Updated script with argparsing. Included csv for potential testing.

bin/Socetnet2ISIS.py

-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)

bin/socet2isis

 #!/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())
\ No newline at end of file
+    main(parse_args())

plio/examples/SocetSet/cub_map.csv

+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

plio/spatial/transformations.py

@@ -45,7 +45,6 @@ def line_sample_size(record, path):
         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
@@ -67,7 +66,6 @@ def known(record):
     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
@@ -140,7 +138,6 @@ def og2oc(dlat, dMajorRadius, dMinorRadius):
         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
@@ -291,7 +288,5 @@ def serial_numbers(image_dict, path):
     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