Updated script with argparsing. Included csv for potential testing.

import os

import warnings

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 *

import plio.io.io_controlnetwork as cn

import pandas as pd

# TODO: Change script to potentially handle configuration files

# Setup the at_file and path to cubes

cub_path = '/Volumes/Blueman/'

at_file = get_path('CTX_Athabasca_Middle_step0.atf')

# Define ipf mapping to cubs

image_dict = {'P01_001540_1889_XI_08N204W' : 'P01_001540_1889_XI_08N204W.lev1.cub',

              'P01_001606_1897_XI_09N203W' : 'P01_001606_1897_XI_09N203W.lev1.cub',

              'P02_001804_1889_XI_08N204W' : 'P02_001804_1889_XI_08N204W.lev1.cub',

              'P03_002226_1895_XI_09N203W' : 'P03_002226_1895_XI_09N203W.lev1.cub',

              'P03_002371_1888_XI_08N204W' : 'P03_002371_1888_XI_08N204W.lev1.cub',

              'P19_008344_1894_XN_09N203W' : 'P19_008344_1894_XN_09N203W.lev1.cub',

              'P20_008845_1894_XN_09N203W' : 'P20_008845_1894_XN_09N203W.lev1.cub'}

##

# End Config

##

# Read in and setup the atf dict of information

atf_dict = read_atf(at_file)

# Get the gpf and ipf files using atf dict

gpf_file = os.path.join(atf_dict['PATH'], atf_dict['GP_FILE']);

ipf_list = [os.path.join(atf_dict['PATH'], i) for i in atf_dict['IMAGE_IPF']]

# Read in the gpf file and ipf file(s) into seperate dataframes

gpf_df = read_gpf(gpf_file)

ipf_df = read_ipf(ipf_list)

# Check for differences between point ids using each dataframes

# point ids as a reference

gpf_pt_idx = pd.Index(pd.unique(gpf_df['point_id']))

ipf_pt_idx = pd.Index(pd.unique(ipf_df['pt_id']))

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\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)

# 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'}

# Rename the columns using the column remap above

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

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

serial_dict = serial_numbers(image_dict, cub_path)

# creates the control network

cn.to_isis('/Volumes/Blueman/test.net', socet_df, serial_dict)

#!/usr/bin/env python

import argparse

import os

import sys

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 *

import plio.io.io_controlnetwork as cn

import pandas as pd

def parse_args():

    parser = argparse.ArgumentParser()

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

                                        required = False)

    return parser.parse_args()

def main(args):

    print('Getting some work done')

    # Setup the at_file, path to cubes, and control network out path

    at_file = args.at_file

    cnet_out = os.path.split(os.path.splitext(at_file)[0])[1]

    cub_path = args.cub_file_path

    if(args.outpath):

        outpath = args.outpath

    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])

    # Read in and setup the atf dict of information

    atf_dict = read_atf(at_file)

    # Get the gpf and ipf files using atf dict

    gpf_file = os.path.join(atf_dict['PATH'], atf_dict['GP_FILE']);

    ipf_list = [os.path.join(atf_dict['PATH'], i) for i in atf_dict['IMAGE_IPF']]

    # Read in the gpf file and ipf file(s) into seperate dataframes

    gpf_df = read_gpf(gpf_file)

    ipf_df = read_ipf(ipf_list)

    # Check for differences between point ids using each dataframes

    # point ids as a reference

    gpf_pt_idx = pd.Index(pd.unique(gpf_df['point_id']))

    ipf_pt_idx = pd.Index(pd.unique(ipf_df['pt_id']))

    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)

    # 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)

    # 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'}

    # Rename the columns using the column remap above

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

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

    serial_dict = serial_numbers(image_dict, cub_path)

    # creates the control network

    cn.to_isis(os.path.join(outpath, cnet_out + '.net'), socet_df, serial_dict)

if __name__ == '__main__':

    main(parse_args())

P01_001540_1889_XI_08N204W,P01_001540_1889_XI_08N204W.lev1.cub

P01_001606_1897_XI_09N203W,P01_001606_1897_XI_09N203W.lev1.cub

P02_001804_1889_XI_08N204W,P02_001804_1889_XI_08N204W.lev1.cub

P03_002226_1895_XI_09N203W,P03_002226_1895_XI_09N203W.lev1.cub

P03_002371_1888_XI_08N204W,P03_002371_1888_XI_08N204W.lev1.cub

P19_008344_1894_XN_09N203W,P19_008344_1894_XN_09N203W.lev1.cub

P20_008845_1894_XN_09N203W,P20_008845_1894_XN_09N203W.lev1.cub

        sample_size = int(sample_size)/2.0 + record['s.'] + 1

        return sample_size, line_size, img_index

# converts known to ISIS keywords

def known(record):

    """

    Converts the known field from a socet dataframe into the

    elif record['known'] == 1 or record['known'] == 2 or record['known'] == 3:

        return 'Constrained'

# converts +/- 180 system to 0 - 360 system

def to_360(num):

    """

    Transforms a given number into 0 - 360 space

        print ("Error in og2oc conversion")

    return dlat

# gets eRadius and pRadius from a .prj file

def get_axis(file):

    """

    Gets eRadius and pRadius from a .prj file

    serial_dict = dict()

    for key in image_dict:

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

        snum = snum.replace('Mars_Reconnaissance_Orbiter', 'MRO')

        serial_dict[key] = snum

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

    return serial_dict