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read_mascs_org_dgal.ipynb
{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"ExecuteTime": {
"end_time": "2018-04-26T09:51:57.619785Z",
"start_time": "2018-04-26T09:51:56.797362Z"
},
"scrolled": true
},
"outputs": [],
"source": [
"# %load https://gist.githubusercontent.com/kidpixo/2ec078d09834b5aa7869/raw/350f79fe4f2e09592404a76db18dcc01a961444b/ipython_inizialization.py\n",
"import matplotlib\n",
"from matplotlib import pyplot as plt\n",
"%matplotlib inline\n",
"import numpy as np\n",
"from __future__ import print_function\n",
"from tabulate import tabulate\n",
"import operator\n",
"import os"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Intro \n",
"\n",
"This notebook aims to read and decode the data from the Mercury Atmospheric and Surface Composition Spectrometer instrument (MASCS ) onboard the Mercury Surface, Space Environment, Geochemistry and Ranging (MESSENGER) mission that orbited Mercury from March 2011 to April 2015, after flybys of Earth, Venus, and Mercury.\n",
"\n",
"To avoit to use IDL, the only official reader provided, and to implement a new reader from scratch (done once, it was enough :-| ), I opted to use gdal/ogr [PDS - Planetary Data Systems TABLE](http://www.gdal.org/drv_pds.html) driver.\n",
"\n",
"__The BIG problem is__ : if a data IEEE_REAL data type is found and the number Bytes is different from 4, it is casted to a 4 Byte IEEE_REAL witouht any WARNING/ERROR to the user.\n",
"\n",
"This is true also for the GDAL v2.3.0beta1 on github.\n",
"\n",
"Code from [gdal/ogr pds layer](https://github.com/OSGeo/gdal/blob/a1df7cb9df2fe3cbcfac974b434b01ac6a1946e5/gdal/ogr/ogrsf_frmts/pds/ogrpdslayer.cpp#L246) :\n",
"\n",
"```c++\n",
"else if (osColumnDataType.compare(\"IEEE_REAL\") == 0)\n",
"{\n",
" if (pasFieldDesc[nFields].nItemBytes != 4) * if nItemBytes is different from 4....\n",
" pasFieldDesc[nFields].nItemBytes = 4; * ...then set it to 4 witouht any WARNING/ERROR\n",
" if (pasFieldDesc[nFields].nItems > 1)\n",
" eFieldType = OFTRealList;\n",
" else\n",
" eFieldType = OFTReal;\n",
" pasFieldDesc[nFields].eFormat = IEEE_REAL;\n",
"}\n",
"```\n",
"\n",
"### My solution\n",
"\n",
"\n",
"My solution is to modify the Label files this way:\n",
"\n",
"For each Object/ Data COLUMNS\n",
"\n",
"For arrays (Objects with ITEMS defined ):\n",
"\n",
" ITEMS = 5\n",
" ITEM_BYTES = 8\n",
"\n",
"to:\n",
"\n",
" ITEMS = 10\n",
" ITEM_BYTES = 4\n",
"\n",
"For NOT arrays (Objects without ITEMS):\n",
"\n",
" BYTES = 8\n",
"\n",
"to:\n",
"\n",
" ITEMS = 2\n",
" ITEM_BYTES = 4\n",
"\n",
"\n",
"This way we double the size of the data readed back, effectively reading all the data block, and we can convert them from float32 2*N element array to float64 N elements array.\n",
"\n",
"\n",
"This is done below in `convert_32b_to_64b`. Example:\n",
"\n",
"- coord : array of all the float32(2*N element arrays)\n",
"- coord_conv : array of all the float64(N element arrays)\n",
"\n",
" # 10 * np.float32 elements array\n",
" print('input size: %s' % coord[0].size,'\\n',coord[0])\n",
" # 5 * np.float64 elements array\n",
" print('output size: %s' % coord_conv[0].size,'\\n',coord_conv[0])\n",
"\n",
"\n",
"## ToDo\n",
"\n",
"My whish would be to read the LABEL file, check the content, change it dinamically and pass a `io.BytesIO` or a `io.StringIO` to `osgeo.ogr.Open`, but this is sadly not possible with the current acrhitecture of `osgeo.ogr`.\n",
"\n",
"The current 'hack' is to search recursivley for each LABEL file in a directory (i.e. a PDS3 dataset), extract the FMT file (where the data structure descption is actually) and create a symbolic link back to the `PDS3_ROOT/label/FMT_FILE`.\n",
"\n",
"This has the advantage to help also the label parser [pvl](http://pvl.readthedocs.io/en/latest/decoding.html), that expect the FMT to be in the same location as the LABEL file.\n",
"\n",
"### Example\n",
"\n",
"In this case: \n",
"\n",
"- `virsvd_orb_11187_050618.lbl` : describe the data file and needs virsvd.fmt\n",
"- `virsvd_orb_11187_050618.dat` : binary data file\n",
"- `data/.../virsvd.fmt` : symlink to `label/.../virsvd.fmt`\n",
"- `label/.../virsvd.fmt` : symlink to `label/.../virsvd_2X4Bytes.fmt`, trick to change the FMT file for the whole dataset on the fly with one command.\n",
"- `virsvd_8Bytes.fmt` : original FMT file with 8 Bytes data type\n",
"- `virsvd_2X4Bytes.fmt` : modified FMT file with only 2x4 Bytes data type\n",
"\n",
"```\n",
"$ tree -C label/ data/ddr/orb/virs/mascs20110706/vis/\n",
"\n",
"├── aareadme.txt\n",
"├── calib\n",
"├── catalog\n",
"├── document\n",
"├── errata.txt\n",
"├── extras\n",
"├── geometry\n",
"├── index\n",
"label/\n",
" ├── virsvd.fmt -> virsvd_2X4Bytes.fmt\n",
" ├── virsvd_2X4Bytes.fmt\n",
" ├── virsvd_8Bytes.fmt\n",
" ├── [...]\n",
"data/ddr/orb/virs/mascs20110706/vis/\n",
" ├── virsvd.fmt -> ../../../../../../label/virsvd.fmt\n",
" ├── virsvd_orb_11187_050618.dat\n",
" ├── virsvd_orb_11187_050618.lbl\n",
" ├── [...]\n",
"└── voldesc.cat\n",
"```\n",
"\n",
"## Read the file \n",
"\n",
"Data from [pds-geosciences.wustl.edu - /messenger/mess-e_v_h-mascs-3-virs-cdr-caldata-v1/messmas_2101/](http://pds-geosciences.wustl.edu/messenger/mess-e_v_h-mascs-3-virs-cdr-caldata-v1/messmas_2101/)\n",
"\n",
"Syntax (PDS == GDAL) :\n",
"\n",
"- File == Layer.\n",
"- Spectrum == Feature : 802 in this case.\n",
"- Spectrum Property == Field : could be a number of a vector.\n",
"\n",
"\n",
"File Properties are not retrievable via osgeo, using pvl (see here below) "
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"ExecuteTime": {
"end_time": "2018-04-26T09:51:57.719089Z",
"start_time": "2018-04-26T09:51:57.623720Z"
}
},
"outputs": [],
"source": [
"# load entire PDS3 label data : I cannot do it directly with OGR\n",
"# see [Parsing Label — pvl 0.2.0 documentation](http://pvl.readthedocs.io/en/latest/decoding.html)\n",
"import pvl"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {
"ExecuteTime": {
"end_time": "2018-04-26T15:24:54.531030Z",
"start_time": "2018-04-26T15:24:54.490797Z"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"============================ ======== ========================================================\n",
"name type val\n",
"============================ ======== ========================================================\n",
"STANDARD_DATA_PRODUCT_ID str VIRSDVIS\n",
"INSTRUMENT_NAME str MERCURY ATMOSPHERIC AND SURFACE COMPOSITION SPECTROMETER\n",
"RECORD_BYTES int 10458\n",
"PRODUCT_TYPE str DDR\n",
"START_TIME datetime 2011-07-06 05:06:19\n",
"SOFTWARE_NAME str PIPE-MASCS2DDR\n",
"PRODUCT_CREATION_TIME datetime 2017-02-18 03:42:45\n",
"SOFTWARE_VERSION_ID str 1.0\n",
"INSTRUMENT_HOST_NAME str MESSENGER\n",
"RECORD_TYPE str FIXED_LENGTH\n",
"PDS_VERSION_ID str PDS3\n",
"INSTRUMENT_ID str MASCS\n",
"DETECTOR_ID str VIRS\n",
"PRODUCT_VERSION_ID str V1\n",
"MISSION_PHASE_NAME str MERCURY ORBIT\n",
"SITE_ID str N/A\n",
"FILE_RECORDS int 802\n",
"SPACECRAFT_CLOCK_STOP_COUNT str 1/218417248.759\n",
"PRODUCT_ID str VIRSVD_ORB_11187_050618_DAT\n",
"SPACECRAFT_CLOCK_START_COUNT str 1/218416246.224\n",
"DATA_SET_ID str MESS-E/V/H-MASCS-4-VIRS-DDR-V2.0\n",
"STOP_TIME datetime 2011-07-06 05:23:01\n",
"TARGET_NAME str MERCURY\n",
"============================ ======== ========================================================\n"
]
}
],
"source": [
"file = \"/user/mars/damo_ma/mess_damo/mess-e_v_h-mascs-3-virs-cdr-caldata-v1/messmas_2101/data/ddr/orb/virs/mascs20110706/vis/virsvd_orb_11187_050618\"\n",
"# file = \"data/virsvd_orb_11187_050618\"\n",
"# file = '/user/mars/damo_ma/mess_damo/mess-e_v_h-mascs-3-virs-cdr-caldata-v1/messmas_2101/data/ddr/ob3/virs/mascs20130509/vis/virsvd_ob3_13129_003702'\n",
"lbl_file = file+\".lbl\"\n",
"label = pvl.load(lbl_file)\n",
"\n",
"outputkeys = [u'PDS_VERSION_ID',\n",
" u'RECORD_TYPE',\n",
" u'RECORD_BYTES',\n",
" u'FILE_RECORDS',\n",
" u'PRODUCT_ID',\n",
" u'PRODUCT_VERSION_ID',\n",
" u'PRODUCT_CREATION_TIME',\n",
" u'PRODUCT_TYPE',\n",
" u'SOFTWARE_NAME',\n",
" u'SOFTWARE_VERSION_ID',\n",
" u'INSTRUMENT_HOST_NAME',\n",
" u'INSTRUMENT_NAME',\n",
" u'INSTRUMENT_ID',\n",
" u'DETECTOR_ID',\n",
" u'DATA_SET_ID',\n",
" u'STANDARD_DATA_PRODUCT_ID',\n",
" u'MISSION_PHASE_NAME',\n",
" u'TARGET_NAME',\n",
" u'SITE_ID',\n",
" u'START_TIME',\n",
" u'STOP_TIME',\n",
" u'SPACECRAFT_CLOCK_START_COUNT',\n",
" u'SPACECRAFT_CLOCK_STOP_COUNT'\n",
"]\n",
"\n",
"outputdict = {i:label[i] for i in outputkeys}\n",
"\n",
"headers = [\"name\", \"type\", \"val\"]\n",
"print(tabulate([(k,type(v).__name__,str(v)) for k,v in outputdict.items()], headers=headers, tablefmt=\"rst\", numalign=\"right\", floatfmt=\".4f\"))"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {},
"outputs": [],
"source": [
"def pprint_color(obj):\n",
" from pygments import highlight\n",
" from pygments.lexers import PythonLexer\n",
" from pygments.formatters import Terminal256Formatter\n",
" from pprint import pformat\n",
" print(highlight(pformat(obj), PythonLexer(), Terminal256Formatter()))"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {
"ExecuteTime": {
"end_time": "2018-04-26T09:52:00.354708Z",
"start_time": "2018-04-26T09:52:00.068903Z"
},
"scrolled": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"FMT file : VIRSVD.FMT\n",
"8 Bytes data type in : \n",
"TARGET_LATITUDE_SET (array) :\n",
"Derived latitude of footprint center, uprange edge (c1), downrange edge (c2), port, (c3) and starboard (c4) edges (5 total values, coordinates as in Figure 4 of VIRS_CDR_DDR_DAP_SIS.PDF; center point, c1, c2, c3, c4) on planet target corresponding to spectral observation. Unit = degrees. PER SPECTRUM column.\n",
"========================================\n",
"TARGET_LONGITUDE_SET (array) :\n",
"Derived longitude of footprint center, uprange edge (c1), downrange edge (c2), port, (c3) and starboard (c4) edges (5 total values, coordinates as in Figure 4 of VIRS_CDR_DDR_DAP_SIS.PDF; center point, c1, c2, c3, c4) on planet target corresponding to spectral observation. Unit = degrees. PER SPECTRUM column.\n",
"========================================\n",
"ALONG_TRACK_FOOTPRINT_SIZE (number)\n",
"Derived length of VIRS footprint along track over the spectrum integration time, accounting for smear across surface. Unit = meters. PER SPECTRUM column.\n",
"========================================\n",
"ACROSS_TRACK_FOOTPRINT_SIZE (number)\n",
"Derived instantaneous width of VIRS footprint across track at the spectrum integration midpoint, accounting for jitter across surface. Unit = meters. PER SPECTRUM column.\n",
"========================================\n",
"INCIDENCE_ANGLE (number)\n",
"Derived solar incidence angle of footprint center. Unit = degrees. PER SPECTRUM column.\n",
"========================================\n",
"EMISSION_ANGLE (number)\n",
"Derived emission angle of footprint center. Unit = degrees. PER SPECTRUM column.\n",
"========================================\n",
"PHASE_ANGLE (number)\n",
"Derived phase angle of footprint center. Unit = degrees. PER SPECTRUM column.\n",
"========================================\n",
"SOLAR_DISTANCE (number)\n",
"Derived subsolar distance to footprint center on planet surface. Unit = kilometers. PER SPECTRUM column.\n",
"========================================\n"
]
}
],
"source": [
"# use BytesIO\n",
"import io\n",
"with open(lbl_file, 'rb') as f:\n",
" label_data = f.read()\n",
"lbl_file_io = io.BytesIO(label_data)\n",
"label = pvl.load(lbl_file_io)\n",
"\n",
"print('FMT file : %s' % label['TABLE']['^STRUCTURE'])\n",
"\n",
"with open('data/'+label['TABLE']['^STRUCTURE'],'r') as f:\n",
" fmt_text = f.read()\n",
"\n",
"#########\n",
"# Changes\n",
"#########\n",
"#\n",
"# For arrays (Objects with ITEMS):\n",
"# ITEMS = 5\n",
"# ITEM_BYTES = 8\n",
"# to:\n",
"# ITEMS = 10\n",
"# ITEM_BYTES = 4\n",
"#\n",
"####\n",
"#\n",
"# For NOT arrays (Objects without ITEMS):\n",
"# BYTES = 8\n",
"# to:\n",
"# ITEMS = 2\n",
"# ITEM_BYTES = 4\n",
"#\n",
"#########\n",
"\n",
"# fmt = pvl.load('data/'+label['TABLE']['^STRUCTURE'])\n",
"fmt = pvl.load('virsvd_coordinates_5x8Bytes.fmt')\n",
"# this is the list of Columns to de-duplictae\n",
"print('8 Bytes data type in : ')\n",
"for k,v in fmt.items():\n",
" if v.get('BYTES') == 8:\n",
" print(v['NAME'],'(number)')\n",
" print(v['DESCRIPTION'])\n",
" print('='*40)\n",
" if v.get('ITEM_BYTES') == 8:\n",
" print(v['NAME'],'(array) :')\n",
" print(v['DESCRIPTION'])\n",
" print('='*40)\n",
" \n",
"items_8bites = [v['NAME'] for k,v in fmt.items() if v.get('BYTES') == 8 or v.get('ITEM_BYTES') == 8]"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"{\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mCOLUMN\u001b[39m\u001b[38;5;124m'\u001b[39m: {\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mBYTES\u001b[39m\u001b[38;5;124m'\u001b[39m: \u001b[38;5;241m4\u001b[39m,\n",
" \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mCOLUMN_NUMBER\u001b[39m\u001b[38;5;124m'\u001b[39m: \u001b[38;5;241m1\u001b[39m,\n",
" \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mDATA_TYPE\u001b[39m\u001b[38;5;124m'\u001b[39m: \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mMSB_UNSIGNED_INTEGER\u001b[39m\u001b[38;5;124m'\u001b[39m,\n",
" \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mDESCRIPTION\u001b[39m\u001b[38;5;124m'\u001b[39m: \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mSpacecraft time in integer seconds that is \u001b[39m\u001b[38;5;124m'\u001b[39m\n",
" \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mtransmitted to MESSENGER subsystems by the \u001b[39m\u001b[38;5;124m'\u001b[39m\n",
" \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mIntegrated Electronics Module. This is assigned to \u001b[39m\u001b[38;5;124m'\u001b[39m\n",
" \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mthe first spectral observation contained in a \u001b[39m\u001b[38;5;124m'\u001b[39m\n",
" \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mscience packet. All spectra contained in the \u001b[39m\u001b[38;5;124m'\u001b[39m\n",
" \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mscience packet will be associated with this start \u001b[39m\u001b[38;5;124m'\u001b[39m\n",
" \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mtime. Unit is in Mission Elapsed Time which is the \u001b[39m\u001b[38;5;124m'\u001b[39m\n",
" \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mnumber of seconds since launch. PACKET column.\u001b[39m\u001b[38;5;124m'\u001b[39m,\n",
" \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mNAME\u001b[39m\u001b[38;5;124m'\u001b[39m: \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mSC_TIME\u001b[39m\u001b[38;5;124m'\u001b[39m,\n",
" \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mSTART_BYTE\u001b[39m\u001b[38;5;124m'\u001b[39m: \u001b[38;5;241m1\u001b[39m},\n",
" \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mCOLUMN\u001b[39m\u001b[38;5;124m'\u001b[39m: {\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mBYTES\u001b[39m\u001b[38;5;124m'\u001b[39m: \u001b[38;5;241m2\u001b[39m,\n",
" \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mCOLUMN_NUMBER\u001b[39m\u001b[38;5;124m'\u001b[39m: \u001b[38;5;241m2\u001b[39m,\n",
" \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mDATA_TYPE\u001b[39m\u001b[38;5;124m'\u001b[39m: \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mMSB_UNSIGNED_INTEGER\u001b[39m\u001b[38;5;124m'\u001b[39m,\n",
" \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mDESCRIPTION\u001b[39m\u001b[38;5;124m'\u001b[39m: \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mSubsecond time in milliseconds that the telemetry \u001b[39m\u001b[38;5;124m'\u001b[39m\n",
" \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mpacket was initiated. All spectra contained in the \u001b[39m\u001b[38;5;124m'\u001b[39m\n",
" \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mscience packet will be associated with this \u001b[39m\u001b[38;5;124m'\u001b[39m\n",
" \u001b[38;5;124m'\u001b[39m\u001b[38;5;124msubsecond start time. Unit is 5 milliseconds (i.e. \u001b[39m\u001b[38;5;124m'\u001b[39m\n",
" \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mall values will be to the nearest 5 milliseconds). \u001b[39m\u001b[38;5;124m'\u001b[39m\n",
" \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mPACKET column.\u001b[39m\u001b[38;5;124m'\u001b[39m,\n",
" \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mNAME\u001b[39m\u001b[38;5;124m'\u001b[39m: \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mPACKET_SUBSECONDS\u001b[39m\u001b[38;5;124m'\u001b[39m,\n",
" \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mSTART_BYTE\u001b[39m\u001b[38;5;124m'\u001b[39m: \u001b[38;5;241m5\u001b[39m},\n",
" \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mCOLUMN\u001b[39m\u001b[38;5;124m'\u001b[39m: {\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mBYTES\u001b[39m\u001b[38;5;124m'\u001b[39m: \u001b[38;5;241m2\u001b[39m,\n",
" \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mCOLUMN_NUMBER\u001b[39m\u001b[38;5;124m'\u001b[39m: \u001b[38;5;241m3\u001b[39m,\n",
" \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mDATA_TYPE\u001b[39m\u001b[38;5;124m'\u001b[39m: \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mMSB_UNSIGNED_INTEGER\u001b[39m\u001b[38;5;124m'\u001b[39m,\n",
" \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mDESCRIPTION\u001b[39m\u001b[38;5;124m'\u001b[39m: \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mAmount of time the array detectors will integrate \u001b[39m\u001b[38;5;124m'\u001b[39m\n",
" \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mphoton counts. Unit is 0.05 second. OBSERVATION \u001b[39m\u001b[38;5;124m'\u001b[39m\n",
" \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mcolumn.\u001b[39m\u001b[38;5;124m'\u001b[39m,\n",
" \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mNAME\u001b[39m\u001b[38;5;124m'\u001b[39m: \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mINT_TIME\u001b[39m\u001b[38;5;124m'\u001b[39m,\n",
" \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mSTART_BYTE\u001b[39m\u001b[38;5;124m'\u001b[39m: \u001b[38;5;241m7\u001b[39m},\n",
" \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mCOLUMN\u001b[39m\u001b[38;5;124m'\u001b[39m: {\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mBYTES\u001b[39m\u001b[38;5;124m'\u001b[39m: \u001b[38;5;241m2\u001b[39m,\n",
" \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mCOLUMN_NUMBER\u001b[39m\u001b[38;5;124m'\u001b[39m: \u001b[38;5;241m4\u001b[39m,\n",
" \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mDATA_TYPE\u001b[39m\u001b[38;5;124m'\u001b[39m: \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mMSB_UNSIGNED_INTEGER\u001b[39m\u001b[38;5;124m'\u001b[39m,\n",
" \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mDESCRIPTION\u001b[39m\u001b[38;5;124m'\u001b[39m: \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mTotal amount of integrations that will be taken. \u001b[39m\u001b[38;5;124m'\u001b[39m\n",
" \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mOBSERVATION column.\u001b[39m\u001b[38;5;124m'\u001b[39m,\n",
" \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mNAME\u001b[39m\u001b[38;5;124m'\u001b[39m: \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mINT_COUNT\u001b[39m\u001b[38;5;124m'\u001b[39m,\n",
" \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mSTART_BYTE\u001b[39m\u001b[38;5;124m'\u001b[39m: \u001b[38;5;241m9\u001b[39m},\n",
" \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mCOLUMN\u001b[39m\u001b[38;5;124m'\u001b[39m: {\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mBYTES\u001b[39m\u001b[38;5;124m'\u001b[39m: \u001b[38;5;241m2\u001b[39m,\n",
" \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mCOLUMN_NUMBER\u001b[39m\u001b[38;5;124m'\u001b[39m: \u001b[38;5;241m5\u001b[39m,\n",
" \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mDATA_TYPE\u001b[39m\u001b[38;5;124m'\u001b[39m: \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mMSB_UNSIGNED_INTEGER\u001b[39m\u001b[38;5;124m'\u001b[39m,\n",
" \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mDESCRIPTION\u001b[39m\u001b[38;5;124m'\u001b[39m: \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mDark counts will be collected every X integrations \u001b[39m\u001b[38;5;124m'\u001b[39m\n",
" \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mduring a VIRS observation. OBSERVATION column.\u001b[39m\u001b[38;5;124m'\u001b[39m,\n",
" \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mNAME\u001b[39m\u001b[38;5;124m'\u001b[39m: \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mDARK_FREQ\u001b[39m\u001b[38;5;124m'\u001b[39m,\n",
" \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mSTART_BYTE\u001b[39m\u001b[38;5;124m'\u001b[39m: \u001b[38;5;241m11\u001b[39m},\n",
" \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mCOLUMN\u001b[39m\u001b[38;5;124m'\u001b[39m: {\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mBYTES\u001b[39m\u001b[38;5;124m'\u001b[39m: \u001b[38;5;241m4\u001b[39m,\n",
" \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mCOLUMN_NUMBER\u001b[39m\u001b[38;5;124m'\u001b[39m: \u001b[38;5;241m6\u001b[39m,\n",
" \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mDATA_TYPE\u001b[39m\u001b[38;5;124m'\u001b[39m: \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mIEEE_REAL\u001b[39m\u001b[38;5;124m'\u001b[39m,\n",
" \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mDESCRIPTION\u001b[39m\u001b[38;5;124m'\u001b[39m: \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mOne of a redundant pair of array temperature \u001b[39m\u001b[38;5;124m'\u001b[39m\n",
" \u001b[38;5;124m'\u001b[39m\u001b[38;5;124msensors. A more complete description of this field \u001b[39m\u001b[38;5;124m'\u001b[39m\n",
" \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mcan be found in the MASCS User\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124ms Guide (Applicable \u001b[39m\u001b[38;5;124m\"\u001b[39m\n",
" \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mDocument 6 in VIRS_CDR_DDR_DAP_SIS.PDF). \u001b[39m\u001b[38;5;124m'\u001b[39m\n",
" \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mConversion from raw counts (DN) to degrees Celsius \u001b[39m\u001b[38;5;124m'\u001b[39m\n",
" \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mis: For the VIS detector it is: -3.34338E-18 * \u001b[39m\u001b[38;5;124m'\u001b[39m\n",
" \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mDN^5 + 8.979E-15 * DN^4 + 3.8131E-11 * DN^3 - \u001b[39m\u001b[38;5;124m'\u001b[39m\n",
" \u001b[38;5;124m'\u001b[39m\u001b[38;5;124m2.703E-8 * DN^2 - 0.0061 * DN - 1.67826 For the \u001b[39m\u001b[38;5;124m'\u001b[39m\n",
" \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mNIR detector it is: TEMP_2 = -8.89100589E-18 * \u001b[39m\u001b[38;5;124m'\u001b[39m\n",
" \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mDN^5 - 9.72043502E-15 * DN^4 + 1.73862825E-10 * \u001b[39m\u001b[38;5;124m'\u001b[39m\n",
" \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mDN^3 + 3.39958815E-7 * DN^2 - 0.00702301101 * DN - \u001b[39m\u001b[38;5;124m'\u001b[39m\n",
" \u001b[38;5;124m'\u001b[39m\u001b[38;5;124m22.2879012 For other temperature data (TEMP_1 and \u001b[39m\u001b[38;5;124m'\u001b[39m\n",
" \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mGRATING_TEMP) see CDR. PER SPECTRUM column.\u001b[39m\u001b[38;5;124m'\u001b[39m,\n",
" \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mNAME\u001b[39m\u001b[38;5;124m'\u001b[39m: \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mTEMP_2\u001b[39m\u001b[38;5;124m'\u001b[39m,\n",
" \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mSTART_BYTE\u001b[39m\u001b[38;5;124m'\u001b[39m: \u001b[38;5;241m13\u001b[39m},\n",
" \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mCOLUMN\u001b[39m\u001b[38;5;124m'\u001b[39m: {\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mBYTES\u001b[39m\u001b[38;5;124m'\u001b[39m: \u001b[38;5;241m2\u001b[39m,\n",
" \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mCOLUMN_NUMBER\u001b[39m\u001b[38;5;124m'\u001b[39m: \u001b[38;5;241m7\u001b[39m,\n",
" \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mDATA_TYPE\u001b[39m\u001b[38;5;124m'\u001b[39m: \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mMSB_UNSIGNED_INTEGER\u001b[39m\u001b[38;5;124m'\u001b[39m,\n",
" \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mDESCRIPTION\u001b[39m\u001b[38;5;124m'\u001b[39m: \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mNumber of pixels that were binned together in the \u001b[39m\u001b[38;5;124m'\u001b[39m\n",
" \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mdata. A flight-software update during the mission \u001b[39m\u001b[38;5;124m'\u001b[39m\n",
" \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mchanged how the pixels are were binned. For data \u001b[39m\u001b[38;5;124m'\u001b[39m\n",
" \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mwith SPECTRUM_UTC_TIME prior to March 16, 2009, \u001b[39m\u001b[38;5;124m'\u001b[39m\n",
" \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mthe pixels were co-added. For data with \u001b[39m\u001b[38;5;124m'\u001b[39m\n",
" \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mSPECTRUM_UTC_TIME on or after March 16, 2009, the \u001b[39m\u001b[38;5;124m'\u001b[39m\n",
" \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mmultiple pixels were averaged. OBSERVATION column.\u001b[39m\u001b[38;5;124m'\u001b[39m,\n",
" \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mNAME\u001b[39m\u001b[38;5;124m'\u001b[39m: \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mBINNING\u001b[39m\u001b[38;5;124m'\u001b[39m,\n",
" \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mSTART_BYTE\u001b[39m\u001b[38;5;124m'\u001b[39m: \u001b[38;5;241m17\u001b[39m},\n",
" \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mCOLUMN\u001b[39m\u001b[38;5;124m'\u001b[39m: {\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mBYTES\u001b[39m\u001b[38;5;124m'\u001b[39m: \u001b[38;5;241m2\u001b[39m,\n",
" \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mCOLUMN_NUMBER\u001b[39m\u001b[38;5;124m'\u001b[39m: \u001b[38;5;241m8\u001b[39m,\n",
" \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mDATA_TYPE\u001b[39m\u001b[38;5;124m'\u001b[39m: \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mMSB_UNSIGNED_INTEGER\u001b[39m\u001b[38;5;124m'\u001b[39m,\n",
" \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mDESCRIPTION\u001b[39m\u001b[38;5;124m'\u001b[39m: \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mStart pixel of data captured by the detector \u001b[39m\u001b[38;5;124m'\u001b[39m\n",
" \u001b[38;5;124m'\u001b[39m\u001b[38;5;124marray. For VIS the value can be from 0-511. For \u001b[39m\u001b[38;5;124m'\u001b[39m\n",
" \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mNIR the value can be from 0-255. OBSERVATION \u001b[39m\u001b[38;5;124m'\u001b[39m\n",
" \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mcolumn.\u001b[39m\u001b[38;5;124m'\u001b[39m,\n",
" \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mNAME\u001b[39m\u001b[38;5;124m'\u001b[39m: \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mSTART_PIXEL\u001b[39m\u001b[38;5;124m'\u001b[39m,\n",
" \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mSTART_BYTE\u001b[39m\u001b[38;5;124m'\u001b[39m: \u001b[38;5;241m19\u001b[39m},\n",
" \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mCOLUMN\u001b[39m\u001b[38;5;124m'\u001b[39m: {\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mBYTES\u001b[39m\u001b[38;5;124m'\u001b[39m: \u001b[38;5;241m2\u001b[39m,\n",
" \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mCOLUMN_NUMBER\u001b[39m\u001b[38;5;124m'\u001b[39m: \u001b[38;5;241m9\u001b[39m,\n",
" \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mDATA_TYPE\u001b[39m\u001b[38;5;124m'\u001b[39m: \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mMSB_UNSIGNED_INTEGER\u001b[39m\u001b[38;5;124m'\u001b[39m,\n",
" \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mDESCRIPTION\u001b[39m\u001b[38;5;124m'\u001b[39m: \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mEnd pixel of data captured by the detector array. \u001b[39m\u001b[38;5;124m'\u001b[39m\n",
" \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mFor VIS the value can be from 0-511. For NIR the \u001b[39m\u001b[38;5;124m'\u001b[39m\n",
" \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mvalue can be from 0-255. The END_PIXEL must be \u001b[39m\u001b[38;5;124m'\u001b[39m\n",
" \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mgreater than START_PIXEL. OBSERVATION column.\u001b[39m\u001b[38;5;124m'\u001b[39m,\n",
" \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mNAME\u001b[39m\u001b[38;5;124m'\u001b[39m: \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mEND_PIXEL\u001b[39m\u001b[38;5;124m'\u001b[39m,\n",
" \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mSTART_BYTE\u001b[39m\u001b[38;5;124m'\u001b[39m: \u001b[38;5;241m21\u001b[39m},\n",
" \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mCOLUMN\u001b[39m\u001b[38;5;124m'\u001b[39m: {\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mBYTES\u001b[39m\u001b[38;5;124m'\u001b[39m: \u001b[38;5;241m2\u001b[39m,\n",
" \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mCOLUMN_NUMBER\u001b[39m\u001b[38;5;124m'\u001b[39m: \u001b[38;5;241m10\u001b[39m,\n",
" \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mDATA_TYPE\u001b[39m\u001b[38;5;124m'\u001b[39m: \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mMSB_UNSIGNED_INTEGER\u001b[39m\u001b[38;5;124m'\u001b[39m,\n",
" \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mDESCRIPTION\u001b[39m\u001b[38;5;124m'\u001b[39m: \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mOrdered number sequence for spectra in a given \u001b[39m\u001b[38;5;124m'\u001b[39m\n",
" \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mobservation. First spectrum is 0, second is 1, \u001b[39m\u001b[38;5;124m'\u001b[39m\n",
" \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mthird is 2, and so on. Note that for observations \u001b[39m\u001b[38;5;124m'\u001b[39m\n",
" \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mwhich span multiple science packets, the numbering \u001b[39m\u001b[38;5;124m'\u001b[39m\n",
" \u001b[38;5;124m'\u001b[39m\u001b[38;5;124msequence is continuous. PER SPECTRUM column.\u001b[39m\u001b[38;5;124m'\u001b[39m,\n",
" \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mNAME\u001b[39m\u001b[38;5;124m'\u001b[39m: \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mSPECTRUM_NUMBER\u001b[39m\u001b[38;5;124m'\u001b[39m,\n",
" \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mSTART_BYTE\u001b[39m\u001b[38;5;124m'\u001b[39m: \u001b[38;5;241m23\u001b[39m},\n",
" \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mCOLUMN\u001b[39m\u001b[38;5;124m'\u001b[39m: {\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mBYTES\u001b[39m\u001b[38;5;124m'\u001b[39m: \u001b[38;5;241m4\u001b[39m,\n",
" \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mCOLUMN_NUMBER\u001b[39m\u001b[38;5;124m'\u001b[39m: \u001b[38;5;241m11\u001b[39m,\n",
" \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mDATA_TYPE\u001b[39m\u001b[38;5;124m'\u001b[39m: \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mMSB_UNSIGNED_INTEGER\u001b[39m\u001b[38;5;124m'\u001b[39m,\n",
" \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mDESCRIPTION\u001b[39m\u001b[38;5;124m'\u001b[39m: \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mA calculated value intended to reflect mission \u001b[39m\u001b[38;5;124m'\u001b[39m\n",
" \u001b[38;5;124m'\u001b[39m\u001b[38;5;124melapsed time (in seconds) since launch (for \u001b[39m\u001b[38;5;124m'\u001b[39m\n",
" \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mpartition 1) or since reset (for partition 2) at \u001b[39m\u001b[38;5;124m'\u001b[39m\n",
" \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mthe start of an individual VIRS spectrum. This is \u001b[39m\u001b[38;5;124m'\u001b[39m\n",
" \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mderived using the following formula: SPECTRUM_MET \u001b[39m\u001b[38;5;124m'\u001b[39m\n",
" \u001b[38;5;124m'\u001b[39m\u001b[38;5;124m= SC_TIME + floor(SPECTRUM_NUMBER*PERIOD*0.050) \u001b[39m\u001b[38;5;124m'\u001b[39m\n",
" \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mPER SPECTRUM column.\u001b[39m\u001b[38;5;124m'\u001b[39m,\n",
" \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mNAME\u001b[39m\u001b[38;5;124m'\u001b[39m: \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mSPECTRUM_MET\u001b[39m\u001b[38;5;124m'\u001b[39m,\n",
" \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mSTART_BYTE\u001b[39m\u001b[38;5;124m'\u001b[39m: \u001b[38;5;241m25\u001b[39m},\n",
" \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mCOLUMN\u001b[39m\u001b[38;5;124m'\u001b[39m: {\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mBYTES\u001b[39m\u001b[38;5;124m'\u001b[39m: \u001b[38;5;241m2\u001b[39m,\n",
" \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mCOLUMN_NUMBER\u001b[39m\u001b[38;5;124m'\u001b[39m: \u001b[38;5;241m12\u001b[39m,\n",
" \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mDATA_TYPE\u001b[39m\u001b[38;5;124m'\u001b[39m: \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mMSB_UNSIGNED_INTEGER\u001b[39m\u001b[38;5;124m'\u001b[39m,\n",
" \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mDESCRIPTION\u001b[39m\u001b[38;5;124m'\u001b[39m: \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mThe calculated subsecond time in milliseconds that \u001b[39m\u001b[38;5;124m'\u001b[39m\n",
" \u001b[38;5;124m'\u001b[39m\u001b[38;5;124ma VIRS integration was started. For each VIRS \u001b[39m\u001b[38;5;124m'\u001b[39m\n",
" \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mintegration, SPECTRUM_MET plus SPECTRUM_SUBSECONDS \u001b[39m\u001b[38;5;124m'\u001b[39m\n",
" \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mgives the spacecraft time of the start of that \u001b[39m\u001b[38;5;124m'\u001b[39m\n",
" \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mintegration. This is derived using the following \u001b[39m\u001b[38;5;124m'\u001b[39m\n",
" \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mformula: SPECTRUM_SUBSECONDS = (PACKET_SUBSECONDS \u001b[39m\u001b[38;5;124m'\u001b[39m\n",
" \u001b[38;5;124m'\u001b[39m\u001b[38;5;124m+ SPECTRUM_NUMBER * PERIOD * 50) mod 1000 PER \u001b[39m\u001b[38;5;124m'\u001b[39m\n",
" \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mSPECTRUM column.\u001b[39m\u001b[38;5;124m'\u001b[39m,\n",
" \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mNAME\u001b[39m\u001b[38;5;124m'\u001b[39m: \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mSPECTRUM_SUBSECONDS\u001b[39m\u001b[38;5;124m'\u001b[39m,\n",
" \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mSTART_BYTE\u001b[39m\u001b[38;5;124m'\u001b[39m: \u001b[38;5;241m29\u001b[39m},\n",
" \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mCOLUMN\u001b[39m\u001b[38;5;124m'\u001b[39m: {\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mBYTES\u001b[39m\u001b[38;5;124m'\u001b[39m: \u001b[38;5;241m17\u001b[39m,\n",
" \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mCOLUMN_NUMBER\u001b[39m\u001b[38;5;124m'\u001b[39m: \u001b[38;5;241m13\u001b[39m,\n",
" \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mDATA_TYPE\u001b[39m\u001b[38;5;124m'\u001b[39m: \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mCHARACTER\u001b[39m\u001b[38;5;124m'\u001b[39m,\n",
" \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mDESCRIPTION\u001b[39m\u001b[38;5;124m'\u001b[39m: \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mUTC time in YYDOYTHH:MM:SS.00 format translated \u001b[39m\u001b[38;5;124m'\u001b[39m\n",
" \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mfrom spacecraft time and packet subseconds (in \u001b[39m\u001b[38;5;124m'\u001b[39m\n",
" \u001b[38;5;124m'\u001b[39m\u001b[38;5;124minteger seconds) that is transmitted to MESSENGER \u001b[39m\u001b[38;5;124m'\u001b[39m\n",
" \u001b[38;5;124m'\u001b[39m\u001b[38;5;124msubsystems by the Integrated Electronics Module. \u001b[39m\u001b[38;5;124m'\u001b[39m\n",
" \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mPER SPECTRUM column.\u001b[39m\u001b[38;5;124m'\u001b[39m,\n",
" \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mNAME\u001b[39m\u001b[38;5;124m'\u001b[39m: \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mSPECTRUM_UTC_TIME\u001b[39m\u001b[38;5;124m'\u001b[39m,\n",
" \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mSTART_BYTE\u001b[39m\u001b[38;5;124m'\u001b[39m: \u001b[38;5;241m31\u001b[39m},\n",
" \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mCOLUMN\u001b[39m\u001b[38;5;124m'\u001b[39m: {\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mBYTES\u001b[39m\u001b[38;5;124m'\u001b[39m: \u001b[38;5;241m2048\u001b[39m,\n",
" \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mCOLUMN_NUMBER\u001b[39m\u001b[38;5;124m'\u001b[39m: \u001b[38;5;241m14\u001b[39m,\n",
" \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mDATA_TYPE\u001b[39m\u001b[38;5;124m'\u001b[39m: \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mIEEE_REAL\u001b[39m\u001b[38;5;124m'\u001b[39m,\n",
" \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mDESCRIPTION\u001b[39m\u001b[38;5;124m'\u001b[39m: \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mDerived column of reflectance-at-sensor spectra. \u001b[39m\u001b[38;5;124m'\u001b[39m\n",
" \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mOne row per spectrum. NIR spectrum has up to 256 \u001b[39m\u001b[38;5;124m'\u001b[39m\n",
" \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mvalues (depending on binning and windowing), VIS \u001b[39m\u001b[38;5;124m'\u001b[39m\n",
" \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mhas up to 512. Reflectance is a unitless \u001b[39m\u001b[38;5;124m'\u001b[39m\n",
" \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mparameter. Reflectance from saturated pixels, or \u001b[39m\u001b[38;5;124m'\u001b[39m\n",
" \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mbinned pixels with one saturated element, are set \u001b[39m\u001b[38;5;124m'\u001b[39m\n",
" \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mto 1e32. PER SPECTRUM column.\u001b[39m\u001b[38;5;124m'\u001b[39m,\n",
" \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mINVALID_CONSTANT\u001b[39m\u001b[38;5;124m'\u001b[39m: \u001b[38;5;241m1e+32\u001b[39m,\n",
" \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mITEMS\u001b[39m\u001b[38;5;124m'\u001b[39m: \u001b[38;5;241m512\u001b[39m,\n",
" \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mITEM_BYTES\u001b[39m\u001b[38;5;124m'\u001b[39m: \u001b[38;5;241m4\u001b[39m,\n",
" \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mNAME\u001b[39m\u001b[38;5;124m'\u001b[39m: \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mIOF_SPECTRUM_DATA\u001b[39m\u001b[38;5;124m'\u001b[39m,\n",
" \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mSTART_BYTE\u001b[39m\u001b[38;5;124m'\u001b[39m: \u001b[38;5;241m48\u001b[39m},\n",
" \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mCOLUMN\u001b[39m\u001b[38;5;124m'\u001b[39m: {\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mBYTES\u001b[39m\u001b[38;5;124m'\u001b[39m: \u001b[38;5;241m2048\u001b[39m,\n",
" \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mCOLUMN_NUMBER\u001b[39m\u001b[38;5;124m'\u001b[39m: \u001b[38;5;241m15\u001b[39m,\n",
" \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mDATA_TYPE\u001b[39m\u001b[38;5;124m'\u001b[39m: \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mIEEE_REAL\u001b[39m\u001b[38;5;124m'\u001b[39m,\n",
" \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mDESCRIPTION\u001b[39m\u001b[38;5;124m'\u001b[39m: \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mDerived column of photometrically normalized \u001b[39m\u001b[38;5;124m'\u001b[39m\n",
" \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mreflectance-at-sensor spectra. One row per \u001b[39m\u001b[38;5;124m'\u001b[39m\n",
" \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mspectrum. NIR spectrum has up to 256 values \u001b[39m\u001b[38;5;124m'\u001b[39m\n",
" \u001b[38;5;124m'\u001b[39m\u001b[38;5;124m(depending on binning and windowing), VIS has up \u001b[39m\u001b[38;5;124m'\u001b[39m\n",
" \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mto 512. Reflectance is a unitless parameter. \u001b[39m\u001b[38;5;124m'\u001b[39m\n",
" \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mReflectance from saturated pixels, or binned \u001b[39m\u001b[38;5;124m'\u001b[39m\n",
" \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mpixels with one saturated element, are set to \u001b[39m\u001b[38;5;124m'\u001b[39m\n",
" \u001b[38;5;124m'\u001b[39m\u001b[38;5;124m1e32. PER SPECTRUM column.\u001b[39m\u001b[38;5;124m'\u001b[39m,\n",
" \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mINVALID_CONSTANT\u001b[39m\u001b[38;5;124m'\u001b[39m: \u001b[38;5;241m1e+32\u001b[39m,\n",
" \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mITEMS\u001b[39m\u001b[38;5;124m'\u001b[39m: \u001b[38;5;241m512\u001b[39m,\n",
" \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mITEM_BYTES\u001b[39m\u001b[38;5;124m'\u001b[39m: \u001b[38;5;241m4\u001b[39m,\n",
" \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mNAME\u001b[39m\u001b[38;5;124m'\u001b[39m: \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mPHOTOM_IOF_SPECTRUM_DATA\u001b[39m\u001b[38;5;124m'\u001b[39m,\n",
" \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mSTART_BYTE\u001b[39m\u001b[38;5;124m'\u001b[39m: \u001b[38;5;241m2096\u001b[39m},\n",
" \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mCOLUMN\u001b[39m\u001b[38;5;124m'\u001b[39m: {\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mBYTES\u001b[39m\u001b[38;5;124m'\u001b[39m: \u001b[38;5;241m2048\u001b[39m,\n",
" \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mCOLUMN_NUMBER\u001b[39m\u001b[38;5;124m'\u001b[39m: \u001b[38;5;241m16\u001b[39m,\n",
" \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mDATA_TYPE\u001b[39m\u001b[38;5;124m'\u001b[39m: \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mIEEE_REAL\u001b[39m\u001b[38;5;124m'\u001b[39m,\n",
" \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mDESCRIPTION\u001b[39m\u001b[38;5;124m'\u001b[39m: \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mDerived column of noise/error in reflectance \u001b[39m\u001b[38;5;124m'\u001b[39m\n",
" \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mpropagated through post-calibration procedure. One \u001b[39m\u001b[38;5;124m'\u001b[39m\n",
" \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mrow per spectrum. NIR spectrum has up to 256 \u001b[39m\u001b[38;5;124m'\u001b[39m\n",
" \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mvalues (depending on binning and windowing), VIS \u001b[39m\u001b[38;5;124m'\u001b[39m\n",
" \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mhas up to 512. Unitless. Noise from saturated \u001b[39m\u001b[38;5;124m'\u001b[39m\n",
" \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mpixels, or binned pixels with one saturated \u001b[39m\u001b[38;5;124m'\u001b[39m\n",
" \u001b[38;5;124m'\u001b[39m\u001b[38;5;124melement, are set to 1e32. PER SPECTRUM column.\u001b[39m\u001b[38;5;124m'\u001b[39m,\n",
" \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mINVALID_CONSTANT\u001b[39m\u001b[38;5;124m'\u001b[39m: \u001b[38;5;241m1e+32\u001b[39m,\n",
" \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mITEMS\u001b[39m\u001b[38;5;124m'\u001b[39m: \u001b[38;5;241m512\u001b[39m,\n",
" \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mITEM_BYTES\u001b[39m\u001b[38;5;124m'\u001b[39m: \u001b[38;5;241m4\u001b[39m,\n",
" \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mNAME\u001b[39m\u001b[38;5;124m'\u001b[39m: \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mIOF_NOISE_SPECTRUM_DATA\u001b[39m\u001b[38;5;124m'\u001b[39m,\n",
" \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mSTART_BYTE\u001b[39m\u001b[38;5;124m'\u001b[39m: \u001b[38;5;241m4144\u001b[39m},\n",
" \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mCOLUMN\u001b[39m\u001b[38;5;124m'\u001b[39m: {\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mBYTES\u001b[39m\u001b[38;5;124m'\u001b[39m: \u001b[38;5;241m2048\u001b[39m,\n",
" \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mCOLUMN_NUMBER\u001b[39m\u001b[38;5;124m'\u001b[39m: \u001b[38;5;241m17\u001b[39m,\n",
" \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mDATA_TYPE\u001b[39m\u001b[38;5;124m'\u001b[39m: \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mIEEE_REAL\u001b[39m\u001b[38;5;124m'\u001b[39m,\n",
" \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mDESCRIPTION\u001b[39m\u001b[38;5;124m'\u001b[39m: \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mDerived column of noise/error in photometrically \u001b[39m\u001b[38;5;124m'\u001b[39m\n",
" \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mnormalized reflectance propagated through \u001b[39m\u001b[38;5;124m'\u001b[39m\n",
" \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mpost-calibration procedure. One row per spectrum. \u001b[39m\u001b[38;5;124m'\u001b[39m\n",
" \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mNIR spectrum has up to 256 values (depending on \u001b[39m\u001b[38;5;124m'\u001b[39m\n",
" \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mbinning and windowing), VIS has up to 512. \u001b[39m\u001b[38;5;124m'\u001b[39m\n",
" \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mUnitless. Noise from saturated pixels, or binned \u001b[39m\u001b[38;5;124m'\u001b[39m\n",
" \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mpixels with one saturated element, are set to \u001b[39m\u001b[38;5;124m'\u001b[39m\n",
" \u001b[38;5;124m'\u001b[39m\u001b[38;5;124m1e32. PER SPECTRUM column.\u001b[39m\u001b[38;5;124m'\u001b[39m,\n",
" \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mINVALID_CONSTANT\u001b[39m\u001b[38;5;124m'\u001b[39m: \u001b[38;5;241m1e+32\u001b[39m,\n",
" \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mITEMS\u001b[39m\u001b[38;5;124m'\u001b[39m: \u001b[38;5;241m512\u001b[39m,\n",
" \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mITEM_BYTES\u001b[39m\u001b[38;5;124m'\u001b[39m: \u001b[38;5;241m4\u001b[39m,\n",
" \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mNAME\u001b[39m\u001b[38;5;124m'\u001b[39m: \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mPHOTOM_IOF_NOISE_SPECTRUM_DATA\u001b[39m\u001b[38;5;124m'\u001b[39m,\n",
" \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mSTART_BYTE\u001b[39m\u001b[38;5;124m'\u001b[39m: \u001b[38;5;241m6192\u001b[39m},\n",
" \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mCOLUMN\u001b[39m\u001b[38;5;124m'\u001b[39m: {\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mBYTES\u001b[39m\u001b[38;5;124m'\u001b[39m: \u001b[38;5;241m4\u001b[39m,\n",
" \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mCOLUMN_NUMBER\u001b[39m\u001b[38;5;124m'\u001b[39m: \u001b[38;5;241m18\u001b[39m,\n",
" \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mDATA_TYPE\u001b[39m\u001b[38;5;124m'\u001b[39m: \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mIEEE_REAL\u001b[39m\u001b[38;5;124m'\u001b[39m,\n",
" \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mDESCRIPTION\u001b[39m\u001b[38;5;124m'\u001b[39m: \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mIndicates version of calibration software used. \u001b[39m\u001b[38;5;124m'\u001b[39m\n",
" \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mIncrements with reprocessing of DDRs. OBSERVATION \u001b[39m\u001b[38;5;124m'\u001b[39m\n",
" \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mcolumn.\u001b[39m\u001b[38;5;124m'\u001b[39m,\n",
" \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mNAME\u001b[39m\u001b[38;5;124m'\u001b[39m: \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mSOFTWARE_VERSION\u001b[39m\u001b[38;5;124m'\u001b[39m,\n",
" \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mSTART_BYTE\u001b[39m\u001b[38;5;124m'\u001b[39m: \u001b[38;5;241m8240\u001b[39m},\n",
" \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mCOLUMN\u001b[39m\u001b[38;5;124m'\u001b[39m: {\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mBYTES\u001b[39m\u001b[38;5;124m'\u001b[39m: \u001b[38;5;241m2048\u001b[39m,\n",
" \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mCOLUMN_NUMBER\u001b[39m\u001b[38;5;124m'\u001b[39m: \u001b[38;5;241m19\u001b[39m,\n",
" \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mDATA_TYPE\u001b[39m\u001b[38;5;124m'\u001b[39m: \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mIEEE_REAL\u001b[39m\u001b[38;5;124m'\u001b[39m,\n",
" \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mDESCRIPTION\u001b[39m\u001b[38;5;124m'\u001b[39m: \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mColumn of wavelengths paired to spectrum data \u001b[39m\u001b[38;5;124m'\u001b[39m\n",
" \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mchannels. Wavelengths derived from calibration \u001b[39m\u001b[38;5;124m'\u001b[39m\n",
" \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mreport equation 6.5. See VIRS CDRDDR SIS. Unit = \u001b[39m\u001b[38;5;124m'\u001b[39m\n",
" \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mnanometers. OBSERVATION column.\u001b[39m\u001b[38;5;124m'\u001b[39m,\n",
" \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mITEMS\u001b[39m\u001b[38;5;124m'\u001b[39m: \u001b[38;5;241m512\u001b[39m,\n",
" \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mITEM_BYTES\u001b[39m\u001b[38;5;124m'\u001b[39m: \u001b[38;5;241m4\u001b[39m,\n",
" \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mNAME\u001b[39m\u001b[38;5;124m'\u001b[39m: \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mCHANNEL_WAVELENGTHS\u001b[39m\u001b[38;5;124m'\u001b[39m,\n",
" \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mSTART_BYTE\u001b[39m\u001b[38;5;124m'\u001b[39m: \u001b[38;5;241m8244\u001b[39m},\n",
" \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mCOLUMN\u001b[39m\u001b[38;5;124m'\u001b[39m: {\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mBYTES\u001b[39m\u001b[38;5;124m'\u001b[39m: \u001b[38;5;241m19\u001b[39m,\n",
" \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mCOLUMN_NUMBER\u001b[39m\u001b[38;5;124m'\u001b[39m: \u001b[38;5;241m20\u001b[39m,\n",
" \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mDATA_TYPE\u001b[39m\u001b[38;5;124m'\u001b[39m: \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mCHARACTER\u001b[39m\u001b[38;5;124m'\u001b[39m,\n",
" \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mDESCRIPTION\u001b[39m\u001b[38;5;124m'\u001b[39m: \u001b[38;5;124m'\u001b[39m\u001b[38;5;124m19 character index of data quality. Each digit \u001b[39m\u001b[38;5;124m'\u001b[39m\n",
" \u001b[38;5;124m'\u001b[39m\u001b[38;5;124msignifies quality factor of measurements. PER \u001b[39m\u001b[38;5;124m'\u001b[39m\n",
" \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mSPECTRUM column. Format: ABCD-EFGH-IJKL-MNOP A: \u001b[39m\u001b[38;5;124m'\u001b[39m\n",
" \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mDark Scan Flag, denotes shutter commanded closed \u001b[39m\u001b[38;5;124m'\u001b[39m\n",
" \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mfor dark observation. 0 = shutter not engaged 1 = \u001b[39m\u001b[38;5;124m'\u001b[39m\n",
" \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mshutter engaged B: Temperature 1 Flag 0 = \u001b[39m\u001b[38;5;124m'\u001b[39m\n",
" \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mTemperature does not exceed 15 deg C threshold. 1 \u001b[39m\u001b[38;5;124m'\u001b[39m\n",
" \u001b[38;5;124m'\u001b[39m\u001b[38;5;124m= Temperature exceeds 15 deg C threshold but less \u001b[39m\u001b[38;5;124m'\u001b[39m\n",
" \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mthan 25 deg C threshold. 2 = Temperature exceeds \u001b[39m\u001b[38;5;124m'\u001b[39m\n",
" \u001b[38;5;124m'\u001b[39m\u001b[38;5;124m25 deg C threshold but less than 40 deg C \u001b[39m\u001b[38;5;124m'\u001b[39m\n",
" \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mthreshold. 3 = Temperature exceeds 40 deg C \u001b[39m\u001b[38;5;124m'\u001b[39m\n",
" \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mthreshold. C: Temperature 2 Flag 0 = Temperature \u001b[39m\u001b[38;5;124m'\u001b[39m\n",
" \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mdoes not exceed 15 deg C threshold. 1 = \u001b[39m\u001b[38;5;124m'\u001b[39m\n",
" \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mTemperature exceeds 15 deg C threshold but less \u001b[39m\u001b[38;5;124m'\u001b[39m\n",
" \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mthan 25 deg C threshold. 2 = Temperature exceeds \u001b[39m\u001b[38;5;124m'\u001b[39m\n",
" \u001b[38;5;124m'\u001b[39m\u001b[38;5;124m25 deg C threshold but less than 40 deg C \u001b[39m\u001b[38;5;124m'\u001b[39m\n",
" \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mthreshold. 3 = Temperature exceeds 40 deg C \u001b[39m\u001b[38;5;124m'\u001b[39m\n",
" \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mthreshold. D: Grating Temperature Flag 0 = \u001b[39m\u001b[38;5;124m'\u001b[39m\n",
" \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mTemperature does not exceed 15 deg C threshold. 1 \u001b[39m\u001b[38;5;124m'\u001b[39m\n",
" \u001b[38;5;124m'\u001b[39m\u001b[38;5;124m= Temperature exceeds 15 deg C threshold but less \u001b[39m\u001b[38;5;124m'\u001b[39m\n",
" \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mthan 25 deg C threshold. 2 = Temperature exceeds \u001b[39m\u001b[38;5;124m'\u001b[39m\n",
" \u001b[38;5;124m'\u001b[39m\u001b[38;5;124m25 deg C threshold but less than 40 deg C \u001b[39m\u001b[38;5;124m'\u001b[39m\n",
" \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mthreshold. 3 = Temperature exceeds 40 deg C \u001b[39m\u001b[38;5;124m'\u001b[39m\n",
" \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mthreshold. E: Anomalous Pixels Integer 0-9 = \u001b[39m\u001b[38;5;124m'\u001b[39m\n",
" \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mIndicates number of hot pixels found. Working \u001b[39m\u001b[38;5;124m'\u001b[39m\n",
" \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mDefinition: The number of pixels with a noise \u001b[39m\u001b[38;5;124m'\u001b[39m\n",
" \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mspike (defined in DQI L, below). Procedure: The \u001b[39m\u001b[38;5;124m'\u001b[39m\n",
" \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mnumber of noise spike channels for spectra i. \u001b[39m\u001b[38;5;124m'\u001b[39m\n",
" \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mRepeat for all N spectra. F: Partial Data Flag 0 = \u001b[39m\u001b[38;5;124m'\u001b[39m\n",
" \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mNo partial data. 1 = Partial data exists. Working \u001b[39m\u001b[38;5;124m'\u001b[39m\n",
" \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mDefinition: If any spectrum has nulls in the \u001b[39m\u001b[38;5;124m'\u001b[39m\n",
" \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mbeginning or end, signifying data drop outs. \u001b[39m\u001b[38;5;124m'\u001b[39m\n",
" \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mProcedure: Given spectra i, evaluate first 10 \u001b[39m\u001b[38;5;124m'\u001b[39m\n",
" \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mchannels and last 10 channels. If any channel has \u001b[39m\u001b[38;5;124m'\u001b[39m\n",
" \u001b[38;5;124m'\u001b[39m\u001b[38;5;124m0 value, set Partial Data Flag = 1. G: Saturation \u001b[39m\u001b[38;5;124m'\u001b[39m\n",
" \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mFlag 0 = No pixels saturated. 1 = Saturated pixels \u001b[39m\u001b[38;5;124m'\u001b[39m\n",
" \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mexist. H: Low Signal Level Flag 0 = Signal level \u001b[39m\u001b[38;5;124m'\u001b[39m\n",
" \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mnot below -32768 threshold. 1 = Signal level below \u001b[39m\u001b[38;5;124m'\u001b[39m\n",
" \u001b[38;5;124m'\u001b[39m\u001b[38;5;124m-32768 threshold. I: Low VIS Wavelength \u001b[39m\u001b[38;5;124m'\u001b[39m\n",
" \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mUncertainty Flag (not yet implemented, set to 0) 0 \u001b[39m\u001b[38;5;124m'\u001b[39m\n",
" \u001b[38;5;124m'\u001b[39m\u001b[38;5;124m= Uncertainty not above TBD threshold at low \u001b[39m\u001b[38;5;124m'\u001b[39m\n",
" \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mwavelengths. 1 = Uncertainty above TBD threshold \u001b[39m\u001b[38;5;124m'\u001b[39m\n",
" \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mat low wavelengths. J: High VIS Wavelength \u001b[39m\u001b[38;5;124m'\u001b[39m\n",
" \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mUncertainty Flag (not yet implemented, set to 0) 0 \u001b[39m\u001b[38;5;124m'\u001b[39m\n",
" \u001b[38;5;124m'\u001b[39m\u001b[38;5;124m= Uncertainty not above TBD threshold at high \u001b[39m\u001b[38;5;124m'\u001b[39m\n",
" \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mwavelengths. 1 = Uncertainty above TBD threshold \u001b[39m\u001b[38;5;124m'\u001b[39m\n",
" \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mat high wavelengths. K: UVVS Operating Flag 0 = \u001b[39m\u001b[38;5;124m'\u001b[39m\n",
" \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mUVVS is not scanning during readout. 1 = UVVS is \u001b[39m\u001b[38;5;124m'\u001b[39m\n",
" \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mscanning during readout. L: UVVS Noise Spike Flag \u001b[39m\u001b[38;5;124m'\u001b[39m\n",
" \u001b[38;5;124m'\u001b[39m\u001b[38;5;124m0 = No noise spike detected. 1 = Noise spike \u001b[39m\u001b[38;5;124m'\u001b[39m\n",
" \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mdetected. Using VIRS VIS (512 channel spectrum, N \u001b[39m\u001b[38;5;124m'\u001b[39m\n",
" \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mspectra per CDR) as an example, Working \u001b[39m\u001b[38;5;124m'\u001b[39m\n",
" \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mDefinition: A noise spike is a data value in 1 or \u001b[39m\u001b[38;5;124m'\u001b[39m\n",
" \u001b[38;5;124m'\u001b[39m\u001b[38;5;124m2 channels that exceeds 3 standard deviations of \u001b[39m\u001b[38;5;124m'\u001b[39m\n",
" \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mthe average for a given channel in a given \u001b[39m\u001b[38;5;124m'\u001b[39m\n",
" \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mobservation. Procedure: Calculate the standard \u001b[39m\u001b[38;5;124m'\u001b[39m\n",
" \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mdeviation for channel 1 through all 1-N spectra. \u001b[39m\u001b[38;5;124m'\u001b[39m\n",
" \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mIf spectra i has stdev >=3, then spectra i has a \u001b[39m\u001b[38;5;124m'\u001b[39m\n",
" \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mnoise spike. Repeat calculation for channels \u001b[39m\u001b[38;5;124m'\u001b[39m\n",
" \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m2-512. Keep track of the number of \u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mnoise spike \u001b[39m\u001b[38;5;124m\"\u001b[39m\n",
" \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mchannels\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m per spectra. M: SPICE Version Epoch. \u001b[39m\u001b[38;5;124m\"\u001b[39m\n",
" \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mIndicates what SPICE is used to determine pointing \u001b[39m\u001b[38;5;124m'\u001b[39m\n",
" \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mfields in CDR. \u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mPredict\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m SPICE may change one or \u001b[39m\u001b[38;5;124m\"\u001b[39m\n",
" \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mmore times before settling on \u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mFinal\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m pointing \u001b[39m\u001b[38;5;124m\"\u001b[39m\n",
" \u001b[38;5;124m'\u001b[39m\u001b[38;5;124msolutions about 2 weeks from data acquisition. 0 = \u001b[39m\u001b[38;5;124m'\u001b[39m\n",
" \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mNo SPICE 1 = Predict 2 = Actual N: Dark Saturation \u001b[39m\u001b[38;5;124m'\u001b[39m\n",
" \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mFlag Denotes that there are minimal unsaturated \u001b[39m\u001b[38;5;124m'\u001b[39m\n",
" \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mdark frames available for the polynomial fit of \u001b[39m\u001b[38;5;124m'\u001b[39m\n",
" \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mone or more pixels. 0 = All pixels in data record \u001b[39m\u001b[38;5;124m'\u001b[39m\n",
" \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mcontain at least four unsaturated dark frames. 1 = \u001b[39m\u001b[38;5;124m'\u001b[39m\n",
" \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mOne or more pixels in data record contain three or \u001b[39m\u001b[38;5;124m'\u001b[39m\n",
" \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mfewer unsaturated dark frames. O-P: Spares\u001b[39m\u001b[38;5;124m'\u001b[39m,\n",
" \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mNAME\u001b[39m\u001b[38;5;124m'\u001b[39m: \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mDATA_QUALITY_INDEX\u001b[39m\u001b[38;5;124m'\u001b[39m,\n",
" \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mSTART_BYTE\u001b[39m\u001b[38;5;124m'\u001b[39m: \u001b[38;5;241m10292\u001b[39m},\n",
" \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mCOLUMN\u001b[39m\u001b[38;5;124m'\u001b[39m: {\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mBYTES\u001b[39m\u001b[38;5;124m'\u001b[39m: \u001b[38;5;241m40\u001b[39m,\n",
" \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mCOLUMN_NUMBER\u001b[39m\u001b[38;5;124m'\u001b[39m: \u001b[38;5;241m21\u001b[39m,\n",
" \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mDATA_TYPE\u001b[39m\u001b[38;5;124m'\u001b[39m: \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mIEEE_REAL\u001b[39m\u001b[38;5;124m'\u001b[39m,\n",
" \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mDESCRIPTION\u001b[39m\u001b[38;5;124m'\u001b[39m: \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mDerived latitude of footprint center, uprange edge \u001b[39m\u001b[38;5;124m'\u001b[39m\n",
" \u001b[38;5;124m'\u001b[39m\u001b[38;5;124m(c1), downrange edge (c2), port, (c3) and \u001b[39m\u001b[38;5;124m'\u001b[39m\n",
" \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mstarboard (c4) edges (5 total values, coordinates \u001b[39m\u001b[38;5;124m'\u001b[39m\n",
" \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mas in Figure 4 of VIRS_CDR_DDR_DAP_SIS.PDF; center \u001b[39m\u001b[38;5;124m'\u001b[39m\n",
" \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mpoint, c1, c2, c3, c4) on planet target \u001b[39m\u001b[38;5;124m'\u001b[39m\n",
" \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mcorresponding to spectral observation. Unit = \u001b[39m\u001b[38;5;124m'\u001b[39m\n",
" \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mdegrees. PER SPECTRUM column.\u001b[39m\u001b[38;5;124m'\u001b[39m,\n",
" \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mINVALID_CONSTANT\u001b[39m\u001b[38;5;124m'\u001b[39m: \u001b[38;5;241m1e+32\u001b[39m,\n",
" \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mITEMS\u001b[39m\u001b[38;5;124m'\u001b[39m: \u001b[38;5;241m5\u001b[39m,\n",
" \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mITEM_BYTES\u001b[39m\u001b[38;5;124m'\u001b[39m: \u001b[38;5;241m8\u001b[39m,\n",
" \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mMISSING_CONSTANT\u001b[39m\u001b[38;5;124m'\u001b[39m: \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1e+32\u001b[39m,\n",
" \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mNAME\u001b[39m\u001b[38;5;124m'\u001b[39m: \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mTARGET_LATITUDE_SET\u001b[39m\u001b[38;5;124m'\u001b[39m,\n",
" \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mSTART_BYTE\u001b[39m\u001b[38;5;124m'\u001b[39m: \u001b[38;5;241m10311\u001b[39m},\n",
" \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mCOLUMN\u001b[39m\u001b[38;5;124m'\u001b[39m: {\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mBYTES\u001b[39m\u001b[38;5;124m'\u001b[39m: \u001b[38;5;241m40\u001b[39m,\n",
" \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mCOLUMN_NUMBER\u001b[39m\u001b[38;5;124m'\u001b[39m: \u001b[38;5;241m22\u001b[39m,\n",
" \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mDATA_TYPE\u001b[39m\u001b[38;5;124m'\u001b[39m: \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mIEEE_REAL\u001b[39m\u001b[38;5;124m'\u001b[39m,\n",
" \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mDESCRIPTION\u001b[39m\u001b[38;5;124m'\u001b[39m: \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mDerived longitude of footprint center, uprange \u001b[39m\u001b[38;5;124m'\u001b[39m\n",
" \u001b[38;5;124m'\u001b[39m\u001b[38;5;124medge (c1), downrange edge (c2), port, (c3) and \u001b[39m\u001b[38;5;124m'\u001b[39m\n",
" \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mstarboard (c4) edges (5 total values, coordinates \u001b[39m\u001b[38;5;124m'\u001b[39m\n",
" \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mas in Figure 4 of VIRS_CDR_DDR_DAP_SIS.PDF; center \u001b[39m\u001b[38;5;124m'\u001b[39m\n",
" \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mpoint, c1, c2, c3, c4) on planet target \u001b[39m\u001b[38;5;124m'\u001b[39m\n",
" \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mcorresponding to spectral observation. Unit = \u001b[39m\u001b[38;5;124m'\u001b[39m\n",
" \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mdegrees. PER SPECTRUM column.\u001b[39m\u001b[38;5;124m'\u001b[39m,\n",
" \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mINVALID_CONSTANT\u001b[39m\u001b[38;5;124m'\u001b[39m: \u001b[38;5;241m1e+32\u001b[39m,\n",
" \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mITEMS\u001b[39m\u001b[38;5;124m'\u001b[39m: \u001b[38;5;241m5\u001b[39m,\n",
" \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mITEM_BYTES\u001b[39m\u001b[38;5;124m'\u001b[39m: \u001b[38;5;241m8\u001b[39m,\n",
" \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mMISSING_CONSTANT\u001b[39m\u001b[38;5;124m'\u001b[39m: \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1e+32\u001b[39m,\n",
" \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mNAME\u001b[39m\u001b[38;5;124m'\u001b[39m: \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mTARGET_LONGITUDE_SET\u001b[39m\u001b[38;5;124m'\u001b[39m,\n",
" \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mSTART_BYTE\u001b[39m\u001b[38;5;124m'\u001b[39m: \u001b[38;5;241m10351\u001b[39m},\n",
" \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mCOLUMN\u001b[39m\u001b[38;5;124m'\u001b[39m: {\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mBYTES\u001b[39m\u001b[38;5;124m'\u001b[39m: \u001b[38;5;241m8\u001b[39m,\n",
" \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mCOLUMN_NUMBER\u001b[39m\u001b[38;5;124m'\u001b[39m: \u001b[38;5;241m23\u001b[39m,\n",
" \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mDATA_TYPE\u001b[39m\u001b[38;5;124m'\u001b[39m: \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mIEEE_REAL\u001b[39m\u001b[38;5;124m'\u001b[39m,\n",
" \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mDESCRIPTION\u001b[39m\u001b[38;5;124m'\u001b[39m: \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mDerived length of VIRS footprint along track over \u001b[39m\u001b[38;5;124m'\u001b[39m\n",
" \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mthe spectrum integration time, accounting for \u001b[39m\u001b[38;5;124m'\u001b[39m\n",
" \u001b[38;5;124m'\u001b[39m\u001b[38;5;124msmear across surface. Unit = meters. PER SPECTRUM \u001b[39m\u001b[38;5;124m'\u001b[39m\n",
" \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mcolumn.\u001b[39m\u001b[38;5;124m'\u001b[39m,\n",
" \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mINVALID_CONSTANT\u001b[39m\u001b[38;5;124m'\u001b[39m: \u001b[38;5;241m1e+32\u001b[39m,\n",
" \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mMISSING_CONSTANT\u001b[39m\u001b[38;5;124m'\u001b[39m: \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1e+32\u001b[39m,\n",
" \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mNAME\u001b[39m\u001b[38;5;124m'\u001b[39m: \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mALONG_TRACK_FOOTPRINT_SIZE\u001b[39m\u001b[38;5;124m'\u001b[39m,\n",
" \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mSTART_BYTE\u001b[39m\u001b[38;5;124m'\u001b[39m: \u001b[38;5;241m10391\u001b[39m},\n",
" \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mCOLUMN\u001b[39m\u001b[38;5;124m'\u001b[39m: {\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mBYTES\u001b[39m\u001b[38;5;124m'\u001b[39m: \u001b[38;5;241m8\u001b[39m,\n",
" \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mCOLUMN_NUMBER\u001b[39m\u001b[38;5;124m'\u001b[39m: \u001b[38;5;241m24\u001b[39m,\n",
" \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mDATA_TYPE\u001b[39m\u001b[38;5;124m'\u001b[39m: \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mIEEE_REAL\u001b[39m\u001b[38;5;124m'\u001b[39m,\n",
" \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mDESCRIPTION\u001b[39m\u001b[38;5;124m'\u001b[39m: \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mDerived instantaneous width of VIRS footprint \u001b[39m\u001b[38;5;124m'\u001b[39m\n",
" \u001b[38;5;124m'\u001b[39m\u001b[38;5;124macross track at the spectrum integration midpoint, \u001b[39m\u001b[38;5;124m'\u001b[39m\n",
" \u001b[38;5;124m'\u001b[39m\u001b[38;5;124maccounting for jitter across surface. Unit = \u001b[39m\u001b[38;5;124m'\u001b[39m\n",
" \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mmeters. PER SPECTRUM column.\u001b[39m\u001b[38;5;124m'\u001b[39m,\n",
" \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mINVALID_CONSTANT\u001b[39m\u001b[38;5;124m'\u001b[39m: \u001b[38;5;241m1e+32\u001b[39m,\n",
" \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mMISSING_CONSTANT\u001b[39m\u001b[38;5;124m'\u001b[39m: \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1e+32\u001b[39m,\n",
" \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mNAME\u001b[39m\u001b[38;5;124m'\u001b[39m: \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mACROSS_TRACK_FOOTPRINT_SIZE\u001b[39m\u001b[38;5;124m'\u001b[39m,\n",
" \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mSTART_BYTE\u001b[39m\u001b[38;5;124m'\u001b[39m: \u001b[38;5;241m10399\u001b[39m},\n",
" \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mCOLUMN\u001b[39m\u001b[38;5;124m'\u001b[39m: {\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mBYTES\u001b[39m\u001b[38;5;124m'\u001b[39m: \u001b[38;5;241m8\u001b[39m,\n",
" \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mCOLUMN_NUMBER\u001b[39m\u001b[38;5;124m'\u001b[39m: \u001b[38;5;241m25\u001b[39m,\n",
" \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mDATA_TYPE\u001b[39m\u001b[38;5;124m'\u001b[39m: \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mIEEE_REAL\u001b[39m\u001b[38;5;124m'\u001b[39m,\n",
" \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mDESCRIPTION\u001b[39m\u001b[38;5;124m'\u001b[39m: \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mDerived solar incidence angle of footprint center. \u001b[39m\u001b[38;5;124m'\u001b[39m\n",
" \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mUnit = degrees. PER SPECTRUM column.\u001b[39m\u001b[38;5;124m'\u001b[39m,\n",
" \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mINVALID_CONSTANT\u001b[39m\u001b[38;5;124m'\u001b[39m: \u001b[38;5;241m1e+32\u001b[39m,\n",
" \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mMISSING_CONSTANT\u001b[39m\u001b[38;5;124m'\u001b[39m: \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1e+32\u001b[39m,\n",
" \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mNAME\u001b[39m\u001b[38;5;124m'\u001b[39m: \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mINCIDENCE_ANGLE\u001b[39m\u001b[38;5;124m'\u001b[39m,\n",
" \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mSTART_BYTE\u001b[39m\u001b[38;5;124m'\u001b[39m: \u001b[38;5;241m10407\u001b[39m},\n",
" \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mCOLUMN\u001b[39m\u001b[38;5;124m'\u001b[39m: {\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mBYTES\u001b[39m\u001b[38;5;124m'\u001b[39m: \u001b[38;5;241m8\u001b[39m,\n",
" \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mCOLUMN_NUMBER\u001b[39m\u001b[38;5;124m'\u001b[39m: \u001b[38;5;241m26\u001b[39m,\n",
" \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mDATA_TYPE\u001b[39m\u001b[38;5;124m'\u001b[39m: \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mIEEE_REAL\u001b[39m\u001b[38;5;124m'\u001b[39m,\n",
" \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mDESCRIPTION\u001b[39m\u001b[38;5;124m'\u001b[39m: \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mDerived emission angle of footprint center. Unit = \u001b[39m\u001b[38;5;124m'\u001b[39m\n",
" \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mdegrees. PER SPECTRUM column.\u001b[39m\u001b[38;5;124m'\u001b[39m,\n",
" \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mINVALID_CONSTANT\u001b[39m\u001b[38;5;124m'\u001b[39m: \u001b[38;5;241m1e+32\u001b[39m,\n",
" \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mMISSING_CONSTANT\u001b[39m\u001b[38;5;124m'\u001b[39m: \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1e+32\u001b[39m,\n",
" \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mNAME\u001b[39m\u001b[38;5;124m'\u001b[39m: \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mEMISSION_ANGLE\u001b[39m\u001b[38;5;124m'\u001b[39m,\n",
" \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mSTART_BYTE\u001b[39m\u001b[38;5;124m'\u001b[39m: \u001b[38;5;241m10415\u001b[39m},\n",
" \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mCOLUMN\u001b[39m\u001b[38;5;124m'\u001b[39m: {\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mBYTES\u001b[39m\u001b[38;5;124m'\u001b[39m: \u001b[38;5;241m8\u001b[39m,\n",
" \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mCOLUMN_NUMBER\u001b[39m\u001b[38;5;124m'\u001b[39m: \u001b[38;5;241m27\u001b[39m,\n",
" \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mDATA_TYPE\u001b[39m\u001b[38;5;124m'\u001b[39m: \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mIEEE_REAL\u001b[39m\u001b[38;5;124m'\u001b[39m,\n",
" \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mDESCRIPTION\u001b[39m\u001b[38;5;124m'\u001b[39m: \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mDerived phase angle of footprint center. Unit = \u001b[39m\u001b[38;5;124m'\u001b[39m\n",
" \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mdegrees. PER SPECTRUM column.\u001b[39m\u001b[38;5;124m'\u001b[39m,\n",
" \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mINVALID_CONSTANT\u001b[39m\u001b[38;5;124m'\u001b[39m: \u001b[38;5;241m1e+32\u001b[39m,\n",
" \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mMISSING_CONSTANT\u001b[39m\u001b[38;5;124m'\u001b[39m: \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1e+32\u001b[39m,\n",
" \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mNAME\u001b[39m\u001b[38;5;124m'\u001b[39m: \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mPHASE_ANGLE\u001b[39m\u001b[38;5;124m'\u001b[39m,\n",
" \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mSTART_BYTE\u001b[39m\u001b[38;5;124m'\u001b[39m: \u001b[38;5;241m10423\u001b[39m},\n",
" \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mCOLUMN\u001b[39m\u001b[38;5;124m'\u001b[39m: {\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mBYTES\u001b[39m\u001b[38;5;124m'\u001b[39m: \u001b[38;5;241m8\u001b[39m,\n",
" \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mCOLUMN_NUMBER\u001b[39m\u001b[38;5;124m'\u001b[39m: \u001b[38;5;241m28\u001b[39m,\n",
" \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mDATA_TYPE\u001b[39m\u001b[38;5;124m'\u001b[39m: \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mIEEE_REAL\u001b[39m\u001b[38;5;124m'\u001b[39m,\n",
" \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mDESCRIPTION\u001b[39m\u001b[38;5;124m'\u001b[39m: \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mDerived subsolar distance to footprint center on \u001b[39m\u001b[38;5;124m'\u001b[39m\n",
" \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mplanet surface. Unit = kilometers. PER SPECTRUM \u001b[39m\u001b[38;5;124m'\u001b[39m\n",
" \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mcolumn.\u001b[39m\u001b[38;5;124m'\u001b[39m,\n",
" \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mMISSING_CONSTANT\u001b[39m\u001b[38;5;124m'\u001b[39m: \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1e+32\u001b[39m,\n",
" \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mNAME\u001b[39m\u001b[38;5;124m'\u001b[39m: \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mSOLAR_DISTANCE\u001b[39m\u001b[38;5;124m'\u001b[39m,\n",
" \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mSTART_BYTE\u001b[39m\u001b[38;5;124m'\u001b[39m: \u001b[38;5;241m10431\u001b[39m},\n",
" \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mCOLUMN\u001b[39m\u001b[38;5;124m'\u001b[39m: {\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mBYTES\u001b[39m\u001b[38;5;124m'\u001b[39m: \u001b[38;5;241m4\u001b[39m,\n",
" \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mCOLUMN_NUMBER\u001b[39m\u001b[38;5;124m'\u001b[39m: \u001b[38;5;241m29\u001b[39m,\n",
" \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mDATA_TYPE\u001b[39m\u001b[38;5;124m'\u001b[39m: \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mIEEE_REAL\u001b[39m\u001b[38;5;124m'\u001b[39m,\n",
" \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mDESCRIPTION\u001b[39m\u001b[38;5;124m'\u001b[39m: \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mSPARE column.\u001b[39m\u001b[38;5;124m'\u001b[39m,\n",
" \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mMISSING_CONSTANT\u001b[39m\u001b[38;5;124m'\u001b[39m: \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1e+32\u001b[39m,\n",
" \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mNAME\u001b[39m\u001b[38;5;124m'\u001b[39m: \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mSPARE_1\u001b[39m\u001b[38;5;124m'\u001b[39m,\n",
" \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mSTART_BYTE\u001b[39m\u001b[38;5;124m'\u001b[39m: \u001b[38;5;241m10439\u001b[39m},\n",
" \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mCOLUMN\u001b[39m\u001b[38;5;124m'\u001b[39m: {\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mBYTES\u001b[39m\u001b[38;5;124m'\u001b[39m: \u001b[38;5;241m4\u001b[39m,\n",
" \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mCOLUMN_NUMBER\u001b[39m\u001b[38;5;124m'\u001b[39m: \u001b[38;5;241m30\u001b[39m,\n",
" \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mDATA_TYPE\u001b[39m\u001b[38;5;124m'\u001b[39m: \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mMSB_INTEGER\u001b[39m\u001b[38;5;124m'\u001b[39m,\n",
" \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mDESCRIPTION\u001b[39m\u001b[38;5;124m'\u001b[39m: \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mSPARE column.\u001b[39m\u001b[38;5;124m'\u001b[39m,\n",
" \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mNAME\u001b[39m\u001b[38;5;124m'\u001b[39m: \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mSPARE_2\u001b[39m\u001b[38;5;124m'\u001b[39m,\n",
" \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mSTART_BYTE\u001b[39m\u001b[38;5;124m'\u001b[39m: \u001b[38;5;241m10443\u001b[39m},\n",
" \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mCOLUMN\u001b[39m\u001b[38;5;124m'\u001b[39m: {\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mBYTES\u001b[39m\u001b[38;5;124m'\u001b[39m: \u001b[38;5;241m4\u001b[39m,\n",
" \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mCOLUMN_NUMBER\u001b[39m\u001b[38;5;124m'\u001b[39m: \u001b[38;5;241m31\u001b[39m,\n",
" \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mDATA_TYPE\u001b[39m\u001b[38;5;124m'\u001b[39m: \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mMSB_INTEGER\u001b[39m\u001b[38;5;124m'\u001b[39m,\n",
" \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mDESCRIPTION\u001b[39m\u001b[38;5;124m'\u001b[39m: \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mSPARE column.\u001b[39m\u001b[38;5;124m'\u001b[39m,\n",
" \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mNAME\u001b[39m\u001b[38;5;124m'\u001b[39m: \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mSPARE_3\u001b[39m\u001b[38;5;124m'\u001b[39m,\n",
" \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mSTART_BYTE\u001b[39m\u001b[38;5;124m'\u001b[39m: \u001b[38;5;241m10447\u001b[39m},\n",
" \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mCOLUMN\u001b[39m\u001b[38;5;124m'\u001b[39m: {\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mBYTES\u001b[39m\u001b[38;5;124m'\u001b[39m: \u001b[38;5;241m4\u001b[39m,\n",
" \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mCOLUMN_NUMBER\u001b[39m\u001b[38;5;124m'\u001b[39m: \u001b[38;5;241m32\u001b[39m,\n",
" \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mDATA_TYPE\u001b[39m\u001b[38;5;124m'\u001b[39m: \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mMSB_INTEGER\u001b[39m\u001b[38;5;124m'\u001b[39m,\n",
" \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mDESCRIPTION\u001b[39m\u001b[38;5;124m'\u001b[39m: \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mSPARE column.\u001b[39m\u001b[38;5;124m'\u001b[39m,\n",
" \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mNAME\u001b[39m\u001b[38;5;124m'\u001b[39m: \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mSPARE_4\u001b[39m\u001b[38;5;124m'\u001b[39m,\n",
" \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mSTART_BYTE\u001b[39m\u001b[38;5;124m'\u001b[39m: \u001b[38;5;241m10451\u001b[39m},\n",
" \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mCOLUMN\u001b[39m\u001b[38;5;124m'\u001b[39m: {\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mBYTES\u001b[39m\u001b[38;5;124m'\u001b[39m: \u001b[38;5;241m4\u001b[39m,\n",
" \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mCOLUMN_NUMBER\u001b[39m\u001b[38;5;124m'\u001b[39m: \u001b[38;5;241m33\u001b[39m,\n",
" \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mDATA_TYPE\u001b[39m\u001b[38;5;124m'\u001b[39m: \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mMSB_INTEGER\u001b[39m\u001b[38;5;124m'\u001b[39m,\n",
" \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mDESCRIPTION\u001b[39m\u001b[38;5;124m'\u001b[39m: \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mSPARE column.\u001b[39m\u001b[38;5;124m'\u001b[39m,\n",
" \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mNAME\u001b[39m\u001b[38;5;124m'\u001b[39m: \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mSPARE_5\u001b[39m\u001b[38;5;124m'\u001b[39m,\n",
" \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mSTART_BYTE\u001b[39m\u001b[38;5;124m'\u001b[39m: \u001b[38;5;241m10455\u001b[39m}}\n",
"\n"
]
}
],
"source": [
"pprint_color(fmt)"
]
},
{
"cell_type": "code",
"execution_count": 104,
"metadata": {
"ExecuteTime": {
"end_time": "2018-04-26T09:52:01.966897Z",
"start_time": "2018-04-26T09:52:01.952873Z"
},
"scrolled": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"{\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mDATA_SET_ID\u001b[39m\u001b[38;5;124m'\u001b[39m: \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mMESS-E/V/H-MASCS-4-VIRS-DDR-V2.0\u001b[39m\u001b[38;5;124m'\u001b[39m,\n",
" \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mDETECTOR_ID\u001b[39m\u001b[38;5;124m'\u001b[39m: \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mVIRS\u001b[39m\u001b[38;5;124m'\u001b[39m,\n",
" \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mFILE_RECORDS\u001b[39m\u001b[38;5;124m'\u001b[39m: \u001b[38;5;241m802\u001b[39m,\n",
" \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mINSTRUMENT_HOST_NAME\u001b[39m\u001b[38;5;124m'\u001b[39m: \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mMESSENGER\u001b[39m\u001b[38;5;124m'\u001b[39m,\n",
" \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mINSTRUMENT_ID\u001b[39m\u001b[38;5;124m'\u001b[39m: \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mMASCS\u001b[39m\u001b[38;5;124m'\u001b[39m,\n",
" \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mINSTRUMENT_NAME\u001b[39m\u001b[38;5;124m'\u001b[39m: \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mMERCURY ATMOSPHERIC AND SURFACE COMPOSITION SPECTROMETER\u001b[39m\u001b[38;5;124m'\u001b[39m,\n",
" \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mMISSION_PHASE_NAME\u001b[39m\u001b[38;5;124m'\u001b[39m: \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mMERCURY ORBIT\u001b[39m\u001b[38;5;124m'\u001b[39m,\n",
" \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mPDS_VERSION_ID\u001b[39m\u001b[38;5;124m'\u001b[39m: \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mPDS3\u001b[39m\u001b[38;5;124m'\u001b[39m,\n",
" \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mPRODUCT_CREATION_TIME\u001b[39m\u001b[38;5;124m'\u001b[39m: datetime\u001b[38;5;241m.\u001b[39mdatetime(\u001b[38;5;241m2017\u001b[39m, \u001b[38;5;241m2\u001b[39m, \u001b[38;5;241m18\u001b[39m, \u001b[38;5;241m3\u001b[39m, \u001b[38;5;241m42\u001b[39m, \u001b[38;5;241m45\u001b[39m),\n",
" \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mPRODUCT_ID\u001b[39m\u001b[38;5;124m'\u001b[39m: \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mVIRSVD_ORB_11187_050618_DAT\u001b[39m\u001b[38;5;124m'\u001b[39m,\n",
" \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mPRODUCT_TYPE\u001b[39m\u001b[38;5;124m'\u001b[39m: \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mDDR\u001b[39m\u001b[38;5;124m'\u001b[39m,\n",
" \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mPRODUCT_VERSION_ID\u001b[39m\u001b[38;5;124m'\u001b[39m: \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mV1\u001b[39m\u001b[38;5;124m'\u001b[39m,\n",
" \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mRECORD_BYTES\u001b[39m\u001b[38;5;124m'\u001b[39m: \u001b[38;5;241m10458\u001b[39m,\n",
" \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mRECORD_TYPE\u001b[39m\u001b[38;5;124m'\u001b[39m: \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mFIXED_LENGTH\u001b[39m\u001b[38;5;124m'\u001b[39m,\n",
" \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mSITE_ID\u001b[39m\u001b[38;5;124m'\u001b[39m: \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mN/A\u001b[39m\u001b[38;5;124m'\u001b[39m,\n",
" \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mSOFTWARE_NAME\u001b[39m\u001b[38;5;124m'\u001b[39m: \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mPIPE-MASCS2DDR\u001b[39m\u001b[38;5;124m'\u001b[39m,\n",
" \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mSOFTWARE_VERSION_ID\u001b[39m\u001b[38;5;124m'\u001b[39m: \u001b[38;5;124m'\u001b[39m\u001b[38;5;124m1.0\u001b[39m\u001b[38;5;124m'\u001b[39m,\n",
" \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mSPACECRAFT_CLOCK_START_COUNT\u001b[39m\u001b[38;5;124m'\u001b[39m: \u001b[38;5;124m'\u001b[39m\u001b[38;5;124m1/218416246.224\u001b[39m\u001b[38;5;124m'\u001b[39m,\n",
" \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mSPACECRAFT_CLOCK_STOP_COUNT\u001b[39m\u001b[38;5;124m'\u001b[39m: \u001b[38;5;124m'\u001b[39m\u001b[38;5;124m1/218417248.759\u001b[39m\u001b[38;5;124m'\u001b[39m,\n",
" \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mSTANDARD_DATA_PRODUCT_ID\u001b[39m\u001b[38;5;124m'\u001b[39m: \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mVIRSDVIS\u001b[39m\u001b[38;5;124m'\u001b[39m,\n",
" \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mSTART_TIME\u001b[39m\u001b[38;5;124m'\u001b[39m: datetime\u001b[38;5;241m.\u001b[39mdatetime(\u001b[38;5;241m2011\u001b[39m, \u001b[38;5;241m7\u001b[39m, \u001b[38;5;241m6\u001b[39m, \u001b[38;5;241m5\u001b[39m, \u001b[38;5;241m6\u001b[39m, \u001b[38;5;241m19\u001b[39m),\n",
" \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mSTOP_TIME\u001b[39m\u001b[38;5;124m'\u001b[39m: datetime\u001b[38;5;241m.\u001b[39mdatetime(\u001b[38;5;241m2011\u001b[39m, \u001b[38;5;241m7\u001b[39m, \u001b[38;5;241m6\u001b[39m, \u001b[38;5;241m5\u001b[39m, \u001b[38;5;241m23\u001b[39m, \u001b[38;5;241m1\u001b[39m),\n",
" \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mTABLE\u001b[39m\u001b[38;5;124m'\u001b[39m: {\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mCOLUMNS\u001b[39m\u001b[38;5;124m'\u001b[39m: \u001b[38;5;241m62\u001b[39m,\n",
" \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mDESCRIPTION\u001b[39m\u001b[38;5;124m'\u001b[39m: \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mThis table contains MESSENGER VIRS spectra \u001b[39m\u001b[38;5;124m'\u001b[39m\n",
" \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mcollected in the visible wavelength, converted to \u001b[39m\u001b[38;5;124m'\u001b[39m\n",
" \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mreflectance and photometrically normalized as well \u001b[39m\u001b[38;5;124m'\u001b[39m\n",
" \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mas instrument engineering data. Detailed \u001b[39m\u001b[38;5;124m'\u001b[39m\n",
" \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mdescriptions for the parameters defined below are \u001b[39m\u001b[38;5;124m'\u001b[39m\n",
" \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mcontained in the CDR/DDR SIS document. The complete \u001b[39m\u001b[38;5;124m'\u001b[39m\n",
" \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mcolumn definitions are contained in an external \u001b[39m\u001b[38;5;124m'\u001b[39m\n",
" \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mfile found in the LABEL directory of the archive \u001b[39m\u001b[38;5;124m'\u001b[39m\n",
" \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mvolume.\u001b[39m\u001b[38;5;124m'\u001b[39m,\n",
" \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mINTERCHANGE_FORMAT\u001b[39m\u001b[38;5;124m'\u001b[39m: \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mBINARY\u001b[39m\u001b[38;5;124m'\u001b[39m,\n",
" \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mNOTE\u001b[39m\u001b[38;5;124m'\u001b[39m: \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mSPICE Kernels: msgr20110705.bc msgr20110706.bc \u001b[39m\u001b[38;5;124m'\u001b[39m\n",
" \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mmsgr20110707.bc msgr_dyn_v600.tf msgr_v231.tf \u001b[39m\u001b[38;5;124m'\u001b[39m\n",
" \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mmsgr_mascs_v100.ti naif0011.tls pck00010_MSGR_v21.tpc \u001b[39m\u001b[38;5;124m'\u001b[39m\n",
" \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mmessenger_2548.tsc msgr_20040803_20150430_od431sc_2.bsp\u001b[39m\u001b[38;5;124m'\u001b[39m,\n",
" \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mROWS\u001b[39m\u001b[38;5;124m'\u001b[39m: \u001b[38;5;241m802\u001b[39m,\n",
" \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mROW_BYTES\u001b[39m\u001b[38;5;124m'\u001b[39m: \u001b[38;5;241m10458\u001b[39m,\n",
" \u001b[38;5;124m'\u001b[39m\u001b[38;5;124m^STRUCTURE\u001b[39m\u001b[38;5;124m'\u001b[39m: \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mVIRSVD.FMT\u001b[39m\u001b[38;5;124m'\u001b[39m},\n",
" \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mTARGET_NAME\u001b[39m\u001b[38;5;124m'\u001b[39m: \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mMERCURY\u001b[39m\u001b[38;5;124m'\u001b[39m,\n",
" \u001b[38;5;124m'\u001b[39m\u001b[38;5;124m^TABLE\u001b[39m\u001b[38;5;124m'\u001b[39m: \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mVIRSVD_ORB_11187_050618.DAT\u001b[39m\u001b[38;5;124m'\u001b[39m}\n",
"\n"
]
}
],
"source": [
"pprint_color(label)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Read the data for one Feature \n",
"\n",
"Read and extract all the Fields for one Feature (==Spectrum)"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {
"ExecuteTime": {
"end_time": "2018-04-26T09:52:03.212390Z",
"start_time": "2018-04-26T09:52:03.077943Z"
},
"scrolled": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"FeatureCount : 802 \n",
"============================== ============ ============== ===================\n",
"name type(GDAL) type(python) val/len\n",
"============================== ============ ============== ===================\n",
"SC_TIME Real float 218416246.0\n",
"PACKET_SUBSECONDS Integer int 45\n",
"INT_TIME Integer int 20\n",
"INT_COUNT Integer int 803\n",
"DARK_FREQ Integer int 40\n",
"TEMP_2 Real float 28.124000549316406\n",
"BINNING Integer int 2\n",
"START_PIXEL Integer int 0\n",
"END_PIXEL Integer int 361\n",
"SPECTRUM_NUMBER Integer int 0\n",
"SPECTRUM_MET Real float 218416246.0\n",
"SPECTRUM_SUBSECONDS Integer int 224\n",
"SPECTRUM_UTC_TIME String str 11187T05:06:19\n",
"IOF_SPECTRUM_DATA RealList list [512]\n",
"PHOTOM_IOF_SPECTRUM_DATA RealList list [512]\n",
"IOF_NOISE_SPECTRUM_DATA RealList list [512]\n",
"PHOTOM_IOF_NOISE_SPECTRUM_DATA RealList list [512]\n",
"SOFTWARE_VERSION Real float 1.0\n",
"CHANNEL_WAVELENGTHS RealList list [512]\n",
"DATA_QUALITY_INDEX String str 0222-9110-0001-2000\n",
"TARGET_LATITUDE_SET RealList list [10]\n",
"TARGET_LONGITUDE_SET RealList list [10]\n",
"ALONG_TRACK_FOOTPRINT_SIZE RealList list [2]\n",
"ACROSS_TRACK_FOOTPRINT_SIZE RealList list [2]\n",
"INCIDENCE_ANGLE RealList list [2]\n",
"EMISSION_ANGLE RealList list [2]\n",
"PHASE_ANGLE RealList list [2]\n",
"SOLAR_DISTANCE RealList list [2]\n",
"SPARE_1 Real float 0.0\n",
"SPARE_2 Integer int 0\n",
"SPARE_3 Integer int 0\n",
"SPARE_4 Integer int 0\n",
"SPARE_5 Integer int 0\n",
"============================== ============ ============== ===================\n"
]
}
],
"source": [
"from osgeo import ogr\n",
"# datamodel : File : Layer[Table for PDS3] > Feature > Fields\n",
"\n",
"# which spectra from the file?\n",
"sp_id = 0\n",
"\n",
"inDataSource = ogr.Open(lbl_file)\n",
"table = inDataSource.GetLayer()\n",
"print('FeatureCount : %s ' % table.GetFeatureCount())\n",
"# print(table[sp_id].GetField('SC_TIME'))\n",
"\n",
"layerDefinition = table.GetLayerDefn()\n",
"\n",
"# printout = [[\"name\", \"type(GDAL)\", \"type(python)\", \"val/len\",'min','max']]\n",
"printout = [[\"name\", \"type(GDAL)\", \"type(python)\", \"val/len\"]]\n",
"for i in range(layerDefinition.GetFieldCount()):\n",
" fielddef = layerDefinition.GetFieldDefn(i)\n",
" fieldName = fielddef.GetName()\n",
" fieldTypeCode = fielddef.GetType()\n",
" fieldType = fielddef.GetFieldTypeName(fieldTypeCode)\n",
" fieldData = table[sp_id].GetField(fieldName)\n",
" tmp = [fieldName,fieldType,type(fieldData).__name__]\n",
" if isinstance(fieldData, list): \n",
" mfieldData = np.ma.masked_greater(fieldData, 1e32)\n",
"# tmp.extend( ['[%s]' % len(fieldData) , np.nanmin(mfieldData),np.nanmax(mfieldData)])\n",
" tmp.extend( ['[%s]' % len(fieldData)])\n",
" else:\n",
"# tmp.extend([table[sp_id].GetField(fieldName), np.nan,np.nan])\n",
" tmp.extend([table[sp_id].GetField(fieldName)])\n",
" printout.extend([tmp])\n",
"\n",
"print(tabulate(printout, headers='firstrow', tablefmt=\"rst\", numalign=\"right\", floatfmt=\".4f\"))"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"ExecuteTime": {
"end_time": "2018-04-26T09:52:08.603321Z",
"start_time": "2018-04-26T09:52:06.092980Z"
}
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"100%|██████████| 802/802 [00:00<00:00, 954.56it/s]\n"
]
}
],
"source": [
"import tqdm\n",
"# which spectra from the file?\n",
"sp_id = 100 \n",
"table_data = []\n",
"layerDefinition = table.GetLayerDefn()\n",
"\n",
"for sp_id in tqdm.trange(table.GetFeatureCount()):\n",
" data = {}\n",
" for i in range(layerDefinition.GetFieldCount()):\n",
" fielddef = layerDefinition.GetFieldDefn(i)\n",
" fieldName = fielddef.GetName()\n",
" fieldData = table[sp_id].GetField(fieldName)\n",
" # transform List to numpy.array\n",
" data[fieldName] = np.array(fieldData) if isinstance(fieldData, list) else fieldData\n",
" table_data.append(data)"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {
"ExecuteTime": {
"end_time": "2018-04-26T09:53:16.249189Z",
"start_time": "2018-04-26T09:53:16.215003Z"
},
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"input size: 10 \n",
" [-2.1693003e+00 -2.0820880e-07 -2.1451390e+00 1.4984515e+38\n",
" -2.1930244e+00 -4.3037403e-07 -2.1697917e+00 1.0847996e-05\n",
" -2.1688092e+00 3.5118337e-20]\n",
"output size: 5 \n",
" [-3.35440389 -3.16111278 -3.54419652 -3.358334 -3.35047364]\n"
]
}
],
"source": [
"def convert_32b_to_64b(coord_32b_x2):\n",
" \"\"\"convert_32b_to_64b : \n",
" Convert elementwise 10 np.float32 elements array to byte-hexstring representation :\n",
" \n",
" 32bits == 4 Bytes * 10 = 40 Bytes\n",
"\n",
" Parameters\n",
" ----------\n",
"\n",
" coord_32b_x2 np.float32: input numpy array, must be 32bits!\n",
"\n",
" Returns\n",
" -------\n",
" \n",
" output np.float64 : 64bits float numpy array with element len(input)/2.\n",
" \n",
" \"\"\"\n",
" try: \n",
" if not np.issubdtype(np.float32,coord_32b_x2.dtype):\n",
" # try to convert to np.float32 array\n",
" try:\n",
" coord_32b_x2 = np.array(coord_32b_x2,dtype=np.float32)\n",
" except ValueError as e:\n",
" print(e)\n",
" # convert elementwise 10 np.float32 elements array to byte-hexstring representation : 32bits == 4 Bytes * 10 = 40 Bytes\n",
" coord_hexstring_32b_iter = iter([x.tostring() for x in coord_32b_x2])\n",
" # join element pairwise in a numpy array: 64bits == 8 Bytes * 5 = 40 Bytes using iterator\n",
" # TODO: here I'm swapping the 2 values due to endianness, this must be automatically checked!!!\n",
" coord_hexstring_64b = np.array([next(coord_hexstring_32b_iter)+x for x in coord_hexstring_32b_iter])\n",
" # convert the byte-hexstring representation to float64 number using ufunc vectorialisation\n",
" coord_64b = np.frombuffer(coord_hexstring_64b,dtype=np.float64)\n",
" return coord_64b\n",
"# else:\n",
"# raise TypeError('numpy float32 array type expected!')\n",
" except AttributeError as e:\n",
" print('Input is not a valid np.arrary:',e)\n",
" \n",
"\n",
"# extract the data from a list of dict: operator module is GREAT!\n",
"array_from_dictlist = lambda dic,key: np.array([x.get(key) for x in dic],dtype=np.float32)\n",
"array_from_dictlist_conv = lambda dic,key: np.array([convert_32b_to_64b(x) for x in array_from_dictlist(dic,key)])\n",
"\n",
"coord = array_from_dictlist(table_data,'TARGET_LATITUDE_SET')\n",
"coord_conv = array_from_dictlist_conv(table_data,'TARGET_LATITUDE_SET')\n",
" \n",
"# 10 * np.float32 elements array\n",
"print('input size: %s' % coord[0].size,'\\n',coord[0])\n",
"# 5 * np.float64 elements array\n",
"print('output size: %s' % coord_conv[0].size,'\\n',coord_conv[0])"
]
},
{
"cell_type": "code",
"execution_count": 108,
"metadata": {
"ExecuteTime": {
"end_time": "2018-04-26T09:53:19.456385Z",
"start_time": "2018-04-26T09:53:19.160184Z"
}
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"100%|██████████| 802/802 [00:00<00:00, 7261.42it/s]\n"
]
}
],
"source": [
"# items_8bites = [\n",
"# 'ALONG_TRACK_FOOTPRINT_SIZE',\n",
"# 'ACROSS_TRACK_FOOTPRINT_SIZE',\n",
"# 'INCIDENCE_ANGLE',\n",
"# 'EMISSION_ANGLE',\n",
"# 'PHASE_ANGLE',\n",
"# 'SOLAR_DISTANCE',\n",
"# 'TARGET_LATITUDE_SET',\n",
"# 'TARGET_LONGITUDE_SET',\n",
"# ]\n",
"\n",
"import copy\n",
"table_data_orig = copy.deepcopy(table_data)\n",
"\n",
"for it in tqdm.tqdm(table_data):\n",
" for k in items_8bites:\n",
" it[k] = convert_32b_to_64b(it[k])[0] if len(it[k]) == 2 else convert_32b_to_64b(it[k])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Reference\n",
"\n",
"From MESSENGER MASCS UVVS Calibrated and Derived Data Record Software Interface Specification\n",
"Version 5.7 April 11, 2017, [UVVS_CDR_DDR_SIS.PDF](http://pds-geosciences.wustl.edu/messenger/mess-e_v_h-mascs-3-virs-cdr-caldata-v1/messmas_2101/document/uvvs_cdr_ddr_sis.pdf), page 79:\n",
"\n",
"\n",
" 16. TARGET_LATITUDE_SET\n",
" Bytes: 40 Type: IEEE_REAL\n",
" Items: 5 Item Bytes: 8\n",
" For exosphere observations, this indicates the latitudes below the tangent points of the UVVS field-of-view center\n",
" and corners. For surface observations, this indicates the latitudes at the locations where the field-of-view center and\n",
" corners intersect the planet surface. Format: (center, corner 1, corner 2, corner 3, corner 4). Unit = degrees. PER\n",
" STEP column."
]
},
{
"cell_type": "code",
"execution_count": 109,
"metadata": {
"ExecuteTime": {
"end_time": "2018-04-26T09:54:32.165457Z",
"start_time": "2018-04-26T09:54:31.266833Z"
},
"scrolled": false
},
"outputs": [
{
"ename": "IndexError",
"evalue": "index 5 is out of bounds for axis 1 with size 5",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mIndexError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-109-6d5dd7c4f588>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 11\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mlb\u001b[0m \u001b[0;32min\u001b[0m \u001b[0menumerate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlabels\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 12\u001b[0m \u001b[0max\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfig\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0madd_subplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m3\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m4\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mi\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 13\u001b[0;31m \u001b[0max\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mscatter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0ml\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0ml\u001b[0m \u001b[0;32min\u001b[0m \u001b[0moperator\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitemgetter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mcoordinates_names\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0md\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0md\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mtable_data\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mT\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0ms\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m50\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0malpha\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m.2\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 14\u001b[0m \u001b[0max\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_title\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Index%s : %s'\u001b[0m \u001b[0;34m%\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mlb\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m<ipython-input-109-6d5dd7c4f588>\u001b[0m in \u001b[0;36m<listcomp>\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m 11\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mlb\u001b[0m \u001b[0;32min\u001b[0m \u001b[0menumerate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlabels\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 12\u001b[0m \u001b[0max\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfig\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0madd_subplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m3\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m4\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mi\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 13\u001b[0;31m \u001b[0max\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mscatter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0ml\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0ml\u001b[0m \u001b[0;32min\u001b[0m \u001b[0moperator\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitemgetter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mcoordinates_names\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0md\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0md\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mtable_data\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mT\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0ms\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m50\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0malpha\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m.2\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 14\u001b[0m \u001b[0max\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_title\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Index%s : %s'\u001b[0m \u001b[0;34m%\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mlb\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mIndexError\u001b[0m: index 5 is out of bounds for axis 1 with size 5"
]
},
{
"data": {
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\n",
"text/plain": [
"<Figure size 1440x720 with 6 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# import geopandas as gpd\n",
"coordinates_names = ['TARGET_LATITUDE_SET','TARGET_LONGITUDE_SET']\n",
"# from shapely.geometry import LineString\n",
"# coord = [l for l in operator.itemgetter(*coordinates_names)(data)]\n",
"# LineString([(x,y) for x,y in zip(*coord)])\n",
"# plt.scatter(coord[0],coord[1])\n",
"\n",
"# labels = ['center', 'corner 1', 'corner 2', 'corner 3', 'corner 4']\n",
"labels = ['center','center.1', 'corner 1','corner 1.1', 'corner 2', 'corner 2.1', 'corner 3', 'corner 3.1', 'corner 4', 'corner 4.1']\n",
"fig = plt.figure(figsize=[20,10])\n",
"for i,lb in enumerate(labels):\n",
" ax = fig.add_subplot(3, 4, i+1)\n",
" ax.scatter(*np.array([np.array([l for l in operator.itemgetter(*coordinates_names)(d)])[:,i] for d in table_data]).T, s=50, alpha=.2)\n",
" ax.set_title('Index%s : %s' % (i,lb))"
]
},
{
"cell_type": "code",
"execution_count": 119,
"metadata": {
"ExecuteTime": {
"end_time": "2018-04-26T09:54:52.060972Z",
"start_time": "2018-04-26T09:54:51.608466Z"
}
},
"outputs": [
{
"data": {
"text/plain": [
"(19.476337, 19.935827)"
]
},
"execution_count": 119,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 720x720 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# import geopandas as gpd\n",
"coordinates_names = ['TARGET_LONGITUDE_SET','TARGET_LATITUDE_SET']\n",
"\n",
"coord = np.array([np.array([l for l in operator.itemgetter(*coordinates_names)(d)],dtype=np.float32) for d in table_data])\n",
"\n",
"plot_coord = coord[100:103]\n",
"\n",
"labels = [' Center', ' Corner 1', ' Corner 2', ' Corner 3', ' Corner 4']\n",
"\n",
"def fov_coord_extractor(x):\n",
" coord_tuples = np.array([(i,j) for i,j in zip(*x)])\n",
" coord_dict = {'center': coord_tuples[0]}\n",
" coord_dict['polygon'] = coord_tuples[[1,3,2,4,1],:]\n",
" return coord_dict\n",
"\n",
"fig, ax = plt.subplots(nrows=1,ncols=1,figsize=[10,10])\n",
"for crd in plot_coord:\n",
" plotdata = fov_coord_extractor(crd)\n",
"# ax.plot(plotdata['polygon'][:,0],plotdata['polygon'][:,1],'r',lw=0)\n",
" ax.scatter(*plotdata['center'], s=200, alpha=1);\n",
" ax.text(*(list(plotdata['center'])+labels[0:1]))\n",
" for indi,row in enumerate(plotdata['polygon'][1:,:]):\n",
" ax.scatter(*row, s=200, alpha=1);\n",
" ax.text(row[0],row[1],labels[indi+1])\n",
"\n",
"ax.set_xlabel('TARGET_LONGITUDE_SET')\n",
"ax.set_ylabel('TARGET_LATITUDE_SET')\n",
"\n",
"from matplotlib.patches import Polygon\n",
"from matplotlib.collections import PatchCollection\n",
"\n",
"patches = []\n",
"Polygon(plotdata['polygon'])\n",
"\n",
"patches = []\n",
"for crd in plot_coord:\n",
" patches.append(Polygon(fov_coord_extractor(crd)['polygon'], True))\n",
"p = PatchCollection(patches, cmap=plt.cm.jet, alpha=0.4)\n",
"color_var = 'PHASE_ANGLE'\n",
"colors = np.vectorize(operator.itemgetter(color_var))(table_data)[100:103]\n",
"\n",
"p.set_array(np.array(colors))\n",
"ax.add_collection(p);\n",
"\n",
"ax.set_xlim(np.min(plot_coord[:,0,:]),np.max(plot_coord[:,0,:]))\n",
"ax.set_ylim(np.min(plot_coord[:,1,:]),np.max(plot_coord[:,1,:]))"
]
},
{
"cell_type": "code",
"execution_count": 148,
"metadata": {
"ExecuteTime": {
"end_time": "2018-04-26T09:55:02.577917Z",
"start_time": "2018-04-26T09:55:02.156730Z"
}
},
"outputs": [
{
"data": {
"text/plain": [
"Text(0,0.5,'PHASE_ANGLE')"
]
},
"execution_count": 148,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 1440x720 with 2 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"color_var = 'PHASE_ANGLE'\n",
"fig, ax = plt.subplots(nrows=1,ncols=1,figsize=[20,10])\n",
"a = ax.scatter(*np.array([fov_coord_extractor(crd)['center'] for crd in coord]).T,\n",
" s=100, alpha=0.4, cmap = plt.cm.Spectral_r,\n",
" c= np.vectorize(operator.itemgetter(color_var))(table_data) \n",
" )\n",
"\n",
"ax.set_xlabel('TARGET_LONGITUDE_SET')\n",
"ax.set_ylabel('TARGET_LATITUDE_SET')\n",
"\n",
"plt.colorbar(mappable=a).ax.set_ylabel(color_var, rotation=90)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Compare with official IDL reader\n",
"\n",
"\n",
"- load the data for one orbit virsvd_orb_11187_050618\n",
"- only not-vector data are there, plus longitude/latitude.\n",
"- compare with output from above"
]
},
{
"cell_type": "code",
"execution_count": 149,
"metadata": {
"ExecuteTime": {
"end_time": "2018-04-26T09:55:05.749827Z",
"start_time": "2018-04-26T09:55:05.717731Z"
}
},
"outputs": [],
"source": [
"data = eval(open('data/'+os.path.basename(file)+\".idltxt\").read())\n",
"data_latitude = data.pop('TARGET_LATITUDE_SET')\n",
"data_longitude = data.pop('TARGET_LONGITUDE_SET')"
]
},
{
"cell_type": "code",
"execution_count": 150,
"metadata": {
"ExecuteTime": {
"end_time": "2018-04-26T11:05:26.651159Z",
"start_time": "2018-04-26T11:05:26.636935Z"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"=========================== =========== =========== ===============\n",
"data name idl python python == idl\n",
"=========================== =========== =========== ===============\n",
"INT_TIME 20 20 1\n",
"SPECTRUM_SUBSECONDS 224 224 1\n",
"SPARE_5 0 0 1\n",
"ACROSS_TRACK_FOOTPRINT_SIZE 1149.27 1149.27 1\n",
"DARK_FREQ 40 40 1\n",
"START_PIXEL 0 0 1\n",
"SPECTRUM_NUMBER 0 0 1\n",
"SPARE_2 0 0 1\n",
"SOFTWARE_VERSION 1 1 1\n",
"SOLAR_DISTANCE 6.17706e+07 6.17706e+07 1\n",
"SC_TIME 2.18416e+08 2.18416e+08 1\n",
"ALONG_TRACK_FOOTPRINT_SIZE 17048.8 17048.8 1\n",
"SPECTRUM_MET 2.18416e+08 2.18416e+08 1\n",
"EMISSION_ANGLE 81.4663 81.4663 1\n",
"BINNING 2 2 1\n",
"TEMP_2 28.124 28.124 1\n",
"SPARE_4 0 0 1\n",
"SPARE_1 0 0 1\n",
"INCIDENCE_ANGLE 3.56776 3.56776 1\n",
"SPARE_3 0 0 1\n",
"PACKET_SUBSECONDS 45 45 1\n",
"INT_COUNT 803 803 1\n",
"END_PIXEL 361 361 1\n",
"PHASE_ANGLE 77.9135 77.9135 1\n",
"=========================== =========== =========== ===============\n"
]
}
],
"source": [
"np.set_printoptions(edgeitems=10,linewidth=175, precision=8)\n",
"\n",
"new_data = table_data[0]\n",
"\n",
"printout = [[k,v,new_data[k], np.isclose(v,new_data[k],atol=1e-6,rtol=0)] for k,v in data.items()]\n",
"\n",
"headers = ('data name','idl','python','python == idl')\n",
"from tabulate import tabulate\n",
"print(tabulate(printout, headers=headers, tablefmt=\"rst\", numalign=\"right\"))"
]
},
{
"cell_type": "code",
"execution_count": 151,
"metadata": {
"ExecuteTime": {
"end_time": "2018-04-26T13:20:26.480293Z",
"start_time": "2018-04-26T13:20:26.463846Z"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"latitude arrays are close with tollerance=1e-6 : True\n",
" difference:\n",
"min : -0.0000042720\n",
"mean : -0.0000000202\n",
"max : 0.0000042979\n",
"\n",
"longitude arrays are close with tollerance=1e-6 : True\n",
" difference:\n",
"min : -0.0000124170\n",
"mean : -0.0000000120\n",
"max : 0.0000124951\n"
]
}
],
"source": [
"conf = {'atol':1e-6}\n",
"conf['rtol'] = .01\n",
"\n",
"describe = lambda x: print('min : {:13.10f}\\nmean : {:13.10f}\\nmax : {:13.10f}'.format(x.min(),x.mean(),x.max()))\n",
"\n",
"print('latitude arrays are close with tollerance=1e-6 : {}\\n difference:'.format(\n",
" (np.isclose(data_latitude,array_from_dictlist(table_data,'TARGET_LATITUDE_SET'),**conf)).all()\n",
" ))\n",
"describe(data_latitude-array_from_dictlist(table_data,'TARGET_LATITUDE_SET'))\n",
"\n",
"print('\\nlongitude arrays are close with tollerance=1e-6 : {}\\n difference:'.format(\n",
" (np.isclose(data_longitude,array_from_dictlist(table_data,'TARGET_LONGITUDE_SET'),**conf)).all()\n",
" ))\n",
"describe(data_longitude-array_from_dictlist(table_data,'TARGET_LONGITUDE_SET'))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Read vectore data for one Feature \n",
"\n",
"Read and extract only Vector Fields (ogr fieldType:RealList) for one Feature (==Spectrum)\n",
"\n",
"Assemble all the spectral properties (not lat/lon) in a numpy array."
]
},
{
"cell_type": "code",
"execution_count": 286,
"metadata": {
"ExecuteTime": {
"end_time": "2018-03-20T14:15:37.841018Z",
"start_time": "2018-03-20T14:15:37.836089Z"
}
},
"outputs": [],
"source": [
"maskbig = lambda x : np.ma.masked_greater(x, 1e32)\n",
"step = lambda x : maskbig(x[1:]-x[0:-1]).mean()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Read from file using OGR driver\n",
"\n",
"Easy on memory, slighlty slower.\n",
"\n",
" %%timeit\n",
" [...]\n",
" 1.84 ms ± 1.79 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)"
]
},
{
"cell_type": "code",
"execution_count": 355,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"401"
]
},
"execution_count": 355,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# which spectra from the file?\n",
"sp_id = int(len(table_data)/2)\n",
"sp_id"
]
},
{
"cell_type": "code",
"execution_count": 358,
"metadata": {
"scrolled": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mIOF_SPECTRUM_DATA\u001b[39m\u001b[38;5;124m'\u001b[39m,\n",
" \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mDerived column of reflectance-at-sensor spectra. One row per spectrum. NIR \u001b[39m\u001b[38;5;124m'\u001b[39m\n",
" \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mspectrum has up to 256 values (depending on binning and windowing), VIS has \u001b[39m\u001b[38;5;124m'\u001b[39m\n",
" \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mup to 512. Reflectance is a unitless parameter. Reflectance from saturated \u001b[39m\u001b[38;5;124m'\u001b[39m\n",
" \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mpixels, or binned pixels with one saturated element, are set to 1e32. PER \u001b[39m\u001b[38;5;124m'\u001b[39m\n",
" \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mSPECTRUM column.\u001b[39m\u001b[38;5;124m'\u001b[39m,\n",
" \u001b[38;5;124m'\u001b[39m\u001b[38;5;124m--------------------\u001b[39m\u001b[38;5;124m'\u001b[39m),\n",
" (\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mPHOTOM_IOF_SPECTRUM_DATA\u001b[39m\u001b[38;5;124m'\u001b[39m,\n",
" \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mDerived column of photometrically normalized reflectance-at-sensor spectra. \u001b[39m\u001b[38;5;124m'\u001b[39m\n",
" \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mOne row per spectrum. NIR spectrum has up to 256 values (depending on \u001b[39m\u001b[38;5;124m'\u001b[39m\n",
" \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mbinning and windowing), VIS has up to 512. Reflectance is a unitless \u001b[39m\u001b[38;5;124m'\u001b[39m\n",
" \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mparameter. Reflectance from saturated pixels, or binned pixels with one \u001b[39m\u001b[38;5;124m'\u001b[39m\n",
" \u001b[38;5;124m'\u001b[39m\u001b[38;5;124msaturated element, are set to 1e32. PER SPECTRUM column.\u001b[39m\u001b[38;5;124m'\u001b[39m,\n",
" \u001b[38;5;124m'\u001b[39m\u001b[38;5;124m--------------------\u001b[39m\u001b[38;5;124m'\u001b[39m),\n",
" (\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mIOF_NOISE_SPECTRUM_DATA\u001b[39m\u001b[38;5;124m'\u001b[39m,\n",
" \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mDerived column of noise/error in reflectance propagated through \u001b[39m\u001b[38;5;124m'\u001b[39m\n",
" \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mpost-calibration procedure. One row per spectrum. NIR spectrum has up to \u001b[39m\u001b[38;5;124m'\u001b[39m\n",
" \u001b[38;5;124m'\u001b[39m\u001b[38;5;124m256 values (depending on binning and windowing), VIS has up to 512. \u001b[39m\u001b[38;5;124m'\u001b[39m\n",
" \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mUnitless. Noise from saturated pixels, or binned pixels with one saturated \u001b[39m\u001b[38;5;124m'\u001b[39m\n",
" \u001b[38;5;124m'\u001b[39m\u001b[38;5;124melement, are set to 1e32. PER SPECTRUM column.\u001b[39m\u001b[38;5;124m'\u001b[39m,\n",
" \u001b[38;5;124m'\u001b[39m\u001b[38;5;124m--------------------\u001b[39m\u001b[38;5;124m'\u001b[39m),\n",
" (\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mPHOTOM_IOF_NOISE_SPECTRUM_DATA\u001b[39m\u001b[38;5;124m'\u001b[39m,\n",
" \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mDerived column of noise/error in photometrically normalized reflectance \u001b[39m\u001b[38;5;124m'\u001b[39m\n",
" \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mpropagated through post-calibration procedure. One row per spectrum. NIR \u001b[39m\u001b[38;5;124m'\u001b[39m\n",
" \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mspectrum has up to 256 values (depending on binning and windowing), VIS has \u001b[39m\u001b[38;5;124m'\u001b[39m\n",
" \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mup to 512. Unitless. Noise from saturated pixels, or binned pixels with one \u001b[39m\u001b[38;5;124m'\u001b[39m\n",
" \u001b[38;5;124m'\u001b[39m\u001b[38;5;124msaturated element, are set to 1e32. PER SPECTRUM column.\u001b[39m\u001b[38;5;124m'\u001b[39m,\n",
" \u001b[38;5;124m'\u001b[39m\u001b[38;5;124m--------------------\u001b[39m\u001b[38;5;124m'\u001b[39m),\n",
" (\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mCHANNEL_WAVELENGTHS\u001b[39m\u001b[38;5;124m'\u001b[39m,\n",
" \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mColumn of wavelengths paired to spectrum data channels. Wavelengths derived \u001b[39m\u001b[38;5;124m'\u001b[39m\n",
" \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mfrom calibration report equation 6.5. See VIRS CDRDDR SIS. Unit = \u001b[39m\u001b[38;5;124m'\u001b[39m\n",
" \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mnanometers. OBSERVATION column.\u001b[39m\u001b[38;5;124m'\u001b[39m,\n",
" \u001b[38;5;124m'\u001b[39m\u001b[38;5;124m--------------------\u001b[39m\u001b[38;5;124m'\u001b[39m)]\n",
"\n"
]
}
],
"source": [
"pprint_color([(lambda x: (x[1].get('NAME'),x[1].get('DESCRIPTION'),'-'*20))(x) for x in \n",
" filter(lambda x: x[1].get('ITEMS') == 512,fmt)])"
]
},
{
"cell_type": "code",
"execution_count": 356,
"metadata": {
"ExecuteTime": {
"end_time": "2018-04-25T11:42:14.382290Z",
"start_time": "2018-04-25T11:42:14.356949Z"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"============================== ====== =========== =========\n",
"VECTOR PARAMETER SIZE MIN MAX\n",
"============================== ====== =========== =========\n",
"IOF_SPECTRUM_DATA [512] -0.0272534 0.0847504\n",
"PHOTOM_IOF_SPECTRUM_DATA [512] -0.0295765 0.0922383\n",
"IOF_NOISE_SPECTRUM_DATA [512] 6.74043e-05 0.0704644\n",
"PHOTOM_IOF_NOISE_SPECTRUM_DATA [512] 7.65381e-05 0.0764708\n",
"CHANNEL_WAVELENGTHS [512] 215.673 1051.83\n",
"============================== ====== =========== =========\n"
]
}
],
"source": [
"# set some cosmetic for numpy\n",
"np.set_printoptions(precision=2, edgeitems=10, linewidth=100)\n",
"\n",
"wav_grid = np.arange(265,1017,2)\n",
"\n",
"wav = np.array(table[sp_id].GetField('CHANNEL_WAVELENGTHS'))\n",
"mwav = maskbig(wav)\n",
"\n",
"printout = []\n",
"for i in range(layerDefinition.GetFieldCount()):\n",
" fieldName = layerDefinition.GetFieldDefn(i).GetName()\n",
" fieldData = table[sp_id].GetField(fieldName)\n",
" if isinstance(fieldData, list):\n",
" # 5*2 = lenght of doubled LAT/LON arrays in data\n",
" if len(fieldData) > 2*len(table_data[0]['TARGET_LATITUDE_SET']):\n",
" mfieldData = np.ma.masked_greater(fieldData, 1e32)\n",
" printout.extend([[fieldName,'[%s]' % ','.join([str(i) for i in mfieldData.shape]),mfieldData.min(),mfieldData.max()]])\n",
" \n",
"headers = ('VECTOR PARAMETER','SIZE','MIN','MAX')\n",
"from tabulate import tabulate\n",
"print(tabulate(printout, headers=headers, tablefmt=\"rst\", numalign=\"right\"))\n",
"\n",
"#TODO: extract columns names\n",
"# np.savetxt('/user/mars/damo_ma/test.dat',data.transpose())"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Extract from data already in memory\n",
"\n",
"Heavvy on memory, slighlty faster.\n",
"\n",
"\n",
" %%timeit\n",
" [...]\n",
" 504 µs ± 4.38 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)"
]
},
{
"cell_type": "code",
"execution_count": 357,
"metadata": {
"ExecuteTime": {
"end_time": "2018-04-25T11:42:14.382290Z",
"start_time": "2018-04-25T11:42:14.356949Z"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"============================== ====== =========== =========\n",
"VECTOR PARAMETER SIZE MIN MAX\n",
"============================== ====== =========== =========\n",
"PHOTOM_IOF_NOISE_SPECTRUM_DATA [512] 7.65381e-05 0.0764708\n",
"CHANNEL_WAVELENGTHS [512] 215.673 1051.83\n",
"IOF_NOISE_SPECTRUM_DATA [512] 6.74043e-05 0.0704644\n",
"PHOTOM_IOF_SPECTRUM_DATA [512] -0.0295765 0.0922383\n",
"IOF_SPECTRUM_DATA [512] -0.0272534 0.0847504\n",
"============================== ====== =========== =========\n"
]
}
],
"source": [
"printout = []\n",
"for k,v in table_data[sp_id].items():\n",
" fieldName = k\n",
" fieldData = v\n",
" if isinstance(v, np.ndarray):\n",
" if fieldData.size > table_data[sp_id]['TARGET_LATITUDE_SET'].size:\n",
" mfieldData = np.ma.masked_greater(fieldData, 1e32)\n",
" printout.extend([[fieldName,'[%s]' % ','.join([str(i) for i in mfieldData.shape]),mfieldData.min(),mfieldData.max()]])\n",
"\n",
"headers = ('VECTOR PARAMETER','SIZE','MIN','MAX')\n",
"from tabulate import tabulate\n",
"print(tabulate(printout, headers=headers, tablefmt=\"rst\", numalign=\"right\"))\n",
"\n",
"#TODO: extract columns names\n",
"# np.savetxt('/user/mars/damo_ma/test.dat',data.transpose())"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Comparison\n",
"\n",
"photometrically normalized spectrum vs. not normalized"
]
},
{
"cell_type": "code",
"execution_count": 388,
"metadata": {
"ExecuteTime": {
"end_time": "2018-03-20T14:15:33.462778Z",
"start_time": "2018-03-20T14:15:33.188492Z"
}
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.legend.Legend at 0x7fb3386c3e80>"
]
},
"execution_count": 388,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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p5f4+MzMTw53l/p49eyIzMxOtWrUKeXxWVhbmzZsHnU6Hu+66C7Nnz8bQoUMjHgcRERERUXmFO4fWagWuugp4+WXgtttO/7guyEB7tpQOruFo2rQpNmzYgBtuuMG9bePGjWjatGnQY1JTU7F3716YzWaYTCaf4+644w6ffbt27YqRI0dixYoVQauo0RpXMHq9Hh07dkTHjh3RvHlzTJs2zR1oIwmuzZo18xtTON544w3cddddmDhxIvr3748NGzYAAFJSUpCbm+tuqz5x4oT7e5e2bdtiy5YtiI+PR1paWkT3CwB2ux2//fYbmjRpguPHj2PZsmXYsmULNE2D3W6HpmkYN25c0A8wfv31V2zfvh033XQTAMBisaB+/foMtERERER0xjgcnspsWRVasxnYuBHYsOHMBFq2HJ9lTz31FJ5++ml32Ny8eTOmTp2KIUOGBD0mISEB/fv3x4gRI2C32wHI6r+FhYV+YW/gwIEYM2aMTzvv6RpXIDk5OT6LMG3evBmXXXZZROdwGT16NJ566ikcPnwYAFBSUoKJEyeGdaxOp8OwYcPgcDiwePFiALIi8WeffQZAgufnn3+O66+/3u/YsWPHlqsya7VaMXr0aNSuXRvp6emYM2cO+vXrh71792LPnj3Yv38/6tWr57N6dGmZmZl44YUXsGfPHuzZswd//fUXDh48iL1790Y8HiIiIiKi8vBa07XMCq0r8JrNZZ930SKgZ0/Aq6ExYqzQnmVdu3bFwYMHcfXVV0PTNJhMJnz++ee49NJLQx43duxYjBw5EmlpadDpdGjcuLH7cjDeatWqhWHDhp2xcZVWUFCAxx9/HHl5eTAYDEhNTcXkyZPLPM7VigzIQlJLly5F586d8ffff6NTp07u9ueBAweGPRZN0/Dss89i3LhxuOWWW/Dcc8/h0UcfRYsWLaCUwq233oo+ffr4HXdbhB8t9e7dG7GxsSgpKUGnTp3w9ddfA5Bw+swzz/js26NHD3zxxRfo0KFDwHNlZWVh0aJFPtu6d++OrKwsPP300xGNi4iIiIioPLwDbVkVWlfgLSgo+7xLlwIzZwJvvAHUrl2+sWmqInH4LGndurUqPQf1jz/+QJMmTc7SiIjOLr7+iYiIiOh0OXYMqF5dvl+yBHDOhgtozx6gXj2gb19g+vTQ5x08GPj4Y+Cbb4AuXXxv0zRtg1KqzLmJbDkmIiIiIiKioE5XhdbVlhzhMkQ+2HL8DzRlyhS88847Ptvat28f8TVmvR0/fhw33nij3/YffvgBKSkp5T7vuap79+7YvXu3z7bXX38dt9xyy1kaERERERHR6eG6Bi0Q3irHQHhzaF2hl4HWqazL3ZAYMGAABgwYENVzpqSkRLzC8/ls3rx5Z3sIbufjtAEiIiIiOn/4LApV4kCoRl9XBfdMVWgvmJbjuLg4HD9+nG/u6R9FKYXjx48jLi7ubA+FiIiIiC5QPi3HJfaQ+5an5XjnTuDkyfKN7YKp0NaqVQsHDhzA0aNHz/ZQiM6ouLg41KpV62wPg4iIiIguUD4V2mI7AGPQfSNtOU5KkjD766/ANddEPraoBFpN024F8A4APYD/KqVeK3V7LIDpAK4AcBzAfUqpPZqmGQH8F0Ar51imK6XGlmcMRqMR9erVq8CjICIiIiIiotK859BGu0Lbvr1cj3bz5vIF2gq3HGuapgfwHoDbADQF0EvTtKaldnsQQK5SKhXABACvO7ffAyBWKdUcEnYf1jStbkXHRERERERERNHhX6ENLpIKrdkMNGoEpKQAv/xSvrFFYw5tWwA7lFK7lFIWAFkA7iy1z50Apjm/nwPgRk1Wb1IAEjRNMwCIB2ABUM7uaSIiIiIiIoo2nzm0FkfIfV2LQlksvseV5nAAp04BJhOQkVH+haGiEWhrAtjv9fMB57aA+yilbADyAaRAwu0pAIcA7AMwXil1IgpjIiIiIiIioigoT4UWCN12XFgIKOUJtL/9VvY1bgOJRqANdJ2c0ksNB9unLQA7gBoA6gF4UtO0+gHvRNMGa5qWrWlaNhd+IiIiIiIiOjN85tBaQ19VJtxA62pJTkyUQFtSAuTkRD62aATaAwBqe/1cC8BfwfZxthcnAzgB4H4A3ymlrEqpIwBWA2gd6E6UUpOVUq2VUq2rV68ehWETERERERFRWfyvQxucd6ANNY/WdZurQguUr+04GoF2PYCGmqbV0zQtBkBPAPNL7TMfQH/n93cDWKbkgrH7ANygiQQAVwH4MwpjIiIiIiIioiiIKNCe8uwcqkLrus206xc0agTExp6lQOucE/sYgMUA/gAwSym1VdO0/2ia1tW52ycAUjRN2wFgBIBnnNvfA5AIYAskGE9RSv1a0TERERERERFRdESyKFS4gdZdoX1xJIzFZlx+eflWOo7KdWiVUt8C+LbUtjFe3xdDLtFT+riCQNuJiIiIiIjo3FBS5ICrFmq1hJ5D632d2nBajhMd+cDGjcjIuA7z58tCUVqgFZiCiEbLMREREREREV2gLEWekFpmhdZrFeSwWo5hBtavR4sWwNGjwKFDkY2NgZaIiIiIiIiCshTK9XQMsJZZobVGWKE1wQxkZ5d7YSgGWiIiIiIiIgrKVaFNwKmyL9vjtWhUWJftQQGwfj3S0+XnSANtVObQEhERERER0YWppNAOPWyIRQmsEQTaUBVaV9hNRAGwKx/J9hOoX78qK7REREREREQUPZZiB2JggQE22Kyh97U5W5JjtZIyK7TxWhEM1avKBmfbcaQrHTPQEhERERERUVCWYjtiYIER1rArtFV0+WXOoU3UTgEdO8oGZ6Ddvj10q3JpDLREREREREQUlKVYIRYlYVVorRYH9LDBpMxlVmhN6iRQpw7QsCGwfj0yMuSyPb/9Fv7YGGiJiIiIiIgoqJJi5VWhDb2vtUTBCCtMjvzQl+0xOyTQJiUBbdq4Ay0Q2cJQDLREREREREQUlKVEuefQWm2h97VaJdAmwgxzfvBr1prz7HLJHpMJaN0aOHgQtfSHUKUKAy0RERERERFFiSvQGmGFrYxAa7MqGGBDIgpQcNIedD9zvkNWOHZVaAFoG2QeLQMtERERERERRUVJiWcOrdWqhdzXaoG0HMMM88ngC0gVmCEV2qQkoGVLQKdzLwzFObREREREREQUFRaL5qnQBi+6AgCsFlfLcUHoRaEK4Gk5TkgAmjZ1z6MtKgp/bAy0REREREREFJTFAq85tGVUaK1eFdpTweOm+ZTO03IMSNtxdjZatVRITw9/bAy0REREREREFJTFCq8KbXiBNhEFKCjSQwXoOlYKKCgyeCq0gCwMdfQoLk/ah19+CX9sDLREREREREQUVIlFQyxK5LI9ttAR0mYDDLDBBDOU0lBY6L9PURHgUJpnDi3gXhgK69dHNDYGWiIiIiIiIgrKYtW5W47LrNDaPBVaAAHn0ZrN8tWn5Tg9HTAagezsiMbGQEtERERERERBWWyeRaGsYbYcmyCpNVSg9Wk5jo2VUMsKLREREREREUWLxeas0BoAmz10hLTaNJ8KrSu8enOFXFOMBTAYPDe0aQNs2AA4HGGPjYGWiIiIiIiIgiqx6hELC4x6BasjvEBrMhQDKKNCW6nUNYBatwby84EdO8IeGwMtERERERERBWWx6xCjt8Ggc8BaZoVWJxXaZD2AwBVa9xzaxFI3uBaGimAeLQMtERERERERBWWx6xGjd8Cos8NWRoXWZgcMmh2JJplrG6hC6245NpW6oWlTID4+onm0DLREREREREQUlARaOwx6B6wOfch9rXYdjJodpiQJtKEqtK593AwGoGVLBloiIiIiIiKKjhK7AbEGO4w6R5kVWqtNB6PO5m45DnnZnsoG/xvbtAE2bgx7bAy0REREREREFJDdDtiVHjFGBwx6FV6FVmeHqYqE1ZAV2qpG/xvbtAGKisIeHwMtERERERERBWS1ytcYgwNGfRgVWocORp0DMcnxMMISdA5tLIphTK7kf+OVVwK33x72+BhoiYiIiIiIKCCLRb7GGh0wGhSsKkCbsBebXQeD3gEkJcGkFQRtOU5EAZCU5H9jaiqwYEHY42OgJSIiIiIiooBKSuRrjBEw6BVsygClgu/vqtDCZEKiMsNs9t/ZnG+HCeYAyxxHjoGWiIiIiIiIAnJVaGOMCkaDhFO7Pfj+VoceRleFFmYU5Dv89inIcwbaQBXaCDHQEhERERERUUDuQBsDGJyB1jWvNhAJtHap0KIA5jz/9Ouu0DLQEhERERER0eninkMbo2B0Tp+12YLvL4FWeSq0JwMFWiVzaNlyTERERERERKeLew5tLGBwBtpQFVqb0sOgV54KbYDL9hQUKFZoiYiIiIiI6PTytBxr7jm0ZVZoDVKhTUSQVY5P6bgoFBEREREREZ1e7kAbp8FglO+DVWgdDsABPYwGWeXYBDPMp/wjp7lQH/yyPRFioCUiIiIiIqKA3HNoY7Uy59C6gq5PhbZI77OPUoC5yMCWYyIiIiIiIjq93HNo43QwllGh9QRauCu0xVaDTwAuKQHsDrYcExERERER0WnmaTnWwWDUAIRRoTV6KrQAfObRuhaJMsEMJCZWeHwMtERERERERBSQpUQWgoqJ08EYI4E2WIXWFXQNBg1ISJDQisCBNjHGCuj1qCgGWiIiIiIiIgrIUiTXkY2Nj6BCa1CATofEODnW+9I9rnBrquR/fdryYKAlIiIiIiKigEpOSXqNqWQos0LraTmWr67QGrDlOFFFZXwMtERERERERBSQpdAZaOP1MDhXOS4z0DqDr2uKrHeF1t1yXPHpswCiFGg1TbtV07QcTdN2aJr2TIDbYzVNm+m8fa2maXW9bkvXNO1/mqZt1TTtN03T4qIxJiIiIiIiIqoYS5En0BpjJT7aSgK3C/tVaJ1VWO8KrbvlOEmLyvgqHGg1TdMDeA/AbQCaAuilaVrTUrs9CCBXKZUKYAKA153HGgB8DuARpVQzAB0BBMn7REREREREdCZZCp1zaBMM7jm01uLAgda9KJRzv8RkiZuBKrSm5Og0C0fjLG0B7FBK7VJKWQBkAbiz1D53Apjm/H4OgBs1TdMA3AzgV6XULwCglDqulIrO7GAiIiIiIiKqkJIiBwDnHFpXhbY48KpQfi3HSbKKccBVjisbojK+aATamgD2e/18wLkt4D5KKRuAfAApANIAKE3TFmuatlHTtKeiMB4iIiIiIiKKAouzGmusZIQhRuJjsAqtO9DGSqA1VZXe44CLQqXERGV80YjFgZqfSy9ZFWwfA4BrALQBUAjgB03TNiilfvC7E00bDGAwANSpU6dCAyYiIiIiIqKyWYoc0MMGfaVYGGOlMht0Dm2xHYAeRmfwTagiodXnsj1mBSOsiK0cH5XxRaNCewBAba+fawH4K9g+znmzyQBOOLf/qJQ6ppQqBPAtgFaB7kQpNVkp1Vop1bp69epRGDYRERERERGFYil2IBYlQEyMu+U4aIXWVc11thzrkk1IQAEKzJ56pznPDhPMQFJSVMYXjUC7HkBDTdPqaZoWA6AngPml9pkPoL/z+7sBLFNKKQCLAaRrmlbJGXSvA/B7FMZEREREREREFVRSrBADCxAbC0OszIkNVqF1za11tSbDZEIiCmDO9+xvzrUhEQVRC7QVbjlWStk0TXsMEk71AD5VSm3VNO0/ALKVUvMBfALgM03TdkAqsz2dx+ZqmvYWJBQrAN8qpRZWdExERERERERUcZYST6B1V2hLHAH3LV2hRVISTDCjILcyXNGzIN8mFVqTKSrji8rSUkqpbyHtwt7bxnh9XwzgniDHfg65dA8RERERERGdQ7wDbVkVWnegjZP93BXaPK8Kbb7jnGs5JiIiIiIiogtQSQlkDm1srDuoBq3QFknLsauS667QnvTsbz4JaTmOUoWWgZaIiIiIiOgcohRgsZztUQiLxVmhjYlxV2jLbDn2CrSJKPBd5fgUWKElIiIiIiK6UE2fDtSqBRQXn+2RSLB2z6GNlxmrNkuQQOsMuq7gC5NJKrSnPPuYT+kYaImIiIiIiC5Uf/wBHD0K7N59tkcCWKya3xxaq0UF3NdmKTWH1lmhLSj0xE5zkYEtx0RERERERBeqvDz5umPH2R0HAJRYNM8cWldtfZ5yAAAgAElEQVSF1hqs5Vi2u/ZzLwpV5FmL2FxsjGqFNiqrHBMREREREVF0uALt9u1ndxyAVGhjXRXaOImPwSq0rpZj7wqtCWYUlBihFGC1Ala7HiYUAAkJURkfK7RERERERETnkIpWaBcuBN5/PzpjsVh17kWhdLFG6GCHLdxAm5CARJyC3aFDcTHci0OZYi2ApkVlfAy0RERERERE55DcXPla3kD79tvAiBFAQUHFx2KxaYjRrIBOBxiNMMIavELr3O4OtJoGU5ws11xQ4Am0iZUCtyyXBwMtERERERHROaSiFdpdu+T6sYsXV3wsJTY9YnVyfVkYDDDABps12KJQzlWO4zwzWxPjZKEos9kTsE2V7BUfmBMDLRERERER0TnEFWj37o38erRWqxwHAF9/XfGxWGw6xOidAdRVobUGuW9XhbaS0b3NlCAh17tCa0oMHIjLg4GWiIiIiIjoHKGUtBzXqAE4HJFfumffPsBulzWXFi4EbLaKjcdi1/sEWgNssAap0FotDuhghy7WE2gTE+Wr2ezVcmyKzvxZgIGWiIiIiIjonFFUJFXWNm3k50jbjnftkq8PPgicOAGsWlWx8ZQOtEZYYQtaoQWMsAJGrwqt83KzBQVeLcdJDLREREREREQXHFe7cevW8jXSQLtzp3x99FEgNrbibccldgNiDaUrtIH3tVqUX6BNTJLI6dNyXCV6V49loCUiIiIiIjpHuFY4btgQSEqK/Fq0O3dKkE1LA268UQKtqsCUVYvDgBiDc1ViV4U2SBuzzaZggM23QusMr94txwy0REREREREFyBXhbZKFSA1tXwV2nr15Co7d94pc3C3bCn/eEoHWgNssAYJtO6W45gY97bEyhJeCwoA80lJ1olVYwIeXx4MtEREREREFfT338B11wFLlpztkdC5wGIBZs6URZ0i5Qq0lT99Cw0bqnLNoW3QQL6/4w75Wt62Y7sdsCs9YozOEq+7Qht4DqzV6j+HNjElFoCE2YI8G/SwIa5qpfINKAAGWiIiIiKiCpo6FfjpJ+DuuytWDaMLw6RJQM+ewNKlkR/rrtDO/ACpF53Enj0IOme1NKWkQlu/vvx86aXAlVeWP9C6LhkUa/RtOQ5aoQ0QaGMrx8MAKwrybDCfsMAEM7QkU/kGFAADLRERERFRBSgFTJsGpKfLiq633w4cPny2R0Vni8UCTJgg369dG/nxrjm0lZGH1Jj9sNuBPXvCO/boUWntdVVoAWk7zs4GDh6MfCyuQOuu0Op0MMAWvEJr8w+0WnISElEA8wkrzLk2JKJAJgdHCQMtEREREVEFZGcDf/wBDB0KfPMNcOwY0K2bXH6F/nkyMyU8xsUB69ZFfryrQpuMfDS0/QEg/Hm0rkv2NLBvkxcjJNACwPz5kY/FHWi9prwaNRus9mCBVpNFobwPMJlgghkFeTYU5DtggtlzLZ8oYKAlIiIiIgIwYwawb1/kx02bJuHl3nuBVq3kPOvWAf37l28OJZ2/lALeeANo3lxeD+vWRb7CcF4eUElfjBhYkZq7HkD4gdZ1yZ4G88YDvXoBhYVo0kQWlypP23GgQGvQHLAFCbS2ABVaJDkrtHl2mE86Ay0rtERERERE0bNyJdCnD/Dss5EdV1IiFblu3YDKlWVbt27A668Ds2cDY8ZEf6yng8OBoJdiofAtWgRs3QqMGiVzV48cifxDktxcoIouHwBw0d71SEwM/9I9rkBb98Aq4NQpYMECaJq8JpctA/LzIxtLSYl8jY31bDPqbLDaAsdIq02TQKt5BV5XhdasYDaDLcdERERERNHmCp5z50oOCNfChcCJE1KN9TZyJPDgg8Arr0jF9lw3cCBw9dUVu14pAePGAbVry4JQbdvKtkjbjvPygMqQ5Knl/BnRpXt27QJq1lSI35cjG7KyAEjbsdUKfPddZGNxV2hjPQHVoHOEbDk2anbfjc4KbUGBgvmUji3HRERERETRtGwZsGIFcM89Ema/+ir8Y6dNk5Vkb7rJd7umAe+/D3ToADz6KLB3b1SHHFV//SWhe/16qTBS+axbB/z4IzB8uHTcpqdLZbNcgdZxQi4ke/gwGta1RNRy3ODSQim516gBfPstkJ+Pdu2A6tUjbzsOPIfWDps9WIVWB6OuVKnfWaE1F+hQUKhjyzERERERUbQoJdXZmjUlnF52GfDZZ+Ede+SI5IU+fQC93v/2mBg5p1JSAT1X59P+97/Sblytmsz/pPJ54w0gORkYNEh+jokBWrYsT6BVqGw/BrRoAQBITTqK3bvDawnfuROon3RcfnjmGekZ/uor6PVAly7yeg33EkBAiAqtI0igtYeo0BbpYC42skJLRERERBQtS5YAq1cD//43EB8P9O4NfP99eJfd+eILCRml24291asHvPWWVIHffz+ysSkl1zH9+GMJS88+Czz2mAToF14ADhyI7HyB2GzA5MnAzTcDTz8tlers7IqfF5DnNCMDsNvL3vd0OHQIKC4+M/e1Ywfw5ZfAkCG+Wa1tW3k+I5mfnHvcgSrIBdq3BwCk6nfDZiu7yl9YKI+5gdG5Y9++QN26Mskb0i2Qnx/ZnF73HNp4T2w06oNXaG12DQZdqU9uXBXaIgPMxUYkaoXyxxYlDLRERERE9I/kqs7WqSPzXQEJiw6HOwOENG0a0Lo10KxZ6P0eegi49VbgqafCX9xn3z6gc2dpZR48WI597TUZ1+rVwH/+I1nlrrsk9Ja3+vvNN3KJmSFD5H6SkoDx48t3Lm/r18t4f/lFFtwqj+Li8gfSrVuBtDTg2msjmxNdXm++KW3Gjz/uu71tWwmav/8e/rlkDm2eLJkdE4OGxb8BKHse7e7d8rWB5U/pL65cWSbzLl0KHD2KKlXk9kgWhrIUywsrJs4TGw06FaJCG6DlOCEBiTiFvKJYlNiNMMVafBeNqiAGWiIiIiK6YC1bBoweHfhN/MKF0g763HOeOYJNmgBXXAF8/nno8/76K7B5c+jqrIumSVtvbKzsH6pi6XAA770nIXnlSuCdd4D9+wGzWVpFjx+X4LJrl6yku3KlhN7GjSX0Dh0ql4u54QaZw9m8uYTLYN5/XxYxuv12CbMPPyyrM7vCUXlYrdJ2e/HFQEKCVLLLo1s34I47Ij8uLw/o3l1+pxs2yPMRSZttpI4cAaZMAfr1k/nU3iJdGMrhAPLNOgm0F10EpKUh9fhaAGUHWvcle3KzJc0DEmjtduDLL92rcEcUaAslnHoHWqPeDlvIQFvq0xVNQ2KsBRa7AQBgiovuL4OBloiIiIguSHv2SAXztdeAyy/3XfDIVZ2tX98/lPbtC2zcGLqqNm2aVOR69QpvLDVrSlD93/+CV0BzcoDrrpO24nbtgC1bgCeeAGrVAhITfYtadesCY8dK2/Hnn8v81/HjgZkzJWxbLECDBhKAe/cOXKXctk2Kd4MHAwbJGhg2TOYDT5gQ3uMK5O23pTI7aZKE0jlzPHMxw7V7N7B4sYwvktZqh0N+f7t3ywJIH3wg80YHD47OCs5DhshaS9WrA1WqyO+lVi15fCNH+u+fmiqF0nADrdkMOByatBxXrQo0boxLdq1BQkL4gbb+X6s8gTY9XT6lycxEcrJsiijQnpLw6RNodQ5YHQEmjQOwOvQw6vw/sTHFeaq2iZWiO5mcgZaIiIiILjhWK3D//RJiZs+WxXo6dwYGDJDrfH71FbBpk4Rao9H32J49JdQFq9LabLIqcJcuQEpK+GPq1Qvo0UPuMztbQvOnn0povfZayR5btki1b/FiCa1liY2VwLpmjRTijh0D/vwTWLUKmDdPWpR37JBqbmkffihB9qGHPNtq1pTn7ZNPJAxHatcu4Pnn5TIxd90l58rNlccTCe/nfs6c8I976SVgwQIJ5NdcI0H2+eeBqVMjv8ZwaXa7fJBxySWyInbfvlLRHjECmD4daNTIa+f9+4E+faDl56Ft2/ADbV6efK2MPHeg1XbvQmoDR5nt6rt2AUlJCilHfvcEWk2TF/TKlUguPORzH+EoOSVBNLaSJ8Aa9Cp0hVbvH1gT4z0h15QY3WtDMdASERERUVQ4HBJezgXPPy/V0MmTgbvvltbTf/9bVjBu1kyqaWlpEgZLu/hiWSRpxozAc1PnzAH+/ju8dmNvmiYVw8qVgTZtpLX5wQcl1NrtUv374w/ggQfKN8Uw0DHXXQc8+aTcr3eFurBQgnOPHhLQvI0cKbd/8EFk96+UXKJIrwfefVfGc9NNEvojaTtWSgLiddfJQr+zZ4d33DffyGJZ/ftL67XL889LC/Srr0qVvLy2b5fn5YknpFV74kSZO/vaazL32sdzz8kL6Ntv0batfFARzlxev0DbpAlgtyP1YnNYFdoGlxZBAzyBFpBAqxSSl80DUM6W43hPoDXqHbA6DAH3tzl0MAQItKYEzzYGWiIiIiI651gsUgGtW1dafc+mpUslZDz0EHDffbItNhZ4+WVg7Vppz921S8KPIfD7cvTpIwszlV7QaPZsCUzp6cBtt3ndYLHIcsZl9MdWry6V02efBWbNkrbfkydloacJE/zDZTS8/LLMpR04UCq4AJCVJeFpyBD//S+/XB7bpEm+izIdPizzkevUkQrlqlW+bbxffCGrRo8dK224gFS/771X2n8LCsIb79q1UlXu10+OXbNGCp6hbNsmv7NWrSSIe4d71zWBu3aVRZu+/DK8cZS2aZN8bdmyjB23b/dc+2nlSrRtKx9YuI4PxSfQVqkik6MBpCYcxq5doedf79wJ1E92ltW9A21aGtCqFZLny5giCrRFcocxlTx/KAa9gk0FqdA69IErtIle3ydFN4Iy0BIRERFRhTgc0sq7eLFc5uOhh6IzX7E8/v5bWkEbN5YFlUq74gpp9129WgpXwXTrJm/Cva9J+/77EpDbtJHL27gWkgIgae7JJ4Hrrwf++ivkGK++Wlpj77kHaNgQ0J3md+SxsdLCe+KEtMgqJaGvWTO5lEsgo0bJYkeffSZhcfBguUbvuHFSNPzhBzm2dWuppv71FzB8OHDllVKl9Xb//UBRkYTacEyfDsTFSWX9nntkW6i248JCzyJQc+cGviKMwSDt123bSrU23HDtbeNGuY+mTcvY8aWX5Elv3dodaAEJ6mVxdThUSbDKoJ19zKnYAas1+CV37Hb5IKmBcZ8k+AYNfHfo2ROG7J+RUMlRrgqtd8ux0aBgVYE/CQoWaE1Jnk8YTMkMtERERER0huTlyQq4kyYFbr9VSnLcF19IS+fEiRJ2Jk8+82N1OKR6mpcniyNVqhR4v5gYCZWh2norVZI5oLNnSxgbM0baWO+4Q65T67oECgB5EiZNktLl4cPAjTdKsj6HpKdLpXbuXFn4KTtbgmew56BjRwn/Tz4pHw5Mny4V3pwc+eBi/36Zg1tUJM/5ZZfJ8z55srQce7v6allJOZy245ISqR537y6rLjdsKBXRWbOCHzNpkizgNWOGjCOYSpWkCp6bK3NhI7Vpk1S6S8+59rFtmwxkyBD5VGTrVlxsOI7LLgtvHq27QutckRgJCUCdOmhYIOXdYG3HBw86FwKz/imvw9Kp3tmqkKw/Fdkc2iAV2qCBVhlg1Pt/muVdlTVVCdIWUU4MtEREREQU1HPPySqxTzwhcxq3bfO9fdw4WdV22DDgmWek+tWpk8zD3Lv3zI51/HgJW2+/LcGjovr0kXbgjh2l6DZwoLSr+lUA166V8t3o0XItoH375Elw9feeI0aMkMWnJk2SnNS3b/B9NU2udVu5sjysvXulqpuaKrcnJEi1d+tWaTPu2lXmk6an+59Lp5MFsZYsKfspWbhQAme/fp5t99wD/Pxz4Opkfj7w+uvSIn3zzWU/B+3aAVddJcE2VPtuaUpJoC2z3fill6S8/NRTnvL36tVhLwzlDrQpXp8KNG6M1L9XAwgeaN2X7Mnf6Ntu7FKnDtC+PSpb/o6w5dh5HdoET4o3GhRsKsQqxwb/QOsdYk1VQ30iEDkGWiIiIiIKaNMmabMdOlRWid2yRRbpGT9ewsCUKRJie/WS6aOa5rnmKnBmW4+nTpWx3HOPtMdGzOGQ9L5xo3vTDTfIdUXXrQP+7//kcQWcc/vuu1JO7NNHEuP8+ZI8brrp3FklC1I5nTZNqsuDBsmQQ+ncWULkK6/IQlmBuBZ++vJL+dAjmPvvl9Why1qx+LPPZB5xp06ebaHajl0V15dfDn1ebyNGSAD85pvwj9m/X1q2QwbanBwpQw8dKteQbdtW2gFWrULbttISfORI6PvJzQU0OJBc3aufvUkTXLpzFeLjVdBAu2uXfPW5ZE9pPXogueQI8o8UB749AEuxs0LrFWgNBkBBF/ADAasywBCoQlvFc3xiSmzY9x8OBloiIiIi8uNwyPvylBQpOvXvL22dt9wi8ytbtpRQdNNNEia954FedpmE3qVLgY8/Pv1j/fxzqZ526iStseVZIRiffy6paPRo9ya9Xh5bVpaEuoDnPXxY+mEHDPCsfHPjjbLyk+sJi6Qkdpq5Fu16881SN7z4oiz5fJo+gUhPl7mnodqOjx2TCu399/t+cJCaKos9lW47Pn5cPkjp0UNuD1f37vI8+D0HIYS1INR//iPVWdcFaePiZMK11zza9etD309eHpCkK4AuxaunvXFj6AoLkHqZNeile3buBAwGhdrmrcEDbe3aSEY+8k6Efx1YS7G8HkpXaAH5gKI0mwpSoU2RgK6DHZVSAkxyrgAGWiIiIiLyM326XPZm3DjPfNFLL5WclpUFHDokcyy//LLU4khOgwdLhfPJJ09v63FWloTtjh3l2rJxceU4idkMPP20TI5csgTeqeHmmz0rJQf08cdy0dvSywXfequUFDdtktWNIulvPc2SkkotRHX4sCxN/MUXMkH4NNA0CaorVwZf2GjmTHkqvduNXe69Vzq7vVfQHjdOFnd68cXIxmIwSIv8qlXhXx9240Z5zgK1VAOQ6y1lZgKPPSbVWZdrrgGys9GqcSF0urLvLy/P65I9Lq6VjlNyQ7Yc172kGAbYgwfaKlWQjPyIPl8pKXbAACt08Z6qqsGZba1W332VAuwwBAy0CdUkxCaiAFqSKfwBhIGBloiIiIh85ObKFMB27fzDhaZJwNu3TwKBKch7U00DPvlEvh80qHyFP4dDFph69VVZVdhi8b19zhzp8m3fXtpHgy0CVaZXX5VQN3u2pJ2PPgrvOKtVVka65ZbAIeKOO+T2pUvlYqjnqokT5cm95BK5WG+0qrQ5OT5LG/fqJV+zsgLvPn26BMYWLfxvK912fOiQzAXu3VtWaw5o1izgl18C3jRwoAT7CRPCeByQzyUaNZK5wwG99JK8AEeN8t3eoQNgsyFx61o0axZOoFWo7DjhG2ibNAEApMYewM6dgT8bCXrJHm9VqqAy8pBvDj8CWooVYmDx+dTK6Kyel67QugJuoEWz9MmJiEchElFQdq97hKK7xBQRERERnffGjJF2zsWLg19SJtClUUqrW1eqaEOGAAsWSL4Lx5Ej0ur78ce+i+AkJMhVcW65RbLDww/LZWIWLgwRNMqyY4f0rfbrB9x5p/SjfvqpBJSyHuRXX8n1akIF4AcflFL3K6/InMquXcMfm1LAsmXA7t1SuvP+d9llkhAvvzz88wVy8qRMlO7RQybNDhwoj6t794qdNy9PytsHDsj469RB/fqyIFNmpnxg4i0nR8Le+PFeG13X1klMRP360hEwe7Z09I4dKxn8hReC3P+HH8oyzomJ8gK59lqfm5OSpItgwgRZVKpOndAPZ9Mm5ymUkusEmc0yPrNZWhCysqTKX62a74Ht28unO6tWoW3b6zFvnpwiWFt87jE7qiDXN9BedBFQuTJS7X/CYrkCBw/6j3fXLqBt3f2SJoM9GFeF9lT4EdBS4gy0sZ4KrTFIhdYTaAN8IJKUBBPMMMEc9UDLCi0RERHRabRvn6wmm5EBPPCArB/0889yuZNIKSX56Ztv5I38iBHSIVu6clkRmzdLvnn00TBWdA3DoEFAjRqSL8qydatUf2vVkmxQo4ZMbT1yRDJWv34yLfXxxyUntmoFLFoUvEoclieflOrTa6/Jz0OGSIl65syyj333XaB+fVlit6z9WrWSBxCsZ7S0jRslQXXqJE/iqFGS4jIz5SK6r78uSzm3aCGfGnj38Sol83Z37PBflrq0jz6SfZ9+Wl6ojRrJ4lgVbZEeOlSuJaOUp1QPyeCbN0vOe/ppeS0fPy6LQel00pbsdvvtPs/tvfdK6P3pJxn2wIH+l1sFIAF26FAJ1LVrS/v30qV+uz3+uHydODH0Qzl2THJ5y5aQT2USE6X/vmFD+b127w4kJ8trqbTKleX35JxHe+KEZ0XiQPKOO/xbjjUNaNIEDfOyAUhnhLfcXPnXwJojT0jAlctkLMnIR4nNgOIw14UqKQFiUeITaA1GSePBKrQGQ4C0bjIhEQUSaCv0BxuAUqrC/wDcCiAHwA4AzwS4PRbATOftawHULXV7HQAFAEaGc39XXHGFIiIiIjqXnTql1AsvKBUfr1RcnFKdOil10UVKyTt8pfR6pVq2VOr115U6eDD4eXJylBo7VqnOnZW6+GLP8TqdUrGx8n1yslL336/UrFlKnTyplMOhlNWqVGGhUnl5Sh09qtSePUr9+qtSq1Yp9e23SmVlKZWZqdSiRUr9739K/fmnUocPK3X11UpVr67UiRPRey7GjFFK05TavTv4PjabUmlp8liGD1fq99+D77t9u1KzZyuVnx/Gnf/4owwg0ANavFiewNde82xzOJRq0kSptm1Dn/eXX+TY8ePDGISSB1+1qlLp6fLiCObwYaUeekiesOrVlZo8Wal9+zy/WJe//1Zq0iSlrrrK86JIS1Pq0kuVMho92wClxo0LfF/FxbL/jTd6ts2cKcd89ll4jyuQzz+Xc/znP0rdeqtSNWvKC1IpVVSk1L//rVS7dr7DNBplV7dNmzw3btmilFJq1y75sWpVpWJi5Gnxk52tVEKCUq1aKWU2y/PUvLn8sSxY4Ld7z55KJSWFfi0tWSL3+8OiEqUMBqVuu02p99+X52jePKW+/16pv/4KfoIhQ5RKTFSbs60KUGrGjOC71r64RD2AT5X66ivfGwYMUEUXX6ZatFDKZJKXn8v69TK+uXWeUKpr1+Ant9vVexiiAHmZheOBdn+q2tjr85/UJ7fMVIBSe/f67nv0qIxjYodZ/ifKzlYtsEldjx+U2rkzrPsGkK3CyaLh7BTyBIAewE4A9QHEAPgFQNNS+wwB8KHz+54AZpa6/UsAsxloiYiI6HzncEgmqFNH3mndd5+ESddt+/bJe2DXm3pXOL31VgmYhYXy/v3FF+V9uOs9fbNmSvXvr9TEiRJKCwpk3/nzlRowQKmUFNlP03yzTHn+TZkS3edk3z55jP/3f8H3mTNH7nvmzCje8W+/ybt/QJ6gDz6Q5KyUUhaLBNcGDSTYeZs4UY7Jzg5+7sGD5dOK48fDH8+iRfIL6tPHN5zabJLSx42TdGUwKPXkk/JpRDh27JDw2L27hOFnnlHqzTeVmjZNqW7d5MlfssT/uI8/lsfpfZvdrlRGhlL168tzFKndu+UxXH21hNh58+Q+vv7ab9fCQvm84ZVXZOgrV3rdOHiwfBJkNMpz4dSmjZxu2LAg933JJfLH5x0wjx1T6oor5Fxz5/ocsm6dnO+tt4I/pNdek32Of+fced68sJ4Kt8xMpQBl/d96ZTDIryeYxHir+hfeVOqnn3xveP11pQC1f0ueqlFDqVq1PBnT9RnEL8YrlBo5MuRQPq80SAHyQVk47r/iD9UA2+U5dJrWZZYC/HPpX3/JOD64IUCgzclR4zFCfYRBknzDEG6gjcYc2rYAdiildgGApmlZAO4E8LvXPncCeMH5/RwA72qapimllKZp3QDsAnAqCmMhIiIiOmOKi6WrMycH+PNP+bppk1yvNSND2mU7dPDsr2nSAVm7NtCtm2zbtk0Ww5k+XdoxY2KkhVjTZIHUd94B7rpL2nADueMO+WezAWvWAMuXS7eo0ej7LzFRpq55/9M0z5TM3Fz5GhMTeJXZiqhdG+jSRTpPn3/ef1VkpaSbNjVVpnJGxdGjnvbQ2bOlpfjRR4EPPpAn9ZdfZGXar7/2aacEIE/AM8/Ivq6L6nrLzZVfbu/evq2hZbn1VukVf/55WY7ZapUXy++/e3rQb79drifTqFH4523QQNqEA+nRQ1b36tkTyM4G6tWT7XY78MYb0jLrfdFXnU4uX9Sli8wlfvjh8Mdht8tzp5Q8PwaDPJ5LLwUmT/abPxwfL13Vpaa3yrzeGTPkDyIvT/qRx44FjEYMGCAtu15XVxK5uTIHuKhIVhK79FLPbSkp0nJ8222yutSMGe6lq9u0kb/Rd96RFuRA3bqbNsmU5ap/rJYNV14Z/nMCuP8TMPy8CrVqtQ66yrPNBhQUGfxbjgH3wlC1zH9gwYKr0KGDvLx/+snTwlzf+ieQFvr3lWxyAIXytIbDYtH8F4Vyfmu1KACe9mL3HNqYAC3HSUl4Em/J96ZJ4d15uMJJvaH+AbgbwH+9fu4L4N1S+2wBUMvr550AqgFIAPA/AImQwMsKLREREZWLwyFdeo0bS2vuJ59Isel02LRJqX79pO3Ru7JZq5Z0b06e7CkEhstuV+qHH5R6/HHpZjx06PSM/bTasEEevHf10enbb+U5mhWgeONq6Zw8OUrjKC5W6pprpMK3bp1sczikDHzZZcrd43rTTQHHqpRSatAgqcCWblUuKFCqRw85x6ZNkY/NbpdyJKBUjRoyhn/9S16wGzdGfr5w7NihVOXKSrVo4Wl3DlUSdzikwlqzppRRXTZuVGrgQKUSE+X5/fhj3yryK6/IOadP9z3fv/8tVeLSParBvPuunGfdOqW++Ua+d7bgOhxKlZSU2t9iUapjR/mdLlsW/LwnT8rjSkhQ6sgR92ZXEXn27MCHpaVJodBwBUkAACAASURBVFv16iV/5OVRr55Sd92lrrtOqfbtA+9y7JiM4x087t/CvG2bT/vEggXylHbtqtQDDyh1cZViuX3FipDDWJk20K8oH8odjXJUBjb6POmz7pujAKV+22T12XfHDhnCtDsCPJEFBXJjTEx4d6zCr9BGI9DeEyDQTiq1z9YAgTYFwHgA9zq3hQy0AAYDyAaQXadOnbCfCCIiIrrwrV2rVIcO8s6mUSNPK+9VVwXvGj1yRLoP586V98CbNknHYl5e4CBss8l76uuuk3MnJMjUuMxMeZ9fUHA6H+F5YMUKeVKCJFObTam6dZW64Qb/Q6+/XrJd6c7fcnE45B0+IBOFSyssVOqll6SHO9RE3Y0b5RwTJni27dwpc2B1uvDnzgYb48mT5T++PFztzr16yf23aSPt1sE+eVm+XB7/2LFKffGFBEFAqUqVZMJ2o0byc1ycTET94ANplb7vPv8PCXbvlvseM6bscToc8rtxFbCsVmkjvvPO4Me89ZYzSU0r+/y//y5jeeop9yabTfLmNdf47242y+4vvqikDbtHj7LvI5C+fZWqXl317etQtWsH3mX7dufDQF+ZbOzNapUw6DVuV+7X65VqV8/Z7xtqLq9S6te2D4YM76XdUn+baouffX6n8/p+KZ/n/M93jH9stStAqS96zPE/kcMhfzcpKeHdsTqzgbYdgMVeP48GMLrUPosBtHN+bwBwDFKfXglgj/NfHoATAB4r6z5ZoSUiIiKlJF/cd5+8o7noInlPbbHIe6dp02Sbpin1yCMy32zpUqWefloWYwo1h1SnU6pKFXmT26qVhLD69eW2OnUky+Tmnu1Hf4bY7Up9+KFSW7cG32fxYqlmNm0qT1ZsbMDq5auvynP455+ebT//LNsqkg99jBsnJ3z++Yqf66qrpDzncEhJq2pVeWEsXlzxc58Nrl+A64/mww9D79+pk+ePIjVVwr3rhe9wyCdJQ4fK8wIoVbt28NXESi0OFdRPP8m5/vtfz7ZRoyS1BVrJ6MgRWUnMZ0WpMvTuLcHc63yuTFz6A7BVq2T7N5/nyTfBFtgqy+TJSgHq2UePKp0u8PRk1+JO82OChOZmzfwWfRo+XI7p02idVM6DdRw47b3tYQVIcT0c19fZrq7RVvls++bBuVJAX2b22f7bhhLpwuj5ZeCTJSfLf6phOpOB1gCZA1sPnkWhmpXaZyh8F4WaFeA8bDkmIiKisOTkSBHOYJD3pWPGBC545ebK4jF6ved9ucGg1LXXKvXyy0qtWSO5a/lyqdR++qmspfPcc0o99pis3XP77dIi2KmTtMuW9X78gvPmm54y0OOP+y+CNH++VI5atJBwceSIBJcGDfwWNTp0SJ7/ESM827p1k4wYlYLl11/LJxj33hudfvNp0+Sx9+0rn3I0by59lecrh8PTLn3xxf5VwNJ+/13+0BYtCv18lpRIa7D3JxWlzZ3rTGvzQ99nr14SfLxbHn7/PfinHg8/LC+qUNX20nJy5Pfp9ULMy5M82Lev766u9cEOTP1ehdPSG9QffygFqMn9VirAs1Cct++dd/FT9bsCn6NHD/mAxYvNptTo0UotbzNKPn0rQ16/xyP6AKn9pTvVDXrfx/zdI/MUoNSaRb5/3xtXnZI1s/r5LrzlVru2/D8RpjMWaOW+0BnANmcr8b+d2/4DoKvz+zjIKsY7AKwDUD/AORhoiYiIzjCHQ9rcvv5aOgv79VOqdWt5r9url1LffRf5XNDT6ZdfpLikaVIQHDYs9CVvvI97/nl5z32mOz3Pa1u3SrW1c2elHn1UQkDVqkq9954k+9mzJUy0aeMbdFetkgDco4dfxeiee+QURUVyeiC8TtQyzZ4twbp169CXxYlEUZGn+njPPRdGX/nJk3LZmdLzXE83i0UuEdSlS/B9/v5b5sE+8YT/bVddJR0A3q+nzZvlNRlwyeMy9O8v7dJeLbqPPy537921O2CAXD3J8dwYuS+z2f9c4XA4lKpWTS3uNE4BsrpzabNny0vt14ZBAu2zz8rfld8kYiUtJD17ljkM+8inlAa7eu658IbduvpudVvM9z7bfhj2tTyGucd8tq/9Pl8BSi0YVOqSQy5Nmwbu6w4i3EAbjVWOoZT6FsC3pbaN8fq+GDLXNtQ5XojGWIiIiKhseXnAtGnAhx/K6rwuNWsCTZvKAqvffgtkZgI1agB9+gD9+8ttp1NJiYztxAng8GHg77/l6+HDwK+/AosWASYT8PTTwL/+BVx0UXjnTU+XfxQBq1VWrDWZZLXbiy8GHnkEGD4cGDoUePttWV61XTt5sSQleY5t315WFR41Cpg0CXjiCfdNjzwiiw7PmSMLz1aqJKvLVsi778p9tGsHfPONnDQa4uKAKVPkhfjQQ7Is9PnOZJLf15lmNAIDB8pqxfv3y9LXpX36qbzuHnnE/7aBA4HBg4H164G2baXhYvhwoEoVWTU6Us89Jysxv/aaLHEMeR2++678v/jii7Lbpk1Ay5aAtm4tcPnlsmp2eTiXLa+TvQTAqIArHefmytcqKbrA52jcWFaS3rHD9z/jkhJgzx75j7oMuqqVkYSTyD+eCIQRBS02PWJ0Np9thhgZn63E7rPdWiT7GWODjL9JE/l9RVmQeyMiIqIL0YYN8r68Rg15L5icLFcm+flnCZIHDgBLlsj7vEOHJHRccYVcRaRZM6BaNfm5e3dg2DDZvmiR54ojkVi7Vq4k0rixXGEjPl7ywyWXyHu1G26Qq3b861/AW2/JFVZefBHYu1feE4cbZqmcXnlFXjAffSRhFpBPBX74AfjyS8DhkMu9fPedb5h1efJJua7IyJHyy3a6/nogLU1+hzNmAIMGyeuqXJQC/u//JIl07SoJOZLL6ISja1cZ5IUQZs+2hx6S39knn/jfZrfLa61jR/clanzcd5/8JzFlivw8dy6wYgXw0kvlC0kNGsindB99JP/xAWjYUK4y9MEHckkuiwXYuhVomaGAdeskSFdEhw6oc0Au/bN3r//NrkvpVL4oxv9GQC6xBADPPuu5Rg4A7Nolf49paWWPoXJlJCMfeUctYQ3ZYtchRu8bXF2B1VpcKtA6fw542R5APsmaPDms+41EVCq0REREdO5SCli2TIoYq1dL8ap3b7kkp+v9USCxsXIJyx49gCNHgFmz5M3d3r3A9u2SHQoKZN/4eMk2t98u/4JdM9XhABYsAMaPB1auBCpXluOqVJHvXf+qVJFge/HF8rVKFeaJMyo7W65F2qePXATXm6bJtu7dPT8HomnSBtCqFXDvvXLd18qVoWlyadMnn5Si3ZP/3959x8dVnfkf/54pKpZtSZYLxka2ZYzBphqbFlqwAZskS1mKSQIsEEiWkMBuEkIgwIYNJCE/FkKWwBJKSEgoYVOAUJYWSACHZppptsE2LuAmybbatPP745mrUZmRJSxpNPLn/Xrd12hm7oyudFXmO89zzvlWjmNIJqVnnrE2gVdftXdSgkVLq6vtBf0559jnOPdc6cYbsy8iioFj4kTp6KNtbd9TT7UEGUrX1x591KqMP/lJ9scOH25/jO6+W7r6anujZI897Gfg0/r+920B6B/9yH5+ZG/0zZkj3XOPtNde9mM2Y8ePrXza0/VnOzrkEA1Rk0YOa9aKFSWd7q6rk8JKqGxUjg6D3XazzogLL7Q/4r/7nf3Mv/++3d+dQFtZqQrVqX5D994EiCXDKgqn2t22tQptJFeFto/+iPNbDwDAIPbMM9Lll0vPPmsh8/rrrShRUdGz5xk9Wjr//Pa3eW8vwF56yULqgw/aJlmFddw4q7xVVdllUZFlj/fekyZMsGM56yzrgBzUUimrJr37rrUs7rmnvbAPbUOjXDJp7b5bttir7nC41w5XTU3WarzDDtYunEt3XpxWVloyOOAAe67LLpNkP4OXXWYV+nadp95bmL77bnvcmjXW4rnvvvaOyi9/aftNmGA/xK+/bmX7yy7jHY9C8c1vSsccY60ZZWX287vPPlYBHTNGOu643I896yxrH5k3z8Lvk09u25sYkybZc/7ylzaOobpaRxxhv6bXX59phd8n/qJ9sK2Bdp99pGhUE8rWa/nyzu/61W70qlCdXFUXXQYXXGC//9/6lv3e/+Y39g6jZG8QbE1lpcpVr/pa361DbklGVFzcoUJbkr1CGwTcnC3HfYRACwDAAPPBB1Y0qKuzluDhwzOXRUXWCtfSYpfNzVZBGDLEXhuWldnr/1jMhoU99ZS18/7859btV9K5KPCpOWd55aijbPvZz6wt+KGHpL//XVq3TvrwQ2n9+kwr3YwZllVOPHE7KKYFQfYHP5Deeqv9fUOHWnVpn32spfWII6xcmU1Li5XWX3rJnmfRIvtGNzfb/ZWVVlI6+mjbcpXH2x5XY6OFYe/th6u0NBMIL73Unv///q/n73xks//+Vra/4QarqpWWqqpKevNNy8ztnHOOtaMWFVnoOfVU6fOftx/wZNIe9Le/2Ts0ixZZpe/ss7f9GNF/5s2zn+MXX7QBqgsX2jtdW7ZI//Efdu5zOewwezPoH/+wDoEjjtj247n0Umtjvvpq6eab5ZxlxnPOsfG0w4ZJk5c9aX9ct3USgUhEmjhRExpX6Z0VnX9P6zYkVanarbfN//u/S4mEhfBIxL5no0Z17/e1slLl+kQr67t3yLFkREWRjhVaewMt3tL+9uB6tKQX32DrhsH+rwQAgIIQi0l//rMNL3riCSvelZVJmzd/+uccPVq67jpr7ywt7b1jzcU5e703bZp00UXt70skpE2btpPW4VRK+tOfLMi+8YZVou6+20LdO+9YKHvjDdt+/WvpF7+wb8xxx1nSnzNHWrXKWjAfecTelWhosOceP94GMwdlpKIi+4F57DEbnyZZlWbIEPumt92amiw0NDZ2PuZIxIJtebm9C/H1r0tHHtl735OLLrIwcscd0nnnSZJqajrs8/LLFma/+lVrAe04LjIclvbe27ZtnkUKeTV9um1nnmnXUyn7md9xx64fFwrZz8d//qeNW+gN1dWWXm+5Rbr4YmniRH3pS/bha69JBx8shV5cIM2c2TudEDU1ql64VI8u31/et/97WLc+rgrVdW8c+EUX2e/1pZfa9+XAA7v3+SsrVa73tWhz96qosVTnQBsE1k4tx83BpFAEWgAACt7mzZZbFi+2bckSu9ywwV6rtG3F9d66Kdets9dWV15pXXDjxtnrvM2bpfp6C4SxWGbypGCLRDJZpaHBLltapFmzLBQPBJFI78/Vk9MHH9g3a++9+/bzeG+VpsWL7XMG27vv2kDjXXaxWY9OOSXzQni//dpPLNPcLD3+uIXR//1fC3wlJZnq66RJ1p87d669ss42+c0Xv2jHsmiRBdvnn7dqZiRiWzhsl6WlVhluu0n2wxVsdXVWQcs1jvHTOuQQq9Ree62Nd81Wnr/4YvuFuOaa7JNMYfAKhbLPepzNd75jAbSqqvc+//e+Z9X+H/9YuvlmlZZabr76amnGXgnpltdsdrreUFOjCX97S42NNpt72y+jbmOq+4FWsgnREgmbIKE742clqaLCxtA2dC8GxnxERdEOFdriHBXaZiq0AAAMaEGnZq5VGxIJ69L89a+t2hpkknDYuuSmTLGiRG2tteF++KEF3IYGK96de64VxdoWAUKhTOGsK8OGMeuv3njDXoH+/vd2subNs+u9HWzXrLFxa7ffbgOCA5WVVnacNcsqSKeeuvW+6pISmwn4C1+wdyGeeMIqszvvbCF2l126V9J2ziq2u+/exSxLeeSctUeecIIF91NOaX//44/beMif/Ywwi66Fw70bZiXrfDj7bAu1l14q7bSTzjvPirZHTlpq4zq2dYbjQE2Nqhufl2Tve7X9UmprpfGqlUaM6v7zXX65/Z3YZ5/u7Z+e5bi+ubhThTibllRUxdEcFdpY9pbjCBVaAAD6jveZDsxk0rZEwopT69fbtmGDXa5ZY6s5fPSRbatW2b5jxlgwnTbNLidMsNfjv/2tzQY8YoS9NgryyMSJXQ8L686LCnRhwQILrg8+aMn+O9+xcPmTn9iLvPnzrewdTJiyYYNVMf/2NxvHJ9k7BhUV7S87frxqlVVQH37YfnAOPtg+14wZVknd1vGmxcWZaaIHo3/6J/uFuOYam/U4+KFPpSzsTppkZTEgHy6+OFOlvfFGjRtnf8/dzx+z+7d1QqhATY0m6G5J0ooV7Wear9sUTldop/bsOefP7/6+0ajKi5qVjIXU0ND1srrJpJRSWEUdhvdHSixCdqzQBgGXCi0AAL0glbJJYN9807pCg8vFi+2fdHcUFdkb9+PHW3bZaScrHi1eLL39ts1jEoxxjUatyHb66VYY7CrAdkSY/RQSCQuwP/+59PTT9i7ClVfaVMxBW+5Xv2rj7K67zqq2n/uc9X6//bbdX1RkryaLimzG1KDldtMme5chm7FjLcSeeWb3W/xgwmGbFOrcc60aO2eO3X7vvTYx0F13WagH8qG6WvqXf7FQe8kl0rhx9rf5H/+wsb1bm2ytu2pqVK0VkizQtlXXEOlZy/GnVFEWl2L2J6+rQBtLL1VbVNT+72FrhTbe/vbWSaFK+zdiEmgBAIPGunVWKX30URtKuHat3e6cdYLusYfNu1NW1n5oYThsQXXkyPZjW8vLu15ZxXur4C5ZYiux9HYXXL9IJm3M55Il7bc1ayzFn3SSTeYzUKYkXrfOXnDedJOVzXfayULrV7/a+ZVZRYWtpXr++dJVV0l//KOdqC9/2b62WbOyT/ucStlA5Lq6TMitr7cxqAPpe1GITjvNWiSvucYCbSxmLZ577WUt2kA+XXKJdWFcc421v0vWxdFb7caSNGmSRmq9SqNxLV+eKX02N0vN8f4JtOXDUlKt/VkbNy73fq2BtkOFNgiszHIMAEA3eC998olVV996yyaJbWmxkBpskg2ffOUV27+qypaROeIIe508bVrfTI7knOWp7s5lMmB4b9+wu+6Sfvc7afXqzH1VVTZ+s7ra7v+f/7Hbjj/ewu1++1nS74uyckuLlbyDWbCCHvBge/99WwanpUWaPduWgfn857ceMIP1VLtaU7WtUMje4WAsZ+8rKbE1Ub73PavK/v3vNpj80Ue3bV1eoDdMnGhtNsGMx0VF9gZfby4NVV4uV1Wl6uQGrViRWbcqWNqsMrzZZinvQ+XDfbvPmUtLi112bJwIWo47VWhjdp0KLQBgQPBeevVVq1RWV29bfqmttSGLzz1nwx1bWqyYNmxY5jISsXeom5oy66tu3GjdoRs2ZJ6rqsrCqfftt0mTbJWUuXOti7Q3VlcYdFatspB611327kA0amt9nnCCpf7Jk9vPotvUZEHj/vutLfTWW+32sjJ7W3/8eLucOtXePZg1q/vVy8ZGawO+9VY7yZs328QrXRk1yhbTPe+8bV8PEvnzta/ZmOcrrrA/CJ/9rL0DBQwEl1xi40l++tPM0lW9NX42UFOjCR+s1PLlnQNtxdBEn49Dqai056/fylq0mZbj9re3VmhjHQMtLccAgAFixQrLDH/5i10fO1Y64ABb5u7AA611N9esu4mErR7y8svWqfXcc3Zdsqyz99722Pp6a9fdssWyTCLReTmaYcMsawWTt+6+OzP5fiqvvGLjSO+9177RBx1ka5+efHLXfdKlpVaZPf54e4fhySetPXnlSgvHq1ZJzzxjAfn737eK5mGHWfX00EPtB2fEiPavht54w6ofd91lPwS77GITmgwfbic82Nr2gI8cac9Dq+/gUFGRGd8s2eRdDCTHQDF5sg1LuPlm+7vnnLTvvr37OWpqVP3eUr2+YmbrTa2Bdngqx4N6T3mV/S3tdqAtbv/7manQdpwUygJupLRDj3If4z8DAKBVMmk555JLbBjhNddY59MLL1gh5Y9/zOw7fLjN7jthglVww2HLTQsXWmFPsuB60EE2NO4zn7Fu1T7upBo83n/fkn7bgDdkiL24CqZqjsWs3J1MWvgcMiTTtplMSg89JP3Xf0nPPmvP8Y1v2DsVO+/c8+MpKck9A+/69TYx05NP2rIzDz7Y/v6yMgukxcXWvldcLJ14ok0OdMghhJnt0YUXWsv4scdaZR8YSC691Jbmuvlm6wbp7eEHNTWasGWRPtlkmbmkxDqZpOxLTfe28lH2JmO3A21J++EA4ZKonFKdmmpoOQYA5NWiRdbNuWCBdPTR9n984kS77+tft8u1a+3+99+39fNWrLDLv//d/vHNmGGFl1mzbJs8mWFxPbJsmXTPPTau9c03O98fClmbcCyWexbekpJMT/bGjfZuw7XX2hiwrS1m+2mNHGnja086KfN1vPSSBd2NGzNbfb39MJ12WoHOoIVeM26cjWmors73kQCdTZkiffGL1knSmxNCBWpqVJ16VpLNbTdlSpsKbVXfj5cpH22DYus2JCXl/nwtDQlJkc6Tj0ejiiiheKz9zfG4l1NK4RIqtACAXpJMWgbKVQBLpaSnnpJ+9SvpvvvsTei77rL/49keM3q0LSWZDWupdtDcbOv7LFvWfquttbfgR4ywLQh2f/6zDTSWrK/7hhvsHYVNm2zbvNkuYzGrcAZbUZGVx5uapIYG2xob7fMffbT1bPd3q+7EiZl3Q4Bcpk/P9xEAuV16qY3znz2795+7pkYT9BtJ9qZwu0A7qu/D4JDRQxVRXPVr45Jyt03FGuKSIp0qtIpEFFVciUTHMbRSVHF747UfEWgBYBCJx23c6tNPW1B9/nkbrrb//vYm8377WeV0wwYLsXfeae8OB0PaLr/c5t35NAZ9mE0krES9ebNVlUpLO++zaZMNPP7DH6SHH7ZgGSgttZA3YoT03ntWsdywIdPTteee0o9+ZONJCYMAkF+77mozwPdFD3CWtWiDluOKHbIsJdbL3IhKlate9esi2nqgLe0caHNWaAm0AICt8N7exV21yv7PBvPyrFolLV1qAbahwfbday8LqXV1ti78Aw+0f65QyCYW/elPbRhbtuU4B5Vg0dgPP8yMO02l7DKZtKBaW9t+W7fO1mNds8bCbNDm65yF2ilTbFKjnXayvuvHH7eAusMO0hln2ARJkyZZQB01qnPq995Cb2Pjp38nAQDQN/pqPdjx4zU+/LFcMqXlyy0s1m1IqERxlYzuh+XCKi3Q1q3vehhKrDEhSSoq7dCWHI0qqiYlEu1vjseliBKdp0XuYwRaAMgD762Yt26dDTMMLhsarHO0sdEum5rs9rbBNZhwqa2qKstUZ5xhq6ccdpgNa2yrrs5mHv7HP+x/zRe/2PWC6gWtrs4C5ksvWTX0vfds4G/bimlXhg2zd+VHjrRv0syZNmPv2LG2ztCHH1o78fvvS7/9rY0NnThROv98a/E98MDuDR52zsa79sUiuQCAgSkSUXTiOO24ulYrVtiwk7pPYqpQfd+F6LYqK1WhOtXXDetyt5YtNutTcWm2Cu3mTpNCJeI+XaHN0sHUhwi0ALCNvLehkS+/bDlo112teNc2z8RiFiSfeMImgn355cyC5bkUFVmX6ogRlqn23dfGr44b134bO7Z71dWKCmnOHNsKVixmA45iMQuDwSbZcjLPPGPba6/ZiQmFLGhOnSodfrhd7ryzfWNDIRt7Gg7bx0OHWoitqOjZmFPvLdCWl28HfdcAgF5RU6MJn6zU8uXpQLs+rgrV9U+grahQuT5WfX3Xk7J1XaGNd67QJhwtxwBQCGprbWmaBQsspC5YYN2obZWUWCfq1KnWyfrss1YcDIUsmJ53nrTjjtZlGmwjR1ogDtZiDff9RIf9q6XF1iB99VXrSwoqk8EWidg+LS02oVFLiwXFxYszVdYPP7T24FxKSqw6esUVVqbef//sY117k3MWggEA6K6aGlU/s1QvrdhLklS7IaVK1fZbhbZc72np5q47iVoD7ZAOL0icBdd4ov2buPFEegxtPy9vQKAFgC6sXm3V1IULrei3cKEVCANTp0rz5kkHHGCTLTU2WqEwyF8LF1ql9ayzbKLEww8vkOzT3Cxdf70t91JZaSl8xgzb9tln6/9wm5psHaDXX7dv4EsvWZjt2J/UHSUlNlZ1772lU06xj0tLrTLadquutpPQaX0BAAAGmJoaTYi9rz985JVKOdXVSaNUJ40Y0/efOxhD29B1FIw12RvIRVnWlY24pBId3l+Ox52iSnTat68RaAEgLRazAPrCC1Z1feGFzOyDzlnF9YADpH/9V8tWs2Zlz3WHHNK/x92rvLf1e777XUvu8+ZZoHzhBVsfNTBqVOfe53DYQusbb9jY0lTK9i0vt0D87/9uY1FnzrT23oYGacuWzFIziURmKZqSErscOtRK2SxmCwAYTGpqVK2nFIs5ffKJVLcprCmqk6qm9f3nLilRRXiL6pu6nryppcn+jxeXdY6MUZfIUqF1ioS66KLqIwRaANu1jz6SHnnEVlh54onMDMHV1RZe/+3frGt1zz23g3l7XnzRvuDnn7cpkm+/3WaYCqxfb4n/1VelDz7IzFL14os2q5VkM/ruuad08sl2ueee0uTJ2QNpx1mrAADYXtTUaILukGTvH9duifbfGFpJ5aUxbd5SrFQq93vGrRXass5jYiMuqUSHQJtIWtDtbwRaAINOS4vlrWAS2tWr7fZQyCqtoZB11D79tPTmm3bfhAnS6adbW/CBB1pRsODF49Lbb1uPc3V19gmLVq6U7r/fFo9//nlbbua222y65I6DeEeOlI480raOWlrs8w0d2jdfCwAAg0mbtWiXL5fqmopU6eptMo1+UD4kLr8lpE2bcg+Fag20Q7JVaJOKJztWaEMEWgDoypYtFk6Dbd26zHI3wdI3K1bYFnS7SlZZDYXstlTKumqds8rrT38qHXOMtNtuBT5BrffShg02S9Xzz9v24ouZZWoqKqxPOtg2brQQ+8ILdv9ee0k//rHNVvVp/pkGrcIAAGDrKio0oWKTVGfvPSd9WBVlsX57mjTNNAAAIABJREFUMVIxLCmttbkXcwXalmZ7MZW1QhtKKp5o37IcTzoCLQAE6uulp56SHn1U+tvfrJC4eXPn/UIhKxyOHGnDOg86yIqLU6ZktsrK/j/+XpFMWil540bp44+3vgXhNRy2iZu+8hVL7Zs324xWr70m3XJLZr+99pKuuko66ST7RgEAgH4zfPIolb++Ra+/bt1NFUP7LwyWl9tlfX3ufWLNXpJUPKzzWNuoSyqR6lChTYYUZQwtgO1FPG45bdMm+2MaXL7zjoXY55+3PDdsmHToodbluuOO7bcxY+xdxYKaL+jjj6XnnrPZf9evt6pqsNXWWthsbratqxmBR4yw9uAxYyy07rCDfVNmzbJJl3IN+E0mrRe7qEiqqembrxEAAGxdTY0mvLlSr7++qySpojy1lQf0nvJKe/HUZaBtsUAbLescaCOhlOLJ9i/A4smQIqH++xpaj6XfPyOA7U5zs/TWW9Irr9h8Qq+8YmNXY7Hs+8+YIV10kTR3ro1n7ef1ubsnkbD1eYJJkhYutJBaVmaTINXU2OXkydYr/dxz0t//Li1dao8Phax0XFVl4XTcOGmPPezxwUK0wVZZaYE12EaP/vTtveGwtOuuvfd9AAAAn05NjarjS/XQMvu/3J8dZeVVFgPr6nLvE2tJKaK4QqWdX3NEQ0m1JDtOCuVURoUWQKFKpSyrvfmmXbbdVqywwqBkf6xnzJAuuECaOFEaPtzaXsrL7eNx46x1eMBIpaRly2xN1bbbO+9YUpcsgO61l62R2tRkM1I99pi0Zk3meUaNkj7zGelrX5MOPti+CUVdT5cPAAAGsZoaTfAftl6tqOq/aFYx0j5XfW1KUvZWt5ZmqUixrG+iR8NJxZPtJ4+MJ8OKhgm0AApALCYtWWJDMoOK66uvWttwoKrKipMHHCB96UuW9/bd10LsgJ18KZmU3nuv/Re1cGH7wbvjx0vTp0uf/ayNU50xwxaojWT5c9rYKH34oQXXnXcewF84AADod5MmqVqPt16tGNV/LWnlY0okSfWfNEsaknWfWMyrWC1Z34CPhFJKdGw5ToUUDdNyDGAAaWmx4ZZvv21Fybfftu39963jVrI37fbay0LrjBk2ge6UKZnJBga0VMp6oZ96yrZnnsmk8pIS+2JOO80ud99dmjatZ1/YkCEWfgEAADqqqdEELW+9WrlD/60WUD7WQmzdx10F2qBCO7zTfdFQSvF4x0AbVrSICi2APrB2rc0SvGFD+3mIGhttKGcoZEMrQyH74/XeexZglyzJtAqHQjYsdPp06dhjLdvtsYddDogxrs3N1uLbdvv4Y7vcuNGWtQkWonXO0vqCBfYNkayCOn++TZO87742zjRb1RUAAKA3VFer2q2UbO6l1pDZH4pHl6tETapfl2NCE0mxmMvZchwJeyVSHVqOU2FFw77Xj3VreLUGDHBr1kg33GDDOMeObb+NHGmZKxzObMmkhdFXX810zK5enf25i4sza7MG67OGQpbtpk+XTjzRAuv06dZVW1rar1+6CQbnrlrVObC23bLNahAO2yzAVVUWYr1v/4Uec4x0xBHWPlxd3f9fGwAA2H5Fo5owLiGtlIZqsyKj+nFWqMpKlate9RtzLxXUEnPWchwOd7ovGk4p3iHQJlIhRWg5BhBYvly65hrpttts9ZaJE9svNbo1oZC0227S7Nk21HPSJMt1wTZiROfKqveZrJc3qZT0xhvSX/9qLcDPPmsV1raKizOpfrfdLJR2TPtB4s/yRxgAAGAg2GHKMEVXxlShOntx1l8qKlSuetVtzF0VjsWdilz2JQQj4ZTingotgA68t+rqtddKd91lRcV/+Rfpu9+1CZa8t/mJgqLkhg02ljWZzGzOWbfsHnvYEM6eCLpx+0wiYX3Mb71l28cf2xcUbJs22SRKtbW2/6RJ1t/8mc9Yog+CakUFEywBAICCF5o8STs9/ZHK1NC/gbayUhVarfpNWwm0oewV3Gi2lmMfUTRChRYYNOJxy2ZLltjESps2WcEw2EaNsknjXn/dJtQNttpaa+39+telb3/bJtUNOGdL2wwfLk2dmr+vrUuxmPTRR1ZiXrbMLj/4IDOrVEuL7RcKWal42DD7goYNszVWZ8yQDj1UOuww2oABAMDgVlOjPfSmUgpJI6b13+etrFS53lH95vE5d4kluqrQ+iwV2oiiESq0QEGoq7MK6v/+ry07GlQ2g1bdNWssxyW7OdFbNGoV1RNPtPmIjj9eGj26746/VyQSmYVn225Ll1opOeCcLS47bZp0/vn2he6xh7UK52VQLgAAwABRU6Nf63T7eMSy/vu8ZWUqd5u0siF3HGyJh1Uczh5ooxGvRMdA68MEWmAg894mxb3lFuneey3I7rmnFRWDsafBnEOzZklf/KItX7PzzpllbIJZhoOtsdEmXNpjj6wTyOWf9zYZ0+LFtlZP28ulS60MLVmSnzLF1u859VSbDnnCBNvGj8+6fhkAAMB2r6ZGw7XZCgD9ueahcyovblZdU+4XoLFkSKWh7NWZSMQr7ttHybiiiuRh6pJeCbTOubmSfiYpLOlW7/2PO9xfLOnXkvaVtEHSKd77Zc65IyX9WFKRpJik73jvn+qNYwJ605//LF12mRUghw61pUnPPdeqqT0RDAHtV6mUtf6uWWP9zG23YN2eoLwcrNuzdKmF1sWL289CVVJiCX3aNOm44zKDdam2AgAA9FxNjV1WVPT7RJYVJS2q31yS8/5YIqzycPZAG42oU4U24cOKRguwQuucC0u6UdKRklZKesk594D3/u02u50tqdZ7v7Nzbr6kn0g6RdJ6SV/w3q92zu0u6TFJ47b1mIDuSqW6ntG3tlb65jetvXj6dKvOzp9vwz37zYsvSjfdZONSR49uv1VWZhaQDS6TSauiBhMvLVqUe2rk4uL2S9mkUvY8kybZOj1HHGGV1112scvx4/M8BTIAAMAgMmKEzSXSnxNCpZWXJdRYV6J4vPPKF5JVaItyBlqvuDIP8l5KKKpoHvp/e+NT7idpiff+A0lyzt0j6VhJbQPtsZL+I/3x/ZL+2znnvPcL2+yzSFKJc67Ye9/SC8cFSLJi4223SQ8/bBPpNjVZvmtqsmLkgQdKX/6ydPLJNllT4C9/kc45R1q3TrriCunSS7P/sveJlhbpvvuk//5vC7RDh1qiXrZMWrvWvpCtGTNG2n13+yKmT7cJliorM1tFhS1iCwAAgPxwzqq0eXhNVj7MZiSur2//GjjQkoyouCj7LMeRiJRSuLU4lEjv1m+vldseSy88xzhJH7W5vlLS/rn28d4nnHP1kqpkFdrAP0taSJhFb2hpkf74R+nWW6Unn7RftMMPt3xXWmrL2QRL2jz0kM0ofMEF0jHH2NjXxx6T7rjD9n/oIZt4t08Fy9m8+ab00kvSnXdacJ06Vfr5z6XTT7d37wLNzZa0a2utIptKZS4lW+Nn1Kg+PmgAAABss0suaT+hZj+pKLfPmSvQxpIRFeVYhicIromETZUSTKtSqIE222KQHc9Il/s456bL2pCPyvlJnDtX0rmSVM1SHuigtlZ67TVp4ULbHnnEJmCaMEG68krpzDPbL3/T1tVXS2+8YW3Fv/2t9MADFoAvuUS6/PIuJmtqbJT+9jcLl8E0x8HaqGvXWjV12TJbu2fZMquqjhhh1dERI2yLRqV33rHlbJqb7bGhkPS5z0nf+IY0e3b2Ft+SEmmnnWwDAABA4TrppLx82vIKe91aX5/9/lgqoqIcsxYHBeWOgTZSoIF2paS2r6rHS1qdY5+VzrmIpHJJGyXJOTde0h8lne69X5rrk3jvb5F0iyTNnDmz/9/CQK/asMGqoI89Jm3caMXEYG3WUaMs5y1fblkw2FatsnA5bJh14A4bJpWVSStW2P2BHXeU5syRzjrLLrc25NM5m5x3r72kH//YMuqoUdal20kiIT3xhPS731kJeMuW3E8cClmKnjjRgumwYbbez8aNlsA/+shC7C67SOedZ1MmM8ESAAAA+kF5lUXB+jqvbPXHmO+iQptewCIIsom4PUc0mq2O2bd6I9C+JGmKc26SpFWS5kv6Yod9HpB0hqQXJJ0o6SnvvXfOVUj6i6Tvee+f64VjwQCVSkkvv2yV00cesWGh3ktVVZb5Fi60DtpYrP3jxoyxPLjffrZfPG4ZcvPmzOWsWTbj8D772LYt67eGw9aa3MnixdINN9h6PevW2fjT+fPtHbWRI9uv2+O9JeLx4/PTdwEAAABsRfkoS6V1HzdL6lxMaUlFVRzNHmgjEQuu8ZgF2XhjXFJRYbYcp8fEni+boTgs6Xbv/SLn3JWSXvbePyDpNkm/cc4tkVVm56cffr6knSVd5py7LH3bUd77tdt6XMi/9eutAvvII3a5fr1VQ2fNslbeefOkmTMzM5R7byF1/XobA1tdnRnnmjcNDdJVV0nXXmsV1y98wQbZzps3QBeOBQAAALauYoy9lq1f06hsgTbmoyrKsQxPtMgCbaLJgmy8KSGpqPX2/tQr02l57x+W9HCH2y5v83GzpE7N4d77H0r6YW8cA/pOIiG98oq14u6yiw3vzLVMVjxu41BvvjlThR05Ujr6aMuARx2Ve64i56wrt1eXxPHeDv6ee2x2qC1bbHrj5ubMNMezZknHH2/bzjtnHnfffdK3vy2tXGmTMv3kJ9IOO/TiwQEAAAD5UT7WKkf1nzRnvT/moyoqyv7YSLq1OAiyVqFV4QZaDC7e22S7Tz4pPfWU9Mwz7VeJmTzZZgQ+80wbyypZNrztNumnP7UxrXvsYUvdzJsn7btvv68TbWuv3nOPbUuWWOvvYYdlxqeWlGTGqT79tHTRRbbtvrt03HHS3/8u/fWv1sN8773SQQf18xcAAAAA9J3hO9oL+bp18U73JZO2LE9R0VYqtM22Xo8FWwIt8qi52cLrgw/aMjUrV9rtU6ZYh+0RR0gHH2w577rrpG9+U7rsMlvidMQI6frrbWLfz3zGqrNz52Ym/O3Sli3W1hvM+NtRIiGtWWMH9PHHNo1aWZltQ4daT/KqVRZg225r1liL8BFHSBdfLJ1wgs0unMuyZdKf/mQTPV11le17883SV76ShzQOAAAA9K3IyAoN1WbVb+i81mxLeiHV4m5VaDPBNlK0ldlY+wCBdjuWSlmAveMO6fHHbRWasjJrC/7BD6Qjj+y8KszJJ9u2YIEF2+uus3dw5s61ZW4OOWQrn3TzZum556z6+de/2kxRyaTdN2yYBduqKgu3K1daME1lH4zeyZAhVoE98kibReqf/7n7LcITJ0oXXmjbhg1WwS0r695jAQAAgEJTWaly1au+tvNr7WCi1lwtx50qtM3Jdrf3JwLtdqipSfrNb2yeo/fft8l4zzxT+vznbYbfkpKtP8cBB1gn7kcfWYF1111z7JgrwEajFjovvlgaO9aWstmwIXMZi9maO8Faq+PH236JRKaq29BgH48ZI02bZqF0a2v0dEdV1bY/BwAAADCQVVaqXKtUv6lzl2SsxWYvLirOHlCjxfaaO6jQtrYcl/R/ZyOBdjvhvXXm3nGH9POf28ozM2daKD3hhMziyDnV1dnkSjU10qRJrTd3rOBKkt56y2aGevrp7AH28MOlAw+kAgoAAADky/DhKtfbqts0ptNdsS0xScU5A23QWpyIWWWWMbTosWRSev11G/f65pu2LOqoUbaNHm1DQJcts33eeMO29evtsZ/7nPSd70iHHppjnKv3NpHSc89Jzz8vvfCCjUv16UHhc+bY2NLjjsssXROP2xjUG2+0WaQiEWn//QmwAAAAwEAUCqki0qB1jZ0jYcuWuKRiFZfkqNAWBWNo04E2aDkuYQwturBsmU3Y9NRT1r1bW2u3jxtnnbf19Z0fU1pqE/cee6y05542vHS33bI8+apVNq1xsK1aZbdXVFh/8ckn2/I2L71k0xnPn2/jXU87zfb55S+l1autenvNNdJZZ9G6CwAAAAxg5cXNWtLUeaBsrMFmPi7KEVBbK7Qt6UDbkkrfTssxOmhqkv7wB+n22y3ISpYZTzhB+uxnbdtxR7u9pcWqsGvX2jDUnXayZVVzTtK7fLmF09//Xnr3XbutqspmBp4922Z42nXX9uNS586VLr3UQu+tt0q/+IVVZ+fOlf7nf2ydHmYFBgAAAAa88tIW1W0q7XR7rMFmhcoVaFvH0KYrs0GwDW7vTwTaPvLhh9Z1m3WM6VZ4b4XQO+6Q7r7bKq81NdKVV9oSOpMnZ39ccbFVa8eN6+LJEwnpL3+x8Pnoo3bb7NnWQjx7tpVxtzaxUihkpd4jj7Tk3NRkkzYBAAAAKBgVZQnVbxjS6fZYg42JzVmhLbYCVlCZDS6jpf0fLwm0vch7Gz567bXWGhwKWafuRRdJ++yz9cevXSvddZdVYxctsnbhE0+07t1DD82RM7dssfGu5eXSyJG2NmswMNZ7W/bm3Xeld96R3n7bxrmuXm1l3e9/Xzr7bGnChE//RdNWDAAAABSk8mFJxXyRmpvbr3RiY2il4iHZOy+DSmxry3HrGFpajge0xYulK66wYuQuu2S2qirr2r32WunVVy1XXnGFFS5vvlm65x4rZl50kRVBnbP24A0bbLbhJUssyD70kBVQDzhAuuUWC8Pl5W0OwHvpgw9skqYXXrAJm954o/06rdGoHUBFha3junlz5r5hwywZ33STdMwx3ZjaGAAAAMBgVT7cLuvr2wfaWGO6QluaPaDmrNASaAeuREL68pdt1mDvM4sNS5YLEwlp6lTr5D3tNKuuStIll9ht119voXaHHaTGRmnTpvbPP2aM9G//ZuvBtpu0yXtp4UJLzPfdZ4FWskrs/vvbeNY99rA1Wdevty1Yz3X2bBsDu+uu9qRjx+aY1hgAAADA9qai0rJBXZ3lkUAsPXtxrkAbBNdOk0IVE2gHrP/6L+nFF21M60knSStWSO+/b9uyZTY50zHHtGkLfustqahI5bvsoosuki64wKqwzz5rS+qMGmWF1JEjLeTut58VVyW1D7G//720dKlNtDRnjvStb0kHHyxNn87kSwAAAAA+tfIqi4P1nzRLUzMl2tYK7ZDscTEItEGQTcQYQzugvfuudPnl0vHHS6ecYkXOSZNsO/roNjtu2mSJ95e/lF55xW47/HDpX/9Vxccdp7PPLtLZZ+f4JN5LC1/LVGKDEDt7tvS979mar4xXBQAAANBLykfZkj31qxskZQJtS6NVXnONoQ0qsUGQZVKoASyZtDbgsjLpF8c/LjfpHCuxTp5sUw9Pnmwl1gcesMGyjY3WAnzDDTaI9qabLAXvsIPNJHzCCVJzs7UE19batmKFTda0ZEkmxF58sYXYkSPz/S0AAAAAMAiVj7EQW7emUVKmeNbaclwWzfaw1uAaj/l2lwTaAej666UFC6TfXrdWO3zjJAumO+5oLcUPPpgZTFtWZmvqnHOONGtWZqzqt74lPfaYrdd61VXSD3/Y+ZOEw7b263e/S4gFAAAA0C92qLYK7QfvJ9vdHmu2imuuQBspsRjZWqEl0A5M771nK9sc+4WkTr3rc3bjI49Yr7Fk5dvVq63CuueeNotwR+GwDa495hhbnPbFF23q4srKzFZR0WYALQAAAAD0vVGThmq63tKTC8bou21ub2nqOtC2jqHtUKENgm5/ItDmkEza+q+lpdJNo/9D7sGXpT/8IRNmJQurO+1kW3cEA28BAAAAIN9GjNAcPaT/WXR+u7VoY+kxscVDc1RoS+32IMgm4ukK7ZD+L9KFtr7L9um662yZ1xtOe0ljb/uhTVN8/PH5PiwAAAAA6B2TJunIoQvUHI/o+eczN8eaLaBubQxtEGRbW44JtAPDc8/ZxMLHH9WgL/3qSBsTe801+T4sAAAAAOg94bAOnVemiOJ6/P98682xlnSgHVqU9WFBa3Fry3E8/XQlBNq8++QT6eSTpYkTUrpj7TFyISfde69UlP1kAgAAAEChGnbsETpAC/TEAw2tt7WkK7TRocVZHxNUYhOJTKCNKiZXRKDNq0RCOvVUqbbW63/3uUrlrz0r3XEH414BAAAADE5z5+pI94ReeWeINm60m2IxC6ih0uyBNqjExtMLvsQTUlRxm2OonxFo27j8cunpp6Wbv/Cw9rz/8swyOgAAAAAwGFVVac70j+UV0lNP2U2xmFSkWM4uVVcUVURxJRJ2PR53iijRTwfcHoE27YEHpB/9SPrq7CU6/b7PS6ecIl19db4PCwAAAAD61KyTJ2mYNumJBxoltQm0zmV/QDisqOKtY2cTCa8ogfbTe+cd6S9/kZYtk1Kpnj9+6VLp9NOlfadu1vXPzpAOOUT61a+k0KD49gAAAABATtF/mqfP6mk98ZiF0paYU5GL536As4ps2wpttKv9+1DBr0NbVycdfri0dq1dLyuTpk+3bcoUadgwuy3Yioqk1aul5cstAC9bJr3xhhRSUvevOVglNTtKf/pTZhEmAAAAABjM9txTc8rv1wNrj9WHH0qxuFTsYl0+JKq44kGgTThFXX4qtAUfaC+/XFq3zut3lyzSlvJxemtlhRa97fTwwzZjcVd23FGaOFE66tAmfWPBlzUx+bH08AvSiBH9cuwAAAAAkHfOac6RTrpfeuLRhGLxkIq2ElAjLqlEwlqS40kC7afy2mvSjTd6/WvJr3Tq1WfZjUOHSlOnSrOnqnH8LmqIVqghUq6G8HA1uKFqcSUam1ypnZreV/HHy6VVq6QFb1mp95lnpJqa/H5RAAAAANDPdv3Svhp3/0o9cV9UyYRTUajrFuKoS7Sp0IYUCX2KsZ+9oGADbSolnXeeVBXdpB/GvyvddZdUXy+995707rvSc89pyIq7NcR7jcr2BKGQlWjHjZMOOkj6+telWbP6+8sAAAAAgLxzc2ZrTugPemjBP2v/ioiKQlup0CqpeLpCm0iKCm1P3Xmn9MIL0h26QJU/vED60pc67+S91NwsNTRIjY122dIijRoljRkjRQr2ywcAAACA3jN0qOZMW6M73yrTSxtrVBNZ0eXu0VBCiWS65TgRUjSU7I+j7KQgE10yKV307ZQOiryk0/d+R/rurdl3dE4qLbUNAAAAAJDTnFOqpLekdbEK7Vr0YZf7RlxS8YStChNP5i/QFuS6NKtWSRs3ev3Cna/Qr39FpRUAAAAAttEO8w/X7npTklQU7jqgRl0yU6FNEWh7ZN066Xz9t/b60Xxpt93yfTgAAAAAUPh23llHVrwsSSqKdB1QI6Gk4kGgTeZvUqiCDLQRxXXlAY9IF16Y70MBAAAAgEFjzuE2uVPxVgJtNJRUImlxMpEKKUqg7b6d3CqV33WjFA7n+1AAAAAAYNA49OwpiiqmokjXATUaSimeDMbQhhUNs2xPt40YXyZNnpzvwwAAAACAQWXoUQfpsqJrtNvEoZIOzrlfJJRSIhWMoSXQ9szorCvLAgAAAAC2RVGRLntwP2ns2C53i4aTakwWSyLQAgAAAAAGiqOO2uoukZBXIpZuOfZhRbfSotxXCnIMLQAAAAAgf6LhlOIpm9MongorEvZ5OQ4CLQAAAACgRyLhlOLeAm3ChxUt5EDrnJvrnHvPObfEOXdxlvuLnXP3pu//h3NuYpv7vpe+/T3n3NG9cTwAAAAAgL4TDXslUkHLcUTRSIEGWudcWNKNkuZJmibpVOfctA67nS2p1nu/s6TrJP0k/dhpkuZLmi5prqRfpJ8PAAAAADBARcJecW9TMsV9RNFogQZaSftJWuK9/8B7H5N0j6RjO+xzrKQ70x/fL2m2c86lb7/He9/ivf9Q0pL08wEAAAAABqhoOKVEMIbWRxTN03TDvRFox0n6qM31lenbsu7jvU9IqpdU1c3HAgAAAAAGkGikTYVWUUUKONC6LLd1rDfn2qc7j7UncO5c59zLzrmX161b18NDBAAAAAD0lkjEKxFMCqXCbjleKWmnNtfHS1qdax/nXERSuaSN3XysJMl7f4v3fqb3fuaoUaN64bABAAAAAJ9GNOIVV0Q+5RVXUUG3HL8kaYpzbpJzrkg2ydMDHfZ5QNIZ6Y9PlPSU996nb5+fngV5kqQpkl7shWMCAAAAAPSRSMSW60m2JCRJ0aJszbf9cBzb+gTe+4Rz7nxJj0kKS7rde7/IOXelpJe99w9Iuk3Sb5xzS2SV2fnpxy5yzt0n6W1JCUlf994nt/WYAAAAAAB9JxqxsbPxhpikqKLR/BxHrxSGvfcPS3q4w22Xt/m4WdJJOR57laSreuM4AAAAAAB9LxKxsbPxxga7Hs1PhbY3Wo4BAAAAANuRaFRKtFZopWhRfo6DQAsAAAAA6JGgIttc3yJJilKhBQAAAAAUgmDMbOPGZrtenJ9oSaAFAAAAAPRIUKFtqktXaPM0yzGBFgAAAADQI0GAbayzMbSRIiq0AAAAAIACEEwC1bQpbteLqdACAAAAAApAJGpRsjXQUqEFAAAAABSCoOW4aXPCrpeE83IcBFoAAAAAQI8EY2YbtyQlMcsxAAAAAKBABGNmm7ak7DoVWgAAAABAIYgUWYBtbPDp61RoAQAAAAAFoLVC25iu0JZG8nIcBFoAAAAAQI+0znLcZNdpOQYAAAAAFIQgwDY2uXbX+xuBFgAAAADQI8GY2aZmuyTQAgAAAAAKQmuFtsUuIyWMoQUAAAAAFIAg0DbF0hXaIdG8HAeBFgAAAADQI5HiINDaJbMcAwAAAAAKQmuFNm6VWQItAAAAAKAgBGNmGxMEWgAAAABAAQkCbFOiSJIUKWUMLQAAAACgAARjaBtTxZJYtgcAAAAAUCBaK7RBoC1yeTkOAi0AAAAAoEeCFuMmX2LX8zOElkALAAAAAOiZoELbqCGKKC6XnwItgRYAAAAA0DPRIekKrUoVVTxvx0GgBQAAAAD0SDApVFIRRVwyb8dBoAUAAAAA9Ei0zSo9UZfI23EQaAEAAAAAPdJ2EigCLQAAAACgYDgnhWXJcq/lAAAKUUlEQVRBlkALAAAAACgokSDQhhhDCwAAAAAoIEFlNuJSeTsGAi0AAAAAoMcissosFVoAAAAAQEEJKrQEWgAAAABAQYmkg2w0TKAFAAAAABSQTIWWMbQAAAAAgAIStBpHwgRaAAAAAEABibhgUigCLQAAAACggERbx9ASaAEAAAAABSSSrsxGIwUaaJ1zI5xzjzvnFqcvK3Psd0Z6n8XOuTPStw1xzv3FOfeuc26Rc+7H23IsAAAAAID+E7QaR8M+b8ewrRXaiyU96b2fIunJ9PV2nHMjJF0haX9J+0m6ok3w/X/e+10l7SPpM865edt4PAAAAACAfhBUaCORwg20x0q6M/3xnZKOy7LP0ZIe995v9N7XSnpc0lzvfaP3/mlJ8t7HJL0qafw2Hg8AAAAAoB8EY2ejBRxox3jv10hS+nJ0ln3GSfqozfWV6dtaOecqJH1BVuUFAAAAAAxwkQEQaCNb28E594SkHbLcdWk3P4fLclvrV+yci0i6W9IN3vsPujiOcyWdK0nV1dXd/NQAAAAAgL4wECq0Ww203vs5ue5zzn3inBvrvV/jnBsraW2W3VZKOrzN9fGS/trm+i2SFnvvr9/KcdyS3lczZ87M33cMAAAAAKBIejKoaDR/x7CtLccPSDoj/fEZkv6cZZ/HJB3lnKtMTwZ1VPo2Oed+KKlc0oXbeBwAAAAAgH4UzG4c3WqZtO9sa6D9saQjnXOLJR2Zvi7n3Ezn3K2S5L3fKOk/Jb2U3q703m90zo2XtS1Pk/Sqc+4159xXtvF4AAAAAAD9IFh/NpLHQLtNn9p7v0HS7Cy3vyzpK22u3y7p9g77rFT28bUAAAAAgAEuErbLQm45BgAAAABsh4IKbbQof8dAoAUAAAAA9FhrhbYof423BFoAAAAAQI9Fo8EsxwRaAAAAAEABCSaDihBoAQAAAACFJJgMipZjAAAAAEBBiUQsyBJoAQAAAAAFpbVCW5y/WEmgBQAAAAD0GIEWAAAAAFCQgsmgIkUEWgAAAABAAQnGzlKhBQAAAAAUlKBCGy0J5+0YCLQAAAAAgB6jQgsAAAAAKEiRHaokSdExI/J2DARaAAAAAECPRWuq7ZJACwAAAAAoJJFI+8t8INACAAAAAHqsdR3aaP6OgUALAAAAAOixoDJLoAUAAAAAFBQqtAAAAACAgnTYYdLXviZNm5a/Y8jj8F0AAAAAQKEaNUq66ab8HgMVWgAAAABAQSLQAgAAAAAKEoEWAAAAAFCQCLQAAAAAgIJEoAUAAAAAFCQCLQAAAACgIBFoAQAAAAAFiUALAAAAAChIBFoAAAAAQEEi0AIAAAAAChKBFgAAAABQkAi0AAAAAICCRKAFAAAAABQk573P9zH0mHNunaTl+T6ODkZKWp/vg8A24zwODpzHwYHzWPg4h4MD53Fw4DwODtvTeZzgvR+1tZ0KMtAORM65l733M/N9HNg2nMfBgfM4OHAeCx/ncHDgPA4OnMfBgfPYGS3HAAAAAICCRKAFAAAAABQkAm3vuSXfB4BewXkcHDiPgwPnsfBxDgcHzuPgwHkcHDiPHTCGFgAAAABQkKjQAgAAAAAKEoG2m5xzJc65F51zrzvnFjnnfpC+fZJz7h/OucXOuXudc0Xp24vT15ek75+Yz+NHhnMu7Jxb6Jx7KH2dc1hgnHPLnHNvOudec869nL5thHPu8fR5fNw5V5m+3TnnbkifxzecczPye/QIOOcqnHP3O+fedc6945w7kPNYWJxzU9O/h8G2yTl3IeexsDjn/i392uYt59zd6dc8/G8sMM65C9LncJFz7sL0bfwuDnDOududc2udc2+1ua3H5805d0Z6/8XOuTPy8bXkC4G2+1okHeG930vS3pLmOucOkPQTSdd576dIqpV0dnr/syXVeu93lnRdej8MDBdIeqfNdc5hYfqs937vNlPXXyzpyfR5fDJ9XZLmSZqS3s6VdFO/Hyly+ZmkR733u0raS/Z7yXksIN7799K/h3tL2ldSo6Q/ivNYMJxz4yR9U9JM7/3uksKS5ov/jQXFObe7pHMk7Sf7e/p559wU8btYCH4laW6H23p03pxzIyRdIWl/2c/AFUEI3h4QaLvJmy3pq9H05iUdIen+9O13Sjou/fGx6etK3z/bOef66XCRg3NuvKTPSbo1fd2JczhYtD1fHc/jr9O/wwskVTjnxubjAJHhnBsu6VBJt0mS9z7mva8T57GQzZa01Hu/XJzHQhORVOqci0gaImmN+N9YaHaTtMB73+i9T0h6RtLx4ndxwPPePytpY4ebe3rejpb0uPd+o/e+VtLj6hySBy0CbQ84a1V9TdJa2Q/KUkl16T8ckrRS0rj0x+MkfSRJ6fvrJVX17xEji+slXSQplb5eJc5hIfKS/s8594pz7tz0bWO892skKX05On1763lMa3uOkT81ktZJusPZEIBbnXNl4jwWsvmS7k5/zHksEN77VZL+n6QVsiBbL+kV8b+x0Lwl6VDnXJVzboikYyTtJH4XC1VPz9t2fT4JtD3gvU+m26rGy8r5u2XbLX2Z7d1KppTOI+fc5yWt9d6/0vbmLLtyDge+z3jvZ8hab77unDu0i305jwNTRNIMSTd57/eR1KBMS1U2nMcBLD2+8p8k/X5ru2a5jfOYR+m2xGMlTZK0o6Qy2d/WjvjfOIB579+RtX8/LulRSa9LSnTxEM5jYcp13rbr80mg/RTSbXF/lXSArNQfSd81XtLq9McrZe+MKX1/uTq3E6B/fUbSPznnlkm6R9ZOdb04hwXHe786fblWNl5vP0mfBO1S6cu16d1bz2Na23OM/FkpaaX3/h/p6/fLAi7nsTDNk/Sq9/6T9HXOY+GYI+lD7/06731c0h8kHST+NxYc7/1t3vsZ3vtDZedksfhdLFQ9PW/b9fkk0HaTc26Uc64i/XGp7B/AO5KelnRierczJP05/fED6etK3/+UZ9HfvPLef897P957P1HWGveU9/5L4hwWFOdcmXNuWPCxpKNkrVZtz1fH83h6embAAyTVB208yB/v/ceSPnLOTU3fNFvS2+I8FqpTlWk3ljiPhWSFpAOcc0PSY2GD30X+NxYY59zo9GW1pBNkv5P8Lhamnp63xyQd5ZyrTHddHJW+bbvg+BvUPc65PWWDssOyNwLu895f6ZyrkVX7RkhaKOnL3vsW51yJpN9I2kf2Ltl87/0H+Tl6dOScO1zSt733n+ccFpb0+fpj+mpE0u+891c556ok3SepWvYC7STv/cb0C7T/lk2O0CjpTO/9y3k4dHTgnNtbNkFbkaQPJJ2p9N9XcR4LRnq83keSarz39enb+H0sIM6WIjxF1qK6UNJXZOPv+N9YQJxzf5ONZ45L+nfv/ZP8Lg58zrm7JR0uaaSkT2SzFf9JPTxvzrmzJF2SftqrvPd39OfXkU8EWgAAAABAQaLlGAAAAABQkAi0AAAAAICCRKAFAAAAABQkAi0AAAAAoCARaAEAAAAABYlACwAAAAAoSARaAAAAAEBBItACAAAAAArS/weRmsxQSfU8LwAAAABJRU5ErkJggg==\n",
"text/plain": [
"<Figure size 1152x432 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"miof_sp = maskbig(table_data[sp_id]['IOF_SPECTRUM_DATA'])\n",
"mphotom_iof_sp = maskbig(table_data[sp_id]['PHOTOM_IOF_SPECTRUM_DATA'])\n",
"\n",
"plt.figure(figsize=[16,6])\n",
"plt.plot( wav[~miof_sp.mask], miof_sp[~miof_sp.mask],'r', label='IOF_SPECTRUM_DATA')\n",
"plt.plot( wav[~mphotom_iof_sp.mask], mphotom_iof_sp[~mphotom_iof_sp.mask],'b',label='PHOTOM_IOF_SPECTRUM_DATA')\n",
"# plt.xlim(wav_grid.min(),wav_grid.max())\n",
"plt.xlim(wav[~miof_sp.mask].min(),wav[~miof_sp.mask].max())\n",
"plt.legend()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Regrid on synt wavelenght grid"
]
},
{
"cell_type": "code",
"execution_count": 362,
"metadata": {
"ExecuteTime": {
"end_time": "2018-03-20T14:15:37.314811Z",
"start_time": "2018-03-20T14:15:37.308705Z"
},
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"266.764404296875 1051.8349609375\n"
]
}
],
"source": [
"print(wav[~miof_sp.mask].min(),wav[~miof_sp.mask].max())"
]
},
{
"cell_type": "code",
"execution_count": 363,
"metadata": {
"ExecuteTime": {
"end_time": "2018-03-20T14:15:38.492393Z",
"start_time": "2018-03-20T14:15:38.484671Z"
}
},
"outputs": [
{
"data": {
"text/plain": [
"(4.645387909116124, 2.0)"
]
},
"execution_count": 363,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"step(wav[~miof_sp.mask]), step(wav_grid)"
]
},
{
"cell_type": "code",
"execution_count": 364,
"metadata": {
"ExecuteTime": {
"end_time": "2018-03-20T14:15:39.132210Z",
"start_time": "2018-03-20T14:15:39.121814Z"
},
"scrolled": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[ 270 272 274 276 278 280 282 284 286 288 290 292 294 296 298 300 302 304 306\n",
" 308 310 312 314 316 318 320 322 324 326 328 330 332 334 336 338 340 342 344\n",
" 346 348 350 352 354 356 358 360 362 364 366 368 370 372 374 376 378 380 382\n",
" 384 386 388 390 392 394 396 398 400 402 404 406 408 410 412 414 416 418 420\n",
" 422 424 426 428 430 432 434 436 438 440 442 444 446 448 450 452 454 456 458\n",
" 460 462 464 466 468 470 472 474 476 478 480 482 484 486 488 490 492 494 496\n",
" 498 500 502 504 506 508 510 512 514 516 518 520 522 524 526 528 530 532 534\n",
" 536 538 540 542 544 546 548 550 552 554 556 558 560 562 564 566 568 570 572\n",
" 574 576 578 580 582 584 586 588 590 592 594 596 598 600 602 604 606 608 610\n",
" 612 614 616 618 620 622 624 626 628 630 632 634 636 638 640 642 644 646 648\n",
" 650 652 654 656 658 660 662 664 666 668 670 672 674 676 678 680 682 684 686\n",
" 688 690 692 694 696 698 700 702 704 706 708 710 712 714 716 718 720 722 724\n",
" 726 728 730 732 734 736 738 740 742 744 746 748 750 752 754 756 758 760 762\n",
" 764 766 768 770 772 774 776 778 780 782 784 786 788 790 792 794 796 798 800\n",
" 802 804 806 808 810 812 814 816 818 820 822 824 826 828 830 832 834 836 838\n",
" 840 842 844 846 848 850 852 854 856 858 860 862 864 866 868 870 872 874 876\n",
" 878 880 882 884 886 888 890 892 894 896 898 900 902 904 906 908 910 912 914\n",
" 916 918 920 922 924 926 928 930 932 934 936 938 940 942 944 946 948 950 952\n",
" 954 956 958 960 962 964 966 968 970 972 974 976 978 980 982 984 986 988 990\n",
" 992 994 996 998 1000 1002 1004 1006 1008 1010 1012 1014 1016 1018 1020 1022 1024 1026 1028\n",
" 1030 1032 1034 1036 1038 1040 1042 1044 1046 1048]\n",
"[ 270 280 290 300 310 320 330 340 350 360 370 380 390 400 410 420 430 440 450\n",
" 460 470 480 490 500 510 520 530 540 550 560 570 580 590 600 610 620 630 640\n",
" 650 660 670 680 690 700 710 720 730 740 750 760 770 780 790 800 810 820 830\n",
" 840 850 860 870 880 890 900 910 920 930 940 950 960 970 980 990 1000 1010 1020\n",
" 1030 1040]\n"
]
}
],
"source": [
"wav_grid_2nm = np.arange(270,1050,2)\n",
"wav_grid_10nm = np.arange(270,1050,10)\n",
"print(wav_grid_2nm)\n",
"print(wav_grid_10nm)"
]
},
{
"cell_type": "code",
"execution_count": 365,
"metadata": {
"ExecuteTime": {
"end_time": "2018-03-20T14:15:41.422347Z",
"start_time": "2018-03-20T14:15:41.412859Z"
}
},
"outputs": [
{
"data": {
"text/plain": [
"array([ 266.76, 271.41, 276.06, 280.7 , 285.35, 289.99, 294.64, 299.29, 303.93, 308.58,\n",
" 313.23, 317.88, 322.52, 327.17, 331.82, 336.47, 341.11, 345.76, 350.41, 355.06,\n",
" 359.71, 364.36, 369. , 373.65, 378.3 , 382.95, 387.6 , 392.25, 396.9 , 401.55,\n",
" 406.2 , 410.85, 415.5 , 420.15, 424.8 , 429.45, 434.09, 438.74, 443.39, 448.04,\n",
" 452.69, 457.34, 461.99, 466.64, 471.29, 475.94, 480.6 , 485.25, 489.9 , 494.55,\n",
" 499.2 , 503.85, 508.5 , 513.15, 517.8 , 522.45, 527.1 , 531.75, 536.4 , 541.05,\n",
" 545.7 , 550.35, 555. , 559.65, 564.3 , 568.95, 573.6 , 578.25, 582.9 , 587.55,\n",
" 592.2 , 596.85, 601.5 , 606.15, 610.8 , 615.45, 620.1 , 624.75, 629.4 , 634.05,\n",
" 638.7 , 643.35, 648. , 652.65, 657.3 , 661.94, 666.59, 671.24, 675.89, 680.54,\n",
" 685.19, 689.84, 694.48, 699.13, 703.78, 708.43, 713.08, 717.72, 722.37, 727.02,\n",
" 731.67, 736.31, 740.96, 745.61, 750.25, 754.9 , 759.55, 764.19, 768.84, 773.49,\n",
" 778.13, 782.78, 787.42, 792.07, 796.71, 801.36, 806. , 810.65, 815.29, 819.94,\n",
" 824.58, 829.22, 833.87, 838.51, 843.15, 847.8 , 852.44, 857.08, 861.72, 866.37,\n",
" 871.01, 875.65, 880.29, 884.93, 889.57, 894.21, 898.86, 903.5 , 908.14, 912.78,\n",
" 917.41, 922.05, 926.69, 931.33, 935.97, 940.61, 945.25, 949.88, 954.52, 959.16,\n",
" 963.79, 968.43, 973.07, 977.7 , 982.34, 986.97, 991.61, 996.24, 1000.88, 1005.51,\n",
" 1010.15, 1014.78, 1019.41, 1024.05, 1028.68, 1033.31, 1037.94, 1042.57, 1047.2 , 1051.83])"
]
},
"execution_count": 365,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"wav[~miof_sp.mask]"
]
},
{
"cell_type": "code",
"execution_count": 366,
"metadata": {
"ExecuteTime": {
"end_time": "2018-03-20T14:15:42.943172Z",
"start_time": "2018-03-20T14:15:42.270067Z"
}
},
"outputs": [],
"source": [
"import scipy.signal"
]
},
{
"cell_type": "code",
"execution_count": 367,
"metadata": {
"ExecuteTime": {
"end_time": "2018-03-20T14:15:43.798536Z",
"start_time": "2018-03-20T14:15:43.790346Z"
},
"scrolled": false
},
"outputs": [
{
"data": {
"text/plain": [
"(512, 170, 170, 390, 78)"
]
},
"execution_count": 367,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"wav.size,wav[~miof_sp.mask].size,mphotom_iof_sp[~mphotom_iof_sp.mask].size, wav_grid_2nm.size, wav_grid_10nm.size"
]
},
{
"cell_type": "code",
"execution_count": 368,
"metadata": {
"ExecuteTime": {
"end_time": "2018-03-20T14:15:45.018755Z",
"start_time": "2018-03-20T14:15:45.003955Z"
}
},
"outputs": [
{
"data": {
"text/plain": [
"(array([0.03, 0.01, 0.02, 0.03, 0.03, 0.04, 0.04, 0.04, 0.05, 0.04]),\n",
" array([266.76, 345.74, 424.72, 503.7 , 582.68, 661.65, 740.63, 819.61, 898.59, 977.56]))"
]
},
"execution_count": 368,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"scipy.signal.resample(mphotom_iof_sp[~mphotom_iof_sp.mask],10,t=wav[~miof_sp.mask])"
]
},
{
"cell_type": "code",
"execution_count": 390,
"metadata": {
"ExecuteTime": {
"end_time": "2018-03-20T14:15:46.649597Z",
"start_time": "2018-03-20T14:15:46.369571Z"
}
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.legend.Legend at 0x7fb338775c18>"
]
},
"execution_count": 390,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 1152x432 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"x, y = wav[~mphotom_iof_sp.mask], mphotom_iof_sp[~mphotom_iof_sp.mask]\n",
"plt.figure(figsize=[16,6])\n",
"interpolator = scipy.interpolate.interp1d(x, y, kind='linear', fill_value='extrapolate')\n",
"plt.plot( x, y, color='black', label = 'original')\n",
"plt.plot( wav_grid_2nm, interpolator(wav_grid_2nm),label='2nm grid',color='b')\n",
"plt.plot(wav_grid_10nm, interpolator(wav_grid_10nm),label='10nm grid',color='r',lw=4, alpha=.5)\n",
"plt.xlim(275,1050)\n",
"plt.legend()"
]
},
{
"cell_type": "code",
"execution_count": 373,
"metadata": {
"ExecuteTime": {
"end_time": "2018-03-20T14:15:48.257275Z",
"start_time": "2018-03-20T14:15:47.943602Z"
}
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.legend.Legend at 0x7fb33a562b38>"
]
},
"execution_count": 373,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 1152x432 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"x, y = wav[~mphotom_iof_sp.mask], mphotom_iof_sp[~mphotom_iof_sp.mask]\n",
"plt.figure(figsize=[16,6])\n",
"interpolator = scipy.interpolate.interp1d(x, y, kind='linear', fill_value='extrapolate')\n",
"plt.plot( x, y, color='black', label = 'original')\n",
"plt.plot( wav_grid_2nm, interpolator(wav_grid_2nm),label='2nm grid',color='b')\n",
"plt.plot(wav_grid_10nm, interpolator(wav_grid_10nm),label='10nm grid',color='r',lw=4, alpha=.5)\n",
"plt.xlim(275,450)\n",
"plt.ylim(0,0.025)\n",
"plt.legend()"
]
},
{
"cell_type": "code",
"execution_count": 387,
"metadata": {
"ExecuteTime": {
"end_time": "2018-03-20T14:16:22.524152Z",
"start_time": "2018-03-20T14:16:22.238080Z"
}
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.legend.Legend at 0x7fb338527898>"
]
},
"execution_count": 387,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 1152x432 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"x, y = wav[~miof_sp.mask], miof_sp[~miof_sp.mask]\n",
"plt.figure(figsize=[16,6])\n",
"interpolator = scipy.interpolate.interp1d(x, y, kind='linear', fill_value='extrapolate')\n",
"plt.plot( x, y, color='black', label = 'original')\n",
"plt.plot( wav_grid_2nm, interpolator(wav_grid_2nm),label='2nm grid',color='b')\n",
"plt.plot(wav_grid_10nm, interpolator(wav_grid_10nm),label='10nm grid',color='r',lw=4, alpha=.5)\n",
"plt.xlim(260,650)\n",
"plt.ylim(0,0.04)\n",
"plt.legend()"
]
},
{
"cell_type": "code",
"execution_count": 386,
"metadata": {
"ExecuteTime": {
"end_time": "2018-03-20T14:15:49.877886Z",
"start_time": "2018-03-20T14:15:49.624237Z"
}
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.legend.Legend at 0x7fb338684c18>"
]
},
"execution_count": 386,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 1152x432 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"x, y = wav[~mphotom_iof_sp.mask], mphotom_iof_sp[~mphotom_iof_sp.mask]\n",
"plt.figure(figsize=[16,6])\n",
"interpolator = scipy.interpolate.interp1d(x, y, kind='linear', fill_value='extrapolate')\n",
"plt.plot( x, y, color='black', label = 'original')\n",
"plt.plot( wav_grid_2nm, interpolator(wav_grid_2nm),label='2nm grid',color='b')\n",
"plt.plot(wav_grid_10nm, interpolator(wav_grid_10nm),label='10nm grid',color='r',lw=4, alpha=.5)\n",
"plt.xlim(950,1060)\n",
"plt.ylim(-0.035,0.10)\n",
"plt.legend()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
"metadata": {
"anaconda-cloud": {},
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.5.5"
}
},
"nbformat": 4,
"nbformat_minor": 1
}
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