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[pre-commit.ci] auto fixes from pre-commit.com hooks
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pre-commit-ci[bot] committed Aug 19, 2024
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79 changes: 43 additions & 36 deletions docs/tutorials/getting_started.ipynb
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Expand Up @@ -25,12 +25,13 @@
},
"outputs": [],
"source": [
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"import numpy as np\n",
"from astropy import units as un\n",
"\n",
"%matplotlib inline\n",
"\n",
"from py21cmsense import GaussianBeam, Observatory, Observation, PowerSpectrum, hera"
"from py21cmsense import GaussianBeam, Observation, Observatory, PowerSpectrum, hera"
]
},
{
Expand Down Expand Up @@ -61,11 +62,11 @@
"outputs": [],
"source": [
"sensitivity = PowerSpectrum(\n",
" observation = Observation(\n",
" observatory = Observatory(\n",
" antpos = hera(hex_num=7, separation=14*un.m),\n",
" beam = GaussianBeam(frequency=135.0*un.MHz, dish_size=14*un.m),\n",
" latitude=38*un.deg\n",
" observation=Observation(\n",
" observatory=Observatory(\n",
" antpos=hera(hex_num=7, separation=14 * un.m),\n",
" beam=GaussianBeam(frequency=135.0 * un.MHz, dish_size=14 * un.m),\n",
" latitude=38 * un.deg,\n",
" )\n",
" )\n",
")"
Expand Down Expand Up @@ -151,9 +152,9 @@
"source": [
"plt.plot(sensitivity.k1d, power_std)\n",
"plt.xlabel(\"k [h/Mpc]\")\n",
"plt.ylabel(r'$\\delta \\Delta^2_{21}$')\n",
"plt.yscale('log')\n",
"plt.xscale('log')"
"plt.ylabel(r\"$\\delta \\Delta^2_{21}$\")\n",
"plt.yscale(\"log\")\n",
"plt.xscale(\"log\")"
]
},
{
Expand Down Expand Up @@ -211,14 +212,14 @@
}
],
"source": [
"plt.plot(sensitivity.k1d, power_std, label='Total')\n",
"plt.plot(sensitivity.k1d, power_std_thermal, label='Thermal')\n",
"plt.plot(sensitivity.k1d, power_std_sample, label='Sample')\n",
"plt.plot(sensitivity.k1d, power_std, label=\"Total\")\n",
"plt.plot(sensitivity.k1d, power_std_thermal, label=\"Thermal\")\n",
"plt.plot(sensitivity.k1d, power_std_sample, label=\"Sample\")\n",
"\n",
"plt.xlabel(\"k [h/Mpc]\")\n",
"plt.ylabel(r'$\\delta \\Delta^2_{21}$')\n",
"plt.yscale('log')\n",
"plt.xscale('log')\n",
"plt.ylabel(r\"$\\delta \\Delta^2_{21}$\")\n",
"plt.yscale(\"log\")\n",
"plt.xscale(\"log\")\n",
"plt.legend();"
]
},
Expand Down Expand Up @@ -412,7 +413,7 @@
}
],
"source": [
"beam.at(160*un.MHz).b_eff/beam.b_eff"
"beam.at(160 * un.MHz).b_eff / beam.b_eff"
]
},
{
Expand Down Expand Up @@ -476,8 +477,8 @@
}
],
"source": [
"plt.figure(figsize=(5,4.5))\n",
"plt.scatter(observatory.baselines_metres[:,:, 0], observatory.baselines_metres[:,:,1], alpha=0.1)\n",
"plt.figure(figsize=(5, 4.5))\n",
"plt.scatter(observatory.baselines_metres[:, :, 0], observatory.baselines_metres[:, :, 1], alpha=0.1)\n",
"plt.xlabel(\"Baseline Length [x, m]\")\n",
"plt.ylabel(\"Baseline Length [y, m]\");"
]
Expand Down Expand Up @@ -621,9 +622,9 @@
"baseline_group_counts = observatory.baseline_weights_from_groups(red_bl)\n",
"\n",
"\n",
"plt.figure(figsize=(7,5))\n",
"plt.scatter(baseline_group_coords[:,0], baseline_group_coords[:,1], c=baseline_group_counts)\n",
"cbar = plt.colorbar();\n",
"plt.figure(figsize=(7, 5))\n",
"plt.scatter(baseline_group_coords[:, 0], baseline_group_coords[:, 1], c=baseline_group_counts)\n",
"cbar = plt.colorbar()\n",
"cbar.set_label(\"Number of baselines in group\", fontsize=15)\n",
"plt.tight_layout();"
]
Expand Down Expand Up @@ -656,9 +657,7 @@
],
"source": [
"coherent_grid = observatory.grid_baselines(\n",
" coherent=True,\n",
" baselines=baseline_group_coords,\n",
" weights=baseline_group_counts\n",
" coherent=True, baselines=baseline_group_coords, weights=baseline_group_counts\n",
")"
]
},
Expand Down Expand Up @@ -693,7 +692,7 @@
}
],
"source": [
"plt.imshow(coherent_grid, extent=(observatory.ugrid().min(), observatory.ugrid().max())*2)\n",
"plt.imshow(coherent_grid, extent=(observatory.ugrid().min(), observatory.ugrid().max()) * 2)\n",
"cbar = plt.colorbar()\n",
"cbar.set_label(\"Effective # of Samples\")"
]
Expand Down Expand Up @@ -758,13 +757,13 @@
}
],
"source": [
"plt.figure(figsize=(7,5))\n",
"plt.figure(figsize=(7, 5))\n",
"x = [bl_group[0] for bl_group in observation.baseline_groups]\n",
"y = [bl_group[1] for bl_group in observation.baseline_groups]\n",
"c = [len(bls) for bls in observation.baseline_groups.values()]\n",
"\n",
"plt.scatter(x, y, c=c)\n",
"cbar = plt.colorbar();\n",
"cbar = plt.colorbar()\n",
"cbar.set_label(\"Number of baselines in group\", fontsize=15)\n",
"plt.tight_layout();"
]
Expand Down Expand Up @@ -800,7 +799,10 @@
}
],
"source": [
"plt.imshow(observation.total_integration_time.to(\"hour\").value, extent=(observation.ugrid.min(), observation.ugrid.max())*2)\n",
"plt.imshow(\n",
" observation.total_integration_time.to(\"hour\").value,\n",
" extent=(observation.ugrid.min(), observation.ugrid.max()) * 2,\n",
")\n",
"cbar = plt.colorbar()\n",
"cbar.set_label(\"Total Integration Time [hours]\")"
]
Expand Down Expand Up @@ -859,8 +861,10 @@
}
],
"source": [
"plt.imshow(observation_2.total_integration_time.to(\"hour\").value, \n",
" extent=(observation_2.ugrid.min(), observation_2.ugrid.max())*2)\n",
"plt.imshow(\n",
" observation_2.total_integration_time.to(\"hour\").value,\n",
" extent=(observation_2.ugrid.min(), observation_2.ugrid.max()) * 2,\n",
")\n",
"cbar = plt.colorbar()\n",
"cbar.set_label(\"Total Integration Time [hours]\")"
]
Expand Down Expand Up @@ -994,8 +998,8 @@
}
],
"source": [
"kperp = un.Quantity(np.linspace(0.01, 0.07, 15), 'littleh/Mpc')\n",
"kpar = un.Quantity( np.linspace(0.1, 2, 15), 'littleh/Mpc')\n",
"kperp = un.Quantity(np.linspace(0.01, 0.07, 15), \"littleh/Mpc\")\n",
"kpar = un.Quantity(np.linspace(0.1, 2, 15), \"littleh/Mpc\")\n",
"sense_gridded = sensitivity.calculate_sensitivity_2d_grid(kperp_edges=kperp, kpar_edges=kpar)"
]
},
Expand Down Expand Up @@ -1028,7 +1032,7 @@
}
],
"source": [
"plt.imshow(np.log10(sense_gridded.value.T), origin='lower')\n",
"plt.imshow(np.log10(sense_gridded.value.T), origin=\"lower\")\n",
"plt.colorbar()"
]
},
Expand Down Expand Up @@ -1059,8 +1063,11 @@
"metadata": {},
"outputs": [],
"source": [
"cable_refl_range = (un.Quantity(1.2, 'littleh/Mpc'), un.Quantity(1.4, 'littleh/Mpc'))\n",
"new_sense = sensitivity.clone(systematics_mask=lambda kperp, kpar: (np.abs(kpar) < cable_refl_range[0]) | (np.abs(kpar) > cable_refl_range[1]))"
"cable_refl_range = (un.Quantity(1.2, \"littleh/Mpc\"), un.Quantity(1.4, \"littleh/Mpc\"))\n",
"new_sense = sensitivity.clone(\n",
" systematics_mask=lambda kperp, kpar: (np.abs(kpar) < cable_refl_range[0])\n",
" | (np.abs(kpar) > cable_refl_range[1])\n",
")"
]
},
{
Expand Down
92 changes: 58 additions & 34 deletions docs/tutorials/reproducing_pober_2015.ipynb
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Original file line number Diff line number Diff line change
Expand Up @@ -56,13 +56,14 @@
"metadata": {},
"outputs": [],
"source": [
"import py21cmsense as p21c\n",
"import matplotlib.pyplot as plt\n",
"import numpy as np\n",
"from astropy import constants\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"
"from astropy.cosmology.units import littleh\n",
"\n",
"import py21cmsense as p21c"
]
},
{
Expand All @@ -78,7 +79,7 @@
"metadata": {},
"outputs": [],
"source": [
"memo_data = np.load(p21c.data.PATH / 'hera331drift_mod_0.135.npz')"
"memo_data = np.load(p21c.data.PATH / \"hera331drift_mod_0.135.npz\")"
]
},
{
Expand All @@ -98,8 +99,8 @@
}
],
"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.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",
Expand Down Expand Up @@ -129,7 +130,7 @@
"metadata": {},
"outputs": [],
"source": [
"hera_ants = p21c.antpos.hera(hex_num=11, row_separation=12.12*un.m)"
"hera_ants = p21c.antpos.hera(hex_num=11, row_separation=12.12 * un.m)"
]
},
{
Expand All @@ -156,7 +157,9 @@
"metadata": {},
"outputs": [],
"source": [
"beam = p21c.GaussianBeam(frequency=135*un.MHz, dish_size=7 * (constants.c / (150 * un.MHz)).to(\"m\"))"
"beam = p21c.GaussianBeam(\n",
" frequency=135 * un.MHz, dish_size=7 * (constants.c / (150 * un.MHz)).to(\"m\")\n",
")"
]
},
{
Expand Down Expand Up @@ -200,8 +203,8 @@
"hera = p21c.Observatory(\n",
" antpos=hera_ants,\n",
" beam=beam,\n",
" latitude=0.6707845*un.rad,\n",
" Trcv=100*un.K,\n",
" latitude=0.6707845 * un.rad,\n",
" Trcv=100 * un.K,\n",
" beam_crossing_time_incl_latitude=False,\n",
")"
]
Expand Down Expand Up @@ -246,17 +249,17 @@
"source": [
"obs = p21c.Observation(\n",
" observatory=hera,\n",
" tsky_amplitude=60*un.K,\n",
" tsky_ref_freq=300*un.MHz,\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",
" time_per_day=6 * un.hour,\n",
" bandwidth=8 * un.MHz,\n",
" n_channels=82,\n",
" integration_time=60*un.s,\n",
" lst_bin_size=beam.at(150*un.MHz).fwhm.value * 12/np.pi * 3600*un.s,\n",
" integration_time=60 * un.s,\n",
" lst_bin_size=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",
" cosmo=Planck15.clone(H0=70.0, Om0=0.266),\n",
")"
]
},
Expand Down Expand Up @@ -328,8 +331,8 @@
"source": [
"sense_moderate = p21c.PowerSpectrum(\n",
" observation=obs,\n",
" foreground_model='moderate',\n",
" horizon_buffer=0.1 *littleh/un.Mpc,\n",
" foreground_model=\"moderate\",\n",
" horizon_buffer=0.1 * littleh / un.Mpc,\n",
" theory_model=p21c.theory.Legacy21cmFAST(),\n",
")"
]
Expand All @@ -351,7 +354,7 @@
],
"source": [
"sense1d = sense_moderate.calculate_sensitivity_1d(thermal=True, sample=True)\n",
"sense1d_t = sense_moderate.calculate_sensitivity_1d(thermal=True, sample=False)\n"
"sense1d_t = sense_moderate.calculate_sensitivity_1d(thermal=True, sample=False)"
]
},
{
Expand Down Expand Up @@ -388,20 +391,37 @@
}
],
"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",
"fig, ax = plt.subplots(\n",
" 2,\n",
" 1,\n",
" sharex=True,\n",
" figsize=(12, 6),\n",
" constrained_layout=True,\n",
" gridspec_kw={\"height_ratios\": [0.65, 0.35]},\n",
")\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",
"ax[0].scatter(\n",
" sense_moderate.k1d, sense1d_t, label=\"Modern 21cmSense (Thermal Only)\", marker=\"x\", color=\"C1\"\n",
")\n",
"ax[0].scatter(\n",
" memo_data[\"ks\"], memo_data[\"T_errs\"], label=\"memo (thermal Only)\", color=\"C1\", facecolor=\"none\"\n",
")\n",
"\n",
"ax[0].set_yscale(\"log\")\n",
"ax[0].set_xscale('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"
"ax[1].plot(\n",
" memo_data[\"ks\"],\n",
" 100 * (sense1d[: len(memo_data[\"ks\"])].value - memo_data[\"errs\"]) / memo_data[\"errs\"],\n",
")\n",
"ax[1].plot(\n",
" memo_data[\"ks\"],\n",
" 100 * (sense1d_t[: len(memo_data[\"ks\"])].value - memo_data[\"T_errs\"]) / memo_data[\"T_errs\"],\n",
")\n",
"ax[0].legend();"
]
},
{
Expand All @@ -426,10 +446,14 @@
"metadata": {},
"outputs": [],
"source": [
"mask = ~np.isinf(memo_data['errs'])\n",
"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-1)\n",
"assert np.allclose(sense1d_t[:len(memo_data['ks'])][mask][:-1].value, memo_data['T_errs'][mask][:-1], rtol=1e-2)\n"
"assert np.allclose(\n",
" sense1d[: len(memo_data[\"ks\"])][mask][:-1].value, memo_data[\"errs\"][mask][:-1], rtol=1e-1\n",
")\n",
"assert np.allclose(\n",
" sense1d_t[: len(memo_data[\"ks\"])][mask][:-1].value, memo_data[\"T_errs\"][mask][:-1], rtol=1e-2\n",
")"
]
}
],
Expand Down
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