Run_Sim.ipynb

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  {
   "cell_type": "code",
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   "id": "fb7d9e5b-83ad-4a90-83f1-545e784e0d41",
   "metadata": {},
   "outputs": [],
   "source": [
    "# python env req:\n",
    "# conda install -c conda-forge rubin-scheduler\n",
    "# git clone ts_fbs_utils\n",
    "# pip install -e . ts_fbs_utils --no-deps \n",
    "# git clone ts_config_ocs \n",
    "# find fbs_config_image_photocal_survey.py in ts_config_ocs/Scheduler/feature_scheduler/auxtel \n",
    "\n",
    "import os\n",
    "import warnings\n",
    "import copy\n",
    "import pickle\n",
    "import json\n",
    "import numpy as np\n",
    "import healpy as hp\n",
    "import matplotlib.pylab as plt\n",
    "from cycler import cycler\n",
    "import colorcet as cc\n",
    "\n",
    "import pandas as pd\n",
    "\n",
    "import datetime\n",
    "from astropy.time import Time, TimeDelta\n",
    "import astropy.units as u\n",
    "import astropy\n",
    "astropy.utils.iers.conf.iers_degraded_accuracy = 'ignore'\n",
    "\n",
    "from rubin_scheduler.scheduler import sim_runner\n",
    "from rubin_scheduler.utils import ra_dec2_hpid\n",
    "from rubin_scheduler.scheduler.model_observatory import ModelObservatory\n",
    "from rubin_scheduler.site_models import Almanac\n",
    "from rubin_scheduler.utils import Site\n",
    "from rubin_scheduler.scheduler.features import Conditions\n",
    "from rubin_scheduler.scheduler.schedulers import CoreScheduler\n",
    "from rubin_scheduler.scheduler.utils import SchemaConverter\n",
    "\n",
    "# Import the actually run config script from ts_config_ocs\n",
    "# (run in directory containing a copy of the script)\n",
    "from fbs_config_image_photocal_survey import get_scheduler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "a090cde8-cfca-4c86-97c7-3f6a85c87769",
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   "outputs": [
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       "(function(root) {\n",
       "  function now() {\n",
       "    return new Date();\n",
       "  }\n",
       "\n",
       "  var force = true;\n",
       "  var py_version = '3.4.0'.replace('rc', '-rc.').replace('.dev', '-dev.');\n",
       "  var reloading = false;\n",
       "  var Bokeh = root.Bokeh;\n",
       "\n",
       "  if (typeof (root._bokeh_timeout) === \"undefined\" || force) {\n",
       "    root._bokeh_timeout = Date.now() + 5000;\n",
       "    root._bokeh_failed_load = false;\n",
       "  }\n",
       "\n",
       "  function run_callbacks() {\n",
       "    try {\n",
       "      root._bokeh_onload_callbacks.forEach(function(callback) {\n",
       "        if (callback != null)\n",
       "          callback();\n",
       "      });\n",
       "    } finally {\n",
       "      delete root._bokeh_onload_callbacks;\n",
       "    }\n",
       "    console.debug(\"Bokeh: all callbacks have finished\");\n",
       "  }\n",
       "\n",
       "  function load_libs(css_urls, js_urls, js_modules, js_exports, callback) {\n",
       "    if (css_urls == null) css_urls = [];\n",
       "    if (js_urls == null) js_urls = [];\n",
       "    if (js_modules == null) js_modules = [];\n",
       "    if (js_exports == null) js_exports = {};\n",
       "\n",
       "    root._bokeh_onload_callbacks.push(callback);\n",
       "\n",
       "    if (root._bokeh_is_loading > 0) {\n",
       "      console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n",
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       "      element.rel = \"stylesheet\";\n",
       "      element.type = \"text/css\";\n",
       "      element.href = url;\n",
       "      console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n",
       "      document.body.appendChild(element);\n",
       "    }    var existing_scripts = []\n",
       "    var scripts = document.getElementsByTagName('script')\n",
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       "      console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n",
       "      document.head.appendChild(element);\n",
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       "      element.async = false;\n",
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       "      element.type = \"module\";\n",
       "      console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n",
       "      document.head.appendChild(element);\n",
       "    }\n",
       "    for (const name in js_exports) {\n",
       "      var url = js_exports[name];\n",
       "      if (skip.indexOf(url) >= 0 || root[name] != null) {\n",
       "\tif (!window.requirejs) {\n",
       "\t  on_load();\n",
       "\t}\n",
       "\tcontinue;\n",
       "      }\n",
       "      var element = document.createElement('script');\n",
       "      element.onerror = on_error;\n",
       "      element.async = false;\n",
       "      element.type = \"module\";\n",
       "      console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n",
       "      element.textContent = `\n",
       "      import ${name} from \"${url}\"\n",
       "      window.${name} = ${name}\n",
       "      window._bokeh_on_load()\n",
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       "    }\n",
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       "      on_load()\n",
       "    }\n",
       "  };\n",
       "\n",
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       "    const element = document.createElement(\"style\");\n",
       "    element.appendChild(document.createTextNode(css));\n",
       "    document.body.appendChild(element);\n",
       "  }\n",
       "\n",
       "  var js_urls = [\"https://cdn.bokeh.org/bokeh/release/bokeh-3.4.0.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-gl-3.4.0.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-3.4.0.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-3.4.0.min.js\", \"https://cdn.holoviz.org/panel/1.4.1/dist/panel.min.js\"];\n",
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       "  var js_exports = {};\n",
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       "  ];\n",
       "\n",
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       "    if ((root.Bokeh !== undefined) || (force === true)) {\n",
       "      for (var i = 0; i < inline_js.length; i++) {\n",
       "\ttry {\n",
       "          inline_js[i].call(root, root.Bokeh);\n",
       "\t} catch(e) {\n",
       "\t  if (!reloading) {\n",
       "\t    throw e;\n",
       "\t  }\n",
       "\t}\n",
       "      }\n",
       "      // Cache old bokeh versions\n",
       "      if (Bokeh != undefined && !reloading) {\n",
       "\tvar NewBokeh = root.Bokeh;\n",
       "\tif (Bokeh.versions === undefined) {\n",
       "\t  Bokeh.versions = new Map();\n",
       "\t}\n",
       "\tif (NewBokeh.version !== Bokeh.version) {\n",
       "\t  Bokeh.versions.set(NewBokeh.version, NewBokeh)\n",
       "\t}\n",
       "\troot.Bokeh = Bokeh;\n",
       "      }} else if (Date.now() < root._bokeh_timeout) {\n",
       "      setTimeout(run_inline_js, 100);\n",
       "    } else if (!root._bokeh_failed_load) {\n",
       "      console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n",
       "      root._bokeh_failed_load = true;\n",
       "    }\n",
       "    root._bokeh_is_initializing = false\n",
       "  }\n",
       "\n",
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       "    // versions of Bokeh and its dependencies at the same time.\n",
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       "    // to determine whether there is an ongoing attempt to initialize\n",
       "    // bokeh, however for backward compatibility we also try to ensure\n",
       "    // that we do not start loading a newer (Panel>=1.0 and Bokeh>3) version\n",
       "    // before older versions are fully initialized.\n",
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       "      root._bokeh_is_initializing = false;\n",
       "      root._bokeh_onload_callbacks = undefined;\n",
       "      console.log(\"Bokeh: BokehJS was loaded multiple times but one version failed to initialize.\");\n",
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       "    } else if (root._bokeh_is_initializing || (typeof root._bokeh_is_initializing === \"undefined\" && root._bokeh_onload_callbacks !== undefined)) {\n",
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       "    } else {\n",
       "      root._bokeh_is_initializing = true\n",
       "      root._bokeh_onload_callbacks = []\n",
       "      var bokeh_loaded = Bokeh != null && (Bokeh.version === py_version || (Bokeh.versions !== undefined && Bokeh.versions.has(py_version)));\n",
       "      if (!reloading && !bokeh_loaded) {\n",
       "\troot.Bokeh = undefined;\n",
       "      }\n",
       "      load_libs(css_urls, js_urls, js_modules, js_exports, function() {\n",
       "\tconsole.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n",
       "\trun_inline_js();\n",
       "      });\n",
       "    }\n",
       "  }\n",
       "  // Give older versions of the autoload script a head-start to ensure\n",
       "  // they initialize before we start loading newer version.\n",
       "  setTimeout(load_or_wait, 100)\n",
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i < js_modules.length; i++) {\n      var url = js_modules[i];\n      if (skip.indexOf(url) !== -1 || existing_scripts.indexOf(url) !== -1) {\n\tif (!window.requirejs) {\n\t  on_load();\n\t}\n\tcontinue;\n      }\n      var element = document.createElement('script');\n      element.onload = on_load;\n      element.onerror = on_error;\n      element.async = false;\n      element.src = url;\n      element.type = \"module\";\n      console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n      document.head.appendChild(element);\n    }\n    for (const name in js_exports) {\n      var url = js_exports[name];\n      if (skip.indexOf(url) >= 0 || root[name] != null) {\n\tif (!window.requirejs) {\n\t  on_load();\n\t}\n\tcontinue;\n      }\n      var element = document.createElement('script');\n      element.onerror = on_error;\n      element.async = false;\n      element.type = \"module\";\n      console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n      element.textContent = `\n      import ${name} from \"${url}\"\n      window.${name} = ${name}\n      window._bokeh_on_load()\n      `\n      document.head.appendChild(element);\n    }\n    if (!js_urls.length && !js_modules.length) {\n      on_load()\n    }\n  };\n\n  function inject_raw_css(css) {\n    const element = document.createElement(\"style\");\n    element.appendChild(document.createTextNode(css));\n    document.body.appendChild(element);\n  }\n\n  var js_urls = [\"https://cdn.bokeh.org/bokeh/release/bokeh-3.4.0.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-gl-3.4.0.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-3.4.0.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-3.4.0.min.js\", \"https://cdn.holoviz.org/panel/1.4.1/dist/panel.min.js\"];\n  var js_modules = [];\n  var js_exports = {};\n  var css_urls = [];\n  var inline_js = [    function(Bokeh) {\n      Bokeh.set_log_level(\"info\");\n    },\nfunction(Bokeh) {} // ensure no trailing comma for IE\n  ];\n\n  function run_inline_js() {\n    if ((root.Bokeh !== undefined) || (force === true)) {\n      for (var i = 0; i < inline_js.length; i++) {\n\ttry {\n          inline_js[i].call(root, root.Bokeh);\n\t} catch(e) {\n\t  if (!reloading) {\n\t    throw e;\n\t  }\n\t}\n      }\n      // Cache old bokeh versions\n      if (Bokeh != undefined && !reloading) {\n\tvar NewBokeh = root.Bokeh;\n\tif (Bokeh.versions === undefined) {\n\t  Bokeh.versions = new Map();\n\t}\n\tif (NewBokeh.version !== Bokeh.version) {\n\t  Bokeh.versions.set(NewBokeh.version, NewBokeh)\n\t}\n\troot.Bokeh = Bokeh;\n      }} else if (Date.now() < root._bokeh_timeout) {\n      setTimeout(run_inline_js, 100);\n    } else if (!root._bokeh_failed_load) {\n      console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n      root._bokeh_failed_load = true;\n    }\n    root._bokeh_is_initializing = false\n  }\n\n  function load_or_wait() {\n    // Implement a backoff loop that tries to ensure we do not load multiple\n    // versions of Bokeh and its dependencies at the same time.\n    // In recent versions we use the root._bokeh_is_initializing flag\n    // to determine whether there is an ongoing attempt to initialize\n    // bokeh, however for backward compatibility we also try to ensure\n    // that we do not start loading a newer (Panel>=1.0 and Bokeh>3) version\n    // before older versions are fully initialized.\n    if (root._bokeh_is_initializing && Date.now() > root._bokeh_timeout) {\n      root._bokeh_is_initializing = false;\n      root._bokeh_onload_callbacks = undefined;\n      console.log(\"Bokeh: BokehJS was loaded multiple times but one version failed to initialize.\");\n      load_or_wait();\n    } else if (root._bokeh_is_initializing || (typeof root._bokeh_is_initializing === \"undefined\" && root._bokeh_onload_callbacks !== undefined)) {\n      setTimeout(load_or_wait, 100);\n    } else {\n      root._bokeh_is_initializing = true\n      root._bokeh_onload_callbacks = []\n      var bokeh_loaded = Bokeh != null && (Bokeh.version === py_version || (Bokeh.versions !== undefined && Bokeh.versions.has(py_version)));\n      if (!reloading && !bokeh_loaded) {\n\troot.Bokeh = undefined;\n      }\n      load_libs(css_urls, js_urls, js_modules, js_exports, function() {\n\tconsole.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n\trun_inline_js();\n      });\n    }\n  }\n  // Give older versions of the autoload script a head-start to ensure\n  // they initialize before we start loading newer version.\n  setTimeout(load_or_wait, 100)\n}(window));"
     },
     "metadata": {},
     "output_type": "display_data"
    },
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       "            var message = result.value;\n",
       "            console.log(message)\n",
       "            var content = {data: message.data, comm_id};\n",
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       "            var metadata = message.metadata || {};\n",
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       "          return messages.next().then(processIteratorResult);\n",
       "        })\n",
       "      }\n",
       "    }\n",
       "\n",
       "    JupyterCommManager.prototype.get_client_comm = function(plot_id, comm_id, msg_handler) {\n",
       "      if (comm_id in window.PyViz.comms) {\n",
       "        return window.PyViz.comms[comm_id];\n",
       "      } else if (window.comm_manager || ((window.Jupyter !== undefined) && (Jupyter.notebook.kernel != null))) {\n",
       "        var comm_manager = window.comm_manager || Jupyter.notebook.kernel.comm_manager;\n",
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       "        var comm = window.PyViz.kernels[plot_id].connectToComm(comm_id);\n",
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       "      } else if (typeof google != 'undefined' && google.colab.kernel != null) {\n",
       "        var comm_promise = google.colab.kernel.comms.open(comm_id)\n",
       "        comm_promise.then((comm) => {\n",
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       "          if (msg_handler) {\n",
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       "        var sendClosure = (data, metadata, buffers, disposeOnDone) => {\n",
       "          return comm_promise.then((comm) => {\n",
       "            comm.send(data, metadata, buffers, disposeOnDone);\n",
       "          });\n",
       "        };\n",
       "        var comm = {\n",
       "          send: sendClosure\n",
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       "      }\n",
       "      window.PyViz.comms[comm_id] = comm;\n",
       "      return comm;\n",
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       "    window.PyViz.comm_manager = new JupyterCommManager();\n",
       "    \n",
       "\n",
       "\n",
       "var JS_MIME_TYPE = 'application/javascript';\n",
       "var HTML_MIME_TYPE = 'text/html';\n",
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       "var CLASS_NAME = 'output';\n",
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       "  if ((output.data == undefined) || (!output.data.hasOwnProperty(EXEC_MIME_TYPE))) {\n",
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       "    var nchildren = toinsert.length;\n",
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       "    html_node.innerHTML = output.data[HTML_MIME_TYPE];\n",
       "    var scripts = [];\n",
       "    var nodelist = html_node.querySelectorAll(\"script\");\n",
       "    for (var i in nodelist) {\n",
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       "    if (JS_MIME_TYPE in output.data) {\n",
       "      toinsert[nchildren-1].children[1].textContent = output.data[JS_MIME_TYPE];\n",
       "    }\n",
       "    output_area._hv_plot_id = id;\n",
       "    if ((window.Bokeh !== undefined) && (id in Bokeh.index)) {\n",
       "      window.PyViz.plot_index[id] = Bokeh.index[id];\n",
       "    } else {\n",
       "      window.PyViz.plot_index[id] = null;\n",
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       "  } else if (output.metadata[EXEC_MIME_TYPE][\"server_id\"] !== undefined) {\n",
       "    var bk_div = document.createElement(\"div\");\n",
       "    bk_div.innerHTML = output.data[HTML_MIME_TYPE];\n",
       "    var script_attrs = bk_div.children[0].attributes;\n",
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       "      toinsert[toinsert.length - 1].childNodes[1].setAttribute(script_attrs[i].name, script_attrs[i].value);\n",
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       "  var id = handle.cell.output_area._hv_plot_id;\n",
       "  var server_id = handle.cell.output_area._bokeh_server_id;\n",
       "  if (((id === undefined) || !(id in PyViz.plot_index)) && (server_id !== undefined)) { return; }\n",
       "  var comm = window.PyViz.comm_manager.get_client_comm(\"hv-extension-comm\", \"hv-extension-comm\", function () {});\n",
       "  if (server_id !== null) {\n",
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       "    return;\n",
       "  } else if (comm !== null) {\n",
       "    comm.send({event_type: 'delete', 'id': id});\n",
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       "  delete PyViz.plot_index[id];\n",
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       "    var doc = window.Bokeh.index[id].model.document\n",
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       "  window.PyViz.plot_index = {}\n",
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       "  handle_add_output(event, handle)\n",
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       "\n",
       "function register_renderer(events, OutputArea) {\n",
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       "    // create a DOM node to render to\n",
       "    var toinsert = this.create_output_subarea(\n",
       "    metadata,\n",
       "    CLASS_NAME,\n",
       "    EXEC_MIME_TYPE\n",
       "    );\n",
       "    this.keyboard_manager.register_events(toinsert);\n",
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       "    var props = {data: data, metadata: metadata[EXEC_MIME_TYPE]};\n",
       "    render(props, toinsert[0]);\n",
       "    element.append(toinsert);\n",
       "    return toinsert\n",
       "  }\n",
       "\n",
       "  events.on('output_added.OutputArea', handle_add_output);\n",
       "  events.on('output_updated.OutputArea', handle_update_output);\n",
       "  events.on('clear_output.CodeCell', handle_clear_output);\n",
       "  events.on('delete.Cell', handle_clear_output);\n",
       "  events.on('kernel_ready.Kernel', handle_kernel_cleanup);\n",
       "\n",
       "  OutputArea.prototype.register_mime_type(EXEC_MIME_TYPE, append_mime, {\n",
       "    safe: true,\n",
       "    index: 0\n",
       "  });\n",
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       "\n",
       "if (window.Jupyter !== undefined) {\n",
       "  try {\n",
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       "    if (OutputArea.prototype.mime_types().indexOf(EXEC_MIME_TYPE) == -1) {\n",
       "      register_renderer(events, OutputArea);\n",
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       "  } catch(err) {\n",
       "  }\n",
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      ],
      "application/vnd.holoviews_load.v0+json": "\nif ((window.PyViz === undefined) || (window.PyViz instanceof HTMLElement)) {\n  window.PyViz = {comms: {}, comm_status:{}, kernels:{}, receivers: {}, plot_index: []}\n}\n\n\n    function JupyterCommManager() {\n    }\n\n    JupyterCommManager.prototype.register_target = function(plot_id, comm_id, msg_handler) {\n      if (window.comm_manager || ((window.Jupyter !== undefined) && (Jupyter.notebook.kernel != null))) {\n        var comm_manager = window.comm_manager || Jupyter.notebook.kernel.comm_manager;\n        comm_manager.register_target(comm_id, function(comm) {\n          comm.on_msg(msg_handler);\n        });\n      } else if ((plot_id in window.PyViz.kernels) && (window.PyViz.kernels[plot_id])) {\n        window.PyViz.kernels[plot_id].registerCommTarget(comm_id, function(comm) {\n          comm.onMsg = msg_handler;\n        });\n      } else if (typeof google != 'undefined' && google.colab.kernel != null) {\n        google.colab.kernel.comms.registerTarget(comm_id, (comm) => {\n          var messages = comm.messages[Symbol.asyncIterator]();\n          function processIteratorResult(result) {\n            var message = result.value;\n            console.log(message)\n            var content = {data: message.data, comm_id};\n            var buffers = []\n            for (var buffer of message.buffers || []) {\n              buffers.push(new DataView(buffer))\n            }\n            var metadata = message.metadata || {};\n            var msg = {content, buffers, metadata}\n            msg_handler(msg);\n            return messages.next().then(processIteratorResult);\n          }\n          return messages.next().then(processIteratorResult);\n        })\n      }\n    }\n\n    JupyterCommManager.prototype.get_client_comm = function(plot_id, comm_id, msg_handler) {\n      if (comm_id in window.PyViz.comms) {\n        return window.PyViz.comms[comm_id];\n      } else if (window.comm_manager || ((window.Jupyter !== undefined) && (Jupyter.notebook.kernel != null))) {\n        var comm_manager = window.comm_manager || Jupyter.notebook.kernel.comm_manager;\n        var comm = comm_manager.new_comm(comm_id, {}, {}, {}, comm_id);\n        if (msg_handler) {\n          comm.on_msg(msg_handler);\n        }\n      } else if ((plot_id in window.PyViz.kernels) && (window.PyViz.kernels[plot_id])) {\n        var comm = window.PyViz.kernels[plot_id].connectToComm(comm_id);\n        comm.open();\n        if (msg_handler) {\n          comm.onMsg = msg_handler;\n        }\n      } else if (typeof google != 'undefined' && google.colab.kernel != null) {\n        var comm_promise = google.colab.kernel.comms.open(comm_id)\n        comm_promise.then((comm) => {\n          window.PyViz.comms[comm_id] = comm;\n          if (msg_handler) {\n            var messages = comm.messages[Symbol.asyncIterator]();\n            function processIteratorResult(result) {\n              var message = 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document.createElement(\"div\");\n    bk_div.innerHTML = output.data[HTML_MIME_TYPE];\n    var script_attrs = bk_div.children[0].attributes;\n    for (var i = 0; i < script_attrs.length; i++) {\n      toinsert[toinsert.length - 1].childNodes[1].setAttribute(script_attrs[i].name, script_attrs[i].value);\n    }\n    // store reference to server id on output_area\n    output_area._bokeh_server_id = output.metadata[EXEC_MIME_TYPE][\"server_id\"];\n  }\n}\n\n/**\n * Handle when an output is cleared or removed\n */\nfunction handle_clear_output(event, handle) {\n  var id = handle.cell.output_area._hv_plot_id;\n  var server_id = handle.cell.output_area._bokeh_server_id;\n  if (((id === undefined) || !(id in PyViz.plot_index)) && (server_id !== undefined)) { return; }\n  var comm = window.PyViz.comm_manager.get_client_comm(\"hv-extension-comm\", \"hv-extension-comm\", function () {});\n  if (server_id !== null) {\n    comm.send({event_type: 'server_delete', 'id': server_id});\n    return;\n  } 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       "</div>\n",
       "<script type=\"application/javascript\">(function(root) {\n",
       "  var docs_json = {\"2eef2ef2-c8fa-439c-92dd-4bdc5baefe2d\":{\"version\":\"3.4.0\",\"title\":\"Bokeh Application\",\"roots\":[{\"type\":\"object\",\"name\":\"panel.models.browser.BrowserInfo\",\"id\":\"p1002\"},{\"type\":\"object\",\"name\":\"panel.models.comm_manager.CommManager\",\"id\":\"p1003\",\"attributes\":{\"plot_id\":\"p1002\",\"comm_id\":\"3762edf915134b78b8eca5e8c0b13751\",\"client_comm_id\":\"70b54d8948ed4ec09c5c4cf9b5a60cbe\"}}],\"defs\":[{\"type\":\"model\",\"name\":\"ReactiveHTML1\"},{\"type\":\"model\",\"name\":\"FlexBox1\",\"properties\":[{\"name\":\"align_content\",\"kind\":\"Any\",\"default\":\"flex-start\"},{\"name\":\"align_items\",\"kind\":\"Any\",\"default\":\"flex-start\"},{\"name\":\"flex_direction\",\"kind\":\"Any\",\"default\":\"row\"},{\"name\":\"flex_wrap\",\"kind\":\"Any\",\"default\":\"wrap\"},{\"name\":\"gap\",\"kind\":\"Any\",\"default\":\"\"},{\"name\":\"justify_content\",\"kind\":\"Any\",\"default\":\"flex-start\"}]},{\"type\":\"model\",\"name\":\"FloatPanel1\",\"properties\":[{\"name\":\"config\",\"kind\":\"Any\",\"default\":{\"type\":\"map\"}},{\"name\":\"contained\",\"kind\":\"Any\",\"default\":true},{\"name\":\"position\",\"kind\":\"Any\",\"default\":\"right-top\"},{\"name\":\"offsetx\",\"kind\":\"Any\",\"default\":null},{\"name\":\"offsety\",\"kind\":\"Any\",\"default\":null},{\"name\":\"theme\",\"kind\":\"Any\",\"default\":\"primary\"},{\"name\":\"status\",\"kind\":\"Any\",\"default\":\"normalized\"}]},{\"type\":\"model\",\"name\":\"GridStack1\",\"properties\":[{\"name\":\"mode\",\"kind\":\"Any\",\"default\":\"warn\"},{\"name\":\"ncols\",\"kind\":\"Any\",\"default\":null},{\"name\":\"nrows\",\"kind\":\"Any\",\"default\":null},{\"name\":\"allow_resize\",\"kind\":\"Any\",\"default\":true},{\"name\":\"allow_drag\",\"kind\":\"Any\",\"default\":true},{\"name\":\"state\",\"kind\":\"Any\",\"default\":[]}]},{\"type\":\"model\",\"name\":\"drag1\",\"properties\":[{\"name\":\"slider_width\",\"kind\":\"Any\",\"default\":5},{\"name\":\"slider_color\",\"kind\":\"Any\",\"default\":\"black\"},{\"name\":\"value\",\"kind\":\"Any\",\"default\":50}]},{\"type\":\"model\",\"name\":\"click1\",\"properties\":[{\"name\":\"terminal_output\",\"kind\":\"Any\",\"default\":\"\"},{\"name\":\"debug_name\",\"kind\":\"Any\",\"default\":\"\"},{\"name\":\"clears\",\"kind\":\"Any\",\"default\":0}]},{\"type\":\"model\",\"name\":\"FastWrapper1\",\"properties\":[{\"name\":\"object\",\"kind\":\"Any\",\"default\":null},{\"name\":\"style\",\"kind\":\"Any\",\"default\":null}]},{\"type\":\"model\",\"name\":\"NotificationAreaBase1\",\"properties\":[{\"name\":\"js_events\",\"kind\":\"Any\",\"default\":{\"type\":\"map\"}},{\"name\":\"position\",\"kind\":\"Any\",\"default\":\"bottom-right\"},{\"name\":\"_clear\",\"kind\":\"Any\",\"default\":0}]},{\"type\":\"model\",\"name\":\"NotificationArea1\",\"properties\":[{\"name\":\"js_events\",\"kind\":\"Any\",\"default\":{\"type\":\"map\"}},{\"name\":\"notifications\",\"kind\":\"Any\",\"default\":[]},{\"name\":\"position\",\"kind\":\"Any\",\"default\":\"bottom-right\"},{\"name\":\"_clear\",\"kind\":\"Any\",\"default\":0},{\"name\":\"types\",\"kind\":\"Any\",\"default\":[{\"type\":\"map\",\"entries\":[[\"type\",\"warning\"],[\"background\",\"#ffc107\"],[\"icon\",{\"type\":\"map\",\"entries\":[[\"className\",\"fas fa-exclamation-triangle\"],[\"tagName\",\"i\"],[\"color\",\"white\"]]}]]},{\"type\":\"map\",\"entries\":[[\"type\",\"info\"],[\"background\",\"#007bff\"],[\"icon\",{\"type\":\"map\",\"entries\":[[\"className\",\"fas fa-info-circle\"],[\"tagName\",\"i\"],[\"color\",\"white\"]]}]]}]}]},{\"type\":\"model\",\"name\":\"Notification\",\"properties\":[{\"name\":\"background\",\"kind\":\"Any\",\"default\":null},{\"name\":\"duration\",\"kind\":\"Any\",\"default\":3000},{\"name\":\"icon\",\"kind\":\"Any\",\"default\":null},{\"name\":\"message\",\"kind\":\"Any\",\"default\":\"\"},{\"name\":\"notification_type\",\"kind\":\"Any\",\"default\":null},{\"name\":\"_destroyed\",\"kind\":\"Any\",\"default\":false}]},{\"type\":\"model\",\"name\":\"TemplateActions1\",\"properties\":[{\"name\":\"open_modal\",\"kind\":\"Any\",\"default\":0},{\"name\":\"close_modal\",\"kind\":\"Any\",\"default\":0}]},{\"type\":\"model\",\"name\":\"BootstrapTemplateActions1\",\"properties\":[{\"name\":\"open_modal\",\"kind\":\"Any\",\"default\":0},{\"name\":\"close_modal\",\"kind\":\"Any\",\"default\":0}]},{\"type\":\"model\",\"name\":\"TemplateEditor1\",\"properties\":[{\"name\":\"layout\",\"kind\":\"Any\",\"default\":[]}]},{\"type\":\"model\",\"name\":\"MaterialTemplateActions1\",\"properties\":[{\"name\":\"open_modal\",\"kind\":\"Any\",\"default\":0},{\"name\":\"close_modal\",\"kind\":\"Any\",\"default\":0}]},{\"type\":\"model\",\"name\":\"copy_to_clipboard1\",\"properties\":[{\"name\":\"fill\",\"kind\":\"Any\",\"default\":\"none\"},{\"name\":\"value\",\"kind\":\"Any\",\"default\":null}]}]}};\n",
       "  var render_items = [{\"docid\":\"2eef2ef2-c8fa-439c-92dd-4bdc5baefe2d\",\"roots\":{\"p1002\":\"d5073ce8-038c-4482-994a-621f901d9392\"},\"root_ids\":[\"p1002\"]}];\n",
       "  var docs = Object.values(docs_json)\n",
       "  if (!docs) {\n",
       "    return\n",
       "  }\n",
       "  const py_version = docs[0].version.replace('rc', '-rc.').replace('.dev', '-dev.')\n",
       "  function embed_document(root) {\n",
       "    var Bokeh = get_bokeh(root)\n",
       "    Bokeh.embed.embed_items_notebook(docs_json, render_items);\n",
       "    for (const render_item of render_items) {\n",
       "      for (const root_id of render_item.root_ids) {\n",
       "\tconst id_el = document.getElementById(root_id)\n",
       "\tif (id_el.children.length && (id_el.children[0].className === 'bk-root')) {\n",
       "\t  const root_el = id_el.children[0]\n",
       "\t  root_el.id = root_el.id + '-rendered'\n",
       "\t}\n",
       "      }\n",
       "    }\n",
       "  }\n",
       "  function get_bokeh(root) {\n",
       "    if (root.Bokeh === undefined) {\n",
       "      return null\n",
       "    } else if (root.Bokeh.version !== py_version) {\n",
       "      if (root.Bokeh.versions === undefined || !root.Bokeh.versions.has(py_version)) {\n",
       "\treturn null\n",
       "      }\n",
       "      return root.Bokeh.versions.get(py_version);\n",
       "    } else if (root.Bokeh.version === py_version) {\n",
       "      return root.Bokeh\n",
       "    }\n",
       "    return null\n",
       "  }\n",
       "  function is_loaded(root) {\n",
       "    var Bokeh = get_bokeh(root)\n",
       "    return (Bokeh != null && Bokeh.Panel !== undefined)\n",
       "  }\n",
       "  if (is_loaded(root)) {\n",
       "    embed_document(root);\n",
       "  } else {\n",
       "    var attempts = 0;\n",
       "    var timer = setInterval(function(root) {\n",
       "      if (is_loaded(root)) {\n",
       "        clearInterval(timer);\n",
       "        embed_document(root);\n",
       "      } else if (document.readyState == \"complete\") {\n",
       "        attempts++;\n",
       "        if (attempts > 200) {\n",
       "          clearInterval(timer);\n",
       "\t  var Bokeh = get_bokeh(root)\n",
       "\t  if (Bokeh == null || Bokeh.Panel == null) {\n",
       "            console.warn(\"Panel: ERROR: Unable to run Panel code because Bokeh or Panel library is missing\");\n",
       "\t  } else {\n",
       "\t    console.warn(\"Panel: WARNING: Attempting to render but not all required libraries could be resolved.\")\n",
       "\t    embed_document(root)\n",
       "\t  }\n",
       "        }\n",
       "      }\n",
       "    }, 25, root)\n",
       "  }\n",
       "})(window);</script>"
      ]
     },
     "metadata": {
      "application/vnd.holoviews_exec.v0+json": {
       "id": "p1002"
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import hvplot.pandas\n",
    "import panel as pn\n",
    "import hvplot\n",
    "import bokeh \n",
    "\n",
    "import schedview.compute\n",
    "import schedview.compute.visits\n",
    "import schedview.collect\n",
    "import schedview.plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "0639e797-54b3-4d1f-9283-d8927598ca6d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "('2024-04-03 22:32:27.047', '2024-04-04 10:04:06.675', 0.4803197658620775)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# What night do you want? \n",
    "DAYOBS = '2024-04-03'\n",
    "site = Site('LSST')\n",
    "almanac = Almanac()\n",
    "night_events = almanac.get_sunset_info(evening_date=DAYOBS, longitude=site.longitude_rad)\n",
    "sunset = Time(night_events['sunset'], format='mjd', scale='utc') \n",
    "sunrise = Time(night_events['sun_n12_rising'], format='mjd', scale='utc')\n",
    "survey_length = sunrise.mjd - sunset.mjd\n",
    "sunset.iso, sunrise.iso, survey_length"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "e56e9ad1-9800-4019-80e5-972f495a7e5f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2024-04-03 23:55:52.405 2024-04-04 10:04:06.675 0.4223873824812472\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING: Tried to get polar motions for times after IERS data is valid. Defaulting to polar motion from the 50-yr mean for those. This may affect precision at the arcsec level. Please check your astropy.utils.iers.conf.iers_auto_url and point it to a newer version if necessary. [astropy.coordinates.builtin_frames.utils]\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>MJD</th>\n",
       "      <th>LST</th>\n",
       "      <th>UTC</th>\n",
       "      <th>Chile/Continental</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>event</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>sunset</th>\n",
       "      <td>60403.939202</td>\n",
       "      <td>100.105580</td>\n",
       "      <td>2024-04-03 22:32:27.047000+00:00</td>\n",
       "      <td>2024-04-03 19:32:27.047000-03:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sun_n12_setting</th>\n",
       "      <td>60403.977841</td>\n",
       "      <td>114.053659</td>\n",
       "      <td>2024-04-03 23:28:05.446000+00:00</td>\n",
       "      <td>2024-04-03 20:28:05.446000-03:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sun_n18_setting</th>\n",
       "      <td>60403.997134</td>\n",
       "      <td>121.018339</td>\n",
       "      <td>2024-04-03 23:55:52.405000+00:00</td>\n",
       "      <td>2024-04-03 20:55:52.405000-03:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sun_n18_rising</th>\n",
       "      <td>60404.400208</td>\n",
       "      <td>266.522036</td>\n",
       "      <td>2024-04-04 09:36:17.943000+00:00</td>\n",
       "      <td>2024-04-04 06:36:17.943000-03:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sun_n12_rising</th>\n",
       "      <td>60404.419522</td>\n",
       "      <td>273.494123</td>\n",
       "      <td>2024-04-04 10:04:06.675000+00:00</td>\n",
       "      <td>2024-04-04 07:04:06.675000-03:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sunrise</th>\n",
       "      <td>60404.458218</td>\n",
       "      <td>287.462974</td>\n",
       "      <td>2024-04-04 10:59:50.046000+00:00</td>\n",
       "      <td>2024-04-04 07:59:50.046000-03:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>moonrise</th>\n",
       "      <td>60404.255211</td>\n",
       "      <td>214.180290</td>\n",
       "      <td>2024-04-04 06:07:30.224000+00:00</td>\n",
       "      <td>2024-04-04 03:07:30.224000-03:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>moonset</th>\n",
       "      <td>60404.838582</td>\n",
       "      <td>64.768963</td>\n",
       "      <td>2024-04-04 20:07:33.506000+00:00</td>\n",
       "      <td>2024-04-04 17:07:33.506000-03:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>night_middle</th>\n",
       "      <td>60404.198710</td>\n",
       "      <td>193.784278</td>\n",
       "      <td>2024-04-04 04:46:08.546000+00:00</td>\n",
       "      <td>2024-04-04 01:46:08.546000-03:00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                          MJD         LST                              UTC  \\\n",
       "event                                                                        \n",
       "sunset           60403.939202  100.105580 2024-04-03 22:32:27.047000+00:00   \n",
       "sun_n12_setting  60403.977841  114.053659 2024-04-03 23:28:05.446000+00:00   \n",
       "sun_n18_setting  60403.997134  121.018339 2024-04-03 23:55:52.405000+00:00   \n",
       "sun_n18_rising   60404.400208  266.522036 2024-04-04 09:36:17.943000+00:00   \n",
       "sun_n12_rising   60404.419522  273.494123 2024-04-04 10:04:06.675000+00:00   \n",
       "sunrise          60404.458218  287.462974 2024-04-04 10:59:50.046000+00:00   \n",
       "moonrise         60404.255211  214.180290 2024-04-04 06:07:30.224000+00:00   \n",
       "moonset          60404.838582   64.768963 2024-04-04 20:07:33.506000+00:00   \n",
       "night_middle     60404.198710  193.784278 2024-04-04 04:46:08.546000+00:00   \n",
       "\n",
       "                               Chile/Continental  \n",
       "event                                             \n",
       "sunset          2024-04-03 19:32:27.047000-03:00  \n",
       "sun_n12_setting 2024-04-03 20:28:05.446000-03:00  \n",
       "sun_n18_setting 2024-04-03 20:55:52.405000-03:00  \n",
       "sun_n18_rising  2024-04-04 06:36:17.943000-03:00  \n",
       "sun_n12_rising  2024-04-04 07:04:06.675000-03:00  \n",
       "sunrise         2024-04-04 07:59:50.046000-03:00  \n",
       "moonrise        2024-04-04 03:07:30.224000-03:00  \n",
       "moonset         2024-04-04 17:07:33.506000-03:00  \n",
       "night_middle    2024-04-04 01:46:08.546000-03:00  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "day_obs_mjd = int(Time(DAYOBS).mjd)\n",
    "timezone = \"Chile/Continental\"\n",
    "day_obs_datetime = Time(day_obs_mjd, format='mjd').datetime\n",
    "day_obs_date = datetime.date(day_obs_datetime.year, day_obs_datetime.month, day_obs_datetime.day)\n",
    "night_events = schedview.compute.astro.night_events(day_obs_date)\n",
    "\n",
    "sunset = Time(night_events.loc['sun_n18_setting', 'MJD'], format='mjd', scale='utc')\n",
    "sunrise = Time(night_events.loc['sun_n12_rising', 'MJD'], format='mjd', scale='utc')\n",
    "survey_length = sunrise.mjd - sunset.mjd\n",
    "print(sunset.iso, sunrise.iso, survey_length)\n",
    "night_events"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "d69fba5f-08f3-4561-9d07-5fb4f9161f35",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "7 2024-04-03 23:55:52.405\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING: TimeDeltaMissingUnitWarning: Numerical value without unit or explicit format passed to TimeDelta, assuming days [astropy.time.core]\n"
     ]
    }
   ],
   "source": [
    "start_times = np.arange(sunset, sunset + TimeDelta(2/24., format='jd'), TimeDelta(20*60, format='sec'))\n",
    "print(len(start_times), start_times[0].iso)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "d3bef5ce-119e-47b4-bc19-47f4e1430346",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING: IllegalSecondWarning: 'second' was found  to be '60.0', which is not in range [0,60). Treating as 0 sec, +1 min [astropy.coordinates.angles.formats]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "progress = 231.99%Skipped 0 observations\n",
      "Flushed 0 observations from queue for being stale\n",
      "Completed 92 observations\n",
      "ran in 0 min = 0.0 hours\n",
      "progress = 228.70%Skipped 0 observations\n",
      "Flushed 0 observations from queue for being stale\n",
      "Completed 90 observations\n",
      "ran in 0 min = 0.0 hours\n",
      "progress = 225.41%Skipped 0 observations\n",
      "Flushed 0 observations from queue for being stale\n",
      "Completed 87 observations\n",
      "ran in 0 min = 0.0 hours\n",
      "progress = 222.13%Skipped 0 observations\n",
      "Flushed 0 observations from queue for being stale\n",
      "Completed 85 observations\n",
      "ran in 0 min = 0.0 hours\n",
      "progress = 218.84%Skipped 0 observations\n",
      "Flushed 0 observations from queue for being stale\n",
      "Completed 83 observations\n",
      "ran in 0 min = 0.0 hours\n",
      "progress = 215.55%Skipped 0 observations\n",
      "Flushed 0 observations from queue for being stale\n",
      "Completed 81 observations\n",
      "ran in 0 min = 0.0 hours\n",
      "progress = 212.26%Skipped 0 observations\n",
      "Flushed 0 observations from queue for being stale\n",
      "Completed 77 observations\n",
      "ran in 0 min = 0.0 hours\n"
     ]
    }
   ],
   "source": [
    "# Set up simulation with scheduler\n",
    "# Keep this cell together (so regenerate scheduler and observatory if rerun)\n",
    "\n",
    "rewards = False \n",
    "\n",
    "obs_examples = []\n",
    "for start_time in start_times:\n",
    "    nside, scheduler=get_scheduler()\n",
    "    scheduler.keep_rewards=rewards\n",
    "    \n",
    "    observatory = ModelObservatory(nside=nside, mjd_start=start_time.mjd, downtimes=\"ideal\", cloud_data=\"ideal\")\n",
    "    observatory.observatory.setup_camera(filter_changetime=5.0, maxspeed=1.5, shutter_2motion_min_time=0.0)\n",
    "    observatory.observatory.setup_telescope(altitude_maxspeed=1.5, azimuth_maxspeed=1.5, settle_time=5.0)\n",
    "    observatory.observatory.setup_dome(azimuth_maxspeed=3.0)\n",
    "    \n",
    "    vals = sim_runner(\n",
    "        observatory,\n",
    "        scheduler,\n",
    "        survey_length=survey_length,\n",
    "        record_rewards=rewards,\n",
    "        verbose=True,\n",
    "    )\n",
    "    observatory = vals[0]\n",
    "    scheduler = vals[1]\n",
    "    observations = vals[2]\n",
    "    if len(vals) == 5:\n",
    "        rewards = vals[3]\n",
    "        obs_rewards = vals[4]\n",
    "    obs_examples.append(observations)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "ec8bd45a-6e90-436d-8c6e-81e726b9dcc3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[19.98098406 19.96002053 20.0735107  20.00193374 19.91517079 20.01240636]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "['2024-04-03 23:56:03.205',\n",
       " '2024-04-04 00:16:02.064',\n",
       " '2024-04-04 00:35:59.665',\n",
       " '2024-04-04 00:56:04.076',\n",
       " '2024-04-04 01:16:04.192',\n",
       " '2024-04-04 01:35:59.102',\n",
       " '2024-04-04 01:55:59.847']"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(np.diff(np.array([Time(o['mjd'][0], format='mjd', scale='utc').mjd for o in obs_examples]))*24*60)\n",
    "[Time(o['mjd'][0], format='mjd', scale='utc').iso for o in obs_examples]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1166,
   "id": "017dc1ce-7fb4-4569-b9ad-b4fb1e1c1636",
   "metadata": {},
   "outputs": [],
   "source": [
    "schema_converter = SchemaConverter()\n",
    "\n",
    "def demangle_note(x):\n",
    "    # remove _expnum\n",
    "    x.target = copy.deepcopy(x.note)\n",
    "    if \"IM\" in x.note:\n",
    "        x.note = x.note.split(\":\")[1].split(\"_\")[0]\n",
    "    return x\n",
    "    \n",
    "visits_examples = []\n",
    "for observations in obs_examples:\n",
    "    visits = schema_converter.obs2opsim(observations)\n",
    "    # add start_date\n",
    "    visits[\"start_date\"] = pd.to_datetime(visits[\"observationStartMJD\"] + 2400000.5, \n",
    "                                          origin=\"julian\", unit=\"D\", utc=True)\n",
    "    visits = visits.apply(demangle_note, axis=1)\n",
    "    visits_examples.append(visits)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "de651792-df20-4597-a024-f619e1df9a82",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>observationId</th>\n",
       "      <th>fieldRA</th>\n",
       "      <th>fieldDec</th>\n",
       "      <th>observationStartMJD</th>\n",
       "      <th>flush_by_mjd</th>\n",
       "      <th>visitExposureTime</th>\n",
       "      <th>filter</th>\n",
       "      <th>rotSkyPos</th>\n",
       "      <th>rotSkyPos_desired</th>\n",
       "      <th>numExposures</th>\n",
       "      <th>...</th>\n",
       "      <th>sunRA</th>\n",
       "      <th>sunDec</th>\n",
       "      <th>moonRA</th>\n",
       "      <th>moonDec</th>\n",
       "      <th>moonDistance</th>\n",
       "      <th>solarElong</th>\n",
       "      <th>moonPhase</th>\n",
       "      <th>cummTelAz</th>\n",
       "      <th>scripted_id</th>\n",
       "      <th>start_date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
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       "      <td>0.0</td>\n",
       "      <td>4</td>\n",
       "      <td>...</td>\n",
       "      <td>13.228926</td>\n",
       "      <td>5.664700</td>\n",
       "      <td>311.981969</td>\n",
       "      <td>-22.096973</td>\n",
       "      <td>132.321217</td>\n",
       "      <td>109.348128</td>\n",
       "      <td>36.678570</td>\n",
       "      <td>8.378161</td>\n",
       "      <td>0</td>\n",
       "      <td>2024-04-03 23:56:03.205026090+00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>307.075000</td>\n",
       "      <td>-87.472194</td>\n",
       "      <td>60403.999541</td>\n",
       "      <td>60404.024134</td>\n",
       "      <td>360.0</td>\n",
       "      <td>r</td>\n",
       "      <td>185.702282</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>13.231009</td>\n",
       "      <td>5.665571</td>\n",
       "      <td>312.027783</td>\n",
       "      <td>-22.086455</td>\n",
       "      <td>65.395356</td>\n",
       "      <td>94.639368</td>\n",
       "      <td>36.655561</td>\n",
       "      <td>182.213471</td>\n",
       "      <td>0</td>\n",
       "      <td>2024-04-03 23:59:20.309489073+00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
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       "      <td>2</td>\n",
       "      <td>192.068625</td>\n",
       "      <td>-44.718767</td>\n",
       "      <td>60404.004199</td>\n",
       "      <td>60404.030825</td>\n",
       "      <td>420.0</td>\n",
       "      <td>r</td>\n",
       "      <td>99.649749</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>13.235262</td>\n",
       "      <td>5.667348</td>\n",
       "      <td>312.121418</td>\n",
       "      <td>-22.065138</td>\n",
       "      <td>93.753431</td>\n",
       "      <td>140.935253</td>\n",
       "      <td>36.608558</td>\n",
       "      <td>127.847390</td>\n",
       "      <td>0</td>\n",
       "      <td>2024-04-04 00:06:02.767857310+00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>86.500000</td>\n",
       "      <td>-32.306444</td>\n",
       "      <td>60404.011318</td>\n",
       "      <td>60404.036178</td>\n",
       "      <td>420.0</td>\n",
       "      <td>r</td>\n",
       "      <td>263.048274</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>13.241762</td>\n",
       "      <td>5.670064</td>\n",
       "      <td>312.264755</td>\n",
       "      <td>-22.032969</td>\n",
       "      <td>110.246776</td>\n",
       "      <td>79.077964</td>\n",
       "      <td>36.536667</td>\n",
       "      <td>-101.491756</td>\n",
       "      <td>0</td>\n",
       "      <td>2024-04-04 00:16:17.874881028+00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>192.068625</td>\n",
       "      <td>-44.718767</td>\n",
       "      <td>60404.017634</td>\n",
       "      <td>60404.043297</td>\n",
       "      <td>420.0</td>\n",
       "      <td>r</td>\n",
       "      <td>96.824892</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>13.247529</td>\n",
       "      <td>5.672474</td>\n",
       "      <td>312.392153</td>\n",
       "      <td>-22.004845</td>\n",
       "      <td>93.955434</td>\n",
       "      <td>140.940096</td>\n",
       "      <td>36.472832</td>\n",
       "      <td>125.244282</td>\n",
       "      <td>0</td>\n",
       "      <td>2024-04-04 00:25:23.585379127+00:00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 46 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   observationId     fieldRA   fieldDec  observationStartMJD  flush_by_mjd  \\\n",
       "0              0  121.936108 -24.572558         60403.997259      0.000000   \n",
       "1              1  307.075000 -87.472194         60403.999541  60404.024134   \n",
       "2              2  192.068625 -44.718767         60404.004199  60404.030825   \n",
       "3              3   86.500000 -32.306444         60404.011318  60404.036178   \n",
       "4              4  192.068625 -44.718767         60404.017634  60404.043297   \n",
       "\n",
       "   visitExposureTime filter   rotSkyPos  rotSkyPos_desired  numExposures  ...  \\\n",
       "0               30.0      r  172.044265                0.0             4  ...   \n",
       "1              360.0      r  185.702282                0.0             1  ...   \n",
       "2              420.0      r   99.649749                0.0             1  ...   \n",
       "3              420.0      r  263.048274                0.0             1  ...   \n",
       "4              420.0      r   96.824892                0.0             1  ...   \n",
       "\n",
       "       sunRA    sunDec      moonRA    moonDec  moonDistance  solarElong  \\\n",
       "0  13.228926  5.664700  311.981969 -22.096973    132.321217  109.348128   \n",
       "1  13.231009  5.665571  312.027783 -22.086455     65.395356   94.639368   \n",
       "2  13.235262  5.667348  312.121418 -22.065138     93.753431  140.935253   \n",
       "3  13.241762  5.670064  312.264755 -22.032969    110.246776   79.077964   \n",
       "4  13.247529  5.672474  312.392153 -22.004845     93.955434  140.940096   \n",
       "\n",
       "   moonPhase   cummTelAz  scripted_id                          start_date  \n",
       "0  36.678570    8.378161            0 2024-04-03 23:56:03.205026090+00:00  \n",
       "1  36.655561  182.213471            0 2024-04-03 23:59:20.309489073+00:00  \n",
       "2  36.608558  127.847390            0 2024-04-04 00:06:02.767857310+00:00  \n",
       "3  36.536667 -101.491756            0 2024-04-04 00:16:17.874881028+00:00  \n",
       "4  36.472832  125.244282            0 2024-04-04 00:25:23.585379127+00:00  \n",
       "\n",
       "[5 rows x 46 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "visits = visits_examples[0]\n",
    "visits.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "3fda74b3-66b4-435b-96a9-b2f1200c3e6f",
   "metadata": {},
   "outputs": [
    {
     "data": {
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r\"]]]}}},\"view\":{\"type\":\"object\",\"name\":\"CDSView\",\"id\":\"p1049\",\"attributes\":{\"filter\":{\"type\":\"object\",\"name\":\"AllIndices\",\"id\":\"p1050\"}}},\"glyph\":{\"type\":\"object\",\"name\":\"Line\",\"id\":\"p1045\",\"attributes\":{\"x\":{\"type\":\"field\",\"field\":\"start_date\"},\"y\":{\"type\":\"field\",\"field\":\"altitude\"},\"line_color\":\"gray\"}},\"nonselection_glyph\":{\"type\":\"object\",\"name\":\"Line\",\"id\":\"p1046\",\"attributes\":{\"x\":{\"type\":\"field\",\"field\":\"start_date\"},\"y\":{\"type\":\"field\",\"field\":\"altitude\"},\"line_color\":\"gray\",\"line_alpha\":0.1}},\"muted_glyph\":{\"type\":\"object\",\"name\":\"Line\",\"id\":\"p1047\",\"attributes\":{\"x\":{\"type\":\"field\",\"field\":\"start_date\"},\"y\":{\"type\":\"field\",\"field\":\"altitude\"},\"line_color\":\"gray\",\"line_alpha\":0.2}}}},{\"type\":\"object\",\"name\":\"GlyphRenderer\",\"id\":\"p1057\",\"attributes\":{\"name\":\"visit_altitude\",\"data_source\":{\"id\":\"p1037\"},\"view\":{\"type\":\"object\",\"name\":\"CDSView\",\"id\":\"p1058\",\"attributes\":{\"filter\":{\"type\":\"object\",\"name\":\"AllIndices\",\"id\":\"p1059\"}}},\"glyph\":{\"type\":\"object\",\"name\":\"Scatter\",\"id\":\"p1054\",\"attributes\":{\"x\":{\"type\":\"field\",\"field\":\"start_date\"},\"y\":{\"type\":\"field\",\"field\":\"altitude\"},\"size\":{\"type\":\"value\",\"value\":5},\"line_color\":{\"type\":\"field\",\"field\":\"note\",\"transform\":{\"type\":\"object\",\"name\":\"CategoricalColorMapper\",\"id\":\"p1040\",\"attributes\":{\"name\":\"note\",\"palette\":[\"#d60000\",\"#8c3bff\",\"#018700\",\"#00acc6\",\"#97ff00\",\"#ff7ed1\"],\"factors\":{\"type\":\"ndarray\",\"array\":[\"Photo0800-1\",\"cwfs\",\"spec:HD111235\",\"spec:HD144334\",\"spec:HD185975\",\"spec_bright:HD38666\"],\"shape\":[6],\"dtype\":\"object\",\"order\":\"little\"}}}},\"fill_color\":{\"type\":\"field\",\"field\":\"note\",\"transform\":{\"id\":\"p1040\"}},\"hatch_color\":{\"type\":\"field\",\"field\":\"note\",\"transform\":{\"id\":\"p1040\"}},\"marker\":{\"type\":\"field\",\"field\":\"filter\",\"transform\":{\"type\":\"object\",\"name\":\"CategoricalMarkerMapper\",\"id\":\"p1041\",\"attributes\":{\"name\":\"filter\",\"factors\":[\"y\",\"z\",\"i\",\"r\",\"g\",\"u\"],\"markers\":[\"circle\",\"hex\",\"star\",\"square\",\"diamond\",\"triangle\"]}}}}},\"nonselection_glyph\":{\"type\":\"object\",\"name\":\"Scatter\",\"id\":\"p1055\",\"attributes\":{\"x\":{\"type\":\"field\",\"field\":\"start_date\"},\"y\":{\"type\":\"field\",\"field\":\"altitude\"},\"size\":{\"type\":\"value\",\"value\":5},\"line_color\":{\"type\":\"field\",\"field\":\"note\",\"transform\":{\"id\":\"p1040\"}},\"line_alpha\":{\"type\":\"value\",\"value\":0.1},\"fill_color\":{\"type\":\"field\",\"field\":\"note\",\"transform\":{\"id\":\"p1040\"}},\"fill_alpha\":{\"type\":\"value\",\"value\":0.1},\"hatch_color\":{\"type\":\"field\",\"field\":\"note\",\"transform\":{\"id\":\"p1040\"}},\"hatch_alpha\":{\"type\":\"value\",\"value\":0.1},\"marker\":{\"type\":\"field\",\"field\":\"filter\",\"transform\":{\"id\":\"p1041\"}}}},\"muted_glyph\":{\"type\":\"object\",\"name\":\"Scatter\",\"id\":\"p1056\",\"attributes\":{\"x\":{\"type\":\"field\",\"field\":\"start_date\"},\"y\":{\"type\":\"field\",\"field\":\"altitude\"},\"size\":{\"type\":\"value\",\"value\":5},\"line_color\":{\"type\":\"field\",\"field\":\"note\",\"transform\":{\"id\":\"p1040\"}},\"line_alpha\":{\"type\":\"value\",\"value\":0.2},\"fill_color\":{\"type\":\"field\",\"field\":\"note\",\"transform\":{\"id\":\"p1040\"}},\"fill_alpha\":{\"type\":\"value\",\"value\":0.2},\"hatch_color\":{\"type\":\"field\",\"field\":\"note\",\"transform\":{\"id\":\"p1040\"}},\"hatch_alpha\":{\"type\":\"value\",\"value\":0.2},\"marker\":{\"type\":\"field\",\"field\":\"filter\",\"transform\":{\"id\":\"p1041\"}}}}}}],\"toolbar\":{\"type\":\"object\",\"name\":\"Toolbar\",\"id\":\"p1013\",\"attributes\":{\"tools\":[{\"type\":\"object\",\"name\":\"PanTool\",\"id\":\"p1026\"},{\"type\":\"object\",\"name\":\"WheelZoomTool\",\"id\":\"p1027\",\"attributes\":{\"renderers\":\"auto\"}},{\"type\":\"object\",\"name\":\"BoxZoomTool\",\"id\":\"p1028\",\"attributes\":{\"overlay\":{\"type\":\"object\",\"name\":\"BoxAnnotation\",\"id\":\"p1029\",\"attributes\":{\"syncable\":false,\"level\":\"overlay\",\"visible\":false,\"left\":{\"type\":\"number\",\"value\":\"nan\"},\"right\":{\"type\":\"number\",\"value\":\"nan\"},\"top\":{\"type\":\"number\",\"value\":\"nan\"},\"bottom\":{\"type\":\"number\",\"value\":\"nan\"},\"left_units\":\"canvas\",\"right_units\":\"canvas\",\"top_units\":\"canvas\",\"bottom_units\":\"canvas\",\"line_color\":\"black\",\"line_alpha\":1.0,\"line_width\":2,\"line_dash\":[4,4],\"fill_color\":\"lightgrey\",\"fill_alpha\":0.5}}}},{\"type\":\"object\",\"name\":\"SaveTool\",\"id\":\"p1034\"},{\"type\":\"object\",\"name\":\"ResetTool\",\"id\":\"p1035\"},{\"type\":\"object\",\"name\":\"HelpTool\",\"id\":\"p1036\"},{\"type\":\"object\",\"name\":\"HoverTool\",\"id\":\"p1062\",\"attributes\":{\"renderers\":[{\"id\":\"p1057\"}],\"tooltips\":[[\"Start time\",\"@start_date{%F %T} UTC (mjd=@observationStartMJD{00000.00}, LST=@observationStartLST\\u00b0)\"],[\"flush by mjd\",\"@flush_by_mjd{00000.00}\"],[\"Note\",\"@note\"],[\"Filter\",\"@filter\"],[\"Field coordinates\",\"RA=@fieldRA\\u00b0, Decl=@fieldDec\\u00b0, Az=@azimuth\\u00b0, Alt=@altitude\\u00b0\"],[\"Parallactic angle\",\"@paraAngle\\u00b0\"],[\"Rotator angle\",\"@rotTelPos\\u00b0\"],[\"Rotator angle (backup)\",\"@rotTelPos_backup\\u00b0\"],[\"Cumulative telescope azimuth\",\"@cummTelAz\\u00b0\"],[\"Airmass\",\"@airmass\"],[\"Moon distance\",\"@moonDistance\\u00b0\"],[\"Moon\",\"RA=@sunRA\\u00b0, Decl=@sunDec\\u00b0, Az=@sunAz\\u00b0, Alt=@sunAlt\\u00b0, phase=@moonPhase\\u00b0\"],[\"Sun\",\"RA=@moonRA\\u00b0, Decl=@moonDec\\u00b0, Az=@moonAz\\u00b0, Alt=@moonAlt\\u00b0, elong=@solarElong\\u00b0\"],[\"Sky brightness\",\"@skyBrightness mag arcsec^-2\"],[\"Exposure time\",\"@visitExposureTime seconds (@numExposures exposures)\"],[\"Visit time\",\"@visitTime seconds\"],[\"Slew distance\",\"@slewDistance\\u00b0\"],[\"Slew time\",\"@slewTime seconds\"],[\"Field ID\",\"@fieldId\"],[\"Proposal ID\",\"@proposalId\"],[\"Block ID\",\"@block_id\"],[\"Scripted ID\",\"@scripted_id\"]],\"formatters\":{\"type\":\"map\",\"entries\":[[\"@start_date\",\"datetime\"]]}}}]}},\"left\":[{\"type\":\"object\",\"name\":\"LinearAxis\",\"id\":\"p1021\",\"attributes\":{\"ticker\":{\"type\":\"object\",\"name\":\"BasicTicker\",\"id\":\"p1022\",\"attributes\":{\"desired_num_ticks\":10,\"mantissas\":[1,2,5]}},\"formatter\":{\"type\":\"object\",\"name\":\"BasicTickFormatter\",\"id\":\"p1023\"},\"major_label_policy\":{\"type\":\"object\",\"name\":\"AllLabels\",\"id\":\"p1024\"}}},{\"type\":\"object\",\"name\":\"Legend\",\"id\":\"p1060\",\"attributes\":{\"items\":[{\"type\":\"object\",\"name\":\"LegendItem\",\"id\":\"p1061\",\"attributes\":{\"label\":{\"type\":\"field\",\"field\":\"note_and_filter\"},\"renderers\":[{\"id\":\"p1057\"}]}}]}}],\"right\":[{\"type\":\"object\",\"name\":\"LinearAxis\",\"id\":\"p1078\",\"attributes\":{\"ticker\":{\"type\":\"object\",\"name\":\"BasicTicker\",\"id\":\"p1079\",\"attributes\":{\"desired_num_ticks\":10,\"mantissas\":[1,2,5]}},\"formatter\":{\"type\":\"object\",\"name\":\"CustomJSTickFormatter\",\"id\":\"p1082\",\"attributes\":{\"code\":\"\\n        const cos_zd = Math.sin(tick * Math.PI / 180.0)\\n        const a = 462.46 + 2.8121/(cos_zd**2 + 0.22*cos_zd + 0.01)\\n        const airmass = Math.sqrt((a*cos_zd)**2 + 2*a + 1) - a * cos_zd\\n        return airmass.toFixed(2)\\n    \"}},\"axis_label\":\"Airmass\",\"major_label_policy\":{\"type\":\"object\",\"name\":\"AllLabels\",\"id\":\"p1081\"},\"minor_tick_line_alpha\":0}}],\"below\":[{\"type\":\"object\",\"name\":\"LinearAxis\",\"id\":\"p1016\",\"attributes\":{\"ticker\":{\"type\":\"object\",\"name\":\"DatetimeTicker\",\"id\":\"p1064\",\"attributes\":{\"tickers\":[{\"type\":\"object\",\"name\":\"AdaptiveTicker\",\"id\":\"p1065\",\"attributes\":{\"num_minor_ticks\":0,\"mantissas\":[1,2,5],\"max_interval\":500.0}},{\"type\":\"object\",\"name\":\"AdaptiveTicker\",\"id\":\"p1066\",\"attributes\":{\"num_minor_ticks\":0,\"base\":60,\"mantissas\":[1,2,5,10,15,20,30],\"min_interval\":1000.0,\"max_interval\":1800000.0}},{\"type\":\"object\",\"name\":\"AdaptiveTicker\",\"id\":\"p1067\",\"attributes\":{\"num_minor_ticks\":0,\"base\":24,\"mantissas\":[1,2,4,6,8,12],\"min_interval\":3600000.0,\"max_interval\":43200000.0}},{\"type\":\"object\",\"name\":\"DaysTicker\",\"id\":\"p1068\",\"attributes\":{\"days\":[1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31]}},{\"type\":\"object\",\"name\":\"DaysTicker\",\"id\":\"p1069\",\"attributes\":{\"days\":[1,4,7,10,13,16,19,22,25,28]}},{\"type\":\"object\",\"name\":\"DaysTicker\",\"id\":\"p1070\",\"attributes\":{\"days\":[1,8,15,22]}},{\"type\":\"object\",\"name\":\"DaysTicker\",\"id\":\"p1071\",\"attributes\":{\"days\":[1,15]}},{\"type\":\"object\",\"name\":\"MonthsTicker\",\"id\":\"p1072\",\"attributes\":{\"months\":[0,1,2,3,4,5,6,7,8,9,10,11]}},{\"type\":\"object\",\"name\":\"MonthsTicker\",\"id\":\"p1073\",\"attributes\":{\"months\":[0,2,4,6,8,10]}},{\"type\":\"object\",\"name\":\"MonthsTicker\",\"id\":\"p1074\",\"attributes\":{\"months\":[0,4,8]}},{\"type\":\"object\",\"name\":\"MonthsTicker\",\"id\":\"p1075\",\"attributes\":{\"months\":[0,6]}},{\"type\":\"object\",\"name\":\"YearsTicker\",\"id\":\"p1076\"}]}},\"formatter\":{\"type\":\"object\",\"name\":\"DatetimeTickFormatter\",\"id\":\"p1077\",\"attributes\":{\"hours\":\"%H:%M\"}},\"major_label_policy\":{\"type\":\"object\",\"name\":\"AllLabels\",\"id\":\"p1019\"}}}],\"center\":[{\"type\":\"object\",\"name\":\"Grid\",\"id\":\"p1020\",\"attributes\":{\"axis\":{\"id\":\"p1016\"}}},{\"type\":\"object\",\"name\":\"Grid\",\"id\":\"p1025\",\"attributes\":{\"dimension\":1,\"axis\":{\"id\":\"p1021\"}}},{\"id\":\"p1060\"},{\"type\":\"object\",\"name\":\"Span\",\"id\":\"p1083\",\"attributes\":{\"location\":1712183547047.0,\"dimension\":\"height\",\"line_color\":\"darkblue\"}},{\"type\":\"object\",\"name\":\"Label\",\"id\":\"p1084\",\"attributes\":{\"text\":\" sunset\",\"text_color\":\"darkblue\",\"x\":1712183547047.0,\"y\":0,\"angle\":90,\"angle_units\":\"deg\"}},{\"type\":\"object\",\"name\":\"Span\",\"id\":\"p1085\",\"attributes\":{\"location\":1712186885446.0002,\"dimension\":\"height\",\"line_color\":\"blue\"}},{\"type\":\"object\",\"name\":\"Label\",\"id\":\"p1086\",\"attributes\":{\"text\":\" sun alt=-12\\u00b0\",\"text_color\":\"blue\",\"x\":1712186885446.0002,\"y\":0,\"angle\":90,\"angle_units\":\"deg\"}},{\"type\":\"object\",\"name\":\"Span\",\"id\":\"p1087\",\"attributes\":{\"location\":1712188552405.0,\"dimension\":\"height\",\"line_color\":\"lightblue\"}},{\"type\":\"object\",\"name\":\"Label\",\"id\":\"p1088\",\"attributes\":{\"text\":\" sun alt=-18\\u00b0\",\"text_color\":\"lightblue\",\"x\":1712188552405.0,\"y\":0,\"angle\":90,\"angle_units\":\"deg\"}},{\"type\":\"object\",\"name\":\"Span\",\"id\":\"p1089\",\"attributes\":{\"location\":1712223377943.0,\"dimension\":\"height\",\"line_color\":\"lightblue\"}},{\"type\":\"object\",\"name\":\"Label\",\"id\":\"p1090\",\"attributes\":{\"text\":\" sun alt=-18\\u00b0\",\"text_color\":\"lightblue\",\"x\":1712223377943.0,\"y\":0,\"angle\":90,\"angle_units\":\"deg\"}},{\"type\":\"object\",\"name\":\"Span\",\"id\":\"p1091\",\"attributes\":{\"location\":1712225046675.0,\"dimension\":\"height\",\"line_color\":\"blue\"}},{\"type\":\"object\",\"name\":\"Label\",\"id\":\"p1092\",\"attributes\":{\"text\":\" sun alt=-12\\u00b0\",\"text_color\":\"blue\",\"x\":1712225046675.0,\"y\":0,\"angle\":90,\"angle_units\":\"deg\"}},{\"type\":\"object\",\"name\":\"Span\",\"id\":\"p1093\",\"attributes\":{\"location\":1712228390046.0002,\"dimension\":\"height\",\"line_color\":\"darkblue\"}},{\"type\":\"object\",\"name\":\"Label\",\"id\":\"p1094\",\"attributes\":{\"text\":\" sunrise\",\"text_color\":\"darkblue\",\"x\":1712228390046.0002,\"y\":0,\"angle\":90,\"angle_units\":\"deg\"}},{\"type\":\"object\",\"name\":\"Span\",\"id\":\"p1095\",\"attributes\":{\"location\":1712210850224.0,\"dimension\":\"height\",\"line_color\":\"green\"}},{\"type\":\"object\",\"name\":\"Label\",\"id\":\"p1096\",\"attributes\":{\"text\":\" moonrise\",\"text_color\":\"green\",\"x\":1712210850224.0,\"y\":0,\"angle\":90,\"angle_units\":\"deg\"}},{\"type\":\"object\",\"name\":\"Span\",\"id\":\"p1097\",\"attributes\":{\"location\":1712261253505.9998,\"dimension\":\"height\",\"line_color\":\"green\"}},{\"type\":\"object\",\"name\":\"Label\",\"id\":\"p1098\",\"attributes\":{\"text\":\" moonset\",\"text_color\":\"green\",\"x\":1712261253505.9998,\"y\":0,\"angle\":90,\"angle_units\":\"deg\"}}],\"frame_width\":800}}],\"defs\":[{\"type\":\"model\",\"name\":\"ReactiveHTML1\"},{\"type\":\"model\",\"name\":\"FlexBox1\",\"properties\":[{\"name\":\"align_content\",\"kind\":\"Any\",\"default\":\"flex-start\"},{\"name\":\"align_items\",\"kind\":\"Any\",\"default\":\"flex-start\"},{\"name\":\"flex_direction\",\"kind\":\"Any\",\"default\":\"row\"},{\"name\":\"flex_wrap\",\"kind\":\"Any\",\"default\":\"wrap\"},{\"name\":\"gap\",\"kind\":\"Any\",\"default\":\"\"},{\"name\":\"justify_content\",\"kind\":\"Any\",\"default\":\"flex-start\"}]},{\"type\":\"model\",\"name\":\"FloatPanel1\",\"properties\":[{\"name\":\"config\",\"kind\":\"Any\",\"default\":{\"type\":\"map\"}},{\"name\":\"contained\",\"kind\":\"Any\",\"default\":true},{\"name\":\"position\",\"kind\":\"Any\",\"default\":\"right-top\"},{\"name\":\"offsetx\",\"kind\":\"Any\",\"default\":null},{\"name\":\"offsety\",\"kind\":\"Any\",\"default\":null},{\"name\":\"theme\",\"kind\":\"Any\",\"default\":\"primary\"},{\"name\":\"status\",\"kind\":\"Any\",\"default\":\"normalized\"}]},{\"type\":\"model\",\"name\":\"GridStack1\",\"properties\":[{\"name\":\"mode\",\"kind\":\"Any\",\"default\":\"warn\"},{\"name\":\"ncols\",\"kind\":\"Any\",\"default\":null},{\"name\":\"nrows\",\"kind\":\"Any\",\"default\":null},{\"name\":\"allow_resize\",\"kind\":\"Any\",\"default\":true},{\"name\":\"allow_drag\",\"kind\":\"Any\",\"default\":true},{\"name\":\"state\",\"kind\":\"Any\",\"default\":[]}]},{\"type\":\"model\",\"name\":\"drag1\",\"properties\":[{\"name\":\"slider_width\",\"kind\":\"Any\",\"default\":5},{\"name\":\"slider_color\",\"kind\":\"Any\",\"default\":\"black\"},{\"name\":\"value\",\"kind\":\"Any\",\"default\":50}]},{\"type\":\"model\",\"name\":\"click1\",\"properties\":[{\"name\":\"terminal_output\",\"kind\":\"Any\",\"default\":\"\"},{\"name\":\"debug_name\",\"kind\":\"Any\",\"default\":\"\"},{\"name\":\"clears\",\"kind\":\"Any\",\"default\":0}]},{\"type\":\"model\",\"name\":\"FastWrapper1\",\"properties\":[{\"name\":\"object\",\"kind\":\"Any\",\"default\":null},{\"name\":\"style\",\"kind\":\"Any\",\"default\":null}]},{\"type\":\"model\",\"name\":\"NotificationAreaBase1\",\"properties\":[{\"name\":\"js_events\",\"kind\":\"Any\",\"default\":{\"type\":\"map\"}},{\"name\":\"position\",\"kind\":\"Any\",\"default\":\"bottom-right\"},{\"name\":\"_clear\",\"kind\":\"Any\",\"default\":0}]},{\"type\":\"model\",\"name\":\"NotificationArea1\",\"properties\":[{\"name\":\"js_events\",\"kind\":\"Any\",\"default\":{\"type\":\"map\"}},{\"name\":\"notifications\",\"kind\":\"Any\",\"default\":[]},{\"name\":\"position\",\"kind\":\"Any\",\"default\":\"bottom-right\"},{\"name\":\"_clear\",\"kind\":\"Any\",\"default\":0},{\"name\":\"types\",\"kind\":\"Any\",\"default\":[{\"type\":\"map\",\"entries\":[[\"type\",\"warning\"],[\"background\",\"#ffc107\"],[\"icon\",{\"type\":\"map\",\"entries\":[[\"className\",\"fas fa-exclamation-triangle\"],[\"tagName\",\"i\"],[\"color\",\"white\"]]}]]},{\"type\":\"map\",\"entries\":[[\"type\",\"info\"],[\"background\",\"#007bff\"],[\"icon\",{\"type\":\"map\",\"entries\":[[\"className\",\"fas fa-info-circle\"],[\"tagName\",\"i\"],[\"color\",\"white\"]]}]]}]}]},{\"type\":\"model\",\"name\":\"Notification\",\"properties\":[{\"name\":\"background\",\"kind\":\"Any\",\"default\":null},{\"name\":\"duration\",\"kind\":\"Any\",\"default\":3000},{\"name\":\"icon\",\"kind\":\"Any\",\"default\":null},{\"name\":\"message\",\"kind\":\"Any\",\"default\":\"\"},{\"name\":\"notification_type\",\"kind\":\"Any\",\"default\":null},{\"name\":\"_destroyed\",\"kind\":\"Any\",\"default\":false}]},{\"type\":\"model\",\"name\":\"TemplateActions1\",\"properties\":[{\"name\":\"open_modal\",\"kind\":\"Any\",\"default\":0},{\"name\":\"close_modal\",\"kind\":\"Any\",\"default\":0}]},{\"type\":\"model\",\"name\":\"BootstrapTemplateActions1\",\"properties\":[{\"name\":\"open_modal\",\"kind\":\"Any\",\"default\":0},{\"name\":\"close_modal\",\"kind\":\"Any\",\"default\":0}]},{\"type\":\"model\",\"name\":\"TemplateEditor1\",\"properties\":[{\"name\":\"layout\",\"kind\":\"Any\",\"default\":[]}]},{\"type\":\"model\",\"name\":\"MaterialTemplateActions1\",\"properties\":[{\"name\":\"open_modal\",\"kind\":\"Any\",\"default\":0},{\"name\":\"close_modal\",\"kind\":\"Any\",\"default\":0}]},{\"type\":\"model\",\"name\":\"copy_to_clipboard1\",\"properties\":[{\"name\":\"fill\",\"kind\":\"Any\",\"default\":\"none\"},{\"name\":\"value\",\"kind\":\"Any\",\"default\":null}]}]}};\n",
       "  const render_items = [{\"docid\":\"ecbd40da-1f3a-4619-bbf3-a90916f14313\",\"roots\":{\"p1005\":\"b6dfba26-0355-4c1e-b675-498fe30e719d\"},\"root_ids\":[\"p1005\"]}];\n",
       "  void root.Bokeh.embed.embed_items_notebook(docs_json, render_items);\n",
       "  }\n",
       "  if (root.Bokeh !== undefined) {\n",
       "    embed_document(root);\n",
       "  } else {\n",
       "    let attempts = 0;\n",
       "    const timer = setInterval(function(root) {\n",
       "      if (root.Bokeh !== undefined) {\n",
       "        clearInterval(timer);\n",
       "        embed_document(root);\n",
       "      } else {\n",
       "        attempts++;\n",
       "        if (attempts > 100) {\n",
       "          clearInterval(timer);\n",
       "          console.log(\"Bokeh: ERROR: Unable to run BokehJS code because BokehJS library is missing\");\n",
       "        }\n",
       "      }\n",
       "    }, 10, root)\n",
       "  }\n",
       "})(window);"
      ],
      "application/vnd.bokehjs_exec.v0+json": ""
     },
     "metadata": {
      "application/vnd.bokehjs_exec.v0+json": {
       "id": "p1005"
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "visits = visits_examples[1]\n",
    "if len(visits):\n",
    "    fig = bokeh.plotting.figure(frame_width=800)\n",
    "    fig = schedview.plot.nightly.plot_alt_vs_time(\n",
    "        visits=visits, almanac_events=night_events,\n",
    "        figure=fig\n",
    "    )\n",
    "    bokeh.io.show(fig)\n",
    "else:\n",
    "    print(\"No visits\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "d4e7c777-50d7-4a1c-b37c-d84488d4491a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "  <div id=\"aaecf570-ccf8-47e9-812f-26112ffa1f39\" data-root-id=\"p1142\" style=\"display: contents;\"></div>\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "(function(root) {\n",
       "  function embed_document(root) {\n",
       "  const docs_json = {\"ca5ab352-49a6-4583-9923-7e2a0127c024\":{\"version\":\"3.4.0\",\"title\":\"Bokeh Application\",\"roots\":[{\"type\":\"object\",\"name\":\"Figure\",\"id\":\"p1142\",\"attributes\":{\"x_range\":{\"type\":\"object\",\"name\":\"DataRange1d\",\"id\":\"p1143\"},\"y_range\":{\"type\":\"object\",\"name\":\"DataRange1d\",\"id\":\"p1144\"},\"x_scale\":{\"type\":\"object\",\"name\":\"LinearScale\",\"id\":\"p1152\"},\"y_scale\":{\"type\":\"object\",\"name\":\"LinearScale\",\"id\":\"p1153\"},\"title\":{\"type\":\"object\",\"name\":\"Title\",\"id\":\"p1145\",\"attributes\":{\"text\":\"Horizon 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fa-exclamation-triangle\"],[\"tagName\",\"i\"],[\"color\",\"white\"]]}]]},{\"type\":\"map\",\"entries\":[[\"type\",\"info\"],[\"background\",\"#007bff\"],[\"icon\",{\"type\":\"map\",\"entries\":[[\"className\",\"fas fa-info-circle\"],[\"tagName\",\"i\"],[\"color\",\"white\"]]}]]}]}]},{\"type\":\"model\",\"name\":\"Notification\",\"properties\":[{\"name\":\"background\",\"kind\":\"Any\",\"default\":null},{\"name\":\"duration\",\"kind\":\"Any\",\"default\":3000},{\"name\":\"icon\",\"kind\":\"Any\",\"default\":null},{\"name\":\"message\",\"kind\":\"Any\",\"default\":\"\"},{\"name\":\"notification_type\",\"kind\":\"Any\",\"default\":null},{\"name\":\"_destroyed\",\"kind\":\"Any\",\"default\":false}]},{\"type\":\"model\",\"name\":\"TemplateActions1\",\"properties\":[{\"name\":\"open_modal\",\"kind\":\"Any\",\"default\":0},{\"name\":\"close_modal\",\"kind\":\"Any\",\"default\":0}]},{\"type\":\"model\",\"name\":\"BootstrapTemplateActions1\",\"properties\":[{\"name\":\"open_modal\",\"kind\":\"Any\",\"default\":0},{\"name\":\"close_modal\",\"kind\":\"Any\",\"default\":0}]},{\"type\":\"model\",\"name\":\"TemplateEditor1\",\"properties\":[{\"name\":\"layout\",\"kind\":\"Any\",\"default\":[]}]},{\"type\":\"model\",\"name\":\"MaterialTemplateActions1\",\"properties\":[{\"name\":\"open_modal\",\"kind\":\"Any\",\"default\":0},{\"name\":\"close_modal\",\"kind\":\"Any\",\"default\":0}]},{\"type\":\"model\",\"name\":\"copy_to_clipboard1\",\"properties\":[{\"name\":\"fill\",\"kind\":\"Any\",\"default\":\"none\"},{\"name\":\"value\",\"kind\":\"Any\",\"default\":null}]}]}};\n",
       "  const render_items = [{\"docid\":\"ca5ab352-49a6-4583-9923-7e2a0127c024\",\"roots\":{\"p1142\":\"aaecf570-ccf8-47e9-812f-26112ffa1f39\"},\"root_ids\":[\"p1142\"]}];\n",
       "  void root.Bokeh.embed.embed_items_notebook(docs_json, render_items);\n",
       "  }\n",
       "  if (root.Bokeh !== undefined) {\n",
       "    embed_document(root);\n",
       "  } else {\n",
       "    let attempts = 0;\n",
       "    const timer = setInterval(function(root) {\n",
       "      if (root.Bokeh !== undefined) {\n",
       "        clearInterval(timer);\n",
       "        embed_document(root);\n",
       "      } else {\n",
       "        attempts++;\n",
       "        if (attempts > 100) {\n",
       "          clearInterval(timer);\n",
       "          console.log(\"Bokeh: ERROR: Unable to run BokehJS code because BokehJS library is missing\");\n",
       "        }\n",
       "      }\n",
       "    }, 10, root)\n",
       "  }\n",
       "})(window);"
      ],
      "application/vnd.bokehjs_exec.v0+json": ""
     },
     "metadata": {
      "application/vnd.bokehjs_exec.v0+json": {
       "id": "p1142"
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "if len(visits):\n",
    "    fig = schedview.plot.nightly.plot_polar_alt_az(visits=visits, legend=True)\n",
    "    bokeh.io.show(fig)\n",
    "else:\n",
    "    print(\"No visits\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "09095849-848e-4009-a27a-c88f6d9aa1f1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Observing</th>\n",
       "      <th>cwfs</th>\n",
       "      <th>spec:HD111235</th>\n",
       "      <th>Photo0800-1</th>\n",
       "      <th>spec_bright:HD38666</th>\n",
       "      <th>spec:HD185975</th>\n",
       "      <th>spec:HD144334</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Start</th>\n",
       "      <td>[2024-04-03 23:56:03.205026090+00:00, 2024-04-...</td>\n",
       "      <td>[2024-04-03 23:56:03.205026090+00:00, 2024-04-...</td>\n",
       "      <td>[2024-04-04 00:06:02.767857310+00:00, 2024-04-...</td>\n",
       "      <td>[2024-04-04 02:38:23.738835454+00:00, 2024-04-...</td>\n",
       "      <td>[2024-04-04 00:16:17.874881028+00:00, 2024-04-...</td>\n",
       "      <td>[2024-04-03 23:59:20.309489073+00:00, 2024-04-...</td>\n",
       "      <td>[2024-04-04 03:48:35.290644768+00:00, 2024-04-...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>End</th>\n",
       "      <td>[2024-04-04 09:54:40.031655727+00:00, 2024-04-...</td>\n",
       "      <td>[2024-04-04 08:14:26.463147106+00:00, 2024-04-...</td>\n",
       "      <td>[2024-04-04 09:46:57.775180932+00:00, 2024-04-...</td>\n",
       "      <td>[2024-04-04 03:26:45.161158741+00:00, 2024-04-...</td>\n",
       "      <td>[2024-04-04 02:30:21.483314038+00:00, 2024-04-...</td>\n",
       "      <td>[2024-04-04 09:54:40.031655727+00:00, 2024-04-...</td>\n",
       "      <td>[2024-04-04 09:38:44.968318638+00:00, 2024-04-...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Nobs</th>\n",
       "      <td>[92, 90, 87, 85, 83, 81, 77]</td>\n",
       "      <td>[5, 5, 5, 5, 5, 5, 4]</td>\n",
       "      <td>[29, 28, 26, 26, 25, 24, 23]</td>\n",
       "      <td>[24, 24, 24, 24, 24, 24, 24]</td>\n",
       "      <td>[8, 7, 6, 5, 4, 3, 1]</td>\n",
       "      <td>[7, 6, 6, 6, 6, 6, 5]</td>\n",
       "      <td>[19, 20, 20, 19, 19, 19, 20]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Airmass Min</th>\n",
       "      <td>NaN</td>\n",
       "      <td>[1.0050213406474164, 1.0069213506963046, 1.005...</td>\n",
       "      <td>[1.0332031615322455, 1.0331912489621253, 1.032...</td>\n",
       "      <td>[1.1922896146442608, 1.189854713243103, 1.1996...</td>\n",
       "      <td>[1.1933098802071387, 1.2305200423706137, 1.336...</td>\n",
       "      <td>[1.8727399057483665, 1.9128039855959438, 1.902...</td>\n",
       "      <td>[1.006749851974111, 1.0067546258574165, 1.0068...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Airmass Max</th>\n",
       "      <td>NaN</td>\n",
       "      <td>[1.4790621418394072, 1.8488935616033864, 1.845...</td>\n",
       "      <td>[2.0230966397999013, 2.12250910621292, 1.97286...</td>\n",
       "      <td>[1.3637401856872815, 1.3597919590860421, 1.377...</td>\n",
       "      <td>[2.0584590707792705, 2.0469344130859457, 2.066...</td>\n",
       "      <td>[2.1491676112718405, 2.1500636984457113, 2.149...</td>\n",
       "      <td>[1.7906108849233244, 1.7995463835409087, 1.828...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                     Observing  \\\n",
       "Start        [2024-04-03 23:56:03.205026090+00:00, 2024-04-...   \n",
       "End          [2024-04-04 09:54:40.031655727+00:00, 2024-04-...   \n",
       "Nobs                              [92, 90, 87, 85, 83, 81, 77]   \n",
       "Airmass Min                                                NaN   \n",
       "Airmass Max                                                NaN   \n",
       "\n",
       "                                                          cwfs  \\\n",
       "Start        [2024-04-03 23:56:03.205026090+00:00, 2024-04-...   \n",
       "End          [2024-04-04 08:14:26.463147106+00:00, 2024-04-...   \n",
       "Nobs                                     [5, 5, 5, 5, 5, 5, 4]   \n",
       "Airmass Min  [1.0050213406474164, 1.0069213506963046, 1.005...   \n",
       "Airmass Max  [1.4790621418394072, 1.8488935616033864, 1.845...   \n",
       "\n",
       "                                                 spec:HD111235  \\\n",
       "Start        [2024-04-04 00:06:02.767857310+00:00, 2024-04-...   \n",
       "End          [2024-04-04 09:46:57.775180932+00:00, 2024-04-...   \n",
       "Nobs                              [29, 28, 26, 26, 25, 24, 23]   \n",
       "Airmass Min  [1.0332031615322455, 1.0331912489621253, 1.032...   \n",
       "Airmass Max  [2.0230966397999013, 2.12250910621292, 1.97286...   \n",
       "\n",
       "                                                   Photo0800-1  \\\n",
       "Start        [2024-04-04 02:38:23.738835454+00:00, 2024-04-...   \n",
       "End          [2024-04-04 03:26:45.161158741+00:00, 2024-04-...   \n",
       "Nobs                              [24, 24, 24, 24, 24, 24, 24]   \n",
       "Airmass Min  [1.1922896146442608, 1.189854713243103, 1.1996...   \n",
       "Airmass Max  [1.3637401856872815, 1.3597919590860421, 1.377...   \n",
       "\n",
       "                                           spec_bright:HD38666  \\\n",
       "Start        [2024-04-04 00:16:17.874881028+00:00, 2024-04-...   \n",
       "End          [2024-04-04 02:30:21.483314038+00:00, 2024-04-...   \n",
       "Nobs                                     [8, 7, 6, 5, 4, 3, 1]   \n",
       "Airmass Min  [1.1933098802071387, 1.2305200423706137, 1.336...   \n",
       "Airmass Max  [2.0584590707792705, 2.0469344130859457, 2.066...   \n",
       "\n",
       "                                                 spec:HD185975  \\\n",
       "Start        [2024-04-03 23:59:20.309489073+00:00, 2024-04-...   \n",
       "End          [2024-04-04 09:54:40.031655727+00:00, 2024-04-...   \n",
       "Nobs                                     [7, 6, 6, 6, 6, 6, 5]   \n",
       "Airmass Min  [1.8727399057483665, 1.9128039855959438, 1.902...   \n",
       "Airmass Max  [2.1491676112718405, 2.1500636984457113, 2.149...   \n",
       "\n",
       "                                                 spec:HD144334  \n",
       "Start        [2024-04-04 03:48:35.290644768+00:00, 2024-04-...  \n",
       "End          [2024-04-04 09:38:44.968318638+00:00, 2024-04-...  \n",
       "Nobs                              [19, 20, 20, 19, 19, 19, 20]  \n",
       "Airmass Min  [1.006749851974111, 1.0067546258574165, 1.0068...  \n",
       "Airmass Max  [1.7906108849233244, 1.7995463835409087, 1.828...  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def summarize_options(visits_examples):\n",
    "    # Find what kinds of observations were present in all simulations\n",
    "    all_notes = []\n",
    "    for visits in visits_examples:\n",
    "        all_notes.extend(list(visits.note.unique()))\n",
    "    all_notes = set(all_notes)\n",
    "    #print('Survey notes across all examples', all_notes)\n",
    "    summary = {}\n",
    "    summary['Observing'] = {}\n",
    "    summary['Observing']['Start'] = []\n",
    "    summary['Observing'][\"End\"] = []\n",
    "    summary['Observing']['Nobs'] = []\n",
    "    for visits in visits_examples:\n",
    "        summary['Observing']['Start'] += [visits.start_date.min()]\n",
    "        summary['Observing']['End'] += [visits.start_date.max()]\n",
    "        summary['Observing']['Nobs'] += [len(visits)]\n",
    "    for note in all_notes:\n",
    "        summary[note] = {}\n",
    "    for note in all_notes:\n",
    "        summary[note][\"Start\"] = []\n",
    "        summary[note][\"End\"] = []\n",
    "        summary[note][\"Nobs\"] = []\n",
    "        summary[note][\"Airmass Min\"] = []\n",
    "        summary[note][\"Airmass Max\"] = []\n",
    "        for visits in visits_examples:\n",
    "            subset = visits.query('note == @note')            \n",
    "            summary[note][\"Start\"] += [subset.start_date.min()]\n",
    "            summary[note][\"End\"] += [subset.start_date.max()]\n",
    "            summary[note][\"Nobs\"] += [len(subset)]\n",
    "            summary[note][\"Airmass Min\"] += [subset.airmass.min()]\n",
    "            summary[note][\"Airmass Max\"] += [subset.airmass.max()]\n",
    "    return pd.DataFrame(summary), all_notes\n",
    "\n",
    "summary, notes = summarize_options(visits_examples)\n",
    "summary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "702ac5d1-95ec-4dc3-99ce-e4c6702470bc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(Timestamp('2024-04-03 23:56:03.205026090+0000', tz='UTC'),\n",
       " '2024-04-03T23:55:52.405')"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "summary.loc[\"Start\", \"Observing\"][0], start_times[0].isot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "4e436d56-482f-4d8a-aa8e-937d1f8f2a3b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for time in summary.loc[\"Start\", \"Observing\"]:\n",
    "    plt.axvline(time, color='k', linestyle=':')\n",
    "colors = cc.glasbey\n",
    "for i, c in enumerate(notes):\n",
    "    x1 = summary.loc['Start', c]\n",
    "    x2 = summary.loc['End', c]\n",
    "    y = summary.loc['Nobs', c]\n",
    "    for xi, xo, yi in zip(x1, x2, y):\n",
    "        plt.plot([xi, xo], [yi, yi], \n",
    "                 linestyle='-', marker='o', color=colors[i])\n",
    "    plt.plot(summary.loc['Start', c][0], \n",
    "             summary.loc['Nobs', c][0], \n",
    "             linestyle='', marker='o', color=colors[i], label=c)\n",
    "plt.xticks(rotation=90)\n",
    "plt.xlabel(\"Start Time for note\")\n",
    "plt.ylabel(\"Number of observations per target\")\n",
    "plt.legend(loc=(1.0, 0.3))\n",
    "plt.grid(True, alpha=0.3)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "444d19fd-46e5-4441-8f2e-52945d62d46c",
   "metadata": {},
   "source": [
    "## Actual visits"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "1c241c6a-ed76-4ca6-bf21-b65dc803d920",
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import warnings\n",
    "import copy\n",
    "import pickle\n",
    "import json\n",
    "import numpy as np\n",
    "import healpy as hp\n",
    "import matplotlib.pylab as plt\n",
    "from cycler import cycler\n",
    "import colorcet as cc\n",
    "\n",
    "import pandas as pd\n",
    "\n",
    "import datetime\n",
    "from astropy.time import Time, TimeDelta\n",
    "import astropy.units as u\n",
    "import astropy\n",
    "astropy.utils.iers.conf.iers_degraded_accuracy = 'ignore'\n",
    "import datetime\n",
    "\n",
    "from rubin_scheduler.site_models import Almanac\n",
    "from rubin_scheduler.utils import Site\n",
    "from rubin_scheduler.scheduler.features import Conditions\n",
    "from rubin_scheduler.scheduler.schedulers import CoreScheduler\n",
    "from rubin_scheduler.scheduler.utils import SchemaConverter\n",
    "\n",
    "\n",
    "from lsst_efd_client import EfdClient\n",
    "from lsst.resources import ResourcePath\n",
    "\n",
    "from rubin_scheduler.scheduler.schedulers import CoreScheduler\n",
    "from rubin_scheduler.scheduler.features import Conditions\n",
    "from rubin_scheduler.site_models import Almanac\n",
    "\n",
    "from urllib.parse import urlparse\n",
    "\n",
    "os.environ[\"S3_ENDPOINT_URL\"] = \"https://s3dfrgw.slac.stanford.edu\"\n",
    "os.environ[\"LSST_DISABLE_BUCKET_VALIDATION\"] = \"1\"\n",
    "os.environ[\"AWS_PROFILE\"] = \"default\"\n",
    "\n",
    "bucket = \"s3://rubin:\"\n",
    "\n",
    "efd_client = EfdClient('usdf_efd')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "87ea072c-6706-49ff-be71-3fbffad2f1b1",
   "metadata": {},
   "outputs": [],
   "source": [
    "minutes_to_days = 1./60/24\n",
    "seconds_to_days = 1./60/60/24\n",
    "\n",
    "def demangle_note(x):    \n",
    "    # remove _expnum\n",
    "    x.target = copy.deepcopy(x.note)\n",
    "    if \"IM\" in x.note:\n",
    "        x.note = x.note.split(\":\")[1].split(\"_\")[0]\n",
    "    return x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "7be0d4de-831e-4e4c-96c4-daa7af84e438",
   "metadata": {},
   "outputs": [],
   "source": [
    "def getDayObsStartTime(dayObs):\n",
    "    \"\"\"Get the start of the given dayObs as an astropy.time.Time object.\n",
    "\n",
    "    The observatory rolls the date over at UTC-12.\n",
    "\n",
    "    Parameters\n",
    "    ----------\n",
    "    dayObs : `int`\n",
    "        The dayObs, as an integer, e.g. 20231225\n",
    "\n",
    "    Returns\n",
    "    -------\n",
    "    time : `astropy.time.Time`\n",
    "        The start of the dayObs as an astropy.time.Time object.\n",
    "    \"\"\"\n",
    "    pythonDateTime = datetime.datetime.strptime(str(dayObs), \"%Y%m%d\")\n",
    "    return Time(pythonDateTime) + 12 * u.hour"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "3144e6d3-abfe-4dc6-857b-e8b5d40be62b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "('2024-04-22 22:11:21.310', '2024-04-23 10:14:54.658', 0.5024693142622709)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# What night do you want? \n",
    "DAYOBS = '2024-04-22'\n",
    "site = Site('LSST')\n",
    "almanac = Almanac()\n",
    "night_events = almanac.get_sunset_info(evening_date=DAYOBS, longitude=site.longitude_rad)\n",
    "sunset = Time(night_events['sunset'], format='mjd', scale='utc') \n",
    "sunrise = Time(night_events['sun_n12_rising'], format='mjd', scale='utc')\n",
    "survey_length = sunrise.mjd - sunset.mjd\n",
    "sunset.iso, sunrise.iso, survey_length"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "96265d89-4a2c-48e6-993c-17f13c6b431c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "On night 2024-04-22 aux tel recorded 90 targets for \n",
      "['cwfs' 'spec:HD111235' 'spec:HD185975' 'spec_bright:HD38666'\n",
      " 'Photo0800-1' 'spec:HD144334' 'Photo1600-1' 'Target']\n"
     ]
    }
   ],
   "source": [
    "# Fetch requested targets\n",
    "topic = 'lsst.sal.Scheduler.logevent_target'\n",
    "all_fields = await efd_client.get_fields(topic)\n",
    "#print(all_fields)\n",
    "targets = await efd_client.select_time_series(topic, all_fields, sunset, sunrise, index=2)\n",
    "\n",
    "if len(targets) == 0:\n",
    "    print(f\"On night {DAYOBS} aux tel recorded {len(targets)} targets\")\n",
    "\n",
    "else:\n",
    "\n",
    "    # target timestamp in EFD = time that the target is pushed to scriptqueue\n",
    "    # this should be at the start of the previous observation\n",
    "    targets[\"target_time\"] = targets.index.copy()\n",
    "    targets[\"target_mjd\"] = Time(targets.index).mjd\n",
    "    # estimate what time this target should be observed (start of observation)\n",
    "    # == target_time + previous exposure time (?) + slew time\n",
    "    cols = [c for c in targets if 'exposureTimes' in c]\n",
    "    targets['total_exptime'] = targets[cols].sum(axis=1)\n",
    "    previous_exposure_time = np.concatenate([np.array([0]), targets['total_exptime'][:-1]])\n",
    "    # matches actual observation time better if we do not include previous observation time --\n",
    "    # is target really being issued at start of previous observation, or end? \n",
    "    targets[\"target_obsmjd\"] = Time(targets.index).mjd  + (targets['slewTime'])*seconds_to_days\n",
    "    \n",
    "    # mangle the note again \n",
    "    targets['orig_note'] = targets.note.copy()\n",
    "    targets = targets.apply(demangle_note, axis=1)\n",
    "    \n",
    "    targets = targets.sort_values(by='target_obsmjd')\n",
    "    targets['target_id'] = np.arange(0, len(targets), 1)\n",
    "    targets = targets.reset_index(drop=True)\n",
    "    \n",
    "    print(f\"On night {DAYOBS} aux tel recorded {len(targets)} targets for \\n{targets.note.unique()}\") "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "bedf39f4-3248-4ed4-b179-d8da465b4847",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "On night 2024-04-22 aux tel recorded 78 observations\n"
     ]
    }
   ],
   "source": [
    "# Observations are the actually acquired observations .. \n",
    "#  ... likely be better to get this from butler or the consDB \n",
    "\n",
    "topic = 'lsst.sal.Scheduler.logevent_observation'\n",
    "all_fields = await efd_client.get_fields(topic)\n",
    "obs = await efd_client.select_time_series(topic, all_fields, sunset, sunrise, index=2)\n",
    "\n",
    "if len(obs) == 0:\n",
    "    print(f\"On night {DAYOBS} aux tel recorded {len(obs)} observations\")\n",
    "\n",
    "else:\n",
    "    # timestamp is the time of successful end of observation/JSON block\n",
    "    obs['obs_time'] = obs.index.copy()\n",
    "    obs['obs_obsmjd'] = Time(obs.index).mjd - (obs['exptime'] * seconds_to_days)\n",
    "    \n",
    "    obs = obs.sort_values(by='obs_obsmjd')\n",
    "    obs['obs_id'] = np.arange(0, len(obs), 1)\n",
    "    obs = obs.reset_index(drop=True)\n",
    "    \n",
    "    print(f\"On night {DAYOBS} aux tel recorded {len(obs)} observations\") "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "01fc870e-a7ef-4132-b66e-e5fc3976f1e3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SchedulerID</th>\n",
       "      <th>additionalInformation</th>\n",
       "      <th>decl</th>\n",
       "      <th>exptime</th>\n",
       "      <th>filter</th>\n",
       "      <th>mjd</th>\n",
       "      <th>nexp</th>\n",
       "      <th>priority</th>\n",
       "      <th>private_efdStamp</th>\n",
       "      <th>private_identity</th>\n",
       "      <th>...</th>\n",
       "      <th>private_revCode</th>\n",
       "      <th>private_seqNum</th>\n",
       "      <th>private_sndStamp</th>\n",
       "      <th>ra</th>\n",
       "      <th>rotSkyPos</th>\n",
       "      <th>salIndex</th>\n",
       "      <th>targetId</th>\n",
       "      <th>obs_time</th>\n",
       "      <th>obs_obsmjd</th>\n",
       "      <th>obs_id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>None</td>\n",
       "      <td></td>\n",
       "      <td>-24.989135</td>\n",
       "      <td>30</td>\n",
       "      <td>r</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>None</td>\n",
       "      <td>1.713828e+09</td>\n",
       "      <td>Scheduler:2</td>\n",
       "      <td>...</td>\n",
       "      <td>3942145a</td>\n",
       "      <td>189</td>\n",
       "      <td>1.713828e+09</td>\n",
       "      <td>121.809569</td>\n",
       "      <td>133.211884</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2024-04-22 23:26:46.373517+00:00</td>\n",
       "      <td>60422.976578</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>None</td>\n",
       "      <td></td>\n",
       "      <td>-44.718767</td>\n",
       "      <td>420</td>\n",
       "      <td>r</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>None</td>\n",
       "      <td>1.713829e+09</td>\n",
       "      <td>Scheduler:2</td>\n",
       "      <td>...</td>\n",
       "      <td>3942145a</td>\n",
       "      <td>190</td>\n",
       "      <td>1.713829e+09</td>\n",
       "      <td>192.068625</td>\n",
       "      <td>348.720441</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2024-04-22 23:36:39.917073+00:00</td>\n",
       "      <td>60422.978934</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>None</td>\n",
       "      <td></td>\n",
       "      <td>-87.472194</td>\n",
       "      <td>360</td>\n",
       "      <td>r</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>None</td>\n",
       "      <td>1.713831e+09</td>\n",
       "      <td>Scheduler:2</td>\n",
       "      <td>...</td>\n",
       "      <td>3942145a</td>\n",
       "      <td>191</td>\n",
       "      <td>1.713831e+09</td>\n",
       "      <td>307.075000</td>\n",
       "      <td>277.472368</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2024-04-23 00:15:16.738277+00:00</td>\n",
       "      <td>60423.006444</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>None</td>\n",
       "      <td></td>\n",
       "      <td>-44.718767</td>\n",
       "      <td>420</td>\n",
       "      <td>r</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>None</td>\n",
       "      <td>1.713832e+09</td>\n",
       "      <td>Scheduler:2</td>\n",
       "      <td>...</td>\n",
       "      <td>3942145a</td>\n",
       "      <td>192</td>\n",
       "      <td>1.713832e+09</td>\n",
       "      <td>192.068625</td>\n",
       "      <td>359.359575</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2024-04-23 00:24:19.012989+00:00</td>\n",
       "      <td>60423.012026</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>None</td>\n",
       "      <td></td>\n",
       "      <td>-32.306444</td>\n",
       "      <td>420</td>\n",
       "      <td>r</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>None</td>\n",
       "      <td>1.713832e+09</td>\n",
       "      <td>Scheduler:2</td>\n",
       "      <td>...</td>\n",
       "      <td>3942145a</td>\n",
       "      <td>193</td>\n",
       "      <td>1.713832e+09</td>\n",
       "      <td>86.500000</td>\n",
       "      <td>19.413568</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2024-04-23 00:31:19.196184+00:00</td>\n",
       "      <td>60423.016889</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73</th>\n",
       "      <td>None</td>\n",
       "      <td></td>\n",
       "      <td>-23.960829</td>\n",
       "      <td>120</td>\n",
       "      <td>g</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>None</td>\n",
       "      <td>1.713866e+09</td>\n",
       "      <td>Scheduler:2</td>\n",
       "      <td>...</td>\n",
       "      <td>3942145a</td>\n",
       "      <td>262</td>\n",
       "      <td>1.713866e+09</td>\n",
       "      <td>239.996109</td>\n",
       "      <td>24.242244</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2024-04-23 09:46:01.786120+00:00</td>\n",
       "      <td>60423.405576</td>\n",
       "      <td>73</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>74</th>\n",
       "      <td>None</td>\n",
       "      <td></td>\n",
       "      <td>-23.960829</td>\n",
       "      <td>120</td>\n",
       "      <td>g</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>None</td>\n",
       "      <td>1.713866e+09</td>\n",
       "      <td>Scheduler:2</td>\n",
       "      <td>...</td>\n",
       "      <td>3942145a</td>\n",
       "      <td>263</td>\n",
       "      <td>1.713866e+09</td>\n",
       "      <td>240.012276</td>\n",
       "      <td>24.310945</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2024-04-23 09:49:58.070936+00:00</td>\n",
       "      <td>60423.408311</td>\n",
       "      <td>74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75</th>\n",
       "      <td>None</td>\n",
       "      <td></td>\n",
       "      <td>-23.960829</td>\n",
       "      <td>120</td>\n",
       "      <td>g</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>None</td>\n",
       "      <td>1.713867e+09</td>\n",
       "      <td>Scheduler:2</td>\n",
       "      <td>...</td>\n",
       "      <td>3942145a</td>\n",
       "      <td>264</td>\n",
       "      <td>1.713867e+09</td>\n",
       "      <td>240.028484</td>\n",
       "      <td>24.387627</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2024-04-23 10:02:53.414239+00:00</td>\n",
       "      <td>60423.417285</td>\n",
       "      <td>75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76</th>\n",
       "      <td>None</td>\n",
       "      <td></td>\n",
       "      <td>-21.789144</td>\n",
       "      <td>30</td>\n",
       "      <td>r</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>None</td>\n",
       "      <td>1.713867e+09</td>\n",
       "      <td>Scheduler:2</td>\n",
       "      <td>...</td>\n",
       "      <td>3942145a</td>\n",
       "      <td>265</td>\n",
       "      <td>1.713867e+09</td>\n",
       "      <td>287.501270</td>\n",
       "      <td>168.999040</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2024-04-23 10:05:19.282512+00:00</td>\n",
       "      <td>60423.420015</td>\n",
       "      <td>76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>77</th>\n",
       "      <td>None</td>\n",
       "      <td></td>\n",
       "      <td>-23.960829</td>\n",
       "      <td>120</td>\n",
       "      <td>g</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>None</td>\n",
       "      <td>1.713867e+09</td>\n",
       "      <td>Scheduler:2</td>\n",
       "      <td>...</td>\n",
       "      <td>3942145a</td>\n",
       "      <td>266</td>\n",
       "      <td>1.713867e+09</td>\n",
       "      <td>240.044651</td>\n",
       "      <td>24.768721</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2024-04-23 10:09:57.288564+00:00</td>\n",
       "      <td>60423.422191</td>\n",
       "      <td>77</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>78 rows × 23 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   SchedulerID additionalInformation       decl  exptime filter  mjd  nexp  \\\n",
       "0         None                       -24.989135       30      r    0     4   \n",
       "1         None                       -44.718767      420      r    0     1   \n",
       "2         None                       -87.472194      360      r    0     1   \n",
       "3         None                       -44.718767      420      r    0     1   \n",
       "4         None                       -32.306444      420      r    0     1   \n",
       "..         ...                   ...        ...      ...    ...  ...   ...   \n",
       "73        None                       -23.960829      120      g    0     1   \n",
       "74        None                       -23.960829      120      g    0     1   \n",
       "75        None                       -23.960829      120      g    0     1   \n",
       "76        None                       -21.789144       30      r    0     4   \n",
       "77        None                       -23.960829      120      g    0     1   \n",
       "\n",
       "   priority  private_efdStamp private_identity  ...  private_revCode  \\\n",
       "0      None      1.713828e+09      Scheduler:2  ...         3942145a   \n",
       "1      None      1.713829e+09      Scheduler:2  ...         3942145a   \n",
       "2      None      1.713831e+09      Scheduler:2  ...         3942145a   \n",
       "3      None      1.713832e+09      Scheduler:2  ...         3942145a   \n",
       "4      None      1.713832e+09      Scheduler:2  ...         3942145a   \n",
       "..      ...               ...              ...  ...              ...   \n",
       "73     None      1.713866e+09      Scheduler:2  ...         3942145a   \n",
       "74     None      1.713866e+09      Scheduler:2  ...         3942145a   \n",
       "75     None      1.713867e+09      Scheduler:2  ...         3942145a   \n",
       "76     None      1.713867e+09      Scheduler:2  ...         3942145a   \n",
       "77     None      1.713867e+09      Scheduler:2  ...         3942145a   \n",
       "\n",
       "    private_seqNum  private_sndStamp          ra   rotSkyPos  salIndex  \\\n",
       "0              189      1.713828e+09  121.809569  133.211884         2   \n",
       "1              190      1.713829e+09  192.068625  348.720441         2   \n",
       "2              191      1.713831e+09  307.075000  277.472368         2   \n",
       "3              192      1.713832e+09  192.068625  359.359575         2   \n",
       "4              193      1.713832e+09   86.500000   19.413568         2   \n",
       "..             ...               ...         ...         ...       ...   \n",
       "73             262      1.713866e+09  239.996109   24.242244         2   \n",
       "74             263      1.713866e+09  240.012276   24.310945         2   \n",
       "75             264      1.713867e+09  240.028484   24.387627         2   \n",
       "76             265      1.713867e+09  287.501270  168.999040         2   \n",
       "77             266      1.713867e+09  240.044651   24.768721         2   \n",
       "\n",
       "    targetId                         obs_time    obs_obsmjd  obs_id  \n",
       "0          0 2024-04-22 23:26:46.373517+00:00  60422.976578       0  \n",
       "1          0 2024-04-22 23:36:39.917073+00:00  60422.978934       1  \n",
       "2          0 2024-04-23 00:15:16.738277+00:00  60423.006444       2  \n",
       "3          0 2024-04-23 00:24:19.012989+00:00  60423.012026       3  \n",
       "4          0 2024-04-23 00:31:19.196184+00:00  60423.016889       4  \n",
       "..       ...                              ...           ...     ...  \n",
       "73         0 2024-04-23 09:46:01.786120+00:00  60423.405576      73  \n",
       "74         0 2024-04-23 09:49:58.070936+00:00  60423.408311      74  \n",
       "75         0 2024-04-23 10:02:53.414239+00:00  60423.417285      75  \n",
       "76         0 2024-04-23 10:05:19.282512+00:00  60423.420015      76  \n",
       "77         0 2024-04-23 10:09:57.288564+00:00  60423.422191      77  \n",
       "\n",
       "[78 rows x 23 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "obs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "be6704b9-1f73-410a-aba1-d85fe712cc8c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\"\\ntime = np.linspace(obs.obs_obsmjd.min(), obs.obs_obsmjd.max(), num=len(obs))\\nplt.plot((time - time[0])/minutes_to_days, time - obs.obs_obsmjd.values, 'k.')\\n\\ntime = np.linspace(targets.target_obsmjd.min(), targets.target_obsmjd.max(), num=len(targets))\\nplt.plot((time - time[0])/minutes_to_days,  time - targets.target_obsmjd.values, 'r.', markersize=10)\\nplt.plot((time - time[0])/minutes_to_days, time - targets.target_mjd.values, 'b.')\\n\""
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\"\"\"\n",
    "time = np.linspace(obs.obs_obsmjd.min(), obs.obs_obsmjd.max(), num=len(obs))\n",
    "plt.plot((time - time[0])/minutes_to_days, time - obs.obs_obsmjd.values, 'k.')\n",
    "\n",
    "time = np.linspace(targets.target_obsmjd.min(), targets.target_obsmjd.max(), num=len(targets))\n",
    "plt.plot((time - time[0])/minutes_to_days,  time - targets.target_obsmjd.values, 'r.', markersize=10)\n",
    "plt.plot((time - time[0])/minutes_to_days, time - targets.target_mjd.values, 'b.')\n",
    "\"\"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "b6fc64b4-dde2-4b17-9c6b-04d24e8e4c9a",
   "metadata": {},
   "outputs": [],
   "source": [
    "def match_obs_and_targets(obs, targets):\n",
    "    # Check targets -> observations\n",
    "    target_to_obs_match = np.zeros(len(targets), int)\n",
    "    obs_to_target_match = np.zeros(len(obs), int)\n",
    "\n",
    "    count = 0\n",
    "    for i, (ri, t) in enumerate(targets.iterrows()):\n",
    "        # Find the soonest observation which matches the same RA/Dec/filter\n",
    "        # and which is not already matched, and is also not more than X minutes later\n",
    "        # Let's just assume targets and obs are already sorted in start_mjd ... \n",
    "        # X needs to be adjusted so that the number of unmatched targets is correct\n",
    "        target_error = 2.0 * minutes_to_days\n",
    "        X = 28 * minutes_to_days\n",
    "        match = np.where((t.target_obsmjd <= (obs.obs_obsmjd + target_error)) # close in time\n",
    "                         & ((obs.obs_obsmjd - t.target_obsmjd) <= X) # not more than X\n",
    "                         & (t.ra == obs.ra) \n",
    "                         & (t.decl == obs.decl) \n",
    "                         & (t['filter'] == obs['filter'])\n",
    "                         & (obs_to_target_match == 0))[0]\n",
    "        if len(match) == 0:\n",
    "            print(f'no obs match for target {t.target_id}')\n",
    "            count += 1\n",
    "            target_to_obs_match[i] = -1\n",
    "        else:\n",
    "            # Consider target to have found the best match \n",
    "            # in the first (soonest) of 'match'\n",
    "            idx = match[0]\n",
    "            target_to_obs_match[i] = obs.iloc[idx]['obs_id']\n",
    "            # And consider observation to have found a match\n",
    "            obs_to_target_match[idx] = t['target_id']\n",
    "\n",
    "    obs['target_id'] = np.array(obs_to_target_match)\n",
    "    targets['obs_id'] = np.array(target_to_obs_match)\n",
    "    \n",
    "    print(f'failed to match {count} targets to observations')\n",
    "    print(f'compare to {len(targets) - len(obs)} expected difference')\n",
    "    return obs, targets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "70a886f3-d151-4b89-9080-1d67cb38f9bf",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "no obs match for target 2\n",
      "no obs match for target 52\n",
      "no obs match for target 56\n",
      "no obs match for target 57\n",
      "no obs match for target 60\n",
      "no obs match for target 62\n",
      "no obs match for target 63\n",
      "no obs match for target 67\n",
      "no obs match for target 68\n",
      "no obs match for target 86\n",
      "no obs match for target 88\n",
      "no obs match for target 89\n",
      "failed to match 12 targets to observations\n",
      "compare to 12 expected difference\n"
     ]
    }
   ],
   "source": [
    "obs, targets = match_obs_and_targets(obs, targets)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "55de6c95-4d36-4c9d-9003-d5a72ae0a5cb",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Join targets + obs\n",
    "jj = obs.join(targets, on='target_id', lsuffix='_o', rsuffix='_t')\n",
    "jj = jj.sort_values(by='obs_obsmjd')\n",
    "\n",
    "jj['delta_obs'] = np.concatenate([np.array([0]), np.diff(jj['obs_obsmjd'])/minutes_to_days]) # minutes\n",
    "jj['delta_request'] = (jj['obs_obsmjd'] - jj['target_obsmjd']) / minutes_to_days # minutes \n",
    "\n",
    "\n",
    "# expected time between observations should be slewtime + exposure time of previous target\n",
    "# Note that in target -- total exposure time is split between exposures. \n",
    "# But in 'observation' -- total exposure time is already in exptime, and nexp means \"to be split between nexp exposures\".\n",
    "prev_exposure_time = np.concatenate([np.array([0]), jj['exptime'][:-1]])\n",
    "jj['expected_overhead'] = (prev_exposure_time + jj['slewTime'])/60 # minutes\n",
    "#jj['expected_overhead'] = (jj['exptime'] + jj['slewTime'])/60"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "244ee304-e84c-4d30-883f-8e881bc43444",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['target_id', 'SchedulerID_o', 'additionalInformation', 'decl_o',\n",
       "       'exptime', 'filter_o', 'mjd', 'nexp', 'priority_o',\n",
       "       'private_efdStamp_o', 'private_identity_o', 'private_kafkaStamp_o',\n",
       "       'private_origin_o', 'private_rcvStamp_o', 'private_revCode_o',\n",
       "       'private_seqNum_o', 'private_sndStamp_o', 'ra_o', 'rotSkyPos',\n",
       "       'salIndex_o', 'targetId_o', 'obs_time', 'obs_obsmjd', 'obs_id_o',\n",
       "       'target_id_o', 'SchedulerID_t', 'airmass', 'cloud', 'decl_t',\n",
       "       'exposureTimes0', 'exposureTimes1', 'exposureTimes2', 'exposureTimes3',\n",
       "       'exposureTimes4', 'exposureTimes5', 'exposureTimes6', 'exposureTimes7',\n",
       "       'exposureTimes8', 'exposureTimes9', 'filter_t', 'isSequence', 'moonAlt',\n",
       "       'moonAz', 'moonDec', 'moonDistance', 'moonPhase', 'moonRa', 'note',\n",
       "       'numExposures', 'numProposals', 'offsetX', 'offsetY', 'priority_t',\n",
       "       'private_efdStamp_t', 'private_host', 'private_identity_t',\n",
       "       'private_kafkaStamp_t', 'private_origin_t', 'private_rcvStamp_t',\n",
       "       'private_revCode_t', 'private_seqNum_t', 'private_sndStamp_t',\n",
       "       'proposalId0', 'proposalId1', 'proposalId2', 'proposalId3',\n",
       "       'proposalId4', 'ra_t', 'requestMjd', 'requestTime', 'salIndex_t',\n",
       "       'seeing', 'sequenceDuration', 'sequenceNVisits', 'sequenceVisits',\n",
       "       'skyAngle', 'skyBrightness', 'slewTime', 'solarElong', 'sunAlt',\n",
       "       'sunAz', 'sunDec', 'sunRa', 'targetId_t', 'target_time', 'target_mjd',\n",
       "       'total_exptime', 'target_obsmjd', 'orig_note', 'target_id_t',\n",
       "       'obs_id_t', 'delta_obs', 'delta_request', 'expected_overhead'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "jj.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "505e1108-53a5-4555-bce2-3b0b2f4eecc1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1a6b28790>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "_ = plt.hist(jj['delta_obs'], bins=np.arange(-2, 25, 0.5), alpha=0.4, histtype='bar', label='actual delta obs')\n",
    "_ = plt.hist(jj['expected_overhead'], bins=np.arange(-2, 25, 0.5), alpha=0.3, histtype='bar', label='calc overhead between obs')\n",
    "#_ = plt.hist(jj['delta_request'], bins=np.arange(-2, 25, 0.5), alpha=0.3, histtype='bar', label='delta expected obs time')\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 615,
   "id": "0c4b1bb7-a375-4457-8151-ba566fbfc0b0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "11\n"
     ]
    }
   ],
   "source": [
    "# Where did the time between observations just take longer than expected \n",
    "# delta obs compared to (slewTime + exptime) BUT sheduler target estimate was correct\n",
    "\n",
    "overhead_error = 4 \n",
    "\n",
    "overheads = np.where(jj.delta_obs - jj.expected_overhead > overhead_error)\n",
    "\n",
    "\n",
    "breaks = []\n",
    "for b in overheads[0]:\n",
    "    b_start = jj.iloc[b-1]['obs_obsmjd'] + jj.iloc[b-1]['exptime']/60/60/24\n",
    "    b_end = jj.iloc[b]['obs_obsmjd'] - jj.iloc[b]['slewTime']/60/60/24\n",
    "    breaks.append([b_start, b_end])\n",
    "\n",
    "print(len(overheads[0]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0c70b224-113b-481a-a399-9c0616bde1c2",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 616,
   "id": "54922b22-9bcc-496e-8a00-c2694e2e03a9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Expected overhead (exptime + slew) (minutes)')"
      ]
     },
     "execution_count": 616,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure()\n",
    "\n",
    "eps = 1\n",
    "for note in jj.note.unique():\n",
    "    j = jj.query('note == @note')\n",
    "    plt.plot(j.delta_obs, j.expected_overhead, '.', label=note)\n",
    "j = jj.iloc[overheads]\n",
    "plt.plot(j.delta_obs, j.expected_overhead, 'o',\n",
    "         markersize=10, color='k', markerfacecolor='none', label='Big Overhead')\n",
    "plt.legend(loc=(1.01, 0.5))\n",
    "for exptime in jj.exptime.unique():\n",
    "    plt.axhline(exptime/60, color='gray', linestyle=':', alpha=0.6)\n",
    "\n",
    "x = np.arange(0, 60)\n",
    "plt.plot(x, x, 'r:')\n",
    "plt.fill_between(x, x-overhead_error, x+overhead_error,  color='r', alpha=0.1)\n",
    "plt.grid(True, alpha=0.3)\n",
    "\n",
    "plt.xlim(0, np.max(jj.delta_obs) + eps)\n",
    "plt.ylim(0, np.max(jj.expected_overhead) + eps/2)\n",
    "\n",
    "plt.xlabel(\"DeltaT between start of observations (minutes)\")\n",
    "plt.ylabel(\"Expected overhead (exptime + slew) (minutes)\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 617,
   "id": "ea7fc5d7-9471-4267-bacd-67a1f5e0ced6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'DeltaT between target request and observation (minutes)')"
      ]
     },
     "execution_count": 617,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "eps = 1\n",
    "for note in jj.note.unique():\n",
    "    j = jj.query('note == @note')\n",
    "    plt.plot(j.delta_obs, j.delta_request, '.', label=note)\n",
    "\n",
    "\"\"\"\n",
    "for d in overheads:\n",
    "    plt.plot([jj.delta_obs.iloc[d], jj.delta_obs.iloc[d-1]], \n",
    "             [jj.delta_request.iloc[d], jj.delta_request.iloc[d-1]],\n",
    "             color='black', alpha=0.5, linestyle=':', zorder=0)\n",
    "\"\"\"\n",
    "\n",
    "j = jj.iloc[overheads]\n",
    "plt.plot(j.delta_obs, j.delta_request, 'o', \n",
    "         markersize=8, color='k', markerfacecolor='none', label='Big Overhead')\n",
    "\n",
    "\n",
    "plt.legend(loc=(1.01, 0.5))\n",
    "x = np.arange(0, 60)\n",
    "plt.plot(x, x, 'r:')\n",
    "plt.fill_between(x, x-overhead_error, x+overhead_error,  color='r', alpha=0.1)\n",
    "plt.grid(True, alpha=0.3)\n",
    "\n",
    "plt.xlim(-0.5, np.max(jj.delta_obs) + eps)\n",
    "plt.ylim(-0.5, np.max(jj.delta_request) + eps)\n",
    "\n",
    "plt.xlabel(\"DeltaT between start of observations (minutes)\")\n",
    "plt.ylabel(\"DeltaT between target request and observation (minutes)\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 618,
   "id": "dfc91133-4582-4cd0-8e36-52d8ad77495e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'airmass')"
      ]
     },
     "execution_count": 618,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "eps = 1\n",
    "plt.figure(figsize=(10, 6))\n",
    "plt.title(f\"Aux Tel DAYOBS {DAYOBS}\")\n",
    "\n",
    "\n",
    "plt.fill_between([night_events['sun_n12_setting'], night_events['sun_n18_setting']],\n",
    "                 2.5, 0.0, color='lightgray', alpha=0.3)\n",
    "plt.fill_between([night_events['sunset'], night_events['sun_n12_setting']], \n",
    "                  2.5, 0.0, color='gray', alpha=0.3)\n",
    "plt.fill_between([night_events['sun_n18_rising'], night_events['sun_n12_rising']],\n",
    "                 2.5, 0.0, color='lightgray', alpha=0.3)\n",
    "plt.fill_between([night_events['sun_n12_rising'], night_events['sunrise']],\n",
    "                 2.5, 0.0, color='gray', alpha=0.3)\n",
    "\n",
    "plt.axhline(night_events['moonrise'])\n",
    "\n",
    "notes = jj.note.unique()\n",
    "note_colors = {}\n",
    "for i, n in enumerate(notes):\n",
    "    note_colors[n] = cc.glasbey[i]\n",
    "\n",
    "for note in jj.note.unique():\n",
    "    j = jj.query('note == @note')\n",
    "    plt.plot(j.obs_obsmjd, j.airmass, '.', color=note_colors[note], label=note)\n",
    "\n",
    "if len(breaks) > 0:\n",
    "    for b in breaks:\n",
    "        plt.fill_between(b, 2.5, 0.0, color='pink', alpha=0.3)\n",
    "\n",
    "j = jj.iloc[overheads]\n",
    "plt.plot(j.obs_obsmjd, j.airmass, 'o', \n",
    "         markersize=10, color='k', markerfacecolor='none', label='Big Overheads')\n",
    "#j = jj.iloc[overheads[0]+1]\n",
    "#plt.plot(j.obs_obsmjd, j.airmass, 's', \n",
    "#         markersize=10, color='k', markerfacecolor='none', label='After Overhead?')\n",
    "\n",
    "# Failed targets\n",
    "i = len(cc.glasbey) - 1\n",
    "for note in targets.note.unique():\n",
    "    try:\n",
    "        color = note_colors[note]\n",
    "    except KeyError:\n",
    "        color = cc.glasbey[i]\n",
    "        i -= 1\n",
    "    t = targets.query('note == @note and obs_id == -1')\n",
    "    plt.plot(t.target_obsmjd, t.airmass, '+', markersize=9, \n",
    "             label=f'Failed {note}', color=color, zorder=1)\n",
    "\n",
    "\n",
    "plt.legend(loc=(1.01, 0.2))\n",
    "\n",
    "plt.xlim(night_events['sunset']+30/60/24, night_events['sunrise']-30/60/24)\n",
    "plt.ylim(2.3, 0.9)\n",
    "\n",
    "plt.xlabel(\"time\")\n",
    "#plt.xticks(rotation='vertical')\n",
    "plt.ylabel(\"airmass\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 624,
   "id": "be3d2c79-b42d-4b60-be19-333e60bcbf99",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['AzEl' 'DaytimeCheckout001' 'DaytimeCheckout002' 'FlatField position'\n",
      " 'cwfs' 'HD  69755' 'spec:HD111235' '*   7 Leo' 'spec:HD185975'\n",
      " 'spec_bright:HD38666' 'HD  77369' 'Photo0800-1' 'spec:HD144334'\n",
      " 'HD 134574' 'HD 112563' 'HD 146284' 'Photo1600-1' 'HD 179609']\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Index(['Exposure id', 'Exposure time', 'Darktime', 'Image type',\n",
       "       'Observation reason', 'dayObs', 'seqNum', 'Group id', 'Target',\n",
       "       'Science program', 'RA', 'Dec', 'Sky angle', 'Azimuth', 'Zenith angle',\n",
       "       'TAI', 'Filter', 'Disperser', 'Airmass', 'Focus-Z', 'Altitude',\n",
       "       'DIMM Seeing', 'Mount jitter RMS', 'Mount motion image degradation',\n",
       "       '_Mount motion image degradation', '50-sigma source count',\n",
       "       '_50-sigma source count', 'Mount timebase errors',\n",
       "       '5-sigma source count', 'PSF FWHM', 'PSF e1', 'PSF e2', 'Sky mean',\n",
       "       'Sky RMS', 'Variance plane mean', 'PSF star count', 'Astrometric bias',\n",
       "       'Astrometric scatter', 'Zeropoint', '_5-sigma source count',\n",
       "       '_PSF FWHM', '_PSF e1', '_PSF e2', '_Sky mean', '_Sky RMS',\n",
       "       '_Variance plane mean', '_PSF star count', '_Astrometric bias',\n",
       "       '_Astrometric scatter', '_Zeropoint', 'obsMjd', 'note'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 624,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fname = 'auxtel_2024-04-22.json'\n",
    "rtv = pd.read_json(fname)\n",
    "rtv.loc['Science program'].unique()\n",
    "\n",
    "def demangle_target(x):\n",
    "    if \"Photo\" in x.Target:\n",
    "        x.note = x.Target.split(\"_\")[0]\n",
    "    if 'cwfs' in x[\"Science program\"]:\n",
    "        x.note = \"cwfs\"\n",
    "    if 'HD1' in x.Target:\n",
    "        x.note = f'spec:{x.Target}'\n",
    "    if 'HD3' in x.Target:\n",
    "        x.note = f'spec_bright:{x.Target}'\n",
    "    return x\n",
    "\n",
    "def get_mjdtime(x):\n",
    "    # ok, the TAI time is ONLY the offset .. \n",
    "    qq = f'{x.dayObs} {x.TAI}'\n",
    "    dtime = datetime.datetime.strptime(qq, '%Y%m%d %H:%M:%S')\n",
    "    x['obsMjd'] = Time(dtime).mjd\n",
    "    # So because it was only the offset, \n",
    "    # we have to do something dumb\n",
    "    # (or go do proper queries in the butler)\n",
    "    if x['obsMjd'] > sunrise.mjd:\n",
    "        x['obsMjd'] -= 1\n",
    "    if x['obsMjd'] < sunset.mjd:\n",
    "        x['obsMjd'] += 1\n",
    "    # Subtract half of the exposure time\n",
    "    x['obsMjd'] -= x['Exposure time']/2*seconds_to_days\n",
    "    return x\n",
    "\n",
    "rtvT = rtv.T\n",
    "\n",
    "rtvT['obsMjd'] = np.zeros(len(rtvT))\n",
    "rtvT = rtvT.apply(get_mjdtime, axis=1)\n",
    "\n",
    "rtvT['note'] = copy.deepcopy(rtvT.Target)\n",
    "rtvT = rtvT.apply(demangle_target, axis=1)\n",
    "print(rtvT['note'].unique())\n",
    "rtvT.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 625,
   "id": "a8465e77-e5dd-40a4-b50e-2a20e2f16138",
   "metadata": {},
   "outputs": [
    {
     "ename": "ModuleNotFoundError",
     "evalue": "No module named 'lsst.summit'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mModuleNotFoundError\u001b[0m                       Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[625], line 2\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[38;5;66;03m# Proper butler query - should work in RSP (?)\u001b[39;00m\n\u001b[0;32m----> 2\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mlsst\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01msummit\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mutils\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mbutlerUtils\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01mbutlerUtils\u001b[39;00m\n\u001b[1;32m      3\u001b[0m butler \u001b[38;5;241m=\u001b[39m butlerUtils\u001b[38;5;241m.\u001b[39mmakeDefaultLatissButler(embargo\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m)\n\u001b[1;32m      5\u001b[0m dayObs \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m20240417\u001b[39m\n",
      "\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'lsst.summit'"
     ]
    }
   ],
   "source": [
    "# Proper butler query - should work in RSP (?)\n",
    "import lsst.summit.utils.butlerUtils as butlerUtils\n",
    "butler = butlerUtils.makeDefaultLatissButler(embargo=True)\n",
    "\n",
    "dayObs = 20240417\n",
    "seqNum = 234\n",
    "\n",
    "where = f\"exposure.day_obs={dayObs} AND exposure.seq_num={seqNum} AND instrument='LATISS'\"\n",
    "(record, ) = butler.registry.queryDimensionRecords('exposure', where=where)\n",
    "print(f\"Exposure {record.id} was {record.exposure_time}s long and started at {record.timespan.begin}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 627,
   "id": "be8046d4-5973-4361-b3e2-16f5e113f365",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'MJD')"
      ]
     },
     "execution_count": 627,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cond1 = np.where(rtvT['Observation reason'] == 'object')[0]\n",
    "cond2 = np.where(rtvT['Observation reason'] == 'imaging')[0]\n",
    "cond3 = np.where(rtvT['note'] == 'cwfs')[0]\n",
    "sci = rtvT.iloc[np.concatenate([cond1, cond2, cond3])]\n",
    "#sci = rtvT\n",
    "for target in sci.note.unique():\n",
    "    s = sci.query('note == @target')\n",
    "    plt.plot(s['obsMjd'], s.Airmass, '.', label=target)\n",
    "plt.legend(loc=(1.01, 0.5))\n",
    "plt.ylim(2.5, 0.9)\n",
    "plt.ylabel('airmass')\n",
    "plt.xlabel('MJD')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 628,
   "id": "1f79088e-9b97-45ab-b16e-d9c76b16fe5f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'airmass')"
      ]
     },
     "execution_count": 628,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "eps = 1\n",
    "plt.figure(figsize=(10, 6))\n",
    "plt.title(f\"Aux Tel DAYOBS {DAYOBS}\")\n",
    "\n",
    "\"\"\"\n",
    "plt.fill_between([night_events['sun_n12_setting'], night_events['sun_n18_setting']],\n",
    "                 2.5, 0.0, color='lightgray', alpha=0.3)\n",
    "plt.fill_between([night_events['sunset'], night_events['sun_n12_setting']], \n",
    "                  2.5, 0.0, color='gray', alpha=0.3)\n",
    "plt.fill_between([night_events['sun_n18_rising'], night_events['sun_n12_rising']],\n",
    "                 2.5, 0.0, color='lightgray', alpha=0.3)\n",
    "plt.fill_between([night_events['sun_n12_rising'], night_events['sunrise']],\n",
    "                 2.5, 0.0, color='gray', alpha=0.3)\n",
    "\n",
    "plt.axhline(night_events['moonrise'])\n",
    "\"\"\"\n",
    "\n",
    "note_colors = {}\n",
    "for i, n in enumerate(notes):\n",
    "    note_colors[n] = cc.glasbey[i]\n",
    "    \n",
    "for note in jj.note.unique():\n",
    "    j = jj.query('note == @note')\n",
    "    plt.plot(j.obs_obsmjd, j.airmass, '.', color=note_colors[note], label=note)\n",
    "# Failed targets\n",
    "i = len(cc.glasbey) - 1\n",
    "for note in targets.note.unique():\n",
    "    try:\n",
    "        color = note_colors[note]\n",
    "    except KeyError:\n",
    "        color = cc.glasbey[i]\n",
    "        i -= 1\n",
    "    t = targets.query('note == @note and obs_id == -1')\n",
    "    plt.plot(t.target_obsmjd, t.airmass, '+', markersize=9, \n",
    "             label=f'Failed {note}', color=color, zorder=1)\n",
    "\n",
    "for note in sci.note.unique():\n",
    "    j = sci.query('note == @note')\n",
    "    plt.plot(j.obsMjd, j.Airmass, 'o', color=note_colors[note], markerfacecolor='none', alpha=0.3, label=note)\n",
    "\n",
    "if len(breaks) > 0:\n",
    "    for b in breaks:\n",
    "        plt.fill_between(b, 2.5, 0.0, color='pink', alpha=0.3)\n",
    "\n",
    "j = jj.iloc[overheads]\n",
    "plt.plot(j.obs_obsmjd, j.airmass, 'o', \n",
    "         markersize=10, color='k', markerfacecolor='none', label='First after delay')\n",
    "\n",
    "plt.legend(loc=(1.01, 0.01))\n",
    "\n",
    "plt.fill_between([night_events['sun_n12_setting'], night_events['sun_n18_setting']],\n",
    "                 2.5, 0.0, color='lightgray', alpha=0.3)\n",
    "plt.fill_between([night_events['sunset'], night_events['sun_n12_setting']], \n",
    "                  2.5, 0.0, color='gray', alpha=0.3)\n",
    "plt.fill_between([night_events['sun_n18_rising'], night_events['sun_n12_rising']],\n",
    "                 2.5, 0.0, color='lightgray', alpha=0.3)\n",
    "plt.fill_between([night_events['sun_n12_rising'], night_events['sunrise']],\n",
    "                 2.5, 0.0, color='gray', alpha=0.3)\n",
    "plt.xlim(night_events['sunset']+30/60/24, night_events['sunrise']-30/60/24)\n",
    "plt.ylim(2.3, 0.9)\n",
    "\n",
    "plt.xlabel(\"time\")\n",
    "#plt.xticks(rotation='vertical')\n",
    "plt.ylabel(\"airmass\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 633,
   "id": "72ff3db2-2b4d-45f6-8a87-a6f57a331a6a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Exposure id</th>\n",
       "      <th>Exposure time</th>\n",
       "      <th>Darktime</th>\n",
       "      <th>Image type</th>\n",
       "      <th>Observation reason</th>\n",
       "      <th>dayObs</th>\n",
       "      <th>seqNum</th>\n",
       "      <th>Group id</th>\n",
       "      <th>Target</th>\n",
       "      <th>Science program</th>\n",
       "      <th>...</th>\n",
       "      <th>_PSF e2</th>\n",
       "      <th>_Sky mean</th>\n",
       "      <th>_Sky RMS</th>\n",
       "      <th>_Variance plane mean</th>\n",
       "      <th>_PSF star count</th>\n",
       "      <th>_Astrometric bias</th>\n",
       "      <th>_Astrometric scatter</th>\n",
       "      <th>_Zeropoint</th>\n",
       "      <th>obsMjd</th>\n",
       "      <th>note</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>663</th>\n",
       "      <td>2024042200663</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.23911</td>\n",
       "      <td>acq</td>\n",
       "      <td>acq</td>\n",
       "      <td>20240422</td>\n",
       "      <td>663</td>\n",
       "      <td>2937863355640000</td>\n",
       "      <td>HD111235</td>\n",
       "      <td>spec-survey</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>60423.305214</td>\n",
       "      <td>spec:HD111235</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>664</th>\n",
       "      <td>2024042200664</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.23293</td>\n",
       "      <td>acq</td>\n",
       "      <td>acq</td>\n",
       "      <td>20240422</td>\n",
       "      <td>664</td>\n",
       "      <td>2937863355640000</td>\n",
       "      <td>HD111235</td>\n",
       "      <td>spec-survey</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>60423.305584</td>\n",
       "      <td>spec:HD111235</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>665</th>\n",
       "      <td>2024042200665</td>\n",
       "      <td>60.0</td>\n",
       "      <td>60.23830</td>\n",
       "      <td>science</td>\n",
       "      <td>object</td>\n",
       "      <td>20240422</td>\n",
       "      <td>665</td>\n",
       "      <td>2937863355640000</td>\n",
       "      <td>HD111235</td>\n",
       "      <td>spec-survey</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>60423.305648</td>\n",
       "      <td>spec:HD111235</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>666</th>\n",
       "      <td>2024042200666</td>\n",
       "      <td>60.0</td>\n",
       "      <td>60.25420</td>\n",
       "      <td>science</td>\n",
       "      <td>object</td>\n",
       "      <td>20240422</td>\n",
       "      <td>666</td>\n",
       "      <td>2937863355640000</td>\n",
       "      <td>HD111235</td>\n",
       "      <td>spec-survey</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>60423.306493</td>\n",
       "      <td>spec:HD111235</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>667</th>\n",
       "      <td>2024042200667</td>\n",
       "      <td>60.0</td>\n",
       "      <td>60.30260</td>\n",
       "      <td>science</td>\n",
       "      <td>x_offset_0</td>\n",
       "      <td>20240422</td>\n",
       "      <td>667</td>\n",
       "      <td>2937863361190000</td>\n",
       "      <td>HD111235</td>\n",
       "      <td>spec-survey</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>60423.307454</td>\n",
       "      <td>spec:HD111235</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>668</th>\n",
       "      <td>2024042200668</td>\n",
       "      <td>60.0</td>\n",
       "      <td>60.24400</td>\n",
       "      <td>science</td>\n",
       "      <td>x_offset_-50</td>\n",
       "      <td>20240422</td>\n",
       "      <td>668</td>\n",
       "      <td>2937866594090000</td>\n",
       "      <td>HD111235</td>\n",
       "      <td>spec-survey</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>60423.308426</td>\n",
       "      <td>spec:HD111235</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>669</th>\n",
       "      <td>2024042200669</td>\n",
       "      <td>60.0</td>\n",
       "      <td>60.22790</td>\n",
       "      <td>science</td>\n",
       "      <td>x_offset_50</td>\n",
       "      <td>20240422</td>\n",
       "      <td>669</td>\n",
       "      <td>2937867438120000</td>\n",
       "      <td>HD111235</td>\n",
       "      <td>spec-survey</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>60423.309410</td>\n",
       "      <td>spec:HD111235</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>7 rows × 52 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Exposure id  Exposure time  Darktime Image type Observation reason  \\\n",
       "663  2024042200663            5.0   5.23911        acq                acq   \n",
       "664  2024042200664            5.0   5.23293        acq                acq   \n",
       "665  2024042200665           60.0  60.23830    science             object   \n",
       "666  2024042200666           60.0  60.25420    science             object   \n",
       "667  2024042200667           60.0  60.30260    science         x_offset_0   \n",
       "668  2024042200668           60.0  60.24400    science       x_offset_-50   \n",
       "669  2024042200669           60.0  60.22790    science        x_offset_50   \n",
       "\n",
       "       dayObs  seqNum          Group id    Target Science program  ...  \\\n",
       "663  20240422     663  2937863355640000  HD111235     spec-survey  ...   \n",
       "664  20240422     664  2937863355640000  HD111235     spec-survey  ...   \n",
       "665  20240422     665  2937863355640000  HD111235     spec-survey  ...   \n",
       "666  20240422     666  2937863355640000  HD111235     spec-survey  ...   \n",
       "667  20240422     667  2937863361190000  HD111235     spec-survey  ...   \n",
       "668  20240422     668  2937866594090000  HD111235     spec-survey  ...   \n",
       "669  20240422     669  2937867438120000  HD111235     spec-survey  ...   \n",
       "\n",
       "     _PSF e2  _Sky mean  _Sky RMS  _Variance plane mean  _PSF star count  \\\n",
       "663      NaN        NaN       NaN                   NaN              NaN   \n",
       "664      NaN        NaN       NaN                   NaN              NaN   \n",
       "665      NaN        NaN       NaN                   NaN              NaN   \n",
       "666      NaN        NaN       NaN                   NaN              NaN   \n",
       "667      NaN        NaN       NaN                   NaN              NaN   \n",
       "668      NaN        NaN       NaN                   NaN              NaN   \n",
       "669      NaN        NaN       NaN                   NaN              NaN   \n",
       "\n",
       "    _Astrometric bias _Astrometric scatter _Zeropoint        obsMjd  \\\n",
       "663               NaN                  NaN        NaN  60423.305214   \n",
       "664               NaN                  NaN        NaN  60423.305584   \n",
       "665               NaN                  NaN        NaN  60423.305648   \n",
       "666               NaN                  NaN        NaN  60423.306493   \n",
       "667               NaN                  NaN        NaN  60423.307454   \n",
       "668               NaN                  NaN        NaN  60423.308426   \n",
       "669               NaN                  NaN        NaN  60423.309410   \n",
       "\n",
       "              note  \n",
       "663  spec:HD111235  \n",
       "664  spec:HD111235  \n",
       "665  spec:HD111235  \n",
       "666  spec:HD111235  \n",
       "667  spec:HD111235  \n",
       "668  spec:HD111235  \n",
       "669  spec:HD111235  \n",
       "\n",
       "[7 rows x 52 columns]"
      ]
     },
     "execution_count": 633,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rtvT.query('note == \"spec:HD111235\" and obsMjd > 60423.3 and obsMjd < 60423.35' )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 623,
   "id": "d7046b3d-d4e6-4f41-b124-7d5081545187",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "https://s3.cp.lsst.org/rubinobs-lfa-cp/Scheduler:2/Scheduler:2/2024/04/22/Scheduler:2_Scheduler:2_2024-04-22T23:10:50.984.p\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>url</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2024-04-22 23:10:15.819373+00:00</th>\n",
       "      <td>https://s3.cp.lsst.org/rubinobs-lfa-cp/Schedul...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-04-22 23:26:16.545699+00:00</th>\n",
       "      <td>https://s3.cp.lsst.org/rubinobs-lfa-cp/Schedul...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-04-22 23:35:11.021632+00:00</th>\n",
       "      <td>https://s3.cp.lsst.org/rubinobs-lfa-cp/Schedul...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-04-22 23:38:50.622913+00:00</th>\n",
       "      <td>https://s3.cp.lsst.org/rubinobs-lfa-cp/Schedul...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-04-23 00:04:36.720182+00:00</th>\n",
       "      <td>https://s3.cp.lsst.org/rubinobs-lfa-cp/Schedul...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-04-23 09:42:13.338472+00:00</th>\n",
       "      <td>https://s3.cp.lsst.org/rubinobs-lfa-cp/Schedul...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-04-23 09:46:09.696161+00:00</th>\n",
       "      <td>https://s3.cp.lsst.org/rubinobs-lfa-cp/Schedul...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-04-23 10:02:56.504990+00:00</th>\n",
       "      <td>https://s3.cp.lsst.org/rubinobs-lfa-cp/Schedul...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-04-23 10:05:26.947234+00:00</th>\n",
       "      <td>https://s3.cp.lsst.org/rubinobs-lfa-cp/Schedul...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-04-23 10:10:04.743889+00:00</th>\n",
       "      <td>https://s3.cp.lsst.org/rubinobs-lfa-cp/Schedul...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>83 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                                                url\n",
       "2024-04-22 23:10:15.819373+00:00  https://s3.cp.lsst.org/rubinobs-lfa-cp/Schedul...\n",
       "2024-04-22 23:26:16.545699+00:00  https://s3.cp.lsst.org/rubinobs-lfa-cp/Schedul...\n",
       "2024-04-22 23:35:11.021632+00:00  https://s3.cp.lsst.org/rubinobs-lfa-cp/Schedul...\n",
       "2024-04-22 23:38:50.622913+00:00  https://s3.cp.lsst.org/rubinobs-lfa-cp/Schedul...\n",
       "2024-04-23 00:04:36.720182+00:00  https://s3.cp.lsst.org/rubinobs-lfa-cp/Schedul...\n",
       "...                                                                             ...\n",
       "2024-04-23 09:42:13.338472+00:00  https://s3.cp.lsst.org/rubinobs-lfa-cp/Schedul...\n",
       "2024-04-23 09:46:09.696161+00:00  https://s3.cp.lsst.org/rubinobs-lfa-cp/Schedul...\n",
       "2024-04-23 10:02:56.504990+00:00  https://s3.cp.lsst.org/rubinobs-lfa-cp/Schedul...\n",
       "2024-04-23 10:05:26.947234+00:00  https://s3.cp.lsst.org/rubinobs-lfa-cp/Schedul...\n",
       "2024-04-23 10:10:04.743889+00:00  https://s3.cp.lsst.org/rubinobs-lfa-cp/Schedul...\n",
       "\n",
       "[83 rows x 1 columns]"
      ]
     },
     "execution_count": 623,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get more recent scheduler snapshots\n",
    "topic = \"lsst.sal.Scheduler.logevent_largeFileObjectAvailable\"\n",
    "fields = [\"url\"]\n",
    "\n",
    "dd = await efd_client.select_time_series(topic, fields, sunset, sunrise, index=2)\n",
    "print(dd.url.iloc[0])\n",
    "dd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 326,
   "id": "2647ed1f-f9a6-4e13-b4d9-f58d968fd2a5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "https://s3.cp.lsst.org/rubinobs-lfa-cp/Scheduler:2/Scheduler:2/2024/04/02/Scheduler:2_Scheduler:2_2024-04-02T23:57:55.593.p\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "ResourcePath(\"s3://rubin:rubinobs-lfa-cp/Scheduler:2/Scheduler:2/2024/04/02/Scheduler:2_Scheduler:2_2024-04-02T23:57:55.593.p\")"
      ]
     },
     "execution_count": 326,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# for scheduler snapshots \n",
    "url = dd.url.iloc[0]\n",
    "print(url)\n",
    "\n",
    "bucket = \"s3://rubin:\"\n",
    "uri = ResourcePath(bucket + urlparse(url).path.lstrip('/'))\n",
    "uri"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 327,
   "id": "cefcd9ea-3374-4b29-adbf-11cf7b70888f",
   "metadata": {},
   "outputs": [],
   "source": [
    "res = uri.read()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 333,
   "id": "601c8e3c-2ef3-42d0-b051-c0bb85e0cdb4",
   "metadata": {},
   "outputs": [],
   "source": [
    "scheduler, conditions = pickle.loads(res)\n",
    "assert isinstance(scheduler, CoreScheduler)\n",
    "assert isinstance(conditions, Conditions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 329,
   "id": "63b29db0-3b9c-46f2-b172-d8e93373f59f",
   "metadata": {},
   "outputs": [],
   "source": [
    "write = True\n",
    "if write:\n",
    "    for k in [res]:\n",
    "        with open(f'pickles/snapshot_{k}.p', 'bw') as file:\n",
    "            pickle.dump(snapshot_pickles[k], file)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "822bf800-34dc-4f6e-b0cb-c9c39e3b044e",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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