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Commit 064ad14c authored by Adam Paquette's avatar Adam Paquette
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Finalized notebook with annotations and reduced imports.

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notebooks/Socet2ISIS.ipynb
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%% Cell type:code id: tags: %% Cell type:code id: tags:
``` python ``` python
import os import os
import sys import sys
import csv
import pvl
import pandas as pd import pandas as pd
import numpy as np
import math import math
import pyproj
from functools import singledispatch
import warnings
from plio.examples import get_path from plio.io.io_bae import read_gpf, read_ipf, read_atf, save_gpf, save_ipf
from plio.io.io_bae import read_gpf, read_ipf, read_atf
from plio.utils.utils import find_in_dict from plio.utils.utils import find_in_dict
from plio.spatial.transformations import * from plio.spatial.transformations import apply_isis_transformations, apply_socet_transformations, serial_numbers
from collections import defaultdict
import plio.io.io_controlnetwork as cn import plio.io.io_controlnetwork as cn
import plio.io.isis_serial_number as sn import plio.io.isis_serial_number as sn
``` ```
%% Cell type:code id: tags: %% Cell type:code id: tags:
``` python ``` python
def socet2isis(at_file, cub_file_path, cub_ipf_map, target_name, outpath=None): def socet2isis(at_file, cub_file_path, extension, target_name, outpath=None):
# Setup the at_file, path to cubes, and control network out path # Setup the at_file, path to cubes, and control network out path
at_file = at_file at_file = at_file
cnet_out = os.path.split(os.path.splitext(at_file)[0])[1] cnet_out = os.path.split(os.path.splitext(at_file)[0])[1]
cub_path = cub_file_path cub_path = cub_file_path
if(outpath): if(outpath):
outpath = outpath outpath = outpath
else: else:
outpath = os.path.split(at_file)[0] outpath = os.path.split(at_file)[0]
with open(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 # Read in and setup the atf dict of information
atf_dict = read_atf(at_file) atf_dict = read_atf(at_file)
# Get the gpf and ipf files using atf dict # Get the gpf and ipf files using atf dict
gpf_file = os.path.join(atf_dict['PATH'], atf_dict['GP_FILE']); 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']] 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 # Read in the gpf file and ipf file(s) into seperate dataframes
gpf_df = read_gpf(gpf_file) gpf_df = read_gpf(gpf_file)
ipf_df = read_ipf(ipf_list) ipf_df = read_ipf(ipf_list)
# Check for differences between point ids using each dataframes # Check for differences between point ids using each dataframes
# point ids as a reference # point ids as a reference
gpf_pt_idx = pd.Index(pd.unique(gpf_df['point_id'])) gpf_pt_idx = pd.Index(pd.unique(gpf_df['point_id']))
ipf_pt_idx = pd.Index(pd.unique(ipf_df['pt_id'])) ipf_pt_idx = pd.Index(pd.unique(ipf_df['pt_id']))
point_diff = ipf_pt_idx.difference(gpf_pt_idx) point_diff = ipf_pt_idx.difference(gpf_pt_idx)
if len(point_diff) != 0: if len(point_diff) != 0:
warnings.warn("The following points found in ipf files missing from gpf file: \n\n{}. \ 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))) \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 # Merge the two dataframes on their point id columns
socet_df = ipf_df.merge(gpf_df, left_on='pt_id', right_on='point_id') socet_df = ipf_df.merge(gpf_df, left_on='pt_id', right_on='point_id')
# Apply the transformations # Apply the transformations
apply_transformations(atf_dict, socet_df) apply_socet_transformations(atf_dict, socet_df)
# Define column remap for socet dataframe # Define column remap for socet dataframe
column_map = {'pt_id': 'id', 'l.': 'y', 's.': 'x', column_map = {'pt_id': 'id', 'l.': 'y', 's.': 'x',
'res_l': 'lineResidual', 'res_s': 'sampleResidual', 'known': 'Type', 'res_l': 'lineResidual', 'res_s': 'sampleResidual', 'known': 'Type',
'lat_Y_North': 'aprioriY', 'long_X_East': 'aprioriX', 'ht': 'aprioriZ', 'lat_Y_North': 'aprioriY', 'long_X_East': 'aprioriX', 'ht': 'aprioriZ',
'sig0': 'aprioriLatitudeSigma', 'sig1': 'aprioriLongitudeSigma', 'sig2': 'aprioriRadiusSigma', 'sig0': 'aprioriLatitudeSigma', 'sig1': 'aprioriLongitudeSigma', 'sig2': 'aprioriRadiusSigma',
'sig_l': 'linesigma', 'sig_s': 'samplesigma'} 'sig_l': 'linesigma', 'sig_s': 'samplesigma'}
# Rename the columns using the column remap above # Rename the columns using the column remap above
socet_df.rename(columns = column_map, inplace=True) 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 + extension for i in pd.unique(socet_df['ipf_file'])}
serial_dict = serial_numbers(image_dict, cub_path) serial_dict = serial_numbers(image_dict, cub_path)
# creates the control network # creates the control network
cn.to_isis(os.path.join(outpath, cnet_out + '.net'), socet_df, serial_dict, targetname = targetname) cn.to_isis(os.path.join(outpath, cnet_out + '.net'), socet_df, serial_dict, targetname = targetname)
return socet_df
``` ```
%% Cell type:code id: tags: %% Cell type:code id: tags:
``` python ``` python
# Setup stuffs for the cub information namely the path and extension def isis2socet(cnet_path, eRadius, eccentricity, cub_path, extension, cub_list, out_gpf, adjusted_flag = False):
cub_path = '/Volumes/Blueman/' pRadius = eRadius * math.sqrt(1 - (eccentricity ** 2))
targetname = 'Mars'
cub_map = '/Users/adampaquette/repos/plio/plio/examples/SocetSet/cub_map2.csv'
# Path to atf file df = cn.from_isis(cnet_path)
atf_file = ('/Users/adampaquette/repos/plio/plio/examples/SocetSet/Relative.atf') # 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'
socet_df = socet2isis(atf_file, cub_path, cub_map, targetname) df.rename(columns = column_map, inplace=True)
```
%% Cell type:code id: tags: apply_isis_transformations(df, eRadius, pRadius, serial_dict, extension, cub_path)
``` python # Save the ipf
def reverse_known(record): save_ipf(df, os.path.split(out_gpf)[0])
"""
Converts the known field from a socet dataframe into the
isis point_type column
Parameters
----------
record : object
Pandas series object
Returns
-------
: str
String representation of a known field
"""
record_type = record['known']
if record_type == 0 or record_type == 2:
return 0
elif record_type == 1 or record_type == 3 or record_type == 4:
return 3
def lat_socet_coord(record, semi_major, semi_minor):
"""
Function to convert lat_Y_North to ISIS_lat
Parameters
----------
record : object
Pandas series object
semi_major : float
Radius from the center of the body to the equater
semi_minor : float
Radius from the pole to the center of mass
Returns
-------
coord_360 : float
Converted latitude into ocentric space, and mapped
into 0 to 360
"""
ographic_coord = oc2og(record['lat_Y_North'], semi_major, semi_minor)
return ((ographic_coord + 180) % 360) - 180
def lon_socet_coord(record, semi_major, semi_minor):
"""
Function to convert lat_Y_North to ISIS_lat
Parameters
----------
record : object
Pandas series object
semi_major : float
Radius from the center of the body to the equater
semi_minor : float
Radius from the pole to the center of mass
Returns
-------
coord_360 : float
Converted latitude into ocentric space, and mapped
into 0 to 360
"""
ographic_coord = oc2og(record['long_X_East'], semi_major, semi_minor)
return ((ographic_coord + 180) % 360) - 180
def fix_sample_line(record, serial_dict, extension, cub_path):
# Cube location to load
cube = pvl.load(os.path.join(cub_path, serial_dict[record['serialnumber']] + extension))
line_size = find_in_dict(cube, 'Lines')
sample_size = find_in_dict(cube, 'Samples')
new_line = record['l.'] - (int(line_size)/2.0) - 1
new_sample = record['s.'] - (int(sample_size)/2.0) - 1
return new_line, new_sample
def ignore_toggle(record):
if record['stat'] == True:
return 0
else:
return 1
```
%% Cell type:code id: tags: # 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()
``` python # Set the gpf_df to only the values we need and do a small rename
return_df = cn.from_isis("/Users/adampaquette/repos/plio/plio/examples/SocetSet/Relative.net") gpf_df = df.iloc[points].copy()
gpf_df.rename(columns = {'pt_id': 'point_id'}, inplace=True)
eRadius = 3.39619000000000e+006 # Save the gpf
pRadius = eRadius * (1 - 1.08339143554195e-001) save_gpf(gpf_df, out_gpf)
```
adjusted_flag = False %% Cell type:code id: tags:
cub_path = '/Volumes/Blueman/' ``` python
extension = '.8bit.cub' # Setup stuffs for the cub information namely the path and extension
cub_list = ['D06_029601_1846_XN_04N224W', cub_path = '/Path/to/cubs'
'F05_037684_1857_XN_05N224W']
# # Name of the target body
cub_dict = {i: i + extension for i in cub_list} targetname = 'Mars'
serial_dict = {sn.generate_serial_number(os.path.join(cub_path, i + extension)): i for i in cub_list} extension = 'cub.-->extension<--'
columns = []
column_index = []
for i, column in enumerate(list(return_df.columns)):
if column not in columns:
column_index.append(i)
columns.append(column)
return_df = return_df.iloc[:, column_index]
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'}
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'
return_df.rename(columns = column_map, inplace=True)
return_df['known'] = return_df.apply(reverse_known, axis = 1)
return_df['ipf_file'] = return_df['serialnumber'].apply(lambda serial_number: serial_dict[serial_number])
return_df['l.'], return_df['s.'] = zip(*return_df.apply(fix_sample_line, serial_dict = serial_dict,
extension = extension,
cub_path = cub_path, axis = 1))
ecef = np.array([[return_df['long_X_East']], [return_df['lat_Y_North']], [return_df['ht']]])
lla = body_fix(ecef, semi_major = eRadius, semi_minor = pRadius, inverse=True)
return_df['long_X_East'], return_df['lat_Y_North'], return_df['ht'] = lla[0][0], lla[1][0], lla[2][0]
return_df['lat_Y_North'] = return_df.apply(lat_socet_coord, semi_major=eRadius, semi_minor=pRadius, axis = 1)
return_df['long_X_East'] = return_df.apply(lon_socet_coord, semi_major=eRadius, semi_minor=pRadius, axis = 1)
return_df['stat'] = return_df.apply(ignore_toggle, axis = 1)
return_df['val'] = return_df['stat']
# Add dumby
x_dummy = lambda x: np.full(len(return_df), x)
return_df['sig0'] = x_dummy(0)
return_df['sig1'] = x_dummy(0)
return_df['sig2'] = x_dummy(0)
return_df['res0'] = x_dummy(0)
return_df['res1'] = x_dummy(0)
return_df['res2'] = x_dummy(0)
return_df['fid_x'] = x_dummy(0) # Path to atf file
return_df['fid_y'] = x_dummy(0) atf_file = 'Path/to/socket/set/at_file.atf'
return_df['no_obs'] = x_dummy(1) socet2isis(atf_file, cub_path, extension, targetname)
return_df['fid_val'] = x_dummy(0)
``` ```
%% Cell type:code id: tags: %% Cell type:code id: tags:
``` python ``` python
return_df[['long_X_East', 'lat_Y_North', 'ht']].iloc[2] # Setup stuffs for the cub information namely the path and extension
``` # along with eRadius and eccentricity
cnet = "Path/to/control/network.net"
%% Cell type:code id: tags: eRadius = 3.39619000000000e+006
eccentricity = 1.08339143554195e-001
``` python cub_path = 'Path/to/cubs'
return_df[['long_X_East', 'lat_Y_North', 'ht']].iloc[2] extension = 'cub.-->extension<--'
```
%% Cell type:code id: tags: # List of cubes to use
cub_list = ['D06_029601_1846_XN_04N224W',
'F05_037684_1857_XN_05N224W']
``` python out_gpf = "/Users/adampaquette/Desktop/InSightE09_XW.gpf"
adjusted_flag = False
isis2socet(cnet, eRadius, eccentricity, cub_path, extension, cub_list, out_gpf, adjusted_flag)
``` ```
%% Cell type:code id: tags: %% Cell type:code id: tags:
``` python ``` python
``` ```
......
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