GFALL levels & lines

gfall_levels_lines.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/epassaro/miniconda3/envs/carsus/lib/python3.6/site-packages/tqdm/autonotebook/__init__.py:18: TqdmExperimentalWarning: Using `tqdm.autonotebook.tqdm` in notebook mode. Use `tqdm.tqdm` instead to force console mode (e.g. in jupyter console)\n",
      "  \" (e.g. in jupyter console)\", TqdmExperimentalWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Initializing the database at sqlite://\n",
      "Ingesting basic atomic data\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from carsus import init_db\n",
    "from carsus.model import Atom, Ion, Level, Line\n",
    "from carsus.io.output import AtomData\n",
    "from carsus.io.nist import NISTIonizationEnergiesIngester\n",
    "from carsus.io.kurucz import GFALLIngester\n",
    "session = init_db()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# This is the usual SQL stuff"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Downloading ionization energies from the NIST Atomic Spectra Database\n",
      "Ingesting ionization energies from nist-asd\n",
      "Ingesting ground levels from nist-asd\n"
     ]
    }
   ],
   "source": [
    "ioniz_energies_ingester = NISTIonizationEnergiesIngester(session, spectra=\"Si\")\n",
    "ioniz_energies_ingester.ingest(ionization_energies=True, ground_levels=True)\n",
    "session.commit()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[\u001b[1mcarsus.io.kurucz.gfall\u001b[0m][\u001b[1;33mWARNING\u001b[0m]  A specific combination to identify unique levels from the gfall data has not been given. Defaulting to [\"energy\", \"j\"]. (\u001b[1mgfall.py\u001b[0m:72)\n",
      "[\u001b[1mcarsus.io.kurucz.gfall\u001b[0m][\u001b[1;37mINFO\u001b[0m   ]  Parsing GFALL ../gfall.dat (\u001b[1mgfall.py\u001b[0m:120)\n",
      "Ingesting levels from ku_latest\n",
      "Ingesting levels for Si 1\n",
      "Ingesting levels for Si 2\n",
      "[\u001b[1mcarsus.io.kurucz.gfall\u001b[0m][\u001b[1;37mINFO\u001b[0m   ]  Extracting line data: atomic_number, ion_charge, energy_lower, j_lower, energy_upper, j_upper, wavelength, loggf (\u001b[1mgfall.py\u001b[0m:296)\n",
      "Ingesting lines from ku_latest\n",
      "Ingesting lines for Si 1\n",
      "Ingesting lines for Si 2\n"
     ]
    }
   ],
   "source": [
    "gfall_ingester = GFALLIngester(session, fname=\"../gfall.dat\", ions=\"Si 1-2\")\n",
    "gfall_ingester.ingest(levels=True, lines=True)\n",
    "session.commit()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "atom_data = AtomData(session, selected_atoms=\"Si\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Now we try to replicate the same DataFrame with our new class"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "from carsus.io.kurucz.gfall import GFALL"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[\u001b[1mcarsus.io.kurucz.gfall\u001b[0m][\u001b[1;33mWARNING\u001b[0m]  A specific combination to identify unique levels from the gfall data has not been given. Defaulting to [\"energy\", \"j\"]. (\u001b[1mgfall.py\u001b[0m:72)\n",
      "Downloading ionization energies from the NIST Atomic Spectra Database\n",
      "[\u001b[1mcarsus.io.kurucz.gfall\u001b[0m][\u001b[1;37mINFO\u001b[0m   ]  Parsing GFALL ./gfall.dat (\u001b[1mgfall.py\u001b[0m:120)\n",
      "[\u001b[1mcarsus.io.kurucz.gfall\u001b[0m][\u001b[1;37mINFO\u001b[0m   ]  Extracting line data: atomic_number, ion_charge, energy_lower, j_lower, energy_upper, j_upper, wavelength, loggf (\u001b[1mgfall.py\u001b[0m:296)\n",
      "[\u001b[1mpy.warnings         \u001b[0m][\u001b[1;33mWARNING\u001b[0m]  /home/epassaro/Desktop/gsoc/notebooks/carsus/io/kurucz/gfall.py:546: PerformanceWarning: indexing past lexsort depth may impact performance.\n",
      "  df = gf.lines.loc[ion]\n",
      " (\u001b[1mwarnings.py\u001b[0m:99)\n",
      "[\u001b[1mpy.warnings         \u001b[0m][\u001b[1;33mWARNING\u001b[0m]  /home/epassaro/Desktop/gsoc/notebooks/carsus/io/kurucz/gfall.py:546: PerformanceWarning: indexing past lexsort depth may impact performance.\n",
      "  df = gf.lines.loc[ion]\n",
      " (\u001b[1mwarnings.py\u001b[0m:99)\n",
      "[\u001b[1mpy.warnings         \u001b[0m][\u001b[1;33mWARNING\u001b[0m]  /home/epassaro/Desktop/gsoc/notebooks/carsus/io/kurucz/gfall.py:549: PerformanceWarning: indexing past lexsort depth may impact performance.\n",
      "  lvl_index2id = levels.set_index(['atomic_number', 'ion_number']).loc[ion]\n",
      " (\u001b[1mwarnings.py\u001b[0m:99)\n",
      "[\u001b[1mpy.warnings         \u001b[0m][\u001b[1;33mWARNING\u001b[0m]  /home/epassaro/Desktop/gsoc/notebooks/carsus/io/kurucz/gfall.py:549: PerformanceWarning: indexing past lexsort depth may impact performance.\n",
      "  lvl_index2id = levels.set_index(['atomic_number', 'ion_number']).loc[ion]\n",
      " (\u001b[1mwarnings.py\u001b[0m:99)\n",
      "[\u001b[1mpy.warnings         \u001b[0m][\u001b[1;33mWARNING\u001b[0m]  /home/epassaro/miniconda3/envs/carsus/lib/python3.6/site-packages/pandas/core/frame.py:6692: FutureWarning: Sorting because non-concatenation axis is not aligned. A future version\n",
      "of pandas will change to not sort by default.\n",
      "\n",
      "To accept the future behavior, pass 'sort=False'.\n",
      "\n",
      "To retain the current behavior and silence the warning, pass 'sort=True'.\n",
      "\n",
      "  sort=sort)\n",
      " (\u001b[1mwarnings.py\u001b[0m:99)\n",
      "[\u001b[1mpy.warnings         \u001b[0m][\u001b[1;33mWARNING\u001b[0m]  /home/epassaro/miniconda3/envs/carsus/lib/python3.6/site-packages/pandas/core/frame.py:6692: FutureWarning: Sorting because non-concatenation axis is not aligned. A future version\n",
      "of pandas will change to not sort by default.\n",
      "\n",
      "To accept the future behavior, pass 'sort=False'.\n",
      "\n",
      "To retain the current behavior and silence the warning, pass 'sort=True'.\n",
      "\n",
      "  sort=sort)\n",
      " (\u001b[1mwarnings.py\u001b[0m:99)\n"
     ]
    }
   ],
   "source": [
    "x = GFALL('./gfall.dat', 'Si 1-2')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Saving to HDF5\n",
    "x.to_hdf('test_si2_levels_lines.h5')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Compare the two DataFrames"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "levels = pd.read_hdf('test_si2_levels_lines.h5', key='levels') \n",
    "lines = pd.read_hdf('test_si2_levels_lines.h5', key='lines') "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[\u001b[1mpy.warnings         \u001b[0m][\u001b[1;33mWARNING\u001b[0m]  /home/epassaro/miniconda3/envs/carsus/lib/python3.6/site-packages/pandas/core/frame.py:6692: FutureWarning: Sorting because non-concatenation axis is not aligned. A future version\n",
      "of pandas will change to not sort by default.\n",
      "\n",
      "To accept the future behavior, pass 'sort=False'.\n",
      "\n",
      "To retain the current behavior and silence the warning, pass 'sort=True'.\n",
      "\n",
      "  sort=sort)\n",
      " (\u001b[1mwarnings.py\u001b[0m:99)\n",
      "[\u001b[1mpy.warnings         \u001b[0m][\u001b[1;33mWARNING\u001b[0m]  /home/epassaro/miniconda3/envs/carsus/lib/python3.6/site-packages/pandas/core/frame.py:6692: FutureWarning: Sorting because non-concatenation axis is not aligned. A future version\n",
      "of pandas will change to not sort by default.\n",
      "\n",
      "To accept the future behavior, pass 'sort=False'.\n",
      "\n",
      "To retain the current behavior and silence the warning, pass 'sort=True'.\n",
      "\n",
      "  sort=sort)\n",
      " (\u001b[1mwarnings.py\u001b[0m:99)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(272, 3)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "atom_data.levels_prepared.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "energy        272\n",
       "g             272\n",
       "metastable    272\n",
       "dtype: int64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "atom_data.levels_prepared.eq(levels).sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1815, 8)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "atom_data.lines_prepared.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "line_id       1815\n",
       "wavelength    1815\n",
       "f_ul          1815\n",
       "f_lu          1815\n",
       "nu            1815\n",
       "B_lu          1815\n",
       "B_ul          1815\n",
       "A_ul          1815\n",
       "dtype: int64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "atom_data.lines_prepared.eq(lines).sum()"
   ]
  }
 ],
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