diff --git a/bin/isisnet2socet b/bin/isisnet2socet
index 263c82d24ae7bc4a2891a7c55bca8871bcab96ce..c72c5e01677d54c1feba3738ec88c0118f91546c 100644
--- a/bin/isisnet2socet
+++ b/bin/isisnet2socet
@@ -2,14 +2,18 @@
import os
import math
import argparse
+import warnings
+import pvl
+import math
+import pyproj
+import numpy as np
import pandas as pd
from plio.io.io_bae import save_gpf, save_ipf
import plio.io.io_controlnetwork as cn
import plio.io.isis_serial_number as sn
-from plio.spatial.transformations import apply_isis_transformations
-from plio.utils.utils import split_all_ext
+from plio.utils.utils import find_in_dict, split_all_ext
def parse_args():
parser = argparse.ArgumentParser()
@@ -25,6 +29,181 @@ def parse_args():
return parser.parse_args()
+def reverse_known(record):
+ """
+ Converts the known field from an isis dataframe into the
+ socet known column
+
+ Parameters
+ ----------
+ record : object
+ Pandas series object
+
+ Returns
+ -------
+ : str
+ String representation of a known field
+ """
+ lookup = {0:0,
+ 2:0,
+ 1:3,
+ 3:3,
+ 4:3}
+ record_type = record['known']
+ return lookup[record_type]
+
+def reproject(record, semi_major, semi_minor, source_proj, dest_proj, **kwargs):
+ """
+ Thin wrapper around PyProj's Transform() function to transform 1 or more three-dimensional
+ point from one coordinate system to another. If converting between Cartesian
+ body-centered body-fixed (BCBF) coordinates and Longitude/Latitude/Altitude coordinates,
+ the values input for semi-major and semi-minor axes determine whether latitudes are
+ planetographic or planetocentric and determine the shape of the datum for altitudes.
+ If semi_major == semi_minor, then latitudes are interpreted/created as planetocentric
+ and altitudes are interpreted/created as referenced to a spherical datum.
+ If semi_major != semi_minor, then latitudes are interpreted/created as planetographic
+ and altitudes are interpreted/created as referenced to an ellipsoidal datum.
+
+ Parameters
+ ----------
+ record : object
+ Pandas series object
+
+ semi_major : float
+ Radius from the center of the body to the equater
+
+ semi_minor : float
+ Radius from the pole to the center of mass
+
+ source_proj : str
+ Pyproj string that defines a projection space ie. 'geocent'
+
+ dest_proj : str
+ Pyproj string that defines a project space ie. 'latlon'
+
+ Returns
+ -------
+ : list
+ Transformed coordinates as y, x, z
+
+ """
+ source_pyproj = pyproj.Proj(proj = source_proj, a = semi_major, b = semi_minor)
+ dest_pyproj = pyproj.Proj(proj = dest_proj, a = semi_major, b = semi_minor)
+
+ y, x, z = pyproj.transform(source_pyproj, dest_pyproj, record[0], record[1], record[2], **kwargs)
+ return y, x, z
+
+def fix_sample_line(record, serial_dict, cub_dict):
+ """
+ Extracts the sample, line data from a cube and computes deviation from the
+ center of the image
+
+ Parameters
+ ----------
+ record : dict
+ Dict containing the key serialnumber, l., and s.
+
+ serial_dict : dict
+ Maps serial numbers to images
+
+ cub_dict : dict
+ Maps basic cub names to their assocated absoluate path cubs
+
+ Returns
+ -------
+ new_line : int
+ new line deviation from the center
+
+ new_sample : int
+ new sample deviation from the center
+
+ """
+ # Cube location to load
+ cube = pvl.load(cub_dict[serial_dict[record['serialnumber']]])
+ line_size = find_in_dict(cube, 'Lines')
+ sample_size = find_in_dict(cube, 'Samples')
+
+ new_line = record['l.'] - (int(line_size / 2.0)) - 1
+ new_sample = record['s.'] - (int(sample_size / 2.0)) - 1
+
+ return new_line, new_sample
+
+def ignore_toggle(record):
+ """
+ Maps the stat column in a record to 0 or 1 based on True or False
+
+ Parameters
+ ----------
+ record : dict
+ Dict containing the key stat
+ """
+ if record['stat'] == True:
+ return 0
+ else:
+ return 1
+
+def apply_isis_transformations(df, eRadius, pRadius, serial_dict, cub_dict):
+ """
+ Takes an ISIS3 control network dataframe and applies the necessary
+ transformations to convert that dataframe into a Socet Set-compatible
+ dataframe
+
+ Parameters
+ ----------
+ df : object
+ Pandas dataframe object
+
+ eRadius : float
+ Equitorial radius of the target body
+
+ pRadius : float
+ Polar radius of the target body
+
+ serial_dict : dict
+ Dictionary mapping serials as keys to images as the values
+
+ cub_dict : str
+ Dictionary mapping the basename of IPF files as keys to image cube names as values
+
+ """
+ # Convert from geocentered coords (x, y, z), to lat lon coords (latitude, longitude, alltitude)
+ ecef = np.array([[df['long_X_East']], [df['lat_Y_North']], [df['ht']]])
+ lla = reproject(ecef, semi_major = eRadius, semi_minor = pRadius,
+ source_proj = 'geocent', dest_proj = 'latlong')
+
+ df['long_X_East'], df['lat_Y_North'], df['ht'] = lla[0][0], lla[1][0], lla[2][0]
+
+ # Convert longitude and latitude from degrees to radians
+ df['long_X_East'] = df['long_X_East'].apply(np.radians)
+ df['lat_Y_North'] = df['lat_Y_North'].apply(np.radians)
+
+ # Update the stat fields and add the val field as it is just a clone of stat
+ df['stat'] = df.apply(ignore_toggle, axis = 1)
+ df['val'] = df['stat']
+
+ # Update the known field, add the ipf_file field for saving, and
+ # update the line, sample using data from the cubes
+ df['known'] = df.apply(reverse_known, axis = 1)
+ df['ipf_file'] = df['serialnumber'].apply(lambda serial_number: serial_dict[serial_number])
+ df['l.'], df['s.'] = zip(*df.apply(fix_sample_line, serial_dict = serial_dict,
+ cub_dict = cub_dict, axis = 1))
+
+ # Add dummy for generic value setting
+ x_dummy = lambda x: np.full(len(df), x)
+
+ df['sig0'] = x_dummy(1)
+ df['sig1'] = x_dummy(1)
+ df['sig2'] = x_dummy(1)
+
+ df['res0'] = x_dummy(0)
+ df['res1'] = x_dummy(0)
+ df['res2'] = x_dummy(0)
+
+ df['fid_x'] = x_dummy(0)
+ df['fid_y'] = x_dummy(0)
+
+ df['no_obs'] = x_dummy(1)
+ df['fid_val'] = x_dummy(0)
def main(args):
# Create cub dict to map ipf to cub
diff --git a/bin/socetnet2isis b/bin/socetnet2isis
index 1bbef96ec726cc008b829502d7b84d76748cc6cd..673b5ce605cd0c936114e12643f54b960f6b784d 100644
--- a/bin/socetnet2isis
+++ b/bin/socetnet2isis
@@ -3,14 +3,17 @@ import os
import sys
import argparse
import warnings
+import pvl
+import math
+import pyproj
+import numpy as np
import pandas as pd
from plio.io.io_bae import read_atf, read_gpf, read_ipf
import plio.io.io_controlnetwork as cn
import plio.io.isis_serial_number as sn
-from plio.spatial.transformations import apply_socet_transformations
-from plio.utils.utils import split_all_ext
+from plio.utils.utils import find_in_dict, split_all_ext
def parse_args():
parser = argparse.ArgumentParser()
@@ -23,7 +26,262 @@ def parse_args():
return parser.parse_args()
+def line_sample_size(record, path):
+ """
+ Converts columns l. and s. to sample size, line size, and generates an
+ image index
+ Parameters
+ ----------
+ record : object
+ Pandas series object
+
+ path : str
+ Path to the associated sup files for a socet project
+
+ Returns
+ -------
+ : list
+ A list of sample_size, line_size, and img_index
+ """
+ with open(os.path.join(path, record['ipf_file'] + '.sup')) as f:
+ for i, line in enumerate(f):
+ if i == 2:
+ img_index = line.split('\\')
+ img_index = img_index[-1].strip()
+ img_index = img_index.split('.')[0]
+
+ if i == 3:
+ line_size = line.split(' ')
+ line_size = line_size[-1].strip()
+ assert int(line_size) > 0, "Line number {} from {} is a negative number: Invalid Data".format(line_size, record['ipf_file'])
+
+ if i == 4:
+ sample_size = line.split(' ')
+ sample_size = sample_size[-1].strip()
+ assert int(sample_size) > 0, "Sample number {} from {} is a negative number: Invalid Data".format(sample_size, record['ipf_file'])
+ break
+
+
+ line_size = int(line_size)/2.0 + record['l.'] + 1
+ sample_size = int(sample_size)/2.0 + record['s.'] + 1
+ return sample_size, line_size, img_index
+
+def get_axis(file):
+ """
+ Gets eRadius and pRadius from a .prj file
+
+ Parameters
+ ----------
+ file : str
+ file with path to a given socet project file
+
+ Returns
+ -------
+ : list
+ A list of the eRadius and pRadius of the project file
+ """
+ with open(file) as f:
+ from collections import defaultdict
+
+ files = defaultdict(list)
+
+ for line in f:
+
+ ext = line.strip().split(' ')
+ files[ext[0]].append(ext[-1])
+
+ eRadius = float(files['A_EARTH'][0])
+ pRadius = eRadius * math.sqrt(1 - (float(files['E_EARTH'][0]) ** 2))
+
+ return eRadius, pRadius
+
+def reproject(record, semi_major, semi_minor, source_proj, dest_proj, **kwargs):
+ """
+ Thin wrapper around PyProj's Transform() function to transform 1 or more three-dimensional
+ point from one coordinate system to another. If converting between Cartesian
+ body-centered body-fixed (BCBF) coordinates and Longitude/Latitude/Altitude coordinates,
+ the values input for semi-major and semi-minor axes determine whether latitudes are
+ planetographic or planetocentric and determine the shape of the datum for altitudes.
+ If semi_major == semi_minor, then latitudes are interpreted/created as planetocentric
+ and altitudes are interpreted/created as referenced to a spherical datum.
+ If semi_major != semi_minor, then latitudes are interpreted/created as planetographic
+ and altitudes are interpreted/created as referenced to an ellipsoidal datum.
+
+ Parameters
+ ----------
+ record : object
+ Pandas series object
+
+ semi_major : float
+ Radius from the center of the body to the equater
+
+ semi_minor : float
+ Radius from the pole to the center of mass
+
+ source_proj : str
+ Pyproj string that defines a projection space ie. 'geocent'
+
+ dest_proj : str
+ Pyproj string that defines a project space ie. 'latlon'
+
+ Returns
+ -------
+ : list
+ Transformed coordinates as y, x, z
+
+ """
+ source_pyproj = pyproj.Proj(proj = source_proj, a = semi_major, b = semi_minor)
+ dest_pyproj = pyproj.Proj(proj = dest_proj, a = semi_major, b = semi_minor)
+
+ y, x, z = pyproj.transform(source_pyproj, dest_pyproj, record[0], record[1], record[2], **kwargs)
+ return y, x, z
+
+# TODO: Does isis cnet need a convariance matrix for sigmas? Even with a static matrix of 1,1,1,1
+def compute_sigma_covariance_matrix(lat, lon, rad, latsigma, lonsigma, radsigma, semimajor_axis):
+
+ """
+ Given geospatial coordinates, desired accuracy sigmas, and an equitorial radius, compute a 2x3
+ sigma covariange matrix.
+ Parameters
+ ----------
+ lat : float
+ A point's latitude in degrees
+
+ lon : float
+ A point's longitude in degrees
+
+ rad : float
+ The radius (z-value) of the point in meters
+
+ latsigma : float
+ The desired latitude accuracy in meters (Default 10.0)
+
+ lonsigma : float
+ The desired longitude accuracy in meters (Default 10.0)
+
+ radsigma : float
+ The desired radius accuracy in meters (Defualt: 15.0)
+
+ semimajor_axis : float
+ The semi-major or equitorial radius in meters (Default: 1737400.0 - Moon)
+ Returns
+ -------
+ rectcov : ndarray
+ (2,3) covariance matrix
+ """
+ lat = math.radians(lat)
+ lon = math.radians(lon)
+
+ # SetSphericalSigmasDistance
+ scaled_lat_sigma = latsigma / semimajor_axis
+
+ # This is specific to each lon.
+ scaled_lon_sigma = lonsigma * math.cos(lat) / semimajor_axis
+
+ # SetSphericalSigmas
+ cov = np.eye(3,3)
+ cov[0,0] = math.radians(scaled_lat_sigma) ** 2
+ cov[1,1] = math.radians(scaled_lon_sigma) ** 2
+ cov[2,2] = radsigma ** 2
+
+ # Approximate the Jacobian
+ j = np.zeros((3,3))
+ cosphi = math.cos(lat)
+ sinphi = math.sin(lat)
+ cos_lmbda = math.cos(lon)
+ sin_lmbda = math.sin(lon)
+ rcosphi = rad * cosphi
+ rsinphi = rad * sinphi
+ j[0,0] = -rsinphi * cos_lmbda
+ j[0,1] = -rcosphi * sin_lmbda
+ j[0,2] = cosphi * cos_lmbda
+ j[1,0] = -rsinphi * sin_lmbda
+ j[1,1] = rcosphi * cos_lmbda
+ j[1,2] = cosphi * sin_lmbda
+ j[2,0] = rcosphi
+ j[2,1] = 0.
+ j[2,2] = sinphi
+ mat = j.dot(cov)
+ mat = mat.dot(j.T)
+ rectcov = np.zeros((2,3))
+ rectcov[0,0] = mat[0,0]
+ rectcov[0,1] = mat[0,1]
+ rectcov[0,2] = mat[0,2]
+ rectcov[1,0] = mat[1,1]
+ rectcov[1,1] = mat[1,2]
+ rectcov[1,2] = mat[2,2]
+
+ return rectcov
+
+def compute_cov_matrix(record, semimajor_axis):
+ cov_matrix = compute_sigma_covariance_matrix(record['lat_Y_North'], record['long_X_East'], record['ht'], record['sig0'], record['sig1'], record['sig2'], semimajor_axis)
+ return cov_matrix.ravel().tolist()
+
+def stat_toggle(record):
+ if record['stat'] == 0:
+ return True
+ else:
+ return False
+
+def known(record):
+ """
+ Converts the known field from a socet dataframe into the
+ isis point_type column
+
+ Parameters
+ ----------
+ record : object
+ Pandas series object
+
+ Returns
+ -------
+ : str
+ String representation of a known field
+ """
+
+ lookup = {0: 'Free',
+ 1: 'Constrained',
+ 2: 'Constrained',
+ 3: 'Constrained'}
+ return lookup[record['known']]
+
+def apply_socet_transformations(atf_dict, df):
+ """
+ Takes a atf dictionary and a socet dataframe and applies the necessary
+ transformations to convert that dataframe into a isis compatible
+ dataframe
+
+ Parameters
+ ----------
+ atf_dict : dict
+ Dictionary containing information from an atf file
+
+ df : object
+ Pandas dataframe object
+
+ """
+ prj_file = os.path.join(atf_dict['PATH'], atf_dict['PROJECT'])
+
+ eRadius, pRadius = get_axis(prj_file)
+
+ # Convert longitude and latitude from radians to degrees
+ df['long_X_East'] = df['long_X_East'].apply(np.degrees)
+ df['lat_Y_North'] = df['lat_Y_North'].apply(np.degrees)
+
+ lla = np.array([[df['long_X_East']], [df['lat_Y_North']], [df['ht']]])
+
+ ecef = reproject(lla, semi_major = eRadius, semi_minor = pRadius,
+ source_proj = 'latlon', dest_proj = 'geocent')
+
+ df['s.'], df['l.'], df['image_index'] = (zip(*df.apply(line_sample_size, path = atf_dict['PATH'], axis=1)))
+ df['known'] = df.apply(known, axis=1)
+ df['long_X_East'] = ecef[0][0]
+ df['lat_Y_North'] = ecef[1][0]
+ df['ht'] = ecef[2][0]
+ df['aprioriCovar'] = df.apply(compute_cov_matrix, semimajor_axis = eRadius, axis=1)
+ df['stat'] = df.apply(stat_toggle, axis=1)
+
def main(args):
# Setup the at_file, path to cubes, and control network out path
at_file = args.at_file