docs/Notebooks/Compare_precisions.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# RV precision comparision\n",
    "This compares the RV precisions by eniric in 2017 which fixed a masking bug in condition #2 and changed the normalization impelentation.\n",
    "it compares RV precision calculated in 2017 to the published results in Figueria et al 2016.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from os.path import join\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "from eniric import config"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load in the precision results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Simulation</th>\n",
       "      <th>RV_Cond_1[m/s]</th>\n",
       "      <th>RV_Cond_2[m/s]</th>\n",
       "      <th>RV_Cond_3[m/s]</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>M0-Z-1.0-60k</td>\n",
       "      <td>8.9</td>\n",
       "      <td>26.1</td>\n",
       "      <td>9.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>M0-Z-1.0-80k</td>\n",
       "      <td>6.0</td>\n",
       "      <td>17.1</td>\n",
       "      <td>6.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>M0-Z-1.0-100k</td>\n",
       "      <td>4.5</td>\n",
       "      <td>12.8</td>\n",
       "      <td>4.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>M0-Z-5.0-60k</td>\n",
       "      <td>13.6</td>\n",
       "      <td>38.9</td>\n",
       "      <td>14.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>M0-Z-5.0-80k</td>\n",
       "      <td>10.6</td>\n",
       "      <td>30.5</td>\n",
       "      <td>10.9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Simulation  RV_Cond_1[m/s]  RV_Cond_2[m/s]  RV_Cond_3[m/s]\n",
       "0   M0-Z-1.0-60k             8.9            26.1             9.3\n",
       "1   M0-Z-1.0-80k             6.0            17.1             6.2\n",
       "2  M0-Z-1.0-100k             4.5            12.8             4.6\n",
       "3   M0-Z-5.0-60k            13.6            38.9            14.0\n",
       "4   M0-Z-5.0-80k            10.6            30.5            10.9"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "path = config.paths[\"precision_results\"]\n",
    "old_precision_file = join(path,\n",
    "    \"precision_figueira_2016.dat\")\n",
    "df_pub = pd.read_csv(old_precision_file, sep=\"\\t\")\n",
    "df_pub.columns\n",
    "# df_old_corrected = ...\n",
    "\n",
    "df_pub.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Simulation</th>\n",
       "      <th>prec_1</th>\n",
       "      <th>prec_2</th>\n",
       "      <th>prec_3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>M0-Z-1.0-60k</td>\n",
       "      <td>9.0</td>\n",
       "      <td>14.8</td>\n",
       "      <td>9.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>M0-Z-1.0-80k</td>\n",
       "      <td>6.0</td>\n",
       "      <td>9.9</td>\n",
       "      <td>6.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>M0-Z-1.0-100k</td>\n",
       "      <td>4.5</td>\n",
       "      <td>7.5</td>\n",
       "      <td>4.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>M0-Z-5.0-60k</td>\n",
       "      <td>13.7</td>\n",
       "      <td>22.4</td>\n",
       "      <td>14.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>M0-Z-5.0-80k</td>\n",
       "      <td>10.7</td>\n",
       "      <td>17.4</td>\n",
       "      <td>11.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      "text/plain": [
       "      Simulation  prec_1  prec_2  prec_3\n",
       "0   M0-Z-1.0-60k     9.0    14.8     9.3\n",
       "1   M0-Z-1.0-80k     6.0     9.9     6.2\n",
       "2  M0-Z-1.0-100k     4.5     7.5     4.7\n",
       "3   M0-Z-5.0-60k    13.7    22.4    14.2\n",
       "4   M0-Z-5.0-80k    10.7    17.4    11.0"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_snrnorm_file = join(path, \"precision_results_2017.dat\")\n",
    "df_new = pd.read_csv(new_snrnorm_file, sep=\"\\t\")\n",
    "df_new = df_new.rename(columns={\"# id\": \"Simulation\"})\n",
    "# Round to 1dp\n",
    "df_new.prec_1 = np.round(df_new.prec_1, 1)\n",
    "df_new.prec_2 = np.round(df_new.prec_2, 1)\n",
    "df_new.prec_3 = np.round(df_new.prec_3, 1)\n",
    "df_new.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Check all rows are the same between tables.\n",
    "assert np.all(df_pub.Simulation == df_new.Simulation)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['Simulation', 'RV_Cond_1[m/s]', 'RV_Cond_2[m/s]', 'RV_Cond_3[m/s]'], dtype='object')\n",
      "Index(['Simulation', 'prec_1', 'prec_2', 'prec_3'], dtype='object')\n"
     ]
    }
   ],
   "source": [
    "# Header names Simulation RV_Cond_1[m/s] RV_Cond_2[m/s] RV_Cond_3[m/s]\n",
    "print(df_pub.columns)\n",
    "print(df_new.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of Simulations that improve precision from all fixes = 0\n",
      "Number of Simulations that worsen precision from all fixes = 110\n",
      "Number of Simulations that improve precision from all fixes = 69\n",
      "Number of Simulations that worsen precision from all fixes = 111\n",
      "Number of Simulations that improve precision from all fixes = 0\n",
      "Number of Simulations that worsen precision from all fixes = 104\n"
     ]
    }
   ],
   "source": [
    "# Compare results of 2017 precsions with fixing clumping and changing the normalization.\n",
    "# Comapre between df_new and df_pub\n",
    "\n",
    "# Calcualte percentage difference inc precsion of cond 1, 2, 3\n",
    "# With 1 and 3 testing the normalization.\n",
    "# Count number of percision in cond 2 that decreased\n",
    "cond_1_up = (df_new[\"prec_1\"] > df_pub[\"RV_Cond_1[m/s]\"]).sum()\n",
    "cond_1_down = (df_new[\"prec_1\"] < df_pub[\"RV_Cond_1[m/s]\"]).sum()\n",
    "cond_2_up = (df_new[\"prec_2\"] > df_pub[\"RV_Cond_2[m/s]\"]).sum()\n",
    "cond_2_down = (df_new[\"prec_2\"] < df_pub[\"RV_Cond_2[m/s]\"]).sum()\n",
    "cond_3_up = (df_new[\"prec_3\"] > df_pub[\"RV_Cond_3[m/s]\"]).sum()\n",
    "cond_3_down = (df_new[\"prec_3\"] < df_pub[\"RV_Cond_3[m/s]\"]).sum()\n",
    "print(\n",
    "    \"Number of Simulations that improve precision from all fixes = {}\".format(\n",
    "        cond_1_down\n",
    "    )\n",
    ")\n",
    "print(\n",
    "    \"Number of Simulations that worsen precision from all fixes = {}\".format(cond_1_up)\n",
    ")\n",
    "print(\n",
    "    \"Number of Simulations that improve precision from all fixes = {}\".format(\n",
    "        cond_2_down\n",
    "    )\n",
    ")\n",
    "print(\n",
    "    \"Number of Simulations that worsen precision from all fixes = {}\".format(cond_2_up)\n",
    ")\n",
    "print(\n",
    "    \"Number of Simulations that improve precision from all fixes = {}\".format(\n",
    "        cond_3_down\n",
    "    )\n",
    ")\n",
    "print(\n",
    "    \"Number of Simulations that worsen precision from all fixes = {}\".format(cond_3_up)\n",
    ")\n",
    "\n",
    "\n",
    "# Percentage of change\n",
    "# 100 * (new - old) / old\n",
    "all_cond1_percent_diff = (\n",
    "    100 * (df_new[\"prec_1\"] - df_pub[\"RV_Cond_1[m/s]\"]) / df_pub[\"RV_Cond_1[m/s]\"]\n",
    ")\n",
    "all_cond2_percent_diff = (\n",
    "    100 * (df_new[\"prec_2\"] - df_pub[\"RV_Cond_2[m/s]\"]) / df_pub[\"RV_Cond_2[m/s]\"]\n",
    ")\n",
    "all_cond3_percent_diff = (\n",
    "    100 * (df_new[\"prec_3\"] - df_pub[\"RV_Cond_3[m/s]\"]) / df_pub[\"RV_Cond_3[m/s]\"]\n",
    ")\n",
    "\n",
    "all_cond1_percent_diff.hist(bins=20, label=\"cond1\")\n",
    "plt.xlabel(\"Percentage of RV Change.\")\n",
    "plt.legend()\n",
    "plt.title(\"RV prevision change due to all fixes.\")\n",
    "plt.show()\n",
    "\n",
    "all_cond2_percent_diff.hist(bins=20, label=\"cond2\")\n",
    "plt.xlabel(\"Percentage of RV Change.\")\n",
    "plt.legend()\n",
    "plt.title(\"RV prevision change due to all fixes.\")\n",
    "plt.show()\n",
    "\n",
    "all_cond3_percent_diff.hist(bins=20, label=\"cond3\")\n",
    "plt.xlabel(\"Percentage of RV Change.\")\n",
    "plt.legend()\n",
    "plt.title(\"RV prevision change due to all fixes.\")\n",
    "plt.show()\n",
    "\n",
    "\n",
    "print(\"The majority of the changes comes from the bug in condition_2.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Find the large discrepencies in precision  >50 %erc\n",
    "\n",
    "# Find the extreme changing precisions\n",
    "\n",
    "percentage_lim = 200\n",
    "mask = all_cond2_percent_diff > percentage_lim\n",
    "dict_for_df = {\n",
    "    \"Simulation\": df_new.Simulation[mask],\n",
    "    \"pub_cond_2\": df_pub[\"RV_Cond_2[m/s]\"][mask],\n",
    "    \"new_cond_2\": df_new.prec_2[mask],\n",
    "    \"pub_cond_1\": df_pub[\"RV_Cond_1[m/s]\"][mask],\n",
    "    \"new_cond_1\": df_new.prec_1[mask],\n",
    "    \"pub_cond_3\": df_pub[\"RV_Cond_3[m/s]\"][mask],\n",
    "    \"new_cond_3\": df_new.prec_3[mask],\n",
    "}\n",
    "\n",
    "# print(df_new.Simulation[all_cond1_percent_diff > 2])\n",
    "df_large = pd.DataFrame(\n",
    "    dict_for_df\n",
    ")  # print(df_new.Simulation[all_cond3_percent_diff > 2])\n",
    "cols = df_large.columns.tolist()\n",
    "new_cols = [\n",
    "    \"Simulation\",\n",
    "    \"pub_cond_1\",\n",
    "    \"new_cond_1\",\n",
    "    \"pub_cond_2\",\n",
    "    \"new_cond_2\",\n",
    "    \"pub_cond_3\",\n",
    "    \"new_cond_3\",\n",
    "]\n",
    "df_large = df_large[new_cols]  # Reorder columns\n",
    "print(\"Simulations that have a large change in precision for condition 2.\")\n",
    "df_large.head()\n",
    "\n",
    "# Most large changes in precision from the K band."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "cols = df_large.columns.tolist()\n",
    "new_cols = [\n",
    "    \"Simulation\",\n",
    "    \"pub_cond_1\",\n",
    "    \"new_cond_1\",\n",
    "    \"pub_cond_2\",\n",
    "    \"new_cond_2\",\n",
    "    \"pub_cond_3\",\n",
    "    \"new_cond_3\",\n",
    "]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plot out RV values with published values.\n",
    "Including published precison 2 values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "res_colour = {\"60k\": \"blue\", \"80k\": \"green\", \"100k\": \"red\"}\n",
    "cond_marker = {1: \".\", 2: \"o\", 3: \"^\"}\n",
    "band_loc = {\"Z\": 1, \"Y\": 2, \"J\": 3, \"H\": 4, \"K\": 5}\n",
    "conditions = {1: [\"prec_1\"], 2: [\"prec_2\"], 3: [\"prec_3\"]}\n",
    "pub_conds = {1: [\"RV_Cond_1[m/s]\"], 2: [\"RV_Cond_2[m/s]\"], 3: [\"RV_Cond_3[m/s]\"]}\n",
    "vel = 1.0\n",
    "\n",
    "print(\"Dashed lines indicate the published cond_2 values.\")\n",
    "\n",
    "# for b in \"ZYJHK\":\n",
    "for star in [\"M0\", \"M3\", \"M6\", \"M9\"]:\n",
    "    # plt.figure()\n",
    "    for res in [\"60k\", \"80k\", \"100k\"]:\n",
    "        for cond in range(1, 4):\n",
    "            x_vals = [band_loc[b] for b in \"ZYJHK\"]\n",
    "            ids = [\"{0:s}-{1:s}-{2:.1f}-{3}\".format(star, b, vel, res) for b in \"ZYJHK\"]\n",
    "            df_masks = [df_new.Simulation == id for id in ids]\n",
    "            # .iat[0,0] gives the first(only element) in dataframe created by mask of index\n",
    "            pub_rv_vals = [df_pub[pub_conds[cond]][mask].iat[0, 0] for mask in df_masks]\n",
    "            plt.plot(\n",
    "                x_vals,\n",
    "                pub_rv_vals,\n",
    "                ls=\"--\",\n",
    "                marker=cond_marker[cond],\n",
    "                color=res_colour[res],\n",
    "            )\n",
    "\n",
    "            rv_vals = [df_new[conditions[cond]][mask].iat[0, 0] for mask in df_masks]\n",
    "            plt.plot(x_vals, rv_vals, marker=cond_marker[cond], color=res_colour[res])\n",
    "            plt.xticks(x_vals, [b for b in \"ZYJHK\"])\n",
    "\n",
    "    plt.ylabel(\"Precision [m/s]\")\n",
    "    plt.title(\"{0} with R_vel = {1} m/s\".format(star, vel))\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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