diff --git a/notebooks/region_search/Region Searching Workbook.ipynb b/notebooks/region_search/Region Searching Workbook.ipynb index 25b8dcfe5..5d7e6da65 100644 --- a/notebooks/region_search/Region Searching Workbook.ipynb +++ b/notebooks/region_search/Region Searching Workbook.ipynb @@ -36,7 +36,7 @@ }, { "cell_type": "code", - "execution_count": 215, + "execution_count": 339, "id": "d0189084", "metadata": {}, "outputs": [], @@ -50,31 +50,34 @@ "import progressbar\n", "from concurrent.futures import ProcessPoolExecutor, as_completed\n", "from astropy.time import Time # for converting Butler visitInfo.date (TAI) to UTC strings\n", - "import pandas as pd" + "from astropy import units as u\n", + "import pandas as pd\n", + "import pickle\n", + "from dateutil import parser" ] }, { "cell_type": "code", - "execution_count": 136, + "execution_count": 476, "id": "ceeec168", "metadata": {}, "outputs": [ { - "data": { - "text/plain": [ - "96" - ] - }, - "execution_count": 136, - "metadata": {}, - "output_type": "execute_result" + "name": "stdout", + "output_type": "stream", + "text": [ + "96 CPUs were reported as available by the multiprocessing module.\n" + ] } ], "source": [ - "# We will use multiprocessing in a few places. Let's see what the sytsem thinks we have available.\n", + "# We will use some kind of multiprocessing in a few places. Let's see what the sytsem thinks we have available.\n", + "# NOTE: we could set limits on executors later using this value, if desired. 2/6/2024 COC\n", + "\n", "import multiprocessing\n", "\n", - "multiprocessing.cpu_count()" + "available_cpus = multiprocessing.cpu_count()\n", + "print(f\"{available_cpus} CPUs were reported as available by the multiprocessing module.\")" ] }, { @@ -124,9 +127,39 @@ }, { "cell_type": "code", - "execution_count": 153, + "execution_count": 465, "id": "e6a546f0", "metadata": {}, + "outputs": [], + "source": [ + "def get_collection_names(butler, basedir, verbose=False, export=True):\n", + " \"\"\"\n", + " Making this a function 2/6/2024 COC.\n", + " \"\"\"\n", + " all_collection_names = []\n", + "\n", + " for c in sorted(butler.registry.queryCollections(\"*\")):\n", + " all_collection_names.append(c)\n", + "\n", + " if export == True:\n", + " outfile = f\"{basedir}/all_collection_names.lst\"\n", + " with open(outfile, \"w\") as f:\n", + " for c in all_collection_names:\n", + " print(c, file=f)\n", + "\n", + " if verbose:\n", + " message = f\"Found {len(all_collection_names)} collections in the Butler.\"\n", + " if export == True:\n", + " message += f' Wrote to \"{outfile}\".'\n", + " print(message)\n", + " return all_collection_names" + ] + }, + { + "cell_type": "code", + "execution_count": 466, + "id": "3006e4fd", + "metadata": {}, "outputs": [ { "name": "stdout", @@ -137,23 +170,72 @@ } ], "source": [ - "all_collection_names = []\n", + "all_collection_names = get_collection_names(butler=butler, basedir=basedir, verbose=True, export=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 468, + "id": "b409c810", + "metadata": {}, + "outputs": [], + "source": [ + "# We looked through the collections already.\n", + "# We will string manipulate to get to what we need.\n", + "# Previously, we used a list file on disk. (This could be a better option for some users.)\n", + "\n", + "\n", + "def get_desired_collections(all_collections_list, desired_collection_list=None):\n", + " \"\"\"\n", + " Produce a list of collections that will be used for querying the Butler.\n", + "\n", + " If desired_collection_list is None, then a hard-wired \"default\" approach\n", + " (for Haden/DEEP) is carried out, requiring:\n", + " 1. \"Pointing\" must be in the collection name.\n", + " 2. \"/imdiff_r/\" must be in the collection name.\n", + " 3. \"/2021\" may not be in the collection name.\n", + "\n", + " Otherwise, desired_collection_list can be either\n", + " 1. a Python list of desired collection names, or\n", + " 2. a filename (ending in .lst) that specifies the desired collections.\n", + " Either way, the collection names are verified against the (required) collections_list.\n", "\n", - "for c in sorted(butler.registry.queryCollections(\"*\")):\n", - " all_collection_names.append(c)\n", + " Made this into a function 2/6/2024 COC.\n", "\n", - "outfile = f\"{basedir}/all_collection_names.lst\"\n", - "with open(outfile, \"w\") as f:\n", - " for c in all_collection_names:\n", - " print(c, file=f)\n", + " NOTE/TODO: untested are the supplied list and list file approaches.\n", + " \"\"\"\n", + "\n", + " desired_collections = []\n", "\n", - "print(f'Found {len(all_collection_names)} collections in the Butler. Wrote to \"{outfile}\".')" + " if desired_collection_list == None:\n", + " for collection_name in all_collection_names:\n", + " if (\n", + " \"Pointing\" in collection_name\n", + " and \"/imdiff_r\" in collection_name\n", + " and \"/2021\" not in collection_name\n", + " ):\n", + " desired_collections.append(collection_name)\n", + " else:\n", + " if type(desired_collection_list) == type(\"\"):\n", + " with open(desired_collection_list, \"r\") as f:\n", + " for line in f:\n", + " desired_collections.append(line.strip())\n", + " else:\n", + " for entry in desired_collection_list:\n", + " desired_collections.append(entry)\n", + " #\n", + " # Validate entries\n", + " for entry in desired_collections:\n", + " if entry not in all_collections_list:\n", + " raise KeyError(f'\"{entry}\" is not in the master list of collections supplied.')\n", + " #\n", + " return desired_collections" ] }, { "cell_type": "code", - "execution_count": 154, - "id": "b409c810", + "execution_count": 473, + "id": "dc9a4efc", "metadata": {}, "outputs": [ { @@ -169,21 +251,13 @@ " 'PointingGroup023/imdiff_r']" ] }, - "execution_count": 154, + "execution_count": 473, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "# We looked through the collections already.\n", - "# We will string manipulate to get to what we need.\n", - "# Previously, I used a list file on disk. (This could be a better option for some users.)\n", - "\n", - "desired_collections = [] # we will store these for future use\n", - "\n", - "for collection_name in all_collection_names:\n", - " if \"Pointing\" in collection_name and \"/imdiff_r\" in collection_name and \"/2021\" not in collection_name:\n", - " desired_collections.append(collection_name)\n", + "desired_collections = get_desired_collections(all_collections_list=all_collection_names)\n", "desired_collections" ] }, @@ -318,6 +392,7 @@ ], "source": [ "%%time\n", + "# TIMING NOTE: this takes roughly 2.5 minutes without cache (2/6/2024 COC)\n", "datasetTypes = getDatasetTypeStats(butler=butler, overwrite=False)" ] }, @@ -417,51 +492,217 @@ }, { "cell_type": "code", - "execution_count": 213, + "execution_count": 608, "id": "c47e5588", "metadata": {}, + "outputs": [], + "source": [ + "def get_vdr_data(butler, desired_collections, desired_datasetTypes):\n", + " \"\"\"\n", + "\n", + " Made as function 2/6/2024 COC.\n", + " \"\"\"\n", + " # VDR === Visit Detector Region\n", + " # VDRs hold what we need in terms of region hashes and unique dataIds.\n", + " # NOTE: this typically takes < 5s to run 2/1/2024 COC\n", + " # NOTE: tried iterating over desired_collections vs supplying desired_collections; same output 2/1/2024 COC\n", + "\n", + " vdr_dict = {\"data_id\": [], \"region\": [], \"detector\": []}\n", + " # vdr_ids = []\n", + " # vdr_regions = []\n", + " # vdr_detectors = []\n", + "\n", + " for dt in desired_datasetTypes:\n", + " datasetRefs = butler.registry.queryDimensionRecords(\n", + " \"visit_detector_region\", datasets=dt, collections=desired_collections\n", + " )\n", + " for ref in datasetRefs:\n", + " vdr_dict[\"data_id\"].append(ref.dataId)\n", + " vdr_dict[\"region\"].append(\n", + " ref.region\n", + " ) # keeping as objects for now; should .encode() for caching/export\n", + " vdr_dict[\"detector\"].append(ref.detector) # 2/2/2024 COC\n", + " # BUT if we decided to export this or cache this, we should write the encode() version to disk\n", + " #\n", + " example_vdr_ref = ref # this leaves a VDR Python object we can play with\n", + " # other data available:\n", + " # id = ref.id# id -- e.g., 1592350 (for DEEP dataset, I think UUIDs for newer Butlers)\n", + " # visit = ref.dataId.full['visit'] # e.g., 946725\n", + " # vdr_filters.append(ref.dataId.full['band']) # e.g., VR\n", + " # vdr_detectors.append(ref.dataId.full['detector']) # e.g., 1\n", + " df = pd.DataFrame.from_dict(vdr_dict)\n", + " return df, example_vdr_ref" + ] + }, + { + "cell_type": "code", + "execution_count": 611, + "id": "eb222e02", + "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "CPU times: user 1.64 s, sys: 85.7 ms, total: 1.72 s\n", - "Wall time: 2.38 s\n" + "CPU times: user 1.82 s, sys: 83.2 ms, total: 1.91 s\n", + "Wall time: 2.4 s\n" ] } ], "source": [ "%%time\n", - "# VDR === Visit Detector Region\n", - "# VDRs hold what we need in terms of region hashes and unique dataIds.\n", - "# NOTE: this typically takes < 5s to run 2/1/2024 COC\n", - "# NOTE: tried iterating over desired_collections vs supplying desired_collections; same output 2/1/2024 COC\n", - "\n", - "vdr_ids = []\n", - "vdr_regions = []\n", - "vdr_detectors = []\n", - "\n", - "for dt in desired_datasetTypes:\n", - " datasetRefs = butler.registry.queryDimensionRecords(\n", - " \"visit_detector_region\", datasets=dt, collections=desired_collections\n", - " )\n", - " for ref in datasetRefs:\n", - " vdr_ids.append(ref.dataId)\n", - " vdr_regions.append(ref.region) # keeping as objects for now; should .encode() for caching/export\n", - " vdr_detectors.append(ref.detector) # 2/2/2024 COC\n", - " # BUT if we decided to export this or cache this, we should write the encode() version to disk\n", - " #\n", - " tmpref = ref # this leaves a VDR Python object we can play with\n", - " # other data available:\n", - " # id = ref.id# id -- e.g., 1592350 (for DEEP dataset, I think UUIDs for newer Butlers)\n", - " # visit = ref.dataId.full['visit'] # e.g., 946725\n", - " # vdr_filters.append(ref.dataId.full['band']) # e.g., VR\n", - " # vdr_detectors.append(ref.dataId.full['detector']) # e.g., 1" + "# TIMING NOTE: requires about 2 seconds 2/6/2024 COC\n", + "df, example_vdr_ref = get_vdr_data(\n", + " butler=butler, desired_collections=desired_collections, desired_datasetTypes=desired_datasetTypes\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 612, + "id": "6e9462e1", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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data_idregiondetector
0(instrument, detector, visit)ConvexPolygon([UnitVector3d(0.9847372525065534...1
1(instrument, detector, visit)ConvexPolygon([UnitVector3d(0.9847381014554984...1
2(instrument, detector, visit)ConvexPolygon([UnitVector3d(0.9847383417970056...1
3(instrument, detector, visit)ConvexPolygon([UnitVector3d(0.9847382159041213...1
4(instrument, detector, visit)ConvexPolygon([UnitVector3d(0.9847381374341414...1
............
47378(instrument, detector, visit)ConvexPolygon([UnitVector3d(0.987608537646486,...62
47379(instrument, detector, visit)ConvexPolygon([UnitVector3d(0.9876085083003562...62
47380(instrument, detector, visit)ConvexPolygon([UnitVector3d(0.9876085761885252...62
47381(instrument, detector, visit)ConvexPolygon([UnitVector3d(0.9876085761885252...62
47382(instrument, detector, visit)ConvexPolygon([UnitVector3d(0.9876086828694174...62
\n", + "

47383 rows × 3 columns

\n", + "
" + ], + "text/plain": [ + " data_id \n", + "0 (instrument, detector, visit) \\\n", + "1 (instrument, detector, visit) \n", + "2 (instrument, detector, visit) \n", + "3 (instrument, detector, visit) \n", + "4 (instrument, detector, visit) \n", + "... ... \n", + "47378 (instrument, detector, visit) \n", + "47379 (instrument, detector, visit) \n", + "47380 (instrument, detector, visit) \n", + "47381 (instrument, detector, visit) \n", + "47382 (instrument, detector, visit) \n", + "\n", + " region detector \n", + "0 ConvexPolygon([UnitVector3d(0.9847372525065534... 1 \n", + "1 ConvexPolygon([UnitVector3d(0.9847381014554984... 1 \n", + "2 ConvexPolygon([UnitVector3d(0.9847383417970056... 1 \n", + "3 ConvexPolygon([UnitVector3d(0.9847382159041213... 1 \n", + "4 ConvexPolygon([UnitVector3d(0.9847381374341414... 1 \n", + "... ... ... \n", + "47378 ConvexPolygon([UnitVector3d(0.987608537646486,... 62 \n", + "47379 ConvexPolygon([UnitVector3d(0.9876085083003562... 62 \n", + "47380 ConvexPolygon([UnitVector3d(0.9876085761885252... 62 \n", + "47381 ConvexPolygon([UnitVector3d(0.9876085761885252... 62 \n", + "47382 ConvexPolygon([UnitVector3d(0.9876086828694174... 62 \n", + "\n", + "[47383 rows x 3 columns]" + ] + }, + "execution_count": 612, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df" ] }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 610, "id": "e04e2f35", "metadata": {}, "outputs": [ @@ -474,12 +715,12 @@ } ], "source": [ - "print(f\"We found {len(vdr_ids)} regions spanning the {len(desired_collections)} desired collections.\")" + "print(f\"We found {len(df['data_id'])} regions spanning the {len(desired_collections)} desired collections.\")" ] }, { "cell_type": "code", - "execution_count": 212, + "execution_count": 559, "id": "08d5b443", "metadata": {}, "outputs": [ @@ -496,19 +737,19 @@ "visit_detector_region.RecordClass(instrument='DECam', detector=62, visit=946176, region=ConvexPolygon([UnitVector3d(0.9876086828694174, -0.13336028508776862, -0.08272922024438323), UnitVector3d(0.9873378171284917, -0.13332652431396907, -0.08595389916869185), UnitVector3d(0.9881047366097594, -0.12752395595185462, -0.08594573955553172), UnitVector3d(0.9883760335240734, -0.12755303452468866, -0.0827226676235914)]))" ] }, - "execution_count": 212, + "execution_count": 559, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# For demonstration purposes we still have the last \"ref\" from the last cell's iteration\n", - "tmpref" + "example_vdr_ref" ] }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 560, "id": "e28031c2", "metadata": {}, "outputs": [ @@ -518,19 +759,19 @@ "{instrument: 'DECam', detector: 62, visit: 946176}" ] }, - "execution_count": 14, + "execution_count": 560, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# This is the unique dataId we would need to retrieve this specific image later\n", - "tmpref.dataId" + "example_vdr_ref.dataId" ] }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 561, "id": "761f3610", "metadata": {}, "outputs": [ @@ -538,20 +779,22 @@ "name": "stdout", "output_type": "stream", "text": [ - "tmpref.instrument = DECam, tmpref.detector = 62, tmpref.visit = 946176\n" + "example_vdr_ref.instrument = DECam, example_vdr_ref.detector = 62, example_vdr_ref.visit = 946176\n" ] } ], "source": [ "# The unique dataId is made up of the following attributes:\n", "print(\n", - " f\"tmpref.instrument = {tmpref.instrument}, tmpref.detector = {tmpref.detector}, tmpref.visit = {tmpref.visit}\"\n", + " f\"example_vdr_ref.instrument = {example_vdr_ref.instrument}, \\\n", + " example_vdr_ref.detector = {example_vdr_ref.detector}, \\\n", + " example_vdr_ref.visit = {example_vdr_ref.visit}\"\n", ")" ] }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 562, "id": "5d6b2106", "metadata": {}, "outputs": [ @@ -561,19 +804,19 @@ "b'p\\xddnE\\x86}\\x9a\\xef?\\x0f\\xc3\\x84\\'\\xf3\\x11\\xc1\\xbf\\x80\\x8a_\\xff\\xbd-\\xb5\\xbf\\x04xUzE\\x98\\xef?\\x94\\x15\\xcf\\xf2\\xd7\\x10\\xc1\\xbf\\x9d\\xa9\\xe4!\\x13\\x01\\xb6\\xbf\\x1d_\\x18\\xd3\\x8d\\x9e\\xef?\\x80\\x87\"z\\xb4R\\xc0\\xbff\\x1d\\x9f<\\x8a\\x00\\xb6\\xbf\\xe4Z\\x84\\xc6\\xc6\\xa0\\xef?\\x1f\\x01\\xe5g\\xa8S\\xc0\\xbf\\xba\\xc9\\x14\\x10P-\\xb5\\xbf'" ] }, - "execution_count": 16, + "execution_count": 562, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# This is the region hash we would use for \"region matching\" later\n", - "tmpref.region.encode()" + "example_vdr_ref.region.encode()" ] }, { "cell_type": "code", - "execution_count": 140, + "execution_count": 563, "id": "aa653a8b", "metadata": {}, "outputs": [ @@ -583,47 +826,83 @@ "False" ] }, - "execution_count": 140, + "execution_count": 563, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# We check to see if we can crawl to other data from the dataId (False means no).\n", - "tmpref.dataId.hasRecords()" + "example_vdr_ref.dataId.hasRecords()" ] }, { "cell_type": "markdown", - "id": "5bdaaf00", + "id": "3f49186c", "metadata": {}, "source": [ - "### Instrument handling\n", - "Some Butler queries require the instrument to be specified.\\\n", - "For now, we are just supplying the one we care about (KLUDGE).\\\n", - "It's a list so we can mix-and-match shift-and-stack across instruments(!).\\\n", - "NOTE: will leave this as a list for future-proofing purpose" + "# Dataframe Assembly\n", + "\n", + "This used to contain more, but after I moved everything to a dictionary, then a DF, this became less important.\n", + "TODO determine where this should go. Or remove." + ] + }, + { + "cell_type": "code", + "execution_count": 587, + "id": "acda18f4", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "CPU times: user 323 ms, sys: 68 ms, total: 391 ms\n", + "Wall time: 582 ms\n" + ] + } + ], + "source": [ + "%%time\n", + "\n", + "# Save the dataframe to a pickle file for easier resuming of the notebook\n", + "# Size was < 20 Mb 2/5/2024 COC\n", + "# COC Note: this was not that useful, maybe TODO remove\n", + "df_filename = f\"{basedir}/region_search_df.pickle\"\n", + "df.to_pickle(df_filename)" ] }, { "cell_type": "code", - "execution_count": 164, - "id": "e6c7af1b", + "execution_count": null, + "id": "bb076b4b", "metadata": {}, "outputs": [], "source": [ - "# [TODO] extract this somehow automatically 2/1/2024 COC\n", - "# desired_instruments = ['DECam']" + "# load the pickle\n", + "# df = pd.read_pickle(file_name)" + ] + }, + { + "cell_type": "markdown", + "id": "5bdaaf00", + "metadata": {}, + "source": [ + "### Instrument handling\n", + "Some Butler queries require the instrument to be specified.\\\n", + "For now, we are just supplying the one we care about (KLUDGE).\\\n", + "It's a list so we can mix-and-match shift-and-stack across instruments(!).\\\n", + "NOTE: will leave this as a list for future-proofing purpose" ] }, { "cell_type": "code", - "execution_count": 176, + "execution_count": 564, "id": "0228bb4d", "metadata": {}, "outputs": [], "source": [ - "def getInstruments(butler, first_instrument_only=True):\n", + "def getInstruments(butler, vdr_ids, first_instrument_only=True):\n", " \"\"\"Iterate through our records to determine which instrument(s) are involved.\n", " Return a list of the identified instruments.\n", " If first_instrument_only is True, stop as soon as we found an instrument.\n", @@ -646,7 +925,7 @@ }, { "cell_type": "code", - "execution_count": 177, + "execution_count": 613, "id": "6c3bb244", "metadata": {}, "outputs": [ @@ -656,14 +935,14 @@ "text": [ "Found DECam. Adding to \"desired_instruments\" now.\n", "WARNING: we are not iterating over all rows to find instruments, just taking the first one.\n", - "CPU times: user 85.5 ms, sys: 8.04 ms, total: 93.5 ms\n", - "Wall time: 126 ms\n" + "CPU times: user 140 ms, sys: 19 ms, total: 159 ms\n", + "Wall time: 199 ms\n" ] } ], "source": [ "%%time\n", - "desired_instruments = getInstruments(butler=butler)" + "desired_instruments = getInstruments(butler=butler, vdr_ids=df[\"data_id\"])" ] }, { @@ -678,7 +957,7 @@ }, { "cell_type": "code", - "execution_count": 143, + "execution_count": 614, "id": "dfc37b54", "metadata": {}, "outputs": [ @@ -686,22 +965,24 @@ "name": "stdout", "output_type": "stream", "text": [ - "CPU times: user 864 ms, sys: 221 ms, total: 1.09 s\n", - "Wall time: 1.5 s\n" + "CPU times: user 875 ms, sys: 108 ms, total: 983 ms\n", + "Wall time: 1.02 s\n" ] } ], "source": [ "%%time\n", "# Back to the dataId, we can actually fetch the image from the Butler\n", - "# NOTE: it takes about 1 second to do this, so we won't be able to do this at scale\n", + "# TIMING NOTE: it takes 1 to 2 seconds to do this, so we won't be able to do this at scale\n", "\n", - "tmp = butler.get(desired_datasetTypes[0], collections=desired_collections, dataId=tmpref.dataId)" + "example_butler_get = butler.get(\n", + " desired_datasetTypes[0], collections=desired_collections, dataId=example_vdr_ref.dataId\n", + ")" ] }, { "cell_type": "code", - "execution_count": 146, + "execution_count": 529, "id": "789f1d42", "metadata": {}, "outputs": [ @@ -711,18 +992,18 @@ "62" ] }, - "execution_count": 146, + "execution_count": 529, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "tmp.detector.getId()" + "example_butler_get.detector.getId()" ] }, { "cell_type": "code", - "execution_count": 149, + "execution_count": 530, "id": "351e8d03", "metadata": {}, "outputs": [ @@ -732,28 +1013,28 @@ "'VR'" ] }, - "execution_count": 149, + "execution_count": 530, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "tmp.filterLabel.bandLabel" + "example_butler_get.filterLabel.bandLabel" ] }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 531, "id": "1c1e5431", "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 19, + "execution_count": 531, "metadata": {}, "output_type": "execute_result" }, @@ -770,12 +1051,12 @@ ], "source": [ "# we can even view the image right here\n", - "plt.imshow(tmp.image.array, cmap=\"gray\")" + "plt.imshow(example_butler_get.image.array, cmap=\"gray\")" ] }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 532, "id": "ba1b7ab6", "metadata": {}, "outputs": [ @@ -788,27 +1069,19 @@ "Pixel Scale: 0.262593 arcsec/pixel" ] }, - "execution_count": 20, + "execution_count": 532, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# There IS a WCS here, which we did not see in the earlier VDR\n", - "tmp.wcs" + "example_butler_get.wcs" ] }, { "cell_type": "code", - "execution_count": 179, - "id": "dc55de3a", - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": 180, + "execution_count": 503, "id": "4270995e", "metadata": { "scrolled": true @@ -1020,7 +1293,7 @@ "# We can access all kinds of metadata this way.\n", "# NOTE: keywords are source data-dependent.\n", "# NOTE: this linkage works with butler.get() but not necessarily elsewhere.\n", - "meta = tmp.getInfo().getMetadata()\n", + "meta = example_butler_get.getInfo().getMetadata()\n", "\n", "print(f\"Here are all the keywords available from this image:\")\n", "for k in meta.keys():\n", @@ -1038,159 +1311,148 @@ }, { "cell_type": "code", - "execution_count": 152, - "id": "6c6d2df2", + "execution_count": 272, + "id": "7bbc916e", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "CPU times: user 6 µs, sys: 0 ns, total: 6 µs\n", - "Wall time: 14.3 µs\n" - ] - } - ], + "outputs": [], "source": [ - "%%time\n", - "\n", - "# this is the single-thead approach; wanted 2 hours to executed\n", - "\n", - "doIt = False\n", - "if doIt == True:\n", - " paths = []\n", - " with progressbar.ProgressBar(max_value=len(vdr_ids)) as bar:\n", - " for i, dataId in enumerate(vdr_ids):\n", - " my_path = butler.getURI(desired_datasetTypes[0], dataId=dataId, collections=desired_collections)\n", - " paths.append(my_path)\n", - " bar.update(i)\n", - "# no progress bar (my original approach)\n", - "# paths = [butler.getURI(desired_datasetTypes[0], dataId=dataId, collections=desired_collections) for dataId in vdr_ids]" + "# NOTE: getURIs() does not work as you'd think (i.e., it only handles a single dataId)\n", + "# butler.getURIs(desired_datasetTypes[0], vdr_ids, collections=desired_collections)" ] }, { "cell_type": "code", - "execution_count": 63, - "id": "92955300", + "execution_count": 567, + "id": "e0f8f8f9", "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "100% (237 of 237) |######################| Elapsed Time: 0:01:03 Time: 0:01:03\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "CPU times: user 1.42 s, sys: 3.65 s, total: 5.08 s\n", - "Wall time: 1min 21s\n" - ] - } - ], + "outputs": [], "source": [ - "%%time\n", + "# The single-thead approach (below) requires some 2 hours to execute. So instead we will multiprocess.\n", + "# paths = [butler.getURI(desired_datasetTypes[0], dataId=dataId, collections=desired_collections) for dataId in vdr_ids]\n", "\n", - "# TODO: functionize\n", "\n", - "paths = []\n", - "\n", - "# vdr_ids[0:1000] took 20s, chunking by 100;\n", - "# vdr_ids[0:100] with chunking by 10 took 8 s\n", - "subset = vdr_ids # doing all with chunksize of 200 took 83s!\n", - "\n", - "# thank you ChatGPT 4 for helping parallelize\n", - "\n", - "from concurrent.futures import ProcessPoolExecutor, as_completed\n", - "import progressbar\n", - "\n", - "\n", - "# Function to divide vdr_ids into chunks of 100\n", "def chunked_dataIds(dataIds, chunk_size=200):\n", " \"\"\"Yield successive chunk_size chunks from dataIds.\"\"\"\n", " for i in range(0, len(dataIds), chunk_size):\n", " yield dataIds[i : i + chunk_size]\n", "\n", "\n", - "# The function that will be executed in parallel, now adjusted to handle a list of dataIds\n", - "def get_uris(dataIds_chunk):\n", - " # Initialize an empty list to store URIs for this chunk\n", + "def get_uris(dataIds_chunk, repo_path, desired_datasetTypes, desired_collections):\n", + " \"\"\"Fetch URIs for a list of dataIds.\"\"\"\n", " chunk_uris = []\n", - " # You might need to reinitialize the Butler instance here if it's not serializable or shareable across processes\n", " butler = dafButler.Butler(repo_path)\n", " for dataId in dataIds_chunk:\n", " try:\n", " uri = butler.getURI(desired_datasetTypes[0], dataId=dataId, collections=desired_collections)\n", - " uri = uri.geturl() # just the text please\n", + " uri = uri.geturl() # Convert to URL string\n", " chunk_uris.append(uri)\n", " except Exception as e:\n", " print(f\"Failed to retrieve path for dataId {dataId}: {e}\")\n", " return chunk_uris\n", "\n", "\n", - "# Use ProcessPoolExecutor to execute get_uris in parallel processes\n", - "with ProcessPoolExecutor() as executor:\n", - " # Prepare your dataId chunks\n", - " dataId_chunks = list(chunked_dataIds(subset))\n", + "def getURIs(butler, dataIds, repo_path, desired_datasetTypes, desired_collections, overwrite=False):\n", + " \"\"\"\n", + " Get URIs from a Butler for a set of dataIDs.\n", + " Cache results to disk for future runs.\n", + " TODO: consider exporting as CSV so we can validate URIs against dataIds. 2/6/2024 COC\n", + " Updated 2/5/2024 COC\n", + " \"\"\"\n", + " paths = []\n", "\n", - " # Initialize progress bar\n", - " with progressbar.ProgressBar(max_value=len(dataId_chunks)) as bar:\n", - " futures = {executor.submit(get_uris, chunk): chunk for chunk in dataId_chunks}\n", + " cache_file = f\"{basedir}/uri_cache.lst\"\n", + " cached_exists = False\n", + " if len(glob.glob(cache_file)) > 0:\n", + " cached_exists = True\n", "\n", - " paths = []\n", - " for i, future in enumerate(as_completed(futures)):\n", - " # Retrieve the result of the future, which is the list of URIs for the chunk\n", - " uris = future.result()\n", - " paths.extend(uris) # Add the retrieved URIs to the main list\n", - " bar.update(i)\n", + " if cached_exists == True and overwrite == False:\n", + " with open(cache_file, \"r\") as f:\n", + " for line in f:\n", + " paths.append(line.strip())\n", + " print(f\"Recycled {len(paths)} paths from {cache_file} as overwrite was {overwrite}.\")\n", + " return paths\n", "\n", - "# At this point, `paths` will contain all successfully retrieved URIs for the chunks processed" - ] - }, + " # Prepare dataId chunks\n", + " dataId_chunks = list(chunked_dataIds(dataIds))\n", + "\n", + " # Execute get_uris in parallel and preserve order\n", + " with ProcessPoolExecutor() as executor:\n", + " # Initialize progress bar\n", + " with progressbar.ProgressBar(max_value=len(dataId_chunks)) as bar:\n", + " # Use map to execute get_uris on each chunk and maintain order\n", + " result_chunks = list(\n", + " executor.map(\n", + " get_uris,\n", + " dataId_chunks,\n", + " [repo_path] * len(dataId_chunks),\n", + " [desired_datasetTypes] * len(dataId_chunks),\n", + " [desired_collections] * len(dataId_chunks),\n", + " )\n", + " )\n", + "\n", + " for i, chunk_uris in enumerate(result_chunks):\n", + " paths.extend(chunk_uris) # Add the retrieved URIs to the main list\n", + " bar.update(i)\n", + "\n", + " with open(cache_file, \"w\") as f:\n", + " for path in paths:\n", + " print(path, file=f)\n", + " print(f\"Wrote {len(paths)} paths to disk for caching purposes.\")\n", + "\n", + " return paths" + ] + }, { "cell_type": "code", - "execution_count": 64, - "id": "d0e4dbc7", + "execution_count": 628, + "id": "6dd2ea6a", "metadata": {}, "outputs": [ { - "data": { - "text/plain": [ - "True" - ] - }, - "execution_count": 64, - "metadata": {}, - "output_type": "execute_result" + "name": "stdout", + "output_type": "stream", + "text": [ + "Recycled 47383 paths from /astro/users/coc123/kbmod_tmp/uri_cache.lst as overwrite was False.\n", + "CPU times: user 45 ms, sys: 32.1 ms, total: 77.1 ms\n", + "Wall time: 76.7 ms\n" + ] } ], "source": [ - "# make sure the number of paths we generated matches the number of vdr_ids\n", - "len(paths) == len(vdr_ids)" + "%%time\n", + "\n", + "# TIMING NOTE: This required 90s uncached 2/5/2024 COC\n", + "\n", + "df[\"uri\"] = getURIs(\n", + " butler=butler,\n", + " dataIds=df[\"data_id\"],\n", + " repo_path=repo_path,\n", + " desired_datasetTypes=desired_datasetTypes,\n", + " desired_collections=desired_collections,\n", + " overwrite=False,\n", + ")" ] }, { "cell_type": "code", - "execution_count": 66, + "execution_count": 617, "id": "eae8d366", "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "'file:///epyc/users/smotherh/DEEP/PointingGroups/butler-repo/PointingGroup008/imdiff_r/20210803T142852Z/deepDiff_differenceExp/20190829/VR/VR_DECam_c0007_6300.0_2600.0/891505/deepDiff_differenceExp_DECam_VR_VR_DECam_c0007_6300_0_2600_0_891505_N28_PointingGroup008_imdiff_r_20210803T142852Z.fits'" + "'file:///epyc/users/smotherh/DEEP/PointingGroups/butler-repo/PointingGroup021/imdiff_r/20210723T174135Z/deepDiff_differenceExp/20190927/VR/VR_DECam_c0007_6300.0_2600.0/898286/deepDiff_differenceExp_DECam_VR_VR_DECam_c0007_6300_0_2600_0_898286_S29_PointingGroup021_imdiff_r_20210723T174135Z.fits'" ] }, - "execution_count": 66, + "execution_count": 617, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# example URI\n", - "paths[0]" + "df[\"uri\"].iloc()[0]" ] }, { @@ -1198,13 +1460,13 @@ "id": "fb728cee", "metadata": {}, "source": [ - "# Timestamp Handling Section\n", + "### Timestamp Handling\n", "Here we will access the timestamp (datetime) information from the Butler for our records." ] }, { "cell_type": "code", - "execution_count": 138, + "execution_count": 619, "id": "df7ff31f", "metadata": {}, "outputs": [ @@ -1212,13 +1474,16 @@ "name": "stdout", "output_type": "stream", "text": [ - "0 DateTime(\"2019-09-27T00:20:59.932016000\", TAI) 120.0 (351.3806941054, -5.2403083277)\n" + "0 DateTime(\"2019-09-27T00:20:59.932016000\", TAI) 120.0 (351.3806941054, -5.2403083277)\n", + "CPU times: user 126 ms, sys: 12 ms, total: 138 ms\n", + "Wall time: 175 ms\n" ] } ], "source": [ + "%%time\n", "# Now we want to get metadata like datetime, exposure time, etc.\n", - "for i, dataId in enumerate(vdr_ids):\n", + "for i, dataId in enumerate(df[\"data_id\"]):\n", " visitInfo = butler.get(\"calexp.visitInfo\", dataId=dataId, collections=desired_collections)\n", " print(i, visitInfo.date, visitInfo.exposureTime, visitInfo.boresightRaDec)\n", " break\n", @@ -1227,7 +1492,7 @@ }, { "cell_type": "code", - "execution_count": 134, + "execution_count": 572, "id": "3ccadfaa", "metadata": {}, "outputs": [ @@ -1237,7 +1502,7 @@ "lsst.daf.base.dateTime.dateTime.DateTime" ] }, - "execution_count": 134, + "execution_count": 572, "metadata": {}, "output_type": "execute_result" } @@ -1249,7 +1514,7 @@ }, { "cell_type": "code", - "execution_count": 139, + "execution_count": 573, "id": "d73d8119", "metadata": {}, "outputs": [ @@ -1259,7 +1524,7 @@ "'2019-09-27T00:20:22.932'" ] }, - "execution_count": 139, + "execution_count": 573, "metadata": {}, "output_type": "execute_result" } @@ -1272,7 +1537,7 @@ }, { "cell_type": "code", - "execution_count": 126, + "execution_count": 574, "id": "89822691", "metadata": {}, "outputs": [], @@ -1305,49 +1570,27 @@ " print(f\"Recycled {len(timestamps)} from {cache_file}.\")\n", " return timestamps\n", "\n", - " # Function to divide vdr_ids into chunks of 100\n", - " def chunked_dataIds(dataIds, chunk_size=200):\n", - " \"\"\"Yield successive chunk_size chunks from dataIds.\"\"\"\n", - " for i in range(0, len(dataIds), chunk_size):\n", - " yield dataIds[i : i + chunk_size]\n", - "\n", - " # The function that will be executed in parallel, now adjusted to handle a list of dataIds\n", - " def get_timestamps(dataIds_chunk):\n", - " # Initialize an empty list to store URIs for this chunk\n", - " chunked_data = []\n", - " # You might need to reinitialize the Butler instance here if it's not serializable or shareable across processes\n", - " butler = dafButler.Butler(repo_path)\n", - " for dataId in dataIds_chunk:\n", - " try:\n", - " visitInfo = butler.get(\"calexp.visitInfo\", dataId=dataId, collections=desired_collections)\n", - " t = Time(str(visitInfo.date).split('\"')[1], format=\"isot\", scale=\"tai\")\n", - " tutc = str(t.utc)\n", - " chunked_data.append(tutc)\n", - " except Exception as e:\n", - " print(f\"Failed to retrieve timestamp for dataId {dataId}: {e}\")\n", - " return chunked_data\n", - "\n", - " # Use ProcessPoolExecutor to execute get_uris in parallel processes\n", - " with ProcessPoolExecutor() as executor:\n", - " # Prepare your dataId chunks\n", - " dataId_chunks = list(chunked_dataIds(dataIds))\n", + " if overwrite or not cache_file_exists:\n", + " timestamps = [] # Re-initialize timestamps here to ensure it's fresh\n", "\n", - " # Initialize progress bar\n", - " with progressbar.ProgressBar(max_value=len(dataId_chunks)) as bar:\n", - " futures = {executor.submit(get_timestamps, chunk): chunk for chunk in dataId_chunks}\n", + " with ProcessPoolExecutor() as executor:\n", + " dataId_chunks = list(chunked_dataIds(dataIds))\n", + " # Initialize progress bar\n", + " with progressbar.ProgressBar(max_value=len(dataId_chunks)) as bar:\n", + " # Use map for preserving order and simplifying the code\n", + " results = executor.map(get_timestamps, dataId_chunks)\n", "\n", - " paths = []\n", - " for i, future in enumerate(as_completed(futures)):\n", - " # Retrieve the result of the future, which is the list of URIs for the chunk\n", - " timestamps = future.result()\n", - " timestamps.extend(timestamps) # Add the retrieved URIs to the main list\n", - " bar.update(i)\n", + " # Process results and maintain the order\n", + " for i, chunk_result in enumerate(results):\n", + " timestamps.extend(chunk_result) # Correctly extend with the result of each future\n", + " bar.update(i)\n", "\n", - " if overwrite == True or cache_file_exists == False:\n", - " with open(cache_file, \"w\") as f:\n", - " for ts in timestamps:\n", - " print(ts, file=f)\n", - " print(f\"Wrote {len(timestamps)} lines to {cache_file} for future use.\")\n", + " # Write to cache if necessary\n", + " if overwrite or not cache_file_exists:\n", + " with open(cache_file, \"w\") as f:\n", + " for ts in timestamps:\n", + " print(ts, file=f)\n", + " print(f\"Wrote {len(timestamps)} lines to {cache_file} for future use.\")\n", "\n", " print(f\"Obtained {len(timestamps)} timestamps.\")\n", " return timestamps" @@ -1355,7 +1598,32 @@ }, { "cell_type": "code", - "execution_count": 128, + "execution_count": 620, + "id": "5ea2f086", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'2019-09-27T00:20:22.932'" + ] + }, + "execution_count": 620, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Double-check that we can convert Butler timestamp (TAI) to UTC string\n", + "visitInfo = butler.get(\"calexp.visitInfo\", dataId=df[\"data_id\"].iloc()[0], collections=desired_collections)\n", + "t = Time(str(visitInfo.date).split('\"')[1], format=\"isot\", scale=\"tai\")\n", + "tutc = str(t.utc)\n", + "tutc" + ] + }, + { + "cell_type": "code", + "execution_count": 621, "id": "7e0d33c0", "metadata": {}, "outputs": [ @@ -1365,25 +1633,120 @@ "text": [ "Overwrite is False, so we will read the timestamps from file now...\n", "Recycled 47383 from /astro/users/coc123/kbmod_tmp/vdr_timestamps.lst.\n", - "CPU times: user 13.5 ms, sys: 10.7 ms, total: 24.2 ms\n", - "Wall time: 23.4 ms\n" + "CPU times: user 24.1 ms, sys: 9.07 ms, total: 33.2 ms\n", + "Wall time: 30.1 ms\n" ] } ], "source": [ "%%time\n", - "vdr_timestamps = getTimestamps(dataIds=vdr_ids, overwrite=False)" + "\n", + "# TIMING NOTE: this took < 5 minutes 2/5/2024 COC\n", + "df[\"ut\"] = getTimestamps(dataIds=df[\"data_id\"], overwrite=False)" ] }, { "cell_type": "code", - "execution_count": 272, - "id": "7ec4fb96", + "execution_count": 622, + "id": "adddf1e4", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "0 2019-09-27T00:20:22.932\n", + "1 2019-09-27T00:22:51.015\n", + "2 2019-09-27T00:25:19.136\n", + "3 2019-09-27T00:27:47.118\n", + "4 2019-09-27T00:30:15.537\n", + " ... \n", + "47378 2020-10-17T04:00:51.409\n", + "47379 2020-10-17T04:03:19.873\n", + "47380 2020-10-17T04:05:48.949\n", + "47381 2020-10-17T04:08:17.445\n", + "47382 2020-10-17T04:10:46.218\n", + "Name: ut, Length: 47383, dtype: object" + ] + }, + "execution_count": 622, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "# NOTE: getURIs() does not work as you'd think (i.e., it only handles a single dataId)\n", - "# butler.getURIs(desired_datasetTypes[0], vdr_ids, collections=desired_collections)" + "df[\"ut\"]" + ] + }, + { + "cell_type": "code", + "execution_count": 364, + "id": "a789a331", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0 : 2019-09-27T00:20:22.932 for {instrument: 'DECam', detector: 1, visit: 898286}. Dict had: 2019-09-27\n", + "1000 : 2019-09-27T02:24:03.066 for {instrument: 'DECam', detector: 12, visit: 898336}. Dict had: 2019-08-29\n", + "2000 : 2019-09-27T00:32:44.405 for {instrument: 'DECam', detector: 23, visit: 898291}. Dict had: 2019-08-29\n", + "3000 : 2019-09-27T02:36:24.497 for {instrument: 'DECam', detector: 33, visit: 898341}. Dict had: 2019-08-29\n", + "4000 : 2019-09-27T00:45:05.306 for {instrument: 'DECam', detector: 44, visit: 898296}. Dict had: 2019-08-29\n", + "5000 : 2019-09-27T02:51:16.295 for {instrument: 'DECam', detector: 54, visit: 898347}. Dict had: 2019-08-29\n", + "6000 : 2019-08-29T07:25:55.714 for {instrument: 'DECam', detector: 4, visit: 891512}. Dict had: 2019-08-30\n", + "7000 : 2019-08-29T06:36:25.673 for {instrument: 'DECam', detector: 14, visit: 891492}. Dict had: 2019-08-30\n", + "8000 : 2019-08-29T05:46:48.259 for {instrument: 'DECam', detector: 24, visit: 891472}. Dict had: 2019-08-29\n", + "9000 : 2019-08-29T09:11:52.823 for {instrument: 'DECam', detector: 33, visit: 891554}. Dict had: 2020-10-19\n", + "10000: 2019-08-29T08:24:43.475 for {instrument: 'DECam', detector: 43, visit: 891535}. Dict had: 2020-10-19\n", + "11000: 2019-08-29T07:39:44.133 for {instrument: 'DECam', detector: 53, visit: 891517}. Dict had: 2020-10-19\n", + "12000: 2020-10-19T03:57:38.040 for {instrument: 'DECam', detector: 1, visit: 946776}. Dict had: 2020-10-19\n", + "13000: 2020-10-19T03:55:07.913 for {instrument: 'DECam', detector: 13, visit: 946775}. Dict had: 2020-10-19\n", + "14000: 2020-10-19T03:52:39.901 for {instrument: 'DECam', detector: 24, visit: 946774}. Dict had: 2020-10-19\n", + "15000: 2020-10-19T03:50:11.828 for {instrument: 'DECam', detector: 35, visit: 946773}. Dict had: 2019-09-27\n", + "16000: 2020-10-19T03:47:43.087 for {instrument: 'DECam', detector: 46, visit: 946772}. Dict had: 2019-09-27\n", + "17000: 2020-10-19T03:45:14.564 for {instrument: 'DECam', detector: 57, visit: 946771}. Dict had: 2019-09-27\n", + "18000: 2019-08-30T07:36:47.123 for {instrument: 'DECam', detector: 7, visit: 891898}. Dict had: 2019-09-27\n", + "19000: 2019-08-30T07:11:50.193 for {instrument: 'DECam', detector: 17, visit: 891888}. Dict had: 2019-09-27\n", + "20000: 2019-08-30T06:47:05.343 for {instrument: 'DECam', detector: 27, visit: 891878}. Dict had: 2019-08-30\n", + "21000: 2019-08-30T06:22:19.105 for {instrument: 'DECam', detector: 37, visit: 891868}. Dict had: 2019-08-30\n", + "22000: 2019-08-30T05:57:29.152 for {instrument: 'DECam', detector: 47, visit: 891858}. Dict had: 2019-08-30\n", + "23000: 2019-08-30T05:32:32.635 for {instrument: 'DECam', detector: 57, visit: 891848}. Dict had: 2019-08-30\n", + "24000: 2019-09-28T01:46:11.541 for {instrument: 'DECam', detector: 6, visit: 898736}. Dict had: 2019-09-28\n", + "25000: 2019-09-28T03:00:31.172 for {instrument: 'DECam', detector: 16, visit: 898766}. Dict had: 2019-09-28\n", + "26000: 2019-09-28T00:17:03.748 for {instrument: 'DECam', detector: 27, visit: 898700}. Dict had: 2019-09-28\n", + "27000: 2019-09-28T01:31:18.723 for {instrument: 'DECam', detector: 37, visit: 898730}. Dict had: 2019-09-28\n", + "28000: 2019-09-28T02:45:39.745 for {instrument: 'DECam', detector: 47, visit: 898760}. Dict had: 2019-09-28\n", + "29000: 2019-09-28T04:00:09.666 for {instrument: 'DECam', detector: 57, visit: 898790}. Dict had: 2019-08-28\n", + "30000: 2019-08-28T06:43:48.523 for {instrument: 'DECam', detector: 7, visit: 891114}. Dict had: 2019-08-28\n", + "31000: 2019-08-28T05:29:20.494 for {instrument: 'DECam', detector: 17, visit: 891084}. Dict had: 2019-08-28\n", + "32000: 2019-08-28T08:31:09.143 for {instrument: 'DECam', detector: 26, visit: 891157}. Dict had: 2019-08-28\n", + "33000: 2019-08-28T07:21:26.793 for {instrument: 'DECam', detector: 36, visit: 891129}. Dict had: 2019-08-28\n", + "34000: 2019-08-28T06:06:34.210 for {instrument: 'DECam', detector: 46, visit: 891099}. Dict had: 2019-08-28\n", + "35000: 2019-08-28T09:08:44.560 for {instrument: 'DECam', detector: 55, visit: 891172}. Dict had: 2019-09-28\n", + "36000: 2019-09-29T00:19:06.349 for {instrument: 'DECam', detector: 5, visit: 899020}. Dict had: 2019-09-29\n", + "37000: 2019-09-29T01:58:29.441 for {instrument: 'DECam', detector: 15, visit: 899060}. Dict had: 2019-09-29\n", + "38000: 2019-09-29T03:37:44.590 for {instrument: 'DECam', detector: 25, visit: 899100}. Dict had: 2019-09-29\n", + "39000: 2019-09-29T01:18:42.912 for {instrument: 'DECam', detector: 36, visit: 899044}. Dict had: 2019-09-29\n", + "40000: 2019-09-29T02:58:02.739 for {instrument: 'DECam', detector: 46, visit: 899084}. Dict had: 2019-09-29\n", + "41000: 2019-09-29T00:38:58.257 for {instrument: 'DECam', detector: 57, visit: 899028}. Dict had: 2019-09-29\n", + "42000: 2020-10-17T03:45:59.925 for {instrument: 'DECam', detector: 6, visit: 946166}. Dict had: 2020-10-17\n", + "43000: 2020-10-17T01:26:44.472 for {instrument: 'DECam', detector: 17, visit: 946110}. Dict had: 2020-10-17\n", + "44000: 2020-10-17T03:06:21.338 for {instrument: 'DECam', detector: 27, visit: 946150}. Dict had: 2020-10-17\n", + "45000: 2020-10-17T00:46:58.038 for {instrument: 'DECam', detector: 38, visit: 946094}. Dict had: 2020-10-17\n", + "46000: 2020-10-17T02:26:33.210 for {instrument: 'DECam', detector: 48, visit: 946134}. Dict had: 2020-10-17\n", + "47000: 2020-10-17T04:05:48.949 for {instrument: 'DECam', detector: 58, visit: 946174}. Dict had: 2020-10-17\n" + ] + } + ], + "source": [ + "# This is for coming back to later to make sure the stamps line up\n", + "# IGNORE FOR NOW 2/6/2024 COC\n", + "# for i in range(0,len(vdr_ids),1000):\n", + "# dataId = vdr_ids[i]\n", + "# visitInfo = butler.get(\"calexp.visitInfo\", dataId=dataId, collections=desired_collections)\n", + "# t = Time(str(visitInfo.date).split('\"')[1], format=\"isot\", scale=\"tai\")\n", + "# tutc = str(t.utc)\n", + "# print(f\"{i!s:5}: {tutc} for {dataId}. Dict had: {id_to_date[dataId]}\")" ] }, { @@ -1407,7 +1770,7 @@ }, { "cell_type": "code", - "execution_count": 183, + "execution_count": 578, "id": "e45d7199", "metadata": {}, "outputs": [], @@ -1430,7 +1793,7 @@ }, { "cell_type": "code", - "execution_count": 194, + "execution_count": 579, "id": "9b278b50", "metadata": {}, "outputs": [ @@ -1443,20 +1806,20 @@ " (352.64644359666477, -4.7450820235469715)]" ] }, - "execution_count": 194, + "execution_count": 579, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Example\n", - "corners = getRegionCorners(tmpref.region)\n", + "corners = getRegionCorners(example_vdr_ref.region)\n", "corners" ] }, { "cell_type": "code", - "execution_count": 190, + "execution_count": 580, "id": "b37bfe47", "metadata": {}, "outputs": [], @@ -1479,7 +1842,7 @@ }, { "cell_type": "code", - "execution_count": 195, + "execution_count": 581, "id": "ef329736", "metadata": {}, "outputs": [ @@ -1490,7 +1853,7 @@ " (-4.930880058593892, -4.7450820235469715))" ] }, - "execution_count": 195, + "execution_count": 581, "metadata": {}, "output_type": "execute_result" } @@ -1513,7 +1876,7 @@ }, { "cell_type": "code", - "execution_count": 181, + "execution_count": 582, "id": "4c89be9e", "metadata": {}, "outputs": [], @@ -1553,7 +1916,7 @@ }, { "cell_type": "code", - "execution_count": 192, + "execution_count": 624, "id": "8e54db29", "metadata": {}, "outputs": [ @@ -1561,19 +1924,19 @@ "name": "stdout", "output_type": "stream", "text": [ - "CPU times: user 483 ms, sys: 99.9 ms, total: 583 ms\n", - "Wall time: 1.1 s\n" + "CPU times: user 363 ms, sys: 19 ms, total: 382 ms\n", + "Wall time: 379 ms\n" ] } ], "source": [ "%%time\n", - "vdr_centers = [getCenterRaDec(i) for i in vdr_regions]" + "df[\"center_coord\"] = [getCenterRaDec(i) for i in df[\"region\"]]" ] }, { "cell_type": "code", - "execution_count": 193, + "execution_count": 625, "id": "64908868", "metadata": {}, "outputs": [ @@ -1583,18 +1946,18 @@ "47383" ] }, - "execution_count": 193, + "execution_count": 625, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "len(vdr_centers)" + "len(df[\"center_coord\"])" ] }, { "cell_type": "code", - "execution_count": 195, + "execution_count": 626, "id": "625de417", "metadata": {}, "outputs": [ @@ -1604,28 +1967,36 @@ "(351.0694028401149, -4.336598368890197)" ] }, - "execution_count": 195, + "execution_count": 626, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "vdr_centers[0]" + "df[\"center_coord\"].iloc()[0]" ] }, { "cell_type": "code", - "execution_count": 196, + "execution_count": 627, "id": "67b57215", "metadata": {}, "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "CPU times: user 1.2 s, sys: 26 ms, total: 1.23 s\n", + "Wall time: 1.22 s\n" + ] + }, { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 196, + "execution_count": 627, "metadata": {}, "output_type": "execute_result" }, @@ -1641,11 +2012,14 @@ } ], "source": [ + "%%time\n", "# Let us visualize where on the sky our chip centers reside\n", "fig = plt.figure(figsize=(4, 4))\n", "plt.xlabel(\"RA\")\n", "plt.ylabel(\"Dec\")\n", - "plt.scatter([i[0] for i in vdr_centers], [i[1] for i in vdr_centers], s=1, alpha=0.5)" + "plt.scatter(\n", + " [i[0] for i in df[\"center_coord\"].iloc()], [i[1] for i in df[\"center_coord\"].iloc()], s=1, alpha=0.5\n", + ")" ] }, { @@ -1661,423 +2035,72 @@ }, { "cell_type": "code", - "execution_count": 214, - "id": "644d94aa", - "metadata": {}, - "outputs": [], - "source": [ - "# NEXT WE WILL ASSEMBLE A DATAFRAME" - ] - }, - { - "cell_type": "code", - "execution_count": 219, - "id": "ba60e68c", - "metadata": {}, - "outputs": [], - "source": [ - "%%time\n", - "df_dict = {}\n", - "df_dict[\"data_id\"] = vdr_ids\n", - "df_dict[\"ut\"] = vdr_timestamps\n", - "df_dict[\"detector\"] = vdr_detectors\n", - "df_dict[\"center_coord\"] = vdr_centers\n", - "# df_dict['region'] = vdr_regions # still mulling this over, maybe we do not need full objects 2/2/2024 COC\n", - "\n", - "df = pd.DataFrame.from_dict(df_dict)" - ] - }, - { - "cell_type": "code", - "execution_count": 221, - "id": "d73a36b2", + "execution_count": 554, + "id": "c2dc4e1a", "metadata": {}, "outputs": [ { "data": { - "text/html": [ - "
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data_idutdetectorcenter_coord
0(instrument, detector, visit)2019-09-27T00:20:22.9321(351.0694028401149, -4.336598368890197)
1(instrument, detector, visit)2019-09-27T00:22:51.0151(351.0695696334572, -4.336293374423113)
2(instrument, detector, visit)2019-09-27T00:25:19.1361(351.0696571334576, -4.336293374415341)
3(instrument, detector, visit)2019-09-27T00:27:47.1181(351.069611300124, -4.336293374419411)
4(instrument, detector, visit)2019-09-27T00:30:15.5371(351.0695696451088, -4.336265319377865)
...............
47378(instrument, detector, visit)2020-10-17T03:33:36.95062(352.4779285369392, -4.838007985003787)
47379(instrument, detector, visit)2020-10-17T03:36:05.01762(352.4778993579928, -4.83798104107715)
47380(instrument, detector, visit)2020-10-17T03:38:33.95562(352.47792852465943, -4.837981041074541)
47381(instrument, detector, visit)2020-10-17T03:41:03.15462(352.47792852465943, -4.837981041074541)
47382(instrument, detector, visit)2020-10-17T03:43:31.99962(352.477974357993, -4.837981041070432)
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47383 rows × 4 columns

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(351.0695696451088, -4.336265319377865) \n", - "... ... \n", - "47378 (352.4779285369392, -4.838007985003787) \n", - "47379 (352.4778993579928, -4.83798104107715) \n", - "47380 (352.47792852465943, -4.837981041074541) \n", - "47381 (352.47792852465943, -4.837981041074541) \n", - "47382 (352.477974357993, -4.837981041070432) \n", - "\n", - "[47383 rows x 4 columns]" + "{instrument: 'DECam', detector: 1, visit: 898286}" ] }, - "execution_count": 221, + "execution_count": 554, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "df" + "# look at the first dataId, which does not show up in a pretty way in the DF\n", + "df[\"data_id\"].iloc[0]" ] }, { "cell_type": "code", - "execution_count": 223, - "id": "c2dc4e1a", + "execution_count": 435, + "id": "580ac4ed", "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "{instrument: 'DECam', detector: 1, visit: 898286}" + "" ] }, - "execution_count": 223, + "execution_count": 435, "metadata": {}, "output_type": "execute_result" + }, + { + "data": { + "image/png": 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" + ] + }, + "metadata": {}, + "output_type": "display_data" } ], "source": [ - "# look at the first dataId, which does not show up in a pretty way in the DF\n", - "df[\"data_id\"].iloc[0]" + "# Let us look at just detector 62 pointings.\n", + "# This will give us a decent idea of how many pointings there are (here, 8).\n", + "\n", + "df62 = df[df[\"detector\"] == 62]\n", + "plt.clf()\n", + "plt.scatter(*zip(*df62[\"center_coord\"]), s=1, alpha=0.5)" ] }, { "cell_type": "code", - "execution_count": 226, - "id": "580ac4ed", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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data_idutdetectorcenter_coord
5644(instrument, detector, visit)2019-08-29T07:16:03.69362(351.69307656022727, -6.143624107225388)
5645(instrument, detector, visit)2019-08-29T07:18:31.27062(351.6932430489372, -6.143319112795237)
5646(instrument, detector, visit)2019-08-29T07:20:59.79062(351.69333054893764, -6.143319112787463)
5647(instrument, detector, visit)2019-08-29T07:23:27.90162(351.69328471560397, -6.143319112791537)
5648(instrument, detector, visit)2019-08-29T07:25:55.71462(351.6932430325679, -6.143291057753383)
...............
47378(instrument, detector, visit)2020-10-17T03:33:36.95062(352.4779285369392, -4.838007985003787)
47379(instrument, detector, visit)2020-10-17T03:36:05.01762(352.4778993579928, -4.83798104107715)
47380(instrument, detector, visit)2020-10-17T03:38:33.95562(352.47792852465943, -4.837981041074541)
47381(instrument, detector, visit)2020-10-17T03:41:03.15462(352.47792852465943, -4.837981041074541)
47382(instrument, detector, visit)2020-10-17T03:43:31.99962(352.477974357993, -4.837981041070432)
\n", - "

781 rows × 4 columns

\n", - "
" - ], - "text/plain": [ - " data_id ut detector \n", - "5644 (instrument, detector, visit) 2019-08-29T07:16:03.693 62 \\\n", - "5645 (instrument, detector, visit) 2019-08-29T07:18:31.270 62 \n", - "5646 (instrument, detector, visit) 2019-08-29T07:20:59.790 62 \n", - "5647 (instrument, detector, visit) 2019-08-29T07:23:27.901 62 \n", - "5648 (instrument, detector, visit) 2019-08-29T07:25:55.714 62 \n", - "... ... ... ... \n", - "47378 (instrument, detector, visit) 2020-10-17T03:33:36.950 62 \n", - "47379 (instrument, detector, visit) 2020-10-17T03:36:05.017 62 \n", - "47380 (instrument, detector, visit) 2020-10-17T03:38:33.955 62 \n", - "47381 (instrument, detector, visit) 2020-10-17T03:41:03.154 62 \n", - "47382 (instrument, detector, visit) 2020-10-17T03:43:31.999 62 \n", - "\n", - " center_coord \n", - "5644 (351.69307656022727, -6.143624107225388) \n", - "5645 (351.6932430489372, -6.143319112795237) \n", - "5646 (351.69333054893764, -6.143319112787463) \n", - "5647 (351.69328471560397, -6.143319112791537) \n", - "5648 (351.6932430325679, -6.143291057753383) \n", - "... ... \n", - "47378 (352.4779285369392, -4.838007985003787) \n", - "47379 (352.4778993579928, -4.83798104107715) \n", - "47380 (352.47792852465943, -4.837981041074541) \n", - "47381 (352.47792852465943, -4.837981041074541) \n", - "47382 (352.477974357993, -4.837981041070432) \n", - "\n", - "[781 rows x 4 columns]" - ] - }, - "execution_count": 226, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df62 = df[df[\"detector\"] == 62]\n", - "df62" - ] - }, - { - "cell_type": "code", - "execution_count": 235, - "id": "811de572", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 235, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "plt.clf()\n", - "plt.scatter(*zip(*df62[\"center_coord\"]), s=1, alpha=0.5)" - ] - }, - { - "cell_type": "code", - "execution_count": 237, - "id": "6ee544b8", + "execution_count": 237, + "id": "6ee544b8", "metadata": {}, "outputs": [], "source": [ "# chip62_coord0 = df62['center_coord'][0]" ] }, - { - "cell_type": "code", - "execution_count": null, - "id": "ac331672", - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "a36fedeb", - "metadata": {}, - "outputs": [], - "source": [] - }, { "cell_type": "markdown", "id": "df7d441d", @@ -2093,29 +2116,10 @@ }, { "cell_type": "code", - "execution_count": 216, + "execution_count": 589, "id": "2c770982", "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - " 3% (1468 of 47383) | | Elapsed Time: 0:01:39 ETA: 0:52:55\n" - ] - }, - { - "ename": "KeyboardInterrupt", - "evalue": "", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)", - "Cell \u001b[0;32mIn[216], line 9\u001b[0m\n\u001b[1;32m 7\u001b[0m my_id \u001b[38;5;241m=\u001b[39m vdr_ids[i]\n\u001b[1;32m 8\u001b[0m matches[my_id] \u001b[38;5;241m=\u001b[39m []\n\u001b[0;32m----> 9\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m j,r \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28menumerate\u001b[39m(vdr_regions):\n\u001b[1;32m 10\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m i \u001b[38;5;241m==\u001b[39m j: \u001b[38;5;28;01mcontinue\u001b[39;00m\n\u001b[1;32m 11\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m l\u001b[38;5;241m.\u001b[39mintersects(r):\n", - "\u001b[0;31mKeyboardInterrupt\u001b[0m: " - ] - } - ], + "outputs": [], "source": [ "# manual region-region matching between all chips\n", "# NOTE: estimate time is 1 hour, so skipping this for now\n", @@ -2140,6 +2144,14 @@ " print(f\"It took {elapsed} seconds.\")" ] }, + { + "cell_type": "markdown", + "id": "ba8db514", + "metadata": {}, + "source": [ + "To be continued..." + ] + }, { "cell_type": "markdown", "id": "f00d9b2f", @@ -2219,7 +2231,7 @@ }, { "cell_type": "code", - "execution_count": 238, + "execution_count": 588, "id": "47c447c5", "metadata": {}, "outputs": [ @@ -2238,6 +2250,14 @@ "print(len(sorted(dsRefs)))" ] }, + { + "cell_type": "markdown", + "id": "d59bcdde", + "metadata": {}, + "source": [ + "To be continued..." + ] + }, { "cell_type": "markdown", "id": "10339ac6", @@ -2245,20 +2265,35 @@ "source": [ "### Brute force approach for (small) discrete datasets\n", "\n", - "The idea here is that we can organize piles of images." + "The idea here is that we can organize discrete piles of images, such as DEEP, DDF." ] }, { "cell_type": "code", - "execution_count": 271, + "execution_count": 441, "id": "048e38e1", "metadata": {}, "outputs": [], "source": [ - "from astropy import units as u\n", + "def find_overlapping_coords(df, uncertainty_radius, overwrite=False):\n", + " \"\"\"\n", "\n", + " Added caching 2/5/2024 COC\n", + " \"\"\"\n", + " import glob\n", + "\n", + " cache_file = f\"{basedir}/overlapping_sets.pickle\"\n", + "\n", + " cache_exists = False\n", + " if len(glob.glob(cache_file)) > 0:\n", + " cache_exists = True\n", + "\n", + " if overwrite == False and cache_exists == True:\n", + " with open(cache_file, \"rb\") as f:\n", + " print(f\"Recycling {cache_file} as overwrite={overwrite}.\")\n", + " overlapping_sets = pickle.load(f)\n", + " return overlapping_sets\n", "\n", - "def find_overlapping_coords(df, uncertainty_radius):\n", " df_copy = df.copy()\n", "\n", " # Assuming uncertainty_radius is provided as a float in arcseconds\n", @@ -2273,8 +2308,8 @@ " set_counter = 1\n", " processed_data_ids = []\n", "\n", + " periodic_update_counter = 0\n", " with progressbar.ProgressBar(max_value=len(all_coords)) as bar:\n", - "\n", " for index, coord in enumerate(all_coords):\n", " data_id = df_copy.iloc[index][\"data_id\"]\n", " if data_id not in processed_data_ids:\n", @@ -2298,940 +2333,584 @@ "\n", " overlapping_sets[f\"set_{set_counter}\"] = overlapping_data_ids\n", " set_counter += 1\n", - " bar.update(index)\n", + " #\n", + " # mitigate too much output 2/5/2024 COC\n", + " periodic_update_counter += 1\n", + " if periodic_update_counter >= 250:\n", + " periodic_update_counter = 0\n", + " bar.update(index)\n", + "\n", + " with open(cache_file, \"wb\") as f:\n", + " pickle.dump(overlapping_sets, f, protocol=pickle.HIGHEST_PROTOCOL)\n", + " print(f\"Saved overlapping_sets to {cache_file} for caching purposes.\")\n", "\n", " return overlapping_sets" ] }, { "cell_type": "code", - "execution_count": 272, - "id": "378f48ab", + "execution_count": 442, + "id": "25710a99", "metadata": {}, "outputs": [ { - "name": "stderr", + "name": "stdout", "output_type": "stream", "text": [ - "100% (781 of 781) |######################| Elapsed Time: 0:00:03 Time: 0:00:03\n" + "Recycling /astro/users/coc123/kbmod_tmp/overlapping_sets.pickle as overwrite=False.\n", + "CPU times: user 173 ms, sys: 22.1 ms, total: 195 ms\n", + "Wall time: 192 ms\n" ] - }, + } + ], + "source": [ + "%%time\n", + "\n", + "# TIMING NOTE: this takes about 1.25 hours [TODO update that number]\n", + "# TODO test caching\n", + "\n", + "overlapping_sets = find_overlapping_coords(df=df, uncertainty_radius=30, overwrite=False)" + ] + }, + { + "cell_type": "code", + "execution_count": 590, + "id": "5f2d5a03", + "metadata": {}, + "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "CPU times: user 3.26 s, sys: 0 ns, total: 3.26 s\n", - "Wall time: 3.23 s\n" + "There are 488 discrete chip-level pointings.\n" ] } ], "source": [ - "%%time\n", - "overlapping_sets = find_overlapping_coords(df=df62, uncertainty_radius=30)" + "print(f\"There are {len(overlapping_sets.keys())} discrete chip-level pointings.\") # should be 488" ] }, { "cell_type": "code", - "execution_count": 267, - "id": "4fd0e6eb", + "execution_count": 591, + "id": "9ed24e28", "metadata": {}, "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "CPU times: user 503 ms, sys: 24.9 ms, total: 527 ms\n", + "Wall time: 526 ms\n" + ] + }, { "data": { "text/plain": [ - "8" + "" ] }, - "execution_count": 267, + "execution_count": 591, "metadata": {}, "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" } ], "source": [ - "len(overlapping_sets)" + "%%time\n", + "\n", + "# Looking at the processed data.\n", + "\n", + "# Create a lookup table for data_id to center_coord\n", + "id_to_coord = df.set_index(\"data_id\")[\"center_coord\"].to_dict()\n", + "\n", + "# Preparing for bulk plotting (if every point uses the same label, adjust as needed)\n", + "coords = [id_to_coord[overlapping_sets[p][0]] for p in overlapping_sets]\n", + "x_coords, y_coords = zip(*coords) # Assuming coords are tuples or lists\n", + "\n", + "# Plotting in bulk\n", + "plt.scatter(x_coords, y_coords, s=1)" ] }, { "cell_type": "code", - "execution_count": 268, - "id": "0fb78d3c", + "execution_count": 594, + "id": "55ffd374", "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "{'set_1': [{instrument: 'DECam', detector: 62, visit: 898287},\n", - " {instrument: 'DECam', detector: 62, visit: 898288},\n", - " {instrument: 'DECam', detector: 62, visit: 898289},\n", - 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" {instrument: 'DECam', detector: 62, visit: 946165},\n", - " {instrument: 'DECam', detector: 62, visit: 946166},\n", - " {instrument: 'DECam', detector: 62, visit: 946167},\n", - " {instrument: 'DECam', detector: 62, visit: 946168},\n", - " {instrument: 'DECam', detector: 62, visit: 946169},\n", - " {instrument: 'DECam', detector: 62, visit: 946170},\n", - " {instrument: 'DECam', detector: 62, visit: 946171},\n", - " {instrument: 'DECam', detector: 62, visit: 946172},\n", - " {instrument: 'DECam', detector: 62, visit: 946173},\n", - " {instrument: 'DECam', detector: 62, visit: 946174},\n", - " {instrument: 'DECam', detector: 62, visit: 946175},\n", - " {instrument: 'DECam', detector: 62, visit: 946176},\n", - " {instrument: 'DECam', detector: 62, visit: 946081}]}" + "'2019-09-27T00:20:22.932'" ] }, - "execution_count": 268, + "execution_count": 594, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "overlapping_sets" + "df[\"ut\"].iloc()[0]" ] }, { "cell_type": "code", - "execution_count": 273, - "id": "25710a99", + "execution_count": 595, + "id": "fcd39c41", "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - " 53% (25405 of 47383) |######### | Elapsed Time: 0:40:26 ETA: 1:07:25IOPub message rate exceeded.\n", - "The notebook server will temporarily stop sending output\n", - "to the client in order to avoid crashing it.\n", - "To change this limit, set the config variable\n", - "`--NotebookApp.iopub_msg_rate_limit`.\n", - "\n", - "Current values:\n", - "NotebookApp.iopub_msg_rate_limit=1000.0 (msgs/sec)\n", - "NotebookApp.rate_limit_window=3.0 (secs)\n", - "\n", - " 73% (34916 of 47383) |############# | Elapsed Time: 1:15:02 ETA: 0:49:16IOPub message rate exceeded.\n", - "The notebook server will temporarily stop sending output\n", - "to the client in order to avoid crashing it.\n", - "To change this limit, set the config variable\n", - "`--NotebookApp.iopub_msg_rate_limit`.\n", - "\n", - "Current values:\n", - "NotebookApp.iopub_msg_rate_limit=1000.0 (msgs/sec)\n", - "NotebookApp.rate_limit_window=3.0 (secs)\n", - "\n", - " 85% (40710 of 47383) |############### | Elapsed Time: 1:41:13 ETA: 0:34:17IOPub message rate exceeded.\n", - "The notebook server will temporarily stop sending output\n", - "to the client in order to avoid crashing it.\n", - "To change this limit, set the config variable\n", - "`--NotebookApp.iopub_msg_rate_limit`.\n", - "\n", - "Current values:\n", - "NotebookApp.iopub_msg_rate_limit=1000.0 (msgs/sec)\n", - "NotebookApp.rate_limit_window=3.0 (secs)\n", - "\n", - " 91% (43160 of 47383) |################ | Elapsed Time: 1:53:20 ETA: 0:21:12IOPub message rate exceeded.\n", - "The notebook server will temporarily stop sending output\n", - "to the client in order to avoid crashing it.\n", - "To change this limit, set the config variable\n", - "`--NotebookApp.iopub_msg_rate_limit`.\n", - "\n", - "Current values:\n", - "NotebookApp.iopub_msg_rate_limit=1000.0 (msgs/sec)\n", - "NotebookApp.rate_limit_window=3.0 (secs)\n", - "\n" + ":19: MatplotlibDeprecationWarning: The get_cmap function was deprecated in Matplotlib 3.7 and will be removed two minor releases later. Use ``matplotlib.colormaps[name]`` or ``matplotlib.colormaps.get_cmap(obj)`` instead.\n" ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "CPU times: user 6.24 s, sys: 526 ms, total: 6.77 s\n", + "Wall time: 6.24 s\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" } ], "source": [ "%%time\n", "\n", - "# TIMING NOTE: this takes about 1.25 hours(?) [TODO update that number]\n", - "# [TODO] caching\n", - "overlapping_sets = find_overlapping_coords(df=df, uncertainty_radius=30)" + "# TIMING NOTE: this requires about 7 seconds\n", + "\n", + "from matplotlib.cm import get_cmap\n", + "from matplotlib.colors import Normalize\n", + "\n", + "# Convert \"ut\" column to datetime\n", + "df[\"ut_datetime\"] = pd.to_datetime(df[\"ut\"])\n", + "\n", + "# Create lookup tables for data_id to center_coord and ut_datetime\n", + "id_to_coord = df.set_index(\"data_id\")[\"center_coord\"].to_dict()\n", + "id_to_date = df.set_index(\"data_id\")[\"ut_datetime\"].dt.date.to_dict()\n", + "\n", + "# Extract unique dates and create a color map\n", + "unique_dates = sorted(set(id_to_date.values()))\n", + "date_to_color = {date: i for i, date in enumerate(unique_dates)}\n", + "norm = Normalize(vmin=0, vmax=len(unique_dates) - 1)\n", + "cmap = get_cmap(\"tab20\", len(unique_dates)) # Choose a colormap that fits the data\n", + "\n", + "# Preparing data for plotting\n", + "coords = [id_to_coord[overlapping_sets[p][0]] for p in overlapping_sets]\n", + "dates = [id_to_date[overlapping_sets[p][0]] for p in overlapping_sets]\n", + "colors = [cmap(norm(date_to_color[date])) for date in dates]\n", + "\n", + "# Plotting\n", + "lcount = 0\n", + "for (x, y), color, date in zip(coords, colors, dates):\n", + " lcount += 1\n", + " plt.scatter(x, y, color=color, label=date.strftime(\"%Y-%m-%d\"), alpha=0.75, s=2)\n", + "\n", + "# To avoid duplicate labels in the legend, handle legend entries manually\n", + "handles, labels = plt.gca().get_legend_handles_labels()\n", + "by_label = dict(zip(labels, handles)) # Removing duplicates\n", + "plt.legend(by_label.values(), by_label.keys())\n", + "plt.savefig(f\"{basedir}/pointings.pdf\")\n", + "# plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 596, + "id": "418c97ee", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Index(['data_id', 'region', 'detector', 'uri', 'center_coord', 'ut',\n", + " 'ut_datetime'],\n", + " dtype='object')" + ] + }, + "execution_count": 596, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.columns" + ] + }, + { + "cell_type": "code", + "execution_count": 597, + "id": "d06ed515", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[datetime.date(2019, 8, 28),\n", + " datetime.date(2019, 8, 29),\n", + " datetime.date(2019, 8, 30),\n", + " datetime.date(2019, 9, 27),\n", + " datetime.date(2019, 9, 28),\n", + " datetime.date(2019, 9, 29),\n", + " datetime.date(2020, 10, 17),\n", + " datetime.date(2020, 10, 19)]" + ] + }, + "execution_count": 597, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Here are the unique dates found in the discrete dataset\n", + "unique_dates = sorted(set(id_to_date.values()))\n", + "unique_dates" + ] + }, + { + "cell_type": "markdown", + "id": "3dc0bb58", + "metadata": {}, + "source": [ + "##### Double-checking a single date" ] }, { "cell_type": "code", - "execution_count": 274, - "id": "22b7212c", + "execution_count": 598, + "id": "58158f47", "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "488" + "data_id (instrument, detector, visit)\n", + "region ConvexPolygon([UnitVector3d(0.9847372525065534...\n", + "detector 1\n", + "uri file:///epyc/users/smotherh/DEEP/PointingGroup...\n", + "center_coord (351.0694028401149, -4.336598368890197)\n", + "ut 2019-09-27T00:20:22.932\n", + "ut_datetime 2019-09-27 00:20:22.932000\n", + "Name: 0, dtype: object" ] }, - "execution_count": 274, + "execution_count": 598, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "len(overlapping_sets)" + "df.iloc()[0]" + ] + }, + { + "cell_type": "code", + "execution_count": 599, + "id": "2ff625e7", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "datetime.date(2019, 9, 27)" + ] + }, + "execution_count": 599, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "id_to_date[df[\"data_id\"].iloc()[0]]" + ] + }, + { + "cell_type": "code", + "execution_count": 600, + "id": "c3b48b13", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(351.0694028401149, -4.336598368890197)" + ] + }, + "execution_count": 600, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "id_to_coord[df[\"data_id\"].iloc()[0]]" + ] + }, + { + "cell_type": "code", + "execution_count": 601, + "id": "16506478", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "6267" + ] + }, + "execution_count": 601, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# We will make a dataframe with just the date we are checking\n", + "df20190828 = df[df[\"ut_datetime\"].dt.date == parser.parse(\"2019-08-28\").date()]\n", + "len(df20190828)" + ] + }, + { + "cell_type": "code", + "execution_count": 603, + "id": "166c9137", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "CPU times: user 933 ms, sys: 33 ms, total: 966 ms\n", + "Wall time: 962 ms\n" + ] + }, + { + "data": { + "image/png": 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", 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", 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", 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", 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", 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", 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", 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "%%time\n", + "# Visualize all of the unique dates, one at a time.\n", + "for dt in unique_dates:\n", + " fig = plt.figure()\n", + " tmpdf = df[df[\"ut_datetime\"].dt.date == parser.parse(\"2019-08-28\").date()]\n", + " plt.scatter(*zip(*tmpdf[\"center_coord\"]), s=1, alpha=0.5)\n", + " plt.title(f\"{dt}\")\n", + "del tmpdf" + ] + }, + { + "cell_type": "markdown", + "id": "337ceedc", + "metadata": {}, + "source": [ + "# Recap and Master Function\n", + "Here is a master function that takes a repo_path and returns \\\n", + "(1) a Pandas dataframe with needed info, and\\\n", + "(2) a dictionary with the images in discrete piles (sets)." + ] + }, + { + "cell_type": "code", + "execution_count": 634, + "id": "fccdee6b", + "metadata": {}, + "outputs": [], + "source": [ + "def retrieve_image_sets(\n", + " repo_path,\n", + " basedir=\"default\",\n", + " desired_datasetTypes=[\"deepDiff_differenceExp\"],\n", + " overwrite=False,\n", + " overlap_uncertainty_radius_arcsec=30,\n", + "):\n", + " \"\"\"2/6/2024 COC\"\"\"\n", + " import lsst\n", + " import lsst.daf.butler as dafButler\n", + " import os\n", + " import time\n", + " from matplotlib import pyplot as plt\n", + " import progressbar\n", + " from concurrent.futures import ProcessPoolExecutor, as_completed\n", + " from astropy.time import Time # for converting Butler visitInfo.date (TAI) to UTC strings\n", + " from astropy import units as u\n", + " import pandas as pd\n", + " import pickle\n", + " from dateutil import parser\n", + "\n", + " if basedir == \"default\":\n", + " basedir = f'{os.environ[\"HOME\"]}/kbmod_tmp'\n", + " print(f'Changing \"default\" basedir to {basedir} now.')\n", + "\n", + " os.makedirs(basedir, exist_ok=True)\n", + "\n", + " # repo_path = f\"/epyc/users/smotherh/DEEP/PointingGroups/butler-repo\"\n", + " butler = dafButler.Butler(repo_path)\n", + "\n", + " all_collection_names = get_collection_names(butler=butler, basedir=basedir, verbose=True, export=True)\n", + " desired_collections = get_desired_collections(all_collections_list=all_collection_names)\n", + " # datasetTypes = getDatasetTypeStats(butler=butler, overwrite=False) # not used 2/6/2024 COC\n", + " # desired_datasetTypes = [\"deepDiff_differenceExp\"]\n", + " df, example_vdr_ref = get_vdr_data(\n", + " butler=butler, desired_collections=desired_collections, desired_datasetTypes=desired_datasetTypes\n", + " )\n", + " desired_instruments = getInstruments(butler=butler, vdr_ids=df[\"data_id\"])\n", + " df[\"uri\"] = getURIs(\n", + " butler=butler,\n", + " dataIds=df[\"data_id\"],\n", + " repo_path=repo_path,\n", + " desired_datasetTypes=desired_datasetTypes,\n", + " desired_collections=desired_collections,\n", + " overwrite=overwrite,\n", + " )\n", + "\n", + " df[\"ut\"] = getTimestamps(dataIds=df[\"data_id\"], overwrite=overwrite)\n", + " df[\"ut_datetime\"] = pd.to_datetime(df[\"ut\"])\n", + " overlapping_sets = find_overlapping_coords(\n", + " df=df, uncertainty_radius=overlap_uncertainty_radius_arcsec, overwrite=overwrite\n", + " )\n", + " return df, overlapping_sets" + ] + }, + { + "cell_type": "code", + "execution_count": 638, + "id": "cf221f77", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Changing \"default\" basedir to /astro/users/coc123/kbmod_tmp now.\n", + "Found 1292 collections in the Butler. Wrote to \"/astro/users/coc123/kbmod_tmp/all_collection_names.lst\".\n", + "Found DECam. Adding to \"desired_instruments\" now.\n", + "WARNING: we are not iterating over all rows to find instruments, just taking the first one.\n", + "Recycled 47383 paths from /astro/users/coc123/kbmod_tmp/uri_cache.lst as overwrite was False.\n", + "Overwrite is False, so we will read the timestamps from file now...\n", + "Recycled 47383 from /astro/users/coc123/kbmod_tmp/vdr_timestamps.lst.\n", + "Recycling /astro/users/coc123/kbmod_tmp/overlapping_sets.pickle as overwrite=False.\n", + "CPU times: user 3.91 s, sys: 413 ms, total: 4.32 s\n", + "Wall time: 5.54 s\n" + ] + } + ], + "source": [ + "%%time\n", + "# TIMING NOTE: this requires about 7 seconds to run ***with everything already cached***.\n", + "df1, overlapping_sets1 = retrieve_image_sets(\n", + " repo_path=f\"/epyc/users/smotherh/DEEP/PointingGroups/butler-repo\"\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "3df17bc4", + "metadata": {}, + "source": [ + "# Next Steps\n", + "\n", + "In no particular order:\n", + "\n", + "1. User-specified (RA, Dec) pair.\n", + "2. Heat map / histogrammed results.\n", + "3. Sky patches approach.\n", + "4. Reflex correction." ] } ],