{ "cells": [ { "cell_type": "markdown", "id": "8c8138ef-e687-444e-95b1-9797654f3f62", "metadata": {}, "source": [ "# On-The-Fly (OTF) Data Reduction\n", "\n", "This notebook shows how to use dysh to calibrate and grid an *On-The-Fly* (OTF) observation. See \n", "[Mangum et al. (2007)](https://ui.adsabs.harvard.edu/abs/2007A%26A...474..679M) \n", "for the background on this method.\n", "\n", "OTF observations can be calibrated in dysh, however, generating FITS cubes requires the use of additional applications.\n", "\n", "The workflow to go from raw data to a FITS cube would then require the following steps:\n", "\n", "1. Calibrate the data using dysh. This can include baseline subtraction, altough in some cases baseline removal is more effective in a FITS cube.\n", "\n", "2. Write the calibrated spectra to a format compatible with the gridding tool being used. For GBT observations the recommended format is SDFITS and the tool to grid the data is the\n", "[gbtgridder](https://github.com/GreenBankObservatory/gbtgridder/tree/release_3.0).\n", "\n", "4. Baseline subtraction in the FITS cube. This is optional, depending on the data quality, and dysh does not provide convenience functions for this.\n", "\n", "## Dysh commands\n", "\n", "The following dysh commands are introduced (leaving out all the function arguments):\n", "\n", " filename = dysh_data()\n", " sdf = GBTFITSLoad()\n", " sb = sdf.getsigref()\n", " ta = sb.timeaverage()\n", " ta.baseline()\n", " ta.plot()\n", "\n", "## Loading Modules\n", "We start by loading the modules we will use for the data reduction. \n" ] }, { "cell_type": "code", "execution_count": 1, "id": "b4967550-2ca1-4931-b53b-6f9868718490", "metadata": { "editable": true, "slideshow": { "slide_type": "" }, "tags": [ "import" ] }, "outputs": [], "source": [ "# We use dysh_data to retrieve the example data set.\n", "from dysh.util.files import dysh_data\n", "from pathlib import Path\n", "\n", "# This is required to load and calibrate the example data set.\n", "from dysh.fits.gbtfitsload import GBTFITSLoad\n", "from dysh.log import init_logging" ] }, { "cell_type": "markdown", "id": "06ed5124-ef17-40e4-8fff-d13900aca5e3", "metadata": {}, "source": [ "## Setup\n", "We start the dysh logging, so we get more information about what is happening.\n", "This is only needed if working on a notebook.\n", "If using the CLI through the ``dysh`` command, then logging is setup for you." ] }, { "cell_type": "code", "execution_count": 2, "id": "8d0b3528-5da1-4390-8bf2-e7268eb2e92a", "metadata": {}, "outputs": [], "source": [ "init_logging(2)\n", "\n", "# also create a local \"output\" directory where temporary notebook files can be stored.\n", "output_dir = Path.cwd() / \"output\"\n", "output_dir.mkdir(exist_ok=True)" ] }, { "cell_type": "markdown", "id": "87669763-8d96-4521-9e81-f69d7213e133", "metadata": {}, "source": [ "## Data Retrieval\n", "\n", "Download the example SDFITS data, if necessary." ] }, { "cell_type": "code", "execution_count": 3, "id": "6bc88bc5-986d-4eae-b1c7-6398cc9ddd5a", "metadata": { "editable": true, "slideshow": { "slide_type": "" }, "tags": [ "wget" ] }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "12:03:27.099 I Resolving example=otf1 -> mapping-L/data/TGBT17A_506_11.raw.vegas/TGBT17A_506_11.raw.vegas.A.fits\n" ] } ], "source": [ "filename = dysh_data(example='otf1')" ] }, { "cell_type": "code", "execution_count": 4, "id": "93a62e3a-c95d-475b-8602-b5b8b7934733", "metadata": { "editable": true, "slideshow": { "slide_type": "" }, "tags": [ "load" ] }, "outputs": [], "source": [ "sdfits = GBTFITSLoad(filename)" ] }, { "cell_type": "code", "execution_count": 5, "id": "90d482e1-3953-49da-9be6-49605ae9cf64", "metadata": { "scrolled": true }, "outputs": [ { "data": { "text/html": [ "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", 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SCANOBJECTVELOCITYPROCPROCSEQNRESTFREQDOPFREQ# IF# POL# INT# FEEDAZIMUTHELEVATION
63C2860.0OnOff11.61681.42040652131248.365772.5531
73C2860.0OnOff21.61681.42040652131250.038573.6777
8SgrB2M57.0Track11.61681.42040652611142.384512.2545
9W33B64.0Track11.61681.42040652611132.205717.9786
10G30.589-0.04438.0Track11.61681.42040652611115.481125.5523
11G31.412+0.30797.0Track11.61681.42040652611115.803427.0986
12G35.577-0.02949.0Track11.61681.42040652611112.313929.0337
13G40.622-0.13732.0Track11.61681.42040652611107.773631.3159
14NGC694645.0DecLatMap11.61681.4204065261138.962641.3644
15NGC694645.0DecLatMap21.61681.4204065261138.991041.4488
16NGC694645.0DecLatMap31.61681.4204065261138.991841.5360
17NGC694645.0DecLatMap41.61681.4204065261139.019341.6203
18NGC694645.0DecLatMap51.61681.4204065261139.019841.7075
19NGC694645.0DecLatMap61.61681.4204065261139.046841.7919
20NGC694645.0DecLatMap71.61681.4204065261139.046541.8791
21NGC694645.0DecLatMap81.61681.4204065261139.073041.9637
22NGC694645.0DecLatMap91.61681.4204065261139.072242.0508
23NGC694645.0DecLatMap101.61681.4204065261139.098342.1355
24NGC694645.0DecLatMap111.61681.4204065261139.096842.2227
25NGC694645.0DecLatMap121.61681.4204065261139.122442.3073
26NGC694645.0DecLatMap131.61681.4204065261139.120242.3945
27NGC694645.0Track11.61681.4204065261139.088142.1493
" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "sdfits.summary()" ] }, { "cell_type": "markdown", "id": "7b2b8ea5-f1a2-496a-928a-885de31aaaa6", "metadata": {}, "source": [ "In this particular observation the OTF slew over the galaxy NGC6946 in scans 14-26, followed by an Off position scan 27. Each **SCAN**, in this case, corresponds to a row, with 61 integrations as the telescope slews slowly over the sky." ] }, { "cell_type": "markdown", "id": "8cbdc5c7-dd31-4d88-a035-9f6780365b22", "metadata": {}, "source": [ "## Data Reduction\n", "\n", "We use `getsigref` to calibrate the OTF scans using scan 27 as the reference position.\n", "`getsigref` takes as input a list of scan numbers, or a single one, and a number, or `Spectrum`, for the reference position.\n", "It calibrates all the input scans using\n", "$$\n", "T_{\\mathrm{A}}=T_{\\mathrm{sys}}\\frac{P_{\\mathrm{sig}}-P_{\\mathrm{ref}}}{P_{\\mathrm{ref}}},\n", "$$\n", "where $T_{\\mathrm{sys}}$ is the system temperature derived from the reference scan, $P_{\\mathrm{sig}}$ is the raw power in the input scans (the signal), and $P_{\\mathrm{ref}}$ is the raw power in the reference scan.\n", "\n", "We start by defining the variables we will use to calibrate the 21 cm-HI line observations present in this data set." ] }, { "cell_type": "code", "execution_count": 6, "id": "2d090f76-3140-44e8-82a1-5508173f0e81", "metadata": {}, "outputs": [], "source": [ "ifnum = 0 # The 21 cm line is in the spectral window labeled 0.\n", "fdnum = 0 # Only one feed in this data set\n", "ref = 27 # The reference (\"OFF\") scan\n", "scans = list(range(14,27)) # The signal (\"ON\") scans 14..26" ] }, { "cell_type": "markdown", "id": "08a3769a-922e-40ca-b47a-539c16aab601", "metadata": {}, "source": [ "Now, we calibrate a single polarization.\n", "The processing for the second polarization should be almost identical." ] }, { "cell_type": "code", "execution_count": 7, "id": "2215b5ab-7261-4a2d-b00c-6e591975d6d0", "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "12:03:30.206 I Ignoring 1 blanked integration(s).\n", "12:03:30.965 I Ignoring 1 blanked integration(s).\n", "12:03:31.166 I Ignoring 1 blanked integration(s).\n", "12:03:31.365 I Ignoring 1 blanked integration(s).\n", "12:03:31.586 I Ignoring 1 blanked integration(s).\n", "12:03:31.788 I Ignoring 1 blanked integration(s).\n", "12:03:31.997 I Ignoring 1 blanked integration(s).\n", "12:03:32.195 I Ignoring 1 blanked integration(s).\n", "12:03:32.417 I Ignoring 1 blanked integration(s).\n", "12:03:32.616 I Ignoring 1 blanked integration(s).\n", "12:03:32.817 I Ignoring 1 blanked integration(s).\n", "12:03:33.031 I Ignoring 1 blanked integration(s).\n", "12:03:33.232 I Ignoring 1 blanked integration(s).\n", "12:03:33.436 I Ignoring 1 blanked integration(s).\n" ] } ], "source": [ "sb0 = sdfits.getsigref(scan=scans, ref=ref, fdnum=fdnum, ifnum=ifnum, plnum=0)" ] }, { "cell_type": "markdown", "id": "ad26efda-ea52-488b-a60b-54b51e83b7ee", "metadata": {}, "source": [ "The return from `getsigref` is a `ScanBlock` with 13 `PSScan`s in it." ] }, { "cell_type": "code", "execution_count": 8, "id": "1e7db301-bc52-4c3b-88b0-b9f4c24aca50", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "ScanBlock([,\n", " ,\n", " ,\n", " ,\n", " ,\n", " ,\n", " ,\n", " ,\n", " ,\n", " ,\n", " ,\n", " ,\n", " ])" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "sb0" ] }, { "cell_type": "markdown", "id": "b0adf513-999d-4fe3-aa20-0ed20770fbb0", "metadata": {}, "source": [ "### Data Inspection\n", "\n", "After calibrating the data, we can inspect the calibrated data using the plotting methods available in dysh.\n", "First, we will extract a single spectrum from the middle of the observations and plot it. \n", "To get the spectrum from the middle of the OTF map, we use `len(sb0)//2` to specify the middle scan and `sb0[len(sb0)//2].nint//2` to specify the middle integration (in this case there are 61 integrations).\n", "We store the number of integrations in the `nint` variable to reuse it later." ] }, { "cell_type": "code", "execution_count": 9, "id": "cbbbbaa6-41c4-4e33-bb05-167fa03995d4", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "60\n" ] } ], "source": [ "nint = sb0[len(sb0)//2].nint\n", "print(nint)\n", "spec = sb0[len(sb0)//2].getspec(nint//2)" ] }, { "cell_type": "markdown", "id": "fcd4605b-13de-47e6-840f-c6aba75b78d5", "metadata": {}, "source": [ "Although `summary` told us there were 61 integrations, only 60 were recorded by `getsigref`. Apparently one integration was blanked." ] }, { "cell_type": "code", "execution_count": 10, "id": "1021d062-21a6-487b-b946-255c66ea0193", "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "b6fa7c02b4264b838ba3a4d9a6f98413", "version_major": 2, "version_minor": 0 }, "text/plain": [ "VBox(children=(HBox(children=(Button(description='Clear All Regions', style=ButtonStyle(), tooltip='Clear all …" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "sp = spec.plot(xaxis_unit=\"km/s\")" ] }, { "cell_type": "markdown", "id": "b8d04134-9e10-4c48-a54a-3ccca507d438", "metadata": {}, "source": [ "A clear and strong signal is present, as well as a ~2 K continuum. There is also an obvious negative signal, most likely local HI since the Off position will never be line free near vlsr ~ 0\n", "\n", "Now we will plot the time average for the middle scan, where the noise should be able $\\sqrt{60} ~ \\approx 7$ times smaller." ] }, { "cell_type": "code", "execution_count": 11, "id": "eb986247-295e-4739-b766-aee5914fa240", "metadata": {}, "outputs": [], "source": [ "spec_avg = sb0[len(sb0)//2].timeaverage()" ] }, { "cell_type": "markdown", "id": "d94a537d-f8ee-471c-859b-99a27fc0358a", "metadata": {}, "source": [ "Again, a clear signal, but the line brightness and continuum are lower due to the dilution from the time averaging. This is because the source is smaller than the area mapped, so the signal gets averaged with empty sky." ] }, { "cell_type": "markdown", "id": "b9f6b0c0-38e1-4785-829c-95ae027e81ca", "metadata": { "editable": true, "slideshow": { "slide_type": "" }, "tags": [ "print_tsys", "test" ] }, "source": [ "### Baseline Subtraction\n", "\n", "If we are only interested in the spectral line, then we use baseline subtraction to remove the continuum.\n", "We will use an order 1 polynomial to remove the continuum, and make sure to exclude the edge channels as well as the channels with 21 cm signal during the baseline fit.\n", "\n", "We explore two approaches to continuum subtraction (baseline removal), using a model derived from a time average and deriving a baseline for each integration.\n", "\n", "#### Using a Time Average\n", "\n", "We start by using the time average of the spectra in one scan to derive a baseline model, and then we will use this baseline model to subtract the continuum from all of the integrations in the scan.\n", "This approach has the benefit of being faster than deriving a baseline from each integration, and the spectrum used to derive the baseline model has a higher signal-to-noise.\n", "However, this approach requires that the baseline be stable in time/sky position.\n", "If that is not the case, then there will be left over baseline/continuum in the baseline subtracted data.\n", "\n", "We start by plotting the time average as a function of channel number to determine where the 21 cm signal is." ] }, { "cell_type": "code", "execution_count": 12, "id": "59e5bfc0-96e1-472a-b701-893d5742e948", "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "f290b29785c14419823e29eba408dfa2", "version_major": 2, "version_minor": 0 }, "text/plain": [ "VBox(children=(HBox(children=(Button(description='Clear All Regions', style=ButtonStyle(), tooltip='Clear all …" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "sp_avg = spec_avg.plot(xaxis_unit=\"channel\")" ] }, { "cell_type": "markdown", "id": "69b6cff7-0c9d-4235-97ff-151e84a06c7d", "metadata": {}, "source": [ "From the plot we see that channels ~1600 to 2250 have 21 cm signal, and the edges are between 0 and 250, and 4095-250 and 4095.\n", "We define an exclude region with these." ] }, { "cell_type": "code", "execution_count": 13, "id": "f1034c62-c528-4997-bce8-5f269038810c", "metadata": {}, "outputs": [], "source": [ "exclude = [(0,250),\n", " (1600,2250),\n", " (4095-250,4095)]" ] }, { "cell_type": "markdown", "id": "9386a874-74b1-403e-8448-fef894974047", "metadata": {}, "source": [ "Now do the baseline fitting, using an order 1 polynomial and removing the best fit model from the data." ] }, { "cell_type": "code", "execution_count": 14, "id": "df2b6cd8-988b-4f31-86b8-930128ed62de", "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "12:03:33.821 I EXCLUDING [Spectral Region, 1 sub-regions:\n", " (1408148687.1396484 Hz, 1409579198.6142578 Hz) \n", ", Spectral Region, 1 sub-regions:\n", " (1418705861.8222656 Hz, 1422425191.65625 Hz) \n", ", Spectral Region, 1 sub-regions:\n", " (1430149953.6191406 Hz, 1431580465.09375 Hz) \n", "]\n" ] } ], "source": [ "spec_avg.baseline(1, model=\"poly\", exclude=exclude, remove=True)" ] }, { "cell_type": "markdown", "id": "40c1d334-fd88-4513-bf8b-1524f6013f82", "metadata": {}, "source": [ "Plot the baseline subtracted data.\n", "(Alternatively, one could use `spec_avg.plot()`.)" ] }, { "cell_type": "code", "execution_count": 15, "id": "f9f0b47a-6aaf-41c2-ab31-1785a27184d8", "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "f290b29785c14419823e29eba408dfa2", "version_major": 2, "version_minor": 0 }, "text/plain": [ "VBox(children=(HBox(children=(Button(description='Clear All Regions', style=ButtonStyle(), tooltip='Clear all …" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "sp_avg.plot(xaxis_unit = \"km/s\")" ] }, { "cell_type": "markdown", "id": "7e37743f-dd09-46ba-9f17-a56314ba26e9", "metadata": {}, "source": [ "The continuum has been removed (the spectrum is centered around 0 K). \n", "We can look at the statistics to verify this. We will be using the left wing where the baseline fits was used." ] }, { "cell_type": "code", "execution_count": 16, "id": "03474a08-776d-41e8-b067-fea381642209", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "{'mean': ,\n", " 'median': ,\n", " 'rms': ,\n", " 'min': ,\n", " 'max': ,\n", " 'npt': 1350,\n", " 'nan': np.int64(0)}" ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ "spec_avg[250:1600].stats() " ] }, { "cell_type": "markdown", "id": "cd0fb104-0836-4c30-a2e1-746a47a33744", "metadata": {}, "source": [ "The mean is around -0.4 mK, the median -1.4 mK and the rms 46 mK." ] }, { "cell_type": "markdown", "id": "55b52676-79ce-4374-a2a8-3a3706eab17d", "metadata": {}, "source": [ "We can subtract this baseline model from all of the integrations using the `subtract_baseline` method of a `ScanBlock` or `Scan`.\n", "The input to `subtract_baseline` should be a baseline model, which can be accessed through the `baseline_model` attribute of a `Spectrum` object.\n", "We could also subtract it from all scans, which we will do below in order to write a whole grid of baseline subtracted spectra." ] }, { "cell_type": "code", "execution_count": 17, "id": "631803ba-a399-455b-a562-535b352ef619", "metadata": {}, "outputs": [], "source": [ "sb0[len(sb0)//2].subtract_baseline(spec_avg.baseline_model)" ] }, { "cell_type": "markdown", "id": "90996cd7-0e13-4b1c-b269-a81eb72f1164", "metadata": {}, "source": [ "Generate a time average again to see how the data changed after the baseline subtraction." ] }, { "cell_type": "code", "execution_count": 18, "id": "2e7c3c12-8402-4ae3-b4a8-ba0e23962275", "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "822f766e5d5041428b0bf24a582408d3", "version_major": 2, "version_minor": 0 }, "text/plain": [ "VBox(children=(HBox(children=(Button(description='Clear All Regions', style=ButtonStyle(), tooltip='Clear all …" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "spec_avg_bsub = sb0[len(sb0)//2].timeaverage()\n", "sp_avg_bsub = spec_avg_bsub.plot()" ] }, { "cell_type": "markdown", "id": "4c628823-5919-46bc-a822-0f533f06c3ac", "metadata": {}, "source": [ "The time average for the middle scan of the OTF map is now centered around zero as expected.\n", "However, since we used the diluted time average for the middle scan as our baseline model, the spectra that cover the source still have continuum left." ] }, { "cell_type": "code", "execution_count": 19, "id": "8e9d6d16-d02f-46db-b912-8c6722c45608", "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "55fd95822db34eada8e7cb9ea2c49a8c", "version_major": 2, "version_minor": 0 }, "text/plain": [ "VBox(children=(HBox(children=(Button(description='Clear All Regions', style=ButtonStyle(), tooltip='Clear all …" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "spec_bsub = sb0[len(sb0)//2].getspec(nint//2)\n", "sp_spec_bsub = spec_bsub.plot()" ] }, { "cell_type": "markdown", "id": "d1644304-1f22-4d16-85e7-ec4f8e798561", "metadata": {}, "source": [ "#### Using the Integrations\n", "\n", "Now we will repeat the calibration, but deriving a baseline model from each integration.\n", "This is slower, so we only do this for the middle scan of the OTF map.\n", "This approach has the benefit of being more flexible than the previous one, however the signal-to-noise is worse.\n", "\n", "To access the data for the integrations we will use the `calibrated` method and the `_calibrated` property of a `Scan`.\n", "`calibrated` returns the data for a specific integration (starting at 0) as a `Spectrum` object, while `_calibrated` is the array that contains the calibrated data for the `Scan`. \n", "We will use the `Spectrum` to derive the baseline, and then update the data by updating the `_calibrated` property of the `Scan`.\n", "\n", "We put the middle scan of the OTF map in a new variable `scan`, then loop over its integrations fitting a baseline model and subtracting it from the calibrated data. \n", "We ignore integrations that were blanked (all values would be NaN).\n", "We use the `math` library to check for NaNs." ] }, { "cell_type": "code", "execution_count": 20, "id": "aa56172a-6d16-440a-b429-8f726f63358f", "metadata": {}, "outputs": [], "source": [ "import math # Load the `math` library. Use this instead of `numpy` to save memory." ] }, { "cell_type": "code", "execution_count": 21, "id": "4abce53d-cde5-46a3-8a72-5ef9facf54ad", "metadata": {}, "outputs": [], "source": [ "scan = sb0[len(sb0)//2]\n", "\n", "for i,_c in enumerate(scan.calibrated):\n", " if math.isnan(_c.data.sum()):\n", " # If the sum is NaN, then skip (continue) this item.\n", " # This is not a great solution, as even a single NaN value\n", " # in the spectrum would cause the sum to be NaN,\n", " # but there should be no NaN values for single channels\n", " # in this data set.\n", " continue\n", " s_i = scan.getspec(i) # Fetch the `Spectrum` for integration `i`.\n", " s_i.baseline(1, model=\"poly\", exclude=exclude, remove=True) # Fit a baseline model.\n", " scan.calibrated[i] -= s_i.baseline_model(s_i.spectral_axis).value # Subtract the baseline model from the data." ] }, { "cell_type": "markdown", "id": "b1800a3d-7823-41bc-afc5-e89145358e88", "metadata": {}, "source": [ "Plot the middle integration of the middle scan." ] }, { "cell_type": "code", "execution_count": 22, "id": "e2ab71a8-7abb-4b6e-98b6-3c3c05247f00", "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "58adfe47e8ea4ebd801dacde6ea479ad", "version_major": 2, "version_minor": 0 }, "text/plain": [ "VBox(children=(HBox(children=(Button(description='Clear All Regions', style=ButtonStyle(), tooltip='Clear all …" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "spec_bsub_int = sb0[len(sb0)//2].getspec(nint//2)\n", "sp_spec_bsub_int = spec_bsub_int.plot()" ] }, { "cell_type": "markdown", "id": "27b2d48a-8bc8-4ee7-9715-58f2bc212bd1", "metadata": {}, "source": [ "Now the continuum has been removed.\n", "\n", "We leave it as an exercise to repeat the above for every scan in the OTF map.\n", "The answer is hidden below." ] }, { "cell_type": "markdown", "id": "d9f2c6ed-6fd9-4b7d-9cd8-9e6c8fd63bb4", "metadata": {}, "source": [ "
\n", " Answer\n", " 
\n",
    "        \n",
    "            for i,_s in enumerate(sb0):\n",
    "                for j,_c in enumerate(_s._calibrated):\n",
    "                    if math.isnan(_c.data.sum()):\n",
    "                        # If the sum is NaN, then skip (continue) this item.\n",
    "                        continue\n",
    "                    s_i = getspec(j)\n",
    "                    s_i.baseline(1, model=\"poly\", exclude=exclude, remove=True)\n",
    "                    _s._calibrated[j] -= s_i.baseline_model(s_i.spectral_axis).value\n",
    "        \n",
    "
\n", " This can be speed up if we only create a Spectrum once, and then update its data attribute with the data for each integration before computing the baseline. That would be:\n", " 
\n",
    "        \n",
    "            sp0 = sb0.timeaverage() \n",
    "            for i,_s in enumerate(sb0):\n",
    "                for j,_c in enumerate(_s._calibrated):\n",
    "                    if math.isnan(_c.data.sum()):\n",
    "                        # If the sum is NaN, then skip (continue) this item.\n",
    "                        continue\n",
    "                    sp0._data = _s._calibrated[j] # Update the data of the `Spectrum`.\n",
    "                    sp0.baseline(1, model=\"poly\", exclude=exclude, remove=True)\n",
    "                    _s._calibrated[j] -= sp0.baseline_model(sp0.spectral_axis).value\n",
    "        \n",
    "
\n", "
\n", "\n" ] }, { "cell_type": "markdown", "id": "12d10680-43c3-43fd-9d64-2709c3771f03", "metadata": {}, "source": [ "## Writing the Data\n", "\n", "After calibration, we write the data to disk in SDFITS format so it can be gridded by the [`gbtgridder`](https://github.com/GreenBankObservatory/gbtgridder) (GBO's supported data gridding tool)." ] }, { "cell_type": "code", "execution_count": 23, "id": "50a0cbee-fe43-4d43-82a6-6bec7175b3fa", "metadata": {}, "outputs": [], "source": [ "sb0.write(output_dir / \"otf_calibrated.fits\", overwrite=True)" ] }, { "cell_type": "markdown", "id": "3e96ea5b-dfc3-4e5b-88aa-64e5ff7fc977", "metadata": { "editable": true, "slideshow": { "slide_type": "" }, "tags": [ "print_tsys", "test" ] }, "source": [ "## Gridding\n", "\n", "To grid GBT data GBO supports the use of the [`gbtgridder`](https://github.com/GreenBankObservatory/gbtgridder).\n", "This is not included as part of `dysh`.\n", "\n", "If you don't have it installed, here's a super short blurb how to get it, with the note that it is important to get the correct release branch at the time of this writing.\n", "\n", "```bash\n", "git clone -b release_3.0 https://github.com/GreenBankObservatory/gbtgridder\n", "cd gbtgridder\n", "pip install .\n", "```\n", "\n", "This was the situation in the summer of 2025, and it may change, be sure to be in touch with the `gbtgridder` developers.\n", "\n", "After installation, either from the shell, or from the notebook, one can grid as follows:\n", "\n", "```bash\n", "gbtgridder --size 32 32 --channels 500:3500 -o otf --auto otf_calibrated.fits\n", "```\n", "This is telling the gridder to produce an output cube with 32 by 32 pixels using only channels between 500 and 3500, and to use \"otf\" as the name of the output files. The `--auto` part is to skip a confirmation prompt. The last argument, `otf_calibrated.fits`, is the input SDFITS (which was created in the previous cell).\n", "This call to the `gbtgridder` will create two files: `otf_cube.fits` and `otf_weight.fits`.\n", "The first file contains the gridded data, and the second the weights used during the gridding process.\n", "\n", "### Working with Cubes\n", "\n", "dysh is not meant to work with image cubes, there are plenty of great tools for this.\n", "Here we show how to use `astropy` to load the FITS cube and visualize its contents.\n", "We use `astropy.io.fits` to load the data, `astropy.wcs.WCS` to generate a World Coordinate System (WCS) representation out of the FITS header, this is handy for plotting.\n", "And, `matplotlib.pyplot` to plot." ] }, { "cell_type": "code", "execution_count": 24, "id": "a45686f2-1578-40c6-8a3b-ea292136ad7d", "metadata": {}, "outputs": [], "source": [ "from astropy.io import fits\n", "from astropy.wcs import WCS\n", "import matplotlib.pyplot as plt" ] }, { "cell_type": "markdown", "id": "80fef28f-4c14-451c-b537-0386dd7a5fb5", "metadata": {}, "source": [ "Before proceeding we download the cube, in case you did not make your own.\n", "\n", "Note that this cube was made using data which had the baseline subtracted using a per integration model. \n", "If you generate your own cubes, there might be differences with respect to the cube used here." ] }, { "cell_type": "code", "execution_count": 25, "id": "a6bd1d23-322c-4371-9390-630d0ccb449e", "metadata": {}, "outputs": [], "source": [ "cube_filename = dysh_data(example='mapping-L/outputs/otf_cube.fits')" ] }, { "cell_type": "markdown", "id": "bba1db93-518a-456b-9f27-c3b4bc3a4550", "metadata": {}, "source": [ "Open the FITS cube, extract the data and header and then create a WCS object." ] }, { "cell_type": "code", "execution_count": 26, "id": "55913d47-0985-4bf5-bdc8-857686f78f3d", "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "WARNING: FITSFixedWarning: 'datfix' made the change 'Set MJD-OBS to 57890.224583 from DATE-OBS'. [astropy.wcs.wcs]\n" ] } ], "source": [ "with fits.open(cube_filename) as hdu:\n", " data = hdu[0].data\n", " head = hdu[0].header\n", "wcs = WCS(head)" ] }, { "cell_type": "markdown", "id": "3c098d58-d8da-4a5b-beff-84c0b72c8aa2", "metadata": {}, "source": [ "The data object is an array with 4 dimensions, the first being the Stokes axis, the second the spectral axis, the third the latitude (e.g., Dec), and the fourth the longitude (e.g., RA). Altough the FITS cube has a Stokes axis, the `gbtgridder` does not handle polarizations properly, so if you have multiple polarizations in the input SDFITS file(s), the `gbtgridder` will average them.\n", "\n", "To plot the spectrum from the center pixel we use." ] }, { "cell_type": "code", "execution_count": 27, "id": "de77a16f-36e7-467f-af0f-2d880f292b1e", "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/home/teuben/GBT/anaconda3/lib/python3.13/site-packages/traitlets/traitlets.py:1385: DeprecationWarning: Passing unrecognized arguments to super(Toolbar).__init__().\n", "NavigationToolbar2WebAgg.__init__() missing 1 required positional argument: 'canvas'\n", "This is deprecated in traitlets 4.2.This error will be raised in a future release of traitlets.\n", " warn(\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "0a80378a6b2b483492283cbd8a9660d6", "version_major": 2, "version_minor": 0 }, "image/png": 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", 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\n", " " ], "text/plain": [ "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.figure()\n", "plt.plot(data[0,:,16,16])\n", "plt.show()" ] }, { "cell_type": "markdown", "id": "d9e6eca2-135c-42f0-a3f3-78fa9b68a489", "metadata": {}, "source": [ "Now plot an image with the mean of the spectral axis.\n", "We use the projection argument of `fig.add_subplot` to tell `matplotlib` to use the projection defined by the WCS object.\n", "This takes care of using sky coordinates in the figure.\n", "While plotting, `imshow`, we tell `matplotlib` to put the origin of the data, pixel (0,0), in the lower left corner (`origin=\"lower\"`), and to automatically adjust the aspect ratio (`aspect=\"auto\"`) for the axes (not really needed since the data is already a square)." ] }, { "cell_type": "code", "execution_count": 28, "id": "4a581590-b4f3-48c7-a128-d84328ccd11c", 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", 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\n", " " ], "text/plain": [ "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig = plt.figure(dpi=150)\n", "ax = fig.add_subplot(111, projection=wcs.celestial)\n", "ax.imshow(data[0].mean(axis=0), origin=\"lower\", aspect=\"auto\")\n", "plt.show()" ] }, { "cell_type": "markdown", "id": "6f458223-50c5-41d3-8f81-fa41079e8717", "metadata": {}, "source": [ "Plot the data for channel 1500. \n", "We use the celestial representation of the WCS object (`wcs.celestial`) to ignore the non celestial axes (e.g., Stokes and spectral)." ] }, { "cell_type": "code", "execution_count": 29, "id": "8ca950bc-e218-4fa5-a1d4-2f63731e2829", "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/home/teuben/GBT/anaconda3/lib/python3.13/site-packages/traitlets/traitlets.py:1385: DeprecationWarning: Passing unrecognized arguments to super(Toolbar).__init__().\n", "NavigationToolbar2WebAgg.__init__() missing 1 required positional argument: 'canvas'\n", "This is deprecated in traitlets 4.2.This error will be raised in a future release of traitlets.\n", " warn(\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "e9e69ee891f840eea08ea0a659b95858", "version_major": 2, "version_minor": 0 }, "image/png": 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In this case the first WCS axis (longitude) goes in the y-axis of the figure, the latitude is fixed to its value at pixel 16, the third dimension (spectral axis) is shown in the x-axis, and the last dimensions (Stokes) is fixed to its value at pixel 0 (the only possibility in this case).\n", "\n", "\n", "See these links for more details on how to use `astropy` for plotting cubes with multiple WCS axes: [link1](https://docs.astropy.org/en/stable/visualization/wcsaxes/slicing_datacubes.html#slicing-the-wcs-object), and [link2](https://docs.astropy.org/en/stable/api/astropy.visualization.wcsaxes.WCSAxes.html#astropy.visualization.wcsaxes.WCSAxes).\n" ] }, { "cell_type": "code", "execution_count": 30, "id": "815f50f7-b3cf-471d-b2ab-2392a3dfada1", "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/home/teuben/GBT/anaconda3/lib/python3.13/site-packages/traitlets/traitlets.py:1385: DeprecationWarning: Passing unrecognized arguments to super(Toolbar).__init__().\n", "NavigationToolbar2WebAgg.__init__() missing 1 required positional argument: 'canvas'\n", "This is deprecated in traitlets 4.2.This error will be raised in a future release of traitlets.\n", " warn(\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "404c20b6260648aa8745bb4cab61c7e0", "version_major": 2, "version_minor": 0 }, "image/png": 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", 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