def compute_residuals(network, sensors):
"""
Compute the error in the observations by taking the difference between the
ground points groundToImage projections and measure values.
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
-------
V : np.array
The control network dataframe with updated ground points
"""
num_meas = len(network)
V = np.zeros((num_meas, 2))
for i in range(num_meas):
row = network.iloc[i]
serial = row["serialnumber"]
ground_pt = row[["adjustedX", "adjustedY", "adjustedZ"]].values
ground_pt = csmapi.EcefCoord(ground_pt[0], ground_pt[1], ground_pt[2])
sensor = sensors[serial]
img_coord = sensor.groundToImage(ground_pt)
V[i, :] = [
row['line'] - img_coord.line, row['sample'] - img_coord.samp
]
V = V.reshape(num_meas * 2)
return V
def compute_sensor_partials(sensor, parameters, ground_pt):
"""
Compute the partial derivatives of the line and sample with respect to a set
of parameters.
Parameters
----------
sensor : CSM sensor
The CSM sensor model
parameters : list
The list of CsmParameter to compute the partials W.R.T.
ground_pt : array
The (x, y, z) ground point to compute the partial derivatives at
Returns
-------
: ndarray
The 2 x n array of partial derivatives. The first array is the line
partials and the second array is the sample partials. The partial
derivatives are in the same order as the parameter list passed in.
"""
partials = np.zeros((2, len(parameters)))
csm_ground = csmapi.EcefCoord(ground_pt[0], ground_pt[1], ground_pt[2])
for i in range(len(parameters)):
partials[:, i] = sensor.computeSensorPartials(parameters[i].index,
csm_ground)
return partials
def compute_ground_partials(sensor, ground_pt):
"""
Compute the partial derivatives of the line and sample with respect to a
ground point.
Parameters
----------
sensor : CSM sensor
The CSM sensor model
ground_pt : array
The (x, y, z) ground point to compute the partial derivatives W.R.T.
Returns
-------
: ndarray
The 2 x 3 array of partial derivatives. The first array is the line
partials and the second array is the sample partials. The partial
derivatives are in (x, y, z) order.
"""
csm_ground = csmapi.EcefCoord(ground_pt[0], ground_pt[1], ground_pt[2])
partials = np.array(sensor.computeGroundPartials(csm_ground))
return np.reshape(partials, (2, 3))
def test_bad_ground_to_image(model):
gnd_coord = csmapi.EcefCoord(-1, -1, 0)
with pytest.warns(Warning) as w:
img = model.groundToImage(gnd_coord, 0)
assert len(w) == 1
def test_ground_to_image(model):
assert hasattr(model, 'groundToImage')
gnd_coord = csmapi.EcefCoord(0, 0, 0)
img = model.groundToImage(gnd_coord, 0)
assert img.samp == 0
assert img.line == 0
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