test_2.ipynb

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      "application/vnd.bokehjs_load.v0+json": ""
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   ],
   "source": [
    "import glob\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import umap\n",
    "import umap.plot as uplot\n",
    "import h5py\n",
    "import pickle\n",
    "from sklearn.utils import Bunch\n",
    "from datetime import datetime\n",
    "import bokeh.plotting as bk\n",
    "import bokeh.transform as btr\n",
    "from bokeh.models import *\n",
    "from bokeh.palettes import *\n",
    "bk.output_notebook()\n",
    "%matplotlib inline\n",
    "sns.set(style='white', context='notebook', rc={'figure.figsize':(14,10)})\n",
    "\n",
    "\n",
    "# get filepaths of all files in '/mnt/c/Users/findm/Desktop/surf2023/waterfall_data/data/'\n",
    "filepaths = glob.glob('/mnt/c/Users/findm/Desktop/surf2023/waterfall_data/data/*.h5')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['calibrated_wfall', 'extent', 'model_spec', 'model_ts', 'model_wfall', 'plot_freq', 'plot_time', 'spec', 'ts', 'wfall']\n",
      "['calibration_observation_date', 'calibration_source_name', 'dm', 'scatterfit', 'tns_name']\n",
      "<class 'numpy.float64'>\n"
     ]
    }
   ],
   "source": [
    "f = h5py.File(filepaths[-1], 'r')\n",
    "print(list(f['frb'].keys()))\n",
    "print(list(f['frb'].attrs.keys()))"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- `extent`: the extent of the waterfall data\n",
    "- `plot_freq`: The values of the frequecy indices in $\\rm{MHz}$\n",
    "- `plot_time`: The value of the time indices in $\\rm{\\mu s}$\n",
    "- `wfall`: waterfall data\n",
    "- `model_wfall`: waterfall from fitted data\n",
    "- `spec`: Dynamic Spectrum\n",
    "- `model_spec`: model-fitted dynamic spectrum\n",
    "- `ts`: time series data\n",
    "- `model_ts`: model-fitted time series\n",
    "- `caliberated_wfall`: The waterfall data with calibration applied\n",
    "- `calibrated_observation_date`\n",
    "- `calibration_source_name`\n",
    "- `dm`\n",
    "- `scatterfit`\n",
    "- `tns_name`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# make a new sklearn-compatible dataset with one entry for every frb file\n",
    "# each entry should have tns_name and dm as metadata, and plot_freq, plot_time, wfall, spec, ts as data\n",
    "\n",
    "tns_names = []\n",
    "dates = []\n",
    "# dms = []\n",
    "# plot_freqs = []\n",
    "# plot_times = []\n",
    "# wfalls = []\n",
    "specs = []\n",
    "tss = []\n",
    "\n",
    "for f in filepaths:\n",
    "    with h5py.File(f, 'r') as frb:\n",
    "        tns_names.append(frb['frb'].attrs['tns_name'].decode())\n",
    "        dates.append(datetime.strptime(frb['frb'].attrs['tns_name'].decode()[3:-1], '%Y%m%d'))\n",
    "        # dms.append(frb['frb'].attrs['dm'])\n",
    "        # plot_freqs.append(frb['frb']['plot_freq'][:])\n",
    "        # plot_times.append(frb['frb']['plot_time'][:])\n",
    "        # wfalls.append(frb['frb']['wfall'][:])\n",
    "        specs.append(frb['frb']['spec'][:])\n",
    "        tss.append(frb['frb']['ts'][:])\n",
    "        \n",
    "frb_dataset = Bunch(tns_name=tns_names, date=dates, spec=specs, ts=tss)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "from cfod import catalog\n",
    "catalog = catalog.as_dataframe()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "| Column Name | Description | Units |\n",
    "| --- | --- | --- |\n",
    "| tns_name | TNS name of the burst | - |\n",
    "| previous_name | Previous name (if applicable) | - |\n",
    "| repeater_name | Associated repeater name (if applicable) | - |\n",
    "| ra | Right ascension (J2000) | degrees |\n",
    "| ra_err | Right ascension error (68% confidence) | degrees |\n",
    "| dec | Declination (J2000) | degrees |\n",
    "| dec_err | Declination error (68% confidence) | degrees |\n",
    "| gl | Galactic longitude | degrees |\n",
    "| gb | Galactic latitude | degrees |\n",
    "| exp_up | Exposure for upper transit of the source | hour |\n",
    "| exp_up_err | Exposure error for upper transit of the source | hour |\n",
    "| exp_low | Exposure for lower transit of the source | hour |\n",
    "| exp_low_err | Exposure error for lower transit of the source | hour |\n",
    "| bonsai_snr | Detection SNR | - |\n",
    "| bonsai_dm | Detection DM | pc cm−3 |\n",
    "| low_ft_68 | Lower limit fluence threshold (68% confidence) | Jy ms |\n",
    "| up_ft_68 | Upper limit fluence threshold (68% confidence) | Jy ms |\n",
    "| low_ft_95 | Lower limit fluence threshold (95% confidence) | Jy ms |\n",
    "| up_ft_95 | Upper limit fluence threshold (95% confidence) | Jy ms |\n",
    "| snr_fitb | SNR determined using the fitting algorithm fitburst | - |\n",
    "| dm_fitb | DM determined using the fitting algorithm fitburst | pc cm−3 |\n",
    "| dm_fitb_err | DM error determined using the fitting algorithm fitburst | pc cm−3 |\n",
    "| dm_exc_ne2001 | DM excess between DM determined by fitburst and NE2001 assuming the best-fit sky position of the source | pc cm−3 |\n",
    "| dm_exc_ymw16 | DM excess between DM determined by fitburst and YMW16 assuming the best-fit sky position of the source | pc cm−3 |\n",
    "| bc_width | Box car width of the pulse | s |\n",
    "| scat_time | Scattering time (at 600MHz) | s |\n",
    "| scat_time_err | Scattering time (at 600MHz) error | s |\n",
    "| flux | Flux | Jy |\n",
    "| flux_err | Flux error | Jy |\n",
    "| fluence | Fluence | Jy ms |\n",
    "| fluence_err | Fluence error | Jy ms |\n",
    "| sub_num | Sub-burst number (1 if the FRB has only one burst) | - |\n",
    "| mjd_400 | Time of arrival in UTC at CHIME location (topocentric) with reference to 400.19 MHz for the specific sub-burst | MJD |\n",
    "| mjd_400_err | Time of arrival in UTC at CHIME location (topocentric) error with reference to 400.19 MHz for the specific sub-burst | MJD |\n",
    "| mjd_inf | Time of arrival in UTC at CHIME location (topocentric) with reference to infinite frequency for the specific sub-burst | MJD |\n",
    "| mjd_inf_err | Time of arrival in UTC at CHIME location (topocentric) error with reference to infinite frequency for the specific sub-burst | MJD |\n",
    "| width_fitb | Width of sub-burst using fitburst | s |\n",
    "| width_fitb_err | Width error of sub-burst using fitburst | s |\n",
    "| sp_idx | Spectral index for the sub-burst | - |\n",
    "| sp_idx_err | Spectral index error for the sub-burst | - |\n",
    "| sp_run | Spectral running for the sub-burst | - |\n",
    "| sp_run_err | Spectral running error for the sub-burst | - |\n",
    "| high_freq | Highest frequency band of detection for the sub-burst | MHz |\n",
    "| low_freq | Lowest frequency band of detection for the sub-burst | MHz |\n",
    "| peak_freq | Peak frequency for the sub-burst | MHz |\n",
    "| chi_sq | Chi-squared from fitburst | - |\n",
    "| dof | Number of degrees of freedom in fitburst | - |\n",
    "| flag_frac | Fraction of spectral channels flagged in fitburst | - |"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# get attribute of frb by name\n",
    "def get_attr(tns_name, attr_name, log=False):\n",
    "    x = catalog[catalog['tns_name'] == tns_name][attr_name].values[0]\n",
    "    if isinstance(x, str):\n",
    "        x = x.replace('<', '')\n",
    "        x = x.replace('>', '')\n",
    "        x = float(x)\n",
    "    if log:\n",
    "        x = np.log10(x)\n",
    "    return x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "UMAP( verbose=True)\n",
      "Wed Jul  5 02:31:22 2023 Construct fuzzy simplicial set\n",
      "Wed Jul  5 02:31:23 2023 Finding Nearest Neighbors\n",
      "Wed Jul  5 02:31:23 2023 Finished Nearest Neighbor Search\n",
      "Wed Jul  5 02:31:23 2023 Construct embedding\n"
     ]
    },
    {
     "data": {
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       "model_id": "419faac772f24275b05442acebbc97db",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Epochs completed:   0%|            0/500 [00:00]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Wed Jul  5 02:31:29 2023 Finished embedding\n"
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   <div>\\n        <div>\\n            <span style=\\\"font-size: 15px; font-weight: bold;\\\">@tns_name</span>\\n        </div>\\n        <div>\\n            <span style=\\\"font-size: 10px; color: #696;\\\">Width (\\u00b5s): </span>\\n            <span style=\\\"font-size: 10px; color: #696;\\\">@width</span>\\n        </div>\\n    </div>\\n\"}}]}},\"toolbar_location\":\"above\",\"left\":[{\"type\":\"object\",\"name\":\"LinearAxis\",\"id\":\"p19018\",\"attributes\":{\"visible\":false,\"ticker\":{\"type\":\"object\",\"name\":\"BasicTicker\",\"id\":\"p19020\",\"attributes\":{\"mantissas\":[1,2,5]}},\"formatter\":{\"type\":\"object\",\"name\":\"BasicTickFormatter\",\"id\":\"p19021\"},\"major_label_policy\":{\"type\":\"object\",\"name\":\"AllLabels\",\"id\":\"p19019\"}}}],\"below\":[{\"type\":\"object\",\"name\":\"LinearAxis\",\"id\":\"p19011\",\"attributes\":{\"visible\":false,\"ticker\":{\"type\":\"object\",\"name\":\"BasicTicker\",\"id\":\"p19013\",\"attributes\":{\"mantissas\":[1,2,5]}},\"formatter\":{\"type\":\"object\",\"name\":\"BasicTickFormatter\",\"id\":\"p19014\"},\"major_label_policy\":{\"type\":\"object\",\"name\":\"AllLabels\",\"id\":\"p19012\"}}}],\"center\":[{\"type\":\"object\",\"name\":\"Grid\",\"id\":\"p19017\",\"attributes\":{\"visible\":false,\"axis\":{\"id\":\"p19011\"}}},{\"type\":\"object\",\"name\":\"Grid\",\"id\":\"p19024\",\"attributes\":{\"visible\":false,\"dimension\":1,\"axis\":{\"id\":\"p19018\"}}}]}}],\"callbacks\":{\"type\":\"map\"}}};\n  const render_items = [{\"docid\":\"c5aa484f-2128-4d16-baf6-547001f56dc7\",\"roots\":{\"p18993\":\"e6439294-2262-42e2-86d2-5cc42a30c40e\"},\"root_ids\":[\"p18993\"]}];\n  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": "p18993"
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "reducer = umap.UMAP(\n",
    "    n_neighbors=15, \n",
    "    min_dist=0.1, \n",
    "    n_components=2, \n",
    "    metric='euclidean', \n",
    "    verbose=True\n",
    ")\n",
    "reducer.fit(np.nan_to_num(frb_dataset.spec))\n",
    "\n",
    "embedding_df = pd.DataFrame(reducer.embedding_, columns=('x', 'y'))\n",
    "embedding_df['tns_name'] = frb_dataset.tns_name\n",
    "embedding_df['date'] = [d.year for d in frb_dataset.date]\n",
    "embedding_df['dm'] = [get_attr(tns_name, 'dm_fitb') for tns_name in frb_dataset.tns_name]\n",
    "embedding_df['width'] = [get_attr(tns_name, 'width_fitb')*1000000 for tns_name in frb_dataset.tns_name]\n",
    "\n",
    "def make_colormap(col_name):\n",
    "    return btr.linear_cmap(col_name, Plasma256, low=min(embedding_df[col_name]), high=max(embedding_df[col_name]))\n",
    "\n",
    "tooltips = \"\"\"\n",
    "    <div>\n",
    "        <div>\n",
    "            <span style=\"font-size: 15px; font-weight: bold;\">@tns_name</span>\n",
    "        </div>\n",
    "        <div>\n",
    "            <span style=\"font-size: 10px; color: #696;\">Width (µs): </span>\n",
    "            <span style=\"font-size: 10px; color: #696;\">@width</span>\n",
    "        </div>\n",
    "    </div>\n",
    "\"\"\"\n",
    "\n",
    "plot = bk.figure(\n",
    "    title='UMAP projection of FRB data',\n",
    "    toolbar_location='above',\n",
    "    tools=(\n",
    "        CopyTool(),\n",
    "        HoverTool(tooltips=tooltips)\n",
    "    )\n",
    ")\n",
    "\n",
    "plot.circle(\n",
    "    x=\"x\",\n",
    "    y=\"y\", \n",
    "    source=embedding_df, \n",
    "    size=7,\n",
    "    line_color=None,\n",
    "    color=make_colormap('width'),\n",
    ")\n",
    "\n",
    "plot.grid.visible = False\n",
    "plot.axis.visible = False\n",
    "\n",
    "bk.show(plot)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_23431/2256696748.py:1: UserWarning: save() called but no resources were supplied and output_file(...) was never called, defaulting to resources.CDN\n",
      "  bk.save(plot, 'umap_frb_spec.html')\n",
      "/tmp/ipykernel_23431/2256696748.py:1: UserWarning: save() called but no title was supplied and output_file(...) was never called, using default title 'Bokeh Plot'\n",
      "  bk.save(plot, 'umap_frb_spec.html')\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "'/home/guutz/dawn/umap_frb_spec.html'"
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bk.save(plot, 'umap_frb_spec.html')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle\n",
    "chime_frb_529_interpolated_ts = pickle.load(open('chime_frb_529_interpolated_ts.pkl','rb'))\n",
    "data = chime_frb_529_interpolated_ts[:][3000:8000]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([], shape=(0, 14962), dtype=float64)"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Missing values in second list: \n",
      "Additional values in second list:\n",
      " FRB20190322B,\n",
      "FRB20190317B,\n",
      "FRB20190317A,\n",
      "FRB20181222D,\n",
      "FRB20190329A,\n",
      "FRB20190228A,\n",
      "FRB20181220A\n"
     ]
    }
   ],
   "source": [
    "list1 = frb_dataset['tns_name']\n",
    "list2 = list(catalog['tns_name'])\n",
    "missing = set(list1).difference(list2)\n",
    "added = set(list2).difference(list1)\n",
    "print('Missing values in second list:', ','.join(missing))\n",
    "print('Additional values in second list:\\n', ',\\n'.join(added))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['FRB20181028A', 'FRB20190501B', 'FRB20190422A', 'FRB20190605B', 'FRB20181117B', 'FRB20181223A', 'FRB20190625E', 'FRB20181226A', 'FRB20181224E', 'FRB20190611A', 'FRB20181019A', 'FRB20190308C', 'FRB20190423B', 'FRB20180917A', 'FRB20181119D', 'FRB20190308B', 'FRB20190519B', 'FRB20181222E', 'FRB20180814B', 'FRB20190109A', 'FRB20190208A', 'FRB20190122C', 'FRB20190301A', 'FRB20181125A', 'FRB20181226B', 'FRB20190104A', 'FRB20181128C', 'FRB20190519A', 'FRB20190421A', 'FRB20190423A', 'FRB20190124C', 'FRB20190604F', 'FRB20190131D', 'FRB20190411C', 'FRB20181222A', 'FRB20190601C', 'FRB20190527A', 'FRB20190213B', 'FRB20181104C', 'FRB20181128A', 'FRB20190609A', 'FRB20190111A']\n",
      "42\n"
     ]
    }
   ],
   "source": [
    "duplicates = [x for x in set(list2) if list2.count(x) > 1]\n",
    "print(duplicates)\n",
    "print(len(duplicates))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "536"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(set(list2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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