diff --git a/docs/notebooks/19-FRB20180916B.ipynb b/docs/notebooks/19-FRB20180916B.ipynb new file mode 100644 index 0000000..3db944b --- /dev/null +++ b/docs/notebooks/19-FRB20180916B.ipynb @@ -0,0 +1,1071 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---\n", + "title: \"Testing known repeaters with limited samples\"\n", + "date: \"2023-08-09\"\n", + "\n", + "---" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# FRB20190915D" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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eventnamerepeater_namemjd_400fluencecatalogmjd
0FRB20180725A-999958324.7498034.00Catalog 158324.749803
1FRB20180727A-999958326.0362622.30Catalog 158326.036262
2FRB20180729A-999958328.03356520.00Catalog 158328.033565
3FRB20180729B-999958328.7280321.20Catalog 158328.728032
4FRB20180730A-999958329.15113430.00Catalog 158329.151134
.....................
146FRB20210313EFRB20201221B59286.2021131.13Catalog 202359286.202113
147FRB20210331FFRB20210323C59304.1477376.10Catalog 202359304.147737
148FRB20210331FFRB20210323C59304.1477376.10Catalog 202359304.147737
149FRB20210426BFRB20210323C59330.0918357.60Catalog 202359330.091835
150FRB20210426BFRB20210323C59330.0918367.60Catalog 202359330.091836
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945 rows × 6 columns

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" + ], + "text/plain": [ + " eventname repeater_name mjd_400 fluence catalog \\\n", + "0 FRB20180725A -9999 58324.749803 4.00 Catalog 1 \n", + "1 FRB20180727A -9999 58326.036262 2.30 Catalog 1 \n", + "2 FRB20180729A -9999 58328.033565 20.00 Catalog 1 \n", + "3 FRB20180729B -9999 58328.728032 1.20 Catalog 1 \n", + "4 FRB20180730A -9999 58329.151134 30.00 Catalog 1 \n", + ".. ... ... ... ... ... \n", + "146 FRB20210313E FRB20201221B 59286.202113 1.13 Catalog 2023 \n", + "147 FRB20210331F FRB20210323C 59304.147737 6.10 Catalog 2023 \n", + "148 FRB20210331F FRB20210323C 59304.147737 6.10 Catalog 2023 \n", + "149 FRB20210426B FRB20210323C 59330.091835 7.60 Catalog 2023 \n", + "150 FRB20210426B FRB20210323C 59330.091836 7.60 Catalog 2023 \n", + "\n", + " mjd \n", + "0 58324.749803 \n", + "1 58326.036262 \n", + "2 58328.033565 \n", + "3 58328.728032 \n", + "4 58329.151134 \n", + ".. ... \n", + "146 59286.202113 \n", + "147 59304.147737 \n", + "148 59304.147737 \n", + "149 59330.091835 \n", + "150 59330.091836 \n", + "\n", + "[945 rows x 6 columns]" + ] + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import os\n", + "from pathlib import Path\n", + "\n", + "import pandas as pd\n", + "import numpy as np\n", + "\n", + "from astropy.timeseries import LombScargle\n", + "from astropy.time import Time\n", + "\n", + "DATAPATH = os.getenv('DATAPATH')\n", + "CATALOG_PATH = Path(DATAPATH, 'raw', 'catalog2023', 'catalog2023_profile.parquet')\n", + "CATALOG_PATH_CSV = Path(DATAPATH, 'raw', 'catalog2023', 'chimefrb2023repeaters.csv')\n", + "CATALOG1_PATH_PARQUET = Path(DATAPATH, 'catalog_1.parquet')\n", + "\n", + "cat1 = pd.read_parquet(CATALOG1_PATH_PARQUET)[[\"eventname\",\"repeater_name\", \"mjd_400\", \"fluence\"]]\n", + "cat1['catalog'] = 'Catalog 1'\n", + "cat2023 = pd.read_csv(CATALOG_PATH_CSV)[[\"tns_name\",\"repeater_name\",\"mjd_400\", \"fluence\"]].rename(columns={'tns_name':'eventname'})\n", + "cat2023['catalog'] = 'Catalog 2023'\n", + "data = pd.concat([cat1, cat2023])\n", + "data['mjd'] = data['mjd_400']\n", + "data" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\LENOVO\\AppData\\Local\\Temp\\ipykernel_16364\\1814019445.py:9: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n", + " to_plot=selected.set_index('datetime').resample('d').sum()\n" + ] + } + ], + "source": [ + "import seaborn as sns\n", + "\n", + "chosen_name = 'FRB20180916B' # (77)\n", + "\n", + "selected = data[['repeater_name', 'mjd', 'catalog']].sort_values(by='mjd')\n", + "selected[chosen_name] = (selected['repeater_name'] == chosen_name).astype(int)\n", + "# detections = selected[chosen_name].sum()\n", + "selected['datetime'] = Time(selected['mjd'], format='mjd').to_datetime()\n", + "to_plot=selected.set_index('datetime').resample('d').sum()" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import astropy.units as u\n", + "\n", + "time = Time(to_plot.index.to_numpy())\n", + "obs = to_plot[chosen_name].to_numpy()\n", + "\n", + "freq_min = 1 / (3 * u.day)\n", + "freq_max = 1/(len(to_plot)*.5 * u.day)\n", + "\n", + "freq_grid = np.linspace(freq_max,freq_min, 10_000)\n", + "\n", + "LS = LombScargle(time, obs)\n", + "power = LS.power(freq_grid)\n", + "\n", + "p = 1/freq_grid[np.nanargmax(power)]\n", + "\n", + "g = sns.lineplot(x=1/freq_grid, y=power)\n", + "g.axvline(p.value, color='red', alpha=1)\n", + "g.set_xscale('log')" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "text/latex": [ + "$16.330021 \\; \\mathrm{d}$" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "1/freq_grid[np.nanargmax(power)]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Limited Window" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "expected = 30\n", + "observation = 3*expected\n", + "offset = 30\n", + "\n", + "page = 0\n", + "window = offset+page*observation\n", + "limited = to_plot[window:window+observation]\n", + "\n", + "g = sns.lineplot(x=to_plot.index.to_numpy(), y=to_plot[chosen_name].to_numpy())\n", + "g.axvspan(to_plot.index[window], to_plot.index[window+observation], color='green', alpha=0.3)\n", + "g.axvline(to_plot.index[-691], color='red', alpha=0.3)\n", + "g.axvline(to_plot.index[53], color='red', alpha=0.3)" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(53, -691)" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from copy import deepcopy\n", + "\n", + "\n", + "def get_first_and_last_index(items: list) -> (int, int):\n", + " first = 0\n", + " last = 0\n", + " for item in deepcopy(items):\n", + " if item == 0:\n", + " first += 1\n", + " elif item > 0:\n", + " break\n", + " for item in deepcopy(items)[::-1]:\n", + " if item == 0:\n", + " last -= 1\n", + " elif item > 0:\n", + " break\n", + " return first, last\n", + "\n", + "get_first_and_last_index(to_plot[chosen_name].to_numpy())" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "limited_time = Time(limited.index.to_numpy())\n", + "limited_obs = limited[chosen_name].to_numpy()\n", + "\n", + "assert np.sum(limited_obs) > 0, \"No Observation found\"\n", + "\n", + "freq_min = 1 / (3 * u.day)\n", + "freq_max = 1/(len(limited) * u.day)\n", + "\n", + "freq_grid = np.linspace(freq_max,freq_min, 10_000)\n", + "\n", + "LS = LombScargle(limited_time, limited_obs)\n", + "power = LS.power(freq_grid)\n", + "\n", + "p = 1/freq_grid[np.nanargmax(power)]\n", + "\n", + "false_alarm_prob = LS.false_alarm_probability(1/p)\n", + "\n", + "g = sns.lineplot(x=1/freq_grid, y=power)\n", + "g.axvline(p.value, color='red', alpha=1)\n", + "g.axvline(2*p.value, color='red', alpha=0.3)\n", + "g.axvline(3*p.value, color='red', alpha=0.3)\n", + "g.axvline(0.5*p.value, color='red', alpha=0.3)\n", + "g.axvline(0.25*p.value, color='red', alpha=0.3)\n", + "# g.set_ylim(0,0.003)\n", + "g.set_title(f\"{chosen_name} (Period={p.value:.2f} d) [{false_alarm_prob:.2f}%]\")\n", + "g.set_xscale('log')" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "def get_first_and_last_index(items: list) -> (int, int):\n", + " first = 0\n", + " last = 0\n", + " for item in deepcopy(items):\n", + " if item == 0:\n", + " first += 1\n", + " elif item > 0:\n", + " break\n", + " for item in deepcopy(items)[::-1]:\n", + " if item == 0:\n", + " last -= 1\n", + " elif item > 0:\n", + " break\n", + " return first, last\n", + " \n", + "def periodicity_within_window(t: np.array, y: np.array, expected_period: float, multiplier: int, offset: int) -> (float, float):\n", + " window = int(np.ceil(multiplier*expected_period))\n", + " limited_t = Time(t[offset+window:offset+(2*window)])\n", + " limited_y = y[offset+window:offset+(2*window)]\n", + "\n", + " if np.sum(limited_y) == 0:\n", + " raise ValueError(\"No Observation found.\")\n", + "\n", + " freq_min = 1 / (3 * u.day)\n", + " freq_max = 1/(len(limited_t) * u.day)\n", + "\n", + " freq_grid = np.linspace(freq_max,freq_min, 10_000)\n", + "\n", + " LS = LombScargle(limited_t, limited_y)\n", + " power = LS.power(freq_grid)\n", + "\n", + " period = 1/freq_grid[np.nanargmax(power)]\n", + " false_alarm_prob = LS.false_alarm_probability(1/period)\n", + " return period, false_alarm_prob" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "d:\\home\\kerja\\sarjana\\.venv\\lib\\site-packages\\numpy\\core\\fromnumeric.py:3464: RuntimeWarning: Mean of empty slice.\n", + " return _methods._mean(a, axis=axis, dtype=dtype,\n", + "d:\\home\\kerja\\sarjana\\.venv\\lib\\site-packages\\numpy\\core\\_methods.py:192: RuntimeWarning: invalid value encountered in scalar divide\n", + " ret = ret.dtype.type(ret / rcount)\n", + "d:\\home\\kerja\\sarjana\\.venv\\lib\\site-packages\\numpy\\core\\_methods.py:269: RuntimeWarning: Degrees of freedom <= 0 for slice\n", + " ret = _var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n", + "d:\\home\\kerja\\sarjana\\.venv\\lib\\site-packages\\numpy\\core\\_methods.py:226: RuntimeWarning: invalid value encountered in divide\n", + " arrmean = um.true_divide(arrmean, div, out=arrmean,\n", + "d:\\home\\kerja\\sarjana\\.venv\\lib\\site-packages\\numpy\\core\\_methods.py:261: RuntimeWarning: invalid value encountered in scalar divide\n", + " ret = ret.dtype.type(ret / rcount)\n" + ] + } + ], + "source": [ + "from collections import defaultdict\n", + "\n", + "\n", + "first, last = get_first_and_last_index(to_plot[chosen_name].to_numpy())\n", + "biggest_window = len(to_plot[first:last])\n", + "# if window > biggest_window:\n", + "# ...\n", + "\n", + "results = defaultdict()\n", + "for multiplier in range(2,30):\n", + " periods = []\n", + " fa_probs = []\n", + " for offset in (offsets:=range(45, biggest_window + first)):\n", + " try:\n", + " p, fa_prob = periodicity_within_window(to_plot.index.to_numpy(), to_plot[chosen_name].to_numpy(), 16, multiplier, offset)\n", + " periods.append(p.value)\n", + " fa_probs.append(fa_prob.value)\n", + " except ValueError:\n", + " pass\n", + " # periods.append(None)\n", + " # fa_probs.append(None)\n", + " try:\n", + " results[multiplier] = np.median(periods), np.std(periods), np.mean(periods), np.min(periods), np.max(periods)\n", + " except ValueError:\n", + " pass\n" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "medians = [v[0] for k, v in results.items()]\n", + "maxi = [v[-1] for k, v in results.items()]\n", + "mini = [v[-2] for k, v in results.items()]\n", + "\n", + "g=sns.scatterplot(x=results.keys(), y=medians)\n", + "sns.scatterplot(x=results.keys(), y=maxi, color='orange', ax=g)\n", + "sns.scatterplot(x=results.keys(), y=mini, color='orange', ax=g)\n", + "g.axhline(p.value)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Leave One Out Estimation" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\LENOVO\\AppData\\Local\\Temp\\ipykernel_16364\\1104562449.py:23: FutureWarning: Using short name for 'orient' is deprecated. Only the options: ('dict', list, 'series', 'split', 'records', 'index') will be used in a future version. Use one of the above to silence this warning.\n", + " stats_ = pd.DataFrame(np.array(best_periods)).describe().transpose().to_dict(orient='record')[0]\n" + ] + }, + { + "data": { + "text/plain": [ + "{'count': 77.0,\n", + " 'mean': 16.3305951869346,\n", + " 'std': 0.002192978856628187,\n", + " 'min': 16.33002105112927,\n", + " '25%': 16.33002105112927,\n", + " '50%': 16.33002105112927,\n", + " '75%': 16.33002105112927,\n", + " 'max': 16.338862742531433}" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import astropy.units as u\n", + "# Estimate error using leave one out resampling\n", + "\n", + "detections = selected.loc[selected[chosen_name] == 1].index\n", + "\n", + "best_periods = []\n", + "for idx in detections:\n", + " reduced=selected[selected.index != idx].set_index('datetime').resample('d').sum(numeric_only=True)\n", + "\n", + " time = Time(reduced.index.to_numpy())\n", + " obs = reduced[chosen_name].to_numpy()\n", + "\n", + " freq_min = 1 / (3 * u.day)\n", + " freq_max = 1/(len(reduced)*.5 * u.day)\n", + "\n", + " freq_grid = np.linspace(freq_max,freq_min, 10_000)\n", + "\n", + " LS = LombScargle(time, obs)\n", + " power = LS.power(freq_grid)\n", + "\n", + " best_periods.append(1/freq_grid[np.nanargmax(power)].value)\n", + "\n", + "stats_ = pd.DataFrame(np.array(best_periods)).describe().transpose().to_dict(orient='record')[0]\n", + "stats_" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import astropy.units as u\n", + "\n", + "time = Time(to_plot.index.to_numpy())\n", + "obs = to_plot[chosen_name].to_numpy()\n", + "\n", + "freq_min = 1 / (3 * u.day)\n", + "freq_max = 1/(len(to_plot)*.5 * u.day)\n", + "\n", + "freq_grid = np.linspace(freq_max,freq_min, 10_000)\n", + "\n", + "LS = LombScargle(time, obs)\n", + "power = LS.power(freq_grid)\n", + "\n", + "p = 1/freq_grid[np.nanargmax(power)]\n", + "\n", + "low = stats_['mean'] - stats_['std']*2\n", + "high = stats_['mean'] + stats_['std']*2\n", + "\n", + "g = sns.lineplot(x=1/freq_grid, y=power)\n", + "g.axvline(p.value, color='red', alpha=1)\n", + "g.axvspan(low, high, alpha=0.3)\n", + "g.set_xscale('log')\n", + "g.set_xlabel('period')\n", + "g.set_ylabel('LS Power')\n", + "g.set_title(f'Lomb-Scargle Periodogram of {chosen_name} ({p.value:.2f}±{2*stats_[\"std\"]:.2f} d)')\n", + "g.figure.savefig(f'{chosen_name}-periodogram-LS.png')" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(16.326209229221345, 16.33002105112927, 16.33498114464786)" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "low, p.value, high" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Phase Folding" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\LENOVO\\AppData\\Local\\Temp\\ipykernel_16364\\1318021332.py:2: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " folded['datetime'] = Time(folded['mjd'], format='mjd').to_datetime()\n" + ] + } + ], + "source": [ + "folded = selected[['mjd', chosen_name]]\n", + "folded['datetime'] = Time(folded['mjd'], format='mjd').to_datetime()" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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mjdFRB20180916Bdatetime
058324.74980302018-07-25 17:59:42.996999
158326.03626202018-07-27 00:52:13.000944
258328.03356502018-07-29 00:48:19.998573
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14759304.14773702021-03-31 03:32:44.442940
14859304.14773702021-03-31 03:32:44.451874
13959310.96607202021-04-06 23:11:08.622372
14959330.09183502021-04-26 02:12:14.531196
15059330.09183602021-04-26 02:12:14.632094
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945 rows × 3 columns

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" + ], + "text/plain": [ + " mjd FRB20180916B datetime\n", + "0 58324.749803 0 2018-07-25 17:59:42.996999\n", + "1 58326.036262 0 2018-07-27 00:52:13.000944\n", + "2 58328.033565 0 2018-07-29 00:48:19.998573\n", + "3 58328.728032 0 2018-07-29 17:28:21.999187\n", + "4 58329.151134 0 2018-07-30 03:37:38.000583\n", + ".. ... ... ...\n", + "147 59304.147737 0 2021-03-31 03:32:44.442940\n", + "148 59304.147737 0 2021-03-31 03:32:44.451874\n", + "139 59310.966072 0 2021-04-06 23:11:08.622372\n", + "149 59330.091835 0 2021-04-26 02:12:14.531196\n", + "150 59330.091836 0 2021-04-26 02:12:14.632094\n", + "\n", + "[945 rows x 3 columns]" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "folded" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [], + "source": [ + "from astropy.timeseries import TimeSeries\n", + "\n", + "reduced = folded.set_index('datetime').resample('d').sum()\n", + "t_frb = TimeSeries(time=reduced.index, data={'detections':reduced[chosen_name].to_numpy().reshape(-1,1)})\n", + "\n", + "frac = []\n", + "trial_p = 1/freq_grid\n", + "for p in trial_p:\n", + " folded_ = t_frb.fold(period=p, wrap_phase=1, normalize_phase=True)\n", + " phases = np.array(folded_['time'])\n", + " counts = np.array(folded_['detections']).flatten()\n", + " phases = pd.DataFrame({'phase':phases, 'detections': counts}).groupby(pd.cut(phases, np.arange(0,1, 0.02)))['detections'].sum()\n", + " phases = phases.reset_index().rename(columns={'index': 'phase_bin'})\n", + " phases['cumsum'] = phases['detections'].cumsum()\n", + " inactive = 0\n", + " state = 0\n", + " prev = 0\n", + " for current in phases['cumsum']:\n", + " if current == prev:\n", + " state += 1\n", + " else:\n", + " prev = current\n", + " inactive = state if state > inactive else inactive\n", + " state = 0\n", + " frac.append(inactive/len(phases))" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "g = sns.lineplot(x=trial_p, y=frac)\n", + "g.axvline(trial_p.value[np.nanargmax(frac)], color='green', alpha=0.2)\n", + "g.set_xscale('log')" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "16.499666532478535" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "trial_p.value[np.nanargmax(frac)]" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\LENOVO\\AppData\\Local\\Temp\\ipykernel_16364\\4185941778.py:34: FutureWarning: Using short name for 'orient' is deprecated. Only the options: ('dict', list, 'series', 'split', 'records', 'index') will be used in a future version. Use one of the above to silence this warning.\n", + " stats = pd.DataFrame(np.array(best_periods)).describe().transpose().to_dict(orient='record')[0]\n" + ] + }, + { + "data": { + "text/plain": [ + "{'count': 77.0,\n", + " 'mean': 16.61153559592509,\n", + " 'std': 0.10664330874314487,\n", + " 'min': 16.59037716812769,\n", + " '25%': 16.59950311910634,\n", + " '50%': 16.59950311910634,\n", + " '75%': 16.59950311910634,\n", + " 'max': 17.535129785128188}" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "detections = selected.loc[selected[chosen_name] == 1].index\n", + "\n", + "def calc_inactive_frac(timeseries: TimeSeries, trial_periods: np.ndarray):\n", + " frac = []\n", + " for period in trial_periods:\n", + " folded_ = timeseries.fold(period=period, wrap_phase=1, normalize_phase=True)\n", + " phases = np.array(folded_['time'])\n", + " counts = np.array(folded_['detections']).flatten()\n", + " phases = pd.DataFrame({'phase':phases, 'detections': counts}).groupby(pd.cut(phases, np.arange(0,1, 0.05)))['detections'].sum()\n", + " phases = phases.reset_index().rename(columns={'index': 'phase_bin'})\n", + " phases['cumsum'] = phases['detections'].cumsum()\n", + " inactive = 0\n", + " state = 0\n", + " prev = 0\n", + " for current in phases['cumsum']:\n", + " if current == prev:\n", + " state += 1\n", + " else:\n", + " prev = current\n", + " inactive = state if state > inactive else inactive\n", + " state = 0\n", + " frac.append(inactive/len(phases))\n", + " return frac\n", + "\n", + "best_periods = []\n", + "for idx in detections:\n", + " folded = selected.loc[selected.index != idx, ['mjd', chosen_name]]\n", + " folded['datetime'] = Time(folded['mjd'], format='mjd').to_datetime()\n", + " reduced = folded.set_index('datetime').resample('d').sum()\n", + " t_frb = TimeSeries(time=reduced.index, data={'detections':reduced[chosen_name].to_numpy().reshape(-1,1)})\n", + " frac = calc_inactive_frac(t_frb, 1/freq_grid)\n", + " best_periods.append(1/freq_grid[np.nanargmax(frac)].value)\n", + "\n", + "stats = pd.DataFrame(np.array(best_periods)).describe().transpose().to_dict(orient='record')[0]\n", + "stats" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "low = stats['mean'] - stats['std']*100\n", + "high = stats['mean'] + stats['std']*100\n", + "\n", + "g = sns.lineplot(x=trial_p, y=frac)\n", + "g.axvline(period:=trial_p.value[np.nanargmax(frac)], color='red', alpha=0.7)\n", + "g.axvline(low, high, alpha=0.3)\n", + "g.set_xscale('log')\n", + "g.set_xlabel('trial periods')\n", + "g.set_ylabel('Inactive fraction')\n", + "g.set_title(f'Phase folding periodogram of {chosen_name}: ({period:.2f}±{2*stats[\"std\"]:.2f} d)')\n", + "g.figure.savefig(f'{chosen_name}-periodogram-phase.png')" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0.42105263157894735" + ] + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "1-np.nanmax(frac)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": ".venv", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.10" + }, + "orig_nbformat": 4 + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/docs/notebooks/FRB20180916B-periodogram-LS.png b/docs/notebooks/FRB20180916B-periodogram-LS.png new file mode 100644 index 0000000..d2daf17 Binary files /dev/null and b/docs/notebooks/FRB20180916B-periodogram-LS.png differ diff --git a/docs/notebooks/FRB20180916B-periodogram-phase.png b/docs/notebooks/FRB20180916B-periodogram-phase.png new file mode 100644 index 0000000..5ac9f28 Binary files /dev/null and b/docs/notebooks/FRB20180916B-periodogram-phase.png differ diff --git a/docs/paper/repeaters.pdf b/docs/paper/repeaters.pdf index 2400297..9d09556 100644 Binary files a/docs/paper/repeaters.pdf and b/docs/paper/repeaters.pdf differ diff --git a/docs/paper/repeaters.qmd b/docs/paper/repeaters.qmd index 24057c7..6c49121 100644 --- a/docs/paper/repeaters.qmd +++ b/docs/paper/repeaters.qmd @@ -102,14 +102,24 @@ The idea is that the uncertainty in the periodicity is tied to the fact that som # Result ## FRB20180916B -- All three methods were able to recover the known periodicity for FRB20180916B of 16 days with considerable standard deviation and false alarm probability of .... +The methodology were first applied to FRB20180916B as a test. +The Lomb--Scargle periodogram points to a periodicity of 16.33$\pm$0.01^[The estimation results in uncertainty below 0.01] days with a false alarm probability of ... . +The Duty Cycle periodogram shows that it has a periodicity of 16.60$\pm$0.21 days with a duty cycle of 42.11% while the Phase Dispersion Minimization periodogram shows a periodicity of 16.39$\pm$ ... days. +The low uncertainty in Lomb--Scargle periodogram can be attributed to the FRB having 77 detections with a well-defined periodicity so leaving one out does not affect the result that much. +This is consistent with the accepted value of 16 days (citation needed). + ## FRB20190915D +The periodicity results for FRB20190915D are 30.06$\pm$10.21 days using the Lomb--Scargle periodogram, 30.00$\pm$0.47 days using the Duty Cycle periodogram and 13.84$\pm$0.01 days using the Phase Dispersion Minimization periodogram. +Its false alarm probability is 4.87% and the duty cycle is ... . +It is surprising that phase dispersion minimization shows a different result. Why is that? + - FRB20190915D shows consistent periodicity of 30 days with false alarm probability of 1% using Lomb--Scargle periodogram and the duty cycle periodogram. - However, the phase dispersion minimization periodogram show a different periodicity of 13.84 days with no standard deviation. The leave one out strategy does not yield a significant standard deviation. It may be because ... + ## FRB20191106C -- In contrast, FRB20191106C shows inconsistent result. +In contrast, FRB20191106C shows inconsistent result. # Discussion diff --git a/docs/slides/jahns_2022/_metadata/gantt/milestones.csv b/docs/slides/jahns_2022/_metadata/gantt/milestones.csv new file mode 100644 index 0000000..78d834c --- /dev/null +++ b/docs/slides/jahns_2022/_metadata/gantt/milestones.csv @@ -0,0 +1,5 @@ +date,name +2023-04-17,"Proposal Defence" +2023-10-16,"Publication / Candidature Defence" +2024-04-15,"Chapters 1,2 & 3" +2024-10-14,"Submission of Dissertation" \ No newline at end of file diff --git a/docs/slides/jahns_2022/_metadata/gantt/progress-got.csv b/docs/slides/jahns_2022/_metadata/gantt/progress-got.csv new file mode 100644 index 0000000..a2f936d --- /dev/null +++ b/docs/slides/jahns_2022/_metadata/gantt/progress-got.csv @@ -0,0 +1,11 @@ +start,end,phase,task +2022-10-17,2023-07-27,Data Exploration,Data Collection +2022-10-17,2022-12-26,Data Exploration,Literature Review +2022-10-17,2022-12-26,Data Exploration,Machine Learning +2022-12-26,2023-03-17,Theoretical Study,Literature Review +2022-12-26,2023-04-17,Theoretical Study,Theoretical Consideration +2023-04-17,2023-07-27,Statistical Study,Literature Review +2023-04-17,2023-10-16,Statistical Study,Statistical Testing +2023-10-16,2024-04-15,Extrapolation,Signal Processing +2024-02-01,2024-04-15,Extrapolation,Analysis +2024-02-01,2024-10-14,Thesis Writing,Thesis Writing \ No newline at end of file diff --git a/docs/slides/jahns_2022/figures/20_energetic.png b/docs/slides/jahns_2022/figures/20_energetic.png new file mode 100644 index 0000000..918f3ce Binary files /dev/null and b/docs/slides/jahns_2022/figures/20_energetic.png differ diff --git a/docs/slides/jahns_2022/figures/DM-sigma_t.png b/docs/slides/jahns_2022/figures/DM-sigma_t.png new file mode 100644 index 0000000..1c92553 Binary files /dev/null and b/docs/slides/jahns_2022/figures/DM-sigma_t.png differ diff --git a/docs/slides/jahns_2022/figures/burst-rate.png b/docs/slides/jahns_2022/figures/burst-rate.png new file mode 100644 index 0000000..0a79224 Binary files /dev/null and b/docs/slides/jahns_2022/figures/burst-rate.png differ diff --git a/docs/slides/jahns_2022/figures/dt-sigma_t-linear-vs-power.png b/docs/slides/jahns_2022/figures/dt-sigma_t-linear-vs-power.png new file mode 100644 index 0000000..a0bc3b4 Binary files /dev/null and b/docs/slides/jahns_2022/figures/dt-sigma_t-linear-vs-power.png differ diff --git a/docs/slides/jahns_2022/figures/gauss.png b/docs/slides/jahns_2022/figures/gauss.png new file mode 100644 index 0000000..83816d4 Binary files /dev/null and b/docs/slides/jahns_2022/figures/gauss.png differ diff --git a/docs/slides/jahns_2022/figures/gauss_waterfall.png b/docs/slides/jahns_2022/figures/gauss_waterfall.png new file mode 100644 index 0000000..c963dfb Binary files /dev/null and b/docs/slides/jahns_2022/figures/gauss_waterfall.png differ diff --git a/docs/slides/jahns_2022/figures/subburst-vs-intraburst-drift.png b/docs/slides/jahns_2022/figures/subburst-vs-intraburst-drift.png new file mode 100644 index 0000000..06d36ec Binary files /dev/null and b/docs/slides/jahns_2022/figures/subburst-vs-intraburst-drift.png differ diff --git a/docs/slides/jahns_2022/gantt.png b/docs/slides/jahns_2022/gantt.png new file mode 100644 index 0000000..7235420 Binary files /dev/null and b/docs/slides/jahns_2022/gantt.png differ diff --git a/docs/slides/jahns_2022/index.qmd b/docs/slides/jahns_2022/index.qmd new file mode 100644 index 0000000..089f847 --- /dev/null +++ b/docs/slides/jahns_2022/index.qmd @@ -0,0 +1,114 @@ +--- +title: 'The FRB 20121102A November rain in 2018 observed with the Arecibo Telescope' +subtitle: '' +author: 'Jahns J. N., et. al. (2022)' +format: + revealjs: + footer: 'Murthadza bin Aznam' + # logo: '../../_common/_assets/radio-cosmology-lab-logo.png' + chalkboard: true + # theme: [dark, ../../_common/_assets/styles.scss] + slide-number: true + center: true +bibliography: references.bib +nocite: | + @* +output-file: 'slides' +--- + +## Data +- 849 bursts detected +- using the 305m Arecibo Telescope +- within 1150 to 1730 MHz +- in the active period of FRB20121102A around Nov 2018 + +## Observation + +![20 most energetic bursts](./figures/20_energetic.png){#fig-obs} + +## Burst Rates + +![The lower panel shows the burst rate in each observation and the upper panel shows DMs selected for dedispersion](./figures/burst-rate.png){#fig-burst-rate} + +## 2D Gaussian Model +![](./figures/gauss.png) + +- The 2D Gaussian is parameterized in such a way that it directly characterizes the center ($t_0$, $\nu_0$) and the drift rate $d_t$. +(As opposed to the commonly used form of an elliptical 2D Gaussian with rotation). + +## 2D Gaussian Fit + +::::{.columns} +:::{.column} +![The resulting 2D Gaussian fits to an example burst](./figures/gauss_waterfall.png){#fig-eg} +::: +:::{.column} +- There are two forms of drift: temporal drift, $d_t$ and frequency drift $d_\nu$ which can be mapped into and from each other. +- There are two types of drift: subburst drift (sad-trombone effect) and intraburst drift. +::: +:::: + +## Drift Relationship +![Relationship between intraburst drift and its center temporal width](./figures/dt-sigma_t-linear-vs-power.png){#fig-drift-width} + +## Inter- vs Intraburst Drift +![Comparison of the temporal drift from the sad-trombone effect (triangles) to the intraburst drift (circles) for the 12 bursts with three or more sub-bursts.](./figures/subburst-vs-intraburst-drift.png){#fig-drift} + +## Temporal Drift vs DM +![The apparent DM difference from the dedispersion DM caused by the intrinsic tilt of the sub-bursts](./figures/DM-sigma_t.png){#fig-DM-sigma_t} + +## Periodicity +- The periodicity is measured using the Lomb--Scargle Periodogram. +- Since it is only measured within the active period, this only represents the short-term periodicity. +- No short-term periodicity is found. + + +## Conclusion +- This paper proposes a new Gaussian fit based on physical parameters +- There is linear relation between subburst drift and its duration +- The intraburst drift is the cause of the apparent short-term variations in DM that have been reported. +- No short-term periodicity despite large burst rate + +## Bibliography +:::{#refs} +::: + +## Progress Report +```{python} +#| echo: false + +import pandas as pd + +from sarjana.gantt import generate_gantt + +milestone = pd.read_csv('./_metadata/gantt/milestones.csv', parse_dates=['date']) +progress = pd.read_csv('./_metadata/gantt/progress-got.csv') +``` + +:::{.content-visible when-format="pptx"} +```{python} +save = 'gantt.png' +``` +::: + +:::{.content-visible when-format="revealjs"} +```{python} +save = None +``` +::: + +```{python} +generate_gantt(progress, milestones=milestone, show=True, savefile=save) +``` + +:::{.content-visible when-format="pptx"} +![](./gantt.png) +::: + +## Progress Report +1. Periodicity of Some Repeaters with Limited Samples from CHIME/FRB Catalog 2023 + - Status: Writing +1. Fast Radio Burst Morphology Consideration of Unsupervised Machine Learning Result + - Status: On Hold +2. BURSTT Collaboration + - Status: Early communication with Taiwan team \ No newline at end of file diff --git a/docs/slides/jahns_2022/references.bib b/docs/slides/jahns_2022/references.bib new file mode 100644 index 0000000..5d06d50 --- /dev/null +++ b/docs/slides/jahns_2022/references.bib @@ -0,0 +1,16 @@ +@article{jahns_FRB20121102ANovember_2022, + title = {The {{FRB 20121102A November}} Rain in 2018 Observed with the {{Arecibo Telescope}}}, + author = {Jahns, J. N. and Spitler, L. G. and Nimmo, K. and Hewitt, D. M. and Snelders, M. P. and Seymour, A. and Hessels, J. W. T. and Gourdji, K. and Michilli, D. and Hilmarsson, G. H.}, + year = {2022}, + month = nov, + journal = {Monthly Notices of the Royal Astronomical Society}, + volume = {519}, + pages = {666--687}, + issn = {0035-8711}, + doi = {10.1093/mnras/stac3446}, + urldate = {2023-07-13}, + abstract = {We present 849 new bursts from FRB 20121102A detected with the 305-m Arecibo Telescope. Observations were conducted as part of our regular campaign to monitor activity and evolution of burst properties. The 10 reported observations were carried out between 1150 and \$1730\textbackslash, \{\textbackslash rm MHz\}\$ and fall in the active period around 2018 November. All bursts were dedispersed at the same dispersion measure and are consistent with a single value of \$(562.4 \textbackslash pm 0.1)\textbackslash, \{\textbackslash rm pc\textbackslash, cm\^\{-3\}\}\$. The rate varies between 0 bursts and 218 {$\pm$} 16 bursts per hour, the highest rate observed to date. The times between consecutive bursts show a bimodal distribution. We find that a Poisson process with varying rate best describes arrival times with separations \$\{\}\{0.1\textbackslash, \{\textbackslash rm s\}\}\$. Clustering on time-scales of \$22\textbackslash, \{\textbackslash rm ms\}\$ reflects a characteristic time-scale of the source and possibly the emission mechanism. We analyse the spectro-temporal structure of the bursts by fitting 2D Gaussians with a temporal drift to each sub-burst in the dynamic spectra. We find a linear relationship between the sub-burst's drift and its duration. At the same time, the drifts are consistent with coming from the sad-trombone effect. This has not been predicted by current models. The energy distribution shows an excess of high-energy bursts and is insufficiently modelled by a single power law even within single observations. We find long-term changes in the energy distribution, the average spectrum, and the sad-trombone drift, compared to earlier and later published observations. Despite the large burst rate, we find no strict short-term periodicity.}, + keywords = {method/modelling,status/hasread,study/Astrophysics/Fast Radio Bursts,supplementary/data}, + annotation = {ADS Bibcode: 2023MNRAS.519..666J}, + file = {C:\Users\LENOVO\Zotero\storage\D2J6RAJU\Jahns et al_2023_The FRB 20121102A November rain in 2018 observed with the Arecibo Telescope.pdf} +} diff --git a/docs/slides/jahns_2022/slides.html b/docs/slides/jahns_2022/slides.html new file mode 100644 index 0000000..df8d08d --- /dev/null +++ b/docs/slides/jahns_2022/slides.html @@ -0,0 +1,829 @@ + + + + + + + + + + + + + + The FRB 20121102A November rain in 2018 observed with the Arecibo Telescope + + + + + + + + + + + + + + + + + +
+
+ +
+

The FRB 20121102A November rain in 2018 observed with the Arecibo Telescope

+

doi:10.1093/mnras/stac3446

+ +
+
+
+Jahns J. N., et. al. (2022) +
+
+
+ +
+
+

Data

+
    +
  • 849 bursts detected
  • +
  • using the 305m Arecibo Telescope
  • +
  • within 1150 to 1730 MHz
  • +
  • in the active period of FRB20121102A around Nov 2018
  • +
+
+
+

Observation

+ +

Figure 1: 20 most energetic bursts

+
+

Burst Rates

+ +

Figure 2: The lower panel shows the burst rate in each observation and the upper panel shows DMs selected for dedispersion

+
+

2D Gaussian Model

+ +
    +
  • The 2D Gaussian is parameterized in such a way that it directly characterizes the center (\(t_0\), \(\nu_0\)) and the drift rate \(d_t\). (As opposed to the commonly used form of an elliptical 2D Gaussian with rotation).
  • +
+
+
+

2D Gaussian Fit

+
+
+
+
+

+
Figure 3: The resulting 2D Gaussian fits to an example burst
+
+
+
+
    +
  • There are two forms of drift: temporal drift, \(d_t\) and frequency drift \(d_\nu\) which can be mapped into and from each other.
  • +
  • There are two types of drift: subburst drift (sad-trombone effect) and intraburst drift.
  • +
+
+
+
+
+

Drift Relationship

+ +

Figure 4: Relationship between intraburst drift and its center temporal width

+
+

Inter- vs Intraburst Drift

+ +

Figure 5: Comparison of the temporal drift from the sad-trombone effect (triangles) to the intraburst drift (circles) for the 12 bursts with three or more sub-bursts.

+
+

Temporal Drift vs DM

+ +

Figure 6: The apparent DM difference from the dedispersion DM caused by the intrinsic tilt of the sub-bursts

+
+

Periodicity

+
    +
  • The periodicity is measured using the Lomb–Scargle Periodogram.
  • +
  • Since it is only measured within the active period, this only represents the short-term periodicity.
  • +
  • No short-term periodicity is found.
  • +
+
+
+

Conclusion

+
    +
  • This paper proposes a new Gaussian fit based on physical parameters
  • +
  • There is linear relation between subburst drift and its duration
  • +
  • The intraburst drift is the cause of the apparent short-term variations in DM that have been reported.
  • +
  • No short-term periodicity despite large burst rate
  • +
+
+
+

Bibliography

+
+
+Jahns, J. N., L. G. Spitler, K. Nimmo, D. M. Hewitt, M. P. Snelders, A. Seymour, J. W. T. Hessels, K. Gourdji, D. Michilli, and G. H. Hilmarsson. 2022. “The FRB 20121102A November Rain in 2018 Observed with the Arecibo Telescope.” Monthly Notices of the Royal Astronomical Society 519 (November): 666–87. https://doi.org/10.1093/mnras/stac3446. +
+
+
+
+

Progress Report

+
+
+

+
+
+
+
+

Progress Report

+
    +
  1. Periodicity of Some Repeaters with Limited Samples from CHIME/FRB Catalog 2023 +
      +
    • Status: Writing
    • +
  2. +
  3. Fast Radio Burst Morphology Consideration of Unsupervised Machine Learning Result +
      +
    • Status: On Hold
    • +
  4. +
  5. BURSTT Collaboration +
      +
    • Status: Early communication with Taiwan team
    • +
  6. +
+ +
+
+
+ + + + + + + + + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/docs/slides/jahns_2022/slides.pptx b/docs/slides/jahns_2022/slides.pptx new file mode 100644 index 0000000..a13eeac Binary files /dev/null and b/docs/slides/jahns_2022/slides.pptx differ