{ "cells": [ { "cell_type": "raw", "metadata": {}, "source": [ "%This notebook demonstrates the use of the workpackage template, replace with your own.\n", "\n", "\\documentclass[english]{workpackage}[1996/06/02]\n", "\n", "% input the common preamble content (required by the ipnb2latex converter)\n", "\\input{header.tex}\n", "\n", "% then follows the rest of the preamble to be placed before the begin document\n", "% this preamble content is special to the documentclass you defined above.\n", "\\WPproject{} % project name\n", "\\WPequipment{} % equipment name\n", "\\WPsubject{MWIR sensor well fill calculations} % main heading \n", "\\WPconclusions{} \n", "\\WPclassification{} \n", "\\WPdocauthor{CJ Willers}\n", "\\WPcurrentpackdate{\\today}\n", "\\WPcurrentpacknumber{} % work package number\n", "\\WPdocnumber{} % this doc number hosts all the work packages\n", "\\WPprevpackdate{} % work package which this one supersedes\n", "\\WPprevpacknumber{} % work package which this one supersedes\n", "\\WPsuperpackdate{} % work package which comes after this one\n", "\\WPsuperpacknumber{} % work package which comes after this one\n", "\\WPdocontractdetails{false}\n", "\\WPcontractname{} % contract name \n", "\\WPorderno{} % contract order number\n", "\\WPmilestonenumber{} % contract milestone number\n", "\\WPmilestonetitle{} % contract milestone title\n", "\\WPcontractline{} % contract milestone line number \n", "\\WPdocECPnumber{} % ecp/ecr number\n", "\\WPdistribution{}\n", "\n", "%and finally the document begin.\n", "\\begin{document}\n", "\\WPlayout\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Prepare Environment" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false, "jupyter": { "outputs_hidden": false } }, "outputs": [], "source": [ "# to prepare the Python environment\n", "import numpy as np\n", "import scipy as sp\n", "import scipy.constants as const\n", "import pandas as pd\n", "import os.path\n", "import time\n", "from scipy.optimize import curve_fit\n", "from scipy import interpolate\n", "import warnings\n", "import gc\n", "\n", "%matplotlib inline\n", "\n", "# %reload_ext autoreload\n", "# %autoreload 2\n", "\n", "import pyradi.ryplot as ryplot\n", "import pyradi.ryplanck as ryplanck\n", "import pyradi.ryfiles as ryfiles\n", "import pyradi.rymodtran as rymodtran\n", "import pyradi.ryutils as ryutils\n", "\n", "from IPython.display import HTML\n", "from IPython.display import Image\n", "from IPython.display import display\n", "from IPython.display import FileLink, FileLinks\n", "\n", "import matplotlib as mpl\n", "mpl.rc(\"savefig\", dpi=72)\n", "mpl.rc('figure', figsize=(10,8))\n", "# %config InlineBackend.figure_format = 'svg'\n", "\n", "pd.set_option('display.max_columns', 80)\n", "pd.set_option('display.max_rows', 80)\n", "pd.set_option('display.width', 100)\n", "pd.set_option('display.max_colwidth', 150)\n", "\n", "# suppress the pytables performance warning\n", "warnings.filterwarnings('ignore',category=pd.io.pytables.PerformanceWarning)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Introduction" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In the visual spectral band, the detector signal is normally dominated by the optical flux from the target object and the path radiance (haze along the path). On a clear day the signal in the detector is almost 100% target object signal. \n", "In the thermal spectral bands the hot optics and any hot obscuration in the optical path also contribute to the signal accumulated in the pixel. The flux emission from these hot objects can be quite significant compared to the target emission.\n", "\n", "At close range the atmosphere has limited effect on the target flux captured in the detector. At longer range the target flux is significantly affects by two atmospheric effects: (1) signal attenuation by absorption and scattering and (2) additive atmospheric flux from path radiance (atmospheric scattering and radiation into the line of sight). As the atmospheric attenuation increases the path radiance also increases, resulting in a double hit on performance: the target flux (signal to noise) decreases and the path radiance flux (well fill) increases. These effects become more prominent as atmospheric conditions degrades (poorer visibility and higher temperature and humidity).\n", "\n", "This document investigates the relative contributions from the various sources of flux in the detector signal. This composite flux is converted to electrical signals, digitised, processes and eventually displayed. Once the flux is captured in the detector, the origins of the various contributions are lost: the detector only counts photons, irrespective of where it comes from. The meaning that we as humans attach to the various contributions becomes irrelevant in the electronics.\n", "\n", "Once the flux is detected and converted to a digital signal, the signal processing algorithms must attempt to remove the spurious flux sources and retain only the target object contribution. For the most part this is easily done, simply by subtracting the more or less constant non-target portion from the total signal. This is done by scanning the whole image and then subtracting the smallest pixel signal from all the pixels in the image. Easily done, but the effect of this procedure is that a significant portion of the usable dynamic range is lost in the process.\n", "\n", "The pixel flux sources include:\n", "\n", "1. Target flux, as propagated through the atmosphere (all sensors).\n", "2. Atmospheric path radiance (all sensors).\n", "3. Hot optics radiance in the unobscured clear aperture (all sensors).\n", "4. The central obscuration radiance (SensorB and SensorC).\n", "\n", "Clearly all flux/radiance contributions, except the target, must be minimised by design choices: cooling down, reflecting cold, or minimising the projected area.\n", "\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Assumptions" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The calculations presented here are based on the following assumptions:\n", "\n", "1. Only thermal emittance is considered for target/scene content and internal radiation content. The sun and its effect does not feature anywhere in this report.\n", "\n", "2. The cold shield is assumed to be perfectly lambertian with unity emissivity.\n", "\n", "3. The effect of external stray light outside the sensor field of view is not considered.\n", "\n", "4. It is assumed that the sensor will be operated at 100% well fill for maximum scene radiance. The well/gain selection and integration time will be adjusted to achieve 100% well fill if possible. At maximum gain and maximum integration time the well fill will be less than 100%.\n", "\n", "5. Signal processing is not considered in this report.\n", "\n", "6. Noise in the signal and electronics is not considered in this report." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Obscuration" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Three configurations are considered: without a central obscuration, with a central obscuration with matching f-numbers, and with a central obscuration with mismatching f-numbers." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Read the input data" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": true, "jupyter": { "outputs_hidden": true } }, "outputs": [], "source": [ "dolong = False\n", "TempTargetDeltaC = 2.\n", "TempDynamicPlot = 40.\n" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false, "jupyter": { "outputs_hidden": false } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Input file: data/well-fill/sensor-definitions.xlsx\n", " created: Wed Oct 19 10:16:27 2016\n", " last modified: Wed Oct 19 10:16:27 2016\n", "\n", "\n", "All available concepts:\n", "[u'MWIR Sensor']\n", " \n", "Selected concept and sensor definitions:\n", "MWIR Sensor\n", " SensorA SensorB SensorC\n", "fno 4 4 6\n", "foclen m 0.6 0.6 0.6\n", "hfov deg 0.91 0.91 0.91\n", "tauopt 0.8 0.74 0.74\n", "centralObs - 0 0.0625 0.0625\n", "fnoColdshield 4 4 4\n", "rprim m 0.08 0.1 0.1\n", "rcentral m 0 0.025 0.025\n", "aprim m2 0.0201062 0.0314159 0.0314159\n", "acentral m2 0 0.0019635 0.0019635\n", "aclear m2 0.0201062 0.0294524 0.0294524\n", "areaDetector m2 2.25e-10 2.25e-10 2.25e-10\n", "sizeDetector m 1.5e-05 1.5e-05 1.5e-05\n", "max IntTime s 0.03 0.03 0.03\n", "tempNETDStd K 300 300 300\n", "netdstd K 0.03 0.03 0.03\n", "wellCapacity [4980000.0] [4980000.0] [4980000.0]\n", "nuloss 0.25 0.25 0.25\n", "wellCapacityUsable [3735000.0] [3735000.0] [3735000.0]\n", "ksyss [0.0, 0.75] [0.0, 0.75] [0.0, 0.75]\n", "spectralCent 4.22 4.22 4.22\n", "spectralWid 1.45 1.45 1.45\n", "spectralLo 3.495 3.495 3.495\n", "spectralHi 4.945 4.945 4.945\n", "quantumEff 0.75 0.75 0.75\n" ] } ], "source": [ "# read the excel spreadsheet containing the sensor data and extract the example data\n", "\n", "xfilename = 'data/well-fill/sensor-definitions.xlsx'\n", "\n", "print('Input file: {}\\n created: {}\\n last modified: {}\\n\\n'.format(xfilename,\n", " time.ctime(os.path.getctime(xfilename)),\n", " time.ctime(os.path.getmtime(xfilename))))\n", "\n", "dfS = pd.read_excel(xfilename, 'Sheet1', index_col=None, na_values=['NA'])\n", "dfS.set_index(keys='Index',inplace=True)\n", "dfS.drop(labels=[-1], inplace=True)\n", "# get rid of empty lines\n", "dfS.dropna(inplace=True)\n", "\n", "\n", "dfsysCols = ['concept','sensor','fno', 'foclen m', 'hfov deg', 'tauopt', \n", " 'centralObs -', 'fnoColdshield', \n", " 'rprim m', 'rcentral m',\n", " 'aprim m2', 'acentral m2','aclear m2', \n", " 'areaDetector m2','sizeDetector m','max IntTime s',\n", " 'tempNETDStd K','netdstd K','wellCapacity','nuloss','wellCapacityUsable',\n", " 'ksyss','spectralCent','spectralWid','spectralLo','spectralHi','quantumEff'\n", " ]\n", "dfsys = pd.DataFrame(columns=dfsysCols)\n", "concepts = dfS.columns.unique()\n", "for column in dfS.columns:\n", " sensors = [s for s in dfS.loc['Sensor'][column].split(',')]\n", " for i,sensor in enumerate(sensors):\n", " rprim = [float(s) for s in dfS.loc['rprim'][column].split(',')]\n", " rcentral = [float(s) for s in dfS.loc['rcentral'][column].split(',')]\n", " foclen = [float(s) for s in dfS.loc['foclen'][column].split(',')]\n", " hfovdeg = [float(s) for s in dfS.loc['hfovdeg'][column].split(',')]\n", " tauopt = [float(s) for s in dfS.loc['tauopt'][column].split(',')]\n", " fnoColdshield = [float(s) for s in dfS.loc['fnoColdshield'][column].split(',')]\n", " fno = [float(s) for s in dfS.loc['fno'][column].split(',')]\n", " nuloss = dfS.loc['nuloss'][column]\n", " \n", " aprim = np.pi * rprim[i] ** 2\n", " acentral = np.pi * rcentral[i] ** 2\n", " aclear = aprim - acentral\n", " areaDetector = dfS.loc['sizeDetector'][column] ** 2\n", " spectralCent = dfS.loc['spectralCent'][column]\n", " spectralWid = dfS.loc['spectralWid'][column]\n", " quantumEff = dfS.loc['quantumEff'][column]\n", " spectralLo = spectralCent - spectralWid / 2.\n", " spectralHi = spectralCent + spectralWid / 2.\n", "\n", " ocentral = acentral / aprim\n", " \n", " if type(dfS.loc['ksyss'][column]) is unicode:\n", " ksyss = [float(s) for s in dfS.loc['ksyss'][column].split(',')]\n", " else:\n", " ksyss = [float(dfS.loc['ksyss'][column])]\n", " \n", " if type(dfS.loc['wellCapacity'][column]) is unicode:\n", " wellCapacity = [float(s) for s in dfS.loc['wellCapacity'][column].split(',')]\n", " wellCapacityUsable = [float(s) * (1.0-nuloss) for s in dfS.loc['wellCapacity'][column].split(',')]\n", " else:\n", " wellCapacity = [float(dfS.loc['wellCapacity'][column])]\n", " wellCapacityUsable = [float(dfS.loc['wellCapacity'][column])* (1.0-nuloss)]\n", "\n", " restults = [column, sensors[i], fno[i], foclen[i], hfovdeg[i], tauopt[i], \n", " ocentral, fnoColdshield[i], \n", " rprim[i], rcentral[i], aprim, acentral, \n", " aclear, areaDetector,\n", " dfS.loc['sizeDetector'][column], dfS.loc['integrationTime'][column],\n", " dfS.loc['tempNETDStd'][column], dfS.loc['netdstd'][column], \n", " wellCapacity, nuloss, wellCapacityUsable, ksyss, \n", " spectralCent, spectralWid, spectralLo, spectralHi, quantumEff]\n", "\n", " dfsys = dfsys.append(pd.DataFrame([restults], columns=dfsysCols))\n", "\n", "# build a multi-index for concepts and sensors\n", "dfsys.index = pd.MultiIndex.from_tuples([(v['concept'],v['sensor']) for k,v in dfsys.iterrows()])\n", "# transpose and clean up\n", "dfsys=dfsys.transpose().drop(['concept','sensor'])\n", "\n", "print('All available concepts:')\n", "print(concepts)\n", "print(' ')\n", "\n", "# filter all sensors of given concept\n", "Cconcept = u'MWIR Sensor'\n", "\n", "dfConcept = dfsys[Cconcept].copy()\n", "CSensors = dfConcept.columns.values\n", "\n", "print('Selected concept and sensor definitions:')\n", "print(Cconcept)\n", "print(dfConcept)\n", "# get one sensor from one concept\n", "# print(dfConcept[CSensors[0]]) \n", "#get particular value for specific variable of given sensor and concept\n", "# print(dfConcept[CSensors[0]]['fno']) \n", "\n", "sizeDetector = dfConcept[CSensors[0]]['sizeDetector m']\n", "integrationTime = dfConcept[CSensors[0]]['max IntTime s']\n", "areaDetector = dfConcept[CSensors[0]]['areaDetector m2']\n", "wellCapacityUsable = dfConcept[CSensors[0]]['wellCapacityUsable']\n", "netdstd = dfConcept[CSensors[0]]['netdstd K']\n", "tempNETDStd = dfConcept[CSensors[0]]['tempNETDStd K']\n", "ksyss = dfConcept[CSensors[0]]['ksyss']\n", "threshold2Noise = 1.\n", "\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Although three sensors are defined above, only SensorA is evaluated in the default mode in this notebook. This is done to limit the execution time and download size, because each additional sensor adds time and a large download file." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Cold Shield" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The detector can receive flux propagating through the clear optics (the intended signal) but also from the optomechanical mounting components such as the optical barrel (undesired signal). The designer's objective is to maximise the intended signal and to minimise the undesired signal in order to minimise detector well fill with non-signal.\n", "\n", "Cold stop efficiency is defined as the ratio of flux in the optical f-number cone (i.e., inside the solid angle subtended by the exit pupil) to total flux on the detector. If there is no vignetting in the system the flux in the exit pupil will be the flux entering the entrance pupil. In the ideal case the cold stop efficiency should be 100%.\n", "\n", "Flux loss/contribution attributable to vignetting is not included in the cold stop efficiency merit, because only the flux in the exit pupil is considered." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Cold shield - intermediate focal plane " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The more sophisticated two-image-plane design specifically focuses (re-images) the entrance pupil onto the cold shield, i.e., the exit pupil is the cold shield aperture. The flux that enters the cold stop can therefore only come from the exit pupil, no other flux emanating behind the exit pupil can reach the detector. This means that only flux (1) entering the entrance pupil plus flux (2) that emanates inside the clear optics areas can reach the detector.\n", "\n", "This optical design has three focusing objectives: (1) the object is imaged onto the intermediate focal plane, (2) the intermediate focal plane is focused onto the detector, and (2) the exit pupil is focused onto the cold stop. Lens 2 therefore has two focussing tasks.\n", "\n", "This optical design is more complex than the single lens design shown before, but has the benefit of achieving a 100% cold stop efficiency. The multiple objectives with this design, especially with respect to lens 2, place some constraints on the design.\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "![''](images/coldshield-ineff-concept.png)\n", "\n", "![''](images/coldshield-ineff-concept2.png)\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Cold shield distance\n", "\n", "The distance between the cold shield and the detector is important. \n", "\n", "In a simple single-focal-plane (Bill Wolfe) design the closer the cold shield is moved towards the exit pupil the better the cold efficiency becomes. This is demonstrated in the graph in the Bill Wolfe extract above. In the case of the single-focal-plane design the cold shield efficiency is improved by increasing the distance between the detector and the cold shield.\n", "\n", "In a the two-focal-plane design that images the exit pupil onto the cold shield the cold shield efficiency is 100% irrespective of the distance between the detector and the cold shield. However, in this case the cold shield distance remains important: if the distance is too short the optical angles in the exit pupil becomes large complicating the optical design and manufacture. This cold stop distance constraint has nothing to do with the cold shield efficiency." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Scene and sensor variations" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "A large number of variations are considered in this report:\n", "\n", "1. Two filter configurations are investigated: a single band response and a filter with a notch around 4.3~$\\mu$m. The notch filter suppresses the path radiance at 4.3 $\\mu$m, but is also a little wider towards the shorter wavelength range. This extra width towards shorter wavelengths attempts to gather more target signal. The effect of the notch filter is evident at longer ranges as the path radiance increases.\n", "\n", "1. Atmospheric transmittance and path radiance play significant roles in the detector electron well fill, particularly under poor atmospheric conditions. For this reason several atmospheric conditions are considered in this analysis.\n", "\n", "1. The background (ambient) temperature also has a significant effect on the target contrast: at cold temperatures the signature contrast is smaller. Several values of ambient temperature are considered in the analysis. \n", "\n", "1. Several different internal temperature differentials relative to ambient outside temperatures are considered. When the equipment is switched on the differential will be zero, but after prolonged operation, an internal temperature rise of 20 C or even 40 C can be expected. The higher temperatures have significant effect on the radiation from the optics and obscurations.\n", "\n", "All these variations result in a multidimensional grid, from which only a few cases a shown in graphical form.\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The target and sensor internal temperatures are all calculated relative to some baseline temperature. This reference-based approach enables us to evaluate the effect of scene or sensor ambient temperature variations.\n", "\n", "Note that the internal sensor temperature is calculated relative to the outside atmospheric temperature: at higher altitudes the internal sensor temperature will be cooler than at lower outside atmospheric temperatures.\n", "In the detailed analysis various combinations of element temperatures are considered. The temperatures are mostly given as deltas from some reference temperatures. The reference temperatures are the cold shield temperature (assumed 100 K) and the scene temperature. There are two scene temperature models: \n", "\n", "- Horizontal paths with temperatures from very cold to very hot.\n", "\n", "- Slant paths not further modelled in this notebook.\n", "\n", "The target temperature is defined as a delta relative to the scene temperature. Typical scene temperature contrast in outdoor scenes ranges from 40 K to 60 K (or even higher).\n", "\n", "The atmospheric temperature is derived from the scene temperature. For the horizontal paths, the atmospheric temperature is the same as the scene temperature. For a slant paths the atmospheric temperature is determined from the vertical profile for the respective atmospheric models.\n", "\n", "The sensor internal temperature is derived from the atmospheric temperature with some delta temperature. The $-100$ K case represents an ideal ice-cold internal temperature (no background flux from the sensor). The $0$ K case represents the case where the internal temperature is the same as the outside temperature. The other two cases represent a hot sensor, compared to the atmosphere around the sensor.\n", "\n", "The optics temperature is at zero delta to the internal temperature.\n", "\n", "The hot shield temperature can be cooler than the interneal temperature, if the mechanics is reflecting the cold detector onto itself. This value is motivated as follows: the hot shield and central obscuration will be designed to reflect the detector cold stop. This reflection is however not optically perfect, there will be inefficiencies, reflecting some portion of the sensor internal optomechanical radiance. The (ineffective) detector reflection will however lower the flux to some degree, hence the -100 C temperature delta.\n", "\n", "The specific variations in the temperatures are given below in `tempCentralObsDeltas`, `tempHotShieldDeltas`, `TempAmbient`, `TempScene`, and `TempInsideDelta`. \n", "The path lengths considered are given below in `pathlenUniqueGen`.\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "![''](images/temperature-series.png)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Graph definitions" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Almost all the graphs in this show percentage well fill, which is the *signal collected in a single pixel*, say the central pixel in the image. The 'well' a small capacitor under the detector element that accumulates electrons during the integration time. \n", "Some detectors have single valued well capacities while others have selectable well capacity values, expressed in electrons.\n", "The well capacity is reduced by the non-uniformity margin, so in practice and in these calculations, the effective well capacity is somewhat smaller than the nominal design value.\n", "\n", "For shorter integration times less electrons will be collected and for longer integration times more electrons are collected. There is a maximum integration time, determined by the frame rate. For this investigation the maximum integration time is assumed to be 19~ms, but it is can be any smaller value.\n", "\n", "When calculating the well fill, the best combination of well capacity and integration is used to achieve the highest well fill (but limited to 100%). the well fill is calculated such that a minimum dynamic range is maintained in the image. For example, most cases consider the case where the temperature difference between the hottest and coldest objects in the scene (dynamic range) differs by 40 K. This means that the well selection and integration is such that the hottest pixel fills the well to 100%. \n", "\n", "The dynamic range is not the target temperature. Somewhere in this dynamic range is the target contrast (for example 2 K). " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "![''](images/well-fill-01.png)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The graph above shows an annotated graph explaining the various contributors to well fill, color coded as defined in the graph legend. The legend also shows the delta temperature for the specific contributor, relative to its reference temperature. This particular graph does not show photon starvation because the well is always 100% filled. For well fill the graph uses the y-axis scale on the left (100% maximum).\n", "\n", "The available well capacity after allowance for the non-uniformity is shown near the bottom of the graph along the x-axis. Also shown is the integration time required to achieve the well fill shown in the stacked graph. The image dynamic range (i.e., 40 K) is shown on the top left of the graph.\n", "\n", "The graph shows well fill versus target distance for a fixed sensor dynamic range setting. The graph below also shows the bits lost to noise (dash-dot line) for a TNR=1 (i.e., RMS noise). The solid line shows the number of bits not available for signal: loss attributable FPA nonuniformity, hot optics well fill and scene minimum radiance well fill (yellow, cyan, magenta, green and red in the graph). The dashed line shows the number of useful bits left for scene radiance above the minimum level, i.e., the dynamic range in the image. The three bit lines map to the scale on the right (14 bits).\n", "\n", "Other graphs show well fill for increasing target contrast at a fixed target distance. For both graph-types the elements' descriptions are the same as shown in the above graph.\n", "\n", "The calculations yield fractional bits resulting from floating point calculations, to be considered as statistical values over large samples.\n", "\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Non uniformity effect on dynamic range" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The detector specfications state quite high values of detector non-uniformity, up to 25% from the mean value. In order to allow headroom for the more responsive detector elements the integration time must be shortened such that the more responsive detector elements do not saturate. This means that the available well capacity for the mean signal is reduced.\n", "\n", "![''](images/nu-well-fill01.png)\n", "\n", "In the calculations below the available well capacity is calculated as \n", "\n", "\\begin{equation}\n", "W_\\textrm{available} = W_\\textrm{true} - (1-NU)\n", "\\end{equation}\n", "where\n", "$W_\\textrm{true}$ is the specified well capacity, and \n", "$NU$ is the per unit non-uniformity, i.e., 0.25. The value for NU used in the calculation is read in per detector from the input data file.\n", "\n", "The number of bits lost to non-uniformity margin is given by $\\log_2(1-NU)$.\n", "An NU of 0.25 results in a bit loss of 0.42 bits.\n", "An NU of 0.15 results in a bit loss of 0.23 bits.\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Signal dynamic range" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The signal dynamic range can be expressed in terms of bits of a binary word. The effect of well fill is to reduce the number of bits from the most-significant end of the word: a 50% well fill removes the MSB and so down. \n", "The number of bits available to the target scene, after well fill by the hot optics, internal sensor stray radiation and path radiance is shown in the figure below and in the following equation:" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "![''](images/well-fill-05.png)\n", "\n", "The word length so obtained is shown in the graphs above: the target percentage of well fill was used to determine the number of bits available to represent the target contrast temperature. Well fill and non-uniformity margin reduces the number of bits from the most significant bit end.\n", "\n", "Noise, EMI and roundoff errors in signal processing reduce the dynamic range from the least-significant bit end. This is accounted for in the present study as follows:\n", "\n", "1. The detector NETD is modified by the ambient conditions, as described in Section~\\ref{netdmodambient}. Under higher temperature background conditions the noise increases (but NETD decreases) and under colder background conditions the noise decreases (NETD increases).\n", "2. A small amount of system noise is added to the detector NETD. This noise is constant and independent of ambient conditions. Presently the assumed system noise increases the NETD by about 20% at 293 K.\n", "3. The noise 'floor' is taken at a threshold to noise ratio (TNR) of one, i.e., the RMS noise value. In practice a higher of TNR (five to eight) would be required for low-false alarm signal detection. The TNR=1 value used here is motivated on the grounds that the human vision system somewhat averages out the noise over multiple frames.\n", "\n", "The number of bits available to represent the target is under pressure from both the well fill end and the noise end --- squeezed somewhere in the middle. Once the top and bottom bits have been removed, the image processing takes place on the centre bits --- but this processing will also introduce bit loss, because of linear and nonlinear scaling and repeated integer resolution processing.\n", "\n", "It is important that the hardware design should strive to provide maximal dynamic range for image processing and \n", "reduce the pressure from both ends: minimise hot optics and noise/EMI/NUC bit loss.\n", "\n", "Seen from this perspective, a 50% well fill is not so severe as originally thought. A 50% well fill does not remove half the number of bits available for the target, it only removes $-\\log_2(W)=1$ bits (where $W$ is the fractional well fill).\n", "However, the detector and sensor noise effectively removes two to four bits on the bottom end, resulting in an overly small number of bits in the useful image.\n", "\n", "In most of the graphs presented in this report the scene dynamic range is 40 K temperature contrast (the hottest object in the image) and the target has a 2 K contrast. The gain was calculated such at a 40 K temperature contrast object would result in a 100% well fill. In the same image the 2 K target would then fill a smaller percentage of the well capacity." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "![''](images/adc-dynamic-range.png)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The work presented in this report shows that the internal sensor radiance present a signal larger than the target. All efforts must therefore be expended to minimise hot optics self-radiance and optical path obscuration radiance." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Sensor Filter" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Two filters are considered: one with a CO$_2$ notch filter and one without. The notch filter is intended to filter out the spectral region where there is no/little target signal, thereby suppressing some of the sensor and path radiance contributions. In the graphs in this document the single band filter is identified in the graph titles by the name `Standard`, whereas the use of notch filter is identified by the name `Notch`.\n", "\n", "The spectral width of the filter is adapted according to the detector specification given in the input data file." ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false, "jupyter": { "outputs_hidden": false } }, "outputs": [], "source": [ "# to define the generic atmospheres: code, description and temperature\n", "atmodef = zip(['MLS23km','tropical5km','SW31km'],\n", " ['Midlatitude Summer, 23 km Urban vis','Tropical, 5 km Urban vis',\n", " 'Scandinavian Winter 31 km vis Navy Maritime'],\n", " [294., 300., 257.])\n", "\n", "#generic atmosphere data are used to extract the wavelength vector for calculations\n", "#get the wavenumber and wavelength vectors from the first atmo file\n", "wn = rymodtran.loadtape7(\"data/well-fill/{}/tape7\".format(atmodef[0][0]), ['FREQ'] ).reshape(-1,)\n", "wl = 1e4 / wn# convert from wavenumber to wavelength" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false, "jupyter": { "outputs_hidden": false } }, "outputs": [ { "data": { "image/png": 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piWRaqIOBscDRknYpUe8ckofrmnWJx6iZmVlnjRxZ+zNquTpqEbG28EUy/dPW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\nT+PHF+Z8gZpEDbfW3xp0KMaE2q4Duxj9H6Np+z9tQYdSlla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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# to define the filter spectral transmittance\n", "TauFilters = np.zeros((wl.shape[0],2))\n", "optFilters = ['Standard','Notch']\n", "\n", "specCent = dfConcept[CSensors[0]]['spectralCent']\n", "specWid = dfConcept[CSensors[0]]['spectralWid']\n", "\n", "#3.4-4.9\n", "# TauFilters[:,1] = ryutils.sfilter(wl, 4.15, 1.45, exponent=40, taupass=0.9, taustop=0.0, filtertype = 'bandpass') \n", "\n", "#3.7-4.8\n", "TauFilters[:,0] = ryutils.sfilter(wl, specCent, specWid, exponent=40, taupass=0.9, taustop=0.0, filtertype = 'bandpass')\n", "TauFilters[:,1] = ryutils.sfilter(wl, specCent, specWid, exponent=40, taupass=0.9, taustop=0.0, filtertype = 'bandpass') - \\\n", " ryutils.sfilter(wl, 4.37, .3 , exponent=40, taupass=0.9, taustop=0.0, filtertype = 'bandpass')\n", "\n", "p = ryplot.Plotter(1,2,1,figsize=(10,6))\n", "for i,optFilter in enumerate(optFilters):\n", " p.plot(1+i,wl,TauFilters[:,i],'Sensor filter transmittance: {}, {:.2f}-{:.2f} $\\mu$m'.format(optFilter,\n", " dfConcept[CSensors[0]]['spectralLo'],dfConcept[CSensors[0]]['spectralHi']), 'Wavelength $\\mu$m',\n", " 'Transmittance'); \n", " \n", "# dfF = pd.DataFrame(TauFilters, columns=['Standard','Notch']) \n", "# dfF['Wavelength'] = wl\n", "# writer = pd.ExcelWriter('MWIR-filters.xlsx')\n", "# dfF.to_excel(writer,'Filters')\n", "# writer.save()\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Atmospheric Transmittance and Path Radiance" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The generic atmospheres are based on two horizontal-path atmospheres (moderate and poor visibility) from which a variety of derived atmospheric conditions are calculated. The horizontal path model assumes that the atmosphere is uniform along the path. The atmospheric transmittance is scaled according to the required path length. The path radiance is furthermore adapted to the respective atmospheric variation. For cooler variations the path radiance is reduced and for warmer atmospheres the path radiance is increased. Hence, the path radiance is always relevant to the ambient temperature in the scenario. The generic atmospheric model is therefore manipulated to estimate the transmittance and path radiance over a multidimensional grid of scenario values.\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Generic Atmospheres" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In this analysis three atmospheric conditions are modelled by Modtran: (1) a very poor atmosphere with Tropical climatic conditions (27 C and 75% relative humidity) and a 5 km visibility Modtran Urban aerosol, (2) a moderate atmosphere with Midlattitude summer climatic conditions (21 C and 76% relative humidity) and a 23 km visibility Urban aerosol, and a good atmoshere with Subarctic Summer (-16C, 80% RH) and 31 km Marine aerosol visibiity aerosol.\n", "\n", "The path radiance graphs below show the fit of a Planck radiance curve to the path radiance curve. It is evident that when the atmospheric transmittance is zero, the path radiates as a blackbody. At wavelengths where the transmittance is not zero the path radiates with an emissivity of (1-$\\tau$). \n", "\n", "In the subsequent calculations the path radiance will be calculated as the the product of $(1-\\tau_a)$ times the Planck radiance at the atmospheric temperature. Atmospheric temperature is assumed equal to the ambient temperature --- this assumption is based on the observation that the atmospheric temperature is normally within a few degrees around ambient temperature." ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false, "jupyter": { "outputs_hidden": false } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Midlatitude Summer, 23 km Urban vis, 5 km path length\n", "Modtran path radiance 1.15e+00 W/(m2.sr)\n", "Modtran path radiance 2.62e+19 q/(s.m2.sr)\n", "(1-tau) path radiance 2.60e+19 q/(s.m2.sr)\n", " \n", "Tropical, 5 km Urban vis, 5 km path length\n", "Modtran path radiance 1.57e+00 W/(m2.sr)\n", "Modtran path radiance 3.56e+19 q/(s.m2.sr)\n", "(1-tau) path radiance 3.60e+19 q/(s.m2.sr)\n", " \n", "Scandinavian Winter 31 km vis Navy Maritime, 5 km path length\n", "Modtran path radiance 1.91e-01 W/(m2.sr)\n", "Modtran path radiance 4.34e+18 q/(s.m2.sr)\n", "(1-tau) path radiance 4.30e+18 q/(s.m2.sr)\n", " \n" ] }, { "data": { "image/png": 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Sx8PDg169elm3Y2NjeeSRR+jQoQMAP/74Ix999JHdtl5eXgCcOnUKHx+fFMc5deoUCxYs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\n800fXlzxIpExkbRo4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ixQrbFwkAZ2dn28x4ah/Ozs5Z9t+wYUPef/99nnrqKdavX0/Dhg2zjY0QQggh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\n6lbVYkfWWcA1i1G3q98CbWPMmmFBtzHGHN9OA74CF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txzp+/DgjRoxg8+bN1hHpzBw7doyxY8eyZcsWAMqXL8+sWbN46qmnsLW1xdfX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\nD0py8KAoH3w8QOBNvjXxfZ/dr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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# to plot selected atmospheric transmittance and path radiance values\n", "sr = ryplot.Plotter(1,1,1,figsize=(12,4));\n", "ss = ryplot.Plotter(2,1,1,figsize=(12,4));\n", "st = ryplot.Plotter(3,1,1,figsize=(12,4));\n", "\n", "for i, (atmo, atmoDesc, tempModAtmo) in enumerate(atmodef):\n", "\n", " colSelect = ['FREQ', 'TOT_TRANS', 'TOTAL_RAD']\n", " skyrad= rymodtran.loadtape7(\"data/well-fill/{}/tape7\".format(atmo), colSelect )\n", "\n", " TauAtmo = skyrad[:,1]\n", " wl, LpathW = ryutils.convertSpectralDensity(skyrad[:,0],skyrad[:,2],'nl')\n", " LpathW = 1.0e4 * LpathW # /cm2 units to /m2 units\n", " LpathQ = LpathW / (ryplanck.const.h * ryplanck.const.c / (wl.reshape(-1,1) * 1e-6))\n", "\n", " if i == 0:\n", " gammaTau = -np.log(TauAtmo).reshape(-1,1) / 5.0 # units of km-1\n", " else:\n", " gammaTau = np.hstack((gammaTau, -np.log(TauAtmo).reshape(-1,1) / 5.0))\n", " \n", " sr.plot(1, wl, TauAtmo, \"Modtran transmittance, 5 km horizontal path\",\"Wavelength [$\\mu$m]\",\"Transmittaance\",\n", " label=[atmoDesc],legendAlpha=0.5, maxNX=10, maxNY=4);\n", " ss.plot(1, wl, LpathW, \"\",\"\",\"\",\n", " label=['Modtran {}'.format(atmoDesc)],legendAlpha=0.5, maxNX=10, maxNY=4);\n", " ss.plot(1, wl, ryplanck.planck(wl,tempModAtmo,type='el')/np.pi, \"\",\"\",\"\",\n", " label=['Planck {} K'.format(tempModAtmo)],legendAlpha=0.5, maxNX=10, maxNY=4);\n", " ss.plot(1, wl, (1-TauAtmo)*ryplanck.planck(wl,tempModAtmo,type='el')/np.pi, \"Path radiance, 5 km horizontal path\",\"Wavelength [$\\mu$m]\",\"L [W/(m$^2$.sr.$\\mu$m)]\",\n", " label=['(1-$\\\\tau$) * Planck {} K'.format(tempModAtmo)],legendAlpha=0.5, maxNX=10, maxNY=4);\n", "\n", " st.plot(1, wl, LpathQ, \"\",\"\",\"\",\n", " label=['Modtran {}'.format(atmoDesc)],legendAlpha=0.5, maxNX=10, maxNY=4);\n", " st.plot(1, wl, ryplanck.planck(wl,tempModAtmo,type='ql')/np.pi, \"\",\"\",\"\",\n", " label=['Planck {} K'.format(tempModAtmo)],legendAlpha=0.5, maxNX=10, maxNY=4);\n", " st.plot(1, wl, (1-TauAtmo)*ryplanck.planck(wl,tempModAtmo,type='ql')/np.pi, \"Path radiance, 5 km horizontal path\",\"Wavelength [$\\mu$m]\",\"L [q/(s.m$^2$.sr.$\\mu$m)]\",\n", " label=['(1-$\\\\tau$) * Planck {} K'.format(tempModAtmo)],legendAlpha=0.5, maxNX=10, maxNY=4);\n", "\n", " LpathQQ = np.trapz((1.0-TauAtmo).reshape(-1,1) * ryplanck.planck(wl,tempModAtmo,type='ql').reshape(-1,1)/np.pi,x=-wl.reshape(-1,1),axis=0)\n", "\n", " print('{}, 5 km path length'.format(atmoDesc))\n", " print('Modtran path radiance {:.2e} W/(m2.sr)'.format(np.trapz(LpathW,x=-wl.reshape(-1,1),axis=0)[0]))\n", " print('Modtran path radiance {:.2e} q/(s.m2.sr)'.format(np.trapz(LpathQ,x=-wl.reshape(-1,1),axis=0)[0]))\n", " print('(1-tau) path radiance {:.2e} q/(s.m2.sr)'.format(LpathQQ[0]))\n", " print(' ')\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Sensor Radiometry Calculations" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The contribution of various flux sources can be calculated from first principles. Of course, it is very difficult to be completely correct, but order of magnitude results can be readily obtained with a little care. More detail on the radiometric theory used here can be obtained in 'Electro-Optical System Design; A Radiometric Perspective', by CJ~Willers, SPIE, 2013.\n", "\n", "In the analysis done here, the effect of the atmosphere is included in terms of target flux attenuation and atmospheric path radiance. Both factors play a significant role in the pixel well fill, particularly under poor atmospheric conditions or for long distance paths." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "For any optical medium $\\epsilon + \\rho + \\tau=1$, where $\\epsilon$ is the medium emissivity, $\\rho$ is the medium reflectance and $\\tau$ is the medium transmittance. Opaque surfaces has $\\tau=0$ and hence $\\epsilon = 1 - \\rho$. Non-reflective surfaces have\n", "$\\epsilon = 1 - \\tau$. Optical elements have emissivity, reflectance and transmittance and this simple analysis is not appropriate. The lens elements will reflect the ambient barrel radiance $\\rho L_{\\textrm{barrel}}$ and will emit $\\epsilon L_{\\lambda\\textrm{optics}}$.\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "![''](images/Radiometry12.png)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Optical design has zero narcissism:** \n", "The total flux emanating from the lens elements will be\n", "$\\Phi=\n", "\\Omega_{f}A_o\\left[\n", "\\tau_{\\textrm{o}} L(T_{\\textrm{object}})\n", "+\n", "\\epsilon_{\\textrm{o}} L(T_{\\textrm{optics}})\n", "+\n", "\\rho_{\\textrm{o}} L(T_{\\textrm{barrel}})\n", "\\right]\n", "$. Assuming that the barrel and optics are at the same temperature $T_{\\textrm{optics}}=T_{\\textrm{barrel}}$,\n", "then \n", "\n", "\\begin{equation}\n", "\\Phi\n", "=\n", "\\Omega_{f}A_o\\left[\n", "\\tau_{\\textrm{o}} L(T_{\\textrm{object}})\n", "+\n", "\\epsilon_{\\textrm{o}} L(T_{\\textrm{optics}})\n", "+\n", "\\rho_{\\textrm{o}} L(T_{\\textrm{optics}})\n", "\\right]\n", "=\n", "\\Omega_{f}A_o\\left[\n", "\\tau_{\\textrm{o}} L(T_{\\textrm{object}})\n", "+\n", " L(T_{\\textrm{optics}})(\\epsilon_{\\textrm{o}}+\\rho_{\\textrm{o}})\n", "\\right],\n", "\\end{equation}\n", "but \n", "$(\\epsilon_{\\textrm{o}}+\\rho_{\\textrm{o}}) L(T_{\\textrm{optics}}) = (1-\\tau_{\\textrm{o}})L(T_{\\textrm{optics}})$. Then\n", "\n", "\n", "\\begin{equation}\n", "\\Phi\n", "=\n", "\\Omega_{f}A_o\\left[\n", "\\tau_{\\textrm{o}} L(T_{\\textrm{object}})\n", "+\n", " L(T_{\\textrm{optics}})(1-\\tau_{\\textrm{o}})\n", "\\right]\n", "\\end{equation}\n", "\n", "Hence the optics' `effective emissivity' can be considered as everything that is not transmittance, leading to the simple model\n", "$\\epsilon_{o\\lambda} = 1 - \\tau_{\\lambda o}$, and similarly for the filter\n", "$\\epsilon_{f\\lambda} = 1 - \\tau_{\\lambda f}$. This model only applies if $T_{\\textrm{optics}}=T_{\\textrm{barrel}}$.\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Optical design has nonzero narcissism:** \n", "\n", "Narcissism is the detector reflected from the optics' elements' surfaces onto itself. If is modelled as a scalar reflective term $\\rho_n$, computed over the full optics aperture, for different field angles. The value of $\\rho_n$ is determined by ray tracing, counting the fraction of rays emanating from the detector, that fall back onto the detector. The object transmittance and the optics' self emittance is not affected by the narcissism and remains the same. The reflectance from the optics now has two components, the reflected narcissism component and the reflected barrel component.\n", "\n", "With narcissism present the total flux emanating from the lens elements will be\n", "\n", "\\begin{equation}\n", "\\Phi=\n", "\\Omega_{f}A_o\\left[\n", "\\tau_{\\textrm{o}} L(T_{\\textrm{object}})\n", "+\n", "\\epsilon_{\\textrm{o}} L(T_{\\textrm{optics}})\n", "+\n", "(\\rho_{\\textrm{o}} - \\rho_{\\textrm{n}}) L(T_{\\textrm{barrel}})\n", "+\n", "\\rho_{\\textrm{n}} L(T_{\\textrm{cold}})\n", "\\right].\n", "\\end{equation}\n", "\n", "Assuming that the barrel and optics are at the same temperature $T_{\\textrm{optics}}=T_{\\textrm{barrel}}$ and $(\\epsilon_{\\textrm{o}}+\\rho_{\\textrm{o}}) L(T_{\\textrm{optics}}) = (1-\\tau_{\\textrm{o}})L(T_{\\textrm{optics}})$,\n", "then \n", "\n", "\\begin{equation}\n", "\\Phi\n", "=\n", "\\Omega_{f}A_o\\left[\n", "\\tau_{\\textrm{o}} L(T_{\\textrm{object}})\n", "+\n", "(1-\\tau_{\\textrm{o}})L(T_{\\textrm{optics}})\n", "- \\rho_{\\textrm{n}}\\left[L(T_{\\textrm{barrel}}) - L(T_{\\textrm{cold}})\\right]\n", "\\right].\n", "\\end{equation}\n", "\n", "$\\left[L(T_{\\textrm{barrel}}) - L(T_{\\textrm{cold}})\\right]$ can be significantly large for a cryogenically cooled detector, and therefore, even small amounts of narcissism reflectance can contribute to the reducing the flux on the detector.\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The interplay between obscuration emissivity and lens transmittance is shown in the diagram below.\n", "Until analysed in more detail later, a simplified approach is taken where:\n", "\n", "1. The relative (percentage) contributions used in the analysis below are given by the projection of the respective obstructions in the pupil.\n", "2. A single lens approximation is used where the lens radiance emanates from a single element in the pupil.\n", "3. The obstruction is located between the lens and the detector, partially obscuring the lens radiance (the second obscuration shown below). Obscuration elements therefore contribute the maximum possible radiance. If the obscuration, optics and barrel are all at the same temperature, this approximation is a reasonable assumption. If the obscuration elements were located in front of the lens, $(1-\\epsilon_o - \\rho_o)L(T_{\\textrm{barrel}})$ will be transmitted, an $\\epsilon_o L(T_{\\textrm{barrel}})$ will be emitted and $\\rho_o L(T_{\\textrm{barrel}})$ will reflect from the optical barrel, adding up to the radiance of an obscuration located behind the lens." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The contribution of various elements to the total flux on the detector can be calculated from the elements' solid angles and radiances within each solid angle. For the first order analysis we assume that the elements' radiance values are uniform in the elements' solid angle. If the radiating sources are not uniform, the values used here can be considered the mean values of the respective radiators.\n", "\n", "The model shown here calculates the flux on the central pixel in the image. The values for other pixels will be slightly different, resulting from different solid angles to these pixels.\n", "\n", "The elements identified in this analysis and their relative contributions are shown in the following figure:" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "![''](images/Radiometry16.png)" ] }, { "cell_type": "raw", "metadata": {}, "source": [ "It is shown in the companion document to this analysis that the detector element irradiance is then\n", "\\begin{eqnarray}\n", "E_{\\lambda\\textrm{det},\\alpha} = \\frac{\\Phi_{\\lambda\\textrm{det},\\alpha}}{A_d}\n", "&=&\n", "(\\Omega_3 -\\Omega_4 - \\Omega_5 - \\Omega_6 ) \\tau_{\\lambda o}\\tau_{\\lambda f} \\cos^4\\alpha L_{\\lambda\\textrm{scene}} \\nonumber\\\\\n", "&&+\n", "(\\Omega_3 -\\Omega_4 - \\Omega_5 - \\Omega_6 )\\tau_{\\lambda f}(1 -\\tau_{\\lambda o}) L_{\\lambda}(T_{\\textrm{optics}})\\nonumber\\\\\n", "&&+\n", "(\\Omega_3 -\\Omega_4 - \\Omega_5 - \\Omega_6 )\\tau_{\\lambda f}\n", "\\rho_{\\lambda n} \\left[L_{\\lambda}(T_{\\textrm{optics}})-L_{\\lambda}(T_{\\textrm{cold}})\\right]\n", "\\nonumber\\\\\n", "&&+\n", "\\Omega_3 (1 -\\tau_{\\lambda f}) L_{\\lambda}(T_{\\textrm{filter}}) \\nonumber\\\\\n", "&&+\n", "\\Omega_4 \\tau_{\\lambda f} \\epsilon_{\\lambda\\textrm{a-obs}} L_{\\lambda}(T_{\\textrm{a-obs}}) \\nonumber\\\\\n", "&&+\n", "\\Omega_5 \\tau_{\\lambda f} \\epsilon_{\\lambda\\textrm{c-obs}} L_{\\lambda}(T_{\\textrm{c-obs}}) \\nonumber\\\\\n", "&&+\n", "\\Omega_6 \\tau_{\\lambda f} \\epsilon_{\\lambda\\textrm{v-obs}} L_{\\lambda}(T_{\\textrm{v-obs}}) \\nonumber\\\\\n", "&&+\n", "\\Omega_{2b}\\tau_{\\lambda f}\\epsilon_{\\lambda\\textrm{barrel}} L_{\\lambda}(T_{{\\textrm{barrel}}})\\nonumber\\\\\n", "&&+\n", "\\Omega_{2a}\\epsilon_{\\lambda\\textrm{barrel}} L_{\\lambda}(T_{{\\textrm{barrel}}})\\nonumber\\\\\n", "&&+\n", "\\Omega_{1}\\epsilon_{\\lambda\\textrm{cold\\;shield}} L_{\\lambda}(T_{{\\textrm{cold\\;shield}}}),\n", "\\end{eqnarray}\n", "where, \n", "$L_{\\lambda}(T)$ is the spectral Planck-law radiation at temperature $T$, and\n", "$T_{\\textrm{optics}}$ is the optics (window and optical elements) temperature,\n", "$T_{\\textrm{filter}}$ is the filter temperature,\n", "$T_{\\textrm{barrel}}$ is the optics barrel temperature,\n", "$T_{\\textrm{cold\\;shield}}$ is the cold shield temperature,\n", "$T_{\\textrm{a-obs}}$ is the asymmetric obscuration temperature,\n", "$T_{\\textrm{c-obs}}$ is the central obscuration temperature,\n", "$T_{\\textrm{v-obs}}$ is the VNS obscuration temperature,\n", "$L_{\\lambda}(T_{\\textrm{cold}})$ is the radiance emanating from the inside of the cold shield, \n", "$\\rho_{\\lambda n}$ is the narcissis reflectance, \n", "$\\epsilon_{\\lambda\\textrm{barrel}}$ is the barrel spectral emissivity,\n", "$\\epsilon_{\\lambda\\textrm{a-obs}}$ is the asymmetric obscuration spectral emissivity,\n", "$\\epsilon_{\\lambda\\textrm{c-obs}}$ is the central obscuration spectral emissivity,\n", "$\\epsilon_{\\lambda\\textrm{c-obs}}$ is the VNS obscuration spectral emissivity,\n", "$\\epsilon_{\\lambda\\textrm{cold\\;shield}}$ is the cold shield spectral emissivity.\n", "The equivalent irradiance $E_{q\\lambda\\textrm{det},\\alpha}$ can be calculated in photon rate terms by using photon rate radiance $L_{q\\lambda}(T)$ in the above equation.\n", "\n", "The optics solid angle is given by\n", "\\begin{equation}\n", "\\Omega_3 = \\pi\\sin^2\\theta = \\pi(NA)^2 = \\frac{\\pi}{(2F_\\sharp)^2}\n", "\\end{equation}\n", "where $\\theta$ is the half apex angle of the f-number cone.\n", "\n", "For small angles $\\cos^4\\alpha\\approx\\cos^2\\alpha\\approx 1$, then \n", "$L_{\\lambda\\textrm{scene}} = \\tau_{\\lambda a}L_{\\lambda\\textrm{target}} + L_{\\lambda\\textrm{path}}$, where \n", "$\\tau_{\\lambda a}$ is the atmospheric transmittance to the target, \n", "$L_{\\lambda\\textrm{target}}$ is the target radiance, and \n", "$L_{\\lambda\\textrm{path}}$ is the atmospheric path radiance.\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The absolute well fill is calculated as follows:\n", " \n", "\\begin{equation}\n", "W_f = 100 \\, \\eta\\, E_{\\textrm{det}}\\, A_d\\, t_i \\,/ \\,W_c\n", "\\end{equation}\n", "where\n", "$\\eta$ is the detector quantum efficiency, \n", "$W_f$ is the actual well fill percentage, \n", "$E_{\\textrm{det}}$ is the irradiance on the detector, \n", "$A_d$ is the area of the detector, \n", "$t_i$ is the integration time, and \n", "$W_c$ is total well capacity of the detector integration capacitor.\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## NETD degradation\\label{netdmodambient}\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Detector NETD components\n", "\n", "The detector noise can be expressed as a noise variation around some nominal background temperature. The NETD noise formulation is a derived form of noise, and hence not directly measureable or provable.\n", "\n", "The detector noise comprises two components: a scene-flux-induced noise (inherent in the signal, non uniformity) and a detector electronics component (read noise, etc.). In the present analysis we do not include the sensor's electronics noise, that will be considered later in the analysis.\n", "\n", "\n", "Assume here that the detector electronics noise adds in quadrature to the detector schene flux noise. This is approximately true if the electronics noise is uncorrelated with the detector noise. The effective detector NETD is then given by\n", "\n", "\\begin{equation}\n", "NETD_\\textrm{eff} = \\sqrt{NETD^2_\\textrm{BLIP}+NETD^2_\\textrm{elec}+NETD^2_\\textrm{sys}}\n", "\\end{equation}\n", "where \n", "$NETD_\\textrm{BLIP}$ is the scene-induced noise, \n", "$NETD^2_\\textrm{elec}$ is the detector electronics noise, and\n", "$NETD^2_\\textrm{sys}$ is the system electronics noise.\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Background limited NETD\n", "\n", "This derivation starts from first principles to find an allowable mechanism whereby the detector supplier's NETD can be adjusted for different scene and internal temperatures. The idea is to use the supplier's measured/specification information and then to rescale to different conditions. The approach used here uses the supplier's NETD at 50% well fill, but does not account for changes in the well fill for the new conditions. Strictly speaking this is not correct, but it is considered adequate for the present analysis.\n", "\n", "The noise equivalent target contrast (NETC) is the target temperature change required to overcome the noise in the sensor. A larger NETC is worse, a smaller NETC is better. The NETC is calculated in kelvin or degrees Celcius from:\n", "\n", "\\begin{equation}\n", "{NETC_\\textrm{BLIP}}=\n", "\\frac{\\sqrt{\\eta_a\\eta_b k_n k_f \\Omega_r F_F }\\;4 f^2_\\textrm{no}}\n", "{\\Omega_p\\sqrt{N}\\;P f }\\cdot\n", "\\frac{1}{\\int_0^\\infty D^{\\ast}_{\\lambda} \\tau_{\\lambda s} \\epsilon_\\lambda \\tau_{\\lambda a} \\frac{d {M_{\\lambda}}(T_t)}{d T}d\\lambda} \n", "\\end{equation}\n", "\n", "\n", "where\n", "$\\sqrt{\\eta_a\\eta_b}$ is the staring array fill factor [-],\n", "$k_f$ is the sensor's time bandwidth product,\n", "$k_n$ is the ratio of sensor's noise bandwidth to the -3 dB electronics bandwidth,\n", "$\\Omega_r$ is the total field of regard/view solid angle [sr],\n", "$F_F$ is the frame frequency [Hz], \n", "$f^2_\\textrm{no}$ is the f-number [-],\n", "$\\Omega_p$ is the pixel field of view solid angle [sr],\n", "$N$ is the number of detectors [-], \n", "$P$ is fraction unobscured optical aperture [-], \n", "$f$ is the focal length [m], \n", "$D^\\ast_{\\lambda}$ is the spectral detector material detectivity [m $\\sqrt{\\textrm{Hz}}$/W] (note that the dimensional unit centimeters is normally used instead of meters),\n", "$\\epsilon_\\lambda$ is the spectral target emissivity [-], \n", "$\\tau_{\\lambda s}$ is the spectral sensor optical transmittance [-], \n", "$\\tau_{\\lambda a}$ is the spectral atmospheric optical transmittance [-], \n", "and \n", "$\\frac{d {M_{\\lambda}}(T_t)}{d T}$ is the spectral derivative of the apparent source exitance with respect to the source temperature; in other words, the rate at which the apparent source exitance changes for a given change in source temperature. \n", "In this calculation the effect of sun irradiance is not accounted for, the predicted performance would be for night time operation.\n", "\n", "In a sensor with viewing hot optics, an atmospheric path, and a scene, the $D^\\ast_{\\lambda}$ depends on sensor internal self radiance, and is given by \n", "\\begin{equation}\n", "\\frac{\\lambda\\eta_\\lambda}\n", "{hc\\sqrt{2}\\sqrt{ \\int_0^{\\lambda_c}\\eta_\\lambda E_q d \\lambda}}\n", "\\end{equation}\n", "where\n", "$\\eta_\\lambda$ is the spectral quantum efficiency and \n", "$E_q$ is total flux on the detector. \n", "Then the NETC is given by \n", "\\begin{equation}\n", "{NETC_\\textrm{BLIP}}=\n", "\\frac{\\sqrt{\\eta_a\\eta_b k_n k_f \\Omega_r F_F }\\;4 f^2_\\textrm{no}}\n", "{\\Omega_p\\sqrt{N}\\;P f }\\cdot\n", "\\frac{hc\\sqrt{2}}{\\int_0^\\infty \\left[\\left(\\frac{\\lambda\\eta_\\lambda}\n", "{\\sqrt{ \\int_0^{\\lambda_c}\\eta_\\lambda E_q d \\lambda}}\n", "\\right) \\tau_{\\lambda s} \\epsilon_\\lambda \\tau_{\\lambda a} \\frac{d {M_{\\lambda}}(T_t)}{d T}\\right]d\\lambda} \n", "\\end{equation}\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\\begin{equation}\n", "{NETC_\\textrm{BLIP}}=\n", "\\frac{\\sqrt{\\eta_a\\eta_b k_n k_f \\Omega_r F_F }\\;4 f^2_\\textrm{no}}\n", "{\\Omega_p\\sqrt{N}\\;P f }\\cdot\n", "\\frac{hc\\sqrt{2 \\int_0^{\\lambda_c}\\eta_\\lambda E_q d \\lambda}}\n", "{\\int_0^\\infty \\lambda\\eta_\\lambda\n", " \\tau_{\\lambda s} \\epsilon_\\lambda \\tau_{\\lambda a} \n", "\\frac{d {M_{\\lambda}}(T_t)}{d T}d\\lambda} \n", "\\end{equation}\n", "\n", "\n", "The $NETD_\\textrm{BLIP}$ is calculated with the same equation, except that $\\tau_{\\lambda a}=1$. Furthermore it is normally assumed that the quantum efficiency is constant over the sensor's usable spectral band.\n", "Then the $NETD_\\textrm{BLIP}$ is given by \n", "\\begin{equation}\n", "{NETD_\\textrm{BLIP}}=\n", "\\frac{\\sqrt{\\eta_a\\eta_b k_n k_f \\Omega_r F_F }\\;4 f^2_\\textrm{no}}\n", "{\\Omega_p\\sqrt{N}\\;P f }\\cdot\n", "\\frac{hc\\,\\sqrt{2 \\int_0^{\\lambda_c} E_q d \\lambda}}\n", "{\\sqrt{\\eta}\\,\\int_0^\\infty \n", "\\lambda \n", " \\tau_{\\lambda s} \\epsilon_\\lambda \\frac{d {M_{\\lambda}}(T_t)}{d T}d\\lambda} \n", "\\end{equation}\n", "Observe that the background flux is a multiplicative term. " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Separating $NETD_\\textrm{BLIP}$ and $NETD_\\textrm{elec}$\n", "\n", "Assuming that $NETD_\\textrm{eff}$ is known and $NETD_\\textrm{BLIP}$ can be calculated from first principles, the detector electronics $NETD_\\textrm{elec}$ can be calculated by\n", "\n", "\\begin{equation}\n", "NETD^2_\\textrm{elec} = \\sqrt{NETD^2_\\textrm{eff} - NETD^2_\\textrm{BLIP}}\n", "\\end{equation}\n", "\n", "Under laboratory test acceptance conditions, the NETD equation can be adapted by noting that the integration time is given by\n", "\n", "\\begin{equation}\n", "t_\\textrm{int} = \\frac{\\Omega_p N}{\\Omega_r F_F \\eta_a \\eta_b}\n", "\\end{equation}\n", "$P=1$, and the pixel field of view is given by\n", "\\begin{equation}\n", "\\Omega_p = \\frac{A_d}{f^2}\n", "\\end{equation}\n", "\n", "hence\n", "\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n", "\\begin{equation}\n", "{NETD_\\textrm{BLIP}}=\n", "\\frac{\\sqrt{k_n k_f }}{\\sqrt{\\Omega_p}}\n", "\\frac{\n", "\\sqrt{\\eta_a\\eta_b \\Omega_r F_F }\\;4 f^2_\\textrm{no}}\n", "{\\sqrt{N \\Omega_p}\\;P f }\\cdot\n", "\\frac{hc\\,\\sqrt{2 \\int_0^{\\lambda_c} E_q d \\lambda}}\n", "{\\sqrt{\\eta}\\,\\int_0^\\infty \n", "\\lambda \n", " \\tau_{\\lambda s} \\epsilon_\\lambda \\frac{d {M_{\\lambda}}(T_t)}{d T}d\\lambda} \n", "\\end{equation}\n", "\n", "\\begin{equation}\n", "{NETD_\\textrm{BLIP}}=\n", "\\frac{\\sqrt{k_n k_f }\n", "\\;4 f^2_\\textrm{no}}\n", "{P\\sqrt{A_d \\,t_\\textrm{int}} }\\cdot\n", "\\frac{hc\\,\\sqrt{2 \\int_0^{\\lambda_c} E_q d \\lambda}}\n", "{\\sqrt{\\eta}\\,\\int_0^\\infty \n", "\\lambda \n", " \\tau_{\\lambda s} \\epsilon_\\lambda \\frac{d {M_{\\lambda}}(T_t)}{d T}d\\lambda} \n", "\\end{equation}\n", "\n", "\n", "Ir is probably safe to assume that the $k_n k_f$ terms for the test equipment will be such as to minimise the noise during measurement, and we reasonably assume $k_n k_f = 1$\n", "\n", "\n", "\\begin{equation}\n", "{NETD_\\textrm{BLIP}}=\n", "\\frac{\n", "\\;4 f^2_\\textrm{no}}\n", "{P\\sqrt{A_d \\,t_\\textrm{int}} }\\cdot\n", "\\frac{hc\\,\\sqrt{2 \\int_0^{\\lambda_c} E_q d \\lambda}}\n", "{\\sqrt{\\eta}\\,\\int_0^\\infty \n", "\\lambda \n", " \\tau_{\\lambda s} \\epsilon_\\lambda \\frac{d {M_{\\lambda}}(T_t)}{d T}d\\lambda} \n", "\\end{equation}\n", "\n", "With this estimate of $NETD_\\textrm{BLIP}$ the electronics noise NETD can be determined.\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Detector suppliers often quote NETD at a given well fill, say 50% against a given background flux. The integration time required to achieve the specified well fill can be determined from \n", "\n", "\\begin{equation}\n", "t_i = \\frac{W_f}{100}\\cdot \\frac{ W_c}{\\eta\\, \\Omega_\\textrm{cs}\\, A_d\\int_0^{\\lambda_c} L_q(T_1) d \\lambda\\, } \n", "\\end{equation}\n", "where\n", "$\\eta$ is the detector quantum efficiency, \n", "$\\Omega_\\textrm{cs}=\\pi/(2f_\\textrm{no})^2$ is the cold shield solid angle,\n", "$W_f$ is the actual well fill percentage, \n", "$\\int_0^{\\lambda_c} E_q(T_1) d \\lambda$ is the irradiance on the detector, \n", "$A_d$ is the area of the detector, \n", "$t_i$ is the integration time, and \n", "$W_c$ is total well capacity of the detector integration capacitor.\n" ] }, { "cell_type": "raw", "metadata": {}, "source": [ "\\begin{equation}\n", "t_i = \\frac{W_f}{100}\\cdot \\frac{ 4 W_c f^2_\\textrm{no}}\n", "{A_d\\,\\eta\\,\\int_0^{\\lambda_c} \\pi L_q(T_1) d\\lambda} \n", "\\end{equation}" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\\begin{equation}\n", "{NETD_\\textrm{BLIP}}=\n", "\\frac{2 f_\\textrm{no}}{P}\n", "\\cdot\n", "\\sqrt{\\frac{\\eta\\,\\int_0^{\\lambda_c} E_q(T_1) d \\lambda}{W_c}}\n", "\\cdot\n", "\\sqrt{\\frac{100}{W_f}}\n", "\\cdot\n", "\\frac{hc\\,\\sqrt{2 \\int_0^{\\lambda_c} E_q(T_1) d \\lambda}}\n", "{\\sqrt{\\eta}\\,\\int_0^\\infty \n", "\\lambda \n", " \\tau_{\\lambda s} \\epsilon_\\lambda \\frac{d {M_{\\lambda}}(T_t)}{d T}d\\lambda} \n", "\\end{equation}\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n", "\\begin{equation}\n", "{NETD_\\textrm{BLIP}}=\n", "\\frac{2\\,\\sqrt{2} \\,h\\,c\\, f_\\textrm{no}}{P}\n", "\\cdot\n", "\\sqrt{\\frac{100}{W_f\\,W_c}}\n", "\\cdot\n", "\\frac{ \\int_0^{\\lambda_c} E_q(T_1) d \\lambda}\n", "{\\int_0^\\infty \n", "\\lambda \n", " \\tau_{\\lambda s} \\epsilon_\\lambda \\frac{d {M_{\\lambda}}(T_t)}{d T}d\\lambda} \n", "\\end{equation}\n", "\n", "where\n", "$T_1$ would be the reference source temperature for NETD measurement and\n", "$W_f$ is typically 50 (50%)." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Background scene influence on $NETD_\\textrm{BLIP}$\n", "\n" ] }, { "cell_type": "markdown", "metadata": { "variables": { "NETD_\\textrm{1BLIP": "

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\n", "NETD_\\textrm{2BLIP": "

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\n" } }, "source": [ "Assuming that the optical transmittance in the pass band is constant, and that the integration time and f-number stays the same, take the ratio of the $NETD_\\textrm{1BLIP}$ at two scene temperatures\n", "\n", "\\begin{equation}\n", "\\frac{{NETD_\\textrm{1BLIP}}}{{NETD_\\textrm{2BLIP}}}\n", "=\n", "\\frac\n", "{P_2\\,\\int_{\\Delta\\lambda}\\lambda \\,\\epsilon_\\lambda\\,\\tau_{2\\lambda s}\\frac{d {M_{\\lambda}}(T_2)}{d T}d\\lambda}\n", "{P_1\\,\\int_{\\Delta\\lambda}\\lambda \\,\\epsilon_\\lambda\\,\\tau_{1\\lambda s}\\frac{d {M_{\\lambda}}(T_1)}{d T}d\\lambda}\n", "\\cdot\n", "\\frac{\\sqrt{ \\int_0^{\\lambda_c} E_q(T_1)\\, d \\lambda}}{\\sqrt{ \\int_0^{\\lambda_c} E_q(T_2)\\, d \\lambda}}\n", "\\end{equation}" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "which allows us to scale the NETD from some baseline background flux value to other background flux levels by\n", "\\begin{equation}\n", "{NETD_\\textrm{2BLIP}}\n", "=\n", "{NETD_\\textrm{1BLIP}}\\cdot\n", "\\frac\n", "{P_1\\,\\int_{\\Delta\\lambda}\\lambda \\,\\epsilon_\\lambda\\,\\tau_{1\\lambda s}\\frac{d {M_{\\lambda}}(T_1)}{d T}d\\lambda}\n", "{P_2\\,\\int_{\\Delta\\lambda}\\lambda \\,\\epsilon_\\lambda\\,\\tau_{2\\lambda s}\\frac{d {M_{\\lambda}}(T_2)}{d T}d\\lambda}\n", "\\cdot\n", "\\sqrt{\\frac\n", "{ \\int_0^{\\lambda_c} E_q(T_2)\\, d \\lambda}\n", "{ \\int_0^{\\lambda_c} E_q(T_1)\\, d \\lambda}\n", "}\n", "\\end{equation}\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The irradiance on the detector for a source with temperature $T$ is \n", "$E_{q}(T)=\\epsilon_\\lambda\\,\\tau_{\\lambda s} \\, \\tau_{\\lambda a} \\, L(T)\\Omega$,\n", "where \n", "$\\epsilon_\\lambda$ is the source emissivity,\n", "$\\tau_{\\lambda s}$ is the sensor filter transmittance (assumed a cold filter), \n", "$\\tau_{\\lambda a}$ is the atmospheric transmittance, \n", "$L(T)$ is black body radiance at temperature $T$, and \n", "$\\Omega$ is the solid angle filled by the source or optics, referenced from the detector.\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n", "\n", "If condition 1 is the 300 K measurement of the detector NETD on its own (unity sensor transmittance and fully clear aperture), the system-level performance will be\n", "\n", "\\begin{equation}\n", "NETD_\\textrm{2BLIP} = \\frac{NETD_\\textrm{1BLIP}(293K)}{P_2} \\cdot\n", "\\frac\n", "{\\int_{\\Delta\\lambda}\\lambda \\,\\epsilon_\\lambda\\,\\tau_{1\\lambda s}\\frac{d {M_{\\lambda}}(293K)}{d T}d\\lambda}\n", "{\\int_{\\Delta\\lambda}\\lambda \\,\\epsilon_\\lambda\\,\\tau_{2\\lambda s}\\frac{d {M_{\\lambda}}(T_2)}{d T}d\\lambda}\n", "\\cdot\n", "\\sqrt{\\left(\n", "\\frac\n", "{ \\int_0^{\\lambda_c} E_q(T_2) d \\lambda} \n", "{\\Omega_\\textrm{cs} \\int_0^{\\lambda_c} \\tau_{1\\lambda s} \\, L(293K)d \\lambda}\n", "\\right)}\n", "\\end{equation}\n", "where $P_2$ is the unobstructed fraction of the sensor aperture, $\\tau_{\\lambda s 2}$ is the effective optics transmittance (including all lenses, windows and filters), $\\Omega_\\textrm{cs}$ is the cold shield open solid angle, and $E_{q}(T_2)$ is the background flux on the detector under the field application condition. For the lab-based NETD measurement the measurement is done without the sensor filter and at short range and with a black body source, hence $\\epsilon_\\lambda=\\tau_{1\\lambda s}=1$. It is fair to assume that the same cold filter is used for both conditions, $\\tau_{1\\lambda s}=\\tau_{2\\lambda s} \\,$.\n", "\n", "Generally speaking $E_q(T_2)$ is a complex value comprising irradiance contributions from the scene, atmosphere, optics and other contributors.\n", "\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "What remains is to determine \n", "the background flux for the reference measurement $\\int_0^{\\lambda_c} E_{q}(T) d \\lambda$, and \n", "the temperature contrast $\\frac{d {M_{\\lambda}}(T)}{d T}$, then the NETD can be calculated at any other flux level." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In the calculation below the background flux terms are calculated as follows:\n", "\n", "1. $\\int_0^{\\lambda_c} E_q(T_2) d \\lambda$ includes all background flux sources: all internal sensor radiation (hot optics and obscurations), atmospheric path radiance and scene radiance (at ambient scene temperature). Ambient scene temperature is used on the assumption that most of the scene will be at scene temperature and that the target is observed against the background scene temperature.\n", "\n", "1. $\\Omega_\\textrm{cs} \\int_0^{\\lambda_c} \\tau_{1\\lambda s} \\, L(293K)d \\lambda$ includes only a single black body at the laboratory acceptance test temperature.\n", "\n", "The temperature derivative terms are calculated as follows:\n", "\n", "1. In $\\int_{\\Delta\\lambda}\\lambda \\,\\epsilon_\\lambda\\,\\tau_{1\\lambda s}\\frac{d {M_{\\lambda}}(293K)}{d T}d\\lambda$ the temperature derivative is calculated at the laboratory acceptance test temperature.\n", "\n", "1. In $\\,\\int_{\\Delta\\lambda}\\lambda \\,\\epsilon_\\lambda\\,\\tau_{2\\lambda s}\\frac{d {M_{\\lambda}}(T_2)}{d T}d\\lambda$ the temperature derivative is calculated at ambient scene temperature. Ambient scene temperature is used on the assumption that most of the scene will be at scene temperature and that the target is observed against the background scene temperature.\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### System noise $NETD_\\textrm{sys}$\n", "\n", "The additional system noise (proximity electronics, etc.) can be referenced to the NETD plane, by a simple model\n", "\n", "\\begin{equation}\n", "NETD_\\textrm{sys} = k_\\textrm{sys} NETD_\\textrm{eff}(T_1)\n", "\\end{equation}\n", "\n", "Discussions with the hardware specialists indicated that the system noise would probably raise the effective system level NETD from, say, 30 mK to 45 mK. This means that the system noise has more or less the same magnitude as the detector NETD at $T_1$=293 K, or $k_\\textrm{sys}=1$.\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Noise equivalent irradiance from NETD" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Calculating scene-induced noise equivalent irradiance from $NETD_\\textrm{BLIP}$ for low scene temperatures\n", "\n", "$NETD_\\textrm{2BLIP}$ can be used to calculate the noise equivalent irradiance at any scene temperature by\n", "\n", "\\begin{equation}\n", "NEE_\\textrm{BLIP}(T_2) = NETD_\\textrm{2BLIP} \\int_{\\Delta\\lambda}\\epsilon_\\lambda\\,\\tau_{2\\lambda s}\\frac{d {M_{\\lambda}}(T_2)}{d T}d\\lambda\n", "\\end{equation}\n", "where the temperature derivative is calculated at the scene temperature" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Calculating detector electronics noise equivalent irradiance from $NETD_\\textrm{elec}$\n", "\n", "$NETD_\\textrm{elec}$ can be used to calculate the noise equivalent irradiance at any scene temperature by\n", "\n", "\\begin{equation}\n", "NEE_\\textrm{elec} = NETD_\\textrm{elec} \\int_{\\Delta\\lambda}\\epsilon_\\lambda\\,\\tau_{1\\lambda s}\\frac{d {M_{\\lambda}}(T_1)}{d T}d\\lambda\n", "\\end{equation}\n", "where the temperature derivative is calculated at the temperature used for the detector FAT measurement (because $NETD_\\textrm{elec}$ was originally determined at the FAT reference temperature).\n", "\n", "Note that $NEE_\\textrm{elec}$ is a noise and must be added in quadrature to other noise sources.\n", "\n", "It is assumed that the detector electronics noise is not a function of the scene flux." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Calculating system noise equivalent irradiance \n", "\n", "$NETD_\\textrm{sys}$ can be used to calculate the noise equivalent irradiance at any scene temperature by\n", "\n", "\\begin{equation}\n", "NEE_\\textrm{sys} = NETD_\\textrm{sys} \\int_{\\Delta\\lambda}\\epsilon_\\lambda\\,\\tau_{1\\lambda s}\\frac{d {M_{\\lambda}}(T_1)}{d T}d\\lambda\n", "\\end{equation}\n", "\n", "\\begin{equation}\n", "NEE_\\textrm{sys} = k_\\textrm{sys} NETD_\\textrm{eff}(T_1) \\int_{\\Delta\\lambda}\\epsilon_\\lambda\\,\\tau_{1\\lambda s}\\frac{d {M_{\\lambda}}(T_1)}{d T}d\\lambda\n", "\\end{equation}\n", "\n", "where the temperature derivative is calculated at the temperature used for the detector FAT measurement (because $NETD_\\textrm{elec}$ was originally determined at the FAT reference temperature).\n", "\n", "Note that $NEE_\\textrm{elec}$ is a noise and must be added in quadrature to other noise sources.\n", "\n", "It is assumed that the detector electronics noise is not a function of the scene flux." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Effective system level noise" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The effective level NETD is given by\n", "\n", "\\begin{equation}\n", "NETD_\\textrm{eff} = \\sqrt{NETD^2_\\textrm{BLIP}+NETD^2_\\textrm{elec}+NETD^2_\\textrm{sys}}\n", "\\end{equation}\n", "\n", "or expressed equivalently in terms of noise equivalent irradiance\n", "\n", "\\begin{equation}\n", "NEE_\\textrm{eff} = \\sqrt{NEE^2_\\textrm{BLIP}+NEE^2_\\textrm{elec}+NEE^2_\\textrm{sys}}\n", "\\end{equation}" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\\begin{equation}\n", "NEE_\\textrm{eff} = \\sqrt{\n", "\\left(NETD_\\textrm{2BLIP} \\int_{\\Delta\\lambda}\\epsilon_\\lambda\\,\\tau_{2\\lambda s}\\frac{d {M_{\\lambda}}(T_2)}{d T}d\\lambda\\right)^2\n", "+\n", "\\left(NETD_\\textrm{elec}\n", "+\n", "k_\\textrm{sys} NETD_\\textrm{eff}(T_1)\n", "\\right)^2\n", "\\left(\\int_{\\Delta\\lambda} \\epsilon_\\lambda\\,\\tau_{1\\lambda s}\\frac{d {M_{\\lambda}}(T_1)}{d T}d\\lambda\\right)^2\n", "}\n", "\\end{equation}" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "It is clear that the electronics noise components, both in the detector and in the rest of the system results in a minimum level of noise equivalent flux $NE\\Phi$ which is intensitive to the scene temperature. The scene-induced flux depends on the scene temperature. \n", "\n", "It is interesting to note that the $NETD_\\textrm{2BLIP}$ increases for decreasing temperatures (higher NETD for lower scene temperatures) but the noise equivalent flux decreases for lower scene temperatures because the derivative term decreases." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Bits lost to noise\n", "\n", "\n", "In order to determine the number of bits lost because of NETD noise the following procedure is followed:\n", "\n", "1. Determine the spectral photon rate Planck radiance derivative with respect to temperature at the scene temperature\n", "\\begin{equation}\n", "\\frac{dL_{q\\lambda}}{dT}(T_\\textrm{scene}) = \\frac{c_{1q\\lambda}x e^x}{\\pi T_\\textrm{scene}\\lambda^4(e^x-1)^2}\n", "\\end{equation}\n", "where $T_\\textrm{scene}$ is the scene temperature.\n", "with units of [q/(s.m$^2$.sr.$\\mu$m.K], where $c_{1q\\lambda}=1.883\\times 10^{27}$. \n", "2. Calculate the in-band irradiance derivative from the scene\n", "\\begin{equation}\n", "\\frac{dE}{dT} = \\Omega_\\textrm{optics} \\cdot \\int_0^\\infty \\tau_{\\lambda f}\\cdot\\tau_{\\lambda o}\\cdot\\frac{dL_{q\\lambda}}{dT}(T_\\textrm{scene}) d\\lambda\n", "\\end{equation}\n", "with units of [q/(s.m$^2$.K]. Note that the atmospheric transmittance is not included in this calculation.\n", "3. Assuming that the detection threshold is given by a scaled NETD, the change in flux associated with the detection threshold is given by\n", "\\begin{equation}\n", "E_\\textrm{th}= \\frac{dE}{dT} \\cdot k_\\textrm{th}\\cdot NETD\n", "\\end{equation}\n", "with units of [q/(s.m$^2$], where $k_\\textrm{th}$ is scale value, with typical values of three (low threshold to noise ratio, high false alarm rate) to eight (high threshold to noise ratio, low false alarm rate). This analysis will work with a threshold to noise ratio of one, i.e., the RMS NETD value. \n", "4. The photon count in the charge well is then \n", "\\begin{equation}\n", "N_\\textrm{th} = E_\\textrm{th}\\cdot t_\\textrm{int}\\cdot A_\\textrm{detector}\n", "\\end{equation}\n", "with units of [q], where $t_\\textrm{int}$ is the integration time, and $A_\\textrm{detector}$ is the detector area.\n", "5. The TNR=1 detection threshold photon count in the charge well is therefore given by\n", "\\begin{equation}\n", "N_\\textrm{th} = k_\\textrm{th}\\cdot NETD\\cdot t_\\textrm{int}\\cdot A_\\textrm{detector}\\cdot \\Omega_\\textrm{optics} \\int_0^\\infty \\tau_{\\lambda f}\\cdot\\tau_{\\lambda o}\\cdot\\frac{dL_{q\\lambda}}{dT}(T_\\textrm{scene}) d\\lambda\n", "\\end{equation}\n", "6. Percentage well fill is then simply $100*N_\\textrm{th}/N_\\textrm{cap}$ where $N_\\textrm{cap}$ is the well capacity.\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Well fill calculation" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The well fill calculation is done for a large number of combinations of sensor and scenario variables (presently 107520 combinations). The various flux contributions and noise values for each combination are calculated and stored in a Pandas DataFrame and subsequently in a data file on disk. The flux from the various contributors is calculated in photon rate units, to accurately represent the electron count that would be accumulated during the detector integration time. Finally all contributions are added together and the relative contributions are calculated as percentages of the total." ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false, "jupyter": { "outputs_hidden": false }, "scrolled": true }, "outputs": [], "source": [ "# to calculate the well fill contribution for one combination of sensor and scenario conditions\n", "def fluxContrib(TauHotOptics, tempAmbient, tempScene, tempAltAtmoK, tempDynamicDelta,\n", " tempInsideDelta, tempHotShieldDelta, tempCentralObsDelta,\n", " tempColdShield, fnoOptics, fnoColdshield, sensor,foclen, hFOV, \n", " centralObs, wl,TauFilter,TauAtmo,LpathQQ,PathLen,atmo,atmoDesc,\n", " tempModAtmo,optFilter,tempNETDStd):\n", "\n", " systems = {}\n", " \n", " systems['PathLen'] = [PathLen]\n", " systems['Atmo'] = [atmo]\n", " systems['AtmoDesc'] = [atmoDesc]\n", " systems['TempModAtmo'] = [tempModAtmo]\n", "\n", " systems['FnoOptics'] = [fnoOptics]\n", " systems['FnoColdshield'] = [fnoColdshield]\n", " systems['Sensor'] = [sensor]\n", " systems['Hfov [deg]'] = [hFOV]\n", " systems['FocLen [m]'] = [foclen]\n", " systems['CentralObs'] = [centralObs]\n", " systems['OptFilter'] = [optFilter]\n", " \n", " systems['Optics Transmittance'] = [TauHotOptics]\n", " systems['OmegaColdShield [sr]'] = [np.pi - (np.pi * (1.0 / (2 * fnoColdshield)) ** 2.)]\n", " \n", " systems['OmegaHotShield [sr]'] = [np.pi - (np.pi * (1.0 / (2 * fnoOptics)) ** 2. ) - systems['OmegaColdShield [sr]'][0]]\n", " systems['OmegaCentralObsc [sr]'] = [systems['CentralObs'][0] * (np.pi * (1.0 / (2 * fnoOptics)) ** 2. )]\n", " systems['OmegaOptics [sr]'] = [np.pi * (1.0 / (2 * fnoOptics)) ** 2. - systems['OmegaCentralObsc [sr]'][0]]\n", " systems['OmegaFilter [sr]'] = [np.pi * (1.0 / (2 * fnoOptics)) ** 2. ]\n", " systems['TempAmbient [C]'] = [tempAmbient]\n", " systems['TempScene [C]'] = [tempScene]\n", " systems['TempInsideDelta [C]'] = [tempInsideDelta]\n", " systems['TempCentralObsDelta [C]'] = [tempCentralObsDelta]\n", " systems['TempHotShieldDelta [C]'] = [tempHotShieldDelta]\n", " \n", " \n", " systems['TempIntern [C]'] = [systems['TempAmbient [C]'][0] + tempInsideDelta]\n", " systems['TempDynamic [C]'] = [systems['TempScene [C]'][0] + tempDynamicDelta]\n", "\n", " systems['TempColdShield [K]'] = [tempColdShield]; EmisColdShield = 1.0; \n", " systems['TempFilter [K]'] = [tempColdShield]; EmisFilter = 1.0; \n", " systems['TempIntern [K]'] = [273.+systems['TempIntern [C]'][0]]\n", " systems['TempCentralObs [K]'] = [273.+systems['TempIntern [C]'][0]+tempCentralObsDelta];EmisCentralObs = 1.0;\n", " systems['TempHotShield [K]'] = [273.+systems['TempIntern [C]'][0]+tempHotShieldDelta]; EmisHotShield = 1.0\n", " systems['TempAmbient [K]'] = [273. + systems['TempAmbient [C]'][0]]; \n", " EmisAmbient = 1.0\n", " systems['TempAltAtmo [K]'] = [273. + tempAltAtmoK]; \n", " EmisTarget = 1.0\n", " systems['TempScene [K]'] = [273. + tempScene]\n", " systems['TempDynamic [K]'] = [273. + systems['TempDynamic [C]'][0]]\n", " \n", " LHotOptics = (1-TauHotOptics) * TauFilter * ryplanck.planck(wl, systems['TempIntern [K]'][0], type='ql')/np.pi\n", " LHotOptics = np.trapz(LHotOptics, x=-wl,axis=0)\n", " systems['EHotOptics [q/(s.m2)]'] = [systems['OmegaOptics [sr]'][0] * LHotOptics]\n", "\n", " LFilter = (1-TauFilter) * ryplanck.planck(wl, systems['TempFilter [K]'][0], type='ql')/np.pi\n", " LFilter = np.trapz(LFilter, x=-wl, axis=0)\n", " systems['EFilter [q/(s.m2)]'] = [systems['OmegaFilter [sr]'][0] * EmisFilter * LFilter]\n", " \n", " LCentralObs = TauFilter * ryplanck.planck(wl, systems['TempCentralObs [K]'][0], type='ql')/np.pi\n", " LCentralObs = np.trapz(LCentralObs, x=-wl, axis=0)\n", " systems['ECentralObs [q/(s.m2)]'] = [systems['OmegaCentralObsc [sr]'][0] * EmisCentralObs * LCentralObs]\n", "\n", " LHotShield = TauFilter * ryplanck.planck(wl, systems['TempHotShield [K]'][0], type='ql')/np.pi\n", " LHotShield = np.trapz(LHotShield, x=-wl, axis=0)\n", " systems['EHotShield [q/(s.m2)]'] = [systems['OmegaHotShield [sr]'][0] * EmisHotShield * LHotShield]\n", "\n", " LColdShield = TauFilter * ryplanck.planck(wl, systems['TempColdShield [K]'][0], type='ql')/np.pi\n", " LColdShield = np.trapz(LColdShield, x=-wl, axis=0)\n", " systems['EColdShield [q/(s.m2)]'] = [systems['OmegaColdShield [sr]'][0] * EmisColdShield * LColdShield]\n", " \n", " systems['EInternal [q/(s.m2)]'] = [systems['EHotOptics [q/(s.m2)]'][0] + systems['EFilter [q/(s.m2)]'][0] + \\\n", " systems['ECentralObs [q/(s.m2)]'][0] + \\\n", " systems['EHotShield [q/(s.m2)]'][0] + systems['EColdShield [q/(s.m2)]'][0]]\n", " \n", " LAmbient = (TauAtmo * TauFilter * TauHotOptics) * \\\n", " ryplanck.planck(wl, systems['TempAmbient [K]'][0], type='ql')/np.pi\n", " LAmbient = np.trapz(LAmbient, x=-wl, axis=0)\n", " systems['EAmbient [q/(s.m2)]'] = [systems['OmegaOptics [sr]'][0] * EmisAmbient * LAmbient]\n", " \n", " LScene = (TauAtmo * TauFilter * TauHotOptics) * \\\n", " ryplanck.planck(wl, systems['TempScene [K]'][0], type='ql')/np.pi\n", " LScene = np.trapz(LScene, x=-wl, axis=0)\n", " systems['EScene [q/(s.m2)]'] = [systems['OmegaOptics [sr]'][0] * EmisTarget * LScene]\n", " \n", " LDynamic = (TauAtmo * TauFilter * TauHotOptics) * \\\n", " ryplanck.planck(wl, systems['TempDynamic [K]'][0], type='ql')/np.pi\n", " LDynamic = np.trapz(LDynamic, x=-wl, axis=0)\n", " systems['EDynamic [q/(s.m2)]'] = [systems['OmegaOptics [sr]'][0] * EmisTarget * LDynamic]\n", " \n", " LpathQQ = TauHotOptics * TauFilter * LpathQQ\n", " LpathQQ = np.trapz(LpathQQ, x=-wl, axis=0)\n", " systems['EPath [q/(s.m2)]'] = [systems['OmegaOptics [sr]'][0] * LpathQQ]\n", "\n", " systems['ETotalAmbient [q/(s.m2)]'] = [systems['EInternal [q/(s.m2)]'][0] + \\\n", " systems['EPath [q/(s.m2)]'][0] + systems['EAmbient [q/(s.m2)]'][0]]\n", "\n", " systems['ETotalDynamic [q/(s.m2)]'] = [systems['EInternal [q/(s.m2)]'][0] + \\\n", " systems['EPath [q/(s.m2)]'][0] + systems['EDynamic [q/(s.m2)]'][0]]\n", "\n", " #prepare detector NETD degradation factor\n", " #obscuration and transmittance effects\n", " effFluxTx = (1. - (centralObs)) * TauHotOptics\n", " systems['EffectiveFluxTx'] = effFluxTx\n", " systems['tempNETDStd [K]'] = tempNETDStd\n", " \n", " #irradiance on detector under standard conditions\n", " Lqstdback = TauFilter * ryplanck.planck(wl, tempNETDStd, type='ql')/np.pi\n", " Eqstdback = np.trapz(Lqstdback, x=-wl,axis=0) * (np.pi - systems['OmegaColdShield [sr]'][0])\n", " systems['Eqstdback [q/(s.m2)'] = Eqstdback\n", " \n", " #irradiance on detector only under scene field conditions\n", " Eqfieldback = systems['ETotalAmbient [q/(s.m2)]'][0]\n", " systems['Eqfieldback [q/(s.m2)'] = Eqfieldback\n", " \n", " # temp derivative at scene ambient temperature note units in q/s\n", " Mderivscnq = (TauAtmo * TauFilter * TauHotOptics) * ryplanck.dplanck(wl, systems['TempAmbient [K]'][0], type='ql')/np.pi\n", " Mderivscnq = np.trapz(Mderivscnq, x=-wl, axis=0)\n", " systems['Mderivscn [q/(s.m2.K)]'] = Mderivscnq \n", " \n", " # temperature derivatives in radiant and photon rate terms\n", " # temp derivative at standard temperature note units in radiant W\n", " Mderivstd = 1e-6 * wl * TauFilter * ryplanck.dplanck(wl, tempNETDStd, type='el')/np.pi\n", " Mderivstd = np.trapz(Mderivstd, x=-wl, axis=0)\n", " systems['Mderivstdm [m.W/(m2.K)]'] = Mderivstd\n", " Mderivstdq = TauFilter * ryplanck.dplanck(wl, tempNETDStd, type='ql')/np.pi\n", " Mderivstdq = np.trapz(Mderivstdq, x=-wl, axis=0)\n", " systems['Mderivstd [q/(s.m2.K)]'] = Mderivstdq\n", "\n", " # temp derivative at lab (not scene) ambient temperature note units in radiant W\n", " Mderivfld = 1e-6 * wl * TauFilter * ryplanck.dplanck(wl, systems['TempAmbient [K]'][0], type='el')/np.pi\n", " Mderivfld = np.trapz(Mderivfld, x=-wl, axis=0)\n", " systems['Mderivfldm [m.W/(m2.K)]'] = Mderivfld\n", " \n", " Mderivfldq = TauFilter * ryplanck.dplanck(wl, systems['TempAmbient [K]'][0], type='ql')/np.pi\n", " Mderivfldq = np.trapz(Mderivfldq, x=-wl, axis=0)\n", " systems['Mderivfld [q/(s.m2.K)]'] = Mderivfldq\n", " \n", " ratioMderiv = systems['Mderivstdm [m.W/(m2.K)]'] / (systems['Mderivfldm [m.W/(m2.K)]'] * (1. - (centralObs)))\n", " systems['ratioMderiv'] = ratioMderiv\n", " \n", " netddegfact = (Eqfieldback / Eqstdback) * ratioMderiv ** 2\n", " systems['NETDDegradeFactor'] = netddegfact\n", "\n", " dLAmbient = ( TauFilter * TauHotOptics) * \\\n", " ryplanck.dplanck(wl, systems['TempAmbient [K]'][0], type='ql')/np.pi\n", " dLAmbient = np.trapz(dLAmbient, x=-wl, axis=0)\n", " systems['dEAmbient [q/(s.m2.K)]'] = [systems['OmegaOptics [sr]'][0] * EmisAmbient * dLAmbient]\n", " \n", " #percentages\n", " systems['EHotOptics [%Amb]'] = [100.* systems['EHotOptics [q/(s.m2)]'][0] / systems['ETotalAmbient [q/(s.m2)]'][0]]\n", " systems['EFilter [%Amb]'] = [100.* systems['EFilter [q/(s.m2)]'][0] / systems['ETotalAmbient [q/(s.m2)]'][0]]\n", " systems['ECentralObs [%Amb]'] = [100.* systems['ECentralObs [q/(s.m2)]'][0] / systems['ETotalAmbient [q/(s.m2)]'][0]]\n", " systems['EHotShield [%Amb]'] = [100.* systems['EHotShield [q/(s.m2)]'][0] / systems['ETotalAmbient [q/(s.m2)]'][0]]\n", " systems['EColdShield [%Amb]'] = [100.* systems['EColdShield [q/(s.m2)]'][0] / systems['ETotalAmbient [q/(s.m2)]'][0]]\n", " systems['EInternal [%Amb]'] = [100.* systems['EInternal [q/(s.m2)]'][0] / systems['ETotalAmbient [q/(s.m2)]'][0]]\n", " systems['EAmbient [%Amb]'] = [100.* systems['EAmbient [q/(s.m2)]'][0] / systems['ETotalAmbient [q/(s.m2)]'][0]]\n", " systems['EPath [%Amb]'] = [100.* systems['EPath [q/(s.m2)]'][0] / systems['ETotalAmbient [q/(s.m2)]'][0]]\n", "\n", " systems['EHotOptics [%Tar]'] = [100.* systems['EHotOptics [q/(s.m2)]'][0] / systems['ETotalDynamic [q/(s.m2)]'][0]]\n", " systems['EFilter [%Tar]'] = [100.* systems['EFilter [q/(s.m2)]'][0] / systems['ETotalDynamic [q/(s.m2)]'][0]]\n", " systems['ECentralObs [%Tar]'] = [100.* systems['ECentralObs [q/(s.m2)]'][0] / systems['ETotalDynamic [q/(s.m2)]'][0]]\n", " systems['EHotShield [%Tar]'] = [100.* systems['EHotShield [q/(s.m2)]'][0] / systems['ETotalDynamic [q/(s.m2)]'][0]]\n", " systems['EColdShield [%Tar]'] = [100.* systems['EColdShield [q/(s.m2)]'][0] / systems['ETotalDynamic [q/(s.m2)]'][0]]\n", " systems['EInternal [%Tar]'] = [100.* systems['EInternal [q/(s.m2)]'][0] / systems['ETotalDynamic [q/(s.m2)]'][0]]\n", " systems['EDynamic [%Tar]'] = [100.* systems['EDynamic [q/(s.m2)]'][0] / systems['ETotalDynamic [q/(s.m2)]'][0]]\n", " systems['EPath [%Tar]'] = [100.* systems['EPath [q/(s.m2)]'][0] / systems['ETotalDynamic [q/(s.m2)]'][0]]\n", "# print(systems.keys())\n", " summary = pd.DataFrame(systems)\n", "# print(summary.columns)\n", " return summary " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Three MWIR sensors are considered in this analysis, designated as 'SensorA', 'SensorB' and 'SensorC'. The primary parameters for each sensor are entered in the code for subsequent processing." ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false, "jupyter": { "outputs_hidden": false } }, "outputs": [], "source": [ "# to define the key optical design parameters\n", "columnsData = ['Atmo', 'AtmoDesc', 'CentralObs', 'EAmbient [%Amb]', 'EAmbient [q/(s.m2)]',\n", " 'ECentralObs [%Amb]', 'ECentralObs [%Tar]', 'ECentralObs [q/(s.m2)]',\n", " 'EColdShield [%Amb]', 'EColdShield [%Tar]', 'EColdShield [q/(s.m2)]', 'EDynamic [%Tar]',\n", " 'EDynamic [q/(s.m2)]', 'EFilter [%Amb]', 'EFilter [%Tar]', 'EFilter [q/(s.m2)]',\n", " 'EHotOptics [%Amb]', 'EHotOptics [%Tar]', 'EHotOptics [q/(s.m2)]', 'EHotShield [%Amb]',\n", " 'EHotShield [%Tar]', 'EHotShield [q/(s.m2)]', 'EInternal [%Amb]', 'EInternal [%Tar]',\n", " 'EInternal [q/(s.m2)]', 'EPath [%Amb]', 'EPath [%Tar]', 'EPath [q/(s.m2)]',\n", " 'EScene [q/(s.m2)]', 'ETotalAmbient [q/(s.m2)]', 'ETotalDynamic [q/(s.m2)]',\n", " 'EffectiveFluxTx', 'Eqfieldback [q/(s.m2)', 'Eqstdback [q/(s.m2)', 'FnoColdshield',\n", " 'FnoOptics', 'FocLen [m]', 'Hfov [deg]', 'Mderivfld [q/(s.m2.K)]',\n", " 'Mderivfldm [m.W/(m2.K)]', 'Mderivscn [q/(s.m2.K)]', 'Mderivstd [q/(s.m2.K)]',\n", " 'Mderivstdm [m.W/(m2.K)]', 'NETDDegradeFactor', 'OmegaCentralObsc [sr]',\n", " 'OmegaColdShield [sr]', 'OmegaFilter [sr]', 'OmegaHotShield [sr]', 'OmegaOptics [sr]',\n", " 'OptFilter', 'Optics Transmittance', 'PathLen', 'Sensor', 'TempAltAtmo [K]',\n", " 'TempAmbient [C]', 'TempAmbient [K]', 'TempCentralObs [K]', 'TempCentralObsDelta [C]',\n", " 'TempColdShield [K]', 'TempDynamic [C]', 'TempDynamic [K]', 'TempFilter [K]',\n", " 'TempHotShield [K]', 'TempHotShieldDelta [C]', 'TempInsideDelta [C]', 'TempIntern [C]',\n", " 'TempIntern [K]', 'TempModAtmo', 'TempScene [C]', 'TempScene [K]',\n", " 'dEAmbient [q/(s.m2.K)]', 'ratioMderiv', 'tempNETDStd [K]']" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Well fill calculations " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The code described in this section sets up the combinations of input parameters to calculate the flux contribution using the generic atmospheric models. The results are all collated and written to an HDF5 data file for subsequent analysis. These calculations are very time intensive and should only done if an input parameter are changed." ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": true, "jupyter": { "outputs_hidden": true } }, "outputs": [], "source": [ "# to define the common parameter variations in scene and sensor conditions\n", "tempColdShield = 100. # K cold shield temperature\n", "tempCentralObsDeltas = [-100., 0] # C central obstruction effective temperature\n", "tempHotShieldDeltas = [-100., 0.] # C warm shield effective temperature\n", "tempInternDeltas = [-100, 0, 20, 40] # delta internal relative to outside\n", "# tempDynamicDeltas = [2., 5., 10., 20., 40., 80.] # dynamic range delta relative to scene\n", "tempDynamicDeltas = [0., 2.0, 5.0, 10.0, 20.0, 30., 40.0, 50., 60., 70., 80.0] # dynamic range delta relative to scene\n", "tempAmbients = [-40., -20, 0, 20, 40] # C sensor sensor ambient ambient \n", "tempScenes = tempAmbients\n", "pathLens = np.linspace(0.001, 10, 11) # varying path lengths\n", "\n", "\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In the first calculation the generic Modtran atmospheres are used for a variety of environmental conditions. The percentage contributions are calculated for all combinations of input parameters. The resulting data set is quite large and takes considerable time to calculate. " ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false, "jupyter": { "outputs_hidden": false }, "scrolled": true }, "outputs": [], "source": [ "# to calculate the well fill, using generic Modtran atmospheres\n", "if False: #QuickTestSet avoid long execution times for dev work\n", " tempCentralObsDeltas = [0] # K central obstruction effective temperature\n", " tempHotShieldDeltas = [0.] # K warm shield effective temperature\n", " tempInternDeltas = [20,]\n", " tempAmbients = [20]\n", " pathLens = np.linspace(0.001, 10, 21)\n", " tempDynamicDeltas = [40.] # dynamic range in image\n", "\n", "if dolong:\n", " for sensor in dfConcept.columns.values:\n", " dfsummary = pd.DataFrame(columns=columnsData)\n", " for i, optFilter in enumerate(optFilters):\n", " TauFilter = TauFilters[:,i]\n", " for tempInsideDelta in tempInternDeltas: # delta-K optics differential wrt outside ambient\n", " for tempScene in tempScenes: # C scene where the target is\n", " #for tempAmbient in tempAmbients: # C ambient sensor outside temperature\n", " tempAmbient = tempScene\n", " for i, (atmo, atmoDesc, tempModAtmo) in enumerate(atmodef): #atmospheres\n", " for pathLen in pathLens: # km, path length \n", " for tempHotShieldDelta in tempHotShieldDeltas: # hotshield temp relative to amb \n", " for tempCentralObsDelta in tempCentralObsDeltas: # central obsc temp relative to amb \n", " for tempDynamicDelta in tempDynamicDeltas: # scene dynamic delta \n", " tempAltAtmoK = 273. + tempAmbient\n", " TauAtmo = np.exp( - gammaTau[:,i] * pathLen)\n", " LpathQQ = (1.0-TauAtmo) * ryplanck.planck(wl, tempAltAtmoK,type='ql')/np.pi\n", " summary = fluxContrib(dfConcept[sensor]['tauopt'], tempAmbient, tempScene, tempAltAtmoK, \n", " tempDynamicDelta, \n", " tempInsideDelta,tempHotShieldDelta, tempCentralObsDelta, \n", " tempColdShield, dfConcept[sensor]['fno'], dfConcept[sensor]['fnoColdshield'], \n", " sensor,dfConcept[sensor]['foclen m'], dfConcept[sensor]['hfov deg'], \n", " dfConcept[sensor]['centralObs -'],\n", " wl,TauFilter,TauAtmo,LpathQQ,pathLen,atmo,atmoDesc,\n", " tempModAtmo, optFilter, dfConcept[sensor]['tempNETDStd K']\n", " )\n", " dfsummary = dfsummary.append(summary)\n", " dfsummary.reset_index(inplace=True, drop=True)\n", "\n", " filename = 'radio-{}-{}-gen.hdf5'.format(Cconcept,sensor)\n", " os.remove(filename) if os.path.exists(filename) else None\n", " store = pd.HDFStore(filename)\n", " store['df{}'.format(sensor)] = dfsummary\n", " store.close() \n", " " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## NETD under different conditions" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This code in this section rescales the detector NETD with scene and internal optics/obscuration temperatures, using the detector supplier's nominal NETD 30 mK at 293 K and 50% well fill. A more accurate approach would be to recalculate the NETD for different well fill values, but for the first approximation this approach is considered adequate in the present analysis." ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": false, "jupyter": { "outputs_hidden": false } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Values available in the HDF5 file data/well-fill/radio-MWIR Sensor-SensorA-gen.hdf5\n", "A total of 58080 combinations are present in this file\n", "Concept: MWIR Sensor\n", "atmoUnique: ['MLS23km' 'tropical5km' 'SW31km']\n", "sensorUnique: [u'SensorA', u'SensorB', u'SensorC']\n", "tempAmbUnique: [ 233. 253. 273. 293. 313.]\n", "tempInternUnique: [ 133. 153. 173. 193. 213. 233. 253. 273. 293. 313. 333. 353.]\n", "tempInternDeltas: TempAmbient + [-100, 0, 20, 40]\n", "pathlenUniqueGen: [ 1.00000000e-03 1.00090000e+00 2.00080000e+00 3.00070000e+00\n", " 4.00060000e+00 5.00050000e+00 6.00040000e+00 7.00030000e+00\n", " 8.00020000e+00 9.00010000e+00 1.00000000e+01]\n", "tempCentralObsDeltas: TempAmbient + [-100.0, 0]\n", "tempHotShieldDeltas: TempAmbient + [-100.0, 0.0]\n", "TempAmbient [C]: [-40. -20. 0. 20. 40.]\n", "TempScene [C]: [-40. -20. 0. 20. 40.]\n", "TempInsideDelta [C]: [-100. 0. 20. 40.]\n", "TempCentralObsDelta [C]: [-100. 0.]\n", "TempHotShieldDelta [C]: [-100. 0.]\n", "TempNETDStd [K]: 300.0\n", "EffectiveFluxTx: 0.8\n", " \n", "tempDynamicDeltas: TempAmbient + [0.0, 2.0, 5.0, 10.0, 20.0, 30.0, 40.0, 50.0, 60.0, 70.0, 80.0]\n", "tempDynamicUnique: [ 233. 235. 238. 243. 253. 263. 273. 283. 293. 303. 313. 255.\n", " 258. 323. 333. 275. 278. 343. 353. 295. 298. 363. 373. 315.\n", " 318. 383. 393.]\n", "TempDynamicDelta [C]: [ -40. -38. -35. -30. -20. -10. 0. 10. 20. 30. 40. -18.\n", " -15. 50. 60. 2. 5. 70. 80. 22. 25. 90. 100. 42.\n", " 45. 110. 120.]\n", " \n", "Index([u'Atmo', u'AtmoDesc', u'CentralObs', u'EAmbient [%Amb]', u'EAmbient [q/(s.m2)]',\n", " u'ECentralObs [%Amb]', u'ECentralObs [%Tar]', u'ECentralObs [q/(s.m2)]',\n", " u'EColdShield [%Amb]', u'EColdShield [%Tar]', u'EColdShield [q/(s.m2)]', u'EDynamic [%Tar]',\n", " u'EDynamic [q/(s.m2)]', u'EFilter [%Amb]', u'EFilter [%Tar]', u'EFilter [q/(s.m2)]',\n", " u'EHotOptics [%Amb]', u'EHotOptics [%Tar]', u'EHotOptics [q/(s.m2)]', u'EHotShield [%Amb]',\n", " u'EHotShield [%Tar]', u'EHotShield [q/(s.m2)]', u'EInternal [%Amb]', u'EInternal [%Tar]',\n", " u'EInternal [q/(s.m2)]', u'EPath [%Amb]', u'EPath [%Tar]', u'EPath [q/(s.m2)]',\n", " u'EScene [q/(s.m2)]', u'ETotalAmbient [q/(s.m2)]', u'ETotalDynamic [q/(s.m2)]',\n", " u'EffectiveFluxTx', u'Eqfieldback [q/(s.m2)', u'Eqstdback [q/(s.m2)', u'FnoColdshield',\n", " u'FnoOptics', u'FocLen [m]', u'Hfov [deg]', u'Mderivfld [q/(s.m2.K)]',\n", " u'Mderivfldm [m.W/(m2.K)]', u'Mderivscn [q/(s.m2.K)]', u'Mderivstd [q/(s.m2.K)]',\n", " u'Mderivstdm [m.W/(m2.K)]', u'NETDDegradeFactor', u'OmegaCentralObsc [sr]',\n", " u'OmegaColdShield [sr]', u'OmegaFilter [sr]', u'OmegaHotShield [sr]', u'OmegaOptics [sr]',\n", " u'OptFilter', u'Optics Transmittance', u'PathLen', u'Sensor', u'TempAltAtmo [K]',\n", " u'TempAmbient [C]', u'TempAmbient [K]', u'TempCentralObs [K]', u'TempCentralObsDelta [C]',\n", " u'TempColdShield [K]', u'TempDynamic [C]', u'TempDynamic [K]', u'TempFilter [K]',\n", " u'TempHotShield [K]', u'TempHotShieldDelta [C]', u'TempInsideDelta [C]', u'TempIntern [C]',\n", " u'TempIntern [K]', u'TempModAtmo', u'TempScene [C]', u'TempScene [K]',\n", " u'dEAmbient [q/(s.m2.K)]', u'ratioMderiv', u'tempNETDStd [K]'],\n", " dtype='object')\n", " \n", "Index into tempDynamicDeltas with TempDynamicPlot: 6 \n" ] } ], "source": [ "# to determine the set of unique parameters in the calculated database\n", "sensor = dfConcept.columns.values[0]\n", "filename = 'data/well-fill/radio-{}-{}-{}.hdf5'.format(Cconcept,sensor,'gen')\n", "with pd.HDFStore(filename) as store:\n", " df = store['df{}'.format(sensor)]\n", " atmoUnique = df['Atmo'].unique()\n", " sensorUnique = sensors\n", " tempAmbUnique = df['TempAmbient [K]'].unique()\n", " tempAmbUniqueC = df['TempAmbient [C]'].unique()\n", " tempSceneUniqueC = df['TempScene [C]'].unique()\n", " tempInsideUniqueC = df['TempInsideDelta [C]'].unique()\n", " tempCentralUniqueC = df['TempCentralObsDelta [C]'].unique()\n", " tempHotAmbUniqueC = df['TempHotShieldDelta [C]'].unique()\n", " tempInternUnique = df['TempIntern [K]'].unique()\n", " pathlenUniqueGen = df['PathLen'].unique()\n", " tempNETDStd = df['tempNETDStd [K]'].unique()[0]\n", " effectiveFluxTx = df['EffectiveFluxTx'].unique()[0]\n", " tempDynamicUnique = df['TempDynamic [K]'].unique()\n", " tempDynamicUniqueC = df['TempDynamic [C]'].unique()\n", " \n", " if True:\n", " print('Values available in the HDF5 file {}'.format(filename))\n", " print('A total of {} combinations are present in this file'.format(df.shape[0]))\n", " print('Concept: {}'.format(Cconcept))\n", " print('atmoUnique: {}'.format(atmoUnique))\n", " print('sensorUnique: {}'.format(sensorUnique))\n", " print('tempAmbUnique: {}'.format(tempAmbUnique)) \n", " print('tempInternUnique: {}'.format(tempInternUnique)) \n", " print('tempInternDeltas: TempAmbient + {}'.format(tempInternDeltas))\n", " print('pathlenUniqueGen: {}'.format(pathlenUniqueGen))\n", " print('tempCentralObsDeltas: TempAmbient + {}'.format(tempCentralObsDeltas))\n", " print('tempHotShieldDeltas: TempAmbient + {}'.format(tempHotShieldDeltas))\n", " print('TempAmbient [C]: {}'.format(tempAmbUniqueC))\n", " print('TempScene [C]: {}'.format(tempSceneUniqueC))\n", " print('TempInsideDelta [C]: {}'.format(tempInsideUniqueC))\n", " print('TempCentralObsDelta [C]: {}'.format(tempCentralUniqueC))\n", " print('TempHotShieldDelta [C]: {}'.format(tempHotAmbUniqueC))\n", " print('TempNETDStd [K]: {}'.format(tempNETDStd))\n", " print('EffectiveFluxTx: {}'.format(effectiveFluxTx))\n", " print(' ')\n", " print('tempDynamicDeltas: TempAmbient + {}'.format(tempDynamicDeltas))\n", " print('tempDynamicUnique: {}'.format(tempDynamicUnique)) \n", " print('TempDynamicDelta [C]: {}'.format(tempDynamicUniqueC))\n", " print(' ')\n", " print(df.columns)\n", " print(' ')\n", " indexTempDynamicPlot = np.where(np.array(tempDynamicDeltas)==TempDynamicPlot)[0][0]\n", " print('Index into tempDynamicDeltas with TempDynamicPlot: {} '.format(indexTempDynamicPlot))" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": true, "jupyter": { "outputs_hidden": true } }, "outputs": [], "source": [ "# print values to support fault condition analysis\n", "def printFilter(dfi,varstr, value):\n", " print('{} : {} {} {}'.format(varstr, value, value in dfi[varstr].unique(), type(value)))" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": false, "jupyter": { "outputs_hidden": false } }, "outputs": [], "source": [ "# to calculate the scene NETD from FAT NETD and scene info\n", "def calcNETDScene(df, dfConcept, sensor, wellfill, wellcapIdx, ksys):\n", "\n", " #first determine BLIP component\n", " netdBLIP1 = 2. * np.sqrt(2.) * const.h * const.c * df['FnoColdshield'].values\n", " netdBLIP2 = np.sqrt(100./ (wellfill * dfConcept[sensor]['wellCapacity'][wellcapIdx])) \n", " netdBLIP3 = df['Eqstdback [q/(s.m2)'] / df['Mderivstdm [m.W/(m2.K)]']\n", " netdBLIPstd = netdBLIP1 * netdBLIP2 * netdBLIP3\n", " P2fracApp = (1. - dfConcept[sensor]['centralObs -'])\n", " netdBLIPscene = np.sqrt(df['NETDDegradeFactor']) * netdBLIPstd / P2fracApp\n", " # then determine detector internal noise NETD \n", " netdElec = np.sqrt(netdstd**2 - netdBLIPstd**2)\n", " # then determine system noise NEDT\n", " netdSys = netdstd * ksys\n", " # finally the quadrature sum\n", " netd = np.sqrt(netdBLIPscene**2 + netdElec**2 + netdSys**2 ) \n", "\n", " # now calculate the noise equivalent flux\n", " nePhiBLIP = netdBLIPscene * df['Mderivfld [q/(s.m2.K)]']\n", " nePhiElec = (netdSys + netdElec) * df['Mderivstd [q/(s.m2.K)]'] \n", " nePhi = np.sqrt(nePhiBLIP**2 + nePhiElec**2)\n", " \n", " if np.abs(df['FnoColdshield'].values.mean() - df['FnoOptics'].values.mean()) > 0.01:\n", " return netdBLIPstd, np.nan, netdElec, netdSys, np.nan, np.nan, nePhiElec, np.nan\n", " \n", "# # print(df['FnoColdshield'].values)\n", "# # print(df['FnoOptics'].values)\n", "# # print(' ')\n", "\n", " return netdBLIPstd, netdBLIPscene, netdElec, netdSys, netd, nePhiBLIP, nePhiElec, nePhi\n" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": false, "jupyter": { "outputs_hidden": false } }, "outputs": [], "source": [ "# to plot the NETD variation under different conditions\n", "def plotNETDAmbient(dfConcept, sensor, atmotype, atmoSet, tempHotShieldDelta, tempCentralObsDelta, tempInsideUnique,\n", " tempDynamicDelta, pathlen, optFilter, netdstd,ksyss,\n", " wellfill, wellcapIdx, plotDetail=False, plotNEE=False):\n", "\n", " p1a = ryplot.Plotter(1,1,1,figsize=(9,4.5));\n", " if plotNEE:\n", " p1b = ryplot.Plotter(2,1,1,figsize=(12,6));\n", " if plotDetail:\n", " p2 = ryplot.Plotter(3,1,2,figsize=(12,4));\n", " p3 = ryplot.Plotter(4,1,2,figsize=(12,4));\n", " p4 = ryplot.Plotter(5,1,1,figsize=(12,4));\n", " #for i,sensor in enumerate(dfConcept.columns.values):\n", " TauHotOptics = dfConcept[sensor]['tauopt']\n", " for j,katmo in enumerate(atmoSet):\n", " for itmpI, tempInsideDelta in enumerate(tempInsideUnique):\n", " filename = 'data/well-fill/radio-{}-{}-{}.hdf5'.format(Cconcept, sensor, atmotype)\n", " for iksys,ksys in enumerate(ksyss):\n", " with pd.HDFStore(filename) as store:\n", " dfi = store['df{}'.format(sensor)]\n", " filt = (dfi.Atmo==katmo) & (dfi.Sensor==sensor) &\\\n", " (np.abs(dfi['TempHotShieldDelta [C]']-(tempHotShieldDelta))<0.01) &\\\n", " (np.abs(dfi['TempCentralObsDelta [C]']-(tempCentralObsDelta))<0.01) & \\\n", " (np.abs(dfi['TempInsideDelta [C]']-(tempInsideDelta))<0.01) & \\\n", " (np.abs(dfi['PathLen']-(pathlen))<0.01) & \\\n", " (dfi['OptFilter']==optFilter) \n", " \n", "# (np.abs(dfi['TempDynamic [C]']-(tempDynamicDelta))<0.01) & \\\n", "\n", " #get intended set only\n", " df = dfi[filt].copy()\n", " if df.empty:\n", " print('df is empty location 1')\n", " printFilter(dfi,'Sensor', sensor)\n", " printFilter(dfi,'Atmo', katmo)\n", " printFilter(dfi,'TempHotShieldDelta [C]', tempHotShieldDelta)\n", " printFilter(dfi,'TempCentralObsDelta [C]', tempCentralObsDelta)\n", " printFilter(dfi,'TempInsideDelta [C]', tempInsideDelta)\n", " printFilter(dfi,'PathLen', pathlen)\n", " printFilter(dfi,'TempDynamic [C]', tempDynamicDelta)\n", " printFilter(dfi,'OptFilter', optFilter)\n", " return\n", " \n", "# print(df['TempScene [C]'])\n", "# printFilter(df,'Sensor', sensor)\n", "# printFilter(df,'Atmo', katmo)\n", "# printFilter(df,'TempHotShieldDelta [C]', tempHotShieldDelta)\n", "# printFilter(df,'TempCentralObsDelta [C]', tempCentralObsDelta)\n", "# printFilter(df,'TempInsideDelta [C]', tempInsideDelta)\n", "# printFilter(df,'PathLen', pathlen)\n", "# printFilter(df,'TempDynamic [C]', tempDynamicDelta)\n", "# printFilter(df,'OptFilter', optFilter)\n", "# print(' ')\n", "\n", " \n", " netdBLIPstd, netdBLIPscene, netdElec, netdSys, netd, nePhiBLIP, nePhiElec, nePhi = \\\n", " calcNETDScene(df, dfConcept, sensor, wellfill, wellcapIdx, ksys)\n", " \n", " title = '{}, {}: NETD (nominal={} K @ {} K): $\\Delta$Tcntrl={}, $\\Delta$Thshld={}'.format(\n", " Cconcept, sensor, netdstd, tempNETDStd, tempCentralObsDelta, tempHotShieldDelta)\n", "\n", " label = '$\\Delta$Tinternal={} C, tau={:.2f}, aperture={:.2f}, effective flux transfer={:.2f}, ksys={}'.format(\n", " tempInsideDelta, TauHotOptics, df['EffectiveFluxTx'].unique()[0]/TauHotOptics, \n", " df['EffectiveFluxTx'].unique()[0],ksys)\n", " \n", " if itmpI==0 and iksys==0 and j==0:\n", " p1a.plot(1,df['TempScene [C]'],netdElec,title,'Scene temperature deg C', 'NETD K',\n", " label=['Detector internal electronics'])\n", " if plotNEE:\n", " p1b.plot(1,df['TempScene [C]'],netdBLIPstd,title,'Scene temperature deg C', 'NETD K',\n", " label=['Detector BLIP at {} K'.format(tempNETDStd)])\n", " \n", "# p1.plot(1,df['TempScene [C]'],netdSys*np.ones(netdElec.shape),title,'Scene temperature deg C', 'NETD K',\n", "# label=['Sys. ksys={:.2f}'.format(ksys)])\n", " p1a.plot(1,df['TempScene [C]'],netd,title,'Scene temperature deg C', 'NETD K',label=[label])\n", " if plotNEE:\n", " p1b.plot(1,df['TempScene [C]'],nePhi,title,'Scene temperature deg C', 'NEE q/(s.m2)',label=[label])\n", " \n", " if iksys==0 and plotDetail:\n", " label = '$\\Delta$Tint={} C, tau={:.2f}, apert={:.2f}'.format(\n", " tempInsideDelta, TauHotOptics, df['EffectiveFluxTx'].unique()[0]/TauHotOptics)\n", " p2.plot(1,df['TempScene [C]'],df['Eqstdback [q/(s.m2)'],\n", " label=['Detector spec {}'.format(label)])\n", " p2.plot(1,df['TempScene [C]'],df['Eqfieldback [q/(s.m2)'],'Background Irradiance [q/(s.m2)',\n", " 'Scene temperature deg C', 'Irradiance [q/(s.m2)',label=['Operational {}'.format(label)])\n", " p2.plot(2,df['TempScene [C]'],df['Eqfieldback [q/(s.m2)']/df['Eqstdback [q/(s.m2)'],\n", " 'Ratio: E-Operational / E-Detector spec',\n", " 'Scene temperature deg C', 'Ratio',label=['{}'.format(label)])\n", " p3.plot(1,df['TempScene [C]'],df['Mderivstdm [m.W/(m2.K)]'],label=['Detector spec {}'.format(label)])\n", " p3.plot(1,df['TempScene [C]'],df['Mderivfldm [m.W/(m2.K)]'],'Temperature derivative [$\\mu$m.q/(s.m$^2$.sr.K)]',\n", " 'Scene temperature deg C', 'Derivative term',label=['Operational{}'.format(label)])\n", " p3.plot(2,df['TempScene [C]'],df['ratioMderiv'],\n", " 'Ratio: $\\Delta$: Detector spec / $\\Delta$: Operational',\n", " 'Scene temperature deg C', 'Ratio',label=['{}'.format(label)])\n", " p4.plot(1,df['TempScene [C]'],np.sqrt(df['NETDDegradeFactor']),\n", " 'Environmental NETD scaling factor (ksys=0)',\n", " 'Scene temperature deg C', 'NETD scaling factor',label=['{}'.format(label)])\n", "\n", "# plotNETDAmbient(dfConcept=dfConcept, sensor=u'SensorA', atmotype='gen', atmoSet=['MLS23km'], \n", "# tempHotShieldDelta=tempHotShieldDeltas[0],\n", "# tempCentralObsDelta=tempCentralObsDeltas[1],tempInsideUnique=tempInsideUniqueC[1:],\n", "# tempDynamicDelta=tempDynamicUniqueC[0], pathlen=pathlenUniqueGen[0], \n", "# optFilter=optFilters[0],netdstd=netdstd,ksyss=ksyss, wellfill=50, wellcapIdx=0, \n", "# plotDetail=True) \n", " " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The following graph shows the NETD for the SensorA for various fractions of additional system noise, from zero (perfectly clean environment with no noise) to a 50% increase in total noise. A value of $k_\\textrm{sys}=0.75$ adds increases the total effective noise by 20% at standard conditions (and less so at higher temperatures). At $k_\\textrm{sys}=0.75$ the system noise is the equivalent of 26 mK (added in quadature to detector noise). Note that because of the optics transmittance the sensor NETD at 40 mK, corresponding to a detector NETD of 30 mK.\n", "\n", "The first graph plots the effective system noise as a function of $k_\\textrm{sys}$. Only one $k_\\textrm{sys}=0.75$ value was eventually used in the detailed analysis.\n", "\n", "The next set of graphs show the contribution of the various NETD degradation terms and ratios. Note how the background flux ratio and the temperature derivative ratio counter-wise for increasing scene temperature. As the scene temperature increases, the background flux increases, but the temperature derivative decreases." ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "collapsed": false, "jupyter": { "outputs_hidden": false } }, "outputs": [ { "data": { "image/png": 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vy8cv52X7rY/O6ejo/N3oAdU1oqwEVGVl7rus2AGl0xb3o3OXR9+cfpvyEXM+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CYPMTHuFdbvPc4nm3YQ3KQZ/QYOpslDIfQb8BjHz10i+Vomy5Z/TPvoaNpGRvLMM88ghLDyh/Dw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\nFsc1jvjv9ifr8yxyvs0hf1s+RYeKUJ5TUp5bzhsz3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cjlcj777DODHBMnTiQkJASAQYMG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\nRtv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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# to plot NETD variations for different values of $k_\\textrm{sys}$ (SensorB)\n", "plotNETDAmbient(dfConcept=dfConcept, sensor=u'SensorA', atmotype='gen', atmoSet=['MLS23km'], \n", " tempHotShieldDelta=tempHotShieldDeltas[0],\n", " tempCentralObsDelta=tempCentralObsDeltas[0],tempInsideUnique=[tempInsideUniqueC[1]],\n", " tempDynamicDelta=tempDynamicUniqueC[0], pathlen=pathlenUniqueGen[0], \n", " optFilter=optFilters[0],netdstd=netdstd,\n", " ksyss=[0, 0.25, 0.5, 0.75, 1.0, 1.25, 1.5, 1.75, 2., 2.25, 2.5],\n", " wellfill=50, wellcapIdx=0, plotDetail=False)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The following graphs above show the NETD for the standard detector cold filter and: \n", "\n", "1. The three different infrared sensors.\n", "2. Three different internal temperature deltas around ambient outside temperature (0, 20, 40) deg C.\n", "3. Different outside scene temperatures of -10 C to +40 C.\n", "4. The central obscuration and hot shield are assumed to reflect radiance at 100 K below the sensor internal temperature.\n", "\n", "The calculation includes all transmittance effects, obscuration effects, hot optics, and cold central obscuration and cold hot shield radiance. These values are therefore representative of the expected NETD under the stated conditions." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The NETD for SensrorB with different internal temperature deltas are shown next. As the sensor heats up the hot optics increases the NETD. For each internal temperature delta two values for system noise are shown: (1) zero system noise and (2) 26 mK system noise. It is evident that the hot optics and hot scene increases the NETD substantially." ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "collapsed": false, "jupyter": { "outputs_hidden": false } }, "outputs": [ { "data": { "image/png": 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2qkJmZqbN/tLHkilK7Dh06BCdOnWKgoODZWXk/GRN/CjBz8+Prl69Wmnb2vJ4\nmLFnqKPSuqhHyg5LusqVrS2Y+lcJlmLrYdVBc2i1WrPxag3WxJZSXau7XihBd12v9f6F/sdHqKqI\ng4MD3njjDfzwww/417/+hfHjx4sfCrYXLly4gIiICIwZMwarV6+udJwkpkL1d6FZWVkICAjAtGnT\nEBQUhCFDhqCoqAhz587F5cuXodFoxO/OJSYmomvXrtBoNJg+fTqICFlZWfDz80NMTAyCg4Nx6NAh\nBAQEYMmSJWJ+hgv8R40ahfDwcAQHB+M///mP1baeOHEC3t7e6NSpE5ycnDBu3Dhs377dahkASEpK\nQsOGDREbGyvuCw4ORo8ePWTPL+UXKZtM7zaXLl2Kt99+u9ps1FNeXo779++jrKwMhYWFaN++vVXp\nDZGzw9/fH9HR0QgICEBUVJT44WtL8RAUFISpU6ciMzNT9Jc5v5jGUnZ2tuQ5lNCzZ080b97cJj/b\n6j9TXSsqKjB9+nRcvnwZQ4cOxfLly422t27dKmvfhg0boFKpoFarERMTA0A69lxcXDBv3jzxQ9wA\nEBcXh2XLlsmWkSmGOsXHx4v75crq5MmTUKlUKCkpwf379zFp0iScO3fOKE9TXaXKVq4tkGqTiouL\nUVhYiOHDh0OtViMkJARbtmyp5F85m6XOb0tsSFGddVCP3ncpKSmSdi9cuFC0FwDmz5+PFStWiLKx\nsbGirCWU6lpVm+oFtd2jq6kfHtIIlSGFhYX0yiuvUIcOHWjv3r0P/XxK+fTTT4lIGA3w8PCg8vJy\no+NvvfVWpREq/V2oVqslJycnSktLIyLhDisxMbHS3VJGRgaNGDGCysrKiIjoxRdfpI0bN5JWq6UG\nDRrQiRMnzOan5/bt20RE9ODBAwoKCqK8vDwjfXr16kVqtbrSb9++fUREtHXrVoqNjRXz27hxI82Y\nMcPINiUyREQJCQk0c+ZMObdKInUXKWWTqdySJUsoLi5O3DZnp1L9iYiWL19OLi4u1Lp1a4qOjrbK\nflPk7GCM0dGjR4mIaPLkybR06VKr4sHQD+b8otVqycHBQUwrdw5L/pM7lylyfrLFf+Z09fDwEOOc\niMjT05Py8vJk06Snp5OPj4+YRl8uUva4urrS6dOnqU+fPuK+gIAAys7ONquTKXqd9HlKnc+wrBYs\nWECzZ8+ml156iT744INK+UmVu2HZGtpl2haYtiFjx46lxMRE2rZtG02bNk1Mf+/evUq6K41LIvur\ng3qfnT9TZKM3AAAgAElEQVR/ntRqNZ09e5aISNJurVZLGo2GiITRNi8vL8rLy5P1UXXYamu7UhVg\nZyNUjrXcn6tXNG7cGMuXL8fw4cMRExODMWPG4P3330ejRo2M5Fic7WvoaKF1C+vz8vLQsmVLAEDn\nzp2h0WiwefNmjB8/XnEenp6e4p1oWFgYtFptpVGaffv2ITU1FeHh4SAiFBUVoU2bNujVqxc8PDwQ\nHh5uNj898fHx+O677wAA2dnZuHjxIiIiIsTjBw8etMp+e0DKpjZt2phNY87Obdu2KTrvnTt3sH37\ndmRlZaFp06YYPXo0EhMTK8WjUuTscHd3R2RkJAAgOjoaCQkJcHZ2RkpKiqJ4sIZOnTqJaeViDrC/\nOJHStW3btuJxMhkZIiJZ++7cuYOoqChxhM3Sy4ZVKhVyc3ORk5OD3NxctGjRAm5ubli5cqWs/0zR\nXzCUsmDBAoSHh6Nx48ZYsWKFojSGZQuYbwsM2xCNRgOtVosxY8Zg1qxZmDt3LoYNGyauHzPU3Zp2\nyh7rYG5uLp566il888038PPzAyCMms+ePdvIbldXV7Rs2RJnzpxBTk4ONBoNmjdvLilbXbZywDtU\nD4OBAwciLS0NL7zwArp06YIvvvgCoaGh4nFrO0WGJCcno2/fvorld+7ciejoaHH71VdfxaxZs6zq\nUDk7O4v/Ozg4iFM6hg0sESEmJgbvvvuuUdqsrCw0adKkUn56OwzzO3DgAPbv34/jx4/D2dkZ/fr1\nE4/p6d27N/Lz8432McawZMkS9O/fH25ubkYPCGRnZ8PNzc1IXokMAAQGBmLr1q3yjtFhrkzkbHJ0\ndER5ebkoZ42dSvXfu3cvOnfujBYtWgAAnn76aRw9ehTR0dGS6W2xQwr9E68TJ05UFA+GWPKLYVq5\nmEtOTsabb75pNk6UIOdnpf43RE5Xc1y4cEEyzccff6w4Dz1RUVHYsmULcnJyMHbsWJt1MsRcWf3x\nxx8oKChAWVkZfvzxRwwePNhifoZlaynepNokb29vnDp1Crt378b8+fPxxBNPYP78+UbnsKadkqqD\n9+/fx7///e+HVgct0bRpU7i7u+PQoUNih8rb2xupqamV7J46dSrWrl2LnJwcTJ482Uh2yZIlRrLV\n0d7YUi/qHbU9RFZTP9TAlJ8pFRUVtGHDBmrZsiUtWrRIHGauCtYsJiwtLaX169dX2h8WFkYHDx4U\nt6Wm/FxcXIhIGGY2XDyqH9a/desWeXh4iPvPnTtHPj4+lJubS0REeXl5lJWVVSm9fltvh+E0wfbt\n22nkyJFEJAzNN2rUiA4cOGCkjyXKysrEhZHFxcWkUqno3LlzVsvoiYyMFKdMiYwXpQ8YMICuXbtm\nVCamfpGzqbS0lFq1akV5eXlUVFREkZGRRlN+VbWRiOj48eMUFBREDx48oIqKCoqJiaGVK1dKptcv\nStfbZIqcHfopv2PHjhER0dSpU2nZsmWK48HUX+b8YppW7hxK64ilhdFyfrLkfykfyulKJEz53bp1\nS5TVb69bt04yTXp6Ovn6+opp9NNZpr4k+rPepKenU/fu3cnX15dycnIs6mSKoY76PM2V1ciRI+nL\nL7+k9957j0aNGlUpP1NdTcvWXFsg1yZdv36dioqKiIho165d4nkNdVcal3LoY+th1EFLMaSf8iss\nLKSePXvSpk2biIjo2rVrknaXlJSQr68veXl5iYvs9bJJSUlGsuZQaqs17Wp1ATub8qt1BWrM0Fro\nUOnRarXUp08f6t27N2m12ho7b2JiIrVu3Zo6duwo/jp06ECPPfYYPf3006KcpTVUcusknnnmGQoO\nDqbXX3+diIg2b95MoaGhFBISQl26dKHjx49btUamuLiYhg4dSgEBATRq1Cjq16+f2Iha82TR999/\nTz4+PvT444/T+++/L+7/29/+RtevXzcrY8r169cpKiqKvLy8KCgoiIYPH06XLl2iiooK8vDwEBsy\nQ5599lnRLyUlJbI2rVixgry8vKhPnz40adIkxR0qpTYSCWXr5+dHwcHB9Nxzz1FJSYlsenM2yZWN\nVqslPz8/mjBhAvn7+9Po0aPpwYMHRET09ddfW4wHU38RCWvXpPwilVbqHEoYP348tWvXjho2bEgd\nO3akzz//XNKHcn6W22/Oh3K6enp6GnWoDLfl0mzYsIGCgoIoNDSUJk2aJOtLw3oTHBxMAwYMsMl/\nhjoZ5ikVwxs2bKDRo0cTkfDkV2RkpGQn11BX07I11xbItSE//PADhYSEUGhoKEVERFBqaqqkf5XG\npSXMtSGGMWRNHSSSjyFDHe/cuUMRERG0c+fOSnanpKSIaV544QWaO3euuG1OtjpsVdquVhe8Q/UI\ndqiIhN774sWLqWXLlrR+/fpqfSyYU/P8+uuvNGvWrNpWo1qxxSald/aPCvUxLjg1S3XFUHl5OYWG\nhtKlS5eqQSv7xN46VPy1CTWEg4MDXnvtNfz0009YvHgxoqKicOvWLavzqS/fYKrrdgQGBmLJkiUA\n6r4tem7evCnaZA2M2c2LigHUbnkYxkVVqS9xVV/sAGrGluqIoYyMDHh7e2PgwIHw8vKqdLw+lYk9\nUaMdKsbYEMbYb4yxC4yxOTIyCYyxi4yx04wxtcF+LWPsDGPsFGPshMH+xYyxDJ38NsbYYzVhi62E\nhobi5MmT6NixI1QqFX788cfaVonDsZlOnTohLS2tttXgcDgG+Pv7IzMzE4sXL65tVR4pauxbfoyx\nBgAuABgA4BqAXwCMI6LfDGSGAniZiIYxxroCWE5EkbpjlwGEEdFtk3yfALCfiCoYYx9AGAKcK3F+\nqilblbJv3z5MmjQJTz31FBYtWoTGjRvXtkocDofD4dQJHuVv+UUAuEhEWURUCuArAE+ayDwJYAMA\nENFxAE0ZY/oXozBI6EtEe4moQrd5DECHh6H8w2DAgAE4c+YMbt68ibCwMKSmpta2ShwOh8PhcGyg\nJjtUbgD+Z7CdrdtnTuaqgQwB+Ikx9gtjLBbSTAbwfTXoWmM0b94cX375JebPn48hQ4bg/fffN3q3\niyn1Ze67vtgB1B9buB32BbfD/qgvttQXO+yNuvRizx5EdJ0x1gpCxyqDiA7rDzLG/gmglIg2yWUw\nceJEeHh4ABDeLhwaGiq+yFAfYLW13b59e6xYsQKrV6/Gf//7X7z00kto165dJXk9ta1vVbdPnz5t\nV/rw7WScPn3arvR51Ld5edjfth570cfW7bra/ur/N/y6hj1Rk2uoIgG8RURDdNtvQFjvtMhA5t8A\nkohos277NwB9iOiGSV4LAeQT0TLd9kQAsQD6E1ExJLDHNVRSVFRU4KOPPsIHH3yAxYsXY+LEiXb3\nFBWHw+FwOLXNo7yG6hcAjzPGOjHGGgIYB2CHicwOAM8BYgfsDhHdYIz9hTHmotvfBMAgAL/qtocA\neA3ASLnOVF2iQYMGmDVrFvbv34+PPvoIf//73/HHH3/UtlocDofD4XDMUGMdKiIqB/AygB8BpAP4\niogyGGPPM8am6WR2A7jCGLsEYDWAF3XJ2wA4zBg7BWHh+U4i0r9vYAUAFwjTgKmMsVU1ZdPDJDg4\nGL/88gu8vLygUqnw/ffC0jDToee6Sn2xA6g/tnA77Atuh/1RX2ypL3bYGzW6hoqI9gDwNdm32mT7\nZYl0VwCEmu7XHfOuTh3tCWdnZ3z44YcYNmwYYmJiMHz4cIwYMaK21eJwOBwOh2NCTU75cWykb9++\nOHPmDO7evYtXX30VJ0+etDqP5ORkzJw5U9zOy8uDWq2GRqNBu3bt0KFDB3G7tLQUPXv2NJvf3bt3\n8cknn1ithx79YkOluLq6Kpbds2cP/Pz84OPjg0WLFtksAwA3btzA+PHj4e3tjfDwcAwfPhyXLl0y\nkjG0pap+UYpS/T/66CMEBQUhJCQEzz77LEpKSmTTW1sm1cHD8JcSO6ZMmYI2bdogJCTErJycn5X6\nXwkJCQkICAjAhAkTjLY/++wzq/KR8qWlemyNjoGBgYiOjraqLgLS5VFT9QSo7N+qYGjLw6iD5sjK\nykJwcHCVbQCsr+tKdL1w4YJ4DVGr1WjatCkSEhIAAB4eHlCpVFCr1YiIiKiq+vZLbX/7pqZ+qOVv\n+VUXX331FbVu3ZreeecdKi0tVZxu6dKl1L59e7p7926lY3FxcZU+jmyJK1eu2PQNN1u/Yaj048jl\n5eXiF89LSkpIpVJRRkaG1TJ6unXrRmvWrBG309LS6PDhw7Lnt9Uv1qBU/6tXr5KnpycVFxcTEVFU\nVBStX7/eKvsfNpmZmTb7qyrfwzx06BCdOnXK7Adx5fxU3f7z8/Ojq1evym4r5WHGnqFO1nyoXA5L\nulbnt05t8ael89dGHbTlA87VgS26lpeXU7t27eh///sfEQkfqM7Ly6t23cC/5cepCm3atEFKSgqS\nk5PRu3dvZGZmWkxz4cIFREREYMyYMVi9enWl4yTx9KP+LjQrKwsBAQGYNm0agoKCMGTIEBQVFWHu\n3Lm4fPkyNBoN5swRviKUmJiIrl27QqPRYPr06SAiZGVlwc/PDzExMQgODsahQ4cQEBCA4cOHi/kV\nF//5LMGoUaMQHh6O4OBg/Oc//7HaPydOnIC3tzc6deoEJycnjBs3Dtu3b7daBgCSkpLQsGFDxMb+\n+dqz4OBg9OjRw0jOcD2ClF+kbDK921y6dCnefvvtarNRT3l5Oe7fv4+ysjIUFhaiffv2suktrauQ\ns8Pf3x/R0dEICAhAVFQUioqKAFiOh6CgIEydOhWZmZmiv8z5xTSWsrOzJc+hZH1Iz5490bx5c5v8\nbI3/DTHVtaKiAtOnT8fly5cxdOhQLF++3Gj75ZdflrQPADZs2CDe8cfExACQjj0XFxfMmzcPq1b9\nubQ0Li4Oy5Ytky0jUwx1io+PF/fLldXJkyehUqlQUlKC+/fvw9PTE+fOnTPK01RXqbKVawuk2qTi\n4mIUFhZi+PDhUKvVCAkJwZYtWyr5V85mqfNLoY+th1UHlaL3XUpKiqTdCxcuFO0FgPnz52PFihWi\nrLe3tyhrCVt03bt3L7y8vNChg/CebSJCRUWF2TT1gtru0dXUD/VkhCopKYmIhDuA+Ph4atmyJX36\n6adm76g+/fRTIhJGAzw8PKi8vNzo+FtvvVVphEp/F6rVasnJyYnS0tKISLjDSkxMrHS3lJGRQSNG\njKCysjIiInrxxRdp48aNpNVqqUGDBnTixAmj/D777DOj/PTcvn2biIgePHhAQUFB4l2NXp9evXqR\nWq2u9Nu3bx8REW3dupViY2PF/DZu3EgzZswwsk2JDBFRQkICzZw5U86tIvoy0dtnehcpZZOp3JIl\nSyguLk7cNmenUv2JiJYvX04uLi7UunVrio6ONmu/oR1SyNnBGKOjR48SEdHkyZNp6dKlVsWDoR/M\n+UWr1ZKDg4OYVu4cSUlJFuNE6lymyPnJGv/rkdOViMjDw8Po7l1/N79+/XrJNOnp6eTj4yOm0ZeL\nlD2urq50+vRp6tOnj7gvICCAsrOzzepkiuEIg2HbIFdWCxYsoNmzZ9NLL71E06ZNq5SfVLkblq2h\nXaZtgWmbNHbsWEpMTKRt27YZnevevXuVdFcal0TSddDb2/uh1kFz6H12/vx5UqvVdPbsWSIiSbu1\nWi1pNBoiEkbbvLy8KC8vT5TV13W9j6qrvdEzefJkWrlypbjt6elJarWaunTpYjTiX1VgZyNUdenF\nnlUmL+8ntGgxsLbVAKrwXqm+ujvIBg0a4P/+7//wxBNPIDo6Grt27cKaNWvQunVrI/m8vDy0bNkS\nANC5c2doNBps3rwZ48ePV3xOT09P8U40LCwMWq220ijNvn37kJqaivDwcBARioqK0KZNG/Tq1Qse\nHh4IDw83ym/y5MlG+emJj4/Hd999BwDIzs7GxYsXjebcDx48qFjvmsLSegQpm9q0aWM2jTk7t23b\npkivO3fuYPv27cjKykLTpk0xevRoJCYmolGjRpLyttrh7u6OyMhIAEB0dDQSEhLg7OyMlJQURfFg\nDZ06dRLTysVcdHS03cWJlK5t27YVj5PJyBARIT8/X9K+O3fuICoqShxha9asmdlzq1Qq5ObmIicn\nB7m5uWjRogXc3NywcuVKyfyl0F8wlLJgwQKEh4ejcePG+PnnnxWlMSxbwHxbYNgmaTQaaLVajBkz\nBrNmzcLcuXMxbNgwcf2Yoe7WtFO1UQctkZubi6eeegrffPMN/Pz8AAij5rNnzzay29XVFS1btsSZ\nM2eQk5MDjUaD5s2bi7ItWrSAo6Oj6KPqsFVPaWkpduzYgQ8++EDcd+TIEbRr1w43b97EwIED4e/v\nX23r++yJR6pD9dtvMXBzmwF39zkQvtVcS1jRMFkiMDAQx44dw8KFCxEaGopPP/0Uw4YNE4/v3LkT\n0dHR4varr76KWbNmWdWhcnZ2Fv93cHAQp3QMG1giQkxMDN59912jtFlZWWjSpImi/A4cOID9+/fj\n+PHjcHZ2Rr9+/cRjenr37o38/HyjfYwxLFmyBP3794ebmxt+//138Vh2djbc3Iy/cKREBhB8u3Xr\nVgmPKEfOJkdHR6NPDFljp1L99+7di86dO6NFixYAgKeffhpHjx5FdHS0ovRK7JCiQQOhbk2cOFFR\nPBhiyS+GaeViDrAcJ0qQ87NS/xtiTldr03z88ceK89ATFRWFLVu2ICcnB2PHjrVZJ0PMldUff/yB\ngoIClJWVoaioSNGH3w3L1lK8SbUh3t7eOHXqFHbv3o358+fjiSeewPz5843OYU07ZW91EACaNm0K\nd3d3HDp0SOxQeXt7IzU1tZLdU6dOxdq1a5GTkyPewMrJVoeter7//nuEhYWhVatW4r527doBAFq1\naoVRo0bhxIkT9bJDVetDZDX1A0APHvyPUlIiKS3tSSotvWM6elgnMDctc+DAAfLw8KDnn3+eCgoK\nqLS0lNavX19JLiwsjA4ePChuS035ubi4EJEwzGy4eFQ/rH/r1i3y8PAQ9587d458fHwoNzeXiIjy\n8vIoKyurUnr9tt4Ow2mC7du308iRI4lIGJpv1KgRHThwwEgfS5SVlYkLKIuLi0mlUtG5c+esltET\nGRkpTpkSGS9KHzBgAF27ds2oTEz9ImdTaWkptWrVivLy8qioqIgiIyONpvyqaiMR0fHjxykoKIge\nPHhAFRUVFBMTQytXrpRMn5GRQUlJSaJNpsjZoZ/yO3bsGBERTZ06lZYtW6Y4Hkz9Zc4vpmnlzmFp\n6lKPpYXRcn6y5H8pH8rpSiRM+d26dUuU1W+vW7dOMk16ejr5+vqKafTTWaa+JPqz3qSnp1P37t3J\n19eXcnJyLOpkiqGO+jzNldXIkSPpyy+/pPfee49GjRpVKT9TXU3L1lxbINcmXb9+nYqKioiIaNeu\nXeJ5DXVXGpdy6GPrYdRBSzGkn/IrLCyknj170qZNm4iI6Nq1a5J2l5SUkK+vL3l5eYlLQvSySUlJ\nRrLmsKa9JCIaN24crVu3Tty+f/8+5efnExFRQUEBde/enX744QeL51UC7GzK75FalN6oUQeEhh6A\ns3MHpKSEo6Dg19pWqVrp3bs3Tp8+jQcPHkCtVuO9997Da6+9Bnd3d/HXsWNHXLx40WhxqRSGn7uR\n+vRNixYt0L17d4SEhGDOnDnw9/fHO++8g0GDBkGlUmHQoEHIycmRTC/3KZ0hQ4agtLQUgYGBmDdv\nHrp162YxjSkODg74+OOPMWjQIAQGBmLcuHHw9/cHAAwbNgw5OTlmZUz59ttv8dNPP+Hxxx9HcHAw\n5s2bh7Zt24KIkJmZKd55GvqlR48eol+GDh0qaZOjoyPefPNNhIeHY/DgwbLnt9VGAIiIiMDo0aOh\nVquhUqlARIiNjZVM7+fnJ2sTYL5sfH19sXLlSgQEBODOnTuYPn06/P398a9//ctiPJj6y9HRUZwu\nkvKLYVpz57DEM888g+7du+PChQtwd3fH2rVrxWPm4sTPz8+s/+V8qNQfhtudOnWSTBMQEIB//vOf\n6NOnD9RqNWbNmiXpS8O8AgICkJ+fjw4dOojTetb4T6o9kIvhjRs3omHDhhg3bhzmzJmD8+fPV3pQ\nwJyugPl4k/IZAKSlpSEiIgJqtRpvv/02FixYUEnWmnIwh6U2RB9D1tRBSzGkp3Hjxti1axfi4+Ox\na9cunD171shu/aick5MT+vXrh6ioKNE2vWxsbKyRbHXYCgCFhYXYu3cvnn76afH4jRs30LNnT6jV\nakRGRmLEiBEYNGiQYl/XKWq7R1dTP5gsSr9+fT0dPtyScnK+lOj31n22bNlCrVu3poULF1r1egWO\nMn799VeaNWtWbatRrdhik9I7+0eF+hgXnJqlumKovLycQkND6dKlS9WglX0COxuhqrGPI9c2Uh9H\nzs8/jfT0v6Nly5Ho3HkxGjRwqiXtHg7Xrl3DpEmTcOfOHXzxxRfw9q63L5Xn1BJZWVkYMWIE0tLS\nalsVDoejIyMjA8OHD8ff//53LF68uLbVeWg8yh9HtjtcXUMRFnYShYUXcObMABQXK5suqE2s+QZT\n+/btsWfPHkyYMAHdu3fH6tWrYS8d6Pr0Lan6YostdnTq1MnuOlOPcnnYI/XFDqDu2OLv74/MzEzZ\nzlRdsaOu8Uh3qADAyak5goN3onnzAUhJ6YK7d4/UtkrVCmMML7/8Mg4ePIjVq1dj5MiRuHHjRm2r\nxeFwOBxOveKRnvIz5dat3fjtt0no1Gk+3NxetmqRYl2gpKQEcXFx+Pzzz8XOFYfD4XA4dRF7m/Lj\nHSoTHjy4jF9/fRpNmgTB13c1HBzk35lTVzly5AgmTJiAAQMG4KOPPoKLi0ttq8ThcDgcjlXYW4fq\nkZ/yM6Vx487QaH4GYw2QmtoNhYWXalslI6pj7rtHjx44ffo0ysvLERoaiqNHj1ZdMSupT3P49cUW\nbod9we2wP+qLLfXFDnuDd6gkcHD4C/z81qN9++k4dao7/vhjZ22rVO089thj+Pzzz7F48WKMGjUK\nb775JkpLS2tbLQ6Hw+Fw6iR8ys8Cd+8exblzUWjbdiI8PN4CYw4PQbva5fr165gyZQpu3ryJL774\nAr6+vrWtEofD4XA4ZuFTfnWMpk27ISzsJO7ePYy0tGEoLb1V2ypVO+3atcN///tfTJo0CT169MCq\nVavs5vUKHA6Hw+HUBXiHSgENG7ZBSMhPcHEJRkpKF+Tnp9aaLg9r7psxhhdffBFHjhzB2rVrjT4n\n8DCoT3P49cUWbod9we2wP+qLLfXFDnvjkepQVWXUpUEDR3h5fYjOnT9EWtpgXL++1nIiOyI5ORkz\nZ84Ut/Py8qBWq6HRaNCuXTt06NABarUa48ePx4EDB/Drr78iNDQU3377rWR+d+/exSeffFJT6sPV\n1VWRXHZ2Nvr374/AwEAEBwcjISFBPLZnzx74+fnBx8cHixYtks1DidyNGzcwfvx4eHt744UXXsDw\n4cNx6ZL5BxhqwmdKbfzoo48QFBSEkJAQPPvssygpKbEqfU1Q0zGmZ8qUKWjTpg1CQkJkZcz5qbp8\nuG3bNgQEBGDChAkAgISEBKNtpUj5sWfPnjbrZYipTkrrqTlqstxt9akllMSAXB1Uml5PVlYWgoOD\nq1V/a1Ciqz21Kw+V2v72TU39ANBx/+OU/Uk2lRWUVfomkDUUFJyjY8d86bffplF5eVGV8qopli5d\nSu3bt6e7d+9WOhYXF0dLly6ttP/nn38mLy8vmjRpEt27d8/o2JUrV2z6hpv+q+fW4urqqkju+vXr\ndOrUKSIiys/PJx8fH8rIyKDy8nLxi+klJSWkUqkoIyOjUnqlct26daM1a9aI22lpaXT48GGzutnq\nM6Uo1f3q1avk6elJxcXFREQUFRVF69evV5y+psjMzKzRGNNz6NAhOnXqFAUHB0seN+en6vShn58f\nXb16VXZbKQ8z7kx1UlpPzWFJ36qWryHW+lTJuZXEgFwdVJreEK1WKxurDxsluj7MdgV29i2/R2qE\nynulN/L25OFop6PIfD0TRVlFNuXTpIk/wsJOoLT0Fk6d6oWiov9Vs6bVy4ULFxAREYExY8Zg9erV\nlY6TxMidq6srunXrhh07duDbb7+Fm5sbOnfujCFDhqCoqAhz587F5cuXodFoxK/FJyYmomvXrtBo\nNJg+fTqICFlZWfDz80NMTAyCg4Nx6NAhBAQEYNq0aQgKCsKQIUNQXFwsnnfUqFEIDw9HcHAw/vOf\n/1hta9u2bREaGgoAcHFxgb+/P65evYoTJ07A29sbnTp1gpOTE8aNG4ft27dXSq9ELikpCQ0bNkRs\nbKy4Lzg4GD169DCrm5TPpOw1veNcunQp3n77bYu2K7URAMrLy3H//n2UlZWhsLAQ7du3tyq9IXI2\n+Pv7Izo6GgEBAYiKikJR0Z/1zVKsBAUFYerUqcjMzBT9JecX0xjLzs6WzF8pPXv2RPPmzWWPm/OT\nLT401bWiogLTp0/H5cuXMXToUCxfvrzStjn7NmzYAJVKBbVajZiYGMydO9fIj8CfI0lz587FqlWr\nxLRxcXFYtmyZbBkZYqqTHnPxe/LkSahUKpSUlOD+/fsICgrCuXPnjPI1rSdS5SsXc1JtS2FhIYYP\nHw61Wo2QkBBs2bJFUn8l7Vd2drbZsrQmBqTqoDXppdD7LSUlRdLuhQsXGpXV/PnzsWLFClkfVYet\nVbGnzlHbPbqa+gmmChRmFtLFf1ykQy0O0dm/n6XbB2/bdNdTUVFBWVmL6ciRtpSXt9fq9LaQlJRk\ndZpPP/2UiIQ7fg8PDyovLzc6/tZbb1UaodLfaWq1WnJycqLly5dT27Ztyd/fn9avX1/prigjI4NG\njBhBZWXC6N+LL75IGzduJK1WSw0aNKATJ04Y5ffZZ58RkXBnlpiYKOZz+/ZtIiJ68OABBQUFUV5e\nnpE+vXr1IrVaXem3b9++SnZfuXKFOnXqRPn5+bR161aKjY0Vj23cuJFmzJhRKY0SuYSEBJo5c6a4\nrbM2+WQAACAASURBVLRMpO4kpew1lVuyZAnFxcVZtF+pjUREy5cvJxcXF2rdujVFR0cTkRAHStMr\nsYExRkePHiUiosmTJ4sxZk2sGPpBzi+m6davXy+ZvyX/GWLurt+cn60pA3O+ICJq27atGP9ERJ6e\nnpSXl2c2TXp6Ovn6+orpbt++LWmLvj6dOnWK+vTpI+4PCAig7Oxss+cwRK+TYb5ZWVlG55s+fboY\nv0RECxYsoNmzZ9NLL71EH3zwQaU8pcrdwcFBLF+9XUSVY87JyYnS0tKIiGjs2LGUmJhI27Zto2nT\npolpDUfbLfnUNLaSkpIsxpDSGJCqg9akN/XX+fPnSa1W09mzZ4mIJO3WarWk0WgoKSmJKioqyMvL\ni/Ly8mR9VB22WmuPNcDORqgca7k/Vys07twYjy97HB5xHshZn4PzU87DwcUBHf6vA1qPa40GzsoG\n7hhjcHd/Da6uYcjIeBYdOryKjh1ft/jJGlaFBYFJVsrn5eWhZcuWAIDOnTtDo9Fg8+bNGD9+vOI8\nPD098corr2Ds2LHo27cv5s6di/Xr1xvJ7Nu3D6mpqQgPDwcRoaioCG3atEGvXr3g4eGB8PBwo/w6\nd+4MAAgLC4NWqxWPxcfH47vvvgMgrIe6ePEiIiIixOMHDx5UpHNBQQFGjx6N5cuX2/Wb4KXsbdOm\njay8Ofu3bdum6Jx37tzB9u3bkZWVhaZNm2L06NFITEy0TnED5Gxwd3dHZGQkACA6OhorVqzAzJkz\nrYoVpRimS0lJkcwfUB4/NYWUL9q2bSseJ5NRISKS9R8A7N+/H2PGjBFH2Jo1a4a7d+/Knj80NBQ3\nb95ETk4OcnNz0aJFC7i5uWHlypWy5zDVx1RHSyxYsADh4eFo3LgxVqxYoShNp06djOJCLuY8PT3F\n0TGNRgOtVosxY8Zg1qxZmDt3LoYNG1Zp/Zg5n0rFZHXEkFQd3LRpE5555hmb8svNzcVTTz2Fb775\nBn5+fgCEUfPZs2cb2e3q6oqWLVvi0qVLKC4uhkajQfPmzSVlq8vWR4ka7VAxxoYAiIewGP4zIqq0\nOo0xlgBgKID7ACYS0WmDYw0AnASQTUQjdftUAP4NoBGAUgAvEtFJJfo4ujqiw8sd4PaiG/L25CE7\nIRuZczLRflp7tJ/eHs7tnBXZ1bx5f2g0J5CePhr37h2Hn986ODo+JitPffsqyrc62LlzJ6Kjo8Xt\nV199FbNmzbKqQ+XsLPihTZs2mDJlCg4ePIioqCg0atQIRKR/FwhiYmLw7rvvGqXNyspCkyZNKuXX\nV+cDBwcHcSrowIED2L9/P44fPw5nZ2f069fPaJoIAHr37o38/HyjfYwxLFmyBP379wcAlJWVYfTo\n0ZgwYQKefPJJAICbmxt+//13MU12djbc3Nwq2apELjAwEFu3bhW3+9pYnnL2Ojo6ory8XJQz9IE5\n+5XauHfvXnTu3BktWrQAADz99NM4evQooqOj8dZbb1lMr8QGKfQ3GtbEiiHm/GKYztvbWzJ/QFn8\nWMKcn5WWgR45XwBAo0aNrE5jC2PGjMGWLVuQk5ODsWPHVvkcpuXk5uZmtP3HH3+goKAAZWVlKCoq\nQuPGjS3maVi+5mJO31YBf7Yt3t7eOHXqFHbv3o358+djwIABWLBggVH+SmOyb9++FmNISQxI1cGf\nf/4ZzzzzjNUxBABNmzaFu7s7Dh06JHaovL29kZqaKtr9xBNPYP78+Zg6dSqOHDmCvXv3YvLkyWZl\nq8NWW+yps9TUUBiETtQlAJ0AOAE4DcDPRGYogP/q/u8K4JjJ8X8A+ALADoN9PwAYZJA+Seb8ZocO\n9RScK6Dz08/ToWaHKP3ZdLp7ovIibjnKy4vo/PkX6NgxHyooSFec7mFRWloqLnQ0JCwsjA4ePChu\nS035ubi4EJEwnGy4QFQ/zXLixAlydnamQYMG0dWrV+ncuXPk4+NDubm5RESUl5dHWVlZldLL5UdE\ntH37dho5ciQRCVMhjRo1ogMHDhjpo4QJEybQP/7xD6N9ZWVl4sLI4uJiUqlUdO7cuUpplcpFRkaK\nU6lExovSBwwYQNeuXauU5tatW+Th4SFuy9lbWlpKrVq1ory8PCoqKqLIyEijKRM5lOp+/PhxCgoK\nogcPHlBFRQXFxMTQypUrLaaXskvOBv2U37Fjx4iIaOrUqbRs2TIiIsWxYuovOb+YppPL3xrMLYw2\n5yepY/oFuFL+M6erh4cH3bp1S5TVb5tLo5/y06fLy8ur5Eci4/qUnp5O3bt3J19fX8rJybHKh6Y6\nurq6UllZmdn4HTlyJH355Zf03nvv0csvv1wpT1N9TcvXXMxJtS3Xr1+noiLh4aFdu3bRqFGjFPvU\nNE8lKKmHUnXw448/lk1vLob0U36FhYXUs2dP2rRpExERXbt2TdLukpIS8vX1JS8vL3Gpi5xsddiq\ntF2yBdjZlF9NLkqPAHCRiLKIqBTAVwCeNJF5EsAGXe/nOICmjLE2AMAY6wDgbwBMVypXAGiq+78Z\ngKtVUbKJfxP4rPJB18td4RLqgvQx6UjtnorczbmoKK0wm7ZBA2f4+HwCd/e5OH26D3Jzv66KKpJY\n8/6Qr7/+Gq+99hrc3d3FX8eOHXHx4kXEx8ebTWs4bSk1hRkeHo5Ro0bh9OnT8Pb2Rnp6Ot555x0M\nGjQIKpUKgwYNEt9jZZqeMSZpx5AhQ1BaWorAwEDMmzcP3bp1M6uDFEeOHEFiYiL2798vvhZiz549\ncHBwwIoVKzBo0CAEBgZi3Lhx8Pf3F9Pp37vl4OCAjz/+WFZOz7fffouffvoJjz/+ODp37ox58+ah\nbdu2ICJkZmaKd56GtGjRAj169EBISAjmzJmDoUOHStrr6OiIN998E+Hh4Rg8eLDk+aWwpLvexoiI\nCIwePRpqtRoqlQpEhNjYWBw6dEg2vZxd5srM19cXK1euREBAAO7cuYPp06cDAPz9/fGvf/3LYqyY\n+svR0VGcLjL1i2G6GzduyOavhGeeeQbdu3fHhQsX4O7ujrVr1xr5z5yfpY75+fnJ+s+cLwwf1jC0\n0VyagIAA/POf/0SfPn2gVqsxa9YstGjRAt27dxf9aOqvgIAA5Ofno0OHDuK0nrlzSOlkiIODg1E5\nNWvWTDy2ceNGNPx/9s47LKqj++Pf6wKCigjSe5XeBRHREDuxRI0oYiEqmpjyaiSJPxJM1Bhj3hfs\nJe2NLZoYSyxENK9lrSiRYgGUoqzSEYTQlrKc3x/rXneX3WUhKGj28zz76Nw7Z+45Z85c5s7MvaOh\ngbCwMCxbtgzXr19vdS+Qrnfp67T3PnHz5k34+/vD29sbq1atQkxMTLt8Kl6mMvdfRfGhqA0uXLhQ\nrryiGBKhpaWF+Ph4bNiwAfHx8bh165ZMu9XV1eHo6Ihp06axtsnL2xm2KntPfSl4Xj03AG8A+E4s\nPQvAJqk8xwEEiqVPA/B58v8DALwAvALJESonADwADwA8BGAh5/rt6fiyCJoEVHqolFKGpdAV8yuU\ntyaPGsoa2pT7668USky0puzspSQQNHXo2rLoyKL0Z83Vq1fJwcGB5syZQ5WVlUrJdEc7Ooq4Lbdv\n36aoqKiuU+ZvoKhO2mtXR57sO4vuGFsdiYvuaEdHeFnsIOpaWzrr3iIQCMje3p5ycnI6QauuBd1s\nhOqFWJTOMMw4ACVElMYwTDAA8ceQRQAWE9ERhmGmAvgRwChZ5bz55puwtrYGIFys6eXlxa5/ET15\nyEobTDFAul466nLqoHtFF0kOSbgXeA8GbxggZF6ITPnk5Co0N2+AltZ23LgxEo8e/Qvq6npKXe9F\nSw8aNAgbN27E9u3b4eXlhV27dqGlpUWhvOhYd9D/76aDg4Ml0rGxsd1Kv/akRUifLysrw/jx4+We\nl04nJiairq5O6fzPsj6e9/Vlpdvrv7bq40VKd8f6eFHTsbGxf0veyMgI48ePh5+fHx4+fAg7O7tu\nZZ8y7YHL5Uq8yNSdeG6bIzMMEwBgBRGNfZL+Pwh7l1+L5fkGwjVQ+5+k70A4IrUYwhGtZgBaALQB\nHCaiOQzDVBJRP7EyqohINAUofn3qLFsbSxtR+F0hCrcXopdjL5gvNkf/8f3BcFoPNxMJkJe3EkVF\nP8LV9Vfo6AR2ig7dlfj4eCxcuBCzZ8/GqlWrJBaJqlChQoUKFZ3FP3lz5D8B2DMMY8UwjAaAMADH\npPIcAzAHYDtglURUQkSfEJElEdk+kTtLRHOeyBQwDPPKE5kRALKetSEahhqwjrFGwP0AmESagLeG\nh2sO1/Bw3UM0VzVL5GUYDmxsVmHAgG9w+/YkFBRsxd/p2Ek/uXY3xo8fj7S0NNy5cweDBg1Cenq6\nzHzd3Y728LLYorKje6Gyo/vxstjystjR3XhuHSoiEgB4D8AfANIB/EJEmQzDvMUwzMIneU4AuM8w\nTA6AbwG8o0TRCwDEMQyTCmA1gIXPxAAZ9NDoAaNwI/he84XzPmdU/1mNqzZXkfVeFuqy6iTy6uuP\nh7f3FRQWfoc7dyIgENTJKfXFx9DQEEeOHMH777+P4OBgbNiwgZ0CVKFChQoVKl5GntuUX1fTmVN+\nimgoaEDB9gIUfVcE7YHaMF9sDt3RuuzbFAJBHe7eXYja2ttwczsELS27Z65TV5Kbm4vZs2ejV69e\n2LlzJ8zNzbtaJRUqVKhQ8RLwT57y+0fQ06wnbFfbIoAXAIOpBsj9OBd/uvyJgu0FENQKwOH0grPz\nHpiYRCIlJRDl5b93tcrPFDs7O1y4cAHBwcHw9fXF/v37u1olFSpUqFChotNRdaieERwtDkzmmWBg\n2kA4bHfA4z8eI9EqETkf5oDP48Pc/D24uf2Gu3ffwv37K0Ck3JTYizj3raamhpiYGPz+++/4/PPP\nMWvWLMTHx3e1Wp3Gi1gnslDZ0b1Q2dH9eFlseVns6G6oOlTPGIZhoBusC7ff3OD7py8AINk3Gben\n3AalucDX909UVp7DrVvj0dRU0cXaPlsGDhyIlJQU6OjoYP78+apGrUKFChUqXhpUa6i6gOaaZpTs\nLkH+pnz00OwBs8VGqA1cj/LHR+Hqegja2t5dreIzJyEhAZGRkQgPD8fq1atVn1dQoUKFChXtorut\noVJ1qLoQaiFU/FGBgo0FqE6phk5MCiq9VsHeIRbGxhFdrd4z59GjR3jrrbeQk5ODn376id0lXoUK\nFSpUqGiL7tahUk35dSFMDwb9x/aHR4IHvM57QePOGNC765B9dQVuX4xES0tDK5mOTpNxuVwsXbqU\nTVdUVLB73ZmYmMDc3JxNNzU1ISgoSGF5VVVV2L59e4d0Eemjr6+PgwcP4oMPPsDw4cOxbt06uZ9X\n0NbWVqrc/Px8DB8+HK6urnB3d8emTZvYcydPnoSTkxMGDBiAr7/+Wm4ZyuQrKSnBjBkz4ODgACcn\nJ4wfPx45OTkKdfu7PlMGZW1cv3493Nzc4OHhgZkzZ6KxsRFcLldp+edBR/31d6eS58+fDyMjI3h4\neMjNo8hPneXD999/Hy4uLpg9ezYAYNOmTRJpZZHlx7bat7JI6ySrnba3Pp5HOxHRXp8qa4syMSCr\nDbZHXgSPx2v3w2hnLrdoS9esrCz2b4u3tzd0dHQk7svW1tbw9PSEt7c3/P39O02vLqGr9755Xj90\ncC+/503j40a6v/42nY8dRud3udHDX1JI0Chgz3d0L6m4uDgyNTWlqqqqVudWrlxJcXFx7Srv/v37\nHdqrTbS7ubQdubm5NGTIEHr11VfpwYMHreS0tbWVKr+oqIhSU1OJiKi6upoGDBhAmZmZJBAI2B3P\nGxsbJXZwF0fZfIMHD6bvvvuOteXmzZt06dIlhbp11GfKoqzuBQUFZGNjQw0Nwj0pp02bRrt27aIz\nZ84oJf+8yM3N7ZC/zp49+7eue/HiRUpNTSV3d3eZ5xX5Wdk6UAZLS0sqKChg005OThJpZXmWcSet\nk6x22t57Vlv6iu4hnUF7fapMbCkTA/LaoLLy4uTl5cmNVXl01p6E7dVVIBCQiYmJxD3exsaGKioq\nOnR9dLO9/FQjVN0M9X7qsF7iiqDF56BvMAm5vUbjymtbkbc6D41ljezeRu0hKysL/v7+CA0Nxbff\nftvqPMmYChU9afJ4PLi4uGDhwoVwc3PD2LFjwefzER0djXv37sHHx4fdEX7v3r0YNGgQfHx8sGjR\nIhAReDwenJycEBERAXd3d1y8eBEuLi7Yt28fW15DQwNsbW1x/vx5lJSUwNbWFhYWFvjhhx/abaux\nsTG8vLwAAH369IGzszMKCgqQlJQEBwcHWFlZQV1dHWFhYTh69GgreWXynTt3DhoaGliwYAEA4X5T\n7u7uGDJkiELdZPls8uTJ8PPzg7u7O2uv9BNnXFwcVq1a1abtytoIAAKBALW1tWhubkZdXR1MTU3R\nq1cvpeXFkWeDs7MzZs2aBRcXF0ybNg18Pp+VaStW3NzcEBkZidzcXNZf8vwiHWP29vYyy1eWoKAg\n6Orqyj2vyM/tqQN5vmhpacGiRYtQXFyMkJAQbNy4EYsWLcK9e/fYtCL7du/ezT7xR0REIDo6WsKP\nwNP2HR0djW3btrGyK1euxLp16+TWkTjSOomQrqfk5GQ2fq9fvw5PT080NjaitrYWbm5uyMjIkChX\nup1I129+fr7cmJO+VzU0NKCurg7jx4+Ht7c3PDw8cODAAZn6K3P/sre3V1iX7YkBWW2wPfKyEPkt\nOTlZpt2ff/45Nm7cyP4diYmJwebNm+X6qLNsFXH69GnY2dnBwsKCPUZEL8+Hn7u6R/e8fnhBRqik\nKS//H13kGtL12A/pQr8LlDk3k6rTqttVxvfff09Ewid+a2trEggEEudXrFjRaoRK9KSZl5dH6urq\ndPPmTSISPknt3bu31VNRZmYmTZgwgZqbm4mI6J133qE9e/ZQXl4e9ejRg5KSkhSWJ+Lx48eUnJxM\njo6OpKOjQ/fu3ZPQZ+jQoeTt7d3qd+bMmVZ2379/n6ysrKi6upoOHjxICxYsYM/t2bOH3n///VYy\nyuTbtGkTLV26tJVsW8h6knz8+DEREdXX15ObmxtVVFS0yhcbG0srV65s035lbSQi2rhxI/Xp04cM\nDQ1p1qxZStsuC3k2MAxDiYmJREQ0b948NsbaEyvifpDnF2k5eeW35T9xFD31K/JTe32oSFdra2uJ\nJ3fRk7wimfT0dHJ0dGTlHj9+LNMWUXtKTU2lV155hT3u4uJC+fn5Cq8hjvTogra2NvF4PLnxS0S0\nfPly+vDDD+ndd9+ltWvXtipTVr1zOBy2fkV2EbWOOfF7y/Tp02nv3r106NAhWrhwISv7119/Ke1T\n6dgiajuGlI0BWW2wPfLS/rp79y55e3vTrVu3iIhk2p2Xl0c+Pj5EJBzps7Ozo4qKCrk+6ixbRcyb\nN4+2bt0qcczGxoa8vb1p4MCB7Ki/sqCbjVCpdXF/7h8Jl+G2I7cagP2oBgAIkKqWiIrXHKHloAXz\nxebQn6gvc1NmERUVFdDX1wcA2NrawsfHB/v378eMGTOU1sDGxoZ94vT19UVeXl6r0ZgzZ84gJSUF\nfn5+ICLw+XwYGRlh6NChsLa2hp+fn0R55eXlEuWJ2LBhA44cOQINDQ3w+XwEBARg37597PkLFy4o\npXNNTQ2mTp2KjRs3ok+fPkrb2hG4XG6HRg6Bp/YCwvVf2dnZMDIykptfkf2HDh1S6pqVlZU4evQo\neDwedHR0MHXqVOzdu7fNNWDykGeDpaUlAgICAACzZs3C5s2bsXTp0nbFirKIy23fvl1m+YDy8fO8\nkOULY2NjAACfz281KkREcv0HAGfPnkVoaCg7wtavXz9UVVXJvb6XlxfKyspQXFyM0tJS6OnpwczM\nDFu3bpV7DWl9pHWUJjc3l7UJAJYvXw4/Pz9oaWlh8+bNSvnJyspKIi7kxZz4vcrHxwd5eXkIDQ1F\nVFQUoqOjMW7cuFbrxxT5VDomuVxup8SQrDa4b98+hIeHd6i80tJSTJo0CYcPH4aTkxMAwN3dHR9+\n+KGE3dra2tDX18cPP/wACwsL+Pj4QFdXV2ZeoHPbS1NTE44dO4a1a9dKHL98+TJMTExQVlaGUaNG\nwdnZudPW+D1vVB2qLiCYgtstIxDwkZPzL2SePYkJ/0lAbYI+Hv77IXKX5sLsPTMYzzeGej/1VnLH\njx/HrFmz2PSSJUsQFRXVrg6V+CcNOBwOO3UjfiMlIkRERODLL7+UkOXxeOjdu7dS5Z0/fx5nz57F\ntWvX0LNnT7z66qsYN24cIiIi0NDQAD6fj9GjR6O6ulqiPIZhEBsbi+HDhwMAmpubMXXqVMyePRuv\nv/46AMDMzAwPHjxgZfLz82FmZtbKVmXyubq64uDBg/LcpTSy7OXz+VBTU4NAIGDziU+VDRs2TK79\nytp4+vRp2NraQk9PDwAwZcoUJCYmwsXFBYmJiW3KK2ODLETbL7UnVsRR5BdpOVnlA4r9J4qftlDk\nZ2XrQIQ8XyiiIzKKCA0NxYEDB1BcXIzp06f/7WtI15P4YmtA+HZvTU0NmpubwefzoaWl1WaZ4vWr\nKOZk3VscHByQmpqKEydOICYmBiNGjMDy5cslym9PTLYVQ8rEgKw2eOXKFYSHh7c7hgBAR0cHlpaW\nuHjxItuhcnBwQEpKCmv3yJEjERMTg8jISOzfvx9qamqYN2+ewrydYauIhIQE+Pr6wsDAQOK4iYkJ\nAMDAwACTJ09GUlLSC9uh6vIhsuf1wws65SdNYeEPdOmSPpWUHCAioqprVZQenk4X+12ku4vuUk1m\nDZu3qamJXegojq+vL124cIFNy5ry69OnDxEJh5PFF4iKhu/Ly8vJ2tqaPZ6RkUEDBgyg0tJSIiKq\nqKggHo/XSl5eeURER48epYkTJxKRcCpEU1OTzp8/T48ePSI1NTVydXWltLS0Nn00e/Zs+uCDDySO\nNTc3s4snGxoayNPTkzIyMlrJKpsvICCAnUolIolF6SNGjKDCwsJWMtI+k2dvU1MTGRgYUEVFBfH5\nfAoICJCYMpGHsrpfu3aN3NzcqL6+nlpaWigiIoK2bt3aprwsu+TZIJryu3r1KhERRUZG0rp164hI\n+ViR9pc8v0jLySu/PShaGK3IT7LOiRbpyvKfIl2tra2pvLyczStKK5IRTfmJ5CoqKlr5kehp+xbJ\nBAYGkqOjIxUXF7fLh9I6amtrU3Nzs8L4nThxIv3888+0Zs0aeu+991qVKa2vdP0qijlZ95aioiLi\n8/lERBQfH0+TJ09W2qfSZSqDMu1QVhvcsmWLXHlFMSSa8qurq6OgoCDat28fEREVFhbKtLuxsZEc\nHR3Jzs6OXeAvL29n2CoiLCyMdu7cKXGstraWqquFS1hqamooMDCQTp06pdS1ibrflN8/a1F6G0PT\nLwImJvPh7p6A3NwPkZv7MfoM7AWXvS7wS/eDen91pL2Shhtjb6A8oRz7f9mPjz76CJaWluzPwsIC\n2dnZ2LBhg8LriEYTpP8vQk9PD4GBgfDw8MCyZcvg7OyML774AqNHj4anpydGjx6N4uJimfKyygOA\nsWPHoqmpCa6urvjkk08wePBgAED//v2hpaWFjz/+GCNHjsS///1viSdgcS5fvoy9e/fi7Nmz7Ku6\nJ0+eBIfDwebNmzF69Gi4uroiLCwMzs7OrNy4ceNQXFwMDoeDLVu2yM0n4rfffsP//vc/2Nvbw93d\nHZ988gmMjY1BRMjNzWWfPKV9NmTIENZnISEhMu1VU1PDZ599Bj8/P4wZM0bm9WXRlu4iG/39/TF1\n6lR4e3vD09MTRIQFCxYolJdnl7w6AwBHR0ds3boVLi4uqKysxKJFiwAAzs7OWL16dZuxIu0vNTU1\ndrpI2i/icorKV4bw8HAEBgYiKysLlpaW2LFjh4T/FPlJ1jknJye5/lPWF+JpRTIuLi749NNP8cor\nr8Db2xtRUVGt2qp02S4uLqiuroa5uTk7raesD2W1ZQ6HI7ee9uzZAw0NDYSFhWHZsmW4fv16q1f4\npetd+jqKYk6WPjdv3oS/vz+8vb2xatUqxMTEtMun8u5X8lAUH4ra4MKFC+XKK4ohEVpaWoiPj8eG\nDRsQHx+PW7duybRbXV0dr776KqZNm8baJi9vZ9gKAHV1dTh9+jSmTJkiIV9SUoKgoCB4e3sjICAA\nEyZMwOjRo9vl725FV/fontcPAJGZGdE77xD98QfRk9dVXzREr7s2NJRRWtooSk0NpoaGEvZ8c30z\nFf5YSEmeSXTV8Srlb8mnpuqmLtJWPh19bff+/fs0dOhQGjZsGOXl5XWuUh1E3Jbbt29TVFRU1ynz\nN1BUJ+21qyNP9p1FZ70S3pl0JC66ox0d4WWxg6hrbemse4tAICB7e3vKycnpBK26FqhGqLqQ06cB\nS0vgs88AY2Ng5kzgwAFAao74RUBDQx8eHgno23cIkpMHoqrqKgCAo8mByVwTDEwdCMfvHPH47GNc\ntbqKnKgc1N+v72Kt/z7W1tY4d+4cxo0bBz8/P+zZs0fUYe4WuLq6IjY2tqvV6HQ6Yld7n+xfZl7W\nuFDx/OiMGMrMzISDgwN8fX1hZ2fXSZqpEPHP3XqmqAg4dgw4cgS4fBkICgImTQImThR2tl4gHj06\nhrt3I2FtvRKmpm+3+kNWn1ePwq2FKNpRBJ0gHZgvNke/4H4v/B+8tLQ09jtH33zzjdyhcBUqVKhQ\n8fLR3bae+ed2qMT56y/g5Elh5yohAXB2FnauJk0CBgx4vop2kLq6bKSnT0GfPr4YMGA7OJzWb84I\nagUo3lOMgk0FYNQZmP/LHIbhhuBocbpA485B9JHRgwcP4scff8SoUaO6WiUVKlSoUPEc6G4dqn/W\nlJ88+vYFpk0D9u0DSkqAFSuAvDzg1VcBFxcgOhq4dg3oBl9zlbcHU69eDvDxuQqiRqSmBqK+/n6r\nPJzeHJi9bQa/dD/Y/ccOZYfLcNXqKu59eg/8fNmvuj8rOmsvKU1NTaxfvx47duzAvHnzsHjx2TDu\nhQAAIABJREFUYtTXP9+pzc7cF6srUdnRvVDZ0f14WWx5Wezobqg6VNJoaACjRwPbtgEPHwI7dwI9\negDz5gHm5sCiRcCpU4DUt1W6AxxObzg774Wx8VykpASgvDxBZj6GYaA3Wg8ev3vA+5I3BH8JcN3j\nOtLD0lGVWNWt1iQpy8iRI3Hjxg2UlJTA19cXKSkpXa2SChUqVKj4B6Ga8msPWVnA0aPCqcGMDGDM\nGOG0YEgIoKPTOYp2EpWVl5CRMR2mpm/ByioGDKO479xc1YyiH4tQsLkA6vrqMF9sDoNQA/TQeLH6\n3ESEn3/+GUuWLMH777+P+fPns3tkqVChQoWKl4fuNuWn6lB1lJIS4PhxYefqwgUgMPDpovZu8ge8\noaEIGRnTwOHowNl5D9TV5W/4KoIEhPLfy5G/MR91mXUwXWQK07dMoWGo8Rw07jwePHiA6OhoJCQk\nwNLSEmPHjkVISAgCAwOhrt76i/IqVKhQoeLFort1qF6s4YfuhJEREBkJxMcDBQXC/1+6BLi5AQEB\nwFdfAZmZnf4x0fbMfffsaQJPz7PQ0rJHcvJA1NTcaFOG4TDQn6gPrzNe8DjlgYYHDUhyTMKduXdQ\nndp5n5d41nP4lpaW2Lt3L0pLS7Ft2zaoq6sjKioKBgYGmDJlCr7//nvk5+d3yrVelvUIKju6Fyo7\nuh8viy0vix3dDVWHqjPQ1gamTgV++kk4crV6NVBYKFyL5eQELFsGJCZ2yaL2Hj3U4eCwATY2q3Hj\nxkgUF/+ktGwf9z5w/N4R/tn+0BqghVsTbiF1WCrKDpWhpbnrF+grg5qaGgIDA/HFF1/g+vXryMrK\nwuTJk3Hu3Dl4eXnB3d0dH330Ec6ePdtqzzEVKlSoUKFCWRRO+TEM40xEmQrOLySi75S+GMOMBbAB\nwo7cf4noaxl5NgEIAVAL4E0iShM71wPAdQD5RDRRSi4KwH8A6BNRhYxyO3fKTxmIgJQU4bTgkSPA\no0fAhAnCqcHhwwFNzeeqTk3NLaSnT4Gu7hjY269Djx7tm8ZraWrBo8OPkL8pHw0FDTB71wwmkSZQ\n130xp9AEAgGuX7+OhIQEnDx5EpmZmQgODkZISAhCQkJgZWXV1SqqUKFChQo5dLcpv7a2aykAYCfn\n3GIA5cp+kh3CTlQOACsA6gDSADhJ5QkB8PuT/w8CcFXq/AcAfgJwTOq4OYCTAO4D0JNzfUVfsH8+\n5OQQxcURDR1KpKNDFBpKtHcv0ePHz/zS586dow8++ICamirp5s2JdPbsQPL0dCVvb28yNjYmMzMz\n8vLyIm9vb2psbKQhQ4YoLO/B2Qf0mf9nwk2Z375LNRk1CvP/XcQ3c1UGgUBA3t7eNGHCBPZYQkIC\nOTo6koODA61du7aVTFlZGe3du5eGDx9OHA6H1NXVKSgoiE6dOkX19fUSeYuLiyksLIzs7e1p4MCB\nNG7cOMrOzlaoU2VlJW3btq1ddrSXtmwUsW7dOnJ1dSV3d3cKDw+nhidbMSkr/zx4Hv6Sxbx588jQ\n0JDc3d3l5lHkp87y4caNG8nZ2ZlmzZolM60ssvzYVvvuqI7tbaeyeJ713lGftoUyMSCvDSorL6Ir\nt3kiUk7XZ3VfQTfbeqatTtCHAB4AsJI6vgxAGQBfpS8EBABIEEv/H4BlUnm+ATBdLJ0JwIiedpr+\nByBYRofqAAD3bt+hEqekhOi//yWaMIFIW5to1CiiLVuIHj5UKNbRvaTi4uLI1NSUqqqqqKVFQHl5\nX9Llyyb0+DGXVq5cSXFxce0q7/79++Tm5kb8Qj7d++weXTK6RGmj0+hR/CNqEbTIlRPtbt5eO7S1\ntduVf926dTRz5ky2QyUQCNhd0RsbGyV2cBdHlO/evXuUmJhIxsbG5OXlRdra2jRu3DjavHkz5eTk\n0ODBg+m7775jbbl58yZdunRJoU4inz0rlLWxoKCAbGxs2Bv4tGnTaNeuXXTmzBml5J8Xubm5HfLX\n2bNn/9Z1L168SKmpqXI7VIr8rGwdKIOlpSUVFBSwaScnJ4m0sjzLuJPWSVY7bW9bb0tf0T2kM2iv\nT5WJLWViQF4bVFZenLy8PIWdf1l01p6EyujamW1Cmu7WoVK4hoqIYgF8B+AcwzBmAMAwzAoASwGM\nIKLktkbAxDAD8FAsnf/kmKI8BWJ51gP4CIDEvB3DMBMBPCSiW+3QpesxNBR+2+rYMeF6q7ffBpKS\nAE9PwM8P+PJLID29Uxa1Z2Vlwd/fH6Ghofj222/BMD1gZfUJnJx2Ij19OiorL4s6nSza2toAAB6P\nBxcXFyxcuBBubm4YO3Ys+3Xye/fuYfC4wfiG/w0G8wbjkvUlvDLtFTj1csLsoNlo+qsJPB4PTk5O\niIiIgLu7Oy5evAgXFxfExsay5TU0NLDXnTx5Mvz8/ODu7o4ffvihQ/bm5+fjxIkTiIyMZI8lJSXB\nwcEBVlZWUFdXR1hYGI4ePdpKVpTPxsYGAQEBWLx4McLCwpCXl4c5c+YgOTkZfn5+SEtLw61bt5CQ\nkICGhga4u7tjyJAhCvUS+czHxwfLli2Tay+Px4O7uzsrFxcXh1WrVrVpt7I2AsLpztraWjQ3N6Ou\nrg6mpqa4c+eO0vLiyLPB2dmZ3Rpo2rRp4POffjx27969GDRoEHx8fLBo0SIQkUSsuLm5ITIyErm5\nuay/5PlFOsbKyspklq8sQUFB0NWV/0asIj+3pw7k+aKlpQWLFi1CUVERQkJCsHHjRixatAj37t1j\n04rs2717Nzw9PeHt7Y2IiAhER0dL+BF42r6jo6Oxbds2VnblypVYt26d3DoSR1onEdL19Ouvv7Lx\ne/36dXh6eqKxsRG1tbVwc3NDRkaGRLnS7US6fvPz8+XGnPS9qqGhAXV1dRg/fjy8vb3h4eGBAwcO\nyNS/rZgUxVZbKBsDstpge+RlIfJbcnKyTLs///xzibqKiYnB5s2b5fqoM2z9O/a8aKi1lYGIVjMM\nowngLMMwJwGEAniViDLaEO00GIYZB6CEiNIYhgkGwDw5rgXgEwDi+43InU998803YW1tDQDo168f\nvLy8EBwcDODpWw9dku7TB1w9PWDuXAT/8ANw6RK4W7YAmzYhuG9fYNIkcC0tARcXBI8Y0e7yL1y4\nAHt7e/j7++PTTz9FVFQULly4AEADAQHXsHv3YPz5Zy7OnHHBiBEhAICWlhZwuVzY2NggJycHH374\nIcLDw7F9+3YcPnwYr7/+OpKSktgPaO7evRv7bu9DWlUaahJrMGX6FMw3mo/54fORnZ2NJUuWYO7c\nubCysmLLs7W1xfbt23Ho0CH2ZrJjxw7069cPf/zxB95++2288cYb0NXVhUAgAJfLxWeffYaamhrU\n1NQAAPr06QMAmDVrFnx8fBAcHIwPPvgA06dPR1oau/wOp06dAofzdIud6upqZGY+XR4o8ld5eTks\nLCzYtLm5OZKSknDz5k0YGhpix44d2LRpE+Lj41FfX4+vvvoKqampcHZ2hr+/P/71r3/BwcEB58+f\nb1Ufr7/+OtLT05GSkgIulwsulyvTXgCoq6sDl8tl5e/fv9+m/dXV1bCwsGCvV11djeonG3+Lx4Op\nqSkmTpwIMzMzaGtrY/To0VBTU0NZWZlS8tLpHTt2IC0tDY2NjYiKisIbb7yBxMRE3L17Fzt27EBA\nQABee+01LF26FNu2bcOdO3ewbds2rFmzBiNGjMC7776LmJgYuLu7Izs7G3v27EFtbS2Ki4vx6NEj\n1l+JiYns/pNcLhe5ubkwfrLvpijGdu3ahTt37mD+/Pmtyh81apRS8QMAiYmJqK2tbRUfwcHBKCgo\nAIfDYevH3Nwchw8fBpfLlRs/8vxnbGyM/fv3Y82aNeBwODhw4AD27duH6dOn48iRI+ByudDV1QWX\ny8XRo0fB5XJRUlIi1z5DQ0OsWbMG//nPf6CtrQ0vLy9UVVUhKSkJ69atY68vat/Tp0/HkiVL4OLi\nAkDY+fnjjz+we/dufPvtt7hy5Qo4HA4mTZqEmJgYfPnll6z+06dPx6lTp8DlcnHjxg3WJoZhJOLX\nzs4Ot2/fZtOvv/465syZg4aGBsyePRsuLi4S/lm7dq2EvjweT6J+AWD+/Pno06cPAgIC4OfnByMj\nI9TW1iInJwf79+9HeHg4Vq1ahUOHDkFTUxMMw2D9+vUIDg5GdXV1K/2PHz+Obdu2tbJ34cKFEvev\n4OBgDBs2DMXFxRLxU1NTg7fffhtLly5VGB8i/2dlZWHixImwtLREr1694OnpCTU14Z9jZeSl46m2\nthZ79uzB+vXrsXv3bjx69AixsbEwMzNDfHw8uFwu6urqMG/ePEyZMgVxcXE4d+4cfvnlF/z555+I\njY0FwzBITU0FAJw4cUKp+60y8d4Re+SlRf/Py8tDd0Rhh4phGNsn//0RgCOACABhAPiic0R0T8lr\nFQCwFEubPzkmncdCRp6pACYyDPMaAC0A2gzD7AbwbwDWAG4wwjutOYBkhmH8iahUWoGdO3fKVU5U\ncc8jzeUyYv+X1IN7GcIu4fvCHxelAMR2GI+cD0yaJOxYaT3dr0/e9SoqKqCvr8+mDx06hP3792PG\njBlsXmPj+dDV/QPa2lGoq7NFr16O4HA47M3MxsYG8+bNAyB82sjLy8PMmTPRu3dvtozq6mrweDz4\n+/uDiMDX4SNoRhCYWgbGMMaQE0Ng7mCOKqqSWV54eDgAYMWKFThy5AgA4PHjx8jOzoa/vz+rj7Aj\nKJ/ff/8dRkZGiIyMBJfLxdmzZwEAbm5uKCh4Gm7Ozs5sZ0HcX4cOHZJI//TTT638yzAM3N3dERcX\nBwCoqqrC6dOnkZCQgOHDh6Nnz57sd69qa2vRu3dv1pfS15Nlr5GRESsjwsbGpk37Dx06xNoYHByM\n/Px8JCUltdK/srISGRkZyM/Ph46ODqZOnYqCggIJHymSl05v2LCBtSE/Px/Z2dkYPHgwLC0tERAQ\nAAD46KOPsHnzZgDAmTNnwOPx8NFHHwljhc/HjBkzMHjwYFhbW8PPzw8AWvlLOp2cnMx2eqytrfH2\n228rLF+Z+BExePBgifiWtt/U1FTimLm5OYKDg5WKH/H01q1bkZKSIqGrsbExZs2aBU1NTXZUKDg4\nmE0rsm/Lli0IDQ3FhAkT2GtVVVW1iidRewKAsrIyODk5obS0FHp6ejAzM2Pbs5+fH3sNHx+fVvqL\npjqk7ZMXvwCwfPly+Pn5QUtLCx9//LFM/0jLi9cvIBzpEo85IyMjGBkZwcbGhh0dE91bQkNDcfv2\nbZw6dQpqamoICgqSKJuIFNorfW1lYkhefIjw8vLCF198AR6Px7bBwsJCpeWl/VVbW4uvvvoKhw8f\nhpOTEwDAzMwMY8aMQXR0NMaNG4fXXnsNAKCvrw9dXV0UFxfDx8cHurq6mDFjBnbu3Nkqb1u2Khvv\n7bVHUVr8/6IOdnehrRGqHAin2MRHfU6I/Z8AKLuz7p8A7BmGsQJQBGHHbIZUnmMA3gWwn2GYAACV\nRFQC4SjUJwDAMMwrAKKIaM4TGWORMMMw9wH4ENFjJXXqEoKDOziNd/8+uJpxCI6NBWbOBEaNAl5/\nHRg3DtDTkyly/PhxzJo1i00vWbIEUVFREh2qHj3UYGg4Hebm2khNDcKAAd9KlNGzZ0/2/xwOh526\nEZ8CICJERESwT7AieDwe+l/qj/7j+iP7X9kobi4Gp5aDMyfPYMTYERLlnT9/HmfPnsW1a9fQs2dP\nvPrqqxLTRAAwbNgwiY4QIOzgxMbGYvjw4bh8+TKOHTuGEydOoL6+HtXV1ZgzZw7eeecdPHjwgJXJ\nz8+HmZn0jLPwJtRWPldXVxw8eJBNp6am4o033sAbb7wBIsLt27eRkJCAuLg4zJgxAwEBAQgJCYGH\nh4dEOfLsVVNTg0AgYPOJ+0CR/croDgCnT5+Gra0t9J7EzJQpU5CYmAgXFxel5JWxQRai0SVFsSLe\niZFGkV/E5bKysmSWD7QdP8qgyM/K1oEIeb4AINePimQ6QmhoKA4cOIDi4mJMnz79b19Dup4yMzNh\nafn0WfrRo0eoqalBc3Mz+Hw+tLRab+QujXj9Koo5WfcqBwcHpKam4sSJE4iJicGIESOwfPlyifKV\njUnRqI2iGFImBmS1wStXriA8PLzdMQQAOjo6sLS0xMWLF9kOlYODA1JSUli7R44ciZiYGERGRuKL\nL76Ampoa+2ArL29b7UUZXTtizwvL81ywBWAsgLsAsgH835NjbwFYKJZnC4QduRsQdo6ky3gFUovS\nxc7dw4uyKL2DsIsJy8qIduwgmjSJqG9fouHDiTZtIuLx2LxNTU3sQkdxfH196cKFC2x6xYoV7KL0\nqqokunLFknr31iCBoKnVGySxsbG0cuVKKi8vJ2tra/Z4RkYGDRgwgEpLS4mIqKKigng8noR8S0sL\npf2URvba9rSp7ybK/b9c+irmK1q5ciURER09epQmTpxIRESZmZmkqalJ58+fJ6KOvT3E5XLZRenN\nzc3swsiGhgby9PSkjIyMVjLK5gsICKDvv/+eiFovSh8xYgQVFhYSEdFff/1Fv/32Gy1cuJBMTU2J\nw+HQW2+9RUeOHKFffvlFpr1NTU1kYGBAFRUVxOfzKSAggPWRIpTV/dq1a+Tm5kb19fXU0tJCERER\ntHXrVjp9+rRCeXG7RMirs7y8PGIYhq5evUpERJGRkbRu3ToiUi5WiKhVjMnzi7Tczp07ZZbfHhQt\njFbkZ1nnRAtwZflPni+IiIyNjam8vJzNa21tTeXl5Qpl0tPTydHRkZWrqKho5UciyfaUnp5OgYGB\n5OjoSMXFxW3qJY5IJxHa2trU3NwsUU8uLi4S8Ttx4kT6+eefac2aNfTee++1KlNaX+n6VRRzsu5V\nRUVFxOfziYgoPj6eJk+erLRPpctUZjG3Mu1QVhvcsmWLXHlFMSRalF5XV0dBQUG0b98+IiIqLCyU\naXdjYyNZWFiQnZ0du8BfXt7OsFXZ+1JHQDdblN7lCjw3QwEqfBIwLx21tURHjhC9+SaRvj6Rjw/R\nqlW096uvyNDQkCwsLNifubk59e3bl6ZMmcKKi3eoiIgaGkqpd28OpaYOp6ysZIk3SEQ3KSKi8PBw\ncnd3p48//piIiPbv309eXl7k4eFBAwcOpGvXrrV6A0WUrs2upax/ZdG7Wu/Su67vUuWlSuLz+RQS\nEkIuLi40efJkevXVV9kOVXvf8iOS7FARCV/dHTBgANnb29NXX30lkfe1116joqKiNvOJKCoqomnT\nppGdnR25ubnR+PHjKScnh1paWsja2pq9OYnT0tJC48ePJxMTE7KysqLevXuTnp4eGRkZ0YgRIyTs\n3bx5M9nZ2dErr7xCc+fOVapD1R4bV6xYQU5OTuTu7k5z5syhxsZGhfLy7GpoaJBZZ3l5eeTk5ESz\nZ88mZ2dnmjp1qsSnJ3799dc2Y4WIaObMmRIxtmnTplZ+kSUnq3xlmTFjBpmYmJCGhgZZWFjQjz/+\n2Mp/ivws65yiuJCnq42NjURnRTytyL7du3eTm5sbeXl50dy5c4modVuVbk/u7u40YsSIdvtQWkdR\nubLqSaTb1KlTiUj49ldAQIDMTop4vUvXr6KYk3WvOnXqFHl4eJCXlxf5+/tTcnKy0j7tyBt0RPLj\nQ5k2KE9eXgyJ61hZWUn+/v50/PhxhXa//fbbFB0dzaYV5e0MW5W5p3aE7tah+kft5ad78SIijI3x\nsYUFTMSGhl8qmpuBy5eFmzj/9hvAMMIPiU6aBAwZAnCUm6ElEuD+/eUoKfkJrq4H0bev/7NR969m\nFO8oRv7mfKj1U4P5YnMYTjNEj54KX0DttqSnp2PHjh2IjY1tM29NTQ24XC4SEhKQkJCAxsZGdu3V\nyJEjodONNtxuj12AcKpk/PjxuHXrxXr59lnRXv+pUCFNZ8VQS0sLfH19cfDgQdjZ2XWSdl3DC/Vh\nz5fphycjVEuys0n34kVanJX1Qo5Ytev7IS0tRDduEK1cSeTtLRy9mjtXOJpVW6tUEaWlh+nSJQMq\nKPi2U7//Im1HS3MLlR0ro9QRqXTZ+DLdX3GfGoobZAt3Mzrjmy4tLS109+5d2rBhA40ZM4b69OlD\nQ4cOpTVr1lBqamqn+l4enfVtGqKOfRuns+hMO7oSlR3djxfdloyMDLK1taXp06d3tSqdArrZCNWL\nOQzQQUx69sR6e3uk+/mhB8PA9c8/sTg7G4Vi30F6qWAYwMMD+Owz4RY4168DXl7Apk2AiQkweTKw\naxdQXi63CAODyfD2voT8/I24ezcSAkH9s1GVw0B/gj68TnvB87QnGgobkOSUhMyITFSndN6mzN0V\nhmEwYMAALF68GCdPnkRJSQmio6NRVFSEadOmwdTUFHPnzsWvv/6Kx4+79TsXAAArKyvcvHmzq9VQ\noUKFGM7OzsjNzZV4a1FF56H0lB/DMI4A9ABUENHdZ6rVM0DWXn7FDQ3498OH2FlcjNlGRlhmaQnT\nl3UqUJqKCuD334V7DJ4+Dfj4CKcFX38dePKtLnGam2tw924k6uuz4ep6CFparfN0Nk3lTSj6oQgF\nWwugaaUJs3+ZQX+yPnqo/aOeAwAAubm57NTgxYsX4e7uzu456O3tjR49/nk+UaFCxT+b7jbl12aH\nimGYOQC+BmCIp59PKIHwLb3u9REIBSjaHLm4oQH/efgQO4qLMetJx8rsn9KxAoD6emGn6uhR4Zfb\nTU2frrvy9BSOdEE4PZyfvxEPHqyFs/Mu6OmNeS7qtTS34NFvj5C/MR8ND55syrzABOp6L+amzH8X\nPp+PCxcusJs6V1RUYMyYMQgJCcGoUaOgr6/f1SqqUKFCxTOnu3WoFD7WMgwzEsBWCL8saQ/hRzXt\nAcQB2MQwzCgF4i8Mxj17Is7eHpn+/tBgGLj/+Sfez85GQTecCuRKfwm0M9DSAiZMAH74ASgqArZs\nAWpqgDfeAGxsgCVLgHPnwAgEsLBYAlfXX3Hnzlzk5a0GUUuHLtkeO3qo9YBhqCF8LvnA7Tc31KbX\n4prdNdx96y5q02vbLuAZ80zqRAGampoYPXo01q9fj8zMTFy9ehWDBw/GL7/8Ajs7OwQEBGDlypW4\ndu2axPeA2uJ52/GsUNnRvXhZ7ABeHlteFju6G23NE/wLwKdEFEdE94mogYjukXCPv08BLH72Kj4/\njDQ0EPukY6XZowfc//wT72VlIV/OB/ZeSjgcICgIiI0FcnKA+HhAXx/46CPA2BiIiEC/c+Xwdb6A\niooE3L49CU1Nlc9NPW1fbTjvdoZfph96mvbEjZE3cGPUDTw6/gjU8s94Y1UaGxsbLFq0CEePHkVp\naSm+/PJL1NTUYP78+TA2NsbMmTOxZ88elJa22jxAhQoVKlR0Egqn/BiGKQLgSTK2cWEYxhDATSIy\nbi3Z/VA05SePksZGxD58iP8WFSHc0BD/Z2kJc03NZ6ThC8DDh8IpwSNHgGvX0DLiFeS+WY8Kg3tw\n9TiKPn3c2y6jk2lpaEHpr6XI35iP5spmmL9vDuO5xlDr2+Y2lf8IHj58iJMnTyIhIQFnz56Fg4MD\n+2mGQYMGSexvqEKFChUvEt1tyq+tDtVfRNRXwflqItJ+Jpp1Mh3pUIkofdKx+qGoCDOedKws/skd\nKwB4/Bg4cQI4ehTFDfHIXdgE+9LpMHplFWBr27Z8J0NE+OvKX8jfmI/Hpx/DaLYRzN41g5aDFrvl\nyT+dpqYmXLlyhV3cnp+fj5EjRyIkJARjx45lNxpWoUKFiheBF65DBUAHknv5sacBPFbU4epO/J0O\nlYjSxkbEPXyI74uKEGZoiOgu6FiJ79DdbeDzUXPuv7jd/An6X2qC3R+26DFhinBRu7c3u6hdnGdp\nB/8hH4XbClH0YxEAoI9XH+HPU/iv1gCtTn1TsFvWiRIUFhayo1enT59G//79MW3aNISEhCAgIADq\n6i/mov8XtT6kUdnR/XhZbHlZ7OhuHaq2/qr0AdAMoEnOT/5Opi8hhhoa+NrODnf9/dGXw4HX9etY\nlJWFB/+kNVay0NREn5B34TuWB/7M4UjbwEEDPQLCwgArK+D994EzZ4CmpuejjoUmbL+yRWBxIHyT\nfWH+L3Oo9VXDo98e4fbrt3FJ5xKS/ZJxd8FdFGwtQOWlSjT/1fxcdOtOmJqaYt68eThw4ADKysqw\nePFiMAyDJUuWwNDQEFOnTsV///tfFBQUdLWqKlSoUNHtaatDZQPAVs5PdO4fh4GGBtba2eGOvz90\nOBx4P8eOVUefKrhcLpYuXcqmKyoq4O3tDR8fH5iYmMDc3JxNNzU1ISgoSGF5VVVV2L59u8QxdfV+\ncHM/Bj2rqUgOOYrKaz8Ap04JP8PwySfCRe2zZwOHDiF44MB26a+t3b6ZZdH2CtPemYb+4/rD6lMr\nPJz/EBGcCESaRCLeLx59vPug5mYNcqNyccXkCq7aX8Xtqbexa84uOJg7wMHWAWvXrpVZfklJCWbM\nmAEHBwd89NFHGD9+PHJychTqJMtnnc3Jkyfh5OSEAQMG4Ouvv5abb/369XBzc4OHhwdmzpyJlpYW\nvP/++xg6dChqa2uhp6eHHj164PTp0/Dw8ICHhweWLVuGc+fOobGx8ZnaIKKj/vq7T97z58+HkZER\nPDw85OZR5Gdl66Atbt68CRcXF8yePRsAsGnTJom0ssjyY1vtW1mkdZLVTttbH8+jnYhor0+VtUWZ\nGJBug+Ltqj0xxOPx4O7evvWrnTk61ZauWVlZ7N8Wb29v6OjoYNOmTex5a2treHp6wtvbG/7+z2aL\ns+eGos+oA/i9qz/l3lk/oanPhrKGBvq/3FzSu3iR3rpzh/LENoHtLsTFxZGpqSlVVVW1Ordy5UqJ\nzZGV4f79+xK7sEvz6FECXbpkSA8erH+6bUp+PrVs3Uo0ZgyRtjbRuHFE339P9GSHe0W0d3PkdevW\n0cyZM9nNkQUCAbvjeWNjo8QO7kREgiYB1WTUUOHeQrLsZ0knhp0griGX7Dn2dNj/MGUQ9MZ1AAAg\nAElEQVR/kE1Fu4qoOq2aBA0CGjx4MH333Xes/M2bN+nSpUsKdWrLZ3+XtmwUUVBQQDY2NtTQINza\nZ9q0abRr1y658s3NzXTlyhX67LPPyM/Pj3R0dGjSpEn0zTffEI/He2b25Obmdshff3ebnosXL1Jq\naqrcrXMU+VnZOlAGJycnKigokJtWlmcZd9I6dWQTc2na0rczt2Fqr0+VubYyMSCvDSorL05XbvPU\nXl0FAgGZmJjQgwcP2GM2NjZUUVHRoevjBdt6Zuiz7tC9DOhraOArW1vc9feHnro6fK5fx1t374L3\nDEasOvL9kKysLPj7+yM0NBTffvttq/MkY22Z6EmTx+PBxcUFCxcuhJubG8aOHQs+n4/o6Gjcu3cP\nPj4+WLZsGQBg7969GDRoEHx8fBATcxTe3olIS/sBdnY6mD07HO5jx+KimxtcHjzA+MBAuN24gbHL\nl6NhwAD2Uw2TR42Cn58f3N3d8cMPP7TbVgDIz8/HiRMnEBkZyR5LSkqCg4MDrKysoK6ujrCwMBw9\nepQ930OtB3o79wbPlgeXABeEnA/BKyWvIGJZBNLc0qBhpIGKkxXImJGBTdqb0HCzAUOvDMXDDQ9x\nfMNxOJk7YciQIQr1kuWzyZMnt7JX+okzLi4Oq1atatPutmwURyAQoLa2Fs3Nzairq4OpqSm++eYb\nmfIcDgeDBw/GypUrkZSUhOzsbEydOhUXL16Er68v+vbtC2NjY9jY2LAjCzweD87Ozpg1axZcXFww\nbdo08MXag3isLFq0CEQEHo8HJycnREREwM3NDZGRkcjNzWX9Jc8v4nLu7u44cOCAzPKVJSgoCLq6\nuh3yc3vqQJ4vWlpasGjRIuTm5iIkJAQbN27EokWLcO/ePTatyL7du3ezT/wRERGIjo6W8CPwtH1H\nR0dj27ZtrOzKlSuxbt06uXUkjrROIqTr6Z133mHj9/r16/D09ERjYyNqa2vh5uaGjIwMiXKl24l0\n/ebn58ttN9L3qoaGBtTV1WH8+PHw9vaGh4cHDhw4IFP/tmJSFFttoWwMyGqD7ZGXhchvycnJMu3+\n/PPPsXHjRvbvSExMDDZv3izXR51lq4jTp0/Dzs4OFhYW7DEiQktLx75n2N1QvVveiehraGCNrS2W\nmptjXX4+fK5fx1QDA3xiZQWrLnwr8MKFC4iMjISpqSlGjBiBqKioNrcqEX8zLicnB/v378d3332H\n6dOn4/Dhw1i7di3S09ORkpICALhz5w7279+PK1eugMPh4N1338Xhw1cQGHgIPJ4zPvvsGr79NgFl\nZT2Rk5ODDz/8EPPmzcP06dNx6LXXEG5kBBw5gh03b6Kfvj7448fDb+1avDFlCnT19Fhdhg0bhpqa\nmlb6xsbGYvjw4QCADz74AP/5z39QVVXFni8oKJBoxObm5khKSmpVjnQ+a2drJFUnwXKZJXssMS4R\ngSmB6BvYFzVpNSg6X4Sry69CTU/t6QL4J4vgNW00WV9K+wwAduzYgX79+oHP58PPzw9vvPFGK/+L\no8j+x48fK2WjqakpoqKiYGlpiV69emH06NEYOXIkLl++rJS8gYEBZs6ciZkzZ0IgEOD8+fO4dOkS\nfv/9d7z33ns4evQohgwZgrt372LHjh0ICAjA/PnzsW3bNixdulRmrOzduxdDhw5FdnY29uzZAz8/\nP/B4PEyYMIH1F4/Hk+sXcbndu3fj4MGDrcqfNWuWUvHTFopiSdk4EyHLF/v27cP27dtx5MgRcLlc\ntnN36tQpcLlclJSU4OOPP5ZpX0ZGBtasWYPExETo6uqisrISVVVVreJO5Mfp06djyZIleOeddwAA\nv/76K/744w+5dTRr1iy2jO3bt7M6iXdAGYaRW08DBw7E66+/jk8//RT19fWYPXs2XFxcJPJItxMe\nj4ecnBy2fgH57Ub8XhUWFoZDhw5BU1MTZmZmiI+PBwBUV1e30l+eT6VjksvlthlDysSAvDYItD+G\nRGRlZSEsLAy7d++Gm5sbDh8+3Mpuf39/TJkyBXFxcSAi/PLLL/jzzz9x8uRJmT7qDFvF2b9/P2bM\nmCFxjGEYjBo1ChwOBwsXLsSCBQvatLW70laHSpNhmN2KMhDRnE7U56VA1LGKsrDAuocP4XP9Ot4w\nMMAnlpaw1vp7r/G350kbEK6VEm1FYmtrCx8fH5lBrQgbGxv2idPX1xd5eXmtRmPOnDmDlJQU+Pn5\ngYjA5/NhZGSEoUOHwtraBqNHf4zU1CD06vUlbGxsMG/evKflFRQAERHA2LHYYGSEIz//DOzcifxH\nj5Dt5AT/0FBAIAAaG3HhwgWFuv7+++8wMjKCl5fXM/sacA+NHtAw1oDpAuET5QAMALUQ6u/Voyat\nBjVpNSj+sRg1aTVo/qtZ+HahZx9UWlSipb4FAr4AHE3h9582bNiAI0eOABCOrGVnZ8PIyEjutRXZ\nf+jQIaX0r6ysxNGjR8Hj8aCjo4OpU6di7969cHNza/cCdA6HgwsXLrA2aGtrIygoCKmpqWAYBhER\nEQgJCYGjoyPOnz+PpUuXthEr1uwfzfYgLlddXS2zfECx/7oCWb4Qfb5CU1OzVXsnIrn+A4CzZ88i\nNDSU7eD069dP4sFCGi8vL5SVlaG4uBilpaXQ09ODmZkZtm7dKvca0vq0dU+ys7NDbe3THQ2WL18O\nPz8/aGlpYfPmzUr5ycrKSiIu5LUb8XuVj48P8vLyEBoaiqioKERHR2PcuHGt1o8p8ql0TAYHB3dK\nDMlqg/v27UN4eHiHyistLcWkSZNw+PBhODk5AQDc3d3x4YcfStitra0NfX196Orq4o8//oCPjw90\ndXVl5gU6t700NTXh2LFjrdalXr58GSYmJigrK8OoUaPg7OzcaWv8njdtdagIQO7zUORlpL+6Or60\ntcVSCwusf/gQvsnJmGJggHt1dbDR0nouOhw/flziqXLJkiWIiopqV4eqp9i+hhwOh526Eb+REhEi\nIiLw5ZdfSsjyeDz07t0bpqZvoU8fL5w+PRkM0wwiARiGI1He+fPncfbcOVy7dQs9e/bEq6++Cv68\neUBhIdDQABgbY5iaGqp79wa0tYVfdYfwCUf0xHT58mUcO3YMJ06cQH19PaqrqzFnzhy88847ePDg\nAatXfn4+zMzMWtlqZmbWZj5XV1ccPHhQ4hjTg0Ev+17oZd8LhlMN2eONjxpRe6MWNTdqUHWpCg35\nDbisexmadprINMlEQm4CEjYnoL9/f4yZNgZ8Ph9qamoSW8aIT5UNGzaMfXpkr/3EfmV0B4TD7ra2\nttB7MvI3ZcoUJCYmYtasWUrJi3P+/HmcPXsW165dY+ts2LBhmD17NpKTk/Hzzz8jISEBe/bswZ07\nd/Daa6+hd+/emDBhQquFx6JYkYciv4jLyYtFQLH/lB2hUuRnZetAGV07U0YRoaGhOHDgAIqLizF9\n+vS/fQ1F9QQAjx49Qk1NDZqbm8Hn86GlxL1QvH5lxZzoGrLuVQ4ODkhNTcWJEycQExODESNGYPny\n5RLlt3X/EqetGFImBmS1wStXriA8PLzdMQQAOjo6sLS0xMWLF9kOlYODA1JSUli7R44ciZiYGERG\nRmLHjh0oLi5mH2zl5e0MW0UkJCTA19cXBgYGEsdNTEwACEe+J0+ejKSkpBe2Q9XWQu6/unqRV2f9\n8AwXpStLeWMjxdy7R3oXL9L8zEy6V1fX7jLOnTundN6mpiZ2oaM4vr6+dOHCBTa9YsWKVovS+/Tp\nQ0TCBY/iC0RjY2Np5cqVVF5eTtbW1uzxjIwMGjBgAJWWlhIRUUVFBfF4vFbyWVnXyd6+N33/vQ81\nNJSx5RERHT16lCZOnEhERJmZmaSpqUnnz59/qk9hIdG33xKFhAgXtYeECNNFRTLt53K57KL05uZm\ndvFkQ0MDeXp6UkZGRisZZfMFBATQ999/T0TCOhFflD5ixAgqLCxsJSPymYAvoL9S/qId7++gETYj\nKOWVFNrTZw9pQIN+DPiR7i67S/379qf8pHyqr62ngIAA1keKUFb3a9eukZubG9XX11NLSwtFRETQ\n1q1b6fTp0wrlZdklr87y8vKIYRi6evUqERFFRkbS6tWr6ddff6UpU6YQh8Mha2treu+99+iXX36h\nO3futIoV6RhramoiAwMDqqioID6fz/pFWm7nzp0yY7E9KFoYrcjPss6JFunK8p+8dkNEZGxsTOXl\n5Wxea2trKi8vVyiTnp5Ojo6OrFxFRUUrPxI9bd8imcDAQHJ0dKTiJy+IKLqGOCKdRGhra1Nzc7NE\nPbm4uEjE78SJE+nnn3+mNWvW0HvvvdeqTGl9petXUczJulcVFRURn88nIqL4+HiaPHmy0j6VLlOZ\n+68y7VBWG9yyZYtceUUxJFqUXldXR0FBQbRv3z4iIiosLJRpd2NjI1lYWJCdnR27yF5e3s6wVURY\nWBjt3LlT4lhtbS1VV1cTEVFNTQ0FBgbSqVOnlLo20Yu3KL3bfDDrZUBPXR1f2Ngge9AgmPTsCb/k\nZETeuYN79fXP5Hq//vorPvroI1haWrI/CwsLZGdnY8OGDQplxaclZU1R6unpITAwkH2d3tnZGV98\n8QVGjx4NT09PjB49GsXFxa3kNTT0oaVlAy2tAUhO9gWf/5A9N3bsWDQ1NcHV1RWffPIJBg8eLKmD\niQmwcKHwC+35+cCbbwJcLuDsDAQGAl9/Ddy9K9MeDoeDLVu2YPTo0XB1dUVYWBicnZ3Z8+PGjUNx\ncXGb+UT89ttv+N///gd7e3vMmzcPn3zyCYyNjUFEyM3NZZ88pX02ZMgQePl5YfUvqzEzbiY0nDQw\nq2wWDo86jMGDB6P/hP7Q0NLAQuuFGBQ4CIO1B8Mw1xCPjj9C4beFqLpaBUGt7A2PlbXR398fU6dO\nhbe3Nzw9PUFEWLBggUJ5eXYpqjNHR0ds3boVLi4uqKysRFRUFEJDQ3Ho0CHs27cPPXv2xMGDB7Fg\nwQJ4e3tj9uzZKC8vx507d0BErL9EMaampsZOF40ZM0bCNvEYs7KywurVq2XGojKEh4cjMDAQWVlZ\nsLS0xI4dOyT8p8hPss45/T97Zx4fV1nv//d3tuyTrUnTpm1SWrrTRaQLIAQKWhAKsolCevF6xeWq\nuNyr+APF671ekesCiop6vVybq0IpyI4iSFygUKC0adOdNm2TtE3SNtskmfX5/XHOTCeTmSxtkjkz\nfd6v13nNeZ7znJnnO+tnvt/v+T5z5iR8/ubOnZtwrrGfu3B7sHPmzZvHXXfdxcUXX8ySJUv48pe/\nPOCzGnvf8+bNo6uriylTpkTCeoM9Rrw5RWO32/u9TtOmncw/rKmpweVycfPNN/PVr36Vt956a0Bo\nPvZ1j32cIb8nYqirq2Pp0qUsWbKEb33rW9x9990jek5HmqIx2PtjsM/g7bffnvD8wd5DYbKysnj2\n2We5//77efbZZ9m6dWtcu51OJ0uWLOGmm26K2JZo7GjYCtDT08NLL73Edddd1+/8o0ePcuGFF7Jk\nyRKWL1/O1Vdfzfvf//4RPd9WYqhK6T9TSn16HOczZoxGpfTR5rjfz/2NjfykqYlrJ0zgrooKzhqn\nUKAVaG19nN27P8X06d9h8uR/GvqERPh88Je/GGsMPvkkuN1GlfarroIFCyA/f/QmPQT19fU8/PDD\nfO973xuV+/O3+/HUeYzcrC1GflbPjh4ypmX0q/6euzgXV5lrzJbZGaldBw4c4KqrrmLr1q3DGt/R\n0cHLL78cqdzucDgiaw5eeuml5Obmns70k85ovy80Zx6j9R4K1+hbv349M2bMGKXZJQerVUofSlD9\ni1Lqe1Hty5VSf4pq/0Ap9aX4Z1sLKwqqMCeihNVqU1jNOEOElcezk/r663C7z+fssx/Ebj/NqyFD\nIXj7bUNY/eEPhscqKwtmzICZM40ten/ChLhL41iZkD9Ez86eiMAKb2KTAVcZZs0e3WV2hkv4Cr26\nuroRn6uUor6+PiKuNm7cyLJlyyICa968eXp9Ro3mFNixYwdXXXUV119/Pffdd1+yp3PapJqg6rc4\nsogcV0oVJTpuZawsqMKc8Pt5oLGRB5uauHrCBO6aNo2Z2dn9xqTLGkzRdgQCXeza9XH6+vYzf/56\nMjMrRu+BlIKjR+Hdd2HvXmOL3g8E4gutGTOMCu9DlJeItSVZKKXwNftOCixTbHkbveTMzyFnUc5J\nobUwF4d74PUoVrAjHl1dXbzyyiuRRZ1DoVBEXK1cuRK3u/9XkFXtGCnaDuuRLrakix1WE1RDXeUX\nO9Gh2prToNDp5JvTp3PHlCk80NjI8k2bEgqrdMLhyGPevEdpbPwBb7+9jLlzaygqunx07lzEWPKm\nrAziFd48fvykwHr3Xfjb3+Dhh439jg4466z43q1p08BhnTJuIkJGeQYZ5RkUf7A40h/oDuDZ6okI\nraM1R/Fs8+Aqcw1YNNqqfzjy8vJYvXo1q1evRinFrl27eOGFF3jooYdYs2YN5557LldccQWrVq0a\ndLkYjUajGUu0h8rCtPv9PNDUxI8bG7mquJi7KyrSWlgBnDhRy44dH6W8/HNMm/ZVRMY/XBWhuxv2\n7Yvv2Tp6FKZOje/dmj4doi7fthoqqOjZ0xMRWZ4thuAKeUMD8rKy52ZjcyXxNRgCj8dDbW1txHvV\n19fHihUrmD59OpWVlVRWVjJ9+nQqKioGLcmg0WhSD6t5qIYSVF3AQk56ojYBS6LaW5RSw161VkRW\nAfdjLMr8K6XUgJUUReRHwBWAB7hNKbU56pgNeAtoVEqtNvsKgUeBCqABuEkpNaCSXSoKqjDtfj8/\namriR42NfNAUVmensbDq62tk+/YbcTonMnfur3E4xi+pfNh4vbB//0Ch9e67cOCA4RGLF0acMQMs\nmmDtO+obkJfVt7+PrNlZ/b1Zi3JxFjmTPd247Nmzh02bNtHQ0EBDQwP79++noaGBAwcOkJeXFxFZ\nYaEV3q+oqCA7jT9TGk06kmqCKoRR3DPRhJVSyj6sBzLE0G5gJdAMvAncrJTaGTXmCuCzSqkPisgy\n4AGl1PKo418EzgXcUYLqu8AxpdR9IvJVoFApdWecx09ZQRWm3e/ni48/zrNTp3JlUVFKC6uhYvih\nkI+9e7/EiRMvMn/+E+TmLhi/yY2QAbYEAnDwYPy8rX37jKsO44URZ8yABJdEJ8UOINgbxLOt/1WG\nni0eY5mdKE9W7uJcMiszEVvyv9vi2REKhWhpaeknsqK3cMXqaJEVvVVUVAyrAOVY25GKpIsdkD62\npIsdVhNUgyaBKKVG09e/FNijlDoAICKPANcAO6PGXAOsNR/7DRHJF5GJSqmjIjIFuBL4NvClmHMu\nNvd/DdQCAwRVOlDgdPIPZWXcv2wZP2ps5Px33uEKU1jNSlFhlQibzcWsWQ9y5Mhatmy5hJkzf8zE\niTcne1rDw+Ewcq/OOgsuj8kFC4Xg8OH+Quv3vz/ZdjgSJ8lPnDjuVyTas+y4z3PjPu9kZD+8zE44\nVHjkYXOZnY5AP5GVsyiHnAU5kWV2konNZqOsrIyysjKWL18+4HgoFOLIkSP9RNamTZt4/PHHaWho\n4NChQxQWFib0cE2bNo3MJK7XqdFoks+gHqpRfSCR64EPKKVuN9u3AkuVUp+PGvMM8B2l1Gtm+yXg\nK0qpTSLyGIaYyge+HOWhis3r6teO6k95D1UsHYEAP25s5IGmJlaZwmp2mgkrgK6uzdTXX8+ECas5\n66z7sNmsGW46bZSCtrb4YcS9e6G3d6BHKyy4pkwZ1hWJY4n/mP9kyNC87d3dS+aMzP7erEW5uEpd\nSZ3rSAmFQhw+fDihh+vQoUMUFxcn9HBNmzat37IoGo3m9EkpD5WIvIIR8kuEUkqtHN0pxZ3HB4Gj\nSqnNIlLF4FcXJpzvbbfdRmVlJWAsGrp48eKI2zNcrTfV2ndXVfH5KVP44vr1LH3pJa5euZKvV1Zy\n2FzxO9nzG412Xt5iurvvp77+23R1rWTevHVs2LDTMvMb9XZJCbVeL0yZQtU3v3nyeHc3VZMnw969\n1L74ImzdSpXHY7RbW6GsjKpFi2DGDGqVgvJyqj70IaispPbVV8dn/pdWUXhpYaR90YqL8Ozw8OIj\nL9L7Ri/zn5uPZ4uHzbbNZM3M4pKVl5C7KJdN3k1klGdwycpLkv/8x2mHF4mtqqriggsuoLa2lgsv\nvDBy/OWXX+bYsWNMmjSJhoYGXnnlFd555x28Xi/79+/n0KFDFBQUMHv2bCorKxERysrKWLVqFZWV\nlezbtw+n02kZe3Vbt63YDu83NDRgRYbKofp4gkPlwOeBbKXUsFwiIrIc+KZSapXZvhNDkH03asxD\nwCtKqUfN9k6McN4dwK1AAMgC8oAnlFJrRGQHUGWGBcvM8wesFSIi6kt/+BLVi6pZNHFRyhYGrB0k\n9t0ZCPDjpiYeaGzk/YWF3F1RwRyLXtk0mB2JUCrEgQP/TnPzL5k//1Hy8+OUQUgCp2LLqNPTc/KK\nxFjvVlOT4cGKl7d11llG4dNxtEMphfegd0DNLF+Lj9xzcvvXzDonF3vOyEKGlng9YggEAjQ3Nw9I\nlg9vzc3NlJaW9gsl9vX1RQTX1KlTcTpT0zNrxdfjVEkXW9LFjpTyUCmlfhXdFpFi4GvAJzCurPvW\nCB7rTWCmiFQAh4GbgY/EjHka+GfgUVOAtSuljgL/z9wQkYsxQn5ros65Dfgu8A/AU4kmkO3M5tpH\nrsWd4aZ6YTW3LLyFyXmTR2CCtXE7HNxVUcHnyst5sKmJizZv5vLCQr5uYWE1EkRsVFbeQ17eeWzb\n9iEqKu6mvPxzKSuOR5XsbGOZnQVxkvd9Pmho6C+0/vxn47ahwagWP3Mm5OTAhg39RZd79KuiiAiZ\nFZlkVmQy4ZoJkf5AR4DuOkNcdb3ZxeFfHjaW2ZmaMaBmlmvS2C2zMxY4HI7IepoXXXTRgOOBQIDG\nxsZ+Iquuro6NGzeyf/9+jhw5QllZ2YBQYlh8TZkyBYeF6qJpNGciw8qhEhE38K/AZ4FnMTxN7474\nwYyyCQ9wsmzCvSLySQxP1S/MMQ8CqzDKJnxMKbUp5j7CgiqcQ1UErAOmAgcwyia0x3lspZQipEL8\n/eDfWbtlLU/seIL3Tn4vaxat4UNzPkSOK/VFRzRdgQAPNjXxw8ZGLjOF1dw0EFYAvb372LbtOnJy\n5jN79i+w29PDrnEnGDQWmk6Ut5WTEz9BfuZMKC4e8yT5kD9Ez66efvWyujd3gzAgAT57TnZSltkZ\nD/x+/wDBFe3lOnr0KJMmTRqQLB/eysvLteDSpB1W81ANFfLLAr4AfBnj6rl7lFL14zO10SVeUnqv\nv5endz1NTV0Nrx56ldWzV1O9sJpLKi/Bbkv+lUmjRVcgwE+amvhBYyMrTWE1Lw2EVTDYw+7dn6K7\nezPz5z9BdvbMZE8pvVAKjhxJvGyPUonXSJw0aczEVmSZneiaWVu68R7ykj0vm+xZ2ThLnbgmunCV\nunBOdBq3Zp89K30+22F8Ph+NjY1xE+b3799Pa2sr5eXlCT1ckydPxm5Pv+dFk96kmqA6iuFN+i+M\ngpoDUEr9eWymNroMdZXf0e6jPLLtEdbWreVo91FuOecW1ixaw/zS+eM4y6E5ndh3WFj9sLGRS5Ms\nrEYrhq+Uorn5IRoa7mH27F8xYcLVpz+5EZIu+QgjtuP48fhC6913obNz4NWI4dupU8dk2Z5AdwBP\nnYeXn3uZ84rPw9fiw3/Uj6/Fh++oD3+LH99RHzaXbYDIit6P7nMUOpJWW2s031der5dDhw4l9HC1\ntbUxZcqUuCUhKisrmTRp0ikLrnT5fED62JIudlhNUA31rdaLcdXcpxMcV8BZozqjJDExdyJ3LL+D\nO5bfQX1LPTV1Naz6zSpKsktYs2gNH1nwESbmTkz2NE+LPIeDOysq+Gx5OT9pbuaSzZu5pKCAr1dW\nMj9FPVYiQnn5p8nNXcz27TfR1bWRyspvIqL/bY85RUWwdKmxxdLVZQirsNB6+21Yt87Yb2kx1kKM\n59mqrDzlZXscuQ7yz8+nyFfE1KqpcccopQh2BvuLLFN49ezsof0v7f36gt1BnCXOIYWXs9QQaLYM\na4YcMzIymDlzJjNnxvfi9vX1cejQoX4i6/nnn4/sHz9+nKlTpyb0cJWVlWGzWdN2jWa8GLc6VMnm\nVOpQBUNBahtqqamr4aldT3H+1POpXljNNbOvIcs5vlWTx4LuQICfNjfz/UOHqCoo4BspLKwAfL6j\nbN9+MyIu5s37LU5n8dAnacafvr6Ty/bEercOHTLChYmW7Rnn92fIFzLEVZTIirSP+vr3tfqxZduG\nFl5mnyPfkTKJ9b29vRw8eDBuOLGhoYH29namTZuWMIdr4sSJWnBpRh2reai0oBomHp+HJ3c+ydq6\ntbzZ9CbXzb2O6oXVvK/ifdiSuYDvKBArrL5eUcECi643NxShUID9+79Ga+t65s9fT17eucmekmYk\n+P3Gsj3xwoj79kFhYeJlewoLkzp1pRSBE4GhhZfZF/KGhiW8XKUunCVObE7rfs/09PREBFe8PK7O\nzk4qKiriFj2dPn06paWlKSMuNdZBC6okMZqV0pu7mvnt1t+ydstaOr2d3LrwVqoXVjN7wuxRuf/B\nGMvYd3cgwM+am/neoUNcXFDAN8ZQWI11DL+lZT27d3+SvLz3Ulp6ExMmXDtmHqt0yUewvB2hkFFT\nK1GSvMsFM2dSm5FB1axZRkiysNDY4u3n5ye1unywN4i/1d8vvytacL228zUW+Rfha/EROBbA7rb3\nE1nRIiy2z55rt4xAqa2t5bzzzuPAgQMJPVwej6ef4Ir1cpWUlFjCHst/RoZJuthhNUGlr6M9BSbn\nTeZfzv8X/uX8f2HLkS3U1NVQ9esqpuVPY83CNXx4wYeZkD1h6DuyGLkOB/86bRqfKS/nZ01NXLZl\nC+8zhdU5KeaxKi29gaKiVRw//jwtLevYu/dLuN0rosRV8hYh1pwiNpuR0D51KjGj3X0AACAASURB\nVMT+GCgFra2GsHrxRZg8GU6cMLb9+0/uHz9+cr+ryxBVg4mu6P3ovtzc076K0Z5lxz7NTua0+GsA\nttS2cF7VeYZ5IYX/mD8SeowWYV1vdg3oQzEs4eWa6MJZ7ETsY/ublJOTw7x585g3b17c493d3RHB\nFRZZGzdujAiv3t7euN6tsPgqLi62hODSnNloD9UoEQgFeGnfS9TU1fDc7ueoqqyiemE1V826igxH\naq7h5QkGeai5mf86eJAL8/P5RmUlC1NMWIUJBLo5fvw5WlrWceLES+Tnn09JyY1aXJ3JBALQ0TFQ\naA1n3+sdvhCL3c8a+/zLoCc4QGQNEGNmGDLQHsBR6BhaeJl99uzxv+Cjq6trgHcrWnx5vV6Ki4vJ\nz8+noKCAgoKCIfej+/Q6i6mJ1TxUWlCNAZ3eTp7Y8QRrt6yl7mgdN8y7gTWL1rBiyoqU/BcVFlbf\nO3SIC9zulBZWYIirY8eepbX1MU6c+BP5+RdQUhL2XCU3D0eTIvh8IxNg4f3jxw1P26mKsTFYfiYU\nCOFv8w9IvI8VXuE+m9M2LOHlmjh+ZSe6uro4ceIE7e3ttLe309HRMaJ9m812SkIsvJ+bm5uS3+2p\njhZUSWI8BVU0BzsO8pu631BTV4Mv6KN6YTW3LryVGUUzTun+khn77gl7rA4d4nxTWC06RWFllRh+\nINDFsWPP0doa9lxdaIqra4Ytrqxiy+mi7RgHlILe3mEJsNq9e6my2U72t7dDZuapibH8fBiFwp1K\nKYJdwWEn3ge7gmzN28qyacv6C6+ogqvRfckoO6GUoq+vb4DQiie+du3aRWZm5oD+vr4+3G73iIVY\neD8/P39cK9lb+jMyAqwmqHQO1RgzLX8aX3vf17jzwjt5+/Db1GypYcWvVjCreBbVC6u5af5NFGal\nhlck227nS1On8qnJk/l5czOr6upY4XbzjYoKFuflJXt6p4TDkcfEiTczceLNprh6ltbWdezdewf5\n+RdSWnoTxcXX4HQWJHuqmnRAxFh3MTsbyssHH1tb2z9XTCkj72swMXbwYPz+ri7Iyzs1MZaXF8kX\nExEcbgcOtwOGsTBByBfC+5SXOTPnDEi892z3DOizZduGJbycE0ev7ISIkJWVRVZWFpMmTRp0bCIh\n4vf76ezsHFSUHThwgC1btsQVax0dHWRnZ49YiEXvZ2Zmai9ZktEeqiTgD/r547t/ZO2Wtbz47otc\ndtZlrFm0hlUzV+Gyu5I9vWHTEwzyi+Zm7jt0iGVuN/eksLCKJRDo5NixZ2lpWUd7+5/Jz7+I0tIb\ntbjSpCbB4Ml8sZGGKvv6oKDg1MRYVtawk/eVUgTaA3GveIzXF+o7WXbCWeLE4XZgz7Vjz7Fjy7FF\n9uPd9jueY8eWZUuqGAmFQnR3dw8Znkx0vL3dWL72VIRYeD83NzflaoVZzUOlBVWSae9r57H6x6ip\nq2Fn204+PP/DVC+q5rzJ56XMv41YYfWNigqWpImwgrC4eoaWlseixNVNFBev1uJKk/74/Ua4caSJ\n+8ePG6UuRponFt53Df7nMtjXv+xEsDtobJ7+tyFPaGB/zLGQL4Q9O47YihJdCY8NJuJy7GN+BWWY\ncNjyVEVZT09PJGx5KqIsPz8f5xjk+A2GFlRJwqqCKpr9J/bzf3X/x9q6tdjFHsm3qiioiIyxcuy7\nNxjkF4cP892DB1mal8c9lZUJhZWV7RiMk+JqHe3tr1BQcDF79pzD6tVfxeHIT/b0TotUfU1i0XZY\niL4+ap97jqo5c0YuxlyukQmw7GzDI5aVdXLf5RqWh0wFVT+hFVeMeYL8fcvfWVq2NLFQM28jx3qC\n2Fy2k2JsmB6zhMei+12n7k2K994KBAKRsOWpiLKOjg4yMjJOyTsW3s/KyhqRI8FqgkrnUFmI6YXT\n+frFX+fui+7mjaY3WLtlLef+4lwWlC5gzaI13DDvhmRPcVCy7HbumDKF2ydN4peHD3PV1q281xRW\n70kTj5XD4WbixFuYOPEWAoEO2tqe4c03f8qGDQ9SUFBlJrSvxuFwJ3uqGk3yycyE4mKYP8JF5pUC\nj2dw0dXY2D+E2dNjbL29J7dAwJhDWGjFCi5zk6wsHOYWeyyylWUxsesA05dPin88O3uAgFNKEeoN\nDSnUwvuBjgC+Zt/QQq07CDC80GacYx37OzgeOD5AxOXn5FM4vfCUoiNKKTwez6BC7Pjx4+zfvz+h\nQAsEAiMSYlZDe6gsjjfg5fk9z1NTV8Of9/+ZK86+gjUL13D5jMtx2Kyth3uDQf778GHuPXgw7YRV\nLIa4eprW1nW0t/+FgoJLzDpXWlxpNEkjGDRywHp7B4qt6G20jg1TwI3GsZAjk6DfTtATGlF4M55Q\nixz3BAn1hbBlj2LYM+qYzTG4V83r9Y6o5MWzzz5rKQ+VFlQpxLGeY6yrX8faurXsP7Gfj57zUaoX\nVrO4bLGl8636gkF+aQqrc01hdW6aCisAv7+dY8eeprX1sYi4MnKurtbiSqNJZ05XwI30nNEWcFlZ\nqIwsgpJFUGUSDLkIBl2Egi6CAQdBn+2kgBuhUAt6gohTRjXsmVWRpQVVMkgHQQUnY997ju2hpq6G\nmroacl25VC+s5pZzbqHcPcSl2EmkL8pjNXXnTn58ww281536AmOwXJewuGppWUdHx99ixJW1RGVa\n5Oyg7bAa6WIHWNCWsIAboRCr3bGDqokTR0/ADUPEqcwsQo5sgrZsgpJNUGUSIvOkcAs4zc1B0Gcn\n5LMR7IVgT3zBFvKEOL/xfEsJKmvHjDQJObv4bL51ybf4ZtU3efXgq6zdspZzfnYO504+l+qF1Vw3\n9zpyXdaqZp5pt/PZKVP4p0mT+GpTE9du28bi3FzuqazkvDQQVvFwOgsoK1tDWdkaU1w9xdGjv2H3\n7k9RWLiSkpIbLSmuNBpNCmC3Q06OsY2E2Bpnw+UUBRy9vUhrC/aeHuyjIeAKsmByNjSO3ISxRHuo\n0ohefy/P7H6Gmroa/nbgb1w9+2rWLFzDpdMvxW4b//W3hqIvGORXR45w78GDLMrJSWthFYvff4K2\ntqdobV1HR8ffKSy8zBRXV2lxpdFoNGAIuEQCrLcXWbnSUh4qLajSlBZPC49se4S1W9ZyuPswt5xz\nC9ULqzln4jnJntoAvKEQvzp8mO8cPMhCU1gtPUOEFYDff9wUV4/R0fGq6bm6yRRX1vIyajQajVWw\nWtmE1CqLqqG2tnZY40pzSvn8ss/z1u1v8afqP2EXO1f+9kqW/HwJP9zwQ450HxnbiQ5BtB0ZNhuf\nKS9n77JlXFVczPX19VxZV8cbnZ3Jm+AIGO5rkgins4hJkz7GwoXPs3z5foqLr+bo0V+zYUM527Zd\nT0vLowQC3aMz2UE4XTusgrbDWqSLHZA+tqSLHVZDC6ozgHkl8/jOZd/hwBcO8P33f5+6ljrm/mQu\nV/7mSn639Xf0+HuSPUXAEFafNoXV6uJibqyv54q6Ol7v6Ej21MaNk+LqBVNcfZDDhx82xdUN4yau\nNBqNRjMydMjvDMXj8/DUrqdYu2UtG5s2cu2ca1mzaA0XVVyETayhs72hEA+bocB5OTncU1HB8vzU\nrkZ+qvj9x2hre5KWlnV0dr5OUdH7zZyrD2K3jzAhVaPRaNIAq4X8xlVQicgq4H4Mz9ivlFLfjTPm\nR8AVgAe4TSm1WUQygL8CLowrE9crpf4t6pzPAZ8BAsBzSqk749yvFlQJONx1mN9u/S01dTWc6DvB\nrefcSvWiauZMmJPsqQGGsPrfI0f4zwMHmJudzT2Vlaw4Q4UVgM/XRlvbk7S2PmaKqw+Y4upKLa40\nGs0Zg9UE1bi5IkTEBjwIfACYD3xERObEjLkCmKGUOhv4JPAQgFLKC1yilFoCLAauEJGl5jmXAFcD\n5yilzgG+N04mJYWxiH1PypvEl8//Mps/tZlnPvIMvqCPS399KUt/uZQfv/FjWj2to/6YI7Ejw2bj\nk5Mns2fZMq4rKeHm7dv5wJYtbLBIKHC88xFcrglMnvxPLFr0R5Yte5fCwvdz+PAvee21ydTX30RL\ny3qCwZGHcdMlr0LbYS3SxQ5IH1vSxQ6rMZ6xnaXAHqXUAaWUH3gEuCZmzDXAWgCl1BtAvohMNNvh\nX4gMDC9V2N30KeBepVTAHNc2plakOQsnLuS/3v9fHPriIf7j0v/gjaY3OPvHZ7P6d6tZv309fYG+\npM3NZbNxuymsbigp4SOmsHrNIsIqGZwUVy+a4upyDh/+Oa+9Non6+g+fsrjSaDQazcgYt5CfiFwP\nfEApdbvZvhVYqpT6fNSYZ4DvKKVeM9svAV9RSm0yPVxvAzOAnyilvmaOeQd4ClgF9AL/qpR6K87j\n65DfKdLl7eKJHU9QU1fDO0fe4Ya5N1C9qJoLpl6Q1CVvfKEQvz5yhG8fOMAsMxR4wRkcCozG52ul\nre33tLSso6vrTYqKrqC09EaKiq7Abs9O9vQ0Go3mtLFayC9lKqUrpULAEhFxA0+KyDyl1HYMGwqV\nUstF5DxgHXBWvPu47bbbqKysBKCgoIDFixdHlhEIu0B1e2A7LyOPivYK7p52NzOumcFvtv6GW39w\nK76gj09c9wmqF1XTWNeYlPl9oqqKfygr464nnuD6V1/lnAsu4JuVlfjfeccyz18y2q+9Vg/Moqrq\nJXy+Fp5++l42bPhPZs/+OEVFq9izZx55ectYufIDlpivbuu2buv2UO3wfkNDA5ZEKTUuG7Ac+ENU\n+07gqzFjHgI+HNXeCUyMc19fB75k7r8AXBx1bC9QHOcclQ688soryZ6CUkqpUCik3mp6S93xwh2q\n9L9K1Yr/XqF+9ubP1LGeY8M6fyzs8AWD6r+bm1Xlhg1q5TvvqFeOH1f+YHDUHycWq7wmw8HrPaqa\nmh5S77xzqfrrX/NVff3NqqXlCRUI9KSUHYOh7bAW6WKHUuljS7rYYf6uj5uOGWobzxyqN4GZIlIh\nIi7gZuDpmDFPA2sARGQ50K6UOioiE0Qk3+zPAi7HEFsATwKXmsdmAU6l1LExt+YMR0Q4d/K53L/q\nfhq/2Mhd77uL2oZapj8wnevXXc+TO5/EF/SN65ycNhsfnzSJ3UuX8pGJE/nc3r0Uvfoql27ezF37\n9vFsWxvH/P5xnZPVcLlKmTz5kyxe/DLLlu0mP/9impp+zGuvTaKh4d9pbf09wWBvsqep0Wg0KUcy\nyiY8wMmyCfeKyCcxVOYvzDEPYuRDeYCPKSN/6hzg1+Z5NuBRpdS3zfFO4H8wrv7zAl9WSv0lzmOr\n8bT1TKWjr4P129eztm4t21u3c9O8m1izaA1Ly5cmJd/quN/PG52dbDC3jZ2dlLlcrHC7WZGfzwq3\nm/k5OdiTmAtmBXy+o7S2PkFr6zq6ut6huPiDlJTcSFHRKuz2zGRPT6PRaAZgtRwqXdhTM2Y0tDfw\nf3X/R01dDQDVC6u5deGtVBZUJm1OQaXY7vHwWmcnGzo62NDZyRGfj6VutyGy3G6Wu90UOp1Jm2Oy\n8XqP0Nb2BK2tj0XEVWnpTRQWfkCLK41GYxm0oEoS6SKoamtrI4l6qYJSio1NG1m7ZS3rtq9jXsk8\nlvqWcveau8nPTP5VeW0+H69HebHe7OpiakZGRGCtyM9nbnY2tgRerFR8TeIRz46wuGppWYfHs4Wi\norC4er9lxVU6vx6pSLrYAeljS7rYYTVBlTJX+WlSFxFh2ZRlLJuyjB+u+iHP73meH/zuB1TcX8Gq\nmauoXljN+2e8H6c9OV6hCS4XV02YwFUTJgAQCIXYZnqx/tLRwb0HD3IsEGBZXl4kTLjM7Sbfkf4f\nn4yMMsrLP0N5+Wfweg/T1vYEhw59n507/4Hi4qvMsOAHsNkykj1VjUajSSraQ6VJGsd7j7Oufh1r\nt6xl34l93LzgZtYsWsOSsiVJrW8Vjxafz/BgmWHCt7u6qMzM5HxTYK1wu5k1iBcr3fB6D9Pa+jit\nrevweLZSXHy1Ka7er8WVRqMZF6zmodKCSmMJ9hzbE8m3ynJmsWbhGm5ZeAtT3FOSPbW4+EMh6jwe\nXjMF1obOTjoCAZa73ZxvhgmX5uWRdwZ4sbzeZlNcPRYlrm6iqOhyLa40Gs2YoQVVkkgXQZUuse9E\ndiilePXQq9RsqWH9jvUsKVtC9cJqrpt7HXkZeeM/0WEQtuWw18vrnZ2RhPfN3d3MyMrq58WamZVl\nOe9bmNF4b50UV+vweOopLr7azLm6HJvNNToTHYJ0/4ykGuliB6SPLelih9UEVfr/fdakFCLChdMu\n5MJpF/LAFQ/w7O5nWbtlLXf84Q6umnUVaxatYeX0ldht9mRPdQCTMjL4UEkJHyopAYylcTZ3d7Oh\ns5Pnjh3j7v376Q2FWG6Kq/Pdbs5zu8mxW8+WUyUjYzJTpnyOKVM+h9fbRGvr4xw48B127KimuHg1\npaU3jqu40mg0mvFCe6g0KUGrp5VHtj1CTV0NjZ2N3HLOLVQvqmbhxIXJntqIaPJ62dDRYXixOjup\n6+5mdna2IbBMT9b0zEzLerFOlb6+RtraHqelZR09PTuYMOEaSkpupLDwMi2uNBrNKWE1D5UWVJqU\nY2fbTmq21FBTV0NRVhHVC6v56DkfZVLepGRPbcR4QyE2dXWxobMzko8VUCpSruF8t5v35uWRlUZe\nrL6+Rlpb19Pa+hg9PTuZMGE1JSU3UVi4UosrjUYzbLSgShLpIqjSJfY9GnaEVIi/Hvgra7es5fc7\nf8+y8mWsWbSGa+dcS7Yze3QmOgxG8zVRSnHI641cUfhaZyf1Hg/zcnJO1sVyu6kYAy9WMt5bfX2H\nIjlXPT27TM9VWFydWhkN/RmxFuliB6SPLelih9UElc6h0qQsNrFRVVlFVWUVD175IE/tfIqauhr+\n+fl/5to51/LRBR/lvPLzKMgsSPZUh42IMC0zk2mZmXy4tBSA3mCQt00v1mOtrXxx715sIv0Kj56b\nm0tmCnqxMjOnMnXqF5g69QumuFrPgQP/xo4dtzBhwrVmWPDUxZVGo9GMF9pDpUk7jnQf4Xdbf8f6\nHeupO1qHO8PN/JL5LChdwPyS+cwvnc+8knm4M9zJnuopoZSioa8vUq7htY4Odvb0cE5OTqTw6Aq3\nm6mZ1qxkPhz6+g5GhQX3MGHCtZSW3khBwaVaXGk0GsB6HiotqDRpjVKKgx0HqW+tp76lnm2t26hv\nqWdH2w6Ks4r7iaz5JYbQynHlJHvaI8YTDPJWV1ek8OhrnZ1k2mz9woRL8vLIsNmSPdUR09d3gNbW\n9bS0PEZv715TXN1EQcElWlxpNGcwWlAliXQRVOkS+062HSEVoqG9gW0thsCqbzW2XW27KMstiwis\nsNiaO2EuWc6suPeVbFvioZTi3d7eiBdrQ2cnu3t6WJybGwkTrnC7mZxxsvCmFe2I5aS4Wkdv77uU\nlHyIkpIb+4mrVLBjOGg7rEe62JIudlhNUOkcKs0ZiU1snFV4FmcVnsXq2asj/cFQkHdPvBsRWc/v\nfZ77XruPvcf3MsU9JSKyFpQuYH7pfGYXz06iFYkREWZmZzMzO5vqsjIAugIB3jRzsR4+fJjbd+0i\n126PlGtweTxcEArhtLAXKzOzgqlTv8zUqV+mt7eB1tb17N9/N319+5kwwRBXoZBlvl81Gs0ZhPZQ\naTTDwB/0s/f4Xupb6w2vlhlC3N++n4r8CuaXzmdByYKIZ2tW8aykLfY8XJRS7O7tjYQJN3R2sq+3\nlyV5ecbyOaYna6LL+qUMenv3mzlX6+ju3kpmZiXZ2XPIzp4d2bKyZuNyTUj2VDUazShhNQ+VFlQa\nzWngC/rYfWz3gNDhwY6DzCicMSB0OLNoJg6bdR3DHYEAG6PChK93dlLocPQrPLowJweHhb1YwWAv\nvb176enZRW/vLnp6Tm4idlNgzYmILON2hq6BpdGkGFpQJYl0EVTpEvtOFzsgvi19gT52tu2MiKyw\nV+tw12HOLj67n8haULqA6QXTk76cTjw7Qkqxs6cnUhdrQ2cnB71ezs3NjQis5W43JRbyYg22TqTP\nd3SAyOrt3UVf30EyM6eRnT0nIrLCm9NZmpTK9enyGUkXOyB9bEkXO6wmqKz7V1mjSWEyHZksLlvM\n4rLF/fp7/D3saN0REVm/3PRL6lvqae1pZXbx7AGhw4qCCmySPG+QTYR5OTnMy8nh45OMSvQn/H7e\nMD1YP25q4tYdOyh1uSJXE56fn8+CnBzsFls+R0TIyCgjI6OMgoKL+x0Lhbz09r4bEVmdna9x5MjD\n9PTsRKlQ3PBhVtZM7PbULU2h0WhGF+2h0mgsQJe3ix1tOwaEDk/0nmBuydyT5R1Mr9ZU91TLrPcX\nVIrtHs/JKwo7Omj2+TgvLy8isJa73RQ5rZ1TFg+lFH5/W9zwYV9fAxkZkweED7OzZ+NyTbLM66PR\npCtW81BpQaXRWJj2vna2t27vJ7K2tWzD4/MMyM9aULqASbnW+CE/5vfzelSY8M2uLia7XJFyDee7\n3czLycFmgbmeKqGQn76+fQPChz09uwiF+gaILKN9Nnb7+C2LpNGkM1pQJYl0EVTpEvtOFzsgObYc\n7z0+ID+rvqUef8g/oLTD/JL5lOYMnQc0lnYElWKbxxNZAHpDZyetPh9LowqPLne7KRgFL5YV3lt+\n//EBIsvwau3D6SztF0IMi66MjCn9XiMr2DEapIsdkD62pIsdVhNUOodKo0lBirKKeF/F+3hfxfv6\n9bd4Wk56s1rqeWz7Y2xr2YZNbAPys+aXzmdC9viUEbCLsCg3l0W5uXy6vByAVp+P182q7vcePMhb\nXV1UZGb2Kzw6Jzs7Jb1YTmcR+fkryM9f0a8/FArQ19cQEVnd3XVmBfhdBAJdZGfPiois48eDdHW5\nycqahcORmyRLNBrNcNEeKo0mzVFKcaT7SERkRXu1Mh2ZA/Kz5pfMpzCrcNzn6Q+F2BrjxWoPBFgW\n5cVa5nbjdqTn/8BAoCNu+LC3dy9OZ3Gc8OFsMjOnIUm8aEGjSSZW81BpQaXRnKEopWjqajLWOAyH\nDVvr2d66PbKgdHR+VjIWlD7i9Ua8WBs6O3mnq4uzsrJOrlGYn8+srCxL5I2NFUqF6Os7SE/PzgGJ\n8YHAcbKyzh4QPszOno3DkZqLf2s0w+WMFlQisgq4H7ABv1JKfTfOmB8BVwAe4Dal1GYRyQD+Crgw\nwpTrlVL/Zo6/D7ga8ALvAh9TSnXGud+0EFTpEvtOFzsgfWwJ2xFSIQ51HOonsqIXlA57scKerfFc\nUNoXCrGlu9tYANr0ZHUHg/3ChF1vvcXqyy5LeZE1nPdVINBFb+/uOJ6t3Tgc+XET4zMzKxEZv5pn\n6fL5gPSxJV3ssJqgGjffuRh+6QeBlUAz8KaIPKWU2hk15gpghlLqbBFZBjwELFdKeUXkEqVUjxjf\nBK+KyAtKqY3Ai8CdSqmQiNwLfM3cNBrNKWATGxUFFVQUVPDBWR+M9AdDQRraGyIC60/7/sT9r9/P\n7mO7mZg7cUDocLAFpU8Vl83GeW4357ndfH7KFACavd5IuYa79++nbudOghkZTHa5mJKRwZSMDMpj\nb10uylwuS1d8Hw4ORx55eeeSl3duv36lQni9TfT07IyIrOPHX6CnZxd+fwuZmTMGhA+NIqbjH+rV\naNKFcfNQichy4B6l1BVm+05ARXupROQh4BWl1KNmewdQpZQ6GjUmG8Nb9Wml1Jsxj3EtcL1SqjrO\n46eFh0qjsRqBUIB9J/YNCB3GLigdDh3OLp5NhiNjTOfkCQZp8npp8nppjL71+SLtNr+fEqczoeAK\nt7Psya1gP9oEgz309u6JiK1oz5bNlh0nfDiHzMzp2Cy8ZJLmzMRqHqrxFFTXAx9QSt1utm8Fliql\nPh815hngO0qp18z2S8BXlFKbTA/X28AM4CdKqQFeKBF5GnhEKfXbOMe0oNJoxhF/0M+e43v61dCq\nb6ln34l9VBZUDggdnl18Ni77+C1h4w+FOBIlsBrjiK4mr5dcuz2h4Ar3FTgcKR9iNJbmOdxPaIVz\ntrzeZrKypg8IH2Znz8HpLE721DVnKFYTVCnzl0MpFQKWiIgbeFJE5imltoePi8hdgD+emApz2223\nUVlZCUBBQQGLFy+OxJFra2sBLN8O91llPqfavv/++1Py+Y/Xjn1tkj2fU21v3ryZL3zhC6N+//NK\n5lFSW0JVSRVVN1bhDXj5zdO/oaGjATVB8ci2R9j46kZaPC2cfa6xzmFOUw6VBZV8+KoPM7NoJn//\n69+H/XgjfT2mZmZSW1vLBOALMccvvvhi2vx+nnzpJdr8fgrf+16afD7Wv/QSbT4fPQsX0uT14t20\niQlOJ7POP5/yjAyC77xDidPJJVVVlGdkcPD11ylwOll5ySVJfz2GamdkTGbLFhswN3L85Zdf5Nix\nJs49N4/e3l28+OIj9PUdYt68w4g42L69jMzMqVRVVZGVNZu33uokI2MSl156edp8PqJtsMp8TrWd\nqt+/4f2GhgasyHiH/L6plFpltocT8tsJXBwd8jP7vw54lFI/MNu3AZ8ALlVKeRM8flp4qGrTJJkw\nXeyA9LEl2Xb0+nvZdWzXgNBhc1czs4pnDSjtcFbhWXEXlE6GHV2BQNywYmNUyPFEIECZyxU3rBi+\nLc/IIMNmS5odI8VYmqfF9Gj192z19R0iM3Ma9fXFXHTRBRGPlpGrVZKSHr1UeE2GQ7rYYTUP1XgK\nKjuwCyMp/TCwEfiIUmpH1JgrgX9WSn3QFGD3K6WWi8gEDO9Th4hkAX8E7lVKPW9eOfh94CKl1LFB\nHj8tBJVGc6bh8XnY0bZjQOiwxdPCnAlz+i3Bs6B0QdIXlE6ENxTicALRFb497POR73AkFFzhMGMq\n1OIKhXzmgtM7B1SMh1BM+DC8HuJMbLaxza/TpA9nrKCCSNmEBzhZNuFeEfkkhqfqF+aYB4FVGGUT\nPmbmT50D/No8zwY8qpT6tjl+D0Y5hbCYel0p9Zk4j60FlUaTRnR5u4x1xszFVQAAGPtJREFUDk2B\nta3VWFj6qOcoRVlFlGSXUJpTSklOCaXZ5m1Oab/+kuwSCrMKLSPAQkrR4vMlFFzhWxEZVHSVZ2RQ\n4nRatsq8z9dmCqz+ifHGgtPlMYnxc8wFp8tS0qulGTvOaEGVTNJFUKWLqzZd7ID0sSVd7Hjp5ZeY\nv3Q+LZ4WWntajVtP68n9qL4WTwsev4firOKT4ssUXf0EWZQQK8gsGJcf9kSvh1KKjkBgSNHVFQwy\naRDBNSUjg0kuFy7b2IrJkbyvjAWn9/cLIZ5ccNpLdvasflcfnlxwenTLcyQiXT4j6WKH1QSV9f3G\nGo1GMwIcdgeT8iYxKW/SsMb7gj7aetr6iayw6Hqr+S1aevoLsh5/jyG4cqJEVxzxFd7Pz8gfVQEm\nIhQ4nRQ4nczPSVxQtTcYpNnn6ye0DvT18VpHRyTseMTno9jh6BdOjHdFY+44hRhtNqe5nuEsjHrN\nJ/H7T0SEVm/vLlpaftdvwWmXqwyns9jcJuBwGLfRfU5nMQ5HMXZ75rjYozmz0B4qjUajGQHegJfW\nntZ+4itWiEV7wfoCff3FV1QIMp4Qc2e4xy20FQiFOOr3J/Ryhb1gGSIJvVzhsGOx05mUkFwoFMDr\nPYTf34Lf34bffyzq1tgPBPr3iTj7iayhRJjTOQGbLVuHHC2G1TxUWlBpNBrNGNIX6OsfcowjvqL7\nfEFfPw/XUF6wPFfemP7QK6U4EQgMKriavF56gsHIlYqJanZZoTq9UopgsDtGbA0twpQKjliE2e3j\nJ47PRLSgShLpIqjSJfadLnZA+tii7bAGvf5eWntaeeFPLzBt0bT+QqxnoCALhoKDiq9YIZbjzBmT\nH3lPMEhzTKmIRq+Xza++ites19Xq91PqdA5amb48I4NsC1anDwZ7efnlZ1mx4uw4Isy47S/C2giF\nenE4ikYkwhyOgjFfazHVPyNhrCaodA6VRqPRWIgsZxbT8qcxe8Jsqs6uGnJ8j79noNfLbO9o2zHA\nC6ZQA/K8BvOCDXfh6xy7nbOzszk7O7tff21zM1XnGmsNxqtO3+Tzsbm7O9Ju9nrJttsTCq7wbeE4\nV6e327NwuUrIy1s87HNCIR9+//G4Hi+f7zAez7YBXrJAoBOHI3+EIqwIm805htZrhoP2UGk0Gs0Z\nhMfnGfYVkK09rQgS92rHRF6wbGf20JMYBKUUbdF5XaYAiw05+pXqJ7gKnU7cdjtuhwO33U6+eet2\nOPrtu+12nEkOOw6GUkH8/hNxRVi096u/CDuBzZY9QhGW+sn5VvNQaUGl0Wg0mrgopej2dcdPvI8K\nQUb3OWyOgVc7DlIHLMt5aiUPoqvTN/t8tAcCdAYCdASDdAYCdJq3HdH75m2GzZZQbA0mxGLbyc4H\nC6NUiECgI27YcWBe2Ml+EUc/kRW+CnJwETY2IeNTQQuqJJEugipdYt/pYgekjy3aDmuRinYopejy\ndfUTX3/7y98omluU0AuW4cgYtAhr7H6m4/S8KkopekKhAWKrMxiM347ab964kdCiRZExmTZbQrGV\n73AM61heEoRZbW0tF198cSQ5f2gRFp2cHxixCHM4Rrd0SBirCSqdQ6XRaDSaUUFEcGe4cWe4mVE0\nAwD3YTdV76uKO14pRae3M26o8WDHQd4+/PaA8hSZjsy4RVjzMvLIdeWS48whx5UT2c915Q5oZ9td\n5GRkMCljZMvc1HZ2UrViRWTunmBwgPcrVogd9fnY3dub0GvWFQySFSvMor1kCYRY7LE8hwP7CESL\niOBw5OFw5AGVwz4vGOxN6PHq62ugq+vtASIsFOoxk/MTibB4ocrCMU/OH220h0qj0Wg0KYFSig5v\nx8DcL08r3b5uun3dePye/rc+T7/9bl83IRUaVHTluHLIdQ4UYgPGxBzLdmaPeBmjUKwwG6HXLHys\nOxgk226PK7Ziw5eDHcuz20d9yaKByflDXyF5Mjk/sQgrL7/dUh4qLag0Go1Gc0bhC/oGCK1Y0RX3\nWJwx0fu9/l6ynFmDe8pMoZbIe5ZItDntg1/FF1KK7tj8sQRCbLBcM08wSE60MDvFHLPc0xRmw0nO\nnzv3V1pQJYN0EVSpmFcRj3SxA9LHFm2HtdB2WI+hbAmpED3+nmGJtH6CzD+EkPN5sNvs/URXQkE2\njDFbN25l5SUryXXlkunI7JffFBZmHQlClMP1mvUEg+QOktA/3ByzXLs9Yf6VzqHSaDQajSYNsYmN\nXFcuua7cUb1fpRTeoDehZyyeSAsv/B1PpLVtbyO4PUi3rxt/yJ/QmxZPpOW6cilz5jAjPD4jh9y8\n8LH8yNgMRxa9ikE9Yx2BAAf6+gb1mvWGQuQlEFtWQ3uoNBqNRqM5QwmEAmMS/vT4PGQ4Mga9OGCo\nCwhyXDlkOXPAno2yZxGwZRCQDHqV0BEI8JGyMu2h0mg0Go1Gk3wcNgf5mfnkZ+aP6v0qpegN9A5b\npHV4O2juah4y/Nnt6wYYdS/gaKA9VClGu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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# to plot the NETD variation for sensorA\n", "plotNETDAmbient(dfConcept=dfConcept, sensor=u'SensorA', atmotype='gen', atmoSet=['MLS23km'], \n", " tempHotShieldDelta=tempHotShieldDeltas[0],\n", " tempCentralObsDelta=tempCentralObsDeltas[0],tempInsideUnique=tempInsideUniqueC[1:],\n", " tempDynamicDelta=tempDynamicUniqueC[2], pathlen=pathlenUniqueGen[0], \n", " optFilter=optFilters[0],netdstd=netdstd,ksyss=ksyss, wellfill=50, wellcapIdx=0,\n", " plotDetail=False)" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "collapsed": false, "jupyter": { "outputs_hidden": false } }, "outputs": [], "source": [ "# to plot the NETD variation for SensorB\n", "# plotNETDAmbient(dfConcept=dfConcept, sensor=u'SensorB', atmotype='gen', atmoSet=['MLS23km'], \n", "# tempHotShieldDelta=tempHotShieldDeltas[0],\n", "# tempCentralObsDelta=tempCentralObsDeltas[0],tempInsideUnique=tempInsideUniqueC[1:],\n", "# tempDynamicDelta=tempDynamicUniqueC[2], pathlen=pathlenUniqueGen[0], \n", "# optFilter=optFilters[0],netdstd=netdstd,ksyss=ksyss, wellfill=50, wellcapIdx=0, \n", "# plotDetail=True)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The NETD for sensorA with different internal temperature deltas are shown next. As the sensor heats up the hot optics increases the NETD. For each internal temperature delta two values for system noise are shown: (1) zero system noise and (2) 26 mK system noise. " ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "collapsed": false, "jupyter": { "outputs_hidden": false } }, "outputs": [ { "data": { "image/png": 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2qkJmZqbN/tLHkilK7Dh06BCdOnWKgoODZWXk/GRN/CjBz8+Prl69Wmnb2vJ4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\npHV4O2juah4y/Nnt6wYYdS/gaKA9VClGu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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# to plot the NETD variation for sensorA\n", "plotNETDAmbient(dfConcept=dfConcept, sensor=u'SensorA', atmotype='gen', atmoSet=['tropical5km'], \n", " tempHotShieldDelta=tempHotShieldDeltas[0],\n", " tempCentralObsDelta=tempCentralObsDeltas[0],tempInsideUnique=tempInsideUniqueC[1:],\n", " tempDynamicDelta=tempDynamicUniqueC[2], pathlen=pathlenUniqueGen[0], \n", " optFilter=optFilters[0],netdstd=netdstd,ksyss=ksyss, wellfill=50, wellcapIdx=0,\n", " plotDetail=False)" ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "collapsed": false, "jupyter": { "outputs_hidden": false } }, "outputs": [], "source": [ "# to plot the NETD variation for sensorB\n", "# plotNETDAmbient(dfConcept=dfConcept, sensor=u'SensorB', atmotype='gen', atmoSet=['tropical5km'], \n", "# tempHotShieldDelta=tempHotShieldDeltas[0],\n", "# tempCentralObsDelta=tempCentralObsDeltas[0],tempInsideUnique=tempInsideUniqueC[1:],\n", "# tempDynamicDelta=tempDynamicUniqueC[2], pathlen=pathlenUniqueGen[0], \n", "# optFilter=optFilters[0],netdstd=netdstd,ksyss=ksyss, wellfill=50, wellcapIdx=0,\n", "# plotDetail=False)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "It is evident that\n", "\n", "1. The low optical transmittance degrades the NETD (transmittance of 0.73 increases NETD by 1/0.73=1.4 times).\n", "2. SensorA central obscuration degrades the NETD (clear aperture of 0.7 of total increases NETD by 1/0.7=1.4 times).\n", "3. For SensorA the combined effect of the low transmittance and obscuration is that NETD is doubled, compared to the raw detector NETD).\n", "4. Note that SensorA has a cold reflective element (central obscuration) that decreases the background flux on the detector, slightly lowering the NETD. This 'lowering' effect is however countered again by the loss in target flux." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n", "## Well-fill Plotting and Analysis " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Given a set of environmental and sensor parameters, this function filters and plots the percentage of well fill versus range for the selected/filtered scenario, and for the selected concept." ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "collapsed": true, "jupyter": { "outputs_hidden": true } }, "outputs": [], "source": [ "# to calculate integration time and well selection\n", "lstContrib = ['EHotOptics [q/(s.m2)]','ECentralObs [q/(s.m2)]','EHotShield [q/(s.m2)]',\n", " 'EPath [q/(s.m2)]','EScene [q/(s.m2)]','EDynamic [q/(s.m2)]',]\n", "\n", "bitsCols = ['DataSet','Atmo','AtmoDesc','Sensor','PathLen','TempAmbient [C]','TempInsideDelta [C]',\n", " 'nubitloss','wellfillbitloss','BitsNoFill','wellfillNETD',\n", " 'BitsNETD','BitsAvail', 'TempAmbient [K]',\n", " 'TempHotShield [K]','TempCentralObs [K]',\n", " 'TempIntern [K]','OptFilter','Gain','WellCapacity','IntTime',\n", " 'atmotype','areaDetector','dynamicRangeK','threshold2Noise','netdstd','ksys',\n", " 'TempDynamic [C]','WfHotOptics','WfCentralObs','WfEHotShield','WfPath','WfScene','WfDynamic',\n", " 'WfPhotonStarve','EScene [q/(s.m2)]','EDynamic [q/(s.m2)]',\n", " 'wellfillTargDiff','TargDiffbitsNoNoise','TargDiffbitsNoise','Mderivscn [q/(s.m2.K)]','DeltaTarget'\n", " ]\n", "\n", "def totalE(row):\n", " total = 0\n", " for item in lstContrib:\n", " if item not in 'EScene [q/(s.m2)]':\n", " total += row[item]\n", " return pd.Series({'EtotalLocal':total})\n", "\n", "def totalEFill(row):\n", " total = 0\n", " for item in lstContrib[:-1]:\n", " if item not in 'EScene [q/(s.m2)]':\n", " total += row[item]\n", " return pd.Series({'EtotalLocalFill':total})\n", "\n", "def calcIntTimeRange(EtotalLocal, intTime, wellCapacities, areaDetector, quantumEff):\n", " for wellCapacity in wellCapacities:\n", " #find well capacity for best well fill\n", " inttime = wellCapacity / (quantumEff * EtotalLocal * areaDetector)\n", " if inttime <= intTime:\n", " break\n", " inttime = inttime if inttime < intTime else intTime\n", " return pd.Series({'IntTime':inttime, 'WellCapacity':wellCapacity})\n" ] }, { "cell_type": "code", "execution_count": 21, "metadata": { "collapsed": false, "jupyter": { "outputs_hidden": false } }, "outputs": [], "source": [ "# to plot well fill for different target ranges\n", "def plotPWellFillRange(sensors, dfBitsLoss, dfConcept, atmoSet, tambient, thotshield, tinternal, \n", " tcentralObs,optFilter, atmotype, intTime,areaDetector, wellCapacity, \n", " dynamicRangeK,ttarget,\n", " threshold2Noise,netdstd,ksys,specNETDwellfill = 50, wellcapIdx=0,\n", " doPlots=False, ADCbits=14.,iplot=1):\n", " \"\"\"This function extracts/filters data from the dataframe, so all parameters must be exact \n", " matches to the parameters in the dataframe.\n", " \"\"\"\n", "\n", " #we later need this, ensure that it is sorted here\n", " wellCapacity.sort(reverse=True)\n", " numAtmo = len(atmoSet)\n", " \n", " pathtype = 'Horizontal range' if atmotype=='gen' else 'Slant range (135-deg zenith)'\n", " if doPlots:\n", " p = ryplot.Plotter(iplot,3,numAtmo,figsize=(9*numAtmo,20))\n", "# print('Input parameters:')\n", "# print('tambient={}'.format(tambient))\n", "# print('tinternal={}'.format(tinternal))\n", "# print('thotshield={}'.format(thotshield))\n", "# print('tcentralObs={}'.format(tcentralObs))\n", "# print('dynamicRangeK={}'.format(dynamicRangeK))\n", "# print('atmoSet={}'.format(atmoSet))\n", " for i,sensor in enumerate(sensors):\n", " for j,katmo in enumerate(atmoSet):\n", " filename = 'data/well-fill/radio-{}-{}-{}.hdf5'.format(Cconcept, sensor, atmotype)\n", " with pd.HDFStore(filename) as store:\n", " dfi = store['df{}'.format(sensor)]\n", "\n", " filt = (dfi.Atmo==katmo) & (dfi.Sensor==sensor) & (dfi['TempAmbient [K]']==tambient) & \\\n", " (np.abs(dfi['TempHotShield [K]']-(tambient+thotshield+tinternal))<0.01) & \\\n", " (np.abs(dfi['TempCentralObs [K]']-(tambient+tcentralObs+tinternal))<0.01) & \\\n", " (np.abs(dfi['TempIntern [K]']-(tambient+tinternal))<0.01) & \\\n", " (dfi['OptFilter']==optFilter)\n", " if dfi.empty:\n", " print('dfi is empty location 1')\n", " return\n", " \n", " # the integration time and well cap setting is determined for the hottest pixel in the image, \n", " # get dynamic range temperature and then calc the integration time and well fill\n", " # use dfd to handle the dynamic range and calc integration time\n", " dfd = dfi[filt & (np.abs(dfi['TempDynamic [K]']-(tambient+dynamicRangeK))<0.01)]\n", " if dfd.empty:\n", " print('dfd is empty location 2')\n", " return\n", " \n", " dfd = dfd.merge(dfd.apply(totalE, axis=1),left_index=True, right_index=True)\n", " dfd = dfd.merge(dfd.apply(totalEFill, axis=1),left_index=True, right_index=True)\n", " #here calculate the integration time and well fill for the given total signal photon count \n", " if dfd.empty:\n", " print('dfd is empty location 3')\n", " return\n", " \n", " quantumEff = dfConcept[sensor]['quantumEff']\n", "\n", " dfd = dfd.merge(dfd.EtotalLocal.apply(calcIntTimeRange,\n", " args=(intTime, wellCapacity, areaDetector, quantumEff)), \n", " left_index=True, right_index=True)\n", " if dfd.empty:\n", " print('dfd is empty location 4')\n", " return\n", " \n", " dfd['Gain'] = (100. * quantumEff * areaDetector / dfd['WellCapacity']) * dfd['IntTime']\n", " \n", " inttimes = dfd['IntTime'].unique()\n", " strIntT = '{:.2f} to {:.2f}'.format(1000.*min(inttimes),1000.*max(inttimes)) \\\n", " if len(inttimes) > 1 else '{:.2f}'.format(1000. * inttimes[0])\n", " wellcaps = dfd['WellCapacity'].unique()\n", " strwellC = '{:.1f} to {:.1f}'.format(1e-6*min(wellcaps),1e-6*max(wellcaps)) \\\n", " if len(wellcaps) > 1 else '{:.2f}'.format(1e-6 * wellcaps[0]) \n", " \n", " #build a stack of contributions, but scaling with intTime and WellCap gain for dynamic range\n", " stack = (dfd[lstContrib[0]] * dfd['Gain']).reshape(1,-1) \n", " for item in lstContrib[1:]:\n", " stack = np.vstack((stack, (dfd[item] * dfd['Gain']).reshape(1,-1)))\n", " #what is 'dynamic' in the stack must be dynamic contrast = dynamic - scene\n", " stack[5,:] = stack[5,:] - stack[4,:]\n", " \n", " #totalWellfill is the total wellfill including the scene dynamic range\n", " totalWellfill = np.sum(stack.T,axis=1)/100.\n", " dfd['totalWellfill'] = totalWellfill\n", " dfd['photstarveloss bits'] = np.where(np.abs(totalWellfill - 1.0) > 0.005,\n", " np.log(totalWellfill)/np.log(2), 0.)\n", "\n", " df = dfd.copy()\n", " \n", " df = df.sort_values(by='PathLen') \n", " \n", " #build a stack of contributions, but scaling with intTime and WellCap gain for hottest pixel\n", " stack = (df[lstContrib[0]] * df['Gain']).reshape(1,-1) \n", " for item in lstContrib[1:]:\n", " stack = np.vstack((stack, (df[item] * df['Gain']).reshape(1,-1)))\n", " #what is 'dynamic' in the stack must be dynamic contrast = dynamic - scene\n", " stack[5,:] = stack[5,:] - stack[4,:]\n", " #photon starvation \n", " stack = np.vstack((stack, 100. - np.sum(stack,axis=0)))\n", "\n", " \n", " df['nuloss'] = dfConcept[sensor]['nuloss']\n", " df['nubitloss'] = np.log(1.0 - df['nuloss'])/np.log(2.)\n", " #bits lost to optics, path & scene DC component\n", " df['EtotalLocalFill'] = df.apply(totalEFill, axis=1)\n", " df['wellfillbitloss'] = np.log(1.0 - (df['Gain']/100.) * quantumEff * \\\n", " (df.EtotalLocalFill+df['EScene [q/(s.m2)]']))/np.log(2.)\n", " \n", " df['BitsNoFill'] = ADCbits + df['wellfillbitloss'] + df['nubitloss'] + \\\n", " df['photstarveloss bits']\n", " \n", " netdBLIPstd, netdBLIPscene, netdElec, netdSys, netd, nePhiBLIP, nePhiElec, nePhi = \\\n", " calcNETDScene(df, dfConcept, sensor, specNETDwellfill, wellcapIdx, ksys)\n", " df['netd'] = netd \n", " df['netdBLIPscene'] = netdBLIPscene\n", " df['netdElec'] = netdElec\n", " df['netdSys'] = netdSys\n", " \n", " df['wellfillNETD'] = threshold2Noise * df['netd'] * (df['Gain']/100.) * df['dEAmbient [q/(s.m2.K)]']\n", " df['BitsNETD'] = ADCbits + np.log(df['wellfillNETD'])/np.log(2.)\n", " df['BitsAvail'] = df['BitsNoFill'] - df['BitsNETD']\n", "\n", " # calculate the differential well fill for a small target contrast at scene temperature\n", " # this is given by 'Mderivscn [q/(s.m2.K)]' * ttarget in units of [q/(s.m2)]\n", " # multiply with gain to get percentage well fill\n", " df['wellfillTargDiff'] = df['Mderivscn [q/(s.m2.K)]'] * ttarget * df['Gain']/100\n", " # convert to bits used from the available bits, BUT IGNORING NOISE\n", " df['TargDiffbitsNoNoise'] = df['BitsNoFill'] + np.log(df['wellfillTargDiff'])/np.log(2.)\n", " # convert to bits used from the available bits, BUT INCLUDING NOISE\n", " df['TargDiffbitsNoise'] = df['BitsAvail'] + np.log(df['wellfillTargDiff'])/np.log(2.)\n", " \n", " df['threshold2Noise'] = threshold2Noise\n", " df['netdstd'] = netdstd\n", " df['ksys'] = ksys\n", " df['atmotype'] = atmotype\n", " df['areaDetector'] = areaDetector\n", " df['dynamicRangeK'] = dynamicRangeK\n", " \n", " # save bits lost data for later use\n", " df['dataset'] = 'plotPWellFillRange'\n", " arb = np.hstack((df['dataset'].reshape(-1,1), \n", " df['Atmo'].reshape(-1,1),\n", " df['AtmoDesc'].reshape(-1,1),\n", " df['Sensor'].reshape(-1,1), \n", " df['PathLen'].reshape(-1,1),\n", " df['TempAmbient [C]'].reshape(-1,1),\n", " df['TempInsideDelta [C]'].reshape(-1,1),\n", " df['nubitloss'].reshape(-1,1),\n", " df['wellfillbitloss'].reshape(-1,1),\n", " df['BitsNoFill'].reshape(-1,1),\n", " df['wellfillNETD'].reshape(-1,1), \n", " df['BitsNETD'].reshape(-1,1),\n", " df['BitsAvail'].reshape(-1,1),\n", " df['TempAmbient [K]'].reshape(-1,1),\n", " df['TempHotShield [K]'].reshape(-1,1),\n", " df['TempCentralObs [K]'].reshape(-1,1),\n", " df['TempIntern [K]'].reshape(-1,1),\n", " df['OptFilter'].reshape(-1,1),\n", " df['Gain'].reshape(-1,1),\n", " df['WellCapacity'].reshape(-1,1),\n", " df['IntTime'].reshape(-1,1),\n", " df['atmotype'].reshape(-1,1),\n", " df['areaDetector'].reshape(-1,1),\n", " df['dynamicRangeK'].reshape(-1,1),\n", " df['threshold2Noise'].reshape(-1,1),\n", " df['netdstd'].reshape(-1,1),\n", " df['ksys'].reshape(-1,1), \n", " df['TempDynamic [C]'].reshape(-1,1), \n", " stack.T[:,0].reshape(-1,1), \n", " stack.T[:,1].reshape(-1,1), \n", " stack.T[:,2].reshape(-1,1), \n", " stack.T[:,3].reshape(-1,1), \n", " stack.T[:,4].reshape(-1,1), \n", " stack.T[:,5].reshape(-1,1), \n", " stack.T[:,6].reshape(-1,1), \n", " df['EScene [q/(s.m2)]'].reshape(-1,1), \n", " df['EDynamic [q/(s.m2)]'].reshape(-1,1), \n", " df['wellfillTargDiff'].reshape(-1,1),\n", " df['TargDiffbitsNoNoise'].reshape(-1,1),\n", " df['TargDiffbitsNoise'].reshape(-1,1),\n", " df['Mderivscn [q/(s.m2.K)]'].reshape(-1,1),\n", " ttarget*np.ones(df['Mderivscn [q/(s.m2.K)]'].reshape(-1,1).shape),\n", " ))\n", " \n", " dfzz = pd.DataFrame(arb, columns=bitsCols)\n", " dfBitsLoss = dfBitsLoss.append(dfzz,ignore_index=True)\n", "\n", " if doPlots:\n", "\n", " #get optical transmittance for this run\n", " tauOpt = df['Optics Transmittance'].unique()[0] * 100.\n", "\n", " title = '{}, {}: {} Ambient={} C, {}'.format(Cconcept, sensor, katmo, tambient-273., optFilter)\n", " label=[x.split()[0][1:] for x in lstContrib]\n", " label[0] = '{} {:.1f} C ({:.0f}%)'.format(label[0], tambient+tinternal-273.,tauOpt ) # hot optics in internal\n", " label[1] = '{} {:.1f} C'.format(label[1], tambient+tcentralObs+tinternal-273. ) # central obscuration\n", " label[2] = '{} {:.1f} C'.format(label[2], tambient+thotshield+tinternal-273. ) # hotshield\n", " label[3] = 'Atmospheric {}'.format(label[3].lower()) \n", " label[4] = 'Scene minimum {:.1f} C'.format(tambient-273.) # scene\n", " label[5] = 'Dynamic range {:.1f} C'.format(dynamicRangeK) \n", " label.append('Photon starvation')\n", " \n", " spidx = j+numAtmo*i+1\n", " p.stackplot(spidx,df['PathLen'], stack.T, title,'{} [km]'.format(pathtype),'Absolute well fill [%]', \n", " plotCol=['y','c','m','g','r','0.75','w'], label=label,legendLoc='lower right',\n", " labelfsize=6,legendAlpha=0.3,pltaxis=[0,np.max(df['PathLen']),0,100])\n", "# plotCol=['y','c','m','b','k','g','r','0.75'], label=label,legendLoc='lower right',\n", "\n", " p.plot(spidx,df['PathLen'],100.*np.ones((df['PathLen'].shape)),plotCol=['k'])\n", "\n", " #write dataframe columns to file for debugging\n", " if False:\n", " with open('test.txt', 'w') as fi:\n", " fi.write('{}'.format(list(df.columns.values)))\n", "\n", " currentP = p.getSubPlot(spidx)\n", " maxplot = np.max(np.sum(stack,axis=0))\n", " currentP.text(df['PathLen'].as_matrix()[0], 92, r'Dynamic range = {} K'.format(dynamicRangeK), \n", " horizontalalignment='left', fontsize=10) \n", " currentP.text(df['PathLen'].as_matrix()[0], 82, r'Integration time = {} ms'.format(strIntT), \n", " horizontalalignment='left', fontsize=10)\n", " currentP.text(df['PathLen'].as_matrix()[0], 72, r'Well capacity = {} Me$^-$'.format(strwellC), \n", " horizontalalignment='left', fontsize=10) \n", "\n", " for ii,idx in enumerate(df.index):\n", " strTxt = '{:.1f} Me$^-$ {:.2f} ms'.format(1e-6*df['WellCapacity'][idx], 1000.*df['IntTime'][idx])\n", " currentP.text(df['PathLen'].as_matrix()[ii], 0.02*maxplot, strTxt, \n", " horizontalalignment='left', verticalalignment='bottom', fontsize=10, rotation=90)\n", "\n", " ax2 = currentP.twinx()\n", " ax2.plot(df['PathLen'].as_matrix(), df['BitsNoFill'].as_matrix(),'k')\n", " ax2.plot(df['PathLen'].as_matrix(), df['BitsNETD'].as_matrix(),'k',linestyle='-.')\n", " ax2.plot(df['PathLen'].as_matrix(), df['BitsAvail'].as_matrix(),'k',linestyle='--')\n", " ax2.set_ylabel('Bits in target range', color='k')\n", " ax2.set_ylim(0, ADCbits)\n", " \n", " \n", " #try to force some garbage collection \n", " dfi = None\n", " dfd = None\n", " df = None\n", " df = None\n", " dfzz = None\n", " n = gc.collect()\n", "# print('Unreachable objects: {}'.format(n))\n", "# print('Remaining Garbage: {}'.format(gc.garbage))\n", "\n", " return dfBitsLoss\n" ] }, { "cell_type": "code", "execution_count": 22, "metadata": { "collapsed": false, "jupyter": { "outputs_hidden": false } }, "outputs": [], "source": [ "dfBitsLoss = pd.DataFrame(columns=bitsCols)" ] }, { "cell_type": "code", "execution_count": 23, "metadata": { "collapsed": false, "jupyter": { "outputs_hidden": false } }, "outputs": [], "source": [ "# to plot well fill for different target temperature contrast values\n", "\n", "lstContribContrast = ['EHotOptics [q/(s.m2)]','ECentralObs [q/(s.m2)]','EHotShield [q/(s.m2)]',\n", " 'EPath [q/(s.m2)]','EScene [q/(s.m2)]','EDynamic [q/(s.m2)]',]\n", "\n", "def totalEContrast(row):\n", " total = 0\n", " for item in lstContribContrast:\n", " if item not in 'EScene [q/(s.m2)]':\n", " total += row[item]\n", " return pd.Series({'EtotalLocal':total})\n", "\n", "def totalEFillContrast(row):\n", " total = 0\n", " for item in lstContribContrast[0:5]:\n", " total += row[item]\n", " return pd.Series({'EtotalLocalFill':total})\n", "\n", "def plotPWellFillTargetContrast(sensors,dfBitsLoss, dfConcept, atmoSet, tambient, thotshield, \n", " tinternal, tcentralObs, trange, optFilter, atmotype, \n", " intTime,areaDetector, wellCapacity, dynamicRangeK,ttarget,\n", " threshold2Noise,netdstd,ksys,specNETDwellfill=50, wellcapIdx=0,\n", " doPlots=False, ADCbits=14,iplot=1):\n", " \"\"\"This function extracts/filters data from the dataframe, so all parameters must be exact \n", " matches to the parameters in the dataframe.\n", " \"\"\"\n", " \n", " #we later need this, ensure that it is sorted here\n", " wellCapacity.sort(reverse=True)\n", " numAtmo = len(atmoSet)\n", " if doPlots:\n", " p = ryplot.Plotter(iplot,3,numAtmo,figsize=(9*numAtmo,20));\n", "# print('Input parameters:')\n", "# print('tambient={}'.format(tambient))\n", "# print('tinternal={}'.format(tinternal))\n", "# # print('ttarget={}'.format(ttarget))\n", "# print('thotshield={}'.format(thotshield))\n", "# print('tcentralObs={}'.format(tcentralObs))\n", "# print('dynamicRangeK={}'.format(dynamicRangeK))\n", "# print('atmoSet={}'.format(atmoSet))\n", " for i,sensor in enumerate(sensors):\n", " for j,katmo in enumerate(atmoSet):\n", " filename = 'data/well-fill/radio-{}-{}-{}.hdf5'.format(Cconcept, sensor, atmotype)\n", " with pd.HDFStore(filename) as store:\n", " dfi = store['df{}'.format(sensor)]\n", " \n", " filt = (dfi.Atmo==katmo) & (dfi.Sensor==sensor) & (dfi['TempAmbient [K]']==tambient) & \\\n", " (np.abs(dfi['TempHotShield [K]']-(tambient+thotshield+tinternal))<0.01) & \\\n", " (np.abs(dfi['TempCentralObs [K]']-(tambient+tcentralObs+tinternal))<0.01) & \\\n", " (np.abs(dfi['TempIntern [K]']-(tambient+tinternal))<0.01) & \\\n", " (np.abs(dfi['PathLen']-trange)<0.001) & \\\n", " (dfi['OptFilter']==optFilter) & \\\n", " (dfi['TempDynamic [K]'] <= tambient + dynamicRangeK )\n", " if dfi.empty:\n", " print('dfi is empty location 1')\n", " return\n", " \n", " #the integration time and well cap setting is determined for the hottest pixel in the image\n", " #which normally is not the target pixel. Calc for hottest pixel independently\n", " #get dynamic range target only and then calc the integration time and well fill for max target\n", " dfd = dfi[filt] #& (np.abs(dfi['TempTarget [K]']-(tambient+dynamicRangeK))<0.01)\n", " if dfd.empty:\n", " print('dfd is empty location 2')\n", " print(tambient+dynamicRangeK)\n", " print('vs')\n", " print(dfi['TempDynamic [K]'].unique())\n", " return\n", " \n", " #after the following EtotalLocalFill will have irradiance from nonscene sources\n", " dfd = dfd.merge(dfd.apply(totalEFillContrast, axis=1),left_index=True, right_index=True)\n", " #after the following EtotalLocal will have irradiance from nonscene + scene sources\n", " dfd = dfd.merge(dfd.apply(totalEContrast, axis=1),left_index=True, right_index=True)\n", " if dfd.empty:\n", " print('dfd is empty location 3')\n", " return\n", " \n", " #here calculate the integration time and well fill for the given max signal photon count\n", " #after this 'IntTime' will have the integration time and 'WellCapacity' ditto\n", " quantumEff = dfConcept[sensor]['quantumEff']\n", " dfd = dfd.merge(dfd.EtotalLocal.apply(calcIntTimeRange,\n", " args=(intTime, wellCapacity, areaDetector,quantumEff)), \n", " left_index=True, right_index=True)\n", " # now find one value for well & integration at max temperature, use for all samples\n", " maxTrow = dfd[dfd['TempDynamic [K]']==np.max(dfd['TempDynamic [K]'])]\n", " dfd['IntTime'] = maxTrow['IntTime'].values * np.ones(dfd['IntTime'].shape)\n", " dfd['WellCapacity'] = maxTrow['WellCapacity'].values * np.ones(dfd['WellCapacity'].shape)\n", " if dfd.empty:\n", " print('dfd is empty location 4')\n", " return\n", " \n", " dfd = dfd.sort_values(by='TempDynamic [K]')\n", " localtempDynamicDeltas = dfd['TempDynamic [C]'].values\n", " \n", " dfd['Gain'] = (100. * quantumEff * areaDetector / dfd['WellCapacity']) * dfd['IntTime']\n", " inttimes = dfd['IntTime'].unique()\n", " strIntT = '{:.2f} to {:.2f}'.format(1000.*min(inttimes),1000.*max(inttimes)) \\\n", " if len(inttimes) > 1 else '{:.2f}'.format(1000. * inttimes[0])\n", " wellcaps = dfd['WellCapacity'].unique()\n", " strwellC = '{:.1f} to {:.1f}'.format(1e-6*min(wellcaps),1e-6*max(wellcaps)) \\\n", " if len(wellcaps) > 1 else '{:.2f}'.format(1e-6 * wellcaps[0]) \n", " \n", " df = dfi[filt].copy()\n", " df['Gain'] = dfd['Gain'].values[0]\n", "\n", " df['WellCapacity'] = wellcaps[0]\n", " df['IntTime'] = inttimes[0]\n", " df['atmotype'] = atmotype\n", " df['areaDetector'] = areaDetector\n", " df['dynamicRangeK'] = dynamicRangeK\n", " \n", " #get optical transmittance for this run\n", " tauOpt = df['Optics Transmittance'].unique()[0] * 100.\n", "\n", " #build a stack of contributions, but scaling with intTime and WellCap gain for hottest pixel\n", " stack = (df[lstContribContrast[0]] * df['Gain']).reshape(1,-1) \n", " for item in lstContribContrast[1:]:\n", " stack = np.vstack((stack, (df[item] * df['Gain']).reshape(1,-1)))\n", " #what is 'dynamic' in the stack must be dynamic contrast = dynamic - scene\n", " stack[5,:] = stack[5,:] - stack[4,:]\n", " # unused dynamic range \n", " stack = np.vstack((stack, np.max(stack[5,:]) - stack[5,:]))\n", " #placeholder for photon starvation\n", " stack = np.vstack((stack, np.zeros(stack[5,:].shape)))\n", " \n", "\n", " #totalWellfill is the wellfill including dynamic range\n", " totalWellfill = np.max(np.sum(stack[0:6,:].T,axis=1)/100.)\n", " \n", " df['totalWellfill'] = totalWellfill\n", " df['photstarveloss bits'] = np.where(np.abs(totalWellfill - 1.0) > 0.005,\n", " np.log(totalWellfill)/np.log(2), 0.)\n", " df['nubitloss'] = np.log(1.0 - dfConcept[sensor]['nuloss'])/np.log(2.)\n", " df['nuloss'] = dfConcept[sensor]['nuloss']\n", " df['Bitsunused'] = np.zeros(df['nuloss'].shape)\n", "# df['Bitsunused'] = -ADCbits*np.log(np.max(stack[6,:]/100))/np.log(2) - \\\n", "# np.where(stack[6,:]>0,np.log(stack[6,:]/100)/(np.log(2)),0)\n", "# df['Bitsunused'] -= np.max(df['Bitsunused']) \n", "# df['Bitsunused'] = np.log((stack[6,:]/np.max(stack[6,:]))/np.log(2))\n", " \n", " \n", " #bits lost to optics, path & scene DC component\n", " df['EtotalLocalFill'] = df.apply(totalEFillContrast, axis=1)\n", " df['wellfillbitloss'] = np.log(1.0 - (df['Gain']/100.) * quantumEff * \\\n", " (df.EtotalLocalFill+df['EScene [q/(s.m2)]']))/np.log(2.)\n", " df['BitsNoFill'] = ADCbits + df['wellfillbitloss'] + df['nubitloss'] + \\\n", " df['photstarveloss bits']\n", "\n", " \n", " \n", " \n", " # additional bits lost to the small target compared to dynamic range\n", " df['UnusedNorm'] = (stack[5,:]-np.min(stack[5,:]))/(np.max(stack[5,:])-np.min(stack[5,:]))\n", " df['BitlostDyn'] = np.log(df['UnusedNorm'])/np.log(2)\n", " df['BitsNoFill'] += df['BitlostDyn']\n", " \n", " netdBLIPstd, netdBLIPscene, netdElec, netdSys, netd, nePhiBLIP, nePhiElec, nePhi = \\\n", " calcNETDScene(df, dfConcept, sensor, specNETDwellfill, wellcapIdx, ksys)\n", " df['netd'] = netd \n", " df['netdBLIPscene'] = netdBLIPscene\n", " df['netdElec'] = netdElec\n", " df['netdSys'] = netdSys\n", " df['threshold2Noise'] = threshold2Noise\n", " df['netdstd'] = netdstd\n", " df['ksys'] = ksys\n", " \n", " df['wellfillNETD'] = threshold2Noise * df['netd'] * df['Gain'] * df['dEAmbient [q/(s.m2.K)]']\n", " df['BitsNETD'] = ADCbits + np.log(df['wellfillNETD']/100.)/np.log(2.)\n", " \n", " df['BitsAvail'] = df['BitsNoFill'] - df['BitsNETD']\n", "\n", " # calculate the differential well fill for a small target contrast at scene temperature\n", " # this is given by 'Mderivscn [q/(s.m2.K)]' * ttarget in units of [q/(s.m2)]\n", " # multiply with gain to get percentage well fill\n", " df['wellfillTargDiff'] = df['Mderivscn [q/(s.m2.K)]'] * ttarget * df['Gain']/100\n", " # convert to bits used from the available bits, BUT IGNORING NOISE\n", " df['TargDiffbitsNoNoise'] = df['BitsNoFill'] + np.log(df['wellfillTargDiff'])/np.log(2.)\n", " # convert to bits used from the available bits, BUT INCLUDING NOISE\n", " df['TargDiffbitsNoise'] = df['BitsAvail'] + np.log(df['wellfillTargDiff'])/np.log(2.)\n", " \n", " \n", " \n", "# print(df[['UnusedNorm','BitlostDyn','wellfillbitloss','totalWellfill','photstarveloss bits','BitsNoFill','BitsAvail']])\n", " \n", " \n", " # save bits lost data for later use\n", " df['dataset'] = 'plotPWellFillTargetContrast'\n", " arb = np.hstack((df['dataset'].reshape(-1,1),\n", " df['Atmo'].reshape(-1,1),\n", " df['AtmoDesc'].reshape(-1,1),\n", " df['Sensor'].reshape(-1,1), \n", " df['PathLen'].reshape(-1,1),\n", " df['TempAmbient [C]'].reshape(-1,1),\n", " df['TempInsideDelta [C]'].reshape(-1,1),\n", " df['nubitloss'].reshape(-1,1),\n", " df['wellfillbitloss'].reshape(-1,1),\n", " df['BitsNoFill'].reshape(-1,1),\n", " df['wellfillNETD'].reshape(-1,1), \n", " df['BitsNETD'].reshape(-1,1),\n", " df['BitsAvail'].reshape(-1,1),\n", " df['TempAmbient [K]'].reshape(-1,1),\n", " df['TempHotShield [K]'].reshape(-1,1),\n", " df['TempCentralObs [K]'].reshape(-1,1),\n", " df['TempIntern [K]'].reshape(-1,1),\n", " df['OptFilter'].reshape(-1,1),\n", " df['Gain'].reshape(-1,1),\n", " df['WellCapacity'].reshape(-1,1),\n", " df['IntTime'].reshape(-1,1),\n", " df['atmotype'].reshape(-1,1),\n", " df['areaDetector'].reshape(-1,1),\n", " df['dynamicRangeK'].reshape(-1,1),\n", " df['threshold2Noise'].reshape(-1,1),\n", " df['netdstd'].reshape(-1,1),\n", " df['ksys'].reshape(-1,1), \n", " df['TempDynamic [C]'].reshape(-1,1), \n", " stack.T[:,0].reshape(-1,1), \n", " stack.T[:,1].reshape(-1,1), \n", " stack.T[:,2].reshape(-1,1), \n", " stack.T[:,3].reshape(-1,1), \n", " stack.T[:,4].reshape(-1,1), \n", " stack.T[:,5].reshape(-1,1), \n", " stack.T[:,6].reshape(-1,1), \n", " df['EScene [q/(s.m2)]'].reshape(-1,1), \n", " df['EDynamic [q/(s.m2)]'].reshape(-1,1),\n", " df['wellfillTargDiff'].reshape(-1,1),\n", " df['TargDiffbitsNoNoise'].reshape(-1,1),\n", " df['TargDiffbitsNoise'].reshape(-1,1),\n", " df['Mderivscn [q/(s.m2.K)]'].reshape(-1,1),\n", " ttarget*np.ones(df['Mderivscn [q/(s.m2.K)]'].reshape(-1,1).shape),\n", " \n", " ))\n", " \n", " dfzz = pd.DataFrame(arb, columns=bitsCols)\n", " dfBitsLoss = dfBitsLoss.append(dfzz,ignore_index=True)\n", "\n", " if doPlots:\n", " \n", " title = '{}, {}: {}, {:.0f} km, Amb={} C, {}'.format(Cconcept, sensor, katmo, trange, tambient-273.,optFilter)\n", " label=[x.split()[0][1:] for x in lstContribContrast]\n", " label[0] = '{} {:.1f} C ({:.0f}%)'.format(label[0], tambient+tinternal-273.,tauOpt ) # hot optics in internal\n", " label[1] = '{} {:.1f} C'.format(label[1], tambient+tcentralObs+tinternal-273. ) # central obscuration\n", " label[2] = '{} {:.1f} C'.format(label[2], tambient+thotshield+tinternal-273. ) # hotshield\n", " label[3] = 'Atmospheric {}'.format(label[3].lower()) \n", " label[4] = 'Scene minimum {:.1f} C'.format(tambient-273.) # scene\n", " label[5] = 'Target signal' # target\n", " label.append('Unused dynamic range' )\n", " label.append('Photon starvation' )\n", "\n", "\n", " spidx = j+numAtmo*i+1\n", " plotCol=['y','c','m','g','r','0.75','.9','w']\n", " \n", " p.stackplot(spidx,localtempDynamicDeltas, stack.T, title,'Target temperature [K]',\n", " 'Absolute well fill [%]', \n", " plotCol=plotCol, label=label,legendLoc='lower right',\n", " legendAlpha=0.5,pltaxis=[np.min(localtempDynamicDeltas),np.max(localtempDynamicDeltas),0, 100.])\n", "\n", " p.plot(spidx,localtempDynamicDeltas,100.*np.ones((localtempDynamicDeltas.shape)),plotCol=['k'])\n", "\n", " currentP = p.getSubPlot(spidx)\n", " maxplot = np.max(np.sum(stack,axis=0))\n", " currentP.text(localtempDynamicDeltas[0], 92, r'Dynamic range = {} K'.format(dynamicRangeK), \n", " horizontalalignment='left', fontsize=10) \n", " currentP.text(localtempDynamicDeltas[0], 82, r'Integration time = {} ms'.format(strIntT), \n", " horizontalalignment='left', fontsize=10)\n", " currentP.text(localtempDynamicDeltas[0], 72, r'Well capacity = {} Me$^-$'.format(strwellC),\n", " horizontalalignment='left', fontsize=10)\n", "\n", " ax2 = currentP.twinx()\n", " # ax2.plot(localtempDynamicDeltas, 6+5*df['wellfillbitloss'].as_matrix(),'b')\n", " # ax2.plot(localtempDynamicDeltas, 4+df['nubitloss'].as_matrix(),'r')\n", " ax2.plot(localtempDynamicDeltas, df['BitsNoFill'].as_matrix(),'k')\n", " ax2.plot(localtempDynamicDeltas, df['BitsNETD'].as_matrix(),'k',linestyle='-.')\n", " ax2.plot(localtempDynamicDeltas, df['BitsAvail'].as_matrix(),'k',linestyle='--')\n", " ax2.set_ylabel('Bits in target range', color='k')\n", " ax2.set_ylim(0, ADCbits)\n", "\n", " #try to force some garbage collection \n", " dfi = None\n", " dfd = None\n", " df = None\n", " dfzz = None\n", " n = gc.collect()\n", "# print('Unreachable objects: {}'.format(n))\n", "# print('Remaining Garbage: {}'.format(gc.garbage))\n", " \n", " return dfBitsLoss\n", " \n" ] }, { "cell_type": "code", "execution_count": 24, "metadata": { "collapsed": false, "jupyter": { "outputs_hidden": false } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Ambient temperature 20.0 C\n", "Internal temperature 40.0 C\n", "Target delta temperature 2.0 C\n", " Atmo PathLen Sensor OptFilter EInternal [%Tar] EPath [%Tar] EDynamic [%Tar]\n", "19801 tropical5km 10.0 SensorA Standard 33.511474 62.866403 3.622123\n", "48841 tropical5km 10.0 SensorA Notch 33.411121 61.919982 4.668897\n", " \n" ] } ], "source": [ "# to display selected results in tabular format\n", "def tableDatasets(sensors, dfConcept, atmoSet, tambient, thotshield, tinternal, tcentralObs, \n", " ttarget, optFilter, trange, atmotype):\n", " \"\"\"This function extracts/filters data from the dataframe, so all temperatures must be exact \n", " matches to the temperatures in the dataframe.\n", " \"\"\"\n", " sumcols = ['Atmo','PathLen', 'Sensor','OptFilter','TempAmbient [C]','TempIntern [C]','TempDynamic [C]',\n", " 'EInternal [%Tar]','EPath [%Tar]','EDynamic [%Tar]',]\n", " dfr = pd.DataFrame(columns=sumcols)\n", " for i,sensor in enumerate(sensors):\n", " for j,katmo in enumerate(atmoSet):\n", " with pd.HDFStore('data/well-fill/radio-{}-{}-{}.hdf5'.format(Cconcept, sensor,atmotype)) as store:\n", " df = store['df{}'.format(sensor)]\n", "# print(df.columns)\n", " filt = (df.Atmo==katmo) & (df.Sensor==sensor) & (df['TempAmbient [K]']==tambient) & \\\n", " (df['TempHotShield [K]']==tambient+thotshield+tinternal) & (df['TempCentralObs [K]']==tambient+tcentralObs+tinternal) & \\\n", " (df['TempIntern [K]']==tambient+tinternal) & (df['TempDynamic [K]']==tambient+ttarget) & \\\n", " (df['PathLen']==trange) \n", " df = df[filt]\n", " df = df[sumcols]\n", " dfr = dfr.append(df)\n", " return dfr\n", "\n", "def printDataset(df):\n", " print('Ambient temperature {} C'.format(df['TempAmbient [C]'].unique()[0]))\n", " print('Internal temperature {} C'.format(df['TempIntern [C]'].unique()[0]))\n", " print('Target delta temperature {} C'.format(df['TempDynamic [C]'].unique()[0]-df['TempAmbient [C]'].unique()[0]))\n", " df = df.drop(['TempDynamic [C]', 'TempIntern [C]', 'TempAmbient [C]'],axis=1)\n", " print(df)\n", " print(' ')\n", " \n", "# to plot the results in tabular format for a very hot scene and internal temperatures at 10 km\n", "tambient = tempAmbUnique[3]\n", "tinternal = tempInternDeltas[2]\n", "ttarget = TempTargetDeltaC\n", "thotshield = tempHotShieldDeltas[0]\n", "tcentralObs = tempCentralObsDeltas[0]\n", "trange = pathlenUniqueGen[-1]\n", "optFilter=optFilters[0]\n", "\n", "# only do for SensorA\n", "sensors = [dfConcept.columns.values[0]]\n", "# dfr=tableDatasets(dfConcept=dfConcept, atmoSet=['MLS23km','tropical5km','SW31km'], tambient=tambient, \n", "dfr=tableDatasets(sensors=sensors,dfConcept=dfConcept, atmoSet=['tropical5km'], tambient=tambient, \n", " thotshield=thotshield,tinternal=tinternal,tcentralObs=tcentralObs,\n", " ttarget=ttarget, optFilter=optFilter, trange=trange,atmotype='gen')\n", "printDataset(dfr) " ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false, "jupyter": { "outputs_hidden": false } }, "outputs": [], "source": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Results" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Generic Atmospheres Well Fill vs Range" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The graphs given in this section shows the percentage of well fill for the various contributors to the total number of electrons in the well, as a function of range. It is clearly evident that the atmospheric transmittance and path radiance has a huge effect on the percentages as a function of range. Particularly, what appears as a reasonable large percentage at short range diminishes to sub=10% values at longer ranges.\n", "\n", "The relevant temperatures are stated in each graph in order to assist in interpretation of the results." ] }, { "cell_type": "code", "execution_count": 25, "metadata": { "collapsed": false, "jupyter": { "outputs_hidden": false } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Values available in the HDF5 file\n", "atmoUnique: ['MLS23km' 'tropical5km' 'SW31km']\n", "sensorUnique: [u'SensorA']\n", "tempAmbUnique: [ 233. 253. 273. 293. 313.]\n", "tempInternUnique: [ 133. 153. 173. 193. 213. 233. 253. 273. 293. 313. 333. 353.]\n", "tempDynamicUnique: [ 233. 235. 238. 243. 253. 263. 273. 283. 293. 303. 313. 255.\n", " 258. 323. 333. 275. 278. 343. 353. 295. 298. 363. 373. 315.\n", " 318. 383. 393.]\n", "tempInternDeltas: TempAmbient + [-100, 0, 20, 40]\n", "tempDynamicDeltas: TempAmbient + [0.0, 2.0, 5.0, 10.0, 20.0, 30.0, 40.0, 50.0, 60.0, 70.0, 80.0]\n", "pathlenUniqueGen: [ 1.00000000e-03 1.00090000e+00 2.00080000e+00 3.00070000e+00\n", " 4.00060000e+00 5.00050000e+00 6.00040000e+00 7.00030000e+00\n", " 8.00020000e+00 9.00010000e+00 1.00000000e+01]\n", "tempCentralObsDeltas: TempAmbient + [-100.0, 0]\n", "tempHotShieldDeltas: TempAmbient + [-100.0, 0.0]\n", "TempAmbient [C]: [-40. -20. 0. 20. 40.]\n", "TempScene [C]: [-40. -20. 0. 20. 40.]\n", "TempInsideDelta [C]: [-100. 0. 20. 40.]\n", "TempCentralObsDelta [C]: [-100. 0.]\n", "TempHotShieldDelta [C]: [-100. 0.]\n", "TempDynamicDelta [C]: [ -40. -38. -35. -30. -20. -10. 0. 10. 20. 30. 40. -18.\n", " -15. 50. 60. 2. 5. 70. 80. 22. 25. 90. 100. 42.\n", " 45. 110. 120.]\n" ] } ], "source": [ "# to determine the unique values in the database for the generic atmosperes\n", "def loadGenericAtmodata():\n", " # The data file is analysed to extract the sets of unique values for subsequent filtering and plotting.\n", " sensor = dfConcept.columns.values[0]\n", " with pd.HDFStore('data/well-fill/radio-{}-{}-{}.hdf5'.format(Cconcept,sensor,'gen')) as store:\n", " df = store['df{}'.format(sensor)]\n", " atmoUnique = df['Atmo'].unique()\n", " sensorUnique = sensors\n", " tempAmbUnique = df['TempAmbient [K]'].unique()\n", " tempAmbUniqueC = df['TempAmbient [C]'].unique()\n", " tempSceneUniqueC = df['TempScene [C]'].unique()\n", " tempInsideUniqueC = df['TempInsideDelta [C]'].unique()\n", " tempCentralUniqueC = df['TempCentralObsDelta [C]'].unique()\n", " tempHotAmbUniqueC = df['TempHotShieldDelta [C]'].unique()\n", " tempInternUnique = df['TempIntern [K]'].unique()\n", " tempDynamicUnique = df['TempDynamic [K]'].unique()\n", " pathlenUniqueGen = df['PathLen'].unique()\n", " tempDynamicUniqueC = df['TempDynamic [C]'].unique()\n", "\n", " print('Values available in the HDF5 file')\n", " print('atmoUnique: {}'.format(atmoUnique))\n", " print('sensorUnique: {}'.format(sensorUnique))\n", " print('tempAmbUnique: {}'.format(tempAmbUnique)) \n", " print('tempInternUnique: {}'.format(tempInternUnique)) \n", " print('tempDynamicUnique: {}'.format(tempDynamicUnique)) \n", " print('tempInternDeltas: TempAmbient + {}'.format(tempInternDeltas))\n", " print('tempDynamicDeltas: TempAmbient + {}'.format(tempDynamicDeltas))\n", " print('pathlenUniqueGen: {}'.format(pathlenUniqueGen))\n", " print('tempCentralObsDeltas: TempAmbient + {}'.format(tempCentralObsDeltas))\n", " print('tempHotShieldDeltas: TempAmbient + {}'.format(tempHotShieldDeltas))\n", " print('TempAmbient [C]: {}'.format(tempAmbUniqueC))\n", " print('TempScene [C]: {}'.format(tempSceneUniqueC))\n", " print('TempInsideDelta [C]: {}'.format(tempInsideUniqueC))\n", " print('TempCentralObsDelta [C]: {}'.format(tempCentralUniqueC))\n", " print('TempHotShieldDelta [C]: {}'.format(tempHotAmbUniqueC))\n", " print('TempDynamicDelta [C]: {}'.format(tempDynamicUniqueC))\n", " \n", " \n", "loadGenericAtmodata()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Perfect optics\n", "The following graph shows the situation for perfectly transmitting and cold optics: there is no flux from any sensor component onto the detector. It is clear that even for perfect sensors the path radiance has a huge well fill contribution and the 2 K contrast target contribution approaches very low well-fill values at long range.\n", "\n", "Note that the noise consumes four bits and about three bits (SensorA).\n", "After losing bits to well fill and noise, the usable 40 K dynamic range image has between 7 and 9 bits (SensorA and SensorB) and 11 bits (SensorA)." ] }, { "cell_type": "code", "execution_count": 26, "metadata": { "collapsed": false, "jupyter": { "outputs_hidden": false }, "scrolled": true }, "outputs": [ { "data": { "image/png": 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kEvgAaAccBsYopXJrO/aGDRtYsWIF991331mT19OYNWsW+fn5lJWVceedd2qD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\nvCLSJIT4EOUzxKJ85SdR/XE/QeVFn0VJD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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# to plot the well fill for perfect optics\n", "tambient = tempAmbUnique[3]\n", "tinternal = tempInternDeltas[0]\n", "ttarget = TempTargetDeltaC\n", "thotshield = tempHotShieldDeltas[0]\n", "tcentralObs = tempCentralObsDeltas[0]\n", "dynamicRangeK = tempDynamicDeltas[9] #TempDynamicPlot\n", "# only do for SensorA\n", "sensors = [dfConcept.columns.values[0]]\n", "\n", "# dfBitsLoss = plotPWellFillRange(dfBitsLoss=dfBitsLoss, dfConcept=dfConcept, atmoSet=['MLS23km','tropical5km'], \n", "dfBitsLoss = plotPWellFillRange(sensors=sensors,dfBitsLoss=dfBitsLoss, dfConcept=dfConcept, atmoSet=['tropical5km'], \n", " tambient=tambient, thotshield=thotshield,tinternal=tinternal,\n", " tcentralObs=tcentralObs, \n", " optFilter=optFilters[0], atmotype='gen', intTime=integrationTime, \n", " areaDetector=areaDetector,wellCapacity=wellCapacityUsable, \n", " dynamicRangeK=dynamicRangeK,ttarget=ttarget,threshold2Noise=threshold2Noise,\n", " netdstd=netdstd,ksys=ksyss[-1],doPlots=True)\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Worst performance optics\n", "\n", "The worst case is for sensors where all obstructions are hot and the sensor and ambient conditions result in high internal temperatures. The following graph shows the situation for hot optics and hot obstructions. Note that the central obscuration are all at internal sensor temperatures, i.e., no cold reflections.\n", "\n", "The contribution of the warm central obscuration in SensorA is quite large: these components have to be cooled or reflect the cold detector. Even in the case of the near-optimal SensorB, the hot optics and path radiance contributions are huge. \n" ] }, { "cell_type": "code", "execution_count": 27, "metadata": { "collapsed": false, "jupyter": { "outputs_hidden": false } }, "outputs": [ { "data": { "image/png": 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yS4HVQBPR1FGIjIyka9euzQZ2Q8Mb1xdE5xNQwZbCwsIG21VVVVRUVFBTU0Ns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VAAAg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\n8sYbR5g5MxatVtCzpw+zZ5v3mHJ11Zq8l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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# to plot the well fill for the worse optics\n", "tambient = tempAmbUnique[4]\n", "tinternal = tempInternDeltas[3]\n", "ttarget = TempTargetDeltaC\n", "thotshield = tempHotShieldDeltas[1]\n", "tcentralObs = tempCentralObsDeltas[1]\n", "dynamicRangeK = TempDynamicPlot\n", "# only do for SensorA\n", "sensors = [dfConcept.columns.values[0]]\n", "\n", "# dfBitsLoss = plotPWellFillRange(dfBitsLoss=dfBitsLoss, dfConcept=dfConcept, atmoSet=['MLS23km','tropical5km'], tambient=tambient, thotshield=thotshield,tinternal=tinternal,\n", "dfBitsLoss = plotPWellFillRange(sensors=sensors,dfBitsLoss=dfBitsLoss, dfConcept=dfConcept, \n", " atmoSet=['tropical5km'], tambient=tambient, thotshield=thotshield,tinternal=tinternal,\n", " tcentralObs=tcentralObs, optFilter=optFilters[0],\n", " atmotype='gen', intTime=integrationTime, areaDetector=areaDetector,wellCapacity=wellCapacityUsable, \n", " dynamicRangeK=dynamicRangeK,ttarget=ttarget,threshold2Noise=threshold2Noise,netdstd=netdstd,ksys=ksyss[-1],doPlots=True)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Well fill as function of target range\n" ] }, { "cell_type": "code", "execution_count": 28, "metadata": { "collapsed": false, "jupyter": { "outputs_hidden": false } }, "outputs": [ { "data": { "image/png": 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M9evXk5SUxH/+8x9uuOEGM+WBwWAwGAxnMT6R01QZqjunqapQVfbs2VMqN+rn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EiPDlVqamqHOlR2u90vUg5AYy1qTznQGna73S+GuGs6Op/WmueklEIIoUNLOXDpaWlYeHl5OVFR\nUV6w6MLxFy2aDnXRXh0Gg4HBgwczePDgZvssFgsHDhwgOzubvLw8vv/+e6qqqlxOVVRUFKmpqfTv\n35/+/fvTu3dv0tLSSE1N9ZgksL06PKUcKC0tbdaMqnZaSjlQUVHR4ggsNdLSEHdNh3fwFx3QcsoB\nIUS8lLJ9s01fAnynE8AF4hxF4As4s9S2hK9o0XSoi47QYTAYyMjI4Je//CXz58/n1Vdf5e233+bT\nTz/lq6++Yt68efTv35+8vDxef/11fvOb35CZmUloaChxcXFkZmZy55138uKLL/L5559z6NAhLBZL\nq+fcsGFDq/vnzJnTbh3e4nxanFOLqJ3s7OxW92s6Ohd/0dFGlnjjpH4TaWoJXxqGfD78RYumQ11c\nah0Gg4GhQ4c2mhrCidlsZv/+/Rw4cIAjR46wefNmqqurXa/o6Gh69+5N//796devnytClZKSct7z\nfvnll5dUhzd58sknvW3CJUHToS78RQeAlPIn3jiv3zhN/pBIMSMjA/B9LZoOdaEmHUFBQQwbNszj\nFBFms5l9+/Zx4MABDh06xMaNG6mpqXE1+8XExNC9e3ciIiJcuXBMJhM9evTghhtuYNy4cYSGhnaa\nloth4sSJgO8kt2wJ5yhNX0+mqOlQL2pLbun1juBCiCRgGRCHMoTwdSnlK0KISOB9oAfwAzBDSnnW\nQ305YcIECgsLmTRpkmuE08mTJ1m/fj2TJk3i1ltv7Sw5F83y5ctZt26dz2vRdKgLX9dRW1vLvn37\n+OSTTzh48CDh4eHY7XYsFgtms9mVRNFgMBATE0NiYiLJycn06NGD5ORkEhISSExMJDExkYSEhAue\nw+9SsnDhQpYvX87MmTNJSkri0KFDZGRksHHjRsaPH8/06dO9bWKb+Oijj1w2R0dHA0pKDk2Hd/A1\nHefJ0zQPuAUlwWWhY3cSMBN4T0rZ6eF+NUSarMDDUsosIYQJ2CmEWAPMAr6WUj7ryAr6R5QPsBl7\n9+71mNzypptuYtasWap/IDjJysriq6++8phM0Ze0aDrUhT/oCAkJwWAwUFRUxMcff9xistHnnnuO\n/Px8jh8/TlFREZs2baKqqgqz2UxdXR11dXXU1NS02bnqqDxVGzZsYMmSJR6TW06dOpX7779fdQ83\nT2RnZ7N27VqPyRTVrmPAgAG8+uqrjB8//rw64uLi6Nq1K+PHj/eStW3Dl69HC8zBc3LLF4D9wI/P\naXL0fi91LFcLIQ6ieJJTgQmOYkuBDbTgNPlaAsLW8Bctmg518WPRkZCQQEJCAmPGjGnxGHa7nZKS\nEo4ePepyrjZu3EhVVRV1dXUuB8vpXMXGxpKQkED37t3p0aMHSUlJjRyrC3Wu2pTcMj4eysrafew2\nExcHpW0bgLR582bmzp3L/v37CQgI4LLLLuP+++/32WSKTScHbk3HjBkzVO8wOXHqWLVqFXfccQdv\nvPEGs2fPdl2PF198kWeffZa6ujqmT5/OokWLmmXYd5KVlcWdd97JwYMH6devH2+88YbH0bROtm3b\nxlNPPcW3336LXq+nd+/e/PrXv+aOO+64UDmqS27p9eY5d4QQPVGcowFAgZQy0m1fuZSy2bhoIYS8\n6aab2LJlS4sJCEeMGNEp9l8Ktm3b5kpC6MtaNB3qQtPRfpzOlTNyVVxczIkTJ1yRK2ezYG1tLUaj\n0RW5as25cs9ftWrVKu6///5GyS379u3rSkI4bNgw6Aynow3PgKqqKrp3785rr73GTTfdhMViYdOm\nTcTHx1NfX89rr73WYjJFT/3X1MjOnTv9Rscrr7zCV199hcFgYOLEifTu3ZuSkhIGDRrEn//8Z775\n5hsSEhK44YYbyMzM5C9/+Uuz4zQ0NJCWlsbDDz/Mvffey7/+9S/+9re/kZub2yyKBfDdd98xefJk\nnnzySWbPnk1UVBS7d+/m2WefZfny5S3ae57muWuBf6BMp9IsuaWUctWFfUoXjmqcJkfT3AbgaSnl\nZ02dJCHEaSlltId6WnJLlaLpUBeajo6zp6ioiKNHj3Ls2DFKS0tdiUDdmwVra2sJCgoiJiaGuLg4\nYmNj6dq1K1arFZ1OR0NDAz//+c9JT08nIiICk8lERGfk1GnDM2Dnzp1cffXVro7FTbHb7SxYsIA3\n33yTEydOkJiYyHvvvcewYcMoKSnhgQceYOPGjYSFhfHQQw/xwAMPAMq8cQcOHCAoKIhPP/2UHj16\nsHTpUtfIy9bqNmXWrFmEhIRw9OhRNm3aREZGBh999BHPPPMMS5cuJT4+nuXLl7siJSkpKSxZsoRJ\nkya57DAajXzyySfExcXxf//3f2RmZrqStrqX3b9/P0ajkc8++4yUlBQ++ugjPv74Y1588UWCgoJ4\n4403uPrqq5udx6k5NzeXt99+m2PHjpGSksK///1vnnjiCWpqavjLX/7CsGHDmDNnDgUFBdx22238\n/e9/b9cl/fWvf018fDwrV65kypQp3H777aSlpXH77beTkpLCggULAPjmm2+49dZbKSkpaXaMtWvX\nMnv2bAoKClzbevToweuvv87kyZOblR83bhxDhgxpd1oDLbnlBSCECAA+At6WUn7m2FwmhIiTUpYJ\nIeKBEy3V//LLL8nOzkYIQc+ePTGZTOzbt4+qqipXIj3nCCJnzho1rjuXjx07Rp8+fZgwYQKrVq1i\nzZo1VFVVMXLkSFXZ29J6bm4u06dPJycnh9zcXNeIp23btrFnzx4uu+wyVdmrXQ/telyK9eTkZLKy\nshg4cKDH/VarlfXr11NcXMypU6cwGo1s3rwZs9mMyWRCp9NhsVgQQqDX62loaKAz5qTPzs52jbpy\n5vlpup6eno5er2fq1KlMmTKFW265hYiICD777DNSU1NZv349ixYt4qWXXiI6Oprc3FxKS0vZu3cv\nt9xyC7feeivvv/8+a9eu5e6776Zv375cffXVlJWVsXLlSlasWMFbb73FXXfdxaxZs9izZw9SSq66\n6iomTZpESUkJBQUFjB8/HqPRyN133+3R3uXLl/Paa6+xYsUKrr32WoYNG8b999/P6dOneeKJJ7jr\nrrtYsmSJq3x+fj6xsbGUlZXx+eef88c//pEPPviAzZs38/e//901otP9syorK+OLL75g5cqV/OEP\nf+Dxxx/nmmuu4a677mL16tWsWLGCe+65h/z8fLKzsxvlH3PWdzZbHjp0CFAiqLm5uSxdupQHHniA\n6667jvXr15OVlcWMGTOYMWMG48aNa/H6ONc/++wzampq+Pbbb/noo4/46quvsFgsHDp0iJqaGnbu\n3NkoFUhgYCAnTpxwJb10P97+/ftJSUlp9P1ISUnh66+/djlNzvK9e/fmu+++Y/bs2W36Prmv5+Tk\nuJymFvKXDQdsUsqPhRD9gClAJPBfT4U7GlVEmoQQy4BTUsqH3bYtBMqllAsdHcEjpZTN+jQJIeSY\nMWMoLy/HZrMxfPhwDh48SEZGBjt37uTyyy/nF7/4RSequXCysrLYs2cPW7du9Wktmg51oelQH560\n5OXlccstt5CXl0daWhoTJ05k4KBBHW7L4ZwcwsPDMZlMhIaGttgPKScnh4ULF/L1119TWlrKtdde\ny8MPP0xxcTGPPvooPXv25I477uDIkSMMGDCArKwswsPDWbx4MT/88IPrOM888wxHjhxhyZIlPPXU\nU2zZsoU1a9YAcPDgQYYPH05NTQ1bt27l5ptvbrFuU2bNmoXBYOC1114D4B//+AeLFi1i//79gNKH\nafz48a5omXsE6L777mPz5s1MnjwZm81G165defLJJ1m2bBlZWVn8+9//5r333nNFmr799ltWr14N\nwBdffMGtt97K2bNnEUJQXV1NeHg4Z86cITw83GOkKS8vj2XLlnHs2DFSU1MpKipy9dOLiYlh0aJF\n3HTTTQBMnz6d8ePH8+CDD573Wu7Zs4cbb7yRzMxMYmNjWbVqFb169eIXv/gFWVlZvPXWWyxbtszl\n9FitVgwGAz/88APdu3dvdKwFCxZw4MAB3n33Xde2X/ziF6Snp/PEE080KltcXOwaAZqenn5eO905\nT/PcfJQmugBgLUrEaQNwNbBaSvnndp3sEuD1SJMQYgxwG5AthNgNSOBRYCHwgRBiNkonsBktHSMn\nJ4f33nvPFd7+4IMPCA0N5eabb+a+++7zmRtpRkYGr7zyCq+//rpPa9F0qAtNh/rwpOWxxx5j0qRJ\njBs3jkWLFrlyOXU0f/rTn2hoaKChoQGbzYbRaCQkJISwsDDCw8MJDw8nMjKSLl26MH36dGbNmkV5\neTmPP/44r7zyCnFxcQQHB/PQQw/xzjvv8NZbbxESEsK0adO44YYbKCoqck3TI6XEbrc36lTt3qk/\nJCQEs9mM3W53ddJvrW5T4uLiXMvBwcHN1qurq1usZ7FYWLhwIVarlRtvvBGbzcaMGTOYNm0ar776\naqvniYmJcTmbzkEBTuepLbh3Pm+P3WFhYYAyQOLAgQNs3LgRo9HI0qVLsVqtvPPOOzz44IPMnDmT\nadOmsWTJEiorK131nY6e8zjumEymRmWd5T2VjYyMRKfTUVJS0m6n6TxMBzIAI8qAsSQpZaUQ4nlg\nK/Djc5qklFuAljojXNWWY+h0OvR6PXq9nsTERFeCO6PRqOrRG55w6vB1LZoOdaHpUB9NtRgMBkBp\nMulMLXPnznUtWywWKioqqKio4OzZs5w9e5bq6mqOHDmC2WymoaEBq9WK1WrFaDSybt06xo8fz6lT\np3j88cdJSUnh3nvvJTg4mODgYCwWCxERETz++OOYTCZMJpNr37fffktpaSmVlZUUFBQQEhLiSloK\nkJycTGpqKjk5OZ3yOTibRfV6fSMn5mK/W6Ghoa48YqDMkXipqKqqarS+fv16jh49SlJSEqCMKn30\n0Uc5ePAgr7zyClFRUezZs8eVdiArK4u4uDiP89H179+fF154odG2vXv3euxTFhwcTGZmJh9//DET\nJkxotv8isDr6LtUKIfKklJUAUso6IYRXRs953Wm6FOj1esxmM0FBQa7QLCievi8Np87KyiIgIMDn\ntWg61IWmQ3140rJr1y4A6urqvOYAGgwG4uLiGkU6AFfnamfOotLSUubOncuoUaPo1q0bv//973np\npZe4++67SUxMJC8vj9raWmJjYzEYDKxYsYKBAwditVo5deoUFouFqKgodu7cSWVlJY888ghWq5Wq\nqirsdjvTp0/HYDBw+vRpMjMzGTlyJGFhYdTU1BAQEED//v0JDQ0lODiYkJAQQkJCqKioIDQ0lOLi\nYoKDg2loaGimr6XuKM5+RvX19RiNRp544gmWLl0KQE1NzUV9phkZGbz33ntMmTKFrKwsPvroI669\n9trz2nQhPPLII8THx/Poo49iNBqZNm0aN910E3PmzKGmpoZ+/fqxZMkSbr31VuLj41mwYAGzZs3y\neKyJEyei1+v5+9//zj333MO//vUvdDqdq5mxKc8++yzXXHMNPXr0YNasWS4H7Zlnnml19Nx5sAgh\nQqSUtYBr+KIQogteSjngF07Trbfe6hrK637jtNlszJvnMbWTann55Zdd/zh9WYumQ11oOtRHS1qc\nTgNAQ3Q0gadPd5gNDdHNBiR7JDQ0lOzsbJYtW0Z1dTVhYWFMmDCBadOm0adPHwICAqitreVvf/sb\nJ0+eJDExkccff5zRo0dzzz338Nxzz/Huu+/S0NBAz549uf/++xk5ciSLFi2ioKCARx99FFD6xqxd\nu5aHHnqImpoaJk2axL///W/efPNNrFYrUVFRjB07FovF4mpOdL727dtHXl4ep06dwmq1kpeXR2Fh\nIdOnT8doNNLQ0IDdbmfu3LmEhoZSWVnJ559/TklJCceOHSM+Pp4tW7ZgNBpdOY0OHz6M3W4nLCzM\nNRqyLU6Ou9P79NNPc8sttxAVFcWECRO47bbbGo1CbOogn2+9NUwmE6+88oor75LRaCQ8PJywsDAq\nKyt58cUXWbFiBVdccQVms5np06czf/58V/3rrruO8ePHM2/ePAIDA1mxYgVz5gZaHTwAACAASURB\nVMxh3rx5XHbZZXz22Wce0w0AZGZmsn79ep544gkWLFiAXq8nLS2N3/zmN2223wPjpZT1AFJKdycp\nEPjVxRz4QlFFR/CLwZlyYMqUKd42RUNDQ+OC2bFjB9dcc423zfArLBYLVVVVrldNTY0rt5YzkanZ\nbMZqtWK327HZbNjt9kbLTqfMbrdjtVqx2Wzo9XoCAwMxGAwYDAaMRiNGo5GgoCDXy9kM6Vx2L+Np\n2X1bZzfRepPzpRzwklkt4heRJg0NDQ0NjaYYDAaio6Ndc7BdCmw2G2az2eWAOXNxOR2wuro66uvr\nqampwWKxYLFYXM5WS46Zu3Pm3BYQEEBgYKDLOXM6WO6OWUhIiMsxc3fAnHXcHbvWtvlarjZvojlN\nKiIrK8uV38WX0XSoC02H+vAXLfn5+aSmpnrbjIumPTr0ej2hoaGuQQgdgdVqdUXE3KNi7s6Z2Wzm\n7NmzrqZKq9VKXV0dgYGBLkfMOeKwpZfTSXPvBN/UWXN3styjas6Xc1tbHbSm22w2r+SovGA0p0lD\nQ0NDQ0NFBAQEuNI9tIcLdWKdDld9fb3LIauvr8disbjena+GhgbOnDnjctScfcs8OWVtcdq2b9/O\nz3/+c5ezZjAYPHbiVwtanyYNDQ0NFaD1adL4MbJ69WoGDRpEdXU1NTU11NTUsHLlSv773/9qfZo0\nNDQ0NDQ0NNwxGAxERUW5Epnu2LGjWRkhRBKwDIhDSTfwupSyfRPdXQJ8J7FJGzhw4IC3TbgonPNU\ngW9r0XSoC02H+vAXLfn5+a7l48ePe9GSi0PToW6EEKMAK/CwlLI/kAn8RgjRt7Nt8Sun6WKTkKkJ\nf9Gi6VAXmg714S9a6uvrvW3CJUHToUrCpZSlUsosACllNXAQ6NbZhviV0+Tr+MNoGtB0qA1Nh/rw\nFy3+MHIONB2+hhCiJ8qcdFs7+9x+5TT5eqd2d/xFi6ZDXWg61Ie/aFGTjqeffprFixdfUF016bgY\n/EWHA1eHcCGECfgI+K0j4tS5hvj6ByuEkCk9exIdE4PFYnFNS+CLVFZWuoaY+rIWTYe60HSoD09a\nAgMDGTVqVKNykydPdk0R1RGYzWbWrFnTprJLliyhtrYWnU6HEILo6Gh69erFsGHDEEJgtVpbnGJD\n7dTW1hISEgKgeh0VFRW8/fbbpKenNxo1fvz4cdatW0dNTQ3x8fFceeWVHifihXPX/fjx4wQHBzNm\nzBj69m25e1BNTQ3ffvstR48epaGhAZPJRHp6OpdffvlFf1bff/89DQ0NVFZWuiYgrq6qoqq62jV6\nTgiRKKUsFkIEAF8A/5VSvnxRJ75A/MJpci6PAcY12b/J8e5p+xaV1TsLXO8Ddp6v3gA861Cbneer\ndwzo4QN2nq9eUx1qtfN89Y4Bx33AzrbU+wLo0mR7WWIi5aGhfHbkCABT09J4qEm05K233gLgjjvu\naLbdOcHsr371K4/7z1dvaloa0/r0abT/05wcAKb16cMf1q1jzuDBXBYTw4cHD/JlXh4A3cPD+dP4\n8S3Wk+B6rcjJ4XOHvuvT0vhpnz7YHfsAvsjJQQLXNan335wcVjvqTU5L42qHnc66ax3nu7JJvXU5\nOWxw1JuQlsZEx/mcdTc6zjekTx9C3ezYlJPDt456mWlpjHGrJ4HvHPVGOexwnm9rTg7bHPWGp6Ux\nws0egO2OesOb2LkrJ4fdjnoZaWlkOPY77dzjqDeoTx/Wb92KzWYjJDiY0JAQ9jvq6YRgxNChdIuL\nY9+hQ5wsLycuNhaAfm7Hk8DXGzZQVa0EaZKTkigpLWX8mDGYwsIAOOQ4X58+fbBYLGzatImoqCgM\nBgNHjx4FICIigkGDBjXKJZXjuA59mnyPcnJyOOKwMy0trdH+Xbt2UVxcjCeaphwQQiwDTkkpH/ZY\noRPwC6fpTeAObxuioaHhs0iUh5MNZYhO01d7tre1rPN8ztexxETGDx3qcgRsQNLvf9+xwoEPXnzR\noV9iA6R0/zyU54Md+OzrdYwYPJiYmBjXw/70mTNs2LyZSRMmYLXZ2Lp9O9dcdRUIgQSKS0o4cuQI\nE8aP5/Dhw1RVVaHX6ykpLSUkOJhhGRlEdOmCAA7n5nL0+HHqLRZCgoPp36cPSQnxAPxQUMDR48eJ\niojkaEEBRkMgo4YOoaq6huxDh7DbJYP79SOlRzIAW3ftJiQ4mEH9lOhJYXEJ2YdyqKmpxWg0MnzI\nIBLiuiofgPOxLGDll2vp3asnx44XUlVdzfSfX8+hQ7nk5R/DbK4nJDSYgQMvo1tSAgg4evQ4R/OO\nERUTxdG8YxgMgWRcPoj4bsqxq2tq2bFlF2crKomKiSA03ERDQwPDxg1BCig/WcH+7QeoPlNNsCmY\n/qP6E50Q3cgm3NyGorwiyn4owxRpoqayhiFXDgEBxw4cozCnkDE3jgEBNquN1W+sZvzM8ZiiTI10\n2hpsrH5tNRNun0BoRCgIyFqVRZApiL7j+jY+N3Bo8yHKcsuYMGtC8w49gmY2trruYd+u73ZRbChu\nvC8HONDYaRJCjAE2Atmc8zcflVKuohNRbwzyApDAO0A+8ATKv9JSYIQ3jbpA/EWLpkNdXCoddsAC\nNDjeL+VyPVAvBGYhsAioR2AWjnISGgQ02CUlUlIDxOsEtVJSL8EgBFak4phIp0OiOAM2ec5BsXPO\naXEug/JMcL6E27sQjmUhlPu6cG5XFoT7w8M50aqjshQgHct2x7IUYBcSqVOcFatZEh8EhdGKndgB\nI3S8ywQFw+1uQj18AE7bt0Dd5VDX99x2k4gg+EAwJ7uX02NiDwyHDZSGnKTrT7tCHRT9s4ikqUnI\na0F+DKWflzH8d8MZNGgwOe/nsPtANmPvHQuAcVsoo/uMwdjFSPH3xWx7bTemOZMwdjFi2Qins8+Q\nNKMHk8dN5vCHh9myeRdxQ+OY+LtJnD54mp0v7STqwXj0Rj0N5RJLtKTqJjsVuRVsXb2b4b8bTkz/\nGMwVZqxmK1UJzafvsG+QHK0qZMSvRxBoD6RmiJ2AjcFkzhiNMVWx6/vXdjLpNqdddsp3nCFxWhKT\n/28yx9cdZ8enu7n6rqsB2PbkTqJGRjFyxijO5J5h27PbiBsWh+0aqCuvY+u8bQz5zRC6Du7KqX2n\n2PHKDq742xUYwpo3PTfUNnD488Nk/l8mx9cfhzJcocrq/GrCB4QrYU1Aj57QL0OpsldhCjXBQKAG\nqIPq6mpEgCD06nPTwISXhXP60GkY1vz7cerTUySMT1C6XXcEhUBik21nAbdMHEKZvTgFeEpK+Sch\nRHcgXkq5rYOsahG/cpruQ/ktr0d5IIQBPwe2e9OodrABmOhY9mUtG9B0XEpsgBmou4D3OuCQEMTq\ndJiBzXY7dqBMSr4N0FNtl2TZ7Vym12NBYpHQ4HoHK5IG2ThSYkNxvvSOl8vREE7nQiCEw8Fw9zx0\nAulYlzqw68CuB7tOYtNLrDqJTYdyV9JLx8txkgCUm36kYz3fcdwzUDhGKk7GDmBCkzo6t+WW3p3L\n+sYOlGeaRuYvMFJ/FOURAEpbnQB5FmzXoHiH3wBXXtih203vNpbTA+Eo18BJGRijjDRYGmAfdEvv\nRtH2IrpO64qlwcLJAycZeO9AV/GoPlF0HaxEYbqN68bR1Udd+xJGJLiWE0clkvtZLmdyzxA3LA6A\nkK4hJI1PUspmJnDksyOk35iOLkBH7MBYhF5QU1ZDePfGU48U/K+A7hO7E9M/BoCgSA99xMpQUiYC\nKekpBFmClG1DIGFUgnI9Uj3bFRwTTPeJ3QFIGp9E9pvZ1J+tx261cyb/DKMeG4VOryOqT5SrDkDR\nliLihsS5Po+YATFEpEZwIusESeOSmpl4+KPDdL+iu0f7rWYrxnBjIx0BtgBspx2O4UAgENgMth42\nAoIbP/oDQgKw1lmbfy5AQ3UDxgijx32dyKsoP81JwJ+AKuBj4PLONsSvnKatwC5giGM9EuX+44v4\nixZ/0rEdGAqcQnFKqlDixE7npDUHphaoFYJqnaAW4ViHWumoLyVmJGYJ9ShRkwaUlw3lfqcH9AL0\nDmdEp3M4JXolDGLXn3s1BNhpCJDYAwGLhDCb8mvPAy4DDsHqDMe2XbBjtO2cAxHo9mq6bnC8As5F\nbZojm7xfItwdjX8BdzveHf+u2YdygXyNQuDXwPuOdQMtfbCqxFxhJtAUCKcg6ZYkNjy0AZvFRsmu\nEqK6RmHscu6B676sN+ixWWxIu0ToBIUbC8n/bz51J+sAsNZbsVRZWqwLYAg3NNpmNTd/8JtPm+k6\npGub9QTbg5VH8X+V9cLvC8lfmU/dhy3YFdHcLqtZKWMwGVzbAIKjgqkrV45Td6qO4u+LKdtVpuyU\nYLfbie4f3cymsz+c5dS+U4z7a9PecQoBQQE01DWer81aa0XfT6/csMD1vdIH6Zs5SA21Dc0cKSeB\npkDqz3g959NIKeVQIcRuACllhRDCKyNB/MppCkS51zgbQU/iWzkVJrot+7KWiW7L3tIhUZyRKqCy\nybv78lmgQqejQgjOOtbPSkmVlNQ4nBdnk5Hzx5Ls0GAGxukFOiEQznCLXiD1YHM4L9YASUOAHVsA\nECghQJ5zQIycc0SMTV5BjvdgpUyDTnGgzqlzf28Hr6P00i8GJqNEbw4Do9t/qE4nxW1ZT+OQUA2N\n+0qonda0mPEZLWeqz1BfUU9UnyjYD0FdgoiMjaRkWwmFGwvpmdqzTcepO1XH3iV7yXwsk8h0JZS1\n8Y8bkZfA8Q6KDqK2rLb1QnFuyzpc16PuVB1739hL5lWZRN7ePruCIoKwVFuwWWwux8npMAEERweT\nNC6JQXcOOu+xTh88Te2pWtY9sA5QnDJpl1QXVTPuz+MwJZko3Fjo0mE1W6mpqiEsMeyc0+T4XpkS\nTEi7pKashtA4pYmu8lglYd3CPJ47dkAspTtKSf95+nnt7EAahBB6HDc9IUQs5wsKdxB+5TQ9CExD\niVA+hpLI4WmvWnTh+IuW9uhwOjpNnZym72eBCr2OMwgqBFRKxdGplpJqJLVScXJ0OIIlOtDrBDol\nTIM9AKwGsATasRglGO2KkxKE4qSEAiGOd+f2PJTOiSVgzkBpb78Czg5wHxvjA4wE3kNxMtbh0uFz\n+IsOOKfFCuwBClCaU1SMtc7K6YOn2f/2frqN7UZYUphi/yZISk4i98Nc6s7UEX97fNuOV28FAYFh\ngUi7pHBjIVWFVZfE1u4Tu7P1ma10HdKV6H7R1J+px1pnxZRo8lwhWdGBGax7rCAhsH/77QqOCSYi\nNYLDHx+mz019OJt/lrJdZcQNVTybbmO6sfnxzZzce5KYATFKc17eGULjQ5s1wfW4sgfdRp9Lfp33\nRR51p+oYOEf5oiQMT+DQu4co2V5C14yuHP7kMF26dcGUa1KcJbfvld6oJ/7yeHI+zGHwXYM5e/Qs\nJ3adYPRTnv85pVyXQtGWIrIWZdHnpj4ExyjRsqNfHSVpQhLhyeEe611iXgE+BeKEEH8GpgOPd8aJ\nm6IKp0kIsQTl/2+ZlHKQY1skSsC6B/ADMENKeba149yG0o9tnWP9M6DTJ6a5CDZwLkrja1pswGng\nBPA1kMo5JycNxYnZKAS9heBNneBFKamSUCkltR4dHYFexzlHJxCsgVAfaKchSILBfs7JCeack2Ny\nvIeB3aDcxxUuwLk5Cjjv+cOA7o5tADOB2PYdzmu4N2sNAhLQdHgbT1qOoHyXx6HkIwBqbDWE6kM9\nHODSUGNr3xQu25/fjtAJhBCYkkz0GteL7tOU/jz0BCIhPiae7OezSRiegL63vrXDuQjrFkav63qx\n5cktCJ0gaVySEr1qDy1E5yJ6RTD4nsHsf3s/dSfrMHYxMmDWgMZOk6MvkEAov/kkZVsYYfSa3Ist\nr12YXUN+M4Ssf2Wx5p41RPSKIDEzEWlX7kPB0cFc/vvLOfDuAXb9YxdCJ4joFcHA2c09Zr1B36iZ\nLyAoAF2gDoNJaaEyhBsY9rth7Ht9H1mvZhHRK4Khfxiq3EzLIHdTLuVl5Yy4XhnyMeCOAexZvIc1\nv16DIczAwDkDW4w0GUwGRj81mpwPctj8xGZs9TaCooJIzEx0Rao6GinlO0KInZzr6TdVSnmoU07e\nBFWkHBBCjAWqgWVuTtNC4LSU8lkhxFwgUko5z0NdmYHy58CJU5HzN7Sy40y/pGwAXmiyzZta6lDu\nJSccL+dyoU5HkU5QLCUn7JJyxygmA2DUC+w6MNula9SQc8SQq1OuHqWTU4jjZULpWW1C+ZGrhaPA\nd+cpc2tnGHKRaDrUhwctiaGJDB3h1ilrQqdadGGUAR4eXeuXr2fQ+EHEdIvxaR2NuAQ6dr2yC1M3\nU8c1dXWSjkvJrg27KE5skqdpI8qoG/i8SXHno1ACSCl/1rHWNUcVkSYp5WYhRI8mm6dy7vIuRfEp\nmjlNALkon+QtKJFu77uBF8ZE4CYUB7AjtNiBCho7QCeAEqBIr6cIKJWSk9LOGceIqWABgXodwiCw\nGqE2xIbVZFecnC5AFBCtvMyBYHZa/CzKSJuBeJ5SseclFNZRpAAf0roOX0DToT48aalC3eFkT8QB\nm1H+/PQEoqBkTwnCKIi5MsarprULDzouBWfyzxAYGkhI1xBO7j1J6a5Sxk4de2kO7okO0uFFMlEa\nFpejjMfxem8/VUSaABxO0+dukaZyKWWU2/5G627b5RKUNA/Lgb3AT1Ccjv6dYvmlxQaspe1a6jnn\n/Lg7Q8U6HYVCUAKUSjvldkkljv7HOkFAgEAaBfVBktoQhxPkHE4cBcSgbLvQXtt2lH5A+xxGpaE8\nHNo+iEUdaDrUhb/ogGZaEtMTGTpzKER42a72YkdJ9nUMvvvgO6qrq8mYk0HsKF9qL6WRDs6gPFR6\ncFHXo2xXGdlvZtNQ3UBQVBBpU9NcaRM6jA7Q0ZGcJ9IUAFyN8hgcBHwJLJdS7u9UI93wJafptJSy\n2VhMIYQcA1zlWDehDBZYAjyJMqUHnOsrtEHF6xtQIks1QDrKSORXgDUoIbdueh17JZQjqZNQISVm\nHIOtAgS6QB31OonZaMcWjeL02B3vfVEcIed309mf4mgHrJei/D9wrttQ/kWvRbkgl3Xw+S/V+rkU\nMsq6FSVfyw6UbCEjVWZvS+va9VDfelNNyZC4M5GhQUOV7cMd2x2j0V2ju9S2fgjlj5ZzvcRRJg/l\nuxVxnvpqWXduc67HoDgdO9GuRwev79q8i+KBjgeT8/dQAKxvlhHciOI8PYeS5PIfeAE1O00HgYlS\nyjIhRDzwjZTyMg/15Json+SXKBGaH4CfAbNRbwS/BtiPEk3aqdOxXcBhm51aXCPXlWzFAvRBAkuE\nVL7w4SjvzmaxCJQ+QmrC2cnVijKcfR/KP54+KH2ZOmWwxSVA06Eu/EUHeNSSSCJDbxiqjKII8ap1\nbceZTNEGFKGkmK9BufFqOjofH9TRWqRJSikczpKz0aUnStfef0spizrZVEAlfZocOJP4O1mJMqXc\nQuBXKAPIPPI6SkTmOhpHl9SAHeX+uBfIArbp9eyx2zkpJaYAgT1MUBlrVzoydQe2ge00NKShCIk7\nN/+Tz5ACfILSTpiGEiaLa7WGOtF0qAt/0QGetdhQ182rLcShdGh3NgO5RzN8CU2HKnFM0DsA+Aol\nurTPyyapI9IkhHgXpYUqGsVXfhJYgdJVMhklUDpDSnnGQ10pUEaZQ2OvSzrWKzvM8sZUoGSI3gvs\n1OvZLiW5djuBAgKD9VRF2bAmonj8qSjDzZoyv4XtTh691FZ3EPPRdKiJ+Wg61MZ8GmlJjE9k6DC3\n0XM3dbZBF8i7tD7qVdPRufiYjvP0aXL2WHEuOxGAlFJ2enxZFU7TxeBsnrujE8/ZgBJR3wtkCcE2\nnY5sm41KwBSow9IFarralc536TSeq6k13HO3+DKaDnWh6VAfHrQkFicydKKPzQPjNteZT6Pp8Brn\na57zilGtoKbmOdUhUb6De1EiSNv0enZKO8ftkmCdQBeq42y0DdnNpkx62R0q9F7J7K6hoaGhoaHR\nwWhOk4M6lJkYsoFdOh3bBByw2WkAgo066iLAHG9T/h32hgaT5JLPqukv/6I1HepC06E+/EWLI6rx\n3YLv6Da2G90ndveuPReKCqIzRVuKKNxUyMh5Iy/8ICrQ4e/86JwmidJBKhtlOp6tej1ZdjulUhKq\nF2DScTbWpvSkSgPiwazTokcaGhqdz7IblhEZ3Nb2/fZTUVfB7Stub1PZdQ+uo76yHp1eh96oJ3ZQ\nLANnDURvvLjhu+t+u47Bdw8mpn/nJsP84rYvuOKFKzptKhB3ak/Wsv6h9fzk7Z8gdEoLVLcx3eg2\nRq3jvTWc+LXTVIkyKnkvsMNtWL9OQFCQnqpIOw2JNqVTdi84a+yA6FF78Jc+G5oOdaHpUB9t1NKR\nDlO7jy9gxP8bQUz/GMwVZrY+s5Uj7xyh72xfS2PugQvoCySlRAiVdbnxwT5NvoZfOE2rUZI3nETp\nmL1Vp2Ov3cYZqXTMtoYJqrralCH96UAM1HrTOWqN71GSDXbxtiEXiaZDXWg61IcPawmKDCJ2cCxV\nR6sgB7BB3ck6tszfQlVBFZFpkQy5f4hrQtnSnaXkvJ+DucJMeI9wBs4eiCnRxO5Xd1N3qs41EXDa\ntDR6Xd+reflZAzF1UybYXffbdfSc3JPCTYWYT5mJHRxLxr0Z6AKaT19QU1bDnsV7qDxWiS5AR0z/\nGIY+MJRv//QtABvnbUToBINmDCI2Ppbd/93NmR/OIO2SyLRIBs4ZSHBUMKA0QUamR3L6wGkqj1WS\ndmMaJVtLGLdgnOt8+V/lc/rgaS7//eWU7S4j58McastqCQwNJHlCsmvOue+eViYeXHXnKoQQjPzj\nSKqLqyn4poDRT44GoPxwOfuX7ae2tJbQhFD6/7I/kemRLlui+kRxav+pxp83BuV6JHFuSLkPI4R4\nEPhUSlngbVuc+IXT9B7wPqDXga2LRPayKQnvUqAiwIea1lJQxGxGSV45AGX+FF/78ms61IWmQ314\n0uJDU8HUna7jZNZJ4kfEK6H8cij6uoiRs0YS1D+IbS9vI/+LfPrO7Et1STW7/7Gby/9wOdGXRZP/\nZT7bntvGxOcnMuS+IZTnlDdqnvNY/nmlvE6vOEYlW0sY9cdR6AJ0bJm/hYL/FdDjyqbTl0LOhzl0\nHdSV0Y+Pxm61cyZfyVoz+onRfHHbF0xYOIGQrkrGR8s7FpJNyQy7YRgySbJn7R72vbWPyx++3HW8\nos1FjJw7ktCEUKxmK7krcqkpq3E18RV9W0Sv63sBEBAUwJD7hhCWFEZlQSVb/7qV8J7hxA+LJ/Px\nTNY/tJ4pS6a4olXVxdWunDmWagvbn9vOgDsGkJiZSPH3xWx7bhtXvHiFyxEt+raIkfNGEhQVxLaF\n5z5vNqJ00DWhjODuDgRdumvfyTwNzBNC5KHkrv5QSnnSmwb5hdNEEMgpYD2BkgV1L0om1CSUBF+t\n5axQG0HAFShJ7/YDX6MkJ/M1LZoOdaHpUB9NtexDyRAejeJIddbd+ej5iwBghR3P70DoBAFBAcT1\niaP34N6uB3Ry32RCq0NhFSSYEijbUwbDoGRTCXHpccSExMAxSO2fytEvj1LxvwqiU6IVzSW4slWX\nfHP+8ilDUzCWGwGIS42jMrtS6WbRBF2djtr8Wuqy6gjuEkxUYFQjvbJAurIAGUwGEjIT4CxQCr1N\nvfl+y/fKNLFRQB0kDUrCZDHBMQgkkLg+cRR/WUzaFWlUn6qmpqiGuKg4OArRQdFKfpqjEE44if0S\nKf++nPioeCWpH0A+5+b3PAmYlfInsk4QGhVKt8RucAy6JXTjh+gfOLHmBElDkqAOkgclE1obCrWQ\n0DuBskNlijYDynyMZ1Geg3tQ/lh09veqrRSiaHen1LWUDwxDmSntZuApIcROFAfqEyllVSdZ6UJt\nH9+FUY+SL9SJBMpRJqHbRevJ8NSEHaVLla9r0XSoC02H+vCkJR5lTsBKlLnCOssB3NHGcvUw/PLh\nxMS6ddjehaKlCozVRsUZkKBv0GOrssF2MOeZCQ4Idp1HIAjSB2HOMsNp5bgcxuVItKW8sdioOCSA\n/qQec43Zo47LEi4j50AOm1/ZjMFgILV3Ksk9kpWdEmVEUKiiwdZgY/8b+zl54iQNDQ0gwWq1IvMl\nIk9AFQRXBDc6T7fgbhzYeoC0sDSKDxYT3zUe/R6lY3xFeQWH9h+iqrIKu7Rjt9tJTExU6tc6zr+T\ncxmZjymfIzvAfNhMsL3xuYLtwZgPmF1zRxpPGV379YV6bKdtsM1xPXa6fQgS73yv2so+3J2kpkgp\npR1lCtY1QohA4FqUKVWeBzp9Vmi/cJp694LXX/e8z2yGIB8JTWZlwT//6ftaNB3qQtOhPjxp2bED\nrrlGWbZYwNBJDuBfnmpbuSlbYc6vYKTbiPj8fPjqK0hNgeuvh2nTlO2ffQaffgrz58NbbwWRm1vV\n6DxXXWXmwd8EMWwYXLtNNDru4sWtl29qx6JFUFDQkg4jMAiA3bvLufvu7/nrgiiSkkL5cgX8v4cg\nKUnRsWBBPnGxNSx7ayxRUUZyciq5+eaN/PlpsFrh3nsbawSwWmO48koLv7q1kkceKeaxR/sxZoyy\n7yc/2c2dc1KYMWMkgYE6nn12P2fOWPjLU1BSItiwFv48H3S6xp/ZX56CL74I4t13Sxtpuv32OqZP\n78rPfgZz5nj+vO+cpVyP++/3fA0783vVVlavhuHDm2+/4gqg8SQfSCkbUKZYWymE8MpMes17zvkg\nTzzR8j5fuYkCZGT4hxZNh7rQdKiP82lR24OtJVJTYebMlvcbDDB5cgIbN5axbdsprFY7b72Vh9Go\nY/BgpVNzTIyRwsJaV53zlW8Pa9YUU1ZWB0BYWCBCCFcfIvfzpqZC795WgoL0mEwBnD1rYdGinEY6\nPBEQoGPy5AReeOEAlZUNZGaeC3zU1toIDw8kMFBHdnYFX311bn7ZyEgDXZIuawAAEBtJREFUOp2g\noKDG02EZN64rx4/X8N//FmGzSVatKiY/v5qJE1vv+NaW6+Fj3NzSDillbUv7OhK/cJqSk1veV17e\neXZcCvxFi6ZDXWg61EdrWqocPTUaGipaLnQJaM/xWxpeH9NKeqWqKujZ08Rf/zqEv/51HxMmrGXT\nphP8/e+XE+AY7TZ7di8WLz7C2LGrWbYs/7zl2zPMf//+s9x22xYyM1fx0EPbmTevP926KQGKe+9N\n57HHshg7djVr1hRz990pmM02xo9fwy9/uYWxY885KFWt9Jy59tpubN16imuuSUCnO2fbY48N4J//\nzGH06FUsXpzLlCmJrn1BQXruvLM3t9/+LWPHriY7u/F16NLFwD/+cTlLl+Yzfvwali7N45//HEF4\n+Pm9nvNdD19CSnm4pX1CiPjOtMV1Xn+Ye+6bb1reP28ePPNM59lzMWRlKf9AW8JXtGg61IWmQ314\n0rJjRyLXXKPMPbd0KfzqV14wrJ3k5yvRjZbQdHQuvqhj9epdDB9e3Gz7FVe0PvecEOJLKeVPOtQ4\nD/hFpKk1fOUm2hb8RYumQ11oOtSH2h5sF4qmQ134iw4AbzhM4EeRpoMHQQjo2xd++AG2bYPu3WHU\nKG9b2H78RYumQ11oOtSHu5bVqxPp3n0osbHQp4+3LWsfBQWKjqQkKCuDI0fQdHgRX9JxvkiTEGIE\nyii67UKIfsAU4JCU8qtmlToBvxg9t3QpbN0KNpvSC//gQSX0vXw55ObCL37hbQvbjr9o0XSoC02H\n+miqJS8PbrkFNm6EkhKYONHbFraNdevg8GGw26F3bygshJQUTYe38BcdAEKIJ1FSDAQIIdYCI4Fv\nUBJeDpFS/rmzbfILp+l//1OG7jY0wM9/Dh98AKGhcPPNcN99vnMjzcryDy2aDnWh6VAfnrQ89hhM\nmgTjxinD6H3h4ZafD/v2wQMPKMPy//pXmDtXGcmo6eh8/EWHG9OBDJTcEaVAkpSyUgjxPEraUc1p\nuhD0+nOvxETlJgpgNCohSl/CX7RoOtSFpkN9NNXiHA4eGOhbWvR6JdeQwQDR0edSP2g6vIO/6HBg\nlVLagFohRJ6UshJASlknhPDKHGl+0RE8IEBJbAfw2mvntldXn0sc5gtkZPiHFk2HutB0qI/WtNTV\n+c7DLTVV+dwtFmX9vvvO7dN0dD7+osMNi1sSy2HOjUKILii5zzsdv+gIvnq156RdZ8/C6dOtD8FU\nGy1lbPU1LZoOdaHpUB9NtWRlhWG1hmE2K05ga/l21ITNpkQ3mqLp8A6+piMgoIqMjOYJpBwZwYOk\nlPVN9wkhYoAEKWV2hxvY9Nxqd5qEEFOAl1CiYkuklAub7G81T5Mvcb48NL6CpkNdaDrUh79o0XSo\nC3/RAZ7zNJ3PH+gMVB3QFkLogH8A1wD9gVuEEH29a1XHkZvrbQsuDZoOdaHpUB/+okXToS78RYcn\n1OIPqNppAkYAR6SUxxwT9b0HTPWyTR1GdbW3Lbg0aDrUhaZDffiLFk2HuvAXHS2gCn9A7U5TN6DA\nbb3QsU1DQ0NDQ0Pjx4Mq/AG1O00/KkpLvW3BpUHToS40HerDX7RoOtSFv+hQM6ruCC6EGAXMl1JO\ncazPQ0mnvtCtjHoFaGhoaGhoaFwQ7h3B2+IPdAZqd5r0QA5wJVACbANukVIe9KphGhoaGhoaGp2G\nWvwBVWcEl1LahBD3A2s4N8RQc5g0NDQ0NDR+RKjFH1B1pElDQ0NDQ0NDQy34TEdwIcQUIcQhIcRh\nIcTcFsq8IoQ4IoTIEkKoMsWXEGKJEKJMCLG3lTK+oCNJCLFeCLFfCJEthHiwhXKq1iKEMAohtgoh\ndjt0PNlCOVXrcCKE0AkhdgkhVrawX/U6hBA/CCH2OK7JthbK+IKOLkKID4UQBx2/k5EeyviCjnTH\ntdjleD/r6ffuI1p+J4TYJ4TYK4R4RwjRLL+8j+j4reN+5VP3Xk/PPyFEpBBijRAiRwix2jFFiqe6\n5/UBOgUppepfKM5dLtADCASygL5NylwLfOlYHgl87227W9AyFmXW5r0t7PcVHfFAhmPZhNLW7KvX\nJMTxrge+B0b4og6Hfb8D/gOs9OHvVj4Q2cp+X9HxFjDLsRwAhPuijiY264BiINnXtACJju+WwbH+\nPnC7D+roD+wFjI571hog1Rd0eHr+AQuBRxzLc4FnWvjeteoDdNbLVyJNbUlqNRVYBiCl3Ap0EULE\nda6Z50dKuRmoaKWIr+golVJmOZargYM0z5nhK1pqHYtGlIdb0zZrn9AhhEgCrgPeaKGIT+gABK1H\nwVWvQwgRDoyTUr4JIKW0SscM7W6oXocHrgLypJQFTbb7ihY9ECqECABCUBxAd3xBx2XAVillvZTS\nBmwEbmxSRpU6Wnj+TQWWOpaXAjd4qKqKxJbgO81zbUlq1bRMkYcyvoDP6RBC9ET597C1yS6f0OJo\n0toNlAJrpZTbmxTxCR3Ai8D/o7nT58RXdEhgrRBiuxDiLg/7fUFHCnBKCPGmo1lrsRAiuEkZX9DR\nlJuB5R62q16LlLIY+BtwHMW+M1LKr5sUU70OYB8wztGsFYLyRym5SRlf0OGkq5SyDJQ/40BXD2VU\nkdgSfMdp0lApQggT8BHwW0fEyeeQUtqllEOAJGCkEKKft21qL0KInwBljuifcLx8lTFSyqEoD4Pf\nCCHGetugCyAAGAr806GlFpjnXZMuDiFEIPAz4ENv23IhCCEiUKITPVCa6kxCiFu9a1X7kVIeQmnS\nWgt8BewGbF416tKi6tFpvuI0FQHd3daTHNualkk+TxlfwGd0OELcHwFvSyk/81DEZ7QAOJpPvgGm\nNNnlCzrGAD8TQuSjRAKuEEIsa1LGF3QgpSxxvJ8EPkUJzbvjCzoKgQIp5Q7H+kcoTpQ7vqDDnWuB\nnY7r0hRf0HIVkC+lLHc0a30CjG5Sxhd0IKV8U0o5XEo5ETgDHG5SxCd0OChzNh0KIeKBEx7KtMUH\n6BR8xWnaDvQWQvRwjHaYCTQdHbQSuB1cmUPPOEN+KqS1SIAv6fg3cEBK+XIL+1WvRQgR4xyt4Wg+\nuRo41KSY6nVIKR+VUnaXUqai/D7WSylvb1JM9TqEECGO6CVCiFBgMkpzhDuq1+Gwp0AIke7YdCVw\noEkx1etowi14bpoD39ByHBglhAgSQgiUa9I0z48v6EAIEet47w5MA95tUkTNOpo+/1YCdziWfwV4\n+gPeFh+gU1B1cksnsoWkVkKIe5TdcrGU8ishxHVCiFygBpjlTZtbQgjxLjARiBZCHAeeBAz4no4x\nwG1AtqM/kAQeRQl9+5KWBGCpEEKH8t1632G3z323POGDOuKAT4UyPVIA8I6Uco0P6gB4EHjH0ayV\nD8zyUR04+s5cBdztts2ntEgptwkhPkJpzmoAdgGLfU2Hg4+FEFEoOu6TUlb6go4Wnn/PAB8KIWYD\nx4AZjrIJwOtSyutb8gG8okFKVTcfamhoaGhoaGioAl9pntPQ0NDQ0NDQ8Cqa06ShoaGhoaGh0QY0\np0lDQ0NDQ0NDow1oTpOGhoaGhoaGRhvQnCYNDQ0NDQ0NjTagOU0aGhoaGhoaGm1Ac5o0NH6kCCEW\nCSEe87Yd7gghjgohJnnbjvYihNgghKgTQmxw23ZJtAghDEKIKiGERQjxp4s9noaGxoWjOU0aGj6A\npwewEOJXQohNF3pMKeW9Uso/X7x1nnFk77U7Eof6OxIlyeDES35gKS1SyjDgnUt9bA0NjfbxY7iZ\naWj4MxeUnbaTHBmBYl+HTB6sQmfMlydJ1tDQaANqu+loaGhcIEKIvkKIb4QQFUKIbCHET932vSmE\neFUI8aUQogqY6Nj2J8f+lY4moErHu00I4Zy7arQQYpvjuFuFEJlux/1GCPEnIcRmR91VjukdAP7n\neD/j2DdSCJEqhFgnhDglhDghhPiPEP+/vfsL0aIK4zj+/bWLRemqZaIuLVJoiEH2BykiKyQQTYNA\nzLC66CJJoi5WKYnSRKMu+qNFhEV4kf8qMjItSSJLzIhCSYhCTSwtszU1FLX8dXGONg6767xh7Lv5\nfGDZd+bMeeaceVl4mHlmj5oqzq+9OYyV9JWk/ZJ2SHqicPyJO1335LY9kmYW2s+TtEhSm6QtkqZL\n2lloHyjprdxvq6QHa/9WTsYaJmmbpEl5e7ukVkmb8vVeKKm/pFX5Wq1RXhMxhFA/ImkKofs6eWdD\nUiPwHvABcDH/rHs2pHD8ZGBOftSzvhjI9gTbvWw3AROB3cBHkvoCK4HngYuA54D38/5i3Hvzec8F\nWvP+Ufl3k+0m2xvzmOcBA4BhpNXKZ9Uw5+IcPgP+AO623RsYB0yVNKHU5wZgCGnttMclXZ73zyKt\nnD6YtFDzFPKdO0kiXc+vSesTjgYeknRrDWMlx7qa9L1Ms72s0HRHjjsUmACsAh4B+gENpO8whFBH\nImkKoftYke+KtElqA14qtF0PXGD7adt/2v6YlOxMLhzzru3PAWwfae8EkoYCi4CJtneREpHvbC+2\nfdz2UuBbYHyh2+u2t+aYy4ER5bAnPuTj1uYx/kZKwm6q4RoU53DU9jrbW/L2N8DSUjwDs/Kxm4FN\nwJW5bSIw1/aBPNf5hX4jgX6259r+y/YPwKuk1dVrMYq0avsU26tLbQts77W9G/gU2Gh7s+2jwDvA\nVTWeK4TwH2vs6gGEECq7PSdDQCoEB+7LmwOBnaXjdwDNhe1y+yny46AVwEzbG/LuQTlOZ3F/Lnw+\nBPTs5Bz9gReAG/NxDUBbZ+MqOWUOkkaSVkm/AuiRf94s9fmlg/ENAn7sIHYL0JyTU0iJ3znAuhrG\nCnA/8Int9gr2i+M63M52h9cxhNA14k5TCN1HZ4XGu4BLSvtagJ8K2x0WjefHUW8Aa22/Voo7+DRx\nO9Le+eYBx4HhtvuQHonVUkBdjrmYlOg153iv1BBvN+nx4Akthc87gW22L8w/fW33tj2e2kwFWiQ9\nW2O/EEIdiqQphP+HjcAhSTMkNUq6GbgNWFKx/zzgfODh0v5VwBBJd0pqyIXMw0j1PqfzKylBuqyw\nrxepDumgpGZgesXxdaQnsM/2sXzX6a5Se2cJ1HLgUUl98limFdq+yGOckQvGGyQNl3RtjeM7CIwB\nRkl6qsa+IYQ6E0lTCN1Dp/9awPYxUp3RWGAv8CKpQPr7Kv1JtTrXAfsKb9FNtt1GSr5ac9xWYJzt\nfaeLa/swMBdYn+uwRgKzgWuA30mJ19s1zLO9tgeAOZL2A48By0rt5T7F7SdJd8y2A2tIj/WO5LEf\nJ817RG7fAywEKr3pVzyX7QOkQvMxkmZXGFcIoU7Jjr/VEEKQNBWYZPuWf9H3Q1LS+aXt0Wd4XD1I\n9U6NwDO255zJ+CGE6iJpCiGclSQNAC4FNpBe+18JzLe9oEsHFkKoW/H2XAjhbNWDVDg+mPS4cAnw\nclcOKIRQ3+JOUwghhBBCBVEIHkIIIYRQQSRNIYQQQggVRNIUQgghhFBBJE0hhBBCCBVE0hRCCCGE\nUEEkTSGEEEIIFfwNobP241gWROwAAAAASUVORK5CYII=\n", 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TqVWrFgEBAZaldu3a+dJ5i4+Pj57NXKPRaDQOT4V7mkQkGPgQaAdkA/8BRgLD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\n3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4bQ55w+7Ph4ByteWEFMbAzjFoxDtCH7OCwhjNG/Hc2+d/dRXVxNeN9wxv9pPAajAVut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\nHAOcKum42jaTgP1J1077R0m/kZdfQLpy+n6kCzV/k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VcezYMUpLSykrK6OkpISgoCDCwsKIiYkhPDwcl8tFaGgoiYmJREREkJ+fT2ho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W5wHNZSKxtf24gl14J/iD9tz2zrRpoAzrzxTFLeTOGnu38irEMYpyWeRm5RLvyJOl2Pvnf0Zfv7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\nlRdd1IY2bepz9NGZXH99Hnv3bmXNGnjsMeegL1wIP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i6v0fGRl5RAPqDQaD4XjHxDTVg1Jqq4g8BXwFlAK/Au66igaqY/ny5WRmZgJ6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p2iPkd00P+NeCSPIQpYzozdSJHDyR6ayr9ZbXDhihqbfA2PHa0Pazs2IxoWVdi67F1hgnYXO8B9\nbLHWBUoF8qhphytM0h47zgxVpkelg0eFaVuFadn6dSXwF5CCUKFy0+N4jStMA3ogLgWzL0RjlRze\nUqiXNCmlvIGLkQjYOGTq+ARJyJqitc5qqsForfOUUr8g+dyZZm+TKTxX63XeQrw1IGQpHJE7iUVI\nyS9Ykqx/MT07w3IKQpgKuyLqE2Dp5JeI/GkeQL74iYi3w3q7/f7OuHwEIRnmvMcT2N5Nt/b4GrPs\nDp/HIWCyg+3Usr+zLh8F7nSi8ZzJ8iHgnlq2Z2Mbcsk0PTvjcg4w2MF2Vxm/eTkLuNSJxtPYZQ1E\nIv+1SQjFCEOIyglgJ/LfVYm0wakC2piWc03P3qb9i0zLRtOzOXyjsJAebXqYvVnK6rW2Wge2oR9t\nWt4PHAWl5GE0ynMZNhiste4O1ZGpDUDDujM3MerNaVJK5SCmvQt8bO71opQ6BiSdKWlSSkUi/exy\nlVL+iDTRU8BIIEdr/bQpETxMa10jEdw6p2kD8ATyH2SOnihg65kMsJmwBmGG+UOxyNpb5zm4ckWN\nxw7ngrUdixBvWb3BbieEu9gBDbbFpXKa1iK+gNDWG85poyXtsPa0VDh47Wi53O5hvY/Ze2NNYgxW\nz3VJQGqrh9X/mlJgMMjDywu8vcHXVx6BgfIICYGoKHlERso+p4OlSzcxaJBtTtOHH8Lbb9vkNC0E\nntVa71RK/QvRjQzWWl98elc9fTQkPLcVcYicDexVSh3QWp+s55jGIBZ4z8QeDcBnWuvvlVJrgc+V\nUv9AeNDU+k50NRKO64tzC2b/hNzIFI1GytYdwV2qgzx2OBfSkQT2MGw9fq5A/qzhLnaA+9hyAsmP\nCMT2D9jVStzNdrTBQj404kVzRHSsiUxtpMaa2JjJjLVHxh7WRMbk1zB7YYxGISh+fhAQAkFBEBYG\nbdtCZSUMGCBE5/nnIScHwkPlGK3lHHfd1YTvVcthCJCilEpDbi+OY4lftCjqJU1a61Gm/nLXIelC\nLyqllgEBiBPvjKC13oYDKSWtdQ4iV9RgRCFxRGfGIoTcFU2gZqtB6zyHNsjdjivCY4dzwdqOa1pt\nFGcOd7ED3McW6xya81rh+hpb8mL9bL+uzOphT3SsiY2Z1BQjxMZ8k/S73XXtSI3ZM2M0go+PkJo2\nIRAcLKQmKgri4uTZ6Fjku0lxww3Nf40WxHjT8yvAHUiAsVUEXxqUCK61PoTIID1m6jJ8HfJ12aKU\nekdrfX8zjrHBeAS4ETgf0SA047LWGU4NvI9oKRRdhpSs1wV3qajx2OFccMXQiSO4ix3gPrY0VDrK\nHJ6qzUNj/VyKhehYb7NONAaL58beC2wmN6b9zN4aLy8TqQmE0FCIiIDoaGjXruVITXMjrPGaoK2K\nlBR5AGy1y6nRWh9SSk0CDmitf1BKjUIaZ7Q4Gi05oLX+HfhdKXUXklJ6XZOP6jSxENiF/K6sc9Kc\ngTS9rBQz0RT9Hehey07WeQ6uXFHjscO5YG2HK8Nd7ADXs0UjZMXspTE/H0fIUrnVeusqKLMXpxxL\neKoKufGoi+SYPDjm3BovL8mn8feXXJrISIiNhY4dhfCcKVJToVOnMz9Pa8OV7UhOlgdITtPmmgKL\nw4CLlVITkfKiIKXU+1rrFuUgp63TpLUuQaroPmm64ZwZ1iPVcs6GJwwG5lFF0XVAxwYeZK4OcnV4\n7PDAg9aHtXfHOkRl/VyCEJ5Sq3XW+TvmHBxld16wSSQ2GoXomElOUBCEh0NMDHToIM+t6ckZP345\njzySxNlnRzbJ+SZPXsWDD/Zh0KBWSbEBYMOGbP7zn828/nqjMlpcClrrB4AHAJRSI4F7W5owwRmQ\nJmfEOUg1pbPc/GvgfoOBV5WmcDr1S4Ba33m2R0pfXbE6yGOHcyERqQjqibS4cFWYP48cRNslD5nA\nI5DqD79WGtfpIBEhI9uR7IzOSMlNGpKcORDbMLCZ9JTBsmPLKNN2BdlnAoXkM5hyGnzLfZhyaCzB\n4ZJc3L49dO0qYSxH+Mc/1rBnTz4rV47B29vAwYOQng5ffJFCt27+3Hhjba5150Ft3pkvvoApU+o+\n9uuvRzb9gOpBUtJ3LFlyHvHxlpioUmLH4cPyufn5QXk5rFoFR4/KupEjwddXU1mpqarSeHsbMBhs\n3X05OaUEBXnj7W2bpX7oUAHFxZVUVWkSE4Pw97dlvlu2nKRLlyACAmxpxerVWZw6VU5VlWbEiLaE\nhPjYbN+0qYguXWr/fimlzkL6zK1XSvVC8ptajbu4FWlai7TaSUR+/60pOVAF3Gow8IlBU3iLlj/C\nxsBdKmo8djgHViKJrOFAH6SPXvO1L2s+rAX2ICpxR5Da2zxErG0Szh3yKkd0b4qQJONfsIS8DKZn\nf+TObwXyJxaGlMObtXIUlCU3IWFygFLvMu5vYJbq0aNFbNx4kpAQL375JZPQ0FjWroVeveDYMfE2\ntTaqqrRJA8iWHBw/XkJoqE81OfjgA/P6PMrKKqis1BQXh1BSItPktdfK9j/+OE7v3qEEBHhhNFrO\n+eOPR8nOLqWyUjNpUhwREbbGf/BBKmPHxhIdbduudcGC3Rw5UkRFhebuu3sQF9fGZvsjj2xl2rTO\nJCTYEqTHH9/O0aNFVFVpbrqpa/W2r76SCrnrr1/DkCF9aN8+mBEjYP9+2fbNN7+xd28+BoPik0+G\n061bsM31Zs7czMyZvenSJchm/Qsv7CItrRCDQfHMMwPo2DHQZvs336QxbVrnGqRp7doTZGaWYDQq\nBg4Mr0GacnMrKS/HIZRSc4EJgJdS6iekfGolUsu81PFRzQu3Ik0/tvYATKgArjEYWOKtKbhNN/zu\n3jrPwZUrajx2OBcOIJPvzYhK8A5kwo5FPDQ9sa2ccFYcADYh/f4MwFCkjec0xDPzKS3TC1BjS4Cs\niVAhohZfYHpdhCXkpZF/XAMWz5EXljwfwLtQiEZJBfTtKdVWV/8dunSxEJDvWkgDuaioAl9fow0x\nADhwoIDCwgrKy6tYvDiL6Ogwxo4N5Ztv0ujVK5aePbM4ejSfffuOsHevYujQAwweHMEFF8Ty9NPb\n6dMnlMzMYjIyShg/vh133tmD2bNT+PPPbPr2DeGll84iKEgKs1euzGD27C0UFlbQpo2Rp58ewPDh\n4u595519fPzxQbKzS4mJ8eORR5I466xIXn11D/v25bNu3Qlyc2U2fuaZ/owfH1dtw65dudx993pA\nMWJEW6ZPTyY310DbtrBhww727j1FeXkVISFBjB6dTMeOQizGj19ObKw/OTmlHD1azNq1E5g0aQWP\nPJJEenoRx4+X8NdfuXz0USr5+RV06BDACy8MIjran+Bgb7y8hKAdPVrEhAkrmD27L59+eoCqKjj3\n3LYEBcmUvH37KZ5+egepqQV4eQlJeuCBPnh5GZg2bQ1aw8aN2RgMirvu6k7btn5oDc8/n8onn+zj\nu+8UN93UlczMIC68UGzu2BFeegk+/7w2nRvBm28Ocbj++ecH1XncnDmOq5vuvbfu2M955wURFZVf\n2+YrEF+IL5ABxJtEsP+LNIl5os6TNwPcijR1aO0BIP+NlxoM/O4LBXfo07+bd5eKGo8dzgMD0MX0\nqAT2IuGhZYBT1L82EFWILZVYZINDsVRSNQYa+dEWUzsByrdab67mAvH8WCsfm6q5lJKS86AgiG0P\nPXrAuedCz56S0FxQALt3w8svwxtvQEkJ/O1v8MwzoqVTXCyT2//+Bxs2QO/econly4+RnBxe04a1\niOhqJZKjYF81tQS5P7dP4fkE6TVVidwUxNpunjFjHbNm9aZnT9u7vtde28Phw4V4eRk4erSYbt26\ncv75oUyf/jsdOpSyY0cOl1/enj178klL8+fddyU89+GHqWgNqakFvPLKWYSG+jBlyq/s2pXHo48m\nceBAAR9/fICPPz7ALbd04+DBAmbN2sxdd/WgQ4cAVqzI4Mknt/Htt+eRnl7Ep58e5LPPzqW8vIrS\n0iobcvfLL5k8+WQSo0bF8MknB5g/fzdjxrSr3mfZsmMsWTIab28D1123mhUr0rjttg589lkuqan5\n/Pe/QxkxIoTbbjvCM8+sZ/Hi86o/7MLCCt58cyihod4217zxxi4sXLifjRuzefPNoSQkBLBnT151\nGOuSS9rX+Og2bMjm55/HcPhwITfeuJa//srj7LMjMRgU99/fiz59QsnIKOG22/7ks88OcfXViSxc\neA5JSd/x9dejiI9vU32e7OxSiosruOuuMZSXH+e55zZy991xHDniTVwcnDjhklWBFVrrSqBIKbVf\na50HoLUuVkq1SmtZtyJNrY1CYILBwKYAKLyjqvF3784cWmgMPHY4FxzZYUR0p3pQo1+B0yIRUXR7\nA4hHJG/Nfc8LkdBWMTUJUBEW8lNotU8JtgTIPKFYESCQiSY0FOI7Sthp+HB5fucdGDsW4uNth/no\no0KKysrg9tuhu11Kz9y5cPPNMHEiXH89VFXB9Omy3kyaxo+nBlJTC+je3YHb2t80ZiOOlfP6IYKT\n9phodVybmpvfe+8cBwfB00+LrN6mTTncdNNa2rWLZflyHwICAti16wgvvNCDoCCxy7rhxDXXdOLD\nDw9w11096NpVPDcDBoQTEeFLt27BdOsWTE5OKevWZQNCbEaOjObqq+ULPGxYFGPGLCcl5STR0X6U\nl2v27s1n0KDwag+OGb16hTBunHiWrr++Mx98cICtW0/Sv7+QzquvTqwOn40cGU12dh4GA+zff5ip\nUztw5EgoixdDYmI8R47sZevWkwwcGFF9bNu2jhPovv76MPfe26s6lGYf+rLHjBnd8PU10rVrMJde\n2p4ffjjC2WdH0quX5XOOjfXn8ssT2LAhu/q9ENh28/DyMjBrVldKSxVLlrQFjOzZU8CuXWGEhEjV\n4eTJdQ7HGVGmlGqjtS5C/MkAKKVCsO3N0GLwkKYmwilgtEGxJxQKb6vyvLMeOBeuqGObTx3bmgPW\nujz+1Jzo9yOiiW0QwlNgeqQgvbLKEQ+JH+JlWYmFAD2L/PbMDgCNDSn09RX9mrgu4sEZORIWLYJL\nL62ZDPzZZ3D8uHiNLr5Yqr6s0a+feJPsceONQhZ8fBwntz73nDx37w7nmQQhIyNh3DjYuFESdnv2\nrHlcdd7KOrsNSTX3tUFNB4fgDIsCFi9OZ+jQSGbPli9QUFA7li9PJyjI8kb27VvzOOtcHz8/o82y\nr6+RoqIKALKySoiNteT/KKWIifEjK6uEQYMiuP/+Xrz66h5SU/M555wo7ruvF5GRQmZiYvxsjouO\n9uP4cYusj/0YzNuOHStmw4Z0vL0PUlkpxA+qOH68tHp/+5wka2RklFR7f+qDUrbnio31Z98+CVMd\nOlTAs8/uZOfOXEpLK6mo0DZEyhFCQ70xGBT+/nDFFfDmm0aGDKmkXz8R2HT0XXUBjNBalwJora1J\nkjdwfWsMyDO1NwGygBFKcbitovimKttE4cbA1bRbaoPHDudCQ+0w/yXZt3bIQCZY+7liC6JdVY60\nmLCvuF6MeIbi7NZ/iEg4GJG8JF8kmfuU6fm4aSzl2HqAyqnW8VFKytlDQ6F9T5mce/QQD5C/3Tir\nqqQEvjbcc4/j9X/7m+P1ZgyqJcWjXX1VsoiIX3KykCUzAgOFxLkCSksrWbbsKBUVMHr0TwCUl1eR\nn1/Onj15dOsWjNGoanwWjUHbtn7VJMKMjIySai/PhAlxTJgQR1FRBY88spXnn9/FE08kV+9nhtaa\nzMySWr1DICFTELJ1001dufHGLrXua5dPboOYGD/S0oro3Ll+hqI1ZGQUVydUZ2QUExUlZO7xx7fT\ns2cwzz47EH9/Ix9+mMrPP2fUe05rnSalROohzv7350IwEyYH608g/z4tDrclTdchCtzNjXRgmFJk\ntofSG6qapumdO5RUg0x+uxE7AIIRYc/GVhK2JhpTFt5SsO8qbsZhhHiUAl2pmYe1Akn6NueufIUo\nv34K7ENsvRax0xopSIjHfgKsRP5B/JHvaS4Wr1CBadufptfmvKASLAnRVUhFnwtdiHQAACAASURB\nVCkUZjRCQADEd4L+/SV8ZU1AzESjNvzwAwwcWHN9XYSpNfHll5Lr1NYFZSyWL8/AaFS88MIIOnUy\nkJ0NeXnw8ssbWbw4nXvv7UVEhA87dxad9jXGjo3lnXf2sW7dCQYMCOfDDw/g62sgOTmMgwcLyMoq\noX//cLy9Dfj5GamqsoSrdu7MZcWKDEaOjOajjw7g42Okb9+6JbIPH4YLL0xg1qyNDBwYQXZ2GAcP\nVpCfn83110cQHl7/dHnZZQksWLCbTp0Cq3OaYmL8CA527M594429zJnTj/T0IhYtSuOppyT0WVhY\nQWCgN/7+Rg4cKODzzw8RHm7xjkVG+pKeXmQjOWCNnBwoLYW1ayWXKTISkpJEhsDVoJTqgdx6/am1\nLrBaP15r3eL1X25BmuYj//9maMRjf8q0/G0zXXcfMFxBdleouErXu3+9MOvpuGpJtRmJyGS4DSlv\nN9/p5AH/Z1p3busMrVFIBL7E4vXYgoR6eiJVaEcQTfzTxTGEjNnnm6wznbsUydmxy5nhY+AshBhZ\n4wBCVH2xrYpINB1TjLB8L6v9P0bsm4ltWAvkh1SMeIsKEMJYgJCjU9jmCP2EpTIMqt8zpeSPOjIS\neiTDBRdYGoo2FnURJoB334UJExp/3tZAcjI89BB88okQw9GjYdSo2rVqrBHh60N2afMlokX41h+v\nXbw4nUsvbc/gwf6sWQN//intRwICOvLttzv41796MnlyAtdfv5Hhw5cyeHAEzz8/qEbZf13o2DGQ\nefP6M2/edrKySunRI5iXXhqMl5eBsrIqXnhhFwcPFuDlpUhKCmfuXEss8Lzzovnxx6M8+GAKCQkB\nPP/8wOqkbUdjCAw0l+qHMnduPx54YDs5OUX4+xtISAgnNDSCadMcH2u97rrrOlFeXsUtt/xJbm4Z\nHTsG8sILgwiuJbVp4MAILrxwBVrDtGmdGTJEXI/33tuTRx/dxsKF++nRI5jx49tV53qB5EI9+GAK\npaVVzJnTt5pQdeoEa9ZITh0ojh+HxETIzYXXXpMwsysphps6j9yOuBHeVkrdrbX+xrT5SVqhaF5p\n3QSTfStCKaUTkDn4RuQ/XwNXIjfQAM3h8d5uOm9uX6i8vAlP/AqWkuoyLCXVp2i5kuqmwIvIV93e\nE1OB2OgqnbZfQdyW+cB7iE1ByJfsVUSnaTNCbu1yXvgO6etTinh07PNUViKVbPY5J3sQMuJr2mbv\n6TcLkDUGryHesQHIZ1AM/IBUVRUjeU3m8FgBFq+QAUvLC3MrjSrpsh4SIgrPQ4ZIQnRtE0NTYvp0\nx+u1FkHFZcuafwxNhZtugtdflzymlStlsuvWTQjUiBHQpg1s2NCOceNq9DN3KsyfDzNmSA7XyZPw\n8cdCCocNkwrBO+5o2fG8+uoe0tIKefLJ/o067vnn4V//ktf2437pJbizCTsCHD1axMSJK9i0aVIN\ncckzxfz5MlaDQYoR3ntPvmunTkl7kpb+POrD0qWbGDToqM26Dz+Et98GZKodqrUuUEp1RG67P9Ba\nz1dKbdZaN+5DbgK4hadpLnLz+wSSB5qMRAyaKz1gPXABkDcY8f40FQ6YnpuypLo1cACZZPOpGSIq\noPETflPhJPJFKUJIjr23/hfEq2NOZziAEIVfTK9LEU9NT6rJAyBfNke/pJHACIT8OKpqqq0rfLd6\n7Kjt/dOIbbkI+clFQr3pprHuRv6CtGk85cAfVHeJNxgkPBYdDUmjxGvT2T5U14pISZFJ+ZlnxDNg\nD2ebDOpCSoqlr9rgwfKoqBCPzYoV4hVYtKi1R1k/UlMtSe8gSfY33QQffSSTtKvck6emyvd+40YJ\n8cbGwpEjNGupfnO8N6mp8mzO46usFOIE4sWsdJU5xAKDOSSntT5oatT7f0qpDrTSTOIWpMkA/AuY\nYnqORua05sAvwIVA4QhgdDNcoL6SalfBeMQzE4HkMoFlEp94hufOQQhQMeLdsdef+RVpd9LDbv1m\n5D31Ryqz7ElTb2qWXg9A8nI0MA4Jne1FiEgf0z721zGjqatVyrCQIXOILBt5L/IQwqSw/KpNPcO8\nvCRvputQCY1t2CBNTteskQoxV8KQIVKS38VBnm5SfVVkTgb7SdPLS7wzw4aJdpOrIChI1L9jTbly\nPj4ipfDll5CZ2bpjawwmT4YlS+CXX6SY4LXXaNZS/UZEKhuFQYPglVek/c3Bg+K1BCgsFO+liyFT\nKZWstU4BMHmcLgTeQTJ9WxxuEZ5bCNxgtW4JsIamlwpdAkwFisYiInLNhSwkN6UtrpU0bY8qJDfH\nXAAThOQ3mfNeckyPYoTp2ifE/o5Ubdn/NH5Dcor8gUGAfYw+G0mab6o2IdaJ7MWma4dQM9foTFCJ\nJV/I7CnKRt4fc4J1BeIhUlTnDBmN8qfeqxdcckntFV32+OMP2L5dvAIetA7S0mRiqwuuEJ7LzRWv\nhqOS9oMHRYnalVBSIh7NqirXLdXPzBS5jOhoyTVzZtQTnmuPCFzWKB1USg3TWq9ukUFawS08TfaY\nBDT1DfSnwHSg6GLE+9CcaIvjxrCbgRaP4DYABcgEX4B4lsy5PQbkK/8H4ilrg3hLzCGfPYjXxuz5\nsbe5D47DWudSdyJ5Uzcbt87VyUJITGOES80J1dZhs5NYvEQFSP6QOZHaKneoTRuZdM47T5I4fZpI\nU2noUHmAVNqEOxCadjW4mh11ESZXsiWkDvmgiKb+LbYA/PwsXjMz8vNdizxFR8vDHq5mh9Y6vY7N\n+1tsIFZwC9JkXz0HUl3dVNVzbyjFv9AUTQHqbqNzZqhPT2clLUeazHlV1thjWm8fjtpuegQi4zNX\nU91s2l6J5NP0RuKbRxHSM8T0qA2t3brkALZ2bETCcz2BVUj127lIblB9YTOw/NpMOUTe3hI2GzRW\nOqk3l55KfaX6zzwDTz3VPNduSriLHeA+tljrAjnCV19JqM7Z4bHDJfE2TZtV3CC4BWnKQeZj6+q5\n9cC9TXDu/yrFXDRF11BTv6Y58Eod2wqb6BoFiMfH/k52C0IG8pGqqgvstgdgr9wvsCc/B7AVuN+J\nxE8DkLDmW7iG5ACIHWZC9DuivpyPEMpfkFBhOTXCZgaD3IF37w2TJknrDWeFK0zODYG72AHuY4u7\nTNAeO5wPWusWJ0zgJqSpOarnNDDHYOB5NEX/oGnzV2pDIkKMrqFm0rdGeHVDkIl4dvKRKjH7cOJB\nhAjY52V1RnKOgnAcfmqoJyQRS0hKI0TCnF/kQ9MIgDYlyhGvkDnHKhPJKTuFeInmI2X35QhJqpSw\nWaWX/AldcolzJ1iaPRqHD8Pq1ZLrAJLrcM45IhvgCqhPp+mHH1xLp6kuuIotZq/G8eOS25SQYBtC\n3rNHZBScHR47nBdKqbMArbVer5TqhZQZ7dJaf98a42l10qSUikfEu6OR6fVNrfWLSqkwJDWpAzLN\nT9Va5zo6R1NXz2ngLoOBdw2awpu0nLCl0A3xAsUiYRxzp3Uj0NFu392IV8e+uWeZ6RGB40TyPg7W\ngYTXHDX2PB2UAK9bLecjZKwUx96q5kYZFlJkT4xKsXiKKkBVSen0oOFSguzjI5VOSoleS0SEVHDd\neSdceWUr2HIa+OQTWL5cNIDMfc2OH4fHHpN1V13VuuNrCriSuGV9cCVbrMUtv/oKLrxQChNAdLNc\nZZL22OF8UErNBSYAXkqpn5AYyEpgllKqv9a6qeu96kWrkyaE39yjtU5RSgUCG5VSyxBJx5+11s8o\npWYC/wFm1XWieOALpMrtdHX2KoFpRgNfGzUFM3TNsvTmQA6SH9MGuMRq/V5EIDEQySOyb7raAXDU\n56o9tTfpbAkcQNirIyjg78103RIcE6NcLCE05LXS8qcybAJcd51jJeaUFPjPfxxfSikhHK6AlBT4\n/ntYuLCmEveUKTBtmmuQppQUIa2OoLVUPLkK3MWW1FRYvx5uv91W3PLkSZFOcBWkpsKsWat45pk+\nDBkSUa8dkyev4sEH+zBokHNlurvL52GFK5DgkS/SBTNea52nlPovIgbz/x9pMpUSZpheFyil/kL4\nzyVYImzvIRkkdZImMyZhyQ7LoKZQc20oA6YaDSz3hoLbddPp7FRgmcCrqBkuq0K8HfYhnh7YJl2b\nvTVm+OF6/eh8EFtPF8VYSFE28r6eQIiRuSRfI+X4Bkm0Hn2FkIKmDKH5+YFvYyroWhlKiUhfjN2P\nITvbeXuzOYK7iFvC6dvS47xleGc3XxuV8ggfdq0cW+9+mzbl8NRTf5Gams+XXyoSE4OYObMXN90U\n6nLilgCXXTaSIabczPpEOr/+2jk7K69cmc6iRQf47LNCAgO9mDAhjhkzevDJJ4pTp6CkpIx//nML\nf/xxgrAwH+66qwcTJ9aed/HBB6ksXLif0tJKLrggloce6ou3d+1/GB99dIAvvzzMkSNFhIR4k5QU\nxi23dKNLl9OeTCu01pVAkVJqv9Y6D0BrXayUqqrn2GZBq5Mma5hk0pORDmzRWutMEGKllDqttpbT\nEc9TfSgGLjQYWOcHBXdUnZ6QZAUygduztFNIx/coHHuAIqkp0OgI3wJXn8a4Whr19ceryw6zqrWZ\nGJ1AyvxPIHlYVci31ooYtWsHYy+GqVObriQf6s87cZUKp+RkmYTvvRfi4y26LVlZonp8l4u0tElO\ndh9xyzOxpTkJU0PPX1hYwZ13rmfOnL6kpcUyZkwVx47l4O1tcElxy06d3EOkMzi4ktGjezNtWii+\nvmXceed6QkL2c8MNXfjyS1i+fDt9+xpZtWosf/2Vyx13rKNHj2A6dapJalavzmLhwv28/fZQIiN9\n+ec/N/DKK3u4+27Har5PPbWd33/P4uGHk0hKCqOqSrN8eQa//pp5JqSpTCnVRmtdhLRIB0ApFYJt\nuVGLwWlIkyk093/A3SaPk/09ymndszSEMOUBYwyKHcFQeFuVeENqg0byjE5SkwCVAsuQXmXWiKRp\nesa5AmFqCK5CvGaOiFE+8h6biVGZlObHx8OkKyXp+nSavTYHXIEwmXHWWfDBB7Brl20iePfuzdMi\norlw//21b5s9u+XG0RRwZVsOHSpAKRg3rp1J3NJIhw6WBEqDAZQ6xObNBxg6tISYGH/mzUumR48Q\njh8vYd687WzcmENAgBfXXJPIVVfJndarr+4hNTUfHx8jK1ZkEBvrz+OPJ9Orl4hB1XWsPWbPTsHP\nz8iRI0Vs2pRD9+4h/O9/A3n77X18+206kZG+PP30ALp3l2SOzz9fTufOScTGRtYYR2SkPzt3WsYx\nfvxyHnkkibPPln3378/Hx8fAypWZxMX589xzA/n55ww++CAVX18jDz/cj6FDo2oca7bZ3Cvv6NEi\nJkxYwaOPJrFgwW6Kiyu5664e9OoVwty5W8jIKGHSpDj+8x/HialTpnSwEhv1Y9KkONavz8ZggAsv\nrGTevAzeeGMkfn5G+vcPZ9SoGBYvPuKQCC1enM7kye1JTBRX6C23dGXWrM0O9z18uJDPPjvERx8N\no1cvS65DXV6sBmKE1roUQGttTZK8gVapBXSK6Ucp5YWlEZ+5g3GmUipaa52plIpBplWHeAvJFAeR\n9jEXOvXBwn9GmZ5/sVv+BvgnkBWuKJpRBYdNGzoi+TfmfnCJSMLTswi/jUU+skNW2wOQUnprvSXr\n4+tbPoD0ck7AkqzdmOOdZTkDUeregITQvJHQZBaSd1SOfEhGqlt9+PiIiOPA8TLBm708KSny3BrL\n5tcnTkgYKytLnqOi4IYbpE9ba46vocv79sEVV1gaeIaE2G7/80+45RbnGW9ty+bXtW3/4QeLl8AZ\nxlvXsr1N9tuPHZNE8PR0W+2d1NSW7R1h7mVmfX2ADh0CMRoVN9+cwtix7Rg7Ngzwrt6+b99RXn99\nL3/722BGjw7Bx6cQLy8D+/dr/v3v9UyYEMOzzw5k3bpi5s5dS2JiIEOHRnHyJKxcmcn8+YN4/PEk\nHn10N3PnbuOLL4ajtebmm9czZEgMK1YMJCOjmGnT1uLnF8hll0U5HO+PPx5j7tyzmT8/iBkz/uTv\nf1/NVVd147fferFgwR4efXQHjz0mKq9KSSjOfI5VqzKZOXMQ//hHEm+9tZsnn9zGo4/aaoekpkqY\n9ddfM3nxxcH84x/JvPTSFm69dR2XX57Am2+OYcWKNB59dBs//DCa1FTpMWh/vBmHTfPPtm2nWLJk\nNN99l80TT6zn3HPb8tZbQ9m3r4p//etXxo6NZeDAiBr2rl4tvwHz8q+/ZhMfL16eQ4cKMBgU5eWW\nVgmRkcFs25bt8PPev7+A3r1jqr9/3bsHk5NTytatZfTr52Oz/8aNJ4iO9sPPL7TG99XR98d6OT3d\n0snA+vcBYCZM9tBan1BKXQRsc7S9OeEUpAnpI7NTaz3fat23iLrP0wg9+cbBcYBoJT5sev0msACY\nDDwCXIRtItQoq9fHkHzlY7GK0puqpAzPPPk/j4gaWt/EGIE7kdwjc98g+5ucM13egugarUOIU29s\n24E09fXOdDkB8brtRPKL0hHStBR5j8zCWVo8RIGBUuL+wgs0CPYhspZe3r8f1q6Ffv1g924Jpfj6\nSqLlP//Z+uNriuV58yykyRnGc7rL775bs5eeM43PetmeLNlvnzdPSFN8vK1YYV3Chc0B++tZlr14\n991zePHF/bzxxlaefLKU4cPb8vDD/QgP9+WZZ9KYNq0z6ekhpmPkT2zbtpMUF5dx881dARg6tA1/\n+1sCP/54lKFDowgLg4EDwxk2TLIxrr02jr///YDp2FMUF5cxc6YcGxcnx27ZcrSaNNmPd8yYGC64\nQLxD558fw+efH2LaNNGPGTculk8/PUinTpbJ3Jpw9O8fzuWXyzgMhjj27Dng8P0IC4MBAyIYMkTG\ncNllscyalcH06Z1RShEX145XXtlKQUE5nTp523jKzccXFMhyQoKQt1tv7Yq3t4HJk6N4/nkjEya0\nIzTUh0GDYPDgcHbtymPgwIga47Ee/9dfH+bQoVz++1+J9RYVVWI0etkck5DgxfbtFhZnva2oqILO\nnb2r1wUEeKE1REVV1th/+fIyoqL86vi+1L68d69l2fz9376dhuARYGGD9mxCtDppUkoNQwJP25RS\nm5Ep9gGELH2ulPoH4s+ZWts5rBs3v4GIOEcB/0Y0Fx1lj9+EZJeXK+ByXVM7aAaOk6ybqp+ZIyQi\n1Xo3I/3NdiCusVjk9rInjWvf0ZSoQpKts0yPdIQk5WH5FpWDj7eUtGdlSdVGZaVUaH3xhYSBtIYb\nb2wdExqL5GR48UV4800Z+5QpMGuWEL6LLoKHHpJtzo7kZJg+3fE2V6rUchc7wD1sSUwM5Pnnk3jx\nRcjNLWDlys1Mn76D884bwM6dxQQGtqlRfHHsWDFZWSUMH74UEFu11gwYYFHajYiw/Mn5+RkpK6uk\nqkqTkVH/sfawPpevr5HwcNtzFxUJYejUCfLy4OuvxfO6aRPk5fny4otyndJSyzgMhpqddsPDfWyu\nExrqgzJ15PX1lfh3UVElgYGO+kLVhPU46xq3PTp1kv+sgwczWL16NxMnDuG992RsJ04YKSuzPa6g\noJyAAMc0oE0bLwoKyq32rUApCAioGc8PDfXh+PGm7zKtlNpa2yZaVgyoGq1OmkwN92rLqrDXpHZ8\nDsTZUYUQKHNkPQCJCm1BhJzN2AV8DlR2RMrfHZGj1qxKMwBdTI9KRHpgO5IvVUceRJNAI3lFZnJ0\nBPEcnTKNywCUg5dBvC43z4H+Dlq7TJsG5eXS/LKkRDpsBwfLuoozEdFqBVRWCmkqL5fEXZC+Tq5k\nh7tUnbmLHeA+thQUwI03BhIX157Fiw9x7bWwdas/PXoUceKE7b7R0f7ExbVh8eLzGn2dMzm2IdAa\nRo6UBP3KSimSuPZa2fa//zXddfz9vSgpsdzqnzhxJuXENbF3bxZbtmxl/vyz6N7dkoBdUhLIt99q\n0tIKad9e7v53786jc2fHSdqdOweyZ08+Y02FlLt25RER4UtwcM2k37PPjmTevO3s3JlbnffVRIgG\nxiFTvDUUsKYpL9RQtDppagqcQDQKgpDI2THEOVNgWmctZbQZOA/IHwD64hYeaH044GCdEYv0QFMX\nzRRiIUdHkTcux+q6FWDQ4jK+/g4YNaphp01JgYkTpfKkqkruqB9+WKrcdu4UMUVXgNmOGTPEe7Z1\nq0XM8tQpIYGugJQU96g6cxc7wPVtOXCggN9+y6Jnz1i6d/cnPb2YVauOMGBAGGFhMHVqe5577i8m\nTAgHQkhLK8Tb20DfvqEEBHjxzjv7uPrqRLy8DBw4UEBpaSW9e4c6vJa53P90jm0oUlOl4KSsTMJl\nfn6SZxlm0ulr377p5BN69Ajmxx+PMmxYFLt35/Hzz8cYNsySRH8m11m06ATr1m1m7tzBDBli/54Y\n6dkzhgULdjN3bhJ//ZXLqlVZfPCBfWsIwUUXxTNnzhYmTmxHZKQvb765l0sucSwAmJAQwNSpHZg5\ncxNz5/YjKSkMrTUrVmRy9GgR//iHgy95w/AdEKi1TrHfoJT65XRPeiZwC9I0FinIGomtv86AOGfM\nX8fViLRo/lCEuzoj7AUsrXG65fQlWMjRMYQgZSNeLC95VpUSD59yO1x66WlexwpTpsB5phvCyEgY\nN07UtSdNsihSuwKuuAIGDpQEzalThUCCiGHOn1/3sc4EV67Usoa72AGnb0t5hE+z6zTVh4AAL7Zt\nO8nChakUF5cTFOTNyJHR3HOP/LjHjm1HXl4577+/ic8+K6VdO3+efLI/MTH+vPzyYJ59dicTJqyg\nvLyKjh0DueOO7rVeyxTlwmBQjTrWHB5rKAICVA0dMzMuvBBeseoL2thzW+9+++3dmTlzE+eeu4yB\nA8OZODGO3Nwyh/s6Wq4LX3yxl/LyCh57bB2PPqpRSjFgQDgLFpwFwOuv92HOnC2MGrWMsDAfZs/u\nWy03kJFRzOTJq/j665HExPgzbFhbbrihM9Onr6WsTHSaZsyoXU581qw+fPzxAZ54YjtHjxYRHOxN\n//7h3Hpr14YbYAetdS1BbNBat4okr9KupD7mAEopvRDJGK8LPyHJ4YWjgRHNPapWQhmWKrUMJLR2\nwrTem+pmspGR4kG59lrnKd/3wAMPamLDhnaMG2evhuuBB+6DpUs3MWjQUZt1H34Ib78NWuvGsdMW\nwP8XU+bXSA/coonAWa08mKaAWUTTTI6OImSpGIsidpmEj84bBzff7NwNZT3wwAMPPPDAFeD2pOld\n4Hag6DKgX+uOpV5Y6zuBhM9yEHKUiXiOspBcJPMnVyaE6OyzpYFsWEv0yqsHKSn1q2m7Ajx2OBfc\nxQ5wH1usNXlcGR47PGgo3Jo0vaQUs9AU/R2oPWTuHMhDhDXTEHJkXc6vgDLw9RG9oDvusOTWeOCB\nBx544IEHLQO3JU2PGww8RRVF1yHq3s6ESoQUpQH7Tc9mlexy8DZCt25w62PQx7FavlPDHe6gwWOH\ns8Fd7AD3scVdvBoeOzxoKNyONGngPoOB15SmcDq2egOthWKEGB1CRCuzEIJUJR/AyJFSSdOUzWY9\n8MADDzzwwF2glIoH3keK5KuAN7XWL7b0OOx1sF0aa4CbDQZeN2gKb9OtQ5g0kqS9GfgS+B/Sr+5L\nGWBYCcx+AFYuhZU/wU8/ibK0j49t352dO1th7E0Ejx3OBY8dzgd3scXcfgQsfdNcER47nBtKqSFI\nCdQ9WuvewFDgdqVUze7BzQy38TRVAP9Rik3emoLbNDSpKGkdKEOq1w4joTaryklVAX37wty5EF67\n0r9DFBY23RBbEx47nAseO5wP7mJLadMKW7caPHY4JYK11hlIvTha6wKl1F9AHNLko8XgFqSpBLjQ\nYGCdt6bkbi2y4M2FPCTUdsD0OImU+VeAvw9MuVJaiJwO3CXPwWOHc8Fjh/PBXWxxlxwajx2uBaVU\nRyAZ+LOlr+0WpGkmUBUAJRObmDDVlbBdKtL6Dz4B3ZuhMs/FNUer4bHDueCxw/ngLrY4sx2PPbaN\n6Gg/br65fnVqZ7ajMXAXO0yoFrlUSgUC/wfcrbUuaOmBuAVpqggzUHR7FRSd4YmKgXTgIK2SsG2t\n3eLKdwweO5wLHjucD2diS07OrWgd0PSDMkGpQsLDX6t3v/Hjl3PiRCk+PgYMBkWHDoFcemk8V1yR\n0Og2I82N2bP71rndWt+otlYqzopDhwq44opfGTMmlhtv7F9tR3r6Ce67bzsZGcX07RvKY48lExvr\n7/AceXllzJmzhT/+OEFYmA933dWDiRPjar3miRMlvPTSbn7/PYvi4kratvVj3Lh2TJvWGT8/42nZ\nkZJiyfXburXG5m0ASikvhDB9oLX+5rQudIZwC9JUNMLEahrTQFUj/dfSEIJ0COnw6w2UQVgo3PFA\n6zWXjYxsnes2NTx2OBc8djgfGmtLcxKmxp5/9uyzuOSSSAoLK9iwIZunntrBtm2nePRRJ+84XAdq\na8RdWakxGp2LDALMm7eDPn1sm/OeOlXG7NkbeOSRJEaOjOall3Zx330b+fDD4Q7P8fjj2/HxMbJq\n1Vj++iuXO+5YR48ewdV96ayRl1fGNdesZsCAcD76aDgxMf5kZhbz/vuppKUV0rXr6XUyT0623Eh8\n+CFs3mzZprU2Zwu/A+zUWrda50+3IE0NQjMlbDcl3CXPwWOHc8Fjh/PBXWwxe2UCArwYOTKaiAhf\nrrnmd66/vhMlJZXcccd6Vqy4oNrz9PPPx3jjjb18/vkIXn11D6mp+fj4GFmxIoPYWH8efzyZXr2k\niuedd/bx5ZeHyckpIybGnzvv7M7o0XLBb75J46uvDtOnTyiLFqUTGurNk0/25+DBAhYs2E15ueZf\n/+rJxRfHAzB7dgoxMf7cfrvkUqxcmcGrr+4hPb2I8HAfHnigL506RWGP8eOXM3VqB77//giHDhXy\n558TePfd/fWMK41+/UL5+us0goO9eeCBPgwf3haAI0eKeOihFHbvzqNvtEs+5wAAIABJREFU31A6\ndAigoKCCJ5/sD8CWLSd57rmd7N9fQFycP/ff35tBgyJqff9/+OEIwcHedOoUxuHDhdVepp9/PkaX\nLkFccEEsADNmdGfkyKUcPFhAx46BNucoLq5k+fIMFi0aiZ+fkf79wxk1KobFi49w9901i9Peey+V\nwECv6jEDREf7c999vWsdZ1NAKTUMuBrYppTajLg+HtBa/9isF7aDW0kOoIEtwC9IwvZ64FPgJeAp\n4BPZ5p8F110JK5fJY8UK6VjfmoTJHlqLHMF778lyZib89Vfrjul04LHDueCxw/ngLrZoDRUVoYSE\n+LNpUw5xcaEEBvqwZs3x6n2WLDnCRRfFVy+vWpXJxIntWLNmHCNHRvPkk9uqt7VvH8D77w/jjz/G\nM2NGV/7zn81kZ1tKwrZtO0X37iH8/vtYJkyI4/77N7FzZy5LlozmySeTmTdvO8XFlTXGuW3bSR56\nKIV77+3FmjXjWbjwHNq1s4SttJYw0fLl5jEf5aGHzmb16nEYDKrecW3ffopOnQL57bex3HBDZx5+\neEv1tlmzNtOvXxi//jqWW2/txnffHanelplZzJ13ruOWW7qyevU47rmnF/fcs4FTp8ocvt8FBeW8\n8soe/v3vXjXyl/bvL6Bbt+BqO/z9jbRrF8Cff+bXOM+hQwV4eYldZnTvHsz+/TX3BfjzzxOcf36s\nw23NBSWsOxF4RGudDFwC3NrShAnchTRVIZ6jd4AVwCrgReAnYBe0bwOvvQIrf4SVy+H770+/wq05\nYa3d8sILsGOHEDqQ/nLzW80h2Th47HAueOxwPriLLceOWV5/+61oA3l5+ZKbW46vL8TExFUTg9zc\nMlavPm6TK9O/fzjDhrVFKcVFF8WxZ49loh4zJpaICF8Axo5tR4cOAWzbdqp6e1xcGy6+OB6lFOPG\nxZKZWcytt3bD29vA0KFReHsr0tJq6jksWpTG5MkJnH22xEWjovyoqrJ4X8x2mPNqrrwykTVr/PDx\nMTZoXO3a+TN5suR1XXxxPMePl5KdXUpGRjE7dpzittu64eVlMHl0oquPW7LkCOeeG82wYeKVGjIk\nkl69QvnttyyH7/2CBXu4/PIE2rb1q15n1mkqKqrg2DFvGzsCA71Ys6YmiSwqqiQw0DboFBDgRWFh\nhcPr5uaWExXl63BbM+IVRJvpStNyPrCgpQcB7kKavkM686aBVyEEBsDSJSIg2akTvP9+81S4NSf+\n+gv++U9L0nlQEJSXt+6YTgceO5wLHjucD+5iS1oaXHwxFBWVEBLijb8/dOoUz6+/ZlJSUsnSpccY\nODC8mnAANq/9/IyUlVVSVSVuk2+/TWfq1F8ZNmwpw4YtZd++fBuvi/2xAGFhliodX18jRUU1J/6M\njBLi42svszbb4e0ty/Hx/lRYneZ0xlVcXEFWVgkhIT74+loSpaOjLR6uY8eKWbbsKMOHL2X4cDl3\nSkoOx4+X1Bjjrl25rF17gmuuSayxDaBNGy9OnCi3saOoqAKjsWaSdps2RgoKbN+ngoJyAgIcZ++E\nhHhz/HiLi0CdrbW+HVEYQmt9EmiVHhpukdN0ycXypzNjBrz8sjz7+MCpU2BwIVponedgNEJlJZiL\nUFzJFo8dzgWPHc4Hd7El1ipKYzDA1q2nKCoqpX//cAoLISjIj379wvj552MsWZLO1KkdG3TeY8eK\nefTRrbz99lCSksIAmDr1V3QT1NHHxPiRnm5bam1dwWgwQFWVZbm01PJ5nMm4oqL8yM0to7S0spo4\nZWYWW43Ln4suimfOnH71nmvDhmyOHSti3LjlaC2EqKpKk5pawKefnkvnzoEsX55ebUdRUQVpaYUM\nGFAzsbtDh0AqKzVpaYXVIbrdu/Po3LnmvgBDhkSxYkUGM2Z0q3ecTYhypZQRScJBKRWFxJhaHC7y\n06wbPUy5apddBnPmwMmT8NZbcNddcNVVrTu204W72OKxw7ngscP54A62FBZW4O+fyR13bCIxMY7U\n1CDeeANGjIALL4xn4cL97NuXzwUX1F3Pb+YexcUVGAwQGupNVZVm0aI09u1znGNjf2x9mDw5gUWL\n0li37gRaa7KySjhwwCL3c8458NFHUFAAxcXw3Xdix+mOy4zYWH969w7l1Vf3UF5exZYtJ1m1KrN6\n+6RJcaxalcmaNcepqtKUllayYUM2WVk1PU1TpnRgyZLRfP75CL74YgRTpnRgxIhoXn/9bADOPz+W\nvLwC5s49Rm5uJbNm7SE4OISLLw6scS5/fyPnnx/DggW7KS6uZNOmHFatyuKiixxLDlx3XSIFBRU8\n+GAKx44J6cvMLOa//93J3r15DXovTgMvAl8D0UqpJ4DfgXnNdbG64BSeJqXU28CFQKbWup9pXRjw\nGdABUU6aqrXOres8Y8ZIGG7jRll+/HFISGjGgTcxrLVbXNkWjx3OBY8dzoczsUWpwmbXaWoo7rhj\nPd7eCqUUnTsHcsMNnenRIwGl4JprICoKu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DfgK4gcXAKuC3SqmMsPVKw5fD0mPG0xQLeDweXC4XlZWVoSnYnRie1lieiByy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\nmBi+6XCwkM5VtiQcKcFKCbDAZuNDrxcxFO40cI+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Nps6A1+tlxYoVvDJnDhVlZdzlcPBH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\nlj5N06bFEBfXNN9LrfWHSqnNwATLqou11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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# to calculate and plot the results\n", "# only do for SensorA\n", "sensors = [dfConcept.columns.values[0]]\n", "\n", "\n", "iplot=1\n", "for tinternal in [2]:\n", " for ttarget in [TempTargetDeltaC]:\n", " for thotshield in [0]:\n", " for tcentralObs in [1]:\n", " for dynamicRangeK in [indexTempDynamicPlot]:\n", " for atmo in ['tropical5km','MLS23km','SW31km']:\n", " for tambient in [0,1,2,3,4]:\n", " doPlots = True if atmo in 'tropical5km' else False\n", " iplot += 1\n", " dfBitsLoss = plotPWellFillRange(sensors=sensors,\n", " dfBitsLoss=dfBitsLoss, \n", " dfConcept=dfConcept, atmoSet=[atmo], \n", " tambient=tempAmbUnique[tambient],\n", " thotshield=tempHotShieldDeltas[thotshield],\n", " tinternal=tempInternDeltas[tinternal], \n", " tcentralObs=tempCentralObsDeltas[tcentralObs],\n", " optFilter=optFilters[0],\n", " atmotype='gen', intTime=integrationTime, \n", " areaDetector=areaDetector,\n", " wellCapacity=wellCapacityUsable, \n", " dynamicRangeK=tempDynamicDeltas[dynamicRangeK],\n", " ttarget=ttarget,\n", " threshold2Noise=threshold2Noise,\n", " netdstd=netdstd,ksys=ksyss[-1],\n", " doPlots=doPlots,iplot=iplot) \n", " " ] }, { "cell_type": "code", "execution_count": 29, "metadata": { "collapsed": false, "jupyter": { "outputs_hidden": false } }, "outputs": [], "source": [ "# to calculate the number of bits lost\n", "def tableBitsLoss(dfBitsLoss, atmoSet, sensorSet, \n", " tranges, tambients,\n", " thotshield, tcentralObs, tinternal,\n", " optFilter,ttarget,atmotype,tdynamic):\n", "\n", " \"\"\"This function extracts/filters data from the dfBitsLoss dataframe\n", " \"\"\"\n", " sumcols = ['Atmo','PathLen', 'Sensor','OptFilter','TempAmbient [C]','TempAmbient [K]','IntTime',\n", " 'BitsAvail','BitsNoFill','BitsNETD','DeltaTarget',\n", " 'WfHotOptics', 'WfCentralObs', 'WfEHotShield',\n", " 'WfPath', 'WfScene','WfDynamic', 'WfPhotonStarve','wellfillTargDiff',\n", " 'Mderivscn [q/(s.m2.K)]','EDynamic [q/(s.m2)]','EScene [q/(s.m2)]',\n", " 'TargDiffbitsNoNoise','TargDiffbitsNoise'\n", " ]\n", " \n", " \n", " for i,sensor in enumerate(sensorSet):\n", " for j,katmo in enumerate(atmoSet):\n", " \n", " print('{}'.format(80*'*'))\n", " print('{}'.format(80*'*'))\n", " print('{}'.format(80*'*'))\n", " \n", " print('Sensor : {} '.format(sensor))\n", " print('Atmosphere : {} '.format(katmo))\n", " print('Sensor dynamic range : {} C'.format(tdynamic))\n", " print('Hot shield temperature : {} C'.format(thotshield+tinternal))\n", " print('Central obsuration temperature : {} C'.format(tcentralObs+tinternal))\n", " print('Internal temperature : {} C'.format(tinternal))\n", " print('Filter : {} '.format(optFilter))\n", "\n", " dfr = pd.DataFrame(columns=sumcols)\n", " \n", " for r,trange in enumerate(tranges):\n", " for t,tambient in enumerate(tambients):\n", " df = dfBitsLoss.copy()\n", " filt = (df.Atmo==katmo) & \\\n", " (df.Sensor==sensor) & \\\n", " (df['PathLen']==trange) & \\\n", " (np.abs(df['TempAmbient [K]']-(tambient))<1.) & \\\n", " (np.abs(df['TempHotShield [K]']-(tambient+thotshield+tinternal))<1.) & \\\n", " (np.abs(df['TempCentralObs [K]']-(tambient+tcentralObs+tinternal))<1.) & \\\n", " (np.abs(df['TempIntern [K]']-(tambient+tinternal))<1.) & \\\n", " (np.abs(df['DeltaTarget']-(ttarget))<1.)\n", " df = df[filt]\n", " df = df[sumcols]\n", " dfr = dfr.append(df)\n", " \n", " dfr['IntTime'] *= 1000.\n", " dfr['WfSceneDyn'] = dfr['WfScene'] + dfr['WfDynamic']\n", "\n", " dfr = dfr.drop(['Sensor','OptFilter','WfHotOptics','WfCentralObs','WfEHotShield','WfPath',\n", " 'WfPhotonStarve','TempAmbient [K]'],axis=1)\n", "\n", " print('\\n{}'.format(80*'*'))\n", "\n", " print('Stare time vs. scene temperature')\n", " dfp = pd.pivot_table(dfr, values='IntTime', \n", " index=['TempAmbient [C]'], \n", " columns=['PathLen'],aggfunc = lambda x: np.mean(x))\n", " print(dfp.round(1))\n", "\n", " print('\\n{}'.format(80*'*'))\n", " print('Available well fill after skimming vs. scene temperature')\n", " dfp = pd.pivot_table(dfr, values='BitsNoFill', \n", " index=['TempAmbient [C]'], \n", " columns=['PathLen'],aggfunc = lambda x: np.mean(x))\n", " print(dfp.round(1))\n", "\n", " print('\\n{}'.format(80*'*'))\n", " print('Temporal noise vs. scene temperature')\n", " dfp = pd.pivot_table(dfr, values='BitsNETD', \n", " index=['TempAmbient [C]'], \n", " columns=['PathLen'],aggfunc = lambda x: np.mean(x))\n", " print(dfp.round(1))\n", "\n", " print('\\n{}'.format(80*'*'))\n", " print('Available dynamic range well fill vs. scene temperature')\n", " dfp = pd.pivot_table(dfr, values='BitsAvail', \n", " index=['TempAmbient [C]'], \n", " columns=['PathLen'],aggfunc = lambda x: np.mean(x))\n", " print(dfp.round(1))\n", "\n", " print('\\n{}'.format(80*'*'))\n", " print('No noise: $\\Delta$T={} K vs. ambient temperature'.format(ttarget))\n", " dfp = pd.pivot_table(dfr, values='TargDiffbitsNoNoise', \n", " index=['TempAmbient [C]'], \n", " columns=['PathLen'],aggfunc = lambda x: np.mean(x))\n", " print(dfp.round(1))\n", "\n", " for TNR in [1.0, 2.0, 3.0]:\n", " dfr['TNR'] = dfr['TargDiffbitsNoise'] - np.log(TNR)/np.log(2)\n", " print('\\n{}'.format(80*'*'))\n", " print('Target $\\Delta$T={} K around minimum scene, TNR = {}, vs. scene temperature'.format(ttarget, TNR))\n", " dfp = pd.pivot_table(dfr, values='TNR', \n", " index=['TempAmbient [C]'], \n", " columns=['PathLen'],aggfunc = lambda x: np.mean(x))\n", " print(dfp.round(1))\n", "\n", " print('\\n{}'.format(80*'*'))\n", "\n" ] }, { "cell_type": "code", "execution_count": 30, "metadata": { "collapsed": false, "jupyter": { "outputs_hidden": false } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "********************************************************************************\n", "********************************************************************************\n", "********************************************************************************\n", "Sensor : SensorA \n", "Atmosphere : tropical5km \n", "Sensor dynamic range : 40.0 C\n", "Hot shield temperature : -80.0 C\n", "Central obsuration temperature : 20 C\n", "Internal temperature : 20 C\n", "Filter : Standard \n", "\n", "********************************************************************************\n", "Stare time vs. scene temperature\n", "PathLen 1.0009 4.0006 7.0003 10.0000\n", "TempAmbient [C] \n", "-40.0 30.0 30.0 30.0 30.0\n", "-20.0 29.6 30.0 30.0 30.0\n", " 0.0 14.0 21.5 25.8 28.2\n", " 20.0 7.2 10.5 12.3 13.2\n", " 40.0 4.0 5.5 6.3 6.8\n", "\n", "********************************************************************************\n", "Available well fill after skimming vs. scene temperature\n", "PathLen 1.0009 4.0006 7.0003 10.0000\n", "TempAmbient [C] \n", "-40.0 12.2 11.4 11.0 10.8\n", "-20.0 13.1 12.4 12.1 11.9\n", " 0.0 13.0 12.6 12.2 12.0\n", " 20.0 12.9 12.5 12.2 12.0\n", " 40.0 12.8 12.4 12.1 11.9\n", "\n", "********************************************************************************\n", "Temporal noise vs. scene temperature\n", "PathLen 1.0009 4.0006 7.0003 10.0000\n", "TempAmbient [C] \n", "-40.0 1.9 1.9 1.9 1.9\n", "-20.0 3.0 3.0 3.0 3.0\n", " 0.0 3.0 3.6 3.9 4.0\n", " 20.0 2.9 3.5 3.7 3.8\n", " 40.0 2.9 3.4 3.6 3.7\n", "\n", "********************************************************************************\n", "Available dynamic range well fill vs. scene temperature\n", "PathLen 1.0009 4.0006 7.0003 10.0000\n", "TempAmbient [C] \n", "-40.0 10.3 9.5 9.1 8.9\n", "-20.0 10.1 9.3 9.0 8.9\n", " 0.0 10.0 9.0 8.4 8.0\n", " 20.0 10.0 9.0 8.5 8.2\n", " 40.0 10.0 9.1 8.6 8.3\n", "\n", "********************************************************************************\n", "No noise: $\\Delta$T=2.0 K vs. ambient temperature\n", "PathLen 1.0009 4.0006 7.0003 10.0000\n", "TempAmbient [C] \n", "-40.0 9.0 6.6 5.0 3.9\n", "-20.0 11.2 9.0 7.5 6.5\n", " 0.0 11.2 9.9 8.7 7.7\n", " 20.0 11.1 9.8 8.6 7.7\n", " 40.0 11.0 9.7 8.5 7.6\n", "\n", "********************************************************************************\n", "Target $\\Delta$T=2.0 K around minimum scene, TNR = 1.0, vs. scene temperature\n", "PathLen 1.0009 4.0006 7.0003 10.0000\n", "TempAmbient [C] \n", "-40.0 7.1 4.6 3.1 1.9\n", "-20.0 8.2 5.9 4.5 3.4\n", " 0.0 8.2 6.3 4.8 3.7\n", " 20.0 8.2 6.3 4.9 3.9\n", " 40.0 8.1 6.3 5.0 4.0\n", "\n", "********************************************************************************\n", "Target $\\Delta$T=2.0 K around minimum scene, TNR = 2.0, vs. scene temperature\n", "PathLen 1.0009 4.0006 7.0003 10.0000\n", "TempAmbient [C] \n", "-40.0 6.1 3.6 2.1 0.9\n", "-20.0 7.2 4.9 3.5 2.4\n", " 0.0 7.2 5.3 3.8 2.7\n", " 20.0 7.2 5.3 3.9 2.9\n", " 40.0 7.1 5.3 4.0 3.0\n", "\n", "********************************************************************************\n", "Target $\\Delta$T=2.0 K around minimum scene, TNR = 3.0, vs. scene temperature\n", "PathLen 1.0009 4.0006 7.0003 10.0000\n", "TempAmbient [C] \n", "-40.0 5.5 3.1 1.5 0.4\n", "-20.0 6.6 4.3 2.9 1.8\n", " 0.0 6.6 4.7 3.3 2.1\n", " 20.0 6.6 4.7 3.3 2.3\n", " 40.0 6.5 4.7 3.4 2.4\n", "\n", "********************************************************************************\n", "********************************************************************************\n", "********************************************************************************\n", "********************************************************************************\n", "Sensor : SensorB \n", "Atmosphere : tropical5km \n", "Sensor dynamic range : 40.0 C\n", "Hot shield temperature : -80.0 C\n", "Central obsuration temperature : 20 C\n", "Internal temperature : 20 C\n", "Filter : Standard \n", "\n", "********************************************************************************\n", "Stare time vs. scene temperature\n", "Empty DataFrame\n", "Columns: []\n", "Index: []\n", "\n", "********************************************************************************\n", "Available well fill after skimming vs. scene temperature\n", "Empty DataFrame\n", "Columns: []\n", "Index: []\n", "\n", "********************************************************************************\n", "Temporal noise vs. scene temperature\n", "Empty DataFrame\n", "Columns: []\n", "Index: []\n", "\n", "********************************************************************************\n", "Available dynamic range well fill vs. scene temperature\n", "Empty DataFrame\n", "Columns: []\n", "Index: []\n", "\n", "********************************************************************************\n", "No noise: $\\Delta$T=2.0 K vs. ambient temperature\n", "Empty DataFrame\n", "Columns: []\n", "Index: []\n", "\n", "********************************************************************************\n", "Target $\\Delta$T=2.0 K around minimum scene, TNR = 1.0, vs. scene temperature\n", "Empty DataFrame\n", "Columns: []\n", "Index: []\n", "\n", "********************************************************************************\n", "Target $\\Delta$T=2.0 K around minimum scene, TNR = 2.0, vs. scene temperature\n", "Empty DataFrame\n", "Columns: []\n", "Index: []\n", "\n", "********************************************************************************\n", "Target $\\Delta$T=2.0 K around minimum scene, TNR = 3.0, vs. scene temperature\n", "Empty DataFrame\n", "Columns: []\n", "Index: []\n", "\n", "********************************************************************************\n", "********************************************************************************\n", "********************************************************************************\n", "********************************************************************************\n", "Sensor : SensorC \n", "Atmosphere : tropical5km \n", "Sensor dynamic range : 40.0 C\n", "Hot shield temperature : -80.0 C\n", "Central obsuration temperature : 20 C\n", "Internal temperature : 20 C\n", "Filter : Standard \n", "\n", "********************************************************************************\n", "Stare time vs. scene temperature\n", "Empty DataFrame\n", "Columns: []\n", "Index: []\n", "\n", "********************************************************************************\n", "Available well fill after skimming vs. scene temperature\n", "Empty DataFrame\n", "Columns: []\n", "Index: []\n", "\n", "********************************************************************************\n", "Temporal noise vs. scene temperature\n", "Empty DataFrame\n", "Columns: []\n", "Index: []\n", "\n", "********************************************************************************\n", "Available dynamic range well fill vs. scene temperature\n", "Empty DataFrame\n", "Columns: []\n", "Index: []\n", "\n", "********************************************************************************\n", "No noise: $\\Delta$T=2.0 K vs. ambient temperature\n", "Empty DataFrame\n", "Columns: []\n", "Index: []\n", "\n", "********************************************************************************\n", "Target $\\Delta$T=2.0 K around minimum scene, TNR = 1.0, vs. scene temperature\n", "Empty DataFrame\n", "Columns: []\n", "Index: []\n", "\n", "********************************************************************************\n", "Target $\\Delta$T=2.0 K around minimum scene, TNR = 2.0, vs. scene temperature\n", "Empty DataFrame\n", "Columns: []\n", "Index: []\n", "\n", "********************************************************************************\n", "Target $\\Delta$T=2.0 K around minimum scene, TNR = 3.0, vs. scene temperature\n", "Empty DataFrame\n", "Columns: []\n", "Index: []\n", "\n", "********************************************************************************\n" ] } ], "source": [ "# to calculate the number of bits lost for Tropical atmosphere\n", "\n", "tranges = [pathlenUniqueGen[i] for i in [1,4,7,10]]\n", "tableBitsLoss(dfBitsLoss=dfBitsLoss, atmoSet=['tropical5km'], \n", " sensorSet=['SensorA','SensorB','SensorC'], \n", " tranges=tranges, tambients=tempAmbUnique,\n", " thotshield=tempHotShieldDeltas[0], tcentralObs=tempCentralObsDeltas[1], tinternal=tempInternDeltas[2],\n", " optFilter=optFilters[0],ttarget=TempTargetDeltaC,atmotype='gen',tdynamic=TempDynamicPlot)" ] }, { "cell_type": "code", "execution_count": 31, "metadata": { "collapsed": false, "jupyter": { "outputs_hidden": false } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "********************************************************************************\n", "********************************************************************************\n", "********************************************************************************\n", "Sensor : SensorA \n", "Atmosphere : MLS23km \n", "Sensor dynamic range : 40.0 C\n", "Hot shield temperature : -80.0 C\n", "Central obsuration temperature : 20 C\n", "Internal temperature : 20 C\n", "Filter : Standard \n", "\n", "********************************************************************************\n", "Stare time vs. scene temperature\n", "PathLen 1.0009 4.0006 7.0003 10.0000\n", "TempAmbient [C] \n", "-40.0 30.0 30.0 30.0 30.0\n", "-20.0 27.5 30.0 30.0 30.0\n", " 0.0 13.1 18.6 22.3 24.7\n", " 20.0 6.8 9.2 10.8 11.8\n", " 40.0 3.8 5.0 5.7 6.1\n", "\n", "********************************************************************************\n", "Available well fill after skimming vs. scene temperature\n", "PathLen 1.0009 4.0006 7.0003 10.0000\n", "TempAmbient [C] \n", "-40.0 12.3 11.7 11.3 11.1\n", "-20.0 13.1 12.6 12.3 12.2\n", " 0.0 13.1 12.7 12.5 12.3\n", " 20.0 13.0 12.7 12.4 12.3\n", " 40.0 12.9 12.6 12.4 12.2\n", "\n", "********************************************************************************\n", "Temporal noise vs. scene temperature\n", "PathLen 1.0009 4.0006 7.0003 10.0000\n", "TempAmbient [C] \n", "-40.0 1.9 1.9 1.9 1.9\n", "-20.0 2.9 3.0 3.0 3.0\n", " 0.0 2.9 3.4 3.6 3.8\n", " 20.0 2.8 3.3 3.5 3.6\n", " 40.0 2.8 3.2 3.4 3.5\n", "\n", "********************************************************************************\n", "Available dynamic range well fill vs. scene temperature\n", "PathLen 1.0009 4.0006 7.0003 10.0000\n", "TempAmbient [C] \n", "-40.0 10.4 9.7 9.4 9.2\n", "-20.0 10.2 9.6 9.3 9.1\n", " 0.0 10.2 9.4 8.9 8.5\n", " 20.0 10.1 9.4 8.9 8.6\n", " 40.0 10.1 9.4 9.0 8.7\n", "\n", "********************************************************************************\n", "No noise: $\\Delta$T=2.0 K vs. ambient temperature\n", "PathLen 1.0009 4.0006 7.0003 10.0000\n", "TempAmbient [C] \n", "-40.0 9.3 7.5 6.2 5.4\n", "-20.0 11.3 9.8 8.7 7.9\n", " 0.0 11.3 10.4 9.7 9.0\n", " 20.0 11.2 10.3 9.6 9.0\n", " 40.0 11.1 10.2 9.5 8.9\n", "\n", "********************************************************************************\n", "Target $\\Delta$T=2.0 K around minimum scene, TNR = 1.0, vs. scene temperature\n", "PathLen 1.0009 4.0006 7.0003 10.0000\n", "TempAmbient [C] \n", "-40.0 7.3 5.5 4.3 3.4\n", "-20.0 8.4 6.8 5.6 4.8\n", " 0.0 8.4 7.0 6.0 5.2\n", " 20.0 8.4 7.0 6.1 5.3\n", " 40.0 8.3 7.0 6.1 5.4\n", "\n", "********************************************************************************\n", "Target $\\Delta$T=2.0 K around minimum scene, TNR = 2.0, vs. scene temperature\n", "PathLen 1.0009 4.0006 7.0003 10.0000\n", "TempAmbient [C] \n", "-40.0 6.3 4.5 3.3 2.4\n", "-20.0 7.4 5.8 4.6 3.8\n", " 0.0 7.4 6.0 5.0 4.2\n", " 20.0 7.4 6.0 5.1 4.3\n", " 40.0 7.3 6.0 5.1 4.4\n", "\n", "********************************************************************************\n", "Target $\\Delta$T=2.0 K around minimum scene, TNR = 3.0, vs. scene temperature\n", "PathLen 1.0009 4.0006 7.0003 10.0000\n", "TempAmbient [C] \n", "-40.0 5.8 3.9 2.7 1.8\n", "-20.0 6.8 5.2 4.1 3.3\n", " 0.0 6.8 5.4 4.4 3.7\n", " 20.0 6.8 5.5 4.5 3.8\n", " 40.0 6.7 5.5 4.5 3.8\n", "\n", "********************************************************************************\n", "********************************************************************************\n", "********************************************************************************\n", "********************************************************************************\n", "Sensor : SensorB \n", "Atmosphere : MLS23km \n", "Sensor dynamic range : 40.0 C\n", "Hot shield temperature : -80.0 C\n", "Central obsuration temperature : 20 C\n", "Internal temperature : 20 C\n", "Filter : Standard \n", "\n", "********************************************************************************\n", "Stare time vs. scene temperature\n", "Empty DataFrame\n", "Columns: []\n", "Index: []\n", "\n", "********************************************************************************\n", "Available well fill after skimming vs. scene temperature\n", "Empty DataFrame\n", "Columns: []\n", "Index: []\n", "\n", "********************************************************************************\n", "Temporal noise vs. scene temperature\n", "Empty DataFrame\n", "Columns: []\n", "Index: []\n", "\n", "********************************************************************************\n", "Available dynamic range well fill vs. scene temperature\n", "Empty DataFrame\n", "Columns: []\n", "Index: []\n", "\n", "********************************************************************************\n", "No noise: $\\Delta$T=2.0 K vs. ambient temperature\n", "Empty DataFrame\n", "Columns: []\n", "Index: []\n", "\n", "********************************************************************************\n", "Target $\\Delta$T=2.0 K around minimum scene, TNR = 1.0, vs. scene temperature\n", "Empty DataFrame\n", "Columns: []\n", "Index: []\n", "\n", "********************************************************************************\n", "Target $\\Delta$T=2.0 K around minimum scene, TNR = 2.0, vs. scene temperature\n", "Empty DataFrame\n", "Columns: []\n", "Index: []\n", "\n", "********************************************************************************\n", "Target $\\Delta$T=2.0 K around minimum scene, TNR = 3.0, vs. scene temperature\n", "Empty DataFrame\n", "Columns: []\n", "Index: []\n", "\n", "********************************************************************************\n", "********************************************************************************\n", "********************************************************************************\n", "********************************************************************************\n", "Sensor : SensorC \n", "Atmosphere : MLS23km \n", "Sensor dynamic range : 40.0 C\n", "Hot shield temperature : -80.0 C\n", "Central obsuration temperature : 20 C\n", "Internal temperature : 20 C\n", "Filter : Standard \n", "\n", "********************************************************************************\n", "Stare time vs. scene temperature\n", "Empty DataFrame\n", "Columns: []\n", "Index: []\n", "\n", "********************************************************************************\n", "Available well fill after skimming vs. scene temperature\n", "Empty DataFrame\n", "Columns: []\n", "Index: []\n", "\n", "********************************************************************************\n", "Temporal noise vs. scene temperature\n", "Empty DataFrame\n", "Columns: []\n", "Index: []\n", "\n", "********************************************************************************\n", "Available dynamic range well fill vs. scene temperature\n", "Empty DataFrame\n", "Columns: []\n", "Index: []\n", "\n", "********************************************************************************\n", "No noise: $\\Delta$T=2.0 K vs. ambient temperature\n", "Empty DataFrame\n", "Columns: []\n", "Index: []\n", "\n", "********************************************************************************\n", "Target $\\Delta$T=2.0 K around minimum scene, TNR = 1.0, vs. scene temperature\n", "Empty DataFrame\n", "Columns: []\n", "Index: []\n", "\n", "********************************************************************************\n", "Target $\\Delta$T=2.0 K around minimum scene, TNR = 2.0, vs. scene temperature\n", "Empty DataFrame\n", "Columns: []\n", "Index: []\n", "\n", "********************************************************************************\n", "Target $\\Delta$T=2.0 K around minimum scene, TNR = 3.0, vs. scene temperature\n", "Empty DataFrame\n", "Columns: []\n", "Index: []\n", "\n", "********************************************************************************\n" ] } ], "source": [ "# to calculate the number of bits lost for Midlatitude Summer atmosphere\n", "\n", "tranges = [pathlenUniqueGen[i] for i in [1,4,7,10]]\n", "tableBitsLoss(dfBitsLoss=dfBitsLoss, atmoSet=['MLS23km'], \n", " sensorSet=['SensorA','SensorB','SensorC'], \n", " tranges=tranges, tambients=tempAmbUnique,\n", " thotshield=tempHotShieldDeltas[0], tcentralObs=tempCentralObsDeltas[1], tinternal=tempInternDeltas[2],\n", " optFilter=optFilters[0],ttarget=TempTargetDeltaC,atmotype='gen',tdynamic=TempDynamicPlot)" ] }, { "cell_type": "code", "execution_count": 32, "metadata": { "collapsed": false, "jupyter": { "outputs_hidden": false } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "********************************************************************************\n", "********************************************************************************\n", "********************************************************************************\n", "Sensor : SensorA \n", "Atmosphere : SW31km \n", "Sensor dynamic range : 40.0 C\n", "Hot shield temperature : -80.0 C\n", "Central obsuration temperature : 20 C\n", "Internal temperature : 20 C\n", "Filter : Standard \n", "\n", "********************************************************************************\n", "Stare time vs. scene temperature\n", "PathLen 1.0009 4.0006 7.0003 10.0000\n", "TempAmbient [C] \n", "-40.0 30.0 30.0 30.0 30.0\n", "-20.0 25.7 30.0 30.0 30.0\n", " 0.0 12.4 16.1 19.1 21.7\n", " 20.0 6.4 8.2 9.5 10.6\n", " 40.0 3.6 4.5 5.1 5.6\n", "\n", "********************************************************************************\n", "Available well fill after skimming vs. scene temperature\n", "PathLen 1.0009 4.0006 7.0003 10.0000\n", "TempAmbient [C] \n", "-40.0 12.4 12.0 11.7 11.4\n", "-20.0 13.2 12.9 12.6 12.4\n", " 0.0 13.1 12.9 12.7 12.5\n", " 20.0 13.0 12.8 12.6 12.5\n", " 40.0 12.9 12.7 12.5 12.4\n", "\n", "********************************************************************************\n", "Temporal noise vs. scene temperature\n", "PathLen 1.0009 4.0006 7.0003 10.0000\n", "TempAmbient [C] \n", "-40.0 1.9 1.9 1.9 1.9\n", "-20.0 2.8 3.0 3.0 3.0\n", " 0.0 2.8 3.2 3.4 3.6\n", " 20.0 2.8 3.1 3.3 3.5\n", " 40.0 2.7 3.1 3.2 3.4\n", "\n", "********************************************************************************\n", "Available dynamic range well fill vs. scene temperature\n", "PathLen 1.0009 4.0006 7.0003 10.0000\n", "TempAmbient [C] \n", "-40.0 10.5 10.0 9.7 9.5\n", "-20.0 10.3 9.8 9.6 9.3\n", " 0.0 10.3 9.7 9.3 8.9\n", " 20.0 10.2 9.7 9.3 9.0\n", " 40.0 10.2 9.7 9.3 9.0\n", "\n", "********************************************************************************\n", "No noise: $\\Delta$T=2.0 K vs. ambient temperature\n", "PathLen 1.0009 4.0006 7.0003 10.0000\n", "TempAmbient [C] \n", "-40.0 9.6 8.4 7.5 6.7\n", "-20.0 11.4 10.6 9.8 9.0\n", " 0.0 11.4 10.9 10.4 9.9\n", " 20.0 11.3 10.8 10.2 9.7\n", " 40.0 11.2 10.7 10.1 9.6\n", "\n", "********************************************************************************\n", "Target $\\Delta$T=2.0 K around minimum scene, TNR = 1.0, vs. scene temperature\n", "PathLen 1.0009 4.0006 7.0003 10.0000\n", "TempAmbient [C] \n", "-40.0 7.6 6.5 5.6 4.8\n", "-20.0 8.6 7.6 6.7 6.0\n", " 0.0 8.6 7.7 6.9 6.2\n", " 20.0 8.5 7.7 6.9 6.2\n", " 40.0 8.5 7.6 6.9 6.2\n", "\n", "********************************************************************************\n", "Target $\\Delta$T=2.0 K around minimum scene, TNR = 2.0, vs. scene temperature\n", "PathLen 1.0009 4.0006 7.0003 10.0000\n", "TempAmbient [C] \n", "-40.0 6.6 5.5 4.6 3.8\n", "-20.0 7.6 6.6 5.7 5.0\n", " 0.0 7.6 6.7 5.9 5.2\n", " 20.0 7.5 6.7 5.9 5.2\n", " 40.0 7.5 6.6 5.9 5.2\n", "\n", "********************************************************************************\n", "Target $\\Delta$T=2.0 K around minimum scene, TNR = 3.0, vs. scene temperature\n", "PathLen 1.0009 4.0006 7.0003 10.0000\n", "TempAmbient [C] \n", "-40.0 6.1 4.9 4.0 3.2\n", "-20.0 7.0 6.0 5.1 4.4\n", " 0.0 7.0 6.1 5.4 4.7\n", " 20.0 7.0 6.1 5.3 4.6\n", " 40.0 6.9 6.0 5.3 4.6\n", "\n", "********************************************************************************\n", "********************************************************************************\n", "********************************************************************************\n", "********************************************************************************\n", "Sensor : SensorB \n", "Atmosphere : SW31km \n", "Sensor dynamic range : 40.0 C\n", "Hot shield temperature : -80.0 C\n", "Central obsuration temperature : 20 C\n", "Internal temperature : 20 C\n", "Filter : Standard \n", "\n", "********************************************************************************\n", "Stare time vs. scene temperature\n", "Empty DataFrame\n", "Columns: []\n", "Index: []\n", "\n", "********************************************************************************\n", "Available well fill after skimming vs. scene temperature\n", "Empty DataFrame\n", "Columns: []\n", "Index: []\n", "\n", "********************************************************************************\n", "Temporal noise vs. scene temperature\n", "Empty DataFrame\n", "Columns: []\n", "Index: []\n", "\n", "********************************************************************************\n", "Available dynamic range well fill vs. scene temperature\n", "Empty DataFrame\n", "Columns: []\n", "Index: []\n", "\n", "********************************************************************************\n", "No noise: $\\Delta$T=2.0 K vs. ambient temperature\n", "Empty DataFrame\n", "Columns: []\n", "Index: []\n", "\n", "********************************************************************************\n", "Target $\\Delta$T=2.0 K around minimum scene, TNR = 1.0, vs. scene temperature\n", "Empty DataFrame\n", "Columns: []\n", "Index: []\n", "\n", "********************************************************************************\n", "Target $\\Delta$T=2.0 K around minimum scene, TNR = 2.0, vs. scene temperature\n", "Empty DataFrame\n", "Columns: []\n", "Index: []\n", "\n", "********************************************************************************\n", "Target $\\Delta$T=2.0 K around minimum scene, TNR = 3.0, vs. scene temperature\n", "Empty DataFrame\n", "Columns: []\n", "Index: []\n", "\n", "********************************************************************************\n", "********************************************************************************\n", "********************************************************************************\n", "********************************************************************************\n", "Sensor : SensorC \n", "Atmosphere : SW31km \n", "Sensor dynamic range : 40.0 C\n", "Hot shield temperature : -80.0 C\n", "Central obsuration temperature : 20 C\n", "Internal temperature : 20 C\n", "Filter : Standard \n", "\n", "********************************************************************************\n", "Stare time vs. scene temperature\n", "Empty DataFrame\n", "Columns: []\n", "Index: []\n", "\n", "********************************************************************************\n", "Available well fill after skimming vs. scene temperature\n", "Empty DataFrame\n", "Columns: []\n", "Index: []\n", "\n", "********************************************************************************\n", "Temporal noise vs. scene temperature\n", "Empty DataFrame\n", "Columns: []\n", "Index: []\n", "\n", "********************************************************************************\n", "Available dynamic range well fill vs. scene temperature\n", "Empty DataFrame\n", "Columns: []\n", "Index: []\n", "\n", "********************************************************************************\n", "No noise: $\\Delta$T=2.0 K vs. ambient temperature\n", "Empty DataFrame\n", "Columns: []\n", "Index: []\n", "\n", "********************************************************************************\n", "Target $\\Delta$T=2.0 K around minimum scene, TNR = 1.0, vs. scene temperature\n", "Empty DataFrame\n", "Columns: []\n", "Index: []\n", "\n", "********************************************************************************\n", "Target $\\Delta$T=2.0 K around minimum scene, TNR = 2.0, vs. scene temperature\n", "Empty DataFrame\n", "Columns: []\n", "Index: []\n", "\n", "********************************************************************************\n", "Target $\\Delta$T=2.0 K around minimum scene, TNR = 3.0, vs. scene temperature\n", "Empty DataFrame\n", "Columns: []\n", "Index: []\n", "\n", "********************************************************************************\n" ] } ], "source": [ "# to calculate the number of bits lost for Subarctic Winter atmosphere\n", "\n", "tranges = [pathlenUniqueGen[i] for i in [1,4,7,10]]\n", "tableBitsLoss(dfBitsLoss=dfBitsLoss, atmoSet=['SW31km'], \n", " sensorSet=['SensorA','SensorB','SensorC'], \n", " tranges=tranges, tambients=tempAmbUnique,\n", " thotshield=tempHotShieldDeltas[0], tcentralObs=tempCentralObsDeltas[1], tinternal=tempInternDeltas[2],\n", " optFilter=optFilters[0],ttarget=TempTargetDeltaC,atmotype='gen',tdynamic=TempDynamicPlot)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Low Contrast Target Results in Tabular Format" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The results shown in the graphs are shown in tabular format for distances of 0, 5, and 10 km.\n" ] }, { "cell_type": "code", "execution_count": 33, "metadata": { "collapsed": false, "jupyter": { "outputs_hidden": false } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Ambient temperature 20.0 C\n", "Internal temperature 40.0 C\n", "Target delta temperature 2.0 C\n", " Atmo PathLen Sensor OptFilter EInternal [%Tar] EPath [%Tar] EDynamic [%Tar]\n", "19801 tropical5km 10.0 SensorA Standard 33.511474 62.866403 3.622123\n", "48841 tropical5km 10.0 SensorA Notch 33.411121 61.919982 4.668897\n", " \n" ] } ], "source": [ "# to plot the results in tabular format for a very hot scene and internal temperatures at 10 km\n", "tambient = tempAmbUnique[3]\n", "tinternal = tempInternDeltas[2]\n", "ttarget = TempTargetDeltaC\n", "thotshield = tempHotShieldDeltas[0]\n", "tcentralObs = tempCentralObsDeltas[0]\n", "trange = pathlenUniqueGen[-1]\n", "optFilter=optFilters[0]\n", "\n", "\n", "# only do for SensorA\n", "sensors = [dfConcept.columns.values[0]]\n", "# dfr=tableDatasets(dfConcept=dfConcept, atmoSet=['MLS23km','tropical5km','SW31km'], tambient=tambient, \n", "dfr=tableDatasets(sensors=sensors,dfConcept=dfConcept, atmoSet=['tropical5km'], tambient=tambient, \n", " thotshield=thotshield,tinternal=tinternal,tcentralObs=tcentralObs,\n", " ttarget=ttarget, optFilter=optFilter, trange=trange,atmotype='gen')\n", "printDataset(dfr)" ] }, { "cell_type": "code", "execution_count": 34, "metadata": { "collapsed": false, "jupyter": { "outputs_hidden": false } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Ambient temperature 20.0 C\n", "Internal temperature 40.0 C\n", "Target delta temperature 2.0 C\n", " Atmo PathLen Sensor OptFilter EInternal [%Tar] EPath [%Tar] EDynamic [%Tar]\n", "19097 MLS23km 5.0005 SensorA Standard 33.166849 49.408930 17.424221\n", "48137 MLS23km 5.0005 SensorA Notch 32.973897 44.815565 22.210538\n", "19581 tropical5km 5.0005 SensorA Standard 33.330343 55.829140 10.840517\n", "48621 tropical5km 5.0005 SensorA Notch 33.181543 52.985809 13.832648\n", "20065 SW31km 5.0005 SensorA Standard 32.967960 40.616290 26.415750\n", "49105 SW31km 5.0005 SensorA Notch 32.720275 33.571716 33.708009\n", " \n" ] } ], "source": [ "# to plot the results in tabular format for a very hot scene and internal temperatures at 5 km\n", "trange = pathlenUniqueGen[5]\n", "# only do for SensorA\n", "sensors = [dfConcept.columns.values[0]]\n", "dfr=tableDatasets(sensors=sensors,dfConcept=dfConcept, atmoSet=['MLS23km','tropical5km','SW31km'], tambient=tambient, \n", " thotshield=thotshield,tinternal=tinternal,tcentralObs=tcentralObs,\n", " ttarget=TempTargetDeltaC, optFilter=optFilter, trange=trange,atmotype='gen')\n", "printDataset(dfr)" ] }, { "cell_type": "code", "execution_count": 35, "metadata": { "collapsed": false, "jupyter": { "outputs_hidden": false } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Ambient temperature 20.0 C\n", "Internal temperature 40.0 C\n", "Target delta temperature 2.0 C\n", " Atmo PathLen Sensor OptFilter EInternal [%Tar] EPath [%Tar] EDynamic [%Tar]\n", "18877 MLS23km 0.001 SensorA Standard 32.082338 4.649823 63.267839\n", "47917 MLS23km 0.001 SensorA Notch 31.900484 0.025767 68.073748\n", "19361 tropical5km 0.001 SensorA Standard 32.082506 4.656795 63.260699\n", "48401 tropical5km 0.001 SensorA Notch 31.900668 0.033449 68.065883\n", "19845 SW31km 0.001 SensorA Standard 32.082159 4.642171 63.275670\n", "48885 SW31km 0.001 SensorA Notch 31.900312 0.018160 68.081528\n", " \n" ] } ], "source": [ "# to plot the results in tabular format for a very hot scene and internal temperatures at close range\n", "trange = pathlenUniqueGen[0]\n", "# only do for SensorA\n", "sensors = [dfConcept.columns.values[0]]\n", "dfr=tableDatasets(sensors=sensors,dfConcept=dfConcept, atmoSet=['MLS23km','tropical5km','SW31km'], tambient=tambient, \n", " thotshield=thotshield,tinternal=tinternal,tcentralObs=tcentralObs,\n", " ttarget=TempTargetDeltaC, optFilter=optFilter, trange=trange,atmotype='gen')\n", "printDataset(dfr)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Well Fill vs Target Temperatures\n", "\n", "The next set of graphs consider well fill for different target temperatures, against constant backgrounds." ] }, { "cell_type": "code", "execution_count": 36, "metadata": { "collapsed": false, "jupyter": { "outputs_hidden": false } }, "outputs": [ { "data": { "image/png": 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mTcqvqNiLlRDpCfwF6KSq570oorW/4h4PZGKaDAaDwWAo3qgqx48fJyEhgaSk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\nUe3atU0gtuGqxV89TQFhNI0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WJ3/btm307NnTZfLGiIgImjRpQtu2bS9TclcC5XyA/xpN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\nEVCANT4uypbnT/e6Rx3+dq970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uhbvoAe6ji9aj5Ll27VqBBlFcXBynT5+mevXqNGjQgHvvvZc2bdrkM5Bq1Khx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\nioiPUipRRHyBpIIamDt3Lr/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w4O00VaIjQU43mpRSx4FQABEpAyQAK7jDfsripF69emzc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\nl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UPb8NxzSzhz5jwAsbEJzliivn2bMnfuZvbtO0Vycipvvrky1/arV4+gefNI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\noiiKB6jTpCiKoiiK4gH/D/FuhqFVGDXAAAAAAElFTkSuQmCC\n", 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B/TCazhKBPfzVA/MZ4GXzenmJnN3Rbc/fBAwP4kUML90YpdS27GWV0Xvzb8CHInKvnXry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\npve46qpX6dDBGnULD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ZuHEjy5cv58iRI3h7e9O2bVuef/55HB0di9u8YkON05QLQojXgdF6caGUcq4Q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\nRqdObxMY6GDs2G4lb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8+uqrJSavszF48GAuXbpEVlYWzzzzjDaYNBqN5jqSkZHBxo0bWb58OUuXLtWj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\n/PF6Fi/eDsBnn1nX3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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# to calculate and plot the results for different ambient temperatures\n", "iplot=1\n", "ttarget = TempTargetDeltaC\n", "# only do for SensorA\n", "sensors = [dfConcept.columns.values[0]]\n", "\n", "for tinternal in [2]:\n", " for trange in [2]:\n", " for thotshield in [0]:\n", " for tcentralObs in [0]:\n", " for dynamicRangeK in [indexTempDynamicPlot]:\n", " for atmo in ['tropical5km','MLS23km','SW31km']:\n", " for tambient in [0,1,2,3,4]:\n", " doPlots = True if atmo in 'tropical5km' else False\n", " iplot += 1\n", " dfBitsLoss = plotPWellFillTargetContrast(sensors=sensors,\n", " dfBitsLoss=dfBitsLoss,\n", " dfConcept=dfConcept, \n", " atmoSet=[atmo], \n", " tambient=tempAmbUnique[tambient], \n", " thotshield=tempHotShieldDeltas[thotshield],\n", " tinternal=tempInternDeltas[tinternal],\n", " tcentralObs=tempCentralObsDeltas[tcentralObs],\n", " trange=pathlenUniqueGen[trange], \n", " optFilter=optFilters[0],\n", " atmotype='gen', \n", " intTime=integrationTime, \n", " areaDetector=areaDetector,\n", " wellCapacity=wellCapacityUsable,\n", " dynamicRangeK=tempDynamicDeltas[dynamicRangeK],\n", " ttarget=ttarget,\n", " threshold2Noise=threshold2Noise,\n", " netdstd=netdstd,ksys=ksyss[-1],\n", " doPlots=doPlots,iplot=iplot)\n", "\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Well Fill vs Target Distance\n", "\n", "The next set of graphs consider well fill for different target distances, against constant backgrounds." ] }, { "cell_type": "code", "execution_count": 37, "metadata": { "collapsed": false, "jupyter": { "outputs_hidden": false } }, "outputs": [ { "data": { "image/png": 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sWVNsWIqi5Fjd0ySEaAusAToBOcB24L/AWCBJSjlHCPEm4CqlLDBhT0k8TZWF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\nbMef8tWr5+/r8OF0n22NHXsNr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ZuHEjy5cv58iRI3h7e9O2bVuef/55HB0di9u8YkON05QLQojXgdF6caGUcq4Q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\nRqdObxMY6GDs2G4lb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kpCS++OILPv7444j76cgGlyOOOII5c+aEqpgqKyvJysqKuo3FixezY8cOCgoK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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# to calculate and plot the results for different ambient temperatures\n", "iplot=1\n", "ttarget = TempTargetDeltaC\n", "# only do for SensorA\n", "sensors = [dfConcept.columns.values[0]]\n", "\n", "for tinternal in [2]:\n", " for thotshield in [0]:\n", " for tcentralObs in [0]:\n", " for dynamicRangeK in [indexTempDynamicPlot]:\n", " for tambient in [3]:\n", " for atmo in ['tropical5km','MLS23km','SW31km']:\n", " for trange in [0,2,10]:\n", " doPlots = True if atmo in 'tropical5km' else False\n", " iplot += 1\n", " dfBitsLoss = plotPWellFillTargetContrast(sensors=sensors,\n", " dfBitsLoss=dfBitsLoss,\n", " dfConcept=dfConcept, \n", " atmoSet=[atmo], \n", " tambient=tempAmbUnique[tambient], \n", " thotshield=tempHotShieldDeltas[thotshield],\n", " tinternal=tempInternDeltas[tinternal],\n", " tcentralObs=tempCentralObsDeltas[tcentralObs],\n", " trange=pathlenUniqueGen[trange], \n", " optFilter=optFilters[0],\n", " atmotype='gen', \n", " intTime=integrationTime, \n", " areaDetector=areaDetector,\n", " wellCapacity=wellCapacityUsable,\n", " dynamicRangeK=tempDynamicDeltas[dynamicRangeK],\n", " ttarget=ttarget,\n", " threshold2Noise=threshold2Noise,\n", " netdstd=netdstd,ksys=ksyss[-1],\n", " doPlots=doPlots,iplot=iplot)\n", "\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Comparison Between Wide Filter and Notch Filter" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The notch filter provides a potential improvement in sensor performance by suppression the atmospheric path radiance and the hot optics sensor radiance in the path radiance spectral range. The lower flux transmitted by the notch filter reduces the well fill in the detector.\n", "\n", "This scenario compares two cold filter configurations by the ratio of bits on target, including all well fill and noise considerations - pretty much the final bits visible to the user. The filter has most effect at longer ranges where the path radiance increases." ] }, { "cell_type": "code", "execution_count": 38, "metadata": { "collapsed": false, "jupyter": { "outputs_hidden": false } }, "outputs": [], "source": [ "# to calculate and plot the ratio of bits on target for the two different filter types\n", "def plotTvsNotch(dfBitsLoss,dfConcept, atmoSet, tambient, thotshield, tinternal,dynamicRangeK,\n", " tcentralObs, ttarget, atmotype,iplot=1):\n", " \"\"\"This function extracts/filters data from the dataframe, so all temperatures must be exact \n", " matches to the temperatures in the dataframe.\n", " \"\"\"\n", "\n", " p = ryplot.Plotter(iplot,1,1,figsize=(12,4))\n", "# for i,sensor in enumerate(['SensorA','SensorB']):\n", " for i,sensor in enumerate(['SensorA']):\n", " for j,katmo in enumerate(atmoSet):\n", " df = dfBitsLoss.copy()\n", " filt = (df.DataSet=='plotPWellFillRange') & \\\n", " (df.Atmo==katmo) & (df.Sensor==sensor) & (df['TempAmbient [K]']==tambient) & \\\n", " (df['TempHotShield [K]']==tambient+thotshield+tinternal) & \\\n", " (df['TempCentralObs [K]']==tambient+tcentralObs+tinternal) & \\\n", " (df['TempIntern [K]']==tambient+tinternal) & \\\n", " (df['dynamicRangeK']==dynamicRangeK) & \\\n", " (df['DeltaTarget']==ttarget) #& \\\n", "\n", " df = df[filt]\n", " df = df.sort_values(by='PathLen')\n", "# print(df)\n", "\n", " P = df[df.OptFilter=='Notch'].as_matrix(['PathLen'])\n", " N = df[df.OptFilter=='Notch'].as_matrix(['TargDiffbitsNoise'])\n", " S = df[df.OptFilter=='Standard'].as_matrix(['TargDiffbitsNoise'])\n", "\n", " spidx = j+2*i+1\n", " title='Bits on target with/out notch filter: Tamb={} C, $\\Delta$Tint={}, $\\Delta$Ttar={}'.format(\n", " tambient-273., tinternal,ttarget )\n", " label='{} {} '.format(sensor, katmo )\n", " p.plot(1,P, N/S, title,'Target Range [km]','Improvement with notch', \n", " label=[label],legendLoc='lower right',legendAlpha=0.5)\n", "\n" ] }, { "cell_type": "code", "execution_count": 39, "metadata": { "collapsed": false, "jupyter": { "outputs_hidden": false } }, "outputs": [ { "data": { "image/png": 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9qf68aCrrVduayngKuV6snxdaLymhtLSUiooKAMrKysiWBn3oT6MPZtYHeKaW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\nTqS4uJj999+fY445hvLycgAOO+wwrrv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QKReRchEpF1F9zsXChVBSEka///e/cBOe/fcPo98HHADdutWu37s+5yLTlItI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\nIlIuMi9nxbi7TwS+qWaXI4EHEvu+Dmx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7Na0CXddFoFwEykWgXKRX0nnSRUSkAMybB+PGRcu778LYsdGNnJWj44MHw7XX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\ndKSkRyX9n6Q1ki7rZlzd5yHpIElPSFqVspjTzbi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5Wrw4KsSTi/KNG2HffWGffaKfe+8dnaQpIiIimZGzedKBu4EbgK3dvUXSrVEU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\nfziLe1CANpqlAAAAAElFTkSuQmCC\n", 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dmc6T/kCuAxERkeZlwYI4Op5aWreOo+OXXhqmQezUqdCRiogUn0bNk17K1O4i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\n7o/r7nIWBc6iwFkUOIsCZ1FQi1k0NjYyadKkXT7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FY2ugBCgCtk3d5+7DE53I7C7geKDC3fvUcczNwFBgJVDi7mXx9kuA7wOVwFvA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\nMSrQRURERCR7VKTXYsoUGDIERo2CK6+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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# to calculate and plot the improvement with the notch filter\n", "plotnotchwellfill = False\n", " \n", "ttarget = TempTargetDeltaC\n", "thotshield = tempHotShieldDeltas[0]\n", "tcentralObs = tempCentralObsDeltas[1]\n", "dynamicRangeK = TempDynamicPlot\n", "atmoUnique = ['tropical5km']\n", "# only do for SensorA\n", "sensors = [dfConcept.columns.values[0]]\n", "\n", "iplot = 1\n", "for iint in [2,3]:\n", " for itamb in [0,2,4]:\n", " for iff in range(0,len(optFilters)):\n", " dfBitsLoss = plotPWellFillRange(sensors=sensors,dfBitsLoss=dfBitsLoss, dfConcept=dfConcept, \n", " atmoSet=['tropical5km'], tambient=tempAmbUnique[itamb], thotshield=thotshield,\n", " tinternal=tempInternDeltas[iint],tcentralObs=tcentralObs,ttarget=ttarget, \n", " optFilter=optFilters[iff], atmotype='gen', intTime=integrationTime, \n", " areaDetector=areaDetector,wellCapacity=wellCapacityUsable, \n", " dynamicRangeK=dynamicRangeK,threshold2Noise=threshold2Noise,netdstd=netdstd,\n", " ksys=ksyss[-1],doPlots=plotnotchwellfill)\n", " dfBitsLoss.drop_duplicates(inplace=True)\n", " plotTvsNotch(dfBitsLoss=dfBitsLoss,dfConcept=dfConcept, atmoSet=atmoUnique, tambient=tempAmbUnique[itamb], \n", " thotshield=thotshield,tinternal=tempInternDeltas[iint],dynamicRangeK=dynamicRangeK,\n", " tcentralObs=tcentralObs,ttarget=ttarget, atmotype='gen', iplot=iplot) \n", " iplot += 1\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Bit Dynamic Range" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The number of bits available to represent the dynamic range of the scene was extracted from the preceding graphs. These numbers are the 14 bits of the ADC minus the bits lost to noise and the bits lost to non-scene well fill, NUC headroom and photon starvation. \n", "\n", "The tables below state the number of available bits for path lengths of 0 km and 10 km. The sensor operating at any range in between will have the number of bits in the stated range." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The statistics of all the samples calculated in this report:" ] }, { "cell_type": "code", "execution_count": 40, "metadata": { "collapsed": false, "jupyter": { "outputs_hidden": false } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Statistical analysis of all the samples in this report:\n", "Minimum = -inf bits\n", "Maximum = 11.8977958509 bits\n", "Mean = -inf bits\n", "StdDev = nan bits\n", "Sample count = 433\n" ] } ], "source": [ "# to calculate the statistics of all the samples in this report\n", "\n", "print('Statistical analysis of all the samples in this report:')\n", "print('Minimum = {} bits'.format(dfBitsLoss['BitsAvail'].min()))\n", "print('Maximum = {} bits'.format(dfBitsLoss['BitsAvail'].max()))\n", "print('Mean = {} bits'.format(dfBitsLoss['BitsAvail'].mean()))\n", "print('StdDev = {} bits'.format(dfBitsLoss['BitsAvail'].std()))\n", "print('Sample count = {}'.format(dfBitsLoss['BitsAvail'].count()))\n" ] }, { "cell_type": "code", "execution_count": 41, "metadata": { "collapsed": false, "jupyter": { "outputs_hidden": false } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "DataSet plotPWellFillTargetContrast\n", "Atmo tropical5km\n", "AtmoDesc Tropical, 5 km urban vis\n", "Sensor SensorA\n", "PathLen 10\n", "TempAmbient [C] 20\n", "TempInsideDelta [C] 20\n", "nubitloss -0.415037\n", "wellfillbitloss -1.7241\n", "BitsNoFill 6.58309\n", "wellfillNETD 0.0853983\n", "BitsNETD 3.8065\n", "BitsAvail 2.7766\n", "TempAmbient [K] 293\n", "TempHotShield [K] 213\n", "TempCentralObs [K] 213\n", "TempIntern [K] 313\n", "OptFilter Standard\n", "Gain 5.97576e-17\n", "WellCapacity 3.735e+06\n", "IntTime 0.0132263\n", "atmotype gen\n", "areaDetector 2.25e-10\n", "dynamicRangeK 40\n", "threshold2Noise 1\n", "netdstd 0.03\n", "ksys 0.75\n", "TempDynamic [C] 22\n", "WfHotOptics 30.2354\n", "WfCentralObs 0\n", "WfEHotShield 0\n", "WfPath 56.7207\n", "WfScene 3.00937\n", "WfDynamic 0.258659\n", "WfPhotonStarve 9.77582\n", "EScene [q/(s.m2)] 5.03597e+16\n", "EDynamic [q/(s.m2)] 5.46881e+16\n", "wellfillTargDiff 0.0508839\n", "TargDiffbitsNoNoise 2.28645\n", "TargDiffbitsNoise -1.52005\n", "Mderivscn [q/(s.m2.K)] 4.25753e+16\n", "DeltaTarget 2\n", "Name: 300, dtype: object\n" ] } ], "source": [ "# to calculate the scenario where the lowest number of available bits occurred.\n", "\n", "print(dfBitsLoss.ix[np.argmin(np.abs(dfBitsLoss['BitsAvail'] - 0.0))])" ] }, { "cell_type": "code", "execution_count": 42, "metadata": { "collapsed": false, "jupyter": { "outputs_hidden": false } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Index([u'DataSet', u'Atmo', u'AtmoDesc', u'Sensor', u'PathLen', u'TempAmbient [C]',\n", " u'TempInsideDelta [C]', u'nubitloss', u'wellfillbitloss', u'BitsNoFill', u'wellfillNETD',\n", " u'BitsNETD', u'BitsAvail', u'TempAmbient [K]', u'TempHotShield [K]', u'TempCentralObs [K]',\n", " u'TempIntern [K]', u'OptFilter', u'Gain', u'WellCapacity', u'IntTime', u'atmotype',\n", " u'areaDetector', u'dynamicRangeK', u'threshold2Noise', u'netdstd', u'ksys',\n", " u'TempDynamic [C]', u'WfHotOptics', u'WfCentralObs', u'WfEHotShield', u'WfPath', u'WfScene',\n", " u'WfDynamic', u'WfPhotonStarve', u'EScene [q/(s.m2)]', u'EDynamic [q/(s.m2)]',\n", " u'wellfillTargDiff', u'TargDiffbitsNoNoise', u'TargDiffbitsNoise',\n", " u'Mderivscn [q/(s.m2.K)]', u'DeltaTarget'],\n", " dtype='object')\n", "['plotPWellFillRange' 'plotPWellFillTargetContrast']\n", " \n", "plotPWellFillRange\n", "['tropical5km' 'MLS23km' 'SW31km']\n", "[u'SensorA']\n", "[0.001 1.0009 2.0008 3.0006999999999997 4.0006 5.000500000000001 6.0004\n", " 7.0003 8.0002 9.0001 10.0]\n", "[20.0 40.0 -40.0 -20.0 0.0]\n", "[-100.0 40.0 20.0]\n", "[70.0 40.0]\n", " \n", "plotPWellFillTargetContrast\n", "['tropical5km' 'MLS23km' 'SW31km']\n", "[u'SensorA']\n", "[2.0008 0.001 10.0]\n", "[-40.0 -20.0 0.0 20.0 40.0]\n", "[20.0]\n", "[40.0]\n", " \n" ] } ], "source": [ "# to display unique values\n", "print(dfBitsLoss.columns)\n", "print(dfBitsLoss['DataSet'].unique())\n", "print(' ')\n", "for dataset in dfBitsLoss['DataSet'].unique():\n", " print(dataset)\n", " print(dfBitsLoss[dfBitsLoss.DataSet==dataset]['Atmo'].unique())\n", " print(dfBitsLoss[dfBitsLoss.DataSet==dataset]['Sensor'].unique())\n", " print(dfBitsLoss[dfBitsLoss.DataSet==dataset]['PathLen'].unique())\n", " print(dfBitsLoss[dfBitsLoss.DataSet==dataset]['TempAmbient [C]'].unique())\n", " print(dfBitsLoss[dfBitsLoss.DataSet==dataset]['TempInsideDelta [C]'].unique())\n", " print(dfBitsLoss[dfBitsLoss.DataSet==dataset]['dynamicRangeK'].unique())\n", "\n", " print(' ')\n" ] }, { "cell_type": "code", "execution_count": 43, "metadata": { "collapsed": false, "jupyter": { "outputs_hidden": false } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Atmospheric condition tropical5km\n", "Sensor dynamic range 70.0 K\n", "Sensor SensorA\n", "PathLen 0.001\n", "TempAmbient [C] TempInsideDelta [C] \n", "20.0 -100.0 11.897796\n", "\n", "********************************************************************************\n", "\n", "Atmospheric condition tropical5km\n", "Sensor dynamic range 70.0 K\n", "Sensor SensorA\n", "PathLen 10.0\n", "TempAmbient [C] TempInsideDelta [C] \n", "20.0 -100.0 8.611029\n", "\n", "********************************************************************************\n", "\n", "Atmospheric condition tropical5km\n", "Sensor dynamic range 40.0 K\n", "Sensor SensorA\n", "PathLen 0.001\n", "TempAmbient [C] TempInsideDelta [C] \n", "-40.0 20.0 10.819596\n", " 40.0 10.715375\n", "-20.0 20.0 10.711192\n", " 0.0 20.0 10.655042\n", " 40.0 10.670588\n", " 20.0 20.0 10.589469\n", " 40.0 20.0 10.531490\n", " 40.0 10.571555\n", "\n", "********************************************************************************\n", "\n", "Atmospheric condition tropical5km\n", "Sensor dynamic range 40.0 K\n", "Sensor SensorA\n", "PathLen 10.0\n", "TempAmbient [C] TempInsideDelta [C] \n", "-40.0 20.0 8.904195\n", " 40.0 9.235631\n", "-20.0 20.0 8.880774\n", " 0.0 20.0 8.024530\n", " 40.0 8.298565\n", " 20.0 20.0 8.158126\n", " 40.0 20.0 8.276895\n", " 40.0 8.492460\n", "\n", "********************************************************************************\n", "\n", "Atmospheric condition MLS23km\n", "Sensor dynamic range 40.0 K\n", "Sensor SensorA\n", "PathLen 0.001\n", "TempAmbient [C] TempInsideDelta [C] \n", "-40.0 20.0 10.819729\n", "-20.0 20.0 10.711346\n", " 0.0 20.0 10.655191\n", " 20.0 20.0 10.589612\n", " 40.0 20.0 10.531627\n", "\n", "********************************************************************************\n", "\n", "Atmospheric condition MLS23km\n", "Sensor dynamic range 40.0 K\n", "Sensor SensorA\n", "PathLen 10.0\n", "TempAmbient [C] TempInsideDelta [C] \n", "-40.0 20.0 9.161420\n", "-20.0 20.0 9.101009\n", " 0.0 20.0 8.534954\n", " 20.0 20.0 8.625664\n", " 40.0 20.0 8.707527\n", "\n", "********************************************************************************\n", "\n", "Atmospheric condition SW31km\n", "Sensor dynamic range 40.0 K\n", "Sensor SensorA\n", "PathLen 0.001\n", "TempAmbient [C] TempInsideDelta [C] \n", "-40.0 20.0 10.819878\n", "-20.0 20.0 10.711514\n", " 0.0 20.0 10.655347\n", " 20.0 20.0 10.589759\n", " 40.0 20.0 10.531764\n", "\n", "********************************************************************************\n", "\n", "Atmospheric condition SW31km\n", "Sensor dynamic range 40.0 K\n", "Sensor SensorA\n", "PathLen 10.0\n", "TempAmbient [C] TempInsideDelta [C] \n", "-40.0 20.0 9.468496\n", "-20.0 20.0 9.335502\n", " 0.0 20.0 8.937662\n", " 20.0 20.0 8.961093\n", " 40.0 20.0 8.989108\n", "\n", "********************************************************************************\n", "\n", "Atmospheric condition tropical5km\n", "Sensor dynamic range 40.0 K\n", "Sensor SensorA\n", "PathLen 0.001\n", "TempAmbient [C] TempInsideDelta [C] \n", "20.0 20.0 -inf\n", "\n", "********************************************************************************\n", "\n", "Atmospheric condition tropical5km\n", "Sensor dynamic range 40.0 K\n", "Sensor SensorA\n", "PathLen 10.0\n", "TempAmbient [C] TempInsideDelta [C] \n", "20.0 20.0 -inf\n", "\n", "********************************************************************************\n", "\n", "Atmospheric condition MLS23km\n", "Sensor dynamic range 40.0 K\n", "Sensor SensorA\n", "PathLen 0.001\n", "TempAmbient [C] TempInsideDelta [C] \n", "20.0 20.0 -inf\n", "\n", "********************************************************************************\n", "\n", "Atmospheric condition MLS23km\n", "Sensor dynamic range 40.0 K\n", "Sensor SensorA\n", "PathLen 10.0\n", "TempAmbient [C] TempInsideDelta [C] \n", "20.0 20.0 -inf\n", "\n", "********************************************************************************\n", "\n", "Atmospheric condition SW31km\n", "Sensor dynamic range 40.0 K\n", "Sensor SensorA\n", "PathLen 0.001\n", "TempAmbient [C] TempInsideDelta [C] \n", "20.0 20.0 -inf\n", "\n", "********************************************************************************\n", "\n", "Atmospheric condition SW31km\n", "Sensor dynamic range 40.0 K\n", "Sensor SensorA\n", "PathLen 10.0\n", "TempAmbient [C] TempInsideDelta [C] \n", "20.0 20.0 -inf\n", "\n", "********************************************************************************\n", "\n" ] } ], "source": [ "# to calculate and display the available number of bits for a few test cases\n", "dynamicRangeK = 40.\n", "TempTargetDeltaC = 2.\n", "dfb = dfBitsLoss.copy()\n", "for dataset in dfb['DataSet'].unique():\n", " rngeU = dfb[dfb.DataSet==dataset]['PathLen'].unique()\n", " rng0 = rngeU[np.argmin(np.abs(rngeU - 0.0))]\n", " rng10 = rngeU[np.argmin(np.abs(rngeU - 10.0))]\n", " for tdyna in dfb[dfb.DataSet==dataset]['dynamicRangeK'].unique():\n", " for atmo in dfb[dfb.DataSet==dataset]['Atmo'].unique():\n", " for rng in [rng0, rng10]:\n", "\n", " filt = (dfb.DataSet==dataset) & (dfb.Atmo==atmo) & (dfb.OptFilter=='Standard') & \\\n", " (np.abs(dfb['dynamicRangeK']-(tdyna))<0.01) & \\\n", " (np.abs(dfb['PathLen']-rng)<0.01) & \\\n", " (np.abs(dfb['DeltaTarget']-(TempTargetDeltaC))<0.01) \n", "\n", " dfd = dfb[filt].copy()\n", " dfd.drop_duplicates(inplace=True)\n", " dfd = dfd[pd.notnull (dfd['BitsAvail'])]\n", " if not dfd.empty:\n", " print('Atmospheric condition {}'.format(atmo))\n", " print('Sensor dynamic range {} K'.format(tdyna))\n", "\n", "# print(dfd)\n", " dfp = pd.pivot_table(dfd, values='BitsAvail', \n", " index=['TempAmbient [C]', 'TempInsideDelta [C]'], \n", " columns=['Sensor','PathLen'],aggfunc = lambda x: np.mean(x))\n", " print(dfp)\n", " print('\\n{}\\n'.format(80*'*'))\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Conclusion" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The following observations are made from the results herein:\n", "\n", "TBDL" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Python and [module versions, and dates](https://github.com/jrjohansson/scientific-python-lectures/blob/master/Lecture-0-Scientific-Computing-with-Python.ipynb)" ] }, { "cell_type": "code", "execution_count": 44, "metadata": { "collapsed": false, "jupyter": { "outputs_hidden": false } }, "outputs": [ { "data": { "application/json": { "Software versions": [ { "module": "Python", "version": "2.7.12 64bit [MSC v.1500 64 bit (AMD64)]" }, { "module": "IPython", "version": "5.1.0" }, { "module": "OS", "version": "Windows 7 6.1.7601 SP1" }, { "module": "numpy", "version": "1.11.1" }, { "module": "scipy", "version": "0.18.1" }, { "module": "matplotlib", "version": "1.5.3" }, { "module": "pyradi", "version": "1.1.0" }, { "module": "pandas", "version": "0.18.1" } ] }, "text/html": [ "
SoftwareVersion
Python2.7.12 64bit [MSC v.1500 64 bit (AMD64)]
IPython5.1.0
OSWindows 7 6.1.7601 SP1
numpy1.11.1
scipy0.18.1
matplotlib1.5.3
pyradi1.1.0
pandas0.18.1
Mon Nov 28 17:41:42 2016 South Africa Standard Time
" ], "text/latex": [ "\\begin{tabular}{|l|l|}\\hline\n", "{\\bf Software} & {\\bf Version} \\\\ \\hline\\hline\n", "Python & 2.7.12 64bit [MSC v.1500 64 bit (AMD64)] \\\\ \\hline\n", "IPython & 5.1.0 \\\\ \\hline\n", "OS & Windows 7 6.1.7601 SP1 \\\\ \\hline\n", "numpy & 1.11.1 \\\\ \\hline\n", "scipy & 0.18.1 \\\\ \\hline\n", "matplotlib & 1.5.3 \\\\ \\hline\n", "pyradi & 1.1.0 \\\\ \\hline\n", "pandas & 0.18.1 \\\\ \\hline\n", "\\hline \\multicolumn{2}{|l|}{Mon Nov 28 17:41:42 2016 South Africa Standard Time} \\\\ \\hline\n", "\\end{tabular}\n" ], "text/plain": [ "Software versions\n", "Python 2.7.12 64bit [MSC v.1500 64 bit (AMD64)]\n", "IPython 5.1.0\n", "OS Windows 7 6.1.7601 SP1\n", "numpy 1.11.1\n", "scipy 0.18.1\n", "matplotlib 1.5.3\n", "pyradi 1.1.0\n", "pandas 0.18.1\n", "Mon Nov 28 17:41:42 2016 South Africa Standard Time" ] }, "execution_count": 44, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# you only need to do this once\n", "#!pip install --upgrade version_information\n", "\n", "%load_ext version_information\n", "%version_information numpy, scipy, matplotlib, pyradi" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true, "jupyter": { "outputs_hidden": 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": "3.8.3" } }, "nbformat": 4, "nbformat_minor": 4 }