Finalized bin scripts for conversions.

import argparse

import os

import pandas as pd

from plio.io.io_bae import save_gpf, save_ipf

from plio.spatial.transformations import apply_isis_transformations

import plio.io.io_controlnetwork as cn

import plio.io.isis_serial_number as sn

def parse_args():

    parser = argparse.ArgumentParser()

    # Add args here

    parser.add_argument('cnet_file', help='Path to an isis control network.')

    parser.add_argument('e_radius', type=float, help='The semimajor radius of a given target.')

    parser.add_argument('p_radius', type=float, help='The semiminor radius of a given target.')

    parser.add_argument('cub_path', help='Path to the cub files associated with a control network.')

    parser.add_argument('cub_extension', help='Extension for all cubes.')

    parser.add_argument('cub_list', help='Path to a list file of all cubes being used')

    parser.add_argument('out_gpf', help='Path to save location of gpf file and new ipf files.')

    parser.add_argument('--adjusted', help='Flag for saving apriori values or adjusted values',

                                      default=False, required = False)

    return parser.parse_args()

def main(args):

    print('Do some stuff')

    # Create cub dict to map ipf to cub

    df = cn.from_isis(args.cnet_file)

    e_radius = args.e_radius

    p_radius = e_radius * (1 - args.p_radius)

    cub_path = args.cub_path

    extension = args.cub_extension

    with open(args.cub_list, 'r') as f:

        lines = f.readlines()

        cub_list = [cub.replace('\n', '') for cub in lines]

    out_gpf = args.out_gpf

    adjusted_flag = args.adjusted

    # Create cub dict to map ipf to cub

    cub_dict = {i: i + extension for i in cub_list}

    # Create serial dict to match serial to ipf

    serial_dict = {sn.generate_serial_number(os.path.join(cub_path, i + extension)): i for i in cub_list}

    # Remove duplicate columns

    # There are better ways to do this but pandas was not having it

    columns = []

    column_index = []

    for i, column in enumerate(list(df.columns)):

        if column not in columns:

            column_index.append(i)

            columns.append(column)

    df = df.iloc[:, column_index]

    # Begin translation

    # Remap the ISIS columns to socet column names

    column_map = {'id': 'pt_id', 'line': 'l.', 'sample': 's.',

                  'lineResidual': 'res_l', 'sampleResidual': 'res_s', 'type': 'known',

                  'aprioriLatitudeSigma': 'sig0', 'aprioriLongitudeSigma': 'sig1', 'aprioriRadiusSigma': 'sig2',

                  'linesigma': 'sig_l', 'samplesigma': 'sig_s', 'ignore': 'stat'}

    # Depending on the adjusted flag, set the renames for columns appropriately

    if adjusted_flag:

        column_map['adjustedY'] = 'lat_Y_North'

        column_map['adjustedX'] = 'long_X_East'

        column_map['adjustedZ'] = 'ht'

    else:

        column_map['aprioriY'] = 'lat_Y_North'

        column_map['aprioriX'] = 'long_X_East'

        column_map['aprioriZ'] = 'ht'

    df.rename(columns = column_map, inplace=True)

    apply_isis_transformations(df, e_radius, p_radius, serial_dict, extension, cub_path)

    # Save the ipf(s)

    save_ipf(df, os.path.split(out_gpf)[0])

    # Get the first record from each group as there all the same, put them

    # into a list, and sort it

    points = [int(i[1].index[0]) for i in df.groupby('pt_id')]

    points.sort()

    # Set the gpf_df to only the values we need and do a small rename

    gpf_df = df.iloc[points].copy()

    gpf_df.rename(columns = {'pt_id': 'point_id'}, inplace=True)

    # Save the gpf

    save_gpf(gpf_df, out_gpf)

if __name__ == '__main__':

    main(parse_args())

#!/usr/bin/env python

import argparse

import os

import sys

import argparse

import warnings

import csv

import numpy as np

from plio.examples import get_path

from plio.io.io_bae import read_atf, read_gpf, read_ipf

from plio.spatial.transformations import *

from plio.spatial.transformations import apply_socet_transformations, serial_numbers

import plio.io.io_controlnetwork as cn

import pandas as pd

    # Add args here

    parser.add_argument('at_file', help='Path to the .atf file for a project.')

    parser.add_argument('cub_file_path', help='Path to cube files related to ipf files.')

    parser.add_argument('cub_ipf_map', help='Path to map file for all ipfs and cubes.')

    parser.add_argument('extension', help='Extension for all cubes being used.')

    parser.add_argument('target_name', help='Name of the target body used in the control net')

    parser.add_argument('--outpath', help='Directory for the control network to be output to.',

                                        required = False)

    parser.add_argument('--outpath', help='Directory for the control network to be output to.')

    return parser.parse_args()

    else:

        outpath = os.path.split(at_file)[0]

    with open(args.cub_ipf_map) as cub_ipf_map:

        reader = csv.reader(cub_ipf_map, delimiter = ',')

        image_dict = dict([(row[0], row[1]) for row in reader])

    # with open(args.cub_ipf_map) as cub_ipf_map:

    #     reader = csv.reader(cub_ipf_map, delimiter = ',')

    #     image_dict = dict([(row[0], row[1]) for row in reader])

        # Read in and setup the atf dict of information

    atf_dict = read_atf(at_file)

    point_diff = ipf_pt_idx.difference(gpf_pt_idx)

    if len(point_diff) != 0:

        warnings.warn("The following points found in ipf files missing from gpf file: " +

        "\n\n{}\n\n".format("\n".join(point_diff)) +

        "Continuing, but these points will be missing from the control " +

        "network.", stacklevel=3)

        warnings.warn("The following points found in ipf files missing from gpf file: \n\n{}. \

                      \n\nContinuing, but these points will be missing from the control network".format(list(point_diff)))

    # Merge the two dataframes on their point id columns

    socet_df = ipf_df.merge(gpf_df, left_on='pt_id', right_on='point_id')

    # Apply the transformations

    apply_transformations(atf_dict, socet_df)

    apply_socet_transformations(atf_dict, socet_df)

    # Define column remap for socet dataframe

    column_remap = {'l.': 'y', 's.': 'x',

                    'res_l': 'LineResidual', 'res_s': 'SampleResidual', 'known': 'Type',

                    'lat_Y_North': 'AprioriY', 'long_X_East': 'AprioriX', 'ht': 'AprioriZ',

                    'sig0': 'AprioriLatitudeSigma', 'sig1': 'AprioriLongitudeSigma',

                    'sig2': 'AprioriRadiusSigma'}

    column_map = {'pt_id': 'id', 'l.': 'y', 's.': 'x',

                  'res_l': 'lineResidual', 'res_s': 'sampleResidual', 'known': 'Type',

                  'lat_Y_North': 'aprioriY', 'long_X_East': 'aprioriX', 'ht': 'aprioriZ',

                  'sig0': 'aprioriLatitudeSigma', 'sig1': 'aprioriLongitudeSigma', 'sig2': 'aprioriRadiusSigma',

                  'sig_l': 'linesigma', 'sig_s': 'samplesigma'}

    # Rename the columns using the column remap above

    socet_df.rename(columns = column_remap, inplace=True)

    images = pd.unique(socet_df['ipf_file'])

    socet_df.rename(columns = column_map, inplace=True)

    # Build an image and serial dict assuming the cubes will be named as the IPFs are

    image_dict = {i: i + args.extension for i in pd.unique(socet_df['ipf_file'])}

    serial_dict = serial_numbers(image_dict, cub_path)

    # creates the control network