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Commit f55cbbfd authored by Tyler Thatcher's avatar Tyler Thatcher
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Updated read_atf

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%% 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
```
......
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