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Commit 1525218b authored by Adam Paquette's avatar Adam Paquette
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Merge branch 'ipf' of https://github.com/acpaquette/plio into ipf

parents 083c74e4 b4b37c37
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.travis.yml
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......@@ -37,7 +37,7 @@ install:
- conda config --add channels conda-forge
- conda install -q gdal h5py pandas sqlalchemy pyyaml networkx affine protobuf scipy pvl
# Development installation
- conda install -q pytest pytest-cov sh
- conda install -q pytest pytest-cov sh coveralls nbsphinx
script:
- pytest --cov=plio
......@@ -51,7 +51,8 @@ after_success:
- conda build --token $CONDA_UPLOAD_TOKEN --python $PYTHON_VERSION recipe -q
# Docs to gh-pages
- source activate test_env # Reactivate the env to have all deps installed.
- source activate test # Reactivate the env to have all deps installed.
- pip install travis-sphinx
- travis-sphinx build --source=docs --nowarn # The sphinx build script
- travis-sphinx deploy --branches=dev
......
bin/Socetnet2ISIS.py deleted 100644 → 0
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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/isis2socet 0 → 100644
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#!/usr/bin/env python
import argparse
import os
def parse_args():
parser = argparse.ArgumentParser()
# Add args here
return parser.parse_args()
def main(args):
print('Do some stuff')
if __name__ == '__main__':
main(parse_args())
bin/socet2isis
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#!/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('target_name', help='Name of the target body used in the control net')
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, targetname = args.target_name)
if __name__ == '__main__':
main(parse_args())
\ No newline at end of file
main(parse_args())
notebooks/Socet2ISIS.ipynb
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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
# 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
```
%% 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()
# Check is needed for split as all do not have a space
if ext in files_ext:
files = np.array(files)
# 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])
# Creates appropriate arrays for certain files in the right format
for file in files:
file = file.strip()
file = file.split(' ')
# 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 IPF files
if file[1].endswith('.ipf'):
ipf.append(file[1])
else:
# Grabs all the SUP files
if file[1].endswith('.sup'):
sup.append(file[1])
# Adds to the dict even if not in files we care about
files[ext.strip()].append(line)
files_dict.append(file)
# 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
# no transform applied
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
# transform
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
# transform but unsure how to handle
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
# transform but unsure how to handle
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):
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)
lon, lat, height = pyproj.transform(lla, ecef, record['long_X_East'], record['lat_Y_North'], record['ht'])
return lon, lat, height
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 np.array(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])
def apply_two_isis_transformations(atf_dict, df):
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['lat_Y_North'] = df.apply(lat_ISIS_coord, semi_major = eRadius, semi_minor = pRadius, axis=1)
df['long_X_East'] = df.apply(lon_ISIS_coord, semi_major = eRadius, semi_minor = pRadius, axis=1)
df['long_X_East'], df['lat_Y_North'], df['ht'] = zip(*df.apply(body_fix, semi_major = eRadius, semi_minor = pRadius, 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)
# apply_two_isis_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'}
# column_map = {'pt_id': 'id', '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)
# socet_df.rename(columns = column_map, 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(image_dict, path):
def serial_numbers(images, path, extension):
serial_dict = dict()
for key in image_dict:
snum = sn.generate_serial_number(os.path.join(path, image_dict[key]))
for image in images:
snum = sn.generate_serial_number(os.path.join(path, image + extension))
snum = snum.replace('Mars_Reconnaissance_Orbiter', 'MRO')
serial_dict[key] = snum
serial_dict[image] = snum
return serial_dict
```
%% Cell type:code id: tags:
``` python
# Setup stuffs for the cub information namely the path and extension
path = '/Volumes/Blueman/'
atf_file = get_path('CTX_Athabasca_Middle_step0.atf')
path = '/home/acpaquette/repos/plio/test_cubes'
targetname = 'Mars'
# Extension of your cub files
extension = '.8bit.cub'
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'}
# Path to atf file
atf_file = ('/home/acpaquette/repos/plio/plio/examples/SocetSet/Relative.atf')
socet_df = socet2isis(atf_file)
images = pd.unique(socet_df['ipf_file'])
# images = pd.unique(socet_df['ipf_file'])
serial_dict = serial_numbers(image_dict, path)
# serial_dict = serial_numbers(images, path, extension)
# creates the control network
cn.to_isis('/Volumes/Blueman/banana.net', socet_df, serial_dict)
# cn.to_isis('/home/acpaquette/repos/plio/plio/examples/SocetSet/cn.net', socet_df, serial_dict, targetname = targetname)
```
%% Cell type:code id: tags:
``` python
return_df = cn.from_isis("/home/acpaquette/repos/plio/plio/examples/SocetSet/cn.net")
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]
```
%% Cell type:code id: tags:
``` python
column_map = {'pt_id': 'id', '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'}
column_map = {k: v for v, k in column_map.items()}
return_df.rename(columns = column_map, inplace=True)
return_df.drop(['chooserName', 'datetime', 'referenceIndex', 'jigsawRejected', 'editLock', 'aprioriSurfPointSource', 'aprioriSurfPointSourceFile','aprioriRadiusSource', 'aprioriRadiusSourceFile'] , axis = 1, inplace=True)
```
%% Cell type:code id: tags:
``` python
return_df[['lat_Y_North', 'long_X_East', 'ht']]
```
%% Output
lat_Y_North long_X_East ht
0 139525.230749 3.390974e+06 4506.496945
1 139525.230749 3.390974e+06 4506.496945
2 139489.278045 3.390969e+06 4516.454802
3 139489.278045 3.390969e+06 4516.454802
4 139823.489797 3.390990e+06 4536.274914
5 139823.489797 3.390990e+06 4536.274914
6 139772.738004 3.390936e+06 4518.050219
7 139772.738004 3.390936e+06 4518.050219
8 139575.914815 3.390952e+06 3816.666542
9 139575.914815 3.390952e+06 3816.666542
10 139614.756296 3.390953e+06 3791.232717
11 139614.756296 3.390953e+06 3791.232717
12 139912.041374 3.390914e+06 3875.608660
13 139912.041374 3.390914e+06 3875.608660
14 139909.452033 3.390930e+06 3845.361327
15 139909.452033 3.390930e+06 3845.361327
16 139669.826849 3.391120e+06 3270.672620
17 139669.826849 3.391120e+06 3270.672620
18 139694.517017 3.391205e+06 3289.744506
19 139694.517017 3.391205e+06 3289.744506
20 139968.793338 3.391126e+06 3274.711397
21 139968.793338 3.391126e+06 3274.711397
22 139979.200780 3.391138e+06 3298.297228
23 139979.200780 3.391138e+06 3298.297228
24 139688.031217 3.391041e+06 4253.956077
25 139688.031217 3.391041e+06 4253.956077
26 139686.910823 3.391089e+06 4216.743792
27 139686.910823 3.391089e+06 4216.743792
28 139786.205284 3.390979e+06 3579.127600
29 139786.205284 3.390979e+06 3579.127600
30 139785.010997 3.391002e+06 3546.549796
31 139785.010997 3.391002e+06 3546.549796
%% Cell type:code id: tags:
``` python
socet_df[['lat_Y_North', 'long_X_East', 'ht']]
```
%% Output
lat_Y_North long_X_East ht
0 0.095708 2.356167 -2342.889214
1 0.095708 2.356167 -2342.889214
2 0.095920 2.355564 -2349.638414
3 0.095920 2.355564 -2349.638414
4 0.096339 2.361186 -2314.316425
5 0.096339 2.361186 -2314.316425
6 0.095954 2.360368 -2370.502882
7 0.095954 2.360368 -2370.502882
8 0.081058 2.357037 -2363.989968
9 0.081058 2.357037 -2363.989968
10 0.080518 2.357691 -2360.922571
11 0.080518 2.357691 -2360.922571
12 0.082311 2.362733 -2388.123298
13 0.082311 2.362733 -2388.123298
14 0.081668 2.362678 -2371.973499
15 0.081668 2.362678 -2371.973499
16 0.069458 2.358505 -2193.309629
17 0.069458 2.358505 -2193.309629
18 0.069861 2.358862 -2106.769773
19 0.069861 2.358862 -2106.769773
20 0.069543 2.363543 -2174.971745
21 0.069543 2.363543 -2174.971745
22 0.070044 2.363710 -2162.103231
23 0.070044 2.363710 -2162.103231
24 0.090342 2.358866 -2269.610862
25 0.090342 2.358866 -2269.610862
26 0.089550 2.358814 -2222.328983
27 0.089550 2.358814 -2222.328983
28 0.076012 2.360565 -2328.281125
29 0.076012 2.360565 -2328.281125
30 0.075320 2.360529 -2305.362047
31 0.075320 2.360529 -2305.362047
%% Cell type:code id: tags:
``` python
```
......
plio/examples/SocetSet/cub_map.csv 0 → 100644
+ 7
− 0
View file @ 1525218b
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/io/io_controlnetwork.py
+ 10
− 9
View file @ 1525218b
from time import gmtime, strftime
import pandas as pd
import numpy as np
import pvl
from plio.io import ControlNetFileV0002_pb2 as cnf
......@@ -186,22 +187,23 @@ class IsisStore(object):
header_bytes = find_in_dict(pvl_header, 'HeaderBytes')
point_start_byte = find_in_dict(pvl_header, 'PointsStartByte')
version = find_in_dict(pvl_header, 'Version')
if version == 2:
point_attrs = [i for i in cnf._CONTROLPOINTFILEENTRYV0002.fields_by_name if i != 'measures']
measure_attrs = [i for i in cnf._CONTROLPOINTFILEENTRYV0002_MEASURE.fields_by_name]
cols = point_attrs + measure_attrs
cp = cnf.ControlPointFileEntryV0002()
self._handle.seek(header_start_byte)
pbuf_header = cnf.ControlNetFileHeaderV0002()
pbuf_header.ParseFromString(self._handle.read(header_bytes))
self._handle.seek(point_start_byte)
cp = cnf.ControlPointFileEntryV0002()
pts = []
for s in pbuf_header.pointMessageSizes:
cp.ParseFromString(self._handle.read(s))
cp.ParseFromString(self._handle.read(s))
pt = [getattr(cp, i) for i in point_attrs if i != 'measures']
for measure in cp.measures:
......@@ -267,24 +269,24 @@ class IsisStore(object):
# As per protobuf docs for assigning to a repeated field.
if attr == 'aprioriCovar':
arr = g.iloc[0]['aprioriCovar']
point_spec.aprioriCovar.extend(arr.ravel().tolist())
if isinstance(arr, np.ndarray):
arr = arr.ravel().tolist()
point_spec.aprioriCovar.extend(arr)
else:
setattr(point_spec, attr, attrtype(g.iloc[0][attr]))
point_spec.type = 2 # Hardcoded to free
point_spec.type = 2 # Hardcoded to free this is bad
# The reference index should always be the image with the lowest index
point_spec.referenceIndex = 0
# A single extend call is cheaper than many add calls to pack points
measure_iterable = []
for node_id, m in g.iterrows():
measure_spec = point_spec.Measure()
# For all of the attributes, set if they are an dict accessible attr of the obj.
for attr, attrtype in self.measure_attrs:
if attr in g.columns:
setattr(measure_spec, attr, attrtype(m[attr]))
measure_spec.serialnumber = serials[m.image_index]
measure_spec.sample = m.x
measure_spec.line = m.y
......@@ -298,7 +300,6 @@ class IsisStore(object):
point_message = point_spec.SerializeToString()
point_sizes.append(point_spec.ByteSize())
point_messages.append(point_message)
return point_messages, point_sizes
def create_buffer_header(self, networkid, targetname,
......
plio/spatial/transformations.py
+ 1
− 6
View file @ 1525218b
......@@ -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
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