Updated read_atf (f55cbbfd) · Commits · aflab / astrogeology / Plio

notebooks/Socet2ISIS.ipynb

+29 −504

Original line number	Diff line number	Diff line
		%% Cell type:code id: tags:

		``` python
		import os
		import sys
		from functools import singledispatch
		import warnings

		import pandas as pd
		import numpy as np
		import math
		import pyproj

		# Path to local plio if wanted
		sys.path.insert(0, "/home/tthatcher/Desktop/Projects/Plio/plio")

		from plio.examples import get_path
		from plio.io.io_bae import read_gpf, read_ipf
		from collections import defaultdict
		import plio.io.io_controlnetwork as cn
		import plio.io.isis_serial_number as sn
		```

		%% Output

		/home/tthatcher/anaconda3/envs/autocnet/lib/python3.6/site-packages/h5py/__init__.py:36: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.
		from ._conv import register_converters as _register_converters

		%% Cell type:code id: tags:

		``` python
		from collections import defaultdict

		atf_file = ('/home/tthatcher/Desktop/Projects/plio_imgs/quest_imgs/CTX_Athabasca_Middle_step0.atf')

		with open(atf_file) as f:

		files_ext = ['.prj', '.sup', '.ipf']
		files_dict = []
		files = defaultdict(list)

		for line in f:
		print(line)
		ext = os.path.splitext(line)[-1]

		if ext in files_ext:
		files[ext.strip()].append(line.strip().split(' '))


		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'] = files['.ipf']

		# Sets the value of IMAGE_SUP to all SUP images
		files_dict['IMAGE_SUP'] = files['.sup']

		# Sets the value of PATH to the path of the ATF file
		files_dict['PATH'] = files['basepath']

		print(files_dict)
		```

		%% Output

		HATS_File_Version_7

		PROJ_FLAG 1

		PROJECT D:\data\CTX_Athabasca_Middle.prj

		ATF_FILE CTX_Athabasca_Middle_step0.atf

		GP_FILE CTX_Athabasca_Middle.gpf

		STRAT_FILE apm.apm_strat

		SOLVE_STRAT_FILE default.solve

		DTM_FILE null

		GPS_INFO_FILE null

		INPUT_COV_FILE null

		TRI_CONSTRAINT_FILE null

		ADJ absolute

		NUM_IMGS 6

		STRIP_SEQ 0

		NUM_STRIPS 3

		STRIP_FLAG 1

		STRIP_FLAG 1

		STRIP_ID 1

		IMAGE_SEQ 0

		NUM_IMGS_STRIP 2

		STRIP_BEGIN 0 0 0 0.0 0.0

		STRIP_END 0 0 0 0.0 0.0

		STRIP_SCAN 1

		IMAGE_FLAG 1

		IMAGE_FLAG 1

		IMAGE_ID 0

		IMAGE_SUP P19_008344_1894_XN_09N203W.sup

		IMAGE_IPF P19_008344_1894_XN_09N203W.ipf

		SENSOR GENERIC_PUSHBROOM

		INCLUDE_IN_SOLUTION 1

		IMG_DATA_1 60.0 0.0

		IMG_DATA_2 1000.0 0.0

		IMG_DATA_3 0.0 228.6

		DEFAULT_FLAG 1

		NUM_ADJ_PARMS 16

		ADJUST_&_SIGMA 0 100

		ADJUST_&_SIGMA 0 100

		ADJUST_&_SIGMA 0 10

		ADJUST_&_SIGMA 0 13

		ADJUST_&_SIGMA 0 13

		ADJUST_&_SIGMA 0 1.3

		ADJUST_&_SIGMA 0 0.0173

		ADJUST_&_SIGMA 0 0.0173

		ADJUST_&_SIGMA 0 0.1

		ADJUST_&_SIGMA 0 0.0017

		ADJUST_&_SIGMA 0 0.0017

		ADJUST_&_SIGMA 0 0.0017

		ADJUST_&_SIGMA 0 0.0002

		ADJUST_&_SIGMA 0 0.0002

		ADJUST_&_SIGMA 0 0.0002

		ADJUST_&_SIGMA 0 0.0353

		IMAGE_FLAG 1

		IMAGE_ID 1

		IMAGE_SUP P20_008845_1894_XN_09N203W.sup

		IMAGE_IPF P20_008845_1894_XN_09N203W.ipf

		SENSOR GENERIC_PUSHBROOM

		INCLUDE_IN_SOLUTION 1

		IMG_DATA_1 60.0 0.0

		IMG_DATA_2 1000.0 0.0

		IMG_DATA_3 0.0 228.6

		DEFAULT_FLAG 1

		NUM_ADJ_PARMS 16

		ADJUST_&_SIGMA 1 100

		ADJUST_&_SIGMA 1 100

		ADJUST_&_SIGMA 1 10

		ADJUST_&_SIGMA 1 13

		ADJUST_&_SIGMA 1 13

		ADJUST_&_SIGMA 1 1.3

		ADJUST_&_SIGMA 0 0.0173

		ADJUST_&_SIGMA 0 0.0173

		ADJUST_&_SIGMA 1 0.1

		ADJUST_&_SIGMA 0 0.0017

		ADJUST_&_SIGMA 0 0.0017

		ADJUST_&_SIGMA 0 0.0017

		ADJUST_&_SIGMA 0 0.0002

		ADJUST_&_SIGMA 0 0.0002

		ADJUST_&_SIGMA 0 0.0002

		ADJUST_&_SIGMA 0 0.0353

		STRIP_FLAG 1

		STRIP_ID 2

		IMAGE_SEQ 0

		NUM_IMGS_STRIP 2

		STRIP_BEGIN 0 0 0 0.0 0.0

		STRIP_END 0 0 0 0.0 0.0

		STRIP_SCAN 1

		IMAGE_FLAG 1

		IMAGE_FLAG 1

		IMAGE_ID 0

		IMAGE_SUP P03_002371_1888_XI_08N204W.sup

		IMAGE_IPF P03_002371_1888_XI_08N204W.ipf

		SENSOR GENERIC_PUSHBROOM

		INCLUDE_IN_SOLUTION 0

		IMG_DATA_1 60.0 0.0

		IMG_DATA_2 1000.0 0.0

		IMG_DATA_3 0.0 228.6

		DEFAULT_FLAG 1

		NUM_ADJ_PARMS 16

		ADJUST_&_SIGMA 0 100

		ADJUST_&_SIGMA 0 100

		ADJUST_&_SIGMA 0 10

		ADJUST_&_SIGMA 0 13

		ADJUST_&_SIGMA 0 13

		ADJUST_&_SIGMA 0 1.3

		ADJUST_&_SIGMA 0 0.0173

		ADJUST_&_SIGMA 0 0.0173

		ADJUST_&_SIGMA 0 0.1

		ADJUST_&_SIGMA 0 0.0017

		ADJUST_&_SIGMA 0 0.0017

		ADJUST_&_SIGMA 0 0.0017

		ADJUST_&_SIGMA 0 0.0002

		ADJUST_&_SIGMA 0 0.0002

		ADJUST_&_SIGMA 0 0.0002

		ADJUST_&_SIGMA 0 0.0353

		IMAGE_FLAG 1

		IMAGE_ID 1

		IMAGE_SUP P01_001540_1889_XI_08N204W.sup

		IMAGE_IPF P01_001540_1889_XI_08N204W.ipf

		SENSOR GENERIC_PUSHBROOM

		INCLUDE_IN_SOLUTION 0

		IMG_DATA_1 60.0 0.0

		IMG_DATA_2 1000.0 0.0

		IMG_DATA_3 0.0 228.6

		DEFAULT_FLAG 1

		NUM_ADJ_PARMS 16

		ADJUST_&_SIGMA 1 100

		ADJUST_&_SIGMA 1 100

		ADJUST_&_SIGMA 1 10

		ADJUST_&_SIGMA 1 13

		ADJUST_&_SIGMA 1 13

		ADJUST_&_SIGMA 1 1.3

		ADJUST_&_SIGMA 0 0.0173

		ADJUST_&_SIGMA 0 0.0173

		ADJUST_&_SIGMA 1 0.1

		ADJUST_&_SIGMA 0 0.0017

		ADJUST_&_SIGMA 0 0.0017

		ADJUST_&_SIGMA 0 0.0017

		ADJUST_&_SIGMA 0 0.0002

		ADJUST_&_SIGMA 0 0.0002

		ADJUST_&_SIGMA 0 0.0002

		ADJUST_&_SIGMA 0 0.0353

		STRIP_FLAG 1

		STRIP_ID 3

		IMAGE_SEQ 0

		NUM_IMGS_STRIP 2

		STRIP_BEGIN 0 0 0 0.0 0.0

		STRIP_END 0 0 0 0.0 0.0

		STRIP_SCAN 1

		IMAGE_FLAG 1

		IMAGE_FLAG 1

		IMAGE_ID 0

		IMAGE_SUP P01_001606_1897_XI_09N203W.sup

		IMAGE_IPF P01_001606_1897_XI_09N203W.ipf

		SENSOR GENERIC_PUSHBROOM

		INCLUDE_IN_SOLUTION 0

		IMG_DATA_1 60.0 0.0

		IMG_DATA_2 1000.0 0.0

		IMG_DATA_3 0.0 228.6

		DEFAULT_FLAG 1

		NUM_ADJ_PARMS 16

		ADJUST_&_SIGMA 0 100

		ADJUST_&_SIGMA 0 100

		ADJUST_&_SIGMA 0 10

		ADJUST_&_SIGMA 0 13

		ADJUST_&_SIGMA 0 13

		ADJUST_&_SIGMA 0 1.3

		ADJUST_&_SIGMA 0 0.0173

		ADJUST_&_SIGMA 0 0.0173

		ADJUST_&_SIGMA 0 0.1

		ADJUST_&_SIGMA 0 0.0017

		ADJUST_&_SIGMA 0 0.0017

		ADJUST_&_SIGMA 0 0.0017

		ADJUST_&_SIGMA 0 0.0002

		ADJUST_&_SIGMA 0 0.0002

		ADJUST_&_SIGMA 0 0.0002

		ADJUST_&_SIGMA 0 0.0353

		IMAGE_FLAG 1

		IMAGE_ID 1

		IMAGE_SUP P03_002226_1895_XI_09N203W.sup

		IMAGE_IPF P03_002226_1895_XI_09N203W.ipf

		SENSOR GENERIC_PUSHBROOM

		INCLUDE_IN_SOLUTION 0

		IMG_DATA_1 60.0 0.0

		IMG_DATA_2 1000.0 0.0

		IMG_DATA_3 0.0 228.6

		DEFAULT_FLAG 1

		NUM_ADJ_PARMS 16

		ADJUST_&_SIGMA 1 100

		ADJUST_&_SIGMA 1 100

		ADJUST_&_SIGMA 1 10

		ADJUST_&_SIGMA 1 13

		ADJUST_&_SIGMA 1 13

		ADJUST_&_SIGMA 1 1.3

		ADJUST_&_SIGMA 0 0.0173

		ADJUST_&_SIGMA 0 0.0173

		ADJUST_&_SIGMA 1 0.1

		ADJUST_&_SIGMA 0 0.0017

		ADJUST_&_SIGMA 0 0.0017

		ADJUST_&_SIGMA 0 0.0017

		ADJUST_&_SIGMA 0 0.0002

		ADJUST_&_SIGMA 0 0.0002

		ADJUST_&_SIGMA 0 0.0002

		ADJUST_&_SIGMA 0 0.0353

		TPP_FILE 3x3.tpp

		TRANS_TF_CTL_IMG 0

		UPDATE_ZERO_SIGMAS 0

		USE_DTM_FILE 0

		USE_GPS_FILE 0

		USE_INPUT_COV_FILE 0

		USE_TRI_CONSTRAINT_FILE 0

		PERCENT_REMOVED_POINTS 50

		{'IMAGE_IPF': [['IMAGE_IPF', 'P19_008344_1894_XN_09N203W.ipf'], ['IMAGE_IPF', 'P20_008845_1894_XN_09N203W.ipf'], ['IMAGE_IPF', 'P03_002371_1888_XI_08N204W.ipf'], ['IMAGE_IPF', 'P01_001540_1889_XI_08N204W.ipf'], ['IMAGE_IPF', 'P01_001606_1897_XI_09N203W.ipf'], ['IMAGE_IPF', 'P03_002226_1895_XI_09N203W.ipf']], 'IMAGE_SUP': [['IMAGE_SUP', 'P19_008344_1894_XN_09N203W.sup'], ['IMAGE_SUP', 'P20_008845_1894_XN_09N203W.sup'], ['IMAGE_SUP', 'P03_002371_1888_XI_08N204W.sup'], ['IMAGE_SUP', 'P01_001540_1889_XI_08N204W.sup'], ['IMAGE_SUP', 'P01_001606_1897_XI_09N203W.sup'], ['IMAGE_SUP', 'P03_002226_1895_XI_09N203W.sup']], 'PATH': '/home/tthatcher/Desktop/Projects/plio_imgs/quest_imgs'}

		%% Cell type:code id: tags:

		``` python
		```

		%% Cell type:code id: tags:

		``` python
		# Reads a .atf file and outputs all of the
		# .ipf, .gpf, .sup, .prj, and path to locate the
		# .apf file (should be the same as all others)
		def read_atf(atf_file):
		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

		# converts columns l. and s. to isis
		def line_sample_size(record, path):
		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

		# converts known to ISIS keywords
		def known(record):
		if record['known'] == 0:
		return 'Free'

		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):
		return num % 360

		# ocentric to ographic latitudes
		def oc2og(dlat, dMajorRadius, dMinorRadius):
		try:
		dlat = math.radians(dlat)
		dlat = math.atan(((dMajorRadius / dMinorRadius)*2) (math.tan(dlat)))
		dlat = math.degrees(dlat)
		except:
		print ("Error in oc2og conversion")
		return dlat

		# ographic to ocentric latitudes
		def og2oc(dlat, dMajorRadius, dMinorRadius):
		try:
		dlat = math.radians(dlat)
		dlat = math.atan((math.tan(dlat) / ((dMajorRadius / dMinorRadius)**2)))
		dlat = math.degrees(dlat)
		except:
		print ("Error in og2oc conversion")
		return dlat

		# gets eRadius and pRadius from a .prj file
		def get_axis(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 * (1 - float(files['E_EARTH'][0]))

		return eRadius, pRadius

		# function to convert lat_Y_North to ISIS_lat
		def lat_ISIS_coord(record, semi_major, semi_minor):
		ocentric_coord = og2oc(record['lat_Y_North'], semi_major, semi_minor)
		coord_360 = to_360(ocentric_coord)
		return coord_360

		# function to convert long_X_East to ISIS_lon
		def lon_ISIS_coord(record, semi_major, semi_minor):
		ocentric_coord = og2oc(record['long_X_East'], semi_major, semi_minor)
		coord_360 = to_360(ocentric_coord)
		return coord_360

		def body_fix(record, semi_major, semi_minor, inverse=False):
		"""
		Parameters
		----------
		record : ndarray
		(n,3) where columns are x, y, height or lon, lat, alt
		"""

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

		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 ignore_toggle(record):
		if record['stat'] == 0:
		return True
		else:
		return False

		# 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)
		coslambda = math.cos(lon)
		sinlambda = math.sin(lon)
		rcosphi = rad * cosphi
		rsinphi = rad * sinphi
		j[0,0] = -rsinphi * coslambda
		j[0,1] = -rcosphi * sinlambda
		j[0,2] = cosphi * coslambda
		j[1,0] = -rsinphi * sinlambda
		j[1,1] = rcosphi * coslambda
		j[1,2] = cosphi * sinlambda
		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
		# return np.array([[1.0, 1.0, 1.0], [1.0, 1.0, 1.0]])


		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()

		# applys transformations to columns
		def apply_transformations(atf_dict, df):
		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['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['ignore'] = df.apply(ignore_toggle, axis=1)

		def socet2isis(prj_file):
		# Read in and setup the atf dict of information
		atf_dict = read_atf(prj_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',
		'sig_l': 'linesigma', 'sig_s': 'samplesigma'}

		# Rename the columns using the column remap above
		socet_df.rename(columns = column_remap, inplace=True)

		# Return the socet dataframe to be converted to a control net
		return socet_df

		# creates a dict of serial numbers with the cub being the key
		def serial_numbers(images, path, extension):
		serial_dict = dict()

		for image in images:
		snum = sn.generate_serial_number(os.path.join(path, image + extension))
		snum = snum.replace('Mars_Reconnaissance_Orbiter', 'MRO')
		serial_dict[image] = snum
		return serial_dict

		```

		%% Cell type:code id: tags:

		``` python
		# path = '/home/tthatcher/Desktop/Projects/plio_imgs/quest_imgs/'
		# extension = '.lev1.cub'
		# atf_file = ('/home/tthatcher/Desktop/Projects/plio_imgs/quest_imgs/CTX_Athabasca_Middle_step0.atf')

		# Setup stuffs for the cub information namely the path and extension
		path = '/path/where/cub/files/are/'

		# Extension of your cub files
		extension = '.8bit.cub'
		extension = '.something.cub'

		# Path to atf file
		atf_file = ('/path/to/atf/file')

		socet_df = socet2isis(atf_file)

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

		serial_dict = serial_numbers(images, path, extension)

		# creates the control network
		cn.to_isis('/path/you/want/the/cnet/to/be/in/cn.net', socet_df, serial_dict)

		# cn.to_isis('/home/tthatcher/Desktop/Projects/plio_imgs/quest_imgs/cn.net', socet_df, serial_dict)
		```

		%% Cell type:code id: tags:

		``` python
		```