- {
- “cells”: [
- {
“cell_type”: “markdown”, “metadata”: {}, “source”: [
“# SLR data reduction: OTF under the hoodn”, “n”, “Here we disect the files (one ifproc and four roach spectrometer boards) for an OTF Map observation. Since an observation is typically one short Cal followed by a long Map, we have 10 files to process for a calibrated observation, in two obsnum’s.n”, “n”, “The ifproc samples various telescope variables at 125 Hz. The spectrometer roach boards at 10 Hz (the integration time). Each roach board has 4 beams, their timestamps are staggered, i.e. beams b0,b1,b2,b3,b0,b1,b2,b3,….. have incremental times at a cadence of about 40 Hz (0.025sec). Note that each board is sampled independantly, thus they needs to be syncd by the software during reduction.n”, “n”, “The information what kind of data the telescope is taking is in the ifproc::BufPos variable, and takes up four values:n”, “* 0 = "On"n”, “* 1 = "Ref"n”, “* 2 = "Sky"n”, “* 3 = "Hot"n”, “n”, “The Cal is two short 2.1" bursts of "Hot" and "Sky", with a 2.5" switch time. The Map are many repetitions of "Ref" and "On". For the IRC benchmark data of ~700" there are five "Ref" and four "On". Slewing is about 7-8". Switching from "Cal" to "Map" is about 14". Each "Ref" is about 10", each "On" about 143"n”, ” n”, ” ObsPgm: <- Cal -> <——————- Map ———————–>n”, ” type: H s S s R s O s R s O s R s O s R s O s Rn”, ” time: 2 3 2 14 10 7 143 8 10 7 143 8 10 7 143 8 10 7 143 8 10n”, ” H=hot S=sky R=ref O=on s=slew/switchn”, ” n”, “In this observation, the integration time was 4x143 = 572", the total run was 708", thus an 81% observing efficiency.n”, “n”, “The Header.Dcs.IntegrationTime = 528.4 differs from this timing analysis, which suggested 572". Is there dead-time between integrations? If so, then the observing efficiency is 75% for the benchmark.n”, ” “
]
}, {
“cell_type”: “code”, “execution_count”: 1, “metadata”: {}, “outputs”: [
- {
“name”: “stdout”, “output_type”: “stream”, “text”: [
“testing TKAggn”, “use() got an unexpected keyword argument ‘warn’n”, “testing Aggn”, “use() got an unexpected keyword argument ‘warn’n”, “testing GTKAggn”, “use() got an unexpected keyword argument ‘warn’n”, “testing Qt4Aggn”, “use() got an unexpected keyword argument ‘warn’n”, “testing WXAggn”, “use() got an unexpected keyword argument ‘warn’n”
]
}
], “source”: [
“%matplotlib inlinen”, “n”, “import osn”, “import numpy as npn”, “import matplotlib.pyplot as pln”, “n”, “import netCDF4n”, “import datetimen”, “n”, “from lmtslr.spec import *n”, “from lmtslr.ifproc import *n”, “from lmtslr.viewer.spec_viewer import *n”, “n”, “from lmtslr.utils.roach_file_utils import create_roach_list, lookup_roach_filesn”, “from lmtslr.utils.ifproc_file_utils import lookup_ifproc_filen”, “n”, “from lmtslr.ifproc.ifproc import IFProcData, IFProcCaln”, “from lmtslr.spec.spec import SpecBankCal, SpecBankDatan”, “n”, “from lmtslr.utils.reader import read_obsnum_ps”
]
}, {
“cell_type”: “code”, “execution_count”: 2, “metadata”: {}, “outputs”: [], “source”: [
“# set root directory for generic LMT datan”, “path = os.environ[‘DATA_LMT’] n”, “example = 1n”, ” n”, “# set obsnum for cal (0) and map (1) for one of the two example, or make your own n”, “if example == 1: n”, ” obsnum0 = 79447n”, ” obsnum1 = 79448n”, “elif example == 2:n”, ” obsnum0 = 91111n”, ” obsnum1 = 91112n”, “else:n”, ” # M51 data with a CAL, but also embedded CAL in the MAPn”, ” obsnum0 = 92625n”, ” obsnum1 = 92626 n”, “n”, “n”, “# get the filenames associated with these obsnums n”, “iffile0 = lookup_ifproc_file(obsnum0,path=path+’/ifproc’)n”, “rofile0,n = lookup_roach_files(obsnum0,path=path+’/spectrometer’)n”, “n”, “iffile1 = lookup_ifproc_file(obsnum1,path=path+’/ifproc’)n”, “rofile1,n = lookup_roach_files(obsnum1,path=path+’/spectrometer’)n”
]
}, {
“cell_type”: “code”, “execution_count”: 3, “metadata”: {}, “outputs”: [
- {
“name”: “stdout”, “output_type”: “stream”, “text”: [
“/media/teuben/P1-2/teuben/LMT/data_lmtn”, “/media/teuben/P1-2/teuben/LMT/data_lmt/ifproc/ifproc_2018-11-16_079447_00_0001.ncn”, “/media/teuben/P1-2/teuben/LMT/data_lmt/ifproc/ifproc_2018-11-16_079448_01_0000.ncn”
]
}
], “source”: [
“print(path)n”, “print(iffile0)n”, “print(iffile1)”
]
}, {
“cell_type”: “code”, “execution_count”: 4, “metadata”: {}, “outputs”: [], “source”: [
“def date_obs(date):n”, ” """ convert to ISO """n”, ” return datetime.datetime.fromtimestamp(date).strftime(‘%Y-%m-%dT%H:%M:%S’)n”, ” “
]
}, {
“cell_type”: “code”, “execution_count”: 5, “metadata”: {}, “outputs”: [], “source”: [
“def get_ifproc(filename):n”, ” """ get some header info from an ifproc file"""n”, ” n”, ” nc = netCDF4.Dataset(filename)n”, ” n”, ” bufpos = nc.variables[‘Data.TelescopeBackend.BufPos’][:]n”, ” dates = nc.variables[‘Data.TelescopeBackend.TelTime’][:]n”, ” date0 = date_obs(dates[0])n”, ” date1 = date_obs(dates[-1])n”, ” tsky = dates[-1] - dates[0]n”, ” sname = filename[filename.rfind(‘/’)+1:]n”, ” n”, ” cnt = []n”, ” for b in [0,1,2,3]:n”, ” idx = np.where(bufpos==b)n”, ” cnt.append(len(idx[0]))n”, ” print(sname,date0,date1,int(tsky),cnt) n”, ” n”, ” nc.close()”
]
}, {
“cell_type”: “code”, “execution_count”: 6, “metadata”: {}, “outputs”: [], “source”: [
“def get_roach(filename):n”, ” """ get some roach board info """n”, ” n”, ” nc = netCDF4.Dataset(filename)n”, ” n”, ” dates = nc.variables[‘Data.Integrate.time’][:]n”, ” date0 = date_obs(dates[0])n”, ” date1 = date_obs(dates[-1])n”, ” tsky = dates[-1] - dates[0]n”, ” sname = filename[filename.rfind(‘/’)+1:]n”, ” nsky = len(dates)n”, ” print(sname,date0,date1,int(tsky),nsky) n”, ” n”, ” nc.close()”
]
}, {
“cell_type”: “code”, “execution_count”: 7, “metadata”: {}, “outputs”: [
- {
“name”: “stdout”, “output_type”: “stream”, “text”: [
“ifproc_2018-11-16_079447_00_0001.nc 2018-11-16T06:48:30 2018-11-16T06:48:38 7 [0, 0, 265, 263]n”, “ifproc_2018-11-16_079448_01_0000.nc 2018-11-16T06:48:52 2018-11-16T07:00:18 685 [57349, 5108, 0, 0]n”, “roach0_79447_0_1_IRC+10216_2018-11-16_114830.nc 2018-11-16T06:48:30 2018-11-16T06:48:38 7 236n”, “roach1_79447_0_1_IRC+10216_2018-11-16_114830.nc 2018-11-16T06:48:30 2018-11-16T06:48:38 7 236n”, “roach2_79447_0_1_IRC+10216_2018-11-16_114830.nc 2018-11-16T06:48:30 2018-11-16T06:48:38 7 240n”, “roach3_79447_0_1_IRC+10216_2018-11-16_114830.nc 2018-11-16T06:48:30 2018-11-16T06:48:38 8 244n”, “roach0_79448_1_0_IRC+10216_2018-11-16_114845.nc 2018-11-16T06:48:52 2018-11-16T07:00:18 686 25108n”, “roach1_79448_1_0_IRC+10216_2018-11-16_114845.nc 2018-11-16T06:48:52 2018-11-16T07:00:18 686 25112n”, “roach2_79448_1_0_IRC+10216_2018-11-16_114845.nc 2018-11-16T06:48:52 2018-11-16T07:00:18 686 25140n”, “roach3_79448_1_0_IRC+10216_2018-11-16_114845.nc 2018-11-16T06:48:52 2018-11-16T07:00:18 686 25144n”
]
}
], “source”: [
“get_ifproc(iffile0)n”, “get_ifproc(iffile1)n”, “for f in rofile0:n”, ” get_roach(f) n”, “for f in rofile1:n”, ” get_roach(f)”
]
}, {
“cell_type”: “markdown”, “metadata”: {}, “source”: [
“### Detailed look at timen”, “n”, “By plotting the time we can see slewing (for the Map, about 7-8") and switching hot/sky (for the Cal, about 2.5").”
]
}, {
“cell_type”: “code”, “execution_count”: 8, “metadata”: {}, “outputs”: [], “source”: [
“def get_dates(filename,varname,varname2=None):n”, ” nc = netCDF4.Dataset(filename)n”, ” var = nc.variables[varname][:]n”, ” if varname2 != None:n”, ” var2 = nc.variables[varname2][:]n”, ” nc.close()n”, ” if varname2 != None:n”, ” return (var,var2)n”, ” return var”
]
}, {
“cell_type”: “code”, “execution_count”: 9, “metadata”: {}, “outputs”: [], “source”: [
“if True:n”, ” # ifproc file contain the bufposn”, ” (dates0,bp0) = get_dates(iffile0,’Data.TelescopeBackend.TelTime’,’Data.TelescopeBackend.BufPos’)n”, ” (dates1,bp1) = get_dates(iffile1,’Data.TelescopeBackend.TelTime’,’Data.TelescopeBackend.BufPos’)n”, ” time0 = dates0[0]n”, “else:n”, ” # roach files don’t have a bufpos, it will be derived latern”, ” dates0 = get_dates(rofile0[0],’Data.Integrate.time’)n”, ” dates1 = get_dates(rofile1[0],’Data.Integrate.time’) n”, ” bp0 = -1 * np.ones(len(dates0)) n”, ” bp1 = -1 * np.ones(len(dates1))n”, ” n”, “dates2 = np.append(dates0,dates1)n”, “bp2 = np.append(bp0,bp1)n”, ” n”
]
}, {
“cell_type”: “code”, “execution_count”: 10, “metadata”: {}, “outputs”: [
- {
“name”: “stdout”, “output_type”: “stream”, “text”: [
“1542368910.3623033n”, “1542368910.3623033n”, “0.00800013542175293n”, “0.040000200271606445n”, “708.210000038147n”
]
}, {
- “data”: {
“image/png”: “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n”, “text/plain”: [
“<Figure size 432x288 with 1 Axes>”
]
}, “metadata”: {
“needs_background”: “light”
}, “output_type”: “display_data”
}
], “source”: [
“dates = dates2 - dates2[0]n”, “dates = dates2 - time0n”, “pl.plot(range(len(dates)),dates);n”, “print(dates0[0])n”, “print(time0)n”, “print(dates[1]-dates[0])n”, “print(dates[4]-dates[0])n”, “print(dates[-1]-dates[0])”
]
}, {
“cell_type”: “code”, “execution_count”: 11, “metadata”: {}, “outputs”: [
- {
“name”: “stdout”, “output_type”: “stream”, “text”: [
“62985 10n”
]
}
], “source”: [
“# find the times where a jump occuredn”, “x=dates[1:]-dates[:-1]n”, “idx = np.where(x>0.1)n”, “d = dates[idx]n”, “y=d[1:]-d[:-1]n”, “print(len(dates),len(d))”
]
}, {
“cell_type”: “code”, “execution_count”: 12, “metadata”: {
“scrolled”: false
}, “outputs”: [
- {
“name”: “stdout”, “output_type”: “stream”, “text”: [
” 1 0.00 2.62 262 3n”, ” 2 2.62 2.49 263 switch/slewn”, ” 3 5.11 2.64 527 2n”, ” 4 7.75 14.65 528 switch/slewn”, ” 5 22.40 10.12 1540 1n”, ” 6 32.52 6.75 1541 switch/slewn”, ” 7 39.27 142.51 15794 0n”, ” 8 181.77 8.11 15795 switch/slewn”, ” 9 189.88 10.56 16851 1n”, ” 10 200.44 7.12 16852 switch/slewn”, ” 11 207.56 143.26 31180 0n”, ” 12 350.82 8.33 31181 switch/slewn”, ” 13 359.15 10.11 32192 1n”, ” 14 369.26 7.21 32193 switch/slewn”, ” 15 376.47 142.61 46456 0n”, ” 16 519.08 8.28 46457 switch/slewn”, ” 17 527.36 10.12 47469 1n”, ” 18 537.48 7.28 47470 switch/slewn”, ” 19 544.75 144.99 61971 0n”, ” 20 689.75 8.35 61972 switch/slewn”, ” 21 698.09 10.12 62984 1n”
]
}
], “source”: [
“# print out the times of the jumps, and if available, the bufposn”, “t = 0n”, “n = 1n”, “for i in idx[0]:n”, ” dt = dates[i]-tn”, ” print("%3d %7.2f %7.2f %6d %d" % (n,t,dt,i,bp2[i]))n”, ” n = n + 1n”, ” t = dates[i]n”, ” dt = dates[i+1]-tn”, ” print("%3d %7.2f %7.2f %6d switch/slew" % (n,t,dt,i+1))n”, ” t = dates[i+1]n”, ” n = n + 1n”, “dt = dates[-1]-tn”, “print("%3d %7.2f %7.2f %6d %d" % (n,t,dt,len(dates)-1,bp2[-1]))”
]
}, {
“cell_type”: “markdown”, “metadata”: {}, “source”: [
“# Looking at raw calibration spectran”, “n”, “The short calibration file is in iffile0, and we will grab some Hot/Sky calibration spectra from rofile0[0], the first 4 pixels”
]
}, {
“cell_type”: “code”, “execution_count”: 13, “metadata”: {}, “outputs”: [
- {
“name”: “stdout”, “output_type”: “stream”, “text”: [
“/media/teuben/P1-2/teuben/LMT/data_lmt/ifproc/ifproc_2018-11-16_079447_00_0001.ncn”, “/media/teuben/P1-2/teuben/LMT/data_lmt/spectrometer/roach0/roach0_79447_0_1_IRC+10216_2018-11-16_114830.ncn”
]
}
], “source”: [
“print(iffile0)n”, “print(rofile0[0])”
]
}, {
“cell_type”: “code”, “execution_count”: 14, “metadata”: {}, “outputs”: [
- {
“name”: “stdout”, “output_type”: “stream”, “text”: [
“2018-11-16T06:48:30 begin /media/teuben/P1-2/teuben/LMT/data_lmt/ifproc/ifproc_2018-11-16_079447_00_0001.ncn”, “2018-11-16T06:48:38 end /media/teuben/P1-2/teuben/LMT/data_lmt/ifproc/ifproc_2018-11-16_079447_00_0001.ncn”, “BufPos 3 5.1 sec 79447n”, “BufPos 2 2.6 sec 79447n”, “before read npixn”, “from pixels npix = 16n”, “from xlen npix = 16n”, “TRACKING Sequoia PIXEL 10n”, “/media/teuben/P1-2/teuben/LMT/data_lmt/ifproc/ifproc_2018-11-16_079447_00_0001.nc does not have map parametersn”, “/media/teuben/P1-2/teuben/LMT/data_lmt/ifproc/ifproc_2018-11-16_079447_00_0001.nc does not have bs parametersn”, “WARNING: 79447 is a Cal observationn”, “get chopn”, “‘Data.Msip1mm.BeamChopperActPos’n”, ” no chopn”
]
}, {
“name”: “stderr”, “output_type”: “stream”, “text”: [
“Traceback (most recent call last):n”, ” File "/media/teuben/P1-2/teuben/LMT/lmtoy/SpectralLineReduction/lmtslr/ifproc/ifproc.py", line 581, in __init__n”, ” self.chop = self.nc.variables[‘Data.Msip1mm.BeamChopperActPos’][:]n”, “KeyError: ‘Data.Msip1mm.BeamChopperActPos’n”
]
}
], “source”: [
“ICal = IFProcData(iffile0)”
]
}, {
“cell_type”: “code”, “execution_count”: 15, “metadata”: {}, “outputs”: [
- {
“name”: “stdout”, “output_type”: “stream”, “text”: [
“read_roach /media/teuben/P1-2/teuben/LMT/data_lmt/spectrometer/roach0/roach0_79447_0_1_IRC+10216_2018-11-16_114830.nc nspec,nchan=236,2048n”, “r:0 inp:0 pix:0 time_offset:-0.030000n”, “r:0 inp:1 pix:1 time_offset:-0.030000n”, “r:0 inp:2 pix:2 time_offset:-0.030000n”, “r:0 inp:3 pix:3 time_offset:-0.030000n”, “read_roach /media/teuben/P1-2/teuben/LMT/data_lmt/spectrometer/roach1/roach1_79447_0_1_IRC+10216_2018-11-16_114830.nc nspec,nchan=236,2048n”, “read_roach /media/teuben/P1-2/teuben/LMT/data_lmt/spectrometer/roach2/roach2_79447_0_1_IRC+10216_2018-11-16_114830.nc nspec,nchan=240,2048n”, “read_roach /media/teuben/P1-2/teuben/LMT/data_lmt/spectrometer/roach3/roach3_79447_0_1_IRC+10216_2018-11-16_114830.nc nspec,nchan=244,2048n”
]
}
], “source”: [
“SCal = SpecBankData(rofile0,ICal,pixel_list=[0,1,2,3])”
]
}, {
“cell_type”: “markdown”, “metadata”: {}, “source”: [
“During the construction of the SpecBankData the time is used to check which integration belongs to which bufpos. Recall I is sampled at 125Hz and S at 10 Hz”
]
}, {
“cell_type”: “code”, “execution_count”: 16, “metadata”: {}, “outputs”: [
- {
“name”: “stdout”, “output_type”: “stream”, “text”: [
“[3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3n”, ” 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3n”, ” 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3n”, ” 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3n”, ” 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3n”, ” 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3n”, ” 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3n”, ” 3 3 3 3 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2n”, ” 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2n”, ” 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2n”, ” 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2n”, ” 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2n”, ” 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2n”, ” 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2n”, ” 2 2 2 2 2 2 2 2 2 2]n”, “[3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 2 2 2 2 2 2 2 2 2 2n”, ” 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2]n”
]
}
], “source”: [
“print(ICal.bufpos)n”, “print(SCal.roach[0].bufpos)”
]
}, {
“cell_type”: “code”, “execution_count”: 17, “metadata”: {}, “outputs”: [
- {
- “data”: {
- “text/plain”: [
“array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16,n”, ” 17, 18, 19, 20, 21, 22, 23, 24, 25, 26])”
]
}, “execution_count”: 17, “metadata”: {}, “output_type”: “execute_result”
}
], “source”: [
“SCal.roach[0].hots”
]
}, {
“cell_type”: “code”, “execution_count”: 18, “metadata”: {}, “outputs”: [
- {
- “data”: {
- “text/plain”: [
“array([27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43,n”, ” 44, 45, 46, 47, 48, 49, 50, 51, 52, 53])”
]
}, “execution_count”: 18, “metadata”: {}, “output_type”: “execute_result”
}
], “source”: [
“SCal.roach[0].skys”
]
}, {
“cell_type”: “code”, “execution_count”: 19, “metadata”: {}, “outputs”: [
- {
- “data”: {
- “text/plain”: [
“(54, 2048)”
]
}, “execution_count”: 19, “metadata”: {}, “output_type”: “execute_result”
}
], “source”: [
“SCal.roach[0].raw_spec.shapen”
]
}, {
“cell_type”: “code”, “execution_count”: 20, “metadata”: {}, “outputs”: [
- {
“name”: “stderr”, “output_type”: “stream”, “text”: [
“<ipython-input-20-2e65f7403d2f>:7: RuntimeWarning: invalid value encountered in true_dividen”, ” skyr = sky1/sky2n”
]
}
], “source”: [
“sky = SCal.roach[0].raw_spec[SCal.roach[0].skys]n”, “hot = SCal.roach[0].raw_spec[SCal.roach[0].hots]n”, “sky1 = sky.mean(axis=0)n”, “sky2 = sky.std(axis=0)n”, “hot1 = hot.mean(axis=0)n”, “hot2 = hot.std(axis=0)n”, “skyr = sky1/sky2n”, “hotr = hot1/hot2”
]
}, {
“cell_type”: “code”, “execution_count”: 21, “metadata”: {}, “outputs”: [
- {
- “data”: {
“image/png”: “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”, “text/plain”: [
“<Figure size 432x288 with 1 Axes>”
]
}, “metadata”: {
“needs_background”: “light”
}, “output_type”: “display_data”
}
], “source”: [
“pl.plot(hot1,label=’HOT’)n”, “pl.plot(sky1,label=’SKY’)n”, “pl.legend();n”, “pl.savefig(‘hot-sky_slr.png’)”
]
}, {
“cell_type”: “markdown”, “metadata”: {}, “source”: [
“The intensity units are "arbitrary" uncalibrated, and channel numbers are plotted horizontally. In this case the channels are 1 km/s at 115GHz, for a bandwidth of 800 MHz.n”
]
}, {
“cell_type”: “code”, “execution_count”: 22, “metadata”: {}, “outputs”: [
- {
- “data”: {
“image/png”: “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n”, “text/plain”: [
“<Figure size 432x288 with 1 Axes>”
]
}, “metadata”: {
“needs_background”: “light”
}, “output_type”: “display_data”
}
], “source”: [
“# compute signal to noise ration”, “pl.plot(hotr)n”, “pl.plot(skyr);”
]
}, {
“cell_type”: “markdown”, “metadata”: {}, “source”: [
“Now construct the Tsys spectrum, but leaving out some edge channels due to low sensitivity”
]
}, {
“cell_type”: “code”, “execution_count”: 23, “metadata”: {}, “outputs”: [
- {
- “data”: {
“image/png”: “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n”, “text/plain”: [
“<Figure size 432x288 with 1 Axes>”
]
}, “metadata”: {
“needs_background”: “light”
}, “output_type”: “display_data”
}
], “source”: [
“tamb = 280n”, “edge = 50n”, “Tsys = tamb * sky1/(hot1-sky1)n”, “pl.plot(Tsys[edge:-edge],label=’Tsys’)n”, “pl.legend()n”, “pl.savefig(‘Tsys_slr.png’)n”
]
}, {
“cell_type”: “markdown”, “metadata”: {}, “source”: [
“# Calibrating a spectrumn”, “n”, “Similar to the calibration file, we grab the I and S for the Map.”
]
}, {
“cell_type”: “code”, “execution_count”: 24, “metadata”: {}, “outputs”: [
- {
“name”: “stdout”, “output_type”: “stream”, “text”: [
“2018-11-16T06:48:52 begin /media/teuben/P1-2/teuben/LMT/data_lmt/ifproc/ifproc_2018-11-16_079448_01_0000.ncn”, “2018-11-16T07:00:18 end /media/teuben/P1-2/teuben/LMT/data_lmt/ifproc/ifproc_2018-11-16_079448_01_0000.ncn”, “BufPos 1 16.9 sec 79448n”, “BufPos 0 150.6 sec 79448n”, “BufPos 1 17.7 sec 79448n”, “BufPos 0 151.6 sec 79448n”, “BufPos 1 17.3 sec 79448n”, “BufPos 0 150.9 sec 79448n”, “BufPos 1 17.4 sec 79448n”, “BufPos 0 153.3 sec 79448n”, “BufPos 1 10.1 sec 79448n”, “before read npixn”, “from pixels npix = 16n”, “from xlen npix = 16n”, “TRACKING Sequoia PIXEL 10n”, “Map Parameters: Ra Continuousn”, “HPBW= 16.0 XLength= 400.0 YLength= 400.0 XStep= 1.00 YStep= 0.70n”, “/media/teuben/P1-2/teuben/LMT/data_lmt/ifproc/ifproc_2018-11-16_079448_01_0000.nc does not have bs parametersn”, “79448 is a Map observationn”, “get chopn”, “‘Data.Msip1mm.BeamChopperActPos’n”, ” no chopn”
]
}, {
“name”: “stderr”, “output_type”: “stream”, “text”: [
“Traceback (most recent call last):n”, ” File "/media/teuben/P1-2/teuben/LMT/lmtoy/SpectralLineReduction/lmtslr/ifproc/ifproc.py", line 581, in __init__n”, ” self.chop = self.nc.variables[‘Data.Msip1mm.BeamChopperActPos’][:]n”, “KeyError: ‘Data.Msip1mm.BeamChopperActPos’n”
]
}
], “source”: [
“IMap = IFProcData(iffile1)”
]
}, {
“cell_type”: “markdown”, “metadata”: {}, “source”: [
“## Intermezzo: total power as function of timen”, “n”, “plotting total power, averages over the middle 1000 channels,n”, “for each pixel as function of time. You will see the On/Off pattern, and the drift in TP,n”, “plus the difference between pixels”
]
}, {
“cell_type”: “code”, “execution_count”: 25, “metadata”: {}, “outputs”: [
- {
“name”: “stdout”, “output_type”: “stream”, “text”: [
“read_roach /media/teuben/P1-2/teuben/LMT/data_lmt/spectrometer/roach0/roach0_79448_1_0_IRC+10216_2018-11-16_114845.nc nspec,nchan=25108,2048n”, “r:0 inp:0 pix:0 time_offset:-0.030000n”, “read_roach /media/teuben/P1-2/teuben/LMT/data_lmt/spectrometer/roach1/roach1_79448_1_0_IRC+10216_2018-11-16_114845.nc nspec,nchan=25112,2048n”, “read_roach /media/teuben/P1-2/teuben/LMT/data_lmt/spectrometer/roach2/roach2_79448_1_0_IRC+10216_2018-11-16_114845.nc nspec,nchan=25140,2048n”, “read_roach /media/teuben/P1-2/teuben/LMT/data_lmt/spectrometer/roach3/roach3_79448_1_0_IRC+10216_2018-11-16_114845.nc nspec,nchan=25144,2048n”, “read_roach /media/teuben/P1-2/teuben/LMT/data_lmt/spectrometer/roach0/roach0_79448_1_0_IRC+10216_2018-11-16_114845.nc nspec,nchan=25108,2048n”, “r:0 inp:1 pix:1 time_offset:-0.030000n”, “read_roach /media/teuben/P1-2/teuben/LMT/data_lmt/spectrometer/roach1/roach1_79448_1_0_IRC+10216_2018-11-16_114845.nc nspec,nchan=25112,2048n”, “read_roach /media/teuben/P1-2/teuben/LMT/data_lmt/spectrometer/roach2/roach2_79448_1_0_IRC+10216_2018-11-16_114845.nc nspec,nchan=25140,2048n”, “read_roach /media/teuben/P1-2/teuben/LMT/data_lmt/spectrometer/roach3/roach3_79448_1_0_IRC+10216_2018-11-16_114845.nc nspec,nchan=25144,2048n”, “read_roach /media/teuben/P1-2/teuben/LMT/data_lmt/spectrometer/roach0/roach0_79448_1_0_IRC+10216_2018-11-16_114845.nc nspec,nchan=25108,2048n”, “r:0 inp:2 pix:2 time_offset:-0.030000n”, “read_roach /media/teuben/P1-2/teuben/LMT/data_lmt/spectrometer/roach1/roach1_79448_1_0_IRC+10216_2018-11-16_114845.nc nspec,nchan=25112,2048n”, “read_roach /media/teuben/P1-2/teuben/LMT/data_lmt/spectrometer/roach2/roach2_79448_1_0_IRC+10216_2018-11-16_114845.nc nspec,nchan=25140,2048n”, “read_roach /media/teuben/P1-2/teuben/LMT/data_lmt/spectrometer/roach3/roach3_79448_1_0_IRC+10216_2018-11-16_114845.nc nspec,nchan=25144,2048n”, “read_roach /media/teuben/P1-2/teuben/LMT/data_lmt/spectrometer/roach0/roach0_79448_1_0_IRC+10216_2018-11-16_114845.nc nspec,nchan=25108,2048n”, “r:0 inp:3 pix:3 time_offset:-0.030000n”, “read_roach /media/teuben/P1-2/teuben/LMT/data_lmt/spectrometer/roach1/roach1_79448_1_0_IRC+10216_2018-11-16_114845.nc nspec,nchan=25112,2048n”, “read_roach /media/teuben/P1-2/teuben/LMT/data_lmt/spectrometer/roach2/roach2_79448_1_0_IRC+10216_2018-11-16_114845.nc nspec,nchan=25140,2048n”, “read_roach /media/teuben/P1-2/teuben/LMT/data_lmt/spectrometer/roach3/roach3_79448_1_0_IRC+10216_2018-11-16_114845.nc nspec,nchan=25144,2048n”
]
}
], “source”: [
“s0 = SpecBankData(rofile1,IMap,pixel_list=[0])n”, “s1 = SpecBankData(rofile1,IMap,pixel_list=[1])n”, “s2 = SpecBankData(rofile1,IMap,pixel_list=[2])n”, “s3 = SpecBankData(rofile1,IMap,pixel_list=[3])”
]
}, {
“cell_type”: “code”, “execution_count”: 26, “metadata”: {}, “outputs”: [
- {
“name”: “stdout”, “output_type”: “stream”, “text”: [
“Total Power Mean over channels 512:1536n”
]
}
], “source”: [
“n = len(s0.roach[0].raw_spec[0])n”, “n0=n//4n”, “n1=3*n0n”, “print("Total Power Mean over channels %d:%d" % (n0,n1))”
]
}, {
“cell_type”: “code”, “execution_count”: 27, “metadata”: {}, “outputs”: [
- {
- “data”: {
“image/png”: “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”, “text/plain”: [
“<Figure size 432x288 with 1 Axes>”
]
}, “metadata”: {
“needs_background”: “light”
}, “output_type”: “display_data”
}
], “source”: [
“w0=s0.roach[0].raw_spec[:,n0:n1].mean(axis=1)n”, “w1=s1.roach[0].raw_spec[:,n0:n1].mean(axis=1)n”, “w2=s2.roach[0].raw_spec[:,n0:n1].mean(axis=1)n”, “w3=s3.roach[0].raw_spec[:,n0:n1].mean(axis=1)n”, “pl.plot(w0,label=’0’)n”, “pl.plot(w1,label=’1’)n”, “pl.plot(w2,label=’2’)n”, “pl.plot(w3,label=’3’)n”, “pl.legend()n”, “pl.xlabel(‘Time’)n”, “pl.ylabel(‘Total Power’)n”, “pl.savefig(‘TP-time.png’)”
]
}, {
“cell_type”: “code”, “execution_count”: 28, “metadata”: {}, “outputs”: [
- {
“name”: “stdout”, “output_type”: “stream”, “text”: [
“read_roach /media/teuben/P1-2/teuben/LMT/data_lmt/spectrometer/roach0/roach0_79448_1_0_IRC+10216_2018-11-16_114845.nc nspec,nchan=25108,2048n”, “r:0 inp:0 pix:0 time_offset:-0.030000n”, “r:0 inp:1 pix:1 time_offset:-0.030000n”, “r:0 inp:2 pix:2 time_offset:-0.030000n”, “r:0 inp:3 pix:3 time_offset:-0.030000n”, “read_roach /media/teuben/P1-2/teuben/LMT/data_lmt/spectrometer/roach1/roach1_79448_1_0_IRC+10216_2018-11-16_114845.nc nspec,nchan=25112,2048n”, “read_roach /media/teuben/P1-2/teuben/LMT/data_lmt/spectrometer/roach2/roach2_79448_1_0_IRC+10216_2018-11-16_114845.nc nspec,nchan=25140,2048n”, “read_roach /media/teuben/P1-2/teuben/LMT/data_lmt/spectrometer/roach3/roach3_79448_1_0_IRC+10216_2018-11-16_114845.nc nspec,nchan=25144,2048n”
]
}
], “source”: [
“SMap = SpecBankData(rofile1,IMap,pixel_list=[0,1,2,3])”
]
}, {
“cell_type”: “code”, “execution_count”: 29, “metadata”: {}, “outputs”: [
- {
- “data”: {
- “text/plain”: [
“[(102, 1528), (1635, 3068), (3170, 4597), (4700, 6149)]”
]
}, “execution_count”: 29, “metadata”: {}, “output_type”: “execute_result”
}
], “source”: [
“SMap.roach[0].on_ranges”
]
}, {
“cell_type”: “code”, “execution_count”: 30, “metadata”: {}, “outputs”: [
- {
- “data”: {
- “text/plain”: [
“[(0, 101), (1529, 1634), (3069, 3169), (4598, 4699), (6150, 6251)]”
]
}, “execution_count”: 30, “metadata”: {}, “output_type”: “execute_result”
}
], “source”: [
“SMap.roach[0].ref_ranges”
]
}, {
“cell_type”: “markdown”, “metadata”: {}, “source”: [
“Thus there are 4 series of On’s. If we take halfway the first row in the 3rd set, we are roughly int the middle of the map, and expect to see a strong signal in this IRC example. We will cheat and just take the Ref from the point before (3169). The only thing we don’t know here is how many rows there are in this section. You have to believe me, there are 10, thus half-way the first row is about 70 points down the line, since there are about 1400 points in an "On" section.n”, “n”, “We also don’t know which of the 4 pixels in this roach it is….. TBD. But to first order we can ignore that.”
]
}, {
“cell_type”: “code”, “execution_count”: 31, “metadata”: {}, “outputs”: [
- {
“name”: “stderr”, “output_type”: “stream”, “text”: [
“<ipython-input-31-95be7b7d23f1>:8: RuntimeWarning: invalid value encountered in true_dividen”, ” Ta = Tsys * (on-ref)/refn”
]
}, {
- “data”: {
“image/png”: “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n”, “text/plain”: [
“<Figure size 432x288 with 1 Axes>”
]
}, “metadata”: {
“needs_background”: “light”
}, “output_type”: “display_data”
}
], “source”: [
“iref = 3169n”, “ion = iref+70 # ~140 points along a single X, doing this 10 timesn”, “#n”, “n”, “ref = SMap.roach[0].raw_spec[iref]n”, “on = SMap.roach[0].raw_spec[ion]n”, “n”, “Ta = Tsys * (on-ref)/refn”, “pl.plot(Ta[edge:-edge]);”
]
}, {
“cell_type”: “markdown”, “metadata”: {}, “source”: [
“Even in this single 0.1sec integration, there is a signal near the middle of the spectrum. A modest hanning smoothing should bring that out a bit more.”
]
}, {
“cell_type”: “code”, “execution_count”: 32, “metadata”: {}, “outputs”: [
- {
- “data”: {
“image/png”: “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n”, “text/plain”: [
“<Figure size 432x288 with 1 Axes>”
]
}, “metadata”: {
“needs_background”: “light”
}, “output_type”: “display_data”
}
], “source”: [
“h = np.hanning(11)n”, “Tasm = np.convolve(Ta,h)n”, “pl.plot(Tasm[edge:-edge]);”
]
}, {
“cell_type”: “markdown”, “metadata”: {}, “source”: [
“Each roach board thus produces about 1400 * 4 points, for 4 boards this will be about 22,000 spectra in a 400 x 400" grid. You have to believe me on that one too. These will need to be gridded in a map, and since the FWHM is about 15", there are about 700 independant resolution elements. Thus we have effectively about 30 integrations, or 3sec integration time, per resolution element.n”, “n”, “Gridding is beyond the scope of this notebook. In addition, it is computationally more intensive, and the gridder is written in C.”
]
}
], “metadata”: {
- “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”: “3.8.8”
}
}, “nbformat”: 4, “nbformat_minor”: 4
}