diff --git a/.travis.yml b/.travis.yml
index 2d2e09d128b335233fa9baaf909e17292fe3f9d1..d05d2d04c1dfbb63fcf1d23242f483701dea28ba 100644
--- a/.travis.yml
+++ b/.travis.yml
@@ -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
diff --git a/bin/Socetnet2ISIS.py b/bin/Socetnet2ISIS.py
deleted file mode 100644
index a410606abc65d9ec3039aae5d7091ba25a5af9de..0000000000000000000000000000000000000000
--- a/bin/Socetnet2ISIS.py
+++ /dev/null
@@ -1,73 +0,0 @@
-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)
diff --git a/bin/isis2socet b/bin/isis2socet
new file mode 100644
index 0000000000000000000000000000000000000000..29b75d891a6bad84c9dd2749e5659c0c94a64598
--- /dev/null
+++ b/bin/isis2socet
@@ -0,0 +1,17 @@
+#!/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())
diff --git a/bin/socet2isis b/bin/socet2isis
index 44255aec300c8ad9f4aed798258a8a9cf8fb89c6..a32aa0fab4d7982fd28a3596f33a2dd6fe061279 100644
--- a/bin/socet2isis
+++ b/bin/socet2isis
@@ -1,15 +1,93 @@
#!/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())
diff --git a/notebooks/Socet2ISIS.ipynb b/notebooks/Socet2ISIS.ipynb
index f9ff8e2dd784c0c36eca6a96d8133d847e531657..a75564baa800ed5f4ac3e93f8935b1fb843635fe 100644
--- a/notebooks/Socet2ISIS.ipynb
+++ b/notebooks/Socet2ISIS.ipynb
@@ -2,7 +2,7 @@
"cells": [
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 56,
"metadata": {},
"outputs": [],
"source": [
@@ -16,17 +16,16 @@
"import math\n",
"import pyproj\n",
"\n",
- "# sys.path.insert(0, \"/home/tthatcher/Desktop/Projects/Plio/plio\")\n",
- "\n",
"from plio.examples import get_path\n",
"from plio.io.io_bae import read_gpf, read_ipf\n",
+ "from collections import defaultdict\n",
"import plio.io.io_controlnetwork as cn\n",
"import plio.io.isis_serial_number as sn"
]
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 85,
"metadata": {},
"outputs": [],
"source": [
@@ -35,49 +34,55 @@
"# .apf file (should be the same as all others) \n",
"def read_atf(atf_file):\n",
" with open(atf_file) as f:\n",
- "\n",
- " files = []\n",
- " ipf = []\n",
- " sup = []\n",
- " files_dict = []\n",
" \n",
- " # Grabs every PRJ, GPF, SUP, and IPF image from the ATF file\n",
+ " # Extensions of files we want\n",
+ " files_ext = ['.prj', '.sup', '.ipf', '.gpf']\n",
+ " files_dict = []\n",
+ " files = defaultdict(list)\n",
+ "\n",
" for line in f:\n",
- " 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':\n",
- " files.append(line)\n",
+ " ext = os.path.splitext(line)[-1].strip()\n",
+ " \n",
+ " # Check is needed for split as all do not have a space\n",
+ " if ext in files_ext:\n",
+ " \n",
+ " # If it is the .prj file, it strips the directory away and grabs file name\n",
+ " if ext == '.prj':\n",
+ " files[ext].append(line.strip().split(' ')[1].split('\\\\')[-1])\n",
+ " \n",
+ " # If the ext is in the list of files we care about, it addes to the dict\n",
+ " files[ext].append(line.strip().split(' ')[-1])\n",
+ " \n",
+ " else:\n",
+ " \n",
+ " # Adds to the dict even if not in files we care about\n",
+ " files[ext.strip()].append(line)\n",
" \n",
- " files = np.array(files)\n",
+ " # Gets the base filepath\n",
+ " files['basepath'] = os.path.dirname(os.path.abspath(atf_file))\n",
" \n",
- " # Creates appropriate arrays for certain files in the right format\n",
- " for file in files:\n",
- " file = file.strip()\n",
- " file = file.split(' ')\n",
- "\n",
- " # Grabs all the IPF files\n",
- " if file[1].endswith('.ipf'):\n",
- " ipf.append(file[1])\n",
- "\n",
- " # Grabs all the SUP files\n",
- " if file[1].endswith('.sup'):\n",
- " sup.append(file[1])\n",
- "\n",
- " files_dict.append(file)\n",
- "\n",
" # Creates a dict out of file lists for GPF, PRJ, IPF, and ATF\n",
" files_dict = (dict(files_dict))\n",
" \n",
" # Sets the value of IMAGE_IPF to all IPF images\n",
- " files_dict['IMAGE_IPF'] = ipf\n",
+ " files_dict['IMAGE_IPF'] = files['.ipf']\n",
" \n",
" # Sets the value of IMAGE_SUP to all SUP images\n",
- " files_dict['IMAGE_SUP'] = sup\n",
+ " files_dict['IMAGE_SUP'] = files['.sup']\n",
+ " \n",
+ " # Sets value for GPF file\n",
+ " files_dict['GP_FILE'] = files['.gpf'][0]\n",
+ " \n",
+ " # Sets value for PRJ file\n",
+ " files_dict['PROJECT'] = files['.prj'][0]\n",
" \n",
" # Sets the value of PATH to the path of the ATF file\n",
- " files_dict['PATH'] = os.path.dirname(os.path.abspath(atf_file))\n",
+ " files_dict['PATH'] = files['basepath']\n",
" \n",
" return files_dict\n",
"\n",
"# converts columns l. and s. to isis\n",
+ "# no transform applied\n",
"def line_sample_size(record, path):\n",
" with open(os.path.join(path, record['ipf_file'] + '.sup')) as f:\n",
" for i, line in enumerate(f):\n",
@@ -103,6 +108,7 @@
" return sample_size, line_size, img_index\n",
" \n",
"# converts known to ISIS keywords\n",
+ "# transform\n",
"def known(record):\n",
" if record['known'] == 0:\n",
" return 'Free'\n",
@@ -115,6 +121,7 @@
" return num % 360\n",
"\n",
"# ocentric to ographic latitudes\n",
+ "# transform but unsure how to handle\n",
"def oc2og(dlat, dMajorRadius, dMinorRadius):\n",
" try: \n",
" dlat = math.radians(dlat)\n",
@@ -125,6 +132,7 @@
" return dlat\n",
"\n",
"# ographic to ocentric latitudes\n",
+ "# transform but unsure how to handle\n",
"def og2oc(dlat, dMajorRadius, dMinorRadius):\n",
" try:\n",
" dlat = math.radians(dlat)\n",
@@ -163,25 +171,134 @@
" coord_360 = to_360(ocentric_coord)\n",
" return coord_360\n",
"\n",
- "def body_fix(record, semi_major, semi_minor):\n",
+ "def body_fix(record, semi_major, semi_minor, inverse=False):\n",
+ " \"\"\"\n",
+ " Parameters\n",
+ " ----------\n",
+ " record : ndarray\n",
+ " (n,3) where columns are x, y, height or lon, lat, alt\n",
+ " \"\"\"\n",
+ " \n",
" ecef = pyproj.Proj(proj='geocent', a=semi_major, b=semi_minor)\n",
" lla = pyproj.Proj(proj='latlon', a=semi_major, b=semi_minor)\n",
- " lon, lat, height = pyproj.transform(lla, ecef, record['long_X_East'], record['lat_Y_North'], record['ht'])\n",
- " return lon, lat, height\n",
" \n",
+ " if inverse:\n",
+ " lon, lat, height = pyproj.transform(ecef, lla, record[0], record[1], record[2])\n",
+ " return lon, lat, height\n",
+ " else:\n",
+ " y, x, z = pyproj.transform(lla, ecef, record[0], record[1], record[2])\n",
+ " return y, x, z\n",
+ "\n",
+ "def ignore_toggle(record):\n",
+ " if record['stat'] == 0:\n",
+ " return True\n",
+ " else:\n",
+ " return False\n",
+ "\n",
+ "# TODO: Does isis cnet need a convariance matrix for sigmas? Even with a static matrix of 1,1,1,1 \n",
+ "def compute_sigma_covariance_matrix(lat, lon, rad, latsigma, lonsigma, radsigma, semimajor_axis):\n",
+ " \n",
+ " \"\"\"\n",
+ " Given geospatial coordinates, desired accuracy sigmas, and an equitorial radius, compute a 2x3\n",
+ " sigma covariange matrix.\n",
+ " \n",
+ " Parameters\n",
+ " ----------\n",
+ " lat : float\n",
+ " A point's latitude in degrees\n",
+ " \n",
+ " lon : float\n",
+ " A point's longitude in degrees\n",
+ " \n",
+ " rad : float\n",
+ " The radius (z-value) of the point in meters\n",
+ " \n",
+ " latsigma : float\n",
+ " The desired latitude accuracy in meters (Default 10.0)\n",
+ " \n",
+ " lonsigma : float\n",
+ " The desired longitude accuracy in meters (Default 10.0)\n",
+ " \n",
+ " radsigma : float\n",
+ " The desired radius accuracy in meters (Defualt: 15.0)\n",
+ " \n",
+ " semimajor_axis : float\n",
+ " The semi-major or equitorial radius in meters (Default: 1737400.0 - Moon)\n",
+ " \n",
+ " Returns\n",
+ " -------\n",
+ " rectcov : ndarray\n",
+ " (2,3) covariance matrix\n",
+ " \"\"\"\n",
+ " \n",
+ " lat = math.radians(lat)\n",
+ " lon = math.radians(lon)\n",
+ " \n",
+ " # SetSphericalSigmasDistance\n",
+ " scaled_lat_sigma = latsigma / semimajor_axis\n",
+ "\n",
+ " # This is specific to each lon.\n",
+ " scaled_lon_sigma = lonsigma * math.cos(lat) / semimajor_axis\n",
+ " \n",
+ " # SetSphericalSigmas\n",
+ " cov = np.eye(3,3)\n",
+ " cov[0,0] = scaled_lat_sigma ** 2\n",
+ " cov[1,1] = scaled_lon_sigma ** 2\n",
+ " cov[2,2] = radsigma ** 2\n",
+ " \n",
+ " # Approximate the Jacobian\n",
+ " j = np.zeros((3,3))\n",
+ " cosphi = math.cos(lat)\n",
+ " sinphi = math.sin(lat)\n",
+ " coslambda = math.cos(lon)\n",
+ " sinlambda = math.sin(lon)\n",
+ " rcosphi = rad * cosphi\n",
+ " rsinphi = rad * sinphi\n",
+ " j[0,0] = -rsinphi * coslambda\n",
+ " j[0,1] = -rcosphi * sinlambda\n",
+ " j[0,2] = cosphi * coslambda\n",
+ " j[1,0] = -rsinphi * sinlambda\n",
+ " j[1,1] = rcosphi * coslambda\n",
+ " j[1,2] = cosphi * sinlambda\n",
+ " j[2,0] = rcosphi\n",
+ " j[2,1] = 0.\n",
+ " j[2,2] = sinphi\n",
+ " mat = j.dot(cov)\n",
+ " mat = mat.dot(j.T)\n",
+ " rectcov = np.zeros((2,3))\n",
+ " rectcov[0,0] = mat[0,0]\n",
+ " rectcov[0,1] = mat[0,1]\n",
+ " rectcov[0,2] = mat[0,2]\n",
+ " rectcov[1,0] = mat[1,1]\n",
+ " rectcov[1,1] = mat[1,2]\n",
+ " rectcov[1,2] = mat[2,2]\n",
+ " \n",
+ " return np.array(rectcov)\n",
+ "# return np.array([[1.0, 1.0, 1.0], [1.0, 1.0, 1.0]])\n",
+ "\n",
+ "\n",
+ "def compute_cov_matrix(record, semimajor_axis):\n",
+ " cov_matrix = compute_sigma_covariance_matrix(record['lat_Y_North'], record['long_X_East'], record['ht'], record['sig0'], record['sig1'], record['sig2'], semimajor_axis)\n",
+ " return cov_matrix.ravel().tolist()\n",
"\n",
"# applys transformations to columns\n",
- "def apply_transformations(atf_dict, df):\n",
- " prj_file = os.path.join(atf_dict['PATH'], atf_dict['PROJECT'].split('\\\\')[-1])\n",
+ "def apply_two_isis_transformations(atf_dict, df):\n",
+ " prj_file = os.path.join(atf_dict['PATH'], atf_dict['PROJECT'])\n",
" \n",
" eRadius, pRadius = get_axis(prj_file)\n",
" \n",
+ " lla = np.array([[df['long_X_East']], [df['lat_Y_North']], [df['ht']]])\n",
+ " \n",
+ " ecef = body_fix(lla, semi_major = eRadius, semi_minor = pRadius, inverse=False)\n",
+ " \n",
" df['s.'], df['l.'], df['image_index'] = (zip(*df.apply(line_sample_size, path = atf_dict['PATH'], axis=1)))\n",
" df['known'] = df.apply(known, axis=1)\n",
- " df['lat_Y_North'] = df.apply(lat_ISIS_coord, semi_major = eRadius, semi_minor = pRadius, axis=1)\n",
- " df['long_X_East'] = df.apply(lon_ISIS_coord, semi_major = eRadius, semi_minor = pRadius, axis=1)\n",
- " df['long_X_East'], df['lat_Y_North'], df['ht'] = zip(*df.apply(body_fix, semi_major = eRadius, semi_minor = pRadius, axis = 1))\n",
- " \n",
+ " df['long_X_East'] = ecef[0][0]\n",
+ " df['lat_Y_North'] = ecef[1][0]\n",
+ " df['ht'] = ecef[2][0] \n",
+ " df['aprioriCovar'] = df.apply(compute_cov_matrix, semimajor_axis = eRadius, axis=1)\n",
+ "# df['ignore'] = df.apply(ignore_toggle, axis=1)\n",
+ " \n",
"def socet2isis(prj_file):\n",
" # Read in and setup the atf dict of information\n",
" atf_dict = read_atf(prj_file)\n",
@@ -209,59 +326,639 @@
" socet_df = ipf_df.merge(gpf_df, left_on='pt_id', right_on='point_id')\n",
" \n",
" # Apply the transformations\n",
- " apply_transformations(atf_dict, socet_df)\n",
+ "# apply_two_isis_transformations(atf_dict, socet_df)\n",
" \n",
" # Define column remap for socet dataframe\n",
- " column_remap = {'l.': 'y', 's.': 'x',\n",
- " 'res_l': 'LineResidual', 'res_s': 'SampleResidual', 'known': 'Type',\n",
- " 'lat_Y_North': 'AprioriY', 'long_X_East': 'AprioriX', 'ht': 'AprioriZ',\n",
- " 'sig0': 'AprioriLatitudeSigma', 'sig1': 'AprioriLongitudeSigma', 'sig2': 'AprioriRadiusSigma'}\n",
+ "# column_map = {'pt_id': 'id', 'l.': 'y', 's.': 'x',\n",
+ "# 'res_l': 'lineResidual', 'res_s': 'sampleResidual', 'known': 'Type',\n",
+ "# 'lat_Y_North': 'aprioriY', 'long_X_East': 'aprioriX', 'ht': 'aprioriZ',\n",
+ "# 'sig0': 'aprioriLatitudeSigma', 'sig1': 'aprioriLongitudeSigma', 'sig2': 'aprioriRadiusSigma',\n",
+ "# 'sig_l': 'linesigma', 'sig_s': 'samplesigma'}\n",
" \n",
" # Rename the columns using the column remap above\n",
- " socet_df.rename(columns = column_remap, inplace=True)\n",
+ "# socet_df.rename(columns = column_map, inplace=True)\n",
" \n",
" # Return the socet dataframe to be converted to a control net\n",
" return socet_df\n",
"\n",
"# creates a dict of serial numbers with the cub being the key\n",
- "def serial_numbers(image_dict, path):\n",
+ "def serial_numbers(images, path, extension):\n",
" serial_dict = dict()\n",
- "\n",
- " for key in image_dict:\n",
- " snum = sn.generate_serial_number(os.path.join(path, image_dict[key]))\n",
+ " \n",
+ " for image in images:\n",
+ " snum = sn.generate_serial_number(os.path.join(path, image + extension))\n",
" snum = snum.replace('Mars_Reconnaissance_Orbiter', 'MRO')\n",
- " serial_dict[key] = snum\n",
+ " serial_dict[image] = snum\n",
" return serial_dict"
]
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 86,
"metadata": {
- "scrolled": false
+ "scrolled": true
},
"outputs": [],
"source": [
"# Setup stuffs for the cub information namely the path and extension\n",
- "path = '/Volumes/Blueman/'\n",
- "atf_file = get_path('CTX_Athabasca_Middle_step0.atf')\n",
+ "path = '/home/acpaquette/repos/plio/test_cubes'\n",
+ "targetname = 'Mars'\n",
+ "# Extension of your cub files\n",
+ "extension = '.8bit.cub'\n",
"\n",
- "image_dict = {'P01_001540_1889_XI_08N204W' : 'P01_001540_1889_XI_08N204W.lev1.cub',\n",
- " 'P01_001606_1897_XI_09N203W' : 'P01_001606_1897_XI_09N203W.lev1.cub',\n",
- " 'P02_001804_1889_XI_08N204W' : 'P02_001804_1889_XI_08N204W.lev1.cub',\n",
- " 'P03_002226_1895_XI_09N203W' : 'P03_002226_1895_XI_09N203W.lev1.cub',\n",
- " 'P03_002371_1888_XI_08N204W' : 'P03_002371_1888_XI_08N204W.lev1.cub',\n",
- " 'P19_008344_1894_XN_09N203W' : 'P19_008344_1894_XN_09N203W.lev1.cub',\n",
- " 'P20_008845_1894_XN_09N203W' : 'P20_008845_1894_XN_09N203W.lev1.cub'}\n",
+ "# Path to atf file\n",
+ "atf_file = ('/home/acpaquette/repos/plio/plio/examples/SocetSet/Relative.atf')\n",
"\n",
"socet_df = socet2isis(atf_file)\n",
"\n",
- "images = pd.unique(socet_df['ipf_file'])\n",
+ "# images = pd.unique(socet_df['ipf_file'])\n",
"\n",
- "serial_dict = serial_numbers(image_dict, path)\n",
+ "# serial_dict = serial_numbers(images, path, extension)\n",
"\n",
"# creates the control network\n",
- "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",
+ "execution_count": 116,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "return_df = cn.from_isis(\"/home/acpaquette/repos/plio/plio/examples/SocetSet/cn.net\")\n",
+ "\n",
+ "columns = []\n",
+ "column_index = []\n",
+ "\n",
+ "for i, column in enumerate(list(return_df.columns)):\n",
+ " if column not in columns:\n",
+ " column_index.append(i)\n",
+ " columns.append(column)\n",
+ "\n",
+ "return_df = return_df.iloc[:, column_index]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 117,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "column_map = {'pt_id': 'id', 'l.': 'y', 's.': 'x',\n",
+ " 'res_l': 'lineResidual', 'res_s': 'sampleResidual', 'known': 'Type',\n",
+ " 'lat_Y_North': 'aprioriY', 'long_X_East': 'aprioriX', 'ht': 'aprioriZ',\n",
+ " 'sig0': 'aprioriLatitudeSigma', 'sig1': 'aprioriLongitudeSigma', 'sig2': 'aprioriRadiusSigma',\n",
+ " 'sig_l': 'linesigma', 'sig_s': 'samplesigma'}\n",
+ "\n",
+ "column_map = {k: v for v, k in column_map.items()}\n",
+ "return_df.rename(columns = column_map, inplace=True)\n",
+ "return_df.drop(['chooserName', 'datetime', 'referenceIndex', 'jigsawRejected', 'editLock', 'aprioriSurfPointSource', 'aprioriSurfPointSourceFile','aprioriRadiusSource', 'aprioriRadiusSourceFile'] , axis = 1, inplace=True)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 129,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "<div>\n",
+ "<style scoped>\n",
+ " .dataframe tbody tr th:only-of-type {\n",
+ " vertical-align: middle;\n",
+ " }\n",
+ "\n",
+ " .dataframe tbody tr th {\n",
+ " vertical-align: top;\n",
+ " }\n",
+ "\n",
+ " .dataframe thead th {\n",
+ " text-align: right;\n",
+ " }\n",
+ "</style>\n",
+ "<table border=\"1\" class=\"dataframe\">\n",
+ " <thead>\n",
+ " <tr style=\"text-align: right;\">\n",
+ " <th></th>\n",
+ " <th>lat_Y_North</th>\n",
+ " <th>long_X_East</th>\n",
+ " <th>ht</th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th>0</th>\n",
+ " <td>139525.230749</td>\n",
+ " <td>3.390974e+06</td>\n",
+ " <td>4506.496945</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>1</th>\n",
+ " <td>139525.230749</td>\n",
+ " <td>3.390974e+06</td>\n",
+ " <td>4506.496945</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>2</th>\n",
+ " <td>139489.278045</td>\n",
+ " <td>3.390969e+06</td>\n",
+ " <td>4516.454802</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>3</th>\n",
+ " <td>139489.278045</td>\n",
+ " <td>3.390969e+06</td>\n",
+ " <td>4516.454802</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>4</th>\n",
+ " <td>139823.489797</td>\n",
+ " <td>3.390990e+06</td>\n",
+ " <td>4536.274914</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>5</th>\n",
+ " <td>139823.489797</td>\n",
+ " <td>3.390990e+06</td>\n",
+ " <td>4536.274914</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>6</th>\n",
+ " <td>139772.738004</td>\n",
+ " <td>3.390936e+06</td>\n",
+ " <td>4518.050219</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>7</th>\n",
+ " <td>139772.738004</td>\n",
+ " <td>3.390936e+06</td>\n",
+ " <td>4518.050219</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>8</th>\n",
+ " <td>139575.914815</td>\n",
+ " <td>3.390952e+06</td>\n",
+ " <td>3816.666542</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>9</th>\n",
+ " <td>139575.914815</td>\n",
+ " <td>3.390952e+06</td>\n",
+ " <td>3816.666542</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>10</th>\n",
+ " <td>139614.756296</td>\n",
+ " <td>3.390953e+06</td>\n",
+ " <td>3791.232717</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>11</th>\n",
+ " <td>139614.756296</td>\n",
+ " <td>3.390953e+06</td>\n",
+ " <td>3791.232717</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>12</th>\n",
+ " <td>139912.041374</td>\n",
+ " <td>3.390914e+06</td>\n",
+ " <td>3875.608660</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>13</th>\n",
+ " <td>139912.041374</td>\n",
+ " <td>3.390914e+06</td>\n",
+ " <td>3875.608660</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>14</th>\n",
+ " <td>139909.452033</td>\n",
+ " <td>3.390930e+06</td>\n",
+ " <td>3845.361327</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>15</th>\n",
+ " <td>139909.452033</td>\n",
+ " <td>3.390930e+06</td>\n",
+ " <td>3845.361327</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>16</th>\n",
+ " <td>139669.826849</td>\n",
+ " <td>3.391120e+06</td>\n",
+ " <td>3270.672620</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>17</th>\n",
+ " <td>139669.826849</td>\n",
+ " <td>3.391120e+06</td>\n",
+ " <td>3270.672620</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>18</th>\n",
+ " <td>139694.517017</td>\n",
+ " <td>3.391205e+06</td>\n",
+ " <td>3289.744506</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>19</th>\n",
+ " <td>139694.517017</td>\n",
+ " <td>3.391205e+06</td>\n",
+ " <td>3289.744506</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>20</th>\n",
+ " <td>139968.793338</td>\n",
+ " <td>3.391126e+06</td>\n",
+ " <td>3274.711397</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>21</th>\n",
+ " <td>139968.793338</td>\n",
+ " <td>3.391126e+06</td>\n",
+ " <td>3274.711397</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>22</th>\n",
+ " <td>139979.200780</td>\n",
+ " <td>3.391138e+06</td>\n",
+ " <td>3298.297228</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>23</th>\n",
+ " <td>139979.200780</td>\n",
+ " <td>3.391138e+06</td>\n",
+ " <td>3298.297228</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>24</th>\n",
+ " <td>139688.031217</td>\n",
+ " <td>3.391041e+06</td>\n",
+ " <td>4253.956077</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>25</th>\n",
+ " <td>139688.031217</td>\n",
+ " <td>3.391041e+06</td>\n",
+ " <td>4253.956077</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>26</th>\n",
+ " <td>139686.910823</td>\n",
+ " <td>3.391089e+06</td>\n",
+ " <td>4216.743792</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>27</th>\n",
+ " <td>139686.910823</td>\n",
+ " <td>3.391089e+06</td>\n",
+ " <td>4216.743792</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>28</th>\n",
+ " <td>139786.205284</td>\n",
+ " <td>3.390979e+06</td>\n",
+ " <td>3579.127600</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>29</th>\n",
+ " <td>139786.205284</td>\n",
+ " <td>3.390979e+06</td>\n",
+ " <td>3579.127600</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>30</th>\n",
+ " <td>139785.010997</td>\n",
+ " <td>3.391002e+06</td>\n",
+ " <td>3546.549796</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>31</th>\n",
+ " <td>139785.010997</td>\n",
+ " <td>3.391002e+06</td>\n",
+ " <td>3546.549796</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "</div>"
+ ],
+ "text/plain": [
+ " lat_Y_North long_X_East ht\n",
+ "0 139525.230749 3.390974e+06 4506.496945\n",
+ "1 139525.230749 3.390974e+06 4506.496945\n",
+ "2 139489.278045 3.390969e+06 4516.454802\n",
+ "3 139489.278045 3.390969e+06 4516.454802\n",
+ "4 139823.489797 3.390990e+06 4536.274914\n",
+ "5 139823.489797 3.390990e+06 4536.274914\n",
+ "6 139772.738004 3.390936e+06 4518.050219\n",
+ "7 139772.738004 3.390936e+06 4518.050219\n",
+ "8 139575.914815 3.390952e+06 3816.666542\n",
+ "9 139575.914815 3.390952e+06 3816.666542\n",
+ "10 139614.756296 3.390953e+06 3791.232717\n",
+ "11 139614.756296 3.390953e+06 3791.232717\n",
+ "12 139912.041374 3.390914e+06 3875.608660\n",
+ "13 139912.041374 3.390914e+06 3875.608660\n",
+ "14 139909.452033 3.390930e+06 3845.361327\n",
+ "15 139909.452033 3.390930e+06 3845.361327\n",
+ "16 139669.826849 3.391120e+06 3270.672620\n",
+ "17 139669.826849 3.391120e+06 3270.672620\n",
+ "18 139694.517017 3.391205e+06 3289.744506\n",
+ "19 139694.517017 3.391205e+06 3289.744506\n",
+ "20 139968.793338 3.391126e+06 3274.711397\n",
+ "21 139968.793338 3.391126e+06 3274.711397\n",
+ "22 139979.200780 3.391138e+06 3298.297228\n",
+ "23 139979.200780 3.391138e+06 3298.297228\n",
+ "24 139688.031217 3.391041e+06 4253.956077\n",
+ "25 139688.031217 3.391041e+06 4253.956077\n",
+ "26 139686.910823 3.391089e+06 4216.743792\n",
+ "27 139686.910823 3.391089e+06 4216.743792\n",
+ "28 139786.205284 3.390979e+06 3579.127600\n",
+ "29 139786.205284 3.390979e+06 3579.127600\n",
+ "30 139785.010997 3.391002e+06 3546.549796\n",
+ "31 139785.010997 3.391002e+06 3546.549796"
+ ]
+ },
+ "execution_count": 129,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "return_df[['lat_Y_North', 'long_X_East', 'ht']]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 128,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "<div>\n",
+ "<style scoped>\n",
+ " .dataframe tbody tr th:only-of-type {\n",
+ " vertical-align: middle;\n",
+ " }\n",
+ "\n",
+ " .dataframe tbody tr th {\n",
+ " vertical-align: top;\n",
+ " }\n",
+ "\n",
+ " .dataframe thead th {\n",
+ " text-align: right;\n",
+ " }\n",
+ "</style>\n",
+ "<table border=\"1\" class=\"dataframe\">\n",
+ " <thead>\n",
+ " <tr style=\"text-align: right;\">\n",
+ " <th></th>\n",
+ " <th>lat_Y_North</th>\n",
+ " <th>long_X_East</th>\n",
+ " <th>ht</th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th>0</th>\n",
+ " <td>0.095708</td>\n",
+ " <td>2.356167</td>\n",
+ " <td>-2342.889214</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>1</th>\n",
+ " <td>0.095708</td>\n",
+ " <td>2.356167</td>\n",
+ " <td>-2342.889214</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>2</th>\n",
+ " <td>0.095920</td>\n",
+ " <td>2.355564</td>\n",
+ " <td>-2349.638414</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>3</th>\n",
+ " <td>0.095920</td>\n",
+ " <td>2.355564</td>\n",
+ " <td>-2349.638414</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>4</th>\n",
+ " <td>0.096339</td>\n",
+ " <td>2.361186</td>\n",
+ " <td>-2314.316425</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>5</th>\n",
+ " <td>0.096339</td>\n",
+ " <td>2.361186</td>\n",
+ " <td>-2314.316425</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>6</th>\n",
+ " <td>0.095954</td>\n",
+ " <td>2.360368</td>\n",
+ " <td>-2370.502882</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>7</th>\n",
+ " <td>0.095954</td>\n",
+ " <td>2.360368</td>\n",
+ " <td>-2370.502882</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>8</th>\n",
+ " <td>0.081058</td>\n",
+ " <td>2.357037</td>\n",
+ " <td>-2363.989968</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>9</th>\n",
+ " <td>0.081058</td>\n",
+ " <td>2.357037</td>\n",
+ " <td>-2363.989968</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>10</th>\n",
+ " <td>0.080518</td>\n",
+ " <td>2.357691</td>\n",
+ " <td>-2360.922571</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>11</th>\n",
+ " <td>0.080518</td>\n",
+ " <td>2.357691</td>\n",
+ " <td>-2360.922571</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>12</th>\n",
+ " <td>0.082311</td>\n",
+ " <td>2.362733</td>\n",
+ " <td>-2388.123298</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>13</th>\n",
+ " <td>0.082311</td>\n",
+ " <td>2.362733</td>\n",
+ " <td>-2388.123298</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>14</th>\n",
+ " <td>0.081668</td>\n",
+ " <td>2.362678</td>\n",
+ " <td>-2371.973499</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>15</th>\n",
+ " <td>0.081668</td>\n",
+ " <td>2.362678</td>\n",
+ " <td>-2371.973499</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>16</th>\n",
+ " <td>0.069458</td>\n",
+ " <td>2.358505</td>\n",
+ " <td>-2193.309629</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>17</th>\n",
+ " <td>0.069458</td>\n",
+ " <td>2.358505</td>\n",
+ " <td>-2193.309629</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>18</th>\n",
+ " <td>0.069861</td>\n",
+ " <td>2.358862</td>\n",
+ " <td>-2106.769773</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>19</th>\n",
+ " <td>0.069861</td>\n",
+ " <td>2.358862</td>\n",
+ " <td>-2106.769773</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>20</th>\n",
+ " <td>0.069543</td>\n",
+ " <td>2.363543</td>\n",
+ " <td>-2174.971745</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>21</th>\n",
+ " <td>0.069543</td>\n",
+ " <td>2.363543</td>\n",
+ " <td>-2174.971745</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>22</th>\n",
+ " <td>0.070044</td>\n",
+ " <td>2.363710</td>\n",
+ " <td>-2162.103231</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>23</th>\n",
+ " <td>0.070044</td>\n",
+ " <td>2.363710</td>\n",
+ " <td>-2162.103231</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>24</th>\n",
+ " <td>0.090342</td>\n",
+ " <td>2.358866</td>\n",
+ " <td>-2269.610862</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>25</th>\n",
+ " <td>0.090342</td>\n",
+ " <td>2.358866</td>\n",
+ " <td>-2269.610862</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>26</th>\n",
+ " <td>0.089550</td>\n",
+ " <td>2.358814</td>\n",
+ " <td>-2222.328983</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>27</th>\n",
+ " <td>0.089550</td>\n",
+ " <td>2.358814</td>\n",
+ " <td>-2222.328983</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>28</th>\n",
+ " <td>0.076012</td>\n",
+ " <td>2.360565</td>\n",
+ " <td>-2328.281125</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>29</th>\n",
+ " <td>0.076012</td>\n",
+ " <td>2.360565</td>\n",
+ " <td>-2328.281125</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>30</th>\n",
+ " <td>0.075320</td>\n",
+ " <td>2.360529</td>\n",
+ " <td>-2305.362047</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>31</th>\n",
+ " <td>0.075320</td>\n",
+ " <td>2.360529</td>\n",
+ " <td>-2305.362047</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "</div>"
+ ],
+ "text/plain": [
+ " lat_Y_North long_X_East ht\n",
+ "0 0.095708 2.356167 -2342.889214\n",
+ "1 0.095708 2.356167 -2342.889214\n",
+ "2 0.095920 2.355564 -2349.638414\n",
+ "3 0.095920 2.355564 -2349.638414\n",
+ "4 0.096339 2.361186 -2314.316425\n",
+ "5 0.096339 2.361186 -2314.316425\n",
+ "6 0.095954 2.360368 -2370.502882\n",
+ "7 0.095954 2.360368 -2370.502882\n",
+ "8 0.081058 2.357037 -2363.989968\n",
+ "9 0.081058 2.357037 -2363.989968\n",
+ "10 0.080518 2.357691 -2360.922571\n",
+ "11 0.080518 2.357691 -2360.922571\n",
+ "12 0.082311 2.362733 -2388.123298\n",
+ "13 0.082311 2.362733 -2388.123298\n",
+ "14 0.081668 2.362678 -2371.973499\n",
+ "15 0.081668 2.362678 -2371.973499\n",
+ "16 0.069458 2.358505 -2193.309629\n",
+ "17 0.069458 2.358505 -2193.309629\n",
+ "18 0.069861 2.358862 -2106.769773\n",
+ "19 0.069861 2.358862 -2106.769773\n",
+ "20 0.069543 2.363543 -2174.971745\n",
+ "21 0.069543 2.363543 -2174.971745\n",
+ "22 0.070044 2.363710 -2162.103231\n",
+ "23 0.070044 2.363710 -2162.103231\n",
+ "24 0.090342 2.358866 -2269.610862\n",
+ "25 0.090342 2.358866 -2269.610862\n",
+ "26 0.089550 2.358814 -2222.328983\n",
+ "27 0.089550 2.358814 -2222.328983\n",
+ "28 0.076012 2.360565 -2328.281125\n",
+ "29 0.076012 2.360565 -2328.281125\n",
+ "30 0.075320 2.360529 -2305.362047\n",
+ "31 0.075320 2.360529 -2305.362047"
+ ]
+ },
+ "execution_count": 128,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "socet_df[['lat_Y_North', 'long_X_East', 'ht']]"
]
},
{
@@ -288,7 +985,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.6.3"
+ "version": "3.6.4"
}
},
"nbformat": 4,
diff --git a/plio/examples/SocetSet/cub_map.csv b/plio/examples/SocetSet/cub_map.csv
new file mode 100644
index 0000000000000000000000000000000000000000..cca0f2c17b832f4df5ac001bafde185ca679cdd7
--- /dev/null
+++ b/plio/examples/SocetSet/cub_map.csv
@@ -0,0 +1,7 @@
+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
diff --git a/plio/io/io_controlnetwork.py b/plio/io/io_controlnetwork.py
index 87b877a604648018d03daedd4f8695890c28ea40..cebe7e6c946a039bd09b64de7f0cad8c0b73edba 100644
--- a/plio/io/io_controlnetwork.py
+++ b/plio/io/io_controlnetwork.py
@@ -1,6 +1,7 @@
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,
diff --git a/plio/spatial/transformations.py b/plio/spatial/transformations.py
index 18160eea9860e78f8ea19d6a73584d809e12b67f..efc4ad609424a1c88e4ca778370b647249655e4c 100644
--- a/plio/spatial/transformations.py
+++ b/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