Added covariance matrix and generic paths

  - 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

  - 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

import os

import numpy as np

# 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 = []

        files_dict = []

        # 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)

        files = np.array(files)

        # Creates appropriate arrays for certain files in the right format

        for file in files:

            file = file.strip()

            file = file.split(' ')

            # Grabs all the IPF files

            if file[1].endswith('.ipf'):

                ipf.append(file[1])

            # Grabs all the SUP files

            if file[1].endswith('.sup'):

                sup.append(file[1])

            files_dict.append(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

        # Sets the value of IMAGE_SUP to all SUP images

        files_dict['IMAGE_SUP'] = sup

        # Sets the value of PATH to the path of the ATF file

        files_dict['PATH'] = os.path.dirname(os.path.abspath(atf_file))

        return files_dict

#!/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('--outpath', help='Directory for the control network to be output to.',

                                        required = False)

    return parser.parse_args()

def main(args):

    # 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)

if __name__ == '__main__':

    main(parse_args())

Object = IsisCube

  Object = Core

    StartByte   = 65537

    Format      = Tile

    TileSamples = 1000

    TileLines   = 1024

    Group = Dimensions

      Samples = 5000

      Lines   = 7168

      Bands   = 1

    End_Group

    Group = Pixels

      Type       = SignedWord

      ByteOrder  = Lsb

      Base       = 0.0

      Multiplier = 1.0

    End_Group

  End_Object

  Group = Instrument

    SpacecraftName        = Mars_Reconnaissance_Orbiter

    InstrumentId          = CTX

    TargetName            = Mars

    MissionPhaseName      = PSP

    StartTime             = 2008-09-17T05:08:10.820

    SpacecraftClockCount  = 0906095311:038

    OffsetModeId          = 196/190/181

    LineExposureDuration  = 1.877 <MSEC>

    FocalPlaneTemperature = 293.4 <K>

    SampleBitModeId       = SQROOT

    SpatialSumming        = 1

    SampleFirstPixel      = 0

  End_Group

  Group = Archive

    DataSetId           = MRO-M-CTX-2-EDR-L0-V1.0

    ProductId           = B01_010045_1878_XN_07N205W

    ProducerId          = MRO_CTX_TEAM

    ProductCreationTime = 2009-04-13T19:24:46

    OrbitNumber         = 10045

  End_Group

  Group = BandBin

    FilterName = BroadBand

    Center     = 0.65 <micrometers>

    Width      = 0.15 <micrometers>

  End_Group

  Group = Kernels

    NaifFrameCode             = -74021

    LeapSecond                = $base/kernels/lsk/naif0012.tls

    TargetAttitudeShape       = $base/kernels/pck/pck00009.tpc

    TargetPosition            = (Table, $base/kernels/spk/de405.bsp)

    InstrumentPointing        = (Table,

                                 $mro/kernels/ck/mro_sc_psp_080916_080922.bc,

                                 $mro/kernels/fk/mro_v15.tf)

    Instrument                = Null

    SpacecraftClock           = $mro/kernels/sclk/MRO_SCLKSCET.00064.65536.tsc

    InstrumentPosition        = (Table, $mro/kernels/spk/mro_psp8.bsp)

    InstrumentAddendum        = $mro/kernels/iak/mroctxAddendum005.ti

    ShapeModel                = $base/dems/molaMarsPlanetaryRadius0005.cub

    InstrumentPositionQuality = Reconstructed

    InstrumentPointingQuality = Reconstructed

    CameraVersion             = 1

  End_Group

End_Object

Object = Label

  Bytes = 65536

End_Object