diff --git a/notebooks/Socet2ISIS.ipynb b/notebooks/Socet2ISIS.ipynb
index e4c81011976bee371c9e092fe6d7906fb01d1f13..bf0637ec03df83e13dcaa2eaa15e31b2eb6347ac 100644
--- a/notebooks/Socet2ISIS.ipynb
+++ b/notebooks/Socet2ISIS.ipynb
@@ -2,7 +2,7 @@
"cells": [
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
@@ -13,16 +13,20 @@
"\n",
"import pandas as pd\n",
"import numpy as np\n",
+ "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"
+ "from plio.io.io_bae import read_gpf, read_ipf\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": 2,
"metadata": {},
"outputs": [],
"source": [
@@ -71,62 +75,16 @@
" # 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",
" \n",
- " return files_dict"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "scrolled": false
- },
- "outputs": [],
- "source": [
- "atf_dict = read_atf(get_path('CTX_Athabasca_Middle_step0.atf'))\n",
- "\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",
- "gpf_df = read_gpf(gpf_file)\n",
- "ipf_df = read_ipf(ipf_list)\n",
- "\n",
- "point_diff = ipf_df.index.difference(gpf_df.index)\n",
- "\n",
- "\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",
- "new_df = ipf_df.merge(gpf_df, left_on='pt_id', right_on='point_id')"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "import math\n",
- "import pyproj\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",
+ " return files_dict\n",
"\n",
"# converts columns l. and s. to isis\n",
- "def line_sample_size(record):\n",
- " with open(atf_dict['PATH'] + '/' + record['ipf_file'] + '.sup') as f:\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",
- " img_index = image_dict[img_index]\n",
" \n",
" if i == 3:\n",
" line_size = line.split(' ')\n",
@@ -178,7 +136,7 @@
"\n",
"# gets eRadius and pRadius from a .prj file\n",
"def get_axis(file):\n",
- " with open(atf_dict['PATH'] + '/' + file) as f:\n",
+ " with open(file) as f:\n",
" from collections import defaultdict\n",
"\n",
" files = defaultdict(list)\n",
@@ -210,76 +168,104 @@
" 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",
"\n",
"# applys transformations to columns\n",
- "def socet2isis(prj_file):\n",
+ "def apply_transformations(atf_dict, df):\n",
+ " prj_file = os.path.join(atf_dict['PATH'], atf_dict['PROJECT'].split('\\\\')[-1])\n",
+ " \n",
" eRadius, pRadius = get_axis(prj_file)\n",
- " new_df['s.'], new_df['l.'], new_df['image_index'] = (zip(*new_df.apply(line_sample_size, axis=1)))\n",
- " new_df['known'] = new_df.apply(known, axis=1)\n",
- " new_df['lat_Y_North'] = new_df.apply(lat_ISIS_coord, semi_major = eRadius, semi_minor = pRadius, axis=1)\n",
- " new_df['long_X_East'] = new_df.apply(lon_ISIS_coord, semi_major = eRadius, semi_minor = pRadius, axis=1)\n",
- " new_df['long_X_East'], new_df['lat_Y_North'], new_df['ht'] = zip(*new_df.apply(body_fix, semi_major = eRadius, semi_minor = pRadius, axis = 1))\n",
- "socet2isis('CTX_Athabasca_Middle.prj')"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "new_df['image_index']"
+ " \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",
+ "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_transformations(atf_dict, socet_df)\n",
+ " \n",
+ " # Define column remap for socet dataframe\n",
+ " column_remap = {'l.': 'x', 's.': 'y',\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",
+ " \n",
+ " # Rename the columns using the column remap above\n",
+ " socet_df.rename(columns = column_remap, 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",
+ " serial_dict[image] = sn.generate_serial_number(os.path.join(path, image + extension))\n",
+ " return serial_dict"
]
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 3,
"metadata": {
- "scrolled": true
+ "scrolled": false
},
- "outputs": [],
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "/Users/adampaquette/anaconda/envs/pysat/lib/python3.6/site-packages/ipykernel_launcher.py:173: UserWarning: The following points found in ipf files missing from gpf file: \n",
+ "\n",
+ "['P03_002226_1895_XI_09N203W_15', 'P03_002226_1895_XI_09N203W_16', 'P03_002226_1895_XI_09N203W_17', 'P03_002226_1895_XI_09N203W_18', 'P03_002226_1895_XI_09N203W_19', 'P03_002226_1895_XI_09N203W_20', 'P03_002226_1895_XI_09N203W_21', 'P03_002226_1895_XI_09N203W_22', 'P03_002226_1895_XI_09N203W_24', 'P03_002226_1895_XI_09N203W_26', 'P03_002226_1895_XI_09N203W_30', 'P03_002226_1895_XI_09N203W_31', 'P03_002226_1895_XI_09N203W_32', 'P03_002226_1895_XI_09N203W_34', 'P03_002226_1895_XI_09N203W_36', 'P03_002226_1895_XI_09N203W_37', 'P03_002226_1895_XI_09N203W_44', 'P03_002226_1895_XI_09N203W_48', 'P03_002226_1895_XI_09N203W_49', 'P03_002226_1895_XI_09N203W_56', 'P03_002226_1895_XI_09N203W_57', 'P03_002226_1895_XI_09N203W_61', 'P03_002226_1895_XI_09N203W_62', 'P03_002226_1895_XI_09N203W_63', 'P03_002226_1895_XI_09N203W_65', 'P19_008344_1894_XN_09N203W_4', 'P20_008845_1894_XN_09N203W_15']. \n",
+ "\n",
+ "Continuing, but these points will be missing from the control network\n"
+ ]
+ }
+ ],
"source": [
- "column_remap = {'l.': 'x', 's.': 'y',\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",
+ "# Setup stuffs for the cub information namely the path and extension\n",
+ "path = '/Volumes/Blueman/'\n",
+ "extension = '.lev1.cub'\n",
"\n",
- "new_df.rename(columns=column_remap, inplace=True)\n",
- "\n",
- "new_df"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "import plio.io.io_controlnetwork as cn\n",
- "import plio.io.isis_serial_number as sn\n",
+ "prj_file = get_path('CTX_Athabasca_Middle_step0.atf')\n",
"\n",
- "# creates a dict of serial numbers with the cub being the key\n",
- "def serial_numbers():\n",
- " serial_dict = {}\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",
- " \n",
- " for key in image_dict:\n",
- " serial_dict[image_dict[key]] = sn.generate_serial_number('/home/tthatcher/Desktop/Projects/Plio/' + image_dict[key])\n",
- " return serial_dict\n",
+ "socet_df = socet2isis(prj_file)\n",
"\n",
- "# serial number dictionary\n",
- "serial_dict = serial_numbers()\n",
+ "images = pd.unique(socet_df['ipf_file'])\n",
"\n",
- "print(serial_dict)\n",
+ "serial_dict = serial_numbers(images, path, extension)\n",
"\n",
"# creates the control network\n",
- "cnet = cn.to_isis('/home/tthatcher/Desktop/Projects/Plio/cn.csv', new_df, serial_dict)"
+ "cnet = cn.to_isis('/Volumes/Blueman/cn.csv', socet_df, serial_dict)"
]
},
{
@@ -287,82 +273,7 @@
"execution_count": null,
"metadata": {},
"outputs": [],
- "source": [
- "@singledispatch\n",
- "def read_ipf(arg):\n",
- " return str(arg)\n",
- "# new_df['known'] = new_df.apply(known, axis=1)\n",
- "\n",
- "@read_ipf.register(str)\n",
- "def read_ipf_str(input_data):\n",
- " \"\"\"AttributeError: 'Series' object has no attribute 'image_index'\n",
- "\n",
- " Read a socet ipf file into a pandas data frame\n",
- "\n",
- " Parameters\n",
- " ----------\n",
- " input_data : str\n",
- " path to the an input data file\n",
- "\n",
- " Returns\n",
- " -------\n",
- " df : pd.DataFrame\n",
- " containing the ipf data with appropriate column names and indices\n",
- " \"\"\"\n",
- "\n",
- " # Check that the number of rows is matching the expected number\n",
- " with open(input_data, 'r') as f:\n",
- " for i, l in enumerate(f):\n",
- " if i == 1:/home/tthatcher/Desktop/Projects/Plio/plio\n",
- " cnt = int(l)\n",
- " elif i == 2:\n",
- " col = l\n",
- " break\n",
- " \n",
- " columns = np.genfromtxt(input_data, skip_header=2, dtype='unicode',\n",
- " max_rows = 1, delimiter = ',')\n",
- "\n",
- " # TODO: Add unicode conversion\n",
- " d = [line.split() for line in open(input_data, 'r')]\n",
- " d = np.hstack(np.array(d[3:]))\n",
- " \n",
- " d = d.reshape(-1, 12)\n",
- " \n",
- " df = pd.DataFrame(d, columns=columns)\n",
- " file = os.path.split(os.path.splitext(input_data)[0])[1]\n",
- " df['ipf_file'] = pd.Series(np.full((len(df['pt_id'])), file), index = df.index)\n",
- "\n",
- " assert int(cnt) == len(df), 'Dataframe length {} does not match point length {}.'.format(int(cnt), len(df))\n",
- " \n",
- " # Soft conversion of numeric types to numerics, allows str in first col for point_id\n",
- " df = df.apply(pd.to_numeric, errors='ignore')\n",
- "\n",
- " return df\n",
- "\n",
- "@read_ipf.register(list)\n",
- "def read_ipf_list(input_data_list):\n",
- " \"\"\"\n",
- " Read a socet ipf file into a pandas data frame\n",
- "\n",
- " Parameters\n",
- " ----------\n",
- " input_data_list : list\n",
- " list of paths to the a set of input data files\n",
- "\n",
- " Returns\n",
- " -------\n",
- " df : pd.DataFrame\n",
- " containing the ipf data with appropriate column names and indices\n",
- " \"\"\"\n",
- " frames = []\n",
- "\n",
- " for input_file in input_data_list:\n",
- " frames.append(read_ipf(input_file))\n",
- "\n",
- " df = pd.concat(frames)\n",
- "\n",
- " return df"
- ]
+ "source": []
}
],
"metadata": {
@@ -381,7 +292,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.6.4"
+ "version": "3.6.3"
}
},
"nbformat": 4,