{ "cells": [ { "cell_type": "markdown", "id": "fc6520b1", "metadata": {}, "source": [ "# Instruments\n", "\n", "While `opticam` was primarily designed for use with the OPTICAM-MX instrument, it includes an interface for compatibility with other instruments. In this notebook, we will explore this interface.\n", "\n", "## Generating observations from a new instrument\n", "\n", "First, let's generate some observations that are not compatible with the instruments currently supported by `opticam`. For the data, we'll generate a 1 s $U$-band exposure from an instrument that has a gain of 100 electrons/ADU. We'll set the time of the observation to 00:00 on January 1st, 2026, with a pointing of 0 RA, 0 DEC:" ] }, { "cell_type": "code", "execution_count": 1, "id": "fa88898c", "metadata": { "execution": { "iopub.execute_input": "2026-02-16T18:31:04.797357Z", "iopub.status.busy": "2026-02-16T18:31:04.797273Z", "iopub.status.idle": "2026-02-16T18:31:05.217595Z", "shell.execute_reply": "2026-02-16T18:31:05.217183Z" } }, "outputs": [], "source": [ "from pathlib import Path\n", "\n", "from photutils.datasets import make_100gaussians_image\n", "from astropy.io import fits\n", "import numpy as np\n", "\n", "\n", "out_dir = Path('out')\n", "\n", "image = make_100gaussians_image()\n", "\n", "hdu = fits.PrimaryHDU(image) # image data\n", "\n", "hdu.header['FILTER'] = 'U' # the observation filter\n", "hdu.header['BINNING'] = '1 1' # the image binning (i.e., 1x1)\n", "hdu.header['GAIN'] = 100. # the detector gain in adu/photon\n", "hdu.header['EXPTIME'] = 1. # the exposure time in seconds\n", "hdu.header['RA'] = '0' # the RA of the image in degrees\n", "hdu.header['DEC'] = '0' # the DEC of the image in degrees\n", "hdu.header[\"DATE-OBS\"] = f\"2026-01-01T00:00:00.000\" # observation timestamp\n", "hdu.header['INSTRUME'] = 'SYNTHETICAM' # name of the instrument\n", "hdu.header['RDNOISE'] = 0. # the instrument's read noise in electrons/pixel\n", "\n", "save_path = out_dir / 'data' / 'simple' # directory to which image will be saved\n", "\n", "# create save directory if it does not already exist\n", "if not save_path.is_dir():\n", " save_path.mkdir(parents=True)\n", "\n", "# save image\n", "file_path = save_path / 'image.fits.gz'\n", "hdu.writeto(\n", " file_path,\n", " overwrite=True,\n", " )" ] }, { "cell_type": "markdown", "id": "3695f8aa", "metadata": {}, "source": [ "Let's take a look at one of these images:" ] }, { "cell_type": "code", "execution_count": 2, "id": "ed49c0f3", "metadata": { "execution": { "iopub.execute_input": "2026-02-16T18:31:05.219205Z", "iopub.status.busy": "2026-02-16T18:31:05.219094Z", "iopub.status.idle": "2026-02-16T18:31:05.449960Z", "shell.execute_reply": "2026-02-16T18:31:05.449639Z" } }, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "from astropy.visualization import simple_norm\n", "from matplotlib import pyplot as plt\n", "\n", "\n", "# get image data from file\n", "with fits.open(file_path) as hdul:\n", " data = np.asarray(hdul[0].data, dtype=np.float64)\n", "\n", "fig, ax = plt.subplots()\n", "\n", "im = ax.imshow(\n", " data,\n", " norm=simple_norm(data, stretch='log'),\n", " cmap='Greys',\n", " origin='lower',\n", " )\n", "\n", "plt.show()" ] }, { "cell_type": "markdown", "id": "ac17ddc2", "metadata": {}, "source": [ "The image looks good.\n", "\n", "Let's see what happens when we try to use the OPTICAM-MX instrument to reduce these data. Before performing reduction with a particular instrument, it's a good idea to call the instrument's `run_checks()` method. This method takes a path to an image and runs a series of checks to ensure that the data are consistent with those produced by the instrument:" ] }, { "cell_type": "code", "execution_count": 3, "id": "130e118a", "metadata": { "execution": { "iopub.execute_input": "2026-02-16T18:31:05.451205Z", "iopub.status.busy": "2026-02-16T18:31:05.451081Z", "iopub.status.idle": "2026-02-16T18:31:06.585796Z", "shell.execute_reply": "2026-02-16T18:31:06.585455Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[OPTICAM] Checking instrument OPTICAM_MX.\n", "[OPTICAM] ERROR: OPTICAM_MX.exptime_kw (EXPOSURE) is not a valid header keyword for file /tmp/tmpczx0sqcu/out/data/simple/image.fits.gz extension 0.\n", "[OPTICAM] ERROR: OPTICAM_MX.dateobs_kw (UT) is not a valid header keyword for file /tmp/tmpczx0sqcu/out/data/simple/image.fits.gz extension 0.\n", "[OPTICAM] ERROR: OPTICAM_MX.pixel_scales does not contain a corresponding value for the camera None.\n", "[OPTICAM] ERROR: Failed to parse the MJD of the image due the following exception: \"Keyword 'UT' not found.\". This is likely due to an incorrect keyword being passed to dateobs_kw and/or your images do not give timestamps in FITS format. In the latter case, you will need to define a custom instrument with a custom get_mjd() method. See https://opticam.readthedocs.io/en/latest/_executed/instruments.html#Defining-an-instrument-from-the-opticam.Instrument-base-class for details.\n", "[OPTICAM] WARNING: OPTICAM_MX.dark_curr_kw (DARKCURR) is not a valid header keyword for file /tmp/tmpczx0sqcu/out/data/simple/image.fits.gz extension 0. If no dark current is listed in the image headers, you will need to use a `opticam.DarkNoiseCorrector` instance to correct for dark noise. See https://opticam.readthedocs.io/en/latest/_executed/applying_corrections.html#Dark-noise for details.\n", "[OPTICAM] OPTICAM_MX failed 4 checks.\n", "[OPTICAM] OPTICAM_MX triggered a warning during 1 check. Warnings may be ignored provided their caveats are satisfied.\n" ] } ], "source": [ "import opticam\n", "\n", "\n", "instrument = opticam.OPTICAM_MX()\n", "instrument.run_checks(file_path)" ] }, { "cell_type": "markdown", "id": "3981e974", "metadata": {}, "source": [ "In this case, we can see that we have several errors, and one warning:\n", "- The first error is caused by `OPTICAM_MX` using a different header keyword for the image's exposure time. Typically, the keyword \"EXPTIME\" is used to identify the image's exposure time; this is the case for these data. However, `OPTICAM_MX` uses the \"EXPOSURE\" keyword instead.\n", "- The second error is again caused by a header keyword mismatch, this time for the timestamp keyword. Typically, the keyword \"DATE-OBS\" is used to identify the image's timestamp; this is the case for these data. However, `OPTICAM_MX` uses the \"UT\" keyword instead.\n", "- The third error is due to the data using the $U$-band filter, which is not supported by `OPTICAM_MX` and therefore has no corresponding pixel scale.\n", "- The final error is related to the second error: the Modified Julian Date (MJD) of the image cannot be inferred because of the timestamp keyword mismatch between these data and `OPTICAM_MX`.\n", "- The warning is due to these data not containing a \"DARKCURR\" keyword in the header. `opticam` can use the dark current value (represented by the header keyword \"DARKCURR\") to infer and subtract the dark noise from the image. If this keyword does not exist, then dark noise can be removed using a `DarkNoiseCorrector` instance instead (see the [corrections tutorial](applying_corrections.ipynb) for more details). This warning can therefore be ignored in this case.\n", "\n", "There are also a number of other issues with using `OPTICAM_MX` that didn't result in any errors being raised. Let's look at `OPTICAM_MX` to see what the instrument represents:" ] }, { "cell_type": "code", "execution_count": 4, "id": "8681a461", "metadata": { "execution": { "iopub.execute_input": "2026-02-16T18:31:06.586717Z", "iopub.status.busy": "2026-02-16T18:31:06.586561Z", "iopub.status.idle": "2026-02-16T18:31:06.588224Z", "shell.execute_reply": "2026-02-16T18:31:06.588037Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "OPTICAM_MX(location=, pixel_scales={'1': 0.1397, '2': 0.1406, '3': 0.1661}, binning_kw='BINNING', camera_kw='INSTRUME', dark_curr_kw='DARKCURR', dateobs_kw='UT', dec_kw='DEC', exptime_kw='EXPOSURE', filter_kw='FILTER', gain_kw='GAIN', ra_kw='RA', read_noise_kw='RDNOISE')\n" ] } ], "source": [ "print(instrument)" ] }, { "cell_type": "markdown", "id": "9babaf8c", "metadata": {}, "source": [ "We can see that `OPTICAM_MX` has a predefined position, a dictionary of pixel scales for each possible filter, a read noise value, and a number of header keywords. Many of these parameters are likely not be representative of our new instrument. Notably, location and read noise values. The location of the instrument is used when applying Barycentric corrections, and the read noise is used to calculate the total photometric error. Our new instrument therefore needs to redefine many of these parameters for accurate results.\n", "\n", "There are two ways to define a new instrument. The simplest is to generate a configuration file template, edit the necessary values, and then use that configuration file to define a new instrument. This works well provided the instrument adheres to the conventional FITS header keywords (discussed in more detail below) and represents values in FITS format. If this is not the case, then you will need to define your instrument as a class that inherits from the `opticam.Instrument` base class.\n", "\n", "## Defining an instrument from a configuration file\n", "\n", "In this case, our data use conventional FITS header keywords and all values are given in FITS format. Explicitly:\n", "- The corresponding filter is given by the \"FILTER\" keyword.\n", "- The image binning is given by the \"BINNING\" keyword.\n", "- The detector's gain value is given by the \"GAIN\" keyword in electrons/ADU.\n", "- The corresponding exposure time is given by the \"EXPTIME\" keyword in seconds.\n", "- The corresponding RA is given by the \"RA\" keyword.\n", "- The corresponding DEC is given by the \"DEC\" keyword.\n", "- The corresponding timestamp is given by the \"DATE-OBS\" keyword in FITS format (YYYY-MM-DDTHH:MM:SS.sss).\n", "\n", "If any of these values were given by different keywords or were in different units/formats, then it would be necessary to define the instrument using a custom class instead of from configuration file.\n", "\n", "To create a configuration file, it is suggested to generate and then edit a configuration template. Configuration templates can be generated using the `generate_instrument_json_template()` function. This function takes a directory path and writes an `instrument_template.json` file to that directory:" ] }, { "cell_type": "code", "execution_count": 5, "id": "eca47b77", "metadata": { "execution": { "iopub.execute_input": "2026-02-16T18:31:06.589030Z", "iopub.status.busy": "2026-02-16T18:31:06.588960Z", "iopub.status.idle": "2026-02-16T18:31:06.590278Z", "shell.execute_reply": "2026-02-16T18:31:06.590070Z" } }, "outputs": [], "source": [ "opticam.generate_instrument_json_template(out_dir)" ] }, { "cell_type": "markdown", "id": "d61aa57a", "metadata": {}, "source": [ "Let's take a look at the contents of this template:" ] }, { "cell_type": "code", "execution_count": 6, "id": "b4932189", "metadata": { "execution": { "iopub.execute_input": "2026-02-16T18:31:06.590890Z", "iopub.status.busy": "2026-02-16T18:31:06.590795Z", "iopub.status.idle": "2026-02-16T18:31:06.592699Z", "shell.execute_reply": "2026-02-16T18:31:06.592432Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "longitude\n", "0.0\n", "\n", "_longitude_description\n", "The East longitude of the observatory in degrees.\n", "\n", "latitude\n", "0.0\n", "\n", "_latitude_description\n", "The latitude of the observatory in degrees.\n", "\n", "height\n", "0.0\n", "\n", "_height_description\n", "The height of the observatory in meters.\n", "\n", "pixel_scales\n", "{'filter_1': 0.0, 'filter_2': 0.0}\n", "\n", "_pixel_scales_description\n", "The pixel-scale in arcsec/pixel for each filter.\n", "\n", "read_noise_kw\n", "RDNOISE\n", "\n", "_read_noise_description\n", "The header keyword that corresponds to the detector's readout noise in electrons/pixel.\n", "\n", "binning_kw\n", "BINNING\n", "\n", "_binning_kw_description\n", "The header keyword that corresponds to the binning mode.\n", "\n", "dark_curr_kw\n", "DARKCURR\n", "\n", "_dark_curr_kw_description\n", "The header keyword that corresponds to the detector's dark current in electrons/pixel/s.\n", "\n", "exptime_kw\n", "EXPTIME\n", "\n", "_exptime_kw_description\n", "The header keyword that corresponds to the exposure time in seconds.\n", "\n", "filter_kw\n", "FILTER\n", "\n", "_filter_kw_description\n", "The header keyword that corresponds to the image filter.\n", "\n", "gain_kw\n", "GAIN\n", "\n", "_gain_kw_description\n", "The header keyword that corresponds to the detector's gain value in electrons/ADU.\n", "\n", "dateobs_kw\n", "DATE-OBS\n", "\n", "_dateobs_kw_description\n", "The header keyword that corresponds to the image's timestamp in ISO 8601/FITS format (i.e., YYYY-MM-DDTHH:MM:SS[.sss]). If your instrument does not give timestamps in this format, you will need to define the instrument with a custom get_mjd() method. See https://opticam.readthedocs.io/en/latest/_executed/instruments.html#Defining-an-instrument-from-the-opticam.Instrument-base-class for details.\n", "\n", "ra_kw\n", "RA\n", "\n", "_ra_kw_description\n", "The header keyword that corresponds to the image's RA in units of hour angle. If your instrument does not give the RA in units of hour angle, you will need to define the instrument with a custom get_sky_coord() method. See https://opticam.readthedocs.io/en/latest/autoapi/opticam/instruments/index.html#opticam.instruments.Instrument.get_sky_coord for details.\n", "\n", "dec_kw\n", "DEC\n", "\n", "_dec_kw_description\n", "The header keyword that corresponds to the image's DEC in units of degrees. If your instrument does not give the DEC in units of degrees, you will need to define the instrument with a custom get_sky_coord() method. See https://opticam.readthedocs.io/en/latest/autoapi/opticam/instruments/index.html#opticam.instruments.Instrument.get_sky_coord for details.\n", "\n" ] } ], "source": [ "import json\n", "\n", "\n", "with open(out_dir / 'instrument_template.json', 'r') as file:\n", " config_dict = json.load(file)\n", "\n", "for k, v in config_dict.items():\n", " print(k)\n", " print(v)\n", " print()" ] }, { "cell_type": "markdown", "id": "0bae2cb3", "metadata": {}, "source": [ "As we can see, the template lists a number of parameters that can be configured, including descriptions for each parameter. In practise, one would probably edit this template in their preferred text editor. For demonstrative purposes, however, I will edit the dictionary in this notebook.\n", "\n", "For this example, I'll ignore the location parameters (longitude, latitude, and height), as well as the read noise, since these values vary on an instrument-by-instrument basis. The only parameter that needs changing to prevent errors is `pixel_scales`, since the `exptime_kw` and `timestamp_kw` parameters of the template are already set to the conventional \"EXPTIME\" and \"DATE-OBS\" values, respectively:" ] }, { "cell_type": "code", "execution_count": 7, "id": "89a606c9", "metadata": { "execution": { "iopub.execute_input": "2026-02-16T18:31:06.593328Z", "iopub.status.busy": "2026-02-16T18:31:06.593261Z", "iopub.status.idle": "2026-02-16T18:31:06.594482Z", "shell.execute_reply": "2026-02-16T18:31:06.594288Z" } }, "outputs": [], "source": [ "config_dict['pixel_scales'] = {\n", " 'SYNTHETICAM': 1.0, # 1.0 arcsec/pixel\n", " }" ] }, { "cell_type": "markdown", "id": "42384d84", "metadata": {}, "source": [ "Let's save this edited config file under a new name:" ] }, { "cell_type": "code", "execution_count": 8, "id": "f3cffb28", "metadata": { "execution": { "iopub.execute_input": "2026-02-16T18:31:06.595016Z", "iopub.status.busy": "2026-02-16T18:31:06.594952Z", "iopub.status.idle": "2026-02-16T18:31:06.596290Z", "shell.execute_reply": "2026-02-16T18:31:06.596115Z" } }, "outputs": [], "source": [ "with open(out_dir / 'new_instrument_config.json', 'w') as file:\n", " json.dump(\n", " config_dict,\n", " file,\n", " indent=4,\n", " )" ] }, { "cell_type": "markdown", "id": "60a2848f", "metadata": {}, "source": [ "Now we can use this file to create our instrument using the `from_json()` method of `opticam.Instrument`. This method takes either a path to a config JSON file, or a config can be passed directly:" ] }, { "cell_type": "code", "execution_count": 9, "id": "eef74d7a", "metadata": { "execution": { "iopub.execute_input": "2026-02-16T18:31:06.596908Z", "iopub.status.busy": "2026-02-16T18:31:06.596843Z", "iopub.status.idle": "2026-02-16T18:31:06.599010Z", "shell.execute_reply": "2026-02-16T18:31:06.598798Z" } }, "outputs": [ { "data": { "text/plain": [ "True" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# using the saved config file\n", "new_instrument = opticam.Instrument.from_json(file_path=out_dir / 'new_instrument_config.json')\n", "\n", "# using the config dictionary\n", "new_instrument_alt = opticam.Instrument.from_json(config=config_dict)\n", "\n", "new_instrument == new_instrument_alt" ] }, { "cell_type": "code", "execution_count": 10, "id": "f1216a82", "metadata": { "execution": { "iopub.execute_input": "2026-02-16T18:31:06.599628Z", "iopub.status.busy": "2026-02-16T18:31:06.599528Z", "iopub.status.idle": "2026-02-16T18:31:06.600789Z", "shell.execute_reply": "2026-02-16T18:31:06.600605Z" }, "tags": [ "remove-cell" ] }, "outputs": [], "source": [ "assert new_instrument == new_instrument_alt" ] }, { "cell_type": "markdown", "id": "1a3f36ec", "metadata": {}, "source": [ "Let's take a look at our new instrument and check it's using the correct values:" ] }, { "cell_type": "code", "execution_count": 11, "id": "0aa625a2", "metadata": { "execution": { "iopub.execute_input": "2026-02-16T18:31:06.601419Z", "iopub.status.busy": "2026-02-16T18:31:06.601352Z", "iopub.status.idle": "2026-02-16T18:31:06.602765Z", "shell.execute_reply": "2026-02-16T18:31:06.602563Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Instrument(location=, pixel_scales={'SYNTHETICAM': 1.0}, binning_kw='BINNING', camera_kw='INSTRUME', dark_curr_kw='DARKCURR', dateobs_kw='DATE-OBS', dec_kw='DEC', exptime_kw='EXPTIME', filter_kw='FILTER', gain_kw='GAIN', ra_kw='RA', read_noise_kw='RDNOISE')\n" ] } ], "source": [ "print(new_instrument)" ] }, { "cell_type": "markdown", "id": "a69d8972", "metadata": {}, "source": [ "Looks good!\n", "\n", "New let's run the instrument checks for this new instrument:" ] }, { "cell_type": "code", "execution_count": 12, "id": "d09d402b", "metadata": { "execution": { "iopub.execute_input": "2026-02-16T18:31:06.603304Z", "iopub.status.busy": "2026-02-16T18:31:06.603236Z", "iopub.status.idle": "2026-02-16T18:31:06.608648Z", "shell.execute_reply": "2026-02-16T18:31:06.608412Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[OPTICAM] Checking instrument Instrument.\n", "[OPTICAM] WARNING: Instrument.dark_curr_kw (DARKCURR) is not a valid header keyword for file /tmp/tmpczx0sqcu/out/data/simple/image.fits.gz extension 0. If no dark current is listed in the image headers, you will need to use a `opticam.DarkNoiseCorrector` instance to correct for dark noise. See https://opticam.readthedocs.io/en/latest/_executed/applying_corrections.html#Dark-noise for details.\n", "[OPTICAM] Instrument sucessfully passed all checks.\n", "[OPTICAM] Instrument triggered a warning during 1 check. Warnings may be ignored provided their caveats are satisfied.\n" ] } ], "source": [ "new_instrument.run_checks(file_path)" ] }, { "cell_type": "markdown", "id": "5624df97", "metadata": {}, "source": [ "No more errors! We still have a warning about the dark current keyword, but we can safely ignore this for now.\n", "\n", "As noted above, creating instruments from a configuration template works well provided the instrument follows the convetional FITS header structure. Now let's take a look at what to do if this is not the case:" ] }, { "cell_type": "code", "execution_count": 13, "id": "5cc9d969", "metadata": { "execution": { "iopub.execute_input": "2026-02-16T18:31:06.609309Z", "iopub.status.busy": "2026-02-16T18:31:06.609239Z", "iopub.status.idle": "2026-02-16T18:31:06.657018Z", "shell.execute_reply": "2026-02-16T18:31:06.656690Z" } }, "outputs": [], "source": [ "from astropy.time import Time\n", "\n", "image = make_100gaussians_image()\n", "\n", "hdu = fits.PrimaryHDU(image) # image data\n", "\n", "hdu.header['FILTER'] = 'I' # the observation filter\n", "hdu.header['BINNING'] = '2x2' # the image binning\n", "hdu.header['GAIN'] = 500. # the detector gain in adu/photon\n", "hdu.header['EXPOSURE'] = 1. # the exposure time in seconds\n", "hdu.header['RA'] = '12 30 49.4' # the RA of the image in degrees\n", "hdu.header['DEC'] = '+12 23 28.0' # the DEC of the image in degrees\n", "hdu.header['INSTRUME'] = 'SYNTHETICAM' # the instrument (synthetic camera)\n", "hdu.header['RDNOISE'] = 0. # the instrument's read noise in electrons/pixel\n", "\n", "\n", "fits_time = f\"2026-01-01T00:00:00.000\" # observation timestamp\n", "mjd_time = float(np.asarray(Time(fits_time, format='fits').mjd)) # convert time from FITS format to MJD\n", "hdu.header['DATE-OBS'] = mjd_time\n", "\n", "save_path = out_dir / 'data' / 'custom' # directory to which image will be saved\n", "\n", "# create save directory if it does not already exist\n", "if not save_path.is_dir():\n", " save_path.mkdir(parents=True)\n", "\n", "# save image\n", "new_file_path = save_path / 'image.fits.gz'\n", "hdu.writeto(\n", " new_file_path,\n", " overwrite=True,\n", " )" ] }, { "cell_type": "markdown", "id": "c436f9cc", "metadata": {}, "source": [ "These new data are very similar to the previous example, but replace the \"EXPTIME\" keyword with \"EXPOSURE\", and store the image's timestamp in MJD format instead of the conventional FITS format.\n", "\n", "Let's look at the header of this image:" ] }, { "cell_type": "code", "execution_count": 14, "id": "7d25c66e", "metadata": { "execution": { "iopub.execute_input": "2026-02-16T18:31:06.658120Z", "iopub.status.busy": "2026-02-16T18:31:06.658051Z", "iopub.status.idle": "2026-02-16T18:31:06.663205Z", "shell.execute_reply": "2026-02-16T18:31:06.662902Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "SIMPLE = T / conforms to FITS standard \n", "BITPIX = -64 / array data type \n", "NAXIS = 2 / number of array dimensions \n", "NAXIS1 = 500 \n", "NAXIS2 = 300 \n", "EXTEND = T \n", "FILTER = 'I ' \n", "BINNING = '2x2 ' \n", "GAIN = 500.0 \n", "EXPOSURE= 1.0 \n", "RA = '12 30 49.4' \n", "DEC = '+12 23 28.0' \n", "INSTRUME= 'SYNTHETICAM' \n", "RDNOISE = 0.0 \n", "DATE-OBS= 61041.0 \n" ] } ], "source": [ "header = fits.getheader(new_file_path)\n", "print(repr(header))" ] }, { "cell_type": "markdown", "id": "beb2c96b", "metadata": {}, "source": [ "Predictably, these new data will fail with both `OPTICAM_MX` and our newly created instrument:" ] }, { "cell_type": "code", "execution_count": 15, "id": "b3493c6c", "metadata": { "execution": { "iopub.execute_input": "2026-02-16T18:31:06.663861Z", "iopub.status.busy": "2026-02-16T18:31:06.663793Z", "iopub.status.idle": "2026-02-16T18:31:06.668936Z", "shell.execute_reply": "2026-02-16T18:31:06.668675Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[OPTICAM] Checking instrument OPTICAM_MX.\n", "[OPTICAM] ERROR: OPTICAM_MX.dateobs_kw (UT) is not a valid header keyword for file /tmp/tmpczx0sqcu/out/data/custom/image.fits.gz extension 0.\n", "[OPTICAM] ERROR: OPTICAM_MX.pixel_scales does not contain a corresponding value for the camera None.\n", "[OPTICAM] ERROR: Failed to parse the MJD of the image due the following exception: \"Keyword 'UT' not found.\". This is likely due to an incorrect keyword being passed to dateobs_kw and/or your images do not give timestamps in FITS format. In the latter case, you will need to define a custom instrument with a custom get_mjd() method. See https://opticam.readthedocs.io/en/latest/_executed/instruments.html#Defining-an-instrument-from-the-opticam.Instrument-base-class for details.\n", "[OPTICAM] WARNING: OPTICAM_MX.dark_curr_kw (DARKCURR) is not a valid header keyword for file /tmp/tmpczx0sqcu/out/data/custom/image.fits.gz extension 0. If no dark current is listed in the image headers, you will need to use a `opticam.DarkNoiseCorrector` instance to correct for dark noise. See https://opticam.readthedocs.io/en/latest/_executed/applying_corrections.html#Dark-noise for details.\n", "[OPTICAM] OPTICAM_MX failed 3 checks.\n", "[OPTICAM] OPTICAM_MX triggered a warning during 1 check. Warnings may be ignored provided their caveats are satisfied.\n" ] } ], "source": [ "instrument.run_checks(new_file_path)" ] }, { "cell_type": "code", "execution_count": 16, "id": "f7ccbc3e", "metadata": { "execution": { "iopub.execute_input": "2026-02-16T18:31:06.669563Z", "iopub.status.busy": "2026-02-16T18:31:06.669497Z", "iopub.status.idle": "2026-02-16T18:31:06.674794Z", "shell.execute_reply": "2026-02-16T18:31:06.674457Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[OPTICAM] Checking instrument Instrument.\n", "[OPTICAM] ERROR: Instrument.exptime_kw (EXPTIME) is not a valid header keyword for file /tmp/tmpczx0sqcu/out/data/custom/image.fits.gz extension 0.\n", "[OPTICAM] ERROR: Failed to parse the MJD of the image due the following exception: Input values did not match the format class fits:\n", "ValueError: Time 61041.0 does not match fits format. This is likely due to an incorrect keyword being passed to dateobs_kw and/or your images do not give timestamps in FITS format. In the latter case, you will need to define a custom instrument with a custom get_mjd() method. See https://opticam.readthedocs.io/en/latest/_executed/instruments.html#Defining-an-instrument-from-the-opticam.Instrument-base-class for details.\n", "[OPTICAM] WARNING: Instrument.dark_curr_kw (DARKCURR) is not a valid header keyword for file /tmp/tmpczx0sqcu/out/data/custom/image.fits.gz extension 0. If no dark current is listed in the image headers, you will need to use a `opticam.DarkNoiseCorrector` instance to correct for dark noise. See https://opticam.readthedocs.io/en/latest/_executed/applying_corrections.html#Dark-noise for details.\n", "[OPTICAM] Instrument failed 2 checks.\n", "[OPTICAM] Instrument triggered a warning during 1 check. Warnings may be ignored provided their caveats are satisfied.\n" ] } ], "source": [ "new_instrument.run_checks(new_file_path)" ] }, { "cell_type": "markdown", "id": "ebe22897", "metadata": {}, "source": [ "## Defining an instrument from the opticam.Instrument base class\n", "\n", "To reduce these data, we cannot create a new instrument from a configuration file because the timestamps are not in FITS format. Instead, we need to define a custom class that inherits from `opticam.Instrument` and implement a custom `get_mjd()` method:" ] }, { "cell_type": "code", "execution_count": 17, "id": "a97b07bc", "metadata": { "execution": { "iopub.execute_input": "2026-02-16T18:31:06.675626Z", "iopub.status.busy": "2026-02-16T18:31:06.675548Z", "iopub.status.idle": "2026-02-16T18:31:06.677300Z", "shell.execute_reply": "2026-02-16T18:31:06.677057Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Writing custom_instrument.py\n" ] } ], "source": [ "%%writefile custom_instrument.py\n", "\n", "import opticam\n", "\n", "\n", "class CustomInstrument(opticam.Instrument):\n", " \n", " def get_mjd(self, file=None, header=None):\n", " \n", " if header is None:\n", " header = file.get_header()\n", " \n", " return float(header[self.dateobs_kw])" ] }, { "cell_type": "markdown", "id": "90533bee", "metadata": {}, "source": [ "Due to compatibility issues, routines defined inside of IPython notebooks cannot be used by `multiprocessing` (used by `opticam`). To overcome this issue, we have used the `%%writefile` magic command to write our `CustomInstrument` class to a temporary module called `custom_routines.py`, such that it can be imported by `multiprocessing`. This is required in Python $\\geq$ 3.14.\n", "\n", "The `get_mjd()` method of an `Instrument` instance takes two inputs: `file` and `header`. `file` will take a [`opticam.MEFSlice`](https://opticam.readthedocs.io/en/latest/autoapi/opticam/mef_slice/index.html#opticam.mef_slice.MEFSlice) instance, while `header` will take an `astropy.io.fits.Header` instance. `opticam`'s `MEFSlice` class is used internally to represent a single extension of a multi-extension FITS file (or the only extension of a standard FITS file). This allows `opticam` to easily get the header of a FITS HDU by calling the `get_header()` method of `MEFSlice`. When called, `opticam` will only define one of these parameters, depending on whether the image is already open or not, and so both parameters should assume default values (i.e., `None`). The value returned by `get_mjd()` should be the MJD of the image as a `float`.\n", "\n", "In this example, the MJD is given by the header, so it can be returned directly. If the header gives the timestamp in an alternative format, it may be convenient to use the [`astropy.time`](https://docs.astropy.org/en/stable/time/index.html) module to convert it to MJD.\n", "\n", "To instance this `CustomInstrument`, we can again generate and edit a configuration template:" ] }, { "cell_type": "code", "execution_count": 18, "id": "428570b5", "metadata": { "execution": { "iopub.execute_input": "2026-02-16T18:31:06.678023Z", "iopub.status.busy": "2026-02-16T18:31:06.677948Z", "iopub.status.idle": "2026-02-16T18:31:06.680336Z", "shell.execute_reply": "2026-02-16T18:31:06.680081Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "CustomInstrument(location=, pixel_scales={'SYNTHETICAM': 1.0}, binning_kw='BINNING', camera_kw='INSTRUME', dark_curr_kw='DARKCURR', dateobs_kw='DATE-OBS', dec_kw='DEC', exptime_kw='EXPOSURE', filter_kw='FILTER', gain_kw='GAIN', ra_kw='RA', read_noise_kw='RDNOISE')\n" ] } ], "source": [ "from custom_instrument import CustomInstrument\n", "\n", "with open(out_dir / 'instrument_template.json', 'r') as file:\n", " custom_config = json.load(file)\n", "\n", "custom_config['exptime_kw'] = 'EXPOSURE' # change EXPTIME keyword\n", "custom_config['pixel_scales'] = {\n", " 'SYNTHETICAM': 1.0, # 1.0 arcsec/pixel\n", " }\n", "\n", "custom_instrument = CustomInstrument.from_json(config=custom_config)\n", "\n", "print(custom_instrument)" ] }, { "cell_type": "code", "execution_count": 19, "id": "178533e8", "metadata": { "execution": { "iopub.execute_input": "2026-02-16T18:31:06.681041Z", "iopub.status.busy": "2026-02-16T18:31:06.680955Z", "iopub.status.idle": "2026-02-16T18:31:06.686300Z", "shell.execute_reply": "2026-02-16T18:31:06.686002Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[OPTICAM] Checking instrument CustomInstrument.\n", "[OPTICAM] WARNING: CustomInstrument.dark_curr_kw (DARKCURR) is not a valid header keyword for file /tmp/tmpczx0sqcu/out/data/custom/image.fits.gz extension 0. If no dark current is listed in the image headers, you will need to use a `opticam.DarkNoiseCorrector` instance to correct for dark noise. See https://opticam.readthedocs.io/en/latest/_executed/applying_corrections.html#Dark-noise for details.\n", "[OPTICAM] CustomInstrument sucessfully passed all checks.\n", "[OPTICAM] CustomInstrument triggered a warning during 1 check. Warnings may be ignored provided their caveats are satisfied.\n" ] } ], "source": [ "custom_instrument.run_checks(new_file_path)" ] }, { "cell_type": "markdown", "id": "ec75f029", "metadata": {}, "source": [ "Alternatively, we could define the instrument by hardcoding the required parameters, which is a bit more convenient for reusing this instrument many times:" ] }, { "cell_type": "code", "execution_count": 20, "id": "f6bdd5ea", "metadata": { "execution": { "iopub.execute_input": "2026-02-16T18:31:06.686998Z", "iopub.status.busy": "2026-02-16T18:31:06.686921Z", "iopub.status.idle": "2026-02-16T18:31:06.688571Z", "shell.execute_reply": "2026-02-16T18:31:06.688337Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Writing custom_instrument_class.py\n" ] } ], "source": [ "%%writefile custom_instrument_class.py\n", "from astropy.coordinates import EarthLocation\n", "from astropy.io import fits\n", "\n", "import opticam\n", "\n", "\n", "__all__ = ['CustomInstrumentClass']\n", "\n", "\n", "class CustomInstrumentClass(opticam.Instrument):\n", " \n", " def __init__(\n", " self,\n", " location = EarthLocation.from_geodetic(\n", " lon=0.0, # observatory longitude in degrees\n", " lat=0.0, # observatory East latitude in degrees\n", " height=0.0, # observatory height in meters\n", " ),\n", " pixel_scales = {\n", " 'SYNTHETICAM': 1.0,\n", " },\n", " exptime_kw = 'EXPOSURE',\n", " ):\n", " \n", " super().__init__(\n", " location=location,\n", " pixel_scales=pixel_scales,\n", " exptime_kw=exptime_kw,\n", " )\n", " \n", " def get_mjd(\n", " self,\n", " file=None,\n", " header=None,\n", " ):\n", " \n", " if header is None:\n", " header = file.get_header()\n", " \n", " return float(header[self.dateobs_kw])" ] }, { "cell_type": "code", "execution_count": 21, "id": "fd97b458", "metadata": { "execution": { "iopub.execute_input": "2026-02-16T18:31:06.689184Z", "iopub.status.busy": "2026-02-16T18:31:06.689106Z", "iopub.status.idle": "2026-02-16T18:31:06.691412Z", "shell.execute_reply": "2026-02-16T18:31:06.691149Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "CustomInstrumentClass(location=, pixel_scales={'SYNTHETICAM': 1.0}, binning_kw='BINNING', camera_kw='INSTRUME', dark_curr_kw='DARKCURR', dateobs_kw='DATE-OBS', dec_kw='DEC', exptime_kw='EXPOSURE', filter_kw='FILTER', gain_kw='GAIN', ra_kw='RA', read_noise_kw='RDNOISE')\n" ] } ], "source": [ "from custom_instrument_class import CustomInstrumentClass\n", "\n", "custom_instrument_class = CustomInstrumentClass()\n", "\n", "print(custom_instrument_class)" ] }, { "cell_type": "code", "execution_count": 22, "id": "ff0f5ce3", "metadata": { "execution": { "iopub.execute_input": "2026-02-16T18:31:06.692090Z", "iopub.status.busy": "2026-02-16T18:31:06.692019Z", "iopub.status.idle": "2026-02-16T18:31:06.697262Z", "shell.execute_reply": "2026-02-16T18:31:06.697040Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[OPTICAM] Checking instrument CustomInstrumentClass.\n", "[OPTICAM] WARNING: CustomInstrumentClass.dark_curr_kw (DARKCURR) is not a valid header keyword for file /tmp/tmpczx0sqcu/out/data/custom/image.fits.gz extension 0. If no dark current is listed in the image headers, you will need to use a `opticam.DarkNoiseCorrector` instance to correct for dark noise. See https://opticam.readthedocs.io/en/latest/_executed/applying_corrections.html#Dark-noise for details.\n", "[OPTICAM] CustomInstrumentClass sucessfully passed all checks.\n", "[OPTICAM] CustomInstrumentClass triggered a warning during 1 check. Warnings may be ignored provided their caveats are satisfied.\n" ] } ], "source": [ "custom_instrument_class.run_checks(new_file_path)" ] }, { "cell_type": "markdown", "id": "3ccde946", "metadata": {}, "source": [ "While creating custom instrument classes is more advanced than editing a configuration template, it is far more configurable. Additionally, instrument classes only need to be created once, and can then be reused indefinitely (unless the instrument changes the format of its data products). If you write a class for an instrument, we encourage you to consider opening a [pull request](https://github.com/OPTICAM-instrument/opticam/pulls) to merge your instrument into `opticam`.\n", "\n", "## Gotchas\n", "\n", "Below, we discuss some common problems you might encounter when trying to write a custom `Instrument` class for your instrument.\n", "\n", "### My images don't contain a binning keyword. What should I do?\n", "\n", "If your images do not contain a binning keyword, you'll need to fake it by writing a custom `get_binning()` method. If your instrument only supports one binning mode, then the `get_binning()` method can simply return an arbitrary string:" ] }, { "cell_type": "code", "execution_count": 23, "id": "cbae0d74", "metadata": { "execution": { "iopub.execute_input": "2026-02-16T18:31:06.697932Z", "iopub.status.busy": "2026-02-16T18:31:06.697864Z", "iopub.status.idle": "2026-02-16T18:31:06.699351Z", "shell.execute_reply": "2026-02-16T18:31:06.699139Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Writing instrument_without_binning.py\n" ] } ], "source": [ "%%writefile instrument_without_binning.py\n", "import opticam\n", "\n", "\n", "class InstrumentWithoutBinning(opticam.Instrument):\n", " \n", " def get_binning(self, file=None, header=None):\n", " \n", " return '1x1'" ] }, { "cell_type": "code", "execution_count": 24, "id": "cb4311d9", "metadata": { "execution": { "iopub.execute_input": "2026-02-16T18:31:06.699895Z", "iopub.status.busy": "2026-02-16T18:31:06.699828Z", "iopub.status.idle": "2026-02-16T18:31:06.701708Z", "shell.execute_reply": "2026-02-16T18:31:06.701521Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "InstrumentWithoutBinning(location=, pixel_scales={'SYNTHETICAM': 1.0}, binning_kw='BINNING', camera_kw='INSTRUME', dark_curr_kw='DARKCURR', dateobs_kw='DATE-OBS', dec_kw='DEC', exptime_kw='EXPTIME', filter_kw='FILTER', gain_kw='GAIN', ra_kw='RA', read_noise_kw='RDNOISE')\n" ] } ], "source": [ "from instrument_without_binning import InstrumentWithoutBinning\n", "\n", "instrument_without_binning = InstrumentWithoutBinning.from_json(config=config_dict)\n", "\n", "print(instrument_without_binning)" ] }, { "cell_type": "markdown", "id": "e7e62995", "metadata": {}, "source": [ "Like the `get_mjd()` method, the `get_binning()` takes one of two inputs: either `file` or `header`. In this example, however, neither are needed. The value returned by `get_binning()` can be any string that uniquely identifies the binning mode used to create the image.\n", "\n", "Now let's generate an image without a binning keyword:" ] }, { "cell_type": "code", "execution_count": 25, "id": "2648b650", "metadata": { "execution": { "iopub.execute_input": "2026-02-16T18:31:06.702339Z", "iopub.status.busy": "2026-02-16T18:31:06.702271Z", "iopub.status.idle": "2026-02-16T18:31:06.738696Z", "shell.execute_reply": "2026-02-16T18:31:06.738275Z" } }, "outputs": [], "source": [ "image = make_100gaussians_image()\n", "\n", "hdu = fits.PrimaryHDU(image) # image data\n", "\n", "hdu.header['FILTER'] = 'U'\n", "hdu.header['GAIN'] = 1.\n", "hdu.header['EXPTIME'] = 1.\n", "hdu.header['RA'] = '12 30 49.4'\n", "hdu.header['DEC'] = '+12 23 28.0'\n", "hdu.header['DATE-OBS'] = f\"2026-01-01T00:00:00.000\"\n", "hdu.header['INSTRUME'] = 'SYNTHETICAM'\n", "hdu.header['RDNOISE'] = 0.\n", "\n", "new_file_path = out_dir / 'data' / 'no_binnning'\n", "hdu.writeto(\n", " new_file_path,\n", " overwrite=True,\n", " )" ] }, { "cell_type": "markdown", "id": "d5015fd9", "metadata": {}, "source": [ "When we check a previous instrument that would otherwise work:" ] }, { "cell_type": "code", "execution_count": 26, "id": "6db9e360", "metadata": { "execution": { "iopub.execute_input": "2026-02-16T18:31:06.739975Z", "iopub.status.busy": "2026-02-16T18:31:06.739892Z", "iopub.status.idle": "2026-02-16T18:31:06.742917Z", "shell.execute_reply": "2026-02-16T18:31:06.742617Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[OPTICAM] Checking instrument Instrument.\n", "[OPTICAM] ERROR: failed to read image binning for file /tmp/tmpczx0sqcu/out/data/no_binnning extension 0 due to the exception \"Keyword 'BINNING' not found.\" This is either due to an incorrect keyword being passed to binning_kw and/or your images do not contain a binning keyword. In the latter case, you will need to define a custom instrument with a custom get_binning() method. See https://opticam.readthedocs.io/en/latest/_executed/instruments.html#My-images-don't-contain-a-binning-keyword.-What-should-I-do? for details.\n", "[OPTICAM] WARNING: Instrument.dark_curr_kw (DARKCURR) is not a valid header keyword for file /tmp/tmpczx0sqcu/out/data/no_binnning extension 0. If no dark current is listed in the image headers, you will need to use a `opticam.DarkNoiseCorrector` instance to correct for dark noise. See https://opticam.readthedocs.io/en/latest/_executed/applying_corrections.html#Dark-noise for details.\n", "[OPTICAM] Instrument failed 1 check.\n", "[OPTICAM] Instrument triggered a warning during 1 check. Warnings may be ignored provided their caveats are satisfied.\n" ] } ], "source": [ "new_instrument.run_checks(new_file_path)" ] }, { "cell_type": "markdown", "id": "d803fad0", "metadata": {}, "source": [ "We see that an error is raised. With our new instrument, however:" ] }, { "cell_type": "code", "execution_count": 27, "id": "0c8fcb5f", "metadata": { "execution": { "iopub.execute_input": "2026-02-16T18:31:06.743705Z", "iopub.status.busy": "2026-02-16T18:31:06.743631Z", "iopub.status.idle": "2026-02-16T18:31:06.745848Z", "shell.execute_reply": "2026-02-16T18:31:06.745531Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[OPTICAM] Checking instrument InstrumentWithoutBinning.\n", "[OPTICAM] WARNING: InstrumentWithoutBinning.dark_curr_kw (DARKCURR) is not a valid header keyword for file /tmp/tmpczx0sqcu/out/data/no_binnning extension 0. If no dark current is listed in the image headers, you will need to use a `opticam.DarkNoiseCorrector` instance to correct for dark noise. See https://opticam.readthedocs.io/en/latest/_executed/applying_corrections.html#Dark-noise for details.\n", "[OPTICAM] InstrumentWithoutBinning sucessfully passed all checks.\n", "[OPTICAM] InstrumentWithoutBinning triggered a warning during 1 check. Warnings may be ignored provided their caveats are satisfied.\n" ] } ], "source": [ "instrument_without_binning.run_checks(new_file_path)" ] }, { "cell_type": "markdown", "id": "6d24c633", "metadata": {}, "source": [ "We don't get any errors.\n", "\n", "Alternatively, the binning information may be inferrable from other keywords (e.g., `NAXIS1` and `NAXIS2`). In this case, you may want your custom `get_binning()` method to infer the binning mode from these parameters. This way, `opticam` will be able to check that all images in a given directory use the same binning mode.\n", "\n", "For example, if your instrument has a resolution of 2048x2048, then you can infer the binning mode by dividing the instrument's resolution by the image resolution:" ] }, { "cell_type": "code", "execution_count": 28, "id": "13589094", "metadata": { "execution": { "iopub.execute_input": "2026-02-16T18:31:06.746543Z", "iopub.status.busy": "2026-02-16T18:31:06.746472Z", "iopub.status.idle": "2026-02-16T18:31:06.748121Z", "shell.execute_reply": "2026-02-16T18:31:06.747899Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Writing instrument_with_inferred_binning.py\n" ] } ], "source": [ "%%writefile instrument_with_inferred_binning.py\n", "import opticam\n", "\n", "\n", "class InstrumentWithInferredBinning(opticam.Instrument):\n", " \n", " def get_binning(self, file=None, header=None):\n", " \n", " if header is None:\n", " header = file.get_header()\n", " \n", " x_size = int(header['NAXIS1'])\n", " y_size = int(header['NAXIS2'])\n", " \n", " # divide instrument resolution by image resolution\n", " x_binning = 2048 // x_size\n", " y_binning = 2048 // y_size\n", " \n", " return f'{x_binning}x{y_binning}'\n" ] }, { "cell_type": "code", "execution_count": 29, "id": "600e9224", "metadata": { "execution": { "iopub.execute_input": "2026-02-16T18:31:06.748672Z", "iopub.status.busy": "2026-02-16T18:31:06.748605Z", "iopub.status.idle": "2026-02-16T18:31:06.751297Z", "shell.execute_reply": "2026-02-16T18:31:06.751059Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[OPTICAM] Checking instrument InstrumentWithInferredBinning.\n", "[OPTICAM] WARNING: InstrumentWithInferredBinning.dark_curr_kw (DARKCURR) is not a valid header keyword for file /tmp/tmpczx0sqcu/out/data/no_binnning extension 0. If no dark current is listed in the image headers, you will need to use a `opticam.DarkNoiseCorrector` instance to correct for dark noise. See https://opticam.readthedocs.io/en/latest/_executed/applying_corrections.html#Dark-noise for details.\n", "[OPTICAM] InstrumentWithInferredBinning sucessfully passed all checks.\n", "[OPTICAM] InstrumentWithInferredBinning triggered a warning during 1 check. Warnings may be ignored provided their caveats are satisfied.\n" ] } ], "source": [ "from instrument_with_inferred_binning import InstrumentWithInferredBinning\n", "\n", "\n", "instrument_with_inferred_binning = InstrumentWithInferredBinning.from_json(config=config_dict)\n", "instrument_with_inferred_binning.run_checks(new_file_path)" ] }, { "cell_type": "markdown", "id": "92d473ec", "metadata": {}, "source": [ "### My instrument has multiple cameras that use the same filters, what should I do?\n", "\n", "If your instrument has multiple cameras that use the same filters, then you'll have to break the degeneracy by specifying a custom `get_camera()` method. Like `get_binning()`, this method parses an image's header to infer the camera that was used to capture the image; in the simplest case, there would be a \"CAMERA\" header keyword which labels each camera. In the case of OPTICAM-MX, however, the camera is inferred from the filter since there is no filter degeneracy between OPTICAM-MX's cameras.\n", "\n", "In this example, I'll generate images from two different cameras, both of which use a $U$-band filter. For clarity, I'll make the images from the second camera slightly smaller to highlight the difference:" ] }, { "cell_type": "code", "execution_count": 30, "id": "89b472b4", "metadata": { "execution": { "iopub.execute_input": "2026-02-16T18:31:06.751947Z", "iopub.status.busy": "2026-02-16T18:31:06.751879Z", "iopub.status.idle": "2026-02-16T18:31:06.970358Z", "shell.execute_reply": "2026-02-16T18:31:06.969976Z" } }, "outputs": [], "source": [ "image = make_100gaussians_image()\n", "\n", "for i in range(3):\n", " # image from first camera\n", " hdu = fits.PrimaryHDU(image) # image data\n", " hdu.header['FILTER'] = 'U'\n", " hdu.header['CAMERA'] = '1' # camera keyword that uniquely identifies the camera used\n", " hdu.header['BINNING'] = '1x1'\n", " hdu.header['GAIN'] = 1.\n", " hdu.header['DARKCURR'] = .1\n", " hdu.header['EXPTIME'] = 1.\n", " hdu.header['RA'] = '12 30 49.4'\n", " hdu.header['DEC'] = '+12 23 28.0'\n", " hdu.header['DATE-OBS'] = f\"2026-01-01T00:00:0{i}.000\"\n", " hdu.header['INSTRUME'] = 'SYNTHETICAM'\n", " hdu.header['RDNOISE'] = 0.\n", " \n", " \n", " save_path = out_dir / 'data' / 'custom_filters'\n", " \n", " # create save directory if it does not already exist\n", " if not save_path.is_dir():\n", " save_path.mkdir(parents=True)\n", " \n", " new_file_path = save_path / f'camera_1_image_{i}.fits.gz'\n", " hdu.writeto(\n", " new_file_path,\n", " overwrite=True,\n", " )\n", " \n", " # smaller image from second camera\n", " hdu = fits.PrimaryHDU(image[:250, :400]) # image data\n", " \n", " hdu.header['FILTER'] = 'U'\n", " hdu.header['CAMERA'] = '2' # camera keyword that uniquely identifies the camera used\n", " hdu.header['BINNING'] = '1x1'\n", " hdu.header['GAIN'] = 1.\n", " hdu.header['DARKCURR'] = .1\n", " hdu.header['EXPTIME'] = 1.\n", " hdu.header['RA'] = '12 30 49.4'\n", " hdu.header['DEC'] = '+12 23 28.0'\n", " hdu.header['DATE-OBS'] = f\"2026-01-01T00:00:0{i}.000\"\n", " hdu.header['INSTRUME'] = 'SYNTHETICAM'\n", " hdu.header['RDNOISE'] = 0.\n", " \n", " new_file_path = save_path / f'camera_2_image_{i}.fits.gz'\n", " hdu.writeto(\n", " new_file_path,\n", " overwrite=True,\n", " )" ] }, { "cell_type": "markdown", "id": "8d245b3b", "metadata": {}, "source": [ "We now have a total of six images taken with two different cameras that use the same filter. When reading these images, we clearly do not want to mix images between cameras since they have different shapes. We therefore need to define an `Instrument` that can disentagle these images:" ] }, { "cell_type": "code", "execution_count": 31, "id": "932d939d", "metadata": { "execution": { "iopub.execute_input": "2026-02-16T18:31:06.971405Z", "iopub.status.busy": "2026-02-16T18:31:06.971333Z", "iopub.status.idle": "2026-02-16T18:31:06.973043Z", "shell.execute_reply": "2026-02-16T18:31:06.972793Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Writing instrument_with_custom_filter.py\n" ] } ], "source": [ "%%writefile instrument_with_custom_filter.py\n", "from astropy.coordinates import EarthLocation\n", "\n", "import opticam\n", "\n", "\n", "class MulticameraInstrument(opticam.Instrument):\n", " \n", " def __init__(\n", " self,\n", " location = EarthLocation.from_geodetic(\n", " lon=0.0,\n", " lat=0.0,\n", " height=0.0,\n", " ),\n", " pixel_scales = {\n", " '1': 1.0, # pixel scale for camera 1\n", " '2': 1.1, # pixel scale for camera 2\n", " },\n", " ):\n", " \n", " super().__init__(\n", " location=location,\n", " pixel_scales=pixel_scales,\n", " )\n", " \n", " def get_camera(self, file_path=None, header=None):\n", " \n", " if header is None:\n", " header = fits.getheader(file_path)\n", " \n", " # return the unique camera identifier\n", " return str(header['CAMERA'])\n" ] }, { "cell_type": "markdown", "id": "a0efa3b6", "metadata": {}, "source": [ "Let's use this instrument in a `Reducer` to see it in action:" ] }, { "cell_type": "code", "execution_count": 32, "id": "d6e1b6ed", "metadata": { "execution": { "iopub.execute_input": "2026-02-16T18:31:06.973694Z", "iopub.status.busy": "2026-02-16T18:31:06.973619Z", "iopub.status.idle": "2026-02-16T18:31:14.307951Z", "shell.execute_reply": "2026-02-16T18:31:14.307537Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[OPTICAM] out/reduced/custom_filters not found, attempting to create ...\n", "[OPTICAM] out/reduced/custom_filters created.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "\r", "[OPTICAM] Scanning data directory: 0%| |[00:00" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "[OPTICAM] Creating source catalogs.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "\r", "[OPTICAM] Aligning 2:U images: 0%| |[00:00" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "[OPTICAM] Plot saved to /tmp/tmpczx0sqcu/out/reduced/custom_filters/diag/background.pdf.\n" ] }, { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "from instrument_with_custom_filter import MulticameraInstrument\n", "\n", "\n", "custom_filter_reducer = opticam.Reducer(\n", " out_directory=out_dir / 'reduced' / 'custom_filters',\n", " data_directory=out_dir / 'data' / 'custom_filters',\n", " instrument=MulticameraInstrument(),\n", " show_plots=True,\n", " finder=opticam.DefaultFinder(npixels=32),\n", " background=opticam.DefaultBackground(25),\n", " )\n", "\n", "custom_filter_reducer.create_catalogs(overwrite=True)" ] }, { "cell_type": "markdown", "id": "5ca778a9", "metadata": {}, "source": [ "As we can see, the two cameras have been uniquely identified thanks to our custom `get_filter()` method." ] } ], "metadata": { "kernelspec": { "display_name": "opticam6", "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": "3.14.2" } }, "nbformat": 4, "nbformat_minor": 5 }