{
"cells": [
{
"cell_type": "code",
"execution_count": 56,
"metadata": {},
"outputs": [],
"source": [
"import os\n",
"import sys\n",
"from functools import singledispatch\n",
"import warnings\n",
"\n",
"import pandas as pd\n",
"import numpy as np\n",
"import math\n",
"import pyproj\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": 85,
"metadata": {},
"outputs": [],
"source": [
"# Reads a .atf file and outputs all of the \n",
"# .ipf, .gpf, .sup, .prj, and path to locate the \n",
"# .apf file (should be the same as all others) \n",
"def read_atf(atf_file):\n",
" with open(atf_file) as f:\n",
" \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",
" 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",
" # Gets the base filepath\n",
" files['basepath'] = os.path.dirname(os.path.abspath(atf_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'] = files['.ipf']\n",
" \n",
" # Sets the value of IMAGE_SUP to all SUP images\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'] = 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",
" if i == 2:\n",
" img_index = line.split('\\\\')\n",
" img_index = img_index[-1].strip()\n",
" img_index = img_index.split('.')[0]\n",
" \n",
" if i == 3:\n",
" line_size = line.split(' ')\n",
" line_size = line_size[-1].strip()\n",
" assert int(line_size) > 0, \"Line number {} from {} is a negative number: Invalid Data\".format(line_size, record['ipf_file'])\n",
" \n",
" if i == 4:\n",
" sample_size = line.split(' ')\n",
" sample_size = sample_size[-1].strip()\n",
" assert int(sample_size) > 0, \"Sample number {} from {} is a negative number: Invalid Data\".format(sample_size, record['ipf_file'])\n",
" break\n",
" \n",
" \n",
" line_size = int(line_size)/2.0 + record['l.'] + 1\n",
" sample_size = int(sample_size)/2.0 + record['s.'] + 1\n",
" 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",
" \n",
" elif record['known'] == 1 or record['known'] == 2 or record['known'] == 3:\n",
" return 'Constrained'\n",
" \n",
"# converts +/- 180 system to 0 - 360 system\n",
"def to_360(num):\n",
" 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",
" dlat = math.atan(((dMajorRadius / dMinorRadius)**2) * (math.tan(dlat)))\n",
" dlat = math.degrees(dlat)\n",
" except:\n",
" print (\"Error in oc2og conversion\")\n",
" 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",
" dlat = math.atan((math.tan(dlat) / ((dMajorRadius / dMinorRadius)**2)))\n",
" dlat = math.degrees(dlat)\n",
" except:\n",
" print (\"Error in og2oc conversion\")\n",
" return dlat\n",
"\n",
"# gets eRadius and pRadius from a .prj file\n",
"def get_axis(file):\n",
" with open(file) as f:\n",
" from collections import defaultdict\n",
"\n",
" files = defaultdict(list)\n",
" \n",
" for line in f:\n",
" \n",
" ext = line.strip().split(' ')\n",
" files[ext[0]].append(ext[-1])\n",
" \n",
" eRadius = float(files['A_EARTH'][0])\n",
" pRadius = eRadius * (1 - float(files['E_EARTH'][0]))\n",
" \n",
" return eRadius, pRadius\n",
" \n",
"# function to convert lat_Y_North to ISIS_lat\n",
"def lat_ISIS_coord(record, semi_major, semi_minor):\n",
" ocentric_coord = og2oc(record['lat_Y_North'], semi_major, semi_minor)\n",
" coord_360 = to_360(ocentric_coord)\n",
" return coord_360\n",
"\n",
"# function to convert long_X_East to ISIS_lon\n",
"def lon_ISIS_coord(record, semi_major, semi_minor):\n",
" ocentric_coord = og2oc(record['long_X_East'], semi_major, semi_minor)\n",
" coord_360 = to_360(ocentric_coord)\n",
" return coord_360\n",
"\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",
" \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_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['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",
" \n",
" # Get the gpf and ipf files using atf dict\n",
" gpf_file = os.path.join(atf_dict['PATH'], atf_dict['GP_FILE']);\n",
" ipf_list = [os.path.join(atf_dict['PATH'], i) for i in atf_dict['IMAGE_IPF']]\n",
" \n",
" # Read in the gpf file and ipf file(s) into seperate dataframes\n",
" gpf_df = read_gpf(gpf_file)\n",
" ipf_df = read_ipf(ipf_list)\n",
"\n",
" # Check for differences between point ids using each dataframes\n",
" # point ids as a reference\n",
" gpf_pt_idx = pd.Index(pd.unique(gpf_df['point_id']))\n",
" ipf_pt_idx = pd.Index(pd.unique(ipf_df['pt_id']))\n",
"\n",
" point_diff = ipf_pt_idx.difference(gpf_pt_idx)\n",
"\n",
" if len(point_diff) != 0:\n",
" warnings.warn(\"The following points found in ipf files missing from gpf file: \\n\\n{}. \\\n",
" \\n\\nContinuing, but these points will be missing from the control network\".format(list(point_diff)))\n",
" \n",
" # Merge the two dataframes on their point id columns\n",
" socet_df = ipf_df.merge(gpf_df, left_on='pt_id', right_on='point_id')\n",
" \n",
" # Apply the transformations\n",
"# apply_two_isis_transformations(atf_dict, socet_df)\n",
" \n",
" # Define column remap for socet dataframe\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_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(images, path, extension):\n",
" serial_dict = dict()\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[image] = snum\n",
" return serial_dict"
]
},
{
"cell_type": "code",
"execution_count": 86,
"metadata": {
"scrolled": true
},
"outputs": [],
"source": [
"# Setup stuffs for the cub information namely the path and extension\n",
"path = '/home/acpaquette/repos/plio/test_cubes'\n",
"targetname = 'Mars'\n",
"# Extension of your cub files\n",
"extension = '.8bit.cub'\n",
"\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",
"\n",
"# serial_dict = serial_numbers(images, path, extension)\n",
"\n",
"# creates the control network\n",
"# 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": [
{
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" </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": [
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"<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']]"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
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