General changes. Near finished isis2socet in notebook.

D06_029601_1846_XN_04N224W,D06_029601_1846_XN_04N224W.8bit.cub

F05_037684_1857_XN_05N224W,F05_037684_1857_XN_05N224W.8bit.cub

import json

import re

import os

import json

from collections import defaultdict

from functools import singledispatch

import numpy as np

                 project

    """

    with open(atf_file) as f:

        files = []

        ipf = []

        sup = []

        # Extensions of files we want

        files_ext = ['.prj', '.sup', '.ipf', '.gpf']

        files_dict = []

        files = defaultdict(list)

        # Grabs every PRJ, GPF, SUP, and IPF image from the ATF file

        for line in f:

            if line[-4:-1] == 'prj' or line[-4:-1] == 'gpf' or line[-4:-1] == 'sup' or line[-4:-1] == 'ipf' or line[-4:-1] == 'atf':

                files.append(line)

            ext = os.path.splitext(line)[-1].strip()

        files = np.array(files)

            # Check is needed for split as all do not have a space

            if ext in files_ext:

        # Creates appropriate arrays for certain files in the right format

        for file in files:

            file = file.strip()

            file = file.split(' ')

                # If it is the .prj file, it strips the directory away and grabs file name

                if ext == '.prj':

                    files[ext].append(line.strip().split(' ')[1].split('\\')[-1])

            # Grabs all the IPF files

            if file[1].endswith('.ipf'):

                ipf.append(file[1])

                # If the ext is in the list of files we care about, it addes to the dict

                files[ext].append(line.strip().split(' ')[-1])

            # Grabs all the SUP files

            if file[1].endswith('.sup'):

                sup.append(file[1])

            else:

            files_dict.append(file)

                # Adds to the dict even if not in files we care about

                files[ext.strip()].append(line)

        # Gets the base filepath

        files['basepath'] = os.path.dirname(os.path.abspath(atf_file))

        # Creates a dict out of file lists for GPF, PRJ, IPF, and ATF

        files_dict = (dict(files_dict))

        # Sets the value of IMAGE_IPF to all IPF images

        files_dict['IMAGE_IPF'] = ipf

        files_dict['IMAGE_IPF'] = files['.ipf']

        # Sets the value of IMAGE_SUP to all SUP images

        files_dict['IMAGE_SUP'] = sup

        files_dict['IMAGE_SUP'] = files['.sup']

        # Sets value for GPF file

        files_dict['GP_FILE'] = files['.gpf'][0]

        # Sets value for PRJ file

        files_dict['PROJECT'] = files['.prj'][0]

        # Sets the value of PATH to the path of the ATF file

        files_dict['PATH'] = os.path.dirname(os.path.abspath(atf_file))

        files_dict['PATH'] = files['basepath']

        return files_dict

import math

import pyproj

import numpy as np

import plio.io.isis_serial_number as sn

def line_sample_size(record, path):

    coord_360 = to_360(ocentric_coord)

    return coord_360

def body_fix(record, semi_major, semi_minor):

def body_fix(record, semi_major, semi_minor, inverse = False):

    """

    Transforms latitude, longitude, and height of a socet point into

    a body fixed point

    """

    ecef = pyproj.Proj(proj='geocent', a=semi_major, b=semi_minor)

    lla = pyproj.Proj(proj='latlon', a=semi_major, b=semi_minor)

    lon, lat, height = pyproj.transform(lla, ecef, record['long_X_East'], record['lat_Y_North'], record['ht'])

    if inverse:

        lon, lat, height = pyproj.transform(ecef, lla, record[0], record[1], record[2])

        return lon, lat, height

    else:

        y, x, z = pyproj.transform(lla, ecef, record[0], record[1], record[2])

        return y, x, z

def stat_toggle(record):

    if record['stat'] == 0:

        return True

    else:

        return False

def apply_transformations(atf_dict, df):

    """

         Pandas dataframe object

    """

    prj_file = os.path.join(atf_dict['PATH'], atf_dict['PROJECT'].split('\\')[-1])

    prj_file = os.path.join(atf_dict['PATH'], atf_dict['PROJECT'])

    eRadius, pRadius = get_axis(prj_file)

    lla = np.array([[df['long_X_East']], [df['lat_Y_North']], [df['ht']]])

    ecef = body_fix(lla, semi_major = eRadius, semi_minor = pRadius, inverse=False)

    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['lat_Y_North'] = df.apply(lat_ISIS_coord, semi_major = eRadius, semi_minor = pRadius, axis=1)

    df['long_X_East'] = df.apply(lon_ISIS_coord, semi_major = eRadius, semi_minor = pRadius, axis=1)

    df['long_X_East'], df['lat_Y_North'], df['ht'] = zip(*df.apply(body_fix, semi_major = eRadius, semi_minor = pRadius, 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 serial_numbers(image_dict, path):

    """

    for key in image_dict:

        serial_dict[key] = sn.generate_serial_number(os.path.join(path, image_dict[key]))

    return serial_dict

# 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] = scaled_lat_sigma ** 2

    cov[1,1] = 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()