diff --git a/CHANGES.rst b/CHANGES.rst index 1e408f7f..31515802 100644 --- a/CHANGES.rst +++ b/CHANGES.rst @@ -4,9 +4,17 @@ New Features ^^^^^^^^^^^^ +- The new FitTrace class (see "API Changes" below) introduces the ability to +take a polynomial trace of an image [#128] + API Changes ^^^^^^^^^^^ +- Renamed KosmosTrace as FitTrace, a conglomerate class for traces that are fit +to images instead of predetermined [#128] +- The default number of bins for FitTrace is now its associated image's number +of dispersion pixels instead of 20. Its default peak_method is now 'max'. [#128] + Bug Fixes ^^^^^^^^^ diff --git a/docs/extraction_quickstart.rst b/docs/extraction_quickstart.rst index a1e45c1e..fa261462 100644 --- a/docs/extraction_quickstart.rst +++ b/docs/extraction_quickstart.rst @@ -16,7 +16,7 @@ the remaining steps of the extraction process. Supported trace types include: * `~specreduce.tracing.ArrayTrace` * `~specreduce.tracing.FlatTrace` -* `~specreduce.tracing.KosmosTrace` +* `~specreduce.tracing.FitTrace` Each of these trace classes takes the 2D spectral image as input, as well as additional information @@ -47,17 +47,17 @@ or, equivalently:: bg = specreduce.tracing.Background.one_sided(image, 15, separation=5, width=2) -The background image can be accessed via `~specreduce.background.Background.bkg_image` and the +The background image can be accessed via `~specreduce.background.Background.bkg_image` and the background-subtracted image via `~specreduce.background.Background.sub_image` (or ``image - bg``). -The background and trace steps can be done iteratively, to refine an automated trace using the +The background and trace steps can be done iteratively, to refine an automated trace using the background-subtracted image as input. Extraction ---------- The `specreduce.extract` module extracts a 1D spectrum from an input 2D spectrum (likely a -background-extracted spectrum from the previous step) and a defined window, using one of the +background-extracted spectrum from the previous step) and a defined window, using one of the implemented methods: * `~specreduce.extract.BoxcarExtract` diff --git a/licenses/KOSMOS_LICENSE b/licenses/KOSMOS_LICENSE index 4e9be95b..95c4e64b 100644 --- a/licenses/KOSMOS_LICENSE +++ b/licenses/KOSMOS_LICENSE @@ -1,4 +1,4 @@ -# NOTE: This license applies only to code used in the KosmosTrace class. +# NOTE: This license applies only to code used in part of the FitTrace class. MIT License diff --git a/notebook_sandbox/compare_extractions.ipynb b/notebook_sandbox/compare_extractions.ipynb index 4ea672d5..ed301bb6 100644 --- a/notebook_sandbox/compare_extractions.ipynb +++ b/notebook_sandbox/compare_extractions.ipynb @@ -22,7 +22,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "id": "fc741345-720e-4189-9748-b3bb98d2d693", "metadata": {}, "outputs": [], @@ -94,8 +94,8 @@ "metadata": {}, "outputs": [], "source": [ - "index_arr = np.tile(np.arange(nrows), (ncols, 1))\n", - "index_arr.T" + "index_arr = np.broadcast_to(np.arange(nrows)[:, np.newaxis], (nrows, ncols))\n", + "index_arr" ] }, { @@ -105,7 +105,7 @@ "metadata": {}, "outputs": [], "source": [ - "img = col_model(index_arr.T) + noise" + "img = col_model(index_arr) + noise" ] }, { @@ -214,7 +214,7 @@ "img_obj = CCDData(img, uncertainty=var_obj, mask=mask, unit=u.DN)\n", "\n", "hrn2 = HorneExtract(img_obj, trace)\n", - "hrn2_result1d_whole = hrn()" + "hrn2_result1d_whole = hrn2()" ] }, { @@ -244,7 +244,7 @@ "metadata": {}, "source": [ "## Compare results\n", - "The whole-image extractions come out as expected, with the Horne-extracted 1D spectrum showing a noticeably better signal-to-noise ratio than its boxcar equivalent." + "The whole-image extractions come out as expected, with the Horne-extracted 1D spectrum showing a noticeably better signal-to-noise ratio than its windowless boxcar equivalent." ] }, { diff --git a/notebook_sandbox/horne_extract/optimal_extract_VLT.ipynb b/notebook_sandbox/horne_extract/optimal_extract_VLT.ipynb index ed9a7a12..063758bb 100644 --- a/notebook_sandbox/horne_extract/optimal_extract_VLT.ipynb +++ b/notebook_sandbox/horne_extract/optimal_extract_VLT.ipynb @@ -8,6 +8,14 @@ "# Optimal/Horne extraction exploration" ] }, + { + "cell_type": "markdown", + "id": "5b9dc060-5262-463c-ab9b-5790dcf3e9e7", + "metadata": {}, + "source": [ + "

Note: This is an experimental notebook created for an older version of specreduce. To learn the package's current best practices, please visit our other notebooks.

" + ] + }, { "cell_type": "markdown", "id": "fc5ad51c-fc94-4dfc-8750-3e58b6224278", diff --git a/notebook_sandbox/jwst_boxcar/boxcar_extraction.ipynb b/notebook_sandbox/jwst_boxcar/boxcar_extraction.ipynb index fec97118..35d9a806 100644 --- a/notebook_sandbox/jwst_boxcar/boxcar_extraction.ipynb +++ b/notebook_sandbox/jwst_boxcar/boxcar_extraction.ipynb @@ -44,7 +44,7 @@ "from jwst import datamodels\n", "\n", "from specreduce.extract import BoxcarExtract\n", - "from specreduce.tracing import FlatTrace, KosmosTrace\n", + "from specreduce.tracing import FitTrace, FlatTrace\n", "from specreduce.background import Background\n", "\n", "import os\n", @@ -86,15 +86,7 @@ "cell_type": "code", "execution_count": 3, "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "DEBUG:jwst.datamodels.util:Opening /var/folders/gj/z56ys0mx1159ky517lwbwhcr0002vj/T/nirspec_fssim_d1_s2d.fits as \n" - ] - } - ], + "outputs": [], "source": [ "# use a jwst datamodel to provide a good interface to the data and wcs info\n", "s2d = datamodels.open(s2dfile)\n", @@ -118,7 +110,7 @@ }, { "data": { - "image/png": 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tuP94LDGEeVGFqvKkqmRytVpenLms9Pmxqqq3uFrKJ6bp5UI2dRVnruKsVHE2rbdVVTic3QGFpGmcNazWnU1PpEIqT2NIq9YX0yrAlJPq0idVeTepmLi8sCpcTn3ev6UlYbDKr5aGsziuzX8psGk2z5Kps6bAFPeR3F3ukslk7vVNs8DtVn/81Cbu3OazvI3pLVmePk6aZjNVtae2CyXbrrIwO2rzvzq0buPL9902gG+qL9ZZ23fDlO0y67y4t/1BZOV2WdvW3ayh6LKBXe0NatPZ9nXK7yuAtO0m/yNare6G5Qd6Q94klK2zjZkvrB/VLO+utu2K2uO9YRmAUcrfB/M/Xg79tPRRhjMrXOWVaZjqqK/zhXuscRQ3GyjHm/UKQpEkdKX7W5jVck8Cly0ErLjL+jZhedOb0so3qoYCSQX5IS895VAhXJons3fzdLdwLMlsk8xl06K+rziet3q9XmgePtN9tR7HojgjNZutagxKcf38uhfekF3qDa7Ndi3Menl7uxq3ycq3bluVM0kqpOl10+Z+Wbb9utZ9gdr0hWwPM0I76XqAO9AXeikZ7MeH9qRsL3yR7fs+2GR2uO11a51qunisDvjxeXDHfOzAEkMNKIc09GMeZTiTqTqdsWHmq8azd922kNK2eZGV8fBfmgpC/d6wvvXUxHQWa6twpvqAIp8sXDFZsVBPPvOS1d/URg+fk0pnyhb+ot30F+5anW2NbF6cn8rXGM6Sy6XBaqFsw4xXW1tWfp6s6XbLNnGCq/D5qZNt9a7a37Li275QpTNQ+x5cDvDFcxSzGU0zTIesZwwO9ccLAAex6uduhj44x3EbZziTVMTPVWx86s4WlS2pY9kpRr5i2+V1Llm37xedPCh1MT7d4S/QM3sIMPEFvHVX+/rs1NI2hKA7WVGwS7XjGUFgOUYDHJwwYAKwT7ym4CIaZTirfcRn04Hjpllu2devt+3OW2+MU1df87/trMYegtNB8eYyGE1fwLHrfgAAAKJRhrOaAw+CRnGa067aZrlaP7Q3AAyOOze2QDK24wEAAP0bbThrGzjxFbUHsOFnw9ANwgMAAMDFMtpw1qbvASvh8Dj0/TgDAADAxXN04axvqwbthLfhI3gBAADgEAhnA5MP/Alr/SCAAQAAoGuEsy0QmMaP30YBAABA10YfzghS2KdNH0+EOQAAAKxrtOFsr6Fs/qNpzcuxtqYfBR/zzxFs9DiMXdP2eMPRINQDAHCcRhnO3LWX4LQQJMI+Y5hoChpN5Y/aGj9A7cp+yHvoP1rdur8dt8+b0+HjZxQBeUSB5thn/M3mfUBQBQAck1GGM7lUnk2afxy5bdCzMDC25nUWbqYBsCXEySVr2o83lW2YUWoq1ySfaVl3YJdWuWwbUziWhrJu1fK8rWHfC8cQ95NmsaK5LevmhYWuM83vp3g7W1/bJq3Ikn1adjuWtaRsevfG/TS1Zx1NB2wt11dt11pH8+KN8vCWQe4gg+x9hcqBBYBRhOUdzF5jFULawO4fAKvF13x3/sgCbGKU4czdpKuFVEoq0hX1y9bglAWmWZjIXlzsXPVwEsftZdgmXqb7KVUPJ0lYqWWEMitTar6PhQNO2mfZMeTbhHKeBYumOmIZn0g2Df9Kn5WLfVBMk22n9YAyuarZK7OVrskzLitdxTNhg8JUnprOrylkZdh/qCf2Vbyb5uGw3gnlaVG1M4Q8L0xeSNPT6tInVj+eUNYn8SBCOTOpmK9XIZUTzZal/eaTUG62D6/WFYt96+n9EvsmXx8PsGl5kd1WVj4N5WlwXKEWJvcRzlZNJG/z5rwypFjtYlWx3erazC6DEW9rcPqEyJd36dADrQPOIh978D0YBt/IpG/VXZ0NkAZCjM+xhPxRhrOZIrsdB8jh0pM3aWt8ZwmnL8qyGYwwQCokcw+nUVYhzT2pN8zemHtVn0tWzE+HjKHMVYWb2YtJw1+ZGmetGppUCwH5tnEXTYPxhhmt2THEsw7LKtCm4UylQqhKdz5XTOeDSXOTTas6ium8eAxKs/3ks3TLZj8tBKikrTEglSc2n0WzhjJp6MnDVxbC6ut9vm1yX89m6xrDWZJ4i3on5yFsYXkW2mb1zY5nxYxbm33N6G27/20GyWu3edP97u+d/OBvHoSLnbgbAe0Q8jM4gB4QyvZraGH3WGZhRxnOzFw+qWZeJNUDQ9tpXeGdxWIQS0NB06MypKH4+bZZSIv1pSEw7ipcl3u1eVgWGlCNwUubV9k2+7XE0tmJtlmVOIFSarWmdnlyPWuHlVZv+5IZQCvn6xvb3SQPVFJ99ivbvha0imwfkjwGp4bwNWtyXt9s+TxE1U+bTNvgzdvlx5ldXwhgG8yQNdr1VMh9zURtNZu2xTZL97efd529vmF0ER7SGdcjQ0A7oGXdegSDKmCM0s8B992OKG3P2ALbKMOZTNJJOQ9JtQ84NZRPRss+S1Dp+uZqZl8Iks1qrf05s6TuhVP2lsyQtVnrtLG2MiuedK31+vKKF7bL+yMbIC6UX3FMtcCUXK/NVmXr01m01tMLG9av9fmyNdq89kxUW4hbu54V6zexxUD2woUVaTCDx06Dw0COeW+a/vg0tmMEsLOxDegPbWj9NbT27NM4w5kkmyTnDK5rww/F1GbK0uULV5o2brm5bht2PYVkn9+O2HSc2/xVftPxaFuzG4POBqfmrbt9U7FdThVsLbt+0dZdXPRZghG/CI/ZIN48h9AGAADWNMpwZumsyUZvzL766/HTepLttjo1qLGuPgbR29fZNs3d13nKGw0GOw5Is10d2SwQNjeIUAMAADo3ynAmaeuBabczDG3TZxdHl2fW7cXgGnR8CB4AAADNRhvO8gHgED7IuBKDVuwJAQgAAODiGW04yw1hsHohAiL2YgiPNwAAAFwsRxPOhoABOwAAAIA2+c80AwAAAAB6QDgDAAAAgAEgnAEAAADAABDOAAAAAGAACGcAAAAAMAA7hTMzu93MHjazR8zsTftqFAAAAAAcm63DmZlNJP28pFdJulXSa8zs1n01DAAAAACOyS4zZ98v6RF3/2N3vyrpPkl37KdZAAAAAHBcdglnN0n6k+T2Z8KyGjO7y8weMLMHpk99bYfqAAAAAGC8Tg5dgbvfK+leSTKzzz/6o2/5mqQvHLpetHqe6P8+0f/9ov/7x33QL/q/X/R/v+j/ftH/cy9sW7FLOHtc0guS2zeHZa3c/QYze8Dd//wO9WIH9H+/6P9+0f/94z7oF/3fL/q/X/R/v+j/9exyWuPHJN1iZi82s0uS7pT0gf00CwAAAACOy9YzZ+5+bmb3SPpNSRNJ73L3h/bWMgAAAAA4Ijt95szdPyjpgxtudu8udWJn9H+/6P9+0f/94z7oF/3fL/q/X/R/v+j/NZi7990GAAAAADh6u3zmDAAAAACwJ4QzAAAAABiAzsKZmd1uZg+b2SNm9qau6j1mZvaYmX3czB40swfCsuea2YfM7A/D5Tf33c4xMbN3mdmTZvYHybLGPrfKz4bnxO+b2ff21/JxaOn/t5rZ4+F58KCZ/VCy7s2h/x82sx/sp9XjYWYvMLP7zewTZvaQmb0+LOc50IEl/c9zoANmdsXMftvMfi/0/78My19sZh8N/fwr4RuuZWaXw+1HwvoX9XoAI7DkPni3mT2aPAduC8t5DToAM5uY2e+a2W+E2zwHNtBJODOziaSfl/QqSbdKeo2Z3dpF3dBfcffbkt+VeJOkD7v7LZI+HG5jf94t6fZsWVufv0rSLeHfXZJ+oaM2jtm7tdj/kvSO8Dy4LXyRkcJr0J2Svits82/DaxW2dy7px939VkkvlXR36GeeA91o63+J50AXnpH0Cnd/iaTbJN1uZi+V9FOq+v/bJX1R0utC+ddJ+mJY/o5QDrtpuw8k6SeS58CDYRmvQYfxekmfTG7zHNhAVzNn3y/pEXf/Y3e/Kuk+SXd0VDfq7pD0nnD9PZL+Vn9NGR93/++S/l+2uK3P75D0H7zyvyRdb2bP76ShI9XS/23ukHSfuz/j7o9KekTVaxW25O5PuPv/DtefUvXmfJN4DnRiSf+34TmwR+Fx/NVw8zT8c0mvkPSrYXn++I/Pi1+V9Eozs25aO05L7oM2vAbtmZndLOmvS/p34baJ58BGugpnN0n6k+T2Z7T8DQP74ZL+m5n9jpndFZbd6O5PhOuflXRjP007Km19zvOiO/eEU1beZfNTeen/Awqnp3yPpI+K50Dnsv6XeA50IpzO9aCkJyV9SNIfSfqSu5+HImkfz/o/rP+ypG/ptMEjlN8H7h6fA28Lz4F3mNnlsIznwP79G0n/VFIZbn+LeA5shC8EGbcfcPfvVTVtf7eZ/eV0pVe/o8BvKXSIPu/FL0j6NlWnuDwh6V/32pojYGbXSvo1SW9w96+k63gOHF5D//Mc6Ii7T939Nkk3q5qF/M5+W3R88vvAzL5b0ptV3RffJ+m5kt7YXwvHy8z+hqQn3f13+m7LRdZVOHtc0guS2zeHZTggd388XD4p6f2q3ig+F6fsw+WT/bXwaLT1Oc+LDrj758KbdSnpFzU/bYv+PwAzO1UVDH7J3d8XFvMc6EhT//Mc6J67f0nS/ZL+oqpT5U7CqrSPZ/0f1j9H0p9229LxSu6D28Mpv+7uz0j69+I5cCgvk/Q3zewxVR9heoWknxHPgY10Fc4+JumW8G0tl1R9APkDHdV9lMzs2WZ2Xbwu6a9J+gNV/f7aUOy1kn69nxYelbY+/4Ckvx++Leqlkr6cnPqFPck+P/C3VT0PpKr/7wzfFvViVR8I/+2u2zcm4bMC75T0SXf/6WQVz4EOtPU/z4FumNkNZnZ9uH6NpL+q6nN/90t6dSiWP/7j8+LVkj4SZpaxpZb74FPJH4dM1eed0ucAr0F74u5vdveb3f1Fqsb6H3H3vyeeAxs5WV1kd+5+bmb3SPpNSRNJ73L3h7qo+4jdKOn94XOVJ5J+2d3/q5l9TNJ7zex1kj4t6Ud6bOPomNl/lvRySc8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+ "image/png": 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" ] @@ -140,9 +132,7 @@ { "cell_type": "code", "execution_count": 5, - "metadata": { - "scrolled": false - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -153,7 +143,7 @@ }, { "data": { - "image/png": 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\n", + "image/png": 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\n", 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" ] @@ -188,7 +178,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "At this point, `specreduce` doesn't provide a tool for finding the trace. Since this is rectified data, we can use the `FlatTrace` class and find the spectrum position and width by eye." + "Since this is rectified data, one option is to use the `FlatTrace` class and find the spectrum position and width by eye." ] }, { @@ -208,7 +198,7 @@ }, { "data": { - "image/png": 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\n", 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\n", 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\n", 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nDPJe++vobIspY2/R7XlYtlWyojou/KXr/6kZAAAA4QwAAGAEwhkAAMAAhDMAAIABCGcAAAADEM4AAAAGIJwBAAAMQJ8zFtI+cVF/nmPzG8x0e8MsodVJt+3Fbr8hRXcZU+rszFOdXjzL6M2x9VBnIb0a0t8WtdNZwISeLFunF1zHhHl627tXQ5Lu9trq1Tlle+8ufhD03uui05dhUqueFRwjrfdryyl19sbvoueCJLUzfyHbD86fvnuk/0Z6y5jSUmirs4wj980/SNqEOncumr/Tjn5+/g7Z/kL/YN+6f8oJobOMT5+aO33nCZfNf/1n7+uuY+d/f+h8Srog25dfPnd6HdmeO709+GB3Hbufv3f+DF/79O4ydi452pk+v86tB/sH4unOMnYvmj9+d7f74/sLj5+/ju37u4vonlN2jvUW0F/HzsW997r4OlpnGbvzd/kkS2mz2nuvc7hyBgAAMADhDAAAYADCGQAAwACEMwAAgAEIZwAAAAMQzgAAAAYgnAEAAAxAOAMAABiAJtQsZPf4hKacnQbPrdeZeQlNqJexjLaMroTdlXSmL6GE3Z0lNP1etCH3hG25jKbf3Ua+q6izZxnbe4KFh86UGlZwiPQs43QxwvuYNL57hfbG75T32VvGMsZmp1vrpFNv51fNtdPpCNsumrCSjknb8zHzJ/fex+6jJ6zjCZ2F9BexsFV8u5xyeWHBQ2SSRdex1T+I2vb+/5w0aXv2bHW+6a5ie0/YniuxwHt15QwAAGAAwhkAAMAAhDMAAIABCGcAAAADEM4AAAAGIJwBAAAMQDgDAAAYgD5nLGTr2M66S1iKlfQwOygGaRGylPZ29uvStGX0s7I7Dpxl7HcY1aE5Jy3ccHMQS9gfh2GXdq+cVdXrquqeqnr3Gc+9uqrurKp3zv69cH/LBAAAONymfKzxl5M8/yzPv7a1du3s328ttywAAIDN0g1nrbXfTfKpFdQCAACwsRa5IcjLqupds489PvZcM1XV9VV1sqpO7py6d4HVAQAAHF4XGs5+IclTk1yb5K4kP3WuGVtrN7TWTrTWTmwfv/QCVwcAAHC4XVA4a63d3Vrbaa3tJvnFJM9ablkAAACb5YLCWVVdecbDb0vy7nPNCwAAQF+3z1lV/VqS5yR5fFXdkeRHkzynqq7NXiuijyT5/v0rkaEdhoYSSeqw9AjhEVawXzdk6BySQ53zNMR+n1LEhhyHcFZDHKgsSzectdZecpanb9yHWgAAADbWIndrBAAAYEmEMwAAgAEIZwAAAAMQzgAAAAYgnAEAAAxAOAMAABiAcAYAADCAbp8zmKcOSOPDpkEp++WAHANwqDkOgUPClTMAAIABCGcAAAADEM4AAAAGIJwBAAAMQDgDAAAYgHAGAAAwAOEMAABgAPqcsRGW0Y9NrzQAAPaTK2cAAAADEM4AAAAGIJwBAAAMQDgDAAAYgHAGAAAwAOEMAABgAMIZAADAAPQ5Y9/pD8YFMW4AWMQSepzCqrlyBgAAMADhDAAAYADCGQAAwACEMwAAgAEIZwAAAAMQzgAAAAYgnAEAAAxAOAMAABiAJtQsZHen3+Gx6nB0E25NN8v/53Ds0r5V7PMpx4exN5ZDck6bxNibrC1hWJTNzWG2SefOBXSvnFXV66rqnqp69xnPPa6qbqmqD8z+f+z+lgkAAHC4TflY4y8nef4jnntlkltba09PcuvsMQAAABeoG85aa7+b5FOPePpFSW6afX1TkhcvtywAAIDNcqE3BLmitXbX7OuPJ7niXDNW1fVVdbKqTu6cuvcCVwcAAHC4LXy3xtZay5zbA7TWbmitnWitndg+fumiqwMAADiULjSc3V1VVybJ7P97llcSAADA5rnQcHZzkutmX1+X5K3LKQcAAGAzdfucVdWvJXlOksdX1R1JfjTJjyd5Y1W9NMlHk3znfhbJuNrn+63yWu9XAL22FxPaYtSivXimtN5YRp27nTqX0QKks4p6aP4MtTthFb06O8vYOt1fR6+/0qQ6d3rr6Lx+yj7trWOfX58kW1OWsYL2Mr190jsXTNmnXUvYp73TSfecNkF3n03ZX506dzun560H+6vYOr34wNl+oLOOnfnrmHKM9PbJkft7A6O/jiP3zR+g2/dPOSnNn/zgY+bvtK2H+vujOvts66H5dbbtCf1Le5uzs0+n2Ll4/k5tE5rC7R6dP8/ORZ3pnddP0bbnT989OmEZvXNSZx1788xfSO98ManOznHYnT7lfXTqnPLjYNuePz675/gp61jg+0T3J+vW2kvOMel5F75aAAAAzrSE3/8BAACwKOEMAABgAMIZAADAAIQzAACAAQhnAAAAAxDOAAAABiCcAQAADKDfQRjm6TTyS9LvCNhr5jel2V9bsOHlMhpdT9kUW52ZVtAouLuOCX1Ue5u712x7Z0KjyeqtZAmNw5fR9Lh2FmyWPWnszZ8+qY/1KsZWz+L9XLsW7UefZCnnpN5xNKUZ9r6bMvY6x/KU7d07BnrrmKT3bWYZx+Fu58S1gn26dXr/17GUY6hnGU18JzUbnj+9t0uXYRmNl3vvdUrD4+7PHws2kE7Sr7P3+invYynn+MWa0k+qYYHLX66cAQAADEA4AwAAGIBwBgAAMADhDAAAYADCGQAAwACEMwAAgAEIZwAAAAPQ54yF1CUTOiwN0c9nFY1b9t+i7dySCbtjGdtqhH5tq7Lo9hrlfRwWB+VQ7+33Ke/D2Fmeg7ItV/G9bIgmfBOsoqfhMqxiex6UbdFzQMZe7fO2cuUMAABgAMIZAADAAIQzAACAAQhnAAAAAxDOAAAABiCcAQAADEA4AwAAGIA+Zyyktg5GT4qD08RmvhHakCQ5LJsTIEnSDkkvzIOiDkg/Kwa0AYeqK2cAAAADEM4AAAAGIJwBAAAMQDgDAAAYgHAGAAAwAOEMAABgAMIZAADAAIQzAACAAWhCzUJqCc0Am16UB88GNIEENoemyMAoFgpnVfWRJKeS7CQ53Vo7sYyiAAAANs0yrpx9U2vtk0tYDgAAwMbyN2cAAAADWDSctSS/U1Vvr6rrzzZDVV1fVSer6uTOqXsXXB0AAMDhtOjHGr+xtXZnVT0hyS1V9b7W2u+eOUNr7YYkNyTJRU+5yl/cAgAAnMVCV85aa3fO/r8nyVuSPGsZRQEAAGyaCw5nVXVpVR1/+Osk35Lk3csqDAAAYJMs8rHGK5K8pfYaXR1J8quttd9eSlVslGX0StsUU3rC9banvnIAAGO64HDWWvtwkmcssRYAAICN5Vb6AAAAAxDOAAAABiCcAQAADEA4AwAAGIBwBgAAMADhDAAAYACL9DkDVmwZPeH0QQMA2D+L/LzmyhkAAMAAhDMAAIABCGcAAAADEM4AAAAGIJwBAAAMQDgDAAAYgHAGAAAwAOEMAABgAJpQAwAAG2GRBtGr4MoZAADAAIQzAACAAQhnAAAAAxDOAAAABiCcAQAADEA4AwAAGIBwBgAAMAB9zoAvMqX/R2v7XwcAwPkYvYfZFK6cAQAADEA4AwAAGIBwBgAAMADhDAAAYADCGQAAwACEMwAAgAEIZwAAAAMQzgAAAAYgnAEAAAxgoXBWVc+vqvdX1Qer6pXLKgoAAGDTXHA4q6rtJD+f5AVJrknykqq6ZlmFAQAAbJJFrpw9K8kHW2sfbq09mOQNSV60nLIAAAA2yyLh7KokHzvj8R2z575IVV1fVSer6uTOqXsXWB0AAMDhte83BGmt3dBaO9FaO7F9/NL9Xh0AAMCBtEg4uzPJ1Wc8fuLsOQAAAM7TIuHsD5I8vaqeXFXHknxXkpuXUxYAAMBmqdbahb+46oVJfibJdpLXtdZe05n/E0k+esZTj0/yyQsuAPaX8cmojE1GZWwyMuOTUXxFa+3ys01YKJwtqqpOttZOrK0AmMP4ZFTGJqMyNhmZ8clBsO83BAEAAKBPOAMAABjAusPZDWteP8xjfDIqY5NRGZuMzPhkeGv9mzMAAAD2rPvKGQAAABHOAAAAhrC2cFZVz6+q91fVB6vqleuqA6rq6qp6W1W9t6reU1Uvnz3/uKq6pao+MPv/seuulc1UVdtV9Y6q+s3Z4ydX1W2z8+evV9WxddfIZqqqy6rqTVX1vqq6vaq+3rmTEVTVP519T393Vf1aVV3s3MlBsJZwVlXbSX4+yQuSXJPkJVV1zTpqgSSnk/xQa+2aJM9O8oOz8fjKJLe21p6e5NbZY1iHlye5/YzHP5Hkta21pyX5dJKXrqUqSH42yW+31r4qyTOyN06dO1mrqroqyT9JcqK19jVJtpN8V5w7OQDWdeXsWUk+2Fr7cGvtwSRvSPKiNdXChmut3dVa+8PZ16ey98PFVdkbkzfNZrspyYvXUiAbraqemORvJvml2eNK8twkb5rNYmyyFlX1mCR/NcmNSdJae7C19pk4dzKGI0kuqaojSR6V5K44d3IArCucXZXkY2c8vmP2HKxVVT0pyTOT3JbkitbaXbNJH09yxbrqYqP9TJJ/kWR39vhLk3ymtXZ69tj5k3V5cpJPJPkPs4/d/lJVXRrnTtastXZnkp9M8qfZC2WfTfL2OHdyALghCMxU1aOT/EaSV7TWPnfmtLbXc0LfCVaqqr41yT2ttbevuxY4iyNJ/mKSX2itPTPJvXnERxidO1mH2d85vih7v0D4siSXJnn+WouCidYVzu5McvUZj584ew7WoqqOZi+Yvb619ubZ03dX1ZWz6VcmuWdd9bGxviHJ366qj2Tv49/Pzd7f+Fw2+6hO4vzJ+tyR5I7W2m2zx2/KXlhz7mTd/nqSP2mtfaK19lCSN2fvfOrcyfDWFc7+IMnTZ3fNOZa9P9K8eU21sOFmf8NzY5LbW2s/fcakm5NcN/v6uiRvXXVtbLbW2g+31p7YWntS9s6T/7W19t1J3pbkO2azGZusRWvt40k+VlVfOXvqeUneG+dO1u9Pkzy7qh41+x7/8Nh07mR4tfeJgzWsuOqF2ftbiu0kr2utvWYthbDxquobk/xekj/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\n", + "image/png": 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\n", "text/plain": [ "
" ] @@ -491,7 +481,7 @@ "source": [ "## Trace\n", "\n", - "Optional: we could now refine the initial flat trace by running an automated `KosmosTrace` on the subtracted image. This process could be iterated as necessary (recreating the subtracted image with the refined trace, etc)." + "We can now refine the initial, flat trace by finding an automated trace on the subtracted image with by fitting another wuth the `FitTrace` class. This process could then be iterated as necessary (recreating the subtracted image with the refined trace, etc)." ] }, { @@ -500,7 +490,7 @@ "metadata": {}, "outputs": [], "source": [ - "auto_trace = KosmosTrace(image-bg, guess=ext_center)" + "auto_trace = FitTrace(image-bg, guess=ext_center, window=ext_width)" ] }, { @@ -527,7 +517,7 @@ "outputs": [ { "data": { - "image/png": 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\n", 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\n", 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" ] @@ -558,7 +548,7 @@ "## About this notebook\n", "\n", "**Author:** Ivo Busko, JWST\n", - "**Updated On:** 2022-02-18" + "**Updated On:** 2022-11-11" ] }, { diff --git a/notebook_sandbox/jwst_boxcar/jwst_boxcar_algorithm.ipynb b/notebook_sandbox/jwst_boxcar/jwst_boxcar_algorithm.ipynb index d68617b3..fd9069d4 100644 --- a/notebook_sandbox/jwst_boxcar/jwst_boxcar_algorithm.ipynb +++ b/notebook_sandbox/jwst_boxcar/jwst_boxcar_algorithm.ipynb @@ -7,6 +7,13 @@ "# Spectral extraction tests (JDAT-1855)" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "

Note: This is an experimental notebook created for an older version of specreduce. To learn the package's current best practices, please visit our other notebooks.

" + ] + }, { "cell_type": "markdown", "metadata": {}, diff --git a/notebook_sandbox/jwst_boxcar/tracing_options.ipynb b/notebook_sandbox/jwst_boxcar/tracing_options.ipynb deleted file mode 100644 index b071a283..00000000 --- a/notebook_sandbox/jwst_boxcar/tracing_options.ipynb +++ /dev/null @@ -1,334 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Automatic Tracing Options" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": { - "slideshow": { - "slide_type": "fragment" - } - }, - "outputs": [], - "source": [ - "%matplotlib inline\n", - "\n", - "from pathlib import Path\n", - "from zipfile import ZipFile\n", - "\n", - "from astropy.visualization import simple_norm\n", - "from astropy.utils.data import download_file\n", - "\n", - "from jwst import datamodels\n", - "\n", - "from specreduce.tracing import KosmosTrace\n", - "from specreduce.background import Background\n", - "\n", - "import tempfile\n", - "\n", - "import numpy as np\n", - "\n", - "import matplotlib.pyplot as plt" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Ingest s2d data" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "# data is taken from s2d file. x1d is used for comparison with pipeline extraction.\n", - "zipped_datapath = Path(download_file('https://stsci.box.com/shared/static/qdj79y7rv99wuui2hot0l3kg5ohq0ah9.zip', cache=True))\n", - "\n", - "data_dir = Path(tempfile.gettempdir())\n", - "\n", - "with ZipFile(zipped_datapath, 'r') as sample_data_zip:\n", - " sample_data_zip.extractall(data_dir)\n", - "\n", - "s2dfile = str(data_dir / \"nirspec_fssim_d1_s2d.fits\")\n", - "x1dfile = str(data_dir / \"nirspec_fssim_d1_x1d.fits\")" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "# use a jwst datamodel to provide a good interface to the data and wcs info\n", - "s2d = datamodels.open(s2dfile)\n", - "image = s2d.slits[0].data\n", - "norm_data = simple_norm(image, \"sqrt\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Background Extraction" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "# extraction parameters based on image above\n", - "ext_center = 27\n", - "ext_width = 4\n", - "\n", - "bkg_sep = 4\n", - "bkg_width = 2" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [], - "source": [ - "bg = Background.two_sided(image, ext_center, bkg_sep, width=bkg_width)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Automatic Traces\n", - "\n", - "Now we'll compare the trace when passing various options to `peak_method` and `window`." - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [], - "source": [ - "auto_trace = KosmosTrace(image-bg, guess=ext_center)" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Text(0.5, 1.0, 'slit[0] slice')" - ] - }, - "execution_count": 16, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "plt.figure(figsize=(15, 15))\n", - "plt.imshow(bg.sub_image(image), norm=norm_data, origin=\"lower\")\n", - "plt.plot(auto_trace.trace, color='r')\n", - "plt.title(\"slit[0] slice\")" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "outputs": [], - "source": [ - "auto_trace_window = KosmosTrace(image-bg, guess=ext_center, window=4)" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[]" - ] - }, - "execution_count": 18, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "plt.plot(auto_trace.trace-auto_trace_window.trace)" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [], - "source": [ - "auto_trace_max = KosmosTrace(image-bg, guess=ext_center, peak_method='max')" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[]" - ] - }, - "execution_count": 10, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "plt.plot(auto_trace.trace-auto_trace_max.trace)" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [], - "source": [ - "auto_trace_centroid = KosmosTrace(image-bg, guess=ext_center, peak_method='centroid', window=4)" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[]" - ] - }, - "execution_count": 12, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "plt.plot(auto_trace.trace-auto_trace_centroid.trace)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "slide" - } - }, - "source": [ - "## About this notebook\n", - "\n", - "**Author:** Kyle Conroy, JWST\n", - "**Updated On:** 2022-07-14" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "[Top of Page](#top)\n", - "\"Space " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "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": "3.8.12" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/notebook_sandbox/tracing_options.ipynb b/notebook_sandbox/tracing_options.ipynb new file mode 100644 index 00000000..9da87301 --- /dev/null +++ b/notebook_sandbox/tracing_options.ipynb @@ -0,0 +1,381 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Automatic Tracing Options" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "slideshow": { + "slide_type": "fragment" + } + }, + "outputs": [], + "source": [ + "%matplotlib inline\n", + "\n", + "from pathlib import Path\n", + "from zipfile import ZipFile\n", + "\n", + "from astropy.modeling import models\n", + "from astropy.visualization import simple_norm\n", + "from astropy.utils.data import download_file\n", + "\n", + "from jwst import datamodels\n", + "\n", + "from specreduce.tracing import FitTrace\n", + "from specreduce.background import Background\n", + "\n", + "import tempfile\n", + "\n", + "import numpy as np\n", + "\n", + "import matplotlib.pyplot as plt" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Ingest s2d data" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "# data is taken from s2d file. x1d is used for comparison with pipeline extraction.\n", + "zipped_datapath = Path(download_file('https://stsci.box.com/shared/static/qdj79y7rv99wuui2hot0l3kg5ohq0ah9.zip', cache=True))\n", + "\n", + "data_dir = Path(tempfile.gettempdir())\n", + "\n", + "with ZipFile(zipped_datapath, 'r') as sample_data_zip:\n", + " sample_data_zip.extractall(data_dir)\n", + "\n", + "s2dfile = str(data_dir / \"nirspec_fssim_d1_s2d.fits\")\n", + "x1dfile = str(data_dir / \"nirspec_fssim_d1_x1d.fits\")" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "# use a jwst datamodel to provide a good interface to the data and wcs info\n", + "s2d = datamodels.open(s2dfile)\n", + "image = s2d.slits[0].data\n", + "norm_data = simple_norm(image, \"sqrt\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Background Extraction" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "# extraction parameters based on image above\n", + "ext_center = 27\n", + "ext_width = 4\n", + "\n", + "bkg_sep = 4\n", + "bkg_width = 2" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "bg = Background.two_sided(image, ext_center, bkg_sep, width=bkg_width)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Automatic Traces\n", + "\n", + "Now we'll compare the trace when passing various options to `peak_method` and `window`." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "auto_trace_gauss = FitTrace(image-bg, guess=ext_center, bins=20,\n", + " peak_method='gaussian', trace_model=models.Polynomial1D(2))" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0.5, 1.0, 'slit[0] slice')" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure(figsize=(15, 15))\n", + "plt.imshow(bg.sub_image(image), norm=norm_data, aspect=5, origin=\"lower\")\n", + "plt.plot(auto_trace_gauss.trace, color='r')\n", + "plt.title(\"slit[0] slice\")" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "auto_trace_window = FitTrace(image-bg, guess=ext_center, bins=20, window=4,\n", + " peak_method='gaussian', trace_model=models.Polynomial1D(2))" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[]" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.plot(auto_trace_gauss.trace-auto_trace_window.trace)" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "auto_trace_centroid = FitTrace(image-bg, guess=ext_center, bins=20, window=4,\n", + " peak_method='centroid', trace_model=models.Polynomial1D(2))" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[]" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.plot(auto_trace_gauss.trace-auto_trace_centroid.trace)" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [], + "source": [ + "auto_trace_max = FitTrace(image-bg, guess=ext_center, bins=20,\n", + " peak_method='max', trace_model=models.Polynomial1D(2))" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[]" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.plot(auto_trace_gauss.trace-auto_trace_max.trace)" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [], + "source": [ + "auto_trace_spline = FitTrace(image-bg, guess=ext_center, bins=20,\n", + " peak_method='max', trace_model=models.Spline1D(degree=2))" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[]" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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20jkXV9o2XRkrUskUFvl48fOtPLd0C03qVONfulG3nIKCXqQSSc3K5XfvrCF+xwFG9W7G1FHnULd6Va/LkiCnoBepBJxzzFmRxqPzE4kw49lre/PzPs29LksqCQW9SJDLzM7jf95PYFnSXoa0b8iTv+pF83rVvS5LKhEFvUgQ+yRhNw99tI6jx4p4eGQ3xg5uQ5UqGjYpp0dBLxKEDuUW8PDc9Xy0Zhc9W9Tl6Wt606GRhk3KmVHQiwSZ5Vv2cv+7CezNyee3l3Zi0kXtNWxSzoqCXiRIHD1WxF8XFM822T62JtNvHkLPFvW8LktCgIJeJAisTj3A799ZS8q+I9x6blseGN5Zs01KmVHQi3joWKGPv3+2hWnLkmlSpxpvTxjIkA6685OULQW9iEc2Zxzmd++sYf3ObK7u24KHr+pGnWq6+EnKnoJepIIVFvl4+csUnluyhVrVInnpxn4MP6eJ12VJCFPQi1SgLRmHue/dtaxNP8Tl5zTh0Z+fQ0ytaK/LkhCnoBepAIVFPv65fBvPLNlMzagI/n5dH67s2VRzxkuFUNCLlLPkzBzue3cta9IOcln3xvzvz3sQW1tH8VJxFPQi5aTI53hleQpPLd5MjagInhvTm6t6NdNRvFQ4Bb1IOdi6N4f7313LqtSDDOvWmD//4hwa1a7mdVkSphT0ImWoyOd49att/G1REtWqRvDstb0Z1VtH8eItBb1IGUnZm8P97yWwcscBLu3aiMd+0YNGdXQUL95T0IucpSKf47Wvt/HkwiSiI6vw9DW9+EWf5jqKl6ChoBc5C5szDvPAewmsSTvIJV0a8dgve9BYR/ESZBT0ImfgWKGPl77Yyt8/20Kt6Ej1xUtQU9CLnKaE9IM88F4Cm/YcZmSvZjw8spuubpWgpqAXCVBeQRHPLN7MP5enEFs7mn/eHMewbo29LkvklBT0IgH4T0oWk99PYHtWLmP6t+TBK7pSt7pmmpTKQUEvchKH8wr464JNzPoulZYNqjNrwkDO1XzxUsko6EVOYNmmTP7fh+vYk53H+PPa8vufdaJGlH5lpPLRT63IcfYfOcbUeRv4aM0uOjaqxft3DKFvq/pelyVyxhT0In7OOeau3cXUeYkcOlrAby7pyJ0XtSc6UvdulcpNQS8CpO3P5aGP1vPl5r30alGXtyYMpGvTOl6XJVImFPQS1gqKfLz6VfENQSLMeGRkN24a3IaIKrrwSUKHgl7C1tq0g0z+YB0bd2czrFtjplzVnWb1qntdlkiZU9BL2MnJL+RvC5N449vtNKodrZtzS8hT0EtYWZyYwZ8+Xs+e7DxuGtSa+y7rTJ1quvBJQpuCXsLCnkN5PDJ3A59u2EPnxrWZdkNfDZmUsKGgl5Dm8zlmfbeDxz9NoqDIxwPDO3Pb+e2oGlHF69JEKkxAP+1mNtzMksws2cwml7J9qJmtMrNCMxt93LaxZrbF/zW2rAoXOZVNe7IZ/dI3/PHjDfRuWY9Fvx3KpAs7KOQl7JzyiN7MIoBpwDAgHVhhZnOdc4kldksFxgH3HffcBsDDQBzggJX+5x4om/JF/ltOfiHPLdnMq19vp271qrrjk4S9QLpuBgDJzrkUADObA4wCfgx659x2/zbfcc+9DFjsnNvv374YGA7MPuvKRY7jnOPT9XuYMi+RPdl5XDegJQ9c1oX6NaO8Lk3EU4EEfXMgrcRyOjAwwNcv7bnNj9/JzCYCEwFatWoV4EuL/J8dWUd4eO4GPk/aS9emdZh2Q1/6tdbJVhEIkpOxzrnpwHSAuLg453E5UonkFxbx8hcpTFuWTGQV449XdmPs4NZEqh9e5EeBBP1OoGWJ5Rb+dYHYCVx43HM/D/C5Iif11ZZ9/PHj9Wzbd4QRPZvyxxHdaFJXN+YWOV4gQb8C6GhmbSkO7jHA9QG+/kLgMTP74X/onwEPnnaVIiVkZOfx6PxE5ifspk3DGsy8dQBDO8V6XZZI0Dpl0DvnCs3sLopDOwJ41Tm3wcymAvHOublm1h/4EKgPjDSzKc657s65/Wb2KMV/LACm/nBiVuR0FRb5mPntDp5evJljRT7uvbQjv76gPdWqahphkZMx54KrSzwuLs7Fx8d7XYYEmdWpB3jow/Uk7s5maKdYpl7VnTYxNb0uSyRomNlK51xcaduC4mSsyInsy8nn8QWbeHdlOk3qVOPFG/py+TlNNCZe5DQo6CUoFRb5ePM/xd00eQVF3H5BO+6+uCO1ovUjK3K69FsjQefbrVk8MncDSRmHOb9jDA+P7E6HRrW8Lkuk0lLQS9DYfegof/5kI/MTdtO8XnVevqkfP+vWWN00ImdJQS+eyy8sYsZX2/j70mR8znHPJR2540KNphEpKwp68dSypEymzktk274jXNa9MX8Y0Y2WDWp4XZZISFHQiyd2ZB3h0fmJLNmYSbuYmrxx6wAu0EVPIuVCQS8VKvdYIS99vpWXvkwhsoox+fIu3HpuW6IiNTeNSHlR0EuF8PkcH6/dyeMLktiTnceo3s148PKumptGpAIo6KXcrU49wJR5iaxJO0iP5nV54fo+xLVp4HVZImFDQS/lZvehozzxaRIfrt5JbO1onhzdk6v7tqBKFQ2XFKlICnopc0ePFTH9yxRe+mIrRc5x50XtuePCDrqqVcQj+s2TMuOcY+7aXTy+YBO7DuUxokdTJl/eRcMlRTymoJcysTbtIFPmbWBV6kG6N6vDM9f2ZmC7hl6XJSIo6OUs7TmUxxMLN/HBqp3E1Iri8at7MLpfSyLUDy8SNBT0ckZyjxXyyvJt/OPzrRT5HL++oD13XtSe2tWqel2aiBxHQS+npcjneH9lOk8tTiIjO5/h3Zvw4BVdaN1QNwERCVYKegnYl5v38ti/N7Jpz2F6t6zHtOv7ajy8SCWgoJdT2rg7m8f+vZHlW/bRskF1Xri+DyN6NNX0wSKVhIJeTmjPoTyeXpzEuyvTqVOtKn8Y0ZWbBrcmOlLTB4tUJgp6+S85+YVM/2Ir05enUORzjD+3LXdd3IF6NaK8Lk1EzoCCXn5UWOTjX/FpPLN4C/ty8rmyZ1MeuKwLrRrqgieRykxBLzjn+GxTJn9ZsInkzBziWtdn+s396NuqvteliUgZUNCHuZU79vPXBZtYsf0AbRrW4KUb+3JZ9yY60SoSQhT0YWpzxmGe+DSJJRsziKkVzaM/P4dr41rqBiAiIUhBH2Z2HjzKM4s38/6qdGpFRXLfzzpx63ltqRGlHwWRUKXf7jCx/8gxXlyWzMz/7ABgwnltmXRhB+rX1EgakVCnoA9xuccKmbF8G9O/TOHIsUKu7tuCe4d1onm96l6XJiIVREEfogqKfMz5PpXnliazLyefYd0ac/9lnenUuLbXpYlIBVPQhxifzzF/3W6eWpTEjqxcBrRpwMs39aVfa81JIxKuFPQhwjnH4sQMnl68mU17DtOlSW1eG9efCzvHaqikSJhT0Fdyzjm+3LKPpxYlkZB+iLYxNXluTG9G9mymm3CLCKCgr9T+k5LFU4uSWLH9AM3rVeeJ0T35ZZ/mREZoLLyI/B8FfSW0KvUATy1K4uvkLBrX0cVOInJyCvpKZP3OQzy9eDOfbcqkYc0o/jCiKzcOak21qpo2WEROLKCgN7PhwHNABPCKc+6vx22PBmYC/YAs4Frn3HYzqwq8AvT1v9dM59xfyrD+sLA54zDPLN7MgvV7qFu9Kvdf1plxQ9pQM1p/p0Xk1E6ZFGYWAUwDhgHpwAozm+ucSyyx23jggHOug5mNAR4HrgV+BUQ753qYWQ0g0cxmO+e2l/UHCUUpe3N4fukWPl67i5pRkdxzSUfGn9+WOroBt4ichkAOCQcAyc65FAAzmwOMAkoG/SjgEf/j94AXrHhMnwNqmlkkUB04BmSXTemhKzkzhxc+28LctbuIiqzC7UPbc/vQdpquQETOSCBB3xxIK7GcDgw80T7OuUIzOwQ0pDj0RwG7gRrAb51z+49/AzObCEwEaNWq1Wl+hNCRnHmY55cmMy9hF9UiI5hwfjtuO78dsbWjvS5NRCqx8u7kHQAUAc2A+sByM1vyw38HP3DOTQemA8TFxblyrinobM44zPNLt/DJut1UrxrBxKHFAR9TSwEvImcvkKDfCbQssdzCv660fdL93TR1KT4pez3wqXOuAMg0s6+BOCAFYdOebJ5fuoV/r9tDzagIfn1Be247vx0N1EUjImUokKBfAXQ0s7YUB/oYigO8pLnAWOBbYDTwmXPOmVkqcDHwppnVBAYBz5ZR7ZVW4q7igP90wx5qRUdy50XtmXCe+uBFpHycMuj9fe53AQspHl75qnNug5lNBeKdc3OBGRSHeTKwn+I/BlA8Wuc1M9sAGPCacy6hPD5IZbB+5yGeX7qFRYkZ1I6O5O6LOzD+vLbUq6GAF5HyY84FV5d4XFyci4+P97qMMrU69QDTlm1lycYMaleL5NZz23LruW2pW0PDJEWkbJjZSudcXGnbdMVNOXHO8c3WLKYtS+abrVnUrV6Vey/tyC3ntqVudQW8iFQcBX0Z8/kcSzZmMO3zraxNO0ij2tE8dEVXrhvYilq6klVEPKDkKSOFRT7mJ+zmxc+T2ZyRQ8sG1fnzL87h6r4tNBeNiHhKQX+W8guLeG9lOi9/kULq/lw6Na7Fs9f25sqeTTVdsIgEBQX9GTqSX8js71OZ/mUKmYfz6dWiLn8Y0Y9LuzbWDT9EJKgo6E/TgSPHmPntDl77ZhsHcwsY0r4hz1zbmyHtG+qWfSISlBT0AUrbn8sry1N4Jz6dowVFXNq1MZMuak/fVvW9Lk1E5KQU9KeQkH6Ql79MYcG63URUMUb1bs7Eoe3o1Li216WJiAREQV8K5xyfJ+3l5S+38p+U/dSOjuS2oe24ZUhbmtSt5nV5IiKnRUFfwrFCHx+v2ck/l6ewOSOHpnWr8dAVXRkzoCW1dbMPEamkFPRAdl4Bb3+XymtfbyMjO58uTWrz9DW9GNmrGVU1RFJEKrmwDvpdB4/y+jfbefu7VHLyCzm3Q0OeGN2LoR1jNIJGREJGWAb9qtQDvPrVNhas3wPAiB5NmTi0Hec0r+txZSIiZS9sgr6gyMen6/cw46ttrEk7SO1qkYw/ry03D25Ni/o1vC5PRKTchHzQH8w9xuzv05j57XZ2H8qjbUxNpo7qztV9W1BTk4yJSBgI2aRLzszh9W+28f7KnRwtKGJI+4b878/P4aLOjTRFgYiElZAKeuccXyXvY8ZX2/g8aS9RkVX4ee9m3HJuW7o2reN1eSIingiZoE/bn8v4N1awOSOHmFrR/PbSTtwwqBUxtaK9Lk1ExFMhE/RN61ajRf0aTBzanpG9mhIdqTngRUQghII+MqIKr47r73UZIiJBR5d9ioiEOAW9iEiIU9CLiIQ4Bb2ISIhT0IuIhDgFvYhIiFPQi4iEOAW9iEiIM+ec1zX8hJntBXacxUvEAPvKqJxQonYpndqldGqX0gVzu7R2zsWWtiHogv5smVm8cy7O6zqCjdqldGqX0qldSldZ20VdNyIiIU5BLyIS4kIx6Kd7XUCQUruUTu1SOrVL6Splu4RcH72IiPxUKB7Ri4hICQp6EZEQFzJBb2bDzSzJzJLNbLLX9VQkM3vVzDLNbH2JdQ3MbLGZbfF/r+9fb2b2vL+dEsysr3eVly8za2lmy8ws0cw2mNk9/vVh3TZmVs3Mvjeztf52meJf39bMvvN//n+ZWZR/fbR/Odm/vY2nH6CcmVmEma02s/n+5UrfLiER9GYWAUwDLge6AdeZWTdvq6pQrwPDj1s3GVjqnOsILPUvQ3EbdfR/TQT+UUE1eqEQ+L1zrhswCLjT/3MR7m2TD1zsnOsF9AaGm9kg4HHgGedcB+AAMN6//3jggH/9M/79Qtk9wMYSy5W/XZxzlf4LGAwsLLH8IPCg13VVcBu0AdaXWE4CmvofNwWS/I9fBq4rbb9Q/wI+BoapbX7SJjWAVcBAiq/4jPSv//F3ClgIDPY/jvTvZ17XXk7t0YLiP/4XA/MBC4V2CYkjeqA5kFZiOd2/Lpw1ds7t9j/eAzT2Pw7LtvL/W90H+A61zQ/dE2uATGAxsBU46Jwr9O9S8rP/2C7+7YeAhhVacMV5FngA8PmXGxIC7RIqQS8n4YoPOcJ2HK2Z1QLeB+51zmWX3BaubeOcK3LO9ab4CHYA0MXbirxnZlcCmc65lV7XUtZCJeh3Ai1LLLfwrwtnGWbWFMD/PdO/PqzaysyqUhzys5xzH/hXq238nHMHgWUUd0nUM7NI/6aSn/3HdvFvrwtkVWylFeJc4Coz2w7Mobj75jlCoF1CJehXAB39Z8ejgDHAXI9r8tpcYKz/8ViK+6d/WH+zf4TJIOBQiW6MkGJmBswANjrnni6xKazbxsxizaye/3F1is9bbKQ48Ef7dzu+XX5or9HAZ/7/hEKKc+5B51wL51wbijPkM+fcDYRCu3h9kqAMT6JcAWymuK/xIa/rqeDPPhvYDRRQ3Ic4nuK+wqXAFmAJ0MC/r1E8QmkrsA6I87r+cmyX8yjulkkA1vi/rgj3tgF6Aqv97bIe+JN/fTvgeyAZeBeI9q+v5l9O9m9v5/VnqIA2uhCYHyrtoikQRERCXKh03YiIyAko6EVEQpyCXkQkxCnoRURCnIJeRCTEKehFREKcgl5EJMT9f99yaVJdOpgkAAAAAElFTkSuQmCC\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.plot(auto_trace_gauss.trace-auto_trace_spline.trace)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "## About this notebook\n", + "\n", + "**Author:** Kyle Conroy, JWST\n", + "**Updated On:** 2022-11-16" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "[Top of Page](#top)\n", + "\"Space " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "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": "3.8.12" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/specreduce/background.py b/specreduce/background.py index 88482695..23d34018 100644 --- a/specreduce/background.py +++ b/specreduce/background.py @@ -145,7 +145,7 @@ def two_sided(cls, image, trace_object, separation, **kwargs): Example: :: - trace = KosmosTrace(image, guess=trace_pos) + trace = FitTrace(image, guess=trace_pos) bg = Background.two_sided(image, trace, bkg_sep, width=bkg_width) Parameters @@ -178,7 +178,7 @@ def one_sided(cls, image, trace_object, separation, **kwargs): Example: :: - trace = KosmosTrace(image, guess=trace_pos) + trace = FitTrace(image, guess=trace_pos) bg = Background.one_sided(image, trace, bkg_sep, width=bkg_width) Parameters diff --git a/specreduce/tests/test_tracing.py b/specreduce/tests/test_tracing.py index 1d0fc983..43e851bd 100644 --- a/specreduce/tests/test_tracing.py +++ b/specreduce/tests/test_tracing.py @@ -3,7 +3,7 @@ from astropy.modeling import models from specreduce.utils.synth_data import make_2dspec_image -from specreduce.tracing import Trace, FlatTrace, ArrayTrace, KosmosTrace +from specreduce.tracing import Trace, FlatTrace, ArrayTrace, FitTrace IM = make_2dspec_image() @@ -66,8 +66,9 @@ def test_array_trace(): assert t_short.shape[0] == IM.shape[1] -# test KOSMOS trace algorithm -def test_kosmos_trace(): +# test fitted traces +@pytest.mark.filterwarnings("ignore:Model is linear in parameters") +def test_fit_trace(): # create image (process adapted from compare_extractions.ipynb) np.random.seed(7) nrows = 200 @@ -82,7 +83,7 @@ def test_kosmos_trace(): img = col_model(index_arr.T) + noise # calculate trace on normal image - t = KosmosTrace(img) + t = FitTrace(img, bins=20) # test shifting shift_up = int(-img.shape[0]/4) @@ -96,17 +97,20 @@ def test_kosmos_trace(): t.shift(shift_out) assert t.trace.mask.all(), 'invalid values not masked' - # test peak_method options - tg = KosmosTrace(img, peak_method='gaussian') - tc = KosmosTrace(img, peak_method='centroid') - tm = KosmosTrace(img, peak_method='max') + # test peak_method and trace_model options + tg = FitTrace(img, bins=20, + peak_method='gaussian', trace_model=models.Legendre1D(3)) + tc = FitTrace(img, bins=20, + peak_method='centroid', trace_model=models.Chebyshev1D(2)) + tm = FitTrace(img, bins=20, + peak_method='max', trace_model=models.Spline1D(degree=3)) # traces should all be close to 100 # (values may need to be updated on changes to seed, noise, etc.) assert np.max(abs(tg.trace-100)) < sigma_pix assert np.max(abs(tc.trace-100)) < 3 * sigma_pix assert np.max(abs(tm.trace-100)) < 6 * sigma_pix with pytest.raises(ValueError): - t = KosmosTrace(img, peak_method='invalid') + t = FitTrace(img, peak_method='invalid') # create same-shaped variations of image with invalid values img_all_nans = np.tile(np.nan, (nrows, ncols)) @@ -116,17 +120,29 @@ def test_kosmos_trace(): img_win_nans = img.copy() img_win_nans[guess - window:guess + window] = np.nan - # ensure a low bin number is rejected + # ensure float bin values trigger a warning but no issues otherwise + with pytest.warns(UserWarning, match='TRACE: Converting bins to int'): + FitTrace(img, bins=20., trace_model=models.Polynomial1D(2)) + + # ensure non-equipped models are rejected + with pytest.raises(ValueError, match=r'trace_model must be one of*'): + FitTrace(img, trace_model=models.Hermite1D(3)) + + # ensure a bin number below 4 is rejected with pytest.raises(ValueError, match='bins must be >= 4'): - KosmosTrace(img, bins=3) + FitTrace(img, bins=3) + + # ensure a bin number below degree of trace model is rejected + with pytest.raises(ValueError, match='bins must be > '): + FitTrace(img, bins=4, trace_model=models.Chebyshev1D(5)) # ensure number of bins greater than number of dispersion pixels is rejected with pytest.raises(ValueError, match=r'bins must be <*'): - KosmosTrace(img, bins=ncols) + FitTrace(img, bins=ncols + 1) # error on trace of otherwise valid image with all-nan window around guess try: - KosmosTrace(img_win_nans, guess=guess, window=window) + FitTrace(img_win_nans, guess=guess, window=window) except ValueError as e: print(f"All-NaN window error message: {e}") else: @@ -134,7 +150,7 @@ def test_kosmos_trace(): # error on trace of all-nan image try: - KosmosTrace(img_all_nans) + FitTrace(img_all_nans) except ValueError as e: print(f"All-NaN image error message: {e}") else: diff --git a/specreduce/tracing.py b/specreduce/tracing.py index 6b2557ca..d22d01e6 100644 --- a/specreduce/tracing.py +++ b/specreduce/tracing.py @@ -1,16 +1,16 @@ # Licensed under a 3-clause BSD style license - see LICENSE.rst from copy import deepcopy -from dataclasses import dataclass +from dataclasses import dataclass, field import warnings -from astropy.modeling import fitting, models +from astropy.modeling import Model, fitting, models from astropy.nddata import CCDData, NDData from astropy.stats import gaussian_sigma_to_fwhm -from scipy.interpolate import UnivariateSpline +from astropy.utils.decorators import deprecated import numpy as np -__all__ = ['Trace', 'FlatTrace', 'ArrayTrace', 'KosmosTrace'] +__all__ = ['Trace', 'FlatTrace', 'ArrayTrace', 'FitTrace'] @dataclass @@ -81,7 +81,7 @@ def __sub__(self, delta): @dataclass class FlatTrace(Trace): """ - Trace that is constant along the axis being traced + Trace that is constant along the axis being traced. Example: :: @@ -114,7 +114,7 @@ def set_position(self, trace_pos): @dataclass class ArrayTrace(Trace): """ - Define a trace given an array of trace positions + Define a trace given an array of trace positions. Parameters ---------- @@ -139,22 +139,20 @@ def __post_init__(self): @dataclass -class KosmosTrace(Trace): +class FitTrace(Trace): """ Trace the spectrum aperture in an image. - Chops image up in bins along the dispersion (wavelength) direction, - fits a Gaussian within each bin to determine the trace's spatial - center. Finally, draws a cubic spline through the bins to up-sample - trace along every pixel in the dispersion direction. - - (The original version of this algorithm is sourced from James - Davenport's ``kosmos`` repository.) + Bins along the image's dispersion (wavelength) direction, finds each + bin's peak cross-dispersion (spatial) pixel, and uses a model to + interpolate the function fitted to the peaks as a final trace. The + number of bins, peak finding algorithm, and model used for fitting + are customizable by the user. Example: :: - trace = KosmosTrace(image, guess=trace_pos) + trace = FitTrace(image, peak_method='gaussian', guess=trace_pos) Parameters ---------- @@ -164,9 +162,10 @@ class KosmosTrace(Trace): direction is axis 1. bins : int, optional The number of bins in the dispersion (wavelength) direction - into which to divide the image. Use fewer if KosmosTrace is - having difficulty, such as with faint targets. - Minimum bin size is 4. [default: 20] + into which to divide the image. If not set, defaults to one bin + per dispersion (wavelength) pixel in the given image. If set, + requires at least 4 or N bins for a degree N ``trace_model``, + whichever is greater. [default: None] guess : int, optional A guess at the trace's location in the cross-dispersion (spatial) direction. If set, overrides the normal max peak @@ -177,25 +176,29 @@ class KosmosTrace(Trace): guess position. Useful for tracing faint sources if multiple traces are present, but potentially bad if the trace is substantially bent or warped. [default: None] + trace_model : one of `~astropy.modeling.polynomial.Chebyshev1D`,\ + `~astropy.modeling.polynomial.Legendre1D`,\ + `~astropy.modeling.polynomial.Polynomial1D`,\ + or `~astropy.modeling.spline.Spline1D`, optional + The 1-D polynomial model used to fit the trace to the bins' peak + pixels. Spline1D models are fit with Astropy's + 'SplineSmoothingFitter', while the other models are fit with the + 'LevMarLSQFitter'. [default: ``models.Polynomial1D(degree=1)``] peak_method : string, optional - One of ``gaussian`` (default), ``centroid``, or ``max``. - gaussian: fits a gaussian to the window within each bin and - adopts the central value. centroid: takes the centroid of the - window within in bin. smooth_max: takes the position with the - maximum flux after smoothing over the window within each bin. - - Improvements Needed - ------------------- - 1) switch to astropy models for Gaussian (done) - 2) return info about trace width (?) - 3) add re-fit trace functionality (or break off into other method) - 4) add other interpolation modes besides spline, maybe via - specutils.manipulation methods? + One of ``gaussian``, ``centroid``, or ``max``. + ``gaussian``: Fits a gaussian to the window within each bin and + adopts the central value as the peak. May work best with fewer + bins on faint targets. (Based on the "kosmos" algorithm from + James Davenport's same-named repository.) + ``centroid``: Takes the centroid of the window within in bin. + ``max``: Saves the position with the maximum flux in each bin. + [default: ``max``] """ - bins: int = 20 + bins: int = None guess: float = None window: int = None - peak_method: str = 'gaussian' + trace_model: Model = field(default=models.Polynomial1D(degree=1)) + peak_method: str = 'max' _crossdisp_axis = 0 _disp_axis = 1 @@ -221,18 +224,30 @@ def __post_init__(self): if self._disp_axis != 1: raise ValueError('dispersion axis must equal 1') - if not isinstance(self.bins, int): - warnings.warn('TRACE: Converting bins to int') - self.bins = int(self.bins) - - if self.bins < 4: - raise ValueError('bins must be >= 4') + valid_models = (models.Spline1D, models.Legendre1D, + models.Chebyshev1D, models.Polynomial1D) + if not isinstance(self.trace_model, valid_models): + raise ValueError("trace_model must be one of " + f"{', '.join([m.name for m in valid_models])}.") cols = img.shape[self._disp_axis] - if self.bins >= cols: - raise ValueError(f"bins must be < {cols}, the length of the " + model_deg = self.trace_model.degree + if self.bins is None: + self.bins = cols + elif self.bins < 4: + # many of the Astropy model fitters require four points at minimum + raise ValueError('bins must be >= 4') + elif self.bins <= model_deg: + raise ValueError(f"bins must be > {model_deg} for " + f"a degree {model_deg} model.") + elif self.bins > cols: + raise ValueError(f"bins must be <= {cols}, the length of the " "image's spatial direction") + if not isinstance(self.bins, int): + warnings.warn('TRACE: Converting bins to int') + self.bins = int(self.bins) + if (self.window is not None and (self.window > img.shape[self._disp_axis] or self.window < 1)): @@ -335,12 +350,26 @@ def __post_init__(self): x_bins = x_bins[y_finite] y_bins = y_bins[y_finite] - # run a cubic spline through the bins; interpolate over wavelengths - ap_spl = UnivariateSpline(x_bins, y_bins, k=3, s=0) + # use given model to bin y-values; interpolate over all wavelengths + fitter = (fitting.SplineSmoothingFitter() + if isinstance(self.trace_model, models.Spline1D) + else fitting.LevMarLSQFitter()) + self.trace_model_fit = fitter(self.trace_model, x_bins, y_bins) + trace_x = np.arange(img.shape[self._disp_axis]) - trace_y = ap_spl(trace_x) + trace_y = self.trace_model_fit(trace_x) else: warnings.warn("TRACE ERROR: No valid points found in trace") - trace_y = np.tile(np.nan, len(x_bins)) + trace_y = np.tile(np.nan, img.shape[self._disp_axis]) self.trace = np.ma.masked_invalid(trace_y) + + +@deprecated('1.3', alternative='FitTrace') +@dataclass +class KosmosTrace(FitTrace): + """ + This class is pending deprecation. Please use `FitTrace` instead. + """ + __doc__ += FitTrace.__doc__ + pass