = plt.subplots(1)
plt.title('Sampled Median Error')
key = 'Input Sampled 16th Percentile Error'
in_16p = np.ma.fix_invalid([e[key] for e in error_dict_list])
if in_16p.count() > 0:
key = 'Input Sampled 84th Percentile Error'
in_84p = np.ma.fix_invalid([e[key] for e in error_dict_list])
key = 'Input Sampled Median Error'
in_med = np.ma.fix_invalid([e[key] for e in error_dict_list])
in_spread = in_84p - in_16p
make_plot(x,in_med,c='r',label=key,yerr=[in_med - in_16p, in_84p - in_med], abs=True)
key = 'Output Sampled 16th Percentile Error'
out_16p = np.ma.fix_invalid([e[key] for e in error_dict_list])
key = 'Output Sampled 84th Percentile Error'
out_84p = np.ma.fix_invalid([e[key] for e in error_dict_list])
key = 'Output Sampled Median Error'
out_med = np.ma.fix_invalid([e[key] for e in error_dict_list])
out_spread = out_84p - out_16p
p = 90.0
out_med_thresh = np.percentile(out_med, p)
out_spread_thresh = np.percentile(out_spread, p)
#print "Outliers with horiz error >= %0.2f (%0.1f%%)" % (p_thresh, p)
print
print "Sampled Error Outliers:"
#idx = (h_mag >= p_thresh).nonzero()[0]
idx = (out_med >= out_med_thresh).nonzero()[0]
idx = np.unique(np.hstack([idx, ((out_spread >= out_spread_thresh).nonzero()[0])]))
#Print all
idx = np.arange(out_med.size)
idx_sort = np.argsort(out_med[idx])
idx = idx[idx_sort]
print 'name, samp_mederrr, samp_errspread, nerr'
for i in idx:
print error_dict_list[i]['File'], out_med[i], out_spread[i], nerr[i]
#Delete from list
if remove_outliers:
print "Removing from calculation"
del error_dict_list[i]
print
print 'Input sampled median error (spread/2): %0.2f (%0.2f)' % (np.median(in_med), np.median(in_spread)/2.)
print 'Output sampled median error (spread/2): %0.2f (%0.2f)' % (np.median(out_med), np.median(out_spread)/2.)
print
make_plot(x,out_med,c='b',label=key,yerr=[out_med - out_16p, out_84p - out_med], abs=True)
fig.autofmt_xdate()
ax.set_ylabel('Median error (m)')
ax.fmt_xdata = mdates.DateFormatter(date_fmt)
ax.xaxis.set_minor_locator(months)
#ax.xaxis.set_major_locator(months_int)
#ax.xaxis.set_major_formatter(date_fmt)
ax.fmt_xdata = date_fmt
ax.set_ylabel('Median error (m)')
plt.legend(scatterpoints=1, loc='upper left', prop={'size':8})
ax.set_ylim(-15,15)
fig_fn = 'sampled_median_error.pdf'
#fig_fn = 'sampled_median_error_2014-2016.pdf'
#from datetime import datetime
#ax.set_xlim(datetime(2014,1,1),datetime(2016,7,1))
plt.savefig(fig_fn, dpi=600, bbox_inches='tight')
def make_plot3d(x, y, z, title=None, orthogonal_fig=True):
cmean = np.mean([x, y, z], axis=1)
cstd = np.std([x, y, z], axis=1)
cmed = np.median([x, y, z], axis=1)
cnmad = malib.mad([x, y, z], axis=1)
x_corr = x - cmean[0]
y_corr = y - cmean[1]
z_corr = z - cmean[2]
ce90 = geolib.CE90(x, y)
ce90_corr = geolib.CE90(x_corr, y_corr)
le90 = geolib.LE90(z)
le90_corr = geolib.LE90(z_corr)
coefs = [ce90, ce90, le90]
#maxdim = np.ceil(np.max([np.max(np.abs([x, y, z])), ce90, le90]))
maxdim = np.ceil(np.max([np.percentile(np.abs([x, y, z]), 99), ce90,
le90]))
if orthogonal_fig:
from matplotlib.patches import Ellipse
#fig_ortho, axa = plt.subplots(1, 3, sharex=True, sharey=True, figsize=(10,5))
fig_ortho, axa = plt.subplots(1, 3, figsize=(10, 5))
title = 'Co-registration Translation Vector Components, n=%i\n' % x.shape[
0]
title += 'mean: (%0.2f, %0.2f, %0.2f), std: (%0.2f, %0.2f, %0.2f)\n' % (
tuple(cmean) + tuple(cstd))
title += 'med: (%0.2f, %0.2f, %0.2f), nmad: (%0.2f, %0.2f, %0.2f)\n' % (
tuple(cmed) + tuple(cnmad))
title += 'CE90: %0.2f (Bias-corrected: %0.2f), LE90: %0.2f (Bias-corrected: %0.2f)' % (
ce90, ce90_corr, le90, le90_corr)
plt.suptitle(title)
dot_prop = {
'color': 'k',
'linestyle': 'None',
'marker': '.',
'ms': 3,
'label': 'ICP correction vector',
'alpha': 0.5
}
mean_prop = {
'color': 'r',
'linestyle': 'None',
'marker': 'o',
'label': 'Mean'
}
for ax in axa:
ax.set_xlim(-maxdim, maxdim)
ax.set_ylim(-maxdim, maxdim)
ax.minorticks_on()
ax.set_aspect('equal')
axa[0].plot(x, y, **dot_prop)
axa[0].plot(cmean[0], cmean[1], **mean_prop)
axa[0].set_xlabel('X offset (m)')
axa[0].set_ylabel('Y offset (m)')
e = Ellipse((0, 0), 2 * ce90, 2 * ce90, linewidth=0, alpha=0.1)
axa[0].add_artist(e)
axa[0].legend(prop={'size': 8}, numpoints=1, loc='upper left')
axa[1].plot(x, z, **dot_prop)
axa[1].plot(cmean[0], cmean[2], **mean_prop)
axa[1].set_xlabel('X offset (m)')
axa[1].set_ylabel('Z offset (m)')
e = Ellipse((0, 0), 2 * ce90, 2 * le90, linewidth=0, alpha=0.1)
axa[1].add_artist(e)
axa[2].plot(y, z, **dot_prop)
axa[2].plot(cmean[1], cmean[2], **mean_prop)
axa[2].set_xlabel('X offset (m)')
axa[2].set_ylabel('Z offset (m)')
e = Ellipse((0, 0), 2 * ce90, 2 * le90, linewidth=0, alpha=0.1)
axa[2].add_artist(e)
plt.tight_layout()
#Note: postscript doesn't properly handle tansparency
fig_fn = '%s_translation_vec_local_meters_orthogonal.pdf' % out_fn_prefix
plt.savefig(fig_fn, dpi=600, bbox_inches='tight')