{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Cruise data analysis in Python\n", "\n", "## WCOA 2013 data set\n", "\n", "To download the data used in this tutorial, use the following command in the Terminal (Mac) or Git Bash (Windows).\n", "\n", "```\n", "git clone https://github.com/mlmldata24/wcoa_cruise.git\n", "```\n", "\n", "The data comes from the West Coast Ocean Acidification (WCOA) cruise in 2013. The goal of this NOAA-supported research cruise is to collect data to help understand the effects of coastal upwelling on ocean acidification, and the impacts of ocean acidification on organisms and ecosystems. [This video](https://www.youtube.com/watch?v=Eesi6e03Yx0&t=134s) gives an idea of life aboard the ship and the type of science operations conducted.\n", "\n", "In this part of the tutorial, we will go over the basics of working with dates in Pandas and Numpy, make some exploratory plots and start a regression analysis. The data exploration will be largely guided by student interest." ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", "import pandas as pd\n", "from matplotlib import pyplot as plt\n", "from scipy import stats" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Introduction to Pandas dataframes" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We use Pandas to import the csv data file. \n", "\n", "Here, there is an optional `parse_dates` argument. The numbers in double brackets `[[8,9]]` indicate which columns to interpret as dates." ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "filename = 'data/wcoa_cruise/WCOA2013_hy1.csv'\n", "df = pd.read_csv(filename,header=31,na_values=-999,\n", " parse_dates=[[8,9]])" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
DATE_TIMEEXPOCODESECT_IDLEGLINESTNNBRCASTNOBTLNBRBTLNBR_FLAG_WLATITUDE...TCARBNTCARBN_FLAG_WALKALIALKALI_FLAG_WPH_TOTPH_TOT_FLAG_WPH_TMPCO32CO32__FLAG_WCHLORA
02013-08-05 02:12:20317W20130803WCOA2013121111248.2...2370.222369.027.294225.0NaN9NaN
12013-08-05 02:12:53317W20130803WCOA2013121112248.2...NaN9NaN97.295225.0NaN9NaN
22013-08-05 02:19:58317W20130803WCOA2013121113248.2...2349.622343.727.282225.043.5213NaN
32013-08-05 02:27:01317W20130803WCOA2013121114248.2...2318.722311.927.287225.045.6412NaN
42013-08-05 02:30:53317W20130803WCOA2013121115248.2...2300.022299.727.308225.047.7412NaN
\n", "

5 rows × 42 columns

\n", "
" ], "text/plain": [ " DATE_TIME EXPOCODE SECT_ID LEG LINE STNNBR CASTNO \\\n", "0 2013-08-05 02:12:20 317W20130803 WCOA2013 1 2 11 1 \n", "1 2013-08-05 02:12:53 317W20130803 WCOA2013 1 2 11 1 \n", "2 2013-08-05 02:19:58 317W20130803 WCOA2013 1 2 11 1 \n", "3 2013-08-05 02:27:01 317W20130803 WCOA2013 1 2 11 1 \n", "4 2013-08-05 02:30:53 317W20130803 WCOA2013 1 2 11 1 \n", "\n", " BTLNBR BTLNBR_FLAG_W LATITUDE ... TCARBN TCARBN_FLAG_W ALKALI \\\n", "0 1 2 48.2 ... 2370.2 2 2369.0 \n", "1 2 2 48.2 ... NaN 9 NaN \n", "2 3 2 48.2 ... 2349.6 2 2343.7 \n", "3 4 2 48.2 ... 2318.7 2 2311.9 \n", "4 5 2 48.2 ... 2300.0 2 2299.7 \n", "\n", " ALKALI_FLAG_W PH_TOT PH_TOT_FLAG_W PH_TMP CO32 CO32__FLAG_W CHLORA \n", "0 2 7.294 2 25.0 NaN 9 NaN \n", "1 9 7.295 2 25.0 NaN 9 NaN \n", "2 2 7.282 2 25.0 43.521 3 NaN \n", "3 2 7.287 2 25.0 45.641 2 NaN \n", "4 2 7.308 2 25.0 47.741 2 NaN \n", "\n", "[5 rows x 42 columns]" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.head()" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Index(['DATE_TIME', 'EXPOCODE', 'SECT_ID', 'LEG', 'LINE', 'STNNBR', 'CASTNO',\n", " 'BTLNBR', 'BTLNBR_FLAG_W', 'LATITUDE', 'LONGITUDE', 'DEPTH', 'CTDPRS',\n", " 'CTDTMP', 'CTDSAL', 'CTDSAL_FLAG_W', 'CTDOXY', 'CTDOXY_FLAG_W',\n", " 'SALNTY', 'SALNTY_FLAG_W', 'OXYGEN', 'OXYGEN_FLAG_W', 'SILCAT',\n", " 'SILCAT_FLAG_W', 'NITRAT', 'NITRAT_FLAG_W', 'NITRIT', 'NITRIT_FLAG_W',\n", " 'PHSPHT', 'PHSPHT_FLAG_W', 'AMMONI', 'AMMONI_FLAG_W', 'TCARBN',\n", " 'TCARBN_FLAG_W', 'ALKALI', 'ALKALI_FLAG_W', 'PH_TOT', 'PH_TOT_FLAG_W',\n", " 'PH_TMP', 'CO32', 'CO32__FLAG_W', 'CHLORA'],\n", " dtype='object')" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.columns" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Instead of strings, the dates are now in a special `datetime64` format. This means that, instead of treating the dates in the same way as any other collection of characters, pandas and NumPy can understand how this variable represents time." ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0 2013-08-05 02:12:20\n", "1 2013-08-05 02:12:53\n", "2 2013-08-05 02:19:58\n", "3 2013-08-05 02:27:01\n", "4 2013-08-05 02:30:53\n", "Name: DATE_TIME, dtype: datetime64[ns]" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df['DATE_TIME'].head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "For example, subtracting `datetime64` objects with pandas gives a `Timedelta` object, which is specifically used to represent differences between times. The first two samples in the cruise data are separated by 33 seconds (the time between firing of bottles)." ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Timedelta('0 days 00:00:33')" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df['DATE_TIME'][1]-df['DATE_TIME'][0]" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([48.2 , 48.3 , 48.37, 48.44, 48.5 , 48.53, 48.61, 48.66, 48.71,\n", " 48.78, 48.81, 48.84, 47.97, 48.14, 47.96, 47.68, 47.13, 47.11,\n", " 47.12, 47.34, 46.13, 46.17, 46.19, 46.25, 46.24, 46.12, 44.65,\n", " 44.66, 44.2 , 41.99, 41.97, 41.96, 41.94, 41.9 , 40.25, 40.23,\n", " 40.22, 40.21, 40.1 , 37.67, 37.94, 37.91, 37.87, 37.76, 37.75,\n", " 36.8 , 36.78, 36.76, 36.73, 36.71, 36.69, 36.52, 36.7 ])" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "pd.unique(df['LATITUDE'])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Exercise\n", "\n", "Create a list of unique station ID’s (“STNNBR”) found in the survey data. Call it `stns`. How many unique stations are there in the data? \n", "\n", "\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Summary statistics" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "A summary of the dataframe is given by the `.describe()` method." ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "count 969.000000\n", "mean 8.993954\n", "std 3.055917\n", "min 1.738200\n", "25% 7.292000\n", "50% 8.328200\n", "75% 10.752200\n", "max 20.747400\n", "Name: CTDTMP, dtype: float64" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df['CTDTMP'].describe()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "These summary statistics can also be accessed individually with similar syntax." ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "8.993954179566563" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df['CTDTMP'].mean()" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "1.7382" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df['CTDTMP'].min()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Alternate method using Numpy functions." ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "1.7382" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "np.min(df['CTDTMP'])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Mathematical operations" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Converting Celcius to Fahrenheit" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [], "source": [ "df['CTDTMP_F'] = 9/5*df['CTDTMP'] + 32 " ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0 38.63894\n", "1 38.64236\n", "2 39.85916\n", "3 41.05328\n", "4 41.82404\n", "Name: CTDTMP_F, dtype: float64" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df['CTDTMP_F'].head()" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0 3.6883\n", "1 3.6902\n", "2 4.3662\n", "3 5.0296\n", "4 5.4578\n", "Name: CTDTMP, dtype: float64" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df['CTDTMP'].head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Plotting" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Plot latitude as a function of time." ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(array([15922., 15926., 15930., 15934., 15938., 15942., 15946.]),\n", " [Text(0, 0, ''),\n", " Text(0, 0, ''),\n", " Text(0, 0, ''),\n", " Text(0, 0, ''),\n", " Text(0, 0, ''),\n", " Text(0, 0, ''),\n", " Text(0, 0, '')])" ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": "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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.figure()\n", "plt.plot(df['DATE_TIME'],df['LATITUDE'],'-o')\n", "plt.xticks(rotation=15)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The `pyplot` library automatically understands `datetime64` objects so it is easy to see how the ship moved between stations from north to south as weeks passed." ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[]" ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": "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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.figure()\n", "plt.plot(df['LONGITUDE'], df['LATITUDE'], 'ro')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The `scatter()` function allows points to be colored according to the value of a variable. In the case of dates, later dates are shown as warmer colors." ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 18, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": "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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.figure()\n", "plt.scatter(df['LONGITUDE'],df['LATITUDE'],c=df['DATE_TIME'])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Note that the vertical coordinate is pressure (not depth, which indicates the bottom depth rather than the depth of the sample). To plot dissolved oxygen with depth:" ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Text(0, 0.5, 'pressure[dbar]')" ] }, "execution_count": 19, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAAkQAAAG0CAYAAADTmjjeAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMywgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/NK7nSAAAACXBIWXMAAA9hAAAPYQGoP6dpAABTc0lEQVR4nO3de1xUdeI//tcBAQF1BLkMKLcsb4EoaIqVqZmXMi/w+2TZurbbbddblrZJ322zT62g++m6ZtvablvttvLZj5etLNPWW4aWIih4yxQEdRBRHBQQFN6/P2hOc+fMMMPMcF7Px8NHzZkzZ97vOTDnxfu8L5IQQoCIiIhIxfw8XQAiIiIiT2MgIiIiItVjICIiIiLVYyAiIiIi1WMgIiIiItVjICIiIiLVYyAiIiIi1evi6QL4ipaWFpw7dw7du3eHJEmeLg4REREpIITAlStXEBsbCz8/2+1ADEQKnTt3DnFxcZ4uBhERETmhoqICffr0sfk8A5FC3bt3B9D6gfbo0cPDpSEiIiIlamtrERcXJ1/HbWEgUshwm6xHjx4MRERERD6mre4u7FRNREREqsdARERERKrHQERERESqx0BEREREqsdARERERKrHQERERESqx0BEREREqsdARERERKrHQERERESqx0BEREREqqeqQLR69WokJSWha9euSE9Px9dff+3pIhEREZEXUM1aZnl5eVi0aBFWr16N22+/He+++y4mT56MI0eOID4+3tPFIyI30+kbUFpdh6SIUADA/rJLkCQJ6QlhiNEEmzwfowk2eU1ooD/KL9Wb7O/ucp6tqUdRxWWMGxCFQbEam2Uz3mZvu5LXNDTdQFHFZUT16IrxA6NRVXsNG4vOoe7adaT06Ynxg6IBAF8dPY9DZy5DEhK6dAEamlowqm8v9A4LkT/fr46cR9WVaxgS1xP1Tc0Wn93Bihp8V3YJtyWGy8eM6tEVg3trUNfUbLWMBadrcKmuEZIkobb+OqqvNqJXtyAkRoQiLizY5BwBlufY2mddcLoGQggM+7Ec6w6cQckZPVJ6a5CZ3sfmz4bx5xYa6G+1zO1l+IxuighFcGAXh851e5kfuz3v5c5yupIkhBCeLkRHGDFiBNLS0vDOO+/I2wYOHIjp06cjJyenzdfX1tZCo9FAr9dzcVcVUMOXgbsv9o7U214YMWwzvoCmxoU5VJa8feXIXl+MFgFIAIy/9CQAmWm9saHwLFoE4CcBOZkpAIDn1hVbHEsCkJuVgpnDf/pDyhXnWKdvwPu7S7Hm61LY+lKWACydPAA9QwLk+vhJwHOTBiCljwbFZ/RYsfmYST0M5czbV46l64ohfjzOY3cm4Zd3JGHX9xfkY7mC+edr/ty9KVrUX2/G9mMX7B7HTwJ+fVdf1DfdwPHzV5F/8mK7yvXEnUn4xR1J8vl5d9dJ5Hx+rM3XZZn9bDx6h+3Pzfwzb4/F/1uEdQfOmmwzPr7xz7TxdiW/S20xP/aMoZa/H0rraKuc5tz5Pan0+q2KQNTU1ISQkBD861//wowZM+TtTz31FIqKirBz506L1zQ2NqKxsVF+XFtbi7i4OAaiTsbaL6G7vwxG94v0WEAy1Lf4rB4rvjhmcRGUACy9dwCeHN3XoS9W87DiyJegcQgwDiPGrx8S1xMHyi/Lr8tK641XHxiiqM4HK2ow/e18mxdpa+xd1PFjOb9ZOg4xmmCLoLF0cms4sfW5ATD5/4LTNfjmh2qs3VcBV38b+0sSdi8dCwAYlbPNap3aqmtnYvj5ulx/HTlftB2G7LH1uRk+8/b8bh+sqMG0t/OtPucvSVg/NwMzVueb/P76SxJ+M6m/RSAGoOh30UCnb8DtudvsBmTjOrbVIml+LGufj9LvC2cpDUSquGVWXV2N5uZmREdHm2yPjo5GZWWl1dfk5OTgpZde6ojidUrOpn13/JVg65i2worxX30tAiZ/pbUI4Pn1JRjdL1JRq4fhQml47dJ1xZCk1v+XpNaL55Oj+7q8ngDsBj1bBICcz4+hqPwyvjxcafHFanzhN7SSmP8le2+yFpt/fK2h3tnrijG6XyQAyLcozumvWYSyFtH65S0ETD434zAEtJ6Te1O0uHug1m79i8/qkfvFMYcv+G3t3yKAf35bjojugXjx30fk/QUgX2itXZAko/06Iog0C4H/HD2PI+dqbb6XWsIQ8NPPlytaw2wdolkIlFXXt+v767uySzafaxYC+8pqLOrQLITJz7rh9w4/ft8YtrX1/VVaXdfm52Ooo3ErmbUgY+1Y5p+PTt9g8Z2r9DvW1VQRiAwkSTJ5LISw2GaQnZ2NZ555Rn5saCGittlL+/buubvjrwR7zcrWfgnffGiIoi+DP+88ifTEcLu3mP662/LWhwDkVgAhWsMHBPDkXT+FIp2+oc2+D8b7llbXmdwqMb7QGoKLedBryxclP/2hYPhibTGrx3PriuEnwaJZ//MSyz8yWgD84v19OFZ5pc33VlrGRz8owIosy9sHHdXi8da2H+w+by3cGZero4LIbzce7qB38g2uujVoi78kITEipF3HMPSrsnX84Ylh8JNM62L+GGj9vTP/QWsrsCVFhFo9lnkZQgL92gwy1o5l/vkoCU0dRRWBKCIiAv7+/hatQVVVVRatRgZBQUEICgrqiOL5lLZacOylfXv33K21zJj/cjnaemSvLLZ+CSGsf7GYez//NN7PP221P4nhvf+yu7TNMgJA7hfH0DssGOkJYdj1/QWTViVbxwdst/iYX3SfW1eMPyoIeva02Nj+7P9Z9rGxRUkYApR9/gZL1xVjgLa7yefgTS0e7r74kvsZ9yEyJgFya6+BvyRheWZyuy/kqXFhyErrbfHHhuH4qXFhyMlMwfPrS9AsROvtssn9LVpc/X4sqL1AYi5GE2xx7OlDY7Gx8Jz8eHlmMuqamtsMMtaOZf75KAlNHUUVgSgwMBDp6enYunWrSR+irVu3Ytq0aR4smW9R0oJjK2gcOF1j9eJtr2XG+JfLkdYjQ3C6VNdk85i2fgnTE1u/aJS2pgi0tgIM0HY3afFS0uxsfIz5HxdabdkwBJqCskuYmKxFw/UWCCEQHx7iUIvP4XN6n+gr4gdgUrIWnxdbv5VtTgD4x7flHRY8fOEzVKOYHkHQ1Ta2vaOD/j1vFFLjwrBkYn+8/00p3ttVihb8FExG94tEWXU9QgL9UN/UgsSIEJe1arz6wBD8PCMB+8tqkBgRgpDAAJPjzxweL7+/YXvP4ACL8AHAbiCxxtqxl0zsb/JYp29QFGSsHcuYktDUUVTRqRpoHXY/e/Zs/OlPf0JGRgb+/Oc/Y82aNTh8+DASEhLafL3aR5kp7Rxna783HkzFgn8W2Tz+qoeGYuHaQovXrZ+bgfJL9XhqbVGb7w1YjiYCLC9g2ZMH4Mm7+iJvX7nFL6EhZH168Kzd8pqTpNZbYIZ+QVNTY9vsmOgrOiIE+EnAY3fchPsGa212JvW0VQ8NRVx4sEVnVnLezOF9MGFQNB7/sMCi5XjD3FEAgJWbj+ObH0eYWftZNHRwr6q9JocHa8f772m34oWNh01e7ycBL029FYP7aLDpUCXWfH2qtXM/gBwbLb+2LuzewloZ3VVue9+hrii3q7BTtZmZM2fi4sWL+O///m/odDokJyfj888/VxSGSHnnuL/uLjUZKSNJwPLMZAxLDLd5K8TQMvPc5AFyk6+hmdbWxcfaPWbzW2S2rlkrNx/H1CGxdv9ysVdea8z7BZVV1+G5yQOw8ovjrbfifJSfBKz5eToe+6DAraFow9zWv8R/v+mIG9+lfeqabsi3KqwNx7elIwKltds37nofR9/C1h8mAPCv/WeQFm95+8dwWwgA/vH4SJOL5a7vL1htTYjRBMuvsXa8mcPjEeDvZ/MCnhoXhl/ckWj3omx4H29mrYzuKndbrT+O8IbPVjUtRO3FFiL7LUTGQ4+N+QH4Jvun4cmGLyPjYxiadY3DzLTUWHxy8JzNL19rLUT5J6sxa823iurz2/sGYnhiGOqamm1OqvbuzpOtAQ0/tQA5QgIwd0xfvL3jpGMv9CKDe/fAy9OTsemQDn/+WlmfKEfNGhGH5TMGKxruC7guYEgAZgyJxfqic4r2Nx7CnpGzzaH3ceeXrCQBuZkpuNxwXdG8Orb4ScBzP7ZufnXkvNxfb3AfDSouNUCSgD5hjrWQSQDem5OOQbEalFXX40xNnUW/M+PPVemFVUlrgq19fKGVh1yLLUTkUub3ef0k4Jd3JAL4qWXG2ndkCyC35Bj/NWF8zx2AxYXw3wdtX6Rs3WNWMjrC4JVNRy22mU96tmLzMbkzsTN/NggAq304DAHAobO1mPZ2Psb2j3TL8SUAC8bdAkDZcF9XEgDuHhSNjQfPKXpfQ6ukcCDe/PL2RPz1mzKny2jPsxP7IbFXKNJ+HImYf7La6WO9cN9A3Ds4Rv6dmp2RaPK88USY5t8DbU3j8NiHBcj98fcq/6TlzobPNaNvL8UBRUlrgq19vKElgryTqtYyI8fp9A3IP1kNnb4Bo/tF4s2HhmDWiDgIAaz5uhS3527D+7tLbX4p+gEICTT9MRMQiOrRVf4CVHoh9APw9qyh2L10rM371I/ekQQ/6zMptMkwTPpghfUO4LbYeztPNL9KaB0Z4//jlBKSZL+MAHD3gCjYmIECALD9uP1ZhZWUyfy8+EmtI+jMh+i2RQB4YnSSXD9n+UsS0hJab9coeV8/AIkRrUtTKHlnSQKmDYm1OLa/JOHeFK31F6Htc2WQFh+O+wbHOvT5+cHyPPhLkkkYasvM4fHYvXQs/vn4SGyYO6rN9xQ/DpzQ6RusltFTI4qIzLGFiGTmw9qVdFBuEcB7X5fa/EuxBcCM1fl2Z0xVOu/F8sxk3Dc4Vg5pjk46qESLAJ7OK3LoOCNuCsPeUzXte2MXuy0p3GRUyMufHrE6PxDQeuF+ZUYyqmqvuaVDs/FcSPZG5Ji3Qto7Xlx4CNbPzUB9UwtCAv1MOsQaMx8ybLzd0Mo4c3g8QgL92+xE/9jon5Z9ePzOJLu3EA0/39aGRxv6rRysqMH+shp8/UM1dhgFzsy03ugX3f2n27Ww/L2zFiKUDpcGTH8PnRnRY9zKYvyetm4tG7cCecuIIiJz7EOkUGfvQ/TurpOts5yKn/oSWFvawZYn7rwJf9ld2vql+OM289EcxhPUAcr6IAGtrRevzEi2Ofx+dL9Ij43oMvSRePSDAudeb6dvkgRgycR++MOX3zt8XOPP1t4yAOaTVTrSN8YePwl46LZ4ZNzUC+mJjq2PZtzH48g5PdZ8fcpq4DSfo0mnb0BBWY3c18U4dBmOaSuMtdV/yXipjrb2N4yQMr7NZK8/i62+eQDw/jelWLOr1OL3xt5oHvP3Mn9svtSIrXmuHGH8HlW11zB9db7Jz7V5nz/246GOxD5EZJdxa9AnRedM1vVpEZDDkRJ+EnDfYK08QuNiXSPmf1xosk9bI8VmDo/HAG13qxfubcerUFV7DQAsJlrMXl+MNx90bNJBV3ZyXTp5ACK6OTaBpyEEGSZTy/3c+tISAkDjdetTIo6+JQLf/HDRZkuKYe6n+wYH4z/HqmyW5b+n3YrR/SKRf7IaJy9cVVyHl6fdig/3nMaJqp9ekxbfE28/nCYHD2dX/zZufYjRBOPugVqr65EJ/LQkiOE1U1Ktv1db/UbMW1ckADA6T+atGIb9zWfwBlp/LuubTLfaen97ozcTI0LwntlCr34A1s/NsLu4rfl7GT827+8n4JplEszPWW4brUDsx0PeiIFIhZQsc+BIu2GL+Om2mKGlwdq08tZaiIyb/VPjwvCElVsRQgDT387HQ7fFWZ3Y8T9HqqyuYA5bdVNetTb1CQvGOwo7TksANs4bhageXeULXml1nd3yRPUIsvwsAaz4/wYDAA6crsGp6qt4bcsJi+PM+7gQVxtvIKJboM3j/3bjYXluFqV9VyQAL35yWC7TzVGhGD8gCnNub72l1Nb6Rs6oa2pus9N+e5kPIQbsj3oyhHhrrSFK+8TYm6XXWlhqgWXYckRHLZPgyuHYRB2FnapVRulcPQaGHxDDPCdA6xf2/LF9TS6ghhmndfoG+a9nQ6dXf0lCTmYKcrNMt1nrO/ALG52iBYCPv6uwWsaNZsPzDeV0+9wvUmvo2Hz4vM19/Iw+s9ys1j4lMZpguUN5aKC/7eMDuHtgtOVn+WNH5F3fX8CCfxbiVSthyCB7XTGq2pjBV5j91x7DqTG+qP5QVYc/7SrFqJxteHfXSavLpej0DQqObputDsOGjs6uYnxujP/fltS4MORmtv1zbe/9zM+v4fXu6IDckZ2alXx+RN6EfYgU6ix9iByZqwdoHdUVHhpk8RdzaXWd1eP88/GRyOjbC4DzM6a2p4O0M/MFOcNai5c1qx4ail7dgmzW19b5sNY/xrxfiNJ+U678TNrq/G7rvYx/LpyVt68cS39cLBVwXf8XV2hvnxhbr3flTMDuPCaRN2MfIrLKWhO9cX8JY4Zhyeb3/gHgEyuT2Zn/pensjKnyrQizPiNKdEQYykqLxZj+UW2OSjLMwA203qoAYFF3W+dj4zzTTrnmn5vSqQokOPaZtBX02lpNXlh5vr0tEIb+bqP7RSJ/6TgcOF0DIeBwZ213cqZPjPmoTlu35Vx964m3s4is4y0zlTFvopdvL1m5wk0fGmv1y1Knb8CKzZYz4v5mcn+XfbmmxoWZ3GLraPbedd2Bczh3+Zrd+VcMf3nv+v4Cbs/dhllrvsXtuduQt6/cZD9rt0wMt9bsUTpnz9LJA6zWxdYv/oyhve1+7v6ShKWTB9h8vfTjezp7C8lc3r5yk89v1/cXcN/gWExJtf6z6SvM62X+c2HMHbeeeDuLyBJvmSnUWW6ZGej0DThwugbzPy50aHkMwPZtHlfcFrFWzrLqehw6e1leF8xfkvDrMTfh7e0nne4nJElA5tDe2FB41mpLS/a9A9AzOMDmmlXyCDGj0XiGMDC4T0+bM3Db+kydueXy7q6TVpdqkCTgoeHxWHD3zQCAUTnbLPpYbZw7CmdqGizOv/kyCuafu+H2ik7fgFXbfsA/vjW9kEsA8rPHya9vTwuE0gWFfU1nrReRt+ItM7IrRhOMsFD7I5xsjT6xNzLGHeU0/DU7NTXWpA/Tqu3Kl8XwlyT8ZlJ/9O4ZDEmCfCtwycT+eP+bUry3qxQtaG05MaznVFpdh3/PG4WNhWfxfv5pk+M1C4HBvXvavYWTf7Ja8YgeZ265pPTWWN3+xweHYkpqrFwG83MsfhwWHhYaaPGc+TIK5p+78dDq+wbHWAQiATi8DIMtHTUiqqN11noR+ToGIhVra4ZoWyHH2oy45rdFzPtHtEXp/hfrGlF99RpC7IzOMiYBWDVrqByADlbU4LuyS+hTGyyHkOfvHYRf3J4kz5+Tt69CnqBQQusCrdYCYEigH0qr6yz6WRnq0tB0w6I8fj/WwTAarz1sBVNDvyVb+xjKEBcWrCjY2gpr7g7GHRm8O1JnrReRr+MtM4U62y0zA+MRJ9Ymo7M3+sTeyBhH5qFRsr+9mayNmc8/ZDwSafH/FmHdgbPyvllpvfHqA0MUvce9KVp8WXLeZEkEw+0280Vh7Y2QM3Q4dtX8PEpGDFmcY6MyzBja22JpB0fK5O4RS511RFRnrReRN1J6/WYgUqizBiLANNgA7ev74Wj/CCX7Kx1ibrgttmLzMYvj/fnnaVaX1/j3j6O5dPoGi742xgzLMRjWzpqxOt/iPdbPzbDY3lZ5XdFvREn/I8OyFgvXFlott7XlLFz5/u3RWZd56Kz1IvI27ENEilmb6t9ZjvaPULK/kiHmL9w3EPcOjrF5vO3HrS9fsb+sBqlxYW3OGG1YjiGjby+bfYP2ldU4NHeSq/qNKOl/FKMJRng365+NoV7ufP/26KzLPHTWehH5Kg67J5dydCZcJfu3NcTcX5Jw7+AYu7P7ju0fZfW1w37sb5MUEWp3qL1xmWy9x/DEMEVD4a0dsyN05CzFRES+hoGIXMreUgTO7m/Yx9rUOOb72zre3QO1yErrbfLarLTe8nw/MZpg5GalWA1FSt8jNS7MZLsxSWp9P1fNz+MMR88NEZGasA+RQp25D5E7ONo/Qmk/GMMQ97jwYLv9Xmwd72BFDfaX1WBYYpjVyQ8NfW0kqXXhVmfew7A9JNAPFZcaTIb5e0O/EW8oAxFRR2EfInI5a0PjbQ2XN+7/Y2DttfvLLuFy/XX0DAnAsMRwAMCnB89CkiTEhQXj0Bk9qq5cw/iB0UiNC8N9g+1fwA3lCQ30hzDqFWRcTkNfI2tD32M0wUhP/KncAgJVtddMym48rP5UdR1CAv0s6m54HNWjq8lnYPyco1MTuAr7rhARWWIgUjmlF2VrQ+MB2Bwub7y/+VBvw2uVDKM3+OO2k8hK640lE/vbLK+1Ie+GoeWGIfLmZXlu0gCk9NHIx7M3bN78WMbMh/Bb+8yemzwAKb1b32vX9xccmppACU8FLCKizoC3zBTqjLfMlM4XZG3Yu7VFQI2XfbA3TN4Pra9rzw+eeXkdWf3d1vGemzTAYsi+I/5ttCCrvfKYz5UEtH8IvqNzPxERqYXS6zc7VauUTt9g0hLSIoDn15dAp2+w2NfaUPYWKyuiG4aRtzVMvgXtC0PWyqt09Xd7x1vxhfNhCGgdwm9grzzWwqDhs3OGI+eSiIisYyBSKXvz/5izNlzbT7JcEd4whLutYfJ+sL+avFLG5VW6+rs9LYDVkWxKDbOyZIZS7Rn+7si5JCIi6xiIVMqROWmsDdfOyUxBbpb1Idzm+0tG4clfkpCTlYJMsyHwzjAur/l7GvgBGH1LhFxXyUqQMz7e0skDrA6bNzxvPHTemPEQfnvlMZAAuUztHf7O+YWIiNqPfYgU6qx9iBxZT8nacG17Q7htLQkCWPYxkgBsnDcKUT26mgx7Lz6rx4XaRowbGIVjlVfaLK/xkPdNhyrx3u5Tcmfqx0cn4Re3J8llOXT2MlZ+cdzieMbHMCzVYTz83vB8fdN1lFXX2xzCb1yeQ2cuY+Vm0/ca3S/SZcPfuTYWEZF1XMvMxTpjIAI8MydN/slqzFrzrcX2fz4+ss0lJJSWV+maah1Zf675RUTU8TgPESniiTlpDLd4zMNKYkRIm0PHlZZX6ZpqHVl/rvlFROS92IeIOpytJSR2fX8Bt+duw6w13+L23G3I21fu9HuwXw0RETmCt8wU6qy3zDzJvI+RkltcjmC/GiIi4i0z8nrGt3jyT1ZbvcW16ZAOwxPDUNfU7PAMzDOHx7u04zIREXVeDETkFaz1KwKAVzYdlf/fmRmY2a+GiIiUYB8i8gptzdsDcAZmIiJyHwYi8hozh8dj99Kx+O19A23uwxmYiYjIHXjLzEe4eiXzto53sKIG35Vdwm2J4a2TJZ6ugRAC8eEhcn8eABbHMD+uTt+Ar46cR9WVaxg/MNrmBIbGonsEQYL19c44UoyIiNyBgcgHuHol87aOt/h/i7DuwFm7xzBesd1wDAAmx50xtLfJcf647SQmJ2vxzs/S2yyXNe1d4oKIiMgWDrtXyFPD7pXOuOyq4x2sqMG0t/MdPq4fAFjpFG3N/LF9sWTigDbLJR9bAt56cCjSE8MYhoiIyCFKr9/sQ+TlXL2SeVvH+67sklPHbYGyMAQAb+84adEx2lq55GMLoFe3IIYhIiJyGwYiL+fqGZfbOt5tieFOHdcPsDiuLULAItAlRYTaXIXeTwL7DRERkVsxEHk5W8tcONta0tbxUuPCkJXWu83jSNJP/Yj8JQk5WSkWx7V1HGuBLkYTjMfvTLK6/2N33MTWISIiciv2IVLI00t3uHol87aOd7CiBvvLajAsMQxRPbriwOkaCAHEhQejvqlFDjTmxzA/rk7fgBVfHMPGonMAYHcJDWv9iPwAfJM9joGIiIicovT6zUCkkKcDka9TGui4/hgREbkS1zIjj7A1v5HSJTS4/hgREXkCAxG5jKvmS+L6Y0RE1NHYqZpcQqdvMJlUkeuOERGRL2EgIpdw9XxJREREHYmBiFwiNNDf6vaQQP6IERGR9+PVilyirqnZ6vb6ppYOLgkREZHjGIhIEZ2+Afknq232CXL1jNpEREQdiaPMOiHD0PfQQH9U1DRACIH48BDUNTXLw+FtDY8/WFHTup6ZAI7oatE3qhsC/PyQu/kYhGidoTrXyuixGE0wnps8ACu+OIYWwZXpiYjItzAQdTLGQ9+t8ZOAGUN7Y0PhWYvh8Yv/twjrDpy1e3whgKXrijG6X6RJ2MnbVy6HIQnAbyb154SKRETkM3jLrBMxH/puTYsA1h04azE8/j9HK9sMQwYCwH+Onrf5vgLAys3HOeSeiIh8BgNRJ2Jt6LsSzUJg+7ELDr3mQm2j3fflkHsiIvIlDESdiLWOzUr4SxLGDoh06DXjBkbZfV92qCYiIl/CQNSJxGiCkZOZAn/JdirylyRkpfWW9zF0fr57oBZZab0VvU9WWm+kxoXZfF92qCYiIl/D1e4V8qXV7g0ry4cE+uFMTQOEAOLCg1Hf1CIvmGpr9fmDFTXYX1aDFiFw9MdRZplpfVBVew37y2owLDHMJAxZe18uykpERN5C6fWbgUghXwpEjrI1BJ+IiMjXKb1+c9i9yrlqhXoiIiJfxj5EKsYV6omIiFoxEKkYh8sTERG1YiBSMQ6XJyIiasVApGIcLk9ERNSKnapVbubweIzuF8nh8kREpGpe3UK0bNkySJJk8k+r1crPCyGwbNkyxMbGIjg4GGPGjMHhw4dNjtHY2IgFCxYgIiICoaGhmDp1Ks6cOdPRVfEqOn0D8k9W42BFDfJPVgMAMvr2YhgiIiLV8voWoltvvRVfffWV/Njf31/+/5UrV+K1117D3/72N/Tr1w+vvPIK7rnnHhw/fhzdu3cHACxatAiffvop1q5di169emHx4sWYMmUKCgoKTI7lTYznBaqqvYbvyi7hpohQ1Dc1Q5IkpCeEIUYTjIMVNdhYdA51164jpU9PjB8ULU+6WHC6BkIIDEsMt1iV3nwBWA63JyIitfPqiRmXLVuGjRs3oqioyOI5IQRiY2OxaNEiPPfccwBaW4Oio6OxYsUKPPnkk9Dr9YiMjMRHH32EmTNnAgDOnTuHuLg4fP7555g4caLisnTUxIzWAos5CcDQ+J44UH7Z4rmstN5Yf+AshNG+uVmtYUenb8DtudusHttfkrB76Vi2EhERUaei9Prt1bfMAODEiROIjY1FUlISHnzwQZw6dQoAUFpaisrKSkyYMEHeNygoCHfddRfy8/MBAAUFBbh+/brJPrGxsUhOTpb3saWxsRG1tbUm/9zNfF4gWwRgNQwBwDqjMGTYN3tdsdzqZOvYHG5PRERq5tWBaMSIEfjwww/x5ZdfYs2aNaisrMSoUaNw8eJFVFZWAgCio6NNXhMdHS0/V1lZicDAQISFhdncx5acnBxoNBr5X1xcnAtrZp29wNIeLQDKquutDrM34HB7IiJSM68ORJMnT0ZWVhZSUlIwfvx4bNq0CQDwwQcfyPtIZiu7CyEstplTsk92djb0er38r6KiwslaKGcvsLSHHyCPIDMeZm/A4fZERKR2Xt+p2lhoaChSUlJw4sQJTJ8+HUBrK1BMTIy8T1VVldxqpNVq0dTUhJqaGpNWoqqqKowaNcruewUFBSEoKMj1lbDDEFieX1+CZjtduyQJGBpn2YdIApCZ1hvrC8/C8HIJQE5Wihx2jIfZhwT6ob6phcPtiYhI9XwqEDU2NuLo0aO48847kZSUBK1Wi61bt2Lo0KEAgKamJuzcuRMrVqwAAKSnpyMgIABbt27FAw88AADQ6XQoKSnBypUrPVYPe8znBaqqvYb9ZTVIjAhBQ1MLJAlIMxpl9u+ic7h67QYGx2lw98DWUWZLJvbHgdM1EAJITwyzCDsxmmAGICIiIiNeHYiWLFmC+++/H/Hx8aiqqsIrr7yC2tpazJkzB5IkYdGiRVi+fDluueUW3HLLLVi+fDlCQkIwa9YsAIBGo8Gjjz6KxYsXo1evXggPD8eSJUvkW3DeyjiwxGiCkRoXZnW/1Lgwq8/FaIJx32AGHiIiIqW8OhCdOXMGDz30EKqrqxEZGYmRI0di7969SEhIAAD85je/QUNDA+bOnYuamhqMGDECW7ZskecgAoDXX38dXbp0wQMPPICGhgbcfffd+Nvf/ua1cxARERFRx/PqeYi8SUfNQ0RERESu02nmISIiIiJyNwYiIiIiUj0GIiIiIlI9BiIiIiJSPa8eZUYwWbk+PjwEdU3NSIoI5TxCRERELsRA5EV0+gZsPVKJ6qtNuHtAFI5VXsFz64ot9vOTgJzM1hXsiYiIqP0YiLxE3r5yk/Dz1n9+sLlviwCeX1+C0f0i2VJERETkAuxD5AV0+garLUH2NAuBsup6N5WIiIhIXRiIvEBpdZ3Dr/GXJCRGhLihNEREROrDQOQFkiJCHdrfX5KwPDOZt8uIiIhchH2IvECMJhgrslIsbpulxfdEUcVltIjWjtRzx/TF7TdHIjEihGGIiIjIhRiIvMTM4fEY3S8SXx05j+qrjRg3IAqpcWHQ6RtQVl3PEERERORGDEReJEYTjNkZiRbbGISIiIjci32IiIiISPUYiIiIiEj1GIiIiIhI9RiIiIiISPUYiIiIiEj1GIi8jE7fgPyT1dDpGzxdFCIiItXgsHsvkrevHNnri+WJGLmiPRERUcdgIPIC/zlaiX/tP4PNh8/L27iiPRERUcdhIPKwzNXf4ED5ZavPGVa0ZyAiIiJyL/Yh8qD/HK20GYYMQgJ5ioiIiNyNV1sP2nasqs196ptaOqAkRERE6sZA5EHjBkTZfd5fkpAYEdJBpSEiIlIvBiIPunugFmnxPa0+5y9JWJ6ZzP5DREREHYCdqj1s/dzb8Z+jldh0SAchBBqampES1xOZaX0YhoiIiDoIA5EXGBSrQe4Xx3Ciqg4AsPlIFf5ztArr594Onb4BpdV1SIoIZUAiIiJyEwYiD8vbV46l64shhOn2A+WXseyTEny45zQnaiQiInIz9iHyIJ2+AdlWwpDB3/JbwxDw00SNXNKDiIjI9RiIPKi0uk4OPEoYJmokIiIi12Ig8qCkiFD4SdafS47tYfEch+ETERG5BwORB8VogpGTmQJ/qTX5+EnAyJvC8Zc56fhs4Z0mz3EYPhERkftIQtjqwULGamtrodFooNfr0aNHD5ceW6dvQFl1PRIjQiwCj73niIiIyD6l12+OMvMwnb4B+8suQZIkhAT6WQyxj9EEMwgRERG5GQORB+XtK8fSdcUwb6KTAORmcYg9ERFRR2EfIg/R6RushiEAEACWrivmEHsiIqIOwkDkIaXVdVbDkIEAUFBW01HFISIiUjUGIg9JigiFjRH3MqmtHYiIiMglGIg8JEYTjNysFLv7pCWEdVBpiIiI1E1xp+q0tDSHDixJEj755BP07t3b4UKpxczh8RjdLxKz1nyL0uo6k+dWZKVwdBkREVEHURyIioqKsHjxYnTr1q3NfYUQyM3NRWNjY7sKpwYxmmBsXzIG/zlaic8P6XBTVDdkpvVhGCIiIupAiidm9PPzQ2VlJaKiohQduHv37jh48CBuuummdhXQW7hzYkYiIiJyD5dPzFhaWorIyEjFBThy5AhiY2MV709ERETkKYo7VSckJECSJNy4cQMvvfQSKioq7O4fFxcHf3//dheQiIiIyN0cHmXWpUsX/OEPf0Bzc7M7ykNERETU4Zwadj9+/Hjs2LHDxUUhIiIi8gyn1jKbPHkysrOzUVJSgvT0dISGhpo8P3XqVJcUjoiIiKgjKB5lZszPz3bDkiRJnfJ2GkeZERER+R6XjzIz1tLS4nTByDqdvgGl1XVIigjlHEREREQdzKlARK6Vt68c2euL0fJjW92EgdG4s18EeoYEYFhiOAMSERGRmzkdiOrq6rBz506Ul5ejqanJ5LmFCxe2u2BqodM3mIQhANhy9Dy2HD0PAJAA5GalYObweM8UkIiISAWcCkSFhYW49957UV9fj7q6OoSHh6O6uhohISGIiopiIHJAaXWdSRgyJwBkryvG6H6RbCkiIiJyE6eG3T/99NO4//77cenSJQQHB2Pv3r04ffo00tPT8T//8z+uLmOnlhQR2uY+LQDKquvdXxgiIiKVcioQGRZ69ff3h7+/PxobGxEXF4eVK1fi+eefd3UZO7UYTTDmjelrdx8/AIkRIR1TICIiIhVyKhAFBARAkiQAQHR0NMrLywEAGo1G/n9S7tlJA3BvitbqcxKAnKwU3i4jIiJyI6f6EA0dOhT79+9Hv379MHbsWPzud79DdXU1PvroI6SkpLi6jKqw+uF0HKyowbajVQgM8IMmOAA9gwORnhjGMERERORmTk3MuH//fly5cgVjx47FhQsXMGfOHOzevRs333wz3n//faSmprqjrB7FiRmJiIh8j9Lrt1OBSI0YiIiIiHyPW2eqNqiqqsLx48chSRL69++PyMjI9hyOiIiIyCOc6lRdW1uL2bNno3fv3rjrrrswevRoxMbG4mc/+xn0er2ry0hERETkVk4FosceewzffvstPvvsM1y+fBl6vR6fffYZ9u/fj8cff9zVZSQiIiJyK6f6EIWGhuLLL7/EHXfcYbL966+/xqRJk1BXV+eyAnoL9iEiIiLyPUqv3061EPXq1QsajcZiu0ajQVhYmOLj7Nq1C/fffz9iY2MhSRI2btxo8rwQAsuWLUNsbCyCg4MxZswYHD582GSfxsZGLFiwABEREQgNDcXUqVNx5swZk31qamowe/ZsaDQaaDQazJ49G5cvX1Zczo6k0zcg/2Q1dPoGTxeFiIhINZwKRL/97W/xzDPPQKfTydsqKyvx7LPP4oUXXlB8nLq6OqSmpmLVqlVWn1+5ciVee+01rFq1Cvv27YNWq8U999yDK1euyPssWrQIGzZswNq1a7F7925cvXoVU6ZMQXNzs7zPrFmzUFRUhM2bN2Pz5s0oKirC7Nmznai5exhC0Ls7TyIjZxtmrfkWGTnbkLePk1wSERF1BMW3zIYOHSrPTg0AJ06cQGNjI+LjW1dhLy8vR1BQEG655RYcOHDA8YJIEjZs2IDp06cDaG0dio2NxaJFi/Dcc88BaG0Nio6OxooVK/Dkk09Cr9cjMjISH330EWbOnAkAOHfuHOLi4vD5559j4sSJOHr0KAYNGoS9e/dixIgRAIC9e/ciIyMDx44dQ//+/a2Wp7GxEY2NjfLj2tpaxMXFufyWWd6+covV7o39e94oVF9txGeHdOgb2Q1Z6X1MJmrU6RtQWl2HpIhQTuBIRERkxuXD7g1BpaOUlpaisrISEyZMkLcFBQXhrrvuQn5+Pp588kkUFBTg+vXrJvvExsYiOTkZ+fn5mDhxIvbs2QONRiOHIQAYOXIkNBoN8vPzbQainJwcvPTSS+6rIFrDjL0wBADT3s43efw/W77HiqwUzBwebxKm/CQgJ7N1OxERETlGcSB68cUX3VkOC5WVlQBa10ozFh0djdOnT8v7BAYGWvRbio6Oll9fWVmJqKgoi+NHRUXJ+1iTnZ2NZ555Rn5saCFypdLqOrthyJal64oxQNvdJEy1COD59SUY3S+SLUVEREQOatfEjB3B+DYd0HorzXybOfN9rO3f1nGCgoIQFBTkYGkdkxQRCj8JDociAWBfWY3F65qFQFl1PQMRERGRgxQHorCwsDaDiMGlS5ecLpCBVtu6+ntlZSViYmLk7VVVVXKrkVarRVNTE2pqakxaiaqqqjBq1Ch5n/Pnz1sc/8KFCxatTx0tRhOMnMwUPL++BM0OzH4gARieGGYRpvwlCYkRIa4vKBERUSenOBC98cYb8v9fvHgRr7zyCiZOnIiMjAwAwJ49e/Dll186NMrMnqSkJGi1WmzduhVDhw4FADQ1NWHnzp1YsWIFACA9PR0BAQHYunUrHnjgAQCATqdDSUkJVq5cCQDIyMiAXq/Hd999h9tuuw0A8O2330Kv18uhyZNmDo/H6H6RKKuuR2JECI6c02PN16X4rvSSzZaj3KwUpMaFmYQpf0nC8sxktg4RERE5wamJGbOysjB27FjMnz/fZPuqVavw1VdfWcwnZMvVq1fxww8/AGgdxfbaa69h7NixCA8PR3x8PFasWIGcnBy8//77uOWWW7B8+XLs2LEDx48fR/fu3QEAv/71r/HZZ5/hb3/7G8LDw7FkyRJcvHgRBQUF8Pf3BwBMnjwZ586dw7vvvgsAeOKJJ5CQkIBPP/1UcZ07emJGnb7BJCR9fkiHm6K6ITPNcpSZYT+GISIiIlOKr9/CCaGhoeLEiRMW27///nsRGhqq+Djbt28XaO0SY/Jvzpw5QgghWlpaxIsvvii0Wq0ICgoSo0ePFsXFxSbHaGhoEPPnzxfh4eEiODhYTJkyRZSXl5vsc/HiRfHwww+L7t27i+7du4uHH35Y1NTUOFRnvV4vAAi9Xu/Q64iIiMhzlF6/nWohSkhIwPz58/Hss8+abP/DH/6AVatWyaPAOhMu3UFEROR7XD4PkbGXXnoJjz76KHbs2CH3Idq7dy82b96M9957z7kSExEREXmIU4HokUcewcCBA/HWW29h/fr1EEJg0KBB+Oabb0wmQCQiIiLyBU7dMlMj3jIjIiLyPS5f7b62ttahAhgvwEpERETkzRQHorCwMFRVVSk+cO/evXHq1CmnCkVERETUkRT3IRJC4L333kO3bt0U7X/9+nWnC0VERETUkRQHovj4eKxZs0bxgbVaLQICApwqFBEREVFHUhyIysrK3FgMIiIiIs9R3IfImqamJhw/fhw3btxwVXmIiIiIOpxTgai+vh6PPvooQkJCcOutt6K8vBwAsHDhQuTm5rq0gERERETu5lQgys7OxsGDB7Fjxw507dpV3j5+/Hjk5eW5rHBEREREHcGpmao3btyIvLw8jBw5EpIkydsHDRqEkydPuqxwRERERB3BqRaiCxcuICoqymJ7XV2dSUAiIiIi8gVOBaLhw4dj06ZN8mNDCFqzZo282CsRERGRr3DqlllOTg4mTZqEI0eO4MaNG3jzzTdx+PBh7NmzBzt37nR1GYmIiIjcyqkWolGjRiE/Px/19fXo27cvtmzZgujoaOzZswfp6emuLiMRERGRWzncQnT9+nU88cQTeOGFF/DBBx+4o0xEREREHcrhFqKAgABs2LDBHWUhIiIi8ginbpnNmDEDGzdudHFRiIiIiDzDqU7VN998M15++WXk5+cjPT0doaGhJs8vXLjQJYUjIiIi6giSEEI4+qKkpCTbB5QknDp1ql2F8ka1tbXQaDTQ6/Xo0aOHp4tDRERECii9fjvVQlRaWup0wYiIiIi8TbtWuyciIiLqDJxqIfrlL39p9/m//vWvThWGiIiIyBOcCkQ1NTUmj69fv46SkhJcvnwZ48aNc0nBiIiIiDqKU4HI2jxELS0tmDt3Lm666aZ2F4qIiIioI7msD5Gfnx+efvppvP766646JBEREVGHcGmn6pMnT+LGjRuuPCQRERGR2zl1y+yZZ54xeSyEgE6nw6ZNmzBnzhyXFIzaR6dvQGl1HUID/VHX1Cz/NykiFDGaYPl5w2MiIiI1cyoQFRYWmjz28/NDZGQkXn311TZHoJH75e0rR/b6YrRYmXLTTwJmDO2NDYVn0SJaH+dkpmDm8PiOLygREZGXcGqmajXylZmqdfoG3J67zWoYssVfkrB76Vi2FBERUaej9PrtVB+ihoYG1NfXy49Pnz6NN954A1u2bHHmcORCpdV1DoUhAGgWAmXV9W3vSERE1Ek5FYimTZuGDz/8EABw+fJl3HbbbXj11Vcxbdo0vPPOOy4tIDkmKSIUfpJjr/GXJCRGhLinQERERD7AqUB04MAB3HnnnQCA//u//4NWq8Xp06fx4Ycf4q233nJpAckxMZpg5GSmwF+ynor8JQlZab3l5/0lCcszk3m7jIiIVM2pTtX19fXo3r07AGDLli3IzMyEn58fRo4cidOnT7u0gOS4mcPjMbpfJMqq6xES6If6phb5v4kRIYjRBGPJxP4oq66XHxMREamZU4Ho5ptvxsaNGzFjxgx8+eWXePrppwEAVVVVXt3hWE1iNMF2g05bzxMREamJU7fMfve732HJkiVITEzEiBEjkJGRAaC1tWjo0KEuLSARERGRuzk97L6yshI6nQ6pqanw82vNVd999x169OiBAQMGuLSQ3sBXht0TERHRT5Rev526ZQYAWq0WWq1WfrNt27ahf//+nTIMERERUefm1C2zBx54AKtWrQLQOifRsGHD8MADD2Dw4MFYt26dSwtIRERE5G5OBaJdu3bJw+43bNgAIQQuX76Mt956C6+88opLC0hERETkbk4FIr1ej/DwcADA5s2bkZWVhZCQENx33304ceKESwtIRERE5G5OBaK4uDjs2bMHdXV12Lx5MyZMmAAAqKmpQdeuXV1aQCIiIiJ3c6pT9aJFi/Dwww+jW7duiI+Px5gxYwC03kpLSUlxZfmIiIiI3M6pQDR37lzcdtttqKiowD333CMPu7/pppvYh4iIiIh8jtPzEAFAU1MTSktL0bdvX3Tp4vQIfp/AeYiIiIh8j9Lrt1N9iOrr6/Hoo48iJCQEt956K8rLywEACxcuRG5urnMlJiIiIvIQpwJRdnY2Dh48iB07dph0oh4/fjzy8vJcVjgiIiKijuDUfa6NGzciLy8PI0eOhCRJ8vZBgwbh5MmTLiscERERUUdwqoXowoULiIqKstheV1dnEpCIiIiIfIFTgWj48OHYtGmT/NgQgtasWSOvfE9ERETkK5y6ZZaTk4NJkybhyJEjuHHjBt58800cPnwYe/bswc6dO11dRiIiIiK3cqqFaNSoUcjPz0d9fT369u2LLVu2IDo6Gnv27EF6erqry0hERETkVg63EF2/fh1PPPEEXnjhBXzwwQfuKBMRERFRh3K4hSggIAAbNmxwR1mIiIiIPMKpW2YzZszAxo0bXVwUIiIiIs9wqlP1zTffjJdffhn5+flIT09HaGioyfMLFy50SeGIiIiIOoJTa5klJSXZPqAk4dSpU+0qlDfiWmZERES+R+n126kWotLSUvn/DXmKEzISERGRr3KqDxEA/OUvf0FycjK6du2Krl27Ijk5Ge+9954ry0ZERETUIZxqIXrhhRfw+uuvY8GCBfLM1Hv27MHTTz+NsrIyvPLKKy4tJBEREZE7OdWHKCIiAn/84x/x0EMPmWz/5z//iQULFqC6utplBfQW7ENERETke5Rev526Zdbc3Ixhw4ZZbE9PT8eNGzecOSQRERGRxzgViH72s5/hnXfesdj+5z//GQ8//LDi4+zatQv3338/YmNjIUmSxdxGjzzyCCRJMvk3cuRIk30aGxuxYMECREREIDQ0FFOnTsWZM2dM9qmpqcHs2bOh0Wig0Wgwe/ZsXL58WXE5iYiIqHNzqg8R0NqpesuWLXJA2bt3LyoqKvDzn/8czzzzjLzfa6+9ZvMYdXV1SE1NxS9+8QtkZWVZ3WfSpEl4//335ceBgYEmzy9atAiffvop1q5di169emHx4sWYMmUKCgoK4O/vDwCYNWsWzpw5g82bNwMAnnjiCcyePRuffvqpc5UnIiKiTsWpQFRSUoK0tDQAwMmTJwEAkZGRiIyMRElJibxfW0PxJ0+ejMmTJ9vdJygoCFqt1upzer0ef/nLX/DRRx9h/PjxAIC///3viIuLw1dffYWJEyfi6NGj2Lx5M/bu3YsRI0YAANasWYOMjAwcP34c/fv3t3rsxsZGNDY2yo9ra2vtllPtdPoGlFbXISkiFDGaYE8Xh4iIyCFOBaLt27e7uhw27dixA1FRUejZsyfuuusu/P73v0dUVBQAoKCgANevX8eECRPk/WNjY5GcnIz8/HxMnDgRe/bsgUajkcMQAIwcORIajQb5+fk2A1FOTg5eeukl91bOi+n0DSg4XQMhBIYlhiNGE2wz9OTtK0f2+mK0CMBPAnIyUzBzeLwHS09EROQYp2+ZdYTJkyfjv/7rv5CQkIDS0lK88MILGDduHAoKChAUFITKykoEBgYiLCzM5HXR0dGorKwEAFRWVsoBylhUVJS8jzXZ2dkmt/5qa2sRFxfnopp5t7x95Vi6rhiG4YcSgMy03thQeNYi9Oj0DXIYAoAWATy/vgSj+0WypYiIiHyGVweimTNnyv+fnJyMYcOGISEhAZs2bUJmZqbN1wkhTG7XWbt1Z76PuaCgIAQFBTlZct9lCDjGczEIAOsOnJUfG4ee0uo6OQwZNAuBsup6BiIiIvIZTs9U7QkxMTFISEjAiRMnAABarRZNTU2oqakx2a+qqgrR0dHyPufPn7c41oULF+R96CfWAo41htCTFBEKP7Nc6S9JSIwIcU8BiYiI3MCnAtHFixdRUVGBmJgYAK3zHgUEBGDr1q3yPjqdDiUlJRg1ahQAICMjA3q9Ht999528z7fffgu9Xi/vQz+xFnCsMYSeGE0wcjJT4P9ja5u/JGF5ZjJbh4iIyKd49JbZ1atX8cMPP8iPS0tLUVRUhPDwcISHh2PZsmXIyspCTEwMysrK8PzzzyMiIgIzZswAAGg0Gjz66KNYvHgxevXqhfDwcCxZsgQpKSnyqLOBAwdi0qRJePzxx/Huu+8CaB12P2XKFJsdqtXMEHCWri+GYQ5zQx+ijYXn0CyEReiZOTweo/tFoqy6Xg5JREREvsSppTtcZceOHRg7dqzF9jlz5uCdd97B9OnTUVhYiMuXLyMmJgZjx47Fyy+/bNK5+dq1a3j22Wfx8ccfo6GhAXfffTdWr15tss+lS5ewcOFCfPLJJwCAqVOnYtWqVejZs6fisqpt6Q6dvgEHTtdACCA9MUweZcbQQ0REvkTp9dujgciXqC0QERERdQZuXcuMiIiIqDNhICIiIiLVYyAiIiIi1WMgIiIiItVjICIiIiLVYyAiIiIi1WMgIiIiItVjICIiIiLVYyAiIiIi1WMgIiIiItVjICIiIiLVYyAiIiIi1WMgIiIiItVjICIiIiLVYyAiIiIi1WMgIiIiItVjICIiIiLVYyAiIiIi1WMgIiIiItVjICIiIiLVYyAiIiIi1WMgIiIiItVjICK30ukbkH+yGjp9g1tfQ0RE1B5dPF0A6rzy9pUje30xWgTgJwE5mSmYOTze5a8hIiJqL7YQkVvo9A1ysAGAFgE8v77EbquPM68hIiJyBQYicovS6jo52Bg0C4Gy6nqXvoaIiMgVGIjILZIiQuEnmW7zlyQkRoS49DVERESuwEBEbhGjCUZOZgr8pdaE4y9JWJ6ZjBhNsEtfQ0RE5AqSEEK0vRvV1tZCo9FAr9ejR48eni6Oz9DpG1BWXY/EiBDFwcaZ1xAREVmj9PrNUWbkNjp9A0qr65AUEWoSbGxtN4jRBDMIERFRh2IgIrewNXyew+qJiMgbsQ8RuZyt4fMHK2o4rJ6IiLwSAxG5nK3h8/vKajisnoiIvBIDEbmcreHzwxPDOKyeiIi8EgMRuZyt4fOpcWEcVk9ERF6Jw+4V4rB7x9kaPs9h9URE1FE47J48ztbweQ6rJyIib8NbZkRERKR6DERERESkegxEREREpHoMRERERKR6DERERESkegxEREREpHoMRERERKR6DERERESkegxEREREpHoMRERERKR6DERERESkegxEREREpHoMRERERKR6DERERESkegxEREREpHoMRERERKR6DERERESkegxEREREpHoMRERERKR6DERERESkegxEREREpHoMRERERKR6DERERESkegxEREREpHoMRERERKR6DERERESkeh4NRDk5ORg+fDi6d++OqKgoTJ8+HcePHzfZRwiBZcuWITY2FsHBwRgzZgwOHz5ssk9jYyMWLFiAiIgIhIaGYurUqThz5ozJPjU1NZg9ezY0Gg00Gg1mz56Ny5cvu7uKRERE5AM8Goh27tyJefPmYe/evdi6dStu3LiBCRMmoK6uTt5n5cqVeO2117Bq1Srs27cPWq0W99xzD65cuSLvs2jRImzYsAFr167F7t27cfXqVUyZMgXNzc3yPrNmzUJRURE2b96MzZs3o6ioCLNnz+7Q+hIREZF3koQQwtOFMLhw4QKioqKwc+dOjB49GkIIxMbGYtGiRXjuuecAtLYGRUdHY8WKFXjyySeh1+sRGRmJjz76CDNnzgQAnDt3DnFxcfj8888xceJEHD16FIMGDcLevXsxYsQIAMDevXuRkZGBY8eOoX///hZlaWxsRGNjo/y4trYWcXFx0Ov16NGjRwd8GkRERNRetbW10Gg0bV6/vaoPkV6vBwCEh4cDAEpLS1FZWYkJEybI+wQFBeGuu+5Cfn4+AKCgoADXr1832Sc2NhbJycnyPnv27IFGo5HDEACMHDkSGo1G3sdcTk6OfHtNo9EgLi7OtZUlIiIir+E1gUgIgWeeeQZ33HEHkpOTAQCVlZUAgOjoaJN9o6Oj5ecqKysRGBiIsLAwu/tERUVZvGdUVJS8j7ns7Gzo9Xr5X0VFRfsqSC6l0zcg/2Q1dPoGq4+JiIgc0cXTBTCYP38+Dh06hN27d1s8J0mSyWMhhMU2c+b7WNvf3nGCgoIQFBSkpOjUwfL2lSN7fTFaBOAnATOG9saGwrPy45zMFMwcHu/pYhIRkQ/xihaiBQsW4JNPPsH27dvRp08febtWqwUAi1acqqoqudVIq9WiqakJNTU1dvc5f/68xfteuHDBovWJvJtO3yCHIQBoEcC6A2dNHj+/voQtRURE5BCPBiIhBObPn4/169dj27ZtSEpKMnk+KSkJWq0WW7dulbc1NTVh586dGDVqFAAgPT0dAQEBJvvodDqUlJTI+2RkZECv1+O7776T9/n222+h1+vlfcg3lFbXyeHHlmYhUFZd3zEFIiKiTsGjt8zmzZuHjz/+GP/+97/RvXt3uSVIo9EgODgYkiRh0aJFWL58OW655RbccsstWL58OUJCQjBr1ix530cffRSLFy9Gr169EB4ejiVLliAlJQXjx48HAAwcOBCTJk3C448/jnfffRcA8MQTT2DKlClWR5iR90qKCIWfBLuhyF+SkBgR0nGFIiIin+fRFqJ33nkHer0eY8aMQUxMjPwvLy9P3uc3v/kNFi1ahLlz52LYsGE4e/YstmzZgu7du8v7vP7665g+fToeeOAB3H777QgJCcGnn34Kf39/eZ9//OMfSElJwYQJEzBhwgQMHjwYH330UYfWl9ovRhOMnMwU+P/Y98tfkpCV1tvk8fLMZMRogj1ZTCIi8jFeNQ+RN1M6jwF1DJ2+AWXV9UiMCEGMJtjiMREREaD8+u01o8yIHBGjCTYJPuaPiYiIHOEVo8yIiIiIPImBiIiIiFSPgYiIiIhUj4GIiIiIVI+BiIiIiFSPgYiIiIhUj4GIiIiIVI+BiIiIiFSPgYiIiIhUj4GIiIiIVI+BiIiIiFSPgYiIiIhUj4GIiIiIVI+BiIiIiFSPgYiIiIhUj4GIiIiIVI+BiIiIiFSPgYiIiIhUj4GIiIiIVI+BiIiIiFSPgYiIiIhUj4GIiIiIVI+BiIiIiFSPgYiIiIhUj4GIiIiIVI+BiIiIiFSPgYiIiIhUj4GIiIiIVI+BiIiIiFSPgYhUR6dvQP7Jauj0DZ4uChEReYkuni4AUUfK21eO7PXFaBGAnwTkZKZg5vB4TxeLiIg8jC1EpBo6fYMchgCgRQDPry9hSxERETEQkXqUVtfJYcigWQiUVdd7pkBEROQ1GIhINZIiQuEnmW7zlyQkRoR4pkBEROQ1GIhINWI0wcjJTIG/1JqK/CUJyzOTEaMJ9nDJiIjI09ipmlRl5vB4jO4XibLqeiRGhDAMERERAAYiUqEYTTCDEBERmeAtMyIiIlI9BiIiIiJSPQYiIiIiUj0GIiIiIlI9BiIiIiJSPQYiIiIiUj0GIiIiIlI9BiIiIiJSPQYiIiIiUj0GIiIiIlI9BiIiIiJSPa5lppAQAgBQW1vr4ZIQERGRUobrtuE6bgsDkUJXrlwBAMTFxXm4JEREROSoK1euQKPR2HxeEm1FJgIAtLS04Ny5c+jevTskSbK5X21tLeLi4lBRUYEePXp0YAk7FuvZuailnoB66sp6dj5qqaur6ymEwJUrVxAbGws/P9s9hdhCpJCfnx/69OmjeP8ePXp06h9YA9azc1FLPQH11JX17HzUUldX1tNey5ABO1UTERGR6jEQERERkeoxELlYUFAQXnzxRQQFBXm6KG7FenYuaqknoJ66sp6dj1rq6ql6slM1ERERqR5biIiIiEj1GIiIiIhI9RiIiIiISPUYiIiIiEj1GIhcaPXq1UhKSkLXrl2Rnp6Or7/+2tNFapdly5ZBkiSTf1qtVn5eCIFly5YhNjYWwcHBGDNmDA4fPuzBEiuza9cu3H///YiNjYUkSdi4caPJ80rq1djYiAULFiAiIgKhoaGYOnUqzpw504G1UKatuj7yyCMW53jkyJEm+3h7XXNycjB8+HB0794dUVFRmD59Oo4fP26yT2c5p0rq2hnO6TvvvIPBgwfLE/NlZGTgiy++kJ/vLOezrXp2hnNpTU5ODiRJwqJFi+Rt3nBOGYhcJC8vD4sWLcL/+3//D4WFhbjzzjsxefJklJeXe7po7XLrrbdCp9PJ/4qLi+XnVq5ciddeew2rVq3Cvn37oNVqcc8998jrvnmruro6pKamYtWqVVafV1KvRYsWYcOGDVi7di12796Nq1evYsqUKWhubu6oaijSVl0BYNKkSSbn+PPPPzd53tvrunPnTsybNw979+7F1q1bcePGDUyYMAF1dXXyPp3lnCqpK+D757RPnz7Izc3F/v37sX//fowbNw7Tpk2TL5Cd5Xy2VU/A98+luX379uHPf/4zBg8ebLLdK86pIJe47bbbxK9+9SuTbQMGDBBLly71UIna78UXXxSpqalWn2tpaRFarVbk5ubK265duyY0Go3405/+1EElbD8AYsOGDfJjJfW6fPmyCAgIEGvXrpX3OXv2rPDz8xObN2/usLI7yryuQggxZ84cMW3aNJuv8cW6VlVVCQBi586dQojOfU7N6ypE5zynQggRFhYm3nvvvU59PoX4qZ5CdL5zeeXKFXHLLbeIrVu3irvuuks89dRTQgjv+R1lC5ELNDU1oaCgABMmTDDZPmHCBOTn53uoVK5x4sQJxMbGIikpCQ8++CBOnToFACgtLUVlZaVJnYOCgnDXXXf5dJ2V1KugoADXr1832Sc2NhbJyck+WfcdO3YgKioK/fr1w+OPP46qqir5OV+sq16vBwCEh4cD6Nzn1LyuBp3pnDY3N2Pt2rWoq6tDRkZGpz2f5vU06Eznct68ebjvvvswfvx4k+3eck65uKsLVFdXo7m5GdHR0Sbbo6OjUVlZ6aFStd+IESPw4Ycfol+/fjh//jxeeeUVjBo1CocPH5brZa3Op0+f9kRxXUJJvSorKxEYGIiwsDCLfXztfE+ePBn/9V//hYSEBJSWluKFF17AuHHjUFBQgKCgIJ+rqxACzzzzDO644w4kJycD6Lzn1Fpdgc5zTouLi5GRkYFr166hW7du2LBhAwYNGiRf/DrL+bRVT6DznEsAWLt2LQ4cOIB9+/ZZPOctv6MMRC4kSZLJYyGExTZfMnnyZPn/U1JSkJGRgb59++KDDz6QO/Z1tjobOFMvX6z7zJkz5f9PTk7GsGHDkJCQgE2bNiEzM9Pm67y1rvPnz8ehQ4ewe/dui+c62zm1VdfOck779++PoqIiXL58GevWrcOcOXOwc+dO+fnOcj5t1XPQoEGd5lxWVFTgqaeewpYtW9C1a1eb+3n6nPKWmQtERETA39/fIqVWVVVZJF5fFhoaipSUFJw4cUIebdbZ6qykXlqtFk1NTaipqbG5j6+KiYlBQkICTpw4AcC36rpgwQJ88skn2L59O/r06SNv74zn1FZdrfHVcxoYGIibb74Zw4YNQ05ODlJTU/Hmm292uvNpq57W+Oq5LCgoQFVVFdLT09GlSxd06dIFO3fuxFtvvYUuXbrIZfX0OWUgcoHAwECkp6dj69atJtu3bt2KUaNGeahUrtfY2IijR48iJiYGSUlJ0Gq1JnVuamrCzp07fbrOSuqVnp6OgIAAk310Oh1KSkp8uu4AcPHiRVRUVCAmJgaAb9RVCIH58+dj/fr12LZtG5KSkkye70zntK26WuOL59QaIQQaGxs71fm0xlBPa3z1XN59990oLi5GUVGR/G/YsGF4+OGHUVRUhJtuusk7zqlLumaTWLt2rQgICBB/+ctfxJEjR8SiRYtEaGioKCsr83TRnLZ48WKxY8cOcerUKbF3714xZcoU0b17d7lOubm5QqPRiPXr14vi4mLx0EMPiZiYGFFbW+vhktt35coVUVhYKAoLCwUA8dprr4nCwkJx+vRpIYSyev3qV78Sffr0EV999ZU4cOCAGDdunEhNTRU3btzwVLWsslfXK1euiMWLF4v8/HxRWloqtm/fLjIyMkTv3r19qq6//vWvhUajETt27BA6nU7+V19fL+/TWc5pW3XtLOc0Oztb7Nq1S5SWlopDhw6J559/Xvj5+YktW7YIITrP+bRXz85yLm0xHmUmhHecUwYiF3r77bdFQkKCCAwMFGlpaSZDYX3RzJkzRUxMjAgICBCxsbEiMzNTHD58WH6+paVFvPjii0Kr1YqgoCAxevRoUVxc7MESK7N9+3YBwOLfnDlzhBDK6tXQ0CDmz58vwsPDRXBwsJgyZYooLy/3QG3ss1fX+vp6MWHCBBEZGSkCAgJEfHy8mDNnjkU9vL2u1uoHQLz//vvyPp3lnLZV185yTn/5y1/K36WRkZHi7rvvlsOQEJ3nfNqrZ2c5l7aYByJvOKeSEEK4pq2JiIiIyDexDxERERGpHgMRERERqR4DEREREakeAxERERGpHgMRERERqR4DEREREakeAxERERGpHgMRERERqR4DEREREakeAxERERGpHgMREanKmDFjIEkSJElCUVGR297nkUcekd9n48aNbnsfInINBiIi8jlFRUV48MEHodVqERgYiL59+2LZsmW4fv26otc//vjj0Ol0SE5Oduh9R48eDUmS8PLLL5tsF0JgxIgRkCQJv/vd7wAAb775JnQ6nUPHJyLPYSAiIp/y/vvv47bbbkN0dDQ+++wzHD16FC+88ALeeustPPLII4qOERISAq1Wiy5duih+XyEEioqKkJCQgOLiYpPnPvjgA5w7dw4AkJaWBgDQaDTQarWKj09EnsVAREQ+Y8eOHXjsscewZs0avPnmmxg2bBj69u2LRx55BH/4wx/w8ccf49ixYw4fd/fu3QgICEBjY6O8rbS0FJIk4fTp0wCAEydO4MqVK3jkkUdMAtGVK1eQnZ0th7H09PT2VZKIPIKBiIh8xlNPPYXJkydjzpw5Fs+NHTsWAHDw4EGHj1tUVISBAwciKCjIZFvPnj2RkJAAACgoKEDXrl3x0EMP4cSJE3J4evnllzFkyBDExMQgIiICcXFxzlSNiDyMgYiIfEJhYSEOHTqEefPmWX2+oaEBANDU1IQxY8Zg0KBBGDx4MP71r3+1eeyDBw9i6NChJtuKioqQmpoqPz5w4AAGDx6Mfv36ITQ0FEePHsWJEyewevVqvP766zhw4ABbh4h8GAMREfmEwsJCAMCQIUOsPn/gwAEAwM0334w33ngDR44cwVdffYWnn34adXV1do9dVFRkcdzCwkKTQFRQUID09HRIkoTBgwejpKQETz/9NJ544gkMGDAABQUFcv8hIvI9DERE5BMMI8i6du1q9fm3334bd955JzIyMuRwExUVhfDwcFy6dMnmcZubm3H48GGLFqIDBw6YhKTCwkI58KSmpuLNN9/Ed999hxdffBFNTU04fPgwAxGRD2MgIiKfYAgsO3futHju1VdfRVFREd58802T7fv370dLS4vdfj3Hjx9HQ0MDYmNj5W179uzB2bNn5RaiU6dO4fLly/ItsSFDhmD//v34/e9/D41Gg+LiYly/fp23zIh8mPIxp0REHnTbbbdh0qRJmDdvHq5fv45hw4bh/PnzeO+99/D3v/8deXl5Jq08Fy9exM9//nO89957do9rmJzxj3/8IxYuXIgffvgBCxcuBAC543RBQQECAwPleYvmzJmD6dOno1evXgBaW5PCwsKQlJTk6moTUQdhCxER+Yx169Zh1qxZWLx4Mfr374/MzExcu3YNRUVFmDZtmrxfY2MjZsyYgezsbIwaNcruMYuKinDPPfegtLQUycnJeP7555Gbm4sePXrg7bffBtAaeJKTkxEQEAAACAgIQEREBCRJkp83v+VGRL5FEkIITxeCiMhVhBCYNWsW+vfvj2XLllk8P2bMGAwZMgRvvPEGAGDixIlIS0tDTk6OW8ojSRI2bNiA6dOnu+X4ROQabCEiok7lm2++QV5eHjZu3IghQ4ZgyJAhFjNLr169Gt26dUNxcTEOHjyIwYMHu7wcv/rVr9CtWzeXH5eI3IMtRESkKmfPnpXnLAoMDERCQgJKSkpw6623uvR9qqqqUFtbCwCIiYlBaGioS49PRK7FQERERESqx1tmREREpHoMRERERKR6DERERESkegxEREREpHoMRERERKR6DERERESkegxEREREpHoMRERERKR6DERERESkegxEREREpHr/P457giYuh5p1AAAAAElFTkSuQmCC", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.figure()\n", "plt.plot(df['OXYGEN'],df['CTDPRS'],'.')\n", "plt.gca().invert_yaxis()\n", "\n", "plt.xlabel('$O_2 [\\mu M]$')\n", "plt.ylabel('pressure[dbar]')" ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 20, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": "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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.figure()\n", "df['CTDTMP'].hist()" ] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 21, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": "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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.figure()\n", "df['CTDTMP'].hist(bins=50)" ] }, { "cell_type": "code", "execution_count": 22, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Index(['DATE_TIME', 'EXPOCODE', 'SECT_ID', 'LEG', 'LINE', 'STNNBR', 'CASTNO',\n", " 'BTLNBR', 'BTLNBR_FLAG_W', 'LATITUDE', 'LONGITUDE', 'DEPTH', 'CTDPRS',\n", " 'CTDTMP', 'CTDSAL', 'CTDSAL_FLAG_W', 'CTDOXY', 'CTDOXY_FLAG_W',\n", " 'SALNTY', 'SALNTY_FLAG_W', 'OXYGEN', 'OXYGEN_FLAG_W', 'SILCAT',\n", " 'SILCAT_FLAG_W', 'NITRAT', 'NITRAT_FLAG_W', 'NITRIT', 'NITRIT_FLAG_W',\n", " 'PHSPHT', 'PHSPHT_FLAG_W', 'AMMONI', 'AMMONI_FLAG_W', 'TCARBN',\n", " 'TCARBN_FLAG_W', 'ALKALI', 'ALKALI_FLAG_W', 'PH_TOT', 'PH_TOT_FLAG_W',\n", " 'PH_TMP', 'CO32', 'CO32__FLAG_W', 'CHLORA', 'CTDTMP_F'],\n", " dtype='object')" ] }, "execution_count": 22, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.keys()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Exercises\n", "\n", "* What scientific questions can be addressed with this data set?\n", "* What relationships might occur between different variables?\n", "* What differences might occur within the same variables, but at different locations or times?\n", "* Create exploratory plots (one PDF, one scatter plot)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Slicing and subsetting data" ] }, { "cell_type": "code", "execution_count": 23, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
DATE_TIMEEXPOCODESECT_IDLEGLINESTNNBRCASTNOBTLNBRBTLNBR_FLAG_WLATITUDE...TCARBN_FLAG_WALKALIALKALI_FLAG_WPH_TOTPH_TOT_FLAG_WPH_TMPCO32CO32__FLAG_WCHLORACTDTMP_F
02013-08-05 02:12:20317W20130803WCOA2013121111248.2...22369.027.294225.0NaN9NaN38.63894
12013-08-05 02:12:53317W20130803WCOA2013121112248.2...9NaN97.295225.0NaN9NaN38.64236
22013-08-05 02:19:58317W20130803WCOA2013121113248.2...22343.727.282225.043.5213NaN39.85916
\n", "

3 rows × 43 columns

\n", "
" ], "text/plain": [ " DATE_TIME EXPOCODE SECT_ID LEG LINE STNNBR CASTNO \\\n", "0 2013-08-05 02:12:20 317W20130803 WCOA2013 1 2 11 1 \n", "1 2013-08-05 02:12:53 317W20130803 WCOA2013 1 2 11 1 \n", "2 2013-08-05 02:19:58 317W20130803 WCOA2013 1 2 11 1 \n", "\n", " BTLNBR BTLNBR_FLAG_W LATITUDE ... TCARBN_FLAG_W ALKALI ALKALI_FLAG_W \\\n", "0 1 2 48.2 ... 2 2369.0 2 \n", "1 2 2 48.2 ... 9 NaN 9 \n", "2 3 2 48.2 ... 2 2343.7 2 \n", "\n", " PH_TOT PH_TOT_FLAG_W PH_TMP CO32 CO32__FLAG_W CHLORA CTDTMP_F \n", "0 7.294 2 25.0 NaN 9 NaN 38.63894 \n", "1 7.295 2 25.0 NaN 9 NaN 38.64236 \n", "2 7.282 2 25.0 43.521 3 NaN 39.85916 \n", "\n", "[3 rows x 43 columns]" ] }, "execution_count": 23, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df[0:3]" ] }, { "cell_type": "code", "execution_count": 24, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
DATE_TIMEEXPOCODESECT_IDLEGLINESTNNBRCASTNOBTLNBRBTLNBR_FLAG_WLATITUDE...TCARBN_FLAG_WALKALIALKALI_FLAG_WPH_TOTPH_TOT_FLAG_WPH_TMPCO32CO32__FLAG_WCHLORACTDTMP_F
02013-08-05 02:12:20317W20130803WCOA2013121111248.2...22369.027.294225.0NaN9NaN38.63894
12013-08-05 02:12:53317W20130803WCOA2013121112248.2...9NaN97.295225.0NaN9NaN38.64236
22013-08-05 02:19:58317W20130803WCOA2013121113248.2...22343.727.282225.043.5213NaN39.85916
\n", "

3 rows × 43 columns

\n", "
" ], "text/plain": [ " DATE_TIME EXPOCODE SECT_ID LEG LINE STNNBR CASTNO \\\n", "0 2013-08-05 02:12:20 317W20130803 WCOA2013 1 2 11 1 \n", "1 2013-08-05 02:12:53 317W20130803 WCOA2013 1 2 11 1 \n", "2 2013-08-05 02:19:58 317W20130803 WCOA2013 1 2 11 1 \n", "\n", " BTLNBR BTLNBR_FLAG_W LATITUDE ... TCARBN_FLAG_W ALKALI ALKALI_FLAG_W \\\n", "0 1 2 48.2 ... 2 2369.0 2 \n", "1 2 2 48.2 ... 9 NaN 9 \n", "2 3 2 48.2 ... 2 2343.7 2 \n", "\n", " PH_TOT PH_TOT_FLAG_W PH_TMP CO32 CO32__FLAG_W CHLORA CTDTMP_F \n", "0 7.294 2 25.0 NaN 9 NaN 38.63894 \n", "1 7.295 2 25.0 NaN 9 NaN 38.64236 \n", "2 7.282 2 25.0 43.521 3 NaN 39.85916 \n", "\n", "[3 rows x 43 columns]" ] }, "execution_count": 24, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.iloc[0:3]" ] }, { "cell_type": "code", "execution_count": 25, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
DATE_TIMEEXPOCODESECT_IDLEGLINESTNNBRCASTNOBTLNBRBTLNBR_FLAG_WLATITUDE...TCARBN_FLAG_WALKALIALKALI_FLAG_WPH_TOTPH_TOT_FLAG_WPH_TMPCO32CO32__FLAG_WCHLORACTDTMP_F
02013-08-05 02:12:20317W20130803WCOA2013121111248.2...22369.027.294225.0NaN9NaN38.63894
12013-08-05 02:12:53317W20130803WCOA2013121112248.2...9NaN97.295225.0NaN9NaN38.64236
22013-08-05 02:19:58317W20130803WCOA2013121113248.2...22343.727.282225.043.5213NaN39.85916
32013-08-05 02:27:01317W20130803WCOA2013121114248.2...22311.927.287225.045.6412NaN41.05328
\n", "

4 rows × 43 columns

\n", "
" ], "text/plain": [ " DATE_TIME EXPOCODE SECT_ID LEG LINE STNNBR CASTNO \\\n", "0 2013-08-05 02:12:20 317W20130803 WCOA2013 1 2 11 1 \n", "1 2013-08-05 02:12:53 317W20130803 WCOA2013 1 2 11 1 \n", "2 2013-08-05 02:19:58 317W20130803 WCOA2013 1 2 11 1 \n", "3 2013-08-05 02:27:01 317W20130803 WCOA2013 1 2 11 1 \n", "\n", " BTLNBR BTLNBR_FLAG_W LATITUDE ... TCARBN_FLAG_W ALKALI ALKALI_FLAG_W \\\n", "0 1 2 48.2 ... 2 2369.0 2 \n", "1 2 2 48.2 ... 9 NaN 9 \n", "2 3 2 48.2 ... 2 2343.7 2 \n", "3 4 2 48.2 ... 2 2311.9 2 \n", "\n", " PH_TOT PH_TOT_FLAG_W PH_TMP CO32 CO32__FLAG_W CHLORA CTDTMP_F \n", "0 7.294 2 25.0 NaN 9 NaN 38.63894 \n", "1 7.295 2 25.0 NaN 9 NaN 38.64236 \n", "2 7.282 2 25.0 43.521 3 NaN 39.85916 \n", "3 7.287 2 25.0 45.641 2 NaN 41.05328 \n", "\n", "[4 rows x 43 columns]" ] }, "execution_count": 25, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.loc[0:3]" ] }, { "cell_type": "code", "execution_count": 26, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
CTDTMPCTDPRS
03.6883999.5
13.69021000.8
24.3662749.0
35.0296503.9
\n", "
" ], "text/plain": [ " CTDTMP CTDPRS\n", "0 3.6883 999.5\n", "1 3.6902 1000.8\n", "2 4.3662 749.0\n", "3 5.0296 503.9" ] }, "execution_count": 26, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.loc[0:3,['CTDTMP','CTDPRS']]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Exercise\n", "\n", "What do you expect to happen when you execute:\n", "```\n", "df[0:1]\n", "df[:4]\n", "df[:-1]\n", "```\n", "\n", "What do you expect to happen when you call:\n", "```\n", "df.iloc[0:4, 1:4]\n", "df.loc[0:4, 1:4]\n", "```\n", "\n", "How are the two commands different?\n", "\n", "Adapted from: https://datacarpentry.org/python-ecology-lesson/03-index-slice-subset/index.html" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Subsetting Data using Criteria" ] }, { "cell_type": "code", "execution_count": 27, "metadata": { "scrolled": true }, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
DATE_TIMEEXPOCODESECT_IDLEGLINESTNNBRCASTNOBTLNBRBTLNBR_FLAG_WLATITUDE...TCARBN_FLAG_WALKALIALKALI_FLAG_WPH_TOTPH_TOT_FLAG_WPH_TMPCO32CO32__FLAG_WCHLORACTDTMP_F
02013-08-05 02:12:20317W20130803WCOA2013121111248.2...22369.027.294225.0NaN9NaN38.63894
12013-08-05 02:12:53317W20130803WCOA2013121112248.2...9NaN97.295225.0NaN9NaN38.64236
22013-08-05 02:19:58317W20130803WCOA2013121113248.2...22343.727.282225.043.5213NaN39.85916
32013-08-05 02:27:01317W20130803WCOA2013121114248.2...22311.927.287225.045.6412NaN41.05328
42013-08-05 02:30:53317W20130803WCOA2013121115248.2...22299.727.308225.047.7412NaN41.82404
\n", "

5 rows × 43 columns

\n", "
" ], "text/plain": [ " DATE_TIME EXPOCODE SECT_ID LEG LINE STNNBR CASTNO \\\n", "0 2013-08-05 02:12:20 317W20130803 WCOA2013 1 2 11 1 \n", "1 2013-08-05 02:12:53 317W20130803 WCOA2013 1 2 11 1 \n", "2 2013-08-05 02:19:58 317W20130803 WCOA2013 1 2 11 1 \n", "3 2013-08-05 02:27:01 317W20130803 WCOA2013 1 2 11 1 \n", "4 2013-08-05 02:30:53 317W20130803 WCOA2013 1 2 11 1 \n", "\n", " BTLNBR BTLNBR_FLAG_W LATITUDE ... TCARBN_FLAG_W ALKALI ALKALI_FLAG_W \\\n", "0 1 2 48.2 ... 2 2369.0 2 \n", "1 2 2 48.2 ... 9 NaN 9 \n", "2 3 2 48.2 ... 2 2343.7 2 \n", "3 4 2 48.2 ... 2 2311.9 2 \n", "4 5 2 48.2 ... 2 2299.7 2 \n", "\n", " PH_TOT PH_TOT_FLAG_W PH_TMP CO32 CO32__FLAG_W CHLORA CTDTMP_F \n", "0 7.294 2 25.0 NaN 9 NaN 38.63894 \n", "1 7.295 2 25.0 NaN 9 NaN 38.64236 \n", "2 7.282 2 25.0 43.521 3 NaN 39.85916 \n", "3 7.287 2 25.0 45.641 2 NaN 41.05328 \n", "4 7.308 2 25.0 47.741 2 NaN 41.82404 \n", "\n", "[5 rows x 43 columns]" ] }, "execution_count": 27, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df[df.LATITUDE > 40].head()" ] }, { "cell_type": "code", "execution_count": 28, "metadata": { "scrolled": true }, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
DATE_TIMEEXPOCODESECT_IDLEGLINESTNNBRCASTNOBTLNBRBTLNBR_FLAG_WLATITUDE...TCARBN_FLAG_WALKALIALKALI_FLAG_WPH_TOTPH_TOT_FLAG_WPH_TMPCO32CO32__FLAG_WCHLORACTDTMP_F
7702013-08-25 21:23:3532P020130821WCOA201321013311237.67...62430.467.493225.073.3462NaN35.12876
7712013-08-25 21:32:1332P020130821WCOA201321013312237.67...22427.627.467225.069.8122NaN35.30336
7722013-08-25 21:40:4432P020130821WCOA201321013313237.67...22424.327.442225.065.5022NaN35.55914
7732013-08-25 21:49:4532P020130821WCOA201321013314237.67...22413.727.400225.060.1702NaN36.10526
7742013-08-25 21:57:5532P020130821WCOA201321013315237.67...22401.427.374225.057.7392NaN36.92894
\n", "

5 rows × 43 columns

\n", "
" ], "text/plain": [ " DATE_TIME EXPOCODE SECT_ID LEG LINE STNNBR CASTNO \\\n", "770 2013-08-25 21:23:35 32P020130821 WCOA2013 2 10 133 1 \n", "771 2013-08-25 21:32:13 32P020130821 WCOA2013 2 10 133 1 \n", "772 2013-08-25 21:40:44 32P020130821 WCOA2013 2 10 133 1 \n", "773 2013-08-25 21:49:45 32P020130821 WCOA2013 2 10 133 1 \n", "774 2013-08-25 21:57:55 32P020130821 WCOA2013 2 10 133 1 \n", "\n", " BTLNBR BTLNBR_FLAG_W LATITUDE ... TCARBN_FLAG_W ALKALI \\\n", "770 1 2 37.67 ... 6 2430.4 \n", "771 2 2 37.67 ... 2 2427.6 \n", "772 3 2 37.67 ... 2 2424.3 \n", "773 4 2 37.67 ... 2 2413.7 \n", "774 5 2 37.67 ... 2 2401.4 \n", "\n", " ALKALI_FLAG_W PH_TOT PH_TOT_FLAG_W PH_TMP CO32 CO32__FLAG_W \\\n", "770 6 7.493 2 25.0 73.346 2 \n", "771 2 7.467 2 25.0 69.812 2 \n", "772 2 7.442 2 25.0 65.502 2 \n", "773 2 7.400 2 25.0 60.170 2 \n", "774 2 7.374 2 25.0 57.739 2 \n", "\n", " CHLORA CTDTMP_F \n", "770 NaN 35.12876 \n", "771 NaN 35.30336 \n", "772 NaN 35.55914 \n", "773 NaN 36.10526 \n", "774 NaN 36.92894 \n", "\n", "[5 rows x 43 columns]" ] }, "execution_count": 28, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df[df.LATITUDE <= 40].head()" ] }, { "cell_type": "code", "execution_count": 29, "metadata": {}, "outputs": [], "source": [ "dfsub = df[(df.CTDPRS <= 10) & (df.LATITUDE > 40)]" ] }, { "cell_type": "code", "execution_count": 30, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
DATE_TIMEEXPOCODESECT_IDLEGLINESTNNBRCASTNOBTLNBRBTLNBR_FLAG_WLATITUDE...TCARBN_FLAG_WALKALIALKALI_FLAG_WPH_TOTPH_TOT_FLAG_WPH_TMPCO32CO32__FLAG_WCHLORACTDTMP_F
182013-08-05 03:00:52317W20130803WCOA20131211119448.20...9NaN9NaN9NaNNaN9NaN56.54660
192013-08-05 03:01:10317W20130803WCOA20131211120248.20...22189.167.983325.0155.0432NaN56.54570
382013-08-05 06:37:22317W20130803WCOA20131212119248.30...92180.067.980325.0152.8682NaN57.72218
392013-08-05 06:37:42317W20130803WCOA20131212120248.30...2NaN97.981225.0NaN5NaN57.72290
582013-08-05 10:41:19317W20130803WCOA20131213119248.37...22178.727.931225.0142.6292NaN55.69844
\n", "

5 rows × 43 columns

\n", "
" ], "text/plain": [ " DATE_TIME EXPOCODE SECT_ID LEG LINE STNNBR CASTNO \\\n", "18 2013-08-05 03:00:52 317W20130803 WCOA2013 1 2 11 1 \n", "19 2013-08-05 03:01:10 317W20130803 WCOA2013 1 2 11 1 \n", "38 2013-08-05 06:37:22 317W20130803 WCOA2013 1 2 12 1 \n", "39 2013-08-05 06:37:42 317W20130803 WCOA2013 1 2 12 1 \n", "58 2013-08-05 10:41:19 317W20130803 WCOA2013 1 2 13 1 \n", "\n", " BTLNBR BTLNBR_FLAG_W LATITUDE ... TCARBN_FLAG_W ALKALI \\\n", "18 19 4 48.20 ... 9 NaN \n", "19 20 2 48.20 ... 2 2189.1 \n", "38 19 2 48.30 ... 9 2180.0 \n", "39 20 2 48.30 ... 2 NaN \n", "58 19 2 48.37 ... 2 2178.7 \n", "\n", " ALKALI_FLAG_W PH_TOT PH_TOT_FLAG_W PH_TMP CO32 CO32__FLAG_W \\\n", "18 9 NaN 9 NaN NaN 9 \n", "19 6 7.983 3 25.0 155.043 2 \n", "38 6 7.980 3 25.0 152.868 2 \n", "39 9 7.981 2 25.0 NaN 5 \n", "58 2 7.931 2 25.0 142.629 2 \n", "\n", " CHLORA CTDTMP_F \n", "18 NaN 56.54660 \n", "19 NaN 56.54570 \n", "38 NaN 57.72218 \n", "39 NaN 57.72290 \n", "58 NaN 55.69844 \n", "\n", "[5 rows x 43 columns]" ] }, "execution_count": 30, "metadata": {}, "output_type": "execute_result" } ], "source": [ "dfsub.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Exercises\n", "\n", "* Select a subset of rows in the `df` DataFrame that contains data from a pressure range between 500 and 1000 dbar. How many rows did you end up with? What did your neighbor get?\n", "\n", "* You can use the isin command in Python to query a DataFrame based upon a list of values as follows:\n", "```\n", "df[df['STNNBR'].isin([listGoesHere])]\n", "```\n", "Use the `isin` function to find all samples from station numbers 11 and 12." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Exercises\n", "\n", "pH values are contained in the `df['PH_TOT']` DataArray\n", "\n", "Quality control flags for pH are contained in the `df['PH_TOT_FLAG_W']` DataArray.\n", "\n", "World Ocean Circulation Experiment (WOCE) quality control flags are used: \n", "* 2 = good value\n", "* 3 = questionable value\n", "* 4 = bad value \n", "* 5 = value not reported\n", "* 6 = mean of replicate measurements\n", "* 9 = sample not drawn.\n", "\n", "\n", "1. 1. Create a new DataFrame called `dfsub1` that includes only data where pressure is less than 10 dbar and the latitude is farher north than $40^{\\circ} \\text{N}$." ] }, { "cell_type": "code", "execution_count": 31, "metadata": { "tags": [ "hide-input" ] }, "outputs": [], "source": [ "dfsub = df[(df['CTDPRS'] > 10) & (df['LATITUDE'] > 40)]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "2. Create a new DataFrame called `dfsub2` that excludes all bad, questionable and missing pH and CTD oxygen values. Plot oxygen vs. pH." ] }, { "cell_type": "code", "execution_count": 32, "metadata": { "tags": [ "hide-input" ] }, "outputs": [], "source": [ "dfsub2 = df[(df['CTDOXY_FLAG_W'] == 2) & (df['PH_TOT_FLAG_W'] == 2)]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Fit a linear model in Python" ] }, { "cell_type": "code", "execution_count": 33, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Text(0, 0.5, 'pH$_{Tot}$')" ] }, "execution_count": 33, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": "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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.plot(df['CTDOXY'],df['PH_TOT'],'.')\n", "plt.xlabel('dissolved oxygen [$\\mu$mol/kg]')\n", "plt.ylabel('pH$_{Tot}$')" ] }, { "cell_type": "code", "execution_count": 34, "metadata": {}, "outputs": [], "source": [ "result = stats.linregress(dfsub2['CTDOXY'],dfsub2['PH_TOT'])" ] }, { "cell_type": "code", "execution_count": 35, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "LinregressResult(slope=0.002305076546103237, intercept=7.202036145940105, rvalue=0.9510631019969654, pvalue=0.0, stderr=2.7737424376777354e-05, intercept_stderr=0.004977802641597415)" ] }, "execution_count": 35, "metadata": {}, "output_type": "execute_result" } ], "source": [ "result" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Exercise\n", "\n", "Plot the linear model with the data." ] } ], "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": "3.10.6" }, "vscode": { "interpreter": { "hash": "0ef88d3abb6b62f34a20525ce337090c4512fe8aecf32c74604482b944e1c3bd" } } }, "nbformat": 4, "nbformat_minor": 4 }