diff --git a/notebooks/Socet2ISIS.ipynb b/notebooks/Socet2ISIS.ipynb
index b060d18a23d1e74383bfa298c88bb5eccde2c375..af97cd92fb1c9fe8bcc5c1b299ef190f4fe2a767 100644
--- a/notebooks/Socet2ISIS.ipynb
+++ b/notebooks/Socet2ISIS.ipynb
@@ -8,21 +8,13 @@
"source": [
"import os\n",
"import sys\n",
- "import csv\n",
- "import pvl\n",
"\n",
"import pandas as pd\n",
- "import numpy as np\n",
"import math\n",
- "import pyproj\n",
- "from functools import singledispatch\n",
- "import warnings\n",
"\n",
- "from plio.examples import get_path\n",
- "from plio.io.io_bae import read_gpf, read_ipf, read_atf\n",
+ "from plio.io.io_bae import read_gpf, read_ipf, read_atf, save_gpf, save_ipf\n",
"from plio.utils.utils import find_in_dict\n",
- "from plio.spatial.transformations import *\n",
- "from collections import defaultdict\n",
+ "from plio.spatial.transformations import apply_isis_transformations, apply_socet_transformations, serial_numbers\n",
"import plio.io.io_controlnetwork as cn\n",
"import plio.io.isis_serial_number as sn"
]
@@ -33,7 +25,7 @@
"metadata": {},
"outputs": [],
"source": [
- "def socet2isis(at_file, cub_file_path, cub_ipf_map, target_name, outpath=None):\n",
+ "def socet2isis(at_file, cub_file_path, extension, target_name, outpath=None):\n",
" # Setup the at_file, path to cubes, and control network out path\n",
" at_file = at_file\n",
" cnet_out = os.path.split(os.path.splitext(at_file)[0])[1]\n",
@@ -43,10 +35,6 @@
" outpath = outpath\n",
" else:\n",
" outpath = os.path.split(at_file)[0]\n",
- "\n",
- " with open(cub_ipf_map) as cub_ipf_map:\n",
- " reader = csv.reader(cub_ipf_map, delimiter = ',')\n",
- " image_dict = dict([(row[0], row[1]) for row in reader])\n",
" \n",
" # Read in and setup the atf dict of information\n",
" atf_dict = read_atf(at_file)\n",
@@ -74,7 +62,7 @@
" 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_socet_transformations(atf_dict, socet_df)\n",
" \n",
" # Define column remap for socet dataframe\n",
" column_map = {'pt_id': 'id', 'l.': 'y', 's.': 'x',\n",
@@ -86,30 +74,79 @@
" # Rename the columns using the column remap above\n",
" socet_df.rename(columns = column_map, inplace=True)\n",
" \n",
+ " # Build an image and serial dict assuming the cubes will be named as the IPFs are\n",
+ " image_dict = {i: i + extension for i in pd.unique(socet_df['ipf_file'])}\n",
" serial_dict = serial_numbers(image_dict, cub_path)\n",
"\n",
" # creates the control network\n",
- " cn.to_isis(os.path.join(outpath, cnet_out + '.net'), socet_df, serial_dict, targetname = targetname)\n",
- " return socet_df"
+ " cn.to_isis(os.path.join(outpath, cnet_out + '.net'), socet_df, serial_dict, targetname = targetname)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
- "scrolled": true
+ "scrolled": false
},
"outputs": [],
"source": [
- "# Setup stuffs for the cub information namely the path and extension\n",
- "cub_path = '/Volumes/Blueman/'\n",
- "targetname = 'Mars'\n",
- "cub_map = '/Users/adampaquette/repos/plio/plio/examples/SocetSet/cub_map2.csv'\n",
+ "def isis2socet(cnet_path, eRadius, eccentricity, cub_path, extension, cub_list, out_gpf, adjusted_flag = False):\n",
+ " pRadius = eRadius * math.sqrt(1 - (eccentricity ** 2))\n",
+ " \n",
+ " df = cn.from_isis(cnet_path)\n",
+ " # Create cub dict to map ipf to cub\n",
+ " cub_dict = {i: i + extension for i in cub_list}\n",
+ "\n",
+ " # Create serial dict to match serial to ipf\n",
+ " serial_dict = {sn.generate_serial_number(os.path.join(cub_path, i + extension)): i for i in cub_list}\n",
+ "\n",
+ " # Remove duplicate columns\n",
+ " # There are better ways to do this but pandas was not having it\n",
+ " columns = []\n",
+ " column_index = []\n",
+ "\n",
+ " for i, column in enumerate(list(df.columns)):\n",
+ " if column not in columns:\n",
+ " column_index.append(i)\n",
+ " columns.append(column)\n",
+ "\n",
+ " df = df.iloc[:, column_index]\n",
+ "\n",
+ " # Begin translation\n",
+ " # Remap the ISIS columns to socet column names\n",
+ " column_map = {'id': 'pt_id', 'line': 'l.', 'sample': 's.', \n",
+ " 'lineResidual': 'res_l', 'sampleResidual': 'res_s', 'type': 'known', \n",
+ " 'aprioriLatitudeSigma': 'sig0', 'aprioriLongitudeSigma': 'sig1', 'aprioriRadiusSigma': 'sig2', \n",
+ " 'linesigma': 'sig_l', 'samplesigma': 'sig_s', 'ignore': 'stat'}\n",
+ "\n",
+ " # Depending on the adjusted flag, set the renames for columns appropriately\n",
+ " if adjusted_flag:\n",
+ " column_map['adjustedY'] = 'lat_Y_North'\n",
+ " column_map['adjustedX'] = 'long_X_East'\n",
+ " column_map['adjustedZ'] = 'ht'\n",
+ " else:\n",
+ " column_map['aprioriY'] = 'lat_Y_North'\n",
+ " column_map['aprioriX'] = 'long_X_East'\n",
+ " column_map['aprioriZ'] = 'ht'\n",
"\n",
- "# Path to atf file\n",
- "atf_file = ('/Users/adampaquette/repos/plio/plio/examples/SocetSet/Relative.atf')\n",
+ " df.rename(columns = column_map, inplace=True)\n",
+ " \n",
+ " apply_isis_transformations(df, eRadius, pRadius, serial_dict, extension, cub_path)\n",
+ "\n",
+ " # Save the ipf\n",
+ " save_ipf(df, os.path.split(out_gpf)[0])\n",
"\n",
- "socet_df = socet2isis(atf_file, cub_path, cub_map, targetname)"
+ " # Get the first record from each group as there all the same, put them\n",
+ " # into a list, and sort it\n",
+ " points = [int(i[1].index[0]) for i in df.groupby('pt_id')]\n",
+ " points.sort()\n",
+ "\n",
+ " # Set the gpf_df to only the values we need and do a small rename\n",
+ " gpf_df = df.iloc[points].copy()\n",
+ " gpf_df.rename(columns = {'pt_id': 'point_id'}, inplace=True)\n",
+ "\n",
+ " # Save the gpf\n",
+ " save_gpf(gpf_df, out_gpf)"
]
},
{
@@ -118,202 +155,46 @@
"metadata": {},
"outputs": [],
"source": [
- "def reverse_known(record):\n",
- " \"\"\"\n",
- " Converts the known field from a socet dataframe into the\n",
- " isis point_type column\n",
- "\n",
- " Parameters\n",
- " ----------\n",
- " record : object\n",
- " Pandas series object\n",
- "\n",
- " Returns\n",
- " -------\n",
- " : str\n",
- " String representation of a known field\n",
- " \"\"\"\n",
- " record_type = record['known']\n",
- " if record_type == 0 or record_type == 2:\n",
- " return 0\n",
- "\n",
- " elif record_type == 1 or record_type == 3 or record_type == 4:\n",
- " return 3\n",
- " \n",
- "def lat_socet_coord(record, semi_major, semi_minor):\n",
- " \"\"\"\n",
- " Function to convert lat_Y_North to ISIS_lat\n",
- "\n",
- " Parameters\n",
- " ----------\n",
- " record : object\n",
- " Pandas series object\n",
- "\n",
- " semi_major : float\n",
- " Radius from the center of the body to the equater\n",
- "\n",
- " semi_minor : float\n",
- " Radius from the pole to the center of mass\n",
- "\n",
- " Returns\n",
- " -------\n",
- " coord_360 : float\n",
- " Converted latitude into ocentric space, and mapped\n",
- " into 0 to 360\n",
- " \"\"\"\n",
- " ographic_coord = oc2og(record['lat_Y_North'], semi_major, semi_minor)\n",
- " return ((ographic_coord + 180) % 360) - 180\n",
- "\n",
- "def lon_socet_coord(record, semi_major, semi_minor):\n",
- " \"\"\"\n",
- " Function to convert lat_Y_North to ISIS_lat\n",
- "\n",
- " Parameters\n",
- " ----------\n",
- " record : object\n",
- " Pandas series object\n",
- "\n",
- " semi_major : float\n",
- " Radius from the center of the body to the equater\n",
- "\n",
- " semi_minor : float\n",
- " Radius from the pole to the center of mass\n",
- "\n",
- " Returns\n",
- " -------\n",
- " coord_360 : float\n",
- " Converted latitude into ocentric space, and mapped\n",
- " into 0 to 360\n",
- " \"\"\"\n",
- " ographic_coord = oc2og(record['long_X_East'], semi_major, semi_minor)\n",
- " return ((ographic_coord + 180) % 360) - 180\n",
+ "# Setup stuffs for the cub information namely the path and extension\n",
+ "cub_path = '/Path/to/cubs'\n",
"\n",
- "def fix_sample_line(record, serial_dict, extension, cub_path):\n",
- " # Cube location to load\n",
- " cube = pvl.load(os.path.join(cub_path, serial_dict[record['serialnumber']] + extension))\n",
- " line_size = find_in_dict(cube, 'Lines')\n",
- " sample_size = find_in_dict(cube, 'Samples')\n",
+ "# Name of the target body\n",
+ "targetname = 'Mars'\n",
+ "extension = 'cub.-->extension<--'\n",
"\n",
- " new_line = record['l.'] - (int(line_size)/2.0) - 1\n",
- " new_sample = record['s.'] - (int(sample_size)/2.0) - 1\n",
- " return new_line, new_sample\n",
+ "# Path to atf file\n",
+ "atf_file = 'Path/to/socket/set/at_file.atf'\n",
"\n",
- "def ignore_toggle(record):\n",
- " if record['stat'] == True:\n",
- " return 0\n",
- " else:\n",
- " return 1"
+ "socet2isis(atf_file, cub_path, extension, targetname)"
]
},
{
"cell_type": "code",
"execution_count": null,
- "metadata": {
- "scrolled": false
- },
+ "metadata": {},
"outputs": [],
"source": [
- "return_df = cn.from_isis(\"/Users/adampaquette/repos/plio/plio/examples/SocetSet/Relative.net\")\n",
+ "# Setup stuffs for the cub information namely the path and extension\n",
+ "# along with eRadius and eccentricity\n",
+ "cnet = \"Path/to/control/network.net\"\n",
"\n",
"eRadius = 3.39619000000000e+006\n",
- "pRadius = eRadius * (1 - 1.08339143554195e-001)\n",
+ "eccentricity = 1.08339143554195e-001\n",
"\n",
- "adjusted_flag = False\n",
+ "cub_path = 'Path/to/cubs'\n",
+ "extension = 'cub.-->extension<--'\n",
"\n",
- "cub_path = '/Volumes/Blueman/'\n",
- "extension = '.8bit.cub'\n",
+ "# List of cubes to use\n",
"cub_list = ['D06_029601_1846_XN_04N224W', \n",
" 'F05_037684_1857_XN_05N224W']\n",
"\n",
- "# \n",
- "cub_dict = {i: i + extension for i in cub_list}\n",
- "serial_dict = {sn.generate_serial_number(os.path.join(cub_path, i + extension)): i for i in cub_list}\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",
+ "out_gpf = \"/Users/adampaquette/Desktop/InSightE09_XW.gpf\"\n",
"\n",
- "return_df = return_df.iloc[:, column_index]\n",
- "\n",
- "column_map = {'id': 'pt_id', 'line': 'l.', 'sample': 's.', \n",
- " 'lineResidual': 'res_l', 'sampleResidual': 'res_s', 'type': 'known', \n",
- " 'aprioriLatitudeSigma': 'sig0', 'aprioriLongitudeSigma': 'sig1', 'aprioriRadiusSigma': 'sig2', \n",
- " 'linesigma': 'sig_l', 'samplesigma': 'sig_s', 'ignore': 'stat'}\n",
- "\n",
- "if adjusted_flag:\n",
- " column_map['adjustedY'] = 'lat_Y_North'\n",
- " column_map['adjustedX'] = 'long_X_East'\n",
- " column_map['adjustedZ'] = 'ht'\n",
- "else:\n",
- " column_map['aprioriY'] = 'lat_Y_North'\n",
- " column_map['aprioriX'] = 'long_X_East'\n",
- " column_map['aprioriZ'] = 'ht'\n",
- "\n",
- "return_df.rename(columns = column_map, inplace=True)\n",
- "\n",
- "return_df['known'] = return_df.apply(reverse_known, axis = 1)\n",
- "return_df['ipf_file'] = return_df['serialnumber'].apply(lambda serial_number: serial_dict[serial_number])\n",
- "return_df['l.'], return_df['s.'] = zip(*return_df.apply(fix_sample_line, serial_dict = serial_dict, \n",
- " extension = extension, \n",
- " cub_path = cub_path, axis = 1))\n",
- "\n",
- "ecef = np.array([[return_df['long_X_East']], [return_df['lat_Y_North']], [return_df['ht']]])\n",
- "lla = body_fix(ecef, semi_major = eRadius, semi_minor = pRadius, inverse=True)\n",
- "return_df['long_X_East'], return_df['lat_Y_North'], return_df['ht'] = lla[0][0], lla[1][0], lla[2][0]\n",
- "\n",
- "return_df['lat_Y_North'] = return_df.apply(lat_socet_coord, semi_major=eRadius, semi_minor=pRadius, axis = 1)\n",
- "return_df['long_X_East'] = return_df.apply(lon_socet_coord, semi_major=eRadius, semi_minor=pRadius, axis = 1)\n",
- "\n",
- "return_df['stat'] = return_df.apply(ignore_toggle, axis = 1)\n",
- "return_df['val'] = return_df['stat']\n",
- "\n",
- "# Add dumby\n",
- "x_dummy = lambda x: np.full(len(return_df), x)\n",
- "\n",
- "return_df['sig0'] = x_dummy(0)\n",
- "return_df['sig1'] = x_dummy(0)\n",
- "return_df['sig2'] = x_dummy(0)\n",
- "\n",
- "return_df['res0'] = x_dummy(0)\n",
- "return_df['res1'] = x_dummy(0)\n",
- "return_df['res2'] = x_dummy(0)\n",
- "\n",
- "return_df['fid_x'] = x_dummy(0)\n",
- "return_df['fid_y'] = x_dummy(0)\n",
+ "adjusted_flag = False\n",
"\n",
- "return_df['no_obs'] = x_dummy(1)\n",
- "return_df['fid_val'] = x_dummy(0)"
+ "isis2socet(cnet, eRadius, eccentricity, cub_path, extension, cub_list, out_gpf, adjusted_flag)"
]
},
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "return_df[['long_X_East', 'lat_Y_North', 'ht']].iloc[2]"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "return_df[['long_X_East', 'lat_Y_North', 'ht']].iloc[2]"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": []
- },
{
"cell_type": "code",
"execution_count": null,