def external_orientation_diff(isd, cube, nx=4, ny=4, width=500, height=500):
csmcam = csm.create_csm(isd)
isdjson = json.load(open(isd))
nlines, nsamples = isdjson['image_lines'], isdjson['image_samples']
xs, ys = np.mgrid[0:nsamples:nsamples/nx, 0:nlines:nlines/ny]
xs, ys = xs.flatten(), ys.flatten()
isis_pts = point_info(cube, xs, ys, "image")
isis_lv_bf = np.asarray([p[1]['LookDirectionBodyFixed'] for p in isis_pts])
isis_pos_bf = np.asarray([p[1]['SpacecraftPosition'].value for p in isis_pts])*1000
isis_ephem_times = np.asarray([p[1]['EphemerisTime'].value for p in isis_pts])
csm_locus = [csmcam.imageToRemoteImagingLocus(csmapi.ImageCoord(y, x)) for x,y in zip(xs,ys)]
csm_lv_bf = np.asarray([[lv.direction.x, lv.direction.y, lv.direction.z] for lv in csm_locus])
csm_pos_bf = np.asarray([[lv.point.x, lv.point.y, lv.point.z] for lv in csm_locus])
csm_ephem_times = np.asarray([csmcam.getImageTime(csmapi.ImageCoord(y, x)) for x,y in zip(xs,ys)])
csm_ephem_times += isdjson['center_ephemeris_time']
csmisis_diff_pos = csm_pos_bf - isis_pos_bf
csmisis_diff_lv = csm_lv_bf - isis_lv_bf
csmisis_diff_ephem = csm_ephem_times - isis_ephem_times
csmisis_diff_pos_mag = np.linalg.norm(csmisis_diff_pos, axis=1)
csmisis_diff_lv_mag = np.linalg.norm(csmisis_diff_lv, axis=1)
csmisis_diff_pos_data = np.asarray([csm_pos_bf.T[0], csm_pos_bf.T[1], csm_pos_bf.T[2],
isis_pos_bf.T[0], isis_pos_bf.T[1], isis_pos_bf.T[2],
csmisis_diff_pos.T[0], csmisis_diff_pos.T[1], csmisis_diff_pos.T[2],
csmisis_diff_pos_mag])
csmisis_diff_pos_data = pd.DataFrame(csmisis_diff_pos_data.T, columns=['csm pos x', 'csm pos y', 'csm pos z',
'isis pos x', 'isis pos y', 'isis pos z',
'diffx', 'diffy', 'diffz',
'magnitude'])
csmisis_diff_pos_plot = plot_diff_3d(csmisis_diff_pos_data, colx='isis pos x', coly='isis pos y', colz='isis pos z',
title='ISIS CSM Position Difference')
csmisis_diff_lv_data = np.asarray([isis_pos_bf.T[0], isis_pos_bf.T[1], isis_pos_bf.T[2],
csm_lv_bf.T[0], csm_lv_bf.T[1], csm_lv_bf.T[2],
isis_lv_bf.T[0], isis_lv_bf.T[1], isis_lv_bf.T[2],
csmisis_diff_pos.T[0], csmisis_diff_pos.T[1], csmisis_diff_pos.T[2],
csmisis_diff_lv.T[0], csmisis_diff_lv.T[1], csmisis_diff_lv.T[2],
csmisis_diff_pos_mag, csmisis_diff_lv_mag, isis_ephem_times, csm_ephem_times, csmisis_diff_ephem])
csmisis_diff_lv_data = pd.DataFrame(csmisis_diff_lv_data.T, columns=['isis pos x', 'isis pos y', 'isis pos z',
'csm lv x', 'csm lv y', 'csm lv z',
'isis lv x', 'isis lv y', 'isis lv z',
'diffx', 'diffy', 'diffz',
'diffu', 'diffv', 'diffw',
'xyz_magnitude', 'uvw_magnitude', 'isis ephem time', 'csm ephem time', 'diff ephem'])
csmisis_diff_lv_plot = plot_diff_3d_cone(csmisis_diff_lv_data, colx='isis pos x', coly='isis pos y', colz='isis pos z',
colu='isis lv x', colv='isis lv y', colw='isis lv z',
title='ISIS CSM Position and Look Vector Difference', width=width, height=height)
csmisis_diff_ephem_plot = go.Figure(go.Scatter(x=np.linspace(0, nlines, ny), y=csmisis_diff_ephem, line_shape='spline')).update_layout(title='ISIS CSM Ephem Time Difference', width=width, height=height/2).update_xaxes(title_text='Line').update_yaxes(title_text='Time Delta Seconds')
return csmisis_diff_lv_plot, csmisis_diff_ephem_plot, csmisis_diff_lv_data
def test_image_to_ground(model):
assert hasattr(model, 'imageToGround')
image_coord = csmapi.ImageCoord()
gnd = model.imageToGround(image_coord, 0)
assert gnd.x == 0
assert gnd.y == 0
assert gnd.z == 0
def compute_apriori_ground_points(network, sensors):
"""
Compute a priori ground points for all of the free points in a control network.
Parameters
----------
network : DataFrame
The control network as a dataframe generated by plio
sensors : dict
A dictionary that maps ISIS serial numbers to CSM sensors
Returns
-------
: DataFrame
The control network dataframe with updated ground points
"""
for point_id, group in network.groupby('id'):
# Free points are type 2 for V2 and V5 control networks
if group.iloc[0]["pointType"] != 2:
continue
positions = []
look_vecs = []
for measure_id, row in group.iterrows():
measure = csmapi.ImageCoord(row["line"], row["sample"])
locus = sensors[row["serialnumber"]].imageToRemoteImagingLocus(
measure)
positions.append([locus.point.x, locus.point.y, locus.point.z])
look_vecs.append(
[locus.direction.x, locus.direction.y, locus.direction.z])
ground_pt, covar_mat = closest_approach(np.array(positions),
np.array(look_vecs))
covar_vec = [
covar_mat[0, 0], covar_mat[0, 1], covar_mat[0, 2], covar_mat[1, 1],
covar_mat[1, 2], covar_mat[2, 2]
]
network.loc[network.id == point_id,
["aprioriX", "aprioriY", "aprioriZ"]] = ground_pt
network.loc[network.id == point_id,
["adjustedX", "adjustedY", "adjustedZ"]] = ground_pt
network.loc[network.id == point_id,
["adjustedX", "adjustedY", "adjustedZ"]] = ground_pt
# We have to do a separate loop to assign a list to a single cell
for measure_id, row in group.iterrows():
network.at[measure_id, 'aprioriCovar'] = covar_vec
return network
def test_bad_get_image_time(model):
img_coord = csmapi.ImageCoord(-1, -1)
with pytest.raises(RuntimeError) as r:
model.getImageTime(img_coord)
def reprojection_diff(isd, cube, nx=4, ny=8):
"""
"""
hv.extension('bokeh')
isdjson = json.load(open(isd))
nlines = isdjson['image_lines']
nsamples = isdjson['image_samples']
# generate meshgrid
xs, ys = np.mgrid[0:nsamples:nsamples / nx, 0:nlines:nlines / ny]
xs, ys = xs.flatten(), ys.flatten()
csmcam = csm.create_csm(isd)
# CS101 C++ programming class style dividers
##############################
isis_pts = point_info(cube, xs, ys, 'image')
isisgnds = np.asarray(
[g[1]['BodyFixedCoordinate'].value for g in isis_pts])
csm_pts = np.asarray([[p.samp, p.line] for p in [
csmcam.groundToImage(csmapi.EcefCoord(*(np.asarray(bf) * 1000)))
for bf in isisgnds
]])
isis2csm_diff = csm_pts - np.asarray([xs, ys]).T
isis2csm_diffmag = np.linalg.norm(isis2csm_diff, axis=1)
isis2csm_angles = np.arctan2(*isis2csm_diff.T[::-1])
data = np.asarray([
csm_pts.T[0], csm_pts.T[1], xs, ys, isis2csm_diff.T[0],
isis2csm_diff.T[1], isis2csm_diffmag, isis2csm_angles
]).T
data = pd.DataFrame(data,
columns=[
'x', 'y', 'isisx', 'isisy', 'diffx', 'diffy',
'magnitude', 'angles'
])
isis2ground2csm_plot = hv.VectorField(
(data['x'], data['y'], data['angles'], data['magnitude']),
group='isis2ground2csmimage').opts(
opts.VectorField(pivot='tail',
colorbar=True,
cmap='coolwarm',
title='ISIS2Ground->CSM2Image Pixel Diff',
arrow_heads=True,
magnitude='Magnitude',
color=dim('Magnitude')))
isis2ground2csm_plot = isis2ground2csm_plot.redim(x='sample', y='line')
isis2ground2csm_plot = isis2ground2csm_plot.opts(
plot=dict(width=500, height=1000))
isis2ground2csm_plot = isis2ground2csm_plot * hv.Points(
data, group='isis2ground2csmimage').opts(
size=5, tools=['hover'], invert_yaxis=True)
isis2ground2csm_plot = isis2ground2csm_plot.redim.range(
a=(data['magnitude'].min(), data['magnitude'].max()))
##############################
##############################
csmgnds = np.asarray([[p.x, p.y, p.z] for p in [
csmcam.imageToGround(csmapi.ImageCoord(y, x), 0)
for x, y in zip(xs, ys)
]])
csmlon, csmlat, _ = reproject(csmgnds.T, isdjson['radii']['semimajor'],
isdjson['radii']['semiminor'], 'geocent',
'latlong')
isis_imgpts = point_info(cube, csmlon, csmlat, 'ground')
isis_imgpts = np.asarray([(p[1]['Sample'], p[1]['Line'])
for p in isis_imgpts])
csm2isis_diff = isis_imgpts - np.asarray([xs, ys]).T
csm2isis_diffmag = np.linalg.norm(csm2isis_diff, axis=1)
csm2isis_angles = np.arctan2(*csm2isis_diff.T[::-1])
csm2isis_data = np.asarray([
xs, ys, isis_imgpts.T[0], isis_imgpts.T[1], csm2isis_diff.T[0],
csm2isis_diff.T[1], csm2isis_diffmag, csm2isis_angles
]).T
csm2isis_data = pd.DataFrame(csm2isis_data,
columns=[
'x', 'y', 'csmx', 'csmy', 'diffx',
'diffy', 'magnitude', 'angles'
])
csm2ground2isis_plot = hv.VectorField(
(csm2isis_data['x'], csm2isis_data['y'], csm2isis_data['angles'],
csm2isis_data['magnitude']),
group='csmground2image2isis').opts(
opts.VectorField(pivot='tail',
colorbar=True,
cmap='coolwarm',
title='CSM2Ground->ISIS2Image Pixel Diff',
arrow_heads=True,
magnitude='Magnitude',
color=dim('Magnitude')))
csm2ground2isis_plot = csm2ground2isis_plot.redim(x='sample', y='line')
csm2ground2isis_plot = csm2ground2isis_plot.opts(
plot=dict(width=500, height=1000))
csm2ground2isis_plot = csm2ground2isis_plot * hv.Points(
csm2isis_data, group='csmground2image2isis').opts(
size=5, tools=['hover'], invert_yaxis=True)
###############################
###############################
isis_lonlat = np.asarray([[
p[1]['PositiveEast360Longitude'].value,
p[1]['PlanetocentricLatitude'].value
] for p in isis_pts])
csm_lonlat = np.asarray([csmlon + 360, csmlat]).T
isiscsm_difflatlon = isis_lonlat - csm_lonlat
isiscsm_difflatlonmag = np.linalg.norm(isiscsm_difflatlon, axis=1)
isiscsm_angleslatlon = np.arctan2(*isiscsm_difflatlon.T[::-1])
isiscsm_latlondata = np.asarray([
isis_lonlat.T[0], isis_lonlat.T[1], csm_lonlat.T[0], csm_lonlat.T[1],
isiscsm_difflatlon.T[0], isiscsm_difflatlon.T[1],
isiscsm_difflatlonmag, isiscsm_angleslatlon
]).T
isiscsm_latlondata = pd.DataFrame(isiscsm_latlondata,
columns=[
'isislon', 'isislat', 'csmlon',
'csmlat', 'difflon', 'difflat',
'magnitude', 'angles'
])
isiscsm_plotlatlon = hv.VectorField(
(isiscsm_latlondata['isislon'], isiscsm_latlondata['isislat'],
isiscsm_latlondata['angles'], isiscsm_latlondata['magnitude']),
group='isisvscsmlatlon').opts(
opts.VectorField(pivot='tail',
colorbar=True,
cmap='coolwarm',
title='Image2Ground latlon Diff',
arrow_heads=True,
magnitude='Magnitude',
color=dim('Magnitude')))
isiscsm_plotlatlon = isiscsm_plotlatlon.redim(x='longitude', y='latitude')
isiscsm_plotlatlon = isiscsm_plotlatlon.opts(
plot=dict(width=500, height=1000))
isiscsm_plotlatlon = isiscsm_plotlatlon * hv.Points(
isiscsm_latlondata, ['isislon', 'isislat'],
group='isisvscsmlatlon').opts(
size=5, tools=['hover'], invert_yaxis=True)
###############################
return isis2ground2csm_plot, csm2ground2isis_plot, isiscsm_plotlatlon, data, csm2isis_data, isiscsm_latlondata
def reprojection_diff(isd, cube, nx=10, ny=50, width=500, height=500):
"""
"""
isdjson = json.load(open(isd))
nlines = isdjson['image_lines']
nsamples = isdjson['image_samples']
# generate meshgrid
xs, ys = np.mgrid[0:nsamples:nsamples/nx, 0:nlines:nlines/ny]
xs, ys = xs.flatten(), ys.flatten()
csmcam = csm.create_csm(isd)
# get data for isis image to ground, csm ground to image
isis_pts = point_info(cube, xs, ys, 'image')
isisgnds = np.asarray([np.asarray(g[1]['BodyFixedCoordinate'].value)*1000 for g in isis_pts])
csm_pts = np.asarray([[p.samp, p.line] for p in [csmcam.groundToImage(csmapi.EcefCoord(*bf)) for bf in isisgnds]])
isis2csm_diff = np.asarray([xs,ys]).T - csm_pts
isis2csm_diffmag = np.linalg.norm(isis2csm_diff, axis=1)
isis2csm_angles = np.arctan2(*isis2csm_diff.T[::-1])
isis2csm_data = np.asarray([csm_pts.T[0], csm_pts.T[1], xs, ys, isis2csm_diff.T[0], isis2csm_diff.T[1], isis2csm_diffmag, isis2csm_angles]).T
isis2csm_data = pd.DataFrame(isis2csm_data, columns=['csm sample', 'csm line', 'isis sample','isis line', 'diff sample', 'diff line', 'magnitude', 'angles'])
isis2csm_plot = plot_diff(isis2csm_data, colx='isis sample', coly='isis line',
coldx='diff sample', coldy='diff line',
title="ISIS2Ground->CSM2Image", width=width, height=height)
# get data for csm image to ground, isis ground to image
csmgnds = np.asarray([[p.x, p.y, p.z] for p in [csmcam.imageToGround(csmapi.ImageCoord(y,x), 0) for x,y in zip(xs,ys)]])
csmlon, csmlat, _ = reproject(csmgnds.T, isdjson['radii']['semimajor'], isdjson['radii']['semimajor'], 'geocent', 'latlong')
isis_imgpts = point_info(cube, (csmlon+360)%360, csmlat, 'ground')
isis_imgpts = np.asarray([(p[1]['Sample'], p[1]['Line']) for p in isis_imgpts])
csm2isis_diff = np.asarray([xs,ys]).T - isis_imgpts
csm2isis_diffmag = np.linalg.norm(csm2isis_diff, axis=1)
csm2isis_angles = np.arctan2(*(csm2isis_diff/csm2isis_diffmag[:,np.newaxis]).T[::-1])
csm2isis_data = np.asarray([xs, ys, isis_imgpts.T[0], isis_imgpts.T[1], csm2isis_diff.T[0], csm2isis_diff.T[1], csm2isis_diffmag, csm2isis_angles]).T
csm2isis_data = pd.DataFrame(csm2isis_data, columns=['csm sample', 'csm line', 'isis sample','isis line', 'diff sample', 'diff line', 'magnitude', 'angles'])
csm2isis_plot = plot_diff(csm2isis_data, colx='csm sample', coly='csm line',
coldx='diff sample', coldy='diff line',
title="CSM2Ground->ISIS2Image", width=width, height=height)
# get data for footprint comparison
isis_lonlat = np.asarray([[p[1]['PositiveEast360Longitude'].value, p[1]['PlanetocentricLatitude'].value] for p in isis_pts])
csm_lonlat = np.asarray([(csmlon+360)%360, csmlat]).T
isiscsm_difflatlon = csm_lonlat - isis_lonlat
isiscsm_difflatlonmag = np.linalg.norm(isiscsm_difflatlon, axis=1)
isiscsm_angleslatlon = np.arctan2(*isiscsm_difflatlon.T[::-1])
isiscsm_latlondata = np.asarray([isis_lonlat.T[0], isis_lonlat.T[1], csm_lonlat.T[0], csm_lonlat.T[1], isiscsm_difflatlon.T[0], isiscsm_difflatlon.T[1], isiscsm_difflatlonmag, isiscsm_angleslatlon]).T
isiscsm_latlondata = pd.DataFrame(isiscsm_latlondata, columns=['isis lon', 'isis lat', 'csm lon','csm lat', 'diff lon', 'diff lat', 'magnitude', 'angles'])
isiscsm_latlonplot = plot_diff(isiscsm_latlondata, colx='isis lon', coly='isis lat',
coldx='diff lon', coldy='diff lat',
title="ISIS Lat/Lon vs CSM Lat/Lon", width=width, height=height)
isiscsm_diffbf = isisgnds - csmgnds
isiscsm_diffbfmag = np.linalg.norm(isiscsm_diffbf, axis=1)
isiscsm_anglesbf = np.arctan2(*isiscsm_diffbf.T[::-1])
isiscsm_bfdata = np.asarray([isisgnds.T[0], isisgnds.T[1], isisgnds.T[2], csmgnds.T[0], csmgnds.T[1], csmgnds.T[2], isiscsm_diffbf.T[0], isiscsm_diffbf.T[1], isiscsm_diffbf.T[2], isiscsm_diffbfmag, isiscsm_anglesbf]).T
isiscsm_bfdata = pd.DataFrame(isiscsm_bfdata, columns=['isisx', 'isisy', 'isisz', 'csmx','csmy', 'csmz', 'diffx', 'diffy', 'diffz', 'magnitude', 'angles'])
isiscsm_bfplot = plot_diff_3d(isiscsm_bfdata, colx='isisx', coly='isisy', colz='isisz',
title='ISIS Body-Fixed vs CSM Body-Fixed', width=width, height=height)
return isis2csm_plot, csm2isis_plot, isiscsm_latlonplot, isiscsm_bfplot, isis2csm_data, csm2isis_data, isiscsm_latlondata, isiscsm_bfdata