{ "cells": [ { "cell_type": "markdown", "id": "6461a0a5-b923-4af1-a937-fe448d3ff90f", "metadata": {}, "source": [ "# Population Comparison\n", "\n", "This is a notebook going through population comparison of the ExoComp table of chemical equilibrium retrieval results. This shows the calculations contained in the Section 3 of the paper \"The Library of Exoplanet Atmospheric Composition Measurements: Population Level Trends in Exoplanet Composition with ExoComp\".\n", "\n", "The paper used ExoComp commit [f55bde2](https://github.com/jlothringer/exocomp/commit/f55bde2d425096b9d6355c063d3fe89587662763)." ] }, { "cell_type": "code", "execution_count": 1, "id": "8397985a-bc96-4562-b7d7-b7d5919fd1e9", "metadata": {}, "outputs": [], "source": [ "from exocomp import Abund\n", "from IPython.display import display\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "import pandas as pd\n", "import os\n", "\n", "# Suppress slice copy warning\n", "pd.options.mode.chained_assignment = None # default='warn'\n", "\n", "cs = ['#1f77b4', '#ff7f0e', '#d62728','#2ca02c', '#9467bd', '#8c564b', '#e377c2', '#7f7f7f', '#bcbd22', '#17becf']\n", "\n", "os.makedirs('./paper_figs/', exist_ok=True)" ] }, { "cell_type": "code", "execution_count": 2, "id": "6193d7e2-5219-4473-b512-3e7c9849127d", "metadata": {}, "outputs": [], "source": [ "# Read in various datasets used throughout\n", "\n", "#data = pd.read_csv('Exoplanet_Atmo_Measurements.csv',comment='#')\n", "#data = pd.read_csv('Exoplanet_Atmo_Measurements_8_13_25.csv',comment='#')\n", "#data = pd.read_csv('Exoplanet_Atmo_Measurements_8_24_25.csv',comment='#')\n", "data = pd.read_csv('Exoplanet_Atmo_Measurements_9_18_25.csv',comment='#')\n", "\n", "stars = pd.read_csv('hypatia-26072025.csv')\n", "bds = pd.read_csv('zalesky_22_BDs.txt') #only M/H and C/O verified after transcription!!\n", "\n", "uhj_list = ['WASP-18 b','WASP-121 b','WASP-76 b', 'WASP-178 b','MASCARA-1 b','WASP-189 b','WASP-33 b','KELT-20 b']" ] }, { "cell_type": "code", "execution_count": 3, "id": "328be75d-622d-4877-bc41-f901a67f3b58", "metadata": { "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Using Asplund09 solar abundances\n", "Using Lodders10 solar abundances\n", "Using Asplund09 solar abundances\n", "Using Asplund09 solar abundances\n", "Using Asplund09 solar abundances\n", "Using Asplund09 solar abundances\n", "Using Lodders10 solar abundances\n", "Using Asplund09 solar abundances\n", "Using Asplund09 solar abundances\n", "Using Asplund09 solar abundances\n", "Using Asplund09 solar abundances\n", "Using Asplund09 solar abundances\n", "Using Asplund09 solar abundances\n", "Using Asplund09 solar abundances\n", "Using Asplund09 solar abundances\n", "Using Asplund09 solar abundances\n", "Using Asplund09 solar abundances\n", "Using Asplund09 solar abundances\n", "Using Asplund09 solar abundances\n", "Using Asplund09 solar abundances\n", "Using Asplund09 solar abundances\n", "Using Asplund09 solar abundances\n", "Using Asplund09 solar abundances\n", "Using Asplund09 solar abundances\n", "Using Asplund09 solar abundances\n", "Using Asplund09 solar abundances\n", "Using Asplund09 solar abundances\n", "Using Asplund09 solar abundances\n", "Using Asplund09 solar abundances\n", "Using Asplund09 solar abundances\n", "Using Asplund09 solar abundances\n", "Using Asplund09 solar abundances\n", "Using Asplund09 solar abundances\n", "Using Asplund09 solar abundances\n", "Using Asplund09 solar abundances\n", "Using Asplund09 solar abundances\n", "Using Asplund09 solar abundances\n", "Using Asplund09 solar abundances\n", "Using Asplund09 solar abundances\n", "Using Asplund09 solar abundances\n", "Using Asplund21 solar abundances\n", "Sometimes POSEIDON varies O/H for C/O (Meech et al. 2025)\n", "Using Asplund09 solar abundances\n", "Sometimes POSEIDON varies O/H for C/O (Meech et al. 2025)\n", "Using Asplund09 solar abundances\n", "Using Asplund09 solar abundances\n", "Using Asplund09 solar abundances\n", "Sometimes POSEIDON varies O/H for C/O (Meech et al. 2025)\n", "Using Asplund09 solar abundances\n", "Using Asplund09 solar abundances\n", "Using Asplund09 solar abundances\n", "Using Asplund09 solar abundances\n", "Using Asplund09 solar abundances\n", "Using Asplund09 solar abundances\n", "Using Asplund09 solar abundances\n", "Using Asplund09 solar abundances\n", "Using Asplund09 solar abundances\n", "Using Asplund09 solar abundances\n", "Using Asplund09 solar abundances\n", "Using Asplund09 solar abundances\n", "Using Asplund09 solar abundances\n", "Using Asplund09 solar abundances\n", "Using Asplund09 solar abundances\n", "Using Asplund09 solar abundances\n", "Using Asplund09 solar abundances\n", "Using Asplund09 solar abundances\n", "Using Asplund09 solar abundances\n", "Using Asplund09 solar abundances\n", "Using Lodders10 solar abundances\n", "Using Asplund09 solar abundances\n", "Using Asplund09 solar abundances\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:32: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['O/H'].iloc[i] = bulk['O']\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:33: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['C/H'].iloc[i] = bulk['C']\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:34: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['Fe/H'].iloc[i] = bulk['Fe']\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:35: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['O/H Upper'].iloc[i] = bulk_err['O'][1]\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:36: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['C/H Upper'].iloc[i] = bulk_err['C'][1]\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:37: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['Fe/H Upper'].iloc[i] = bulk_err['Fe'][1]\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:38: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['O/H Lower'].iloc[i] = bulk_err['O'][0]\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:39: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['C/H Lower'].iloc[i] = bulk_err['C'][0]\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:40: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['Fe/H Lower'].iloc[i] = bulk_err['Fe'][0]\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:32: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['O/H'].iloc[i] = bulk['O']\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:33: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['C/H'].iloc[i] = bulk['C']\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:34: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['Fe/H'].iloc[i] = bulk['Fe']\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:35: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['O/H Upper'].iloc[i] = bulk_err['O'][1]\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:36: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['C/H Upper'].iloc[i] = bulk_err['C'][1]\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:37: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['Fe/H Upper'].iloc[i] = bulk_err['Fe'][1]\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:38: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['O/H Lower'].iloc[i] = bulk_err['O'][0]\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:39: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['C/H Lower'].iloc[i] = bulk_err['C'][0]\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:40: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['Fe/H Lower'].iloc[i] = bulk_err['Fe'][0]\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:32: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['O/H'].iloc[i] = bulk['O']\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:33: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['C/H'].iloc[i] = bulk['C']\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:34: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['Fe/H'].iloc[i] = bulk['Fe']\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:35: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['O/H Upper'].iloc[i] = bulk_err['O'][1]\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:36: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['C/H Upper'].iloc[i] = bulk_err['C'][1]\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:37: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['Fe/H Upper'].iloc[i] = bulk_err['Fe'][1]\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:38: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['O/H Lower'].iloc[i] = bulk_err['O'][0]\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:39: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['C/H Lower'].iloc[i] = bulk_err['C'][0]\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:40: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['Fe/H Lower'].iloc[i] = bulk_err['Fe'][0]\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:32: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['O/H'].iloc[i] = bulk['O']\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:33: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['C/H'].iloc[i] = bulk['C']\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:34: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['Fe/H'].iloc[i] = bulk['Fe']\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:35: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['O/H Upper'].iloc[i] = bulk_err['O'][1]\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:36: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['C/H Upper'].iloc[i] = bulk_err['C'][1]\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:37: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['Fe/H Upper'].iloc[i] = bulk_err['Fe'][1]\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:38: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['O/H Lower'].iloc[i] = bulk_err['O'][0]\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:39: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['C/H Lower'].iloc[i] = bulk_err['C'][0]\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:40: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['Fe/H Lower'].iloc[i] = bulk_err['Fe'][0]\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:32: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['O/H'].iloc[i] = bulk['O']\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:33: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['C/H'].iloc[i] = bulk['C']\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:34: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['Fe/H'].iloc[i] = bulk['Fe']\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:35: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['O/H Upper'].iloc[i] = bulk_err['O'][1]\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:36: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['C/H Upper'].iloc[i] = bulk_err['C'][1]\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:37: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['Fe/H Upper'].iloc[i] = bulk_err['Fe'][1]\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:38: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['O/H Lower'].iloc[i] = bulk_err['O'][0]\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:39: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['C/H Lower'].iloc[i] = bulk_err['C'][0]\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:40: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['Fe/H Lower'].iloc[i] = bulk_err['Fe'][0]\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:32: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['O/H'].iloc[i] = bulk['O']\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:33: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['C/H'].iloc[i] = bulk['C']\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:34: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['Fe/H'].iloc[i] = bulk['Fe']\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:35: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['O/H Upper'].iloc[i] = bulk_err['O'][1]\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:36: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['C/H Upper'].iloc[i] = bulk_err['C'][1]\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:37: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['Fe/H Upper'].iloc[i] = bulk_err['Fe'][1]\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:38: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['O/H Lower'].iloc[i] = bulk_err['O'][0]\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:39: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['C/H Lower'].iloc[i] = bulk_err['C'][0]\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:40: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['Fe/H Lower'].iloc[i] = bulk_err['Fe'][0]\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:32: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['O/H'].iloc[i] = bulk['O']\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:33: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['C/H'].iloc[i] = bulk['C']\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:34: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['Fe/H'].iloc[i] = bulk['Fe']\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:35: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['O/H Upper'].iloc[i] = bulk_err['O'][1]\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:36: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['C/H Upper'].iloc[i] = bulk_err['C'][1]\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:37: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['Fe/H Upper'].iloc[i] = bulk_err['Fe'][1]\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:38: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['O/H Lower'].iloc[i] = bulk_err['O'][0]\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:39: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['C/H Lower'].iloc[i] = bulk_err['C'][0]\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:40: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['Fe/H Lower'].iloc[i] = bulk_err['Fe'][0]\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:32: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['O/H'].iloc[i] = bulk['O']\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:33: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['C/H'].iloc[i] = bulk['C']\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:34: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['Fe/H'].iloc[i] = bulk['Fe']\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:35: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['O/H Upper'].iloc[i] = bulk_err['O'][1]\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:36: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['C/H Upper'].iloc[i] = bulk_err['C'][1]\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:37: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['Fe/H Upper'].iloc[i] = bulk_err['Fe'][1]\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:38: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['O/H Lower'].iloc[i] = bulk_err['O'][0]\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:39: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['C/H Lower'].iloc[i] = bulk_err['C'][0]\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:40: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['Fe/H Lower'].iloc[i] = bulk_err['Fe'][0]\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:32: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['O/H'].iloc[i] = bulk['O']\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:33: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['C/H'].iloc[i] = bulk['C']\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:34: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['Fe/H'].iloc[i] = bulk['Fe']\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:35: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['O/H Upper'].iloc[i] = bulk_err['O'][1]\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:36: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['C/H Upper'].iloc[i] = bulk_err['C'][1]\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:37: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['Fe/H Upper'].iloc[i] = bulk_err['Fe'][1]\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:38: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['O/H Lower'].iloc[i] = bulk_err['O'][0]\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:39: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['C/H Lower'].iloc[i] = bulk_err['C'][0]\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:40: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['Fe/H Lower'].iloc[i] = bulk_err['Fe'][0]\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:32: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['O/H'].iloc[i] = bulk['O']\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:33: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['C/H'].iloc[i] = bulk['C']\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:34: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['Fe/H'].iloc[i] = bulk['Fe']\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:35: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['O/H Upper'].iloc[i] = bulk_err['O'][1]\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:36: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['C/H Upper'].iloc[i] = bulk_err['C'][1]\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:37: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['Fe/H Upper'].iloc[i] = bulk_err['Fe'][1]\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:38: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['O/H Lower'].iloc[i] = bulk_err['O'][0]\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:39: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['C/H Lower'].iloc[i] = bulk_err['C'][0]\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:40: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['Fe/H Lower'].iloc[i] = bulk_err['Fe'][0]\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:32: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['O/H'].iloc[i] = bulk['O']\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:33: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['C/H'].iloc[i] = bulk['C']\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:34: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['Fe/H'].iloc[i] = bulk['Fe']\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:35: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['O/H Upper'].iloc[i] = bulk_err['O'][1]\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:36: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['C/H Upper'].iloc[i] = bulk_err['C'][1]\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:37: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['Fe/H Upper'].iloc[i] = bulk_err['Fe'][1]\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:38: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['O/H Lower'].iloc[i] = bulk_err['O'][0]\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:39: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['C/H Lower'].iloc[i] = bulk_err['C'][0]\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:40: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['Fe/H Lower'].iloc[i] = bulk_err['Fe'][0]\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:32: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['O/H'].iloc[i] = bulk['O']\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:33: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['C/H'].iloc[i] = bulk['C']\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:34: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['Fe/H'].iloc[i] = bulk['Fe']\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:35: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['O/H Upper'].iloc[i] = bulk_err['O'][1]\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:36: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['C/H Upper'].iloc[i] = bulk_err['C'][1]\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:37: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['Fe/H Upper'].iloc[i] = bulk_err['Fe'][1]\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:38: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['O/H Lower'].iloc[i] = bulk_err['O'][0]\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:39: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['C/H Lower'].iloc[i] = bulk_err['C'][0]\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:40: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['Fe/H Lower'].iloc[i] = bulk_err['Fe'][0]\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:32: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['O/H'].iloc[i] = bulk['O']\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:33: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['C/H'].iloc[i] = bulk['C']\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:34: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['Fe/H'].iloc[i] = bulk['Fe']\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:35: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['O/H Upper'].iloc[i] = bulk_err['O'][1]\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:36: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['C/H Upper'].iloc[i] = bulk_err['C'][1]\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:37: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['Fe/H Upper'].iloc[i] = bulk_err['Fe'][1]\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:38: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['O/H Lower'].iloc[i] = bulk_err['O'][0]\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:39: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['C/H Lower'].iloc[i] = bulk_err['C'][0]\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:40: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['Fe/H Lower'].iloc[i] = bulk_err['Fe'][0]\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:32: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['O/H'].iloc[i] = bulk['O']\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:33: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['C/H'].iloc[i] = bulk['C']\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:34: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['Fe/H'].iloc[i] = bulk['Fe']\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:35: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['O/H Upper'].iloc[i] = bulk_err['O'][1]\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:36: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['C/H Upper'].iloc[i] = bulk_err['C'][1]\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:37: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['Fe/H Upper'].iloc[i] = bulk_err['Fe'][1]\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:38: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['O/H Lower'].iloc[i] = bulk_err['O'][0]\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:39: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['C/H Lower'].iloc[i] = bulk_err['C'][0]\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:40: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['Fe/H Lower'].iloc[i] = bulk_err['Fe'][0]\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:32: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['O/H'].iloc[i] = bulk['O']\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:33: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['C/H'].iloc[i] = bulk['C']\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:34: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['Fe/H'].iloc[i] = bulk['Fe']\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:35: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['O/H Upper'].iloc[i] = bulk_err['O'][1]\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:36: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['C/H Upper'].iloc[i] = bulk_err['C'][1]\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:37: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['Fe/H Upper'].iloc[i] = bulk_err['Fe'][1]\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:38: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['O/H Lower'].iloc[i] = bulk_err['O'][0]\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:39: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['C/H Lower'].iloc[i] = bulk_err['C'][0]\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:40: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['Fe/H Lower'].iloc[i] = bulk_err['Fe'][0]\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:32: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['O/H'].iloc[i] = bulk['O']\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:33: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['C/H'].iloc[i] = bulk['C']\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:34: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['Fe/H'].iloc[i] = bulk['Fe']\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:35: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['O/H Upper'].iloc[i] = bulk_err['O'][1]\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:36: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['C/H Upper'].iloc[i] = bulk_err['C'][1]\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:37: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['Fe/H Upper'].iloc[i] = bulk_err['Fe'][1]\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:38: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['O/H Lower'].iloc[i] = bulk_err['O'][0]\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:39: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['C/H Lower'].iloc[i] = bulk_err['C'][0]\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:40: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['Fe/H Lower'].iloc[i] = bulk_err['Fe'][0]\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:32: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['O/H'].iloc[i] = bulk['O']\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:33: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['C/H'].iloc[i] = bulk['C']\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:34: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['Fe/H'].iloc[i] = bulk['Fe']\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:35: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['O/H Upper'].iloc[i] = bulk_err['O'][1]\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:36: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['C/H Upper'].iloc[i] = bulk_err['C'][1]\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:37: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['Fe/H Upper'].iloc[i] = bulk_err['Fe'][1]\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:38: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['O/H Lower'].iloc[i] = bulk_err['O'][0]\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:39: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['C/H Lower'].iloc[i] = bulk_err['C'][0]\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:40: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['Fe/H Lower'].iloc[i] = bulk_err['Fe'][0]\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:32: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['O/H'].iloc[i] = bulk['O']\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:33: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['C/H'].iloc[i] = bulk['C']\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:34: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['Fe/H'].iloc[i] = bulk['Fe']\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:35: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['O/H Upper'].iloc[i] = bulk_err['O'][1]\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:36: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['C/H Upper'].iloc[i] = bulk_err['C'][1]\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:37: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['Fe/H Upper'].iloc[i] = bulk_err['Fe'][1]\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:38: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['O/H Lower'].iloc[i] = bulk_err['O'][0]\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:39: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['C/H Lower'].iloc[i] = bulk_err['C'][0]\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:40: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['Fe/H Lower'].iloc[i] = bulk_err['Fe'][0]\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:32: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['O/H'].iloc[i] = bulk['O']\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:33: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['C/H'].iloc[i] = bulk['C']\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:34: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['Fe/H'].iloc[i] = bulk['Fe']\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:35: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['O/H Upper'].iloc[i] = bulk_err['O'][1]\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:36: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['C/H Upper'].iloc[i] = bulk_err['C'][1]\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:37: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['Fe/H Upper'].iloc[i] = bulk_err['Fe'][1]\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:38: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['O/H Lower'].iloc[i] = bulk_err['O'][0]\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:39: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['C/H Lower'].iloc[i] = bulk_err['C'][0]\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:40: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['Fe/H Lower'].iloc[i] = bulk_err['Fe'][0]\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:32: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['O/H'].iloc[i] = bulk['O']\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:33: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['C/H'].iloc[i] = bulk['C']\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:34: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['Fe/H'].iloc[i] = bulk['Fe']\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:35: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['O/H Upper'].iloc[i] = bulk_err['O'][1]\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:36: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['C/H Upper'].iloc[i] = bulk_err['C'][1]\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:37: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['Fe/H Upper'].iloc[i] = bulk_err['Fe'][1]\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:38: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['O/H Lower'].iloc[i] = bulk_err['O'][0]\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:39: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['C/H Lower'].iloc[i] = bulk_err['C'][0]\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:40: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['Fe/H Lower'].iloc[i] = bulk_err['Fe'][0]\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:32: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['O/H'].iloc[i] = bulk['O']\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:33: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['C/H'].iloc[i] = bulk['C']\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:34: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['Fe/H'].iloc[i] = bulk['Fe']\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:35: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['O/H Upper'].iloc[i] = bulk_err['O'][1]\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:36: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['C/H Upper'].iloc[i] = bulk_err['C'][1]\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:37: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['Fe/H Upper'].iloc[i] = bulk_err['Fe'][1]\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:38: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['O/H Lower'].iloc[i] = bulk_err['O'][0]\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:39: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['C/H Lower'].iloc[i] = bulk_err['C'][0]\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:40: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['Fe/H Lower'].iloc[i] = bulk_err['Fe'][0]\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:32: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['O/H'].iloc[i] = bulk['O']\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:33: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['C/H'].iloc[i] = bulk['C']\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:34: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['Fe/H'].iloc[i] = bulk['Fe']\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:35: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['O/H Upper'].iloc[i] = bulk_err['O'][1]\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:36: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['C/H Upper'].iloc[i] = bulk_err['C'][1]\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:37: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['Fe/H Upper'].iloc[i] = bulk_err['Fe'][1]\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:38: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['O/H Lower'].iloc[i] = bulk_err['O'][0]\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:39: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['C/H Lower'].iloc[i] = bulk_err['C'][0]\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:40: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['Fe/H Lower'].iloc[i] = bulk_err['Fe'][0]\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:32: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['O/H'].iloc[i] = bulk['O']\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:33: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['C/H'].iloc[i] = bulk['C']\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:34: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['Fe/H'].iloc[i] = bulk['Fe']\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:35: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['O/H Upper'].iloc[i] = bulk_err['O'][1]\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:36: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['C/H Upper'].iloc[i] = bulk_err['C'][1]\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:37: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['Fe/H Upper'].iloc[i] = bulk_err['Fe'][1]\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:38: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['O/H Lower'].iloc[i] = bulk_err['O'][0]\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:39: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['C/H Lower'].iloc[i] = bulk_err['C'][0]\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:40: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['Fe/H Lower'].iloc[i] = bulk_err['Fe'][0]\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:32: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['O/H'].iloc[i] = bulk['O']\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:33: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['C/H'].iloc[i] = bulk['C']\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:34: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['Fe/H'].iloc[i] = bulk['Fe']\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:35: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['O/H Upper'].iloc[i] = bulk_err['O'][1]\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:36: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['C/H Upper'].iloc[i] = bulk_err['C'][1]\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:37: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['Fe/H Upper'].iloc[i] = bulk_err['Fe'][1]\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:38: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['O/H Lower'].iloc[i] = bulk_err['O'][0]\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:39: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['C/H Lower'].iloc[i] = bulk_err['C'][0]\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:40: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['Fe/H Lower'].iloc[i] = bulk_err['Fe'][0]\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:32: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['O/H'].iloc[i] = bulk['O']\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:33: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['C/H'].iloc[i] = bulk['C']\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:34: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['Fe/H'].iloc[i] = bulk['Fe']\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:35: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['O/H Upper'].iloc[i] = bulk_err['O'][1]\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:36: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['C/H Upper'].iloc[i] = bulk_err['C'][1]\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:37: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['Fe/H Upper'].iloc[i] = bulk_err['Fe'][1]\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:38: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['O/H Lower'].iloc[i] = bulk_err['O'][0]\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:39: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['C/H Lower'].iloc[i] = bulk_err['C'][0]\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:40: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['Fe/H Lower'].iloc[i] = bulk_err['Fe'][0]\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:32: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['O/H'].iloc[i] = bulk['O']\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:33: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['C/H'].iloc[i] = bulk['C']\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:34: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['Fe/H'].iloc[i] = bulk['Fe']\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:35: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['O/H Upper'].iloc[i] = bulk_err['O'][1]\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:36: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['C/H Upper'].iloc[i] = bulk_err['C'][1]\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:37: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['Fe/H Upper'].iloc[i] = bulk_err['Fe'][1]\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:38: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['O/H Lower'].iloc[i] = bulk_err['O'][0]\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:39: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['C/H Lower'].iloc[i] = bulk_err['C'][0]\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:40: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['Fe/H Lower'].iloc[i] = bulk_err['Fe'][0]\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:32: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['O/H'].iloc[i] = bulk['O']\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:33: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['C/H'].iloc[i] = bulk['C']\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:34: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['Fe/H'].iloc[i] = bulk['Fe']\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:35: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['O/H Upper'].iloc[i] = bulk_err['O'][1]\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:36: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['C/H Upper'].iloc[i] = bulk_err['C'][1]\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:37: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['Fe/H Upper'].iloc[i] = bulk_err['Fe'][1]\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:38: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['O/H Lower'].iloc[i] = bulk_err['O'][0]\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:39: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['C/H Lower'].iloc[i] = bulk_err['C'][0]\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:40: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['Fe/H Lower'].iloc[i] = bulk_err['Fe'][0]\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:32: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['O/H'].iloc[i] = bulk['O']\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:33: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['C/H'].iloc[i] = bulk['C']\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:34: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['Fe/H'].iloc[i] = bulk['Fe']\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:35: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['O/H Upper'].iloc[i] = bulk_err['O'][1]\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:36: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['C/H Upper'].iloc[i] = bulk_err['C'][1]\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:37: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['Fe/H Upper'].iloc[i] = bulk_err['Fe'][1]\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:38: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['O/H Lower'].iloc[i] = bulk_err['O'][0]\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:39: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['C/H Lower'].iloc[i] = bulk_err['C'][0]\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:40: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['Fe/H Lower'].iloc[i] = bulk_err['Fe'][0]\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:32: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['O/H'].iloc[i] = bulk['O']\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:33: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['C/H'].iloc[i] = bulk['C']\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:34: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['Fe/H'].iloc[i] = bulk['Fe']\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:35: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['O/H Upper'].iloc[i] = bulk_err['O'][1]\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:36: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['C/H Upper'].iloc[i] = bulk_err['C'][1]\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:37: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['Fe/H Upper'].iloc[i] = bulk_err['Fe'][1]\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:38: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['O/H Lower'].iloc[i] = bulk_err['O'][0]\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:39: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['C/H Lower'].iloc[i] = bulk_err['C'][0]\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:40: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['Fe/H Lower'].iloc[i] = bulk_err['Fe'][0]\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:32: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['O/H'].iloc[i] = bulk['O']\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:33: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['C/H'].iloc[i] = bulk['C']\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:34: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['Fe/H'].iloc[i] = bulk['Fe']\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:35: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['O/H Upper'].iloc[i] = bulk_err['O'][1]\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:36: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['C/H Upper'].iloc[i] = bulk_err['C'][1]\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:37: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['Fe/H Upper'].iloc[i] = bulk_err['Fe'][1]\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:38: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['O/H Lower'].iloc[i] = bulk_err['O'][0]\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:39: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['C/H Lower'].iloc[i] = bulk_err['C'][0]\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:40: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['Fe/H Lower'].iloc[i] = bulk_err['Fe'][0]\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:32: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['O/H'].iloc[i] = bulk['O']\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:33: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['C/H'].iloc[i] = bulk['C']\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:34: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['Fe/H'].iloc[i] = bulk['Fe']\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:35: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['O/H Upper'].iloc[i] = bulk_err['O'][1]\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:36: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['C/H Upper'].iloc[i] = bulk_err['C'][1]\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:37: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['Fe/H Upper'].iloc[i] = bulk_err['Fe'][1]\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:38: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['O/H Lower'].iloc[i] = bulk_err['O'][0]\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:39: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['C/H Lower'].iloc[i] = bulk_err['C'][0]\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:40: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['Fe/H Lower'].iloc[i] = bulk_err['Fe'][0]\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:32: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['O/H'].iloc[i] = bulk['O']\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:33: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['C/H'].iloc[i] = bulk['C']\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:34: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['Fe/H'].iloc[i] = bulk['Fe']\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:35: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['O/H Upper'].iloc[i] = bulk_err['O'][1]\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:36: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['C/H Upper'].iloc[i] = bulk_err['C'][1]\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:37: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['Fe/H Upper'].iloc[i] = bulk_err['Fe'][1]\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:38: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['O/H Lower'].iloc[i] = bulk_err['O'][0]\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:39: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['C/H Lower'].iloc[i] = bulk_err['C'][0]\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:40: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['Fe/H Lower'].iloc[i] = bulk_err['Fe'][0]\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:32: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['O/H'].iloc[i] = bulk['O']\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:33: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['C/H'].iloc[i] = bulk['C']\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:34: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['Fe/H'].iloc[i] = bulk['Fe']\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:35: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['O/H Upper'].iloc[i] = bulk_err['O'][1]\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:36: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['C/H Upper'].iloc[i] = bulk_err['C'][1]\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:37: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['Fe/H Upper'].iloc[i] = bulk_err['Fe'][1]\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:38: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['O/H Lower'].iloc[i] = bulk_err['O'][0]\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:39: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['C/H Lower'].iloc[i] = bulk_err['C'][0]\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:40: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['Fe/H Lower'].iloc[i] = bulk_err['Fe'][0]\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:32: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['O/H'].iloc[i] = bulk['O']\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:33: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['C/H'].iloc[i] = bulk['C']\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:34: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['Fe/H'].iloc[i] = bulk['Fe']\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:35: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['O/H Upper'].iloc[i] = bulk_err['O'][1]\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:36: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['C/H Upper'].iloc[i] = bulk_err['C'][1]\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:37: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['Fe/H Upper'].iloc[i] = bulk_err['Fe'][1]\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:38: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['O/H Lower'].iloc[i] = bulk_err['O'][0]\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:39: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['C/H Lower'].iloc[i] = bulk_err['C'][0]\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:40: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['Fe/H Lower'].iloc[i] = bulk_err['Fe'][0]\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:32: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['O/H'].iloc[i] = bulk['O']\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:33: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['C/H'].iloc[i] = bulk['C']\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:34: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['Fe/H'].iloc[i] = bulk['Fe']\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:35: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['O/H Upper'].iloc[i] = bulk_err['O'][1]\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:36: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['C/H Upper'].iloc[i] = bulk_err['C'][1]\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:37: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['Fe/H Upper'].iloc[i] = bulk_err['Fe'][1]\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:38: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['O/H Lower'].iloc[i] = bulk_err['O'][0]\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:39: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['C/H Lower'].iloc[i] = bulk_err['C'][0]\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:40: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['Fe/H Lower'].iloc[i] = bulk_err['Fe'][0]\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:32: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['O/H'].iloc[i] = bulk['O']\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:33: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['C/H'].iloc[i] = bulk['C']\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:34: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['Fe/H'].iloc[i] = bulk['Fe']\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:35: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['O/H Upper'].iloc[i] = bulk_err['O'][1]\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:36: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['C/H Upper'].iloc[i] = bulk_err['C'][1]\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:37: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['Fe/H Upper'].iloc[i] = bulk_err['Fe'][1]\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:38: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['O/H Lower'].iloc[i] = bulk_err['O'][0]\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:39: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['C/H Lower'].iloc[i] = bulk_err['C'][0]\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:40: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['Fe/H Lower'].iloc[i] = bulk_err['Fe'][0]\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:32: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['O/H'].iloc[i] = bulk['O']\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:33: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['C/H'].iloc[i] = bulk['C']\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:34: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['Fe/H'].iloc[i] = bulk['Fe']\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:35: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['O/H Upper'].iloc[i] = bulk_err['O'][1]\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:36: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['C/H Upper'].iloc[i] = bulk_err['C'][1]\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:37: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['Fe/H Upper'].iloc[i] = bulk_err['Fe'][1]\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:38: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['O/H Lower'].iloc[i] = bulk_err['O'][0]\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:39: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['C/H Lower'].iloc[i] = bulk_err['C'][0]\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:40: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['Fe/H Lower'].iloc[i] = bulk_err['Fe'][0]\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:32: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['O/H'].iloc[i] = bulk['O']\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:33: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['C/H'].iloc[i] = bulk['C']\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:34: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['Fe/H'].iloc[i] = bulk['Fe']\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:35: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['O/H Upper'].iloc[i] = bulk_err['O'][1]\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:36: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['C/H Upper'].iloc[i] = bulk_err['C'][1]\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:37: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['Fe/H Upper'].iloc[i] = bulk_err['Fe'][1]\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:38: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['O/H Lower'].iloc[i] = bulk_err['O'][0]\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:39: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['C/H Lower'].iloc[i] = bulk_err['C'][0]\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:40: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['Fe/H Lower'].iloc[i] = bulk_err['Fe'][0]\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:32: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['O/H'].iloc[i] = bulk['O']\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:33: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['C/H'].iloc[i] = bulk['C']\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:34: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['Fe/H'].iloc[i] = bulk['Fe']\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:35: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['O/H Upper'].iloc[i] = bulk_err['O'][1]\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:36: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['C/H Upper'].iloc[i] = bulk_err['C'][1]\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:37: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['Fe/H Upper'].iloc[i] = bulk_err['Fe'][1]\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:38: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['O/H Lower'].iloc[i] = bulk_err['O'][0]\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:39: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['C/H Lower'].iloc[i] = bulk_err['C'][0]\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:40: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['Fe/H Lower'].iloc[i] = bulk_err['Fe'][0]\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:32: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['O/H'].iloc[i] = bulk['O']\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:33: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['C/H'].iloc[i] = bulk['C']\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:34: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['Fe/H'].iloc[i] = bulk['Fe']\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:35: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['O/H Upper'].iloc[i] = bulk_err['O'][1]\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:36: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['C/H Upper'].iloc[i] = bulk_err['C'][1]\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:37: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['Fe/H Upper'].iloc[i] = bulk_err['Fe'][1]\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:38: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['O/H Lower'].iloc[i] = bulk_err['O'][0]\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:39: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['C/H Lower'].iloc[i] = bulk_err['C'][0]\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:40: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['Fe/H Lower'].iloc[i] = bulk_err['Fe'][0]\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:32: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['O/H'].iloc[i] = bulk['O']\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:33: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['C/H'].iloc[i] = bulk['C']\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:34: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['Fe/H'].iloc[i] = bulk['Fe']\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:35: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['O/H Upper'].iloc[i] = bulk_err['O'][1]\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:36: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['C/H Upper'].iloc[i] = bulk_err['C'][1]\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:37: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['Fe/H Upper'].iloc[i] = bulk_err['Fe'][1]\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:38: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['O/H Lower'].iloc[i] = bulk_err['O'][0]\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:39: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['C/H Lower'].iloc[i] = bulk_err['C'][0]\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:40: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['Fe/H Lower'].iloc[i] = bulk_err['Fe'][0]\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:32: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['O/H'].iloc[i] = bulk['O']\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:33: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['C/H'].iloc[i] = bulk['C']\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:34: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['Fe/H'].iloc[i] = bulk['Fe']\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:35: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['O/H Upper'].iloc[i] = bulk_err['O'][1]\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:36: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['C/H Upper'].iloc[i] = bulk_err['C'][1]\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:37: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['Fe/H Upper'].iloc[i] = bulk_err['Fe'][1]\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:38: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['O/H Lower'].iloc[i] = bulk_err['O'][0]\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:39: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['C/H Lower'].iloc[i] = bulk_err['C'][0]\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:40: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['Fe/H Lower'].iloc[i] = bulk_err['Fe'][0]\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:32: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['O/H'].iloc[i] = bulk['O']\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:33: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['C/H'].iloc[i] = bulk['C']\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:34: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['Fe/H'].iloc[i] = bulk['Fe']\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:35: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['O/H Upper'].iloc[i] = bulk_err['O'][1]\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:36: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['C/H Upper'].iloc[i] = bulk_err['C'][1]\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:37: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['Fe/H Upper'].iloc[i] = bulk_err['Fe'][1]\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:38: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['O/H Lower'].iloc[i] = bulk_err['O'][0]\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:39: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['C/H Lower'].iloc[i] = bulk_err['C'][0]\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:40: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['Fe/H Lower'].iloc[i] = bulk_err['Fe'][0]\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:32: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['O/H'].iloc[i] = bulk['O']\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:33: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['C/H'].iloc[i] = bulk['C']\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:34: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['Fe/H'].iloc[i] = bulk['Fe']\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:35: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['O/H Upper'].iloc[i] = bulk_err['O'][1]\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:36: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['C/H Upper'].iloc[i] = bulk_err['C'][1]\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:37: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['Fe/H Upper'].iloc[i] = bulk_err['Fe'][1]\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:38: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['O/H Lower'].iloc[i] = bulk_err['O'][0]\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:39: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['C/H Lower'].iloc[i] = bulk_err['C'][0]\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:40: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['Fe/H Lower'].iloc[i] = bulk_err['Fe'][0]\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:32: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['O/H'].iloc[i] = bulk['O']\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:33: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['C/H'].iloc[i] = bulk['C']\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:34: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['Fe/H'].iloc[i] = bulk['Fe']\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:35: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['O/H Upper'].iloc[i] = bulk_err['O'][1]\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:36: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['C/H Upper'].iloc[i] = bulk_err['C'][1]\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:37: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['Fe/H Upper'].iloc[i] = bulk_err['Fe'][1]\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:38: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['O/H Lower'].iloc[i] = bulk_err['O'][0]\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:39: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['C/H Lower'].iloc[i] = bulk_err['C'][0]\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:40: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['Fe/H Lower'].iloc[i] = bulk_err['Fe'][0]\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:32: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['O/H'].iloc[i] = bulk['O']\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:33: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['C/H'].iloc[i] = bulk['C']\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:34: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['Fe/H'].iloc[i] = bulk['Fe']\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:35: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['O/H Upper'].iloc[i] = bulk_err['O'][1]\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:36: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['C/H Upper'].iloc[i] = bulk_err['C'][1]\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:37: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['Fe/H Upper'].iloc[i] = bulk_err['Fe'][1]\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:38: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['O/H Lower'].iloc[i] = bulk_err['O'][0]\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:39: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['C/H Lower'].iloc[i] = bulk_err['C'][0]\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:40: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['Fe/H Lower'].iloc[i] = bulk_err['Fe'][0]\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:32: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['O/H'].iloc[i] = bulk['O']\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:33: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['C/H'].iloc[i] = bulk['C']\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:34: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['Fe/H'].iloc[i] = bulk['Fe']\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:35: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['O/H Upper'].iloc[i] = bulk_err['O'][1]\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:36: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['C/H Upper'].iloc[i] = bulk_err['C'][1]\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:37: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['Fe/H Upper'].iloc[i] = bulk_err['Fe'][1]\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:38: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['O/H Lower'].iloc[i] = bulk_err['O'][0]\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:39: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['C/H Lower'].iloc[i] = bulk_err['C'][0]\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:40: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['Fe/H Lower'].iloc[i] = bulk_err['Fe'][0]\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:32: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['O/H'].iloc[i] = bulk['O']\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:33: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['C/H'].iloc[i] = bulk['C']\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:34: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['Fe/H'].iloc[i] = bulk['Fe']\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:35: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['O/H Upper'].iloc[i] = bulk_err['O'][1]\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:36: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['C/H Upper'].iloc[i] = bulk_err['C'][1]\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:37: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['Fe/H Upper'].iloc[i] = bulk_err['Fe'][1]\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:38: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['O/H Lower'].iloc[i] = bulk_err['O'][0]\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:39: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['C/H Lower'].iloc[i] = bulk_err['C'][0]\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:40: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['Fe/H Lower'].iloc[i] = bulk_err['Fe'][0]\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:32: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['O/H'].iloc[i] = bulk['O']\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:33: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['C/H'].iloc[i] = bulk['C']\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:34: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['Fe/H'].iloc[i] = bulk['Fe']\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:35: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['O/H Upper'].iloc[i] = bulk_err['O'][1]\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:36: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['C/H Upper'].iloc[i] = bulk_err['C'][1]\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:37: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['Fe/H Upper'].iloc[i] = bulk_err['Fe'][1]\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:38: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['O/H Lower'].iloc[i] = bulk_err['O'][0]\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:39: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['C/H Lower'].iloc[i] = bulk_err['C'][0]\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:40: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['Fe/H Lower'].iloc[i] = bulk_err['Fe'][0]\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:32: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['O/H'].iloc[i] = bulk['O']\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:33: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['C/H'].iloc[i] = bulk['C']\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:34: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['Fe/H'].iloc[i] = bulk['Fe']\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:35: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['O/H Upper'].iloc[i] = bulk_err['O'][1]\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:36: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['C/H Upper'].iloc[i] = bulk_err['C'][1]\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:37: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['Fe/H Upper'].iloc[i] = bulk_err['Fe'][1]\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:38: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['O/H Lower'].iloc[i] = bulk_err['O'][0]\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:39: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['C/H Lower'].iloc[i] = bulk_err['C'][0]\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:40: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['Fe/H Lower'].iloc[i] = bulk_err['Fe'][0]\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:32: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['O/H'].iloc[i] = bulk['O']\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:33: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['C/H'].iloc[i] = bulk['C']\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:34: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['Fe/H'].iloc[i] = bulk['Fe']\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:35: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['O/H Upper'].iloc[i] = bulk_err['O'][1]\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:36: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['C/H Upper'].iloc[i] = bulk_err['C'][1]\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:37: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['Fe/H Upper'].iloc[i] = bulk_err['Fe'][1]\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:38: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['O/H Lower'].iloc[i] = bulk_err['O'][0]\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:39: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['C/H Lower'].iloc[i] = bulk_err['C'][0]\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:40: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['Fe/H Lower'].iloc[i] = bulk_err['Fe'][0]\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:32: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['O/H'].iloc[i] = bulk['O']\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:33: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['C/H'].iloc[i] = bulk['C']\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:34: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['Fe/H'].iloc[i] = bulk['Fe']\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:35: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['O/H Upper'].iloc[i] = bulk_err['O'][1]\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:36: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['C/H Upper'].iloc[i] = bulk_err['C'][1]\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:37: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['Fe/H Upper'].iloc[i] = bulk_err['Fe'][1]\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:38: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['O/H Lower'].iloc[i] = bulk_err['O'][0]\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:39: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['C/H Lower'].iloc[i] = bulk_err['C'][0]\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:40: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['Fe/H Lower'].iloc[i] = bulk_err['Fe'][0]\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:32: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['O/H'].iloc[i] = bulk['O']\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:33: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['C/H'].iloc[i] = bulk['C']\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:34: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['Fe/H'].iloc[i] = bulk['Fe']\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:35: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['O/H Upper'].iloc[i] = bulk_err['O'][1]\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:36: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['C/H Upper'].iloc[i] = bulk_err['C'][1]\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:37: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['Fe/H Upper'].iloc[i] = bulk_err['Fe'][1]\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:38: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['O/H Lower'].iloc[i] = bulk_err['O'][0]\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:39: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['C/H Lower'].iloc[i] = bulk_err['C'][0]\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:40: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['Fe/H Lower'].iloc[i] = bulk_err['Fe'][0]\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:32: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['O/H'].iloc[i] = bulk['O']\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:33: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['C/H'].iloc[i] = bulk['C']\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:34: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['Fe/H'].iloc[i] = bulk['Fe']\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:35: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['O/H Upper'].iloc[i] = bulk_err['O'][1]\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:36: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['C/H Upper'].iloc[i] = bulk_err['C'][1]\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:37: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['Fe/H Upper'].iloc[i] = bulk_err['Fe'][1]\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:38: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['O/H Lower'].iloc[i] = bulk_err['O'][0]\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:39: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['C/H Lower'].iloc[i] = bulk_err['C'][0]\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:40: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['Fe/H Lower'].iloc[i] = bulk_err['Fe'][0]\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:32: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['O/H'].iloc[i] = bulk['O']\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:33: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['C/H'].iloc[i] = bulk['C']\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:34: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['Fe/H'].iloc[i] = bulk['Fe']\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:35: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['O/H Upper'].iloc[i] = bulk_err['O'][1]\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:36: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['C/H Upper'].iloc[i] = bulk_err['C'][1]\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:37: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['Fe/H Upper'].iloc[i] = bulk_err['Fe'][1]\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:38: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['O/H Lower'].iloc[i] = bulk_err['O'][0]\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:39: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['C/H Lower'].iloc[i] = bulk_err['C'][0]\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:40: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['Fe/H Lower'].iloc[i] = bulk_err['Fe'][0]\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:32: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['O/H'].iloc[i] = bulk['O']\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:33: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['C/H'].iloc[i] = bulk['C']\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:34: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['Fe/H'].iloc[i] = bulk['Fe']\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:35: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['O/H Upper'].iloc[i] = bulk_err['O'][1]\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:36: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['C/H Upper'].iloc[i] = bulk_err['C'][1]\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:37: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['Fe/H Upper'].iloc[i] = bulk_err['Fe'][1]\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:38: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['O/H Lower'].iloc[i] = bulk_err['O'][0]\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:39: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['C/H Lower'].iloc[i] = bulk_err['C'][0]\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:40: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['Fe/H Lower'].iloc[i] = bulk_err['Fe'][0]\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:32: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['O/H'].iloc[i] = bulk['O']\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:33: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['C/H'].iloc[i] = bulk['C']\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:34: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['Fe/H'].iloc[i] = bulk['Fe']\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:35: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['O/H Upper'].iloc[i] = bulk_err['O'][1]\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:36: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['C/H Upper'].iloc[i] = bulk_err['C'][1]\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:37: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['Fe/H Upper'].iloc[i] = bulk_err['Fe'][1]\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:38: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['O/H Lower'].iloc[i] = bulk_err['O'][0]\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:39: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['C/H Lower'].iloc[i] = bulk_err['C'][0]\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:40: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['Fe/H Lower'].iloc[i] = bulk_err['Fe'][0]\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:32: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['O/H'].iloc[i] = bulk['O']\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:33: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['C/H'].iloc[i] = bulk['C']\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:34: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['Fe/H'].iloc[i] = bulk['Fe']\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:35: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['O/H Upper'].iloc[i] = bulk_err['O'][1]\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:36: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['C/H Upper'].iloc[i] = bulk_err['C'][1]\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:37: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['Fe/H Upper'].iloc[i] = bulk_err['Fe'][1]\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:38: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['O/H Lower'].iloc[i] = bulk_err['O'][0]\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:39: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['C/H Lower'].iloc[i] = bulk_err['C'][0]\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:40: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['Fe/H Lower'].iloc[i] = bulk_err['Fe'][0]\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:32: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['O/H'].iloc[i] = bulk['O']\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:33: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['C/H'].iloc[i] = bulk['C']\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:34: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['Fe/H'].iloc[i] = bulk['Fe']\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:35: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['O/H Upper'].iloc[i] = bulk_err['O'][1]\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:36: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['C/H Upper'].iloc[i] = bulk_err['C'][1]\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:37: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['Fe/H Upper'].iloc[i] = bulk_err['Fe'][1]\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:38: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['O/H Lower'].iloc[i] = bulk_err['O'][0]\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:39: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['C/H Lower'].iloc[i] = bulk_err['C'][0]\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:40: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['Fe/H Lower'].iloc[i] = bulk_err['Fe'][0]\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:32: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['O/H'].iloc[i] = bulk['O']\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:33: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['C/H'].iloc[i] = bulk['C']\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:34: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['Fe/H'].iloc[i] = bulk['Fe']\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:35: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['O/H Upper'].iloc[i] = bulk_err['O'][1]\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:36: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['C/H Upper'].iloc[i] = bulk_err['C'][1]\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:37: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['Fe/H Upper'].iloc[i] = bulk_err['Fe'][1]\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:38: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['O/H Lower'].iloc[i] = bulk_err['O'][0]\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:39: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['C/H Lower'].iloc[i] = bulk_err['C'][0]\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:40: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['Fe/H Lower'].iloc[i] = bulk_err['Fe'][0]\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:32: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['O/H'].iloc[i] = bulk['O']\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:33: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['C/H'].iloc[i] = bulk['C']\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:34: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['Fe/H'].iloc[i] = bulk['Fe']\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:35: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['O/H Upper'].iloc[i] = bulk_err['O'][1]\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:36: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['C/H Upper'].iloc[i] = bulk_err['C'][1]\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:37: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['Fe/H Upper'].iloc[i] = bulk_err['Fe'][1]\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:38: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['O/H Lower'].iloc[i] = bulk_err['O'][0]\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:39: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['C/H Lower'].iloc[i] = bulk_err['C'][0]\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:40: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['Fe/H Lower'].iloc[i] = bulk_err['Fe'][0]\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:32: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['O/H'].iloc[i] = bulk['O']\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:33: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['C/H'].iloc[i] = bulk['C']\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:34: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['Fe/H'].iloc[i] = bulk['Fe']\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:35: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['O/H Upper'].iloc[i] = bulk_err['O'][1]\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:36: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['C/H Upper'].iloc[i] = bulk_err['C'][1]\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:37: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['Fe/H Upper'].iloc[i] = bulk_err['Fe'][1]\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:38: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['O/H Lower'].iloc[i] = bulk_err['O'][0]\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:39: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['C/H Lower'].iloc[i] = bulk_err['C'][0]\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:40: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['Fe/H Lower'].iloc[i] = bulk_err['Fe'][0]\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:32: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['O/H'].iloc[i] = bulk['O']\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:33: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['C/H'].iloc[i] = bulk['C']\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:34: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['Fe/H'].iloc[i] = bulk['Fe']\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:35: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['O/H Upper'].iloc[i] = bulk_err['O'][1]\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:36: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['C/H Upper'].iloc[i] = bulk_err['C'][1]\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:37: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['Fe/H Upper'].iloc[i] = bulk_err['Fe'][1]\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:38: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['O/H Lower'].iloc[i] = bulk_err['O'][0]\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:39: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['C/H Lower'].iloc[i] = bulk_err['C'][0]\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:40: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['Fe/H Lower'].iloc[i] = bulk_err['Fe'][0]\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:32: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['O/H'].iloc[i] = bulk['O']\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:33: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['C/H'].iloc[i] = bulk['C']\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:34: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['Fe/H'].iloc[i] = bulk['Fe']\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:35: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['O/H Upper'].iloc[i] = bulk_err['O'][1]\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:36: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['C/H Upper'].iloc[i] = bulk_err['C'][1]\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:37: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['Fe/H Upper'].iloc[i] = bulk_err['Fe'][1]\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:38: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['O/H Lower'].iloc[i] = bulk_err['O'][0]\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:39: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['C/H Lower'].iloc[i] = bulk_err['C'][0]\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:40: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['Fe/H Lower'].iloc[i] = bulk_err['Fe'][0]\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:32: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['O/H'].iloc[i] = bulk['O']\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:33: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['C/H'].iloc[i] = bulk['C']\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:34: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['Fe/H'].iloc[i] = bulk['Fe']\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:35: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['O/H Upper'].iloc[i] = bulk_err['O'][1]\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:36: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['C/H Upper'].iloc[i] = bulk_err['C'][1]\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:37: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['Fe/H Upper'].iloc[i] = bulk_err['Fe'][1]\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:38: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['O/H Lower'].iloc[i] = bulk_err['O'][0]\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:39: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['C/H Lower'].iloc[i] = bulk_err['C'][0]\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:40: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['Fe/H Lower'].iloc[i] = bulk_err['Fe'][0]\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:32: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['O/H'].iloc[i] = bulk['O']\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:33: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['C/H'].iloc[i] = bulk['C']\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:34: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['Fe/H'].iloc[i] = bulk['Fe']\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:35: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['O/H Upper'].iloc[i] = bulk_err['O'][1]\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:36: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['C/H Upper'].iloc[i] = bulk_err['C'][1]\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:37: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['Fe/H Upper'].iloc[i] = bulk_err['Fe'][1]\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:38: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['O/H Lower'].iloc[i] = bulk_err['O'][0]\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:39: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['C/H Lower'].iloc[i] = bulk_err['C'][0]\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:40: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['Fe/H Lower'].iloc[i] = bulk_err['Fe'][0]\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:32: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['O/H'].iloc[i] = bulk['O']\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:33: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['C/H'].iloc[i] = bulk['C']\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:34: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['Fe/H'].iloc[i] = bulk['Fe']\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:35: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['O/H Upper'].iloc[i] = bulk_err['O'][1]\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:36: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['C/H Upper'].iloc[i] = bulk_err['C'][1]\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:37: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['Fe/H Upper'].iloc[i] = bulk_err['Fe'][1]\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:38: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['O/H Lower'].iloc[i] = bulk_err['O'][0]\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:39: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['C/H Lower'].iloc[i] = bulk_err['C'][0]\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:40: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['Fe/H Lower'].iloc[i] = bulk_err['Fe'][0]\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:32: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['O/H'].iloc[i] = bulk['O']\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:33: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['C/H'].iloc[i] = bulk['C']\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:34: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['Fe/H'].iloc[i] = bulk['Fe']\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:35: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['O/H Upper'].iloc[i] = bulk_err['O'][1]\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:36: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['C/H Upper'].iloc[i] = bulk_err['C'][1]\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:37: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['Fe/H Upper'].iloc[i] = bulk_err['Fe'][1]\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:38: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['O/H Lower'].iloc[i] = bulk_err['O'][0]\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:39: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['C/H Lower'].iloc[i] = bulk_err['C'][0]\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3076383045.py:40: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " data['Fe/H Lower'].iloc[i] = bulk_err['Fe'][0]\n" ] }, { "data": { "text/html": [ "
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36HD 149026 bBean et al. 2023PublishedEclipseNIRCAM/F322W2+NIRCam/F444W0.8400.030.032.090000...0.33010.78000010.7042799.5900000.3200000.3214030.3200000.3500000.3512830.350000
47HD 149026 bGagnebin et al. 2024PublishedEclipseNIRCAM/F322W2+NIRCam/F444W0.6700.270.061.146128...0.3309.8061289.6322038.5961280.3679770.3728360.3679770.2688450.3810220.268845
..................................................................
6165WASP-189 bLesjak et al. 2025PublishedEclipseVLT/CRIRES+0.3200.140.411.400000...0.07010.0900009.5951508.9000001.3900001.4492071.3900000.6000000.6161170.600000
6266WASP-121 bPelletier et al. 2025SubmittedEclipseNIRISS/SOSS0.8200.090.051.240000...0.0809.8601329.7739468.7400000.3700000.3700000.3700000.3500000.3500000.350000
6367V1298 Tau bBarat et al. 2025PublishedTransitNIRSpec/G395H0.2200.050.060.600000...0.0609.3603238.7027468.0500000.4000000.4000000.4000000.6000000.6000000.600000
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" ], "text/plain": [ " index Planet Reference Status Geometry \\\n", "0 3 WASP-178 b Lothringer et al. 2025 Published Transit \n", "1 4 HD 189733 b Fu et al. 2024 Published Transit \n", "2 5 HD 209458 b Xue et al. 2024 Published Transit \n", "3 6 HD 149026 b Bean et al. 2023 Published Eclipse \n", "4 7 HD 149026 b Gagnebin et al. 2024 Published Eclipse \n", ".. ... ... ... ... ... \n", "61 65 WASP-189 b Lesjak et al. 2025 Published Eclipse \n", "62 66 WASP-121 b Pelletier et al. 2025 Submitted Eclipse \n", "63 67 V1298 Tau b Barat et al. 2025 Published Transit \n", "64 68 WD 0806 b Voyer et al. 2025 Published Eclipse \n", "65 69 KELT-7 b Ahrer et al. 2025 In Press Transit \n", "\n", " Obs C/O C/O Lower C/O Upper \\\n", "0 WFC3/G280+WFC3/G102+WFC3/G141+NIRSpec/G395H 0.010 0.01 0.01 \n", "1 NIRCAM/F322W2+NIRCam/F444W 0.200 0.20 0.00 \n", "2 NIRCAM/F322W2+NIRCam/F444W 0.080 0.05 0.09 \n", "3 NIRCAM/F322W2+NIRCam/F444W 0.840 0.03 0.03 \n", "4 NIRCAM/F322W2+NIRCam/F444W 0.670 0.27 0.06 \n", ".. ... ... ... ... \n", "61 VLT/CRIRES+ 0.320 0.14 0.41 \n", "62 NIRISS/SOSS 0.820 0.09 0.05 \n", "63 NIRSpec/G395H 0.220 0.05 0.06 \n", "64 MIRI/LRS 0.340 0.06 0.06 \n", "65 NIRSpec/G395H 0.585 0.16 0.16 \n", "\n", " Metallicity ... Stellar Mass Upper O/H C/H Fe/H \\\n", "0 1.470000 ... 0.110 10.160000 8.160000 8.970000 \n", "1 0.602060 ... 0.080 9.403082 8.704112 8.102060 \n", "2 0.477121 ... 0.090 10.004031 8.907121 7.977121 \n", "3 2.090000 ... 0.330 10.780000 10.704279 9.590000 \n", "4 1.146128 ... 0.330 9.806128 9.632203 8.596128 \n", ".. ... ... ... ... ... ... \n", "61 1.400000 ... 0.070 10.090000 9.595150 8.900000 \n", "62 1.240000 ... 0.080 9.860132 9.773946 8.740000 \n", "63 0.600000 ... 0.060 9.360323 8.702746 8.050000 \n", "64 -0.130000 ... -99.000 8.623098 8.154577 7.370000 \n", "65 0.930000 ... 0.066 9.592844 9.360000 8.430000 \n", "\n", " O/H Upper C/H Upper Fe/H Upper O/H Lower C/H Lower Fe/H Lower \n", "0 0.280000 0.280179 0.280000 1.100000 1.100045 1.100000 \n", "1 0.096910 0.096910 0.096910 0.124939 0.124939 0.124939 \n", "2 0.378823 0.367977 0.367977 0.183052 0.176091 0.176091 \n", "3 0.320000 0.321403 0.320000 0.350000 0.351283 0.350000 \n", "4 0.367977 0.372836 0.367977 0.268845 0.381022 0.268845 \n", ".. ... ... ... ... ... ... \n", "61 1.390000 1.449207 1.390000 0.600000 0.616117 0.600000 \n", "62 0.370000 0.370000 0.370000 0.350000 0.350000 0.350000 \n", "63 0.400000 0.400000 0.400000 0.600000 0.600000 0.600000 \n", "64 0.070000 0.070000 0.070000 0.060000 0.060000 0.060000 \n", "65 0.313847 0.270000 0.270000 0.943663 0.930000 0.930000 \n", "\n", "[66 rows x 41 columns]" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Convert to O/H, C/H, and Ref/H to standardize solar\n", "\n", "data['O/H'] = 0.0\n", "data['C/H'] = 0.0\n", "data['Fe/H'] = 0.0\n", "data['O/H Upper'] = 0.0\n", "data['C/H Upper'] = 0.0\n", "data['Fe/H Upper'] = 0.0\n", "data['O/H Lower'] = 0.0\n", "data['C/H Lower'] = 0.0\n", "data['Fe/H Lower'] = 0.0\n", "droplist = [0, 1, 2] # In case we want to neglect a data point. Here, we drop WASP-39b, WASP-94Ab, WASP-96b b/c in prep.\n", "for i in range(1,len(data)):\n", " if data['Retrieval Type'].iloc[i] in ['ForMoSA']: # Not including grid-search techniques right now\n", " droplist.append(i)\n", " continue\n", " g = Abund(retrieval = data['Retrieval Type'].iloc[i])\n", " if data['Metallicity Type'].iloc[i] == 'O/H':\n", " g.mh_type = 'O/H'\n", " g.co_type = 'C/H'\n", " elif data['Metallicity Type'].iloc[i] == 'C/H':\n", " g.mh_type = 'C/H'\n", " g.co_type = 'O/H'\n", " else:\n", " g.mh_type = '(O+C)/H'\n", " g.co_type = 'MH_Preserve'\n", "\n", " # Use exocomp to convert from metallicity and C/O to O/H and C/H\n", " bulk,bulk_err = g.convert_bulk_abundance(data['Metallicity'].iloc[i],data['C/O'].iloc[i],\n", " mh_err = [data['Metallicity Lower'].iloc[i],data['Metallicity Upper'].iloc[i]],\n", " co_err = [data['C/O Lower'].iloc[i],data['C/O Upper'].iloc[i]],)\n", " data['O/H'].iloc[i] = bulk['O']\n", " data['C/H'].iloc[i] = bulk['C']\n", " data['Fe/H'].iloc[i] = bulk['Fe']\n", " data['O/H Upper'].iloc[i] = bulk_err['O'][1]\n", " data['C/H Upper'].iloc[i] = bulk_err['C'][1]\n", " data['Fe/H Upper'].iloc[i] = bulk_err['Fe'][1]\n", " data['O/H Lower'].iloc[i] = bulk_err['O'][0]\n", " data['C/H Lower'].iloc[i] = bulk_err['C'][0]\n", " data['Fe/H Lower'].iloc[i] = bulk_err['Fe'][0]\n", "\n", "# Drop entries in the droplist\n", "for i in droplist:\n", " data = data.drop(i)\n", "data.reset_index()" ] }, { "cell_type": "code", "execution_count": 4, "id": "52bb1384-4b55-4761-b035-6fb8e6533b0b", "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/2177664126.py:46: FutureWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.\n", " result = grouped.apply(process_group).reset_index(drop=True)\n" ] }, { "data": { "text/html": [ "
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PlanetReferenceStatusGeometryObsC/OC/O LowerC/O UpperMetallicityMetallicity Lower...Stellar Mass UpperO/HC/HFe/HO/H UpperC/H UpperFe/H UpperO/H LowerC/H LowerFe/H Lower
02M0122 bXuan et al. 2024PublishedDirectKeck/KPIC0.3700000.0800000.080000-0.3000000.220000...0.0208.5617988.1300007.2000000.1700000.1500000.1500000.2340940.2200000.220000
151 Eri bBrown-Sevilla et al. 2023PublishedDirectVLT/SPHERE0.3800000.0900000.0900000.2600000.300000...0.0509.1102168.6900007.7600000.3132090.3000000.3000000.3132090.3000000.300000
2AB Pic bGandhi et al. 2025PublishedDirectVLT/CRIRES+0.5900000.0100000.0100000.5400000.080000...0.1009.2300009.0008528.0000000.0800000.0806230.0800000.0800000.0806230.080000
3AF Lep bZhang et al. 2023PublishedDirectVLT/SPHERE0.6114750.0768220.0768221.2682930.156174...0.0609.8654199.6982938.7682930.1762940.1561740.1561740.1762940.1561740.156174
4DH Tau bXuan et al. 2024PublishedDirectKeck/KPIC0.5400000.0500000.060000-0.3000000.200000...0.0008.3976068.1300007.2000000.1615550.1500000.1500000.2061550.2000000.200000
5GJ 3470 bBeatty et al. 2024PublishedTransitNIRCAM/F322W2+NIRCam/F444W0.3500000.1000000.1000002.1000000.120000...0.05010.84986910.3939379.6000000.1200000.1200000.1200000.1200000.1200000.120000
6GQ Lup bXuan et al. 2024PublishedDirectKeck/KPIC0.5400000.0089440.0089440.4588750.134518...0.1609.1355588.8888757.9588750.1348710.1345180.1345180.1348710.1345180.134518
7GSC 6214-210 bXuan et al. 2024PublishedDirectKeck/KPIC0.7000000.0600000.0700000.1500000.300000...0.1108.7349028.5800007.6500000.1931320.1800000.1800000.3059410.3000000.300000
8HAT-P-14 bLiu et al. 2025PublishedTransitNIRISS/SOSS+NIRSpec/G395H0.4100000.2000000.240000-0.0800000.980000...0.0458.7372168.3500007.4200000.9217920.8900000.8900001.0002000.9800000.980000
9HD 149026 bBean et al. 2023PublishedEclipseNIRCAM/F322W2+NIRCam/F444W0.8379270.0298170.0298171.6416820.253605...0.33010.31743310.2117069.1179330.2536050.2582690.2536050.2536050.2582690.253605
10HD 189733 bZhang et al. 2025PublishedEclipseNIRCAM/F322W2+NIRCam/F444W0.4300000.0500000.0600000.6800000.110000...0.0809.4048679.0383358.1800000.1500000.1500000.1500000.1100000.1100000.110000
11HD 189733 bFu et al. 2024PublishedTransitNIRCAM/F322W2+NIRCam/F444W0.3476270.1089980.1089980.5674600.090081...0.0809.3348538.7855738.0674600.0900810.0900810.0900810.0900810.0900810.090081
12HD 209458 bXue et al. 2024PublishedTransitNIRCAM/F322W2+NIRCam/F444W0.1638340.0535170.0535170.1922180.161513...0.0909.1874648.6364647.6922180.1628680.1732030.1615130.1628680.1732030.161513
13HD 80606 bSikora et al. 2025SubmittedEclipseNIRSpec/G395H0.6900000.1400000.140000-0.1100000.550000...0.2708.5800008.4188497.3900000.4800000.5000000.4800000.5500000.5675390.550000
14HIP 65 bBazinet et al. 2024PublishedEclipseGemini/IGRINS0.7200000.3000000.130000-0.7600000.480000...0.0277.8846757.7420076.7400000.4200000.4200000.4200000.4800000.4800000.480000
15HR 8799 bNasedkin et al. 2024PublishedDirectVLT/GRAVITY0.7800000.0400000.0300000.9600000.080000...0.3009.4979059.3900008.4600000.0854400.0800000.0800000.0894430.0800000.080000
16HR 8799 cNasedkin et al. 2025PublishedDirectVLT/GRAVITY0.6600000.0100000.0100001.2700000.060000...0.3009.8804569.7000008.7700000.0509900.0500000.0500000.0608280.0600000.060000
17HR 8799 dNasedkin et al. 2026PublishedDirectVLT/GRAVITY0.6000000.0600000.0400001.2000000.200000...0.3009.8518499.6300008.7000000.2039610.2000000.2000000.2088060.2000000.200000
18HR 8799 eMolliere et al. 2020PublishedDirectVLT/GRAVITY0.8635290.0194030.0194031.4423050.164643...0.0409.9985949.8723058.9423050.1671260.1646430.1646430.1671260.1646430.164643
19KELT-20 bFinnerty et al. 2025PublishedTransitKeck/KPIC0.1000000.1000000.4000001.0000000.700000...0.1409.8388108.8388108.5000000.7000000.7000000.7000000.7000000.7000000.700000
20KELT-7 bAhrer et al. 2025In PressTransitNIRSpec/G395H0.5850000.1600000.1600000.9300000.930000...0.0669.5928449.3600008.4300000.3138470.2700000.2700000.9436630.9300000.930000
21MASCARA-1 bRamkumar et al. 2025PublishedEclipseVLT/CRIRES+0.7400000.1400000.110000-0.3300000.000000...0.0608.3600008.2292327.1700001.0000001.0060321.0000000.0000000.1400000.000000
22PDS 70 bHsu et al. 2024PublishedDirectKeck/KPIC0.2800000.1200000.200000-0.2000000.500000...0.0208.7828428.2300007.3000000.8246210.8000000.8000000.5141980.5000000.500000
23ROXs 12 bXuan et al. 2024PublishedDirectKeck/KPIC0.5400000.0500000.050000-0.3000000.200000...0.0808.3976068.1300007.2000000.1581140.1500000.1500000.2061550.2000000.200000
24ROXs 42B bXuan et al. 2024PublishedDirectKeck/KPIC0.4800000.0800000.0800000.0000000.520000...-99.0008.7487598.4300007.5000000.2435160.2300000.2300000.5261180.5200000.520000
25TOI-5205 bCañas et al. 2025SubmittedTransitNIRSpec/PRISM1.3000000.4000000.400000-0.7600000.100000...0.0207.9300008.0439436.7400000.1700000.4346260.1700000.1000000.4123110.100000
26Tau Boo bPanwar et al. 2024PublishedEclipseVLT/CRIRES0.8325900.1992100.199210-0.5092711.018595...0.2448.1101998.0239146.9907291.0185951.0185951.0185951.0185951.0185951.018595
27TrES-4 bMeech et al. 2025PublishedTransitNIRSpec/G395H0.3500000.1000000.120000-0.1500000.260000...0.3808.7359328.2800007.3500000.2954660.2700000.2700000.2785680.2600000.260000
28V1298 Tau bBarat et al. 2025PublishedTransitNIRSpec/G395H0.2200000.0500000.0600000.6000000.600000...0.0609.3603238.7027468.0500000.4000000.4000000.4000000.6000000.6000000.600000
29WASP-107 bSing et al. 2024PublishedTransitNIRSpec/G395H0.3384710.0585710.0585711.4534850.108070...0.02010.1769889.8081568.9534850.1080700.1080700.1080700.1080700.1080700.108070
30WASP-121 bSmith et al. 2024bPublishedEclipseGemini/IGRINS0.8900840.0249040.0249040.7903280.123681...0.0709.4527449.3924028.2903280.1236810.1249560.1236810.1236810.1249560.123681
31WASP-121 bGapp et al. 2025PublishedTransitNIRSpec/G395H0.9780000.0060000.0040001.1600000.080000...0.0709.8500009.8403398.6600000.0600000.0601330.0600000.0800000.0802250.080000
32WASP-127 bKanumalla et al. 2024PublishedEclipseGemini/IGRINS0.6800000.6800000.0000001.5900000.300000...0.02010.28000010.1125099.0900000.3000000.3000000.3000000.3000000.7432360.300000
33WASP-15 bKirk et al. 2025PublishedTransitNIRSpec/G395H0.5300000.1500000.0900001.3400000.260000...0.16010.0457249.7700008.8400000.2941090.2800000.2800000.3001670.2600000.260000
34WASP-166 bMayo et al. 2025SubmittedTransitNIRISS/SOSS+NIRSpec/PRISM0.2820000.0530000.0780001.5700000.180000...0.06010.3423159.7925649.0700000.1700000.1700000.1700000.1800000.1800000.180000
35WASP-178 bLothringer et al. 2025PublishedTransitWFC3/G280+WFC3/G102+WFC3/G141+NIRSpec/G395H0.0100000.0100000.0100001.4700001.100000...0.11010.1600008.1600008.9700000.2800000.2801790.2800001.1000001.1000451.100000
36WASP-18 bCoulombe et al. 2023PublishedEclipseNIRISS/SOSS0.4031960.2958080.2958080.2374600.228827...0.0608.9274608.6348207.7374600.2288270.3732640.2288270.2288270.3732640.228827
37WASP-189 bLesjak et al. 2025PublishedEclipseVLT/CRIRES+0.3200000.1400000.4100001.4000000.600000...0.07010.0900009.5951508.9000001.3900001.4492071.3900000.6000000.6161170.600000
38WASP-19 bSaha et al. 2025SubmittedEclipseNIRSpec/PRISM0.9400000.0300000.0300001.7000000.700000...0.09010.29240110.2655299.2000001.2000001.2000001.2000000.7000000.7000000.700000
39WASP-33 bFinnerty et al. 2023PublishedEclipseKEck/KPIC0.8000000.2000000.1000000.7400000.440000...0.3509.3649319.2680218.2400000.4400000.4400000.4400000.4400000.4400000.440000
40WASP-43 bYang et al. 2024PublishedEclipseMIRI/LRS0.7833680.0820730.0820730.2000000.069544...0.0508.7408438.6312747.7000000.1120770.0695440.0695440.1120770.0695440.069544
41WASP-69 bSchlawin et al. 2024PublishedEclipseNIRCAM/F322W2+NIRCam/F444W+MIRI/LRS0.7500000.1000000.1900000.9600000.170000...0.1409.5971659.4722268.4600000.2000000.2000000.2000000.1700000.1700000.170000
42WASP-76 bMansfield et al. 2024PublishedEclipseGemini/IGRINS0.5900000.1400000.130000-0.7400000.170000...0.0207.9388067.7096586.7600000.2300000.2300000.2300000.1700000.1700000.170000
43WASP-77 A bLine et al. 2021PublishedEclipseGemini/IGRINS0.5486320.0380620.038062-0.6336940.068547...0.0708.0440067.8020546.8663060.0732540.0711210.0685470.0732540.0711210.068547
44WASP-80 bWiser et al. 2025In PressEclipseNIRCAM/F322W2+NIRCam/F444W+MIRI/LRS0.4800000.0700000.0600000.5500000.100000...0.0509.2599418.9411838.0500000.1200000.1200000.1200000.1000000.1000000.100000
45WASP-94A bAhrer et al. 2025PublishedTransitNIRSpec/G395H0.4900000.1300000.0800000.3400000.160000...0.1909.0470178.7372137.8400000.1200000.1200000.1200000.1600000.1600000.160000
46WD 0806 bVoyer et al. 2025PublishedEclipseMIRI/LRS0.3400000.0600000.060000-0.1300000.060000...-99.0008.6230988.1545777.3700000.0700000.0700000.0700000.0600000.0600000.060000
47beta Pic bNowak et al. 2020PublishedDirectVLT/GRAVITY0.4400000.0700000.0500000.6600000.110000...0.0279.4465479.0900008.1600000.1392840.1300000.1300000.1303840.1100000.110000
48kap And bXuan et al. 2024PublishedDirectKeck/KPIC0.5800000.0400000.050000-0.1000000.200000...0.2008.5665728.3300007.4000000.1486610.1400000.1400000.2039610.2000000.200000
\n", "

49 rows × 40 columns

\n", "
" ], "text/plain": [ " Planet Reference Status Geometry \\\n", "0 2M0122 b Xuan et al. 2024 Published Direct \n", "1 51 Eri b Brown-Sevilla et al. 2023 Published Direct \n", "2 AB Pic b Gandhi et al. 2025 Published Direct \n", "3 AF Lep b Zhang et al. 2023 Published Direct \n", "4 DH Tau b Xuan et al. 2024 Published Direct \n", "5 GJ 3470 b Beatty et al. 2024 Published Transit \n", "6 GQ Lup b Xuan et al. 2024 Published Direct \n", "7 GSC 6214-210 b Xuan et al. 2024 Published Direct \n", "8 HAT-P-14 b Liu et al. 2025 Published Transit \n", "9 HD 149026 b Bean et al. 2023 Published Eclipse \n", "10 HD 189733 b Zhang et al. 2025 Published Eclipse \n", "11 HD 189733 b Fu et al. 2024 Published Transit \n", "12 HD 209458 b Xue et al. 2024 Published Transit \n", "13 HD 80606 b Sikora et al. 2025 Submitted Eclipse \n", "14 HIP 65 b Bazinet et al. 2024 Published Eclipse \n", "15 HR 8799 b Nasedkin et al. 2024 Published Direct \n", "16 HR 8799 c Nasedkin et al. 2025 Published Direct \n", "17 HR 8799 d Nasedkin et al. 2026 Published Direct \n", "18 HR 8799 e Molliere et al. 2020 Published Direct \n", "19 KELT-20 b Finnerty et al. 2025 Published Transit \n", "20 KELT-7 b Ahrer et al. 2025 In Press Transit \n", "21 MASCARA-1 b Ramkumar et al. 2025 Published Eclipse \n", "22 PDS 70 b Hsu et al. 2024 Published Direct \n", "23 ROXs 12 b Xuan et al. 2024 Published Direct \n", "24 ROXs 42B b Xuan et al. 2024 Published Direct \n", "25 TOI-5205 b Cañas et al. 2025 Submitted Transit \n", "26 Tau Boo b Panwar et al. 2024 Published Eclipse \n", "27 TrES-4 b Meech et al. 2025 Published Transit \n", "28 V1298 Tau b Barat et al. 2025 Published Transit \n", "29 WASP-107 b Sing et al. 2024 Published Transit \n", "30 WASP-121 b Smith et al. 2024b Published Eclipse \n", "31 WASP-121 b Gapp et al. 2025 Published Transit \n", "32 WASP-127 b Kanumalla et al. 2024 Published Eclipse \n", "33 WASP-15 b Kirk et al. 2025 Published Transit \n", "34 WASP-166 b Mayo et al. 2025 Submitted Transit \n", "35 WASP-178 b Lothringer et al. 2025 Published Transit \n", "36 WASP-18 b Coulombe et al. 2023 Published Eclipse \n", "37 WASP-189 b Lesjak et al. 2025 Published Eclipse \n", "38 WASP-19 b Saha et al. 2025 Submitted Eclipse \n", "39 WASP-33 b Finnerty et al. 2023 Published Eclipse \n", "40 WASP-43 b Yang et al. 2024 Published Eclipse \n", "41 WASP-69 b Schlawin et al. 2024 Published Eclipse \n", "42 WASP-76 b Mansfield et al. 2024 Published Eclipse \n", "43 WASP-77 A b Line et al. 2021 Published Eclipse \n", "44 WASP-80 b Wiser et al. 2025 In Press Eclipse \n", "45 WASP-94A b Ahrer et al. 2025 Published Transit \n", "46 WD 0806 b Voyer et al. 2025 Published Eclipse \n", "47 beta Pic b Nowak et al. 2020 Published Direct \n", "48 kap And b Xuan et al. 2024 Published Direct \n", "\n", " Obs C/O C/O Lower \\\n", "0 Keck/KPIC 0.370000 0.080000 \n", "1 VLT/SPHERE 0.380000 0.090000 \n", "2 VLT/CRIRES+ 0.590000 0.010000 \n", "3 VLT/SPHERE 0.611475 0.076822 \n", "4 Keck/KPIC 0.540000 0.050000 \n", "5 NIRCAM/F322W2+NIRCam/F444W 0.350000 0.100000 \n", "6 Keck/KPIC 0.540000 0.008944 \n", "7 Keck/KPIC 0.700000 0.060000 \n", "8 NIRISS/SOSS+NIRSpec/G395H 0.410000 0.200000 \n", "9 NIRCAM/F322W2+NIRCam/F444W 0.837927 0.029817 \n", "10 NIRCAM/F322W2+NIRCam/F444W 0.430000 0.050000 \n", "11 NIRCAM/F322W2+NIRCam/F444W 0.347627 0.108998 \n", "12 NIRCAM/F322W2+NIRCam/F444W 0.163834 0.053517 \n", "13 NIRSpec/G395H 0.690000 0.140000 \n", "14 Gemini/IGRINS 0.720000 0.300000 \n", "15 VLT/GRAVITY 0.780000 0.040000 \n", "16 VLT/GRAVITY 0.660000 0.010000 \n", "17 VLT/GRAVITY 0.600000 0.060000 \n", "18 VLT/GRAVITY 0.863529 0.019403 \n", "19 Keck/KPIC 0.100000 0.100000 \n", "20 NIRSpec/G395H 0.585000 0.160000 \n", "21 VLT/CRIRES+ 0.740000 0.140000 \n", "22 Keck/KPIC 0.280000 0.120000 \n", "23 Keck/KPIC 0.540000 0.050000 \n", "24 Keck/KPIC 0.480000 0.080000 \n", "25 NIRSpec/PRISM 1.300000 0.400000 \n", "26 VLT/CRIRES 0.832590 0.199210 \n", "27 NIRSpec/G395H 0.350000 0.100000 \n", "28 NIRSpec/G395H 0.220000 0.050000 \n", "29 NIRSpec/G395H 0.338471 0.058571 \n", "30 Gemini/IGRINS 0.890084 0.024904 \n", "31 NIRSpec/G395H 0.978000 0.006000 \n", "32 Gemini/IGRINS 0.680000 0.680000 \n", "33 NIRSpec/G395H 0.530000 0.150000 \n", "34 NIRISS/SOSS+NIRSpec/PRISM 0.282000 0.053000 \n", "35 WFC3/G280+WFC3/G102+WFC3/G141+NIRSpec/G395H 0.010000 0.010000 \n", "36 NIRISS/SOSS 0.403196 0.295808 \n", "37 VLT/CRIRES+ 0.320000 0.140000 \n", "38 NIRSpec/PRISM 0.940000 0.030000 \n", "39 KEck/KPIC 0.800000 0.200000 \n", "40 MIRI/LRS 0.783368 0.082073 \n", "41 NIRCAM/F322W2+NIRCam/F444W+MIRI/LRS 0.750000 0.100000 \n", "42 Gemini/IGRINS 0.590000 0.140000 \n", "43 Gemini/IGRINS 0.548632 0.038062 \n", "44 NIRCAM/F322W2+NIRCam/F444W+MIRI/LRS 0.480000 0.070000 \n", "45 NIRSpec/G395H 0.490000 0.130000 \n", "46 MIRI/LRS 0.340000 0.060000 \n", "47 VLT/GRAVITY 0.440000 0.070000 \n", "48 Keck/KPIC 0.580000 0.040000 \n", "\n", " C/O Upper Metallicity Metallicity Lower ... Stellar Mass Upper \\\n", "0 0.080000 -0.300000 0.220000 ... 0.020 \n", "1 0.090000 0.260000 0.300000 ... 0.050 \n", "2 0.010000 0.540000 0.080000 ... 0.100 \n", "3 0.076822 1.268293 0.156174 ... 0.060 \n", "4 0.060000 -0.300000 0.200000 ... 0.000 \n", "5 0.100000 2.100000 0.120000 ... 0.050 \n", "6 0.008944 0.458875 0.134518 ... 0.160 \n", "7 0.070000 0.150000 0.300000 ... 0.110 \n", "8 0.240000 -0.080000 0.980000 ... 0.045 \n", "9 0.029817 1.641682 0.253605 ... 0.330 \n", "10 0.060000 0.680000 0.110000 ... 0.080 \n", "11 0.108998 0.567460 0.090081 ... 0.080 \n", "12 0.053517 0.192218 0.161513 ... 0.090 \n", "13 0.140000 -0.110000 0.550000 ... 0.270 \n", "14 0.130000 -0.760000 0.480000 ... 0.027 \n", "15 0.030000 0.960000 0.080000 ... 0.300 \n", "16 0.010000 1.270000 0.060000 ... 0.300 \n", "17 0.040000 1.200000 0.200000 ... 0.300 \n", "18 0.019403 1.442305 0.164643 ... 0.040 \n", "19 0.400000 1.000000 0.700000 ... 0.140 \n", "20 0.160000 0.930000 0.930000 ... 0.066 \n", "21 0.110000 -0.330000 0.000000 ... 0.060 \n", "22 0.200000 -0.200000 0.500000 ... 0.020 \n", "23 0.050000 -0.300000 0.200000 ... 0.080 \n", "24 0.080000 0.000000 0.520000 ... -99.000 \n", "25 0.400000 -0.760000 0.100000 ... 0.020 \n", "26 0.199210 -0.509271 1.018595 ... 0.244 \n", "27 0.120000 -0.150000 0.260000 ... 0.380 \n", "28 0.060000 0.600000 0.600000 ... 0.060 \n", "29 0.058571 1.453485 0.108070 ... 0.020 \n", "30 0.024904 0.790328 0.123681 ... 0.070 \n", "31 0.004000 1.160000 0.080000 ... 0.070 \n", "32 0.000000 1.590000 0.300000 ... 0.020 \n", "33 0.090000 1.340000 0.260000 ... 0.160 \n", "34 0.078000 1.570000 0.180000 ... 0.060 \n", "35 0.010000 1.470000 1.100000 ... 0.110 \n", "36 0.295808 0.237460 0.228827 ... 0.060 \n", "37 0.410000 1.400000 0.600000 ... 0.070 \n", "38 0.030000 1.700000 0.700000 ... 0.090 \n", "39 0.100000 0.740000 0.440000 ... 0.350 \n", "40 0.082073 0.200000 0.069544 ... 0.050 \n", "41 0.190000 0.960000 0.170000 ... 0.140 \n", "42 0.130000 -0.740000 0.170000 ... 0.020 \n", "43 0.038062 -0.633694 0.068547 ... 0.070 \n", "44 0.060000 0.550000 0.100000 ... 0.050 \n", "45 0.080000 0.340000 0.160000 ... 0.190 \n", "46 0.060000 -0.130000 0.060000 ... -99.000 \n", "47 0.050000 0.660000 0.110000 ... 0.027 \n", "48 0.050000 -0.100000 0.200000 ... 0.200 \n", "\n", " O/H C/H Fe/H O/H Upper C/H Upper Fe/H Upper O/H Lower \\\n", "0 8.561798 8.130000 7.200000 0.170000 0.150000 0.150000 0.234094 \n", "1 9.110216 8.690000 7.760000 0.313209 0.300000 0.300000 0.313209 \n", "2 9.230000 9.000852 8.000000 0.080000 0.080623 0.080000 0.080000 \n", "3 9.865419 9.698293 8.768293 0.176294 0.156174 0.156174 0.176294 \n", "4 8.397606 8.130000 7.200000 0.161555 0.150000 0.150000 0.206155 \n", "5 10.849869 10.393937 9.600000 0.120000 0.120000 0.120000 0.120000 \n", "6 9.135558 8.888875 7.958875 0.134871 0.134518 0.134518 0.134871 \n", "7 8.734902 8.580000 7.650000 0.193132 0.180000 0.180000 0.305941 \n", "8 8.737216 8.350000 7.420000 0.921792 0.890000 0.890000 1.000200 \n", "9 10.317433 10.211706 9.117933 0.253605 0.258269 0.253605 0.253605 \n", "10 9.404867 9.038335 8.180000 0.150000 0.150000 0.150000 0.110000 \n", "11 9.334853 8.785573 8.067460 0.090081 0.090081 0.090081 0.090081 \n", "12 9.187464 8.636464 7.692218 0.162868 0.173203 0.161513 0.162868 \n", "13 8.580000 8.418849 7.390000 0.480000 0.500000 0.480000 0.550000 \n", "14 7.884675 7.742007 6.740000 0.420000 0.420000 0.420000 0.480000 \n", "15 9.497905 9.390000 8.460000 0.085440 0.080000 0.080000 0.089443 \n", "16 9.880456 9.700000 8.770000 0.050990 0.050000 0.050000 0.060828 \n", "17 9.851849 9.630000 8.700000 0.203961 0.200000 0.200000 0.208806 \n", "18 9.998594 9.872305 8.942305 0.167126 0.164643 0.164643 0.167126 \n", "19 9.838810 8.838810 8.500000 0.700000 0.700000 0.700000 0.700000 \n", "20 9.592844 9.360000 8.430000 0.313847 0.270000 0.270000 0.943663 \n", "21 8.360000 8.229232 7.170000 1.000000 1.006032 1.000000 0.000000 \n", "22 8.782842 8.230000 7.300000 0.824621 0.800000 0.800000 0.514198 \n", "23 8.397606 8.130000 7.200000 0.158114 0.150000 0.150000 0.206155 \n", "24 8.748759 8.430000 7.500000 0.243516 0.230000 0.230000 0.526118 \n", "25 7.930000 8.043943 6.740000 0.170000 0.434626 0.170000 0.100000 \n", "26 8.110199 8.023914 6.990729 1.018595 1.018595 1.018595 1.018595 \n", "27 8.735932 8.280000 7.350000 0.295466 0.270000 0.270000 0.278568 \n", "28 9.360323 8.702746 8.050000 0.400000 0.400000 0.400000 0.600000 \n", "29 10.176988 9.808156 8.953485 0.108070 0.108070 0.108070 0.108070 \n", "30 9.452744 9.392402 8.290328 0.123681 0.124956 0.123681 0.123681 \n", "31 9.850000 9.840339 8.660000 0.060000 0.060133 0.060000 0.080000 \n", "32 10.280000 10.112509 9.090000 0.300000 0.300000 0.300000 0.300000 \n", "33 10.045724 9.770000 8.840000 0.294109 0.280000 0.280000 0.300167 \n", "34 10.342315 9.792564 9.070000 0.170000 0.170000 0.170000 0.180000 \n", "35 10.160000 8.160000 8.970000 0.280000 0.280179 0.280000 1.100000 \n", "36 8.927460 8.634820 7.737460 0.228827 0.373264 0.228827 0.228827 \n", "37 10.090000 9.595150 8.900000 1.390000 1.449207 1.390000 0.600000 \n", "38 10.292401 10.265529 9.200000 1.200000 1.200000 1.200000 0.700000 \n", "39 9.364931 9.268021 8.240000 0.440000 0.440000 0.440000 0.440000 \n", "40 8.740843 8.631274 7.700000 0.112077 0.069544 0.069544 0.112077 \n", "41 9.597165 9.472226 8.460000 0.200000 0.200000 0.200000 0.170000 \n", "42 7.938806 7.709658 6.760000 0.230000 0.230000 0.230000 0.170000 \n", "43 8.044006 7.802054 6.866306 0.073254 0.071121 0.068547 0.073254 \n", "44 9.259941 8.941183 8.050000 0.120000 0.120000 0.120000 0.100000 \n", "45 9.047017 8.737213 7.840000 0.120000 0.120000 0.120000 0.160000 \n", "46 8.623098 8.154577 7.370000 0.070000 0.070000 0.070000 0.060000 \n", "47 9.446547 9.090000 8.160000 0.139284 0.130000 0.130000 0.130384 \n", "48 8.566572 8.330000 7.400000 0.148661 0.140000 0.140000 0.203961 \n", "\n", " C/H Lower Fe/H Lower \n", "0 0.220000 0.220000 \n", "1 0.300000 0.300000 \n", "2 0.080623 0.080000 \n", "3 0.156174 0.156174 \n", "4 0.200000 0.200000 \n", "5 0.120000 0.120000 \n", "6 0.134518 0.134518 \n", "7 0.300000 0.300000 \n", "8 0.980000 0.980000 \n", "9 0.258269 0.253605 \n", "10 0.110000 0.110000 \n", "11 0.090081 0.090081 \n", "12 0.173203 0.161513 \n", "13 0.567539 0.550000 \n", "14 0.480000 0.480000 \n", "15 0.080000 0.080000 \n", "16 0.060000 0.060000 \n", "17 0.200000 0.200000 \n", "18 0.164643 0.164643 \n", "19 0.700000 0.700000 \n", "20 0.930000 0.930000 \n", "21 0.140000 0.000000 \n", "22 0.500000 0.500000 \n", "23 0.200000 0.200000 \n", "24 0.520000 0.520000 \n", "25 0.412311 0.100000 \n", "26 1.018595 1.018595 \n", "27 0.260000 0.260000 \n", "28 0.600000 0.600000 \n", "29 0.108070 0.108070 \n", "30 0.124956 0.123681 \n", "31 0.080225 0.080000 \n", "32 0.743236 0.300000 \n", "33 0.260000 0.260000 \n", "34 0.180000 0.180000 \n", "35 1.100045 1.100000 \n", "36 0.373264 0.228827 \n", "37 0.616117 0.600000 \n", "38 0.700000 0.700000 \n", "39 0.440000 0.440000 \n", "40 0.069544 0.069544 \n", "41 0.170000 0.170000 \n", "42 0.170000 0.170000 \n", "43 0.071121 0.068547 \n", "44 0.100000 0.100000 \n", "45 0.160000 0.160000 \n", "46 0.060000 0.060000 \n", "47 0.110000 0.110000 \n", "48 0.200000 0.200000 \n", "\n", "[49 rows x 40 columns]" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Define some functions for combining multiple entries for the same planet\n", "# Should spin this off into a .py file, but useful to look at in here\n", "\n", "def weighted_avg_and_se(df, val_col, err_low_col, err_up_col):\n", " # Use the maximum of the lower and upper errors as the weight\n", " weights = 1 / np.maximum(df[err_low_col], df[err_up_col])**2\n", " avg = np.average(df[val_col], weights=weights)\n", " se = np.sqrt(1 / weights.sum())\n", " return avg, se\n", "\n", "\n", "def process_group(group):\n", " if len(group) == 1:\n", " return group # no duplicates, keep as is\n", "\n", " row = group.iloc[0].copy()\n", " \n", " # C/O\n", " row['C/O'], co_se = weighted_avg_and_se(group, 'C/O', 'C/O Lower', 'C/O Upper')\n", " row['C/O Lower'] = co_se\n", " row['C/O Upper'] = co_se\n", "\n", " # Metallicity\n", " row['Metallicity'], met_se = weighted_avg_and_se(group, 'Metallicity', 'Metallicity Lower', 'Metallicity Upper')\n", " row['Metallicity Lower'] = met_se\n", " row['Metallicity Upper'] = met_se\n", "\n", " #O/H\n", " row['O/H'], oh_se = weighted_avg_and_se(group, 'O/H', 'O/H Lower', 'O/H Upper')\n", " row['O/H Lower'] = oh_se\n", " row['O/H Upper'] = oh_se\n", " \n", " #C/H\n", " row['C/H'], ch_se = weighted_avg_and_se(group, 'C/H', 'C/H Lower', 'C/H Upper')\n", " row['C/H Lower'] = ch_se\n", " row['C/H Upper'] = ch_se\n", " \n", " #Fe/H\n", " row['Fe/H'], feh_se = weighted_avg_and_se(group, 'Fe/H', 'Fe/H Lower', 'Fe/H Upper')\n", " row['Fe/H Lower'] = feh_se\n", " row['Fe/H Upper'] = feh_se\n", " \n", " return pd.DataFrame([row])\n", " \n", "grouped = data.groupby(['Planet', 'Geometry'], group_keys=False)\n", "result = grouped.apply(process_group).reset_index(drop=True)\n", "result" ] }, { "cell_type": "code", "execution_count": 5, "id": "4a698a45-6867-47a2-8ea9-dff271bd5ee5", "metadata": {}, "outputs": [], "source": [ "# Define some subgroups by geometry\n", "direct = result[result['Geometry'] == 'Direct']\n", "emission = result[result['Geometry'] == 'Eclipse']\n", "transit = result[result['Geometry'] == 'Transit']\n", "\n", "# Split off UHJs so we can mark them as special\n", "# Because they are :). \n", "# Also dissociation...\n", "uhjs = result[result['Planet'].isin(uhj_list)]" ] }, { "cell_type": "code", "execution_count": 6, "id": "245e6429-a665-4a79-a859-432decb6d327", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Number of final entries: 66\n", "Number of unique planets measured: 47\n", "Number of planet measurements accounting for multiples: 49\n", "Number of direct spectroscopy entries: 16\n", "Number of transit spectroscopy entries: 15\n", "Number of emission spectroscopy entries: 18\n", "Number of UHJ entries: 8\n" ] } ], "source": [ "print(f'Number of final entries: {len((data['Planet']))}')\n", "print(f'Number of unique planets measured: {len(np.unique(data['Planet']))}')\n", "# number after account for multiple entires per planet\n", "# can be more than unique planets, because we can measure planets with different geometries and they'll count towards this\n", "print(f'Number of planet measurements accounting for multiples: {len((result['Planet']))}')\n", "print(f'Number of direct spectroscopy entries: {len((direct['Planet']))}')\n", "print(f'Number of transit spectroscopy entries: {len((transit['Planet']))}')\n", "print(f'Number of emission spectroscopy entries: {len((emission['Planet']))}')\n", "print(f'Number of UHJ entries: {len((uhjs['Planet']))}')" ] }, { "cell_type": "code", "execution_count": 7, "id": "943c1947-2b9c-4547-922a-c3cf5b45b68b", "metadata": {}, "outputs": [], "source": [ "# Make a generic plotting script for pop comparisons\n", "# so that we can make aesthetic changes easier to all plots\n", "\n", "def pop_plot(fig,ax,axis1,axis2,plot_direct = True,\n", " axis1_label = None, axis2_label = None):\n", " if axis1_label is None:\n", " axis1_label = axis1\n", " if axis2_label is None:\n", " axis2_label = axis2\n", " if plot_direct is True:\n", " ds = [direct, emission, transit]\n", " labels = ['Direct', 'Eclipse', 'Transmission','UHJ']\n", " markers = ['o', 's', 'd', '*']\n", " colors = ['#2E86AB', '#A23B72', '#F18F01', 'grey']\n", " else:\n", " ds = [emission, transit]\n", " labels = ['Eclipse', 'Transmission','UHJ']\n", " markers = ['s', 'd', '*']\n", " colors = ['#A23B72', '#F18F01', 'grey']\n", " \n", " \n", " for d, label, marker, color in zip(ds,labels,markers,colors):\n", " ax.errorbar(d[f'{axis1}'], d[f'{axis2}'],\n", " xerr=[d[f'{axis1} Lower'], d[f'{axis1} Upper']],\n", " yerr=[d[f'{axis2} Lower'], d[f'{axis2} Upper']],\n", " fmt=marker, label=label, color=color,\n", " markersize=7, capsize=2, alpha=1.0,\n", " markeredgecolor='k',markeredgewidth=0.5)\n", "\n", " for _, row in uhjs.iterrows():\n", " color_map = {'Direct': '#2E86AB', 'Eclipse': '#A23B72', 'Transit': '#F18F01'}\n", " ax.errorbar(row[f'{axis1}'], row[f'{axis2}'],\n", " xerr=[[row[f'{axis1} Lower']], [row[f'{axis1} Upper']]],\n", " yerr=[[row[f'{axis2} Lower']], [row[f'{axis2} Upper']]],\n", " fmt='*', color=color_map[row['Geometry']], markersize=12,\n", " markeredgecolor='k',markeredgewidth=0.5)\n", " \n", " ax.set_xlabel(f'{axis1_label}', fontsize=15)\n", " ax.set_ylabel(f'{axis2_label}', fontsize=15)\n", " ax.legend(frameon=True, fancybox=True, shadow=False,fontsize=12)\n", " ax.grid(True, alpha=0.3)\n", " ax.minorticks_on()\n", " \n", " ax.tick_params(labelsize=12)\n", " ax.yaxis.set_ticks_position('both')\n", " ax.xaxis.set_ticks_position('both')\n", " plt.tight_layout()" ] }, { "cell_type": "markdown", "id": "7c0de562-729e-4917-8686-3af75257606c", "metadata": {}, "source": [ "## Metallicity & CO Plots" ] }, { "cell_type": "code", "execution_count": 8, "id": "1b1b50ca-b810-48b2-a5ad-ca834900df7c", "metadata": {}, "outputs": [ { "data": { "image/png": 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig, ax = plt.subplots(figsize=(9, 6))\n", "pop_plot(fig,ax,'Metallicity','C/O')\n", "\n", "plt.savefig('./paper_figs/metallicity_co.pdf')" ] }, { "cell_type": "code", "execution_count": 9, "id": "088ea030-9fb2-4edf-8531-29eff39ac77a", "metadata": {}, "outputs": [], "source": [ "data_nircam = data[data['Obs'] == 'NIRCAM/F322W2+NIRCam/F444W']\n", "data_nirspec_g395 = data[data['Obs'] == \"NIRSpec/G395H\"]\n", "data_soss = data[data['Obs'] == 'NIRISS/SOSS'].drop_duplicates().reset_index(drop=True)\n", "data_nir = pd.concat([data_nircam,data_nirspec_g395]).drop_duplicates().reset_index(drop=True)\n", "\n", "#hires\n", "data_gemini = data[data['Obs'].str.contains(\"Gemini\")]\n", "data_espresso = data[data['Obs'].str.contains(\"ESPRESSO\")]\n", "data_crires = data[data['Obs'].str.contains(\"CRIRES\")]\n", "data_hires = pd.concat([data_gemini,data_espresso,data_crires]).drop_duplicates().reset_index(drop=True)" ] }, { "cell_type": "code", "execution_count": 10, "id": "83e84df1-3efc-46e9-a449-c52ab90c2d15", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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PlanetReferenceStatusGeometryObsC/OC/O LowerC/O UpperMetallicityMetallicity Lower...Stellar Mass UpperO/HC/HFe/HO/H UpperC/H UpperFe/H UpperO/H LowerC/H LowerFe/H Lower
0WASP-18 bCoulombe et al. 2023PublishedEclipseNIRISS/SOSS0.600.600.000.0128370.317577...0.068.7028378.4809887.5128370.2968340.2968340.2968340.3175770.6788630.317577
1WASP-121 bPelletier et al. 2025SubmittedEclipseNIRISS/SOSS0.820.090.051.2400000.350000...0.089.8601329.7739468.7400000.3700000.3700000.3700000.3500000.3500000.350000
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2 rows × 40 columns

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" ], "text/plain": [ " Planet Reference Status Geometry Obs C/O \\\n", "0 WASP-18 b Coulombe et al. 2023 Published Eclipse NIRISS/SOSS 0.60 \n", "1 WASP-121 b Pelletier et al. 2025 Submitted Eclipse NIRISS/SOSS 0.82 \n", "\n", " C/O Lower C/O Upper Metallicity Metallicity Lower ... \\\n", "0 0.60 0.00 0.012837 0.317577 ... \n", "1 0.09 0.05 1.240000 0.350000 ... \n", "\n", " Stellar Mass Upper O/H C/H Fe/H O/H Upper C/H Upper \\\n", "0 0.06 8.702837 8.480988 7.512837 0.296834 0.296834 \n", "1 0.08 9.860132 9.773946 8.740000 0.370000 0.370000 \n", "\n", " Fe/H Upper O/H Lower C/H Lower Fe/H Lower \n", "0 0.296834 0.317577 0.678863 0.317577 \n", "1 0.370000 0.350000 0.350000 0.350000 \n", "\n", "[2 rows x 40 columns]" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data_soss" ] }, { "cell_type": "code", "execution_count": 34, "id": "a4c3e6a4-5cad-45c0-910b-cabaad652981", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\\multicolumn{7}{c}{\\textbf{Direct Spectroscopy (16 planets)}} \\\\\n", " Average & $0.611 \\pm 0.090$ & $0.824 \\pm 0.496$ & $9.138 \\pm 0.545$ & $8.870 \\pm 0.610$ & $13.02 \\pm 7.21$ & $100.5 \\pm 1.9$ \\\\\n", " Median & 0.560 & 0.359 & 9.123 & 8.789 & 11.85 & 100.0 \\\\\n", "\\multicolumn{7}{c}{\\textbf{Eclipse Spectroscopy (18 planets)}} \\\\\n", " Average & $0.773 \\pm 0.187$ & $0.065 \\pm 0.564$ & $9.070 \\pm 0.815$ & $8.869 \\pm 0.829$ & $2.70 \\pm 2.73$ & $1678.9 \\pm 773.3$ \\\\\n", " Median & 0.705 & 0.394 & 9.094 & 8.788 & 1.72 & 1686.5 \\\\\n", "\\multicolumn{7}{c}{\\textbf{Eclipse \n", " No UHJ Spectroscopy (13 planets)}} \\\\\n", " Average & $0.729 \\pm 0.204$ & $0.020 \\pm 0.545$ & $9.115 \\pm 0.829$ & $8.929 \\pm 0.864$ & $2.36 \\pm 2.32$ & $1360.5 \\pm 672.5$ \\\\\n", " Median & 0.720 & 0.550 & 9.260 & 8.941 & 1.67 & 1411.0 \\\\\n", "\\multicolumn{7}{c}{\\textbf{Transit Spectroscopy (15 planets)}} \\\\\n", " Average & $0.705 \\pm 0.427$ & $0.957 \\pm 0.710$ & $9.546 \\pm 0.727$ & $9.033 \\pm 0.710$ & $1.47 \\pm 2.12$ & $1518.0 \\pm 613.5$ \\\\\n", " Median & 0.350 & 0.930 & 9.593 & 8.786 & 0.78 & 1543.0 \\\\\n", "\\multicolumn{7}{c}{\\textbf{Transit \n", " No UHJ Spectroscopy (12 planets)}} \\\\\n", " Average & $0.294 \\pm 0.125$ & $0.885 \\pm 0.813$ & $9.445 \\pm 0.777$ & $9.055 \\pm 0.713$ & $1.32 \\pm 2.30$ & $1299.3 \\pm 478.9$ \\\\\n", " Median & 0.350 & 0.584 & 9.348 & 8.761 & 0.69 & 1371.0 \\\\\n", "\\multicolumn{7}{c}{\\textbf{UHJs Spectroscopy (8 planets)}} \\\\\n", " Average & $0.728 \\pm 0.419$ & $0.860 \\pm 0.523$ & $9.327 \\pm 0.779$ & $8.800 \\pm 0.709$ & $3.02 \\pm 2.88$ & $2463.7 \\pm 155.8$ \\\\\n", " Median & 0.497 & 0.895 & 9.646 & 8.737 & 1.82 & 2459.0 \\\\\n", "\\multicolumn{7}{c}{\\textbf{1-3um Spectroscopy (2 planets)}} \\\\\n", " Average & $0.815 \\pm 0.032$ & $0.533 \\pm 0.606$ & $9.281 \\pm 0.579$ & $9.127 \\pm 0.646$ & $5.68 \\pm 4.52$ & $2476.5 \\pm 27.5$ \\\\\n", " Median & 0.710 & 0.626 & 9.281 & 9.127 & 5.68 & 2476.5 \\\\\n", "\\multicolumn{7}{c}{\\textbf{3-5um Spectroscopy (18 planets)}} \\\\\n", " Average & $0.943 \\pm 0.132$ & $0.762 \\pm 0.828$ & $9.473 \\pm 0.809$ & $9.119 \\pm 0.856$ & $1.05 \\pm 0.93$ & $1451.2 \\pm 563.6$ \\\\\n", " Median & 0.500 & 0.641 & 9.477 & 8.973 & 0.95 & 1585.5 \\\\\n", "\\multicolumn{7}{c}{\\textbf{Hi-Res Spectroscopy (16 planets)}} \\\\\n", " Average & $0.553 \\pm 0.063$ & $0.057 \\pm 0.534$ & $8.710 \\pm 0.749$ & $8.502 \\pm 0.711$ & $4.98 \\pm 6.27$ & $1739.4 \\pm 763.1$ \\\\\n", " Median & 0.635 & -0.190 & 8.430 & 8.341 & 1.89 & 1691.0 \\\\\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3604463298.py:178: UserWarning: set_ticklabels() should only be used with a fixed number of ticks, i.e. after set_ticks() or using a FixedLocator.\n", " ax[0].set_xticklabels(ax[0].get_xticklabels(),rotation=45,fontsize=15)\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_45813/3604463298.py:179: UserWarning: set_ticklabels() should only be used with a fixed number of ticks, i.e. after set_ticks() or using a FixedLocator.\n", " ax[1].set_xticklabels(ax[1].get_xticklabels(),rotation=45,fontsize=15)\n" ] }, { "data": { "image/png": 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ahYWFatmypZ544gn97W9/K/dk3ffff78WL16sFStW6OjRozKbzWrevLkefvhhjRw5Uk2bNvXyUQAA/NWiRYs8rnosnZond+HChT7ICgAAAPid2WzWsXyTDEMei4WGIR3LZyQMSldrCoXjxo1z6x1YmdhRo0Zp1KhR1ZsYAKBWiI2N1Y4dO9S7d29t2LDBtT0pKUkLFy5kQmgAALxoy6rl2rpquce2nIMH5CwultliUfS5MR5jLuzSTW27dPNminXKxo0btWnTJo9tJpNJhmHIZDIpIyPDY0z79u2VmJjozRTrtLgW7bRoww9ucxSWWLhBatqSkTAoXa0pFAIA4G2xsbFav369LBaLa7jG+vXrfZ0WAAC1XtsyCn2v33+ncn89Kts5DXTj38fWcGZ1U2JiYqmFvhdeeEHHjx9XRESEUlJSajgzSNL09xbpquRWMuRQ76TfVz1euEF65F2rlmUyEgalo1AIAAAAAABQS8TGxmpZ5g7dndJbj2VsUHS4lJMvxbVK0rJMRsKgbBQKAQAAAAAAapHY2Fh9uny9NPP3kTAavN7XaSEAMHslAAAAAABALcW6JagI3i4AAAAAAAAAKBQCAAAAAAAAYI5CoE77OfOAsjIPeW4sWRrLZNInr/7gMSQ+ubHaJMd4MUMAAAAAAFBTKBR6SXp6utLT01VcXOzrVIBStUmOKbXQN+OxVco75lB4VIj6DOtQw5kBALwpOTlZFotFqampSk1N9XU6FZaZmam0tDR9/fXXKiwsVGJiokaMGKFbbrmlQvs5dOiQJk2apAULFmjPnj2KiIhQmzZtdMcdd+j+++/3UvYA6rL89YeUv+Gwx7bi3ELXzyNvbvYYE57USOEdG3stPwCgUOglJTfedrtd0dHRvk4HAADAJTMzU1FRUb5Oo1KWLl2qnj17KjQ0VIMGDVJkZKTmzp2rgQMHas+ePRo5cmS59rN+/Xpdd911+u2339SnTx/dfPPNys3N1ZYtWzR//nwKhQC8Irxj41ILfXtHf3XqPwxDDe9sV4NZAcDvKBQCAAAgIBQVFenee++V2WzWihUr1LFjR0nS2LFjdemll2rMmDG6+eab1bx58zL3Y7fbdcMNN0iSvv/+e3Xo4N5zvqioyCv5AwAA+DsWMwFQ6zmdTl+nAACoBkuWLNH27ds1ePBgV5FQkqKjozVmzBgVFBTozTffPOt+Xn31Ve3evVuTJ08+o0goSUFBfJcOAADqJu6CANRK2dnZuuXGP2nb1i0KCwnWiYJCfdD5Us3+8CPFxsb6Oj2Pypqz5vTFZZizBkBdtWzZMknSddddd0Zbz549JUnLly8/635mzZolk8mkm266ST/99JM+++wznThxQhdeeKGuv/56hYSEVGveAAAAgYJCIYBaJzs7Wxe3b6e+7eP1p55dZTKZZBiGth44rIvbt9PaTZv9slhY1pw1+yd+q2J7gSy2YOasAVBnZWVlSZLi4+PPaIuJiZHNZnPFlKagoEAbN25Uo0aN9PLLLystLc2t53mrVq300UcfKTExsXqTBwAACAAMPQZQ69xy45/Ut3282jZpLJPJJEkymUxq26Sx+rY7XwP/fKOPMwQAVEZOTo4klbpQXFRUlCumNL/++quKi4t19OhRTZgwQc8884wOHjyovXv36sknn9TOnTvVr18/nTx5stR9OBwO2e12twcAAIA/+eO9isPhKNfz6FEIoNbZkfWz/nRNF49tFzZprEVfrKrhjAAA/qKk92BxcbGGDx/utkryhAkT9NNPP2n27Nl6//33ddttt3ncx6RJkzR+/PgayRcA/NHGjRu1adMmj22hoaEKCQmR2WxWRkaGx5j27dvTcxvwsmbNmrn9npaWpnHjxp31eRQKAdQqTqdToUFBrp6Ef2QymWQNCpLT6ZTZTKdqAAgkJT0JS+s1aLfbVb9+/XLtQ5L69+9/Rnv//v01e/Zsfffdd6UWCkePHq0RI0a4ve4fb8Yr4+P1+zR/Q7bHtiO5DtfPIW9meozplxSrGzrGVTkPADibxMRECn2An9uzZ4+ioqJcv1ut1nI9j0IhgFrFbDbrZFGRDMPwWCw0DEMni4ooEgJAACqZmzArK0uXXHKJW9uBAweUm5urSy+9tMx9REREKC4uTvv27VO9evXOaC/ZduLEiVL3YbVay32zXRE3dIwrtdDXavQnkiSnYWjancnV/toAAKB2iYqKcisUlheFQgC1Tqv4Ntp64LDaNjlzYZCt+w+pdZsLfJAVAKCqunXrpkmTJumzzz7ToEGD3NoWL17sijmbq6++Wm+//bZ+/PFHXXzxxW5tP/74oySpRYsWlcrx5MmTKi4uVkhIiIKDgyWdGuZ88uRJmUwmhYeHlxnrdDpdRcqIiAhXrFFUKGdxsUxBFte20mIdDoeKiooUHBzsWsHZMAzl5+dLksLDw11fphUUFKiwsLBCsUFBQW6F0ry8PElSWFiY64u40mLzi4tV5HTKZhiubYWFhSooKJDFYlFoaOjvsfn5MgxDoaGhslgsFY4tKiqSw+GQ2WxWWFiYK/bEiROnRiBUINZqtSooKMh1PotPFsvpcEq/n85SY6t67isSe7Zz/8dYR8EJFRUXS7KeEVuRc1/Z90lp57Misfn5+XIUFsrp/P09Vdr5rMz7xNP5rEhsec59SeyJwpMKCwotM7Yi5z4QrxEVPffevEYYxtnfUxU59169RuzIUMj+uQoOMv8v1tAJR/GpWJNZMpySySzH531UVGwoOMiskGDz7+ez8Z+l5rdU+X3izWvE6ar6PvHlNaKE0+lUXl5eha4R5X2flPz7VBZdagDUOrM//EgLNmVpS/ZB1//gDcPQluyDWrB5m2Z98KGPMwQAVEaPHj3UqlUrzZw5U+vXr3dtz8nJ0cSJExUSEqI77rjDtX3//v3aunXrGUOV77vvPknS5MmTdezYMdf2AwcOaMqUKTKbzbrpppsqleNNN90km82md99917Vt48aNstlsZ6zWfPvtt8tms2nq1Kmubdu3b5fNZlNcnHvPwiOfvqI9L96snO/muR2fzWY7o2fkiBEjZLPZNHHiRNe2nJwc2Ww22Ww2FRUVubY//vjjstlsevzxx13bioqKXLGn/9tNnDhRNpvNbdi1dKoXps1m0/79+13bpkyZIpvNpqFDh7rFXrn6G3XK+lm7T/7eY3Pq1Kmy2Wy6/fbb3WLj4+Nls9m0ceNG17Z3331XNpvtjPOTlJQkm82m1atXu7Z9+OGHstls6tWrl1vs5ZdfLpvNpiVLlri2LV68WDabTV27dnWLveaaa2Sz2bRgwQLXtq+++krL71iu7eO3u8X2799fNptNs2bNcm1bu3atbDabEhIS3GIHDRokm82mGTNmuLZt2bJFNpvtjCL1PffcI5vNpvT0dNe23bt3y2azqXFj9y9Fhw8fLpvNpmeffda17ciRI67zebpRo0bpvhev1cLv33Jty8/Pd8WWFAMkafz48bLZbBo1apTbPkpijxw54tr27LPPymazafjw4W6xjRs3ls1m0+7du13b0tPTZbPZdM8997jFtmjRQjabTVu2bHFtmzFjhmw22xlfEiQkJOih/2Zo77Hf36uzZs2SzWY7Y3qB5ORk2Ww2ffXVV65tCxYskM1m0zXXXOMW27VrV9lsNteXEJK0ZMkS2Ww2XX755W6xvXr1ks1m04cf/n6PuXr1atlsNiUlJbnFlnWNuPL1wW6xFblGDB06VDabTVOmTHFtC8RrRFxcnGw2m7Zv//3vy1fXiNMXfqjoNcJmsyk52b33t1evEe3uVvrGa6Ru86Ru87S75cuy9Vqkxjctl0LPPRUYeq6Gz46VrdciPfvt5a7YI+3ekK3d3R6vETabzW1O3kC9RthsNq1du9a1zR+uEbt27arwNaK89xGxsbGqCgqFAGqd2NhYrd20WT8VmvX84hV65cuv9fziFfqp0Ky1mzZX+cIJAPCNoKAgTZs2TU6nU127dtVf//pXjRw5UklJSfr55581ceJEtw9Qo0ePVtu2bd1uzCXp//7v/zRixAht3rxZHTp0UGpqqv76178qKSlJ+/bt0z/+8Q+1adOmho8OAADA90zG6f1pUe3sdruio6OVk5NTqbHh5XHZxC91wH5SMVGhWj2mh1deoyZkdbtKRQcPKujccxW/fJmv06m0pLeS5DScMpvM2nDHBl+nU2kzHlulvGMORdSz6q7JnlcQDgQvDOqv4uIiWSxBGvHevLM/wU/tn/itiu0FskSFqMmYzr5Op9ImTJig4uJiWSwWjR071tfpVJrFYnEtiFNcXOzrdIByq4n7kpqwZs0apaWl6euvv1ZhYaESExM1YsQIDRw40C3urrvu0ptvvqnp06frrrvuOmM/M2bMUHp6un788UeZTCZddNFFGjFihG688cYK5XP6v2tISIhXhh63/NtHp66fQRbtfOZPZcb687DCDVdcqaJDh2SLidEFK5ZLCsyhxz1m9tDhE4cVUz9GXw74ssxYfx56PGP0Vzp5vFjRDWy6a3KXgB16PO3BIXLk5CiqYUMN/febATv0ODvt61NDj01S00lXMvTYx0OP27Rpo+zsbMXFxemXX37x76HHZcV+doF0Yp8UFidH7+0Vukb409Dj777vIYfjgKzWGF3U8bMqvU98eY2YMmWKjh8/roiICA0bNswrQ48PHjyo2NjYSt/vMUchgFqPhUt8Lzs7WwMGDFBWVpZCQ0N18uRJLV68WHPmzKGHJ4AKu/TSS7Vo0aKzxs2YMcNt2NYf3XXXXR4LiFVx+oeTEhaLxe3DV1mxZrPZY6wpKFhmS7BOX6ertFhPi62YTCaPsSEhIa4PgJWJlVSh2HCLRUVms8ynHUhwcLDrA45b7GkfhioTGxQU5PrwdrrTP7xVJtZiscgSapHZaS5XbFXPfUViK3LurVarrCFhKrI4zhpbHefeU2xp57MiseHh4bIGB6vQ/Pt7qrTzWdX3SWnns6rnviQ2LDhUMsqOrci5D8RrREXPfVViz3buT18Y0R/eJz65RlThfSL5zzXij/zhGlGR81kd75OKoFDoJenp6UpPT6enCYA6Lzs7WxdddJGuvfZaXXvttTKZTDIMQ9u2bdNFF12kdevWUSwEalhycrIsFotSU1OVmprq63QAAADgJygUeknJjXfJUBQAqKsGDBiga6+91m3yXZPJpPj4eBmGoVtuuUUrV670YYZA3ZOZmRnQQ48BAADgHRQKAS9wOp0yWxjuCkinVt669tprPbbFx8e7rTrpTzIyMpSRkeGxzWw2u+Yo/ONqaSVSUlKUkpLizRQBAAAAoFpRKESNylnwieyffOKxreh/S6YXHTmiPfcP8xgT1aePovv28Vp+VZGdna1+g/rppx0/yRJuUXF+sS6ZdonmvzefYZWos0omiz99jpfTmUwmWa1WV9HNn5RV6GvatKn27dunc889V/PmBe4iOQAAAIC/OHBgng4eWuCxLcgSKUtYmEymIG344a8eY85t3FcxMZ6/xEf5UShEjYruW3qhb0tCu1P/YRhq9u9XazCrqsvOztYFyRfonEHn6Py7z3fNwXZk4xFdkHyBfsr8iWIh6iSz2ayTJ0/KMAyPxULDMFwrgwEAAACou2Ji+lPo8wN8MgOqQb9B/XTOoHMU1SHKVQwxmUyK6hClcwaeo/4pXOxQd7Vu3Vrbtm3z2JaVlaXzzz+/hjMCAAAAAHhCj0KgGmzduVUt727psS2yQ6S2vr+1hjMC/MecOXN00UUXyTAMxcfHu3rcZmVl6YsvvtC6det8nSIABATmQAYAAN5GoTBAfLx+n+ZvyPbYdiTX4fo55M1MjzH9kmJ1Q8c4r+VXlzmdTpnDzWXOwWYKM/nlHGxATYiNjdW6det0yy236IsvvpDVapXD4VB8fLzWrVvHsHwAKEN2drb63jxYe7N2ymyNkNORp4uXt9SC92dy/QQAANWOQmGAuKFjXKmFvlajTy0O4jQMTbszuSbTgv63+mm+s8w52Jz5FAlRt8XGxmrlypWaMGGCiouLZbFYNHbsWF+nBQB+LTs7W/EdOyui2xA1ufJRV4/svTu+V3zHzspa/y3FQgAAUK2oXADV4MKWF+r4xuMe247/cFxtW7Wt4YwA/0XRHADKp+/NgxXRbYjCW3dymwM5vHUnRXS7R/0G3OrjDAEAQG3DpzWgGsx/b75+fe9X2TfYZRiGpFM9Ce0b7Pp11q+alzHPxxkCAIBAs2X7LwprdYnHtrBWnfTjtl9qNiEAAFDrMfQYqAaxsbH6KfMn9U/pr02zN8kSblFxfrHat26vbzK/YVgQAACoEKfTKZM1ouw5kK3hzIEMAACqFYVCoJrExsbqu+XfKemtJBUVFynIEqTv7vjO12kBAIAAZDabZTjyypwD2XDkUSQEAADVikIh4AXctAMAgKpq27qF9u74XuGtO53RdmLHd0o4v6UPsgKA2iMjI0MZGRke2w4ePOj62b9/f48xKSkpSklJ8Vp+gC9QKAQAAACqQV5eniwWiyQpKChIVqtV0qnef/n5+ZKkiIgIV/zJkydVXFxc6v5mvzVNSZd3l2QorFUn16rHJ3Z8p7zl/9X89d8qLy9PkhQeHu7qeehwOFRUVFSh3M1ms8LCwtyORZLCwsJcX4AWFBSosLCwQvs1mUwKDw93/X6iuFiFTqci/jensyQVFhaqoKCgQvuV3P8tT5w4IafTKavVqqCgUx9xioqK5HA4qrTfknMUEhKi4OBgSVJxcbGKTxbL6XCq2Fzs+rc6G0/nKDg4WCEhIZJODTc/ceJEhfP1dI5Ke//9kaPghByFDgUVOM84jtDQUNf7ueQcWSwWhYaGumLKe+yn83SOSnv/lZejsFDFTqfr9+LiYp08efKM919+fr5rPvHyKu0ceXr/VYSnc5RfcELhwb//O5ztGuFJaecoUK4RJefI0/uvospzjejfv3+pRcDIyEhJp95PpxcT/3iNyMvLO+MacfLkyQrn67VrhNNwLU5R0WtEWQLpGiHJ4znyxTXC4XCooKBADoejzOOoyn1EZf593BjwqpycHEOSkZOT47XXaPnYAqP5qAVGy8cWeO01asKPbROMHy+40PixbYKvU6mSDm92MNrPaG90eLODr1OpkumjVhqvDP3SmD5qpa9TqZLnB/Yznrulj/H8wH6+TqVKsv+52tgzaoWR/c/Vvk6lSsaPH2+MGzfOGD9+vK9TqZK4uDhDkhEXF+frVIAKqYn7krrklVdeMdq2bWu0adPGkOT2GDZsmCvu0KFDru2nu/nmm894nqeHKTzaCDqnqRESe4ER1KCZcfH/XWXs27fPMAzDFXPo0CHXfocNG1au/Z7+6Natm1tuDRs2NCQZmzZtcm1LS0ur8H4TEtzv684PDzckGW93SHL7d6zofhs2bOi2327duhmSjNmzZ7u2zZ49u8L7Le0cvfLKK65tS5curdR+PZ2jtLQ017ZNmzZVar+ezlFp77+KPJYuXXrGObr55pvd/n0qs19P56i0919FHjde1M547b473M7RH99/CQkJFd6vp3NU2vuvIo/SztGeUSuMPY+tcHv/VeRR2jkKlGtEyTny9P6ryINrxO+PTS81Nox3ZRgfxNXpa4SncxSI1whP77/SHm3atDHatm3rduzlQY9CL0lPT1d6enqFvwECAADwtuTkZFksFqWmpio1NdXX6QSskn8/u92u6Ohor72OkZ+jovwcSVL4BV30/aqlXnst/G7hjoVad2idJGn2T7O19cutkqS96/f6Mi0AAMolMzNTUVFRFX6e6X9VXnhJyY1jTk5OpU5QebQa/YmchmQ2STsm9fHKa9SELQntJKdTMpvV9sfNvk6n0pLeSpLTcMpsMmvDHRt8nU6lzXhslfKOORRRz6q7JnfxdTqV9sKg/jIMp0wms0a8N8/X6VTa/onfqtheIEtUiJqM6ezrdCptwoQJron5x44d6+t0Kq1p06bat2+f4uLitHcvHxgROGrivqQuKvl3zc7Odv27VsfQ49O1G/vpqfs9i1m/PHuja3ugDSv84YorVXjokCJiYnThiuWSAnPosV8NK6zC0ON3nvxGeTkORURbddtTl7u1BdKwwv8+dK9O5hxTdMNGGvrvNwN26PG+satODT02SU0nXcnQ4xoYelzWNSIyMtJ133r8+HGP+w2Ia8SnbWR2ZEthcSros4Ohx/Lt0OMpU6YoNzdXNptNDz30UKn7rcp9hN1uV2xsbKXv9+hRCAAAAFSDiIgItxv4EiaTyeP20z9MnY05JFT63xfDf3zNP7Jara4PF5Xlab8hISGuD0OVFWaxKNhsluW0lZyDg4NdH94qvd/TPkCWCAoKcn3YrCxP58hisXj896kIT+fIbDZXeb+ezlFp7z9JsoaEqSjYLGuItczXLu0cVTXf0s5RRfdrDQ5W4WmLCZZ2jk4vCFRGaefI0/uvIkrOUXhI2KkBg/9TkWtEaQLtGuHpHPnqGlEyL2xZf0MBcY047X8cFb1GlJe/XyP+yJfXCKvVqoKCAlmtZV93T1fR+4iqjmylUAgAQG32S4a0y/Nqfjq+XTKKJFOQFNnac0zzFKkFq/kBAAAAdQGFQgAAarMWZRT6PmwqndgnhcVJ3QJ3aD4AAACA6mE+ewgAAAAAAACA2o5CIQAAAAAAAAAKhbVFRVfZAgAAAAAAAE5XoTkKS5a+rgyTyVThJdhRtuzsbPW9ebD2Zu2U2RohpyNPFy9vqQXvz1RsbKyv0wMAAHXQ1q1btWjRIq1fv15HjhyRJDVs2FAdO3ZUr169dOGFF/o4QwAAAJSmQoVCwzDOHuSF5+JM2dnZiu/YWRHdhqjJlY+6lm7fu+N7xXfsrKz131IsBAAANWbv3r0aOnSoPv30U0ln3vu98847evTRR9WrVy/9+9//VrNmzXyRJgAAAMpQoUJhWcNbzWaz7rrrLr3xxhtVTgpn1/fmwYroNkThrTu5tplMpv/9bqjfgFv1/aqlvksQAADUGT/88IOuvvpq/frrr7JarerZs6cuueQSNW7cWJJ06NAhrV27VosXL9bChQt18cUXa+nSpWrfvr2PMwcAAMDpKlQohP/Ysv0XNb7yEo9tYa066ccVb9ZwRgAA4Gw2btyojRs3emz77bff5HQ6ZTabVb9+fY8xiYmJSkxM9GaKFXbixAn17dtXv/76q+68804999xzatCggcfYX3/9VX/72980ffp09e3bV1u2bFFYWFgNZwwAAIDSUCgMQE6nUyZrhEwmk8d2k8kkkzXc9WEjkARizvC9LauWa+uq5R7bTGaTjOJTPz98ZoLHmAu7dFPbLt28mSIASCq70Pf888/r+PHjioyM1ODBg2s4s8pLT0/X3r17NXz4cP3rX/8qM/acc87Rf//7X0VERCg9PV2vvvqqRo4cWUOZ+r+P1+/T/A3ZHtvMJpOchiGzyaQhb2Z6jOmXFKsbOsZ5M0UAAFDLUSgMQGazWYYjT4ZheCwWGoYhw5EXMAW37Oxs3d6vn3Zv36ZIs0XHncU675JL9Pb8+cyziHJpW0ah7/X771Tur0cVHl1PN/59bA1nBgC134cffqioqCg988wz5X7O008/rbfeeksffPABhcLT3NAxrtRC32UTv9QB+0k1tFk17c7kGs4MAADUFRQKA1Tb1i20d8f3bnMUljix4zslnN/SB1lVXHZ2trq0aaPH6tXXlS1auhZl+ergIXVp00arfv6ZYiEAAH7sp59+0hVXXKHQ0NByPycsLExXXHGFVq9e7cXMAAAAUFEUCgPUgvdnKr5jZ0mGwlp1chXYTuz4TnnL/6v567/1dYrlcnu/fnqsXn11tdlc20wmk7rabDJk6I7+/fXFd9/5MEMAAFCWvLw8RUdHV/h5UVFRysvL80JG8LWcBZ/I/sknHtvMkZEKCQuTgoK05/5hHmOi+vRRdN8+3kwRAACUgkKhl6Snpys9PV3FxcVe2X9sbKyy1n+rfgNu1cZlM2S2hsvpyFdim5aav/7bgOmFt2frVl3ZtJnHtq4RNr2wZUsNZwQAQO2XnJwsi8Wi1NRUpaamVmlfjRo10vbt2yv8vB07dqhRo0ZVem34p+i+FPoAAAhUFSoUvvXWW2W2b9u2rcyYO+64oyIvF9BKbrztdnulvmUvj9jYWH2/aqlajf5ERcVOBVnM+n5S4NyUOZ1ORZpMZS7KEmkyscAJAADVLDMzU1FRUdWyr8svv1xz587Vjz/+qISEhHI958cff1RmZqYGDBhQLTkAAACgelSoUHjXXXeVWtSRpJUrV2rVqlWlttelQmFNC8RCmtls1nHDKHNRluOGEZDHBgBAXXH33Xdrzpw5SklJ0ZIlS9SgQYMy448ePaqUlBTXcwEAAOA/KlQovOOOO8osFAIV1ezCC/XVwUNucxSWWJGXq/PatvVBVgAAoLx69uypAQMGaM6cOWrfvr3S0tI0aNAg1atXzy0uJydH7733niZMmKADBw5owIABuu6663yTNAAApcjOzlavXr3kdDolnRoJl5SUpEWLFgXMFF9AVVSoUDhjxgwvpYG66u3589WlTRsZMtQ1wuZalGVFXq6ePnZMq9as8XWKAADgLN566y0VFhbqo48+UmpqqoYPH65WrVq55iA8fPiwduzYIeN/Iwn+9Kc/nXVKm0CUl5cni8XilX0XF5yQs8Ch4gKDRWBQbRwFJ+QodCiowBnQ7ytHYaEcRUUKLiwM6OPILzghZ7FTZos5oI8jkO3fv1/t2rVTQUGB2/YffvhBLVu21ObNm9WkSRMfZVdJJ53SSUkmp8T7yuccDocKCgrkcDi89nde1f1WqFC4fPlyXXnllQwFRbWJjY3Vqp9/1h39++uFjRtlM1uU6yzWeYmJWrVmDd/YAAAQAKxWqz744AO99957euGFF/Tdd99p27Zt2rZtm1tcp06dNGLECA0aNMhHmXpXTdy37JFkG+/1l0EddN+Lvs6gejz03wxfp1ApDcPrKzrUpujQSOWcPK6c13J1JP83X6eF0xQUFCg+Pt7XaVTBfklnjuSD7zz++OO+TsGjChUKu3fvrvr166t3797q16+frr/++mqbCBt1V2xsrL747jttSWgnZ1GRzEFBavvdd75OCwAAVNCgQYM0aNAgHTlyRBs2bNDRo0clSQ0aNFBSUpIaNmzo4wwBwP/ERjbWpJ6Pqnurzq4RVkt3rNboxc8r+/ghX6cHoI6pUKHwqaee0oIFCzRz5kzNnDlTQUFB6tatm/r166d+/fqpRYsWXkoTdQW9VQEACCxvvvmm+vbt67aIScOGDdWjRw8fZlUz0tPTlZ6eruLiYkmn5rXy1pfoVz27VAftDp0bZdWyv3X3ymug7nnnyW+Ul+NQRLRVtz11ua/TqbT/PnSvcn/7Vbb65+ieKf/xdToV0q9Hb93VrK+ubn2Za5vJZNLVrS/XxJ4j9dbehZr3xSc+zLDucDqdqlevnmtuQk/MZrOOHTsWWJ9b58VLJ/ZLYU2k/lm+zqbOmzJlinJzc2Wz2fTQQw955TXsdrtiY2OVnJwsi8Wi1NRUpaamlvv5FSoUPv7443r88cd16NAhzZ8/X/PmzdOXX36pL774Qg8//LASEhJ0ww03qG/fvrrsssvOvkMAAAAEtL/85S+yWCzq3Lmz6z6wbR1ZjKzkxttutys6OloRERGKiIjwymtZQsJkDjHJEhLqtddA3WMNCVNRsFnWEGtAv6+swcEqDAqSNTg44I4je+dede/W2WPb1a0u06QVrwfcMQWysy3eajKZFBkZWUPZVJNQs2T87yfvJZ+zWq0qKCiQ1eq9627JF5iZmZmV+gKzUmXwxo0b65577tHHH3+so0ePat68eRoyZIh+++03TZw4UV26dFFMTIyGDBmiefPm6cSJE5V5mQpxOp165ZVXdPHFFys8PFxRUVHq2rWr5s2bV+H9vPzyy0pMTFRYWJgaNWqklJQU7dixw0uZAwAABK63335bN910kzZv3qxRo0apffv2io+P18iRI7Vs2TLXzSoAwJ3T6VSUNaLU4pTJZFKkNaLMHm6oXu3atSuzvX379jWUCeA7Ve4va7Va1bdvX73++uvau3evMjMz9fjjjys2NlZvvPGGbrzxRjVo0ED9+vXT1KlTdfjw4erI241hGLrlllv0wAMPyG6365577tGgQYP0008/6YYbbtArr7xS7n0NHTpUDz74oAzD0IMPPqjrr79eH3zwgZKTk5WVRTddAACA091666167733dPjwYX3xxRcaPny4nE6nXnzxRfXo0UONGzfWbbfdplmzZslut/s6XQDwG2azWXZHngzD8NhuGIaOO/ICa5hrgFu0aJGsVqvHNqvVqoULF9ZwRkDNq9DQ4/K45JJLdMkll2jChAnau3ev5s2bp3nz5umLL77QwoULdeDAAY0dO7ZaX3Pu3LmaO3euunTpos8//1xhYWGSpIkTJ6pTp0569NFH1bdv37POobh06VJNmzZNXbt21eeff66QkBBJ0uDBg9W7d28NHz5cixcvrtbcEZgW7lioRTsXeWwzyeT6+cCXD3iM6dWyl3q36u21/AAAqGlBQUG6+uqrdfXVV2vKlCnavHmz6z7wvffeU0ZGhoKCgnTllVeqf//+6tu3r1q1auXrtAHAp5q2Pk9Ld6zW1a3PnCNyyY7VanZ+cx9kVXfFxsZqx44d6t27tzZs2ODanpSUpIULF9bI6vaAr1VLoXDnzp06cuSIpFOTV7ds2VKS1LRpUw0bNkzDhg1TXl6eFi9e7JXx/B9//LEkacyYMa4iYUkujzzyiB5++GFNnz5d48ePL3M///nPqYlvn3rqKVeRUJJ69eqlq666Sp999pl2796t8847r9qPAYGld6vepRb6eszpoUP5h9QgrIFe7vFyDWcGAIB/aNeundq1a6fRo0fr8OHDmj9/vubPn68vvvhCS5Ys0SOPPKK2bduqf//++stf/qL4+HhfpwwANe6t99/VlR0vl6FTcxKWrHq8ZMdqTVierq/Wf+PrFOuc2NhYrV+/XhaLRU6nU2azWevXr/d1WkCNqXQf5jVr1mjQoEE655xzdP755+uyyy7TZZddpvPPP1/nnHOOUlJStGbNGld8RESE/vznP+vaa6+tlsRPd+DAAUlyFShPV7JtyZIlZ93PsmXLFBERoS5dupzR1rNnT0nS8uXLq5IqAABAndOoUSPdfffd+vDDD3XkyBF98sknuvfee5WTk6PJkycrIyPD1ykCgE/Exsbqq/Xf6J3sT3XtG3fpxneG6do37tI72Z/qq/Xf0IMNQI2rVI/Cv/3tb3rxxRddk6qGhISofv36kqTffvtNx44d06xZszRnzhw98sgjevbZZ6svYw8aNmwo6VTPxj+usrdz505J0s8//1zmPvLy8rR//361b99eFovljPaSb7nPNk+hw+GQw+Fw/c5cPAAAwN/88f7EarWWOidTeSxZskR79+5Vp06dlJCQUGbs9u3bdfjwYQ0cOFCvvfaa1q5dy4InAOq02NhYfb5qifaO/krOYqfMFrOaTrrS12kBqKMq3KNwxIgRev755xUSEqKRI0dq7dq1riLb/v37lZeXp3Xr1unRRx+V1WrVCy+8oEceecQbubv06tVLkjR58mSdPHnStf3o0aN66aWXJEnHjh0rcx85OTmSpOjoaI/tJUtKl8SVZtKkSYqOjnY9mjVrVp5DAAAAqDHNmjVzu1+ZNGlSpfe1Z88e9enTR//4xz/Kdd/TrFkz/fOf/1S/fv20f/9+XXzxxUpOTq706wNAbcLCJQB8rUJXodWrV+ull15Ss2bNtHbtWj377LPq2LGjWw88i8WipKQkPfPMM1q7dq3i4uL0r3/9S99++221J19i8ODB6t69u7766islJibqgQce0H333ad27dq5Cnw1dcEdPXq0cnJyXI89e/bUyOsCAACU1549e9zuV0aPHl3pfU2bNk0FBQV65plnyjUXdWRkpJ599lnl5+dr2rRplX5dAAAAVL8KDT1+7bXXZDKZlJGRoQsvvPCs8RdccIHee+89XXHFFXrttdfUuXPnSidalqCgIC1atEiTJ0/WzJkzNXXqVEVHR+vGG2/Uo48+qjZt2qhx48Zl7qOkJ2FpPQZLhuiU1uOwRFWH7pTm4/X7NH9Dtsc2s8kkp2HIbDJpyJuZHmP6JcXqho5x1Z4XAAAIPFFRUa4vU6vq888/V6NGjfSnP/2p3M/p37+/zj33XC1atEhPPvlkteQBAAAQCEqm8fNXFSoUrlixQh06dND//d//lfs5//d//6ekpCQtW7asorlViNVqVVpamtLS0ty2l7xup06dynx+RESEmjRpop07d6q4uPiMeQpL5ib01Yp8N3SMK7XQd9nEL3XAflINbVZNu5OhOwAAoOZs3brV40JwZ9OpUyd9/fXXXsgIAADAv2RnZ2vAgAHKyspSaGioTp48qQ8++EBz5szxu0WLKlQoPHDggC677LIKv8iFF16ojz/+uMLPqw7vvvuuJGnQoEFnje3WrZvee+89rVq1Sl27dnVrW7x4sSSdsR0AAKAuy8vLO+uIC0+io6OVm5vrhYwAAAB8Z+PGjdq4caPr999++01paWnq2bOnrrnmGplMJhmGoW3btqlDhw4aP368a4FgSUpMTFRiYqIvUpdUwUJhaGio8vPzK/wiJ06c8Mpw3NPZ7fYzhtC8//77euONN5ScnKw///nPru1HjhzRkSNH1LBhQ9eKyZL017/+Ve+9956efPJJff755woJCZEkLVq0SMuWLdN1112n5s2be/U4AAAAAkn9+vV18ODBCj/v4MGDbjfFAAAAtcEfC31dunRRz5493UaomkwmxcfHyzAMZWRkaOXKlb5I1aMKFQpbt26tr7/+2uPQ3NIUFxfr66+/VuvWrSuVYHl17txZzZo1U9u2bRUaGqo1a9Zo2bJlatWqlebMmeOW7yuvvKLx48crLS1N48aNc23v3r27hgwZomnTpuniiy9Wnz59tH//fs2aNUvnnHOOXn75Za8eAwAAQKBJSEjQ6tWrdeLECYWFhZXrOfn5+frmm2906aWXejk7AADqgF8ypF0ZntuCo6WgCMkUJC3v7zmmeYrUIsV7+dVx27dv1zXXXOOxLT4+XkuXLq3hjMpWoUJhnz599NRTT+npp5/WmDFjyvWcZ555RkePHlVqamqlEiyvgQMH6oMPPtDq1atVWFioli1b6oknntDf/va3Ck3W/frrrysxMVFTp07VlClTZLPZdOONN+qf//yn14udAAAAgaZv375atmyZ/vGPf+if//xnuZ7zj3/8QydOnFC/fv28nB0AAHVACwp9/srpdMpqtcpkMnlsN5lMCgkJkdPplNlsruHsPKtQofChhx7Sv/71L40dO1ZOp1OjR48utWdhcXGxJk+erLFjx6p+/fp68MEHqyXh0owbN86td2BlY81msx588EGv5wsAAFAb3HfffXr22Wc1efJkhYWFacyYMaXe6DqdTv3zn//U5MmTFRMTo6FDh9ZwtgAAADXHbDbL4XDIMAyPxULDMORwOPymSChVsFBYv359zZkzR3369FFaWppef/11DRgwQJ06dVKjRo0kSYcPH9Z3332n999/X/v27VNwcLBmz57NHDQAAAC1UHh4uObOnatrrrlGaWlp+s9//qMBAwbo4osvdrs/XLt2rebMmaO9e/cqNDRUc+fOVXh4uI+zBwAA8K7WrVtr27ZtbnMUlsjKytL555/vg6xKV6FCoST16NFDX331le68805t3bpVU6ZMOSPGMAxJp1Y7fuutt9SpU6eqZwoAAAC/9H//93/6+uuvdfvtt2vz5s168cUXz4gpuT9s166d3nnnHSUlJdV0mjXi5MmTKi4uLrU9IiKi3LHh4eGu3gfOogI5C06quMBQXl5embEOh0NFRUWl7jcsLMzVc6GgoECFhYXVEhsaGuoabVSR2MLCQhUUFJQaa7VaFRQUVOHYoqIiORyOUmNDQkIUHBxc4dji4mKdPHmy1Njg4GDXoogViXU6nTpx4kS1xAYFBbkWkzQMo9QFKR0FJ1RYXCTp7LGSZLFYFBoa6vrd03uxMrFms9ltjtOKxObn58tRWChHUZGCCwvdnmsymdy+kMjPz3ddi/7oj7EnTpyQ0+ksNY/T/5YrElvW331+wQmFB4eVK1aq2N8914gzY8/2d3/6e6WuXiMqGuuv14jy/t176xrx9ttv6/LLL5dhGIqPj3etepyVlaUvvvhC69evd8VWx31EWf8+5WJUwcKFC43U1FTjiiuuMNq2bWu0bdvWuOKKK4zU1FTjk08+MZxOZ1V2Xyvk5OQYkoycnByvvUbnf35hNB+1wOj8zy+89ho14ce2CcaPF1xo/Ng2wdepVMnVs6822s9ob1w9+2pfp1Il00etNF4Z+qUxfdRKX6dSJa/dd4fx3C19jNfuu8PXqVRJ9j9XG3tGrTCy/7na16lUyfjx441x48YZ48eP93UqVRIXF2dIMuLi4nydStV8EGcY7+rUT/jcc889Z6SlpRnPPfec116jJu5LFi1aZAwfPvyM+8Phw4cbixYt8trr+tLp/669e/c2JJX6ON3NN99cZmxubq4rtuFF15UZe+jQIVfssGHDyozduXOnK/bRRx8tM3bTpk2u2LS0tDJj16xZ44p95plnyoxdunSpK/aVV14pM3bBggWu2OnTp5cZO3v2bFfs7Nmzy4ydPn26K3bBggVlxr7yyiuu2KVLl5YZ+8wzz7hi16xZU2ZsWlqaK3bTpk1lxj766KOu2J07d5YZO2zYMFfsoUOHyozt3OY61/1ebm5umbE333yz23u4rNjevXu7xYaHh5ca261bN7fYhg0blhrbqVMnt9jmzZuXGpuQ4P65IiEhodTY5s2bu8V26tSp1NiGDRu6xXbr1q3U2PDwcLfYs10j9oxaYex5bIVhGBW7Rtx5551lxnKNOPWoyDXCZDIZkgyz2VynrxF33nmnK5ZrxO/KukbUr1/fePfdd12Ptm3bGtKp60GDBg2Mpk2bGg0aNDDCw8ON4OBgt9iOHTuW+e92urNdIyp7v1fhHoWn69Wrl3r16lWVXQAAAKAWuf7663X99df7Og0AAACfsFgsGjx4sOv3qVOnasuWLcrPzz+jR2Z4eLhb7LvvvuvWw9AXTP+r8sJL7Ha7oqOjlZOTU6HVlyvisolf6oD9pGKiQrV6TA+vvEZN2JLQTnI6JbNZbX/c7Ot0Kq3HnB46lH9IjcMb68sBX/o6nUqb8dgq5R1zKKKeVXdN7uLrdCrt9fvvVO6vR2U7p4GG/vtNX6dTafsnfqtie4EsUSFqMqazr9OptAkTJrgm8h07dqyv06m0pk2bat++fYqLi9PevXt9nU7lfdhUOrFPCouTbgzg46glnn/+eR0/flyRkZEaOXKkV16jJu5L6qLT/11DQkK8MvT40gmLdOBYvs6NsmrZ37qXGcuwQoYel3eo4DtPfqMTx4tUr0Gk7prcJWCHFU57cIhyf/tVtvrn6J4p/3G1BdrQ431jV50aemySmk66kqHHPr5GREZGyjAM12IUdfEaUdFYf71GlPfvviauEVOmTFFubq5sNpseeuihM2KrY+ix3W5XbGxspe/3qtSjEAAAAMDvTv/QU52x5qAQmUOcsoSEun1I8MRqtbo+1J1NSEiI64Olr2KDg4NdH7CrMzYoKMhVEKjOWIvFctZzUJlYs9nslViTyVRqrDUkTEUWR7liPfGH2PDwcFmDg1UYFCRrcHCZz63IAkqnFxqqM7asv/vwkLBTAwbLEftHFfm75xpxytn+7kvmkStP7Olq0zWiKrGS/1wjvBFb2WuE1WpVQUGBrFarx+OojvuIsgqN5eE/6y8DAAAAAAAA8Bl6FHpJenq60tPTq1zJrW1yFnwi+yefeG78X1d4mUzac/8wjyFRffooum8fL2UHAEDdkJycLIvFotTUVKWmpvo6nYDF/R4AAPBXlb3fo1DoJSUnomTOGpwS3bf0Ql9Wt6tUdPCggho2VLN/v1rDmQEAUHdkZmYyR2E14H4PAAD4q8re71EoBAAAAAAAdU5GRoYyMjI8tpnNZjmdTpnNZvXv399jTEpKilJSUryZIlDjKBQCAAAAAIA6p6xCX9OmTbVv3z6de+65mjdvXg1nBvgOhUIAQLXauHGjNm3a5LGtZPU4k8lU6re37du3V2JiojdTBAAAAAB4QKEQqMN+zjygrMxDHttCwiwKDgmTyWLSJ6/+4DEmPrmx2iTHeDPFOiV//SHlbzjssc0UGiRLiEUms0lH3tzsMSY8qZHCOzb2ZorlkpiYWGqh74UXXtDx48cVERHBMA0AAAAA8DMUCoE6rE1yDIU+PxLesbFfFPoAAAAAAHWT2dcJAAAAAAAAAPA9ehQCAAAAQC1V1lQz+fYC109/n2pmy6rl2rpquce2/Jxjrp8fPjPBY8yFXbqpbZdu3koPAGoNCoUAAAAAUEuVNdXMq/cvOfUfhqE+wzrUYFYV17aMQt8Lg/pLkgynoRv/PrYm0wKAWoehxwAAAAAAAAAoFAIAAAAAAACgUAgAAAAAAABAFAoBAAAAAAAAiEIhAAAAAAAAAFEo9Jr09HQlJCQoOTnZ16kAAAC4SU5OVkJCgtLT032dCgAAAPxIkK8TqK1SU1OVmpoqu92u6OhoX6cDAADgkpmZqaioKF+nAQAAAD9DoRAAAAABJTMzU2lpafr6669VWFioxMREjRgxQrfcckul9vfbb7+pffv2ys7OVs+ePfXpp59Wc8bl8/H6fZq/IdtjW2RokMJCIhRkNmnIm5keY/olxeqGjnHeTBEAANRyFAoBAAAQMJYuXaqePXsqNDRUgwYNUmRkpObOnauBAwdqz549GjlyZIX3OXz4cOXk5Hgh24q5oWMchT4AAOBTzFEIAACAgFBUVKR7771XZrNZK1as0NSpU/X8889rw4YNatOmjcaMGaNdu3ZVaJ9z587VzJkz9fTTT3spawAAgMBBj8IakpeXJ4vF4pV9FxeckLPAoeICQ3l5eV55jZqQX1ysIqdTQcXFAX0cxSeL5XQ4VWwO7OOoLRyFhXIUFSm4sJDz4QccDocKCgrkcDgC+nw4nU7Xz0A+Dp10SiclmZxSIB9HLVETfx8B/X6VtGTJEm3fvl1/+ctf1LFjR9f26OhojRkzRnfddZfefPNNjR07tlz7O3z4sO6//37dfvvt6tOnj4YPH+6lzAEAAAIDhcIaEhsb6/XX2CPJNt7rL+N9WT9LNpuvs6gWtjtqx3HUFg/9N8PXKeA0Y8aM8XUKVbZ//37ZasX1ar+k2nActcfjjz/u6xT80rJlyyRJ11133RltPXv2lCQtX7683Pu77777ZLFYNGXKFL8YegwAAOBrFAoBAAAQELKysiRJ8fHxZ7TFxMTIZrO5Ys7mnXfe0QcffKCPPvpI9evXr1Ch0OFwyOFwuH632+3lfi4AAEBN+OP9idVqldVqPevzKBTWkOzsbEVFRXll31c9u1QH7Q6dG2XVsr9198pr1IRtPa9X0aFDCmrcWOcv9s1qg9Whzwd9dPjEYTUKa6RP/vyJr9Op8/770L3K/e1X2eqfo3um/MfX6dR5//rXv5SbmyubzaYHH3zQ1+lUWnx8vPbv368mTZqUuyjhl+bFSyf2S2FNpP4BfBy1xJQpU1x/Hw899JBXXsNut9fIKAdvKSnmRUdHe2yPiooqV8EvOztbDz74oFJSUnTDDTdUOI9JkyZp/PjaMIwDAADUVs2aNXP7PS0tTePGjTvr8ygU1pCIiAhZLBYVFxeXGVPi5MmTZcaGh4fLZDJJkkzmsuc+PD3W4XCoqKio1NiwsDCZzafWuCkoKFBhYWG1xIaGhrrmaDxbrNVsdv1bFBYWqqCgoPRYq1VBQUEVji0qKnLrCfBHISEhCg4OrnBsWedMkoKDgxUSEuKKPXnyZLlinU6nTpw4US2xQUFBrm8RDMNQfn5+tcRaLBaFhoa6fi9rHqyKxJrNZoWFhVUqtqx8TSaTwsPD3WINwyhX7IkTJ1xz1Hly+t9yRWLP9ndf2WvE2f7uA+0acXqsr64RJf9eZrNZVqu1QteI8v7dc42omWtEef/ua+oa8cfneesaUdcNGTJEwcHB+te//lWp548ePVojRoxw/W6328+4GQcAAPClPXv2uHVYK09vQkmSAa/KyckxJBk5OTlG7969DUmlPk538803lxmbm5vrim140XVlxh46dMgVO2zYsDJjd+7c6Yp99NFHy4zdtGmTKzYtLa3M2DVr1rhin3nmmTJj3+6Q5Ip95ZVXyoxdsGCBK3b69Ollxs6ePdsVO3v27DJjp0+f7opdsGBBmbGvvPKKK3bp0qVlxj7zzDOu2DVr1pQZm5aW5ordtGlTmbGPPvqoK3bnzp1lxg4bNswVe+jQoTJj77zzTldsbm5umbE333yz23u4rNjevXu7xYaHh5ca261bN7fYhg0blhrbqVMnt9jmzZuXGpuQkOAWm5CQUGps8+bN3WI7depUamzDhg3dYrt161ZqbHh4uFust64Rd955Z5mxgXaNWLp0qSvWV9eIevXqGZKMuLg4rhH/wzXid5W5RoSHhxsNGjQwmjZtajRo0MAIDw83QkND3WKr8xqRk5NjBKKS4/ruu+88tttsNqNZs2Zl7mPGjBmGJGPOnDlu20v+Nnr27FnhvE6/3wMCSfp9XxqvDP3SSL/vS1+nUiXPD+xnPHdLH+P5gf18nUqV7HlshbFn1Apjz2MrfJ0KDMOIi4tz3e8B1eW5554z0tLSjOeee85rr1HV+xJ6FAIAAPjIyZMnFRUVpb59++r888+XyWSSYRjatm2bPvnkE2VnZwf0UOHqVjI3YVZWli655BK3tgMHDig3N1eXXnppmftYt26dJGnAgAEe2xcvXiyTyaSkpCStX7++6kkDAAAEEJNhlDKOBtXCbrcrOjpaOTk5CgkJ8cqwwksnLNKBY/mlzlEYKMMKS+YojIiJ0YUrTq1YGIhDj3vM7FHqHIUMK6x4bFWHFU57cIjHOQoZely52KpeI0qbozDQhh63adNG2dnZiouL0y+//BK4Q489zFFY164R5f27r65rxKZNm7R9+3bX70899ZQuvvhij4tz/Pzzz9qwYYNrBeSCggK1bdtW7du397jv8lwjSuYozMnJ8drcyd60ePFiXX/99frLX/6iN954w63tzTff1F133aXx48dr7Nixpe5j1qxZ+vzzz8/Ynpubq1mzZqlp06bq2bOnzjvvvDL3c7rT7/cC8d8Vdder9y+RYUgmkzTs31f7Op1Ke2FQfxmGUyaTWSPem+frdCpt7+ivTvX7NklNJ13p63TqvKZNm2rfvn2Ki4vT3r17fZ0Oaonnn39ex48fV2RkpEaOHOmV16jqfQk9CmvQ6R96qjPWHBQic4hTlpBQtw8JnpR3lRvp1Ifbkg+WNREbbrGoyGyW5X8FC+nUh9uSD9hnU5HYoKAgV0GgOmMtFossoRaZnWZZQi1lng+Lpez205lPm7exOmNNJpNXYiX5RWx4eLiswcEqDAqSNTi4zOee/iH/bE4vNFRnrLeuERX5u/f2NcJqtaqgoEBWq7XU81GR/frqGmE67TpV0WtEed/DNXKNCDWf+kASapY8PL8uXCO8EVvW333nzp3VuXNn1+8jRozQ+eef7zE2Pj5eS5cu1eDBg8v92iVKu0YE+ryFPXr0UKtWrTRz5kw9+OCD6tixo6RTi5xMnDhRISEhuuOOO1zx+/fvV05Ojpo0aeJaAGXgwIEaOHDgGfv+5ZdfNGvWLLVr107Tpk2rkeMBAADwN2ZfJwAAAFAXOZ1OWa1Wt8Lz6Uwmk0JCQsrslVzXBAUFadq0aXI6neratav++te/auTIkUpKStLPP/+siRMnqkWLFq740aNHq23btvrwww99lzQAAEAAoUchAADwewcOzNPBQws8tp3I3y3DKJLJFKSw8PM8xpzbuK9iYvp7M8UKM5vNcjgcMgzDY7HQMAw5HA7XEH6c0r17d61cuVJpaWmaNWuWCgsLlZiYqKefftpjT0EAAACUH4VCL0lPT1d6errbEJ+S+YI8zVf1x/mNPMWePq/U6UOrnEUFchaclLPI8vu2UmJL5h87fb6q0+eVOn2uspJ5wioSe/p8VdLvc0V5mqvsj7H5xcUqcjplO20OqJI5xf44X1XJXFGe5iorT2zJ/GN/nK+qZF6pisSePq9ZcXGxik8Wy+lwSqeNUisttqrnviKxZzv3VX2flHbuK/s+Ke18ViQ2Pz9fjsJCOZ2/v6dKO5+VeZ94Op8ViS3Pua+O94mn8+nNa8SaNWv0448/uvWUKioqUlFRkSwWixo0aCCz2ayMjAzXHHwhISGu83nBBRfoggsuqNL7xNvXiNPnqqvM+6S0Oe1q/Bqh3/n7NSIysocaN+7r8dx/930PORwHZLXG6PzWL0ry/D45efJkpc+9t64RLVu21LZt2zzOUZiVleU2LLk6rhEl/+7JycmyWCxKTU1VamrqGa/t7y699FItWrTorHEzZszQjBkzyrXPFi1alDoPJQAAQF3BV9Rekpqaqh9//FGZmZmubTfddJNsNpveffdd17aNGzfKZrOd8QHh9ttvl81m09SpU13btm/fLpvNpri4OLfYnR+9pD0v3qwD33zg2rZ//37ZbDbVq1fPLXbEiBGy2WyaOHGia1tOTo5sNptsNpvbIgaPP/64bDabaxJ16dSHopLYnJwc1/aJEyfKZrNpxIgRbq9Xr1492Ww27d+/37VtypQpstlsGjp0qFvslau/Uaesn7X75O8T8k+dOlU2m0233367W2x8fLxsNps2btzo2vbuu+/KZrPppptucotNSkqSzWbT6tWrXds+/PBD2Ww29erVyy328ssvl81m05IlS1zbFi9eLJvNpq5du7rFXnPNNbLZbFqw4PceLl999ZWW37Fc28dvd4vt37+/bDabZs2a5dq2du1a2Ww2JSQkuMUOGjRINpvN7YPNli1bZLPZ3IZTSdI999wjm82m9PR017bdu3fLZrOpcePGbrHDhw+XzWbTs88+69p25MgR1/k83ahRo2Sz2TR+/HjXtvz8fFfs6QsWjB8/XjabTaNGjXLbR0nskSNHXNueffZZ2Ww2DR8+3C22cePGstls2r17t2tbenq6bDab7rnnHrfYFi1ayGazacuWLa5tM2bMkM1m06BBg9xiExIS9NB/M7T32O/v1VmzZslms6l/f/eeRcnJybLZbPrqq69c2xYsWCCbzaZrrrnGLbZr166y2WxavHixa9uSJUtks9l0+eWXu8X26tVLNpvNbdjb6tWrZbPZlJSU5BbrrWvE0KFDZbPZNGXKFNc2b14jLrvsMt19993q3bu3UlJSlJKSou3bt+vuu+/Wzz//rOHDh2vYsGFKSUnRX//6V91999266qqrXLFffvmlx2tEXFycbDab20IQvrpGnL54SUWvETabTcnJyW6xXCNO8cU1wmazae3ata5t3r5GXHTRRZo5c6brceTIES1YsEA///yzq0hlGIZ+/vlnLVy4UCkpKa7YkvdfVa4RJSsoZ2Zm6scffwzIIiEAAAC8hx6FAAAANSQ0NNRtcZKpU6dq27Zt2rBhg7788ktZrVadPHlSeXl5aty4sVsh791339X69et9kDUAoLrkrz+k/A2HPTeaTCpZhvrIm5s9hoQnNVJ4x8Ye2wCgOpgMxlh41enLUoeEhHhlWOGlExbpwLF8xdSL0Jqx15cZ689DjzdccaWKDh2SLSZGF6xYLikwhx73mNlDh08cVkz9GH054MsyYxl67B7rjaHH0x4cIkdOjqIaNtTQf7/J0OMaGHrsrWuEPw09btOmjbKzsxUXF6dffvklcIcef3aBdGKfFBYnR+/tAXuNOH3o8UUdP6vS+8SX14gpU6YoJydHkZGRGjZsmFeuEQcPHlRsbKxycnIUFRUlVI/T7/f4d0UgefX+JSV1KQ3799W+TqfSXhjUX4bhlMlk1oj35vk6nUrbP/FbFdsLZIkKUZMxnX2dTp3XtGlT7du3T3Fxcdq7d6+v00Et8fzzz+v48eOKjIzUyJEjvfIaVb0voUdhDTr9w0kJi8Xi9uGrrFiz2ewx1hwUInOIU+ag4LPGWq1Wtw9f0qlVFT3FhoSEuD4AViZWUoViwy0WFZnNMp82oXtwcLDrA45b7GkfhioTGxQU5PrwdrrTP7xVJtZiscgSapHZaS5XbFXPfUViK3Luq/o+kSp27j3FlnY+KxIbHh4ua3CwCs2/v6dKO59VfZ+Udj6reu6r433i6XwG4jWioue+KrFnO/enLzzhD+8TrhGVi63Iua+pa4TZbK7Q+ayO9wkAAABQgkIhAAAeZGRkKCMjw2NbdHS0IiIiFBQUdMZcdiVK5lwEAAAAgEBBoRAAAA8o9AEAAACoa1j1GAAAAAAAAACFQgAAAAAAAAAUCgEAAAAAAACIQiEAAAAAAAAAUSgEAAAAgDrL6XT6OoVqUVuOAwB8jVWPAQAAAKAOyc7O1sCbBmv7T7sUZrXphCNXGeuba9bcmYqNjfV1euWWnZ2tW278k7Zt3aKwkGCdKCjUB50v1ewPPwqo4wAAf0KhEACA2uyXDGlXhue2kwd//7m8v+eY5ilSixTv5AYAqHHZ2dm6pMNluvHS+zVwwKUymUwyDEM/7lmjSzpcpu9/WB0QRbbs7Gxd3L6d+raP1596dnUdx9YDh3Vx+3Zau2lzQBwHAPgbCoUAANRmLcoo9M20nPppOKVu82ouJwCAzwy8abBuvPR+tTuvs2ubyWRSu/M6yzAMDbr5Vq34eqkPMyyfW278k/q2j1fbJo1d20wm06nfDUMD/3yjvlr9rQ8zBIDAxByFAAAAAFBH7MjarYRml3psa3deZ+3I2lXDGVXOjqyfdWFMI49tFzZprB0//1TDGQFA7UCh0EvS09OVkJCg5ORkX6cCAADgJjk5WQkJCUpPT/d1KgGN+z0EGqfTqbCQCJlMJo/tJpNJocERfr8wiNPpVGhQUJnHYQ0K8vvjAABvquz9HkOPvSQ1NVW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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Plot and print out some properties\n", "do_stats = False\n", "def analyze_and_plot_population(data, population_name, ax, print_stats=True, color='k', print_latex=True):\n", " \"\"\"\n", " Analyze a population dataset and add error bar plots to existing axes.\n", " \n", " Parameters:\n", " -----------\n", " data : dict or DataFrame\n", " Dictionary/DataFrame containing the population data with keys:\n", " 'C/O', 'C/O Lower', 'C/O Upper', 'Metallicity', 'Metallicity Lower', \n", " 'Metallicity Upper', 'Mass', 'T_eq', 'O/H', 'C/H'\n", " population_name : str\n", " Name of the population for labeling (e.g., 'Eclipse', 'Transit', 'UHJs')\n", " ax : array of matplotlib axes\n", " Array with at least 2 axes: ax[0] for O/H plot, ax[1] for C/O plot\n", " print_stats : bool, optional\n", " Whether to print statistics (default True)\n", " \n", " Returns:\n", " --------\n", " dict : Dictionary containing all calculated statistics\n", " \"\"\"\n", " \n", " # Calculate weighted averages\n", " co_weights = 1/np.max([data['C/O Lower'], data['C/O Upper']], axis=0)**2\n", " mh_weights = 1/np.max([data['Metallicity Lower'], data['Metallicity Upper']], axis=0)**2\n", " \n", " avg_co = np.average(data['C/O'], weights=co_weights)\n", " avg_mh = np.average(data['Metallicity'], weights=mh_weights)\n", " avg_mass = np.average(data['Mass'])\n", " avg_temp = np.average(data['T_eq'])\n", " avg_oh = np.average(data['O/H'])\n", " avg_ch = np.average(data['C/H'])\n", " \n", " # Weighted standard deviations\n", " std_co = np.sqrt(np.average((data['C/O'] - avg_co)**2, weights=co_weights))\n", " std_mh = np.sqrt(np.average((data['Metallicity'] - avg_mh)**2, weights=mh_weights))\n", " \n", " # Unweighted standard deviations\n", " std_mass = np.std(data['Mass'])\n", " std_temp = np.std(data['T_eq'])\n", " std_oh = np.std(data['O/H'])\n", " std_ch = np.std(data['C/H'])\n", " \n", " # Calculate medians\n", " med_oh = np.median(data['O/H'])\n", " med_ch = np.median(data['C/H'])\n", " med_co = np.median(data['C/O'])\n", " med_mh = np.median(data['Metallicity'])\n", " med_mass = np.median(data['Mass'])\n", " med_temp = np.median(data['T_eq'])\n", " \n", " # Count number of planets\n", " n_planets = len(data)\n", " \n", " # Print statistics if requested\n", " if print_stats:\n", " print(f'{population_name} Spectroscopy ({n_planets} planets):')\n", " print(f' Average C/O = {avg_co:.3f} ± {std_co:.3f}, Average M/H = {avg_mh:.3f} ± {std_mh:.3f}')\n", " print(f' Average Mass = {avg_mass:.2f} ± {std_mass:.2f}, Average T_eq = {avg_temp:.1f} ± {std_temp:.1f}')\n", " print(f' Average O/H = {avg_oh:.3f} ± {std_oh:.3f}, Average C/H = {avg_ch:.3f} ± {std_ch:.3f}')\n", " print(f' Median C/O = {med_co:.3f}, Median M/H = {med_mh:.3f}')\n", " print(f' Median Mass = {med_mass:.2f}, Median T_eq = {med_temp:.1f}')\n", " print(f' Median O/H = {med_oh:.3f}, Median C/H = {med_ch:.3f}')\n", "\n", " if print_latex:\n", " latex_output = f\"\"\"\\\\multicolumn{{7}}{{c}}{{\\\\textbf{{{population_name} Spectroscopy ({n_planets} planets)}}}} \\\\\\\\\n", " Average & ${avg_co:.3f} \\\\pm {std_co:.3f}$ & ${avg_mh:.3f} \\\\pm {std_mh:.3f}$ & ${avg_oh:.3f} \\\\pm {std_oh:.3f}$ & ${avg_ch:.3f} \\\\pm {std_ch:.3f}$ & ${avg_mass:.2f} \\\\pm {std_mass:.2f}$ & ${avg_temp:.1f} \\\\pm {std_temp:.1f}$ \\\\\\\\\n", " Median & {med_co:.3f} & {med_mh:.3f} & {med_oh:.3f} & {med_ch:.3f} & {med_mass:.2f} & {med_temp:.1f} \\\\\\\\\"\"\"\n", " print(latex_output)\n", "\n", " \n", " # Add error bar plots\n", " oh_err = np.std(data['O/H']) / np.sqrt(len(data['O/H']))\n", " co_err = np.std(data['C/O']) / np.sqrt(len(data['C/O']))\n", " \n", " ax[0].errorbar(population_name, med_oh, yerr=oh_err, fmt='o',capsize=6,color=color,markeredgecolor='k',markeredgewidth=0.5,linewidth=2)\n", " ax[1].errorbar(population_name, med_co, yerr=co_err, fmt='o',capsize=6,color=color,markeredgecolor='k',markeredgewidth=0.5,linewidth=2)\n", "\n", " ax[0].errorbar(population_name, med_oh, yerr=oh_err*np.sqrt(len(data['O/H'])), fmt='o',capsize=6,color=color,markeredgecolor='k',markeredgewidth=0.5,linewidth=2)\n", " ax[1].errorbar(population_name, med_co, yerr=co_err*np.sqrt(len(data['C/O'])), fmt='o',capsize=6,color=color,markeredgecolor='k',markeredgewidth=0.5,linewidth=2)\n", " \n", " # Return all statistics as a dictionary\n", " stats = {\n", " 'avg_co': avg_co, 'avg_mh': avg_mh, 'avg_mass': avg_mass, 'avg_temp': avg_temp,\n", " 'avg_oh': avg_oh, 'avg_ch': avg_ch, 'med_oh': med_oh, 'med_ch': med_ch,\n", " 'med_co': med_co, 'med_mh': med_mh, 'med_mass': med_mass, 'med_temp': med_temp\n", " }\n", " \n", " return stats\n", "\n", "fig,ax = plt.subplots(1,2,figsize=(13,6))\n", "\n", "direct_stats = analyze_and_plot_population(direct, 'Direct', ax, print_stats=do_stats,color=cs[0])\n", "\n", "# Remove HR 8799 planets to see how much that's changing things... not much actually\n", "data_nohr8799 = data[~data['Planet'].isin(['HR 8799 b','HR 8799 c','HR 8799 d','HR 8799 e'])]\n", "direct_nohr8799 = data_nohr8799[(data_nohr8799['Geometry'] == 'Direct')]\n", "#direct_nohr8799_stats = analyze_and_plot_population(direct_nohr8799, 'Direct \\n No HR 8799', ax, print_stats=do_stats,color=cs[1])\n", "\n", "#Eclipse\n", "emission_stats = analyze_and_plot_population(emission, 'Eclipse', ax, print_stats=do_stats,color=cs[2])\n", "\n", "# Plot non-ultra hots by removing UHJ list\n", "data_nouhj = result[~result['Planet'].isin(uhj_list)]\n", "data_nouhj.reset_index()\n", "emission_nouhj = data_nouhj[(data_nouhj['Geometry'] == 'Eclipse')]\n", "emission_nouhj_stats = analyze_and_plot_population(emission_nouhj, 'Eclipse \\n No UHJ', ax, print_stats=do_stats,color=cs[3])\n", "\n", "transit_stats = analyze_and_plot_population(transit, 'Transit', ax, print_stats=do_stats,color=cs[4])\n", "transit_nouhj = data_nouhj[(data_nouhj['Geometry'] == 'Transit')]\n", "transit_nouhj_stats = analyze_and_plot_population(transit_nouhj, 'Transit \\n No UHJ', ax, print_stats=do_stats,color=cs[5])\n", "\n", "uhjs_stats = analyze_and_plot_population(uhjs, 'UHJs', ax, print_stats=do_stats,color=cs[6])\n", "\n", "# Obs coverage - use 'data', not 'result' here because we want to see how individual obs compare.\n", "data_nircam = data[data['Obs'] == 'NIRCAM/F322W2+NIRCam/F444W']\n", "data_nirspec_g395 = data[data['Obs'].str.startswith(\"NIRSpec/G395\")]\n", "data_soss = data[data['Obs'] == 'NIRISS/SOSS'].drop_duplicates().reset_index(drop=True)\n", "data_nir = pd.concat([data_nircam,data_nirspec_g395]).drop_duplicates().reset_index(drop=True)\n", "\n", "#hires\n", "data_gemini = data[data['Obs'].str.contains(\"Gemini\")]\n", "data_espresso = data[data['Obs'].str.contains(\"ESPRESSO\")]\n", "data_crires = data[data['Obs'].str.contains(\"CRIRES\")]\n", "data_hires = pd.concat([data_gemini,data_espresso,data_crires]).drop_duplicates().reset_index(drop=True)\n", "\n", "nir_soss =analyze_and_plot_population(data_soss, '1-3um', ax, print_stats=do_stats, color='grey')\n", "nir_stats =analyze_and_plot_population(data_nir, '3-5um', ax, print_stats=do_stats, color='k')\n", "\n", "hires_stats = analyze_and_plot_population(data_hires, 'Hi-Res', ax, print_stats=do_stats, color='orange')\n", "\n", "# Include T-dwarfs and Stars\n", "ax[0].errorbar('T-Dwarfs',np.nanmedian(bds['M_H'])+8.69,yerr = np.nanstd(bds['M_H'])/np.sqrt(len(bds['M_H'])),color=cs[8],capsize=6,fmt='o',\n", " markeredgecolor='k',markeredgewidth=0.5,linewidth=2)\n", "ax[0].errorbar('T-Dwarfs',np.nanmedian(bds['M_H'])+8.69,yerr = np.nanstd(bds['M_H']),color=cs[8],capsize=6,fmt='o',\n", " markeredgecolor='k',markeredgewidth=0.5,linewidth=2)\n", "\n", "ax[0].errorbar('Stars',np.nanmedian(stars['O'])+8.69,yerr = np.nanstd(stars['O'])/np.sqrt(len(stars['O'])),color=cs[7],capsize=6,fmt='o',\n", " markeredgecolor='k',markeredgewidth=0.5,linewidth=2)\n", "ax[0].errorbar('Stars',np.nanmedian(stars['O'])+8.69,yerr = np.nanstd(stars['O']),color=cs[7],capsize=6,fmt='o',\n", " markeredgecolor='k',markeredgewidth=0.5,linewidth=2)\n", "\n", "ax[1].errorbar('T-Dwarfs',np.nanmedian(bds['C_O']),yerr = np.nanstd(bds['C_O'])/np.sqrt(len(bds['C_O'])),color=cs[8],capsize=6,fmt='o',\n", " markeredgecolor='k',markeredgewidth=0.5,linewidth=2)\n", "ax[1].errorbar('T-Dwarfs',np.nanmedian(bds['C_O']),yerr = np.nanstd(bds['C_O']),color=cs[8],capsize=6,fmt='o',\n", " markeredgecolor='k',markeredgewidth=0.5,linewidth=2)\n", "\n", "# Now include C/O of stars from Hypatia\n", "# We need to convert C and O relative to solar to absolute\n", "aspund09_co = 10**8.43/10**8.69\n", "ax[1].errorbar('Stars',np.nanmedian(10**stars['C']/10**stars['O'])*aspund09_co,\n", " yerr = np.nanstd(aspund09_co*10**stars['C']/10**stars['O'])/np.sqrt(len(stars['O'])),\n", " color=cs[7],capsize=6,fmt='o',markeredgecolor='k',markeredgewidth=0.5,linewidth=2)\n", "\n", "ax[1].errorbar('Stars',np.nanmedian(10**stars['C']/10**stars['O'])*aspund09_co,\n", " yerr = np.nanstd(aspund09_co*10**stars['C']/10**stars['O']),\n", " color=cs[7],capsize=6,fmt='o',markeredgecolor='k',markeredgewidth=0.5,linewidth=2)\n", "\n", "# Plot range of solar values\n", "ax[0].hlines(8.73,-2,12,'k',linestyle='--',label='Lodders+10')\n", "ax[0].hlines(8.76,-2,12,'k',linestyle='-',label='Lodders+25')\n", "ax[0].hlines(8.69,-2,12,'k',linestyle=':',label='Asplund+09/21')\n", "\n", "ax[1].hlines(10**8.39/10**8.73,-2,12,'k',linestyle='--',label='Lodders+10')\n", "ax[1].hlines(10**8.46/10**8.76,-2,12,'k',linestyle='-',label='Lodders+25')\n", "ax[1].hlines(10**8.43/10**8.69,-2,12,'k',linestyle='-.',label='Asplund+09')\n", "ax[1].hlines(10**8.46/10**8.69,-2,12,'k',linestyle=':',label='Asplund+21')\n", "\n", "# Formatting\n", "ax[0].set_ylabel('O/H',fontsize=15)\n", "ax[1].set_ylabel('C/O',fontsize=15)\n", "\n", "ax[1].legend(frameon=True,loc='lower left',fontsize=12)\n", "\n", "\n", "ax[0].set_xticklabels(ax[0].get_xticklabels(),rotation=45,fontsize=15)\n", "ax[1].set_xticklabels(ax[1].get_xticklabels(),rotation=45,fontsize=15)\n", "\n", "ax[0].set_xlim(-0.5,10.5)\n", "\n", "ax[1].set_ylim(0.0,1.0)\n", "ax[1].set_xlim(-0.5,10.5)\n", "\n", "ax[0].tick_params(labelsize=14)\n", "ax[0].yaxis.set_ticks_position('both')\n", "ax[0].xaxis.set_ticks_position('both')\n", "ax[1].tick_params(labelsize=14)\n", "ax[1].yaxis.set_ticks_position('both')\n", "ax[1].xaxis.set_ticks_position('both')\n", "ax[0].minorticks_on()\n", "ax[1].minorticks_on()\n", "\n", "\n", "plt.tight_layout()\n", "\n", "plt.savefig('./paper_figs/metallicity_co_geometry.pdf')" ] }, { "cell_type": "code", "execution_count": 12, "id": "9506c818-9ad2-4489-b67c-bffdd44b8709", "metadata": {}, "outputs": [ { "data": { "image/png": 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C7du3L9lgqERxyhUiIioTgk8BWTnAyhVL8cn8FaV67B9//FHh9Q8//IAjR44otdevX780wypR1apVQ3p6OszMzHS+7/T0dJialq1So2y9GyIiMmoxMbG4GAXsHAsM2r4dse/PgKura6kdf9iwYQqvL1y4gCNHjii1v+7ly5ewtrYuydBKjCAIsLS0LJF9l9R+9Yl9nIiIyGB8OmsyPuma92H+cZsYzJk+Qd8hKWnfvj0aNmyIy5cvo23btrC2tsYnn3wCAPj9998REBAAd3d3WFhYoGbNmli4cCFyc3NV7uPmzZvo0KEDrK2tUaVKFXzxxRdKx1u1ahUaNGgAa2trODg4oFmzZti2bZt8+bx582Bubo47d+5g2LBhsLe3h7OzM+bOnQtRFBEdHY1evXrBzs4Orq6u+PLLLxX2r6qPU2xsLEaNGgUPDw9YWFjAzc0NvXr1wv379+XrREREoEuXLnBycoKVlRW8vLwwevRohX2r6uN09epVdOvWDXZ2drCxsUHHjh1x4cIFhXVCQkIgCALOnj2LadOmwdnZGRUqVECfPn0QHx9f6PeoJLFwIiKiUqWus3dERASkTy/BxzOvn5GvJ5Abcx6XL1/WeB+lJSEhAd26dYOPjw++/vprdOjQAUDeB76NjQ2mTZuGb775Bk2bNsVnn32Gjz/+WGkfz58/R9euXfHGG2/gyy+/RL169TBr1iyEhYXJ19mwYQMmT54Mb29vfP3115g/fz58fHxw8eJFpf0NGjQIUqkUn3/+Ofz9/bFo0SJ8/fXX6NSpE6pUqYJly5ahVq1amDFjBv74448C31/fvn3x66+/YtSoUVizZg0mT56MlJQUPHz4EAAQFxeHzp074/79+/j444+xatUqDB06VKkAet3ff/+NNm3a4Pr165g5cybmzp2LqKgotG/fXuV7mjRpEq5fv46goCC8//772Lt3LyZOnFjgMUqcSMWWlJQkAhCTkpIU2nNzc8WYmBgxNzdXT5GVDYaaR2lWqpi0UhCTVgqiNCtV3+FoxFBzaWzKex7T09PFmzdviunp6UXaPvXX7qI0K1XMzUwRM9Oei7mZKWJuZorYtU0j8c58yH+uklYK4u15ELu1bSzmZqaI0qxU+b+033vq+F2pFhgYKL7+UdmuXTsRgLhu3Tql9V++fKnUNmHCBNHa2lrMyMhQ2scPP/wgb8vMzBRdXV3Fvn37ytt69eolNmjQoMAYP/vsMxGAOG7cOHlbTk6O6OHhIQqCIH7++efy9ufPn4tWVlbiiBEj5G1RUVEiAHHz5s3ydQCIy5cvV3vMX3/9VQQghoeHFxgbADEoKEj+unfv3qK5ubn477//ytuePHki2traim3btpW3bd68WQQgvv3226JUKpW3T506VTQxMRFfvHih9piFnZ/qPrM1xStORERUqnLvhyEl2Bapa+yQsb4SUtfYYet4W/jb/YnKdopPtbnaC/CzvYGt422REvzqn75ZWFhg1KhRSu1WVlbyr1NSUvDs2TO0adMGL1++RGRkpMK6NjY2Cn2nzM3N0bx5c9y7d0/eVrFiRTx69Ajh4eGFxjR27Fj51yYmJmjWrBlEUcSYMWMU9le3bl2FY6h6D+bm5jh58iSeP3+ucp2KFSsCAPbt24fs7OxCYwOA3NxcHD58GL1790aNGjXk7W5ubhgyZAjOnDmD5ORkhW3Gjx+v8KRjmzZtkJubiwcPHmh0zJLAwomIiPRuywVgdCvVy8a0yltuSKpUqQJzc3Ol9r///ht9+vSBvb097Ozs4OzsLC+OkpKSFNb18PBQGv7AwcFBoViZNWsWbGxs0Lx5c9SuXRuBgYE4e/asypiqVq2q8Nre3h6WlpZwcnJSaldXEAF5ReGyZcsQFhaGypUro23btvjiiy8QGxsrX6ddu3bo27cv5s+fDycnJ/Tq1QubN29GZmam2v3Gx8fj5cuXqFu3rtKy+vXrQyqVIjo6usD35ODgAAAFxl/SWDgREVGpMqneDbaBKbD5IBmW4xNh80EyJszdhM0RqsfU+T7cHhM+2wTbwBT5P33Lf2VJ5sWLF2jXrh2uX7+OBQsWYO/evThy5AiWLVsGAJBKpQrrm5iYqNx33h2uPPXr18ft27exY8cOtG7dGr/88gtat26NoKAgpe1U7U+TY6gyZcoU3LlzB0uXLoWlpSXmzp2L+vXr4+rVqwDyOn2Hhobi/PnzmDhxIh4/fozRo0ejadOmSE1NLXDf2ihq/CWJhRMREZUqQWIKwayCwr/+Q0bhYkINPE1W/ECMTRIRnuiF/oNHKaxviE6ePImEhASEhITgww8/RI8ePfD222/Lr5IUVYUKFTBw4EBs3rwZDx8+REBAABYvXoyMjAwdRa5azZo1MX36dBw+fBh//fUXsrKylJ7Ia9GiBRYvXoyIiAj89NNP+Pvvv7Fjxw6V+3N2doa1tTVu376ttCwyMhISiQSenp4l8l50iYVTMXDKFSIi7ZnVGaTUJggCFqzYgMUnFW8rLTnlggVfblS6paVqH/omuzqS/2pIVlYW1qxZU+R9JiQkKLw2NzeHt7c3RFHUuG+Rtl6+fKlUlNWsWRO2trbyW3HPnz9Xuurj4+MDAGpv15mYmKBz5874/fffFYY1ePr0KbZt24bWrVsXaSTvouKUK3rAKVeIiLRnVm+wyvZmzZpBUrk5rkUfgI+ngKvRgIlbCzRt2lTjfehTy5Yt4eDggBEjRmDy5MkQBAE//vhjsW4rde7cGa6urmjVqhUqV66MW7duYfXq1QgICICtbcl0kr9z5w46duyIAQMGwNvbG6ampvj111/x9OlTDBqUV7Bu2bIFa9asQZ8+fVCzZk2kpKRgw4YNsLOzQ/fu3dXue9GiRThy5Ahat26NDz74AKampvjuu++QmZmpcgyrksQpV4iIyOgtWvYtRnc7gJ1jRSz9ww0he7/Td0gac3R0xL59+zB9+nR8+umncHBwwLBhw9CxY0d06dKlSPucMGECfvrpJ3z11VdITU2Fh4cHJk+ejE8//VTH0b/i6emJwYMH49ixY/jxxx9hamqKevXqYdeuXejbty+AvM7hly5dwo4dO/D06VPY29ujefPm+Omnn+Dl5aV23w0aNMDp06cxe/ZsLF26FFKpFP7+/ti6dSv8/f1L7D3pkiDqs4dVGSG74pSUlKRQvUqlUsTFxcHFxQUSCe+KFpWh5lHMTpM/Fm0bmGKw/S7yM9RcGpvynseMjAxERUXBy8urWFNqiKKInJwcmJqaym/FidlpmNfHBneeAo26Ty/1ueqMkao8lmeFnZ/qPrM1Vf5+4omIyKAFtgPMTYGpM2brOxQiJQZbOKWmpiIoKAhdu3ZFpUqVlObRKYhsjhtV//KPQyGzZ88eNGnSBJaWlqhatSqCgoKQk5Oj43dERESasDQTsGpgyU08S1QcBtvH6dmzZ1iwYAGqVq2KN954AydPntR6HwsWLFC61yob7VQmLCwMvXv3Rvv27bFq1Sr8+eefWLRoEeLi4rB27dpivAMiItJGduR2ZN/ZAdMa7wAA0sOGAMh7gs4QO4NT+WSwhZObmxtiYmLg6uqKiIiIIj3y361bNzRr1qzAdWbMmIHGjRvj8OHDMDXNS4ednR2WLFmCDz/8EPXq1StS/EREpB2zeoNZIJHBM9hbdRYWFnB1dS32flJSUpCbm6ty2c2bN3Hz5k2MHz9eXjQBwAcffABRFBEaGlrs4xMREVHZYbCFky506NABdnZ2sLa2Rs+ePXH37l2F5bKh41+/KuXu7g4PDw/5ciIiIiLAgG/VFYe1tTVGjhwpL5wuX76Mr776Ci1btsSVK1fkQ7rHxMQAyLst+Do3Nzc8efJE5f4zMzMVRkaVzeYslUoV5iKSSqUQRVFpfiLSjqHmUXztey0YWHyqGGoujU15z6Ps/cv+FYdse46MUzzM4yuy8/L1z2QZWZvss1vGwsICFhYWhe6/TBZOAwYMwIABA+Sve/fujS5duqBt27ZYvHgx1q1bBwBIT08HAJWJsrS0VEqqzNKlSzF//nyl9vj4eIVh6qVSKZKSkiCKYrkc60VXDDaPOS9h/d+X8fHxgGmaXsPRhMHm0siU9zxmZ2dDKpUiJyenWE8gi6Io70rB8YeKjnlUlJOTA6lUioSEBJiZmSktT0nJmyT69XnxgoKCMG/evEL3XyYLJ1Vat24Nf39/HD16VN4mm91a1bw6GRkZKme/BoDZs2dj2rRp8tfJycnw9PSEs7Oz0gCYgiDA2dm5XP5y1RVDzaOYnQZZqeTs7Gw0A2AaYi6NTXnPY25uLlJTU5GZmQkbG5ti70/Vhxtpj3nMk5mZCRMTE7i6usrnD8xPNsxFdHS0wme2JlebgHJUOAF51WX+WZllt+hiYmKUKs+YmBg0b95c5X7UXc6TSCRKv0QFQVDZTtoxxDyK+WKRSCQQDCi2ghhiLo1Rec6jRCJBxYoVER8fj8zMTNjZ2RVp1GrZiNe5ubm8UlIMzOOrHCQnJyM5ORkVK1ZUW0jKfmbt7Ow4V11h7t27B2dnZ/lr2UzOERERCkXSkydP8OjRI4wfP760QyQiMgqurq6wsrJCXFyc2m4NhZH1Q5FIJOX2A18XmMdXTExM4ObmBnt7+xI7htEXTjExMUhKSkLNmjXl1WV8fLxCgQQABw4cwOXLlzF58mR5W4MGDVCvXj2sX78eEyZMkF/SW7t2LQRBQL9+/UrvjRARGRFBEFCxYkXY29sjNze3SH2dZP1QHB0dy+WVO11hHvOYmprCxMSkxItHgy6cVq9ejRcvXsifbtu7dy8ePXoEAJg0aRLs7e0xe/ZsbNmyBVFRUahevToAoGXLlvD19UWzZs1gb2+PK1eu4Pvvv4enpyc++eQThWMsX74cPXv2ROfOnTFo0CD89ddfWL16NcaOHYv69euX6vslIjI2giDA1NRUYSw8TUmlUpiZmcHS0rJcf+AXF/NYugy6cFqxYgUePHggf717927s3r0bADBs2DC1l+IGDhyI/fv34/Dhw3j58iXc3Nwwbtw4BAUFoXLlygrr9ujRA7t378b8+fMxadIkODs745NPPsFnn31Wcm+MiIiIjJIgctCHYktOToa9vT2SkpKUnqqLi4uDi4sL/wooBkPNo5idhpRgWwCAbWCK0TxVZ4i5NDbMo24wj7rBPGpH3We2pphhIiIiIg2xcCIiIiLSEAsnIiIiIg2xcCqG4OBgeHt7w8/PT9+hEBERkRb8/Pzg7e2N4OBgrbYz6KfqDF1gYCACAwPlHc2IiIjIOISHh7NzOBEREVFJYuFEREREpCEWTkREREQaYuFEREREpCEWTkREREQaYuFEREREpCEWTkREREQaYuFEREREpCEWTkREREQaYuFUDJxyhYiIyDhxyhU94JQrRERExolTrhARERGVMBZORERERBpi4URERESkIRZORERERBpi4URERESkIRZORERERBpi4URERESkIRZORERERBpi4URERESkIRZOxcApV4iIiIwTp1zRA065QkREZJw45QoRERFRCWPhRERERKQhFk5ExZCRLWLSThEZGRn6DoWIiEoBCyeiYgg+BWTlACtXLNV3KEREVApYOBEVUUxMLC5GAeuGAKfDtiM2NlbfIRERUQlj4URURJ/OmoxPugKCIODjNjGYM32CvkMiIqISxsKJqAgiIiIgfXIaPp4CAMDXE8iNOY+LvyzWc2RERFSSWDgRaUkURcydPhZzOqYotH/SLh5Bi76AKIp6ioyIiEoaCyciLYXu3IYWTvdR2U5QaHe1F+Dv8RKhO7fpKTIiIippLJyKgVOulE8bVi/DqGZJKpeNaZmDDauXlXJERESkraJOucLCqRgCAwNx8+ZNhIeH6zsUKkXjJs7C5gjVU+xsOmeKcZM+LuWIiIhIW+Hh4bh58yYCAwO12o6FE5GW+g0cgosJXniarNiXKTZJxMVH1ug3YLCeIiMiopLGwolIS4IgYMGKDVh8zFahfckpF8z/dCYEQVCzJRERGTsWTkRF0KxZM0jc2+BadN5Vp6vRgIlbC/j3naPnyIiIqCQZZOGUmpqKoKAgdO3aFZUqVYIgCAgJCdFo22PHjmH06NGoU6cOrK2tUaNGDYwdOxYxMTFK67Zv3x6CICj969q1q47fEZVFi5Z9iyUH84YnWPqHKxZ/+Z2+QyIiohJmqu8AVHn27BkWLFiAqlWr4o033sDJkyc13nbWrFlITExE//79Ubt2bdy7dw+rV6/Gvn37cO3aNbi6uiqs7+HhgaVLFecZc3d318XboDLOzc0V/l7Ae9uAtt2HKJ1bRERU9hhk4eTm5oaYmBi4uroiIiJCq8f9v/rqK7Ru3RoSyauLaV27dkW7du2wevVqLFq0SGF9e3t7DBs2TGexU/kS2A74aDcwdcZsfYdCRESlwCBv1VlYWBT5r/e2bdsqFE2ytkqVKuHWrVsqt8nJyUFqamqRjkflm6WZgFUDBVhaWuo7FCIiKgUGWTjpWmpqKlJTU+Hk5KS07M6dO6hQoQJsbW3h6uqKuXPnIjs7Ww9REhERkaEzyFt1uvb1118jKysLAwcOVGivWbMmOnTogEaNGiEtLQ2hoaFYtGgR7ty5g507d6rdX2ZmJjIzM+Wvk5OTAQBSqRRSqVTeLpVKIYqiQhtpz1DzKL72vRYMLD5VDDWXxoZ51A3mUTeYR+3I8iT77JaxsLCAhYVFoduX+cLpjz/+wPz58zFgwAC89dZbCss2bdqk8Prdd9/F+PHjsWHDBkydOhUtWrRQuc+lS5di/vz5Su3x8fHIyMiQv5ZKpUhKSoIoikq3D0lzBpvHnJew/u/L+Ph4wDRNr+FowmBzaWSYR91gHnWDedROSkreBO2enp4K7UFBQZg3b16h25fpwikyMhJ9+vRBw4YNsXHjRo22mT59OjZs2ICjR4+qLZxmz56NadOmyV8nJyfD09MTzs7OsLOzk7dLpVIIggBnZ2eezMVgqHkUs9MgK5WcnZ0hmFXQazyaMNRcGhvmUTeYR91gHrUj65MaHR2t8JmtydUmoAwXTtHR0ejcuTPs7e1x4MAB2NraFr4RXlWgiYmJatdRdzlPIpEonbSCIKhsJ+0YYh7FfLFIJBIIBhRbQQwxl8aIedQN5lE3mEfNyXJkZ2enUDhpqkwWTgkJCejcuTMyMzNx7NgxuLm5abztvXv3AORdQSAiIiLKz6hL05iYGERGRio8BZeWlobu3bvj8ePHOHDgAGrXrq1y2+TkZIUO3kDeCNCycZ66dOlScoETERGRUTLYK06rV6/Gixcv8OTJEwDA3r178ejRIwDApEmTYG9vj9mzZ2PLli2IiopC9erVAQBDhw7FpUuXMHr0aNy6dUth7CYbGxv07t0bAHDlyhUMHjwYgwcPRq1atZCeno5ff/0VZ8+exfjx49GkSZNSfb9ERERk+Ay2cFqxYgUePHggf717927s3r0bADBs2DDY29ur3O7atWsAgO+//x7ff/+9wrJq1arJC6dq1aqhTZs2+PXXXxEbGwuJRIL69etj3bp1GD9+vO7fEBERERk9gy2c7t+/X+g6ISEhSpP/arIdAHh5eWHXrl3aB0ZERETlllH3cSIiIiIqTSyciIiIiDTEwomIiIhIQyyciiE4OBje3t7w8/PTdyhERESkBT8/P3h7eyM4OFir7Qy2c7gxCAwMRGBgIJKTk9U+5UdERESGJzw8vEgjh/OKExEREZGGWDgRERERaYiFExEREZGGWDgRERERaYiFExEREZGGWDgRERERaYiFExEREZGGWDgRERERaYiFExEREZGGWDgVA6dcISIiMk6cckUPOOUKERGRceKUK0REREQljIUTERERkYZYOBERERFpiIUTERERkYZYOBERERFpiIUTERERkYZYOBERERFpiIUTERERkYZYOBERERFpiIVTMXDKFSIiIuPEKVf0gFOuEBERGSdOuUJERERUwlg4EREREWmIhRMRERGRhlg4EREREWmIhRMRERGRhlg4EREREWmIhRMRERGRhlg4EREREWmIhRMRERGRhlg4FQOnXCEiIjJOnHJFDzjlChERkXHilCtEREREJYyFExEREZGGeKuOiMjARR25gnuHLuPJhUgAgHuLepCYSFD9bV94dWqi5+iIyheDveKUmpqKoKAgdO3aFZUqVYIgCAgJCdF4+xcvXmD8+PFwdnZGhQoV0KFDB1y5ckXlunv27EGTJk1gaWmJqlWrIigoCDk5OTp6J0RExePVqQnaLhwuf9124XB0WDaGRRORHhhs4fTs2TMsWLAAt27dwhtvvKHVtlKpFAEBAdi2bRsmTpyIL774AnFxcWjfvj3u3r2rsG5YWBh69+6NihUrYtWqVejduzcWLVqESZMm6fLtEBERURlgsLfq3NzcEBMTA1dXV0RERGj1yH9oaCjOnTuHn3/+Gf369QMADBgwAHXq1EFQUBC2bdsmX3fGjBlo3LgxDh8+DFPTvHTY2dlhyZIl+PDDD1GvXj3dvjEiIiIyWgZ7xcnCwgKurq5F2jY0NBSVK1fG//73P3mbs7MzBgwYgN9//x2ZmZkAgJs3b+LmzZsYP368vGgCgA8++ACiKCI0NLR4b4KIiKiYpFIppk6aAKlUqu9QCAZcOBXH1atX0aRJE0gkim+vefPmePnyJe7cuSNfDwCaNWumsJ67uzs8PDzky4mIiPRl39692Ll1I/bv26fvUAgGfKuuOGJiYtC2bVuldjc3NwDAkydP0KhRI8TExCi0v77ukydPVO4/MzNTftUKAJKTkwHk/VWQ/y8CqVQKURT5V0IxGWoexde+14KBxaeKoebS2Ogjj6JUVPi6LHwPeT5qZumnE7FzjBSzPg1EQI8eSsuZR+3I8iT77JaxsLCAhYVFoduXycIpPT1d5Zu3tLSUL8//v7p1X0+qzNKlSzF//nyl9vj4eGRkZMhfS6VSJCUlQRRFpatfpDmDzWPOS1j/92V8fDxgmqbXcDRhsLk0MvrIY25Glvzr+Pg4mFial8pxSxLPx8L99ddfsMx+gjc8BFhkPcHJkyfh7e2tsA7zqJ2UlBQAgKenp0J7UFAQ5s2bV+j2ZbJwsrKyUrgiJCMraqysrBT+V7eubPnrZs+ejWnTpslfJycnw9PTE87OzgrDt0ulUgiCAGdnZ57MxWCoeRSz0yArlZydnSGYVdBrPJow1FwaG33kMSf9VeHk7OwCU6uyUTjxfCzYvJnvYXp7KQABk9pJ8dlH4/FH+B2FdZhH7cguokRHRyt8ZmtytQkoo4WT7Im818na3N3d5evJ2l+vPGNiYtC8eXOV+1d3OU8ikSidtIIgqGwn7RhiHsV8sUgkEggGFFtBDDGXxqi08yhIBIWvy8r3j+djnk9nTcXhfT/DpdKrP8AyMjIgpDxChzp5r9+qC6w8fg+d/b3kH/4A8DQxDa3f6o4vv1lX7vOoCVmO7OzsijRXXZksnHx8fHD69GlIpVKFk+jixYuwtrZGnTp15OsBQEREhEKR9OTJEzx69Ajjx48v1biJiKh8mvLRHFy/fA59qv6Jfo0zXluaVzRLJAL2BwJAtHxJ6A1L/PqwEca9P6W0Qi33jL40jYmJQWRkJLKzs+Vt/fr1w9OnT7F7925527Nnz/Dzzz/jnXfekV8tatCgAerVq4f169cjNzdXvu7atWshCIJ8DCgiIqKS5OTkhF8PnsXfFQZj7iEH5OSKBa6fkyvi00MO+LvCYPxy4DQqVapUSpGSQV9xWr16NV68eCF/um3v3r149OgRAGDSpEmwt7fH7NmzsWXLFkRFRaF69eoA8gqnFi1aYNSoUbh58yacnJywZs0a5ObmKnXqXr58OXr27InOnTtj0KBB+Ouvv7B69WqMHTsW9evXL9X3S0RE5ZepqSm+XLUJP21piR6L3sO2kTmoVEFQWi8hVcTQEFNM/Gw5hgwfzafpSplBF04rVqzAgwcP5K93794tv4o0bNgw2Nvbq9zOxMQEBw4cwEcffYRvv/0W6enp8PPzQ0hICOrWrauwbo8ePbB7927Mnz8fkyZNgrOzMz755BN89tlnJffGiIhIJ8riBMhDR4zBkyexeHfTp9g/UXn5uyFAn7HzMWT46FKPjQy8cLp//36h64SEhKic/NfBwQEbN27Exo0bC91H79690bt3b+0DJCIivfLq1AQerRtgR6dPAORNgGxmpdnTUYas/dudcftAEADlq0l1XU3Q4e3OpR8UATDwwomIiEiVfgG98exxLABAFEWkRD8DAGxscQaC8Or2llMVV4Tu/00fIRbLrh/X4d3muQAE3HgsYuF+YG4A0LiKgHeb52LXj+vQrFnhFwZI91g4ERGR0Xn2OBZjbFq9avBWvd6mx2dLJyAdEkURVy6cxKeDgW3hEuwIz8VX/YCpocBgPwkGN8vFwu0nIIqiQpFIpcPon6ojIqLyJ/ul8sDFZcXly5fRwDEJnx2uhBuW/WFh74aazoCFvRtuWPbHZ4crwdsxCVeuXNF3qOUSC6diCA4Ohre3N/z8/PQdChFRuZKV9vpYR2XHzh/W4uCNDPgPXo41m7ajXcBwvLfTAh16jMCaTdvRfNAXOHQjAzt/WKvvUI2an58fvL29ERwcrNV2vFVXDIGBgQgMDERycrLaJ/yIiEj3cvNNQVPW2Ds4Yee+0/D19QUATPt4HiZGRWHqrCAAeU/deTduggO/7dRnmEYvPDycI4cTkSJVj2pn5WSjbnd/1OjcVM/REZEqn85fpvDa0tISG7coFkm+vr7ywopKFwsnojLs9Ue128x/F4kpL+Di4qLnyIiKx6QMTHJMxol9nIiIyOjwWTLSFxZORERkdMwqWOo7hFIjlUox6f2JnFrFQLBwIiIio2Nmbfyjg2vq0MFD2LV1Ow4fOqzvUAjF7OMUGRmJsLAwXLt2Dc+e5Y3a6uTkBB8fH3Tr1g316tXTSZBERET5OVVxlQ9umX/kcFtPJ6WRw41V1JEr+OdAOOZvXYmJNbtjSeBsWPR/bNTz8JUFRSqcHj16hAkTJuDgwYMA8k7a/LZu3YoZM2agW7duWLt2LTw9PYsfKRER0X/yT6OSnZ4pfwBi0JElRj9XXf7pZDIyMpCanY7qNi54Hv0Cc/YFw/xQXsd4Y51OxthpXTjduHEDb731FhITE2FhYYEuXbqgadOm8qd04uLicOXKFRw6dAgHDhxAkyZNcOLECTRs2FDnwRMRUfkWdeQK7h+9Co9WeXOunJm3FQCM+qpM/ulk9jy/CK8qNQEAXV2b4EFaHN7xaA7AOKeTKQu0KpzS09PRo0cPJCYmYsSIEVixYgUcHR1VrpuYmIiPPvoImzdvRo8ePXDr1i1YWVnpJGgiIiIgb8gNYy2Q8vt4+kzs/3UPHO0qIvrBQ6wU4vMWCAJ6/FcoNaxYDcdir2Plzd8AAEliOtr7tEBC8gt06NIRXwev0lP05YtWhVNwcDAePXqEiRMn4ttvvy1w3UqVKmHTpk2oUKECgoODsWbNGkyfPr1YwRqa4OBgBAcHIzc3V9+hUCnLjtyO7Ds7YFrjHQBAetgQAIBZnUEwqzdYn6ERkRGaMXsmrlwKR70ke4xp0ErlOhJBwNT6vRTaLiX/i9vuVnhvUmBphKkzqgbnlZhISvVKoZ+fH0xMTOSzgGhKEF/voFSAVq1a4e+//0ZsbCwsLTV7FDQ9PR1ubm5o0KABzp4tm5cVZVOuJCUlKQzfLpVKERcXBxcXF0gkfICxqJjH4snf/2PgocXyATCZy6LTxzlZ1vrxAPzZfl1OTg6mBU7G33vP4V2vDjAR1OckV5Tix6gTaPBOS6xY9TUSExONLo/6OqfVfWZrSqsM3759G61bt9a4aAIAKysrtG7dGrdv39Y6OCIiovLC1NQU3363BnFmL/Ft5F6kZKerXC8lOx3f3tqTt953a2BqyklASpNWhVNaWlqRJrO1s7NDWlqa1tsRERGVN44OlfA/zzex/u5Blcu/uxuG/1VtCUeHSqUcGQFaFk7Ozs74999/tT7IvXv34OzsrPV2RERE5VGuKIWHtZPKZZ7WTpBC4142pGNaFU5vvvkmIiIicPPmTY23uXnzJsLDw9GyZUutgyMiIiqPwhPuoLlTHQDAg9Q4fHnzVzxIjQMA+DnWwaVnd/QZXrmmVeE0evRoSKVSDB48GAkJCYWun5CQgMGDB8u3JSIiooKJooj7qXGoXsEF5+MjsTv6PIZ4tcfuh+dwPj4SXjaVcT/1qdLg01Q6tOpR1qVLF/Tv3x8///wzGjZsiKCgIAwaNAgVK1ZUWC8pKQk7duzAggULEBsbi/79+6Nz5866jJuIqFCG8MhzSZA9iVRW3g8pMrWxhIu1A7ZEHcO/aU/hXKEiQmPPw8TSDAdiL+N2yiM4W1eExJZjI+qD1l3xf/jhB2RnZ+O3335DYGAgJk6ciBo1asj7MMXHx+PevXsQRRGiKKJ379744YcfdB44EVFhvDo1gUfrBvJCo+3C4Ub5GH+/gN6IfxQjn49Nxjblv3nZDuW95hQcZUPDho2wOzIUS1Ysw7ujRigt/2FzCObM+Bh9G7bVQ3SkdeFkYWGB3bt3Y8eOHfjqq68QERGBf/75B//884/Ces2aNcO0adMwaNAgnQVLRFQePXsci7G2rQHvgtfjFBxlg4OjA/YcDYOvr6/K5cNHjUQjnzewJ/TXUo6MgCJO8gsAgwYNwqBBg/Ds2TNcv35d3ufJ0dERb7zxBpycVD8NQEREmukX0Bsxdx8gNv7pqyk4VLAytcB7dbqpXR515Apv5RmReUsWFrqOr6+v2sKKSpZWhdOWLVvQo0cPhfnpnJyc0LFjR50HZgw45QqR8TGmfk/PHsfiPZe3AJeC15PNXSaKIrLTM5WW3z961eDeG5U9UUeu4J8D4YgNz3viz9WvDkzNTQ3yZwso+pQrWhVOo0aNgomJCfz9/dGrVy/06NED9evX1zrYskKWbNnw7URk+PTZ76mkr/ykRD+Tv6/8PFq9usfHq09UUrw6NYFr09oI7TkPANB67hBYVbLVb1AFCA8PL/kpV3788Uf07dsXf//9N2bNmoWGDRuidu3amD59Ok6ePMkrL0REBbh36DKy0zM1/qerx82luVL5Pu8fvaqTfRKVV1pdcRo6dCiGDh2KnJwc/PHHH/j999+xb98+rFy5El9//TUqVqyIbt264Z133kG3bt2KVMkREZVVTy5EqrwipE5K9LNCO4Rre9z8V5+ISHtF6hxuamqKt956C2+99Ra++eYb/P3339izZw/27NmDHTt2YPv27TA1NUWbNm3Qs2dP9OjRAzVq1NB17GQEjKk/CRERUWF0MqVygwYN0KBBA8yePRvx8fHYu3cv9u7di6NHj+L48eOYOnUq6tevj549e2LUqFGoXbu2Lg5LRqCsjKNDpAvuLeqh7cLhGq+/scUZrfZv6+mEQUeWKLU/OH4d1d56AwBwZt5WrfZJRIp0Ujjl5+zsjNGjR2P06NHIzMzE8ePH8fvvv2P//v34/PPPYWlpic8++0zXhyUiMngSE4lWfzgIgqDV/gVBULn/WgHNtdoPUUnpF9Abzx7HAsh7ClQ2qOvGFmcUzndDHsxV68Lp+PHjePToEZo1awZv74Lvlf/777+Ij4/HwIEDsW7dOly5coUdyImo3Kr+dsmMu5MkpmNT6lk4VXHVWwxEmnj2OBZjbFq9alBTRhjyYK5aFU7R0dEICAiAp6cnLl++XOj6np6e6NOnDx4/foy7d++iSRP2aSGi8quk+vV5VquKk9cu6DUGooIUdRgMQxw+Q6vCaePGjcjKysIXX3wBW9vCx2awtbXF8uXL0bt3b2zcuBFz584tcqBERJoq7HaArM3K1AKDoNwnyFA4VXGV/+Wd/32YWJkj/008Z083PURHpLl7hy7Do3UDjYfYkA3maoiDt2pVOB05cgTOzs7o3bu3xtv07NkTlStXRlhYGAsnIioVGt0O8H414rahyt/HIzs9U/6QxaAjS/iQBRkV2ZAYmg6xIRvM1RCHz9BqAMzIyEj4+flpfZBmzZrh9u3bWm9n6IKDg+Ht7V2knBAREZH++Pn5wdvbG8HBwVptp1XhlJaWVqSpRezt7ZGamqr1doYuMDAQN2/eRHh4uL5DISIiMljuLeph0JElsPV00mh9dUNr6FJ4eDhu3ryp1Tx1gJaFk4ODA54+farVAQDg6dOncHBw0Ho7IiIiMn6yoTg0HWJD3dAahkCrwsnb2xsXLlxAenq6xtu8fPkS58+fL3ToAiIiIiqbijoMhiEOn6FV4dSjRw+kpaVh0aJFGm+zaNEipKen45133tE6OCIiIjJ+RX0yztCeqAO0LJzee+89VK5cGZ9//jkWLVoEqVSqdl2pVIqFCxfi888/R+XKlTFhwgStAsvMzMSsWbPg7u4OKysr+Pv748iRI4VuV716dQiCoPLf61O9qFvv888/1ypWIiIiyiMVRfwUdVJljeBUxRWbUs9iU+pZbEw5g5U3f8PKm79hY8oZebumg7nqi1bDEVhbW+OXX37B22+/jaCgIGzYsAH9+/dHkyZN4OzsDACIj4/HlStX8PPPP+PRo0ewtLTEL7/8Amtra60CGzlyJEJDQzFlyhTUrl0bISEh6N69O06cOIHWrVur3e7rr79W6oj+4MEDfPrpp+jcubPS+p06dcLw4YpzR/n6Gt6lQSJtqB3H6M0zyJVKYWJqAgGGPa0BERmnv148wOWEf3Ds+HH0HNBHYVlZGGJD6ylXWrZsiXPnzuHdd9/F33//jZUrVyqtIxvgqkGDBti6dSveeOMNrY5x6dIl7NixA8uXL8eMGTMAAMOHD0fDhg0xc+ZMnDt3Tu22qsaYkt1aHDp0qNKyOnXqYNiwYVrFR2ToysK0BkRknE7H/Y3J9Xpiw5p1SoVTWVCkSX59fHzw559/4uDBg9i/fz+uXbuGhIQEAICjoyN8fHwQEBCArl27Fimo0NBQmJiYYPz48fI2S0tLjBkzBp988gmio6Ph6emp8f62bdsGLy8vtGzZUuXy9PR0CIIAS0vLIsVLRIZF3Yjbtp5OSiOHE1Hx5L/CnZGRgdTsdFS3ccGuP8+gZYMmMDczB1B2rnAXqXCS6dq1a5GLo4JcvXoVderUgZ2dnUJ78+Z5M3xfu3ZN48Lp6tWruHXrFubMmaNyeUhICNasWQNRFFG/fn18+umnGDJkSPHeABHpVWG3A/K3EVHx5L/Cvef5RXhVqQkA6OraBA/S4vCOR95nd1m5wl2swqmkxMTEwM1Nee4lWduTJ0803tdPP/0EQPVtupYtW2LAgAHw8vLCkydPEBwcjKFDhyIpKQnvv/++2n1mZmYiMzNT/jo5ORlAXof4/J3hpFIpRFEssBN9eSBKRYWvtc0H86g9zWaDyluvrOdV1flnjOdkcWM2RPzZ1g195HH2jFk48NteVLKriEcPHmKlEJ+3QBDQ479CqWHFajgWe10+tVGSmI52Pi2QmPwC3Xu/g4ULF8r3V5rntOw4ss9uGQsLC1hYFH4V2iALp/T0dJXBy26laTqOlFQqxY4dO+Dr64v69esrLT97VrH6HT16NJo2bYpPPvkEI0eOhJWVlcr9Ll26FPPnz1dqj4+PR0ZGhsLxk5KSIIoiJBKtHmAsU3IzsuRfx8fHwcTSXKvtmUft5ebkarxeXFxcCUejX6rOP2M8J4sbsyHiz7Zu6COPw8eMxKWzF9AwvRLGNmilch2JIGBq/V4KbZeS/sFfzmYYMXYU4uNf/e4pzXM6JSUFAJTuXAUFBWHevHmFbm+QhZOVlZXCFR0ZWVGirqB53alTp/D48WNMnTpVo/XNzc0xceJEvPfee7h8+bLap/dmz56NadOmyV8nJyfD09MTzs7OCrcXpVIpBEGAs7Nzuf6lkJP+6he+s7MLTK20/5BiHrVjYmqi8XouLi4lHI1+yc6/dXfCsLHnGUAiAFIRKY/+e9JQ1vYfZ/fK2LXv1wL3qY9zsrg/R4ZIXR6zb+9ATuRPyH0QBgAwqdYNkJjAtM4gmNUdpK9wDZY+zkcXFxccOn0M0ydOQci+Y3jXqwNMBPXHzhWl+DHqBLx7tMSh1T/D1NRUb+e07CJMdHS0wme2JlebAAMtnNzc3PD48WOl9piYGACAu7u7Rvv56aefIJFIMHjwYI2PLatAExMT1a6j7nKeRCJROmkFQVDZXp4I+T6UBIlQpFwwj9rRbFKDvPXKek5l5196Tiam2nV7tUDdk4ZPzmqUk9I+J3Xxc2SIVOXRov4QmNfqhZRgWwCAdY9dEMwq6CtEo6CP35Hm5uZYtX4NGlWrg28j92Jsrc6wNVO+sJGSnY6Ndw8h29YEx9aveRWzns5p2XHs7OyU+lJrtL2uA9IFHx8f3LlzR+n+48WLF+XLC5OZmYlffvkF7du317jQAoB79+4BgHxcKiIiIlLP0aES/uf5JtbfPahy+Xd3w/C/qi3h6FCplCMrGQZ5xalfv35YsWIF1q9fLx/HKTMzE5s3b4a/v7/8qtDDhw/x8uVL1KtXT2kfBw4cwIsXL1R2Cgfy+iO9XhylpKTg66+/hpOTE5o2barjd0VERGQcoo5cwT8HwhEbfgcA4OpXB6bmpqj+tq/KaVByRSk8rJ1U7svT2glSjR9ZMXwGWTj5+/ujf//+mD17NuLi4lCrVi1s2bIF9+/fx6ZNm+TrDR8+HKdOnZIPuJnfTz/9BAsLC/Tt21flMYKDg/Hbb7/hnXfeQdWqVRETE4Pvv/8eDx8+xI8//ghzc+PvP0Dll9pxjDyclEYOJyJ6nVenJnBtWhuhPecBAFrPHQKrSrZq1w9PuIPmTnUBAA9S4xD68Cz6VW2FajYu8HOsg0vP7gBOBnmTS2sGWTgBwA8//IC5c+fixx9/xPPnz9G4cWPs27cPbdu2LXTb5ORk7N+/HwEBAbC3t1e5TqtWrXDu3Dls3LgRCQkJqFChApo3b47vv/8eb731lq7fDlGpUjeO0cBDi5GY8gIuLi5lpo8MkS5oe4WFXhFFEfdT4zCgWhucj4/EhWe3McSrPXZEnUIL53po4VQXPz84A0fHsvGHmsEWTpaWlli+fDmWL1+udp2TJ0+qbLezsyt0yIJOnTqhU6dOxQmRiIjKCG2vsNArpjaWcLF2wJao47ifnQA7qRl2RJ1CBfdKOBz3J26nPIazdUVIbDV7It7QGWzhRMZN7SSzLc5AEF49SVFWhuAnIiqvGjZshN2RoViyYhn69fqfvPjst2cerCrZ4ofNIZgz42P0bVj4HSNjwMKJSgQnmSUiKh8cHB2w52gYfH19kZ6YorR8+KiRaOTzBvaEFjw+mrFg4URE5cq6O2FIz1EeYFcmSUxHe58WvBpKpKF5SxYWuo6vry98fX1LIZqSx8KpGIKDgxEcHIzcXM2mtyCi0hV15AruH70Kj1beqBj3B1ZFHUB8+nMseEP1MCX58WooGQN2ai86Pz8/mJiYIDAwEIGBgRpvx8KpGGTJTk5OVvv0HhHpj1enJvIPjw7LxgAA2vu00GdIRDrl1akJKjephV965c2fyk7tmgsPDy87I4cTERERGSIWTkREREQaYuFEREREpCEWTkREREQaYuFEREREpCE+VUclQu0ks55OSiOHExERGQsWTlQi1E0yO+jIEphZWegpKiIiouJh4URE5YpTFVdsfHRGfhVUhldDiUgTLJyIqFwJ3f+bwlVQGV4NJSJNsHAqBk65QoYu/5QjAHBm/k/IzMpE3e7+qNG5qZ6jo7IgO3I7siK3Ivd+GADApHo3CBJTmNUZBLN6g/UcHZF6nHJFDzjlChm6/FOOAIBUKkVcXBxcXFz0GBWVJWb1BsO0Zk+kBOdN82EdsAuCWQU9R0VUOE65QkRERFTCWDgRERERaYi36oiIDJxSX7V5WwEA1d/2VbgVS0Qlj4UTEZGBe72vGpUcqShi+/1T+J9Uqu9QyECxcCIi0iM+lWZY/nrxAJcT/sGx48fRc0AffYdDBoh9nIiI9Mis3mBYB+ySv7YO2AXrnr+zaAKQkS1i0k4RGRkZpXbM03F/Y3K9ntiwZl2pHZOMC684ERGRQQo+BWTlACtXLMUn81eUyDH6BfTGs8exAICMjAykZqejuo0Ldv15Bi0bNIG5mTmAvJHk808lReUXrzgREZHBiYmJxcUoYN0Q4HTYdsTGxpbIcZ49jsUYm1YYY9MKnuk26F6lGQCgq2sTeKRVkC+TFVdELJyIiMjgfDprMj7pCgiCgI/bxGDO9Alq1406ckWrfX88fSYa1aiH9j4tEP3gIVbe/A0rb/6Gf1Nj0aBiNQBAw4rV8E9KjHxZ9IOHaO/TAo1q1MPH02dqdTxt4yPDxlt1xcApV4iIdCs7cjuup9aG9Okl+PjnTbrs6wlsvnoely9fRuMKd5T6f/1zIByuTWtrfIzA9z5AxLmLqJ/mgDENWqlcRyIImFq/l0LbpaR/cKuyBSa+/wHSE1M0Pt79o1fLxVORrw+bcWFZXt89VcNmGMIQG5xyRQ845QoRkW5l3voRc1c+wrdvPwMgyNs/aRePKdNG4+dp1ZQKp9jwOwjtOU+r4wyTNMGuhNP4Oy4Kw2u+BRNB/Q2YXFGKH/49BksTcwyza4MTI1dpdSxZcVDWaTNshiEMscEpV4iIyOjt/j0M/nZ/orKdoNDuai/Az/YGfj37SCfHMREkGOzVDg/T4vBt5F6kZKerXC8lOx3f3tqDh2nxGOzVrsACi8oHXnEiIjICUUeu4N6hy3hyIRIA4N6iHiQmkjI3eviWC8DmEaqXjWkFjN7+D4Z9odju6lcHrecOKdLxNrQ7jc4mdbH+7kFM91Yet+m7u2HoX7U1DufeRr8984p0DNktKyobWDgRERkBr05N4NG6AXZ0+gQA0HbhcJhZWeg5Kt0b9c4b2BwRhaltlPsQfR9uj5Fdayq1m5qbwqqSbZGOJzGRIFeUwsPaSeVyT2snSCFCYiIp8jGobOE1RyIiMhh9WlfFxYQaeJosKrTHJokIT/RCn1ZVlLap/rZvsY4ZnnAHzZ3qAAAepMbhy5u/4kFqHADAz7EOLj27U6z9Fzc+Miy84kREpAMlNXVK/gEaRVFESvQzAMDGFmcgCK/6AZWVARrN6w7GghW1sXhmN3zbM0HevuSUCxZ8uRHmFZSLmOLcqhRFEfdT4zCgWhucj4/EhWe3McSrPXZEnUIL53po4VQXPz84A0dH1yIfoyzdSiUWTkREOmFWbzBMa/ZESnDe7RzrgF0QzCoUe7+yARrl1Dygtenx2WIfyxCY1RuMZgAklZvjWvQB+HgKuBoNmLi1QNOmTQE01enxTG0s4WLtgC1Rx3E/OwF2UjPsiDqFCu6VcDjuT9xOeQxn64qQ2Frp9LhkvHirjojIAOhjXjZDtmjZt1hyMO+K0NI/XLH4y+9K5DgNGzbC3cxYjFw8FX/dvolpDfpgqndvHP7jOP6Je4gRi6fgn8ynaNSwUYkcn4wPCyciIgOQf142AtzcXOHvBby3DWjbfQhcXYt+q6wgDo4O2HM0DO+OUv0o3/BRI7HnaBgcHB1K5PhkfFg4ERHpWWnNy2ZsAtsB5qbA1BmzS+wY85YshK9vwZ23fX19EbR4QYnFQMaFhVMxBAcHw9vbG35+fvoOxWBFHbmCM/O2wqOVNzxaeePMvK04MWsT524iykebednKE0szAasGCrC0tNR3KFQG+fn5wdvbG8HBwVptx87hxcApVwpnCMPqExmi7MjtMKs3GBEREWrnZWvatCnELM3nRCMizRV1yhUWTkSlrLyMAE0Fy4rcCpMa72DutNFq52Xbd+QsCyciA8PCiaiUlZcRoKlguffDsHW8LfztoHZetq3jbQF46idAIlKJfZyIiPRkywVgdCvVy8a0yltORIbFYAunzMxMzJo1C+7u7rCysoK/vz+OHDlS6Hbz5s2DIAhK/9R1Lty0aRPq168PS0tL1K5dG6tWrdL1WyEiUmJSvRsmzN2EzRGq+1h8H26PCZ9tglMlCTalnsWm1LPYmHIGK2/+hpU3f8PGlDPy9k2pZ+FUpWQe1yciRQZ7q27kyJEIDQ3FlClTULt2bYSEhKB79+44ceIEWrduXej2a9euhY2Njfy1iYmJ0jrfffcd3nvvPfTt2xfTpk3D6dOnMXnyZLx8+RKzZs3S6fshovIhI1vER7uBdWMyYFXAyOGCxBT9h4xCwIZVeJp8TeF2nWxetqDBo9DD5ndY9/wdAJCdnim/xTvoyBLe4iXSA4MsnC5duoQdO3Zg+fLlmDFjBgBg+PDhaNiwIWbOnIlz584Vuo9+/frByUn1bNcAkJ6ejjlz5iAgIAChoaEAgHHjxkEqlWLhwoUYP348HBw44BkRaSf/QJafzF+hdj2zOoMgCAIWrNigdl42QRBgVmdQaYRNRBoyyFt1oaGhMDExwfjx4+VtlpaWGDNmDM6fP4/o6OhC9yGKIpKTkyGKosrlJ06cQEJCAj744AOF9sDAQKSlpWH//v3FexNEVO5oM5ClbOLfZs2a/TcvW97vKsV52VCsCYKJSPcMsnC6evUq6tSpozS+QvPmzQEA165dK3QfNWrUgL29PWxtbTFs2DA8ffpU6RhA3i+t/Jo2bQqJRCJfTkSkqaIOZKnJvGwcTJbIMBjkrbqYmBi4ubkptcvanjx5onZbBwcHTJw4EW+++SYsLCxw+vRpBAcH49KlS4iIiJAXYzExMTAxMYGLi4vC9ubm5nB0dCzwGJmZmcjMzJS/Tk5OBgBIpVJIpVJ5u1QqhSiKCm2kvbKSxwE9+iD+yX8FvFREyqNnAICN/mcAyav+Lc7ulbFr368lEkNZyWVxiVLlK9GiVPO8qMpjREQEpLGKA1l+f+U8wsPD5VeP1Klc2UU+L1ubboPh4uKiFEu1jj6o1tFHbTz6JL72e08oRh6Lu8/iyH+Hwph+TvLHqcu4jTUfhZG9D9lnt4yFhQUsLArvN2iQhVN6errK4GVPxqWnp6vd9sMPP1R43bdvXzRv3hxDhw7FmjVr8PHHH8v3YW5urnIflpaWBR5j6dKlmD9/vlJ7fHy8wszmUqkUSUlJEEUREolBXtwzCmUlj7EPn2BCxbavGrxVr/fdwz8QFxdXIjGUlVwWV25GllJbfHwcTCxV/0543et5FEURc6aOxpouigNZzmkfj4lTRmNL6GEIgqB+hzkvEdgO+Gg3MHT42BL7/peYnJew/u/L+Ph4wDRNo80KPB+LuM/iyHrx6hjP4uNhnv0SJg9+hcm9XTB9eiovrMrtABNz5FbrjdxqfUo8Jk1kPE+Vfy2LWxdU5aMsSEnJG1TW01NxjLSgoCDMmzev0O0NsnCysrJSuKIjIytKrKystNrfkCFDMH36dBw9elReOFlZWSErS/mXp+w4BR1j9uzZmDZtmvx1cnIyPD094ezsrHB7USqVQhAEODs7l+sPqeIqK3k0MVV+slPdeq9fCdWVspLL4spJV/7Zd3Z2gamV5oVT/jyG7tyG1q6PVA5k2bJyNE6fPIZ+A4eo3Z+YnYY0MwGrBgIVPD0gFPA0niESs9Mg+4h1dnbWOP6Czsei7rM4MsxeFSBOzs6wdLABXCZA2qAPXm50BwDYvbMdEuuS+fksqpcmr4bbkcetAyrzUQbILsJER0crfGZrcrUJMNDCyc3NDY8fP1Zqj4mJAQC4u7trvU9PT08kJiYqHCM3NxdxcXEKH1JZWVlISEgo8BjqLudJJBKlH35BEFS2k3bKQh4LuN6gtF5Jvs/SzqUhTjEjSJS/G4JE0Con+fO4MfgLbAxIgqrv8mi/JIwN/gIDBg9Tuy8x33ElEgkEIzvPixO/uvNRHznJf1VQFtd/ASjEYmi/h/LHoxB3ManNh5GTvQ87O7sizVVnkFnw8fHBnTt3lO4/Xrx4Ub5cG6Io4v79+3B2dlY4BpDXLyG/iIgISKVSrY9BRKp5dWqCtguHy1+3XTgcHZaNKVPz8o2bOAubI1RP9P19hD3GTfq4lCMiopJikIVTv379kJubi/Xr18vbMjMzsXnzZvj7+8vvSz58+BCRkZEK28bHxyvtb+3atYiPj0fXrl3lbW+99RYqVaqEtWvXKq1rbW2NgIAAXb4lIirD+g0cgosJXniarNjpPDZJRHiCF/oN4JACRGWFQd6q8/f3R//+/TF79mzExcWhVq1a2LJlC+7fv49NmzbJ1xs+fDhOnTql0PO/WrVqGDhwIBo1agRLS0ucOXMGO3bsgI+PDyZMePVosJWVFRYuXIjAwED0798fXbp0wenTp7F161YsXrwYlSpVKtX3TETGS5OBLImobDDIwgkAfvjhB8ydOxc//vgjnj9/jsaNG2Pfvn1o27ZtgdsNHToU586dwy+//IKMjAxUq1YNM2fOxJw5c2Btba2w7gcffAAzMzN8+eWX2LNnDzw9PbFy5UqlJ/OIiArzaiDLA/DxFJQGstSl7MjtyIrcitz7YQDy5r0TJKYwqzOIA2YSlTCDLZwsLS2xfPlyLF++XO06J0+eVGrbsGGDVscZN24cxo0bp214RERKFi37FqO7HcDOsSKW/uGGkL3KA1nqglm9wTCt2RMpwbYAAOuAXUb3JB6RsTLIPk5ERMbIzc1VPpBl2+5D4Orqqu+QiEjHDPaKE1FZ41TFFZsenwWQ96RnSnTeyOG2nk4KfWCcqpStD9vsyO3I/PsH9Op3CACQeeAmss3My+xtJdlAllNnzNZ3KERUAlg4EZWS0P2/yb/OTs/Ejk6fAAAGHVkCMyvNBl4zJv0CeuPZ47xJbkWpFGJSRQCAYJ/w35g83wD4Bk5VXBVyY+ws/xvIUjbIHhGVLSyciiE4OBjBwcHIzc3VdyhUTOxsq3vPHsdijE2rVw12bVSuJ7sKR6RPUUeu4P7Rq/BolTcX0oVluwAA1d/2RbVWHvoMjUqIn58fTExMEBgYiMDAQI23Y+FUDLJkJycnw95e9eB3ZBzY2ZaofPPq1ETtoKzSl0Y2dyBpJDw8vOyMHE5ERMYlI1vEpJ2iwkTnVHqkooifok5CKpXqO5Qyj4UTEREVW/ApICsHWLliqb5DKZf+evEAlxP+wbHjx/UdSpnHwomIiIolJiYWF6OAdUOA02HbERsbq++Qyp3TcX9jcr2e2LBmnb5DKfPYx4mIiIrl01mT8UnXvKlnPm4TgznTJ2DTT78XeX/ZkduRfWcHTGu8AwBIDxsCAHxYI5/8T61mZGQgNTsd1W1csOvPM2jZoAnMzcwBoMw9tWoIWDgRUbmS/+kpaa4UTy7kTRT+4Ph11AporufojEd25HaY1RuMiIgISJ9ego9/3lhkvp7A5qvncfnyZTRt2lS+njbM6g1mgVSI/E+t7nl+EV5VagIAuro2wYO0OLzjkXcu86lV3WPhRETlSv6np/KPp1XtrTf0GZbRyYrcCpMa72DutNH49u1nAF4N4vpJu3hMmTYa+46cRfadHSyCiijqyBWFJ/0+nj4T+3/dA0e7ioh+8BArhfi8BYKAHv8VSg0rVsOx2OtYefM3AECSmI72Pi2QkPwCAX164vMvvyjwGFQ4Fk5ERKS13Pth2DreFv52QGU7QWGZq70AP9sb2DreFv3+11NPERq/fw6Ew7VpbfnrwPc+QMS5i6if5oAxDVqp3EYiCJhav5dC26Wkf3CrsgUmvv8B0hNTFJbdP3qVhZOWWDgRlbLXB9o7M28rgLyB9srSLzCFKWakUohJ/wAABPta/40c/mo9Mk5bLgCbR6heNqYVMGoL0O9/pRtTWRIbfgehPecptA2TNMGuhNP4Oy4Kw2u+BRNB/TNeuaIUP/x7DJYm5hhm1wYnRq5SWkf2e4g0x8KJqJQVNNBeWZK/Q2pWciIyvncCAFiOPghzu0p6iop0xaR6N0yY2xebd0/F1DYpSsu/D7fHhM++AlD0TuKkzESQYLBXO8y/vg3fRu7F2FqdYWtmpbReSnY6Nt49hOTslwh6Y4geIi27WDgVQ0lOuRJ15AruHbos77jq3qIeJCaSMndVgoiMkyAxRf8hoxCwYRWeJl9TuF0XmyQiPNELQYNHIX0vC6eicvWrg9ZzVRc9G9qdRmeTulh/9yCme/dRWv7d3TD0r9oah3Nvo9+eeWqPIZtapjzilCt6UJJTrnh1agKP1g3kHVfbLhxeJieCNXac447KK7M6gyAIAhas2IDFM7vh254J8mVLTrlgwZcbIQgCzOoM0mOUxs3U3BRWlWxVLpOYSJArSuFh7aRyuae1E6QQITGRqN1HeccpV4j0wKzeYFgHvPqLzTpgF6x7/s6iico82TnerFkzSCo3x7VoEQBwNRowcWuBpk2bKqxH2qv+tm+By8MT7qC5Ux0AwIPUOHx581c8SM2bV8/PsQ4uPbtT7GOQMhZORERULIuWfYslBwFRFLH0D1cs/vI7fYdUJhTULUMURdxPjUP1Ci44Hx+J3dHnMcSrPXY/PIfz8ZHwsqmM+6lPIYpikY9BqvFWHVE+GdkiPtoNrBuTASuzCvoOp0yIOnIFD49dQH3HvNwOffQjzExs2F/vP8UZJdtQzlc3N1f4ewHvbQPadh8CV1c+KVnSTG0s4WLtgC1Rx3E/OwF2UjPsiDqFCu6VcDjuT9xOeQxn64qQ2Cp3HKfi4RUnonw4UanueXVqgrYLR8hzG1EhGh2WjWHR9B+zeoNh3fN3pX+a3OIypPM1sB1gbgpMnTFb36GUCw0bNsLdzFiMXDwVf0b+jWkN+mCqd28c/uM4/ol7iBGLp+CfzKdo1LCRvkMtc1g4Ef2nOBOVZmSLmLRTREZGRglGaLw4CazuGVpOLc0ErBoowNLSUq9xlBcOjg7YczQM745SPZDW8FEjsedoGBwcHUo5srKPhRPRf1RNVKopQ/rL3xAVJ7ekGnNavs1bshC+vgV37Pb19UXQ4gWlFFH5wT5OBib/jNeiKCIl+hkAYGOLMxCEV+OkcMZr9YoyqWhhE5UWRPaX/86xwKDt2xH9dk14tnm/yPGXNapy+334SY1yS8qyI7fjempttedr4wp3+CQbUQli4WRg8s94DQBQMxo+Z7xWLytyK0xraj4/liiKhU5Umr9ofd2nH32g8Jf/Z/MWYPMxFk7Af7mdPhbfdlTM7ZyOyZg6fSz2nbiiNrfGNkZWcTp5ayPz1o+Yu/KR2vP152nVDDI/RGUFCycqc3LvhyElWPMB3367JhY6UWlvH9Uf7lejReQ+BHzefPWX/6YLibj4y2L4951T9DdRRoTu3IYWTvdV57ZSFEJ3bkP/QUNVbmtWbzBMa/aUfy+tA3ZBMOAnHc3qDS6VgmX372EFnq+/njXBsF5qNibSklQUsf3+KfxPKtV3KAaDfZyKITg4GN7e3vDz89N3KFQMWy4Ao1VPNI4xrfKWqyKKIpaEAZ92U2z/tHMWghZ9Uej4KeXBhtXLMKpZksplo/2SsGH1slKOyPgVdr6GHPyndAOiMu2vFw9wOeEfHDt+XN+h6Jyfnx+8vb0RHBys1Xa84lQMJTnlChWdSfVuCqN5F2aC885CJyq17T9QadnPu3bizRpjVP7l7+/xssCrKeXFuImzsPnnQExtk6y07PsIe4yb9LEeojJuo955A5sjotSeryO71tRDVFRWnY77G5Pr9cSGNevQc4DynHjGjFOuEP1HkJhCMKug8b/+Q0bhYkINPE1WvEIkm6i0/+BRKrfbuPZr9X/5t8zh1RQA/QYOwcUEL9W5TfBCvwHsi6OtPq2rFni+9mlVRU+RUVnQL6A32vu0QHufFujQsg1Ss9NR3cYF9/+8g5YNmsiX9Qvore9Q9YaFE5U52k4qKp+o9KTiZJn5JypVZdzEWfj+vOqLtpvOmfJqCtTndvExuwJzS+qZ1x1c4PlqXpfFKBWd7AGlMTat4Jlug+5VmgEAuro2gUdaBfky2dPf5RELJypzitJBt7CJSlXpN3AILkRbq/zL/+Ija15N+Y+q3Jp6tudQBEVkVm9wgecrn6gjbX08fSYa1aiHVnV8Ef3gIVbe/A0rb/6Gf1Nj0aBiNQBAw4rV8E9KjHxZ9IOHaO/TAo1q1MPH02fq+R2ULvZxIvrPomXfYnS3A9g5VsTSP9wQsrfgiUoFQcC8OTOxeM3X+LZngrx9ySkXzP90Mq+m5KNtbqlwzCnpyozZM3HlUjhqP7NBiwZvqVxHIgiYWl/xcc1Lyf/idhUrfPTJrNII02DwihPRf4oyUWmLfnNU/uXPoQgUcRJY3WNOSVecnJxw4MQRRCXHYPM/R5ErFjz0QK4oRci9YxDedMeBE0fg6OhYSpEaBl5xMjBOVVzlg1vmHznc1tNJaeRw0r3AdsBHu7WbqJR/+WumKLmlgjGnpCumpqYY7NUO869vw7eRezG2VmfYmlkprZeSnY6Ndw8h29YEx75bo4dI9Y+Fk4HJP41KdnomdnT6BAAw6MgSmFlZ6Cmq8iNvolJoNVEp//LXTFFyC+RNoPzRbmDdmAxYGfAAmPpQ1JwSqeLqVwfuSZ7obFIX6+8exHRv5eEHvrsbhv5VW+Mo7uohQsPAW3VEOhDYDjA35V/+JYETKBOVDlNzU0hMJMgVpfCwdlK5jqe1E6Qo34P7snAi0oG8v/wF/uWvY7IJlNcNAU6HbUdsbPl9BJqopFV/2xcAEJ5wB82d6gAAHqTG4cubv+JBahwAwM+xDi49u6O3GA0BC6di4JQrRCXr01mTFSZQnjN9gr5DIiqzvDo1gSiKuJ8ah+oVXHA+PhK7o89jiFd77H54DufjI+FlUxn3U5+WiSmlOOWKHnDKFSLdy47cDrN6gxEREQHp00vw8X81gfLmq+dx+fJlNK5wh+MVEZUAUxtLuFg7YEvUcdzPToCd1Aw7ok6hgnslHI77E7dTHsPZuiIktsodx40Np1whojIhK3IrpFmpmDttNOa0f6aw7JN28Zg7bTSybm/XU3REZVvDho1wNzMWIxdPxV+3b2Jagz6Y6t0bh/84jn/iHmLE4in4J/MpGjVspO9Q9YaFExEZlNz7Ydg63hb+dn+qnEDZz/YGfj37SE/REZVtDo4O2HM0DO+OGqFy+fBRI7HnaBgcHB1KOTLDwVt1BirqyBXcP3oVHq28AQBn5m0FkNd5z6tTE32GVmZl3/lZ/nVKsC1MqneDIDGFWZ1Bam8LZUduR/adHTCt8Q4AID1sCAAUuA0VbssFYLPq39sY0woYvf0fDPuidGMiKg/mLVlY6Dq+vr7w9fUthWgMk8EWTpmZmfjss8/w448/4vnz52jcuDEWLVqETp06Fbjd7t27sXPnToSHhyM2Nhaenp7o0aMH5s6di4oVKyqsW716dTx48EBpHxMmTMC6det0+Xa05tWpCQukUmZWpz8yjoyWv7YO2AWhkHGDzOoNZoGkYybVu2HC3L7YvHsqprZJUVr+fbg9RnatqYfIiIgMuHAaOXIkQkNDMWXKFNSuXRshISHo3r07Tpw4gdatW6vdbvz48XB3d8ewYcNQtWpV/Pnnn1i9ejUOHDiAK1euwMpKsUObj48Ppk+frtBWp06dEnlPRFQ4QWKK/kNGIWDDKjxNvqZwuy42SUR4ohc+alVFjxESUXlmkIXTpUuXsGPHDixfvhwzZswAAAwfPhwNGzbEzJkzce7cObXbhoaGon379gptTZs2xYgRI/DTTz9h7NixCsuqVKmCYcOG6fw9EFHRmNUZBEEQsGDFBiye2U1pAuUFX26EeYXyPY4MEemPQXYODw0NhYmJCcaPHy9vs7S0xJgxY3D+/HlER0er3fb1ogkA+vTJGzb+1q1bKrfJyspCWlpa8YImIp2Q3fps1qyZygmUmzZtytujRKQ3Blk4Xb16FXXq1FEaX6F58+YAgGvXrmm1P9low05OykPIHz9+HNbW1rCxsUH16tXxzTffFC1oItK5Rcu+xZKDeRNeL/3DFYu/5ATKRKRfBnmrLiYmBm5ubkrtsrYnT55otb9ly5bBxMQE/fr1U2hv3LgxWrdujbp16yIhIQEhISGYMmUKnjx5gmXLlqndX2ZmJjIzM+Wvk5OTAQBSqRRSqVTeLpVKIYqiQhtpr7TyKL62f6lUCqGMfe/0dU6Kr/1caJrXypVd5BMot+k2GC4uLjqNXZSKCl9rum9D+Nkuak4NKRZDyKMmXv+9DgOLN398usxn/tHBjeH7pCnZ+5B9dstYWFjAwsKi0O0NsnBKT09XGbxsHrD09HSN97Vt2zZs2rQJM2fORO3atRWW7dmzR+H1qFGj0K1bN3z11VeYNGkSPDw8VO5z6dKlmD9/vlJ7fHw8MjIy5K+lUimSkpIgiiIkEoO8uGcUSiOPJg9+hcmD3wC3t5Dz8Bg+2g0sdewJszoDkFtNeYZwY6W3czLnJaz/+zI+Ph4w1fDWeM5LBLYDPtoNDB0+FnFxcToNKzcjS/51fHwcTCzNNdpO3z/b8vPVPe8p46Tf8/4ozK3WWz/naxG/v/rOo8Yynsnf37NnzwADm5Iy43mq/Otn8fEwz36pk/1mvXj1fdTlfvUtJSXvaV1PT0+F9qCgIMybN6/Q7Q2ycLKyslK4oiMjK0pefzJOndOnT2PMmDHo0qULFi9eXOj6giBg6tSpOHToEE6ePKm20/js2bMxbdo0+evk5GR4enrC2dlZ4faiVCqFIAhwdnY27F8KBq5U8ugyAfCbADE7DQv62iErB9h8twlmDy1bc6Pp65wUs9Mg+xXs7Oxc6DAPCtuZCVg1EKjg6aHxdprKSX9VODk7u8DUSvPCSa8/2/+dr4aiqN9fvedRQ9KXgKxkcHJygsTaRa/xvO6lyatKzsnZGZYONjrZb4bZq4JMl/vVN9lFmOjoaIXPbE2uNgEGWji5ubnh8ePHSu0xMTEAAHd390L3cf36dfTs2RMNGzZEaGgoTE01e6uyCjQxMVHtOuou50kkEqUffkEQVLaTdkorj0+exuFiFLBzLDBo+w7EffARXF1dS/SYpa20z8nXBwnNPJT3B4kmg4SK+WKUSCQQdByzIBEUvtYmJ/zZfqU43yejyONr78/QYs0fjyyfuiAIgsLXhva+i0r2Puzs7Io0V51BFk4+Pj44ceIEkpOTFd7UxYsX5csL8u+//6Jr165wcXHBgQMHYGOjeZV87949AHl/NVH58+msyfika94viY/bxGDO9AnY9NPv+g7LqHGQUCIqSwyyfOzXrx9yc3Oxfv16eVtmZiY2b94Mf39/+VWhhw8fIjIyUmHb2NhYdO7cGRKJBIcOHVJbACUmJiI3N1ehLTs7G59//jnMzc3RoUMHHb8rMmTZkdsREREB6dNL8PHM+yvL1xPIjTmPy5cvIzuSk8oSEZGBXnHy9/dH//79MXv2bMTFxaFWrVrYsmUL7t+/j02bNsnXGz58OE6dOqXQ879r1664d+8eZs6ciTNnzuDMmTPyZZUrV5ZP2bJnzx4sWrQI/fr1g5eXFxITE7Ft2zb89ddfWLJkSZm7PUMFy7z1I+aufIRv334G4NXl6U/axWPKtNH4eVo1XjUpZZwHkIgMkUEWTgDwww8/YO7cuQpz1e3btw9t27YtcLvr168DAL74QnkG0Hbt2skLp0aNGsHb2xtbt25FfHw8zM3N4ePjg127dqF///66f0Nk0Hb/HgZ/OyhM7wEArvYC/Gxv4NezJhjWS0/BlVO8xUdEhshgCydLS0ssX74cy5cvV7vOyZMnldryX30qSNOmTZWGI6Dya8sFYPMI1cvGtAJGb/8Hw5RrcSrDsiO3IytyK3LvhwHIm3xYkJjCrM4gmNQZqOfoiEhfDLKPE1FpG/XOG9gcofrpiu/D7TGya22Vy6jsMqs3GNYBu+SvrQN2wbrn77wKRlTOGewVJ6LS1Kd1VfQPF/A0+ZrC7brYJBHhiV74qFUVPUZHZLjYF43KGxZORADM6w7GghW1sXhmN3zbM0HevuSUCxZ8uRHmFe7oMToiw8W+aFTe8FZdMQQHB8Pb2xt+fn76DoWKyazeYDRr1gySys1xLTqvn9zVaMDErQWaNm3KDwYiojLGz88P3t7eCA4O1mo7Fk7FEBgYiJs3byI8PFzfoZCOLFr2LZYczHvIYOkfrlj85Xf6Don0LCNbxKSdosI8lERk/MLDw3Hz5k0EBgZqtR0LJ6J83Nxc4e8FvLcNaNt9CMfzIgSfArJygJUrluo7FCIyACyciF4T2A4wNwWmzpit71BIz2JiYnExClg3BDgdth2xsbH6DomI9Iydw4leY2kmYNXAVzNoU/nFuQupvIo6cgX3j16FRytvAMCFZXlDc1R/2xdenZroMzS9Y+FERKRCREQEpE9Ow8f/1dyF34efxOXLl+Hr66vn6IhKllenJuW+QFKHt+qIiF4jiiLmTh+LOR1TFNrndEzGZ9PHajxDARGVPSyciP6THbkd6WFDYFrjHZjWeAfpYUPwck8vZEdu13doVMpCd25DC6f7qucurBSFX3bxnCAqr3irjug/HMiPZDasXoaNAUkABKVlo/2SMGb1F2jb4e3SD4yI9I5XnIiIXjNu4ixsjrBXuez7CHuMmzSzlCMiIkPBwomI6DX9Bg7BxQQvPE1W7MsUmyQiPMELffvzyiRRecXCqRg45QpR2SQIAhas2IDFx2wV2hcfs8OCLzdCEJRv4RGRcSnqlCvs41QMgYGBCAwMRHJyMuztVV/WJyLj1KxZM0jc2+Ba9AH4eAq4Gg2YerZH06ZNIZVK9R0eERVTeHg47OzstN6OV5yIiNTg3IVE9DoWTkREanDuQpKRSkV88pvIq43EwomIqCCcu5AA4NBNYPc14EDYYX2HQnrGwomIqAB5cxcKnLuwnPvqGLBzLPD5AhbQ5R0LJyIiogJcu/4XKpgDb3gIsMyOxY0bN/QdEukRCyciIqICTP5gDCZ1yPt6YjspPhjVV78BkV5xOAIiIiqXsiO3K0yz9OmsqTi872e4VKogb8vIyICQGo0OdfJev1UXWHn8X7ztV03h9m1cYho69+iPRctWqt0/lQ0snIiIVMiO3I7sOztgWuMdAEB62BAAgFmdQTCpM1CfoZGOZN4MgUnVjvLXkwPH43r4KfSpFol+b2S8tnbeoKcSiYD9gQAQLV8Set0Svz6ohw8nToD0ZZy8PfvOjhIvnKKOXEHUkatwalYTFuYWuLBsFwCg+tu+8OrUpESPXV6xcCIiUqGgSZ/5SHrZIH14BKnrXw0xYQlgS4CIefuBaw+AeQGAqYn6UeJzckUE7QMEIR1bAq7C9BdvpOZbLiu6S5JXpyao1tEHcXFxcHFxgUTCHjgljRkuBk65QkRUtpiaCFjUU8AbVYAewUBimqhyvYRUEQHBgK8nsKinUGCBRYaJU67oAadcISIyXpKqnWDd9UeVy0YBSFixEu9uXob9E5WXvxsioM/IWRg5Y6ra/WccHaejSKkkFHXKFRZORERULklMLSGxdlG7vEO3/+HOkeUAlG/N1nWV4K3ufQvcnsom3qojIqJyyazOoAKX7/pxHd5tngsAuPFYRP/1Im48zrt1927zXOz6cV2x9k/GiYUTERGVSwU98SaKIq5cOAkfD2BbuASf/g4s7Q3M+T3vta8ncPn8CYii6j5Qhe2fjBcLJyIiotdcvnwZDRyT8NnhSrhu+g7MTYGazoCFXWXcsOyPzw5XgrdjEq5cuaLvUKmUsXAiIiJ6zc4f1uLgjQz4D16O4LXf4c0awHvbgPZdB2DNpu1oPugLHLqRgZ0/rNV3qFTKWDgRERG9xt7BCTv3ncaQ4aMBAIHtAHNTYMq0GQCAoSPGYOe+07Cv6KTPMEkP+FQdERHRaz6dv0zhtaWZgFUDoTDNiq+vL3x9fUs7NNIzXnEiIiIi0hALJyIiIiINsXAqBk65QkREZJyKOuUKC6diCAwMxM2bNxEeHq7vUIiIqByQSqWYOmkCJ5rWgfDwcNy8eROBgYFabcfCiYiIyEjs27sXO7duxP59+/QdSrnFwomIiMhILP10InaOkWLppypmHqZSwcKJiIjICFy7dg1W2U/whocAy6zHuHHjhr5DKpcMtnDKzMzErFmz4O7uDisrK/j7++PIkSMabfv48WMMGDAAFStWhJ2dHXr16oV79+6pXHfTpk2oX78+LC0tUbt2baxatUqXb4OIiEgnJo7uh0nt8/o2TWwnxQej+uo5ovLJYAfAHDlyJEJDQzFlyhTUrl0bISEh6N69O06cOIHWrVur3S41NRUdOnRAUlISPvnkE5iZmWHlypVo164drl27BkdHR/m63333Hd577z307dsX06ZNw+nTpzF58mS8fPkSs2bNKo23SUREBADIjtwOs3qD8emsqTi872e4VKogX5aRkQEh5RE61Ml7/VZdYOXxf/G2XzVYWFgiNzcHJiamiH+ehs49+mPRspXy/ZFuGWThdOnSJezYsQPLly/HjBl5w9sPHz4cDRs2xMyZM3Hu3Dm1265ZswZ3797FpUuX5MMEdOvWDQ0bNsSXX36JJUuWAADS09MxZ84cBAQEIDQ0FAAwbtw4SKVSLFy4EOPHj4eDg0MJv1MiIqI8mTdDYFK1IyYHjsf18FPoUy0S/d7IeG0tAQAgkQjYHwgA0fIlodct8euDevhw4gRIX8Yh+84OFk4lwCBv1YWGhsLExATjx4+Xt1laWmLMmDE4f/48oqOjC9zWz89PYWylevXqoWPHjti1a5e87cSJE0hISMAHH3ygsH1gYCDS0tKwf/9+Hb4jIiKigkkfHkHqeldY7m6ALQFXce1BOj7dIyInVyxwu5xcEXN+F3HtQTq2BFyFxS/eSF3vWkpRlz8GWThdvXoVderUgZ2dnUJ78+bNAeR1kFNFKpXixo0baNasmdKy5s2b499//0VKSor8GACU1m3atCkkEol8ORERUWkzNRGwqKeAN6oAPYKBxDTVxVNCqoiAYMDXE1jUU4CpiVDKkZY/BnmrLiYmBm5ubkrtsrYnT56o3C4xMRGZmZmFblu3bl3ExMTAxMQELi4uCuuZm5vD0dFR7TGAvI7rmZmZ8tfJyckA8gq3/IOSSaVSiKLIgcqKiXnUHeZSkSgVFb7WNC/Mo24YSx5f/72OEopX4tkJll22KLSNGAvEf/U13t38BfarGIHg3RABPUd8hG7Dh8HS0RESyavrIZnHxht8bvVBlhPZZ7eMhYUFLCwsCt3eIAun9PR0lcHLZqVOT09Xux0AjbZNT0+Hubm5yv1YWlqqPQYALF26FPPnz1dqj4+PR0bGq/vRUqkUSUlJEEVR4WQm7TCPusNcvhJz5hZiz9yCU7OaAIBjc74HALi2rg+31vUL3JZ51A1jyKPJg19h8uA3wL0TACD5wAgAQG613sit1kenxzLPBVJTldt9mrfHP4eXA1C+6lS3sgCf5u3xIsMUYiqQP43mmZlIiYvTaYxlgezOk6enp0J7UFAQ5s2bV+j2Blk4WVlZKVzRkZEVJVZWVmq3A6DRtlZWVsjKylK5n4yMDLXHAIDZs2dj2rRp8tfJycnw9PSEs7Ozwu1FqVQKQRDg7OxssL8UjAHzqDvM5Ssu/3PBG/9rV6RtmUfdMIo8ukwA/CaUyqGyG41AxdfuggDA0f278K6/FICAG49FLNwPzA0AGlcR8K6/FPsP/IxpnyxWyqO6/ZV3sgsp0dHRCp/ZmlxtAgy0cHJzc8Pjx4+V2mNiYgAA7u7uKrerVKkSLCws5OsVtK2bmxtyc3MRFxencLsuKysLCQkJao8BqL+cJ5FIlH74BUFQ2U7aYR51h7nUDeZRN5jHVyzqD1FqE0URVy+ewtzBwLZwCXaE5+KrfsDUUGCwnwSDm+Vi4fZTAJQ/g1TtjyDPkZ2dnVJfao2213VAuuDj44M7d+4o3X+8ePGifLkqEokEjRo1QkREhNKyixcvokaNGrC1tVXYx+vrRkREQCqVqj0GERFRabl8+TIaOCbhs8OVcMOyPyzs3VDTGbCwd8MNy/747HAleDsmcRTxUmSQhVO/fv2Qm5uL9evXy9syMzOxefNm+Pv7y+9LPnz4EJGRkUrbhoeHKxREt2/fxvHjx9G/f39521tvvYVKlSph7dq1CtuvXbsW1tbWCAgIKIm3RkREpLGdP6zFwRsZ8B+8HGs2bUe7gOF4b6cFOvQYgTWbtqP5oC9w6EYG9v3yg75DLTcM8ladv78/+vfvj9mzZyMuLg61atXCli1bcP/+fWzatEm+3vDhw3Hq1CmI4qsOcx988AE2bNiAgIAAzJgxA2ZmZvjqq69QuXJlTJ8+Xb6elZUVFi5ciMDAQPTv3x9dunTB6dOnsXXrVixevBiVKlUq1fdMRET0OnsHJ+zcdxq+vr4AgGkfz8PEqChMnRUEABg6YgzqNfTBL9s36zPM8kU0UOnp6eKMGTNEV1dX0cLCQvTz8xMPHjyosE67du1EVW8hOjpa7Nevn2hnZyfa2NiIPXr0EO/evavyOOvXrxfr1q0rmpubizVr1hRXrlwpSqVSrWJNSkoSAYhJSUkK7bm5uWJMTIyYm5ur1f5IEfOoO8ylbjCPusE86gbzqB11n9maEkRRLHhIUipUcnIy7O3tkZSUpPRUnazzOTs+Fh3zqDvMpW4wj7rBPOoG86gddZ/ZmmKGiYiIiDTEwomIiIhIQyyciIiIiDTEwqkYgoOD4e3tDT8/P32HQkRERFrw8/ODt7c3goODtdrOIIcjMBaBgYEIDAyUdzQjIiIi4xAeHs7O4UREREQliYUTERERkYZYOBERERFpiIUTERERkYZYOBERERFpiE/V6YBs1prk5GSFdqlUipSUFFhaWnIY/GJgHnWHudQN5lE3mEfdYB61I/usLuqMcyycdCAlJQUA4OnpqedIiIiISBMpKSlFGkqIk/zqgFQqxZMnT2BrawtBEOTtycnJ8PT0RHR0dJHGipDx8/NDeHi43rbXdwzMo+6210Uuy0IemEfDiKGs/Gwzj7rZvrRiEEURKSkpcHd3L9IVOl5x0gGJRAIPDw+1y+3s7Ip1MpuYmOh1e0OJgXnUTQxA8XJZFvLAPBpODIDx/2wzj7rZvjRjKM6g1bwZagQCAwP1ur2hxFBchvAeDCGG4ioLeWAeDScGXdB3HphH3WxvKDEUhrfqSpBsKpakpCSd/HVbXjGPusNc6gbzqBvMo24wj6WLV5xKkIWFBYKCgmBhYaHvUIwa86g7zKVuMI+6wTzqBvNYunjFiYiIiEhDvOJEREREpCEWTkREREQaYuFEREREpCEWTiUgMzMTs2bNgru7O6ysrODv748jR47oOyyDcPLkSQiCoPLfhQsXFNY9d+4cWrduDWtra7i6umLy5MlITU1V2mdZz3dqaiqCgoLQtWtXVKpUCYIgICQkROW6t27dQteuXWFjY4NKlSrh3XffRXx8vNJ6UqkUX3zxBby8vGBpaYnGjRtj+/btxdqnMdA0lyNHjlR5jtarV09p3fKWy/DwcEycOBENGjRAhQoVULVqVQwYMAB37txRWpfno3qa5pHnouHhAJglYOTIkQgNDcWUKVNQu3ZthISEoHv37jhx4gRat26t7/AMwuTJk+Hn56fQVqtWLfnX165dQ8eOHVG/fn189dVXePToEVasWIG7d+8iLCxMYbuynu9nz55hwYIFqFq1Kt544w2cPHlS5XqPHj1C27ZtYW9vjyVLliA1NRUrVqzAn3/+iUuXLsHc3Fy+7pw5c/D5559j3Lhx8PPzw++//44hQ4ZAEAQMGjSoSPs0BprmEsh7Umnjxo0KbaoGzStvuVy2bBnOnj2L/v37o3HjxoiNjcXq1avRpEkTXLhwAQ0bNgTA87EwmuYR4LlocETSqYsXL4oAxOXLl8vb0tPTxZo1a4pvvvmmHiMzDCdOnBABiD///HOB63Xr1k10c3MTk5KS5G0bNmwQAYiHDh2St5WHfGdkZIgxMTGiKIpieHi4CEDcvHmz0nrvv/++aGVlJT548EDeduTIERGA+N1338nbHj16JJqZmYmBgYHyNqlUKrZp00b08PAQc3JytN6nsdA0lyNGjBArVKhQ6P7KYy7Pnj0rZmZmKrTduXNHtLCwEIcOHSpv4/lYME3zyHPR8LBw0rGPPvpINDExUfjAF0VRXLJkiQhAfPjwoZ4iMwz5C6fk5GQxOztbaZ2kpCTR1NRU/OijjxTaMzMzRRsbG3HMmDHytvKW74I+7F1cXMT+/fsrtdepU0fs2LGj/HVwcLAIQPz7778V1tu2bZsIQDx9+rTW+zRGmhROOTk5SudWfszlK02aNBGbNGkif83zsWhezyPPRcPDPk46dvXqVdSpU0dp9NbmzZsDyLsFRcCoUaNgZ2cHS0tLdOjQAREREfJlf/75J3JyctCsWTOFbczNzeHj44OrV6/K25jvPI8fP0ZcXJxSzoC8XLyeswoVKqB+/fpK68mWa7vPsujly5ews7ODvb09KlWqhMDAQKU+dsxlHlEU8fTpUzg5OQHg+VhUr+dRhueiYWEfJx2LiYmBm5ubUrus7cmTJ6UdkkExNzdH37590b17dzg5OeHmzZtYsWIF2rRpg3PnzsHX1xcxMTEAoDaPp0+flr9mvvMUlrPExERkZmbCwsICMTExqFy5MgRBUFoPeJUzbfZZ1ri5uWHmzJlo0qQJpFIpDh48iDVr1uD69es4efIkTE3zfnUyl3l++uknPH78GAsWLADA87GoXs8jwHPRELFw0rH09HSVJ5ylpaV8eXnWsmVLtGzZUv66Z8+e6NevHxo3bozZs2fj4MGD8hypy2P+HDLfeQrLmWwdCwsLjXOmzT7LmqVLlyq8HjRoEOrUqYM5c+YgNDRU3tGWuQQiIyMRGBiIN998EyNGjADA87EoVOUR4LloiHirTsesrKyQmZmp1J6RkSFfTopq1aqFXr164cSJE8jNzZXnSF0e8+eQ+c5TWM7yr6NpzrTZZ3kwdepUSCQSHD16VN5W3nMZGxuLgIAA2NvbIzQ0FCYmJgB4PmpLXR7V4bmoXyycdMzNzU1+KTQ/WZu7u3tph2QUPD09kZWVhbS0NPklZHV5zJ9D5jtPYTmrVKmS/C9INzc3xMbGQnxtmsrXc6bNPssDKysrODo6IjExUd5WnnOZlJSEbt264cWLFzh48KDSzyXA81ETBeVRHZ6L+sXCScd8fHxw584dJCcnK7RfvHhRvpyU3bt3D5aWlrCxsUHDhg1hamqq0GEcALKysnDt2jWFHDLfeapUqQJnZ2elnAHApUuXlHL28uVL3Lp1S2G913OmzT7Lg5SUFDx79gzOzs7ytvKay4yMDLzzzju4c+cO9u3bB29vb4XlPB81U1ge1eG5qGf6fKSvLLpw4YLSuEIZGRlirVq1RH9/fz1GZhji4uKU2q5duyaamZmJPXv2lLd17dpVdHNzE5OTk+VtGzduFAGIYWFh8rbylu+CHqF/7733RCsrK4UhGI4ePSoCENeuXStvi46OVjveS5UqVRTGe9F0n8ZIXS7T09MVzjuZjz76SAQg7t69W95WHnOZk5Mj9uzZUzQ1NRX379+vdj2ejwXTJI88Fw0TC6cS0L9/f/k4RN99953YsmVL0dTUVDx16pS+Q9O7Dh06iN27dxcXLVokrl+/XpwyZYpobW0t2tvbizdv3pSvd/nyZdHCwkL09fUV165dK86ZM0e0tLQUO3furLTP8pDvVatWiQsXLhTff/99EYD4v//9T1y4cKG4cOFC8cWLF6IoiuLDhw9FR0dHsWbNmuK3334rLlmyRHRwcBAbNWokZmRkKOxP9ot3/Pjx4oYNG8SAgAARgPjTTz8prKfNPo1FYbmMiooSK1asKL7//vviN998I37zzTdi9+7dRQBi165dxdzcXIX9lbdcfvjhhyIA8Z133hF//PFHpX8yPB8LpkkeeS4aJhZOJSA9PV2cMWOG6OrqKlpYWIh+fn7iwYMH9R2WQfjmm2/E5s2bi5UqVRJNTU1FNzc3cdiwYeLdu3eV1j19+rTYsmVL0dLSUnR2dhYDAwNV/vVVHvJdrVo1EYDKf1FRUfL1/vrrL7Fz586itbW1WLFiRXHo0KFibGys0v5yc3PFJUuWiNWqVRPNzc3FBg0aiFu3blV5bE33aSwKy+Xz58/FYcOGibVq1RKtra1FCwsLsUGDBuKSJUvErKwspf2Vt1y2a9dObf5ev4nB81E9TfLIc9EwCaL4Wk8yIiIiIlKJncOJiIiINMTCiYiIiEhDLJyIiIiINMTCiYiIiEhDLJyIiIiINMTCiYiIiEhDLJyIiIiINMTCiYiIiEhDLJyIiIiINMTCico0QRAK/Tdy5Ei9xDZv3jwIgoCQkBCF9pEjR0IQBJw8eVKhvX379hAEAffv31doFwQB1atX13l8JbXfknD//n2Nvtf5/7Vv317fYZMeyX7+ZP8aNmyosFx2Tqn7GUhNTUW7du0gCAL8/f3x4sULAMDXX3+tsF9j+RkizZnqOwCi0jBixAi1y1q3bl2KkVBJsLGxUfk9PnjwIJ4+fYpWrVqhVq1aCsvq1atXWuGVOSdPnkSHDh0wYsQIpcLf2MjODQ8PD423SU1NRbdu3XDmzBm0aNEChw4dgp2dHQDA29tbfi5u2bKlRGIm/WLhROWCIf5ynzhxIgYNGgQ3NzeN1v/hhx/w8uVLVKlSpYQjy3Pr1i2YmZmVyrGKy8nJSeX3uH379nj69CnGjh2rtyuLZNi0PTdSUlLQrVs3nD17Fq1atUJYWBhsbW3lyzt37ozOnTsDYOFUVrFwItITJycnODk5abx+1apVSzAaZbwiQ6QoOTkZXbt2xfnz59G2bVvs378fNjY2+g6LShn7OBGpcPbsWbz99tuwtbVFxYoV0aVLF1y8eBEhISEQBAHz5s1TWL969eoQBEHlvk6ePKmyL5W6Pk7qqOvjJJOVlYWgoCDUrFkTlpaWqFGjBj777DNkZGQUuK9t27ahRYsW8vcqo6p/hrr3X1CMsr4i7du3R1paGqZNmwZPT09YWVmhSZMm2Lt3r3zdn3/+Gf7+/qhQoQIqV66MyZMnIz09XaP8aOvly5dYunQpfH19YWNjAxsbG7Ro0ULtVQJZPnJycrBw4ULUqlULVlZWqF+/PjZv3ixf7/jx4+jQoQPs7Ozg4OCA4cOHIyEhQWl/+XO1detWNG3aFNbW1nBxccGIESPw+PFjtbEfPHgQAQEBcHZ2hoWFBWrUqIFp06apPE7+PnOHDh1Chw4dULFiRQiCIO+Xc/r0aUycOBGNGzeGg4MDrKysUK9ePXz88cfydfLvr0OHDgDyrqjk788jOy/UnfOqYlKV46ysLCxYsAD16tWDhYUFevfuLV9H2++briQnJ6NLly44f/482rdvjwMHDrBoKqd4xYnoNfv27UOfPn2Qk5OD5s2bo0aNGrh+/Tratm1rsLd7RFFE3759cezYMXTs2BE+Pj44duwYFi5ciHPnzuHQoUMwMTFR2m7p0qXYuHEjWrVqhR49eiA6OrrEYszKykLHjh0RFRWFtm3b4tmzZ/jjjz/Qp08fHDx4EH/++SdmzpyJdu3aoUuXLvjjjz+watUqJCQk4KefftJpLHFxcejUqRNu3LgBV1dXtGvXDqIo4ty5cxg5ciQiIiKwatUqldsOGDBAXhzVrFkTp06dwujRowEAtra2GDx4MFq0aCH/kP3xxx8RFRWFP/74Q2VxvWLFCqxZswZt2rRBr169cOHCBfzwww84fvw4zp8/r9T35uOPP8ayZctgbm4OPz8/uLm54fr161i5ciX27NmDs2fPonLlykrH2bZtGzZu3IhmzZqhW7du+Pfff+XxfPTRR7h+/ToaN26Mjh07IiMjA1euXMGyZcuwb98+XLhwQV4ktG7dGrGxsTh06BBq1qyp0EfQx8enSN+P/KRSKXr37o0//vgD7dq1Q+PGjeHo6AigeN+34khKSkLnzp1x6dIldOzYEXv27IG1tbXOj0NGQiQqwwCI2pzmycnJorOzswhA/P777+XtUqlUnDVrlnx/QUFBCttVq1ZN7XFOnDghAhBHjBih0B4UFCQCEDdv3qzQPmLECBGAeOLECYX2du3aiQDEqKgole/Rw8ND/Pfff+XtcXFxYsOGDUUA4sqVK1Xuy9LSUjx58qTKuAGI1apVU2jbvHmzyvdfUIxRUVHyGN966y0xNTVVaX+1atUSHRwcxPDwcPmyx48fiy4uLiIAhfelDVk8r+e4e/fuIgDxww8/FDMyMuTtsbGxYrNmzUQAYlhYmMI2svfQsGFDMS4uTt5+/PhxEYDo5uYmOjo6ivv27ZMvS0pKEhs0aCACEI8fP64yNlNTU3H//v3y9qysLHHo0KEiALFXr14K2+zatUsew927d+XtUqlU/Oyzz0QA4sCBAxW2kZ1PAMQdO3aozNOBAwfEFy9eKLRlZGSI48ePFwGI8+fPV1im7pzWdLm6c1wWZ61atcRHjx4pbVeU75s66n7+ZGTnrYuLi+jn5ycCEDt37iy+fPlSo/3L3s/rP0Nk/HirjsqFgh5L/+233+TrhYaGIj4+Hm3btsWoUaMUtl+4cKFWT96Uts8++ww1atSQv3Z2dsby5csBAKtXr1a5zZgxY9CuXbtSiU8ikWDt2rWoUKGCvG348OFwcnLCP//8g8DAQDRr1ky+zN3dHUOHDgUA/PHHHzqL49q1azhw4AD8/Pzw1VdfwcLCQr6scuXKWL9+PQBg7dq1Krf/+uuv4ezsLH/doUMH+Pr6IiYmBt26dUNAQIB8mZ2dHcaPHw8AOHXqlMr9DRgwAN27d5e/NjMzwzfffANra2vs2bNH4Srg4sWLAQDbt29XeEpQdpvMx8cHoaGhePbsmdJxAgICMHDgQJUxdOvWDfb29gptFhYW+Prrr2Fqaorff/9d5XYlZenSpUoPQRT3+1ZUcXFxCA8Ph7W1NbZu3QorKyud7p+MD2/VUblQ0HAE+Ttdnz59GgAwaNAgpfXMzMzQr18/fP311zqPTxdUxdy1a1c4ODjg33//RUxMjNITfD179iyt8FC9enXUqVNHoU0ikaBatWp49uyZ/Emk/GSFYExMjM7iOHz4MACgd+/ekEiU/3aU9Z25dOmS0jIzMzOV4z/VqFEDV69eLdJ7UPV9c3R0ROfOnfHbb7/hzJkzGDx4MOLi4nD9+nXUrl1bacwhIK94atWqFa5du4bLly+jS5cuCssL+14/fvwYe/fuRWRkJJKTkyGVSgEA5ubmuHv3boHb6pIgCHjnnXeU2ovzfSsOJycnODo64vbt2+jfvz/CwsJYPJVzLJyoXNC0A/aTJ08AANWqVVO53FAHs3NwcFB4JDq/atWq4fnz53jy5IlS4VSaT+qpG0ZB1ndG1XLZsszMTJ3FIeu4PmfOHMyZM0fteqo61bu6uqrsK1ac91DYuSY7J2Vx3717V+2DCDKqrjgV9L3+6quv8PHHHyM7O7vA/ZYGFxcXhatJMsX5vhVHhQoVcPToUbRu3RqnTp1C37598fvvvxvNUB2keyyciEqY7C93Q2RpaanT/RX0XlVdJdBmua7IYmzdujVq1qyp1bb6fA+yuF1dXZWuJr1OVTGm7nt94cIFTJ8+Hfb29vjmm2/Qvn17uLq6yosXd3d3nV7xAwo+T9TFWZzvW3F5eHjg2LFjaNOmDcLCwjB06FBs375dZRFNZR8LJ6J8ZFdkHjx4oHK5unZzc3MAeSMKv/6Ickk+qSbz/PlzpKSkqLzq9PDhQwB5H4DFlf99qlIa77W4ZP3UevfujenTp+s5mrxzqnHjxirbgVffN1nc6gb7LKpff/0VQF7/qddvaaenpyM2NlbrfZbEeaLv71vNmjVx9OhRtGvXDj///DNsbGywadOmQq/+UdnDzuFE+bRp0wYAsGvXLqVlOTk5+OWXX1RuJyu47ty5o7TsyJEjOoxQPVUxHz58GImJiahRo4bGI5QXpKD3eefOHXmRZsg6deoE4FXBoG+qvm+JiYk4fPiwvN8SkFc41KtXDzdv3lSZ/6J6/vy5fP+v+/nnnyGKolK7rDDKyclRuc+CzpPExERcuXJF6zgN4fvm7e2NQ4cOwd7eHps3b8aHH36ot1hIf1g4EeXTv39/ODo64uTJkwoD6omiiKCgILWFgezJtKVLlyI3N1fevn37dmzfvr1kg/7P/PnzFQaefPbsGT766CMAQGBgoE6O4efnB2tra4SFheHy5csKxxo7dqxB35aU8ff3R6dOnXD27FkEBgYiOTlZaZ3r16/j4MGDpRLPzp07cejQIfnrnJwcTJ06FWlpaejRo4dC36S5c+dCKpWib9++uHbtmtK+EhISsGHDBq2OL+uwv2nTJoU+Tjdv3sSsWbNUbiO7Cnb79m2Vy728vFC1alX8+eefCk/kpaWlYfz48SpzXhhD+b41adIE+/fvh7W1NVatWlVgfysqm3irjsqFggaurFq1KhYsWAAgbwDDTZs2oW/fvhg5ciTWrl0rHwDz7t27GDdunMoPpsDAQKxbtw6hoaHw9vZG48aNcffuXfz111/48MMPsXLlypJ6a/L30LhxYzRo0AAdO3aEmZkZjh8/jhcvXqBDhw6YPHmyTo5jY2ODGTNmYMGCBWjdurV8dviLFy+ifv36ePPNN3H+/HmdHKskbd26FV27dsWaNWuwbds2+Pj4wN3dHUlJSbhx4waio6Px4YcfomvXriUey/jx49GtWze0bdsWbm5uuHjxIqKiouDu7q40jMSQIUPw999/Y8mSJWjatCl8fHxQs2ZNiKKIf//9Fzdu3ICNjQ3GjRun8fFHjRqFL7/8Env37kXdunXh5+eHxMREnDp1Cr1798alS5eUblFXr14djRs3RkREBJo3b44GDRrAxMQEPXv2lD+9FxQUhDFjxqBv375o27at/Ik3Ozs79OrVq0hDHBjK961Vq1b4/fff0aNHDyxZsgR2dnZqi0wqe3jFicqFLVu2qP23Z88ehXV79eqFEydOoEOHDvjrr7+wf/9+uLm54dSpU2jZsqXK/VeuXBl//PEHevTogZiYGISFhcHe3h5HjhwplUf+BUFAaGgopkyZgj///BP79u2Dvb095syZg/3798PUVHd/I82bNw/Lly+Hh4cHjh8/jr/++gujR4/GkSNH5LdwDJ2LiwvOnTuHb7/9Ft7e3rh69SpCQ0Nx48YN1KhRA8uXL8eMGTNKJZYZM2bg+++/R1JSEn777TckJyfj3XffxcWLF1U+Cbd48WL5012xsbH47bffcOLECeTm5uL9999XOp8L4+joiPDwcAwZMgRZWVnYs2cPHj9+jIULFxZ4tfSXX35B7969ce/ePfzwww/YtGmTwi240aNHY/Pmzahfvz7Onj2LS5cu4Z133sH58+cVpvbRhiF9395++23s2rULpqam+Pjjj7FmzZpSOS7pnyCquoFNRCqFhIRg1KhRCAoKUjtfG5Em2rdvj1OnTiEqKspgh7koy+bNm4f58+dj8+bNJTaVkiAIqFatmtr5Jck48VYdERGVWxs3bsTJkyfh4eGBRYsWFXt/hw8fxrZt23QQGRkqFk5ERFRunT37//bt2AZgEAiCIP3QHCXQHB0g2sGZJbILiOwZifTjFTyjjDFKrfVKOK21jo8lfI8dJwB+p/de9t7vmXNemdtaO+Z6pvseO04AACE3TgAAIeEEABASTgAAIeEEABASTgAAIeEEABASTgAAIeEEABASTgAAoQdXKwzP2a+yngAAAABJRU5ErkJggg==", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# C/O temp\n", "# If low transiting C/O is methane depletion...\n", "fig, ax = plt.subplots(figsize=(6, 5))\n", "\n", "pop_plot(fig,ax,'T_eq','C/O',plot_direct=False,\n", " axis1_label='Equilibrium Temperature [K]')\n", "\n", "plt.savefig('./paper_figs/temp_co.pdf')" ] }, { "cell_type": "code", "execution_count": 13, "id": "27c53007-531f-48aa-baf6-7797a53ac69d", "metadata": {}, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Metallicity temp\n", "# If low transiting C/O is methane depletion...\n", "fig, ax = plt.subplots(figsize=(6.0, 5))\n", "\n", "pop_plot(fig,ax,'T_eq','Metallicity',plot_direct=False,\n", " axis1_label='Equilibrium Temperature [K]')\n", "\n", "plt.savefig('./paper_figs/temp_met.pdf')" ] }, { "cell_type": "markdown", "id": "3ef495d3-a5e7-49e0-9287-121a8b0db316", "metadata": {}, "source": [ "## Metallicity & CO Statistical Tests\n", "### Correlations" ] }, { "cell_type": "code", "execution_count": 14, "id": "30868434-751e-443b-abb3-13d0816dfe84", "metadata": {}, "outputs": [], "source": [ "# Define weighted correlation tests\n", "from scipy import stats\n", "def weighted_correlation(x, y, sigma_x, sigma_y, verbose=False):\n", " \"\"\"Calculate weighted Pearson correlation coefficient with uncertainties in both x and y\"\"\"\n", " # Combined weights from both uncertainties\n", " weights = 1 / (sigma_x**2 + sigma_y**2)\n", " # Weighted means\n", " x_mean = np.average(x, weights=weights)\n", " y_mean = np.average(y, weights=weights)\n", " \n", " # Weighted covariance and variances\n", " cov = np.average((x - x_mean) * (y - y_mean), weights=weights)\n", " var_x = np.average((x - x_mean)**2, weights=weights)\n", " var_y = np.average((y - y_mean)**2, weights=weights)\n", " \n", " # Correlation coefficient\n", " cc = cov / np.sqrt(var_x * var_y)\n", " if verbose:\n", " print(cc)\n", " return cc\n", "\n", "def wrap_weighted_correlation(d,v1='Metallicity',v2='C/O'):\n", " wc = weighted_correlation(d[v1],d[v2],\n", " np.max([d[f'{v1} Lower'].values,d[f'{v1} Upper'].values],axis=0),\n", " np.max([d[f'{v2} Lower'].values,d[f'{v2} Upper'].values],axis=0))\n", " return wc" ] }, { "cell_type": "code", "execution_count": 15, "id": "294c03b1-a2c7-4299-878f-4f3d8c670f55", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Pearson CC Metallicity versus C/O: data, direct, emission, transit:\n" ] }, { "data": { "text/plain": [ "PearsonRResult(statistic=-0.013964296535937832, pvalue=0.9113909131915993)" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "PearsonRResult(statistic=0.6405768421023071, pvalue=0.007508049261887619)" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "PearsonRResult(statistic=0.16209677980002138, pvalue=0.5204719148285988)" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "PearsonRResult(statistic=-0.39424649091285213, pvalue=0.1459127592394487)" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "--\n", "Weighted CC Metallicity versus C/O: data, direct, emission, transit:\n" ] }, { "data": { "text/plain": [ "0.28797775474405374" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "0.6229019517669526" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "0.4261101561877167" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "0.029766884481706887" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Metallicity versus C/O\n", "print('Pearson CC Metallicity versus C/O: data, direct, emission, transit:')\n", "display(stats.pearsonr(data['Metallicity'],data['C/O']))\n", "display(stats.pearsonr(direct['Metallicity'],direct['C/O']))\n", "display(stats.pearsonr(emission['Metallicity'],emission['C/O']))\n", "display(stats.pearsonr(transit['Metallicity'],transit['C/O']))\n", "print('--')\n", "print('Weighted CC Metallicity versus C/O: data, direct, emission, transit:')\n", "display(weighted_correlation(data['Metallicity'],data['C/O'],\n", " np.max([data['Metallicity Lower'].values,data['Metallicity Upper'].values],axis=0),\n", " np.max([data['C/O Lower'].values,data['C/O Upper'].values],axis=0)))\n", "display(weighted_correlation(direct['Metallicity'],direct['C/O'],\n", " np.max([direct['Metallicity Lower'].values,direct['Metallicity Upper'].values],axis=0),\n", " np.max([direct['C/O Lower'].values,direct['C/O Upper'].values],axis=0)))\n", "display(weighted_correlation(emission['Metallicity'],emission['C/O'],\n", " np.max([emission['Metallicity Lower'].values,emission['Metallicity Upper'].values],axis=0),\n", " np.max([emission['C/O Lower'].values,emission['C/O Upper'].values],axis=0)))\n", "display(weighted_correlation(transit['Metallicity'],transit['C/O'],\n", " np.max([transit['Metallicity Lower'].values,transit['Metallicity Upper'].values],axis=0),\n", " np.max([transit['C/O Lower'].values,transit['C/O Upper'].values],axis=0)))" ] }, { "cell_type": "code", "execution_count": 16, "id": "290d66c2-9414-41eb-b742-19def9570e58", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Pearson CC Teq versus C/O: emission, transit:\n" ] }, { "data": { "text/plain": [ "PearsonRResult(statistic=0.22019578716672644, pvalue=0.3799482827121262)" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "PearsonRResult(statistic=-0.11813616594638135, pvalue=0.674982546425338)" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "--\n", "Weighted CC Teq versus C/O: emission, transit:\n" ] }, { "data": { "text/plain": [ "0.9620219472463741" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "0.4101054171440646" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Teq versus C/O\n", "print('Pearson CC Teq versus C/O: emission, transit:')\n", "display(stats.pearsonr(emission['T_eq'],emission['C/O']))\n", "display(stats.pearsonr(transit['T_eq'],transit['C/O']))\n", "print('--')\n", "print('Weighted CC Teq versus C/O: emission, transit:')\n", "display(weighted_correlation(emission['T_eq'],emission['C/O'],\n", " np.max([emission['T_eq Lower'].values,emission['T_eq Upper'].values],axis=0),\n", " np.max([emission['C/O Lower'].values,emission['C/O Upper'].values],axis=0)))\n", "display(weighted_correlation(transit['T_eq'],transit['C/O'],\n", " np.max([transit['T_eq Lower'].values,transit['T_eq Upper'].values],axis=0),\n", " np.max([transit['C/O Lower'].values,transit['C/O Upper'].values],axis=0)))" ] }, { "cell_type": "code", "execution_count": 17, "id": "82031815-887b-41c2-924c-68fcd986843f", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Pearson CC Teq versus Metallicity: emission, transit:\n" ] }, { "data": { "text/plain": [ "PearsonRResult(statistic=0.13952504176937403, pvalue=0.5808316118170909)" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "PearsonRResult(statistic=0.05664763617958711, pvalue=0.8410727165796158)" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "--\n", "Weighted CC Teq versus Metallicity: emission, transit:\n" ] }, { "data": { "text/plain": [ "-0.16821920576265195" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "0.4863353999169412" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Teq versus Metallicity\n", "print('Pearson CC Teq versus Metallicity: emission, transit:')\n", "display(stats.pearsonr(emission['T_eq'],emission['Metallicity']))\n", "display(stats.pearsonr(transit['T_eq'],transit['Metallicity']))\n", "print('--')\n", "print('Weighted CC Teq versus Metallicity: emission, transit:')\n", "#wrap_weighted_correlation(data)\n", "display(weighted_correlation(emission['T_eq'],emission['Metallicity'],\n", " np.max([emission['T_eq Lower'].values,emission['T_eq Upper'].values],axis=0),\n", " np.max([emission['Metallicity Lower'].values,emission['Metallicity Upper'].values],axis=0)))\n", "display(weighted_correlation(transit['T_eq'],transit['Metallicity'],\n", " np.max([transit['T_eq Lower'].values,transit['T_eq Upper'].values],axis=0),\n", " np.max([transit['Metallicity Lower'].values,transit['Metallicity Upper'].values],axis=0)))" ] }, { "cell_type": "markdown", "id": "40c81f28-67a8-4ced-9373-c3e7da17dd8e", "metadata": {}, "source": [ "### C/O KS Tests" ] }, { "cell_type": "code", "execution_count": 18, "id": "704c9f36-8607-40fa-a39f-59b232557ff9", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Test to show weighted and unweighted agree - use asymp p-val calc\n" ] }, { "data": { "text/plain": [ "0.010916465714889769" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "0.010916465714889769" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "from scipy.stats import distributions\n", "from scipy.stats import kstest\n", "\n", "#see https://stackoverflow.com/questions/40044375/how-to-calculate-the-kolmogorov-smirnov-statistic-between-two-weighted-samples\n", "def ks_weighted(data1, data2, wei1, wei2, alternative='two-sided'):\n", " ix1 = np.argsort(data1)\n", " ix2 = np.argsort(data2)\n", " data1 = data1[ix1]\n", " data2 = data2[ix2]\n", " wei1 = wei1[ix1]\n", " wei2 = wei2[ix2]\n", " data = np.concatenate([data1, data2])\n", " cwei1 = np.hstack([0, np.cumsum(wei1)/sum(wei1)])\n", " cwei2 = np.hstack([0, np.cumsum(wei2)/sum(wei2)])\n", " cdf1we = cwei1[np.searchsorted(data1, data, side='right')]\n", " cdf2we = cwei2[np.searchsorted(data2, data, side='right')]\n", " d = np.max(np.abs(cdf1we - cdf2we))\n", " # calculate p-value\n", " n1 = data1.shape[0]\n", " n2 = data2.shape[0]\n", " m, n = sorted([float(n1), float(n2)], reverse=True)\n", " en = m * n / (m + n)\n", " if alternative == 'two-sided':\n", " prob = distributions.kstwo.sf(d, np.round(en))\n", " else:\n", " z = np.sqrt(en) * d\n", " # Use Hodges' suggested approximation Eqn 5.3\n", " # Requires m to be the larger of (n1, n2)\n", " expt = -2 * z**2 - 2 * z * (m + 2*n)/np.sqrt(m*n*(m+n))/3.0\n", " prob = np.exp(expt)\n", " return d, prob\n", "\n", "print('Test to show weighted and unweighted agree - use asymp p-val calc')\n", "display(ks_weighted(transit['C/O'].values,direct['C/O'].values,\n", " np.ones(len(transit)),np.ones(len(direct)))[1])\n", "display(kstest(transit['C/O'],direct['C/O'],method='asymp')[1])" ] }, { "cell_type": "code", "execution_count": 19, "id": "02198050-47dd-4984-9466-d2675876de0d", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "KstestResult(statistic=0.3292360221937687, pvalue=6.568675759987819e-06, statistic_location=0.5754399373371571, statistic_sign=-1)" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# planets vs. stars\n", "# Can only do unweighted b/c of stellar weights??\n", "stars['C/O'] = (10**stars['C']/10**stars['O'])*aspund09_co\n", "display(kstest(data['C/O'],stars['C/O'].dropna(),method='asymp'))" ] }, { "cell_type": "code", "execution_count": 20, "id": "7928bee1-d82f-411c-b008-3f62aac485de", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "---Unweighted---\n", "Transit versus Direct\n" ] }, { "data": { "text/plain": [ "KstestResult(statistic=0.5375, pvalue=0.010916465714889769, statistic_location=0.35, statistic_sign=1)" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Emission versus Direct\n" ] }, { "data": { "text/plain": [ "KstestResult(statistic=0.4236111111111111, pvalue=0.08117625333702128, statistic_location=0.66, statistic_sign=-1)" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Transit versus Emission\n" ] }, { "data": { "text/plain": [ "KstestResult(statistic=0.5333333333333334, pvalue=0.01187611196535396, statistic_location=0.585, statistic_sign=-1)" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Transit (no UHJs) versus Direct\n" ] }, { "data": { "text/plain": [ "KstestResult(statistic=0.42234848484848486, pvalue=0.012834670132515047, statistic_location=0.585, statistic_sign=1)" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "---Weighted---\n", "Transit versus Direct\n" ] }, { "data": { "text/plain": [ "(0.7072116351573585, 0.00015060630669413172)" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Emission versus Direct\n" ] }, { "data": { "text/plain": [ "(0.6427173229112506, 0.0009552228173997642)" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Transit versus Emission\n" ] }, { "data": { "text/plain": [ "(0.7072116351573585, 0.00015060630669413172)" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Transit (no UHJs) versus Direct\n" ] }, { "data": { "text/plain": [ "(0.9062119853595603, 1.1973158621674008e-08)" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "#KS tests\n", "print('---Unweighted---')\n", "print('Transit versus Direct')\n", "display(kstest(transit['C/O'],direct['C/O'],method='asymp'))\n", "print('Emission versus Direct')\n", "display(kstest(emission['C/O'],direct['C/O'],method='asymp'))\n", "print('Transit versus Emission')\n", "display(kstest(emission['C/O'],transit['C/O'],method='asymp'))\n", "print('Transit (no UHJs) versus Direct')\n", "display(kstest(transit_nouhj['C/O'],data['C/O'],method='asymp'))\n", "\n", "print('---Weighted---')\n", "print('Transit versus Direct')\n", "display(ks_weighted(transit['C/O'].values,direct['C/O'].values,\n", " 1/(np.max([transit['C/O Lower'].values,transit['C/O Upper'].values],axis=0))**2,\n", " 1/np.max([direct['C/O Lower'].values,direct['C/O Upper'].values],axis=0)**2))\n", "print('Emission versus Direct')\n", "display(ks_weighted(emission['C/O'].values,direct['C/O'].values,\n", " 1/np.max([emission['C/O Lower'].values,emission['C/O Upper'].values],axis=0)**2,\n", " 1/np.max([direct['C/O Lower'].values,direct['C/O Upper'].values],axis=0)**2))\n", "print('Transit versus Emission')\n", "display(ks_weighted(transit['C/O'].values,emission['C/O'].values,\n", " 1/np.max([transit['C/O Lower'].values,transit['C/O Upper'].values],axis=0)**2,\n", " 1/np.max([emission['C/O Lower'].values,emission['C/O Upper'].values],axis=0)**2))\n", "print('Transit (no UHJs) versus Direct')\n", "display(ks_weighted(transit_nouhj['C/O'].values,direct['C/O'].values,\n", " 1/np.max([transit_nouhj['C/O Lower'].values,transit_nouhj['C/O Upper'].values],axis=0)**2,\n", " 1/np.max([direct['C/O Lower'].values,direct['C/O Upper'].values],axis=0)**2))" ] }, { "cell_type": "markdown", "id": "19cc9b58-c0a9-4b77-a8ed-b286d32cf8a3", "metadata": {}, "source": [ "### Metallicity KS Tests" ] }, { "cell_type": "code", "execution_count": 21, "id": "3f565efe-cda2-44c7-a2a1-54a01a802790", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "KstestResult(statistic=0.5627575757575758, pvalue=6.300939371659787e-19, statistic_location=0.33, statistic_sign=-1)" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "(0.6682059498627333, 8.216470695742154e-28)" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# planets vs. stars\n", "# Can only do unweighted b/c of stellar weights??\n", "display(kstest(data['Metallicity'],stars['Fe'].dropna()))\n", "\n", "display(ks_weighted(data['Metallicity'].values,stars['Fe'].dropna().values,\n", " 1/np.max([data['Metallicity Lower'].values,data['Metallicity Upper'].values],axis=0)**2,\n", " np.ones(len(stars['Fe'].dropna()))))" ] }, { "cell_type": "code", "execution_count": 22, "id": "811b916c-eea3-488d-8006-8e81115d6570", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "---Unweighted---\n", "Transit versus Direct\n" ] }, { "data": { "text/plain": [ "KstestResult(statistic=0.2916666666666667, pvalue=0.42629346134549484, statistic_location=0.54, statistic_sign=-1)" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Direct versus Eclipse\n" ] }, { "data": { "text/plain": [ "KstestResult(statistic=0.2777777777777778, pvalue=0.4293158524103572, statistic_location=-0.33, statistic_sign=1)" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Transit versus Eclipse\n" ] }, { "data": { "text/plain": [ "KstestResult(statistic=0.25555555555555554, pvalue=0.5736808272434241, statistic_location=-0.11, statistic_sign=1)" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Transit (nouhj) versus All\n" ] }, { "data": { "text/plain": [ "KstestResult(statistic=0.16477272727272727, pvalue=0.8130717670668356, statistic_location=-0.17, statistic_sign=-1)" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Transit versus All\n" ] }, { "data": { "text/plain": [ "KstestResult(statistic=0.22727272727272727, pvalue=0.4821048957740845, statistic_location=0.55, statistic_sign=-1)" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Eclipse versus All\n" ] }, { "data": { "text/plain": [ "KstestResult(statistic=0.1414141414141414, pvalue=0.902997004829324, statistic_location=-0.33, statistic_sign=1)" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Eclipse (nouhj) versus All\n" ] }, { "data": { "text/plain": [ "KstestResult(statistic=0.20095693779904306, pvalue=0.5133585327036325, statistic_location=-0.11, statistic_sign=1)" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "---Weighted---\n", "Transit versus Direct\n" ] }, { "data": { "text/plain": [ "(0.305294586723762, 0.3691800454471108)" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Direct versus Eclipse\n" ] }, { "data": { "text/plain": [ "(0.6184583866087447, 0.0017776670392841256)" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Transit versus Eclipse\n" ] }, { "data": { "text/plain": [ "(0.6080472113228086, 0.002293123054953894)" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Transit (nouhj) versus All\n" ] }, { "data": { "text/plain": [ "(0.2664885800746464, 0.263817706785329)" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Transit versus All\n" ] }, { "data": { "text/plain": [ "(0.31479056536667005, 0.14828438020084733)" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Direct versus All\n" ] }, { "data": { "text/plain": [ "(0.24774593219207436, 0.34388271291168093)" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Eclipse versus All\n" ] }, { "data": { "text/plain": [ "(0.3876742339838617, 0.02098430995441003)" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Eclipse (nouhj) versus All\n" ] }, { "data": { "text/plain": [ "(0.4355717930387687, 0.004178134718310855)" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "#KS tests\n", "from scipy.stats import kstest\n", "print('---Unweighted---')\n", "print('Transit versus Direct')\n", "display(kstest(transit['Metallicity'],direct['Metallicity']))\n", "print('Direct versus Eclipse')\n", "display(kstest(emission['Metallicity'],direct['Metallicity']))\n", "print('Transit versus Eclipse')\n", "display(kstest(emission['Metallicity'],transit['Metallicity']))\n", "print('Transit (nouhj) versus All')\n", "display(kstest(transit_nouhj['Metallicity'],data['Metallicity']))\n", "print('Transit versus All')\n", "display(kstest(transit['Metallicity'],data['Metallicity']))\n", "print('Eclipse versus All')\n", "display(kstest(emission['Metallicity'],data['Metallicity']))\n", "print('Eclipse (nouhj) versus All')\n", "display(kstest(emission_nouhj['Metallicity'],data['Metallicity']))\n", "\n", "\n", "print('---Weighted---')\n", "print('Transit versus Direct')\n", "display(ks_weighted(transit['Metallicity'].values,direct['Metallicity'].values,\n", " 1/np.max([transit['Metallicity Lower'].values,transit['Metallicity Upper'].values],axis=0)**2,\n", " 1/np.max([direct['Metallicity Lower'].values,direct['Metallicity Upper'].values],axis=0)**2))\n", "print('Direct versus Eclipse')\n", "display(ks_weighted(emission['Metallicity'].values,direct['Metallicity'].values,\n", " 1/np.max([emission['Metallicity Lower'].values,emission['Metallicity Upper'].values],axis=0)**2,\n", " 1/np.max([direct['Metallicity Lower'].values,direct['Metallicity Upper'].values],axis=0)**2))\n", "print('Transit versus Eclipse')\n", "display(ks_weighted(transit['Metallicity'].values,emission['Metallicity'].values,\n", " 1/np.max([transit['Metallicity Lower'].values,transit['Metallicity Upper'].values],axis=0)**2,\n", " 1/np.max([emission['Metallicity Lower'].values,emission['Metallicity Upper'].values],axis=0)**2))\n", "print('Transit (nouhj) versus All')\n", "display(ks_weighted(transit_nouhj['Metallicity'].values,data['Metallicity'].values,\n", " 1/np.max([transit_nouhj['Metallicity Lower'].values,transit_nouhj['Metallicity Upper'].values],axis=0)**2,\n", " 1/np.max([data['Metallicity Lower'].values,data['Metallicity Upper'].values],axis=0)**2))\n", "print('Transit versus All')\n", "display(ks_weighted(transit['Metallicity'].values,data['Metallicity'].values,\n", " 1/np.max([transit['Metallicity Lower'].values,transit['Metallicity Upper'].values],axis=0)**2,\n", " 1/np.max([data['Metallicity Lower'].values,data['Metallicity Upper'].values],axis=0)**2))\n", "print('Direct versus All')\n", "display(ks_weighted(direct['Metallicity'].values,data['Metallicity'].values,\n", " 1/np.max([direct['Metallicity Lower'].values,direct['Metallicity Upper'].values],axis=0)**2,\n", " 1/np.max([data['Metallicity Lower'].values,data['Metallicity Upper'].values],axis=0)**2))\n", "print('Eclipse versus All')\n", "display(ks_weighted(emission['Metallicity'].values,data['Metallicity'].values,\n", " 1/np.max([emission['Metallicity Lower'].values,emission['Metallicity Upper'].values],axis=0)**2,\n", " 1/np.max([data['Metallicity Lower'].values,data['Metallicity Upper'].values],axis=0)**2))\n", "print('Eclipse (nouhj) versus All')\n", "display(ks_weighted(emission_nouhj['Metallicity'].values,data['Metallicity'].values,\n", " 1/np.max([emission_nouhj['Metallicity Lower'].values,emission_nouhj['Metallicity Upper'].values],axis=0)**2,\n", " 1/np.max([data['Metallicity Lower'].values,data['Metallicity Upper'].values],axis=0)**2))" ] }, { "cell_type": "code", "execution_count": 23, "id": "372f24b6-0480-4b69-9c48-0b7a26936131", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0.02285733851666528 0.5166666666666667\n", "0.011270580115818643 0.56875\n", "0.03661180335504245 0.4826388888888889\n" ] } ], "source": [ "# 2D KS\n", "#import ndtest\n", "#P, D = ndtest.ks2d2s(transit['C/O'].values, transit['Metallicity'].values, \n", " emission['C/O'].values, emission['Metallicity'].values, extra=True)\n", "#print(P,D)\n", "#P, D = ndtest.ks2d2s(transit['C/O'].values, transit['Metallicity'].values, \n", " direct['C/O'].values, direct['Metallicity'].values, extra=True)\n", "#print(P,D)\n", "#P, D = ndtest.ks2d2s(direct['C/O'].values, direct['Metallicity'].values, \n", " emission['C/O'].values, emission['Metallicity'].values, extra=True)\n", "#print(P,D)" ] }, { "cell_type": "markdown", "id": "22970ce7-bdd2-4afb-ad4c-282c6ca7faad", "metadata": {}, "source": [ "### Mass-Composition Correlation Tests" ] }, { "cell_type": "code", "execution_count": 24, "id": "4b71bb0f-6878-4208-858e-414f306b029c", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Pearson CC Mass versus C/O: data, direct, emission, transit:\n", "---Unweighted---\n", "All\n" ] }, { "data": { "text/plain": [ "PearsonRResult(statistic=0.01604223520875822, pvalue=0.8982710068045661)" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Direct\n" ] }, { "data": { "text/plain": [ "PearsonRResult(statistic=0.017497217154245483, pvalue=0.9487187648127204)" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Eclipse\n" ] }, { "data": { "text/plain": [ "PearsonRResult(statistic=-0.3611613117200738, pvalue=0.14088237034975143)" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Transit\n" ] }, { "data": { "text/plain": [ "PearsonRResult(statistic=-0.062147557254192076, pvalue=0.8258505462205231)" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "---Weighted Coefficients (not Pvalues)---\n", "All\n" ] }, { "data": { "text/plain": [ "0.020829836583343985" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Direct\n" ] }, { "data": { "text/plain": [ "0.011180010818847534" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Direct No HR8799\n" ] }, { "data": { "text/plain": [ "0.2716153613615166" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Eclipse\n" ] }, { "data": { "text/plain": [ "-0.3653973780283877" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Transit\n" ] }, { "data": { "text/plain": [ "-0.052119346609028075" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "print('Pearson CC Mass versus C/O: data, direct, emission, transit:')\n", "\n", "print('---Unweighted---')\n", "print('All')\n", "display(stats.pearsonr(data['Mass'],data['C/O']))\n", "print('Direct')\n", "display(stats.pearsonr(direct['Mass'],direct['C/O']))\n", "print('Eclipse')\n", "display(stats.pearsonr(emission['Mass'],emission['C/O']))\n", "print('Transit')\n", "display(stats.pearsonr(transit['Mass'],transit['C/O']))\n", "\n", "\n", "print('---Weighted Coefficients (not Pvalues)---')\n", "print('All')\n", "display(weighted_correlation(data['Mass'],data['C/O'],\n", " np.ones(len(data['Mass'])),\n", " np.max([data['C/O Lower'].values,data['C/O Upper'].values],axis=0)))\n", "print('Direct')\n", "display(weighted_correlation(direct['Mass'],direct['C/O'],\n", " np.ones(len(direct['Mass'])),\n", " np.max([direct['C/O Lower'].values,direct['C/O Upper'].values],axis=0)))\n", "print('Direct No HR8799')\n", "display(weighted_correlation(direct_nohr8799['Mass'],direct_nohr8799['C/O'],\n", " np.ones(len(direct_nohr8799['Mass'])),\n", " np.max([direct_nohr8799['C/O Lower'].values,direct_nohr8799['C/O Upper'].values],axis=0)))\n", "print('Eclipse')\n", "display(weighted_correlation(emission['Mass'],emission['C/O'],\n", " np.ones(len(emission['Mass'])),\n", " np.max([emission['C/O Lower'].values,emission['C/O Upper'].values],axis=0)))\n", "print('Transit')\n", "display(weighted_correlation(transit['Mass'],transit['C/O'],\n", " np.ones(len(transit['Mass'])),\n", " np.max([transit['C/O Lower'].values,transit['C/O Upper'].values],axis=0)))" ] }, { "cell_type": "code", "execution_count": 25, "id": "efe975db-7c2b-47d6-8279-59fdd3216ce3", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Pearson CC Mass versus Metallicity: data, direct, emission, transit:\n", "---Unweighted---\n", "All\n" ] }, { "data": { "text/plain": [ "PearsonRResult(statistic=-0.21471841745908585, pvalue=0.0833969415460411)" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Direct\n" ] }, { "data": { "text/plain": [ "PearsonRResult(statistic=-0.4352904938817366, pvalue=0.09195537151044374)" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Eclipse\n" ] }, { "data": { "text/plain": [ "PearsonRResult(statistic=-0.43267290747770776, pvalue=0.07291038332355068)" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Transit\n" ] }, { "data": { "text/plain": [ "PearsonRResult(statistic=-0.3352155150829301, pvalue=0.22194149282066594)" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "---Weighted Coefficients (not Pvalues)---\n", "All\n" ] }, { "data": { "text/plain": [ "-0.20106386496302864" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Direct\n" ] }, { "data": { "text/plain": [ "-0.49159819788765846" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Direct No HR8799\n" ] }, { "data": { "text/plain": [ "-0.42817930486335526" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Eclipse\n" ] }, { "data": { "text/plain": [ "-0.3946300723318043" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Transit\n" ] }, { "data": { "text/plain": [ "-0.3004807833305506" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "print('Pearson CC Mass versus Metallicity: data, direct, emission, transit:')\n", "\n", "print('---Unweighted---')\n", "print('All')\n", "display(stats.pearsonr(data['Mass'],data['Metallicity']))\n", "print('Direct')\n", "display(stats.pearsonr(direct['Mass'],direct['Metallicity']))\n", "print('Eclipse')\n", "display(stats.pearsonr(emission['Mass'],emission['Metallicity']))\n", "print('Transit')\n", "display(stats.pearsonr(transit['Mass'],transit['Metallicity']))\n", "\n", "\n", "print('---Weighted Coefficients (not Pvalues)---')\n", "print('All')\n", "display(weighted_correlation(data['Mass'],data['Metallicity'],\n", " np.ones(len(data['Mass'])),\n", " np.max([data['Metallicity Lower'].values,data['Metallicity Upper'].values],axis=0)))\n", "print('Direct')\n", "display(weighted_correlation(direct['Mass'],direct['Metallicity'],\n", " np.ones(len(direct['Mass'])),\n", " np.max([direct['Metallicity Lower'].values,direct['Metallicity Upper'].values],axis=0)))\n", "print('Direct No HR8799')\n", "display(weighted_correlation(direct_nohr8799['Mass'],direct_nohr8799['Metallicity'],\n", " np.ones(len(direct_nohr8799['Mass'])),\n", " np.max([direct_nohr8799['Metallicity Lower'].values,direct_nohr8799['Metallicity Upper'].values],axis=0)))\n", "print('Eclipse')\n", "display(weighted_correlation(emission['Mass'],emission['Metallicity'],\n", " np.ones(len(emission['Mass'])),\n", " np.max([emission['Metallicity Lower'].values,emission['Metallicity Upper'].values],axis=0)))\n", "print('Transit')\n", "display(weighted_correlation(transit['Mass'],transit['Metallicity'],\n", " np.ones(len(transit['Mass'])),\n", " np.max([transit['Metallicity Lower'].values,transit['Metallicity Upper'].values],axis=0)))" ] }, { "cell_type": "markdown", "id": "353ae0e0-d5f5-42d7-8511-bb921fd68630", "metadata": {}, "source": [ "## C/O versus Mass" ] }, { "cell_type": "code", "execution_count": 26, "id": "fa99042b-ec6f-4142-99e4-c1255e517696", "metadata": {}, "outputs": [ { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "#fig, ax = plt.subplots(figsize=(9, 6))\n", "\n", "# Mass C/O\n", "# If low transiting C/O is methane depletion...\n", "fig, ax = plt.subplots(figsize=(8, 6))\n", "\n", "pop_plot(fig,ax,'Mass','C/O',plot_direct=True,\n", " axis1_label='Mass [M$_J$]')\n", "#plt.gca().set_xscale('log')\n", "plt.xlim(-1,30)\n", "plt.savefig('./paper_figs/mass_co_geometry.pdf')" ] }, { "cell_type": "markdown", "id": "07ca771e-27d3-471f-9e29-46f26b03c42c", "metadata": {}, "source": [ "## Stellar Property vs. Metallicity Plots" ] }, { "cell_type": "code", "execution_count": 27, "id": "e73a728f-08bc-4e4b-bc0a-55f4d1fe53f7", "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_20318/1782682592.py:10: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " d['Stellar Metallicity'].iloc[np.where(d['Stellar Metallicity'] == -99)[0]] = np.nan\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_20318/1782682592.py:11: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " d['Stellar Metallicity Upper'].iloc[np.where(d['Stellar Metallicity Upper'] == -99)[0]] = np.nan\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_20318/1782682592.py:12: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " d['Stellar Metallicity Lower'].iloc[np.where(d['Stellar Metallicity Lower'] == -99)[0]] = np.nan\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_20318/1782682592.py:14: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " d['Stellar Metallicity'].iloc[np.where(d['Stellar Metallicity'] == 0.0)[0]] = np.nan\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_20318/1782682592.py:15: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " d['Stellar Metallicity Upper'].iloc[np.where(d['Stellar Metallicity Upper'] == 0.0)[0]] = np.nan\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_20318/1782682592.py:16: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " d['Stellar Metallicity Lower'].iloc[np.where(d['Stellar Metallicity Lower'] == 0.0)[0]] = np.nan\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_20318/1782682592.py:18: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " d['Stellar Mass'].iloc[np.where(d['Stellar Mass'] == -99)[0]] = np.nan\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_20318/1782682592.py:19: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " d['Stellar Mass Upper'].iloc[np.where(d['Stellar Mass Upper'] == -99)[0]] = np.nan\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_20318/1782682592.py:20: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " d['Stellar Mass Lower'].iloc[np.where(d['Stellar Mass Lower'] == -99)[0]] = np.nan\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_20318/1782682592.py:22: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " d['Stellar Mass'].iloc[np.where(d['Stellar Mass'] == 0.0)[0]] = np.nan\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_20318/1782682592.py:23: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " d['Stellar Mass Upper'].iloc[np.where(d['Stellar Mass Upper'] == 0.0)[0]] = np.nan\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_20318/1782682592.py:24: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " d['Stellar Mass Lower'].iloc[np.where(d['Stellar Mass Lower'] == 0.0)[0]] = np.nan\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_20318/1782682592.py:10: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " d['Stellar Metallicity'].iloc[np.where(d['Stellar Metallicity'] == -99)[0]] = np.nan\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_20318/1782682592.py:11: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " d['Stellar Metallicity Upper'].iloc[np.where(d['Stellar Metallicity Upper'] == -99)[0]] = np.nan\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_20318/1782682592.py:12: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " d['Stellar Metallicity Lower'].iloc[np.where(d['Stellar Metallicity Lower'] == -99)[0]] = np.nan\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_20318/1782682592.py:14: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " d['Stellar Metallicity'].iloc[np.where(d['Stellar Metallicity'] == 0.0)[0]] = np.nan\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_20318/1782682592.py:15: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " d['Stellar Metallicity Upper'].iloc[np.where(d['Stellar Metallicity Upper'] == 0.0)[0]] = np.nan\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_20318/1782682592.py:16: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " d['Stellar Metallicity Lower'].iloc[np.where(d['Stellar Metallicity Lower'] == 0.0)[0]] = np.nan\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_20318/1782682592.py:18: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " d['Stellar Mass'].iloc[np.where(d['Stellar Mass'] == -99)[0]] = np.nan\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_20318/1782682592.py:19: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " d['Stellar Mass Upper'].iloc[np.where(d['Stellar Mass Upper'] == -99)[0]] = np.nan\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_20318/1782682592.py:20: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " d['Stellar Mass Lower'].iloc[np.where(d['Stellar Mass Lower'] == -99)[0]] = np.nan\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_20318/1782682592.py:22: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " d['Stellar Mass'].iloc[np.where(d['Stellar Mass'] == 0.0)[0]] = np.nan\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_20318/1782682592.py:23: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " d['Stellar Mass Upper'].iloc[np.where(d['Stellar Mass Upper'] == 0.0)[0]] = np.nan\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_20318/1782682592.py:24: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " d['Stellar Mass Lower'].iloc[np.where(d['Stellar Mass Lower'] == 0.0)[0]] = np.nan\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_20318/1782682592.py:10: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " d['Stellar Metallicity'].iloc[np.where(d['Stellar Metallicity'] == -99)[0]] = np.nan\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_20318/1782682592.py:11: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " d['Stellar Metallicity Upper'].iloc[np.where(d['Stellar Metallicity Upper'] == -99)[0]] = np.nan\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_20318/1782682592.py:12: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " d['Stellar Metallicity Lower'].iloc[np.where(d['Stellar Metallicity Lower'] == -99)[0]] = np.nan\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_20318/1782682592.py:14: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " d['Stellar Metallicity'].iloc[np.where(d['Stellar Metallicity'] == 0.0)[0]] = np.nan\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_20318/1782682592.py:15: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " d['Stellar Metallicity Upper'].iloc[np.where(d['Stellar Metallicity Upper'] == 0.0)[0]] = np.nan\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_20318/1782682592.py:16: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " d['Stellar Metallicity Lower'].iloc[np.where(d['Stellar Metallicity Lower'] == 0.0)[0]] = np.nan\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_20318/1782682592.py:18: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " d['Stellar Mass'].iloc[np.where(d['Stellar Mass'] == -99)[0]] = np.nan\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_20318/1782682592.py:19: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " d['Stellar Mass Upper'].iloc[np.where(d['Stellar Mass Upper'] == -99)[0]] = np.nan\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_20318/1782682592.py:20: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " d['Stellar Mass Lower'].iloc[np.where(d['Stellar Mass Lower'] == -99)[0]] = np.nan\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_20318/1782682592.py:22: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " d['Stellar Mass'].iloc[np.where(d['Stellar Mass'] == 0.0)[0]] = np.nan\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_20318/1782682592.py:23: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " d['Stellar Mass Upper'].iloc[np.where(d['Stellar Mass Upper'] == 0.0)[0]] = np.nan\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_20318/1782682592.py:24: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " d['Stellar Mass Lower'].iloc[np.where(d['Stellar Mass Lower'] == 0.0)[0]] = np.nan\n" ] }, { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Planet Metallcity vs. Stellar Metallicity\n", "#Custom plotting this one b/c we want to filter bad measurements\n", "fig, ax = plt.subplots(figsize=(6, 5))\n", "\n", "for d, label, marker, color in zip([direct, emission, transit], \n", " ['Direct', 'Eclipse', 'Transmission','UHJ'],\n", " ['o', 's', 'd', '*'],\n", " ['#2E86AB', '#A23B72', '#F18F01', 'grey']):\n", " # If stellar metallicity is -99, that means it doesn't have a main sequence host!\n", " d['Stellar Metallicity'].iloc[np.where(d['Stellar Metallicity'] == -99)[0]] = np.nan\n", " d['Stellar Metallicity Upper'].iloc[np.where(d['Stellar Metallicity Upper'] == -99)[0]] = np.nan\n", " d['Stellar Metallicity Lower'].iloc[np.where(d['Stellar Metallicity Lower'] == -99)[0]] = np.nan\n", " # If stellar metallicity is exactly 0.0, that means we don't have a measurement and assume solar... let's ignore those\n", " d['Stellar Metallicity'].iloc[np.where(d['Stellar Metallicity'] == 0.0)[0]] = np.nan\n", " d['Stellar Metallicity Upper'].iloc[np.where(d['Stellar Metallicity Upper'] == 0.0)[0]] = np.nan\n", " d['Stellar Metallicity Lower'].iloc[np.where(d['Stellar Metallicity Lower'] == 0.0)[0]] = np.nan\n", " #Same for mass\n", " d['Stellar Mass'].iloc[np.where(d['Stellar Mass'] == -99)[0]] = np.nan\n", " d['Stellar Mass Upper'].iloc[np.where(d['Stellar Mass Upper'] == -99)[0]] = np.nan\n", " d['Stellar Mass Lower'].iloc[np.where(d['Stellar Mass Lower'] == -99)[0]] = np.nan\n", " # If stellar metallicity is exactly 0.0, that means we don't have a measurement and assume solar... let's ignore those\n", " d['Stellar Mass'].iloc[np.where(d['Stellar Mass'] == 0.0)[0]] = np.nan\n", " d['Stellar Mass Upper'].iloc[np.where(d['Stellar Mass Upper'] == 0.0)[0]] = np.nan\n", " d['Stellar Mass Lower'].iloc[np.where(d['Stellar Mass Lower'] == 0.0)[0]] = np.nan\n", "\n", " ax.errorbar(d['Stellar Metallicity'], d['Metallicity'],\n", " xerr=[d['Stellar Metallicity Lower'], d['Stellar Metallicity Upper']],\n", " yerr=[d['Metallicity Lower'], d['Metallicity Upper']],\n", " fmt=marker, label=label, color=color,\n", " markersize=7, capsize=2, alpha=1.0,\n", " markeredgecolor='k',markeredgewidth=0.5)\n", "\n", "for _, row in uhjs.iterrows():\n", " color_map = {'Direct': '#2E86AB', 'Eclipse': '#A23B72', 'Transit': '#F18F01'}\n", " if row['Stellar Metallicity'] == -99:\n", " continue\n", " if row['Stellar Metallicity'] == 0.0:\n", " continue\n", " ax.errorbar(row['Stellar Metallicity'], row['Metallicity'],\n", " xerr=[[row['Stellar Metallicity Lower']], [row['Stellar Metallicity Upper']]],\n", " yerr=[[row['Metallicity Lower']], [row['Metallicity Upper']]],\n", " fmt='*', color=color_map[row['Geometry']], markersize=12,\n", " markeredgecolor='k',markeredgewidth=0.5)\n", "\n", "#ax.set_xscale('log')\n", "\n", "ax.set_xlabel('Stellar Metallicity [M/H]', fontsize=15)\n", "ax.set_ylabel('Planet Metallicity [M/H]', fontsize=15)\n", "#ax.legend(frameon=True, fancybox=True, shadow=False,loc='upper left')\n", "ax.grid(True, alpha=0.3)\n", "ax.minorticks_on()\n", "plt.tight_layout()\n", "\n", "plt.savefig('./paper_figs/metallicity_metallicity.pdf')" ] }, { "cell_type": "code", "execution_count": 28, "id": "c04643d6-f5c9-4761-b889-9a3c6dc00067", "metadata": { "scrolled": true }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_20318/3468103133.py:10: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " d['Stellar Metallicity'].iloc[np.where(d['Stellar Metallicity'] == -99)[0]] = np.nan\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_20318/3468103133.py:11: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " d['Stellar Metallicity Upper'].iloc[np.where(d['Stellar Metallicity Upper'] == -99)[0]] = np.nan\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_20318/3468103133.py:12: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " d['Stellar Metallicity Lower'].iloc[np.where(d['Stellar Metallicity Lower'] == -99)[0]] = np.nan\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_20318/3468103133.py:14: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " d['Stellar Metallicity'].iloc[np.where(d['Stellar Metallicity'] == 0.0)[0]] = np.nan\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_20318/3468103133.py:15: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " d['Stellar Metallicity Upper'].iloc[np.where(d['Stellar Metallicity Upper'] == 0.0)[0]] = np.nan\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_20318/3468103133.py:16: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " d['Stellar Metallicity Lower'].iloc[np.where(d['Stellar Metallicity Lower'] == 0.0)[0]] = np.nan\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_20318/3468103133.py:18: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " d['Stellar Mass'].iloc[np.where(d['Stellar Mass'] == -99)[0]] = np.nan\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_20318/3468103133.py:19: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " d['Stellar Mass Upper'].iloc[np.where(d['Stellar Mass Upper'] == -99)[0]] = np.nan\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_20318/3468103133.py:20: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " d['Stellar Mass Lower'].iloc[np.where(d['Stellar Mass Lower'] == -99)[0]] = np.nan\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_20318/3468103133.py:22: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " d['Stellar Mass'].iloc[np.where(d['Stellar Mass'] == 0.0)[0]] = np.nan\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_20318/3468103133.py:23: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " d['Stellar Mass Upper'].iloc[np.where(d['Stellar Mass Upper'] == 0.0)[0]] = np.nan\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_20318/3468103133.py:24: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " d['Stellar Mass Lower'].iloc[np.where(d['Stellar Mass Lower'] == 0.0)[0]] = np.nan\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_20318/3468103133.py:10: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " d['Stellar Metallicity'].iloc[np.where(d['Stellar Metallicity'] == -99)[0]] = np.nan\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_20318/3468103133.py:11: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " d['Stellar Metallicity Upper'].iloc[np.where(d['Stellar Metallicity Upper'] == -99)[0]] = np.nan\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_20318/3468103133.py:12: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " d['Stellar Metallicity Lower'].iloc[np.where(d['Stellar Metallicity Lower'] == -99)[0]] = np.nan\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_20318/3468103133.py:14: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " d['Stellar Metallicity'].iloc[np.where(d['Stellar Metallicity'] == 0.0)[0]] = np.nan\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_20318/3468103133.py:15: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " d['Stellar Metallicity Upper'].iloc[np.where(d['Stellar Metallicity Upper'] == 0.0)[0]] = np.nan\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_20318/3468103133.py:16: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " d['Stellar Metallicity Lower'].iloc[np.where(d['Stellar Metallicity Lower'] == 0.0)[0]] = np.nan\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_20318/3468103133.py:18: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " d['Stellar Mass'].iloc[np.where(d['Stellar Mass'] == -99)[0]] = np.nan\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_20318/3468103133.py:19: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " d['Stellar Mass Upper'].iloc[np.where(d['Stellar Mass Upper'] == -99)[0]] = np.nan\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_20318/3468103133.py:20: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " d['Stellar Mass Lower'].iloc[np.where(d['Stellar Mass Lower'] == -99)[0]] = np.nan\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_20318/3468103133.py:22: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " d['Stellar Mass'].iloc[np.where(d['Stellar Mass'] == 0.0)[0]] = np.nan\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_20318/3468103133.py:23: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " d['Stellar Mass Upper'].iloc[np.where(d['Stellar Mass Upper'] == 0.0)[0]] = np.nan\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_20318/3468103133.py:24: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " d['Stellar Mass Lower'].iloc[np.where(d['Stellar Mass Lower'] == 0.0)[0]] = np.nan\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_20318/3468103133.py:10: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " d['Stellar Metallicity'].iloc[np.where(d['Stellar Metallicity'] == -99)[0]] = np.nan\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_20318/3468103133.py:11: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " d['Stellar Metallicity Upper'].iloc[np.where(d['Stellar Metallicity Upper'] == -99)[0]] = np.nan\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_20318/3468103133.py:12: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " d['Stellar Metallicity Lower'].iloc[np.where(d['Stellar Metallicity Lower'] == -99)[0]] = np.nan\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_20318/3468103133.py:14: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " d['Stellar Metallicity'].iloc[np.where(d['Stellar Metallicity'] == 0.0)[0]] = np.nan\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_20318/3468103133.py:15: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " d['Stellar Metallicity Upper'].iloc[np.where(d['Stellar Metallicity Upper'] == 0.0)[0]] = np.nan\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_20318/3468103133.py:16: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " d['Stellar Metallicity Lower'].iloc[np.where(d['Stellar Metallicity Lower'] == 0.0)[0]] = np.nan\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_20318/3468103133.py:18: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " d['Stellar Mass'].iloc[np.where(d['Stellar Mass'] == -99)[0]] = np.nan\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_20318/3468103133.py:19: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " d['Stellar Mass Upper'].iloc[np.where(d['Stellar Mass Upper'] == -99)[0]] = np.nan\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_20318/3468103133.py:20: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " d['Stellar Mass Lower'].iloc[np.where(d['Stellar Mass Lower'] == -99)[0]] = np.nan\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_20318/3468103133.py:22: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " d['Stellar Mass'].iloc[np.where(d['Stellar Mass'] == 0.0)[0]] = np.nan\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_20318/3468103133.py:23: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " d['Stellar Mass Upper'].iloc[np.where(d['Stellar Mass Upper'] == 0.0)[0]] = np.nan\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_20318/3468103133.py:24: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " d['Stellar Mass Lower'].iloc[np.where(d['Stellar Mass Lower'] == 0.0)[0]] = np.nan\n" ] }, { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Planet C/O vs. Stellar Metallicity\n", "#Custom plotting this one b/c we want to filter bad measurements\n", "fig, ax = plt.subplots(figsize=(6, 5))\n", "\n", "for d, label, marker, color in zip([direct, emission, transit], \n", " ['Direct', 'Eclipse', 'Transmission','UHJ'],\n", " ['o', 's', 'd', '*'],\n", " ['#2E86AB', '#A23B72', '#F18F01', 'grey']):\n", " # If stellar metallicity is -99, that means it doesn't have a main sequence host!\n", " d['Stellar Metallicity'].iloc[np.where(d['Stellar Metallicity'] == -99)[0]] = np.nan\n", " d['Stellar Metallicity Upper'].iloc[np.where(d['Stellar Metallicity Upper'] == -99)[0]] = np.nan\n", " d['Stellar Metallicity Lower'].iloc[np.where(d['Stellar Metallicity Lower'] == -99)[0]] = np.nan\n", " # If stellar metallicity is exactly 0.0, that means we don't have a measurement and assume solar... let's ignore those\n", " d['Stellar Metallicity'].iloc[np.where(d['Stellar Metallicity'] == 0.0)[0]] = np.nan\n", " d['Stellar Metallicity Upper'].iloc[np.where(d['Stellar Metallicity Upper'] == 0.0)[0]] = np.nan\n", " d['Stellar Metallicity Lower'].iloc[np.where(d['Stellar Metallicity Lower'] == 0.0)[0]] = np.nan\n", " #Same for mass\n", " d['Stellar Mass'].iloc[np.where(d['Stellar Mass'] == -99)[0]] = np.nan\n", " d['Stellar Mass Upper'].iloc[np.where(d['Stellar Mass Upper'] == -99)[0]] = np.nan\n", " d['Stellar Mass Lower'].iloc[np.where(d['Stellar Mass Lower'] == -99)[0]] = np.nan\n", " # If stellar metallicity is exactly 0.0, that means we don't have a measurement and assume solar... let's ignore those\n", " d['Stellar Mass'].iloc[np.where(d['Stellar Mass'] == 0.0)[0]] = np.nan\n", " d['Stellar Mass Upper'].iloc[np.where(d['Stellar Mass Upper'] == 0.0)[0]] = np.nan\n", " d['Stellar Mass Lower'].iloc[np.where(d['Stellar Mass Lower'] == 0.0)[0]] = np.nan\n", "\n", " ax.errorbar(d['Stellar Metallicity'], d['C/O'],\n", " xerr=[d['Stellar Metallicity Lower'], d['Stellar Metallicity Upper']],\n", " yerr=[d['C/O Lower'], d['C/O Upper']],\n", " fmt=marker, label=label, color=color,\n", " markersize=7, capsize=2, alpha=1.0,\n", " markeredgecolor='k',markeredgewidth=0.5)\n", "\n", "for _, row in uhjs.iterrows():\n", " color_map = {'Direct': '#2E86AB', 'Eclipse': '#A23B72', 'Transit': '#F18F01'}\n", " if row['Stellar Metallicity'] == -99:\n", " continue\n", " if row['Stellar Metallicity'] == 0.0:\n", " continue\n", " ax.errorbar(row['Stellar Metallicity'], row['C/O'],\n", " xerr=[[row['Stellar Metallicity Lower']], [row['Stellar Metallicity Upper']]],\n", " yerr=[[row['C/O Lower']], [row['C/O Upper']]],\n", " fmt='*', color=color_map[row['Geometry']], markersize=12,\n", " markeredgecolor='k',markeredgewidth=0.5)\n", "\n", "#ax.set_xscale('log')\n", "\n", "ax.set_xlabel('Stellar Metallicity [M/H]', fontsize=15)\n", "ax.set_ylabel('Planet C/O', fontsize=15)\n", "#ax.legend(frameon=True, fancybox=True, shadow=False,loc='upper left')\n", "ax.grid(True, alpha=0.3)\n", "ax.minorticks_on()\n", "plt.tight_layout()\n", "\n", "plt.savefig('./paper_figs/co_stellarmetallicity.pdf')" ] }, { "cell_type": "code", "execution_count": 29, "id": "f2f5cb84-66e2-4339-a14a-2d8e0ad58066", "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_20318/3704831077.py:9: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " d['Stellar Metallicity'].iloc[np.where(d['Stellar Metallicity'] == -99)[0]] = np.nan\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_20318/3704831077.py:10: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " d['Stellar Metallicity Upper'].iloc[np.where(d['Stellar Metallicity Upper'] == -99)[0]] = np.nan\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_20318/3704831077.py:11: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " d['Stellar Metallicity Lower'].iloc[np.where(d['Stellar Metallicity Lower'] == -99)[0]] = np.nan\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_20318/3704831077.py:13: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " d['Stellar Metallicity'].iloc[np.where(d['Stellar Metallicity'] == 0.0)[0]] = np.nan\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_20318/3704831077.py:14: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " d['Stellar Metallicity Upper'].iloc[np.where(d['Stellar Metallicity Upper'] == 0.0)[0]] = np.nan\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_20318/3704831077.py:15: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " d['Stellar Metallicity Lower'].iloc[np.where(d['Stellar Metallicity Lower'] == 0.0)[0]] = np.nan\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_20318/3704831077.py:17: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " d['Stellar Mass'].iloc[np.where(d['Stellar Mass'] == -99)[0]] = np.nan\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_20318/3704831077.py:18: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " d['Stellar Mass Upper'].iloc[np.where(d['Stellar Mass Upper'] == -99)[0]] = np.nan\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_20318/3704831077.py:19: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " d['Stellar Mass Lower'].iloc[np.where(d['Stellar Mass Lower'] == -99)[0]] = np.nan\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_20318/3704831077.py:21: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " d['Stellar Mass'].iloc[np.where(d['Stellar Mass'] == 0.0)[0]] = np.nan\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_20318/3704831077.py:22: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " d['Stellar Mass Upper'].iloc[np.where(d['Stellar Mass Upper'] == 0.0)[0]] = np.nan\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_20318/3704831077.py:23: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " d['Stellar Mass Lower'].iloc[np.where(d['Stellar Mass Lower'] == 0.0)[0]] = np.nan\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_20318/3704831077.py:9: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " d['Stellar Metallicity'].iloc[np.where(d['Stellar Metallicity'] == -99)[0]] = np.nan\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_20318/3704831077.py:10: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " d['Stellar Metallicity Upper'].iloc[np.where(d['Stellar Metallicity Upper'] == -99)[0]] = np.nan\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_20318/3704831077.py:11: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " d['Stellar Metallicity Lower'].iloc[np.where(d['Stellar Metallicity Lower'] == -99)[0]] = np.nan\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_20318/3704831077.py:13: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " d['Stellar Metallicity'].iloc[np.where(d['Stellar Metallicity'] == 0.0)[0]] = np.nan\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_20318/3704831077.py:14: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " d['Stellar Metallicity Upper'].iloc[np.where(d['Stellar Metallicity Upper'] == 0.0)[0]] = np.nan\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_20318/3704831077.py:15: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " d['Stellar Metallicity Lower'].iloc[np.where(d['Stellar Metallicity Lower'] == 0.0)[0]] = np.nan\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_20318/3704831077.py:17: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " d['Stellar Mass'].iloc[np.where(d['Stellar Mass'] == -99)[0]] = np.nan\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_20318/3704831077.py:18: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " d['Stellar Mass Upper'].iloc[np.where(d['Stellar Mass Upper'] == -99)[0]] = np.nan\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_20318/3704831077.py:19: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " d['Stellar Mass Lower'].iloc[np.where(d['Stellar Mass Lower'] == -99)[0]] = np.nan\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_20318/3704831077.py:21: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " d['Stellar Mass'].iloc[np.where(d['Stellar Mass'] == 0.0)[0]] = np.nan\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_20318/3704831077.py:22: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " d['Stellar Mass Upper'].iloc[np.where(d['Stellar Mass Upper'] == 0.0)[0]] = np.nan\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_20318/3704831077.py:23: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " d['Stellar Mass Lower'].iloc[np.where(d['Stellar Mass Lower'] == 0.0)[0]] = np.nan\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_20318/3704831077.py:9: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " d['Stellar Metallicity'].iloc[np.where(d['Stellar Metallicity'] == -99)[0]] = np.nan\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_20318/3704831077.py:10: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " d['Stellar Metallicity Upper'].iloc[np.where(d['Stellar Metallicity Upper'] == -99)[0]] = np.nan\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_20318/3704831077.py:11: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " d['Stellar Metallicity Lower'].iloc[np.where(d['Stellar Metallicity Lower'] == -99)[0]] = np.nan\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_20318/3704831077.py:13: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " d['Stellar Metallicity'].iloc[np.where(d['Stellar Metallicity'] == 0.0)[0]] = np.nan\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_20318/3704831077.py:14: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " d['Stellar Metallicity Upper'].iloc[np.where(d['Stellar Metallicity Upper'] == 0.0)[0]] = np.nan\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_20318/3704831077.py:15: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " d['Stellar Metallicity Lower'].iloc[np.where(d['Stellar Metallicity Lower'] == 0.0)[0]] = np.nan\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_20318/3704831077.py:17: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " d['Stellar Mass'].iloc[np.where(d['Stellar Mass'] == -99)[0]] = np.nan\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_20318/3704831077.py:18: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " d['Stellar Mass Upper'].iloc[np.where(d['Stellar Mass Upper'] == -99)[0]] = np.nan\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_20318/3704831077.py:19: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " d['Stellar Mass Lower'].iloc[np.where(d['Stellar Mass Lower'] == -99)[0]] = np.nan\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_20318/3704831077.py:21: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " d['Stellar Mass'].iloc[np.where(d['Stellar Mass'] == 0.0)[0]] = np.nan\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_20318/3704831077.py:22: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " d['Stellar Mass Upper'].iloc[np.where(d['Stellar Mass Upper'] == 0.0)[0]] = np.nan\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_20318/3704831077.py:23: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " d['Stellar Mass Lower'].iloc[np.where(d['Stellar Mass Lower'] == 0.0)[0]] = np.nan\n" ] }, { "data": { "image/png": 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H2bVrFw8++CC1atXi6NGjxMfH07JlSwBmz57Niy++iJeXF3379iUzM5MTJ05w69YtpkyZYvHzUN5I4iSEEKJSadg7qPhGFtyuMFu3bqVOnTr5ljVv3pzIyEh++uknpk2bxqhRo0hLS6NJkya8//77Ju/7gQceoGnTpoSEhJCZmcmoUaOYNWsWAJ6enuzfv5/PP/+c5ORkGjRowCeffEK/fv0AmDBhAm5ubnz00Ue88soruLu706ZNG15++WVLvfRyTVFNHVZQgSUnJ+Pl5UVSUhKenp62Dsei9Ho9cXFx1KpVC41G7sxamjY9k1V9ZgIwcsfcfN9K5dzbjpx72zB23tNvprB20CwAhm+ahWsND4seMyMjg6ioKAICAnBxcSnRtj3adWF81a7Ftluceoi9EUfMDbFMqKqKTqfD0dGRcePG8c8//7BhwwZbh2VXinqvlCQPqNRXnMLCwggLCyM7O9vWoQghhChj3vV8Wfz3ISB/5XAPf+8ClcOFyFWpE6fQ0FBCQ0MNmaYQQojKI+80KkVdPRYir0qdOAkhhBBRO04RvfO0oU7TwVnLgZw+TWVRKdzSli5dausQKjRJnIQQQlRqAX3al8sESdiG9JoUQgghhDCRJE5CCCGEECaSxEkIIYQQwkSSOAkhhBBCmEgSJyGEEEIIE0niJIQQolLTRq4kbUN/4j5SeOo+hcQ1fbm96RG0kSttHZrdmzVrFu3atSv1fpYuXUq1atVKvZ+yIImTEEKISs2pxSjc+q8hbB9k6eCb31vjNmgjTi1GWeV4iqIU+ZM7p1x5MG3aNHbt2lXq/YwYMYILFy5YICLrkzpOQgghKr3r12M5GgWrJ8DIlSuJ/c80fH2tM9XK9evXDY9Xr17NW2+9xfnz5w3LqlatanisqirZ2dk4Otrnn+uqVavmi9dcrq6uuLq6WiAi65MrTkIIISq9N157kZl9c64GTQ++zutTJ1rtWL6+voYfLy8vFEUxPI+MjMTDw4Pw8HA6dOhAlSpVOHjwIH/99RePPPIItWvXpmrVqnTq1ImdO3fm22/Tpk2ZO3cuTz/9NB4eHtSvX59vv/3WsD4rK4vnn3+eOnXq4OLiQoMGDZg3b55hvaIofPPNNwwYMAA3NzdatmzJ4cOHuXjxIj169MDd3Z3777+fv/76y7DN3bfq9u7dy7333ou7uzvVqlWja9euXL58GYAzZ87Qs2dPPDw88PT0pEOHDpw4cQIwfqtuwYIFNG7cGGdnZ5o3b87//d//5VuvKAqLFi1iyJAhuLm50bRpUzZt2lSq340pJHESQghRqdzdd+nEiRPobxyjnX/OxL5B/pB9/TAnT54scjtrmj59Ou+//z5//PEHbdu2JTU1lYcffphdu3Zx+vRp+vbty8CBA7ly5Uq+7T799FM6duzI6dOnee655/jPf/5juJo1f/58Nm3axJo1azh//jwrVqygYcOG+bZ/9913GTNmDBEREbRo0YLRo0czceJEZsyYwYkTJ1BVleeff95ozDqdjsGDB9O9e3d+/fVXDh8+zLPPPmuYMPnxxx/Hz8+P48ePc/LkSaZPn46Tk5PRfa1fv56XXnqJqVOncvbsWSZOnMi4cePYs2dPvnazZ8/mscce49dff+Xhhx/m8ccf5+bNm+acctOpQk1KSlIBNSkpydahWFx2drZ6/fp1NTs729ahVEhZtzPU77tOUb/vOkXNup2Rb52ce9uRc28bxs777cRkw/+R24nJFj9menq6eu7cOTU9Pd3kbVLXP6zqs1JVfVaqmp2ZovYNbqNemI2a9Jli+Dk/C7VfSFs1OzPF0DZt4yCLx79kyRLVy8vL8HzPnj0qoG7YsKHYbVu1aqV+8cUXqqqqql6vVxs0aKA+8cQThvV6vV6tVauWumDBAlVVVfWFF15Qe/Xqper1eqP7A9Q33njD8Pzw4cMqoC5evNiwbOXKlaqLi4vh+dtvv63ec889qqqqamJiogqoe/fuNbp/Dw8PdenSpUbX3X0e7r//fvWZZ57J1+bRRx9VH3744ULjTU1NVQE1PDzc6DGKeq+UJA+QK05CCCEqlezocFLCPEgJ82D5sx509vyN2p5Kvja+XgqdPH5l+bMehrZlqWPHjvmep6amMm3aNFq2bEm1atWoWrUqf/zxR4ErTm3atDE8zr0FGBcXB8DYsWOJiIigefPmvPjii2zfvr3Acdu2bWt4XLt27QL7rF27NhkZGSQnJxfYtkaNGowdO5aHHnqIgQMH8t///jdff64pU6YwYcIEevfuzfvvv5/vlt/d/vjjD7p27ZpvWdeuXfnjjz8Kjdfd3R1PT0/D67UWSZyEEEJUWsuOwNNdja8b3zVnvS24u7vnez5t2jTWr1/P3LlzOXDgABEREbRp04asrKx87e6+9aUoCnq9HoD27dsTFRXFu+++S3p6Oo899hjDhw8vdPvcW2zGluXu825Llizh8OHD3H///axevZpmzZpx5EjOSZw1axa///47/fv3Z/fu3QQGBrJ+/XqTz4kxRb1ea6nUiVNYWBiBgYF06tTJ1qEIIYQoIw4N++ERmoJHaAoT31zMkhOeRtt9d9yLiW8tNrS1pUOHDjF27FiGDBlCmzZt8PX1JTo6usT78fT0ZMSIESxcuJDVq1fz008/WbxPUFBQEDNmzOB///sfrVu35ocffjCsa9asGZMnT2b79u0MHTqUJUuWGN1Hy5YtOXToUL5lhw4dIjAw0KKxmsM+xzeWkdDQUEJDQ0lOTsbLy8vW4QghhCgDisYRxSnnis6jo8fRf+EX3EiOyHe7LjZJ5fjNAN4eNc5wlcWWmjZtyrp16xg4cCCKovDmm2+W+MrKp59+Sp06dQgKCkKj0fDjjz/i6+trscKTUVFRfPvttwwaNIi6dety/vx5/vzzT8aMGUN6ejqvvPIKw4cPJyAggKtXr3L8+HGGDRtmdF+vvPIKjz32GEFBQfTu3Zuff/6ZdevWFRhJaAuVOnESQghR+Tg1G2l4rCgK73y8kDmv9mP+oETD8rn7avHOJ4vyJU15tytrn376KU8//TT3338/3t7evPbaa0b7GRXFw8ODDz/8kD///BMHBwc6derEli1b0Ggsc/PJzc2NyMhIli1bRmJiInXq1CE0NJSJEyei0+lITExkzJgx3LhxA29vb4YOHcrs2bON7mvw4MH897//5eOPP+all14iICCAJUuW0KNHD4vEWhrKvz3TK7XcK05JSUl4ehq/ZFte6fV64uLiqFWrlsX+c4g7tOmZrOozE4CRO+bi5FrFsE7Ove3IubcNY+c9/WYKawfNAmD4plm41rBsJ+uMjAyioqIICAjAxcXF7P08Pao/Y+tuoZ2/wukY+D52IItXbLRgpNalqio6nQ5HR0e7uEJmj4p6r5QkD5BPFCGEEJXeex/MZ+7WnARk3n5f5nzyja1DEnZKEichhBCVmjZyJdUipnBfUBMmrdRwf3N3PI9NlEl+hVHSx0kIIUSl5tRiFE4tRvHagxk8P/EpXv1mWalu+4mKTRInIYQQAnBxcWHRstW2DkPYOblVJ4QQQghhIkmchBBClHsyQFwUx1LvEUmchBBClFsODg4ABaYeEeJut2/fBgpO01JS0sdJCCFEueXo6Iibmxvx8fE4OTlV2rpdUsepcKqqcvv2beLi4qhWrZoh2TaXJE5CCCHKLUVRqFOnDlFRUVy+fNnW4diMqqro9Xo0Go0kToWoVq0avr6+pd6PJE5CCCHKNWdnZ5o2bVqpb9fp9XoSExOpWbNmpb3qVhQnJ6dSX2nKJYmTEEKIck+j0VTq2kt6vR4nJydcXFwkcbIyObtCCCGEECaSK06VlDZyJVmRy8mODgfAoWE/FI0jTs1G4tRilI2jE0IIIeyTJE6VlFOLUTg2HkRKWM5M5W7916A4uds4KiGEEMK+VepbdWFhYQQGBtKpUydbhyKEEEKIcqBSJ06hoaGcO3eO48eP2zoUm8nQqrywWiUjI8PWoQghhBB2r1InTgLC9kGWDj77eJ6tQxFCCCHsniROldj167EcjYKvR8OB8JXExsbaOiQhhBDCrkniVIm98dqLzOybU3l3evB1Xp860dYhCSGEEHZNEqdKwOHy+gLLTpw4gf7GMdr555TmD/KH7OuHOXnypNF9aCNXWjVGIYQQojyQxKkScIj+CVWbZvjRZ6Xy5pSneb1HQr52M7vH8+aUp9FnpeZrr2rT0F5YZaPohRBCCPshdZwqAcfru0lb4GV4viFCpbMn1PbMPxGkr5dCJ49fWf6sB4Pb5V/n2GhgmcQqhBBC2DO54lQJLTsCT3c1vm5815z1QgghhChIrjhVAro6vfAavN4w8eNEn9UsWTeZycEpBdp+d9yLiW99isejI/ItTw8fXSaxCiGEEPZMrjhVBooDipO74efR0eM4mtiIG8lqvmaxSSrHbwbw6Khx+drLVCxCCCFEDkmcKoHsBoPzPVcUhXc+Xsicvd75ls/dV4t3PlmEouTv3wTg1GykNUMUQgghygWTbtU9/fTTFjmYoigsXrzYIvvKNW/ePNatW0dkZCSurq7cf//9fPDBBzRv3tyixynPshsMKbCsY8eOaGrfS0TMFtr5K5yOAYc6XejQoYPRfTi1GGXtMIUQQgi7Z1LitHTpUhRFQVXV4hsXwRqJ0759+wgNDaVTp07odDpmzpzJgw8+yLlz53B3l1tMRXnvg/k83W8LqyeozNtfh6U/f2PrkIQQQgi7ZnLn8IceeojXXnvN7AO9//77bN++3eztC7N169Z8z5cuXUqtWrU4efIkISEhFj9eRVKnji+dA2DSDxDy8Gh8fX1tHZIQQghh10xOnHx9fenevbvZB1q6dKnZ25ZEUlISADVq1CiT45V3od3hlXUwedoMW4cihBBC2D2TEqdHHnmE9u3bl+pA7du3559//inVPoqj1+t5+eWX6dq1K61bty60XWZmJpmZmYbnycnJhu31er1VYyxrer0eVVWNvi5Vr8fFSeGLEeDs7FzhXntZUPVqvsd5z2FR515Yl5x72zB23vN28ZDfifXIe750SnLeTEqc1q8vONdZSb3wwgu88MILpd5PUUJDQzl79iwHDx4sst28efOYPXt2geXx8fFkZGRYKzyb0Ov1JCUloaqqoY6Tge42bv8+jI+PB8e0Mo+vvMvOyDI8jo+Pw8HF2fC8yHMvrErOvW0YO+9Z/9z5XEmIj8dZe9tW4VVo8p4vnZSUgnUNC1NhCmA+//zzbN68mf379+Pn51dk2xkzZjBlyhTD8+TkZPz9/fHx8cHT09PaoZYpvV6Poij4+PgU+M+katPI/Ujz8fGRek1m0KXfSZx8fGrh6Jo/cSrs3AvrknNvG8bOe4ZTqmG9t48PLtWr2iq8Ck3e86Xj4uJicttynzipqsoLL7zA+vXr2bt3LwEBAcVuU6VKFapUqVJguUajqZBvOEVRCrw2beRKtBdWGeagy9z2BJBTr0lKD5hO0Sj5Ht/9/jF27kXZkHNvG3ef97x14XLXCeuQ97z5SnLOTEqc9u/fb3YwgFVHt4WGhvLDDz+wceNGPDw8iI2NBcDLywtXV1erHbe8c2oxShIkIYQQooRMSpx69OhhtJq0KRRFQafTmbWtKRYsWADkxJjXkiVLGDt2rNWOK4QQQojKx6TEKSQkpNDEad++fdSuXZsWLVpYNDBTlbYopxBCCCGEqUxKnPbu3VvoOo1GQ79+/fjuu+8sFZMQQgghhF2SHmRCCCGEECaSxEkIIYQQwkTlvhyBEGUlascpLm07ybUjkQDU7WKbfn0VmbFzrHHQ0LB3EAF9Sjd7gRBCWIIkTkKYKKBPe/y6tWJVn5kAhLw7BsDwXJSesXPs5Fqw5poQQtiK3KoTQgghhDCRJE5CCCGEECYy6Vbd008/XeT6gwcPFtpGURQWL15c8siEEEIIIeyMSYnT0qVLi1x/8eJFLl68aHSdJE5CCCGEqChMSpyWLFli7TiEEEIIIeyeSYnTU089Ze04hBBCCCHsnkmdw2fPnk1ERISVQyl7YWFhBAYG0qlTJ1uHIoQQQohywOTEqUOHDtSvX5/Q0FDCw8PJysqydmxWFxoayrlz5zh+/LitQxFCCCFEOWBS4nTw4EFeeeUVPDw8WLBgAQMGDKBmzZoMGzaMZcuWER8fb+04hRBCCCFszqQ+Tvfffz/3338/77//PpcuXWLjxo38/PPP/Pzzz6xfvx4HBwfuvfdeBg0axMCBAwkMDLR23EIIIYQQZa7EU640atSIyZMnM3nyZJKSktiyZQubNm1i27ZtHD58mJkzZxIQEMCgQYMYMGAA3bt3x8HBwRqxC2ETX18IJ12XyaIuBwFIiUkAYFGXgyiKYmjnXbc2X373jU1iFMLeaSNXknluKforOwDQ1O+DxtEFp2YjcWoxysbRCVG4Us1V5+XlxahRoxg1ahTZ2dns37+fTZs2sXnzZj7//HP++9//4unpSb9+/Zg8ebJ0whYVQrouk8mBg+8sKOQC66Jrh8oknuJoI1eSFbmc7OhwABwa9kPROMofKGFTTi1G4VD/AVK/9QXAre//oXGrZeOohCiexaZccXBwoGfPnnz22Wf8+eef/P7778yZM4fAwEDWrFlDeHi4pQ4lhEVE7Thl6xDysVY8Ti1G4dZ/jeG5W/81uA3aKEmTEEKYoVRXnIrSsmVLWrZsyfTp00lISODWrVvWOpQQZrm07SR+3VqVaBtdeglGk+pVsjOy0KVnoWiUYptH7zxNQJ/2JYpHCCFE2bJa4pSXt7c33t7eZXEoIUx27Ugkq/rMtNr+U64msPuJ/5rc3q+rDKoQQgh7Z5FJfosic9UJIYozvP9gEv6OBUBV1cI73NfzZe0vG2wRYpmRTtNC2DeTJ/lVFAVVVUt8AEmchL2q26UFIe+OKdE2uvQsPmuywaS2Hn7e9Fr+Ej4+tUy6VXdw1vISxVKRJPwdy/iqXe8sKOTi2+K/7aPDvTVJp2kh7FuJbtV16NCB0aNHU7duXWvFI0SZ0ThocHKtYsUDKDi4OOPo6oxGY/44jKgdp6TvUyVzed+v1LR1EEIIo0xKnCZNmsSPP/7IyZMniYiIoEePHjz++OMMHz6cqlWrWjtGIayiYe8gW4eQT2HxmNOJ/W6qNtPwWJueiaIrk+6NJjP1araqqmjTM4tvV8KO+fbm6v7fqFnb1lEIIYwx6dPzq6++Yv78+WzZsoXly5ezefNmdu/eTWhoKIMGDeKJJ56gb9++UuhSlCumXsW5u//NzazUMo3HEp3YHRy09B6g8so66PnTGziorqXan6WlxCQUenvu7nbW7NBvLxoG+9k6BCFEIUz+2uno6MigQYMYNGgQKSkprF27luXLl/Pjjz+yZs0aatasyYgRI3j88cfp0qWLNWMWokzd3f/ma7cUPju3oUC7JH06/g3rG5771LWvSwZh+yBLBzsTj/FQre62DkcIIcols67Xe3h4MG7cOMaNG8e1a9dYsWIFK1asICwsjK+++oqgoCBOnDhh6ViFsAuTmvUzunxRykH2RhwxPNfr9cTFxZX6eOZ0Yr/btZi/+H7IF6yeACN+uEaP/3sRX1/fUsdmKbnT1xTHw9+bkTvmFttO1avEx8eZ3DHf3hx55ytbhyCEKESpOzrUrVuXV155hQcffJAZM2awdetWrly5YonYhBCY34k971Qrb65Smdk3Z5TrjJBYZr3xIotXbLRCtObJW3KguHamnAu9Xm+RjvlCCHG3UiVOMTEx/PDDD6xYsYLff/8dVVVp2rRpqeo+laWwsDDCwsLIzs62dShCFKq4TuzayJVG6/s4tRiFY+NB7J9eFUWBdv45yUmQPyw5fZiTJ0/SoUOHEu/X3oX/foXNZy9z8NINALo18sVBo9Av0J9+reoXs7V98AtpA+dtHYUQwpgSJ05JSUmsWbOGFStWcPDgQfR6PbVq1eKFF17g8ccfL1cT+YaGhhIaGkpycjJeXl62DkcIo4rrxJ4VuRzHxoOMrtNnpTI3HL4cmX/5zO7xvDzlaTbvOFTo1R7thVXlMnHq16o+IY19uf+zTQB8NKQLbs72NYqwOA26tyVVEich7JJJnyZZWVls3ryZ5cuXEx4eTmZmJu7u7owaNYrHH3+cPn36yIg6IWwkOzqclDAPo+s2RKjc1whqe+ZPjny9FDp5/MryZz0Y3M544uTYaKDFYy2Mdz1fQ3HLvJXDPfy9C1QOF0IIWzIpcapduzbJyck4ODjQp08fnnjiCR555BHc3NysHZ8QohSWHYElTxlfN74rjFsGg9uVaUhG5Z1GRZueaSg5MHLHXOsWKRVCiBIyKXFKSkpCURQCAwPRarUsWbKEJUuWmHQARVHYtm1bqYIU5VfeDsoADg37oWgcZd4tC3Jo2A+3/muMrptYYxnfrQllSu+C67477sXEtz7F49ERRrdNDx9tyTCFMEqvV3ljE3w+QY904xflgck3/lVV5ddffy3xAUwdLSMqptwOyrm3ktz6r0FxcrdxVBWLonEs9JwOHzmGhz8I5Uaymu92XWySyvGbAbw9apz8HxU2te0crIuAB8O3M+jR0pXdEKIsmJQ47dmzx9pxiAouQ5tTtfrr8Rm4lrPEqaj+N4DheUDn0k2LYi6nZiMLXacoCjP6wnvh8EWeC0tz99XinU8WFZk0FbVfISzl012wegK89s4MSZxEuWBS4tS9u1QZFqWTW7X6s4/nMXP2x7YOp0SK6n8D3Hm+rvjCjNZQ3C3PIH8FVVWJiFFp569wOgYc6nQpshSBKfsVorQizpzF3Rnu8VNw0cby66+/0rZtW1uHJUSR5JaysLrr12M5GgVfj4YD4SuJjY21dUiVzhv9YO7WnCtm8/b7MueTb2wdkhC8+Nx4XuiZ8/j57nqeGzfMtgEJYYLyVdxElEtvvPaioWr19ODrvD51ol1Vra4MfL0UOgeoTPoBQh4ebVfTrViaqqoc2L+PmHUL0KUmMSJiPdOee5aQkBDpz2UhJS2OuuHvPUSm/cnKAZvQOOR8X8/IyEBJjaFns5w2vZrDZ7v/onenBri4uBi2jbuZxoMDHuW9Dz6zaoxCmMqkxGnQoEH07t2bF1980ewD/fe//2XXrl1s2rTJ7H2I8ufI2jnobxyjXeeCVavbul+QD7YyFNodXlkHk6fNsHUoVnPr1i36DxtBnEtNavccjpNXTS4mJTLhg2/wmT2HLevWUK1aNVuHWe5lnluKQ/0HCizX6/WQkYD+NvDvVDf69FR6+nTmeMJfDG0Ww2Ptsu7aKuezQaNR+CUUIMawZu0ZF9ZfbsFLz09Ef7tk8z6W1wKuwv6ZlDht3rwZb2/vUh0oIiKCX375pVT7EOWLqqq8/d4HfDk0hdwPR7hTtfrHKQ3kg60MuTgpfDGCfN/mKxJVVek/bAQJLXvh7tfEsNy5ug/OwUNJvHqRh4eN4NDOrYYrT+G/Xyk307DYE/2VHaR+a/yqpRtwO89znc6Ry2kDSdfCzyez+PWKyqz+4OhQ+NU/XbbK25tBUdJZ1v80jj8FklrCGMuygKuoXEy+VRcbG8v+/fvNPpD0a6l81q7+gfv8UgqtWr3+kANPPGKj4ESFs3//fuJdvHHLkzTl5eLXhPioM+zYvZduwcEAhJ+LkcSpDByI+50XWwziSMYG7qmXyoAw+GG8Sg33gslTYqrK6O/gmW4wvL3cWhX2x+TEadu2baUqZKmqqvQvqGQWfvkBi/obXze+Kzy98iJPfFi2MYmK67MFC3FsHVxkG8fWwYyaPge/oZMA6N6kTlmEVuFo6vfBre//FViu1+tJSEggdNIrJFzLKdORmZlJqjaJhlVrsSbGm0UnGpOYEccTS66z5fmC+35yqcKQsa8xdtrkUsWYsfOZUm0vRGFMSpyeeqqQORuEKMIzz7/Gdz+MZUqv7ALrvjvuxdi+jW0QlaioYm/cwKlNzSLbOHnVRJeWVEYRVVwaRxc0brUKrtDrwQUSYm8xwTMEgE0xRwmo1xSAvr7tuZwWR1+fQK64rzK67+a+Gno9PMz4/oWwAyYlTqZOryJEXsNHjGbxB89xIznZaNXqV7rWs2F0oqLxrV2b80mJOFf3KbSNNimR7m2asWrqYACmbzxaRtFVLLeyOnP3TKXTp77KL+s34eXmwfW//+YzJT5nhaIwwO9eAFpXa8Cu2DOcvvkn1T2qMeAzyNTBlX+y6NMqiy9H63jy3mzW/N/XdOy4iKgdpwjo096sGKWAq7AWKUcgrEZRFGa9/ipzvvqc+YMSDctzq1Y7u1+wYXSVR27V9o+G5nQQ7/LxejJwoVsjXxw0Cv0C/StEP5/J/3mGCR98g3Pw0ELbZJ89yLTpk3Bzlo++0jh3qCY1eqXkWxY66TlOHDpKy7RqTGwVYnQ7jaIwuWX+jo0H4n7D0ekQFxO1/HDcgVEds3lnxS5uJyYTvfO0+YmTDDwRViKfHsKqugx/nW9/+h8RMVuMVK0uunK1sIzcqu1f7SPfZL8fDelSoRKIkJAQfGbPIeHqRVyNdBDPuHoR78xEgoPv9IPqF+hfliFWGLHHL7B20KwCy59waM+amwf4PT6aMY174aAUXmM5W9Wz9K8dxGRe5tjrtxn8pRsbD/oTEX2ZJs6xfPzAcwQ/1NOKr0II81TqyuFhYWEEBgbSqVMnW4dSob33wXypWm0jeau2H4mCG8mqrUOyGkVRmDznE9L+t4nYHavIuhWHqteTdSuOpL0/UjNyD1t+Wp1vkEpFuNJmTxwUDaMCunMlLY75kT+Tok032i5Fm84n59ZxNS2RzjVb8p8fHKjv1IrR/v1xvBnMjshsIpLPlHH0Qpim4nzdNENoaCihoaEkJyfj5eVl63AqrDp1fOkcQKWoWm1v8lZtn9FX5b19tXn74f8CoP8zA1o9YeMI84vacYronafx6xoIwMFZywFo2DvIpFs2j97XhoeP7KPtC/OI27MOXVoS3ds0Y9rroQQHB8vIXgvx7dSMbm+OLrBcVVUS4uOp++gBHnRozrd/bmVq4JAC7b46/wsjG4awXXeOjx8/zfQNej7bugy36n4MBx7/9Qzbw38qg1ciRMlV6sRJlJ3KULW6JMpiOogTJ07cVbVdQT2l56Wo4bjVa8zhpoOtenwo+esM6NPe7D4tuRRFoWpAIFUDcpKvVVMHV6hbkvbA0dkR1xoeBZbr9XqctbdRHDRkq3r83IwXTm5YtRZ6VBSNhqqu2Xw5CqrWqYnGLWef9/Xoxn09urHntcVWfR1CmEM+TUSZqOhVq0sqK3I5jo0HWW3/qqry5pSnmd87gbxV29/oEc/DqxZQ5cl3UbVpqIp1PwJk2ouKqWHvoGLbHE+8wL3ezQG4nBrH2iuHGF6/Kw2q1qJTzWYcS7gANUp/HCHKmiROwqq0kSvRXlhlmP4gPTzn8r5Ts5GV+g9qdnQ4KWEFv7FbyoYIlc6eGK3a/njjyzS+MIbshQophWxvKTLtRcVU3FVBVVWJTo3jsQbBHI6P5EjCeUYH9GBV1D66+LSgi3dzfrx8kBpFlI4w5ThC2IJZiVN0dDQNGza0cCiiInJqMapSJ0i2suwILCmkbu34rjBuGQxuV6YhiUrEqaoLtdyqsyxqN9HaRDz1TqyK2od73Rpsj/uN8yl/4+NWDU3VKrYOVYgSMytxatKkCb179+aZZ57hkUcewdFRLlwJURIODfvh1n+N1fY/0Wc1S9ZNZnJwwWtKn+9x5ESaH6MP3cu0SeMICe5mtU7TuVcYy4qqquzft4+YdQvQpSbhWNWLA51q8OADPaVjeBlq1bo16yN/Yu7HHzD8kaGG0gXDN83CtYYH3y9ZyuvTpjOsdSfgsC1DFaLEzMp4GjduzPbt29mxYwc+Pj6MHTuW8ePH07RpU0vHJ0SFpGgcUZzcrbb/R0ePo//CL7iRHFGgavv6aF/qT3yfv5Jv8swn3+Mz51O2rFtDtWrVrBZPWbh16xZdHx5MknttavccjpNXTbRJiTz9/td4vTmb/4VvLPevsbyoXqM6m3aGExQURPrNgsn7mHFjadPuHjasXG6D6IQoHbPqOJ0/f549e/YwatQokpOT+fDDD2nRogW9evVi1apVZGVlWTpOISoUS08HEf77lXzPFUXhnY8XMmdv/lFNU7d44vjg82gcHHCu7oNb8FASW/bi4WEjUFXjNZ7u3ndJlNW0F6qq0n/YCLLb98P7gcdwru6DotHgXN0H7wceI7t9vyJfY3lUmt+Ltb095x2Cgoru2B0UFMTbs14ro4iEsByz77F1796d7t27888///B///d/LFq0iL1797Jv3z6qV6/OmDFjeOaZZ2jZsqUl4xXCZozVGNJn683al6X7fW0+e4XuTevmWxbYth2qT0dOx2wlyF/h1BU4pzajar38kyu7+DUh7tIZNm/fxf3dupm0b5M1ehRtls68bUvgwP79xLl4426kYjjkvMb4qDPs2L2Xbnkqh9srfZ5zdjtLh8ax4DkMPxcjBTyFsAFFteBXsGPHjrFw4UJWr15NWloaAPfffz/PPvssjz32GFWq2GdHwNwCmElJSXh6eto6HIvS6/XExcVRq1YtNJpKXSjeKrTpmazqMxOAkTvm4uR65z1eluf+nnlrjS7XJMfitOpFdryo0vPb6mQM+QAnj+oF2mXdiiNuzzr8hk6yapzWErNuAbV7Di9ygt/y9BqrkcTPrs8AMDB9If9QsEBv9yZ1mP9o17IOrUjG3vPpN1MK9HEytL8dR+q3OQVxqz4bi8atVlmHXGHIZ33plCQPsOjZvffee5k2bRojR45EVVVUVeXQoUOMHTuW+vXr8+WXX1rycEKIYjh7VuMfrTNP/+BEcoPuRpMmACevmujSkso4OsvRpSbh5FWzyDbl/TUKIeyDRYbDZWRk8OOPP7Jw4UIOHTqEqqr4+vry9NNP06tXL1avXs3y5ct56aWXuHXrFm+++aYlDiuE+Fe3Rr58NKRLgeWqNo3h61w4om1MjfsfLXR7bVIi3Vo35fsXBxRY99bmE0b3XVJ6vZ4ZU0KZ92mYxb8Rj4hYz8WkRJyqeZMWdY5bEfsNo+qqB3XHvWFLtEmJdG/TjFVTB1v02Nagvx2HfmnO41/+08/olZjpG4+WbVBCCKCUidOvv/7KwoULWbFiBUlJOd/kevbsyaRJkxg8eLChTEGvXr2YOXMmXbp04dtvv5XESQgLc9AoRqcVURVHXuicxIt/1ELj5Fzo9tlnD/La9EnUdC9Y2b2wfZfUpo0b+WnldzzYdyADB1m2avq0555l3Htf8PfVaFzrNMw3qu7miV3EH9iEj19Dpr35YrmYfkWvcyT138duzo5oykHMQlQWZn3tW7RoEZ07dyYoKIiwsDAcHByYPHkykZGR7Ny5k+HDhxeo7dSgQQP69OnDtWvXLBK4EOKOfoH+ha7r2hhqxh4h4+pFo+szrl7EOzOR4EI6TRe175KY98bzrB6vZ94bz1tkf3kFBwdz69xxavUchm+fkflG1fn2GUmtnsO4ee4E3Yx0fi+v6ni62ToEISols77GPPvss0BOx+9Jkybx6KOPmtTxu23btsTExJhzSCFEEYoaXaUoCj8+8Q8jd+wkPuoMjq2DcfLyRpuUgO7sAWpmJLJ21Q+ka7ONbt+9aV1ul2JknP7PVURs+RLXrKvc46fgkhnD4c9DaNPnWTRNLVOu4MD+/dRoex/u/sZrybn7N4W2Xdi5Z1+FGVV3Pfl2WYYkhPiXWYlTaGgoEydOpHXr1iXabtq0aUybNs2cQwohSqGaq8LBrRvYsf8Yo2fMRZeWhKN7Tv+f2w1b0G/xXise3YWkby/x2UM5z57vASP++yfV012ADRY5Qu6ouqI4tQlh1PQ55WhUXc7j/gvCCx1VJ4Qoe2YlTq+88gpVq1Yttt2tW7dISUmhfn37rDUSFhZGWFgY2dnGv2kLUZEoikJwSAh+Q2+Waj8POBxkV7bxW15JO5aiXjyMR545yPQ6LX4OifRslvO8V3Pw33WDW0v+g8bRydAuJTUTpcl9ePUZa/Lxcpk6qq7x7VPsdH2Mw9lBZKNhZ3bXYvcthBB5mZU4BQQEMHbsWBYvXlxku1dffZUlS5ag01m/AJ45QkNDCQ0NNdRvEKIy2f3iAFydSv4RoNv0Le/0ftXouoTHWhA64UmGNTzP8Hsy7lqbM/WLRqMQ/jxAomHN2jMu/BTdnK8WvU/NGjXybaXuXcZ7D39cZEy5o+qKquOkTUqkgUdO2bpuk3ajOLnTA3ivyD3bhoyqE8J+mZU45dZoMrWtEML+uDo5mjXCLPnqTvRL/YyuqwH83wCVWb9AxGWY1R8cHQqfXFeXrfL2ZlCUdP5vQASOm9pydy12x0YDi41z2nPPMuGDb3AOHlr4sc4e4LlO/wA5owQVM5JGY6J2nOLStpNcOxIJQN0uLdA4aGjYO4iAPu3N2qeMqhPCfln1f2NCQgKurq7WPIQQdiO3gnjuH84GD7TDrW09G0dV9hwdFN4bBD+eVBkQBj+MV6nhXjB5SkxVGf0dPNMNhrcvPLkyRUhICD6z55B49SIuRqZdybh6EZ+MeO5vVKrDGBXQpz1+3VoZfv8h747JV0HeWiw12lEIUTImJ0779+/P9zw2NrbAslw6nY7z58+zbds2WrVqVboIhShncv9w5k6BUNFo6vfBre//FdtuHJD48Wc8ueQDfjFSgeDJpQpDxr7G2GmTi9xPxs5nij2WoihsWbeGh4eNKDByMPvsQbwzE/ll9TKUFRUn2ZB56oSwDZMTpx49eqAod74Vbtu2jW3bthXaXlVVFEVh6tSppYtQCGFXNI4uJs8p1rPfUC7s+AgK3ICD5r4aej08zGLzk1WrVo1DO7eyfdcew8jB7m2aMW36pJwaVbrbpFjkSEKIyszkxGnMmDGGxGnZsmU0btyYrl2NTzDp7OxM3bp1GThwIO3bm3ePXwhhn5yamV57ac3/fc2T92YDCr/+rfLuL/Bmf2hbT+HJe7NZ839f07HjIosd7+6RgysnD8LdJadiuvS2FEJYgsmJ09KlSw2Ply1bRrdu3fjuu++sEZMQwo45tRhlUjtVVTl1ZC9vjIIfjmtYdTybT4fD5LUwqpOGUR2zeXflHsPV6dIeTwghyoJZncP1+oKX3YUQ9m37HzH8cj7RUDgxdzh7v0B/q/SXOXnyJK1qJvHW9hpk1QrB2XEDjX2gimdtfnXpyR/btxNYM4lTp07RoUMHix9fCCGsQca4ClFJPNjSn4fatiiz463+fgFbf81g1rz5jBw+gNmPbWDSD9Cj72PMfG8+K5YtZvbMl3D9fgEdOhR9u64y0utV3tgEn0/QmzepqBDCKkxKnN555x0URSE0NJQaNWrwzjvvmHwARVF48803zQ5QCGEebeRKtBdW4dhoIADp4aOBnD5DZXH7y6u6N6s3H6C1ayQZO5/hpbH9mfr1aSa1u8TtTY/wWOeRBG4+wJYNq60eS3m07Rysi4AHw7cz6NExtg5HCPEvkxKnWbNmoSgKI0aMoEaNGobnphS3lMRJCNtwajHKpv2D3pj9wb+PgnBqMQo3YMld08kFAUFBQWUcWfnw6S5YPQFee2eGJE5C2BGTEqclS5YAUKdOnXzPhRBCWF7EmbO4O8M9fgou2lh+/fVX2rZta+uwhBCYmDg99dRTRT63tf379/PRRx9x8uRJrl+/zvr16xk8eLCtwxJCCLO8+Nx4pvbMefx8dz3PjRvGwZN/2jYoIQRQQTqHp6Wlcc899/D0008zdGjhc1UJISq28N+vsPG3y4bnU9cfwdnRgX6B/vRtVrPY7bWRK8v09uYbr01m++YfqVXD3bAsIyMDJTWGns1ynvdqDp/t/ovenRrg4uJiaBd3M40HBzzKex98Vuo4yvp1C1GemZU43bhxg6NHj9KmTRsCAgKMtomKiuK3336jS5cu1KplmcrAhenXrx/9+vWz6jGEEPavX6v63NuwFr3mbwbgvQEd8fZwA0DVphW7fea5pTjUf6DEx9WnZ+LsfDvn8e049Kppc9W9GPosZ47vY0iDSIbfk3HX2pzaVhqNwi+hADGGNWvPuLD+cgteen4i+tuln9ZHe2GVJE5CmMisxOnTTz/l448/5uzZs4W2SU9PZ8iQIUyfPp05c+aYHaA1ZGZmkpmZaXienJwM5NSnqmg1qvR6PaqqVrjXZQ8eGzCE+KuxpFxNyLd8UeeDoFFAhexsHbXr1+PHzettFGXloqoqB/ftJWbdAnSpSTx5ci2vhk4kJCQE9HoytCqvrIOvxt3G1aPgBOT6KztI/dbXrGP3G5Tzb+b/fUNm0U0NXIBl/VVm/QIRl2FW/5xJkgujy1Z5ezMoSjrL+p/G8adAUs2KNj+HgAEW+Yww9nmTdxDR3esKPJbPKbPJZ33plOS8mZU4hYeH06pVK1q2bFlom8DAQFq1asUvv/xid4nTvHnzmD17doHl8fHxZGTc/a2vfNPr9SQlJaGqKhqNVIOxpKvnowit0wcCi273zZV95W6yX4fL63GI/gnH67sB0NXpBYoD2Q0Gk91giI2jM+6ff/7hkTETSPWsQ+2ew3Hyqkl0UiJPzPmKqq+9yaYlX/LDPsjSwbx33+T5abMK7MOt7MPG0UHhvUHw40mVAWHww3iVGu4Fk6fEVJXR38Ez3WB4+8KTK3NkZmaSYoH3qLHPm6x/7lzpS4iPx1l7+84GGQmGc56QkJCTSQqzyGd96aSkmD6TpVmJ0+XLl+nTp0+x7Zo2bcqePXvMOYRVzZgxgylTphieJycn4+/vj4+PD56enjaMzPL0ej2KouDj41Op/zNF7zxNw96WHfZexcO0P7MODo5WuV1dmtcUfi6GfoH+hTeoNRG13ROkLfACwGvwehQn98Lb25iqqgwZ/SR0GoC3XxPDcufqPng/MIKMqxd5/JkX8E7KGeI/cuUm9FPewtc3/9Wl2/59cHloWYmPr0vPYuOIeQA8snoGjq7OJd7HUxMg/tPPeXLJh/zyfMH1Ty5VeGTsq4yZ8jIAuy78zQPN6pX4OMZk7nqWahZ4jxr7vMlwunNNzNvHB5fqVe+0vw25aZS3t7fFJnyujOSzvnTy9h8sjlmJU3Z2tkntFEXJd0vMXlSpUoUqVQr2QdBoNBXyDacoSoV9baaK2n4K/+DWlt2pCXXMcttlZ2ote2zg8q4IGj1o3lQlW36PoWcxf3RV3Z1L1xk6PYpiv7cADuzfT7yLD+55kqa8XPyakBB+iU+G5Px/mB4cy+tTJxG2bF2+dqqDM1nO3iU+vi47i6ysnEQ6y9kbvXPJEyeArr0Hc3HHx0DBc93cV0O3PkMM8f3y11881N6824p3y/r3M8IS7v68yTsPoXL3cfI8ruyfUZYgn/XmK8k5MytxatSoEYcPH0an0+HoaHwXOp2Ow4cPU7++5efAEqKkrh2JZFWfmRbdZ0pMQrG36QBSriZY/NgAfl1NOHghDl6K5b5PNhTZxoUMdvzbDajX/J/JsOP7KDHrFlC75/BC19/++y/aeicT5J/zRzzIHz45uI97pnyCW73GhnYPODRm1x8bCmz/gMNBdmV3K3T/TrpsXvr3ca/5P6N1dDDrddze+hWL7s0GFH79W+XdX+DN/tC2nsKT92YzbsZM3B76D4BhzkFLcGo20mL7EqKiMystHThwILGxsUyfPr3Q6uEzZswgNjaWQYMGlSpAU6SmphIREUFERASQM6IvIiKCK1euWP3YQgjb06Um4eRlvNyAqqpk71rAJwPyd6P++OFk1F0LqKKm40IGLmRwKLuj4XHen94Oh6z+GlRVRXP9d9r5wfJj8MZGmDcYXt8I/3csJ9nTXPvdpBkbSkpG1AlhOrOuOE2dOpXvv/+ezz77jB07djB+/HgaN8751vbXX3+xePFizp49i6+vL6+88opFAzbmxIkT9OzZ0/A8t//SU089xdKlS61+fGH/6nZpQci7lp22YlGXgya18/DzZuSOuRY9NsDBWcvN3rZbI18+GtKlyDaqNo3shTmPd7840K77OI2IWM/FpEScq/sUWHf77EGGNoqntmf+DtW+XgqPN75M4wtjGNyu6M7WSsP+HH54cKHrdelZbNx2DMg5V+b0cTp18gSrD+h4c3t1MryDcXbcRGMfcPaoxa9VevLm9u082EjLyF71CWrfgekbj5b4GEKI0jMrcapRowbbt29nyJAh/Pbbb0yePDnfelVVadasGT/99BPe3iXvL1BSPXr0sMq3MFFxaBw0OLmaVlvHVHn7bhR9cMXixy4tB42Cm3PR//1VxZHccSZuzo4oTvZbL3fac88y/p33ce49vsA63YkNvDjuNrl1kfIa3xXGLYPB7Yrev4NGU+T50ubp9+nq7IBTMefWmI0rF7Lt10xmzZvPyOEDmP3YJib9AD37jWDme/NZsWwxs2e+hOvKhXTt0rnE+xdCWIbZn4QtW7bk999/Z926dezcuZOYmJzibP7+/vTu3ZuhQ4fi4GDefX4hLM3SI+rsQWleU5Ej6sqhkJAQat58in+uXsTlrg7ijh0HM//At7z1YMGBKt8d92LiW5/i8eiIIvefHj7aovEa41Xdm9WbDxAUFIT+dhyh3eGVdfDylGkAPP7UeALbtmfLhtVAxfsdClFelOorpIODA48++iiPPvqopeIRwioC+rS3dQgWV5rX1K9V2Q3aiNpxiotbjhN7/AIAvp2a4ejsSMPeQRb7vSiKwvpXAhm+eA/xUWdwbB2Mk5c32qQElFtxbPzNiYldMvLdrotNUjl+M4C3R40z/eqhFb0x+4N8z12cFL4YkX+YdFBQEEFBOQlzWf4OhRB32O+1dyHsnHc9Xxb/ndNpWFXVnFF2gIe/N4qioALZumx869e1YZS2F9CnPb4dmrJ20CwAur05GtcaHhY/jk/7Jzk0aiTbd+1h9Iy56NKS6N6mGdNm/AdX1ynMee1h5g9KNLSfu68W73yyyKSkSUadCSFymZQ45Y5Oq1evHg4ODiUerSYlCURFtPaXDYbH2vRMQ8mBkTvm4uRaBb1eT1xcnNXnaiwv9KrKyuh9DLXSlBC5I8OCQ0LwG3oTgJWTB+HuktNRW1P7XiJittDOX+F0DDjU6UKHDqbVwZJRZ0KIXCYlTg0bNkSj0XDu3DmaNWtGw4YNTb60rSgKOp2uVEEKIcq/s/9c5mTiRXbt3s2gx6wzbUv471cIPxdDSBNfsjKzmP7zMRQU+gX6894H83m63xZWT1CZt78OS3/+xioxCCEqNpMSp5CQEBRFwc3NLd9zIYQw1YG433mxxSAWfvW11RKnfq3q069V/XxX+3IrAqvaNDoHwKQfIOTh0QWmWxFCCFOYlDjt3bu3yOdCCHG34f0Hk/B3LAAZGRmkatNpWLUWa347yP2t2uPslHMLzbueb77bntaUO1Jt8rQZZXI8IUTFI53DhRBWkfB3LOOrdgVg062jBPw7tUlf3/ZcTotjoN+9AIYO9mXB2Eg1IYQoCUmcRLkTteMUl7ad5NqRSCCnKrjGQWPR4e2icFE7ThV6nqdPfZVf1m+ipmc1Yi5f4TMlPmeFojDg30SpdbUG7Io9w2fnNgCQpKbTo10XEpP/of+QQbz/yYcmH08IIcqaSYnT/v37S3WQkJCQUm0vRF4Bfdrj162VYRRbyLtj7K4yd0V2cctxfDs0NboudNJznPjfUVqmVWd8q65G22gUhcktH8m37FjSRf6oXYXn//Mc6TdT8q2L3nlaEichhN0wKXHq0aNHqTqDZ+eZjkAIUb7FHr9gqMlkzBOa9qxJPMDvcVGMadwLB6XwucSzVT3f/7ULFwdnnvAMZs/YLwq08esaaImwhRDCIkxKnMaMGVMhR9GFhYURFhYmiZ0olagdp4jeedrwBz538t0GD7TDrW09W4ZmEw6KhlEB3Zl95gfmR/7MhCYP4uHkWqBdijadRX9uI1l7m7fvsf6UJpaWt/N73gKoi7oczPd5aanO7+G/X2Hz2SscvJRzzG6NfHHQ5JRasNcq4nf/3zjywRoAua0uyjWTEqelS5daOQzbCA0NJTQ0lOTkZLy8vGwdjiinAvq0N/pHIHdIfEXj26kZ3d4sPtFZ2P0ADzo059s/tzI1sGD5gW/+DOfR+t3YdvMXhq17BcXR3eh+cv/Y2pu8nd8BKOTCmKU6v/drVZ/uTety3ycbAPhoSJdiJ2q2tcL+bwhRntn3/zohhN1xdHY0acoUjYOGbFWPn5u30fX+bt7oUVEUcK3ugeJkPHEqinQct0/RO0/T6EHTqrILUd5I4iSEKJGGvYNMbns88QL3ejcH4HJqHGuvHGJ4/a40qFqLTjWbcSzhAjiYf7xL207i161VgeWqXiU7IwtdehaKJue2marNNKzXpmei6Er38aeqqsnttOmZxTfMQ5+eP1aNkvNcl5WNky6na4EuPQutnXUzyD3v0TsjJHESFZZJnxzff/99qQ4yZsyYUm0vhLAfpl7hUVWV6NQ4HmsQzOH4SI4knGd0QA9WRe2ji08Lung358fLB6nuWXQCUtTxrh2JNIyuLI6Dg5YB/94xXDtwFtnZTiZtV5iUmIRCb8/d3c7UGHM5O9+m36CcxxsenUtWlpth3Uv//rtx27ES7bMs1btfOvSLisukxGns2LFmdQ5XVRVFUSRxEhZR1p1xRek4VnWhllt1lkXtJlqbiKfeiVVR+3CvW4Ptcb9xPuVvfFw9wKni9QMTQlRcJiVOb731VoUcVSfKl7LujCtKp3XrNqyLXMvcjz9g+CNDDSUMhm+ahWsND5Yt/IY3pkxlUBPzj1G3SwtC3i34xUzVq8THx+HjUyvPrbo0Mr77MieGn2eZ1acqr0VdDprUzsPfm5E75pZo3/rbcWT+X84kxIN/nInGrRYA6VnZ9Jr/MwC7XxyIq3Mx9znLWO55P//VDluHIoTVmJQ4zZo1y8phiMrMVh18pWOxdVWvWZ1NO8MJCgoqUNQSYMzYJ2h0fhLhvzmbfQyNg8Zo8VO9Xo+DizOOrs53Jvl11JHx73on1yooTqUrmmrql0lFUUpcoFWvViG3l5OTaxU0rlVQVZX/HdzNpU3foktN4vGzm5j23LN2Nel67nkXoiKTzuHC5grr4Hs3S3fGlYrU1jVr7rvFtrnHT+EeP63ZxyhJR/Xy7NatW/QfNoI4F29q9xyOk1dNLiYlMuGDb/CZPYct69ZQrVo1W4dp0LB3O1uHIITVSOIkbM7UDr6W7owrFanLv8qQ+O6MjGHWtBkktuyFu9+d+5rO1X1wDh5K4tWLPDxsBId2bmXruRi7KIZZWRJaUTmVKnG6cuUKP//8M3/++ScpKSlGrwgoisLixYtLcxghhI1kaFVeWQdfj8/AtZR9gioa73q+hv50eQcrePh7FxisUBrfrd9OnIt3vqQpLxe/JsRHnWHH7r2E33S2i8RJiIrM7MTpnXfe4d1330Wv1xuW5SZOuR8auaPqJHEqX6J2nOLStpNcOxIJ5HTA1ThorDZNQmEdfO9m6c64uVOjiMKF7YMsHXz28Txmzv7YrH0UOu1GrxbUtFikZS/vyE1teqbhKufIHXMtOun0nq3h1Og5qsg2jq2DGTV9Do+/Otvk/UbtOMXFLceJPX4ByKkI7+jsKNOhCFEMsxKn1atXM2vWLBo2bMjrr7/Ojz/+yI4dO9i2bRuXLl1i9erV7N27lylTpjBw4EBLxyysLKBPe/y6tTL8IQh5d4xF/xDcrbAOvnezZmdcUdD167EcjYLVE2DkypXE/mcavr4lv3pS2LQbqjaNlD8sEWnFpk1Nwcmr6BTTyasmurSkEu03oE97fDs0NYx27PbmaJMqwgtR2RU+bXkRvvrqK5ydndmzZw/jx4+nTp06APTp04eJEyeye/duPvnkE/773//i4GBfw2WF/TG3P0SWXsf30ZvJ0uvK9LiVxRuvvcjMvjmJ6PTg67w+daKtQ6qUfHy80SYlFtlGm5RI9zbNyigiISo3sxKnX3/9lfvvv58GDRoA+W/N5Zo8eTLNmzfnvffes0CYoiIz97bArrjDVK/1F7vjD5fpcSsCbeTKItefOHEC/Y1jtPPP+b8d5A/Z1w9z8uTJUu9blEyH7r3QnT1QZJvssweZFiqJrRBlwaxbdZmZmfku2bu4uADwzz//UL16dcPye+65h61bt5YyRCFy5O2Mm5GZQbb2V3Y+qdL7vxFE3dLh7ORsaFdS4b9fYfPZKxy8lFOZvFsjXxw0Cv0C/StkZ9usyOU4Nh5kdJ2qqrw55Wnm904A7twendk9npenPM3mHYeKvG2qvbAKpxZF98kRphs75EGiD+0h8epFXIx0EM+4ehHvzESCg4NJOxdjgwiFqFzMSpzq1KlDXNydaRLq1asHwO+//063bt0My69evUq2nU1CKQpn71Oa5D3muMf6Mc7/DIqi8PEwHd/H+rN4xUaz992vVX26N63LfZ/kHOOjIV1wc6641Tqyo8NJCTPen2VDhEpnT6jtmT858vVS6OTxK8uf9WBwu8ITJ8dG0q/Rknq38OfedWt4eNgI4qPO4Ng6GCcvb7RJCWSfPYh3ZiJbflqNoigVMskXwt6Y9ZehTZs2+S7Z9+jRA1VVefvtt9m0aRPu7u6sWbOGAwcOcN9991ksWEsLCwsjLCxMkrt/lcWUJsau7ARl7KJex3FGP/SNtU+MOo8m9hjt7r9zG2nJ6ZzbSB06mD4juzZypeHKiKqq7N+3j5h1C9ClJjEiYr3dVWUuK8uOwJKnjK8b3xXGLYPB7co0pEqvWrVqHNq5le279jB6xlx0aUl0b9OMadMnERwcXOneo0LYklmJ08CBA9m0aRO7d++mV69edO3alZ49e7Jnzx6qV6+Op6cnt27dQlEU3nzzTUvHbDGhoaGEhoaSnJyMl5eXrcMpc5accsTUfRm7sqNumYdbs2mo2rQC7fs2q0lIQy96zY8G4IP+rXn04eeY3/sm5txGyiv3llJ5q8psCQ4N++HWf43RdRN9VrNk3WQmBxecJuW7415MfOtTPB4dUei+08NHWyxOcYeiKASHhOA39CYAq6YOrtBXRYWwV2b9r3viiSfo1q0bPj4+hmXr16/n1VdfZcOGDdy6dYvAwEBmzJhB3759LRassKy7pzopzZQmpZm+pKjbRrl2uOb8u+750t1Gysux0UBUVaX/sBEmVWU251v9nkvxjKhVq8TbWZuicSx0kttHR4+j/8IvuJEcke88xyapHL8ZwNujxskVDiFEpWVW4lSlShWaN2+eb5mnpydff/01X3/9tUUCE9Z391QnpZnSpKymL7HkbaRsvZ7tu/aYXJW5W3BwiWJV9Xr2XopnRJcSbVYmnJqNLHSdoii88/FC5rzaj/mD7gyDn7uvFu98sqjYpKmofYvC5b0tXY0kfv73y8Ku81fpE2R/ybcQlZVZidP+/fvx9fWlWbOi64b8+eefXL9+nZCQELOCE5VDUbeNAG5n6eg1/2cAXpiZzpKN04zeRpp/0I1r7Z+iT/r9Jh337b/+yxOr5lC75/Ai2+VWZfYbOsmk/ebVxb9GibcpC8WNeuvYsSOa2vcSEbOFdv4Kp2PAoU4Xk/qQyYg689x9GzvXA839gJzEKvxcDN2b5NTNm77xaM52FXTkpxD2yqzEqUePHowbN67YqVQ+/PBDvvvuO+l8bafunuqkNFOalGb6kqJuGwEoqo4MckpeDBkxgke/+8robaT1l2rh/mRPMjD9NpIuNckqVZkrgvc+mM/T/baweoLKvP11WPrzN7YOqVLr16q+JEhC2AGzexaa0h/G1D4zwjbunurEVlOalOTWTmG3kWZsq873S1YT1N70flb6PzPofiyci0mJOFf3KbRdblXmVVMHm7xvyLlVN2WtacmoPapTx5fOATDpBwh5eLRZ060IIURFY1blcFNdu3aNqlWrWvMQohQsOeWIqfvKO+w/6vv3GfHkU/zvRt0SJdl3biPlbHM6BmKqtKJrl3txc3Y0+adqqyeY9tyzJldlLsm+3Zwdca0AI55Cu4OzI0yeNsPWoQghhF0w+ZP9+++/z/f84sWLBZbl0ul0nD9/np07d9Klix32jBWAZaccMWVflhz2f/dtpJc+/sysuENCQvCZPcekqszm6NGo8CtZ5YGLk8IXI+7MDlCYqB2nuLTtJNeORAI5t4E1Dhoa9g6q1FPbCCEqHpMTp7Fjxxpu5SiKwqFDhzh0qPBCiKqq4uLiwltvvVX6KIVFhf9+xWhfibxTmuStHO7h712gcnhJWXrY/923kR7v3tHkWPK+fkVR2GJiVWZz9CzniZOpAvq0x69bK8Noy5B3x1j0dq4QQtgLkxOnt956C0VRUFWVd955h3bt2vHII48Ybevs7EzdunV58MEHqVOnjsWCFZax+ewVujetW2D59+vXGh7r0rPY2D+neOkjv7yLo6tzvra3s3QlOuaB/fvNHvafrtXle6z/cxXqn6t5fkw/pn0TwTOtzpGyYSBK0xFomhbfXyr8XEy+xFGqMgshhDCVyYnTrFmzDI+XLl1K7969efvtt60Rk7Cyg5diCwx5vpuTLpuX/n3ca/7PaB0dSnXMmHULLDLsv9f8zYAL8G8xp97Q+69/V/4BsKHYWHKHc+clVZlNE7XjFBe3HCf2+AUAfDs1w9HZkYa9g/IVUxVCiIrKrL8M0dHRFg5DVHT2Puy/PNbI0UauJCtyOdnR4UBOPSxF44hTs5FWq6UU0Kc9vh2asnbQLAC6vTka1xo5Vd/vriYvhBAVUam/Up85c4Zjx46RkJBAq1atGDRoEACZmZlkZmbi6elZ6iCFZXVr5MtHQwrvtB+zK4KY3RFwX0sA3k/OKTbp36sd/g+0M+uYIyLWmz3sP12r+/dKE+x+cQCuTqV72+YmRXmVxxo5Ti1G4dh4kGG6Grf+a4qshyWEEKL0zP4LdP78ecaNG8fRo3f+CD311FOGxOmHH35gwoQJ/PLLLzJfnZ1x0ChF3oZq3q8jzfuZ3tnaFNOee5YJH3yDc/DQQttknz3ItOmTiozN1clRbqEJIYSwGbPqOMXExBASEsKRI0cYOHAgH374YYE6PI899hjOzs789NNPFglUWE6/QP8yP2ZISAg+GQlkXL1odH1ph/2XhC1evzVlaFVeWK2SkZFh61CEEKLCMytxeuedd0hISGDRokVs2LCBqVOnFmjj7u5Ou3bt8l2REvbBFrekcof914zcw+0DP5F1Kw5VryfrVhzpB9ZRM3JPqYb9l0R5uyVXnLB9kKWDzz6eZ+tQhBCiwjMrcdq6dStt27bl6aefLrJdw4YN+fvvv80KrCyEhYURGBhIp06dbB1KpZA77P/LKc8Qt2cdl1d8RNPLh1g0fRKHdm41ufiluOP69ViORsHXo+FA+EpiY2NtHZIQQlRoZnUWiYuLo2vXrsW202q13L5925xDlInQ0FBCQ0NJTk7Gy8vL1uFUCjLs37LeeO1FZvbNOa/Tg6/z+tSJLF6x0dZhCSFEhWXWFaeaNWty5cqVYttduHBBCmAKYYQ2cmWp93HixAn0N47Rzj/n9maQP2RfP8zJkydLve/S0l1cbesQhBDCKsz6qt+1a1c2bNhAREQE7dq1M9pm3759nD17lrFjx5YiPCEqpqzI5Tg2HmT29qqq8uaUp5nfOwG40y9sZvd4Jox6hHeGzKBBr3Y07NXO/GNo04w/1uV/rGo1/7bJxMFBC0D2xR+hzRizjy0qPm3kSrQXVuHYaCAAGTufAbBqHTIhLMGsxGnatGmsX7+eRx55hK+//poHH3ww3/rdu3czduxYHB0defnlly0RpxAVSnZ0uKH+kjk2RKh09oTanvk70/t6KfRs9jdXbjxP+z8UUv4obaQ5Ur+9Mz+hTucIvABAxvf10TnemRJnwJDcR/0tc2BRYTm1GCUJkiiXzLpV17lzZ+bPn8+1a9cYMGAA1apVQ1EUfvrpJ6pXr06fPn24du0aX375JW3btrV0zEJUesuOwNOFdDMc3zVnvRBCCMszu1fuc889R/v27Xn//ffZvXs3qqqSkpKCi4sLDz30EK+//rpJHchF5RH++xU2n73CwUt3Rn69sv4IA1qXv6rdpeXQsB9u/deYvf1En9UsWTeZycEpBdYtPu7JxLc+w+PRESbtS9WmGa4oVX021lB9vLDl6bdS0K//kJXR+xjyRDTuNXMGVmjTM1k7cBYAg16OMvu1CSGEPSvVcKYuXbqwYcMGVFUlISEBvV6Pt7c3Dg6lmxBWVEz9WtUnU6c3JE7dGvnioFEIPxdjWF9ZKBrHUk2P8ujocfRf+AU3kiPy3a6LTVI5lhDArFHjzKqJpTi5G40r73LFUc/Zfy5zMvEiu/cfZdBjOffnFJ0j2dlOZr4ioaoq+/ftI2bdAmJSbzLa04vnOv3DQ3cVFxZC2JZFxoErioKPT+FzkAmR68GWfry95QQAHw3pUmlLETg1G1mq7RVF4Z2PFzLn1X7MH5RoWP7mBjdmfbnA6oVED8T9zostBrHwq68NiVNeDk0eterxK5pbt27Rf9gI4ly8qd1zOE5eNfkrKZEXT27Cp/9IwjeslzpnQtiJyvlXSwgbs0Sn2I4dO6KpfS8RMVto569w+orKPzfr0L59ewtEmN+jj4wk4Vo8ABkZGaRq02lYtRZrfjvI/a3a4+zkjKqq6K6nMKlZPxybmHabsCKI2nGK6J2n8esaCMDBWcsBaNg7iIA++X8X4b9fKXBlVVVV+g8bQWLLXrj7NTEsd67ug3Pv8dy8eoG+DwSxf/kcnFuOLnJfQgjrMylx6tWrl9kHUBSFXbt2mb29EKJw730wn6f7bWH1BJW3NrgzvHZvqxwn/u84Jnh0A2DTraME1GsMQF/f9lxOi2Og370AfBazwSrHt2cBfdoXSJAKs/nsFbo3rZtv2YH9+4lz8c6XNOXl4teMxNpd2HutDt0a3xnBGH4uRhInIWzApMRp7969KIpSYCJfU5TF3GPCsqJ2nOLStpNcOxIJQN0uLdA4aIx+gxa2VaeOL50DYNIP4O/YgmrO5vWbytCqvLIOvh6fgeu/fZmmv/oGv6xwo4arM38nxfCZZkNOY0VhwL+JUutqDdgVe4bPzuWsu5mVymfnNvBh4Gb6D32E9z/5EMh5T8l7Bw5eiuW+TzbkWxazbgG1ew4vcjvH1sGMmj4Hv6GTDMu6N5HiwkLYQolu1XXo0IEnnniCLl26WCseYUPD+w8m4e+cjtuqqpISkwCAR4p3TgK8Laeddz1f1v6ywUZRiruFdodX1kHP2veCmf2Iw/ZBpg4+em8Wr7w2N2e/z07g+M8LaeXciWf8WxvdTqMoTG75SL5lh+MjOV8tmef/8xzpN3NG/UXvPC2JUyF0qUk4edUsso2TV010aUllFJEQoigmJU7vv/8+K1as4OTJk5w6dYrGjRvz+OOP8/jjj9OkifHLy6L8Sfg7lvFV85SQCDTebvHfh8omIGESFyeFL0bA5vWOZGeXfPvciYJXT4BBXyyl+h6t4crVmNrPsCb6AL/f3MmYxr1wUAov/Zat6vn+r124ODjzpHcwe8Z+YViX2/+nsuvWyJePhuT/4jkiYj0XkxJxrl74ABttUiLd2zRj1dTBhmXTNx61VphCiCKYVADz1Vdf5cyZM5w5c4apU6eSkZHB7Nmzad68Offddx9hYWEkJCRYO1ZhJVE7TtnFPoRt5J0o+J3BqWy+sdOwzkHRMCqgO1fS4pgf+TMp2nSj+0jRpjP/j01cSYtnVED3IhOsysxBo+Dm7JjvZ9pzz6I7e6DI7bLPHmRa6MR82wkhbKNEn25t2rThww8/5PLly+zatYuxY8cSGRnJCy+8QL169RgwYACrVq0iIyPDWvEKK7i07STa9EyT+7Cpqoo2PTPfT/TO01aOUlhDgYmC6yv4BiTR6J1HGLbuFfoP/oL+g7+gbkM/hvrfx7d/bjW6n2/+DGdo/fvxdHKjTr3zDF4zk+GbZhl+RI5+gf4FloWEhOCTkUDG1YtGt8m4ehHvzESCg4OL3ZcQwvrM+tqiKAo9e/akZ8+efPXVV2zatInly5ezdetWwsPD8ff3Jzo62sKhCmu5diSSVX1m5vRpMuGOSkpMAqv6zMy3TG7F2F7uBLuqNg01z/xxhSlsouDXeybw8uwX+HnLdsM8dIqScyvOz83b6L783bzRo4Ki4t8gEpfqVXFyrVL6F1XBGBsFpygKW9at4eFhI4iPOoNj62CcvLzRJiWQffYg3pmJbPlpdYGBNjKiTgjbKPX13ipVqjBkyBCcnZ1JS0tj9+7d3L592xKxWV1YWBhhYWFkm9MxRJRY3srIutQkRkSsZ9pzzxISEiKjLy2g36BvAMj47ktMueZb1ETBnTx+ZUWoL4Pb5axTk/7ieDbc690cgMupcay9cojh9bvSoGotOtVsxrGEC1St+g9Qo8CxGvYOKs1Lq/CqVavGoZ1b2b5rD6NnzEWXlkT3Ns2YNn0SwcHB8v9DCDtSqsTp4MGDrFixgh9//JFbt26h0Wjo06cPY8eOtVB41hUaGkpoaCjJycl4eXnZOhybqdulBSHvjmFRl4Mmtffw92bkjrn5luUW/SuMscrIF5MSmfDBN/jMnsOWdWukMnIZW3YEljxlfN34rjBuGQxul/NcVVWiU+N4rEEwh+MjOZJwntEBPVgVtY8uPi3o4t2cHy8fpLqn8du9MqKueIqiEBwSgt/QmwCsmjpY+jIJYYdK/L/yjz/+YPny5fzwww9cuXIFVVVp3749b7zxBiNHjsTX19cacQor0jhocHKtYvK3WkVRjN6GKayScZGVkYOHknj1Ig8PG8GhnVtL9c26slVS1kauRHthFY6NBqK7doTYqJyRcP4jZ+DSenQxWxc9UfB3x72YOHMe3AwFwLleILV0qSyL2k20NgFPvTOrovbhXrc62+N+5XzK3/i4VeNWqlwZEUJUbCZ1Dr9+/Tqffvop7du3p3Xr1sybNw+NRsPMmTM5d+4cJ06c4OWXX5akqZyyxG2Uhr2D2Hz2CrezdAV+tu/aQ5yLNy6FVkZuQnyVGuzYvdfo9unaO/110rUF1+f+5E4WXFk4tRiF26CNuA3aiKb2vRw9NISjh4bg2GKMYVLeon4eHT2Oo4mNuJGc/ypRbJLK8ZsBDB/5pGFZ69at+TPzBmPnTObs+T+Y0moIkwMHs33/Hi7GxfDUnJe5mHEDP7daZX0ahBCiTJl0xcnf3x9VValZsyaTJk3iiSee4L777rN2bKKMWOI2SkCf9hyct7ZAVWQwvzKyMb3mby50XWWupJwzqe6ZEm1T2ETBc/fV4p1PFuW7+letRnU27QwnKCjIUNQyrzHjxtKyRUs+HDmVv2Oa08jsVyKEEPbNpMRJr9ejKAru7u7s2LGDHTt2mHwARVE4f/682QGKsuNdz9dQ3DJf5XB/73x/RL3rlezKolRGtr6cSXVLljiBkYmCY8ChThc6dOiAqk0ztJv13psoTkVP59KuXTsG+Xfm78p14U8IUcmY3MdJVVUuX75c4gPIaJDyI3caldzZ3u9W3Fx1xqoig/mVkXOla3WGK027XxyAq5Pxt61UUjZP3omC5+2vw9Kfv7F1SEIIYbdMSpyioqKsHYewIyWZ7T2v3KrId5v23LNM+OAbnIOHFrpt9tmDTJs+qdhRRK5OFbtqsjZyJVmRy8mODgfAoWE/FI0jTs1G4tRilFWOmXei4JCHR0tfRSGEKIJJf4EaNGhg7ThEBVBYJeOQkBB8Zs8h8epFox3EC6uMbKnjlydOLUbh2HgQKWEeALj1X1PsLTJLyJ0oePK0GVY/lhBClGcyoZSwmMJKAeRWRq4ZuYfbB34i61Ycql5P1q040g+so2bkHqOVkS11fFG8nImCFVxcXGwdihBC2LWKe89D2BWpjCyEEKIikMRJlBmpjCyEEKK8k1t1QgghhBAmqjCJU1hYGA0bNsTFxYXOnTtz7NgxW4ckhBBCiAqmQtwnWb16NVOmTOHrr7+mc+fOfP755zz00EOcP3+eWrVkCgghhBCiOOG/X2Hz2SscvBQL5NTmc9Ao9Av0l8E3eVSIxOnTTz/lmWeeYdy4cQB8/fXX/PLLL3z33XdMnz7dxtEJYb/yThQMkB6eMzmwU7ORODYeZMvQhBBlrF+r+nRvWtcwddZHQ7pIP1QjzDojvXr1om/fvrz66qtFtvv444/ZsmULu3fvNis4U2RlZXHy5ElmzLhTf0aj0dC7d28OHz5steMKURE4tRhVaGHNvFOuCCGEyGFW4rR3714aNmxYbLvz58+zb98+cw5hsoSEBLKzs6ldu3a+5bVr1yYyMtLoNpmZmWRmZhqeJycnAzlz8un1eusFawN6vR5VVe3mdal54lBNPN/mbGMPzD33eV+vXq9HMekcqfkeW+IcGYsjeudpondGUO/+QAAOv78GgIa92+HXtVWJYjDndZrK2Lm35vEspazf66qq5ntc2uPZ2+dNZWKpc19eP29LqySv06rX4DIyMnB0tL/LfPPmzWP27NkFlsfHx5ORkWGDiKxHr9eTlJSEqqpoNLYfC5ChzTY8jo+Px8XJwSrb2AOzz73uNm7/PoyPjwfH4q/8ZGdkGR7Hx8fh4OJcwmjzc7i8HofLG6BuHwCSNg4HwKPBYAKnDDG6TXx8XMli0N1Go1V5ZR3MHnAVl6rVSxVzXnef+8JeT3aDwWQ3MP56bKGs3+tZ/9x5byXEx+OsvV2q/dnb501lYqlzX14/b0srJSXF5LZWy2qSk5P53//+R506dax1CAC8vb1xcHDgxo0b+ZbfuHGj0Dm3ZsyYwZQpU/LF6u/vj4+PD56enlaNt6zp9XoURcHHx8cuPsjSs3SGxz4+PriacP/cnG3sgbnnXtWmkfvnzMfHx6QpV3TpdxInH59aOLqWLnGi1kToNLFEm5Q0BlWbxjv7IEsHK75fxIxZH5kVqjEFzr0Zr8cWyvq9nuGUanjs7eODS/WqpdqfvX3eVCaWOvfl9fO2tEoya4LJZ6RRo0b5nq9du5a9e/cabavT6bhx4wY6nY7nn3/e5GDM4ezsTIcOHdi1axeDBw8Gct5Au3btKvTYVapUoUqVKgWWazSaCvmfXVEUu3ltSp4YFBNjMmcbe2HOuVfztNVoNPlef6HH0Sj5HtviHJU0hms34jgaBasnwMiVq4h77hWLTjBsT+97U5X1ez1vxf7c82WJfZa3815RWOLcl+fP29Ioyes0OXGKjo42PFYUhdTUVFJTU422dXJyom7dugwaNIh58+aZHIy5pkyZwlNPPUXHjh259957+fzzz0lLSzOMshNC2J83XnuRmX1zPk+mB1/n9akTWbxio63Dspnw368Qfi6G7k1yrtJP33gUQIaCizKjqir79+0jZt0CdKlJjIhYz7TnniUkJESmxcrD5MQpb8cpjUbD2LFj+e6776wSVEmNGDGC+Ph43nrrLWJjY2nXrh1bt24t0GFciIpkeP/BJPydU29FVVVSYhIAWNTlYL4POe96vqz9ZYMtQkQbudLoqL0TJ06gv3GMdp1z4gzyhyWnD3Py5Ek6dOhg8n4qkn6t6kuCJGzm1q1b9B82gjgXb2r3HI6TV00uJiUy4YNv8Jk9hy3r1lCtWjVbh2kXzLp5uWTJEpo0aWLpWErl+eeft/ptQSHsScLfsYyv2vXOgkDj7Rb/fahsAjIiK3J5gXpQqqry5pSnmd87AbiT4M3sHs/LU55m845DBb7dai+sqvCJk6icwn+/YvOEWVVV+g8bQWLLXrj73fnb7lzdB+fgoSRevcjDw0ZwaOdWm195sofzZVbi9NRTT1k6DiFsJmrHKQL6tLd1GPlk/Dva7OvxGbia0DncXmVHh5MS5pFv2YYIlc6eUNsz/wewr5dCJ49fWf6sB4Pb5V+XW6BTiIpm89krdG9at9T7UfV6MrTZpGfpTOoXmdeB/fuJc/HOlzTl5eLXhPioM+zYvZduwcGljrU0ws/FlM/EKdf27dtZsGABx44dIyEhgSeeeILFixcDsG3bNrZt28a0adOoW7f0bwohrOXStpP4dWtVfMMSUvUq2RlZ6NKz8nWcLnY7bSZh/442+2Teu7z25gfG2+WpwVPk/lQVbXpm8Q1LKe+ouryP77bsCCwp5LvX+K4wbhkMbmfh4ISwUwcvxRoqddtKzLoF1O45vMg2jq2DGTV9Dn5DJ5VRVMbl9gG0JbMTp5deeokvv/wSVVWpWrUqWq023wd5nTp1+Pzzz/H392fy5MkWCVYIa7h2JJJVfWbaOgyDZN0/HE3JGW32yBeL8NqZQTXngledUmISCr09d3e7sn59awfNAqBz14acODIg37q2jpF8u3cfr/bTFtjum73OtHXoweb1zal3XwtC3s3JsHKnghFCWJ4uNQknr5pFtnHyqokuLamMIrJvZiVO33//PV988QUdO3bk22+/pV27dgWG8rVt2xZ/f39+/vlnSZyEKIFN1/fw+vCc0WazB6cx96edPOH/iK3DMouqasjOdsq3LMirNQvORvBU1/h8t+tik1R2n/XiPw1bkZ2toFermFTDSojyrFsjXz4a0qXU+1H1euLj43Nqv5XwVt2IiPVcTErEubpPoW20SYl0b9OMVVMHlzLS0skdbWpLZiVOCxYsoFq1avzyyy/4+BR+otu2bctvv/1mdnCiYtP/uQpaPWHrMKjbpQUh746x+H5VvUp8fBw+PrVMvlV38tRJ6rzxPe38/x1tVl/Bt1Eyzd4bTvv2+fthLepy0KR9evh7M3LH3JIFbwZdepbhStPwTbNwdHVGd/EeGs0eUaBts1PDmfPGI8x/JNGwbM5eHz5fs9HwOg/OWm5Y59RsZIF9VIaRdqLic9AoFplIV6/X4+LkgKuzY4lrL0177lkmfPANzsFDC22TffYg06ZPkkl/MTNxOnv2LN27dy8yaQLw8vIqUNFbiFz68z+gNit+ugtVq8OFjH8fp6Eqlv2P6+ioxdFRV3zDEtLr9Tg46nBw1Jr0QaaqKrNnTCow2uz1HvG8PHOS0dFmprLG67ub6qjFwSHn9puDoxZHRw2OLYYBBY/d+d57+LZWeyJittPOX+F0DDj63su997Y1tNcomYaJhh0bDyow6bCMtBMVQb9Af1uHQEhICD6z55B49SIuRjqIZ1y9iHdmIsE27hgO9nG+zP4LZMoH+LVr13B1dTX3EKKiu7K1wIiru6mqysGL4HOiGjdSFUZseIHnOv1D18amvQdNEeQLKWHWKWXhBhQ/01yOko42U/+pBh7dit2v+s+fxZ5nSxnwbx6c8d2XFDfr4/RAlRfXwOoJKnPD4cuRv+SLs7jfi4y0ExWBrUeIQc5n6ZZ1a3h42Ajio87g2DoYJy9vtEkJZJ89iHdmIlt+Wm3zUgRgH+fLrMSpadOmnDp1Cq1Wi5OTk9E2KSkpRERE0KqV5UcrlQfhv19h89krHLyUU6CweS0v4lMzuHk7Z3RTt0a+OGiUSlUV+O7KyPxddPtbt1UeW1GNRN8uOHUfhJNXTf5KSuTFk5uouecIPz7xD9Vcbf8f2VIq22gzXy+FzgEqk36A+xoVTBiFEGWnWrVqHNq5le279jB6xlx0aUl0b9OMadMnERwcbBdJk70wK3F69NFHef3115k+fTqffPKJ0TYzZswgKSmJkSML9k2oDPq1qk/3pnUNw0yXPtkTwPD8oyFdKt294rsrI6dt6Idb/zVG26qqyn3dH0bX4+GCBdl6j+fW1e6M3LGTg1s3lPg/9O0sHb3m/wzA7hcHWu33oM/TWdOUW3UTfVazZN1kJgcXnKX7u+NeTHzrUzwevdNfqNb2kSz6+99+TqpKyt85lcM96nlDnnNSq1VXPEJXlfLVFE+bnsnagbMAGP7zLJxcC84HmZeqTSNUW5tX1sGr31/G1aPoUT13k5F2QliWoigEh4TgN/QmAKumDq50f6dMYdYZefnll1m1ahWff/45//vf/3jkkZwRP3/99RefffYZ69ev5+DBg7Rv355nnnnGogGLikPROBY6amr/vn384+6LdyEF2Vz9mpAQ9SsHj5wiJCSkZMdVdWSQMxO24uSO4mSdDwZFrwfHtJxjmJA4PTp6HP0XfsGN5IgCo82O3wzg7VHj8iWJa7f8bHisTc80lBwYuWNusUmLNSg6R8MIupzzWnwMLk4KX4wAV4+aMoJOCFEumDXtsaurKzt37qRv374cPXqU119/HYADBw4wdepUDh48SJ8+fQgPD8fZ2dmiAYuKw9hIqVyfLViIZ/ueRW7v0Lobny1YaOmwbEZRFN75eCFz9nrnWz53Xy3e+WSRXCq/S1HvHyGEsBazv2r7+Pjwyy+/cObMGbZv3050dDR6vR4/Pz/69OnDvffea8k4rSIsLIywsDCys7NtHUqlVNSIqNgbN3BqU3xBttgTFWvUZseOHdHUvpeImC2G0WYOdboYnfi2spMRdUIIWyj1PYp77rmHe+65xxKxlLnQ0FBCQ0NJTk7Gy8vL1uGIPHxr1+a8CQXZfGvXLsOoysZ7H8zn6X5bWD1BZd7+Oiz9+RtbhySEEOJfZt2qE8LaJv/nGZJP7ymyTfbZg0z+T8XrQ1enji+dA2DSDxDy8Gh8fX1tHZIQQoh/leqKU1RUFAcOHOD69etkZhqfRFRRFN58883SHEZUQiEhIXilvkVGIQXZ0u2oIJs1hHaHV9bB5GkzbB2KqOD0qsrK6H0M1ettHYoQ5YJZiVNWVhYTJkxgxYoVQNGztFfWxElVVfbv20fMugXoUpN47PQ6Qjp35MpPm8lOS2ZExHqmPfcsISEh0unXCEVR+F/4RosXZLv792Kvv4fc0WYuLi62DkVUcGf/uczJxIvs2r2bQY8VX8lfiMrOrMTprbfeYvny5VSrVo0nnniCZs2a4eFRNpWJy4Nbt27Rf9gI4ly8qd1zuKFw47GNW9El36T+Yy9xMSuDCR98g8/sOWxZt4Zq1arZOmy7Y+mCbMZ+LxeTEuX3ICq1A3G/82KLQSz86mtJnIQwgVmJ0w8//EC1atU4ffo0DRo0sHRM5ZqqqvQfNoLElr0KFG6s0+9Jbl+9yNV1X9HgiVdxDh5K4tWLdH14MGcP7bGrKx72oriCbOG/XzGp8npRv5fc38PDw0ZwaOdWk34Pph5XCHszvP9gEv7OmdEgIyODVG06DavWYs1vB7m/VXucnXJKyHjX82XtLxtsGKkQ9smszuFxcXEEBwdL0mTE/v37iXfxNtovB8DNrwkuvg24HR0JgItfE/5x8+HAgQNlGWaFsfnsFW5n6Yr92b5rD3FF/F5c/JoQX6UGO3bvNWl/4ediyviVCmEZCX/HMr5qV8ZX7Yp/elUertcRgL6+7fFLczesy02uhBD5mXXFSRKmwn22YCGOrYvusFyjYy/i9qzDPaAlAJ7te/LZgoUlroAt4OClWMM0NkWJWbeA2j2HF9nGsXUwo6bPwW/opGL3Z5hvTwg7E7XjFAF92udbNn3qq/yyfhM1PasRc/kKnynxOSsUhQF+OTX3WldrwK7YM3x2bgMASWo6Pdp1ITH5H/oPGcT7n3xY5DGEqCzMSpyefvpp5s6da5iHS9xhauFGXVpSvucVrZCjvdGlJuHkVbLfixDl0cUtx/Ht0DTfstBJz3Hif0dpmVad8a26Gt1OoyhMbvlIvmXHki7yR+0qPP+f50i/eWcOxeidpyVxqoDunoh9+sajAJVqMnpTmJU4vfLKK5w+fZqePXvyxRdf0KNHD+mf8y9TCzc6unvle14RCzmWhW6NfPloSJdi242IWM9FE34v3ds0Y9XUwcXuL/cDRQh7E3v8AmsHzSqw/AlNe9YkHuD3uCjGNO6Fg1J4T41sVc/3f+3CxcGZJzyD2TP2i3zr/boGWjpsYQfunohdGGdWH6cmTZpw7Ngx/vjjD3r37o2rqysNGzakUaNGBX4aN25s6Zjt2uT/PIPubNH9lW6e2E31oO6G58mn9lTIQo5lwUGj4ObsWOzPtOeeLfb3kn32INNCJ5q0PyHKGwdFw6iA7lxJi2N+5M+kaNONtkvRpjP/j01cSYtnVED3IhMsISojs/4CREdH53uelZXFlStXLBFPuRcSEoLP7DkkFlK48fbVi2TEXqZ27xEAZFy9SLXb8RW2kKOlxeyK4Oqu01w7ktO5/v7WDdlzIpKGvYOKvHVQ3O8lo4QFNfsF+pv3AoSwMt9Ozej25uhC1y/sfoAHHZrz7Z9bmRpYsPzAN3+G82j9bmzPPs/wTbOM7uPIB2ssFK0Q5Y9ZiZNeKswWSlEUtqxbY7RwY+LhraTfuEL9x15C+0/8nUKOWzbIrU4T+T/QjkY92rCqz0wAhn32LE6uVYrdrqjfizkFNeVytrBXjs6OuNYovK6exkFDtqrHz83b6Hp/N2/0qGgcNEXuR4jKSu45WEFhhRvHD+nF3B9+5uq6r0pVyFGYx9IFNYWwRw17BxXb5njiBe71bg7A5dQ41l45xPD6XWlQtRadajbjWMIF8C78Fp0pxxCiopLEyUqMFW4E+L9MP8Nz6StT9oorqClEeVfcaDdVVYlOjeOxBsEcjo/kSMJ5Rgf0YFXUPrr4tKCLd3N+vHyQmjULn1xaRtSJyqzUfzFSUlL466+/SElJKXTOOqlPJIQQ9sGxqgu13KqzLGo3l3U3qedXhy3Z5/Dw92b79d84n/I3Pm7V0Hi42jpUIeyS2YnT2bNnefnll9m7d2+Rk/wCZGdnm3sYqwoLCyMsLMxu4xNCCEtr3boN6yLXMvfjD3hy3FMF1n+/ZCmvT5vOsNbyhVcIY8xKnP7880+6detGcnIyXbt25fr160RFRTFy5EguXbrEqVOn0Ol0DBo0yK4nTQ0NDSU0NJTk5GS8vLyK30AIK9NGrkR7YRWOjQYCkB6eMzrKqdlInFqMsmVoooKoXrM6m3aGExRkvJ/SmHFjadPuHjatXV/GkQlRPpiVOL333nukpKSwZMkSnnrqKcaNG0dUVBQrVqwA4OLFi4wfP55z585x5MgRiwYsREXm1GKUJEjCqmbNfbfYNkFBQYUmVkJUdmZVNtu9ezctW7bkqacKXuaFnAKZGzduJD4+njfffLNUAQohhBBC2AuzEqe4uDgCA++U3HdycgIgIyPDsKxatWr06NGDzZs3lzJEIYQQQgj7YNatuho1apCZmZnvOcDly5dp3rx5vrZxcXGlCE8IYc+G9x9Mwt+xQM4w95SYBAAWdTmYry6Wdz1f1v6ywRYhCiGERZmVOAUEBHD58mXD83bt2qGqKqtXr+att94CICEhgb1791K/vlRYFqKiSvg7lvFVu95ZUMjcr4v/PlQ2AQkhhJWZdavuwQcf5OzZs4bkaeDAgXh7e/POO+8wcuRIpk6dSqdOnUhKSuKxxx6zaMBCiIIu7z5j6xDKvagdp2wdghCiHDDritOTTz5JZmYmN27coEGDBri7u7Nq1Soee+wx1qy5M/ljnz59eP311y0WrBDCuOhdpw2PdelZZXbc4mq45W2nTc/Mv0x757k2PRNFZ7kK7qpeJTsjC116ForGtKl0oneelorYQohimfVJ1bhxY+bNm5dvWa9evbh8+TIHDhzg1q1bNGvWjA4dOlgkyPIo/PcrhJ+LoXuTOgBM33iUbL1pf2SE9Rj7vQD0C/Qv1xP3Xj92wfB47aBZZXbclJiEQm/P3d0ud2LmXA4OWgYMyXm8duAssrOdrBCh6fy6mvBChBCVnkUn6XJ3d6dv376W3GW51a9V/QJ/iG9n6bjvkw22CUgAxn8vQgghhKlkdtMyUlGvdAj7UOfeZoarTsM3zcLR1blMjruoy0GT2nn4ezNyx9x8y1RtGhnffQnA8J9noTi5m7SvjFupbHgsZ1+D18zEpXrVAm1UvUp8fBw+PrVMvlV3cNZyk9oJISo3kxKn77//vlQHGTNmTKm2rwjkSoewJkVzZ5yHo6szTq5Vyua4imlJiaIoBWJSHXXkVn5zcq2C4mRazHn7cDm6GH+ter0eBxdnHF2d0WjMGgMjhBBGmZQ4jR071uQPyLxUVUVRFEmchLCyBj3v4dqRSFuHUa417C1TjAghimdS4vTWW2+ZlTgJUV5E7TjFpW0nDclH3S4t0DhoaNg7qFyMtGrQ6x4Oz1tt6zDKtfLwexZC2J5JidOsWbOsHIYQtlFY5WuPFO+cLwvbctpJ5WvjvOv5Gopb5jt//t4FKocLIURFIJ3DRaUmla9LJ28yqU3PNJQcGLljbpn1sxJCiLJUosRpy5YtbNiwgZiYGKpUqULbtm0ZN24cAQEB1orPqsLCwggLCyM7O9vWoaCNXElW5HKyo8MBcGjYD0XjiFOzkTi1GGXj6Mq/qB2nrHorxtr7F0IIYR9MTpwef/xxVq1aBdypFvzzzz/z8ccfs2rVKgYNGmSdCK0oNDSU0NBQkpOT8fLysmksTi1G4dh4EClhHgC49V9j8vBsUbxL207i161VgeWlqXydl1SdFkKIysGkxGnx4sWsXLkSR0dHnnzySYKCgkhJSWHz5s0cPnyYMWPGcPnyZZsnH0IU5tqRyAKVq6F0la/zkqrTQghROZiUOC1btgyNRkN4eDgPPPCAYfmMGTMYN24c33//PevWrWPcuHFWC1QIIYQQwtZMSpx+++03unTpki9pyjVz5kyWLVvGb7/9ZvHghLCUul1aEPJuwXpipal8nZdUnRZCiMrBpMQpOTmZxo0bG12Xuzw5OdlyUQlhYRoHjdFRXqWpfC1sR6+qrIzex1C93tahCCEqGZMSJ1VVcXBwMLoudzoDvXyACTtm7arQtqw6HbXjFNE7Txv6WeVe/SovxTvNcfafy5xMvMiu3bsZ9NgQW4cjhKhEpI6TncnQqryyDr4en4GrjKqzGGsnELZMUAL6tK+wCVJhDsT9zostBrHwq68lcRJClCmTE6dly5axbNkyo+sURSl0vaIo6HQ68yOsZML2QZYOPvt4HjNnf2zrcCo8qXxdPuSt8J6RkUGqNp2GVWux5reD3N+qPc5OzoBUeBdCWJ/JiZOp9W4stV1ldP16LEejYPUEGLlyJbH/mYavb+X+gx3++xXCz8Wg/+lLsm/douOid0EFl39SgZzO3XcnOCX5w5nbNvd2190q8u2u8iRvhfdNt44SUC+nb2Vf3/ZcTotjoN+9gFR4F0JYn0mJk/RfKhtvvPYiM/vmXKWbHnyd16dOZPGKjbYOy6b6tapPv1b16TEng/F1et9ZUdd4e3P/cFbG2132bvrUV/ll/SZqelYj5vIVPlPic1YoCgP+TZRaV2vArtgzfHZuAwBJajo92nUhMfkfej70AJ+HfWGj6IUQFZX0cbID2siVnEltiv7GMdp1zrl6EuQPS04f5uhPc+g87HUbR2hbUTtOmdxWe7vw6t6ifJk241VOHTtOiyQvxrfqarSNRlGY3PKRfMuOJf/F+bquTHohtCzCFEJUMhpbByAg84//480pT/N6j4R8y2d2j+ft9z6s9Lc7L207afI50KZlWDkaUVa8vb3ZsmcH3FeXpZd2ka0WfeU7W9Wz9NIulPvqsnn3NmrUqFFGkQohKhNJnOzAuo3hdPb8jdqe+WsK+XopdPa7zdrVP9goMvtw7UikodO2qFwcHR2Z/81XxDndZn7kz6Ro0422S9GmM/+PTTntvvkKR0e5mC6EsA5JnOzAsiPwtPE7EYy/X8fCLz8o24CEsDM1q9dgqP99fPvnVqPrv/kznKH176dmdbnKJISwLkmc7MC4gfew5ISn0XWL/+fIMy9ML+OI7EvdLi3w8Pe2dRjCxrJVPX5uxt8H/m7e6Knct7SFEGVDEic7MKRbfY4mNuJGcv4P/tgklaNX3Rj+2CgbRWYfNA4ak6dGERXX8cQL3OvdDIDLqXF8cm49l1PjAOhUsxnHEi7YMjwhRCUhiZMdcG4+inc+Xsicvfm/Tc/dV4vZb7xa6ZOGkkxn4uTuYsVIhK2oqkp0ahwN3WtxOD6SdTGHGR3Qg3VX/sfh+EgCqtYmOvVGpR9IIYSwvkrdgzIsLIywsDCys7NtGodTi1F0BDS17yUiZgvt/BVOx4BDnS6VvhQBlGw6Eyc3mYi3InKs6kItt+osi9pNtDYRT70Tq6L24V63BtvjfuN8yt/4uFVD4+Fq61CFEBVcpU6cQkNDCQ0NJTk5GS8vL1uHw3sfzOfpfltYPUFl3v46LP35G1uHZDdkapTKrXXrNqyLXMvcjz9g+CNDWTtoFgDDN83CtYYH3y9ZyuvTpjOsdYhN4xRCVHyVOnGyN3Xq+NI5ACb9ACEPj670063klXcaFW16Jqv6zARg5I65OLnKVaaKrnrN6mzaGU5QUBDpN1MKrB8zbixt2t3DprXrbRCdEKIykcTJzoR2h1fWweRpM2wdihB2Y9bcd4ttExQURFCQ6f3hhBDCHJI42RkXJ4UvRoCLi3RyFkIIIeyNJE52Qhu5Eu2FVTg2GghAevhoAJyajcSpReUuRyCEEELYC0mc7IRTi1GSIAkhhBB2Tuo4CSGEEEKYSBInIYQQQggTya06IUS5EbXjFNE7T+PXNRCAIx+sAXKqy5ekUKoQQphLEichRLkR0Ke9JEhCCJuSW3VCCCGEECaSxEkIIYQQwkTlPnGaM2cO999/P25ublSrVs3W4QghhBCiAiv3iVNWVhaPPvoo//nPf2wdihBCCCEquHLfOXz27NkALF261LaBCCGEEKLCK/eJkzkyMzPJzMw0PE9OTgZAr9ej1+ttFZZV6PV6VFWtUK9L1av5Htvra6uI574oJf29qHnW6/V6FAuep8p27u2FnHfbkXNfOiU5b5UycZo3b57hSlVe8fHxZGRk2CAi69Hr9SQlJaGqKhpNub8zC0B2RpbhcXx8HA4uzjaMpnAV8dwXpcS/F91t3Azt48ExzWKxVLZzby/kvNuOnPvSSUlJMbmtXSZO06dP54MPPiiyzR9//EGLFi3M2v+MGTOYMmWK4XlycjL+/v74+Pjg6elp1j7tlV6vR1EUfHx8Ksx/Jl36nT/QPj61cHS138Spop37opT096Jq00gztPdBcXK3WCyV7dzbCznvtiPnvnRcXFxMbmuXidPUqVMZO3ZskW0aNWpk9v6rVKlClSpVCizXaDQV8g2nKEqFem2KRsn32J5fV0U790Up6e9FzbNeo9GgWPgcVaZzb0/kvNuOnHvzleSc2WXi5OPjg4+Pj63DEEIIIYTIxy4Tp5K4cuUKN2/e5MqVK2RnZxMREQFAkyZNqFq1qm2DE0IIIUSFUu4Tp7feeotly5YZngcFBQGwZ88eevToYaOohBBF0UauRHthFY6NBgKQHj4aAKdmI3FqMcqWoQkhRJHKfeK0dOlSqeEkRDnj1GKUJEhCiHJJepAJIYQQQphIEichhBBCCBNJ4iSEEEIIYSJJnIQQQgghTCSJkxBCCCGEiSRxEkIIIYQwkSROQgghhBAmksRJCCGEEMJEkjgJIYQQQphIEichhBBCCBNV6sQpLCyMwMBAOnXqZOtQhBBCCFEOVOrEKTQ0lHPnznH8+HFbhyKEEEKIcqBSJ05CCCGEECUhiZMQQgghhIkcbR2AECURteMU0TtP49c1EICDs5YD0LB3EAF92tsyNCGEEJWAJE6iXAno014SJCGEEDYjt+qEEEIIIUwkiZMQQgghhIkkcRJCCCGEMJEkTkIIIYQQJpLESQghhBDCRJI4CSGEEEKYSBInIYQQQggTSeIkhBBCCGEiSZyEEEIIIUwkiZMQQgghhIkkcRJCCCGEMJEkTkIIIYQQJqrUiVNYWBiBgYF06tTJ1qEIIYQQohyo1IlTaGgo586d4/jx47YORQghhBDlQKVOnIQQQgghSkISJyGEEEIIE0niJIQQQghhIkmchBBCCCFMJImTEEIIIYSJJHESQgghhDCRJE5CCCGEECaSxEkIIYQQwkSSOAkhhBBCmMjR1gEIIcq/qB2niN55Gr+ugQAcnLUcgIa9gwjo096WoQkhhEVJ4iSEKLWAPu0lQRJCVApyq04IIYQQwkSSOAkhhBBCmEgSJyGEEEIIE0niJIQQQghhokqdOIWFhREYGEinTp1sHYoQQgghyoFKnTiFhoZy7tw5jh8/butQhBBCCFEOVOrESQghhBCiJCRxEkIIIYQwkSROQgghhBAmksRJCCGEEMJEkjgJIYQQQphIEichhBBCCBNJ4iSEEEIIYSJJnIQQQgghTCSJkxBCCCGEiSRxEkIIIYQwkaOtA7AHqqoCkJycbONILE+v15OSkoKLiwsajeTJZUnOve3IubcNOe+2I+e+dHL//ufmA0WRxAlISUkBwN/f38aRCCGEEMJWUlJS8PLyKrKNopqSXlVwer2ea9eu4eHhgaIoZu+nU6dOFpsw2FL7Sk5Oxt/fn5iYGDw9Pe0mrsqwLzn3ttuXvZ57ezxXltyXvZ73yrAvOfel25eqqqSkpFC3bt1ir9jJFSdAo9Hg5+dX6v04ODhY5A1r6X0BeHp6WmR/9voa7XVfIOfeVvsC+zv39nquKvp5ryz7Ajn3pdlXcVeacsmNUAsKDQ21y31Zkr2+RnvdlyXZ62u0131ZkqXistdzVdHPe2XZlyXZ62u0h33JrboKLjk5GS8vL5KSkiz6rUYUT8697ci5tw0577Yj577syBWnCq5KlSq8/fbbVKlSxdahVDpy7m1Hzr1tyHm3HTn3ZUeuOAkhhBBCmEiuOAkhhBBCmEgSJyGEEEIIE0niJIQQQghhIkmcKoCwsDAaNmyIi4sLnTt35tixY4W2Xbp0KYqi5PtxcXEpw2grjv379zNw4EDq1q2Loihs2LCh2G327t1L+/btqVKlCk2aNGHp0qVWj7OiKel537t3b4H3vKIoxMbGlk3AFcS8efPo1KkTHh4e1KpVi8GDB3P+/Plit/vxxx9p0aIFLi4utGnThi1btpRBtBWLOedePuutRxKncm716tVMmTKFt99+m1OnTnHPPffw0EMPERcXV+g2np6eXL9+3fBz+fLlMoy44khLS+Oee+4hLCzMpPZRUVH079+fnj17EhERwcsvv8yECRPYtm2blSOtWEp63nOdP38+3/u+Vq1aVoqwYtq3bx+hoaEcOXKEHTt2oNVqefDBB0lLSyt0m//973+MGjWK8ePHc/r0aQYPHszgwYM5e/ZsGUZe/plz7kE+661GFeXavffeq4aGhhqeZ2dnq3Xr1lXnzZtntP2SJUtULy+vMoqu8gDU9f/f3p0HRXWlbQB/mt1GkEUEBAKiQoREUYwElIiIy4gKE8OqhMRxiUtFQzRaNcloMs6kYhSsmigap1zQmOi4i7sCbiE6CpQbo4Ki4wIRQVFARXi+P/Jxx7YbaIyKyPur6io595xz33vP5fbL6dvHTZvqrfPZZ5/R29tboywqKoqDBg16jpG92vQ57+np6QTA0tLSFxJTS/Hrr78SAA8cOFBnncjISIaGhmqU+fn5cfz48c87vFeaPude7vXPj8w4NWMPHz7EiRMnEBISopQZGBggJCQEmZmZdba7d+8eXF1d4eLigrCwMJw5c+ZFhNviZWZmaowVAAwaNKjesRLPjo+PDxwdHTFgwAAcOXKkqcNp9u7cuQMAsLGxqbOOXPPPhz7nHpB7/fMiiVMzVlxcjOrqatjb22uU29vb1/n8hqenJ5YtW4YtW7Zg9erVqKmpQUBAAK5evfoiQm7RCgsLdY5VWVkZKisrmyiqV5+joyMWL16MDRs2YMOGDXBxcUFQUBCysrKaOrRmq6amBlOnTkXv3r3xxhtv1Fmvrmteni97evqee7nXPz/yn/y2MP7+/vD391d+DggIQJcuXbBkyRL89a9/bcLIhHg+PD094enpqfwcEBCA/Px8JCUlYdWqVU0YWfM1adIknD59GocPH27qUFocfc+93OufH5lxasbatm0LQ0NDFBUVaZQXFRXBwcFBrz6MjY3RvXt35OXlPY8QxWMcHBx0jpWlpSVatWrVRFG1TL169ZJr/ilNnjwZqampSE9Ph7Ozc71167rm9b0/CU2NOfdPknv9syOJUzNmYmICX19f7N+/XymrqanB/v37Nf7SqE91dTVOnToFR0fH5xWm+H/+/v4aYwUAe/fu1XusxLOTk5Mj13wjkcTkyZOxadMmpKWloUOHDg22kWv+2Xiac/8kudc/Q039dLr4fX766SeamppyxYoVPHv2LMeNG0crKysWFhaSJOPi4jhz5kyl/pdffsndu3czPz+fJ06cYHR0NM3MzHjmzJmmOoRm6+7du8zOzmZ2djYBMDExkdnZ2bx8+TJJcubMmYyLi1PqX7x4kWq1mtOnT2dubi4XLlxIQ0ND7tq1q6kOoVlq7HlPSkri5s2beeHCBZ46dYpTpkyhgYEB9+3b11SH0CxNmDCBbdq0YUZGBm/cuKG8KioqlDpP3m+OHDlCIyMjzps3j7m5uZw1axaNjY156tSppjiEZutpzr3c658fSZxeAf/4xz/42muv0cTEhL169eIvv/yibOvbty/j4+OVn6dOnarUtbe355AhQ5iVldUEUTd/tV9zf/JVe77j4+PZt29frTY+Pj40MTGhu7s7ly9f/sLjbu4ae96/+eYbduzYkWZmZrSxsWFQUBDT0tKaJvhmTNc5B6BxDT95vyHJdevW0cPDgyYmJvT29ub27dtfbOCvgKc593Kvf35UJPni5reEEEIIIZovecZJCCGEEEJPkjgJIYQQQuhJEichhBBCCD1J4iSEEEIIoSdJnIQQQggh9CSJkxBCCCGEniRxEkIIIYTQkyROQgghhBB6ksRJvJLS09MxYsQIODk5wcTEBNbW1vD09ERERAS+++473Llz57nHEBQUBJVKhYKCAo1yNzc3qFSq577/hqhUKuWVmZlZZ71169Yp9dzc3F5cgM9IRkaGxrGqVCoUFxdr1cvKykJcXBxcXV1hamoKS0tLdOrUCcOGDcO8efNw48aN3x3LyzL24uksWLBA4zpqjr8P4vczauoAhHjWvvrqK8yaNQsA0KVLF/j5+cHY2Bjnzp3Dxo0bsX79evTs2RNvv/220sbNzQ2XL19GS11I/4cffqjzP15dvXr1C47m+ejYsSP69OkDADAzM9PYtnz5cowdOxbV1dVwc3PDoEGDYG5ujosXL2L37t1ITU2Fs7MzoqOjmyJ08ZLw8vJCfHw8AGDlypVNHI1oKpI4iVfKiRMnMHv2bBgbG2PdunUIDw/X2F5YWIjVq1fDysqqSeJ72RgaGsLLywtr167FggULYGSkeUu4desWdu3ahR49eiArK6uJonw2+vTpgxUrVmiVX7t2DRMnTkR1dTUWLVqE8ePHw8Dgf5PxpaWlWLduHZycnF5gtOJlNHDgQAwcOBCAJE4tmXxUJ14pGzduBElERkZqJU0A4ODggGnTpuH1119/8cG9pEaOHIni4mLs3r1ba9vatWtRVVWFUaNGNUFkL8aOHTtw//599O7dGxMmTNBImgDA2toa48ePR2BgYBNFKIR4mUjiJF4pN2/eBADY2dnpVb/2+ZfLly8DQL3PLzx69AjJycnw9/eHpaUlWrVqBR8fHyxYsACPHj363bFv374do0ePRpcuXWBpaQlzc3N069YNf//73/HgwQOt+itWrIBKpcLs2bNx/vx5REdHw97eHgYGBti8ebPe+42NjYVKpdL5kdzq1avRunVrhIWF6WxLEj/++COio6Ph4eEBc3NzWFhYoFevXli0aBFqamp0tvnhhx/Qp08f2Nvbw8zMDC4uLggJCcHChQs16j58+BCLFi3CW2+9BVtbW6jVari5uWHo0KH46aef9D7G+jT2mnncrVu3MH36dHTu3BlmZmawsbHB4MGDsWfPnkb18yLHvqCgACqVCkFBQSgvL0dCQgJcXFzQqlUr9OjRA9u2bVPq/utf/4Kfnx/Mzc1hb2+Pjz/+GJWVlVp95uTk4LPPPoOvry/s7OxgamoKd3d3TJw4EdevX9cZx+nTpzFq1Ci4u7vDzMwMdnZ28PHxwdSpU7WeJ/v5558RHh6uPH/m4OCAXr16YebMmbh3755W30ePHkVERAQcHR1hYmICZ2dnjBkzBleuXNGq25jrUQgAAIV4hXz11VcEQBcXFxYVFTVYPzc3l/Hx8TQ3NycAxsfHK69PP/1UqVdRUcF+/foRAG1sbDhgwAAOGzaM7dq1IwAOHz6c1dXVGn337duXAHjp0iWNcldXV+r61bO3t6elpSUDAgIYGRnJQYMG0dramgAYHBzMR48eadRfvnw5ATA6OpqWlpbs0KEDo6KiOHDgQKampjZ47ABoaGioxKpWq3n37l1le35+PgEwLi6ON27cIAC6urpq9FFZWUkAtLW1ZWBgIKOiohgSEkK1Wq2czydNmzaNAGhqasoBAwYwJiaG/fr1o52dnVb/7733HgHQwsKCQ4YMYXR0NAMDA9mmTRv27du3wWMkyfT09DpjIcmUlBRlH//5z3/06pMkr169Snd3dwLga6+9xqioKAYHB9PQ0JAAmJiYqNXmZRj7S5cuEQD9/f3p5+fHdu3a8b333mNQUBANDAxoaGjIvXv3MjExkUZGRuzfvz//+Mc/0tbWlgAYGxur1WdUVBSNjIzYo0cPhoeHMzw8nG5ubgRAR0dHXrt2TaP+8ePHaWZmRgDs2rUrIyMjOXToUHp5eREA09PTlbpbt26lgYEBVSoV/fz8GB0dzcGDB7Njx446f78WLlxIAwMDGhgY0M/PjxEREezatSsB0M7OjmfPntWo35jr8XG6fh9EyyCJk3il5Ofns1WrVsobYXx8PJcuXcqsrCytN5/H1fWGVmvixIkEwKioKN6+fVspLysr45AhQwiAycnJGm0amzht3ryZFRUVGmVlZWUcOnQoAXDlypUa22rfPAFw8uTJ9R6fLo8nTkuXLtXaR20Sunv37joTp6qqKm7atIkPHz7UKP/111/Zs2dPAuCBAweU8srKSpqamtLCwoIXL17U6uvgwYPKzxcvXlT2WVxcrFG3srKSP//8s17H2VDidPv2bSUBNjU1ZUREBBcuXMjMzEw+ePCgzn5rxyU2Nlaj3qFDh6hWq2loaMjs7GyNNi/D2NcmTrVJ2b1797T67dSpE62trfnvf/9b2Xbt2jXlPOXn52v0mZaWxsLCQo2y6upqfvnllwTADz/8UGPb+++/TwCcN2+eVny5ubm8fv268vM777xDAFy/fr1W3WPHjrGsrEz5OTMzk4aGhnRycuLx48c16v7zn/8kAPr5+SlljbkenySJU8sliZN45ezbt48uLi7Km0Pty8rKihMmTNC4KdeqL3EqKiqisbExXVxctN7cSPLGjRs0MTFh165dNcobmzjV5cKFCwTAd999V6O89k3Ozs6O5eXlevdX6/HEqbS0lKamphw4cKCy3dPTk46Ojnz06FGdiVN99u7dSwBMSEhQyoqKigiAPj4+DbY/evQoATA8PFz/g9KhocSJJLOzs5XZjsdfarWasbGxPH/+vEb92tm41q1b89atW1r9JSQkEADHjBmjUf4yjH1t4mRgYMBz585pbKuurmbbtm0JgJ9//rlW208++YQAuHz5cr335+TkRFtbW42yP/zhDwTAnJycBtt36dKFADT+YKlLWFgYAXDbtm06tw8fPpwAmJWVRbJx1+OTJHFqueRbdeKV079/f+Tl5WH79u3Ys2cPjh07hpMnT+L27dtITk7Ghg0bcPDgQXh6eurVX0ZGBqqqqjB48GC0atVKa7uDgwM6d+6MU6dOobKyUmcdfV24cAE7duxAXl4eysvLUVNToyyRcOHCBZ1tQkJCoFarn3qfAGBlZYXQ0FBs2bIFhYWF+O9//4tz587hk08+gaGhYYPtc3JysGfPHly+fBkVFRUgibt372rF3a5dOzg7OyMnJwczZ87EuHHj4O7urrPP119/Hebm5ti+fTu+/fZbjBw5Eu3bt/9dx1kXHx8fnDp1Cvv27cPOnTvxyy+/ICcnBxUVFVizZg22bNmCnTt3Kg+IHz58GAAwePBg2NjYaPUXFxeHxMREHDp0SO8YXvTYu7m5wcPDQ6PMwMAArq6uKC4uVr499rjasdK1ptWtW7ewdetWnD59Grdv30Z1dTUAoKqqCrdu3UJJSYlyrnx9fbFz505MmjQJc+bMQZ8+fbS+0VnL19cXubm5iIuLwxdffAFfX1+tB/gBoKamBvv374darcagQYN09hUYGIitW7fi2LFj6N69e6OuRyEUTZy4CfFClJaWMjk5WXluJCQkRGN7fTMB33zzjdZMRF2vq1evKu0aM+NUU1PDhIQEqlSqOvt2c3PTaFM76zBjxoynOid4bMaJJDdu3EgATEpK4scff0wAPHHiBEnWOeP04MEDxsTE1HtOgoKCNNrs37+fdnZ2ynZXV1e+//773LFjh1aMa9asYevWrZW6Hh4eHD9+PA8fPqz3ceoz46RLeXk516xZQ2dnZwJg586dlW1ff/01AXDatGk625aWliozUo97Gca+dsYpMDBQ5/ba6/bJj+Me3++sWbM0yp8cJ12vgoICpf6dO3cYFBSkbGvdujUHDBjABQsWaM0sXblyhd26dVPqWltbc9iwYVy6dCkrKyuVerWzR/q85syZo7RrzPX4OF2/D6JlkBkn0SJYWVnho48+Qvv27REWFob09HRUVFTo9dd67TfDfHx80K1bt3rrmpqaPlV8a9euRWJiIlxcXJCUlAR/f3/Y2dnB2NgYDx8+hKmpaZ2Lcz65mOPTGjJkCKysrJCSkoLr16+jS5cu6NGjR71tEhMT8eOPP+LNN9/E3Llz0aNHD1hbW8PY2Bjnz5+Hp6enVtzBwcHIy8tDamoqdu3ahYyMDKSkpCAlJQUjRozA+vXrlboxMTEICQnBli1bsGfPHhw4cABLlizBkiVLkJCQgPnz5z+TY9dFrVYjJiYG3t7e6NatGy5cuIDz589rzdLo0pjVwZtq7HXN2jRme63Lly/jgw8+APDbytqhoaFwcnJSZl4DAgKQmZmpcQyWlpZIS0vDkSNHsG3bNmRkZCAtLQ179+7F119/jUOHDqFz584AABcXFxw/fhxpaWlITU3FgQMHsG3bNmzbtg1z585FZmYmbG1tld/T1q1bY8SIEfXG7O3trfy7MdejEABkxkm0LHfv3lX+snz8mz71zTitWrVKeQi3MRoz4xQZGUkAOr8RlZubq/Ov27r++tcXnphxIsmxY8cq5+dvf/ubUl7XjFOvXr0IgKdPn9bqf+fOnQSg17ffMjMzlZmd7du311mvpqaGO3fupKWlZZ37fdLTzjg9rvYbZUeOHCFJrly5kgAYERGhs35OTg4B0NPTU6P8ZRj72hmnusalruu2rv0uWrSo3tk3e3v7Ovt7XFFRkTJ7Wdd5rVVQUMDg4GAC4PTp00n+9jC3mZkZLS0tWVNTU2/7huhzPeoaF9EyyDpO4pXCBv7LlLy8PACAiYkJ2rZtq5SbmJgAgM71mPr16wdDQ0OkpqaiqqrqGUb7P6WlpQAAZ2dnrW3r1q17LvvUJS4uDra2tmjbti1GjhzZYP1nFffbb7+NuLg4AL+t71MXlUqFwYMHIzQ0FABw5swZvfdRl4aumZKSEpSUlACAsnp47X/dsmvXLty+fVurTe2aWPosmvmyjP3Tqi/+gwcPoqioSK9+2rVrh9mzZwOo/xoAAFdXV8yYMUOjrpGREYKCglBWVob9+/frG75O+l6PomWSxEm8Ur744gtMnz4d+fn5WtuuXbuG8ePHAwCGDx+uJEsAlIeOz507p9XOyckJo0ePRkFBAWJiYnS+EeTl5WHDhg1PHXftxz/ff/+9xhv5oUOH8O233z51v40VGBiI4uJi3Lx5E66urg3Wr4178eLFGuXr169HSkqKVv0rV65gxYoVqKio0Ci/f/8+0tPTAfz20QwAZGdnY+PGjXj48KFG3ZKSEhw9elSj7u+RnJyMcePG4eTJk1rbSkpK8MEHH4AkevbsqZwTd3d3hIaG4u7du5gyZYpGQp2ZmYnk5GQYGhpi0qRJDe7/ZRn7p1Ub/+rVq1FeXq6UX7t2DR999JHONosXL8alS5e0ynfs2AFAc1yTkpJQWFioV90///nPMDAwwIcffoiMjAytNvfu3cOyZcuURTwbcz0KoWjS+S4hnrEpU6ZoPEgcHh7O6Oho9unTh8bGxgR+W6Pm8Ye4SXL+/PkEQHt7e0ZHR/NPf/qTxoO3FRUVHDBgAAHQ3NycvXv3ZkxMDIcPH85OnToRAMPCwjT6bMxHdefOnVMW4fTy8lIWelSpVMoCfS/io7q61PVR3YEDB5QFH319fRkTE6Os31Qb9+MfCWVnZxP47Wv+77zzDmNjYxkWFqY8nNuzZ0/ev3+fJLlp0yYCYJs2bdi/f3+OHDmSoaGhtLCwIAAOGzZMr9gb+qguKSlJ48HgYcOGKYsg1o6JnZ0dT548qdHu6tWr7NChg9IuOjqa/fv3V87H/Pnztfb1Moz9s/6o7sGDB/T29iYAOjg4cMSIEQwNDaVarWZAQAADAgK0+qt92NvLy4sjRoxgVFSUUmZmZqbx8H+bNm1oYGDA7t27MzIykhEREfTw8CDw22K0Ty4VkZycrIzBG2+8wXfffZdRUVH08/OjqakpAbC0tJRk467HJ+kaF9EySOIkXik3b97kqlWrOGrUKL755pu0tbWlkZERbWxs2Lt3b86dO1djwb9aVVVV/Pzzz9mxY0clwXrypvjo0SOuXLmSwcHBtLGxobGxMdu3b09/f39++eWXWmviNHYdp9zcXGU1crVaze7du/P7778nqfsm/TIkTuRvz4MEBwfT2tqaFhYWDAgI4IYNG3S+QZeVlXH+/PkcMmQI3dzcaGZmRltbW/bs2ZNJSUkaaxLduHGDc+bMYXBwMJ2dnWliYkJ7e3v27t2by5Yt01p0sy4NJU537tzh+vXrOXbsWHbv3p3t2rWjkZER27Rpw7feeot/+ctfePPmTZ1ti4uL+emnn7Jjx440MTGhlZUVBw4cyN27d+us/zKM/bNOnEiypKSEEyZMoJubG01NTenu7s4ZM2awvLxcZ39bt27l6NGj6e3tTSsrK6rVanp4eHDMmDFaq7enpKQwNjaWnp6etLCwoIWFBb28vJiQkKD1B1Ct7OxsxsfH09XVVRkXb29vjh49mqmpqcozUI25Hp8kiVPLpSIb+IBfCCGasYyMDPTr1w/x8fFYsWJFU4cjXhEqlQqurq4oKCho6lDECybLEQghWoTDhw8rX5v/7rvv0Lp166YNSDQ7e/bswZo1a5o6DNHEJHESQrQI+fn5ypcG5s2bJ4mTaLSzZ89i5cqVTR2GaGLyUZ0QQgghhJ5kOQIhhBBCCD1J4iSEEEIIoSdJnIQQQggh9CSJkxBCCCGEniRxEkIIIYTQkyROQgghhBB6ksRJCCGEEEJPkjgJIYQQQuhJEichhBBCCD1J4iSEEEIIoaf/A/A57wz5FWhFAAAAAElFTkSuQmCC", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Planet Metallicity vs. Stellar Mass\n", "fig, ax = plt.subplots(figsize=(6, 5))\n", "\n", "for d, label, marker, color in zip([direct, emission, transit], \n", " ['Direct', 'Eclipse', 'Transmission','UHJ'],\n", " ['o', 's', 'd', '*'],\n", " ['#2E86AB', '#A23B72', '#F18F01', 'grey']):\n", " # If stellar metallicity is -99, that means it doesn't have a main sequence host!\n", " d['Stellar Metallicity'].iloc[np.where(d['Stellar Metallicity'] == -99)[0]] = np.nan\n", " d['Stellar Metallicity Upper'].iloc[np.where(d['Stellar Metallicity Upper'] == -99)[0]] = np.nan\n", " d['Stellar Metallicity Lower'].iloc[np.where(d['Stellar Metallicity Lower'] == -99)[0]] = np.nan\n", " # If stellar metallicity is exactly 0.0, that means we don't have a measurement and assume solar... let's ignore those\n", " d['Stellar Metallicity'].iloc[np.where(d['Stellar Metallicity'] == 0.0)[0]] = np.nan\n", " d['Stellar Metallicity Upper'].iloc[np.where(d['Stellar Metallicity Upper'] == 0.0)[0]] = np.nan\n", " d['Stellar Metallicity Lower'].iloc[np.where(d['Stellar Metallicity Lower'] == 0.0)[0]] = np.nan\n", " #Same for mass\n", " d['Stellar Mass'].iloc[np.where(d['Stellar Mass'] == -99)[0]] = np.nan\n", " d['Stellar Mass Upper'].iloc[np.where(d['Stellar Mass Upper'] == -99)[0]] = np.nan\n", " d['Stellar Mass Lower'].iloc[np.where(d['Stellar Mass Lower'] == -99)[0]] = np.nan\n", " # If stellar metallicity is exactly 0.0, that means we don't have a measurement and assume solar... let's ignore those\n", " d['Stellar Mass'].iloc[np.where(d['Stellar Mass'] == 0.0)[0]] = np.nan\n", " d['Stellar Mass Upper'].iloc[np.where(d['Stellar Mass Upper'] == 0.0)[0]] = np.nan\n", " d['Stellar Mass Lower'].iloc[np.where(d['Stellar Mass Lower'] == 0.0)[0]] = np.nan\n", "\n", " ax.errorbar(d['Stellar Mass'], d['Metallicity'],\n", " xerr=[d['Stellar Mass Lower'], d['Stellar Mass Upper']],\n", " yerr=[d['Metallicity Lower'], d['Metallicity Upper']],\n", " fmt=marker, label=label, color=color,\n", " markersize=7, capsize=2, alpha=1.0,\n", " markeredgecolor='k',markeredgewidth=0.5)\n", "\n", "for _, row in uhjs.iterrows():\n", " color_map = {'Direct': '#2E86AB', 'Eclipse': '#A23B72', 'Transit': '#F18F01'}\n", " if row['Stellar Metallicity'] == -99:\n", " continue\n", " if row['Stellar Metallicity'] == 0.0:\n", " continue\n", " ax.errorbar(row['Stellar Mass'], row['Metallicity'],\n", " xerr=[[row['Stellar Mass Lower']], [row['Stellar Mass Upper']]],\n", " yerr=[[row['Metallicity Lower']], [row['Metallicity Upper']]],\n", " fmt='*', color=color_map[row['Geometry']], markersize=12,\n", " markeredgecolor='k',markeredgewidth=0.5)\n", "\n", "#ax.set_xscale('log')\n", "\n", "ax.set_xlabel('Stellar Mass [Solar masses]', fontsize=15)\n", "ax.set_ylabel('Planet Metallicity [M/H]', fontsize=15)\n", "ax.legend(frameon=True, fancybox=True, shadow=False,loc='upper right')\n", "ax.grid(True, alpha=0.3)\n", "ax.minorticks_on()\n", "plt.tight_layout()\n", "\n", "plt.savefig('./paper_figs/stellarmass_metallicity.pdf')" ] }, { "cell_type": "code", "execution_count": 30, "id": "f77b837b-4ecc-4e4f-b8a3-cb6898fe7ff3", "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_20318/2445045271.py:9: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " d['Stellar Metallicity'].iloc[np.where(d['Stellar Metallicity'] == -99)[0]] = np.nan\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_20318/2445045271.py:10: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " d['Stellar Metallicity Upper'].iloc[np.where(d['Stellar Metallicity Upper'] == -99)[0]] = np.nan\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_20318/2445045271.py:11: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " d['Stellar Metallicity Lower'].iloc[np.where(d['Stellar Metallicity Lower'] == -99)[0]] = np.nan\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_20318/2445045271.py:13: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " d['Stellar Metallicity'].iloc[np.where(d['Stellar Metallicity'] == 0.0)[0]] = np.nan\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_20318/2445045271.py:14: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " d['Stellar Metallicity Upper'].iloc[np.where(d['Stellar Metallicity Upper'] == 0.0)[0]] = np.nan\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_20318/2445045271.py:15: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " d['Stellar Metallicity Lower'].iloc[np.where(d['Stellar Metallicity Lower'] == 0.0)[0]] = np.nan\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_20318/2445045271.py:17: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " d['Stellar Mass'].iloc[np.where(d['Stellar Mass'] == -99)[0]] = np.nan\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_20318/2445045271.py:18: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " d['Stellar Mass Upper'].iloc[np.where(d['Stellar Mass Upper'] == -99)[0]] = np.nan\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_20318/2445045271.py:19: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " d['Stellar Mass Lower'].iloc[np.where(d['Stellar Mass Lower'] == -99)[0]] = np.nan\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_20318/2445045271.py:21: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " d['Stellar Mass'].iloc[np.where(d['Stellar Mass'] == 0.0)[0]] = np.nan\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_20318/2445045271.py:22: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " d['Stellar Mass Upper'].iloc[np.where(d['Stellar Mass Upper'] == 0.0)[0]] = np.nan\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_20318/2445045271.py:23: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " d['Stellar Mass Lower'].iloc[np.where(d['Stellar Mass Lower'] == 0.0)[0]] = np.nan\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_20318/2445045271.py:9: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " d['Stellar Metallicity'].iloc[np.where(d['Stellar Metallicity'] == -99)[0]] = np.nan\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_20318/2445045271.py:10: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " d['Stellar Metallicity Upper'].iloc[np.where(d['Stellar Metallicity Upper'] == -99)[0]] = np.nan\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_20318/2445045271.py:11: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " d['Stellar Metallicity Lower'].iloc[np.where(d['Stellar Metallicity Lower'] == -99)[0]] = np.nan\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_20318/2445045271.py:13: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " d['Stellar Metallicity'].iloc[np.where(d['Stellar Metallicity'] == 0.0)[0]] = np.nan\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_20318/2445045271.py:14: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " d['Stellar Metallicity Upper'].iloc[np.where(d['Stellar Metallicity Upper'] == 0.0)[0]] = np.nan\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_20318/2445045271.py:15: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " d['Stellar Metallicity Lower'].iloc[np.where(d['Stellar Metallicity Lower'] == 0.0)[0]] = np.nan\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_20318/2445045271.py:17: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " d['Stellar Mass'].iloc[np.where(d['Stellar Mass'] == -99)[0]] = np.nan\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_20318/2445045271.py:18: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " d['Stellar Mass Upper'].iloc[np.where(d['Stellar Mass Upper'] == -99)[0]] = np.nan\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_20318/2445045271.py:19: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " d['Stellar Mass Lower'].iloc[np.where(d['Stellar Mass Lower'] == -99)[0]] = np.nan\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_20318/2445045271.py:21: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " d['Stellar Mass'].iloc[np.where(d['Stellar Mass'] == 0.0)[0]] = np.nan\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_20318/2445045271.py:22: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " d['Stellar Mass Upper'].iloc[np.where(d['Stellar Mass Upper'] == 0.0)[0]] = np.nan\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_20318/2445045271.py:23: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " d['Stellar Mass Lower'].iloc[np.where(d['Stellar Mass Lower'] == 0.0)[0]] = np.nan\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_20318/2445045271.py:9: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " d['Stellar Metallicity'].iloc[np.where(d['Stellar Metallicity'] == -99)[0]] = np.nan\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_20318/2445045271.py:10: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " d['Stellar Metallicity Upper'].iloc[np.where(d['Stellar Metallicity Upper'] == -99)[0]] = np.nan\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_20318/2445045271.py:11: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " d['Stellar Metallicity Lower'].iloc[np.where(d['Stellar Metallicity Lower'] == -99)[0]] = np.nan\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_20318/2445045271.py:13: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " d['Stellar Metallicity'].iloc[np.where(d['Stellar Metallicity'] == 0.0)[0]] = np.nan\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_20318/2445045271.py:14: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " d['Stellar Metallicity Upper'].iloc[np.where(d['Stellar Metallicity Upper'] == 0.0)[0]] = np.nan\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_20318/2445045271.py:15: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " d['Stellar Metallicity Lower'].iloc[np.where(d['Stellar Metallicity Lower'] == 0.0)[0]] = np.nan\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_20318/2445045271.py:17: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " d['Stellar Mass'].iloc[np.where(d['Stellar Mass'] == -99)[0]] = np.nan\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_20318/2445045271.py:18: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " d['Stellar Mass Upper'].iloc[np.where(d['Stellar Mass Upper'] == -99)[0]] = np.nan\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_20318/2445045271.py:19: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " d['Stellar Mass Lower'].iloc[np.where(d['Stellar Mass Lower'] == -99)[0]] = np.nan\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_20318/2445045271.py:21: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " d['Stellar Mass'].iloc[np.where(d['Stellar Mass'] == 0.0)[0]] = np.nan\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_20318/2445045271.py:22: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " d['Stellar Mass Upper'].iloc[np.where(d['Stellar Mass Upper'] == 0.0)[0]] = np.nan\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_20318/2445045271.py:23: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", "A typical example is when you are setting values in a column of a DataFrame, like:\n", "\n", "df[\"col\"][row_indexer] = value\n", "\n", "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", " d['Stellar Mass Lower'].iloc[np.where(d['Stellar Mass Lower'] == 0.0)[0]] = np.nan\n" ] }, { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Planet C/O vs. Stellar Mass\n", "fig, ax = plt.subplots(figsize=(6, 5))\n", "\n", "for d, label, marker, color in zip([direct, emission, transit], \n", " ['Direct', 'Eclipse', 'Transmission','UHJ'],\n", " ['o', 's', 'd', '*'],\n", " ['#2E86AB', '#A23B72', '#F18F01', 'grey']):\n", " # If stellar metallicity is -99, that means it doesn't have a main sequence host!\n", " d['Stellar Metallicity'].iloc[np.where(d['Stellar Metallicity'] == -99)[0]] = np.nan\n", " d['Stellar Metallicity Upper'].iloc[np.where(d['Stellar Metallicity Upper'] == -99)[0]] = np.nan\n", " d['Stellar Metallicity Lower'].iloc[np.where(d['Stellar Metallicity Lower'] == -99)[0]] = np.nan\n", " # If stellar metallicity is exactly 0.0, that means we don't have a measurement and assume solar... let's ignore those\n", " d['Stellar Metallicity'].iloc[np.where(d['Stellar Metallicity'] == 0.0)[0]] = np.nan\n", " d['Stellar Metallicity Upper'].iloc[np.where(d['Stellar Metallicity Upper'] == 0.0)[0]] = np.nan\n", " d['Stellar Metallicity Lower'].iloc[np.where(d['Stellar Metallicity Lower'] == 0.0)[0]] = np.nan\n", " #Same for mass\n", " d['Stellar Mass'].iloc[np.where(d['Stellar Mass'] == -99)[0]] = np.nan\n", " d['Stellar Mass Upper'].iloc[np.where(d['Stellar Mass Upper'] == -99)[0]] = np.nan\n", " d['Stellar Mass Lower'].iloc[np.where(d['Stellar Mass Lower'] == -99)[0]] = np.nan\n", " # If stellar metallicity is exactly 0.0, that means we don't have a measurement and assume solar... let's ignore those\n", " d['Stellar Mass'].iloc[np.where(d['Stellar Mass'] == 0.0)[0]] = np.nan\n", " d['Stellar Mass Upper'].iloc[np.where(d['Stellar Mass Upper'] == 0.0)[0]] = np.nan\n", " d['Stellar Mass Lower'].iloc[np.where(d['Stellar Mass Lower'] == 0.0)[0]] = np.nan\n", "\n", " ax.errorbar(d['Stellar Mass'], d['C/O'],\n", " xerr=[d['Stellar Mass Lower'], d['Stellar Mass Upper']],\n", " yerr=[d['C/O Lower'], d['C/O Upper']],\n", " fmt=marker, label=label, color=color,\n", " markersize=7, capsize=2, alpha=1.0,\n", " markeredgecolor='k',markeredgewidth=0.5)\n", "\n", "for _, row in uhjs.iterrows():\n", " color_map = {'Direct': '#2E86AB', 'Eclipse': '#A23B72', 'Transit': '#F18F01'}\n", " if row['Stellar Metallicity'] == -99:\n", " continue\n", " if row['Stellar Metallicity'] == 0.0:\n", " continue\n", " ax.errorbar(row['Stellar Mass'], row['C/O'],\n", " xerr=[[row['Stellar Mass Lower']], [row['Stellar Mass Upper']]],\n", " yerr=[[row['C/O Lower']], [row['C/O Upper']]],\n", " fmt='*', color=color_map[row['Geometry']], markersize=12,\n", " markeredgecolor='k',markeredgewidth=0.5)\n", "\n", "#ax.set_xscale('log')\n", "\n", "ax.set_xlabel('Stellar Mass [Solar masses]', fontsize=15)\n", "ax.set_ylabel('Planet C/O', fontsize=15)\n", "#ax.legend(frameon=True, fancybox=True, shadow=False,loc='upper right')\n", "ax.grid(True, alpha=0.3)\n", "ax.minorticks_on()\n", "plt.tight_layout()\n", "\n", "plt.savefig('./paper_figs/stellarmass_co.pdf')" ] }, { "cell_type": "markdown", "id": "7d6012ce-3c1c-4afa-8f88-b343d52453bb", "metadata": {}, "source": [ "## Stellar Correlations\n", "I'm not sure how to deal with the stellar metallicity and mass uncertainties because many are missing. Let's keep unweighted for now.\n", "### Stellar metallicity versus Planet metallicity" ] }, { "cell_type": "code", "execution_count": 31, "id": "8d3f5d0a-3a4a-4164-9bc8-1fc982f363bc", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "PearsonRResult(statistic=0.28287791960527015, pvalue=0.3271040020337429)" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "PearsonRResult(statistic=-0.184878106324994, pvalue=0.493046811049206)" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "PearsonRResult(statistic=0.4898049934495137, pvalue=0.08931398516289683)" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "PearsonRResult(statistic=0.10364735322511971, pvalue=0.40756830245359443)" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "from scipy import stats\n", "display(stats.pearsonr(transit['Stellar Metallicity'].dropna(),transit.dropna(subset=['Stellar Metallicity'])['Metallicity']))\n", "display(stats.pearsonr(emission['Stellar Metallicity'].dropna(),emission.dropna(subset=['Stellar Metallicity'])['Metallicity']))\n", "display(stats.pearsonr(direct['Stellar Metallicity'].dropna(),direct.dropna(subset=['Stellar Metallicity'])['Metallicity']))\n", "display(stats.pearsonr(data['Stellar Metallicity'].dropna(),data.dropna(subset=['Stellar Metallicity'])['Metallicity']))" ] }, { "cell_type": "markdown", "id": "723643dc-d823-4949-b695-14cc9cda7420", "metadata": {}, "source": [ "### Stellar metallicity versus Planet C/O" ] }, { "cell_type": "code", "execution_count": 32, "id": "98020f47-e6ec-4eed-bcb0-2aef258bbbe9", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "PearsonRResult(statistic=0.2322659676227221, pvalue=0.4242596853164645)" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "PearsonRResult(statistic=-0.1006723115297697, pvalue=0.7106608752856736)" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "PearsonRResult(statistic=0.528463494790637, pvalue=0.06336574285876743)" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "PearsonRResult(statistic=0.273496265681139, pvalue=0.3440810570481956)" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "PearsonRResult(statistic=0.11742954808199495, pvalue=0.3477160461870806)" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "from scipy import stats\n", "display(stats.pearsonr(transit['Stellar Metallicity'].dropna(),transit.dropna(subset=['Stellar Metallicity'])['C/O']))\n", "display(stats.pearsonr(emission['Stellar Metallicity'].dropna(),emission.dropna(subset=['Stellar Metallicity'])['C/O']))\n", "display(stats.pearsonr(direct['Stellar Metallicity'].dropna(),direct.dropna(subset=['Stellar Metallicity'])['C/O']))\n", "display(stats.pearsonr(direct_nohr8799['Stellar Metallicity'].dropna(),direct_nohr8799.dropna(subset=['Stellar Metallicity'])['C/O']))\n", "display(stats.pearsonr(data['Stellar Metallicity'].dropna(),data.dropna(subset=['Stellar Metallicity'])['C/O']))" ] }, { "cell_type": "markdown", "id": "31d1c010-aaba-454f-8c34-d79b75c76155", "metadata": {}, "source": [ "### Stellar Mass versus Planet metallicity" ] }, { "cell_type": "code", "execution_count": 33, "id": "4eb9aa0f-99de-49d6-b3fa-247c88b741fa", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "PearsonRResult(statistic=0.181561924979298, pvalue=0.5172442724803548)" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "PearsonRResult(statistic=0.07138764199875626, pvalue=0.7854174883480484)" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "PearsonRResult(statistic=0.38353137311029917, pvalue=0.15818470613154015)" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "PearsonRResult(statistic=0.13298790386205964, pvalue=0.2871054484482557)" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "from scipy import stats\n", "display(stats.pearsonr(transit['Stellar Mass'].dropna(),transit.dropna(subset=['Stellar Mass'])['Metallicity']))\n", "display(stats.pearsonr(emission['Stellar Mass'].dropna(),emission.dropna(subset=['Stellar Mass'])['Metallicity']))\n", "display(stats.pearsonr(direct['Stellar Mass'].dropna(),direct.dropna(subset=['Stellar Mass'])['Metallicity']))\n", "display(stats.pearsonr(data['Stellar Mass'].dropna(),data.dropna(subset=['Stellar Mass'])['Metallicity']))" ] }, { "cell_type": "markdown", "id": "769a412b-09e9-4d57-9c61-c8a25244e03a", "metadata": {}, "source": [ "### Stellar Mass versus Planet C/O" ] }, { "cell_type": "code", "execution_count": 34, "id": "c36a3738-ee01-496f-b283-f731af2916ce", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "PearsonRResult(statistic=-0.4819463782197407, pvalue=0.06887902557809111)" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "PearsonRResult(statistic=-0.045797326861969315, pvalue=0.8614451694612288)" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "PearsonRResult(statistic=0.22201775901534723, pvalue=0.4264536055036452)" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "PearsonRResult(statistic=0.11049689881198713, pvalue=0.3771075653157047)" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "from scipy import stats\n", "display(stats.pearsonr(transit['Stellar Mass'].dropna(),transit.dropna(subset=['Stellar Mass'])['C/O']))\n", "display(stats.pearsonr(emission['Stellar Mass'].dropna(),emission.dropna(subset=['Stellar Mass'])['C/O']))\n", "display(stats.pearsonr(direct['Stellar Mass'].dropna(),direct.dropna(subset=['Stellar Mass'])['C/O']))\n", "display(stats.pearsonr(data['Stellar Mass'].dropna(),data.dropna(subset=['Stellar Mass'])['C/O']))" ] }, { "cell_type": "markdown", "id": "21e8da60-9072-4d97-a870-d2af243a25b4", "metadata": {}, "source": [ "## Mass-Metallicity\n", "There are two cells at the end with the functions used here. I don't want to put them here because they're long. I should put them in a separate script to import, but they're useful to work with directly here.\n", "\n", "Let's first calculate with the metallicity as given, then calculate for O/H and C/H individually. For each, we'll run dynesty to fit a line to the sample AND fit the scenario where there is no mass-metallicity realtionship, so we can compare evidences.\n", "\n", "But first, let's read in the Solar System data, b/c we'll compare to that. For the Solar System, we take the atmospheric metallicities from Kreidberg et al. 2014b. These are measurements of CH4, so metallicity here is equal to [C/H]. We'll use the solar C/O to convert to O/H BUT this may not be the case in reality as the Solar System planets may not have accreted C and O in equal proportion." ] }, { "cell_type": "code", "execution_count": 37, "id": "169afbb6-2f06-45d4-991a-e1ef1e37ddd6", "metadata": {}, "outputs": [], "source": [ "# Input intervals (times solar)\n", "metallicity_bounds = {\n", " \"Jupiter\": (3.3, 5.5),\n", " \"Saturn\": (9.5, 10.3),\n", " \"Neptune\": (71, 100),\n", " \"Uranus\": (67, 111)\n", "}\n", "\n", "# Build the DataFrame\n", "rows = []\n", "for planet, (low, high) in metallicity_bounds.items():\n", " median = (low + high) / 2\n", " log_median = np.log10(median)\n", " log_low = np.log10(low)\n", " log_high = np.log10(high)\n", " err_lower = log_median - log_low\n", " err_upper = log_high - log_median\n", "\n", " rows.append({\n", " \"Planet\": planet,\n", " \"Metallicity\": log_median,\n", " \"Metallicity Lower\": err_lower,\n", " \"Metallicity Upper\": err_upper,\n", " \"O/H\" : np.log10(median*10**8.69),\n", " \"O/H Lower\": np.log10(median*10**8.69) - np.log10(low*10**8.69),\n", " \"O/H Upper\": np.log10(high*10**8.69) - np.log10(median*10**8.69),\n", " \"C/H\" : np.log10(median*10**8.43),\n", " \"C/H Lower\": np.log10(median*10**8.43) - np.log10(low*10**8.43),\n", " \"C/H Upper\": np.log10(high*10**8.43) - np.log10(median*10**8.43),\n", " })\n", "\n", "solar_system = pd.DataFrame(rows)\n", "solar_system['Mass'] = [ 1.0, 568/1898, 86.8/1898, 102/ 1898]\n", "solar_system['Mass Lower'] = [ 0,0,0,0]\n", "solar_system['Mass Upper'] = [ 0,0,0,0]" ] }, { "cell_type": "markdown", "id": "480a5ff9-3bf0-423a-8c03-aef7b643a340", "metadata": {}, "source": [ "## Metallicity as given" ] }, { "cell_type": "code", "execution_count": 56, "id": "a1e35d78-c85b-45bf-81aa-6d37448e1ef0", "metadata": { "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Running nested sampling...\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "3978it [00:01, 2036.01it/s, +500 | bound: 5 | nc: 1 | ncall: 22058 | eff(%): 20.772 | loglstar: -inf < -203.746 < inf | logz: -209.342 +/- 0.099 | dlogz: 0.000 > 0.100]\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "Nested Sampling Results:\n", "Log Evidence: -209.34 ± 0.13\n", "Number of likelihood evaluations: 3978\n", "\n", "Parameter Estimates:\n", "Parameter Median 68% CI - / +\n", "---------------------------------------------\n", "slope (m) 0.710 [0.680, 0.739] [0.030, 0.029]\n", "Running nested sampling...\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "3796it [00:01, 1917.98it/s, +500 | bound: 5 | nc: 1 | ncall: 21179 | eff(%): 20.775 | loglstar: -inf < -200.582 < inf | logz: -205.814 +/- 0.095 | dlogz: 0.000 > 0.100]\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "Nested Sampling Results:\n", "Log Evidence: -205.81 ± 0.12\n", "Number of likelihood evaluations: 3796\n", "\n", "Parameter Estimates:\n", "Parameter Median 68% CI - / +\n", "---------------------------------------------\n", "slope (m) 0.926 [0.886, 0.964] [0.040, 0.038]\n", "Running nested sampling...\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "3931it [00:01, 1996.93it/s, +500 | bound: 5 | nc: 1 | ncall: 21718 | eff(%): 20.883 | loglstar: -inf < -215.753 < inf | logz: -221.256 +/- 0.097 | dlogz: 0.000 > 0.100]\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "Nested Sampling Results:\n", "Log Evidence: -221.26 ± 0.12\n", "Number of likelihood evaluations: 3931\n", "\n", "Parameter Estimates:\n", "Parameter Median 68% CI - / +\n", "---------------------------------------------\n", "slope (m) 0.117 [0.086, 0.150] [0.032, 0.032]\n", "Running nested sampling...\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "4240it [00:01, 2211.55it/s, +500 | bound: 6 | nc: 1 | ncall: 22459 | eff(%): 21.586 | loglstar: -inf < -99.398 < inf | logz: -105.517 +/- 0.104 | dlogz: 0.000 > 0.100]" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "Nested Sampling Results:\n", "Log Evidence: -105.52 ± 0.13\n", "Number of likelihood evaluations: 4240\n", "\n", "Parameter Estimates:\n", "Parameter Median 68% CI - / +\n", "---------------------------------------------\n", "slope (m) 1.041 [1.024, 1.058] [0.017, 0.017]\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "\n" ] } ], "source": [ "# Run the flat (no relationship) cases\n", "results_direct_flat = mass_mh_dynesty_flat(direct, plot_it=False, xerrs=False, oh=False)\n", "results_transit_flat = mass_mh_dynesty_flat(transit, plot_it=False, xerrs=False, oh=False)\n", "results_emission_flat = mass_mh_dynesty_flat(emission, plot_it=False, xerrs=False, oh=False)\n", "results_ss_flat = mass_mh_dynesty_flat(solar_system, plot_it=False, xerrs=False, oh=False)" ] }, { "cell_type": "code", "execution_count": 57, "id": "1e6d5206-9a52-4797-a2ee-02ae71d79773", "metadata": { "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Running nested sampling...\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "2422it [00:01, 908.05it/s, bound: 9 | nc: 25 | ncall: 24523 | eff(%): 9.876 | loglstar: -inf < -200.795 < inf | logz: -207.769 +/- 0.111 | dlogz: 90.719 > 0.100] /Users/jlothringer/miniconda3/envs/research/lib/python3.12/site-packages/dynesty/bounding.py:617: UserWarning: The enlargement factor for the ellipsoidal bounds determined from bootstrapping is very large. If you are using uniform sampling that may mean that the sampling will be inefficient. This may be caused by a very complex posterior shape. You may consider using more livepoints or different sampler (i.e. rslice or rwalk) or alternatively disable bootstrap (bootstrap=0)\n", " warnings.warn(\n", "7092it [00:04, 1504.82it/s, +500 | bound: 35 | nc: 1 | ncall: 43869 | eff(%): 17.506 | loglstar: -inf < -111.576 < inf | logz: -123.404 +/- 0.146 | dlogz: 0.000 > 0.100]\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "Nested Sampling Results:\n", "Log Evidence: -123.40 ± 0.18\n", "Number of likelihood evaluations: 7092\n", "\n", "Parameter Estimates:\n", "Parameter Median 68% CI - / +\n", "---------------------------------------------\n", "slope (m) -1.749 [-2.164, -1.439] [0.415, 0.311]\n", "intercept (b) -0.286 [-0.749, 0.070] [0.462, 0.356]\n", "cutoff (s) -0.513 [-0.638, -0.366] [0.125, 0.147]\n", "Running nested sampling...\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "2752it [00:03, 381.36it/s, bound: 21 | nc: 29 | ncall: 32770 | eff(%): 8.398 | loglstar: -inf < -216.003 < inf | logz: -223.108 +/- 0.112 | dlogz: 68.497 > 0.100] /Users/jlothringer/miniconda3/envs/research/lib/python3.12/site-packages/dynesty/bounding.py:617: UserWarning: The enlargement factor for the ellipsoidal bounds determined from bootstrapping is very large. If you are using uniform sampling that may mean that the sampling will be inefficient. This may be caused by a very complex posterior shape. You may consider using more livepoints or different sampler (i.e. rslice or rwalk) or alternatively disable bootstrap (bootstrap=0)\n", " warnings.warn(\n", "2805it [00:03, 223.85it/s, bound: 28 | nc: 558 | ncall: 38040 | eff(%): 7.374 | loglstar: -inf < -215.805 < inf | logz: -222.662 +/- 0.111 | dlogz: 67.943 > 0.100]/Users/jlothringer/miniconda3/envs/research/lib/python3.12/site-packages/dynesty/bounding.py:617: UserWarning: The enlargement factor for the ellipsoidal bounds determined from bootstrapping is very large. If you are using uniform sampling that may mean that the sampling will be inefficient. This may be caused by a very complex posterior shape. You may consider using more livepoints or different sampler (i.e. rslice or rwalk) or alternatively disable bootstrap (bootstrap=0)\n", " warnings.warn(\n", "/Users/jlothringer/miniconda3/envs/research/lib/python3.12/site-packages/dynesty/bounding.py:617: UserWarning: The enlargement factor for the ellipsoidal bounds determined from bootstrapping is very large. If you are using uniform sampling that may mean that the sampling will be inefficient. This may be caused by a very complex posterior shape. You may consider using more livepoints or different sampler (i.e. rslice or rwalk) or alternatively disable bootstrap (bootstrap=0)\n", " warnings.warn(\n", "/Users/jlothringer/miniconda3/envs/research/lib/python3.12/site-packages/dynesty/bounding.py:617: UserWarning: The enlargement factor for the ellipsoidal bounds determined from bootstrapping is very large. If you are using uniform sampling that may mean that the sampling will be inefficient. This may be caused by a very complex posterior shape. You may consider using more livepoints or different sampler (i.e. rslice or rwalk) or alternatively disable bootstrap (bootstrap=0)\n", " warnings.warn(\n", "/Users/jlothringer/miniconda3/envs/research/lib/python3.12/site-packages/dynesty/bounding.py:617: UserWarning: The enlargement factor for the ellipsoidal bounds determined from bootstrapping is very large. If you are using uniform sampling that may mean that the sampling will be inefficient. This may be caused by a very complex posterior shape. You may consider using more livepoints or different sampler (i.e. rslice or rwalk) or alternatively disable bootstrap (bootstrap=0)\n", " warnings.warn(\n", "/Users/jlothringer/miniconda3/envs/research/lib/python3.12/site-packages/dynesty/bounding.py:617: UserWarning: The enlargement factor for the ellipsoidal bounds determined from bootstrapping is very large. If you are using uniform sampling that may mean that the sampling will be inefficient. This may be caused by a very complex posterior shape. You may consider using more livepoints or different sampler (i.e. rslice or rwalk) or alternatively disable bootstrap (bootstrap=0)\n", " warnings.warn(\n", "/Users/jlothringer/miniconda3/envs/research/lib/python3.12/site-packages/dynesty/bounding.py:617: UserWarning: The enlargement factor for the ellipsoidal bounds determined from bootstrapping is very large. If you are using uniform sampling that may mean that the sampling will be inefficient. This may be caused by a very complex posterior shape. You may consider using more livepoints or different sampler (i.e. rslice or rwalk) or alternatively disable bootstrap (bootstrap=0)\n", " warnings.warn(\n", "/Users/jlothringer/miniconda3/envs/research/lib/python3.12/site-packages/dynesty/bounding.py:617: UserWarning: The enlargement factor for the ellipsoidal bounds determined from bootstrapping is very large. If you are using uniform sampling that may mean that the sampling will be inefficient. This may be caused by a very complex posterior shape. You may consider using more livepoints or different sampler (i.e. rslice or rwalk) or alternatively disable bootstrap (bootstrap=0)\n", " warnings.warn(\n", "/Users/jlothringer/miniconda3/envs/research/lib/python3.12/site-packages/dynesty/bounding.py:617: UserWarning: The enlargement factor for the ellipsoidal bounds determined from bootstrapping is very large. If you are using uniform sampling that may mean that the sampling will be inefficient. This may be caused by a very complex posterior shape. You may consider using more livepoints or different sampler (i.e. rslice or rwalk) or alternatively disable bootstrap (bootstrap=0)\n", " warnings.warn(\n", "/Users/jlothringer/miniconda3/envs/research/lib/python3.12/site-packages/dynesty/bounding.py:617: UserWarning: The enlargement factor for the ellipsoidal bounds determined from bootstrapping is very large. If you are using uniform sampling that may mean that the sampling will be inefficient. This may be caused by a very complex posterior shape. You may consider using more livepoints or different sampler (i.e. rslice or rwalk) or alternatively disable bootstrap (bootstrap=0)\n", " warnings.warn(\n", "2844it [00:07, 74.29it/s, bound: 47 | nc: 82 | ncall: 51953 | eff(%): 5.474 | loglstar: -inf < -215.756 < inf | logz: -222.423 +/- 0.110 | dlogz: 67.624 > 0.100] /Users/jlothringer/miniconda3/envs/research/lib/python3.12/site-packages/dynesty/bounding.py:617: UserWarning: The enlargement factor for the ellipsoidal bounds determined from bootstrapping is very large. If you are using uniform sampling that may mean that the sampling will be inefficient. This may be caused by a very complex posterior shape. You may consider using more livepoints or different sampler (i.e. rslice or rwalk) or alternatively disable bootstrap (bootstrap=0)\n", " warnings.warn(\n", "/Users/jlothringer/miniconda3/envs/research/lib/python3.12/site-packages/dynesty/bounding.py:617: UserWarning: The enlargement factor for the ellipsoidal bounds determined from bootstrapping is very large. If you are using uniform sampling that may mean that the sampling will be inefficient. This may be caused by a very complex posterior shape. You may consider using more livepoints or different sampler (i.e. rslice or rwalk) or alternatively disable bootstrap (bootstrap=0)\n", " warnings.warn(\n", "8593it [00:10, 784.50it/s, +500 | bound: 80 | nc: 1 | ncall: 77523 | eff(%): 11.806 | loglstar: -inf < -142.271 < inf | logz: -157.108 +/- 0.163 | dlogz: 0.000 > 0.100]\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "Nested Sampling Results:\n", "Log Evidence: -157.11 ± 0.20\n", "Number of likelihood evaluations: 8593\n", "\n", "Parameter Estimates:\n", "Parameter Median 68% CI - / +\n", "---------------------------------------------\n", "slope (m) -1.752 [-1.908, -1.594] [0.156, 0.158]\n", "intercept (b) 0.290 [0.250, 0.328] [0.040, 0.038]\n", "cutoff (s) 0.221 [0.208, 0.232] [0.013, 0.012]\n", "Running nested sampling...\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "2630it [00:04, 271.66it/s, bound: 33 | nc: 73 | ncall: 42380 | eff(%): 6.206 | loglstar: -inf < -203.760 < inf | logz: -210.176 +/- 0.108 | dlogz: 67.937 > 0.100] /Users/jlothringer/miniconda3/envs/research/lib/python3.12/site-packages/dynesty/bounding.py:617: UserWarning: The enlargement factor for the ellipsoidal bounds determined from bootstrapping is very large. If you are using uniform sampling that may mean that the sampling will be inefficient. This may be caused by a very complex posterior shape. You may consider using more livepoints or different sampler (i.e. rslice or rwalk) or alternatively disable bootstrap (bootstrap=0)\n", " warnings.warn(\n", "/Users/jlothringer/miniconda3/envs/research/lib/python3.12/site-packages/dynesty/bounding.py:617: UserWarning: The enlargement factor for the ellipsoidal bounds determined from bootstrapping is very large. If you are using uniform sampling that may mean that the sampling will be inefficient. This may be caused by a very complex posterior shape. You may consider using more livepoints or different sampler (i.e. rslice or rwalk) or alternatively disable bootstrap (bootstrap=0)\n", " warnings.warn(\n", "/Users/jlothringer/miniconda3/envs/research/lib/python3.12/site-packages/dynesty/bounding.py:617: UserWarning: The enlargement factor for the ellipsoidal bounds determined from bootstrapping is very large. If you are using uniform sampling that may mean that the sampling will be inefficient. This may be caused by a very complex posterior shape. You may consider using more livepoints or different sampler (i.e. rslice or rwalk) or alternatively disable bootstrap (bootstrap=0)\n", " warnings.warn(\n", "5956it [00:07, 747.54it/s, +500 | bound: 57 | nc: 1 | ncall: 60438 | eff(%): 10.771 | loglstar: -inf < -136.788 < inf | logz: -146.340 +/- 0.131 | dlogz: 0.000 > 0.100]\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "Nested Sampling Results:\n", "Log Evidence: -146.34 ± 0.16\n", "Number of likelihood evaluations: 5956\n", "\n", "Parameter Estimates:\n", "Parameter Median 68% CI - / +\n", "---------------------------------------------\n", "slope (m) -1.703 [-1.855, -1.557] [0.152, 0.146]\n", "intercept (b) 2.509 [2.350, 2.667] [0.159, 0.158]\n", "cutoff (s) 3.096 [1.980, 4.407] [1.116, 1.311]\n", "Running nested sampling...\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "2782it [00:02, 689.30it/s, bound: 20 | nc: 7 | ncall: 32119 | eff(%): 8.662 | loglstar: -inf < -99.614 < inf | logz: -107.659 +/- 0.120 | dlogz: 100.811 > 0.100] /Users/jlothringer/miniconda3/envs/research/lib/python3.12/site-packages/dynesty/bounding.py:617: UserWarning: The enlargement factor for the ellipsoidal bounds determined from bootstrapping is very large. If you are using uniform sampling that may mean that the sampling will be inefficient. This may be caused by a very complex posterior shape. You may consider using more livepoints or different sampler (i.e. rslice or rwalk) or alternatively disable bootstrap (bootstrap=0)\n", " warnings.warn(\n", "6289it [00:05, 1520.81it/s, bound: 52 | nc: 1 | ncall: 56227 | eff(%): 11.185 | loglstar: -inf < -1.645 < inf | logz: -12.112 +/- 0.137 | dlogz: 0.611 > 0.100] /Users/jlothringer/miniconda3/envs/research/lib/python3.12/site-packages/dynesty/bounding.py:617: UserWarning: The enlargement factor for the ellipsoidal bounds determined from bootstrapping is very large. If you are using uniform sampling that may mean that the sampling will be inefficient. This may be caused by a very complex posterior shape. You may consider using more livepoints or different sampler (i.e. rslice or rwalk) or alternatively disable bootstrap (bootstrap=0)\n", " warnings.warn(\n", "/Users/jlothringer/miniconda3/envs/research/lib/python3.12/site-packages/dynesty/bounding.py:617: UserWarning: The enlargement factor for the ellipsoidal bounds determined from bootstrapping is very large. If you are using uniform sampling that may mean that the sampling will be inefficient. This may be caused by a very complex posterior shape. You may consider using more livepoints or different sampler (i.e. rslice or rwalk) or alternatively disable bootstrap (bootstrap=0)\n", " warnings.warn(\n", "7250it [00:06, 1096.75it/s, +500 | bound: 60 | nc: 1 | ncall: 62740 | eff(%): 12.452 | loglstar: -inf < 0.280 < inf | logz: -11.880 +/- 0.144 | dlogz: 0.000 > 0.100]\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "Nested Sampling Results:\n", "Log Evidence: -11.88 ± 0.17\n", "Number of likelihood evaluations: 7250\n", "\n", "Parameter Estimates:\n", "Parameter Median 68% CI - / +\n", "---------------------------------------------\n", "slope (m) -1.127 [-1.210, -1.043] [0.083, 0.084]\n", "intercept (b) 0.412 [0.360, 0.462] [0.051, 0.051]\n", "cutoff (s) 1.729 [-0.169, 3.844] [1.899, 2.114]\n" ] }, { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "results_transit = mass_mh_dynesty(transit, plot_it=False, xerrs=False)\n", "results_emission = mass_mh_dynesty(emission, plot_it=False, xerrs=False)\n", "results_direct = mass_mh_dynesty(direct, plot_it=False, xerrs=False)\n", "results_ss = mass_mh_dynesty(solar_system, plot_it=True, xerrs=False, oh=False)" ] }, { "cell_type": "code", "execution_count": 58, "id": "d1907894-b0fa-42fb-ab74-3d7819f0d048", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "Slope & $-1.79\\pm0.35$ & $-1.75\\pm0.16$ & $-1.71\\pm0.15$ & $-1.13\\pm0.09$ \\\\\n", "Intercept (M/H) & $-0.33\\pm0.40$ & $0.29\\pm0.04$ & $2.51\\pm0.16$ & $0.41\\pm0.07$ \\\\\n", "Mass Cutoff (log10) & $-0.51\\pm0.13$ & $0.22\\pm0.02$ & $3.15\\pm1.05$ & $1.84\\pm1.70$ \\\\\n", "$\\ln E$ & $-123.40 \\pm 0.2$ & $-157.11 \\pm 0.2$ & $-146.34 \\pm 0.2$ & $-11.88 \\pm 0.2$ \\\\\n", "\\hline \\\n", "\\multicolumn{4}{c}{\\textbf{Flat-Line}} \\\\\n", "\\textbf{Parameter} & \textbf{Transiting} & \\textbf{Eclipse} & \\textbf{Direct} & \\textbf{Solar System} \\\\\n", "\\hline \\\n", "Intercept (M/H) & $0.93\\pm0.04$ & $0.12\\pm0.03$ & $0.71\\pm0.03$ & $1.04\\pm0.02$ \\\\\n", "$\\ln E$ & $-205.81 \\pm 0.2$ & $-221.26 \\pm 0.2$ & $-209.34 \\pm 0.2$ & $-105.52 \\pm 0.2$ \\\\\n", "\\hline \\\n", "\n" ] } ], "source": [ "from dynesty import utils as dyfunc\n", "\n", "# Transit\n", "samples_transit, weights_transit = results_transit.samples, results_transit.importance_weights()\n", "mean_transit, cov_transit = dyfunc.mean_and_cov(samples_transit, weights_transit)\n", "cov_transit = np.sqrt(np.diag(cov_transit))\n", "\n", "# Emission\n", "samples_emission, weights_emission = results_emission.samples, results_emission.importance_weights()\n", "mean_emission, cov_emission = dyfunc.mean_and_cov(samples_emission, weights_emission)\n", "cov_emission = np.sqrt(np.diag(cov_emission))\n", "\n", "# Direct\n", "samples_direct, weights_direct = results_direct.samples, results_direct.importance_weights()\n", "mean_direct, cov_direct = dyfunc.mean_and_cov(samples_direct, weights_direct)\n", "cov_direct = np.sqrt(np.diag(cov_direct))\n", "\n", "# SS\n", "samples_ss, weights_ss = results_ss.samples, results_ss.importance_weights()\n", "mean_ss, cov_ss = dyfunc.mean_and_cov(samples_ss, weights_ss)\n", "cov_ss = np.sqrt(np.diag(cov_ss))\n", "\n", "# Transit (flat)\n", "samples_transit_flat, weights_transit_flat = results_transit_flat.samples, results_transit_flat.importance_weights()\n", "mean_transit_flat, cov_transit_flat = dyfunc.mean_and_cov(samples_transit_flat, weights_transit_flat)\n", "cov_transit_flat = np.sqrt(np.diag(cov_transit_flat))\n", "\n", "# Emission (flat)\n", "samples_emission_flat, weights_emission_flat = results_emission_flat.samples, results_emission_flat.importance_weights()\n", "mean_emission_flat, cov_emission_flat = dyfunc.mean_and_cov(samples_emission_flat, weights_emission_flat)\n", "cov_emission_flat = np.sqrt(np.diag(cov_emission_flat))\n", "\n", "# Direct (flat)\n", "samples_direct_flat, weights_direct_flat = results_direct_flat.samples, results_direct_flat.importance_weights()\n", "mean_direct_flat, cov_direct_flat = dyfunc.mean_and_cov(samples_direct_flat, weights_direct_flat)\n", "cov_direct_flat = np.sqrt(np.diag(cov_direct_flat))\n", "\n", "# SS (flat)\n", "samples_ss_flat, weights_ss_flat = results_ss_flat.samples, results_ss_flat.importance_weights()\n", "mean_ss_flat, cov_ss_flat = dyfunc.mean_and_cov(samples_ss_flat, weights_ss_flat)\n", "cov_ss_flat = np.sqrt(np.diag(cov_ss_flat))\n", "\n", "# Print LaTeX format\n", "latex_string = f'''\n", "Slope & ${mean_transit[0]:0.2f}\\\\pm{cov_transit[0]:0.2f}$ & ${mean_emission[0]:0.2f}\\\\pm{cov_emission[0]:0.2f}$ & ${mean_direct[0]:0.2f}\\\\pm{cov_direct[0]:0.2f}$ & ${mean_ss[0]:0.2f}\\\\pm{cov_ss[0]:0.2f}$ \\\\\\\\\n", "Intercept (M/H) & ${mean_transit[1]:0.2f}\\\\pm{cov_transit[1]:0.2f}$ & ${mean_emission[1]:0.2f}\\\\pm{cov_emission[1]:0.2f}$ & ${mean_direct[1]:0.2f}\\\\pm{cov_direct[1]:0.2f}$ & ${mean_ss[1]:0.2f}\\\\pm{cov_ss[1]:0.2f}$ \\\\\\\\\n", "Mass Cutoff (log10) & ${mean_transit[2]:0.2f}\\\\pm{cov_transit[2]:0.2f}$ & ${mean_emission[2]:0.2f}\\\\pm{cov_emission[2]:0.2f}$ & ${mean_direct[2]:0.2f}\\\\pm{cov_direct[2]:0.2f}$ & ${mean_ss[2]:0.2f}\\\\pm{cov_ss[2]:0.2f}$ \\\\\\\\\n", "$\\\\ln E$ & ${np.max(results_transit.logz):0.2f} \\\\pm 0.2$ & ${np.max(results_emission.logz):0.2f} \\\\pm 0.2$ & ${np.max(results_direct.logz):0.2f} \\\\pm 0.2$ & ${np.max(results_ss.logz):0.2f} \\\\pm 0.2$ \\\\\\\\\n", "\\\\hline \\\\\n", "\\\\multicolumn{{4}}{{c}}{{\\\\textbf{{Flat-Line}}}} \\\\\\\\\n", "\\\\textbf{{Parameter}} & \\textbf{{Transiting}} & \\\\textbf{{Eclipse}} & \\\\textbf{{Direct}} & \\\\textbf{{Solar System}} \\\\\\\\\n", "\\\\hline \\\\\n", "Intercept (M/H) & ${mean_transit_flat[0]:0.2f}\\\\pm{cov_transit_flat[0]:0.2f}$ & ${mean_emission_flat[0]:0.2f}\\\\pm{cov_emission_flat[0]:0.2f}$ & ${mean_direct_flat[0]:0.2f}\\\\pm{cov_direct_flat[0]:0.2f}$ & ${mean_ss_flat[0]:0.2f}\\\\pm{cov_ss_flat[0]:0.2f}$ \\\\\\\\\n", "$\\\\ln E$ & ${np.max(results_transit_flat.logz):0.2f} \\\\pm 0.2$ & ${np.max(results_emission_flat.logz):0.2f} \\\\pm 0.2$ & ${np.max(results_direct_flat.logz):0.2f} \\\\pm 0.2$ & ${np.max(results_ss_flat.logz):0.2f} \\\\pm 0.2$ \\\\\\\\\n", "\\\\hline \\\\\n", "'''\n", "print(latex_string)" ] }, { "cell_type": "markdown", "id": "325ae879-08df-4c0f-9ea7-923769147eca", "metadata": {}, "source": [ "## O/H" ] }, { "cell_type": "code", "execution_count": 59, "id": "aca58ffa-cf5d-4575-887b-1b5d3fd0ea26", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Running nested sampling...\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "3868it [00:02, 1918.49it/s, +500 | bound: 5 | nc: 1 | ncall: 22037 | eff(%): 20.281 | loglstar: -inf < -193.449 < inf | logz: -198.825 +/- 0.096 | dlogz: 0.000 > 0.100]\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "Nested Sampling Results:\n", "Log Evidence: -198.82 ± 0.12\n", "Number of likelihood evaluations: 3868\n", "\n", "Parameter Estimates:\n", "Parameter Median 68% CI - / +\n", "---------------------------------------------\n", "slope (m) 9.673 [9.635, 9.713] [0.039, 0.040]\n", "Running nested sampling...\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "3966it [00:01, 2007.03it/s, +500 | bound: 5 | nc: 1 | ncall: 22782 | eff(%): 20.043 | loglstar: -inf < -206.282 < inf | logz: -211.853 +/- 0.098 | dlogz: 0.000 > 0.100]\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "Nested Sampling Results:\n", "Log Evidence: -211.85 ± 0.13\n", "Number of likelihood evaluations: 3966\n", "\n", "Parameter Estimates:\n", "Parameter Median 68% CI - / +\n", "---------------------------------------------\n", "slope (m) 8.816 [8.784, 8.851] [0.032, 0.035]\n", "Running nested sampling...\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "3963it [00:01, 2045.17it/s, +500 | bound: 5 | nc: 1 | ncall: 21632 | eff(%): 21.120 | loglstar: -inf < -161.703 < inf | logz: -167.268 +/- 0.098 | dlogz: 0.000 > 0.100]\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "Nested Sampling Results:\n", "Log Evidence: -167.27 ± 0.12\n", "Number of likelihood evaluations: 3963\n", "\n", "Parameter Estimates:\n", "Parameter Median 68% CI - / +\n", "---------------------------------------------\n", "slope (m) 9.374 [9.339, 9.405] [0.035, 0.031]\n", "Running nested sampling...\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "4274it [00:01, 2157.30it/s, +500 | bound: 6 | nc: 1 | ncall: 22607 | eff(%): 21.595 | loglstar: -inf < -99.398 < inf | logz: -105.586 +/- 0.104 | dlogz: 0.000 > 0.100]" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "Nested Sampling Results:\n", "Log Evidence: -105.59 ± 0.13\n", "Number of likelihood evaluations: 4274\n", "\n", "Parameter Estimates:\n", "Parameter Median 68% CI - / +\n", "---------------------------------------------\n", "slope (m) 9.732 [9.715, 9.747] [0.017, 0.015]\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "\n" ] } ], "source": [ "results_transit_flat = mass_mh_dynesty_flat(transit, plot_it=False, xerrs=False, oh=True)\n", "results_emission_flat = mass_mh_dynesty_flat(emission, plot_it=False, xerrs=False, oh=True)\n", "results_direct_flat = mass_mh_dynesty_flat(direct, plot_it=False, xerrs=False, oh=True)\n", "results_ss_flat = mass_mh_dynesty_flat(solar_system, plot_it=False, xerrs=False, oh=True)" ] }, { "cell_type": "code", "execution_count": 60, "id": "1ca55d33-509a-4abe-a23b-33fd34fe8b44", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Running nested sampling...\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "2423it [00:01, 789.34it/s, bound: 10 | nc: 71 | ncall: 24889 | eff(%): 9.735 | loglstar: -inf < -194.190 < inf | logz: -201.226 +/- 0.112 | dlogz: 90.702 > 0.100] /Users/jlothringer/miniconda3/envs/research/lib/python3.12/site-packages/dynesty/bounding.py:617: UserWarning: The enlargement factor for the ellipsoidal bounds determined from bootstrapping is very large. If you are using uniform sampling that may mean that the sampling will be inefficient. This may be caused by a very complex posterior shape. You may consider using more livepoints or different sampler (i.e. rslice or rwalk) or alternatively disable bootstrap (bootstrap=0)\n", " warnings.warn(\n", "/Users/jlothringer/miniconda3/envs/research/lib/python3.12/site-packages/dynesty/bounding.py:617: UserWarning: The enlargement factor for the ellipsoidal bounds determined from bootstrapping is very large. If you are using uniform sampling that may mean that the sampling will be inefficient. This may be caused by a very complex posterior shape. You may consider using more livepoints or different sampler (i.e. rslice or rwalk) or alternatively disable bootstrap (bootstrap=0)\n", " warnings.warn(\n", "/Users/jlothringer/miniconda3/envs/research/lib/python3.12/site-packages/dynesty/bounding.py:617: UserWarning: The enlargement factor for the ellipsoidal bounds determined from bootstrapping is very large. If you are using uniform sampling that may mean that the sampling will be inefficient. This may be caused by a very complex posterior shape. You may consider using more livepoints or different sampler (i.e. rslice or rwalk) or alternatively disable bootstrap (bootstrap=0)\n", " warnings.warn(\n", "7098it [00:05, 1285.55it/s, +500 | bound: 38 | nc: 1 | ncall: 46204 | eff(%): 16.624 | loglstar: -inf < -104.892 < inf | logz: -116.733 +/- 0.146 | dlogz: 0.000 > 0.100]\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "Nested Sampling Results:\n", "Log Evidence: -116.73 ± 0.18\n", "Number of likelihood evaluations: 7098\n", "\n", "Parameter Estimates:\n", "Parameter Median 68% CI - / +\n", "---------------------------------------------\n", "slope (m) -1.776 [-2.186, -1.458] [0.410, 0.318]\n", "intercept (b) 8.429 [7.955, 8.787] [0.474, 0.358]\n", "cutoff (s) -0.524 [-0.644, -0.385] [0.120, 0.139]\n", "Running nested sampling...\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "2777it [00:04, 225.28it/s, bound: 32 | nc: 26 | ncall: 42114 | eff(%): 6.594 | loglstar: -inf < -206.620 < inf | logz: -213.633 +/- 0.112 | dlogz: 72.004 > 0.100] /Users/jlothringer/miniconda3/envs/research/lib/python3.12/site-packages/dynesty/bounding.py:617: UserWarning: The enlargement factor for the ellipsoidal bounds determined from bootstrapping is very large. If you are using uniform sampling that may mean that the sampling will be inefficient. This may be caused by a very complex posterior shape. You may consider using more livepoints or different sampler (i.e. rslice or rwalk) or alternatively disable bootstrap (bootstrap=0)\n", " warnings.warn(\n", "2896it [00:06, 107.42it/s, bound: 47 | nc: 46 | ncall: 53583 | eff(%): 5.405 | loglstar: -inf < -206.287 < inf | logz: -212.881 +/- 0.110 | dlogz: 71.009 > 0.100] /Users/jlothringer/miniconda3/envs/research/lib/python3.12/site-packages/dynesty/bounding.py:617: UserWarning: The enlargement factor for the ellipsoidal bounds determined from bootstrapping is very large. If you are using uniform sampling that may mean that the sampling will be inefficient. This may be caused by a very complex posterior shape. You may consider using more livepoints or different sampler (i.e. rslice or rwalk) or alternatively disable bootstrap (bootstrap=0)\n", " warnings.warn(\n", "/Users/jlothringer/miniconda3/envs/research/lib/python3.12/site-packages/dynesty/bounding.py:617: UserWarning: The enlargement factor for the ellipsoidal bounds determined from bootstrapping is very large. If you are using uniform sampling that may mean that the sampling will be inefficient. This may be caused by a very complex posterior shape. You may consider using more livepoints or different sampler (i.e. rslice or rwalk) or alternatively disable bootstrap (bootstrap=0)\n", " warnings.warn(\n", "2910it [00:07, 65.88it/s, bound: 52 | nc: 1048 | ncall: 57409 | eff(%): 5.069 | loglstar: -inf < -206.283 < inf | logz: -212.820 +/- 0.109 | dlogz: 70.921 > 0.100]/Users/jlothringer/miniconda3/envs/research/lib/python3.12/site-packages/dynesty/bounding.py:617: UserWarning: The enlargement factor for the ellipsoidal bounds determined from bootstrapping is very large. If you are using uniform sampling that may mean that the sampling will be inefficient. This may be caused by a very complex posterior shape. You may consider using more livepoints or different sampler (i.e. rslice or rwalk) or alternatively disable bootstrap (bootstrap=0)\n", " warnings.warn(\n", "8468it [00:10, 817.97it/s, +500 | bound: 77 | nc: 1 | ncall: 76016 | eff(%): 11.876 | loglstar: -inf < -131.416 < inf | logz: -146.002 +/- 0.162 | dlogz: 0.000 > 0.100]\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "Nested Sampling Results:\n", "Log Evidence: -146.00 ± 0.20\n", "Number of likelihood evaluations: 8468\n", "\n", "Parameter Estimates:\n", "Parameter Median 68% CI - / +\n", "---------------------------------------------\n", "slope (m) -1.811 [-1.969, -1.658] [0.158, 0.153]\n", "intercept (b) 8.961 [8.923, 9.000] [0.038, 0.039]\n", "cutoff (s) 0.223 [0.211, 0.238] [0.011, 0.015]\n", "Running nested sampling...\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "2773it [00:09, 37.19it/s, bound: 68 | nc: 585 | ncall: 67791 | eff(%): 4.091 | loglstar: -inf < -161.718 < inf | logz: -168.038 +/- 0.107 | dlogz: 46.872 > 0.100] /Users/jlothringer/miniconda3/envs/research/lib/python3.12/site-packages/dynesty/bounding.py:617: UserWarning: The enlargement factor for the ellipsoidal bounds determined from bootstrapping is very large. If you are using uniform sampling that may mean that the sampling will be inefficient. This may be caused by a very complex posterior shape. You may consider using more livepoints or different sampler (i.e. rslice or rwalk) or alternatively disable bootstrap (bootstrap=0)\n", " warnings.warn(\n", "2800it [00:10, 40.28it/s, bound: 74 | nc: 184 | ncall: 72610 | eff(%): 3.856 | loglstar: -inf < -161.703 < inf | logz: -167.929 +/- 0.107 | dlogz: 46.708 > 0.100]/Users/jlothringer/miniconda3/envs/research/lib/python3.12/site-packages/dynesty/bounding.py:617: UserWarning: The enlargement factor for the ellipsoidal bounds determined from bootstrapping is very large. If you are using uniform sampling that may mean that the sampling will be inefficient. This may be caused by a very complex posterior shape. You may consider using more livepoints or different sampler (i.e. rslice or rwalk) or alternatively disable bootstrap (bootstrap=0)\n", " warnings.warn(\n", "6112it [00:12, 507.06it/s, +500 | bound: 90 | nc: 1 | ncall: 84637 | eff(%): 7.859 | loglstar: -inf < -115.351 < inf | logz: -125.231 +/- 0.132 | dlogz: 0.000 > 0.100]\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "Nested Sampling Results:\n", "Log Evidence: -125.23 ± 0.16\n", "Number of likelihood evaluations: 6112\n", "\n", "Parameter Estimates:\n", "Parameter Median 68% CI - / +\n", "---------------------------------------------\n", "slope (m) -1.531 [-1.693, -1.360] [0.162, 0.171]\n", "intercept (b) 10.994 [10.809, 11.172] [0.185, 0.177]\n", "cutoff (s) 3.192 [1.895, 4.417] [1.298, 1.225]\n", "Running nested sampling...\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "2737it [00:02, 635.84it/s, bound: 20 | nc: 38 | ncall: 33820 | eff(%): 8.093 | loglstar: -inf < -102.393 < inf | logz: -110.951 +/- 0.124 | dlogz: 103.887 > 0.100]/Users/jlothringer/miniconda3/envs/research/lib/python3.12/site-packages/dynesty/bounding.py:617: UserWarning: The enlargement factor for the ellipsoidal bounds determined from bootstrapping is very large. If you are using uniform sampling that may mean that the sampling will be inefficient. This may be caused by a very complex posterior shape. You may consider using more livepoints or different sampler (i.e. rslice or rwalk) or alternatively disable bootstrap (bootstrap=0)\n", " warnings.warn(\n", "/Users/jlothringer/miniconda3/envs/research/lib/python3.12/site-packages/dynesty/bounding.py:617: UserWarning: The enlargement factor for the ellipsoidal bounds determined from bootstrapping is very large. If you are using uniform sampling that may mean that the sampling will be inefficient. This may be caused by a very complex posterior shape. You may consider using more livepoints or different sampler (i.e. rslice or rwalk) or alternatively disable bootstrap (bootstrap=0)\n", " warnings.warn(\n", "/Users/jlothringer/miniconda3/envs/research/lib/python3.12/site-packages/dynesty/bounding.py:617: UserWarning: The enlargement factor for the ellipsoidal bounds determined from bootstrapping is very large. If you are using uniform sampling that may mean that the sampling will be inefficient. This may be caused by a very complex posterior shape. You may consider using more livepoints or different sampler (i.e. rslice or rwalk) or alternatively disable bootstrap (bootstrap=0)\n", " warnings.warn(\n", "/Users/jlothringer/miniconda3/envs/research/lib/python3.12/site-packages/dynesty/bounding.py:617: UserWarning: The enlargement factor for the ellipsoidal bounds determined from bootstrapping is very large. If you are using uniform sampling that may mean that the sampling will be inefficient. This may be caused by a very complex posterior shape. You may consider using more livepoints or different sampler (i.e. rslice or rwalk) or alternatively disable bootstrap (bootstrap=0)\n", " warnings.warn(\n", "/Users/jlothringer/miniconda3/envs/research/lib/python3.12/site-packages/dynesty/bounding.py:617: UserWarning: The enlargement factor for the ellipsoidal bounds determined from bootstrapping is very large. If you are using uniform sampling that may mean that the sampling will be inefficient. This may be caused by a very complex posterior shape. You may consider using more livepoints or different sampler (i.e. rslice or rwalk) or alternatively disable bootstrap (bootstrap=0)\n", " warnings.warn(\n", "/Users/jlothringer/miniconda3/envs/research/lib/python3.12/site-packages/dynesty/bounding.py:617: UserWarning: The enlargement factor for the ellipsoidal bounds determined from bootstrapping is very large. If you are using uniform sampling that may mean that the sampling will be inefficient. This may be caused by a very complex posterior shape. You may consider using more livepoints or different sampler (i.e. rslice or rwalk) or alternatively disable bootstrap (bootstrap=0)\n", " warnings.warn(\n", "/Users/jlothringer/miniconda3/envs/research/lib/python3.12/site-packages/dynesty/bounding.py:617: UserWarning: The enlargement factor for the ellipsoidal bounds determined from bootstrapping is very large. If you are using uniform sampling that may mean that the sampling will be inefficient. This may be caused by a very complex posterior shape. You may consider using more livepoints or different sampler (i.e. rslice or rwalk) or alternatively disable bootstrap (bootstrap=0)\n", " warnings.warn(\n", "/Users/jlothringer/miniconda3/envs/research/lib/python3.12/site-packages/dynesty/bounding.py:617: UserWarning: The enlargement factor for the ellipsoidal bounds determined from bootstrapping is very large. If you are using uniform sampling that may mean that the sampling will be inefficient. This may be caused by a very complex posterior shape. You may consider using more livepoints or different sampler (i.e. rslice or rwalk) or alternatively disable bootstrap (bootstrap=0)\n", " warnings.warn(\n", "2884it [00:05, 251.94it/s, bound: 39 | nc: 992 | ncall: 48352 | eff(%): 5.965 | loglstar: -inf < -99.399 < inf | logz: -106.975 +/- 0.118 | dlogz: 99.585 > 0.100] /Users/jlothringer/miniconda3/envs/research/lib/python3.12/site-packages/dynesty/bounding.py:617: UserWarning: The enlargement factor for the ellipsoidal bounds determined from bootstrapping is very large. If you are using uniform sampling that may mean that the sampling will be inefficient. This may be caused by a very complex posterior shape. You may consider using more livepoints or different sampler (i.e. rslice or rwalk) or alternatively disable bootstrap (bootstrap=0)\n", " warnings.warn(\n", "/Users/jlothringer/miniconda3/envs/research/lib/python3.12/site-packages/dynesty/bounding.py:617: UserWarning: The enlargement factor for the ellipsoidal bounds determined from bootstrapping is very large. If you are using uniform sampling that may mean that the sampling will be inefficient. This may be caused by a very complex posterior shape. You may consider using more livepoints or different sampler (i.e. rslice or rwalk) or alternatively disable bootstrap (bootstrap=0)\n", " warnings.warn(\n", "4213it [00:07, 565.04it/s, bound: 55 | nc: 18 | ncall: 59774 | eff(%): 7.048 | loglstar: -inf < -9.267 < inf | logz: -19.498 +/- 0.137 | dlogz: 10.781 > 0.100] /Users/jlothringer/miniconda3/envs/research/lib/python3.12/site-packages/dynesty/bounding.py:617: UserWarning: The enlargement factor for the ellipsoidal bounds determined from bootstrapping is very large. If you are using uniform sampling that may mean that the sampling will be inefficient. This may be caused by a very complex posterior shape. You may consider using more livepoints or different sampler (i.e. rslice or rwalk) or alternatively disable bootstrap (bootstrap=0)\n", " warnings.warn(\n", "4397it [00:07, 338.74it/s, bound: 63 | nc: 39 | ncall: 66103 | eff(%): 6.652 | loglstar: -inf < -7.173 < inf | logz: -17.514 +/- 0.136 | dlogz: 8.428 > 0.100]/Users/jlothringer/miniconda3/envs/research/lib/python3.12/site-packages/dynesty/bounding.py:617: UserWarning: The enlargement factor for the ellipsoidal bounds determined from bootstrapping is very large. If you are using uniform sampling that may mean that the sampling will be inefficient. This may be caused by a very complex posterior shape. You may consider using more livepoints or different sampler (i.e. rslice or rwalk) or alternatively disable bootstrap (bootstrap=0)\n", " warnings.warn(\n", "4445it [00:08, 301.05it/s, bound: 65 | nc: 12 | ncall: 67753 | eff(%): 6.561 | loglstar: -inf < -6.672 < inf | logz: -17.079 +/- 0.136 | dlogz: 7.896 > 0.100]/Users/jlothringer/miniconda3/envs/research/lib/python3.12/site-packages/dynesty/bounding.py:617: UserWarning: The enlargement factor for the ellipsoidal bounds determined from bootstrapping is very large. If you are using uniform sampling that may mean that the sampling will be inefficient. This may be caused by a very complex posterior shape. You may consider using more livepoints or different sampler (i.e. rslice or rwalk) or alternatively disable bootstrap (bootstrap=0)\n", " warnings.warn(\n", "/Users/jlothringer/miniconda3/envs/research/lib/python3.12/site-packages/dynesty/bounding.py:617: UserWarning: The enlargement factor for the ellipsoidal bounds determined from bootstrapping is very large. If you are using uniform sampling that may mean that the sampling will be inefficient. This may be caused by a very complex posterior shape. You may consider using more livepoints or different sampler (i.e. rslice or rwalk) or alternatively disable bootstrap (bootstrap=0)\n", " warnings.warn(\n", "7246it [00:10, 700.20it/s, +500 | bound: 85 | nc: 1 | ncall: 82710 | eff(%): 9.422 | loglstar: -inf < 0.294 < inf | logz: -11.858 +/- 0.144 | dlogz: 0.000 > 0.100]" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "Nested Sampling Results:\n", "Log Evidence: -11.86 ± 0.18\n", "Number of likelihood evaluations: 7246\n", "\n", "Parameter Estimates:\n", "Parameter Median 68% CI - / +\n", "---------------------------------------------\n", "slope (m) -1.125 [-1.206, -1.049] [0.081, 0.076]\n", "intercept (b) 9.101 [9.052, 9.147] [0.049, 0.046]\n", "cutoff (s) 1.587 [-0.180, 3.848] [1.767, 2.261]\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "\n" ] } ], "source": [ "results_transit = mass_mh_dynesty(transit, plot_it=False, xerrs=False, oh=True)\n", "results_emission = mass_mh_dynesty(emission, plot_it=False, xerrs=False, oh=True)\n", "results_direct = mass_mh_dynesty(direct, plot_it=False, xerrs=False, oh=True)\n", "results_ss = mass_mh_dynesty(solar_system, plot_it=False, xerrs=False, oh=True)" ] }, { "cell_type": "code", "execution_count": 62, "id": "88c3f690-4455-425e-82d9-96b4821334ee", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "Slope & $-1.81\\pm0.35$ & $-1.81\\pm0.16$ & $-1.53\\pm0.16$ & $-1.13\\pm0.09$ \\\\\n", "Intercept (M/H) & $8.38\\pm0.40$ & $8.96\\pm0.04$ & $10.99\\pm0.18$ & $9.10\\pm0.07$ \\\\\n", "Mass Cutoff (log10) & $-0.52\\pm0.12$ & $0.22\\pm0.02$ & $3.18\\pm1.07$ & $1.77\\pm1.70$ \\\\\n", "$\\ln E$ & $-116.73 \\pm 0.2$ & $-146.00 \\pm 0.2$ & $-125.23 \\pm 0.2$ & $-11.86 \\pm 0.2$ \\\\\n", "\\hline \\\n", "\\multicolumn{5}{c}{\\textbf{Flat-Line}} \\\\\n", "\\textbf{Parameter} & \\textbf{Transiting} & \\textbf{Eclipse} & \\textbf{Direct} & \\textbf{Solar System} \\\\\n", "\\hline \\\n", "Intercept (M/H) & $9.67\\pm0.04$ & $8.82\\pm0.03$ & $9.37\\pm0.03$ & $9.73\\pm0.02$ \\\\\n", "$\\ln E$ & $-198.82 \\pm 0.2$ & $-211.85 \\pm 0.2$ & $-167.27 \\pm 0.2$ & $-105.59 \\pm 0.2$ \\\\\n", "\\hline \\\n", "\n" ] } ], "source": [ "from dynesty import utils as dyfunc\n", "\n", "# Transit\n", "samples_transit, weights_transit = results_transit.samples, results_transit.importance_weights()\n", "mean_transit, cov_transit = dyfunc.mean_and_cov(samples_transit, weights_transit)\n", "cov_transit = np.sqrt(np.diag(cov_transit))\n", "\n", "# Emission\n", "samples_emission, weights_emission = results_emission.samples, results_emission.importance_weights()\n", "mean_emission, cov_emission = dyfunc.mean_and_cov(samples_emission, weights_emission)\n", "cov_emission = np.sqrt(np.diag(cov_emission))\n", "\n", "# Direct\n", "samples_direct, weights_direct = results_direct.samples, results_direct.importance_weights()\n", "mean_direct, cov_direct = dyfunc.mean_and_cov(samples_direct, weights_direct)\n", "cov_direct = np.sqrt(np.diag(cov_direct))\n", "\n", "# SS\n", "samples_ss, weights_ss = results_ss.samples, results_ss.importance_weights()\n", "mean_ss, cov_ss = dyfunc.mean_and_cov(samples_ss, weights_ss)\n", "cov_ss = np.sqrt(np.diag(cov_ss))\n", "\n", "# Transit (flat)\n", "samples_transit_flat, weights_transit_flat = results_transit_flat.samples, results_transit_flat.importance_weights()\n", "mean_transit_flat, cov_transit_flat = dyfunc.mean_and_cov(samples_transit_flat, weights_transit_flat)\n", "cov_transit_flat = np.sqrt(np.diag(cov_transit_flat))\n", "\n", "# Emission (flat)\n", "samples_emission_flat, weights_emission_flat = results_emission_flat.samples, results_emission_flat.importance_weights()\n", "mean_emission_flat, cov_emission_flat = dyfunc.mean_and_cov(samples_emission_flat, weights_emission_flat)\n", "cov_emission_flat = np.sqrt(np.diag(cov_emission_flat))\n", "\n", "# Direct (flat)\n", "samples_direct_flat, weights_direct_flat = results_direct_flat.samples, results_direct_flat.importance_weights()\n", "mean_direct_flat, cov_direct_flat = dyfunc.mean_and_cov(samples_direct_flat, weights_direct_flat)\n", "cov_direct_flat = np.sqrt(np.diag(cov_direct_flat))\n", "\n", "# SS (flat)\n", "samples_ss_flat, weights_ss_flat = results_ss_flat.samples, results_ss_flat.importance_weights()\n", "mean_ss_flat, cov_ss_flat = dyfunc.mean_and_cov(samples_ss_flat, weights_ss_flat)\n", "cov_ss_flat = np.sqrt(np.diag(cov_ss_flat))\n", "\n", "# Print LaTeX format\n", "latex_string = f'''\n", "Slope & ${mean_transit[0]:0.2f}\\\\pm{cov_transit[0]:0.2f}$ & ${mean_emission[0]:0.2f}\\\\pm{cov_emission[0]:0.2f}$ & ${mean_direct[0]:0.2f}\\\\pm{cov_direct[0]:0.2f}$ & ${mean_ss[0]:0.2f}\\\\pm{cov_ss[0]:0.2f}$ \\\\\\\\\n", "Intercept (M/H) & ${mean_transit[1]:0.2f}\\\\pm{cov_transit[1]:0.2f}$ & ${mean_emission[1]:0.2f}\\\\pm{cov_emission[1]:0.2f}$ & ${mean_direct[1]:0.2f}\\\\pm{cov_direct[1]:0.2f}$ & ${mean_ss[1]:0.2f}\\\\pm{cov_ss[1]:0.2f}$ \\\\\\\\\n", "Mass Cutoff (log10) & ${mean_transit[2]:0.2f}\\\\pm{cov_transit[2]:0.2f}$ & ${mean_emission[2]:0.2f}\\\\pm{cov_emission[2]:0.2f}$ & ${mean_direct[2]:0.2f}\\\\pm{cov_direct[2]:0.2f}$ & ${mean_ss[2]:0.2f}\\\\pm{cov_ss[2]:0.2f}$ \\\\\\\\\n", "$\\\\ln E$ & ${np.max(results_transit.logz):0.2f} \\\\pm 0.2$ & ${np.max(results_emission.logz):0.2f} \\\\pm 0.2$ & ${np.max(results_direct.logz):0.2f} \\\\pm 0.2$ & ${np.max(results_ss.logz):0.2f} \\\\pm 0.2$ \\\\\\\\\n", "\\\\hline \\\\\n", "\\\\multicolumn{{5}}{{c}}{{\\\\textbf{{Flat-Line}}}} \\\\\\\\\n", "\\\\textbf{{Parameter}} & \\\\textbf{{Transiting}} & \\\\textbf{{Eclipse}} & \\\\textbf{{Direct}} & \\\\textbf{{Solar System}} \\\\\\\\\n", "\\\\hline \\\\\n", "Intercept (M/H) & ${mean_transit_flat[0]:0.2f}\\\\pm{cov_transit_flat[0]:0.2f}$ & ${mean_emission_flat[0]:0.2f}\\\\pm{cov_emission_flat[0]:0.2f}$ & ${mean_direct_flat[0]:0.2f}\\\\pm{cov_direct_flat[0]:0.2f}$ & ${mean_ss_flat[0]:0.2f}\\\\pm{cov_ss_flat[0]:0.2f}$ \\\\\\\\\n", "$\\\\ln E$ & ${np.max(results_transit_flat.logz):0.2f} \\\\pm 0.2$ & ${np.max(results_emission_flat.logz):0.2f} \\\\pm 0.2$ & ${np.max(results_direct_flat.logz):0.2f} \\\\pm 0.2$ & ${np.max(results_ss_flat.logz):0.2f} \\\\pm 0.2$ \\\\\\\\\n", "\\\\hline \\\\\n", "'''\n", "print(latex_string)" ] }, { "cell_type": "code", "execution_count": 63, "id": "cc638a0e-a6bc-4d8f-a057-b95f9eba22d7", "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_20318/3002674758.py:73: RuntimeWarning: invalid value encountered in log10\n", " MF = np.log10(factorZ*CM * 1e12)\n" ] }, { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig, ax = plt.subplots(figsize=(9, 6))\n", "\n", "#x = 10**(np.linspace(-2,2.0))\n", "#fit = np.log10(9.7*(x**-0.45)) #[M/H] ~ log10(Z/Zs)\n", "#ax.plot(x,fit,'k--',label='Thorgren+ 2016',zorder=1,alpha=0.75)\n", "#plot w/o solar system for o/h\n", "for data, result, label, marker, color in zip([direct, emission, transit], \n", " [results_direct,results_emission,results_transit],\n", " ['Direct', 'Eclipse', 'Transmission'],\n", " ['o', 's', 'd'],\n", " ['#2E86AB', '#A23B72', '#F18F01']):\n", " ax.errorbar(data['Mass'], data['O/H'],\n", " xerr=[data['Mass Lower'], data['Mass Upper']],\n", " yerr=[data['O/H Lower'], data['O/H Upper']],\n", " fmt=marker, label=label, color=color,\n", " markersize=6, capsize=2, alpha=1.0,\n", " markeredgecolor='k',markeredgewidth=0.5)\n", "\n", " n_samples = min(50, len(result['samples']))\n", " indices = np.random.choice(len(result['samples']), n_samples, p=result.importance_weights()/np.sum(result.importance_weights()))\n", " xfake = np.linspace(-2,2.5)\n", " for idx in indices: # Plot subset for clarity\n", " m_sample, b_sample, s_sample = result['samples'][idx, 0], result['samples'][idx, 1], result['samples'][idx, 2]\n", " y_sample = m_sample * xfake + b_sample\n", " y_sample[np.where(xfake > s_sample)[0]] = m_sample * s_sample + b_sample\n", " \n", " ax.plot(10**xfake, y_sample, '-', alpha=0.05, color=color)\n", "\n", "for _, row in uhjs.iterrows():\n", " color_map = {'Direct': '#2E86AB', 'Eclipse': '#A23B72', 'Transit': '#F18F01'}\n", " ax.errorbar(row['Mass'], row['O/H'],\n", " xerr=[[row['Mass Lower']], [row['Mass Upper']]],\n", " yerr=[[row['O/H Lower']], [row['O/H Upper']]],\n", " fmt='*', color=color_map[row['Geometry']], markersize=12,\n", " markeredgecolor='k',markeredgewidth=0.5)\n", "\n", "ax.set_xlim(0.025,50)\n", "ax.set_xscale('log')\n", "\n", "ax.set_ylim(7.5,12)\n", "\n", "ax.set_xlabel(r'Mass [M$_{\\mathrm{Jup}}$]', fontsize=16)\n", "ax.set_ylabel('O/H', fontsize=16)\n", "ax.legend(frameon=True, fancybox=True, shadow=False,fontsize=13)\n", "ax.grid(True, alpha=0.3)\n", "\n", "ax.minorticks_on()\n", "\n", "ax.yaxis.set_ticks_position('both')\n", "ax.xaxis.set_ticks_position('both')\n", "\n", "plt.tight_layout()\n", "\n", "ax.tick_params(labelsize=12)\n", "\n", "#8.69\n", "#9.7*M&-0.45\n", "#ax.plot( np.logspace(-2,2), 8.69 + np.log10((9.7*np.logspace(-1,1)**-0.45)),'--k') #Thorngren, kinda - mass fraction\n", "#plt.savefig('mass_metallicity_fits_oh.pdf')\n", "\n", "X = 0.7381\n", "Y = 0.2485\n", "Z = 0.0134\n", "ZM = (9.7*np.logspace(-2,2)**-0.45) * Z\n", "ZH = (10**8.69+10**8.43+10**7.83) / 1e12\n", "muZ = 18\n", "muH = 2.0\n", "\n", "factorZ = (1+(Y/X)) / (((1/ZM)-1)*(muZ/muH))\n", "\n", "#C/M\n", "CM = 10**8.69 / (1.04e-3*1e12)\n", "MF = np.log10(factorZ*CM * 1e12)\n", "\n", "#8.69\n", "#9.7*M&-0.45\n", "#ax.plot( np.logspace(-2,2), 8.69 + np.log10((9.7*np.logspace(-1,1)**-0.45)),'--k') #Thorngren, kinda - mass fraction\n", "ax.plot(np.logspace(-2,2),MF,'--k',label='Thorngren+ 16 (Interior)')\n", "\n", "ax.hlines(8.69,-2,100,'k',linestyle=':',label='Solar (Asplund+09)')\n", "\n", "ax.legend(frameon=True, fancybox=True, shadow=False,fontsize=13)\n", "\n", "plt.savefig('./paper_figs/mass_metallicity_fits_oh.pdf')" ] }, { "cell_type": "code", "execution_count": 64, "id": "4c7f684b-9ffe-487d-b0e3-8555b0ca9a97", "metadata": {}, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "params = ['Slope', 'Intercept', 'Cutoff']\n", "color_map = {'Direct': '#2E86AB', 'Eclipse': '#A23B72', 'Transit': '#F18F01','Solar System': '#808080'}\n", "\n", "# Extract posterior samples\n", "samples = results_transit['samples']\n", "weights = results_transit.importance_weights()\n", "lw = 2\n", "\n", "fig,ax = plt.subplots(1,3,figsize=(15,6))\n", "ax[0].hist(results_transit['samples'][:, 0],weights = results_transit.importance_weights(),density=True,bins=100,label='Transit',\n", " ec=color_map['Transit'],fc=color_map['Transit'] + '60',histtype='stepfilled',linewidth=lw)\n", "ax[0].hist(results_emission['samples'][:, 0],weights = results_emission.importance_weights(),density=True,bins=100,label='Eclipse',\n", " ec=color_map['Eclipse'],fc=color_map['Eclipse'] + '60',histtype='stepfilled',linewidth=lw)\n", "ax[0].hist(results_direct['samples'][:, 0],weights = results_direct.importance_weights(),density=True,bins=100,label='Direct',\n", " ec=color_map['Direct'],fc=color_map['Direct'] + '60',histtype='stepfilled',linewidth=lw)\n", "#ax[0].hist(results_ss['samples'][:, 0],weights = results_ss.importance_weights(),density=True,bins=100,label='Solar System',\n", "# ec=color_map['Solar System'],fc=color_map['Solar System'] + '60',histtype='stepfilled',linewidth=lw)\n", "\n", "\n", "#ax[0].set_title(f'{params[0]}')\n", "ax[0].set_xlim(-3.5,-0.5)\n", "ax[0].set_ylabel('Frequency',fontsize=16)\n", "ax[0].set_xlabel(f'{params[0]}',fontsize=16)\n", "ax[0].tick_params(labelsize=12)\n", "\n", "ax[1].hist(results_transit['samples'][:, 1],weights = results_transit.importance_weights(),density=True,bins=100,label='Transit',\n", " ec=color_map['Transit'],fc=color_map['Transit'] + '60',histtype='stepfilled',linewidth=lw)\n", "ax[1].hist(results_emission['samples'][:, 1],weights = results_emission.importance_weights(),density=True,bins=100,label='Eclipse',\n", " ec=color_map['Eclipse'],fc=color_map['Eclipse'] + '60',histtype='stepfilled',linewidth=lw)\n", "ax[1].hist(results_direct['samples'][:, 1],weights = results_direct.importance_weights(),density=True,bins=100,label='Direct',\n", " ec=color_map['Direct'],fc=color_map['Direct'] + '60',histtype='stepfilled',linewidth=lw)\n", "#ax[1].hist(results_ss['samples'][:, 1],weights = results_ss.importance_weights(),density=True,bins=100,label='Solar System',\n", "# ec=color_map['Solar System'],fc=color_map['Solar System'] + '60',histtype='stepfilled',linewidth=lw)\n", "\n", "#ax[1].set_title(f'{params[1]}')\n", "ax[1].set_xlim(7,12.5)\n", "ax[1].set_xlabel(f'{params[1]}',fontsize=16)\n", "ax[1].tick_params(labelsize=12)\n", "\n", "ax[2].hist(results_transit['samples'][:, 2],weights = results_transit.importance_weights(),density=True,bins=100,label='Transit',\n", " ec=color_map['Transit'],fc=color_map['Transit'] + '60',histtype='stepfilled',linewidth=lw)\n", "ax[2].hist(results_emission['samples'][:, 2],weights = results_emission.importance_weights(),density=True,bins=100,label='Eclipse',\n", " ec=color_map['Eclipse'],fc=color_map['Eclipse'] + '60',histtype='stepfilled',linewidth=lw)\n", "ax[2].hist(results_direct['samples'][:, 2],weights = results_direct.importance_weights(),density=True,bins=100,label='Direct',\n", " ec=color_map['Direct'],fc=color_map['Direct'] + '60',histtype='stepfilled',linewidth=lw)\n", "#ax[2].hist(results_ss['samples'][:, 2],weights = results_ss.importance_weights(),density=True,bins=100,label='Solar System',\n", "# ec=color_map['Solar System'],fc=color_map['Solar System'] + '60',histtype='stepfilled',linewidth=lw)\n", "\n", "#ax[2].set_title(f'{params[2]}')\n", "ax[2].set_xlim(-1,2)\n", "ax[2].set_xlabel(f'{params[2]}',fontsize=16)\n", "ax[2].tick_params(labelsize=12)\n", "ax[2].legend(fontsize=13)\n", "\n", "ax[0].minorticks_on()\n", "ax[1].minorticks_on()\n", "ax[2].minorticks_on()\n", "\n", "ax[0].yaxis.set_ticks_position('both')\n", "ax[0].xaxis.set_ticks_position('both')\n", "ax[1].yaxis.set_ticks_position('both')\n", "ax[1].xaxis.set_ticks_position('both')\n", "ax[2].yaxis.set_ticks_position('both')\n", "ax[2].xaxis.set_ticks_position('both')\n", "\n", "plt.tight_layout()\n", "\n", "#ax.tick_params(labelsize=12)\n", "\n", "plt.savefig('./paper_figs/mass_metallicity_posteriors_oh.pdf')" ] }, { "cell_type": "markdown", "id": "17d8436e-bdd6-45e8-9d2a-0c706ecdab6a", "metadata": {}, "source": [ "## C/H" ] }, { "cell_type": "code", "execution_count": 65, "id": "a7dcdba0-aa3f-48fd-ae69-608b3e386f82", "metadata": { "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Running nested sampling...\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "3764it [00:01, 1941.57it/s, +500 | bound: 5 | nc: 1 | ncall: 20887 | eff(%): 20.915 | loglstar: -inf < -175.093 < inf | logz: -180.261 +/- 0.094 | dlogz: 0.000 > 0.100]\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "Nested Sampling Results:\n", "Log Evidence: -180.26 ± 0.12\n", "Number of likelihood evaluations: 3764\n", "\n", "Parameter Estimates:\n", "Parameter Median 68% CI - / +\n", "---------------------------------------------\n", "slope (m) 9.395 [9.355, 9.435] [0.040, 0.040]\n", "Running nested sampling...\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "3892it [00:02, 1838.08it/s, +500 | bound: 5 | nc: 1 | ncall: 21241 | eff(%): 21.175 | loglstar: -inf < -219.076 < inf | logz: -224.500 +/- 0.097 | dlogz: 0.000 > 0.100]\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "Nested Sampling Results:\n", "Log Evidence: -224.50 ± 0.12\n", "Number of likelihood evaluations: 3892\n", "\n", "Parameter Estimates:\n", "Parameter Median 68% CI - / +\n", "---------------------------------------------\n", "slope (m) 8.518 [8.485, 8.548] [0.033, 0.030]\n", "Running nested sampling...\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "3977it [00:02, 1931.50it/s, +500 | bound: 5 | nc: 1 | ncall: 22033 | eff(%): 20.791 | loglstar: -inf < -202.967 < inf | logz: -208.560 +/- 0.098 | dlogz: 0.000 > 0.100]\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "Nested Sampling Results:\n", "Log Evidence: -208.56 ± 0.13\n", "Number of likelihood evaluations: 3977\n", "\n", "Parameter Estimates:\n", "Parameter Median 68% CI - / +\n", "---------------------------------------------\n", "slope (m) 9.144 [9.114, 9.172] [0.030, 0.029]\n", "Running nested sampling...\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "4189it [00:01, 2104.86it/s, +500 | bound: 6 | nc: 1 | ncall: 22249 | eff(%): 21.560 | loglstar: -inf < -99.398 < inf | logz: -105.416 +/- 0.103 | dlogz: 0.000 > 0.100]" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "Nested Sampling Results:\n", "Log Evidence: -105.42 ± 0.13\n", "Number of likelihood evaluations: 4189\n", "\n", "Parameter Estimates:\n", "Parameter Median 68% CI - / +\n", "---------------------------------------------\n", "slope (m) 9.470 [9.455, 9.486] [0.016, 0.016]\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "\n" ] } ], "source": [ "results_transit_flat = mass_mh_dynesty_flat(transit, plot_it=False, xerrs=False, ch=True)\n", "results_emission_flat = mass_mh_dynesty_flat(emission, plot_it=False, xerrs=False, ch=True)\n", "results_direct_flat = mass_mh_dynesty_flat(direct, plot_it=False, xerrs=False, ch=True)\n", "results_ss_flat = mass_mh_dynesty_flat(solar_system, plot_it=False, xerrs=False,ch=True)" ] }, { "cell_type": "code", "execution_count": 66, "id": "eb0c6ce2-e5c4-4ed2-b337-c141f34fd8c6", "metadata": { "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Running nested sampling...\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "2539it [00:02, 671.05it/s, bound: 13 | nc: 17 | ncall: 27712 | eff(%): 9.162 | loglstar: -inf < -176.167 < inf | logz: -183.361 +/- 0.113 | dlogz: 61.472 > 0.100] /Users/jlothringer/miniconda3/envs/research/lib/python3.12/site-packages/dynesty/bounding.py:617: UserWarning: The enlargement factor for the ellipsoidal bounds determined from bootstrapping is very large. If you are using uniform sampling that may mean that the sampling will be inefficient. This may be caused by a very complex posterior shape. You may consider using more livepoints or different sampler (i.e. rslice or rwalk) or alternatively disable bootstrap (bootstrap=0)\n", " warnings.warn(\n", "/Users/jlothringer/miniconda3/envs/research/lib/python3.12/site-packages/dynesty/bounding.py:617: UserWarning: The enlargement factor for the ellipsoidal bounds determined from bootstrapping is very large. If you are using uniform sampling that may mean that the sampling will be inefficient. This may be caused by a very complex posterior shape. You may consider using more livepoints or different sampler (i.e. rslice or rwalk) or alternatively disable bootstrap (bootstrap=0)\n", " warnings.warn(\n", "2651it [00:03, 329.54it/s, bound: 23 | nc: 17 | ncall: 35137 | eff(%): 7.545 | loglstar: -inf < -175.162 < inf | logz: -181.914 +/- 0.110 | dlogz: 61.022 > 0.100]/Users/jlothringer/miniconda3/envs/research/lib/python3.12/site-packages/dynesty/bounding.py:617: UserWarning: The enlargement factor for the ellipsoidal bounds determined from bootstrapping is very large. If you are using uniform sampling that may mean that the sampling will be inefficient. This may be caused by a very complex posterior shape. You may consider using more livepoints or different sampler (i.e. rslice or rwalk) or alternatively disable bootstrap (bootstrap=0)\n", " warnings.warn(\n", "/Users/jlothringer/miniconda3/envs/research/lib/python3.12/site-packages/dynesty/bounding.py:617: UserWarning: The enlargement factor for the ellipsoidal bounds determined from bootstrapping is very large. If you are using uniform sampling that may mean that the sampling will be inefficient. This may be caused by a very complex posterior shape. You may consider using more livepoints or different sampler (i.e. rslice or rwalk) or alternatively disable bootstrap (bootstrap=0)\n", " warnings.warn(\n", "7044it [00:06, 1010.35it/s, +500 | bound: 51 | nc: 1 | ncall: 56357 | eff(%): 13.506 | loglstar: -inf < -115.278 < inf | logz: -127.012 +/- 0.145 | dlogz: 0.000 > 0.100]\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "Nested Sampling Results:\n", "Log Evidence: -127.01 ± 0.18\n", "Number of likelihood evaluations: 7044\n", "\n", "Parameter Estimates:\n", "Parameter Median 68% CI - / +\n", "---------------------------------------------\n", "slope (m) -1.639 [-2.061, -1.277] [0.422, 0.362]\n", "intercept (b) 8.153 [7.656, 8.568] [0.497, 0.415]\n", "cutoff (s) -0.598 [-0.717, -0.433] [0.120, 0.165]\n", "Running nested sampling...\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "2807it [00:05, 121.94it/s, bound: 41 | nc: 128 | ncall: 47905 | eff(%): 5.860 | loglstar: -inf < -219.108 < inf | logz: -225.789 +/- 0.110 | dlogz: 68.809 > 0.100]/Users/jlothringer/miniconda3/envs/research/lib/python3.12/site-packages/dynesty/bounding.py:617: UserWarning: The enlargement factor for the ellipsoidal bounds determined from bootstrapping is very large. If you are using uniform sampling that may mean that the sampling will be inefficient. This may be caused by a very complex posterior shape. You may consider using more livepoints or different sampler (i.e. rslice or rwalk) or alternatively disable bootstrap (bootstrap=0)\n", " warnings.warn(\n", "2837it [00:06, 79.83it/s, bound: 47 | nc: 53 | ncall: 52595 | eff(%): 5.394 | loglstar: -inf < -219.079 < inf | logz: -225.630 +/- 0.110 | dlogz: 68.589 > 0.100] /Users/jlothringer/miniconda3/envs/research/lib/python3.12/site-packages/dynesty/bounding.py:617: UserWarning: The enlargement factor for the ellipsoidal bounds determined from bootstrapping is very large. If you are using uniform sampling that may mean that the sampling will be inefficient. This may be caused by a very complex posterior shape. You may consider using more livepoints or different sampler (i.e. rslice or rwalk) or alternatively disable bootstrap (bootstrap=0)\n", " warnings.warn(\n", "7999it [00:10, 759.72it/s, +500 | bound: 78 | nc: 1 | ncall: 75748 | eff(%): 11.295 | loglstar: -inf < -148.332 < inf | logz: -161.996 +/- 0.154 | dlogz: 0.000 > 0.100]\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "Nested Sampling Results:\n", "Log Evidence: -162.00 ± 0.19\n", "Number of likelihood evaluations: 7999\n", "\n", "Parameter Estimates:\n", "Parameter Median 68% CI - / +\n", "---------------------------------------------\n", "slope (m) -1.721 [-1.927, -1.519] [0.206, 0.202]\n", "intercept (b) 8.742 [8.695, 8.786] [0.047, 0.045]\n", "cutoff (s) 0.313 [0.221, 0.388] [0.092, 0.075]\n", "Running nested sampling...\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "2478it [00:03, 402.70it/s, bound: 21 | nc: 19 | ncall: 33339 | eff(%): 7.433 | loglstar: -inf < -204.039 < inf | logz: -211.231 +/- 0.112 | dlogz: 69.485 > 0.100] /Users/jlothringer/miniconda3/envs/research/lib/python3.12/site-packages/dynesty/bounding.py:617: UserWarning: The enlargement factor for the ellipsoidal bounds determined from bootstrapping is very large. If you are using uniform sampling that may mean that the sampling will be inefficient. This may be caused by a very complex posterior shape. You may consider using more livepoints or different sampler (i.e. rslice or rwalk) or alternatively disable bootstrap (bootstrap=0)\n", " warnings.warn(\n", "2572it [00:05, 188.70it/s, bound: 37 | nc: 132 | ncall: 45844 | eff(%): 5.610 | loglstar: -inf < -203.241 < inf | logz: -209.950 +/- 0.110 | dlogz: 68.007 > 0.100]/Users/jlothringer/miniconda3/envs/research/lib/python3.12/site-packages/dynesty/bounding.py:617: UserWarning: The enlargement factor for the ellipsoidal bounds determined from bootstrapping is very large. If you are using uniform sampling that may mean that the sampling will be inefficient. This may be caused by a very complex posterior shape. You may consider using more livepoints or different sampler (i.e. rslice or rwalk) or alternatively disable bootstrap (bootstrap=0)\n", " warnings.warn(\n", "2637it [00:09, 85.19it/s, bound: 66 | nc: 118 | ncall: 67130 | eff(%): 3.928 | loglstar: -inf < -203.006 < inf | logz: -209.433 +/- 0.108 | dlogz: 67.357 > 0.100] /Users/jlothringer/miniconda3/envs/research/lib/python3.12/site-packages/dynesty/bounding.py:617: UserWarning: The enlargement factor for the ellipsoidal bounds determined from bootstrapping is very large. If you are using uniform sampling that may mean that the sampling will be inefficient. This may be caused by a very complex posterior shape. You may consider using more livepoints or different sampler (i.e. rslice or rwalk) or alternatively disable bootstrap (bootstrap=0)\n", " warnings.warn(\n", "/Users/jlothringer/miniconda3/envs/research/lib/python3.12/site-packages/dynesty/bounding.py:617: UserWarning: The enlargement factor for the ellipsoidal bounds determined from bootstrapping is very large. If you are using uniform sampling that may mean that the sampling will be inefficient. This may be caused by a very complex posterior shape. You may consider using more livepoints or different sampler (i.e. rslice or rwalk) or alternatively disable bootstrap (bootstrap=0)\n", " warnings.warn(\n", "6031it [00:12, 486.81it/s, +500 | bound: 90 | nc: 1 | ncall: 85654 | eff(%): 7.670 | loglstar: -inf < -136.648 < inf | logz: -146.350 +/- 0.132 | dlogz: 0.000 > 0.100]\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "Nested Sampling Results:\n", "Log Evidence: -146.35 ± 0.16\n", "Number of likelihood evaluations: 6031\n", "\n", "Parameter Estimates:\n", "Parameter Median 68% CI - / +\n", "---------------------------------------------\n", "slope (m) -1.702 [-1.854, -1.559] [0.153, 0.143]\n", "intercept (b) 10.943 [10.785, 11.104] [0.158, 0.160]\n", "cutoff (s) 3.211 [1.932, 4.409] [1.279, 1.198]\n", "Running nested sampling...\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "2486it [00:02, 858.77it/s, bound: 14 | nc: 17 | ncall: 27479 | eff(%): 9.047 | loglstar: -inf < -107.551 < inf | logz: -116.390 +/- 0.125 | dlogz: 109.319 > 0.100]/Users/jlothringer/miniconda3/envs/research/lib/python3.12/site-packages/dynesty/bounding.py:617: UserWarning: The enlargement factor for the ellipsoidal bounds determined from bootstrapping is very large. If you are using uniform sampling that may mean that the sampling will be inefficient. This may be caused by a very complex posterior shape. You may consider using more livepoints or different sampler (i.e. rslice or rwalk) or alternatively disable bootstrap (bootstrap=0)\n", " warnings.warn(\n", "/Users/jlothringer/miniconda3/envs/research/lib/python3.12/site-packages/dynesty/bounding.py:617: UserWarning: The enlargement factor for the ellipsoidal bounds determined from bootstrapping is very large. If you are using uniform sampling that may mean that the sampling will be inefficient. This may be caused by a very complex posterior shape. You may consider using more livepoints or different sampler (i.e. rslice or rwalk) or alternatively disable bootstrap (bootstrap=0)\n", " warnings.warn(\n", "/Users/jlothringer/miniconda3/envs/research/lib/python3.12/site-packages/dynesty/bounding.py:617: UserWarning: The enlargement factor for the ellipsoidal bounds determined from bootstrapping is very large. If you are using uniform sampling that may mean that the sampling will be inefficient. This may be caused by a very complex posterior shape. You may consider using more livepoints or different sampler (i.e. rslice or rwalk) or alternatively disable bootstrap (bootstrap=0)\n", " warnings.warn(\n", "/Users/jlothringer/miniconda3/envs/research/lib/python3.12/site-packages/dynesty/bounding.py:617: UserWarning: The enlargement factor for the ellipsoidal bounds determined from bootstrapping is very large. If you are using uniform sampling that may mean that the sampling will be inefficient. This may be caused by a very complex posterior shape. You may consider using more livepoints or different sampler (i.e. rslice or rwalk) or alternatively disable bootstrap (bootstrap=0)\n", " warnings.warn(\n", "2688it [00:04, 332.34it/s, bound: 33 | nc: 33 | ncall: 41775 | eff(%): 6.434 | loglstar: -inf < -99.744 < inf | logz: -107.445 +/- 0.118 | dlogz: 100.483 > 0.100] /Users/jlothringer/miniconda3/envs/research/lib/python3.12/site-packages/dynesty/bounding.py:617: UserWarning: The enlargement factor for the ellipsoidal bounds determined from bootstrapping is very large. If you are using uniform sampling that may mean that the sampling will be inefficient. This may be caused by a very complex posterior shape. You may consider using more livepoints or different sampler (i.e. rslice or rwalk) or alternatively disable bootstrap (bootstrap=0)\n", " warnings.warn(\n", "/Users/jlothringer/miniconda3/envs/research/lib/python3.12/site-packages/dynesty/bounding.py:617: UserWarning: The enlargement factor for the ellipsoidal bounds determined from bootstrapping is very large. If you are using uniform sampling that may mean that the sampling will be inefficient. This may be caused by a very complex posterior shape. You may consider using more livepoints or different sampler (i.e. rslice or rwalk) or alternatively disable bootstrap (bootstrap=0)\n", " warnings.warn(\n", "/Users/jlothringer/miniconda3/envs/research/lib/python3.12/site-packages/dynesty/bounding.py:617: UserWarning: The enlargement factor for the ellipsoidal bounds determined from bootstrapping is very large. If you are using uniform sampling that may mean that the sampling will be inefficient. This may be caused by a very complex posterior shape. You may consider using more livepoints or different sampler (i.e. rslice or rwalk) or alternatively disable bootstrap (bootstrap=0)\n", " warnings.warn(\n", "/Users/jlothringer/miniconda3/envs/research/lib/python3.12/site-packages/dynesty/bounding.py:617: UserWarning: The enlargement factor for the ellipsoidal bounds determined from bootstrapping is very large. If you are using uniform sampling that may mean that the sampling will be inefficient. This may be caused by a very complex posterior shape. You may consider using more livepoints or different sampler (i.e. rslice or rwalk) or alternatively disable bootstrap (bootstrap=0)\n", " warnings.warn(\n", "7136it [00:09, 777.33it/s, +500 | bound: 73 | nc: 1 | ncall: 71983 | eff(%): 10.682 | loglstar: -inf < 0.308 < inf | logz: -11.628 +/- 0.142 | dlogz: 0.000 > 0.100]" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "Nested Sampling Results:\n", "Log Evidence: -11.63 ± 0.17\n", "Number of likelihood evaluations: 7136\n", "\n", "Parameter Estimates:\n", "Parameter Median 68% CI - / +\n", "---------------------------------------------\n", "slope (m) -1.128 [-1.210, -1.042] [0.082, 0.086]\n", "intercept (b) 8.840 [8.791, 8.893] [0.050, 0.052]\n", "cutoff (s) 1.725 [-0.180, 3.973] [1.905, 2.248]\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "\n" ] } ], "source": [ "results_transit = mass_mh_dynesty(transit, plot_it=False, xerrs=False, ch=True)\n", "results_emission = mass_mh_dynesty(emission, plot_it=False, xerrs=False, ch=True)\n", "results_direct = mass_mh_dynesty(direct, plot_it=False, xerrs=False, ch=True)\n", "results_ss = mass_mh_dynesty(solar_system, plot_it=False, xerrs=False,ch=True)" ] }, { "cell_type": "code", "execution_count": 67, "id": "859ce8ee-1860-4964-a0d7-a9c05944a983", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "Slope & $-1.67\\pm0.38$ & $-1.72\\pm0.20$ & $-1.70\\pm0.15$ & $-1.13\\pm0.09$ \\\\\n", "Intercept (M/H) & $8.11\\pm0.43$ & $8.74\\pm0.04$ & $10.94\\pm0.16$ & $8.84\\pm0.07$ \\\\\n", "Mass Cutoff (log10) & $-0.58\\pm0.13$ & $0.31\\pm0.08$ & $3.19\\pm1.06$ & $1.83\\pm1.73$ \\\\\n", "$\\ln E$ & $-127.01 \\pm 0.2$ & $-162.00 \\pm 0.2$ & $-146.35 \\pm 0.2$ & $-11.63 \\pm 0.2$ \\\\\n", "\\hline \\\n", "\\multicolumn{5}{c}{\\textbf{Flat-Line}} \\\\\n", "\\textbf{Parameter} & \\textbf{Transiting} & \\textbf{Eclipse} & \\textbf{Direct} & \\textbf{Solar System} \\\\\n", "\\hline \\\n", "Intercept (M/H) & $9.39\\pm0.04$ & $8.52\\pm0.03$ & $9.14\\pm0.03$ & $9.47\\pm0.02$ \\\\\n", "$\\ln E$ & $-180.26 \\pm 0.2$ & $-224.50 \\pm 0.2$ & $-208.56 \\pm 0.2$ & $-105.42 \\pm 0.2$ \\\\\n", "\\hline \\\n", "\n" ] } ], "source": [ "from dynesty import utils as dyfunc\n", "\n", "# Transit\n", "samples_transit, weights_transit = results_transit.samples, results_transit.importance_weights()\n", "mean_transit, cov_transit = dyfunc.mean_and_cov(samples_transit, weights_transit)\n", "cov_transit = np.sqrt(np.diag(cov_transit))\n", "\n", "# Emission\n", "samples_emission, weights_emission = results_emission.samples, results_emission.importance_weights()\n", "mean_emission, cov_emission = dyfunc.mean_and_cov(samples_emission, weights_emission)\n", "cov_emission = np.sqrt(np.diag(cov_emission))\n", "\n", "# Direct\n", "samples_direct, weights_direct = results_direct.samples, results_direct.importance_weights()\n", "mean_direct, cov_direct = dyfunc.mean_and_cov(samples_direct, weights_direct)\n", "cov_direct = np.sqrt(np.diag(cov_direct))\n", "\n", "# SS\n", "samples_ss, weights_ss = results_ss.samples, results_ss.importance_weights()\n", "mean_ss, cov_ss = dyfunc.mean_and_cov(samples_ss, weights_ss)\n", "cov_ss = np.sqrt(np.diag(cov_ss))\n", "\n", "# Transit (flat)\n", "samples_transit_flat, weights_transit_flat = results_transit_flat.samples, results_transit_flat.importance_weights()\n", "mean_transit_flat, cov_transit_flat = dyfunc.mean_and_cov(samples_transit_flat, weights_transit_flat)\n", "cov_transit_flat = np.sqrt(np.diag(cov_transit_flat))\n", "\n", "# Emission (flat)\n", "samples_emission_flat, weights_emission_flat = results_emission_flat.samples, results_emission_flat.importance_weights()\n", "mean_emission_flat, cov_emission_flat = dyfunc.mean_and_cov(samples_emission_flat, weights_emission_flat)\n", "cov_emission_flat = np.sqrt(np.diag(cov_emission_flat))\n", "\n", "# Direct (flat)\n", "samples_direct_flat, weights_direct_flat = results_direct_flat.samples, results_direct_flat.importance_weights()\n", "mean_direct_flat, cov_direct_flat = dyfunc.mean_and_cov(samples_direct_flat, weights_direct_flat)\n", "cov_direct_flat = np.sqrt(np.diag(cov_direct_flat))\n", "\n", "# SS (flat)\n", "samples_ss_flat, weights_ss_flat = results_ss_flat.samples, results_ss_flat.importance_weights()\n", "mean_ss_flat, cov_ss_flat = dyfunc.mean_and_cov(samples_ss_flat, weights_ss_flat)\n", "cov_ss_flat = np.sqrt(np.diag(cov_ss_flat))\n", "\n", "# Print LaTeX format\n", "latex_string = f'''\n", "Slope & ${mean_transit[0]:0.2f}\\\\pm{cov_transit[0]:0.2f}$ & ${mean_emission[0]:0.2f}\\\\pm{cov_emission[0]:0.2f}$ & ${mean_direct[0]:0.2f}\\\\pm{cov_direct[0]:0.2f}$ & ${mean_ss[0]:0.2f}\\\\pm{cov_ss[0]:0.2f}$ \\\\\\\\\n", "Intercept (M/H) & ${mean_transit[1]:0.2f}\\\\pm{cov_transit[1]:0.2f}$ & ${mean_emission[1]:0.2f}\\\\pm{cov_emission[1]:0.2f}$ & ${mean_direct[1]:0.2f}\\\\pm{cov_direct[1]:0.2f}$ & ${mean_ss[1]:0.2f}\\\\pm{cov_ss[1]:0.2f}$ \\\\\\\\\n", "Mass Cutoff (log10) & ${mean_transit[2]:0.2f}\\\\pm{cov_transit[2]:0.2f}$ & ${mean_emission[2]:0.2f}\\\\pm{cov_emission[2]:0.2f}$ & ${mean_direct[2]:0.2f}\\\\pm{cov_direct[2]:0.2f}$ & ${mean_ss[2]:0.2f}\\\\pm{cov_ss[2]:0.2f}$ \\\\\\\\\n", "$\\\\ln E$ & ${np.max(results_transit.logz):0.2f} \\\\pm 0.2$ & ${np.max(results_emission.logz):0.2f} \\\\pm 0.2$ & ${np.max(results_direct.logz):0.2f} \\\\pm 0.2$ & ${np.max(results_ss.logz):0.2f} \\\\pm 0.2$ \\\\\\\\\n", "\\\\hline \\\\\n", "\\\\multicolumn{{5}}{{c}}{{\\\\textbf{{Flat-Line}}}} \\\\\\\\\n", "\\\\textbf{{Parameter}} & \\\\textbf{{Transiting}} & \\\\textbf{{Eclipse}} & \\\\textbf{{Direct}} & \\\\textbf{{Solar System}} \\\\\\\\\n", "\\\\hline \\\\\n", "Intercept (M/H) & ${mean_transit_flat[0]:0.2f}\\\\pm{cov_transit_flat[0]:0.2f}$ & ${mean_emission_flat[0]:0.2f}\\\\pm{cov_emission_flat[0]:0.2f}$ & ${mean_direct_flat[0]:0.2f}\\\\pm{cov_direct_flat[0]:0.2f}$ & ${mean_ss_flat[0]:0.2f}\\\\pm{cov_ss_flat[0]:0.2f}$ \\\\\\\\\n", "$\\\\ln E$ & ${np.max(results_transit_flat.logz):0.2f} \\\\pm 0.2$ & ${np.max(results_emission_flat.logz):0.2f} \\\\pm 0.2$ & ${np.max(results_direct_flat.logz):0.2f} \\\\pm 0.2$ & ${np.max(results_ss_flat.logz):0.2f} \\\\pm 0.2$ \\\\\\\\\n", "\\\\hline \\\\\n", "'''\n", "print(latex_string)" ] }, { "cell_type": "code", "execution_count": 49, "id": "99b8e255-fbae-4aaf-930f-55323b63a71d", "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_20318/2982232166.py:68: RuntimeWarning: invalid value encountered in log10\n", " MF = np.log10(factorZ*CM * 1e12)\n", "/var/folders/mv/w837twcj1x15d56059x6tb640005r5/T/ipykernel_20318/2982232166.py:80: RuntimeWarning: invalid value encountered in log10\n", " MF2 = np.log10(factorZ2*CM * 1e12)\n" ] }, { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig, ax = plt.subplots(figsize=(9, 6))\n", "\n", "#x = 10**(np.linspace(-2,2.0))\n", "#fit = np.log10(9.7*(x**-0.45)) #[M/H] ~ log10(Z/Zs)\n", "#ax.plot(x,fit,'k--',label='Thorgren+ 2016',zorder=1,alpha=0.75)\n", "\n", "for data, result, label, marker, color in zip([direct, emission, transit,solar_system], \n", " [results_direct,results_emission,results_transit,results_ss],\n", " ['Direct', 'Eclipse', 'Transmission','Solar System'],\n", " ['o', 's', 'd', 'H'],\n", " ['#2E86AB', '#A23B72', '#F18F01', '#808080']):\n", " ax.errorbar(data['Mass'], data['C/H'],\n", " xerr=[data['Mass Lower'], data['Mass Upper']],\n", " yerr=[data['C/H Lower'], data['C/H Upper']],\n", " fmt=marker, label=label, color=color,\n", " markersize=6, capsize=2, alpha=1.0,\n", " markeredgecolor='k',markeredgewidth=0.5)\n", "\n", " n_samples = min(50, len(result['samples']))\n", " indices = np.random.choice(len(result['samples']), n_samples, p=result.importance_weights()/np.sum(result.importance_weights()))\n", " xfake = np.linspace(-2,2.5)\n", " for idx in indices: # Plot subset for clarity\n", " m_sample, b_sample, s_sample = result['samples'][idx, 0], result['samples'][idx, 1], result['samples'][idx, 2]\n", " y_sample = m_sample * xfake + b_sample\n", " y_sample[np.where(xfake > s_sample)[0]] = m_sample * s_sample + b_sample\n", " \n", " ax.plot(10**xfake, y_sample, '-', alpha=0.05, color=color)\n", "\n", "for _, row in uhjs.iterrows():\n", " color_map = {'Direct': '#2E86AB', 'Eclipse': '#A23B72', 'Transit': '#F18F01'}\n", " ax.errorbar(row['Mass'], row['C/H'],\n", " xerr=[[row['Mass Lower']], [row['Mass Upper']]],\n", " yerr=[[row['C/H Lower']], [row['C/H Upper']]],\n", " fmt='*', color=color_map[row['Geometry']], markersize=12,\n", " markeredgecolor='k',markeredgewidth=0.5)\n", "\n", "ax.set_xlim(0.025,50)\n", "ax.set_xscale('log')\n", "\n", "ax.set_ylim(7.0,11.5)\n", "\n", "ax.set_xlabel(r'Mass [M$_{\\mathrm{Jup}}$]', fontsize=16)\n", "ax.set_ylabel('C/H', fontsize=16)\n", "ax.legend(frameon=True, fancybox=True, shadow=False,fontsize=13)\n", "ax.grid(True, alpha=0.3)\n", "\n", "ax.minorticks_on()\n", "\n", "ax.yaxis.set_ticks_position('both')\n", "ax.xaxis.set_ticks_position('both')\n", "\n", "plt.tight_layout()\n", "\n", "ax.tick_params(labelsize=12)\n", "\n", "X = 0.7381\n", "Y = 0.2485\n", "Z = 0.0134\n", "ZM = (9.7*np.logspace(-2,2)**-0.45) * Z\n", "ZH = (10**8.69+10**8.43+10**7.83) / 1e12\n", "muZ = 18\n", "muH = 2.0\n", "\n", "factorZ = (1+(Y/X)) / (((1/ZM)-1)*(muZ/muH))\n", "\n", "#C/M\n", "CM = 10**8.43 / (1.04e-3*1e12)\n", "MF = np.log10(factorZ*CM * 1e12)\n", "\n", "#8.69\n", "#9.7*M&-0.45\n", "#ax.plot( np.logspace(-2,2), 8.69 + np.log10((9.7*np.logspace(-1,1)**-0.45)),'--k') #Thorngren, kinda - mass fraction\n", "ax.plot(np.logspace(-2,2),MF,'--k',label='Thorngren+ 16 (Interior)')\n", "\n", "#yayaati's paper\n", "a = 2.6\n", "b = 3.3\n", "ZM2 = (a/np.logspace(-2,2) + b) * Z\n", "factorZ2 = (1+(Y/X)) / (((1/ZM2)-1)*(muZ/muH))\n", "MF2 = np.log10(factorZ2*CM * 1e12)\n", "\n", "#ax.plot(np.logspace(-2,2),MF2,'-.k',label='Chachan+ 25 (Interior)')\n", "\n", "ax.hlines(8.43,-2,100,'k',linestyle=':',label='Asplund+09/21 Solar')\n", "\n", "ax.legend(frameon=True, fancybox=True, shadow=False,fontsize=13,framealpha=1.0)\n", "\n", "plt.savefig('./paper_figs/mass_metallicity_fits_ch.pdf')" ] }, { "cell_type": "code", "execution_count": 50, "id": "fff13656-d9fb-425c-a8ef-4cb27cdb1b23", "metadata": {}, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "params = ['Slope', 'Intercept', 'Cutoff']\n", "color_map = {'Direct': '#2E86AB', 'Eclipse': '#A23B72', 'Transit': '#F18F01','Solar System': '#808080'}\n", "\n", "# Extract posterior samples\n", "samples = results_transit['samples']\n", "weights = results_transit.importance_weights()\n", "lw = 2\n", "\n", "fig,ax = plt.subplots(1,3,figsize=(15,6))\n", "ax[0].hist(results_transit['samples'][:, 0],weights = results_transit.importance_weights(),density=True,bins=100,label='Transit',\n", " ec=color_map['Transit'],fc=color_map['Transit'] + '60',histtype='stepfilled',linewidth=lw)\n", "ax[0].hist(results_emission['samples'][:, 0],weights = results_emission.importance_weights(),density=True,bins=100,label='Eclipse',\n", " ec=color_map['Eclipse'],fc=color_map['Eclipse'] + '60',histtype='stepfilled',linewidth=lw)\n", "ax[0].hist(results_direct['samples'][:, 0],weights = results_direct.importance_weights(),density=True,bins=100,label='Direct',\n", " ec=color_map['Direct'],fc=color_map['Direct'] + '60',histtype='stepfilled',linewidth=lw)\n", "ax[0].hist(results_ss['samples'][:, 0],weights = results_ss.importance_weights(),density=True,bins=100,label='Solar System',\n", " ec=color_map['Solar System'],fc=color_map['Solar System'] + '60',histtype='stepfilled',linewidth=lw)\n", "\n", "\n", "#ax[0].set_title(f'{params[0]}')\n", "ax[0].set_xlim(-3.5,-0.5)\n", "ax[0].set_ylabel('Frequency',fontsize=16)\n", "ax[0].set_xlabel(f'{params[0]}',fontsize=16)\n", "ax[0].tick_params(labelsize=12)\n", "\n", "ax[1].hist(results_transit['samples'][:, 1],weights = results_transit.importance_weights(),density=True,bins=100,label='Transit',\n", " ec=color_map['Transit'],fc=color_map['Transit'] + '60',histtype='stepfilled',linewidth=lw)\n", "ax[1].hist(results_emission['samples'][:, 1],weights = results_emission.importance_weights(),density=True,bins=100,label='Eclipse',\n", " ec=color_map['Eclipse'],fc=color_map['Eclipse'] + '60',histtype='stepfilled',linewidth=lw)\n", "ax[1].hist(results_direct['samples'][:, 1],weights = results_direct.importance_weights(),density=True,bins=100,label='Direct',\n", " ec=color_map['Direct'],fc=color_map['Direct'] + '60',histtype='stepfilled',linewidth=lw)\n", "ax[1].hist(results_ss['samples'][:, 1],weights = results_ss.importance_weights(),density=True,bins=100,label='Solar System',\n", " ec=color_map['Solar System'],fc=color_map['Solar System'] + '60',histtype='stepfilled',linewidth=lw)\n", "\n", "#ax[1].set_title(f'{params[1]}')\n", "ax[1].set_xlim(7,12.5)\n", "ax[1].set_xlabel(f'{params[1]}',fontsize=16)\n", "ax[1].tick_params(labelsize=12)\n", "\n", "ax[2].hist(results_transit['samples'][:, 2],weights = results_transit.importance_weights(),density=True,bins=100,label='Transit',\n", " ec=color_map['Transit'],fc=color_map['Transit'] + '60',histtype='stepfilled',linewidth=lw)\n", "ax[2].hist(results_emission['samples'][:, 2],weights = results_emission.importance_weights(),density=True,bins=100,label='Eclipse',\n", " ec=color_map['Eclipse'],fc=color_map['Eclipse'] + '60',histtype='stepfilled',linewidth=lw)\n", "ax[2].hist(results_direct['samples'][:, 2],weights = results_direct.importance_weights(),density=True,bins=100,label='Direct',\n", " ec=color_map['Direct'],fc=color_map['Direct'] + '60',histtype='stepfilled',linewidth=lw)\n", "ax[2].hist(results_ss['samples'][:, 2],weights = results_ss.importance_weights(),density=True,bins=100,label='Solar System',\n", " ec=color_map['Solar System'],fc=color_map['Solar System'] + '60',histtype='stepfilled',linewidth=lw)\n", "\n", "#ax[2].set_title(f'{params[2]}')\n", "ax[2].set_xlim(-1,2.0)\n", "ax[2].set_xlabel(f'{params[2]}',fontsize=16)\n", "ax[2].tick_params(labelsize=12)\n", "ax[2].legend(fontsize=13)\n", "\n", "ax[0].minorticks_on()\n", "ax[1].minorticks_on()\n", "ax[2].minorticks_on()\n", "\n", "ax[0].yaxis.set_ticks_position('both')\n", "ax[0].xaxis.set_ticks_position('both')\n", "ax[1].yaxis.set_ticks_position('both')\n", "ax[1].xaxis.set_ticks_position('both')\n", "ax[2].yaxis.set_ticks_position('both')\n", "ax[2].xaxis.set_ticks_position('both')\n", "\n", "plt.tight_layout()\n", "\n", "#ax.tick_params(labelsize=12)\n", "\n", "plt.savefig('./paper_figs/mass_metallicity_posteriors_ch.pdf')" ] }, { "cell_type": "code", "execution_count": 51, "id": "b7865864-6d4c-40b1-b552-b1a3b818a93d", "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", "import matplotlib.pyplot as plt\n", "from dynesty import NestedSampler\n", "from dynesty.plotting import runplot, traceplot, cornerplot\n", "\n", "# Calculate weighted posterior statistics\n", "def weighted_quantile(values, weights, quantiles):\n", " \"\"\"Calculate weighted quantiles\"\"\"\n", " sorted_indices = np.argsort(values)\n", " sorted_values = values[sorted_indices]\n", " sorted_weights = weights[sorted_indices]\n", " cumsum = np.cumsum(sorted_weights)\n", " cumsum /= cumsum[-1] # normalize\n", " return np.interp(quantiles, cumsum, sorted_values)\n", "\n", "def mass_mh_dynesty(dataset, plot_it=True, xerrs=True, oh = False, ch = False):\n", " # Generate synthetic data\n", " #np.random.seed(42)\n", " #n_data = 50\n", " #x_true = np.linspace(0, 10, n_data)\n", " #m_true, b_true, sigma_true = 2.5, 1.0, 0.5\n", " \n", " # True line with noise\n", " #y_true = m_true * x_true + b_true\n", " #y_obs = y_true + np.random.normal(0, sigma_true, n_data)\n", " \n", " y_obs = dataset['Metallicity'].values\n", " ylow = dataset['Metallicity Lower'].values\n", " yhigh = dataset['Metallicity Upper'].values\n", " if oh is True:\n", " y_obs = dataset['O/H'].values\n", " ylow = dataset['O/H Lower'].values\n", " yhigh = dataset['O/H Upper'].values\n", " if ch is True:\n", " y_obs = dataset['C/H'].values\n", " ylow = dataset['C/H Lower'].values\n", " yhigh = dataset['C/H Upper'].values\n", " #y_err_u = \n", " x_true = np.log10(dataset['Mass'].values)\n", " if xerrs is True:\n", " x_err = np.max([np.log10(dataset['Mass'].values+dataset['Mass Upper'].values) - np.log10(dataset['Mass'].values),\n", " np.log10(dataset['Mass'].values) - np.log10(dataset['Mass'].values)-dataset['Mass Lower'].values],\n", " axis=0)\n", " else:\n", " x_err = np.zeros(len(x_true))\n", " \n", " # Define the log-likelihood function\n", " def loglike(theta):\n", " \"\"\"Log-likelihood function for linear model y = mx + b + noise\"\"\"\n", " m, b, s = theta\n", " #sigma = np.exp(log_sigma) # Use log(sigma) to ensure positivity\n", " \n", " # Model prediction\n", " y_model = m * x_true + b\n", " y_model[np.where(x_true > s)[0]] = m * s + b\n", "\n", " # to incorporate errors in x, just make an effective error that is yerr**2+(dy/dx * x_err)**2 = yerr**2+(m*xerr)**2\n", " chi2 = 0\n", " for i in range(len(y_model)):\n", " # skipping measurements with only upper or lower limits\n", " # until better implemented\n", " if yhigh[i] == 0.0: \n", " continue\n", " elif ylow[i] == 0.0:\n", " continue\n", " else:\n", " xerr_eff = x_err[i]*m #okay if xerr = 0.0\n", " if y_model[i] > y_obs[i]:\n", " chi2 += ((y_model[i] - y_obs[i]) / np.sqrt(yhigh[i]**2+xerr_eff**2)) ** 2\n", " else:\n", " chi2 += ((y_model[i] - y_obs[i]) / np.sqrt(ylow[i]**2+xerr_eff**2)) ** 2\n", " \n", " \n", " # Gaussian likelihood\n", " #chi2 = np.sum((y_obs - y_model)**2 / sigma**2)\n", " #for calculating absolute likelihood, let's take max err\n", " sigma = np.sum(np.max([ylow,yhigh],axis=0))\n", " logL = -0.5 * (chi2 + len(y_obs) * np.log(2 * np.pi * sigma**2))\n", " \n", " return logL\n", " \n", " # Define the prior transform function\n", " def ptform(u):\n", " \"\"\"Prior transform function (uniform to parameter space)\"\"\"\n", " theta = np.array(u) # u is in [0,1]^ndim\n", " \n", " # Transform uniform [0,1] to parameter ranges:\n", " # m: slope [-5, 5]\n", " # b: intercept [-5, 5] \n", " # log_sigma: log(noise) [log(0.01), log(2)]\n", " theta[0] = -5 + 8 * u[0] # m\n", " theta[1] = -5 + 20 * u[1] # b \n", " #theta[1] = -5 + 6 * u[1] #keep metallicity at Mj < 10x solar\n", " #theta[2] = np.log(0.01) + (np.log(2) - np.log(0.01)) * u[2] # log_sigma\n", " theta[2] = -5+10*u[2]\n", " \n", " return theta\n", " \n", " # Set up the nested sampler\n", " ndim = 3 # m, b, log_sigma\n", " sampler = NestedSampler(loglike, ptform, ndim, nlive=500)\n", " \n", " # Run the nested sampling\n", " print(\"Running nested sampling...\")\n", " sampler.run_nested(dlogz=0.1, print_progress=True)\n", " \n", " # Get results\n", " results = sampler.results\n", " \n", " # Print summary statistics\n", " print(f\"\\nNested Sampling Results:\")\n", " print(f\"Log Evidence: {results.logz[-1]:.2f} ± {results.logzerr[-1]:.2f}\")\n", " print(f\"Number of likelihood evaluations: {results.niter}\")\n", " \n", " # Extract posterior samples\n", " samples = results['samples']\n", " #weights = results['importance_weights']\n", " #weights = np.exp(results['logwt'])\n", " #weights = results['logwt']\n", " weights = results.importance_weights()\n", " \n", " # Calculate weighted posterior statistics\n", " def weighted_quantile(values, weights, quantiles):\n", " \"\"\"Calculate weighted quantiles\"\"\"\n", " sorted_indices = np.argsort(values)\n", " sorted_values = values[sorted_indices]\n", " sorted_weights = weights[sorted_indices]\n", " cumsum = np.cumsum(sorted_weights)\n", " cumsum /= cumsum[-1] # normalize\n", " return np.interp(quantiles, cumsum, sorted_values)\n", " \n", " # Get parameter estimates (median and 68% credible intervals)\n", " params = ['slope (m)', 'intercept (b)', 'cutoff (s)']\n", " #true_values = [m_true, b_true, np.log(sigma_true)]\n", " \n", " print(f\"\\nParameter Estimates:\")\n", " print(f\"{'Parameter':<12} {'Median':<8} {'68% CI'} {'- / +'}\")\n", " print(\"-\" * 45)\n", " \n", " for i in range(ndim):\n", " median = weighted_quantile(samples[:, i], weights, [0.5])[0]\n", " ci_low, ci_high = weighted_quantile(samples[:, i], weights, [0.16, 0.84])\n", " \n", " #true_val = true_values[i]\n", " median_val = median\n", " ci_low_val = ci_low\n", " ci_high_val = ci_high\n", " param_name = params[i]\n", " \n", " print(f\"{param_name:<12} {median_val:<8.3f} [{ci_low_val:.3f}, {ci_high_val:.3f}] [{median_val-ci_low_val:.3f}, {ci_high_val-median_val:.3f}]\")\n", "\n", " if plot_it is True:\n", " # Create plots\n", " fig, axes = plt.subplots(2, 3, figsize=(15, 10))\n", " \n", " # Plot 1: Data and fitted line\n", " ax1 = axes[0, 0]\n", " #ax1.scatter(x_true, y_obs, alpha=1.0, label='Observed data',zorder=999)\n", " #ax1.plot(x_true, y_true, 'r-', label='True line', linewidth=2)\n", " ax1.errorbar(x_true, y_obs, yerr = [ylow,yhigh], zorder=999,fmt='.',color='k',label='Observed Data')\n", " \n", " \n", " # Plot posterior samples of the line\n", " n_samples = min(100, len(samples))\n", " indices = np.random.choice(len(samples), n_samples, p=weights/np.sum(weights))\n", " xfake = np.linspace(-2,1.5)\n", " for idx in indices[:50]: # Plot subset for clarity\n", " m_sample, b_sample, s_sample = samples[idx, 0], samples[idx, 1], samples[idx, 2]\n", " y_sample = m_sample * xfake + b_sample\n", " y_sample[np.where(xfake > s_sample)[0]] = m_sample * s_sample + b_sample\n", " \n", " ax1.plot(xfake, y_sample, 'k-', alpha=0.1)\n", " \n", " ax1.set_xlabel('x')\n", " ax1.set_ylabel('y')\n", " ax1.legend()\n", " ax1.set_title('Data and Posterior Fits')\n", " \n", " # Plot 2: Trace plot (evolution of live points)\n", " ax2 = axes[0, 1]\n", " ax2.plot(results['logl'])\n", " ax2.set_xlabel('Iteration')\n", " ax2.set_ylabel('Log Likelihood')\n", " ax2.set_title('Nested Sampling Trace')\n", " ax2.set_yscale('symlog')\n", " \n", " # Plot 3: Prior vs Posterior for slope\n", " ax3 = axes[1, 0]\n", " ax3.hist(samples[:, 0], weights=weights, bins=100, alpha=0.7, \n", " density=True, label='Posterior')\n", " #ax3.axvline(m_true, color='r', linestyle='--', label='True value')\n", " ax3.axhline(1/10, color='g', linestyle=':', label='Prior', alpha=0.7) # Prior is uniform over [-5,5]\n", " ax3.set_xlabel('Slope (m)')\n", " ax3.set_ylabel('Density')\n", " ax3.legend()\n", " ax3.set_title('Slope: Prior vs Posterior')\n", " \n", " # Plot 4: Prior vs Posterior for intercept \n", " ax4 = axes[1, 1]\n", " ax4.hist(samples[:, 1], weights=weights, bins=100, alpha=0.7,\n", " density=True, label='Posterior')\n", " #ax4.axvline(b_true, color='r', linestyle='--', label='True value')\n", " ax4.axhline(1/10, color='g', linestyle=':', label='Prior', alpha=0.7) # Prior is uniform over [-5,5]\n", " ax4.set_xlabel('Intercept (b)')\n", " ax4.set_ylabel('Density')\n", " ax4.legend()\n", " ax4.set_title('Intercept: Prior vs Posterior')\n", " \n", " # Plot 4: Prior vs Posterior for intercept \n", " ax5 = axes[1, 2]\n", " ax5.hist(samples[:, 2], weights=weights, bins=100, alpha=0.7,\n", " density=True, label='Posterior')\n", " #ax4.axvline(b_true, color='r', linestyle='--', label='True value')\n", " #ax4.axhline(1/10, color='g', linestyle=':', label='Prior', alpha=0.7) # Prior is uniform over [-5,5]\n", " ax5.set_xlabel('Cutoff (s)')\n", " ax5.set_ylabel('Density')\n", " ax5.legend()\n", " ax5.set_title('Cutoff: Prior vs Posterior')\n", " \n", " plt.tight_layout()\n", " plt.show()\n", " \n", " # Optional: Create corner plot if you want to see parameter correlations\n", " # Uncomment the following lines:\n", " fig_corner = cornerplot(results)\n", " plt.show()\n", " return results" ] }, { "cell_type": "code", "execution_count": 52, "id": "0990e265-3e46-421a-a94e-b8c7a3a36cbc", "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", "import matplotlib.pyplot as plt\n", "from dynesty import NestedSampler\n", "from dynesty.plotting import runplot, traceplot, cornerplot\n", "\n", "# Calculate weighted posterior statistics\n", "def weighted_quantile(values, weights, quantiles):\n", " \"\"\"Calculate weighted quantiles\"\"\"\n", " sorted_indices = np.argsort(values)\n", " sorted_values = values[sorted_indices]\n", " sorted_weights = weights[sorted_indices]\n", " cumsum = np.cumsum(sorted_weights)\n", " cumsum /= cumsum[-1] # normalize\n", " return np.interp(quantiles, cumsum, sorted_values)\n", "\n", "def mass_mh_dynesty_flat(dataset, plot_it=True, xerrs=True, oh = False, ch = False):\n", " # Generate synthetic data\n", " #np.random.seed(42)\n", " #n_data = 50\n", " #x_true = np.linspace(0, 10, n_data)\n", " #m_true, b_true, sigma_true = 2.5, 1.0, 0.5\n", " \n", " # True line with noise\n", " #y_true = m_true * x_true + b_true\n", " #y_obs = y_true + np.random.normal(0, sigma_true, n_data)\n", " \n", " y_obs = dataset['Metallicity'].values\n", " ylow = dataset['Metallicity Lower'].values\n", " yhigh = dataset['Metallicity Upper'].values\n", " if oh is True:\n", " y_obs = dataset['O/H'].values\n", " ylow = dataset['O/H Lower'].values\n", " yhigh = dataset['O/H Upper'].values\n", " if ch is True:\n", " y_obs = dataset['C/H'].values\n", " ylow = dataset['C/H Lower'].values\n", " yhigh = dataset['C/H Upper'].values\n", " #y_err_u = \n", " x_true = np.log10(dataset['Mass'].values)\n", " if xerrs is True:\n", " x_err = np.max([np.log10(dataset['Mass'].values+dataset['Mass Upper'].values) - np.log10(dataset['Mass'].values),\n", " np.log10(dataset['Mass'].values) - np.log10(dataset['Mass'].values)-dataset['Mass Lower'].values],\n", " axis=0)\n", " else:\n", " x_err = np.zeros(len(x_true))\n", " \n", " # Define the log-likelihood function\n", " def loglike(theta):\n", " \"\"\"Log-likelihood function for linear model y = mx + b + noise\"\"\"\n", " b = theta\n", " #sigma = np.exp(log_sigma) # Use log(sigma) to ensure positivity\n", " \n", " # Model prediction\n", " #y_model = m * x_true + b\n", " #y_model[np.where(x_true > s)[0]] = m * s + b\n", " y_model = b*np.ones(len(x_true))\n", "\n", " # to incorporate errors in x, just make an effective error that is yerr**2+(dy/dx * x_err)**2 = yerr**2+(m*xerr)**2\n", " chi2 = 0\n", " for i in range(len(y_model)):\n", " # skipping measurements with only upper or lower limits\n", " # until better implemented\n", " if yhigh[i] == 0.0: \n", " continue\n", " elif ylow[i] == 0.0:\n", " continue\n", " else:\n", " xerr_eff = 0.0 #okay if xerr = 0.0\n", " if y_model[i] > y_obs[i]:\n", " chi2 += ((y_model[i] - y_obs[i]) / np.sqrt(yhigh[i]**2+xerr_eff**2)) ** 2\n", " else:\n", " chi2 += ((y_model[i] - y_obs[i]) / np.sqrt(ylow[i]**2+xerr_eff**2)) ** 2\n", " \n", " \n", " # Gaussian likelihood\n", " #chi2 = np.sum((y_obs - y_model)**2 / sigma**2)\n", " #for calculating absolute likelihood, let's take max err\n", " sigma = np.sum(np.max([ylow,yhigh],axis=0))\n", " logL = -0.5 * (chi2 + len(y_obs) * np.log(2 * np.pi * sigma**2))\n", " \n", " return logL\n", " \n", " # Define the prior transform function\n", " def ptform(u):\n", " \"\"\"Prior transform function (uniform to parameter space)\"\"\"\n", " theta = np.array(u) # u is in [0,1]^ndim\n", " \n", " # Transform uniform [0,1] to parameter ranges:\n", " # m: slope [-5, 5]\n", " # b: intercept [-5, 5] \n", " # log_sigma: log(noise) [log(0.01), log(2)]\n", " #theta[0] = -5 + 8 * u[0] # m\n", " theta[0] = -5 + 20 * u[0] # b \n", " #theta[1] = -5 + 6 * u[1] #keep metallicity at Mj < 10x solar\n", " #theta[2] = np.log(0.01) + (np.log(2) - np.log(0.01)) * u[2] # log_sigma\n", " #theta[2] = -5+10*u[2]\n", " \n", " return theta\n", " \n", " # Set up the nested sampler\n", " ndim = 1 # m, b, log_sigma\n", " sampler = NestedSampler(loglike, ptform, ndim, nlive=500)\n", " \n", " # Run the nested sampling\n", " print(\"Running nested sampling...\")\n", " sampler.run_nested(dlogz=0.1, print_progress=True)\n", " \n", " # Get results\n", " results = sampler.results\n", " \n", " # Print summary statistics\n", " print(f\"\\nNested Sampling Results:\")\n", " print(f\"Log Evidence: {results.logz[-1]:.2f} ± {results.logzerr[-1]:.2f}\")\n", " print(f\"Number of likelihood evaluations: {results.niter}\")\n", " \n", " # Extract posterior samples\n", " samples = results['samples']\n", " #weights = results['importance_weights']\n", " #weights = np.exp(results['logwt'])\n", " #weights = results['logwt']\n", " weights = results.importance_weights()\n", " \n", " # Calculate weighted posterior statistics\n", " def weighted_quantile(values, weights, quantiles):\n", " \"\"\"Calculate weighted quantiles\"\"\"\n", " sorted_indices = np.argsort(values)\n", " sorted_values = values[sorted_indices]\n", " sorted_weights = weights[sorted_indices]\n", " cumsum = np.cumsum(sorted_weights)\n", " cumsum /= cumsum[-1] # normalize\n", " return np.interp(quantiles, cumsum, sorted_values)\n", " \n", " # Get parameter estimates (median and 68% credible intervals)\n", " params = ['slope (m)', 'intercept (b)', 'cutoff (s)']\n", " #true_values = [m_true, b_true, np.log(sigma_true)]\n", " \n", " print(f\"\\nParameter Estimates:\")\n", " print(f\"{'Parameter':<12} {'Median':<8} {'68% CI'} {'- / +'}\")\n", " print(\"-\" * 45)\n", " \n", " for i in range(ndim):\n", " median = weighted_quantile(samples[:, i], weights, [0.5])[0]\n", " ci_low, ci_high = weighted_quantile(samples[:, i], weights, [0.16, 0.84])\n", " \n", " #true_val = true_values[i]\n", " median_val = median\n", " ci_low_val = ci_low\n", " ci_high_val = ci_high\n", " param_name = params[i]\n", " \n", " print(f\"{param_name:<12} {median_val:<8.3f} [{ci_low_val:.3f}, {ci_high_val:.3f}] [{median_val-ci_low_val:.3f}, {ci_high_val-median_val:.3f}]\")\n", "\n", " if plot_it is True:\n", " # Create plots\n", " fig, axes = plt.subplots(2, 3, figsize=(15, 10))\n", " \n", " # Plot 1: Data and fitted line\n", " ax1 = axes[0, 0]\n", " #ax1.scatter(x_true, y_obs, alpha=1.0, label='Observed data',zorder=999)\n", " #ax1.plot(x_true, y_true, 'r-', label='True line', linewidth=2)\n", " ax1.errorbar(x_true, y_obs, yerr = [ylow,yhigh], zorder=999,fmt='.',color='k',label='Observed Data')\n", " \n", " \n", " # Plot posterior samples of the line\n", " n_samples = min(100, len(samples))\n", " indices = np.random.choice(len(samples), n_samples, p=weights/np.sum(weights))\n", " xfake = np.linspace(-2,1.5)\n", " for idx in indices[:50]: # Plot subset for clarity\n", " m_sample, b_sample, s_sample = samples[idx, 0], samples[idx, 1], samples[idx, 2]\n", " y_sample = m_sample * xfake + b_sample\n", " y_sample[np.where(xfake > s_sample)[0]] = m_sample * s_sample + b_sample\n", " \n", " ax1.plot(xfake, y_sample, 'k-', alpha=0.1)\n", " \n", " ax1.set_xlabel('x')\n", " ax1.set_ylabel('y')\n", " ax1.legend()\n", " ax1.set_title('Data and Posterior Fits')\n", " \n", " # Plot 2: Trace plot (evolution of live points)\n", " ax2 = axes[0, 1]\n", " ax2.plot(results['logl'])\n", " ax2.set_xlabel('Iteration')\n", " ax2.set_ylabel('Log Likelihood')\n", " ax2.set_title('Nested Sampling Trace')\n", " ax2.set_yscale('symlog')\n", " \n", " # Plot 3: Prior vs Posterior for slope\n", " ax3 = axes[1, 0]\n", " ax3.hist(samples[:, 0], weights=weights, bins=100, alpha=0.7, \n", " density=True, label='Posterior')\n", " #ax3.axvline(m_true, color='r', linestyle='--', label='True value')\n", " ax3.axhline(1/10, color='g', linestyle=':', label='Prior', alpha=0.7) # Prior is uniform over [-5,5]\n", " ax3.set_xlabel('Slope (m)')\n", " ax3.set_ylabel('Density')\n", " ax3.legend()\n", " ax3.set_title('Slope: Prior vs Posterior')\n", " \n", " # Plot 4: Prior vs Posterior for intercept \n", " ax4 = axes[1, 1]\n", " ax4.hist(samples[:, 1], weights=weights, bins=100, alpha=0.7,\n", " density=True, label='Posterior')\n", " #ax4.axvline(b_true, color='r', linestyle='--', label='True value')\n", " ax4.axhline(1/10, color='g', linestyle=':', label='Prior', alpha=0.7) # Prior is uniform over [-5,5]\n", " ax4.set_xlabel('Intercept (b)')\n", " ax4.set_ylabel('Density')\n", " ax4.legend()\n", " ax4.set_title('Intercept: Prior vs Posterior')\n", " \n", " # Plot 4: Prior vs Posterior for intercept \n", " ax5 = axes[1, 2]\n", " ax5.hist(samples[:, 2], weights=weights, bins=100, alpha=0.7,\n", " density=True, label='Posterior')\n", " #ax4.axvline(b_true, color='r', linestyle='--', label='True value')\n", " #ax4.axhline(1/10, color='g', linestyle=':', label='Prior', alpha=0.7) # Prior is uniform over [-5,5]\n", " ax5.set_xlabel('Cutoff (s)')\n", " ax5.set_ylabel('Density')\n", " ax5.legend()\n", " ax5.set_title('Cutoff: Prior vs Posterior')\n", " \n", " plt.tight_layout()\n", " plt.show()\n", " \n", " # Optional: Create corner plot if you want to see parameter correlations\n", " # Uncomment the following lines:\n", " fig_corner = cornerplot(results)\n", " plt.show()\n", " return results" ] }, { "cell_type": "code", "execution_count": null, "id": "dc4461e6-f299-4981-9fed-4ba5dbc687a4", "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "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": "3.12.2" } }, "nbformat": 4, "nbformat_minor": 5 }