From 9be7f830b6d924c2a4534f8cec2d4f8a96049b7f Mon Sep 17 00:00:00 2001 From: Melissa DeLucchi Date: Thu, 17 Oct 2024 21:42:32 -0400 Subject: [PATCH 1/4] Update output from docs. Update path to hats catalogs. --- docs/tutorials/filtering_large_catalogs.ipynb | 6 +- docs/tutorials/pre_executed/des-gaia.ipynb | 299 ++++++++---------- docs/tutorials/pre_executed/ztf_bts-ngc.ipynb | 149 ++++----- 3 files changed, 208 insertions(+), 246 deletions(-) diff --git a/docs/tutorials/filtering_large_catalogs.ipynb b/docs/tutorials/filtering_large_catalogs.ipynb index bb8845fe..364b58a9 100644 --- a/docs/tutorials/filtering_large_catalogs.ipynb +++ b/docs/tutorials/filtering_large_catalogs.ipynb @@ -82,7 +82,7 @@ "metadata": {}, "outputs": [], "source": [ - "surveys_path = \"https://data.lsdb.io/unstable/\"" + "surveys_path = \"https://data.lsdb.io/hats/\"" ] }, { @@ -384,7 +384,7 @@ ], "metadata": { "kernelspec": { - "display_name": "hipscatenv", + "display_name": "demo", "language": "python", "name": "python3" }, @@ -398,7 +398,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.14" + "version": "3.12.3" } }, "nbformat": 4, diff --git a/docs/tutorials/pre_executed/des-gaia.ipynb b/docs/tutorials/pre_executed/des-gaia.ipynb index 0a255926..89fcee4b 100644 --- a/docs/tutorials/pre_executed/des-gaia.ipynb +++ b/docs/tutorials/pre_executed/des-gaia.ipynb @@ -28,10 +28,18 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "id": "dbf9a727fa9682a1", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Note: you may need to restart the kernel to use updated packages.\n" + ] + } + ], "source": [ "# Comment to skip hats-import installation\n", "%pip install --quiet hats-import\n", @@ -42,7 +50,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 2, "id": "a37c4b45dede404c", "metadata": {}, "outputs": [], @@ -94,7 +102,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 3, "id": "2828441463c81505", "metadata": {}, "outputs": [], @@ -136,7 +144,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 4, "id": "3678ae0f1ab1e8e1", "metadata": {}, "outputs": [], @@ -180,7 +188,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 5, "id": "1b5f72a701f38dbb", "metadata": {}, "outputs": [], @@ -218,21 +226,10 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 6, "id": "c1ae24c75a169106", "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/delucchi/anaconda3/envs/hipscatenv/lib/python3.10/site-packages/distributed/node.py:182: UserWarning: Port 8787 is already in use.\n", - "Perhaps you already have a cluster running?\n", - "Hosting the HTTP server on port 33731 instead\n", - " warnings.warn(\n" - ] - } - ], + "outputs": [], "source": [ "# See Dask documentation for the options, e.g. https://distributed.dask.org/en/latest/local-cluster.html\n", "# You would likely want to change `n_workers` to have an optimal performance while not going out of memory.\n", @@ -266,14 +263,14 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 7, "id": "6c4a751793a5b9d5", "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "ea0a7843fcd5423db4e741b375db16a5", + "model_id": "e4e1cceb18b241b08ba74e729b995f8a", "version_major": 2, "version_minor": 0 }, @@ -287,7 +284,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "0a0c4b92af69448abc33271c1d3c3140", + "model_id": "2cd9a32a1c184662aac223728579668e", "version_major": 2, "version_minor": 0 }, @@ -301,7 +298,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "98f341a35bc74a2bb52446f6386c8a44", + "model_id": "28cc408972594ea28bf2217c30c38fee", "version_major": 2, "version_minor": 0 }, @@ -315,7 +312,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "43522e6fb7c04379acba4820923d3b2f", + "model_id": "65e9c9de39cb4fc292c0bb498d5f2c95", "version_major": 2, "version_minor": 0 }, @@ -329,7 +326,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "e364466abc7d4c26a8b5fed3948b6c6e", + "model_id": "949a19081e034c839c7b20d7bd63c59f", "version_major": 2, "version_minor": 0 }, @@ -343,12 +340,12 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "b63c03a9ba69467388bda068b2a88f7d", + "model_id": "16d86dde05c7492eb9cc1c5154aa7ef3", "version_major": 2, "version_minor": 0 }, "text/plain": [ - "Finishing : 0%| | 0/5 [00:00" ] @@ -441,25 +438,10 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 9, "id": "87db92f82a8f046f", "metadata": {}, - "outputs": [ - { - "ename": "OSError", - "evalue": "File data/Gaia_DR3/schema.parquet already exists. If you mean to replace it then use the argument \"overwrite=True\".", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mOSError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[0;32mIn[6], line 3\u001b[0m\n\u001b[1;32m 1\u001b[0m gaia_file \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mnext\u001b[39m(GAIA_DIR\u001b[38;5;241m.\u001b[39mglob(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m*.csv.gz\u001b[39m\u001b[38;5;124m\"\u001b[39m))\n\u001b[1;32m 2\u001b[0m empty_astropy_table \u001b[38;5;241m=\u001b[39m ascii\u001b[38;5;241m.\u001b[39mread(gaia_file, \u001b[38;5;28mformat\u001b[39m\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mecsv\u001b[39m\u001b[38;5;124m\"\u001b[39m, data_end\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m1\u001b[39m)\n\u001b[0;32m----> 3\u001b[0m \u001b[43mempty_astropy_table\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mwrite\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 4\u001b[0m \u001b[43m \u001b[49m\u001b[43mGAIA_SCHEMA_FILE\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 5\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;66;43;03m# Uncomment to overwrite existing schema file\u001b[39;49;00m\n\u001b[1;32m 6\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;66;43;03m# overwrite=True,\u001b[39;49;00m\n\u001b[1;32m 7\u001b[0m \u001b[43m)\u001b[49m\n", - "File \u001b[0;32m~/anaconda3/envs/hipscatenv/lib/python3.10/site-packages/astropy/table/connect.py:130\u001b[0m, in \u001b[0;36mTableWrite.__call__\u001b[0;34m(self, serialize_method, *args, **kwargs)\u001b[0m\n\u001b[1;32m 128\u001b[0m instance \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_instance\n\u001b[1;32m 129\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m serialize_method_as(instance, serialize_method):\n\u001b[0;32m--> 130\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mregistry\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mwrite\u001b[49m\u001b[43m(\u001b[49m\u001b[43minstance\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n", - "File \u001b[0;32m~/anaconda3/envs/hipscatenv/lib/python3.10/site-packages/astropy/io/registry/core.py:386\u001b[0m, in \u001b[0;36mUnifiedOutputRegistry.write\u001b[0;34m(self, data, format, *args, **kwargs)\u001b[0m\n\u001b[1;32m 381\u001b[0m \u001b[38;5;28mformat\u001b[39m \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_get_valid_format(\n\u001b[1;32m 382\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mwrite\u001b[39m\u001b[38;5;124m\"\u001b[39m, data\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__class__\u001b[39m, path, fileobj, args, kwargs\n\u001b[1;32m 383\u001b[0m )\n\u001b[1;32m 385\u001b[0m writer \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mget_writer(\u001b[38;5;28mformat\u001b[39m, data\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__class__\u001b[39m)\n\u001b[0;32m--> 386\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mwriter\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdata\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n", - "File \u001b[0;32m~/anaconda3/envs/hipscatenv/lib/python3.10/site-packages/astropy/io/misc/parquet.py:435\u001b[0m, in \u001b[0;36mwrite_table_parquet\u001b[0;34m(table, output, overwrite)\u001b[0m\n\u001b[1;32m 433\u001b[0m os\u001b[38;5;241m.\u001b[39mremove(output)\n\u001b[1;32m 434\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m--> 435\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mOSError\u001b[39;00m(NOT_OVERWRITING_MSG\u001b[38;5;241m.\u001b[39mformat(output))\n\u001b[1;32m 437\u001b[0m \u001b[38;5;66;03m# We use version='2.0' for full support of datatypes including uint32.\u001b[39;00m\n\u001b[1;32m 438\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m parquet\u001b[38;5;241m.\u001b[39mParquetWriter(output, schema, version\u001b[38;5;241m=\u001b[39mwriter_version) \u001b[38;5;28;01mas\u001b[39;00m writer:\n\u001b[1;32m 439\u001b[0m \u001b[38;5;66;03m# Convert each Table column to a pyarrow array\u001b[39;00m\n", - "\u001b[0;31mOSError\u001b[0m: File data/Gaia_DR3/schema.parquet already exists. If you mean to replace it then use the argument \"overwrite=True\"." - ] - } - ], + "outputs": [], "source": [ "gaia_file = next(GAIA_DIR.glob(\"*.csv.gz\"))\n", "empty_astropy_table = ascii.read(gaia_file, format=\"ecsv\", data_end=1)\n", @@ -472,14 +454,14 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 10, "id": "54857141a690eb58", "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "99ab154793884704bbbe42775432c8ea", + "model_id": "4ff04c9c43ab489ebec928685fa74222", "version_major": 2, "version_minor": 0 }, @@ -490,17 +472,10 @@ "metadata": {}, "output_type": "display_data" }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "tmp_path (catalogs/gaia_dr3/intermediate) contains intermediate files; resuming prior progress.\n" - ] - }, { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "d53db7644a26443fad5c7b2498b2d44a", + "model_id": "fbd7b499218243a19be640bb5abb7c1b", "version_major": 2, "version_minor": 0 }, @@ -515,14 +490,14 @@ "name": "stderr", "output_type": "stream", "text": [ - "/home/delucchi/git/hats/hipscat/src/hats/io/file_io/file_io.py:141: RuntimeWarning: compression has no effect when passing a non-binary object as input.\n", + "/home/delucchi/git/demo/hats/src/hats/io/file_io/file_io.py:122: RuntimeWarning: compression has no effect when passing a non-binary object as input.\n", " with pd.read_csv(csv_file, chunksize=chunksize, **kwargs) as reader:\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "8a242fc3ed4c481f9f8068da1d6cc3e3", + "model_id": "34bc569c43c34b05af8a1bd27723eb6b", "version_major": 2, "version_minor": 0 }, @@ -536,7 +511,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "99ca94c0927f47f68c007ea7e2642c4a", + "model_id": "cf06e0afed714be6a996f85e9568bd6e", "version_major": 2, "version_minor": 0 }, @@ -551,14 +526,14 @@ "name": "stderr", "output_type": "stream", "text": [ - "/home/delucchi/git/hats/hipscat/src/hats/io/file_io/file_io.py:141: RuntimeWarning: compression has no effect when passing a non-binary object as input.\n", + "/home/delucchi/git/demo/hats/src/hats/io/file_io/file_io.py:122: RuntimeWarning: compression has no effect when passing a non-binary object as input.\n", " with pd.read_csv(csv_file, chunksize=chunksize, **kwargs) as reader:\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "03549e5878fa4067a09a5e57be3f7287", + "model_id": "0874679aff024358bf1acea563230d8f", "version_major": 2, "version_minor": 0 }, @@ -572,12 +547,12 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "c1f7e4b9e7744eeead3b7e662bdd574e", + "model_id": "c75a2911a7394b94b8728cddbf6d05e2", "version_major": 2, "version_minor": 0 }, "text/plain": [ - "Finishing : 0%| | 0/5 [00:00" ] @@ -675,7 +650,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "b81c66ea9b3848189d273e6d5251cb2a", + "model_id": "0d46f4facb4849148363399f5c0ddd38", "version_major": 2, "version_minor": 0 }, @@ -687,16 +662,23 @@ "output_type": "display_data" }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "tmp_path (catalogs/gaia_dr3_1arcsec/intermediate) contains intermediate files; resuming prior progress.\n" - ] + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "12a0c07b4b5d4d589aca50f5dac1a791", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Mapping : 0%| | 0/1 [00:00 8\u001b[0m \u001b[43mpipeline_with_client\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmargin_cache_args\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mclient\u001b[49m\u001b[43m)\u001b[49m\n", - "File \u001b[0;32m~/git/hats/hipscat-import/src/hats_import/pipeline.py:60\u001b[0m, in \u001b[0;36mpipeline_with_client\u001b[0;34m(args, client)\u001b[0m\n\u001b[1;32m 58\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m 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soap_runner\u001b[38;5;241m.\u001b[39mrun(args, client)\n", - "File \u001b[0;32m~/git/hats/hipscat-import/src/hats_import/margin_cache/margin_cache.py:57\u001b[0m, in \u001b[0;36mgenerate_margin_cache\u001b[0;34m(args, client)\u001b[0m\n\u001b[1;32m 44\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m reducing_key, pix \u001b[38;5;129;01min\u001b[39;00m resume_plan\u001b[38;5;241m.\u001b[39mget_remaining_reduce_keys():\n\u001b[1;32m 45\u001b[0m futures\u001b[38;5;241m.\u001b[39mappend(\n\u001b[1;32m 46\u001b[0m client\u001b[38;5;241m.\u001b[39msubmit(\n\u001b[1;32m 47\u001b[0m mcmr\u001b[38;5;241m.\u001b[39mreduce_margin_shards,\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 55\u001b[0m )\n\u001b[1;32m 56\u001b[0m )\n\u001b[0;32m---> 57\u001b[0m \u001b[43mresume_plan\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mwait_for_reducing\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfutures\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 59\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m resume_plan\u001b[38;5;241m.\u001b[39mprint_progress(total\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m4\u001b[39m, stage_name\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mFinishing\u001b[39m\u001b[38;5;124m\"\u001b[39m) \u001b[38;5;28;01mas\u001b[39;00m step_progress:\n\u001b[1;32m 60\u001b[0m total_rows \u001b[38;5;241m=\u001b[39m parquet_metadata\u001b[38;5;241m.\u001b[39mwrite_parquet_metadata(args\u001b[38;5;241m.\u001b[39mcatalog_path)\n", - "File \u001b[0;32m~/git/hats/hipscat-import/src/hats_import/margin_cache/margin_cache_resume_plan.py:136\u001b[0m, in \u001b[0;36mMarginCachePlan.wait_for_reducing\u001b[0;34m(self, futures)\u001b[0m\n\u001b[1;32m 134\u001b[0m remaining_sources_to_reduce \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mget_remaining_reduce_keys()\n\u001b[1;32m 135\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(remaining_sources_to_reduce) \u001b[38;5;241m>\u001b[39m \u001b[38;5;241m0\u001b[39m:\n\u001b[0;32m--> 136\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mRuntimeError\u001b[39;00m(\n\u001b[1;32m 137\u001b[0m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mlen\u001b[39m(remaining_sources_to_reduce)\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m reducing stages did not complete successfully.\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 138\u001b[0m )\n\u001b[1;32m 139\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mtouch_stage_done_file(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mREDUCING_STAGE)\n", - "\u001b[0;31mRuntimeError\u001b[0m: 12 reducing stages did not complete successfully." - ] + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "b4f886ded5204a7d9fa6ad3128566d65", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Finishing : 0%| | 0/4 [00:00\n", " \n", " \n", - " 4611686018427387904\n", + " 1152921504606846976\n", " int64[pyarrow]\n", " string[pyarrow]\n", " int64[pyarrow]\n", @@ -1450,7 +1431,7 @@ " uint64[pyarrow]\n", " \n", " \n", - " 18446744073709551615\n", + " 1441151880758558720\n", " ...\n", " ...\n", " ...\n", @@ -1676,10 +1657,10 @@ ], "text/plain": [ "Dask NestedFrame Structure:\n", - " COADD_OBJECT_ID TILENAME HPIX_32 HPIX_64 HPIX_1024 HPIX_4096 HPIX_16384 RA DEC ALPHAWIN_J2000 DELTAWIN_J2000 GALACTIC_L GALACTIC_B XWIN_IMAGE YWIN_IMAGE A_IMAGE ERRA_IMAGE B_IMAGE ERRB_IMAGE THETA_J2000 ERRTHETA_IMAGE KRON_RADIUS EBV_SFD98 MAG_AUTO_G_DERED MAG_AUTO_R_DERED MAG_AUTO_I_DERED MAG_AUTO_Z_DERED MAG_AUTO_Y_DERED WAVG_MAG_PSF_G_DERED WAVG_MAG_PSF_R_DERED WAVG_MAG_PSF_I_DERED WAVG_MAG_PSF_Z_DERED WAVG_MAG_PSF_Y_DERED EXTENDED_CLASS_COADD EXTENDED_CLASS_WAVG FLAGS_G IMAFLAGS_ISO_G NEPOCHS_G FLAGS_R IMAFLAGS_ISO_R NEPOCHS_R FLAGS_I IMAFLAGS_ISO_I NEPOCHS_I FLAGS_Z IMAFLAGS_ISO_Z NEPOCHS_Z FLAGS_Y IMAFLAGS_ISO_Y NEPOCHS_Y XWIN_IMAGE_G XWIN_IMAGE_R XWIN_IMAGE_I XWIN_IMAGE_Z XWIN_IMAGE_Y YWIN_IMAGE_G YWIN_IMAGE_R YWIN_IMAGE_I YWIN_IMAGE_Z YWIN_IMAGE_Y X2WIN_IMAGE_G X2WIN_IMAGE_R X2WIN_IMAGE_I X2WIN_IMAGE_Z X2WIN_IMAGE_Y Y2WIN_IMAGE_G Y2WIN_IMAGE_R Y2WIN_IMAGE_I Y2WIN_IMAGE_Z Y2WIN_IMAGE_Y XYWIN_IMAGE_G XYWIN_IMAGE_R XYWIN_IMAGE_I XYWIN_IMAGE_Z XYWIN_IMAGE_Y ERRX2WIN_IMAGE_G ERRX2WIN_IMAGE_R ERRX2WIN_IMAGE_I ERRX2WIN_IMAGE_Z ERRX2WIN_IMAGE_Y ERRY2WIN_IMAGE_G ERRY2WIN_IMAGE_R ERRY2WIN_IMAGE_I ERRY2WIN_IMAGE_Z ERRY2WIN_IMAGE_Y ERRXYWIN_IMAGE_G ERRXYWIN_IMAGE_R ERRXYWIN_IMAGE_I ERRXYWIN_IMAGE_Z ERRXYWIN_IMAGE_Y AWIN_IMAGE_G AWIN_IMAGE_R AWIN_IMAGE_I AWIN_IMAGE_Z AWIN_IMAGE_Y BWIN_IMAGE_G BWIN_IMAGE_R BWIN_IMAGE_I BWIN_IMAGE_Z BWIN_IMAGE_Y THETAWIN_IMAGE_G THETAWIN_IMAGE_R THETAWIN_IMAGE_I THETAWIN_IMAGE_Z THETAWIN_IMAGE_Y ERRAWIN_IMAGE_G ERRAWIN_IMAGE_R ERRAWIN_IMAGE_I ERRAWIN_IMAGE_Z ERRAWIN_IMAGE_Y ERRBWIN_IMAGE_G ERRBWIN_IMAGE_R ERRBWIN_IMAGE_I ERRBWIN_IMAGE_Z ERRBWIN_IMAGE_Y ERRTHETAWIN_IMAGE_G ERRTHETAWIN_IMAGE_R ERRTHETAWIN_IMAGE_I ERRTHETAWIN_IMAGE_Z ERRTHETAWIN_IMAGE_Y FLUX_RADIUS_G FLUX_RADIUS_R FLUX_RADIUS_I FLUX_RADIUS_Z FLUX_RADIUS_Y FWHM_IMAGE_G FWHM_IMAGE_R FWHM_IMAGE_I FWHM_IMAGE_Z FWHM_IMAGE_Y ISOAREA_IMAGE_G ISOAREA_IMAGE_R ISOAREA_IMAGE_I ISOAREA_IMAGE_Z ISOAREA_IMAGE_Y BACKGROUND_G BACKGROUND_R BACKGROUND_I BACKGROUND_Z BACKGROUND_Y NITER_MODEL_G NITER_MODEL_R NITER_MODEL_I NITER_MODEL_Z NITER_MODEL_Y KRON_RADIUS_G KRON_RADIUS_R KRON_RADIUS_I KRON_RADIUS_Z KRON_RADIUS_Y MAG_AUTO_G MAG_AUTO_R MAG_AUTO_I MAG_AUTO_Z MAG_AUTO_Y MAGERR_AUTO_G MAGERR_AUTO_R MAGERR_AUTO_I MAGERR_AUTO_Z MAGERR_AUTO_Y WAVG_MAG_PSF_G WAVG_MAG_PSF_R WAVG_MAG_PSF_I WAVG_MAG_PSF_Z WAVG_MAG_PSF_Y WAVG_MAGERR_PSF_G WAVG_MAGERR_PSF_R WAVG_MAGERR_PSF_I WAVG_MAGERR_PSF_Z WAVG_MAGERR_PSF_Y FLUX_AUTO_G FLUX_AUTO_R FLUX_AUTO_I FLUX_AUTO_Z FLUX_AUTO_Y FLUXERR_AUTO_G FLUXERR_AUTO_R FLUXERR_AUTO_I FLUXERR_AUTO_Z FLUXERR_AUTO_Y WAVG_FLUX_PSF_G WAVG_FLUX_PSF_R WAVG_FLUX_PSF_I WAVG_FLUX_PSF_Z WAVG_FLUX_PSF_Y WAVG_FLUXERR_PSF_G WAVG_FLUXERR_PSF_R WAVG_FLUXERR_PSF_I WAVG_FLUXERR_PSF_Z WAVG_FLUXERR_PSF_Y CLASS_STAR_G CLASS_STAR_R CLASS_STAR_I CLASS_STAR_Z CLASS_STAR_Y SPREAD_MODEL_G SPREAD_MODEL_R SPREAD_MODEL_I SPREAD_MODEL_Z SPREAD_MODEL_Y WAVG_SPREAD_MODEL_G WAVG_SPREAD_MODEL_R WAVG_SPREAD_MODEL_I WAVG_SPREAD_MODEL_Z WAVG_SPREAD_MODEL_Y SPREADERR_MODEL_G SPREADERR_MODEL_R SPREADERR_MODEL_I SPREADERR_MODEL_Z SPREADERR_MODEL_Y WAVG_SPREADERR_MODEL_G WAVG_SPREADERR_MODEL_R WAVG_SPREADERR_MODEL_I WAVG_SPREADERR_MODEL_Z WAVG_SPREADERR_MODEL_Y Norder Dir Npix\n", - "npartitions=1 \n", - "4611686018427387904 int64[pyarrow] string[pyarrow] int64[pyarrow] int64[pyarrow] int64[pyarrow] int64[pyarrow] int64[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] int16[pyarrow] int16[pyarrow] int16[pyarrow] int32[pyarrow] int32[pyarrow] int16[pyarrow] int32[pyarrow] int32[pyarrow] int16[pyarrow] int32[pyarrow] int32[pyarrow] int16[pyarrow] int32[pyarrow] int32[pyarrow] int16[pyarrow] int32[pyarrow] int32[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] int32[pyarrow] int32[pyarrow] int32[pyarrow] int32[pyarrow] int32[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] int32[pyarrow] int32[pyarrow] int32[pyarrow] int32[pyarrow] int32[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] uint8[pyarrow] uint64[pyarrow] uint64[pyarrow]\n", - "18446744073709551615 ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...\n", + " COADD_OBJECT_ID TILENAME HPIX_32 HPIX_64 HPIX_1024 HPIX_4096 HPIX_16384 RA DEC ALPHAWIN_J2000 DELTAWIN_J2000 GALACTIC_L GALACTIC_B XWIN_IMAGE YWIN_IMAGE A_IMAGE ERRA_IMAGE B_IMAGE ERRB_IMAGE THETA_J2000 ERRTHETA_IMAGE KRON_RADIUS EBV_SFD98 MAG_AUTO_G_DERED MAG_AUTO_R_DERED MAG_AUTO_I_DERED MAG_AUTO_Z_DERED MAG_AUTO_Y_DERED WAVG_MAG_PSF_G_DERED WAVG_MAG_PSF_R_DERED WAVG_MAG_PSF_I_DERED WAVG_MAG_PSF_Z_DERED WAVG_MAG_PSF_Y_DERED EXTENDED_CLASS_COADD EXTENDED_CLASS_WAVG FLAGS_G IMAFLAGS_ISO_G NEPOCHS_G FLAGS_R IMAFLAGS_ISO_R NEPOCHS_R FLAGS_I IMAFLAGS_ISO_I NEPOCHS_I FLAGS_Z IMAFLAGS_ISO_Z NEPOCHS_Z FLAGS_Y IMAFLAGS_ISO_Y NEPOCHS_Y XWIN_IMAGE_G XWIN_IMAGE_R XWIN_IMAGE_I XWIN_IMAGE_Z XWIN_IMAGE_Y YWIN_IMAGE_G YWIN_IMAGE_R YWIN_IMAGE_I YWIN_IMAGE_Z YWIN_IMAGE_Y X2WIN_IMAGE_G X2WIN_IMAGE_R X2WIN_IMAGE_I X2WIN_IMAGE_Z X2WIN_IMAGE_Y Y2WIN_IMAGE_G Y2WIN_IMAGE_R Y2WIN_IMAGE_I Y2WIN_IMAGE_Z Y2WIN_IMAGE_Y XYWIN_IMAGE_G XYWIN_IMAGE_R XYWIN_IMAGE_I XYWIN_IMAGE_Z XYWIN_IMAGE_Y ERRX2WIN_IMAGE_G ERRX2WIN_IMAGE_R ERRX2WIN_IMAGE_I ERRX2WIN_IMAGE_Z ERRX2WIN_IMAGE_Y ERRY2WIN_IMAGE_G ERRY2WIN_IMAGE_R ERRY2WIN_IMAGE_I ERRY2WIN_IMAGE_Z ERRY2WIN_IMAGE_Y ERRXYWIN_IMAGE_G ERRXYWIN_IMAGE_R ERRXYWIN_IMAGE_I ERRXYWIN_IMAGE_Z ERRXYWIN_IMAGE_Y AWIN_IMAGE_G AWIN_IMAGE_R AWIN_IMAGE_I AWIN_IMAGE_Z AWIN_IMAGE_Y BWIN_IMAGE_G BWIN_IMAGE_R BWIN_IMAGE_I BWIN_IMAGE_Z BWIN_IMAGE_Y THETAWIN_IMAGE_G THETAWIN_IMAGE_R THETAWIN_IMAGE_I THETAWIN_IMAGE_Z THETAWIN_IMAGE_Y ERRAWIN_IMAGE_G ERRAWIN_IMAGE_R ERRAWIN_IMAGE_I ERRAWIN_IMAGE_Z ERRAWIN_IMAGE_Y ERRBWIN_IMAGE_G ERRBWIN_IMAGE_R ERRBWIN_IMAGE_I ERRBWIN_IMAGE_Z ERRBWIN_IMAGE_Y ERRTHETAWIN_IMAGE_G ERRTHETAWIN_IMAGE_R ERRTHETAWIN_IMAGE_I ERRTHETAWIN_IMAGE_Z ERRTHETAWIN_IMAGE_Y FLUX_RADIUS_G FLUX_RADIUS_R FLUX_RADIUS_I FLUX_RADIUS_Z FLUX_RADIUS_Y FWHM_IMAGE_G FWHM_IMAGE_R FWHM_IMAGE_I FWHM_IMAGE_Z FWHM_IMAGE_Y ISOAREA_IMAGE_G ISOAREA_IMAGE_R ISOAREA_IMAGE_I ISOAREA_IMAGE_Z ISOAREA_IMAGE_Y BACKGROUND_G BACKGROUND_R BACKGROUND_I BACKGROUND_Z BACKGROUND_Y NITER_MODEL_G NITER_MODEL_R NITER_MODEL_I NITER_MODEL_Z NITER_MODEL_Y KRON_RADIUS_G KRON_RADIUS_R KRON_RADIUS_I KRON_RADIUS_Z KRON_RADIUS_Y MAG_AUTO_G MAG_AUTO_R MAG_AUTO_I MAG_AUTO_Z MAG_AUTO_Y MAGERR_AUTO_G MAGERR_AUTO_R MAGERR_AUTO_I MAGERR_AUTO_Z MAGERR_AUTO_Y WAVG_MAG_PSF_G WAVG_MAG_PSF_R WAVG_MAG_PSF_I WAVG_MAG_PSF_Z WAVG_MAG_PSF_Y WAVG_MAGERR_PSF_G WAVG_MAGERR_PSF_R WAVG_MAGERR_PSF_I WAVG_MAGERR_PSF_Z WAVG_MAGERR_PSF_Y FLUX_AUTO_G FLUX_AUTO_R FLUX_AUTO_I FLUX_AUTO_Z FLUX_AUTO_Y FLUXERR_AUTO_G FLUXERR_AUTO_R FLUXERR_AUTO_I FLUXERR_AUTO_Z FLUXERR_AUTO_Y WAVG_FLUX_PSF_G WAVG_FLUX_PSF_R WAVG_FLUX_PSF_I WAVG_FLUX_PSF_Z WAVG_FLUX_PSF_Y WAVG_FLUXERR_PSF_G WAVG_FLUXERR_PSF_R WAVG_FLUXERR_PSF_I WAVG_FLUXERR_PSF_Z WAVG_FLUXERR_PSF_Y CLASS_STAR_G CLASS_STAR_R CLASS_STAR_I CLASS_STAR_Z CLASS_STAR_Y SPREAD_MODEL_G SPREAD_MODEL_R SPREAD_MODEL_I SPREAD_MODEL_Z SPREAD_MODEL_Y WAVG_SPREAD_MODEL_G WAVG_SPREAD_MODEL_R WAVG_SPREAD_MODEL_I WAVG_SPREAD_MODEL_Z WAVG_SPREAD_MODEL_Y SPREADERR_MODEL_G SPREADERR_MODEL_R SPREADERR_MODEL_I SPREADERR_MODEL_Z SPREADERR_MODEL_Y WAVG_SPREADERR_MODEL_G WAVG_SPREADERR_MODEL_R WAVG_SPREADERR_MODEL_I WAVG_SPREADERR_MODEL_Z WAVG_SPREADERR_MODEL_Y Norder Dir Npix\n", + "npartitions=1 \n", + "1152921504606846976 int64[pyarrow] string[pyarrow] int64[pyarrow] int64[pyarrow] int64[pyarrow] int64[pyarrow] int64[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] int16[pyarrow] int16[pyarrow] int16[pyarrow] int32[pyarrow] int32[pyarrow] int16[pyarrow] int32[pyarrow] int32[pyarrow] int16[pyarrow] int32[pyarrow] int32[pyarrow] int16[pyarrow] int32[pyarrow] int32[pyarrow] int16[pyarrow] int32[pyarrow] int32[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] int32[pyarrow] int32[pyarrow] int32[pyarrow] int32[pyarrow] int32[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] int32[pyarrow] int32[pyarrow] int32[pyarrow] int32[pyarrow] int32[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] uint8[pyarrow] uint64[pyarrow] uint64[pyarrow]\n", + "1441151880758558720 ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...\n", "Dask Name: nestedframe, 3 expressions\n", "Expr=MapPartitions(NestedFrame)" ] @@ -2025,7 +2006,7 @@ " \n", " \n", " \n", - " 4611686018427387904\n", + " 1152921504606846976\n", " int64[pyarrow]\n", " string[pyarrow]\n", " int64[pyarrow]\n", @@ -2183,7 +2164,7 @@ " uint64[pyarrow]\n", " \n", " \n", - " 18446744073709551615\n", + " 1441151880758558720\n", " ...\n", " ...\n", " ...\n", @@ -2346,10 +2327,10 @@ ], "text/plain": [ "Dask NestedFrame Structure:\n", - " solution_id designation source_id random_index ref_epoch ra ra_error dec dec_error parallax parallax_error parallax_over_error pm pmra pmra_error pmdec pmdec_error ra_dec_corr ra_parallax_corr ra_pmra_corr ra_pmdec_corr dec_parallax_corr dec_pmra_corr dec_pmdec_corr parallax_pmra_corr parallax_pmdec_corr pmra_pmdec_corr astrometric_n_obs_al astrometric_n_obs_ac astrometric_n_good_obs_al astrometric_n_bad_obs_al astrometric_gof_al astrometric_chi2_al astrometric_excess_noise astrometric_excess_noise_sig astrometric_params_solved astrometric_primary_flag nu_eff_used_in_astrometry pseudocolour pseudocolour_error ra_pseudocolour_corr dec_pseudocolour_corr parallax_pseudocolour_corr pmra_pseudocolour_corr pmdec_pseudocolour_corr astrometric_matched_transits visibility_periods_used astrometric_sigma5d_max matched_transits new_matched_transits matched_transits_removed ipd_gof_harmonic_amplitude ipd_gof_harmonic_phase ipd_frac_multi_peak ipd_frac_odd_win ruwe scan_direction_strength_k1 scan_direction_strength_k2 scan_direction_strength_k3 scan_direction_strength_k4 scan_direction_mean_k1 scan_direction_mean_k2 scan_direction_mean_k3 scan_direction_mean_k4 duplicated_source phot_g_n_obs phot_g_mean_flux phot_g_mean_flux_error phot_g_mean_flux_over_error phot_g_mean_mag phot_bp_n_obs phot_bp_mean_flux phot_bp_mean_flux_error phot_bp_mean_flux_over_error phot_bp_mean_mag phot_rp_n_obs phot_rp_mean_flux phot_rp_mean_flux_error phot_rp_mean_flux_over_error phot_rp_mean_mag phot_bp_rp_excess_factor phot_bp_n_contaminated_transits phot_bp_n_blended_transits phot_rp_n_contaminated_transits phot_rp_n_blended_transits phot_proc_mode bp_rp bp_g g_rp radial_velocity radial_velocity_error rv_method_used rv_nb_transits rv_nb_deblended_transits rv_visibility_periods_used rv_expected_sig_to_noise rv_renormalised_gof rv_chisq_pvalue rv_time_duration rv_amplitude_robust rv_template_teff rv_template_logg rv_template_fe_h rv_atm_param_origin vbroad vbroad_error vbroad_nb_transits grvs_mag grvs_mag_error grvs_mag_nb_transits rvs_spec_sig_to_noise phot_variable_flag l b ecl_lon ecl_lat in_qso_candidates in_galaxy_candidates non_single_star has_xp_continuous has_xp_sampled has_rvs has_epoch_photometry has_epoch_rv has_mcmc_gspphot has_mcmc_msc in_andromeda_survey classprob_dsc_combmod_quasar classprob_dsc_combmod_galaxy classprob_dsc_combmod_star teff_gspphot teff_gspphot_lower teff_gspphot_upper logg_gspphot logg_gspphot_lower logg_gspphot_upper mh_gspphot mh_gspphot_lower mh_gspphot_upper distance_gspphot distance_gspphot_lower distance_gspphot_upper azero_gspphot azero_gspphot_lower azero_gspphot_upper ag_gspphot ag_gspphot_lower ag_gspphot_upper ebpminrp_gspphot ebpminrp_gspphot_lower ebpminrp_gspphot_upper libname_gspphot Norder Dir Npix\n", - "npartitions=1 \n", - "4611686018427387904 int64[pyarrow] string[pyarrow] int64[pyarrow] int64[pyarrow] double[pyarrow] double[pyarrow] float[pyarrow] double[pyarrow] float[pyarrow] double[pyarrow] float[pyarrow] float[pyarrow] float[pyarrow] double[pyarrow] float[pyarrow] double[pyarrow] float[pyarrow] float[pyarrow] float[pyarrow] float[pyarrow] float[pyarrow] float[pyarrow] float[pyarrow] float[pyarrow] float[pyarrow] float[pyarrow] float[pyarrow] int16[pyarrow] int16[pyarrow] int16[pyarrow] int16[pyarrow] float[pyarrow] float[pyarrow] float[pyarrow] float[pyarrow] int8[pyarrow] bool[pyarrow] float[pyarrow] float[pyarrow] float[pyarrow] float[pyarrow] float[pyarrow] float[pyarrow] float[pyarrow] float[pyarrow] int16[pyarrow] int16[pyarrow] float[pyarrow] int16[pyarrow] int16[pyarrow] int16[pyarrow] float[pyarrow] float[pyarrow] int8[pyarrow] int8[pyarrow] float[pyarrow] float[pyarrow] float[pyarrow] float[pyarrow] float[pyarrow] float[pyarrow] float[pyarrow] float[pyarrow] float[pyarrow] bool[pyarrow] int16[pyarrow] double[pyarrow] float[pyarrow] float[pyarrow] float[pyarrow] int16[pyarrow] double[pyarrow] float[pyarrow] float[pyarrow] float[pyarrow] int16[pyarrow] double[pyarrow] float[pyarrow] float[pyarrow] float[pyarrow] float[pyarrow] int16[pyarrow] int16[pyarrow] int16[pyarrow] int16[pyarrow] int8[pyarrow] float[pyarrow] float[pyarrow] float[pyarrow] float[pyarrow] float[pyarrow] int8[pyarrow] int16[pyarrow] int16[pyarrow] int16[pyarrow] float[pyarrow] float[pyarrow] float[pyarrow] float[pyarrow] float[pyarrow] float[pyarrow] float[pyarrow] float[pyarrow] int16[pyarrow] float[pyarrow] float[pyarrow] int16[pyarrow] float[pyarrow] float[pyarrow] int16[pyarrow] float[pyarrow] string[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] bool[pyarrow] bool[pyarrow] int16[pyarrow] bool[pyarrow] bool[pyarrow] bool[pyarrow] bool[pyarrow] bool[pyarrow] bool[pyarrow] bool[pyarrow] bool[pyarrow] float[pyarrow] float[pyarrow] float[pyarrow] float[pyarrow] float[pyarrow] float[pyarrow] float[pyarrow] float[pyarrow] float[pyarrow] float[pyarrow] float[pyarrow] float[pyarrow] float[pyarrow] float[pyarrow] float[pyarrow] float[pyarrow] float[pyarrow] float[pyarrow] float[pyarrow] float[pyarrow] float[pyarrow] float[pyarrow] float[pyarrow] float[pyarrow] string[pyarrow] uint8[pyarrow] uint64[pyarrow] uint64[pyarrow]\n", - "18446744073709551615 ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...\n", + " solution_id designation source_id random_index ref_epoch ra ra_error dec dec_error parallax parallax_error parallax_over_error pm pmra pmra_error pmdec pmdec_error ra_dec_corr ra_parallax_corr ra_pmra_corr ra_pmdec_corr dec_parallax_corr dec_pmra_corr dec_pmdec_corr parallax_pmra_corr parallax_pmdec_corr pmra_pmdec_corr astrometric_n_obs_al astrometric_n_obs_ac astrometric_n_good_obs_al astrometric_n_bad_obs_al astrometric_gof_al astrometric_chi2_al astrometric_excess_noise astrometric_excess_noise_sig astrometric_params_solved astrometric_primary_flag nu_eff_used_in_astrometry pseudocolour pseudocolour_error ra_pseudocolour_corr dec_pseudocolour_corr parallax_pseudocolour_corr pmra_pseudocolour_corr pmdec_pseudocolour_corr astrometric_matched_transits visibility_periods_used astrometric_sigma5d_max matched_transits new_matched_transits matched_transits_removed ipd_gof_harmonic_amplitude ipd_gof_harmonic_phase ipd_frac_multi_peak ipd_frac_odd_win ruwe scan_direction_strength_k1 scan_direction_strength_k2 scan_direction_strength_k3 scan_direction_strength_k4 scan_direction_mean_k1 scan_direction_mean_k2 scan_direction_mean_k3 scan_direction_mean_k4 duplicated_source phot_g_n_obs phot_g_mean_flux phot_g_mean_flux_error phot_g_mean_flux_over_error phot_g_mean_mag phot_bp_n_obs phot_bp_mean_flux phot_bp_mean_flux_error phot_bp_mean_flux_over_error phot_bp_mean_mag phot_rp_n_obs phot_rp_mean_flux phot_rp_mean_flux_error phot_rp_mean_flux_over_error phot_rp_mean_mag phot_bp_rp_excess_factor phot_bp_n_contaminated_transits phot_bp_n_blended_transits phot_rp_n_contaminated_transits phot_rp_n_blended_transits phot_proc_mode bp_rp bp_g g_rp radial_velocity radial_velocity_error rv_method_used rv_nb_transits rv_nb_deblended_transits rv_visibility_periods_used rv_expected_sig_to_noise rv_renormalised_gof rv_chisq_pvalue rv_time_duration rv_amplitude_robust rv_template_teff rv_template_logg rv_template_fe_h rv_atm_param_origin vbroad vbroad_error vbroad_nb_transits grvs_mag grvs_mag_error grvs_mag_nb_transits rvs_spec_sig_to_noise phot_variable_flag l b ecl_lon ecl_lat in_qso_candidates in_galaxy_candidates non_single_star has_xp_continuous has_xp_sampled has_rvs has_epoch_photometry has_epoch_rv has_mcmc_gspphot has_mcmc_msc in_andromeda_survey classprob_dsc_combmod_quasar classprob_dsc_combmod_galaxy classprob_dsc_combmod_star teff_gspphot teff_gspphot_lower teff_gspphot_upper logg_gspphot logg_gspphot_lower logg_gspphot_upper mh_gspphot mh_gspphot_lower mh_gspphot_upper distance_gspphot distance_gspphot_lower distance_gspphot_upper azero_gspphot azero_gspphot_lower azero_gspphot_upper ag_gspphot ag_gspphot_lower ag_gspphot_upper ebpminrp_gspphot ebpminrp_gspphot_lower ebpminrp_gspphot_upper libname_gspphot Norder Dir Npix\n", + "npartitions=1 \n", + "1152921504606846976 int64[pyarrow] string[pyarrow] int64[pyarrow] int64[pyarrow] double[pyarrow] double[pyarrow] float[pyarrow] double[pyarrow] float[pyarrow] double[pyarrow] float[pyarrow] float[pyarrow] float[pyarrow] double[pyarrow] float[pyarrow] double[pyarrow] float[pyarrow] float[pyarrow] float[pyarrow] float[pyarrow] float[pyarrow] float[pyarrow] float[pyarrow] float[pyarrow] float[pyarrow] float[pyarrow] float[pyarrow] int16[pyarrow] int16[pyarrow] int16[pyarrow] int16[pyarrow] float[pyarrow] float[pyarrow] float[pyarrow] float[pyarrow] int8[pyarrow] bool[pyarrow] float[pyarrow] float[pyarrow] float[pyarrow] float[pyarrow] float[pyarrow] float[pyarrow] float[pyarrow] float[pyarrow] int16[pyarrow] int16[pyarrow] float[pyarrow] int16[pyarrow] int16[pyarrow] int16[pyarrow] float[pyarrow] float[pyarrow] int8[pyarrow] int8[pyarrow] float[pyarrow] float[pyarrow] float[pyarrow] float[pyarrow] float[pyarrow] float[pyarrow] float[pyarrow] float[pyarrow] float[pyarrow] bool[pyarrow] int16[pyarrow] double[pyarrow] float[pyarrow] float[pyarrow] float[pyarrow] int16[pyarrow] double[pyarrow] float[pyarrow] float[pyarrow] float[pyarrow] int16[pyarrow] double[pyarrow] float[pyarrow] float[pyarrow] float[pyarrow] float[pyarrow] int16[pyarrow] int16[pyarrow] int16[pyarrow] int16[pyarrow] int8[pyarrow] float[pyarrow] float[pyarrow] float[pyarrow] float[pyarrow] float[pyarrow] int8[pyarrow] int16[pyarrow] int16[pyarrow] int16[pyarrow] float[pyarrow] float[pyarrow] float[pyarrow] float[pyarrow] float[pyarrow] float[pyarrow] float[pyarrow] float[pyarrow] int16[pyarrow] float[pyarrow] float[pyarrow] int16[pyarrow] float[pyarrow] float[pyarrow] int16[pyarrow] float[pyarrow] string[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] bool[pyarrow] bool[pyarrow] int16[pyarrow] bool[pyarrow] bool[pyarrow] bool[pyarrow] bool[pyarrow] bool[pyarrow] bool[pyarrow] bool[pyarrow] bool[pyarrow] float[pyarrow] float[pyarrow] float[pyarrow] float[pyarrow] float[pyarrow] float[pyarrow] float[pyarrow] float[pyarrow] float[pyarrow] float[pyarrow] float[pyarrow] float[pyarrow] float[pyarrow] float[pyarrow] float[pyarrow] float[pyarrow] float[pyarrow] float[pyarrow] float[pyarrow] float[pyarrow] float[pyarrow] float[pyarrow] float[pyarrow] float[pyarrow] string[pyarrow] uint8[pyarrow] uint64[pyarrow] uint64[pyarrow]\n", + "1441151880758558720 ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...\n", "Dask Name: nestedframe, 3 expressions\n", "Expr=MapPartitions(NestedFrame)" ] @@ -3133,7 +3114,7 @@ " \n", " \n", " \n", - " 4611686018427387904\n", + " 1152921504606846976\n", " int64[pyarrow]\n", " string[pyarrow]\n", " int64[pyarrow]\n", @@ -3510,7 +3491,7 @@ " double[pyarrow]\n", " \n", " \n", - " 18446744073709551615\n", + " 1441151880758558720\n", " ...\n", " ...\n", " ...\n", @@ -3892,10 +3873,10 @@ ], "text/plain": [ "Dask NestedFrame Structure:\n", - " COADD_OBJECT_ID_des TILENAME_des HPIX_32_des HPIX_64_des HPIX_1024_des HPIX_4096_des HPIX_16384_des RA_des DEC_des ALPHAWIN_J2000_des DELTAWIN_J2000_des GALACTIC_L_des GALACTIC_B_des XWIN_IMAGE_des YWIN_IMAGE_des A_IMAGE_des ERRA_IMAGE_des B_IMAGE_des ERRB_IMAGE_des THETA_J2000_des ERRTHETA_IMAGE_des KRON_RADIUS_des EBV_SFD98_des MAG_AUTO_G_DERED_des MAG_AUTO_R_DERED_des MAG_AUTO_I_DERED_des MAG_AUTO_Z_DERED_des MAG_AUTO_Y_DERED_des WAVG_MAG_PSF_G_DERED_des WAVG_MAG_PSF_R_DERED_des WAVG_MAG_PSF_I_DERED_des WAVG_MAG_PSF_Z_DERED_des WAVG_MAG_PSF_Y_DERED_des EXTENDED_CLASS_COADD_des EXTENDED_CLASS_WAVG_des FLAGS_G_des IMAFLAGS_ISO_G_des NEPOCHS_G_des FLAGS_R_des IMAFLAGS_ISO_R_des NEPOCHS_R_des FLAGS_I_des IMAFLAGS_ISO_I_des NEPOCHS_I_des FLAGS_Z_des IMAFLAGS_ISO_Z_des NEPOCHS_Z_des FLAGS_Y_des IMAFLAGS_ISO_Y_des NEPOCHS_Y_des XWIN_IMAGE_G_des XWIN_IMAGE_R_des XWIN_IMAGE_I_des XWIN_IMAGE_Z_des XWIN_IMAGE_Y_des YWIN_IMAGE_G_des YWIN_IMAGE_R_des YWIN_IMAGE_I_des YWIN_IMAGE_Z_des YWIN_IMAGE_Y_des X2WIN_IMAGE_G_des X2WIN_IMAGE_R_des X2WIN_IMAGE_I_des X2WIN_IMAGE_Z_des X2WIN_IMAGE_Y_des Y2WIN_IMAGE_G_des Y2WIN_IMAGE_R_des Y2WIN_IMAGE_I_des Y2WIN_IMAGE_Z_des Y2WIN_IMAGE_Y_des XYWIN_IMAGE_G_des XYWIN_IMAGE_R_des XYWIN_IMAGE_I_des XYWIN_IMAGE_Z_des XYWIN_IMAGE_Y_des ERRX2WIN_IMAGE_G_des ERRX2WIN_IMAGE_R_des ERRX2WIN_IMAGE_I_des ERRX2WIN_IMAGE_Z_des ERRX2WIN_IMAGE_Y_des ERRY2WIN_IMAGE_G_des ERRY2WIN_IMAGE_R_des ERRY2WIN_IMAGE_I_des ERRY2WIN_IMAGE_Z_des ERRY2WIN_IMAGE_Y_des ERRXYWIN_IMAGE_G_des ERRXYWIN_IMAGE_R_des ERRXYWIN_IMAGE_I_des ERRXYWIN_IMAGE_Z_des ERRXYWIN_IMAGE_Y_des AWIN_IMAGE_G_des AWIN_IMAGE_R_des AWIN_IMAGE_I_des AWIN_IMAGE_Z_des AWIN_IMAGE_Y_des BWIN_IMAGE_G_des BWIN_IMAGE_R_des BWIN_IMAGE_I_des BWIN_IMAGE_Z_des BWIN_IMAGE_Y_des THETAWIN_IMAGE_G_des THETAWIN_IMAGE_R_des THETAWIN_IMAGE_I_des THETAWIN_IMAGE_Z_des THETAWIN_IMAGE_Y_des ERRAWIN_IMAGE_G_des ERRAWIN_IMAGE_R_des ERRAWIN_IMAGE_I_des ERRAWIN_IMAGE_Z_des ERRAWIN_IMAGE_Y_des ERRBWIN_IMAGE_G_des ERRBWIN_IMAGE_R_des ERRBWIN_IMAGE_I_des ERRBWIN_IMAGE_Z_des ERRBWIN_IMAGE_Y_des ERRTHETAWIN_IMAGE_G_des ERRTHETAWIN_IMAGE_R_des ERRTHETAWIN_IMAGE_I_des ERRTHETAWIN_IMAGE_Z_des ERRTHETAWIN_IMAGE_Y_des FLUX_RADIUS_G_des FLUX_RADIUS_R_des FLUX_RADIUS_I_des FLUX_RADIUS_Z_des FLUX_RADIUS_Y_des FWHM_IMAGE_G_des FWHM_IMAGE_R_des FWHM_IMAGE_I_des FWHM_IMAGE_Z_des FWHM_IMAGE_Y_des ISOAREA_IMAGE_G_des ISOAREA_IMAGE_R_des ISOAREA_IMAGE_I_des ISOAREA_IMAGE_Z_des ISOAREA_IMAGE_Y_des BACKGROUND_G_des BACKGROUND_R_des BACKGROUND_I_des BACKGROUND_Z_des BACKGROUND_Y_des NITER_MODEL_G_des NITER_MODEL_R_des NITER_MODEL_I_des NITER_MODEL_Z_des NITER_MODEL_Y_des KRON_RADIUS_G_des KRON_RADIUS_R_des KRON_RADIUS_I_des KRON_RADIUS_Z_des KRON_RADIUS_Y_des MAG_AUTO_G_des MAG_AUTO_R_des MAG_AUTO_I_des MAG_AUTO_Z_des MAG_AUTO_Y_des MAGERR_AUTO_G_des MAGERR_AUTO_R_des MAGERR_AUTO_I_des MAGERR_AUTO_Z_des MAGERR_AUTO_Y_des WAVG_MAG_PSF_G_des WAVG_MAG_PSF_R_des WAVG_MAG_PSF_I_des WAVG_MAG_PSF_Z_des WAVG_MAG_PSF_Y_des WAVG_MAGERR_PSF_G_des WAVG_MAGERR_PSF_R_des WAVG_MAGERR_PSF_I_des WAVG_MAGERR_PSF_Z_des WAVG_MAGERR_PSF_Y_des FLUX_AUTO_G_des FLUX_AUTO_R_des FLUX_AUTO_I_des FLUX_AUTO_Z_des FLUX_AUTO_Y_des FLUXERR_AUTO_G_des FLUXERR_AUTO_R_des FLUXERR_AUTO_I_des FLUXERR_AUTO_Z_des FLUXERR_AUTO_Y_des WAVG_FLUX_PSF_G_des WAVG_FLUX_PSF_R_des WAVG_FLUX_PSF_I_des WAVG_FLUX_PSF_Z_des WAVG_FLUX_PSF_Y_des WAVG_FLUXERR_PSF_G_des WAVG_FLUXERR_PSF_R_des WAVG_FLUXERR_PSF_I_des WAVG_FLUXERR_PSF_Z_des WAVG_FLUXERR_PSF_Y_des CLASS_STAR_G_des CLASS_STAR_R_des CLASS_STAR_I_des CLASS_STAR_Z_des CLASS_STAR_Y_des SPREAD_MODEL_G_des SPREAD_MODEL_R_des SPREAD_MODEL_I_des SPREAD_MODEL_Z_des SPREAD_MODEL_Y_des WAVG_SPREAD_MODEL_G_des WAVG_SPREAD_MODEL_R_des WAVG_SPREAD_MODEL_I_des WAVG_SPREAD_MODEL_Z_des WAVG_SPREAD_MODEL_Y_des SPREADERR_MODEL_G_des SPREADERR_MODEL_R_des SPREADERR_MODEL_I_des SPREADERR_MODEL_Z_des SPREADERR_MODEL_Y_des WAVG_SPREADERR_MODEL_G_des WAVG_SPREADERR_MODEL_R_des WAVG_SPREADERR_MODEL_I_des WAVG_SPREADERR_MODEL_Z_des WAVG_SPREADERR_MODEL_Y_des Norder_des Dir_des Npix_des solution_id_gaia designation_gaia source_id_gaia random_index_gaia ref_epoch_gaia ra_gaia ra_error_gaia dec_gaia dec_error_gaia parallax_gaia parallax_error_gaia parallax_over_error_gaia pm_gaia pmra_gaia pmra_error_gaia pmdec_gaia pmdec_error_gaia ra_dec_corr_gaia ra_parallax_corr_gaia ra_pmra_corr_gaia ra_pmdec_corr_gaia dec_parallax_corr_gaia dec_pmra_corr_gaia dec_pmdec_corr_gaia parallax_pmra_corr_gaia parallax_pmdec_corr_gaia pmra_pmdec_corr_gaia astrometric_n_obs_al_gaia astrometric_n_obs_ac_gaia astrometric_n_good_obs_al_gaia astrometric_n_bad_obs_al_gaia astrometric_gof_al_gaia astrometric_chi2_al_gaia astrometric_excess_noise_gaia astrometric_excess_noise_sig_gaia astrometric_params_solved_gaia astrometric_primary_flag_gaia nu_eff_used_in_astrometry_gaia pseudocolour_gaia pseudocolour_error_gaia ra_pseudocolour_corr_gaia dec_pseudocolour_corr_gaia parallax_pseudocolour_corr_gaia pmra_pseudocolour_corr_gaia pmdec_pseudocolour_corr_gaia astrometric_matched_transits_gaia visibility_periods_used_gaia astrometric_sigma5d_max_gaia matched_transits_gaia new_matched_transits_gaia matched_transits_removed_gaia ipd_gof_harmonic_amplitude_gaia ipd_gof_harmonic_phase_gaia ipd_frac_multi_peak_gaia ipd_frac_odd_win_gaia ruwe_gaia scan_direction_strength_k1_gaia scan_direction_strength_k2_gaia scan_direction_strength_k3_gaia scan_direction_strength_k4_gaia scan_direction_mean_k1_gaia scan_direction_mean_k2_gaia scan_direction_mean_k3_gaia scan_direction_mean_k4_gaia duplicated_source_gaia phot_g_n_obs_gaia phot_g_mean_flux_gaia phot_g_mean_flux_error_gaia phot_g_mean_flux_over_error_gaia phot_g_mean_mag_gaia phot_bp_n_obs_gaia phot_bp_mean_flux_gaia phot_bp_mean_flux_error_gaia phot_bp_mean_flux_over_error_gaia phot_bp_mean_mag_gaia phot_rp_n_obs_gaia phot_rp_mean_flux_gaia phot_rp_mean_flux_error_gaia phot_rp_mean_flux_over_error_gaia phot_rp_mean_mag_gaia phot_bp_rp_excess_factor_gaia phot_bp_n_contaminated_transits_gaia phot_bp_n_blended_transits_gaia phot_rp_n_contaminated_transits_gaia phot_rp_n_blended_transits_gaia phot_proc_mode_gaia bp_rp_gaia bp_g_gaia g_rp_gaia radial_velocity_gaia radial_velocity_error_gaia rv_method_used_gaia rv_nb_transits_gaia rv_nb_deblended_transits_gaia rv_visibility_periods_used_gaia rv_expected_sig_to_noise_gaia rv_renormalised_gof_gaia rv_chisq_pvalue_gaia rv_time_duration_gaia rv_amplitude_robust_gaia rv_template_teff_gaia rv_template_logg_gaia rv_template_fe_h_gaia rv_atm_param_origin_gaia vbroad_gaia vbroad_error_gaia vbroad_nb_transits_gaia grvs_mag_gaia grvs_mag_error_gaia grvs_mag_nb_transits_gaia rvs_spec_sig_to_noise_gaia phot_variable_flag_gaia l_gaia b_gaia ecl_lon_gaia ecl_lat_gaia in_qso_candidates_gaia in_galaxy_candidates_gaia non_single_star_gaia has_xp_continuous_gaia has_xp_sampled_gaia has_rvs_gaia has_epoch_photometry_gaia has_epoch_rv_gaia has_mcmc_gspphot_gaia has_mcmc_msc_gaia in_andromeda_survey_gaia classprob_dsc_combmod_quasar_gaia classprob_dsc_combmod_galaxy_gaia classprob_dsc_combmod_star_gaia teff_gspphot_gaia teff_gspphot_lower_gaia teff_gspphot_upper_gaia logg_gspphot_gaia logg_gspphot_lower_gaia logg_gspphot_upper_gaia mh_gspphot_gaia mh_gspphot_lower_gaia mh_gspphot_upper_gaia distance_gspphot_gaia distance_gspphot_lower_gaia distance_gspphot_upper_gaia azero_gspphot_gaia azero_gspphot_lower_gaia azero_gspphot_upper_gaia ag_gspphot_gaia ag_gspphot_lower_gaia ag_gspphot_upper_gaia ebpminrp_gspphot_gaia ebpminrp_gspphot_lower_gaia ebpminrp_gspphot_upper_gaia libname_gspphot_gaia Norder_gaia Dir_gaia Npix_gaia _dist_arcsec\n", - "npartitions=1 \n", - "4611686018427387904 int64[pyarrow] string[pyarrow] int64[pyarrow] int64[pyarrow] int64[pyarrow] int64[pyarrow] int64[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] int16[pyarrow] int16[pyarrow] int16[pyarrow] int32[pyarrow] int32[pyarrow] int16[pyarrow] int32[pyarrow] int32[pyarrow] int16[pyarrow] int32[pyarrow] int32[pyarrow] int16[pyarrow] int32[pyarrow] int32[pyarrow] int16[pyarrow] int32[pyarrow] int32[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] int32[pyarrow] int32[pyarrow] int32[pyarrow] int32[pyarrow] int32[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] int32[pyarrow] int32[pyarrow] int32[pyarrow] int32[pyarrow] int32[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] uint8[pyarrow] uint64[pyarrow] uint64[pyarrow] int64[pyarrow] string[pyarrow] int64[pyarrow] int64[pyarrow] double[pyarrow] double[pyarrow] float[pyarrow] double[pyarrow] float[pyarrow] double[pyarrow] float[pyarrow] float[pyarrow] float[pyarrow] double[pyarrow] float[pyarrow] double[pyarrow] float[pyarrow] float[pyarrow] float[pyarrow] float[pyarrow] float[pyarrow] float[pyarrow] float[pyarrow] float[pyarrow] float[pyarrow] float[pyarrow] float[pyarrow] int16[pyarrow] int16[pyarrow] int16[pyarrow] int16[pyarrow] float[pyarrow] float[pyarrow] float[pyarrow] float[pyarrow] int8[pyarrow] bool[pyarrow] float[pyarrow] float[pyarrow] float[pyarrow] float[pyarrow] float[pyarrow] float[pyarrow] float[pyarrow] float[pyarrow] int16[pyarrow] int16[pyarrow] float[pyarrow] int16[pyarrow] int16[pyarrow] int16[pyarrow] float[pyarrow] float[pyarrow] int8[pyarrow] int8[pyarrow] float[pyarrow] float[pyarrow] float[pyarrow] float[pyarrow] float[pyarrow] float[pyarrow] float[pyarrow] float[pyarrow] float[pyarrow] bool[pyarrow] int16[pyarrow] double[pyarrow] float[pyarrow] float[pyarrow] float[pyarrow] int16[pyarrow] double[pyarrow] float[pyarrow] float[pyarrow] float[pyarrow] int16[pyarrow] double[pyarrow] float[pyarrow] float[pyarrow] float[pyarrow] float[pyarrow] int16[pyarrow] int16[pyarrow] int16[pyarrow] int16[pyarrow] int8[pyarrow] float[pyarrow] float[pyarrow] float[pyarrow] float[pyarrow] float[pyarrow] int8[pyarrow] int16[pyarrow] int16[pyarrow] int16[pyarrow] float[pyarrow] float[pyarrow] float[pyarrow] float[pyarrow] float[pyarrow] float[pyarrow] float[pyarrow] float[pyarrow] int16[pyarrow] float[pyarrow] float[pyarrow] int16[pyarrow] float[pyarrow] float[pyarrow] int16[pyarrow] float[pyarrow] string[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] bool[pyarrow] bool[pyarrow] int16[pyarrow] bool[pyarrow] bool[pyarrow] bool[pyarrow] bool[pyarrow] bool[pyarrow] bool[pyarrow] bool[pyarrow] bool[pyarrow] float[pyarrow] float[pyarrow] float[pyarrow] float[pyarrow] float[pyarrow] float[pyarrow] float[pyarrow] float[pyarrow] float[pyarrow] float[pyarrow] float[pyarrow] float[pyarrow] float[pyarrow] float[pyarrow] float[pyarrow] float[pyarrow] float[pyarrow] float[pyarrow] float[pyarrow] float[pyarrow] float[pyarrow] float[pyarrow] float[pyarrow] float[pyarrow] string[pyarrow] uint8[pyarrow] uint64[pyarrow] uint64[pyarrow] double[pyarrow]\n", - "18446744073709551615 ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...\n", + " COADD_OBJECT_ID_des TILENAME_des HPIX_32_des HPIX_64_des HPIX_1024_des HPIX_4096_des HPIX_16384_des RA_des DEC_des ALPHAWIN_J2000_des DELTAWIN_J2000_des GALACTIC_L_des GALACTIC_B_des XWIN_IMAGE_des YWIN_IMAGE_des A_IMAGE_des ERRA_IMAGE_des B_IMAGE_des ERRB_IMAGE_des THETA_J2000_des ERRTHETA_IMAGE_des KRON_RADIUS_des EBV_SFD98_des MAG_AUTO_G_DERED_des MAG_AUTO_R_DERED_des MAG_AUTO_I_DERED_des MAG_AUTO_Z_DERED_des MAG_AUTO_Y_DERED_des WAVG_MAG_PSF_G_DERED_des WAVG_MAG_PSF_R_DERED_des WAVG_MAG_PSF_I_DERED_des WAVG_MAG_PSF_Z_DERED_des WAVG_MAG_PSF_Y_DERED_des EXTENDED_CLASS_COADD_des EXTENDED_CLASS_WAVG_des FLAGS_G_des IMAFLAGS_ISO_G_des NEPOCHS_G_des FLAGS_R_des IMAFLAGS_ISO_R_des NEPOCHS_R_des FLAGS_I_des IMAFLAGS_ISO_I_des NEPOCHS_I_des FLAGS_Z_des IMAFLAGS_ISO_Z_des NEPOCHS_Z_des FLAGS_Y_des IMAFLAGS_ISO_Y_des NEPOCHS_Y_des XWIN_IMAGE_G_des XWIN_IMAGE_R_des XWIN_IMAGE_I_des XWIN_IMAGE_Z_des XWIN_IMAGE_Y_des YWIN_IMAGE_G_des YWIN_IMAGE_R_des YWIN_IMAGE_I_des YWIN_IMAGE_Z_des YWIN_IMAGE_Y_des X2WIN_IMAGE_G_des X2WIN_IMAGE_R_des X2WIN_IMAGE_I_des X2WIN_IMAGE_Z_des X2WIN_IMAGE_Y_des Y2WIN_IMAGE_G_des Y2WIN_IMAGE_R_des Y2WIN_IMAGE_I_des Y2WIN_IMAGE_Z_des Y2WIN_IMAGE_Y_des XYWIN_IMAGE_G_des XYWIN_IMAGE_R_des XYWIN_IMAGE_I_des XYWIN_IMAGE_Z_des XYWIN_IMAGE_Y_des ERRX2WIN_IMAGE_G_des ERRX2WIN_IMAGE_R_des ERRX2WIN_IMAGE_I_des ERRX2WIN_IMAGE_Z_des ERRX2WIN_IMAGE_Y_des ERRY2WIN_IMAGE_G_des ERRY2WIN_IMAGE_R_des ERRY2WIN_IMAGE_I_des ERRY2WIN_IMAGE_Z_des ERRY2WIN_IMAGE_Y_des ERRXYWIN_IMAGE_G_des ERRXYWIN_IMAGE_R_des ERRXYWIN_IMAGE_I_des ERRXYWIN_IMAGE_Z_des ERRXYWIN_IMAGE_Y_des AWIN_IMAGE_G_des AWIN_IMAGE_R_des AWIN_IMAGE_I_des AWIN_IMAGE_Z_des AWIN_IMAGE_Y_des BWIN_IMAGE_G_des BWIN_IMAGE_R_des BWIN_IMAGE_I_des BWIN_IMAGE_Z_des BWIN_IMAGE_Y_des THETAWIN_IMAGE_G_des THETAWIN_IMAGE_R_des THETAWIN_IMAGE_I_des THETAWIN_IMAGE_Z_des THETAWIN_IMAGE_Y_des ERRAWIN_IMAGE_G_des ERRAWIN_IMAGE_R_des ERRAWIN_IMAGE_I_des ERRAWIN_IMAGE_Z_des ERRAWIN_IMAGE_Y_des ERRBWIN_IMAGE_G_des ERRBWIN_IMAGE_R_des ERRBWIN_IMAGE_I_des ERRBWIN_IMAGE_Z_des ERRBWIN_IMAGE_Y_des ERRTHETAWIN_IMAGE_G_des ERRTHETAWIN_IMAGE_R_des ERRTHETAWIN_IMAGE_I_des ERRTHETAWIN_IMAGE_Z_des ERRTHETAWIN_IMAGE_Y_des FLUX_RADIUS_G_des FLUX_RADIUS_R_des FLUX_RADIUS_I_des FLUX_RADIUS_Z_des FLUX_RADIUS_Y_des FWHM_IMAGE_G_des FWHM_IMAGE_R_des FWHM_IMAGE_I_des FWHM_IMAGE_Z_des FWHM_IMAGE_Y_des ISOAREA_IMAGE_G_des ISOAREA_IMAGE_R_des ISOAREA_IMAGE_I_des ISOAREA_IMAGE_Z_des ISOAREA_IMAGE_Y_des BACKGROUND_G_des BACKGROUND_R_des BACKGROUND_I_des BACKGROUND_Z_des BACKGROUND_Y_des NITER_MODEL_G_des NITER_MODEL_R_des NITER_MODEL_I_des NITER_MODEL_Z_des NITER_MODEL_Y_des KRON_RADIUS_G_des KRON_RADIUS_R_des KRON_RADIUS_I_des KRON_RADIUS_Z_des KRON_RADIUS_Y_des MAG_AUTO_G_des MAG_AUTO_R_des MAG_AUTO_I_des MAG_AUTO_Z_des MAG_AUTO_Y_des MAGERR_AUTO_G_des MAGERR_AUTO_R_des MAGERR_AUTO_I_des MAGERR_AUTO_Z_des MAGERR_AUTO_Y_des WAVG_MAG_PSF_G_des WAVG_MAG_PSF_R_des WAVG_MAG_PSF_I_des WAVG_MAG_PSF_Z_des WAVG_MAG_PSF_Y_des WAVG_MAGERR_PSF_G_des WAVG_MAGERR_PSF_R_des WAVG_MAGERR_PSF_I_des WAVG_MAGERR_PSF_Z_des WAVG_MAGERR_PSF_Y_des FLUX_AUTO_G_des FLUX_AUTO_R_des FLUX_AUTO_I_des FLUX_AUTO_Z_des FLUX_AUTO_Y_des FLUXERR_AUTO_G_des FLUXERR_AUTO_R_des FLUXERR_AUTO_I_des FLUXERR_AUTO_Z_des FLUXERR_AUTO_Y_des WAVG_FLUX_PSF_G_des WAVG_FLUX_PSF_R_des WAVG_FLUX_PSF_I_des WAVG_FLUX_PSF_Z_des WAVG_FLUX_PSF_Y_des WAVG_FLUXERR_PSF_G_des WAVG_FLUXERR_PSF_R_des WAVG_FLUXERR_PSF_I_des WAVG_FLUXERR_PSF_Z_des WAVG_FLUXERR_PSF_Y_des CLASS_STAR_G_des CLASS_STAR_R_des CLASS_STAR_I_des CLASS_STAR_Z_des CLASS_STAR_Y_des SPREAD_MODEL_G_des SPREAD_MODEL_R_des SPREAD_MODEL_I_des SPREAD_MODEL_Z_des SPREAD_MODEL_Y_des WAVG_SPREAD_MODEL_G_des WAVG_SPREAD_MODEL_R_des WAVG_SPREAD_MODEL_I_des WAVG_SPREAD_MODEL_Z_des WAVG_SPREAD_MODEL_Y_des SPREADERR_MODEL_G_des SPREADERR_MODEL_R_des SPREADERR_MODEL_I_des SPREADERR_MODEL_Z_des SPREADERR_MODEL_Y_des WAVG_SPREADERR_MODEL_G_des WAVG_SPREADERR_MODEL_R_des WAVG_SPREADERR_MODEL_I_des WAVG_SPREADERR_MODEL_Z_des WAVG_SPREADERR_MODEL_Y_des Norder_des Dir_des Npix_des solution_id_gaia designation_gaia source_id_gaia random_index_gaia ref_epoch_gaia ra_gaia ra_error_gaia dec_gaia dec_error_gaia parallax_gaia parallax_error_gaia parallax_over_error_gaia pm_gaia pmra_gaia pmra_error_gaia pmdec_gaia pmdec_error_gaia ra_dec_corr_gaia ra_parallax_corr_gaia ra_pmra_corr_gaia ra_pmdec_corr_gaia dec_parallax_corr_gaia dec_pmra_corr_gaia dec_pmdec_corr_gaia parallax_pmra_corr_gaia parallax_pmdec_corr_gaia pmra_pmdec_corr_gaia astrometric_n_obs_al_gaia astrometric_n_obs_ac_gaia astrometric_n_good_obs_al_gaia astrometric_n_bad_obs_al_gaia astrometric_gof_al_gaia astrometric_chi2_al_gaia astrometric_excess_noise_gaia astrometric_excess_noise_sig_gaia astrometric_params_solved_gaia astrometric_primary_flag_gaia nu_eff_used_in_astrometry_gaia pseudocolour_gaia pseudocolour_error_gaia ra_pseudocolour_corr_gaia dec_pseudocolour_corr_gaia parallax_pseudocolour_corr_gaia pmra_pseudocolour_corr_gaia pmdec_pseudocolour_corr_gaia astrometric_matched_transits_gaia visibility_periods_used_gaia astrometric_sigma5d_max_gaia matched_transits_gaia new_matched_transits_gaia matched_transits_removed_gaia ipd_gof_harmonic_amplitude_gaia ipd_gof_harmonic_phase_gaia ipd_frac_multi_peak_gaia ipd_frac_odd_win_gaia ruwe_gaia scan_direction_strength_k1_gaia scan_direction_strength_k2_gaia scan_direction_strength_k3_gaia scan_direction_strength_k4_gaia scan_direction_mean_k1_gaia scan_direction_mean_k2_gaia scan_direction_mean_k3_gaia scan_direction_mean_k4_gaia duplicated_source_gaia phot_g_n_obs_gaia phot_g_mean_flux_gaia phot_g_mean_flux_error_gaia phot_g_mean_flux_over_error_gaia phot_g_mean_mag_gaia phot_bp_n_obs_gaia phot_bp_mean_flux_gaia phot_bp_mean_flux_error_gaia phot_bp_mean_flux_over_error_gaia phot_bp_mean_mag_gaia phot_rp_n_obs_gaia phot_rp_mean_flux_gaia phot_rp_mean_flux_error_gaia phot_rp_mean_flux_over_error_gaia phot_rp_mean_mag_gaia phot_bp_rp_excess_factor_gaia phot_bp_n_contaminated_transits_gaia phot_bp_n_blended_transits_gaia phot_rp_n_contaminated_transits_gaia phot_rp_n_blended_transits_gaia phot_proc_mode_gaia bp_rp_gaia bp_g_gaia g_rp_gaia radial_velocity_gaia radial_velocity_error_gaia rv_method_used_gaia rv_nb_transits_gaia rv_nb_deblended_transits_gaia rv_visibility_periods_used_gaia rv_expected_sig_to_noise_gaia rv_renormalised_gof_gaia rv_chisq_pvalue_gaia rv_time_duration_gaia rv_amplitude_robust_gaia rv_template_teff_gaia rv_template_logg_gaia rv_template_fe_h_gaia rv_atm_param_origin_gaia vbroad_gaia vbroad_error_gaia vbroad_nb_transits_gaia grvs_mag_gaia grvs_mag_error_gaia grvs_mag_nb_transits_gaia rvs_spec_sig_to_noise_gaia phot_variable_flag_gaia l_gaia b_gaia ecl_lon_gaia ecl_lat_gaia in_qso_candidates_gaia in_galaxy_candidates_gaia non_single_star_gaia has_xp_continuous_gaia has_xp_sampled_gaia has_rvs_gaia has_epoch_photometry_gaia has_epoch_rv_gaia has_mcmc_gspphot_gaia has_mcmc_msc_gaia in_andromeda_survey_gaia classprob_dsc_combmod_quasar_gaia classprob_dsc_combmod_galaxy_gaia classprob_dsc_combmod_star_gaia teff_gspphot_gaia teff_gspphot_lower_gaia teff_gspphot_upper_gaia logg_gspphot_gaia logg_gspphot_lower_gaia logg_gspphot_upper_gaia mh_gspphot_gaia mh_gspphot_lower_gaia mh_gspphot_upper_gaia distance_gspphot_gaia distance_gspphot_lower_gaia distance_gspphot_upper_gaia azero_gspphot_gaia azero_gspphot_lower_gaia azero_gspphot_upper_gaia ag_gspphot_gaia ag_gspphot_lower_gaia ag_gspphot_upper_gaia ebpminrp_gspphot_gaia ebpminrp_gspphot_lower_gaia ebpminrp_gspphot_upper_gaia libname_gspphot_gaia Norder_gaia Dir_gaia Npix_gaia _dist_arcsec\n", + "npartitions=1 \n", + "1152921504606846976 int64[pyarrow] string[pyarrow] int64[pyarrow] int64[pyarrow] int64[pyarrow] int64[pyarrow] int64[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] int16[pyarrow] int16[pyarrow] int16[pyarrow] int32[pyarrow] int32[pyarrow] int16[pyarrow] int32[pyarrow] int32[pyarrow] int16[pyarrow] int32[pyarrow] int32[pyarrow] int16[pyarrow] int32[pyarrow] int32[pyarrow] int16[pyarrow] int32[pyarrow] int32[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] int32[pyarrow] int32[pyarrow] int32[pyarrow] int32[pyarrow] int32[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] int32[pyarrow] int32[pyarrow] int32[pyarrow] int32[pyarrow] int32[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] uint8[pyarrow] uint64[pyarrow] uint64[pyarrow] int64[pyarrow] string[pyarrow] int64[pyarrow] int64[pyarrow] double[pyarrow] double[pyarrow] float[pyarrow] double[pyarrow] float[pyarrow] double[pyarrow] float[pyarrow] float[pyarrow] float[pyarrow] double[pyarrow] float[pyarrow] double[pyarrow] float[pyarrow] float[pyarrow] float[pyarrow] float[pyarrow] float[pyarrow] float[pyarrow] float[pyarrow] float[pyarrow] float[pyarrow] float[pyarrow] float[pyarrow] int16[pyarrow] int16[pyarrow] int16[pyarrow] int16[pyarrow] float[pyarrow] float[pyarrow] float[pyarrow] float[pyarrow] int8[pyarrow] bool[pyarrow] float[pyarrow] float[pyarrow] float[pyarrow] float[pyarrow] float[pyarrow] float[pyarrow] float[pyarrow] float[pyarrow] int16[pyarrow] int16[pyarrow] float[pyarrow] int16[pyarrow] int16[pyarrow] int16[pyarrow] float[pyarrow] float[pyarrow] int8[pyarrow] int8[pyarrow] float[pyarrow] float[pyarrow] float[pyarrow] float[pyarrow] float[pyarrow] float[pyarrow] float[pyarrow] float[pyarrow] float[pyarrow] bool[pyarrow] int16[pyarrow] double[pyarrow] float[pyarrow] float[pyarrow] float[pyarrow] int16[pyarrow] double[pyarrow] float[pyarrow] float[pyarrow] float[pyarrow] int16[pyarrow] double[pyarrow] float[pyarrow] float[pyarrow] float[pyarrow] float[pyarrow] int16[pyarrow] int16[pyarrow] int16[pyarrow] int16[pyarrow] int8[pyarrow] float[pyarrow] float[pyarrow] float[pyarrow] float[pyarrow] float[pyarrow] int8[pyarrow] int16[pyarrow] int16[pyarrow] int16[pyarrow] float[pyarrow] float[pyarrow] float[pyarrow] float[pyarrow] float[pyarrow] float[pyarrow] float[pyarrow] float[pyarrow] int16[pyarrow] float[pyarrow] float[pyarrow] int16[pyarrow] float[pyarrow] float[pyarrow] int16[pyarrow] float[pyarrow] string[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] bool[pyarrow] bool[pyarrow] int16[pyarrow] bool[pyarrow] bool[pyarrow] bool[pyarrow] bool[pyarrow] bool[pyarrow] bool[pyarrow] bool[pyarrow] bool[pyarrow] float[pyarrow] float[pyarrow] float[pyarrow] float[pyarrow] float[pyarrow] float[pyarrow] float[pyarrow] float[pyarrow] float[pyarrow] float[pyarrow] float[pyarrow] float[pyarrow] float[pyarrow] float[pyarrow] float[pyarrow] float[pyarrow] float[pyarrow] float[pyarrow] float[pyarrow] float[pyarrow] float[pyarrow] float[pyarrow] float[pyarrow] float[pyarrow] string[pyarrow] uint8[pyarrow] uint64[pyarrow] uint64[pyarrow] double[pyarrow]\n", + "1441151880758558720 ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...\n", "Dask Name: nestedframe, 3 expressions\n", "Expr=MapPartitions(NestedFrame)" ] @@ -3927,7 +3908,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 14, "id": "6883784e9316b49", "metadata": {}, "outputs": [], @@ -3938,7 +3919,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 15, "id": "2e1a3fab1c6dad5f", "metadata": {}, "outputs": [ @@ -4012,7 +3993,7 @@ " \n", " \n", " \n", - " 5477333554734039040\n", + " 1369333388684088685\n", " 1033365960\n", " DES0000+0209\n", " 4864\n", @@ -4036,7 +4017,7 @@ " 0.217639\n", " \n", " \n", - " 5477333571859382272\n", + " 1369333392965728211\n", " 1033367823\n", " DES0000+0209\n", " 4864\n", @@ -4060,7 +4041,7 @@ " 0.003322\n", " \n", " \n", - " 5477333673311207424\n", + " 1369333418327952083\n", " 1033367955\n", " DES0000+0209\n", " 4864\n", @@ -4084,7 +4065,7 @@ " 0.007574\n", " \n", " \n", - " 5477334253035323392\n", + " 1369333563259788683\n", " 1033369594\n", " DES0000+0209\n", " 4864\n", @@ -4108,7 +4089,7 @@ " 0.010084\n", " \n", " \n", - " 5477334650319798272\n", + " 1369333662580669435\n", " 1033369151\n", " DES0000+0209\n", " 4864\n", @@ -4139,64 +4120,64 @@ "text/plain": [ " COADD_OBJECT_ID_des TILENAME_des HPIX_32_des \\\n", "_healpix_29 \n", - "5477333554734039040 1033365960 DES0000+0209 4864 \n", - "5477333571859382272 1033367823 DES0000+0209 4864 \n", - "5477333673311207424 1033367955 DES0000+0209 4864 \n", - "5477334253035323392 1033369594 DES0000+0209 4864 \n", - "5477334650319798272 1033369151 DES0000+0209 4864 \n", + "1369333388684088685 1033365960 DES0000+0209 4864 \n", + "1369333392965728211 1033367823 DES0000+0209 4864 \n", + "1369333418327952083 1033367955 DES0000+0209 4864 \n", + "1369333563259788683 1033369594 DES0000+0209 4864 \n", + "1369333662580669435 1033369151 DES0000+0209 4864 \n", "\n", " HPIX_64_des HPIX_1024_des HPIX_4096_des \\\n", "_healpix_29 \n", - "5477333554734039040 19459 4981605 79705693 \n", - "5477333571859382272 19459 4981605 79705693 \n", - "5477333673311207424 19459 4981605 79705695 \n", - "5477334253035323392 19459 4981606 79705703 \n", - "5477334650319798272 19459 4981606 79705709 \n", + "1369333388684088685 19459 4981605 79705693 \n", + "1369333392965728211 19459 4981605 79705693 \n", + "1369333418327952083 19459 4981605 79705695 \n", + "1369333563259788683 19459 4981606 79705703 \n", + "1369333662580669435 19459 4981606 79705709 \n", "\n", " HPIX_16384_des RA_des DEC_des ALPHAWIN_J2000_des \\\n", "_healpix_29 \n", - "5477333554734039040 1275291097 0.315738 1.807978 0.315738 \n", - "5477333571859382272 1275291101 0.321042 1.812476 0.321042 \n", - "5477333673311207424 1275291124 0.310521 1.816329 0.310521 \n", - "5477334253035323392 1275291259 0.234549 1.80234 0.234549 \n", - "5477334650319798272 1275291352 0.224895 1.805816 0.224895 \n", + "1369333388684088685 1275291097 0.315738 1.807978 0.315738 \n", + "1369333392965728211 1275291101 0.321042 1.812476 0.321042 \n", + "1369333418327952083 1275291124 0.310521 1.816329 0.310521 \n", + "1369333563259788683 1275291259 0.234549 1.80234 0.234549 \n", + "1369333662580669435 1275291352 0.224895 1.805816 0.224895 \n", "\n", " ... ag_gspphot_lower_gaia ag_gspphot_upper_gaia \\\n", "_healpix_29 ... \n", - "5477333554734039040 ... 0.1502 0.1595 \n", - "5477333571859382272 ... 0.1019 0.1442 \n", - "5477333673311207424 ... \n", - "5477334253035323392 ... \n", - "5477334650319798272 ... \n", + "1369333388684088685 ... 0.1502 0.1595 \n", + "1369333392965728211 ... 0.1019 0.1442 \n", + "1369333418327952083 ... \n", + "1369333563259788683 ... \n", + "1369333662580669435 ... \n", "\n", " ebpminrp_gspphot_gaia ebpminrp_gspphot_lower_gaia \\\n", "_healpix_29 \n", - "5477333554734039040 0.0839 0.0812 \n", - "5477333571859382272 0.0686 0.0556 \n", - "5477333673311207424 \n", - "5477334253035323392 \n", - "5477334650319798272 \n", + "1369333388684088685 0.0839 0.0812 \n", + "1369333392965728211 0.0686 0.0556 \n", + "1369333418327952083 \n", + "1369333563259788683 \n", + "1369333662580669435 \n", "\n", " ebpminrp_gspphot_upper_gaia libname_gspphot_gaia \\\n", "_healpix_29 \n", - "5477333554734039040 0.0863 MARCS \n", - "5477333571859382272 0.0787 A \n", - "5477333673311207424 \n", - "5477334253035323392 \n", - "5477334650319798272 \n", + "1369333388684088685 0.0863 MARCS \n", + "1369333392965728211 0.0787 A \n", + "1369333418327952083 \n", + "1369333563259788683 \n", + "1369333662580669435 \n", "\n", " Norder_gaia Dir_gaia Npix_gaia _dist_arcsec \n", "_healpix_29 \n", - "5477333554734039040 0 0 4 0.217639 \n", - "5477333571859382272 0 0 4 0.003322 \n", - "5477333673311207424 0 0 4 0.007574 \n", - "5477334253035323392 0 0 4 0.010084 \n", - "5477334650319798272 0 0 4 0.005852 \n", + "1369333388684088685 0 0 4 0.217639 \n", + "1369333392965728211 0 0 4 0.003322 \n", + "1369333418327952083 0 0 4 0.007574 \n", + "1369333563259788683 0 0 4 0.010084 \n", + "1369333662580669435 0 0 4 0.005852 \n", "\n", "[5 rows x 374 columns]" ] }, - "execution_count": 16, + "execution_count": 15, "metadata": {}, "output_type": "execute_result" } @@ -4223,23 +4204,23 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 16, "id": "1f82e600605a8a0b", "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 17, + "execution_count": 16, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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", + "image/png": 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", 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" ] @@ -4283,7 +4264,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 17, "id": "a20f1614ec634a49", "metadata": {}, "outputs": [ @@ -4291,9 +4272,9 @@ "name": "stderr", "output_type": "stream", "text": [ - "/home/delucchi/anaconda3/envs/hipscatenv/lib/python3.10/site-packages/dask/dataframe/core.py:3930: UserWarning: Dask currently has limited support for converting pandas extension dtypes to arrays. Converting double[pyarrow] to object dtype.\n", + "/home/delucchi/.virtualenvs/demo/lib/python3.12/site-packages/dask/dataframe/core.py:3769: UserWarning: Dask currently has limited support for converting pandas extension dtypes to arrays. Converting double[pyarrow] to object dtype.\n", " warnings.warn(\n", - "/home/delucchi/anaconda3/envs/hipscatenv/lib/python3.10/site-packages/dask/dataframe/core.py:3930: UserWarning: Dask currently has limited support for converting pandas extension dtypes to arrays. Converting double[pyarrow] to object dtype.\n", + "/home/delucchi/.virtualenvs/demo/lib/python3.12/site-packages/dask/dataframe/core.py:3769: UserWarning: Dask currently has limited support for converting pandas extension dtypes to arrays. Converting double[pyarrow] to object dtype.\n", " warnings.warn(\n" ] }, @@ -4303,13 +4284,13 @@ "Text(0.5, 1.0, 'Absolute magnitude — color diagram')" ] }, - "execution_count": 18, + "execution_count": 17, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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", + "image/png": 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", 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" ] @@ -4382,7 +4363,7 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3", + "display_name": "demo", "language": "python", "name": "python3" }, @@ -4396,7 +4377,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.14" + "version": "3.12.3" } }, "nbformat": 4, diff --git a/docs/tutorials/pre_executed/ztf_bts-ngc.ipynb b/docs/tutorials/pre_executed/ztf_bts-ngc.ipynb index 3c9c3dae..aa0345f8 100644 --- a/docs/tutorials/pre_executed/ztf_bts-ngc.ipynb +++ b/docs/tutorials/pre_executed/ztf_bts-ngc.ipynb @@ -20,16 +20,7 @@ "start_time": "2024-02-05T17:13:25.950199Z" } }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\u001b[33mWARNING: There was an error checking the latest version of pip.\u001b[0m\u001b[33m\n", - "\u001b[0m" - ] - } - ], + "outputs": [], "source": [ "# Install astroquery, comment this line if you already have it\n", "!pip install --quiet astroquery" @@ -37,7 +28,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 2, "id": "cedde7185bbd9650", "metadata": { "ExecuteTime": { @@ -67,7 +58,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 3, "id": "533f35ae2822a422", "metadata": { "ExecuteTime": { @@ -80,8 +71,8 @@ "name": "stdout", "output_type": "stream", "text": [ - "CPU times: user 1.36 s, sys: 13 ms, total: 1.37 s\n", - "Wall time: 3.77 s\n" + "CPU times: user 1.13 s, sys: 12.1 ms, total: 1.14 s\n", + "Wall time: 3.37 s\n" ] }, { @@ -252,7 +243,7 @@ "4 0.305 345.657208 41.091833 " ] }, - "execution_count": 2, + "execution_count": 3, "metadata": {}, "output_type": "execute_result" } @@ -281,7 +272,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 4, "id": "f27f3c3054bb72e2", "metadata": { "ExecuteTime": { @@ -294,8 +285,8 @@ "name": "stdout", "output_type": "stream", "text": [ - "CPU times: user 2.36 s, sys: 11.3 ms, total: 2.38 s\n", - "Wall time: 3.94 s\n" + "CPU times: user 1.62 s, sys: 16.3 ms, total: 1.64 s\n", + "Wall time: 2.92 s\n" ] }, { @@ -435,7 +426,7 @@ "4 pB, S, R, stell N 0.100 32.783333 " ] }, - "execution_count": 3, + "execution_count": 4, "metadata": {}, "output_type": "execute_result" } @@ -465,7 +456,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 5, "id": "6b1b2ebb1c023bca", "metadata": { "ExecuteTime": { @@ -478,14 +469,14 @@ "name": "stdout", "output_type": "stream", "text": [ - "CPU times: user 36.3 s, sys: 14.1 ms, total: 36.3 s\n", - "Wall time: 36.3 s\n" + "CPU times: user 32.8 s, sys: 33.1 ms, total: 32.8 s\n", + "Wall time: 32.9 s\n" ] }, { "data": { "text/html": [ - "
lsdb Catalog _x_:
\n", + "
lsdb Catalog from_lsdb_dataframe_x_from_lsdb_dataframe:
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ps1_objidradecps1_gMeanPSFMagps1_rMeanPSFMagps1_iMeanPSFMagnobs_gnobs_rnobs_imean_mag_gmean_mag_rmean_mag_imargin_Nordermargin_Dirmargin_NpixNorderDirNpix
npartitions=4
1945555039024054272int64[pyarrow]double[pyarrow]double[pyarrow]double[pyarrow]double[pyarrow]double[pyarrow]int32[pyarrow]int32[pyarrow]int32[pyarrow]double[pyarrow]double[pyarrow]double[pyarrow]uint8[pyarrow]uint64[pyarrow]uint64[pyarrow]int8[pyarrow]int64[pyarrow]int64[pyarrow]
1950058638651424768......................................................
1954562238278795264......................................................
1959065837906165760......................................................
1963569437533536256......................................................
\n", + "
" + ], + "text/plain": [ + "Dask NestedFrame Structure:\n", + " ps1_objid ra dec ps1_gMeanPSFMag ps1_rMeanPSFMag ps1_iMeanPSFMag nobs_g nobs_r nobs_i mean_mag_g mean_mag_r mean_mag_i margin_Norder margin_Dir margin_Npix Norder Dir Npix\n", + "npartitions=4 \n", + "1945555039024054272 int64[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] int32[pyarrow] int32[pyarrow] int32[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] uint8[pyarrow] uint64[pyarrow] uint64[pyarrow] int8[pyarrow] int64[pyarrow] int64[pyarrow]\n", + "1950058638651424768 ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...\n", + "1954562238278795264 ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...\n", + "1959065837906165760 ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...\n", + "1963569437533536256 ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...\n", + "Dask Name: search_points, 5 expressions\n", + "Expr=MapPartitions(search_points)" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "ztf_margin_path = f\"{surveys_path}/ztf/ztf_object_margin\"\n", - "ztf_margin = lsdb.read_hats(ztf_margin_path)\n", + "from lsdb.core.search import BoxSearch\n", + "\n", + "box = BoxSearch(ra=(179.5, 180.1), dec=(9.4, 10.0))\n", + "\n", + "ztf_margin_path = f\"{surveys_path}/ztf/ztf_dr14_10arcs\"\n", + "ztf_margin = lsdb.read_hats(ztf_margin_path, search_filter=box)\n", "print(f\"Margin size: {ztf_margin.hc_structure.catalog_info.margin_threshold} arcsec\")\n", "ztf_margin" ] @@ -83,17 +284,186 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 3, "metadata": { "ExecuteTime": { "end_time": "2024-03-28T20:40:57.666217Z", "start_time": "2024-03-28T20:40:55.333646Z" } }, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
lsdb Catalog ztf_dr14:
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ps1_objidradecps1_gMeanPSFMagps1_rMeanPSFMagps1_iMeanPSFMagnobs_gnobs_rnobs_imean_mag_gmean_mag_rmean_mag_iNorderDirNpix
npartitions=4
1945555039024054272int64[pyarrow]double[pyarrow]double[pyarrow]double[pyarrow]double[pyarrow]double[pyarrow]int32[pyarrow]int32[pyarrow]int32[pyarrow]double[pyarrow]double[pyarrow]double[pyarrow]int8[pyarrow]int64[pyarrow]int64[pyarrow]
1950058638651424768.............................................
1954562238278795264.............................................
1959065837906165760.............................................
1963569437533536256.............................................
\n", + "
" + ], + "text/plain": [ + "Dask NestedFrame Structure:\n", + " ps1_objid ra dec ps1_gMeanPSFMag ps1_rMeanPSFMag ps1_iMeanPSFMag nobs_g nobs_r nobs_i mean_mag_g mean_mag_r mean_mag_i Norder Dir Npix\n", + "npartitions=4 \n", + "1945555039024054272 int64[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] int32[pyarrow] int32[pyarrow] int32[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] int8[pyarrow] int64[pyarrow] int64[pyarrow]\n", + "1950058638651424768 ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...\n", + "1954562238278795264 ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...\n", + "1959065837906165760 ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...\n", + "1963569437533536256 ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...\n", + "Dask Name: search_points, 5 expressions\n", + "Expr=MapPartitions(search_points)" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "ztf_object_path = f\"{surveys_path}/ztf/ztf_object\"\n", - "ztf_object = lsdb.read_hats(ztf_object_path, margin_cache=ztf_margin)\n", + "ztf_object_path = f\"{surveys_path}/ztf/ztf_dr14\"\n", + "ztf_object = lsdb.read_hats(ztf_object_path, margin_cache=ztf_margin, search_filter=box)\n", "ztf_object" ] }, @@ -108,7 +478,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 4, "metadata": { "ExecuteTime": { "end_time": "2024-03-28T20:49:47.638073Z", @@ -171,7 +541,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 5, "metadata": { "ExecuteTime": { "end_time": "2024-03-28T20:49:50.702578Z", @@ -180,24 +550,24 @@ }, "outputs": [], "source": [ - "# the healpix pixel to plot\n", - "order = 3\n", - "pixel = 434\n", + "# # the healpix pixel to plot\n", + "# order = 3\n", + "# pixel = 434\n", "\n", - "# Plot the points from the specified ztf pixel in green, and from the pixel's margin cache in red\n", - "plot_points(\n", - " [\n", - " ztf_object.get_partition(order, pixel).compute(),\n", - " ztf_object.margin.get_partition(order, pixel).compute(),\n", - " ],\n", - " order,\n", - " pixel,\n", - " [\"green\", \"red\"],\n", - " [\"ra\", \"ra\"],\n", - " [\"dec\", \"dec\"],\n", - " xlim=[179.5, 180.1],\n", - " ylim=[9.4, 10.0],\n", - ")" + "# # Plot the points from the specified ztf pixel in green, and from the pixel's margin cache in red\n", + "# plot_points(\n", + "# [\n", + "# ztf_object.get_partition(order, pixel).compute(),\n", + "# ztf_object.margin.get_partition(order, pixel).compute(),\n", + "# ],\n", + "# order,\n", + "# pixel,\n", + "# [\"green\", \"red\"],\n", + "# [\"ra\", \"ra\"],\n", + "# [\"dec\", \"dec\"],\n", + "# xlim=[179.5, 180.1],\n", + "# ylim=[9.4, 10.0],\n", + "# )" ] }, { @@ -215,16 +585,1165 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 6, "metadata": { "ExecuteTime": { "end_time": "2024-03-28T20:49:55.217215Z", "start_time": "2024-03-28T20:49:52.586757Z" } }, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
lsdb Catalog gaia:
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solution_iddesignationsource_idrandom_indexref_epochrara_errordecdec_errorparallaxparallax_errorparallax_over_errorpmpmrapmra_errorpmdecpmdec_errorra_dec_corrra_parallax_corrra_pmra_corrra_pmdec_corrdec_parallax_corrdec_pmra_corrdec_pmdec_corrparallax_pmra_corrparallax_pmdec_corrpmra_pmdec_corrastrometric_n_obs_alastrometric_n_obs_acastrometric_n_good_obs_alastrometric_n_bad_obs_alastrometric_gof_alastrometric_chi2_alastrometric_excess_noiseastrometric_excess_noise_sigastrometric_params_solvedastrometric_primary_flagnu_eff_used_in_astrometrypseudocolourpseudocolour_errorra_pseudocolour_corrdec_pseudocolour_corrparallax_pseudocolour_corrpmra_pseudocolour_corrpmdec_pseudocolour_corrastrometric_matched_transitsvisibility_periods_usedastrometric_sigma5d_maxmatched_transitsnew_matched_transitsmatched_transits_removedipd_gof_harmonic_amplitudeipd_gof_harmonic_phaseipd_frac_multi_peakipd_frac_odd_winruwescan_direction_strength_k1scan_direction_strength_k2scan_direction_strength_k3scan_direction_strength_k4scan_direction_mean_k1scan_direction_mean_k2scan_direction_mean_k3scan_direction_mean_k4duplicated_sourcephot_g_n_obsphot_g_mean_fluxphot_g_mean_flux_errorphot_g_mean_flux_over_errorphot_g_mean_magphot_bp_n_obsphot_bp_mean_fluxphot_bp_mean_flux_errorphot_bp_mean_flux_over_errorphot_bp_mean_magphot_rp_n_obsphot_rp_mean_fluxphot_rp_mean_flux_errorphot_rp_mean_flux_over_errorphot_rp_mean_magphot_bp_rp_excess_factorphot_bp_n_contaminated_transitsphot_bp_n_blended_transitsphot_rp_n_contaminated_transitsphot_rp_n_blended_transitsphot_proc_modebp_rpbp_gg_rpradial_velocityradial_velocity_errorrv_method_usedrv_nb_transitsrv_nb_deblended_transitsrv_visibility_periods_usedrv_expected_sig_to_noiserv_renormalised_gofrv_chisq_pvaluerv_time_durationrv_amplitude_robustrv_template_teffrv_template_loggrv_template_fe_hrv_atm_param_originvbroadvbroad_errorvbroad_nb_transitsgrvs_maggrvs_mag_errorgrvs_mag_nb_transitsrvs_spec_sig_to_noisephot_variable_flaglbecl_lonecl_latin_qso_candidatesin_galaxy_candidatesnon_single_starhas_xp_continuoushas_xp_sampledhas_rvshas_epoch_photometryhas_epoch_rvhas_mcmc_gspphothas_mcmc_mscin_andromeda_surveyclassprob_dsc_combmod_quasarclassprob_dsc_combmod_galaxyclassprob_dsc_combmod_starteff_gspphotteff_gspphot_lowerteff_gspphot_upperlogg_gspphotlogg_gspphot_lowerlogg_gspphot_uppermh_gspphotmh_gspphot_lowermh_gspphot_upperdistance_gspphotdistance_gspphot_lowerdistance_gspphot_upperazero_gspphotazero_gspphot_lowerazero_gspphot_upperag_gspphotag_gspphot_lowerag_gspphot_upperebpminrp_gspphotebpminrp_gspphot_lowerebpminrp_gspphot_upperlibname_gspphotNorderDirNpix
npartitions=3933
0int64[pyarrow]string[pyarrow]int64[pyarrow]int64[pyarrow]double[pyarrow]double[pyarrow]double[pyarrow]double[pyarrow]double[pyarrow]double[pyarrow]double[pyarrow]double[pyarrow]double[pyarrow]double[pyarrow]double[pyarrow]double[pyarrow]double[pyarrow]double[pyarrow]double[pyarrow]double[pyarrow]double[pyarrow]double[pyarrow]double[pyarrow]double[pyarrow]double[pyarrow]double[pyarrow]double[pyarrow]int64[pyarrow]int64[pyarrow]int64[pyarrow]int64[pyarrow]double[pyarrow]double[pyarrow]double[pyarrow]double[pyarrow]int64[pyarrow]bool[pyarrow]double[pyarrow]double[pyarrow]double[pyarrow]double[pyarrow]double[pyarrow]double[pyarrow]double[pyarrow]double[pyarrow]int64[pyarrow]int64[pyarrow]double[pyarrow]int64[pyarrow]int64[pyarrow]int64[pyarrow]double[pyarrow]double[pyarrow]int64[pyarrow]int64[pyarrow]double[pyarrow]double[pyarrow]double[pyarrow]double[pyarrow]double[pyarrow]double[pyarrow]double[pyarrow]double[pyarrow]double[pyarrow]bool[pyarrow]int64[pyarrow]double[pyarrow]double[pyarrow]double[pyarrow]double[pyarrow]int64[pyarrow]double[pyarrow]double[pyarrow]double[pyarrow]double[pyarrow]int64[pyarrow]double[pyarrow]double[pyarrow]double[pyarrow]double[pyarrow]double[pyarrow]double[pyarrow]double[pyarrow]double[pyarrow]double[pyarrow]double[pyarrow]double[pyarrow]double[pyarrow]double[pyarrow]double[pyarrow]double[pyarrow]double[pyarrow]double[pyarrow]double[pyarrow]double[pyarrow]double[pyarrow]double[pyarrow]double[pyarrow]double[pyarrow]double[pyarrow]double[pyarrow]double[pyarrow]double[pyarrow]double[pyarrow]double[pyarrow]double[pyarrow]double[pyarrow]double[pyarrow]double[pyarrow]double[pyarrow]double[pyarrow]string[pyarrow]double[pyarrow]double[pyarrow]double[pyarrow]double[pyarrow]bool[pyarrow]bool[pyarrow]int64[pyarrow]bool[pyarrow]bool[pyarrow]bool[pyarrow]bool[pyarrow]bool[pyarrow]bool[pyarrow]bool[pyarrow]bool[pyarrow]double[pyarrow]double[pyarrow]double[pyarrow]double[pyarrow]double[pyarrow]double[pyarrow]double[pyarrow]double[pyarrow]double[pyarrow]double[pyarrow]double[pyarrow]double[pyarrow]double[pyarrow]double[pyarrow]double[pyarrow]double[pyarrow]double[pyarrow]double[pyarrow]double[pyarrow]double[pyarrow]double[pyarrow]double[pyarrow]double[pyarrow]double[pyarrow]string[pyarrow]int8[pyarrow]int64[pyarrow]int64[pyarrow]
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" + ], + "text/plain": [ + "Dask NestedFrame Structure:\n", + " solution_id designation source_id random_index ref_epoch ra ra_error dec dec_error parallax parallax_error parallax_over_error pm pmra pmra_error pmdec pmdec_error ra_dec_corr ra_parallax_corr ra_pmra_corr ra_pmdec_corr dec_parallax_corr dec_pmra_corr dec_pmdec_corr parallax_pmra_corr parallax_pmdec_corr pmra_pmdec_corr astrometric_n_obs_al astrometric_n_obs_ac astrometric_n_good_obs_al astrometric_n_bad_obs_al astrometric_gof_al astrometric_chi2_al astrometric_excess_noise astrometric_excess_noise_sig astrometric_params_solved astrometric_primary_flag nu_eff_used_in_astrometry pseudocolour pseudocolour_error ra_pseudocolour_corr dec_pseudocolour_corr parallax_pseudocolour_corr pmra_pseudocolour_corr pmdec_pseudocolour_corr astrometric_matched_transits visibility_periods_used astrometric_sigma5d_max matched_transits new_matched_transits matched_transits_removed ipd_gof_harmonic_amplitude ipd_gof_harmonic_phase ipd_frac_multi_peak ipd_frac_odd_win ruwe scan_direction_strength_k1 scan_direction_strength_k2 scan_direction_strength_k3 scan_direction_strength_k4 scan_direction_mean_k1 scan_direction_mean_k2 scan_direction_mean_k3 scan_direction_mean_k4 duplicated_source phot_g_n_obs phot_g_mean_flux phot_g_mean_flux_error phot_g_mean_flux_over_error phot_g_mean_mag phot_bp_n_obs phot_bp_mean_flux phot_bp_mean_flux_error phot_bp_mean_flux_over_error phot_bp_mean_mag phot_rp_n_obs phot_rp_mean_flux phot_rp_mean_flux_error phot_rp_mean_flux_over_error phot_rp_mean_mag phot_bp_rp_excess_factor phot_bp_n_contaminated_transits phot_bp_n_blended_transits phot_rp_n_contaminated_transits phot_rp_n_blended_transits phot_proc_mode bp_rp bp_g g_rp radial_velocity radial_velocity_error rv_method_used rv_nb_transits rv_nb_deblended_transits rv_visibility_periods_used rv_expected_sig_to_noise rv_renormalised_gof rv_chisq_pvalue rv_time_duration rv_amplitude_robust rv_template_teff rv_template_logg rv_template_fe_h rv_atm_param_origin vbroad vbroad_error vbroad_nb_transits grvs_mag grvs_mag_error grvs_mag_nb_transits rvs_spec_sig_to_noise phot_variable_flag l b ecl_lon ecl_lat in_qso_candidates in_galaxy_candidates non_single_star has_xp_continuous has_xp_sampled has_rvs has_epoch_photometry has_epoch_rv has_mcmc_gspphot has_mcmc_msc in_andromeda_survey classprob_dsc_combmod_quasar classprob_dsc_combmod_galaxy classprob_dsc_combmod_star teff_gspphot teff_gspphot_lower teff_gspphot_upper logg_gspphot logg_gspphot_lower logg_gspphot_upper mh_gspphot mh_gspphot_lower mh_gspphot_upper distance_gspphot distance_gspphot_lower distance_gspphot_upper azero_gspphot azero_gspphot_lower azero_gspphot_upper ag_gspphot ag_gspphot_lower ag_gspphot_upper ebpminrp_gspphot ebpminrp_gspphot_lower ebpminrp_gspphot_upper libname_gspphot Norder Dir Npix\n", + "npartitions=3933 \n", + "0 int64[pyarrow] string[pyarrow] int64[pyarrow] int64[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] int64[pyarrow] int64[pyarrow] int64[pyarrow] int64[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] int64[pyarrow] bool[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] int64[pyarrow] int64[pyarrow] double[pyarrow] int64[pyarrow] int64[pyarrow] int64[pyarrow] double[pyarrow] double[pyarrow] int64[pyarrow] int64[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] bool[pyarrow] int64[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] int64[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] int64[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] string[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] bool[pyarrow] bool[pyarrow] int64[pyarrow] bool[pyarrow] bool[pyarrow] bool[pyarrow] bool[pyarrow] bool[pyarrow] bool[pyarrow] bool[pyarrow] bool[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] string[pyarrow] int8[pyarrow] int64[pyarrow] int64[pyarrow]\n", + "18014398509481984 ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...\n", + "... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...\n", + "3454260914193170432 ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...\n", + "3458764513820540928 ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...\n", + "Dask Name: nestedframe, 3 expressions\n", + "Expr=MapPartitions(NestedFrame)" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "gaia = lsdb.read_hats(f\"{surveys_path}/gaia\")\n", + "gaia = lsdb.read_hats(f\"{surveys_path}/gaia/gaia\")\n", "gaia" ] }, @@ -239,14 +1758,1275 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 7, "metadata": { "ExecuteTime": { "end_time": "2024-03-28T20:49:55.477297Z", "start_time": "2024-03-28T20:49:55.222520Z" } }, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
lsdb Catalog gaia_x_ztf_dr14:
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solution_id_gaiadesignation_gaiasource_id_gaiarandom_index_gaiaref_epoch_gaiara_gaiara_error_gaiadec_gaiadec_error_gaiaparallax_gaiaparallax_error_gaiaparallax_over_error_gaiapm_gaiapmra_gaiapmra_error_gaiapmdec_gaiapmdec_error_gaiara_dec_corr_gaiara_parallax_corr_gaiara_pmra_corr_gaiara_pmdec_corr_gaiadec_parallax_corr_gaiadec_pmra_corr_gaiadec_pmdec_corr_gaiaparallax_pmra_corr_gaiaparallax_pmdec_corr_gaiapmra_pmdec_corr_gaiaastrometric_n_obs_al_gaiaastrometric_n_obs_ac_gaiaastrometric_n_good_obs_al_gaiaastrometric_n_bad_obs_al_gaiaastrometric_gof_al_gaiaastrometric_chi2_al_gaiaastrometric_excess_noise_gaiaastrometric_excess_noise_sig_gaiaastrometric_params_solved_gaiaastrometric_primary_flag_gaianu_eff_used_in_astrometry_gaiapseudocolour_gaiapseudocolour_error_gaiara_pseudocolour_corr_gaiadec_pseudocolour_corr_gaiaparallax_pseudocolour_corr_gaiapmra_pseudocolour_corr_gaiapmdec_pseudocolour_corr_gaiaastrometric_matched_transits_gaiavisibility_periods_used_gaiaastrometric_sigma5d_max_gaiamatched_transits_gaianew_matched_transits_gaiamatched_transits_removed_gaiaipd_gof_harmonic_amplitude_gaiaipd_gof_harmonic_phase_gaiaipd_frac_multi_peak_gaiaipd_frac_odd_win_gaiaruwe_gaiascan_direction_strength_k1_gaiascan_direction_strength_k2_gaiascan_direction_strength_k3_gaiascan_direction_strength_k4_gaiascan_direction_mean_k1_gaiascan_direction_mean_k2_gaiascan_direction_mean_k3_gaiascan_direction_mean_k4_gaiaduplicated_source_gaiaphot_g_n_obs_gaiaphot_g_mean_flux_gaiaphot_g_mean_flux_error_gaiaphot_g_mean_flux_over_error_gaiaphot_g_mean_mag_gaiaphot_bp_n_obs_gaiaphot_bp_mean_flux_gaiaphot_bp_mean_flux_error_gaiaphot_bp_mean_flux_over_error_gaiaphot_bp_mean_mag_gaiaphot_rp_n_obs_gaiaphot_rp_mean_flux_gaiaphot_rp_mean_flux_error_gaiaphot_rp_mean_flux_over_error_gaiaphot_rp_mean_mag_gaiaphot_bp_rp_excess_factor_gaiaphot_bp_n_contaminated_transits_gaiaphot_bp_n_blended_transits_gaiaphot_rp_n_contaminated_transits_gaiaphot_rp_n_blended_transits_gaiaphot_proc_mode_gaiabp_rp_gaiabp_g_gaiag_rp_gaiaradial_velocity_gaiaradial_velocity_error_gaiarv_method_used_gaiarv_nb_transits_gaiarv_nb_deblended_transits_gaiarv_visibility_periods_used_gaiarv_expected_sig_to_noise_gaiarv_renormalised_gof_gaiarv_chisq_pvalue_gaiarv_time_duration_gaiarv_amplitude_robust_gaiarv_template_teff_gaiarv_template_logg_gaiarv_template_fe_h_gaiarv_atm_param_origin_gaiavbroad_gaiavbroad_error_gaiavbroad_nb_transits_gaiagrvs_mag_gaiagrvs_mag_error_gaiagrvs_mag_nb_transits_gaiarvs_spec_sig_to_noise_gaiaphot_variable_flag_gaial_gaiab_gaiaecl_lon_gaiaecl_lat_gaiain_qso_candidates_gaiain_galaxy_candidates_gaianon_single_star_gaiahas_xp_continuous_gaiahas_xp_sampled_gaiahas_rvs_gaiahas_epoch_photometry_gaiahas_epoch_rv_gaiahas_mcmc_gspphot_gaiahas_mcmc_msc_gaiain_andromeda_survey_gaiaclassprob_dsc_combmod_quasar_gaiaclassprob_dsc_combmod_galaxy_gaiaclassprob_dsc_combmod_star_gaiateff_gspphot_gaiateff_gspphot_lower_gaiateff_gspphot_upper_gaialogg_gspphot_gaialogg_gspphot_lower_gaialogg_gspphot_upper_gaiamh_gspphot_gaiamh_gspphot_lower_gaiamh_gspphot_upper_gaiadistance_gspphot_gaiadistance_gspphot_lower_gaiadistance_gspphot_upper_gaiaazero_gspphot_gaiaazero_gspphot_lower_gaiaazero_gspphot_upper_gaiaag_gspphot_gaiaag_gspphot_lower_gaiaag_gspphot_upper_gaiaebpminrp_gspphot_gaiaebpminrp_gspphot_lower_gaiaebpminrp_gspphot_upper_gaialibname_gspphot_gaiaNorder_gaiaDir_gaiaNpix_gaiaps1_objid_ztf_dr14ra_ztf_dr14dec_ztf_dr14ps1_gMeanPSFMag_ztf_dr14ps1_rMeanPSFMag_ztf_dr14ps1_iMeanPSFMag_ztf_dr14nobs_g_ztf_dr14nobs_r_ztf_dr14nobs_i_ztf_dr14mean_mag_g_ztf_dr14mean_mag_r_ztf_dr14mean_mag_i_ztf_dr14Norder_ztf_dr14Dir_ztf_dr14Npix_ztf_dr14_dist_arcsec
npartitions=4
1945555039024054272int64[pyarrow]string[pyarrow]int64[pyarrow]int64[pyarrow]double[pyarrow]double[pyarrow]double[pyarrow]double[pyarrow]double[pyarrow]double[pyarrow]double[pyarrow]double[pyarrow]double[pyarrow]double[pyarrow]double[pyarrow]double[pyarrow]double[pyarrow]double[pyarrow]double[pyarrow]double[pyarrow]double[pyarrow]double[pyarrow]double[pyarrow]double[pyarrow]double[pyarrow]double[pyarrow]double[pyarrow]int64[pyarrow]int64[pyarrow]int64[pyarrow]int64[pyarrow]double[pyarrow]double[pyarrow]double[pyarrow]double[pyarrow]int64[pyarrow]bool[pyarrow]double[pyarrow]double[pyarrow]double[pyarrow]double[pyarrow]double[pyarrow]double[pyarrow]double[pyarrow]double[pyarrow]int64[pyarrow]int64[pyarrow]double[pyarrow]int64[pyarrow]int64[pyarrow]int64[pyarrow]double[pyarrow]double[pyarrow]int64[pyarrow]int64[pyarrow]double[pyarrow]double[pyarrow]double[pyarrow]double[pyarrow]double[pyarrow]double[pyarrow]double[pyarrow]double[pyarrow]double[pyarrow]bool[pyarrow]int64[pyarrow]double[pyarrow]double[pyarrow]double[pyarrow]double[pyarrow]int64[pyarrow]double[pyarrow]double[pyarrow]double[pyarrow]double[pyarrow]int64[pyarrow]double[pyarrow]double[pyarrow]double[pyarrow]double[pyarrow]double[pyarrow]double[pyarrow]double[pyarrow]double[pyarrow]double[pyarrow]double[pyarrow]double[pyarrow]double[pyarrow]double[pyarrow]double[pyarrow]double[pyarrow]double[pyarrow]double[pyarrow]double[pyarrow]double[pyarrow]double[pyarrow]double[pyarrow]double[pyarrow]double[pyarrow]double[pyarrow]double[pyarrow]double[pyarrow]double[pyarrow]double[pyarrow]double[pyarrow]double[pyarrow]double[pyarrow]double[pyarrow]double[pyarrow]double[pyarrow]double[pyarrow]string[pyarrow]double[pyarrow]double[pyarrow]double[pyarrow]double[pyarrow]bool[pyarrow]bool[pyarrow]int64[pyarrow]bool[pyarrow]bool[pyarrow]bool[pyarrow]bool[pyarrow]bool[pyarrow]bool[pyarrow]bool[pyarrow]bool[pyarrow]double[pyarrow]double[pyarrow]double[pyarrow]double[pyarrow]double[pyarrow]double[pyarrow]double[pyarrow]double[pyarrow]double[pyarrow]double[pyarrow]double[pyarrow]double[pyarrow]double[pyarrow]double[pyarrow]double[pyarrow]double[pyarrow]double[pyarrow]double[pyarrow]double[pyarrow]double[pyarrow]double[pyarrow]double[pyarrow]double[pyarrow]double[pyarrow]string[pyarrow]int8[pyarrow]int64[pyarrow]int64[pyarrow]int64[pyarrow]double[pyarrow]double[pyarrow]double[pyarrow]double[pyarrow]double[pyarrow]int32[pyarrow]int32[pyarrow]int32[pyarrow]double[pyarrow]double[pyarrow]double[pyarrow]int8[pyarrow]int64[pyarrow]int64[pyarrow]double[pyarrow]
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\n", + "
" + ], + "text/plain": [ + "Dask NestedFrame Structure:\n", + " solution_id_gaia designation_gaia source_id_gaia random_index_gaia ref_epoch_gaia ra_gaia ra_error_gaia dec_gaia dec_error_gaia parallax_gaia parallax_error_gaia parallax_over_error_gaia pm_gaia pmra_gaia pmra_error_gaia pmdec_gaia pmdec_error_gaia ra_dec_corr_gaia ra_parallax_corr_gaia ra_pmra_corr_gaia ra_pmdec_corr_gaia dec_parallax_corr_gaia dec_pmra_corr_gaia dec_pmdec_corr_gaia parallax_pmra_corr_gaia parallax_pmdec_corr_gaia pmra_pmdec_corr_gaia astrometric_n_obs_al_gaia astrometric_n_obs_ac_gaia astrometric_n_good_obs_al_gaia astrometric_n_bad_obs_al_gaia astrometric_gof_al_gaia astrometric_chi2_al_gaia astrometric_excess_noise_gaia astrometric_excess_noise_sig_gaia astrometric_params_solved_gaia astrometric_primary_flag_gaia nu_eff_used_in_astrometry_gaia pseudocolour_gaia pseudocolour_error_gaia ra_pseudocolour_corr_gaia dec_pseudocolour_corr_gaia parallax_pseudocolour_corr_gaia pmra_pseudocolour_corr_gaia pmdec_pseudocolour_corr_gaia astrometric_matched_transits_gaia visibility_periods_used_gaia astrometric_sigma5d_max_gaia matched_transits_gaia new_matched_transits_gaia matched_transits_removed_gaia ipd_gof_harmonic_amplitude_gaia ipd_gof_harmonic_phase_gaia ipd_frac_multi_peak_gaia ipd_frac_odd_win_gaia ruwe_gaia scan_direction_strength_k1_gaia scan_direction_strength_k2_gaia scan_direction_strength_k3_gaia scan_direction_strength_k4_gaia scan_direction_mean_k1_gaia scan_direction_mean_k2_gaia scan_direction_mean_k3_gaia scan_direction_mean_k4_gaia duplicated_source_gaia phot_g_n_obs_gaia phot_g_mean_flux_gaia phot_g_mean_flux_error_gaia phot_g_mean_flux_over_error_gaia phot_g_mean_mag_gaia phot_bp_n_obs_gaia phot_bp_mean_flux_gaia phot_bp_mean_flux_error_gaia phot_bp_mean_flux_over_error_gaia phot_bp_mean_mag_gaia phot_rp_n_obs_gaia phot_rp_mean_flux_gaia phot_rp_mean_flux_error_gaia phot_rp_mean_flux_over_error_gaia phot_rp_mean_mag_gaia phot_bp_rp_excess_factor_gaia phot_bp_n_contaminated_transits_gaia phot_bp_n_blended_transits_gaia phot_rp_n_contaminated_transits_gaia phot_rp_n_blended_transits_gaia phot_proc_mode_gaia bp_rp_gaia bp_g_gaia g_rp_gaia radial_velocity_gaia radial_velocity_error_gaia rv_method_used_gaia rv_nb_transits_gaia rv_nb_deblended_transits_gaia rv_visibility_periods_used_gaia rv_expected_sig_to_noise_gaia rv_renormalised_gof_gaia rv_chisq_pvalue_gaia rv_time_duration_gaia rv_amplitude_robust_gaia rv_template_teff_gaia rv_template_logg_gaia rv_template_fe_h_gaia rv_atm_param_origin_gaia vbroad_gaia vbroad_error_gaia vbroad_nb_transits_gaia grvs_mag_gaia grvs_mag_error_gaia grvs_mag_nb_transits_gaia rvs_spec_sig_to_noise_gaia phot_variable_flag_gaia l_gaia b_gaia ecl_lon_gaia ecl_lat_gaia in_qso_candidates_gaia in_galaxy_candidates_gaia non_single_star_gaia has_xp_continuous_gaia has_xp_sampled_gaia has_rvs_gaia has_epoch_photometry_gaia has_epoch_rv_gaia has_mcmc_gspphot_gaia has_mcmc_msc_gaia in_andromeda_survey_gaia classprob_dsc_combmod_quasar_gaia classprob_dsc_combmod_galaxy_gaia classprob_dsc_combmod_star_gaia teff_gspphot_gaia teff_gspphot_lower_gaia teff_gspphot_upper_gaia logg_gspphot_gaia logg_gspphot_lower_gaia logg_gspphot_upper_gaia mh_gspphot_gaia mh_gspphot_lower_gaia mh_gspphot_upper_gaia distance_gspphot_gaia distance_gspphot_lower_gaia distance_gspphot_upper_gaia azero_gspphot_gaia azero_gspphot_lower_gaia azero_gspphot_upper_gaia ag_gspphot_gaia ag_gspphot_lower_gaia ag_gspphot_upper_gaia ebpminrp_gspphot_gaia ebpminrp_gspphot_lower_gaia ebpminrp_gspphot_upper_gaia libname_gspphot_gaia Norder_gaia Dir_gaia Npix_gaia ps1_objid_ztf_dr14 ra_ztf_dr14 dec_ztf_dr14 ps1_gMeanPSFMag_ztf_dr14 ps1_rMeanPSFMag_ztf_dr14 ps1_iMeanPSFMag_ztf_dr14 nobs_g_ztf_dr14 nobs_r_ztf_dr14 nobs_i_ztf_dr14 mean_mag_g_ztf_dr14 mean_mag_r_ztf_dr14 mean_mag_i_ztf_dr14 Norder_ztf_dr14 Dir_ztf_dr14 Npix_ztf_dr14 _dist_arcsec\n", + "npartitions=4 \n", + "1945555039024054272 int64[pyarrow] string[pyarrow] int64[pyarrow] int64[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] int64[pyarrow] int64[pyarrow] int64[pyarrow] int64[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] int64[pyarrow] bool[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] int64[pyarrow] int64[pyarrow] double[pyarrow] int64[pyarrow] int64[pyarrow] int64[pyarrow] double[pyarrow] double[pyarrow] int64[pyarrow] int64[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] bool[pyarrow] int64[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] int64[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] int64[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] string[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] bool[pyarrow] bool[pyarrow] int64[pyarrow] bool[pyarrow] bool[pyarrow] bool[pyarrow] bool[pyarrow] bool[pyarrow] bool[pyarrow] bool[pyarrow] bool[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] string[pyarrow] int8[pyarrow] int64[pyarrow] int64[pyarrow] int64[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] int32[pyarrow] int32[pyarrow] int32[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] int8[pyarrow] int64[pyarrow] int64[pyarrow] double[pyarrow]\n", + "1950058638651424768 ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...\n", + "1954562238278795264 ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...\n", + "1959065837906165760 ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...\n", + "1963569437533536256 ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...\n", + "Dask Name: nestedframe, 3 expressions\n", + "Expr=MapPartitions(NestedFrame)" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "gaia.crossmatch(ztf_object)" ] @@ -262,14 +3042,23 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 8, "metadata": { "ExecuteTime": { "end_time": "2024-03-28T20:49:57.849868Z", "start_time": "2024-03-28T20:49:57.843828Z" } }, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/delucchi/git/demo/lsdb/src/lsdb/dask/crossmatch_catalog_data.py:108: RuntimeWarning: Right catalog does not have a margin cache. Results may be incomplete and/or inaccurate.\n", + " warnings.warn(\n" + ] + } + ], "source": [ "try:\n", " ztf_object.crossmatch(gaia)\n", @@ -288,7 +3077,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 9, "metadata": { "ExecuteTime": { "end_time": "2024-03-28T20:50:01.240040Z", @@ -297,23 +3086,26 @@ }, "outputs": [], "source": [ - "crossmatch_result = gaia.crossmatch(ztf_object)\n", + "# crossmatch_result = gaia.crossmatch(ztf_object)\n", + "\n", + "# order = 3\n", + "# pixel = 434\n", "\n", - "plot_points(\n", - " [\n", - " crossmatch_result.get_partition(order, pixel).compute(),\n", - " crossmatch_result.get_partition(order, pixel).compute(),\n", - " ],\n", - " order,\n", - " pixel,\n", - " [\"green\", \"red\"],\n", - " [\"ra_gaia_halfdegree\", \"ra_ztf_object_halfdegree\"],\n", - " [\"dec_gaia_halfdegree\", \"dec_ztf_object_halfdegree\"],\n", - " xlim=[179.5, 180.1],\n", - " ylim=[9.4, 10.0],\n", - " markers=[\"+\", \"x\"],\n", - " alpha=0.8,\n", - ")" + "# plot_points(\n", + "# [\n", + "# crossmatch_result.get_partition(order, pixel).compute(),\n", + "# crossmatch_result.get_partition(order, pixel).compute(),\n", + "# ],\n", + "# order,\n", + "# pixel,\n", + "# [\"green\", \"red\"],\n", + "# [\"ra_gaia_halfdegree\", \"ra_ztf_object_halfdegree\"],\n", + "# [\"dec_gaia_halfdegree\", \"dec_ztf_object_halfdegree\"],\n", + "# xlim=[179.5, 180.1],\n", + "# ylim=[9.4, 10.0],\n", + "# markers=[\"+\", \"x\"],\n", + "# alpha=0.8,\n", + "# )" ] }, { @@ -329,7 +3121,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 10, "metadata": { "ExecuteTime": { "end_time": "2024-03-28T20:50:05.236767Z", @@ -338,22 +3130,22 @@ }, "outputs": [], "source": [ - "small_sky_box_filter = ztf_object.box_search(ra=[179.9, 180], dec=[9.5, 9.7])\n", + "# small_sky_box_filter = ztf_object.box_search(ra=[179.9, 180], dec=[9.5, 9.7])\n", "\n", - "# Plot the points from the filtered ztf pixel in green, and from the pixel's margin cache in red\n", - "plot_points(\n", - " [\n", - " small_sky_box_filter.get_partition(order, pixel).compute(),\n", - " small_sky_box_filter.margin.get_partition(order, pixel).compute(),\n", - " ],\n", - " order,\n", - " pixel,\n", - " [\"green\", \"red\"],\n", - " [\"ra\", \"ra\"],\n", - " [\"dec\", \"dec\"],\n", - " xlim=[179.5, 180.1],\n", - " ylim=[9.4, 10.0],\n", - ")" + "# # Plot the points from the filtered ztf pixel in green, and from the pixel's margin cache in red\n", + "# plot_points(\n", + "# [\n", + "# small_sky_box_filter.get_partition(order, pixel).compute(),\n", + "# small_sky_box_filter.margin.get_partition(order, pixel).compute(),\n", + "# ],\n", + "# order,\n", + "# pixel,\n", + "# [\"green\", \"red\"],\n", + "# [\"ra\", \"ra\"],\n", + "# [\"dec\", \"dec\"],\n", + "# xlim=[179.5, 180.1],\n", + "# ylim=[9.4, 10.0],\n", + "# )" ] }, { @@ -370,12 +3162,21 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3 (ipykernel)", + "display_name": "demo", "language": "python", "name": "python3" }, "language_info": { - "name": "python" + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.3" } }, "nbformat": 4, diff --git a/docs/tutorials/remote_data.ipynb b/docs/tutorials/remote_data.ipynb index f5f4ddb5..0f42127a 100644 --- a/docs/tutorials/remote_data.ipynb +++ b/docs/tutorials/remote_data.ipynb @@ -44,7 +44,7 @@ "source": [ "from upath import UPath\n", "\n", - "test_path = UPath(\"https://data.lsdb.io/unstable/gaia_dr3/gaia/\")\n", + "test_path = UPath(\"https://data.lsdb.io/hats/gaia/gaia/\")\n", "test_path.exists()" ] }, @@ -56,7 +56,7 @@ "source": [ "import lsdb\n", "\n", - "cat = lsdb.read_hats(\"https://data.lsdb.io/unstable/gaia_dr3/gaia/\")\n", + "cat = lsdb.read_hats(\"https://data.lsdb.io/hats/gaia/gaia/\")\n", "cat" ] }, diff --git a/src/lsdb/io/to_hats.py b/src/lsdb/io/to_hats.py index 092150a8..a8ddda4f 100644 --- a/src/lsdb/io/to_hats.py +++ b/src/lsdb/io/to_hats.py @@ -65,6 +65,7 @@ def to_hats( **kwargs: Arguments to pass to the parquet write operations """ # Create the output directory for the catalog + base_catalog_path = hc.io.file_io.get_upath(base_catalog_path) if hc.io.file_io.directory_has_contents(base_catalog_path): if not overwrite: raise ValueError( From a89efb53de33e51ea43137f9a57131c70a95f187 Mon Sep 17 00:00:00 2001 From: Sean McGuire Date: Fri, 18 Oct 2024 13:07:08 -0400 Subject: [PATCH 3/4] fix notebooks --- docs/getting-started.rst | 2 +- docs/index.rst | 1 - docs/tutorials/import_catalogs.ipynb | 12 +- docs/tutorials/margins.ipynb | 2815 +------------------------- docs/tutorials/remote_data.ipynb | 61 - 5 files changed, 22 insertions(+), 2869 deletions(-) diff --git a/docs/getting-started.rst b/docs/getting-started.rst index 4a59c20b..4fa87eac 100644 --- a/docs/getting-started.rst +++ b/docs/getting-started.rst @@ -1,5 +1,5 @@ Getting Started with LSDB -============ +========================== Installation -------------------------- diff --git a/docs/index.rst b/docs/index.rst index 629a5e09..2d4e058c 100644 --- a/docs/index.rst +++ b/docs/index.rst @@ -57,7 +57,6 @@ Using this Guide :hidden: Home page - Installation Getting Started Tutorials API Reference diff --git a/docs/tutorials/import_catalogs.ipynb b/docs/tutorials/import_catalogs.ipynb index ca62131f..8f71dfbe 100644 --- a/docs/tutorials/import_catalogs.ipynb +++ b/docs/tutorials/import_catalogs.ipynb @@ -17,7 +17,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": null, "id": "c2de4ed424644058", "metadata": { "ExecuteTime": { @@ -45,7 +45,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": null, "id": "6b5f76fe439a32b9", "metadata": { "ExecuteTime": { @@ -122,7 +122,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": null, "id": "46bca7773cf6b261", "metadata": { "ExecuteTime": { @@ -137,7 +137,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": null, "id": "d2d1324d21d62c81", "metadata": { "ExecuteTime": { @@ -227,7 +227,7 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": null, "id": "b9d176a8dc303aa0", "metadata": { "ExecuteTime": { @@ -256,7 +256,7 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": null, "id": "a90fcbe0", "metadata": {}, "outputs": [], diff --git a/docs/tutorials/margins.ipynb b/docs/tutorials/margins.ipynb index fe4bef21..229db313 100644 --- a/docs/tutorials/margins.ipynb +++ b/docs/tutorials/margins.ipynb @@ -32,7 +32,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "metadata": { "ExecuteTime": { "end_time": "2024-03-28T20:38:39.721106Z", @@ -48,211 +48,14 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "metadata": { "ExecuteTime": { "end_time": "2024-03-28T20:39:05.137393Z", "start_time": "2024-03-28T20:39:02.808295Z" } }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Margin size: 10.0 arcsec\n" - ] - }, - { - "data": { - "text/html": [ - "
lsdb Catalog ztf_dr14_10arcs:
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lsdb Catalog ztf_dr14:
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solution_iddesignationsource_idrandom_indexref_epochrara_errordecdec_errorparallaxparallax_errorparallax_over_errorpmpmrapmra_errorpmdecpmdec_errorra_dec_corrra_parallax_corrra_pmra_corrra_pmdec_corrdec_parallax_corrdec_pmra_corrdec_pmdec_corrparallax_pmra_corrparallax_pmdec_corrpmra_pmdec_corrastrometric_n_obs_alastrometric_n_obs_acastrometric_n_good_obs_alastrometric_n_bad_obs_alastrometric_gof_alastrometric_chi2_alastrometric_excess_noiseastrometric_excess_noise_sigastrometric_params_solvedastrometric_primary_flagnu_eff_used_in_astrometrypseudocolourpseudocolour_errorra_pseudocolour_corrdec_pseudocolour_corrparallax_pseudocolour_corrpmra_pseudocolour_corrpmdec_pseudocolour_corrastrometric_matched_transitsvisibility_periods_usedastrometric_sigma5d_maxmatched_transitsnew_matched_transitsmatched_transits_removedipd_gof_harmonic_amplitudeipd_gof_harmonic_phaseipd_frac_multi_peakipd_frac_odd_winruwescan_direction_strength_k1scan_direction_strength_k2scan_direction_strength_k3scan_direction_strength_k4scan_direction_mean_k1scan_direction_mean_k2scan_direction_mean_k3scan_direction_mean_k4duplicated_sourcephot_g_n_obsphot_g_mean_fluxphot_g_mean_flux_errorphot_g_mean_flux_over_errorphot_g_mean_magphot_bp_n_obsphot_bp_mean_fluxphot_bp_mean_flux_errorphot_bp_mean_flux_over_errorphot_bp_mean_magphot_rp_n_obsphot_rp_mean_fluxphot_rp_mean_flux_errorphot_rp_mean_flux_over_errorphot_rp_mean_magphot_bp_rp_excess_factorphot_bp_n_contaminated_transitsphot_bp_n_blended_transitsphot_rp_n_contaminated_transitsphot_rp_n_blended_transitsphot_proc_modebp_rpbp_gg_rpradial_velocityradial_velocity_errorrv_method_usedrv_nb_transitsrv_nb_deblended_transitsrv_visibility_periods_usedrv_expected_sig_to_noiserv_renormalised_gofrv_chisq_pvaluerv_time_durationrv_amplitude_robustrv_template_teffrv_template_loggrv_template_fe_hrv_atm_param_originvbroadvbroad_errorvbroad_nb_transitsgrvs_maggrvs_mag_errorgrvs_mag_nb_transitsrvs_spec_sig_to_noisephot_variable_flaglbecl_lonecl_latin_qso_candidatesin_galaxy_candidatesnon_single_starhas_xp_continuoushas_xp_sampledhas_rvshas_epoch_photometryhas_epoch_rvhas_mcmc_gspphothas_mcmc_mscin_andromeda_surveyclassprob_dsc_combmod_quasarclassprob_dsc_combmod_galaxyclassprob_dsc_combmod_starteff_gspphotteff_gspphot_lowerteff_gspphot_upperlogg_gspphotlogg_gspphot_lowerlogg_gspphot_uppermh_gspphotmh_gspphot_lowermh_gspphot_upperdistance_gspphotdistance_gspphot_lowerdistance_gspphot_upperazero_gspphotazero_gspphot_lowerazero_gspphot_upperag_gspphotag_gspphot_lowerag_gspphot_upperebpminrp_gspphotebpminrp_gspphot_lowerebpminrp_gspphot_upperlibname_gspphotNorderDirNpix
npartitions=3933
0int64[pyarrow]string[pyarrow]int64[pyarrow]int64[pyarrow]double[pyarrow]double[pyarrow]double[pyarrow]double[pyarrow]double[pyarrow]double[pyarrow]double[pyarrow]double[pyarrow]double[pyarrow]double[pyarrow]double[pyarrow]double[pyarrow]double[pyarrow]double[pyarrow]double[pyarrow]double[pyarrow]double[pyarrow]double[pyarrow]double[pyarrow]double[pyarrow]double[pyarrow]double[pyarrow]double[pyarrow]int64[pyarrow]int64[pyarrow]int64[pyarrow]int64[pyarrow]double[pyarrow]double[pyarrow]double[pyarrow]double[pyarrow]int64[pyarrow]bool[pyarrow]double[pyarrow]double[pyarrow]double[pyarrow]double[pyarrow]double[pyarrow]double[pyarrow]double[pyarrow]double[pyarrow]int64[pyarrow]int64[pyarrow]double[pyarrow]int64[pyarrow]int64[pyarrow]int64[pyarrow]double[pyarrow]double[pyarrow]int64[pyarrow]int64[pyarrow]double[pyarrow]double[pyarrow]double[pyarrow]double[pyarrow]double[pyarrow]double[pyarrow]double[pyarrow]double[pyarrow]double[pyarrow]bool[pyarrow]int64[pyarrow]double[pyarrow]double[pyarrow]double[pyarrow]double[pyarrow]int64[pyarrow]double[pyarrow]double[pyarrow]double[pyarrow]double[pyarrow]int64[pyarrow]double[pyarrow]double[pyarrow]double[pyarrow]double[pyarrow]double[pyarrow]double[pyarrow]double[pyarrow]double[pyarrow]double[pyarrow]double[pyarrow]double[pyarrow]double[pyarrow]double[pyarrow]double[pyarrow]double[pyarrow]double[pyarrow]double[pyarrow]double[pyarrow]double[pyarrow]double[pyarrow]double[pyarrow]double[pyarrow]double[pyarrow]double[pyarrow]double[pyarrow]double[pyarrow]double[pyarrow]double[pyarrow]double[pyarrow]double[pyarrow]double[pyarrow]double[pyarrow]double[pyarrow]double[pyarrow]double[pyarrow]string[pyarrow]double[pyarrow]double[pyarrow]double[pyarrow]double[pyarrow]bool[pyarrow]bool[pyarrow]int64[pyarrow]bool[pyarrow]bool[pyarrow]bool[pyarrow]bool[pyarrow]bool[pyarrow]bool[pyarrow]bool[pyarrow]bool[pyarrow]double[pyarrow]double[pyarrow]double[pyarrow]double[pyarrow]double[pyarrow]double[pyarrow]double[pyarrow]double[pyarrow]double[pyarrow]double[pyarrow]double[pyarrow]double[pyarrow]double[pyarrow]double[pyarrow]double[pyarrow]double[pyarrow]double[pyarrow]double[pyarrow]double[pyarrow]double[pyarrow]double[pyarrow]double[pyarrow]double[pyarrow]double[pyarrow]string[pyarrow]int8[pyarrow]int64[pyarrow]int64[pyarrow]
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" - ], - "text/plain": [ - "Dask NestedFrame Structure:\n", - " solution_id designation source_id random_index ref_epoch ra ra_error dec dec_error parallax parallax_error parallax_over_error pm pmra pmra_error pmdec pmdec_error ra_dec_corr ra_parallax_corr ra_pmra_corr ra_pmdec_corr dec_parallax_corr dec_pmra_corr dec_pmdec_corr parallax_pmra_corr parallax_pmdec_corr pmra_pmdec_corr astrometric_n_obs_al astrometric_n_obs_ac astrometric_n_good_obs_al astrometric_n_bad_obs_al astrometric_gof_al astrometric_chi2_al astrometric_excess_noise astrometric_excess_noise_sig astrometric_params_solved astrometric_primary_flag nu_eff_used_in_astrometry pseudocolour pseudocolour_error ra_pseudocolour_corr dec_pseudocolour_corr parallax_pseudocolour_corr pmra_pseudocolour_corr pmdec_pseudocolour_corr astrometric_matched_transits visibility_periods_used astrometric_sigma5d_max matched_transits new_matched_transits matched_transits_removed ipd_gof_harmonic_amplitude ipd_gof_harmonic_phase ipd_frac_multi_peak ipd_frac_odd_win ruwe scan_direction_strength_k1 scan_direction_strength_k2 scan_direction_strength_k3 scan_direction_strength_k4 scan_direction_mean_k1 scan_direction_mean_k2 scan_direction_mean_k3 scan_direction_mean_k4 duplicated_source phot_g_n_obs phot_g_mean_flux phot_g_mean_flux_error phot_g_mean_flux_over_error phot_g_mean_mag phot_bp_n_obs phot_bp_mean_flux phot_bp_mean_flux_error phot_bp_mean_flux_over_error phot_bp_mean_mag phot_rp_n_obs phot_rp_mean_flux phot_rp_mean_flux_error phot_rp_mean_flux_over_error phot_rp_mean_mag phot_bp_rp_excess_factor phot_bp_n_contaminated_transits phot_bp_n_blended_transits phot_rp_n_contaminated_transits phot_rp_n_blended_transits phot_proc_mode bp_rp bp_g g_rp radial_velocity radial_velocity_error rv_method_used rv_nb_transits rv_nb_deblended_transits rv_visibility_periods_used rv_expected_sig_to_noise rv_renormalised_gof rv_chisq_pvalue rv_time_duration rv_amplitude_robust rv_template_teff rv_template_logg rv_template_fe_h rv_atm_param_origin vbroad vbroad_error vbroad_nb_transits grvs_mag grvs_mag_error grvs_mag_nb_transits rvs_spec_sig_to_noise phot_variable_flag l b ecl_lon ecl_lat in_qso_candidates in_galaxy_candidates non_single_star has_xp_continuous has_xp_sampled has_rvs has_epoch_photometry has_epoch_rv has_mcmc_gspphot has_mcmc_msc in_andromeda_survey classprob_dsc_combmod_quasar classprob_dsc_combmod_galaxy classprob_dsc_combmod_star teff_gspphot teff_gspphot_lower teff_gspphot_upper logg_gspphot logg_gspphot_lower logg_gspphot_upper mh_gspphot mh_gspphot_lower mh_gspphot_upper distance_gspphot distance_gspphot_lower distance_gspphot_upper azero_gspphot azero_gspphot_lower azero_gspphot_upper ag_gspphot ag_gspphot_lower ag_gspphot_upper ebpminrp_gspphot ebpminrp_gspphot_lower ebpminrp_gspphot_upper libname_gspphot Norder Dir Npix\n", - "npartitions=3933 \n", - "0 int64[pyarrow] string[pyarrow] int64[pyarrow] int64[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] int64[pyarrow] int64[pyarrow] int64[pyarrow] int64[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] int64[pyarrow] bool[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] int64[pyarrow] int64[pyarrow] double[pyarrow] int64[pyarrow] int64[pyarrow] int64[pyarrow] double[pyarrow] double[pyarrow] int64[pyarrow] int64[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] bool[pyarrow] int64[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] int64[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] int64[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] string[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] bool[pyarrow] bool[pyarrow] int64[pyarrow] bool[pyarrow] bool[pyarrow] bool[pyarrow] bool[pyarrow] bool[pyarrow] bool[pyarrow] bool[pyarrow] bool[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] string[pyarrow] int8[pyarrow] int64[pyarrow] int64[pyarrow]\n", - "18014398509481984 ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...\n", - "... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...\n", - "3454260914193170432 ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...\n", - "3458764513820540928 ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...\n", - "Dask Name: nestedframe, 3 expressions\n", - "Expr=MapPartitions(NestedFrame)" - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "gaia = lsdb.read_hats(f\"{surveys_path}/gaia/gaia\")\n", "gaia" @@ -1758,1275 +243,14 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": null, "metadata": { "ExecuteTime": { "end_time": "2024-03-28T20:49:55.477297Z", "start_time": "2024-03-28T20:49:55.222520Z" } }, - "outputs": [ - { - "data": { - "text/html": [ - "
lsdb Catalog gaia_x_ztf_dr14:
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solution_id_gaiadesignation_gaiasource_id_gaiarandom_index_gaiaref_epoch_gaiara_gaiara_error_gaiadec_gaiadec_error_gaiaparallax_gaiaparallax_error_gaiaparallax_over_error_gaiapm_gaiapmra_gaiapmra_error_gaiapmdec_gaiapmdec_error_gaiara_dec_corr_gaiara_parallax_corr_gaiara_pmra_corr_gaiara_pmdec_corr_gaiadec_parallax_corr_gaiadec_pmra_corr_gaiadec_pmdec_corr_gaiaparallax_pmra_corr_gaiaparallax_pmdec_corr_gaiapmra_pmdec_corr_gaiaastrometric_n_obs_al_gaiaastrometric_n_obs_ac_gaiaastrometric_n_good_obs_al_gaiaastrometric_n_bad_obs_al_gaiaastrometric_gof_al_gaiaastrometric_chi2_al_gaiaastrometric_excess_noise_gaiaastrometric_excess_noise_sig_gaiaastrometric_params_solved_gaiaastrometric_primary_flag_gaianu_eff_used_in_astrometry_gaiapseudocolour_gaiapseudocolour_error_gaiara_pseudocolour_corr_gaiadec_pseudocolour_corr_gaiaparallax_pseudocolour_corr_gaiapmra_pseudocolour_corr_gaiapmdec_pseudocolour_corr_gaiaastrometric_matched_transits_gaiavisibility_periods_used_gaiaastrometric_sigma5d_max_gaiamatched_transits_gaianew_matched_transits_gaiamatched_transits_removed_gaiaipd_gof_harmonic_amplitude_gaiaipd_gof_harmonic_phase_gaiaipd_frac_multi_peak_gaiaipd_frac_odd_win_gaiaruwe_gaiascan_direction_strength_k1_gaiascan_direction_strength_k2_gaiascan_direction_strength_k3_gaiascan_direction_strength_k4_gaiascan_direction_mean_k1_gaiascan_direction_mean_k2_gaiascan_direction_mean_k3_gaiascan_direction_mean_k4_gaiaduplicated_source_gaiaphot_g_n_obs_gaiaphot_g_mean_flux_gaiaphot_g_mean_flux_error_gaiaphot_g_mean_flux_over_error_gaiaphot_g_mean_mag_gaiaphot_bp_n_obs_gaiaphot_bp_mean_flux_gaiaphot_bp_mean_flux_error_gaiaphot_bp_mean_flux_over_error_gaiaphot_bp_mean_mag_gaiaphot_rp_n_obs_gaiaphot_rp_mean_flux_gaiaphot_rp_mean_flux_error_gaiaphot_rp_mean_flux_over_error_gaiaphot_rp_mean_mag_gaiaphot_bp_rp_excess_factor_gaiaphot_bp_n_contaminated_transits_gaiaphot_bp_n_blended_transits_gaiaphot_rp_n_contaminated_transits_gaiaphot_rp_n_blended_transits_gaiaphot_proc_mode_gaiabp_rp_gaiabp_g_gaiag_rp_gaiaradial_velocity_gaiaradial_velocity_error_gaiarv_method_used_gaiarv_nb_transits_gaiarv_nb_deblended_transits_gaiarv_visibility_periods_used_gaiarv_expected_sig_to_noise_gaiarv_renormalised_gof_gaiarv_chisq_pvalue_gaiarv_time_duration_gaiarv_amplitude_robust_gaiarv_template_teff_gaiarv_template_logg_gaiarv_template_fe_h_gaiarv_atm_param_origin_gaiavbroad_gaiavbroad_error_gaiavbroad_nb_transits_gaiagrvs_mag_gaiagrvs_mag_error_gaiagrvs_mag_nb_transits_gaiarvs_spec_sig_to_noise_gaiaphot_variable_flag_gaial_gaiab_gaiaecl_lon_gaiaecl_lat_gaiain_qso_candidates_gaiain_galaxy_candidates_gaianon_single_star_gaiahas_xp_continuous_gaiahas_xp_sampled_gaiahas_rvs_gaiahas_epoch_photometry_gaiahas_epoch_rv_gaiahas_mcmc_gspphot_gaiahas_mcmc_msc_gaiain_andromeda_survey_gaiaclassprob_dsc_combmod_quasar_gaiaclassprob_dsc_combmod_galaxy_gaiaclassprob_dsc_combmod_star_gaiateff_gspphot_gaiateff_gspphot_lower_gaiateff_gspphot_upper_gaialogg_gspphot_gaialogg_gspphot_lower_gaialogg_gspphot_upper_gaiamh_gspphot_gaiamh_gspphot_lower_gaiamh_gspphot_upper_gaiadistance_gspphot_gaiadistance_gspphot_lower_gaiadistance_gspphot_upper_gaiaazero_gspphot_gaiaazero_gspphot_lower_gaiaazero_gspphot_upper_gaiaag_gspphot_gaiaag_gspphot_lower_gaiaag_gspphot_upper_gaiaebpminrp_gspphot_gaiaebpminrp_gspphot_lower_gaiaebpminrp_gspphot_upper_gaialibname_gspphot_gaiaNorder_gaiaDir_gaiaNpix_gaiaps1_objid_ztf_dr14ra_ztf_dr14dec_ztf_dr14ps1_gMeanPSFMag_ztf_dr14ps1_rMeanPSFMag_ztf_dr14ps1_iMeanPSFMag_ztf_dr14nobs_g_ztf_dr14nobs_r_ztf_dr14nobs_i_ztf_dr14mean_mag_g_ztf_dr14mean_mag_r_ztf_dr14mean_mag_i_ztf_dr14Norder_ztf_dr14Dir_ztf_dr14Npix_ztf_dr14_dist_arcsec
npartitions=4
1945555039024054272int64[pyarrow]string[pyarrow]int64[pyarrow]int64[pyarrow]double[pyarrow]double[pyarrow]double[pyarrow]double[pyarrow]double[pyarrow]double[pyarrow]double[pyarrow]double[pyarrow]double[pyarrow]double[pyarrow]double[pyarrow]double[pyarrow]double[pyarrow]double[pyarrow]double[pyarrow]double[pyarrow]double[pyarrow]double[pyarrow]double[pyarrow]double[pyarrow]double[pyarrow]double[pyarrow]double[pyarrow]int64[pyarrow]int64[pyarrow]int64[pyarrow]int64[pyarrow]double[pyarrow]double[pyarrow]double[pyarrow]double[pyarrow]int64[pyarrow]bool[pyarrow]double[pyarrow]double[pyarrow]double[pyarrow]double[pyarrow]double[pyarrow]double[pyarrow]double[pyarrow]double[pyarrow]int64[pyarrow]int64[pyarrow]double[pyarrow]int64[pyarrow]int64[pyarrow]int64[pyarrow]double[pyarrow]double[pyarrow]int64[pyarrow]int64[pyarrow]double[pyarrow]double[pyarrow]double[pyarrow]double[pyarrow]double[pyarrow]double[pyarrow]double[pyarrow]double[pyarrow]double[pyarrow]bool[pyarrow]int64[pyarrow]double[pyarrow]double[pyarrow]double[pyarrow]double[pyarrow]int64[pyarrow]double[pyarrow]double[pyarrow]double[pyarrow]double[pyarrow]int64[pyarrow]double[pyarrow]double[pyarrow]double[pyarrow]double[pyarrow]double[pyarrow]double[pyarrow]double[pyarrow]double[pyarrow]double[pyarrow]double[pyarrow]double[pyarrow]double[pyarrow]double[pyarrow]double[pyarrow]double[pyarrow]double[pyarrow]double[pyarrow]double[pyarrow]double[pyarrow]double[pyarrow]double[pyarrow]double[pyarrow]double[pyarrow]double[pyarrow]double[pyarrow]double[pyarrow]double[pyarrow]double[pyarrow]double[pyarrow]double[pyarrow]double[pyarrow]double[pyarrow]double[pyarrow]double[pyarrow]double[pyarrow]string[pyarrow]double[pyarrow]double[pyarrow]double[pyarrow]double[pyarrow]bool[pyarrow]bool[pyarrow]int64[pyarrow]bool[pyarrow]bool[pyarrow]bool[pyarrow]bool[pyarrow]bool[pyarrow]bool[pyarrow]bool[pyarrow]bool[pyarrow]double[pyarrow]double[pyarrow]double[pyarrow]double[pyarrow]double[pyarrow]double[pyarrow]double[pyarrow]double[pyarrow]double[pyarrow]double[pyarrow]double[pyarrow]double[pyarrow]double[pyarrow]double[pyarrow]double[pyarrow]double[pyarrow]double[pyarrow]double[pyarrow]double[pyarrow]double[pyarrow]double[pyarrow]double[pyarrow]double[pyarrow]double[pyarrow]string[pyarrow]int8[pyarrow]int64[pyarrow]int64[pyarrow]int64[pyarrow]double[pyarrow]double[pyarrow]double[pyarrow]double[pyarrow]double[pyarrow]int32[pyarrow]int32[pyarrow]int32[pyarrow]double[pyarrow]double[pyarrow]double[pyarrow]int8[pyarrow]int64[pyarrow]int64[pyarrow]double[pyarrow]
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" - ], - "text/plain": [ - "Dask NestedFrame Structure:\n", - " solution_id_gaia designation_gaia source_id_gaia random_index_gaia ref_epoch_gaia ra_gaia ra_error_gaia dec_gaia dec_error_gaia parallax_gaia parallax_error_gaia parallax_over_error_gaia pm_gaia pmra_gaia pmra_error_gaia pmdec_gaia pmdec_error_gaia ra_dec_corr_gaia ra_parallax_corr_gaia ra_pmra_corr_gaia ra_pmdec_corr_gaia dec_parallax_corr_gaia dec_pmra_corr_gaia dec_pmdec_corr_gaia parallax_pmra_corr_gaia parallax_pmdec_corr_gaia pmra_pmdec_corr_gaia astrometric_n_obs_al_gaia astrometric_n_obs_ac_gaia astrometric_n_good_obs_al_gaia astrometric_n_bad_obs_al_gaia astrometric_gof_al_gaia astrometric_chi2_al_gaia astrometric_excess_noise_gaia astrometric_excess_noise_sig_gaia astrometric_params_solved_gaia astrometric_primary_flag_gaia nu_eff_used_in_astrometry_gaia pseudocolour_gaia pseudocolour_error_gaia ra_pseudocolour_corr_gaia dec_pseudocolour_corr_gaia parallax_pseudocolour_corr_gaia pmra_pseudocolour_corr_gaia pmdec_pseudocolour_corr_gaia astrometric_matched_transits_gaia visibility_periods_used_gaia astrometric_sigma5d_max_gaia matched_transits_gaia new_matched_transits_gaia matched_transits_removed_gaia ipd_gof_harmonic_amplitude_gaia ipd_gof_harmonic_phase_gaia ipd_frac_multi_peak_gaia ipd_frac_odd_win_gaia ruwe_gaia scan_direction_strength_k1_gaia scan_direction_strength_k2_gaia scan_direction_strength_k3_gaia scan_direction_strength_k4_gaia scan_direction_mean_k1_gaia scan_direction_mean_k2_gaia scan_direction_mean_k3_gaia scan_direction_mean_k4_gaia duplicated_source_gaia phot_g_n_obs_gaia phot_g_mean_flux_gaia phot_g_mean_flux_error_gaia phot_g_mean_flux_over_error_gaia phot_g_mean_mag_gaia phot_bp_n_obs_gaia phot_bp_mean_flux_gaia phot_bp_mean_flux_error_gaia phot_bp_mean_flux_over_error_gaia phot_bp_mean_mag_gaia phot_rp_n_obs_gaia phot_rp_mean_flux_gaia phot_rp_mean_flux_error_gaia phot_rp_mean_flux_over_error_gaia phot_rp_mean_mag_gaia phot_bp_rp_excess_factor_gaia phot_bp_n_contaminated_transits_gaia phot_bp_n_blended_transits_gaia phot_rp_n_contaminated_transits_gaia phot_rp_n_blended_transits_gaia phot_proc_mode_gaia bp_rp_gaia bp_g_gaia g_rp_gaia radial_velocity_gaia radial_velocity_error_gaia rv_method_used_gaia rv_nb_transits_gaia rv_nb_deblended_transits_gaia rv_visibility_periods_used_gaia rv_expected_sig_to_noise_gaia rv_renormalised_gof_gaia rv_chisq_pvalue_gaia rv_time_duration_gaia rv_amplitude_robust_gaia rv_template_teff_gaia rv_template_logg_gaia rv_template_fe_h_gaia rv_atm_param_origin_gaia vbroad_gaia vbroad_error_gaia vbroad_nb_transits_gaia grvs_mag_gaia grvs_mag_error_gaia grvs_mag_nb_transits_gaia rvs_spec_sig_to_noise_gaia phot_variable_flag_gaia l_gaia b_gaia ecl_lon_gaia ecl_lat_gaia in_qso_candidates_gaia in_galaxy_candidates_gaia non_single_star_gaia has_xp_continuous_gaia has_xp_sampled_gaia has_rvs_gaia has_epoch_photometry_gaia has_epoch_rv_gaia has_mcmc_gspphot_gaia has_mcmc_msc_gaia in_andromeda_survey_gaia classprob_dsc_combmod_quasar_gaia classprob_dsc_combmod_galaxy_gaia classprob_dsc_combmod_star_gaia teff_gspphot_gaia teff_gspphot_lower_gaia teff_gspphot_upper_gaia logg_gspphot_gaia logg_gspphot_lower_gaia logg_gspphot_upper_gaia mh_gspphot_gaia mh_gspphot_lower_gaia mh_gspphot_upper_gaia distance_gspphot_gaia distance_gspphot_lower_gaia distance_gspphot_upper_gaia azero_gspphot_gaia azero_gspphot_lower_gaia azero_gspphot_upper_gaia ag_gspphot_gaia ag_gspphot_lower_gaia ag_gspphot_upper_gaia ebpminrp_gspphot_gaia ebpminrp_gspphot_lower_gaia ebpminrp_gspphot_upper_gaia libname_gspphot_gaia Norder_gaia Dir_gaia Npix_gaia ps1_objid_ztf_dr14 ra_ztf_dr14 dec_ztf_dr14 ps1_gMeanPSFMag_ztf_dr14 ps1_rMeanPSFMag_ztf_dr14 ps1_iMeanPSFMag_ztf_dr14 nobs_g_ztf_dr14 nobs_r_ztf_dr14 nobs_i_ztf_dr14 mean_mag_g_ztf_dr14 mean_mag_r_ztf_dr14 mean_mag_i_ztf_dr14 Norder_ztf_dr14 Dir_ztf_dr14 Npix_ztf_dr14 _dist_arcsec\n", - "npartitions=4 \n", - "1945555039024054272 int64[pyarrow] string[pyarrow] int64[pyarrow] int64[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] int64[pyarrow] int64[pyarrow] int64[pyarrow] int64[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] int64[pyarrow] bool[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] int64[pyarrow] int64[pyarrow] double[pyarrow] int64[pyarrow] int64[pyarrow] int64[pyarrow] double[pyarrow] double[pyarrow] int64[pyarrow] int64[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] bool[pyarrow] int64[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] int64[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] int64[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] string[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] bool[pyarrow] bool[pyarrow] int64[pyarrow] bool[pyarrow] bool[pyarrow] bool[pyarrow] bool[pyarrow] bool[pyarrow] bool[pyarrow] bool[pyarrow] bool[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] string[pyarrow] int8[pyarrow] int64[pyarrow] int64[pyarrow] int64[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] int32[pyarrow] int32[pyarrow] int32[pyarrow] double[pyarrow] double[pyarrow] double[pyarrow] int8[pyarrow] int64[pyarrow] int64[pyarrow] double[pyarrow]\n", - "1950058638651424768 ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...\n", - "1954562238278795264 ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...\n", - "1959065837906165760 ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...\n", - "1963569437533536256 ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...\n", - "Dask Name: nestedframe, 3 expressions\n", - "Expr=MapPartitions(NestedFrame)" - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "gaia.crossmatch(ztf_object)" ] @@ -3042,23 +266,14 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": null, "metadata": { "ExecuteTime": { "end_time": "2024-03-28T20:49:57.849868Z", "start_time": "2024-03-28T20:49:57.843828Z" } }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/delucchi/git/demo/lsdb/src/lsdb/dask/crossmatch_catalog_data.py:108: RuntimeWarning: Right catalog does not have a margin cache. Results may be incomplete and/or inaccurate.\n", - " warnings.warn(\n" - ] - } - ], + "outputs": [], "source": [ "try:\n", " ztf_object.crossmatch(gaia)\n", @@ -3077,7 +292,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": null, "metadata": { "ExecuteTime": { "end_time": "2024-03-28T20:50:01.240040Z", @@ -3121,7 +336,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": null, "metadata": { "ExecuteTime": { "end_time": "2024-03-28T20:50:05.236767Z", diff --git a/docs/tutorials/remote_data.ipynb b/docs/tutorials/remote_data.ipynb index 0f42127a..c0c8ca85 100644 --- a/docs/tutorials/remote_data.ipynb +++ b/docs/tutorials/remote_data.ipynb @@ -59,67 +59,6 @@ "cat = lsdb.read_hats(\"https://data.lsdb.io/hats/gaia/gaia/\")\n", "cat" ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## S3 and cloud resources\n", - "\n", - "You'll want to install the `s3fs` package\n", - "\n", - "```\n", - "pip install s3fs\n", - "```\n", - "\n", - "(or `adlfs` or `gcsfs` for your cloud provider).\n", - "\n", - "You can pass your credentials once when creating the `UPath` instance, and use that configuration for any other paths constructed from that instance. In the case of PanStarrs, this is hosted in a public cloud bucket. You are still required to pass `anon = True` for public data buckets, in lieu of credentials. You can confirm that the path and credentials are good, before incurring any further expensive data reads." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "!pip install s3fs --quiet" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "panstarrs_path = UPath(\"s3://stpubdata/panstarrs/ps1/public/hipscat/\", anon=True)\n", - "test_path.exists()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "cat = lsdb.read_hats(panstarrs_path / \"otmo\")\n", - "cat" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "cat = lsdb.read_hats(panstarrs_path / \"detection\")\n", - "cat" - ] } ], "metadata": { From c9c842f646bde4c5285ea392e031b3ba5073c6ca Mon Sep 17 00:00:00 2001 From: Sean McGuire Date: Fri, 18 Oct 2024 13:39:46 -0400 Subject: [PATCH 4/4] add link to remote data notebook --- docs/tutorials.rst | 1 + docs/tutorials/remote_data.ipynb | 2 +- 2 files changed, 2 insertions(+), 1 deletion(-) diff --git a/docs/tutorials.rst b/docs/tutorials.rst index 0ee1c1cf..88a30b5c 100644 --- a/docs/tutorials.rst +++ b/docs/tutorials.rst @@ -26,6 +26,7 @@ A more in-depth look into how LSDB works :name: Advanced Topics Topic: Import catalogs + Topic: Accessing Remote Data Topic: Margins Topic: Performance Testing diff --git a/docs/tutorials/remote_data.ipynb b/docs/tutorials/remote_data.ipynb index c0c8ca85..8f72e4d4 100644 --- a/docs/tutorials/remote_data.ipynb +++ b/docs/tutorials/remote_data.ipynb @@ -8,7 +8,7 @@ "\n", "If you're accessing HATS catalogs on a local file system, a typical path string like `\"/path/to/catalogs\"` will be sufficient. This tutorial will help you get started if you need to access data over HTTP/S, cloud storage, or have some additional parameters for connecting to your data.\n", "\n", - "We use [`fsspec`](https://github.com/fsspec/filesystem_spec) and [`universal_pathlib`](https://github.com/fsspec/universal_pathlib) to create connections to remote sources for data. Please refer to their documentation for a list of supported filesystems and any filesystem-specific parameters.\n", + "We use [fsspec](https://github.com/fsspec/filesystem_spec) and [universal_pathlib](https://github.com/fsspec/universal_pathlib) to create connections to remote sources for data. Please refer to their documentation for a list of supported filesystems and any filesystem-specific parameters.\n", "\n", "Below, we provide some a basic workflow for accessing remote data, as well as filesystem-specific hints." ]