Week09_Volcanic_Geochemistry/Week09_Assignment_Cobalt_Resources.ipynb

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    "import pandas as pd\n",
    "pd.set_option('display.max_columns', None)\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
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    "## Where is the cobalt?\n",
    "\n",
    "To electrify more of the electric grid and transportation, more metals are being mined. Batteries are driving a particular demand for cobalt as lithium-ion batteries utilize a chemical reaction between lithium and cobalt oxide. There are major problems with the current cobolt supply particularly with there being small-scale mines that are unregulated with regard to safety, child labor and environmental impact. Watch this video to learn more:\n",
    "https://www.youtube.com/watch?v=ll7aUgeK3-o\n",
    "\n",
    "There is a start-up in Berkeley (with significant venture capital funding) called Kobold that is focused on using basic ore-deposit science, big data, and scientific computing to discover and develop deposits related to battery materials. https://www.koboldmetals.com"
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    "**Your task in this assignment is to track down what types of igneous rocks have the most cobalt (Co) by completing the following assessments**\n",
    "\n",
    "1) The lowest grade of ore that is mined for cobalt is 0.01%. How many data points have concentrations of cobalt that might be this high (recall that the data are in ppm).\n",
    "\n",
    "2) Compare the distributions of cobalt in the COMPOSITION categories of 'FELSIC', 'INTERMEDIATE', 'MAFIC', 'ULTRAMAFIC' using both probability density estimates and box-plots. For the box plot, I recommend using `sns.boxenplot` and setting the ylim so it is zoomed in on the boxes. Answer this question, if you were going to target one of these compositions for mineral exploration, which one would you target?\n",
    "\n",
    "3) Let's say you needed to make a choice whether to explore in mafic intrusive (plutonic) rocks or mafic volcanics rocks. The coarser-grained plutonic rocks that crystallized underground have the 'PLUTONIC' label in the 'TYPE' column while the volcanic rocks have the 'VOLCANIC' label. Use the A/B test framework and determine whether both the mean and the median are statistically distinct from that which would result from random permutations. Describe the results of your test in a paragraph that demonstrates that you understand what the test is and what the results (including calculated p-values) mean. It will be helpful to read this Inferential Thinking chapter: https://www.inferentialthinking.com/chapters/12/Comparing_Two_Samples.html"
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   "source": [
    "#import all the data\n",
    "igneous_data = pd.read_csv('./data/ign.csv',sep='\\t')\n",
    "#drop data points that don't have Co (ppm) values\n",
    "igneous_data_Co = igneous_data.dropna(subset=['CO'])"
   ]
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    "sns.boxenplot?"
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   "source": [
    "More info on cobalt can be found here for the curious:\n",
    "\n",
    "https://pubs.usgs.gov/of/2017/1155/ofr20171155.pdf"
   ]
  },
  {
   "cell_type": "markdown",
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   "source": [
    "## Make sure that you can run the three cells below before uploading the assignment\n",
    "\n",
    "You will not be able to run this on DataHub. Instead you need to download this notebook to your local computer and run the following cells. The first one will install astropy and astroscrappy (as well as other dependencies) which are necessary for what we will be doing in class on Tuesday."
   ]
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  {
   "cell_type": "code",
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   "source": [
    "%conda install astroscrappy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from astropy.io import fits\n",
    "import astroscrappy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "astroscrappy.detect_cosmics?"
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