diff --git a/.gitignore b/.gitignore index e15ef748..acd34266 100644 --- a/.gitignore +++ b/.gitignore @@ -5,6 +5,7 @@ __pycache__/ build/ dist/ *.egg-info/ +.DS_Store # default directory for downloaded model files SNEWPY_models/ diff --git a/doc/source/nb/AnalyticFluence.ipynb b/doc/source/nb/AnalyticFluence.ipynb index 6705162e..1b6f0ff3 100644 --- a/doc/source/nb/AnalyticFluence.ipynb +++ b/doc/source/nb/AnalyticFluence.ipynb @@ -14,9 +14,17 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Using folder `/Users/shlok2223/.astropy/cache/snewpy/models/AnalyticFluence/`.\n" + ] + } + ], "source": [ "import os\n", "\n", @@ -47,7 +55,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ @@ -103,9 +111,30 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 3, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "%matplotlib inline\n", "filename = \"AnalyticFluence_demo.dat\"\n", @@ -140,9 +169,29 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 4, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Preparing fluences ...\n" + ] + }, + { + "ename": "AttributeError", + "evalue": "module 'snewpy.models.ccsn_loaders' has no attribute 'Analytic3Species'", + "output_type": "error", + "traceback": [ + "\u001b[31m---------------------------------------------------------------------------\u001b[39m", + "\u001b[31mAttributeError\u001b[39m Traceback (most recent call last)", + "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[4]\u001b[39m\u001b[32m, line 22\u001b[39m\n\u001b[32m 20\u001b[39m \u001b[38;5;66;03m#first generated integrated fluence files for SNOwGLoBES\u001b[39;00m\n\u001b[32m 21\u001b[39m \u001b[38;5;28mprint\u001b[39m(\u001b[33m\"\u001b[39m\u001b[33mPreparing fluences ...\u001b[39m\u001b[33m\"\u001b[39m)\n\u001b[32m---> \u001b[39m\u001b[32m22\u001b[39m tarredoutfile = \u001b[43msnowglobes\u001b[49m\u001b[43m.\u001b[49m\u001b[43mgenerate_fluence\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmodel_folder\u001b[49m\u001b[43m \u001b[49m\u001b[43m+\u001b[49m\u001b[43m \u001b[49m\u001b[43mfilename\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmodeltype\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtransformation\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdistance\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43moutfile\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 24\u001b[39m \u001b[38;5;66;03m#run the fluence file through SNOwGLoBES \u001b[39;00m\n\u001b[32m 25\u001b[39m \u001b[38;5;28mprint\u001b[39m(\u001b[33m\"\u001b[39m\u001b[33mRunning SNOwGLoBES ...\u001b[39m\u001b[33m\"\u001b[39m)\n", + "\u001b[36mFile \u001b[39m\u001b[32m/opt/anaconda3/envs/snewpyDevelopments/lib/python3.12/site-packages/snewpy/snowglobes.py:132\u001b[39m, in \u001b[36mgenerate_fluence\u001b[39m\u001b[34m(model_path, model_type, transformation_type, d, output_filename, tstart, tend, snmodel_dict)\u001b[39m\n\u001b[32m 102\u001b[39m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34mgenerate_fluence\u001b[39m(model_path, model_type, transformation_type, d, output_filename=\u001b[38;5;28;01mNone\u001b[39;00m, tstart=\u001b[38;5;28;01mNone\u001b[39;00m, tend=\u001b[38;5;28;01mNone\u001b[39;00m, snmodel_dict={}):\n\u001b[32m 103\u001b[39m \u001b[38;5;250m \u001b[39m\u001b[33;03m\"\"\"Generate fluence files in SNOwGLoBES format.\u001b[39;00m\n\u001b[32m 104\u001b[39m \n\u001b[32m 105\u001b[39m \u001b[33;03m This version will subsample the times in a supernova model, produce energy\u001b[39;00m\n\u001b[32m (...)\u001b[39m\u001b[32m 130\u001b[39m \u001b[33;03m Path of NumPy archive file with neutrino fluence data.\u001b[39;00m\n\u001b[32m 131\u001b[39m \u001b[33;03m \"\"\"\u001b[39;00m\n\u001b[32m--> \u001b[39m\u001b[32m132\u001b[39m model_class = \u001b[38;5;28;43mgetattr\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43msnewpy\u001b[49m\u001b[43m.\u001b[49m\u001b[43mmodels\u001b[49m\u001b[43m.\u001b[49m\u001b[43mccsn_loaders\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmodel_type\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 134\u001b[39m \u001b[38;5;66;03m# Choose flavor transformation. Use dict to associate the transformation name with its class.\u001b[39;00m\n\u001b[32m 135\u001b[39m NMO = MixingParameters(\u001b[33m'\u001b[39m\u001b[33mNORMAL\u001b[39m\u001b[33m'\u001b[39m)\n", + "\u001b[31mAttributeError\u001b[39m: module 'snewpy.models.ccsn_loaders' has no attribute 'Analytic3Species'" + ] + } + ], "source": [ "#set model type as \n", "modeltype = 'Analytic3Species'\n", @@ -215,7 +264,8 @@ "hash": "12a4e164d15418f42fe0584c567a58ace9669f8a11b564e22ebd17e6959ef919" }, "kernelspec": { - "display_name": "Python 3.9.5 64-bit ('snews': conda)", + "display_name": "Python 3 (ipykernel)", + "language": "python", "name": "python3" }, "language_info": { @@ -228,9 +278,9 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.11.4" + "version": "3.12.2" } }, "nbformat": 4, - "nbformat_minor": 2 + "nbformat_minor": 4 } diff --git a/python/snewpy/models/ccsn.py b/python/snewpy/models/ccsn.py index 0bf0ad9a..92125abd 100644 --- a/python/snewpy/models/ccsn.py +++ b/python/snewpy/models/ccsn.py @@ -369,6 +369,57 @@ def __init__(self, axion_mass:u.Quantity, axion_coupling:u.Quantity): self.metadata['PNS mass'] = pns_mass*u.Msun return super().__init__(filename, self.metadata) + +@RegistryModel( + _param_validator = lambda p: \ + (p['progenitor_mass'].to_value('Msun') in (11.2, 20, 25) and p['axion_mass'] == 0 and p['axion_coupling'] == 0) or \ + (p['progenitor_mass'].to_value('Msun') in (11.2, 20, 25) and p['axion_mass'].to_value('MeV') in (40, 100, 150, 200, 300, 600, 400, 800) and p['axion_coupling'].to_value('1e-10/GeV') in (2, 4, 6, 8, 10)), + + progenitor_mass = Parameter(values=[11.2, 20, 25]<`_. """ diff --git a/python/snewpy/models/ccsn_loaders.py b/python/snewpy/models/ccsn_loaders.py index f687b840..6b385d80 100644 --- a/python/snewpy/models/ccsn_loaders.py +++ b/python/snewpy/models/ccsn_loaders.py @@ -846,6 +846,47 @@ def __init__(self, filename, metadata={}): super().__init__(simtab, metadata) +class Takata_2025(PinchedModel): + def __init__(self, filename, metadata={}): + """ + Parameters + ---------- + filename: str + Absolute or relative path to file prefix. + + """ + + # Open the requested filename using the model downloader.\ + datafile = self.request_file(filename) + + self.metadata = metadata + + #Read ASCII data. + simtab = Table.read(datafile, format='ascii') + + #Remove the first table row, which appears to have zero input. + simtab = simtab[simtab['1:t_sim[s]'] > 0] + + #Get grid of model times. + simtab['TIME'] = simtab['2:t_pb[s]'] << u.s + for j, (f, fkey) in enumerate(zip(["NU_E", "NU_E_BAR", "NU_X"], 'ebx')): + simtab[f'L_{f}'] = simtab[f'{6+j}:Le{fkey}[e/s]'] << u.erg / u.s + # Compute the pinch parameter from E_rms and E_avg + # E_rms^2/^2 = (2+a)/(1+a) + Eavg = simtab[f'{9+j}:Em{fkey}[MeV]'] + Erms = simtab[f'{12+j}:Er{fkey}[MeV]'] + x = Erms**2 / Eavg**2 + alpha = (2-x)/(x-1) + + simtab[f'E_{f}'] = Eavg << u.MeV + simtab[f'Erms_{f}'] = Erms << u.MeV + simtab[f'ALPHA_{f}'] = alpha + + self.filename = os.path.basename(filename) + + super().__init__(simtab, metadata) + + class Bugli_2021(PinchedModel): """Model based on `Buggli (2021) `_. """ diff --git a/python/snewpy/models/model_files.yml b/python/snewpy/models/model_files.yml index 3e9b9840..2db59a68 100644 --- a/python/snewpy/models/model_files.yml +++ b/python/snewpy/models/model_files.yml @@ -54,6 +54,9 @@ models: Mori_2023: repository: *ccsn_repository + Takata_2025: + repository: *ccsn_repository + Bugli_2021: repository: *ccsn_repository