notebooks/prototype-db.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Exploring a redesign of the snowexsql database\n",
    "\n",
    "This notebook works with a toy database located on AWS to explore what could be possible with a redesign of the [NASA Snowexsql](https://github.com/SnowEx/snowexsql) database.\n",
    "\n",
    "\n",
    "A. Arendt and J. Meyer, SnowEx hackweek 2022"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [],
   "source": [
    "from snowexsql.db import get_db\n",
    "from sqlalchemy import text\n",
    "from sqlalchemy import inspect\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Connect to the existing snowexsql database \n",
    "\n",
    "We'll follow the great [database tutorials](https://snowex.hackweek.io/tutorials/database/index.html) by Micah$^2$ from the 2022 snowexhackweek."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['spatial_ref_sys', 'points', 'layers', 'sites', 'images']\n"
     ]
    }
   ],
   "source": [
    "db_snowex_engine = get_db('snow:hackweek@db.snowexdata.org/snowex')[0]\n",
    "# Output the list of tables in the database \n",
    "tables = inspect(db_snowex_engine).get_table_names()\n",
    "print(tables)"
   ]
  },
  {
   "attachments": {
    "cc68277c-3f14-475b-b7e4-c6a9e0e89a42.png": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### The entity relationship diagram (ERD) for the current Snowexsql database\n",
    "\n",
    "(generated using dbeaver software)\n",
    "\n",
    "![image.png](attachment:cc68277c-3f14-475b-b7e4-c6a9e0e89a42.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Connect to the beaver-db database\n",
    "\n",
    "We've temporarily created a free, micro instance of a postgres database on Amazon Relational Database System (RDS). It's open to any IP address at the moment."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [],
   "source": [
    "db_beaver_engine = get_db('db-beaver.c64qrzqeobgb.us-west-2.rds.amazonaws.com', '../creds.json')[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Run this to create the beaver-db database from a SQL script\n",
    "\n",
    "Run this anytime you change the schema of the database in the sql file. If nothing changes you only need to run this once."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open(\"../sql_scripts/layers_with_constraints.sql\") as file:\n",
    "    query = text(file.read())\n",
    "    results = db_beaver_engine.execute(query)"
   ]
  },
  {
   "attachments": {
    "d78ada49-68d6-4be1-ad95-ecb2066dd0fa.png": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### This is the entity relationship diagram for the test beaver-db database\n",
    "\n",
    "For now we won't be working with any image data. Just trying some new things with the point and layer observations.\n",
    "\n",
    "![image.png](attachment:d78ada49-68d6-4be1-ad95-ecb2066dd0fa.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now let's take a look at some data from the snowexsql database and think about how that might fit into our proposed new schema.\n",
    "\n",
    "We'll do some tests with data described in the [Snowex mission and datasets tutorial](https://snowex.hackweek.io/tutorials/core-datasets/02_data_descriptions.html).\n",
    "\n",
    "Suppose we want to work with the data shown in this fieldbook snippet:\n",
    "\n",
    "![field-book-1](https://snowex.hackweek.io/_images/02_SnowEx20_SnowPits_GMIOP_20200212_2C12_book1_v01.jpg)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "'Pit_ID' on the field book refers to 'site_id' in the database. Let's formulate a SQL query to look for any records with site_id = 2C12"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [],
   "source": [
    "qry = \"SELECT * FROM layers WHERE site_id = '2C12' LIMIT 5\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For readability, let's read the data into a Pandas dataframe:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>site_name</th>\n",
       "      <th>date</th>\n",
       "      <th>time_created</th>\n",
       "      <th>time_updated</th>\n",
       "      <th>id</th>\n",
       "      <th>doi</th>\n",
       "      <th>date_accessed</th>\n",
       "      <th>instrument</th>\n",
       "      <th>type</th>\n",
       "      <th>units</th>\n",
       "      <th>...</th>\n",
       "      <th>depth</th>\n",
       "      <th>site_id</th>\n",
       "      <th>pit_id</th>\n",
       "      <th>bottom_depth</th>\n",
       "      <th>comments</th>\n",
       "      <th>sample_a</th>\n",
       "      <th>sample_b</th>\n",
       "      <th>sample_c</th>\n",
       "      <th>value</th>\n",
       "      <th>flags</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Grand Mesa</td>\n",
       "      <td>2020-02-12</td>\n",
       "      <td>2022-06-30 22:28:33.067228+00:00</td>\n",
       "      <td>None</td>\n",
       "      <td>6287</td>\n",
       "      <td>https://doi.org/10.5067/DUD2VZEVBJ7S</td>\n",
       "      <td>2022-06-30</td>\n",
       "      <td>None</td>\n",
       "      <td>density</td>\n",
       "      <td>None</td>\n",
       "      <td>...</td>\n",
       "      <td>90.0</td>\n",
       "      <td>2C12</td>\n",
       "      <td>COGM2C12_20200212</td>\n",
       "      <td>80.0</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>195.0</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Grand Mesa</td>\n",
       "      <td>2020-02-12</td>\n",
       "      <td>2022-06-30 22:28:33.067228+00:00</td>\n",
       "      <td>None</td>\n",
       "      <td>6288</td>\n",
       "      <td>https://doi.org/10.5067/DUD2VZEVBJ7S</td>\n",
       "      <td>2022-06-30</td>\n",
       "      <td>None</td>\n",
       "      <td>density</td>\n",
       "      <td>None</td>\n",
       "      <td>...</td>\n",
       "      <td>80.0</td>\n",
       "      <td>2C12</td>\n",
       "      <td>COGM2C12_20200212</td>\n",
       "      <td>70.0</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>241.5</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Grand Mesa</td>\n",
       "      <td>2020-02-12</td>\n",
       "      <td>2022-06-30 22:28:33.067228+00:00</td>\n",
       "      <td>None</td>\n",
       "      <td>6289</td>\n",
       "      <td>https://doi.org/10.5067/DUD2VZEVBJ7S</td>\n",
       "      <td>2022-06-30</td>\n",
       "      <td>None</td>\n",
       "      <td>density</td>\n",
       "      <td>None</td>\n",
       "      <td>...</td>\n",
       "      <td>70.0</td>\n",
       "      <td>2C12</td>\n",
       "      <td>COGM2C12_20200212</td>\n",
       "      <td>60.0</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>297.0</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Grand Mesa</td>\n",
       "      <td>2020-02-12</td>\n",
       "      <td>2022-06-30 22:28:33.067228+00:00</td>\n",
       "      <td>None</td>\n",
       "      <td>6290</td>\n",
       "      <td>https://doi.org/10.5067/DUD2VZEVBJ7S</td>\n",
       "      <td>2022-06-30</td>\n",
       "      <td>None</td>\n",
       "      <td>density</td>\n",
       "      <td>None</td>\n",
       "      <td>...</td>\n",
       "      <td>60.0</td>\n",
       "      <td>2C12</td>\n",
       "      <td>COGM2C12_20200212</td>\n",
       "      <td>50.0</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>287.0</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Grand Mesa</td>\n",
       "      <td>2020-02-12</td>\n",
       "      <td>2022-06-30 22:28:33.067228+00:00</td>\n",
       "      <td>None</td>\n",
       "      <td>6291</td>\n",
       "      <td>https://doi.org/10.5067/DUD2VZEVBJ7S</td>\n",
       "      <td>2022-06-30</td>\n",
       "      <td>None</td>\n",
       "      <td>density</td>\n",
       "      <td>None</td>\n",
       "      <td>...</td>\n",
       "      <td>50.0</td>\n",
       "      <td>2C12</td>\n",
       "      <td>COGM2C12_20200212</td>\n",
       "      <td>40.0</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>262.5</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 29 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    site_name        date                     time_created time_updated    id  \\\n",
       "0  Grand Mesa  2020-02-12 2022-06-30 22:28:33.067228+00:00         None  6287   \n",
       "1  Grand Mesa  2020-02-12 2022-06-30 22:28:33.067228+00:00         None  6288   \n",
       "2  Grand Mesa  2020-02-12 2022-06-30 22:28:33.067228+00:00         None  6289   \n",
       "3  Grand Mesa  2020-02-12 2022-06-30 22:28:33.067228+00:00         None  6290   \n",
       "4  Grand Mesa  2020-02-12 2022-06-30 22:28:33.067228+00:00         None  6291   \n",
       "\n",
       "                                    doi date_accessed instrument     type  \\\n",
       "0  https://doi.org/10.5067/DUD2VZEVBJ7S    2022-06-30       None  density   \n",
       "1  https://doi.org/10.5067/DUD2VZEVBJ7S    2022-06-30       None  density   \n",
       "2  https://doi.org/10.5067/DUD2VZEVBJ7S    2022-06-30       None  density   \n",
       "3  https://doi.org/10.5067/DUD2VZEVBJ7S    2022-06-30       None  density   \n",
       "4  https://doi.org/10.5067/DUD2VZEVBJ7S    2022-06-30       None  density   \n",
       "\n",
       "  units  ... depth  site_id             pit_id  bottom_depth  comments  \\\n",
       "0  None  ...  90.0     2C12  COGM2C12_20200212          80.0      None   \n",
       "1  None  ...  80.0     2C12  COGM2C12_20200212          70.0      None   \n",
       "2  None  ...  70.0     2C12  COGM2C12_20200212          60.0      None   \n",
       "3  None  ...  60.0     2C12  COGM2C12_20200212          50.0      None   \n",
       "4  None  ...  50.0     2C12  COGM2C12_20200212          40.0      None   \n",
       "\n",
       "  sample_a  sample_b sample_c  value  flags  \n",
       "0     None      None     None  195.0   None  \n",
       "1     None      None     None  241.5   None  \n",
       "2     None      None     None  297.0   None  \n",
       "3     None      None     None  287.0   None  \n",
       "4     None      None     None  262.5   None  \n",
       "\n",
       "[5 rows x 29 columns]"
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_sql(qry, db_snowex_engine)\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's see if we can narrow in on specific records based on who collected the data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>observers</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Kate Hale</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   observers\n",
       "0  Kate Hale\n",
       "1       None"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "qry = \"SELECT DISTINCT(observers) FROM layers WHERE site_id = '2C12'\"\n",
    "df = pd.read_sql(qry, db_snowex_engine)\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Looks like the names J. Lund and S. Tanner didn't get included. Let's try by time:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>15:48:00+00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>16:00:00+00:00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             time\n",
       "0  15:48:00+00:00\n",
       "1  16:00:00+00:00"
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "qry = \"SELECT DISTINCT(time) FROM layers WHERE site_id = '2C12'\"\n",
    "df = pd.read_sql(qry, db_snowex_engine)\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "OK, we'll assume the fieldbook entry refers to all database entries collected at time = 15:48:00+00:00. Let's try it:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>site_name</th>\n",
       "      <th>date</th>\n",
       "      <th>time_created</th>\n",
       "      <th>time_updated</th>\n",
       "      <th>id</th>\n",
       "      <th>doi</th>\n",
       "      <th>date_accessed</th>\n",
       "      <th>instrument</th>\n",
       "      <th>type</th>\n",
       "      <th>units</th>\n",
       "      <th>...</th>\n",
       "      <th>depth</th>\n",
       "      <th>site_id</th>\n",
       "      <th>pit_id</th>\n",
       "      <th>bottom_depth</th>\n",
       "      <th>comments</th>\n",
       "      <th>sample_a</th>\n",
       "      <th>sample_b</th>\n",
       "      <th>sample_c</th>\n",
       "      <th>value</th>\n",
       "      <th>flags</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Grand Mesa</td>\n",
       "      <td>2020-02-12</td>\n",
       "      <td>2022-06-30 22:28:33.067228+00:00</td>\n",
       "      <td>None</td>\n",
       "      <td>6287</td>\n",
       "      <td>https://doi.org/10.5067/DUD2VZEVBJ7S</td>\n",
       "      <td>2022-06-30</td>\n",
       "      <td>None</td>\n",
       "      <td>density</td>\n",
       "      <td>None</td>\n",
       "      <td>...</td>\n",
       "      <td>90.0</td>\n",
       "      <td>2C12</td>\n",
       "      <td>COGM2C12_20200212</td>\n",
       "      <td>80.0</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>195.0</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Grand Mesa</td>\n",
       "      <td>2020-02-12</td>\n",
       "      <td>2022-06-30 22:28:33.067228+00:00</td>\n",
       "      <td>None</td>\n",
       "      <td>6288</td>\n",
       "      <td>https://doi.org/10.5067/DUD2VZEVBJ7S</td>\n",
       "      <td>2022-06-30</td>\n",
       "      <td>None</td>\n",
       "      <td>density</td>\n",
       "      <td>None</td>\n",
       "      <td>...</td>\n",
       "      <td>80.0</td>\n",
       "      <td>2C12</td>\n",
       "      <td>COGM2C12_20200212</td>\n",
       "      <td>70.0</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>241.5</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Grand Mesa</td>\n",
       "      <td>2020-02-12</td>\n",
       "      <td>2022-06-30 22:28:33.067228+00:00</td>\n",
       "      <td>None</td>\n",
       "      <td>6289</td>\n",
       "      <td>https://doi.org/10.5067/DUD2VZEVBJ7S</td>\n",
       "      <td>2022-06-30</td>\n",
       "      <td>None</td>\n",
       "      <td>density</td>\n",
       "      <td>None</td>\n",
       "      <td>...</td>\n",
       "      <td>70.0</td>\n",
       "      <td>2C12</td>\n",
       "      <td>COGM2C12_20200212</td>\n",
       "      <td>60.0</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>297.0</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Grand Mesa</td>\n",
       "      <td>2020-02-12</td>\n",
       "      <td>2022-06-30 22:28:33.067228+00:00</td>\n",
       "      <td>None</td>\n",
       "      <td>6290</td>\n",
       "      <td>https://doi.org/10.5067/DUD2VZEVBJ7S</td>\n",
       "      <td>2022-06-30</td>\n",
       "      <td>None</td>\n",
       "      <td>density</td>\n",
       "      <td>None</td>\n",
       "      <td>...</td>\n",
       "      <td>60.0</td>\n",
       "      <td>2C12</td>\n",
       "      <td>COGM2C12_20200212</td>\n",
       "      <td>50.0</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>287.0</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Grand Mesa</td>\n",
       "      <td>2020-02-12</td>\n",
       "      <td>2022-06-30 22:28:33.067228+00:00</td>\n",
       "      <td>None</td>\n",
       "      <td>6291</td>\n",
       "      <td>https://doi.org/10.5067/DUD2VZEVBJ7S</td>\n",
       "      <td>2022-06-30</td>\n",
       "      <td>None</td>\n",
       "      <td>density</td>\n",
       "      <td>None</td>\n",
       "      <td>...</td>\n",
       "      <td>50.0</td>\n",
       "      <td>2C12</td>\n",
       "      <td>COGM2C12_20200212</td>\n",
       "      <td>40.0</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>262.5</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>81</th>\n",
       "      <td>Grand Mesa</td>\n",
       "      <td>2020-02-12</td>\n",
       "      <td>2022-06-30 22:28:48.913444+00:00</td>\n",
       "      <td>None</td>\n",
       "      <td>11726</td>\n",
       "      <td>https://doi.org/10.5067/DUD2VZEVBJ7S</td>\n",
       "      <td>2022-06-30</td>\n",
       "      <td>None</td>\n",
       "      <td>temperature</td>\n",
       "      <td>None</td>\n",
       "      <td>...</td>\n",
       "      <td>40.0</td>\n",
       "      <td>2C12</td>\n",
       "      <td>COGM2C12_20200212</td>\n",
       "      <td>NaN</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>-4.8</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>82</th>\n",
       "      <td>Grand Mesa</td>\n",
       "      <td>2020-02-12</td>\n",
       "      <td>2022-06-30 22:28:48.913444+00:00</td>\n",
       "      <td>None</td>\n",
       "      <td>11727</td>\n",
       "      <td>https://doi.org/10.5067/DUD2VZEVBJ7S</td>\n",
       "      <td>2022-06-30</td>\n",
       "      <td>None</td>\n",
       "      <td>temperature</td>\n",
       "      <td>None</td>\n",
       "      <td>...</td>\n",
       "      <td>30.0</td>\n",
       "      <td>2C12</td>\n",
       "      <td>COGM2C12_20200212</td>\n",
       "      <td>NaN</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>-3.7</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>83</th>\n",
       "      <td>Grand Mesa</td>\n",
       "      <td>2020-02-12</td>\n",
       "      <td>2022-06-30 22:28:48.913444+00:00</td>\n",
       "      <td>None</td>\n",
       "      <td>11728</td>\n",
       "      <td>https://doi.org/10.5067/DUD2VZEVBJ7S</td>\n",
       "      <td>2022-06-30</td>\n",
       "      <td>None</td>\n",
       "      <td>temperature</td>\n",
       "      <td>None</td>\n",
       "      <td>...</td>\n",
       "      <td>20.0</td>\n",
       "      <td>2C12</td>\n",
       "      <td>COGM2C12_20200212</td>\n",
       "      <td>NaN</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>-3.2</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>84</th>\n",
       "      <td>Grand Mesa</td>\n",
       "      <td>2020-02-12</td>\n",
       "      <td>2022-06-30 22:28:48.913444+00:00</td>\n",
       "      <td>None</td>\n",
       "      <td>11729</td>\n",
       "      <td>https://doi.org/10.5067/DUD2VZEVBJ7S</td>\n",
       "      <td>2022-06-30</td>\n",
       "      <td>None</td>\n",
       "      <td>temperature</td>\n",
       "      <td>None</td>\n",
       "      <td>...</td>\n",
       "      <td>10.0</td>\n",
       "      <td>2C12</td>\n",
       "      <td>COGM2C12_20200212</td>\n",
       "      <td>NaN</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>-2.7</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>85</th>\n",
       "      <td>Grand Mesa</td>\n",
       "      <td>2020-02-12</td>\n",
       "      <td>2022-06-30 22:28:48.913444+00:00</td>\n",
       "      <td>None</td>\n",
       "      <td>11730</td>\n",
       "      <td>https://doi.org/10.5067/DUD2VZEVBJ7S</td>\n",
       "      <td>2022-06-30</td>\n",
       "      <td>None</td>\n",
       "      <td>temperature</td>\n",
       "      <td>None</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2C12</td>\n",
       "      <td>COGM2C12_20200212</td>\n",
       "      <td>NaN</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>-1.8</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>86 rows × 29 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     site_name        date                     time_created time_updated  \\\n",
       "0   Grand Mesa  2020-02-12 2022-06-30 22:28:33.067228+00:00         None   \n",
       "1   Grand Mesa  2020-02-12 2022-06-30 22:28:33.067228+00:00         None   \n",
       "2   Grand Mesa  2020-02-12 2022-06-30 22:28:33.067228+00:00         None   \n",
       "3   Grand Mesa  2020-02-12 2022-06-30 22:28:33.067228+00:00         None   \n",
       "4   Grand Mesa  2020-02-12 2022-06-30 22:28:33.067228+00:00         None   \n",
       "..         ...         ...                              ...          ...   \n",
       "81  Grand Mesa  2020-02-12 2022-06-30 22:28:48.913444+00:00         None   \n",
       "82  Grand Mesa  2020-02-12 2022-06-30 22:28:48.913444+00:00         None   \n",
       "83  Grand Mesa  2020-02-12 2022-06-30 22:28:48.913444+00:00         None   \n",
       "84  Grand Mesa  2020-02-12 2022-06-30 22:28:48.913444+00:00         None   \n",
       "85  Grand Mesa  2020-02-12 2022-06-30 22:28:48.913444+00:00         None   \n",
       "\n",
       "       id                                   doi date_accessed instrument  \\\n",
       "0    6287  https://doi.org/10.5067/DUD2VZEVBJ7S    2022-06-30       None   \n",
       "1    6288  https://doi.org/10.5067/DUD2VZEVBJ7S    2022-06-30       None   \n",
       "2    6289  https://doi.org/10.5067/DUD2VZEVBJ7S    2022-06-30       None   \n",
       "3    6290  https://doi.org/10.5067/DUD2VZEVBJ7S    2022-06-30       None   \n",
       "4    6291  https://doi.org/10.5067/DUD2VZEVBJ7S    2022-06-30       None   \n",
       "..    ...                                   ...           ...        ...   \n",
       "81  11726  https://doi.org/10.5067/DUD2VZEVBJ7S    2022-06-30       None   \n",
       "82  11727  https://doi.org/10.5067/DUD2VZEVBJ7S    2022-06-30       None   \n",
       "83  11728  https://doi.org/10.5067/DUD2VZEVBJ7S    2022-06-30       None   \n",
       "84  11729  https://doi.org/10.5067/DUD2VZEVBJ7S    2022-06-30       None   \n",
       "85  11730  https://doi.org/10.5067/DUD2VZEVBJ7S    2022-06-30       None   \n",
       "\n",
       "           type units  ... depth  site_id             pit_id  bottom_depth  \\\n",
       "0       density  None  ...  90.0     2C12  COGM2C12_20200212          80.0   \n",
       "1       density  None  ...  80.0     2C12  COGM2C12_20200212          70.0   \n",
       "2       density  None  ...  70.0     2C12  COGM2C12_20200212          60.0   \n",
       "3       density  None  ...  60.0     2C12  COGM2C12_20200212          50.0   \n",
       "4       density  None  ...  50.0     2C12  COGM2C12_20200212          40.0   \n",
       "..          ...   ...  ...   ...      ...                ...           ...   \n",
       "81  temperature  None  ...  40.0     2C12  COGM2C12_20200212           NaN   \n",
       "82  temperature  None  ...  30.0     2C12  COGM2C12_20200212           NaN   \n",
       "83  temperature  None  ...  20.0     2C12  COGM2C12_20200212           NaN   \n",
       "84  temperature  None  ...  10.0     2C12  COGM2C12_20200212           NaN   \n",
       "85  temperature  None  ...   0.0     2C12  COGM2C12_20200212           NaN   \n",
       "\n",
       "    comments sample_a  sample_b sample_c  value  flags  \n",
       "0       None     None      None     None  195.0   None  \n",
       "1       None     None      None     None  241.5   None  \n",
       "2       None     None      None     None  297.0   None  \n",
       "3       None     None      None     None  287.0   None  \n",
       "4       None     None      None     None  262.5   None  \n",
       "..       ...      ...       ...      ...    ...    ...  \n",
       "81      None     None      None     None   -4.8   None  \n",
       "82      None     None      None     None   -3.7   None  \n",
       "83      None     None      None     None   -3.2   None  \n",
       "84      None     None      None     None   -2.7   None  \n",
       "85      None     None      None     None   -1.8   None  \n",
       "\n",
       "[86 rows x 29 columns]"
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "qry = \"SELECT * FROM layers WHERE site_id = '2C12' AND time = '15:48:00+00:00'\"\n",
    "df = pd.read_sql(qry, db_snowex_engine)\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Great! Let's look at the different types of measurements as well as the  and cross-check with the field entries:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>density</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>grain_size</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>grain_type</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>hand_hardness</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>lwc_vol</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>manual_wetness</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>permittivity</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>temperature</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             type\n",
       "0         density\n",
       "1      grain_size\n",
       "2      grain_type\n",
       "3   hand_hardness\n",
       "4         lwc_vol\n",
       "5  manual_wetness\n",
       "6    permittivity\n",
       "7     temperature"
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "qry = \"SELECT DISTINCT(type) FROM layers WHERE site_id = '2C12' AND time = '15:48:00+00:00'\"\n",
    "df = pd.read_sql(qry, db_snowex_engine)\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Populating the beaver-db database"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's grab data for this fieldbook record and systematically import it into our new schema.\n",
    "\n",
    "We'll need to progress through the tables in the same order in which they were created in the SQL script used above. This is because of all the foreign key relationships. The database won't let us reference a foreign key to a table that does not yet exist. It will be particularly unhappy especially because we require the foreign key variables to be `NOT NULL`.\n",
    "\n",
    "For each new table we'll start by querying the existing snowexsql database and then use `INSERT` statements to populate the beaver-db tables."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## `beaver-db.instruments` table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>instrument</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  instrument\n",
       "0       None"
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "qry = \"SELECT DISTINCT(instrument) \\\n",
    "       FROM layers \\\n",
    "       WHERE site_id = '2C12' AND time = '15:48:00+00:00'\"\n",
    "df = pd.read_sql(qry, db_snowex_engine)\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "OK, this is missing. Below we'll ingest some density data. So let's assume it was a Federal Snow Sampler:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INSERT INTO instruments (name)        VALUES ('Federal Snow Sampler')\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<sqlalchemy.engine.cursor.LegacyCursorResult at 0x7f974b1b8970>"
      ]
     },
     "execution_count": 111,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "qry = \"INSERT INTO instruments (name) \\\n",
    "       VALUES ('Federal Snow Sampler')\"\n",
    "print(qry)\n",
    "db_beaver_engine.execute(qry)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### `beaver-db.measurement_type` table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>type</th>\n",
       "      <th>units</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>density</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>grain_size</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>grain_type</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>hand_hardness</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>lwc_vol</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>manual_wetness</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>permittivity</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>temperature</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             type units\n",
       "0         density  None\n",
       "1      grain_size  None\n",
       "2      grain_type  None\n",
       "3   hand_hardness  None\n",
       "4         lwc_vol  None\n",
       "5  manual_wetness  None\n",
       "6    permittivity  None\n",
       "7     temperature  None"
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "qry = \"SELECT DISTINCT(type), units \\\n",
    "       FROM layers \\\n",
    "       WHERE site_id = '2C12' AND time = '15:48:00+00:00'\"\n",
    "df = pd.read_sql(qry, db_snowex_engine)\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's manually insert a few rows for now:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INSERT INTO measurement_types (name, unit)        VALUES ('density','kg m-3'), ('grain_size','mm'), ('temperature','degrees Celcius')\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<sqlalchemy.engine.cursor.LegacyCursorResult at 0x7f974b1b8130>"
      ]
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "qry = \"INSERT INTO measurement_types (name, unit) \\\n",
    "       VALUES ('density','kg m-3'), ('grain_size','mm'), ('temperature','degrees Celcius')\"\n",
    "print(qry)\n",
    "db_beaver_engine.execute(qry)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### `beaver-db.observers` table\n",
    "\n",
    "This contains the names of any snowex observer. Each record must refer only to a single unique person.\n",
    "\n",
    "Let's create records for J. Lund and S. Tanner.\n",
    "\n",
    "**Developer note**: we should separate out family and given names in some systematic way."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INSERT INTO observers (name)        VALUES ('Jewell Lund'), ('Steve Tanner')\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<sqlalchemy.engine.cursor.LegacyCursorResult at 0x7f974b1bb250>"
      ]
     },
     "execution_count": 114,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "qry = \"INSERT INTO observers (name) \\\n",
    "       VALUES ('Jewell Lund'), ('Steve Tanner')\"\n",
    "print(qry)\n",
    "db_beaver_engine.execute(qry)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## `beaver-db.sites` table \n",
    "\n",
    "The `beaver-db.sites` table will describe general information about the sample site location. Eventually it would be expanded to include many of the other fields currently included in the `snowexsql.sites` table. Right now we just have the site name."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Grand Mesa\n"
     ]
    }
   ],
   "source": [
    "qry = \"SELECT DISTINCT(site_name) \\\n",
    "       FROM layers \\\n",
    "       WHERE site_id = '2C12' AND time = '15:48:00+00:00'\"\n",
    "df = pd.read_sql(qry, db_snowex_engine)\n",
    "site_name = df['site_name'][0]\n",
    "print(site_name)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Still a bit of confusion about the distinction between 'site' and 'location' but we'll sort that out later! Let's populate the beaver-db.sites table with this information:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INSERT INTO sites (name) VALUES ('Grand Mesa')\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<sqlalchemy.engine.cursor.LegacyCursorResult at 0x7f974b1bb5e0>"
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "qry = \"INSERT INTO sites (name) VALUES ('{site_name}')\".format(site_name = site_name)\n",
    "print(qry)\n",
    "db_beaver_engine.execute(qry)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### `beaver-db.points` table\n",
    "\n",
    "The `beaver-db.sites` table has a foreign key relation to the `beaver-db.points` table, so let's populate that one next. For that we'll need to grab the observation date. But importantly, we'll also need to match the point observation to the specific associated record in the `beaver-db.sites` table, in order to enact the foreign key relationship.\n",
    "\n",
    "First we'll grab the date and pit_id from the `snowexsql.layers` table:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2020-02-12 COGM2C12_20200212\n"
     ]
    }
   ],
   "source": [
    "qry = \"SELECT DISTINCT(date), pit_id \\\n",
    "       FROM layers \\\n",
    "       WHERE site_id = '2C12' AND time = '15:48:00+00:00'\"\n",
    "df = pd.read_sql(qry, db_snowex_engine)\n",
    "date = df['date'][0]\n",
    "pit_id = df['pit_id'][0]\n",
    "print(date, pit_id)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now the query to create the new `beaver-db.points` record. Here we'll embed another SELECT statement in order to get the correct foreign key id from the `beaver-db.sites` table. Note that this is fine because there should only ever be one unique row entry for each site."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INSERT INTO points (sites_id, pit_id, observation_date)        SELECT id, 'COGM2C12_20200212', '2020-02-12' FROM        sites WHERE name = 'Grand Mesa'\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<sqlalchemy.engine.cursor.LegacyCursorResult at 0x7f974b1bb910>"
      ]
     },
     "execution_count": 118,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "qry = \"INSERT INTO points (sites_id, pit_id, observation_date) \\\n",
    "       SELECT id, '{pit_id}', '{date}' FROM \\\n",
    "       sites WHERE name = '{site_name}'\".format(pit_id = pit_id, date = date, site_name = site_name)\n",
    "print(qry)\n",
    "db_beaver_engine.execute(qry)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### `beaver-db.layers` table\n",
    "\n",
    "This table has metadata about snowpit samples, or any information collected along a 3rd spatial dimension involving more than one observation at a given location."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>bottom_depth</th>\n",
       "      <th>sample_a</th>\n",
       "      <th>sample_b</th>\n",
       "      <th>sample_c</th>\n",
       "      <th>value</th>\n",
       "      <th>time_created</th>\n",
       "      <th>time_updated</th>\n",
       "      <th>type</th>\n",
       "      <th>pit_id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>80.0</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>195.0</td>\n",
       "      <td>2022-06-30 22:28:33.067228+00:00</td>\n",
       "      <td>None</td>\n",
       "      <td>density</td>\n",
       "      <td>COGM2C12_20200212</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>70.0</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>241.5</td>\n",
       "      <td>2022-06-30 22:28:33.067228+00:00</td>\n",
       "      <td>None</td>\n",
       "      <td>density</td>\n",
       "      <td>COGM2C12_20200212</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>60.0</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>297.0</td>\n",
       "      <td>2022-06-30 22:28:33.067228+00:00</td>\n",
       "      <td>None</td>\n",
       "      <td>density</td>\n",
       "      <td>COGM2C12_20200212</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>50.0</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>287.0</td>\n",
       "      <td>2022-06-30 22:28:33.067228+00:00</td>\n",
       "      <td>None</td>\n",
       "      <td>density</td>\n",
       "      <td>COGM2C12_20200212</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>40.0</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>262.5</td>\n",
       "      <td>2022-06-30 22:28:33.067228+00:00</td>\n",
       "      <td>None</td>\n",
       "      <td>density</td>\n",
       "      <td>COGM2C12_20200212</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>30.0</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>312.0</td>\n",
       "      <td>2022-06-30 22:28:33.067228+00:00</td>\n",
       "      <td>None</td>\n",
       "      <td>density</td>\n",
       "      <td>COGM2C12_20200212</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>20.0</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>305.5</td>\n",
       "      <td>2022-06-30 22:28:33.067228+00:00</td>\n",
       "      <td>None</td>\n",
       "      <td>density</td>\n",
       "      <td>COGM2C12_20200212</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>10.0</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>307.5</td>\n",
       "      <td>2022-06-30 22:28:33.067228+00:00</td>\n",
       "      <td>None</td>\n",
       "      <td>density</td>\n",
       "      <td>COGM2C12_20200212</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>80.0</td>\n",
       "      <td>193.0</td>\n",
       "      <td>197.0</td>\n",
       "      <td>None</td>\n",
       "      <td>195.0</td>\n",
       "      <td>2022-06-30 22:28:36.123840+00:00</td>\n",
       "      <td>None</td>\n",
       "      <td>density</td>\n",
       "      <td>COGM2C12_20200212</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>70.0</td>\n",
       "      <td>248.0</td>\n",
       "      <td>235.0</td>\n",
       "      <td>None</td>\n",
       "      <td>241.5</td>\n",
       "      <td>2022-06-30 22:28:36.123840+00:00</td>\n",
       "      <td>None</td>\n",
       "      <td>density</td>\n",
       "      <td>COGM2C12_20200212</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>60.0</td>\n",
       "      <td>303.0</td>\n",
       "      <td>291.0</td>\n",
       "      <td>None</td>\n",
       "      <td>297.0</td>\n",
       "      <td>2022-06-30 22:28:36.123840+00:00</td>\n",
       "      <td>None</td>\n",
       "      <td>density</td>\n",
       "      <td>COGM2C12_20200212</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>50.0</td>\n",
       "      <td>280.0</td>\n",
       "      <td>294.0</td>\n",
       "      <td>None</td>\n",
       "      <td>287.0</td>\n",
       "      <td>2022-06-30 22:28:36.123840+00:00</td>\n",
       "      <td>None</td>\n",
       "      <td>density</td>\n",
       "      <td>COGM2C12_20200212</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>40.0</td>\n",
       "      <td>264.0</td>\n",
       "      <td>261.0</td>\n",
       "      <td>None</td>\n",
       "      <td>262.5</td>\n",
       "      <td>2022-06-30 22:28:36.123840+00:00</td>\n",
       "      <td>None</td>\n",
       "      <td>density</td>\n",
       "      <td>COGM2C12_20200212</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>30.0</td>\n",
       "      <td>313.0</td>\n",
       "      <td>311.0</td>\n",
       "      <td>None</td>\n",
       "      <td>312.0</td>\n",
       "      <td>2022-06-30 22:28:36.123840+00:00</td>\n",
       "      <td>None</td>\n",
       "      <td>density</td>\n",
       "      <td>COGM2C12_20200212</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>20.0</td>\n",
       "      <td>314.0</td>\n",
       "      <td>297.0</td>\n",
       "      <td>None</td>\n",
       "      <td>305.5</td>\n",
       "      <td>2022-06-30 22:28:36.123840+00:00</td>\n",
       "      <td>None</td>\n",
       "      <td>density</td>\n",
       "      <td>COGM2C12_20200212</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>10.0</td>\n",
       "      <td>309.0</td>\n",
       "      <td>306.0</td>\n",
       "      <td>None</td>\n",
       "      <td>307.5</td>\n",
       "      <td>2022-06-30 22:28:36.123840+00:00</td>\n",
       "      <td>None</td>\n",
       "      <td>density</td>\n",
       "      <td>COGM2C12_20200212</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    bottom_depth sample_a sample_b sample_c  value  \\\n",
       "0           80.0     None     None     None  195.0   \n",
       "1           70.0     None     None     None  241.5   \n",
       "2           60.0     None     None     None  297.0   \n",
       "3           50.0     None     None     None  287.0   \n",
       "4           40.0     None     None     None  262.5   \n",
       "5           30.0     None     None     None  312.0   \n",
       "6           20.0     None     None     None  305.5   \n",
       "7           10.0     None     None     None  307.5   \n",
       "8           80.0    193.0    197.0     None  195.0   \n",
       "9           70.0    248.0    235.0     None  241.5   \n",
       "10          60.0    303.0    291.0     None  297.0   \n",
       "11          50.0    280.0    294.0     None  287.0   \n",
       "12          40.0    264.0    261.0     None  262.5   \n",
       "13          30.0    313.0    311.0     None  312.0   \n",
       "14          20.0    314.0    297.0     None  305.5   \n",
       "15          10.0    309.0    306.0     None  307.5   \n",
       "\n",
       "                       time_created time_updated     type             pit_id  \n",
       "0  2022-06-30 22:28:33.067228+00:00         None  density  COGM2C12_20200212  \n",
       "1  2022-06-30 22:28:33.067228+00:00         None  density  COGM2C12_20200212  \n",
       "2  2022-06-30 22:28:33.067228+00:00         None  density  COGM2C12_20200212  \n",
       "3  2022-06-30 22:28:33.067228+00:00         None  density  COGM2C12_20200212  \n",
       "4  2022-06-30 22:28:33.067228+00:00         None  density  COGM2C12_20200212  \n",
       "5  2022-06-30 22:28:33.067228+00:00         None  density  COGM2C12_20200212  \n",
       "6  2022-06-30 22:28:33.067228+00:00         None  density  COGM2C12_20200212  \n",
       "7  2022-06-30 22:28:33.067228+00:00         None  density  COGM2C12_20200212  \n",
       "8  2022-06-30 22:28:36.123840+00:00         None  density  COGM2C12_20200212  \n",
       "9  2022-06-30 22:28:36.123840+00:00         None  density  COGM2C12_20200212  \n",
       "10 2022-06-30 22:28:36.123840+00:00         None  density  COGM2C12_20200212  \n",
       "11 2022-06-30 22:28:36.123840+00:00         None  density  COGM2C12_20200212  \n",
       "12 2022-06-30 22:28:36.123840+00:00         None  density  COGM2C12_20200212  \n",
       "13 2022-06-30 22:28:36.123840+00:00         None  density  COGM2C12_20200212  \n",
       "14 2022-06-30 22:28:36.123840+00:00         None  density  COGM2C12_20200212  \n",
       "15 2022-06-30 22:28:36.123840+00:00         None  density  COGM2C12_20200212  "
      ]
     },
     "execution_count": 119,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "qry = \"SELECT bottom_depth, sample_a, sample_b, sample_c, value, time_created, time_updated, type, pit_id \\\n",
    "       FROM layers \\\n",
    "       WHERE site_id = '2C12' AND time = '15:48:00+00:00' AND type = 'density'\"\n",
    "df = pd.read_sql(qry, db_snowex_engine)\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It appears that the 'value' column is the average of the multiple samples? \n",
    "\n",
    "**Developer notes**:\n",
    "\n",
    "* propose removing the `value` column and let users calcuate statistics if multiple samples are taken, on demand. If there is a need, could add a boolean column indicating YES/NO for multi-sampling. Note this would be moved over to our `measurements` table.\n",
    "\n",
    "Next for this table we'll need to do a bit more data manipulation:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "80.0\n",
      "<DatetimeArray>\n",
      "['2022-06-30 22:28:33.067228+00:00', '2022-06-30 22:28:36.123840+00:00']\n",
      "Length: 2, dtype: datetime64[ns, UTC]\n",
      "COGM2C12_20200212\n"
     ]
    }
   ],
   "source": [
    "depth = pd.unique(df['bottom_depth'])\n",
    "print(bottom_depth)\n",
    "time_created = pd.unique(df['time_created'])\n",
    "print(time_created)\n",
    "pit_id = pd.unique(df['pit_id'])[0]\n",
    "print(pit_id)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Developer note**:\n",
    "\n",
    "We might want to rethink the `time_created` variable. Shouldn't it refer to the time at which the data were collected in the field, not the time of a caluclation being made in the database? We'll select the latter time for our purposes for now as these records should all have been created at the same time. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2022-06-30 22:28:36.123840+00:00\n",
      "80.0\n"
     ]
    }
   ],
   "source": [
    "time_created = time_created[1]\n",
    "print(time_created)\n",
    "bottom_depth = depth.max()\n",
    "print(bottom_depth)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INSERT INTO layers (depth, bottom_depth, time_created, points_id)            SELECT '80.0', '80.0', '2022-06-30 22:28:36.123840+00:00', id FROM            points WHERE pit_id = 'COGM2C12_20200212'\n",
      "INSERT INTO layers (depth, bottom_depth, time_created, points_id)            SELECT '70.0', '80.0', '2022-06-30 22:28:36.123840+00:00', id FROM            points WHERE pit_id = 'COGM2C12_20200212'\n",
      "INSERT INTO layers (depth, bottom_depth, time_created, points_id)            SELECT '60.0', '80.0', '2022-06-30 22:28:36.123840+00:00', id FROM            points WHERE pit_id = 'COGM2C12_20200212'\n",
      "INSERT INTO layers (depth, bottom_depth, time_created, points_id)            SELECT '50.0', '80.0', '2022-06-30 22:28:36.123840+00:00', id FROM            points WHERE pit_id = 'COGM2C12_20200212'\n",
      "INSERT INTO layers (depth, bottom_depth, time_created, points_id)            SELECT '40.0', '80.0', '2022-06-30 22:28:36.123840+00:00', id FROM            points WHERE pit_id = 'COGM2C12_20200212'\n",
      "INSERT INTO layers (depth, bottom_depth, time_created, points_id)            SELECT '30.0', '80.0', '2022-06-30 22:28:36.123840+00:00', id FROM            points WHERE pit_id = 'COGM2C12_20200212'\n",
      "INSERT INTO layers (depth, bottom_depth, time_created, points_id)            SELECT '20.0', '80.0', '2022-06-30 22:28:36.123840+00:00', id FROM            points WHERE pit_id = 'COGM2C12_20200212'\n",
      "INSERT INTO layers (depth, bottom_depth, time_created, points_id)            SELECT '10.0', '80.0', '2022-06-30 22:28:36.123840+00:00', id FROM            points WHERE pit_id = 'COGM2C12_20200212'\n"
     ]
    }
   ],
   "source": [
    "for d in depth:\n",
    "    qry = \"INSERT INTO layers (depth, bottom_depth, time_created, points_id) \\\n",
    "           SELECT '{depth}', '{bottom_depth}', '{time_created}', id FROM \\\n",
    "           points WHERE pit_id = '{pit_id}'\".format(depth = d, bottom_depth = bottom_depth, time_created = time_created, pit_id = pit_id)\n",
    "    print(qry)\n",
    "    db_beaver_engine.execute(qry)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### `beaver-db.measurements` table\n",
    "\n",
    "Here we ingest the snow observations."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [],
   "source": [
    "sample_a = df[['bottom_depth','sample_a']].dropna(axis = 'rows')\n",
    "sample_a.rename(columns={\"bottom_depth\": \"depth\", \"sample_a\": \"value\"}, inplace = True)\n",
    "sample_b = df[['bottom_depth','sample_b']].dropna(axis = 'rows')\n",
    "sample_b.rename(columns={\"bottom_depth\": \"depth\", \"sample_b\": \"value\"}, inplace = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We're creating a unique row for every observation, regardless of whether it's taken in the same layer or not."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>depth</th>\n",
       "      <th>value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>80.0</td>\n",
       "      <td>193.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>70.0</td>\n",
       "      <td>248.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>60.0</td>\n",
       "      <td>303.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>50.0</td>\n",
       "      <td>280.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>40.0</td>\n",
       "      <td>264.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>30.0</td>\n",
       "      <td>313.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>20.0</td>\n",
       "      <td>314.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>10.0</td>\n",
       "      <td>309.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>80.0</td>\n",
       "      <td>197.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>70.0</td>\n",
       "      <td>235.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>60.0</td>\n",
       "      <td>291.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>50.0</td>\n",
       "      <td>294.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>40.0</td>\n",
       "      <td>261.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>30.0</td>\n",
       "      <td>311.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>20.0</td>\n",
       "      <td>297.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>10.0</td>\n",
       "      <td>306.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    depth  value\n",
       "8    80.0  193.0\n",
       "9    70.0  248.0\n",
       "10   60.0  303.0\n",
       "11   50.0  280.0\n",
       "12   40.0  264.0\n",
       "13   30.0  313.0\n",
       "14   20.0  314.0\n",
       "15   10.0  309.0\n",
       "8    80.0  197.0\n",
       "9    70.0  235.0\n",
       "10   60.0  291.0\n",
       "11   50.0  294.0\n",
       "12   40.0  261.0\n",
       "13   30.0  311.0\n",
       "14   20.0  297.0\n",
       "15   10.0  306.0"
      ]
     },
     "execution_count": 124,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2 = pd.concat([sample_a,sample_b])\n",
    "df2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INSERT INTO measurements (value, uncertainty, layers_id, measurement_types_id, instruments_id)     SELECT '193.0', 0.01, l.id, m.id, i.id FROM layers as l, measurement_types as m, instruments as i     WHERE l.depth = '80.0' and m.name = 'density' and i.name = 'Federal Snow Sampler'\n",
      "INSERT INTO measurements (value, uncertainty, layers_id, measurement_types_id, instruments_id)     SELECT '248.0', 0.01, l.id, m.id, i.id FROM layers as l, measurement_types as m, instruments as i     WHERE l.depth = '70.0' and m.name = 'density' and i.name = 'Federal Snow Sampler'\n",
      "INSERT INTO measurements (value, uncertainty, layers_id, measurement_types_id, instruments_id)     SELECT '303.0', 0.01, l.id, m.id, i.id FROM layers as l, measurement_types as m, instruments as i     WHERE l.depth = '60.0' and m.name = 'density' and i.name = 'Federal Snow Sampler'\n",
      "INSERT INTO measurements (value, uncertainty, layers_id, measurement_types_id, instruments_id)     SELECT '280.0', 0.01, l.id, m.id, i.id FROM layers as l, measurement_types as m, instruments as i     WHERE l.depth = '50.0' and m.name = 'density' and i.name = 'Federal Snow Sampler'\n",
      "INSERT INTO measurements (value, uncertainty, layers_id, measurement_types_id, instruments_id)     SELECT '264.0', 0.01, l.id, m.id, i.id FROM layers as l, measurement_types as m, instruments as i     WHERE l.depth = '40.0' and m.name = 'density' and i.name = 'Federal Snow Sampler'\n",
      "INSERT INTO measurements (value, uncertainty, layers_id, measurement_types_id, instruments_id)     SELECT '313.0', 0.01, l.id, m.id, i.id FROM layers as l, measurement_types as m, instruments as i     WHERE l.depth = '30.0' and m.name = 'density' and i.name = 'Federal Snow Sampler'\n",
      "INSERT INTO measurements (value, uncertainty, layers_id, measurement_types_id, instruments_id)     SELECT '314.0', 0.01, l.id, m.id, i.id FROM layers as l, measurement_types as m, instruments as i     WHERE l.depth = '20.0' and m.name = 'density' and i.name = 'Federal Snow Sampler'\n",
      "INSERT INTO measurements (value, uncertainty, layers_id, measurement_types_id, instruments_id)     SELECT '309.0', 0.01, l.id, m.id, i.id FROM layers as l, measurement_types as m, instruments as i     WHERE l.depth = '10.0' and m.name = 'density' and i.name = 'Federal Snow Sampler'\n",
      "INSERT INTO measurements (value, uncertainty, layers_id, measurement_types_id, instruments_id)     SELECT '197.0', 0.01, l.id, m.id, i.id FROM layers as l, measurement_types as m, instruments as i     WHERE l.depth = '80.0' and m.name = 'density' and i.name = 'Federal Snow Sampler'\n",
      "INSERT INTO measurements (value, uncertainty, layers_id, measurement_types_id, instruments_id)     SELECT '235.0', 0.01, l.id, m.id, i.id FROM layers as l, measurement_types as m, instruments as i     WHERE l.depth = '70.0' and m.name = 'density' and i.name = 'Federal Snow Sampler'\n",
      "INSERT INTO measurements (value, uncertainty, layers_id, measurement_types_id, instruments_id)     SELECT '291.0', 0.01, l.id, m.id, i.id FROM layers as l, measurement_types as m, instruments as i     WHERE l.depth = '60.0' and m.name = 'density' and i.name = 'Federal Snow Sampler'\n",
      "INSERT INTO measurements (value, uncertainty, layers_id, measurement_types_id, instruments_id)     SELECT '294.0', 0.01, l.id, m.id, i.id FROM layers as l, measurement_types as m, instruments as i     WHERE l.depth = '50.0' and m.name = 'density' and i.name = 'Federal Snow Sampler'\n",
      "INSERT INTO measurements (value, uncertainty, layers_id, measurement_types_id, instruments_id)     SELECT '261.0', 0.01, l.id, m.id, i.id FROM layers as l, measurement_types as m, instruments as i     WHERE l.depth = '40.0' and m.name = 'density' and i.name = 'Federal Snow Sampler'\n",
      "INSERT INTO measurements (value, uncertainty, layers_id, measurement_types_id, instruments_id)     SELECT '311.0', 0.01, l.id, m.id, i.id FROM layers as l, measurement_types as m, instruments as i     WHERE l.depth = '30.0' and m.name = 'density' and i.name = 'Federal Snow Sampler'\n",
      "INSERT INTO measurements (value, uncertainty, layers_id, measurement_types_id, instruments_id)     SELECT '297.0', 0.01, l.id, m.id, i.id FROM layers as l, measurement_types as m, instruments as i     WHERE l.depth = '20.0' and m.name = 'density' and i.name = 'Federal Snow Sampler'\n",
      "INSERT INTO measurements (value, uncertainty, layers_id, measurement_types_id, instruments_id)     SELECT '306.0', 0.01, l.id, m.id, i.id FROM layers as l, measurement_types as m, instruments as i     WHERE l.depth = '10.0' and m.name = 'density' and i.name = 'Federal Snow Sampler'\n"
     ]
    }
   ],
   "source": [
    "for depth, value in zip(df2['depth'], df2['value']):\n",
    "    qry = \"INSERT INTO measurements (value, uncertainty, layers_id, measurement_types_id, instruments_id) \\\n",
    "    SELECT '{value}', 0.01, l.id, m.id, i.id FROM layers as l, measurement_types as m, instruments as i \\\n",
    "    WHERE l.depth = '{depth}' and m.name = 'density' and i.name = 'Federal Snow Sampler'\".format(depth = depth, value = value)\n",
    "    print(qry)\n",
    "    db_beaver_engine.execute(qry)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Developer note**:\n",
    "What is `profile_identifier`?"
   ]
  }
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