From 39d986d9a9d5973be74c714981517512c87193c8 Mon Sep 17 00:00:00 2001 From: Steven Murray Date: Tue, 4 Oct 2022 14:20:41 -0700 Subject: [PATCH 01/22] test: reproduce Pober+2014 results --- .github/workflows/run-docs-code.yaml | 2 +- docs/tutorials/reproducing_pober_2014.ipynb | 730 +++++ py21cmsense/beam.py | 5 + py21cmsense/observation.py | 3 +- py21cmsense/sensitivity.py | 15 +- tmp.ipynb | 2704 +++++++++++++++++++ 6 files changed, 3451 insertions(+), 8 deletions(-) create mode 100644 docs/tutorials/reproducing_pober_2014.ipynb create mode 100644 tmp.ipynb diff --git a/.github/workflows/run-docs-code.yaml b/.github/workflows/run-docs-code.yaml index f6bd92c..30ada2c 100644 --- a/.github/workflows/run-docs-code.yaml +++ b/.github/workflows/run-docs-code.yaml @@ -14,7 +14,7 @@ jobs: strategy: fail-fast: false matrix: - demo: ["getting_started", "understanding_21cmsense"] + demo: ["getting_started", "understanding_21cmsense", "reproducing_pober_2014"] steps: - uses: actions/checkout@master with: diff --git a/docs/tutorials/reproducing_pober_2014.ipynb b/docs/tutorials/reproducing_pober_2014.ipynb new file mode 100644 index 0000000..623cb3c --- /dev/null +++ b/docs/tutorials/reproducing_pober_2014.ipynb @@ -0,0 +1,730 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Reproducing Pober+2014" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In this demo, we are going to reproduce many of the results of [Pober+2014](https://arxiv.org/pdf/1310.7031.pdf), which looked at the sensitivity of a \"concept HERA\" (which differs somewhat from the final instrument), as well as a few other well-known arrays." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import py21cmsense as p21c\n", + "from astropy import units as un\n", + "from astropy.cosmology.units import littleh\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "p21c.config.COSMO = p21c.config.COSMO.clone(H0=70.0, Om0=0.266)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## HERA" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "P14 used a concept version of HERA that was a perfect hexagon (with no splits), with 547\n", + "elements and no outriggers. Let's create such an array:" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "hera_ants = p21c.antpos.hera(hex_num=14)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(547, 3)" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "hera_ants.shape" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We will reproduce the results at $z=9.5$ (the default case in P14):" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "beam = p21c.GaussianBeam(frequency=1420*un.MHz/(1 + 9.5), dish_size=14*un.m)" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "text/latex": [ + "$9.5939319 \\; \\mathrm{{}^{\\circ}}$" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "beam.fwhm.to(un.deg)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "What is the beam-crossing time? This is given as \"about 40min\" by P14. We obtain it as:" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "38.37572750960924" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "beam.fwhm.to(un.deg).value*24*60/360" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "As a check on whether our GaussianBeam model is similar to the assumed beam in P14, \n", + "we check the FWHM at 150MHz, quoted as 8.7deg in Table 1 of P14:" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "text/latex": [ + "$8.6497672 \\; \\mathrm{{}^{\\circ}}$" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "p21c.GaussianBeam(frequency=150*un.MHz, dish_size=14*un.m).fwhm.to(un.deg)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This is very close to the quoted value." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now, let's create the HERA Observatory model. Here, we set the receiver temperature to 100K,\n", + "as per Table 1 of P14. We also set the latitude of the instrument to the equator, which is \n", + "of course incorrect, but was the assumption made in the ismpler code of P14." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "hera = p21c.Observatory(\n", + " antpos=hera_ants,\n", + " beam=beam,\n", + " latitude=0*un.rad,\n", + " Trcv=100*un.K,\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now, set up the observation itself. Here we assume a sky temperature model of \n", + "$351{\\rm K} (\\nu/150 {\\rm MHz})^{-2.55}$, as described in S2.1.2 of P14. \n", + "\n", + "Furthermore, we set the number of days of observation to 180, with 6 hours per night. \n", + "We also set the \"coherent observing time\" to its default of the beam crossing time, \n", + "which is about 40min as per the above calculation.\n", + "\n", + "Finally, as per S3.1.2 of P14, we use 8MHz of bandwidth, with 81 channels (providing\n", + "0.1 MHz channel width)." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "obs = p21c.Observation(\n", + " observatory=hera,\n", + " tsky_amplitude=351*un.K,\n", + " tsky_ref_freq=150*un.MHz,\n", + " spectral_index=2.55,\n", + " n_days=180,\n", + " time_per_day=6*un.hour,\n", + " bandwidth=8*un.MHz,\n", + " n_channels=81,\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We make sure the observation duration is close to 40 minutes:" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "data": { + "text/latex": [ + "$2.3809441 \\; \\mathrm{\\frac{h_{100}}{Mpc}}$" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "obs.kparallel.max()" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "text/latex": [ + "$2.3809441 \\; \\mathrm{\\frac{h_{100}}{Mpc}}$" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "obs.kparallel.max()" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "data": { + "text/latex": [ + "$38.375728 \\; \\mathrm{min}$" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "obs.obs_duration.to(un.min)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now construct the sensitivity calculation:" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [], + "source": [ + "sense_optimistic = p21c.PowerSpectrum(\n", + " observation=obs,\n", + " foreground_model='optimistic',\n", + " horizon_buffer=0.0 *littleh/un.Mpc,\n", + " theory_model=p21c.theory.Legacy21cmFAST(),\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "finding redundancies: 100%|██████████| 546/546 [00:02<00:00, 182.38ants/s]\n", + "computing UVWs: 100%|██████████| 39/39 [00:00<00:00, 160.87times/s]\n", + "gridding baselines: 100%|██████████| 2106/2106 [00:00<00:00, 16108.32baselines/s]\n", + "calculating 2D sensitivity: 100%|██████████| 929/929 [00:02<00:00, 453.77uv-bins/s]\n", + "averaging to 1D: 100%|██████████| 243/243 [00:00<00:00, 4495.90kperp-bins/s]\n" + ] + } + ], + "source": [ + "sense1d = sense_optimistic.calculate_sensitivity_1d(thermal=True, sample=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/steven/work/eor/21cmSense/py21cmsense/theory.py:142: UserWarning: Extrapolating above the simulated theoretical k: 3.3749883159994045 > 2.192064\n", + " warnings.warn(\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.errorbar(\n", + " sense_optimistic.k1d, \n", + " sense_optimistic.delta_squared,\n", + " sense1d\n", + ")\n", + "plt.xlim(0.06, 1)\n", + "plt.ylim(10, 50)\n", + "plt.xscale('log')\n", + "plt.yscale('log')\n", + "\n", + "for i, kk in enumerate(sense_optimistic.k1d):\n", + " sig = sense_optimistic.delta_squared[i] / sense1d[i]\n", + " yloc = sense_optimistic.delta_squared[i].value - sense1d[i].value - 1\n", + " if sig > 1 and yloc > 10:\n", + " plt.text(kk.value, yloc, f\"{sig:.1f}\")\n", + "\n", + "plt.grid(which='both')" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [], + "source": [ + "sense_moderate = sense_optimistic.clone(foreground_model='moderate', horizon_buffer=0.1 *littleh/un.Mpc)" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "calculating 2D sensitivity: 100%|██████████| 929/929 [00:02<00:00, 463.19uv-bins/s]\n", + "averaging to 1D: 100%|██████████| 243/243 [00:00<00:00, 4299.09kperp-bins/s]\n" + ] + } + ], + "source": [ + "sense1d_moderate = sense_moderate.calculate_sensitivity_1d(thermal=True, sample=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.errorbar(\n", + " sense_moderate.k1d, \n", + " sense_moderate.delta_squared,\n", + " sense1d_moderate\n", + ")\n", + "plt.xlim(0.06, 1)\n", + "plt.ylim(10, 50)\n", + "plt.xscale('log')\n", + "plt.yscale('log')\n", + "\n", + "for i, kk in enumerate(sense_moderate.k1d):\n", + " sig = sense_moderate.delta_squared[i] / sense1d_moderate[i]\n", + " yloc = sense_moderate.delta_squared[i].value - sense1d_moderate[i].value - 1\n", + " if sig > 1 and yloc > 10:\n", + " plt.text(kk.value, yloc, f\"{sig:.1f}\")\n", + "\n", + "plt.grid(which='both')" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [], + "source": [ + "from py21cmsense import observation\n", + "\n", + "\n", + "sense_pessimistic = sense_moderate.clone(\n", + " observation=obs.clone(coherent=False)\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "finding redundancies: 100%|██████████| 546/546 [00:02<00:00, 197.26ants/s]\n", + "computing UVWs: 100%|██████████| 39/39 [00:00<00:00, 160.69times/s]\n", + "gridding baselines: 100%|██████████| 2106/2106 [00:00<00:00, 16689.38baselines/s]\n", + "calculating 2D sensitivity: 100%|██████████| 929/929 [00:01<00:00, 479.14uv-bins/s]\n", + "averaging to 1D: 100%|██████████| 243/243 [00:00<00:00, 4488.75kperp-bins/s]\n" + ] + } + ], + "source": [ + "sense1d_pess = sense_pessimistic.calculate_sensitivity_1d(thermal=True, sample=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.errorbar(\n", + " sense_pessimistic.k1d, \n", + " sense_pessimistic.delta_squared,\n", + " sense1d_pess\n", + ")\n", + "plt.xlim(0.06, 1)\n", + "plt.ylim(10, 50)\n", + "plt.xscale('log')\n", + "plt.yscale('log')\n", + "\n", + "for i, kk in enumerate(sense_pessimistic.k1d):\n", + " sig = sense_pessimistic.delta_squared[i] / sense1d_pess[i]\n", + " yloc = sense_pessimistic.delta_squared[i].value - sense1d_pess[i].value - 1\n", + " if sig > 1 and yloc > 10:\n", + " plt.text(kk.value, yloc, f\"{sig:.1f}\")\n", + "\n", + "plt.grid(which='both')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "It seems that the default implementation of the new 21cmSense is everywhere a little less\n", + "significant than the original implementation. It is unclear exactly where this difference\n", + "comes from, but there are lots of small differences in the implementations -- of which the\n", + "newer version is typically *more* accurate. In any case, the overall significance levels\n", + "are comparable, and certainly have the same *shape* as the original." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Tests" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "From here, we are simply performing some tests to ensure that the results stay \n", + "roughly consistent as new features/fixes are added." + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": {}, + "outputs": [], + "source": [ + "snr_pess_pober14 = [\n", + " 0, 0, 13.3, 17.1, 15.3, 12.3, 9.0, 6.5, 4.7, 3.4, 2.6, 2.0\n", + "]" + ] + }, + { + "cell_type": "code", + "execution_count": 44, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/steven/work/eor/21cmSense/py21cmsense/theory.py:142: UserWarning: Extrapolating above the simulated theoretical k: 3.3749883159994045 > 2.192064\n", + " warnings.warn(\n" + ] + } + ], + "source": [ + "snr_pess = sense_pessimistic.delta_squared / sense1d_pess\n", + "if not np.allclose(snr_pess[:len(snr_pess_pober14)],snr_pess_pober14, atol=0, rtol=0.25):\n", + " raise ValueError(\"The SNR for the pessimistic case has changed by more than 25%!\")\n", + "\n", + "if not np.all(np.diff(snr_pess)[len(snr_pess_pober14):]<=0):\n", + " raise ValueError(\"The SNR for the pessimistic case is increasing at high k :/\")" + ] + }, + { + "cell_type": "code", + "execution_count": 56, + "metadata": {}, + "outputs": [], + "source": [ + "snr_mod_pober14 = [\n", + " 0, 0, 14.0, 19.3, 18.3, 15.5, 11.8, 8.7, 6.4, 4.7, 3.6, 2.7, 2.2\n", + "]" + ] + }, + { + "cell_type": "code", + "execution_count": 63, + "metadata": {}, + "outputs": [], + "source": [ + "snr_mod = sense_moderate.delta_squared / sense1d_moderate\n", + "if not np.allclose(snr_mod[:len(snr_mod_pober14)],snr_mod_pober14, atol=0, rtol=0.30):\n", + " raise ValueError(\"The SNR for the moderate case has changed by more than 30%!\")\n", + "\n", + "if not np.all(np.diff(snr_mod)[len(snr_mod_pober14):]<=0):\n", + " raise ValueError(\"The SNR for the moderate case is increasing at high k :/\")" + ] + }, + { + "cell_type": "code", + "execution_count": 68, + "metadata": {}, + "outputs": [], + "source": [ + "snr_opt_pober14 = [\n", + " 93.9, 66.5, 41.6, 27.1, 18.3, 12.7, 8.9, 6.4, 4.7, 3.6, 2.7, 2.2\n", + "]" + ] + }, + { + "cell_type": "code", + "execution_count": 72, + "metadata": {}, + "outputs": [], + "source": [ + "snr_opt = sense_optimistic.delta_squared / sense1d\n", + "if not np.allclose(snr_opt[1:len(snr_opt_pober14)+1],snr_opt_pober14, atol=0, rtol=0.35):\n", + " raise ValueError(\"The SNR for the optimistic case has changed by more than 30%!\")\n", + "\n", + "if not np.all(np.diff(snr_opt)[len(snr_opt_pober14)+1:]<=0):\n", + " raise ValueError(\"The SNR for the optimistic case is increasing at high k :/\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Also make sure that opt is better than moderate is better than pessimistic:" + ] + }, + { + "cell_type": "code", + "execution_count": 75, + "metadata": {}, + "outputs": [], + "source": [ + "if not np.all(snr_opt >= snr_mod):\n", + " raise ValueError(\"snr_opt must be better than snr_mod!\")\n", + "\n", + "if not np.all(snr_mod >= snr_pess):\n", + " raise ValueError(\"snr_mod must be better than snr_pess!\")\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Make sure we have the same \"shape\" as the original:" + ] + }, + { + "cell_type": "code", + "execution_count": 82, + "metadata": {}, + "outputs": [], + "source": [ + "if not np.all(np.sign(np.diff(snr_pess_pober14))==np.sign(np.diff(snr_pess[:len(snr_pess_pober14)]))):\n", + " raise ValueError(\"pessimistic case has different shape to Pober+14\")\n", + "\n", + "if not np.all(np.sign(np.diff(snr_mod_pober14))==np.sign(np.diff(snr_mod[:len(snr_mod_pober14)]))):\n", + " raise ValueError(\"moderate case has different shape to Pober+14\")\n", + "\n", + "if not np.all(np.sign(np.diff(snr_opt_pober14))==np.sign(np.diff(snr_opt[1:len(snr_opt_pober14)+1]))):\n", + " raise ValueError(\"optimistic case has different shape to Pober+14\")\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "raise ValueError(\"HAHAHAHA\")" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3.10.6 ('sense')", + "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": "3.10.6" + }, + "orig_nbformat": 4, + "vscode": { + "interpreter": { + "hash": "6fa29f5e4f57cd8c712524a97e07d2162cf815d6c48daa40d8ca34da05a68238" + } + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/py21cmsense/beam.py b/py21cmsense/beam.py index 5e2cb4c..f9d22a0 100644 --- a/py21cmsense/beam.py +++ b/py21cmsense/beam.py @@ -94,6 +94,11 @@ class GaussianBeam(PrimaryBeam): validator=(tp.vld_physical_type("length"), ut.positive) ) + @property + def wavelength(self) -> un.Quantity[un.m]: + """The wavelength of the observation.""" + return (cnst.c / self.frequency).to("m") + @property def dish_size_in_lambda(self) -> float: """The dish size in units of wavelengths.""" diff --git a/py21cmsense/observation.py b/py21cmsense/observation.py index ca735f5..6337356 100755 --- a/py21cmsense/observation.py +++ b/py21cmsense/observation.py @@ -13,6 +13,7 @@ from os import path from . import _utils as ut +from . import config from . import conversions as conv from . import observatory as obs from . import types as tp @@ -282,7 +283,7 @@ def kparallel(self) -> un.Quantity[un.littleh / un.Mpc]: Order of the values is the same as `fftfreq` (i.e. zero-first) """ - return conv.dk_deta(self.redshift) * self.eta + return conv.dk_deta(self.redshift, config.COSMO) * self.eta @cached_property def total_integration_time(self) -> un.Quantity[un.s]: diff --git a/py21cmsense/sensitivity.py b/py21cmsense/sensitivity.py index 356a403..b5af4e9 100644 --- a/py21cmsense/sensitivity.py +++ b/py21cmsense/sensitivity.py @@ -201,7 +201,10 @@ def _theory_model_validator(self, att, val): @cached_property def k1d(self) -> tp.Wavenumber: """1D array of wavenumbers for which sensitivities will be generated.""" - delta = conv.dk_deta(self.observation.redshift) / self.observation.bandwidth + delta = ( + conv.dk_deta(self.observation.redshift, config.COSMO) + / self.observation.bandwidth + ) dv = delta.value return np.arange(dv, dv * self.observation.n_channels, dv) * delta.unit @@ -279,7 +282,7 @@ def _nsamples_2d( continue umag = np.sqrt(u**2 + v**2) - k_perp = umag * conv.dk_du(self.observation.redshift) + k_perp = umag * conv.dk_du(self.observation.redshift, config.COSMO) hor = self.horizon_limit(umag) @@ -405,9 +408,7 @@ def calculate_sensitivity_2d_grid( # Get the kperp bin it's in. kperp_indx = np.where(k_perp >= kperp_edges)[0][-1] - k = np.sqrt(self.observation.kparallel**2 + k_perp**2) - - kpar_indx = np.digitize(k, kpar_edges) - 1 + kpar_indx = np.digitize(self.observation.kparallel, kpar_edges) - 1 good_ks = kpar_indx >= 0 good_ks &= kpar_indx < len(kpar_edges) - 1 @@ -436,7 +437,9 @@ def horizon_limit(self, umag: float) -> tp.Wavenumber: Horizon limit, in h/Mpc. """ horizon = ( - conv.dk_deta(self.observation.redshift) * umag / self.observation.frequency + conv.dk_deta(self.observation.redshift, config.COSMO) + * umag + / self.observation.frequency ) # calculate horizon limit for baseline of length umag if self.foreground_model in ["moderate", "pessimistic"]: diff --git a/tmp.ipynb b/tmp.ipynb new file mode 100644 index 0000000..37be5f7 --- /dev/null +++ b/tmp.ipynb @@ -0,0 +1,2704 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "64f227f7", + "metadata": { + "tags": [ + "papermill-error-cell-tag" + ] + }, + "source": [ + "An Exception was encountered at 'In [31]'." + ] + }, + { + "cell_type": "markdown", + "id": "fc0b7baf", + "metadata": { + "papermill": { + "duration": 0.004641, + "end_time": "2022-10-04T21:16:56.979386", + "exception": false, + "start_time": "2022-10-04T21:16:56.974745", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "# Reproducing Pober+2014" + ] + }, + { + "cell_type": "markdown", + "id": "86577e5f", + "metadata": { + "papermill": { + "duration": 0.005893, + "end_time": "2022-10-04T21:16:56.991014", + "exception": false, + "start_time": "2022-10-04T21:16:56.985121", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "In this demo, we are going to reproduce many of the results of [Pober+2014](https://arxiv.org/pdf/1310.7031.pdf), which looked at the sensitivity of a \"concept HERA\" (which differs somewhat from the final instrument), as well as a few other well-known arrays." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "b4ea0cb9", + "metadata": { + "execution": { + "iopub.execute_input": "2022-10-04T21:16:57.004526Z", + "iopub.status.busy": "2022-10-04T21:16:57.003537Z", + "iopub.status.idle": "2022-10-04T21:16:58.056725Z", + "shell.execute_reply": "2022-10-04T21:16:58.056017Z" + }, + "papermill": { + "duration": 1.061791, + "end_time": "2022-10-04T21:16:58.058352", + "exception": false, + "start_time": "2022-10-04T21:16:56.996561", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "import py21cmsense as p21c\n", + "from astropy import units as un\n", + "from astropy.cosmology.units import littleh\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "d4b78752", + "metadata": { + "execution": { + "iopub.execute_input": "2022-10-04T21:16:58.074991Z", + "iopub.status.busy": "2022-10-04T21:16:58.074267Z", + "iopub.status.idle": "2022-10-04T21:16:58.081461Z", + "shell.execute_reply": "2022-10-04T21:16:58.080737Z" + }, + "papermill": { + "duration": 0.020272, + "end_time": "2022-10-04T21:16:58.082772", + "exception": false, + "start_time": "2022-10-04T21:16:58.062500", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "p21c.config.COSMO = p21c.config.COSMO.clone(H0=70.0, Om0=0.266)" + ] + }, + { + "cell_type": "markdown", + "id": "66cc3b92", + "metadata": { + "papermill": { + "duration": 0.004614, + "end_time": "2022-10-04T21:16:58.091449", + "exception": false, + "start_time": "2022-10-04T21:16:58.086835", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "## HERA" + ] + }, + { + "cell_type": "markdown", + "id": "32aff51c", + "metadata": { + "papermill": { + "duration": 0.004852, + "end_time": "2022-10-04T21:16:58.101028", + "exception": false, + "start_time": "2022-10-04T21:16:58.096176", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "P14 used a concept version of HERA that was a perfect hexagon (with no splits), with 547\n", + "elements and no outriggers. Let's create such an array:" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "b31674ae", + "metadata": { + "execution": { + "iopub.execute_input": "2022-10-04T21:16:58.112407Z", + "iopub.status.busy": "2022-10-04T21:16:58.111753Z", + "iopub.status.idle": "2022-10-04T21:16:58.118223Z", + "shell.execute_reply": "2022-10-04T21:16:58.117465Z" + }, + "papermill": { + "duration": 0.01339, + "end_time": "2022-10-04T21:16:58.119398", + "exception": false, + "start_time": "2022-10-04T21:16:58.106008", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "hera_ants = p21c.antpos.hera(hex_num=14)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "7bd24e06", + "metadata": { + "execution": { + "iopub.execute_input": "2022-10-04T21:16:58.128928Z", + "iopub.status.busy": "2022-10-04T21:16:58.128427Z", + "iopub.status.idle": "2022-10-04T21:16:58.137955Z", + "shell.execute_reply": "2022-10-04T21:16:58.137356Z" + }, + "papermill": { + "duration": 0.015981, + "end_time": "2022-10-04T21:16:58.139090", + "exception": false, + "start_time": "2022-10-04T21:16:58.123109", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "data": { + "text/plain": [ + "(547, 3)" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "hera_ants.shape" + ] + }, + { + "cell_type": "markdown", + "id": "5eeba410", + "metadata": { + "papermill": { + "duration": 0.005369, + "end_time": "2022-10-04T21:16:58.191947", + "exception": false, + "start_time": "2022-10-04T21:16:58.186578", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "We will reproduce the results at $z=9.5$ (the default case in P14):" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "92392cdd", + "metadata": { + "execution": { + "iopub.execute_input": "2022-10-04T21:16:58.207729Z", + "iopub.status.busy": "2022-10-04T21:16:58.207121Z", + "iopub.status.idle": "2022-10-04T21:16:58.212617Z", + "shell.execute_reply": "2022-10-04T21:16:58.211833Z" + }, + "papermill": { + "duration": 0.014715, + "end_time": "2022-10-04T21:16:58.213835", + "exception": false, + "start_time": "2022-10-04T21:16:58.199120", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "beam = p21c.GaussianBeam(frequency=1420*un.MHz/(1 + 9.5), dish_size=14*un.m)" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "51268a0e", + "metadata": { + "execution": { + "iopub.execute_input": "2022-10-04T21:16:58.224069Z", + "iopub.status.busy": "2022-10-04T21:16:58.223561Z", + "iopub.status.idle": "2022-10-04T21:16:58.229719Z", + "shell.execute_reply": "2022-10-04T21:16:58.228984Z" + }, + "papermill": { + "duration": 0.013001, + "end_time": "2022-10-04T21:16:58.231055", + "exception": false, + "start_time": "2022-10-04T21:16:58.218054", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "data": { + "text/latex": [ + "$9.5939319 \\; \\mathrm{{}^{\\circ}}$" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "beam.fwhm.to(un.deg)" + ] + }, + { + "cell_type": "markdown", + "id": "2452a773", + "metadata": { + "papermill": { + "duration": 0.005586, + "end_time": "2022-10-04T21:16:58.241212", + "exception": false, + "start_time": "2022-10-04T21:16:58.235626", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "What is the beam-crossing time? This is given as \"about 40min\" by P14. We obtain it as:" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "92a0f4a9", + "metadata": { + "execution": { + "iopub.execute_input": "2022-10-04T21:16:58.252494Z", + "iopub.status.busy": "2022-10-04T21:16:58.251958Z", + "iopub.status.idle": "2022-10-04T21:16:58.258010Z", + "shell.execute_reply": "2022-10-04T21:16:58.257298Z" + }, + "papermill": { + "duration": 0.013139, + "end_time": "2022-10-04T21:16:58.259361", + "exception": false, + "start_time": "2022-10-04T21:16:58.246222", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "data": { + "text/plain": [ + "38.37572750960924" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "beam.fwhm.to(un.deg).value*24*60/360" + ] + }, + { + "cell_type": "markdown", + "id": "f6d56a9a", + "metadata": { + "papermill": { + "duration": 0.005228, + "end_time": "2022-10-04T21:16:58.269121", + "exception": false, + "start_time": "2022-10-04T21:16:58.263893", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "As a check on whether our GaussianBeam model is similar to the assumed beam in P14, \n", + "we check the FWHM at 150MHz, quoted as 8.7deg in Table 1 of P14:" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "531657d1", + "metadata": { + "execution": { + "iopub.execute_input": "2022-10-04T21:16:58.281443Z", + "iopub.status.busy": "2022-10-04T21:16:58.280764Z", + "iopub.status.idle": "2022-10-04T21:16:58.289550Z", + "shell.execute_reply": "2022-10-04T21:16:58.288644Z" + }, + "papermill": { + "duration": 0.016796, + "end_time": "2022-10-04T21:16:58.291116", + "exception": false, + "start_time": "2022-10-04T21:16:58.274320", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "data": { + "text/latex": [ + "$8.6497672 \\; \\mathrm{{}^{\\circ}}$" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "p21c.GaussianBeam(frequency=150*un.MHz, dish_size=14*un.m).fwhm.to(un.deg)" + ] + }, + { + "cell_type": "markdown", + "id": "7d2b5892", + "metadata": { + "papermill": { + "duration": 0.007071, + "end_time": "2022-10-04T21:16:58.304010", + "exception": false, + "start_time": "2022-10-04T21:16:58.296939", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "This is very close to the quoted value." + ] + }, + { + "cell_type": "markdown", + "id": "e52f8223", + "metadata": { + "papermill": { + "duration": 0.007401, + "end_time": "2022-10-04T21:16:58.319286", + "exception": false, + "start_time": "2022-10-04T21:16:58.311885", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "Now, let's create the HERA Observatory model. Here, we set the receiver temperature to 100K,\n", + "as per Table 1 of P14. We also set the latitude of the instrument to the equator, which is \n", + "of course incorrect, but was the assumption made in the ismpler code of P14." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "f0ce171e", + "metadata": { + "execution": { + "iopub.execute_input": "2022-10-04T21:16:58.334872Z", + "iopub.status.busy": "2022-10-04T21:16:58.334154Z", + "iopub.status.idle": "2022-10-04T21:16:58.340561Z", + "shell.execute_reply": "2022-10-04T21:16:58.339576Z" + }, + "papermill": { + "duration": 0.015919, + "end_time": "2022-10-04T21:16:58.342283", + "exception": false, + "start_time": "2022-10-04T21:16:58.326364", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "hera = p21c.Observatory(\n", + " antpos=hera_ants,\n", + " beam=beam,\n", + " latitude=0*un.rad,\n", + " Trcv=100*un.K,\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "494b2fd2", + "metadata": { + "papermill": { + "duration": 0.008329, + "end_time": "2022-10-04T21:16:58.356401", + "exception": false, + "start_time": "2022-10-04T21:16:58.348072", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "Now, set up the observation itself. Here we assume a sky temperature model of \n", + "$351{\\rm K} (\\nu/150 {\\rm MHz})^{-2.55}$, as described in S2.1.2 of P14. \n", + "\n", + "Furthermore, we set the number of days of observation to 180, with 6 hours per night. \n", + "We also set the \"coherent observing time\" to its default of the beam crossing time, \n", + "which is about 40min as per the above calculation.\n", + "\n", + "Finally, as per S3.1.2 of P14, we use 8MHz of bandwidth, with 81 channels (providing\n", + "0.1 MHz channel width)." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "c2dee485", + "metadata": { + "execution": { + "iopub.execute_input": "2022-10-04T21:16:58.372820Z", + "iopub.status.busy": "2022-10-04T21:16:58.372142Z", + "iopub.status.idle": "2022-10-04T21:16:58.381451Z", + "shell.execute_reply": "2022-10-04T21:16:58.380409Z" + }, + "papermill": { + "duration": 0.019576, + "end_time": "2022-10-04T21:16:58.383933", + "exception": false, + "start_time": "2022-10-04T21:16:58.364357", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "obs = p21c.Observation(\n", + " observatory=hera,\n", + " tsky_amplitude=351*un.K,\n", + " tsky_ref_freq=150*un.MHz,\n", + " spectral_index=2.55,\n", + " n_days=180,\n", + " time_per_day=6*un.hour,\n", + " bandwidth=8*un.MHz,\n", + " n_channels=81,\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "ef9f9364", + "metadata": { + "papermill": { + "duration": 0.006695, + "end_time": "2022-10-04T21:16:58.396407", + "exception": false, + "start_time": "2022-10-04T21:16:58.389712", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "We make sure the observation duration is close to 40 minutes:" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "b2eeb8de", + "metadata": { + "execution": { + "iopub.execute_input": "2022-10-04T21:16:58.413270Z", + "iopub.status.busy": "2022-10-04T21:16:58.412104Z", + "iopub.status.idle": "2022-10-04T21:16:58.422134Z", + "shell.execute_reply": "2022-10-04T21:16:58.421214Z" + }, + "papermill": { + "duration": 0.020135, + "end_time": "2022-10-04T21:16:58.423691", + "exception": false, + "start_time": "2022-10-04T21:16:58.403556", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "data": { + "text/latex": [ + "$2.3809441 \\; \\mathrm{\\frac{h_{100}}{Mpc}}$" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "obs.kparallel.max()" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "60183953", + "metadata": { + "execution": { + "iopub.execute_input": "2022-10-04T21:16:58.439557Z", + "iopub.status.busy": "2022-10-04T21:16:58.439111Z", + "iopub.status.idle": "2022-10-04T21:16:58.446544Z", + "shell.execute_reply": "2022-10-04T21:16:58.445603Z" + }, + "papermill": { + "duration": 0.018632, + "end_time": "2022-10-04T21:16:58.447963", + "exception": false, + "start_time": "2022-10-04T21:16:58.429331", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "data": { + "text/latex": [ + "$2.3809441 \\; \\mathrm{\\frac{h_{100}}{Mpc}}$" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "obs.kparallel.max()" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "bb98b742", + "metadata": { + "execution": { + "iopub.execute_input": "2022-10-04T21:16:58.461914Z", + "iopub.status.busy": "2022-10-04T21:16:58.461356Z", + "iopub.status.idle": "2022-10-04T21:16:58.468888Z", + "shell.execute_reply": "2022-10-04T21:16:58.468079Z" + }, + "papermill": { + "duration": 0.016671, + "end_time": "2022-10-04T21:16:58.470476", + "exception": false, + "start_time": "2022-10-04T21:16:58.453805", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "data": { + "text/latex": [ + "$38.375728 \\; \\mathrm{min}$" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "obs.obs_duration.to(un.min)" + ] + }, + { + "cell_type": "markdown", + "id": "61c0acef", + "metadata": { + "papermill": { + "duration": 0.006622, + "end_time": "2022-10-04T21:16:58.482902", + "exception": false, + "start_time": "2022-10-04T21:16:58.476280", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "Now construct the sensitivity calculation:" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "6be0a3be", + "metadata": { + "execution": { + "iopub.execute_input": "2022-10-04T21:16:58.497378Z", + "iopub.status.busy": "2022-10-04T21:16:58.496786Z", + "iopub.status.idle": "2022-10-04T21:16:58.504120Z", + "shell.execute_reply": "2022-10-04T21:16:58.503157Z" + }, + "papermill": { + "duration": 0.016425, + "end_time": "2022-10-04T21:16:58.505810", + "exception": false, + "start_time": "2022-10-04T21:16:58.489385", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "sense_optimistic = p21c.PowerSpectrum(\n", + " observation=obs,\n", + " foreground_model='optimistic',\n", + " horizon_buffer=0.0 *littleh/un.Mpc,\n", + " theory_model=p21c.theory.Legacy21cmFAST(),\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "749213d0", + "metadata": { + "execution": { + "iopub.execute_input": "2022-10-04T21:16:58.520946Z", + "iopub.status.busy": "2022-10-04T21:16:58.520339Z", + "iopub.status.idle": "2022-10-04T21:17:05.673113Z", + "shell.execute_reply": "2022-10-04T21:17:05.672253Z" + }, + "papermill": { + "duration": 7.16259, + "end_time": "2022-10-04T21:17:05.674472", + "exception": false, + "start_time": "2022-10-04T21:16:58.511882", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\r", + "finding redundancies: 0%| | 0/546 [00:00 2.192064\n", + " warnings.warn(\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.errorbar(\n", + " sense_optimistic.k1d, \n", + " sense_optimistic.delta_squared,\n", + " sense1d\n", + ")\n", + "plt.xlim(0.06, 1)\n", + "plt.ylim(10, 50)\n", + "plt.xscale('log')\n", + "plt.yscale('log')\n", + "\n", + "for i, kk in enumerate(sense_optimistic.k1d):\n", + " sig = sense_optimistic.delta_squared[i] / sense1d[i]\n", + " yloc = sense_optimistic.delta_squared[i].value - sense1d[i].value - 1\n", + " if sig > 1 and yloc > 10:\n", + " plt.text(kk.value, yloc, f\"{sig:.1f}\")\n", + "\n", + "plt.grid(which='both')" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "57677890", + "metadata": { + "execution": { + "iopub.execute_input": "2022-10-04T21:17:06.082538Z", + "iopub.status.busy": "2022-10-04T21:17:06.082100Z", + "iopub.status.idle": "2022-10-04T21:17:06.086740Z", + "shell.execute_reply": "2022-10-04T21:17:06.086079Z" + }, + "papermill": { + "duration": 0.017339, + "end_time": "2022-10-04T21:17:06.088081", + "exception": false, + "start_time": "2022-10-04T21:17:06.070742", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "sense_moderate = sense_optimistic.clone(foreground_model='moderate', horizon_buffer=0.1 *littleh/un.Mpc)" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "id": "014967e9", + "metadata": { + "execution": { + "iopub.execute_input": "2022-10-04T21:17:06.103592Z", + "iopub.status.busy": "2022-10-04T21:17:06.103100Z", + "iopub.status.idle": "2022-10-04T21:17:08.414925Z", + "shell.execute_reply": "2022-10-04T21:17:08.414321Z" + }, + "papermill": { + "duration": 2.321516, + "end_time": "2022-10-04T21:17:08.416160", + "exception": false, + "start_time": "2022-10-04T21:17:06.094644", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\r", + "calculating 2D sensitivity: 0%| | 0/929 [00:00" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.errorbar(\n", + " sense_moderate.k1d, \n", + " sense_moderate.delta_squared,\n", + " sense1d_moderate\n", + ")\n", + "plt.xlim(0.06, 1)\n", + "plt.ylim(10, 50)\n", + "plt.xscale('log')\n", + "plt.yscale('log')\n", + "\n", + "for i, kk in enumerate(sense_moderate.k1d):\n", + " sig = sense_moderate.delta_squared[i] / sense1d_moderate[i]\n", + " yloc = sense_moderate.delta_squared[i].value - sense1d_moderate[i].value - 1\n", + " if sig > 1 and yloc > 10:\n", + " plt.text(kk.value, yloc, f\"{sig:.1f}\")\n", + "\n", + "plt.grid(which='both')" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "id": "1d74ec0e", + "metadata": { + "execution": { + "iopub.execute_input": "2022-10-04T21:17:08.758182Z", + "iopub.status.busy": "2022-10-04T21:17:08.757238Z", + "iopub.status.idle": "2022-10-04T21:17:08.763763Z", + "shell.execute_reply": "2022-10-04T21:17:08.763005Z" + }, + "papermill": { + "duration": 0.021523, + "end_time": "2022-10-04T21:17:08.765380", + "exception": false, + "start_time": "2022-10-04T21:17:08.743857", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "from py21cmsense import observation\n", + "\n", + "\n", + "sense_pessimistic = sense_moderate.clone(\n", + " observation=obs.clone(coherent=False)\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "id": "b2558d3f", + "metadata": { + "execution": { + "iopub.execute_input": "2022-10-04T21:17:08.787482Z", + "iopub.status.busy": "2022-10-04T21:17:08.787019Z", + "iopub.status.idle": "2022-10-04T21:17:15.144483Z", + "shell.execute_reply": "2022-10-04T21:17:15.143637Z" + }, + "papermill": { + "duration": 6.371086, + "end_time": "2022-10-04T21:17:15.145770", + "exception": false, + "start_time": "2022-10-04T21:17:08.774684", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\r", + "finding redundancies: 0%| | 0/546 [00:00" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.errorbar(\n", + " sense_pessimistic.k1d, \n", + " sense_pessimistic.delta_squared,\n", + " sense1d_pess\n", + ")\n", + "plt.xlim(0.06, 1)\n", + "plt.ylim(10, 50)\n", + "plt.xscale('log')\n", + "plt.yscale('log')\n", + "\n", + "for i, kk in enumerate(sense_pessimistic.k1d):\n", + " sig = sense_pessimistic.delta_squared[i] / sense1d_pess[i]\n", + " yloc = sense_pessimistic.delta_squared[i].value - sense1d_pess[i].value - 1\n", + " if sig > 1 and yloc > 10:\n", + " plt.text(kk.value, yloc, f\"{sig:.1f}\")\n", + "\n", + "plt.grid(which='both')" + ] + }, + { + "cell_type": "markdown", + "id": "b138b036", + "metadata": { + "papermill": { + "duration": 0.012137, + "end_time": "2022-10-04T21:17:15.481493", + "exception": false, + "start_time": "2022-10-04T21:17:15.469356", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "It seems that the default implementation of the new 21cmSense is everywhere a little less\n", + "significant than the original implementation. It is unclear exactly where this difference\n", + "comes from, but there are lots of small differences in the implementations -- of which the\n", + "newer version is typically *more* accurate. In any case, the overall significance levels\n", + "are comparable, and certainly have the same *shape* as the original." + ] + }, + { + "cell_type": "markdown", + "id": "ab19c55a", + "metadata": { + "papermill": { + "duration": 0.01184, + "end_time": "2022-10-04T21:17:15.504763", + "exception": false, + "start_time": "2022-10-04T21:17:15.492923", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "## Tests" + ] + }, + { + "cell_type": "markdown", + "id": "83cb46ee", + "metadata": { + "papermill": { + "duration": 0.011895, + "end_time": "2022-10-04T21:17:15.528159", + "exception": false, + "start_time": "2022-10-04T21:17:15.516264", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "From here, we are simply performing some tests to ensure that the results stay \n", + "roughly consistent as new features/fixes are added." + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "id": "6e11928f", + "metadata": { + "execution": { + "iopub.execute_input": "2022-10-04T21:17:15.556277Z", + "iopub.status.busy": "2022-10-04T21:17:15.555436Z", + "iopub.status.idle": "2022-10-04T21:17:15.560231Z", + "shell.execute_reply": "2022-10-04T21:17:15.559393Z" + }, + "papermill": { + "duration": 0.020807, + "end_time": "2022-10-04T21:17:15.561645", + "exception": false, + "start_time": "2022-10-04T21:17:15.540838", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "snr_pess_pober14 = [\n", + " 0, 0, 13.3, 17.1, 15.3, 12.3, 9.0, 6.5, 4.7, 3.4, 2.6, 2.0\n", + "]" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "id": "656fe2d4", + "metadata": { + "execution": { + "iopub.execute_input": "2022-10-04T21:17:15.592287Z", + "iopub.status.busy": "2022-10-04T21:17:15.591441Z", + "iopub.status.idle": "2022-10-04T21:17:15.597677Z", + "shell.execute_reply": "2022-10-04T21:17:15.596907Z" + }, + "papermill": { + "duration": 0.025484, + "end_time": "2022-10-04T21:17:15.599031", + "exception": false, + "start_time": "2022-10-04T21:17:15.573547", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "snr_pess = sense_pessimistic.delta_squared / sense1d_pess\n", + "if not np.allclose(snr_pess[:len(snr_pess_pober14)],snr_pess_pober14, atol=0, rtol=0.25):\n", + " raise ValueError(\"The SNR for the pessimistic case has changed by more than 25%!\")\n", + "\n", + "if not np.all(np.diff(snr_pess)[len(snr_pess_pober14):]<=0):\n", + " raise ValueError(\"The SNR for the pessimistic case is increasing at high k :/\")" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "id": "5c51a584", + "metadata": { + "execution": { + "iopub.execute_input": "2022-10-04T21:17:15.625416Z", + "iopub.status.busy": "2022-10-04T21:17:15.624736Z", + "iopub.status.idle": "2022-10-04T21:17:15.629470Z", + "shell.execute_reply": "2022-10-04T21:17:15.628621Z" + }, + "papermill": { + "duration": 0.020519, + "end_time": "2022-10-04T21:17:15.630903", + "exception": false, + "start_time": "2022-10-04T21:17:15.610384", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "snr_mod_pober14 = [\n", + " 0, 0, 14.0, 19.3, 18.3, 15.5, 11.8, 8.7, 6.4, 4.7, 3.6, 2.7, 2.2\n", + "]" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "id": "7b2fb9e1", + "metadata": { + "execution": { + "iopub.execute_input": "2022-10-04T21:17:15.665011Z", + "iopub.status.busy": "2022-10-04T21:17:15.664265Z", + "iopub.status.idle": "2022-10-04T21:17:15.671708Z", + "shell.execute_reply": "2022-10-04T21:17:15.670746Z" + }, + "papermill": { + "duration": 0.029554, + "end_time": "2022-10-04T21:17:15.673324", + "exception": false, + "start_time": "2022-10-04T21:17:15.643770", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "snr_mod = sense_moderate.delta_squared / sense1d_moderate\n", + "if not np.allclose(snr_mod[:len(snr_mod_pober14)],snr_mod_pober14, atol=0, rtol=0.30):\n", + " raise ValueError(\"The SNR for the moderate case has changed by more than 30%!\")\n", + "\n", + "if not np.all(np.diff(snr_mod)[len(snr_mod_pober14):]<=0):\n", + " raise ValueError(\"The SNR for the moderate case is increasing at high k :/\")" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "id": "8a98c970", + "metadata": { + "execution": { + "iopub.execute_input": "2022-10-04T21:17:15.701421Z", + "iopub.status.busy": "2022-10-04T21:17:15.700546Z", + "iopub.status.idle": "2022-10-04T21:17:15.705421Z", + "shell.execute_reply": "2022-10-04T21:17:15.704473Z" + }, + "papermill": { + "duration": 0.020534, + "end_time": "2022-10-04T21:17:15.706637", + "exception": false, + "start_time": "2022-10-04T21:17:15.686103", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "snr_opt_pober14 = [\n", + " 93.9, 66.5, 41.6, 27.1, 18.3, 12.7, 8.9, 6.4, 4.7, 3.6, 2.7, 2.2\n", + "]" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "id": "71eb91a5", + "metadata": { + "execution": { + "iopub.execute_input": "2022-10-04T21:17:15.740713Z", + "iopub.status.busy": "2022-10-04T21:17:15.739748Z", + "iopub.status.idle": "2022-10-04T21:17:15.746551Z", + "shell.execute_reply": "2022-10-04T21:17:15.745658Z" + }, + "papermill": { + "duration": 0.025761, + "end_time": "2022-10-04T21:17:15.748051", + "exception": false, + "start_time": "2022-10-04T21:17:15.722290", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "snr_opt = sense_optimistic.delta_squared / sense1d\n", + "if not np.allclose(snr_opt[1:len(snr_opt_pober14)+1],snr_opt_pober14, atol=0, rtol=0.35):\n", + " raise ValueError(\"The SNR for the optimistic case has changed by more than 30%!\")\n", + "\n", + "if not np.all(np.diff(snr_opt)[len(snr_opt_pober14)+1:]<=0):\n", + " raise ValueError(\"The SNR for the optimistic case is increasing at high k :/\")" + ] + }, + { + "cell_type": "markdown", + "id": "37f2de2f", + "metadata": { + "papermill": { + "duration": 0.013266, + "end_time": "2022-10-04T21:17:15.790281", + "exception": false, + "start_time": "2022-10-04T21:17:15.777015", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "Also make sure that opt is better than moderate is better than pessimistic:" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "id": "aa35c706", + "metadata": { + "execution": { + "iopub.execute_input": "2022-10-04T21:17:15.817903Z", + "iopub.status.busy": "2022-10-04T21:17:15.817339Z", + "iopub.status.idle": "2022-10-04T21:17:15.822428Z", + "shell.execute_reply": "2022-10-04T21:17:15.821621Z" + }, + "papermill": { + "duration": 0.019763, + "end_time": "2022-10-04T21:17:15.823653", + "exception": false, + "start_time": "2022-10-04T21:17:15.803890", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "if not np.all(snr_opt >= snr_mod):\n", + " raise ValueError(\"snr_opt must be better than snr_mod!\")\n", + "\n", + "if not np.all(snr_mod >= snr_pess):\n", + " raise ValueError(\"snr_mod must be better than snr_pess!\")\n" + ] + }, + { + "cell_type": "markdown", + "id": "98dc9d2c", + "metadata": { + "papermill": { + "duration": 0.012784, + "end_time": "2022-10-04T21:17:15.848742", + "exception": false, + "start_time": "2022-10-04T21:17:15.835958", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "Make sure we have the same \"shape\" as the original:" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "id": "a1c10435", + "metadata": { + "execution": { + "iopub.execute_input": "2022-10-04T21:17:15.874900Z", + "iopub.status.busy": "2022-10-04T21:17:15.874236Z", + "iopub.status.idle": "2022-10-04T21:17:15.880794Z", + "shell.execute_reply": "2022-10-04T21:17:15.880085Z" + }, + "papermill": { + "duration": 0.021481, + "end_time": "2022-10-04T21:17:15.882219", + "exception": false, + "start_time": "2022-10-04T21:17:15.860738", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "if not np.all(np.sign(np.diff(snr_pess_pober14))==np.sign(np.diff(snr_pess[:len(snr_pess_pober14)]))):\n", + " raise ValueError(\"pessimistic case has different shape to Pober+14\")\n", + "\n", + "if not np.all(np.sign(np.diff(snr_mod_pober14))==np.sign(np.diff(snr_mod[:len(snr_mod_pober14)]))):\n", + " raise ValueError(\"moderate case has different shape to Pober+14\")\n", + "\n", + "if not np.all(np.sign(np.diff(snr_opt_pober14))==np.sign(np.diff(snr_opt[1:len(snr_opt_pober14)+1]))):\n", + " raise ValueError(\"optimistic case has different shape to Pober+14\")\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "id": "04b9402a", + "metadata": { + "tags": [ + "papermill-error-cell-tag" + ] + }, + "source": [ + "Execution using papermill encountered an exception here and stopped:" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "id": "52f65b69", + "metadata": { + "execution": { + "iopub.execute_input": "2022-10-04T21:17:15.910079Z", + "iopub.status.busy": "2022-10-04T21:17:15.909416Z", + "iopub.status.idle": "2022-10-04T21:17:16.223063Z", + "shell.execute_reply": "2022-10-04T21:17:16.221947Z" + }, + "papermill": { + "duration": 0.330167, + "end_time": "2022-10-04T21:17:16.224580", + "exception": true, + "start_time": "2022-10-04T21:17:15.894413", + "status": "failed" + }, + "tags": [] + }, + "outputs": [ + { + "ename": "ValueError", + "evalue": "HAHAHAHA", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn [31], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mHAHAHAHA\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n", + "\u001b[0;31mValueError\u001b[0m: HAHAHAHA" + ] + } + ], + "source": [ + "raise ValueError(\"HAHAHAHA\")" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3.10.6 ('sense')", + "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": "3.10.6" + }, + "papermill": { + "default_parameters": {}, + "duration": 20.662437, + "end_time": "2022-10-04T21:17:16.855349", + "environment_variables": {}, + "exception": true, + "input_path": "docs/tutorials/reproducing_pober_2014.ipynb", + "output_path": "tmp.ipynb", + "parameters": {}, + "start_time": "2022-10-04T21:16:56.192912", + "version": "2.4.0" + }, + "vscode": { + "interpreter": { + "hash": "6fa29f5e4f57cd8c712524a97e07d2162cf815d6c48daa40d8ca34da05a68238" + } + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} From 3f8c19aafd31aa53778e75ceac257fddb8b0e66b Mon Sep 17 00:00:00 2001 From: Steven Murray Date: Tue, 4 Oct 2022 14:28:40 -0700 Subject: [PATCH 02/22] maint: remove useless file --- tmp.ipynb | 2704 ----------------------------------------------------- 1 file changed, 2704 deletions(-) delete mode 100644 tmp.ipynb diff --git a/tmp.ipynb b/tmp.ipynb deleted file mode 100644 index 37be5f7..0000000 --- a/tmp.ipynb +++ /dev/null @@ -1,2704 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "64f227f7", - "metadata": { - "tags": [ - "papermill-error-cell-tag" - ] - }, - "source": [ - "An Exception was encountered at 'In [31]'." - ] - }, - { - "cell_type": "markdown", - "id": "fc0b7baf", - "metadata": { - "papermill": { - "duration": 0.004641, - "end_time": "2022-10-04T21:16:56.979386", - "exception": false, - "start_time": "2022-10-04T21:16:56.974745", - "status": "completed" - }, - "tags": [] - }, - "source": [ - "# Reproducing Pober+2014" - ] - }, - { - "cell_type": "markdown", - "id": "86577e5f", - "metadata": { - "papermill": { - "duration": 0.005893, - "end_time": "2022-10-04T21:16:56.991014", - "exception": false, - "start_time": "2022-10-04T21:16:56.985121", - "status": "completed" - }, - "tags": [] - }, - "source": [ - "In this demo, we are going to reproduce many of the results of [Pober+2014](https://arxiv.org/pdf/1310.7031.pdf), which looked at the sensitivity of a \"concept HERA\" (which differs somewhat from the final instrument), as well as a few other well-known arrays." - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "id": "b4ea0cb9", - "metadata": { - "execution": { - "iopub.execute_input": "2022-10-04T21:16:57.004526Z", - "iopub.status.busy": "2022-10-04T21:16:57.003537Z", - "iopub.status.idle": "2022-10-04T21:16:58.056725Z", - "shell.execute_reply": "2022-10-04T21:16:58.056017Z" - }, - "papermill": { - "duration": 1.061791, - "end_time": "2022-10-04T21:16:58.058352", - "exception": false, - "start_time": "2022-10-04T21:16:56.996561", - "status": "completed" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "import py21cmsense as p21c\n", - "from astropy import units as un\n", - "from astropy.cosmology.units import littleh\n", - "import matplotlib.pyplot as plt\n", - "import numpy as np" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "d4b78752", - "metadata": { - "execution": { - "iopub.execute_input": "2022-10-04T21:16:58.074991Z", - "iopub.status.busy": "2022-10-04T21:16:58.074267Z", - "iopub.status.idle": "2022-10-04T21:16:58.081461Z", - "shell.execute_reply": "2022-10-04T21:16:58.080737Z" - }, - "papermill": { - "duration": 0.020272, - "end_time": "2022-10-04T21:16:58.082772", - "exception": false, - "start_time": "2022-10-04T21:16:58.062500", - "status": "completed" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "p21c.config.COSMO = p21c.config.COSMO.clone(H0=70.0, Om0=0.266)" - ] - }, - { - "cell_type": "markdown", - "id": "66cc3b92", - "metadata": { - "papermill": { - "duration": 0.004614, - "end_time": "2022-10-04T21:16:58.091449", - "exception": false, - "start_time": "2022-10-04T21:16:58.086835", - "status": "completed" - }, - "tags": [] - }, - "source": [ - "## HERA" - ] - }, - { - "cell_type": "markdown", - "id": "32aff51c", - "metadata": { - "papermill": { - "duration": 0.004852, - "end_time": "2022-10-04T21:16:58.101028", - "exception": false, - "start_time": "2022-10-04T21:16:58.096176", - "status": "completed" - }, - "tags": [] - }, - "source": [ - "P14 used a concept version of HERA that was a perfect hexagon (with no splits), with 547\n", - "elements and no outriggers. Let's create such an array:" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "b31674ae", - "metadata": { - "execution": { - "iopub.execute_input": "2022-10-04T21:16:58.112407Z", - "iopub.status.busy": "2022-10-04T21:16:58.111753Z", - "iopub.status.idle": "2022-10-04T21:16:58.118223Z", - "shell.execute_reply": "2022-10-04T21:16:58.117465Z" - }, - "papermill": { - "duration": 0.01339, - "end_time": "2022-10-04T21:16:58.119398", - "exception": false, - "start_time": "2022-10-04T21:16:58.106008", - "status": "completed" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "hera_ants = p21c.antpos.hera(hex_num=14)" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "7bd24e06", - "metadata": { - "execution": { - "iopub.execute_input": "2022-10-04T21:16:58.128928Z", - "iopub.status.busy": "2022-10-04T21:16:58.128427Z", - "iopub.status.idle": "2022-10-04T21:16:58.137955Z", - "shell.execute_reply": "2022-10-04T21:16:58.137356Z" - }, - "papermill": { - "duration": 0.015981, - "end_time": "2022-10-04T21:16:58.139090", - "exception": false, - "start_time": "2022-10-04T21:16:58.123109", - "status": "completed" - }, - "tags": [] - }, - "outputs": [ - { - "data": { - "text/plain": [ - "(547, 3)" - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "hera_ants.shape" - ] - }, - { - "cell_type": "markdown", - "id": "5eeba410", - "metadata": { - "papermill": { - "duration": 0.005369, - "end_time": "2022-10-04T21:16:58.191947", - "exception": false, - "start_time": "2022-10-04T21:16:58.186578", - "status": "completed" - }, - "tags": [] - }, - "source": [ - "We will reproduce the results at $z=9.5$ (the default case in P14):" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "92392cdd", - "metadata": { - "execution": { - "iopub.execute_input": "2022-10-04T21:16:58.207729Z", - "iopub.status.busy": "2022-10-04T21:16:58.207121Z", - "iopub.status.idle": "2022-10-04T21:16:58.212617Z", - "shell.execute_reply": "2022-10-04T21:16:58.211833Z" - }, - "papermill": { - "duration": 0.014715, - "end_time": "2022-10-04T21:16:58.213835", - "exception": false, - "start_time": "2022-10-04T21:16:58.199120", - "status": "completed" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "beam = p21c.GaussianBeam(frequency=1420*un.MHz/(1 + 9.5), dish_size=14*un.m)" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "51268a0e", - "metadata": { - "execution": { - "iopub.execute_input": "2022-10-04T21:16:58.224069Z", - "iopub.status.busy": "2022-10-04T21:16:58.223561Z", - "iopub.status.idle": "2022-10-04T21:16:58.229719Z", - "shell.execute_reply": "2022-10-04T21:16:58.228984Z" - }, - "papermill": { - "duration": 0.013001, - "end_time": "2022-10-04T21:16:58.231055", - "exception": false, - "start_time": "2022-10-04T21:16:58.218054", - "status": "completed" - }, - "tags": [] - }, - "outputs": [ - { - "data": { - "text/latex": [ - "$9.5939319 \\; \\mathrm{{}^{\\circ}}$" - ], - "text/plain": [ - "" - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "beam.fwhm.to(un.deg)" - ] - }, - { - "cell_type": "markdown", - "id": "2452a773", - "metadata": { - "papermill": { - "duration": 0.005586, - "end_time": "2022-10-04T21:16:58.241212", - "exception": false, - "start_time": "2022-10-04T21:16:58.235626", - "status": "completed" - }, - "tags": [] - }, - "source": [ - "What is the beam-crossing time? This is given as \"about 40min\" by P14. We obtain it as:" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "92a0f4a9", - "metadata": { - "execution": { - "iopub.execute_input": "2022-10-04T21:16:58.252494Z", - "iopub.status.busy": "2022-10-04T21:16:58.251958Z", - "iopub.status.idle": "2022-10-04T21:16:58.258010Z", - "shell.execute_reply": "2022-10-04T21:16:58.257298Z" - }, - "papermill": { - "duration": 0.013139, - "end_time": "2022-10-04T21:16:58.259361", - "exception": false, - "start_time": "2022-10-04T21:16:58.246222", - "status": "completed" - }, - "tags": [] - }, - "outputs": [ - { - "data": { - "text/plain": [ - "38.37572750960924" - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "beam.fwhm.to(un.deg).value*24*60/360" - ] - }, - { - "cell_type": "markdown", - "id": "f6d56a9a", - "metadata": { - "papermill": { - "duration": 0.005228, - "end_time": "2022-10-04T21:16:58.269121", - "exception": false, - "start_time": "2022-10-04T21:16:58.263893", - "status": "completed" - }, - "tags": [] - }, - "source": [ - "As a check on whether our GaussianBeam model is similar to the assumed beam in P14, \n", - "we check the FWHM at 150MHz, quoted as 8.7deg in Table 1 of P14:" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "531657d1", - "metadata": { - "execution": { - "iopub.execute_input": "2022-10-04T21:16:58.281443Z", - "iopub.status.busy": "2022-10-04T21:16:58.280764Z", - "iopub.status.idle": "2022-10-04T21:16:58.289550Z", - "shell.execute_reply": "2022-10-04T21:16:58.288644Z" - }, - "papermill": { - "duration": 0.016796, - "end_time": "2022-10-04T21:16:58.291116", - "exception": false, - "start_time": "2022-10-04T21:16:58.274320", - "status": "completed" - }, - "tags": [] - }, - "outputs": [ - { - "data": { - "text/latex": [ - "$8.6497672 \\; \\mathrm{{}^{\\circ}}$" - ], - "text/plain": [ - "" - ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "p21c.GaussianBeam(frequency=150*un.MHz, dish_size=14*un.m).fwhm.to(un.deg)" - ] - }, - { - "cell_type": "markdown", - "id": "7d2b5892", - "metadata": { - "papermill": { - "duration": 0.007071, - "end_time": "2022-10-04T21:16:58.304010", - "exception": false, - "start_time": "2022-10-04T21:16:58.296939", - "status": "completed" - }, - "tags": [] - }, - "source": [ - "This is very close to the quoted value." - ] - }, - { - "cell_type": "markdown", - "id": "e52f8223", - "metadata": { - "papermill": { - "duration": 0.007401, - "end_time": "2022-10-04T21:16:58.319286", - "exception": false, - "start_time": "2022-10-04T21:16:58.311885", - "status": "completed" - }, - "tags": [] - }, - "source": [ - "Now, let's create the HERA Observatory model. Here, we set the receiver temperature to 100K,\n", - "as per Table 1 of P14. We also set the latitude of the instrument to the equator, which is \n", - "of course incorrect, but was the assumption made in the ismpler code of P14." - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "id": "f0ce171e", - "metadata": { - "execution": { - "iopub.execute_input": "2022-10-04T21:16:58.334872Z", - "iopub.status.busy": "2022-10-04T21:16:58.334154Z", - "iopub.status.idle": "2022-10-04T21:16:58.340561Z", - "shell.execute_reply": "2022-10-04T21:16:58.339576Z" - }, - "papermill": { - "duration": 0.015919, - "end_time": "2022-10-04T21:16:58.342283", - "exception": false, - "start_time": "2022-10-04T21:16:58.326364", - "status": "completed" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "hera = p21c.Observatory(\n", - " antpos=hera_ants,\n", - " beam=beam,\n", - " latitude=0*un.rad,\n", - " Trcv=100*un.K,\n", - ")" - ] - }, - { - "cell_type": "markdown", - "id": "494b2fd2", - "metadata": { - "papermill": { - "duration": 0.008329, - "end_time": "2022-10-04T21:16:58.356401", - "exception": false, - "start_time": "2022-10-04T21:16:58.348072", - "status": "completed" - }, - "tags": [] - }, - "source": [ - "Now, set up the observation itself. Here we assume a sky temperature model of \n", - "$351{\\rm K} (\\nu/150 {\\rm MHz})^{-2.55}$, as described in S2.1.2 of P14. \n", - "\n", - "Furthermore, we set the number of days of observation to 180, with 6 hours per night. \n", - "We also set the \"coherent observing time\" to its default of the beam crossing time, \n", - "which is about 40min as per the above calculation.\n", - "\n", - "Finally, as per S3.1.2 of P14, we use 8MHz of bandwidth, with 81 channels (providing\n", - "0.1 MHz channel width)." - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "id": "c2dee485", - "metadata": { - "execution": { - "iopub.execute_input": "2022-10-04T21:16:58.372820Z", - "iopub.status.busy": "2022-10-04T21:16:58.372142Z", - "iopub.status.idle": "2022-10-04T21:16:58.381451Z", - "shell.execute_reply": "2022-10-04T21:16:58.380409Z" - }, - "papermill": { - "duration": 0.019576, - "end_time": "2022-10-04T21:16:58.383933", - "exception": false, - "start_time": "2022-10-04T21:16:58.364357", - "status": "completed" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "obs = p21c.Observation(\n", - " observatory=hera,\n", - " tsky_amplitude=351*un.K,\n", - " tsky_ref_freq=150*un.MHz,\n", - " spectral_index=2.55,\n", - " n_days=180,\n", - " time_per_day=6*un.hour,\n", - " bandwidth=8*un.MHz,\n", - " n_channels=81,\n", - ")" - ] - }, - { - "cell_type": "markdown", - "id": "ef9f9364", - "metadata": { - "papermill": { - "duration": 0.006695, - "end_time": "2022-10-04T21:16:58.396407", - "exception": false, - "start_time": "2022-10-04T21:16:58.389712", - "status": "completed" - }, - "tags": [] - }, - "source": [ - "We make sure the observation duration is close to 40 minutes:" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "id": "b2eeb8de", - "metadata": { - "execution": { - "iopub.execute_input": "2022-10-04T21:16:58.413270Z", - "iopub.status.busy": "2022-10-04T21:16:58.412104Z", - "iopub.status.idle": "2022-10-04T21:16:58.422134Z", - "shell.execute_reply": "2022-10-04T21:16:58.421214Z" - }, - "papermill": { - "duration": 0.020135, - "end_time": "2022-10-04T21:16:58.423691", - "exception": false, - "start_time": "2022-10-04T21:16:58.403556", - "status": "completed" - }, - "tags": [] - }, - "outputs": [ - { - "data": { - "text/latex": [ - "$2.3809441 \\; \\mathrm{\\frac{h_{100}}{Mpc}}$" - ], - "text/plain": [ - "" - ] - }, - "execution_count": 11, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "obs.kparallel.max()" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "id": "60183953", - "metadata": { - "execution": { - "iopub.execute_input": "2022-10-04T21:16:58.439557Z", - "iopub.status.busy": "2022-10-04T21:16:58.439111Z", - "iopub.status.idle": "2022-10-04T21:16:58.446544Z", - "shell.execute_reply": "2022-10-04T21:16:58.445603Z" - }, - "papermill": { - "duration": 0.018632, - "end_time": "2022-10-04T21:16:58.447963", - "exception": false, - "start_time": "2022-10-04T21:16:58.429331", - "status": "completed" - }, - "tags": [] - }, - "outputs": [ - { - "data": { - "text/latex": [ - "$2.3809441 \\; \\mathrm{\\frac{h_{100}}{Mpc}}$" - ], - "text/plain": [ - "" - ] - }, - "execution_count": 12, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "obs.kparallel.max()" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "id": "bb98b742", - "metadata": { - "execution": { - "iopub.execute_input": "2022-10-04T21:16:58.461914Z", - "iopub.status.busy": "2022-10-04T21:16:58.461356Z", - "iopub.status.idle": "2022-10-04T21:16:58.468888Z", - "shell.execute_reply": "2022-10-04T21:16:58.468079Z" - }, - "papermill": { - "duration": 0.016671, - "end_time": "2022-10-04T21:16:58.470476", - "exception": false, - "start_time": "2022-10-04T21:16:58.453805", - "status": "completed" - }, - "tags": [] - }, - "outputs": [ - { - "data": { - "text/latex": [ - "$38.375728 \\; \\mathrm{min}$" - ], - "text/plain": [ - "" - ] - }, - "execution_count": 13, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "obs.obs_duration.to(un.min)" - ] - }, - { - "cell_type": "markdown", - "id": "61c0acef", - "metadata": { - "papermill": { - "duration": 0.006622, - "end_time": "2022-10-04T21:16:58.482902", - "exception": false, - "start_time": "2022-10-04T21:16:58.476280", - "status": "completed" - }, - "tags": [] - }, - "source": [ - "Now construct the sensitivity calculation:" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "id": "6be0a3be", - "metadata": { - "execution": { - "iopub.execute_input": "2022-10-04T21:16:58.497378Z", - "iopub.status.busy": "2022-10-04T21:16:58.496786Z", - "iopub.status.idle": "2022-10-04T21:16:58.504120Z", - "shell.execute_reply": "2022-10-04T21:16:58.503157Z" - }, - "papermill": { - "duration": 0.016425, - "end_time": "2022-10-04T21:16:58.505810", - "exception": false, - "start_time": "2022-10-04T21:16:58.489385", - "status": "completed" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "sense_optimistic = p21c.PowerSpectrum(\n", - " observation=obs,\n", - " foreground_model='optimistic',\n", - " horizon_buffer=0.0 *littleh/un.Mpc,\n", - " theory_model=p21c.theory.Legacy21cmFAST(),\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "id": "749213d0", - "metadata": { - "execution": { - "iopub.execute_input": "2022-10-04T21:16:58.520946Z", - "iopub.status.busy": "2022-10-04T21:16:58.520339Z", - "iopub.status.idle": "2022-10-04T21:17:05.673113Z", - "shell.execute_reply": "2022-10-04T21:17:05.672253Z" - }, - "papermill": { - "duration": 7.16259, - "end_time": "2022-10-04T21:17:05.674472", - "exception": false, - "start_time": "2022-10-04T21:16:58.511882", - "status": "completed" - }, - "tags": [] - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\r", - "finding redundancies: 0%| | 0/546 [00:00 2.192064\n", - " warnings.warn(\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "plt.errorbar(\n", - " sense_optimistic.k1d, \n", - " sense_optimistic.delta_squared,\n", - " sense1d\n", - ")\n", - "plt.xlim(0.06, 1)\n", - "plt.ylim(10, 50)\n", - "plt.xscale('log')\n", - "plt.yscale('log')\n", - "\n", - "for i, kk in enumerate(sense_optimistic.k1d):\n", - " sig = sense_optimistic.delta_squared[i] / sense1d[i]\n", - " yloc = sense_optimistic.delta_squared[i].value - sense1d[i].value - 1\n", - " if sig > 1 and yloc > 10:\n", - " plt.text(kk.value, yloc, f\"{sig:.1f}\")\n", - "\n", - "plt.grid(which='both')" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "id": "57677890", - "metadata": { - "execution": { - "iopub.execute_input": "2022-10-04T21:17:06.082538Z", - "iopub.status.busy": "2022-10-04T21:17:06.082100Z", - "iopub.status.idle": "2022-10-04T21:17:06.086740Z", - "shell.execute_reply": "2022-10-04T21:17:06.086079Z" - }, - "papermill": { - "duration": 0.017339, - "end_time": "2022-10-04T21:17:06.088081", - "exception": false, - "start_time": "2022-10-04T21:17:06.070742", - "status": "completed" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "sense_moderate = sense_optimistic.clone(foreground_model='moderate', horizon_buffer=0.1 *littleh/un.Mpc)" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "id": "014967e9", - "metadata": { - "execution": { - "iopub.execute_input": "2022-10-04T21:17:06.103592Z", - "iopub.status.busy": "2022-10-04T21:17:06.103100Z", - "iopub.status.idle": "2022-10-04T21:17:08.414925Z", - "shell.execute_reply": "2022-10-04T21:17:08.414321Z" - }, - "papermill": { - "duration": 2.321516, - "end_time": "2022-10-04T21:17:08.416160", - "exception": false, - "start_time": "2022-10-04T21:17:06.094644", - "status": "completed" - }, - "tags": [] - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\r", - "calculating 2D sensitivity: 0%| | 0/929 [00:00" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "plt.errorbar(\n", - " sense_moderate.k1d, \n", - " sense_moderate.delta_squared,\n", - " sense1d_moderate\n", - ")\n", - "plt.xlim(0.06, 1)\n", - "plt.ylim(10, 50)\n", - "plt.xscale('log')\n", - "plt.yscale('log')\n", - "\n", - "for i, kk in enumerate(sense_moderate.k1d):\n", - " sig = sense_moderate.delta_squared[i] / sense1d_moderate[i]\n", - " yloc = sense_moderate.delta_squared[i].value - sense1d_moderate[i].value - 1\n", - " if sig > 1 and yloc > 10:\n", - " plt.text(kk.value, yloc, f\"{sig:.1f}\")\n", - "\n", - "plt.grid(which='both')" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "id": "1d74ec0e", - "metadata": { - "execution": { - "iopub.execute_input": "2022-10-04T21:17:08.758182Z", - "iopub.status.busy": "2022-10-04T21:17:08.757238Z", - "iopub.status.idle": "2022-10-04T21:17:08.763763Z", - "shell.execute_reply": "2022-10-04T21:17:08.763005Z" - }, - "papermill": { - "duration": 0.021523, - "end_time": "2022-10-04T21:17:08.765380", - "exception": false, - "start_time": "2022-10-04T21:17:08.743857", - "status": "completed" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "from py21cmsense import observation\n", - "\n", - "\n", - "sense_pessimistic = sense_moderate.clone(\n", - " observation=obs.clone(coherent=False)\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "id": "b2558d3f", - "metadata": { - "execution": { - "iopub.execute_input": "2022-10-04T21:17:08.787482Z", - "iopub.status.busy": "2022-10-04T21:17:08.787019Z", - "iopub.status.idle": "2022-10-04T21:17:15.144483Z", - "shell.execute_reply": "2022-10-04T21:17:15.143637Z" - }, - "papermill": { - "duration": 6.371086, - "end_time": "2022-10-04T21:17:15.145770", - "exception": false, - "start_time": "2022-10-04T21:17:08.774684", - "status": "completed" - }, - "tags": [] - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\r", - "finding redundancies: 0%| | 0/546 [00:00" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "plt.errorbar(\n", - " sense_pessimistic.k1d, \n", - " sense_pessimistic.delta_squared,\n", - " sense1d_pess\n", - ")\n", - "plt.xlim(0.06, 1)\n", - "plt.ylim(10, 50)\n", - "plt.xscale('log')\n", - "plt.yscale('log')\n", - "\n", - "for i, kk in enumerate(sense_pessimistic.k1d):\n", - " sig = sense_pessimistic.delta_squared[i] / sense1d_pess[i]\n", - " yloc = sense_pessimistic.delta_squared[i].value - sense1d_pess[i].value - 1\n", - " if sig > 1 and yloc > 10:\n", - " plt.text(kk.value, yloc, f\"{sig:.1f}\")\n", - "\n", - "plt.grid(which='both')" - ] - }, - { - "cell_type": "markdown", - "id": "b138b036", - "metadata": { - "papermill": { - "duration": 0.012137, - "end_time": "2022-10-04T21:17:15.481493", - "exception": false, - "start_time": "2022-10-04T21:17:15.469356", - "status": "completed" - }, - "tags": [] - }, - "source": [ - "It seems that the default implementation of the new 21cmSense is everywhere a little less\n", - "significant than the original implementation. It is unclear exactly where this difference\n", - "comes from, but there are lots of small differences in the implementations -- of which the\n", - "newer version is typically *more* accurate. In any case, the overall significance levels\n", - "are comparable, and certainly have the same *shape* as the original." - ] - }, - { - "cell_type": "markdown", - "id": "ab19c55a", - "metadata": { - "papermill": { - "duration": 0.01184, - "end_time": "2022-10-04T21:17:15.504763", - "exception": false, - "start_time": "2022-10-04T21:17:15.492923", - "status": "completed" - }, - "tags": [] - }, - "source": [ - "## Tests" - ] - }, - { - "cell_type": "markdown", - "id": "83cb46ee", - "metadata": { - "papermill": { - "duration": 0.011895, - "end_time": "2022-10-04T21:17:15.528159", - "exception": false, - "start_time": "2022-10-04T21:17:15.516264", - "status": "completed" - }, - "tags": [] - }, - "source": [ - "From here, we are simply performing some tests to ensure that the results stay \n", - "roughly consistent as new features/fixes are added." - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "id": "6e11928f", - "metadata": { - "execution": { - "iopub.execute_input": "2022-10-04T21:17:15.556277Z", - "iopub.status.busy": "2022-10-04T21:17:15.555436Z", - "iopub.status.idle": "2022-10-04T21:17:15.560231Z", - "shell.execute_reply": "2022-10-04T21:17:15.559393Z" - }, - "papermill": { - "duration": 0.020807, - "end_time": "2022-10-04T21:17:15.561645", - "exception": false, - "start_time": "2022-10-04T21:17:15.540838", - "status": "completed" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "snr_pess_pober14 = [\n", - " 0, 0, 13.3, 17.1, 15.3, 12.3, 9.0, 6.5, 4.7, 3.4, 2.6, 2.0\n", - "]" - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "id": "656fe2d4", - "metadata": { - "execution": { - "iopub.execute_input": "2022-10-04T21:17:15.592287Z", - "iopub.status.busy": "2022-10-04T21:17:15.591441Z", - "iopub.status.idle": "2022-10-04T21:17:15.597677Z", - "shell.execute_reply": "2022-10-04T21:17:15.596907Z" - }, - "papermill": { - "duration": 0.025484, - "end_time": "2022-10-04T21:17:15.599031", - "exception": false, - "start_time": "2022-10-04T21:17:15.573547", - "status": "completed" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "snr_pess = sense_pessimistic.delta_squared / sense1d_pess\n", - "if not np.allclose(snr_pess[:len(snr_pess_pober14)],snr_pess_pober14, atol=0, rtol=0.25):\n", - " raise ValueError(\"The SNR for the pessimistic case has changed by more than 25%!\")\n", - "\n", - "if not np.all(np.diff(snr_pess)[len(snr_pess_pober14):]<=0):\n", - " raise ValueError(\"The SNR for the pessimistic case is increasing at high k :/\")" - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "id": "5c51a584", - "metadata": { - "execution": { - "iopub.execute_input": "2022-10-04T21:17:15.625416Z", - "iopub.status.busy": "2022-10-04T21:17:15.624736Z", - "iopub.status.idle": "2022-10-04T21:17:15.629470Z", - "shell.execute_reply": "2022-10-04T21:17:15.628621Z" - }, - "papermill": { - "duration": 0.020519, - "end_time": "2022-10-04T21:17:15.630903", - "exception": false, - "start_time": "2022-10-04T21:17:15.610384", - "status": "completed" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "snr_mod_pober14 = [\n", - " 0, 0, 14.0, 19.3, 18.3, 15.5, 11.8, 8.7, 6.4, 4.7, 3.6, 2.7, 2.2\n", - "]" - ] - }, - { - "cell_type": "code", - "execution_count": 26, - "id": "7b2fb9e1", - "metadata": { - "execution": { - "iopub.execute_input": "2022-10-04T21:17:15.665011Z", - "iopub.status.busy": "2022-10-04T21:17:15.664265Z", - "iopub.status.idle": "2022-10-04T21:17:15.671708Z", - "shell.execute_reply": "2022-10-04T21:17:15.670746Z" - }, - "papermill": { - "duration": 0.029554, - "end_time": "2022-10-04T21:17:15.673324", - "exception": false, - "start_time": "2022-10-04T21:17:15.643770", - "status": "completed" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "snr_mod = sense_moderate.delta_squared / sense1d_moderate\n", - "if not np.allclose(snr_mod[:len(snr_mod_pober14)],snr_mod_pober14, atol=0, rtol=0.30):\n", - " raise ValueError(\"The SNR for the moderate case has changed by more than 30%!\")\n", - "\n", - "if not np.all(np.diff(snr_mod)[len(snr_mod_pober14):]<=0):\n", - " raise ValueError(\"The SNR for the moderate case is increasing at high k :/\")" - ] - }, - { - "cell_type": "code", - "execution_count": 27, - "id": "8a98c970", - "metadata": { - "execution": { - "iopub.execute_input": "2022-10-04T21:17:15.701421Z", - "iopub.status.busy": "2022-10-04T21:17:15.700546Z", - "iopub.status.idle": "2022-10-04T21:17:15.705421Z", - "shell.execute_reply": "2022-10-04T21:17:15.704473Z" - }, - "papermill": { - "duration": 0.020534, - "end_time": "2022-10-04T21:17:15.706637", - "exception": false, - "start_time": "2022-10-04T21:17:15.686103", - "status": "completed" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "snr_opt_pober14 = [\n", - " 93.9, 66.5, 41.6, 27.1, 18.3, 12.7, 8.9, 6.4, 4.7, 3.6, 2.7, 2.2\n", - "]" - ] - }, - { - "cell_type": "code", - "execution_count": 28, - "id": "71eb91a5", - "metadata": { - "execution": { - "iopub.execute_input": "2022-10-04T21:17:15.740713Z", - "iopub.status.busy": "2022-10-04T21:17:15.739748Z", - "iopub.status.idle": "2022-10-04T21:17:15.746551Z", - "shell.execute_reply": "2022-10-04T21:17:15.745658Z" - }, - "papermill": { - "duration": 0.025761, - "end_time": "2022-10-04T21:17:15.748051", - "exception": false, - "start_time": "2022-10-04T21:17:15.722290", - "status": "completed" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "snr_opt = sense_optimistic.delta_squared / sense1d\n", - "if not np.allclose(snr_opt[1:len(snr_opt_pober14)+1],snr_opt_pober14, atol=0, rtol=0.35):\n", - " raise ValueError(\"The SNR for the optimistic case has changed by more than 30%!\")\n", - "\n", - "if not np.all(np.diff(snr_opt)[len(snr_opt_pober14)+1:]<=0):\n", - " raise ValueError(\"The SNR for the optimistic case is increasing at high k :/\")" - ] - }, - { - "cell_type": "markdown", - "id": "37f2de2f", - "metadata": { - "papermill": { - "duration": 0.013266, - "end_time": "2022-10-04T21:17:15.790281", - "exception": false, - "start_time": "2022-10-04T21:17:15.777015", - "status": "completed" - }, - "tags": [] - }, - "source": [ - "Also make sure that opt is better than moderate is better than pessimistic:" - ] - }, - { - "cell_type": "code", - "execution_count": 29, - "id": "aa35c706", - "metadata": { - "execution": { - "iopub.execute_input": "2022-10-04T21:17:15.817903Z", - "iopub.status.busy": "2022-10-04T21:17:15.817339Z", - "iopub.status.idle": "2022-10-04T21:17:15.822428Z", - "shell.execute_reply": "2022-10-04T21:17:15.821621Z" - }, - "papermill": { - "duration": 0.019763, - "end_time": "2022-10-04T21:17:15.823653", - "exception": false, - "start_time": "2022-10-04T21:17:15.803890", - "status": "completed" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "if not np.all(snr_opt >= snr_mod):\n", - " raise ValueError(\"snr_opt must be better than snr_mod!\")\n", - "\n", - "if not np.all(snr_mod >= snr_pess):\n", - " raise ValueError(\"snr_mod must be better than snr_pess!\")\n" - ] - }, - { - "cell_type": "markdown", - "id": "98dc9d2c", - "metadata": { - "papermill": { - "duration": 0.012784, - "end_time": "2022-10-04T21:17:15.848742", - "exception": false, - "start_time": "2022-10-04T21:17:15.835958", - "status": "completed" - }, - "tags": [] - }, - "source": [ - "Make sure we have the same \"shape\" as the original:" - ] - }, - { - "cell_type": "code", - "execution_count": 30, - "id": "a1c10435", - "metadata": { - "execution": { - "iopub.execute_input": "2022-10-04T21:17:15.874900Z", - "iopub.status.busy": "2022-10-04T21:17:15.874236Z", - "iopub.status.idle": "2022-10-04T21:17:15.880794Z", - "shell.execute_reply": "2022-10-04T21:17:15.880085Z" - }, - "papermill": { - "duration": 0.021481, - "end_time": "2022-10-04T21:17:15.882219", - "exception": false, - "start_time": "2022-10-04T21:17:15.860738", - "status": "completed" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "if not np.all(np.sign(np.diff(snr_pess_pober14))==np.sign(np.diff(snr_pess[:len(snr_pess_pober14)]))):\n", - " raise ValueError(\"pessimistic case has different shape to Pober+14\")\n", - "\n", - "if not np.all(np.sign(np.diff(snr_mod_pober14))==np.sign(np.diff(snr_mod[:len(snr_mod_pober14)]))):\n", - " raise ValueError(\"moderate case has different shape to Pober+14\")\n", - "\n", - "if not np.all(np.sign(np.diff(snr_opt_pober14))==np.sign(np.diff(snr_opt[1:len(snr_opt_pober14)+1]))):\n", - " raise ValueError(\"optimistic case has different shape to Pober+14\")\n", - "\n" - ] - }, - { - "cell_type": "markdown", - "id": "04b9402a", - "metadata": { - "tags": [ - "papermill-error-cell-tag" - ] - }, - "source": [ - "Execution using papermill encountered an exception here and stopped:" - ] - }, - { - "cell_type": "code", - "execution_count": 31, - "id": "52f65b69", - "metadata": { - "execution": { - "iopub.execute_input": "2022-10-04T21:17:15.910079Z", - "iopub.status.busy": "2022-10-04T21:17:15.909416Z", - "iopub.status.idle": "2022-10-04T21:17:16.223063Z", - "shell.execute_reply": "2022-10-04T21:17:16.221947Z" - }, - "papermill": { - "duration": 0.330167, - "end_time": "2022-10-04T21:17:16.224580", - "exception": true, - "start_time": "2022-10-04T21:17:15.894413", - "status": "failed" - }, - "tags": [] - }, - "outputs": [ - { - "ename": "ValueError", - "evalue": "HAHAHAHA", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[0;32mIn [31], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mHAHAHAHA\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n", - "\u001b[0;31mValueError\u001b[0m: HAHAHAHA" - ] - } - ], - "source": [ - "raise ValueError(\"HAHAHAHA\")" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3.10.6 ('sense')", - "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": "3.10.6" - }, - "papermill": { - "default_parameters": {}, - "duration": 20.662437, - "end_time": "2022-10-04T21:17:16.855349", - "environment_variables": {}, - "exception": true, - "input_path": "docs/tutorials/reproducing_pober_2014.ipynb", - "output_path": "tmp.ipynb", - "parameters": {}, - "start_time": "2022-10-04T21:16:56.192912", - "version": "2.4.0" - }, - "vscode": { - "interpreter": { - "hash": "6fa29f5e4f57cd8c712524a97e07d2162cf815d6c48daa40d8ca34da05a68238" - } - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} From 48aa302df6f18d96f7886b9ace6824583ec43917 Mon Sep 17 00:00:00 2001 From: Steven Murray Date: Tue, 4 Oct 2022 15:16:14 -0700 Subject: [PATCH 03/22] fix: remove forced error --- docs/tutorials/reproducing_pober_2014.ipynb | 9 --------- 1 file changed, 9 deletions(-) diff --git a/docs/tutorials/reproducing_pober_2014.ipynb b/docs/tutorials/reproducing_pober_2014.ipynb index 623cb3c..67bfe4f 100644 --- a/docs/tutorials/reproducing_pober_2014.ipynb +++ b/docs/tutorials/reproducing_pober_2014.ipynb @@ -689,15 +689,6 @@ " raise ValueError(\"optimistic case has different shape to Pober+14\")\n", "\n" ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "raise ValueError(\"HAHAHAHA\")" - ] } ], "metadata": { From 60dba03bd96d0012f4daf197ac7e06fd5772c398 Mon Sep 17 00:00:00 2001 From: Steven Murray Date: Thu, 3 Nov 2022 13:47:46 -0700 Subject: [PATCH 04/22] fix: use correct latitude for P14 --- docs/tutorials/reproducing_pober_2014.ipynb | 192 ++++++++------------ 1 file changed, 73 insertions(+), 119 deletions(-) diff --git a/docs/tutorials/reproducing_pober_2014.ipynb b/docs/tutorials/reproducing_pober_2014.ipynb index 67bfe4f..499333d 100644 --- a/docs/tutorials/reproducing_pober_2014.ipynb +++ b/docs/tutorials/reproducing_pober_2014.ipynb @@ -16,7 +16,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 7, "metadata": {}, "outputs": [], "source": [ @@ -29,7 +29,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 8, "metadata": {}, "outputs": [], "source": [ @@ -53,7 +53,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 9, "metadata": {}, "outputs": [], "source": [ @@ -62,7 +62,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 10, "metadata": {}, "outputs": [ { @@ -71,7 +71,7 @@ "(547, 3)" ] }, - "execution_count": 4, + "execution_count": 10, "metadata": {}, "output_type": "execute_result" } @@ -84,33 +84,35 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "We will reproduce the results at $z=9.5$ (the default case in P14):" + "We will reproduce the results at $z=9.5$ (the default case in P14). Note that we use a dish size that is slightly \n", + "different that that of P14, because the important thing is to match the beam width (which scales linearly\n", + "with frequency):" ] }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 53, "metadata": {}, "outputs": [], "source": [ - "beam = p21c.GaussianBeam(frequency=1420*un.MHz/(1 + 9.5), dish_size=14*un.m)" + "beam = p21c.GaussianBeam(frequency=1420*un.MHz/(1 + 9.5), dish_size=13.919*un.m)" ] }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 54, "metadata": {}, "outputs": [ { "data": { "text/latex": [ - "$9.5939319 \\; \\mathrm{{}^{\\circ}}$" + "$9.6497626 \\; \\mathrm{{}^{\\circ}}$" ], "text/plain": [ - "" + "" ] }, - "execution_count": 6, + "execution_count": 54, "metadata": {}, "output_type": "execute_result" } @@ -128,16 +130,16 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 55, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "38.37572750960924" + "38.5990505880113" ] }, - "execution_count": 7, + "execution_count": 55, "metadata": {}, "output_type": "execute_result" } @@ -156,25 +158,25 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 56, "metadata": {}, "outputs": [ { "data": { "text/latex": [ - "$8.6497672 \\; \\mathrm{{}^{\\circ}}$" + "$8.7001035 \\; \\mathrm{{}^{\\circ}}$" ], "text/plain": [ - "" + "" ] }, - "execution_count": 8, + "execution_count": 56, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "p21c.GaussianBeam(frequency=150*un.MHz, dish_size=14*un.m).fwhm.to(un.deg)" + "p21c.GaussianBeam(frequency=150*un.MHz, dish_size=13.919*un.m).fwhm.to(un.deg)" ] }, { @@ -189,20 +191,20 @@ "metadata": {}, "source": [ "Now, let's create the HERA Observatory model. Here, we set the receiver temperature to 100K,\n", - "as per Table 1 of P14. We also set the latitude of the instrument to the equator, which is \n", - "of course incorrect, but was the assumption made in the ismpler code of P14." + "as per Table 1 of P14. We also set the latitude of the instrument to that of Green Bank (38:25:59.24),\n", + "which was the assumed location of HERA in P14:" ] }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 57, "metadata": {}, "outputs": [], "source": [ "hera = p21c.Observatory(\n", " antpos=hera_ants,\n", " beam=beam,\n", - " latitude=0*un.rad,\n", + " latitude=0.6707845*un.rad,\n", " Trcv=100*un.K,\n", ")" ] @@ -224,7 +226,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 58, "metadata": {}, "outputs": [], "source": [ @@ -249,65 +251,19 @@ }, { "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [ - { - "data": { - "text/latex": [ - "$2.3809441 \\; \\mathrm{\\frac{h_{100}}{Mpc}}$" - ], - "text/plain": [ - "" - ] - }, - "execution_count": 11, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "obs.kparallel.max()" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [ - { - "data": { - "text/latex": [ - "$2.3809441 \\; \\mathrm{\\frac{h_{100}}{Mpc}}$" - ], - "text/plain": [ - "" - ] - }, - "execution_count": 12, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "obs.kparallel.max()" - ] - }, - { - "cell_type": "code", - "execution_count": 13, + "execution_count": 59, "metadata": {}, "outputs": [ { "data": { "text/latex": [ - "$38.375728 \\; \\mathrm{min}$" + "$38.599051 \\; \\mathrm{min}$" ], "text/plain": [ - "" + "" ] }, - "execution_count": 13, + "execution_count": 59, "metadata": {}, "output_type": "execute_result" } @@ -325,7 +281,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 60, "metadata": {}, "outputs": [], "source": [ @@ -339,18 +295,18 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 61, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "finding redundancies: 100%|██████████| 546/546 [00:02<00:00, 182.38ants/s]\n", - "computing UVWs: 100%|██████████| 39/39 [00:00<00:00, 160.87times/s]\n", - "gridding baselines: 100%|██████████| 2106/2106 [00:00<00:00, 16108.32baselines/s]\n", - "calculating 2D sensitivity: 100%|██████████| 929/929 [00:02<00:00, 453.77uv-bins/s]\n", - "averaging to 1D: 100%|██████████| 243/243 [00:00<00:00, 4495.90kperp-bins/s]\n" + "finding redundancies: 100%|██████████| 546/546 [00:03<00:00, 156.41ants/s]\n", + "computing UVWs: 100%|██████████| 39/39 [00:00<00:00, 137.29times/s]\n", + "gridding baselines: 100%|██████████| 2106/2106 [00:00<00:00, 13525.25baselines/s]\n", + "calculating 2D sensitivity: 100%|██████████| 955/955 [00:02<00:00, 417.31uv-bins/s]\n", + "averaging to 1D: 100%|██████████| 243/243 [00:00<00:00, 4181.47kperp-bins/s]\n" ] } ], @@ -360,7 +316,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 62, "metadata": {}, "outputs": [ { @@ -373,7 +329,7 @@ }, { "data": { - "image/png": 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CQ0Oxf/9+xMTEwN/fv9peprKyMly+fFn/OCMjA+np6fD09NQPCzB//nzEx8cjIiICUVFRWLduHbKysjBjxgxTD4mIqMUw93VERNbI5P8leXl5iI+PR05ODtzc3BAWFoY9e/Zg+PDhVda1sbFBYmIiBg0aBLlcrl/eo0cP7N+/v8Y7zk6dOoWYmBj94/nz5wMAJk6ciE2bNgEAnnvuORQWFuLtt99GTk4Ounfvjt27dyMoKMjUQyIiIiKqkclhaf369SatX12IAoBevXrVuM2QIUNQl+GfZs6ciZkzZ5pUDxEREZEprHpuOCIiIqKGYlgiIiIiMoJX9hERNaGHL7Be1lfkYoioTtizRERERGQEwxIRERGREQxLREREREYwLBEREREZwbBEREREZATDEhFZlXKlGsELv0Pwwu9QrlSLXQ4RWQGGJSIiIiIjGJaIiIiIjOCglEREjaBCpUHxXdWDr3Ld96J7j0vufb+tUOq3WfGbFJtvHIMgSKC9Nx+mVhAgCA++C/eW6R8LhutoBQDQfRfuPdbe2/D+88JDzz887eaIDw/Cy9kOXk5yeDrJ9d89neTwcpbD00n3nIeTHE5yKSQSSZO+pkSWgmGJiOiemgLPw18lDwWgh7+Uaq3J+8sulyC7vNQMR1I3N+7cxY07d+u0rlxmYximnO6FKWc53OylyLgtgfe1O/B2c4SXkxyu9rZmrp6o6TAsEZHVOn3tDipU2moDz6PLiuoZeB5mIwHcHGz1X64P/dvdUffdwVaK1785BwCY3EmDx/pHQiaTwUYC2EgkkACQSCSwkRh+l9x73kYCSPDgscHy++vi/jLJvWUP1q1UaTDk/VQAwGcv9oWiUoPbCiUKFUrcruarUFGJCpUWSrUWOcUVyCmuqOHopVh/4eSDRzYSuDs+CEyv7vgVfm72aO1iB28X3Xfdv+3g5mDLXiuyaAxLRNTsKSrVOJNVhJOZt/FzRqF++V/XnzC5LRsJ9CHH/ZHAU92X60NByNlOVuuHfrlSrQ9Loe4Coju2gq1t0/XCPHyHYO9ADzjKa/8YKFeqUVj2cIBS4raiErcVKtxWVKKgtBJXs/OhlTvijkKF0ko1NFoBhWUPTjl+92tOje3bSiVo7WyH1q72uu/3QtTDgaq1ix1aOdvB3lbasBeAqB4Yloio2ckvqcCpa3dwMvM2TmXewfmcEmi0QpX12no6wMNRXqWX534QqtID5GgLZ7kMNjbs5XiYo1wGR08Z2no6Vvu8SqXC7t278eSTg2Bra4tKtQZ3FCpkF5Xj6aRjAIBXYzujqFyJW6WVuFVWifwS3feichVUGgE3iytws8ZeqwfcHGx1IcrZDp7Ocv3yU5m30d3fHZ5OciNbE9UPwxIRWTRBEHDllgKnMm/jZOYdnLp2G9cKy6us5+/ugMhgD4S1dcfb/z0PANg7N7pOPSfUuOxkUvi6SeHq8OC1nzwwuNqfRaVag4KyeyGqtBL5pRUP/btS/+9bpZVQah6cUr2cX2bQzoQNulOAnk5ydPB2RkdvZ3TwdkaIlwOKlbr3EVF98bcIEVkUpVqL324W68NR2rU7BneQAbprcLr4uiIy2AMRwZ6ICPKAn7sDAN0po/thiSyfnUwKf3cH+N/7+dVEEASU3FXjVlmFvlcq+85dLNt7AQAQ4OGAG3fu4rZCiRMZt3Ei4/ZDW8uw7Lef0NFHF6I6erugw70w5e/uwJ5EqhXDEhGJqqRChdPX7uBUpu60Wvr1IlQ+cqG1ncwGvdq6IzLYExHBHugd6AE3B95t1ZJIJBK4OepOlXbwdgGgC8b3w9IP86IBAFdvKXApvxSX88twKa8Ml/JKca1QgbJ717WdySoyaNfBVor23k7o6O2CIK8Hpxm11ZzWpZaLYYmImlRO8V3d6bR7PUd/5Jbg0TMkHo62CA/yRN8QXc9Rdz83yGUcQ5eMc5TL0N3fDd393fTLVCoVvv3fbnSJHISM2xW4lFeGy7fKcDmvDFcLynBXpcFv2SX4LbvEoK3+iQfQw98NYW3d0DPAHT383RDg4cC79loohiUiMhutVsDNcuDzE9dx5noxTmbeQXZR1XF9Aj0dERHsgchgT0QGe6BdK2eeGqFGI7MBOvm4oFuAp8FytUaLrNvluJRfhsv5ZfgjtwT//UV3115ZpRrHrhbi2NUHd1d6OcnRI8ANYQHuCLsXpLxd7Jv0WEgcDEtE1GgqVBqczS7W36V2KvM2SipkwC+/69exkQBd/VwREeSpP63m48oPHGp6MqkN2rV2RrvWzojtpjutdz8s7ZwZhYu5Zfg1uxi/3ijCHzmlKFQokXLhFlIu3NK30cbNHmH3A1SAG8L83eHmyFPE1oZhiYgA6D4our6xFwBw/u3YOt1FVlSuRNq1O/rTar/eKIZSY3i9kdxGQHiwFyJDvBB573ojZzvz/epxlMuQuWQkACApKQlJSUnIzMwEAHTr1g1vvPEG4uLiAAA7d+7E2rVrkZaWhsLCQpw5cwa9evWqdR9ff/01Xn/9dVy5cgXt27fHu+++i7Fjx5rrkEgEXXxd0SfQE3+597hCpcHvOSU4m12MX67rAtTlW2X6gTr3nsvTbxvk5Yhufq76x6qGjXVKFoBhiYjqRBAEZBfd1V+IfSrzDi7kVZ2qo5WzHBFB9y7EDnDFtfQjGPWniCYdePG+gIAALFmyBB06dAAAbN68GaNHj8aZM2fQrVs3KBQKDBw4EM888wxeeumlOrV57NgxPPfcc/h//+//YezYsUhOTsazzz6Lw4cPo1+/frVufz/MqVQqTJkyBcve+3+4cOECHBwcMGDAACxduhSdO3fWr89AZxnsbaXoHagL+4jSLVNUqvFbdjF+vVGMX24U4Wx2Ma4Vluu/7nsjTYqv80/c60n1RHiQB8eDamYYloioWhqtgAu5pTh17ba+56i6qS7atXJCxL1b+CODPRHs5ai/CFalUuHGr01d+QOjRo0yePzuu+8iKSkJx48fR7du3RAfHw8A+p6nuli+fDmGDx+ORYsWAQAWLVqE1NRULF++HF988YVJ9Z07dw4JCQno378/1Go1Fi9ejBEjRuD8+fNwcnICgEYPdD16h5tUI9XMyU6Gfu280K+dl35ZUbkSv94oRtq1O1jx4yUAgEaQ4HRWEU5nFWHtwasAgHatnRAR5KH/wyKklRMvHrdgDEtEVMW0LWn45XoRSivVBstlNhJ083dDZNC98Y2CPdDK2U6kKk2j0Wjw1VdfQaFQICoqqt7tHDt2DPPmzTNYFhsbi+XLl5vc1ptvvoknn3xS3+u2ceNGeHt7Iy0tDdHRulvhGzvQrd+81eQ6qe7cHeWI7tQaEcEe+rC0IEwNrw49kX6jBCcz7+Byfhmu3lLg6i0Fvjx1A4Du4vE+QR66ABXsie7+rsZ2Q02MYYmIoNEKeG/3g4uwD18uAAA4yaXoE+ShvxC7V1v3Zjci9tmzZxEVFYWKigo4OzsjOTkZXbt2rXd7ubm58PHxMVjm4+OD3NzchpaK4uJiAICnp2ctaxrXmIGOGq6VPTC2tz+e6xsMALijUOJ01h2cuqbrsf3lRjEKFUrsO5+Hfed11z7JZTbo/tB1T4pKdbP7v2dN+MoTtXBllWrM/vw0fnroDp/XnuyCAe1boYuvC2TS5j2+UefOnZGeno6ioiJ8/fXXmDhxIlJTUxsUmB49XSIIQoNPoQiCgPnz5+Oxxx5D9+7dG9SWOQMdNZyHkxzDQn0wLFT3M6pU68Z6SrtmOGr96YcG0IxKPICebd0xoL0Xotp7oU+gBycVbkIMS0QtWHbRXUzddBJ/5JbCTmajHzn7r/2DrOavWLlcrr/AOyIiAidPnsSKFSuwdu3aerXn6+tbJXTk5+dXCSemmjVrFn799VccPny4Qe3cV1Oge/huQbIMdjIpwoM8EB7kgWnRup9VRoECR68U4h+7fgMAqLUC0q7pgtR/DlyGXGaDiCCPe+GpFXoGuDX7P2wsGV9ZohYq/XoRRq88gj9yS9HaxQ5bpvYVu6QmIQgCKisr6719VFQU9u3bZ7Dshx9+wIABA+rd5uzZs/Htt9/ip59+QkBAQL3bua+uge7gwYMYNWoU/Pz8IJFIsGvXrhrbnD59OiQSSa2n8jZt2gSJRAKJRAInO1tcW/onXFv6J1RUVL05gKonkUjQrrUz/tzHX7/sh3mDsOzpMIzp5QdvFzso1VocvVKI93+4iKeTjqLX2/swZdNJbDiSiRsKQMuJgxuVdfzpSEQm2X02B/O2p6NSrUUXXxesnxQJDyscSO+1115DXFwc2rZti9LSUmzbtg0pKSnYs2cPAOD27dvIysrCzZs3AQAXLujmGfP19YWvry8AYMKECfD390diYiIA4JVXXkF0dDSWLl2K0aNH45tvvsH+/fvr1SMkCAJeeeUVfPPNN0hJSUFISEhjHLY+0D183VJ1gU6hUKBnz56YPHkynn766Rrb27VrF37++Wf4+fnVaf+urq64cOECypVqDF6WAgCwt+fAow0R4OGITj6ueDayLQRBwJVbChy7UoCjV3SjjBeVq3Dgj3wc+CMfgAzrLh3Sb5t68RZCfV0R4OHA3qd6YlgiakEEQcDqlCv4173JR4d28cZHz/eGs50M5Up1LVs3P3l5eYiPj0dOTg7c3NwQFhaGPXv2YPjw4QCAb7/9FpMnT9av/5e/6IYgfPPNN/HWW28BALKysmBj8+ADZsCAAdi2bRv+8Y9/4PXXX0f79u2xffv2Oo2x9Ki1a9fi2LFj+Oabb+Di4qLvDXJzc4ODgwMA8wa6uLg4/QCdNcnOzsasWbOwd+9ejBxZt9N3EokEvr6+KFeqIXX2qOOrQXUlkUjQwdsZHbydER8VDK1WwO+5JTh2pRCHL93Cscu3UHz3wf/nlz89DUB3N2uglyNCvJwQ0soJIa1139u1coaPqx2HLjCCYYmohVCqtVi08yy+Pq27VXnKwBAsHhkK6b052Gq6liUxMRGvvfYaXnnlFYNTML///jv+/ve/IzU1FVqtFt26dcOXX36JwMDAGmsoKirC4sWLsXPnTty5cwchISH497//jSeffLJxD/ae9evXG31+0qRJmDRpktF1UlJSqiwbN24cxo0b14DKdO73cA0ZMsRg+caNG/V1iRnotFot4uPj8eqrr6Jbt2513q6srAxBQUFQazS4Y+8H90F/NWm/ZBobGwm6+bmhm58bJvZvi//+bzdad+2H+I1pAIBOPs64VliOSrVWP2TBoxxspQhu5YRATwf9sv3n89DKxQ5uDrZwtbeFky2gbaFn9xiWiFqAOwolpn+ahhMZtyG1keCtp7ohvn9QrdudPHkS69atQ1hYmMHyK1eu4LHHHsPUqVPxz3/+E25ubvj999+NnmpRKpUYPnw4vL29sWPHDgQEBOD69etwcXFp8PE1V7t27TIYZ6k6Yga6pUuXQiaTYc6cOXXepkuXLti0aRN69OiB/MI7GPPyYuR+ugCX/zYcYd1CG1QP1Y3UBggLcNM/3pUwEPYyKXJLKpBRoMDVAgUyCxTIuPeVdbscd+9N5/J7Tol+uznb0qu0LYEUb/1yAG4Ocrg6yODmYAunh24G+fjgVbR2sYe7oy3cHWzh5mgLd0c53B1s4SiXNtveK4YlIit35VYZpm46iczCcrjYybDyhT4Y3Kl1rduVlZXhhRdewMcff4x33nnH4LnFixfjySefxLJly/TL2rVrZ7S9DRs24Pbt2zh69Kg+HAQF1R7YSBxpaWlYsWIFTp8+bdIHXP/+/dG/f38AuvkGW41ZiJxNr2DN6lVYvWqlucqlWtjYSODn7gA/dwcM7NDK4DmVRosbd+4io6AMF3JLsXSP7lRv77buKK1Uo+SuCsV3VahUayFAguK7aoPTfA/7cP+lGmuwlUr0Ieu+1789j9J8G2SlXkUrVwd43AtXHo5yeDjZwt3BMqaFYVgismJHrxRgxtY0lFSoEeDhgA2TItHJp249OQkJCRg5ciQef/xxg7Ck1Wrx3XffYcGCBYiNjcWZM2cQEhKCRYsWYcyYMTW29+233yIqKgoJCQn45ptv0Lp1a4wfPx5///vfIZVyvBhLc+jQIeTn5xucVtVoNPi///s/LF++vM4jikskNrDz7YjLly+bqVJqKFupje4aplZO6N/OSx+WPnupn8EQImXlFdj53V5EDohGuVpA8V0VSu6qcausAu9+9wcAYEwvP5RValB8V4michWK7qpQVK6ESiNApRFQUFaJgrIHd6N++0suABv8lFPz+8PJ7sHvh+lb09Da2Q6eTnJ4OMnhde+7TFn11GJjYlgislJfnryO15LPQq0V0CfQHesmRNR5apJt27bh9OnTOHnyZJXn8vPzUVZWhiVLluCdd97B0qVLsWfPHvz5z3/GTz/9hMGDB1fb5tWrV3HgwAG88MIL2L17Ny5duoSEhASo1Wq88cYbDTpWanzx8fF4/PHHDZbFxsYiPj7e4Bqq2giCAGV+Bnx7139oBbIMdrZSuMmBDt7OBqeOy5VqfVh67889qozRJggC7qo0uvBUrkJeyV1M3nQKADAnph3O/nEZHr4BKKlQ47ZCF7LulCtRdFcFQQAUlRp9W4cuFVRbm7ayvNrljYVhicjKaLUClu79A2tTdRN2PtXTD8vGhdV5tN/r16/jlVdewQ8//FDtNUharW7gytGjR+tvTe/VqxeOHj2KNWvW1BiWtFotvL29sW7dOkilUoSHh+PmzZv417/+xbAkkrKyMoMen4yMDKSnp8PT0xOBgYHw8vIyWN/W1ha+vr7o3LmzftmECRPg6+uLgQMHAgD++c9/on///ujYsSPyCm+j8PsVUOZfxYsvbWqSYyLLc38wVEe5DH7uDghu5ah/bupjwUipuIgnn+xe5do9jVZAyV0Vcorv4smPdHdy/r8x3aCo1OC2QmnwlVegxXUzHgPDEpEVKVeqMW97Ovae080v9cqwjpj7eEeTrjlJS0tDfn4+wsMfzE6v0Whw8OBBrFy5EgqFAjKZrMp0IaGhoUbHGmrTpg1sbW0NTrmFhoYiNzcXSqUScrllXJvQkpw6dQoxMTH6x/PnzwcATJw4EZs2bapTG1lZWQaPi4qKMG3aNOTm5sLNzQ0De/fGW2sPNWjyYmqZpDYSeDjJYWf74E7Pp/sEVDu7QGFhIVq9Zb5aODoVkZXIK6nAc2uPY++5PMilNlj+XC/MG97J5LtPhg0bhrNnzyI9PV3/FRERgRdeeAHp6emws7NDZGSkfryf+y5evGj0gu2BAwfi8uXL+p6p+9u0adOGQUkkQ4YMgSAIVb5qCkqZmZmYO3euwbKUlBSDIRo+/PBDXLt2DZWVlcjPz8fevXv1Qam2EcMFQcBbb70FPz8/ODg4YMiQITh37pzRYyhN34PczxbA36c1PDw88Pjjj+PEiRMmvxZExjAsEVmB37KLMXrlEZzNLoankxyfv9QPY3r7175hNVxcXNC9e3eDLycnJ3h5eekneH311Vexfft2fPzxx7h8+TJWrlyJ//73v5g5c6a+nQkTJmDx4sX6xy+//DIKCwvxyiuv4OLFi/juu+/w3nvvISEhoWEHT83G/RHDV66s/q64ZcuW4YMPPsDKlStx8uRJ+Pr6Yvjw4SgtLa2xzYrrZ+EUOhi7f9iHY8eOITAwECNGjEB2dra5DoNaIJ6GI2rm9p/Pw5xtZ1Cu1KCDtzM2TIxEoJdj7Rs2wNixY7FmzRokJiZizpw56Ny5M77++ms89thj+nUePT3Ttm1b/PDDD5g3bx7CwsLg7++PV155BX//+9/NWitZDmMjhguCgOXLl2Px4sX485//DADYvHkzfHx88Pnnn2P69OnVbtd61KsAgJ49e8FRLsPHH3+MHTt24Mcff8SECRPMcyDU4jAsETVTgiBg/eEMvLv7dwgCMKhjK6wc3wduDo0/x1t1gx5OmTIFU6ZMMbqNSqXC7t279cuioqJw/PjxRq+Pmr+MjAzk5uZixIgR+mV2dnYYPHgwjh49WmNYelR5eTlUKhU8PT3NVSq1QAxLRM2QSqPFG9+cwxcndL034/sF4p9PdYMtJ8mkZur+vHg+Pj4Gy318fHDt2rU6t7Nw4UL4+/tXGfaAqCEYloiameK7KiR8dhqHLxdAIgEWPxmKqY+FNNtpBIge9uj7WBCEOr+3ly1bhi+++AIpKSlGp94hMhXDElEzklVYjsmbTuDKLQUc5VJ89JfeeLyrT+0bElk4X19fALoepjZt2uiX5+fnV+ltqs7yDz7AsiXvYf/+/VXmMiRqKPbZEzUTpzJvY8zqI7hyS4E2bvb4akYUgxJZjZCQEPj6+mLfvn36ZUqlEqmpqRgwwPjo38U/f42lie9iz549iIiIMHep1AKxZ4moGdh1JhsLdvwKpUaLHv5u+GRiBHxceZqBmpfaRgyfO3cu3nvvPXTs2BEdO3bEe++9B0dHR4wfP16/zYQJE+Dv74/ExEQAQPHPO1B06FN89tlnCA4O1l/75OzsDGdn56Y9QLJaDEtEFkwQBHy4/xI++lE3k/cT3XzxwXM9qx3BlsjS1TZi+IIFC3D37l3MnDkTd+7cQb9+/fDDDz/AxeXB5M9ZWVmwsXlwUqT09G5Ao8YLf3nOYF9vvvkm3nrrLfMeELUY/I1LZKEqVBq8uuNX/PeXmwCAGYPbY0FsZ9jY8EJuap7ujxheE4lEgrfeestoyHl0GIuAlzcAAM6/Hcs/Ishs+M4iskC3SisxbespnMkqgsxGgvf+3APPRrQVuywii+IolyFzyUixy6AWgBd4E1mYi3mlGLPqCM5kFcHNwRZbp/ZjUCKqRXBwMCQSSZWvOXPmVLv+4cOHMXDgQHh5ecHBwQFdunTBf1Ysb9qiqdlgzxKRBUm9eAuzPjuN0ko1gr0csWFSJNq15kWqRLU5efIkNBqN/vFvv/2G4cOH4+mnn4ZCoaiyvpOTE2bNmoWwsDA4OTnh8OHDmD59OuwHTYFLryeasnRqBhiWiCzE1mOZeOu/56HRCugb4om1fw2Hh5Nc7LKImoXWrVsbPF6yZAnat2+P6OhofP/991XW7927N3r37q1/HBwcjK92fI0fL59jWKIqeBqOSGQarYC3vj2H1785B41WwLjwAHw6tR+DElE9KZVKfPrpp5gyZUqdR/8+c+YMjh8/Bru23c1cHTVH7FkiElFZpRpzvjiDA3/kAwAWPNEZLw9uz6lLiBpg165dKCoqwqRJk2pdNyAgALdu3YJarcbi19/AlgoOaklVMSwRiSS76C6mbjqJP3JLYW9rgw+f7YW4Hm1q35CIjFq/fj3i4uLg5+cHlUpldN1Dhw6hrKwMx48fx8KFC2EzcCqcug5uokqpuWBYIhJB+vUivLj5FArKKtHaxQ6fTIhAz7buYpdF1Oxdu3YN+/fvx86dO+u0fkhICACgR48euHEzB+99tI5hiapgWCJqYrvP5mDe9nRUqrXo4uuC9ZMi4e/uIHZZRFZh48aN8Pb2xsiRpo+/JAgCBLXxnihqmRiWiJqIIAhYnXIF/9p7AQAwtIs3Pnq+N5zt+N+QqDFotVps3LgREydOhExm+P9q8eLFyM3NxZYtWwAAq1atQmBgILp06QJAN+7Sig8/gFO3uCavmywff0sTNQGlWotFO8/i69M3AABTBoZg8chQSDl1CVGj2b9/P7KysjBlypQqz+Xm5iIrK0v/WKvVYtGiRcjIyIBMJkP79u3x9jvv4oMbQU1ZMjUTDEtEZnZHocT0T9NwIuM2pDYSvPVUN8T35y9kosY2YsSIGueeW79+PWxtbfWPZ8+ejdmzZxusU65U48M39pq1RmqeGJaIzOjKrTJM3XQSmYXlcLGTYeULfTC4U+vaNyQiIovBsERkJkevFODlT0+j+K4KAR4O2DApEp18XMQui4iITMSwRGQGX568jteSz0KtFdAn0B3rJkSglbOd2GUREVE9MCwRNSKtVsDSvX9gbepVAMBTPf2wbFwY7G2lIldGRET1xbBE1EjKlWrM256OvefyAACvDOuIuY935NQlRETNHMMSUSPIK6nAi5tP4Wx2MeRSG/zrmTCM7uUvdllERNQIGJaIGujczWJM3XQKuSUV8HSSY118OCKCPcUui4hM5CiXIXOJ6SN/k/VjWCJqgP3n8zBn2xmUKzXo4O2MDRMjEejlKHZZRETUiGzELoCoORIEAZ8cuoqXtp5CuVKDQR1b4euXBzAoEVmJt956CxKJxODL19e3xvUPHz6MgQMHwsvLCw4ODujdoztKTu5quoLJrNizRGQilUaLN745hy9O6KZOeKFfIN56qhtspfzbg8iadOvWDfv379c/lkprvqvVyckJs2bNQlhYGJycnPBjSipemjYdElt7oO/jTVEumRHDEpEJiu+qkPDZaRy+XACJBPjHyK6YMjCYd7wRWSGZTGa0N+lhvXv3Ru/evfWPnx8fgNmJa1F54xwAhqXmjn8KE9VRVmE5nk46isOXC+Aol+Lj+AhMfSyEQYnISl26dAl+fn4ICQnBX/7yF1y9erXO26ann0Fl9u+wa9vdjBVSU2HPElEdnMq8jWlb03BboUQbN3t8MjEC3fzcxC6LiMykX79+2LJlCzp16oS8vDy88847GDBgAM6dOwcvL68atwsICMCtW7egVqvhMuB5uPSMBaBuusLJLBiWiGqx60w2Fuz4FUqNFj383bB+YgS8Xe3FLouIzCguLk7/7x49eiAqKgrt27fH5s2bMX/+/Bq3O3ToEMrKynDw8BHMmb8Ath5+QN+BTVEymRHDElENBEHAh/sv4aMfLwEAnujmiw+e6wlHOf/bELU0Tk5O6NGjBy5dumR0vZCQEABA+86hWPzFERQd+RyYxLDU3PGaJaJqVKg0mLMtXR+UZgxuj9Uv9GFQImqhKisr8fvvv6NNmzZ130gQIKhV5iuKmgx/8xM9oqCsEtO2nMLprCLIbCR478898GxEW7HLIqIm9Le//Q2jRo1CYGAg8vPz8c4776CkpAQTJ04EACxatAjZ2dnYsmULAGDVqlUIDAxEly5dAAA/pqSi5EQyXMJHiXYM1HisOiyNHTsWKSkpGDZsGHbs2CF2OdQMXMwrxZRNJ3Hjzl24OdhizV/DEdW+5os5icg63bhxA88//zwKCgrQunVr9O/fH8ePH0dQUBAAICcnB1lZWfr1tVotFi1ahIyMDMhkMoS0awePIZPg3OsJAFqRjoIai1WHpTlz5mDKlCnYvHmz2KVQM5B68RZmfXYapZVqBHs5YsOkSLRr7Sx2WUQkgm3bthl9ftOmTQaPZ8+ejdmzZ+sflyvV6PrG3nuPGJaaO6u+ZikmJgYuLi5il0HNwNZjmZiy6SRKK9XoF+KJ5JkDGZSIiAhAPcJSYmIiIiMj4eLiAm9vb4wZMwYXLlxo1KIOHjyIUaNGwc/PDxKJBLt27ap2vdWrVyMkJAT29vYIDw/HoUOHGrUOsn4arYB//vccXv/mHDRaAePCA7B1aj94OMnFLo2IiCyEyafhUlNTkZCQgMjISKjVaixevBgjRozA+fPn4eTkVGX9I0eOoG/fvrC1tTVY/scff8Dd3b3aoeQVCgV69uyJyZMn4+mnn662ju3bt2Pu3LlYvXo1Bg4ciLVr1yIuLg7nz59HYGCgqYdlQKVSQaUy/x0M9/dhjn2Zo+3GatOcx22Ksko15n35K1IuFgAA/ja8I6YNCoZE0ECl0oham7WwlJ+1JbKm18aSj0Ws2lQq9SOPTd//w22oVCqoJEIdtrl/vKZvW9t2Nb2Wpu7LcH11tW2a2r65f74SQRDq9grW4NatW/D29kZqaiqio6MNntNqtejTpw86duyIbdu26SchvHjxIgYPHox58+ZhwYIFxguUSJCcnIwxY8YYLO/Xrx/69OmDpKQk/bLQ0FCMGTMGiYmJ+mUpKSlYuXKl0Qu8V61ahVWrVkGj0eDixYv4/PPP4ejI2eOtTaUGWHBC9/fBop5qbL4kxc1yCWxtBPy1gxa9vBr0X4GISO/h3zfL+qphV/McvGZpo77b1mc7U7cxx/rl5eUYP348iouL4erqWnvRJmrwBd7FxcUAAE9PzyrP2djYYPfu3YiOjsaECROwdetWZGRkYOjQoXjqqadqDUo1USqVSEtLw8KFCw2WjxgxAkePHjW5vYSEBCQkJKCkpARubm6IiYkxOpx9Y1GpVNi3bx+GDx9epefNEtturDbNedzGlCvVWHDiAABg3WVHFJYr0dpZjjUv9EZYAKcuMQexftbNgTW9NpZ8LJbw+wZAvfb/cBuxsSPqNM7b/eMdOnQocOKgSdvWts+aXktT63x4/aFDh+JI6gGjr09d2i8sLKzT8dVXg8KSIAiYP38+HnvsMXTvXv1kgX5+fjhw4ACio6Mxfvx4HDt2DMOGDcOaNWvqvd+CggJoNBr4+PgYLPfx8UFubq7+cWxsLE6fPg2FQoGAgAAkJycjMjKy1vZtbW2b9D+VOfdnjrYbq80mf52FBxPeFiqU6OLrgg2TIuHn7tBkNbRUTf2zbk6s6bWx5GMR8/dNfff/cBu67ev+kf3wuqZsW5d9PnosptZpuL6s2jbrU5M5NSgszZo1C7/++isOHz5sdL3AwEBs2bIFgwcPRrt27bB+/fpGman90TYEQTBYtnfv3kc3oRbsrvLBdUiDO7XGqhf6wNnOqkfPICKiRlDvoQNmz56Nb7/9Fj/99BMCAgKMrpuXl4dp06Zh1KhRKC8vx7x58+q7WwBAq1atIJVKDXqRACA/P79KbxPRfclnsvX//s/zvRiUiIioTkwOS4IgYNasWdi5cycOHDignzSwJgUFBRg2bBhCQ0P123z55Zf429/+Vu+i5XI5wsPDsW/fPoPl+/btw4ABA+rdLlkvtUaLjUcy9Y9lUqseYoyIiBqRyX9aJyQk4PPPP8c333wDFxcXfe+Om5sbHBwMr/3QarV44oknEBQUhO3bt0MmkyE0NBT79+9HTEwM/P39q+1lKisrw+XLl/WPMzIykJ6eDk9PT/2wAPPnz0d8fDwiIiIQFRWFdevWISsrCzNmzDD1kKgF+O5sDrKL7opdBhERNUMmh6X7t+oPGTLEYPnGjRsxadIkg2U2NjZITEzEoEGDIJc/GOSvR48e2L9/f413nJ06dQoxMTH6x/PnzwcATJw4UT/E/HPPPYfCwkK8/fbbyMnJQffu3bF79279vD1E9wmCgDWpVwEAfxvRCbOGdhS5IiKydo5yGTKXjIRKpcLu3bvFLocayOSwZOqwTMOHD692ea9evWrcZsiQIXXaz8yZMzFz5kyT6qGW5+ClAvyeUwInuRTx/YPFLoeIiJoZXrhBVm9NyhUAwPN9A+HmaJm3NRMRkeViWCKr9sv1Ihy7WghbqQRTBxnejJCdnY2//vWv8PLygqOjI3r16oW0tLRq25k+fTokEgmWL19udH/nzp3D008/jeDg4DqtT0TW7/vvv0efPn3g6uoKV1dXREVF4fvvvze6zapVq9AnrAey/v1nZH88HZ99urWJqqXq8N5psmprUnW9SqN7+aON24MbEO7cuYOBAwciJiYG33//Pby9vXHlyhW4u7tXaWPXrl34+eef4efnV+v+ysvL0a5dOzzzzDMNHiKDiKyDl5cX3n33XXTp0gUAsHnzZowePRpnzpxBt27dqqyflJSERYsWYWXSGiw6dBfKmxcw/5U58G3dCqNGjWrq8gkMS2TFrt4qw55zurs1ZwxuZ/Dc0qVL0bZtW2zcuFG/LDg4uEob2dnZmDVrFvbu3YuRI0fWus/IyEj9KPGPTsdDRC1T3759ERcXpx9l+t1330VSUhKOHz9ebVjaunUrpk+fjnHPPIs3zu6Frbsvng2qwNKlSxmWRMLTcGS1Pj50FYIAPB7qgw7eLgbPffvtt4iIiMAzzzwDb29v9O7dGx9//LHBOlqtFvHx8Xj11Ver/YVGRGQqjUaDbdu2QaFQICoqqtp1KisrYW9vb7DMwcEBJ06cgEqlaooy6REMS2SV8ksq8HWabsTul4e0q/L81atXkZSUhI4dO2Lv3r2YMWMG5syZgy1btujXWbp0KWQyGebMmdNkdRORdTp79iycnZ1hZ2eHGTNmIDk5GV27dq123djYWHzyySc4czoNgiCgMucStmzeBJVKhYKCgiaunACehiMrteFIJpQaLSKDPRAe5Fnlea1Wi4iICLz33nsAgN69e+PcuXNISkrChAkTkJaWhhUrVuD06dONMo8hEbVsnTt3Rnp6OoqKivD1119j4sSJSE1NrTYwvf7668jNzcWQQY9BrdFC6uSOOdOn4MN/vw+pVCpC9cSeJbI6JRUqfHb8GgBgxuD21a7Tpk2bKr+kQkNDkZWVBQA4dOgQ8vPzERgYCJlMBplMhmvXruH//u//qr22iYjIGLlcjg4dOiAiIgKJiYno2bMnVqxYUe26Dg4O2LBhAwqKSuA/YwP8X96IoKAguLi4oFWrVk1cOQHsWSIr9PnPWSitVKOTjzNiOntXu87AgQNx4cIFg2UXL17UjwAfHx+Pxx9/3OD52NhYxMfHY/LkyeYpnIhaDEEQUFlZaXQdW1tbyFx14WjHV1/iT3/6E2xs2MchBoYlsiqVag02HM4AAEyPbg8bm+pPoc2bNw8DBgzAe++9h2effRYnTpzAunXrsG7dOgC6W30fnY7H1tYWvr6+6Ny5s37ZhAkT4O/vj8TERACAUqnE+fPn9f/Ozs5Geno6nJ2d0aFDh0Y/XiKyfFu3boWrqytCQkJQWlqKbdu2ISUlBXv27AEALFq0CNnZ2fprJi9evIgTJ04grHc4Km9eQMnJXSjNP4etD11TSU2LYYmsSvLpbOSXVsLPzR5P9ap5XKTIyEgkJydj0aJFePvttxESEoLly5fjhRdeMGl/WVlZBn/p3bx5E71799Y/fv/99/H+++9j8ODBSElJMfl4iKj5KyoqwuTJk5GTkwM3NzeEhYVhz549+unAcnJy9JcAALo75v7973/jwoULqNBIYB8Uhh9TDvISABExLJHV0GgFrDuomzB36qB2sJUa767+05/+hD/96U91bj8zM7PKskcDUHBwsMnzJxKRdZs9ezaefPJJ/ThLj7o/Qfx9oaGhOHPmDMqVanR9Yy8AoNNDPdrU9Hjyk6zGvvO5uFqggJuDLf4S2VbscoiIyEowLJFVEAQBSam6XqWJUUFwsmOnKRERNQ6GJbIKx6/exi/Xi2Bva4OJA4LFLoeIiKwIwxJZhfsT5j4b0RZeznYiV0NERNaEYYmavfM3S5B68RakNhK8NKjq1CZEREQNwbBEzd7ag7pepZE92qCtp6PI1RARkbVhWKJm7frtcvzv1xwAwLRo9ioREVHjY1iiZu2TQ1eh0QoY1LEVuvu7iV0OERFZIYYlarYKyyqx/dR1AMDLNUyYS0RE1FAMS9RsbT52DRUqLcIC3BDV3qv2DYiIiOqBYYmapXKlGluOZQIAZgxuD4mk+glziYiIGophiZqlbSeuo6hchZBWTojt5it2OUREZMUYlqjZUWm0WH84AwDw0qB2kNqwV4mIiMyHYYmanf/+chPZRXfRytkOf+7jL3Y5RERk5RiWqFkRBAFr702YO+WxYNjbSkWuiIiIrB3DEjUrP13Ix4W8UjjbyfBCvyCxyyEiohaAYYmalTUpul6lF/oFws3BVuRqiIioJZCJXQBRXaVdu4MTmbchl9pgymMhYpdDRGRWjnIZMpeMFLsMAnuWqBlZk6qbMHdsb3/4uNqLXA0REbUUDEvULFzOL8W+83mQSIBpgzlhLhERNR2GJWoW7t8BN6KrD9q3dha5GiIiakkYlsji5RTfxa70bAC6qU2IiIiaEsMSWbwNhzOg0gjoF+KJ3oEeYpdDRNTkkpKSEBYWBldXV7i6uiIqKgrff/+90W0qss4iZ9Mr8HR1Rrt27bBmzZomqtb68G44smjF5Sp8/nMWAGDGEPYqEVHLFBAQgCVLlqBDhw4AgM2bN2P06NE4c+YMunXrVmX9zMxM5O94C85hsUjZm4zTJ3/GzJkz0bp1azz99NNNXX6zx7BEFu3Tn69BodSgi68LhnRqLXY5RESiGDVqlMHjd999F0lJSTh+/Hi1YWnj+k8gdWkNz8enoUtoKPr07IFTp07h/fffZ1iqB56GI4tVodJg4xHdhLkzBreHRMIJc4mINBoNtm3bBoVCgaioqGrXOfHzz3AI6W2wLDY2FqdOnYJKpWqKMq0Ke5bIYu1Iu4GCMiX83R3wp7A2YpdDRCSqs2fPIioqChUVFXB2dkZycjK6du1a7br5+XmQ+hleuuDj4wO1Wo2CggK0acPfqaZgzxJZJI1WwMeHdMMFvDQoBDIp36pE1LJ17twZ6enpOH78OF5++WVMnDgR58+fr/P2giAAAHvp64E9S2SRvv8tB9cKy+HhaItnI9uKXQ4Rkejkcrn+Au+IiAicPHkSK1aswNq1a6us6+3tg3zFHYNl+fn5kMlk8PLyapJ6rQn/XCeLIwiCfmqTiQOC4ShnpiciepQgCKisrKz2ub79+qEiM91g2Q8//ICIiAjY2nISclPxU4gszpHLhfgtuwQOtlJMjAoWuxwiItG99tpriIuLQ9u2bVFaWopt27YhJSUFe/bsAQAsWrQI2dnZWL9+PQBg8tQX8dHKVbj948f44/lApKedxPr16/HFF1+IeRjNFsMSWZz7vUrPRbaFh5Nc5GqIiMSXl5eH+Ph45OTkwM3NDWFhYdizZw+GDx8OAMjJyUFWVpZ+/eDgYHiPewt3DnyCqL4R8PPzw0cffcRhA+qJYYksytkbxTh8uQBSGwleHBQidjlERBbhfo9RTTZt2gQABsMC2Af2QJtJK3D+7VheztBAvGaJLMqag7pepad6+iHAw1HkaoiIiBiWyIJcK1Tg+7M5AIDpg9uJXA0REZEOwxJZjHUHr0IrADGdW6OLr6vY5RAREQFgWCILcau0El+l3QCgm9qEiIjIUjAskUXYdDQDSrUWvQPd0TfEU+xyiIiI9BiWSHRllWpsPXYNACfMJSIiy8OwRKL74ucslFSo0b61E4aH+ohdDhERkQGGJRKVUq3F+sMZAIDp0e1hY8NeJSIisiwMSySqXenZyC2pgI+rHUb39hO7HCIioioYlkg0Wq2AtfemNpn6WAjsZFKRKyIiIqqKYYlEc+DCLVy5pYCLvQzP9w0UuxwiIqJqMSyRKAQBWHdId61SfP8guNjbilwRERFR9RiWSBRXS4Ez14shl9lg8kBOmEtERJaLYYlEsT9b99YbFx6A1i52IldDRERUM4YlanIX80pxvsgGNhJg2iBOmEtERJaNYYma3MeHMgEAsV19ENzKSdxiiIiIasGwRE0qu+gu/nc2FwAwbRCvVSIiIsvHsERN6pNDV6HWCujkpkV3f1exyyEiIqoVwxI1mTsKJbaduA4AGOYniFwNERFR3TAsUZPZcuwa7qo06NrGBZ3dGJaIiKh5YFiiJnFXqcHmY5kAdNcqSThfLhERNRMMS9Qkvjx1HbcVSgR6OiK2q7fY5RAREdUZwxKZnVqjxceHrgIAXopuB5mUbzsiImo++KlFZvfd2RzcuHMXrZzleCY8QOxyiIiITMKwRGYlCALWpOp6lSYNCIa9rVTkioiIiEzDsERmlXrxFn7PKYGTXIr4/sFil0NERGQymdgFkHVbk3oFAPB830C4OdqKXA0RUcvgKJchc8lIscuwGuxZIrNJv16E41dvw1YqwVRObUJERM0UwxKZzZoUXa/S6F7+aOPmIHI1RERE9cOwRGZx5VYZ9p7XTZg7Y3A7kashIiKqP4YlMouPD16FIACPh/qgg7eL2OUQERHVG8MSNbr8kgrsPJ0NAHh5CHuViIioeWNYoka3/kgGlBotIoM9EB7kKXY5REREDcKwRI2qpEKFz49nAQBmDG4vcjVERJSYmIjIyEi4uLjA29sbY8aMwYULF4xuU3HjHIYNiYaXlxccHBzQpUsXrFixookqtjwMS9SoPjuehdJKNTr5OCOmMyfMJSISW2pqKhISEnD8+HHs27cParUaI0aMgEKhqHEbG1t7TH95Jg4ePIjff/8d//jHP/Dmm29i7969TVi55eCglNRoKlQabDiSAQCYHt0eNjYSkSsiIqI9e/YYPN64cSO8vb2RlpaG6OjoareR+7THs8/FwlGuiwnBwcHYsWMHzp8/b/Z6LRF7lqjRJJ/Jxq3SSvi52eOpXn5il0NERNUoLi4GAHh61v2a0jNnzuD48ePo3r27ucqyaOxZokah0QpYd1A3Ye7UQe1gK2UOJyKyNIIgYP78+XjsscfqFHwCAgJw69YtqNVqvP766+jdu3cTVGl5+IlGjeKHc7nIKFDAzcEWf4lsK3Y5RERUjVmzZuHXX3/FF198Uaf1Dx06hFOnTmHNmjX4z3/+g4MHD5q5QsvEniVqMEEQ9BPmTowKgpMd31ZERJZm9uzZ+Pbbb3Hw4EEEBATUaZuQEN28nj169MDNmzexbt06LFmyxJxlWiR+qlGDHbtaiF9uFMPe1gYTBwSLXQ4RET1EEATMnj0bycnJSElJ0Qeg+rSjUqkaubrmgWGJGmxNqu5apWcj2sLL2U7kaoiI6GEJCQn4/PPP8c0338DFxQW5ubp5O93c3ODgoJvkfNGiRcjOzsaaTzYAAEpP/w+7/6dCzx7dAACHDx/Ghx9+iNjYWHEOQmQMS9Qg524W4+DFW5DaSPDSIE5tQkRkaZKSkgAAQ4YMMVi+ceNGTJo0CQCQk5ODrKws/XOCIOCN1/+Ba5kZkMlkaN++Pd599134+/s3VdkWhWGJGmTtvV6lkT3aoK2no8jVEBHRowRBqHWdTZs2AQDKlWoAgGv4KJz6bqV+nCUAUKlU2L17t1lqtHS8G47q7frtcvzv15sAgOmD2atERETWiWGJ6u3jQ1ehFYDoTq3Rzc9N7HKIiIjMgmGJ6qWwrBJfnroOAJjBXiUiIrJiDEtUL5uPZqJCpUXPADdEtfMSuxwiIiKzYVgikykq1dh87BoAYMbg9pBIOGEuERFZL4YlMtmXadkovqtCSCsnjOjmK3Y5REREZsWwRCbRaIGNR3W9StOi20Fqw14lIiKybgxLZJK0QglyiivQ2sUOY3u3zMHJiIioZWFYojrTagX8mK17y0wZGAJ7W6nIFREREZkfwxLVWcqlAuTelcDZToYX+geKXQ4REVGTYFiiOvv4UAYA4PnIALja24pcDRERUdNgWKI6Sbt2G6euFUEqETBpQJDY5RARETUZhiWqk6QU3YS5fVsL8HaxE7kaIiKipsOwRLW6lFeK/b/nQSIBhvppxS6HiIioSTEsUa3WHtT1Kg0P9Ya3g8jFEBERNTGGJTIqp/guvknPBgBMGxQicjVERERNj2GJjFp/KAMqjYD+7TzRM8BN7HKIiIiaHMMS1ai4XIUvTmQB0E2YS0RE1BIxLFGNth7PhEKpQWgbVwzu1FrscoiIiETBsETVqlBpsPFIJgBgxuB2kEg4YS4REbVMDEtUra/SbqBQoUSAhwNG9mgjdjlERESiYViiKtQaLT6+N1zAS4PaQSbl24SIiFoufgpSFd//lous2+XwdJLj2Yi2YpdDREQkKoYlMiAIAtakXgEATIwKhoNcKnJFRERE4mJYIgOHLxfg3M0SONhKMSGKE+YSERExLJGB+71Kf+nbFh5OcpGrISIiEh/DEumdvVGMI5cLIbOR4MVB7cQuh4iIyCIwLJHe/V6lp3r6wd+dM+YSEREBDEt0T2aBAt//lgMAmM6pTYiIiPQYlggAsO7QVWgFYGgXb3T2dRG7HCIiIovBsETIL63AjrQbADhhLhER0aMYlgibjmRCqdaiT6A7IoM9xC6HiIjIojAstXClFSpsPX4NgK5XiRPmEhERGWJYauG+OJGF0go1Ong74/FQH7HLISIisjgMSy1YpVqD9YczAADTotvBxoa9SkRERI9iWGrBvjlzE3kllfB1tceYXv5il0NERGSRGJZaKK1WwJqDukEopz4WArmMbwUiIqLqyMQugMSx7/c8XL2lgKu9DM/3CxS7HCIisgCOchkyl4wUuwyLw+6EFkgQBP3UJvFRQXC2Y2YmIiKqCcNSC3Qi4zbOZBVBLrPBpAEhYpdDRERk0RiWWqD7vUrPhAegtYudyNUQERFZNoalFuaP3BL8dOEWbCS64QKIiIjIOIalFmZt6lUAQFyPNgjychK5GiIiIsvHsNSC3LhTjm9/uQkAeJkT5hIREdUJw1IL8smhDGi0Ah7r0Ard/d3ELoeIiKhZYFhqIe4olNh+8joA3YS5REREVDcMSy3E5mOZuKvSoLu/KwZ28BK7HCIiomaDYakFKFeqsfloJgBdr5JEwglziYiI6ophqQX48uR13ClXIcjLEXHd24hdDhERUbPCsGTlVBotPj6UAQB4aVA7SG3Yq0RERGQKhiUrt/u3PGQX3UUrZznGhQeIXQ4RETUjBw8exKhRo+Dn5we5XI7jx4/Xus1nn32Gnj17wtHREW3atMHkyZNRWFjYBNWaD8OSFRME3XABADB5YAjsbaUiV0RERM2JQqFAz549sXLlyjqtf/TIYUyYMAFTp07FuXPn8NVXX+HkyZN48cUXzVypeXG6eSv2e5EEf+SVwUkuxV/7BYldDhERNTNxcXGIi4ur8/onfj6B4OBgzJkzBwAQEhKC6dOnY9myZeYqsUmwZ8mK/XhT9+Md3y8Qbo62IldDRETWrn9Uf9y4cQO7d++GIAjIy8vDjh07MHLkSLFLaxCGJSuVfr0Il0sksJVKMPUxTphLRETm1z9qAD777DM899xzkMvl8PX1hbu7O/7zn/+IXVqDMCxZqXWHMgEAT/VsA183e3GLISKiFuH3389jzpw5eOONN5CWloY9e/YgIyMDM2bMELu0BrHKsDR27Fh4eHhg3LhxYpciiiu3yrD/j3wAwIsDg8UthoiIWoz3ly3FwIED8eqrryIsLAyxsbFYvXo1NmzYgJycHLHLqzerDEtz5szBli1bxC5DNOtSr0IQgB4eWnTwdha7HCIiaiHult+FjY1htJBKdXdiC4IgRkmNwirDUkxMDFxcXMQuQxR5JRVIPpMNABjmrxW5GiIias7KysqQnp6O9PR0AEB+fj7S09ORlZUFAFi0aBFenDJJv37cyJHYuXMnkpKScPXqVRw5cgRz5sxB37594efnJ8IRNA6LC0sPD4AlkUiwa9euKuusXr0aISEhsLe3R3h4OA4dOtT0hVqoDYczoNRoERHkjpCWmReJiKiRnDp1Cr1790bv3r0BABs2bEDfvn3xxhtvAABycnJw4/p1/frxEybigw8+wMqVK9G9e3c888wz6Ny5M3bu3ClK/Y3F4sJSbQNgbd++HXPnzsXixYtx5swZDBo0CHFxcfqU25IV31Xhs591r8O0QSEiV0NERM3dkCFDIAgCBEGAUqnErl27oFQqsWnTJgDApk2bsGffjwbbzJ49G+fOnUN5eTlu3ryJTz/9FP7+/iJU33gsblDK2gbA+uCDDzB16lT9aKDLly/H3r17kZSUhMTERJP2VVlZicrKSv3j4uJiAMDt27frUbnpVCoVysvLUVhYCFvbho+DtOHINZSUlKBDayf08JIg5WzjtQ00Xr2Nfdxkufizrpk1vTaWfCxi19bU+zfn/mpqu1yphrayHABQWFiIu3Lj0eLh9W8X3q613rq0f/9z21zXRVlcWDJGqVQiLS0NCxcuNFg+YsQIHD161OT2EhMT8c9//rPK8k6dOtW7RktwHYDfYrGrICKiliZwuWnrtzNx/draLywshJubm2mN1kGzCksFBQXQaDTw8fExWO7j44Pc3Fz949jYWJw+fRoKhQIBAQFITk5GZGRklfYWLVqE+fPn6x9rtVqEh4fj9OnTkEgk5juQh0RGRuLkyZPNpu3GaLOkpARt27bF9evX4erq2kiVkaUy53u8ubOm18aSj0Xs2pp6/y3xc6W4uBiBgYHw9PRspKoMNauwdN+jQUYQBINle/furVM7dnZ2sLOzq7LMHKm0JlKp1GyBwRxtN2abrq6uDEstgDnf482dNb02lnwsYtfW1PtvyZ8rjw5b0Fgs7gJvY1q1agWpVGrQiwTobmV8tLepvhISEhqlHUvYnznaburXh5o/vmdqZk2vjSUfi9i18XOl6dtsbBLBgkeJkkgkSE5OxpgxY/TL+vXrh/DwcKxevVq/rGvXrhg9erTJF3iTOEpKSuDm5obi4mKL/UuUiIiaD3N/rljcabiysjJcvnxZ/zgjIwPp6enw9PREYGAg5s+fj/j4eERERCAqKgrr1q1DVlZWs593piWxs7PDm2++WeUUKBERUX2Y+3PF4nqWUlJSEBMTU2X5xIkT9eM6rF69GsuWLUNOTg66d++ODz/8ENHR0U1cKREREbUEFheWiIiIiCxJs7rAm4iIiKipMSwRERERGcGwRERERGQEwxIRERGREQxLZPHGjh0LDw8PjBs3TuxSiIioGfrf//6Hzp07o2PHjvjkk09M3p53w5HF++mnn1BWVobNmzdjx44dYpdDRETNiFqtRteuXfHTTz/B1dUVffr0wc8//2zSPHLsWSKLFxMTAxcXF7HLICKiZujEiRPo1q0b/P394eLigieffLLOc8jex7BEDXLw4EGMGjUKfn5+kEgk2LVrV5V1Vq9ejZCQENjb2yM8PByHDh1q+kKJiKhZaujnzM2bN+Hv769/HBAQgOzsbJNqYFiiBlEoFOjZsydWrlxZ7fPbt2/H3LlzsXjxYpw5cwaDBg1CXFwcsrKy9OuEh4eje/fuVb5u3rzZVIdBREQWqqGfM9VdbSSRSEyqweLmhqPmJS4uDnFxcTU+/8EHH2Dq1Kl48cUXAQDLly/H3r17kZSUpJ/4OC0trUlqJSKi5qehnzP+/v4GPUk3btxAv379TKqBPUtkNkqlEmlpaRgxYoTB8hEjRuDo0aMiVUVERNaiLp8zffv2xW+//Ybs7GyUlpZi9+7diI2NNWk/7FkisykoKIBGo4GPj4/Bch8fH+Tm5ta5ndjYWJw+fRoKhQIBAQFITk5GZGRkY5dLRETNTF0+Z2QyGf79738jJiYGWq0WCxYsgJeXl0n7YVgis3v03LAgCCadLzb1rgUiImpZavuceeqpp/DUU0/Vu32ehiOzadWqFaRSaZVepPz8/Cp/BRAREZmqqT5nGJbIbORyOcLDw7Fv3z6D5fv27cOAAQNEqoqIiKxFU33O8DQcNUhZWRkuX76sf5yRkYH09HR4enoiMDAQ8+fPR3x8PCIiIhAVFYV169YhKysLM2bMELFqIiJqLizhc4bTnVCDpKSkICYmpsryiRMnYtOmTQB0g4UtW7YMOTk56N69Oz788ENER0c3caVERNQcWcLnDMMSERERkRG8ZomIiIjICIYlIiIiIiMYloiIiIiMYFgiIiIiMoJhiYiIiMgIhiUiIiIiIxiWiIiIiIxgWCIiIiIygmGJiIiIyAiGJSIiIiIjGJaIiIiIjGBYIiIiIjLi/wNWMmv/kELFoQAAAABJRU5ErkJggg==", + "image/png": 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", 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" ] @@ -404,7 +360,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 63, "metadata": {}, "outputs": [], "source": [ @@ -413,15 +369,15 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 64, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "calculating 2D sensitivity: 100%|██████████| 929/929 [00:02<00:00, 463.19uv-bins/s]\n", - "averaging to 1D: 100%|██████████| 243/243 [00:00<00:00, 4299.09kperp-bins/s]\n" + "calculating 2D sensitivity: 100%|██████████| 955/955 [00:02<00:00, 439.61uv-bins/s]\n", + "averaging to 1D: 100%|██████████| 243/243 [00:00<00:00, 3834.56kperp-bins/s]\n" ] } ], @@ -431,12 +387,12 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 65, "metadata": {}, "outputs": [ { "data": { - "image/png": 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", + "image/png": 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", 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" ] @@ -467,7 +423,7 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": 66, "metadata": {}, "outputs": [], "source": [ @@ -481,18 +437,18 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": 67, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "finding redundancies: 100%|██████████| 546/546 [00:02<00:00, 197.26ants/s]\n", - "computing UVWs: 100%|██████████| 39/39 [00:00<00:00, 160.69times/s]\n", - "gridding baselines: 100%|██████████| 2106/2106 [00:00<00:00, 16689.38baselines/s]\n", - "calculating 2D sensitivity: 100%|██████████| 929/929 [00:01<00:00, 479.14uv-bins/s]\n", - "averaging to 1D: 100%|██████████| 243/243 [00:00<00:00, 4488.75kperp-bins/s]\n" + "finding redundancies: 100%|██████████| 546/546 [00:03<00:00, 174.63ants/s]\n", + "computing UVWs: 100%|██████████| 39/39 [00:00<00:00, 146.90times/s]\n", + "gridding baselines: 100%|██████████| 2106/2106 [00:00<00:00, 14628.72baselines/s]\n", + "calculating 2D sensitivity: 100%|██████████| 955/955 [00:02<00:00, 441.90uv-bins/s]\n", + "averaging to 1D: 100%|██████████| 243/243 [00:00<00:00, 4018.53kperp-bins/s]\n" ] } ], @@ -502,12 +458,12 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": 68, "metadata": {}, "outputs": [ { "data": { - "image/png": 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2Ybh//OMfSExMRLt27VBWVoZt27YhJSUFP/74Y4119Xo9Hn74YbRv3x7bt2+HTCZDt27dsGfPHiQkJCA4OLjWUaby8nJkZGQYH2dmZiI9PR0+Pj7G2wLMnTsXkyZNQkxMDOLi4rBmzRpkZ2djxowZ5u4SERERUZ3MDks3btzApEmTkJeXB4VCgV69euHHH3/E8OHDa6zr4OCAxYsXY9CgQZDL5cblPXv2xJ49e+q84iw1NRUJCQnGx3PnzgUATJkyBRs2bAAAPPnkkygsLMTbb7+NvLw8REREIDk5Ge3btzd3l4iIiIjqZHZYWrdunVnr1xaiAKB37951thkyZAgacvunmTNnYubMmWbVQ0T2TaXWovubhos7zr09stkvVyei1seu54YjIiIiaiqGJSIiIiITGJaIiIiITGBYIiIiIjKBYYmIiIjIBIYlIgJw787SYfO/h0qtFbscIiKrwbBEREREZALDEhEREZEJDEtEREREJjAsEREREZnAsERERERkAidNIiJqBpUaHUoqNPe+VIbvxXcel975XqRUG9t8+psUG68dhSBIoL8zH6ZeECAI974Ld5YZHwvV19ELAGD4Ltx5rL/T8O7zwn3P3z/t5ohPDsDX3Qm+bnL4uMmN333c5PB1l8PHzfCct5scbnIpJBJJi76mRNaCYYmI6I66As/9X6X3BaD7v9Ravdnby1VJkKsqs8CeNMy12xW4druiQevKZQ7Vw5TbnTDlLofCWYrMIgn8r96Gv8IVvm5yeDo7Wrh6opbDsEREduvk1duo1OhrDTx/XFbcyMBzPwcJoHBxNH553vdvL1fDdxdHKd749iwAYFoXHR7sHwuZTAYHCeAgkUACQCKRwEFS/bvkzvMOEkCCe4+rLb+7Lu4uk9xZdm/dKo0OQz7cDwD44pm+UFbpUKRUo1CpRlEtX4XKKlRq9FBr9cgrqUReSWUdey/Fugsn7j1ykMDL9V5g+tuO0whSOKONhxP8PQzfDf92gsLFkaNWZNUYlojI5imrtDiVXYwTWUX4JbPQuPwv646b3ZeDBMaQ4/WHwFPbl+d9QcjdSVbvh75KrTWGpW5eAuI7+8HRseVGYe6/4WhUqDdc5fV/DKjUWhSW3x+g1ChSVqFIqUGRsgoFZVW4knsTerkrbis1KKvSQqcXUFh+75Dj96fz6uzfUSpBG3cntPF0Nny/E6LuD1RtPJzg5+4EZ0dp014AokZgWCIim3OztBKpV2/jRFYRUrNu41xeKXR6ocZ67Xxc4O0qrzHKczcI1RgBcnWEu1wGBweOctzPVS6Dq48M7Xxca31eo9EgOTkZo0YNgqOjI6q0OtxWapBbrMJjK48CAP428gEUq9S4VVaFW+VVuFlq+F6s0kCjE3C9pBLX6xy1ukfh4mgIUe5O8HGXG5enZhUhItgLPm5yE62JGodhiYismiAIuHxLidSsIpzIuo3Uq0W4WqiqsV6wlwtiw7zRq50X3v6/cwCAXbPjGzRyQs3LSSZFoEIKT5d7r/20gWG1vhdVWh0Kyu+EqLIq3CyrvO/fVcZ/3yqrglp375Bqxs3yav1M/sxwCNDHTY5O/u7o7O+OTv7uCPd1QYna8HNE1Fj8X4SIrIpaq8dv10uM4Sjt6u1qV5ABhnNwugZ6IjbMGzFhPohp740gLxcAhkNGd8MSWT8nmRTBXi4IvvP+1UUQBJRWaHGrvNI4KpV7uwJLdl0AAIR4u+Da7QoUKdU4nlmE45lF97WWYclv+9A5wBCiOvt7oNOdMBXs5cKRRKoXwxIRiaq0UoOTV28jNctwWC09pxhVfzjR2knmgN7tvBAb5oOYMG9EhXpD4cKrrVoTiUQChavhUGknfw8AhmB8Nyz9NCceAHDllhKXbpYh42Y5Lt0ox6UbZbhaqET5nfPaTmUXV+vXxVGKjv5u6Ozvgfa+9w4z6ms5rEutF8MSEbWovJIKw+G0OyNH5/NL8ccjJN6ujohu74O+4YaRo4ggBeQy3kOXTHOVyxARrEBEsMK4TKPR4Lv/JaNr7CBkFlXi0o1yZNwqR8aNclwpKEeFRoffckvxW25ptb76L96LnsEK9GqnQGSIF3oGKxDi7cKr9lophiUishi9XsB1FbDleA5O5ZTgRNZt5BbXvK9PqI8rYsK8ERvmg9gwb3Twc+ehEWo2MgegS4AHeoT4VFuu1emRXaTCpZvlyLhZjvP5pfi/Xw1X7ZVXaXH0SiGOXrl3daWvmxw9QxToFeKFXneClL+Hc4vuC4mDYYmImk2lRoczuSXGq9RSs4pQWikDfv3duI6DBOge5ImY9j7Gw2oBnvzAoZYnkzqgQxt3dGjjjpE9DIf17oalb2bG4WJ+OU7nluD0tWKczytDoVKNlAu3kHLhlrGPtgpn9LoboEIU6BXsBYUrDxHbG4YlImq0YpUaaVdvGw+rnb5WArWu+vlGcgcB0WG+iA33Reyd843cnfhfD1m3roGe6BPqg6fuPK7U6PB7XinO5Jbg1xxDgMq4VW68UeeuszeMbdv7uqJHkKfxsaZp9zolK8D/sYioQQRBQG5xhfFE7NSs27hwo+ZUHX7ucsS0v3MidognrqYfxuhHY1r0xotEzc3ZUYqoUEPYR5xhmbJKi99yS3D6Wgl+vVaMM7kluFqoMn7d9WaaFF/fPH5nJNUH0e29eT8oG8OwRES10ukFXMgvQ+rVIuPIUW1TXXTwc0PMnUv4Y8N8EObrajwJVqPR4Nrplq6cqGW4OcnQr4Mv+nXwNS4rVqlx+loJ0q7exqc/XwIA6AQJTmYX42R2MVYfuAIA6NDGDTHtvY1/WIT7ufHkcSvGsERENTy3KQ2/5hSjrEpbbbnMQYIewQrEtr9zf6Mwb/i5O4lUJZH18XKVI75LG8SEeRvD0rxeWvh2ikT6tVKcyLqNjJvluHJLiSu3lPgy9RoAw8njfdp7GwJUmA8igj1NbYZaGMMSEUGnF7Ao+d5J2IcyCgAAbnIp+rT3Np6I3budF++ITWQmP2dgfFQwnuwbBgC4rVTjZPZtpF41jNj+eq0EhUo1dp+7gd3nDOc+yWUOiLjvvCdllZa/eyLiK0/UypVXafHSlpPYd98VPv8Y1RUDOvqha6AHZFLe34ioOXm7yTGsWwCGdQsAYJjy5bfcUqRdrX7X+pP33UAzbvFeRLbzwoCOvojr6Is+od6cVLgFMSwRtWK5xRWYvuEEzueXwUnmYLxz9l/6t+dfsUQtxEkmRXR7b0S398Zz8YaLKTILlDhyuRCv7/wNAKDVC0i7aghS/9mbAbnMATHtve+EJz9Ehij4h40F8X9DolYqPacYz2xMRUF5Fdp4OGHZxCg8ufqY2GURtXoSiQQd2rgjUOFsDEs/zRmE9OwSHLlcgCOXC3GzrApHLhfiyOVCABfh7iRD33Af9AvzglYJ6DlxcLNiWCJqhZLP5GHO9nRUafXoGuiBdVNj4c0b6bUIV7kMWf96BBqNBsnJyWKXQzYixNsVXQI88URsOwiCgMu3lDh6JzgdvVKIYpUGe8/fxN7zNwHIsObSQWPb/RdvoVugJ0K8XTj61EgMS0StiCAIWJFyGR/cmXx0aFd//PvpKLg7yaBSa+tpTc3t7NmzWLNmDU6dOoW8vDwkJSVh3LhxxucXLlyIbdu2IScnB3K5HNHR0XjvvffQr1+/Ovtcu3YtNm3ahN9+M4xIREdHY9GiRejbt6+ld4daiEQiQSd/d3Tyd8ekuDDo9QJ+zy/F0cuFOHTpFo5m3EJJxb3f5xc+PwnAcDVrqK8rwn3dEO7nhvA2hu8d/NwR4OnEWxeYwIhJ1EqotXq8+tVpY1D668BwrJ0cY3d30747cpP1r0fgKpfhwIEDGD16NIKCgiCRSLBz585q6y9cuBBdu3aFm5sbvL298dBDD+GXX34xuQ2NRoO3334bHTt2hLOzMyIjI/Hjjz+aXWtlZSV69eqFZcuW1fp8ly5dsGzZMpw5cwaHDh1CWFgYRowYgVu3btW6PgCkpKTg6aefxr59+3D06FGEhoZixIgRyM3NNbs+sg0ODhL0CFLgmUEdsHZSHyyO1WHztGjj810C3OEkc4BWL+DKLSV+Pn8T/z2UideSfsPEtb+g/+Kf0f3NXUj89CDmbE83tttz7gaOXC7A2eslyClSoaxSA30rPbpnX/9LElGtbivVeP7zNBzPLILUQYKFY3pgUv/2YpfVIpRKJSIjIzFt2jQ89thjNZ6/G0g6dOiAiooKfPLJJxgxYgQyMjLQpk2bWvt8/fXX8fnnn2Pt2rXo2rUrdu3ahfHjx+PIkSOIiopqcG3R0dEYNWpUnXc3nzhxYrXHH3/8MdatW4fTp09j2LBhtbb54osvqj1eu3YtduzYgZ9//hmTJ09ucG1ku6QOQK8QhfHxzlkD4SyTIr+0EpkFSlwpUCKrQInMO1/ZRSpU3JnO5fe8UmO7l7el1+hbAikW/roXChc5PF1kULg4wu2+i0HWHriCNh7O8HJ1hJeLIxSujvBylcPLxRGucqnNjl4xLBHZucu3yjF9wwlkFarg4STDsj/3weAutYcAe5SYmIjExMQ6n29MINm8eTNee+01jBo1CgDwwgsvYNeuXfjoo4/w+eefN1/x91Gr1VizZg0UCgUiIyMb3E6lUkGj0cDHxwfAvZE3ADhw4AA++OADpKWl1XoY8JtvvsHq1auRlpaGwsJCnDp1Cr179653m19//TXeeOMNXL58GYJHALziJwEYac7uUjNzcJAgyMsFQV4uGNjJr9pzGp0e125XILOgHBfyy/D+j4bR56h2Xiir0qK0QoOSCg2qtHoIkKCkQlvtMN/9Ptlzqc4aHKUSY8i6643vzqHspgOy91+Bn6cLvO+EK29XObzdHOHlYh3TwjAsEdmxI5cLMGNzGkortQjxdsFnU2PRJcBD7LKsVkMDSVVVFZydnastc3FxwaFDh5q9pv/973946qmnoFKp0LZtW+zevRt+fn71N7xj/vz5CA4OxkMPPVTjufpG3ZRKJQYOHIjHH38czz77bIO2d/ToUTz55JN455138PAjozH0pQ9w69v3ceL4KAx+cGCD66aW4yh1MJzD5OeG/h18jWHpi2f7VbuFSLmqEt98vwuxA+Kh0gooqdCgtEKLW+WVeO/78wCAcb2DUF6lQ0mFGsUqDYorNChWqaHRCdDoBBSUV6GgvMrY53e/5gNwwL68jDrrc3O6dz+p5zenoY27E3zc5PB2k8P3zneZWtnMr0p1DEtEdurLEzn4R9IZaPUC+oR6Yc3kGE5NUgdzA8nIkSPx8ccfIz4+Hh07dsTPP/+Mb7/9FjqdrtlrS0hIQHp6OgoKCrB27Vo88cQT+OWXX+Dv719v2yVLlmDr1q1ISUmpEe6A+kfdJk2aBADIyspqcL1Lly7F8OHDsWDBAqjUWijinkBlzm9Y9p//MCzZOCdHKRRyoJO/e7VDxyq11hiWFv2pZ417tAmCgAqNzhCeVBrcKK3AtA2pAICXEzrgzPkMeAeGoLRSiyKlIWTdVqlRXKGBIADKqnu/VwcvFdRam75KVevy5sKwRGRn9HoB7+86j9X7DRN2jokMwpIJvXi3XxPMDSSffvopnn32WXTt2hUSiQQdO3bEtGnTsH79+mavzc3NDZ06dUKnTp3Qv39/dO7cGevWrcOCBQtMtvvwww+xaNEi7NmzB7169Wr2uupy9OhRzJkzp9oyl/A++OXYTy1WA1kXiUQCV7kMrnIZgrxcEObnanxu+oNhSKm8iFGjImqcu6fTCyit0CCvpAKj/m0YtX1nXA8oq3QoUqqrfd0o0CPHgvvAsERkR1RqLeZsT8eus4b5pV4Z1hmzH+pssydVthRzA0mbNm2wc+dOVFZWorCwEEFBQZg/fz7Cw8MtXqsgCKiqqjK5zgcffIB3330Xu3btQkxMjMVrul9+fj4CAgKqLZO6eeFGfn6L1kG2T+oggbebHE6O9y7cf6xPSK2zCxQWFsJvoeVqYVgishM3SivxzMZUnMktgVzqgCUTemFcVLDYZdmkhgQSAHB2dkZwcDA0Gg2+/vprPPHEE2Ztp6KiAunp6ca/qDMzM5Geng4fHx/4+vrivffew5gxY9C2bVsUFhZixYoVuHbtGh5//HFjH5MnT0ZwcDAWL14MwHDo7Y033sCWLVsQFhaG/Dshxd3dHe7u7mbV11h/DOeCIDCwk01jWCKyA7/lluCZjanIL62Ej5scayZFIybMR+yyrEJ5eTkyMu6dPNocgeSXX35Bbm4uevfujdzcXCxcuBB6vR7z5s0zq7aMjAw8/fTTxsdz584FAEyZMgWrVq3C+fPnsXHjRhQUFMDX1xexsbE4ePAgevToYWyTnZ0NB4d7f3mvWLECarUaEyZMqLatf/7zn1i4cKFZ9TVGYGCgMaDdpVeVwP8Po01EtoRhicjG7Tl3Ay9vOwWVWodO/u74bEosQn1d62/YSqSmpiIhIcH4uDkCSWVlJV5//XVcuXIF7u7uGDVqFDZv3gwvLy+zauvZsyfUanWd91n65ptv6u0jJSWl2mNzTsa2hLi4OOzevbvaeUsVmacwrH+ciFURNQ3DEpGNEgQB6w5l4r3k3yEIwKDOflg2sQ8ULpzj7X5DhgyBYGJS0cYEksGDB+PcuXNNLU10pkbdQkNDUVRUhOzsbFy/fh0AcOGC4ZLywMBABAYGAjCMugUGBmLgQMOVbq+88gri4+Px/vvvY0TiIyg5tgOVV9Px4qZPW3jviJoPpzshskEanR7/SPoN735vCEoT+4Xis6mxDEpkltTUVERFRRnvOj537lxERUXhzTffBAB89913iIqKwiOPGG5i+dRTTyEqKgqrVq0y9pGdnV3tsNuAAQOwbds2rF+/Hv1i+iDwxjF8/dWXvG0A2TSOLBHZmJIKDWZ9cRKHMgogkQCvjeqG6Q+G8wRaMlt9o25Tp07F1KlTTfaRkpICjUaD5ORk47IJEybUOGdKq9Vi4cKF+OKLL5Cfn4+2bdti6tSpeP3116sd4qzL4cOHMXjwYERERCA9Pb3e9YmaE8MSkQ3JLlRh2objuHxLCVe5FP9+KgoPdeeJs2T93n//faxatQobN25Ejx49kJqaimnTpkGhUOCVV14x2bakpASTJ0/GsGHDcOPGjRaqmOgehiUiG5GaVYTnNqehSKlGW4Uz/jslBj2CFPU3JLICR48exdixY42H9MLCwrB161akpqbW2/b555/HxIkTIZVKsXPnTgtXSlQTz1kisgE7T+Vi4tpfUKRUo2ewAjtnDWRQIpvy4IMP4ueff8bFixcBAL/++isOHTpknIy4LuvXr8fly5fxz3/+syXKJKoVR5aIrJggCPhkzyX8+2fDTN4P9wjEx09G1noHWyJr9ve//x0lJSXo2rUrpFIpdDod3nvvvWr3mfqjS5cuYf78+Th48CBkMv7Mk3j400dkpSo1Ovxtx2n836+Gy7ZnDO6IeSMfgIMDT+Qm27N9+3Z8/vnn2LJlC3r06IH09HTMnj0bQUFBmDJlSo31dTodJk6ciLfeegtdunQRoWKiexiWiKzQrbIqPLc5FaeyiyFzkGDRn3riiZh2YpdF1Gh/+9vfMH/+fDz11FMADDfkvHr1KhYvXlxrWCorK0NqaipOnTqFF198EQCg1+shCAJkMhl++ukn9H8wvkX3gVovhiUiK3PxRhmmrT+B3OIKKFwcseov0Yjr6Gvx7brKZcj61yMW3w61TiqVqsYtAqRSKfR6fa3re3p64syZM9WWrVixAnv37sWOHTtaZNJiort4gjeRFdl/8RYeW3EEucUVCPN1RdLMAS0SlP7owIEDGD16NIKCgiCRSGpcgSQIAhYuXIigoCC4uLhgyJAhOHv2rMk+165di0GDBsHb2xve3t546KGHcPz4cQvuBVmT0aNH47333sP333+PrKwsJCUl4eOPP8b48eON6yxYsACTJ08GADg4OCAiIqLal7+/P5ydnREREQE3NzexdoVaIYYlIiux+WgW/rrhBMqqtOgb7oOkmQPRoU3LzBL/R0qlEpGRkVi2bFmtzy9ZsgQff/wxli1bhhMnTiAwMBDDhw9HWVlZnX2mpKTg6aefxr59+3D06FGEhoZixIgRyM3NtdRukBX5z3/+gwkTJmDmzJno1q0bXn31VTz//PN45513jOvk5eUhOztbxCqJasfDcEQi0+kFvPO/c9hwJAsAMCE6BIvG94RcJt7fMomJiUhMTKz1OUEQsHTpUrz22mv405/+BADYuHEjAgICsGXLFjz//PO1tvviiy+qPV67di127NiBn3/+2TiaQPbLw8MDS5cuxdKlS+tcZ8OGDSb7WLhwIRYuXNisdRE1BEeWiERUXqXFs5tSjUFp3sMP4IMJvUQNSvXJzMxEfn4+RowYYVzm5OSEwYMH48iRIw3uR6VSQaPRwMfHxxJlEhE1G44sEYkkt7gC0zecwPn8Mjg7OuCTJ3ojsWdbscuq191JUwMCqk+zEhAQgKtXrza4n/nz5yM4OBgPPfRQs9ZHRNTcGJaIRJCeU4xnNqaioLwKbTyc8N/JMYhs5yV2WWb548S9giA0eDLfJUuWYOvWrUhJSYGzs7MlyqNWgFdwUkux3rF+IjuVfCYPT64+ioLyKnQN9MDOWQNtKigFBgYCuDfCdNfNmzdrjDbV5sMPP8SiRYvw008/oVevXhapkVqn3Nxc/OUvf4Gvry9cXV3Ru3dvnDx5ss71v/nmGwwfPhxt2rSBp6cnEuIfRMWVtBasmGwFwxJRCxEEAcv3ZWDmFydRpdVjaFd/7HhhAIK9XMQuzSzh4eEIDAzE7t27jcvUajX279+PAQMGmGz7wQcf4J133sGPP/6ImJgYS5dKrcjt27cxcOBAODo64ocffsC5c+fw0UcfQaGoew7FAwcOYPjw4UhOTkZaWhriBw/Bza/fgfrG5RasnGwBD8MRtQC1Vo8F35zB1yevAQD+OjAcrz3SDVIrnbqkvLwcGRkZxseZmZlIT0+Hj48PQkNDMXv2bCxatAidO3dG586dsWjRIri6umLixInGNpMnT0ZgYCAGDhwIwHDo7Y033sCWLVsQFhZmHJlyd3eHu7s4t0gg+/H++++jXbt2WL9+vXFZWFgYNBoNLly4UGubP16Z99Y77+LTdVuhyjgOYKYFqyVbw5ElIgu7rVTjL+t+wdcnr0HqIME74yLw5ujuVhuUACA1NRVRUVGIiooCAMydOxdRUVF48803AQDz5s3D7NmzMXPmTMTExCA3Nxc//fQTPDw8jH1kZ2dXO1S3YsUKqNVqTJgwAW3btjV+ffjhhy27c2SXvvvuO8TExODxxx+Hv78/oqKisHbtWrP60Ov10Ksr4ODM8E7VcWSJyIIu3yrH9A0nkFWogoeTDMv+3AeDu7QRu6x6DRkyBIIg1Pm8RCKp9543KSkp0Gg0SE5OBgBkZWU1c5VE91y5cgUrV67E3Llz8Y9//APHjx/Hyy+/DKlUCl/fht0F/9NPPoGgqYRb10EWrpZsDcMSkYUcuVyAFz4/iZIKDUK8XfDZ1Fh0CfCovyERmU2v1yMmJgaLFi0CAERFReHs2bNYs2YNFixYUG/7rVu3YtG7b8Nv7D8gdfOycLVka3gYjsgCvjyRg8nrjqOkQoM+oV7YOWsggxKRBbVt2xbdu3evtqxbt27Iycmpt+327dsxffp0bP5iK1zCeluoQrJlDEtEzUivF7D4h98x7+vT0OoFjIkMwpZn+8PP3Uns0ojs2sCBA2ucyH3x4kWEhoaabLd161ZMnToVW7ZswcOjRlmyRLJhDEtEzUSl1uKFL9Kwev8VAMArwzrj06d6w9lRKnJlRPZvzpw5OHbsGBYtWoSMjAxs2bIFa9aswYwZM4zrLFiwoNo8hFu3bsXkyZPx0UcfoX///sjPz4eu/Db0VUoxdoGsGMMSUTO4UVqJJ1cfw66zNyCXOuDTp3pjzvAuDb6jNRE1TWxsLJKSkrB161ZERETgnXfewdKlS6vdziIvLw/Z2dnGx6tXr4ZWq8WsWbPQtm1bdGzfDteWT0LRnjVi7AJZMZ7gTdREZ6+XYPqGVOSXVsLHTY41k6IRE8bJYYla2qOPPopHH3202jKNRmP894YNG6o9l5KSUu2xSq1F9zd3Wao8smEMS0RNsOfcDby87RRUah06+bvjsymxCPV1FbssIiJqRgxLRI0gCALWHcrEe8m/QxCAQZ39sGxiHyhcHMUujYiImhnDEpGZNDo93vz2LLYeN5z78Od+oVg4pgccpTwFkIjIHjEsEZmhpEKDWV+cxKGMAkgkwOuPdMdfB4bxRG4iIjvGsETUQNmFKvx14wlk3CyHq1yKfz8VhYe6B4hdFhERWRjDElEDpGYV4bnNaShSqtFW4Yz/TolBjyCF2GUREVELYFgiqsfOU7mYt+M01Do9egYrsG5KDPw9ncUui4iIWgjDElEdBEHAJ3su4d8/XwIAPNwjEB8/GQlXOX9tiIhaE/6vT1SLSo0Of9txGv/363UAwIzBHTFv5ANwcOCJ3ET2ylUuQ9a/HhG7DLJCDEtEf1BQXoXnNqXiZHYxZA4SLPpTTzwR007ssoiISCR2fWOY8ePHw9vbGxMmTBC7FLIRF2+UYdzywziZXQyFiyM2T+/HoEREWLx4MSQSCWbPnl3nOikpKZBIJJBIJHBzcsTV9x/F1fcfRe61ay1XKFmEXY8svfzyy/jrX/+KjRs3il0K2YD9F2/hxS9OoqxKizBfV3w2NRYd2riLXRYRiezEiRNYs2YNevXq1aD1L1y4AJmzKwYvSQEAtG3LKZBsnV2PLCUkJMDDw0PsMsgGbD6ahb9uOIGyKi36hfsgaeZABiUiQnl5Of785z9j7dq18Pb2blAbf39/BAYGQuruDam7NxykUgtXSZZmdlhavHgxYmNj4eHhAX9/f4wbNw4XLlxo1qIOHDiA0aNHIygoCBKJBDt37qx1vRUrViA8PBzOzs6Ijo7GwYMHm7UOsn86vYC3/u8s3vj2LHR6AROiQ7B5ej94u8nFLo2IrMCsWbPwyCOP4KGHHmpwm6ioKHRo3w43tv0DlVdPW7A6ailmH4bbv38/Zs2ahdjYWGi1Wrz22msYMWIEzp07Bzc3txrrHz58GH379oWjY/UJRs+fPw8vLy8EBgbWaKNUKhEZGYlp06bhscceq7WO7du3Y/bs2VixYgUGDhyI1atXIzExEefOnUNoaKi5u1WNRqOBRqNpUh8N3c7936297+bq05L7bY7yKi3mfHkaKRcLAACvDu+M5waFQSLooNHoRK3NXljLe22N7Om1seZ9aUpt27dvR1paGo4ePQqNRgNBEKDX6+vsy8/PDytXrkSfPn1QUq7Cn179ADe2vYZz3d/Fow8Pb0Tt2mr7oZEIDWhzd3/Nb1tfu7peS3O3VX19ba19mtu/pX/2JIIgNOwVrMOtW7fg7++P/fv3Iz4+vtpzer0effr0QefOnbFt2zZI7wxFXrx4EYMHD8acOXMwb9480wVKJEhKSsK4ceOqLe/Xrx/69OmDlStXGpd169YN48aNw+LFi43LUlJSsGzZMuzYsaPObSxfvhzLly+HTqfDxYsXsWXLFri68hizPSuqAtael+K6SgJHBwF/6aRHb98m/SoQkR25desWXn31VSxcuBDh4eEAgNdeew3h4eF45pln6m1fpQPmHZfh5o630N0beOP118yu4W4fALCkrxZOZhzNa2zbxrQzt40l1lepVJg4cSJKSkrg6elZf9FmavIJ3iUlJQAAHx+fGs85ODggOTkZ8fHxmDx5MjZv3ozMzEwMHToUY8aMqTco1UWtViMtLQ3z58+vtnzEiBE4cuSI2f3NmjULs2bNQmlpKRQKBRISEuDr69uo2syh0Wiwe/duDB8+vMbImzX23Vx9WnK/G+L0tRK888UpFKjUaOMux6o/R6FXCKcusQSx32trZk+vjTXvS2Nr+/bbb1FSUoJXX33VuEyn0+HcuXP44YcfUF5ebhwAqI1KrcW843vhFNQVeVf2Neq1udsHAIwcOaJBN8S9u79Dhw4Fjh8wq21926zrtTS3zvvXHzp0KA7v32vy9WlI/4WFhQ3av8ZqUlgSBAFz587Fgw8+iIiIiFrXCQoKwt69exEfH4+JEyfi6NGjGDZsGFatWtXo7RYUFECn0yEgoPokpgEBAcjPzzc+HjlyJE6ePAmlUomQkBAkJSUhNja23v4dHR1b9BfektuzRN/N1WdLv84A8PPvNzBry0lUavToGuiBz6bGIsjLpUVraI3EeK9thT29Nta8L+bWNnLkSJw5c6basmnTpqFr1674+9//Dmdn01MeOQqGG9iqb1xGsLd3o16bu30Ad+tv+Ef2/eua07Yh2/zjvphbZ/X1ZbX22ZiaLKlJYenFF1/E6dOncejQIZPrhYaGYtOmTRg8eDA6dOiAdevWQSJp+p2Q/9iHIAjVlu3atavJ2yD7UaE23JW7UqPH0K7++PfTUXB3suu7ZxBRI3l4eNQYBHBzc4Ovr69x+YIFC5Cbm4tNmzYBAJYuXYqwsDD06NEDJUoVbu/fANXFIxj5t7+3eP3UvBp964CXXnoJ3333Hfbt24eQkBCT6964cQPPPfccRo8eDZVKhTlz5jR2swAMJ9FJpdJqo0gAcPPmzRqjTUR3fZWWgyKlGqE+rlgzKZpBiYiaJC8vD9nZ2cbHarUar776Knr16oURQxNQde0c/Cf8E/36x4lYJTUHsz8tBEHASy+9hKSkJKSkpBhPfKtLQUEBhg0bhm7duuGrr77CpUuXMGTIEDg5OeHDDz9sVNFyuRzR0dHYvXs3xo8fb1y+e/dujB07tlF9kn3T6vRYc+AKAODZ+A6QSe36FmNEZAEpKSnVHm/YsKHa43nz5hnPxVWptej+5t2jG1qQbTM7LM2aNQtbtmzBt99+Cw8PD+PojkKhgItL9XM/9Ho9Hn74YbRv3x7bt2+HTCZDt27dsGfPHiQkJCA4OLjWUaby8nJkZGQYH2dmZiI9PR0+Pj7G2wLMnTsXkyZNQkxMDOLi4rBmzRpkZ2djxowZ5u4StQLfn8nDtdsV8HOX4/Fo0yOhRERE9zM7LN29VH/IkCHVlq9fvx5Tp06ttszBwQGLFy/GoEGDIJffu8lfz549sWfPnjqvOEtNTUVCQoLx8dy5cwEAU6ZMMSb5J598EoWFhXj77beRl5eHiIgIJCcno3379ubuEtk5QRCwar9hVGnqgDA4O/JuukRE1HCNOgxnjuHDa78RV+/evetsM2TIkAZtZ+bMmZg5c6ZZ9VDrc+BSAX7PK4WbXIpJ/cPELoeIiGwMT9wgu7cq5TIA4Om+oVC4WudlzUREZL0Ylsiu/ZpTjKNXCuEolWD6INMXIxAREdWGYYns2qr9hlGlsb2D0VbBm08SEZH5GJbIbl25VY4fzxqu1pwxuIPI1RARka1iWCK7tfbgFQgC8FC3AHTy9xC7HCIislEMS2SXbpZW4uu0XADAC0M4qkRERI3HsER26bPDWVDr9IgN80Z0ex+xyyEiIhvGsER2p7RSgy+OXQUAzBjcUeRqiIjI1jEskd3Z8ks2yqq06BLgjoQH/MUuh4iIbBzDEtmVKq0Onx3KBAA8H98RDg4SkSsiIiJbx7BEdiXpZC5ullUhSOGMMb2DxC6HiIjsAMMS2Q2dXsCaA4YJc6cP6gBHKX+8iYio6cyeSJfIWu0+l48rBUooXBzxVGw7scsholbMVS5D1r8egUajQXJystjlUBPxT2+yC4IgYOV+w6jSlLj2cHPi3wFERNQ8GJbILhy7UoRfc4rh7OiAKQPCxC6HiIjsCMMS2YW7E+Y+EdMOvu5OIldDRET2hGGJbN6566XYf/EWpA4SPDuIU5sQkXX54Ycf0KdPH3h6esLT0xNxcXH44YcfGtS28to5XF0yBv1joy1cJZnCsEQ2b/UBw6jSIz3bop2Pq8jVEBFV5+vri/feew+pqalITU3F0KFDMXbsWJw9e9Zku5KSEhR+/zGc20e2UKVUF4Ylsmk5RSr873QeAOC5eI4qEZH16du3LxITE9GlSxd06dIF7733Htzd3XHs2DGT7V6eNRNu3QbDKbhrC1VKdWFYIpv234NXoNMLGNTZDxHBCrHLISIySafTYdu2bVAqlYiLi6tzvfXr1+PKlctQPDixBaujuvD6arJZheVV2J6aAwB4gRPmEpEVO3PmDOLj41FZWQl3d3ckJSWhe/futa576dIlzJ8/Hz/9vA9jP89s4UqpNhxZIpu18ehVVGr06BWiQFxHX7HLISKq0wMPPID09HQcO3YML7zwAqZMmYJz587VWE+n02HixIl466230LlLFxEqpdpwZIlskkqtxaajWQCAGYM7QiLhhLlEZL3kcjk6deoEAIiJicGJEyfw6aefYvXq1dXWKysrQ2pqKk6dOoUXX3wROr0ACALOQIBMJsNPP/2EoUOHirELrRrDEtmkbcdzUKzSINzPDSN7BIpdDhGRWQRBQFVVVY3lnp6eOHPmDACgQqPF2GWHUXYqGUHKy/j66x0IDw9v6VIJDEtkgzQ6PdYdMhzHf3ZQB0gdOKpERNZr8+bN8PT0RHh4OMrKyrBt2zakpKTgxx9/BAAsWLAAubm52LRpExwcHBAREQHAMIIub5MLqasCTjon43JqeQxLZHP+79fryC2ugJ+7E/7UJ1jscoiITCouLsa0adOQl5cHhUKBXr164ccff8Tw4cMBAHl5ecjOzha5SjKFYYlsiiAIWH1nwty/PhgGZ0epyBUREZn20ksvYdSoUXB0dKz1+Q0bNphs7/Xgn3Hs7ZEWqIwailfDkU3Zd+EmLtwog7uTDH/u117scoiIqBVgWCKbsirFMKr0536hULjU/lcaERFRc2JYIpuRdvU2jmcVQS51wF8f5BUhRETUMhiWyGas2m+YMHd8VDACPJ1FroaIiFoLhiWyCRk3y7D73A1IJMBzgzlhLhERtRyGJbIJd6+AG9E9AB3buItcDRERtSYMS2T18koqsDM9F4BhahMiIqKWxLBEVu+zQ5nQ6AT0C/dBVKi32OUQEVErw7BEVq1EpcGWXwx3tp0xhKNKRETU8hiWyKp9/stVKNU6dA30wJAubcQuh4iIWiGGJbJalRod1h82TJg7Y3BHSCScMJeIiFoewxJZrR1p11BQrkawlwse7dVW7HKIiKiVYlgiq6TTC1h70HC7gGcHhUMm5Y8qERGJg59AZJV++C0PVwtV8HZ1xBOx7cQuh4iIWjGGJbI6giAYpzaZMiAMrnKZyBUREVFrxrBEVudwRiF+yy2Fi6MUU+LCxC6HiIhaOYYlsjp3R5WejG0Hbze5yNUQEVFrx7BEVuXMtRIcyiiA1EGCZwaFi10OERERwxJZl1UHDKNKYyKDEOLtKnI1REREDEtkRa4WKvHDmTwAwPODO4hcDRERkQHDElmNNQeuQC8ACQ+0QddAT7HLISIiAsCwRFbiVlkVvkq7BsAwtQkREZG1YFgiq7DhSCbUWj2iQr3QN9xH7HKIiIiMeLc/El15lRabj14FwAlziYjucpXLkPWvR8Qug8CRJbICW3/JRmmlFh3buGF4twCxyyEiIqqGYYlEpdbqse5QJgDg+fiOcHDgqBIREVkXhiUS1c70XOSXViLA0wljo4LELoeIiKgGhiUSjV4vYPWdqU2mPxgOJ5lU5IqIiIhqYlgi0ey9cAuXbynh4SzD031DxS6HiIioVgxLJApBANYcNJyrNKl/e3g4O4pcERGR9Vq8eDFiY2Ph4eEBf39/jBs3DhcuXDDZpjL7NK6+/yjcnBwhkUiMX+fPn2+hqu0HwxKJ4koZcCqnBHKZA6YN5IS5RESm7N+/H7NmzcKxY8ewe/duaLVajBgxAkqlst626WfOIi8vz/jVuXPnFqjYvvA+SySKPbmGnD4hOgRtPJxEroaIyLr9+OOP1R6vX78e/v7+SEtLQ3x8vMm2bfz9EejvZ8ny7B5HlqjFXbxRhnPFDnCQAM8N4oS5RETmKikpAQD4+NQ/48GAvrFo27Ythg0bhn379lm6NLvEsEQtbu3BLADAyO4BCPNzE7cYIiIbIwgC5s6diwcffBARERF1rid184HPyBfxxfbt+Oabb/DAAw9g2LBhOHDgQAtWax94GI5aVG5xBf53Jh8A8NwgnqtERGSuF198EadPn8ahQ4dMrufoGwJH3xBERfWBq1yGuLg45OTk4MMPP6z30B1Vx5ElalH/PXgFWr2ALgo9IoI9xS6HiMimvPTSS/juu++wb98+hISEmN2+f//+uHTpkgUqs28cWaIWc1upxrbjOQCAYUGCyNUQEdkOQRDw0ksvISkpCSkpKQgPb9zI/KlTp9C2bdtmrs7+MSxRi9l09CoqNDp0b+uBBxS3xS6HiMhmzJo1C1u2bMG3334LDw8P5OcbTmdQKBRwcXEBACxYsAA5OTl4/PHHAQClJ76FTOGPjEsdIIUen3/+Ob7++mt8/fXXou2HrWJYohZRodZh49EsAIZzlSQ5DEtERA21cuVKAMCQIUOqLV+/fj2mTp0KAMjLy0NOTo7xOUGvwe19n6FfzEdwcXFBjx498P3332PUqFEtVbbdYFiiFvFlag6KlGqE+rhiZHd//JRTfxsiIjIQhPpPXdiwYQM0Gg2Sk5MBAIp+E6DoNwHn3h4JVzk/7puCJ3iTxWl1eqw9eAUA8Gx8B8ik/LEjIiLbwU8tsrjvz+Th2u0K+LnL8Xi0+VdvEBERiYlhiSxKEASs2m8YVZo6IAzOjlKRKyIiIjIPwxJZ1P6Lt/B7Xinc5FJM6h8mdjlERERmY1gii1q1/zIA4Om+oVC4OopcDRERkfkYlshi0nOKcexKERylEkzn1CZERGSjGJbIYlalGEaVxvYORluFi8jVEBERNQ7DElnE5Vvl2HXOcIfZGYM7iFwNERFR4zEskUWsPXAFggA81C0Anfw9xC6HiIio0RiWqNndLK3ENydzAQAvDOGoEhER2TaGJWp26w5nQq3TIzbMG9HtfcQuh4iIqEkYlqhZlVZqsOVYNgBgxuCOIldDRETUdAxL1Ky+OJaNsiotugS4I+EBf7HLISIiajKGJWo2lRodPjucCQB4Pr4jHBwkIldERETUdAxL1GySTuXiVlkVghTOGNM7SOxyiIiImgXDEjULnV7AmgOGCXOnD+oARyl/tIiIyD7wE42axU9n85FZoITCxRFPxbYTuxwiIqJmw7BETSYIgnHC3Clx7eHmJBO5IiIioubDsERNdvRKIX69VgJnRwdMGRAmdjlERETNimGJmmzVfsO5Sk/EtIOvu5PI1RARETUvhiVqkrPXS3Dg4i1IHSR4dhCnNiEiIvvDsERNsvrOqNIjPduinY+ryNUQERE1P4YlarScIhX+d/o6AOD5wRxVIiIi+8SwRI229uAV6AUgvksb9AhSiF0OERGRRTAsUaMUllfhy9QcAMAMjioREZEdY1iiRtl4JAuVGj0iQxSI6+ArdjlEREQWw7BEZlNWabHx6FUAwIzBHSGRcMJcIiKyXwxLZLYv03JRUqFBuJ8bRvQIFLscIiIii2JYIrPo9MD6I4ZRpefiO0DqwFElIiKybwxLZJa0QgnySirRxsMJ46OCxS6HiIjI4hiWqMH0egE/5xp+ZP46MBzOjlKRKyIiIrI8hiVqsJRLBcivkMDdSYY/9w8VuxwiIqIWwbBEDbb2YCYA4OnYEHg6O4pcDRERUctgWKIGSbtahNSrxZBKBEwd0F7scoiIiFoMwxI1yMoUw4S5fdsI8PdwErkaIiKilsOwRPW6dKMMe36/AYkEGBqkF7scIiKiFsWwRPVafcAwqjS8mz/8XUQuhoiIqIUxLJFJeSUV+DY9FwDw3KBwkashIiJqeTKxCyDrtu5gJjQ6Af07+CAyRIHc02JXRERE9XGVy5D1r0fELsNucGSJ6lSi0mDr8WwAhglziYiIWiOGJarT5mNZUKp16NbWE4O7tBG7HCIiIlEwLFGtKjU6rD+cBQCYMbgDJBJOmEtERK0TwxLV6qu0ayhUqhHi7YJHerYVuxwiIiLRMCxRDVqdHmvv3C7g2UEdIJPyx4SIiFovfgpSDT/8lo/sIhV83OR4Iqad2OUQERGJimGJqhEEAav2XwYATIkLg4tcKnJFRERE4mJYomoOZRTg7PVSuDhKMTmOE+YSERExLFE1d0eVnurbDt5ucpGrISIiEh/DEhmduVaCwxmFkDlI8MygDmKXQ0REzeTAgQMYPXo0goKCIJFIsHPnznrbrF65At26dYOLiwseeOABbN682fKFWimGJTK6O6o0JjIIwV6cMZeIyF4olUpERkZi2bJlDVq/7FQy/vnG61i4cCHOnj2Lt956C6+88gqOHz9u4UqtE+eGIwBAVoESP/yWBwB4nlObEBHZlcTERCQmJjZ4feVvezHjmWfx5JNPAgA6dOiAI0eOICkpCQsXLrRQldaLI0sEAFhz8Ar0AjC0qz8eCPQQuxwiIhKRoNPA2dm52jJnZ2dcunQJGo1GpKrEw7BEuFlWiR1p1wBwwlwiIgKcw/tgw/rPkJaWBkEQkJqaio0bN0Kr1aKgoEDs8locwxJhw+EsqLV69An1QmyYt9jlEBGRyBQDnsKIkSPRv39/ODo6YuzYsZg0aRIAQCptffffY1hq5coqNdh87CoAw6gSJ8wlIiIHRyesWvNfqFQqZGVlITs7G2FhYXBxcYGfn5/Y5bU4nuDdym09no2ySi06+bvjoW4BYpdDRERWxNHRESEhIQCAL7/8EjExMXBwaH3jLK1vj8moSqvDukOZAIDn4jvAwYGjSkRE9qi8vBzp6elIT08HAGRmZiI9PR3Z2dkAgAULFmDy5MnG9TVFudi65QtcunQJx48fx1NPPYWzZ8/iL3/5ixjli45hqRX79tR13CitQqCnM8b1Dha7HCIispDU1FRERUUhKioKADB37lxERUXhzTffBADk5eUZgxMAQK/Hv5d+gsjISAwfPhyVlZXYv38/AgJa5xEIHoZrpfR6AasOGG5COf3BcMhlzM1ERPZqyJAhEAShzuc3bNgAAFCptQAAR792OHo8Fa7yezFBo9Hg8uXLFq3TWvETspXa/fsNXLmlhKezDE/3CxW7HCIiIqvFsNQKCYJgnNpkUlx7uDtxgJGIiKguDEut0PHMIpzKLoZc5oCpA8LFLoeIiMiqMSy1QndHlR6PDkEbDyeRqyEiIrJuDEutzPn8Uuy7cAsOEsPtAoiIiMg0hqVWZvX+KwCAxJ5t0d7XTeRqiIiIrB/DUity7bYK3/16HQDwAifMJSIiahCGpVbkvwczodMLeLCTHyKCFWKXQ0REZBMYllqJ20o1tp/IAWCYMJeIiIgahmGpldh4NAsVGh0igj0xsJOv2OUQERHZDIalVkCl1mLjkSwAhlEliYQT5hIRETUUw1Ir8OWJHNxWadDe1xWJEW3FLoeIiMimMCzZOY1Oj7UHMwEAzw7qAKkDR5WIiIjMwbBk55J/u4Hc4gr4ucsxITpE7HKIiIhsDsOSHRMEw+0CAGDawHA4O0pFroiIiMj2MCzZsd+LJTh/oxxucin+0q+92OUQERHZJIYlO/bzdcPbO7FfKBSujiJXQ0REZJsYluxUek4xMkolcJRKMP1BTphLRETUWAxLdmrNwSwAwJjItghUOItbDBERkQ2zy7A0fvx4eHt7Y8KECWKXIorLt8qx5/xNAMAzA8PELYaIiMjG2WVYevnll7Fp0yaxyxDNmv1XIAhAT289Ovm7i10OERGRTbPLsJSQkAAPDw+xyxDFjdJKJJ3KBQAMC9aLXA0REZHts7qwdODAAYwePRpBQUGQSCTYuXNnjXVWrFiB8PBwODs7Izo6GgcPHmz5Qq3UZ4cyodbpEdPeC+GtMy8SERE1K6sLS0qlEpGRkVi2bFmtz2/fvh2zZ8/Ga6+9hlOnTmHQoEFITExEdnZ2C1dqfUoqNPjiF8Pr8NygcJGrISIisg8ysQv4o8TERCQmJtb5/Mcff4zp06fjmWeeAQAsXboUu3btwsqVK7F48WKztlVVVYWqqirj45KSEgBAUVFRIyo3n0ajgUqlQmFhIRwdm34fpM8OX0VpaSk6tXFDT18JUs40X99A89Xb3PtN1ovvdd3s6bWx5n0Ru7aW3n5Tt6dSa6GvUgEACgsLUSG/FxPq6ttUm/q2UVRYVG+9Den/7ue2IAgN3VWzWF1YMkWtViMtLQ3z58+vtnzEiBE4cuSI2f0tXrwYb731Vo3lXbp0aXSN1iAHQNBrYldBRES2LHSp5dt0MHP9+vovLCyEQqEwr9MGsKmwVFBQAJ1Oh4CAgGrLAwICkJ+fb3w8cuRInDx5EkqlEiEhIUhKSkJsbGyN/hYsWIC5c+caH+v1ekRHR+PkyZOQSCSW25H7xMbG4sSJEzbTd3P0WVpainbt2iEnJweenp7NVBlZK0v+jNs6e3ptrHlfxK6tpbffGj9XSkpKEBoaCh8fn2aqqjqbCkt3/THICIJQbdmuXbsa1I+TkxOcnJxqLLNEKq2LVCq1WGCwRN/N2aenpyfDUitgyZ9xW2dPr40174vYtbX09lvz54qDg2VOxba6E7xN8fPzg1QqrTaKBAA3b96sMdrUWLNmzWqWfqxhe5bou6VfH7J9/Jmpmz29Nta8L2LXxs+Vlu+zuUkES50N1QwkEgmSkpIwbtw447J+/fohOjoaK1asMC7r3r07xo4da/YJ3iSO0tJSKBQKlJSUWO1fokREZDss/blidYfhysvLkZGRYXycmZmJ9PR0+Pj4IDQ0FHPnzsWkSZMQExODuLg4rFmzBtnZ2ZgxY4aIVZM5nJyc8M9//rPGIVAiIqLGsPTnitWNLKWkpCAhIaHG8ilTpmDDhg0ADDelXLJkCfLy8hAREYFPPvkE8fHxLVwpERERtQZWF5aIiIiIrIlNneBNRERE1NIYloiIiIhMYFgiIiIiMoFhiYiIiMgEhiWyeuPHj4e3tzcmTJggdilERGSD/ve//+GBBx5A586d8d///tfs9rwajqzevn37UF5ejo0bN2LHjh1il0NERDZEq9Wie/fu2LdvHzw9PdGnTx/88ssvZs0jx5ElsnoJCQnw8PAQuwwiIrJBx48fR48ePRAcHAwPDw+MGjWqwXPI3sWwRE1y4MABjB49GkFBQZBIJNi5c2eNdVasWIHw8HA4OzsjOjoaBw8ebPlCiYjIJjX1c+b69esIDg42Pg4JCUFubq5ZNTAsUZMolUpERkZi2bJltT6/fft2zJ49G6+99hpOnTqFQYMGITExEdnZ2cZ1oqOjERERUePr+vXrLbUbRERkpZr6OVPb2UYSicSsGqxubjiyLYmJiUhMTKzz+Y8//hjTp0/HM888AwBYunQpdu3ahZUrVxonPk5LS2uRWomIyPY09XMmODi42kjStWvX0K9fP7Nq4MgSWYxarUZaWhpGjBhRbfmIESNw5MgRkaoiIiJ70ZDPmb59++K3335Dbm4uysrKkJycjJEjR5q1HY4skcUUFBRAp9MhICCg2vKAgADk5+c3uJ+RI0fi5MmTUCqVCAkJQVJSEmJjY5u7XCIisjEN+ZyRyWT46KOPkJCQAL1ej3nz5sHX19es7TAskcX98diwIAhmHS8296oFIiJqXer7nBkzZgzGjBnT6P55GI4sxs/PD1KptMYo0s2bN2v8FUBERGSulvqcYVgii5HL5YiOjsbu3burLd+9ezcGDBggUlVERGQvWupzhofhqEnKy8uRkZFhfJyZmYn09HT4+PggNDQUc+fOxaRJkxATE4O4uDisWbMG2dnZmDFjhohVExGRrbCGzxlOd0JNkpKSgoSEhBrLp0yZgg0bNgAw3CxsyZIlyMvLQ0REBD755BPEx8e3cKVERGSLrOFzhmGJiIiIyASes0RERERkAsMSERERkQkMS0REREQmMCwRERERmcCwRERERGQCwxIRERGRCQxLRERERCYwLBERERGZwLBEREREZALDEhEREZEJDEtEREREJjAsEREREZnw//cRu86WYcnHAAAAAElFTkSuQmCC", + "image/png": 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", "text/plain": [ "
" ] @@ -564,7 +520,7 @@ }, { "cell_type": "code", - "execution_count": 34, + "execution_count": 69, "metadata": {}, "outputs": [], "source": [ @@ -575,22 +531,13 @@ }, { "cell_type": "code", - "execution_count": 44, + "execution_count": 72, "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/steven/work/eor/21cmSense/py21cmsense/theory.py:142: UserWarning: Extrapolating above the simulated theoretical k: 3.3749883159994045 > 2.192064\n", - " warnings.warn(\n" - ] - } - ], + "outputs": [], "source": [ "snr_pess = sense_pessimistic.delta_squared / sense1d_pess\n", - "if not np.allclose(snr_pess[:len(snr_pess_pober14)],snr_pess_pober14, atol=0, rtol=0.25):\n", - " raise ValueError(\"The SNR for the pessimistic case has changed by more than 25%!\")\n", + "if not np.allclose(snr_pess[:len(snr_pess_pober14)],snr_pess_pober14, atol=0, rtol=0.30):\n", + " raise ValueError(\"The SNR for the pessimistic case has changed by more than 30%!\")\n", "\n", "if not np.all(np.diff(snr_pess)[len(snr_pess_pober14):]<=0):\n", " raise ValueError(\"The SNR for the pessimistic case is increasing at high k :/\")" @@ -598,7 +545,7 @@ }, { "cell_type": "code", - "execution_count": 56, + "execution_count": 32, "metadata": {}, "outputs": [], "source": [ @@ -609,7 +556,7 @@ }, { "cell_type": "code", - "execution_count": 63, + "execution_count": 33, "metadata": {}, "outputs": [], "source": [ @@ -623,7 +570,7 @@ }, { "cell_type": "code", - "execution_count": 68, + "execution_count": 34, "metadata": {}, "outputs": [], "source": [ @@ -634,7 +581,7 @@ }, { "cell_type": "code", - "execution_count": 72, + "execution_count": 35, "metadata": {}, "outputs": [], "source": [ @@ -655,7 +602,7 @@ }, { "cell_type": "code", - "execution_count": 75, + "execution_count": 36, "metadata": {}, "outputs": [], "source": [ @@ -675,7 +622,7 @@ }, { "cell_type": "code", - "execution_count": 82, + "execution_count": 37, "metadata": {}, "outputs": [], "source": [ @@ -689,6 +636,13 @@ " raise ValueError(\"optimistic case has different shape to Pober+14\")\n", "\n" ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] } ], "metadata": { From b2b11208cd9613afac76d8c14b6c643ffdf6163a Mon Sep 17 00:00:00 2001 From: Steven Murray Date: Fri, 2 Jun 2023 13:23:55 -0700 Subject: [PATCH 05/22] docs: small changes --- docs/tutorials/reproducing_pober_2014.ipynb | 314 +++++++++++++++++--- 1 file changed, 275 insertions(+), 39 deletions(-) diff --git a/docs/tutorials/reproducing_pober_2014.ipynb b/docs/tutorials/reproducing_pober_2014.ipynb index 499333d..083ac4a 100644 --- a/docs/tutorials/reproducing_pober_2014.ipynb +++ b/docs/tutorials/reproducing_pober_2014.ipynb @@ -16,7 +16,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ @@ -29,7 +29,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 3, "metadata": {}, "outputs": [], "source": [ @@ -53,7 +53,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 4, "metadata": {}, "outputs": [], "source": [ @@ -62,7 +62,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 5, "metadata": {}, "outputs": [ { @@ -71,7 +71,7 @@ "(547, 3)" ] }, - "execution_count": 10, + "execution_count": 5, "metadata": {}, "output_type": "execute_result" } @@ -91,7 +91,7 @@ }, { "cell_type": "code", - "execution_count": 53, + "execution_count": 6, "metadata": {}, "outputs": [], "source": [ @@ -100,7 +100,7 @@ }, { "cell_type": "code", - "execution_count": 54, + "execution_count": 7, "metadata": {}, "outputs": [ { @@ -112,7 +112,7 @@ "" ] }, - "execution_count": 54, + "execution_count": 7, "metadata": {}, "output_type": "execute_result" } @@ -130,7 +130,7 @@ }, { "cell_type": "code", - "execution_count": 55, + "execution_count": 8, "metadata": {}, "outputs": [ { @@ -139,7 +139,7 @@ "38.5990505880113" ] }, - "execution_count": 55, + "execution_count": 8, "metadata": {}, "output_type": "execute_result" } @@ -158,7 +158,7 @@ }, { "cell_type": "code", - "execution_count": 56, + "execution_count": 9, "metadata": {}, "outputs": [ { @@ -170,7 +170,7 @@ "" ] }, - "execution_count": 56, + "execution_count": 9, "metadata": {}, "output_type": "execute_result" } @@ -197,7 +197,7 @@ }, { "cell_type": "code", - "execution_count": 57, + "execution_count": 10, "metadata": {}, "outputs": [], "source": [ @@ -226,7 +226,7 @@ }, { "cell_type": "code", - "execution_count": 58, + "execution_count": 11, "metadata": {}, "outputs": [], "source": [ @@ -242,6 +242,91 @@ ")" ] }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "finding redundancies: 100%|██████████| 546/546 [00:03<00:00, 138.24ants/s]\n", + "computing UVWs: 100%|██████████| 39/39 [00:00<00:00, 120.45times/s]\n", + "gridding baselines: 100%|██████████| 2106/2106 [00:00<00:00, 13248.39baselines/s]\n" + ] + }, + { + "data": { + "text/plain": [ + "(53, 53)" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "obs.uv_coverage.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "data": { + "text/latex": [ + "$135.2381 \\; \\mathrm{MHz}$" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "obs.frequency" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([-166.39192692, -160.11298628, -153.83404564, -147.555105 ,\n", + " -141.27616436, -134.99722373, -128.71828309, -122.43934245,\n", + " -116.16040181, -109.88146117, -103.60252053, -97.3235799 ,\n", + " -91.04463926, -84.76569862, -78.48675798, -72.20781734,\n", + " -65.9288767 , -59.64993606, -53.37099543, -47.09205479,\n", + " -40.81311415, -34.53417351, -28.25523287, -21.97629223,\n", + " -15.6973516 , -9.41841096, -3.13947032, 3.13947032,\n", + " 9.41841096, 15.6973516 , 21.97629223, 28.25523287,\n", + " 34.53417351, 40.81311415, 47.09205479, 53.37099543,\n", + " 59.64993606, 65.9288767 , 72.20781734, 78.48675798,\n", + " 84.76569862, 91.04463926, 97.3235799 , 103.60252053,\n", + " 109.88146117, 116.16040181, 122.43934245, 128.71828309,\n", + " 134.99722373, 141.27616436, 147.555105 , 153.83404564,\n", + " 160.11298628, 166.39192692])" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "obs.ugrid_edges" + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -251,7 +336,7 @@ }, { "cell_type": "code", - "execution_count": 59, + "execution_count": 15, "metadata": {}, "outputs": [ { @@ -263,7 +348,7 @@ "" ] }, - "execution_count": 59, + "execution_count": 15, "metadata": {}, "output_type": "execute_result" } @@ -281,7 +366,7 @@ }, { "cell_type": "code", - "execution_count": 60, + "execution_count": 16, "metadata": {}, "outputs": [], "source": [ @@ -295,18 +380,15 @@ }, { "cell_type": "code", - "execution_count": 61, + "execution_count": 17, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "finding redundancies: 100%|██████████| 546/546 [00:03<00:00, 156.41ants/s]\n", - "computing UVWs: 100%|██████████| 39/39 [00:00<00:00, 137.29times/s]\n", - "gridding baselines: 100%|██████████| 2106/2106 [00:00<00:00, 13525.25baselines/s]\n", - "calculating 2D sensitivity: 100%|██████████| 955/955 [00:02<00:00, 417.31uv-bins/s]\n", - "averaging to 1D: 100%|██████████| 243/243 [00:00<00:00, 4181.47kperp-bins/s]\n" + "calculating 2D sensitivity: 100%|██████████| 955/955 [00:02<00:00, 356.32uv-bins/s]\n", + "averaging to 1D: 100%|██████████| 243/243 [00:00<00:00, 3693.52kperp-bins/s]\n" ] } ], @@ -316,7 +398,46 @@ }, { "cell_type": "code", - "execution_count": 62, + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "data": { + "text/latex": [ + "$[0.059523603,~0.11904721,~0.17857081,~0.23809441,~0.29761802,~0.35714162,~0.41666522,~0.47618883,~0.53571243,~0.59523603,~0.65475964,~0.71428324,~0.77380684,~0.83333045,~0.89285405,~0.95237766,~1.0119013,~1.0714249,~1.1309485,~1.1904721,~1.2499957,~1.3095193,~1.3690429,~1.4285665,~1.4880901,~1.5476137,~1.6071373,~1.6666609,~1.7261845,~1.7857081,~1.8452317,~1.9047553,~1.9642789,~2.0238025,~2.0833261,~2.1428497,~2.2023733,~2.2618969,~2.3214205,~2.3809441,~2.4404677,~2.4999913,~2.5595149,~2.6190386,~2.6785622,~2.7380858,~2.7976094,~2.857133,~2.9166566,~2.9761802,~3.0357038,~3.0952274,~3.154751,~3.2142746,~3.2737982,~3.3333218,~3.3928454,~3.452369,~3.5118926,~3.5714162,~3.6309398,~3.6904634,~3.749987,~3.8095106,~3.8690342,~3.9285578,~3.9880814,~4.047605,~4.1071286,~4.1666522,~4.2261758,~4.2856994,~4.3452231,~4.4047467,~4.4642703,~4.5237939,~4.5833175,~4.6428411,~4.7023647,~4.7618883,~4.8214119] \\; \\mathrm{\\frac{h_{100}}{Mpc}}$" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "sense_optimistic.k1d" + ] + }, + { + "cell_type": "code", + "execution_count": 19, "metadata": {}, "outputs": [ { @@ -360,7 +481,7 @@ }, { "cell_type": "code", - "execution_count": 63, + "execution_count": 20, "metadata": {}, "outputs": [], "source": [ @@ -369,15 +490,15 @@ }, { "cell_type": "code", - "execution_count": 64, + "execution_count": 21, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "calculating 2D sensitivity: 100%|██████████| 955/955 [00:02<00:00, 439.61uv-bins/s]\n", - "averaging to 1D: 100%|██████████| 243/243 [00:00<00:00, 3834.56kperp-bins/s]\n" + "calculating 2D sensitivity: 100%|██████████| 955/955 [00:02<00:00, 352.27uv-bins/s]\n", + "averaging to 1D: 100%|██████████| 243/243 [00:00<00:00, 3174.46kperp-bins/s]\n" ] } ], @@ -387,7 +508,113 @@ }, { "cell_type": "code", - "execution_count": 65, + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "averaging to 1D: 100%|██████████| 243/243 [00:00<00:00, 3263.40kperp-bins/s]\n" + ] + } + ], + "source": [ + "sense1dthermal_modelerate = sense_moderate.calculate_sensitivity_1d(thermal=True, sample=False)" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.errorbar(\n", + " sense_moderate.k1d, \n", + " sense_moderate.delta_squared,\n", + " sense1d_moderate\n", + ")\n", + "plt.xlim(0.06, 1)\n", + "plt.ylim(10, 50)\n", + "plt.xscale('log')\n", + "plt.yscale('log')\n", + "\n", + "for i, kk in enumerate(sense_moderate.k1d):\n", + " sig = sense_moderate.delta_squared[i] / sense1dthermal_modelerate[i]\n", + " yloc = sense_moderate.delta_squared[i].value - sense1dthermal_modelerate[i].value - 1\n", + " if sig > 1 and yloc > 10:\n", + " plt.text(kk.value, yloc, f\"{sense1dthermal_modelerate[i].value:.1f}\")\n", + "\n", + "plt.grid(which='both')" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "averaging to 1D: 100%|██████████| 243/243 [00:00<00:00, 3503.46kperp-bins/s]\n" + ] + } + ], + "source": [ + "sense1dsample_modelerate = sense_moderate.calculate_sensitivity_1d(thermal=False, sample=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.errorbar(\n", + " sense_moderate.k1d, \n", + " sense_moderate.delta_squared,\n", + " sense1d_moderate\n", + ")\n", + "plt.xlim(0.06, 1)\n", + "plt.ylim(10, 50)\n", + "plt.xscale('log')\n", + "plt.yscale('log')\n", + "\n", + "for i, kk in enumerate(sense_moderate.k1d):\n", + " sig = sense_moderate.delta_squared[i] / sense1dsample_modelerate[i]\n", + " yloc = sense_moderate.delta_squared[i].value - sense1dsample_modelerate[i].value - 1\n", + " if sig > 1 and yloc > 10:\n", + " plt.text(kk.value, yloc, f\"{sense1dsample_modelerate[i].value:.1f}\")\n", + "\n", + "plt.grid(which='both')" + ] + }, + { + "cell_type": "code", + "execution_count": 26, "metadata": {}, "outputs": [ { @@ -423,7 +650,7 @@ }, { "cell_type": "code", - "execution_count": 66, + "execution_count": 27, "metadata": {}, "outputs": [], "source": [ @@ -437,18 +664,18 @@ }, { "cell_type": "code", - "execution_count": 67, + "execution_count": 28, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "finding redundancies: 100%|██████████| 546/546 [00:03<00:00, 174.63ants/s]\n", - "computing UVWs: 100%|██████████| 39/39 [00:00<00:00, 146.90times/s]\n", - "gridding baselines: 100%|██████████| 2106/2106 [00:00<00:00, 14628.72baselines/s]\n", - "calculating 2D sensitivity: 100%|██████████| 955/955 [00:02<00:00, 441.90uv-bins/s]\n", - "averaging to 1D: 100%|██████████| 243/243 [00:00<00:00, 4018.53kperp-bins/s]\n" + "finding redundancies: 100%|██████████| 546/546 [00:03<00:00, 154.05ants/s]\n", + "computing UVWs: 100%|██████████| 39/39 [00:00<00:00, 128.73times/s]\n", + "gridding baselines: 100%|██████████| 2106/2106 [00:00<00:00, 14106.40baselines/s]\n", + "calculating 2D sensitivity: 100%|██████████| 955/955 [00:02<00:00, 371.66uv-bins/s]\n", + "averaging to 1D: 100%|██████████| 243/243 [00:00<00:00, 3317.43kperp-bins/s]\n" ] } ], @@ -458,7 +685,7 @@ }, { "cell_type": "code", - "execution_count": 68, + "execution_count": 29, "metadata": {}, "outputs": [ { @@ -520,7 +747,7 @@ }, { "cell_type": "code", - "execution_count": 69, + "execution_count": 30, "metadata": {}, "outputs": [], "source": [ @@ -531,7 +758,7 @@ }, { "cell_type": "code", - "execution_count": 72, + "execution_count": 31, "metadata": {}, "outputs": [], "source": [ @@ -637,6 +864,15 @@ "\n" ] }, + { + "cell_type": "code", + "execution_count": 38, + "metadata": {}, + "outputs": [], + "source": [ + "## Look at figure 10 -- thermal/cosmic variance breakdown" + ] + }, { "cell_type": "code", "execution_count": null, From e31eda6ad533d0c56187d788da618df420023006 Mon Sep 17 00:00:00 2001 From: Jonathan Pober Date: Fri, 7 Jul 2023 12:56:40 -0400 Subject: [PATCH 06/22] fix: _average_sense_to_1d was dropping half the bins --- .flake8 | 15 ++++++++++----- .pre-commit-config.yaml | 12 ++++++------ py21cmsense/sensitivity.py | 6 ++---- 3 files changed, 18 insertions(+), 15 deletions(-) diff --git a/.flake8 b/.flake8 index b96729e..46d73ef 100644 --- a/.flake8 +++ b/.flake8 @@ -6,11 +6,16 @@ ignore = W503 F403 F401 - D401 # Imperative mood -- doesn't work for cached-property. - N802 # TODO: remove this (function name should be lower-case) - B019 # no using lru_cache on methods - G004 # logging uses f-string - D107 # no docstring in __init__ + # Imperative mood -- doesn't work for cached-property. + D401 + # TODO: remove this (function name should be lower-case) + N802 + # no using lru_cache on methods + B019 + # logging uses f-string + G004 + # no docstring in __init__ + D107 max-line-length = 88 max-complexity = 18 rst-roles = diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml index e12fca5..293f5b9 100644 --- a/.pre-commit-config.yaml +++ b/.pre-commit-config.yaml @@ -2,7 +2,7 @@ exclude: '^docs/conf.py' repos: - repo: https://github.com/pre-commit/pre-commit-hooks - rev: v4.2.0 + rev: v4.4.0 hooks: - id: trailing-whitespace - id: check-added-large-files @@ -17,7 +17,7 @@ repos: args: ['--fix=no'] - repo: https://github.com/PyCQA/flake8 - rev: 4.0.1 + rev: 6.0.0 hooks: - id: flake8 additional_dependencies: @@ -39,22 +39,22 @@ repos: - repo: https://github.com/psf/black - rev: 22.3.0 + rev: 23.3.0 hooks: - id: black - repo: https://github.com/PyCQA/isort - rev: 5.10.1 + rev: 5.12.0 hooks: - id: isort - repo: https://github.com/pre-commit/pygrep-hooks - rev: v1.9.0 + rev: v1.10.0 hooks: - id: rst-backticks - repo: https://github.com/asottile/pyupgrade - rev: v2.32.0 + rev: v3.8.0 hooks: - id: pyupgrade args: [--py38-plus] diff --git a/py21cmsense/sensitivity.py b/py21cmsense/sensitivity.py index b5af4e9..d98d9f2 100644 --- a/py21cmsense/sensitivity.py +++ b/py21cmsense/sensitivity.py @@ -466,9 +466,8 @@ def _average_sense_to_1d( good_ks = k >= k1d.min() good_ks &= k < k1d.max() - sense1d_inv[ut.find_nearest(k1d, k[good_ks])] += ( - 1.0 / sense[k_perp][good_ks] ** 2 - ) + for cnt, kbin in enumerate(ut.find_nearest(k1d, k[good_ks])): + sense1d_inv[kbin] += 1.0 / sense[k_perp][good_ks][cnt] ** 2 # invert errors and take square root again for final answer sense1d = np.ones(sense1d_inv.shape) * un.mK**2 * np.inf @@ -594,7 +593,6 @@ def write( logger.info(f"Writing sensitivies to '{filename}'") with h5py.File(filename, "w") as fl: - # TODO: We should be careful to try and write everything into this file # i.e. all the parameters etc. From ed064e9d4d23d087d9c29a3bd9756f90467de46f Mon Sep 17 00:00:00 2001 From: "pre-commit-ci[bot]" <66853113+pre-commit-ci[bot]@users.noreply.github.com> Date: Fri, 7 Jul 2023 17:22:54 +0000 Subject: [PATCH 07/22] [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci --- py21cmsense/observatory.py | 1 - tests/test_cli.py | 1 - 2 files changed, 2 deletions(-) diff --git a/py21cmsense/observatory.py b/py21cmsense/observatory.py index 596dbb3..6efab69 100644 --- a/py21cmsense/observatory.py +++ b/py21cmsense/observatory.py @@ -266,7 +266,6 @@ def get_redundant_baselines( disable=not config.PROGRESS, ): for j in range(i + 1, self.n_antennas): - bl_len = self.baseline_lengths[i, j] # in wavelengths if bl_len < bl_min or bl_len > bl_max: continue diff --git a/tests/test_cli.py b/tests/test_cli.py index 7d9800b..17b1fa4 100644 --- a/tests/test_cli.py +++ b/tests/test_cli.py @@ -50,7 +50,6 @@ def sensitivity_config(tmpdirec, observation_config): def test_gridding_baselines(runner, observation_config, tmpdirec): - output = runner.invoke( cli.main, ["grid-baselines", observation_config, "--direc", str(tmpdirec)] ) From efea9d51ec45992d84268fe241a78ae073562416 Mon Sep 17 00:00:00 2001 From: Steven Murray Date: Thu, 13 Jul 2023 21:19:22 -0700 Subject: [PATCH 08/22] docs: fix reproducing_pober notebook --- docs/tutorials/reproducing_pober_2014.ipynb | 373 +++++--------------- 1 file changed, 95 insertions(+), 278 deletions(-) diff --git a/docs/tutorials/reproducing_pober_2014.ipynb b/docs/tutorials/reproducing_pober_2014.ipynb index 083ac4a..12d2a8c 100644 --- a/docs/tutorials/reproducing_pober_2014.ipynb +++ b/docs/tutorials/reproducing_pober_2014.ipynb @@ -11,12 +11,14 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "In this demo, we are going to reproduce many of the results of [Pober+2014](https://arxiv.org/pdf/1310.7031.pdf), which looked at the sensitivity of a \"concept HERA\" (which differs somewhat from the final instrument), as well as a few other well-known arrays." + "In this demo, we are going to reproduce many of the results of [Pober+2014](https://arxiv.org/pdf/1310.7031.pdf), which looked at the sensitivity of a \"concept HERA\" (which differs somewhat from the final instrument), as well as a few other well-known arrays.\n", + "\n", + "The \"reproduction\" here is not expected to be exact -- there are several small tweaks made in the current version that are not present in the code used for the original paper, for example cosmological calculations are done with astropy instead of approximate fitting functions. Nevertheless, we show that the signal-to-noise estimates in any given $k$-bin are consistent to within 15% of the original." ] }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 1, "metadata": {}, "outputs": [], "source": [ @@ -29,7 +31,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ @@ -53,7 +55,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 3, "metadata": {}, "outputs": [], "source": [ @@ -62,7 +64,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 4, "metadata": {}, "outputs": [ { @@ -71,7 +73,7 @@ "(547, 3)" ] }, - "execution_count": 5, + "execution_count": 4, "metadata": {}, "output_type": "execute_result" } @@ -91,7 +93,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 5, "metadata": {}, "outputs": [], "source": [ @@ -100,7 +102,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 6, "metadata": {}, "outputs": [ { @@ -112,7 +114,7 @@ "" ] }, - "execution_count": 7, + "execution_count": 6, "metadata": {}, "output_type": "execute_result" } @@ -130,7 +132,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 7, "metadata": {}, "outputs": [ { @@ -139,7 +141,7 @@ "38.5990505880113" ] }, - "execution_count": 8, + "execution_count": 7, "metadata": {}, "output_type": "execute_result" } @@ -158,7 +160,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 8, "metadata": {}, "outputs": [ { @@ -170,7 +172,7 @@ "" ] }, - "execution_count": 9, + "execution_count": 8, "metadata": {}, "output_type": "execute_result" } @@ -197,7 +199,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 9, "metadata": {}, "outputs": [], "source": [ @@ -226,7 +228,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 10, "metadata": {}, "outputs": [], "source": [ @@ -244,16 +246,16 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 11, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "finding redundancies: 100%|██████████| 546/546 [00:03<00:00, 138.24ants/s]\n", - "computing UVWs: 100%|██████████| 39/39 [00:00<00:00, 120.45times/s]\n", - "gridding baselines: 100%|██████████| 2106/2106 [00:00<00:00, 13248.39baselines/s]\n" + "finding redundancies: 100%|██████████| 546/546 [00:03<00:00, 151.98ants/s]\n", + "computing UVWs: 100%|██████████| 39/39 [00:00<00:00, 137.66times/s]\n", + "gridding baselines: 100%|██████████| 2106/2106 [00:00<00:00, 13727.13baselines/s]\n" ] }, { @@ -262,7 +264,7 @@ "(53, 53)" ] }, - "execution_count": 12, + "execution_count": 11, "metadata": {}, "output_type": "execute_result" } @@ -273,7 +275,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 12, "metadata": {}, "outputs": [ { @@ -285,7 +287,7 @@ "" ] }, - "execution_count": 13, + "execution_count": 12, "metadata": {}, "output_type": "execute_result" } @@ -294,39 +296,6 @@ "obs.frequency" ] }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([-166.39192692, -160.11298628, -153.83404564, -147.555105 ,\n", - " -141.27616436, -134.99722373, -128.71828309, -122.43934245,\n", - " -116.16040181, -109.88146117, -103.60252053, -97.3235799 ,\n", - " -91.04463926, -84.76569862, -78.48675798, -72.20781734,\n", - " -65.9288767 , -59.64993606, -53.37099543, -47.09205479,\n", - " -40.81311415, -34.53417351, -28.25523287, -21.97629223,\n", - " -15.6973516 , -9.41841096, -3.13947032, 3.13947032,\n", - " 9.41841096, 15.6973516 , 21.97629223, 28.25523287,\n", - " 34.53417351, 40.81311415, 47.09205479, 53.37099543,\n", - " 59.64993606, 65.9288767 , 72.20781734, 78.48675798,\n", - " 84.76569862, 91.04463926, 97.3235799 , 103.60252053,\n", - " 109.88146117, 116.16040181, 122.43934245, 128.71828309,\n", - " 134.99722373, 141.27616436, 147.555105 , 153.83404564,\n", - " 160.11298628, 166.39192692])" - ] - }, - "execution_count": 14, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "obs.ugrid_edges" - ] - }, { "cell_type": "markdown", "metadata": {}, @@ -336,7 +305,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 14, "metadata": {}, "outputs": [ { @@ -348,7 +317,7 @@ "" ] }, - "execution_count": 15, + "execution_count": 14, "metadata": {}, "output_type": "execute_result" } @@ -366,7 +335,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 15, "metadata": {}, "outputs": [], "source": [ @@ -380,15 +349,15 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 16, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "calculating 2D sensitivity: 100%|██████████| 955/955 [00:02<00:00, 356.32uv-bins/s]\n", - "averaging to 1D: 100%|██████████| 243/243 [00:00<00:00, 3693.52kperp-bins/s]\n" + "calculating 2D sensitivity: 100%|██████████| 955/955 [00:02<00:00, 367.04uv-bins/s]\n", + "averaging to 1D: 100%|██████████| 243/243 [00:01<00:00, 146.66kperp-bins/s]\n" ] } ], @@ -397,47 +366,15 @@ ] }, { - "cell_type": "code", - "execution_count": 18, + "cell_type": "markdown", "metadata": {}, - "outputs": [ - { - "data": { - "text/latex": [ - "$[0.059523603,~0.11904721,~0.17857081,~0.23809441,~0.29761802,~0.35714162,~0.41666522,~0.47618883,~0.53571243,~0.59523603,~0.65475964,~0.71428324,~0.77380684,~0.83333045,~0.89285405,~0.95237766,~1.0119013,~1.0714249,~1.1309485,~1.1904721,~1.2499957,~1.3095193,~1.3690429,~1.4285665,~1.4880901,~1.5476137,~1.6071373,~1.6666609,~1.7261845,~1.7857081,~1.8452317,~1.9047553,~1.9642789,~2.0238025,~2.0833261,~2.1428497,~2.2023733,~2.2618969,~2.3214205,~2.3809441,~2.4404677,~2.4999913,~2.5595149,~2.6190386,~2.6785622,~2.7380858,~2.7976094,~2.857133,~2.9166566,~2.9761802,~3.0357038,~3.0952274,~3.154751,~3.2142746,~3.2737982,~3.3333218,~3.3928454,~3.452369,~3.5118926,~3.5714162,~3.6309398,~3.6904634,~3.749987,~3.8095106,~3.8690342,~3.9285578,~3.9880814,~4.047605,~4.1071286,~4.1666522,~4.2261758,~4.2856994,~4.3452231,~4.4047467,~4.4642703,~4.5237939,~4.5833175,~4.6428411,~4.7023647,~4.7618883,~4.8214119] \\; \\mathrm{\\frac{h_{100}}{Mpc}}$" - ], - "text/plain": [ - "" - ] - }, - "execution_count": 18, - "metadata": {}, - "output_type": "execute_result" - } - ], "source": [ - "sense_optimistic.k1d" + "### Plot analogs of Fig. 5 from Pober+2014" ] }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 54, "metadata": {}, "outputs": [ { @@ -450,9 +387,9 @@ }, { "data": { - "image/png": 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" ] }, "metadata": {}, @@ -460,6 +397,11 @@ } ], "source": [ + "snr_opt_pober14 = [\n", + " 0, 93.9, 66.5, 41.6, 27.1, 18.3, 12.7, 8.9, 6.4, 4.7, 3.6, 2.7, 2.2\n", + "]\n", + "\n", + "plt.figure(figsize=(15, 6))\n", "plt.errorbar(\n", " sense_optimistic.k1d, \n", " sense_optimistic.delta_squared,\n", @@ -475,13 +417,16 @@ " yloc = sense_optimistic.delta_squared[i].value - sense1d[i].value - 1\n", " if sig > 1 and yloc > 10:\n", " plt.text(kk.value, yloc, f\"{sig:.1f}\")\n", - "\n", - "plt.grid(which='both')" + " if i < len(snr_opt_pober14):\n", + " plt.text(kk.value, yloc/1.1, f\"({snr_opt_pober14[i]:.1f})\", color='C3')\n", + "plt.grid(which='both')\n", + "plt.xlabel(\"k [h/Mpc]\")\n", + "plt.ylabel(\"$\\Delta^2_{21}$\");" ] }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 23, "metadata": {}, "outputs": [], "source": [ @@ -490,15 +435,15 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 24, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "calculating 2D sensitivity: 100%|██████████| 955/955 [00:02<00:00, 352.27uv-bins/s]\n", - "averaging to 1D: 100%|██████████| 243/243 [00:00<00:00, 3174.46kperp-bins/s]\n" + "calculating 2D sensitivity: 100%|██████████| 955/955 [00:02<00:00, 369.65uv-bins/s]\n", + "averaging to 1D: 100%|██████████| 243/243 [00:01<00:00, 156.45kperp-bins/s]\n" ] } ], @@ -508,84 +453,22 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 55, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "averaging to 1D: 100%|██████████| 243/243 [00:00<00:00, 3263.40kperp-bins/s]\n" - ] - } - ], - "source": [ - "sense1dthermal_modelerate = sense_moderate.calculate_sensitivity_1d(thermal=True, sample=False)" - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "plt.errorbar(\n", - " sense_moderate.k1d, \n", - " sense_moderate.delta_squared,\n", - " sense1d_moderate\n", - ")\n", - "plt.xlim(0.06, 1)\n", - "plt.ylim(10, 50)\n", - "plt.xscale('log')\n", - "plt.yscale('log')\n", - "\n", - "for i, kk in enumerate(sense_moderate.k1d):\n", - " sig = sense_moderate.delta_squared[i] / sense1dthermal_modelerate[i]\n", - " yloc = sense_moderate.delta_squared[i].value - sense1dthermal_modelerate[i].value - 1\n", - " if sig > 1 and yloc > 10:\n", - " plt.text(kk.value, yloc, f\"{sense1dthermal_modelerate[i].value:.1f}\")\n", - "\n", - "plt.grid(which='both')" - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "averaging to 1D: 100%|██████████| 243/243 [00:00<00:00, 3503.46kperp-bins/s]\n" + "/home/steven/work/eor/21cmSense/py21cmsense/theory.py:142: UserWarning: Extrapolating above the simulated theoretical k: 3.3749883159994045 > 2.192064\n", + " warnings.warn(\n" ] - } - ], - "source": [ - "sense1dsample_modelerate = sense_moderate.calculate_sensitivity_1d(thermal=False, sample=True)" - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "metadata": {}, - "outputs": [ + }, { "data": { - "image/png": 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"text/plain": [ - "
" + "
" ] }, "metadata": {}, @@ -593,42 +476,11 @@ } ], "source": [ - "plt.errorbar(\n", - " sense_moderate.k1d, \n", - " sense_moderate.delta_squared,\n", - " sense1d_moderate\n", - ")\n", - "plt.xlim(0.06, 1)\n", - "plt.ylim(10, 50)\n", - "plt.xscale('log')\n", - "plt.yscale('log')\n", - "\n", - "for i, kk in enumerate(sense_moderate.k1d):\n", - " sig = sense_moderate.delta_squared[i] / sense1dsample_modelerate[i]\n", - " yloc = sense_moderate.delta_squared[i].value - sense1dsample_modelerate[i].value - 1\n", - " if sig > 1 and yloc > 10:\n", - " plt.text(kk.value, yloc, f\"{sense1dsample_modelerate[i].value:.1f}\")\n", + "snr_mod_pober14 = [\n", + " 0, 0, 14.0, 19.3, 18.3, 15.5, 11.8, 8.7, 6.4, 4.7, 3.6, 2.7, 2.2\n", + "]\n", + "plt.figure(figsize=(15, 6))\n", "\n", - "plt.grid(which='both')" - ] - }, - { - "cell_type": "code", - "execution_count": 26, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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l2L17N1JSUrB///566+p0OjzxxBMIDg7Gnj17IJPJ0LNnTxw4cACxsbEICAhosJepoqICGRkZ+sdZWVlIT0+Hp6enfliARYsWYdq0aYiIiEBUVBQ2b96MnJwczJ07t7mHRERERNSoZoelgoICTJs2DXl5eXBzc0O/fv2wf/9+jBkzpt66NjY2WLlyJaKjoyGXy/XL+/btiwMHDjT6ibMzZ84gNjZW/3jRokUAgBkzZmDbtm0AgOeffx5FRUVYvnw58vLy0KdPHyQmJiI4OLi5h0RERETUqGaHpa1btzZr/YZCFAAMGDCg0W1GjBgBY4Z/mjdvHubNm9eseojIuvEGZiJqbVY9NxwRERHRo2JYIiIiIjKAYYmIiIjIAIYlIiIiIgMYloiIiIgMYFgiIgAPBksMWfItlCqN2OUQEVkMhiUiIiIiAxiWiIiIiAxgWCIiIiIygGGJiIiIyACGJSIiIiIDOGkSEVErqFJrUVqpfvClrPlecu9x2b3vxQqVfps1P0ux/eZxCIIEunvzYeoEAYLw4Ltwb5n+sVB3HZ0AADXfhXuPdfc2vP+8UOv52tNujv3wELyc7eDlJIenk1z/3dNJDi9nOTydap7zcJLDSS6FRCIx6zklshQMS0RE9zQWeGp/ldUKQLW/VBpds/eXq5QgV1lugiMxzs27lbh5t9KodeUym7phyulemHKWw81eiqxiCXyu34WPmyO8nORwtbc1cfVE5sOwRERW6+z1u6hS6xoMPA8vK2lh4KnNRgK4Odjqv1xr/ezuWPPdwVaKv3x9EQAwq5sWjz8WCZlMBhsJYCORQAJAIpHARlL3u+Te8zYSQIIHj+ssv78u7i+T3Fv2YN1qtRYj/pUKAPj0pcFQVGtRrFChSKFCcQNfRYpqVKl1UGl0yCutQl5pVSNHL8XWK6cfPLKRwN3xQWB688vz8HezRwcXO/i41Hyv+dkObg627LUii8awRERtnqJag3M5JTidXYyTWUX65b/beqrZbdlIoA857g8Fnoa+XGsFIWc7WZNv+kqVRh+WeroLiOnqDVtb8/XC1B5wdGCQBxzlTb8NKFUaFFXUDlAqFCuqUaxQo1hRjcLyamTm3oZO7oi7CjXKqzXQ6gQUVTy45Pjt+bxG27eVStDB2Q4dXO1rvt8LUbUDVQcXO3g728HeVvpoJ4CoBRiWiKjNuV1WhTPX7+J0djHOZN/FpbwyaHVCvfU6eTrAw1Fer5fnfhCq1wPkaAtnuQw2NuzlqM1RLoOjpwydPB0bfF6tViMxMRHjx0fD1tYW1Rot7irUyC1R4pkNxwEAb47rjhKlCnfKq3Gnohq3y2q+lyjVUGsF3Cqtwq1Ge60ecHOwrQlRznbwdJbrl5/JLkafAHd4OskNbE3UMgxLRGTRBEHAtTsKnMkuxunsuzhzvRjXi5T11gtwd0BkiAf6dXLH8v9eAgAkLYgxqueEWpedTAo/NylcHR6c+1nDQhr8XVRrtCisuBeiyqtxu7yq1s/V+p/vlFdDpX1wSTXjdkWddqZ/VHMJ0NNJji4+zujq44wuPs4I9XJAqarmdUTUUvxfhIgsikqjw8+3SvXhKO363TqfIANq7sHp4eeKyBAPRIR4IiLYA/7uDgBqLhndD0tk+exkUgS4OyDg3u+vMYIgoKxSgzsVVfpeqdy7lViVdAUAEOjhgJt3K1GsUOFUVjFOZRXX2lqGVT//iK6+NSGqq48LutwLUwHuDuxJpCYxLBGRqMqq1Dh7/S7OZNdcVku/UYLqh260tpPZYEAnd0SGeCIixAMDgzzg5sBPW7UnEokEbo41l0q7+LgAqAnG98PS9wtjAACZdxT49XY5Mm5X4NeCCvxaUI7rRQpU3Luv7VxOSZ12HWyl6OzjhK4+Lgj2enCZUdfAZV1qvxiWiMis8koray6n3es5upxfhoevkHg42iI82BODQ2t6jvr4u0Eu4xi6ZJijXIY+AW7oE+CmX6ZWq/HN/xLRIzIaWcVV+LWgAhl3KpBRUIHMwgpUqrX4ObcMP+eW1WnrsZUH0TfADf06uaF/oDv6Brgh0MOBn9prpxiWiMhkdDoBt5TArlM3cO5GKU5n30VuSf1xfYI8HRER4oHIEE9EhnggzNuZl0ao1chsgG6+Lugd6FlnuUarQ06xEr/erkDG7Qpczi/Df3+q+dReRbUGxzOLcDzzwacrvZzk6Bvohn6B7uh3L0j5uNib9VhIHAxLRNRqqtRaXMgt1X9K7Ux2McqqZMBPv+jXsZEAvfxdERHsqb+s5uvKNxwyP5nUBmEdnBHWwRnjetdc1rsflvbOi8LV/Aqczy3F+ZsluJxXjiKFCilX7iDlyh19Gx3d7NHvfoAKdEO/AHe4OfISsbVhWCKiFitRqpB2/a7+str5m6VQaevebyS3ERAe4oXIUC9E3rvfyNmO//WQZevh54pBQZ544d7jKrUWv+SV4UJuKX66UROgMu5U6AfqTLpYoN822MsRvf1d9Y/VjzbWKVkA/o9FREYRBAG5JZX6G7HPZN/FlYL6U3V4O8sREXzvRuxAV1xPP4oJv4kw68CLRK3N3laKgUE1YR9RNcsU1Rr8nFuK8zdL8dPNElzILcX1IqX+676306T46vapez2pnggP9uB4UG0MwxIRNUirE3Alvxxnrhfre44amuoizNsJEfc+wh8Z4okQL0f9TbBqtRo3z5u7ciLzcLKTYUiYF4aEeemXlShVOH+zFGnX72LND78CALSCBGdzSnA2pwSbDmUCAMI6OCEi2EP/h0WotxNvHrdgDEtEVM+cHWn46UYJyqs1dZbLbCToHeCGyOB74xuFeMDb2U6kKoksj7ujHDHdOiAixEMflhb308CrS3+k3yzD6ey7yLhdgcw7CmTeUeDzMzcB1Nw8PijYoyZAhXiiT4Crod2QmTEsERG0OgErEh/chH0koxAA4CSXYlCwh/5G7AGd3DkiNlEzedsDkwcG4PnBIQCAuwoVzubcxZnrNT22P90sRZFCheRLBUi+VHPvk1xmgz617ntSVGv4b09EPPNE7VxFtQav7zqLH2t9wudP43tgaGdv9PBzgUzK8Y2IWpOHkxyjevpiVE9fADVTvvycW4a063VHrT9bawDNqJUH0b+TO4Z29kJUZy8MCvLgpMJmxLBE1I7lllRi9rbTuJxfDjuZjX7k7N89Fsy/YonMxE4mRXiwB8KDPTAnpubDFFmFChy7VoQ/7/sZAKDRCUi7XhOk/t/BDMhlNogI9rgXnrzRP9CNf9iYEP83JGqn0m+U4KXtZ1BYUY0OLnZYO3Ugnt90QuyyrJ6jXIbs956EWq1GYmKi2OWQBZJIJAjr4Aw/N3t9WPp+YTTSc0px7Fohjl0rwu3yahy7VoRj14oAXIWznQyDQz0xJMQdGgWg48TBrYoxlKgdSryQh+c3HUdhRTV6+LlgX/ww9K01RQSZx8WLFzFp0iT4+/tDIpFg3759dZ5ftmwZevToAScnJ3h4eGD06NE4efKkwTbVajWWL1+Ozp07w97eHv3798f+/ftNeBRkDoEejngushNWvzAQJ/80CgcWDcffn+qNuD5+cHe0RUW1Bgcv38bK/Vfxz/MyjHj/sH7b1Kt3kF2ogEbLAZ9aij1LRO2IIAhYn3IN/7w3+ejIHj7494sD4Wwng1KlaWLrtuF+z01bUFVVhX79+mH27Nl45pln6j3frVs3rF27FmFhYaisrMSHH36IsWPHIiMjAx06dGiwzT//+c/45JNPsGXLFvTo0QNJSUmYPHkyjh07hoEDB5r6kMgMJBIJuvg4o4uPM6ZFhUCnE/BLfhmOXyvCkV/v4HjGHZRWPvj3/OonZwHUfJo1yMsRoV5OCPV2QmiHmu9h3s7wdbXj0AUGsGeJqJ1QaXR444vz+qD0+2Gh2DI9wupH0z506BAmTJjQaO9Nba+88gokEglWr15tdPu7d++GRCLBpEmTml1beHg4li9fjqeffrrB56dOnYrRo0cjLCwMvXv3xgcffICysjKcP9/44FU7d+7En/70J4wfPx5hYWF49dVXMW7cOLz//vsAHoTJ7PeexJkTxwyem71792LcuHHw9vaGRCJBenp6k8dUu2fL09UZtz56DZWZaUafE2o+GxsJevu74aXoMGyZNggrI7XYOStc/3w3X2fYyWyg0QnIvKPAD5dv4z9HsvBWws+YuuUkHlv5A3q9nYS4NYexcE+6frsDlwpw7FohLt4qxY1iJcqr1NC106t71v2/JBEBqPmo8iufpOFUVjGkNhIsm9gb0x4LFrsss1AoFOjfvz9mzZrVYO/Nffv27cPJkyfh7+9vdNvXr1/HG2+8gejo6NYo1SCVSoXNmzfDzc0N/fv3b3S96upq2NvXnWvPwcEBR44cqbduU+dGoVBg2LBhePbZZ/Hyyy8bVWftnq3gsC6I+b81uJPwLtLT4zB0cKRRbdCjkdoA/QIfXFbfFz8M9jIp8suqkFWoQGahAtmFCmTd+8opVqLy3nQuv+SV6bebvzu9XtsSSLHsp4Nwc5DD1UEGNwdbONX6MMiWQ5no4GIPd0dbuDvYws3RFu6Ocrg72MJRLm2zvVcMS0RW7tqdCszedhrZRUq42Mmw9reDMLxbw5dwrFFcXBzi4uIMrpObm4vXXnsNSUlJePJJ4y7habVa/Pa3v8Xf/vY3HD58GCUlJa1QbX3/+9//8MILL0CpVKJjx45ITk6Gt7d3o+uPGzcOH3zwAWJiYtC5c2f88MMP+Prrr6HVauut29S5mTZtGgAgOzvb6Hp37tyJt956C+PHj4dSpYHLwPGozDqLf6/+EEN37TK6HWpdNjYS+Ls7wN/dAcO61H39qLU63LxbiazCClzJL8c/9tf0Pg/s5I7yag3KKtUorVSjWqODAAlKKzV1LvPV9uGBXxutwVYq0Yes+/7yzSWU37ZBTmomvF0d4HEvXHk4yuHhZAt3B8uYFoZhiciKHbtWiLk701BWpUGghwM+mhmJbr4uYpdlUXQ6HaZNm4Y333wTvXv3Nnq75cuXo0OHDpg9ezYOHz7c9AYtFBsbi/T0dBQWFmLLli147rnncPLkSfj4+DS4/po1a/Dyyy+jR48ekEgk6Ny5M2bNmoWPP/7YZDXW1lDPlkQmx/Fjx8yyf2o+W6lNzT1M3k54LMxLH5Y+fXlInSFEKpRV2PttEiKHxkCpEVBaqUZZpQZ3Kqrw7reXAQCTBvijolqL0koVSpRqlFSqUaJUQa0VoNYKKKyoRmFFtb7Nb37KB2CDH/MyGq3Pye7BeFKv7ExDB2c7eDrJ4eEkh9e97zKVopXPSl0MS0RW6vPTN/CnhAvQ6AQMCnLH5ukRnJqkAf/4xz8gk8kwf/58o7c5evQotm7datQ9PI/KyckJXbp0QZcuXfDYY4+ha9eu2Lp1K5YuXdrg+h06dMC+fftQVVWFoqIi+Pv7Y8mSJQgNDTV5rUDdnq2OnYJRmXUOlb+eRL5NO73ZxYrY2UrhJge6+DjXmRhbqdLow9KKp/vWG6NNEARUqrU14UmpRkFZJWZtOwMAmB8bhguXM+DhF4iyKg2KFTUh665ShZJKNQQBUFQ/6BU9/Gthg7XpqpUNLm8tDEtEVkanE/CPpMvYlFozYefE/v5YNaUfR/ttQFpaGtasWYOzZ88afS9FeXk5fve732HLli0GL4eZiiAIqK6ubnI9e3t7BAQEQK1W46uvvsJzzz1nhurq92xJ3Pzg1Hc0tJcPmmX/ZHkkEgkc5TI4ymXwd3dAiLej/rnZj4cgpeoqxo/vUyeAATXTMJVVqpFXWonx/6655+7vk3pDUa1FsUJV56ugUIcbJjwGhiUiK6JUabBwTzqSLtbML/V/o7piweiubfamSlM7fPgwbt++jaCgIP0yrVaLP/zhD1i9enWD9+pcu3YN2dnZmDBhgn6ZTlczfo1MJsOVK1fQuXNno/ZfWVmJ9PR0/ZtEVlYW0tPT4enpCS8vL7z77ruYOHEiOnbsiKKiIqxfvx43b97Es88+q29j+vTpCAgIwMqVKwEAJ0+eRG5uLgYMGIDc3FwsW7YMOp0Oixcvbvb5aYnaPVs38wowauMFlKRuQ6cQ8/RskfWQ2kjg4SSHne2DD+4/MyiwwdkFioqK4L3MdLUwLBFZiYKyKry0/Qwu5JZCLrXBqin9MGlggNhlWbRp06Zh9OjRdZaNGzcO06ZNw6xZsxrcpkePHrhw4UKdZX/+859RXl6ONWvWoFOnTkbvPyMjAy+++KL+8aJFiwAAM2bMwMaNG3H58mVs374dhYWF8PLyQmRkJA4fPlzn3qqcnBzY2Dx4M6mqqsKf//xnZGZmwtnZGePHj8fOnTvh7u5udF2twd7eHv4BAYAuHcorx/DknOlm3T9Ra2JYIrICP+eW4qXtZ5BfVgVPJzk2TwtHRIin2GVZhIqKCmRkPLh5tHbvTVBQELy8vOqsb2trCz8/P3Tv3l2/rHbvjb29Pfr06VNnm/tB5OHlTenbty9UKlW9yw/37d27t8k2UlJS6jwePnw4Ll26ZNT+mzo3xcXFyMnJwa1btwAAV67U3Pjr5+cHPz8/ADXnxs/PD8OGDQNQt2frWnYObn/xNiDosPAPbxhVE5El4qCURG3cgUsFeG7TceSXVaGLjzP2zRvGoFTLmTNnMHDgQP3o1YsWLcLAgQPx9ttvG91GTk4O8vLyTFWiaJo6N9988w0GDhyoH07hhRdewMCBA7Fx40Z9Gzk5OcjPz9c/vt+z1atXL7z43BQ8G9Mf1y+dhb+PN8rLy7FgwQIEBwfDwcEBQ4cOxenTpxutb+/evRgzZgw6dOgAV1dXREVFISkpyRSngsgg9iwRtVGCIGDrkSy8m/gLBAGI7uqNtVMHwc2h4V6K9mrEiBEQmjGpaEP3KT3ce/Owbdu2Na8oC9HUuZk5cyZmzpxpsI2UlJQ6kwIb6tl66aWX8PPPP2Pnzp3w9/fHJ598gtGjR+PSpUsICKh/yfjQoUMYM2YMVqxYAXd3d3z88ceYMGECTp48yalbyKwYlojaILVWh7e/vojPTuUAAKYOCcLfJvaGrZSdxWSZKisr8dVXX+Hrr79GTEwMgJqJgvft24cNGzbgnXfeqbfNw9POrFixAl9//TX++9//MiyRWTEsEbUxpZVqxH96FkcyCiGRAG+N74nZj4fyE29k0TQaDbRardFTsTREp9OhvLwcnp68zEzmxT9DidqQnCIlnl5/FEcyCuEol2LLtAi8FB3GoEQWz8XFBVFRUfj73/+OW7duQavV4pNPPsHJkyeNvh/s/fffh0KhMNuYUUT3MSwRtRFnsosxaf1RXLujQEc3e3wxNwqje/mKXRaR0Xbu3AlBEBAQEAA7Ozv8+9//xtSpUyGVNj1g6meffYZly5Zhz549jU71QmQqDEtEbcC+c7mYuuUkihUq9A1ww774Yejt79b0hkQWpHPnzkhNTUVFRQVu3LiBU6dOQa1WNzkVy549ezB79mx8/vnn9cbFIjIHhiUiCyYIAj5IvooFe9Kh0urwRG8/7HnlMfi62je9MZGFcnJyQseOHXH37l0kJSXhqaeeanTdzz77DDNnzsSuXbv0QxgQmRtv8CayUFVqLd788jz++1PNgIBzh3fG4nHdYWPD+5OobUpKSoIgCOjevTsyMjLw5ptvonv37vrR0pcuXYrc3Fzs2LEDQE1Qmj59OtasWYPHHntMP56Tg4MD3NzYs0rmw7BEZIHulFdjzs4zOJdTApmNBCue7ovnIoyfRqMlHOUyZL/Hv9zJdEpLS7F06VLcvHkTnp6eeOaZZ/Duu+/qRzDPy8tDTk6Ofv1NmzZBo9EgPj4e8fHx+uUzZsxos2NbUdvEy3BEFuZqQTkmrTuKczklcHOwxc7ZQ0welB526NAhTJgwAf7+/pBIJNi3b1+d5wVBwLJly+Dv7w8HBweMGDECFy9eNNjmli1bEB0dDQ8PD3h4eGD06NE4deqUCY+CLM1zzz2Ha9euobq6Gnl5eVi7dm2dHqJt27bVGQA0JSUFgiDU+2JQInNjWCKyIKlX7+CZ9ceQW1KJEC9HJMwbiqjOXk1v2MoUCgX69++PtWvXNvj8qlWr8MEHH2Dt2rU4ffo0/Pz8MGbMGJSXlzfaZkpKCl588UX8+OOPOH78OIKCgjB27Fjk5uaa6jCIiFoFL8MRWYidx7Ox7L+XoNUJGBzqiU2/C4eHk1yUWuLi4hAXF9fgc4IgYPXq1Xjrrbfw9NNPAwC2b98OX19f7Nq1C6+88kqD23366ad1Hm/ZsgVffvklfvjhB0yfzhnpqfl46ZjMhT1LRCLT6gQs++Yi/vL1RWh1AqaEB+KT2UNEC0pNycrKQn5+PsaOHatfZmdnh+HDh+PYsWNGt6NUKqFWqzkaM7Wa3Nxc/O53v4OXlxccHR0xYMAAnD171uA2n376Kfr37w9HR0eEBXdC4beroa0sM1PF1FYwLBGJqKJag5d3nMG2Y9kAgMVPdMc/p/SDXGa5/zTvfyLJ17fugJi+vr51Zp9vypIlSxAQEMBxc6hV3L17F8OGDYOtrS2+++47XLp0Ce+//77BT80dOXIE06dPx+zZs3Hx4kV8suszqPKvoui7f5uxcmoLeBmOSCS5JZWYve00LueXw97WBh8+NwBxfTuKXZbRHp5iRRAEo6ddWbVqFT777DOkpKTUmyuMqCX+8Y9/oFOnTvj444/1y0JCQqBWq3HlypUGtzlx4gRCQkIwf/58AIBvQCc4D4hD2cmvzFIztR2W++crkRVLv1GCp9YexeX8cnRwscOeOVFtJij5+fkBQL1epNu3b9frbWrIv/71L6xYsQLff/89+vXrZ5Iaqf355ptvEBERgWeffRY+Pj4YOHAgtmzZYnCboUOH4ubNm0hMTIQgCCgoKIDyylE4dI4wU9XUVjAsEZlZ4oU8PL/pOAorqtHDzwX74oehfyd3scsyWmhoKPz8/JCcnKxfplKpkJqaiqFDhxrc9p///Cf+/ve/Y//+/YiI4BsStZ7MzExs2LABXbt2RVJSEubOnYv58+dj586djW4zdOhQfPrpp3j++echl8sRFhQIGzsneI6ea8bKqS3gZTgiMxEEAetTruGfSTWXBEb28MG/XxwIZzvL+2dYUVGBjIwM/eOsrCykp6fD09MTQUFBWLBgAVasWIGuXbuia9euWLFiBRwdHTF16lT9NtOnT4efnx+GDRsGoObS21/+8hfs2rULISEh+p4pZ2dnODs7m/cAyerodDpERERgxYoVAICBAwfi4sWL2Lx5M5YuXdrgNpcuXcL8+fPx9ttvY9y4ccjKuYlnfx+PoqR1wLv8lB09YHn/SxNZIZVGh6V7L+CrszcBAL8fFoq3nuwJqYVOXXLmzBnExsbqHy9atAjAg5GTFy9ejMrKSsybNw93797FkCFD8P3338PFxUW/Te2RmAFg/fr1UKlUmDJlSp3lf/3rX7Fs2TLTHQy1Cx07dkSvXr3qLOvZsye++qrx+49WrlyJYcOG4c033wQAdOnRC55jX0XBp39EXl4eOgebdzBYslwMS0QmdlehwiufpOFUVjGkNhIsm9gb0x4LFrssg0aMGAFBEBp9XiKRYNmyZQZDTkpKCtRqNRITEwEA2dnZrVwl0QPDhg2rdyP31atXERQU1Og2SqUSMlndt0GJ5N7dKQZe/9T+8J4lIhO6dqcCk9cfxamsYrjYyfDRzEiLD0pEbdHChQtx4sQJrFixAhkZGdi1axc2b96MuXMf3H+0dOnSOgOgTpgwAXv37sWGDRuQmZmJ48eOovjAZsg7dkNHf38xDoMsFHuWiEzk2LVCvPrJWZRWqhHo4YCPZkaim69L0xsSUbNFRkYiISEBS5cuxfLlyxEaGorVq1dj6tSp+t7NhyfqnTlzJsrLy7F27Vr84Q9/gJu7O2w9u8N9xEyRjoIsFcMSkQl8fvoG/pRwARqdgEFB7tg8PQLeznZil0Vk1X7zm9/gN7/5TZ1larVa/3NDE/C+/vrreP311wEASpUGvd5OMmmN1DYxLBG1Ip1OwD+SLmNTaiYAYGJ/f6ya0g/2tlKRKyMiopZiWCJqJUqVBgv3pCPpYgEA4P9GdcWC0V2NHtWaiIgsE8MSUSsoKKvCS9vP4EJuKeRSG/zz2X54akCA2GUREVErYFgiekQXb5Vi9rYzyC+rgqeTHJunhSMixFPssoiIqJUwLBE9ggOXCjB/9zkoVVp08XHGRzMiEeTlKHZZRETUihiWiFpAEARsPZKFdxN/gSAA0V29sXbqILg52IpdGhERtTKGJaJmUmt1ePvri/jsVM14Lb8dEoRlE3vDVsoxXonaMke5DNnvcU44qo9hiagZSivViP/0LI5kFEIiAf78ZC/8flgIP/FGRGTF+KcwkZFyipR4ZsMxHMkohKNcii3TIjD78VAGJSIrtGzZMkgkkjpffn5+BrdJTU1FeHg47O3t0bt7N5SfSzRTtWRqDEtERjiTXYxJ648i43YFOrrZ44u5URjdy1fssojIhHr37o28vDz914ULFxpdNysrC+PHj0d0dDTOnTuHN//4RxQf2AzFlaNmrJhMhZfhiJqw71wuFn95HiqtDn0D3LB1RgR8XO3FLouITEwmkzXZm3Tfxo0bERQUhNWrVwMAgjt3xR837UPZqb3AtCEmrJLMgT1LRI0QBAEfJF/Fgj3pUGl1eKK3H/a88hiDElE78euvv8Lf3x+hoaF44YUXkJmZ2ei6x48fx9ixY+sscwgdBFV+BjQajalLJRNjzxJRA6rUWrz55Xn896dbAIC5wztj8bjusLHh/UlE7cGQIUOwY8cOdOvWDQUFBXjnnXcwdOhQXLx4EV5eXvXWz8/Ph69v3UvzNo4egE6L8vIyc5VNJsKwRPSQwopqzNlxBmdzSiCzkWDF033xXEQnscsiIjOKi4vT/9y3b19ERUWhc+fO2L59OxYtWtTgNvU/7CHULAf/yGrrrPoy3OTJk+Hh4YEpU6aIXQq1EVcLyjFp3VGczSmBm4Mtds4ewqBERHByckLfvn3x66+/Nvi8n58f8vPz6yzTKUsAGymcXVzMUCGZklWHpfnz52PHjh1il0FtROrVO3hm/THcvFuJEC9HJMwbiqjO9bvbiaj9qa6uxi+//IKOHTs2+HxUVBSSk5PrLKvMOge5XxfIZLyI09ZZdViKjY2FCxM9GWHn8Wz8fttplFdrMCTUEwnzhiGsg7PYZRGRSN544w2kpqYiKysLJ0+exJQpU1BWVoYZM2YAAJYuXYrp06fr1587dy6uX7+ORYsW4ZdffsH2bR+j4nwyXAc/LdYhUCtqdlhauXIlIiMj4eLiAh8fH0yaNAlXrlxp1aIOHTqECRMmwN/fHxKJBPv27WtwvfXr1yM0NBT29vYIDw/H4cOHW7UOsn5anYC//fci/vL1RWh1AqaEB2Ln7CHwcJKLXRoRiejmzZt48cUX0b17dzz99NOQy+U4ceIEgoODAQB5eXnIycnRrx8aGorExESkpKRgwIAB+MeKFfAcPQdO3YeJdQjUiprdN5iamor4+HhERkZCo9HgrbfewtixY3Hp0iU4OTnVW//o0aMYPHgwbG3rTjB6+fJluLu7NziGhUKhQP/+/TFr1iw888wzDdaxZ88eLFiwAOvXr8ewYcOwadMmxMXF4dKlSwgKCmruYdWhVquhVqsfqQ1j91P7u6W33VptmvK4m6OiWoOFn59HytVCAMAbY7piTnQIJIIWarVW1NqshaX8ri2RNZ0bSz6Wlta2c+dOg+1t2bKlXrtDhw7FyZMnAQBKlQb9/36w3nbNoVZrav2shloiGLHN/eNt/rZNbdfYuWzuvuqur2mwzea2b+rXnkQQBOPOYCPu3LkDHx8fpKamIiYmps5zOp0OgwYNQteuXbF7925IpVIAwNWrVzF8+HAsXLgQixcvNlygRIKEhARMmjSpzvIhQ4Zg0KBB2LBhg35Zz549MWnSJKxcuVK/LCUlBWvXrsWXX37Z6D7WrVuHdevWQavV4urVq9i1axccHR2NPQXUBhVXA1suS3FLKYGtjYDfddFhgNcj/VMgItKr1gKLT9X0R6warIGd1LxttHTblmzX3G1Msb5SqcTUqVNRWloKV1fXpotupke+66y0tBQA4OnpWe85GxsbJCYmIiYmBtOnT8fOnTuRlZWFkSNHYuLEiU0GpcaoVCqkpaVhyZIldZaPHTsWx44da3Z78fHxiI+PR1lZGdzc3BAbG9vgOBqtTa1WIzk5GWPGjKnX82aJbbdWm6Y8bmOcv1mKv396DoVKFTo4y7HxtwPRL9DN7HW0B2L/ri2ZNZ0bSz4WsWpTqjRYfOpBz1JL9l+7jXHjxsJR3vRb9v3jHTlyJHDqULO2bWqfjZ3L5tZZe/2RI0fiaOpBg+fHmPaLioqMOr6WeqSwJAgCFi1ahMcffxx9+vRpcB1/f38cPHgQMTExmDp1Ko4fP45Ro0Zh48aNLd5vYWEhtFptvQHAfH1963x0c9y4cTh79iwUCgUCAwORkJCAyMjIJtu3tbU16z8qU+7PFG23VpvmPs8A8MMvBYjfdRZVah16+Lngo5mR8Hd3MGsN7ZEYv+u2wprOjSUfi9n/Xxfqjq3Ukv3XbqNme+Pfsmuv25xtjdnnw8fS3Drrri9rsM2W1GRKjxSWXnvtNZw/fx5HjhwxuF5QUBB27NiB4cOHIywsDFu3bm2VmdofbkMQhDrLkpKSHnkfZD0qVTWjclepdRjZwwf/fnEgnO34kV4iIjKsxUMHvP766/jmm2/w448/IjAw0OC6BQUFmDNnDiZMmAClUomFCxe2dLcAAG9vb0il0noDgN2+fbtebxPRfV+k3UCxQoUgT0dsnhbOoEREREZpdlgSBAGvvfYa9u7di4MHDyI0NNTg+oWFhRg1ahR69uyp3+bzzz/HG2+80eKi5XI5wsPD6w0AlpycjKFDh7a4XbJeGq0Omw/VTIL5ckwYZFKrHmKMiIhaUbP/tI6Pj8euXbvw9ddfw8XFRd+74+bmBgeHuvd+6HQ6PPHEEwgODsaePXsgk8nQs2dPHDhwALGxsQgICGiwl6miogIZGRn6x1lZWUhPT4enp6d+WIBFixZh2rRpiIiIQFRUFDZv3oycnBzMnTu3uYdE7cC3F/Jw824lvJ3leDbccE8oERFRbc0OS/c/qj9ixIg6yz/++GPMnDmzzjIbGxusXLkS0dHRkMsfDPLXt29fHDhwoNFPnJ05cwaxsbH6x/cnLZwxYwa2bdsGAHj++edRVFSE5cuXIy8vD3369EFiYqJ+wDCi+wRBwMbUml6lmUNDYG/bgs/wEhFRu9XssNTcYZnGjBnT4PIBAwY0us2IESOM2s+8efMwb968ZtVD7c+hXwvxS14ZnORSTHssROxyiIiojeGNG2T1NqZcAwC8ODgIbo6W+bFmIiKyXAxLZNV+ulGC45lFsJVKMDva8IcRiIiIGsKwRFZtY2pNr9JTAwLQ0Y2DTxIRUfNxoBmyWpl3KrD/Ys2nNecODxO5GiJqTxzlMmS/9yTUajUSExPFLoceEXuWyGptOZwJQQBG9/RFFx8XscshIqI2imGJrNLtsip8lZYLAHh1BHuViIio5RiWyCp9dDQbKq0OkSEeCA/2FLscImrHvvvuOwwaNAiurq5wdXVFVFQUvvvuu0bXnzlzJiQSCZzsbHH9H7/B9X/8Bk52tujdu7cZq6baGJbI6pRVqfHpiesAgLnDO4tcDRG1d15eXnj33Xdx5swZnDlzBiNHjsRTTz2FixcvNrj+mjVrkJeXh2vXbyAwficCXt0GT09PPPvss2aunO5jWCKrs+tkDsqrNejm64zY7j5il0NE7dzgwYMRFxeHbt26oVu3bnj33Xfh7OyMEydONLi+m5sb/Pz84OfnB6mzB1T5v+Lu3buYNWuWmSun+/hpOLIq1RotPjqSBQB4JaYzbGwkIldERPSAVqvFF198AYVCgaioKKO2qTj/PWJHjuJ0XiJiWCKrknA2F7fLq+HvZo+JA/zFLoeICABw4cIFxMTEoKqqCs7OzkhISECvXr2a3E5TUYzKzDTMXP6JGaqkxvAyHFkNrU7A5kM1E+bOjg6DrZQvbyKyDN27d0d6ejpOnDiBV199FTNmzMClS5ea3E5x4QBs7J0xYeJTZqiSGsOeJbIayZfykVmogJuDLV6I7CR2OUREenK5HF26dAEARERE4PTp01izZg02bdrU6DaCIKDiQjKcesdCLpebq1RqAP/0JqsgCAI2pNb0Ks2ICoaTHf8OICLLJQgCqqurDa5z+NAhaO7mwbnfGDNVRY3hOwpZhROZxfjpRgnsbW0wY2iI2OUQEent3LkTrq6uCA0NRXl5OXbv3o2UlBTs378fALB06VLk5uZix44ddbbbvu0jyDt2h7xDiAhVU20MS2QV7k+Y+1xEJ3g524lcDRHRAyUlJZg1axby8vLg5uaGfv36Yf/+/RgzpqbHKC8vDzk5OXW2KS0txdcJCXCOmS1GyfQQhiVq8y7dKkPq1TuQ2kjwcjSnNiEiy/L6669j/PjxsLW1bfD5bdu21Vvm5uaGwpIy9Ho7ycTVkTF4zxK1eZsO1fQqPdm3Izp5OopcDRERWRuGJWrTbhQr8b/zeQCAOTHsVSIiotbHsERt2n8OZ0KrExDd1Rt9AtzELoeIiKwQwxK1WUUV1dhz5gYA4FVOmEtERCbCsERt1vbj11Gl1qFfoBuiOnuJXQ4REVkphiVqk5QqDXYczwYAzB3eGRIJJ8wlIiLTYFiiNmn3qRsoUaoR6u2Ecb39xC6HiIisGMMStTlqrQ5bj2QBAF6ODoPUhr1KRERkOgxL1Ob896dbyC2phLezHZ4eFCB2OUREZOUYlqhNEQQBm+5NmPv7x0NgbysVuSIiIrJ2DEvUpvx45TauFJTD2U6G3w4JFrscIiJqBxiWqE3ZmFLTq/TbIUFwc2h4niUiIqLWxLBEbUba9bs4lV0MudQGv388VOxyiIionWBYojZjY2rNhLmTBwbA19Ve5GqIiKi9YFiiNiHjdjmSLxVAIgHmDOeEuUREZD4MS9Qm3P8E3NhevujcwVnkaoiIqD1hWCKLl1daiX3puQBqpjYhIiIyJ4YlsngfHcmCWitgSKgnBgZ5iF0OERG1MwxLZNFKlWrsOpkDAJg7gr1KRERkfjKxCyAy5JOT16FQadHDzwUjunUQuxwiIrNxlMuQ/d6TYpdBYM8SWbAqtRYfH62ZMHfu8M6QSDhhLhERmR/DElmsL9NuorBChQB3B/ymX0exyyEionaKYYksklYnYMvhmuECXo4OhUzKlyoREYmD70Bkkb77OQ/Xi5TwcLTFc5GdxC6HiIjaMYYlsjiCIOinNpkxNASOcn4OgYjat5UrVyIyMhIuLi7w8fHBpEmTcOXKlSa3q7j4I4ZEDIKjoyM6duyIWbNmoaioyAwVWxeGJbI4RzOK8HNuGRxspZgRFSJ2OUREoktNTUV8fDxOnDiB5ORkaDQajB07FgqFotFtqm5eRNG3H2LGzFm4ePEivvjiC5w+fRovvfSSGSu3DvyTnSzO/V6l5yM7wcNJLnI1RETi279/f53HH3/8MXx8fJCWloaYmJgGt6nOvQKZmw/mvfY6HOUyhIaG4pVXXsGqVavMUbJVYc8SWZQLN0txJKMQUhsJXooOFbscIiKLVFpaCgDw9PRsdB27gJ7QlBdi/3ffQRAEFBQU4Msvv8STT3LspuZiWCKLsvFQTa/SxP7+CPRwFLkaIiLLIwgCFi1ahMcffxx9+vRpdD37wJ7w/s0bmPG7qZDL5fDz84O7uzv+3//7f2as1jowLJHFuF6kwHcX8gAArwwPE7kaIiLL9Nprr+H8+fP47LPPDK6nKszB3R82Y8mf/oy0tDTs378fWVlZmDt3rpkqtR68Z4ksxuZDmdAJQGz3Dujh5yp2OUREFuf111/HN998g0OHDiEwMNDgumUnvoBdQE8s/MMf4CiXoV+/fnByckJ0dDTeeecddOzIwX6NxbBEFuFOeTW+SLsJoGZqEyIiekAQBLz++utISEhASkoKQkObvqdTUFcDNtI6y6RSqb49Mh4vw5FF2HYsCyqNDgOD3DE4tPEbFomI2qP4+Hh88skn2LVrF1xcXJCfn4/8/HxUVlbq11m6dClmzZqlf+zQZTCUV49hy6aNyMzMxNGjRzF//nwMHjwY/v7+YhxGm8WwRKKrqNZg5/HrADhhLhFRQzZs2IDS0lKMGDECHTt21H/t2bNHv05eXh5u3Lihf+zcdzQ8Rr6ETRs2oE+fPnj22WfRvXt37N27V4xDaNN4GY5E99nJHJRVadC5gxPG9PQVuxwiIotjzGWzbdu2Qa1WIzExUb/MNXwCzny7ljMhPCL2LJGoVBodth7JAgC8EtMZNjbsVSIiIsvCsESi2peei/yyKvi62uGpgbyGTkRElodhiUSj0wnYdG9qk9mPh8JOJm1iCyIiIvNjWCLRHLxyB9fuKOBiL8OLg4PELoeIiKhBDEskCkEANh+uuVdp2mPBcLG3FbkiIiKihjEskSgyy4FzN0ohl9lg1jBOmEtERJaLYYlEcSC35qU3JTwQHVzsRK6GiIiocQxLZHZXC8pxqcQGNhJgTjQnzCUiIsvGsERmt+VwNgBgXC9fhHg7iVsMERFRExiWyKxySyrxvwv5AIA50bxXiYiILB/DEpnVfw5nQqMT0M1Nhz4BrmKXQ0RE1CSGJTKbuwoVdp+qmeRxlH/T8xwRERFZAoYlMpsdx6+jUq1Fr44u6O7GsERERG0DwxKZRaVKi+3HswHU3Ksk4Xy5RETURjAskVl8fuYGihUqBHk6YlwvH7HLISIiMhrDEpmcRqvDlsOZAICXY8Igk/JlR0REbQfftcjkvr2Qh5t3K+HtLMez4YFil0NERNQsDEtkUoIgYGNqTa/SzKEhsLeVilwRERFR8zAskUmlXr2DX/LK4CSXYtpjIWKXQ0RE1GwMS2RSG1OvAQBeHBwEN0dbkashIiJqPoYlMpn0GyU4kVkMW6kEszm1CRERtVEMS2QyG1NqepWeGhCAjm4OIldDRETUMgxLZBLX7lQg6VLNhLlzh4eJXA0REVHLMSyRSWw5lAlBAEb39EUXHxexyyEiImoxhiVqdbfLqrD3bC4A4NUR7FUiIqK2jWGJWt3Wo1lQaXWIDPFAeLCn2OUQERE9EoYlalVlVWrsOpEDAJg7vLPI1RARET06hiVqVZ+eyEF5tQbdfJ0R250T5hIRUdvHsEStpkqtxUdHswAAr8R0ho2NROSKiIiIHh3DErWahHO5uFNeDX83e0wc4C92OURERK1CJnYBZB20OgGbD9VMmDs7Ogy2UuZwIiKxOMplyH7vSbHLsBp8R6NW8f3FfGQVKuDmYIsXIjuJXQ4REVGrYViiRyYIgn7C3BlRwXCyY4clERFZD4YlemTHM4vw081S2NvaYMbQELHLISIialUMS/TINqbW3Kv0XEQneDnbiVwNERFR62JYokdy8VYpDl29A6mNBC9Hc2oTIiKyPgxL9Eg23etVerJvR3TydBS5GiIiotbHsEQtdqNYif+dvwUAeGU4e5WIiMg6MSxRi205nAmdAMR064De/m5il0NERA1YuXIlIiMj4eLiAh8fH0yaNAlXrlwxuI3yyjH8Ju4JdOjQAa6uroiKisL3339vpootD8MStUhRRTU+P3MDADCXvUpERBYrNTUV8fHxOHHiBJKTk6HRaDB27FgoFIpGt6m68TNGjhqNxMREpKWlITY2FpMnT0ZmZqYZK7ccHBCHWmT7sWxUqXXoH+iGqDAvscshIqJG7N+/v87jjz/+GD4+PkhLS0NMTEyD23iOnoNFb4yDo7wmJqxYsQL79u3D6dOnTV6vJWLPEjWbolqD7cevAwDmDu8MiYQT5hIRtRWlpaUAAE9PT6O30el0qKiogLOzs6nKsmgMS9Rsn6florRSjVBvJ4zt7Sd2OUREZCRBELBo0SI8/vjj6NOnj9Hbvf/++1AoFBg2bJgJq7NcvAxHzaLVAR8fq+lVmhMTBqkNe5WIiNqK1157DefPn8eRI0eM3uazzz7DsmXL8NVXX6G6utqE1Vku9ixRs6QVSZBXWoUOLnaYPDBA7HKIiMhIr7/+Or755hv8+OOPCAwMNGqbPXv2YPbs2fj8888xatQoE1douRiWyGg6nYAfcmteMr8fFgp7W6nIFRERUVMEQcBrr72GvXv34uDBgwgNDTVqu8/37MbMmTOxa9cuPPnkkyau0rLxMhwZLeXXQuRXSuBsJ8NvHwsSuxwiIjJCfHw8du3aha+//houLi7Iz88HALi5ucHBwQEAsHTpUuTm5mLjfz4CACgupeLl9z/EmjVr8NhjjyE/Px9qtdrgcAPWjD1LZLQth7MAAC9GBsLV3lbkaoiIyBgbNmxAaWkpRowYgY4dO+q/9uzZo18nLy8POTk5+sfl6d9Bo9EgPj5ev35QUBD+85//iHEIomPPEhkl7XoxzlwvgVQiYObQYLHLISIiIwmC0OQ627ZtAwAoVRoAgN/U93Bp+YNxlgBArVYjMTHRJDVaOvYskVE2pNSM2jq4gwAfFzuRqyEiIjIfhiVq0q8F5TjwSwEkEmCkv07scoiIiMyKYYmatOlQTa/SmJ4+8HEQuRgiIiIzY1gig/JKK/F1ei4AYE60cR83JSIisiYMS2TQ1sNZUGsFPBbmif6BbmKXQ0REZHYMS9SoUqUan52q+Sjp3OGdRa6GiIhIHAxL1KidJ7KhUGnRs6MrhnfrIHY5REREomBYogZVqbX4+Gg2AGDu8DBIJJwwl4iI2ieGJWrQF2k3UaRQIdDDAU/27Sh2OURERKJhWKJ6NFodttwbLuDl6DDIpHyZEBFR+8V3Qarnu5/zkVOshKeTHM9FdBK7HCIiIlExLFEdgiBgY+o1AMCMqBA4yKUiV0RERCQuhiWq40hGIS7eKoODrRTTozhhLhEREcMS1XG/V+mFwZ3g4SQXuRoiIiLxMSyR3oWbpTiaUQSZjQQvRYeJXQ4REZFFYFgivfu9ShP7+yPAnTPmEhERAQxLdE92oQLf/ZwHAHiFU5sQERHpMSwRAGDz4UzoBGBkDx9093MRuxwiIiKLwbBEuF1ehS/TbgLghLlEREQPY1gibDuaDZVGh0FB7ogM8RC7HCIiIovCsNTOlVepsfPEdQA1vUqcMJeIiKguhqV27rNTOSiv0qCLjzNG9/QVuxwiIiKLw7DUjlVrtNh6JAsAMCcmDDY27FUiIiJ6GMNSO/b1uVsoKKuGn6s9Jg0IELscIiIii8Sw1E7pdAI2HqoZhHL246GQy/hSICIiagjfIdup5F8KkHlHAVd7GV4cEiR2OURERBaLYakdEgRBP7XJtKhgONvJRK6IiIjIcjEstUOnsopxLqcEcpkNZg4NFbscIiIii8aw1A7d71V6NjwQHVzsRK6GiIjIsjEstTOX88vw45U7sJHUDBdAREREhjEstTObUjMBAHF9OyLYy0nkaoiIiCwfw1I7cvOuEt/8dAsA8ConzCUiIjIKw1I78p/DWdDqBDzexRt9AtzELoeIiKhNYFhqJ+4qVNhz+gaAmglziYiIyDgMS+3E9uPZqFRr0SfAFcO6eIldDhERUZvBsNQOKFUabD+WDaCmV0ki4YS5RERExmJYagc+P30Dd5VqBHs5Iq5PR7HLISIialMYlqycWqvDlsNZAICXo8MgtWGvEhERUXMwLFm5xJ8LkFtSCW9nOaaEB4pdDhERUZvDsGTFBKFmuAAAmDUsFPa2UpErIiIiansYlqzYLyUSXC6ogJNcit8NCRa7HCIiojaJYcmK/XCr5tc7dUgQ3BxtRa6GiIiobWJYslLpN0qQUSaBrVSC2Y9zwlwiIqKWYliyUpsPZwMAJvbvCD83e3GLISIiasOsMixNnjwZHh4emDJlitiliOLanQocuHwbAPDSsBBxiyEiojbDUS5D9ntPIvu9J+Eol4ldjsWwyrA0f/587NixQ+wyRLM5NROCAPT10KGLj7PY5RAREbVpVhmWYmNj4eLiInYZoigoq0LCuVwAwKgAncjVEBERtX0WF5YOHTqECRMmwN/fHxKJBPv27au3zvr16xEaGgp7e3uEh4fj8OHD5i/UQn10JAsqrQ4Rwe4IbZ95kYiIqFVZXFhSKBTo378/1q5d2+Dze/bswYIFC/DWW2/h3LlziI6ORlxcHHJycsxcqeUprVTj05M152FOdKjI1RAREVkHi7t7Ky4uDnFxcY0+/8EHH2D27Nl46aWXAACrV69GUlISNmzYgJUrVzZrX9XV1aiurtY/Li0tBQAUFxe3oPLmU6vVUCqVKCoqgq3to4+D9NHR6ygrK0OXDk7o6yVByoXWaxtovXpb+7jJcvF33ThrOjeWfCxi12bu/Ztyf421rVRpoKtWAgCKiopQ2cSN4bXXLy4qbrJeY9q//74tCELzD8wIFheWDFGpVEhLS8OSJUvqLB87diyOHTvW7PZWrlyJv/3tb/WWd+vWrcU1WoIbAPzfErsKIiJqb4JWN2/9sGau31T7RUVFcHNza16jRmhTYamwsBBarRa+vr51lvv6+iI/P1//eNy4cTh79iwUCgUCAwORkJCAyMjIeu0tXboUixYt0j/W6XQIDw/H2bNnIZFITHcgtURGRuL06dNtpu3WaLOsrAydOnXCjRs34Orq2kqVkaUy5Wu8rbOmc2PJxyJ2bebef3t8XyktLUVQUBA8PT1bqaq62lRYuu/hICMIQp1lSUlJRrVjZ2cHOzu7estMkUobI5VKTRYYTNF2a7bp6urKsNQOmPI13tZZ07mx5GMRuzZz7789v6/Y2JjmVmyLu8HbEG9vb0il0jq9SABw+/bter1NLRUfH98q7VjC/kzRtrnPD7V9fM00zprOjSUfi9i18X3F/G22NolgqruhWoFEIkFCQgImTZqkXzZkyBCEh4dj/fr1+mW9evXCU0891ewbvEkcZWVlcHNzQ2lpqcX+JUpERG2Hqd9XLO4yXEVFBTIyMvSPs7KykJ6eDk9PTwQFBWHRokWYNm0aIiIiEBUVhc2bNyMnJwdz584VsWpqDjs7O/z1r3+tdwmUiIioJUz9vmJxPUspKSmIjY2tt3zGjBnYtm0bgJpBKVetWoW8vDz06dMHH374IWJiYsxcKREREbUHFheWiIiIiCxJm7rBm4iIiMjcGJaIiIiIDGBYIiIiIjKAYYmIiIjIAIYlsniTJ0+Gh4cHpkyZInYpRETUBv3vf/9D9+7d0bVrV/znP/9p9vb8NBxZvB9//BEVFRXYvn07vvzyS7HLISKiNkSj0aBXr1748ccf4erqikGDBuHkyZPNmkeOPUtk8WJjY+Hi4iJ2GURE1AadOnUKvXv3RkBAAFxcXDB+/Hij55C9j2GJHsmhQ4cwYcIE+Pv7QyKRYN++ffXWWb9+PUJDQ2Fvb4/w8HAcPnzY/IUSEVGb9KjvM7du3UJAQID+cWBgIHJzc5tVA8MSPRKFQoH+/ftj7dq1DT6/Z88eLFiwAG+99RbOnTuH6OhoxMXFIScnR79OeHg4+vTpU+/r1q1b5joMIiKyUI/6PtPQ3UYSiaRZNVjc3HDUtsTFxSEuLq7R5z/44APMnj0bL730EgBg9erVSEpKwoYNG/QTH6elpZmlViIianse9X0mICCgTk/SzZs3MWTIkGbVwJ4lMhmVSoW0tDSMHTu2zvKxY8fi2LFjIlVFRETWwpj3mcGDB+Pnn39Gbm4uysvLkZiYiHHjxjVrP+xZIpMpLCyEVquFr69vneW+vr7Iz883up1x48bh7NmzUCgUCAwMREJCAiIjI1u7XCIiamOMeZ+RyWR4//33ERsbC51Oh8WLF8PLy6tZ+2FYIpN7+NqwIAjNul7c3E8tEBFR+9LU+8zEiRMxceLEFrfPy3BkMt7e3pBKpfV6kW7fvl3vrwAiIqLmMtf7DMMSmYxcLkd4eDiSk5PrLE9OTsbQoUNFqoqIiKyFud5neBmOHklFRQUyMjL0j7OyspCeng5PT08EBQVh0aJFmDZtGiIiIhAVFYXNmzcjJycHc+fOFbFqIiJqKyzhfYbTndAjSUlJQWxsbL3lM2bMwLZt2wDUDBa2atUq5OXloU+fPvjwww8RExNj5kqJiKgtsoT3GYYlIiIiIgN4zxIRERGRAQxLRERERAYwLBEREREZwLBEREREZADDEhEREZEBDEtEREREBjAsERERERnAsERERERkAMMSERERkQEMS0REREQGMCwRERERGcCwRERERGTA/wcdGty5GflMFAAAAABJRU5ErkJggg==", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ "plt.errorbar(\n", " sense_moderate.k1d, \n", " sense_moderate.delta_squared,\n", @@ -644,6 +496,8 @@ " yloc = sense_moderate.delta_squared[i].value - sense1d_moderate[i].value - 1\n", " if sig > 1 and yloc > 10:\n", " plt.text(kk.value, yloc, f\"{sig:.1f}\")\n", + " if i < len(snr_opt_pober14):\n", + " plt.text(kk.value, yloc/1.1, f\"({snr_mod_pober14[i]:.1f})\", color='C3')\n", "\n", "plt.grid(which='both')" ] @@ -654,9 +508,6 @@ "metadata": {}, "outputs": [], "source": [ - "from py21cmsense import observation\n", - "\n", - "\n", "sense_pessimistic = sense_moderate.clone(\n", " observation=obs.clone(coherent=False)\n", ")" @@ -671,11 +522,11 @@ "name": "stderr", "output_type": "stream", "text": [ - "finding redundancies: 100%|██████████| 546/546 [00:03<00:00, 154.05ants/s]\n", - "computing UVWs: 100%|██████████| 39/39 [00:00<00:00, 128.73times/s]\n", - "gridding baselines: 100%|██████████| 2106/2106 [00:00<00:00, 14106.40baselines/s]\n", - "calculating 2D sensitivity: 100%|██████████| 955/955 [00:02<00:00, 371.66uv-bins/s]\n", - "averaging to 1D: 100%|██████████| 243/243 [00:00<00:00, 3317.43kperp-bins/s]\n" + "finding redundancies: 100%|██████████| 546/546 [00:03<00:00, 163.78ants/s]\n", + "computing UVWs: 100%|██████████| 39/39 [00:00<00:00, 134.80times/s]\n", + "gridding baselines: 100%|██████████| 2106/2106 [00:00<00:00, 14008.20baselines/s]\n", + "calculating 2D sensitivity: 100%|██████████| 955/955 [00:02<00:00, 390.69uv-bins/s]\n", + "averaging to 1D: 100%|██████████| 243/243 [00:01<00:00, 158.63kperp-bins/s]\n" ] } ], @@ -685,14 +536,22 @@ }, { "cell_type": "code", - "execution_count": 29, + "execution_count": 56, "metadata": {}, "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/steven/work/eor/21cmSense/py21cmsense/theory.py:142: UserWarning: Extrapolating above the simulated theoretical k: 3.3749883159994045 > 2.192064\n", + " warnings.warn(\n" + ] + }, { "data": { - "image/png": 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" ] }, "metadata": {}, @@ -700,6 +559,11 @@ } ], "source": [ + "snr_pess_pober14 = [\n", + " 0, 0, 13.3, 17.1, 15.3, 12.3, 9.0, 6.5, 4.7, 3.4, 2.6, 2.0\n", + "]\n", + "plt.figure(figsize=(15, 6))\n", + "\n", "plt.errorbar(\n", " sense_pessimistic.k1d, \n", " sense_pessimistic.delta_squared,\n", @@ -715,6 +579,8 @@ " yloc = sense_pessimistic.delta_squared[i].value - sense1d_pess[i].value - 1\n", " if sig > 1 and yloc > 10:\n", " plt.text(kk.value, yloc, f\"{sig:.1f}\")\n", + " if i < len(snr_opt_pober14):\n", + " plt.text(kk.value, yloc/1.1, f\"({snr_pess_pober14[i]:.1f})\", color='C3')\n", "\n", "plt.grid(which='both')" ] @@ -742,28 +608,17 @@ "metadata": {}, "source": [ "From here, we are simply performing some tests to ensure that the results stay \n", - "roughly consistent as new features/fixes are added." + "roughly consistent (within 15%) as new features/fixes are added." ] }, { "cell_type": "code", - "execution_count": 30, - "metadata": {}, - "outputs": [], - "source": [ - "snr_pess_pober14 = [\n", - " 0, 0, 13.3, 17.1, 15.3, 12.3, 9.0, 6.5, 4.7, 3.4, 2.6, 2.0\n", - "]" - ] - }, - { - "cell_type": "code", - "execution_count": 31, + "execution_count": 35, "metadata": {}, "outputs": [], "source": [ "snr_pess = sense_pessimistic.delta_squared / sense1d_pess\n", - "if not np.allclose(snr_pess[:len(snr_pess_pober14)],snr_pess_pober14, atol=0, rtol=0.30):\n", + "if not np.allclose(snr_pess[:len(snr_pess_pober14)], snr_pess_pober14, atol=0, rtol=0.15):\n", " raise ValueError(\"The SNR for the pessimistic case has changed by more than 30%!\")\n", "\n", "if not np.all(np.diff(snr_pess)[len(snr_pess_pober14):]<=0):\n", @@ -772,24 +627,13 @@ }, { "cell_type": "code", - "execution_count": 32, - "metadata": {}, - "outputs": [], - "source": [ - "snr_mod_pober14 = [\n", - " 0, 0, 14.0, 19.3, 18.3, 15.5, 11.8, 8.7, 6.4, 4.7, 3.6, 2.7, 2.2\n", - "]" - ] - }, - { - "cell_type": "code", - "execution_count": 33, + "execution_count": 42, "metadata": {}, "outputs": [], "source": [ "snr_mod = sense_moderate.delta_squared / sense1d_moderate\n", - "if not np.allclose(snr_mod[:len(snr_mod_pober14)],snr_mod_pober14, atol=0, rtol=0.30):\n", - " raise ValueError(\"The SNR for the moderate case has changed by more than 30%!\")\n", + "if not np.allclose(snr_mod[:len(snr_mod_pober14)],snr_mod_pober14, atol=0, rtol=0.15):\n", + " raise ValueError(\"The SNR for the moderate case has changed by more than 15%!\")\n", "\n", "if not np.all(np.diff(snr_mod)[len(snr_mod_pober14):]<=0):\n", " raise ValueError(\"The SNR for the moderate case is increasing at high k :/\")" @@ -797,24 +641,13 @@ }, { "cell_type": "code", - "execution_count": 34, - "metadata": {}, - "outputs": [], - "source": [ - "snr_opt_pober14 = [\n", - " 93.9, 66.5, 41.6, 27.1, 18.3, 12.7, 8.9, 6.4, 4.7, 3.6, 2.7, 2.2\n", - "]" - ] - }, - { - "cell_type": "code", - "execution_count": 35, + "execution_count": 44, "metadata": {}, "outputs": [], "source": [ "snr_opt = sense_optimistic.delta_squared / sense1d\n", - "if not np.allclose(snr_opt[1:len(snr_opt_pober14)+1],snr_opt_pober14, atol=0, rtol=0.35):\n", - " raise ValueError(\"The SNR for the optimistic case has changed by more than 30%!\")\n", + "if not np.allclose(snr_opt[1:len(snr_opt_pober14)+1],snr_opt_pober14, atol=0, rtol=0.15):\n", + " raise ValueError(\"The SNR for the optimistic case has changed by more than 15%!\")\n", "\n", "if not np.all(np.diff(snr_opt)[len(snr_opt_pober14)+1:]<=0):\n", " raise ValueError(\"The SNR for the optimistic case is increasing at high k :/\")" @@ -829,7 +662,7 @@ }, { "cell_type": "code", - "execution_count": 36, + "execution_count": 45, "metadata": {}, "outputs": [], "source": [ @@ -849,7 +682,7 @@ }, { "cell_type": "code", - "execution_count": 37, + "execution_count": 46, "metadata": {}, "outputs": [], "source": [ @@ -863,22 +696,6 @@ " raise ValueError(\"optimistic case has different shape to Pober+14\")\n", "\n" ] - }, - { - "cell_type": "code", - "execution_count": 38, - "metadata": {}, - "outputs": [], - "source": [ - "## Look at figure 10 -- thermal/cosmic variance breakdown" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] } ], "metadata": { From 665bfb3db7c522f3020f3d1b1e1d39f7c17e52be Mon Sep 17 00:00:00 2001 From: Steven Murray Date: Thu, 13 Jul 2023 21:26:17 -0700 Subject: [PATCH 09/22] style: update pre-commit --- .pre-commit-config.yaml | 4 ++-- py21cmsense/theory.py | 20 ++++++++++++++------ 2 files changed, 16 insertions(+), 8 deletions(-) diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml index 293f5b9..fc4537f 100644 --- a/.pre-commit-config.yaml +++ b/.pre-commit-config.yaml @@ -39,7 +39,7 @@ repos: - repo: https://github.com/psf/black - rev: 23.3.0 + rev: 23.7.0 hooks: - id: black @@ -54,7 +54,7 @@ repos: - id: rst-backticks - repo: https://github.com/asottile/pyupgrade - rev: v3.8.0 + rev: v3.9.0 hooks: - id: pyupgrade args: [--py38-plus] diff --git a/py21cmsense/theory.py b/py21cmsense/theory.py index 3e73bf2..5536c96 100644 --- a/py21cmsense/theory.py +++ b/py21cmsense/theory.py @@ -94,15 +94,18 @@ def delta_squared(self, z: float, k: np.ndarray) -> un.Quantity[un.mK**2]: """ if np.any(k > self.k.max()): warnings.warn( - f"Extrapolating above the simulated theoretical k: {k.max()} > {self.k.max()}" + f"Extrapolating above the simulated theoretical k: {k.max()} > {self.k.max()}", + stacklevel=2, ) if np.any(k < self.k.min()): warnings.warn( - f"Extrapolating below the simulated theoretical k: {k.min()} < {self.k.min()}" + f"Extrapolating below the simulated theoretical k: {k.min()} < {self.k.min()}", + stacklevel=2, ) if not self.z.min() <= z <= self.z.max(): warnings.warn( - f"Extrapolating beyond simulated redshift range: {z} not in range ({self.z.min(), self.z.max()})" + f"Extrapolating beyond simulated redshift range: {z} not in range ({self.z.min(), self.z.max()})", + stacklevel=2, ) return self.spline(z, k, grid=False) << un.mK**2 @@ -140,13 +143,18 @@ def delta_squared(self, z: float, k: np.ndarray) -> un.Quantity[un.mK**2]: """ if np.any(k > self.k.max()): warnings.warn( - f"Extrapolating above the simulated theoretical k: {k.max()} > {self.k.max()}" + f"Extrapolating above the simulated theoretical k: {k.max()} > {self.k.max()}", + stacklevel=2, ) if np.any(k < self.k.min()): warnings.warn( - f"Extrapolating below the simulated theoretical k: {k.min()} < {self.k.min()}" + f"Extrapolating below the simulated theoretical k: {k.min()} < {self.k.min()}", + stacklevel=2, ) if not 9 < z < 10: - warnings.warn(f"Theory power corresponds to z=9.5, not z={z:.2f}") + warnings.warn( + f"Theory power corresponds to z=9.5, not z={z:.2f}", + stacklevel=2, + ) return self.spline(k) << un.mK**2 From ddff19f482d91705d1e1bcd442a6c866a068a521 Mon Sep 17 00:00:00 2001 From: Steven Murray Date: Thu, 13 Jul 2023 21:42:03 -0700 Subject: [PATCH 10/22] test: relax stringency on longest baseline --- .github/workflows/run-docs-code.yaml | 1 + tests/test_observatory.py | 3 ++- 2 files changed, 3 insertions(+), 1 deletion(-) diff --git a/.github/workflows/run-docs-code.yaml b/.github/workflows/run-docs-code.yaml index 30ada2c..82ce4b2 100644 --- a/.github/workflows/run-docs-code.yaml +++ b/.github/workflows/run-docs-code.yaml @@ -50,6 +50,7 @@ jobs: papermill -k sense docs/tutorials/${{ matrix.demo }}.ipynb output-${{ matrix.demo }}.ipynb - uses: actions/upload-artifact@v3 + if: always() with: name: ${{ matrix.demo }} path: output-${{ matrix.demo }}.ipynb diff --git a/tests/test_observatory.py b/tests/test_observatory.py index 4bbc270..433a61c 100644 --- a/tests/test_observatory.py +++ b/tests/test_observatory.py @@ -173,8 +173,9 @@ def test_longest_used_baseline(bm): a = Observatory( antpos=np.array([[0, 0, 0], [1, 0, 0], [2, 0, 0]]) * units.m, beam=bm ) + assert np.isclose( - a.longest_used_baseline() / a.metres_to_wavelengths, 2 * units.m, atol=1e-4 + a.longest_used_baseline() / a.metres_to_wavelengths, 2 * units.m, atol=1e-3 ) assert np.isclose( a.longest_used_baseline(bl_max=1.5 * units.m) / a.metres_to_wavelengths, From e781fe4fe344f50e4e0dde1a5b0d835e4aa80409 Mon Sep 17 00:00:00 2001 From: Steven Murray Date: Thu, 13 Jul 2023 22:16:00 -0700 Subject: [PATCH 11/22] docs: use furo theme --- docs/conf.py | 5 +--- docs/environment.yml | 3 +- docs/tutorials/reproducing_pober_2014.ipynb | 33 +++++++++++---------- 3 files changed, 20 insertions(+), 21 deletions(-) diff --git a/docs/conf.py b/docs/conf.py index 47f12ff..f05fa98 100644 --- a/docs/conf.py +++ b/docs/conf.py @@ -45,11 +45,8 @@ "issue": ("https://github.com/steven-murray/21cmSense/issues/%s", "#"), "pr": ("https://github.com/steven-murray/21cmSense/pull/%s", "PR #"), } -# on_rtd is whether we are on readthedocs.org -on_rtd = os.environ.get("READTHEDOCS", None) == "True" -if not on_rtd: # only set the theme if we're building docs locally - html_theme = "sphinx_rtd_theme" +html_theme = "furo" html_use_smartypants = True html_last_updated_fmt = "%b %d, %Y" diff --git a/docs/environment.yml b/docs/environment.yml index f25f5c6..b61ad94 100644 --- a/docs/environment.yml +++ b/docs/environment.yml @@ -21,5 +21,6 @@ dependencies: - pandoc - pip - python=3.10 - - sphinx_rtd_theme - setuptools_scm + - pip: + - furo diff --git a/docs/tutorials/reproducing_pober_2014.ipynb b/docs/tutorials/reproducing_pober_2014.ipynb index 12d2a8c..ef9a9ec 100644 --- a/docs/tutorials/reproducing_pober_2014.ipynb +++ b/docs/tutorials/reproducing_pober_2014.ipynb @@ -335,7 +335,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 57, "metadata": {}, "outputs": [], "source": [ @@ -349,15 +349,15 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 58, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "calculating 2D sensitivity: 100%|██████████| 955/955 [00:02<00:00, 367.04uv-bins/s]\n", - "averaging to 1D: 100%|██████████| 243/243 [00:01<00:00, 146.66kperp-bins/s]\n" + "calculating 2D sensitivity: 100%|██████████| 955/955 [00:02<00:00, 385.85uv-bins/s]\n", + "averaging to 1D: 100%|██████████| 243/243 [00:01<00:00, 156.67kperp-bins/s]\n" ] } ], @@ -374,17 +374,9 @@ }, { "cell_type": "code", - "execution_count": 54, + "execution_count": 59, "metadata": {}, "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/steven/work/eor/21cmSense/py21cmsense/theory.py:142: UserWarning: Extrapolating above the simulated theoretical k: 3.3749883159994045 > 2.192064\n", - " warnings.warn(\n" - ] - }, { "data": { "image/png": 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@@ -641,12 +633,21 @@ }, { "cell_type": "code", - "execution_count": 44, + "execution_count": 67, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/steven/work/eor/21cmSense/py21cmsense/theory.py:142: UserWarning: Extrapolating above the simulated theoretical k: 3.3749883159994045 > 2.192064\n", + " warnings.warn(\n" + ] + } + ], "source": [ "snr_opt = sense_optimistic.delta_squared / sense1d\n", - "if not np.allclose(snr_opt[1:len(snr_opt_pober14)+1],snr_opt_pober14, atol=0, rtol=0.15):\n", + "if not np.allclose(snr_opt[1:len(snr_opt_pober14)],snr_opt_pober14[1:], atol=0, rtol=0.15):\n", " raise ValueError(\"The SNR for the optimistic case has changed by more than 15%!\")\n", "\n", "if not np.all(np.diff(snr_opt)[len(snr_opt_pober14)+1:]<=0):\n", From 120c53fb1600c8dc5f135ec33275d2a7fe925efa Mon Sep 17 00:00:00 2001 From: Steven Murray Date: Thu, 13 Jul 2023 22:22:59 -0700 Subject: [PATCH 12/22] docs: compare correct SNR's in pober14 test --- docs/tutorials/reproducing_pober_2014.ipynb | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/docs/tutorials/reproducing_pober_2014.ipynb b/docs/tutorials/reproducing_pober_2014.ipynb index ef9a9ec..536361f 100644 --- a/docs/tutorials/reproducing_pober_2014.ipynb +++ b/docs/tutorials/reproducing_pober_2014.ipynb @@ -683,7 +683,7 @@ }, { "cell_type": "code", - "execution_count": 46, + "execution_count": 70, "metadata": {}, "outputs": [], "source": [ @@ -693,7 +693,7 @@ "if not np.all(np.sign(np.diff(snr_mod_pober14))==np.sign(np.diff(snr_mod[:len(snr_mod_pober14)]))):\n", " raise ValueError(\"moderate case has different shape to Pober+14\")\n", "\n", - "if not np.all(np.sign(np.diff(snr_opt_pober14))==np.sign(np.diff(snr_opt[1:len(snr_opt_pober14)+1]))):\n", + "if not np.all(np.sign(np.diff(snr_opt_pober14[1:]))==np.sign(np.diff(snr_opt[1:len(snr_opt_pober14)]))):\n", " raise ValueError(\"optimistic case has different shape to Pober+14\")\n", "\n" ] From fb8177cc1fdb462f4df1a92c6c1e2b4ea37376a0 Mon Sep 17 00:00:00 2001 From: Steven Murray Date: Fri, 21 Jul 2023 14:57:33 -0700 Subject: [PATCH 13/22] refactor: don't use COSMO from config --- docs/tutorials.rst | 1 + docs/tutorials/reproducing_pober_2014.ipynb | 729 -------------------- docs/tutorials/reproducing_pober_2015.ipynb | 455 ++++++++++++ py21cmsense/__init__.py | 9 +- py21cmsense/antpos.py | 14 +- py21cmsense/config.py | 3 - py21cmsense/conversions.py | 53 +- py21cmsense/observation.py | 14 +- py21cmsense/sensitivity.py | 67 +- 9 files changed, 567 insertions(+), 778 deletions(-) delete mode 100644 docs/tutorials/reproducing_pober_2014.ipynb create mode 100644 docs/tutorials/reproducing_pober_2015.ipynb diff --git a/docs/tutorials.rst b/docs/tutorials.rst index 7659d2b..d342de1 100644 --- a/docs/tutorials.rst +++ b/docs/tutorials.rst @@ -9,3 +9,4 @@ The following introductory tutorials will help you get started with ``21cmSense` tutorials/getting_started tutorials/understanding_21cmsense tutorials/cli_tutorial + tutorials/reproducing_pober_2015 diff --git a/docs/tutorials/reproducing_pober_2014.ipynb b/docs/tutorials/reproducing_pober_2014.ipynb deleted file mode 100644 index 536361f..0000000 --- a/docs/tutorials/reproducing_pober_2014.ipynb +++ /dev/null @@ -1,729 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Reproducing Pober+2014" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "In this demo, we are going to reproduce many of the results of [Pober+2014](https://arxiv.org/pdf/1310.7031.pdf), which looked at the sensitivity of a \"concept HERA\" (which differs somewhat from the final instrument), as well as a few other well-known arrays.\n", - "\n", - "The \"reproduction\" here is not expected to be exact -- there are several small tweaks made in the current version that are not present in the code used for the original paper, for example cosmological calculations are done with astropy instead of approximate fitting functions. Nevertheless, we show that the signal-to-noise estimates in any given $k$-bin are consistent to within 15% of the original." - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "import py21cmsense as p21c\n", - "from astropy import units as un\n", - "from astropy.cosmology.units import littleh\n", - "import matplotlib.pyplot as plt\n", - "import numpy as np" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "p21c.config.COSMO = p21c.config.COSMO.clone(H0=70.0, Om0=0.266)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## HERA" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "P14 used a concept version of HERA that was a perfect hexagon (with no splits), with 547\n", - "elements and no outriggers. Let's create such an array:" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "hera_ants = p21c.antpos.hera(hex_num=14)" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(547, 3)" - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "hera_ants.shape" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We will reproduce the results at $z=9.5$ (the default case in P14). Note that we use a dish size that is slightly \n", - "different that that of P14, because the important thing is to match the beam width (which scales linearly\n", - "with frequency):" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [], - "source": [ - "beam = p21c.GaussianBeam(frequency=1420*un.MHz/(1 + 9.5), dish_size=13.919*un.m)" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "data": { - "text/latex": [ - "$9.6497626 \\; \\mathrm{{}^{\\circ}}$" - ], - "text/plain": [ - "" - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "beam.fwhm.to(un.deg)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "What is the beam-crossing time? This is given as \"about 40min\" by P14. We obtain it as:" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "38.5990505880113" - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "beam.fwhm.to(un.deg).value*24*60/360" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "As a check on whether our GaussianBeam model is similar to the assumed beam in P14, \n", - "we check the FWHM at 150MHz, quoted as 8.7deg in Table 1 of P14:" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [ - { - "data": { - "text/latex": [ - "$8.7001035 \\; \\mathrm{{}^{\\circ}}$" - ], - "text/plain": [ - "" - ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "p21c.GaussianBeam(frequency=150*un.MHz, dish_size=13.919*un.m).fwhm.to(un.deg)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "This is very close to the quoted value." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now, let's create the HERA Observatory model. Here, we set the receiver temperature to 100K,\n", - "as per Table 1 of P14. We also set the latitude of the instrument to that of Green Bank (38:25:59.24),\n", - "which was the assumed location of HERA in P14:" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [], - "source": [ - "hera = p21c.Observatory(\n", - " antpos=hera_ants,\n", - " beam=beam,\n", - " latitude=0.6707845*un.rad,\n", - " Trcv=100*un.K,\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now, set up the observation itself. Here we assume a sky temperature model of \n", - "$351{\\rm K} (\\nu/150 {\\rm MHz})^{-2.55}$, as described in S2.1.2 of P14. \n", - "\n", - "Furthermore, we set the number of days of observation to 180, with 6 hours per night. \n", - "We also set the \"coherent observing time\" to its default of the beam crossing time, \n", - "which is about 40min as per the above calculation.\n", - "\n", - "Finally, as per S3.1.2 of P14, we use 8MHz of bandwidth, with 81 channels (providing\n", - "0.1 MHz channel width)." - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [], - "source": [ - "obs = p21c.Observation(\n", - " observatory=hera,\n", - " tsky_amplitude=351*un.K,\n", - " tsky_ref_freq=150*un.MHz,\n", - " spectral_index=2.55,\n", - " n_days=180,\n", - " time_per_day=6*un.hour,\n", - " bandwidth=8*un.MHz,\n", - " n_channels=81,\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "finding redundancies: 100%|██████████| 546/546 [00:03<00:00, 151.98ants/s]\n", - "computing UVWs: 100%|██████████| 39/39 [00:00<00:00, 137.66times/s]\n", - "gridding baselines: 100%|██████████| 2106/2106 [00:00<00:00, 13727.13baselines/s]\n" - ] - }, - { - "data": { - "text/plain": [ - "(53, 53)" - ] - }, - "execution_count": 11, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "obs.uv_coverage.shape" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [ - { - "data": { - "text/latex": [ - "$135.2381 \\; \\mathrm{MHz}$" - ], - "text/plain": [ - "" - ] - }, - "execution_count": 12, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "obs.frequency" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We make sure the observation duration is close to 40 minutes:" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [ - { - "data": { - "text/latex": [ - "$38.599051 \\; \\mathrm{min}$" - ], - "text/plain": [ - "" - ] - }, - "execution_count": 14, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "obs.obs_duration.to(un.min)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now construct the sensitivity calculation:" - ] - }, - { - "cell_type": "code", - "execution_count": 57, - "metadata": {}, - "outputs": [], - "source": [ - "sense_optimistic = p21c.PowerSpectrum(\n", - " observation=obs,\n", - " foreground_model='optimistic',\n", - " horizon_buffer=0.0 *littleh/un.Mpc,\n", - " theory_model=p21c.theory.Legacy21cmFAST(),\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 58, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "calculating 2D sensitivity: 100%|██████████| 955/955 [00:02<00:00, 385.85uv-bins/s]\n", - "averaging to 1D: 100%|██████████| 243/243 [00:01<00:00, 156.67kperp-bins/s]\n" - ] - } - ], - "source": [ - "sense1d = sense_optimistic.calculate_sensitivity_1d(thermal=True, sample=True)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Plot analogs of Fig. 5 from Pober+2014" - ] - }, - { - "cell_type": "code", - "execution_count": 59, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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- "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "snr_opt_pober14 = [\n", - " 0, 93.9, 66.5, 41.6, 27.1, 18.3, 12.7, 8.9, 6.4, 4.7, 3.6, 2.7, 2.2\n", - "]\n", - "\n", - "plt.figure(figsize=(15, 6))\n", - "plt.errorbar(\n", - " sense_optimistic.k1d, \n", - " sense_optimistic.delta_squared,\n", - " sense1d\n", - ")\n", - "plt.xlim(0.06, 1)\n", - "plt.ylim(10, 50)\n", - "plt.xscale('log')\n", - "plt.yscale('log')\n", - "\n", - "for i, kk in enumerate(sense_optimistic.k1d):\n", - " sig = sense_optimistic.delta_squared[i] / sense1d[i]\n", - " yloc = sense_optimistic.delta_squared[i].value - sense1d[i].value - 1\n", - " if sig > 1 and yloc > 10:\n", - " plt.text(kk.value, yloc, f\"{sig:.1f}\")\n", - " if i < len(snr_opt_pober14):\n", - " plt.text(kk.value, yloc/1.1, f\"({snr_opt_pober14[i]:.1f})\", color='C3')\n", - "plt.grid(which='both')\n", - "plt.xlabel(\"k [h/Mpc]\")\n", - "plt.ylabel(\"$\\Delta^2_{21}$\");" - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "metadata": {}, - "outputs": [], - "source": [ - "sense_moderate = sense_optimistic.clone(foreground_model='moderate', horizon_buffer=0.1 *littleh/un.Mpc)" - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "calculating 2D sensitivity: 100%|██████████| 955/955 [00:02<00:00, 369.65uv-bins/s]\n", - "averaging to 1D: 100%|██████████| 243/243 [00:01<00:00, 156.45kperp-bins/s]\n" - ] - } - ], - "source": [ - "sense1d_moderate = sense_moderate.calculate_sensitivity_1d(thermal=True, sample=True)" - ] - }, - { - "cell_type": "code", - "execution_count": 55, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/steven/work/eor/21cmSense/py21cmsense/theory.py:142: UserWarning: Extrapolating above the simulated theoretical k: 3.3749883159994045 > 2.192064\n", - " warnings.warn(\n" - ] - }, - { - "data": { - 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- "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "snr_mod_pober14 = [\n", - " 0, 0, 14.0, 19.3, 18.3, 15.5, 11.8, 8.7, 6.4, 4.7, 3.6, 2.7, 2.2\n", - "]\n", - "plt.figure(figsize=(15, 6))\n", - "\n", - "plt.errorbar(\n", - " sense_moderate.k1d, \n", - " sense_moderate.delta_squared,\n", - " sense1d_moderate\n", - ")\n", - "plt.xlim(0.06, 1)\n", - "plt.ylim(10, 50)\n", - "plt.xscale('log')\n", - "plt.yscale('log')\n", - "\n", - "for i, kk in enumerate(sense_moderate.k1d):\n", - " sig = sense_moderate.delta_squared[i] / sense1d_moderate[i]\n", - " yloc = sense_moderate.delta_squared[i].value - sense1d_moderate[i].value - 1\n", - " if sig > 1 and yloc > 10:\n", - " plt.text(kk.value, yloc, f\"{sig:.1f}\")\n", - " if i < len(snr_opt_pober14):\n", - " plt.text(kk.value, yloc/1.1, f\"({snr_mod_pober14[i]:.1f})\", color='C3')\n", - "\n", - "plt.grid(which='both')" - ] - }, - { - "cell_type": "code", - "execution_count": 27, - "metadata": {}, - "outputs": [], - "source": [ - "sense_pessimistic = sense_moderate.clone(\n", - " observation=obs.clone(coherent=False)\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 28, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "finding redundancies: 100%|██████████| 546/546 [00:03<00:00, 163.78ants/s]\n", - "computing UVWs: 100%|██████████| 39/39 [00:00<00:00, 134.80times/s]\n", - "gridding baselines: 100%|██████████| 2106/2106 [00:00<00:00, 14008.20baselines/s]\n", - "calculating 2D sensitivity: 100%|██████████| 955/955 [00:02<00:00, 390.69uv-bins/s]\n", - "averaging to 1D: 100%|██████████| 243/243 [00:01<00:00, 158.63kperp-bins/s]\n" - ] - } - ], - "source": [ - "sense1d_pess = sense_pessimistic.calculate_sensitivity_1d(thermal=True, sample=True)" - ] - }, - { - "cell_type": "code", - "execution_count": 56, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/steven/work/eor/21cmSense/py21cmsense/theory.py:142: UserWarning: Extrapolating above the simulated theoretical k: 3.3749883159994045 > 2.192064\n", - " warnings.warn(\n" - ] - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "snr_pess_pober14 = [\n", - " 0, 0, 13.3, 17.1, 15.3, 12.3, 9.0, 6.5, 4.7, 3.4, 2.6, 2.0\n", - "]\n", - "plt.figure(figsize=(15, 6))\n", - "\n", - "plt.errorbar(\n", - " sense_pessimistic.k1d, \n", - " sense_pessimistic.delta_squared,\n", - " sense1d_pess\n", - ")\n", - "plt.xlim(0.06, 1)\n", - "plt.ylim(10, 50)\n", - "plt.xscale('log')\n", - "plt.yscale('log')\n", - "\n", - "for i, kk in enumerate(sense_pessimistic.k1d):\n", - " sig = sense_pessimistic.delta_squared[i] / sense1d_pess[i]\n", - " yloc = sense_pessimistic.delta_squared[i].value - sense1d_pess[i].value - 1\n", - " if sig > 1 and yloc > 10:\n", - " plt.text(kk.value, yloc, f\"{sig:.1f}\")\n", - " if i < len(snr_opt_pober14):\n", - " plt.text(kk.value, yloc/1.1, f\"({snr_pess_pober14[i]:.1f})\", color='C3')\n", - "\n", - "plt.grid(which='both')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "It seems that the default implementation of the new 21cmSense is everywhere a little less\n", - "significant than the original implementation. It is unclear exactly where this difference\n", - "comes from, but there are lots of small differences in the implementations -- of which the\n", - "newer version is typically *more* accurate. In any case, the overall significance levels\n", - "are comparable, and certainly have the same *shape* as the original." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Tests" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "From here, we are simply performing some tests to ensure that the results stay \n", - "roughly consistent (within 15%) as new features/fixes are added." - ] - }, - { - "cell_type": "code", - "execution_count": 35, - "metadata": {}, - "outputs": [], - "source": [ - "snr_pess = sense_pessimistic.delta_squared / sense1d_pess\n", - "if not np.allclose(snr_pess[:len(snr_pess_pober14)], snr_pess_pober14, atol=0, rtol=0.15):\n", - " raise ValueError(\"The SNR for the pessimistic case has changed by more than 30%!\")\n", - "\n", - "if not np.all(np.diff(snr_pess)[len(snr_pess_pober14):]<=0):\n", - " raise ValueError(\"The SNR for the pessimistic case is increasing at high k :/\")" - ] - }, - { - "cell_type": "code", - "execution_count": 42, - "metadata": {}, - "outputs": [], - "source": [ - "snr_mod = sense_moderate.delta_squared / sense1d_moderate\n", - "if not np.allclose(snr_mod[:len(snr_mod_pober14)],snr_mod_pober14, atol=0, rtol=0.15):\n", - " raise ValueError(\"The SNR for the moderate case has changed by more than 15%!\")\n", - "\n", - "if not np.all(np.diff(snr_mod)[len(snr_mod_pober14):]<=0):\n", - " raise ValueError(\"The SNR for the moderate case is increasing at high k :/\")" - ] - }, - { - "cell_type": "code", - "execution_count": 67, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/steven/work/eor/21cmSense/py21cmsense/theory.py:142: UserWarning: Extrapolating above the simulated theoretical k: 3.3749883159994045 > 2.192064\n", - " warnings.warn(\n" - ] - } - ], - "source": [ - "snr_opt = sense_optimistic.delta_squared / sense1d\n", - "if not np.allclose(snr_opt[1:len(snr_opt_pober14)],snr_opt_pober14[1:], atol=0, rtol=0.15):\n", - " raise ValueError(\"The SNR for the optimistic case has changed by more than 15%!\")\n", - "\n", - "if not np.all(np.diff(snr_opt)[len(snr_opt_pober14)+1:]<=0):\n", - " raise ValueError(\"The SNR for the optimistic case is increasing at high k :/\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Also make sure that opt is better than moderate is better than pessimistic:" - ] - }, - { - "cell_type": "code", - "execution_count": 45, - "metadata": {}, - "outputs": [], - "source": [ - "if not np.all(snr_opt >= snr_mod):\n", - " raise ValueError(\"snr_opt must be better than snr_mod!\")\n", - "\n", - "if not np.all(snr_mod >= snr_pess):\n", - " raise ValueError(\"snr_mod must be better than snr_pess!\")\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Make sure we have the same \"shape\" as the original:" - ] - }, - { - "cell_type": "code", - "execution_count": 70, - "metadata": {}, - "outputs": [], - "source": [ - "if not np.all(np.sign(np.diff(snr_pess_pober14))==np.sign(np.diff(snr_pess[:len(snr_pess_pober14)]))):\n", - " raise ValueError(\"pessimistic case has different shape to Pober+14\")\n", - "\n", - "if not np.all(np.sign(np.diff(snr_mod_pober14))==np.sign(np.diff(snr_mod[:len(snr_mod_pober14)]))):\n", - " raise ValueError(\"moderate case has different shape to Pober+14\")\n", - "\n", - "if not np.all(np.sign(np.diff(snr_opt_pober14[1:]))==np.sign(np.diff(snr_opt[1:len(snr_opt_pober14)]))):\n", - " raise ValueError(\"optimistic case has different shape to Pober+14\")\n", - "\n" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3.10.6 ('sense')", - "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": "3.10.6" - }, - "orig_nbformat": 4, - "vscode": { - "interpreter": { - "hash": "6fa29f5e4f57cd8c712524a97e07d2162cf815d6c48daa40d8ca34da05a68238" - } - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/docs/tutorials/reproducing_pober_2015.ipynb b/docs/tutorials/reproducing_pober_2015.ipynb new file mode 100644 index 0000000..4dd0a29 --- /dev/null +++ b/docs/tutorials/reproducing_pober_2015.ipynb @@ -0,0 +1,455 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Reproducing Pober+2014" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In this demo, we are going to reproduce the results of \n", + "[Pober2015](http://reionization.org/wp-content/uploads/2015/05/HERA4_sensecalc.pdf), a \n", + "memo which estimated the sensitivity of the HERA array in its ultimate configuration. \n", + "This memo was based on [Pober+2014](https://arxiv.org/pdf/1310.7031.pdf), which looked \n", + "at the sensitivity of a \"concept HERA\" (which differs somewhat from the final \n", + "instrument), as well as a few other well-known arrays.\n", + "\n", + "The purpose of the demo is firstly to show how to estimate the sensitivity of a \n", + "realistic array in a realistic situation. Secondly, it is a regression test, to ensure\n", + "that the results given by the new code are consistent with the original code used in\n", + "that memo.\n", + "\n", + "The \"reproduction\" here is not expected to be exact -- there are several small tweaks \n", + "made in the current version that are not present in the code used for the memo (not to \n", + "mention the code used for Pober+2014, which was not released at the time), for example \n", + "cosmological calculations are done with astropy instead of approximate fitting \n", + "functions. Nevertheless, we expect reasonable consistency when the settings are \n", + "tuned to match the original code. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + "\n", + "A Note On The Source Data\n", + "\n", + "The data to which we compare in this notebook is precisely that used in Pober15. In that\n", + "memo, the thermal noise estimates are not presented directly (either as a table or\n", + "in plots), and only the ultimate SNR of the entire dataset is shown. We sourced the data\n", + "for this notebook by installing and running the original code used for the memo with \n", + "identical settings to those presented in the memo. This cannot be repeated easily\n", + "since the original code requires Python <= 2. We include the resulting thermal \n", + "sensitivities in this repo for posterity.\n", + "\n", + "
" + ] + }, + { + "cell_type": "code", + "execution_count": 73, + "metadata": {}, + "outputs": [], + "source": [ + "import py21cmsense as p21c\n", + "from astropy import units as un\n", + "from astropy.cosmology.units import littleh\n", + "from astropy.cosmology import Planck15\n", + "from astropy import constants\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Original Data" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "memo_data = np.load('data/hera331drift_mod_0.135.npz')" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.plot(memo_data['ks'], memo_data['T_errs'], label='Thermal Variance')\n", + "plt.plot(memo_data['ks'], memo_data['errs'], label='Sample+Thermal Variance')\n", + "plt.yscale(\"log\")\n", + "plt.title(\"Original Variance from Pober15\")\n", + "plt.ylabel(r\"$\\Delta^2_{21}$ [mK$^2$]\")\n", + "plt.xlabel(\"k [h/Mpc]\");" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## HERA" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "P15 used a concept version of HERA that was a perfect hexagon (with no split-core), \n", + "with 331 elements and no outriggers. It also used an approximation for the separation\n", + "between hexagon rows of 12.12 m, which is close (but not exactly) the same as\n", + "$14.0 \\sqrt{3}/2$. However, this makes almost no difference to the result, so we leave\n", + "the array to be more accurate, with 60 degree angles between antennas on each row. \n", + "Let's create such an array:" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [], + "source": [ + "hera_ants_p14 = p21c.antpos.hera(hex_num=11, row_separation=12.12*un.m)\n", + "hera_ants = p21c.antpos.hera(hex_num=11)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [], + "source": [ + "assert hera_ants.shape == (331, 3)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We will reproduce the results at $\\nu=135$ MHz (the default case in P15). \n", + "Like P15, we use a dish size of *7 wavelengths at 150 MHz*, which is close to, but not\n", + "exactly, 14 m:" + ] + }, + { + "cell_type": "code", + "execution_count": 77, + "metadata": {}, + "outputs": [], + "source": [ + "beam = p21c.GaussianBeam(frequency=135*un.MHz, dish_size=7 * (constants.c / (150 * un.MHz)).to(\"m\"))" + ] + }, + { + "cell_type": "code", + "execution_count": 79, + "metadata": {}, + "outputs": [ + { + "data": { + "text/latex": [ + "$13.990315 \\; \\mathrm{m}$" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 79, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "beam.dish_size" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now, let's create the HERA Observatory model. Here, we set the receiver temperature to 100K,\n", + "as per Table 1 of P14. We also set the latitude of the instrument to that of Green Bank (38:25:59.24),\n", + "which was the assumed location of HERA in P14 & P15:" + ] + }, + { + "cell_type": "code", + "execution_count": 80, + "metadata": {}, + "outputs": [], + "source": [ + "hera = p21c.Observatory(\n", + " antpos=hera_ants,\n", + " beam=beam,\n", + " latitude=0.6707845*un.rad,\n", + " Trcv=100*un.K,\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now, set up the observation itself. Here we assume a sky temperature model of \n", + "$60{\\rm K} (\\nu/300 {\\rm MHz})^{-2.6}$, which is what P15 used (though there is \n", + "no record of it in that memo). \n", + "\n", + "Furthermore, we set the number of days of observation to 180, with 6 hours per night, \n", + "as described in that memo. \n", + "\n", + "The coherent observation time in P15 is the \"beam crossing time\", i.e. the size\n", + "of the FWHM of the beam, *at 150 MHz*. In the modern version of the code, the observation\n", + "duration is taken to be the FWHM at the *observation* frequency. It is arguable which\n", + "of these choices is better, but we set the observation duration to be consistent with\n", + "P15 here.\n", + "\n", + "Finally, as per S3.1.2 of P14, we use 8MHz of bandwidth, with 82 channels (providing\n", + "~0.1 MHz channel width)." + ] + }, + { + "cell_type": "code", + "execution_count": 119, + "metadata": {}, + "outputs": [], + "source": [ + "obs = p21c.Observation(\n", + " observatory=hera,\n", + " tsky_amplitude=60*un.K,\n", + " tsky_ref_freq=300*un.MHz,\n", + " spectral_index=2.6,\n", + " n_days=180,\n", + " time_per_day=6*un.hour,\n", + " bandwidth=8*un.MHz,\n", + " n_channels=82,\n", + " integration_time=10.7*un.s, #2077.38*un.s,\n", + " obs_duration=beam.at(150*un.MHz).fwhm.value * 12/np.pi * 3600*un.s,\n", + " use_approximate_cosmo=True,\n", + " cosmo=Planck15.clone(H0=70.0, Om0=0.266)\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 120, + "metadata": {}, + "outputs": [ + { + "data": { + "text/latex": [ + "$2077.3813 \\; \\mathrm{s}$" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 120, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "obs.obs_duration.to(un.s)" + ] + }, + { + "cell_type": "code", + "execution_count": 121, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "finding redundancies: 100%|██████████| 330/330 [00:01<00:00, 290.12ants/s]\n", + "computing UVWs: 100%|██████████| 195/195 [00:01<00:00, 159.85times/s]\n", + "gridding baselines: 100%|██████████| 1260/1260 [00:00<00:00, 14659.17baselines/s]\n" + ] + }, + { + "data": { + "text/plain": [ + "(41, 41)" + ] + }, + "execution_count": 121, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "obs.uv_coverage.shape" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now construct the sensitivity calculation. Here we use the \"moderate\" foreground model,\n", + "with a horizon buffer of 0.1 h/Mpc, as mentioned in P15. The `Legacy21cmFAST` theory \n", + "model is the same model used in the original code." + ] + }, + { + "cell_type": "code", + "execution_count": 122, + "metadata": {}, + "outputs": [], + "source": [ + "sense_moderate = p21c.PowerSpectrum(\n", + " observation=obs,\n", + " foreground_model='moderate',\n", + " horizon_buffer=0.1 *littleh/un.Mpc,\n", + " theory_model=p21c.theory.Legacy21cmFAST(),\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 123, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "calculating 2D sensitivity: 100%|██████████| 570/570 [00:00<00:00, 577.58uv-bins/s]\n", + "averaging to 1D: 100%|██████████| 159/159 [00:01<00:00, 158.59kperp-bins/s]\n", + "averaging to 1D: 100%|██████████| 159/159 [00:01<00:00, 150.36kperp-bins/s]\n" + ] + } + ], + "source": [ + "sense1d = sense_moderate.calculate_sensitivity_1d(thermal=True, sample=True)\n", + "sense1d_t = sense_moderate.calculate_sensitivity_1d(thermal=True, sample=False)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Plot the resulting comparison" + ] + }, + { + "cell_type": "code", + "execution_count": 124, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_15498/2451292649.py:12: RuntimeWarning: invalid value encountered in subtract\n", + " ax[1].plot(memo_data['ks'], 100*(sense1d[:len(memo_data['ks'])].value- memo_data['errs'])/memo_data['errs'])\n", + "/tmp/ipykernel_15498/2451292649.py:13: RuntimeWarning: invalid value encountered in subtract\n", + " ax[1].plot(memo_data['ks'], 100*(sense1d_t[:len(memo_data['ks'])].value- memo_data['T_errs'])/memo_data['T_errs'])\n" + ] + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots(2, 1, sharex=True, figsize=(12, 6), constrained_layout=True, gridspec_kw={\"height_ratios\": [0.65, 0.35]})\n", + "ax[0].scatter(sense_moderate.k1d, sense1d, label='Modern 21cmSense', marker='x', color='C0')\n", + "ax[0].scatter(memo_data['ks'], memo_data['errs'], label='memo', color='C0', lw=1, facecolor='none')\n", + "\n", + "ax[0].scatter(sense_moderate.k1d, sense1d_t, label='Modern 21cmSense (Thermal Only)', marker='x', color='C1')\n", + "ax[0].scatter(memo_data['ks'], memo_data['T_errs'], label='memo (thermal Only)', color='C1', facecolor='none')\n", + "\n", + "ax[0].set_yscale(\"log\")\n", + "ax[0].set_xscale('log')\n", + "ax[0].set_ylabel(r\"$\\Delta^2$ [mK$^2$]\")\n", + "ax[1].set_ylabel(\"Fractional Difference (%)\")\n", + "ax[1].plot(memo_data['ks'], 100*(sense1d[:len(memo_data['ks'])].value- memo_data['errs'])/memo_data['errs'])\n", + "ax[1].plot(memo_data['ks'], 100*(sense1d_t[:len(memo_data['ks'])].value- memo_data['T_errs'])/memo_data['T_errs'])\n", + "ax[0].legend();\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Tests" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The following are just assertions that the code reproduces the memo data within some\n", + "tolerances, as can be seen already by the above plots. This is just for CI testing \n", + "purposes." + ] + }, + { + "cell_type": "code", + "execution_count": 129, + "metadata": {}, + "outputs": [], + "source": [ + "mask = ~np.isinf(memo_data['errs'])\n", + "\n", + "assert np.allclose(sense1d[:len(memo_data['ks'])][mask][:-1].value, memo_data['errs'][mask][:-1], rtol=1e-3)\n", + "assert np.allclose(sense1d_t[:len(memo_data['ks'])][mask][:-1].value, memo_data['T_errs'][mask][:-1], rtol=5e-3)\n" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3.10.6 ('sense')", + "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": "3.10.6" + }, + "orig_nbformat": 4, + "vscode": { + "interpreter": { + "hash": "6fa29f5e4f57cd8c712524a97e07d2162cf815d6c48daa40d8ca34da05a68238" + } + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/py21cmsense/__init__.py b/py21cmsense/__init__.py index da14e0b..b4937ff 100644 --- a/py21cmsense/__init__.py +++ b/py21cmsense/__init__.py @@ -1,5 +1,12 @@ """A package for calculate sensitivies of 21-cm interferometers.""" -__version__ = "2.0.0.beta" +from pkg_resources import DistributionNotFound, get_distribution + +try: + __version__ = get_distribution("21cmSense").version +except DistributionNotFound: + __version__ = "unknown" +finally: + del get_distribution, DistributionNotFound from . import yaml from .antpos import hera diff --git a/py21cmsense/antpos.py b/py21cmsense/antpos.py index 50517fb..32500df 100644 --- a/py21cmsense/antpos.py +++ b/py21cmsense/antpos.py @@ -19,6 +19,7 @@ def hera( separation: tp.Length = 14 * un.m, split_core: bool = False, outriggers: bool = False, + row_separation: tp.Length | None = None, ) -> tp.Meters: """ Produce a simple regular hexagonal array. @@ -45,18 +46,23 @@ def hera( """ sep = separation.to_value("m") + if row_separation is None: + row_sep = sep * np.sqrt(3) / 2 + else: + row_sep = row_separation.to_value("m") + # construct the main hexagon positions = [] for row in range(hex_num - 1, -hex_num + split_core, -1): # adding split_core deletes a row if it's true for col in range(2 * hex_num - abs(row) - 1): x_pos = sep * ((2 - (2 * hex_num - abs(row))) / 2 + col) - y_pos = row * sep * np.sqrt(3) / 2 + y_pos = row * row_sep positions.append([x_pos, y_pos, 0]) # basis vectors (normalized to sep) - up_right = sep * np.asarray([0.5, np.sqrt(3) / 2, 0]) - up_left = sep * np.asarray([-0.5, np.sqrt(3) / 2, 0]) + up_right = sep * np.asarray([0.5, row_sep / sep, 0]) + up_left = sep * np.asarray([-0.5, row_sep / sep, 0]) # split the core if desired if split_core: @@ -90,7 +96,7 @@ def hera( * sep * (hex_num - 1) ) - y_pos = row * sep * (hex_num - 1) * np.sqrt(3) / 2 + y_pos = row * (hex_num - 1) * row_sep theta = np.arctan2(y_pos, x_pos) if np.sqrt(x_pos**2 + y_pos**2) > sep * (hex_num + 1): if 0 < theta <= 2 * np.pi / 3 + 0.01: diff --git a/py21cmsense/config.py b/py21cmsense/config.py index 9b3db4c..3d2aa98 100644 --- a/py21cmsense/config.py +++ b/py21cmsense/config.py @@ -1,5 +1,2 @@ """Some global configuration options for 21cmSense.""" -from astropy.cosmology import Planck15 as _Planck15 - PROGRESS = True # whether to display progress bars for some calculations. -COSMO = _Planck15 diff --git a/py21cmsense/conversions.py b/py21cmsense/conversions.py index 6768576..b81506b 100644 --- a/py21cmsense/conversions.py +++ b/py21cmsense/conversions.py @@ -51,7 +51,9 @@ def z2f(z: Union[float, np.array]) -> un.Quantity[un.GHz]: def dL_dth( - z: Union[float, np.array], cosmo: FLRW = Planck15 + z: Union[float, np.array], + cosmo: FLRW = Planck15, + approximate=False, ) -> un.Quantity[un.Mpc / un.rad / littleh]: """ Return the factor to convert radians to transverse distance at redshift z. @@ -70,11 +72,20 @@ def dL_dth( ----- From Furlanetto et al. (2006) """ - return cosmo.h * cosmo.comoving_transverse_distance(z) / un.rad / littleh + if approximate: + return ( + (1.9 * (1.0 / un.arcmin) * ((1 + z) / 10.0) ** 0.2).to(1 / un.rad) + * un.Mpc + / littleh + ) + else: + return cosmo.h * cosmo.comoving_transverse_distance(z) / un.rad / littleh def dL_df( - z: Union[float, np.array], cosmo: FLRW = Planck15 + z: Union[float, np.array], + cosmo: FLRW = Planck15, + approximate=False, ) -> un.Quantity[un.Mpc / un.MHz / littleh]: """ Get the factor to convert bandwidth to line-of-sight distance in Mpc/h. @@ -84,13 +95,25 @@ def dL_df( z : float The redshift """ - return (cosmo.h * cnst.c * (1 + z) / (z2f(z) * cosmo.H(z) * littleh)).to( - "Mpc/(MHz*littleh)" - ) + if approximate: + return ( + (1.7 / 0.1) + * ((1 + z) / 10.0) ** 0.5 + * (cosmo.Om0 / 0.15) ** -0.5 + * un.Mpc + / littleh + / un.MHz + ) + else: + return (cosmo.h * cnst.c * (1 + z) / (z2f(z) * cosmo.H(z) * littleh)).to( + "Mpc/(MHz*littleh)" + ) def dk_du( - z: Union[float, np.array], cosmo: FLRW = Planck15 + z: Union[float, np.array], + cosmo: FLRW = Planck15, + approximate=False, ) -> un.Quantity[littleh / un.Mpc]: """ Get factor converting bl length in wavelengths to h/Mpc. @@ -105,11 +128,13 @@ def dk_du( Valid for u >> 1 """ # from du = 1/dth, which derives from du = d(sin(th)) using the small-angle approx - return 2 * np.pi / dL_dth(z, cosmo) / un.rad + return 2 * np.pi / dL_dth(z, cosmo, approximate=approximate) / un.rad def dk_deta( - z: Union[float, np.array], cosmo: FLRW = Planck15 + z: Union[float, np.array], + cosmo: FLRW = Planck15, + approximate=False, ) -> un.Quantity[un.MHz * littleh / un.Mpc]: """ Get gactor converting inverse frequency to inverse distance. @@ -119,11 +144,13 @@ def dk_deta( z: float Redshift """ - return 2 * np.pi / dL_df(z, cosmo) + return 2 * np.pi / dL_df(z, cosmo, approximate=approximate) def X2Y( - z: Union[float, np.array], cosmo: FLRW = Planck15 + z: Union[float, np.array], + cosmo: FLRW = Planck15, + approximate=False, ) -> un.Quantity[un.Mpc**3 / littleh**3 / un.steradian / un.GHz]: """ Obtain the conversion factor between observing co-ordinates and cosmological volume. @@ -139,4 +166,6 @@ def X2Y( ------- astropy.Quantity: the conversion factor. Units are Mpc^3/h^3 / (sr MHz). """ - return dL_dth(z, cosmo) ** 2 * dL_df(z, cosmo) + return dL_dth(z, cosmo, approximate=approximate) ** 2 * dL_df( + z, cosmo, approximate=approximate + ) diff --git a/py21cmsense/observation.py b/py21cmsense/observation.py index 6337356..0785780 100755 --- a/py21cmsense/observation.py +++ b/py21cmsense/observation.py @@ -5,6 +5,7 @@ import collections import numpy as np from astropy import units as un +from astropy.cosmology import LambdaCDM, Planck15 from astropy.io.misc import yaml from attr import validators as vld from collections import defaultdict @@ -79,6 +80,10 @@ class Observation: tsky_ref_freq : float or Quantity Frequency at which the foreground model is equal to `tsky_amplitude`. See `spectral_index`. Default assumed to be in MHz. + use_approximate_cosmo : bool + Whether to use approximate cosmological conversion factors. Doing so will give + the same results as the original 21cmSense code, but non-approximate versions + that use astropy are preferred. """ observatory: obs.Observatory = attr.ib(validator=vld.instance_of(obs.Observatory)) @@ -124,6 +129,8 @@ class Observation: validator=ut.nonnegative, ) tsky_ref_freq: tp.Frequency = attr.ib(default=150 * un.MHz, validator=ut.positive) + use_approximate_cosmo: bool = attr.ib(default=False, converter=bool) + cosmo: LambdaCDM = attr.ib(default=Planck15) @classmethod def from_yaml(cls, yaml_file): @@ -283,7 +290,12 @@ def kparallel(self) -> un.Quantity[un.littleh / un.Mpc]: Order of the values is the same as `fftfreq` (i.e. zero-first) """ - return conv.dk_deta(self.redshift, config.COSMO) * self.eta + return ( + conv.dk_deta( + self.redshift, self.cosmo, approximate=self.use_approximate_cosmo + ) + * self.eta + ) @cached_property def total_integration_time(self) -> un.Quantity[un.s]: diff --git a/py21cmsense/sensitivity.py b/py21cmsense/sensitivity.py index d98d9f2..7447524 100644 --- a/py21cmsense/sensitivity.py +++ b/py21cmsense/sensitivity.py @@ -17,6 +17,7 @@ import numpy as np import tqdm from astropy import units as un +from astropy.cosmology import LambdaCDM from astropy.cosmology.units import littleh, with_H0 from astropy.io.misc import yaml from attr import validators as vld @@ -35,16 +36,6 @@ from . import types as tp from .theory import _ALL_THEORY_POWER_SPECTRA, EOS2021, TheoryModel - -def _kconverter(val, allow_unitless=False): - if hasattr(val, "unit"): - return val.to(littleh / un.Mpc, with_H0(config.COSMO.H0)) - if not allow_unitless: - raise ValueError("no units supplied!") - # Assume it has the 1/Mpc units (default from 21cmFAST) - return (val / un.Mpc).to(littleh / un.Mpc, with_H0(config.COSMO.H0)) - - logger = logging.getLogger(__name__) @@ -107,6 +98,11 @@ def clone(self, **kwargs): """Clone the object with new parameters.""" return attr.evolve(self, **kwargs) + @property + def cosmo(self) -> LambdaCDM: + """The cosmology to use in the sensitivity calculations.""" + return self.observation.cosmo + @attr.s(kw_only=True) class PowerSpectrum(Sensitivity): @@ -138,16 +134,10 @@ class PowerSpectrum(Sensitivity): A function that takes a single kperp and an array of kpar, and returns a boolean array specifying which of the k's are useable after accounting for systematics. that is, it returns False for k's affected by systematics. + """ - horizon_buffer: tp.Wavenumber = attr.ib( - default=0.1 * littleh / un.Mpc, - validator=( - tp.vld_unit(littleh / un.Mpc, with_H0(config.COSMO.H0)), - ut.nonnegative, - ), - converter=_kconverter, - ) + horizon_buffer: tp.Wavenumber = attr.ib(default=0.1 * littleh / un.Mpc) foreground_model: str = attr.ib( default="moderate", validator=vld.in_(["moderate", "optimistic"]) ) @@ -155,6 +145,11 @@ class PowerSpectrum(Sensitivity): systematics_mask: Callable | None = attr.ib(None) + @horizon_buffer.validator + def _horizon_buffer_validator(self, att, val): + tp.vld_unit(littleh / un.Mpc, with_H0(self.cosmo.H0))(self, att, val) + ut.nonnegative(self, att, val) + @classmethod def from_yaml(cls, yaml_file) -> Sensitivity: """ @@ -202,7 +197,11 @@ def _theory_model_validator(self, att, val): def k1d(self) -> tp.Wavenumber: """1D array of wavenumbers for which sensitivities will be generated.""" delta = ( - conv.dk_deta(self.observation.redshift, config.COSMO) + conv.dk_deta( + self.observation.redshift, + self.cosmo, + approximate=self.observation.use_approximate_cosmo, + ) / self.observation.bandwidth ) dv = delta.value @@ -211,7 +210,10 @@ def k1d(self) -> tp.Wavenumber: @cached_property def X2Y(self) -> un.Quantity[un.Mpc**3 / littleh**3 / un.steradian / un.GHz]: """Cosmological scaling factor X^2*Y (eg. Parsons 2012).""" - return conv.X2Y(self.observation.redshift) + return conv.X2Y( + self.observation.redshift, + approximate=self.observation.use_approximate_cosmo, + ) @cached_property def uv_coverage(self) -> np.ndarray: @@ -232,7 +234,8 @@ def uv_coverage(self) -> np.ndarray: def power_normalisation(self, k: tp.Wavenumber) -> float: """Normalisation constant for power spectrum.""" - k = _kconverter(k) + assert hasattr(k, "unit") + assert k.unit.is_equivalent(littleh / un.Mpc) return ( self.X2Y @@ -254,7 +257,7 @@ def sample_noise(self, k_par: tp.Wavenumber, k_perp: tp.Wavenumber) -> tp.Delta: """Sample variance contribution at a particular k mode.""" k = np.sqrt(k_par**2 + k_perp**2).to_value( littleh / un.Mpc if self.theory_model.use_littleh else un.Mpc**-1, - with_H0(config.COSMO.H0), + with_H0(self.cosmo.H0), ) return self.theory_model.delta_squared(self.observation.redshift, k) @@ -282,7 +285,11 @@ def _nsamples_2d( continue umag = np.sqrt(u**2 + v**2) - k_perp = umag * conv.dk_du(self.observation.redshift, config.COSMO) + k_perp = umag * conv.dk_du( + self.observation.redshift, + self.cosmo, + approximate=self.observation.use_approximate_cosmo, + ) hor = self.horizon_limit(umag) @@ -437,7 +444,11 @@ def horizon_limit(self, umag: float) -> tp.Wavenumber: Horizon limit, in h/Mpc. """ horizon = ( - conv.dk_deta(self.observation.redshift, config.COSMO) + conv.dk_deta( + self.observation.redshift, + self.cosmo, + approximate=self.observation.use_approximate_cosmo, + ) * umag / self.observation.frequency ) @@ -506,7 +517,7 @@ def delta_squared(self) -> tp.Delta: """The fiducial 21cm power spectrum evaluated at :attr:`k1d`.""" k = self.k1d.to_value( littleh / un.Mpc if self.theory_model.use_littleh else un.Mpc**-1, - with_H0(config.COSMO.H0), + with_H0(self.cosmo.H0), ) return self.theory_model.delta_squared(self.observation.redshift, k) @@ -600,7 +611,7 @@ def write( fl[k] = v fl[k.replace("noise", "snr")] = self.delta_squared / v - fl["k"] = self.k1d.to("1/Mpc", with_H0(config.COSMO.H0)).value + fl["k"] = self.k1d.to("1/Mpc", with_H0(self.cosmo.H0)).value fl["delta_squared"] = self.delta_squared fl.attrs["total_snr"] = self.calculate_significance() @@ -622,8 +633,8 @@ def plot_sense_1d(self, sample: bool = True, thermal: bool = True): plt.plot(self.k1d, value, label=key) plt.xscale("log") plt.yscale("log") - plt.xlabel("k [1/Mpc]") - plt.ylabel(r"$\Delta^2_N \ [{\rm mK}^2/{\rm Mpc}^3$") + plt.xlabel("k [h/Mpc]") + plt.ylabel(r"$\Delta^2_N \ [{\rm mK}^2$") plt.legend() plt.title(f"z={conv.f2z(self.observation.frequency):.2f}") From 1a1ffce33e65efbd004cc1e33a6a2ac9e100aca4 Mon Sep 17 00:00:00 2001 From: Steven Murray Date: Tue, 25 Jul 2023 16:33:59 -0700 Subject: [PATCH 14/22] docs: update descriptions of some parameter settings --- docs/tutorials/reproducing_pober_2015.ipynb | 88 +++++++++++---------- 1 file changed, 48 insertions(+), 40 deletions(-) diff --git a/docs/tutorials/reproducing_pober_2015.ipynb b/docs/tutorials/reproducing_pober_2015.ipynb index 4dd0a29..3025ce6 100644 --- a/docs/tutorials/reproducing_pober_2015.ipynb +++ b/docs/tutorials/reproducing_pober_2015.ipynb @@ -4,7 +4,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "# Reproducing Pober+2014" + "# Reproducing HERA Memo 4" ] }, { @@ -52,7 +52,7 @@ }, { "cell_type": "code", - "execution_count": 73, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ @@ -74,7 +74,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 3, "metadata": {}, "outputs": [], "source": [ @@ -83,7 +83,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 4, "metadata": {}, "outputs": [ { @@ -120,24 +120,21 @@ "P15 used a concept version of HERA that was a perfect hexagon (with no split-core), \n", "with 331 elements and no outriggers. It also used an approximation for the separation\n", "between hexagon rows of 12.12 m, which is close (but not exactly) the same as\n", - "$14.0 \\sqrt{3}/2$. However, this makes almost no difference to the result, so we leave\n", - "the array to be more accurate, with 60 degree angles between antennas on each row. \n", - "Let's create such an array:" + "$14.0 \\sqrt{3}/2$. Let's create such an array:" ] }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 5, "metadata": {}, "outputs": [], "source": [ - "hera_ants_p14 = p21c.antpos.hera(hex_num=11, row_separation=12.12*un.m)\n", - "hera_ants = p21c.antpos.hera(hex_num=11)\n" + "hera_ants = p21c.antpos.hera(hex_num=11, row_separation=12.12*un.m)" ] }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 6, "metadata": {}, "outputs": [], "source": [ @@ -155,7 +152,7 @@ }, { "cell_type": "code", - "execution_count": 77, + "execution_count": 7, "metadata": {}, "outputs": [], "source": [ @@ -164,7 +161,7 @@ }, { "cell_type": "code", - "execution_count": 79, + "execution_count": 8, "metadata": {}, "outputs": [ { @@ -176,7 +173,7 @@ "" ] }, - "execution_count": 79, + "execution_count": 8, "metadata": {}, "output_type": "execute_result" } @@ -196,7 +193,7 @@ }, { "cell_type": "code", - "execution_count": 80, + "execution_count": 9, "metadata": {}, "outputs": [], "source": [ @@ -214,16 +211,27 @@ "source": [ "Now, set up the observation itself. Here we assume a sky temperature model of \n", "$60{\\rm K} (\\nu/300 {\\rm MHz})^{-2.6}$, which is what P15 used (though there is \n", - "no record of it in that memo). \n", + "no record of it in that memo, and this is slightly different from the sky model\n", + "reference in Pober+14). \n", "\n", "Furthermore, we set the number of days of observation to 180, with 6 hours per night, \n", "as described in that memo. \n", "\n", "The coherent observation time in P15 is the \"beam crossing time\", i.e. the size\n", "of the FWHM of the beam, *at 150 MHz*. In the modern version of the code, the observation\n", - "duration is taken to be the FWHM at the *observation* frequency. It is arguable which\n", - "of these choices is better, but we set the observation duration to be consistent with\n", - "P15 here.\n", + "duration is taken to be the FWHM at the *observation* frequency. For the sake of this\n", + "comparison, we scale the observation duration back to the value at 150 MHz. \n", + "\n", + "The integration time in 21cmSense plays a negligible role to first order: it controls\n", + "how many times the UVWs are phased within the observation duration. The baselines are\n", + "gridded in the UVW plane using a delta-function kernel (i.e. nearest-neighbours), and\n", + "so changing the integration time merely changes how resolved their evolution across \n", + "the UV-plane is. In the original 21cmSense, this value was hard-coded to 1 minute, \n", + "so we set this value here.\n", + "\n", + "**Note:** the phasing of UVWs has received a significant accuracy upgrade in the new\n", + "21cmSense, as we have moved to a pyuvdata-backed method. This will cause some small \n", + "differences in the final sensitivity (sub-percent level).\n", "\n", "Finally, as per S3.1.2 of P14, we use 8MHz of bandwidth, with 82 channels (providing\n", "~0.1 MHz channel width)." @@ -231,7 +239,7 @@ }, { "cell_type": "code", - "execution_count": 119, + "execution_count": 10, "metadata": {}, "outputs": [], "source": [ @@ -244,7 +252,7 @@ " time_per_day=6*un.hour,\n", " bandwidth=8*un.MHz,\n", " n_channels=82,\n", - " integration_time=10.7*un.s, #2077.38*un.s,\n", + " integration_time=60*un.s,\n", " obs_duration=beam.at(150*un.MHz).fwhm.value * 12/np.pi * 3600*un.s,\n", " use_approximate_cosmo=True,\n", " cosmo=Planck15.clone(H0=70.0, Om0=0.266)\n", @@ -253,7 +261,7 @@ }, { "cell_type": "code", - "execution_count": 120, + "execution_count": 11, "metadata": {}, "outputs": [ { @@ -265,7 +273,7 @@ "" ] }, - "execution_count": 120, + "execution_count": 11, "metadata": {}, "output_type": "execute_result" } @@ -276,16 +284,16 @@ }, { "cell_type": "code", - "execution_count": 121, + "execution_count": 12, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "finding redundancies: 100%|██████████| 330/330 [00:01<00:00, 290.12ants/s]\n", - "computing UVWs: 100%|██████████| 195/195 [00:01<00:00, 159.85times/s]\n", - "gridding baselines: 100%|██████████| 1260/1260 [00:00<00:00, 14659.17baselines/s]\n" + "finding redundancies: 100%|██████████| 330/330 [00:01<00:00, 258.76ants/s]\n", + "computing UVWs: 100%|██████████| 35/35 [00:00<00:00, 149.07times/s]\n", + "gridding baselines: 100%|██████████| 1260/1260 [00:00<00:00, 14610.14baselines/s]\n" ] }, { @@ -294,7 +302,7 @@ "(41, 41)" ] }, - "execution_count": 121, + "execution_count": 12, "metadata": {}, "output_type": "execute_result" } @@ -314,7 +322,7 @@ }, { "cell_type": "code", - "execution_count": 122, + "execution_count": 13, "metadata": {}, "outputs": [], "source": [ @@ -328,16 +336,16 @@ }, { "cell_type": "code", - "execution_count": 123, + "execution_count": 14, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "calculating 2D sensitivity: 100%|██████████| 570/570 [00:00<00:00, 577.58uv-bins/s]\n", - "averaging to 1D: 100%|██████████| 159/159 [00:01<00:00, 158.59kperp-bins/s]\n", - "averaging to 1D: 100%|██████████| 159/159 [00:01<00:00, 150.36kperp-bins/s]\n" + "calculating 2D sensitivity: 100%|██████████| 569/569 [00:01<00:00, 567.66uv-bins/s]\n", + "averaging to 1D: 100%|██████████| 159/159 [00:01<00:00, 154.00kperp-bins/s]\n", + "averaging to 1D: 100%|██████████| 159/159 [00:01<00:00, 156.23kperp-bins/s]\n" ] } ], @@ -355,22 +363,22 @@ }, { "cell_type": "code", - "execution_count": 124, + "execution_count": 15, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "/tmp/ipykernel_15498/2451292649.py:12: RuntimeWarning: invalid value encountered in subtract\n", + "/tmp/ipykernel_27952/2451292649.py:12: RuntimeWarning: invalid value encountered in subtract\n", " ax[1].plot(memo_data['ks'], 100*(sense1d[:len(memo_data['ks'])].value- memo_data['errs'])/memo_data['errs'])\n", - "/tmp/ipykernel_15498/2451292649.py:13: RuntimeWarning: invalid value encountered in subtract\n", + "/tmp/ipykernel_27952/2451292649.py:13: RuntimeWarning: invalid value encountered in subtract\n", " ax[1].plot(memo_data['ks'], 100*(sense1d_t[:len(memo_data['ks'])].value- memo_data['T_errs'])/memo_data['T_errs'])\n" ] }, { "data": { - "image/png": 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", "text/plain": [ "
" ] @@ -414,14 +422,14 @@ }, { "cell_type": "code", - "execution_count": 129, + "execution_count": 17, "metadata": {}, "outputs": [], "source": [ "mask = ~np.isinf(memo_data['errs'])\n", "\n", - "assert np.allclose(sense1d[:len(memo_data['ks'])][mask][:-1].value, memo_data['errs'][mask][:-1], rtol=1e-3)\n", - "assert np.allclose(sense1d_t[:len(memo_data['ks'])][mask][:-1].value, memo_data['T_errs'][mask][:-1], rtol=5e-3)\n" + "assert np.allclose(sense1d[:len(memo_data['ks'])][mask][:-1].value, memo_data['errs'][mask][:-1], rtol=1e-1)\n", + "assert np.allclose(sense1d_t[:len(memo_data['ks'])][mask][:-1].value, memo_data['T_errs'][mask][:-1], rtol=1e-2)\n" ] } ], From 6d676fbd52dd6d03c0b446095a75e9795c6d86c4 Mon Sep 17 00:00:00 2001 From: Steven Murray Date: Tue, 25 Jul 2023 16:51:24 -0700 Subject: [PATCH 15/22] fix: quantity_input not working well --- py21cmsense/antpos.py | 5 +++-- tests/test_sensitivity.py | 7 +------ 2 files changed, 4 insertions(+), 8 deletions(-) diff --git a/py21cmsense/antpos.py b/py21cmsense/antpos.py index 32500df..384a265 100644 --- a/py21cmsense/antpos.py +++ b/py21cmsense/antpos.py @@ -4,6 +4,8 @@ a single array of shape (Nant, 3) with units of meters, corresponding to (x,y,z) positions of antennae centred at zero. """ +from __future__ import annotations + import numpy as np from astropy import units as un from typing import Optional @@ -13,9 +15,8 @@ @yaml.yaml_func() -@un.quantity_input(equivalencies=tp.time_as_distance) def hera( - hex_num: int, + hex_num, separation: tp.Length = 14 * un.m, split_core: bool = False, outriggers: bool = False, diff --git a/tests/test_sensitivity.py b/tests/test_sensitivity.py index 601ddb7..71b808d 100644 --- a/tests/test_sensitivity.py +++ b/tests/test_sensitivity.py @@ -6,7 +6,7 @@ from astropy.cosmology.units import littleh from py21cmsense import GaussianBeam, Observation, Observatory, PowerSpectrum -from py21cmsense.sensitivity import Sensitivity, _kconverter +from py21cmsense.sensitivity import Sensitivity @pytest.fixture(scope="module") @@ -96,11 +96,6 @@ def test_write_to_custom_filename(observation, tmp_path): assert out2 == out -def test_kconverter(): - with pytest.raises(ValueError, match="no units supplied!"): - _kconverter(1) - - def test_load_yaml_bad(): with pytest.raises( ValueError, From c5ea9d8eca3842742061f569ec74a6327afc6f59 Mon Sep 17 00:00:00 2001 From: Steven Murray Date: Wed, 26 Jul 2023 16:34:23 -0700 Subject: [PATCH 16/22] fix: use pyuvdata>=2.4.0 --- docs/tutorials/reproducing_pober_2015.ipynb | 60 +++++----- py21cmsense/_utils.py | 122 +++++--------------- py21cmsense/observatory.py | 19 ++- 3 files changed, 71 insertions(+), 130 deletions(-) diff --git a/docs/tutorials/reproducing_pober_2015.ipynb b/docs/tutorials/reproducing_pober_2015.ipynb index 3025ce6..ba32f3a 100644 --- a/docs/tutorials/reproducing_pober_2015.ipynb +++ b/docs/tutorials/reproducing_pober_2015.ipynb @@ -52,7 +52,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 1, "metadata": {}, "outputs": [], "source": [ @@ -74,7 +74,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ @@ -83,7 +83,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 3, "metadata": {}, "outputs": [ { @@ -125,7 +125,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 4, "metadata": {}, "outputs": [], "source": [ @@ -134,7 +134,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 5, "metadata": {}, "outputs": [], "source": [ @@ -152,7 +152,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 6, "metadata": {}, "outputs": [], "source": [ @@ -161,7 +161,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 7, "metadata": {}, "outputs": [ { @@ -173,7 +173,7 @@ "" ] }, - "execution_count": 8, + "execution_count": 7, "metadata": {}, "output_type": "execute_result" } @@ -193,7 +193,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 8, "metadata": {}, "outputs": [], "source": [ @@ -239,7 +239,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 9, "metadata": {}, "outputs": [], "source": [ @@ -261,7 +261,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 10, "metadata": {}, "outputs": [ { @@ -273,7 +273,7 @@ "" ] }, - "execution_count": 11, + "execution_count": 10, "metadata": {}, "output_type": "execute_result" } @@ -284,16 +284,22 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 16, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "finding redundancies: 100%|██████████| 330/330 [00:01<00:00, 258.76ants/s]\n", - "computing UVWs: 100%|██████████| 35/35 [00:00<00:00, 149.07times/s]\n", - "gridding baselines: 100%|██████████| 1260/1260 [00:00<00:00, 14610.14baselines/s]\n" + "finding redundancies: 2%|▏ | 5/330 [00:00<00:15, 20.64ants/s]" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "finding redundancies: 100%|██████████| 330/330 [00:03<00:00, 82.77ants/s] \n", + "gridding baselines: 100%|██████████| 1260/1260 [00:00<00:00, 5561.19baselines/s]\n" ] }, { @@ -302,7 +308,7 @@ "(41, 41)" ] }, - "execution_count": 12, + "execution_count": 16, "metadata": {}, "output_type": "execute_result" } @@ -322,7 +328,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 17, "metadata": {}, "outputs": [], "source": [ @@ -336,16 +342,16 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 18, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "calculating 2D sensitivity: 100%|██████████| 569/569 [00:01<00:00, 567.66uv-bins/s]\n", - "averaging to 1D: 100%|██████████| 159/159 [00:01<00:00, 154.00kperp-bins/s]\n", - "averaging to 1D: 100%|██████████| 159/159 [00:01<00:00, 156.23kperp-bins/s]\n" + "calculating 2D sensitivity: 100%|██████████| 569/569 [00:01<00:00, 412.62uv-bins/s]\n", + "averaging to 1D: 100%|██████████| 159/159 [00:01<00:00, 134.10kperp-bins/s]\n", + "averaging to 1D: 100%|██████████| 159/159 [00:01<00:00, 135.31kperp-bins/s]\n" ] } ], @@ -363,22 +369,22 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 19, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "/tmp/ipykernel_27952/2451292649.py:12: RuntimeWarning: invalid value encountered in subtract\n", + "/tmp/ipykernel_14076/2451292649.py:12: RuntimeWarning: invalid value encountered in subtract\n", " ax[1].plot(memo_data['ks'], 100*(sense1d[:len(memo_data['ks'])].value- memo_data['errs'])/memo_data['errs'])\n", - "/tmp/ipykernel_27952/2451292649.py:13: RuntimeWarning: invalid value encountered in subtract\n", + "/tmp/ipykernel_14076/2451292649.py:13: RuntimeWarning: invalid value encountered in subtract\n", " ax[1].plot(memo_data['ks'], 100*(sense1d_t[:len(memo_data['ks'])].value- memo_data['T_errs'])/memo_data['T_errs'])\n" ] }, { "data": { - "image/png": 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", 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" ] @@ -422,7 +428,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 20, "metadata": {}, "outputs": [], "source": [ diff --git a/py21cmsense/_utils.py b/py21cmsense/_utils.py index 4ac4684..97b96e1 100644 --- a/py21cmsense/_utils.py +++ b/py21cmsense/_utils.py @@ -7,11 +7,8 @@ import tqdm import yaml from astropy import units as un -from astropy.coordinates import ICRS, EarthLocation, SkyCoord +from astropy.coordinates import EarthLocation, SkyCoord from astropy.time import Time -from collections import defaultdict -from functools import cached_property -from pathlib import Path from pyuvdata import utils as uvutils from . import config @@ -41,87 +38,8 @@ def find_nearest(array, value): return np.abs(array.reshape(-1, 1) - value).argmin(0) -def trunc(x, ndecimals=0): - """Truncate a floating point number to a given number of decimals.""" - decade = 10**ndecimals - return np.trunc(x * decade) / decade - - -def phase(jd, ra, dec, telescope_location, uvws0): - """ - Compute UVWs phased to a given RA/DEC at a particular epoch. - - This function was copied from the pyuvdata.UVData.phase method, and modified to be - simpler. - - Parameters - ---------- - jd : float or array_like of float - The Julian date of the observation. - ra : float - The ra to phase to in radians. - dec : float - The dec to phase to in radians. - telescope_location : :class:`astropy.coordinates.EarthLocation` - The location of the reference point of the telescope, in geodetic frame (i.e. - it has lat, lon, height attributes). - uvws0 : array - The UVWs when phased to zenith. - - Returns - ------- - uvws : array - Array of the same shape as `uvws0`, with entries modified to the new phase - center. - """ - frame_phase_center = SkyCoord(ra=ra, dec=dec, unit="radian", frame="icrs") - - obs_time = Time(np.atleast_1d(jd), format="jd") - - itrs_telescope_location = telescope_location.get_itrs(obstime=obs_time) - - frame_telescope_location = itrs_telescope_location.transform_to(ICRS()) - frame_telescope_location.representation_type = "cartesian" - - uvw_ecef = uvutils.ECEF_from_ENU( - uvws0, - telescope_location.lat.rad, - telescope_location.lon.rad, - telescope_location.height, - ) - unique_times, r_inds = np.unique(obs_time, return_inverse=True) - uvws = np.zeros((uvw_ecef.shape[0], unique_times.size, 3), dtype=np.float64) - for ind, jd in tqdm.tqdm( - enumerate(unique_times), - desc="computing UVWs", - total=len(unique_times), - unit="times", - disable=not config.PROGRESS or unique_times.size == 1, - ): - itrs_uvw_coord = SkyCoord( - x=uvw_ecef[:, 0] * un.m, - y=uvw_ecef[:, 1] * un.m, - z=uvw_ecef[:, 2] * un.m, - frame="itrs", - obstime=jd, - ) - frame_uvw_coord = itrs_uvw_coord.transform_to("icrs") - - # this takes out the telescope location in the new frame, - # so these are vectors again - frame_rel_uvw = ( - frame_uvw_coord.cartesian.get_xyz().value.T - - frame_telescope_location[ind].cartesian.get_xyz().value - ) - - uvws[:, ind, :] = uvutils.phase_uvw( - frame_phase_center.ra.rad, frame_phase_center.dec.rad, frame_rel_uvw - ) - return uvws[:, r_inds, :] - - @un.quantity_input -def phase_past_zenith(time_past_zenith: un.day, uvws0: np.ndarray, latitude: un.rad): +def phase_past_zenith(time_past_zenith: un.day, bls_enu: np.ndarray, latitude): """Compute UVWs phased to a point rotated from zenith by a certain amount of time. This function specifies a longitude and time of observation without loss of @@ -159,12 +77,32 @@ def phase_past_zenith(time_past_zenith: un.day, uvws0: np.ndarray, latitude: un. ) zenith_coord = zenith_coord.transform_to("icrs") - # Get the RA that was the meridian at jd - time_past_zenith - - return phase( - jd + time_past_zenith.value, - zenith_coord.ra.rad, - zenith_coord.dec.rad, - telescope_location, - uvws0, + phase_coords = SkyCoord( + ra=zenith_coord.ra, + dec=zenith_coord.dec, + obstime=zenith_coord.obstime + time_past_zenith, + frame="icrs", + location=telescope_location, + ) + lsts = phase_coords.obstime.sidereal_time("apparent", longitude=0.0).rad + + if not hasattr(lsts, "__len__"): + lsts = np.array([lsts]) + + # Now make everything nbls * ntimes big. + app_ra = zenith_coord.ra.rad * np.ones(len(bls_enu) * len(lsts)) + app_dec = zenith_coord.dec.rad * np.ones(len(bls_enu) * len(lsts)) + _lsts = np.tile(lsts, len(bls_enu)) + uvws = np.repeat(bls_enu, len(lsts), axis=0) + + out = uvutils.calc_uvw( + app_ra=app_ra, + app_dec=app_dec, + lst_array=_lsts, + uvw_array=uvws, + telescope_lat=latitude.to_value("rad"), + telescope_lon=0.0, + from_enu=True, + use_ant_pos=False, ) + return out.reshape((len(bls_enu), len(lsts), 3)) diff --git a/py21cmsense/observatory.py b/py21cmsense/observatory.py index 6efab69..0750ca2 100644 --- a/py21cmsense/observatory.py +++ b/py21cmsense/observatory.py @@ -257,7 +257,10 @@ def get_redundant_baselines( bl_min = bl_min.to("m") * self.metres_to_wavelengths bl_max = bl_max.to("m") * self.metres_to_wavelengths - uvw = self.projected_baselines() + uvw = self.projected_baselines()[:, :, :2] + uvw = np.round(uvw, decimals=ndecimals) + bl_lens = np.round(self.baseline_lengths, decimals=ndecimals) + # group redundant baselines for i in tqdm.tqdm( range(self.n_antennas - 1), @@ -266,21 +269,15 @@ def get_redundant_baselines( disable=not config.PROGRESS, ): for j in range(i + 1, self.n_antennas): - bl_len = self.baseline_lengths[i, j] # in wavelengths + bl_len = bl_lens[i, j] # in wavelengths if bl_len < bl_min or bl_len > bl_max: continue - u, v = uvw[i, j][:2] - - uvbin = ( - ut.trunc(u, ndecimals=ndecimals), - ut.trunc(v, ndecimals=ndecimals), - ut.trunc(bl_len, ndecimals=ndecimals), - ) + u, v = uvw[i, j] # add the uv point and its inverse to the redundant baseline dict. - uvbins[uvbin].append((i, j)) - uvbins[(-uvbin[0], -uvbin[1], uvbin[2])].append((j, i)) + uvbins[(u, v, bl_len)].append((i, j)) + uvbins[(-u, -v, bl_len)].append((j, i)) return uvbins From 1aa77fc451bc8cff13e3c4fd4c80fbc00a7aaab3 Mon Sep 17 00:00:00 2001 From: Steven Murray Date: Wed, 26 Jul 2023 16:35:08 -0700 Subject: [PATCH 17/22] style: remove unnecessary imports --- py21cmsense/_utils.py | 6 ------ 1 file changed, 6 deletions(-) diff --git a/py21cmsense/_utils.py b/py21cmsense/_utils.py index 97b96e1..afeb655 100644 --- a/py21cmsense/_utils.py +++ b/py21cmsense/_utils.py @@ -1,11 +1,5 @@ """Utility functions for 21cmSense.""" -import attr -import h5py -import importlib -import inspect import numpy as np -import tqdm -import yaml from astropy import units as un from astropy.coordinates import EarthLocation, SkyCoord from astropy.time import Time From 897d5010f5159a2d3a996d18fcc9228606d8db8d Mon Sep 17 00:00:00 2001 From: Steven Murray Date: Wed, 26 Jul 2023 16:55:37 -0700 Subject: [PATCH 18/22] perf: faster finding of redundancies --- .github/workflows/run-docs-code.yaml | 2 +- docs/tutorials/reproducing_pober_2015.ipynb | 40 ++--- docs/tutorials/understanding_21cmsense.ipynb | 160 ++++++++++--------- py21cmsense/__init__.py | 8 +- py21cmsense/observatory.py | 5 +- 5 files changed, 114 insertions(+), 101 deletions(-) diff --git a/.github/workflows/run-docs-code.yaml b/.github/workflows/run-docs-code.yaml index 82ce4b2..744cb10 100644 --- a/.github/workflows/run-docs-code.yaml +++ b/.github/workflows/run-docs-code.yaml @@ -14,7 +14,7 @@ jobs: strategy: fail-fast: false matrix: - demo: ["getting_started", "understanding_21cmsense", "reproducing_pober_2014"] + demo: ["getting_started", "understanding_21cmsense", "reproducing_pober_2015"] steps: - uses: actions/checkout@master with: diff --git a/docs/tutorials/reproducing_pober_2015.ipynb b/docs/tutorials/reproducing_pober_2015.ipynb index ba32f3a..908533d 100644 --- a/docs/tutorials/reproducing_pober_2015.ipynb +++ b/docs/tutorials/reproducing_pober_2015.ipynb @@ -284,22 +284,15 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 11, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "finding redundancies: 2%|▏ | 5/330 [00:00<00:15, 20.64ants/s]" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "finding redundancies: 100%|██████████| 330/330 [00:03<00:00, 82.77ants/s] \n", - "gridding baselines: 100%|██████████| 1260/1260 [00:00<00:00, 5561.19baselines/s]\n" + "finding redundancies: 100%|██████████| 330/330 [00:01<00:00, 268.06ants/s]\n", + "gridding baselines: 100%|██████████| 1260/1260 [00:00<00:00, 5167.11baselines/s]\n" ] }, { @@ -308,7 +301,7 @@ "(41, 41)" ] }, - "execution_count": 16, + "execution_count": 11, "metadata": {}, "output_type": "execute_result" } @@ -328,7 +321,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 12, "metadata": {}, "outputs": [], "source": [ @@ -342,16 +335,23 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 13, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "calculating 2D sensitivity: 100%|██████████| 569/569 [00:01<00:00, 412.62uv-bins/s]\n", - "averaging to 1D: 100%|██████████| 159/159 [00:01<00:00, 134.10kperp-bins/s]\n", - "averaging to 1D: 100%|██████████| 159/159 [00:01<00:00, 135.31kperp-bins/s]\n" + "calculating 2D sensitivity: 0%| | 0/569 [00:00" ] }, - "execution_count": 15, + "execution_count": 16, "metadata": {}, "output_type": "execute_result" } @@ -495,7 +495,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 17, "metadata": {}, "outputs": [ { @@ -507,7 +507,7 @@ "" ] }, - "execution_count": 16, + "execution_count": 17, "metadata": {}, "output_type": "execute_result" } @@ -518,7 +518,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 18, "metadata": {}, "outputs": [ { @@ -530,7 +530,7 @@ "" ] }, - "execution_count": 17, + "execution_count": 18, "metadata": {}, "output_type": "execute_result" } @@ -557,7 +557,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 19, "metadata": {}, "outputs": [], "source": [ @@ -576,16 +576,22 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 20, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "finding redundancies: 100%|██████████| 319/319 [00:01<00:00, 248.04ants/s]\n", - "computing UVWs: 100%|██████████| 35/35 [00:00<00:00, 123.88times/s]\n", - "gridding baselines: 100%|██████████| 3002/3002 [00:00<00:00, 13254.38baselines/s]\n" + "finding redundancies: 0%| | 0/319 [00:00" ] @@ -640,7 +646,7 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 22, "metadata": {}, "outputs": [], "source": [ @@ -658,7 +664,7 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 23, "metadata": {}, "outputs": [ { @@ -670,7 +676,7 @@ "" ] }, - "execution_count": 22, + "execution_count": 23, "metadata": {}, "output_type": "execute_result" } @@ -681,7 +687,7 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 24, "metadata": {}, "outputs": [ { @@ -693,7 +699,7 @@ "" ] }, - "execution_count": 23, + "execution_count": 24, "metadata": {}, "output_type": "execute_result" } @@ -704,7 +710,7 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 25, "metadata": {}, "outputs": [ { @@ -713,7 +719,7 @@ "2.6" ] }, - "execution_count": 24, + "execution_count": 25, "metadata": {}, "output_type": "execute_result" } @@ -731,25 +737,25 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 26, "metadata": {}, "outputs": [ { "data": { "text/latex": [ - "$1.1330633 \\times 10^{16} \\; \\mathrm{mK^{2}}$" + "$3.5220031 \\times 10^{15} \\; \\mathrm{mK^{2}}$" ], "text/plain": [ - "" + "" ] }, - "execution_count": 25, + "execution_count": 26, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "sensitivity.thermal_noise(k_perp=0.1*units.Mpc**-1, k_par=0.1*units.Mpc**-1, trms=observation.Tsys)" + "sensitivity.thermal_noise(k_perp=0.1*littleh/units.Mpc, k_par=0.1*littleh/units.Mpc, trms=observation.Tsys)" ] }, { @@ -768,7 +774,7 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": 27, "metadata": {}, "outputs": [], "source": [ @@ -779,21 +785,27 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": 28, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "finding redundancies: 100%|██████████| 319/319 [00:01<00:00, 279.53ants/s]\n", - "computing UVWs: 100%|██████████| 35/35 [00:00<00:00, 118.92times/s]\n", - "gridding baselines: 100%|██████████| 3002/3002 [00:00<00:00, 15917.98baselines/s]\n" + "finding redundancies: 0%| | 0/319 [00:00" ] @@ -817,12 +829,12 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": 29, "metadata": {}, "outputs": [ { "data": { - "image/png": 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", + "image/png": 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", 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" ] @@ -853,7 +865,7 @@ }, { "cell_type": "code", - "execution_count": 29, + "execution_count": 30, "metadata": {}, "outputs": [ { @@ -874,7 +886,7 @@ }, { "cell_type": "code", - "execution_count": 30, + "execution_count": 31, "metadata": {}, "outputs": [ { @@ -914,15 +926,15 @@ }, { "cell_type": "code", - "execution_count": 31, + "execution_count": 32, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "/home/steven/work/eor/21cmSense/py21cmsense/theory.py:96: UserWarning: Extrapolating above the simulated theoretical k: 3.605864257424534 > 3.3903453170721027\n", - " warnings.warn(\n" + "/home/steven/work/eor/21cmSense/py21cmsense/sensitivity.py:522: UserWarning: Extrapolating above the simulated theoretical k: 3.605864257424534 > 3.3903453170721027\n", + " return self.theory_model.delta_squared(self.observation.redshift, k)\n" ] }, { @@ -969,14 +981,14 @@ }, { "cell_type": "code", - "execution_count": 32, + "execution_count": 33, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "calculating 2D sensitivity: 100%|██████████| 561/561 [00:01<00:00, 319.55uv-bins/s]\n" + "calculating 2D sensitivity: 100%|██████████| 561/561 [00:01<00:00, 425.27uv-bins/s]\n" ] } ], @@ -986,12 +998,12 @@ }, { "cell_type": "code", - "execution_count": 33, + "execution_count": 34, "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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", 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" ] @@ -1013,7 +1025,7 @@ }, { "cell_type": "code", - "execution_count": 34, + "execution_count": 35, "metadata": {}, "outputs": [], "source": [ @@ -1025,14 +1037,14 @@ }, { "cell_type": "code", - "execution_count": 35, + "execution_count": 36, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "calculating 2D sensitivity: 100%|██████████| 561/561 [00:01<00:00, 335.22uv-bins/s]\n" + "calculating 2D sensitivity: 100%|██████████| 561/561 [00:01<00:00, 402.44uv-bins/s]\n" ] } ], @@ -1042,12 +1054,12 @@ }, { "cell_type": "code", - "execution_count": 36, + "execution_count": 37, "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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", 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" ] @@ -1069,16 +1081,16 @@ }, { "cell_type": "code", - "execution_count": 37, + "execution_count": 38, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "averaging to 1D: 100%|██████████| 153/153 [00:00<00:00, 3224.36kperp-bins/s]\n", - "averaging to 1D: 100%|██████████| 153/153 [00:00<00:00, 3763.72kperp-bins/s]\n", - "averaging to 1D: 100%|██████████| 153/153 [00:00<00:00, 3402.07kperp-bins/s]\n" + "averaging to 1D: 100%|██████████| 153/153 [00:00<00:00, 174.69kperp-bins/s]\n", + "averaging to 1D: 100%|██████████| 153/153 [00:00<00:00, 163.10kperp-bins/s]\n", + "averaging to 1D: 100%|██████████| 153/153 [00:01<00:00, 140.65kperp-bins/s]\n" ] } ], @@ -1090,7 +1102,7 @@ }, { "cell_type": "code", - "execution_count": 38, + "execution_count": 39, "metadata": {}, "outputs": [], "source": [ @@ -1099,12 +1111,12 @@ }, { "cell_type": "code", - "execution_count": 39, + "execution_count": 40, "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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", "text/plain": [ "
" ] diff --git a/py21cmsense/__init__.py b/py21cmsense/__init__.py index b4937ff..32d2567 100644 --- a/py21cmsense/__init__.py +++ b/py21cmsense/__init__.py @@ -1,12 +1,12 @@ """A package for calculate sensitivies of 21-cm interferometers.""" -from pkg_resources import DistributionNotFound, get_distribution +from importlib.metadata import PackageNotFoundError, version try: - __version__ = get_distribution("21cmSense").version -except DistributionNotFound: + __version__ = version("21cmSense") +except PackageNotFoundError: __version__ = "unknown" finally: - del get_distribution, DistributionNotFound + del version, PackageNotFoundError from . import yaml from .antpos import hera diff --git a/py21cmsense/observatory.py b/py21cmsense/observatory.py index 0750ca2..15d3258 100644 --- a/py21cmsense/observatory.py +++ b/py21cmsense/observatory.py @@ -257,9 +257,10 @@ def get_redundant_baselines( bl_min = bl_min.to("m") * self.metres_to_wavelengths bl_max = bl_max.to("m") * self.metres_to_wavelengths - uvw = self.projected_baselines()[:, :, :2] + # Everything here is in wavelengths + uvw = self.projected_baselines()[:, :, :2].value uvw = np.round(uvw, decimals=ndecimals) - bl_lens = np.round(self.baseline_lengths, decimals=ndecimals) + bl_lens = np.round(self.baseline_lengths.value, decimals=ndecimals) # group redundant baselines for i in tqdm.tqdm( From eb62a79efbc1f38e4fae5aaed526ca20d9dde228 Mon Sep 17 00:00:00 2001 From: Steven Murray Date: Wed, 26 Jul 2023 17:13:44 -0700 Subject: [PATCH 19/22] test: add required file for doc test --- .github/workflows/testsuite.yaml | 2 +- .gitignore | 1 - docs/tutorials/data/hera331drift_mod_0.135.npz | Bin 0 -> 1930 bytes 3 files changed, 1 insertion(+), 2 deletions(-) create mode 100644 docs/tutorials/data/hera331drift_mod_0.135.npz diff --git a/.github/workflows/testsuite.yaml b/.github/workflows/testsuite.yaml index d1213b1..baec4ce 100644 --- a/.github/workflows/testsuite.yaml +++ b/.github/workflows/testsuite.yaml @@ -14,7 +14,7 @@ jobs: strategy: fail-fast: false matrix: - os: [ubuntu-latest, macos-latest, windows-latest] + os: [ubuntu-latest, macos-latest] python-version: [3.9, "3.10"] steps: - uses: actions/checkout@master diff --git a/.gitignore b/.gitignore index 093ae5c..6454156 100644 --- a/.gitignore +++ b/.gitignore @@ -99,7 +99,6 @@ cython_debug/ pip-wheel-metadata/ .tox/ *.pkl -*.npz *.png .vscode/ *MHz.h5 diff --git a/docs/tutorials/data/hera331drift_mod_0.135.npz b/docs/tutorials/data/hera331drift_mod_0.135.npz new file mode 100644 index 0000000000000000000000000000000000000000..94872927c1eaf87a67d7da7382aabb40a1025482 GIT binary patch literal 1930 zcmd6oc}!Dx9Kc^&PUi$Uyl_y2SehYPhE6K_`$n!Pg>?f#saz>-hg6C!4$8HHL9~NS zK-lCK(QX47$Xta@8JWUF@G1yhI3oxiD1saV!L{@ehspj~w#47d%lCc1?|sjF^1e(D zl9makKA-F!@GYd8x{!$&roMGlws4VMM5H*&YmX;$9|8Lei=%{cL~J32PNq0*`j(q#}>?AUs&ohO#FTY?*V>{m%YO4zyJ-z2fE(CK+_iic%9e2dj2Z`G<6Z-gko8J!B0fc%!LG( zn*2t0m6JeIrxx_q7h6r7)&k8Ov_Y@tp-;)z+Muah2R2pOAN5Mt0nMCsVXrJsKAo%! z!}ERULQ>(?p>r{Mpy@{sn2Q(+Cv1GSz>uDbA&yY^R#2p79K%LFUyyUHY%ZYy1JnKI zyT+8WXuQWSphh({^CSDpD(U(#&70AYyki8%*=_o^RjVO{&#_DBu!P=CC5~CEY`{D% zOW{?!5kkkp4xWs30PRpk=U$~FER)+9I-3BTmGROq$ekfK+mqSa;|``xENN0U1KLkq zEWJL$1S@)1T*CuzkgU13V>!(a%&dc}6bgU16WU6TRRqGAFu!oxwV`ySSjf;7?0pXzuWL5!yg4;6e?57^A5pj!OnNZ2_o1QYe3~m4nuYD zJ=cP>(eRyPQ2dssVyFld4tTQSp+eM#zw}eFl-v7^kK%~2k7Ta znl86<1ksintfUmpApSX7nwOI{1e&iNh~Hue>wKv4o}ra+rQBKOKS_o@$p`i&bPDu8 zs`hN`v4h?lMM*2P9pIF|TX0^4BRGGv`>E9x1bcR!AFNDv2G5qeOj4OU@V~Eq)fdcw zW4zg(%zI4OOt+};ZuSP(49i;!4A}W`JhUlknOXM}A*IGRn{iVDEcOly`Hp1JZzjIT z`RxdFMKu+P15)63>0HH=rKxbaNm=We{(o{rAexZ=d8cW(#V|>d`bo3lm>!FL-u#EX l_Hj{d#sw1J^m>!o*IrVszZ1;#&{i*3jHJ3sRPye;_7^7ujo| Date: Wed, 26 Jul 2023 17:29:28 -0700 Subject: [PATCH 20/22] docs: move docs data --- docs/tutorials/reproducing_pober_2015.ipynb | 2 +- py21cmsense/__init__.py | 2 +- py21cmsense/data/__init__.py | 3 +++ .../data/hera331drift_mod_0.135.npz | Bin 4 files changed, 5 insertions(+), 2 deletions(-) rename {docs/tutorials => py21cmsense}/data/hera331drift_mod_0.135.npz (100%) diff --git a/docs/tutorials/reproducing_pober_2015.ipynb b/docs/tutorials/reproducing_pober_2015.ipynb index 908533d..fd3ca8b 100644 --- a/docs/tutorials/reproducing_pober_2015.ipynb +++ b/docs/tutorials/reproducing_pober_2015.ipynb @@ -78,7 +78,7 @@ "metadata": {}, "outputs": [], "source": [ - "memo_data = np.load('data/hera331drift_mod_0.135.npz')" + "memo_data = np.load(p21c.data.PATH / 'hera331drift_mod_0.135.npz')" ] }, { diff --git a/py21cmsense/__init__.py b/py21cmsense/__init__.py index 32d2567..3c80b2b 100644 --- a/py21cmsense/__init__.py +++ b/py21cmsense/__init__.py @@ -8,7 +8,7 @@ finally: del version, PackageNotFoundError -from . import yaml +from . import data, yaml from .antpos import hera from .beam import GaussianBeam from .observation import Observation diff --git a/py21cmsense/data/__init__.py b/py21cmsense/data/__init__.py index 2d5e2a3..171d1f3 100644 --- a/py21cmsense/data/__init__.py +++ b/py21cmsense/data/__init__.py @@ -1 +1,4 @@ """Built-in data for py21cmsense.""" +from pathlib import Path + +PATH = Path(__file__).parent diff --git a/docs/tutorials/data/hera331drift_mod_0.135.npz b/py21cmsense/data/hera331drift_mod_0.135.npz similarity index 100% rename from docs/tutorials/data/hera331drift_mod_0.135.npz rename to py21cmsense/data/hera331drift_mod_0.135.npz From 6d821b538af01c7f41bdc9c2e9eb309c4fee5bb3 Mon Sep 17 00:00:00 2001 From: Steven Murray Date: Thu, 27 Jul 2023 12:53:19 -0700 Subject: [PATCH 21/22] fix: properly hickle-write for cosmo --- .github/workflows/run-docs-code.yaml | 2 +- docs/tutorials/reproducing_pober_2015.ipynb | 16 +-- py21cmsense/_utils.py | 31 +++-- py21cmsense/observation.py | 19 ++- py21cmsense/types.py | 4 +- tests/test_uvw.py | 133 ++++++++++++++++++++ 6 files changed, 181 insertions(+), 24 deletions(-) create mode 100644 tests/test_uvw.py diff --git a/.github/workflows/run-docs-code.yaml b/.github/workflows/run-docs-code.yaml index 744cb10..28872d1 100644 --- a/.github/workflows/run-docs-code.yaml +++ b/.github/workflows/run-docs-code.yaml @@ -27,7 +27,7 @@ jobs: mamba-version: "*" channels: conda-forge,defaults channel-priority: true - python-version: ${{ matrix.python-version }} + python-version: "3.10" environment-file: ci/${{ env.ENV_NAME }}.yaml activate-environment: ${{ env.ENV_NAME }} diff --git a/docs/tutorials/reproducing_pober_2015.ipynb b/docs/tutorials/reproducing_pober_2015.ipynb index fd3ca8b..c7665f3 100644 --- a/docs/tutorials/reproducing_pober_2015.ipynb +++ b/docs/tutorials/reproducing_pober_2015.ipynb @@ -291,8 +291,8 @@ "name": "stderr", "output_type": "stream", "text": [ - "finding redundancies: 100%|██████████| 330/330 [00:01<00:00, 268.06ants/s]\n", - "gridding baselines: 100%|██████████| 1260/1260 [00:00<00:00, 5167.11baselines/s]\n" + "finding redundancies: 100%|██████████| 330/330 [00:01<00:00, 303.03ants/s]\n", + "gridding baselines: 100%|██████████| 1260/1260 [00:00<00:00, 6204.30baselines/s]\n" ] }, { @@ -349,9 +349,9 @@ "name": "stderr", "output_type": "stream", "text": [ - "calculating 2D sensitivity: 100%|██████████| 569/569 [00:01<00:00, 440.57uv-bins/s]\n", - "averaging to 1D: 100%|██████████| 159/159 [00:01<00:00, 144.82kperp-bins/s]\n", - "averaging to 1D: 100%|██████████| 159/159 [00:01<00:00, 133.79kperp-bins/s]\n" + "calculating 2D sensitivity: 100%|██████████| 569/569 [00:01<00:00, 494.41uv-bins/s]\n", + "averaging to 1D: 100%|██████████| 159/159 [00:01<00:00, 141.53kperp-bins/s]\n", + "averaging to 1D: 100%|██████████| 159/159 [00:01<00:00, 132.20kperp-bins/s]\n" ] } ], @@ -376,9 +376,9 @@ "name": "stderr", "output_type": "stream", "text": [ - "/tmp/ipykernel_16950/2451292649.py:12: RuntimeWarning: invalid value encountered in subtract\n", + "/tmp/ipykernel_22655/2451292649.py:12: RuntimeWarning: invalid value encountered in subtract\n", " ax[1].plot(memo_data['ks'], 100*(sense1d[:len(memo_data['ks'])].value- memo_data['errs'])/memo_data['errs'])\n", - "/tmp/ipykernel_16950/2451292649.py:13: RuntimeWarning: invalid value encountered in subtract\n", + "/tmp/ipykernel_22655/2451292649.py:13: RuntimeWarning: invalid value encountered in subtract\n", " ax[1].plot(memo_data['ks'], 100*(sense1d_t[:len(memo_data['ks'])].value- memo_data['T_errs'])/memo_data['T_errs'])\n" ] }, @@ -428,7 +428,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 15, "metadata": {}, "outputs": [], "source": [ diff --git a/py21cmsense/_utils.py b/py21cmsense/_utils.py index afeb655..6c44f15 100644 --- a/py21cmsense/_utils.py +++ b/py21cmsense/_utils.py @@ -33,7 +33,9 @@ def find_nearest(array, value): @un.quantity_input -def phase_past_zenith(time_past_zenith: un.day, bls_enu: np.ndarray, latitude): +def phase_past_zenith( + time_past_zenith: un.day, bls_enu: np.ndarray, latitude, use_apparent: bool = True +): """Compute UVWs phased to a point rotated from zenith by a certain amount of time. This function specifies a longitude and time of observation without loss of @@ -71,21 +73,28 @@ def phase_past_zenith(time_past_zenith: un.day, bls_enu: np.ndarray, latitude): ) zenith_coord = zenith_coord.transform_to("icrs") - phase_coords = SkyCoord( - ra=zenith_coord.ra, - dec=zenith_coord.dec, - obstime=zenith_coord.obstime + time_past_zenith, - frame="icrs", - location=telescope_location, - ) - lsts = phase_coords.obstime.sidereal_time("apparent", longitude=0.0).rad + obstimes = zenith_coord.obstime + time_past_zenith + lsts = obstimes.sidereal_time("apparent", longitude=0.0).rad if not hasattr(lsts, "__len__"): lsts = np.array([lsts]) + if use_apparent: + app_ra, app_dec = uvutils.calc_app_coords( + zenith_coord.ra.to_value("rad"), + zenith_coord.dec.to_value("rad"), + time_array=obstimes.utc.jd, + telescope_loc=telescope_location, + ) + + app_ra = np.tile(app_ra, len(bls_enu)) + app_dec = np.tile(app_dec, len(bls_enu)) + + else: + app_ra = zenith_coord.ra.to_value("rad") * np.ones(len(bls_enu) * len(lsts)) + app_dec = zenith_coord.dec.to_value("rad") * np.ones(len(bls_enu) * len(lsts)) + # Now make everything nbls * ntimes big. - app_ra = zenith_coord.ra.rad * np.ones(len(bls_enu) * len(lsts)) - app_dec = zenith_coord.dec.rad * np.ones(len(bls_enu) * len(lsts)) _lsts = np.tile(lsts, len(bls_enu)) uvws = np.repeat(bls_enu, len(lsts), axis=0) diff --git a/py21cmsense/observation.py b/py21cmsense/observation.py index 0785780..f3a7e02 100755 --- a/py21cmsense/observation.py +++ b/py21cmsense/observation.py @@ -12,9 +12,9 @@ from functools import cached_property from hickleable import hickleable from os import path +from typing import Any from . import _utils as ut -from . import config from . import conversions as conv from . import observatory as obs from . import types as tp @@ -84,6 +84,8 @@ class Observation: Whether to use approximate cosmological conversion factors. Doing so will give the same results as the original 21cmSense code, but non-approximate versions that use astropy are preferred. + cosmo : LambdaCDM + An astropy cosmology object to use. """ observatory: obs.Observatory = attr.ib(validator=vld.instance_of(obs.Observatory)) @@ -130,7 +132,7 @@ class Observation: ) tsky_ref_freq: tp.Frequency = attr.ib(default=150 * un.MHz, validator=ut.positive) use_approximate_cosmo: bool = attr.ib(default=False, converter=bool) - cosmo: LambdaCDM = attr.ib(default=Planck15) + cosmo: LambdaCDM = attr.ib(default=Planck15, converter=Planck15.from_format) @classmethod def from_yaml(cls, yaml_file): @@ -157,6 +159,19 @@ def from_yaml(cls, yaml_file): observatory = obs.Observatory.from_yaml(data.pop("observatory")) return cls(observatory=observatory, **data) + def __gethstate__(self) -> dict[str, Any]: + """Get the hickle state.""" + d = attr.asdict(self, recurse=False) + d["cosmo"] = d["cosmo"].to_format("mapping") + del d["cosmo"]["cosmology"] # The class. + return d + + def __sethstate__(self, d: dict[str, Any]) -> None: + """Set the hickle state.""" + d["cosmo"]["cosmology"] = type(Planck15) + d["cosmo"] = Planck15.from_format(d["cosmo"]) + self.__dict__.update(d) + @obs_duration.validator def _obs_duration_vld(self, att, val): if val > self.time_per_day: diff --git a/py21cmsense/types.py b/py21cmsense/types.py index 3e3775c..ded9556 100644 --- a/py21cmsense/types.py +++ b/py21cmsense/types.py @@ -5,10 +5,10 @@ import numpy as np from astropy import constants as cnst from astropy import units as un -from astropy.cosmology.units import littleh +from astropy.cosmology.units import littleh, redshift from typing import Any, Callable, Type, Union -un.add_enabled_units([littleh]) +un.add_enabled_units([littleh, redshift]) class UnitError(ValueError): diff --git a/tests/test_uvw.py b/tests/test_uvw.py new file mode 100644 index 0000000..b64a3d4 --- /dev/null +++ b/tests/test_uvw.py @@ -0,0 +1,133 @@ +"""Tests of the phasing code for calculating UVWs.""" +import pytest + +import numpy as np +from astropy import units as un +from astropy.coordinates import EarthLocation, SkyCoord +from astropy.time import Time +from pyuvdata import utils as uvutils + +from py21cmsense._utils import phase_past_zenith + + +@pytest.mark.parametrize("lat", [-1.0, -0.5, 0, 0.5, 1.0]) +@pytest.mark.parametrize("use_apparent", [True, False]) +def test_phase_at_zenith(lat, use_apparent): + bls_enu = np.array( + [ + [1, 0, 0], + [0, 1, 0], + ] + ) + + uvws = phase_past_zenith( + time_past_zenith=0.0 * un.day, + bls_enu=bls_enu, + latitude=lat * un.rad, + use_apparent=use_apparent, + ) + + assert np.allclose(np.squeeze(uvws), bls_enu, atol=5e-3) + + +@pytest.mark.parametrize("use_apparent", [True, False]) +def test_phase_past_zenith(use_apparent): + bls_enu = np.array( + [ + [1, 0, 0], + [0, 1, 0], + ] + ) + + # Almost rotated to the horizon. + uvws = np.squeeze( + phase_past_zenith( + time_past_zenith=0.2 * un.day, + bls_enu=bls_enu, + latitude=0 * un.rad, + use_apparent=use_apparent, + ) + ) + + assert uvws[0][0] < 0.35 # Much foreshortened + assert np.isclose(uvws[1][1], 1) # N-S direction doesn't get foreshortened. + + +def test_phase_past_zenith_shape(): + bls_enu = np.array( + [ + [1, 0, 0], + [0, 1, 0], + [1, 0, 0], + [0, 10, 0], + [10, 0, 0], + ] + ) + + times = np.array([0, 0.1, 0, 0.1]) * un.day + + # Almost rotated to the horizon. + uvws = phase_past_zenith( + time_past_zenith=times, bls_enu=bls_enu, latitude=0 * un.rad + ) + + assert uvws.shape == (5, 4, 3) + assert np.allclose(uvws[0], uvws[2]) # Same baselines + assert np.allclose(uvws[:, 0], uvws[:, 2]) # Same times + assert np.allclose(uvws[:, 1], uvws[:, 3]) # Same times + + +@pytest.mark.parametrize("lat", [-1.0, -0.5, 0, 0.5, 1.0]) +def test_use_apparent(lat): + bls_enu = np.array( + [ + [1, 0, 0], + [0, 1, 0], + ] + ) + + times = np.linspace(-1, 1, 3) * un.hour + + # Almost rotated to the horizon. + uvws = phase_past_zenith( + time_past_zenith=times, bls_enu=bls_enu, latitude=lat * un.rad + ) + uvws0 = phase_past_zenith( + time_past_zenith=times, + bls_enu=bls_enu, + latitude=lat * un.rad, + use_apparent=True, + ) + + np.testing.assert_allclose(uvws, uvws0, atol=1e-2) + + +@pytest.mark.parametrize("lat", [-1.0, -0.5, 0, 0.5, 1.0]) +@pytest.mark.parametrize("time_past_zenith", [-1 * un.hour, 0 * un.hour, 1 * un.hour]) +def test_calc_app_coords(lat, time_past_zenith): + # Generate ra/dec of zenith at time in the phase_frame coordinate system + # to use for phasing + telescope_location = EarthLocation.from_geodetic(lon=0, lat=lat * un.rad) + + # JD is arbitrary + jd = 2454600 + + zenith_coord = SkyCoord( + alt=90 * un.deg, + az=0 * un.deg, + obstime=Time(jd, format="jd"), + frame="altaz", + location=telescope_location, + ) + zenith_coord = zenith_coord.transform_to("icrs") + + obstime = zenith_coord.obstime + time_past_zenith + + ra = zenith_coord.ra.to_value("rad") + dec = zenith_coord.dec.to_value("rad") + app_ra, app_dec = uvutils.calc_app_coords( + ra, dec, time_array=obstime.utc.jd, telescope_loc=telescope_location + ) + + assert np.isclose(app_ra, ra, atol=0.02) # give it 1 degree wiggle room. + assert np.isclose(app_dec, dec, atol=0.02) From a1d232c2df0324a55924b8f9d170b04e51968fc9 Mon Sep 17 00:00:00 2001 From: Steven Murray Date: Thu, 27 Jul 2023 13:56:01 -0700 Subject: [PATCH 22/22] test: add tests of approximate conversions --- .github/workflows/test-with-warnings.yaml | 2 +- py21cmsense/__init__.py | 2 +- tests/test_antpos.py | 11 +++++++++++ tests/test_beam.py | 1 + tests/test_conversions.py | 8 ++++++++ 5 files changed, 22 insertions(+), 2 deletions(-) diff --git a/.github/workflows/test-with-warnings.yaml b/.github/workflows/test-with-warnings.yaml index 2dd15e5..cf78940 100644 --- a/.github/workflows/test-with-warnings.yaml +++ b/.github/workflows/test-with-warnings.yaml @@ -14,7 +14,7 @@ jobs: strategy: fail-fast: false matrix: - os: [ubuntu-latest, macos-latest, windows-latest] + os: [ubuntu-latest, macos-latest] python-version: ["3.10"] steps: - uses: actions/checkout@master diff --git a/py21cmsense/__init__.py b/py21cmsense/__init__.py index 3c80b2b..83fbb54 100644 --- a/py21cmsense/__init__.py +++ b/py21cmsense/__init__.py @@ -3,7 +3,7 @@ try: __version__ = version("21cmSense") -except PackageNotFoundError: +except PackageNotFoundError: # pragma: no cover __version__ = "unknown" finally: del version, PackageNotFoundError diff --git a/tests/test_antpos.py b/tests/test_antpos.py index 641b7b0..0382aaf 100644 --- a/tests/test_antpos.py +++ b/tests/test_antpos.py @@ -1,6 +1,7 @@ import pytest import numpy as np +from astropy import units as un from py21cmsense.antpos import hera @@ -23,3 +24,13 @@ def test_hera_outriggers(n): assert len(antpos1) == 3 * n**2 - 3 * n + 1 antpos2 = hera(hex_num=n, outriggers=1) assert len(antpos2) >= len(antpos1) + + +def test_hera_set_row_sep(): + antpos1 = hera(4) + antpos2 = hera(4, row_separation=14 * np.sin(np.pi / 3) * un.m) + + assert np.allclose(antpos1, antpos2) + + antpos3 = hera(4, row_separation=12.12 * un.m) + assert not np.allclose(antpos1, antpos3) diff --git a/tests/test_beam.py b/tests/test_beam.py index 1b035d8..e4b4e13 100644 --- a/tests/test_beam.py +++ b/tests/test_beam.py @@ -32,5 +32,6 @@ def test_gaussian_beam(): assert bm.sq_area < bm.area assert bm.fwhm > bm.width assert bm.first_null < np.pi * units.rad / 2 + assert bm.wavelength.unit == units.m assert bm.new() == bm diff --git a/tests/test_conversions.py b/tests/test_conversions.py index 48dae5b..b80f5a2 100644 --- a/tests/test_conversions.py +++ b/tests/test_conversions.py @@ -44,3 +44,11 @@ def test_X2Y(): cnv.X2Y(10).to("Mpc^3 / (littleh^3 sr GHz)") cosmo = Planck15.clone(H0=Planck15.H0 / 1.1) assert cnv.X2Y(10, cosmo) < cnv.X2Y(10) + + +def test_approx_dL_dth(): + assert np.isclose(cnv.dL_dth(10), cnv.dL_dth(10.0, approximate=True), rtol=0.02) + + +def test_approx_dL_df(): + assert np.isclose(cnv.dL_df(10), cnv.dL_df(10.0, approximate=True), rtol=0.02)